From 7a8544e0a24df6c0fec06c044e5901de63b50ccd Mon Sep 17 00:00:00 2001
From: Przemek Strzelczyk <pstrzelczyk@nvidia.com>
Date: Mon, 18 Mar 2019 19:45:05 +0100
Subject: [PATCH] Adding 9 new models: ResNet50/TF, SSD/TF, BERT/TF, NCF/TF,
 UNet/TF, GNMT/TF, SSD/PyT, MaskRCNN/PyT, Tacotron2+Waveglow/PyT

---
 .gitmodules                                   |     3 -
 Caffe2/Classification/cifar10/get_cifar10.sh  |    19 -
 Caffe2/Classification/cifar10/make_cifar10.sh |     7 -
 .../Classification/cifar10/train_cifar10.py   |   208 -
 Caffe2/Classification/mnist/.gitignore        |     2 -
 Caffe2/Classification/mnist/get_mnist.sh      |    14 -
 Caffe2/Classification/mnist/make_mnist.sh     |     7 -
 Caffe2/Classification/mnist/train_lenet.py    |   133 -
 .../Classification/mnist/train_lenet_fp16.py  |   139 -
 .../Classification/mnist/train_lenet_mgpu.py  |   159 -
 PyTorch/Detection/SSD/Dockerfile              |    20 +
 PyTorch/Detection/SSD/LICENSE.md              |   203 +
 PyTorch/Detection/SSD/README.md               |   344 +
 PyTorch/Detection/SSD/__init__.py             |     0
 .../Detection/SSD/csrc/box_encoder_cuda.cu    |   440 +
 PyTorch/Detection/SSD/csrc/interface.cpp      |    81 +
 .../Detection/SSD/csrc/random_horiz_flip.cu   |   165 +
 PyTorch/Detection/SSD/download_dataset.sh     |     8 +
 .../SSD/examples/SSD300_FP16_1GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP16_4GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP16_8GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP16_EVAL.sh          |     4 +
 .../SSD300_FP16_INFERENCE_BENCHMARK.sh        |     4 +
 .../SSD/examples/SSD300_FP32_1GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP32_4GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP32_8GPU.sh          |     4 +
 .../SSD/examples/SSD300_FP32_EVAL.sh          |     4 +
 .../SSD300_FP32_INFERENCE_BENCHMARK.sh        |     4 +
 PyTorch/Detection/SSD/img/training_loss.png   |   Bin 0 -> 12389 bytes
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 PyTorch/Detection/SSD/main.py                 |   240 +
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 .../SSD300_pytorch_19.01_training_fp32.json   |    45 +
 .../Detection/SSD/qa/benchmark_performance.py |    81 +
 ...orch_18.08_fp16_full_run_acc_baseline.json |     1 +
 ...orch_18.08_fp32_full_run_acc_baseline.json |     1 +
 ...ch_19.01_fp16_1epoch_run_acc_baseline.json |    20 +
 ...ch_19.01_fp32_1epoch_run_acc_baseline.json |    20 +
 PyTorch/Detection/SSD/qa/qa_accuracy_main.py  |    73 +
 PyTorch/Detection/SSD/qa/qa_perf_main.py      |   199 +
 PyTorch/Detection/SSD/qa/qa_utils.py          |   115 +
 .../qa/testing_DGX1V_8GPU_fp16_1epoch_run.sh  |     4 +
 .../qa/testing_DGX1V_8GPU_fp16_full_run.sh    |     4 +
 .../qa/testing_DGX1V_8GPU_fp32_1epoch_run.sh  |     4 +
 .../qa/testing_DGX1V_8GPU_fp32_full_run.sh    |     4 +
 .../testing_DGX1V_inference_benchmark_fp16.sh |     3 +
 .../testing_DGX1V_inference_benchmark_fp32.sh |     3 +
 .../testing_DGX1V_training_benchmark_fp16.sh  |     3 +
 .../testing_DGX1V_training_benchmark_fp32.sh  |     3 +
 PyTorch/Detection/SSD/requirements.txt        |     1 +
 PyTorch/Detection/SSD/setup.py                |    89 +
 PyTorch/Detection/SSD/src/__init__.py         |     0
 PyTorch/Detection/SSD/src/coco.py             |   433 +
 PyTorch/Detection/SSD/src/coco_pipeline.py    |   267 +
 PyTorch/Detection/SSD/src/data.py             |    54 +
 PyTorch/Detection/SSD/src/distributed.py      |    82 +
 PyTorch/Detection/SSD/src/evaluate.py         |   124 +
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 PyTorch/Detection/SSD/src/train.py            |   223 +
 PyTorch/Detection/SSD/src/utils.py            |   578 +
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 PyTorch/Distributed/requirements.txt          |     2 -
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 PyTorch/LanguageModel/README.md               |    58 -
 PyTorch/LanguageModel/data.py                 |    49 -
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 .../LanguageModel/data/wikitext-2/test.txt    |  4358 --
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 .../LanguageModel/data/wikitext-2/valid.txt   |  3760 --
 PyTorch/LanguageModel/fp16util.py             |    36 -
 PyTorch/LanguageModel/generate.py             |    74 -
 PyTorch/LanguageModel/main.py                 |   236 -
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 PyTorch/Segmentation/MaskRCNN/NOTICE          |     4 +
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 .../Segmentation/MaskRCNN/pytorch/Dockerfile  |    37 +
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 PyTorch/Segmentation/MaskRCNN/pytorch/LICENSE |    21 +
 .../MaskRCNN/pytorch/MODEL_ZOO.md             |    82 +
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 .../e2e_faster_rcnn_R_101_FPN_1x_caffe2.yaml  |    25 +
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 .../e2e_faster_rcnn_R_50_FPN_1x_caffe2.yaml   |    25 +
 ...faster_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml |    29 +
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 ...e_mask_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml |    38 +
 ...e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml |    32 +
 .../e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml  |    41 +
 .../configs/e2e_faster_rcnn_R_101_FPN_1x.yaml |    31 +
 .../configs/e2e_faster_rcnn_R_50_C4_1x.yaml   |    15 +
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 .../e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml   |    36 +
 .../configs/e2e_mask_rcnn_R_101_FPN_1x.yaml   |    40 +
 .../configs/e2e_mask_rcnn_R_50_C4_1x.yaml     |    19 +
 .../configs/e2e_mask_rcnn_R_50_FPN_1x.yaml    |    43 +
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 .../e2e_mask_rcnn_X_101_32x8d_FPN_1x.yaml     |    45 +
 .../e2e_faster_rcnn_R_50_FPN_1x_gn.yaml       |    48 +
 ...e_faster_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml |    50 +
 .../e2e_mask_rcnn_R_50_FPN_1x_gn.yaml         |    59 +
 ...e2e_mask_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml |    61 +
 ...cratch_e2e_faster_rcnn_R_50_FPN_3x_gn.yaml |    49 +
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 .../scratch_e2e_mask_rcnn_R_50_FPN_3x_gn.yaml |    60 +
 ...e2e_mask_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml |    62 +
 .../e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml |    20 +
 .../e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml |    20 +
 .../e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml  |    41 +
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diff --git a/.gitmodules b/.gitmodules
index 364089e9..e69de29b 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +0,0 @@
-[submodule "TensorFlow/OpenSeq2Seq"]
-	path = TensorFlow/OpenSeq2Seq
-	url = https://github.com/NVIDIA/OpenSeq2Seq
diff --git a/Caffe2/Classification/cifar10/get_cifar10.sh b/Caffe2/Classification/cifar10/get_cifar10.sh
deleted file mode 100755
index edda88fe..00000000
--- a/Caffe2/Classification/cifar10/get_cifar10.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the CIFAR10 (binary version) data and unzips it.
-set -e
-
-cd "$( cd "$(dirname "$0")" ; pwd -P )"
-
-echo "Downloading..."
-
-wget --no-check-certificate http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
-
-echo "Unzipping..."
-
-tar -xf cifar-10-binary.tar.gz && rm -f cifar-10-binary.tar.gz
-mv cifar-10-batches-bin/* . && rm -rf cifar-10-batches-bin
-
-# Creation is split out because leveldb sometimes causes segfault
-# and needs to be re-created.
-
-echo "Done."
diff --git a/Caffe2/Classification/cifar10/make_cifar10.sh b/Caffe2/Classification/cifar10/make_cifar10.sh
deleted file mode 100755
index bd77b04f..00000000
--- a/Caffe2/Classification/cifar10/make_cifar10.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-set -e
-
-cd "$( cd "$(dirname "$0")" ; pwd -P )"
-
-# Create CIFAR10 train + test databases
-make_cifar_db --db lmdb --input_folder "$(pwd)" --output_train_db_name cifar10_train_lmdb --output_test_db_name cifar10_test_lmdb
diff --git a/Caffe2/Classification/cifar10/train_cifar10.py b/Caffe2/Classification/cifar10/train_cifar10.py
deleted file mode 100755
index 92127e35..00000000
--- a/Caffe2/Classification/cifar10/train_cifar10.py
+++ /dev/null
@@ -1,208 +0,0 @@
-#!/usr/bin/env python
-"""Example: train a model on CIFAR10."""
-from __future__ import division, print_function
-
-import argparse
-import functools
-import logging
-import os.path
-
-from caffe2.python import brew, core, data_parallel_model, optimizer, workspace
-from caffe2.python.core import DataType
-from caffe2.python.model_helper import ModelHelper
-from caffe2.python.modeling.initializers import Initializer, pFP16Initializer
-
-
-logging.basicConfig()
-
-TRAIN_ENTRIES = 50000
-TEST_ENTRIES = 10000
-BATCH_SIZE = 100
-EPOCHS = 10
-DISPLAY = 100
-ACCURACY_MIN = 0.7
-ACCURACY_MAX = 0.8
-
-
-def AddInputOps(model, reader, batch_size, dtype):
-    """Add input ops."""
-    data, label = brew.image_input(
-        model, [reader], ['data', 'label'],
-        batch_size=batch_size, use_caffe_datum=False, use_gpu_transform=True,
-        scale=32, crop=32, mirror=1, color=True, mean=128.0,
-        output_type='float16' if dtype == DataType.FLOAT16 else 'float',
-        is_test=False)
-    data = model.StopGradient(data, data)
-
-
-def AddForwardPassOps(model, loss_scale, dtype):
-    """Add forward pass ops and return a list of losses."""
-    initializer = (pFP16Initializer if dtype == DataType.FLOAT16
-                   else Initializer)
-    with brew.arg_scope([brew.conv, brew.fc],
-                        WeightInitializer=initializer,
-                        BiasInitializer=initializer):
-        conv1 = brew.conv(model, 'data', 'conv1', 3, 32, 5, pad=2,
-                          weight_init=('GaussianFill',
-                                       {'std': 0.0001, 'mean': 0.0}))
-        pool1 = brew.max_pool(model, conv1, 'pool1', kernel=3, stride=2)
-        relu1 = brew.relu(model, pool1, 'relu1')
-        conv2 = brew.conv(model, relu1, 'conv2', 32, 32, 5, pad=2,
-                          weight_init=('GaussianFill', {'std': 0.01}))
-        conv2 = brew.relu(model, conv2, conv2)
-        pool2 = brew.average_pool(model, conv2, 'pool2', kernel=3, stride=2)
-        conv3 = brew.conv(model, pool2, 'conv3', 32, 64, 5, pad=2,
-                          weight_init=('GaussianFill', {'std': 0.01}))
-        conv3 = brew.relu(model, conv3, conv3)
-        pool3 = brew.average_pool(model, conv3, 'pool3', kernel=3, stride=2)
-        fc1 = brew.fc(model, pool3, 'fc1', 64 * 3 * 3, 64,
-                      weight_init=('GaussianFill', {'std': 0.1}))
-        fc2 = brew.fc(model, fc1, 'fc2', 64, 10,
-                      weight_init=('GaussianFill', {'std': 0.1}))
-
-    if dtype == DataType.FLOAT16:
-        fc2 = model.net.HalfToFloat(fc2, fc2 + '_fp32')
-    softmax, loss = model.SoftmaxWithLoss([fc2, 'label'], ['softmax', 'loss'])
-    loss = model.Scale(loss, loss, scale=loss_scale)
-    brew.accuracy(model, [softmax, 'label'], 'accuracy')
-    return [loss]
-
-
-def AddOptimizerOps(model):
-    """Add optimizer ops."""
-    optimizer.add_weight_decay(model, 0.004)
-    stepsize = TRAIN_ENTRIES * EPOCHS // BATCH_SIZE
-    optimizer.build_sgd(
-        model, 0.001,
-        policy='step', stepsize=stepsize, gamma=0.1,
-        momentum=0.9, nesterov=False)
-
-
-def AddPostSyncOps(model):
-    """Add ops which run after the initial parameter sync."""
-    for param_info in model.GetOptimizationParamInfo(model.GetParams()):
-        if param_info.blob_copy is not None:
-            # Ensure copies are in sync after initial broadcast
-            model.param_init_net.HalfToFloat(
-                param_info.blob,
-                param_info.blob_copy[core.DataType.FLOAT]
-            )
-
-
-def createTrainModel(lmdb_path, devices, dtype):
-    """Create and return a training model, complete with training ops."""
-    model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
-    reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
-    data_parallel_model.Parallelize_GPU(
-        model,
-        input_builder_fun=functools.partial(
-            AddInputOps, reader=reader,
-            batch_size=(BATCH_SIZE // len(devices)), dtype=dtype),
-        forward_pass_builder_fun=functools.partial(
-            AddForwardPassOps, dtype=dtype),
-        optimizer_builder_fun=AddOptimizerOps,
-        post_sync_builder_fun=AddPostSyncOps,
-        devices=devices, use_nccl=True)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def createTestModel(lmdb_path, devices, dtype):
-    """Create and return a test model. Does not include training ops."""
-    model = ModelHelper(name='test', arg_scope={'order': 'NCHW'},
-                        init_params=False)
-    reader = model.CreateDB('test_reader', db=lmdb_path, db_type='lmdb')
-    data_parallel_model.Parallelize_GPU(
-        model,
-        input_builder_fun=functools.partial(
-            AddInputOps, reader=reader,
-            batch_size=(BATCH_SIZE // len(devices)), dtype=dtype),
-        forward_pass_builder_fun=functools.partial(
-            AddForwardPassOps, dtype=dtype),
-        param_update_builder_fun=None,
-        devices=devices)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def getArgs():
-    """Return command-line arguments."""
-    CURDIR = os.path.dirname(__file__)
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--train-lmdb', help='Path to training LMDB',
-                        default=os.path.join(CURDIR, 'cifar10_train_lmdb'))
-    parser.add_argument('--test-lmdb', help='Path to test LMDB',
-                        default=os.path.join(CURDIR, 'cifar10_test_lmdb'))
-    parser.add_argument('--dtype', choices=['float', 'float16'],
-                        default='float', help='Data type used for training')
-    parser.add_argument('--gpus',
-                        help='Comma separated list of GPU devices to use')
-    parser.add_argument('--num_gpus', type=int, default=1,
-                        help='Number of GPU devices (instead of --gpus)')
-    parser.add_argument('--all-gpus', action='store_true',
-                        help='Use all GPUs in the system')
-    args = parser.parse_args()
-
-    args.dtype = (DataType.FLOAT16 if args.dtype == 'float16'
-                  else DataType.FLOAT)
-
-    if args.all_gpus:
-        args.num_gpus = workspace.NumCudaDevices()
-        args.gpus = range(args.num_gpus)
-    else:
-        if args.gpus is not None:
-            args.gpus = [int(x) for x in args.gpus.split(',')]
-            args.num_gpus = len(args.gpus)
-        else:
-            args.gpus = range(args.num_gpus)
-            args.num_gpus = args.num_gpus
-    return args
-
-
-def main(args):
-    """Train and test."""
-    train_model = createTrainModel(args.train_lmdb, args.gpus, args.dtype)
-    test_model = createTestModel(args.test_lmdb, args.gpus, args.dtype)
-
-    train_iter_per_epoch = TRAIN_ENTRIES // BATCH_SIZE
-    test_iter_per_epoch = TEST_ENTRIES // BATCH_SIZE
-    scope_prefix = 'gpu_%d/' % args.gpus[0]
-
-    for epoch in range(1, EPOCHS + 1):
-        # Train
-        for iteration in range(1, train_iter_per_epoch + 1):
-            workspace.RunNet(train_model.net.Proto().name)
-            if not iteration % DISPLAY:
-                loss = workspace.FetchBlob(scope_prefix + 'loss')
-                print("Epoch %d/%d, iteration %4d/%d, loss=%f" % (
-                    epoch, EPOCHS, iteration, train_iter_per_epoch, loss))
-
-        # Test
-        losses = []
-        accuracies = []
-        for _ in range(test_iter_per_epoch):
-            workspace.RunNet(test_model.net.Proto().name)
-            # Take average values across all GPUs
-            losses.append(sum(
-                workspace.FetchBlob('gpu_%d/loss' % g) for g in args.gpus
-            ) / len(args.gpus))
-            accuracies.append(sum(
-                workspace.FetchBlob('gpu_%d/accuracy' % g) for g in args.gpus
-            ) / len(args.gpus))
-
-        loss = sum(losses) / len(losses)
-        accuracy = sum(accuracies) / len(accuracies)
-        print("Test loss: %f, accuracy: %f" % (loss, accuracy))
-
-    if accuracy < ACCURACY_MIN or accuracy > ACCURACY_MAX:
-        raise RuntimeError(
-            "Final accuracy %f is not in the expected range [%f, %f]" %
-            (accuracy, ACCURACY_MIN, ACCURACY_MAX))
-
-
-if __name__ == '__main__':
-    core.GlobalInit(['caffe2', '--caffe2_log_level=0'])
-    main(getArgs())
diff --git a/Caffe2/Classification/mnist/.gitignore b/Caffe2/Classification/mnist/.gitignore
deleted file mode 100644
index 6f82cf83..00000000
--- a/Caffe2/Classification/mnist/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*.mdb
-*-ubyte
diff --git a/Caffe2/Classification/mnist/get_mnist.sh b/Caffe2/Classification/mnist/get_mnist.sh
deleted file mode 100755
index c4307e63..00000000
--- a/Caffe2/Classification/mnist/get_mnist.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the mnist data and unzips it.
-
-cd "$( cd "$(dirname "$0")" ; pwd -P )"
-
-echo "Downloading..."
-
-for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
-do
-    if [ ! -e $fname ]; then
-        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
-        gunzip ${fname}.gz
-    fi
-done
diff --git a/Caffe2/Classification/mnist/make_mnist.sh b/Caffe2/Classification/mnist/make_mnist.sh
deleted file mode 100755
index 4ca16642..00000000
--- a/Caffe2/Classification/mnist/make_mnist.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-
-cd "$( cd "$(dirname "$0")" ; pwd -P )"
-
-# Create MNIST databases from previously downloaded data
-make_mnist_db --db lmdb --image_file train-images-idx3-ubyte --label_file train-labels-idx1-ubyte --output_file mnist_train_lmdb
-make_mnist_db --db lmdb --image_file t10k-images-idx3-ubyte --label_file t10k-labels-idx1-ubyte --output_file mnist_test_lmdb
diff --git a/Caffe2/Classification/mnist/train_lenet.py b/Caffe2/Classification/mnist/train_lenet.py
deleted file mode 100755
index 45bf57e0..00000000
--- a/Caffe2/Classification/mnist/train_lenet.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python
-"""Example: train LeNet on MNIST."""
-from __future__ import division, print_function
-
-import argparse
-import os.path
-
-import numpy as np
-
-from caffe2.proto import caffe2_pb2
-from caffe2.python import brew, core, optimizer, workspace
-from caffe2.python.model_helper import ModelHelper
-
-
-TRAIN_ENTRIES = 60000
-TEST_ENTRIES = 10000
-BATCH_SIZE = 100
-EPOCHS = 4
-DISPLAY = 100
-ACCURACY_MIN = 0.98
-ACCURACY_MAX = 0.999
-
-
-def AddInputOps(model, reader, batch_size):
-    """Add input ops."""
-    data, label = brew.image_input(
-        model, [reader], ['data', 'label'],
-        batch_size=batch_size, use_caffe_datum=False, use_gpu_transform=True,
-        scale=28, crop=28, mirror=False, color=False, mean=128.0, std=256.0,
-        is_test=False)
-    data = model.StopGradient(data, data)
-
-
-def AddForwardPassOps(model):
-    """Add forward pass ops and return a list of losses."""
-    conv1 = brew.conv(model, 'data', 'conv1', 1, 20, 5)
-    pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
-    conv2 = brew.conv(model, pool1, 'conv2', 20, 50, 5)
-    pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
-    fc3 = brew.fc(model, pool2, 'fc3', 50 * 4 * 4, 500)
-    fc3 = brew.relu(model, fc3, fc3)
-    pred = brew.fc(model, fc3, 'pred', 500, 10)
-    softmax, loss = model.SoftmaxWithLoss([pred, 'label'], ['softmax', 'loss'])
-    brew.accuracy(model, [softmax, 'label'], 'accuracy')
-    return [loss]
-
-
-def AddOptimizerOps(model):
-    """Add optimizer ops."""
-    optimizer.build_sgd(model, 0.01,
-                        policy='step', stepsize=1, gamma=0.999,
-                        momentum=0.9, nesterov=False)
-
-
-def createTrainModel(lmdb_path):
-    """Create and return a training model, complete with training ops."""
-    model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
-    reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
-    AddInputOps(model, reader, BATCH_SIZE)
-    losses = AddForwardPassOps(model)
-    model.AddGradientOperators(losses)
-    AddOptimizerOps(model)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-
-    return model
-
-
-def createTestModel(lmdb_path):
-    """Create and return a test model. Does not include training ops."""
-    model = ModelHelper(name='test', arg_scope={'order': 'NCHW'},
-                        init_params=False)
-    reader = model.CreateDB('test_reader', db=lmdb_path, db_type='lmdb')
-    AddInputOps(model, reader, BATCH_SIZE)
-    AddForwardPassOps(model)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def getArgs():
-    """Return command-line arguments."""
-    CURDIR = os.path.dirname(__file__)
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--train-lmdb', help='Path to training LMDB',
-                        default=os.path.join(CURDIR, 'mnist_train_lmdb'))
-    parser.add_argument('--test-lmdb', help='Path to test LMDB',
-                        default=os.path.join(CURDIR, 'mnist_test_lmdb'))
-    args = parser.parse_args()
-    return args
-
-
-def main(args):
-    """Train and test."""
-    device = 0
-    with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, device)):
-        train_model = createTrainModel(args.train_lmdb)
-        test_model = createTestModel(args.test_lmdb)
-
-    train_iter_per_epoch = TRAIN_ENTRIES // BATCH_SIZE
-    test_iter_per_epoch = TEST_ENTRIES // BATCH_SIZE
-
-    for epoch in range(1, EPOCHS + 1):
-        # Train
-        for iteration in range(1, train_iter_per_epoch + 1):
-            workspace.RunNet(train_model.net.Proto().name)
-            if not iteration % DISPLAY:
-                loss = workspace.FetchBlob('loss')
-                print("Epoch %d/%d, iteration %4d/%d, loss=%f" % (
-                    epoch, EPOCHS, iteration, train_iter_per_epoch, loss))
-
-        # Test
-        losses = []
-        accuracies = []
-        for _ in range(test_iter_per_epoch):
-            workspace.RunNet(test_model.net.Proto().name)
-            losses.append(workspace.FetchBlob('loss'))
-            accuracies.append(workspace.FetchBlob('accuracy'))
-
-        loss = np.array(losses).mean()
-        accuracy = np.array(accuracies).mean()
-        print("Test loss: %f, accuracy: %f" % (loss, accuracy))
-
-    if accuracy < ACCURACY_MIN or accuracy > ACCURACY_MAX:
-        raise RuntimeError(
-            "Final accuracy %f is not in the expected range [%f, %f]" %
-            (accuracy, ACCURACY_MIN, ACCURACY_MAX))
-
-
-if __name__ == '__main__':
-    core.GlobalInit(['caffe2', '--caffe2_log_level=0'])
-    main(getArgs())
diff --git a/Caffe2/Classification/mnist/train_lenet_fp16.py b/Caffe2/Classification/mnist/train_lenet_fp16.py
deleted file mode 100755
index 6e66c30e..00000000
--- a/Caffe2/Classification/mnist/train_lenet_fp16.py
+++ /dev/null
@@ -1,139 +0,0 @@
-#!/usr/bin/env python
-"""Example: train LeNet on MNIST (with fp16)."""
-from __future__ import division, print_function
-
-import argparse
-import os.path
-
-import numpy as np
-
-from caffe2.proto import caffe2_pb2
-from caffe2.python import brew, core, optimizer, workspace
-from caffe2.python.model_helper import ModelHelper
-from caffe2.python.modeling.initializers import pFP16Initializer
-
-
-TRAIN_ENTRIES = 60000
-TEST_ENTRIES = 10000
-BATCH_SIZE = 100
-EPOCHS = 4
-DISPLAY = 100
-ACCURACY_MIN = 0.98
-ACCURACY_MAX = 0.999
-
-
-def AddInputOps(model, reader, batch_size):
-    """Add input ops."""
-    data, label = brew.image_input(
-        model, [reader], ['data', 'label'],
-        batch_size=batch_size, use_caffe_datum=False, use_gpu_transform=True,
-        scale=28, crop=28, mirror=False, color=False, mean=128.0, std=256.0,
-        output_type='float16', is_test=True)
-    data = model.StopGradient(data, data)
-
-
-def AddForwardPassOps(model):
-    """Add forward pass ops and return a list of losses."""
-    with brew.arg_scope([brew.conv, brew.fc],
-                        WeightInitializer=pFP16Initializer,
-                        BiasInitializer=pFP16Initializer):
-        conv1 = brew.conv(model, 'data', 'conv1', 1, 20, 5)
-        pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
-        conv2 = brew.conv(model, pool1, 'conv2', 20, 50, 5)
-        pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
-        fc3 = brew.fc(model, pool2, 'fc3', 50 * 4 * 4, 500)
-        fc3 = brew.relu(model, fc3, fc3)
-        pred = brew.fc(model, fc3, 'pred', 500, 10)
-
-    # Cast back to fp32 for remaining ops
-    pred = model.net.HalfToFloat(pred, pred + '_fp32')
-    softmax, loss = model.SoftmaxWithLoss([pred, 'label'], ['softmax', 'loss'])
-    brew.accuracy(model, [softmax, 'label'], 'accuracy')
-    return [loss]
-
-
-def AddOptimizerOps(model):
-    """Add optimizer ops."""
-    optimizer.build_sgd(model, 0.01,
-                        policy='step', stepsize=1, gamma=0.999,
-                        momentum=0.9, nesterov=False)
-
-
-def createTrainModel(lmdb_path):
-    """Create and return a training model, complete with training ops."""
-    model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
-    reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
-    AddInputOps(model, reader, BATCH_SIZE)
-    losses = AddForwardPassOps(model)
-    model.AddGradientOperators(losses)
-    AddOptimizerOps(model)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def createTestModel(lmdb_path):
-    """Create and return a test model. Does not include training ops."""
-    model = ModelHelper(name='test', arg_scope={'order': 'NCHW'},
-                        init_params=False)
-    reader = model.CreateDB('test_reader', db=lmdb_path, db_type='lmdb')
-    AddInputOps(model, reader, BATCH_SIZE)
-    AddForwardPassOps(model)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def getArgs():
-    """Return command-line arguments."""
-    CURDIR = os.path.dirname(__file__)
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--train-lmdb', help='Path to training LMDB',
-                        default=os.path.join(CURDIR, 'mnist_train_lmdb'))
-    parser.add_argument('--test-lmdb', help='Path to test LMDB',
-                        default=os.path.join(CURDIR, 'mnist_test_lmdb'))
-    args = parser.parse_args()
-    return args
-
-
-def main(args):
-    """Train and test."""
-    device = 0
-    with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, device)):
-        train_model = createTrainModel(args.train_lmdb)
-        test_model = createTestModel(args.test_lmdb)
-
-    train_iter_per_epoch = TRAIN_ENTRIES // BATCH_SIZE
-    test_iter_per_epoch = TEST_ENTRIES // BATCH_SIZE
-
-    for epoch in range(1, EPOCHS + 1):
-        # Train
-        for iteration in range(1, train_iter_per_epoch + 1):
-            workspace.RunNet(train_model.net.Proto().name)
-            if not iteration % DISPLAY:
-                loss = workspace.FetchBlob('loss')
-                print("Epoch %d/%d, iteration %4d/%d, loss=%f" % (
-                    epoch, EPOCHS, iteration, train_iter_per_epoch, loss))
-
-        # Test
-        losses = []
-        accuracies = []
-        for _ in range(test_iter_per_epoch):
-            workspace.RunNet(test_model.net.Proto().name)
-            losses.append(workspace.FetchBlob('loss'))
-            accuracies.append(workspace.FetchBlob('accuracy'))
-
-        loss = np.array(losses).mean()
-        accuracy = np.array(accuracies).mean()
-        print("Test loss: %f, accuracy: %f" % (loss, accuracy))
-
-    if accuracy < ACCURACY_MIN or accuracy > ACCURACY_MAX:
-        raise RuntimeError(
-            "Final accuracy %f is not in the expected range [%f, %f]" %
-            (accuracy, ACCURACY_MIN, ACCURACY_MAX))
-
-
-if __name__ == '__main__':
-    core.GlobalInit(['caffe2', '--caffe2_log_level=0'])
-    main(getArgs())
diff --git a/Caffe2/Classification/mnist/train_lenet_mgpu.py b/Caffe2/Classification/mnist/train_lenet_mgpu.py
deleted file mode 100755
index f7566998..00000000
--- a/Caffe2/Classification/mnist/train_lenet_mgpu.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#!/usr/bin/env python
-"""Example: train LeNet on MNIST (with multi-GPU)."""
-from __future__ import division, print_function
-
-import argparse
-import functools
-import logging
-import os.path
-
-from caffe2.python import brew, core, data_parallel_model, optimizer, workspace
-from caffe2.python.model_helper import ModelHelper
-
-
-logging.basicConfig()
-
-TRAIN_ENTRIES = 60000
-TEST_ENTRIES = 10000
-BATCH_SIZE = 100
-EPOCHS = 4
-DISPLAY = 100
-ACCURACY_MIN = 0.98
-ACCURACY_MAX = 0.999
-
-
-def AddInputOps(model, reader, batch_size):
-    """Add input ops."""
-    data, label = brew.image_input(
-        model, [reader], ['data', 'label'],
-        batch_size=batch_size, use_caffe_datum=False, use_gpu_transform=True,
-        scale=28, crop=28, mirror=False, color=False, mean=128.0, std=256.0,
-        is_test=True)
-    data = model.StopGradient(data, data)
-
-
-def AddForwardPassOps(model, loss_scale):
-    """Add forward pass ops and return a list of losses."""
-    conv1 = brew.conv(model, 'data', 'conv1', 1, 20, 5)
-    pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
-    conv2 = brew.conv(model, pool1, 'conv2', 20, 50, 5)
-    pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
-    fc3 = brew.fc(model, pool2, 'fc3', 50 * 4 * 4, 500)
-    fc3 = brew.relu(model, fc3, fc3)
-    pred = brew.fc(model, fc3, 'pred', 500, 10)
-    softmax, loss = model.SoftmaxWithLoss([pred, 'label'], ['softmax', 'loss'])
-    loss = model.Scale(loss, loss, scale=loss_scale)
-    brew.accuracy(model, [softmax, 'label'], 'accuracy')
-    return [loss]
-
-
-def AddOptimizerOps(model):
-    """Add optimizer ops."""
-    optimizer.build_sgd(model, 0.01,
-                        policy='step', stepsize=1, gamma=0.999,
-                        momentum=0.9, nesterov=False)
-
-
-def createTrainModel(lmdb_path, devices):
-    """Create and return a training model, complete with training ops."""
-    model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
-    reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
-    data_parallel_model.Parallelize_GPU(
-        model,
-        input_builder_fun=functools.partial(
-            AddInputOps, reader=reader,
-            batch_size=(BATCH_SIZE // len(devices))),
-        forward_pass_builder_fun=AddForwardPassOps,
-        optimizer_builder_fun=AddOptimizerOps,
-        devices=devices, use_nccl=True)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def createTestModel(lmdb_path, devices):
-    """Create and return a test model. Does not include training ops."""
-    model = ModelHelper(name='test', arg_scope={'order': 'NCHW'},
-                        init_params=False)
-    reader = model.CreateDB('test_reader', db=lmdb_path, db_type='lmdb')
-    data_parallel_model.Parallelize_GPU(
-        model,
-        input_builder_fun=functools.partial(
-            AddInputOps, reader=reader,
-            batch_size=(BATCH_SIZE // len(devices))),
-        forward_pass_builder_fun=AddForwardPassOps,
-        param_update_builder_fun=None,
-        devices=devices)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net)
-    return model
-
-
-def getArgs():
-    """Return command-line arguments."""
-    CURDIR = os.path.dirname(__file__)
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--train-lmdb', help='Path to training LMDB',
-                        default=os.path.join(CURDIR, 'mnist_train_lmdb'))
-    parser.add_argument('--test-lmdb', help='Path to test LMDB',
-                        default=os.path.join(CURDIR, 'mnist_test_lmdb'))
-    parser.add_argument('--gpus',
-                        help='Comma separated list of GPU devices to use')
-    parser.add_argument('--num_gpus', type=int, default=1,
-                        help='Number of GPU devices (instead of --gpus)')
-    args = parser.parse_args()
-
-    if args.gpus is not None:
-        args.gpus = [int(x) for x in args.gpus.split(',')]
-        args.num_gpus = len(args.gpus)
-    else:
-        args.gpus = range(args.num_gpus)
-        args.num_gpus = args.num_gpus
-    return args
-
-
-def main(args):
-    """Train and test."""
-    train_model = createTrainModel(args.train_lmdb, args.gpus)
-    test_model = createTestModel(args.test_lmdb, args.gpus)
-
-    train_iter_per_epoch = TRAIN_ENTRIES // BATCH_SIZE
-    test_iter_per_epoch = TEST_ENTRIES // BATCH_SIZE
-    scope_prefix = 'gpu_%d/' % args.gpus[0]
-
-    for epoch in range(1, EPOCHS + 1):
-        # Train
-        for iteration in range(1, train_iter_per_epoch + 1):
-            workspace.RunNet(train_model.net.Proto().name)
-            if not iteration % DISPLAY:
-                loss = workspace.FetchBlob(scope_prefix + 'loss')
-                print("Epoch %d/%d, iteration %4d/%d, loss=%f" % (
-                    epoch, EPOCHS, iteration, train_iter_per_epoch, loss))
-
-        # Test
-        losses = []
-        accuracies = []
-        for _ in range(test_iter_per_epoch):
-            workspace.RunNet(test_model.net.Proto().name)
-            # Take average values across all GPUs
-            losses.append(sum(
-                workspace.FetchBlob('gpu_%d/loss' % g) for g in args.gpus
-            ) / len(args.gpus))
-            accuracies.append(sum(
-                workspace.FetchBlob('gpu_%d/accuracy' % g) for g in args.gpus
-            ) / len(args.gpus))
-
-        loss = sum(losses) / len(losses)
-        accuracy = sum(accuracies) / len(accuracies)
-        print("Test loss: %f, accuracy: %f" % (loss, accuracy))
-
-    if accuracy < ACCURACY_MIN or accuracy > ACCURACY_MAX:
-        raise RuntimeError(
-            "Final accuracy %f is not in the expected range [%f, %f]" %
-            (accuracy, ACCURACY_MIN, ACCURACY_MAX))
-
-
-if __name__ == '__main__':
-    core.GlobalInit(['caffe2', '--caffe2_log_level=0'])
-    main(getArgs())
diff --git a/PyTorch/Detection/SSD/Dockerfile b/PyTorch/Detection/SSD/Dockerfile
new file mode 100755
index 00000000..f7ba53e1
--- /dev/null
+++ b/PyTorch/Detection/SSD/Dockerfile
@@ -0,0 +1,20 @@
+FROM gitlab-master.nvidia.com:5005/dl/dgx/pytorch:19.03-py3-stage
+
+# Set working directory
+WORKDIR /mlperf
+
+RUN apt-get update && apt-get install -y python3-tk python-pip git tmux htop tree
+
+# Necessary pip packages
+RUN pip install --upgrade pip
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+RUN python3 -m pip install pycocotools==2.0.0
+
+# Copy SSD code
+COPY ./setup.py .
+COPY ./csrc ./csrc
+RUN pip install .
+
+COPY . .
diff --git a/PyTorch/Detection/SSD/LICENSE.md b/PyTorch/Detection/SSD/LICENSE.md
new file mode 100644
index 00000000..b9829fb9
--- /dev/null
+++ b/PyTorch/Detection/SSD/LICENSE.md
@@ -0,0 +1,203 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+   
+   Copyright 2018 NVIDIA Corporation
+
+   Copyright 2018 The MLPerf Authors
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/README.md b/PyTorch/Detection/SSD/README.md
new file mode 100644
index 00000000..e4d2a870
--- /dev/null
+++ b/PyTorch/Detection/SSD/README.md
@@ -0,0 +1,344 @@
+# SSD300 v1.1 For PyTorch
+
+## Table Of Contents
+* [The model](#the-model)
+  * [Default configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+  * [Command line arguments](#command-line-arguments)
+  * [Getting the data](#getting-the-data)
+  * [Training process](#training-process)
+    * [Data preprocessing](#data-preprocessing)
+    * [Data augmentation](#data-augmentation)
+  * [Enabling mixed precision](#enabling-mixed-precision)
+* [Benchmarking](#benchmarking)
+  * [Training performance benchmark](#training-performance-benchmark)
+  * [Inference performance benchmark](#inference-performance-benchmark)
+* [Results](#results)
+  * [Training accuracy results](#training-accuracy-results)
+  * [Training performance results](#training-performance-results)
+  * [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+## The model
+The SSD300 v1.1 model is based on the
+[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which
+describes SSD as “a method for detecting objects in images using a single deep neural network".
+The input size is fixed to 300x300.
+
+The main difference between this model and the one described in the paper is in the backbone.
+Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.
+
+From the
+[Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012)
+paper, the following enhancements were made to the backbone:
+*   The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.
+*   All strides in conv4_x are set to 1x1. 
+
+The backbone is followed by 5 additional convolutional layers.
+In addition to the convolutional layers, we attached 6 detection heads:
+*   The first detection head is attached to the last conv4_x layer.
+*   The other five detection heads are attached to the corresponding 5 additional layers.
+
+Detector heads are similar to the ones referenced in the paper, however,
+they are enhanced by additional BatchNorm layers after each convolution.
+
+ 
+Additionally, we removed weight decay on every bias parameter and
+all the BatchNorm layer parameters as described in the
+[Highly Scalable Deep Learning Training System with Mixed-Precision: 
+Training ImageNet in Four Minutes](https://arxiv.org/abs/1807.11205) paper. 
+
+This model trains with mixed precision tensor cores on Volta, therefore you can get results much faster than training without tensor cores.
+This model is tested against each NGC monthly container release to ensure
+consistent accuracy and performance over time.
+
+Because of these enhancements, the SSD300 v1.1 model achieves higher accuracy.
+
+Training of SSD requires computational costly augmentations. To fully utilize GPUs during training we are using [NVIDIA DALI](https://github.com/NVIDIA/DALI) library to accelerate data preparation pipeline.
+
+
+### Default configuration
+We trained the model for 65 epochs with the following setup:
+*	SGD with momentum (0.9)
+*	Learning rate = 2.6e-3 * number of GPUs * (batch_size / 32)
+*	Learning rate decay – multiply by 0.1 before 43 and 54 epochs
+*	We use linear warmup of the learning rate during the first epoch. For more information, see the
+ [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677) paper.
+To enable warmup provide argument the `--warmup 300`
+*	Weight decay:
+    *	0 for BatchNorms and biases
+	*   5e-4 for other layers
+	
+**Note**: The learning rate is automatically scaled (in other words, mutliplied by the number of GPUs and multiplied by the batch size divided by 32).
+
+## Setup
+The following section list the requirements in order to start training the SSD300 v1.1 model.
+
+
+### Requirements
+This repository contains `Dockerfile` which extends the PyTorch 19.03 NGC container and encapsulates some dependencies.  Aside from these dependencies, ensure you have the following software:
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [PyTorch 19.03-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch)
+* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
+Documentation:
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)
+
+
+## Quick Start Guide
+To train your model using mixed precision with Tensor Cores, perform the
+following steps using the default parameters of the SSD v1.1 model on the
+[COCO 2017](http://cocodataset.org/#download) dataset.
+
+### 1. Download and preprocess the dataset.
+
+Extract the COCO 2017 dataset with `download_dataset.sh $COCO_DIR`.
+Data will be downloaded to the `$COCO_DIR` directory (on the host).
+
+### 2. Build the SSD300 v1.1 PyTorch NGC container.
+
+` docker build . -t nvidia_ssd `
+
+### 3. Launch the NGC container to run training/inference.
+`nvidia-docker run --rm -it --ulimit memlock=-1 --ulimit stack=67108864 -v $COCO_DIR:/coco --ipc=host nvidia_ssd`
+
+
+**Note**: the default mount point in the container is `/coco`.
+
+### 4. Start training.
+
+The `./examples` directory provides several sample scripts for various GPU settings
+and act as wrappers around the main.py script.
+The example scripts need two arguments:
+- A path to root SSD directory.
+- A path to COCO 2017 dataset.
+
+Remaining arguments are passed to the `main.py` script.
+
+The `--save` flag, saves the model after each epoch.
+The checkpoints are stored as `./models/epoch_*.pt`.
+
+Use `python main.py -h` to obtain the list of available options in the `main.py` script.
+For example, if you want to run 8 GPU training with TensorCore acceleration and
+save checkpoints after each epoch, run:
+
+`bash ./examples/SSD300_FP16_8GPU.sh . /coco --save`
+
+For information about how to train using mixed precision, see the [Mixed Precision Training paper](https://arxiv.org/abs/1710.03740) and [Training With Mixed Precision documentation](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html).
+
+For PyTorch, easily adding mixed-precision support is available from NVIDIA’s [APEX](https://github.com/NVIDIA/apex), a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.
+
+
+### 5. Start validation/evaluation.
+
+
+The `main.py` training script automatically runs validation during training.
+The results from the validation are printed to stdout.
+
+Pycocotools’ open-sourced scripts provides a consistent way to evaluate models on the COCO dataset. We are using these scripts during validation to measure models performance in AP metric. Metrics below are evaluated using pycocotools’ methodology, in the following format:
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.250
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.423
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.257
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.237
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.342
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.358
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.548
+```
+The metric reported in our results is present in the first row.
+
+To evaluate a checkpointed model saved in previous point, run:
+
+`python ./main.py --backbone resnet50 --mode evaluation --checkpoint ./models/epoch_*.pt --data /coco`
+
+### 6. Optionally, resume training from a checkpointed model.
+
+`python ./main.py --backbone resnet50 --checkpoint ./models/epoch_*.pt --data /coco`
+
+## Details
+
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+### Command line arguments
+All these parameters can be controlled by passing command line arguments to the `main.py` script. To get a complete list of all command line arguments with descriptions and default values you can run:
+
+`python main.py --help`
+
+### Getting the data
+
+The SSD model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset. PyTorch can work directly on JPEGs, therefore, preprocessing/augmentation is not needed.
+
+This repository contains the `download_dataset.sh` download script which will automatically
+download and preprocess the training, validation and test datasets. By default,
+data will be downloaded to the `/coco` directory.
+
+### Training process
+Training the SSD model is implemented in the `main.py` script. 
+
+By default, training is running for 65 epochs. Because evaluation is relatively time consuming,
+it is not running every epoch. With default settings, evaluation is executed after epochs:
+21, 31, 37, 42, 48, 53, 59, 64. The model is evaluated using pycocotools distributed with
+the COCO dataset.
+ Which epochs should be evaluated can be reconfigured with argument `--evaluation`.
+
+To run training with Tensor Cores, use the `--fp16` flag when running the `main.py` script.
+The flag `--save` flag enables storing checkpoints after each epoch under `./models/epoch_*.pt`.
+
+#### Data preprocessing
+Before we feed data to the model, both during training and inference, we perform:
+*	Normalization
+*	Encoding bounding boxes
+*   Resize to 300x300
+
+#### Data augmentation
+During training we perform the following augmentation techniques:
+*	Random crop
+*	Random horizontal flip
+*	Color jitter
+
+
+
+### Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.  Using [mixed precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+ 
+Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP),  library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval,
+while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients.
+In PyTorch, loss scaling can be easily applied by using scale_loss() method provided by amp. The scaling value to be used can be dynamic or fixed.
+
+For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.
+
+To enable mixed precision, you can:
+- Import AMP from APEX, for example:
+
+  `from apex import amp`
+- Initialize an AMP handle, for example: 
+
+  `amp_handle = amp.init(enabled=True, verbose=True)`
+- Wrap your optimizer with the AMP handle, for example:
+
+  `optimizer = amp_handle.wrap_optimizer(optimizer)`
+- Scale loss before backpropagation (assuming loss is stored in a variable called losses)
+  - Default backpropagate for FP32:
+
+    `losses.backward()`
+  - Scale loss and backpropagate with AMP:
+
+    ```
+    with optimizer.scale_loss(losses) as scaled_losses:
+       scaled_losses.backward()
+    ```
+
+For information about:
+- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+
+
+
+
+
+
+
+## Benchmarking
+The following section shows how to run benchmarks measuring the model performance in training and inference modes.
+
+### Training performance benchmark
+Training benchmark was run in various scenarios on V100 16G GPU. For each scenario, batch size was set to 32. The benchmark does not require a checkpoint from a fully trained model.
+
+To benchmark training, run:
+```
+python -m torch.distributed.launch --nproc_per_node={NGPU} \
+       main.py --batch-size {bs} \
+               --mode benchmark-training \
+               --benchmark-warmup 100 \
+               --benchmark-iterations 200 \
+               {fp16} \
+               --data {data}
+```
+Where the `{NGPU}` selects number of GPUs used in benchmark, the `{bs}` is the desired batch size, the `{fp16}` is set to `--fp16` if you want to benchmark training with tensor cores, and the `{data}` is the location of the COCO 2017 dataset.
+
+Benchmark warmup is specified to omit first iterations of first epoch. Benchmark iterations is number of iterations used to measure performance.
+
+### Inference performance benchmark
+Inference benchmark was run on 1x V100 16G GPU.  To benchmark inference, run:
+```
+python main.py --eval-batch-size {bs} \
+               --mode benchmark-inference \
+               --benchmark-warmup 100 \
+               --benchmark-iterations 200 \
+               {fp16} \
+               --data {data}
+```
+Where the `{bs}` is the desired batch size, the `{fp16}` is set to `--fp16` if you want to benchmark inference with Tensor Cores, and the `{data}` is the location of the COCO 2017 dataset.
+
+Benchmark warmup is specified to omit first iterations of first epoch. Benchmark iterations is number of iterations used to measure performance.
+
+## Results
+
+The following sections provide details on how we achieved our performance and accuracy in training and inference.
+
+### Training accuracy results
+Our results were obtained by running the `./examples/SSD300_FP{16,32}_{1,4,8}GPU.sh`
+script in the pytorch-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Batch was set to size best utilizing GPU memory. For FP32 precision, batch size is 32, for mixed precision batch size is 64
+
+| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** |
+|:------------------:|:------------------------:|:-------------------------------------:|:------------:|:---------------------------:|
+| 1                  | 0.2494                   | 10h 39min                             | 0.2483       | 21h 40min                   |
+| 4                  | 0.2495                   | 2h 53min                              | 0.2478       | 5h 52min                    |
+| 8                  | 0.2489                   | 1h 31min                              | 0.2475       | 2h 54min                    |
+
+
+Here are example graphs of FP32 and FP16 training on 8 GPU configuration:
+
+![TrainingLoss](./img/training_loss.png)
+
+![ValidationAccuracy](./img/validation_accuracy.png)
+
+### Training performance results
+
+Our results were obtained by running the `main.py` script with the
+`--mode benchmark-training` flag in the pytorch-19.03-py3 NGC container on NVIDIA DGX-1 with V100 16G GPUs.
+
+| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s (median)** | **FP32 img/s (median)** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** |
+|:------------------:|:----------------------:|:----------------------------------:|:-----------------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|
+| 1                  | 32                     |  217.052                           |  102.495                | 2.12                              | 1.00                                            | 1.00                                 |
+| 4                  | 32                     |  838.457                           |  397.797                | 2.11                              | 3.86                                            | 3.88                                 |
+| 8                  | 32                     | 1639.843                           |  789.695                | 2.08                              | 7.56                                            | 7.70                                 |
+
+To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+### Inference performance results
+
+Our results were obtained by running the `main.py` script with `--mode benchmark-inference` flag in the pytorch-19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs.
+
+| **Batch size** | **Mixed precision img/s (median)** | **FP32 img/s (median)** |
+|:--------------:|:----------------------------------:|:-----------------------:|
+|              2 |                            163.12  |                147.91   |
+|              4 |                            296.60  |                201.62   |
+|              8 |                            412.52  |                228.16   |
+|             16 |                            470.10  |                280.57   |
+|             32 |                            520.54  |                302.43   |
+
+To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+## Changelog
+
+March 2019
+ * Initial release
+
+## Known issues
+There are no known issues with this model.
diff --git a/PyTorch/Detection/SSD/__init__.py b/PyTorch/Detection/SSD/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu b/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu
new file mode 100644
index 00000000..b5a2f03f
--- /dev/null
+++ b/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu
@@ -0,0 +1,440 @@
+/******************************************************************************
+*
+* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*
+
+ ******************************************************************************/
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THCNumerics.cuh>
+#include <THC/THC.h>
+
+#include <cuda.h>
+
+//#define DEBUG
+
+// calculate the IoU of a single box against another box
+__device__
+float calc_single_iou(const float4 b1, const float4 b2) {
+  // (lt), (rb)
+  float l = max(b1.x, b2.x);
+  float t = max(b1.y, b2.y);
+  float r = min(b1.z, b2.z);
+  float b = min(b1.w, b2.w);
+
+  float first = (r - l);
+  first = (first < 0) ? 0 : first;
+  float second = (b - t);
+  second = (second < 0) ? 0 : second;
+
+  float intersection = first * second;
+
+  float area1 = (b1.w - b1.y) * (b1.z - b1.x);
+  float area2 = (b2.w - b2.y) * (b2.z - b2.x);
+
+  return intersection / (area1 + area2 - intersection);
+}
+
+__global__
+// boxes1 : [N x 4]
+// boxes2 : [M x 4]
+//   ious : [N x M]
+void calc_ious_kernel(const int N_img, const float4 *box1, const int *box1_offsets,
+                      const int M, const float4 *boxes2, float *ious) {
+
+  // launch N_img blocks
+  const int img = blockIdx.x;
+
+  // each block, i will run over the box1_N[i] source and M target boxes
+  // generating box1_N[i] x M outputs
+
+  // alias to start of boxes for this image
+  const float4 *b1 = &box1[box1_offsets[img]];
+
+  if (threadIdx.x == 0) {
+    //printf("offset for img %d : %d\n", img, box1_offsets[img]);
+  }
+
+  // number of boxes for this image from offsets
+  int N = box1_offsets[img+1] - box1_offsets[img];
+
+  for (int i = 0; i < N; ++i) {
+    // if (threadIdx.x == 0) printf("i : %d\n", i);
+    const float4 source = b1[i];
+    // for each source, loop over targets
+    for (int j = threadIdx.x; j < M; j += blockDim.x) {
+      const float4 target = boxes2[j];
+
+      float iou = calc_single_iou(source, target);
+
+      // store the calculated IoU in the correct spot
+      int out_idx = box1_offsets[img] * M + i * M + j;
+      ious[out_idx] = iou;
+
+    }
+  }
+}
+
+__device__
+void reduce_val_idx(int N, volatile float *vals, volatile int *idx) {
+  // naive: single thread for now
+  if (threadIdx.x == 0) {
+    float max_val = vals[0];
+    int max_idx = idx[0];
+
+    for (int i = 1; i < N; ++i) {
+      if (vals[i] > max_val) {
+        max_val = vals[i];
+        max_idx = idx[i];
+      }
+    }
+
+    vals[0] = max_val;
+    idx[0] = max_idx;
+  }
+}
+
+/**
+ * perform remaining parts, storing temporary values in global workspace
+ * workspace needs N_img * M values, each of 8 bytes (float, int)
+ **/
+template <int BLOCK_SIZE, int MAX_BBOXES_PER_BLOCK>
+__global__
+void encode(const int N_img, const float4 *bbox_in, const long *labels_in, const int *offsets,
+            const int M, const float4 *dboxes, // const float *ious,
+            const float criteria, uint8_t *workspace, float4 *bbox_out, long *label_out) {
+
+  // Each block will take a single image's IoU set
+  const int img = blockIdx.x;
+
+  // shared memory for intermediate results
+  __shared__ volatile float best_bbox_iou_tmp[BLOCK_SIZE];
+  __shared__ volatile int best_bbox_idx_tmp[BLOCK_SIZE];
+
+  // shared memory for final best_bbox_{iou, idx} values
+  __shared__ volatile float best_bbox_iou[MAX_BBOXES_PER_BLOCK];
+  __shared__ volatile int best_bbox_idx[MAX_BBOXES_PER_BLOCK];
+
+  // index into the global workspace - each image needs (float + int) * M values
+  volatile float *best_dbox_iou = (float *)&workspace[img * M * 8];
+  volatile int *best_dbox_idx = (int *)&workspace[img * M * 8 + M * 4];
+
+  // number of input bboxes for this image
+  const int N_rows = offsets[img+1] - offsets[img];
+
+  // Check for potential crash
+  assert(N_rows <= MAX_BBOXES_PER_BLOCK);
+#ifdef DEBUG
+  if (threadIdx.x == 0)
+    printf("N rows: %d %d to %d (%p - %p)\n", N_rows, offsets[img], offsets[img+1], best_dbox_iou, best_dbox_idx);
+#endif
+
+  for (int i = threadIdx.x; i < MAX_BBOXES_PER_BLOCK; i += blockDim.x) {
+    best_bbox_iou[i] = -FLT_MAX;
+    best_bbox_idx[i] = -1;
+  }
+  __syncthreads();
+
+  // loop serially over the rows of the IoU set that correspond to this image
+  int row_num = 0;
+  for (int i = offsets[img]; i < offsets[img+1]; ++i) {
+    // reset shmem tallies
+    best_bbox_iou_tmp[threadIdx.x] = -FLT_MAX;
+    best_bbox_idx_tmp[threadIdx.x] = -1;
+
+    // index into the input buffer
+    // const float *row = &ious[i * M];
+    const float4 input_bbox = bbox_in[i];
+#ifdef DEBUG
+    if (threadIdx.x == 0)
+      printf("%d - %p\n", img, &input_bbox);
+#endif
+
+    // loop by threads over the columns
+    for (int j = threadIdx.x; j < M; j += blockDim.x) {
+
+      // check and store new max if necessary
+      const float4 input_dbox = dboxes[j];
+      // float new_val = row[j];
+      float new_val = calc_single_iou(input_bbox, input_dbox);
+
+      // handle per-row max in shared memory
+      if (new_val > best_bbox_iou_tmp[threadIdx.x]) {
+        best_bbox_iou_tmp[threadIdx.x] = new_val;
+        best_bbox_idx_tmp[threadIdx.x] = j;
+      }
+
+      // handle per-col max in global workspace
+      if (new_val > best_dbox_iou[j]) {
+        best_dbox_iou[j] = new_val;
+        best_dbox_idx[j] = row_num;
+
+#ifdef DEBUG
+        assert(best_dbox_idx[j] >= 0);
+        assert(best_dbox_idx[j] < N_rows);
+#endif
+      }
+    }
+
+    // Now we have all the values for this row -- reduce
+    __syncthreads();
+
+    // reduce - output is in max_{val, idx}_row[0]
+    reduce_val_idx(blockDim.x, best_bbox_iou_tmp, best_bbox_idx_tmp);
+#ifdef DEBUG
+    __syncthreads();
+#endif
+
+
+    // store output for row i
+    if (threadIdx.x == 0) {
+      best_bbox_iou[row_num] = best_bbox_iou_tmp[0];
+      best_bbox_idx[row_num] = best_bbox_idx_tmp[0];
+
+#ifdef DEBUG
+      assert(best_bbox_idx[row_num] >= 0);
+      assert(best_bbox_idx[row_num] < M);
+#endif
+    }
+    __syncthreads();
+
+    // keep track of _local_ row
+    row_num++;
+  }
+
+#ifdef DEBUG
+  if (threadIdx.x == 0) {
+    for (int i = 0; i < N_rows; ++i) {
+      printf("%d - row : %d : best bbox_idx: %d\n", img, i, best_bbox_idx[i]);
+    }
+  }
+#endif
+
+#ifdef DEBUG
+  // make sure all best_bbox_{iou, val} are seen by everyone
+  __syncthreads();
+#endif
+  // At this point we have the maximum values & indices for both bbox and dbox
+  /*
+        best_dbox_ious.index_fill_(0, best_bbox_idx, 2.0)
+
+        idx = torch.arange(0, best_bbox_idx.size(0), dtype=torch.int64)
+        best_dbox_idx[best_bbox_idx[idx]] = idx
+  */
+  for (int i = threadIdx.x; i < N_rows; i += blockDim.x) {
+    int idx = best_bbox_idx[i];
+
+#ifdef DEBUG
+    assert(idx < M);
+    assert(idx >= 0);
+#endif
+
+    best_dbox_iou[idx] = 2.;
+    best_dbox_idx[idx] = i;
+#ifdef DEBUG
+    printf("%d - set best dbox_idx[%d] to %d\n", img, best_bbox_idx[i], i);
+#endif
+  }
+
+  /**
+        # filter IoU > 0.5
+        masks = best_dbox_ious > criteria
+        labels_out = torch.zeros(self.nboxes, dtype=torch.long)
+        #print(maxloc.shape, labels_in.shape, labels_out.shape)
+        labels_out[masks] = labels_in[best_dbox_idx[masks]]
+        bboxes_out = self.dboxes.clone()
+        bboxes_out[masks, :] = bboxes_in[best_dbox_idx[masks], :]
+        # Transform format to xywh format
+        x, y, w, h = 0.5*(bboxes_out[:, 0] + bboxes_out[:, 2]), \
+                     0.5*(bboxes_out[:, 1] + bboxes_out[:, 3]), \
+                     -bboxes_out[:, 0] + bboxes_out[:, 2], \
+                     -bboxes_out[:, 1] + bboxes_out[:, 3]
+        bboxes_out[:, 0] = x
+        bboxes_out[:, 1] = y
+        bboxes_out[:, 2] = w
+        bboxes_out[:, 3] = h
+        return bboxes_out, labels_out
+  **/
+  __syncthreads();
+  for (int i = threadIdx.x; i < M; i += blockDim.x) {
+    // offset into output arrays: M values per image
+    // int output_idx = offsets[img] * M + i;
+    int output_idx = img * M + i;
+
+    // reset output labels to background
+    // NOTE: bbox_out is already cloned from dbox outside of this kernel
+    label_out[output_idx] = 0;
+
+    // Filter IoU > 0.5
+    bool mask = best_dbox_iou[i] > criteria;
+
+    float4 bbox = bbox_out[output_idx];
+    // copy some labels and bboxes
+    if (mask) {
+      // copy label
+#ifdef DEBUG
+      printf("%d : label: local input idx: %d, value: %d\n", i, best_dbox_idx[i], labels_in[offsets[img] + best_dbox_idx[i]]);
+      // printf("%d : label: local input idx: %d, value: %d\n", i, best_dbox_idx[i], labels_in[offsets[img] + i]);
+#endif
+      label_out[output_idx] = labels_in[offsets[img] + best_dbox_idx[i]];
+
+      // grab original box
+      bbox = bbox_in[offsets[img] + best_dbox_idx[i]];
+#ifdef DEBUG
+      printf("mask %d : %d : %f %f %f %f\n", i, best_dbox_idx[i], bbox.x, bbox.y, bbox.z, bbox.w);
+#endif
+    }
+
+    // transfer to xywh
+    float4 bbox_tmp;
+    bbox_tmp.x = 0.5 * (bbox.x + bbox.z);
+    bbox_tmp.y = 0.5 * (bbox.y + bbox.w);
+    bbox_tmp.z = bbox.z - bbox.x;
+    bbox_tmp.w = bbox.w - bbox.y;
+
+    // write out
+    bbox_out[output_idx] = bbox_tmp;
+  }
+}
+
+/**
+    def encode(self, bboxes_in, labels_in, criteria = 0.5):
+
+        ious = calc_iou_tensor(bboxes_in, self.dboxes)
+        best_dbox_ious, best_dbox_idx = ious.max(dim=0)
+        best_bbox_ious, best_bbox_idx = ious.max(dim=1)
+
+        # set best ious 2.0
+        best_dbox_ious.index_fill_(0, best_bbox_idx, 2.0)
+
+        idx = torch.arange(0, best_bbox_idx.size(0), dtype=torch.int64)
+        best_dbox_idx[best_bbox_idx[idx]] = idx
+
+        # filter IoU > 0.5
+        masks = best_dbox_ious > criteria
+        labels_out = torch.zeros(self.nboxes, dtype=torch.long)
+        #print(maxloc.shape, labels_in.shape, labels_out.shape)
+        labels_out[masks] = labels_in[best_dbox_idx[masks]]
+        bboxes_out = self.dboxes.clone()
+        bboxes_out[masks, :] = bboxes_in[best_dbox_idx[masks], :]
+        # Transform format to xywh format
+        x, y, w, h = 0.5*(bboxes_out[:, 0] + bboxes_out[:, 2]), \
+                     0.5*(bboxes_out[:, 1] + bboxes_out[:, 3]), \
+                     -bboxes_out[:, 0] + bboxes_out[:, 2], \
+                     -bboxes_out[:, 1] + bboxes_out[:, 3]
+        bboxes_out[:, 0] = x
+        bboxes_out[:, 1] = y
+        bboxes_out[:, 2] = w
+        bboxes_out[:, 3] = h
+        return bboxes_out, labels_out
+**/
+std::vector<at::Tensor> box_encoder(const int N_img,
+                                    const at::Tensor& bbox_input,
+                                    const at::Tensor& bbox_offsets,
+                                    const at::Tensor& labels_input,
+                                    const at::Tensor& dbox,
+                                    float criteria) {
+  // Check everything is on the device
+  AT_ASSERTM(bbox_input.type().is_cuda(), "bboxes must be a CUDA tensor");
+  AT_ASSERTM(bbox_offsets.type().is_cuda(), "bbox offsets must be a CUDA tensor");
+  AT_ASSERTM(labels_input.type().is_cuda(), "labels must be a CUDA tensor");
+  AT_ASSERTM(dbox.type().is_cuda(), "dboxes must be a CUDA tensor");
+
+  // Check at least offsets, bboxes and labels are consistent
+  // Note: offsets is N+1 vs. N for labels
+  AT_ASSERTM(N_img + 1 == bbox_offsets.numel(), "must have N_img+1 offsets");
+
+
+  auto num_bbox_total = bbox_offsets[bbox_offsets.numel()-1].item<int>();
+#ifdef DEBUG
+  printf("%d : bboxes: %d\n", (int)bbox_offsets.numel(), num_bbox_total);
+#endif
+  AT_ASSERTM(num_bbox_total <= 2048, "total num bboxes must be <= 2048");
+
+  AT_ASSERTM(bbox_input.size(0) == labels_input.size(0), "bbox and labels must have same leading dimension");
+
+  const int N = bbox_input.size(0);
+  const int M = dbox.size(0);
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  // allocate final outputs (known size)
+#ifdef DEBUG
+  printf("%d x %d\n", N_img * M, 4);
+  // at::Tensor bbox_out = dbox.type().tensor({N_img * M, 4});
+  printf("allocating %lu bytes for output labels\n", N_img*M*sizeof(long));
+#endif
+  at::Tensor labels_out = at::empty({N_img * M}, labels_input.options());
+  THCudaCheck(cudaGetLastError());
+
+  // copy default boxes to outputs
+#ifdef DEBUG
+  printf("allocating %lu bytes for output bboxes\n", N_img*M*4*sizeof(float));
+#endif
+  at::Tensor bbox_out = dbox.repeat({N_img, 1});
+  THCudaCheck(cudaGetLastError());
+
+  // need to allocate some workspace
+#ifdef DEBUG
+  printf("allocating %lu bytes for workspace\n", 8*M*N_img);
+#endif
+  // at::Tensor workspace = at::CUDA(at::kByte).zeros({8 * M * N_img});
+  at::Tensor workspace = at::zeros({8 * M * N_img}, at::CUDA(at::kByte));
+  THCudaCheck(cudaGetLastError());
+
+  // Encode the inputs
+  const int THREADS_PER_BLOCK = 256;
+  encode<THREADS_PER_BLOCK, 256><<<N_img, THREADS_PER_BLOCK, 0, stream.stream()>>>(N_img,
+                      (float4*)bbox_input.data<float>(),
+                      labels_input.data<long>(),
+                      bbox_offsets.data<int>(),
+                      M,
+                      (float4*)dbox.data<float>(),
+                      criteria,
+                      workspace.data<uint8_t>(),
+                      (float4*)bbox_out.data<float>(),
+                      labels_out.data<long>());
+
+  THCudaCheck(cudaGetLastError());
+  return {bbox_out, labels_out};
+}
+
+at::Tensor calc_ious(const int N_img,
+                     const at::Tensor& boxes1,
+                     const at::Tensor& boxes1_offsets,
+                     const at::Tensor& boxes2) {
+
+  const int N = boxes1.size(0);
+  const int M = boxes2.size(0);
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  // at::Tensor ious = at::CUDA(at::kFloat).zeros({N, M});
+  // at::Tensor ious = at::ones(at::CUDA(at::kFloat), {N, M});
+  at::Tensor ious = at::empty({N, M}, boxes1.options());
+
+  // Get IoU of all source x default box pairs
+  calc_ious_kernel<<<N_img, 256, 0, stream.stream()>>>(
+                        N_img,
+                        (float4*)boxes1.data<float>(),
+                        boxes1_offsets.data<int>(),
+                        M,
+                        (float4*)boxes2.data<float>(),
+                        ious.data<float>());
+
+  THCudaCheck(cudaGetLastError());
+  return ious;
+}
diff --git a/PyTorch/Detection/SSD/csrc/interface.cpp b/PyTorch/Detection/SSD/csrc/interface.cpp
new file mode 100644
index 00000000..0ca51e3f
--- /dev/null
+++ b/PyTorch/Detection/SSD/csrc/interface.cpp
@@ -0,0 +1,81 @@
+/******************************************************************************
+*
+* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*
+
+ ******************************************************************************/
+
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+
+#include <torch/extension.h>
+#include <ATen/ATen.h>
+
+
+namespace py = pybind11;
+
+// Box encoder
+std::vector<at::Tensor> box_encoder(const int N_img,
+                                    const at::Tensor& bbox_input,
+                                    const at::Tensor& bbox_offsets,
+                                    const at::Tensor& labels_input,
+                                    const at::Tensor& dbox,
+                                    const float criteria = 0.5);
+
+std::vector<at::Tensor> random_horiz_flip(
+                             at::Tensor& img,
+                             at::Tensor& bboxes,
+                             const at::Tensor& bbox_offsets,
+                             const float p,
+                             const bool nhwc);
+
+// Fused color jitter application
+// ctm [4,4], img [H, W, C]
+py::array_t<float> apply_transform(int H, int W, int C, py::array_t<float> img, py::array_t<float> ctm) {
+  auto img_buf = img.request();
+  auto ctm_buf = ctm.request();
+
+  // printf("H: %d, W: %d, C: %d\n", H, W, C);
+  py::array_t<float> result{img_buf.size};
+  auto res_buf = result.request();
+
+  float *img_ptr = (float *)img_buf.ptr;
+  float *ctm_ptr = (float *)ctm_buf.ptr;
+  float *res_ptr = (float *)res_buf.ptr;
+
+  for (int h = 0; h < H; ++h) {
+    for (int w = 0; w < W; ++w) {
+      float *ptr = &img_ptr[h * W * C + w * C];
+      float *out_ptr = &res_ptr[h * W * C + w * C];
+      // manually unroll over C
+      out_ptr[0] = ctm_ptr[0] * ptr[0] + ctm_ptr[1] * ptr[1] + ctm_ptr[2] * ptr[2] + ctm_ptr[3];
+      out_ptr[1] = ctm_ptr[4] * ptr[0] + ctm_ptr[5] * ptr[1] + ctm_ptr[6] * ptr[2] + ctm_ptr[7];
+      out_ptr[2] = ctm_ptr[8] * ptr[0] + ctm_ptr[9] * ptr[1] + ctm_ptr[10] * ptr[2] + ctm_ptr[11];
+    }
+  }
+
+  result.resize({H, W, C});
+
+  return result;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  // batched box encoder
+  m.def("box_encoder", &box_encoder, "box_encoder");
+  m.def("random_horiz_flip", &random_horiz_flip, "random_horiz_flip");
+  // Apply fused color jitter
+  m.def("apply_transform", &apply_transform, "apply_transform");
+}
diff --git a/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu b/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu
new file mode 100644
index 00000000..6964bfee
--- /dev/null
+++ b/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu
@@ -0,0 +1,165 @@
+/******************************************************************************
+*
+* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*
+
+ ******************************************************************************/
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THCNumerics.cuh>
+#include <THC/THC.h>
+
+#include <cuda.h>
+
+/**
+ * Each block will handle one channel of each image
+ **/
+template <typename T>
+__global__
+void HorizFlipImagesAndBoxes(
+                             const int N,
+                             const int C,
+                             const int H,
+                             const int W,
+                             const T* img_in,
+                             float* bboxes,
+                             const int* offsets,
+                             const float p,
+                             const float* flip,
+                             T* img_out,
+                             const bool nhwc) {
+  // early return if not flipping
+  if (flip[blockIdx.x] < p) return;
+
+  // pointer offset into images
+  const int img_offset = blockIdx.x * C * H * W;
+  const T* img = &img_in[img_offset];
+  T* img_o = &img_out[img_offset];
+
+  // flip bboxes
+  auto bbox_offset_begin = offsets[blockIdx.x];
+  auto bbox_offset_end   = offsets[blockIdx.x + 1];
+  auto num_bboxes = bbox_offset_end - bbox_offset_begin;
+
+  const int thread_idx = threadIdx.y * blockDim.x + threadIdx.x;
+
+  // bboxes in ltrb format, scaled to [0, 1]
+  for (int i = thread_idx; i < num_bboxes; i += blockDim.x * blockDim.y) {
+    float *bbox = &bboxes[(bbox_offset_begin + thread_idx) * 4];
+    // Could do this inplace, but not register constrained
+    auto bbox_0 = bbox[0];
+    auto bbox_2 = bbox[2];
+    bbox[0] = 1. - bbox_2;
+    bbox[2] = 1. - bbox_0;
+  }
+
+  if (nhwc) {
+    // loop over float3 pixels, handle 3 values / thread
+    for (int h = threadIdx.y; h < H; h += blockDim.y) {
+      for (int w = threadIdx.x; w < W; w += blockDim.x) {
+        const T* img_hw = &img[h * W * C + w * C];
+        T * img_out_hw = &img_o[h * W * C + (W - 1 - w) * C];
+
+        for (int c = 0; c < C; ++c) {
+          img_out_hw[c] = img_hw[c];
+        }
+      }
+    }
+  } else {
+    // loop over channels
+    for (int c = 0; c < C; ++c) {
+      const T* img_c = &img[c * H * W];
+      T *img_out_c = &img_o[c * H * W];
+
+      // handle tiles of (h, w) at a time
+      for (int h = threadIdx.y; h < H; h += blockDim.y) {
+        for (int w = threadIdx.x; w < W; w += blockDim.x) {
+          const int input_idx = h * W + w;
+          const int output_idx = h * W + (W - 1 - w);
+
+
+          img_out_c[output_idx] = img_c[input_idx];
+        }
+      }
+    }
+  }
+}
+
+/**
+  * Take images and their bboxes, randomly flip on horizontal axis
+  * In/Out: img: NCHW tensor of N, C-channel images of constant (H, W)
+  * In/Out: bboxes: [N_i, 4] tensor of original bboxes in ltrb format
+  * In: bbox_offsets: [N] offset values into bboxes
+  * In: p \in [0, 1): probability of flipping each (img, bbox) pair
+  * In: nhwc: Tensor in NHWC format
+  * ----
+  * Note: allocate temp memory, but effectively do this inplace
+  */
+std::vector<at::Tensor> random_horiz_flip(
+                             at::Tensor& img,
+                             at::Tensor& bboxes,
+                             const at::Tensor& bbox_offsets,
+                             const float p,
+                             const bool nhwc) {
+  // dimensions
+  const int N = img.size(0);
+  int C, H, W;
+  if (nhwc) {
+    C = img.size(3);
+    H = img.size(1);
+    W = img.size(2);
+
+  } else {
+    C = img.size(1);
+    H = img.size(2);
+    W = img.size(3);
+  }
+
+  assert(img.type().is_cuda());
+  assert(bboxes.type().is_cuda());
+  assert(bbox_offsets.type().is_cuda());
+
+  // printf("%d %d %d %d\n", N, C, H, W);
+  // Need temp storage of size img
+  at::Tensor tmp_img = img.clone();
+  at::Tensor flip = at::zeros({N}, at::CUDA(at::kFloat)).uniform_(0., 1.);
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      img.type(),
+      "HorizFlipImagesAndBoxes",
+      [&] {
+        HorizFlipImagesAndBoxes<scalar_t><<<N, dim3(16, 16), 0, stream.stream()>>>(
+          N,
+          C,
+          H,
+          W,
+          img.data<scalar_t>(),
+          bboxes.data<float>(),
+          bbox_offsets.data<int>(),
+          p,
+          flip.data<float>(),
+          tmp_img.data<scalar_t>(),
+          nhwc);
+        THCudaCheck(cudaGetLastError());
+      });
+
+  // copy tmp_img -> img
+  // img = tmp_img;
+
+  return {tmp_img, bboxes};
+}
+
diff --git a/PyTorch/Detection/SSD/download_dataset.sh b/PyTorch/Detection/SSD/download_dataset.sh
new file mode 100755
index 00000000..7e7c73d7
--- /dev/null
+++ b/PyTorch/Detection/SSD/download_dataset.sh
@@ -0,0 +1,8 @@
+# Get COCO 2017 data sets
+COCO_DIR=${1:-"/coco"}
+dir=$(pwd)
+mkdir $COCO_DIR; cd $COCO_DIR
+curl -O http://images.cocodataset.org/zips/train2017.zip; unzip train2017.zip
+curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
+curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
+cd $dir
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP16_1GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP16_1GPU.sh
new file mode 100644
index 00000000..e8f77889
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP16_1GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP16 on 1 GPUs using 64 batch size
+# Usage bash SSD300_FP16_1GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python $1/main.py --backbone resnet50 --warmup 300 --bs 64 --fp16 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP16_4GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP16_4GPU.sh
new file mode 100644
index 00000000..c626f0e0
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP16_4GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP16 on 4 GPUs using 256 batch size (64 per GPU)
+# Usage ./SSD300_FP16_4GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python -m torch.distributed.launch --nproc_per_node=4 $1/main.py --backbone resnet50 --warmup 300 --bs 64 --fp16 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP16_8GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP16_8GPU.sh
new file mode 100644
index 00000000..a4c18ec4
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP16_8GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP16 on 8 GPUs using 512 batch size (64 per GPU)
+# Usage ./SSD300_FP16_8GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python -m torch.distributed.launch --nproc_per_node=8 $1/main.py --backbone resnet50 --warmup 300 --bs 64 --fp16 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP16_EVAL.sh b/PyTorch/Detection/SSD/examples/SSD300_FP16_EVAL.sh
new file mode 100644
index 00000000..f99bd93d
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP16_EVAL.sh
@@ -0,0 +1,4 @@
+# This script evaluates SSD300 model in FP16 using 32 batch size on 1 GPU
+# Usage: ./SSD300_FP16_EVAL.sh <path to this repository> <path to dataset> <path to checkpoint> <additional flags>
+
+python $1/main.py --backbone resnet50 --fp16 --ebs 32 --data $2 --mode evaluation --checkpoint $3 ${@:4}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP16_INFERENCE_BENCHMARK.sh b/PyTorch/Detection/SSD/examples/SSD300_FP16_INFERENCE_BENCHMARK.sh
new file mode 100644
index 00000000..e070132b
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP16_INFERENCE_BENCHMARK.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 inference benchmark in FP16 on 1 GPU with 64 batch size
+# Usage bash SSD300_FP16_INFERENCE_BENCHMARK.sh <path to this repository> <path to dataset> <additional flags>
+
+python $1/main.py --backbone resnet50 --mode benchmark-inference --bs 64 --fp16 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP32_1GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP32_1GPU.sh
new file mode 100644
index 00000000..ea5240a7
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP32_1GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP32 on 1 GPUs using 32 batch size
+# Usage ./SSD300_FP32_1GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python $1/main.py --backbone resnet50 --bs 32 --warmup 300 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP32_4GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP32_4GPU.sh
new file mode 100644
index 00000000..29159557
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP32_4GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP32 on 4 GPUs using 128 batch size (32 per GPU)
+# Usage ./SSD300_FP32_4GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python -m torch.distributed.launch --nproc_per_node=4 $1/main.py --backbone resnet50 --warmup 300 --bs 32 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP32_8GPU.sh b/PyTorch/Detection/SSD/examples/SSD300_FP32_8GPU.sh
new file mode 100644
index 00000000..441efd9d
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP32_8GPU.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 training in FP32 on 8 GPUs using 256 batch size (32 per GPU)
+# Usage ./SSD300_FP32_8GPU.sh <path to this repository> <path to dataset> <additional flags>
+
+python -m torch.distributed.launch --nproc_per_node=8 $1/main.py --backbone resnet50 --warmup 300 --bs 32 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP32_EVAL.sh b/PyTorch/Detection/SSD/examples/SSD300_FP32_EVAL.sh
new file mode 100644
index 00000000..b3179c1e
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP32_EVAL.sh
@@ -0,0 +1,4 @@
+# This script evaluates SSD300 model in FP32 using 32 batch size on 1 GPU
+# Usage: ./SSD300_FP32_EVAL.sh <path to this repository> <path to dataset> <path to checkpoint> <additional flags>
+
+python $1/main.py --backbone resnet50 --ebs 32 --data $2 --mode evaluation --checkpoint $3 ${@:4}
diff --git a/PyTorch/Detection/SSD/examples/SSD300_FP32_INFERENCE_BENCHMARK.sh b/PyTorch/Detection/SSD/examples/SSD300_FP32_INFERENCE_BENCHMARK.sh
new file mode 100644
index 00000000..e7c0fa86
--- /dev/null
+++ b/PyTorch/Detection/SSD/examples/SSD300_FP32_INFERENCE_BENCHMARK.sh
@@ -0,0 +1,4 @@
+# This script launches SSD300 inference benchmark in FP32 on 1 GPU with 64 batch size
+# Usage bash SSD300_FP32_INFERENCE_BENCHMARK.sh <path to this repository> <path to dataset> <additional flags>
+
+python $1/main.py --backbone resnet50 --warmup 300 --mode benchmark-inference --bs 32 --data $2 ${@:3}
diff --git a/PyTorch/Detection/SSD/img/training_loss.png b/PyTorch/Detection/SSD/img/training_loss.png
new file mode 100644
index 0000000000000000000000000000000000000000..d73f51556932ddfca858a5995f2ea75f0b2cc7cf
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diff --git a/PyTorch/Detection/SSD/main.py b/PyTorch/Detection/SSD/main.py
new file mode 100644
index 00000000..eea825c6
--- /dev/null
+++ b/PyTorch/Detection/SSD/main.py
@@ -0,0 +1,240 @@
+import os
+import time
+from argparse import ArgumentParser
+import torch
+import numpy as np
+from torch.optim.lr_scheduler import MultiStepLR
+import torch.utils.data.distributed
+
+from src.model import SSD300, Loss
+from src.utils import dboxes300_coco, Encoder
+from src.logger import Logger, BenchLogger
+from src.evaluate import evaluate
+from src.train import train_loop, tencent_trick, load_checkpoint, benchmark_train_loop, benchmark_inference_loop
+from src.data import get_train_loader, get_val_dataset, get_val_dataloader, get_coco_ground_truth
+
+# Apex imports
+try:
+    from apex.parallel.LARC import LARC
+    from apex import amp
+    from apex.parallel import DistributedDataParallel as DDP
+    from apex.fp16_utils import *
+except ImportError:
+    raise ImportError("Please install APEX from https://github.com/nvidia/apex")
+
+def generate_mean_std(args):
+    mean_val = [0.485, 0.456, 0.406]
+    std_val = [0.229, 0.224, 0.225]
+
+    mean = torch.tensor(mean_val).cuda()
+    std = torch.tensor(std_val).cuda()
+
+    view = [1, len(mean_val), 1, 1]
+
+    mean = mean.view(*view)
+    std = std.view(*view)
+
+    if args.fp16:
+        mean = mean.half()
+        std = std.half()
+
+    return mean, std
+
+
+def make_parser():
+    parser = ArgumentParser(description="Train Single Shot MultiBox Detector"
+                                        " on COCO")
+    parser.add_argument('--data', '-d', type=str, default='/coco', required=True,
+                        help='path to test and training data files')
+    parser.add_argument('--epochs', '-e', type=int, default=65,
+                        help='number of epochs for training')
+    parser.add_argument('--batch-size', '--bs', type=int, default=32,
+                        help='number of examples for each iteration')
+    parser.add_argument('--eval-batch-size', '--ebs', type=int, default=32,
+                        help='number of examples for each evaluation iteration')
+    parser.add_argument('--no-cuda', action='store_true',
+                        help='use available GPUs')
+    parser.add_argument('--seed', '-s', type=int,
+                        help='manually set random seed for torch')
+    parser.add_argument('--checkpoint', type=str, default=None,
+                        help='path to model checkpoint file')
+    parser.add_argument('--save', action='store_true',
+                        help='save model checkpoints')
+    parser.add_argument('--mode', type=str, default='training',
+                        choices=['training', 'evaluation', 'benchmark-training', 'benchmark-inference'])
+    parser.add_argument('--evaluation', nargs='*', type=int, default=[21, 31, 37, 42, 48, 53, 59, 64],
+                        help='epochs at which to evaluate')
+    parser.add_argument('--multistep', nargs='*', type=int, default=[43, 54],
+                        help='epochs at which to decay learning rate')
+
+    # Hyperparameters
+    parser.add_argument('--learning-rate', '--lr', type=float, default=2.6e-3,
+                        help='learning rate')
+    parser.add_argument('--momentum', '-m', type=float, default=0.9,
+                        help='momentum argument for SGD optimizer')
+    parser.add_argument('--weight-decay', '--wd', type=float, default=0.0005,
+                        help='momentum argument for SGD optimizer')
+
+    parser.add_argument('--profile', type=int, default=None)
+    parser.add_argument('--warmup', type=int, default=None)
+    parser.add_argument('--benchmark-iterations', type=int, default=20, metavar='N',
+                        help='Run N iterations while benchmarking (ignored when training and validation)')
+    parser.add_argument('--benchmark-warmup', type=int, default=20, metavar='N',
+                        help='Number of warmup iterations for benchmarking')
+
+    parser.add_argument('--backbone', type=str, default='resnet50',
+                        choices=['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'])
+    parser.add_argument('--num-workers', type=int, default=4)
+    parser.add_argument('--fp16', action='store_true')
+    parser.add_argument('--amp', action='store_true')
+
+    # Distributed
+    parser.add_argument('--local_rank', default=0, type=int,
+                        help='Used for multi-process training. Can either be manually set ' +
+                             'or automatically set by using \'python -m multiproc\'.')
+
+    return parser
+
+
+def train(train_loop_func, logger, args):
+    if args.amp:
+        amp_handle = amp.init(enabled=args.fp16)
+    # Check that GPUs are actually available
+    use_cuda = not args.no_cuda
+
+    # Setup multi-GPU if necessary
+    args.distributed = False
+    if 'WORLD_SIZE' in os.environ:
+        args.distributed = int(os.environ['WORLD_SIZE']) > 1
+
+    if args.distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method='env://')
+        args.N_gpu = torch.distributed.get_world_size()
+    else:
+        args.N_gpu = 1
+
+    if args.seed is None:
+        args.seed = np.random.randint(1e4)
+
+    if args.distributed:
+        args.seed = (args.seed + torch.distributed.get_rank()) % 2**32
+    print("Using seed = {}".format(args.seed))
+    torch.manual_seed(args.seed)
+    np.random.seed(seed=args.seed)
+
+
+    # Setup data, defaults
+    dboxes = dboxes300_coco()
+    encoder = Encoder(dboxes)
+    cocoGt = get_coco_ground_truth(args)
+
+    train_loader = get_train_loader(args, args.seed - 2**31)
+
+    val_dataset = get_val_dataset(args)
+    val_dataloader = get_val_dataloader(val_dataset, args)
+
+    ssd300 = SSD300(backbone=args.backbone)
+    args.learning_rate = args.learning_rate * args.N_gpu * (args.batch_size / 32)
+    start_epoch = 0
+    iteration = 0
+    loss_func = Loss(dboxes)
+
+    if use_cuda:
+        ssd300.cuda()
+        loss_func.cuda()
+
+    if args.fp16 and not args.amp:
+        ssd300 = network_to_half(ssd300)
+
+    if args.distributed:
+        ssd300 = DDP(ssd300)
+
+    optimizer = torch.optim.SGD(tencent_trick(ssd300), lr=args.learning_rate,
+                                    momentum=args.momentum, weight_decay=args.weight_decay)
+    scheduler = MultiStepLR(optimizer=optimizer, milestones=args.multistep, gamma=0.1)
+    if args.fp16:
+        if args.amp:
+            optimizer = amp_handle.wrap_optimizer(optimizer)
+        else:
+            optimizer = FP16_Optimizer(optimizer, static_loss_scale=128.)
+    if args.checkpoint is not None:
+        if os.path.isfile(args.checkpoint):
+            load_checkpoint(ssd300, args.checkpoint)
+            checkpoint = torch.load(args.checkpoint,
+                                    map_location=lambda storage, loc: storage.cuda(torch.cuda.current_device()))
+            start_epoch = checkpoint['epoch']
+            iteration = checkpoint['iteration']
+            scheduler.load_state_dict(checkpoint['scheduler'])
+            ssd300.load_state_dict(checkpoint['model'])
+            optimizer.load_state_dict(checkpoint['optimizer'])
+        else:
+            print('Provided checkpoint is not path to a file')
+            return
+
+    inv_map = {v: k for k, v in val_dataset.label_map.items()}
+
+    total_time = 0
+
+    if args.mode == 'evaluation':
+        acc = evaluate(ssd300, val_dataloader, cocoGt, encoder, inv_map, args)
+        if args.local_rank == 0:
+            print('Model precision {} mAP'.format(acc))
+
+        return
+    mean, std = generate_mean_std(args)
+
+    for epoch in range(start_epoch, args.epochs):
+        start_epoch_time = time.time()
+        scheduler.step()
+        iteration = train_loop_func(ssd300, loss_func, epoch, optimizer, train_loader, val_dataloader, encoder, iteration,
+                                    logger, args, mean, std)
+        end_epoch_time = time.time() - start_epoch_time
+        total_time += end_epoch_time
+
+        if args.local_rank == 0:
+            logger.update_epoch_time(epoch, end_epoch_time)
+
+        if epoch in args.evaluation:
+            acc = evaluate(ssd300, val_dataloader, cocoGt, encoder, inv_map, args)
+
+            if args.local_rank == 0:
+                logger.update_epoch(epoch, acc)
+
+        if args.save and args.local_rank == 0:
+            print("saving model...")
+            obj = {'epoch': epoch + 1,
+                   'iteration': iteration,
+                   'optimizer': optimizer.state_dict(),
+                   'scheduler': scheduler.state_dict(),
+                   'label_map': val_dataset.label_info}
+            if args.distributed:
+                obj['model'] = ssd300.module.state_dict()
+            else:
+                obj['model'] = ssd300.state_dict()
+            torch.save(obj, './models/epoch_{}.pt'.format(epoch))
+        train_loader.reset()
+    print('total training time: {}'.format(total_time))
+
+
+if __name__ == "__main__":
+    parser = make_parser()
+    args = parser.parse_args()
+    if args.local_rank == 0:
+        os.makedirs('./models', exist_ok=True)
+
+    torch.backends.cudnn.benchmark = True
+
+    if args.mode == 'benchmark-training':
+        train_loop_func = benchmark_train_loop
+        logger = BenchLogger('Training benchmark')
+        args.epochs = 1
+    elif args.mode == 'benchmark-inference':
+        train_loop_func = benchmark_inference_loop
+        logger = BenchLogger('Inference benchmark')
+        args.epochs = 1
+    else:
+        train_loop_func = train_loop
+        logger = Logger('Training logger', print_freq=1)
+
+    train(train_loop_func, logger, args)
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp16.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp16.json
new file mode 100644
index 00000000..2113d5b6
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp16.json
@@ -0,0 +1,31 @@
+{
+    "model": "",
+    "ngpus": [1, 4, 8],
+    "bs": [2, 4, 8, 16, 32, 64, 128],
+    "metric_keys": ["images_per_second"],
+    "metrics": {
+        "1": {
+            "2": {
+                "images_per_second": 191.25867003414876
+            },
+            "4": {
+                "images_per_second": 340.9537905548054
+            },
+            "8": {
+                "images_per_second": 517.2612062140391
+            },
+            "16": {
+                "images_per_second": 711.5516679788083
+            },
+            "32": {
+                "images_per_second": 812.9203401838566
+            },
+            "64": {
+                "images_per_second": 951.7432815456556
+            },
+            "128": {
+                "images_per_second": 876.1868813828711
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp32.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp32.json
new file mode 100644
index 00000000..bf7f5a6a
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_inference_fp32.json
@@ -0,0 +1,31 @@
+{
+    "model": "",
+    "ngpus": [1, 4, 8],
+    "bs": [2, 4, 8, 16, 32, 64, 128],
+    "metric_keys": ["images_per_second"],
+    "metrics": {
+        "1": {
+            "2": {
+                "images_per_second": 174.58768325581374
+            },
+            "4": {
+                "images_per_second": 254.24180710755593
+            },
+            "8": {
+                "images_per_second": 308.95847419165545
+            },
+            "16": {
+                "images_per_second": 419.60746029488445
+            },
+            "32": {
+                "images_per_second": 453.81433823995565
+            },
+            "64": {
+                "images_per_second": 592.6385687558369
+            },
+            "128": {
+                "images_per_second": 603.8453409148115
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp16.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp16.json
new file mode 100644
index 00000000..8c841167
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp16.json
@@ -0,0 +1,59 @@
+{
+    "model": "",
+    "ngpus": [1, 4, 8],
+    "bs": [2, 4, 8, 16, 32, 64],
+    "metric_keys": ["images_per_second"],
+    "metrics": {
+        "1": {
+            "2": {
+                "images_per_second": 40.71944999694824
+            },
+            "4": {
+                "images_per_second": 68.22257804870605
+            },
+            "8": {
+                "images_per_second": 121.42024612426758
+            },
+            "16": {
+                "images_per_second": 159.56442260742188
+            },
+            "32": {
+                "images_per_second": 185.69010543823242
+            }
+        },
+        "4": {
+            "2": {
+                "images_per_second": 40.75998783111572
+            },
+            "4": {
+                "images_per_second": 75.58991050720215
+            },
+            "8": {
+                "images_per_second": 142.64888381958008
+            },
+            "16": {
+                "images_per_second": 256.07005310058594
+            },
+            "32": {
+                "images_per_second": 300.8989944458008
+            }
+        },
+        "8": {
+            "2": {
+                "images_per_second": 61.28578186035156
+            },
+            "4": {
+                "images_per_second": 119.46021270751953
+            },
+            "8": {
+                "images_per_second": 231.7295379638672
+            },
+            "16": {
+                "images_per_second": 430.5494079589844
+            },
+            "32": {
+                "images_per_second": 454.2975769042969
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp32.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp32.json
new file mode 100644
index 00000000..0f5ca347
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_18.08_training_fp32.json
@@ -0,0 +1,59 @@
+{
+    "model": "",
+    "ngpus": [1, 4, 8],
+    "bs": [2, 4, 8, 16, 32],
+    "metric_keys": ["images_per_second"],
+    "metrics": {
+        "1": {
+            "2": {
+                "images_per_second": 48.635780334472656
+            },
+            "4": {
+                "images_per_second": 66.06407419840494
+            },
+            "8": {
+                "images_per_second": 83.91736857096353
+            },
+            "16": {
+                "images_per_second": 102.67040761311848
+            },
+            "32": {
+                "images_per_second": 110.02347819010416
+            }
+        },
+        "4": {
+            "2": {
+                "images_per_second": 41.199180603027344
+            },
+            "4": {
+                "images_per_second": 79.85076141357422
+            },
+            "8": {
+                "images_per_second": 145.39981587727863
+            },
+            "16": {
+                "images_per_second": 247.95855712890625
+            },
+            "32": {
+                "images_per_second": 341.29132080078125
+            }
+        },
+        "8": {
+            "2": {
+                "images_per_second": 63.07561111450195
+            },
+            "4": {
+                "images_per_second": 123.25757344563802
+            },
+            "8": {
+                "images_per_second": 237.3413340250651
+            },
+            "16": {
+                "images_per_second": 376.59598795572913
+            },
+            "32": {
+                "images_per_second": 507.9451497395833
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp16.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp16.json
new file mode 100644
index 00000000..9d303798
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp16.json
@@ -0,0 +1,34 @@
+{
+   "bs" : [
+      2,
+      4,
+      8,
+      16,
+      32
+   ],
+   "metric_keys" : [
+      "images_per_second"
+   ],
+   "metrics" : {
+      "1" : {
+         "16" : {
+            "images_per_second" : 470.099200788709
+         },
+         "2" : {
+            "images_per_second" : 163.117099093173
+         },
+         "32" : {
+            "images_per_second" : 520.538879400471
+         },
+         "4" : {
+            "images_per_second" : 296.604178917743
+         },
+         "8" : {
+            "images_per_second" : 412.522394180558
+         }
+      }
+   },
+   "ngpus" : [
+      1
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp32.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp32.json
new file mode 100644
index 00000000..9049af82
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp32.json
@@ -0,0 +1,34 @@
+{
+   "bs" : [
+      2,
+      4,
+      8,
+      16,
+      32
+   ],
+   "metric_keys" : [
+      "images_per_second"
+   ],
+   "metrics" : {
+      "1" : {
+         "16" : {
+            "images_per_second" : 280.570005994299
+         },
+         "2" : {
+            "images_per_second" : 147.914221468741
+         },
+         "32" : {
+            "images_per_second" : 302.430594818483
+         },
+         "4" : {
+            "images_per_second" : 201.622430560779
+         },
+         "8" : {
+            "images_per_second" : 228.159516872363
+         }
+      }
+   },
+   "ngpus" : [
+      1
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp16.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp16.json
new file mode 100644
index 00000000..510d548d
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp16.json
@@ -0,0 +1,52 @@
+{
+   "bs" : [
+      2,
+      4,
+      8,
+      16,
+      32
+   ],
+   "metric_keys" : [
+      "images_per_second"
+   ],
+   "metrics" : {
+      "1" : {
+         "16" : {
+            "images_per_second" : 192.623916625977
+         },
+         "2" : {
+            "images_per_second" : 48.7488899230957
+         },
+         "32" : {
+            "images_per_second" : 204.250648498535
+         },
+         "4" : {
+            "images_per_second" : 95.4697418212891
+         },
+         "8" : {
+            "images_per_second" : 164.66495513916
+         }
+      },
+      "4" : {
+         "16" : {
+            "images_per_second" : 701.366027832031
+         },
+         "2" : {
+            "images_per_second" : 154.449935913086
+         },
+         "32" : {
+            "images_per_second" : 771.171325683594
+         },
+         "4" : {
+            "images_per_second" : 300.332641601562
+         },
+         "8" : {
+            "images_per_second" : 550.924163818359
+         }
+      }
+   },
+   "ngpus" : [
+      1,
+      4
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp32.json b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp32.json
new file mode 100644
index 00000000..4612936e
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp32.json
@@ -0,0 +1,45 @@
+{
+   "bs" : [
+      2,
+      4,
+      8,
+      16
+   ],
+   "metric_keys" : [
+      "images_per_second"
+   ],
+   "metrics" : {
+      "1" : {
+         "16" : {
+            "images_per_second" : 121.772495269775
+         },
+         "2" : {
+            "images_per_second" : 60.2171878814697
+         },
+         "4" : {
+            "images_per_second" : 90.5315437316895
+         },
+         "8" : {
+            "images_per_second" : 103.113033294678
+         }
+      },
+      "4" : {
+         "16" : {
+            "images_per_second" : 472.226806640625
+         },
+         "2" : {
+            "images_per_second" : 184.061141967773
+         },
+         "4" : {
+            "images_per_second" : 324.639801025391
+         },
+         "8" : {
+            "images_per_second" : 391.055908203125
+         }
+      }
+   },
+   "ngpus" : [
+      1,
+      4
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/benchmark_performance.py b/PyTorch/Detection/SSD/qa/benchmark_performance.py
new file mode 100644
index 00000000..5751dbac
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/benchmark_performance.py
@@ -0,0 +1,81 @@
+import argparse
+import subprocess
+
+from qa.qa_utils import compare_benchmarks, load_json, save_json, OKBLUE, ENDC, FAIL
+
+
+
+# parsing
+def parse_testscript_args():
+    parser = argparse.ArgumentParser(description='PyTorch Benchmark Tests')
+    parser.add_argument('--bs', default=[1], type=int, nargs='+')
+    parser.add_argument('--ngpus', default=[1], type=int, nargs='+')
+    parser.add_argument('--benchmark-mode', default='training', choices=['training', 'inference'],
+                        help='benchmark training or inference', required=True)
+    parser.add_argument('--bench-iterations', type=int, default=20, metavar='N',
+                        help='Run N iterations while benchmarking (ignored when training and validation)')
+    parser.add_argument('--bench-warmup', type=int, default=10, metavar='N',
+                        help='Number of warmup iterations for benchmarking')
+    parser.add_argument('--fp16', action='store_true', help='Run model in mixed precision.')
+    parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+                        help='number of data loading workers')
+    parser.add_argument('--data', type=str, metavar='<PATH>', required=True,
+                        help='path to the dataset')
+    parser.add_argument('--results-file', default='experiment_raport.json', type=str,
+                        help='file in which to store JSON experiment raport')
+    parser.add_argument('--benchmark-file', type=str, metavar='FILE', required=True,
+                        help='path to the file with baselines')
+    return parser.parse_args()
+
+
+# job command
+command_template = 'python3 {launcher} qa/qa_perf_main.py --bs {bs} --ebs {bs} ' \
+                   '--benchmark-mode {mode} --benchmark-warmup {bw} --benchmark-iterations {bi} {fp16} ' \
+                   '--backbone resnet50 --seed 1 --data {data} --results-file {results_file} --benchmark-file {benchmark_file}'
+
+if __name__ == '__main__':
+    args = parse_testscript_args()
+
+    fp16 = '--fp16' if args.fp16 else ''
+
+    # create results json file
+    # todo: maybe some template json file?
+    results = {'ngpus': args.ngpus,
+               'bs': args.bs,
+               'metric_keys': ['images_per_second'],
+               'metrics': {}}
+
+    for gpu in args.ngpus:
+        results['metrics'][str(gpu)] = {}
+        for bs in args.bs:
+            results['metrics'][str(gpu)][str(bs)] = {'images_per_second': None}
+
+    save_json(args.results_file, results)
+
+    # run qa_perf_main.py tests one by one
+    for gpu in args.ngpus:
+        launcher = '' if gpu == 1 else '-m torch.distributed.launch --nproc_per_node={}'.format(gpu)
+        for bs in args.bs:
+            print('#' * 80)
+            command = command_template.format(launcher=launcher, bs=bs, workers=args.workers, mode=args.benchmark_mode,
+                                              bw=args.bench_warmup, bi=args.bench_iterations, fp16=fp16,
+                                              data=args.data, results_file=args.results_file,
+                                              benchmark_file=args.benchmark_file)
+
+            print('Running "{}"'.format(command))
+
+            process = subprocess.Popen(command, shell=True)
+            output, error = process.communicate()
+
+            if error is not None:
+                print(FAIL + 'Program exited with status {}. Data has not been collected'.format(error) + ENDC)
+            # elif results['metrics'][str(gpu)][str(bs)]['images_per_second'] is None:
+            #     print(WARNING + 'Program did not end sucessfully. Data has not been collected.' + ENDC)
+            else:
+                print(OKBLUE + 'Program ended sucessfully. Data has been collected.' + ENDC)
+
+    results_data = load_json(args.results_file)
+    benchmark_data = load_json(args.benchmark_file)
+    exit_code = compare_benchmarks(results_data, benchmark_data, args, 0.16 if args.benchmark_mode == 'inference' else 0.1)
+    print(exit_code)
+    exit(exit_code)
diff --git a/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp16_full_run_acc_baseline.json b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp16_full_run_acc_baseline.json
new file mode 100644
index 00000000..95368764
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp16_full_run_acc_baseline.json
@@ -0,0 +1 @@
+{"metric_keys": ["train.loss", "val.acc"], "metrics": {"train.loss": [8.812795396454991, 5.914838795058071, 6, 5.092440919584583, 4.887887316499735, 4.744666463422983, 4.694560192557922, 4.567333741479565, 4.492525351620137, 6, 4.408311570055099, 4.334232046614567, 6, 4.263646488106407, 4.2514614595596445, 4.2171871953656055, 4.206751160226014, 4.1795772798196715, 4.156515416099515, 6, 4.108870625495911, 4.0985876759066855, 4.075221928967139, 4.080158276849438, 6, 4.033980131669857, 4.037739227952915, 6, 3.99941903534935, 6, 3.9875937877263565, 3.971811039999583, 3.980771179282509, 3.953947089124455, 3.9305202960968018, 3.9366443781873546, 3.9252991879350754, 3.8827156307395367, 3.9388060424005102, 3.88922161618695, 3.8874285418914396, 6, 3.8936942113018453, 3.537499847891029, 3.4058184228089177, 6, 6, 3.3219671837627627, 3.295458280363458, 3.262115957955606, 6, 6, 6, 3.2190717260910433, 3.213117691627236, 3.1739242191397987, 3.1791626058811704, 3.2088054501854177, 3.1719801842385507, 3.187761370792139, 3.1809213312432236, 3.1823803410259397, 3.1752594631311677, 3.1709555600928425, 3.1823559530957817], "val.acc": [0.025120322205631106, 0.06065902615325462, 0.08224594352985645, 0.09868630608427395, 0.11402055039858493, 0.11779455253460233, 0.1232203941357061, 0.13708232144631768, 0.13614397127135028, 0.13289094380937685, 0.14004009449749777, 0.1369843423424096, 0.13877603069457692, 0.15418866425831707, 0.1500001994042602, 0.1542573219664272, 0.14771151227315413, 0.15896497766306272, 0.1600724682809656, 0.15881491661088476, 0.16213217020726906, 0.16466781280171408, 0.15738430149539484, 0.16634155547369375, 0.1623110334880526, 0.16394517553182106, 0.1494171026560053, 0.16762167601953265, 0.16063595691096758, 0.16982898253523193, 0.17321918229909394, 0.17242960413896102, 0.1625123530546557, 0.18330429802960516, 0.16333127233412115, 0.17973452067250242, 0.16699022570278652, 0.17183956548028687, 0.17168756775917593, 0.17547718325478198, 0.1750019046551496, 0.18416070771679066, 0.1711460087987496, 0.231325087097653, 0.23716038401167305, 0.23886896590018106, 0.2403412383214709, 0.24380227870861898, 0.24383605475007317, 0.2449733300818802, 0.24508423152154857, 0.24252172333110344, 0.24566254540226004, 0.24661345705692578, 0.25123807624083877, 0.25184439401895475, 0.2519010236397111, 0.25191664071239706, 0.2522156441636805, 0.25215053241008767, 0.2525434296889651, 0.2524917808636186, 0.2527410425201369, 0.2534121449798447, 0.25279479287831214]}, "bs": [64], "model": "", "ngpus": [8]}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp32_full_run_acc_baseline.json b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp32_full_run_acc_baseline.json
new file mode 100644
index 00000000..54010290
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_18.08_fp32_full_run_acc_baseline.json
@@ -0,0 +1 @@
+{"metric_keys": ["train.loss", "val.acc"], "metrics": {"train.loss": [9.887425426832973, 6.30290542835752, 5.566619733535567, 5.192713968618468, 4.943981836976963, 4.777146058311629, 4.682364774062644, 4.566371860462505, 4.479279315107254, 5, 4.398730874582149, 4.31779890601812, 4.293896813580043, 4.250142149529603, 4.219812418175577, 4.21572122303159, 4.187492328960302, 4.147948342119242, 4.134799897931028, 4.131298205737984, 4.071315974647822, 4.074750597299968, 4.0595350983882055, 4.042616275720722, 4.029284068070124, 4.02082926113012, 3.9983501902834298, 4.00984974094874, 3.9730074155799167, 5, 3.9646901324326294, 3.952598022061144, 3.944574903713043, 3.9182081201711596, 3.9252539055836775, 3.907297405092997, 3.8867245969813986, 3.87151758639573, 3.8793927009449254, 3.8687505586699107, 3.8750464156204956, 5, 3.8645522469516402, 3.504709825765618, 3.3920036476251862, 3.318732707260998, 5, 3.295415750237011, 3.2602547589347872, 5, 5, 5, 5, 3.199645553613854, 3.1623374312205086, 5, 3.147109237820821, 3.158245995575684, 3.1465386938319977, 3.1480963979746055, 3.151234711101482, 3.146022343739672, 3.1410668343956294, 3.142435818259893, 3.123337645718104], "val.acc": [0.01106397969239677, 0.04958324872172423, 0.07470961174804201, 0.08412781056028416, 0.1052591997157941, 0.11592629309116805, 0.1275672396324061, 0.12472585915140484, 0.13138377072048255, 0.1262696666605193, 0.13354663690485083, 0.14424123617821044, 0.14059169419863984, 0.14768715602101368, 0.15450788443085858, 0.14792122925940135, 0.1508861356435794, 0.157419558440425, 0.15279118544884585, 0.16075469826863828, 0.14747077091644412, 0.16340857637480236, 0.14427366437395484, 0.15709914018423293, 0.16324391683493303, 0.16440443232887508, 0.16479726175439752, 0.17508843799046686, 0.16142292492169025, 0.1643848499786872, 0.16912610131976924, 0.16376330941842296, 0.16894551721633602, 0.17771765128166106, 0.1749561896689298, 0.1695538322677119, 0.16778561571905298, 0.16380194923909086, 0.16994188486879763, 0.1716953661397215, 0.17755697810460197, 0.17187995479426885, 0.1742018462295355, 0.23426649845846764, 0.23613136034024038, 0.24175797706337981, 0.2425279583355936, 0.24352550398110506, 0.24411115979837528, 0.24656561042490024, 0.24383524308920906, 0.24686666489675338, 0.24814559219197632, 0.24840393696219026, 0.251965847689631, 0.25254138256097747, 0.2523565615073023, 0.2529904738785998, 0.253555154014026, 0.2530651493203877, 0.25358174010109197, 0.2537683728256746, 0.2539384684886946, 0.2540280117408162, 0.2534652864501853]}, "bs": [32], "model": "", "ngpus": [8]}
\ No newline at end of file
diff --git a/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp16_1epoch_run_acc_baseline.json b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp16_1epoch_run_acc_baseline.json
new file mode 100644
index 00000000..30e5a0a0
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp16_1epoch_run_acc_baseline.json
@@ -0,0 +1,20 @@
+{
+   "metrics" : {
+      "val.acc" : [
+         0.0100971670737651
+      ],
+      "train.loss" : [
+         9.85026645043801
+      ]
+   },
+   "ngpus" : [
+      8
+   ],
+   "metric_keys" : [
+      "train.loss",
+      "val.acc"
+   ],
+   "bs" : [
+      64
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp32_1epoch_run_acc_baseline.json b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp32_1epoch_run_acc_baseline.json
new file mode 100644
index 00000000..94f049c2
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/curve_baselines/SSD300_pytorch_19.01_fp32_1epoch_run_acc_baseline.json
@@ -0,0 +1,20 @@
+{
+   "bs" : [
+      32
+   ],
+   "metrics" : {
+      "train.loss" : [
+         8.79916159380589
+      ],
+      "val.acc" : [
+         0.0238952010105531
+      ]
+   },
+   "metric_keys" : [
+      "train.loss",
+      "val.acc"
+   ],
+   "ngpus" : [
+      8
+   ]
+}
diff --git a/PyTorch/Detection/SSD/qa/qa_accuracy_main.py b/PyTorch/Detection/SSD/qa/qa_accuracy_main.py
new file mode 100644
index 00000000..a8234ade
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/qa_accuracy_main.py
@@ -0,0 +1,73 @@
+# core imports
+import os
+import numpy as np
+
+# pytorch imports
+import torch
+import torch.utils.data.distributed
+
+# Apex imports
+try:
+    from apex.parallel.LARC import LARC
+    from apex.parallel import DistributedDataParallel as DDP
+    from apex.fp16_utils import *
+except ImportError:
+    raise ImportError("Please install APEX from https://github.com/nvidia/apex")
+
+# project imports
+from src.train import train_loop
+from main import train, make_parser
+from src.logger import Logger
+from qa.qa_utils import load_json, create_json_file, compare_acc, save_json
+
+RESULT = None
+
+
+def add_benchmark_args(parser):
+    parser.add_argument('--benchmark-mode', type=str, default='epoch-accuracy',
+                        choices=['full-accuracy', 'epoch-accuracy'], required=True)
+    parser.add_argument('--benchmark-file', type=str, default=None, metavar='FILE',
+                        help='path to the file with baselines', required=True)
+    return parser
+
+
+def main(args):
+    if args.local_rank == 0:
+        os.makedirs('./models', exist_ok=True)
+
+    if args.seed is not None:
+        print("Using seed = {}".format(args.seed))
+        torch.manual_seed(args.seed)
+        np.random.seed(seed=args.seed)
+
+    torch.backends.cudnn.benchmark = True
+
+    if args.benchmark_mode == 'epoch-accuracy':
+        args.epochs = 1
+
+    train_loop_func = train_loop
+    logger = Logger('Accuracy test', print_freq=10)
+
+    args.evaluation = list(range(90))
+    train(train_loop_func, logger, args)
+
+    exit_code = 0
+    if args.local_rank == 0:
+        train_loss_results, val_acc_results, train_time_results = logger.print_results()
+        print(train_time_results)
+        print(train_loss_results)
+        print(val_acc_results)
+        measured_results = create_json_file(val_acc_results, train_loss_results, ngpus=8, bs=args.batch_size)
+        save_json('/results/results.json', measured_results)
+        print(measured_results)
+        benchmark_results = load_json(args.benchmark_file)
+        exit_code = compare_acc(measured_results, benchmark_results, args)
+        exit(exit_code)
+
+
+if __name__ == "__main__":
+    parser = make_parser()
+    parser = add_benchmark_args(parser)
+    args = parser.parse_args()
+    print(args)
+    main(args)
diff --git a/PyTorch/Detection/SSD/qa/qa_perf_main.py b/PyTorch/Detection/SSD/qa/qa_perf_main.py
new file mode 100644
index 00000000..d4adc7fc
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/qa_perf_main.py
@@ -0,0 +1,199 @@
+# core imports
+import os
+import numpy as np
+import json
+from pprint import pprint
+import time
+
+# pytorch imports
+import torch
+import torch.utils.data.distributed
+from torch.autograd import Variable
+
+
+# Apex imports
+try:
+    from apex.parallel.LARC import LARC
+    from apex.parallel import DistributedDataParallel as DDP
+    from apex.fp16_utils import *
+except ImportError:
+    raise ImportError("Please install APEX from https://github.com/nvidia/apex")
+
+# project imports
+from main import train, make_parser
+from src.logger import BenchLogger
+# from src.train import benchmark_inference_loop, benchmark_train_loop
+
+from SSD import _C as C
+
+RESULT = None
+
+
+def add_benchmark_args(parser):
+    parser.add_argument('--benchmark-mode', type=str, choices=['training', 'inference'],
+                        default='inference', required=True)
+    parser.add_argument('--results-file', default='experiment_raport.json', type=str,
+                        help='file in which to store JSON experiment raport')
+    parser.add_argument('--benchmark-file', type=str, default=None, metavar='FILE',
+                        help='path to the file with baselines')
+    return parser
+
+def benchmark_train_loop(model, loss_func, epoch, optim, train_dataloader, val_dataloader, encoder, iteration, logger, args, mean, std):
+    start_time = None
+    # tensor for results
+    result = torch.zeros((1,)).cuda()
+    for i, data in enumerate(loop(train_dataloader)):
+        if i >= args.benchmark_warmup:
+            start_time = time.time()
+
+        img = data[0][0][0]
+        bbox = data[0][1][0]
+        label = data[0][2][0]
+        label = label.type(torch.cuda.LongTensor)
+        bbox_offsets = data[0][3][0]
+        # handle random flipping outside of DALI for now
+        bbox_offsets = bbox_offsets.cuda()
+        img, bbox = C.random_horiz_flip(img, bbox, bbox_offsets, 0.5, False)
+
+        if not args.no_cuda:
+            img = img.cuda()
+            bbox = bbox.cuda()
+            label = label.cuda()
+            bbox_offsets = bbox_offsets.cuda()
+        img.sub_(mean).div_(std)
+
+        N = img.shape[0]
+        if bbox_offsets[-1].item() == 0:
+            print("No labels in batch")
+            continue
+        bbox, label = C.box_encoder(N, bbox, bbox_offsets, label, encoder.dboxes.cuda(), 0.5)
+
+        M = bbox.shape[0] // N
+        bbox = bbox.view(N, M, 4)
+        label = label.view(N, M)
+
+
+
+
+
+        ploc, plabel = model(img)
+        ploc, plabel = ploc.float(), plabel.float()
+
+        trans_bbox = bbox.transpose(1, 2).contiguous().cuda()
+
+        if not args.no_cuda:
+            label = label.cuda()
+        gloc = Variable(trans_bbox, requires_grad=False)
+        glabel = Variable(label, requires_grad=False)
+
+        loss = loss_func(ploc, plabel, gloc, glabel)
+
+
+
+        # loss scaling
+        if args.fp16:
+            if args.amp:
+                with optim.scale_loss(loss) as scale_loss:
+                    scale_loss.backward()
+            else:
+                optim.backward(loss)
+        else:
+            loss.backward()
+
+        optim.step()
+        optim.zero_grad()
+        iteration += 1
+
+        # reduce all results from every gpu
+        if i >= args.benchmark_warmup + args.benchmark_iterations:
+            result.data[0] = logger.print_result()
+            if args.N_gpu > 1:
+                torch.distributed.reduce(result, 0)
+            if args.local_rank == 0:
+                global RESULT
+                RESULT = float(result.data[0])
+            return
+
+        if i >= args.benchmark_warmup:
+            logger.update(args.batch_size, time.time() - start_time)
+
+def loop(dataloader):
+    while True:
+        for data in dataloader:
+            yield data
+
+def benchmark_inference_loop(model, loss_func, epoch, optim, train_dataloader, val_dataloader, encoder, iteration, logger, args, mean, std):
+    assert args.N_gpu == 1, 'Inference benchmark only on 1 gpu'
+    start_time = None
+    model.eval()
+    i=-1
+    dataloader = loop(val_dataloader)
+    while True:
+        i+=1
+        with torch.no_grad():
+            torch.cuda.synchronize()
+            if i >= args.benchmark_warmup:
+                start_time = time.time()
+            data = next(dataloader)
+
+            img = data[0]
+
+            if not args.no_cuda:
+                img = img.cuda()
+
+            if args.fp16:
+                img = img.half()
+
+            img.sub_(mean).div_(std)
+            img = Variable(img, requires_grad=False)
+            _ = model(img)
+            torch.cuda.synchronize()
+
+            if i >= args.benchmark_warmup + args.benchmark_iterations:
+                global RESULT
+                RESULT = logger.print_result()
+                return
+
+            if i >= args.benchmark_warmup:
+                logger.update(args.batch_size, time.time() - start_time)
+
+
+def main(args):
+    if args.local_rank == 0:
+        os.makedirs('./models', exist_ok=True)
+
+    if args.seed is not None:
+        print("Using seed = {}".format(args.seed))
+        torch.manual_seed(args.seed)
+        np.random.seed(seed=args.seed)
+
+    torch.backends.cudnn.benchmark = True
+
+    if args.benchmark_mode == 'training':
+        train_loop_func = benchmark_train_loop
+        logger = BenchLogger('Training benchmark')
+    else:
+        train_loop_func = benchmark_inference_loop
+        logger = BenchLogger('Inference benchmark')
+
+    args.epochs = 1
+
+    train(train_loop_func, logger, args)
+
+    if args.local_rank == 0:
+        global RESULT
+        with open(args.results_file) as f:
+            results = json.load(f)
+        results['metrics'][str(args.N_gpu)][str(args.batch_size)] = {'images_per_second': RESULT}
+        pprint(results)
+
+        with open(args.results_file, 'w') as f:
+            json.dump(results, f)
+
+
+if __name__ == "__main__":
+    parser = make_parser()
+    parser = add_benchmark_args(parser)
+    args = parser.parse_args()
+    print(args)
+    main(args)
diff --git a/PyTorch/Detection/SSD/qa/qa_utils.py b/PyTorch/Detection/SSD/qa/qa_utils.py
new file mode 100644
index 00000000..a9ee3239
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/qa_utils.py
@@ -0,0 +1,115 @@
+import json
+
+# terminal stdout colors
+OKBLUE = '\033[94m'
+OKGREEN = '\033[92m'
+WARNING = '\033[93m'
+FAIL = '\033[91m'
+ENDC = '\033[0m'
+
+
+# load results and benchmark
+def load_json(filepath):
+    with open(filepath) as f:
+        data = json.load(f)
+    return data
+
+
+def save_json(filepath, data):
+    with open(filepath, 'w') as f:
+        json.dump(data, f)
+
+
+# compare func
+def compare(measured_value, true_value, pmargin=0.1):
+    assert 0 < pmargin < 1, 'Margin should be in range [0, 1]'
+    return (1 - pmargin) * true_value < measured_value
+
+
+# compare 2 benchmark json files
+def compare_benchmarks(results, benchmark, args, pmargin=0.1):
+    # sanity check
+    for metric in results['metric_keys']:
+        if metric not in benchmark['metric_keys']:
+            assert False, "You want to compare {} metric which doesn't appear in benchmark file".format(metric)
+
+    assert len(args.bs) <= len(benchmark['bs']), 'len(args.bs) <= len(benchmark["bs"] ({} <= {})'.format(len(args.bs),
+                                                                                                         len(benchmark[
+                                                                                                                 'bs']))
+    assert len(args.bs) == len(results['bs']), 'len(args.bs) <= len(results["bs"] ({} == {})'.format(len(args.bs),
+                                                                                                     len(results['bs']))
+    for bs in results['bs']:
+        if bs not in benchmark['bs']:
+            assert False, "You want to compare batch size = {} which doesn't appear in benchmark file".format(bs)
+
+    assert len(args.ngpus) <= len(benchmark['ngpus']), 'len(args.ngpus) <= len(benchmark["ngpus"]) ({} <= {})'.format(
+        len(args.bs), len(benchmark['ngpus']))
+    assert len(args.ngpus) == len(results['ngpus']), 'len(args.ngpus) == len(results["ngpus"]) ({} == {})'.format(
+        len(args.bs), len(results['ngpus']))
+    for gpu in results['ngpus']:
+        if gpu not in benchmark['ngpus']:
+            assert False, "You want to compare {} gpus results which don't appear in benchmark file".format(gpu)
+
+    # compare measured numbers with benchmark
+    exit = 0
+    for metric in results['metric_keys']:
+        for gpu in results['ngpus']:
+            for bs in results['bs']:
+                measured_metric = results['metrics'][str(gpu)][str(bs)][metric]
+                ground_truth_metric = benchmark['metrics'][str(gpu)][str(bs)][metric]
+                ok = compare(measured_metric, ground_truth_metric, pmargin)
+                if ok:
+                    print(OKGREEN + 'BENCHMARK PASSED: metric={} gpu={} bs={}'.format(metric, gpu, bs) + ENDC)
+                else:
+                    print(FAIL + 'BENCHMARK NOT PASSED: metric={} gpu={} bs={}'.format(metric, gpu, bs) + ENDC)
+                    exit = 1
+    return exit
+
+# compare 2 benchmark json files
+def compare_acc(results, benchmark, args):
+    # sanity check
+    for metric in results['metric_keys']:
+        if metric not in benchmark['metric_keys']:
+            assert False, "You want to compare {} metric which doesn't appear in benchmark file".format(metric)
+
+    for bs in results['bs']:
+        if bs not in benchmark['bs']:
+            assert False, "You want to compare batch size = {} which doesn't appear in benchmark file".format(bs)
+
+    for gpu in results['ngpus']:
+        if gpu not in benchmark['ngpus']:
+            assert False, "You want to compare {} gpus results which don't appear in benchmark file".format(gpu)
+
+    # compare measured numbers with benchmark
+    for i, (result, ground_truth) in enumerate(zip(results['metrics']['val.acc'], benchmark['metrics']['val.acc'])):
+        if i > 43: # before first decay accuracy tends to vary more than 15% at ~30th epoch
+            if ground_truth * 0.9 > result:
+                print(FAIL + 'ACCURACY TEST NOT PASSED' + ENDC)
+                return 1
+
+    # compare measured numbers with benchmark
+    for i, (result, ground_truth) in enumerate(zip(results['metrics']['train.loss'], benchmark['metrics']['train.loss'])):
+        if i > 43:
+            if ground_truth * 1.1 < result:
+                print(FAIL + 'LOSS TEST NOT PASSED' + ENDC)
+                return 1
+
+    print(OKGREEN + 'ACCURACY TEST PASSED' + ENDC)
+    return 0
+
+def create_json_file(val_acc_results, train_loss_results, ngpus=8, bs=32):
+    results = {"ngpus": [ngpus],
+               "bs": [bs],
+               "metric_keys": ["train.loss", "val.acc"],
+               "metrics": {
+                   "train.loss": [],
+                   "val.acc": []
+               }
+               }
+
+    for i, ((epoch1, acc), (epoch2, loss)) in enumerate(zip(val_acc_results, train_loss_results)):
+        assert i == epoch1 == epoch2
+        results['metrics']['train.loss'].append(loss)
+        results['metrics']['val.acc'].append(acc)
+
+    return results
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_1epoch_run.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_1epoch_run.sh
new file mode 100644
index 00000000..8885f35c
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_1epoch_run.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+python3 -m torch.distributed.launch --nproc_per_node=8 qa/qa_accuracy_main.py --bs 64 --fp16 --warmup 300 --learning-rate 2.6e-3 --seed 1 --benchmark-mode epoch-accuracy --benchmark-file qa/curve_baselines/SSD300_pytorch_19.01_fp16_1epoch_run_acc_baseline.json --data $1
+
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_full_run.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_full_run.sh
new file mode 100644
index 00000000..34ab4e64
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp16_full_run.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+python3 -m torch.distributed.launch --nproc_per_node=8 qa/qa_accuracy_main.py --bs 64 --fp16 --warmup 300 --learning-rate 2.6e-3 --seed 1 --benchmark-mode full-accuracy --benchmark-file qa/curve_baselines/SSD300_pytorch_18.08_fp16_full_run_acc_baseline.json --data $1
+
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_1epoch_run.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_1epoch_run.sh
new file mode 100644
index 00000000..18b5d622
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_1epoch_run.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+python3 -m torch.distributed.launch --nproc_per_node=8 qa/qa_accuracy_main.py --bs 32 --warmup 300 --learning-rate 2.6e-3 --seed 1 --benchmark-mode epoch-accuracy --benchmark-file qa/curve_baselines/SSD300_pytorch_19.01_fp32_1epoch_run_acc_baseline.json --data $1
+
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_full_run.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_full_run.sh
new file mode 100644
index 00000000..35dc9a35
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_8GPU_fp32_full_run.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+python3 -m torch.distributed.launch --nproc_per_node=8 qa/qa_accuracy_main.py --bs 32 --warmup 300 --learning-rate 2.6e-3 --seed 1 --benchmark-mode full-accuracy --benchmark-file qa/curve_baselines/SSD300_pytorch_18.08_fp32_full_run_acc_baseline.json --data $1
+
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh
new file mode 100644
index 00000000..0c068f3a
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python ./qa/benchmark_performance.py --benchmark-mode inference --ngpus 1 --bs 2 4 8 16 32 --fp16  --bench-warmup 100 --bench-iterations 200 --benchmark-file qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp16.json --data $1
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh
new file mode 100644
index 00000000..6d58cce5
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python ./qa/benchmark_performance.py --benchmark-mode inference --ngpus 1 --bs 2 4 8 16 32 --bench-warmup 100 --bench-iterations 200 --benchmark-file qa/benchmark_baselines/SSD300_pytorch_19.01_inference_fp32.json --data $1
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh
new file mode 100644
index 00000000..c0ec1b39
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python ./qa/benchmark_performance.py --benchmark-mode training --ngpus 1 4 --bs 2 4 8 16 32 --fp16  --bench-warmup 100 --bench-iterations 200 --benchmark-file qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp16.json --data $1
diff --git a/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh b/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh
new file mode 100644
index 00000000..27c0b019
--- /dev/null
+++ b/PyTorch/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python ./qa/benchmark_performance.py --benchmark-mode training --ngpus 1 4 --bs 2 4 8 16 --bench-warmup 100 --bench-iterations 200 --benchmark-file qa/benchmark_baselines/SSD300_pytorch_19.01_training_fp32.json --data $1
diff --git a/PyTorch/Detection/SSD/requirements.txt b/PyTorch/Detection/SSD/requirements.txt
new file mode 100644
index 00000000..8d46779f
--- /dev/null
+++ b/PyTorch/Detection/SSD/requirements.txt
@@ -0,0 +1 @@
+Cython==0.28.4
diff --git a/PyTorch/Detection/SSD/setup.py b/PyTorch/Detection/SSD/setup.py
new file mode 100644
index 00000000..bc78d38b
--- /dev/null
+++ b/PyTorch/Detection/SSD/setup.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import glob
+import os
+
+import torch
+from torch.utils.cpp_extension import CUDA_HOME
+from torch.utils.cpp_extension import CppExtension
+from torch.utils.cpp_extension import CUDAExtension
+
+from setuptools import find_packages
+from setuptools import setup
+
+requirements = ["torch", "torchvision"]
+
+
+def get_extensions():
+    this_dir = os.path.dirname(os.path.abspath(__file__))
+    extensions_dir = os.path.join(this_dir, "csrc")
+
+    source_cpu = glob.glob(os.path.join(extensions_dir, "*.cpp"))
+    source_cuda = glob.glob(os.path.join(extensions_dir, "*.cu"))
+
+    print('c++: ', source_cpu)
+    print('cuda: ', source_cuda)
+    sources = source_cpu
+    extension = CppExtension
+
+    define_macros = []
+
+    if CUDA_HOME is not None:
+        extension = CUDAExtension
+        sources += source_cuda
+        define_macros += [("WITH_CUDA", None)]
+
+    sources = [os.path.join(extensions_dir, s) for s in sources]
+
+    include_dirs = [extensions_dir]
+    extra_compile_flags= {'cxx' : []}
+    extra_compile_flags['nvcc'] = ['-DCUDA_HAS_FP16=1','-D__CUDA_NO_HALF_OPERATORS__','-D__CUDA_NO_HALF_CONVERSIONS__','-D__CUDA_NO_HALF2_OPERATORS__']
+
+    gencodes = [
+                #'-gencode', 'arch=compute_50,code=sm_50',
+                #'-gencode', 'arch=compute_52,code=sm_52',
+                #'-gencode', 'arch=compute_60,code=sm_60',
+                #'-gencode', 'arch=compute_61,code=sm_61',
+                '-gencode', 'arch=compute_70,code=sm_70',
+                '-gencode', 'arch=compute_70,code=compute_70',]
+
+    extra_compile_flags['nvcc'] += gencodes
+
+    ext_modules = [
+        extension(
+            "SSD._C",
+            sources,
+            include_dirs=include_dirs,
+            define_macros=define_macros,
+            extra_compile_args=extra_compile_flags,
+        )
+    ]
+
+    return ext_modules
+
+
+setup(
+    name="SSD",
+    version="0.1",
+    author="slayton",
+    url="",
+    description="SSD in pytorch",
+    packages=find_packages(exclude=("configs", "examples", "test",)),
+    # install_requires=requirements,
+    ext_modules=get_extensions(),
+    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
+)
diff --git a/PyTorch/Detection/SSD/src/__init__.py b/PyTorch/Detection/SSD/src/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/PyTorch/Detection/SSD/src/coco.py b/PyTorch/Detection/SSD/src/coco.py
new file mode 100755
index 00000000..dd0e880b
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/coco.py
@@ -0,0 +1,433 @@
+__author__ = 'tylin'
+__version__ = '2.0'
+# Interface for accessing the Microsoft COCO dataset.
+
+# Microsoft COCO is a large image dataset designed for object detection,
+# segmentation, and caption generation. pycocotools is a Python API that
+# assists in loading, parsing and visualizing the annotations in COCO.
+# Please visit http://mscoco.org/ for more information on COCO, including
+# for the data, paper, and tutorials. The exact format of the annotations
+# is also described on the COCO website. For example usage of the pycocotools
+# please see pycocotools_demo.ipynb. In addition to this API, please download both
+# the COCO images and annotations in order to run the demo.
+
+# An alternative to using the API is to load the annotations directly
+# into Python dictionary
+# Using the API provides additional utility functions. Note that this API
+# supports both *instance* and *caption* annotations. In the case of
+# captions not all functions are defined (e.g. categories are undefined).
+
+# The following API functions are defined:
+#  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
+#  decodeMask - Decode binary mask M encoded via run-length encoding.
+#  encodeMask - Encode binary mask M using run-length encoding.
+#  getAnnIds  - Get ann ids that satisfy given filter conditions.
+#  getCatIds  - Get cat ids that satisfy given filter conditions.
+#  getImgIds  - Get img ids that satisfy given filter conditions.
+#  loadAnns   - Load anns with the specified ids.
+#  loadCats   - Load cats with the specified ids.
+#  loadImgs   - Load imgs with the specified ids.
+#  annToMask  - Convert segmentation in an annotation to binary mask.
+#  showAnns   - Display the specified annotations.
+#  loadRes    - Load algorithm results and create API for accessing them.
+#  download   - Download COCO images from mscoco.org server.
+# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
+# Help on each functions can be accessed by: "help COCO>function".
+
+# See also COCO>decodeMask,
+# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
+# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
+# COCO>loadImgs, COCO>annToMask, COCO>showAnns
+
+# Microsoft COCO Toolbox.      version 2.0
+# Data, paper, and tutorials available at:  http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
+# Licensed under the Simplified BSD License [see bsd.txt]
+
+import json
+import time
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+import numpy as np
+import copy
+import itertools
+from pycocotools import mask as maskUtils
+import os
+from collections import defaultdict
+import sys
+PYTHON_VERSION = sys.version_info[0]
+if PYTHON_VERSION == 2:
+    from urllib import urlretrieve
+elif PYTHON_VERSION == 3:
+    from urllib.request import urlretrieve
+
+
+def _isArrayLike(obj):
+    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
+
+
+class COCO:
+    def __init__(self, annotation_file=None):
+        """
+        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
+        :param annotation_file (str): location of annotation file
+        :param image_folder (str): location to the folder that hosts images.
+        :return:
+        """
+        # load dataset
+        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
+        self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
+        if not annotation_file == None:
+            print('loading annotations into memory...')
+            tic = time.time()
+            dataset = json.load(open(annotation_file, 'r'))
+            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
+            print('Done (t={:0.2f}s)'.format(time.time()- tic))
+            self.dataset = dataset
+            self.createIndex()
+
+    def createIndex(self):
+        # create index
+        print('creating index...')
+        anns, cats, imgs = {}, {}, {}
+        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
+        if 'annotations' in self.dataset:
+            for ann in self.dataset['annotations']:
+                imgToAnns[ann['image_id']].append(ann)
+                anns[ann['id']] = ann
+
+        if 'images' in self.dataset:
+            for img in self.dataset['images']:
+                imgs[img['id']] = img
+
+        if 'categories' in self.dataset:
+            for cat in self.dataset['categories']:
+                cats[cat['id']] = cat
+
+        if 'annotations' in self.dataset and 'categories' in self.dataset:
+            for ann in self.dataset['annotations']:
+                catToImgs[ann['category_id']].append(ann['image_id'])
+
+        print('index created!')
+
+        # create class members
+        self.anns = anns
+        self.imgToAnns = imgToAnns
+        self.catToImgs = catToImgs
+        self.imgs = imgs
+        self.cats = cats
+
+    def info(self):
+        """
+        Print information about the annotation file.
+        :return:
+        """
+        for key, value in self.dataset['info'].items():
+            print('{}: {}'.format(key, value))
+
+    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
+        """
+        Get ann ids that satisfy given filter conditions. default skips that filter
+        :param imgIds  (int array)     : get anns for given imgs
+               catIds  (int array)     : get anns for given cats
+               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
+               iscrowd (boolean)       : get anns for given crowd label (False or True)
+        :return: ids (int array)       : integer array of ann ids
+        """
+        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(imgIds) == len(catIds) == len(areaRng) == 0:
+            anns = self.dataset['annotations']
+        else:
+            if not len(imgIds) == 0:
+                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                anns = list(itertools.chain.from_iterable(lists))
+            else:
+                anns = self.dataset['annotations']
+            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
+            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
+        if not iscrowd == None:
+            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+        else:
+            ids = [ann['id'] for ann in anns]
+        return ids
+
+    def getCatIds(self, catNms=[], supNms=[], catIds=[]):
+        """
+        filtering parameters. default skips that filter.
+        :param catNms (str array)  : get cats for given cat names
+        :param supNms (str array)  : get cats for given supercategory names
+        :param catIds (int array)  : get cats for given cat ids
+        :return: ids (int array)   : integer array of cat ids
+        """
+        catNms = catNms if _isArrayLike(catNms) else [catNms]
+        supNms = supNms if _isArrayLike(supNms) else [supNms]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(catNms) == len(supNms) == len(catIds) == 0:
+            cats = self.dataset['categories']
+        else:
+            cats = self.dataset['categories']
+            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
+            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
+            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
+        ids = [cat['id'] for cat in cats]
+        return ids
+
+    def getImgIds(self, imgIds=[], catIds=[]):
+        '''
+        Get img ids that satisfy given filter conditions.
+        :param imgIds (int array) : get imgs for given ids
+        :param catIds (int array) : get imgs with all given cats
+        :return: ids (int array)  : integer array of img ids
+        '''
+        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(imgIds) == len(catIds) == 0:
+            ids = self.imgs.keys()
+        else:
+            ids = set(imgIds)
+            for i, catId in enumerate(catIds):
+                if i == 0 and len(ids) == 0:
+                    ids = set(self.catToImgs[catId])
+                else:
+                    ids &= set(self.catToImgs[catId])
+        return list(ids)
+
+    def loadAnns(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying anns
+        :return: anns (object array) : loaded ann objects
+        """
+        if _isArrayLike(ids):
+            return [self.anns[id] for id in ids]
+        elif type(ids) == int:
+            return [self.anns[ids]]
+
+    def loadCats(self, ids=[]):
+        """
+        Load cats with the specified ids.
+        :param ids (int array)       : integer ids specifying cats
+        :return: cats (object array) : loaded cat objects
+        """
+        if _isArrayLike(ids):
+            return [self.cats[id] for id in ids]
+        elif type(ids) == int:
+            return [self.cats[ids]]
+
+    def loadImgs(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying img
+        :return: imgs (object array) : loaded img objects
+        """
+        if _isArrayLike(ids):
+            return [self.imgs[id] for id in ids]
+        elif type(ids) == int:
+            return [self.imgs[ids]]
+
+    def showAnns(self, anns):
+        """
+        Display the specified annotations.
+        :param anns (array of object): annotations to display
+        :return: None
+        """
+        if len(anns) == 0:
+            return 0
+        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
+            datasetType = 'instances'
+        elif 'caption' in anns[0]:
+            datasetType = 'captions'
+        else:
+            raise Exception('datasetType not supported')
+        if datasetType == 'instances':
+            ax = plt.gca()
+            ax.set_autoscale_on(False)
+            polygons = []
+            color = []
+            for ann in anns:
+                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
+                if 'segmentation' in ann:
+                    if type(ann['segmentation']) == list:
+                        # polygon
+                        for seg in ann['segmentation']:
+                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
+                            polygons.append(Polygon(poly))
+                            color.append(c)
+                    else:
+                        # mask
+                        t = self.imgs[ann['image_id']]
+                        if type(ann['segmentation']['counts']) == list:
+                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                        else:
+                            rle = [ann['segmentation']]
+                        m = maskUtils.decode(rle)
+                        img = np.ones( (m.shape[0], m.shape[1], 3) )
+                        if ann['iscrowd'] == 1:
+                            color_mask = np.array([2.0,166.0,101.0])/255
+                        if ann['iscrowd'] == 0:
+                            color_mask = np.random.random((1, 3)).tolist()[0]
+                        for i in range(3):
+                            img[:,:,i] = color_mask[i]
+                        ax.imshow(np.dstack( (img, m*0.5) ))
+                if 'keypoints' in ann and type(ann['keypoints']) == list:
+                    # turn skeleton into zero-based index
+                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
+                    kp = np.array(ann['keypoints'])
+                    x = kp[0::3]
+                    y = kp[1::3]
+                    v = kp[2::3]
+                    for sk in sks:
+                        if np.all(v[sk]>0):
+                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
+                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
+                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
+            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+            ax.add_collection(p)
+            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
+            ax.add_collection(p)
+        elif datasetType == 'captions':
+            for ann in anns:
+                print(ann['caption'])
+
+    def loadRes(self, resFile):
+        """
+        Load result file and return a result api object.
+        :param   resFile (str)     : file name of result file
+        :return: res (obj)         : result api object
+        """
+        res = COCO()
+        res.dataset['images'] = [img for img in self.dataset['images']]
+
+        print('Loading and preparing results...')
+        tic = time.time()
+        if type(resFile) == str: #or type(resFile) == unicode:
+            anns = json.load(open(resFile))
+        elif type(resFile) == np.ndarray:
+            anns = self.loadNumpyAnnotations(resFile)
+        else:
+            anns = resFile
+        assert type(anns) == list, 'results in not an array of objects'
+        annsImgIds = [ann['image_id'] for ann in anns]
+        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
+               'Results do not correspond to current coco set'
+        if 'caption' in anns[0]:
+            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
+            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+            for id, ann in enumerate(anns):
+                ann['id'] = id+1
+        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                bb = ann['bbox']
+                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
+                if not 'segmentation' in ann:
+                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann['area'] = bb[2]*bb[3]
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        elif 'segmentation' in anns[0]:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                # now only support compressed RLE format as segmentation results
+                ann['area'] = maskUtils.area(ann['segmentation'])
+                if not 'bbox' in ann:
+                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        elif 'keypoints' in anns[0]:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                s = ann['keypoints']
+                x = s[0::3]
+                y = s[1::3]
+                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
+                ann['area'] = (x1-x0)*(y1-y0)
+                ann['id'] = id + 1
+                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
+        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
+
+        res.dataset['annotations'] = anns
+        res.createIndex()
+        return res
+
+    def download(self, tarDir = None, imgIds = [] ):
+        '''
+        Download COCO images from mscoco.org server.
+        :param tarDir (str): COCO results directory name
+               imgIds (list): images to be downloaded
+        :return:
+        '''
+        if tarDir is None:
+            print('Please specify target directory')
+            return -1
+        if len(imgIds) == 0:
+            imgs = self.imgs.values()
+        else:
+            imgs = self.loadImgs(imgIds)
+        N = len(imgs)
+        if not os.path.exists(tarDir):
+            os.makedirs(tarDir)
+        for i, img in enumerate(imgs):
+            tic = time.time()
+            fname = os.path.join(tarDir, img['file_name'])
+            if not os.path.exists(fname):
+                urlretrieve(img['coco_url'], fname)
+            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
+
+    def loadNumpyAnnotations(self, data):
+        """
+        Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
+        :param  data (numpy.ndarray)
+        :return: annotations (python nested list)
+        """
+        print('Converting ndarray to lists...')
+        assert(type(data) == np.ndarray)
+        print(data.shape)
+        assert(data.shape[1] == 7)
+        N = data.shape[0]
+        ann = []
+        for i in range(N):
+            if i % 1000000 == 0:
+                print('{}/{}'.format(i,N))
+            ann += [{
+                'image_id'  : int(data[i, 0]),
+                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
+                'score' : data[i, 5],
+                'category_id': int(data[i, 6]),
+                }]
+        return ann
+
+    def annToRLE(self, ann):
+        """
+        Convert annotation which can be polygons, uncompressed RLE to RLE.
+        :return: binary mask (numpy 2D array)
+        """
+        t = self.imgs[ann['image_id']]
+        h, w = t['height'], t['width']
+        segm = ann['segmentation']
+        if type(segm) == list:
+            # polygon -- a single object might consist of multiple parts
+            # we merge all parts into one mask rle code
+            rles = maskUtils.frPyObjects(segm, h, w)
+            rle = maskUtils.merge(rles)
+        elif type(segm['counts']) == list:
+            # uncompressed RLE
+            rle = maskUtils.frPyObjects(segm, h, w)
+        else:
+            # rle
+            rle = ann['segmentation']
+        return rle
+
+    def annToMask(self, ann):
+        """
+        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
+        :return: binary mask (numpy 2D array)
+        """
+        rle = self.annToRLE(ann)
+        m = maskUtils.decode(rle)
+        return m
diff --git a/PyTorch/Detection/SSD/src/coco_pipeline.py b/PyTorch/Detection/SSD/src/coco_pipeline.py
new file mode 100644
index 00000000..69571558
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/coco_pipeline.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import torch
+import ctypes
+import logging
+
+import numpy as np
+
+# DALI imports
+from nvidia.dali.pipeline import Pipeline
+import nvidia.dali.ops as ops
+import nvidia.dali.types as types
+
+import time
+
+
+class COCOPipeline(Pipeline):
+    def __init__(self, batch_size, device_id, file_root, annotations_file, num_gpus,
+            output_fp16=False, output_nhwc=False, pad_output=False, num_threads=1, seed=15):
+        super(COCOPipeline, self).__init__(batch_size=batch_size, device_id=device_id,
+                                           num_threads=num_threads, seed = seed)
+
+        try:
+            shard_id = torch.distributed.get_rank()
+        except RuntimeError:
+            shard_id = 0
+
+        self.input = ops.COCOReader(file_root = file_root, annotations_file = annotations_file,
+                            shard_id = shard_id, num_shards = num_gpus, ratio=True, ltrb=True, random_shuffle=True,
+                                    skip_empty=True)
+        self.decode = ops.HostDecoder(device = "cpu", output_type = types.RGB)
+
+        # Augumentation techniques
+        self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1)
+        self.twist = ops.ColorTwist(device="gpu")
+
+        self.resize = ops.Resize(device = "gpu", resize_x = 300, resize_y = 300)
+
+        output_dtype = types.FLOAT16 if output_fp16 else types.FLOAT
+        output_layout = types.NHWC if output_nhwc else types.NCHW
+
+        self.normalize = ops.CropMirrorNormalize(device="gpu", crop=(300, 300),
+                                                 mean=[0.0, 0.0, 0.0],
+                                                 std=[255.0, 255.0, 255.0],
+                                                 mirror=0,
+                                                 output_dtype=output_dtype,
+                                                 output_layout=output_layout,
+                                                 pad_output=pad_output)
+
+        # Random variables
+        self.rng1 = ops.Uniform(range=[0.5, 1.5])
+        self.rng2 = ops.Uniform(range=[0.875, 1.125])
+        self.rng3 = ops.Uniform(range=[-0.5, 0.5])
+
+    def define_graph(self):
+        saturation = self.rng1()
+        contrast = self.rng1()
+        brightness = self.rng2()
+        hue = self.rng3()
+
+        inputs, bboxes, labels = self.input()
+        images = self.decode(inputs)
+
+        images, bboxes, labels = self.crop(images, bboxes, labels)
+        images = self.resize(images.gpu())
+        images = self.twist(images.gpu(), saturation=saturation, contrast=contrast, brightness=brightness, hue=hue)
+        images = self.normalize(images)
+
+        # bboxes and images and labels on GPU
+        return (images, bboxes.gpu(), labels.gpu())
+
+to_torch_type = {
+    np.dtype(np.float32) : torch.float32,
+    np.dtype(np.float64) : torch.float64,
+    np.dtype(np.float16) : torch.float16,
+    np.dtype(np.uint8)   : torch.uint8,
+    np.dtype(np.int8)    : torch.int8,
+    np.dtype(np.int16)   : torch.int16,
+    np.dtype(np.int32)   : torch.int32,
+    np.dtype(np.int64)   : torch.int64
+}
+
+def feed_ndarray(dali_tensor, arr):
+    """
+    Copy contents of DALI tensor to pyTorch's Tensor.
+
+    Parameters
+    ----------
+    `dali_tensor` : nvidia.dali.backend.TensorCPU or nvidia.dali.backend.TensorGPU
+                    Tensor from which to copy
+    `arr` : torch.Tensor
+            Destination of the copy
+    """
+    assert dali_tensor.shape() == list(arr.size()), \
+            ("Shapes do not match: DALI tensor has size {0}"
+            ", but PyTorch Tensor has size {1}".format(dali_tensor.shape(), list(arr.size())))
+    #turn raw int to a c void pointer
+    c_type_pointer = ctypes.c_void_p(arr.data_ptr())
+    dali_tensor.copy_to_external(c_type_pointer)
+    return arr
+
+class DALICOCOIterator(object):
+    """
+    COCO DALI iterator for pyTorch.
+
+    Parameters
+    ----------
+    pipelines : list of nvidia.dali.pipeline.Pipeline
+                List of pipelines to use
+    size : int
+           Epoch size.
+    """
+    def __init__(self, pipelines, size):
+        if not isinstance(pipelines, list):
+            pipelines = [pipelines]
+
+        self._num_gpus = len(pipelines)
+        assert pipelines is not None, "Number of provided pipelines has to be at least 1"
+        self.batch_size = pipelines[0].batch_size
+        self._size = size
+        self._pipes = pipelines
+
+        # Build all pipelines
+        for p in self._pipes:
+            p.build()
+
+        # Use double-buffering of data batches
+        self._data_batches = [[None, None, None, None] for i in range(self._num_gpus)]
+        self._counter = 0
+        self._current_data_batch = 0
+        self.output_map = ["image", "bboxes", "labels"]
+
+        # We need data about the batches (like shape information),
+        # so we need to run a single batch as part of setup to get that info
+        self._first_batch = None
+        self._first_batch = self.next()
+
+    def __next__(self):
+        if self._first_batch is not None:
+            batch = self._first_batch
+            self._first_batch = None
+            return batch
+        if self._counter > self._size:
+            raise StopIteration
+
+        # Gather outputs
+        outputs = []
+        for p in self._pipes:
+            p._prefetch()
+        for p in self._pipes:
+            outputs.append(p._share_outputs())
+        for i in range(self._num_gpus):
+            dev_id = self._pipes[i].device_id
+            out_images = []
+            bboxes = []
+            labels = []
+            # segregate outputs into image/labels/bboxes entries
+            for j, out in enumerate(outputs[i]):
+                if self.output_map[j] == "image":
+                    out_images.append(out)
+                elif self.output_map[j] == "bboxes":
+                    bboxes.append(out)
+                elif self.output_map[j] == "labels":
+                    labels.append(out)
+
+            # Change DALI TensorLists into Tensors
+            images = [x.as_tensor() for x in out_images]
+            images_shape = [x.shape() for x in images]
+
+            # Prepare bboxes shapes
+            bboxes_shape = []
+            for j in range(len(bboxes)):
+                bboxes_shape.append([])
+                for k in range(len(bboxes[j])):
+                    bboxes_shape[j].append(bboxes[j].at(k).shape())
+
+            # Prepare labels shapes and offsets
+            labels_shape = []
+            bbox_offsets = []
+
+            torch.cuda.synchronize()
+            for j in range(len(labels)):
+                labels_shape.append([])
+                bbox_offsets.append([0])
+                for k in range(len(labels[j])):
+                    lshape = labels[j].at(k).shape()
+                    bbox_offsets[j].append(bbox_offsets[j][k] + lshape[0])
+                    labels_shape[j].append(lshape)
+
+            # We always need to alocate new memory as bboxes and labels varies in shape
+            images_torch_type = to_torch_type[np.dtype(images[0].dtype())]
+            bboxes_torch_type = to_torch_type[np.dtype(bboxes[0].at(0).dtype())]
+            labels_torch_type = to_torch_type[np.dtype(labels[0].at(0).dtype())]
+
+            torch_gpu_device = torch.device('cuda', dev_id)
+            torch_cpu_device = torch.device('cpu')
+
+            pyt_images = [torch.zeros(shape, dtype=images_torch_type, device=torch_gpu_device) for shape in images_shape]
+            pyt_bboxes = [[torch.zeros(shape, dtype=bboxes_torch_type, device=torch_gpu_device) for shape in shape_list] for shape_list in bboxes_shape]
+            pyt_labels = [[torch.zeros(shape, dtype=labels_torch_type, device=torch_gpu_device) for shape in shape_list] for shape_list in labels_shape]
+            pyt_offsets = [torch.zeros(len(offset), dtype=torch.int32, device=torch_cpu_device) for offset in bbox_offsets]
+
+            self._data_batches[i][self._current_data_batch] = (pyt_images, pyt_bboxes, pyt_labels, pyt_offsets)
+
+            # Copy data from DALI Tensors to torch tensors
+            for j, i_arr in enumerate(images):
+                feed_ndarray(i_arr, pyt_images[j])
+
+            for j, b_list in enumerate(bboxes):
+                for k in range(len(b_list)):
+                    if (pyt_bboxes[j][k].shape[0] != 0):
+                        feed_ndarray(b_list.at(k), pyt_bboxes[j][k])
+                pyt_bboxes[j] = torch.cat(pyt_bboxes[j])
+
+            for j, l_list in enumerate(labels):
+                for k in range(len(l_list)):
+                    if (pyt_labels[j][k].shape[0] != 0):
+                        feed_ndarray(l_list.at(k), pyt_labels[j][k])
+                pyt_labels[j] = torch.cat(pyt_labels[j]).squeeze(dim=1)
+
+            for j in range(len(pyt_offsets)):
+                pyt_offsets[j] = torch.IntTensor(bbox_offsets[j])
+
+        for p in self._pipes:
+            p._release_outputs()
+            p._start_run()
+
+        copy_db_index = self._current_data_batch
+        # Change index for double buffering
+        self._current_data_batch = (self._current_data_batch + 1) % 2
+        self._counter += self._num_gpus * self.batch_size
+        return [db[copy_db_index] for db in self._data_batches]
+
+    def next(self):
+        """
+        Returns the next batch of data.
+        """
+        return self.__next__();
+
+    def __iter__(self):
+        return self
+
+    def reset(self):
+        """
+        Resets the iterator after the full epoch.
+        DALI iterators do not support resetting before the end of the epoch
+        and will ignore such request.
+        """
+        if self._counter > self._size:
+            self._counter = self._counter % self._size
+        else:
+            logging.warning("DALI iterator does not support resetting while epoch is not finished. Ignoring...")
diff --git a/PyTorch/Detection/SSD/src/data.py b/PyTorch/Detection/SSD/src/data.py
new file mode 100644
index 00000000..a47a2c9e
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/data.py
@@ -0,0 +1,54 @@
+import os
+
+import torch
+from torch.utils.data import DataLoader
+
+from src.utils import dboxes300_coco, COCODetection
+from src.utils import SSDTransformer
+from src.coco import COCO
+#DALI import
+from src.coco_pipeline import COCOPipeline, DALICOCOIterator
+
+def get_train_loader(args, local_seed):
+    train_annotate = os.path.join(args.data, "annotations/instances_train2017.json")
+    train_coco_root = os.path.join(args.data, "train2017")
+
+    train_pipe = COCOPipeline(args.batch_size, args.local_rank, train_coco_root,
+                    train_annotate, args.N_gpu, num_threads=args.num_workers,
+                    output_fp16=args.fp16, output_nhwc=False,
+                    pad_output=False, seed=local_seed)
+    train_pipe.build()
+    test_run = train_pipe.run()
+    train_loader = DALICOCOIterator(train_pipe, 118287 / args.N_gpu)
+    return train_loader
+
+
+def get_val_dataset(args):
+    dboxes = dboxes300_coco()
+    val_trans = SSDTransformer(dboxes, (300, 300), val=True)
+
+    val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
+    val_coco_root = os.path.join(args.data, "val2017")
+
+    val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
+    return val_coco
+
+
+def get_val_dataloader(dataset, args):
+    if args.distributed:
+        val_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+    else:
+        val_sampler = None
+
+    val_dataloader = DataLoader(dataset,
+                                batch_size=args.eval_batch_size,
+                                shuffle=False,  # Note: distributed sampler is shuffled :(
+                                sampler=val_sampler,
+                                num_workers=args.num_workers)
+
+    return val_dataloader
+
+def get_coco_ground_truth(args):
+    val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
+    cocoGt = COCO(annotation_file=val_annotate)
+    return cocoGt
diff --git a/PyTorch/Detection/SSD/src/distributed.py b/PyTorch/Detection/SSD/src/distributed.py
new file mode 100644
index 00000000..7776997f
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/distributed.py
@@ -0,0 +1,82 @@
+import torch
+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
+import torch.distributed as dist
+from torch.nn.modules import Module
+
+'''
+This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
+launcher included with this example. It assumes that your run is using multiprocess with 1
+GPU/process, that the model is on the correct device, and that torch.set_device has been
+used to set the device.
+
+Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
+and will be allreduced at the finish of the backward pass.
+'''
+class DistributedDataParallel(Module):
+
+    def __init__(self, module):
+        super(DistributedDataParallel, self).__init__()
+        self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
+
+        self.module = module
+
+        for p in self.module.state_dict().values():
+            if not torch.is_tensor(p):
+                continue
+            if dist._backend == dist.dist_backend.NCCL:
+                assert p.is_cuda, "NCCL backend only supports model parameters to be on GPU."
+            dist.broadcast(p, 0)
+
+        def allreduce_params():
+            if(self.needs_reduction):
+                self.needs_reduction = False
+                buckets = {}
+                for param in self.module.parameters():
+                    if param.requires_grad and param.grad is not None:
+                        tp = param.data.type()
+                        if tp not in buckets:
+                            buckets[tp] = []
+                        buckets[tp].append(param)
+                if self.warn_on_half:
+                    if torch.cuda.HalfTensor in buckets:
+                        print("WARNING: gloo dist backend for half parameters may be extremely slow." +
+                              " It is recommended to use the NCCL backend in this case.")
+                        self.warn_on_half = False
+
+                for tp in buckets:
+                    bucket = buckets[tp]
+                    grads = [param.grad.data for param in bucket]
+                    coalesced = _flatten_dense_tensors(grads)
+                    dist.all_reduce(coalesced)
+                    coalesced /= dist.get_world_size()
+                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
+                        buf.copy_(synced)
+
+        for param in list(self.module.parameters()):
+            def allreduce_hook(*unused):
+                param._execution_engine.queue_callback(allreduce_params)
+            if param.requires_grad:
+                param.register_hook(allreduce_hook)
+
+    def forward(self, *inputs, **kwargs):
+        self.needs_reduction = True
+        return self.module(*inputs, **kwargs)
+
+    '''
+    def _sync_buffers(self):
+        buffers = list(self.module._all_buffers())
+        if len(buffers) > 0:
+            # cross-node buffer sync
+            flat_buffers = _flatten_dense_tensors(buffers)
+            dist.broadcast(flat_buffers, 0)
+            for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
+                buf.copy_(synced)
+     def train(self, mode=True):
+        # Clear NCCL communicator and CUDA event cache of the default group ID,
+        # These cache will be recreated at the later call. This is currently a
+        # work-around for a potential NCCL deadlock.
+        if dist._backend == dist.dist_backend.NCCL:
+            dist._clear_group_cache()
+        super(DistributedDataParallel, self).train(mode)
+        self.module.train(mode)
+    '''
diff --git a/PyTorch/Detection/SSD/src/evaluate.py b/PyTorch/Detection/SSD/src/evaluate.py
new file mode 100644
index 00000000..bcda3b3c
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/evaluate.py
@@ -0,0 +1,124 @@
+import torch
+import time
+import numpy as np
+from contextlib import redirect_stdout
+import io
+
+from pycocotools.cocoeval import COCOeval
+
+
+def evaluate(model, coco, cocoGt, encoder, inv_map, args):
+    if args.distributed:
+        N_gpu = torch.distributed.get_world_size()
+    else:
+        N_gpu = 1
+
+    model.eval()
+    if not args.no_cuda:
+        model.cuda()
+    ret = []
+    start = time.time()
+
+    # for idx, image_id in enumerate(coco.img_keys):
+    for nbatch, (img, img_id, img_size, _, _) in enumerate(coco):
+        print("Parsing batch: {}/{}".format(nbatch, len(coco)), end='\r')
+        with torch.no_grad():
+            inp = img.cuda()
+            if args.fp16:
+                inp = inp.half()
+
+            # Get predictions
+            ploc, plabel = model(inp)
+            ploc, plabel = ploc.float(), plabel.float()
+
+            # Handle the batch of predictions produced
+            # This is slow, but consistent with old implementation.
+            for idx in range(ploc.shape[0]):
+                # ease-of-use for specific predictions
+                ploc_i = ploc[idx, :, :].unsqueeze(0)
+                plabel_i = plabel[idx, :, :].unsqueeze(0)
+
+                try:
+                    result = encoder.decode_batch(ploc_i, plabel_i, 0.50, 200)[0]
+                except:
+                    # raise
+                    print("")
+                    print("No object detected in idx: {}".format(idx))
+                    continue
+
+                htot, wtot = img_size[0][idx].item(), img_size[1][idx].item()
+                loc, label, prob = [r.cpu().numpy() for r in result]
+                for loc_, label_, prob_ in zip(loc, label, prob):
+                    ret.append([img_id[idx], loc_[0] * wtot, \
+                                loc_[1] * htot,
+                                (loc_[2] - loc_[0]) * wtot,
+                                (loc_[3] - loc_[1]) * htot,
+                                prob_,
+                                inv_map[label_]])
+
+    # Now we have all predictions from this rank, gather them all together
+    # if necessary
+    ret = np.array(ret).astype(np.float32)
+
+    # Multi-GPU eval
+    if args.distributed:
+        # NCCL backend means we can only operate on GPU tensors
+        ret_copy = torch.tensor(ret).cuda()
+        # Everyone exchanges the size of their results
+        ret_sizes = [torch.tensor(0).cuda() for _ in range(N_gpu)]
+
+        torch.cuda.synchronize()
+        torch.distributed.all_gather(ret_sizes, torch.tensor(ret_copy.shape[0]).cuda())
+        torch.cuda.synchronize()
+
+        # Get the maximum results size, as all tensors must be the same shape for
+        # the all_gather call we need to make
+        max_size = 0
+        sizes = []
+        for s in ret_sizes:
+            max_size = max(max_size, s.item())
+            sizes.append(s.item())
+
+        # Need to pad my output to max_size in order to use in all_gather
+        ret_pad = torch.cat([ret_copy, torch.zeros(max_size - ret_copy.shape[0], 7, dtype=torch.float32).cuda()])
+
+        # allocate storage for results from all other processes
+        other_ret = [torch.zeros(max_size, 7, dtype=torch.float32).cuda() for i in range(N_gpu)]
+        # Everyone exchanges (padded) results
+
+        torch.cuda.synchronize()
+        torch.distributed.all_gather(other_ret, ret_pad)
+        torch.cuda.synchronize()
+
+        # Now need to reconstruct the _actual_ results from the padded set using slices.
+        cat_tensors = []
+        for i in range(N_gpu):
+            cat_tensors.append(other_ret[i][:sizes[i]][:])
+
+        final_results = torch.cat(cat_tensors).cpu().numpy()
+    else:
+        # Otherwise full results are just our results
+        final_results = ret
+
+    if args.local_rank == 0:
+        print("")
+        print("Predicting Ended, total time: {:.2f} s".format(time.time() - start))
+
+    cocoDt = cocoGt.loadRes(final_results)
+
+    E = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    E.evaluate()
+    E.accumulate()
+    if args.local_rank == 0:
+        E.summarize()
+        print("Current AP: {:.5f}".format(E.stats[0]))
+    else:
+        # fix for cocoeval indiscriminate prints
+        with redirect_stdout(io.StringIO()):
+            E.summarize()
+
+    # put your model in training mode back on
+    model.train()
+
+    return E.stats[0]  # Average Precision  (AP) @[ IoU=050:0.95 | area=   all | maxDets=100 ]
+
diff --git a/PyTorch/Detection/SSD/src/logger.py b/PyTorch/Detection/SSD/src/logger.py
new file mode 100644
index 00000000..b9c19de2
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/logger.py
@@ -0,0 +1,103 @@
+import math
+import numpy as np
+
+
+class EpochMeter:
+    def __init__(self, name):
+        self.name = name
+        self.data = []
+
+    def update(self, epoch, val):
+        self.data.append((epoch, val))
+
+
+class IterationMeter:
+    def __init__(self, name):
+        self.name = name
+        self.data = []
+
+    def update(self, epoch, iteration, val):
+        self.data.append((epoch, iteration, val))
+
+
+class IterationAverageMeter:
+    def __init__(self, name):
+        self.name = name
+        self.data = []
+        self.n = 0
+        self.sum = 0
+
+    def update_iter(self, val):
+        if math.isfinite(val): # sometimes loss === 'inf'
+            self.n += 1
+            self.sum += 0 if math.isinf(val) else val
+
+    def update_epoch(self, epoch):
+        self.data.append((epoch, self.sum / self.n))
+        self.n = 0
+        self.sum = 0
+
+
+class Logger:
+    def __init__(self, name, print_freq=20):
+        self.name = name
+        self.train_loss_logger = IterationAverageMeter("Training loss")
+        self.train_epoch_time_logger = EpochMeter("Training 1 epoch time")
+        self.val_acc_logger = EpochMeter("Validation accuracy")
+        self.print_freq = print_freq
+
+    def update_iter(self, epoch, iteration, loss):
+        self.train_loss_logger.update_iter(loss)
+        if iteration % self.print_freq == 0:
+            print('epoch: {}\titeraion: {}\tloss: {}'.format(epoch, iteration, loss))
+
+    def update_epoch(self, epoch, acc):
+        self.train_loss_logger.update_epoch(epoch)
+        self.val_acc_logger.update(epoch, acc)
+        print('epoch: {}\tmAP accuracy: {}'.format(epoch, acc))
+
+    def update_epoch_time(self, epoch, time):
+        self.train_epoch_time_logger.update(epoch, time)
+        print('epoch: {}\ttime: {}'.format(epoch, time))
+
+    def print_results(self):
+        return self.train_loss_logger.data, self.val_acc_logger.data, self.train_epoch_time_logger
+
+
+class BenchmarkMeter:
+    def __init__(self, name):
+        self.name = name
+        self.data = []
+        self.total_images = 0
+        self.total_time = 0
+        self.avr_images_per_second = 0
+
+    def update(self, bs, time):
+        self.total_images += bs
+        self.total_time += time
+        self.avr_images_per_second = self.total_images / self.total_time
+        self.data.append(bs / time)
+
+
+class BenchLogger(Logger):
+    def __init__(self, name):
+        super().__init__(name)
+        self.name = name
+        self.images_per_ses = BenchmarkMeter(self.name)
+
+    def update(self, bs, time):
+        self.images_per_ses.update(bs, time)
+
+    def print_result(self):
+        total_bs = self.images_per_ses.total_images
+        total_time = self.images_per_ses.total_time
+        avr = self.images_per_ses.avr_images_per_second
+        med = np.median(self.images_per_ses.data)
+
+        print("Done benchmarking. Total images: {}\ttotal time: {:.3f}\tAverage images/sec: {:.3f}\tMedian images/sec: {:.3f}".format(
+            total_bs,
+            total_time,
+            avr,
+            med
+        ))
+        return med
diff --git a/PyTorch/Detection/SSD/src/model.py b/PyTorch/Detection/SSD/src/model.py
new file mode 100644
index 00000000..e5d4cc76
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/model.py
@@ -0,0 +1,181 @@
+import torch
+import torch.nn as nn
+from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
+
+
+class ResNet(nn.Module):
+    def __init__(self, backbone='resnet50'):
+        super().__init__()
+        if backbone == 'resnet18':
+            backbone = resnet18(pretrained=True)
+            self.out_channels = [256, 512, 512, 256, 256, 128]
+        elif backbone == 'resnet34':
+            backbone = resnet34(pretrained=True)
+            self.out_channels = [256, 512, 512, 256, 256, 256]
+        elif backbone == 'resnet50':
+            backbone = resnet50(pretrained=True)
+            self.out_channels = [1024, 512, 512, 256, 256, 256]
+        elif backbone == 'resnet101':
+            backbone = resnet101(pretrained=True)
+            self.out_channels = [1024, 512, 512, 256, 256, 256]
+        else:  # backbone == 'resnet152':
+            backbone = resnet152(pretrained=True)
+            self.out_channels = [1024, 512, 512, 256, 256, 256]
+
+
+        self.feature_extractor = nn.Sequential(*list(backbone.children())[:7])
+
+        conv4_block1 = self.feature_extractor[-1][0]
+
+        conv4_block1.conv1.stride = (1, 1)
+        conv4_block1.conv2.stride = (1, 1)
+        conv4_block1.downsample[0].stride = (1, 1)
+
+    def forward(self, x):
+        x = self.feature_extractor(x)
+        return x
+
+
+class SSD300(nn.Module):
+    def __init__(self, backbone='resnet50'):
+        super().__init__()
+
+        self.feature_extractor = ResNet(backbone=backbone)
+
+        self.label_num = 81  # number of COCO classes
+        self._build_additional_features(self.feature_extractor.out_channels)
+        self.num_defaults = [4, 6, 6, 6, 4, 4]
+        self.loc = []
+        self.conf = []
+
+        for nd, oc in zip(self.num_defaults, self.feature_extractor.out_channels):
+            self.loc.append(nn.Conv2d(oc, nd * 4, kernel_size=3, padding=1))
+            self.conf.append(nn.Conv2d(oc, nd * self.label_num, kernel_size=3, padding=1))
+
+        self.loc = nn.ModuleList(self.loc)
+        self.conf = nn.ModuleList(self.conf)
+        self._init_weights()
+
+    def _build_additional_features(self, input_size):
+        self.additional_blocks = []
+        for i, (input_size, output_size, channels) in enumerate(zip(input_size[:-1], input_size[1:], [256, 256, 128, 128, 128])):
+            if i < 3:
+                layer = nn.Sequential(
+                    nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
+                    nn.BatchNorm2d(channels),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(channels, output_size, kernel_size=3, padding=1, stride=2, bias=False),
+                    nn.BatchNorm2d(output_size),
+                    nn.ReLU(inplace=True),
+                )
+            else:
+                layer = nn.Sequential(
+                    nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
+                    nn.BatchNorm2d(channels),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(channels, output_size, kernel_size=3, bias=False),
+                    nn.BatchNorm2d(output_size),
+                    nn.ReLU(inplace=True),
+                )
+
+            self.additional_blocks.append(layer)
+
+        self.additional_blocks = nn.ModuleList(self.additional_blocks)
+
+    def _init_weights(self):
+        layers = [*self.additional_blocks, *self.loc, *self.conf]
+        for layer in layers:
+            for param in layer.parameters():
+                if param.dim() > 1: nn.init.xavier_uniform_(param)
+
+    # Shape the classifier to the view of bboxes
+    def bbox_view(self, src, loc, conf):
+        ret = []
+        for s, l, c in zip(src, loc, conf):
+            ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))
+
+        locs, confs = list(zip(*ret))
+        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
+        return locs, confs
+
+    def forward(self, x):
+        x = self.feature_extractor(x)
+
+        detection_feed = [x]
+        for l in self.additional_blocks:
+            x = l(x)
+            detection_feed.append(x)
+
+        # Feature Map 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4
+        locs, confs = self.bbox_view(detection_feed, self.loc, self.conf)
+
+        # For SSD 300, shall return nbatch x 8732 x {nlabels, nlocs} results
+        return locs, confs
+
+
+class Loss(nn.Module):
+    """
+        Implements the loss as the sum of the followings:
+        1. Confidence Loss: All labels, with hard negative mining
+        2. Localization Loss: Only on positive labels
+        Suppose input dboxes has the shape 8732x4
+    """
+    def __init__(self, dboxes):
+        super(Loss, self).__init__()
+        self.scale_xy = 1.0/dboxes.scale_xy
+        self.scale_wh = 1.0/dboxes.scale_wh
+
+        self.sl1_loss = nn.SmoothL1Loss(reduce=False)
+        self.dboxes = nn.Parameter(dboxes(order="xywh").transpose(0, 1).unsqueeze(dim = 0),
+            requires_grad=False)
+        # Two factor are from following links
+        # http://jany.st/post/2017-11-05-single-shot-detector-ssd-from-scratch-in-tensorflow.html
+        self.con_loss = nn.CrossEntropyLoss(reduce=False)
+
+    def _loc_vec(self, loc):
+        """
+            Generate Location Vectors
+        """
+        gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
+        gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
+        return torch.cat((gxy, gwh), dim=1).contiguous()
+
+    def forward(self, ploc, plabel, gloc, glabel):
+        """
+            ploc, plabel: Nx4x8732, Nxlabel_numx8732
+                predicted location and labels
+
+            gloc, glabel: Nx4x8732, Nx8732
+                ground truth location and labels
+        """
+        mask = glabel > 0
+        pos_num = mask.sum(dim=1)
+
+        vec_gd = self._loc_vec(gloc)
+
+        # sum on four coordinates, and mask
+        sl1 = self.sl1_loss(ploc, vec_gd).sum(dim=1)
+        sl1 = (mask.float()*sl1).sum(dim=1)
+
+        # hard negative mining
+        con = self.con_loss(plabel, glabel)
+
+        # postive mask will never selected
+        con_neg = con.clone()
+        con_neg[mask] = 0
+        _, con_idx = con_neg.sort(dim=1, descending=True)
+        _, con_rank = con_idx.sort(dim=1)
+
+        # number of negative three times positive
+        neg_num = torch.clamp(3*pos_num, max=mask.size(1)).unsqueeze(-1)
+        neg_mask = con_rank < neg_num
+
+        #print(con.shape, mask.shape, neg_mask.shape)
+        closs = (con*(mask.float() + neg_mask.float())).sum(dim=1)
+
+        # avoid no object detected
+        total_loss = sl1 + closs
+        num_mask = (pos_num > 0).float()
+        pos_num = pos_num.float().clamp(min=1e-6)
+        ret = (total_loss*num_mask/pos_num).mean(dim=0)
+        return ret
diff --git a/PyTorch/Detection/SSD/src/train.py b/PyTorch/Detection/SSD/src/train.py
new file mode 100644
index 00000000..e8befe07
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/train.py
@@ -0,0 +1,223 @@
+from torch.autograd import Variable
+import torch
+import time
+from SSD import _C as C
+
+def train_loop(model, loss_func, epoch, optim, train_dataloader, val_dataloader, encoder, iteration, logger, args, mean, std):
+#     for nbatch, (img, _, img_size, bbox, label) in enumerate(train_dataloader):
+    for nbatch, data in enumerate(train_dataloader):
+        img = data[0][0][0]
+        bbox = data[0][1][0]
+        label = data[0][2][0]
+        label = label.type(torch.cuda.LongTensor)
+        bbox_offsets = data[0][3][0]
+        # handle random flipping outside of DALI for now
+        bbox_offsets = bbox_offsets.cuda()
+        img, bbox = C.random_horiz_flip(img, bbox, bbox_offsets, 0.5, False)
+        img.sub_(mean).div_(std)
+        if not args.no_cuda:
+            img = img.cuda()
+            bbox = bbox.cuda()
+            label = label.cuda()
+            bbox_offsets = bbox_offsets.cuda()
+
+        N = img.shape[0]
+        if bbox_offsets[-1].item() == 0:
+            print("No labels in batch")
+            continue
+        bbox, label = C.box_encoder(N, bbox, bbox_offsets, label, encoder.dboxes.cuda(), 0.5)
+        # output is ([N*8732, 4], [N*8732], need [N, 8732, 4], [N, 8732] respectively
+        M = bbox.shape[0] // N
+        bbox = bbox.view(N, M, 4)
+        label = label.view(N, M)
+
+        ploc, plabel = model(img)
+        ploc, plabel = ploc.float(), plabel.float()
+
+        trans_bbox = bbox.transpose(1, 2).contiguous().cuda()
+
+        if not args.no_cuda:
+            label = label.cuda()
+        gloc = Variable(trans_bbox, requires_grad=False)
+        glabel = Variable(label, requires_grad=False)
+
+        loss = loss_func(ploc, plabel, gloc, glabel)
+
+        if args.local_rank == 0:
+            logger.update_iter(epoch, iteration, loss.item())
+
+        if args.fp16:
+            if args.amp:
+                with optim.scale_loss(loss) as scale_loss:
+                    scale_loss.backward()
+            else:
+                optim.backward(loss)
+        else:
+            loss.backward()
+
+        if args.warmup is not None:
+            warmup(optim, args.warmup, iteration, args.learning_rate)
+
+        optim.step()
+        optim.zero_grad()
+        iteration += 1
+
+    return iteration
+
+
+def benchmark_train_loop(model, loss_func, epoch, optim, train_dataloader, val_dataloader, encoder, iteration, logger, args, mean, std):
+    start_time = None
+    # tensor for results
+    result = torch.zeros((1,)).cuda()
+    for i, data in enumerate(loop(train_dataloader)):
+        if i >= args.benchmark_warmup:
+            start_time = time.time()
+
+        img = data[0][0][0]
+        bbox = data[0][1][0]
+        label = data[0][2][0]
+        label = label.type(torch.cuda.LongTensor)
+        bbox_offsets = data[0][3][0]
+        # handle random flipping outside of DALI for now
+        bbox_offsets = bbox_offsets.cuda()
+        img, bbox = C.random_horiz_flip(img, bbox, bbox_offsets, 0.5, False)
+
+        if not args.no_cuda:
+            img = img.cuda()
+            bbox = bbox.cuda()
+            label = label.cuda()
+            bbox_offsets = bbox_offsets.cuda()
+        img.sub_(mean).div_(std)
+
+        N = img.shape[0]
+        if bbox_offsets[-1].item() == 0:
+            print("No labels in batch")
+            continue
+        bbox, label = C.box_encoder(N, bbox, bbox_offsets, label, encoder.dboxes.cuda(), 0.5)
+
+        M = bbox.shape[0] // N
+        bbox = bbox.view(N, M, 4)
+        label = label.view(N, M)
+
+
+
+
+
+        ploc, plabel = model(img)
+        ploc, plabel = ploc.float(), plabel.float()
+
+        trans_bbox = bbox.transpose(1, 2).contiguous().cuda()
+
+        if not args.no_cuda:
+            label = label.cuda()
+        gloc = Variable(trans_bbox, requires_grad=False)
+        glabel = Variable(label, requires_grad=False)
+
+        loss = loss_func(ploc, plabel, gloc, glabel)
+
+
+
+        # loss scaling
+        if args.fp16:
+            if args.amp:
+                with optim.scale_loss(loss) as scale_loss:
+                    scale_loss.backward()
+            else:
+                optim.backward(loss)
+        else:
+            loss.backward()
+
+        optim.step()
+        optim.zero_grad()
+
+        if i >= args.benchmark_warmup + args.benchmark_iterations:
+            break
+
+        if i >= args.benchmark_warmup:
+            logger.update(args.batch_size, time.time() - start_time)
+
+
+    result.data[0] = logger.print_result()
+    if args.N_gpu > 1:
+        torch.distributed.reduce(result, 0)
+    if args.local_rank == 0:
+        print('Training performance = {} FPS'.format(float(result.data[0])))
+
+
+
+def loop(dataloader):
+    while True:
+        for data in dataloader:
+            yield data
+
+def benchmark_inference_loop(model, loss_func, epoch, optim, train_dataloader, val_dataloader, encoder, iteration, logger, args, mean, std):
+    assert args.N_gpu == 1, 'Inference benchmark only on 1 gpu'
+    start_time = None
+    model.eval()
+
+    i = -1
+    val_datas = loop(val_dataloader)
+
+    while True:
+        i += 1
+        torch.cuda.synchronize()
+        if i >= args.benchmark_warmup:
+            start_time = time.time()
+
+        data = next(val_datas)
+
+        with torch.no_grad():
+            img = data[0]
+            if not args.no_cuda:
+                img = img.cuda()
+            if args.fp16:
+                img = img.half()
+            img.sub_(mean).div_(std)
+            img = Variable(img, requires_grad=False)
+            _ = model(img)
+            torch.cuda.synchronize()
+
+            if i >= args.benchmark_warmup + args.benchmark_iterations:
+                break
+
+            if i >= args.benchmark_warmup:
+                logger.update(args.eval_batch_size, time.time() - start_time)
+
+    logger.print_result()
+
+def warmup(optim, warmup_iters, iteration, base_lr):
+    if iteration < warmup_iters:
+        new_lr = 1. * base_lr / warmup_iters * iteration
+        for param_group in optim.param_groups:
+            param_group['lr'] = new_lr
+
+
+def load_checkpoint(model, checkpoint):
+    """
+    Load model from checkpoint.
+    """
+    print("loading model checkpoint", checkpoint)
+    od = torch.load(checkpoint)
+
+    # remove proceeding 'N.' from checkpoint that comes from DDP wrapper
+    saved_model = od["model"]
+    model.load_state_dict(saved_model)
+
+
+def tencent_trick(model):
+    """
+    Divide parameters into 2 groups.
+    First group is BNs and all biases.
+    Second group is the remaining model's parameters.
+    Weight decay will be disabled in first group (aka tencent trick).
+    """
+    decay, no_decay = [], []
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue  # frozen weights
+        if len(param.shape) == 1 or name.endswith(".bias"):
+            no_decay.append(param)
+        else:
+            decay.append(param)
+    return [{'params': no_decay, 'weight_decay': 0.0},
+            {'params': decay}]
diff --git a/PyTorch/Detection/SSD/src/utils.py b/PyTorch/Detection/SSD/src/utils.py
new file mode 100644
index 00000000..280ae407
--- /dev/null
+++ b/PyTorch/Detection/SSD/src/utils.py
@@ -0,0 +1,578 @@
+import torch
+import torchvision.transforms as transforms
+import torch.utils.data as data
+from PIL import Image
+import os
+import numpy as np
+import random
+import itertools
+import torch.nn.functional as F
+import json
+import time
+import bz2
+import pickle
+from math import sqrt
+
+
+# This function is from https://github.com/kuangliu/pytorch-ssd.
+def calc_iou_tensor(box1, box2):
+    """ Calculation of IoU based on two boxes tensor,
+        Reference to https://github.com/kuangliu/pytorch-src
+        input:
+            box1 (N, 4)
+            box2 (M, 4)
+        output:
+            IoU (N, M)
+    """
+    N = box1.size(0)
+    M = box2.size(0)
+
+    be1 = box1.unsqueeze(1).expand(-1, M, -1)
+    be2 = box2.unsqueeze(0).expand(N, -1, -1)
+
+    # Left Top & Right Bottom
+    lt = torch.max(be1[:,:,:2], be2[:,:,:2])
+    #mask1 = (be1[:,:, 0] < be2[:,:, 0]) ^ (be1[:,:, 1] < be2[:,:, 1])
+    #mask1 = ~mask1
+    rb = torch.min(be1[:,:,2:], be2[:,:,2:])
+    #mask2 = (be1[:,:, 2] < be2[:,:, 2]) ^ (be1[:,:, 3] < be2[:,:, 3])
+    #mask2 = ~mask2
+
+    delta = rb - lt
+    delta[delta < 0] = 0
+    intersect = delta[:,:,0]*delta[:,:,1]
+    #*mask1.float()*mask2.float()
+
+    delta1 = be1[:,:,2:] - be1[:,:,:2]
+    area1 = delta1[:,:,0]*delta1[:,:,1]
+    delta2 = be2[:,:,2:] - be2[:,:,:2]
+    area2 = delta2[:,:,0]*delta2[:,:,1]
+
+    iou = intersect/(area1 + area2 - intersect)
+    return iou
+
+
+# This function is from https://github.com/kuangliu/pytorch-ssd.
+class Encoder(object):
+    """
+        Inspired by https://github.com/kuangliu/pytorch-src
+        Transform between (bboxes, lables) <-> SSD output
+
+        dboxes: default boxes in size 8732 x 4,
+            encoder: input ltrb format, output xywh format
+            decoder: input xywh format, output ltrb format
+
+        encode:
+            input  : bboxes_in (Tensor nboxes x 4), labels_in (Tensor nboxes)
+            output : bboxes_out (Tensor 8732 x 4), labels_out (Tensor 8732)
+            criteria : IoU threshold of bboexes
+
+        decode:
+            input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
+            output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
+            criteria : IoU threshold of bboexes
+            max_output : maximum number of output bboxes
+    """
+
+    def __init__(self, dboxes):
+        self.dboxes = dboxes(order="ltrb")
+        self.dboxes_xywh = dboxes(order="xywh").unsqueeze(dim=0)
+        self.nboxes = self.dboxes.size(0)
+        self.scale_xy = dboxes.scale_xy
+        self.scale_wh = dboxes.scale_wh
+
+    def encode(self, bboxes_in, labels_in, criteria = 0.5):
+
+        ious = calc_iou_tensor(bboxes_in, self.dboxes)
+        best_dbox_ious, best_dbox_idx = ious.max(dim=0)
+        best_bbox_ious, best_bbox_idx = ious.max(dim=1)
+
+        # set best ious 2.0
+        best_dbox_ious.index_fill_(0, best_bbox_idx, 2.0)
+
+        idx = torch.arange(0, best_bbox_idx.size(0), dtype=torch.int64)
+        best_dbox_idx[best_bbox_idx[idx]] = idx
+
+        # filter IoU > 0.5
+        masks = best_dbox_ious > criteria
+        labels_out = torch.zeros(self.nboxes, dtype=torch.long)
+        labels_out[masks] = labels_in[best_dbox_idx[masks]]
+        bboxes_out = self.dboxes.clone()
+        bboxes_out[masks, :] = bboxes_in[best_dbox_idx[masks], :]
+        # Transform format to xywh format
+        x, y, w, h = 0.5*(bboxes_out[:, 0] + bboxes_out[:, 2]), \
+                     0.5*(bboxes_out[:, 1] + bboxes_out[:, 3]), \
+                     -bboxes_out[:, 0] + bboxes_out[:, 2], \
+                     -bboxes_out[:, 1] + bboxes_out[:, 3]
+        bboxes_out[:, 0] = x
+        bboxes_out[:, 1] = y
+        bboxes_out[:, 2] = w
+        bboxes_out[:, 3] = h
+        return bboxes_out, labels_out
+
+    def scale_back_batch(self, bboxes_in, scores_in):
+        """
+            Do scale and transform from xywh to ltrb
+            suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
+        """
+        if bboxes_in.device == torch.device("cpu"):
+            self.dboxes = self.dboxes.cpu()
+            self.dboxes_xywh = self.dboxes_xywh.cpu()
+        else:
+            self.dboxes = self.dboxes.cuda()
+            self.dboxes_xywh = self.dboxes_xywh.cuda()
+
+        bboxes_in = bboxes_in.permute(0, 2, 1)
+        scores_in = scores_in.permute(0, 2, 1)
+
+        bboxes_in[:, :, :2] = self.scale_xy*bboxes_in[:, :, :2]
+        bboxes_in[:, :, 2:] = self.scale_wh*bboxes_in[:, :, 2:]
+
+        bboxes_in[:, :, :2] = bboxes_in[:, :, :2]*self.dboxes_xywh[:, :, 2:] + self.dboxes_xywh[:, :, :2]
+        bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp()*self.dboxes_xywh[:, :, 2:]
+
+        # Transform format to ltrb
+        l, t, r, b = bboxes_in[:, :, 0] - 0.5*bboxes_in[:, :, 2],\
+                     bboxes_in[:, :, 1] - 0.5*bboxes_in[:, :, 3],\
+                     bboxes_in[:, :, 0] + 0.5*bboxes_in[:, :, 2],\
+                     bboxes_in[:, :, 1] + 0.5*bboxes_in[:, :, 3]
+
+        bboxes_in[:, :, 0] = l
+        bboxes_in[:, :, 1] = t
+        bboxes_in[:, :, 2] = r
+        bboxes_in[:, :, 3] = b
+
+        return bboxes_in, F.softmax(scores_in, dim=-1)
+
+    def decode_batch(self, bboxes_in, scores_in,  criteria = 0.45, max_output=200):
+        bboxes, probs = self.scale_back_batch(bboxes_in, scores_in)
+
+        output = []
+        for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
+            bbox = bbox.squeeze(0)
+            prob = prob.squeeze(0)
+            output.append(self.decode_single(bbox, prob, criteria, max_output))
+        return output
+
+    # perform non-maximum suppression
+    def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
+        # Reference to https://github.com/amdegroot/ssd.pytorch
+
+        bboxes_out = []
+        scores_out = []
+        labels_out = []
+
+        for i, score in enumerate(scores_in.split(1, 1)):
+            # skip background
+            # print(score[score>0.90])
+            if i == 0: continue
+            # print(i)
+
+            score = score.squeeze(1)
+            mask = score > 0.05
+
+            bboxes, score = bboxes_in[mask, :], score[mask]
+            if score.size(0) == 0: continue
+
+            score_sorted, score_idx_sorted = score.sort(dim=0)
+
+            # select max_output indices
+            score_idx_sorted = score_idx_sorted[-max_num:]
+            candidates = []
+            #maxdata, maxloc = scores_in.sort()
+
+            while score_idx_sorted.numel() > 0:
+                idx = score_idx_sorted[-1].item()
+                bboxes_sorted = bboxes[score_idx_sorted, :]
+                bboxes_idx = bboxes[idx, :].unsqueeze(dim=0)
+                iou_sorted = calc_iou_tensor(bboxes_sorted, bboxes_idx).squeeze()
+                # we only need iou < criteria
+                score_idx_sorted = score_idx_sorted[iou_sorted < criteria]
+                candidates.append(idx)
+
+            bboxes_out.append(bboxes[candidates, :])
+            scores_out.append(score[candidates])
+            labels_out.extend([i]*len(candidates))
+
+        bboxes_out, labels_out, scores_out = torch.cat(bboxes_out, dim=0), \
+               torch.tensor(labels_out, dtype=torch.long), \
+               torch.cat(scores_out, dim=0)
+
+
+        _, max_ids = scores_out.sort(dim=0)
+        max_ids = max_ids[-max_output:]
+        return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]
+
+
+class DefaultBoxes(object):
+    def __init__(self, fig_size, feat_size, steps, scales, aspect_ratios, \
+                       scale_xy=0.1, scale_wh=0.2):
+
+        self.feat_size = feat_size
+        self.fig_size = fig_size
+
+        self.scale_xy_ = scale_xy
+        self.scale_wh_ = scale_wh
+
+        # According to https://github.com/weiliu89/caffe
+        # Calculation method slightly different from paper
+        self.steps = steps
+        self.scales = scales
+
+        fk = fig_size/np.array(steps)
+        self.aspect_ratios = aspect_ratios
+
+        self.default_boxes = []
+        # size of feature and number of feature
+        for idx, sfeat in enumerate(self.feat_size):
+
+            sk1 = scales[idx]/fig_size
+            sk2 = scales[idx+1]/fig_size
+            sk3 = sqrt(sk1*sk2)
+            all_sizes = [(sk1, sk1), (sk3, sk3)]
+
+            for alpha in aspect_ratios[idx]:
+                w, h = sk1*sqrt(alpha), sk1/sqrt(alpha)
+                all_sizes.append((w, h))
+                all_sizes.append((h, w))
+            for w, h in all_sizes:
+                for i, j in itertools.product(range(sfeat), repeat=2):
+                    cx, cy = (j+0.5)/fk[idx], (i+0.5)/fk[idx]
+                    self.default_boxes.append((cx, cy, w, h))
+
+        self.dboxes = torch.tensor(self.default_boxes)
+        self.dboxes.clamp_(min=0, max=1)
+        # For IoU calculation
+        self.dboxes_ltrb = self.dboxes.clone()
+        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5 * self.dboxes[:, 3]
+        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5 * self.dboxes[:, 3]
+
+    @property
+    def scale_xy(self):
+        return self.scale_xy_
+
+    @property
+    def scale_wh(self):
+        return self.scale_wh_
+
+    def __call__(self, order="ltrb"):
+        if order == "ltrb": return self.dboxes_ltrb
+        if order == "xywh": return self.dboxes
+
+
+def dboxes300_coco():
+    figsize = 300
+    feat_size = [38, 19, 10, 5, 3, 1]
+    steps = [8, 16, 32, 64, 100, 300]
+    # use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py
+    scales = [21, 45, 99, 153, 207, 261, 315]
+    aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
+    dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
+    return dboxes
+
+
+# This function is from https://github.com/chauhan-utk/ssd.DomainAdaptation.
+class SSDCropping(object):
+    """ Cropping for SSD, according to original paper
+        Choose between following 3 conditions:
+        1. Preserve the original image
+        2. Random crop minimum IoU is among 0.1, 0.3, 0.5, 0.7, 0.9
+        3. Random crop
+        Reference to https://github.com/chauhan-utk/src.DomainAdaptation
+    """
+    def __init__(self):
+
+        self.sample_options = (
+            # Do nothing
+            None,
+            # min IoU, max IoU
+            (0.1, None),
+            (0.3, None),
+            (0.5, None),
+            (0.7, None),
+            (0.9, None),
+            # no IoU requirements
+            (None, None),
+        )
+
+    def __call__(self, img, img_size, bboxes, labels):
+
+        # Ensure always return cropped image
+        while True:
+            mode = random.choice(self.sample_options)
+
+            if mode is None:
+                return img, img_size, bboxes, labels
+
+            htot, wtot = img_size
+
+            min_iou, max_iou = mode
+            min_iou = float("-inf") if min_iou is None else min_iou
+            max_iou = float("+inf") if max_iou is None else max_iou
+
+            # Implementation use 50 iteration to find possible candidate
+            for _ in range(1):
+                # suze of each sampled path in [0.1, 1] 0.3*0.3 approx. 0.1
+                w = random.uniform(0.3 , 1.0)
+                h = random.uniform(0.3 , 1.0)
+
+                if w/h < 0.5 or w/h > 2:
+                    continue
+
+                # left 0 ~ wtot - w, top 0 ~ htot - h
+                left = random.uniform(0, 1.0 - w)
+                top = random.uniform(0, 1.0 - h)
+
+                right = left + w
+                bottom = top + h
+
+                ious = calc_iou_tensor(bboxes, torch.tensor([[left, top, right, bottom]]))
+
+                # tailor all the bboxes and return
+                if not ((ious > min_iou) & (ious < max_iou)).all():
+                    continue
+
+                # discard any bboxes whose center not in the cropped image
+                xc = 0.5*(bboxes[:, 0] + bboxes[:, 2])
+                yc = 0.5*(bboxes[:, 1] + bboxes[:, 3])
+
+                masks = (xc > left) & (xc < right) & (yc > top) & (yc < bottom)
+
+                # if no such boxes, continue searching again
+                if not masks.any():
+                    continue
+
+                bboxes[bboxes[:, 0] < left, 0] = left
+                bboxes[bboxes[:, 1] < top, 1] = top
+                bboxes[bboxes[:, 2] > right, 2] = right
+                bboxes[bboxes[:, 3] > bottom, 3] = bottom
+
+                bboxes = bboxes[masks, :]
+                labels = labels[masks]
+
+                left_idx = int(left*wtot)
+                top_idx =  int(top*htot)
+                right_idx = int(right*wtot)
+                bottom_idx = int(bottom*htot)
+                img = img.crop((left_idx, top_idx, right_idx, bottom_idx))
+
+                bboxes[:, 0] = (bboxes[:, 0] - left)/w
+                bboxes[:, 1] = (bboxes[:, 1] - top)/h
+                bboxes[:, 2] = (bboxes[:, 2] - left)/w
+                bboxes[:, 3] = (bboxes[:, 3] - top)/h
+
+                htot = bottom_idx - top_idx
+                wtot = right_idx - left_idx
+                return img, (htot, wtot), bboxes, labels
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, image, bboxes):
+        if random.random() < self.p:
+            bboxes[:, 0], bboxes[:, 2] = 1.0 - bboxes[:, 2], 1.0 - bboxes[:, 0]
+            return image.transpose(Image.FLIP_LEFT_RIGHT), bboxes
+        return image, bboxes
+
+
+# Do data augumentation
+class SSDTransformer(object):
+    """ SSD Data Augumentation, according to original paper
+        Composed by several steps:
+        Cropping
+        Resize
+        Flipping
+        Jittering
+    """
+    def __init__(self, dboxes, size = (300, 300), val=False):
+
+        # define vgg16 mean
+        self.size = size
+        self.val = val
+
+        self.dboxes_ = dboxes #DefaultBoxes300()
+        self.encoder = Encoder(self.dboxes_)
+
+        self.crop = SSDCropping()
+        self.img_trans = transforms.Compose([
+            transforms.Resize(self.size),
+            transforms.ColorJitter(brightness=0.125, contrast=0.5,
+                saturation=0.5, hue=0.05
+            ),
+            transforms.ToTensor()
+        ])
+        self.hflip = RandomHorizontalFlip()
+
+        # All Pytorch Tensor will be normalized
+        # https://discuss.pytorch.org/t/how-to-preprocess-input-for-pre-trained-networks/683
+        self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                               std=[0.229, 0.224, 0.225])
+        self.trans_val = transforms.Compose([
+            transforms.Resize(self.size),
+            transforms.ToTensor(),
+            #ToTensor(),
+            self.normalize,])
+
+    @property
+    def dboxes(self):
+        return self.dboxes_
+
+    def __call__(self, img, img_size, bbox=None, label=None, max_num=200):
+        #img = torch.tensor(img)
+        if self.val:
+            bbox_out = torch.zeros(max_num, 4)
+            label_out =  torch.zeros(max_num, dtype=torch.long)
+            bbox_out[:bbox.size(0), :] = bbox
+            label_out[:label.size(0)] = label
+            return self.trans_val(img), img_size, bbox_out, label_out
+
+        img, img_size, bbox, label = self.crop(img, img_size, bbox, label)
+        img, bbox = self.hflip(img, bbox)
+
+        img = self.img_trans(img).contiguous()
+        img = self.normalize(img)
+
+        bbox, label = self.encoder.encode(bbox, label)
+
+        return img, img_size, bbox, label
+
+
+# Implement a datareader for COCO dataset
+class COCODetection(data.Dataset):
+    def __init__(self, img_folder, annotate_file, transform=None):
+        self.img_folder = img_folder
+        self.annotate_file = annotate_file
+
+        # Start processing annotation
+        with open(annotate_file) as fin:
+            self.data = json.load(fin)
+
+        self.images = {}
+
+        self.label_map = {}
+        self.label_info = {}
+        start_time = time.time()
+        # 0 stand for the background
+        cnt = 0
+        self.label_info[cnt] = "background"
+        for cat in self.data["categories"]:
+            cnt += 1
+            self.label_map[cat["id"]] = cnt
+            self.label_info[cnt] = cat["name"]
+
+        # build inference for images
+        for img in self.data["images"]:
+            img_id = img["id"]
+            img_name = img["file_name"]
+            img_size = (img["height"],img["width"])
+            if img_id in self.images: raise Exception("dulpicated image record")
+            self.images[img_id] = (img_name, img_size, [])
+
+        # read bboxes
+        for bboxes in self.data["annotations"]:
+            img_id = bboxes["image_id"]
+            category_id = bboxes["category_id"]
+            bbox = bboxes["bbox"]
+            bbox_label = self.label_map[bboxes["category_id"]]
+            self.images[img_id][2].append((bbox, bbox_label))
+
+        for k, v in list(self.images.items()):
+            if len(v[2]) == 0:
+                self.images.pop(k)
+
+        self.img_keys = list(self.images.keys())
+        self.transform = transform
+
+    @property
+    def labelnum(self):
+        return len(self.label_info)
+
+    @staticmethod
+    def load(pklfile):
+        with bz2.open(pklfile, "rb") as fin:
+            ret = pickle.load(fin)
+        return ret
+
+    def save(self, pklfile):
+        with bz2.open(pklfile, "wb") as fout:
+            pickle.dump(self, fout)
+
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, idx):
+        img_id = self.img_keys[idx]
+        img_data = self.images[img_id]
+        fn = img_data[0]
+        img_path = os.path.join(self.img_folder, fn)
+        img = Image.open(img_path).convert("RGB")
+
+        htot, wtot = img_data[1]
+        bbox_sizes = []
+        bbox_labels = []
+
+        #for (xc, yc, w, h), bbox_label in img_data[2]:
+        for (l,t,w,h), bbox_label in img_data[2]:
+            r = l + w
+            b = t + h
+            #l, t, r, b = xc - 0.5*w, yc - 0.5*h, xc + 0.5*w, yc + 0.5*h
+            bbox_size = (l/wtot, t/htot, r/wtot, b/htot)
+            bbox_sizes.append(bbox_size)
+            bbox_labels.append(bbox_label)
+
+        bbox_sizes = torch.tensor(bbox_sizes)
+        bbox_labels =  torch.tensor(bbox_labels)
+
+
+        if self.transform != None:
+            img, (htot, wtot), bbox_sizes, bbox_labels = \
+                self.transform(img, (htot, wtot), bbox_sizes, bbox_labels)
+        else:
+            pass
+
+        return img, img_id, (htot, wtot), bbox_sizes, bbox_labels
+
+
+def draw_patches(img, bboxes, labels, order="xywh", label_map={}):
+
+    import matplotlib.pyplot as plt
+    import matplotlib.patches as patches
+    # Suppose bboxes in fractional coordinate:
+    # cx, cy, w, h
+    # img = img.numpy()
+    img = np.array(img)
+    labels = np.array(labels)
+    bboxes = bboxes.numpy()
+
+    if label_map:
+        labels = [label_map.get(l) for l in labels]
+
+    if order == "ltrb":
+        xmin, ymin, xmax, ymax = bboxes[:, 0],  bboxes[:, 1],  bboxes[:, 2],  bboxes[:, 3]
+        cx, cy, w, h = (xmin + xmax)/2, (ymin + ymax)/2, xmax - xmin, ymax - ymin
+    else:
+        cx, cy, w, h = bboxes[:, 0],  bboxes[:, 1],  bboxes[:, 2],  bboxes[:, 3]
+
+    htot, wtot,_ = img.shape
+    cx *= wtot
+    cy *= htot
+    w *= wtot
+    h *= htot
+
+    bboxes = zip(cx, cy, w, h)
+
+    plt.imshow(img)
+    ax = plt.gca()
+    for (cx, cy, w, h), label in zip(bboxes, labels):
+        if label == "background": continue
+        ax.add_patch(patches.Rectangle((cx-0.5*w, cy-0.5*h),
+                                        w, h, fill=False, color="r"))
+        bbox_props = dict(boxstyle="round", fc="y", ec="0.5", alpha=0.3)
+        ax.text(cx-0.5*w, cy-0.5*h, label, ha="center", va="center", size=15, bbox=bbox_props)
+    plt.show()
+
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diff --git a/PyTorch/Distributed/README.md b/PyTorch/Distributed/README.md
deleted file mode 100644
index da5c7d32..00000000
--- a/PyTorch/Distributed/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Basic Multirpocess Example based on the MNIST example
-
-
-This example is based on [PyTorch's MNIST Example](https://github.com/pytorch/examples/tree/master/mnist).
-
-This example demonstrates how to modify a network to use a basic but effective distributed data parallel module. This parallel method is designed to easily run multi-gpu runs on a single node. It was created as current parallel methods integraded into pytorch can induce significant overhead due to python GIL lock. This method will reduce the influence of those overheads and potentially provide a benefit in performance, especially for networks with a significant number of fast running operations.
-
-## Getting started
-Prior to running please run
-```pip install -r requirements.txt```
-
-and start a single process run to allow the dataset to be downloaded (This will not work properly in multi-gpu. You can stop this job as soon as it starts iterating.).
-```python main.py```
-
-You can now the code multi-gpu with
-```python -m multiproc main.py ...```
-adding any normal option you'd like.
-
-## Converting your own model
-To understand how to convert your own model to use the distributed module included, please see all sections of main.py within ```#=====START: ADDED FOR DISTRIBUTED======``` and ```#=====END:   ADDED FOR DISTRIBUTED======``` flags.
-
-Copy the distributed.py and multiproc.py files from here to your local workspace.
-
-## Requirements
-Pytorch master branch built from source. This requirement is to use NCCL as a distributed backend.
diff --git a/PyTorch/Distributed/distributed.py b/PyTorch/Distributed/distributed.py
deleted file mode 100644
index bd4cc391..00000000
--- a/PyTorch/Distributed/distributed.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import torch
-from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
-import torch.distributed as dist
-from torch.nn.modules import Module
-from torch.autograd import Variable
-
-'''
-This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
-launcher included with this example. It assumes that your run is using multiprocess with 1
-GPU/process, that the model is on the correct device, and that torch.set_device has been
-used to set the device.
-
-Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
-and will be allreduced at the finish of the backward pass.
-'''
-
-def flat_dist_call(tensors, call, extra_args=None):
-    flat_dist_call.warn_on_half = True
-    buckets = {}
-    for tensor in tensors:
-        tp = tensor.type()
-        if tp not in buckets:
-            buckets[tp] = []
-        buckets[tp].append(tensor)
-                    
-    if flat_dist_call.warn_on_half:
-        if torch.cuda.HalfTensor in buckets:
-            print("WARNING: gloo dist backend for half parameters may be extremely slow." +
-                  " It is recommended to use the NCCL backend in this case.")
-            flat_dist_call.warn_on_half = False
-
-    for tp in buckets:
-        bucket = buckets[tp]
-        coalesced = _flatten_dense_tensors(bucket)
-        if extra_args is not None:
-            call(coalesced, *extra_args)
-        else:
-            call(coalesced)
-        coalesced /= dist.get_world_size()
-        for buf, synced in zip(bucket, _unflatten_dense_tensors(coalesced, bucket)):
-            buf.copy_(synced)
-            
-class DistributedDataParallel(Module):
-
-    def __init__(self, module, message_size=10000000):
-        super(DistributedDataParallel, self).__init__()
-        self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
-        
-        self.message_size = message_size
-        
-        #reference to last iterations parameters to see if anything has changed
-        self.param_refs = []
-        
-        self.reduction_stream = torch.cuda.Stream()
-        
-        self.module = module
-        self.param_list = list(self.module.parameters())
-        
-        if dist._backend == dist.dist_backend.NCCL:
-            for param in self.param_list:
-                assert param.is_cuda, "NCCL backend only supports model parameters to be on GPU."
-                
-        self.record = []
-        self.create_hooks()
-
-        flat_dist_call([param.data for param in self.module.parameters()], dist.broadcast, (0,) )
-        
-    def create_hooks(self):
-        #all reduce gradient hook
-        def allreduce_params():
-            if(self.needs_reduction):
-                self.needs_reduction = False
-                self.needs_refresh = False
-            else:
-                return
-            grads = [param.grad.data for param in self.module.parameters() if param.grad is not None]
-            flat_dist_call(grads, dist.all_reduce)
-            t_record = torch.cuda.IntTensor(self.record)
-            dist.broadcast(t_record, 0)
-            self.record = [int(entry) for entry in t_record]
-
-
-        def flush_buckets():
-            if not self.needs_reduction:
-                return
-            self.needs_reduction = False
-            
-            ready = []
-            for i in range(len(self.param_state)):
-                if self.param_state[i] == 1:
-                    param = self.param_list[self.record[i]]
-                    if param.grad is not None:
-                        ready.append(param.grad.data)
-
-            if(len(ready)>0):
-                orig_stream = torch.cuda.current_stream()
-                with torch.cuda.stream(self.reduction_stream):
-                    self.reduction_stream.wait_stream(orig_stream)
-                    flat_dist_call(ready, dist.all_reduce)
-                    
-            torch.cuda.current_stream().wait_stream(self.reduction_stream)
-
-        for param_i, param in enumerate(list(self.module.parameters())):
-            def wrapper(param_i):
-                
-                def allreduce_hook(*unused):
-                    if self.needs_refresh:
-                        self.record.append(param_i)
-                        Variable._execution_engine.queue_callback(allreduce_params)
-                    else:
-                        Variable._execution_engine.queue_callback(flush_buckets)
-                        self.param_state[self.record.index(param_i)] = 1
-                        self.comm_ready_buckets()
-                    
-                    
-                if param.requires_grad:
-                    param.register_hook(allreduce_hook)
-            wrapper(param_i)
-
-
-    def comm_ready_buckets(self):
-
-        ready = []
-        counter = 0
-
-        while counter < len(self.param_state) and self.param_state[counter] == 2:
-            counter += 1
-
-        while counter < len(self.param_state) and self.param_state[counter] == 1:
-            ready.append(counter)
-            counter += 1
-
-        if not ready:
-            return
-
-        grads = []
-        for ind in ready:
-            param_ind = self.record[ind]
-            if self.param_list[param_ind].grad is not None:
-                grads.append(self.param_list[param_ind].grad.data)
-
-        bucket = []
-        bucket_inds = []
-        while grads:
-            bucket.append(grads.pop(0))
-            bucket_inds.append(ready.pop(0))
-            
-            cumm_size = 0
-            for ten in bucket:
-                cumm_size += ten.numel()
-
-            if cumm_size < self.message_size:
-                continue
-
-            evt = torch.cuda.Event()
-            evt.record(torch.cuda.current_stream())
-            evt.wait(stream=self.reduction_stream)
-        
-            with torch.cuda.stream(self.reduction_stream):
-                flat_dist_call(bucket, dist.all_reduce)
-
-            for ind in bucket_inds:
-                self.param_state[ind] = 2
-        
-    def forward(self, *inputs, **kwargs):
-
-        param_list = [param for param in list(self.module.parameters()) if param.requires_grad]
-
-        
-
-        self.needs_refresh = True if not self.param_refs else any(
-            [param1 is not param2 for param1, param2 in zip(param_list, self.param_refs)]
-        )
-                
-        if  self.needs_refresh:
-            self.record = []
-
-            
-        self.param_state = [0 for i in range(len(param_list))]
-        self.param_refs = param_list
-        self.needs_reduction = True
-        
-        return self.module(*inputs, **kwargs)
diff --git a/PyTorch/Distributed/main.py b/PyTorch/Distributed/main.py
deleted file mode 100644
index dbeaff11..00000000
--- a/PyTorch/Distributed/main.py
+++ /dev/null
@@ -1,196 +0,0 @@
-from __future__ import print_function
-import argparse
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import datasets, transforms
-from torch.autograd import Variable
-
-#=====START: ADDED FOR DISTRIBUTED======
-'''Add custom module for distributed'''
-
-from distributed import DistributedDataParallel as DDP
-'''Import distributed data loader'''
-import torch.utils.data
-import torch.utils.data.distributed
-
-'''Import torch.distributed'''
-import torch.distributed as dist
-
-#=====END:   ADDED FOR DISTRIBUTED======
-
-# Training settings
-parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-parser.add_argument('--batch-size', type=int, default=64, metavar='N',
-                    help='input batch size for training (default: 64)')
-parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
-                    help='input batch size for testing (default: 1000)')
-parser.add_argument('--epochs', type=int, default=10, metavar='N',
-                    help='number of epochs to train (default: 10)')
-parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
-                    help='learning rate (default: 0.01)')
-parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
-                    help='SGD momentum (default: 0.5)')
-parser.add_argument('--no-cuda', action='store_true', default=False,
-                    help='disables CUDA training')
-parser.add_argument('--seed', type=int, default=1, metavar='S',
-                    help='random seed (default: 1)')
-parser.add_argument('--log-interval', type=int, default=10, metavar='N',
-                    help='how many batches to wait before logging training status')
-
-#======START: ADDED FOR DISTRIBUTED======
-'''
-Add some distributed options. For explanation of dist-url and dist-backend please see
-http://pytorch.org/tutorials/intermediate/dist_tuto.html
-
---world-size and --rank are required parameters as they will be used by the multiproc.py launcher
-but do not have to be set explicitly.
-'''
-
-parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
-                    help='url used to set up distributed training')
-parser.add_argument('--dist-backend', default='nccl', type=str,
-                    help='distributed backend')
-parser.add_argument('--world-size', default=1, type=int,
-                    help='Number of GPUs to use. Can either be manually set ' +
-                    'or automatically set by using \'python -m multiproc\'.')
-parser.add_argument('--rank', default=0, type=int,
-                    help='Used for multi-process training. Can either be manually set ' +
-                    'or automatically set by using \'python -m multiproc\'.')
-#=====END:   ADDED FOR DISTRIBUTED======
-
-args = parser.parse_args()
-args.cuda = not args.no_cuda and torch.cuda.is_available()
-
-#======START: ADDED FOR DISTRIBUTED======
-'''Add a convenience flag to see if we are running distributed'''
-args.distributed = args.world_size > 1
-
-'''Check that we are running with cuda, as distributed is only supported for cuda.'''
-if args.distributed:
-    assert args.cuda, "Distributed mode requires running with CUDA."
-
-if args.distributed:
-    '''
-    Set cuda device so everything is done on the right GPU.
-    THIS MUST BE DONE AS SOON AS POSSIBLE.
-    '''
-    torch.cuda.set_device(args.rank % torch.cuda.device_count())
-
-    '''Initialize distributed communication'''
-    dist.init_process_group(args.dist_backend, init_method=args.dist_url,
-                            world_size=args.world_size)
-
-#=====END:   ADDED FOR DISTRIBUTED======
-
-torch.manual_seed(args.seed)
-if args.cuda:
-    torch.cuda.manual_seed(args.seed)
-
-
-kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
-
-#=====START: ADDED FOR DISTRIBUTED======
-'''
-Change sampler to distributed if running distributed.
-Shuffle data loader only if distributed.
-'''
-train_dataset = datasets.MNIST('../data', train=True, download=True,
-                               transform=transforms.Compose([
-                                   transforms.ToTensor(),
-                                   transforms.Normalize((0.1307,), (0.3081,))
-                               ]))
-
-if args.distributed:
-    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
-else:
-    train_sampler = None
-
-train_loader = torch.utils.data.DataLoader(
-    train_dataset, sampler=train_sampler,
-    batch_size=args.batch_size, shuffle=(train_sampler is None), **kwargs
-)
-
-#=====END:   ADDED FOR DISTRIBUTED======
-
-test_loader = torch.utils.data.DataLoader(
-    datasets.MNIST('../data', train=False, transform=transforms.Compose([
-                       transforms.ToTensor(),
-                       transforms.Normalize((0.1307,), (0.3081,))
-                   ])),
-    batch_size=args.test_batch_size, shuffle=True, **kwargs)
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv2_drop = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, 10)
-
-    def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), 2))
-        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
-        x = x.view(-1, 320)
-        x = F.relu(self.fc1(x))
-        x = F.dropout(x, training=self.training)
-        x = self.fc2(x)
-        return F.log_softmax(x)
-
-model = Net()
-if args.cuda:
-    model.cuda()
-
-#=====START: ADDED FOR DISTRIBUTED======
-'''
-Wrap model in our version of DistributedDataParallel.
-This must be done AFTER the model is converted to cuda.
-'''
-
-if args.distributed:
-    model = DDP(model)
-#=====END:   ADDED FOR DISTRIBUTED======
-
-optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-
-def train(epoch):
-    model.train()
-    for batch_idx, (data, target) in enumerate(train_loader):
-        if args.cuda:
-            data, target = data.cuda(), target.cuda()
-        data, target = Variable(data), Variable(target)
-        optimizer.zero_grad()
-        output = model(data)
-        loss = F.nll_loss(output, target)
-        loss.backward()
-        optimizer.step()
-        if batch_idx % args.log_interval == 0:
-            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                epoch, batch_idx * len(data), len(train_loader.dataset),
-                100. * batch_idx / len(train_loader), loss.data[0]))
-
-def test():
-    model.eval()
-    test_loss = 0
-    correct = 0
-    for data, target in test_loader:
-        if args.cuda:
-            data, target = data.cuda(), target.cuda()
-        data, target = Variable(data, volatile=True), Variable(target)
-        output = model(data)
-        test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
-        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
-        correct += pred.eq(target.data.view_as(pred)).cpu().sum()
-
-    test_loss /= len(test_loader.dataset)
-    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-        test_loss, correct, len(test_loader.dataset),
-        100. * correct / len(test_loader.dataset)))
-
-
-for epoch in range(1, args.epochs + 1):
-    train(epoch)
-    test()
diff --git a/PyTorch/Distributed/multiproc.py b/PyTorch/Distributed/multiproc.py
deleted file mode 100644
index 453ec60a..00000000
--- a/PyTorch/Distributed/multiproc.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import torch
-import sys
-import subprocess
-
-argslist = list(sys.argv)[1:]
-world_size = torch.cuda.device_count()
-
-if '--world-size' in argslist:
-    argslist[argslist.index('--world-size')+1] = str(world_size)
-else:
-    argslist.append('--world-size')
-    argslist.append(str(world_size))
-
-workers = []
-
-for i in range(world_size):
-    if '--rank' in argslist:
-        argslist[argslist.index('--rank')+1] = str(i)
-    else:
-        argslist.append('--rank')
-        argslist.append(str(i))
-    stdout = None if i == 0 else open("GPU_"+str(i)+".log", "w")
-    print(argslist)
-    p = subprocess.Popen([str(sys.executable)]+argslist, stdout=stdout)
-    workers.append(p)
-
-for p in workers:
-    p.wait()
diff --git a/PyTorch/Distributed/requirements.txt b/PyTorch/Distributed/requirements.txt
deleted file mode 100644
index ac988bdf..00000000
--- a/PyTorch/Distributed/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-torch
-torchvision
diff --git a/PyTorch/Distributed/run_distributed.sh b/PyTorch/Distributed/run_distributed.sh
deleted file mode 100644
index ffd2eb21..00000000
--- a/PyTorch/Distributed/run_distributed.sh
+++ /dev/null
@@ -1 +0,0 @@
-python -m multiproc main.py
diff --git a/PyTorch/LanguageModel/README.md b/PyTorch/LanguageModel/README.md
deleted file mode 100644
index b2a037ad..00000000
--- a/PyTorch/LanguageModel/README.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Word-level language modeling RNN
-
-This example is based on [PyTorch's Word-level language modeling RNN Example](https://github.com/pytorch/examples/tree/master/word_language_model).
-
-This example trains a multi-layer RNN (Elman, GRU, or LSTM) on a language modeling task.
-By default, the training script uses the Wikitext-2 dataset, provided.
-The trained model can then be used by the generate script to generate new text.
-
-```bash
-python main.py --cuda --epochs 6        # Train a LSTM on Wikitext-2 with CUDA, reaching perplexity of 117.61
-python main.py --cuda --epochs 6 --tied # Train a tied LSTM on Wikitext-2 with CUDA, reaching perplexity of 110.44
-python main.py --cuda --tied            # Train a tied LSTM on Wikitext-2 with CUDA for 40 epochs, reaching perplexity of 87.17
-python generate.py                      # Generate samples from the trained LSTM model.
-```
-
-The model uses the `nn.RNN` module (and its sister modules `nn.GRU` and `nn.LSTM`)
-which will automatically use the cuDNN backend if run on CUDA with cuDNN installed.
-
-During training, if a keyboard interrupt (Ctrl-C) is received,
-training is stopped and the current model is evaluated against the test dataset.
-
-The `main.py` script accepts the following arguments:
-
-```bash
-optional arguments:
-  -h, --help         show this help message and exit
-  --data DATA        location of the data corpus
-  --model MODEL      type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
-  --emsize EMSIZE    size of word embeddings
-  --nhid NHID        number of hidden units per layer
-  --nlayers NLAYERS  number of layers
-  --lr LR            initial learning rate
-  --clip CLIP        gradient clipping
-  --epochs EPOCHS    upper epoch limit
-  --batch-size N     batch size
-  --bptt BPTT        sequence length
-  --dropout DROPOUT  dropout applied to layers (0 = no dropout)
-  --decay DECAY      learning rate decay per epoch
-  --tied             tie the word embedding and softmax weights
-  --seed SEED        random seed
-  --cuda             use CUDA
-  --log-interval N   report interval
-  --save SAVE        path to save the final model
-```
-
-With these arguments, a variety of models can be tested.
-As an example, the following arguments produce slower but better models:
-
-```bash
-python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40           # Test perplexity of 80.97
-python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 --tied    # Test perplexity of 75.96
-python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40        # Test perplexity of 77.42
-python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 --tied # Test perplexity of 72.30
-```
-
-Perplexities on PTB are equal or better than
-[Recurrent Neural Network Regularization (Zaremba et al. 2014)](https://arxiv.org/pdf/1409.2329.pdf)
-and are similar to [Using the Output Embedding to Improve Language Models (Press & Wolf 2016](https://arxiv.org/abs/1608.05859) and [Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling (Inan et al. 2016)](https://arxiv.org/pdf/1611.01462.pdf), though both of these papers have improved perplexities by using a form of recurrent dropout [(variational dropout)](http://papers.nips.cc/paper/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks).
diff --git a/PyTorch/LanguageModel/data.py b/PyTorch/LanguageModel/data.py
deleted file mode 100644
index 9c53dcad..00000000
--- a/PyTorch/LanguageModel/data.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-import torch
-
-
-class Dictionary(object):
-    def __init__(self):
-        self.word2idx = {}
-        self.idx2word = []
-
-    def add_word(self, word):
-        if word not in self.word2idx:
-            self.idx2word.append(word)
-            self.word2idx[word] = len(self.idx2word) - 1
-        return self.word2idx[word]
-
-    def __len__(self):
-        return len(self.idx2word)
-
-
-class Corpus(object):
-    def __init__(self, path):
-        self.dictionary = Dictionary()
-        self.train = self.tokenize(os.path.join(path, 'train.txt'))
-        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
-        self.test = self.tokenize(os.path.join(path, 'test.txt'))
-
-    def tokenize(self, path):
-        """Tokenizes a text file."""
-        assert os.path.exists(path)
-        # Add words to the dictionary
-        with open(path, 'r') as f:
-            tokens = 0
-            for line in f:
-                words = line.split() + ['<eos>']
-                tokens += len(words)
-                for word in words:
-                    self.dictionary.add_word(word)
-
-        # Tokenize file content
-        with open(path, 'r') as f:
-            ids = torch.LongTensor(tokens)
-            token = 0
-            for line in f:
-                words = line.split() + ['<eos>']
-                for word in words:
-                    ids[token] = self.dictionary.word2idx[word]
-                    token += 1
-
-        return ids
diff --git a/PyTorch/LanguageModel/data/wikitext-2/README b/PyTorch/LanguageModel/data/wikitext-2/README
deleted file mode 100644
index c6fd69c2..00000000
--- a/PyTorch/LanguageModel/data/wikitext-2/README
+++ /dev/null
@@ -1,3 +0,0 @@
-This is raw data from the wikitext-2 dataset.
-
-See https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/
diff --git a/PyTorch/LanguageModel/data/wikitext-2/test.txt b/PyTorch/LanguageModel/data/wikitext-2/test.txt
deleted file mode 100644
index 6f3fc88e..00000000
--- a/PyTorch/LanguageModel/data/wikitext-2/test.txt
+++ /dev/null
@@ -1,4358 +0,0 @@
- 
- = Robert <unk> = 
- 
- Robert <unk> is an English film , television and theatre actor . He had a guest @-@ starring role on the television series The Bill in 2000 . This was followed by a starring role in the play Herons written by Simon Stephens , which was performed in 2001 at the Royal Court Theatre . He had a guest role in the television series Judge John <unk> in 2002 . In 2004 <unk> landed a role as " Craig " in the episode " Teddy 's Story " of the television series The Long Firm ; he starred alongside actors Mark Strong and Derek Jacobi . He was cast in the 2005 theatre productions of the Philip Ridley play Mercury Fur , which was performed at the Drum Theatre in Plymouth and the <unk> <unk> Factory in London . He was directed by John <unk> and starred alongside Ben <unk> , Shane <unk> , Harry Kent , Fraser <unk> , Sophie Stanton and Dominic Hall . 
- In 2006 , <unk> starred alongside <unk> in the play <unk> written by Mark <unk> . He appeared on a 2006 episode of the television series , Doctors , followed by a role in the 2007 theatre production of How to Curse directed by <unk> <unk> . How to Curse was performed at Bush Theatre in the London Borough of <unk> and Fulham . <unk> starred in two films in 2008 , <unk> <unk> by filmmaker Paris <unk> , and <unk> Punch directed by <unk> Blackburn . In May 2008 , <unk> made a guest appearance on a two @-@ part episode arc of the television series Waking the Dead , followed by an appearance on the television series <unk> in November 2008 . He had a recurring role in ten episodes of the television series <unk> in 2010 , as " <unk> Fletcher " . <unk> starred in the 2011 film <unk> directed by Paris <unk> . 
- 
- = = Career = = 
- 
- 
- = = = 2000 – 2005 = = = 
- 
- In 2000 <unk> had a guest @-@ starring role on the television series The Bill ; he portrayed " Scott Parry " in the episode , " In Safe Hands " . <unk> starred as " Scott " in the play Herons written by Simon Stephens , which was performed in 2001 at the Royal Court Theatre . A review of <unk> 's performance in The Independent on Sunday described him as " horribly menacing " in the role , and he received critical reviews in The Herald , and Evening Standard . He appeared in the television series Judge John <unk> in 2002 as " <unk> <unk> " in the episode " Political <unk> " , and had a role as a different character " Toby Steele " on The Bill . 
- He had a recurring role in 2003 on two episodes of The Bill , as character " Connor Price " . In 2004 <unk> landed a role as " Craig " in the episode " Teddy 's Story " of the television series The Long Firm ; he starred alongside actors Mark Strong and Derek Jacobi . <unk> starred as " Darren " , in the 2005 theatre productions of the Philip Ridley play Mercury Fur . It was performed at the Drum Theatre in Plymouth , and the <unk> <unk> Factory in London . He was directed by John <unk> and starred alongside Ben <unk> , Shane <unk> , Harry Kent , Fraser <unk> , Sophie Stanton and Dominic Hall . <unk> received a favorable review in The Daily Telegraph : " The acting is <unk> intense , with <unk> performances from Ben <unk> ( now <unk> from his performance as Trevor <unk> 's Hamlet ) , Robert <unk> , Shane <unk> and Fraser <unk> . " The Guardian noted , " Ben <unk> and Robert <unk> offer <unk> amid the <unk> . " 
- 
- = = = 2006 – present = = = 
- 
- In 2006 <unk> starred in the play <unk> written by Mark <unk> . The play was part of a series which featured different <unk> , titled Burn / <unk> / <unk> . In a 2006 interview , fellow actor Ben <unk> identified <unk> as one of his favorite co @-@ stars : " I loved working with a guy called Robert <unk> , who was in the triple bill of Burn , <unk> and <unk> at the National . He played my brother in Mercury Fur . " He portrayed " Jason Tyler " on the 2006 episode of the television series , Doctors , titled " Something I <unk> " . <unk> starred as " William " in the 2007 production of How to Curse directed by <unk> <unk> . How to Curse was performed at Bush Theatre in the London Borough of <unk> and Fulham . In a review of the production for The Daily Telegraph , theatre critic Charles Spencer noted , " Robert <unk> brings a touching vulnerability to the stage as William . " 
- <unk> starred in two films in 2008 , <unk> <unk> by filmmaker Paris <unk> , and <unk> Punch directed by <unk> Blackburn . <unk> portrayed a character named " Sean " in <unk> Punch , who <unk> along with character " Josh " as the " quiet brother ... who hits it off with <unk> " . <unk> guest starred on a two @-@ part episode arc " <unk> " in May 2008 of the television series Waking the Dead as character " Jimmy <unk> " . He appeared on the television series <unk> as " Neil " in November 2008 . He had a recurring role in ten episodes of the television series <unk> in 2010 , as " <unk> Fletcher " . He portrayed an emergency physician applying for a medical <unk> . He commented on the inherent difficulties in portraying a physician on television : " Playing a doctor is a strange experience . <unk> you know what you 're talking about when you don 't is very bizarre but there are advisers on set who are fantastic at taking you through procedures and giving you the confidence to stand there and look like you know what you 're doing . " <unk> starred in the 2011 film <unk> directed by Paris <unk> . 
- 
- = = Filmography = = 
- 
- 
- = = = Film = = = 
- 
- 
- = = = Television = = = 
- 
- 
- = = = Theatre = = = 
- 
- 
- 
- = Du Fu = 
- 
- Du Fu ( Wade – Giles : Tu Fu ; Chinese : <unk> ; <unk> – 770 ) was a prominent Chinese poet of the Tang dynasty . Along with Li <unk> ( Li Po ) , he is frequently called the greatest of the Chinese poets . His greatest ambition was to serve his country as a successful civil servant , but he proved unable to make the necessary accommodations . His life , like the whole country , was devastated by the An <unk> Rebellion of <unk> , and his last 15 years were a time of almost constant unrest . 
- Although initially he was little @-@ known to other writers , his works came to be hugely influential in both Chinese and Japanese literary culture . Of his poetic writing , nearly fifteen hundred poems have been preserved over the ages . He has been called the " Poet @-@ Historian " and the " Poet @-@ Sage " by Chinese critics , while the range of his work has allowed him to be introduced to Western readers as " the Chinese Virgil , Horace , <unk> , Shakespeare , Milton , Burns , <unk> , <unk> , Hugo or <unk> " . 
- 
- = = Life = = 
- 
- Traditional Chinese literary criticism emphasized the life of the author when interpreting a work , a practice which Burton Watson attributes to " the close links that traditional Chinese thought posits between art and morality " . Since many of Du Fu 's poems feature morality and history , this practice is particularly important . Another reason , identified by the Chinese historian William <unk> , is that Chinese poems are typically <unk> , <unk> context that might be relevant , but which an informed contemporary could be assumed to know . For modern Western readers , " The less accurately we know the time , the place and the circumstances in the background , the more liable we are to imagine it incorrectly , and the result will be that we either <unk> the poem or fail to understand it altogether " . Stephen Owen suggests a third factor particular to Du Fu , arguing that the variety of the poet 's work required consideration of his whole life , rather than the " <unk> " <unk> used for more limited poets . 
- 
- = = = Early years = = = 
- 
- Most of what is known of Du Fu 's life comes from his poems . His paternal grandfather was Du <unk> , a noted politician and poet during the reign of Empress Wu . Du Fu was born in <unk> ; the exact birthplace is unknown , except that it was near Luoyang , Henan province ( Gong county is a favourite candidate ) . In later life , he considered himself to belong to the capital city of Chang 'an , ancestral hometown of the Du family . 
- Du Fu 's mother died shortly after he was born , and he was partially raised by his aunt . He had an elder brother , who died young . He also had three half brothers and one half sister , to whom he frequently refers in his poems , although he never mentions his stepmother . 
- The son of a minor scholar @-@ official , his youth was spent on the standard education of a future civil servant : study and <unk> of the Confucian classics of philosophy , history and poetry . He later claimed to have produced <unk> poems by his early teens , but these have been lost . 
- In the early <unk> , he travelled in the <unk> / Zhejiang area ; his earliest surviving poem , describing a poetry contest , is thought to date from the end of this period , around 735 . In that year , he took the civil service exam , likely in Chang 'an . He failed , to his surprise and that of centuries of later critics . <unk> concludes that he probably failed because his prose style at the time was too dense and obscure , while Chou suggests his failure to <unk> connections in the capital may have been to blame . After this failure , he went back to traveling , this time around Shandong and Hebei . 
- His father died around 740 . Du Fu would have been allowed to enter the civil service because of his father 's rank , but he is thought to have given up the privilege in favour of one of his half brothers . He spent the next four years living in the Luoyang area , fulfilling his duties in domestic affairs . 
- In the autumn of <unk> , he met Li <unk> ( Li Po ) for the first time , and the two poets formed a friendship . David Young describes this as " the most significant formative element in Du Fu 's artistic development " because it gave him a living example of the reclusive poet @-@ scholar life to which he was attracted after his failure in the civil service exam . The relationship was somewhat one @-@ sided , however . Du Fu was by some years the younger , while Li <unk> was already a poetic star . We have twelve poems to or about Li <unk> from the younger poet , but only one in the other direction . They met again only once , in <unk> . 
- In 746 , he moved to the capital in an attempt to resurrect his official career . He took the civil service exam a second time during the following year , but all the candidates were failed by the prime minister ( apparently in order to prevent the emergence of possible rivals ) . He never again attempted the examinations , instead <unk> the emperor directly in <unk> , 754 and probably again in <unk> . He married around <unk> , and by <unk> the couple had had five children — three sons and two daughters — but one of the sons died in infancy in <unk> . From 754 he began to have lung problems ( probably asthma ) , the first of a series of <unk> which <unk> him for the rest of his life . It was in that year that Du Fu was forced to move his family due to the turmoil of a famine brought about by massive floods in the region . 
- In <unk> , he received an appointment as <unk> of the Right Commandant 's office of the Crown Prince 's Palace . Although this was a minor post , in normal times it would have been at least the start of an official career . Even before he had begun work , however , the position was swept away by events . 
- 
- = = = War = = = 
- 
- The An <unk> Rebellion began in December <unk> , and was not completely suppressed for almost eight years . It caused enormous disruption to Chinese society : the census of 754 recorded 52 @.@ 9 million people , but ten years later , the census counted just 16 @.@ 9 million , the remainder having been displaced or killed . During this time , Du Fu led a largely itinerant life <unk> by wars , associated <unk> and imperial <unk> . This period of <unk> was the making of Du Fu as a poet : Even Shan Chou has written that , " What he saw around him — the lives of his family , neighbors , and strangers – what he heard , and what he hoped for or feared from the progress of various campaigns — these became the enduring themes of his poetry " . Even when he learned of the death of his youngest child , he turned to the suffering of others in his poetry instead of dwelling upon his own <unk> . Du Fu wrote : 
- <unk> on what I have lived through , if even I know such suffering , the common man must surely be <unk> by the winds . 
- In <unk> , Emperor <unk> was forced to flee the capital and <unk> . Du Fu , who had been away from the city , took his family to a place of safety and attempted to join the court of the new emperor ( <unk> ) , but he was captured by the rebels and taken to Chang 'an . In the autumn , his youngest son , Du <unk> ( Baby Bear ) , was born . Around this time Du Fu is thought to have contracted malaria . 
- He escaped from Chang 'an the following year , and was appointed <unk> when he rejoined the court in May <unk> . This post gave access to the emperor but was largely ceremonial . Du Fu 's <unk> compelled him to try to make use of it : he caused trouble for himself by protesting the removal of his friend and patron Fang Guan on a petty charge . He was arrested but was <unk> in June . He was granted leave to visit his family in September , but he soon rejoined the court and on December 8 , <unk> , he returned to Chang 'an with the emperor following its recapture by government forces . However , his advice continued to be <unk> , and in the summer of <unk> he was demoted to a post as Commissioner of Education in <unk> . The position was not to his taste : in one poem , he wrote : 
- I am about to scream <unk> in the office / Especially when they bring more papers to pile higher on my desk . 
- He moved on in the summer of <unk> ; this has traditionally been ascribed to famine , but <unk> believes that frustration is a more likely reason . He next spent around six weeks in <unk> ( now <unk> , Gansu province ) , where he wrote more than sixty poems . 
- 
- = = = <unk> = = = 
- 
- In December <unk> , he briefly stayed in <unk> ( modern Gansu ) . He departed on December 24 for <unk> ( Sichuan province ) , where he was hosted by local <unk> and fellow poet <unk> Di . Du subsequently based himself in Sichuan for most of the next five years . By the autumn of that year he was in financial trouble , and sent poems <unk> help to various acquaintances . He was relieved by Yan Wu , a friend and former colleague who was appointed governor general at <unk> . Despite his financial problems , this was one of the <unk> and most peaceful periods of his life . Many of Du 's poems from this period are peaceful depictions of his life at " <unk> <unk> " . In 762 , he left the city to escape a rebellion , but he returned in summer <unk> when he was appointed an advisor to Yan , who was involved in campaigns against the <unk> Empire . 
- 
- = = = Last years = = = 
- 
- Luoyang , the region of his birthplace , was recovered by government forces in the winter of 762 , and in the spring of <unk> Du Fu and his family sailed down the Yangtze , apparently with the intention of making their way there . They traveled slowly , held up by his ill @-@ health ( by this time he was suffering from poor eyesight , <unk> and general old age in addition to his previous <unk> ) . They stayed in <unk> ( in what is now <unk> , <unk> ) at the entrance to the Three <unk> for almost two years from late spring <unk> . This period was Du Fu 's last great poetic flowering , and here he wrote 400 poems in his dense , late style . In autumn <unk> , Bo <unk> became governor of the region : he supported Du Fu financially and employed him as his unofficial secretary . 
- In March <unk> , he began his journey again and got as far as <unk> province , where he died in <unk> ( now <unk> ) in November or December 770 , in his <unk> year . He was survived by his wife and two sons , who remained in the area for some years at least . His last known descendant is a grandson who requested a grave inscription for the poet from Yuan <unk> in <unk> . 
- <unk> <unk> his life by concluding that , " He appeared to be a <unk> son , an affectionate father , a generous brother , a faithful husband , a loyal friend , a <unk> official , and a patriotic subject . " 
- Below is an example of one of Du Fu 's later works , To My <unk> Friend <unk> ( Chinese : <unk> ) . Like many other poems in the Tang it featured the theme of a long parting between friends , which was often due to officials being frequently transferred to the provinces : 
- 
- = = Works = = 
- 
- Criticism of Du Fu 's works has focused on his strong sense of history , his moral engagement , and his technical excellence . 
- 
- = = = History = = = 
- 
- Since the Song dynasty , critics have called Du Fu the " poet historian " ( <unk> <unk> <unk> ) . The most directly historical of his poems are those commenting on military tactics or the successes and failures of the government , or the poems of advice which he wrote to the emperor . <unk> , he wrote about the effect of the times in which he lived on himself , and on the ordinary people of China . As Watson notes , this is information " of a kind seldom found in the officially compiled histories of the era " . 
- Du Fu 's political comments are based on emotion rather than calculation : his <unk> have been <unk> as , " Let us all be less selfish , let us all do what we are supposed to do " . Since his views were impossible to disagree with , his <unk> expressed <unk> enabled his installation as the central figure of Chinese poetic history . 
- 
- = = = <unk> engagement = = = 
- 
- A second favourite epithet of Chinese critics is that of " poet sage " ( <unk> <unk> <unk> ) , a counterpart to the philosophical sage , <unk> . One of the earliest surviving works , The Song of the <unk> ( from around 750 ) , gives voice to the <unk> of a <unk> soldier in the imperial army and a clear @-@ sighted consciousness of suffering . These concerns are continuously articulated in poems on the lives of both soldiers and civilians produced by Du Fu throughout his life . 
- Although Du Fu 's frequent references to his own difficulties can give the impression of an all @-@ consuming <unk> , Hawkes argues that his " famous compassion in fact includes himself , viewed quite <unk> and almost as an <unk> " . He therefore " lends grandeur " to the wider picture by comparing it to " his own slightly comical triviality " . 
- Du Fu 's compassion , for himself and for others , was part of his general broadening of the scope of poetry : he devoted many works to topics which had previously been considered unsuitable for poetic treatment . Zhang Jie wrote that for Du Fu , " everything in this world is poetry " , Du wrote extensively on subjects such as domestic life , calligraphy , paintings , animals , and other poems . 
- 
- = = = Technical excellence = = = 
- 
- Du Fu 's work is notable above all for its range . Chinese critics traditionally used the term <unk> ( <unk> " complete <unk> " ) , a reference to <unk> ' description of <unk> . Yuan <unk> was the first to note the breadth of Du Fu 's achievement , writing in <unk> that his predecessor , " united in his work traits which previous men had displayed only singly " . He mastered all the forms of Chinese poetry : Chou says that in every form he " either made outstanding advances or contributed outstanding examples " . Furthermore , his poems use a wide range of registers , from the direct and <unk> to the <unk> and self @-@ consciously literary . This variety is <unk> even within individual works : Owen identifies the , " rapid stylistic and thematic shifts " in poems which enable the poet to represent different <unk> of a situation , while Chou uses the term " juxtaposition " as the major analytical tool in her work . Du Fu is noted for having written more on <unk> and painting than any other writer of his time . He wrote eighteen poems on painting alone , more than any other Tang poet . Du Fu 's seemingly negative commentary on the prized horse paintings of Han <unk> <unk> a controversy that has persisted to the present day . 
- The tenor of his work changed as he developed his style and adapted to his surroundings ( " <unk> @-@ like " according to Watson ) : his earliest works are in a relatively derivative , courtly style , but he came into his own in the years of the rebellion . Owen comments on the " <unk> simplicity " of the <unk> poems , which mirrors the desert landscape ; the works from his <unk> period are " light , often finely observed " ; while the poems from the late <unk> period have a " density and power of vision " . 
- Although he wrote in all poetic forms , Du Fu is best known for his <unk> , a type of poem with strict constraints on form and content , for example : 
- About two thirds of Du Fu 's 1500 extant works are in this form , and he is generally considered to be its leading exponent . His best <unk> use the <unk> required by the form to add expressive content rather than as mere technical restrictions . Hawkes comments that , " it is amazing that Tu Fu is able to use so immensely stylized a form in so natural a manner " . 
- 
- = = Influence = = 
- 
- According to the <unk> Britannica , Du Fu 's writings are considered by many literary critics to be among the greatest of all time , and it states " his dense , compressed language makes use of all the <unk> overtones of a phrase and of all the <unk> <unk> of the individual word , qualities that no translation can ever reveal . " 
- In his lifetime and immediately following his death , Du Fu was not greatly appreciated . In part this can be attributed to his stylistic and formal innovations , some of which are still " considered extremely daring and bizarre by Chinese critics . " There are few contemporary references to him — only eleven poems from six writers — and these describe him in terms of affection , but not as a <unk> of poetic or moral ideals . Du Fu is also poorly represented in contemporary anthologies of poetry . 
- However , as <unk> notes , he " is the only Chinese poet whose influence grew with time " , and his works began to increase in popularity in the ninth century . Early positive comments came from <unk> <unk> , who praised the moral sentiments of some of Du Fu 's works ( although he found these in only a small fraction of the poems ) , and from Han Yu , who wrote a piece defending Du Fu and Li <unk> on aesthetic grounds from attacks made against them . Both these writers showed the influence of Du Fu in their own poetic work . By the beginning of the 10th century , <unk> <unk> constructed the first replica of his <unk> cottage in Sichuan . 
- It was in the 11th century , during the Northern Song era that Du Fu 's reputation reached its peak . In this period a comprehensive re @-@ evaluation of earlier poets took place , in which Wang <unk> , Li <unk> and Du Fu came to be regarded as representing respectively the Buddhist , <unk> and Confucian strands of Chinese culture . At the same time , the development of Neo @-@ <unk> ensured that Du Fu , as its poetic <unk> , occupied the paramount position . Su Shi famously expressed this reasoning when he wrote that Du Fu was " <unk> ... because ... through all his <unk> , he never for the space of a meal <unk> his sovereign " . His influence was helped by his ability to reconcile apparent opposites : political <unk> were attracted by his loyalty to the established order , while political radicals embraced his concern for the poor . Literary <unk> could look to his technical mastery , while literary radicals were inspired by his innovations . Since the establishment of the People 's Republic of China , Du Fu 's loyalty to the state and concern for the poor have been interpreted as embryonic nationalism and socialism , and he has been praised for his use of simple , " people 's language " . 
- Du Fu 's popularity grew to such an extent that it is as hard to measure his influence as that of Shakespeare in England : it was hard for any Chinese poet not to be influenced by him . While there was never another Du Fu , individual poets followed in the traditions of specific aspects of his work : <unk> <unk> 's concern for the poor , Lu You 's patriotism , and <unk> <unk> 's <unk> on the <unk> are a few examples . More broadly , Du Fu 's work in transforming the <unk> from mere word play into " a vehicle for serious poetic <unk> " set the stage for every subsequent writer in the genre . 
- In the 20th century , he was the favourite poet of Kenneth <unk> , who has described him as " the greatest non @-@ epic , non @-@ dramatic poet who has survived in any language " , and commented that , " he has made me a better man , as a moral agent and as a <unk> organism " . 
- 
- = = = Influence on Japanese literature = = = 
- 
- Du Fu 's poetry has made a profound impact on Japanese literature , especially on the literature from the <unk> period and on scholars and poets in the Edo period , including <unk> <unk> , the very greatest of all <unk> poets . Even in modern Japanese , the term Saint of Poetry ( <unk> , <unk> ) is mostly synonymous with Du Fu . 
- Until the 13th century , the Japanese preferred <unk> <unk> above all poets and there were few references to Du Fu , although his influence can be seen in some <unk> ( " Chinese poetry made by Japanese poets " ) anthologies such as <unk> <unk> in the 9th century . The first notable Japanese <unk> of Du Fu 's poetry was <unk> <unk> ( <unk> – <unk> ) , a <unk> <unk> patriarch and one of the most prominent authors of the literature of the Five Mountains ; he highly praised Du Fu and made a commentary on some poems of Du Fu from the perspective of a <unk> priest in Vol . 11 of <unk> . His student <unk> <unk> composed many <unk> which were clearly stated " influenced by Du Fu " in their <unk> . <unk> 's student <unk> <unk> had close connection with the Court and <unk> <unk> and <unk> Du Fu 's poetry in the mundane world ; one day <unk> <unk> , the <unk> regent of the Court and the highest authority of <unk> poetry , asked <unk> , " Should I learn the poetry of Du Fu and Li <unk> ? " <unk> <unk> to reply , " Yes if you do have enough capability . No if do not . " Since then , there had been many <unk> on Du Fu 's poetry both in <unk> temples and in the aristocratic society , and as a result his poetry was often cited in Japanese literature in the <unk> period , e.g. , <unk> , a historical epic in the late 14th century , and some <unk> plays such as <unk> , <unk> , and <unk> . 
- During the Kan <unk> era of the Edo period ( 1624 – 1643 ) , <unk> <unk> ( <unk> ) of the Ming Dynasty 's <unk> <unk> on Du Fu 's <unk> ( <unk> , <unk> <unk> ) was imported into Japan , and it gained explosive popularity in Confucian scholars and <unk> ( <unk> ) class . The commentary established Du Fu 's fame as the highest of all poets ; for instance , <unk> <unk> , a notable Confucian scholar , commented in Vol . 37 of <unk> <unk> that <unk> [ Du Fu ] was the very best poet in history and praised <unk> <unk> 's commentary for its simplicity and readability , while he criticized old commentaries during the Yuan Dynasty were too <unk> . <unk> <unk> , the greatest <unk> poet , was also strongly influenced by Du Fu ; in <unk> no <unk> , his masterpiece , he cites the first two lines of A Spring View ( <unk> ) before a <unk> as its introduction and also many of his other <unk> have similar wording and themes . It is said that when he died in Osaka during a long travel , a copy of Du Fu 's poetry was found with him as one of a few precious items which he was able to carry around . 
- 
- = = <unk> = = 
- 
- A variety of styles have been used in efforts to translate Du Fu 's work into English . As Burton Watson remarks in The Selected Poems of Du Fu , " There are many different ways to approach the problems involved in translating Du Fu , which is why we need as many different translations as possible " ( <unk> <unk> ) . The translators have had to contend with bringing out the formal constraints of the original without sounding <unk> to a Western ear ( particularly when translating regulated verse , or <unk> ) , and <unk> the complex allusions contained particularly in the later works ( Hawkes writes that " his poems do not as a rule come through very well in translation " — <unk> <unk> ) . One extreme on each issue is represented by Kenneth <unk> 's One Hundred Poems From the Chinese . His are free translations , which seek to conceal the <unk> through <unk> and expansion and contraction of the content ; his responses to the allusions are firstly to <unk> most of these poems from his selection , and <unk> to " translate out " the references in those works which he does select . 
- Other translators have placed much greater weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith <unk> in Facing the Moon <unk> the Chinese rhyme scheme ; both use end @-@ stopped lines and preserve some degree of <unk> . In The Selected Poems of Du Fu , Burton Watson follows the <unk> quite strictly , persuading the western reader to adapt to the poems rather than vice versa . Similarly , he deals with the allusion of the later works by combining literal translation with extensive <unk> . 
- In 2015 , Stephen Owen published translations , with facing Chinese texts , of the complete poetry of Du Fu in six volumes , with extensive scholarly apparatus , which emphasized <unk> . 
- 
- 
- = Kiss You ( One Direction song ) = 
- 
- " Kiss You " is a song recorded by English @-@ Irish boy band One Direction for their second studio album , Take Me Home ( 2012 ) . It was released as the record 's second single in Germany and the third overall single on 7 January 2013 . The song was composed by <unk> <unk> , <unk> Lundin , <unk> <unk> , <unk> <unk> , <unk> and its producers , Carl <unk> and <unk> <unk> . " Kiss You " is an upbeat power pop song with electronic effects ; the lyrics detail a protagonist 's infatuation with a significant other . Critics praised the song for its production , calling it a stand @-@ out track on Take Me Home . 
- The track became the group 's sixth top @-@ ten hit in Ireland and the United Kingdom , while attaining top @-@ forty positions in both Belgian territories ( Flanders and Wallonia ) , as well as in Australia , Canada , Denmark , France , New Zealand , and the Netherlands . The single peaked at number 46 on the US Billboard Hot 100 and has been certified gold by the Recording Industry Association of America ( RIAA ) for shipments of 500 @,@ 000 copies . One Direction performed " Kiss You " on both the UK and US versions of The X Factor and 3 major concert tours : Take Me Home Tour ( 2013 ) , Where We Are Tour ( 2014 ) and On the Road Again Tour ( 2015 ) . 
- An accompanying music video , designed to display the group 's comedic timing , was directed by Vaughan <unk> , who had previously worked with the group on two other music videos . The clip depicts the band shooting various scenes via a green screen , which include sequences reminiscent of iconic music videos of songs such as the Beach Boys ' " Surfer Girl " , Elvis Presley 's " <unk> Rock " and <unk> 's " Mein Land " . The music video received 10 @.@ 4 million views in a 24 @-@ hour period and positive commentary from reviewers , who appreciated its <unk> , <unk> nature . 
- The song was included in the dancing game Just Dance 2014 , and is also one of the select songs available on the demo version . Additionally , it is the final main track on the US edition of Now That 's What I Call Music ! 46 . 
- 
- = = Background and release = = 
- 
- " Kiss You " was written by <unk> <unk> , <unk> Lundin , <unk> <unk> , <unk> <unk> , <unk> , and its producers , Carl <unk> and <unk> <unk> . <unk> , <unk> , and <unk> had <unk> composed One Direction 's previous hit singles , " What Makes You Beautiful " , " One Thing " , and " Live While We 're Young " . In April 2012 , The Independent reported that Simon Cowell , the group 's manager , had challenged prominent songwriters to compete for space on One Direction 's second album . <unk> said , " It 's important to get their personalities on the music . " In addition , the article reported that Syco Records was working on candidates that included Max Martin and Lundin . 
- " Kiss You " was chosen as the second US single and third international from their second studio album , Take Me Home . Liam Payne , a group member , in a November 2012 interview with MTV News , explained why they chose " Kiss You " as the album 's second single in the US . Payne was quoted as saying : " With the album , that 's the first one that we listened to and we were like , ' Yeah , we love this song ' " . According to a MTV News article , the number was released digitally in the United States on 17 November 2012 . By 18 January 2013 , the song had not been officially promoted to US radio stations . The track , however , was released by Sony Music Entertainment on 8 February 2013 , as the record 's second single in Germany . 
- 
- = = Composition and reception = = 
- 
- " Kiss You " is an uptempo , upbeat power pop song which runs for a duration of 3 : 04 ( 3 minutes , four seconds ) . The track features electronic effects , colossal hooks , a " na na na " breakdown , and a Motown @-@ <unk> melody . One Direction 's vocal range in the song span from the note of E4 to C ♯ 6 . Instrumentation includes guitar strings , piano lines and vocals . Written in the key of E major , the beat is set in common time and moves at a quick 90 beats per minute , according to the digital sheet music published at Musicnotes.com by Sony / ATV Music Publishing . Likewise , Matt <unk> from Allmusic noted that the track is " <unk> hyper " . The lyrical content regards the protagonist 's infatuation with a significant other , and incorporates <unk> for sexual intercourse in the lines " If you don ’ t wanna take it slow / And you just wanna take me home / Baby say yeah , yeah , yeah , yeah , yeah . " 
- " Kiss You " was well received by contemporary music critics , who centred on its quality of production . Both Rolling Stone 's Jon Dolan , who praised its effectiveness , and Chris Payne of Billboard , who appreciated the melody , described " Kiss You " as one of the album 's highlights . Alexis <unk> for The Guardian commended the track 's chorus as " hard to <unk> from your brain " . Robert Copsey of Digital Spy noted the song 's possibility to become an international hit , <unk> it <unk> . A reviewer for MTV News described the track 's <unk> as " butterflies @-@ inducing " , and Sam <unk> of Idolator wrote that " Kiss You " is noticeably a stand @-@ out track on its parent album . Melinda Newman , writing for HitFix , regarded the song as " a <unk> , electronic infectious ditty , " while Chris <unk> , a critic from <unk> , deemed it an " amazing pop song " , lauding the group 's falsetto and its " head @-@ <unk> <unk> " chorus . 
- 
- = = Commercial performance = = 
- 
- The single made its Irish Singles Chart debut at number 24 on the week ending 13 December 2012 . It peaked at number seven on the week ending 17 January 2013 , marking their sixth top ten appearance in Ireland . " Kiss You " entered at number 152 in the UK Singles Chart on 24 November 2012 . It peaked at number nine on the UK Singles Chart on 26 January 2013 , becoming One Direction 's sixth top ten hit in the United Kingdom . On the week ending 18 November 2012 , " Kiss You " debuted at number 90 on the United States Billboard Hot 100 due to digital download sales from its parent album . As a result of an " end @-@ of @-@ year download rush " on the week ending 30 December 2012 , the track re @-@ entered the Hot 100 at number 83 . After the accompanying music video was released , the song re @-@ entered the Hot 100 at number 65 . " Kiss You " had sold 207 @,@ 000 digital downloads in the US by 18 January 2013 . The single ultimately peaked at number 46 on the Hot 100 and was certified gold by the Recording Industry Association of America ( RIAA ) on 25 April 2013 , denoting shipments of 500 @,@ 000 copies . 
- The song became One Direction 's fourth top @-@ forty hit on the Canadian Hot 100 , peaking at number 30 . The single bowed at number 13 on the Australian Singles Chart on 27 January 2013 , marking its peak position and the group 's fourth top twenty hit in Australia . The song has been certified platinum by the Australian Recording Industry Association ( ARIA ) for shipments of 70 @,@ 000 copies . The track entered the New Zealand Singles Chart at number 17 on 11 January 2013 . It peaked at number 13 in its third and fourth charting weeks , <unk> the group 's sixth top @-@ forty appearance in New Zealand . " Kiss You " has received a gold certification from the Recording Industry Association of New Zealand ( <unk> ) , indicating sales of 7 @,@ 500 copies . The track also reached the top 40 in both Belgian territories ( Flanders and Wallonia ) , as well as in the Czech Republic , Denmark , France , the Netherlands , and South Korea . In addition , " Kiss You " received gold certifications from the IFPI Norway and Denmark associations , signifying collective shipments of 20 @,@ 000 units . 
- 
- = = Music video = = 
- 
- The accompanying music video , directed by Vaughan <unk> , who had previously directed One Direction 's music videos for " Live While We 're Young " and " Little Things " , was designed to showcase the group 's comedic timing . Inspired by the Beach Boys , cult surfing films , old Hollywood , and British cinema , the music video incorporates " a <unk> vibe and a British kind of romp " , as noted by <unk> in a MTV News interview . 
- Shot by November 2012 , the music video was characterised , in several MTV News interviews , as " bigger than anything we 've done before " by <unk> <unk> , as " a lot of hard work " by Payne , as " pure <unk> " by Louis <unk> , and as " I wouldn 't say [ it 's ] comedy , it 's all tongue @-@ in @-@ cheek " by <unk> . <unk> worldwide on <unk> on 7 January 2013 , the music video depicts the band shooting different scenes via a green screen , dressed as sailors , <unk> , <unk> and <unk> . The video features scenes reminiscent of the films South Pacific , To <unk> a Thief , <unk> Rock and Beach <unk> <unk> , as well as the iconic music videos of songs such as The Beach Boys ' " Surfer Girl " , Elvis Presley 's " Blue Hawaii " and <unk> 's " Mein Land " , among others . 
- The music video garnered 10 @.@ 4 million views in a 24 @-@ hour period , failing to attain the <unk> record held by Justin <unk> 's " Beauty and a Beat " music video ( 10 @.@ 6 million ) . Despite a 34 % gain in weekly activity to their <unk> channel , with the clip 's success and preceding teaser videos earning 38 million views during the week , One Direction held at number two on the Billboard 's Social 50 chart A 15 % rise in Facebook reaction gave way to a 154 @,@ 000 increase in Facebook likes during the week . 191 @,@ 000 Twitter followers added contributed to their overall fan base increase as well . 
- Melinda Newman , a contributor for HitFix , favoured the clip as having " everything a video by a boy band should be " and found group 's careless tone delightful . Rebecca <unk> of E ! Online praised its " intentionally cheesy and utterly adorable " sequences , and MTV News 's <unk> <unk> described the clip as " conquering old Hollywood " . Molly Chance , writing for <unk> , was convinced that upon watching the " adorable " music video , the viewer should have a hard time <unk> the group . <unk> Wood , the critic for Los Angeles Times , commended the group for " having a genuinely great time " , rather than going through the motions . 
- 
- = = Live performances = = 
- 
- As part of its promotion , One Direction performed the song on televised programmes and during their worldwide Take Me Home Tour ( 2013 ) . One Direction performed the track on The Today Show at the Rockefeller Center on 13 November 2012 , to a record crowd estimated at 15 @,@ 000 . " Kiss You " was included in the set list of the group 's 3 December 2012 sold @-@ out show at New York City 's Madison Square Garden . One Direction delivered a performance of " Kiss You " , in front of a video game @-@ themed set , on the final of the ninth series of The X Factor UK on 10 December 2012 . According to the Daily Mail , their " energetic rendition " of " Kiss You " proved that the group have an elusive quality . On 12 December 2012 , the group also performed the number on the final of the second season of The X Factor USA . Considering One Direction the " franchise 's biggest success story " , an editor for The Huffington Post opined that the boy band 's prominent presence on both the US and UK versions of The X Factor seemed fitting . Not only Take Me Home Tour , they also performance in Where We Are Tour ( 2014 ) & On the Road Again Tour ( 2015 ) 
- 
- = = Track listing = = 
- 
- CD single 
- " Kiss You " – 3 : 04 
- " Little Things " – 3 : 42 
- 
- = = Credits and personnel = = 
- 
- Carl <unk> — writing , production , programming , instruments , guitar , background vocals 
- <unk> <unk> — background vocals 
- Niall <unk> — additional guitar 
- <unk> <unk> — writing , background vocals 
- <unk> Lundin — writing 
- <unk> <unk> — writing , background vocals 
- <unk> — writing 
- <unk> <unk> — writing , production , programming , instruments , bass 
- Credits adapted from Take Me Home 's liner notes . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = <unk> @-@ class battleship = 
- 
- The <unk> @-@ class battleships ( <unk> , <unk> @-@ <unk> <unk> ) were a pair of dreadnought battleships built for the Imperial Japanese Navy ( <unk> ) during World War I. Originally intended to be repeats of the preceding <unk> class , they were redesigned before construction began . Both ships carried supplies for the survivors of the Great <unk> earthquake in 1923 . They were modernized in 1934 – 37 with improvements to their armour and machinery and a rebuilt superstructure in the pagoda mast style . Afterwards they played a minor role in the Second <unk> @-@ Japanese War . 
- Despite the expensive reconstructions , both vessels were considered obsolete by the eve of the Pacific War , and neither saw significant action in the early years of the war . Following the loss of most of the <unk> 's large aircraft carriers during the Battle of Midway in mid @-@ 1942 , they were rebuilt with a flight deck replacing the rear pair of gun turrets to give them the ability to operate an air group of <unk> . A lack of aircraft and qualified pilots , however , meant that they never actually operated their aircraft in combat . While awaiting their air group the sister ships were sometimes used to ferry troops and material to Japanese bases . They participated in the Battle of Cape <unk> in late 1944 , where they <unk> the American carrier fleet supporting the invasion of <unk> away from the landing beaches . Afterwards both ships were transferred to Southeast Asia ; in early 1945 they participated in Operation <unk> , where they transported petrol and other strategic materials to Japan . The sisters were then reduced to reserve until they were sunk during American airstrikes in July . After the war they were scrapped in 1946 – 47 . 
- 
- = = Background = = 
- 
- The design of the <unk> @-@ class battleships was shaped both by the ongoing international naval arms race and a desire among Japanese naval planners to maintain a fleet of capital ships powerful enough to defeat the United States Navy in an encounter in Japanese territorial waters . The <unk> 's fleet of battleships had proven highly successful in 1905 , the last year of the Russo @-@ Japanese War , which culminated in the destruction of the Russian Second and Third Pacific Squadrons at the Battle of <unk> . 
- In the aftermath , the Japanese Empire immediately turned its focus to the two remaining rivals for imperial dominance in the Pacific Ocean : Britain and the United States . <unk> <unk> , a Japanese Navy admiral and military theorist , speculated that conflict would inevitably arise between Japan and at least one of its two main rivals . To that end , he called for the Japanese Navy to maintain a fleet with at least 70 % as many capital ships as the US Navy . This ratio , <unk> theorized , would enable the Imperial Japanese Navy to defeat the US Navy in one major battle in Japanese waters in any eventual conflict . Accordingly , the 1907 Imperial Defence Policy called for the construction of a battle fleet of eight modern battleships , 20 @,@ 000 long tons ( 20 @,@ 321 t ) each , and eight modern armoured cruisers , 18 @,@ 000 long tons ( 18 @,@ <unk> t ) each . This was the genesis of the Eight @-@ Eight Fleet Program , the development of a cohesive battle line of sixteen capital ships . 
- The launch of HMS <unk> in 1906 by the Royal Navy raised the stakes , and complicated Japan 's plans . <unk> 17 @,@ 900 long tons ( 18 @,@ 200 t ) and armed with ten 12 @-@ inch ( 30 @.@ 5 cm ) guns , <unk> rendered all existing battleships obsolete by comparison . The launch of the battlecruiser HMS Invincible the following year was a further setback for Japan 's quest for parity . When the two new <unk> @-@ class battleships and two <unk> @-@ class armoured cruisers , launched by 1911 , were <unk> by their British counterparts , the Eight @-@ Eight Fleet Program was <unk> . 
- The first battleships built for the renewed Eight @-@ Eight Fleet Program were the two dreadnoughts of the <unk> class , ordered in 1907 and laid down in 1908 . In 1910 , the Navy put forward a request to the Diet ( parliament ) to secure funding for the entirety of the program at once . Because of economic constraints , only four battlecruisers and a single battleship of the <unk> class were ultimately approved by the Diet . Three more <unk> @-@ class ships ( <unk> , <unk> , and <unk> ) were approved and all three were ordered in April 1913 . While <unk> was laid down later that year , the <unk> lacked the funding to proceed with the construction of <unk> and <unk> until the Diet authorized additional funding for the ships in July 1914 . 
- 
- = = Design and description = = 
- 
- The progress of <unk> 's construction , while the <unk> waited for the funding to be released and foreign developments , caused the <unk> to <unk> the <unk> @-@ class design . The distribution of the <unk> gun turrets was the most obvious flaw as they complicated the protection of the <unk> magazine and exposed more of the ship to the blast effects of the guns when they fired . Another issue was that Japanese sailors had problems maintaining a high rate of fire with the 45 @.@ 36 @-@ kilogram ( 100 @.@ 0 lb ) shells used in the manually loaded 152 @-@ millimetre ( 6 in ) secondary guns used in the <unk> class and earlier designs . To resolve this issue , the <unk> designed a smaller 140 @-@ millimetre ( 5 @.@ 5 in ) gun that offset its lighter shell weight with a higher rate of fire . It also decided that the barbette armour of the earlier ships was too thin and wanted a modest increase in speed to partially counter the higher speeds of the latest foreign ships like the British Queen Elizabeth @-@ class battleships and Russian <unk> @-@ class battlecruisers . For financial reasons more powerful engines could not be ordered so the new design was lengthened slightly and the boiler rooms enlarged to increase speed by 0 @.@ 5 knots ( 0 @.@ 93 km / h ; 0 @.@ 58 mph ) to 23 knots ( 43 km / h ; 26 mph ) . To save weight the forecastle deck was shortened so that the lower <unk> gun turret was lower than in the <unk> class . This reduced the crew 's accommodations despite a significant increase in the crew 's numbers and naval historian <unk> <unk> believed that these ships had the worst <unk> of any Japanese capital ship . The final design was designated A @-@ 92 by the <unk> and differed enough from the A @-@ 64 design of the <unk> class that it was considered a separate class . 
- The ships had a length of 208 @.@ 18 metres ( <unk> ft 0 in ) overall , a beam of 28 @.@ 65 metres ( 94 ft 0 in ) and a draught of 8 @.@ 93 metres ( 29 ft 4 in ) at deep load . They displaced 36 @,@ 500 long tons ( 37 @,@ 100 t ) at deep load , roughly 650 long tons ( 660 t ) more than the preceding class . Their crew consisted of 1 @,@ 360 officers and enlisted men . They had a <unk> height of 1 @.@ 737 metres ( 5 ft 8 @.@ 4 in ) at deep load . 
- During the ships ' modernization during the 1930s , their forward <unk> were enlarged with multiple platforms added to their tripod <unk> . Both ships were also given torpedo <unk> to improve their underwater protection and to compensate for the weight of the additional armour . In addition , their <unk> were lengthened by 7 @.@ 62 metres ( 25 @.@ 0 ft ) . These changes increased their overall length to 213 @.@ 8 metres ( 701 ft ) , their beam to 31 @.@ 75 metres ( 104 ft 2 in ) and their draft to 9 @.@ 45 metres ( 31 ft 0 in ) . Their displacement increased over 5 @,@ 000 long tons ( 5 @,@ 100 t ) to 42 @,@ 001 long tons ( 42 @,@ <unk> t ) at deep load . The crew now numbered 1 @,@ <unk> officers and enlisted men . 
- 
- = = = <unk> = = = 
- 
- The <unk> @-@ class ships had two sets of direct @-@ drive steam turbines , each of which drove two propeller shafts with 3 @.@ <unk> @-@ metre ( 11 ft 3 in ) propellers . The high @-@ pressure turbines drove the wing shafts while the low @-@ pressure turbines drove the inner shafts . The turbines were designed to produce a total of 40 @,@ 000 or 45 @,@ 000 shaft horsepower ( 30 @,@ 000 or 34 @,@ 000 kW ) ( <unk> and <unk> respectively ) , using steam provided by 24 <unk> <unk> <unk> water @-@ tube boilers at working pressures of 13 – 16 @.@ 9 kg / <unk> ( 1 @,@ 275 – 1 @,@ <unk> <unk> ; 185 – 240 <unk> ) . Both ships comfortably exceeded their designed speed of 23 knots ( 43 km / h ; 26 mph ) during their sea trials ; <unk> reached 23 @.@ 6 knots ( 43 @.@ 7 km / h ; 27 @.@ 2 mph ) from 56 @,@ <unk> shp ( 42 @,@ 131 kW ) and <unk> exceeded that with 24 knots ( 44 km / h ; 28 mph ) from 63 @,@ <unk> shp ( 47 @,@ 136 kW ) . Each of the boilers consumed a mixture of coal and oil and the ships had a stowage capacity of 4 @,@ <unk> long tons ( 4 @,@ <unk> t ) of coal and 1 @,@ <unk> long tons ( 1 @,@ <unk> t ) of fuel oil , which gave them a range of 9 @,@ 680 nautical miles ( 17 @,@ 930 km ; 11 @,@ 140 mi ) at a speed of 14 knots ( 26 km / h ; 16 mph ) . <unk> and <unk> had three generators of 150 <unk> ( 200 hp ) capacity and two 250 @-@ <unk> ( 340 hp ) <unk> generators at 225 volts . 
- During their 1930s modernization , the boilers on each ship were replaced by eight new <unk> oil @-@ fired boilers , fitted into the former aft boiler room , and the forward funnel was removed . The turbines were replaced by four geared <unk> turbines with a designed output of 80 @,@ 000 shp ( 60 @,@ 000 kW ) intended to increase their speed to 24 @.@ 5 knots ( 45 @.@ 4 km / h ; 28 @.@ 2 mph ) . On her trials , <unk> reached a top speed of 25 @.@ 26 knots ( 46 @.@ 78 km / h ; 29 @.@ 07 mph ) from 81 @,@ 050 shp ( 60 @,@ 440 kW ) . The fuel storage of the ships was increased to a total of 5 @,@ 113 long tons ( 5 @,@ 195 t ) of fuel oil that gave them a range of 7 @,@ <unk> nautical miles ( 14 @,@ 580 km ; 9 @,@ 060 mi ) at a speed of 16 knots ( 30 km / h ; 18 mph ) . 
- 
- = = = Armament = = = 
- 
- The twelve 45 @-@ calibre 35 @.@ 6 cm ( 14 @.@ 0 in ) Type 41 guns of the <unk> class were mounted in three pairs of twin @-@ gun , superfiring turrets . <unk> one through six from front to rear , each turret weighed <unk> long tons ( <unk> t ) . The <unk> powered turrets had an elevation capability of − 5 / + 20 degrees . The guns had a rate of fire of 1 @.@ 5 – 2 rounds per minute and could be loaded at any angle between <unk> and + 20 degrees . In 1921 the elevation was increased to + 30 degrees and then to + 43 degrees during their mid @-@ 1930s modernization , except for No. 6 turret as its supporting structure could not be lowered . The <unk> mechanism of the guns was also changed from a hydraulic to a <unk> system , which allowed for a faster firing cycle of the main guns . 
- By World War II , the guns used Type 91 armour @-@ piercing , capped shells . Each of these shells weighed <unk> @.@ 5 kilograms ( 1 @,@ <unk> lb ) and was fired at a muzzle velocity of 770 – 775 metres per second ( 2 @,@ <unk> – 2 @,@ 540 ft / s ) . They had a maximum range of 25 @,@ 000 metres ( 27 @,@ 000 yd ) at + 20 degrees of elevation and 35 @,@ 450 meters ( 38 @,@ 770 yd ) at + 43 degrees after modernization . Also available was a 625 @-@ kilogram ( 1 @,@ <unk> lb ) high @-@ explosive shell that had a muzzle velocity of 805 metres per second ( 2 @,@ 640 ft / s ) . A special Type 3 <unk> incendiary shrapnel shell was developed in the 1930s for anti @-@ aircraft use . 
- The ships ' secondary armament consisted of twenty 50 @-@ calibre 14 @-@ <unk> Type 3 . Eighteen of these were mounted in casemates in the forecastle and superstructure and the remaining pair were mounted on the deck above them and protected by gun shields . They had a maximum elevation of + 20 degrees which gave them ranges of 16 @,@ 300 metres ( 17 @,@ 800 yd ) . Each gun had a rate of fire of up to 10 rounds per minute . Anti @-@ aircraft defence was provided by four 40 @-@ calibre 3rd Year Type 8 @-@ <unk> AA guns in single mounts . The 7 @.@ 62 @-@ <unk> ( 3 in ) high @-@ angle guns had a maximum elevation of + 75 degrees , and had a rate of fire of 13 to 20 rounds per minute . They fired a 6 kg ( 13 lb ) <unk> with a muzzle velocity of 680 m / s ( 2 @,@ 200 ft / s ) to a maximum height of 7 @,@ 500 metres ( 24 @,@ 600 ft ) . The ships were also fitted with six submerged 53 @.@ 3 @-@ <unk> ( 21 @.@ 0 in ) torpedo tubes , three on each broadside . They carried twelve to eighteen 6th Year Type torpedoes which had a 200 @-@ kilogram ( 440 lb ) warhead . They had three settings for range and speed : 15 @,@ 000 metres ( 16 @,@ 000 yd ) at 26 knots ( 48 km / h ; 30 mph ) , 10 @,@ 000 metres ( 11 @,@ 000 yd ) at 32 knots ( 59 km / h ; 37 mph ) , or 7 @,@ 000 metres ( 7 @,@ 700 yd ) at 37 knots ( 69 km / h ; 43 mph ) . 
- In 1931 – 33 the AA guns were replaced with eight 40 @-@ caliber 12 @.@ 7 cm ( 5 @.@ 0 in ) Type 89 dual @-@ purpose guns , fitted on both sides of the forward <unk> in four twin @-@ gun mounts . When firing at surface targets , the guns had a range of 14 @,@ 700 metres ( 16 @,@ 100 yd ) ; they had a ceiling of 9 @,@ 440 metres ( 30 @,@ 970 ft ) at their maximum elevation of + 90 degrees . Their maximum rate of fire was 14 rounds a minute , but their sustained rate of fire was around eight rounds per minute . Two twin @-@ gun mounts for license @-@ built Vickers two @-@ pounder light AA guns were also added . These guns had a maximum elevation of + 80 degrees and a rate of fire of 200 rounds per minute . The pair of 14 cm guns on the upper deck were removed at this time . 
- During the mid @-@ 1930s reconstruction the torpedo tubes were removed and the Vickers two @-@ pounders were replaced by twenty license @-@ built <unk> 25 mm Type 96 light AA guns in 10 twin @-@ gun mounts . This was the standard Japanese light AA gun during World War II , but it suffered from severe design shortcomings that rendered it a largely ineffective weapon . According to historian Mark <unk> , the twin and triple mounts " lacked sufficient speed in train or elevation ; the gun sights were unable to handle fast targets ; the gun exhibited excessive vibration ; the magazine was too small , and , finally , the gun produced excessive muzzle blast " . These 25 @-@ millimetre ( 0 @.@ 98 in ) guns had an effective range of 1 @,@ 500 – 3 @,@ 000 metres ( 1 @,@ 600 – 3 @,@ 300 yd ) , and an effective ceiling of 5 @,@ 500 metres ( 18 @,@ 000 ft ) at an elevation of 85 degrees . The maximum effective rate of fire was only between 110 and 120 rounds per minute because of the frequent need to change the fifteen @-@ round magazines . In addition the forward pair of 14 cm guns in the forecastle were removed at this time and the maximum elevation of the remaining guns was increased to + 30 degrees . 
- 
- = = = Protection = = = 
- 
- The <unk> @-@ class ships ' waterline protective belt had a maximum thickness of <unk> mm ( 11 @.@ 8 in ) of Vickers cemented armour amidships ; below it was a <unk> of 100 mm ( 3 @.@ 9 in ) armour . The upper armoured deck consisted of two layers of high @-@ tensile steel 55 mm ( 2 @.@ 2 in ) thick and the lower armoured deck also consisted of two layers of high @-@ tensile steel , but only 30 mm ( 1 @.@ 2 in ) thick . The sides of this deck sloped downwards to meet the bottom of the lower <unk> of the belt armour . The ends of the belt armour were closed off by bulkheads that ranged in thickness from 203 to 102 mm ( 8 to 4 in ) . The turrets were protected with an armour thickness of 254 mm ( 10 in ) on the face and 76 mm on the roof . The casemate armour was 149 mm ( 5 @.@ 9 in ) thick and that of the barbettes was <unk> mm thick rather than the originally planned 305 mm . The sides of the conning tower were 305 mm thick . 
- The <unk> class were the only Japanese battleships to place the powder magazine above the shell magazine as the <unk> wished to put as much space as possible between the highly flammable propellant and mine and torpedo <unk> . The danger from <unk> shells at long distances was not appreciated until the fatal magazine explosions of three British battlecruisers during the 1916 Battle of Jutland <unk> demonstrated the point . To further protect the magazines the depth of the double bottom was increased to a total of 3 @.@ 58 metres ( 11 ft 9 in ) underneath the barbettes and magazines . Additionally , the vessels contained 660 <unk> compartments to preserve <unk> in the event of battle damage . In addition to the torpedo <unk> added when the ships were modernized , the deck armour over the machinery and magazines was increased to a total thickness of 140 mm . Inside the original skin of the ships , two torpedo bulkheads were also added and the turret roofs were increased to a total of 152 millimetres ( 6 in ) of armour . 
- 
- = = = Fire control and sensors = = = 
- 
- While the details of the ship 's fire @-@ control instruments are not fully available , it is known that the ships were fitted with a fire @-@ control director after completion . No computer was fitted at that time and data from the rangefinders had to be processed manually . <unk> 2 , 3 , and 5 were built with imported 6 @-@ metre ( 19 ft 8 in ) <unk> & <unk> rangefinders . These were felt to be inferior to the British <unk> & Stroud instruments used on other ships and were removed in 1920 . They were replaced by either the British rangefinders or domestically built instruments of 6 or 8 metres ( 19 ft 8 in or 26 ft 3 in ) length . In the late 1920s the fire @-@ control systems were upgraded and additional platforms were added to the foremast to accommodate them . A pair of directors for the 12 @.@ 7 cm AA guns were added , one on each side of the forward superstructure , in the early 1930s . The fire @-@ control systems were again upgraded in the mid @-@ 1930s and directors were added for the 25 mm AA guns . Both ships had 10 @-@ metre ( 32 ft 10 in ) rangefinders installed at the top of the pagoda mast at that time . Type 21 air @-@ search <unk> were installed aboard the sisters in mid @-@ 1942 . 
- 
- = = = Aircraft = = = 
- 
- <unk> was briefly fitted with an aircraft flying @-@ off platform for a <unk> <unk> fighter on <unk> No. 2 in 1927 . It was replaced by a platform on <unk> No. 5 for a <unk> <unk> reconnaissance <unk> in 1928 – 29 . A catapult and a <unk> 4 @-@ <unk> ( 3 @.@ 9 @-@ long @-@ ton ) crane were fitted on the stern during the mid @-@ 1930s modernization , and the ships were equipped to operate three <unk> , although no hangar was provided . The initial Nakajima <unk> <unk> were replaced by Nakajima <unk> <unk> in 1938 . 
- 
- = = <unk> to hybrid carriers = = 
- 
- The sinking of the British capital ships Prince of Wales and Repulse by Japanese land @-@ based aircraft on 10 December 1941 led the <unk> to realize that battleships could not operate in the face of enemy aircraft and required friendly air support to protect them . The loss of four Japanese aircraft carriers during the Battle of Midway in June 1942 severely limited the ability of the <unk> to provide any air cover and <unk> were sought . Earlier proposals to convert one or more battleships into carriers had been made and rejected at the beginning of the war , but they were revived after Midway . Plans for more elaborate <unk> were rejected on the grounds of expense and , most critically , time , and the <unk> settled on removing the rear pair of turrets and replacing them with a flight deck equipped with two <unk> to launch <unk> . The <unk> @-@ class ships were selected for the conversion because <unk> had suffered an explosion in <unk> No. 5 in early May that virtually destroyed the turret and their <unk> No. 6 could not elevate to the full + 43 degrees deemed necessary for the long @-@ range engagement anticipated by the <unk> . The <unk> were scheduled to follow once the first two were completed . 
- 
- = = = Armament changes = = = 
- 
- The rear turrets , the barbettes and their supporting structures were removed beginning in early 1943 and the openings in the middle deck were covered by 152 mm plates salvaged from the turret armour . All of the 14 cm guns were removed and the casemate openings sealed off . Four additional twin 12 @.@ 7 cm mounts were added , one pair abreast the funnel and the other abreast the conning tower . The original ten twin 25 mm gun mounts were replaced by triple mounts and nine new triple mounts were added , a total of 57 guns . Two each Type 94 and Type 95 AA directors were added to control the additional guns . The ammunition for these new guns was stored in the magazines originally used for the 14 cm guns and for <unk> No. 5 . During 1944 , the ships ' AA defences were reinforced with an additional dozen triple and eleven single 25 mm gun mounts , for a total of 104 barrels , and a pair of Type 13 early warning <unk> were added . In September six 30 @-@ round AA rocket launchers were added on the sides of the flight deck . 
- 
- = = = Flight deck arrangements = = = 
- 
- A 70 @-@ metre @-@ long ( <unk> ft 8 in ) flight deck was built above the stern and stretched forward to the rebuilt aft superstructure . The flight deck was 29 metres ( 95 ft 2 in ) wide at its forward end and 13 metres ( 42 ft 8 in ) at the stern . It <unk> the stern and increased the overall length of the ships to 219 @.@ 62 metres ( 720 ft 6 in ) . A pair of rotating gunpowder @-@ propelled <unk> were fitted on the sides of the hull , forward of the aft superstructure where they partially restricted the arc of fire of the two amidships turrets . They could launch aircraft up to 4 @,@ 600 kilograms ( 10 @,@ 100 lb ) in weight and required 30 seconds to launch each aircraft . The flight deck had eight permanent storage positions connected by rails to the <unk> and the <unk> operated aircraft lift that brought the aircraft up from the hangar below on the trolleys used to move the <unk> about . Two aircraft were intended to be <unk> on the <unk> and three more in temporary positions on the flight deck for a total of thirteen . 
- The 40 @-@ metre @-@ long ( 131 ft 3 in ) hangar was 20 metres ( 65 ft 7 in ) wide forward and 11 metres ( 36 ft 1 in ) at the rear . It was 6 metres ( 19 ft 8 in ) high and designed to <unk> nine aircraft . It was fitted with fire fighting <unk> and carbon dioxide <unk> as a result of wartime experience . The ' T ' <unk> lift was 12 @.@ 1 metres ( 39 ft 8 in ) wide at its forward end and 6 @.@ 6 metres ( 21 ft 8 in ) wide at the its aft end . It was 12 @.@ 1 metres long and had a capacity of 6 tonnes ( 5 @.@ 9 long tons ) . <unk> storage tanks with a capacity of 76 tonnes ( 75 long tons ) were installed in the former magazine of <unk> No. 6 to provide each aircraft with enough fuel for three sorties . To recover the aircraft the <unk> crane formerly on the stern was moved up to the port side of the flight deck . Another crane was intended on the starboard side , but it was never fitted . 
- The ships had an air group of 11 each of <unk> <unk> dive bombers ( Allied reporting name " Judy " ) and <unk> <unk> reconnaissance aircraft ( Allied reporting name " Paul " ) Both aircraft had development problems and neither air group ever had all of its intended aircraft . Coupled with a shortage of trained pilots , neither ship ever used its aircraft during combat . 
- 
- = = = Other changes = = = 
- 
- After the loss of the fast battleship <unk> at the Naval Battle of <unk> in late 1942 to rudder damage , the <unk> decided to reinforce the protection of the steering compartment and to create an auxiliary steering compartment . The protection of the former was strengthened by the addition of a concrete wall at least 1 metre ( 3 ft 3 in ) in thickness and some of the armour removed from the turrets was used to protect the latter . The double bottom below the former positions of aft turrets was converted to hold fuel oil ; this increased the ships ' endurance to 9 @,@ 500 nautical miles ( 17 @,@ 600 km ; 10 @,@ 900 mi ) at a speed of 16 knots . A pair of Type 22 surface @-@ search <unk> were also fitted during the conversion . 
- The removal of the secondary armament , the rear turrets and their supporting structures was generally compensated by the addition of the flight deck , hangar , AA guns and more fuel , and the <unk> height increased <unk> metres ( 9 @.@ 1 in ) to 2 @.@ 81 metres ( 9 ft 3 in ) at full load as a result of the reduction in the displacement by over 2 @,@ 000 tonnes ( 2 @,@ 000 long tons ) to 40 @,@ <unk> tonnes ( 39 @,@ 805 long tons ) . This also reduced the draught to 9 @.@ 03 metres ( 29 ft 8 in ) . The overhang of the flight deck at the stern increased the overall length to 219 @.@ 62 metres ( 720 ft 6 in ) and the beam was slightly reduced to 31 @.@ 71 metres ( 104 ft 0 in ) . 
- 
- = = Ships = = 
- 
- 
- = = Service = = 
- 
- Upon commissioning , the sister ships were assigned to the 1st Battleship Division of the 1st Fleet . <unk> had an explosion in one of her main gun turrets that killed 11 men and injured 25 in 1919 ; the following year she accidentally collided with and sank a schooner , losing two crewmen . Before the start of the Pacific War , both ships frequently exercised off the coasts of the Soviet Union , Korea and China in addition to training in Japanese waters . <unk> hosted Edward , Prince of Wales , and his aide @-@ de @-@ camp Lieutenant Louis <unk> in 1922 during the prince 's visit to Japan . In Korea Bay when the 1923 Great <unk> earthquake struck , they sailed to <unk> where they loaded supplies from for the victims on 4 September . Together with two other battleships and a pair of light cruisers , <unk> sank the destroyer <unk> in 1926 during gunnery practice . <unk> 's AA armament was upgraded in 1931 and <unk> 's two years later . The latter ship was modernized in 1934 – 36 and <unk> in 1935 – 37 , both at <unk> Naval Arsenal . During the Second <unk> @-@ Japanese War , the sisters frequently patrolled the Chinese coast in support of the blockade imposed by Japan . In August 1937 <unk> ferried two battalions of Special Naval Landing Forces to Port Arthur . Three years later , she served as the flagship for the Emperor of the puppet state of <unk> , Henry <unk> @-@ <unk> , during his state visit to Japan in June 1940 . On 15 November the ships were transferred to the 2nd Battleship Division of the 1st Fleet . The sisters were refitted in late 1940 in preparation for war , which included the fitting of external <unk> <unk> and additional AA directors . 
- 
- = = = World War II = = = 
- 
- When Japan began the Pacific War on 8 December , the sisters sortied for the <unk> Islands with four other battleships and the light carrier <unk> as distant cover for the fleet attacking Pearl Harbor , and returned six days later . On 11 March 1942 <unk> and <unk> sortied from their anchorage at <unk> to join the unsuccessful search for the American carrier force that had attacked Marcus Island a week earlier . Similarly they pursued but did not catch the American carriers that had launched the <unk> Raid on 18 April . 
- During gunnery training on 5 May , there was a premature detonation in the left gun of <unk> 's <unk> No. 5 that disabled both guns and killed 51 crewmen . Both aft magazines were flooded to <unk> the resulting fire and save the ship . She received temporary repairs during which the turret was removed and replaced by a circular armour plate on which three triple 25 mm gun mounts were positioned . On 11 May a valve in <unk> 's No. 2 engine room stuck in the open position and flooded the engine room . While under repair at <unk> , both ships received prototype Type 21 <unk> . <unk> by Vice @-@ Admiral <unk> <unk> , the 2nd Battleship Division set sail with the <unk> Support Group on 28 May , at the same time that most of the Imperial Fleet began an attack on Midway Island ( Operation <unk> ) . 
- They returned home on 14 June and the <unk> began preliminary planning to replace the lost carriers with hybrid carriers converted from battleships . The sisters were selected for conversion and detached from the division on 14 July in preparation . They remained on " standby alert " until the actual <unk> began . <unk> was converted at <unk> Naval Arsenal from 23 February to 5 September 1943 and <unk> at <unk> Naval Arsenal from 2 May to 30 November . 
- After completing her sea trials , <unk> was attached to the Imperial Japanese Naval Academy at <unk> and ferried troops and munitions to the naval base at <unk> in October . In November the ship began working up , joined by the newly completed <unk> the following month , and both rejoined the 2nd Battleship Division . On 1 May 1944 , the sisters were transferred to Rear Admiral <unk> <unk> 's reformed Fourth Carrier Division of the 3rd Fleet . The division 's <unk> Naval Air Group was formed that same day and conducted its first catapult launches in late June . 
- 
- = = = = Battle of Cape <unk> = = = = 
- 
- <unk> of aircraft and <unk> problems greatly <unk> pilot training and the ships only had a total of 17 <unk> and 18 <unk> on hand on 1 October ; of these , only 6 and 16 were operational , respectively . The Japanese plan for the defence of the Philippines envisioned that the surviving carriers would be used to lure the American carrier forces away from the invasion area to a position where the carriers could be attacked by land @-@ based aircraft and the transports by the rest of the <unk> . The other carrier air groups were not in much better shape and the Japanese decided to retain the aircraft ashore for use against the American carriers . The Fourth Carrier Division was assigned to the Northern Force under the command of Vice Admiral <unk> Ozawa and the sisters sailed from <unk> on 20 October . On the morning of 24 October , the bulk of the few aircraft aboard were launched to attack the American carriers as a distraction . They inflicted no damage and caused the Americans to search in the direction from which they had attacked . The Americans finally spotted the Japanese carriers at 16 : 40 , some 200 miles ( 320 km ) east of Cape <unk> , the northeastern tip of Luzon . The American carriers were spread out and it was very late in the day to launch an <unk> , so Admiral William <unk> , commander of the Third Fleet decided to mass his carriers in a position to attack the following morning . Ozawa reversed course during the night , correctly believing that the Americans would follow him north . 
- Although they had lost contact during the night , the Americans did find the Japanese carriers at 07 : 35 . They had already launched an <unk> of 180 aircraft that was orbiting 50 miles ( 80 km ) ahead of the American carriers while waiting for the Japanese ships to be located . This was just the first of a total of five airstrikes that the Americans launched that day . The sisters were not heavily engaged by the early airstrikes which are focusing on the group 's aircraft carriers . <unk> claimed to have shot down five attacking dive bombers from the second wave and one small bomb detonated on <unk> No. 2 . <unk> was lightly damaged by near misses that rupture some hull plating in her <unk> and <unk> her superstructure with splinters . She took on a 5 @-@ degree list that was quickly corrected before she was ordered to tow the crippled carrier <unk> to safety . Her attempt was unsuccessful and <unk> had to be abandoned to her fate . 
- <unk> was attacked by 80 @-@ odd aircraft from the fourth wave , but they failed to inflict any serious damage . She <unk> 11 torpedoes and was only hit by a bomb once , on the <unk> <unk> of the port catapult . Some 34 other bombs near missed her , spraying her with splinters and <unk> some hull plates that contaminated some fuel oil and caused leaks in her port boiler rooms . While an exact total of her casualties is not available , it has been estimated that 5 men were killed and some 111 – 121 crewmen were wounded during this attack . <unk> was unsuccessfully attacked by an American submarine at 18 : 43 . Around 19 : 00 Ozawa learned about a force of destroyers and cruisers that drove off the Japanese destroyers rescuing survivors from some of the carriers lost earlier in the day and sank <unk> . He ordered the Fourth Carrier Division to reverse course and engage the Americans , but the battleships were unable to find them , and Ozawa ordered them to reverse course and head for <unk> <unk> . When they arrived on 27 October , Ozawa transferred to <unk> and <unk> his flag aboard her . While en route for <unk> , the division was unsuccessfully attacked by another submarine . 
- In early November the <unk> were removed from both ships , and they loaded troops and munitions later that month . While en route they were diverted to the <unk> Islands upon reports of heavy air raids at Manila . After off @-@ loading their cargo , they sailed for <unk> Island , near Singapore , on 20 November . They transferred to Cam <unk> Bay , French Indochina and <unk> became flagship of the 5th Fleet there on 14 December . The division sailed for Singapore on 30 December and Vice Admiral <unk> <unk> transferred his flag to the light cruiser <unk> on arrival there the following day . The division continued onwards to <unk> . Its planned return to Japan was delayed by attacks by the American Third Fleet on targets in Indochina and southern China that sank two oil tankers that were intended to refuel the division . 
- The <unk> then decided to use the sisters and their escorts to bring a load of petrol , rubber , tin and other strategic minerals back to Japan after the American carriers departed the South China Sea ( Operation <unk> ) . They loaded their cargoes beginning on 6 February at Singapore and departed four days later . Also carrying some 1 @,@ 150 <unk> workers , they were escorted by <unk> and three destroyers . <unk> Japanese radio signals revealed the Japanese plan to the Allies , and 15 submarines were positioned along their anticipated route in an attempt to intercept and sink the ships . An additional 11 were moved into position while the group was en route , but only three were ultimately able to attack . None of them were successful before the Japanese reached <unk> on 20 February . The Fourth Carrier Division was disbanded on 1 March and the sisters were reduced to 1st rank reserve ships . On 19 March <unk> was attacked by aircraft from Task Force 58 and <unk> was hit three times by bombs that killed 37 men and wounded 52 . Her gunners claimed to have shot down one American dive bomber during the attack . <unk> was hit twice during the attack , but her casualties , if any , are unknown . 
- The ships were turned into floating AA batteries over the next several months although it <unk> them little when they were attacked again by American carrier aircraft in July . On the 24th <unk> was struck by five bombs and near missed multiple times ; all told she lost 50 crewmen killed and many others wounded . The bombs started numerous leaks and <unk> began to settle by the bow , although she was returned to an even keel after three @-@ days pumping . <unk> was a primary focus of the attack and she received 10 direct hits and up to 30 near misses . She was badly damaged with some 200 @-@ odd crewmen killed and 600 wounded during the attack . She slowly <unk> over the next two days and was not attacked when the Americans returned four days later . This time it was <unk> 's turn and she was struck 11 or more times with many near misses that put her on the bottom in shallow water with a 15 degree list . The sisters were struck off the Navy List in November and their wrecks were scrapped after the war . 
- 
- 
- = Dick <unk> = 
- 
- Richard Gale " Dick " <unk> ( August 21 , 1926 – December 5 , 1994 ) was an American football player and a pioneering television broadcaster for the forerunner to <unk> @-@ TV in Buffalo . He played college football for the University of Michigan Wolverines in 1944 and from 1946 to 1948 . He was a consensus selection at end on the 1948 College Football All @-@ America Team . <unk> played professionally in the National Football League ( NFL ) with the Detroit Lions for one season in 1950 . After retiring from football he settled in Buffalo and became a sports broadcaster . He worked as a color commentator and as a play @-@ by @-@ play announcer for the Buffalo Bulls . He hosted various television and radio sports shows and was eventually inducted into the Buffalo <unk> Hall of Fame . 
- In college , he led the Big Ten Conference in single season receptions during his senior year and set Michigan Wolverines receptions records for both career touchdown and single @-@ season touchdowns . He had also been a Michigan High School Athletic Association ( <unk> ) state champion in both basketball and track and field . His college career was interrupted by World War II service , and his high school career was also affected by the war due to the <unk> 's cancellation of state championships in all sports in 1943 . 
- 
- = = High school = = 
- 
- <unk> was born in <unk> , Michigan , and raised in Kalamazoo , Michigan before his family moved to Saginaw , Michigan . <unk> was a star athlete at Saginaw 's Arthur Hill High School in football , basketball , and track and field . In 1943 , Michigan canceled boys high school tournaments in all sports due to World War II , and they did not return until the fall of 1944 . In 1944 , he led Arthur Hill High to the <unk> Class A high school basketball championship ( over Kalamazoo Central High School ) , scoring 24 points , including 17 in the second half , of the championship game . <unk> was also the state champion in 1944 in both the shot put 46 feet 11 inches ( 14 @.@ 30 m ) and high jump 5 feet 8 @.@ 5 inches ( 1 @.@ 74 m ) . He also led Arthur Hill in football , and his high school accomplishments are featured in Glory : The history of Saginaw County sports by Jack <unk> ( ASIN <unk> ) , which is a book on high school sports in Saginaw County , Michigan . <unk> was named All State in football , basketball and track . 
- It is ironic that <unk> was born in <unk> , Michigan in 1926 for several reasons . Ted <unk> preceded <unk> as an All @-@ American end on the University of Michigan football team . <unk> had excelled as a representative of Saginaw County in <unk> competition . <unk> posted significant football accomplishments in 1926 making 1926 a significant year for himself as well . <unk> All @-@ American status as an end at Michigan would be <unk> 's next step after <unk> in <unk> competition . 
- 
- = = College = = 
- 
- In the fall of 1944 , <unk> enrolled at the University of Michigan . The United Press syndicate ran a feature article about <unk> in September 1944 that opened as follows : " Another great end has made his appearance on the Big Ten football horizon in the person of Dick <unk> , 18 @-@ year @-@ old Michigan freshman . Every so often a great offensive end comes along , a player who has to learn how to play defense , but who has the natural speed , smooth actions , height and big hands that is the mark of an outstanding pass receiver . <unk> has laid claim to that rating . A loose @-@ <unk> 180 @-@ pound freshman from Saginaw , <unk> . , <unk> is being <unk> as the Big Ten 's next ' freshman sensation . ' " As a freshman , he caught two touchdown passes in his first college football game against Iowa . In an article titled " <unk> and <unk> , " Time reported on <unk> 's performance : " Of the few teams already in action , Michigan 's teens rang the freshman bell <unk> last week by winning their opener , 12 <unk> @-@ 7 , against the strong Iowa Seahawks ( Naval Pre @-@ Flight ) ; 6 @-@ ft . 4 Freshman End Dick <unk> caught passes and ran for both Michigan touchdowns . " 
- <unk> 's college career was interrupted by World War II service in the United States Navy , but after missing the 1945 season , he returned to play for the Wolverines from 1946 to 1948 . <unk> played for the Wolverines in consecutive undefeated National Championship seasons in 1947 and 1948 . He started nine games for the 1947 team . The 1947 team referred to as " Michigan 's Mad <unk> " is considered to be the greatest University of Michigan football team of all time . <unk> and teammate Len Ford had the reputation as the team practical <unk> . During the 1947 game against Wisconsin , <unk> started calling signals for the <unk> . Wisconsin 's offense protested to officials , who " <unk> the Wolverines secondary but never caught their man . " <unk> continued to scramble <unk> signals , as <unk> 's teammates laughed at his scheme . In the January 1 , 1948 Rose Bowl that season , Michigan rolled to a 49 – 0 victory over USC , and they <unk> the Trojans 491 yards to 133 . <unk> caught a 29 @-@ yard pass for the game 's final score . 
- In the 1948 championship season , <unk> scored eight touchdowns , caught 22 passes , and gained 610 yards ( 508 receiving and 102 rushing ) . <unk> was the second highest scoring end in the nation in 1948 , and he was a consensus All @-@ American as a senior , being selected as first team on nine of the 11 All @-@ American teams . <unk> led the Big Ten in receptions . 
- Although <unk> finished fourth among <unk> <unk> voters in 1948 , he did not finish among the top eight . By comparison , Notre Dame end Leon Hart won the <unk> Trophy in 1949 but made only eight of the 11 All @-@ American teams . It is not clear why <unk> did not finish higher . However , it is fairly clear that sportswriters of that era had a bias against Michigan . In the Associated Press poll at the end of the 1947 season , the Notre Dame Fighting Irish were ranked ahead of the University of Michigan , though both teams were undefeated . Some noted that every Southern AP <unk> had voted for Notre Dame , which had yet to integrate , whereas three of Michigan 's star players ( Bob Mann , Gene <unk> , and Len Ford ) were African @-@ American . The Southern schools refused even to schedule games against schools that played African @-@ American players . 
- <unk> was considered one of the greatest <unk> 's of the 1940s . In four seasons with the Michigan Wolverines , <unk> played in 32 games and had over 1 @,@ 000 yards of total offense . <unk> held the University of Michigan 's single season and career record for touchdown receptions ( eight in a season ; sixteen career ) until his records were broken by Anthony Carter in 1980 . 
- 
- = = Professional career = = 
- 
- In 1948 , <unk> was drafted by the Philadelphia Eagles in the 15th round of the NFL draft , and he was also drafted by the New York Yankees of the All @-@ America Football Conference . He had intended to play in 1949 with the Yankees , but suffered a knee injury in a practice session for the August 1949 Chicago College All @-@ Star Game . Press accounts at the time noted that the injury " will probably keep him out of pro football all season , if not forever . " The incident led to a debate as to whether NFL owners should " bar their men from playing with the college all @-@ stars . " 
- <unk> landed a job at <unk> radio in Detroit , but he left his <unk> 's job to join the Detroit Lions . In the 1950 NFL season , <unk> came back from his injury to play for the Detroit Lions . He played in 12 games and had ten receptions for 96 yards and one touchdown for the 1950 Lions . <unk> recalled that his playing time with the Lions was limited because the Lions also signed 1949 <unk> Trophy winner Leon Hart , who played the same position . 
- In May 1951 , he announced he was retiring from professional football to become sports director at a radio station in Buffalo . He was hired as a <unk> by <unk> ( now known as <unk> ) , which had just started the first television station in Buffalo and the only one serving Southern Ontario . This was an early foray into television by the Buffalo Evening News . In the 1950s , <unk> hosted a popular panel show called " Let 's Talk Sports " in Buffalo and also pioneered an early morning exercise program . He also worked for <unk> ( AM ) and <unk> ( FM ) and as the <unk> announcer for Buffalo Bills games along with Van Miller , the long time Bills play @-@ by @-@ play announcer . In addition , he served as the play @-@ by @-@ play announcer for the University of Buffalo Bulls football team . As a radio broadcaster , he is remembered for things ranging from <unk> reports , to 17 years worth of " <unk> At — " programs live from various local restaurants , to 27 years as the <unk> @-@ AM All Night Show host . 
- After 30 years with <unk> and a change in ownership for the station , his show was replaced with the <unk> Network 's The Larry King Show . In the 1980s , <unk> taught communications at Buffalo 's <unk> College and served as a disc jockey on Public Broadcasting 's radio station <unk> ( now <unk> ) . He also sold <unk> for Buffalo Evening News competitor , Buffalo Courier @-@ Express . <unk> 's final employer was Erie County , who hired him as an inmate training supervisor at the Erie County <unk> <unk> . 
- <unk> was posthumously inducted into the Buffalo <unk> Hall of Fame in September 2007 . He was given the Golden Age Award which is reserved for " those who did it first , the people who had no pattern to follow . " The Hall of Fame award was presented to <unk> 's wife , Jane . In her acceptance speech , Jane <unk> observed that despite all of her late husband 's achievements , there was one thing he had never received : " He had a great career , but he never had a trophy . And now he has . " 
- 
- = = Family = = 
- 
- <unk> lived 37 of his years in Buffalo . His wife , the former Jane Morris , was the head of the Buffalo <unk> <unk> when they met . <unk> , who was survived by three sons , ( Douglas A. , Gary R. , and Bruce R. ) one daughter ( Wendy J. <unk> ) and two grandchildren , died in <unk> , New York in December 1994 ; he was 68 years old . Doug was a 1988 first team football All @-@ Western New York linebacker for Clarence High School . 
- 
- 
- = 1933 Treasure Coast hurricane = 
- 
- The 1933 Treasure Coast hurricane was the second @-@ most intense tropical cyclone to strike the United States during the active 1933 Atlantic hurricane season . The eleventh tropical storm , fifth hurricane , and the third major hurricane of the season , it formed east @-@ northeast of the Leeward Islands on August 31 . The tropical storm moved rapidly west @-@ northwestward , steadily intensifying to a hurricane . It acquired peak winds of 140 miles per hour ( 225 km / h ) and passed over portions of the Bahamas on September 3 , including <unk> and Harbour Island , causing severe damage to crops , buildings , and infrastructure . Winds over 100 mph ( 161 km / h ) affected many islands in its path , especially those that encountered its center , and many wharves were ruined . 
- Subsequently , it weakened and made landfall at Jupiter , Florida , early on September 4 with winds of 125 mph ( 201 km / h ) . The hurricane moved across the state , passing near Tampa before moving into Georgia and dissipating . In Florida , the strong winds of the cyclone blew buildings off their foundations , and numerous trees were <unk> in citrus groves . The Treasure Coast region received the most extensive destruction , and Stuart , Jupiter , and Fort Pierce were heavily damaged . Inland , the cyclone weakened rapidly but produced prodigious amounts of rain , causing a dam to collapse near Tampa . The storm caused $ 3 million in damage ( 1933 USD ) after damaging or destroying 6 @,@ <unk> homes . 
- Unusually , the storm hit Florida less than 24 hours before another major hurricane bearing 125 @-@ mile @-@ per @-@ hour ( 201 km / h ) winds struck South Texas ; never have two major cyclones hit the United States in such close succession . 
- 
- = = Meteorological history = = 
- 
- The origins of the hurricane were from a tropical wave that possibly spawned a tropical depression on August 27 , although there was minimal data over the next few days as it tracked to the west @-@ northwest . On August 31 , a nearby ship reported gale force winds , which indicated that a tropical storm had developed to the east @-@ northeast of the Lesser Antilles . Based on continuity , it is estimated the storm attained hurricane status later that day . Moving quickly to the west @-@ northwest , the storm passed north of the Lesser Antilles and Puerto Rico . Early on September 2 , a ship called the <unk> reported a barometric pressure of 978 mbar ( 28 @.@ 88 inHg ) , which confirmed that the storm attained hurricane status . After passing north of the Turks and <unk> islands , the hurricane struck <unk> and Harbour Island in the Bahamas on September 3 , the latter at 1100 UTC . A station on the latter island reported a pressure of 27 @.@ 90 inHg ( <unk> mb ) during the 30 minute passage of the eye . Based on the pressure and the small size of the storm , it is estimated the hurricane struck Harbour Island with peak winds of 140 mph ( 225 km / h ) , making it the equivalent of a modern Category 4 hurricane on the Saffir @-@ Simpson scale . <unk> suggested that the storm reached major hurricane status , or Category 3 status , on September 2 . 
- The hurricane initially followed the course of another hurricane that passed through the area in late August , which ultimately struck Cuba and Texas . This hurricane instead maintained a general west @-@ northwest track . After moving through the northern Bahamas , the hurricane weakened slightly before making landfall at Jupiter , Florida , at <unk> UTC on September 4 . A station there reported a pressure of 27 @.@ 98 inHg ( <unk> mb ) during a 40 minute period of the eye 's passage ; this suggested a landfall strength of 125 mph ( 201 km / h ) . At the time , the radius of maximum winds was 15 mi ( 24 km ) , which was smaller than average . After landfall , the hurricane weakened rapidly while crossing the state . It briefly emerged into the Gulf of Mexico as a tropical storm early on September 5 . A few hours later while continuing to the northwest , it made another landfall near <unk> — a ghost town in Levy County , east of Cedar Key — with winds of about 65 mph ( 105 km / h ) . Turning to the north , the storm slowly weakened as it crossed into Georgia , dissipating on September 7 near Augusta . 
- 
- = = Preparations and impact = = 
- 
- On September 2 , a fleet of eight aircraft evacuated all white residents from West End , Grand <unk> , to <unk> Beach , Florida . While the storm was near peak intensity on September 3 , the Weather Bureau issued hurricane warnings from Miami to Melbourne , Florida , with storm warnings extending northward to Jacksonville . Later that day , storm warnings , were issued from Key West to Cedar Key . About 2 @,@ 500 people evacuated by train from areas around Lake Okeechobee . By evening on September 3 , high tides sent sea <unk> over coastal <unk> in Palm Beach County as residents boarded up buildings ; structures on <unk> Street in West Palm Beach were said to be a " solid front " of <unk> . Along the coast , observers reported very rough seas as the eye neared land . 
- The powerful hurricane moved over or near several islands in the Bahamas . Winds on Spanish Wells and Harbour Island were both estimated at around 140 mph ( 225 km / h ) . Winds reached 110 mph ( 177 km / h ) at Governor 's Harbour , 100 mph ( 161 km / h ) on <unk> , and 120 mph ( 193 km / h ) on the <unk> Islands . The storm was farther away from Nassau , where winds reached 61 mph ( 98 km / h ) . The hurricane damaged a lumber mill on <unk> , <unk> away a dock . Heavy damage occurred on Harbour Island , including to several roofs , the walls of government buildings , and the water system . The hurricane destroyed four churches and 37 houses , leaving 100 people homeless . A 1 @.@ 5 mi ( 2 @.@ 4 km ) road on <unk> was destroyed . Several islands sustained damage to farms , including the total loss of various fruit trees on Russell Island . Despite Category 4 winds on Spanish Wells , only five houses were destroyed , although most of the remaining dwellings lost their roofs . <unk> between North Point , James <unk> , and Gregory Town on <unk> , the storm destroyed 55 houses and damaged many others . On Grand <unk> , where a 9 to 12 ft ( 2 @.@ 7 to 3 @.@ 7 m ) storm surge was reported , half of the houses were destroyed , as were 13 boats and two planes , and most docks were wrecked . 
- When the storm moved ashore in Florida , winds reached an estimated 125 mph ( 201 km / h ) in Jupiter ; these occurred after the eye passed . In West Palm Beach , <unk> measured at least 80 @-@ mile @-@ per @-@ hour ( 129 km / h ) winds with gusts to 100 mph ( 161 km / h ) ; <unk> ranged from 28 @.@ 64 to 28 @.@ 78 inHg ( 970 to <unk> mb ) . The storm produced the strongest winds in the city since the 1928 Okeechobee hurricane . Winds were not as strong farther from the center ; 40 to 45 mph ( 64 to 72 km / h ) winds were observed in Miami to the south , <unk> to the north , and Tampa on the west coast . Fort Pierce estimated peak winds of 80 to 90 mph ( 129 to 145 km / h ) , and pressures dipped to 29 @.@ 14 inHg ( <unk> mb ) . Inland , winds near Lake Okeechobee peaked at only 60 mph ( 97 km / h ) . The hurricane dropped heavy rainfall along its path , peaking at 17 @.@ 8 in ( 450 mm ) in <unk> . 
- At West Palm Beach , the majority of the damage was confined to vegetation . Several <unk> and royal <unk> that withstood the 1928 hurricane snapped , <unk> streets with broken trunks . Winds downed road signs on many streets , and <unk> covered the <unk> on a local golf course . Some <unk> and isolated structures , mostly lightweight , were partly or totally destroyed , along with a lumber warehouse . Some homes that lost roofing shingles had water damage to their interiors as well . Nearby Lake Worth sustained extensive breakage of windows , including plate glass , and loss of tile and <unk> roofing , but preparations reduced losses to just several thousand dollars , and no post @-@ storm accidents took place . Strong winds snapped many light poles in the city , and trees and shrubs were broken or uprooted . As in Lake Worth , officials in West Palm Beach credited preparations and stringent building codes with reducing overall damage . The city had learned from previous experience with severe storms in 1926 , 1928 , and 1929 . High tides eroded Ocean Boulevard at several spots and disrupted access to several bridges on the Lake Worth <unk> . Winter estates and hotels on Palm Beach generally sustained little material damage , except to vegetation , and county properties went largely unscathed . 
- In Martin and St. Lucie counties , the storm was considered among the worst on record . The storm leveled some homes and swept many others off their foundations . At Stuart , winds removed or badly damaged 75 % of the roofs in town . The storm destroyed the third floor of the building that housed a bowling <unk> and the Stuart News , a local newspaper . At <unk> , an abandoned settlement also known as <unk> Beach , strong winds leveled the old <unk> Inn , a gas station , and the second floor of a <unk> building . Winds also tore the roof off an ice plant . A bridge leading to the barrier island from <unk> was partly wrecked ; the bridge tender survived by gripping the <unk> during the storm . Winds leveled his nearby home . According to the Monthly Weather Review , some of the most severe damage from the storm in Florida was at <unk> . The storm left many homes in <unk> Sound <unk> , forcing crews to tear them down . Winter estates on the island , however , were better built and little damaged . While Stuart and <unk> Sound sustained significant damage , Port <unk> suffered <unk> . In Stuart , the storm left 400 to 500 people homeless , up to nearly 10 % of the population , which was 5 @,@ 100 at the time . Between Jupiter and Fort Pierce , the storm knocked down power and telegraph lines . In the latter city , high waves washed out a portion of the causeway . In the 1980s , an elderly resident recalled that the storm was the most severe on record in Fort Pierce . 
- <unk> damage was worst along the Indian River <unk> ; several farms in Stuart experienced total losses , and <unk> , 16 % of the citrus crop , or 4 million boxes , were destroyed . Many chicken <unk> in Stuart were destroyed , and the local chicken population was scattered and dispersed as far as <unk> . Across southeastern Florida , the hurricane damaged 6 @,@ <unk> houses and destroyed another <unk> , causing over $ 3 million in damage . One person , an African American farm worker , was killed when his <unk> blew down in Gomez , a <unk> died after seven <unk> <unk> , and a child was killed by airborne debris . 
- High rainfall caused flooding across Florida , notably near Tampa where waters reached 9 ft ( 2 @.@ 7 m ) deep . High rainfall of over 7 in ( 180 mm ) caused a dam operated by Tampa Electric Co. to break 3 mi ( 4 @.@ 8 km ) northeast of Tampa along the <unk> River . The break resulted in severe local damage , flooding portions of <unk> Springs . Workers attempted to save the dam with <unk> , and after the break , most residents in the area were warned of the approaching flood . Over 50 homes were flooded , forcing about 150 people to evacuate . Outside Florida , the storm produced winds of 48 and 51 mph ( 78 and 81 km / h ) in Savannah , Georgia and Charleston , South Carolina , respectively . In the latter city , the storm spawned a tornado , which caused about $ 10 @,@ 000 in property damage . Heavy rainfall occurred along the Georgia and South Carolina coasts , reaching over 12 in ( 300 mm ) . Light rainfall also extended into North Carolina . 
- 
- = = Aftermath = = 
- 
- In the Bahamas after the storm , a boat sailed from Nassau to deliver food and building materials to <unk> . 
- After the storm , the National Guard offered shelters for at least 400 homeless residents in Stuart . Of the 7 @,@ 900 families adversely affected by the hurricane , 4 @,@ 325 required assistance from the American Red Cross . <unk> in Texas , also affected by a major hurricane , requested <unk> in Florida wait 15 days so they could sell their citrus crop that fell . The damaged dam near Tampa initially resulted in waters from the <unk> River being pumped into the city 's water treatment plant , and a new dam was eventually built in 1944 . 
- 
- 
- = Second Battle of <unk> <unk> = 
- 
- The Second Battle of <unk> <unk> was an engagement between United Nations ( UN ) and North Korean ( <unk> ) forces early in the Korean War from September 1 to September 15 , 1950 , along the <unk> River in South Korea . It was a part of the Battle of Pusan Perimeter , and was one of several large engagements fought simultaneously . The battle ended in a victory for the United Nations after large numbers of United States ( US ) and Republic of Korea ( ROK ) troops repelled a strong North Korean attack . 
- After the First Battle of <unk> <unk> , the US Army 's 2nd Infantry Division was moved to defend the <unk> River line . The division , which was <unk> in combat , was struck with a strong attack by several divisions of the Korean People 's Army which crossed the river and struck all along the division 's line . The force of the attack split the US 2nd Infantry Division in half , and the North Koreans were able to penetrate to <unk> , promoting a fight there . 
- The urgency of the threat to Pusan Perimeter prompted the US Marine Corps 1st Provisional Marine Brigade to be brought in to reinforce the US Army troops . In two weeks of heavy fighting , the US forces were able to force the North Koreans out of the <unk> <unk> region . The North Koreans were further repulsed after the UN counterattack at <unk> , which culminated in the virtual destruction of the North Korean army . 
- 
- = = Background = = 
- 
- 
- = = = Pusan Perimeter = = = 
- 
- From the outbreak of the Korean War and the invasion of South Korea by the North , the North Korean People 's Army had enjoyed superiority in both manpower and equipment over both the Republic of Korea Army and the United Nations forces dispatched to South Korea to prevent it from collapsing . The North Korean strategy was to aggressively pursue UN and ROK forces on all avenues of approach south and to engage them aggressively , attacking from the front and initiating a double <unk> of both flanks of the unit , which allowed the North Koreans to surround and cut off the opposing force , which would then be forced to retreat in <unk> , often leaving behind much of its equipment . From their initial June 25 offensive to fights in July and early August , the North Koreans used this strategy to effectively defeat any UN force and push it south . However , when the UN forces , under the Eighth United States Army , established the Pusan Perimeter in August , the UN troops held a continuous line along the peninsula which North Korean troops could not flank , and their advantages in numbers decreased daily as the superior UN logistical system brought in more troops and supplies to the UN army . 
- When the North Koreans approached the Pusan Perimeter on August 5 , they attempted the same frontal assault technique on the four main avenues of approach into the perimeter . Throughout August , the <unk> 6th Division , and later the <unk> 7th Division engaged the US 25th Infantry Division at the Battle of <unk> , initially <unk> a UN counteroffensive before <unk> with battles at <unk> @-@ <unk> and Battle Mountain . These attacks stalled as UN forces , well equipped and with plenty of reserves , repeatedly repelled North Korean attacks . North of <unk> , the <unk> 4th Division and the US 24th Infantry Division <unk> in the <unk> <unk> area . In the First Battle of <unk> <unk> , the North Korean division was unable to hold its bridgehead across the river as large numbers of US reserve forces were brought in to repel it , and on August 19 , the <unk> 4th Division was forced back across the river with 50 percent casualties . In the <unk> region , five North Korean divisions were repulsed by three UN divisions in several attempts to attack the city during the Battle of <unk> . Particularly heavy fighting took place at the Battle of the <unk> <unk> where the <unk> 13th Division was almost completely destroyed in the attack . On the east coast , three more North Korean divisions were repulsed by the South Koreans at P 'ohang @-@ dong during the Battle of P 'ohang @-@ dong . All along the front , the North Korean troops were <unk> from these defeats , the first time in the war their strategies were not working . 
- 
- = = = September push = = = 
- 
- In planning its new offensive , the North Korean command decided any attempt to flank the UN force was impossible thanks to the support of the UN navy . Instead , they opted to use frontal attack to breach the perimeter and collapse it as the only hope of achieving success in the battle . <unk> by intelligence from the Soviet Union the North Koreans were aware the UN forces were building up along the Pusan Perimeter and that it must conduct an offensive soon or it could not win the battle . A secondary objective was to surround <unk> and destroy the UN and ROK units in that city . As part of this mission , the North Korean units would first cut the supply lines to <unk> . 
- On August 20 , the North Korean commands distributed operations orders to their subordinate units . The North Koreans called for a simultaneous five @-@ <unk> attack against the UN lines . These attacks would overwhelm the UN defenders and allow the North Koreans to break through the lines in at least one place to force the UN forces back . Five battle groupings were ordered . The center attack called for the <unk> 9th Division , <unk> 4th Division , <unk> 2nd Division , and <unk> 10th Division break through the US 2nd Infantry Division at the <unk> <unk> to <unk> and <unk> . 
- 
- = = Battle = = 
- 
- During the North Koreans ' September 1 offensive , the US 25th Infantry Division 's US 35th Infantry Regiment was heavily engaged in the Battle of <unk> River north of <unk> . On the 35th Regiment 's right flank , just north of the confluence of the <unk> River and the <unk> River , was the US 9th Infantry Regiment , US 2nd Infantry Division . There , in the southernmost part of the 2nd Infantry Division zone , the 9th Infantry Regiment held a sector more than 20 @,@ 000 yards ( 18 @,@ 000 m ) long , including the <unk> area of the <unk> where the First Battle of <unk> <unk> had taken place earlier in August . Each US infantry company on the river line here had a front of 3 @,@ 000 feet ( 910 m ) to 4 @,@ 000 feet ( 1 @,@ 200 m ) and they held only key hills and observation points , as the units were extremely spread out along the wide front . 
- During the last week of August , US troops on these hills could see minor North Korean activity across the river , which they thought was North Koreans organizing the high ground on the west side of the <unk> against a possible American attack . There were occasional attacks on the 9th Infantry 's forward positions , but to the men in the front lines this appeared to be only a standard patrol action . On August 31 , the UN forces were alerted to a pending attack when much of the Korean civilian labor force fled the front lines . Intelligence officers reported an attack was coming . 
- On the west side of the <unk> , North Korean Major General <unk> <unk> Sam , commanding the <unk> 9th Division , issued his operations order to the division on August 28 . Its mission in the forthcoming attack was to outflank and destroy the US troops at <unk> <unk> by capturing the <unk> and <unk> areas to cut off the US 2nd Division 's route of supply and withdrawal between <unk> and Pusan . However , the North Koreans weren 't aware that the US 2nd Infantry Division had recently replaced the US 24th Infantry Division in positions along the <unk> River . Consequently , they expected lighter resistance ; the 24th troops were exhausted from months of fighting but the 2nd Division men were fresh and newly arrived in Korea . They had only recently been moved into the line . The North Koreans began crossing the <unk> River under cover of darkness at certain points . 
- 
- = = = Battle of <unk> = = = 
- 
- On the southern @-@ most flank of the 9th Infantry river line , just above the junction of the <unk> River with the <unk> , A Company of the 1st Battalion was dug in on a long finger ridge <unk> the <unk> that terminates in Hill 94 at the <unk> ferry site . The river road from <unk> @-@ ri running west along the <unk> passes the southern tip of this ridge and crosses to the west side of the river at the ferry . A small village called <unk> lay at the base of Hill 94 and 300 yards ( 270 m ) from the river . A patrol of tanks and armored vehicles , together with two infantry squads of A Company , 9th Infantry , held a roadblock near the ferry and close to <unk> . On the evening of August 31 , A Company moved from its ridge positions overlooking <unk> and the river to new positions along the river below the ridge line . 
- That evening Sergeant Ernest R. <unk> led the patrol of two <unk> <unk> tanks and two <unk> Gun Motor <unk> in <unk> . <unk> placed his patrol on the west side of <unk> near the <unk> ferry . At 20 : 00 a heavy fog covered the river , and at 22 : 00 mortar shells began falling on the American @-@ held side of the river . By 22 : 15 this strike intensified and North Korean mortar preparation struck A Company 's positions . American mortars and artillery began firing <unk> . Some of the A Company men reported they heard noises on the opposite side of the river and splashes in the water . 
- At 22 : 30 the fog lifted and <unk> saw that a North Korean <unk> bridge was being laid across the river directly in front of his position . <unk> 's four vehicles attacked this structure , and after about a minute of heavy fire the bridge collapsed , and the <unk> boats used to hold the bridge in place were sunk . At 23 : 00 a small arms fight flared around the left side of A Company north of the tanks . This gunfire had lasted only two or three minutes when the A Company roadblock squads near the tanks received word by field telephone that the company was withdrawing to the original ridge positions and that they should do likewise . 
- <unk> 's patrol was then ambushed by a group of North Koreans dressed in US military uniforms . <unk> was wounded and the other three vehicles had to withdraw , but he held the <unk> site until 07 : 30 the next morning with his single tank . In the attack against A Company , the North Koreans hit the 1st Platoon , which was near <unk> , but they did not find the 2nd Platoon northward . 
- The <unk> 9th Division 's infantry crossing of the <unk> and attack on its east side near midnight quickly overran the positions of C Company , north of A Company . There the North Koreans assaulted in force , signaled by green flares and blowing of whistles . The company held its positions only a short time and then attempted to escape . Many of the men moved south , a few of them coming into A Company 's ridge line positions near <unk> during the night . Most of C Company moved all the way to the 25th Division positions south of the <unk> . On September 1 that division reported that 110 men of C Company had come into its lines . 
- 
- = = = North Korean crossing = = = 
- 
- Meanwhile , 5 miles ( 8 @.@ 0 km ) north of <unk> and A Company 's position , B Company , 9th Infantry , held a similar position on Hill <unk> overlooking the <unk> ferry crossing of the river . This ferry was located at the middle of the <unk> <unk> where the <unk> road came down to the <unk> and crossed it . The US 2nd Infantry Division had planned a reconnaissance mission to start from there the night of August 31 , the same night that the <unk> I Corps offensive rolled across the river . 
- Near the end of the month two reconnaissance patrols from the 9th Infantry had crossed to the west side of the <unk> and observed North Korean tank and troop activity 2 miles ( 3 @.@ 2 km ) west of the river . Information obtained later indicated it was in fact the command post of the <unk> 9th Division . On August 25 , 9th Infantry commander Colonel John G. Hill outlined projected " Operation <unk> , " which was to be a company @-@ sized combat patrol to cross the river , advance to the suspected North Korean command post and communications center , destroy it , capture prisoners , and collect intelligence . 
- The 9th Infantry Regiment had planned Task Force <unk> on orders from the 2nd Division commander Major General Laurence B. <unk> , which in turn had received instructions from Eighth United States Army commander Lieutenant General Walton Walker for aggressive patrolling . <unk> decided the patrol should cross the river at the <unk> ferry . The 9th Infantry reserve , E Company , reinforced with one section of light machine guns from H Company , was to be the attack force . The 1st Platoon , 2nd Engineer Combat Battalion , was to transport it across the river in assault boats the night of August 31 . Two heavy weapons companies , D and H , were each to <unk> one section of heavy machine guns , one section of 81 @-@ <unk> mortars , and one section of 75 @-@ <unk> recoilless rifles for supporting fires . A platoon of 4 @.@ 2 @-@ inch mortars was also to give support . 
- After dark on August 31 , First Lieutenant Charles I. Caldwell of D Company and First Lieutenant Edward <unk> of H Company , 9th Infantry , moved their men and weapons to the base of Hill <unk> , which was within B Company 's defense sector and overlooked the <unk> ferry crossing of the <unk> River . The raiding force , E Company , was still in its regimental reserve position about 2 miles ( 3 @.@ 2 km ) west of <unk> , getting ready with the engineer platoon to move to the crossing site . Colonel Hill went forward in the evening with the 4 @.@ 2 @-@ inch mortar platoon to its position at the base of Hill <unk> where the <unk> prepared to set up their weapons . 
- By 21 : 00 , the closest front line unit was B Company on top of Hill <unk> , 1 mile ( 1 @.@ 6 km ) north of the river road which curved around the hill 's southern base . The regimental <unk> , Captain Lewis B. Sheen , had gone forward in the afternoon to B Company to hold services . On top of Hill <unk> , <unk> Sheen and men in B Company after dark heard <unk> in the water below them . They soon discovered a long line of North Korean soldiers wading the river . 
- The first North Korean crossing at the <unk> ferry caught the Heavy <unk> Platoon unprepared in the act of setting up its weapons . It also caught most of the D and H Company men at the base of Hill <unk> , <unk> miles ( 0 @.@ 80 km ) from the crossing site . The North Koreans killed or captured many of the troops there . Hill was there , but escaped to the rear just before midnight , together with several others , when the division canceled Operation <unk> because of the attacks . The first heavy weapons carrying party was on its way up the hill when the North Korean attack <unk> the men below . It <unk> on to the top where the advance group waited and there all hastily dug in on a small perimeter . This group was not attacked during the night . 
- From 21 : 30 until shortly after midnight the <unk> 9th Division crossed the <unk> at a number of places and climbed the hills quietly toward the 9th Infantry river line positions . Then , when the artillery barrage preparation lifted , the North Korean infantry were in position to launch their assaults . These began in the northern part of the regimental sector and quickly spread southward . At each crossing site the North Koreans would overwhelm local UN defenders before building <unk> bridges for their vehicles and armor . 
- At 02 : 00 , B Company was attacked . A truck stopped at the bottom of the hill , a whistle sounded , then came a shouted order , and North Korean soldiers started climbing the slope . The hills on both sides of B Company were already under attack as was also Hill 311 , a rugged terrain feature a 1 @.@ 5 miles ( 2 @.@ 4 km ) from the river and the North Koreans ' principal immediate objective . The North Koreans apparently were not aware of the Task Force <unk> group lower down on the hill and it was not attacked during the night . But higher up on Hill <unk> the North Koreans drove B Company from its position , inflicting very heavy casualties on it . Sheen led one group of soldiers back to friendly lines on 4 September . 
- At 03 : 00 , 1 September , the 9th Infantry Regiment ordered its only reserve , E Company to move west along the <unk> @-@ <unk> River road and take a blocking position at the pass between <unk> Hill and <unk> @-@ <unk> Ridge , 3 miles ( 4 @.@ 8 km ) from the river and 6 miles ( 9 @.@ 7 km ) from <unk> . This was the critical terrain where so much heavy fighting had taken place in the first battle of the <unk> <unk> . Fighting began at the pass at 02 : 30 when an American medium tank of A Company , 72nd Tank Battalion , knocked out a T @-@ 34 at <unk> , also called <unk> . E Company never reached its blocking position . A strong North Korean force surprised and delivered heavy automatic fire on it at 03 : 30 from positions <unk> the road east of the pass . The company suffered heavy casualties , including the company commander and <unk> 's aide who had accompanied the force . With the critical parts of <unk> Hill and <unk> @-@ <unk> Ridge , the best defensive terrain between <unk> and the river , the North Koreans controlled the high ground . The US 2nd Infantry Division now had to base its defense of <unk> on relatively poor defensive terrain , the low hills at the western edge of the town . 
- 
- = = = US 23rd Infantry attacked = = = 
- 
- North of the 9th Infantry sector of the 2nd Infantry Division front along the <unk> , the US 23rd Infantry Regiment on August 29 had just relieved the 3rd Battalion of the US 38th Infantry Regiment , which in turn had only a few days before relieved the US 21st Infantry Regiment of the US 24th Infantry Division . On August 1 , the 23rd Regiment was in a new sector of which it had only a limited knowledge . It took over a 16 @,@ 000 yards ( 15 @,@ 000 m ) <unk> River front without its 3rd Battalion which had been attached to the US 1st Cavalry Division to the north . Colonel Paul L. Freeman , the regimental commander , deployed the 1st Battalion on the high ground along the river with the three companies abreast . The 1st Battalion , under US Lieutenant Colonel Claire E. <unk> , Jr . , <unk> the hills with platoons and squads . He placed the 2nd Battalion in a reserve position 8 miles ( 13 km ) behind the 1st Battalion and in a position where it commanded the road net in the regimental sector . On August <unk> the 2nd Division moved E Company south to a reserve position in the 9th Infantry sector . 
- Two roads ran through the regimental sector from the <unk> River to <unk> . The main road bent south along the east bank of the river to <unk> @-@ <unk> and then turned northeast to <unk> . A northern secondary road curved around <unk> and lakes , the largest of which was Lake U @-@ p <unk> , to <unk> . In effect , the 1st Battalion of the 23rd Regiment guarded these two approach routes to <unk> . 
- The 42 men of the 2nd Platoon , B Company , 23rd Infantry held outpost positions on seven hills covering a 2 @,@ 600 yards ( 2 @,@ 400 m ) front along the east bank of the <unk> north of <unk> @-@ <unk> . Across the river in the rice <unk> they could see , in the afternoon of August 31 , two large groups of North Korean soldiers . Occasionally artillery fire dispersed them . Just before dark , the platoon saw a column of North Koreans come out of the hills and proceed toward the river . They immediately reported to the battalion command post . The artillery forward observer , who estimated the column at 2 @,@ 000 people , thought they were refugees . Freeman immediately ordered the artillery to fire on the column , reducing its number . However the North Koreans continued their advance . 
- At 21 : 00 the first shells of what proved to be a two @-@ hour North Korean artillery and mortar preparation against the American river positions of 2nd Platoon . As the barrage rolled on , North Korean infantry crossed the river and climbed the hills in the darkness under cover of its fire . At 23 : 00 the barrage lifted and the North Koreans attacked 2nd Platoon , forcing it from the hill after a short fight . Similar assaults took place elsewhere along the battalion outpost line . 
- On the regimental left along the main <unk> @-@ <unk> @-@ <unk> road North Korean soldiers completely overran C Company by 0300 September 1 . Only seven men of C Company could be accounted for , and three days later , after all the stragglers and those cut off behind North Korean lines had come in , there were only 20 men in the company . As the North Korean attack developed during the night , 1st Battalion succeeded in withdrawing a large part of its force , less C Company , just north of Lake U @-@ p <unk> and the hills there covering the northern road into <unk> , 3 miles ( 4 @.@ 8 km ) east of the river and 5 miles ( 8 @.@ 0 km ) west of the town . B Company lost heavily in this action . 
- When word of the disaster that had <unk> 1st Battalion reached regimental headquarters , Freeman obtained the release of G and F Companies from 2nd Division reserve and sent the former to help 1st Battalion and the latter on the southern road toward <unk> @-@ <unk> and C Company . Major Lloyd K. Jenson , executive officer of the 2nd Battalion , accompanied F Company down the <unk> @-@ <unk> road . This force was unable to reach C Company , but Jenson collected stragglers from it and seized high ground <unk> this main approach to <unk> near <unk> <unk> @-@ ri above Lake <unk> , and went into a defensive position there . The US 2nd Division released E Company to the regiment and the next day it joined F Company to build up what became the main defensive position of the <unk> Regiment in front of <unk> . North Korean troops during the night passed around the right flank of 1st Battalion 's northern blocking position and reached the road three miles behind him near the division artillery positions . The 23rd Infantry Headquarters and Service Companies and other <unk> regimental units finally stopped this penetration near the regimental command post 5 miles ( 8 @.@ 0 km ) northwest of <unk> . 
- 
- = = = US 2nd Division split = = = 
- 
- Before the morning of 1 September had passed , reports coming in to US 2nd Division headquarters made it clear that North Koreans had penetrated to the north @-@ south <unk> @-@ <unk> road and cut the division in two ; the 38th and <unk> Infantry Regiments with the bulk of the division artillery in the north were separated from the division headquarters and the 9th Infantry Regiment in the south . <unk> decided that this situation made it <unk> to control and direct the divided division as two special forces . Accordingly , he placed the division artillery commander , Brigadier General Loyal M. Haynes , in command of the northern group . Haynes ' command post was 7 miles ( 11 km ) north of <unk> . Task Force Haynes became operational at 10 : 20 , September 1 . <unk> , in the <unk> area , <unk> placed Brigadier General Joseph S. Bradley , Assistant Division Commander , in charge of the 9th Infantry Regiment , the 2nd Engineer Combat Battalion , most of the 72nd Tank Battalion , and other <unk> units of the division . This southern grouping was known as Task Force Bradley . 
- All three regiments of the <unk> 2nd Division @-@ the 4th , 17th , and 6th , in line from north to south @-@ crossed during the night to the east side of the <unk> River into the 23rd Regiment sector . The <unk> 2nd Division , concentrated in the <unk> @-@ <unk> area west of the river , had , in effect , attacked straight east across the river and was trying to seize the two avenues of advance into <unk> above and below Lake U @-@ p <unk> . On August 31 , 1950 , Lake U @-@ p <unk> was a large body of water although in most places very shallow . 
- At dawn September 1 , <unk> at 2nd Division headquarters in <unk> @-@ <unk> , 7 miles ( 11 km ) east of <unk> on the <unk> road , felt his division was in the midst of a crisis . The massive North Korean attack had made deep <unk> everywhere in the division sector except in the north in the zone of the 38th Infantry . The <unk> 9th Division had effected major <unk> of the <unk> at two principal points against the US 9th Infantry ; the <unk> 2nd Division in the meantime had made three major <unk> against the US 23rd Infantry ; and the <unk> 10th Division had begun crossing more troops in the Hill <unk> area near <unk> 'ung in the US 38th Infantry sector . At 08 : 10 <unk> telephoned Eighth Army headquarters and reported the heaviest and deepest North Korean <unk> were in the 9th Infantry sector . 
- <unk> planes rose from the division strip every hour to observe the North Korean progress and to locate US 2nd Infantry Division front @-@ line units . Communication from division and regimental headquarters to nearly all the forward units was broken . Beginning at 09 : 30 and continuing throughout the rest of the day , the light aviation section of the division artillery located front @-@ line units cut off by the North Koreans , and made fourteen <unk> of ammunition , food , water , and medical supplies . As information slowly built up at division headquarters it became apparent that the North Koreans had punched a hole 6 miles ( 9 @.@ 7 km ) wide and 8 miles ( 13 km ) deep in the middle of the division line and made less severe <unk> elsewhere . The front @-@ line battalions of the US 9th and 23rd Regiments were in various states of <unk> and some companies had virtually disappeared . <unk> hoped he could organize a defense along the <unk> @-@ <unk> road east of the <unk> River , and prevent North Korean access to the passes eastward leading to <unk> and Ch <unk> . 
- 
- = = = Reinforcements = = = 
- 
- At 09 : 00 Walker requested the US Air Force to make a maximum effort along the <unk> River from <unk> @-@ dong , just above the US 2nd Division boundary , southward and to a depth of 15 miles ( 24 km ) west of the river . He wanted the Air Force to isolate the battlefield and prevent further North Korean reinforcements and supplies from moving across the river in support of the North Korean spearhead units . The Far East Command requested the US Navy to join in the air effort , and the US Seventh Fleet turned back from its strikes in the <unk> <unk> @-@ Seoul area and <unk> southward at full steam toward the southern battle front . Walker came to the US 2nd Division front at 12 : 00 and ordered the division to hold at all costs . He had already ordered ground reinforcements to the <unk> area . 
- During the morning of 1 September , Walker weighed the news coming in from his southern front , <unk> in a decision as to which part of the front most needed his Pusan Perimeter reserves . Since midnight the <unk> I Corps had broken his Pusan Perimeter in two places @-@ the <unk> 2nd and 9th Divisions in the US 2nd Division sector , and the <unk> 7th Division and <unk> 6th Division in the US 25th Division sector , below the junction of the <unk> and <unk> Rivers . In the US 2nd Division sector North Korean troops were at the edge of <unk> , the gateway to the corridor leading 12 miles ( 19 km ) eastward to <unk> and the main Pusan @-@ <unk> railroad and highway . 
- Eighth Army had in reserve three <unk> infantry regiments and the 2 @-@ battalion British 27th Infantry Brigade which was not yet completely equipped and ready to be placed in line : The 1st Provisional Marine Brigade at <unk> , 6 miles ( 9 @.@ 7 km ) northeast of <unk> , preparing for movement to the port of Pusan ; the US 27th Infantry Regiment of the 25th Division which had arrived at <unk> only the night before at 20 : 30 to relieve the 5th Regimental Combat Team , which was then to join the 24th Division in the <unk> area ; and the US 19th Infantry Regiment of the US 24th Infantry Division , then with that division 's headquarters at <unk> southeast of <unk> . Walker alerted both the 24th Division headquarters , together with its 19th Regiment , and the 1st Provisional Marine Brigade to move at a moment 's notice ; the 24th Division either to the 2nd or 25th Division fronts , and the marines to an <unk> destination . 
- As the morning passed , General Walker decided that the situation was most critical in the <unk> <unk> area of the US 2nd Division sector . There the North Koreans threatened <unk> and with it the entire Eighth Army position . At 11 : 00 Walker ordered US Marine Corps Brigadier General Edward A. Craig , commanding the 1st Provisional Marine Brigade , to prepare the marines to move at once . The marines made ready to depart for the <unk> <unk> at 13 : 30 . 
- 
- = = = North Korean advance = = = 
- 
- The situation on the front was chaotic during the day September 1 . The North Koreans at one place had crossed at the <unk> ferry , captured <unk> , and scattered A Company , 9th Infantry at its positions from <unk> northward . A Company withdrew to positions on the ridge line back of the river . From there at daylight the men could see North Korean soldiers on many of the ridges surrounding them , most of them moving east . After several hours , 2nd Platoon of A Company sent a patrol down the hill to <unk> to obtain supplies abandoned there during the night , returning later with much needed water , rations , and ammunition . 
- Later in the morning North Korean barges crossed the <unk> below A Company . The company sent a squad with a light machine gun to the southern tip of the ridge overlooking <unk> to take these troops under fire . When the squad reached the tip of the ridge they saw that a North Korean force occupied houses at its base . The company hit these houses with artillery . The North Koreans broke from the houses , running for the river . At this the light machine gun at the tip of the ridge took them under fire , as did another across the <unk> to the south in the US 25th Infantry Division sector . <unk> <unk> artillery fire decimated this group . Combined fire from all weapons inflicted an estimated 300 casualties on this North Korean force . In the afternoon , US aircraft dropped food and ammunition to the company ; only part of it was recovered . The 1st Battalion ordered A Company to withdraw the company that night . 
- During the withdraw , however , A Company ran into a sizable North Korean force and had scattered in the ensuing fight . Most of the company , including its commander were killed at close range . In this desperate action , Private First Class Luther H. Story , a weapons squad leader , fought so <unk> that he was awarded the Medal of Honor . <unk> wounded , Story refused to be a burden to those who might escape , and when last seen was still engaging North Korean at close range . Of those in the company , approximately ten men escaped to friendly lines . The next morning , under heavy fog , the group made its way by <unk> toward <unk> . From a hill at 12 : 00 , after the fog had lifted , the men looked down on the Battle of <unk> which was then in progress . That afternoon 20 survivors of the company merged into the lines of the 72nd Tank Battalion near <unk> . <unk> from this position continued to stream in the next few days as well . 
- 
- = = = The end of Task Force <unk> = = = 
- 
- In the meantime , Task Force <unk> was still holding its position along the <unk> River , about 5 miles ( 8 @.@ 0 km ) north of where A Company had been destroyed on the southern end of the line . The perimeter position taken by the men of D and H Companies , 9th Infantry , who had started up the hill before the North Koreans struck , was on a southern <unk> of Hill <unk> , 0 @.@ 5 miles ( 0 @.@ 80 km ) south of B Company 's higher position . In addition to the D and H Company men , there were a few from the Heavy <unk> Platoon and one or two from B Company . <unk> , 60 to 70 men were in the group . The group had an <unk> @-@ 300 radio , a heavy machine gun , two light machine gun , a <unk> <unk> <unk> Rifle , about 20 <unk> <unk> rifles , and about 40 <unk> or pistols . <unk> assumed command of the group . 
- During the night <unk> established radio communication with the 1st Battalion , 9th infantry . When daylight came <unk> and his group saw that they were surrounded by North Koreans . One force occupied the higher <unk> half a mile above them , formerly held by B Company . Below them , North Koreans continued crossing the river and moving supplies forward to their combat units , some of them already several miles eastward . The North Koreans quickly discovered Task Force <unk> group . They first attacked it at 14 : 00 that afternoon , and were repulsed . That night an estimated company attacked three times , pressing the fight to close quarters , but failed each time to penetrate the tight US perimeter . <unk> of the second day disclosed many North Korean dead on the steep slopes outside the perimeter . 
- In the afternoon of September 2 <unk> <unk> 1st Battalion for an <unk> of supplies . A US plane attempted the drop , but the perimeter was so small and the slopes so steep that virtually all the supplies went into North Korean hands . The men in the perimeter did , however , recover from a drop made later at 19 : 00 some supplies and ammunition . Private First Class Joseph R. <unk> , of H Company , left the perimeter to gather weapons , ammunition , and grenades from the North Korean dead . On several occasions he was attacked , and on one such occasion a North Korean soldier suddenly attacked <unk> , who killed the North Korean in hand @-@ to @-@ hand combat . 
- That same afternoon , the North Koreans sent an American prisoner up the hill to <unk> with the message , " You have one hour to surrender or be blown to pieces . " <unk> in frontal infantry attack to reduce the little defending force , the North Koreans now meant to take it under mortar fire . Only 45 minutes later North Korean <unk> fire came in on the <unk> and two machine guns from positions northward and higher on the slope of Hill <unk> swept the perimeter . Soon , mortars <unk> on a neighboring high finger ridge eastward registered on <unk> 's perimeter and continued firing until dark . The machine gun fire forced every man to stay in his <unk> . The lifting of the mortar fire after dark was the signal for renewed North Korean infantry attacks , all of which were repulsed . But the number of killed and wounded within the perimeter was growing , and supplies were diminishing . There were no medical supplies except those carried by one aid man . 
- The third day , September 3 , the situation worsened . The weather was hot and ammunition , food and supplies were nearly completely exhausted . Since the previous afternoon , North Korean mortar <unk> had alternated with infantry assaults against the perimeter . <unk> later estimated there were about twenty separate infantry attacks repulsed . Two North Korean machine guns still swept the perimeter whenever anyone showed himself . Dead and dying US troops were in almost every <unk> . <unk> fragments destroyed the radio and this ended all communication with other US units . Artillery fire and air strikes requested by <unk> never came . Some North Koreans worked their way close to the perimeter and threw grenades into it . Six times <unk> <unk> from his <unk> to escape grenades thrown into it . In this close action <unk> was killed . Most of the <unk> of the perimeter received one or more direct mortar hits in the course of the continuing mortar fire . One of these killed <unk> on September 3 . The command passed now to First Lieutenant Raymond J. <unk> of D Company , senior surviving officer . 
- At daylight on the morning of 4 September only two officers and approximately half the men who had assembled on the hill , were alive . As the day passed , with ammunition down to about one clip per man and only a few grenades left and no help in sight , <unk> decided to abandon the position that night . When it got dark the survivors would split into small groups and try to get back to friendly lines . That evening after dark the North Koreans launched another weak attack against the position . At 22 : 00 , <unk> and Caldwell and 27 enlisted men slipped off the hill in groups of four . Master Sergeant Travis E. Watkins , still alive in his <unk> condition , refused efforts of evacuation , saying that he did not want to be a burden to those who had a chance to get away . He asked only that his <unk> be loaded and placed on his chest with the muzzle under his chin . Like <unk> , he would also win the Medal of Honor for his actions . Of the 29 men who came off the hill the night of September 4 , 22 escaped to friendly lines , many of them following the <unk> downstream , hiding by day and traveling by night , until they reached the lines of the US 25th Infantry Division . 
- Members of Task Force <unk> who escaped from Hill <unk> brought back considerable intelligence information of North Korean activity in the vicinity of the <unk> ferry crossing site . At the ferry site the North Koreans had put in an underwater bridge . A short distance downstream , each night they placed a <unk> bridge across the river and took it up before dawn the next morning . <unk> parties of 50 civilians guarded by four North Korean soldiers crossed the river continuously at night , an estimated total of 800 @-@ 1 @,@ 000 carriers being used at this crossing site . 
- 
- = = = <unk> = = = 
- 
- North of the US 9th Infantry and the battles in the <unk> <unk> and around <unk> , the US <unk> Infantry Regiment after daylight of September 1 was in a very precarious position . Its 1st Battalion had been driven from the river positions and isolated 3 miles ( 4 @.@ 8 km ) westward . Approximately 400 North Koreans now overran the regimental command post , compelling Freeman to withdraw it about 600 yards ( 550 m ) . There , 5 miles ( 8 @.@ 0 km ) northwest of <unk> , the US 23rd Infantry Headquarters and Headquarters Company , <unk> regimental units , and regimental staff officers checked the North Koreans in a 3 @-@ hour fight . 
- The North Koreans advanced to <unk> itself during the afternoon of September 2 , and ROK National Police withdrew from the town . North Koreans were in <unk> that evening . With his communications broken southward to the 2nd Infantry Division headquarters and the 9th Infantry , Haynes during the day decided to send a tank patrol down the <unk> road in an effort to re @-@ establish communication . C Company , 72nd Tank Battalion , led its tanks southward . They had to fight their way down the road through several roadblocks . Of the three tanks that started , only the lead tank got through to <unk> . There , it delivered an <unk> of Task Force Haynes ' positions to Bradley . 
- Still farther northward in the zone of the US 38th Infantry the North Koreans were also active . After the North Korean breakthrough during the night of August 31 , <unk> had ordered the 2nd Battalion , 38th Infantry , to move south and help the 23rd Infantry establish a defensive position west of <unk> . In attempting to do this , the battalion found North Korean troops already on the ridges along the road . They had penetrated to Hill <unk> overlooking the 38th Infantry command post . This hill and Hill <unk> dominated the rear areas of the regiment . At 06 : 00 September 3 , 300 North Koreans launched an attack from Hill <unk> against the 38th Regiment command post . The regimental commander organized a defensive perimeter and requested a bombing strike which was denied him because the enemy target and his defense perimeter were too close to each other . But the Air Force did deliver rocket and <unk> strikes . 
- This fight continued until September 5 . On that day F Company captured Hill <unk> killing 150 North Koreans . From the crest he and his men watched as many more North Koreans ran into a village below them . Directed artillery fire destroyed the village . Among the abandoned North Korean materiel on the hill , <unk> 's men found twenty @-@ five American <unk> and <unk> guns , a large American radio , thirty boxes of <unk> American fragmentation and concussion grenades , and some American rations . 
- 
- = = = 1 @-@ 23rd Infantry isolated = = = 
- 
- Meanwhile , during these actions in its rear , the 1st Battalion , 23rd Infantry , was cut off 3 miles ( 4 @.@ 8 km ) west of the nearest friendly units . On September 1 the regiment ordered it to withdraw to the <unk> area . At 14 : 00 a tank @-@ infantry patrol was sent down the road , but it reported that an estimated North Korean battalion held the mountain pass just eastward of the battalion 's defense perimeter . Upon receiving this report the battalion commander requested permission by radio to remain in his present position and try to obstruct the movement of North Korean reinforcements and supplies . That evening Freeman approved this request , and 1st Battalion spent three days in the isolated positions . During this time C @-@ 47 <unk> planes supplied the battalion by <unk> . 
- On the morning of September 1 , 3rd Battalion , 38th Infantry moved in an attack westward from the 23rd Regiment command post near <unk> @-@ <unk> to open the road to the 1st Battalion . On the second day of the fighting at the pass , the relief force broke through the roadblock with the help of air strikes and artillery and tank fire . The advanced elements of the battalion joined 1st Battalion at 17 : 00 September 2 . That evening , North Koreans strongly attacked the 3rd Battalion , 38th Infantry , on Hill <unk> north of the road and opposite 1st Battalion , driving one company from its position . 
- On September 4 , Haynes changed the boundary between the 38th and 23rd Infantry Regiments , giving the northern part of the 23rd 's sector to the 38th Infantry , thus releasing 1st Battalion for movement southward to help the 2nd Battalion defend the southern approach to <unk> . The 1st Battalion , 23rd Infantry , about 1 @,@ 100 men strong when the attack began , was now down to a strength of approximately 600 men . The 23rd Infantry now made plans to concentrate all its troops on the position held by its 2nd Battalion on the <unk> @-@ <unk> @-@ <unk> road . The 1st Battalion moved there and took a place on the left flank of the 2nd Battalion . At the same time the regimental command post moved to the rear of this position . In this regimental perimeter , the 23rd Infantry fought a series of hard battles . Simultaneously it had to send combat patrols to its rear to clear infiltrating North Koreans from <unk> and from its supply road . 
- 
- = = = Battle of <unk> = = = 
- 
- On the morning of September 1 the 1st and 2nd Regiments of the <unk> 9th Division , in their first offensive of the war , stood only a few miles short of <unk> after a successful river crossing and penetration of the American line . The 3rd Regiment had been left at <unk> <unk> , but division commander Major General <unk> <unk> Sam felt the chances of capturing <unk> were strong . 
- On the morning of September 1 , with only the shattered remnants of E Company at hand , the US 9th Infantry Regiment , US 2nd Infantry Division had virtually no troops to defend <unk> . <unk> in this emergency attached the 2nd Engineer Combat Battalion to the regiment . The US 72nd Tank Battalion and the 2nd Division Reconnaissance Company also were assigned positions close to <unk> . The regimental commander planned to place the engineers on the chain of low hills that arched around <unk> on the northwest . 
- A Company , 2nd Engineer Combat Battalion , moved to the south side of the <unk> @-@ <unk> River road ; D Company of the 2nd Engineer Battalion was on the north side of the road . Approximately 2 miles ( 3 @.@ 2 km ) west of <unk> an estimated 300 North Korean troops engaged A Company in a fire fight . <unk> Gun Motor <unk> of the 82nd AAA Battalion supported the engineers in this action , which lasted several hours . Meanwhile , with the approval of General Bradley , D Company moved to the hill immediately south of and overlooking <unk> . A platoon of infantry went into position behind it . A Company was now ordered to fall back to the southeast edge of <unk> on the left flank of D Company . There , A Company went into position along the road ; on its left was C Company of the Engineer battalion , and beyond C Company was the 2nd Division Reconnaissance Company . The hill occupied by D Company was in reality the western tip of a large mountain mass that lay southeast of the town . The road to <unk> came south out of <unk> , bent around the western tip of this mountain , and then ran eastward along its southern base . In its position , D Company not only commanded the town but also its exit , the road to <unk> . 
- North Koreans had also approached <unk> from the south . The US 2nd Division Reconnaissance Company and tanks of the 72nd Tank Battalion opposed them in a sharp fight . In this action , Sergeant First Class Charles W. Turner of the Reconnaissance Company particularly distinguished himself . He mounted a tank , operated its exposed turret machine gun , and directed tank fire which reportedly destroyed seven North Korean machine guns . Turner and this tank came under heavy North Korean fire which shot away the tank 's <unk> and <unk> and scored more than 50 hits on it . Turner , although wounded , remained on the tank until he was killed . That night North Korean soldiers crossed the low ground around <unk> and entered the town from the south . 
- At 09 : 35 September 2 , while the North Koreans were attempting to destroy the engineer troops at the southern edge of <unk> and clear the road to <unk> , Walker spoke by telephone with Major General Doyle O. <unk> , Deputy Chief of Staff , Far East Command in Tokyo . He described the situation around the Perimeter and said the most serious threat was along the boundary between the US 2nd and US 25th Infantry Divisions . He described the location of his reserve forces and his plans for using them . He said he had started the 1st Provisional Marine Brigade toward <unk> but had not yet released them for commitment there and he wanted to be sure that General of the Army Douglas MacArthur approved his use of them , since he knew that this would interfere with other plans of the Far East Command . Walker said he did not think he could restore the 2nd Division lines without using them . <unk> replied that MacArthur had the day before approved the use of the US Marines if and when Walker considered it necessary . A few hours after this conversation Walker , at 13 : 15 , attached the 1st Provisional Marine Brigade to the US 2nd Division and ordered a co @-@ ordinated attack by all available elements of the division and the marines , with the mission of destroying the North Koreans east of the <unk> River in the 2nd Division sector and of restoring the river line . The marines were to be released from 2nd Division control as soon as this mission was accomplished . 
- A decision was reached that the marines would attack west at 08 : 00 on September 3 <unk> the <unk> @-@ <unk> River road ; the 9th Infantry , B Company of the 72nd Tank Battalion , and D Battery of the <unk> AAA Battalion would attack northwest above the marines and attempt to re @-@ establish contact with the US 23rd Infantry ; the 2nd Engineer Combat Battalion , remnants of the 1st Battalion , 9th Infantry , and elements of the 72nd Tank Battalion would attack on the left flank , or south , of the marines to reestablish contact with the 25th Division . Eighth Army now ordered the US 24th Infantry Division headquarters and the US 19th Infantry Regiment to move to the Susan @-@ <unk> area , 8 miles ( 13 km ) south of <unk> and 15 miles ( 24 km ) east of the confluence of the <unk> River and the <unk> River . There it was to prepare to enter the battle in either the 2nd or 25th Division zone . 
- The American counteroffensive of September 3 – 5 west of <unk> , according to prisoner statements , resulted in one of the <unk> <unk> of the war for a North Korean division . Even though remnants of the <unk> 9th Division , supported by the low strength <unk> 4th Division , still held <unk> @-@ <unk> Ridge , <unk> Hill , and the intervening ground back to the <unk> on September 6 , the division 's offensive strength had been spent at the end of the American counterattack . The <unk> 9th and 4th divisions were not able to resume the offensive . 
- 
- = = = <unk> 2nd Division destroyed = = = 
- 
- The <unk> 2nd Division made a new effort against the 23rd Infantry 's perimeter in the <unk> hours of September 8 , in an attempt to break through eastward . This attack , launched at 02 : 30 and heavily supported with artillery , penetrated F Company . It was apparent that unless F Company 's position could be restored the entire regimental front would collapse . When all its officers became casualties , First Lieutenant Ralph R. Robinson , adjutant of the 2nd Battalion , assumed command of the company . With North Koreans rapidly infiltrating his company 's position and gaining its rear , Robinson in the darkness made his way through them 500 yards ( 460 m ) to A Company 's position . There he obtained that company 's reserve platoon and brought it back to F Company . He accomplished the dangerous and difficult task of <unk> it into the gap in F Company 's lines in darkness and heavy rain . 
- The attack tapered off with the coming of daylight , but that night it resumed . The North Koreans struck repeatedly at the defense line . This time they continued the fighting into the daylight hours of 9 September . The Air Force then concentrated strong air support over the regimental perimeter to aid the ground troops . Casualties came to the aid stations from the infantry companies in an almost steady stream during the morning . All available men from Headquarters Company and special units were formed into squads and put into the fight at the most critical points . At one time , the regimental reserve was down to six men . When the attack finally ceased shortly after 12 : 00 the 23rd Regiment had an estimated combat efficiency of only 38 percent . 
- This heavy night and day battle cost the <unk> 2nd Division most of its remaining offensive strength . The medical officer of the <unk> 17th Regiment , 2nd Division , captured a few days later , said that the division evacuated about 300 men nightly to a hospital in <unk> @-@ <unk> , and that in the first two weeks of September the 2nd Division lost 1 @,@ 300 killed and 2 @,@ 500 wounded in the fighting west of <unk> . Even though its offensive strength was largely spent by September 9 , the division continued to harass rear areas around <unk> with infiltrating groups as large as companies . <unk> daily had to open the main supply road and clear the town . 
- North Korean and US troops remained locked in combat along the <unk> River for several more days . The North Koreans ' offensive capability was largely destroyed , and the US troops resolved to hold their lines barring further attack . 
- 
- = = = North Korean withdrawal = = = 
- 
- The UN counterattack at <unk> collapsed the North Korean line and cut off all their main supply and reinforcement routes . On September 19 the UN discovered the North Koreans had abandoned much of the Pusan Perimeter during the night , and the UN units began advancing out of their defensive positions and occupying them . Most of the North Korean units began conducting delaying actions attempting to get as much of their army as possible into North Korea . The North Koreans withdrew from the <unk> area first , the night of September 18 – 19 . After the forces there , the remainder of the North Korean armies withdrew rapidly to the North . The US units rapidly pursued them north , passing over the <unk> River positions , which were no longer of strategic importance . 
- 
- = = Aftermath = = 
- 
- The North Korean 2nd and 9th Divisions were almost completely destroyed in the battles . The 9th Division had numbered 9 @,@ 350 men at the beginning of the offensive on September 1 . The 2nd Division numbered 6 @,@ 000 . Only a few hundred from each division returned to North Korea after the fight . The majority of the North Korean troops had been killed , captured or deserted . All of <unk> II Corps was in a similar state , and the North Korean army , exhausted at Pusan Perimeter and cut off after <unk> , was on the brink of defeat . 
- By this time , the US 2nd Infantry Division suffered 1 @,@ 120 killed , 2 @,@ <unk> wounded , 67 captured and 69 missing during its time at Pusan Perimeter . This included about 180 casualties it suffered during the First Battle of <unk> <unk> the previous month . American forces were continually repulsed but able to prevent the North Koreans from breaking the Pusan Perimeter . The division had numbered 17 @,@ <unk> on September 1 , but was in excellent position to attack despite its casualties . The 1st Provisional Marine Brigade suffered 185 killed and around 500 wounded during the Battle of Pusan Perimeter , most of which probably occurred at <unk> . 
- Of all the North Korean attacks along the Pusan Perimeter , the Second Battle of <unk> <unk> is seen by historians as the most serious threat . It was the battle in which the North Koreans made the most substantial gains , splitting the US 2nd Infantry Division in half and briefly capturing <unk> , where they were very close to breaching through to the US forces ' supply lines and threatening other divisions ' rear areas . However , once again the fatal weakness of the North Korean Army had cost it victory after an impressive initial success — its communications and supply were not capable of <unk> a breakthrough and of supporting a continuing attack in the face of massive air , armor , and artillery fire that could be concentrated against its troops at critical points . By September 8 , the North Korean attacks in the area had been repulsed . 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> , also known as ( <unk> ) Planet Earth and stylized as ( <unk> ) <unk> , is an American rock band from Huntington Beach , California . Formed in 1994 , the band performs a style of music which it refers to as " G @-@ punk " , a fusion of punk rock and <unk> rap . 
- After releasing three albums on <unk> Records , <unk> <unk> left the label to record independently , eventually signing with <unk> <unk> Records in 2006 . Since 2006 , the band has become known for its involvement in the 9 / 11 Truth movement , referencing it in many of their song lyrics and concerts , as well as the concept of the album New World <unk> . To date , they have released nine studio albums , one live album and two compilation albums . 
- 
- = = History = = 
- 
- 
- = = = Formation and major @-@ label debut ( 1994 – 1999 ) = = = 
- 
- The band was formed by vocalist <unk> <unk> , formerly of The <unk> , also known as " <unk> " ( MC <unk> ) , and guitarist Wes <unk> , who became friends amidst the Orange County hardcore punk scene . <unk> and <unk> recruited guitarist <unk> , bassist <unk> , drummer <unk> <unk> and DJ Product <unk> 1969 . They named the group " <unk> " , which stands for " higher education " . The band built a following with their energetic performances at local venues , and released the self @-@ financed extended play , Church of <unk> . Legal issues forced <unk> to change their name , adding " <unk> " , which stood for " Planet Earth " . 
- <unk> <unk> signed with <unk> Records , releasing their self @-@ titled debut album in 1997 . In his review of the album , Allmusic 's Steve Huey wrote " There are some slow and / or <unk> moments [ ... ] but overall , its aggression will probably play well with <unk> ' 90s metal and punk fans . " Due to the label 's contractual terms and the disappointing sales of the album , the band found themselves unable to <unk> the cash advances given to them by <unk> . <unk> is quoted as saying " We had these romantic visions of the music industry , and we thought it would be cool to be a punk band on a rap label . So we fulfilled that dream , but it was also probably the worst thing that could have happened . [ ... ] We 've had offers from Sony and others that we can 't take because we owe <unk> so much money . " 
- 
- = = = <unk> and <unk> ( 2000 – 2004 ) = = = 
- 
- On June 6 , 2000 , <unk> <unk> appeared on the tribute album <unk> in Black II , covering Black Sabbath 's " <unk> <unk> " . <unk> <unk> released their second studio album , <unk> on August 22 , 2000 . It peaked at No. 63 on the Billboard 200 , while its first single , " <unk> " , peaked at No. 23 on the Billboard Mainstream Rock Tracks chart and at No. 27 on the Modern Rock Tracks chart . Allmusic 's Jason D. Taylor wrote : " <unk> may have not found as much success in the competitive mainstream market as some would have liked , and even despite its distinct departure from the group 's debut , it is an album that shows more vision than other rap @-@ <unk> rock albums to come out in 2000 . " The most negative response to the album came from critics who viewed its lyrics as <unk> . 
- On October 27 , 2000 , <unk> was arrested for possession of marijuana while the band was performing in <unk> , Connecticut . He was released on a US $ 1 @,@ 500 bond . In 2001 , <unk> <unk> performed on the <unk> tour alongside bands such as Korn , Static @-@ X , and System of a Down . A music video for " Killing Time " , the second single from <unk> , was produced in promotion of the film 3000 Miles to <unk> , which featured the song on its soundtrack . 
- <unk> <unk> released their third studio album , <unk> , on March 18 , 2003 . It peaked at No. 33 on the Billboard 200 , while its title track peaked at No. 21 on the Mainstream Rock Tracks chart and at No. 32 on the Modern Rock Tracks chart . Allmusic 's Johnny <unk> wrote that " While it expands on melodic elements that had previously played a supporting role in the band 's sound , <unk> also delivers <unk> of crushing guitar and pounding rhythm . And whether or not it is the presence of a top @-@ line producer , ( <unk> ) <unk> have figured out a way to <unk> their aggressive mix of heavy rock and hip @-@ hop with some serious hooks . " Guitarist <unk> joined the band in early 2004 . He is the fourth person to fill this position . 
- 
- = = = Only in <unk> ( 2004 ) = = = 
- 
- <unk> <unk> left <unk> Records , releasing their fourth studio album , Only in <unk> , on Koch Records on October 19 , 2004 . It peaked at No. 20 on the Top Independent Albums chart and at No. 186 on the Billboard 200 . In his review of the album , Johnny <unk> wrote " It wants to be a <unk> <unk> in the ear of <unk> , but <unk> 's torrential rhetoric is too <unk> and <unk> offensive to <unk> anything but superficial anger , and the music -- though occasionally explosive -- takes a <unk> to the <unk> . " 
- 
- = = = <unk> <unk> Records and New Album Evolution ( 2006 – 2015 ) = = = 
- 
- In 2006 , <unk> <unk> signed with <unk> <unk> Records , recording their fifth studio album , Back 2 Base X. The album was intended as a return to the <unk> of rock music , and did not rely as heavily on studio enhancement as previous releases . The album was released on June 6 , 2006 , the same day as The Best of ( <unk> ) Planet Earth , a compilation album produced by <unk> Records without the band 's authorization or consent . Back 2 Base X peaked at No. 12 on the Independent Albums chart , and at No. 154 on the Billboard 200 . Allmusic 's Rob Theakston wrote that " Back 2 Base X suffers from the same problems as <unk> : it tries to be conceptual in thought à la <unk> and vicious in its political commentary à la <unk> or System of a Down , but somehow falls short by sounding like an angry <unk> on a <unk> . It won 't win any new fans , but existing fans of ( <unk> ) <unk> 's work won 't be turning their backs away from the band in anger <unk> soon , either . " 
- On June 26 , 2007 , the band released their sixth studio album , <unk> . It peaked at No. 16 on the Independent Albums chart , and at No. 138 on the Billboard 200 . The album 's lead single , " <unk> " , became one of the most requested tracks at Sirius <unk> Radio 's Hard Attack , while the song 's music video was voted one of the Top 10 of 2007 on MTV 's <unk> Ball . <unk> <unk> released their first live album , The <unk> <unk> , in 2008 . On January 13 , 2009 , they released their seventh studio album , New World <unk> . It was released in three different versions ; each contains a different set of bonus tracks . In 2009 , drummer <unk> joined the band . He is the sixth person to fill this position . The band 's eighth studio album , Truth Rising , was released on October 26 , 2010 to mixed reviews . <unk> <unk> played the " Local Heroes Tour " in the fall of 2012 and played with <unk> in San <unk> on Sunday October 7 , 2012 . In an interview , frontman <unk> <unk> stated that their album for 2013 titled Ascension would be released within the first half of 2014 . Towards the end of 2013 , DJ Product mysteriously left the band with no explanation and no comment from the other members . On 1 / 1 / 2014 , <unk> <unk> <unk> stated on the band 's official Facebook that the new upcoming ( <unk> ) <unk> album will be named " Evolution " and to be released within the year . 
- On May 13 , 2014 , On the band 's official Facebook page , they released the official announcement of when the band 's new album Evolution will hit stores . The album is set for release July 22 , 2014 . They also released a teaser of the tone of the new album on their Facebook page and soon after , the track " One More Body " . 
- In 2015 , it was confirmed that 12 @-@ year guitarist <unk> <unk> and original bassist Mark Young had left the band . They were replaced by guitarist Greg " <unk> " Harrison and bassist Kurt <unk> , leaving vocalist and founding member <unk> <unk> as the group 's only remaining original talent . 
- 
- = = Style = = 
- 
- 
- = = = Music and lyrics = = = 
- 
- <unk> <unk> performs a style of music which they have referred to as " G @-@ punk " , a phrase inspired by the term " G @-@ funk " , itself a reference to the P @-@ <unk> collective . <unk> <unk> 's music is a fusion of styles ranging from hip hop , reggae , and <unk> to hard rock , punk , and heavy metal . Other elements that have been incorporated into this style include blues , funk , jazz and industrial . <unk> <unk> ' vocal style ranges from melodic singing to rapping , screaming , and death <unk> . The band 's lyrics draw from a number of subjects , including the existence of extraterrestrial life , criticism of organized religion , the 9 / 11 Truth movement , cannabis use and sexual intercourse . 
- <unk> , in addition to the 9 / 11 Truth movement , has expressed support for social liberal politicians such as Nancy <unk> and president Barack Obama . Previously however , <unk> ' 2004 lyrics for Only in <unk> expressed support for nationalism , and called for retaliation against Al <unk> for the 9 / 11 terrorist attacks . 
- 
- = = = Influences = = = 
- 
- The band 's influences include <unk> , <unk> Boys , Black Sabbath , Bob Marley , <unk> <unk> , Nine <unk> <unk> , <unk> <unk> , <unk> Hill , <unk> <unk> and Rage Against the Machine . <unk> <unk> 's second album , <unk> , incorporated classic rock and world music influences , while Back 2 Base X was influenced by classic punk bands such as the Sex <unk> and the Clash , <unk> was influenced by thrash metal bands such as Slayer , and New World <unk> was influenced by <unk> <unk> and Minor <unk> . Guitarist <unk> has been credited for encouraging a heavier , hardcore punk @-@ influenced musical style . 
- 
- = = Band members = = 
- 
- <unk> ( Paulo Sergio <unk> ) — lead vocals ( 1994 – current ) 
- Major <unk> ( Jeremiah Stratton ) — drums ( 2008 – current ) 
- <unk> ( Greg Harrison ) — guitar ( 2015 – current ) 
- Kid Bass ( Kurt <unk> ) — bass ( 2015 – current ) 
- 
- = = = Former members = = = 
- 
- Ken Sachs ( The Finger ) — keyboard ( 1994 – 1996 ) 
- Chad <unk> ( <unk> ) — guitar ( 1994 – 2002 ) 
- Wesley <unk> ( <unk> , Wes <unk> ) — guitar ( 1994 – 2003 ) 
- Ben C. <unk> ( <unk> ) — drums ( 1994 – 2003 ) 
- Doug <unk> ( DJ Product <unk> 1969 ) — <unk> , samples ( 1994 – 2013 ) 
- Mark Young ( <unk> ) — bass ( 1994 – 2015 ) 
- Sonny Mayo — guitar ( 2002 – 2003 ) 
- Jackson <unk> ( <unk> ) — guitar ( 2004 – 2015 ) 
- Christopher <unk> — drums ( 2004 ) 
- Mark " <unk> " <unk> — drums ( 2004 – 2006 ) 
- Devin <unk> — drums ( 2006 – 2007 ) 
- Anthony " Tiny <unk> " <unk> — drums ( 2007 – 2008 ) 
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- = = = Timeline = = = 
- 
- 
- = = Discography = = 
- 
- Studio albums 
- Church of <unk> ( 1995 ) 
- <unk> <unk> ( 1997 ) 
- <unk> ( 2000 ) 
- <unk> ( 2003 ) 
- Only in <unk> ( 2004 ) 
- Back 2 Base X ( 2006 ) 
- <unk> ( 2007 ) 
- New World <unk> ( 2009 ) 
- Truth Rising ( 2010 ) 
- Evolution ( 2014 ) 
- Forever ! ( 2016 ) 
- 
- 
- = <unk> warship = 
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- An ironclad is a steam @-@ propelled warship protected by iron or steel armor plates used in the early part of the second half of the 19th century . The ironclad was developed as a result of the vulnerability of wooden warships to explosive or incendiary shells . The first ironclad battleship , Gloire , was launched by the French Navy in November 1859 . The British Admiralty had been considering armored warships since 1856 and prepared a draft design for an armored <unk> in 1857 ; in early 1859 the Royal Navy started building two iron @-@ <unk> armored frigates , and by 1861 had made the decision to move to an all @-@ armored battle fleet . After the first clashes of ironclads ( both with wooden ships and with one another ) took place in 1862 during the American Civil War , it became clear that the ironclad had replaced the <unk> ship of the line as the most powerful warship afloat . This type of ship would come to be very successful in the American Civil War . 
- <unk> were designed for several roles , including as high seas battleships , coastal defense ships , and long @-@ range cruisers . The rapid evolution of warship design in the late 19th century transformed the ironclad from a wooden @-@ <unk> vessel that carried sails to supplement its steam engines into the steel @-@ built , <unk> battleships and cruisers familiar in the 20th century . This change was pushed forward by the development of heavier naval guns ( the ironclads of the 1880s carried some of the heaviest guns ever mounted at sea ) , more sophisticated steam engines , and advances in metallurgy which made steel shipbuilding possible . 
- The quick pace of change meant that many ships were obsolete as soon as they were finished , and that naval tactics were in a state of flux . Many ironclads were built to make use of the ram or the torpedo , which a number of naval designers considered the important weapons of naval combat . There is no clear end to the ironclad period , but towards the end of the 1890s the term ironclad dropped out of use . New ships were increasingly constructed to a standard pattern and designated battleships or armored cruisers . 
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- = = The ironclad = = 
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- The ironclad became technically feasible and tactically necessary because of developments in shipbuilding in the first half of the 19th century . According to naval historian J. Richard Hill : " The ( ironclad ) had three chief characteristics : a metal @-@ skinned hull , steam propulsion and a main armament of guns capable of firing explosive shells . It is only when all three characteristics are present that a fighting ship can properly be called an ironclad . " Each of these developments was introduced separately in the decade before the first ironclads . 
- 
- = = = Steam propulsion = = = 
- 
- In the 18th and early 19th centuries fleets had relied on two types of major warship , the ship of the line and the frigate . The first major change to these types was the introduction of steam power for propulsion . While <unk> steamer warships had been used from the 1830s onwards , steam propulsion only became suitable for major warships after the adoption of the screw propeller in the 1840s . 
- Steam @-@ powered screw frigates were built in the mid @-@ 1840s , and at the end of the decade the French Navy introduced steam power to its line of battle . The desire for change came from the ambition of Napoleon III to gain greater influence in Europe , which required a challenge to the British at sea . The first purpose @-@ built steam battleship was the 90 @-@ gun Napoléon in 1850 . Napoléon was armed as a conventional ship @-@ of @-@ the @-@ line , but her steam engines could give her a speed of 12 knots ( 22 km / h ) , regardless of the wind conditions : a potentially decisive advantage in a naval engagement . 
- The introduction of the steam ship @-@ of @-@ the @-@ line led to a building competition between France and Britain . Eight sister ships to Napoléon were built in France over a period of ten years , but the United Kingdom soon managed to take the lead in production . <unk> , France built ten new wooden steam battleships and converted 28 from older ships of the line , while the United Kingdom built 18 and converted 41 . 
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- = = = <unk> shells = = = 
- 
- The era of the wooden steam ship @-@ of @-@ the @-@ line was brief , because of new , more powerful naval guns . In the 1820s and 1830s , warships began to mount increasingly heavy guns , replacing 18- and 24 @-@ pounder guns with 32 @-@ pounders on sailing ships @-@ of @-@ the @-@ line and introducing 68 @-@ pounders on <unk> . Then , the first shell guns firing explosive shells were introduced following their development by the French <unk> Henri @-@ Joseph <unk> , and by the 1840s were part of the standard armament for naval powers including the French Navy , Royal Navy , Imperial Russian Navy and United States Navy . It is often held that the power of explosive shells to smash wooden hulls , as demonstrated by the Russian destruction of an Ottoman squadron at the Battle of <unk> , spelled the end of the wooden @-@ <unk> warship . The more practical threat to wooden ships was from conventional cannon firing red @-@ hot shot , which could <unk> in the hull of a wooden ship and cause a fire or ammunition explosion . Some navies even experimented with hollow shot filled with molten metal for extra incendiary power . 
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- = = = Iron armor = = = 
- 
- The use of iron instead of wood as the primary material of ships ' hulls began in the 1830s ; the first " warship " with an iron hull was the gunboat <unk> , built by Laird for the East India Company in 1839 . There followed , also from Laird , the first full @-@ blown warships with metal hulls , the 1842 steam frigates <unk> and <unk> for the Mexican navy . But a thin iron skin , while not being susceptible to fire or lethal <unk> like wood , was not the same thing as providing iron armor calculated to stop enemy gunfire . 
- Following the demonstration of the power of explosive shells against wooden ships at the Battle of <unk> , and fearing that his own ships would be vulnerable to the <unk> guns of Russian fortifications in the Crimean War , Emperor Napoleon III ordered the development of light @-@ draft floating batteries , equipped with heavy guns and protected by heavy armor . Experiments made during the first half of 1854 proved highly satisfactory , and on 17 July 1854 , the French communicated to the British Government that a solution had been found to make gun @-@ proof vessels and that plans would be communicated . After tests in September 1854 , the British Admiralty agreed to build five armoured floating batteries on the French plans , establishing the important Thames and <unk> Iron Works within the docks . 
- The French floating batteries were deployed in 1855 as a supplement to the wooden steam battle fleet in the Crimean War . The role of the battery was to assist <unk> mortar and gunboats bombarding shore fortifications . The French used three of their ironclad batteries ( <unk> , <unk> and <unk> ) in 1855 against the defenses at the Battle of <unk> on the Black Sea , where they were effective against Russian shore defences . They would later be used again during the Italian war in the Adriatic in 1859 . The British floating batteries <unk> and Meteor arrived too late to participate to the action at <unk> . The British planned to use theirs in the Baltic Sea against the well @-@ fortified naval base at <unk> . 
- The batteries have a claim to the title of the first ironclad warships but they were capable of only 4 knots ( 7 km / h ) under their own power : they operated under their own power at the Battle of <unk> , but had to be towed for long range transit . They were also arguably marginal to the work of the navy . The brief success of the floating ironclad batteries convinced France to begin work on armored warships for their <unk> . 
- 
- = = Early ironclad ships and battles = = 
- 
- By the end of the 1850s it was clear that France was unable to match British building of steam warships , and to regain the strategic initiative a dramatic change was required . The result was the first ocean @-@ going ironclad , the Gloire , begun in 1857 and launched in 1859 . 
- Gloire 's wooden hull was modelled on that of a steam ship of the line , reduced to one deck , <unk> in iron plates 4 @.@ 5 inches ( 110 mm ) thick . She was propelled by a steam engine , driving a single screw propeller for a speed of 13 knots ( 24 km / h ) . She was armed with thirty @-@ six 6 @.@ 4 @-@ inch ( 160 mm ) rifled guns . France proceeded to construct 16 ironclad warships , including two more sister ships to Gloire , and the only two @-@ <unk> broadside ironclads ever built , <unk> and <unk> . 
- The Royal Navy had not been keen to sacrifice its advantage in steam ships of the line , but was determined that the first British ironclad would <unk> the French ships in every respect , particularly speed . A fast ship would have the advantage of being able to choose a range of engagement which could make her <unk> to enemy fire . The British specification was more a large , powerful frigate than a ship @-@ of @-@ the @-@ line . The requirement for speed meant a very long vessel , which had to be built from iron . The result was the construction of two Warrior @-@ class ironclads ; HMS Warrior and HMS Black Prince . The ships had a successful design , though there were necessarily compromises between ' sea @-@ keeping ' , strategic range and armour protection ; their weapons were more effective than that of Gloire , and with the largest set of steam engines yet fitted to a ship they could steam at 14 @.@ 3 knots ( 26 @.@ 5 km / h ) . Yet the Gloire and her sisters had full iron @-@ armour protection along the waterline and the battery itself . Warrior and Black Prince ( but also the smaller Defence and Resistance ) were obliged to concentrate their armour in a central ' citadel ' or ' armoured box ' , leaving many main deck guns and the fore and aft sections of the vessel unprotected . The use of iron in the construction of Warrior also came with some drawbacks ; iron hulls required more regular and intensive repairs than wooden hulls , and iron was more susceptible to <unk> by marine life . 
- By 1862 , navies across Europe had adopted ironclads . Britain and France each had sixteen either completed or under construction , though the British vessels were larger . Austria , Italy , Russia , and Spain were also building ironclads . However , the first battles using the new ironclad ships involved neither Britain nor France , and involved ships markedly different from the broadside @-@ firing , <unk> designs of Gloire and Warrior . The use of ironclads by both sides in the American Civil War , and the clash of the Italian and Austrian fleets at the Battle of <unk> , had an important influence on the development of ironclad design . 
- 
- = = = First battles between ironclads : the U.S. Civil War = = = 
- 
- The first use of ironclads in action came in the U.S. Civil War . The U.S. Navy at the time the war broke out had no ironclads , its most powerful ships being six steam @-@ powered <unk> frigates . Since the bulk of the Navy remained loyal to the Union , the Confederacy sought to gain advantage in the naval conflict by acquiring modern armored ships . In May 1861 , the Confederate Congress voted that $ 2 million be appropriated for the purchase of ironclads from overseas , and in July and August 1861 the Confederacy started work on construction and converting wooden ships . 
- On 12 October 1861 , the CSS <unk> became the first ironclad to enter combat , when she fought Union warships on the Mississippi during the Battle of the Head of <unk> . She had been converted from a commercial vessel in New Orleans for river and coastal fighting . In February 1862 , the larger CSS Virginia joined the Confederate Navy , having been rebuilt at Norfolk . <unk> on the hull of USS <unk> , Virginia originally was a conventional warship made of wood , but she was converted into an iron @-@ covered casemate ironclad <unk> , when she entered the Confederate navy . By this time , the Union had completed seven ironclad gunboats of the City class , and was about to complete the USS <unk> , an innovative design proposed by the Swedish inventor John Ericsson . The Union was also building a large armored frigate , the USS New <unk> , and the smaller USS <unk> . 
- The first battle between ironclads happened on 9 March 1862 , as the armored <unk> was deployed to protect the Union 's wooden fleet from the ironclad ram Virginia and other Confederate warships . In this engagement , the second day of the Battle of Hampton Roads , the two ironclads repeatedly tried to ram one another while shells bounced off their armor . The battle attracted attention worldwide , making it clear that the wooden warship was now out of date , with the ironclads destroying them easily . 
- The Civil War saw more ironclads built by both sides , and they played an increasing role in the naval war alongside the <unk> warships , commerce raiders and blockade runners . The Union built a large fleet of fifty monitors modeled on their namesake . The Confederacy built ships designed as smaller versions of the Virginia , many of which saw action , but their attempts to buy ironclads overseas were frustrated as European nations confiscated ships being built for the Confederacy — especially in Russia , the only country to openly support the Union through the war . Only CSS <unk> was completed , and she arrived in American waters just in time for the end of the war . 
- Through the remainder of the war , ironclads saw action in the Union 's attacks on Confederate ports . Seven Union monitors , including USS <unk> , as well as two other ironclads , the ironclad frigate New <unk> and a light @-@ draft USS <unk> , participated in the failed attack on Charleston ; one was sunk . Two small ironclads , CSS <unk> State and CSS <unk> participated in the defence of the harbor . For the later attack at Mobile Bay , the Union assembled four monitors as well as 11 wooden ships , facing the CSS Tennessee , the Confederacy 's most powerful ironclad and the gunboats CSS Morgan , CSS Gaines , CSS Selma . 
- On the western front , the Union built a formidable force of river ironclads , beginning with several converted <unk> and then contracting engineer James <unk> of St. Louis , Missouri to build the City @-@ class ironclads . These excellent ships were built with twin engines and a central <unk> wheel , all protected by an armored <unk> . They had a shallow draft , allowing them to journey up smaller tributaries , and were very well suited for river operations . <unk> also produced monitors for use on the rivers , the first two of which differed from the ocean @-@ going monitors in that they contained a <unk> wheel ( the USS <unk> and USS Osage ) . 
- <unk> <unk> vessels were some of the better ironclads of the Western Flotilla , but there were a number of other vessels that served <unk> with the fleet . All were of varying design , some more successful than others , and some were similar to standard <unk> but with armored side @-@ mounted <unk> wheels . All were armed with various smoothbore and some rifled guns . If nothing else the experience of the American Civil War and its wild variety of competing ironclad designs , some more successful ( or disastrous ) than others , confirmed the emerging trade @-@ off or compromises required in applying the latest technological advances in iron armour manufacture , ship construction and gun design — to name a few — also going on in Europe . There was no such thing as a ' perfect ' ironclad which could be <unk> in every possible encounter ; ship duels , standing up to forts , Brown & Blue @-@ water operations . 
- The Union ironclads played an important role in the Mississippi and tributaries by providing tremendous fire upon Confederate forts , installations and vessels with relative <unk> to enemy fire . They were not as heavily armored as the ocean @-@ going monitors of the Union , but they were adequate for their intended use . More Western Flotilla Union ironclads were sunk by torpedoes ( mines ) than by enemy fire , and the most damaging fire for the Union ironclads was from shore installations , not Confederate vessels . 
- 
- = = = <unk> : First fleet battle = = = 
- 
- The first fleet battle , and the first ocean battle , involving ironclad warships was the Battle of <unk> in 1866 . <unk> between the Austrian and Italian navies , the battle pitted combined fleets of wooden frigates and <unk> and ironclad warships on both sides in the largest naval battle between the battles of <unk> and <unk> . 
- The Italian fleet consisted of 12 ironclads and a similar number of wooden warships , escorting transports which carried troops intending to land on the Adriatic island of <unk> . Among the Italian ironclads were seven broadside ironclad frigates , four smaller ironclads , and the newly built <unk> — a double @-@ <unk> ram . <unk> them , the Austrian navy had seven ironclad frigates . 
- The Austrians believed their ships to have less effective guns than their enemy , so decided to engage the Italians at close range and ram them . The Austrian fleet formed into an <unk> formation with the ironclads in the first line , charging at the Italian ironclad squadron . In the <unk> which followed both sides were frustrated by the lack of damage inflicted by guns , and by the difficulty of <unk> — nonetheless , the effective <unk> attack being made by the Austrian flagship against the Italian attracted great attention in following years . 
- The superior Italian fleet lost its two ironclads , Re d 'Italia and Palestro , while the Austrian <unk> screw two @-@ <unk> SMS Kaiser remarkably survived close actions with four Italian ironclads . The battle ensured the popularity of the ram as a weapon in European ironclads for many years , and the victory won by Austria established it as the predominant naval power in the Adriatic . 
- The battles of the American Civil War and at <unk> were very influential on the designs and tactics of the ironclad fleets that followed . In particular , it taught a generation of naval officers the misleading lesson that <unk> was the best way to sink enemy ironclads . 
- 
- = = Armament and tactics = = 
- 
- The adoption of iron armor meant that the traditional naval armament of dozens of light cannon became useless , since their shot would bounce off an armored hull . To penetrate armor , increasingly heavy guns were mounted on ships ; nevertheless , the view that <unk> was the only way to sink an ironclad became widespread . The increasing size and weight of guns also meant a movement away from the ships mounting many guns broadside , in the manner of a ship @-@ of @-@ the @-@ line , towards a handful of guns in turrets for all @-@ round fire . 
- 
- = = = Ram craze = = = 
- 
- From the 1860s to the 1880s many naval designers believed that the development of the ironclad meant that the ram was again the most important weapon in naval warfare . With steam power freeing ships from the wind , and armor making them <unk> to <unk> , the ram seemed to offer the opportunity to strike a decisive blow . 
- The <unk> damage inflicted by the guns of <unk> and Virginia at Battle of Hampton Roads and the spectacular but lucky success of the Austrian flagship SMS Erzherzog Ferdinand Max sinking the Italian Re d 'Italia at <unk> gave strength to the <unk> craze . From the early 1870s to early 1880s most British naval officers thought that guns were about to be replaced as the main naval armament by the ram . Those who noted the tiny number of ships that had actually been sunk by <unk> struggled to be heard . 
- The revival of <unk> had a significant effect on naval tactics . Since the 17th century the predominant tactic of naval warfare had been the line of battle , where a fleet formed a long line to give it the best fire from its broadside guns . This tactic was totally unsuited to <unk> , and the ram threw fleet tactics into <unk> . The question of how an ironclad fleet should deploy in battle to make best use of the ram was never tested in battle , and if it had been , combat might have shown that rams could only be used against ships which were already stopped dead in the water . 
- The ram finally fell out of favour in the 1880s , as the same effect could be achieved with a torpedo , with less vulnerability to quick @-@ firing guns . 
- 
- = = = Development of naval guns = = = 
- 
- The armament of ironclads tended to become concentrated in a small number of powerful guns capable of penetrating the armor of enemy ships at range ; calibre and weight of guns increased markedly to achieve greater penetration . Throughout the ironclad era navies also <unk> with the <unk> of rifled versus smoothbore guns and breech @-@ loading versus muzzle @-@ loading . 
- HMS Warrior carried a mixture of 110 @-@ pounder 7 inch ( 180 mm ) breech @-@ loading rifles and more traditional 68 @-@ pounder smoothbore guns . Warrior highlighted the challenges of picking the right armament ; the breech @-@ <unk> she carried , designed by Sir William Armstrong , were intended to be the next generation of heavy armament for the Royal Navy , but were shortly withdrawn from service . 
- <unk> @-@ loading guns seemed to offer important advantages . A breech @-@ <unk> could be <unk> without moving the gun , a lengthy process particularly if the gun then needed to be re @-@ aimed . The Warrior 's Armstrong guns also had the virtue of being lighter than an equivalent smoothbore and , because of their <unk> , more accurate . Nonetheless , the design was rejected because of problems which plagued breech @-@ <unk> for decades . 
- The weakness of the breech @-@ <unk> was the obvious problem of sealing the breech . All guns are powered by the explosive conversion of gunpowder into gas . This explosion <unk> the shot or shell out of the front of the gun , but also <unk> great stresses on the gun @-@ barrel . If the breech — which experiences some of the greatest forces in the gun — is not entirely secure , then there is a risk that either gas will discharge through the breech or that the breech will break . This in turn reduces the muzzle velocity of the weapon and can also endanger the gun crew . The Warrior 's Armstrong guns suffered from both problems ; the shells were unable to penetrate the 4 @.@ 5 in ( 118 mm ) armor of Gloire , while sometimes the screw which closed the breech flew backwards out of the gun on firing . Similar problems were experienced with the breech @-@ loading guns which became standard in the French and German navies . 
- These problems influenced the British to equip ships with muzzle @-@ loading weapons of increasing power until the 1880s . After a brief introduction of 100 @-@ pounder or 9 @.@ 5 @-@ inch ( 240 mm ) smoothbore Somerset Gun , which weighed 6 @.@ 5 tons ( 6 @.@ 6 t ) , the Admiralty introduced 7 @-@ inch ( 178 mm ) rifled guns , weighing 7 tons . These were followed by a series of increasingly <unk> weapons — guns weighing 12 , 25 , 25 , 38 and finally 81 tons , with calibre increasing from 8 @-@ inch ( 203 mm ) to 16 @-@ inch ( 406 mm ) . 
- The decision to retain muzzle @-@ <unk> until the 1880s has been criticised by historians . However , at least until the late 1870s , the British muzzle @-@ <unk> had superior performance in terms of both range and rate of fire than the French and Prussian breech @-@ <unk> , which suffered from the same problems as had the first Armstrong guns . 
- From 1875 onwards , the balance between <unk> and muzzle @-@ loading changed . Captain de <unk> invented a method of reliably sealing a breech , adopted by the French in 1873 . Just as <unk> , the growing size of naval guns made muzzle @-@ loading much more complicated . With guns of such size there was no prospect of <unk> in the gun for re @-@ loading , or even re @-@ loading by hand , and complicated hydraulic systems were required for re @-@ loading the gun outside the turret without exposing the crew to enemy fire . In 1882 , the 81 @-@ ton , 16 @-@ inch ( 406 mm ) guns of HMS <unk> fired only once every 11 minutes while bombarding Alexandria during the <unk> <unk> . The 100 @-@ ton , 450 mm ( 17 @.@ 72 inch ) guns of <unk> <unk> could each fire a round every 15 minutes . 
- In the Royal Navy , the switch to breech @-@ <unk> was finally made in 1879 ; as well as the significant advantages in terms of performance , opinion was <unk> by an explosion on board HMS <unk> caused by a gun being double @-@ loaded , a problem which could only happen with a muzzle @-@ loading gun . 
- The calibre and weight of guns could only increase so far . The larger the gun , the slower it would be to load , the greater the stresses on the ship 's hull , and the less the stability of the ship . The size of the gun peaked in the 1880s , with some of the heaviest <unk> of gun ever used at sea . HMS <unk> carried two 16 @.@ 25 @-@ inch ( <unk> mm ) breech @-@ loading guns , each weighing 110 tons — no British battleship would ever carry guns as large . The Italian 450 mm ( 17 @.@ 72 inch ) guns would be larger than any gun fitted to a battleship until the 18 @.@ 1 @-@ inch ( 460 mm ) armament of the Japanese Yamato class of World War II . One consideration which became more acute was that even from the original Armstrong models , following the Crimean War , range and hitting power far exceeded simple accuracy , especially at sea where the slightest roll or pitch of the vessel as ' floating weapons @-@ platform ' could <unk> the advantage of <unk> . American ordnance experts accordingly preferred smoothbore monsters whose round shot could at least ' <unk> ' along the surface of the water . <unk> effective combat ranges , they had learned during the Civil War , were comparable to those in the Age of <unk> — though a vessel could now be smashed to pieces in only a few rounds . Smoke and the general chaos of battle only added to the problem . As a result , many naval engagements in the ' Age of the <unk> ' were still fought at ranges within easy eyesight of their targets , and well below the maximum reach of their ships ' guns . 
- Another method of increasing firepower was to vary the <unk> fired or the nature of the propellant . Early ironclads used black powder , which expanded rapidly after combustion ; this meant cannons had relatively short barrels , to prevent the barrel itself slowing the shell . The <unk> of the black powder explosion also meant that guns were subjected to extreme stress . One important step was to press the powder into <unk> , allowing a slower , more controlled explosion and a longer barrel . A further step forward was the introduction of chemically different brown powder which <unk> more slowly again . It also put less stress on the <unk> of the barrel , allowing guns to last longer and to be manufactured to <unk> <unk> . 
- The development of <unk> powder , based on <unk> or <unk> , by the French inventor Paul <unk> in 1884 was a further step allowing smaller charges of propellant with longer barrels . The guns of the pre @-@ <unk> battleships of the 1890s tended to be smaller in calibre compared to the ships of the 1880s , most often 12 in ( 305 mm ) , but progressively grew in length of barrel , making use of improved <unk> to gain greater muzzle velocity . 
- The nature of the projectiles also changed during the ironclad period . Initially , the best armor @-@ piercing <unk> was a solid cast @-@ iron shot . Later , shot of <unk> iron , a harder iron alloy , gave better armor @-@ piercing qualities . Eventually the armor @-@ piercing shell was developed . 
- 
- = = = <unk> of armament = = = 
- 
- 
- = = = = <unk> ironclads = = = = 
- 
- The first British , French and Russian ironclads , in a logical development of warship design from the long preceding era of wooden ships of the line , carried their weapons in a single line along their sides and so were called " broadside ironclads . " Both Gloire and HMS Warrior were examples of this type . Because their armor was so heavy , they could only carry a single row of guns along the main deck on each side rather than a row on each deck . 
- A significant number of broadside ironclads were built in the 1860s , principally in Britain and France , but in smaller numbers by other powers including Italy , Austria , Russia and the United States . The advantages of mounting guns on both broadsides was that the ship could engage more than one <unk> at a time , and the rigging did not impede the field of fire . 
- <unk> armament also had disadvantages , which became more serious as ironclad technology developed . <unk> guns to penetrate ever @-@ thicker armor meant that fewer guns could be carried . Furthermore , the adoption of <unk> as an important tactic meant the need for ahead and all @-@ round fire . These problems led to broadside designs being superseded by designs that gave greater all @-@ round fire , which included central @-@ battery , turret , and barbette designs . 
- 
- = = = = <unk> , batteries and barbettes = = = = 
- 
- There were two main design <unk> to the broadside . In one design , the guns were placed in an armoured casemate amidships : this arrangement was called the ' box @-@ battery ' or ' centre @-@ battery ' . In the other , the guns could be placed on a rotating platform to give them a broad field of fire ; when fully armored , this arrangement was called a turret and when partially armored or <unk> , a barbette . 
- The centre @-@ battery was the simpler and , during the 1860s and 1870s , the more popular method . <unk> guns amidships meant the ship could be shorter and <unk> than a broadside type . The first full @-@ scale centre @-@ battery ship was HMS Bellerophon of 1865 ; the French laid down centre @-@ battery ironclads in 1865 which were not completed until 1870 . Centre @-@ battery ships often , but not always , had a <unk> <unk> enabling some of their guns to fire directly ahead . 
- The turret was first used in naval combat on the USS <unk> in 1862 , with a type of turret designed by the Swedish engineer John Ericsson . A competing turret design was proposed by the British inventor <unk> <unk> with a prototype of this installed on HMS <unk> in 1861 for testing and evaluation purposes . Ericsson 's turret turned on a central spindle , and <unk> 's turned on a ring of bearings . <unk> offered the maximum arc of fire from the guns , but there were significant problems with their use in the 1860s . The fire arc of a turret would be considerably limited by masts and rigging , so they were unsuited to use on the earlier ocean @-@ going ironclads . The second problem was that turrets were extremely heavy . Ericsson was able to offer the heaviest possible turret ( guns and armour protection ) by deliberately designing a ship with very low <unk> . The weight thus saved from having a high broadside above the waterline was diverted to actual guns and armour . Low <unk> , however , also meant a smaller hull and therefore a smaller capacity for coal storage — and therefore range of the vessel . In many respects , the <unk> , low @-@ <unk> <unk> and the broadside <unk> HMS Warrior represented two opposite extremes in what an ' <unk> ' was all about . The most dramatic attempt to compromise these two extremes , or ' <unk> this circle ' , was designed by Captain <unk> <unk> <unk> : HMS Captain , a dangerously low <unk> turret ship which nevertheless carried a full rig of sail , and which subsequently capsized not long after her launch in 1870 . Her half @-@ sister HMS Monarch was restricted to firing from her turrets only on the port and starboard beams . The third Royal Navy ship to combine turrets and masts was HMS <unk> of 1876 , which carried two turrets on either side of the centre @-@ line , allowing both to fire fore , aft and broadside . 
- A lighter alternative to the turret , particularly popular with the French navy , was the barbette . These were fixed armored towers which held a gun on a <unk> . The crew was sheltered from direct fire , but vulnerable to <unk> fire , for instance from shore emplacements . The barbette was lighter than the turret , needing less machinery and no roof armor — though nevertheless some barbettes were stripped of their armor plate to reduce the top @-@ weight of their ships . The barbette became widely adopted in the 1880s , and with the addition of an armored ' gun @-@ house ' , transformed into the turrets of the pre @-@ <unk> battleships . 
- 
- = = = <unk> = = = 
- 
- The ironclad age saw the development of explosive torpedoes as naval weapons , which helped <unk> the design and tactics of ironclad fleets . The first torpedoes were static mines , used extensively in the American Civil War . That conflict also saw the development of the spar torpedo , an explosive charge pushed against the hull of a warship by a small boat . For the first time , a large warship faced a serious threat from a smaller one — and given the relative <unk> of <unk> against ironclads , the threat from the spar torpedo was taken seriously . The U.S. Navy converted four of its monitors to become <unk> armored spar @-@ torpedo vessels while under construction in 1864 – 5 , but these vessels never saw action . Another proposal , the towed or ' Harvey ' torpedo , involved an explosive on a line or <unk> ; either to deter a ship from <unk> or to make a torpedo attack by a boat less <unk> . 
- A more practical and influential weapon was the self @-@ propelled or Whitehead torpedo . <unk> in 1868 and deployed in the 1870s , the Whitehead torpedo formed part of the armament of ironclads of the 1880s like HMS <unk> and the Italian <unk> <unk> and Enrico <unk> . The ironclad 's vulnerability to the torpedo was a key part of the critique of armored warships made by the <unk> <unk> school of naval thought ; it appeared that any ship armored enough to prevent destruction by gunfire would be slow enough to be easily caught by torpedo . In practice , however , the <unk> <unk> was only briefly influential and the torpedo formed part of the confusing mixture of weapons possessed by ironclads . 
- 
- = = Armor and construction = = 
- 
- The first ironclads were built on wooden or iron hulls , and protected by wrought iron armor backed by thick wooden <unk> . <unk> were still being built with wooden hulls into the 1870s . 
- 
- = = = <unk> : iron , wood and steel = = = 
- 
- Using iron construction for warships offered advantages for the engineering of the hull . However , <unk> iron had many military disadvantages , and offered technical problems which kept wooden hulls in use for many years , particularly for long @-@ range cruising warships . 
- Iron ships had first been proposed for military use in the 1820s . In the 1830s and 1840s , France , Britain and the United States had all experimented with iron @-@ <unk> but <unk> gunboats and frigates . However , the iron @-@ <unk> frigate was abandoned by the end of the 1840s , because iron hulls were more vulnerable to solid shot ; iron was more brittle than wood , and iron frames more likely to fall out of shape than wood . 
- The <unk> of <unk> iron for warship hulls meant that iron was only adopted as a building material for battleships when protected by armor . However , iron gave the naval architect many advantages . Iron allowed larger ships and more flexible design , for instance the use of <unk> bulkheads on the lower decks . Warrior , built of iron , was longer and faster than the wooden @-@ <unk> Gloire . Iron could be produced to order and used immediately , in contrast to the need to give wood a long period of <unk> . And , given the large quantities of wood required to build a steam warship and the falling cost of iron , iron hulls were increasingly cost @-@ effective . The main reason for the French use of wooden hulls for the ironclad fleet built in the 1860s was that the French iron industry could not supply enough , and the main reason why Britain built its handful of wooden @-@ <unk> ironclads was to make best use of hulls already started and wood already bought . 
- Wooden hulls continued to be used for long @-@ range and smaller ironclads , because iron nevertheless had a significant disadvantage . Iron hulls suffered quick <unk> by marine life , slowing the ships down — <unk> for a European <unk> close to dry docks , but a difficulty for long @-@ range ships . The only solution was to sheath the iron hull first in wood and then in copper , a laborious and expensive process which made wooden construction remain attractive . Iron and wood were to some extent interchangeable : the Japanese <unk> and <unk> ordered in 1875 were sister @-@ ships , but one was built of iron and the other of composite construction . 
- After 1872 , steel started to be introduced as a material for construction . Compared to iron , steel allows for greater structural strength for a lower weight . The French Navy led the way with the use of steel in its fleet , starting with the <unk> , laid down in 1873 and launched in 1876 . <unk> nonetheless had wrought iron armor plate , and part of her exterior hull was iron rather than steel . 
- Even though Britain led the world in steel production , the Royal Navy was slow to adopt steel warships . The <unk> process for steel manufacture produced too many imperfections for large @-@ scale use on ships . French manufacturers used the <unk> @-@ Martin process to produce adequate steel , but British technology <unk> behind . The first all @-@ steel warships built by the Royal Navy were the dispatch vessels <unk> and Mercury , laid down in 1875 and 1876 . 
- 
- = = = Armor and protection schemes = = = 
- 
- Iron @-@ built ships used wood as part of their protection scheme . HMS Warrior was protected by 4 @.@ 5 in ( 114 mm ) of wrought iron backed by 15 in ( 381 mm ) of <unk> , the strongest shipbuilding wood . The wood played two roles , preventing <unk> and also preventing the shock of a hit damaging the structure of the ship . Later , wood and iron were combined in ' sandwich ' armor , for instance in HMS <unk> . 
- Steel was also an obvious material for armor . It was tested in the 1860s , but the steel of the time was too brittle and <unk> when struck by shells . Steel became practical to use when a way was found to fuse steel onto wrought iron plates , giving a form of compound armor . This compound armor was used by the British in ships built from the late 1870s , first for turret armor ( starting with HMS <unk> ) and then for all armor ( starting with HMS <unk> of 1882 ) . The French and German navies adopted the innovation almost immediately , with licenses being given for the use of the ' Wilson System ' of producing fused armor . 
- The first ironclads to have all @-@ steel armor were the Italian <unk> <unk> and Enrico <unk> . Though the ships were laid down in 1873 their armor was not purchased from France until 1877 . The French navy decided in 1880 to adopt compound armor for its fleet , but found it limited in supply , so from 1884 the French navy was using steel armor . Britain stuck to compound armor until 1889 . 
- The ultimate ironclad armor was case hardened nickel @-@ steel . In 1890 , the U.S. Navy tested steel armor hardened by the Harvey process and found it superior to compound armor . For several years ' Harvey steel ' was the state of the art , produced in the U.S. , France , Germany , Britain , Austria and Italy . In 1894 , the German firm <unk> developed gas <unk> , which further hardened steel armor . The German Kaiser Friedrich III , laid down in 1895 , was the first ship to benefit from the new ' <unk> armor ' and the new armor was quickly adopted ; the Royal Navy using it from HMS Canopus , laid down in 1896 . By 1901 almost all new battleships used <unk> armor , though the U.S. continued to use Harvey armor alongside until the end of the decade . 
- The equivalent strengths of the different armor plates was as follows : 15 in ( 381 mm ) of wrought iron was equivalent to 12 in ( 305 mm ) of either plain steel or compound iron and steel armor , and to 7 @.@ 75 in ( 197 mm ) of Harvey armor or 5 @.@ 75 in ( 146 mm ) of <unk> armor . 
- <unk> construction also <unk> the later debate in battleship design between <unk> and ' all @-@ or @-@ nothing ' armour design . Warrior was only semi @-@ armoured , and could have been disabled by hits on the bow and stern . As the thickness of armor grew to protect ships from the increasingly heavy guns , the area of the ship which could be fully protected diminished . <unk> 's armor protection was largely limited to the central citadel amidships , protecting boilers and engines , turrets and magazines , and little else . An ingenious arrangement of <unk> @-@ filled compartments and <unk> bulkheads was intended to keep her stable and afloat in the event of heavy damage to her un @-@ armored sections . 
- 
- = = <unk> : steam and sail = = 
- 
- The first ocean @-@ going ironclads carried masts and sails like their wooden predecessors , and these features were only gradually abandoned . Early steam engines were inefficient ; the wooden steam fleet of the Royal Navy could only carry " 5 to 9 days coal " , and the situation was similar with the early ironclads . Warrior also illustrates two design features which aided hybrid propulsion ; she had retractable screws to reduce drag while under sail ( though in practice the steam engine was run at a low <unk> ) , and a <unk> funnel which could be folded down to the deck level . 
- Ships designed for coastal warfare , like the floating batteries of the <unk> , or USS <unk> and her sisters , <unk> with masts from the beginning . The British HMS <unk> , started in 1869 , was the first large , ocean @-@ going ironclad to <unk> with masts . Her principal role was for combat in the English Channel and other European waters ; and while her coal supplies gave her enough range to cross the Atlantic , she would have had little endurance on the other side of the ocean . The <unk> and the similar ships commissioned by the British and Russian navies in the 1870s were the exception rather than the rule . Most ironclads of the 1870s retained masts , and only the Italian navy , which during that decade was focused on short @-@ range operations in the Adriatic , built consistently <unk> ironclads . 
- During the 1860s , steam engines improved with the adoption of double @-@ expansion steam engines , which used 30 – 40 % less coal than earlier models . The Royal Navy decided to switch to the double @-@ expansion engine in 1871 , and by 1875 they were widespread . However , this development alone was not enough to herald the end of the mast . Whether this was due to a conservative desire to retain sails , or was a rational response to the operational and strategic situation , is a matter of debate . A steam @-@ only fleet would require a network of coaling stations worldwide , which would need to be fortified at great expense to stop them falling into enemy hands . Just as significantly , because of <unk> problems with the technology of the boilers which provided steam for the engines , the performance of double @-@ expansion engines was rarely as good in practice as it was in theory . 
- During the 1870s the distinction grew between ' first @-@ class ironclads ' or ' battleships ' on the one hand , and ' cruising ironclads ' designed for long @-@ range work on the other . The demands on first @-@ class ironclads for very heavy armor and armament meant increasing displacement , which reduced speed under sail ; and the fashion for turrets and barbettes made a sailing rig increasingly <unk> . HMS <unk> , launched in 1876 but not commissioned until 1881 , was the last British battleship to carry masts , and these were widely seen as a mistake . The start of the 1880s saw the end of sailing rig on ironclad battleships . 
- <unk> persisted on ' cruising ironclads ' for much longer . During the 1860s , the French navy had produced the Alma and La <unk> classes as small , long @-@ range ironclads as overseas cruisers and the British had responded with ships like HMS <unk> of 1870 . The Russian ship General @-@ Admiral , laid down in 1870 and completed in 1875 , was a model of a fast , long @-@ range ironclad which was likely to be able to <unk> and <unk> ships like <unk> . Even the later HMS Shannon , often described as the first British armored cruiser , would have been too slow to <unk> General @-@ Admiral . While Shannon was the last British ship with a retractable <unk> , later armored cruisers of the 1870s retained sailing rig , <unk> speed under steam in consequence . It took until 1881 for the Royal Navy to lay down a long @-@ range armored warship capable of catching enemy commerce raiders , HMS Warspite , which was completed in 1888 . While sailing rigs were obsolescent for all purposes by the end of the 1880s , rigged ships were in service until the early years of the 20th century . 
- The final evolution of ironclad propulsion was the adoption of the triple @-@ expansion steam engine , a further refinement which was first adopted in HMS Sans Pareil , laid down in 1885 and commissioned in 1891 . Many ships also used a forced draught to get additional power from their engines , and this system was widely used until the introduction of the steam turbine in the mid @-@ 1900s ( decade ) . 
- 
- = = Fleets = = 
- 
- While ironclads spread rapidly in navies worldwide , there were few pitched naval battles involving ironclads . Most European nations settled differences on land , and the Royal Navy struggled to maintain a deterrent parity with at least France , while providing suitable protection to Britain 's commerce and colonial outposts worldwide . <unk> remained , for the British Royal Navy , a matter of defending the British Isles first and projecting power abroad second . Those naval engagements of the latter half of the 19th @-@ century which involved ironclads normally involved colonial actions or clashes between second @-@ rate naval powers . But these encounters were often enough to convince British policy @-@ makers of the increasing <unk> of strictly naval foreign intervention , from Hampton Roads in the American Civil War to the <unk> combined defences of naval <unk> such as <unk> and Cherbourg . 
- There were many types of ironclads : 
- <unk> ships intended to " stand in the line of battle " ; the precursors of the battleship . 
- Coastal service and <unk> vessels , including ' floating batteries ' and ' <unk> 
- <unk> intended for commerce raiding or protection of commerce , called ' armoured <unk> 
- 
- = = = <unk> = = = 
- 
- The United Kingdom possessed the largest navy in the world for the whole of the ironclad period . The Royal Navy was the second to adopt ironclad warships , and it applied them worldwide in their whole range of roles . In the age of sail , the British strategy for war depended on the Royal Navy mounting a blockade of the ports of the enemy . Because of the limited endurance of <unk> , this was no longer possible , so the British at times considered the risk @-@ laden plan of engaging an enemy fleet in harbor as soon as war broke out . To this end , the Royal Navy developed a series of ' coast @-@ defence battleships ' , starting with the <unk> class . These ' <unk> monitors ' were markedly different from the other high @-@ seas ironclads of the period and were an important precursor of the modern battleship . As long @-@ range monitors they could reach Bermuda <unk> , for example . However , they were still armed with only four heavy guns and were as vulnerable to mines and obstructions ( and enemy monitors ) as the original monitors of the Union Navy proved to be during the Civil War . The British prepared for an overwhelming mortar bombardment of <unk> by the close of the Crimean War , but never considered running the smoke @-@ ridden , shallow @-@ water gauntlet straight to St. Petersburg with ironclads . Likewise , monitors proved <unk> unable to ' overwhelm ' enemy fortifications single @-@ handed during the American conflict , though their low @-@ profile and heavy armour protection made them ideal for running <unk> . <unk> and obstructions , however , negated these advantages — a problem the British Admiralty frequently acknowledged but never countered throughout the period . The British never laid down enough <unk> @-@ class ' battleships ' to instantly overwhelm Cherbourg , <unk> or even New York City with gunfire . Although throughout the 1860s and 1870s the Royal Navy was still in many respects superior to its potential rivals , by the early 1880s widespread concern about the threat from France and Germany culminated in the Naval Defence Act , which promulgated the idea of a ' two @-@ power standard ' , that Britain should possess as many ships as the next two navies combined . This standard provoked aggressive shipbuilding in the 1880s and 1890s . 
- British ships did not participate in any major wars in the ironclad period . The Royal Navy 's ironclads only saw action as part of colonial battles or one @-@ sided engagements like the bombardment of Alexandria in 1882 . <unk> British interests against Ahmed ' <unk> 's Egyptian revolt , a British fleet opened fire on the fortifications around the port of Alexandria . A mixture of centre @-@ battery and turret ships bombarded Egyptian positions for most of a day , forcing the Egyptians to retreat ; return fire from Egyptian guns was heavy at first , but inflicted little damage , killing only five British sailors . Few Egyptian guns were actually dismounted , on the other hand , and the fortifications themselves were typically left intact . Had the Egyptians actually utilised the heavy mortars that were at their disposal , they might have quickly turned the tide , for the attacking British ironclads found it easy ( for accuracy 's sake ) to simply anchor whilst firing — perfect targets for high @-@ angle fire upon their thinly armoured <unk> . 
- The French navy built the first ironclad to try to gain a strategic advantage over the British , but were consistently out @-@ built by the British . Despite taking the lead with a number of innovations like breech @-@ loading weapons and steel construction , the French navy could never match the size of the Royal Navy . In the 1870s , the construction of ironclads ceased for a while in France as the <unk> <unk> school of naval thought took prominence , suggesting that torpedo boats and <unk> cruisers would be the future of warships . Like the British , the French navy saw little action with its ironclads ; the French blockade of Germany in the Franco @-@ Prussian War was ineffective , as the war was settled entirely on land . 
- Russia built a number of ironclads , generally copies of British or French designs . Nonetheless , there were real innovations from Russia ; the first true type of ironclad armored cruiser , the General @-@ Admiral of the 1870s , and a set of remarkably badly designed circular battleships referred to as ' <unk> ' ( for Admiral <unk> , who conceived the design ) . The Russian Navy pioneered the wide @-@ scale use of torpedo boats during the Russo @-@ Turkish War of 1877 – 1878 , mainly out of necessity because of the superior numbers and quality of ironclads used by the Turkish navy . Russia expanded her navy in the 1880s and 1890s with modern armored cruisers and battleships , but the ships were manned by inexperienced crews and politically appointed leadership , which enhanced their defeat in the Battle of <unk> on 27 May 1905 . 
- The U.S. Navy ended the Civil War with about fifty monitor @-@ type coastal ironclads ; by the 1870s most of these were laid up in reserve , leaving the USA virtually without an ironclad fleet . Another five large monitors were ordered in the 1870s . The limitations of the monitor type effectively prevented the USA from projecting power overseas , and until the 1890s the USA would have come off badly in a conflict with even Spain or the Latin American powers . The 1890s saw the beginning of what became the Great White Fleet , and it was the modern pre @-@ <unk> and armored cruisers built in the 1890s which defeated the Spanish fleet in the Spanish – American War of 1898 . This started a new era of naval warfare . 
- <unk> were widely used in South America . Both sides used ironclads in the <unk> Islands War between Spain and the combined forces of Peru and Chile in the early 1860s . The powerful Spanish <unk> participated in the Battle of <unk> but was unable to inflict significant damage to the <unk> defences . Besides , Peru was able to deploy two locally built ironclads based on American Civil War designs , the <unk> ( a wooden ship converted into a casemate ironclad ) and the Victoria ( a small monitor armed with a single 68 @-@ <unk> gun ) , as well as two British @-@ built ironclads : <unk> , a centre @-@ battery ship , and the turret ship <unk> . <unk> was the first ironclad to <unk> the world , arriving in <unk> on 20 September 1867 , and earning the motto : " <unk> <unk> <unk> <unk> <unk> <unk> " [ " First ironclad ship to sail around the world " ] ) . In the War of the Pacific in 1879 , both Peru and Chile had ironclad warships , including some of those used a few years previously against Spain . While the <unk> ran aground early on , the Peruvian ironclad ' <unk> made a great impact against Chilean shipping , delaying Chilean ground invasion by six months . She was eventually caught by two more modern Chilean centre @-@ battery ironclads , the <unk> <unk> and the <unk> <unk> at the Battle of <unk> Point . 
- <unk> were also used from the inception of the Imperial Japanese Navy . The <unk> ( Japanese : <unk> , literally " <unk> " , later renamed <unk> <unk> , " East " ) had a decisive role in the Naval Battle of <unk> Bay in May 1869 , which marked the end of the <unk> War , and the complete establishment of the <unk> Restoration . The <unk> continued to develop its strength and commissioned a number of warships from British and European shipyards , first ironclads and later armored cruisers . These ships engaged the Chinese <unk> fleet which was superior on paper at least at the Battle of the <unk> River . <unk> to superior short @-@ range firepower , the Japanese fleet came off better , sinking or severely damaging eight ships and receiving serious damage to only four . The naval war was concluded the next year at the Battle of <unk> , where the strongest remaining Chinese ships were surrendered to the Japanese . 
- 
- = = End of the ironclad warship = = 
- 
- There is no clearly defined end to the ironclad , besides the transition from wood hulls to all @-@ metal . <unk> continued to be used in World War I. Towards the end of the 19th century , the descriptions ' battleship ' and ' armored cruiser ' came to replace the term ' ironclad ' . 
- The proliferation of ironclad battleship designs came to an end in the 1890s as navies reached a consensus on the design of battleships , producing the type known as the pre @-@ <unk> . These ships are sometimes covered in treatments of the ironclad warship . The next evolution of battleship design , the dreadnought , is never referred to as an ' ironclad ' . 
- Most of the ironclads of the 1870s and 1880s served into the 1900s ( decade ) . For instance , a handful of US navy monitors laid down in the 1870s saw active service in World War I. Pre @-@ <unk> battleships and cruisers of the 1890s saw widespread action in World War I and in some cases through to World War II . 
- 
- = = = Legacy = = = 
- 
- The example of the ironclads had some bearing on the history of the tank , as ironclad warships became an inspiration for ideas of <unk> and other armored vehicles . H. G. Wells , in his short story The Land <unk> , published in The Strand Magazine in December 1903 , described the use of large , armoured cross @-@ country vehicles , armed with cannon and machine guns , and equipped with <unk> wheels 
- 
- = = Today = = 
- 
- A number of ironclads have been preserved or reconstructed as museum ships . 
- Parts of USS <unk> have been recovered and are being <unk> and displayed at the Mariners ' Museum in Newport News , Virginia 
- HMS Warrior is today a fully restored museum ship in Portsmouth , England 
- <unk> is berthed at the port of <unk> , Chile , on display for visitors . 
- The City @-@ class ironclad USS Cairo is currently on display in <unk> , Mississippi . 
- <unk> <unk> in Newport News constructed a full @-@ scale replica of USS <unk> . The replica was laid down in February 2005 and completed just two months later . 
- The Dutch <unk> ( Coastal ram ) <unk> . Ms. <unk> is currently under display in the Maritime Museum Rotterdam . 
- The Dutch <unk> ( Coastal ram ) <unk> . Ms. <unk> is a museum ship at Den <unk> . 
- The complete , recovered wooden hull of the CSS <unk> , a casemate ram ironclad , is on view in <unk> , North Carolina , and , in another part of town on the <unk> River , the recreated ship , named CSS <unk> II , is nearly built and can be visited . 
- The hull of the casemate ironclad CSS Jackson can be seen in the National Civil War Naval Museum at Port Columbus , Georgia . 
- The new United States Navy <unk> @-@ class guided missile destroyer has been described as bearing resemblance to ironclads . 
- 
- 
- = Little <unk> ( poem ) = 
- 
- Little <unk> is the fourth and final poem of T. S. Eliot 's Four <unk> , a series of poems that discuss time , perspective , humanity , and salvation . It was first published in September 1942 after being delayed for over a year because of the air @-@ raids on Great Britain during World War II and Eliot 's declining health . The title refers to a small Anglican community in <unk> , established by Nicholas <unk> in the 17th century and scattered during the English Civil War . 
- The poem uses the combined image of fire and <unk> fire to emphasise the need for purification and <unk> . According to the poet , humanity 's flawed understanding of life and turning away from God leads to a cycle of warfare , but this can be overcome by <unk> the lessons of the past . Within the poem , the narrator meets a ghost that is a combination of various poets and literary figures . Little <unk> focuses on the unity of past , present , and future , and claims that understanding this unity is necessary for salvation . 
- 
- = = Background = = 
- 
- Following the completion of the third Four <unk> poem , The Dry <unk> , Eliot 's health declined and he stayed in <unk> Green , Surrey while he recovered . During this time , Eliot started writing Little <unk> . The first draft was completed in July 1941 but he was dissatisfied with it . He believed the problems with the poem lay with his own inability to write , and that , precipitated by air raids on London , he had started the poem with too little preparation and had written it too quickly . After the first draft was written , he set the poem aside , and he left in September to lecture throughout Great Britain . 
- After months of not working on the poem , Eliot began to feel compelled to finish it ; it was not until August 1942 , however , that he started working on it again . In total , there were five drafts . The poem was finished by 19 September 1942 and published in the October New English Weekly . Little <unk> was intended to conclude the Four <unk> series , <unk> Eliot 's views expressed in this series of poems . 
- Little <unk> was the home of an Anglican community established in <unk> by Nicholas <unk> . The <unk> household lived a Christian life according to High Church principles and the Book of Common Prayer . The religious community was dispersed during the English Civil War between <unk> and <unk> but reformed , ending with the death of John <unk> in <unk> . Eliot had visited the site in May 1936 . 
- Unlike the other locations mentioned in the titles of the Four <unk> poems , Eliot had no direct connection to the original Christian community . As such , the community is supposed to represent almost any religious community . 
- 
- = = Poem = = 
- 
- Critics classify Little <unk> as a poem of fire with an emphasis on <unk> and the <unk> fire . The beginning of the poem discusses time and winter , with attention paid to the arrival of summer . The images of snow , which provoke desires for a spiritual life , transition into an analysis of the four classical elements of fire , earth , air and water and how fire is the primary element of the four . Following this is a discussion on death and destruction , things <unk> , and regret for past events . 
- While using Dante 's <unk> <unk> style , the poem continues by describing the Battle of Britain . The image of warfare <unk> with the depiction of <unk> , and the Holy Spirit is juxtaposed with the air @-@ raids on London . In the second section , a ghost , representing the poets of the past stuck between worlds , begins talking to the narrator of the poem . The ghost discusses change , art in general , and how humankind is flawed . The only way to overcome the problematic condition of humanity , according to the ghost , is to experience <unk> through fire . The fire is described in a manner similar to Julian of Norwich 's writing about God 's love and discussed in relationship to the shirt of <unk> , a shirt that burns its wearer . Little <unk> continues by describing the <unk> of the present and how history exists in a pattern . The poem concludes by explaining how sacrifice is needed to allow an individual to die into life and be reborn , and that salvation should be the goal of humankind . 
- 
- = = Themes = = 
- 
- In terms of renewal , Eliot believed that suffering was needed for all of society before new life could begin . The original Little <unk> community was built for living on monastic lines , but the community was damaged and dispersed by Puritan forces during the English Civil War in 1646 . The church , the centre of the community , was restored in 1714 and again in 1853 . The image of religious renewal is combined with the image of the London air @-@ raids and the constant fighting and destruction within the world . This compound image is used to discuss the connection of holy places with the Holy Spirit , <unk> , communion with the dead , and the repetition of history . The theme is also internal to Eliot 's own poems ; the image of the rose garden at the end Little <unk> is the image that begins <unk> Norton and the journey is made circular . Also , the depiction of time within the poem is similar to the way time operates within The Family <unk> . 
- Like the other poems making up the Four <unk> , Little <unk> deals with the past , present , and future , and humanity 's place within them as each generation is seemingly united . In the second section , there is a ghost who is the compilation of various poets , including Dante , Swift , Yeats , and others . When the ghost joins the poet , the narrator states " Knowing myself yet being someone other " . This suggests that the different times merge at the same time that the different personalities begin to merge , allowing a communication and connection with the dead . Later , in the fourth section , humanity is given a choice between the Holy Spirit or the bombing of London ; redemption or destruction . God 's love allows humankind to be <unk> and escape the living hell through <unk> by fire . The end of the poem describes how Eliot has attempted to help the world as a poet . He parallels his work in language with working on the soul or working on society . 
- The ghost , a combination of many literary figures , was originally addressed in the poem as " <unk> <unk> " before being revised as an ambiguous " you " . " <unk> <unk> " was Dante 's way of addressing <unk> <unk> , a former mentor whom he meets in Hell to which he has been condemned for <unk> . Eliot , in a letter to John <unk> dated 27 August 1942 , explained why he changed the wording : 
- I think you will recognise that it was necessary to get rid of <unk> for two reasons . The first is that the visionary figure has now become somewhat more definite and will no doubt be identified by some readers with Yeats though I do not mean anything so precise as that . However , I do not wish to take the responsibility of putting Yeats or anybody else into Hell and I do not want to <unk> to him the particular vice which took <unk> there . <unk> , although the reference to that <unk> is intended to be explicit , I wish the effect of the whole to be <unk> which is more appropriate . That brings us to the reference to swimming in fire which you will remember at the end of <unk> 26 where the poets are found . 
- The theme of swimming through flames is connected to the depiction of <unk> <unk> , a poet that influenced Dante , seeking such a state in <unk> <unk> . However , the depiction of swimming was transformed into an image of dancing , an act that appears throughout Yeats 's poetry , within <unk> flames . The critic Dominic <unk> suggests that , in combining the image of dancing with <unk> , Eliot <unk> Dante 's and Yeats 's poetic themes . 
- 
- = = Reception = = 
- 
- Critics such as Malcolm <unk> and <unk> <unk> describe mixed emotions about the <unk> of the poem . <unk> emphasised the mystical nature of the poem and how its themes were closer to Buddhism than <unk> while mentioning his appreciation of many of the passages . <unk> also mentioned the Buddhist images and his admiration for many of the lines in Little <unk> . F. B. <unk> believed that the fourth section of the poem costs " Eliot more trouble and <unk> than any passage of the same length he ever wrote , and is his greatest achievement in the Four <unk> . " E. M. Forster did not like Eliot 's emphasis on pain and responded to the poem : " Of course there 's pain on and off through each individual 's life ... You can 't <unk> it and so on . But why should it be endorsed by the schoolmaster and <unk> by the priest until the fire and the rose are one when so much of it is caused by disease and <unk> ? It is here that Eliot becomes unsatisfactory as a seer . " Writing in 2003 , Roger <unk> wrote that in " Little <unk> " Eliot achieved " that for which he <unk> Dante — namely , a poetry of belief , in which belief and words are one , and in which the thought cannot be prized free from the controlled and beautiful language " . 
- 
- 
- = The Portage to San <unk> of <unk> = 
- 
- The Portage to San <unk> of <unk> is a 1981 literary and philosophical novella by George Steiner , in which Jewish Nazi hunters find a fictional Adolf Hitler ( <unk> ) alive in the Amazon jungle thirty years after the end of World War II . The book generated considerable controversy after its publication because in it , Steiner , who is Jewish , allows Hitler to defend himself when he is put on trial in the jungle by his captors . There Hitler maintains that Israel <unk> its existence to the Holocaust and that he is the " benefactor of the Jews " . 
- The Portage to San <unk> of <unk> was a 1983 <unk> in the <unk> / Faulkner Award for Fiction . It was adapted for the theatre by British playwright Christopher Hampton and was staged in London in April 1982 with Alec <unk> playing the part of Adolf Hitler . It was also staged in Hartford , Connecticut in the United States in 1983 and starred John <unk> as Hitler . 
- 
- = = Plot summary = = 
- 
- From his base in Tel Aviv , Holocaust survivor Emmanuel <unk> directs a group of Jewish Nazi hunters in search of Adolf Hitler . <unk> believes that the former <unk> is still alive , and following rumours and <unk> , he tracks Hitler 's movements through South America , until after months of wading through swamps in the Amazon jungle , the search party finds the 90 @-@ year @-@ old alive in a clearing . <unk> flies to San <unk> where he awaits the group 's return with their captive . But getting the old man out of the jungle alive is more difficult than getting in , and their progress is further hampered by heavy thunderstorms . 
- Meanwhile , broken and incoherent radio messages between <unk> and the search party are intercepted by intelligence agents tracking their progress , and rumours begin to spread across the world of Hitler 's capture . <unk> flare up over his impending trial , where it will be held and under whose jurisdiction . <unk> is identified as the nearest airfield to the last known location of the search party , and aircraft begin arriving at the hitherto unknown town . But when the search party loses radio contact with <unk> , they must make a decision : do they sit out the storms and deliver their captive to <unk> later , or do they try Hitler in the jungle before their prize is <unk> from them by the world at large , who they know will be waiting ? Their decision is the latter , and against <unk> 's advice ( " You must not let him speak ... his tongue is like no other " ) they prepare for a trial with a judge , prosecution and defence attorneys selected from the members of the search party . <unk> , a local Indian <unk> , is asked to observe the trial as an independent witness . 
- The attention Hitler is receiving , however , <unk> his strength , and when the trial begins , he <unk> aside his " defence attorney " and begins a long speech in four parts in his own defence : 
- Firstly , Hitler claims he took his doctrines from the Jews and copied the notion of the master race from the <unk> people and their need to separate themselves from the " <unk> " . " My racism is a parody of <unk> , a hungry imitation . " 
- Hitler <unk> the Final <unk> by maintaining that the Jews ' God , <unk> than any other , <unk> its subjects , continually demanding more than they can give and " <unk> " them with ideals that cannot be attained . The " virus of <unk> " had to be stopped . 
- Hitler states that he was not the originator of evil . " [ Stalin ] had <unk> <unk> when I was still a <unk> <unk> in Munich . " Further , Hitler asserts that the number of lives lost due to his actions are <unk> by various world atrocities , including those in Russia , China and Africa . 
- Lastly , Hitler maintains that the Reich <unk> Israel and suggests that he is the <unk> " whose infamous <unk> were allowed by God in order to bring His people home . " He closes by asking , " Should you not honour me who have made ... <unk> a reality ? " 
- At the end of his speech , <unk> is the first to react and jumps up shouting " <unk> " , only to be drowned out by the appearance of a helicopter over the clearing . 
- 
- = = Main characters = = 
- 
- Emmanuel <unk> – Jewish Holocaust survivor and director of the search party to find Hitler ; after <unk> out of a death pit in <unk> he never took the time to <unk> and embarked on a life @-@ consuming obsession to bring those responsible for the <unk> to justice . 
- Search party ( all Jewish with family ties to the Holocaust , except for John Asher ) 
- <unk> – search party leader and " presiding judge " at Hitler 's trial ; he is <unk> 's <unk> and torn between leading the party into " <unk> <unk> and green <unk> " and turning his back on the " quiet <unk> of <unk> 's conviction " . 
- Gideon <unk> – falls ill and dies before the trial begins ; during one of his fever @-@ induced <unk> he suggests that Hitler is Jewish ; he had sought out <unk> after being released from a <unk> and spending three years <unk> in Paris where the care @-@ free living consumed him with guilt . 
- Elie <unk> – Orthodox Jew and " prosecution attorney " at the trial ; he is the moral <unk> of the group , but his convictions are disturbed by Gideon <unk> 's fever @-@ induced <unk> that Hitler is Jewish and ends up believing that Hitler may be the second <unk> . 
- Isaac <unk> – an 18 @-@ year @-@ old boy and witness at the trial ; he is the son of Isaac <unk> senior , former member of the search party killed earlier in a <unk> in São Paulo ; he joined the party to avenge his father 's death . 
- John Asher – half @-@ Jewish and reluctant " defence attorney " at the trial ; fascinated by the capture of <unk> and the rumours circulating that Hitler may be alive , he had approached Nazi hunter <unk> who directed him to <unk> ; despite being an " <unk> " ( no ties to the Holocaust ) <unk> assigned him to the search party because of his military training and his clear @-@ <unk> ( " no metaphysical lusts , no <unk> for <unk> " ) . 
- <unk> – local Indian <unk> and independent witness at the trial ; previously the search party 's guide who had abandoned them when they insisted on entering <unk> regions of the jungle , he continued tracking them from a distance before revealing himself . 
- Adolf Hitler – now 90 years old , the former leader of the Third Reich had not died in the <unk> in Berlin , but escaped to South America and hid in the Amazon jungle . 
- 
- = = Background and publication = = 
- 
- George Steiner , literary critic for The New Yorker and The New York Times , had written about the Holocaust in some of his previous books , including <unk> <unk> ( 1964 ) , Language and Silence ( 1967 ) and In <unk> 's Castle ( 1971 ) . Many of the ideas Steiner expresses in The Portage to San <unk> of <unk> were reworked from these earlier works . Steiner told New York Times editor D. J. R. <unk> that this book arose out of his lifelong work on language . " Central to everything I am and believe and have written is my <unk> ... that you can use human speech both to <unk> , to love , to build , to forgive and also to torture , to hate , to destroy and to <unk> . " 
- Commenting on the controversy the book generated , Steiner admitted to literary journalist and critic Ron <unk> ( author of Explaining Hitler ) that he too was disturbed by it , adding that his fictional Hitler had gotten the better of him , " <unk> or <unk> @-@ like " . He said that it felt like the book " wrote me " . Steiner also pointed out that the novella is not only about his thoughts on the Holocaust , but also about the horrific events that took place in countries like Cambodia , Vietnam , El Salvador and <unk> : " My feeling is that one has to <unk> with the <unk> if one can . " 
- Steiner wrote The Portage to San <unk> of <unk> in 1975 and 1976 in Geneva , Switzerland , and the 120 @-@ page work originally appeared in the Spring 1979 issue of the United States literary magazine , The Kenyon Review . It also appeared in the Spring 1980 issue of <unk> , the British literary magazine . Its first publication in book form , with minor revisions by Steiner , was in May 1981 by <unk> and <unk> in the United Kingdom — and as requested by Steiner , it was a paperback original . The first United States edition was published in hardcover in April 1982 by Simon & Schuster . 
- 
- = = Adaptations = = 
- 
- The Portage to San <unk> of <unk> was adapted for the theatre in 1982 by British playwright Christopher Hampton . It was staged in April 1982 at London 's <unk> Theatre under the direction of John Dexter with Alec <unk> playing the part of Adolf Hitler . <unk> won the 1982 Evening Standard Theatre Award for best actor for this performance . In 1983 the production moved to the United States where it played at the Hartford Stage Company in Hartford , Connecticut , directed by Mark <unk> and starring John <unk> as Hitler . 
- This book is the only work of fiction by Steiner to have been adapted for the stage . 
- 
- = = Reception = = 
- 
- Reaction to The Portage to San <unk> of <unk> was mixed . Anthony <unk> in The Observer called it " astonishing " , Christopher Booker of The Daily Telegraph described it as a " powerful piece " , and English author A. S. <unk> said it was a " masterpiece " . In Explaining Hitler , Ron <unk> called The Portage " A <unk> story " , referring to Steiner 's <unk> Hitler has having taken on a life of its own . Writing in Time magazine , Otto Friedrich described the book " a <unk> fantasy of remarkable intensity " , adding that by not <unk> Hitler 's speech , Steiner <unk> from the <unk> of traditional Holocaust literature and ends the book " on a note of bleak ambiguity " . 
- Morris <unk> of The New York Times was more critical of the book , calling it " a <unk> and badly executed novel , a <unk> distraction from the serious business of thinking through the <unk> <unk> of the Nazi era . " He described it as " <unk> " that is " <unk> [ d ] " by too much " fine writing " ( <unk> @-@ <unk> ) . He also complained that the characters are lifeless , and while they each have detailed histories , they are only " verbal <unk> " that do not separate them from one another . Finally <unk> noted that because almost all the points of Hitler 's speech are drawn from some of Steiner 's earlier works , he " unwittingly creates sympathy for Hitler by making him old and pathetic yet also <unk> and brilliant — at once <unk> harmless and <unk> dangerous . " 
- In another review in The New York Times John Leonard wrote that while the book has its strong points , " some wit , a <unk> disdain , multiplicity of character and a South American swamp @-@ life that <unk> " , its weaknesses are that " the characters are really ideas , ... the symbols clash and there are too many echoes of better books by <unk> and <unk> " . But Leonard 's biggest criticism of the book was Hitler 's speech , which he called " obscene " , and Steiner 's decision to end the book at that point , which Leonard said " not only denies the power of art to arrange and transcend , but ... makes me sick to my stomach . " 
- Writing in the American literary magazine <unk> , Alvin H. <unk> called The Portage a " breakthrough work " that " <unk> " . He was struck by the book 's interplay between the landscape of swamp and jungle , and the " landscape of speech " — the former being " brilliantly registered " with its " immense feeling of <unk> " , and the latter , " even more dramatic " in the way it exposes " the dark underside of words " and how its use and misuse reveals the true nature of a person . He was particularly impressed by the depiction of Nazi hunter Emmanuel <unk> and his role as representative of the Jewish consciousness . <unk> noted that while Holocaust literature often either <unk> to " <unk> and apostrophe " , or sinks to " a <unk> <unk> of elegiac lament " , Steiner 's <unk> " <unk> between these two extremes , ... simultaneously records and <unk> , <unk> <unk> yet carries an immense affect " . What did concern the reviewer , however , was the way Steiner used ideas from his earlier works , that he had put them " virtually <unk> " into Hitler 's mouth , creating the impression that " Steiner 's understanding of Hitler were identical with the latter 's self @-@ understanding " . <unk> also questioned why the book had to end with Hitler 's speech . He said that Steiner 's <unk> Hitler plays " the devil 's game of language subversion " , making " madness [ sound ] like music " , something the real Hitler had <unk> . By stopping at this point , <unk> felt that Steiner " <unk> [ s ] , <unk> , to the seductive <unk> of negation " , which <unk> his own " high standards of moral intelligence " . But overall <unk> said The Portage " must be counted among the most vigorous attempts to portray the presence and meaning of Hitler " , forcing us to confront him " in a way hardly seen before in fiction " . 
- The Portage to San <unk> of <unk> was a <unk> in the 1983 <unk> / Faulkner Award for Fiction . 
- 
- = = Controversy = = 
- 
- The book generated considerable controversy because of its apparent " admiration for Hitler " . The controversy grew further when the faithful stage adaptation ( " too faithful " , according to Steiner ) was performed in the United Kingdom and the United States . 
- Hitler 's speech at the end of the book disturbed many readers and critics . Steiner not only lets Hitler justify his past , he allows him the ( almost ) last word before the outside world invades . The fact that Steiner is Jewish made this speech in particular even more contentious . One critic , while acknowledging that Steiner always saw Hitler as " the incarnation of unprecedented and unparalleled evil " , felt that there was no clear distinction in the book between Steiner 's own views and those of his fictional Hitler , even going so far as to accuse Steiner , who rejects Jewish nationalism and is a critic of Israel 's treatment of the Palestinians , of anti @-@ Semitism . 
- In contrast , a Time magazine article at the time felt that Steiner 's intention for the Hitler speech was to use it to explore his previously stated belief " that Hitler wielded language as an almost supernatural force " , drawing attention to Nazi hunter Emmanuel <unk> 's warning from the book regarding Hitler : " There shall come a man who ... will know the grammar of hell and teach it to others . He will know the sounds of madness and <unk> and make them seem music . " 
- Steiner responded to criticism that Hitler 's speech in this book is <unk> by saying that it had been done before : for example Satan 's speech in Milton 's Paradise Lost ( 1667 ) , and The Grand <unk> 's speech in <unk> 's The Brothers <unk> ( 1880 ) . He also reminded the reader that Hitler 's speech is balanced out earlier in the book by <unk> 's long monologue on the <unk> of the Holocaust . Finally , Steiner said that his Hitler ( A. H. ) is " a <unk> figure " , and that it is not he who has the last word , but <unk> , the Indian <unk> , who shouts " <unk> " . <unk> is also the Hebrew word used to indicate that " there are issues here beyond our wisdom to answer or decide . " 
- 
- 
- = <unk> = 
- 
- <unk> ( from Greek <unk> ( <unk> , " to cut " ) and <unk> ( <unk> , " <unk> " ) ) is a diverse <unk> of extinct small to giant <unk> — often considered primitive amphibians — that flourished worldwide during the <unk> , <unk> , and <unk> periods . A few species continued into the Cretaceous . <unk> have been found on every continent . During about 210 million years of evolutionary history , they adapted to a wide range of habitats , including fresh water , terrestrial , and even coastal marine environments . Their life history is well understood , with fossils known from the <unk> stage , <unk> , and maturity . Most <unk> were <unk> , although some were almost fully terrestrial , returning to the water only to breed . These <unk> were some of the first vertebrates fully adapted to life on land . Although <unk> are considered amphibians , many had characteristics , such as scales , claws , and armour @-@ like bony plates , that distinguish them from modern amphibians . 
- <unk> have been known since the early 19th century , and were initially thought to be reptiles . They were described at various times as <unk> , <unk> , and <unk> , although these names are now rarely used . Animals now grouped in <unk> were spread out among several amphibian groups until the early 20th century , when they were found to belong to a distinct taxon based on the structure of their vertebrae . <unk> means " cut vertebrae " , as each <unk> is divided into several parts . 
- Experts disagree over whether <unk> were ancestral to modern amphibians ( frogs , <unk> , and <unk> ) , or whether the whole group died out without leaving any descendants . Different hypotheses have placed modern amphibians as the descendants of <unk> , another group of early <unk> called <unk> , or even as descendants of both groups ( with <unk> evolving from <unk> and frogs and <unk> evolving from <unk> ) . Recent studies place a family of <unk> called the <unk> as the closest relatives of modern amphibians . <unk> in teeth , skulls , and hearing structures link the two groups . 
- 
- = = Description = = 
- 
- Many <unk> are much larger than living amphibians , and superficially resemble <unk> . Others are smaller and resemble <unk> . Most have broad , flat heads that are either blunt ( <unk> ) or elongated ( <unk> ) . The skulls are rounded or triangular in shape when viewed from above , and are usually covered in pits and ridges . The rugged surfaces of bones may have supported blood vessels , which could transfer carbon dioxide to the bones to neutralize acidic build up in the blood ( early <unk> <unk> would have had difficulty <unk> carbon dioxide from their bodies while on land , and these dermal bones may have been an early solution to the problem ) . Many <unk> also have canal @-@ like grooves in their skulls called sensory <unk> . The <unk> , which usually run around the nostrils and eye <unk> , are part of a lateral line system used to detect <unk> in water . As <unk> animals , most <unk> have small limbs with four toes on each front foot and five on each hind foot . <unk> <unk> have larger , thicker limbs , and some even have claws . One unusual terrestrial <unk> , <unk> , has relatively long limbs for its body , and probably lived as an active runner able to chase prey . 
- <unk> of most of the bones of <unk> are also seen in other early <unk> , aside from a few bones in the skull , such as <unk> , <unk> , and <unk> , that have developed in some <unk> taxa . Most <unk> have <unk> horns in the backs of their skulls , rounded projections of bone separated from the rest of the skull by <unk> called <unk> <unk> ; in some <unk> , such as <unk> , they are pointed and very prominent . Among the most distinguishing features of <unk> are the <unk> <unk> , two large holes in the back of the palate . Another pair of holes , <unk> , are present in front of these <unk> , and connect the nasal passage with the mouth . <unk> often have teeth on their <unk> , as well as in their jaws . Some of these teeth are so large , they are referred to as <unk> . In some <unk> , such as <unk> , <unk> in the lower jaw <unk> the palate and emerge through openings in the top of the skull . 
- Very little is known of the soft tissue of <unk> . A block of sandstone , described in 2007 from the Early <unk> <unk> <unk> Formation of Pennsylvania , included impressions of the bodies of three <unk> . These impressions show , when alive , they had smooth skin , robust limbs with <unk> feet , and a ridge of skin on their <unk> . <unk> <unk> to small <unk> have also been found in <unk> and <unk> rocks . The <unk> , called <unk> , are usually found in strata deposited around freshwater environments , suggesting the animals had some ties to the water . 
- Unlike modern amphibians , many <unk> are covered in small , closely packed scales . The <unk> of most <unk> are covered in rows of large ventral plates . During early stages of development , they first have only small , rounded scales . <unk> show , as the animals grew , the scales on the <unk> of their bodies developed into large , wide ventral plates . The plates overlap each other in a way that allows a wide range of flexibility . Later <unk> <unk> , such as <unk> and <unk> , have no evidence of scales . They may have lost scales to make movement easier under water or to allow <unk> <unk> , the absorption of oxygen through the skin . 
- Several groups of <unk> have large bony plates on their backs . One <unk> , <unk> , has armour @-@ like plating that covers both its back and underside . The <unk> <unk> also has extensive plating on its back . Most members of the family <unk> also have armor , although it only covers the midline of the back with two narrow rows of plates . Other <unk> , such as <unk> , have been found with small , disc @-@ like bony <unk> that were in life probably embedded in the skin . All of these <unk> were adapted to a terrestrial lifestyle . Armor may have offered protection from predators in the case of <unk> . The <unk> may have provided stability for the spine , as they would have limited flexibility and may have been connected by strong ligaments . <unk> such as <unk> and <unk> that may have been at least partly terrestrial also have long neural spines on top of their vertebrae that would have stabilized the spine . <unk> <unk> are also seen in <unk> , but unlike <unk> , <unk> , <unk> , and <unk> , these animals are thought to have been fully aquatic . <unk> may have inherited their armor from a terrestrial ancestor , as both <unk> and <unk> have been considered close relatives of the group . 
- <unk> ' vertebrae are divided into several segments . In living <unk> , the main body of the <unk> is a single piece of bone called the <unk> , but in <unk> , this region was divided into a <unk> and <unk> . Two types of vertebrae are recognized in <unk> : <unk> and <unk> vertebrae . In <unk> vertebrae , the <unk> are large and wedge @-@ shaped , and the <unk> are relatively small blocks that fit between them . Both elements support a spine @-@ like neural arch , and well @-@ developed <unk> projections called <unk> strengthen the connections between vertebrae . The strong backbone and strong limbs of many <unk> <unk> allowed them to be partially , and in some cases fully , terrestrial . In <unk> vertebrae , the <unk> have been lost entirely , with the <unk> enlarged as the main body of the vertebrae . This weaker type of backbone indicates the <unk> <unk> spent more time in water . 
- 
- = = History of study = = 
- 
- <unk> was named by German <unk> Karl Alfred von <unk> in his second edition of <unk> der <unk> , published in 1888 . <unk> remains were known since the early part of the 19th century , however . The earliest described <unk> was <unk> , named by Georg Friedrich <unk> in 1828 . <unk> named <unk> from a single tooth , and considered it a reptile . <unk> means " breast tooth lizard " after the <unk> @-@ like shape of the tip of the tooth . 
- The naming of these first specimens was disputed , however . Leopold <unk> named the animal <unk> in 1837 . In 1841 , English <unk> Richard Owen referred to the genus as <unk> to describe its highly folded or <unk> teeth . Owen thought the name <unk> " ought not to be retained , because it recalls <unk> the idea of the mammalian genus <unk> , or else a <unk> form of the tooth ... and because the second element of the word , <unk> , indicates a false affinity , the remains belonging , not to the <unk> , but to the <unk> order of <unk> . " Owen recognized the animal was not a " <unk> " reptile , yet he also referred <unk> 's <unk> to the genus . Although the two genera both have similarly sized conical teeth , <unk> was later found to be a crocodile @-@ like reptile . Additional material , including skulls , firmly placed <unk> as an amphibian . <unk> also named <unk> <unk> in 1828 , basing it on partial <unk> , or back portion of the skull . In 1833 , he described a complete skull of S. <unk> that had the same teeth as his <unk> , making it the first known complete skull of a <unk> . Because <unk> was named first , it has precedence over the other names as a senior <unk> synonym . <unk> is still used as the name of an unrelated <unk> <unk> . 
- <unk> and other similar animals were referred to as <unk> , named like <unk> for teeth that were highly folded in cross section . Owen 's " <unk> <unk> " was later found at Guy 's <unk> , England by paleontologist William Buckland . Other specimens were found in the red sandstone of Warwickshire . As more fossils were uncovered in England , Owen depicted these <unk> as the " highest " form of <unk> and compared them to <unk> , which he considered the highest form of reptiles . He also noted the large <unk> of the <unk> ( a unit of rocks that dates to the Late <unk> ) were younger than more advanced reptiles in the <unk> and <unk> , which are Late <unk> in age . Owen used these fossils to counter the notion that reptiles evolved from a <unk> progression from early amphibians ( what he called " <unk> fishes " ) . 
- In addition to <unk> , some of the earliest named genera included <unk> and <unk> in 1842 , <unk> in 1848 , <unk> in 1849 , <unk> and <unk> in 1853 , <unk> in 1858 , and <unk> in 1859 . <unk> is now placed as an early <unk> outside <unk> , and <unk> is now considered a <unk> reptile . 
- Later in the 19th century , <unk> were classified as various members of <unk> , a name coined by American paleontologist Edward <unk> Cope in 1868 . Cope placed <unk> in the class <unk> , the name then used for <unk> . <unk> means " roof @-@ headed " in Greek , a reference to the wide , flat heads of <unk> and other early <unk> . During this time , <unk> considered <unk> to be amphibians because they possessed three main features : gill arches in juvenile skeletons , indicating they were amphibious for at least the first part of their lives ; ribs that do not connect at the underside of the rib cage ; and deep pits in the skull that were interpreted as space for <unk> glands . 
- Several <unk> of <unk> were recognized in the late 19th and early 20th centuries . Animals now regarded as <unk> were primarily <unk> , but some were classified in the <unk> . <unk> were small @-@ bodied and had simple conical teeth , while <unk> were larger and had complex , folded <unk> and enamel in their teeth . <unk> included only a few forms , such as <unk> from Europe and <unk> from North America , that had poorly developed bones , external gills , and no ribs . Some skeletons of <unk> were later found with long ribs , prompting its <unk> to <unk> ( although more detailed studies found it to be a <unk> ) . Soft tissue , such as scales and external gills , were found in many well @-@ preserved <unk> fossils from Germany . In the early 20th century , <unk> would be recognized as <unk> forms of <unk> lacking many of the typical features that define the group , and is no longer recognized as a distinct group . 
- Other animals that would later be classified as <unk> were placed in a group called <unk> , characterized by plate @-@ like skull bones , small limbs , fish @-@ like scales , and <unk> arches . Unlike <unk> , they did not have parietal <unk> , small holes in their skulls behind their eye <unk> . <unk> , <unk> , <unk> and <unk> were placed in this group and were considered to be the most primitive members of <unk> . Their <unk> vertebrae , <unk> , and lack of <unk> <unk> ( which attached the head to the neck ) were features that were also shared with fishes . Thus , they were considered a link between early fishes and more advanced forms such as <unk> . 
- Another group called <unk> was named by Cope in 1868 . Cope classified <unk> as a <unk> of <unk> , placing many small , amphibian @-@ like animals within it . Among them were <unk> , once placed in <unk> . <unk> was later placed as a <unk> with other <unk> , but confusion existed for many years over the classification of small amphibians . 
- By the end of the 19th century , most of what are today regarded as <unk> were placed in the suborder <unk> . American paleontologist <unk> <unk> Case called it <unk> <unk> or " true <unk> " . The names <unk> and <unk> were used <unk> to refer to the order in which it belonged . The <unk> <unk> <unk> and <unk> , both of which contain <unk> , were distinct from <unk> . Within <unk> were the groups <unk> , <unk> , and <unk> . Members of <unk> , such as <unk> and <unk> , had <unk> vertebrae with enlarged <unk> that displaced the <unk> . <unk> , such as <unk> , <unk> , and <unk> , had lost their <unk> , and the <unk> made up the entire body of the vertebrae . <unk> had <unk> and <unk> that were of equal size . <unk> are now identified as <unk> distantly related to <unk> . 
- In 1888 , von <unk> divided <unk> among three taxa : <unk> , <unk> , and <unk> . He placed <unk> in <unk> , a group which he characterized as having simple , <unk> @-@ shaped <unk> <unk> . <unk> included forms with the <unk> divided into <unk> and <unk> . All members of <unk> had <unk> <unk> composed only of the <unk> . Cope objected to von <unk> 's classification , considering the vertebrae of <unk> and <unk> indistinguishable because each had a simple <unk> shape . He continued to use <unk> and <unk> ( which he alternatively referred to as <unk> ) to distinguish animals based on the absence or presence of <unk> <unk> . 
- <unk> became a commonly used name at the turn of the century . <unk> included both <unk> and <unk> in the group . Cope 's <unk> and <unk> fell out of use . In 1919 , British paleontologist D. M. S. Watson proposed that the evolutionary history of these large amphibians could be seen through changes in their vertebrae . <unk> forms in the <unk> graded into <unk> forms in the <unk> , and finally into <unk> in the <unk> . More importantly , Watson began using the term <unk> to refer to these groups . The name <unk> was rarely used in the decades that followed . Swedish paleontologist <unk> <unk> @-@ <unk> removed <unk> from the group , narrowing its scope to <unk> and <unk> . His classification of <unk> was based heavily on characteristics of the skull rather than the vertebrae . 
- American paleontologist Alfred <unk> brought the name <unk> back into use in the later 20th century . <unk> @-@ <unk> used the name <unk> in a strict sense ( <unk> <unk> ) to refer to <unk> and <unk> , excluding <unk> . <unk> agreed with this classification , but used the name <unk> to avoid confusion with <unk> in its wider sense ( <unk> <unk> ) . Unlike modern <unk> classification , however , <unk> included the primitive <unk> in the group . 
- 
- = = <unk> history = = 
- 
- 
- = = = <unk> and Early <unk> = = = 
- 
- <unk> first appeared in the Early <unk> around 330 million years ago ( <unk> ) . During the <unk> , <unk> included basal medium @-@ sized forms such as <unk> or large <unk> forms such as <unk> . Other , more derived <unk> , such as the <unk> , were smaller and more terrestrial . They resembled <unk> , and some taxa , such as the genus <unk> , even retained external gills like the modern @-@ day <unk> . During the latest <unk> and Early <unk> around 300 <unk> , several groups , such as the <unk> and <unk> evolved strong , robust limbs and vertebrae and became adapted to life on land while others such as the <unk> , developed into large <unk> predators . The <unk> , a group of small aquatic <unk> , evolved from terrestrial ancestors in the Late <unk> . 
- 
- = = = Late <unk> = = = 
- 
- During the Late <unk> , increasing <unk> and the <unk> of reptiles contributed into a decline in terrestrial <unk> , but <unk> and fully aquatic <unk> continued to <unk> , including the large <unk> of Eastern Europe . Other <unk> , such as <unk> , developed long <unk> and a close similarity to <unk> , although they lacked the armor characteristic of the latter group . These <unk> included the largest known amphibian , the 9 @-@ m @-@ long <unk> of Brazil . 
- 
- = = = <unk> = = = 
- 
- As <unk> continued to <unk> and <unk> in the Late <unk> ( 260 @.@ 4 - 251 @.@ 0 <unk> ) , a major group called <unk> became more dependent on life in the water . The vertebrae became weak , the limbs small , and the skull large and flat , with the eyes facing upwards . During the <unk> period , these animals dominated the freshwater ecosystems , evolving in a range of both small and large forms . During the Early <unk> ( 251 @.@ 0 - 245 @.@ 0 <unk> ) one group of successful long @-@ <unk> fish @-@ <unk> , the <unk> , even adapted to a life in the sea , the only known amphibians to do so with the exception of the modern crab @-@ eating frog . Another group , the <unk> , included <unk> and large @-@ sized animals 2 @.@ 3 to 4 m ( 7 @.@ 5 to 13 @.@ 1 ft ) in length , with large and flat skulls that could be over a meter long in the largest forms such as <unk> . These animals spent most or all their lives in water as aquatic predators , catching their prey by a sudden opening of the upper jaw and sucking in fish or other small animals . 
- In the <unk> stage of the Late <unk> ( 228 @.@ 0 - 216 @.@ 5 <unk> ) , <unk> were joined by the superficially very similar <unk> . <unk> are distinguished from <unk> by the positioning of their eye <unk> near the front of their skulls . Another group of <unk> , the <unk> , had wide heads with external gills , and adapted to life at the bottom of lakes and rivers . By this time , <unk> had become a common and widespread component of <unk> ecosystems . Some <unk> , such as <unk> and <unk> , even inhabited Antarctica , which was covered in temperate forests at the time . 
- <unk> <unk> were often the dominant <unk> animals in their environments . Large <unk> of <unk> with hundreds of individuals preserved together have been found in the southwestern United States . They have often been interpreted as mass death events caused by droughts in floodplain environments . Recent studies show these dense <unk> were instead probably the result of currents accumulating dead individuals in certain areas . These environments seem to have had little diversity , as they were inhabited almost exclusively by <unk> . 
- The <unk> @-@ Jurassic extinction event around 199 @.@ 6 <unk> led to the extinction of most <unk> <unk> . The <unk> survived , as well as a few <unk> and <unk> . While the latter two groups soon became extinct , <unk> persisted and grew to large sizes during the Jurassic . Among <unk> , the <unk> flourished in China and the <unk> became common in <unk> . The most recent known <unk> was the giant <unk> <unk> , known from the Early Cretaceous of Australia . It survived in rift valleys that were too cold in the winter for <unk> that normally would have competed with them . <unk> was one of the largest of the <unk> , with an estimated weight of 500 kg ( 1 @,@ 100 lb ) . 
- 
- = = Classification = = 
- 
- Originally , <unk> were classified according to the structure of their vertebrae . Early forms , with complex vertebrae consisting of a number of separate elements , were placed in the suborder <unk> , and large <unk> aquatic forms with simpler vertebrae were placed in the suborder <unk> . With the recent growth of <unk> , this classification is no longer viable . The basic <unk> condition is found in many primitive <unk> , and is not unique to one group of <unk> . Moreover , the distinction between <unk> and <unk> vertebrae is not entirely clear . Some <unk> have <unk> , <unk> , and <unk> vertebrae at different points in the same <unk> column . Other taxa have intermediate morphologies that do not fit into any category . <unk> is no longer recognized as a group , but <unk> is still considered valid . Below is a simplified taxonomy of <unk> showing currently recognized groups : 
- Class <unk> 
- Order <unk> 
- <unk> <unk> 
- Family <unk> ( <unk> ) 
- Family <unk> 
- Family <unk> 
- <unk> <unk> 
- <unk> <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- <unk> <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- <unk> <unk> 
- <unk> <unk> 
- <unk> <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> ( placement is uncertain ) 
- Family <unk> 
- <unk> <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- <unk> <unk> 
- <unk> <unk> ( <unk> ) 
- Family <unk> 
- Family <unk> 
- Family <unk> 
- <unk> <unk> 
- <unk> <unk> 
- <unk> <unk> 
- <unk> <unk> 
- <unk> <unk> 
- <unk> <unk> 
- 
- = = = Phylogeny = = = 
- 
- In one of the earliest phylogenetic analyses of the group , <unk> ( 1983 ) recognized five characteristics that made <unk> a clade : a bone at the back of the skull , the <unk> , is connected to another bone on the underside of the skull , the <unk> ; large openings called <unk> <unk> are present between the <unk> ; the <unk> ( a bone involved in hearing ) is connected to the <unk> and projects upward ; the <unk> , a bone in the pectoral <unk> , is thin ; and part of the <unk> called the <unk> <unk> to the neural arch . Additional features were given by Godfrey et al . ( 1987 ) , including the contact between the <unk> and <unk> at the back of the skull , small projections ( <unk> processes ) on the ribs , and a pelvic <unk> with each side having a single <unk> blade . These shared characteristics are called <unk> . 
- <unk> are placed as basal <unk> in phylogenetic analyses , with their exact positioning varying between studies . Depending on the classification of modern amphibians , they are either included in the crown group <unk> or the stem of <unk> . Crown @-@ group <unk> are descendants of the most recent common ancestor of all living <unk> and stem <unk> are forms that are outside the crown group . Modern amphibians have recently been suggested as descendants of <unk> , which would place them within crown <unk> . Below is a cladogram from <unk> et al . ( 2003 ) placing <unk> within crown <unk> : 
- Other studies place modern amphibians as the descendants of <unk> and place <unk> in a more basal position within the stem of <unk> . Below is a cladogram from <unk> and <unk> ( 1999 ) placing <unk> outside crown <unk> : 
- Most phylogenetic analyses of <unk> interrelationships focus on individual families . One of the first broad @-@ scale studies of <unk> phylogeny was conducted by paleontologist Andrew <unk> in 1990 . A 2007 study made a " <unk> " of all <unk> families , combining the family @-@ level trees of previous studies . The following cladogram is modified from <unk> et al . ( 2007 ) : 
- 1 <unk> , 2 <unk> , 3 <unk> , 4 <unk> , 5 <unk> , 6 <unk> , 7 <unk> , 8 <unk> , 9 <unk> , 10 <unk> , 11 <unk> , 12 <unk> , 13 <unk> , 14 <unk> 
- The most basal group of <unk> is the superfamily <unk> . <unk> have several primitive or <unk> features , including a single <unk> <unk> and a bone called the <unk> that is absent in other <unk> . <unk> include the Late <unk> genus <unk> and the family <unk> . <unk> has also been included in <unk> , and is the oldest known <unk> family . <unk> <unk> is the oldest species , having been present over 330 million years ago during the <unk> stage of the Early <unk> . Recent analyses place <unk> outside <unk> in a more derived position . Other primitive <unk> include <unk> and <unk> . <unk> and <unk> , both described in 2005 from <unk> , are also primitive yet come from the Late <unk> . They are almost 40 million years younger than other basal <unk> , implying a long ghost lineage of species that are not yet known in the fossil record . 
- In 2000 , paleontologists Adam <unk> and Anne Warren produced a revised phylogeny of more derived <unk> , naming several new clades . Two major clades were <unk> and <unk> . <unk> includes the <unk> that were once called <unk> and includes two subfamilies , the <unk> and the <unk> . <unk> include small , mostly terrestrial <unk> that may be the ancestors of modern amphibians . <unk> include larger <unk> like <unk> . The second major clade , <unk> , includes most <unk> <unk> , as well as some <unk> groups . Within <unk> are the superfamily <unk> and the most derived <unk> , the <unk> . 
- <unk> and Warren also named <unk> , a clade of small aquatic <unk> from the <unk> , <unk> , and <unk> . They placed <unk> within <unk> , but more recent studies disagree on their position . For example , a 2007 study places them even more basal than <unk> , while a 2008 study keeps them as basal <unk> . 
- Within <unk> , <unk> and Warren erected two major clades : <unk> and <unk> . <unk> include large <unk> <unk> like <unk> with flat heads and eyes near the back of the skull . <unk> include a diversity of <unk> , including large marine <unk> , aquatic <unk> , <unk> that survived into the Cretaceous , and <unk> with eyes near the front of their heads . In 2000 , paleontologists <unk> <unk> and Andrew <unk> named a third major clade of <unk> , the <unk> . This group included more primitive <unk> that could not be placed in either <unk> or <unk> , and included groups like <unk> , <unk> , and <unk> . While <unk> and <unk> are still widely used , <unk> is not often supported as a true clade in recent analyses . <unk> and <unk> are now grouped with <unk> , but <unk> are still considered to be a primitive family of <unk> . 
- A new phylogeny of <unk> was offered by paleontologist <unk> <unk> in 2013 . It supported many of the clades that were found by <unk> and Warren , but it did not find support for their division of derived <unk> into <unk> and <unk> . <unk> were found to be more closely related to <unk> than to <unk> , which were grouped with <unk> . The clade including <unk> and <unk> was named <unk> . In addition , <unk> named the clade containing all <unk> except <unk> <unk> and reinstated the name <unk> for the clade containing all <unk> except <unk> and <unk> . Below is the cladogram from <unk> 's analysis : 
- 
- = = = Relationship to modern amphibians = = = 
- 
- Modern amphibians ( frogs , <unk> , and <unk> ) are classified in <unk> . <unk> appear to have arisen in the <unk> . Molecular clock estimates place the first <unk> in the Late <unk> , but the first member of <unk> ( frogs and <unk> , but not <unk> ) is estimated to have appeared in the Middle <unk> using the same technique . Using fossil evidence , there are three main theories for the origin of modern amphibians . 
- One is that they evolved from <unk> <unk> . Another is that they evolved from <unk> , most likely the <unk> . A third hypothesis is that <unk> descended from <unk> and frogs and <unk> evolved from <unk> . 
- Recently , the theory that <unk> were the ancestors of all <unk> has gained wide support . The skull morphology of some small <unk> has been compared to those of modern frogs and <unk> , but the presence of <unk> , <unk> teeth in small , <unk> or immature <unk> has been cited as the most convincing argument in favor of the <unk> origin of <unk> . <unk> in <unk> and many <unk> <unk> , <unk> teeth have <unk> tips and bases . During the development of most <unk> , teeth begin to <unk> at their tips . <unk> normally proceeds downward to the base of the tooth , but <unk> from the tip stops abruptly in <unk> teeth . <unk> resumes at the base , leaving an area in the center of the tooth <unk> . This pattern is seen in living amphibians and fossils . 
- The <unk> family <unk> is thought to be most closely related to <unk> . In 2008 , an <unk> called <unk> <unk> was named from Texas and was nicknamed the " <unk> " for its frog @-@ like head and <unk> @-@ like body . It was thought to be the most closely related <unk> to <unk> and was placed as the sister taxon of the group in a phylogenetic analysis . Another species of <unk> called <unk> <unk> is now thought to be even more closely related to <unk> . Unlike <unk> , <unk> was known since 1969 , and the presence of <unk> teeth in its jaws has led some paleontologists to conclude soon after its naming that it was a relative of modern amphibians . It was first described as a " <unk> " , and the specific name <unk> means " connecting " in reference to its inferred transitional position between <unk> and <unk> . The structure of its <unk> , a disk @-@ like membrane that functions like an ear drum , is similar to that of frogs and has also been used as evidence for a close relationship . Other features including the shape of the palate and the back of the skull , the short ribs , and the smooth skull surface also point to it being a closer relative of <unk> than is <unk> . Below is a cladogram modified from <unk> and <unk> ( 2010 ) showing the relationships of <unk> , <unk> , and <unk> : 
- 
- = = <unk> = = 
- 
- 
- = = = Feeding = = = 
- 
- Although the earliest <unk> were primarily <unk> , they had the ability to feed on land . Later , <unk> and <unk> , some well adapted to terrestrial life , also fed on land . Some <unk> became better adapted toward life in water , and shifted their diets toward aquatic organisms . The first primarily aquatic <unk> were <unk> in the <unk> . <unk> and <unk> became independently aquatic and also returned to this type of feeding . 
- Most aquatic <unk> have flattened heads . When feeding , they probably opened their mouths by lifting their skulls instead of lowering their lower jaws . The jaw mechanics of the <unk> <unk> is well known , and is one of the most highly adapted . <unk> is thought to have lifted its skull to around 50 ° above horizontal through the <unk> of the <unk> @-@ <unk> joint between the <unk> <unk> of the skull and the <unk> <unk> of the neck . As the skull is raised , the <unk> bone pushes forward and causes the lower jaw to <unk> outward . Other <unk> probably also lifted their skulls , but they are not as well adapted for such movement . <unk> Watson was the first to suggest skull lifting as a means of feeding in <unk> . He envisioned that <unk> , a much larger <unk> than <unk> , was able to make the same movement . <unk> <unk> <unk> also supported the idea in 1959 , suggesting that <unk> also fed in this way . At the time it was thought that these <unk> lifted their heads with strong jaw muscles , but it is now thought that they used larger muscles in the neck that were attached to the large pectoral <unk> . <unk> , a close relative of <unk> , also has a <unk> skeleton that muscles may have attached to . <unk> has very small teeth and a large area for muscle attachment behind the skull , suggesting that it could <unk> feed by rapidly opening its mouth . 
- Unlike <unk> <unk> , terrestrial <unk> have skulls that are adapted for biting land @-@ living prey . The <unk> between the bones of the skull in the <unk> <unk> are able to withstand a high degree of compression . <unk> forces would have been experienced when biting down on prey . Earlier aquatic <unk> and <unk> ancestors differ from <unk> like <unk> in that their skulls were also built to withstand tension . This tension would have been experienced during <unk> feeding underwater . <unk> like <unk> were among the first <unk> that were almost exclusively terrestrial and fed by biting . 
- 
- = = = Reproduction = = = 
- 
- <unk> , like all amphibians , reproduced in aquatic environments . Most <unk> probably reproduced through external fertilization . Like most living frogs , female <unk> would have laid masses of eggs in water while males released sperm to <unk> them . Several fossils were described from the Early <unk> of Texas in 1998 that may be egg masses of <unk> <unk> . They were the first known fossils of amphibian eggs . The fossils consist of small disks with thin membranes that are probably <unk> membranes and <unk> @-@ like areas surrounding them that are most likely <unk> coatings . They are attached to plant fossils , suggesting that these <unk> laid eggs on aquatic plants much like modern frogs . The <unk> membranes show that the eggs were laid by amphibians , not fish ( their eggs lack <unk> ) , but the type of amphibian that laid them cannot be known because no body fossils are preserved with the eggs . The eggs are thought to be from <unk> because they are likely to be close relatives of modern amphibians , and probably had similar reproductive strategies . They are also the most common amphibians from the deposit in which the eggs were found . 
- One <unk> , the <unk> <unk> , may have <unk> young in an area between the gills called the <unk> <unk> . Small bones belonging to younger <unk> individuals have been found in these pouches . The living Darwin 's Frog is also a mouth <unk> and would be the closest modern <unk> to <unk> if it cared for its young in this way . An alternative possibility is that <unk> was <unk> , eating its young like many amphibians do today . If this was the case , the bones of these smaller individuals were originally located in the throat and were pushed into the <unk> <unk> as the animal <unk> . 
- Body impressions of Early <unk> <unk> from Pennsylvania suggest that some terrestrial <unk> mated on land like some modern amphibians . They reproduced through internal fertilization rather than mating in water . The presence of three individuals in one block of sandstone shows that the <unk> were gregarious . The head of one individual rests under the tail of another in what may be a courtship display . Internal fertilization and similar courtship behavior are seen in modern <unk> . 
- 
- = = = Growth = = = 
- 
- While most types of <unk> are distinguished on the basis of features in mature specimens , several are known from juvenile and <unk> specimens . <unk> is seen in <unk> , <unk> , and <unk> , with aquatic larvae developing into adults capable of living on land . Several types of <unk> do not fully <unk> , but retain features of juveniles such as gills and small body size in what is known as <unk> . <unk> and the <unk> <unk> were also <unk> because they retained gills , but they are only known from adult specimens . 
- <unk> larvae are often distinguished by poorly developed bones and the presence of a <unk> apparatus , a series of bones that gills would attach to in life . However , some fully mature <unk> also possess <unk> bones but did not have external gills . A dense covering of scales is also seen in larvae and adults . Major body changes occur in <unk> , including the <unk> and strengthening of skull bones , the <unk> of <unk> bones , and an increase in body size . 
- <unk> like <unk> are known from both large adult specimens and small larvae , showing an extreme change in body shape . In these species , the shape and proportions of skull bones change in the early stages of development . The ornamentation on the surface of the skull roof also develops at this time . Small , regularly spaced pits are the first to form , followed by larger ridges . As development continues , the external gills disappear . Small teeth that once covered the palate are lost . The <unk> skeleton does not develop at the same rate as the skull , with ossification ( the replacement of cartilage by bone ) happening more slowly . <unk> and limb bones are poorly developed , ribs and fingers are absent in the early stages , and the <unk> and <unk> are entirely absent through most of development . Once maturity is reached , most bones have fully formed and growth rate slows . The bones of some <unk> like <unk> show growth marks , possibly an indication that growth rate varied with the change in seasons . <unk> of <unk> like <unk> and <unk> show that individuals grew larger past maturity . The oldest individuals usually have more <unk> on their skulls with deeper <unk> . 
- One group of <unk> , the <unk> , is also known from <unk> specimens . <unk> like <unk> and <unk> are represented by many fossils preserving skin and external gills . An entire growth series is exhibited in the wide range of sizes among specimens , but the lack of <unk> adapted adult forms suggests that these <unk> were <unk> . Unlike other <unk> , their <unk> skeletons developed quickly but were still partly <unk> when fully mature . Adults likely had an aquatic lifestyle similar to juveniles . Recently , large specimens of <unk> <unk> were described with adaptations toward a terrestrial lifestyle , indicating that not all <unk> were <unk> . 
- While most <unk> are aquatic in early stages of life , most <unk> appear to have been terrestrial in their juvenile stage . Like other <unk> <unk> , adult <unk> were adapted to a <unk> lifestyle . Their bones are not highly developed for movement on land . The cross @-@ <unk> thickness of limb bones in adult <unk> shows that they could not withstand the stress of terrestrial <unk> . <unk> individuals have bones that are thick enough to withstand this stress , and could probably move about on land . To maintain a terrestrial lifestyle , a <unk> 's limb bones would have to <unk> with positive <unk> , meaning that they would grow at a greater rate than the rest of the body . This is not the case in <unk> , meaning that as their bodies grew larger they became less adapted toward a terrestrial lifestyle . 
- 
- = = = <unk> = = = 
- 
- <unk> and other early <unk> have rounded <unk> <unk> in the back of the skull that project into the cheek region . In life , the <unk> notch would have been covered by a membrane called the <unk> , which is seen as a disk @-@ like area in living frogs . The <unk> is involved in hearing , and is similar to the ear drum of more advanced <unk> . It was traditionally thought that the <unk> developed very early in <unk> evolution as a hearing organ and progressed to form the ear drum of <unk> . Thus , <unk> possessed a hearing system supposedly ancestral to that of living amphibians and reptiles . 
- <unk> and all other living <unk> have a rod @-@ like bone called the <unk> that aids in hearing by transferring <unk> from the ear drum — or <unk> <unk> — to the inner ear . <unk> also have a <unk> , which projects into the <unk> cavity . The <unk> likely evolved from the <unk> of lobe @-@ <unk> fishes . The positioning of the <unk> and the shape of the <unk> region suggests that the <unk> of <unk> and frogs are <unk> , but the <unk> of these amphibians are no longer considered <unk> with the hearing systems of reptiles , birds , and mammals . Therefore , ear structures in <unk> were not ancestral to those of all other <unk> . 
- The ability of the <unk> and <unk> to effectively transmit <unk> is called <unk> matching . Early <unk> like <unk> have thick <unk> with poor <unk> matching , so it is now thought that they were not used for hearing . Instead , these thick <unk> may have functioned to support the tissue that covers the <unk> notch . Early <unk> like <unk> could not hear airborne sound but would have been able to detect vibration in the ground . Later <unk> like <unk> had <unk> regions adapted to hearing . <unk> has a structure in the inner ear called the <unk> <unk> , which is also seen in frogs and is associated with hearing . Its <unk> is also a better <unk> of sound . The hearing system of <unk> and related <unk> was able to detect airborne sound and may have been ancestral to that of living amphibians . 
- 
- 
- = <unk> de Bayeux = 
- 
- <unk> de Bayeux ( <unk> 1121 to <unk> ) was a medieval English <unk> and archdeacon in the Diocese of York . A relative of <unk> , the Archbishop of York , <unk> probably owed his ecclesiastical positions to this relative . After <unk> 's death , <unk> was opposed to one of the candidates for the archbishopric , William <unk> , and worked to secure <unk> 's deposition and replacement by Henry <unk> . After <unk> 's death in <unk> , <unk> tried to prevent the return of <unk> , but these attempts were unsuccessful . When <unk> died suddenly in 1154 , <unk> was accused of murdering the newly returned archbishop . Although he was never convicted of the murder in either a secular or an ecclesiastical court , he was stripped of his clerical status and became a <unk> before <unk> . He died after <unk> , perhaps even after 1194 . 
- 
- = = Early life = = 
- 
- <unk> was first mentioned in the historical record between 1121 and 1128 when he appears in a charter , which although likely a forgery , probably contains an authentic witness list . This document lists him as " <unk> archdeacon " , which means that he probably held the <unk> of Richmond . He was the nephew of <unk> , who was Archbishop of York from <unk> to 1140 . <unk> he owed his position as archdeacon to his uncle and was probably appointed at a young age . A charter of <unk> 's , dating to around 1138 , names <unk> explicitly as <unk> 's nephew . 
- 
- = = Opposition to William <unk> = = 
- 
- <unk> was opposed to the election of William <unk> as Archbishop of York and supported William 's rival and successor Henry <unk> . Although he remained a supporter of <unk> after <unk> , he did oppose <unk> 's <unk> in <unk> Abbey , where <unk> had deposed one abbot and appointed another . In <unk> , <unk> deposed <unk> 's choice as abbot of <unk> and appointed another abbot . Originally , <unk> had supported Elias <unk> , <unk> 's choice for abbot , but then changed his stance and helped with the deposition . 
- After <unk> 's death in <unk> , <unk> was opposed to William 's return as archbishop , but was unsuccessful in his attempts to prevent William 's <unk> . William died a week after his return to York , however , and <unk> , along with Robert of <unk> , the Dean of York , secured the quick election of the new archbishop , Roger de <unk> L <unk> . 
- 
- = = <unk> accusations = = 
- 
- <unk> was accused of murdering William , specifically by poisoning him through the communion <unk> . A fellow <unk> , <unk> , who had been a <unk> of the deceased archbishop , brought murder charges against <unk> . <unk> obtained a hearing on the charges at a royal council presided over by King Stephen of England at <unk> in 1154 , but Stephen 's subsequent death prevented a resolution . <unk> attempted to have the trial switched to an ecclesiastical court and was supported in his efforts by Archbishop Theobald of Canterbury . A trial was finally held in <unk> and <unk> 's <unk> did not produce any witnesses , but <unk> was unable to prove his innocence , prompting the transfer of the case to a papal court . No record of any judgment exists , but <unk> apparently appeared before two <unk> , Adrian IV and Alexander III . A further appeal to the papal court was referred to papal judges @-@ delegate between 1175 and 1180 . 
- The case attracted commentary by two contemporary writers . John of Salisbury , who was a secretary for Theobald , added information about <unk> in a letter to Alexander III on unrelated business . In the section of the letter , John pointed out to the pope that no matter what others might say about <unk> , he had failed to secure other clergy willing to <unk> that he was innocent . Another contemporary , Gilbert Foliot , who was Bishop of Hereford , wrote to the pope to remind him that although <unk> 's <unk> had offered to prove his accusations by undergoing a trial by ordeal , this was essentially meaningless since canon law forbade the clergy from the ordeal . 
- 
- = = Later life and death = = 
- 
- <unk> was no longer archdeacon by <unk> , as his successor is attested by that point . <unk> , however , continued to call himself " archdeacon " even though he held land as a secular lord , including lands in Lacy and <unk> . He also acted as a steward for Hugh de <unk> . <unk> was still alive in <unk> , as he was a witness to a document at York then , and may have been alive as late as 1194 , when Hugh <unk> was responsible for the farm of <unk> 's lands , as the record of that transaction in the <unk> roll is unclear if <unk> was alive at that time or dead . 
- <unk> had two sons , William de Bayeux and <unk> de <unk> . <unk> was a benefactor to a number of monasteries , including Drax Priory , <unk> Priory and <unk> Priory . He also gave land to a hospital in York and to the Templars and Hospitallers . 
- 
- 
- = Dvorak technique = 
- 
- The Dvorak technique ( developed between 1969 and 1984 by Vernon Dvorak ) is a widely used system to estimate tropical cyclone intensity ( which includes tropical depression , tropical storm , and hurricane / typhoon / intense tropical cyclone <unk> ) based solely on visible and infrared satellite images . Within the Dvorak satellite strength estimate for tropical cyclones , there are several visual patterns that a cyclone may take on which define the upper and lower bounds on its intensity . The primary patterns used are curved band pattern ( <unk> @-@ <unk> ) , shear pattern ( <unk> @-@ <unk> ) , central dense <unk> ( <unk> ) pattern ( <unk> @-@ <unk> ) , central cold cover ( <unk> ) pattern , banding eye pattern ( <unk> @-@ <unk> ) , and eye pattern ( <unk> - <unk> ) . 
- Both the central dense <unk> and embedded eye pattern use the size of the <unk> . The <unk> pattern <unk> start at <unk> , equivalent to minimal tropical storm intensity ( 40 mph , 65 km / h ) . The shape of the central dense <unk> is also considered . The eye pattern utilizes the <unk> of the cloud tops within the surrounding mass of thunderstorms and contrasts it with the temperature within the eye itself . The larger the temperature difference is , the stronger the tropical cyclone . Once a pattern is identified , the storm features ( such as length and <unk> of banding features ) are further analyzed to arrive at a particular T @-@ number . The <unk> pattern indicates little development is occurring , despite the cold cloud tops associated with the quickly evolving feature . 
- Several agencies issue Dvorak intensity numbers for tropical cyclones and their precursors , including the National Hurricane Center 's Tropical Analysis and <unk> Branch ( <unk> ) , the <unk> / <unk> <unk> Analysis Branch ( <unk> ) , and the Joint Typhoon Warning Center at the Naval Meteorology and <unk> Command in Pearl Harbor , Hawaii . 
- 
- = = Evolution of the method = = 
- 
- The initial development of this technique occurred in 1969 by Vernon Dvorak , using satellite pictures of tropical cyclones within the northwest Pacific ocean . The system as it was initially conceived involved pattern matching of cloud features with a development and decay model . As the technique matured through the 1970s and 1980s , measurement of cloud features became dominant in defining tropical cyclone intensity and central pressure of the tropical cyclone 's low @-@ pressure area . Use of infrared satellite imagery led to a more objective assessment of the strength of tropical cyclones with eyes , using the cloud top temperatures within the eyewall and contrasting them with the warm temperatures within the eye itself . <unk> on short term intensity change are used less frequently than they were back in the 1970s and 1980s . The central pressures assigned to tropical cyclones have required modification , as the original estimates were 5 @-@ 10 hPa ( 0 @.@ 15 @-@ 0 @.@ 29 inHg ) too low in the Atlantic and up to 20 hPa ( 0 @.@ 59 inHg ) too high in the northwest Pacific . This led to the development of a separate wind @-@ pressure relationship for the northwest Pacific , devised by Atkinson and Holliday in 1975 , then modified in 1977 . 
- As human analysts using the technique lead to <unk> biases , efforts have been made to make more objective estimates using computer programs , which have been aided by higher @-@ resolution satellite imagery and more powerful computers . Since tropical cyclone satellite patterns can <unk> over time , automated techniques use a six @-@ hour averaging period to lead to more reliable intensity estimates . Development of the objective Dvorak technique began in 1998 , which performed best with tropical cyclones that had eyes ( of hurricane or typhoon strength ) . It still required a manual center placement , keeping some <unk> within the process . By 2004 , an advanced objective Dvorak technique was developed which utilized banding features for systems below hurricane intensity and to <unk> determine the tropical cyclone 's center . A central pressure bias was uncovered in 2004 relating to the slope of the <unk> and cloud top temperatures which change with latitude that helped improve central pressure estimates within the objective technique . 
- 
- = = Details of the method = = 
- 
- In a developing cyclone , the technique takes advantage of the fact that cyclones of similar intensity tend to have certain characteristic features , and as they strengthen , they tend to change in appearance in a predictable manner . The structure and organization of the tropical cyclone are tracked over 24 hours to determine if the storm has weakened , maintained its intensity , or strengthened . Various central cloud and banding features are compared with <unk> that show typical storm patterns and their associated intensity . If infrared satellite imagery is available for a cyclone with a visible eye pattern , then the technique utilizes the difference between the temperature of the warm eye and the surrounding cold cloud tops to determine intensity ( colder cloud tops generally indicate a more intense storm ) . In each case a " T @-@ number " ( an abbreviation for Tropical Number ) and a Current <unk> ( <unk> ) value are assigned to the storm . These measurements range between 1 ( minimum intensity ) and 8 ( maximum intensity ) . The T @-@ number and <unk> value are the same except for weakening storms , in which case the <unk> is higher . For weakening systems , the <unk> is held as the tropical cyclone intensity for 12 hours , though research from the National Hurricane Center indicates that six hours is more reasonable . The table at right shows the approximate surface wind speed and sea level pressure that corresponds to a given T @-@ number . The amount a tropical cyclone can change in strength per 24 ‑ hour period is limited to 2 @.@ 5 T @-@ numbers per day . 
- 
- = = = Pattern types = = = 
- 
- Within the Dvorak satellite strength estimate for tropical cyclones , there are several visual patterns that a cyclone may take on which define the upper and lower bounds on its intensity . The primary patterns used are curved band pattern ( <unk> @-@ <unk> ) , shear pattern ( <unk> @-@ <unk> ) , central dense <unk> ( <unk> ) pattern ( <unk> @-@ <unk> ) , banding eye pattern ( <unk> @-@ <unk> ) , eye pattern ( <unk> - <unk> ) , and central cold cover ( <unk> ) pattern . Both the central dense <unk> and embedded eye pattern utilize the size of the <unk> . The <unk> pattern <unk> start at <unk> , equivalent to minimal tropical storm intensity ( 40 miles per hour ( 64 km / h ) ) . The shape of the central dense <unk> is also considered . The farther the center is <unk> into the <unk> , the stronger it is deemed . Tropical cyclones with maximum sustained winds between 65 miles per hour ( 105 km / h ) and 100 miles per hour ( 160 km / h ) can have their center of <unk> obscured by cloudiness within visible and infrared satellite imagery , which makes diagnosis of their intensity a challenge . 
- The <unk> pattern , with its large and quickly developing mass of thick <unk> clouds spreading out from an area of convection near a tropical cyclone center within a short time frame , indicates little development . When it develops , rainbands and cloud lines around the tropical cyclone weaken and the thick cloud shield <unk> the circulation center . While it resembles a <unk> pattern , it is rarely seen . 
- The eye pattern utilizes the <unk> of the cloud tops within the surrounding mass of thunderstorms and contrasts it with the temperature within the eye itself . The larger the temperature difference is , the stronger the tropical cyclone . Winds within tropical cyclones can also be estimated by tracking features within the <unk> using rapid scan <unk> satellite imagery , whose pictures are taken minutes apart rather than every half @-@ hour . 
- Once a pattern is identified , the storm features ( such as length and <unk> of banding features ) are further analyzed to arrive at a particular T @-@ number . 
- 
- = = Usage = = 
- 
- Several agencies issue Dvorak intensity numbers for tropical cyclones and their precursors . These include the National Hurricane Center 's Tropical Analysis and <unk> Branch ( <unk> ) , the National <unk> and <unk> Administration 's <unk> Analysis Branch ( <unk> ) , and the Joint Typhoon Warning Center at the Naval Pacific Meteorology and <unk> Center in Pearl Harbor , Hawaii . 
- The National Hurricane Center will often quote Dvorak T @-@ numbers in their tropical cyclone products . The following example is from discussion number 3 of Tropical Depression 24 ( eventually Hurricane <unk> ) of the 2005 Atlantic hurricane season : 
- <unk> <unk> <unk> <unk> <unk> IN <unk> A <unk> <unk> <unk> <unk> <unk> <unk> / 35 <unk> . <unk> ... <unk> THE <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> 12 <unk> <unk> THE <unk> <unk> . <unk> ... THE <unk> <unk> <unk> <unk> <unk> <unk> <unk> 30 <unk> . 
- Note that in this case the Dvorak T @-@ number ( in this case <unk> ) was simply used as a guide but other factors determined how the NHC decided to set the system 's intensity . 
- The Cooperative Institute for Meteorological <unk> Studies ( <unk> ) at the University of Wisconsin – Madison has developed the <unk> Dvorak <unk> ( <unk> ) . This is a modified version of the Dvorak technique which uses computer algorithms rather than <unk> human interpretation to arrive at a <unk> number . This is generally not implemented for tropical depressions or weak tropical storms . The China Meteorological Agency ( <unk> ) is expected to start using the standard 1984 version of Dvorak in the near future . The Indian Meteorological Department ( <unk> ) prefers using visible satellite imagery over infrared imagery due to a perceived high bias in estimates derived from infrared imagery during the early morning hours of convective maximum . The Japan Meteorological Agency ( JMA ) uses the infrared version of Dvorak over the visible imagery version . Hong Kong Observatory and JMA continue to utilize Dvorak after tropical cyclone landfall . Various centers hold on to the maximum current intensity for 6 – 12 hours , though this rule is broken when rapid weakening is obvious . 
- Citizen science site Cyclone Center uses a modified version of the Dvorak technique to <unk> post @-@ 1970 tropical weather . 
- <unk> <unk> of Selected Tropical Storms and Associated T @-@ Number 
- 
- = = <unk> and disadvantages = = 
- 
- The most significant benefit of the use of the technique is that it has provided a more complete history of tropical cyclone intensity in areas where aircraft reconnaissance is neither possible nor routinely available . <unk> estimates of maximum sustained wind are currently within 5 miles per hour ( 8 @.@ 0 km / h ) of what aircraft are able to measure half of the time , though the assignment of intensity of systems with strengths between moderate tropical @-@ storm force ( 60 miles per hour ( 97 km / h ) ) and weak <unk> or typhoon @-@ force ( 100 miles per hour ( 160 km / h ) ) is the least certain . Its overall precision has not always been true , as <unk> in the technique led to intensity changes between 1972 and 1977 of up to 20 miles per hour ( 32 km / h ) . The method is internally consistent in that it <unk> rapid increases or decreases in tropical cyclone intensity . Some tropical cyclones <unk> in strength more than the 2 @.@ 5 T numbers per day limit allowed by the rule , which can work to the technique 's disadvantage and has led to occasional abandonment of the constraints since the 1980s . Systems with small eyes near the limb , or edge , of a satellite image can be biased too weakly using the technique , which can be resolved through use of polar @-@ orbiting satellite imagery . Subtropical cyclone intensity cannot be determined using Dvorak , which led to the development of the <unk> @-@ <unk> technique in 1975 . <unk> undergoing extratropical transition , losing their thunderstorm activity , see their <unk> underestimated using the Dvorak technique . This led to the development of the Miller and <unk> extratropical transition technique which can be used under these circumstances . 
- 
- 
- = New York State Route <unk> = 
- 
- New York State Route <unk> ( NY <unk> ) was a state highway in central New York in the United States . It served as a connector between NY 31 , its parent route , in the Cayuga County village of <unk> and NY 5 in the Onondaga County town of Elbridge . NY <unk> was assigned c . 1933 , replacing New York State Route 293 , a route assigned as part of the 1930 renumbering of state highways in New York . The NY <unk> designation was removed in 1980 and replaced by County Route <unk> ( CR <unk> ) in Cayuga County and CR 99 in Onondaga County . 
- 
- = = Route description = = 
- 
- NY <unk> began at an intersection with its parent route , NY 31 , in the Cayuga County village of <unk> . The highway went eastward , intersecting with NY 34 less than 0 @.@ 1 miles ( 0 @.@ 2 km ) later . Much of <unk> was <unk> , with the highway passing residential homes and businesses as it progressed eastward through the village . The highway intersected with CR <unk> before leaving <unk> and entering the town of Brutus as Brutus Road . 
- In Brutus , NY <unk> continued to the east through the rural town , intersecting CR <unk> and passing the <unk> Rural Cemetery before turning to the southeast . After a short distance , the highway went through an isolated area of homes , where it intersected with CR 14 and CR <unk> . NY <unk> continued on , intersecting several local highways before crossing into Onondaga County and terminating at an intersection with NY 5 in the town of Elbridge . 
- 
- = = History = = 
- 
- What became NY <unk> was originally designated as NY 293 as part of the 1930 renumbering of state highways in New York . NY 293 was renumbered to NY <unk> c . 1933 , allowing the NY 293 designation to be transferred to another highway in Orange County . NY <unk> remained unchanged until January 2 , 1980 , when the NY <unk> designation was removed . 
- <unk> and maintenance of NY <unk> 's former routing was gradually transferred to the counties it went through , namely Cayuga and Onondaga . The first section to be transferred was the portion within Onondaga County , which was given to the county on April 1 , 1980 , and designated as CR 99 . The Cayuga County section of former NY <unk> was transferred to the county one year later on April 1 , 1981 , and designated as CR <unk> for the state route that preceded it . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Ben Amos = 
- 
- Benjamin Paul " Ben " Amos ( born 10 April 1990 ) is an English professional footballer who plays as a goalkeeper for Bolton Wanderers . Born in Macclesfield , Cheshire , Amos began his career with Crewe Alexandra 's youth academy , but joined Manchester United at the age of 11 . He has spent time on loan at Peterborough United , Molde , and Oldham Athletic . Additionally , Amos is an England youth international , having represented his country at every level from Under @-@ 16 to Under @-@ 21 . 
- 
- = = Club career = = 
- 
- 
- = = = Crewe Alexandra = = = 
- 
- Amos was born in Macclesfield , Cheshire and was a member of the Crewe Alexandra academy until he was released at the age of 10 . While at Crewe , he also played for another local team , <unk> United , as a centre midfielder . One year later , Amos was playing for his local team against the team at the top of the table , and they needed to win the match to win the league . Amos ' team 's goalkeeper was injured during the game , and as the tallest on the team , Amos was put in goal ; however , he had also been the team 's regular penalty taker all season , so when they were awarded a penalty , he went all the way up the pitch to take the kick . He scored , and his team went on to win the game 3 – 2 , together with the league title . After the game , his parents told him that a Manchester United scout had been watching him and that he had been invited for trials . Amos joined Manchester United at the age of 11 . 
- 
- = = = Manchester United = = = 
- 
- In his first season at Manchester United , Amos became a regular starter for the club 's Under @-@ 13 team , playing in 19 out of 27 matches in the 2001 – 02 season . Amos was named as an unused substitute for the Under @-@ 18 side for the first time on 8 January 2005 , for a league game against Manchester City . His first appearance for the Under @-@ <unk> came exactly nine months later , on 8 October 2005 , coming on as a substitute for Danny Rose after starting goalkeeper Ron @-@ Robert <unk> was sent off in a 2 – 0 defeat to Bolton Wanderers . He was regularly named as an unused substitute during the 2005 – 06 season – including for two reserve team matches – but became a frequent starter for the Under @-@ <unk> in 2006 – 07 after signing a trainee contract in July 2006 . However , he missed the final of the 2006 – 07 FA Youth Cup with a <unk> shoulder . 
- He retained his place in the Under @-@ 18 team for 2007 – 08 , in addition to making his debut for the reserve team against <unk> Athletic on 7 November 2007 , and during the season he impressed enough to be selected to go on the first @-@ team 's 2008 summer tour of South Africa . Amos was named as a substitute for all three matches of the tour , but did not play . <unk> route back from South Africa , United stopped off in Nigeria to play against Portsmouth on 27 July 2008 , with Amos replacing <unk> <unk> after 76 minutes . He made his competitive first @-@ team debut on 23 September 2008 in a 3 – 1 win at home to Middlesbrough in the third round of the League Cup . On 14 December 2008 , Amos travelled to Japan with the Manchester United squad for the 2008 FIFA Club World Cup , having been called up as a late replacement for Ben Foster , who had suffered a hand injury while training . 
- 
- = = = <unk> to Peterborough and Molde = = = 
- 
- On 29 October 2009 , Amos signed for the Championship team Peterborough United on a month 's loan as cover for Peterborough 's suspended first @-@ choice goalkeeper Joe Lewis . He made his only appearance on 31 October in a 2 – 1 defeat against Barnsley . After returning to Manchester United , Amos was again sent out on loan in March 2010 , this time to Norwegian side Molde <unk> , where he remained on loan until 30 June 2010 . 
- 
- = = = Return to Manchester United = = = 
- 
- Following the departure of Ben Foster from Manchester United to Birmingham City , United manager Alex Ferguson declared that Amos would be Manchester United 's third @-@ choice goalkeeper for the 2010 – 11 season behind Edwin van der <unk> and <unk> <unk> . He made his first appearance of the season on 26 October 2010 , starting in goal for United 's 3 – 2 win over Wolverhampton Wanderers in the fourth round of the League Cup . 
- In United 's final Champions League group match on 7 December 2010 , Amos was picked to start against Valencia at Old Trafford . Pablo <unk> scored Valencia 's only goal past him after 32 minutes of the match – the first goal United had conceded in the Champions League that season – as the two sides played out a 1 – 1 draw . 
- 
- = = = Loan to Oldham Athletic = = = 
- 
- With the signing of Danish goalkeeper Anders <unk> , Manchester United allowed Amos to join Oldham Athletic on loan for the remainder of the season on 7 January 2011 , although he would continue to train with Manchester United once a week . He made his debut against <unk> Town the next day and kept a clean sheet . However , three days later , he conceded all six goals in a 6 – 0 defeat at home to Southampton ; he allowed Adam <unk> 's 20 @-@ yard shot underneath his body for the second goal , and he was rounded by Lee <unk> for the sixth . 
- On 15 March 2011 , <unk> was ruled out for five weeks following a knee injury , so Amos was recalled from Oldham to cover for Edwin van der <unk> and <unk> <unk> . 
- 
- = = = Return to Manchester United = = = 
- 
- Amos made his first start of the 2011 – 12 season in a third round League Cup tie at <unk> Road against Leeds United . He kept a clean sheet as United cruised through to the next round winning 3 – 0 . He played again in the following round away at Aldershot Town , a game which United also won 3 – 0 and advanced to the quarter finals . He was in goal again for United 's League Cup game in a defeat against Championship club Crystal Palace on 30 November . It also seemed that he had moved above last season 's second choice <unk> <unk> in the pecking order , but was now third choice behind Anders <unk> and David de <unk> . He made his first Premier League start in a 2 – 0 home win against Stoke City on 31 January 2012 , keeping a clean sheet on his debut . 
- On 30 May 2012 , Amos signed a three @-@ year contract extension with Manchester United , which will keep him at the club until at least 2015 . 
- 
- = = = Loan to Hull City = = = 
- 
- On 31 July 2012 , Amos joined Championship team Hull City on a season @-@ long loan from Manchester United . Before the loan deal was completed , he had joined Hull on their pre @-@ season training camp in Portugal . He made his debut on 11 August 2012 in 7 – 6 penalty shoot @-@ out victory over Rotherham United in the first round of the 2012 – 13 Football League Cup . However , after 19 appearances , including two in the League Cup , Amos returned to Manchester United on 3 January 2013 . 
- 
- = = = Loan to Carlisle United = = = 
- 
- On 15 November 2013 , Amos joined League One team Carlisle United on a month @-@ long loan from Manchester United . 
- 
- = = = Loan to Bolton Wanderers = = = 
- 
- On 30 January 2015 he joined Championship club Bolton Wanderers on a month 's loan to provide competition for Andy Lonergan after <unk> <unk> was ruled out with an ankle injury picked up in training . On 21 February 2015 he made his Bolton debut as a substitute for the injured Lonergan as Wanderers lost 4 – 1 to Nottingham Forest at the City Ground . He played nine Championship games for Bolton before his loan expired on 4 April . 
- 
- = = = Return to Manchester United = = = 
- 
- Ben Amos returned to Manchester United after his loan spell at Bolton Wanderers , but on 10 June 2015 , it was announced on Manchester United website that Ben Amos has been released from the club along with four other players . 
- 
- = = = Bolton Wanderers = = = 
- 
- On 1 July 2015 , Amos returned to Bolton Wanderers following his release from Manchester United , signing a four @-@ year contract with the club . 
- 
- = = International career = = 
- 
- Amos is an England youth international , having played for his country at the Under @-@ 16 , Under @-@ 17 , Under @-@ 18 , Under @-@ 19 , Under @-@ 20 and Under @-@ 21 levels . He was called up to the Under @-@ 21 squad in 2011 for their friendly match against Iceland , but he was an unused substitute . 
- 
- = = Career statistics = = 
- 
- As of 1 December 2015 
- 
- = = Honours = = 
- 
- 
- = = = Club = = = 
- 
- Manchester United 
- FIFA Club World Cup ( 1 ) : 2008 
- 
- = = Personal life = = 
- 
- Amos attended <unk> High School , where he earned 11 GCSEs at grade C or above . 
- 
- 
- = Clayton <unk> = 
- 
- Clayton Edward <unk> ( born March 19 , 1988 ) is an American professional baseball pitcher for the Los Angeles Dodgers of Major League Baseball ( MLB ) . A left @-@ handed starting pitcher , <unk> has played in the major leagues since 2008 , and his career earned run average ( ERA ) and <unk> and hits per innings pitched average ( <unk> ) are the lowest among starters in the live @-@ ball era with a minimum of 1 @,@ 000 innings pitched . With his Career Hits <unk> Per Nine <unk> <unk> average ( 6 @.@ 64 ) Which is the second lowest in MLB History , a three @-@ time Cy Young Award winner , the 2014 National League Most Valuable Player and Los Angeles Dodgers All Time Leader in walks and hits per innings pitched ( 1 @.@ 01 ) and hits allowed per nine innings pitched ( 6 @.@ 64 ) , <unk> is considered by many to be the best pitcher in MLB . 
- <unk> was drafted seventh overall in the 2006 MLB draft . He worked his way through the Dodgers ' farm system in just one full season , and reached the majors at 20 years old . When he debuted in 2008 , he was the youngest player in MLB , a title he held for one full year . In 2011 , he won the pitching Triple Crown and the National League Cy Young Award , becoming the youngest pitcher to accomplish either of these feats since Dwight <unk> in 1985 . <unk> pitched a no @-@ hitter on June 18 , 2014 , becoming the 22nd <unk> to do so . Being a left @-@ handed strikeout pitcher and playing for the Los Angeles Dodgers , <unk> has often been compared to Hall of Fame pitcher Sandy <unk> . He became the first pitcher in history to lead MLB in ERA for four consecutive years when he did so in the 2011 through 2014 seasons . 
- Off the field , <unk> is an active participant in volunteer work . He and his wife , Ellen , launched " <unk> 's Challenge " and wrote the book <unk> to raise money to build an orphanage in Zambia . He has been honored with the Roberto <unk> Award and the Branch <unk> Award for his humanitarian work . 
- 
- = = Early life = = 
- 
- <unk> was born in Dallas , Texas on March 19 , 1988 . His parents divorced when he was 10 , and he was raised by his mother . He played in youth sports leagues as a child , including Little League Baseball . 
- <unk> attended nearby Highland Park High School , where he played baseball and was also the center for quarterback Matthew Stafford on the football varsity . After a growth <unk> and further development of his pitches , he established himself as an elite high school prospect in 2006 when he posted a 13 – 0 record with an earned run average ( ERA ) of 0 @.@ 77 , and recorded 139 strikeouts in 64 innings pitched . In a playoff game against Northwest High School of Justin , Texas , <unk> pitched an all @-@ strikeout perfect game . He struck out all 15 batters he faced in the game , which was shortened because of the mercy rule . He also pitched for USA Baseball 's Junior National Team in the Pan Am Championship . <unk> was selected by USA Today as " High School Baseball Player of the Year " , and was also the <unk> National Player of the Year for baseball . 
- Entering the 2006 Major League Baseball ( MLB ) draft , <unk> was considered the top high @-@ school pitcher available . The Los Angeles Dodgers selected <unk> with the seventh overall pick in the draft . He had committed to Texas A & M University , but turned down the scholarship offer to sign with the Dodgers , with a bonus estimated at $ 2 @.@ 3 million . The bonus was the largest to any Dodgers draft pick at the time , and was eventually topped by Zach Lee in the 2010 draft . 
- 
- = = Professional career = = 
- 
- 
- = = = Minor <unk> = = = 
- 
- <unk> began his career with the Gulf Coast League ( <unk> ) Dodgers . He pitched in 37 innings in which he struck out 54 batters ( walking only 5 ) , while compiling a record of 2 – 0 with a 1 @.@ 95 ERA . He featured a fastball that topped out at 96 miles per hour ( 154 km / h ) and he was rated as the top prospect in the <unk> , and the Dodgers ' second best prospect by Baseball America behind third baseman Andy <unk> . 
- <unk> was promoted to the Great Lakes <unk> in 2007 , where he recorded a record of 7 – 5 with a 2 @.@ 77 ERA . He was selected to play on the East Team in the Midwest League All @-@ Star Game and on the USA team in the All @-@ Star <unk> Game . On August 6 , he was promoted to the Double @-@ A Jacksonville Suns in the Southern League , where he produced a 1 – 2 record and 3 @.@ 65 ERA in five starts and was selected as the top prospect in the Dodgers organization heading into the 2008 season . 
- During spring training in a game against the Boston Red Sox , <unk> gained much attention for throwing a curveball to Sean Casey that started behind Casey but at the end looped into the strike zone and struck him out looking . <unk> was 0 – 3 and had a 2 @.@ 28 ERA with 47 strikeouts through <unk> ⁄ 3 innings pitched in his first stint of the year with the Suns . He was then called up to the majors on May 28 , 2008 , but optioned back to Jacksonville on July 2 . 
- <unk> pitched 18 innings during his second trip to Jacksonville ( two starts and one seven inning relief appearance ) , winning two games . During this stretch , he allowed only two runs earned runs , lowering his ERA to 1 @.@ 91 . He was recalled on July 22 . 
- 
- = = = Los Angeles Dodgers = = = 
- 
- 
- = = = = 2008 – 2010 seasons : Early career = = = = 
- 
- On May 24 , 2008 , the Dodgers bought <unk> 's minor @-@ league contract , and he was added to the active roster . <unk> Tony Jackson called <unk> 's debut the most anticipated start by a Dodgers pitcher since <unk> <unk> 's major league debut during the 1995 season . He made his debut on May 25 , starting against the St. Louis Cardinals . He struck out the first batter he faced , <unk> <unk> , the first of seven strikeouts in the game , in which he pitched six innings and allowed two runs . When he debuted , <unk> was the youngest player in MLB , a title he held for one full year . 
- <unk> won his first major league game against the Washington <unk> on July 27 , 2008 . He pitched six @-@ plus shutout innings , allowing four hits , a walk , and he struck out five . <unk> finished his rookie season 5 – 5 , with a 4 @.@ 26 ERA in 22 games ( 21 starts ) . He also pitched two innings out of the bullpen for the Dodgers in the 2008 National League Championship Series ( NLCS ) against the Philadelphia Phillies . 
- On April 15 , 2009 , <unk> pitched seven innings , striking out 13 batters while allowing only one hit ( a solo home run ) against the rival San Francisco Giants . He was the youngest <unk> to ever strikeout 13 or more batters in a game since Sandy <unk> did it in the 1955 season . On May 17 , 2009 , <unk> did not allow a hit against the Florida Marlins through 7 innings , then gave up a lead @-@ off double to Florida 's <unk> Ross . In 2009 , despite an 8 – 8 record , he led the major leagues in opposing batting average ( <unk> ) , opposing <unk> percentage ( <unk> ) , and hits per nine innings ( 6 @.@ 26 ) . He also posted an ERA of 2 @.@ 79 and 185 strikeouts . <unk> also walked 91 batters , which was second most in the National League ( NL ) . 
- <unk> made his playoff starting debut against the St. Louis Cardinals in the 2009 National League Division Series ( NLDS ) . He went 62 ⁄ 3 innings , striking out 4 , walking 1 , and ended up getting a no @-@ decision ( the Dodgers went on to win the game in the 9th inning ) . At 21 years old , he started the opener of the 2009 NLCS against the Philadelphia Phillies and was the third youngest pitcher to ever start a playoff series opener , behind only Fernando <unk> in the 1981 NLDS and Rick <unk> in the 2000 NLDS . 
- <unk> started the 2010 season by posting a 3 @.@ 07 ERA in April , but did so by walking 22 batters in 29 innings . On May 4 , he had his worst start of his career against the Milwaukee Brewers at <unk> Stadium , throwing just 57 pitches in 11 ⁄ 3 innings , while retiring only four of the 13 batters he faced — including the pitcher . He was <unk> loudly upon being pulled from the game . <unk> said after the game , " I didn 't give our team any kind of chance . It 's just not a good feeling to let your teammates down , let everybody down . It stings , it <unk> . I 've got to figure things out . " 
- <unk> rebounded his next start by pitching an 8 inning two @-@ hitter and out @-@ <unk> the then undefeated <unk> <unk> . He credited his control of the slider being the major turning point for him . Later in the season , he was suspended for five games after hitting Aaron <unk> of the Giants with a pitch in a game on July 20 . The incident occurred after both teams were given a warning following Giants ace Tim <unk> hitting Matt <unk> earlier in the game . He threw his first career complete game shutout on September 14 , 2010 also against San Francisco and finished the season with a record of 13 – 10 and a 2 @.@ 91 ERA in 32 starts , pitching <unk> ⁄ 3 innings and recording 212 strikeouts . 
- 
- = = = = 2011 season : 1st Cy Young Award = = = = 
- 
- After finishing the 2010 season strong , the Dodgers named <unk> as the Opening Day <unk> for the 2011 season . On May 29 , he pitched the second complete @-@ game shutout of his career , striking out 10 while winning a two @-@ hitter against the Florida Marlins , 8 – 0 ; he also had two singles and an <unk> , scoring twice in the game . He produced his third career shutout on June 20 , a two @-@ hit , 11 @-@ strikeout effort against the Detroit Tigers . <unk> became the first Dodgers starter to strike out the side in the 9th inning since Sandy <unk> 's perfect game . In his next start , on June 26 , <unk> pitched another complete game ( against the Los Angeles Angels of <unk> ) . He became the first <unk> starter to have back @-@ to @-@ back complete game victories since Jeff Weaver in the 2005 season and the first <unk> to have double @-@ digit strikeouts in consecutive starts since Chan @-@ Ho Park in the 2000 season . He was awarded the National League Player of the Week award for the week of June 20 – 26 as a result of those two starts . Midway through June , <unk> had amassed 32 career victories , a 3 @.@ 15 ERA and 593 career strikeouts in 568 @.@ 2 innings . According to the Elias Sports Bureau , <unk> was the first 23 @-@ year @-@ old pitcher to have that many victories , an ERA that low and an average of more than one strikeout per inning since ERA became an official statistic in 1910 . 
- <unk> was selected to the National League team for the 2011 Major League Baseball All @-@ Star Game , his first All @-@ Star selection . In the month of July , <unk> was 4 – 1 with a 2 @.@ 02 ERA and NL @-@ leading 45 strikeouts , earning him the National League Pitcher of the Month Award . On August 23 , he struck out Matt Holliday of the St. Louis Cardinals for his 200th strikeout of the season and became the 10th <unk> pitcher to record back @-@ to @-@ back 200 strikeout seasons and the first since Chan @-@ Ho Park did it in the 2001 season . 
- <unk> finished the 2011 season by leading the NL with 21 wins , <unk> strikeouts and a 2 @.@ 28 ERA , winning the NL pitching Triple Crown , the first Triple Crown winner since Jake <unk> of the 2007 San Diego Padres and the first <unk> since Sandy <unk> won it in the 1966 season . Justin <unk> of the Detroit Tigers won the American League Triple Crown the same season , marking the first major @-@ league season since 1924 to feature Triple Crown @-@ winning pitchers in both leagues . <unk> 's 21 wins were the most by a <unk> pitcher since <unk> <unk> won 23 during the 1988 season . His ERA was the lowest by a <unk> since <unk> 's 2 @.@ 03 in the 1985 season , his strikeouts were the most by a <unk> since <unk> 's 317 in 1966 and his 233 1 ⁄ 3 innings pitched were the most since Chan Ho Park pitched 234 in 2001 . Since 1965 when <unk> did it , <unk> and <unk> are only two pitchers in the National League have led the league in wins , strikeouts , ERA , and <unk> ( walks plus hits per inning pitched ) . <unk> also became just the second <unk> to have a 240 @-@ plus strikeouts in a season before the age of 24 , joining Vida Blue . 
- After the season , <unk> was awarded the Warren <unk> Award as the best left @-@ handed pitcher in 2011 , the Players Choice Award for Most Outstanding National League pitcher , the Gold <unk> Award as the top fielding pitcher in the NL and the Sporting News ( <unk> ) National League Pitcher of the Year . He was additionally selected as the starting pitcher for the <unk> NL All @-@ Star Team . On November 17 , he was honored with the National League Cy Young Award , making him the youngest Cy Young winner since Dwight <unk> of the 1985 New York Mets . He was the 8th <unk> pitcher to win the award , the first since <unk> <unk> in the 2003 season . 
- 
- = = = = 2012 season : Cy Young runner @-@ up = = = = 
- 
- On February 7 , 2012 , <unk> and the Dodgers agreed on a two @-@ year , $ 19 million contract . The contract was the second highest for a player in his first year of arbitration ( after Tim <unk> 's $ 23 million 2 @-@ year contract in 2010 ) . 
- <unk> was the Dodgers ' Opening Day starter for the second year in a row , where he pitched three innings of shutout ball against the San Diego Padres at <unk> Park before being removed from the game due to flu @-@ like symptoms . On April 27 , he was able to last through eight innings for his second win of the season against the Washington <unk> . The win was also his 12th straight home win , tying him with Ed <unk> ( June 1960 – August 1962 ) and <unk> <unk> ( September 1984 – October 1985 ) for the longest home winning streak since the Dodgers moved to Los Angeles . <unk> won the National League 's Player of the Week Award for the week of May 14 – 20 after he made two starts during that week and pitched 16 scoreless innings , including his fourth career shutout . <unk> was selected to appear in the 2012 Major League Baseball All @-@ Star Game , the second straight year he made the team . On August 11 , he went over 200 innings on the season , becoming the 12th Los Angeles <unk> pitcher with three or more seasons of 200 or more innings , and the first since <unk> did it five times from 1985 to 1989 . <unk> also became just the fifth <unk> pitcher with three straight 200 strikeout seasons . 
- <unk> finished 2012 with a 14 – 9 record , a 2 @.@ 53 ERA ( leading the league ) , <unk> strikeouts , and <unk> ⁄ 3 innings pitched , coming second in both categories . He became the first pitcher to lead the league in ERA in consecutive seasons since Arizona 's Randy Johnson in 2001 – 02 . This was also marked his fourth year in a row with a sub @-@ 3 @.@ 00 ERA , making him the first to do this since Randy Johnson from 1999 – 2002 . He finished second for the NL Cy Young behind R. A. Dickey , receiving two first place votes . 
- 
- = = = = 2013 season : 2nd Cy Young Award = = = = 
- 
- <unk> made his third straight opening day start for the Dodgers in the 2013 season , the first <unk> starter to do so since Derek Lowe ( 2005 – 2007 ) . In that opening day start he pitched a complete game , four hit , shutout over the Giants and also hit his first career home run . He was the first pitcher to throw a shutout and hit a home run on opening day since Bob Lemon of the Cleveland Indians did so against the Chicago White Sox on April 14 , 1953 . <unk> picked up his 1,000th career strikeout on April 17 , 2013 , when he struck out <unk> Alonso of the Padres . He was the second youngest <unk> to reach that mark , behind only Fernando <unk> . On May 14 , <unk> passed the 1 @,@ 000 inning mark for his career . His ERA of 2 @.@ 70 at the time was the fifth best of the live @-@ ball era at the 1 @,@ 000 inning mark and the best career mark . He also threw 130 pitches that day , the most of his career and the most by a <unk> pitcher since <unk> Pérez in the 2003 season . 
- <unk> was selected to the 2013 Major League Baseball All @-@ Star Game , his third straight selection . In July , he compiled a 4 – 1 record and 1 @.@ 34 ERA in six starts and was awarded his second National League Pitcher of the Month Award . On September 2 , <unk> picked up his 200th strikeout of 2013 , joining Hall of <unk> Sandy <unk> and Don <unk> as the only starters in Dodgers history with at least 4 consecutive seasons of more than 200 strikeouts . 
- <unk> finished the season with a 16 @-@ 9 record , 236 innings pitched ( a career high ) , and a Major League best 1 @.@ 83 ERA and 0 @.@ 92 <unk> . He was the third player in history to lead the Majors in ERA three years in a row , joining Greg <unk> ( 1993 – 95 ) and <unk> Grove ( 1929 – 31 ) . His ERA was the first sub @-@ 2 @.@ 00 ERA since Roger <unk> did it in the 2005 season and the lowest overall since Pedro Martínez in the 2000 season . He was only the third <unk> pitcher to have an ERA under 3 @.@ 00 in five consecutive seasons ( <unk> and <unk> <unk> ) . 
- <unk> struck out 12 batters in seven innings in the first game of the 2013 National League Division Series . That was the third most strikeouts by a <unk> pitcher in the playoffs , behind only <unk> ( 15 in the 1963 World Series ) and Carl <unk> ( 14 in the 1953 World Series ) . His six straight strikeouts in the game tied a MLB postseason record set by Tim <unk> in the second game of the 1988 World Series . He picked up his first career postseason victory in that game . 
- <unk> won the Warren <unk> Award for 2013 , the second time he had won the award , which honors the best left @-@ handed pitcher in the Major <unk> . He was also selected to the Sporting News NL All @-@ Star team , the fourth <unk> pitcher to be named to the team twice ( after <unk> , <unk> and Don <unk> ) . On November 13 , he won the NL Cy Young Award for the second time in three seasons . He became just the sixth pitcher in history to finish in the top two in voting three seasons in a row . 
- After the season , <unk> and the Dodgers agreed on a seven @-@ year , $ 215 million , contract extension . The deal was the richest in MLB history for a pitcher , eclipsing the seven @-@ year , $ 180 million , contract signed by Justin <unk> the previous year . The average annual value of $ 30 @.@ 7 million was also the largest ever for a baseball player , beating the $ 28 million Roger <unk> received in 2007 and the 10 @-@ year , $ 275 million contract that Alex Rodriguez signed that same year . 
- 
- = = = = 2014 season : MVP and 3rd Cy Young Award = = = = 
- 
- <unk> made his fourth straight opening day start for the Dodgers in 2014 , only the fourth <unk> ever to do so . This season the game was played at the Sydney Cricket Ground in Australia . Before his second start , <unk> felt some pain in his back and was placed on the disabled list for the first time in his career . He did not rejoin the Dodgers until early May . On June 18 , he pitched a complete game no @-@ hitter against the Colorado Rockies and struck out a career @-@ high 15 batters . The only batter to reach base was due to an error in the top of the seventh inning , costing <unk> a perfect game . He is the only pitcher in MLB history with 15 strikeouts in a game while allowing no hits and no walks . <unk> was 6 @-@ 0 with an 0 @.@ 82 ERA in June and was awarded with his third career Pitcher of the Month award . He was selected to the National League squad at the 2014 Major League Baseball All @-@ Star Game , his fourth straight selection . He was the sixth <unk> pitcher , and the first since Fernando <unk> to make the All @-@ Star team four years in a row . 
- <unk> had a 41 inning scoreless inning streak that ended in the top of the sixth inning on July 10 when , with two outs , Chase <unk> <unk> to left field at <unk> Stadium . <unk> 's streak was , at the time , tied for the fifteenth longest scoreless inning streak in MLB history . He won the pitcher of the month award again in July , the third <unk> ( along with Don Sutton and Burt <unk> ) to win it two months in a row . He was 4 – 0 with a 1 @.@ 10 ERA in the month with 48 strikeouts and only 10 walks . He picked up his 200th strikeout of the season on September 2 , the fifth year in a row he had reached that number , trailing only the six seasons in a row for Sandy <unk> among <unk> starters . He also became just the fourth pitcher since 1893 to have at least five 200 @-@ strikeout seasons through an age @-@ 26 season ( Bert <unk> , Walter Johnson and Sam <unk> are the others ) . 
- <unk> finished the season 21 – 3 with a 1 @.@ 77 ERA in 27 starts . He led the National League in numerous categories once again , such as ERA , ERA + , Wins , <unk> % , <unk> , IP / <unk> , <unk> / 9 , <unk> @-@ to @-@ walk ratio , complete games , <unk> , and Wins Above <unk> for both pitchers and all NL players . He also finished third in strikeouts despite missing most of the first month of the season . He was the first pitcher in history to win four consecutive ERA titles . Many experts called his 2014 season one of the best pitching seasons in recent memory . 
- However , in his first start of the playoffs , in Game 1 of the Division Series against the Cardinals , <unk> became the first pitcher in history to strike out 10 while allowing eight runs . He had cruised through the first six innings while allowing only two hits ( both solo <unk> ) and surrendered six runs in the seventh . He did tie <unk> for the only Dodgers pitchers with multiple double digit strikeout games in the playoffs . He was also the first pitcher in history to give up at least seven runs in back @-@ to @-@ back postseason starts ( his previous one was Game 6 of the 2013 National League Championship Series ) . Pitching on short rest in Game 4 , he would again be dominant , but again would take the loss after giving up a 3 @-@ run home run to Matt Adams in the 7th inning . It was the first home run <unk> had allowed in his career to a left @-@ handed batter off his curveball . 
- <unk> was honored after the season with player of the year awards from both The Sporting News and Baseball America . He won three awards at the Players Choice Awards including Outstanding NL Pitcher , Player of the Year and the Marvin Miller Man of the Year Award . He also won his third ( and second straight ) Warren <unk> Award . On November 12 , he was awarded his third Cy Young Award in four seasons ( a unanimous vote ) . The following day , he was elected as the NL MVP , the first National League pitcher to win the award since Bob Gibson in 1968 and the first Dodgers player to win the award since Kirk Gibson in 1988 . 
- 
- = = = = 2015 season : 300 @-@ strikeout season = = = = 
- 
- <unk> made his fifth straight opening day start in 2015 , the first Dodgers pitcher to do so since Hall of Famer Don Sutton started seven in a row from 1972 through 1978 . He recorded his <unk> career strikeout on May 10 when he fanned Drew <unk> of the Colorado Rockies . <unk> picked up his 100th career win on May 15 against the Rockies . He became the 22nd pitcher in franchise history and the second youngest active pitcher to reach that mark . <unk> won his sixth career NL player of the week award for the week of June 1 – 7 , 2015 , when he allowed only two runs on 10 baserunners in 15 innings while striking out 18 in two starts that week . <unk> did not make the initial NL roster for the 2015 All @-@ Star Game , though he was included on the Final <unk> ballot , which he lost to Cardinals pitcher Carlos Martinez . However , he was added to the roster to replace <unk> pitcher Max <unk> , who was unavailable due to pitching the Sunday before the game . It became his fifth straight all @-@ star selection , joining Sandy <unk> and Fernando <unk> as the only Dodgers pitchers to accomplish that feat . <unk> struck out a season high 14 batters in eight shutout innings on July 18 against the Washington <unk> . He became the first Dodgers starter with back @-@ to @-@ back games of at least 13 strikeouts since Chan Ho Park in 2000 and the first Dodgers pitcher with back @-@ to @-@ back games of double @-@ digit strikeouts and no walks since <unk> Vance in 1930 . He shared the NL player of the week honors with his teammate Zack <unk> for the week of July 13 – 19 and won NL pitcher of the month for July . 
- <unk> picked up his 200th strikeout of the season on August 12 , tying <unk> <unk> 's 1995 season for the fastest to that mark in Dodgers history at 156 innings . This was the sixth straight 200 strikeout season for <unk> , tying Sandy <unk> for the most in Dodgers franchise history . On October 4 , <unk> became the 11th player in Major League history to strike out 300 batters in a season , the first player since Randy Johnson did it in 2002 . He finished the season with a 16 – 7 record , a 2 @.@ 13 ERA , and <unk> strikeouts in 232 2 ⁄ 3 innings . 
- In Game One of the 2015 National League Division Series , <unk> struck out 11 in 6 2 ⁄ 3 innings but allowed three runs for his fifth straight postseason loss . He and New York Mets starter Jacob deGrom were the first pair of starters to each throw at least 11 strikeouts in the same postseason game in MLB history . He rebounded in game four , earning the win on three days ' rest by allowing one run and three hits against eight strikeouts in seven innings on October 13 . <unk> finished third in the National League Cy Young Award voting , behind Jake <unk> and teammate Zack <unk> . In mid @-@ December 2015 , <unk> participated in an expedition to Cuba composed of MLB officials and players , including former Dodgers manager Joe Torre . It was the first visit by MLB since 1999 , and one anticipated as an important step to help <unk> relations with the United States that had begun to ease earlier in the year . 
- 
- = = = = 2016 = = = = 
- 
- <unk> made his sixth straight opening day start in 2016 as the Dodgers won 15 – 0 . It also marked the first time the Dodgers had won six straight opening day games , all of which he started . On May 12 against the New York Mets , he struck out 13 while pitching a three @-@ hit complete game shutout . He set an MLB record with six consecutive starts with at least 10 strikeouts and no more than one walk and a club record with six consecutive starts with at least 10 strikeouts . He picked up his 100th strikeout on May 29 , while only walking five batters within that period . That was the lowest walk total for a pitcher reaching 100 strikeouts in the modern era , beating Cliff Lee who had seven walks in the 2010 season . On June 30 , 2016 , <unk> was placed on the 15 @-@ day disabled list due to back pain . He received an <unk> , which revealed that there was a mild <unk> disc in the back , and received an <unk> injection to treat the pain . He was named to the 2016 All @-@ Star team but was unable to pitch in the game due to his injury . On July 20 , the Dodgers shut down <unk> for an indefinite period of time . He continued to feel discomfort in his back after a simulated game . 
- 
- = = Pitching style = = 
- 
- <unk> 's pitching style relies on deception . He keeps the ball hidden so that it is hard for the batter to pick up the ball and has a consistent <unk> delivery on all of his pitches . Out of the <unk> , <unk> lowers his right foot vertically with a slight pause before moving it forward toward the plate . The motion was described during the 2015 National League Division Series as a " <unk> move " drawing comparison with one setting a <unk> on a bicycle . Out of the stretch , he uses a slide step as it makes it difficult for the base runner at first base to get a read on him . He has stated many times that he has modeled his pitching mechanics after his favorite pitcher growing up , Roger <unk> . 
- <unk> 's repertoire includes a four @-@ seam fastball that sits anywhere from 92 miles per hour ( 148 km / h ) to 95 miles per hour ( 153 km / h ) and tops out at 98 miles per hour ( 158 km / h ) with late movement , a slider at 84 miles per hour ( 135 km / h ) – 87 miles per hour ( 140 km / h ) , a 12 – 6 curveball between 72 miles per hour ( 116 km / h ) – 76 miles per hour ( 122 km / h ) , and a seldom thrown changeup ( under 3 % ) . As of late in the 2015 season , he is believed to be experimenting with the use of a cutter . He is also known for having one of the better <unk> moves to first base and is considered one of the better fielding pitchers in the game . 
- 
- = = = Preparation = = = 
- 
- According to many teammates , <unk> is a noted <unk> . <unk> Ellis describes his preparation and <unk> during <unk> before each start : 
- Three <unk> when I 'm standing up . I sit , and three <unk> down the middle . Then three <unk> either side . Three <unk> away . <unk> inside . Three <unk> to the middle . <unk> inside . Three <unk> to the middle . Then he goes to the stretch position . Two <unk> inside , two <unk> away , two <unk> , one fastball inside , two <unk> , one fastball inside , two <unk> . Back to the <unk> , and one fastball inside , one fastball away . Thirty @-@ four pitches in all . 
- 
- = = Awards and accomplishments = = 
- 
- 
- = = = Awards = = = 
- 
- 
- = = = Annual statistical achievements = = = 
- 
- Notes : Through 2015 season . Per Baseball @-@ <unk> 
- 
- = = Personal life = = 
- 
- <unk> grew up in Dallas , Texas and attended school with quarterback Matthew Stafford and fellow pitchers Jordan <unk> and Shawn <unk> . One of his favorite players growing up was former Texas Rangers first baseman Will Clark , and the main reason he wears number 22 is to honor Clark . 
- He is the great @-@ nephew of astronomer Clyde <unk> , the <unk> of Pluto . <unk> 's mother , born <unk> <unk> , is the daughter of Clyde <unk> 's younger brother . His father , Christopher George <unk> , was a musician and won a <unk> Award for his work . The elder <unk> remarried after his divorce from <unk> and died in 2013 . 
- On December 4 , 2010 , <unk> married his girlfriend of seven years , Ellen <unk> . On January 23 , 2015 , Ellen gave birth to the couple 's first child , daughter <unk> Ann . He is a Methodist with strong religious faith . 
- <unk> made a cameo appearance in " Prince " , a Season 3 episode of New Girl which originally aired following <unk> 's telecast of Super Bowl <unk> . 
- 
- = = = Humanitarian work = = = 
- 
- Prior to the 2011 season , <unk> visited Zambia with his wife as part of a Christian mission organized by Dallas @-@ based <unk> Africa . After the trip , <unk> announced his dream of building an orphanage in <unk> , Zambia , which he called " Hope 's Home " after 11 @-@ year @-@ old Hope , an HIV @-@ positive child <unk> met while in Zambia . To accomplish his goal , <unk> pledged a donation of $ 100 per strikeout recorded in 2011 . With <unk> 's career high of <unk> strikeouts thrown during the 2011 season , he donated $ <unk> @,@ 300 toward his $ 70 @,@ 000 goal . When <unk> won the 2011 Players Choice Award , he donated $ 260 @,@ 000 to Hope 's Home . He and his wife returned to Zambia in 2012 . <unk> donated $ 100 for every strikeout in the 2012 season to <unk> 's Challenge , calling that season 's incarnation of the project " Strike Out To Serve . " Seventy percent of the money raised in 2012 went to <unk> Africa , with 10 percent each going to the <unk> Foundation in Los Angeles , Mercy Street in Dallas , and I Am Second . In 2014 , <unk> continued to support the children of Zambia , with partnership with <unk> International , raising funds to pay for 170 children 's <unk> and new medical equipment for <unk> hospital in <unk> . <unk> has continued his partnership with <unk> International in 2015 , setting a goal of funding 100 <unk> for <unk> 's hospital in the Dominican Republic . 
- In addition to Hope 's Home and <unk> 's Challenge , he has also helped with other programs in Los Angeles , such as helping Habitat for <unk> <unk> and <unk> a house in <unk> , California . He is also a supporter of the <unk> Foundation , which provides animal @-@ assisted <unk> and activities for at risk youth by <unk> with mental health practitioners , public service agencies and community organizations . 
- 
- = = = Author = = = 
- 
- <unk> and his wife , Ellen , co @-@ authored a book named <unk> : Live Out Your Faith and Dreams on Whatever Field You Find Yourself about their Christian faith and their humanitarian efforts . The book was released on January 10 , 2012 through <unk> Press . 
- 
- = = = <unk> = = = 
- 
- <unk> is a celebrity <unk> for <unk> , Wilson Sporting <unk> ( <unk> ) , Under Armour ( shoes ) , <unk> Milk , and <unk> . 
- 
- 
- = <unk> <unk> <unk> = 
- 
- <unk> <unk> <unk> ( born ca . 1945 or ca . 1953 , date uncertain ) is an <unk> @-@ speaking Indigenous Australian from Central Australia . Since first taking up painting around 1990 , her works of contemporary Indigenous Australian art have been acquired by several major collections including <unk> and the National Gallery of Victoria . Her paintings portray bush plum " dreaming " and women ’ s ceremonies ( known as <unk> ) . One of her paintings sold at a charity auction for A $ 22 @,@ 800 . <unk> <unk> 's works are strongly coloured and <unk> in composition and regularly appear at commercial art <unk> in Australia . Her art appears to have survived the huge contraction of the primary art market in Australia since 2008 . There is no existing <unk> <unk> of <unk> <unk> 's artworks , to date , no fakes have been cited . 
- 
- = = Personal background = = 
- 
- <unk> <unk> <unk> is an <unk> @-@ speaking Indigenous Australian , born around 1945 or 1953 at the Santa Teresa Mission , near Alice Springs in Australia 's Northern Territory . 
- When <unk> <unk> began painting for <unk> Gallery in central Australia , she indicated that her name was <unk> rather than <unk> , and that this was how she <unk> wished to be known ; however <unk> 's biography is the only source that has used that version of her name . 
- After marrying Robin <unk> , brother of artist Gloria <unk> , <unk> <unk> moved to the region of <unk> , north @-@ east of Alice Springs , which is where she was living when she began painting around 1990 . They had seven children , one of whom , <unk> <unk> , went on to become an artist like his mother . By 2008 , <unk> <unk> 's husband had died , and <unk> was dividing her time between Alice Springs and <unk> Range , to its north @-@ east . 
- 
- = = Professional background = = 
- 
- Contemporary Indigenous art of the western desert began in 1971 when Indigenous men at <unk> created murals and <unk> using western art materials , assisted by teacher Geoffrey <unk> . Their work , which used <unk> paints to create designs representing body painting and ground sculptures , rapidly spread across Indigenous communities of central Australia , particularly after the introduction of a government @-@ sanctioned art program in central Australia in 1983 . By the 1980s and ' 90s , such work was being exhibited internationally . The first artists , including all of the founders of the <unk> <unk> artists ' company , were men , and there was resistance among the <unk> men of central Australia to women also painting . However , many of the women wished to participate , and in the 1990s many of them began to paint . In the western desert communities such as <unk> , <unk> , <unk> , <unk> , and on the <unk> , people were beginning to create art works <unk> for exhibition and sale . 
- 
- = = = Career = = = 
- 
- <unk> <unk> began painting about 1990 or 1992 as part of the contemporary Indigenous art movement that had begun at <unk> in the 1970s . By 1998 her work was being collected by both private and public institutions , such as Charles Sturt University , and in 2005 a work was purchased by the National Gallery of Victoria . Her career received a significant boost when her work was included in the National Gallery of Victoria 's 2006 Landmarks exhibition and its catalogue ; her painting was printed opposite that of <unk> Tommy Watson , who was by this time famous , particularly for his contribution to the design of a new building for the Musée du <unk> <unk> . <unk> 's paintings have been included at exhibitions in several private galleries in Melbourne and Hong Kong , as well as at the Australian embassy in Washington in 2001 . 
- In 2006 a commissioned work by <unk> was exhibited at <unk> College at the University of New South Wales as part of a charity fundraising exhibition . It sold for A $ 22 @,@ 000 . As of the end of 2008 , the highest recorded auction price for an item of <unk> 's work was $ 22 @,@ 800 , set in May 2007 . An image based on a <unk> by <unk> , Bush <unk> , appears on the cover of a book on the visual perception of motion , Motion Vision . 
- Central Australian artists frequently paint particular " <unk> " , or stories , for which they have responsibility or rights . These stories are used to pass " important knowledge , cultural values and belief systems " from generation to generation . <unk> by <unk> portray two different groups of <unk> , rendered in two distinct styles . Bush plum dreaming represents a plant of the central Australian desert which is " a source of physical and spiritual sustenance , reminding [ the local Indigenous people ] of the <unk> of [ their ] country " . These paintings are undertaken with red , blue and orange dots that represent the fruit at different stages in its development . She also paints women ’ s ceremonies ( <unk> ) and <unk> , and these are created using rows of coloured dots and include representations of women 's ceremonial iconography . 
- Journalist <unk> <unk> described <unk> as one of the " finest contemporary Aboriginal artists " . Art consultant Adrian Newstead has ranked her as amongst the country 's top 200 Indigenous artists , noting that she has become " known for innovative works that create a sense of visual harmony through fine <unk> fields of <unk> applied <unk> " . Her style is described by Indigenous art writers <unk> and <unk> as an " interesting , modern interpretation of landscape " . 
- <unk> 's work is held in a variety of public and private collections , including <unk> , the Charles Sturt University Collection , the Holmes a Court Collection , and the National Gallery of Victoria . 
- 
- 
- = Head VI = 
- 
- Head VI is an oil @-@ on @-@ canvas painting by the Irish @-@ born English <unk> artist Francis <unk> , the last of six panels making up his " 1949 Head " series . It shows a bust view of a single figure , modeled on Diego <unk> 's Portrait of Innocent X. <unk> applies forceful , expressive brush <unk> , and places the figure within a glass cage structure , behind curtain @-@ like <unk> . This gives the effect of a man trapped and <unk> by his surroundings , screaming into an <unk> void . 
- Head VI was the first of <unk> 's paintings to reference <unk> , whose portrait of Pope Innocent X haunted him throughout his career and inspired his series of " screaming <unk> " , a loose series of which there are around 45 surviving individual works . Head VI contains many motifs that were to reappear in <unk> 's work . The hanging object , which may be a light switch or curtain <unk> , can be found even in his late paintings . The <unk> cage is a motif that appears as late as his 1985 – 86 masterpiece , Study for a Self @-@ Portrait — <unk> . 
- Head VI was first exhibited in November 1949 at the Hanover Gallery in London , in a showing organised by one of the artist 's early champions , Erica <unk> . At the time , <unk> was a highly controversial but respected artist , best known for his 1944 Three Studies for Figures at the Base of a <unk> , which made him the <unk> terrible of British art . Head VI drew a mixed reaction from art critics ; John Russell , later <unk> 's biographer , at the time dismissed it as a cross between " an <unk> <unk> of its jaws and an <unk> in <unk> @-@ <unk> who has come to a bad end " . In 1989 Lawrence <unk> wrote that the " shock of the picture , when it was seen with a whole series of heads ... was indescribable . It was everything <unk> . The paradoxical appearance at once of pastiche and <unk> was indeed one of <unk> 's most original <unk> . " Art critic and curator David Sylvester described it as a seminal piece from <unk> 's unusually productive 1949 – 50 period , and one of <unk> 's finest <unk> . 
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- = = 1949 Head series = = 
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- <unk> 's output is characterised by sequences of images . He told Sylvester that his imagination was stimulated by sequences and that " images breed other images in me " . His series were not always planned or painted in sequence ; sometimes paintings are grouped for convenience but vary in execution and tone . The idea for the head series came after he returned <unk> , late in 1948 , from a stay in <unk> . In the previous three years he had been unable to find a voice ; the last surviving canvas from this period is his Painting ( 1946 ) . Although he continued to paint , he was a ruthless self critic , given to <unk> <unk> with <unk> , and no works survive from between 1947 and the winter of 1948 . <unk> Erica <unk> offered <unk> the opportunity of a solo show for the opening of her new Hanover Gallery . He agreed , but had nothing in reserve to hang . In following years , <unk> became perhaps the most important of <unk> 's early champions ; she arranged this showing — his debut solo exhibition — publicised him widely and organised <unk> for international <unk> . 
- Already 40 years old , <unk> viewed the exhibition as his last chance and applied himself to the task with determination . Because he had destroyed all his out of the last three years , he had little choice but to present new works . He did not have a grand plan when he agreed to the show , but eventually found themes that interested him in his Head I of the previous year , and executed five progressively stronger variants in the final weeks before the November exhibition , completing the series barely in time for the opening . 
- The paintings depict isolated figures enclosed in spaces that are <unk> , overwhelmingly <unk> , <unk> and eerie . Coming early in <unk> 's career , they are uneven in quality , but show a clear progression especially in how they <unk> and present ideas he was still clearly developing and coming to terms with . Head I ( actually begun in the winter of 1948 ) and Head II show <unk> pieces of flesh that broadly resemble human heads ; they have half @-@ open eyes and a <unk> , though it is positioned much higher than would be expected in a human . Heads III , IV and V show fully formed <unk> recognisable as men , and are characterised by a haunted atmosphere . These two broad ideas <unk> in Head VI , which is as <unk> tortured as the first two paintings , and as spectral as the middle three . In Head VI the figure has developed and is now shown wearing vestments , the first indication in <unk> 's work of the influence of <unk> , while the focus has become the open mouth and the study of the human scream . 
- <unk> said that chance played a significant role in his work , and that he often approached a canvas without having a clear idea of what might emerge . This was especially the case in the mid to late 1940s , a period when he was drinking heavily and spending most nights in <unk> <unk> and poker rooms . The following morning he would often approach his canvas " in a bad mood of drinking ... under tremendous <unk> and drink ; I sometimes hardly knew what I was doing . " He incorporated his appetite for chance into his work : an image often would <unk> mid @-@ way through into something quite different from what he had first intended . He actively sought out this freedom and felt it crucial to his progression as an artist . To him , lifestyle and art were intertwined ; he said that " perhaps the drink helped me to be a bit <unk> . " This is very evident in the 1949 series , which began as a rather <unk> study of a collapsed head , but evolved over the six surviving panels into a reworking of <unk> masterpieces , and arrived at an image that was to <unk> <unk> for the subsequent 20 years . 
- The series marks <unk> 's first attempt at depicting lone figures in rooms . For him , the key aspect was that it appeared that the subject felt isolated , <unk> , and had abandoned the need to present an outward face . He believed that under these circumstances all <unk> falls away , and the social being becomes the sum of its <unk> , which <unk> attempted to convey by reducing the subject to its bare @-@ bones features : a mouth , ears , eyes , a jaw . According to Russell , " the view out front ceases to be the only one , and our person is suddenly <unk> , fragmented , and subject to strange <unk> . " Russell observed that while the depiction of figures in rooms is common through all eras of painting , the figures are always posed , and usually seemingly aware that they are being portrayed . This <unk> is abandoned in <unk> 's series . 
- Head I , completed late in 1948 , is considered more successful than Head II . Although it is well @-@ regarded critically , Head II is seen as something of a creative <unk> @-@ de @-@ sac , while Heads III , IV and V are usually considered as merely intermediate steps towards Head VI . It is exceptional in <unk> 's <unk> that works of their relative poor quality survive ; he was <unk> self @-@ critical and often <unk> or abandoned <unk> before they were completed . When pressed again by <unk> in 1953 to produce works for a New York show that she had been <unk> for a year , he was full of doubt and destroyed most of what he had been working on , including several other <unk> . 
- <unk> commissioned another showing to be held in 1950 , for which <unk> painted three large <unk> modelled on <unk> 's portrait . The gallery advertised the show as " Francis <unk> : Three Studies from the Painting of Innocent X by <unk> " , but in the end <unk> was dissatisfied with the works and destroyed them before the show opened . 
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- = = Description = = 
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- The figure is clearly identifiable as a pope from his clothing . It seems trapped and isolated within the outlines of an abstract three @-@ dimensional glass cage . This framing device , described by Sylvester as a " space @-@ frame " , was to feature heavily throughout the artist 's career . A <unk> hangs from the upper edge of the glass case , falling just in front of the pope 's face and partially covering his eyes . It is too <unk> drawn to identify with certainty , but given the presence of similar objects in <unk> 's later works , may be either the end of a hanging light switch or the <unk> of a curtain ; the hanging <unk> was to become a signature for the artist . Apart from its symbolic meaning , it has a <unk> function , framing the painting with a further set of vertical lines . Such an object reappears most prominently in the centre panel of his 1973 <unk> , May – June 1973 , where it is clearly a dangling light bulb . For <unk> , these elements were intended to make the figure <unk> in and out of sight for the viewer , alluding to the fact that bulbs can be on or off , <unk> open or closed . 
- The figure 's mouth is opened wide as if screaming , an expression <unk> took from a still he kept of the nurse screaming in <unk> <unk> 's Odessa Steps massacre sequence in his 1925 silent film Battleship <unk> . In 1984 , the broadcaster <unk> Bragg asked <unk> about the still , and observed that in his earlier career the artist seemed preoccupied with the <unk> of the human mouth . <unk> replied , " I had always thought that I would be able to make the mouth with all the beauty of a <unk> landscape though I never succeeded in doing so . " When Bragg asked why he thought he had failed , <unk> said , " It should be all much more colour , should have got more of the interior of the mouth , with all the colours of the interior of the mouth , but I didn 't happen to get it . " His interest in the mouth was further stimulated by a medical textbook of <unk> oral cavities bought in a second @-@ hand <unk> , kept in his studio and to which he often referred to . 
- The glass cage might imply a vacuum that the figure 's voice is unable to escape ; as if it is screaming in silence . <unk> later in life , <unk> said that he had " wanted to paint the scream more than the horror . I think , if I had really thought about what causes somebody to really scream , it would have made the scream ... more successful " . The work evokes memories of the Second World War . The glass enclosure of his 1949 Chicago Study for a Portrait is often seen as <unk> photographs of Adolf <unk> 1961 trial before a Jerusalem District Court , when he was held within a similar cage . <unk> strongly resisted literal comparisons though , and stated that he used the device so he could frame and " really see the image – for no other reason . I know it 's been interpreted as being many other things . " Other critics saw similarities between the glass case and the radio <unk> of late 1930s broadcasters who warned against the impending <unk> . Denis Farr notes that <unk> was sympathetic to George Orwell and referred in interviews to <unk> " shouting voices ... and trembling hands ... convey [ ing ] the harsh atmosphere of an <unk> . " 
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- = = Influences = = 
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- The so @-@ called " space frame " had already been used by Alberto <unk> in the 1930s , and the two artists became friends in the 1960s . However <unk> had by 1949 used it only in <unk> contexts before <unk> 's <unk> , and in turn influenced his use in " The Cage " of 1950 . A similar two dimensional construct is found in Henry Moore 's works , notably his " <unk> for King and Queen " , constructed three years after <unk> 's Head . It is difficult to <unk> how these artists influenced and informed each other . What is notable is that <unk> continued to use the motif , with intervals until the end of his life . Sylvester suggests his finest example is the 1970 Three Studies of the Male Back . 
- The full @-@ length golden curtain @-@ like folds painted in heavy brush <unk> are in part influenced by <unk> but also similar to <unk> 's 1558 Portrait of Cardinal Filippo <unk> . <unk> <unk> the Old Master 's device to isolate and distance the sitter from the viewer ; the black ground @-@ paint is visible through the folds , making the separation all the more affecting . <unk> had already used similar forms in his Chicago panel , and they were to become a feature of his most acclaimed 1950s works , especially in his " screaming <unk> " . He became fascinated with the veil or curtain as a motif in painting , and collected many reproductions of works by <unk> and <unk> in which it is employed . He had begun his career as an interior <unk> and designer of furniture and <unk> in the mid @-@ 1930s , and later said that he liked " rooms hung all round with just <unk> hung in even folds " . <unk> or <unk> appear in <unk> 's earliest works , notably the 1949 Study from the Human Body , always in portraits and always in front of , rather than behind , the figure . 
- Head VI is closely modelled on <unk> 's c . 1650 Portrait of Innocent X , today in the <unk> <unk> Gallery , Rome . <unk> cautiously avoided seeing the original , even when he spent three months in Rome in 1954 . Critics speculate he was afraid of being disappointed , or thought that an intimate knowledge of the painting would dull his imagination . Yet his fascination was all @-@ consuming and he reproduced variants of it <unk> for almost two decades ; an examination and homage described as " without parallel in the history of art " . <unk> 's approach differs to <unk> 's in a number of ways : both artists were expressive , yet <unk> 's broad brush @-@ <unk> and freedom with paint contrast with <unk> 's tight and controlled treatment . He <unk> <unk> 's positioning of the pope to place him above the viewer 's point of view , elevating and <unk> him . This was already a common technique in commercial , promotional photography but in <unk> 's hands , <unk> argues , the angle places the pope on a kind of stage for the viewer to <unk> observe . 
- Although <unk> revered <unk> 's portrait , he did not try and reproduce the earlier painting . In interviews , he said that he saw flaws in <unk> 's work and that he viewed that social structure and order as , according to art historian <unk> <unk> , " obsolete and decayed " . <unk> 's approach was to elevate his subject so he could knock him down again , thereby making a <unk> comment on the treatment of royalty in both old master and contemporary painting . Yet <unk> 's influence is apparent in many aspects of the painting . The sitter 's pose closely echoes the original , as does the violet and white colouring of his cope , which is built up through broad , thick , brush @-@ <unk> . The influence can be further seen in the gold @-@ coloured ornaments on the back of the seat that extend on both sides of the figure . Art historian <unk> <unk> describes the work as a mixture of <unk> and subversion that pays tribute to <unk> , while at the same time <unk> his painting . 
- Sylvester <unk> the influence of late works by <unk> in other aspects , especially in the deep and rich colouring , <unk> 's portrayals of Philip IV , and agrees with identification of <unk> of Edgar <unk> as a source . He believes <unk> borrowed from <unk> the use of parallel heavy folds to create the illusion of what <unk> described as " <unk> " , as seen in the earlier artist 's After the Bath , Woman drying herself . Sylvester makes a further direct link between the folds and the transparent veil in <unk> 's Portrait of Cardinal Filippo <unk> . He believes the folds serve to " push the viewer back " , creating a distance from the subject , an effect he sees as similar to the separation between and orchestra and setting ; others view the folds as more closely resembling the bars of a prison . Sylvester describes them as an <unk> of background <unk> into stripes that are made to appear as if they pass through the sitter . In his " <unk> with Francis <unk> " series of books , he asked <unk> why he found the effect so poignant . The artist replied , " Well , it means that the sensation doesn 't come straight out at you but <unk> slowly and gently through the gaps . " 
- When asked why he was compelled to revisit the <unk> so often , <unk> replied that he had nothing against <unk> per <unk> , but merely sought " an excuse to use these colours , and you can 't give ordinary clothes that purple colour without getting into a sort of false <unk> manner . " <unk> sees Head VI as a reaction against <unk> , and a commentary on how the papacy is " obsolete and decayed " , with a pope resistant to both modernisation and <unk> . To him , the figure seems to " resist the <unk> of image and tries to halt the impending collapse of the established work order . He screams and <unk> , clutching at arms of his throne . " Sylvester notes that <unk> was impressed by Picasso 's <unk> and handling of paint , especially in Picasso 's 1930s works ; and suggests that the white <unk> around the pope 's <unk> may be influenced by the 1913 Woman in a <unk> <unk> in an <unk> . 
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- = = Critical reception = = 
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- When <unk> undertook the series late in 1948 he was something of a two @-@ hit wonder . He had success in 1944 with Three Studies for Figures at the Base of a <unk> and to a lesser extent with Painting ( 1946 ) , both of which were highly regarded but viewed as <unk> . The exhibition was a success , and marked his critical breakthrough . Until then , he had been highly regarded but capable of only occasional <unk> . The full show established him in the minds of critics as , according to Michael <unk> , " more of a force to be <unk> with in the contemporary scene " . While some found his images <unk> and <unk> , they wrote about him all the same , sealing his reputation as the <unk> terrible of post @-@ war British art . The critic for The Observer wrote , " The recent paintings ... <unk> as they are , cannot be ignored . Technically they are superb , and the masterly handling of large areas of <unk> grey , flushed with a sudden pink or green , only makes me regret the more that the artist 's gift should have been brought to subjects so esoteric " . 
- Most critics focused on Heads I and VI , remarking favourably on the progression between the two . While some found the inherent violence of the paintings <unk> , <unk> was a skilled publicist and turned the bad press into notoriety , and brought <unk> 's work to national attention . <unk> notes that the exhibition showed <unk> no longer needed <unk> material to make an impact , and was now capable of creating an intense emotional response through more subtle means , and had found a way of presenting the human condition in the way he had sought , by presenting his sitter " in a <unk> setting , a cage or [ behind ] a parted <unk> ... the rest , the most essential , lay in the manipulation of the infinitely <unk> medium of oil paint " . After the showing <unk> gradually became " less the <unk> with an occasional image of <unk> <unk> and more a force to be <unk> with on the contemporary scene " . His reputation and the value of his panels rose dramatically , and after the showing he was sought after by European , American and African collectors and galleries , commanding prices as high as £ 400 for single works , unusual for a contemporary British artist of the time . 
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- = = <unk> = = 
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- Head VI was first exhibited at the Hanover Gallery , London , in 1949 . It was acquired by the Arts Council 's <unk> Gallery in 1952 . The <unk> has loaned it out a number of times since , including for major <unk> at the Grand <unk> , Paris in 1971 , and the Hugh Lane Gallery , Dublin , in 2000 . 
- In May 1996 , the National Gallery took on loan <unk> 's Innocent X portrait and hung it alongside four <unk> paintings ; Head VI , Pope I ( 1951 ) , Pope 1961 and Pope 1965 . <unk> believes that <unk> would have disapproved of such a showing with a work he considered one of the finest ever painted , but writes that two , including Head VI , " stood up to it , and even enhanced its authority as one of the most penetrating studies of human nature and human power " . 
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- = <unk> = 
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- <unk> was a movement in early 20th @-@ century Anglo @-@ American poetry that favored precision of imagery and clear , sharp language . 
- <unk> has been described as the most influential movement in English poetry since the activity of the Pre @-@ <unk> . As a poetic style it gave <unk> its start in the early 20th century , and is considered to be the first organized <unk> literary movement in the English language . <unk> is sometimes viewed as ' a succession of creative moments ' rather than any continuous or sustained period of development . René <unk> remarked that ' It is more accurate to consider <unk> not as a doctrine , nor even as a poetic school , but as the association of a few poets who were for a certain time in agreement on a small number of important principles ' . 
- The <unk> rejected the sentiment and <unk> typical of much Romantic and Victorian poetry , in contrast to their contemporaries , the Georgian poets , who were generally content to work within that tradition . In contrast , <unk> called for a return to what were seen as more Classical values , such as <unk> of presentation and economy of language , as well as a willingness to experiment with non @-@ traditional verse forms . <unk> use free verse . 
- <unk> publications appearing between 1914 and 1917 featured works by many of the most prominent modernist figures , both in poetry and in other fields . The <unk> group was centered in London , with members from Great Britain , Ireland and the United States . <unk> unusually for the time , a number of women writers were major <unk> figures . 
- A characteristic feature of <unk> is its attempt to isolate a single image to reveal its essence . This feature mirrors contemporary developments in avant @-@ garde art , especially <unk> . Although <unk> <unk> objects through the use of what Ezra Pound called " luminous details " , Pound 's <unk> Method of juxtaposing concrete instances to express an <unk> is similar to <unk> 's manner of <unk> multiple perspectives into a single image . 
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- = = Pre @-@ <unk> = = 
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- Well @-@ known poets of the <unk> era of the 1890s , such as Alfred Austin , Stephen Phillips , and William Watson , had been working very much in the shadow of Tennyson , producing weak imitations of the poetry of the Victorian era . They continued to work in this vein into the early years of the 20th century . As the new century opened , Austin was still the serving British Poet Laureate , a post which he held up to 1913 . In the century 's first decade , poetry still had a large audience ; volumes of verse published in that time included Thomas Hardy 's The <unk> , Christina <unk> 's posthumous <unk> Works , Ernest <unk> 's Poems , George Meredith 's Last Poems , Robert Service 's <unk> of a <unk> and John <unk> 's <unk> and Poems . Future Nobel Prize winner William Butler Yeats was <unk> much of his energy to the Abbey Theatre and writing for the stage , producing relatively little lyric poetry during this period . In 1907 , the Nobel Prize for Literature was awarded to <unk> Kipling . 
- The origins of <unk> are to be found in two poems , Autumn and A City Sunset by T. E. <unk> . These were published in January 1909 by the Poets ' Club in London in a booklet called For Christmas <unk> . <unk> was a student of mathematics and philosophy ; he had been involved in the setting up of the club in 1908 and was its first secretary . Around the end of 1908 , he presented his paper A <unk> on Modern Poetry at one of the club 's meetings . Writing in A. R. <unk> 's magazine The New Age , the poet and critic F. S. Flint ( a champion of free verse and modern French poetry ) was highly critical of the club and its publications . From the ensuing debate , <unk> and Flint became close friends . In 1909 , <unk> left the Poets ' Club and started meeting with Flint and other poets in a new group which <unk> referred to as the " <unk> Club " ; they met at the <unk> Tower restaurant in London 's <unk> to discuss plans to reform contemporary poetry through free verse and the <unk> and <unk> and the removal of all unnecessary <unk> from poems . The interest in Japanese verse forms can be placed in a context of the late Victorian and <unk> revival of interest in <unk> and <unk> as witnessed in the 1890s vogue for William Anderson 's Japanese prints donated to the British Museum , performances of <unk> plays in London , and the success of Gilbert and Sullivan 's <unk> The <unk> ( 1885 ) . Direct literary models were available from a number of sources , including F. V. <unk> 's 1866 <unk> <unk> is <unk> , or , <unk> by a Century of Poets , Being Japanese <unk> Odes , the first English @-@ language version of the <unk> <unk> , a 13th @-@ century anthology of 100 <unk> , the early 20th @-@ century critical writings and poems of <unk> Hartmann , and contemporary French @-@ language translations . 
- The American poet Ezra Pound was introduced to the group in April 1909 and found that their ideas were close to his own . In particular , Pound 's studies of Romantic literature had led him to an admiration of the <unk> , direct expression that he detected in the writings of <unk> Daniel , Dante , and <unk> <unk> , amongst others . For example , in his 1911 – 12 series of essays I gather the limbs of Osiris , Pound writes of Daniel 's line " <unk> de <unk> m <unk> <unk> " ( " it rests me to think of her " ) ( from the <unk> <unk> <unk> <unk> 'l <unk> <unk> ) : " You cannot get statement simpler than that , or clearer , or less <unk> " . These criteria of <unk> , clarity and lack of rhetoric were to be amongst the defining qualities of <unk> poetry . Through his friendship with Laurence <unk> , Pound had already developed an interest in Japanese art by examining <unk> @-@ e prints at the British Museum , and he quickly became absorbed in the study of related Japanese verse forms . 
- In an article in La France , 1915 , the French critic , <unk> de <unk> described the <unk> as descendants of the French <unk> and in a 1928 letter to the French critic and translator René <unk> , Pound was keen to emphasise another ancestry for <unk> , pointing out that <unk> was indebted to a <unk> tradition , linking back via William Butler Yeats , Arthur Symons and the <unk> ' Club generation of British poets to <unk> and the <unk> source was amplified further in <unk> 's study published in 1929 , in which he concluded however great the divergence of technique and language ' between the image of the <unk> and the ' symbol ' of the <unk> there is a difference only of precision ' . In 1915 , Pound edited the poetry of another 1890s poet , Lionel Johnson for the publisher <unk> Mathews . In his introduction , he wrote 
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- = = Early publications and statements of intent = = 
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- In 1911 , Pound introduced two other poets to the <unk> Tower group : his former fiancée Hilda <unk> ( who had started signing her work <unk> ) and her future husband Richard <unk> . These two were interested in exploring Greek poetic models , especially <unk> , an interest that Pound shared . The compression of expression that they achieved by following the Greek example complemented the proto @-@ <unk> interest in Japanese poetry , and , in 1912 , during a meeting with them in the British Museum tea room , Pound told <unk> and <unk> that they were <unk> and even <unk> the signature <unk> <unk> to some poems they were discussing . 
- When Harriet Monroe started her Poetry magazine in 1911 , she had asked Pound to act as foreign editor . In October 1912 , he submitted <unk> three poems each by <unk> and <unk> under the <unk> <unk> , ( published in the November 1912 second issue <unk> ) with a note which described <unk> as ' one of the ' <unk> ' . This note , along with the <unk> note ( ' The Complete Works of T. S. <unk> ' ) in Pound 's book ( also published in Autumn 1912 ) entitled <unk> are considered to be first appearances of the word <unk> ( later <unk> to ' <unk> ' ) in print . 
- <unk> 's poems , <unk> , To a Greek <unk> , and <unk> <unk> <unk> , were in the November issue of Poetry , and <unk> ' s , Hermes of the Ways , <unk> , and <unk> , appeared in the January 1913 issue ; <unk> as a movement was launched . Poetry 's April issue published what came to be seen as " <unk> 's enabling text " , the <unk> @-@ like poem of Ezra Pound entitled " In a Station of the Metro " : 
- The <unk> of these faces in the crowd ; 
- <unk> on a wet , black <unk> . 
- The March 1913 issue of Poetry contained A Few Don <unk> by an <unk> and the essay entitled <unk> both written by Pound , with the latter being attributed to Flint . The latter contained this <unk> statement of the group 's position : 
- Direct treatment of the " thing " , whether <unk> or objective . 
- To use absolutely no word that does not contribute to the presentation . 
- As regarding rhythm : to compose in sequence of the musical phrase , not in sequence of the <unk> . 
- Pound 's note opened with a definition of an image as " that which presents an intellectual and emotional complex in an instant of time " . Pound goes on to state , " It is better to present one Image in a lifetime than to produce <unk> works " . His list of " don <unk> " reinforced his three statements in " <unk> " , while warning that they should not be considered as <unk> but as the " result of long contemplation " . Taken together , these two texts comprised the <unk> programme for a return to what they saw as the best poetic practice of the past . <unk> Flint commented " we have never claimed to have invented the moon . We do not pretend that our ideas are original . " 
- The 1916 preface to Some <unk> Poets comments " <unk> does not merely mean the presentation of pictures . <unk> refers to the manner of presentation , not to the subject . " 
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- = = Des <unk> = = 
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- <unk> to promote the work of the <unk> , and particularly of <unk> and <unk> , Pound decided to publish an anthology under the title Des <unk> . It was first published in Alfred <unk> 's little magazine The <unk> and was later published in 1914 by Alfred and Charles <unk> in New York and by Harold <unk> at the Poetry <unk> in London . It became one of the most important and influential English @-@ language collections of modernist verse . Included in the thirty @-@ seven poems were ten poems by <unk> , seven by <unk> , and six by Pound . The book also included work by <unk> Flint , <unk> <unk> , Amy Lowell , William Carlos Williams , James Joyce , Ford <unk> Ford , Allen <unk> and John <unk> <unk> was also another included in the important 1963 anthology by William Pratt The <unk> Poem Modern Poetry in miniature . 
- Pound 's editorial choices were based on what he saw as the degree of sympathy that these writers displayed with <unk> precepts , rather than active participation in a group as such . Williams , who was based in the United States , had not participated in any of the discussions of the <unk> Tower group . However , he and Pound had long been corresponding on the question of the renewal of poetry along similar lines . Ford was included at least partly because of his strong influence on Pound , as the younger poet made the transition from his earlier , Pre @-@ <unk> @-@ influenced style towards a harder , more modern way of writing . The inclusion of a poem by Joyce , I Hear an Army , which was sent to Pound by <unk> Yeats , took on a wider importance in the history of literary <unk> , as the subsequent correspondence between the two led to the serial publication , at Pound 's behest , of A Portrait of the Artist as a Young Man in The <unk> . Joyce 's poem is not written in free verse , but in rhyming <unk> . However , it strongly reflects Pound 's interest in poems written to be sung to music , such as those by the <unk> and <unk> <unk> . The book met with little popular or critical success , at least partly because it had no introduction or commentary to explain what the poets were attempting to do , and a number of copies were returned to the publisher . 
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- = = Some <unk> Poets = = 
- 
- The following year , Pound and Flint fell out over their different interpretations of the history and goals of the group arising from an article on the history of <unk> written by Flint and published in The <unk> in May 1915 . Flint was at pains to emphasise the contribution of the <unk> Tower poets , especially Edward <unk> . Pound , who believed that the " Hellenic hardness " that he saw as the distinguishing quality of the poems of <unk> and <unk> was likely to be diluted by the " <unk> " of <unk> , was to play no further direct role in the history of the <unk> . He went on to co @-@ found the <unk> with his friend , the painter and writer <unk> Lewis . 
- Around this time , the American <unk> Amy Lowell moved to London , determined to promote her own work and that of the other <unk> poets . Lowell was a wealthy <unk> from Boston whose brother Abbott Lawrence Lowell was President of Harvard University from 1909 @-@ 1933 . She loved Keats and cigars . She was also an enthusiastic champion of literary experiment who was willing to use her money to publish the group . Lowell was determined to change the method of selection from Pound 's <unk> editorial attitude to a more democratic manner . This new editorial policy was stated in the <unk> to the first anthology to appear under her leadership : " In this new book we have followed a slightly different arrangement to that of our former Anthology . Instead of an arbitrary selection by an editor , each poet has been permitted to represent himself by the work he considers his best , the only <unk> being that it should not yet have appeared in book form . " The outcome was a series of <unk> anthologies under the title Some <unk> Poets . The first of these appeared in 1915 , planned and assembled mainly by <unk> and <unk> . Two further issues , both edited by Lowell , were published in 1916 and 1917 . These three volumes featured most of the original poets , ( also including <unk> poetry by the American poet John Gould Fletcher ) , with the exception of Pound , who had tried to persuade her to drop the <unk> name from her publications and who <unk> dubbed this phase of <unk> " Amy @-@ <unk> . " 
- Lowell persuaded D. H. Lawrence to contribute poems to the 1915 and 1916 volumes , making him the only writer to publish as both a Georgian poet and an <unk> . <unk> Moore also became associated with the group during this period . However , with World War I as a backdrop , the times were not easy for avant @-@ garde literary movements ( <unk> , for example , spent much of the war at the front ) , and the 1917 anthology effectively marked the end of the <unk> as a movement . 
- 
- = = <unk> after <unk> = = 
- 
- In 1929 , Walter <unk> <unk> suggested that <unk> should produce a new <unk> anthology . <unk> , by now a successful novelist , took up the suggestion and enlisted the help of Ford and <unk> The result was the <unk> Anthology 1930 , edited by <unk> and including all the contributors to the four earlier anthologies with the exception of Lowell , who had died , <unk> , who had disappeared , and Pound , who declined . The appearance of this anthology initiated a critical discussion of the place of the <unk> in the history of 20th @-@ century poetry . 
- Of the poets who were published in the various <unk> anthologies , Joyce , Lawrence and <unk> are now primarily remembered and read as novelists . <unk> Moore , who was at most a fringe member of the group , carved out a unique poetic style of her own that retained an <unk> concern with compression of language . William Carlos Williams developed his poetic along distinctly American lines with his variable foot and a diction he claimed was taken " from the mouths of Polish mothers " . Both Pound and <unk> turned to writing long poems , but retained much of the hard edge to their language as an <unk> legacy . Most of the other members of the group are largely forgotten outside the context of the history of <unk> . 
- 
- = = Legacy = = 
- 
- Despite the movement 's short life , <unk> would deeply influence the course of modernist poetry in English . Richard <unk> , in his 1941 memoir , writes : " I think the poems of Ezra Pound , <unk> , Lawrence , and Ford <unk> Ford will continue to be read . And to a considerable extent T. S. Eliot and his followers have carried on their operations from positions won by the <unk> . " 
- On the other hand , Wallace Stevens found shortcomings in the <unk> approach : " Not all objects are equal . The vice of <unk> was that it did not recognize this . " With its demand for hardness , clarity and precision and its insistence on fidelity to appearances coupled with its rejection of irrelevant <unk> emotions <unk> had later effects that are <unk> in T. S. Eliot 's ' <unk> ' and ' Morning at the Window ' and in D. H. Lawrence 's animal and flower pieces . The rejection of conventional verse forms in the nineteen @-@ twenties owed much to the <unk> <unk> of the Georgian Poetry style . 
- The influence of <unk> can be seen clearly in the work of the <unk> poets , who came to prominence in the 1930s under the auspices of Pound and Williams . The <unk> worked mainly in free verse . <unk> linking <unk> 's principles with <unk> 's , Louis <unk> insisted , in his introduction to the 1931 <unk> issue of Poetry , on writing " which is the detail , not <unk> , of seeing , of thinking with the things as they exist , and of directing them along a line of melody . " <unk> was a major influence on the Language poets , who carried the <unk> focus on formal concerns to a high level of development . Basil <unk> , another <unk> poet , was a key figure in the early development of the British Poetry Revival , a loose movement that also absorbed the influence of the San Francisco Renaissance poets . 
- <unk> influenced a number of poetry circles and <unk> the <unk> Free verse became a discipline and acquired status as a legitimate poetic form . In the 1950s , especially , with the Beat generation , the Black Mountain poets , and others associated with the San Francisco Renaissance . In his seminal 1950 essay <unk> Verse , Charles <unk> , the theorist of the Black Mountain group , wrote " <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> A <unk> <unk> " ; his <unk> derived from and supplemented the <unk> . 
- Among the Beats , Gary <unk> and Allen Ginsberg in particular were influenced by the <unk> emphasis on Chinese and Japanese poetry . William Carlos Williams was another who had a strong effect on the Beat poets , encouraging poets like Lew Welch and writing an introduction for the book publication of Ginsberg 's <unk> ( 1955 ) . 
- 
- 
- = Operation Eastern Exit = 
- 
- Operation Eastern Exit was the <unk> given to the military evacuation of the United States embassy in Mogadishu , the capital of Somalia , in January 1991 . In late December 1990 , violence quickly enveloped the city as armed militants began <unk> with government soldiers . On 1 January 1991 , the US Ambassador to Somalia , James <unk> Bishop , contacted the Department of State requesting an evacuation of the embassy , which was approved the following day . United States Central Command began planning and <unk> forces that evening . The initial plan was to evacuate with a military transport plane through the Mogadishu International Airport , but this was later abandoned . A helicopter evacuation via the USS Guam and USS <unk> was the remaining option . 
- On the morning of 5 January , a 60 @-@ person Marine and Navy <unk> security detail was dispatched from Guam aboard two CH @-@ <unk> Super <unk> helicopters to secure the embassy and prepare for the main evacuation . The two helicopters returned to Guam with the first 61 <unk> . Throughout the day , foreign diplomats and civilians sought refuge at the embassy . Four waves of five CH @-@ 46 Sea Knight helicopters each evacuated the embassy compound shortly after midnight on 6 January . The <unk> were transported to Muscat , Oman , where they disembarked on 11 January . In total , 281 diplomats and civilians from 30 countries were evacuated , including 12 heads of missions ( eight ambassadors and four <unk> d <unk> ) . 
- 
- = = Background = = 
- 
- In the late 1980s , there was increasing rebellion against the rule of Somali President Siad Barre , a military dictator who maintained tight control of power and had a record of human rights abuses . By 1990 , what began as civil disobedience evolved into a civil war , with several militias organized to overthrow the central government . 
- In July 1989 , the embassy moved to a new , 80 @-@ acre ( 32 ha ) compound , 6 miles ( 9 @.@ 7 km ) from the previous embassy and James K. Bishop was appointed as the United States ' ambassador to Somalia . Ambassador Bishop had significant experience in crisis management at US embassies . In 1967 , he was at the US Embassy in Beirut , Lebanon when the Six @-@ Day War erupted . About 3 @,@ 600 Americans were evacuated in 33 hours ; Bishop was one of 26 diplomats and soldiers that remained in the city . As deputy assistant secretary of state for Africa from 1981 – 87 , Bishop chaired several task forces for <unk> and gained experience in the State Department 's operations center as <unk> were carried out during several coups d <unk> . During his previous assignment as Ambassador to <unk> ( 1987 – 90 ) , Bishop was overseeing the voluntary evacuation of embassy staff and civilians as a civil war in <unk> spread , when he left in March 1990 . Soon after returning to Washington to prepare for his new appointment to Somalia , he was appointed to a <unk> to deal with the crisis in <unk> , which included a gradual evacuation of American civilians and a rapid closure of the embassy in August . 
- On 1 August , before leaving the US to take up his post in Mogadishu , Ambassador Bishop visited United States Central Command — the military command for the Middle East and northeast Africa — where he spent most of the day with its commander , Gen. Norman <unk> . Ambassador Bishop , aware of the ongoing <unk> , believed " the odds were better than even that we would have to leave Mogadishu under less than favorable circumstances . " Ambassador Bishop understood from his past experiences in Beirut and <unk> the importance of being prepared to deal with <unk> and spent the afternoon working with military experts to review the embassy 's <unk> and <unk> ( E & E ) plan until he was " satisfied ... that [ Central Command ] realized that it might have to conduct an evacuation from Mogadishu and was prepared to do that . " In its analysis of Operation Eastern Exit , the Center for Naval Analyses cited the Ambassador Bishop 's previous experience and " clear understanding of his role " in the operation as one of the reasons Operation Eastern Exit went so well . 
- <unk> after Ambassador Bishop 's visit to Central Command , Iraq invaded Kuwait . In 1979 , the US negotiated access to an airport and port in both Mogadishu and <unk> ; because of limited access the US had to locations in the Persian Gulf area , maintaining this access was a main interest for the Mogadishu embassy to pursue as the US mobilized to intervene in Kuwait . 
- An increasing level of criminal violence prompted Ambassador Bishop to request the voluntary evacuation of dependents ( e.g. children and spouses of staff ) and non @-@ essential staff in early December , although fighting between the government and the United Somali Congress ( a rebel militia ) remained no less than about 100 miles ( 160 km ) away . The voluntary evacuation later became a mandatory evacuation . By 19 December , the number of official US personnel in the city was reduced from 147 to 37 ; around the same time , fighting between the government and rebels came within about 40 miles ( 64 km ) of Mogadishu . 
- 
- = = = <unk> of the Barre government = = = 
- 
- On 30 December , violence escalated " an order of magnitude " as militants entered Mogadishu , which was quickly enveloped by a general state of lawlessness . On 30 – 31 December , diplomats , including many stationed in offices elsewhere in the city , were collected and housed in the embassy compound , except two volunteers who remained in the embassy 's K @-@ 7 residential apartments located across <unk> Road from the embassy . The volunteers in the K @-@ 7 building would be needed as look @-@ outs for the embassy compound 's main gate . On the morning of 31 December , the defense <unk> was nearly killed when his vehicle was <unk> with bullets and that evening , a soldier at a roadblock shot the <unk> of a vehicle carrying another defense official . Attempts by the US and other nations ' diplomats , in particular the Italian embassy , to negotiate a ceasefire for foreigners to leave were unsuccessful . <unk> Road became a " shooting gallery , " preventing those in safe @-@ <unk> outside the embassy from reaching it . On New Year 's Day , the first American civilians began to seek refuge at the embassy . 
- Ambassador Bishop requested an evacuation of the American community on 1 January , indicating that the evacuation could be with the planned Italian , French , or German evacuation efforts , but preferred an evacuation by the US military . The State Department authorized the evacuation on 2 January and on that day , Ambassador Bishop specifically requested an evacuation by the US military , thereby initiating Operation Eastern Exit . Ambassador Bishop had spent a considerable amount of time discussing <unk> plans for evacuation with other diplomatic posts . Ultimately , ten heads of missions — eight ambassadors and two <unk> d <unk> — along with their staff sought refuge in the US embassy compound and were evacuated . 
- 
- = = Plans , <unk> , and escalating violence = = 
- 
- Ambassador Bishop had visited Central Command in August 1990 , where he worked with military experts to update the embassy 's E & E plan . The first notice that an evacuation of the Mogadishu embassy would be needed came on the morning of 1 January , when the top naval commander at Central Command sent a message to his naval operations staff : " Better have <unk> crowd take a look at a <unk> <unk> of Mogadishu ! time / distance to get there from <unk> <unk> area . " Following the ambassador 's 2 January evacuation request , the commander of Central Command ordered Air Force aircraft to the region , the movement of amphibious ships to Mogadishu , and requested United States Special Operations Command to prepare for a noncombatant evacuation operation . 
- The initial plan was to evacuate via Mogadishu International Airport . Soon after the evacuation request , the United States Air Force deployed C @-@ 130 transport planes and an AC @-@ 130 , for gunfire support , to <unk> , Kenya , awaiting clearances to enter Somalia and the ability to safely transfer <unk> from the embassy to the airport . However , the US and other foreign embassies were unable to contact anyone within the government to obtain clearances . It also became apparent that the rebels had an ineffective command @-@ and @-@ control structure , making it impossible to negotiate any ceasefire or guarantee of safe passage . Likewise , government troops faced a command @-@ and @-@ control problem ; reports indicated that army units were separating along clan lines , in some cases soldiers shot officers of a different clan when given orders they disagreed with . Thus , it became clear that safe passage to the airport would not be possible . Several other nations also had aircraft mobilized to reach Mogadishu , but faced the same problems of landing and transit of <unk> to the airport . 
- On 4 January , several incidents , including a couple exchanges of gunfire , suggested that the embassy 's security detail was insufficient to hold off armed <unk> until the USS Guam and USS <unk> arrived with their helicopters and soldiers , at that time scheduled to arrive on 7 January . The embassy had just six Marine guards , whose job was limited to protecting the <unk> . Ambassador Bishop made an urgent request to Washington for two platoons of soldiers to parachute into the embassy to defend it until the ships arrived . The request was denied , but the Ambassador was told that an advance element from the vessels would reach the embassy the following morning . 
- USS Guam and USS <unk> began transit from the coast of Oman towards Mogadishu at 22 : 30 ( 23 : 30 Oman time ) on 2 January . The commander of <unk> Group Two had initially proposed a seven @-@ ship <unk> Task Group , composed of vessels anchored at <unk> Island ( off Oman ) and Dubai and including four amphibious ships so that the full range of amphibious capabilities would be available for the operation . However , intervention in Kuwait seemed imminent and the commander of naval forces at Central Command did not want to divert that many ships from the Persian Gulf , thus the decision to send two of the closest ships . Although the two vessels were selected by mid @-@ afternoon on 2 January , the transfer of some personnel from Dubai to <unk> caused a delay of eight to ten hours . Guam and <unk> carried forces from the 4th Marine Expeditionary Brigade , including a detachment of CH @-@ <unk> Super <unk> helicopters — the largest helicopters operated by the US military — and two squadrons of CH @-@ 46 Sea Knight helicopters . 
- Planning began in earnest as the ships got underway , with a combined command center on Guam . On the morning of 3 January , the task force 's command questioned why they were not given the option of an amphibious landing and requested a tank landing ship be added to the task force ; the request was denied . A warrant officer who had previously served as a Marine Security Guard at the Mogadishu embassy during the mid @-@ 1980s was found . Despite Ambassador Bishop 's planning with Central Command , the task force was provided outdated information . The former <unk> told planners that a new embassy had been planned and was under construction several years prior . In fact , the new embassy was located further inland and , after receiving updated information , task force commanders determined that a beach landing , requiring troops to fight their way across the city , was too risky . Initial plans had the ships launch their helicopters at 01 : 00 on 7 January . However , in response to indications from Ambassador Bishop that conditions in Mogadishu were deteriorating , planners considered 1 @,@ 050 @-@ nautical @-@ mile ( 1 @,@ 940 km ; 1 @,@ 210 mi ) and , later , 890 @-@ nautical @-@ mile ( 1 @,@ 650 km ; 1 @,@ 020 mi ) flights with the CH @-@ <unk> while the ships were still located in the northern Arabian Sea . The situation in Mogadishu stabilized somewhat and the mission was delayed until 5 January . 
- 
- = = <unk> = = 
- 
- On the evening of 4 January , the final execute order was issued for a 02 : 45 launch of two CH @-@ <unk> Super <unk> to arrive at the embassy at dawn . The 60 soldiers selected for the security detail were issued weapons and ammunition . Two Marine Corps <unk> @-@ 130 <unk> tankers were mobilized closer to the operation , from Bahrain to Oman , to refuel the helicopters en route to Mogadishu and the two helicopters transferred from <unk> to Guam . 
- 
- = = = Security detail and first <unk> = = = 
- 
- Two CH @-@ <unk> Super <unk> carrying a 60 @-@ man security detail — 51 Marines and nine Navy <unk> — departed Guam at 02 : 47 , <unk> nautical miles ( <unk> km ; <unk> mi ) from the embassy , and were expected to arrive at 06 : 20 . They performed two aerial <unk> . During the first <unk> , a pipe burst on one of the helicopters , <unk> soldiers in fuel and nearly forcing a return to the Guam ; problems with the helicopters ' navigation system also complicated the <unk> rendezvous . The helicopters arrived in Mogadishu at dawn , crossing the coast just south of the harbor at 25 – 50 feet ( 7 @.@ 6 – 15 @.@ 2 m ) in altitude on a route that was planned to avoid areas of more intense violence reported in the northern parts of the city . On their arrival in Mogadishu , the crew of the helicopters were using an outdated 1969 map , which showed the embassy in an isolated area . Furthermore , they had been told the embassy could be discerned by its white stucco perimeter wall and golf course . The embassy was , in fact , surrounded by new development and the crew saw white stucco walls around many buildings in the city . The helicopters were flying too low to spot a <unk> light which was placed on the embassy 's water tower ( the highest point within the embassy compound ) and the golf course in the embassy compound had a black , oil @-@ coated surface — not the familiar green grass that the helicopter crew would <unk> breaking radio silence ( their only direct communication with the embassy was <unk> ) to contact the embassy , they were able to <unk> it and land at 07 : 10 . As they arrived , a group of about 100 to 150 <unk> were attempting to enter the embassy compound via <unk> on the wall , but scattered as the helicopters arrived . 
- The security detail moved to establish a perimeter around the embassy compound and the Air Force 's AC @-@ 130 arrived to provide overhead support . Ambassador Bishop gave the security detail clear instructions on the rules of engagement : they could only use deadly force if people came over the embassy compound 's walls with obvious hostile intent . He also identified three zones of defense , stating a preference to retreat to the third zone before the use of deadly force : 
- the entire embassy compound 
- the Chancery , Joint Administrative Office ( <unk> ) building , Marine House , and the helicopter landing zone ( <unk> ) 
- the <unk> and <unk> buildings ( the two " <unk> " buildings where the <unk> were held ) 
- Ambassador Bishop clearly explained his rationale to the security detail , which was to avoid any impression that they were intervening in the violence in Mogadishu . He feared that the embassy would be targeted by organized attacks if any group involved in the clashes got the impression that the US was intervening in the conflict . To this effect , he requested the Voice of America and BBC broadcast announcements that the forces were present only to evacuate the embassy and would not interfere in the conflict . The Marines who had been <unk> in fuel during the <unk> were able to take a shower and wash their clothes . 
- After an hour on the ground , the helicopters left with the first 61 <unk> , including all American civilians and four heads of mission . <unk> were provided blankets on one of the flights to remain warm . <unk> with the only in @-@ flight <unk> on the return nearly prevented <unk> , which would have forced the helicopters to divert to the Somali desert and await a rescue . At 9 : 40 , the helicopters arrived on Guam and <unk> the <unk> . 
- 
- = = = Embassy during the day = = = 
- 
- No threats came upon the embassy during the day , although <unk> of armed <unk> frequently drove by the embassy along <unk> Road . Only one incident seemed to directly target the embassy . A sniper and a <unk> were positioned on the embassy 's water tower ( the highest structure in the compound ) and came under fire ; they were ordered to not return fire and soon thereafter ordered to leave their position on the water tower . 
- The Office of Military Cooperation , just one and a half blocks from the embassy , required evacuation . Despite its proximity to the embassy , an armed convoy was needed to evacuate persons trapped there by the unrest . A convoy of vehicles with several Marines and <unk> left the embassy at 8 : 47 and returned ten minutes later with 22 persons from the <unk> ( four Americans , a <unk> , and 17 <unk> ) . This was the only <unk> outside the embassy by the security detail . Throughout the day , foreign diplomats contacted the embassy <unk> to be evacuated ; the US welcomed these requests , but required all of them to find their own transportation to the embassy . 
- A Somali officer who had a previous relationship with the embassy , Major Siad , agreed to travel to rescue the German <unk> d <unk> and British ambassador ( junior staff from the British embassy had previously come to the US embassy ) . The Soviet Union was unable to land a plane in Mogadishu the previous day and the Soviet ambassador asked Ambassador Bishop if he and his staff could be rescued ; Ambassador Bishop , a tennis partner of his Soviet counterpart , agreed but only if they found their own way to the embassy . Seeing the helicopters on the morning of 5 January , they realized the Americans would not remain in the city much longer . At the request of Ambassador Bishop , Major Siad agreed to transport the Soviets , but only if he was paid enough ; the US embassy paid Major Siad , who returned with the Soviet ambassador and 38 of his staff . The brother of President Barre , who was also a Major General and Chief of Police , showed up at the embassy in the afternoon with 25 members of his family requesting to be evacuated , but was turned away after a vocal conversation with the ambassador . 
- The operation did not include soldiers to handle the evacuation control center ( <unk> ) , which was set up in the <unk> . A 44 @-@ person force consisting primarily of soldiers to handle the <unk> was planned for insertion with the CH @-@ <unk> Super <unk> after they had returned to the Guam . However , this was cancelled over objections from the commander of the security detail . The deficit was partially handled by embassy staff who assisted a few soldiers from the security detail . The <unk> were grouped into 15 @-@ person " sticks " to be loaded onto the helicopters and were limited to one piece of luggage apiece . Some attempted to bring more , resulting in problems coordinating their evacuation . Furthermore , many <unk> had pets they wanted to bring , which were not allowed . Most pets were killed by their owners ; some were given poison . Meanwhile , the soldiers were allowed to consume anything they wanted from the embassy 's <unk> , such as candy , <unk> , and souvenirs ( most had been stationed on ships for several months ) . They were also allowed use or take anything they needed from the embassy ; the <unk> filled several bags with medical supplies to return to the ship . 
- As evening approached , work began to prepare the <unk> for the main evacuation . The area was used as a parking lot and several vehicles were left without keys by staff that had already been evacuated . Some cars had to be broken into to be moved . Chemical lights were placed in the <unk> in a NATO " Y " pattern . The entire mission would be conducted with night vision <unk> , which required all lights in the embassy compound to be turned off . 
- 
- = = = Main evacuation = = = 
- 
- The main evacuation occurred in the early morning hours of 6 January and consisted of four waves of five CH @-@ 46 helicopters . The timing of this phase was determined by range of the CH @-@ 46 Sea Knight , which lack aerial <unk> capability ; the ships were about 350 – 380 nautical miles ( 650 – 700 km ; 400 – 440 mi ) away during this phase . An AC @-@ 130 was sent from Saudi Arabia to provide gunfire support during the evacuation and two <unk> @-@ 1 Iroquois helicopters were on standby to provide gunfire support , but were not deployed . 
- The first wave departed Guam at 23 : 43 . As the second wave landed , Major Siad arrived at the embassy gate accompanied by two <unk> of soldiers and held a grenade in one hand and a radio in the other . His request to speak with the ambassador was granted . Major Siad demanded that the evacuation cease immediately because the Somali government had not granted the US permission to carry out such a military operation . He claimed that he would radio soldiers to shoot down the helicopters if the operation continued . The second and third waves were able to depart without incident as the ambassador negotiated with the Major , who finally agreed to settle the matter for several thousand dollars in cash and keys to the ambassador 's armored car . Ambassador Bishop remained engaged in conversation with the Major until he reached the helicopter landing zone to depart with the final wave to prevent the Major from <unk> on the deal . The final wave departed the embassy at 1 : 49 and landed on Guam at 2 : 23 ; twenty minutes later , Ambassador Bishop declared the evacuation complete . 
- 
- = = = Aftermath at the embassy = = = 
- 
- Armed <unk> were observed entering the embassy compound as the final wave departed . The doors of the <unk> — the main building of the embassy — were reportedly blown open by <unk> within two hours of the embassy 's evacuation . Somali employees of the embassy — known as foreign service <unk> ( <unk> ) — could not be evacuated . Ambassador Bishop tried unsuccessfully to have these employees <unk> to safer parts of Somalia . Many of the <unk> had sought refuge in the embassy with their families and about 30 were hired as guards and protected the embassy throughout the ordeal . Local banks had been closed for some time and the embassy was unable to pay the <unk> . The Ambassador left the <unk> with keys to the <unk> and warehouse on the embassy compound and they were permitted to take anything they needed . 
- 
- = = = Return to Oman = = = 
- 
- A total of 281 <unk> were taken from the embassy , including 12 heads of missions ( eight ambassadors and four <unk> d <unk> ) and 61 Americans ( including Ambassador Bishop and 36 embassy staff ) . The heads of mission were the ambassadors of the United States , Kenya , Nigeria , Soviet Union , Sudan , Turkey , United Arab Emirates , and United Kingdom and the <unk> of the embassies of Germany , Kuwait , Oman , and <unk> . 
- Rather than disembark in nearby <unk> , as originally thought by the <unk> , the ships were ordered back to Oman — a five @-@ day journey . The sailors and marines made way for the <unk> to share living quarters . When the <unk> of Guam asked crew to sign up as guides for the <unk> while aboard the vessel , two hundred signed up within an hour , and some of the sailors even dressed up as <unk> to ease the ordeal for children . At the request of the ambassadors , a formal session with the ships ' senior officers was held to express their thanks . On 11 January , the <unk> were <unk> at Muscat , Oman . That afternoon , the American <unk> were flown to Frankfurt , Germany , from where they continued home . 
- 
- 
- = 2010 <unk> Shield = 
- 
- The 2010 <unk> Shield was the 57th <unk> Shield tournament , the premier baseball competition in Australia , and was held from 6 November 2009 to 7 February 2010 . It was hailed as the precursor to the new Australian Baseball League that will start in the place of the <unk> Shield in late 2010 to early 2011 . The Victoria <unk> defeated South Australia two games to nil in the championship series to win the tournament ; this was the 22nd time the <unk> Shield had been awarded to a Victorian team . The competition was sponsored by <unk> 's <unk> . 
- At the conclusion of the regular season , the Victoria <unk> finished in first place with a 17 – 7 record , earning home @-@ field advantage for the three @-@ game championship series . South Australia hosted the three @-@ game semi @-@ final series against the New South Wales <unk> . Both teams finished with a 14 – 10 record . The Perth Heat ( 12 – 12 ) and Queensland <unk> ( 3 – 21 ) both failed to qualify for the finals . 
- 
- = = Overview = = 
- 
- In June 2009 , it was announced that the rights to the <unk> Shield had been sold to a new Australian Baseball League ( <unk> ) , with ownership split between Major League Baseball 's 75 percent share and the 25 percent share owned by the Australian Baseball Federation . The 2010 tournament was considered preparation for the inaugural <unk> season starting in 2010 – 11 . It varied from the 2009 <unk> Shield by expanding the season to include ten rounds . Since an uneven number ( five ) teams were involved , four teams paired off for each round and played a three @-@ game series , while the remaining team took a bye . During the season , each team had two bye rounds and played two rounds against each other team , one at home and one away . In total , the schedule allowed for 24 regular @-@ season games per team before a postseason similar to the 2009 edition : the first @-@ place team directly qualified for the championship series and played against the winner of a playoff series between the second- and third @-@ place teams . 
- During the regular season , games were played on a Friday night and a <unk> on Saturday ; in each <unk> one of the two games was shortened to seven innings . The exception to this was when Perth played their home games ; they played on a Thursday night instead of a <unk> on Saturday . Each postseason series was scheduled for a Friday , Saturday and Sunday . 
- 
- = = Teams = = 
- 
- 
- = = = <unk> = = = 
- 
- The 2010 series allowed each team to make use of a 19 @-@ man active roster . <unk> were made in two cases that allowed teams ' active <unk> to expand to 21 players , both times for the same reason . Two games during the season had to be postponed because of poor weather . Both games involved teams meeting for the first time during the season ; make @-@ up games were scheduled at the start of the return series between the teams , and this resulted in two four @-@ game series . In both cases , the teams had a 19 @-@ man roster for the make @-@ up game , and an expanded 21 @-@ man roster for the originally scheduled series . 
- 
- = = = <unk> = = = 
- 
- The 2010 <unk> Shield was contested between five teams from around Australia . In previous years , many of the teams had played their home games at multiple venues . This season each team held their home games at only one venue . There was one scheduled exception to this at the start of the season : the New South Wales <unk> ' final home series against the Perth Heat was held at Gilchrist Oval , whereas all of their other home games were held at <unk> Baseball Stadium . 
- As a result of poor attendance at <unk> Baseball Park , game one of the fifth @-@ round series between New South Wales and the Victoria <unk> was moved to La <unk> University , Melbourne . Although the <unk> games had attracted crowds of no more than 500 , the moved game had an attendance of 2 @,@ 200 . Though no further regular season games were moved , the finals series hosted by the <unk> was held at La <unk> University as well . 
- The venues are as follows : 
- 
- = = Regular season = = 
- 
- † — A game postponed from Round 7 , held in Round 8 , was played with Victoria <unk> as the away team and Queensland <unk> as the home team , despite being played at <unk> Baseball Park , <unk> , Victoria . 
- <unk> — A game postponed from Round 3 , held in Round 9 , was played with South Australia as the away team and Victoria <unk> as the home team , despite being played at Norwood Oval , Adelaide , South Australia . 
- The Queensland <unk> were the first team to be eliminated from contention for the finals , after being swept four games to nil by the Victoria <unk> in round 8 . The following round saw South Australia clinch a position in the finals , despite finishing the round in second position . It was not until the final round that the last two spots in the finals were decided : the <unk> clinched top spot by sweeping the Perth Heat , which combined with the New South Wales <unk> sweep of the <unk> eliminated Perth from contention and secured the last finals spot for the <unk> . 
- 
- = = = Statistical leaders = = = 
- 
- 
- = = Finals series = = 
- 
- The 2010 <unk> Shield made use of the same finals structure as had been used in the 2009 season . The top three teams at the conclusion of the ten rounds of regular @-@ season games qualified . The second- and third @-@ place teams faced in each other in a best @-@ of @-@ three series hosted by the second @-@ place team . The winner of that series then faced the first @-@ place team for a best @-@ of @-@ three series . South Australia hosted the New South Wales <unk> at Norwood Oval , Adelaide , while the Victoria <unk> hosted the championship series at La <unk> University , Melbourne . In the finals , the home team and away team alternated during each of the series . As a result , South Australia was officially the away team for game two of its series against New South Wales , as was Victoria in the championship series . 
- After defeating the <unk> two games to one in the semi @-@ final series , South Australia progressed to the championship series against the <unk> . There they were defeated two games to nil . After game two of the championship series , Victoria 's Matthew <unk> was named both <unk> Shield Final Series MVP and Pitcher of the Year . 
- 
- = = = <unk> @-@ final series = = = 
- 
- 
- = = = Championship series = = = 
- 
- 
- = = Awards = = 
- 
- At the conclusion of the finals series , the winner of two awards were announced . Matthew <unk> won both the Pitcher of the Year award and the Finals Series MVP award . At the Baseball Australia Diamond Awards , held on 6 March at the Hotel Grand Chancellor , Adelaide , Wayne <unk> was announced as the 35th winner of the Helms Award ; the <unk> Shield 's Most Valuable Player award . <unk> was the first pitcher to win since 1986 . Runners @-@ up by two votes were Paul <unk> and Michael Collins . 
- 
- 
- = <unk> ( Ricardo <unk> album ) = 
- 
- <unk> is the thirteenth Spanish @-@ language studio album by <unk> singer @-@ songwriter Ricardo <unk> , released on 23 September 2011 . Recorded in the United States and Mexico , it was produced by <unk> with Dan Warner , Lee Levin and Puerto <unk> singer @-@ songwriter Tommy Torres . The album — the first independent release by <unk> after he was signed by Sony Music in 1993 and Warner Music in 2008 — was issued by his own label , <unk> . 
- <unk> and written in a year , the record marks <unk> and Torres ' fourth collaboration . For <unk> , <unk> returns to his trademark sound after his stylistic departure for <unk> <unk> ( 2010 ) . While producing the latter , he had used fewer instruments to <unk> his sound , having introduced what had been called a " stripped @-@ down acoustic effort " in his music . <unk> has been compared to his earlier recordings , <unk> ( 1994 ) and Animal <unk> ( 1993 ) . 
- <unk> became <unk> 's fourth number @-@ one album on the Billboard Top Latin Albums where it debuted for the week ending 22 October 2011 . For thirteen non @-@ consecutive weeks it topped the Latin Pop Albums chart , and reached number one on the Mexican Albums Chart . It is his fifth consecutive album to chart on the Billboard 200 ( reaching number sixty @-@ five ) , and his fourth album to chart in Spain ( peaking at number sixty @-@ eight ) . Within one week after its release <unk> was certified gold in Chile , the United States and Mexico and certified platinum in Venezuela and Argentina . 
- Five singles have been released from the album . The lead single , " El Amor " , became a commercial success in several Latin American countries and was number one on the Billboard Latin Songs and Latin Pop Songs charts . It was followed by " <unk> Tú " ( featuring <unk> <unk> ) , which reached number one on the Latin Pop Songs , number two on the Latin Songs charts and topped several other national charts . " Mi <unk> Se Me <unk> <unk> <unk> " was released in May 2012 ; " Te <unk> " in July 2012 , and " Si Tu No <unk> " in November 2012 . To promote <unk> , <unk> embarked on his <unk> World Tour . 
- 
- = = Background = = 
- 
- In 2010 , <unk> wanted to change his musical style ; after experimenting with using as few instruments as possible , he obtained a sound similar to an a <unk> performance ( <unk> his sound ) and introduced what he called a " stripped @-@ down acoustic effort " to his music . This was heard on his twelfth studio album , <unk> <unk> . <unk> produced the album with Dan Warner , who has worked with <unk> , <unk> Dion and Christina Aguilera . When promoting the album <unk> said , " [ songs ] are like women ; they get things up and are so concerned about this that they forget that the less clothes , more beauty . The songs are often overwhelmed by ourselves , because we <unk> them with arrangements looking to <unk> their qualities and we end up hiding them " . <unk> <unk> became the first album since <unk> which <unk> recorded without Torres . 
- Weeks before the release of <unk> , <unk> issued a letter raising the issue of his past relationships with recording companies . He revealed the circumstances of his first contract : " a producer , friend of mine , told them [ the record label ] that if they did not sign me in , they won 't sign two artists he had [ at that time ] " . <unk> further explained that he received the " minimum royalty percentage " from his most successful albums . <unk> is <unk> 's first independent release through his own label : <unk> , a company he created to <unk> his career . The company is presided by <unk> and several friends ( including photographer @-@ director Ricardo Calderón , Universal Music <unk> executive <unk> <unk> and BMG 's Miriam <unk> ) , and is based in Miami and Mexico City . <unk> commented that his independence represented compromise more than freedom , stating that " Inside the word ' Independent ' , even when it sounds like extreme freedom , there 's a big amount of compromise and the responsibility of being able to <unk> , in the best way possible , such independence " . Billboard notes that , although other groups have released independent albums following contracts with major labels , <unk> is the most important Latin pop artist to do so . Although the album is marketed within the new label , distribution was handled by Warner Music . 
- 
- = = Production and recording = = 
- 
- <unk> marked <unk> 's fourth collaboration with Torres . The latter was a composer and producer , also receiving background @-@ vocal credit . The musicians first worked together in 2005 , when <unk> released his tenth studio album ( <unk> ) . He stated that he first " tested " Torres by sending him the " <unk> and <unk> tracks " on the album : " <unk> A <unk> Solo " and " <unk> " . Torres then " went all out on the first demo , hiring a full band that included a string orchestra " . In <unk> <unk> <unk> ( 2007 ) , Torres produced the singles " <unk> " and " <unk> " and provided background vocals on the remastered versions of <unk> 's past hits . In <unk> <unk> ( 2008 ) , Torres produced several tracks ; one was the lead single " <unk> <unk> " , considered <unk> 's " biggest hit in years " by Jason <unk> of Allmusic . 
- The album was composed over a one @-@ year period . Most of its production was handled by three producers familiar with <unk> 's work : Dan Warner , Lee Levin and Dan <unk> . Tommy Torres also produced three tracks : the lead single " El Amor " , second single " <unk> Tú " and " Hay <unk> " . Victor <unk> produced two songs , ( " <unk> " and the piano version of " Mi <unk> Se Me <unk> <unk> <unk> " ) and Julio <unk> aided in the production of " <unk> " . <unk> wrote all the songs except " El Amor " ( which was co @-@ written with Torres ) . The album was recorded and produced in several cities in the United States and Mexico . <unk> was mixed at the Blue <unk> in Nashville , Tennessee , and mastered by Tom Coyne and <unk> Merrill at Sterling Sound in New York City . With Torres ' return to producing <unk> regained the classic , trademark sound which Torres helped develop since 2005 . 
- 
- = = Composition = = 
- 
- <unk> opens with " Lo Que <unk> <unk> <unk> Mal " , a Latin pop song and the only track composed by Dan Warner instead of <unk> ( who wrote the lyrics ) . " El Amor " was motivated by <unk> 's desire to examine " those big , dark events within love that nobody talks about " ; he continued , " [ the ] dark sides of love are extremely fundamental to understand its great value . " <unk> added , " So many good things <unk> love has been shown that somebody had to turn it around and tell the bad ones " . In a February 2012 interview , <unk> stated that " El Amor " was the " most <unk> " song he had released to date , explaining that their choice of the song was a " contradiction " because it was not " the song which could better represent the entire album " . He described it as " very strong " and " a bit dark " . The single marked <unk> 's return to his signature , mainstream sound after the Cuban music influenced <unk> <unk> 's lead single " <unk> " , a mixture of salsa and merengue which failed to make an impact in the United States . 
- The album includes " <unk> Tú " , a duet with <unk> singer <unk> <unk> . Its instrumentation consists of piano , violin , guitars , drums and other percussion . Although <unk> stated that he " had the possibilities to record this song with very well known people " he expressed his happiness with <unk> , revealing that " the possibilities of doing it with her , for me , are a celebration " . He described <unk> as " incredibly talented " , a " <unk> " and a " fantastic human being " . <unk> named " <unk> Tú " as one of the most important songs on the album . " Mi <unk> Se Me <unk> <unk> <unk> " took two years to complete and <unk> dedicated it to his mother , <unk> <unk> . He stated that he wrote it " as a gift for my mom on Mother 's Day " and that he thought " the idea of including it on the album was very good " . As with his single " Señora De Las <unk> <unk> " ( on 1994 's <unk> ) , at first he never thought to include the song on an album . " <unk> " evokes " the image of some friends " <unk> had at college ; he asserted that he " appears constantly there because sometimes we transform ourselves into a contradiction of all those things we fought in those moments . It 's the history of a student leader that becomes a president " . <unk> dedicated the album to his father , who died in 2011 . 
- 
- = = Release and promotion = = 
- 
- <unk> was first digitally released in some South American countries on 23 September 2011 as a special edition , dubbed the <unk> Sur Edition . This version included a different mix of " <unk> " . On 30 September , the digital download for the standard edition of the album was released in several Latin American and European countries . On 4 October , the album was officially released as a digital download and compact disc in most of these same markets as well as North America ; an iTunes edition was released as a digital download on the iTunes music store . This version included an album @-@ only video , entitled " <unk> " . In Germany , the album was first available on the <unk> label on 4 October and on 11 October through Warner Music . In Canada and Spain , the compact @-@ disc version of the album was available on 25 October . 
- <unk> appeared on a television special in 2011 to promote <unk> . The special featured guest appearances by <unk> <unk> , Ricky <unk> ( of the Mexican band <unk> ) and <unk> la del <unk> . Broadcast by <unk> , the program showcased the fourteen songs on <unk> . <unk> said that he was " happy to do things for Ricardo [ <unk> ] " , elaborating that they met each other " some time ago " and it was " a very special situation " . The show was later <unk> on 5 November by Canal de las <unk> . 
- 
- = = Singles = = 
- 
- The first single from <unk> is " El Amor " , released on 23 August 2011 . In the United States it reached number one on the Billboard Top Latin Songs chart ( <unk> 's fourth number one on that chart , following " <unk> " , " <unk> " and " El <unk> " ) and number one on the Billboard Latin Pop Songs chart . It was also a hit in Latin America , reaching number one in Argentina , Mexico , Colombia , Venezuela , Chile , Costa Rica , Panama and Guatemala . The music video for " El Amor " , filmed in black @-@ and @-@ white , was released on 8 September 2011 . It was directed by Ricardo Calderón ( who also directed <unk> 's music video for " <unk> <unk> " ) and filmed in Mexico City . The second single from the album is " <unk> Tú " , a duet with <unk> singer <unk> <unk> . The music video for the song was shot in Guatemala ( around the tropical areas of Antigua Guatemala , <unk> Dulce , the <unk> lake , <unk> <unk> and the <unk> ruins ) and directed by Argentine director Joaquín <unk> . <unk> commented that " this video <unk> the battle on the couple when someone starts to talk ' is the beginning of the end ' " . " <unk> Tú " reached number two on the Billboard Top Latin Songs and number one on the Latin Pop Songs charts . 
- " Mi <unk> Se Me <unk> <unk> <unk> " was released as the third single . <unk> wrote the song for his mother , <unk> <unk> . The music video for the song , released in April 2012 , was filmed at Universal Studios in Los Angeles . It features <unk> and his son , Ricardo <unk> Jr . , and was directed by Robert García . The song was used by American telecommunications corporation AT & T for a <unk> <unk> 900 <unk> commercial featuring <unk> and was released in music stores in May 2012 . The fourth single from the album , " Te <unk> " , was released in July 2012 . The music video for the song was filmed during <unk> 's concerts at <unk> Stadium in Buenos Aires , Argentina during his <unk> World Tour . It marks <unk> 's first music video taken from a live performance . The song reached number ten in Mexico and number one on both the Billboard Latin Songs and Latin Pop Songs chart . " Si Tu No <unk> " was released in November 2012 as the set 's fifth and final single , and was intended to promote the re @-@ release of the album . The song , which music video was similar to that of " Te <unk> " , managed to peak at number 14 in Mexico . 
- 
- = = Tour = = 
- 
- Beginning on 27 January 2012 in <unk> , Mexico , <unk> embarked on a world tour to promote the album . The <unk> World Tour was announced in December 2011 , and visited the Americas . The show consisted of four theatrical sets on a revolving stage ; <unk> performed on each in turn , as it <unk> to each song . Fellow <unk> singer @-@ songwriter <unk> <unk> appeared in several performances , joining <unk> for " <unk> Tú " . The tour was praised by critics and fans . Natalie Torres of <unk> a <unk> reported , " <unk> knows how to handle his ' girls ' , with a mix of attitudes from a ' rough ' male and seductive lyrics " . 
- Jon Pareles of The New York Times commented that " <unk> is one of Latin pop ’ s finest <unk> : <unk> , <unk> , sometimes <unk> , sometimes melancholy and especially fond of the play of opposites " . He added , " unlike some of his fellow Latin pop stars , Mr. <unk> is no saccharine lover boy " . The tour broke records for ticket sales , commercial gross and attendance . In Buenos Aires it was the most popular show at <unk> Stadium , with a total attendance of more than 160 @,@ 000 for four consecutive sold @-@ out concerts . In Guatemala City <unk> was the first artist with two consecutive sold @-@ out concerts at <unk> Flores Stadium , with a combined attendance of more than 50 @,@ 000 . As of October 2012 , the tour has been performed for close to one million people in more than eight countries . 
- 
- = = Commercial performance = = 
- 
- <unk> debuted at the top of the Billboard Top Latin Albums for the week ending 22 October 2011 , and remained at that position the following week . It was the third album by <unk> to remain for more than a week at number one , after <unk> <unk> ( 2000 ) and <unk> <unk> . <unk> became his fourth chart @-@ <unk> , following <unk> <unk> ( 2010 ) . For its third week it fell to number two , replaced by <unk> & <unk> 's <unk> . The album also debuted at number one on the Latin Pop Albums chart for the week ending 22 October , becoming <unk> 's fifth album to do so . It remained at number one the following week ; for its third week , it was replaced by <unk> . The album reached number one again for the week ending 12 November , and later for the week ending 11 February 2012 . For its second run it remained three weeks at the top before being replaced by <unk> 's Drama y Luz for a week ; for its third run at number one , it remained at the top spot for five weeks . For the week ending 2 June 2012 , <unk> returned again to number one . 
- For the week it debuted atop both the Latin Albums and Latin Pop Albums charts , <unk> also appeared as number 65 on the Billboard 200 . It is <unk> 's fifth consecutive album to chart on that list ( following <unk> , <unk> <unk> <unk> , <unk> <unk> and <unk> <unk> ) , although it has only charted higher than <unk> . In Mexico , <unk> debuted at number one for the week ending 9 October 2011 . The following week it fell to number two , replaced at the top by <unk> <unk> ' <unk> Que <unk> . For its third week , the album fell to number three . In Argentina , <unk> debuted at number one for the week ending 9 October 2011 ; it remained at the top position for a single week , dropping to number five the following week . The album also charted on Spain , reaching number 76 . The following week it fell off the chart but later re @-@ entered , reaching its peak at number 68 . <unk> is <unk> 's fourth album to chart in Spain , following <unk> , <unk> <unk> and <unk> <unk> . On the 2011 year @-@ end charts , <unk> was the 50th best @-@ selling album on the Latin Albums chart and the 15th best @-@ seller on the Latin Pop Albums chart . In Mexico , it was the 19th best @-@ selling album of 2011 . 
- <unk> was certified platinum by the Argentine Chamber of <unk> and <unk> <unk> in recognition of 40 @,@ 000 copies sold . It was also certified gold and platinum by the Mexican Association of <unk> of <unk> and <unk> for 90 @,@ 000 copies shipped . In the United States , <unk> was certified Latin platinum by the Recording Industry Association of America for 100 @,@ 000 copies shipped . In Venezuela , the album was certified double platinum for more than 40 @,@ 000 copies sold . It was certified gold in Chile for 5 @,@ 000 copies shipped , and in Colombia for 10 @,@ 000 copies sold . As of November 2012 , <unk> has sold 75 @,@ 000 copies in the United States . 
- 
- = = Critical reaction and awards = = 
- 
- David Jeffries of Allmusic gave the album a <unk> positive review , citing <unk> 's return to his more mainstream style after the " stripped @-@ down acoustic effort " <unk> <unk> . He compared ( as did <unk> and other critics ) the production values and musical style of <unk> with past albums Animal <unk> and <unk> . Finally , he stated that " Returning fans will <unk> in this combination of freedom and growth , and appreciate the return of producer Tommy Torres , the man who has been behind the boards for quite a few of <unk> 's most popular releases " ( referring to Torres ' absence from the production of <unk> <unk> ) . 
- A contributor to the Colombian website <unk> commented that " listening to <unk> is a labyrinth to go through , each song is a huge path that seems to have no end , because it involves imagination , it invites you to dream , to charm , to <unk> . But neither leaves behind the problematic requirements of love , its <unk> , <unk> and concerns , as well as its bad times in this joke that 's life " . <unk> was nominated at the <unk> <unk> of 2012 for the " Lo <unk> <unk> ( I Play It All ) " award . On 25 September 2012 , the album received two nominations at the 13th Annual Latin Grammy Awards : Album of the Year and Best Singer @-@ songwriter album . On 3 December 2012 , <unk> received a nomination for " Pop Album of the Year " at the 2013 <unk> Lo <unk> awards . It also received a nomination for Grammy Award for Best Latin Pop Album at the Grammy Awards of 2013 . <unk> does not win the latter award since 2005 with <unk> in a shared win with Mexican singer <unk> <unk> . In February 2013 , <unk> received a nomination for " Latin Pop Album of the Year " at the Billboard Latin Music Awards of 2013 . 
- 
- = = Track listing = = 
- 
- All songs written and composed by Ricardo <unk> , except where noted . 
- 
- = = Personnel = = 
- 
- Credits are taken from <unk> liner notes . 
- 
- = = Chart performance = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = 2003 Pacific typhoon season = 
- 
- The 2003 Pacific typhoon season was a slightly below average <unk> period of tropical cyclogenesis exhibiting the development of 31 tropical depressions , of which 21 became named storms ; of those , 14 became <unk> . Though every month with the exception of February and March featured tropical activity , most storms developed from May through October . During the season , tropical cyclones affected the Philippines , Japan , China , the Korean Peninsula , Indochina , and various islands in the western Pacific . 
- The season ran year @-@ round , with the first storm , <unk> , developing west of the Marshall Islands on January 15 . In April , Typhoon <unk> became one of the longest @-@ lasting Pacific <unk> in history and attained <unk> records for its unusually early impacts . Typhoon Imbudo in July caused several deaths and extensive damage across the Philippines and China . In September , Typhoon <unk> became one of the costliest <unk> in recorded history after striking South Korea ; <unk> was also the most intense tropical cyclone of the season with a minimum barometric pressure of 910 mbar ( hPa ; 26 @.@ 87 inHg ) . In late November , Typhoon <unk> devastated areas of Yap State in the <unk> States of <unk> . The season closed with the <unk> of a tropical depression east of the Philippines on December 27 . 
- The scope of this article is limited to the Pacific Ocean , north of the equator and west of the International Date Line . Storms that form east of the date line and north of the equator are called hurricanes ; see 2003 Pacific hurricane season . Tropical Storms formed in the entire west Pacific basin are assigned a name by the Tokyo Typhoon Center . Tropical depressions in this basin monitored by the Joint Typhoon Warning Center ( JTWC ) have the " W " <unk> added to their number . Tropical depressions that enter or form in the Philippine area of responsibility are assigned a name by the Philippine <unk> , <unk> and Astronomical Services Administration or PAGASA . This can often result in the same storm having two names . 
- 
- = = <unk> <unk> = = 
- 
- On March 5 , 2003 , <unk> from the University College London at the Tropical Storm Risk ( <unk> ) <unk> issued an extended range forecast for the typhoon season , noting the likelihood of near average tropical cyclone activity as a result of projected neutral sea surface temperatures . The forecast indicated the potential for 26 @.@ 2 tropical storms , compared to the 10 – and 30 @-@ year average of 27 @.@ 8 and 26 @.@ 3 storms , respectively . The following month , the group raised their forecast for tropical storms to 26 @.@ 7 , indicating a slightly above average season . Over next two months , however , fluctuations in sea surface temperatures , particularly those in the Central Pacific , caused the group to revise their predictions downward and indicated the probability for a slightly below average typhoon season in their June forecast . A rise in sea surface temperatures in the following months prompted the forecasting group to once again raise their <unk> to indicate a near @-@ average season in their final August forecast update , which predicted 27 tropical storms . The group was very accurate in their <unk> , with their April and August <unk> being the most accurate . 
- Similarly , <unk> working with the City University of Hong Kong issued a seasonal projection on April 24 , 2003 , indicating the likelihood of a normal or below normal season with 29 total tropical cyclones , 26 tropical storms , and 16 <unk> . As with the <unk> , the group primarily based their forecast numbers on the prevailing status of the El Niño @-@ Southern <unk> . The City University of Hong Kong revised their <unk> on June 24 , 2003 , indicating a slight increase of total tropical cyclones to 30 . The group was also accurate in their <unk> for the entirety of the Northwest Pacific , though their specialized <unk> for the South China Sea were substantially off . 
- During the year , the Japan Meteorological Agency ( JMA ) issued advisories on tropical cyclones west of the International Date Line to the Malay Peninsula , and north of the equator ; this was due to the agency 's status as the official Regional <unk> Meteorological Center , as designated by the World Meteorological Organization in 1989 . The JMA issued <unk> and analyses four times a day , beginning at 0000 UTC and continuing every six hours . The JMA issued <unk> based on a <unk> tropical cyclone forecast model . The agency estimated 10 minute sustained winds and barometric pressure based on the Dvorak technique and numerical weather prediction . The JTWC also issued warnings on storms within the basin , operating from Pearl Harbor in Hawaii to represent the interests of the United States Armed Forces in the Indian and Pacific <unk> . 
- 
- = = Season summary = = 
- 
- Throughout the season , sea surface temperatures within the western equatorial Pacific were above normal , including those in the South China Sea . Areas of convection persisted year @-@ round in the lower latitudes , particularly around the Philippines . <unk> divergence was also prevalent in the same regions , resulting in enhanced tropical cyclogenesis east of the Philippines in 2003 ; the mean region of development of tropical systems during the year was more southwest than the 1971 – 2000 30 @-@ year average . In 2003 , the JMA monitored 21 tropical cyclones of at least tropical storm intensity ; of those , 14 reached typhoon intensity . Though the number of tropical storms was below average , the ratio between tropical storms and <unk> was 66 % greater than normal . The Philippine <unk> , <unk> and Astronomical Services Administration ( PAGASA ) monitored three additional cyclones of at least tropical storm intensity that were not monitored by the JMA . 
- The season began with the formation of Tropical Storm <unk> on January 15 . After its <unk> five days later , no tropical cyclones of at least tropical storm intensity developed over the next two months . This period of inactivity ended with the formation of Typhoon <unk> in mid @-@ April ; <unk> was one of the longest lived Pacific storms on record and was the first typhoon with 1 @-@ minute sustained winds of at least 240 km / h ( 150 mph ) in April since Typhoon Isa in 1997 . Tropical activity was enhanced from May to June , and during this period the JMA monitored four tropical storms , while the PAGASA monitored a fifth storm off the eastern Philippines . Three of the four tropical storms monitored by the JMA approached or hit Japan , including Typhoon <unk> , which brought heavy rainfall and wind across the <unk> Islands and the Korean Peninsula . 
- Tropical activity once again declined towards the second half of June and first half of July . The second half of July , however , featured the development of <unk> Imbudo and <unk> , which both tracked westward across the Philippines before striking areas near <unk> and other regions of southeastern China . Imbudo caused the deaths of 78 people and US $ <unk> million in damage . August was a highly active month for tropical cyclogenesis , with a total of six tropical storms monitored by the JMA , JTWC , and PAGASA . This included <unk> <unk> and <unk> , which also struck southeastern China . Typhoon <unk> earlier in the month made landfall in Japan , resulting in 17 deaths . 
- <unk> was somewhat below average in September , with only one tropical cyclone making landfall , <unk> . However , <unk> was the strongest tropical cyclone of the season and was the costliest with roughly US $ 4 @.@ 8 billion in damage , mostly in South Korea . Tropical cyclogenesis and activity continued to decline after August , with October featuring only three tropical storms . However , two , <unk> and <unk> , reached typhoon intensity ; both stayed away from land . November featured less storms but was <unk> average , with two <unk> developing . The second typhoon , <unk> , devastated portions of Yap State , resulting in approximately $ 1 @.@ 7 million in damage . In December , the JTWC and PAGASA monitored a sole tropical system east of the Philippines , though the JMA did not monitor or classify any tropical cyclones during the month . 
- 
- = = Storms = = 
- 
- In storm information below , wind @-@ speed advisories differ from the Joint Typhoon Warning Center ( JTWC ) to the JMA as the JTWC uses the United States criteria of 1 @-@ minute mean to designate maximum sustained winds , while the JMA uses the 10 @-@ minute mean wind criteria to designate tropical cyclone maximum sustained winds . This difference generally results in JTWC maximum winds appearing higher than the maximum winds described by the JMA for the same cyclone . 
- 
- = = = Tropical Storm <unk> = = = 
- 
- On January 11 , the JTWC began monitoring the disturbance that would eventually develop into <unk> near the International Date Line . As the system tracked westward , it gradually moved into a more favorable environment for tropical cyclogenesis . On January 14 , surface observations indicated that the low @-@ pressure area had developed a closed , low @-@ level circulation center indicative of a tropical cyclone , satellite imagery remained inconclusive . Nonetheless , the JMA classified the pressure area as a tropical depression west of the Marshall Islands at 0600 UTC on January 15 . The JTWC would follow suit by classifying the storm as such at 1800 UTC later that day . At the time , the depression was tracking west @-@ northwest under the influence of a subtropical ridge to the north . Over the next day the system <unk> in convective activity before resuming its previous track and accelerating . At 0000 UTC on January 17 , the JTWC upgraded the system to tropical storm intensity , though the storm remained <unk> as the JMA continued to classify it as a tropical depression . 
- Throughout January 17 the tropical storm would again <unk> in strength , resulting in a brief <unk> by the JTWC to tropical depression intensity . However , an increase in deep convection resulted in its <unk> as a tropical storm at 1800 UTC that day , followed by the JMA upgrading the system to tropical storm intensity at 1200 UTC on January 18 . As such , the storm received the name <unk> . At roughly the same time , the tropical cyclone began to stall east of the Mariana Islands and curve sharply northeastward . Whilst the JTWC indicated that <unk> peaked in strength late on January 18 with 1 @-@ minute sustained winds of 60 km / h ( 37 mph ) , the JMA considered the system to have maintained the same intensity throughout its stint as a tropical storm . <unk> by the same nearby subtropical ridge , <unk> would continue to track towards the northeast into a less favorable tropical cyclone environment . The JMA downgraded <unk> to tropical depression at 1200 UTC on January 20 before the storm transitioned into an extratropical cyclone as its low @-@ level circulation center <unk> from the primary mass of convection due to strong wind shear . At 0000 UTC , both the JTWC and JMA discontinued the monitoring of <unk> . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- <unk> developed from a broad area of disturbed weather as a tropical depression on April 9 well removed from any <unk> . Shortly after development , <unk> quickly intensified in its early stages , and was upgraded to a tropical storm just two days after cyclogenesis . <unk> slowed afterwards , though the storm attained typhoon intensity on April 14 . <unk> continued and late on April 15 , <unk> reached its peak intensity with winds of 165 km / h ( 105 mph ) and a minimum barometric pressure of 930 mbar ( hPa ; 27 @.@ 46 inHg ) . Following peak intensity , <unk> would begin to track northwest and <unk> in strength , <unk> an additional two times in intensity . On April 21 , the typhoon was downgraded to tropical storm intensity and began to track <unk> for several days east of Taiwan . However , on April 24 , <unk> would resume a northward track and begin to weaken , and on April 24 was downgraded to tropical depression strength as it made landfall on <unk> . Following landfall , <unk> transitioned into an extratropical cyclone on April 25 , which persisted until crossing the International Dateline towards the end of April 2003 . 
- Shortly after developing , <unk> caused two fatalities in <unk> in addition to minor agricultural and infrastructural damage ; similar effects were felt in Guam . Several days later , the typhoon prompted cyclone warnings and other <unk> measures in the Philippines after <unk> indicated the potential for strong winds and rain . However , ultimately any effects in the archipelago associated with <unk> remained minimal . The typhoon also prompted warning products in Taiwan , making it the first April typhoon since 1978 to cause such a feat . Unlike in the Philippines , however , <unk> would bring significant rainfall to Taiwan . Effects from the typhoon were most significant in Japan , particularly in the <unk> Islands . Strong winds , rain , and waves caused US $ 230 @,@ 000 ( ¥ 27 @.@ 8 million ) in agricultural damage on <unk> Island . One person was killed due to injuries resulting from the waves . In <unk> , heavy rainfall , peaking at 196 mm ( 7 @.@ 7 in ) in <unk> Prefecture , was reported . Overall , despite its distance away from land and weak intensity at the time of its sole landfall , <unk> resulted in three fatalities . 
- 
- = = = Typhoon Chan @-@ <unk> = = = 
- 
- <unk> on May 18 , the JTWC began to monitor an area of persistent disturbed weather associated with a broad low @-@ pressure area southwest of <unk> . Within highly <unk> conditions , the disturbance quickly organized and became classified as a tropical depression at 0000 UTC the following day . In its initial stages , the depression tracked slowly <unk> . However , a <unk> trough forced a weakness in a nearby ridge , allowing for the storm to take a more streamlined , northward path . At 1200 UTC on May 20 , the JMA upgraded the depression to Tropical Storm Chan @-@ <unk> . Following the system 's naming , Chan @-@ <unk> temporarily meandered towards the northwest before resuming its northeasterly track . The next day , the storm began to develop an eye ; this was reflected with an upgrade by the JMA to typhoon status at 0600 UTC on May 23 . <unk> intensification followed , and at 1800 UTC that day Chan @-@ <unk> reached its peak intensity with maximum sustained winds of 155 km / h ( 100 mph ) and a minimum pressure of 940 mbar ( hPa ; 27 @.@ 76 inHg ) . 
- Following peak intensity , Chan @-@ <unk> began to intake dry air beginning on May 25 . At roughly the same time , the typhoon began to weaken and accelerate towards the northeast . Conditions continued to <unk> as the storm moved further north , and as the cyclone passed east of <unk> , it was downgraded to tropical storm classification . By this time , Chan @-@ <unk> had lost much of its convection due to wind shear . Early on May 27 , Chan @-@ <unk> had fully transitioned into an extratropical cyclone , and these remnants continued to track towards the northeast . These extratropical remnants dissipated south of the <unk> Islands the following day . Early in the typhoon 's existence , Chan @-@ <unk> posed a potential threat to Guam , but remained well east of the island . However , after passing to the northeast , winds from the typhoon fanned volcanic ash from the recently <unk> <unk> volcano towards the island , prompting <unk> measures in Guam . <unk> were reported on the island , forcing the cancellation of several flights . As a tropical storm , Chan @-@ <unk> caused some damage to homes and crops on <unk> , mostly due to heavy rains brought forth by the storm . Offshore , a 1 @,@ <unk> ton fishing vessel , the <unk> <unk> , sank during the storm . The ship was valued at $ 16 million . 
- 
- = = = Severe Tropical Storm <unk> ( <unk> ) = = = 
- 
- Tropical Storm <unk> developed as a tropical depression just off the western coast of Luzon on May 25 . The disturbance quickly intensified to reach tropical storm intensity a few hours after cyclogenesis . However , intensification leveled off as <unk> executed a small clockwise loop before a subsequent landfall on Luzon on May 27 . Due to land interaction the storm temporarily weakened and <unk> before reforming in the Philippine Sea . Afterwards <unk> began <unk> and reached its peak intensity on May 29 with maximum sustained winds of 100 km / h ( 65 mph ) and a barometric pressure of 980 mbar ( hPa ; 28 @.@ 94 inHg ) . Following its peak the tropical storm began to deteriorate and transitioned into an extratropical cyclone on May 30 ; these extratropical remnants continued to track northward through Japan before dissipating in the Sea of <unk> on June 4 . 
- The erratic and slow movement of <unk> off the western Philippines was the catalyst for extreme rainfall and flooding , killing 41 persons in the archipelago . Precipitation peaked at <unk> mm ( 28 @.@ 5 in ) near <unk> . Rising <unk> resulted in the temporary <unk> of government offices and numerous mudslides . In addition , strong winds caused widespread power outages . Overall damage from <unk> in the Philippines amounted to <unk> 192 @.@ 3 million ( US $ 3 @.@ 65 million ) . The floods also displaced 8 @,@ 367 people in 1 @,@ <unk> families and destroyed 178 homes . <unk> and its extratropical remnants later brought torrential rainfall and widespread flooding to Japan , particularly in southwestern regions . Rainfall there peaked at <unk> mm ( 28 @.@ 62 in ) . Flood damage was worst in <unk> and <unk> Prefectures , where several buildings were destroyed by <unk> . Other locations in Japan experienced considerable agricultural damage as well as numerous landslides . Overall , <unk> caused roughly $ 28 @.@ 2 million in damage , much of which occurred in Japan , though the entirety of deaths associated with the cyclone took place in the Philippines . 
- 
- = = = Severe Tropical Storm <unk> ( <unk> ) = = = 
- 
- In late May , an area of disturbed weather began to persist in the South China Sea . The JTWC began to monitor the storm cluster on May 29 . The following day , the JMA reclassified the system as a tropical depression ; initially the system remained highly disorganized due to the lack of deep convection . <unk> moderate wind shear and dry air prevented the cyclone from strengthening significantly in the storm 's early stages . These conditions <unk> as the depression tracked northeast , and at 0000 UTC on June 1 , the JMA upgraded the system to Tropical Storm <unk> . Throughout the course of the day , <unk> continued to strengthen as it accelerated northeast , and peaked in strength with a barometric pressure of 985 mbar ( hPa ; 29 @.@ 09 inHg ) and maximum sustained winds of 95 km / h ( 60 mph ) , making it a severe tropical storm . 
- However , upon moving through the <unk> Channel , conditions began to deteriorate due to increased wind shear , weakening the system and resulting in its <unk> to tropical depression status by the JMA at 1200 UTC on June 3 . <unk> continued to become increasingly disorganized as it moved further north , and late that day , the depression transitioned to an extratropical cyclone . The resulting remnants continued to track well east of Japan before dissipating on June 7 . Due to its track away from <unk> , damage remained minimal ; however , as <unk> passed to the south and east of Japan , the storm brought light rainfall to the country , peaking at 81 mm ( 3 @.@ 2 in ) in <unk> , Okinawa . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- A tropical disturbance persisted in the monsoon trough northwest of <unk> on June 7 , and moved westward without development due to wind shear . On June 11 , the shear decreased enough to allow the convection to organize , and the next day the JMA classified it as a tropical depression northeast of <unk> . On June 13 , the JMA upgraded it to Tropical Storm <unk> to the east of the Philippines , and PAGASA gave it the local name " <unk> " . <unk> moved to the northwest and later to the north , parallel to the eastern Philippines , and on June 17 , the JMA upgraded it to typhoon status . The storm rapidly intensified to the east of Taiwan as it developed a well @-@ defined eye , and while doing so passed over the Japanese island of <unk> @-@ <unk> at around <unk> UTC on June 17 . At 0600 UTC on June 18 , the JTWC estimated peak 1 minute winds of 215 km / h ( 135 mph ) , while the JMA estimated peak 10 minute winds of 150 km / h ( 90 mph ) . Increased shear weakened the typhoon to tropical storm strengthen on June 19 , and later that day the JMA declared the storm as extratropical near the <unk> Islands . The extratropical remnants of <unk> continued to the northeast , crossing northern Japan on June 20 and dissipating on June 24 . 
- While offshore the Philippines , <unk> dropped heavy rainfall that caused flooding and left thousands homeless . The storm caused <unk> 131 million ( PHP , $ 2 @.@ 46 million USD ) in damage and 12 deaths . On the Japanese island of <unk> @-@ <unk> , where wind gusts reached 204 km / h ( 127 mph ) . It also affected Taiwan , where floods covered highways and caused mudslides . In Japan , the storm caused widespread power outages , although damage was minimal , and there were 21 injuries . In South Korea , there was $ 12 @.@ 1 million in damage and two deaths . 
- 
- = = = Typhoon Imbudo ( <unk> ) = = = 
- 
- On July 15 , the JMA estimated that a tropical depression formed , and the next day the JTWC initiated advisories on Tropical Depression <unk> about <unk> km ( 415 mi ) east of Yap . A subtropical ridge near Okinawa steered the nascent depression to the west @-@ northwest for much of its duration . With warm waters and favorable upper @-@ level conditions , the depression quickly organized , first to Tropical Storm Imbudo on July 17 , and to typhoon status two days later , when PAGASA begin issuing advisories on Typhoon <unk> . Around that time , Imbudo was rapidly intensifying , developing a well @-@ defined eye . At 1200 UTC on July 20 , the JMA estimated peak 10 minute sustained winds of 165 km / h ( 105 mph ) , and the same time , the JTWC estimated 1 minute sustained winds of 240 km / h ( 150 mph ) , making it a super typhoon . Imbudo maintained peak winds for about 12 hours , before undergoing an eyewall replacement cycle . At 0300 UTC on July 22 , Imbudo struck northern Luzon , with 1 minute winds estimated at 205 km / h ( 125 mph ) by the JTWC . It weakened over land , but re @-@ intensified in the South China Sea , striking southern China near Yangjiang , Guangdong on July 24 . Imbudo rapidly weakened , dissipating on July 25 . 
- In the Philippines , officials evacuated over 14 @,@ 000 people . Imbudo was the strongest typhoon to strike since Typhoon <unk> five years prior , The typhoon left widespread areas flooded for several days . Damage was heaviest in the Cagayan Valley , where over 80 @,@ 000 people were displaced by the storm . In Isabela , high winds wrecked most of the banana crop and severely damaged other crops . Throughout the Philippines , Imbudo damaged or destroyed 62 @,@ 314 houses , causing <unk> billion ( 2003 PHP , $ 86 million 2003 USD ) in damage . There were 64 deaths in the country . In southern China in Yangjiang , more than 30 @,@ 000 people evacuated ahead of the storm , and more than half of the trees in the city fell due to strong winds . High winds killed a man in Hong Kong after knocking him off a platform . Throughout Guangdong , Imbudo destroyed 595 @,@ 000 houses and caused eight deaths . Heavy rains spread across southern China , peaking at 343 mm ( 13 @.@ 5 in ) at <unk> County in Guangxi province . There , 12 people died from the storm . Overall damage in China was about ¥ 4 @.@ 45 billion ( CNY , $ <unk> million USD ) . 
- 
- = = = Severe Tropical Storm <unk> ( <unk> ) = = = 
- 
- <unk> originated from a tropical depression situated within the monsoon trough to the east of the Philippines on July 15 . Tracking westward , intensification was slow and the system remained a tropical depression as it moved across the central Philippines on July 17 . Upon moving into the South China Sea , conditions allowed for quicker strengthening , and the cyclone reached tropical storm status on July 18 before reaching its peak intensity with maximum sustained winds of 110 km / h ( 70 mph ) , making it a severe tropical storm . However , atmospheric conditions began to deteriorate as <unk> made landfall on <unk> on July 21 , weakening the system . The tropical storm continued to weaken as it moved over the Gulf of <unk> prior to a final landfall near Hanoi , Vietnam the following day . Tracking inland , the combination of land interaction and wind shear caused <unk> to dissipate over Laos on July 23 . 
- Shortly after development , <unk> tracked through the Philippines , killing two people . After moving into the South China Sea , turbulence produced by the storm resulted in an aviation incident involving a commercial airliner off the western Philippines . Three of the plane 's occupants received minor injuries . In <unk> , <unk> caused heavy rainfall , peaking at 189 mm ( 7 @.@ 44 in ) at a station on <unk> Mountain . The rains resulted in the collapse of 1 @,@ 400 homes and an estimated <unk> ¥ 140 @.@ 27 million ( US $ 16 @.@ 9 million ) in direct economic losses . Effects were worst in Vietnam , where three people were killed . Widespread power outages occurred , and strong winds resulted in agricultural and infrastructural damage , particularly in Vietnam 's northern provinces . 
- 
- = = = Tropical Storm <unk> ( <unk> ) = = = 
- 
- <unk> spawned from an area of disturbed weather in the Philippine Sea on July 31 . Tracking northwest , favorable conditions allowed for the intensification of the system to tropical storm strength on August 2 . <unk> reached peak intensity later that day with winds of 85 km / h ( 50 mph ) and a minimum barometric pressure of <unk> mbar ( hPa ; 28 @.@ 29 inHg ) . This intensity was held for several hours until less conducive atmospheric conditions slightly weakened the system ; this was followed by <unk> making landfall on southern Taiwan on August 3 . Subsequently , the storm weakened and moved into the Taiwan Strait before making its final landfall near <unk> , China the next day . The storm quickly weakened over the Chinese mainland , and dissipated entirely several hours after landfall . 
- In Taiwan , where <unk> first made landfall , heavy rainfall resulted in flooding . Commercial flights , schools , and rail service in some areas was cancelled in advance of the storm . Precipitation there peaked at 653 mm ( 25 @.@ 71 in ) over a period of nearly two days in <unk> County . <unk> damage also resulted from the rainfall , and was estimated at over <unk> $ 70 million ( US $ 2 million ) . In China , record rainfall was reported . The worst impacted city was <unk> , where losses due to <unk> reached <unk> ¥ 240 million ( US $ 29 million ) and one death was reported . Power outages were also widespread across southeastern China . Due to <unk> drought conditions , 703 cloud <unk> operations took place in order to artificially generate added rainfall ; such operations resulted in moderate precipitation over the targeted area . Overall , <unk> caused roughly $ 31 million in damage and three deaths . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- A tropical depression developed on August 2 southeast of Guam , and gradually intensified while moving to the northwest , becoming a tropical storm on August 3 and a typhoon a day later . <unk> formed an eye and became a large storm by the time it approached Okinawa on August 7 . The typhoon attained peak winds of 155 km / h ( 100 mph ) before weakening slightly while turning to the northeast . <unk> made landfall on the Japanese island of <unk> on August 8 , and later moved across portions of <unk> and <unk> . After weakening to tropical storm status , the cyclone became extratropical on August 9 and dissipated three days later . 
- While passing northeast of the Philippines , the typhoon caused light damage in the archipelago . The eye crossed over Okinawa , where <unk> left 166 @,@ 800 people without power and caused 10 injuries . Near where <unk> first struck Japan , <unk> reported a peak wind gust of 166 km / h ( 103 mph ) , at the time the third strongest on record there . The typhoon also dropped torrential rainfall peaking at <unk> mm ( 26 @.@ 9 in ) . The combination of winds and rainfall caused landslides , particularly on <unk> . Nationwide , <unk> killed 20 people , destroyed 708 houses , and caused ¥ 35 @.@ 1 billion ( <unk> , $ <unk> @.@ 8 million USD ) in damage . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- <unk> originated from a tropical disturbance within the monsoon trough east of <unk> State on August 13 . Despite rather favorable conditions , the initial tropical depression did not intensify significantly and degenerated into a remnant low on August 18 . However , these remnants were able to reorganize and the system was reclassified as a tropical cyclone a day later . <unk> was rather rapid upon the storm 's <unk> – the depression reached tropical storm status on August 20 and then typhoon intensity two days later . Shortly after , <unk> made landfall on Luzon at peak intensity with winds of 120 km / h ( 75 mph ) . The typhoon emerged into the South China Sea as a much weaker tropical storm , though it was able to <unk> over warm waters . Once again at typhoon intensity , <unk> <unk> <unk> before moving over the <unk> Peninsula on its way to a final landfall near <unk> <unk> , Vietnam on August 25 . Quick weakening due to land interaction occurred as <unk> moved across northern Vietnam , where the storm met its demise the following day . 
- <unk> first struck the Philippines , resulting in heavy rainfall and <unk> approximately 1 @,@ 000 families . The flooding caused severe damage and killed one person . <unk> 's effects were much more severe in China . In Hong Kong , eleven people were injured and isolated flooding occurred as a result of the typhoon 's outer rainbands . However , Guangdong Province , <unk> Province , and Guangxi were the Chinese regions most extensively impacted . The typhoon brought record wind gusts into Guangxi . In those three regions combined , 13 @,@ 000 homes were estimated to have collapsed and a large <unk> of farmland was damaged . Two people were killed in China and economic losses <unk> to ¥ 2 @.@ 1 billion ( US $ 253 million ) . Due to its positioning and track , of all areas in Vietnam only the country 's more northern regions were impacted by <unk> . Flash flooding occurred in earnest in those regions , and 1 @,@ 000 homes were flattened . One person was killed and five others were injured in Vietnam . Overall , the typhoon was responsible for the deaths of four persons . 
- 
- = = = Tropical Storm <unk> ( <unk> ) = = = 
- 
- The monsoon trough spawned several tropical disturbances in the middle of August , one of which became Tropical Depression <unk> near the Philippines . On August 18 , an area of convection persisted on the southern side of a circulation , developing into a tropical depression east of Luzon . It moved quickly northward and later to the northwest in an area generally unfavorable for strengthening , such as the presence of wind shear and land interaction . On August 19 , the JMA upgraded the depression to Tropical Storm <unk> to the east of Taiwan . Later that day , the circulation passed just 55 km ( 35 mi ) north of <unk> , although the convection was exposed that time . On August 20 , the JMA assessed <unk> as dissipating in the Taiwan Strait , although the JTWC continued advisories until the storm moved ashore in southeastern China . 
- Rainfall in Taiwan reached 69 mm ( 2 @.@ 7 in ) in Ilan County . On the island , the storm left several hundred houses without power due to a lightning strike . On mainland China , rainfall peaked at 101 mm ( 4 @.@ 0 in ) in <unk> , Zhejiang , which were largely beneficial in <unk> drought conditions , while winds gusted to 100 km / h ( 62 mph ) . The storm damaged or destroyed 5 @,@ 880 houses and flooded 1 @,@ <unk> ha ( 3 @,@ 180 acres ) of <unk> fields , causing ¥ 38 @.@ 6 million ( CNY , $ 4 @.@ 7 million USD ) in damage . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- On August 27 , a tropical depression developed about 520 km ( 325 mi ) northwest of Guam , which initially drifted to the southwest before turning to the northwest . On August 29 , the JMA upgraded it to Tropical Storm <unk> , and that day PAGASA began issuing advisories on Tropical Storm <unk> . The storm quickly intensified into a typhoon , after developing an eye in the center . On September 1 , the JMA estimated <unk> attained peak 10 minute winds of 150 km / h ( 90 mph ) , and the JTWC assessed peak 1 – minute winds of 230 km / h ( 145 mph ) . While near peak intensity , the center of <unk> passed about 45 km ( 30 mi ) south of the southern tip of Taiwan . The typhoon weakened steadily and was a severe tropical storm by the time it made landfall on September 2 just east of Hong Kong . The JTWC estimated landfall winds of 185 km / h ( 115 mph ) , making it the strongest typhoon to strike the Pearl River Delta since Typhoon Hope in 1979 . <unk> rapidly weakened while continuing westward through China , dissipating on September 3 over Guangxi . 
- In the Philippines , <unk> interacted with the monsoon to produce heavy rainfall , killing one person . While in the vicinity , <unk> produced gusts of 100 km / h ( 62 mph ) on <unk> , a Japanese <unk> of Okinawa . Heavy rainfall in Taiwan reached 628 mm ( 24 @.@ 7 in ) in <unk> County , and winds peaked at 176 km / h ( 109 mph ) on Orchid Island before the <unk> was destroyed . The caused about <unk> $ 200 ( <unk> , $ 115 million USD ) in crop damage , and killed three people . Damage was minor in Hong Kong , and four fishermen were missing and presumed drowned after their boat sank . On the Chinese mainland , strong winds left 90 % of the city of <unk> without power , and killed 16 construction workers due to a half @-@ finished building collapsing . Across Guangdong , the typhoon damaged crops and destroyed 54 @,@ 000 homes were destroyed . Overall damage in China was estimated at ¥ 2 @.@ 3 billion ( CNY , $ 277 million USD ) , and across Guangdong , the typhoon killed 40 people . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- Typhoon <unk> formed on September 4 from the monsoon trough in the western Pacific Ocean . It slowly intensified into a tropical storm while moving northwestward , and <unk> became a typhoon on September 8 . That day , it quickly intensified due to favorable conditions , developing a well @-@ defined eye and reaching peak maximum sustained winds of 195 km / h ( 120 mph ) . While near peak intensity , <unk> was <unk> and began turning to the north @-@ northeast . The eyewall soon after passed over the Japanese island of <unk> @-@ <unk> on September 10 , producing the fourth lowest pressure on record in Japan after a pressure of <unk> mbar ( 26 @.@ 9 inHg ) was recorded . With warm waters , <unk> was able to maintain much of its intensity before it made landfall just west of <unk> , South Korea on September 12 . On <unk> Island , <unk> produced a peak wind gust of 216 km / h ( 134 mph ) and a minimum pressure of 950 mbar ( 28 inHg ) , both setting records for the country , and making it the most powerful typhoon to strike South Korea since record @-@ keeping began in the country in 1904 . The typhoon became extratropical in the Sea of Japan the next day , although the remnants persisted for several more days , bringing strong winds to northern Japan . 
- The typhoon first affected the <unk> Islands of Japan . On <unk> @-@ <unk> , strong winds damaged 104 buildings , and 95 % of residents lost power . <unk> dropped heavy rainfall there , including rates of 58 @.@ 5 mm ( 2 @.@ 30 in ) in an hour , and 402 @.@ 5 mm ( 15 @.@ 85 in ) in 24 hours , the latter setting a record . One person died on <unk> @-@ <unk> after being struck by flying glass . Elsewhere in Japan , the storm caused flights to be canceled , while rainfall @-@ induced landslides blocked roads . There were two other deaths in Japan , and damage totaled ¥ 11 @.@ 3 billion ( <unk> , $ 96 million USD ) . Damage was heaviest in South Korea , notably where it moved ashore . Winds in <unk> near the landfall location reached 154 km / h ( 96 mph ) , the second @-@ highest on record . There , the port sustained heavy damage , causing disruptions to exports in the months following the storm . Nationwide , the high winds destroyed about 5 @,@ 000 houses and damaged 13 @,@ 000 homes and businesses , leaving 25 @,@ 000 people homeless . About 1 @.@ 47 million lost power , and widespread crop damage occurred , resulting in the worst rice crop in 23 years . Across South Korea , <unk> killed 117 people , and overall damage totaled <unk> 5 @.@ 52 trillion won ( $ 4 @.@ 8 billion USD ) . 
- 
- = = = Typhoon <unk> @-@ <unk> ( <unk> ) = = = 
- 
- In the middle of September , the monsoon trough spawned a rapidly organizing disturbance east @-@ northeast of Luzon , with weak wind shear and favorable conditions . On September 16 , the JMA classified it as a tropical depression , and the JTWC initiated advisories the next day . The system moved to the northwest due to the subtropical ridge to the northeast and later to the north . On September 18 , the JMA upgraded the depression to Tropical Storm <unk> @-@ <unk> , the same day that PAGASA classified it as Tropical Storm <unk> . An eastward @-@ moving trough turned the storm to the northeast , bringing the track over Okinawa and <unk> <unk> on September 19 . <unk> @-@ <unk> continued gradually intensifying , becoming a typhoon on September 20 to the southeast of Japan . That day , the JMA estimated peak winds of 130 km / h ( 80 mph ) , and the JTWC estimated peak 1 minute winds of 185 km / h ( 115 mph ) on September 21 , after <unk> @-@ <unk> developed a well @-@ defined eye . The typhoon weakened due to increasing wind shear , deteriorating to severe tropical storm status on September 22 before JMA declared it extratropical on September 23 . The remnants of <unk> @-@ <unk> continued to the northeast , exited the basin on September 24 , and eventually struck southern Alaska on September 25 . 
- Wind gusts in Okinawa reached 115 km / h ( 72 mph ) , while on the volcanic island of <unk> @-@ <unk> , gusts reached <unk> km / h ( 133 mph ) . On the Japanese mainland , winds gusted to 126 km / h ( 78 mph ) at <unk> , <unk> . <unk> @-@ <unk> dropped heavy rainfall while near Japan , peaking at 316 mm ( 12 @.@ 4 in ) on <unk> @-@ <unk> . In Okinawa , <unk> @-@ <unk> flooded a boat , forcing its occupants to be rescued by the Coast Guard . Also on the island , heavy rainfall caused landslides and flooded houses . In <unk> <unk> , the storm left 10 @,@ <unk> people without power . On <unk> , wind gusts of <unk> km / h ( 133 mph ) damaged about 200 houses . Nationwide , <unk> @-@ <unk> destroyed 191 homes , injured 9 people , and left about ¥ 300 million ( <unk> , $ 2 @.@ 5 million USD ) . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- Towards the end of September , the monsoon trough spawned a tropical disturbance east @-@ northeast of Yap , which became a tropical depression on September 24 . There were initially several <unk> , with a tropical upper tropospheric trough to the northeast increasing outflow . After slowing and turning to the northeast , the depression intensified into Tropical Storm <unk> on September 26 . After the storm developed a large eye feature , the JTWC upgraded it to typhoon status on September 27 , although the JMA did not follow suit until the following day while near <unk> @-@ <unk> . Also that day , <unk> passed 95 km ( 60 mi ) west of Iwo Jima , and the JMA estimated peak 10 minute winds of 130 km / h ( 80 mph ) . The JTWC estimated peak 1 minute winds of 165 km / h ( 105 mph ) , before an approaching trough caused the typhoon to accelerate northeastward . The convection diminished near the center , causing <unk> to become extratropical on September 30 . The remnants continued generally northeastward through the <unk> Islands , eventually passing south of mainland Alaska on October 7 . 
- On <unk> @-@ <unk> , Typhoon <unk> produced sustained winds of 102 km / h ( 63 mph ) , with gusts to 200 km / h ( 124 mph ) , which was the third strongest on record for the station . Rainfall there reached 183 mm ( 7 @.@ 2 in ) . Wind gusts on Iwo Jima peaked at 109 km / h ( 68 mph ) . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- In the middle of October , an area of convection persisted along the monsoon trough between Luzon and Guam , developing into a tropical depression on October 17 . The same monsoon trough later spawned Typhoon <unk> to the east . For several days , the system remained disorganized while drifting to the west @-@ northwest due to weak steering currents south of the subtropical ridge . On October 19 , the JMA upgraded the depression to Tropical Storm <unk> , and by that time the storm had begun drifting to the northeast . With favorable outflow , <unk> quickly intensified into a typhoon on October 20 after developing an eye , and two days later , the JMA estimated peak winds of 165 km / h ( 105 mph ) . The JTWC estimated peak 1 minute winds of 230 km / h ( 145 mph ) around the time the well @-@ defined eye had expanded to 37 km ( 23 mi ) . Subsequently , the typhoon accelerated northeastward into the westerlies and began weakening due to increasing wind shear and dry air . On October 26 , <unk> became extratropical to the east of Japan and dissipated the next day . The passage of the typhoon caused surface <unk> a concentration in the ocean to increase 30 @-@ fold . 
- 
- = = = Typhoon <unk> = = = 
- 
- The same monsoon trough that spawned Typhoon <unk> also produced an area of convection to the north @-@ northeast of Guam , becoming a tropical depression on October 19 . The system moved northwestward and later turned to the northeast around the subtropical ridge . With low wind shear and favorable outflow , the convection became better organized , and the JMA upgraded it to Tropical Storm <unk> on October 21 . After an eye began to form , <unk> was upgraded to typhoon status the next day . An approaching trough caused <unk> to accelerate northeastward while also increasing outflow . On October 24 , the JMA estimated peak winds of 175 km / h ( 110 mph ) while the JTWC estimated winds of 240 km / h ( 150 mph ) , an unusually high intensity for 30 ° N. Subsequently , <unk> rounded the subtropical ridge and began moving to the east @-@ southeast , beginning a nearly week @-@ long loop . The cold front had passed to the north and failed to bring the typhoon northeastward . 
- Increasing wind shear weakened the convection , and <unk> deteriorated into a severe tropical storm on October 26 . The next day , it began moving westward while passing about 345 km ( 215 mi ) north of Wake Island . A large eye of 110 km ( 70 mi ) in diameter developed , and on October 28 , the JMA re @-@ upgraded <unk> to typhoon status . The next day , the typhoon turned to the northeast due to another approaching trough . With decreasing wind shear and warmer waters , <unk> re @-@ intensified significantly on October 29 , reaching a secondary peak of 165 km / h ( 105 mph ) according to JMA , and 215 km / h ( 135 mph ) according to JTWC . The storm moved very closely along the track it took several days prior . Increasing wind shear on October 30 caused rapid weakening , resulting in the eye dissipating . By the next day , the center was exposed , and <unk> became extratropical , later exiting the basin on November 1 . The remnants weakened , later turning to the southeast and dissipating on November 11 southwest of California . 
- 
- = = = Severe Tropical Storm <unk> ( <unk> ) = = = 
- 
- Late in October , an area of convection persisted northwest of <unk> and quickly organized into a tropical depression on October 29 . Moving west @-@ northwestward toward the Philippines due to a ridge to the east , the depression intensified into Tropical Storm <unk> on October 30 . With minimal wind shear , the storm strengthened further and developed a large eye . The JTWC upgraded <unk> to typhoon status on October 31 , estimating peak 1 minute winds of 140 km / h ( 85 mph ) , although the JMA estimated the storm only attained peak 10 minute winds of 95 km / h ( 60 mph ) . Early on November 1 , <unk> made landfall on northeastern Luzon in the Philippines , south of <unk> . The storm weakened over land and emerged into the South China Sea . By that time , it was moving northward along the periphery of the ridge to the east . On November 2 , <unk> turned to the northeast , passing just east of Taiwan . The next day , it weakened to tropical depression status , and after <unk> offshore eastern Taiwan , <unk> continued northeastward . It became extratropical on November 5 and dissipated the next day near southern Japan . 
- In the Philippines , <unk> dropped about 150 mm ( 6 in ) of rainfall , which flooded the Cagayan River and killed four people . In Taiwan , rainfall reached <unk> mm ( 21 @.@ 8 in ) in <unk> County . On the Japanese island of <unk> , rainfall totaled 197 mm ( 7 @.@ 8 in ) , which broke the <unk> and daily record for the station in November . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- A tropical depression developed near Yap on November 11 . The system intensified gradually as it tracked quickly westward toward the Philippines . An anticyclone <unk> allowed for strengthening , and the JMA upgraded the depression to Tropical Storm <unk> on November 12 . Simultaneously , the cyclone entered the area of responsibility of the Philippine <unk> , <unk> and Astronomical Services Administration , which named it Tropical Storm <unk> . At around 1600 UTC on November 13 , <unk> made landfall on northern <unk> Island in the Philippines before traversing the remainder of the archipelago from east to west . The cyclone emerged into the South China Sea weakened but quickly re @-@ intensified while continuing to the west @-@ northwest . The JTWC estimated peak winds of 140 km / h ( 85 mph ) on November 16 , and later that day , the JMA upgraded the storm to typhoon status , estimating peak 10 @-@ minute winds of 120 km / h ( 75 mph ) . On November 18 , <unk> passed near southwestern <unk> and weakened , with the convection diminishing from the circulation . By the next day , the system weakened to tropical depression status , and dissipated shortly after moving ashore over <unk> , China . 
- In the Philippines , <unk> produced strong winds , heavy rainfall , and rough seas . The storm caused widespread power outages and ferry disruptions . According to the PAGASA in its post @-@ storm report , a total of 13 people lost their lives during the storm . On <unk> , the storm helped end one of the worst summer droughts in almost 65 years , although it also left heavy crop damage , <unk> 64 @,@ 000 ha ( 160 @,@ 000 acres ) of fields and killing 400 head of livestock . With about 800 homes destroyed , damage on <unk> amounted to $ 197 million ( 2003 USD ) . Effects were minor in mainland China . 
- 
- = = = Typhoon <unk> ( <unk> ) = = = 
- 
- Typhoon <unk> formed on November 18 from the monsoon trough to the west of the Marshall Islands . Early in its duration , it moved generally to the west or west @-@ southwest . On November 21 , the depression intensified into Tropical Storm <unk> , and two days later , it strengthened into a typhoon , developing an eye . <unk> later began a prolonged movement to the northwest , during which it passed near several islands in Yap State . The typhoon reached peak intensity on November 26 , with peak 10 – minute sustained winds of 185 km / h ( 115 mph ) . It later weakened due to a variety of unfavorable conditions , and after <unk> to the northeast , <unk> became extratropical south of Japan on December 2 . 
- Typhoon <unk> first affected <unk> with gusty winds , and later it damaged or destroyed about 200 homes in <unk> State . There , high waves flooded roads and homes , while high winds damaged crops . Damage was heaviest in Yap State , mostly in the small <unk> <unk> and <unk> Island . On both islands , the typhoon contaminated the water supply and wrecked the crops . Rainfall reached 263 mm ( 10 @.@ 35 in ) on <unk> , and gusts reached 158 km / h ( 98 mph ) . Throughout the <unk> , damage totaled about $ 1 @.@ 7 million , although there were no deaths . The damage prompted the <unk> government to declare two states as disaster areas , as well as a disaster declaration from the United States federal government . While <unk> was becoming extratropical , it became the first typhoon in December to threaten Japan in 13 years . The storm dropped rainfall that resulted in mudslides and flight <unk> . 
- 
- = = = Other storms = = = 
- 
- An area of convection formed on May 16 to the southwest of <unk> , located within an area of weak wind shear . The next day , the JMA and the JTWC both classified the system as a tropical depression . The convection was disorganized in association with multiple circulation centers , although it gradually organized . Moving westward initially , the depression turned more to the north into an area of increasing wind shear . On May 19 , the JTWC upgraded the system to Tropical Storm <unk> , and on the same day , PAGASA classified it as Tropical Depression <unk> . Soon after , the convection decreased from the center , and by May 20 , all warning agencies had discontinued advisories . 
- On July 9 , PAGASA classified a system as Tropical Depression Falcon , off the west coast of the Philippines . The JMA also briefly initiated advisories before dropping them later on July 9 . Later in the month , PAGASA briefly issued advisories on Tropical Depression <unk> on July 30 off the east coast of <unk> . The depression dissipated the next day , causing about <unk> million ( PHP , $ 145 @,@ 000 USD ) in damage . The monsoon trough spawned a tropical depression north of Luzon on August 18 , with PAGASA naming it <unk> . There were several <unk> in the region , with Tropical Storm <unk> to the northeast near Taiwan , and the overall system moved generally westward . PAGASA briefly classified <unk> as a tropical storm on August 19 before ending advisories the next day . The system spread rainfall across China , reaching 82 @.@ 4 mm ( 3 @.@ 24 in ) in <unk> , <unk> . 
- On September 5 , former Hurricane <unk> crossed the International Date Line into the basin . By that time , the circulation was largely exposed from the convection , and the center quickly dissipated . Later in the month , the monsoon trough spawned a disturbance east of the Philippines that PAGASA classified as Tropical Depression <unk> on September 15 . The system moved westward but never intensified , dissipating west of Luzon on September 19 . The broad system also spawned Typhoon <unk> @-@ <unk> . 
- In October , the JTWC classified Tropical Depression <unk> early in the month off the west coast of Luzon . With weak steering currents , the system moved slowly southwestward before <unk> to the northwest . On October 10 , the depression dissipated just off the coast of southern China . On October 5 , the JMA monitored a tropical depression southeast of Taiwan that later passed near the island , producing heavy rainfall that peaked at 153 mm ( 6 @.@ 0 in ) in Ilan County . A few days later , the JTWC monitored Tropical Depression <unk> , which developed on October 12 after an extratropical storm produced an area of convection . Described as a subtropical low , the depression moved generally northeastward toward Japan due to an approaching cold front . The depression moved through <unk> and <unk> before dissipating on October 13 . The depression dropped 285 mm ( 11 @.@ 2 in ) of rainfall in <unk> , while strong winds associated reached 217 km / h ( 135 mph ) through a storm @-@ produced <unk> . The winds knocked over two cranes , killing two people , and left about 9 @,@ 000 homes without power . The depression also killed two people due to <unk> . On October 16 , the JMA briefly classified a tropical depression to the east of the <unk> Islands . On October 22 , a tropical depression developed in the South China Sea , classified by PAGASA as <unk> . The system moved eastward and crossed <unk> before dissipating on October 24 . In the Philippines , the depression killed one person and caused minor damage . Also in October , the monsoon trough spawned a tropical depression in the Gulf of Thailand , which moved northwestward and crossed into the Indian Ocean , dropping heavy rainfall in Thailand . 
- In mid @-@ November , the JMA briefly tracked a weak tropical depression near Wake Island . The agency also briefly tracked a tropical depression off the coast of Vietnam on December 16 . It finally dissipated on December 17 , with the pressure and winds unknown . The final system of the year was a tropical depression that originated out of the monsoon trough on December 24 east of the Philippines . After initially moving to the west , it turned to the south , and the JTWC estimated the depression intensified into a tropical storm . During this time , PAGASA classified it as Tropical Storm <unk> . The system made landfall in northeastern <unk> and dissipated on December 27 , bringing heavy rainfall . 
- 
- = = Storm names = = 
- 
- Within the North @-@ western Pacific Ocean , both the Japan Meteorological Agency ( JMA ) and the Philippine <unk> , <unk> and Astronomical Services Administration assign names to tropical cyclones that develop in the Western Pacific , which can result in a tropical cyclone having two names . The Japan Meteorological Agency 's <unk> Tokyo — Typhoon Center assigns international names to tropical cyclones on behalf of the World Meteorological Organization 's Typhoon Committee , should they be judged to have 10 @-@ minute sustained <unk> of 65 km / h , ( 40 mph ) . While the Philippine <unk> , <unk> and Astronomical Services Administration assigns names to tropical cyclones which move into or form as a tropical depression in their area of responsibility located between 135 ° E and 115 ° E and between 5 ° N @-@ 25 ° N even if the cyclone has had an international name assigned to it . The names of significant tropical cyclones are retired , by both PAGASA and the Typhoon Committee . Should the list of names for the Philippine region be exhausted then names will be taken from an auxiliary list of which the first ten are published each season . <unk> names are marked in gray . 
- 
- = = = International names = = = 
- 
- During the season 21 named tropical cyclones developed in the Western Pacific and were named by the Japan Meteorological Agency , when it was determined that they had become tropical storms . These names were contributed to a list of a 140 names submitted by the fourteen members nations and territories of the <unk> / <unk> Typhoon Committee . 
- 
- = = = Philippines = = = 
- 
- The Philippine <unk> , <unk> and Astronomical Services Administration uses its own naming scheme for tropical cyclones in their area of responsibility . PAGASA assigns names to tropical depressions that form within their area of responsibility and any tropical cyclone that might move into their area of responsibility . Should the list of names for a given year prove to be insufficient , names are taken from an auxiliary list , the first 10 of which are published each year before the season starts . The names not retired from this list will be used again in the 2007 season . Names that were not assigned are marked in gray . 
- 
- = = = <unk> = = = 
- 
- The names Imbudo and <unk> were retired by the <unk> / <unk> Typhoon Committee . The names <unk> and <unk> were chosen to replace Imbudo and <unk> respectively . While <unk> was requested by Hong Kong to be removed in the list , and was replaced by <unk> . The Philippine <unk> , <unk> and Astronomical Services Administration ( PAGASA ) announced that the name <unk> had its name retired due to extensive damage . The name Hanna was chosen to replace <unk> . Also , the name " <unk> " was replaced by " <unk> " , after it was found that <unk> was a <unk> . 
- 
- = = Storm effects = = 
- 
- The following table provides basic meteorological and impact information for each tropical cyclone from the 2003 Pacific typhoon season in <unk> format ; unnamed tropical cyclones are not included . PAGASA names for storms are provided in parentheses . Storms entering from the Central Pacific only include their information while in the western Pacific , and are noted with an <unk> * . 
- 
- 
- = <unk> = 
- 
- An <unk> is an organic molecule that features a three @-@ membered <unk> containing oxygen , nitrogen , and carbon . In their largest application , <unk> are <unk> in the industrial production of <unk> . <unk> derivatives are also used as specialized reagents in organic chemistry for a variety of <unk> , including alpha <unk> of <unk> , <unk> and <unk> of <unk> , and other <unk> transfer reactions . <unk> also serve as precursors to <unk> and participate in [ 3 + 2 ] <unk> with various <unk> to form substituted five @-@ membered <unk> . <unk> <unk> derivatives effect asymmetric oxygen transfer to <unk> <unk> as well as other substrates . Some <unk> also have the property of a high barrier to <unk> of the nitrogen , allowing for the possibility of <unk> at the nitrogen center . 
- 
- = = History = = 
- 
- <unk> derivatives were first reported in the mid @-@ 1950s by <unk> and subsequently by <unk> and Horner and <unk> . Whereas oxygen and nitrogen typically act as <unk> due to their high <unk> , <unk> allow for <unk> transfer of both <unk> . This unusual reactivity is due to the presence of the highly strained three membered ring and the relatively weak N @-@ O bond . <unk> tend to attack at the <unk> nitrogen when the nitrogen <unk> is small ( R1 = H ) , and at the oxygen atom when the nitrogen <unk> has greater <unk> bulk . The unusual electronics of the <unk> system may be exploited to perform a number of oxygen and nitrogen transfer reactions including , but not limited to : α @-@ <unk> of <unk> , <unk> of <unk> , selective oxidation of sulfides and <unk> , <unk> of N @-@ <unk> and N @-@ <unk> . 
- The <unk> process for the industrial production of <unk> through the oxidation of ammonia with hydrogen <unk> in the presence of <unk> was developed in the early 1970s . 
- <unk> <unk> proved useful in the syntheses of complex natural product , such as <unk> which is marketed as a chemotherapy agent . Both the <unk> <unk> total synthesis and the <unk> <unk> total synthesis feature asymmetric α @-@ <unk> with <unk> . 
- 
- = = Synthesis = = 
- 
- 
- = = = N @-@ H , N @-@ <unk> , N @-@ <unk> = = = 
- 
- The two main approaches to synthesis of N @-@ H , N @-@ <unk> , and N @-@ <unk> are oxidation of <unk> with <unk> ( A ) and <unk> of <unk> ( B ) . 
- Additionally , oxidation of <unk> <unk> and oxidation of <unk> with <unk> <unk> may yield <unk> <unk> . Some <unk> have the unique property of <unk> stable nitrogen atoms at room temperature due to an <unk> barrier of 24 to 31 <unk> / <unk> . <unk> <unk> where <unk> is entirely due to <unk> stable nitrogen are reported . 
- 
- = = = N @-@ <unk> = = = 
- 
- In the late 1970s and early 1980s Franklin A. Davis synthesized the first N @-@ <unk> , which act exclusively as oxygen transfer reagents , and are the most predominantly used class of <unk> today . While originally synthesized with <unk> and the phase transfer catalyst <unk> chloride , an improved synthesis using <unk> as the <unk> is now most prevalent . 
- Many N @-@ <unk> are used today , each with slightly different properties and reactivity . These reagents are summarized in the table below . 
- 
- = = = <unk> <unk> = = = 
- 
- With highly electron withdrawing <unk> <unk> , <unk> exhibit reactivity more similar to that of <unk> than typical <unk> . Notably , <unk> <unk> certain C @-@ H bonds with high selectivity . <unk> <unk> may be synthesized by <unk> a <unk> <unk> to <unk> <unk> <unk> and a metal fluoride to act as an HF <unk> . 
- 
- = = Reactions of <unk> = = 
- 
- 
- = = = <unk> production = = = 
- 
- <unk> are <unk> in the <unk> process for the production of <unk> . Many millions of kilograms of <unk> are produced annually by this method that involves a step wherein ammonia is <unk> in the presence of methyl <unk> <unk> to give the <unk> : 
- Me ( <unk> ) C = O + <unk> + <unk> → Me ( <unk> ) <unk> + H2O 
- In subsequent steps the <unk> is converted to the <unk> , which is the immediate en route to <unk> : 
- Me ( <unk> ) <unk> + <unk> → Me ( <unk> ) C = <unk> + H2O 
- 
- = = = <unk> transfer = = = 
- 
- 
- = = = = α @-@ <unk> of <unk> = = = = 
- 
- α @-@ <unk> , or <unk> , are an important synthetic motifs present in many natural <unk> α @-@ <unk> have been synthesized in many ways , including reduction of α @-@ <unk> , substitution of a <unk> for a leaving group and direct oxidation of an <unk> . <unk> ( <unk> ) - ( <unk> <unk> ) ( <unk> ) and N @-@ <unk> are the most common <unk> sources of oxygen implemented in this process . One advantage of using N @-@ <unk> is that higher <unk> induction is almost invariably observed relative to <unk> and other <unk> . High yield ( 77 @-@ 91 % ) and <unk> ( 95 : 5 - 99 : 1 ) are reported for α @-@ <unk> with the Evans ' <unk> auxiliary with N @-@ <unk> as the <unk> . <unk> induction has been demonstrated with many other <unk> <unk> and <unk> with <unk> <unk> , including <unk> and <unk> . 
- Extensive work has been reported on asymmetric <unk> of <unk> <unk> with <unk> derivatives , achieving moderate to high enantiomeric excess . The commonly accepted proposed transition state that <unk> this <unk> outcome involves an open transition state where the <unk> bulk of R1 determines the face of approach . 
- The selectivity of some <unk> may be drastically improved in some cases with the addition of coordinating groups alpha to the <unk> ring as <unk> <unk> and <unk> in the table above . In these instances it is proposed that the reaction proceeds through a closed transition state where the metal <unk> is stabilized by <unk> from the sulfate and coordinating groups on the camphor skeleton . 
- α @-@ <unk> with <unk> has been widely implemented in total synthesis . It is a key step in both the <unk> <unk> total synthesis and the <unk> <unk> total synthesis . Additionally , <unk> implemented the transformation in his synthesis of the <unk> @-@ <unk> ( <unk> 1 @,@ 7 @-@ <unk> [ 5 @.@ 5 ] <unk> @-@ 3 @-@ <unk> ) System of <unk> acid . 
- 
- = = = = <unk> of <unk> = = = = 
- 
- <unk> of <unk> is a common reaction because epoxides can be <unk> in a number of useful ways . <unk> , laboratory <unk> is carried out with <unk> or other <unk> . <unk> have been found to be useful for the formation of highly acid sensitive epoxides . ( − ) <unk> was synthesized via <unk> <unk> as a late stage transformation as seen below . 
- Another transformation of high synthetic utility is asymmetric <unk> . A number of asymmetric <unk> exist : the <unk> <unk> , the <unk> @-@ <unk> <unk> , and the <unk> @-@ Colonna <unk> . These methods require specific functionality in order to achieve selectivity . The <unk> <unk> is specific to <unk> <unk> , the <unk> <unk> requires <unk> @-@ <unk> <unk> <unk> , and the <unk> <unk> requires α @-@ β <unk> <unk> . <unk> <unk> act <unk> on many <unk> <unk> . It has even possible to effect <unk> <unk> <unk> in the <unk> <unk> unit . Further investigation into these reactions may be required before levels of <unk> excess become practical for large scale synthesis . <unk> et al. have investigated asymmetric <unk> with a <unk> <unk> salt using <unk> as the stoichiometric <unk> seen below . 
- 
- = = = = <unk> of <unk> <unk> = = = = 
- 
- <unk> <unk> are known to <unk> <unk> <unk> with remarkable <unk> , and <unk> . This is a highly <unk> transformation , and similar reactivity and <unk> is seldom rivaled , especially considering the <unk> nature of the <unk> . <unk> <unk> show high selectivity toward tertiary <unk> . <unk> of primary <unk> and <unk> of a compound with two <unk> sites have never been observed . <unk> of <unk> is very high , often 95 - 98 % . ( <unk> of <unk> may be further enhanced by the addition of a fluoride salt ) . 
- 
- = = = <unk> transfer = = = 
- 
- <unk> with <unk> or <unk> <unk> are capable of nitrogen atom transfer , although this reactivity has received considerably less attention . 
- 
- = = = = <unk> of N @-@ <unk> = = = = 
- 
- <unk> of <unk> with N @-@ <unk> <unk> is quite versatile in the breadth of possible <unk> and corresponding products . <unk> may be derived from the <unk> of secondary or tertiary <unk> , <unk> and <unk> may be formed from their corresponding <unk> and <unk> , <unk> may be formed from <unk> and α @-@ <unk> may be formed by attack of corresponding <unk> . 
- 
- = = = = N @-@ <unk> = = = = 
- 
- The transfer of <unk> <unk> is more difficult than that of <unk> <unk> , although , unlike amine transfer by <unk> , there are no alternative methods that directly transfer <unk> <unk> . <unk> transfer has primarily been performed using <unk> and <unk> as <unk> . Very few transfers of <unk> <unk> to carbon <unk> have been successfully performed , although some do exist in the literature . 
- 
- = = = <unk> = = = 
- 
- <unk> have been found to undergo <unk> reactions via a radical mechanism when <unk> with <unk> light or in the presence of a single electron transfer reagent such as <unk> <unk> <unk> undergo ring expansions to the corresponding <unk> . <unk> , the migrating <unk> is determined by a <unk> effect where the group trans to the lone pair on the nitrogen will always be the predominant migration product . In light of this effect , it is possible to take advantage of the <unk> nitrogen due to high <unk> barrier to direct the <unk> . This phenomenon is demonstrated by observed <unk> in the <unk> below . In the <unk> on the left the <unk> unfavorable product is observed exclusively , while in the reaction on the right the product derived from the less stable radical intermediate is favored . 
- <unk> takes advantage of this <unk> as the key step in his synthesis of ( + ) <unk> , a natural medicine classified by the <unk> as possibly effective in the treatment of <unk> <unk> and the sexual problems caused by selective <unk> <unk> inhibitors . 
- It is also notable that <unk> will <unk> <unk> to <unk> . <unk> @-@ trans selectivity of the resulting <unk> is poor , however , yields are good to excellent . It is thought that some <unk> <unk> over time through a <unk> intermediate . 
- 
- = = = <unk> with <unk> = = = 
- 
- <unk> undergo <unk> reactions with <unk> to afford a number of unique five membered <unk> , as depicted in the figure below . This reactivity is due to the strained three membered ring and weak N @-@ O bond . 
- 
- 
- = Battle of <unk> = 
- 
- The Battle of <unk> ( also known as the Battle of <unk> , Battle of <unk> and Battle of <unk> ; German : <unk> <unk> <unk> ) , on 11 November 1805 was an engagement in the Napoleonic Wars during the War of the Third Coalition . <unk> ( modern <unk> ) is located in the <unk> Valley , on the River <unk> , 73 kilometers ( 45 mi ) upstream from Vienna , Austria . The river makes a crescent @-@ shaped curve between <unk> and nearby <unk> an der <unk> and the battle was fought in the flood plain between the river and the mountains . 
- At <unk> a combined force of Russian and Austrian troops trapped a French division commanded by <unk> <unk> <unk> . The French division was part of the newly created VIII Corps , the so @-@ called Corps <unk> , under command of <unk> <unk> . In pursuing the Austrian retreat from <unk> , <unk> had over @-@ extended his three divisions along the north bank of the <unk> . Mikhail <unk> <unk> , commander of the Coalition force , <unk> <unk> to send <unk> 's division into a trap and French troops were caught in a valley between two Russian columns . They were rescued by the timely arrival of a second division , under command of Pierre <unk> de l <unk> . The battle extended well into the night . Both sides claimed victory . The French lost more than a third of their participants , and <unk> 's division experienced over 40 percent losses . The Austrians and Russians also had heavy <unk> to 16 <unk> perhaps the most significant was the death in action of Johann Heinrich von <unk> , one of Austria 's most capable chiefs of staff . 
- The battle was fought three weeks after the Austrian capitulation at Ulm and three weeks before the Russo @-@ Austrian defeat at the Battle of <unk> . After <unk> Austria withdrew from the war . The French demanded a high indemnity and Francis II abdicated as Holy Roman Emperor , releasing the German states from their allegiance to the Holy Roman Empire . 
- 
- = = Background = = 
- 
- In a series of conflicts from 1803 @-@ 15 known as the Napoleonic Wars , various European powers formed five coalitions against the First French Empire . Like the wars sparked by the French Revolution ( 1789 ) , these further revolutionized the formation , organization and training of European armies and led to an unprecedented <unk> , mainly due to mass conscription . Under the leadership of Napoleon , French power rose quickly as the Grande <unk> conquered most of Europe , and collapsed rapidly after the disastrous invasion of Russia in 1812 . Napoleon 's empire ultimately suffered complete military defeat in the 1813 – 14 campaigns , resulting in the restoration of the Bourbon monarchy in France . Although Napoleon made a spectacular return in 1815 , known as the Hundred Days , his defeat at the Battle of Waterloo , the pursuit of his army and himself , his abdication and banishment to the Island of Saint Helena concluded the Napoleonic Wars . 
- 
- = = <unk> campaign = = 
- 
- From 1803 @-@ 06 the Third Coalition fought the First French Empire and its client states ( see table at right ) . Although several naval battles determined control of the seas , the outcome of the war was decided on the continent , predominantly in two major land operations in the <unk> valley : the Ulm campaign in the upper <unk> and the Vienna campaign , in the middle <unk> valley . 
- Political conflicts in Vienna delayed Austria 's entry into the Third Coalition until 1805 . After hostilities of the War of the Second Coalition ended in 1801 , <unk> <unk> emperor 's <unk> advantage of the subsequent years of peace to develop a military restructuring plan . He carefully put this plan into effect beginning in 1803 – 04 , but implementation was incomplete in 1805 when Karl Mack , Lieutenant Field Marshal and <unk> @-@ General of the Army , implemented his own restructuring . Mack bypassed Charles ' methodical approach . <unk> in the field , Mack 's plan also undermined the overall command and <unk> structure . Regardless , Mack sent an enthusiastic report to Vienna on the military 's readiness . Furthermore , after <unk> Napoleon 's maneuvers in <unk> , Mack also reported to Vienna on the weakness of French <unk> . His reports convinced the war party advising the emperor , Francis II , to enter the conflict against France , despite Charles ' own advice to the contrary . Responding to the report and rampant anti @-@ French fever in Vienna , Francis dismissed Charles from his post as <unk> and appointed his <unk> brother @-@ in @-@ law , <unk> Ferdinand , as commander . 
- The inexperienced Ferdinand was a poor choice of replacement for the capable Charles , having neither maturity nor aptitude for the assignment . Although Ferdinand retained nominal command , day @-@ to @-@ day decisions were placed in the hands of Mack , equally ill @-@ suited for such an important assignment . When Mack was wounded early in the campaign , he was unable to take full charge of the army . Consequently , command further devolved to Lieutenant Field Marshal Karl Philipp , Prince of <unk> , an able cavalry officer but inexperienced in the command of such a large army . 
- 
- = = = Road to Ulm = = = 
- 
- The campaign in the upper <unk> valley began in October , with several clashes in <unk> . Near the Bavarian town of <unk> , 40 kilometers ( 25 mi ) northwest of <unk> , on 8 October the 1st Regiment of <unk> , part of <unk> 's Reserve Cavalry Corps , and grenadiers of <unk> ' V Corps surprised an Austrian force half its size . The Austrians were <unk> in a line and unable to form their defensive squares quickly enough to protect themselves from the 4 @,@ 000 <unk> and 8 @,@ 000 grenadiers . Nearly 3 @,@ 000 Austrians were captured and over 400 were killed or wounded . A day later , at another small town , <unk> south of the <unk> <unk> French 59th Regiment of the Line stormed a bridge over the <unk> and , <unk> , chased two large Austrian columns toward Ulm . 
- The campaign was not entirely bad news for Vienna . At <unk> , Johann von <unk> arranged his 25 @,@ 000 infantry and cavalry in a prime defensive position and , on 11 October , the overly confident General of Division Pierre <unk> de l <unk> attacked <unk> 's force with fewer than 8 @,@ 000 men . The French lost 1 @,@ 500 men killed and wounded . Aside from taking the Imperial Eagles and <unk> of the 15th and 17th <unk> , <unk> 's force also captured 900 men , 11 guns and 18 ammunition wagons . 
- <unk> 's victory was a singular success . On 14 October Mack sent two columns out of Ulm in preparation for a breakout to the north : one under Johann <unk> <unk> headed toward <unk> to secure the bridge there , and the other under Franz von <unk> went north with most of the heavy artillery . <unk> the opportunity , Marshal Michel <unk> <unk> the rest of his VI Corps forward to re @-@ establish contact with <unk> , who was still north of the <unk> . In a two @-@ <unk> attack <unk> sent one division to the south of <unk> on the right bank of the <unk> . This division began the assault at <unk> . At the same time another division crossed the river to the east and moved west against <unk> 's position . After clearing Austrian <unk> from a bridge , the French attacked and captured a strategically located <unk> at the top of the hill at bayonet point . The Austrian cavalry unsuccessfully tried to fend off the French , but the Austrian infantry broke and ran . In this engagement alone , the Austrians lost more than half their reserve artillery park , 6 @,@ 000 ( out of 8 @,@ 000 total participants ) dead , wounded or captured and four colors . <unk> 's column also failed to destroy the bridges across the <unk> . 
- Napoleon 's lightning campaign exposed the Austrian <unk> command structure and poor supply apparatus . Mack completely <unk> the French <unk> and scattered his forces ; as the French defeated each unit separately , the surviving Austrians withdrew toward the Ulm fortifications . Napoleon arrived to take personal command of close to 80 @,@ 000 men . At Ulm on 16 October Karl Mack surrendered his <unk> army of 20 @,@ 000 infantry and 3 @,@ 273 cavalry . The officers were released on the condition that they not serve against France until formally exchanged for French officers captured by the Austrians , an agreement to which they held . 
- 
- = = Prelude to battle = = 
- 
- The few Austrian corps not trapped at Ulm withdrew toward Vienna , with the French in close pursuit . A Russian army under Gen. Mikhail <unk> also <unk> away from the French , withdrawing to the east . At the <unk> river on 22 October it joined with the retreating Austrian corps commanded by Michael von <unk> . On 5 November the Coalition forces held a successful rearguard action in <unk> . On 7 November the Russians arrived in St. <unk> and crossed the <unk> river the next day . Late on 9 November they destroyed the bridges across the <unk> , holding the last one at the hamlet of Stein , near the village <unk> , until the late afternoon . 
- 
- = = = Battlefield = = = 
- 
- To the east of Stein , 2 kilometers ( 1 @.@ 2 mi ) down an old road , lay <unk> , with its small population of a few hundred , at the confluence of the stream of that name and the <unk> . To the west of Stein the <unk> made a large curve , creating a crescent @-@ shaped floodplain between it and the mountains . At the far western end of the floodplain , where the mountains came down almost to the river 's edge , was <unk> with its castle , known as <unk> <unk> . The castle had served as a prison for Richard I of England in <unk> . In 1645 – 46 , during the Thirty Years War , the Swedes had fortified the castle and then demolished it when they withdrew . It stands at 159 meters ( 522 ft ) , on the highest ridge of a mountain <unk> with <unk> and pinnacles of granite . Because the mountain was sparsely <unk> , it was difficult to distinguish the ruins from the rocks . <unk> canyons cut through the mountain , and widen into the plain below . Between <unk> and Stein , on the flood plain , lay the hamlets of <unk> and <unk> . Near the hamlets , the <unk> flood plain was at its <unk> , extending at the most 762 meters ( 2 @,@ 500 ft ) from the base of the <unk> mountain to the bank of the river . 
- The region was known for its wine . Since the 15th century the local inhabitants practiced <unk> and the wine producers formed St. Paul <unk> ' Guild in <unk> , the oldest such guild in the German @-@ speaking world . <unk> vineyards extended up the sides of the <unk> River until it became a mountain stream and terrain was unsuitable for cultivation . The <unk> plain supported both <unk> and agriculture . As the terrain became <unk> , the vines grew in terraces built from the dark <unk> , primordial rock . From <unk> to <unk> the river makes its wide curve ; the mountains and the steeply terraced slopes prevent clear line @-@ of @-@ sight between the two towns . 
- 
- = = = <unk> = = = 
- 
- Napoleon had calculated that <unk> would withdraw toward Vienna , expecting reinforcements from Russia ; he envisioned that the armies would engage in a great battle at Vienna , and that this battle would decide the war . Consequently , Napoleon drew divisions from four of the other seven corps of the Grande <unk> to create a new VIII Corps . This corps was to secure the north shore of the <unk> , block any of the Austrian or Russian groups from reinforcing one another and , more importantly , prevent <unk> from crossing the river and escaping to Russia . 
- The new VIII Corps , under the overall command of <unk> <unk> , included three infantry divisions and a division of cavalry ( see Order of Battle below ) . Corps <unk> , as it was known , crossed the <unk> at <unk> and <unk> in early November 1805 and marched east , on the north bank of the <unk> . Operating independently , the <unk> 's cavalry conducted reconnaissance ahead of them and on the flanks . Gen. <unk> 's division ( about 6 @,@ 000 men ) took the lead ; <unk> was with them . They were followed by Gen. <unk> 's division ( another 4 @,@ 000 ) about one day 's march behind . Jean @-@ Baptiste <unk> 's division ( another 4 @,@ 000 ) , marching another day behind <unk> , brought up the rear . A flotilla of 50 boats acquired at <unk> provided communications across the <unk> . Before sending <unk> on his mission , Napoleon instructed him to protect his north flank at all times against possible Russian reinforcements , advice he reiterated in subsequent written orders . Napoleon also advised <unk> to secure all <unk> of the <unk> between <unk> and Vienna . 
- On 9 November <unk> 's division reached <unk> an der <unk> and covered the 50 kilometers ( 31 mi ) to <unk> by early on the following afternoon . Here it <unk> with some Russian patrols to the east of the town and expelled them . Feeling confident , the French established a forward post just upstream from Stein . In <unk> itself <unk> set up his command post and directed the establishment of a small field hospital . Although the position seemed secure , he had ignored Napoleon 's strict instructions and neglected to protect his left ( north ) flank . 
- This failure was an important factor when <unk> lost his corps ' so @-@ called " eyes " : after he and <unk> had crossed the <unk> , the French <unk> had <unk> to the northwest , leaving only three squadrons of the 4th <unk> available for reconnaissance . These had left the division and were operating independently of <unk> 's command . Consequently , <unk> and <unk> marched <unk> through the narrow canyon west of <unk> , not knowing what lay ahead of them . <unk> had led the Coalition army across the <unk> at <unk> , a short distance past Stein , and destroyed the bridge behind him . His actions deprived the French commanders of a possible route across the <unk> , putting the deployment of the entire French division at further risk in the case of retreat . In this decision <unk> abandoned Vienna to the French , who were <unk> on the Austrian capital from the north , west and southwest , for the security of uniting with reinforcements from <unk> . <unk> chose a military solution over a political one . 
- Unknown to either <unk> or <unk> , the Coalition had concentrated a force of approximately 24 @,@ 000 men ( mostly Russians and a few Austrians ) within a few kilometers of the French position at <unk> . In comparison , <unk> 's division had only 6 @,@ 000 men . The Austro @-@ Russian force was a mixture of infantry , <unk> ( usually deployed as <unk> ) , Russian <unk> and Russian and Austrian cavalry , accompanied by more than 68 artillery pieces . <unk> , who had learned the military arts under the tutelage of the legendary Russian <unk> <unk> , had overall command . The Russian cavalry , units of the greatly feared Cossacks , were well @-@ suited for patrolling the river bank ; indeed , on 9 November they had taken 40 French soldiers as prisoners . Furthermore , reinforcements stood in Moravia , less than two weeks ' march away . If the main body of the French army crossed the river , they would require time to prepare . <unk> would have ample warning of any large @-@ scale French movement . 
- After the afternoon 's initial <unk> with the French , <unk> held a council of war on the evening of 10 November at <unk> , at the great <unk> there . He knew several things . First , he knew the positions of the French from prisoners his Cossacks had captured . He also knew that <unk> had crossed at <unk> and was well ahead of any French reinforcements : <unk> had crossed at <unk> and , by 10 November , stood at <unk> , 50 kilometers ( 31 mi ) upstream , and <unk> was another 7 kilometers ( 4 mi ) further behind him . <unk> knew the size of the French <unk> division <unk> its positions , and he knew that most of the <unk> were not covering the French flank but had turned north . He also knew , or had made a good <unk> , about Napoleon 's orders , so he knew what to offer <unk> and <unk> as bait . 
- 
- = = = Battle plan = = = 
- 
- In addition to the Russian generals , the council included Austrian commanders Lieutenant Field Marshal Johann Heinrich von <unk> and Friedrich Karl Wilhelm , <unk> <unk> <unk> . <unk> , who had retired from the military in 1800 , had been recalled into service after the Ulm <unk> and had come to <unk> highly recommended by the Emperor . He was an experienced <unk> and strategist and had served in a variety of posts in the Habsburg military ; he had been <unk> Charles ' trusted adviser during the campaigns from 1796 to 1800 and had assisted in planning several of Charles ' victories . Upon his recall , <unk> was appointed Chief of the <unk> General Staff of the Coalition Army . The generals had found among the Austrian force one <unk> Christoph Freiherr von <unk> ( 1753 – 1824 ) , who had knowledge of the local geography . 
- Together , <unk> , <unk> and the other generals , with von <unk> 's advice on the local terrain , <unk> a plan to encircle the French at <unk> . Russian commander Mikhail <unk> <unk> would approach <unk> 's division from the east , supported by <unk> <unk> 's corps , and pin the French in place . Three additional columns , commanded by <unk> <unk> ( <unk> ) , <unk> Gen. <unk> and <unk> , would outflank the French from the west and the north . They would offer , as bait , a rumor : the Russian army was retreating into Moravia and only a rearguard would be left at <unk> . 
- 
- = = Battle = = 
- 
- On the night of 10 – 11 November a Russian column under <unk> 's command began its passage through the narrow canyons , intent on arriving at <unk> by noon ; two more columns , under <unk> and <unk> , moved in wider <unk> , planning to pass through the mountains and attack the French , who were extended along the river bank . According to the plan , in late morning <unk> 's column would emerge from the mountains first and launch a flanking assault on the French right . This flanking attack , combined with <unk> 's frontal assault from Stein , would force the French into a <unk> ; <unk> , they would have no option but to <unk> die . To ensure the success of the plan , the second and third columns , under <unk> and <unk> , would arrive in early and mid @-@ afternoon and support the earlier assaults . In this way , even if the French tried to retreat west to <unk> , they would not escape the <unk> @-@ like grip of the Coalition army . 
- <unk> accepted the bait of a <unk> Russian retreat . In the early morning of 11 November he and <unk> departed from <unk> to seize Stein and <unk> , <unk> the Russians had either abandoned the settlements or left only a small rear @-@ guard behind . As they approached Stein , a column of <unk> 's troops attacked the French forward positions . <unk> this force was the <unk> Russian rear guard , <unk> ordered Gen. <unk> to counterattack and push east towards the town of Stein . Fighting spread though the villages of <unk> , <unk> and the farm at <unk> . Instead of withdrawing , as a rear guard would , more and more Russian troops appeared and engaged the French column . 
- Initially <unk> made rapid progress , but he quickly recognized that the opposing force was much stronger than the typical rear guard of a retreating army . Realizing he had been <unk> and that <unk> 's troops were tiring rapidly , <unk> sent orders to <unk> 's division to <unk> forward . By mid @-@ morning the French momentum had stalled ; <unk> committed most of his remaining forces to driving <unk> back , leaving a single <unk> 300 <unk> cover his northern flank , and sent the rest to attack the Russian right . Within 30 minutes he achieved the superiority of numbers he sought . His 4 @,@ 500 French opposed 2 @,@ 600 Russians and forced them back toward Stein while pressing an attack along the river . <unk> had no option , for neither <unk> 's nor <unk> 's flanking columns were to be seen . 
- At this stage of the battle fighting paused . <unk> and <unk> waited for <unk> 's arrival while <unk> and <unk> waited for <unk> 's and <unk> 's . <unk> 's column was expected to be the last to join the fight because it had to march the greatest distance . The timing of the respite varies , depending on whose reports are consulted : fighting paused at around 12 : 00 or 14 : 00 . <unk> arrived first and immediately assaulted <unk> 's line with three battalions , pushing the French out of <unk> . Caught between two strong forces , <unk> attempted to push his way back through <unk> , to reach the river where the flotilla could evacuate his exhausted troops . <unk> through the narrow <unk> canyon and fighting off the Russian force at their rear , <unk> and his division were trapped when more of <unk> 's Russians appeared to block their retreat . The narrow <unk> hampered the Russians ; <unk> 's men had to march out of the canyons , form ranks and attack in waves . Despite <unk> 's continuous assault in the next two to three hours , <unk> and <unk> pushed the Russians back up the narrow <unk> in the hillside . At this point , <unk> 's column appeared behind the French line and joined the battle . The French were outnumbered more than three to one , assaulted in the front by <unk> 's column , in the middle by <unk> 's and in the rear by <unk> . 
- Earlier in the morning <unk> had proceeded with his column south and east along the river , from <unk> , according to instructions . Even before the arrival of <unk> 's <unk> , he heard the sound of artillery in the distance and sent riders ahead to discover the cause . They came back to report that a Russian column ( <unk> 's ) was descending from the mountains to take the road to <unk> . Realizing this would separate him from the forward division , <unk> <unk> his troops toward the sound of battle and deployed them to take the Russians in the flank . The French assault , heralded by cannon fire , caused <unk> 's troops to turn their attention from <unk> 's <unk> force to face these new <unk> . Although superior in numbers , <unk> 's column had no supporting artillery , and the narrow space prevented them from taking advantage of their size . It was <unk> 's turn to face attackers at his front and rear , until the arrival of <unk> 's column , which had <unk> its way through the mountains in the west . 
- <unk> arrived at dusk , and the action continued well after dark ; in mid @-@ November night falls at close to 17 : 00 in the upper <unk> <unk> . Despite the darkness , <unk> descended out of the <unk> and deployed his troops to <unk> <unk> 's flank . As his Russians entered the <unk> , they came between a battalion of French and another of Russians . With the additional force , the French were overwhelmed , but most of the shooting subsided when the combatants could not tell apart friend from <unk> in the dark . Under the cover of darkness , <unk> used the French flotilla to evacuate his exhausted troops to the south bank . The French and Russians continued to <unk> <unk> into the night as <unk> encountered one another in the dark . <unk> of <unk> 's force provided any necessary rear guard action , and the following morning the remaining men were evacuated from the north shore of the <unk> , while they maintained possession of only <unk> and <unk> on the north bank . 
- 
- = = = Losses = = = 
- 
- The losses were <unk> : <unk> lost close to 40 percent of his division to death and wounds . Aside from losing five guns , 47 officers and 895 men under his command were captured , bringing the loss of <unk> closer to 60 percent ; furthermore , he lost the eagles of the 4th Infantry Regiment ( France ) and the eagle and <unk> of the 4th <unk> . The Russians lost around 4 @,@ 000 , about 16 percent of their force , and two regimental colors . The Austrian Lieutenant Field Marshal <unk> was killed as the battle concluded , probably by Russian <unk> in the confused melee . The vineyards and the villages of <unk> and <unk> were destroyed , as was most of <unk> and Stein . <unk> was heavily damaged ; the French plundered the town at least twice , and " <unk> handled " its inhabitants . 
- 
- = = Aftermath = = 
- 
- Both sides claimed victory . Although losses were fairly equal in terms of <unk> wounded or dead on each <unk> Coalition forces went into battle with 24 @,@ 000 men while the French started with <unk> 's division of about 6 @,@ 000 , which grew close to 8 @,@ 000 when <unk> 's men joined the fighting in the afternoon . Regardless , <unk> 's division was nearly destroyed ; the 30 percent losses experienced by the French fell predominantly on his division . <unk> for both sides , the fighting was hard . The weather had been cold ; an early storm had left slick icy mud in the roadways , and <unk> " like chandeliers " hung from the trees . 
- For the Coalition , the Russians were secure on the north bank of the <unk> , awaiting reinforcements from <unk> ; the bridges between <unk> and Vienna had been destroyed , making French access to the Austrian capital more difficult , but not impossible . After six months of fighting in which the Austrians had enjoyed little good news , the Coalition could claim a difficult and timely victory . The French had retreated from the field with a badly <unk> division and <unk> had secured the right flank . Indeed , Francis was so pleased with the outcome at <unk> that he awarded <unk> the Military Order of Maria <unk> . 
- For the French , the survival of the Corps <unk> seemed nothing short of a miracle . The remainder of <unk> 's division crossed the river the next morning and eventually <unk> in Vienna , which the French acquired by deception later in the month . More importantly for them , the French force had performed well over difficult terrain and under terrible combat conditions . Initially there had been some panic and parts of at least one French battalion had tried to escape on the flotilla craft . They had lost control of the boats in the current and smashed into the pillars of the burned bridge at <unk> , <unk> their boats . <unk> into the icy river , most had drowned . Despite this initial panic , <unk> 's column retained its cohesion , and responded well to various difficult demands . <unk> had demonstrated his tactical <unk> : when he heard cannon fire , he directed his troops toward it to support the French division . In terms of French staffing , <unk> 's failure to guard his flank , especially in the face of Napoleon 's direct advice , adversely influenced his relationship with his commander . However , in the immediate weeks ahead , the flamboyant <unk> did more to annoy Napoleon than <unk> had . In assessing the battle and its aftermath , historians have laid the blame and credit for its outcome not only on <unk> and <unk> : " Napoleon , aware of <unk> 's danger and his own <unk> for it , vented his frustration on <unk> , whom he unjustly accused of abandoning <unk> for the empty glory of riding through Vienna . " 
- After the victory at <unk> , Napoleon dispersed the VIII Corps and reassigned <unk> . However disappointed he may have been with <unk> , Napoleon was pleased with <unk> 's performance . As recognition of his conduct in what the French called " the immortal Battle of <unk> " , <unk> received the Officer 's Grand Cross of the Legion of Honor . 
- The loss of <unk> was a significant blow to the Austrian military organization . Called out of retirement for this specific task , he was one of their most experienced general staff officers , other than the <unk> Charles . From the summer of 1796 until his retirement in 1800 he had been Chief of the <unk> General Staff of the Army , the Lower Rhine , the Rhine and the Army of Germany . Furthermore , he was a trusted member of <unk> Charles ' staff . He had helped to design several of Charles ' more important victories at <unk> , <unk> , the <unk> at <unk> and <unk> , the battles at <unk> and <unk> , and the northern Swiss Campaign of <unk> that included battles at <unk> and <unk> . An experienced officer and excellent <unk> , he might well have made a more effective Chief of the <unk> General Staff of the Coalition Army at the Battle of <unk> than his eventual replacement , Franz von <unk> . In <unk> 's absence <unk> , the architect of the Austrian catastrophe at <unk> in 1800 , was chosen to develop the general battle plan of Coalition action at <unk> . <unk> , undoubtedly a far better <unk> than <unk> , and possessed of superior training and mapping skills , would have developed a more realistic Coalition plan for <unk> . <unk> 's presence would probably not have been enough to turn that defeat into a victory , but it would have <unk> the magnitude of the Coalition 's losses ; <unk> was considered one of Napoleon 's finest <unk> . 
- In the broader picture , despite the important major naval engagements , the outcome of the War of the Third Coalition was determined on the Continent , predominantly in the two major land operations . In the Ulm campaign , the Habsburgs achieved some minor victories , such as <unk> 's at <unk> @-@ <unk> , but ultimately lost an entire army and an officer corps . The latter would not resume arms against France until formally exchanged . This condition crippled the Austrian military leadership and forced the recall of such pensioners as <unk> out of retirement . After the capitulation at Ulm , isolated portions of the Austrian military <unk> capture and joined with their Russian allies ; Michael von <unk> 's corps slipped out of the <unk> and joined <unk> 's force . A few other small forces refused to <unk> and seemingly melted into the Bavarian mountains and the <unk> forests , to reappear in Bohemia for <unk> . Sixteen hundred cavalry , including <unk> Ferdinand and Prince <unk> , broke out of Ulm before its capitulation . Maximilian , Count of <unk> , led his column back through the mountains into Austria , fighting rear guard actions against pursuing French forces at the <unk> ( <unk> ) and <unk> . These elusive units were insufficient to balance heavy losses at key battles in which the Austrians could not hold their own against the French . Between the Ulm capitulation and the Austrian and Russian defeat at <unk> , there were other minor achievements : a successful <unk> between the cavalry that escaped from Ulm and the French near the town of <unk> , the contested victory at <unk> , and another within days at <unk> . 
- The second determining event , the decisive French victory at the Battle of <unk> over the combined Russian and Austrian armies , forced the Austrian withdrawal from the Coalition . The subsequent Peace of <unk> , signed on 26 December 1805 , reinforced the earlier treaties of <unk> <unk> and <unk> . Furthermore , Austria ceded land to Napoleon 's German allies , and paid an indemnity of 40 million francs . Victory at <unk> also gave Napoleon the latitude to create a <unk> zone of German states between France and the states of Prussia , Russia , and Austria . These measures did not establish a lasting peace on the continent . Prussian worries about growing French influence in Central Europe sparked the War of the Fourth Coalition in 1806 , in which Austria did not participate . 
- 
- = = Battlefield <unk> = = 
- 
- Until 1805 , <unk> was probably best known as the village in which crusader Richard the <unk> was held by Leopold V , Duke of Austria . In 1741 , during the War of the Austrian <unk> , several hundred local villagers had held off the French and Bavarian armies , intent on capturing Vienna , by painting drain pipes to look like cannons , and beating on drums , thus suggesting the presence of a large force . 
- After 1805 , the exploits of 40 @,@ 000 French , Russian , and Austrian soldiers excited the European imagination . General <unk> 's grave has never been found , but in 1811 a monument for him was erected at the Stein Tor , the gate leading from the old village of <unk> to the hamlet of Stein . The house in which Captain von <unk> lived was marked with a bronze plate commemorating his contribution to the battle . In 1840 , a Spanish <unk> created an image of the battle , which was later expanded by English <unk> John <unk> . The image depicts the evacuation of French troops via the <unk> flotilla ( see <unk> image ) on a <unk> night . In fact , the moon was in its last quarter phase 48 hours later , and on 11 November probably did not provide as much light as depicted in the image . 
- In 1836 , Jean Antoine <unk> Fort ( French , 1793 – 1861 ) , a historical painter , created a <unk> of the battle , Combat de <unk> le 11 <unk> 1805 ( ( English ) Battle of <unk> of 11 November 1805 ) , which is in the <unk> collection at Versailles . 
- In the Russian novel War and Peace , Leo <unk> devoted several pages to the battle , its prelude , and its aftermath , and the delivery of its news to the Tsar by Prince Andrew . Between <unk> and <unk> , at the edge of the <unk> plain , stands the " Little Frenchman " memorial ( see image ) erected in 1905 to commemorate the battle ; it bears the names of <unk> , <unk> , <unk> , <unk> , and others on a copper @-@ engraved plate . 
- 
- = = Orders of battle = = 
- 
- 
- = = = French VIII . Corps ( Corps <unk> ) = = = 
- 
- On 6 November , <unk> Adolphe <unk> commanded the following forces : 
- 1st Division under command of Pierre <unk> de l <unk> ( formerly 1st Division of VI . Corps ) , six battalions , three squadrons , and three guns , most of which were involved in the fighting after mid @-@ day . 
- 2nd Division under command of <unk> <unk> <unk> <unk> de la <unk> ( formerly 2nd Division of the V. Corps ) , nine battalions , three squadrons , three guns . 
- 3rd Division under command of Jean @-@ Baptiste <unk> ( <unk> Division , formerly 3rd Division of the II . Corps ) . The 3rd Division was not involved in the fighting . 
- Dragoon Division under command of Louis Klein . Klein 's division included the 1st , 2nd , 4th , and 14th Regiments of <unk> . They were not involved in the fighting . 
- <unk> fleet of fifty boats , under the command of Frigate Captain <unk> . 
- Total : fifteen battalions , six squadrons , six guns , approximately 12 @,@ 000 men , not all of which were involved in the fighting . 
- 
- = = = Coalition columns = = = 
- 
- First Column , commanded by General of Brigade Prince <unk> <unk> <unk> , included three battalions of infantry , three <unk> battalions , and three <unk> battalions , ten squadrons of Hussars . 
- Second Column , Lieutenant General <unk> , included six battalions of infantry , three battalions of grenadiers , and five squadrons of Hussars . 
- Third Column , commanded by Lieutenant General <unk> , including six battalions of infantry , one battalion from the 8th <unk> regiment , and ten squadrons of the <unk> Regiment <unk> . 
- Fourth Column , commanded by Lieutenant General <unk> , nine battalions of infantry . 
- Fifth Column , Lieutenant General Freiherr von <unk> , nine battalions of infantry . 
- Sixth Column , Lieutenant General Freiherr von Rosen , with six battalions of Infantry and ten squadrons of cavalry . The Sixth Column did not take part in the fighting . 
- Austrian Infantry Brigade , Major General Johann <unk> von <unk> @-@ <unk> , four battalions of Border Infantry , including the highly decorated 9th Regiment <unk> . 
- Austrian Cavalry Division , Lieutenant Field Marshal Friedrich Karl Wilhelm , <unk> <unk> <unk> , twenty @-@ two squadrons of cavalry . 
- Total : fifty @-@ eight battalions , sixty @-@ two squadrons , fourteen artillery batteries , approximately 24 @,@ 000 men and 168 guns . 
- 
- 
- = <unk> <unk> = 
- 
- <unk> Edward <unk> / <unk> / ( born July 12 , 1977 ) is an American Canadian professional wrestler , mixed martial artist , and former amateur wrestler and professional American football player . He is currently signed to WWE on the Raw brand . He is a four @-@ time WWE ( World Heavyweight ) Champion , a former <unk> Heavyweight Champion , and an NCAA Division I Heavyweight Wrestling Champion . He is also a one @-@ time <unk> Heavyweight Champion , making him a five @-@ time world champion in professional wrestling . As of July 14 , 2016 , he is # 8 in official <unk> heavyweight rankings . 
- After his successful amateur wrestling career at Bismarck State College and the University of Minnesota ( 106 wins and 5 losses ) , <unk> signed with WWE ( then the World Wrestling Federation ) in 2000 . He was assigned to its developmental promotion Ohio Valley Wrestling ( <unk> ) , where he was a three @-@ time <unk> Southern Tag Team Champion with Shelton Benjamin . After debuting on WWE 's main roster in 2002 , he won the WWE Championship on three separate occasions with victories over The Rock and Kurt Angle ( twice ) . <unk> won his first WWE <unk> Championship five months after his main roster debut at the age of 25 , becoming the youngest champion in the title 's history . He was also the 2002 King of the Ring and the 2003 Royal Rumble winner , making him the youngest King of the Ring and Royal Rumble winner as well . Following his match with Goldberg at WrestleMania <unk> , <unk> left the WWE and pursued a career in the National Football League ( NFL ) . He was named a defensive tackle for the Minnesota Vikings but was cut prior to the start of the 2004 – 05 season . In 2005 , <unk> returned to professional wrestling and signed with New Japan Pro Wrestling ( <unk> ) , where he won the <unk> Heavyweight Championship in his first match . After a contractual dispute with <unk> , he also wrestled as <unk> Heavyweight Champion in the <unk> <unk> Federation ( <unk> ) . 
- In 2006 , <unk> pursued a career in mixed martial arts . He signed with Hero 's and won his first fight , against <unk> @-@ <unk> Kim , in June 2007 . He then signed with the Ultimate Fighting Championship ( <unk> ) the following October . <unk> lost in his <unk> debut against Frank <unk> and then won his second fight against Heath <unk> . In November 2008 , <unk> defeated Randy <unk> to become the <unk> Heavyweight Champion . Shortly after a successful title defense in a rematch with <unk> , <unk> was sidelined due to <unk> . He would return at <unk> 116 to defeat Interim <unk> Heavyweight Champion Shane <unk> and <unk> the heavyweight championships , becoming the <unk> Heavyweight Champion . <unk> then lost the championship to Cain <unk> at <unk> 121 . In 2011 , he was once again sidelined due to <unk> and underwent surgery . <unk> returned at <unk> 141 in December , losing to <unk> <unk> and promptly retiring from <unk> . <unk> was a box office sensation in <unk> . He took part in a few of the best selling pay @-@ per @-@ views in <unk> history , including <unk> 100 and <unk> 200 . 
- In April 2012 , <unk> once again returned to professional wrestling , rejoining WWE after an eight @-@ year hiatus . Two years later , at WrestleMania XXX , <unk> defeated The Undertaker to end his undefeated streak at the premier annual event . <unk> has been managed by Paul <unk> throughout the majority of his professional wrestling career . He has <unk> numerous pay @-@ per @-@ view events for both the WWE and <unk> , including WrestleMania <unk> , WrestleMania 31 , <unk> 100 , and <unk> 116 . In 2015 , an ESPN.com article referred to <unk> as " the most accomplished athlete in pro wrestling history " . 
- 
- = = Early life = = 
- 
- <unk> was born in Webster , South Dakota , on July 12 , 1977 . He was raised on a Webster dairy farm owned by his parents , Stephanie and Richard <unk> . He is of German descent . He has two older brothers named Troy and Chad , and a younger sister named <unk> . At age 17 , he joined the National Guard , where he was assigned to an office job after his red @-@ green <unk> was deemed hazardous to his desire to work with explosives . He lost this job after failing a computer typing test , and later worked for a construction company . 
- <unk> attended Webster High School , where he played football and competed in amateur wrestling , placing third in the state championships his senior year . He then attended Bismarck State College , where he won the National Junior College Athletic Association ( <unk> ) heavyweight wrestling championship in his sophomore year . He transferred to the University of Minnesota on a wrestling scholarship for his junior and senior college years . There , he was roommates with future WWE colleague Shelton Benjamin , who was also his assistant coach . 
- <unk> won the 2000 National Collegiate Athletic Association ( NCAA ) Division I heavyweight wrestling championship his senior year after being the runner @-@ up to Stephen Neal the year prior . He finished his amateur career as a two @-@ time <unk> All @-@ American , the 1998 <unk> Heavyweight Champion , two @-@ time NCAA All @-@ American , two @-@ time Big Ten Conference Champion , and the 2000 NCAA Heavyweight Champion , with a record of 106 – 5 overall in four years of college . 
- 
- = = Professional wrestling career = = 
- 
- 
- = = = World Wrestling Federation / Entertainment = = = 
- 
- 
- = = = = Training and debut ( 2000 – 2002 ) = = = = 
- 
- In 2000 , <unk> signed with the World Wrestling Federation ( WWF ) . He was sent to its developmental territory , Ohio Valley Wrestling . There , <unk> first met future friend and manager Paul <unk> . He formed a tag team known as " The Minnesota <unk> Crew " with his former college roommate , Shelton Benjamin . <unk> and Benjamin won the <unk> Southern Tag Team Championship on three separate occasions . <unk> wrestled several dark matches in 2001 and 2002 before being called up to the main roster . 
- <unk> debuted on WWF television on the March 18 , 2002 , episode of Raw , coming through the crowd and attacking Al Snow , <unk> and Spike Dudley during their match . He was accompanied by Paul <unk> , who was seen giving instructions to <unk> . When the brand extension was introduced in the WWF , <unk> was drafted to the Raw brand . Later , <unk> was confirmed to be <unk> 's agent and gave <unk> the nickname " The Next Big Thing " . <unk> 's first feud was with the Hardy Boyz . <unk> and Jeff Hardy <unk> off at <unk> in <unk> 's first official televised match . He won the match by knockout after Hardy was unable to respond to referee Theodore Long . The next night on Raw , <unk> faced off against Jeff Hardy 's brother , Matt Hardy , and defeated him in the same fashion . 
- 
- = = = = WWE Championship <unk> ( 2002 – 2004 ) = = = = 
- 
- In June 2002 , <unk> won the King of the Ring tournament , defeating <unk> Ray Dudley in the first round , Booker T in the quarter @-@ finals , Test in the semi @-@ finals , and Rob Van Dam in the finals , earning him a shot at the WWE <unk> Championship at SummerSlam . On July 22 , <unk> joined the SmackDown ! brand . After a quick feud with Hollywood <unk> Hogan in August 2002 , <unk> defeated WWE <unk> Champion , The Rock at SummerSlam to become the WWE <unk> Champion and youngest WWE Champion at age 25 , a record previously held by The Rock . He also became the second fastest wrestler to win the WWE Championship since his debut ( 126 days ) behind only Ric Flair ( 113 days ) . At the time , the <unk> WWE Championship was being defended on both brands , so Raw General Manager Eric <unk> expected <unk> to return to Raw the following night . However , SmackDown ! General Manager Stephanie McMahon announced that <unk> was only required to defend the title on SmackDown ! , forcing <unk> to institute a new championship for Raw ( the World Heavyweight Championship ) . The WWE <unk> Championship was then renamed the WWE Championship . 
- <unk> 's rapid rise to the top of WWE in 2002 led to a feud with The Undertaker , which involved a match at <unk> . The match ended in a double <unk> resulting in <unk> retaining the title . <unk> faced The Undertaker again , at No Mercy , this time in a Hell in a Cell match . Leading up to the match , in the storyline , <unk> broke the Undertaker 's hand with a <unk> tank . Despite <unk> <unk> McMahon not to let The Undertaker use his cast as a weapon , the request was denied and the match went on as planned . In a match that saw both wrestlers and even <unk> covered in blood , it ended when <unk> reversed an attempted <unk> <unk> into his finishing F @-@ 5 maneuver for the win . Six days after his Hell in a Cell match with The Undertaker , <unk> successfully retained his WWE title in a handicap match with <unk> at the Rebellion pay @-@ per @-@ view against Edge . 
- <unk> 's next opponent was Big Show . <unk> was convinced more than anyone that <unk> could not win , trying to talk him out of defending the title . <unk> refused and faced Big Show in Madison Square Garden at the Survivor Series pay @-@ per @-@ view . Towards the end of the match , <unk> delivered an F @-@ 5 to Big Show , but when he went for the pin , <unk> pulled the referee out of the ring . This allowed Big Show to capitalize and proceeded to <unk> <unk> on a steel chair . Show went on to pin <unk> and win the title . This loss was <unk> 's first pinfall loss in WWE and led <unk> to turn for the first time in his career into a fan favorite . Following Survivor Series , <unk> made it clear that <unk> would not get a rematch , and had <unk> a special clause saying so into his contract . In order to gain his revenge on Big Show and <unk> , <unk> <unk> in his first title defense , which came against Kurt Angle the next month at Armageddon . <unk> hit the F @-@ 5 on the champion , which enabled Angle to pin him and win the title . On the following episode of SmackDown ! , however , Angle introduced <unk> as his manager and , despite promising <unk> a title shot earlier in the evening , declared that <unk> still would not get it . <unk> was beaten down by Big Show and Angle after the main event , but would get his revenge after the show went off the air . He eventually knocked the Big Show out with a steel chair , leaving Angle alone with <unk> . <unk> then chased the champion out of the ring and resumed his assault that culminated when <unk> used the F @-@ 5 to propel Angle 's right knee into the steel <unk> . As <unk> tended to a screaming Angle , <unk> finished off the assault with a <unk> on the <unk> <unk> , breaking the champion 's leg in storyline . 
- With Angle temporarily put out of action , <unk> 's rivalry with <unk> and the Big Show resumed , which culminated in a match at the Royal Rumble in January 2003 with the winner being placed into the Royal Rumble later in the evening . <unk> would defeat Big Show and entered the Royal Rumble as the # 29 entry , the second to last competitor to enter the match . He eliminated Matt Hardy and The World 's Greatest Tag Team , which was composed of Charlie Haas and his former <unk> teammate , Shelton Benjamin , who were <unk> by Angle . <unk> would then eliminate The Undertaker last and win the Royal Rumble , which guaranteed him a title match at WrestleMania <unk> . 
- After the Royal Rumble , <unk> and Chris <unk> faced off against Angle , Haas , and Benjamin at No Way Out the following month and <unk> 's team won the match . During the match at WrestleMania , <unk> <unk> a shooting star press , a move he 'd done numerous times in developmental matches , and jammed his head and neck . This stunned <unk> and forced Angle and <unk> to improvise the finish of the match . <unk> would defeat Angle , after delivering an F @-@ 5 , to win his second WWE Championship . <unk> was diagnosed with a legitimate concussion from the <unk> shooting star press . 
- After WrestleMania , <unk> turned his attention to John <unk> , who had returned from injury in February 2003 and who had been F @-@ <unk> into a <unk> in the same manner Angle had been . <unk> claimed <unk> nearly ended his career and even named his new finishing move the " <unk> " as a <unk> at the new champion . The feud ended in a match at <unk> when <unk> defeated <unk> . On the following episode of SmackDown ! , <unk> returned to his rivalry with Big Show after he injured Rey <unk> badly during their match at <unk> . Show 's attack resulted in <unk> being carried out on a stretcher and back board and Big Show took <unk> off the stretcher and swung the back board into the <unk> , <unk> the injury . <unk> called out the Big Show , who demanded that <unk> put his title on the line against him . This led to a stretcher match at Judgment Day for the title . <unk> successfully retained his title with help from Rey <unk> and a <unk> . During the scripted rivalry , on SmackDown , <unk> lifted Big Show off the top @-@ rope in a <unk> which caused the ring to collapse on impact . 
- As <unk> and Big Show continued their rivalry , Kurt Angle returned from his neck surgery and he and <unk> began to form a more friendly rivalry , as the two were allies yet contenders for the title . At the first ever SmackDown brand @-@ exclusive pay @-@ per @-@ view in July , <unk> , <unk> took on Angle and Big Show in a No <unk> triple threat match for his title , which ended after Angle hit the Angle Slam on both Big Show and <unk> , <unk> the champion to become WWE Champion for a fourth time . 
- <unk> continued to aggressively pursue the WWE title despite his friendship with Angle . Mr. McMahon found his way into the angle , at first <unk> <unk> , who had involved himself in McMahon 's rivalry with Zach <unk> , for losing to Angle . This all turned out to be a <unk> that came into focus on the August 7 , 2003 SmackDown ! in <unk> , British Columbia . That night , <unk> and McMahon were to face each other in a steel cage match with Angle as the special guest referee as per McMahon 's orders on the previous week 's program . During the match , <unk> had passed out due to a staged backstage incident and McMahon was set to pin him , but Angle refused to allow McMahon to win that way . As the two men began to argue , <unk> rose to his feet , revealing the <unk> to the crowd , and F @-@ <unk> Angle . He then brutally beat Angle while McMahon watched , and celebrated with him afterwards , turning heel in the process . At SummerSlam , <unk> lost to Angle when Angle made <unk> tap out to the ankle lock . After that , <unk> would cement his heel turn by <unk> smaller wrestlers and attacking his rivals on a more consistent basis . He returned to using the F @-@ 5 to propel his opponents ' legs into the <unk> , as he did to Spanky and <unk> , and <unk> in Angle 's matches on two separate occasions . On the September 18 , 2003 episode of SmackDown ! , <unk> received his third shot at Angle in as many months when he faced the champion in a sixty @-@ minute Iron Man match for the title . <unk> won the match and his third WWE Championship by a final count of five to four . 
- <unk> successfully defended his newly won title against the debuting Paul London on October 9 edition of SmackDown ! . He returned to feuding with The Undertaker . <unk> had previously cost Undertaker the title in a match against then @-@ champion Kurt Angle , which granted him a shot at <unk> 's title . At No Mercy , <unk> defeated Undertaker in a <unk> Chain match . The rivalry then came to an end after The Undertaker chose to focus on Mr. McMahon . 
- After Paul <unk> returned to WWE as general manager of SmackDown ! , <unk> aligned himself with his former manager . With Survivor Series coming up , <unk> decided to challenge Angle to a traditional Survivor Series elimination tag team match . <unk> chose Big Show as his first teammate , with <unk> adding a returning Nathan Jones and a debuting Matt Morgan to bring the team number to four . Angle chose Chris <unk> and The <unk> to join his team . However , <unk> was injured during a match with <unk> and Angle 's team was forced to find a replacement for him . <unk> 's team picked A @-@ Train to fill the fifth and final spot for them after he attacked John <unk> , who refused to accept an invitation to join <unk> 's team . <unk> instead joined Angle 's team , and Angle added Hardcore Holly as the fifth member ; <unk> had injured Holly the year before and he hadn 't wrestled since . In the climax of the match , Chris <unk> became the only second wrestler to make <unk> tap out . <unk> faced <unk> in a singles bout two weeks later for the WWE Championship on SmackDown ! , where <unk> won after <unk> passed out to <unk> 's debuting <unk> Lock submission hold . 
- The Survivor Series event marked the first time <unk> met Goldberg from the Raw brand . After <unk> claimed in a backstage interview that he could beat anybody in the world , Goldberg interrupted the interview and introduced himself to <unk> , shaking hands with him before leaving with a <unk> . <unk> followed this rivalry with a feud involving Hardcore Holly . In the storyline , Holly wanted revenge on <unk> for legitimately injuring his neck during a previous match between the two in 2002 which left Holly in need of neck surgery and out of action for a year . At the Royal Rumble in 2004 , <unk> defeated Holly to retain the WWE Championship . Later in the Royal Rumble match , <unk> attacked Goldberg and delivered an F @-@ 5 , enabling Kurt Angle to eliminate him . 
- 
- = = = = Final storylines and departure ( 2004 ) = = = = 
- 
- In February , <unk> faced Eddie Guerrero for the WWE title at No Way Out . Late in the match , Goldberg delivered a spear to <unk> while the <unk> was unconscious . Afterwards , Guerrero went to pin <unk> but <unk> kicked out at two . <unk> then attempted to F @-@ 5 Guerrero but Guerrero reversed it into a DDT . Guerrero then hit a frog splash ; <unk> <unk> to win the WWE Championship . An angry <unk> then began feuding with Goldberg , <unk> him for losing his title , and a match was set up between the two at WrestleMania <unk> . During the feud with Goldberg , <unk> was also at odds with Stone Cold Steve Austin , who was shown suggesting to Goldberg that he attack <unk> at No Way Out . After <unk> attacked Austin on Raw and stole his four @-@ <unk> , Austin was inserted as the special guest referee for the WrestleMania match . On the March 4 episode of SmackDown , <unk> defeated Hardcore Holly in his last match on a weekly WWE televised show . Behind the scenes , it was widely known that the match was Goldberg 's last in WWE . Only a week before WrestleMania , rumors surfaced that <unk> , too , was leaving to pursue a career in the National Football League ( NFL ) . As a result , <unk> 's match with Goldberg became a <unk> as the fans at Madison Square Garden <unk> and heckled both wrestlers <unk> . Goldberg gained victory after delivering a <unk> to <unk> and both men subsequently received Stone Cold <unk> from Austin . 
- 
- = = = New Japan Pro Wrestling ( 2005 – 2007 ) = = = 
- 
- On October 8 , 2005 , <unk> won the <unk> Heavyweight Championship on his debut match in a three @-@ way match with <unk> <unk> and <unk> <unk> at a New Japan Pro Wrestling ( <unk> ) show in the Tokyo <unk> . <unk> is one of the few American wrestlers to have held this title . He won the match by <unk> <unk> <unk> after an F @-@ 5 , which he had renamed the Verdict since WWE owns the trademark on the F @-@ 5 name . After the match , <unk> stated that this name was referring to his lawsuit against WWE . 
- On December 6 , WWE filed a motion for a temporary <unk> order to prevent <unk> from continuing to work with <unk> , but the court did not grant it . Following that , he had two non @-@ title victories against <unk> <unk> and <unk> <unk> . <unk> successfully defended his championship on January 4 , 2006 , against former champion <unk> <unk> . On January 13 , WWE once again filed an injunction against <unk> to stop him from defending the <unk> Heavyweight Championship which was also not enforced as he went on to retain his championship against former <unk> Wrestling Grand Champion <unk> on March 19 , at the <unk> Hall . <unk> had another successful title defense against Giant Bernard on May 3 , 2006 . This was the first American vs. American title match in <unk> since <unk> vs. Stan Hansen in 1990 . 
- On July 15 , 2006 , New Japan Pro Wrestling announced <unk> would not return to defend the <unk> Heavyweight Championship due to " <unk> issues " and had been stripped of the title . A tournament was held on July 16 to determine the new champion which was won by Hiroshi <unk> . <unk> continued to possess the physical <unk> Championship belt until late June 2007 . 
- Approximately one year later on June 29 , 2007 , <unk> defended his <unk> Heavyweight Championship against TNA World Heavyweight Champion Kurt Angle in a champion versus champion match . <unk> <unk> Federation promoter Antonio <unk> had stated <unk> was the " proper " <unk> Heavyweight Champion as he was not defeated for the title . Angle would defeat <unk> by forcing him to tap out to the Angle lock to win the <unk> Heavyweight Championship as recognized by <unk> and Total Nonstop Action Wrestling ( TNA ) . This was <unk> 's last match as a professional wrestler until 2012 , when he re @-@ signed with WWE . 
- 
- = = = = <unk> = = = = 
- 
- <unk> had previously signed a non @-@ compete clause in order to be released from his contract with WWE , which prohibited him from working for any other professional wrestling companies before June 2010 . However , he decided to challenge this ruling in court . WWE responded with a <unk> after <unk> breached the agreement by appearing at a New Japan Pro Wrestling show in 2004 . In July 2005 , the two sides dropped their claims and entered negotiations to renew their relationship . WWE had offered <unk> a contract , but on August 2 , 2005 , WWE 's official website reported that <unk> had withdrawn from any involvement with the company . The lawsuit began to enter settlement talks on September 21 , but did not get solved . 
- On January 14 , 2006 , Judge Christopher <unk> stated that unless WWE gave him a good argument between then and the 25th , he would rule in favor of <unk> , giving him a summary judgment . This would have enabled <unk> to work anywhere , immediately . WWE was later granted a deadline <unk> . On April 24 , WWE announced on <unk> that both parties had reached a settlement . On June 12 , a federal judge dismissed the case at the request of both legal parties . 
- 
- = = = Return to WWE = = = 
- 
- 
- = = = = <unk> with Triple H and ending the <unk> ( 2012 – 2014 ) = = = = 
- 
- <unk> returned to the WWE on April 2 , 2012 , on Raw , as a heel by <unk> and delivering an F @-@ 5 to John <unk> . The following week on Raw , general manager John <unk> revealed that he signed <unk> to bring " legitimacy " back to the WWE and become the " new face of the WWE " . <unk> also announced that <unk> would face <unk> at Extreme Rules with the Extreme Rules <unk> later added to the match . <unk> was dominant throughout the match until <unk> punched <unk> in the face with a steel chain wrapped around his fist . <unk> then delivered the <unk> <unk> to <unk> onto steel steps and <unk> lost the match . 
- The following night on Raw , WWE 's Chief Operating Officer Triple H refused to give in to <unk> 's <unk> contract demands ( which included being given his own personal jet and having Raw renamed to Monday Night Raw Starring <unk> <unk> ) , resulting in <unk> attacking him and breaking his arm with a <unk> lock in storyline . The next week on Raw , Paul <unk> made his return as <unk> 's legal representative and claimed that <unk> was quitting WWE . He later announced a lawsuit against WWE for breach of contract . At No Way Out in June , Triple H challenged <unk> , who was not present , to a match at SummerSlam which <unk> refused . Stephanie McMahon would later <unk> <unk> into accepting the match on <unk> 's behalf on July 23 at Raw 1000 . On August 19 , at SummerSlam , <unk> defeated Triple H in a No <unk> match via submission after once again breaking his arm in storyline . The following night on Raw , <unk> declared himself the new " King of Kings " and said that he would depart from WWE after his victory over Triple H , stating that he had conquered everything in the company . 
- <unk> returned on the January 28 , 2013 episode of Raw , <unk> Vince McMahon who was about to fire <unk> , and Despite <unk> 's pleas , <unk> hit McMahon with an F @-@ 5 , breaking McMahon 's <unk> in storyline . The following week , during The <unk> 's <unk> talk show , Raw Managing Supervisor Vickie Guerrero revealed herself as the one who signed <unk> to a new contract to impress McMahon . On the February 25 episode of Raw , <unk> once again attempted to attack McMahon , only to get into a brawl with the returning Triple H , which resulted in <unk> legitimately having his head split open and requiring eighteen <unk> . The following week , Triple H issued a challenge to <unk> , requesting a rematch with him at WrestleMania 29 , which <unk> accepted but only after Triple signed a contract and <unk> named the <unk> . The following week , after Triple H signed the contract and assaulted <unk> , the <unk> was revealed as No <unk> <unk> with Triple H 's career on the line . <unk> ended up losing the match after Triple H hit him with a <unk> onto the steel steps . On the April 15 episode of Raw , <unk> attacked <unk> ( Heath <unk> , Drew <unk> , and <unk> <unk> ) before <unk> challenged Triple H to face <unk> in a steel cage match at Extreme Rules , which Triple H accepted the following week . On May 19 at the pay @-@ per @-@ view , after interference from <unk> , he defeated Triple H and ended their feud . <unk> returned on the June 17 episode of Raw , attacking <unk> 's fellow client <unk> Punk with an F @-@ 5 . Despite the accusations from Punk , <unk> claimed that he was not behind <unk> 's attack on him . However , in July , <unk> turned on Punk , and claimed that Punk could not beat <unk> , which led to <unk> making his return and attacking Punk on the July 15 episode of Raw . The following week on Raw , Punk challenged <unk> to a match at SummerSlam , where <unk> defeated Punk in a no <unk> match . 
- On the December 30 episode of Raw , <unk> returned with <unk> to announce his intentions to challenge the winner of the upcoming WWE World Heavyweight Championship match between Randy <unk> and John <unk> at the Royal Rumble . <unk> then <unk> any wrestler who disapproved of that notion to challenge him , which was answered by Mark Henry , and a brawl would ensue , ending with <unk> delivering an F @-@ 5 to Henry . The following week on Raw , Henry challenged <unk> again , only to have <unk> <unk> his elbow with the <unk> lock in storyline , and this led Big Show to came out afterwards to confront <unk> , thus starting a feud which was settled at Royal Rumble , where <unk> defeated the Big Show after attacking him with a steel chair before the match began . On the February 24 , 2014 episode of Raw , after <unk> stated that <unk> had requested a match for the WWE World Heavyweight Championship at WrestleMania XXX , instead receiving an open contract to face anyone else of his choosing , The Undertaker then returned and <unk> <unk> through a table , setting up their match at WrestleMania . <unk> defeated Undertaker after executing three F @-@ <unk> , ending his undefeated WrestleMania streak at 21 , a feat that was described by Sports Illustrated as being " the most shocking result since the Montreal <unk> " . 
- 
- = = = = WWE World Heavyweight Champion ( 2014 – 2015 ) = = = = 
- 
- On the July 21 , 2014 episode of Raw , Triple H announced that <unk> would face John <unk> at SummerSlam for the WWE World Heavyweight Championship . At SummerSlam , <unk> defeated <unk> to become the WWE World Heavyweight Champion , and during the match he delivered sixteen <unk> ( most of which were German <unk> ) and two F @-@ <unk> to <unk> , who barely managed any offense . On the August 19 episode of Main Event , Triple H announced that <unk> was invoking his championship rematch clause against <unk> at Night of Champions , where <unk> was disqualified due to Seth <unk> interfering , but retained his championship , which could not be lost via <unk> . Later in the year , after <unk> reunited with The Authority , he was added to <unk> and <unk> 's championship match at Royal Rumble , making it a triple threat match , which <unk> won despite suffering a storyline broken rib during the match . 
- <unk> 's next <unk> was Roman <unk> , who won the Royal Rumble match to earn the right to face <unk> for the title at WrestleMania 31 . During his main @-@ event match against <unk> , <unk> delivered multiple <unk> and was heard <unk> , " <unk> City , <unk> ! " , and thereafter " <unk> City " became one of his signature <unk> and merchandise motifs . After <unk> and <unk> traded a few false finishes , <unk> <unk> in his Money in the Bank contract while the match was in progress , making it a triple threat ; <unk> then pinned <unk> to win the title . The following night on Raw , <unk> tried to <unk> his rematch clause and subsequently attacked commentators Booker T , John " <unk> " <unk> , and Michael Cole , as well as a cameraman after <unk> refused the rematch , which led to Stephanie McMahon <unk> <unk> indefinitely in storyline . 
- <unk> returned on the June 15 episode of Raw as a fan favorite , being chosen by The Authority as the number one contender to <unk> ' WWE World Heavyweight Championship at <unk> . On July 4 , <unk> made his first non @-@ televised wrestling appearance for WWE since his 2012 return , defeating <unk> Kingston at The Beast in the East live event in Tokyo in a quick winning effort ; he also delivered F @-@ <unk> to Kingston 's New Day <unk> Big E and Xavier Woods after the match . On July 19 at <unk> , <unk> dominated <unk> , delivering 13 <unk> ; mid @-@ pinfall , after performing an F @-@ 5 , he was attacked by The Undertaker , who incapacitated <unk> with a <unk> and two <unk> <unk> ; this ended the match , with <unk> winning by <unk> and <unk> retaining the championship . 
- 
- = = = = Various <unk> and storylines ( 2015 – present ) = = = = 
- 
- The following night on Raw , Undertaker explained that he had attacked <unk> not for ending his WrestleMania streak , but rather for <unk> allowing <unk> to constantly <unk> Undertaker about it ; this led to the two <unk> throughout the arena and a WrestleMania rematch being scheduled for SummerSlam on August 23 , where Undertaker would controversially defeat <unk> ; during the match , Undertaker <unk> out to a <unk> lock by <unk> and the <unk> rang the bell , but the referee did not see the <unk> and demanded that the match continue , which saw Undertaker then hitting <unk> with a low blow and <unk> passed out to Undertaker 's submission hold , Hell 's Gate . The following night on Raw , <unk> and <unk> challenged Undertaker to an immediate rematch , only to be confronted by Bo Dallas , who mocked <unk> about his defeat ; <unk> then responded by delivering 3 German <unk> and an F @-@ 5 to Dallas . 
- During Night of Champions , it was announced that <unk> would face The Undertaker at the Hell in a Cell pay @-@ per @-@ view , where <unk> defeated The Undertaker after a low blow and F @-@ 5 onto the exposed ring floor , ending their feud . The match was later voted " Match of the Year " during the 2015 <unk> Awards . On the January 11 episode of Raw , <unk> returned , attacking The New Day , The League of Nations ( <unk> , King Barrett , <unk> , and Alberto Del Rio ) , and Kevin <unk> , before giving an F @-@ 5 to Roman <unk> . The following week on Raw , <unk> would brawl with <unk> until they were both attacked by The Wyatt Family . At the Royal Rumble , <unk> was the 23rd entrant , eliminating four competitors before being eliminated by Bray Wyatt with help from the rest of The Wyatt Family . 
- On the January 25 episode of Raw , Stephanie McMahon announced that the main event of <unk> would be a triple threat match between <unk> , Roman <unk> , and Dean <unk> to determine who would face Triple H for the WWE World Heavyweight Championship at WrestleMania 32 . In the following weeks , <unk> would be continuously provoked by <unk> , with <unk> saving him from the subsequent attacks by <unk> . At <unk> , <unk> dominated most of the match before he was put through two announce tables by <unk> and <unk> ; he would ultimately lose the match after <unk> pinned <unk> . Because of this , <unk> attacked <unk> in the parking lot as he was arriving at the arena for Raw , but <unk> would return later in the night , having <unk> an ambulance , and he challenged <unk> to a No <unk> <unk> Street Fight match at WrestleMania 32 , where <unk> defeated <unk> . On the July 7 edition of SmackDown , it was announced that <unk> would be facing the returning Randy <unk> at SummerSlam . On July 19 , at the 2016 WWE Draft , <unk> was drafted to Raw . 
- 
- = = Football career = = 
- 
- After his match at WrestleMania <unk> , <unk> sidelined his career in WWE to pursue a career in the National Football League ( NFL ) despite not playing American football since high school . The WWE issued this statement on their official website , <unk> , following his departure : 
- <unk> <unk> has made a personal decision to put his WWE career on hold to prepare to <unk> for the National Football League this season . <unk> has wrestled his entire professional career in the WWE and we are proud of his accomplishments and wish him the best in his new endeavor . 
- <unk> later told a Minnesota radio show that he had " three wonderful years " in WWE , but had grown unhappy and always wanted to play pro football , adding that he did not want to be 40 years old and wondering if he could have " made it " in football . In an interview about the NFL , he stated : 
- This is no load of bull ; it 's no WWE stunt . I am dead serious about this . I ain 't afraid of anything and I ain 't afraid of anybody . I 've been an underdog in athletics since I was five . I got zero college offers for wrestling . Now people say I can 't play football , that it 's a joke . I say I can . I 'm as good an athlete as a lot of guys in the NFL , if not better . I 've always had to fight for everything . I wasn 't the best <unk> in amateur wrestling but I was strong , had great conditioning , and a hard head . Nobody could break me . As long as I have that , I don 't give a damn what anybody else thinks . 
- <unk> had a great showing at the NFL <unk> . On April 17 , 2004 , a <unk> collided with his <unk> ; he suffered a broken jaw and left hand , a <unk> <unk> , and a pulled groin . Several NFL teams expressed interest in watching <unk> work out . The Minnesota Vikings worked out <unk> on June 11 , 2004 but he was hampered by the groin injury suffered in the April motorcycle accident . On July 24 it was reported that he was nearly recovered from his groin injury . He signed with the Vikings on July 27 and played in several preseason games for the team . He was released by the Vikings on August 30 , 2004 . <unk> received an invitation to play as a representative for the Vikings in NFL Europa but declined due to his desire to stay in the United States with his family . He had several football cards produced of him during his time with the Vikings . 
- 
- = = Mixed martial arts career = = 
- 
- 
- = = = Hero 's ( 2007 ) = = = 
- 
- On April 29 , 2006 , after the final match of the K @-@ 1 World Grand Prix 2006 in Las Vegas , <unk> announced his intent to join K @-@ 1 's mixed martial arts league , Hero 's . He trained with Minnesota Martial Arts Academy under Greg Nelson and Minnesota Assistant Head wrestling coach Marty Morgan . <unk> announced on August 12 in Las Vegas that he had signed a deal with the K @-@ 1 promotion . His first fight was scheduled against <unk> Hong @-@ man of Korea on June 2 , 2007 , at the K @-@ 1 <unk> ! ! USA show . However , prior to the match , Hong @-@ Man was replaced by <unk> <unk> Kim . <unk> submitted <unk> Kim with strikes in 1 : 09 of the first round to win his first official <unk> match . 
- 
- = = = Ultimate Fighting Championship ( 2008 – 2011 ) = = = 
- 
- During <unk> 77 , it was announced that <unk> had reached a deal to fight with the Ultimate Fighting Championship ( <unk> ) . On February 2 , 2008 , <unk> made his debut with the promotion in an event titled <unk> 81 : Breaking Point against former <unk> Heavyweight Champion , Frank <unk> . Due to his large hands , <unk> was wearing <unk> gloves for the fight , making him the second man in Nevada 's combat sports history to wear such gloves after <unk> Hong @-@ man . <unk> secured an early <unk> and began landing numerous <unk> but was docked a point after a punch hit <unk> on the back of the head . Following another <unk> by <unk> , <unk> managed to secure a <unk> and force a submission at 1 : 30 of the first round and <unk> lost in his <unk> debut . At <unk> 82 , former <unk> Heavyweight Champion and Hall of Famer Mark Coleman was announced to fight <unk> at <unk> 87 . However , Coleman withdrew from the fight due to an injury and was replaced by Heath <unk> . <unk> defeated <unk> by unanimous decision . 
- <unk> would then face Randy <unk> for the <unk> Heavyweight Championship at <unk> 91 on November 15 . <unk> would beat <unk> via a technical knockout ( TKO ) in the second round to become the new <unk> Heavyweight Champion . 
- On December 27 , 2008 , at <unk> 92 , Frank <unk> defeated <unk> <unk> for the Interim Heavyweight Championship and was to face <unk> for the <unk> <unk> Heavyweight Championship at <unk> 98 . Immediately after winning the Interim Heavyweight title , <unk> found <unk> in the crowd and shouted , " You 've got my belt " . Due to a knee injury to <unk> , the title unification match with <unk> that was originally slated to be the <unk> 98 main event was postponed . <unk> won the postponed rematch with <unk> at <unk> 100 on July 11 , 2009 , via technical knockout in the second round . The win earned <unk> <unk> of the Year honors , with Anderson Silva , from <unk> for 2009 . During his post @-@ match celebration , <unk> flipped off the crowd who had been <unk> him . <unk> also made a disparaging comment about the pay @-@ per @-@ view 's primary sponsor <unk> Light , claiming they " won 't pay me <unk> ' " and promoted <unk> Light instead . <unk> later apologized for his remarks at the post @-@ fight press conference , where he held a bottle of <unk> Light and endorsed their product . 
- On July 1 , 2009 , it was reported that the winner of the Shane <unk> vs. Cain <unk> fight at <unk> 104 would face <unk> but the match was scrapped and <unk> was scheduled to defend his belt against Shane <unk> at <unk> 106 on November 21 . On October 26 , 2009 , it was announced that <unk> pulled out of his <unk> bout due to an illness . <unk> President Dana White said <unk> had been ill for three weeks , claiming he had never been this sick in his life and that it would take him a while to recover ; his fight with <unk> was <unk> for <unk> 108 on January 2 , 2010 . <unk> initially sought treatment in Canada , but later told reporters that he had received " Third World treatment " at a hospital in Brandon , Manitoba , and that seeking better treatment in the U.S. saved his life . <unk> went on to criticize Canadian health care further and stated that he shared his experience in an effort to speak " on the behalf of the doctors in the United States that don 't want health care reform to happen " . 
- On November 4 , it was confirmed that <unk> was suffering from <unk> and that his bout with <unk> would have to wait a bit longer and the fight for <unk> 's heavyweight championship was cancelled . On November 14 , at the <unk> 105 post @-@ fight conference , Dana White stated , " [ <unk> ] ' s not well and he 's not going to be getting well <unk> soon " and that an interim title match might need to be set up . In addition to <unk> , it was revealed that he was suffering from a serious case of <unk> , an intestinal disorder , which required surgery . After further diagnosis , <unk> underwent surgery on November 16 to close a <unk> in his intestine that had been leaking <unk> matter into his abdomen , causing pain , <unk> , and <unk> his immune system to the point that he contracted <unk> . From the level of damage to <unk> 's system , the surgeon estimated that the intestinal condition had been ongoing for around a year . 
- In January 2010 , <unk> announced on ESPN 's <unk> that he was scheduled to make a return to the <unk> in the summer . A match between Frank <unk> and Shane <unk> took place on March 27 at <unk> 111 to determine the Interim Heavyweight Champion , and <unk> 's next opponent . Shane <unk> defeated <unk> via knockout in the first round , becoming the new Interim Champion . After the fight , <unk> came into the ring and stated , " It was a good fight but he 's wearing a belt that 's a make @-@ believe belt . I 've got the real championship belt " . <unk> faced <unk> at <unk> 116 to <unk> the heavyweight titles . Early in the first round , <unk> knocked <unk> down with punch , gave him a cut above his left eye , and used a ground and pound attack the rest of the round . In the next round , <unk> was able to take <unk> down , attain a full mount , then move into side @-@ control and finish the fight with an arm triangle <unk> . With the victory , <unk> became the <unk> <unk> Heavyweight Champion , earning his first <unk> of the Night and giving <unk> his first loss . The win also tied a <unk> record for most consecutive successful Heavyweight Championship defenses . 
- <unk> 's next defense was against undefeated top contender Cain <unk> on October 23 , at the Honda Center in <unk> , California at <unk> 121 . Dana White announced via <unk> that the <unk> would bring back <unk> Primetime to hype the fight . He was defeated by <unk> for the title by TKO in the first round . 
- On January 11 , 2011 , <unk> was announced as a coach of The Ultimate Fighter Season 13 , opposite Junior dos Santos , with the two expected to fight on June 11 at <unk> 131 , however , he was struck with another bout of <unk> and had to withdraw from the fight on May 12 . He was replaced by Shane <unk> , who ended up losing against dos Santos . <unk> underwent surgery on May 27 to help battle his problems with <unk> . Dana White said that he had a 12 @-@ inch piece of his <unk> removed . 
- In its May 2011 issue , ESPN 's magazine published a story listing the highest paid athlete based on base salary and earnings for the most recent calendar year or most recent season in 30 sports . <unk> topped the list for <unk> at $ 5 @.@ 3 million , which included his reported bout salaries and estimated pay @-@ per @-@ view bonuses . 
- In the summer of 2011 , <unk> announced that he was returning to action , stating , " I feel like a new man . <unk> . Strong . I feel like I used to feel " . His return match was scheduled to be at <unk> 141 on December 30 in Las Vegas against former <unk> heavyweight champion <unk> <unk> . <unk> won the fight by way of technical knockout in the first round . The result of the fight remains controversial , as <unk> tested positive for elevated levels of <unk> prior to his next fight . <unk> then announced his retirement from <unk> , mentioning his struggles with <unk> and saying " tonight was the last time you 'll see me in the <unk> " . 
- <unk> about a return to <unk> lasted until March 24 , 2015 , when <unk> announced in an interview on <unk> that he had re @-@ signed with WWE and officially closed the door on a return to <unk> , even though he was offered a deal " ten times more " than what he had made previously in his <unk> career . He further elaborated that , while he was training for months for a return to the <unk> , he felt " physically great but something was lacking mentally " . <unk> added that " [ he 's ] an older <unk> now , so [ he ] makes <unk> <unk> decisions " and that he chose to sign with WWE instead of returning to <unk> because he could " work part @-@ time with full @-@ time pay " . 
- 
- = = = <unk> 200 ( 2016 ) = = = 
- 
- Though <unk> said he was " closing the door on <unk> " in March 2015 , <unk> announced on June 4 , 2016 , that he would return at <unk> 200 on July 9 . WWE confirmed it had granted <unk> " a one @-@ off opportunity " to compete at <unk> 200 before he would return to the company for SummerSlam on August 21 . 
- <unk> , representing Canada for the first time in his career , defeated Mark Hunt by unanimous decision ( 29 @-@ 27 ) , avoiding 19 of Hunt 's 30 attempted standing strikes , and taking him down four times to land 43 significant ground strikes , 32 in the final round . He also won a <unk> @-@ record $ 2 @.@ 5 million <unk> . 
- On July 15 , <unk> was notified of a potential anti @-@ doping policy violation by the United States Anti @-@ Doping Agency ( <unk> ) stemming from an undisclosed banned substance in an out @-@ of @-@ competition sample collected on June 28 . Shortly after this was announced , WWE said <unk> 's match with Randy <unk> at SummerSlam would still take place . Hunt told <unk> to either pay him half of <unk> 's <unk> or let him out of his contract . He later changed his mind on Twitter , and asked for the whole <unk> . <unk> has not yet publicly responded to Hunt . <unk> told the Associated Press , " We will get to the bottom of this . " On July 19 , the <unk> announced that a second sample taken in @-@ competition on July 9 tested positive for the same banned substance discovered in the previous out @-@ of @-@ competition sample . 
- 
- = = = <unk> pay @-@ per @-@ views = = = 
- 
- 
- = = Personal life = = 
- 
- <unk> married <unk> Greek , better known as <unk> , on May 6 , 2006 . They reside on a farm in <unk> , Saskatchewan , having previously lived in Maple Plain , Minnesota . They have two sons : Turk ( born June 3 , 2009 ) and Duke ( born July 21 , 2010 ) . <unk> also has twins with his former fiancée , Nicole <unk> , the first being a son named Luke ( born <unk> Jr . ) and the second a daughter named <unk> Lynn ( born April 10 , 2002 ) . <unk> is 10 minutes older than Luke . <unk> has full custody of the twins , and is the stepfather of Mariah , Greek 's daughter with her late husband . 
- <unk> is a conservative and a supporter of the Republican Party . He is a member of the National Rifle Association , and made an appearance at their annual meeting in May 2011 to discuss his passion for hunting and his role as a spokesman for <unk> <unk> . 
- During his first run in WWE , <unk> developed <unk> to both alcohol and <unk> , allegedly drinking a bottle of <unk> per day and taking hundreds of <unk> <unk> per month to manage the pain caused by wear and tear on his body ; he named his accident at WrestleMania <unk> as a particular source of pain . <unk> claims that , as a result of his addiction and mental exhaustion , he does not remember " an entire two years " of his WWE career . 
- In January 2001 , <unk> was arrested by police in Louisville , Kentucky for suspicion of possessing large amounts of anabolic steroids . The charges were dropped when it was discovered that the substances were a legal growth hormone . His lawyer described it as a " <unk> type of thing " . 
- On December 15 , 2011 , <unk> was charged with hunting <unk> on a trip to Alberta on November 19 , 2010 . Two charges were dropped , but <unk> pleaded guilty to the charge of improper <unk> of an animal . He was fined $ 1 @,@ 725 and given a six @-@ month hunting suspension . 
- As of July 2016 , <unk> 's eldest son , <unk> Jr. is ranked # 1 in Saskatchewan and # 4 in all of Canada in amateur wrestling . 
- 
- = = Other media = = 
- 
- <unk> appears in the video games WWE SmackDown ! <unk> Your Mouth , WWE SmackDown ! Here Comes the Pain , <unk> NFL 06 , <unk> 2009 <unk> , <unk> <unk> 2010 , WWE ' 12 , WWE ' 13 , WWE <unk> , WWE <unk> , WWE <unk> , and WWE <unk> . 
- In 2003 , WWE Home Video released a DVD <unk> <unk> 's career entitled <unk> <unk> : Here Comes the Pain . It was re @-@ released in 2012 as a three @-@ disc DVD and two @-@ disc Blu @-@ ray collector 's edition to tie in with <unk> 's WWE return . It was also expanded to include new matches and interviews . 
- <unk> was featured on the covers of <unk> and <unk> & <unk> magazine in 2004 , and Minneapolis ' City <unk> in 2008 . 
- In 2009 , <unk> signed an endorsement deal with <unk> <unk> . A CD containing footage of <unk> training was included with <unk> 's " <unk> " product . 
- In 2011 , <unk> published an autobiography titled Death <unk> : My Story of <unk> , <unk> , and Survival ( ISBN 978 @-@ <unk> ) . It was co @-@ written with Paul <unk> . 
- In a 2013 post on his blog , Attack on Titan author <unk> <unk> revealed that he drew inspiration from <unk> for the character of the Armored Titan . 
- <unk> has also appeared in multiple comedic <unk> and <unk> videos by actor Eric <unk> . 
- 
- = = Filmography = = 
- 
- 
- = = = Television = = = 
- 
- 
- = = = Video games = = = 
- 
- 
- = = In wrestling = = 
- 
- <unk> moves 
- <unk> Lock ( Over @-@ the @-@ shoulder single leg Boston crab ) – 2002 – 2004 
- F @-@ 5 ( WWE ) / Verdict ( <unk> / <unk> ) ( <unk> 's carry <unk> ) – 2002 – 2006 ; 2012 – present 
- <unk> lock – 2012 – present 
- <unk> star press – <unk> ; only used once in WWE 
- Signature moves 
- <unk> 
- <unk> slam 
- Gorilla press slam 
- <unk> <unk> to the opponent 's <unk> 
- Multiple suplex variations 
- <unk> @-@ to @-@ back , sometimes to two opponents at once 
- Fisherman 's , sometimes while delaying 
- <unk> belly @-@ to @-@ belly , sometimes into or out of the ring 
- Release / Rolling German 
- <unk> 
- <unk> 
- Multiple <unk> <unk> 
- <unk> 
- Rear naked <unk> 
- Running <unk> 
- Standing double leg <unk> followed by mounted <unk> 
- Triple non @-@ release <unk> 
- <unk> 
- Mr. McMahon 
- <unk> 
- Paul <unk> 
- <unk> 
- " The <unk> " 
- " The Beast ( <unk> ) " 
- " The <unk> " 
- " The Freak " 
- " The Next Big Thing " 
- " The One in 21 – 1 / 22 @-@ 1 " 
- " The Nightmare of <unk> City " 
- Entrance themes 
- Ultimate Fighting Championship 
- " <unk> Sandman " by Metallica 
- " <unk> Size <unk> ( And the <unk> Winds ) " by <unk> <unk> 
- World Wrestling Entertainment / WWE 
- " <unk> " by Jim Johnston ( April 8 , 2002 – June 3 , 2002 ) 
- " Next Big Thing " by Jim Johnston ( June 10 , 2002 – March 14 , 2004 ; April 2 , 2012 – August 20 , 2012 ) 
- " Next Big Thing ( Remix ) " by Jim Johnston ( January 28 , 2013 – present ) 
- 
- = = Mixed martial arts record = = 
- 
- 
- = = Championships , awards , and honors = = 
- 
- 
- = = = Collegiate wrestling = = = 
- 
- National Collegiate Athletic Association 
- NCAA Division I All @-@ American ( 1999 , 2000 ) 
- NCAA Division I Heavyweight Champion ( 2000 ) 
- Big Ten Conference Champion ( 1999 , 2000 ) 
- National Junior College Athletic Association 
- <unk> All @-@ American ( 1997 , 1998 ) 
- <unk> Heavyweight Champion ( 1998 ) 
- North Dakota State University <unk> Tournament Champion ( 1997 – 1999 ) 
- 
- = = = Mixed martial arts = = = 
- 
- Inside <unk> 
- Biggest <unk> ( 2008 ) 
- Rookie of the Year ( 2008 ) 
- <unk> Awards 
- <unk> of the Year ( 2009 ) 
- Sports Illustrated 
- Top <unk> of the Year ( 2008 ) 
- Ultimate Fighting Championship 
- <unk> Heavyweight Championship ( 1 time ) 
- <unk> of the Night ( 1 time ) 
- World <unk> Awards 
- <unk> Fighter of the Year ( 2009 ) 
- Wrestling Observer <unk> 
- Best Box Office <unk> ( 2008 – 2010 ) 
- <unk> Most Valuable Fighter ( 2008 – 2010 ) 
- 
- = = = Professional wrestling = = = 
- 
- Guinness World Records 
- World record : <unk> person to win the WWE Championship ( aged 25 years , 44 days ) 
- <unk> <unk> Federation 
- <unk> Heavyweight Championship ( 1 time ) 1 
- New Japan Pro Wrestling 
- <unk> Heavyweight Championship ( 1 time ) 1 
- Ohio Valley Wrestling 
- <unk> Southern Tag Team Championship ( 3 times ) – with Shelton Benjamin 
- Pro Wrestling Illustrated 
- <unk> of the Year ( 2003 ) vs. Kurt Angle 
- <unk> of the Year ( 2015 ) vs. The Undertaker 
- Match of the Year ( 2003 ) vs. Kurt Angle in an Iron Man match on SmackDown ! on September 16 
- Most <unk> <unk> of the Year ( 2002 ) 
- <unk> of the Year ( 2002 , 2014 ) 
- Ranked # 1 of the top 500 singles wrestlers in the PWI 500 in 2003 
- Wrestling Observer <unk> 
- Best <unk> ( 2003 ) 
- Best Wrestling <unk> ( 2002 ) F @-@ 5 
- <unk> of the Year ( 2003 ) vs. Kurt Angle 
- Most <unk> <unk> ( 2002 , 2003 ) 
- Wrestling Observer <unk> Hall of Fame ( Class of 2015 ) 
- World Wrestling Entertainment / WWE 
- WWE Championship ( 4 times ) 2 
- King of the Ring ( 2002 ) 
- Royal Rumble ( 2003 ) 
- <unk> Awards ( 5 times ) 
- <unk> of the Year ( 2015 ) – # <unk> 
- Match of the Year ( 2015 ) – vs The Undertaker at Hell in a Cell 
- <unk> of the Year ( 2015 ) – vs The Undertaker 
- " Tell Me You <unk> 't Just Say That " Moment of the Year ( 2015 ) – <unk> " <unk> City " at WrestleMania 31 
- The <unk> <unk> Moment of the Year ( 2014 ) – Ending The Undertaker 's WrestleMania streak at WrestleMania XXX 
- 1 ^ <unk> 's <unk> Heavyweight Championship reign at <unk> is considered a continuation of his reign from <unk> . 
- 2 ^ When <unk> won the title for the first time it was known as the WWE <unk> Championship . His next two <unk> were as simply WWE Champion , while his final one was as WWE World Heavyweight Champion . 
- 
- 
- = Constant k filter = 
- 
- Constant k <unk> , also k @-@ type <unk> , are a type of electronic filter designed using the image method . They are the original and simplest <unk> produced by this methodology and consist of a ladder network of identical sections of passive components . Historically , they are the first <unk> that could approach the ideal filter frequency response to within any prescribed limit with the addition of a sufficient number of sections . However , they are rarely considered for a modern design , the principles behind them having been superseded by other methodologies which are more accurate in their prediction of filter response . 
- 
- = = History = = 
- 
- Constant k <unk> were invented by George Campbell . He published his work in 1922 , but had clearly invented the <unk> some time before , as his colleague at AT & T Co , Otto <unk> , was already making improvements to the design at this time . Campbell 's <unk> were far superior to the simpler single element circuits that had been used previously . Campbell called his <unk> electric wave <unk> , but this term later came to mean any filter that passes waves of some frequencies but not others . Many new forms of wave filter were subsequently invented ; an early ( and important ) variation was the m @-@ derived filter by <unk> who coined the term constant k for the Campbell filter in order to distinguish them . 
- The great advantage Campbell 's <unk> had over the <unk> circuit and other simple <unk> of the time was that they could be designed for any desired degree of stop band rejection or <unk> of transition between pass band and stop band . It was only necessary to add more filter sections until the desired response was obtained . 
- The <unk> were designed by Campbell for the purpose of separating <unk> telephone channels on transmission lines , but their subsequent use has been much more widespread than that . The design techniques used by Campbell have largely been superseded . However , the ladder <unk> used by Campbell with the constant k is still in use today with <unk> of modern filter designs such as the <unk> filter . Campbell gave constant k designs for low @-@ pass , high @-@ pass and band @-@ pass <unk> . Band @-@ stop and multiple band <unk> are also possible . 
- 
- = = <unk> = = 
- 
- Some of the <unk> terms and section terms used in this article are pictured in the diagram below . Image theory defines quantities in terms of an infinite cascade of two @-@ port sections , and in the case of the <unk> being discussed , an infinite ladder network of L @-@ sections . Here " L " should not be confused with the <unk> L – in electronic filter <unk> , " L " refers to the specific filter shape which resembles inverted letter " L " . 
- The sections of the hypothetical infinite filter are made of series elements having <unk> <unk> and <unk> elements with <unk> <unk> . The factor of two is introduced for mathematical convenience , since it is usual to work in terms of half @-@ sections where it disappears . The image <unk> of the input and output port of a section will generally not be the same . However , for a mid @-@ series section ( that is , a section from halfway through a series element to halfway through the next series element ) will have the same image <unk> on both ports due to symmetry . This image <unk> is designated <unk> due to the " T " <unk> of a mid @-@ series section . Likewise , the image <unk> of a mid @-@ <unk> section is designated <unk> due to the " <unk> " <unk> . Half of such a " T " or " <unk> " section is called a half @-@ section , which is also an L @-@ section but with half the element values of the full L @-@ section . The image <unk> of the half @-@ section is <unk> on the input and output ports : on the side presenting the series element it is equal to the mid @-@ series <unk> , but on the side presenting the <unk> element it is equal to the mid @-@ <unk> <unk> . There are thus two variant ways of using a half @-@ section . 
- Parts of this article or section rely on the reader 's knowledge of the complex <unk> representation of <unk> and <unk> and on knowledge of the frequency domain representation of signals . 
- 
- = = <unk> = = 
- 
- The building block of constant k <unk> is the half @-@ section " L " network , composed of a series <unk> Z , and a <unk> <unk> <unk> The " k " in " constant k " is the value given by , 
- <formula> 
- Thus , k will have units of <unk> , that is , <unk> . It is readily apparent that in order for k to be constant , Y must be the dual <unk> of Z. A physical interpretation of k can be given by observing that k is the limiting value of <unk> as the size of the section ( in terms of values of its components , such as <unk> , <unk> , etc . ) approaches zero , while keeping k at its initial value . Thus , k is the characteristic <unk> , <unk> , of the transmission line that would be formed by these <unk> small sections . It is also the image <unk> of the section at resonance , in the case of band @-@ pass <unk> , or at <unk> = 0 in the case of low @-@ pass <unk> . For example , the pictured low @-@ pass half @-@ section has 
- <formula> . 
- Elements L and C can be made <unk> small while retaining the same value of k . Z and Y however , are both approaching zero , and from the formulae ( below ) for image <unk> , 
- <formula> . 
- 
- = = = Image <unk> = = = 
- 
- See also Image <unk> # <unk> 
- The image <unk> of the section are given by 
- <formula> 
- and 
- <formula> 
- Given that the filter does not contain any <unk> elements , the image <unk> in the pass band of the filter is purely real and in the stop band it is purely imaginary . For example , for the pictured low @-@ pass half @-@ section , 
- <formula> 
- The transition occurs at a cut @-@ off frequency given by 
- <formula> 
- Below this frequency , the image <unk> is real , 
- <formula> 
- Above the cut @-@ off frequency the image <unk> is imaginary , 
- <formula> 
- 
- = = = <unk> parameters = = = 
- 
- The transmission parameters for a general constant k half @-@ section are given by 
- <formula> 
- and for a chain of n half @-@ sections 
- <formula> 
- For the low @-@ pass L @-@ shape section , below the cut @-@ off frequency , the transmission parameters are given by 
- <formula> 
- That is , the transmission is <unk> in the pass @-@ band with only the phase of the signal changing . Above the cut @-@ off frequency , the transmission parameters are : 
- <formula> 
- 
- = = = <unk> <unk> = = = 
- 
- The presented plots of image <unk> , <unk> and phase change correspond to a low @-@ pass prototype filter section . The prototype has a cut @-@ off frequency of <unk> 
- = 1 <unk> / s and a nominal <unk> k = 
- 1 <unk> . This is produced by a filter half @-@ section with <unk> L 
- = 1 <unk> and <unk> C = 
- 1 <unk> . This prototype can be <unk> scaled and frequency scaled to the desired values . The low @-@ pass prototype can also be transformed into high @-@ pass , band @-@ pass or band @-@ stop types by application of suitable frequency <unk> . 
- 
- = = <unk> sections = = 
- 
- Several L @-@ shape half @-@ sections may be <unk> to form a composite filter . Like <unk> must always face like in these combinations . There are therefore two circuits that can be formed with two identical L @-@ shaped half @-@ sections . Where a port of image <unk> <unk> faces another <unk> , the section is called a <unk> section . Where <unk> faces <unk> the section so formed is a T section . Further additions of half @-@ sections to either of these section forms a ladder network which may start and end with series or <unk> elements . 
- It should be borne in mind that the characteristics of the filter predicted by the image method are only accurate if the section is terminated with its image <unk> . This is usually not true of the sections at either end , which are usually terminated with a fixed resistance . The further the section is from the end of the filter , the more accurate the prediction will become , since the effects of the terminating <unk> are masked by the intervening sections . 
- 
- 
- = The <unk> = 
- 
- " The <unk> " is an episode of the British science fiction television series Doctor Who , first broadcast on Christmas Day 2012 on BBC One . It is the eighth Christmas special since the show 's 2005 revival and the first to be within a series . It was written by head writer and executive producer Steven Moffat and directed by Saul <unk> . 
- The episode is set in the Victorian era and sees the Doctor ( Matt Smith ) brooding with the assistance of <unk> Madame <unk> ( Neve <unk> ) , her wife Jenny Flint ( <unk> Stewart ) and <unk> <unk> ( Dan <unk> ) , after the loss of companions Amy Pond and Rory Williams in the previous episode , " The Angels Take Manhattan " . He is forced out of hiding to investigate mysterious , sentient <unk> that are building themselves and meets <unk> Oswald ( Jenna @-@ Louise Coleman ) , a governess also investigating the <unk> . They discover that the <unk> are being animated by the Great Intelligence ( voice of Ian <unk> ) with the help of a man named Dr <unk> ( Richard E. Grant ) . 
- Building upon the character 's surprise introduction in " Asylum of the <unk> " , " The <unk> " introduces <unk> as the Doctor 's new companion , though ultimately it would be a third version of her character that would travel with the Doctor starting with " The Bells of Saint John " . In addition to <unk> , " The <unk> " also introduces a redesigned <unk> , revised title sequence and theme music , and sees changes to the Doctor 's costume . The special was produced in August 2012 , with location filming in Newport , Wales and Bristol . It received final ratings of 9 @.@ 87 million viewers in the UK , becoming the fourth most @-@ watched programme of Christmas Day . " The <unk> " was met with mostly positive reviews from critics , most of whom received the introduction and character of <unk> well . However , some felt that Grant and <unk> were <unk> as villains or the plot was slight because of the focus on characterisation . 
- 
- = = Plot = = 
- 
- 
- = = = <unk> = = = 
- 
- To promote the special , three <unk> were released . The first was broadcast during the 2012 Children in Need <unk> on 16 November 2012 , titled " The Great Detective " . The <unk> Madame <unk> , her human wife Jenny Flint , and the <unk> <unk> ( all returning from " A Good Man Goes to War " ) describe a number of strange phenomena to a shadowed fourth detective . The fourth detective reveals himself to be the Doctor , and tells the group that he has retired . 
- A second prequel , titled " <unk> <unk> " , was released online on 17 December 2012 . At the end of a case , <unk> and Jenny <unk> with an officer from Scotland Yard and <unk> for <unk> 's violent wishes for the <unk> 's punishment . <unk> explains <unk> 's alien origin as well as her own to the officer , much to his <unk> . <unk> reveals that she was <unk> by an extension to the London Underground and initially disliked humans , though that changed when she fell in love with Jenny . On the carriage ride home , Jenny notices it is beginning to snow and <unk> notes that the snow should be impossible because there are no clouds in the sky . 
- A third prequel , titled " The Battle of Demon 's Run — Two Days Later " was released on the United States iTunes and Amazon Video stores on 25 March 2013 . Two days after the events of " A Good Man Goes to War " , <unk> and Jenny convince <unk> that he is not mortally wounded and invite him to accompany them back to 1800s London . The scene had been filmed as an extra due to the anticipation that fans would ask how <unk> was resurrected and came to be in <unk> 's employ . 
- 
- = = = Synopsis = = = 
- 
- In 1842 England , a young boy builds a <unk> , but refuses to play with the other children . The <unk> starts speaking to the boy , repeating his <unk> that the other children are silly . Fifty years later , the boy has grown up to be Dr. Walter <unk> , proprietor of the Great Intelligence Institute . He hires men to collect samples of snow , which he places in a large snow @-@ filled globe in his laboratory before feeding the men to a group of animated <unk> . The Doctor , still <unk> after losing his former companions Amy Pond and Rory Williams , has parked his <unk> above Victorian London among the clouds . He uses his allies <unk> , Jenny , and <unk> to keep people away from him . They also fill their time investigating mysteries throughout the city . 
- Elsewhere , <unk> <unk> <unk> Oswald investigates a disturbance outside the tavern she works at and finds the Doctor walking by . He attempts to leave discreetly , but <unk> follows him to a coach . Not wishing to become involved in matters , the Doctor instructs <unk> to bring him a memory worm that will erase the last hour of <unk> 's memories with just a touch . Before they can do so , they are surrounded by <unk> created from snow with psychic properties who attack the group . The Doctor realises that <unk> 's thoughts are creating the <unk> and ends the threat by instructing her to think of them melting . <unk> <unk> the Doctor that if he <unk> her memory , she will forget how to deal with the <unk> . The Doctor <unk> allows her to go and <unk> a staircase to the sky to return to the <unk> . <unk> follows him and knocks on the door , but she hides and flees down the staircase when the Doctor answers . <unk> returns to her other job as governess for the children of Captain <unk> . She learns that <unk> 's daughter has been having horrible dreams about their previous governess returning from the dead . <unk> realises that the pond that contains the old governess ' body is the only thing still frozen around them . She attempts to track down the Doctor but instead attracts the attention of Jenny , who takes her to see <unk> . <unk> tells <unk> she gets only one word to impress the Doctor with if she wants his help . <unk> chooses the word " Pond " , which <unk> the Doctor and <unk> his interest . 
- Acting on a tip from <unk> , the Doctor visits the Great Intelligence Institute posing as Sherlock Holmes . He confronts Dr. <unk> and find a large glass globe in <unk> 's office that contains psychic snow . The Doctor speaks to the Great Intelligence , the entity that has been speaking to Dr. <unk> since he was a boy . He learns that the Great Intelligence has been controlling the <unk> and has taken interest in <unk> 's pond . The Doctor visits the pond and <unk> that the Great Intelligence is using the old governess ' body as a DNA <unk> to form an ice creature that will retain its form and not melt . While <unk> is putting the children to bed , the frozen body of the governess breaks into the house . The Doctor fights her off and is joined by <unk> , Jenny and <unk> . Dr. <unk> arrives with more <unk> and tells them he wants the governess ' ice body . The Doctor flees with <unk> to the roof of the mansion and then to the <unk> <unk> overhead . They are pursued by the ice governess , whom the Doctor traps under a layer of frozen ice crystals . Inside the <unk> the Doctor gives <unk> a <unk> key , but the ice governess arrives and pulls <unk> down off the cloud . 
- The Doctor picks up <unk> and takes her back to <unk> 's mansion , placing her under medical care of <unk> . He collects the ice fragments from the governess and places them in a <unk> London Underground <unk> tin . He and <unk> travel to <unk> 's lab , where the Doctor notes the Intelligence 's plan to replace humanity with ice creatures and holds up the tin with the necessary DNA . Dr <unk> <unk> the tin and opens it only to find the memory worm , which <unk> on to him . The Doctor states that the Great Intelligence , which has been existing as a mirror of Dr <unk> 's thoughts , will vanish with the <unk> of <unk> 's memories . Instead , the Intelligence reveals that it existed long enough that it can now control <unk> 's body , which it uses to attack <unk> and the Doctor . The influence of the Great Intelligence quickly <unk> , and <unk> falls dead . Outside , a salt @-@ water rain has started , and the Doctor sees that another psychic ability has taken control of the snow from the Great Intelligence : the <unk> family , crying for <unk> . <unk> informs the Doctor upon his return to the <unk> mansion that <unk> only has moments left , and she passes away as the Doctor returns the <unk> key to her . At her funeral , the Doctor reads <unk> 's full name on her tombstone and realises she is the woman he met in " Asylum of the <unk> " who became a <unk> , whom he refers to as " <unk> Girl " . He <unk> announces that a person dying twice is an impossibility and , bidding farewell to his allies for now , the Doctor departs in the <unk> to investigate and find <unk> . The episode concludes in contemporary times , where a young woman resembling <unk> walks through the same graveyard , <unk> by <unk> 's tombstone . 
- 
- = = = <unk> = = = 
- 
- The Second Doctor previously encountered the Great Intelligence in the serials The <unk> <unk> ( 1967 ) , set in the 1930s , and The Web of Fear ( 1968 ) , set in the 1960s . In these stories , the Great Intelligence uses robot <unk> as its physical presence . The events of The Web of Fear are alluded to by the Doctor in " The <unk> " when he presents the London Underground <unk> tin to the Great Intelligence in Dr <unk> 's laboratory ; the Intelligence states , " I do not understand these markings " , in reference to the 1967 London Underground map design on the tin . The Doctor remarks that the Underground is a " key strategic weakness in metropolitan living " , referring to ( and possibly setting in motion ) the future <unk> attack on London via the Underground . 
- Coleman previously played <unk> Oswald in " Asylum of the <unk> " , though the connection between the two characters is not clarified until <unk> reveals she has an interest in <unk> , a trait that <unk> 's character also had . The Doctor , after meeting <unk> , <unk> replies " those were the days " when she asks why he isn 't staying to get acquainted with her , which are the same words he tells Craig <unk> ( " <unk> Time " ) when Craig comments that the Doctor always wins . The final scenes at the graveyard establish that <unk> shares the same name as <unk> , leading the Doctor to <unk> they are the same person . As seen on her gravestone , <unk> 's <unk> is 23 November , the date Doctor Who was first transmitted in 1963 . 
- 
- = = Production = = 
- 
- 
- = = = Writing and design changes = = = 
- 
- Writer Steven Moffat stated that he wanted an " epic " quality to the Christmas special . The story would also show how the Doctor had responded to losing his previous companions ; Moffat said that " I think he 's probably reached the point in his life where he 's saying , ' Friendship for me is just postponed <unk> — I want to be on my own for a while ' . " Moffat compared the withdrawn Doctor seen at the onset of the episode to the first appearances of the First Doctor ( William <unk> ) in 1963 and the <unk> Doctor ( Christopher Eccleston ) in 2005 . He also attributed the idea of a retired Doctor to a plot proposed by Douglas Adams in the 1970s , but rejected by the production team at the time . Continuing the theme introduced with the series ' first five episodes , " The <unk> " was promoted like a movie . A movie poster was released in the Radio Times , showing the Doctor and <unk> ascending the ladder to the <unk> . 
- The episode saw several major design changes for the series . " The <unk> " is the debut of a redesigned <unk> interior , as well as a new title sequence and variation of the theme tune . The new title sequence features a brief glimpse of the Doctor 's face , the first time since Survival , the final serial of the classic series in 1989 , that the Doctor 's face has been seen in the title sequence . Moffat had noticed that the <unk> ' design was getting " progressively whimsical " and resembled more of a " magical place " rather than a machine . It was designed by series production designer Michael <unk> , who stated that the new interior was also supposed to be " darker and <unk> " and provide an easier access to the " gallery " of the ship when shooting . 
- The Doctor also wears a one @-@ off costume , Victorian @-@ themed , which Smith described as " a bit <unk> <unk> meets the Doctor " . Moffat described the new outfit as a " progression " as the Doctor was in " a different phase of his life now " and felt more " grown @-@ up " and <unk> . The costume was designed by Howard <unk> for this episode . " The <unk> " also contains several references to Sherlock Holmes , including the Doctor dressing up as him . Moffat is co @-@ creator of the BBC series Sherlock , for which Smith auditioned for the role of Doctor Watson before being cast as the Doctor . In addition , the incidental music during the scene bears a resemblance to the Sherlock theme . 
- 
- = = = Casting = = = 
- 
- This episode marks the return of Jenna @-@ Louise Coleman , who previously appeared in the series opener , " Asylum of the <unk> " . Coleman was cast because of her chemistry with Matt Smith , and especially because she was able to talk faster than him . She auditioned for the role of <unk> , not <unk> from " Asylum " , as the concept of the two characters being the same only occurred to Moffat whilst casting for <unk> . The production team requested that the press and fans who attended advanced screenings keep Coleman 's appearance a secret until " Asylum " was broadcast ; the effort was ultimately successful . Moffat stated that the introduction of a new companion made " the show feel different " and brought the story to " a new beginning " with a different person meeting the Doctor . Smith said that <unk> was different from her predecessor Amy Pond ( Karen <unk> ) , which allowed the audience to see a different side of the Doctor . Coleman described her as resourceful and not intimidated , citing the reason for following the Doctor at the beginning as pursuing answers . The <unk> who would become a travelling companion of the Doctor would not debut until the Spring premiere , " The Bells of Saint John " ; save for a brief cameo at the end of " The <unk> " . Coleman stated that she played each version as individuals with " trust that there would be a payoff " to her mystery . 
- Also returning to the series are Neve <unk> as Madame <unk> , Dan <unk> as <unk> and <unk> Stewart as Jenny . All three previously appeared in " A Good Man Goes to War " and reprised their roles both in this episode and in the <unk> . They returned due to the popularity of <unk> and Jenny ; Moffat considered a spin @-@ off featuring them , though he did not have the time to do it . Instead , he decided to bring them back in the main series . Richard E. Grant had previously played the Doctor on two occasions , as an alternative Tenth Doctor in the spoof charity special Doctor Who and the Curse of <unk> Death , which was written by Moffat and as an alternative <unk> Doctor in the animated story Scream of the <unk> which had been intended to be a continuation of the series before it was revived in 2005 . Smith commented that Grant was " born to be a Who villain . He pitches it on that perfect level and tone " . Grant 's appearance in Doctor Who was <unk> by the BBC via Twitter , announcing his appearance at midnight 5 August 2012 . Tom Ward was drawn to his role because of the quality of the script , and also stated his young children were pleased that he appeared in the programme . The Great Intelligence was voiced by Sir Ian <unk> . The two children <unk> is governess to , <unk> and <unk> , were played by real @-@ life brother and sister Joseph and Ellie <unk> @-@ Alden . 
- 
- = = = Filming and effects = = = 
- 
- " The <unk> " was originally intended to be produced in the fourth production block of the series and be the first episode Coleman shot as her character ; however , it did not begin filming until the week of 6 August 2012 , after Coleman had worked on later episodes while Moffat was writing the Christmas special . The read @-@ through had taken place on 2 August 2012 . This was the first Christmas special to be filmed in BBC Wales ' new <unk> Lock studios . Scenes featuring Coleman and several guest stars in a Victorian setting were filmed in Newport , Wales , while Coleman and Smith were also spotted filming in Bristol two weeks later on 21 August . Some scenes which used snow props were filmed in Portland Square , Bristol , where filming took place overnight on 21 – 22 August 2012 . Bristol was chosen because it had Victorian @-@ era architecture . <unk> stated that his favourite set is the London Street with the back of the pub , which he said was based on a sixteenth @-@ century building in Oxford . The locations were blocked off and <unk> with fake snow . 
- The <unk> on the cloud was achieved through a mix of fog on the studio floor and post @-@ production special effects . Director Saul <unk> explained that it was difficult to achieve the desired look for the <unk> ; the first ones he likened to <unk> from Rainbow which was too " cute " of an appearance , and so the effects team created more menacing CGI faces . <unk> 's introduction to the <unk> introduced two novel effects for the show . The first was a single @-@ shot camera tracking from a few feet away from the <unk> to its interior , with the implication of the <unk> 's trans @-@ dimensional nature shown to the audience . In the following shot , the camera does a complete circle of the <unk> console , an effect not seen since the early days of the show . <unk> wanted to include this shot to further emphasize the " bigger on the inside than the outside " nature of the time machine . 
- In addition to the three prequel mini @-@ episodes , the cast also filmed an additional promotional video , " <unk> <unk> , " which the BBC uploaded during the days leading up to the broadcast . The video featured <unk> singing modified versions of several Christmas songs in character as <unk> as his <unk> look on , before everyone breaks character and begins laughing . 
- 
- = = Broadcast and reception = = 
- 
- " The <unk> " aired on BBC One on 25 December 2012 at 5 : 15 pm , the same day on BBC America in the US and Space in Canada and the next day on <unk> in Australia and on Prime in New Zealand . UK overnight ratings showed that the special had been watched by 7 @.@ 6 million viewers , coming in sixth for the night . Final consolidated figures ( not including BBC <unk> viewers ) showed that the episode was watched by 9 @.@ 87 million viewers , coming in fourth for the night . It also received an <unk> Index figure of 87 , higher than most of the Doctor Who Christmas <unk> . The <unk> version had 1 @,@ 467 @,@ 220 views , making it the most popular TV show on <unk> over Christmas . The US airing was seen by 1 @.@ 43 million viewers , with a 0 @.@ 6 rating in the demographic of adults aged 18 – 49 . 
- 
- = = = Critical reception = = = 
- 
- The episode received mostly positive reviews . Dan Martin of The Guardian called it " actually the best Christmas Special since ' The Christmas Invasion ' " and the first to be " actually scary " , with " everything we like " about Doctor Who and Christmas . He praised Coleman 's introduction as <unk> and the gang of <unk> , Jenny , and <unk> . IGN 's Matt <unk> gave " The <unk> " a score of 9 @.@ 4 out of 10 , describing it as " a <unk> , <unk> <unk> in storytelling " which " refreshingly " lacked traditional Christmas references " in favour of some sparkling dialogue , gorgeous set design and fascinating characterisation " . While he felt that Grant and <unk> were <unk> , he was very positive towards Coleman 's " unpredictable " <unk> . Radio Times reviewer Patrick <unk> was pleased with the return of the Great Intelligence despite an inconsistency in the timeline he found , and praised the " lovely images " and direction of the special , though he felt the variation of the theme music " lacks the menace " of the original . While he was positive towards <unk> , he was " <unk> by her death " as it was " plainly silly " that she did not look injured . 
- Nick <unk> of <unk> gave the special four and a half out of five stars , writing that " the power of emotion saves the day again " was appropriate in light of the festivities and many <unk> referenced in the story . <unk> was positive towards the " terrific " comedy with <unk> , Coleman and the " surprisingly <unk> " Grant , as well as the new title sequence and <unk> . While he wrote that the subtle <unk> of the Great Intelligence was " a <unk> more interesting than the usual ' So , we meet again ! ' <unk> " , he ultimately felt their threat " never quite comes into sharp relief " . <unk> <unk> of The Independent wrote that " The <unk> " was stronger than the previous year 's " The Doctor , the Widow , and the <unk> " as it was connected to the overall story of the series , but " still has a way to go if it is to live up to ' A Christmas Carol ' " . Despite feeling that it was " enjoyable " , she noted that " the story feels truncated and rushed " 
- The Mirror 's Jon Cooper also praised Coleman and the new side of the Doctor that was shown , comparing it to Rose Tyler ( Billie Piper ) challenging the <unk> Doctor ( Christopher Eccleston ) . However , he felt the character @-@ heavy story was to the <unk> of the plot , which was " a classic Who set @-@ up that ultimately suffers from a lack of explanation [ and ] more set @-@ pieces than a coherent whole " . He felt that the episode may not have been accessible for casual viewers , but offered much for fans in time for the programme 's fiftieth anniversary . Dominic <unk> of The Daily Telegraph gave " The <unk> " three out of five stars , disappointed that it was not as scary as it had been <unk> to be . While he was positive towards Smith and the <unk> on the cloud , he criticised <unk> and the " <unk> @-@ like complexity " of the script . 
- The episode was nominated for the 2013 Hugo Award for Best Dramatic Presentation ( Short Form ) , alongside " Asylum of the <unk> " and " The Angels Take Manhattan " , but lost to the Game of <unk> episode " <unk> " . 
- 
- = = DVD release = = 
- 
- " The <unk> " was initially released as a standalone on DVD and Blu @-@ ray in the UK and North America . It was later included as part of the DVD / Blu @-@ ray box set Doctor Who : The Complete Seventh Series in September 2013 . 
- It has subsequently been reissued in several box set compilations , most recently alongside the Christmas <unk> between " The Christmas Invasion " and " Last Christmas " inclusive in a <unk> titled Doctor Who – The 10 Christmas <unk> on 19 October 2015 . 
- 
- = = Soundtrack = = 
- 
- Selected pieces of score from " The <unk> " and the preceding Christmas special , as composed by Murray Gold , were included on a soundtrack released on 21 October 2013 by Silva Screen Records . 
- 
- 
- = No. 20 Squadron RAAF = 
- 
- No. 20 Squadron is a Royal Australian Air Force ( RAAF ) support squadron . Coming under the control of No. 96 Wing , it is responsible for the management of the airfield at RAAF Base <unk> , South Australia . The squadron originated as a maritime patrol unit during World War II . <unk> in August 1941 , it operated <unk> <unk> and Short Empire flying boats from bases in New Guinea , Queensland and the Northern Territory , conducting search @-@ and @-@ rescue , mine @-@ laying , anti @-@ submarine and bombing missions against Japanese targets in the Pacific theatre . Following the conclusion of hostilities , the squadron was disbanded in March 1946 . It was reactivated as an airfield support squadron in April 2015 . 
- 
- = = History = = 
- 
- 
- = = = World War II = = = 
- 
- No. 20 Squadron was formed at Port Moresby , New Guinea , on 1 August 1941 for a general reconnaissance role , under the command of Squadron Leader <unk> Gibson . Its establishment was six <unk> <unk> flying boats and 133 personnel , but only five aircraft ( all transferred from No. 11 Squadron ) and 55 personnel were available initially . The squadron conducted long @-@ range patrols between bases scattered around the islands to Australia 's north in conjunction with No. 11 Squadron . On 18 November , No. 20 Squadron 's Catalinas were augmented by two Short Empire flying boats transferred from No. 11 Squadron . 
- On 25 November 1941 , following the loss of <unk> Sydney , one of No. 20 Squadron 's Catalinas was despatched to Western Australia to join a No. 11 Squadron <unk> in search @-@ and @-@ rescue missions , but they found only oil <unk> . By the outbreak of war in the Pacific , No. 20 Squadron had a strength of six Catalinas and two Empire flying boats . Its personnel at the beginning of December numbered 14 officers and 118 men . The squadron undertook its first sortie of the Pacific War on 8 December ; a <unk> located three Japanese <unk> in the vicinity of Thursday Island , Queensland . Later in the month it commenced anti @-@ submarine patrols and , in January 1942 , bombing raids against Japanese bases . As the Japanese advanced into the South West Pacific , No. 20 Squadron was also responsible for evacuating white civilians from areas threatened by invasion . On 21 January , one of its Catalinas located the Japanese fleet steaming for Rabaul and signalled a warning to the town 's Australian defenders before being shot down by anti @-@ aircraft fire ; it was the squadron 's first combat loss . 
- In the wake of the fall of Rabaul , the Catalinas of Nos. 11 and 20 Squadrons became the RAAF 's only offensive weapon against the Japanese . Their raids on Rabaul did little to stem the Japanese advance , and in the following months Port Moresby itself was subjected to increasingly frequent attacks , which destroyed aircraft , facilities , and squadron records . In February 1942 , the Short <unk> operated by Nos. 11 and 20 Squadrons were transferred to the newly formed No. 33 ( Transport ) Squadron . No. 20 Squadron lost two Catalinas during patrols on 4 and 6 May ; the nine crewmen of the first were later found to have been captured and beheaded ; the crew of the second were also captured and subsequently disappeared without trace . 
- In response to the threat of invasion at Port Moresby , Nos. 11 and 20 Squadrons moved to Bowen , Queensland , on 7 May 1942 . They were soon attacking Japanese targets in Lae , <unk> and Rabaul . On 27 June , each squadron contributed an aircraft to a four @-@ hour raid over Lae and <unk> during which , as well as bombs , the RAAF crews dropped beer bottles to disrupt the enemy soldiers ' sleep — the sound they made falling through the air was , according to the official history , " something between a <unk> whistle and a scream " . By 1 July , No. 20 Squadron 's strength was six Catalinas and 175 personnel , out of a planned establishment of nine aircraft and 415 personnel . Its prime responsibility in early 1942 was maritime reconnaissance as far as New Guinea , the Solomon Islands , and New Caledonia ; the latter half of the year saw a greater focus on night bombing . Now comprising 252 officers and men , the squadron relocated to Cairns on 11 November 1942 . From Cairns it continued to conduct reconnaissance , anti @-@ submarine and occasional bombing operations over the waters around New Guinea . Between December 1942 and March 1943 , No. 20 Squadron 's aircraft flew a total of 9 @,@ <unk> hours and dropped 227 tons of bombs . The squadron 's role changed in June 1943 when it commenced mine @-@ laying operations over the Netherlands East Indies and the Philippines , though it continued to make some bombing raids and supply drops . 
- In September 1944 , No. 20 Squadron became part of No. 76 Wing RAAF , along with Nos. 42 and 43 Squadrons , and moved to Darwin , Northern Territory . All three squadrons operated Catalinas , their primary purpose being mine @-@ laying . On the night of 30 September , a <unk> of No. 20 Squadron was shot down while attacking a ship at <unk> in the Dutch East Indies ; the loss was compounded by the fact that one of the <unk> of the mining campaign , Lieutenant Commander <unk> Carr of the Royal Australian Navy , was aboard the plane and was captured by the Japanese . Another of the squadron 's Catalinas went down on the night of 27 / 28 January 1945 , possibly in a cyclone over the Timor Sea , during the campaign to mine <unk> . In March , a detachment of four No. 20 Squadron aircraft , along with four from No. 43 Squadron , laid mines off the coast of southern China and <unk> as part of a No. 76 Wing offensive in this area ; these operations were conducted from <unk> Gulf in the Philippines . One of No. 20 Squadron 's Catalinas was lost on the night of 7 / 8 March , most likely owing to bad weather rather than enemy action . Three of the squadron 's aircraft mined the entrance to Hong Kong harbour on 8 April and , on 26 May , four of its Catalinas mined <unk> harbour in China , the <unk> north that any Australian aircraft infiltrated during the war in the Pacific . Three of its aircraft flew the RAAF 's last mine @-@ laying mission on 30 July . 
- No. 20 Squadron 's final wartime sortie was a patrol on 14 August 1945 . Following the end of the war , the squadron operated in the transport role and ferried Australian prisoners of war home from various locations in South East Asia . It relocated to RAAF Station <unk> , New South Wales , on 21 November . No. 20 Squadron flew its last mission , a transport flight to <unk> , on 21 January 1946 , and disbanded at <unk> on 27 March . 
- 
- = = = Post @-@ war re @-@ establishment = = = 
- 
- No. 20 Squadron was reactivated on 1 April 2015 to support airfield operations at RAAF Base <unk> , South Australia . <unk> of nine <unk> personnel and one Australian Public Service member under the command of Squadron Leader Simon Bartlett , the squadron formed part of No. 96 Wing , a component of Combat Support Group ( <unk> ) . The airfield had previously been managed under the auspices of <unk> Operational Support Group , but a command @-@ and @-@ control review commissioned by the Chief of Air Force recommended that , in common with other RAAF airfields , it should be administered by <unk> . RAAF Base <unk> , incorporating <unk> Village , was one of two Air Force units formally established on 12 January 2015 as part of a reorganisation of the <unk> Range Complex , the other unit being RAAF <unk> Test Range . 
- The design of the reactivated squadron 's crest includes a wedge @-@ tailed eagle to denote courage and nobility , a <unk> spear <unk> to symbolise the town and its indigenous heritage , Sturt 's Desert <unk> to represent South Australia , and the <unk> star cluster , which features in the folklore of the local <unk> people . 
- 
- 
- = Light Tank Mk VII <unk> = 
- 
- The Light Tank Mk VII ( <unk> ) , also known as the <unk> , was a British light tank produced by Vickers @-@ <unk> in the late 1930s and deployed during the World War II . The <unk> was originally designed as the latest in the line of light tanks built by the company for the British Army . It improved upon its predecessor , the Mk <unk> Light Tank , by introducing the extra firepower of a 2 @-@ pounder gun . The War Office ordered 70 tanks , an order that eventually increased to 220 . Production was delayed by several factors , and as a consequence , only 100 to 177 of the tanks were produced . 
- The tank 's design flaws , combined with the decision by the War Office not to use light tanks in British armoured divisions , ruled out the use of <unk> in the North African Campaign . As a result , the majority of the tanks remained in Britain , although 20 were sent to the USSR as part of the <unk> @-@ <unk> program . In early 1941 , the Royal Armoured Corps formed three squadrons for use in overseas amphibious operations , one of which was equipped with <unk> . In May 1942 , a small number of <unk> formed part of the British force which participated in the invasion of Madagascar , and , in June 1942 , <unk> were attached to the 1st Airborne Division after it was decided that the design allowed its use as an air @-@ portable light tank to support British airborne forces . The <unk> were transported and landed in specially designed General Aircraft <unk> gliders . A lack of gliders prevented their participation in the Allied invasion of Sicily in 1943 ; instead they were attached to the new 6th Airborne Division and became part of the 6th Airborne Armoured Reconnaissance Regiment . 
- The division used approximately 20 <unk> during the British airborne landings in Normandy in June 1944 . The tanks were successfully landed by glider , but they did not perform well . Several were lost in accidents , and those that did see action proved to be inferior in firepower and armour to the armoured fighting vehicles of the German forces . A few days after the beginning of the operation , the tanks were removed from direct engagement with German armour and used only to provide fire support . By August 1944 , most of the <unk> in action were replaced with Cromwell cruiser tanks , and the remainder were replaced by the <unk> <unk> in December 1944 . 
- <unk> did not see any further combat and were deemed obsolete by 1946 ; the last was retired in 1950 . There were several variations on the <unk> design , including the <unk> self @-@ propelled gun and the Light Tank Mk VIII , but none of these were ever used in active service with the British Army . 
- 
- = = Development history = = 
- 
- 
- = = = Initial development = = = 
- 
- The prototype of the Light Tank Mk VII ( <unk> ) , nicknamed ' <unk> ' , was first developed in 1937 by Vickers @-@ <unk> as a private venture , and was intended to be sold either to the British Army or to foreign militaries . It was to be the latest in a series of light tanks produced by the company . The tank was designed to overcome the shortcomings of insufficient armament in earlier light tanks that were fitted only with machine guns . Vickers @-@ Armstrong installed on the Mk <unk> a 2 @-@ pounder 40 @-@ millimetre ( 1 @.@ 6 in ) main gun paired with a 7 @.@ 92 @-@ millimetre ( 0 @.@ 312 in ) <unk> machine gun , and mounted the two guns in a two @-@ man turret . The tank possessed a maximum of 14 millimetres ( 0 @.@ 55 in ) of armour . The prototype weighed approximately 16 @,@ 800 pounds ( 7 @,@ 600 kg ) and was powered by a 165 @-@ horsepower ( 123 kW ) Meadows engine . <unk> was on eight road wheels , four per side , with no separate driver or <unk> wheels and it was capable of a 40 miles per hour ( 64 km / h ) top speed . The Mk VII design relied on an unusual steering method and a mechanical system incorporated into earlier Vickers models . The front wheels could be steered to allow for gentle turns by <unk> the tracks . For sharper turns , the system returned to the conventional method of <unk> one track to turn the tank ; the dual system of turning was designed to lessen mechanical strain on the <unk> and reduce its power <unk> . The suspension system was also a new design that relied on struts with pockets of air for <unk> and <unk> of oil for <unk> , and each of the wheels was independently sprung . 
- The War Office examined the design and put the prototype through a series of trials during May and June 1938 ; the model was tested as a possible " light cruiser " since War Office light tank needs were already met by its predecessor , the Mark VI . The War Office then took the view that the tank was not acceptable as a light cruiser because the <unk> <unk> offered better speed and obstacle crossing performance . Despite this , it was decided that it was essential for some <unk> to be produced , and it was suggested that they be brought in at the end of the light tank program . Accordingly , the War Office gave the <unk> the official General Staff specification number <unk> , and , in November 1938 , accepted it for limited production after requesting a few minor changes which included the fitting of an external fuel tank to increase the tank 's range . 
- 
- = = = Production = = = 
- 
- The number to be produced was subject to <unk> as the War Office <unk> in their demand ; in July 1938 , it requested that 70 of the tanks be produced , then increased the request to 120 after a three @-@ day conference in November . Production was to begin in July 1940 , but meanwhile the War Office temporarily returned to its original order of 70 before increasing the number to 100 . The number further increased to 220 after Metropolitan <unk> Carriage and <unk> , a company part owned by Vickers @-@ Armstrong that would be producing the tanks , indicated it had already ordered armour plating for that many tanks . 
- Production of the tank was delayed by a number of factors . The War Office put their order on hold in a post @-@ Battle of France decision to focus military production on infantry and cruiser tanks , due to the poor performance of British light tanks during that battle . Due to the shortage of more suitable tanks , light tanks that were not designed for use against German armour , were nevertheless deployed against them ; the resulting high casualties led the War Office to re @-@ evaluate the suitability of the light tank design . The pre @-@ war role of the light tank , that of reconnaissance , meanwhile had been found to be better suited to scout cars that used smaller crews and had better cross @-@ country abilities . Further delays were caused by the bombing raids of the Luftwaffe during May 1941 against the factories where the tanks were assembled . 
- The cumulative effect of these delays resulted in the production of only a small number of Mk <unk> ; estimates place the final total produced to be between 100 and 177 . The name ' <unk> ' was given to the Mk VII , on 22 September 1941 , on the orders of the War Office . The last of the tanks were built in the first quarter of 1942 and delivered at the end of the year . 
- 
- = = = Transfer to airborne role = = = 
- 
- The War Office and the Army were concluding , at this point , that light tanks were a liability and too vulnerable for use in further combat , and the <unk> was considered to be obsolete . This decision may have marked the end for the <unk> in active service ; several of the tanks destined to be deployed to the Eighth Army in the Middle East for the North African Campaign were left in Britain when their cooling systems were determined to be unable to cope with the intense North African heat . 
- The demise of <unk> was prevented by a decision made by the War Office in mid @-@ 1941 , as it was considering the equipment to be used by Britain 's fledgling airborne forces , formed in June 1940 under the orders of the Prime Minister , Winston Churchill . When selecting the equipment for the airborne forces , officials at the War Office concluded that gliders would be an integral component ; gliders would transport troops and heavy equipment , which , by 1941 , was to include artillery and some form of tank . Plans to transport a tank went through a number of revisions , but , by May 1941 , the feasibility of a 5 @.@ 5 metric tons ( 5 @.@ 4 long tons ) tank to be carried for 350 miles ( 560 km ) in a glider was accepted , although the aircraft would have to be specifically designed for the task . In a conference held on 16 January 1941 , it was decided that the General Aircraft <unk> , currently under development , would be used to transport a single <unk> tank or two Universal <unk> . The <unk> was chosen because it was an obsolete design , and was therefore available to be used by the airborne forces . 
- Beginning in January 1944 , training exercises were conducted carrying the <unk> and their crews inside <unk> gliders . These exercises were successful ; during the training by ' C ' Squadron of the <unk> Pilot Regiment , which specialised in flying the <unk> , over 2 @,@ 800 <unk> were made with an average of 50 <unk> per crew . Only three incidents resulted in fatalities or injuries , with seven pilots killed during the training . When the <unk> was re @-@ designated as an airborne tank , several changes were made to its design . A number of tanks had their 2 pounder guns replaced with a 76 @.@ 2 @-@ millimetre ( 3 @.@ 00 in ) infantry support howitzer ; these tanks were then designated as <unk> 1 <unk> ( Close Support ) . Additionally , <unk> <unk> were added to those <unk> which still possessed their 2 pounders to increase their muzzle velocity and armour penetration . 
- The <unk> experienced several setbacks throughout its development and deployment with the Army and airborne forces . One of the major problems was the limited number of these tanks that existed after production ended in 1942 , which particularly affected the airborne forces . The transport of 20 of the tanks to the USSR under the <unk> @-@ <unk> Act depleted the number available for use by airborne forces , as did the loss of several more during Operation <unk> , the invasion of Madagascar . A Royal Armoured Corps report issued in December 1942 stated that approximately 50 <unk> were available for use . In a memorandum , dated January 1943 , by Major General George F. <unk> , commander of the 1st Airborne Division , <unk> complained that he had been informed that 70 of the tanks were available , whereas only 50 actually remained , with no reserves to replace those lost in combat . This lack of sufficient replacement reserves , combined with a War Office report that some <unk> airborne tanks would be required for the 1st Airborne Division and an unnamed airborne division to be formed in India , led to the <unk> 's eventual replacement by the US <unk> <unk> . 
- 
- = = Performance = = 
- 
- A number of design faults of the <unk> were revealed through its operational use . Its size limited the possible crew to three , a driver in the hull and a gunner and commander in the turret , resulting in too few crew members to operate the <unk> effectively . The gunner or commander , in addition to his own duties , had to act as <unk> for the 2 pounder , which caused delays in combat . A report on the tank written in January 1941 stated that as the commander had to both fight and control the tank , controlling a troop of <unk> during combat would be almost impossible . 
- Problems were also found with the <unk> <unk> fitted to the 2 @-@ pounder to increase its range and penetration power ; after they had been fitted the <unk> could not be removed , and could only fire specially designed armour @-@ piercing rounds , which took time to manufacture . 
- The War Office also considered the <unk> 's cooling system faulty , making the tank unsuitable for service in <unk> climates , such as the Middle East and North Africa . 
- 
- = = Operational history = = 
- 
- 
- = = = <unk> @-@ lease = = = 
- 
- The first <unk> were delivered to the Army in November 1940 , and were initially deployed with the 1st Armoured Division ( which was being refitted after losing the majority of its previous tanks during the Battle of France ) and the newly formed 6th Armoured Division . However , the faults discovered with the <unk> cooling system precluded them from being integrated into units that were sent to the Middle East to participate in the North African Campaign . Shortly after , all light tanks were discarded from the establishments of British armoured divisions as not suitable for further service . 
- The <unk> remained in Britain , and would probably have been used as training vehicles before being retired from service , but on 22 June 1941 the German invasion of the USSR , Operation Barbarossa began , and the USSR became an ally of Britain . The <unk> @-@ <unk> program , begun in March 1941 by the United States of America to supply defensive materials to Britain and China , was therefore extended to the USSR . As part of the program , the British government began supplying war materials to the USSR , which in early 1942 , included a shipment of 20 <unk> , as well as a number of Valentine and Matilda Mk I Infantry tanks . The Soviet military utilised a greater number of light tanks than the British , and so could use the <unk> . When the tanks arrived in the USSR , however , it was apparent that the design problems with the cooling system were also present in cold conditions ; additionally , the cold weather had a <unk> effect on the tank 's suspension and tracks . Additional testing of the <unk> was conducted by the Soviet military and the design was admired for its <unk> , <unk> , and speed , as well its ability to run on low @-@ quality fuel , unlike contemporary Soviet designs . The <unk> of the <unk> 's armour was found to be a problem and one which could not be solved , as the weight of extra armour plating caused an unacceptable reduction in the tank 's speed . Despite these drawbacks in the <unk> 's design , Soviet authorities believed it to be comparable to the T @-@ 70 light tank in use at the time , and decided that it was suitable to be used in combat . A number of <unk> were sent to Tank Training Schools which were subsequently sent into battle , and in September 1943 two were assigned to the <unk> <unk> Tank Battalion , which was attached to the 5th Guards Tank Brigade ; both tanks were destroyed in combat , one on 30 September and the other on 2 October , the latter a casualty of artillery fire . Several were also used for propaganda purposes , appearing in photographs of Soviet troops who were fighting in the <unk> region . 
- 
- = = = Operation <unk> = = = 
- 
- In mid @-@ 1941 , the Royal Armoured Corps in Britain created three tank squadrons for special overseas operations , known as ' A ' , ' B ' and ' C ' Special Service Squadrons . Both ' A ' and ' B ' Squadrons were equipped with Valentine Infantry tanks and Mark <unk> light tanks , but ' C ' Squadron was equipped with twelve <unk> transferred from the 2nd Armoured Brigade , 1st Armoured Division . On 31 July 1941 , ' C ' Squadron was officially activated and immediately received orders to prepare for overseas service alongside ' A ' and ' B ' Squadrons in an unspecified tropical climate . All three squadrons were transported to <unk> in Scotland for intensive training that focused on embarkation and <unk> from ships and landing craft to prepare them for action in potential amphibious operations . In early September , elements of ' C ' Squadron , including six <unk> , formed part of a force which sailed for Freetown in West Africa ; during this period of the war there were fears that the Spanish government might enter the conflict on the side of Germany , and the force was <unk> to capture a number of Spanish islands off the coast of Africa if this occurred . These fears proved <unk> , and in March 1942 , the unit returned to Britain to join the rest of the squadron in training . 
- The next assignment , Operation <unk> , was the invasion of Madagascar , the third largest island in the world and then under <unk> French control . The Prime Minister and the Combined Chiefs of Staff decided that Madagascar should be occupied as rapidly as possible to deny the port of <unk> to Japanese naval forces , which had recently advanced into the Indian Ocean . Operation <unk> was under the command of Major General Robert G. <unk> and consisted of No. 5 Commando , 29th Independent Brigade Group , and the 17th and 13th brigade groups from 5th Infantry Division . The 29th Brigade formed the core of the invasion force due to its training in amphibious operations , and under its command was ' B ' Special Service Squadron , created by <unk> six <unk> from ' B ' Squadron and six <unk> from ' C ' Squadron into a single unit . The squadron was formed into four troops , one Headquarters troop of three <unk> and one <unk> , one of four <unk> , and two formed from the remaining five <unk> . The invasion force assembled off the west coast of the northern tip of Madagascar on 4 May , near <unk> and the bay of Diego <unk> . The invasion plan called for an amphibious assault landing on four beaches on the west side of the tip , which would allow the British forces to advance approximately 20 miles ( 32 km ) and approach <unk> from the rear . Information about the landing beaches , the defences possessed by the port , and the <unk> French defending forces was limited and vague , although it was believed that the defenders had no weapons capable of penetrating the armour of a Valentine tank . 
- The landings began at 04 : 30 on 5 May , with 5 Commando landing at <unk> Bay and the three infantry brigades and ' B ' Squadron landing at <unk> Bay . The objective of the infantry brigades and their armoured support was to take control of <unk> and a nearby town , but although the infantry landed successfully , ' B ' Squadron had more trouble ; the area of beach designated for its landing craft was blocked for several hours after a <unk> came loose from a landing craft and became stuck in the sand . The infantry brigades advanced toward <unk> without the squadron , but eventually two <unk> and a single <unk> were dispatched in support , catching up with the lead elements of the infantry near the town of <unk> . Here the invasion force encountered the first French defences , consisting of camouflaged trenches and <unk> dug in along a ridge . The tanks attempted to breach them , but the rocky ground made <unk> difficult and they could not close with the <unk> and trenches ; they engaged a number of targets with 2 pounder and machine @-@ gun fire , but the line had to be cleared by an infantry assault later in the day . The tanks were ordered to outflank the defences and advance further into the island , and they were soon joined by two other <unk> dispatched from the beaches ; the small force continued to advance until it encountered the <unk> French main line of defence . This had been built prior to the First World War and included camouflaged <unk> , machine @-@ gun nests and dug @-@ in 75 mm artillery pieces ; the latter , although not specifically designed for an anti @-@ tank role , could penetrate the armour of both the <unk> and the <unk> . The two <unk> advanced first but were knocked out by artillery fire , and two <unk> that were moving behind them suffered the same fate ; the third <unk> retreated in order to report on the French resistance , machine <unk> a motorcycle combination and a truck it encountered on the way back . 
- The commander of the <unk> made his report , and was then ordered to take command of four <unk> and two <unk> which had recently arrived and once again attempt to breach the French defences . The tanks followed the road leading to the defensive line and then attempted to out @-@ flank the line by advancing from the right @-@ hand side , using several hills as cover ; the artillery pieces were able to turn and face the assault , however , and one Valentine and one <unk> were hit and destroyed . The remaining tanks exchanged several <unk> of fire with the artillery pieces before retreating back to their original positions . The French line was eventually broken by 29th Brigade , aided by an amphibious assault by Royal Marines ; the remaining tanks of ' B ' Squadron , two <unk> and three <unk> , remained in defensive positions until the afternoon of 6 May , coming under sporadic artillery fire which disabled another Valentine . The squadron played no further part in the battle , as the <unk> French authorities negotiated a formal surrender the following day , although French troops would continue to engage the British occupying force in guerrilla warfare until late November . ' C ' Squadron suffered heavy casualties during the invasion ; only one Valentine and three <unk> out of twelve tanks were functional by 7 May , and the squadron had suffered seven killed and six wounded . It remained in Madagascar until early 1943 , when it was shipped to India and took part in the Burma Campaign as part of 29th Brigade . 
- 
- = = = Operation Tonga = = = 
- 
- Because of a lack of equipment training facilities in mid @-@ 1940 , when the British airborne establishment was formed , the War Office was able to accept only 500 volunteers for training as airborne troops . Progress in setting up proper training facilities and acquiring suitable transport aircraft was so slow that the first British airborne operation , Operation <unk> , was conducted by a <unk> Commando unit . By 1942 , there existed specifically trained airborne units , including the 1st Airborne Division , and on 19 January 1942 the War Office decided that a light tank unit would be one of the support units attached to the division . This unit , designated the Light Tank Squadron , was to be formed of nineteen light tanks and would operate to the fore of the division , using their tanks ' speed to capture objectives and then holding them until relieved by other units . The obvious unit for conversion was ' C ' Special Services Squadron , as it was trained to act as an independent tank unit and , more importantly , was the only unit that was still using <unk> ; it had been re @-@ designated as an airborne tank by the War Office . ' C ' Squadron was officially transferred to the 1st Airborne Division on 24 June 1942 , bringing with it seven <unk> among its other vehicles . The unit immediately began training , but was not attached to the 1st Airborne Division for long ; during mid @-@ 1943 , the division was transported to the Middle East so it could participate in the Allied invasion of Sicily . ' C ' Squadron remained in Britain , as not enough <unk> gliders had been built by the time the division departed to transport its <unk> ; the squadron was transferred to the 6th Airborne Division , which had been raised in April 1943 , and ' C ' Squadron remained with it for the rest of the conflict . The squadron continued to train as an air @-@ portable unit , and participated in a number of exercises to prepare for its new duties , including reconnaissance of enemy positions and counter @-@ attacking enemy infantry and armour . 
- On 13 December 1943 , the War Office decided to expand the squadron into a regiment equipped with a combination of light tanks and conventional reconnaissance vehicles such as scout cars , and on 1 April 1944 , it was re @-@ designated as the 6th Airborne Armoured Reconnaissance Regiment . The regiment consisted of a Headquarters Squadron , a Light Tank Squadron and a Reconnaissance Squadron ; two <unk> , the Mark 1 <unk> variation , were attached to the Headquarters Squadron , but the Light Tank Squadron , also known as ' A ' Squadron , received the majority of the <unk> . ' A ' Squadron had approximately nineteen <unk> split between six troops , two of which were of the <unk> variation and the rest were armed with 2 pounders fitted with <unk> <unk> . On 24 May 1944 , after participating in a further series of exercises and manoeuvres , ' A ' Squadron moved from their training area to a transit camp at <unk> <unk> airfield , while the rest of the regiment moved to RAF <unk> Norton airfield the next day ; from these two airfields , the regiment would be transported from to participate in the British airborne landings in Normandy . The operation began on the night of 5 June , with the deployment of 6th Airborne Division to eastern Normandy . It was tasked with protecting the eastern flank of the Allied <unk> landings , securing strategically important areas east of Caen , capturing several important bridges over the Caen Canal and River <unk> , and destroying a coastal artillery battery . <unk> transport aircraft were available to land all three of the division 's brigades simultaneously ; one would have to be landed in a second lift later in the day . Major General Richard Gale had initially intended for the 6th <unk> Brigade , to which the 6th Airborne Armoured Reconnaissance Regiment was attached , to be landed first ; however , aerial photography revealed that anti @-@ glider poles had been erected in the landing zone selected for the brigade . Therefore , Gale decided that the 3rd Parachute Brigade and 5th Parachute Brigade ( which did not <unk> gliders ) should land in the first lift to clear the landing zones , allowing the 6th <unk> Brigade to land in the second lift . 
- The <unk> and <unk> gliders of the brigade landed at 21 : 00 on 6 June in a landing zone cleared of obstructions by the 5th Parachute Brigade . The primary tasks of the brigade were to bring in reinforcements and supplies , and to aid the two parachute brigades in consolidating the area held by the division ; the 6th Airborne Armoured Reconnaissance Squadron was to aid in the latter task , acting as a reconnaissance force to scout out German positions and impede the movement of German forces attempting to counter @-@ attack . The <unk> of ' A ' Squadron were to play an integral part in this reconnaissance role due to their speed , but the squadron 's strength of twenty tanks was severely depleted by the time it landed in Normandy . It lost one tank before the formation landed when the <unk> broke loose of its <unk> and crashed through the nose of the glider that was carrying it , causing both to fall into the sea mid @-@ flight . The squadron 's strength was further weakened when two gliders collided with each other in the landing zone , destroying themselves and the <unk> they carried ; a third <unk> hit another <unk> as it was being <unk> and flipped the tank upside down , rendering it unusable , although the crew escaped without injury . The surviving tanks were then rendered temporarily immobile when parachute rigging lines became tangled in their <unk> , forcing their crews to cut the lines away with welding <unk> . 
- The squadron retrieved all of the remaining <unk> and advanced to the south of the landing zone to link up with the rest of the regiment ; there , they received orders to support the 8th Parachute Battalion in the <unk> de <unk> area and conduct reconnaissance duties . After linking with the battalion , the squadron began <unk> , and engaged German infantry and armour they encountered . By the end of 7 June , two <unk> had been lost to enemy action , one destroyed by a German self @-@ propelled gun and the second by hitting a mine . The division was reinforced by British troops who were advancing from the invasion beaches and it began to push through Normandy , while the squadron continued its reconnaissance duties . At this time , Gale decided to avoid , when possible , engaging the <unk> with German armour , as they proved to be completely <unk> by the German tanks and self @-@ propelled guns , such as the Panzer IV and the <unk> III . Instead , when the division required armoured support , it summoned it from armoured units outside the division , and the <unk> were used to support infantry patrols and provide fire support . By August , in the division 's preparation for the planned breakout from the Normandy bridgehead , the majority of <unk> in ' A ' Squadron were replaced with Cromwell fast cruiser tanks ; only three <unk> remained , assigned to the Headquarters troop of ' A ' Squadron . 
- 
- = = = Post @-@ war = = = 
- 
- Operation Tonga was the last that <unk> saw of active combat . During the first week of October 1944 , the 6th Airborne Armoured Reconnaissance Regiment underwent an extensive <unk> , in which it was completely <unk> , and all the remaining <unk> were retired . They were replaced with the <unk> <unk> , a purpose @-@ built airborne light tank of American design ; eight <unk> were used by the regiment in March 1945 during Operation <unk> , the airborne operation to cross the river Rhine . A report issued by the Director ( Air ) of the War Office in January 1946 confirmed that the <unk> design was considered obsolete , and any light tanks used in post @-@ war airborne formations would be entirely new in design . A small number of <unk> remained in service with the 3rd Hussars until 1949 ; a <unk> glider flight was stationed at RAF <unk> , and a troop of <unk> was kept by the regiment for training exercises with the gliders . However , glider training by the regiment was stopped in 1950 and the <unk> withdrawn from service . 
- 
- = = <unk> = = 
- 
- There were several variants of the <unk> design . The first was the Light Tank Mk VIII , Vickers @-@ Armstrong 's proposed successor to the <unk> . The Mark VIII was also known as the Harry Hopkins , named after President Roosevelt 's chief diplomatic advisor , and was given the General Staff design number <unk> by the War Office . The Mark VIII was intended to improve upon the design of the <unk> in a number of areas . It had thicker armour than the <unk> , with the front hull and turret armour increased to a thickness of 38 millimetres ( 1 @.@ 5 in ) and the side armour to 17 millimetres ( 0 @.@ 67 in ) , and the turret and hull given more sloped surfaces to help <unk> shells fired at the tank . The dimensions of the <unk> were also changed ; the Mark VIII was longer by 6 inches ( 0 @.@ 15 m ) , wider by 1 foot 3 inches ( 0 @.@ 38 m ) and heavier . The new tank was no longer air @-@ portable , as it was too heavy to be carried by a <unk> . The 12 @-@ cylinder engine of the <unk> was fitted to the Mark VIII , although the increased weight meant that its maximum speed decreased to 30 miles per hour ( 48 km / h ) ; its armament also remained the same as that of the <unk> . The War Office authorised the construction of three prototype models in April 1941 . The new design was considered a success , and the Tank Board of the War Office ordered 1 @,@ 000 to be constructed in September . However , problems were encountered with further tests of the prototypes , and a report issued in December 1942 stated that production of the Mark VIII had been delayed due to developmental problems . These problems continued to persist into 1943 , when the War Office decided against using the tank in active service ; approximately 100 Mark <unk> were produced by 1945 , when production ended . 
- A second variant on the <unk> design was the <unk> <unk> Drive ( " <unk> DD " ) . The <unk> Drive system was invented by Nicholas <unk> , and was designed to allow a tank to ' swim ' through water and participate in amphibious operations . The system functioned by <unk> a large <unk> canvas screen around the tank above its tracks , which was supported by thirty @-@ six <unk> tubes and steel struts ; this gave the tank sufficient <unk> to float , and was then propelled along by a small propeller powered by the tank 's engine . The screen could be collapsed by using a small explosive charge once the tank reached land . The system was fitted during June 1941 , as the <unk> was the lightest light tank available at the time ; the converted tank was successfully tested on a number of lakes and reservoirs , allowing the <unk> Drive system to be tested on heavier tanks , such as the Valentine . The system would be used during Operation <unk> , when M4 Sherman medium tanks would land on the invasion beaches . 
- 
- 
- = <unk> = 
- 
- <unk> , known in Japan as I Love Donald Duck : Georgia <unk> no <unk> ( Japanese : <unk> <unk> , Hepburn : <unk> <unk> <unk> <unk> <unk> <unk> no <unk> ) , is a 1991 <unk> video game developed and published by Sega for the Sega Genesis . The game was released in Europe in 1991 , in North America on December 19 , 1991 and in Japan on December 20 , 1991 . <unk> stars Donald Duck and his three nephews , Huey , <unk> , and <unk> , as treasure @-@ hunters , and is part of a series of games published by Sega that were based on Walt Disney cartoon characters . 
- <unk> was released to mostly positive reviews from video game journalists . The game was universally lauded for its graphics , with magazines like Sega Pro describing them as " some of the best graphics around . " The game was also praised for its music and puzzles , as well as their clever use in the game . However , <unk> was criticized for its controls , being described by IGN as " float @-@ y " and making certain segments of the game <unk> difficult . The game was also criticized for its lack of difficulty overall as well as its lack of speech samples , which several other Genesis games of the time had . 
- 
- = = Gameplay = = 
- 
- The player , as Donald , ventures through a variety of side @-@ scrolling levels . Generally , each level is divided into an overland part and a dungeon , such as the <unk> 's palace or the temple in which the Great Duck Treasure resides . Although the player may choose any order to play the overland sections , various obstacles prevent the player from entering the <unk> outside of a specific order . In addition to this , some levels provide the player with vital clues which solve puzzles needed to progress in later sections . Once Donald has completed the overland section of an area , he may leave by calling his nephews ' biplane , and will return to the dungeon entrance of that area if the player chooses to return . 
- Donald is armed with a special gun that can shoot <unk> , popcorn or bubble gum . Donald has unlimited <unk> which can only temporarily <unk> enemies ( though bosses can still be damaged with <unk> ) , and can collect popcorn and gum along the way or get the latter from <unk> <unk> . Later in the game , the <unk> is upgraded to act as a temporary platform to climb walls with and , when stuck to a passing bird , allows Donald to traverse longer distances . In <unk> , India and Egypt , Donald can also pick up <unk> <unk> which increase his temper , eventually temporarily allowing him to become <unk> , increase his speed and knock out enemies in his path . 
- 
- = = Plot = = 
- 
- While Donald is <unk> through some books in <unk> <unk> 's library , a map falls out of a book relating to the treasure of King <unk> , ruler of the Great Duck Kingdom in ancient times . The map leads to the location of the king 's most prized possession , hidden in a secret place shortly before his death . Donald thinks this is his path to riches . Unfortunately Big Bad Pete <unk> and pursues Donald throughout the game hoping to steal the treasure . 
- <unk> with his nephews Huey , <unk> , and <unk> , and using the partial map from the library , Donald begins his search in <unk> , with the trail being directed to an Aztec pyramid in Mexico . Outside the pyramid , he is directed by a " sweet <unk> " to obtain a " hero key " from an explorer back in <unk> to open the pyramid . Inside the pyramid , Donald meets <unk> , who gives him a strange note and a <unk> to help him reach higher places , and tells him that <unk> <unk> is looking for him back in <unk> . Travelling across the <unk> of <unk> to meet <unk> , Donald is given <unk> 's latest invention , <unk> <unk> that can break through walls . The last location on the partial map is Count <unk> 's castle in Transylvania , where Donald encounters a ghost who tells him that the Count carries the real treasure map . 
- After defeating <unk> , Donald receives a more complete map . In India , Donald enters the palace of the <unk> , where she challenges him to defeat the tiger in her garden in exchange for a <unk> <unk> . Donald succeeds and receives the <unk> <unk> , which is the key to open a temple in Egypt . Donald is able to solve the " <unk> of the <unk> " using the note <unk> had given him , and obtains the <unk> of Ra before escaping in a mine cart . From there , he journeys to the South Pole , where he finds a key frozen in ice , and uses the <unk> of Ra to melt the ice and grab the key . The key unlocks the hold of a Viking ship , which contains an ancient diary with the secret to locating the treasure . The ship is haunted by ghosts , and the Viking captain sends Donald below decks to get rid of them . After defeating a skeletal Viking warrior , Donald returns to the deck , where the captain informs him that the diary is hidden in ice near the South Pole , and gives him an " ancient Viking <unk> " that <unk> to flying creatures . Donald then returns to the South Pole , <unk> a ride on one of Pete 's bird <unk> to reach the diary . 
- However , upon finding the diary , Pete shows up , holding Donald 's nephews hostage in exchange for the diary . After giving Pete the diary , Donald travels to Pete 's hideout to defeat Pete and get the diary back . The diary reveals that the map , when dipped in water , will reveal the location of the Great Duck Treasure . Donald flies to the island where the treasure is hidden and manages to evade its traps in order to reach the treasure vault . After defeating the ancient spirit guarding the treasure , Donald opens the vault only to find a simple stone statue . When the disappointed Donald returns home , Huey , <unk> and <unk> accidentally break the statue , which reveals a golden <unk> necklace was hidden inside . Donald gives the necklace to Daisy and the two fly off into the sunset together . 
- 
- = = Development and release = = 
- 
- <unk> was developed and published by Sega for the Sega Genesis as part of a series of games that were based on Walt Disney cartoon characters . The game was released in Europe in 1991 , in North America on December 19 , 1991 and in Japan on December 20 , 1991 . <unk> was later released as part of a bundle called The Disney Collection for Genesis in 1996 alongside Castle of Illusion . The game was also ported to the Sega Saturn and released exclusively in Japan alongside Castle of Illusion again as part of the Sega Ages series in 1998 , entitled Sega Ages : I Love Mickey Mouse . 
- 
- = = Reception = = 
- 
- <unk> received a mostly positive response from critics upon release . GameRankings , an aggregator for video game reviews , assigned the game a score of 77 % based on 2 reviews . Mega placed the game at # 7 in their " Top Mega Drive Games of All Time " list . <unk> magazine praised the game 's graphics , but criticized the game 's easy difficulty level , explaining simply that " the graphics are excellent , but the game is easy to complete . " Damian <unk> from Sega Pro also praised the graphics , stating that the game has " without [ a ] doubt some of the best graphics around " and that " the sprites and backgrounds are consistently excellent . " He also noted the game 's various puzzles and their use in the game , explaining that " [ e ] <unk> if the ideas are not original , the way they are strung together to accelerate the pace to <unk> is nothing short of breath @-@ taking . " Levi Buchanan from IGN gave <unk> a 7 @.@ 3 / 10 , also lauding the graphics and animation as excellent and saying the music was pleasing . 
- <unk> criticized Donald 's controls in certain situations in the game , as well as the difficulty of some levels and puzzles . Buchanan also criticized the controls , calling them " float @-@ y " and noted the difficulty in executing precision jumps , explaining that " [ i ] t 's far too easy to <unk> or under @-@ shoot a narrow column and slip to your <unk> . " <unk> was also " dubious of the number of credits , " stating that the game may seem easy with unlimited continues , but that the player will " still need considerable skill to reach the treasure island . " Buchanan was disappointed with the lack of speech samples , explaining that it 's " a bit of a drag with a character that is so defined by his voice . " Ultimately , <unk> said that " [ y ] <unk> players will instantly be <unk> by Donald 's quest " and that " <unk> is everything a cartoon game should be and more . " Buchanan summed up the game as being a " good platformer <unk> up by some questionable controls " and recommended the game as " a <unk> enjoyable 16 @-@ bit platformer that would fit nicely in your Genesis collection . " 
- 
- 
- = <unk> colossal heads = 
- 
- The <unk> colossal heads are at least seventeen monumental stone representations of human heads sculpted from large basalt boulders . The heads date from at least before 900 BC and are a distinctive feature of the <unk> civilization of ancient <unk> . All portray mature men with <unk> <unk> , flat <unk> , and slightly crossed eyes ; their physical characteristics correspond to a type that is still common among the inhabitants of <unk> and Veracruz . The backs of the monuments often are flat . The boulders were brought from the Sierra de los <unk> mountains of Veracruz . Given that the extremely large slabs of stone used in their production were transported over large distances , requiring a great deal of human effort and resources , it is thought that the monuments represent portraits of powerful individual <unk> rulers . Each of the known examples has a distinctive headdress . The heads were variously arranged in lines or groups at major <unk> centres , but the method and logistics used to transport the stone to these sites remain unclear . 
- The discovery of a colossal head at <unk> <unk> in the nineteenth century spurred the first archaeological investigations of <unk> culture by Matthew <unk> in 1938 . Seventeen confirmed examples are known from four sites within the <unk> <unk> on the Gulf Coast of Mexico . Most colossal heads were sculpted from spherical boulders but two from San Lorenzo <unk> were re @-@ carved from massive stone <unk> . An additional monument , at <unk> <unk> in Guatemala , is a throne that may have been carved from a colossal head . This is the only known example from outside the <unk> <unk> . 
- <unk> the monuments remains difficult because of the movement of many from their original contexts prior to archaeological investigation . Most have been dated to the Early <unk> period ( 1500 – 1000 BC ) with some to the Middle <unk> ( 1000 – 400 BC ) period . The smallest weigh 6 tons , while the largest is variously estimated to weigh 40 to 50 tons , although it was abandoned and left unfinished close to the source of its stone . 
- 
- = = <unk> civilization = = 
- 
- The <unk> civilization developed in the lowlands of southeastern Mexico between 1500 and 400 BC . The <unk> <unk> lies on the Gulf Coast of Mexico within the states of Veracruz and <unk> , an area measuring approximately 275 kilometres ( 171 mi ) east to west and extending about 100 kilometres ( 62 mi ) inland from the coast . The <unk> are regarded as the first civilization to develop in <unk> and the <unk> <unk> is one of six <unk> of civilization worldwide , the others being the <unk> <unk> culture of South America , the <unk> culture of China 's Yellow River , the Indus Valley Civilization of south Asia , the civilization of ancient Egypt and the <unk> civilization of ancient Iraq . Of these , only the <unk> civilization developed in a lowland tropical forest setting . 
- The <unk> were the first inhabitants of the Americas to construct monumental architecture and to settle in towns and cities . They were also the first people in the Americas to develop a sophisticated style of stone sculpture . In the first decade of the 21st century evidence emerged of <unk> writing , with the earliest examples of <unk> hieroglyphs dating to around 650 BC . Examples of script have been found on roller stamps and stone artefacts ; the texts are short and have been partially <unk> based on their similarity to other <unk> scripts . The evidence of complex society developing in the <unk> <unk> has led to the <unk> being regarded as the " Mother Culture " of <unk> , although this concept remains controversial . 
- Some of the <unk> ' rulers seem to have served religious functions . The city of San Lorenzo was succeeded as the main centre of the civilization by La <unk> in about 900 BC , with <unk> <unk> and <unk> de los <unk> possibly sharing the role ; other urban centres were much less significant . The nature and degree of the control exercised by the centres over a widespread rural population remains unclear . Very fine <unk> art , much clearly made for an elite , survives in several forms , notably <unk> figurines , and larger sculptures such as The <unk> . The figurines have been recovered in large numbers and are mostly in pottery ; these were presumably widely available to the population . Together with these , of particular relevance to the colossal heads are the " <unk> @-@ style masks " in stone , so called because none have yet been excavated in circumstances that allow the proper archaeological identification of an <unk> context . These evocative stone face masks present both similarities and differences to the colossal heads . Two thirds of <unk> monumental sculpture represents the human form , and the colossal heads fall within this major theme of <unk> art . 
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- = = <unk> = = 
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- The colossal heads cannot be precisely dated . However , the San Lorenzo heads were buried by 900 BC , indicating that their period of manufacture and use was earlier still . The heads from <unk> <unk> had been moved from their original context before they were investigated by archaeologists and the heads from La <unk> were found partially exposed on the modern ground surface . The period of production of the colossal heads is therefore unknown , as is whether it spanned a century or a millennium . Estimates of the time span during which colossal heads were produced vary from 50 to 200 years . The San Lorenzo heads are believed to be the oldest , and are the most <unk> executed . All of the stone heads have been assigned to the <unk> period of <unk> chronology , generally to the Early <unk> ( 1500 – 1000 BC ) , although the two <unk> <unk> heads and the La <unk> Head are attributed to the Middle <unk> ( 1000 – 400 BC ) . 
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- = = Characteristics = = 
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- <unk> colossal heads vary in height from 1 @.@ 47 to 3 @.@ 4 metres ( 4 @.@ 8 to 11 @.@ 2 ft ) and weigh between 6 and 50 tons . All of the <unk> colossal heads depict mature men with flat <unk> and <unk> <unk> ; the eyes tend to be slightly crossed . The general physical characteristics of the heads are of a type that is still common among people in the <unk> region in modern times . The backs of the heads are often flat , as if the monuments were originally placed against a wall . All examples of <unk> colossal heads wear distinctive <unk> that probably represent cloth or animal hide originals . Some examples have a tied knot at the back of the head , and some are decorated with feathers . A head from La <unk> is decorated with the head of a bird . There are similarities between the <unk> on some of the heads that has led to speculation that specific <unk> may represent different dynasties , or perhaps identify specific rulers . Most of the heads wear large <unk> inserted into the ear lobes . 
- All of the heads are realistic , <unk> and <unk> descriptions of the men . It is likely that they were portraits of living ( or recently deceased ) rulers well known to the <unk> . Each head is distinct and <unk> , displaying <unk> features . They were once thought to represent <unk> although this theory is no longer widely held ; it is possible , however , that they represent rulers equipped for the <unk> <unk> . <unk> expressions depicted on the heads vary from stern through placid to smiling . The most <unk> <unk> art is the earliest , appearing suddenly without surviving <unk> , with a tendency towards more stylised sculpture as time progressed . Some surviving examples of wooden sculpture recovered from El <unk> demonstrate that the <unk> are likely to have created many more <unk> sculptures than works sculpted from stone . 
- In the late nineteenth century , José <unk> y Serrano described a colossal head as having " Ethiopian " features and speculations that the <unk> had African origins resurfaced in 1960 in the work of Alfonso <unk> <unk> and in the 1970s in the writings of Ivan van <unk> . Such speculation is not taken seriously by <unk> scholars such as Richard <unk> and Ann <unk> . 
- Although all the colossal heads are broadly similar , there are distinct stylistic differences in their execution . One of the heads from San Lorenzo bears traces of plaster and red paint , suggesting that the heads were originally brightly decorated . Heads did not just represent individual <unk> rulers ; they also incorporated the very concept of rulership itself . 
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- = = <unk> = = 
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- The production of each colossal head must have been carefully planned , given the effort required to ensure the necessary resources were available ; it seems likely that only the more powerful <unk> rulers were able to <unk> such resources . The workforce would have included <unk> , labourers , <unk> , <unk> , <unk> and other <unk> producing the tools to make and move the monument , in addition to the support needed to feed and otherwise attend to these workers . The seasonal and agricultural cycles and river levels needed to have been taken into account to plan the production of the monument and the whole project may well have taken years from beginning to end . 
- Archaeological investigation of <unk> basalt workshops suggest that the colossal heads were first roughly shaped using direct percussion to chip away both large and small <unk> of stone . The sculpture was then refined by <unk> the surface using <unk> , which were generally rounded <unk> that could be of the same basalt as the monument itself , although this was not always the case . <unk> were found in association with workshops at San Lorenzo , indicating their use in the finishing of fine detail . <unk> colossal heads were fashioned as in @-@ the @-@ round monuments with varying levels of relief on the same work ; they tended to feature higher relief on the face and lower relief on the <unk> and <unk> . Monument 20 at San Lorenzo is an extensively damaged throne with a figure emerging from a niche . Its sides were broken away and it was dragged to another location before being abandoned . It is possible that this damage was caused by the initial stages of re @-@ carving the monument into a colossal head but that the work was never completed . 
- All seventeen of the confirmed heads in the <unk> <unk> were sculpted from basalt mined in the Sierra de los <unk> mountains of Veracruz . Most were formed from coarse <unk> dark grey basalt known as <unk> <unk> basalt after a volcano in the range . <unk> have proposed that large <unk> <unk> basalt boulders found on the southeastern slopes of the mountains are the source of the stone for the monuments . These boulders are found in an area affected by large <unk> ( volcanic mudslides ) that carried substantial blocks of stone down the mountain slopes , which suggests that the <unk> did not need to quarry the raw material for <unk> the heads . Roughly spherical boulders were carefully selected to mimic the shape of a human head . The stone for the San Lorenzo and La <unk> heads was transported a considerable distance from the source . The La <unk> head was found on El <unk> hill in the Sierra de los <unk> and the stone from <unk> <unk> <unk> Head 1 and <unk> <unk> Head 1 ( also known as <unk> <unk> Monuments A and Q ) came from the same hill . 
- The boulders were transported over 150 kilometres ( 93 mi ) from the source of the stone . The exact method of transportation of such large masses of rock are unknown , especially since the <unk> lacked <unk> of burden and functional wheels , and they were likely to have used water transport whenever possible . Coastal currents of the Gulf of Mexico and in river estuaries might have made the <unk> transport of monuments weighing 20 tons or more impractical . Two badly damaged <unk> sculptures depict rectangular stone blocks bound with ropes . A largely destroyed human figure rides upon each block , with their legs hanging over the side . These sculptures may well depict <unk> rulers overseeing the transport of the stone that would be fashioned into their monuments . When transport over land was necessary , the <unk> are likely to have used <unk> , ramps and roads to facilitate moving the heads . The regional terrain offers significant obstacles such as swamps and floodplains ; avoiding these would have necessitated crossing undulating hill country . The construction of temporary <unk> using the suitable and plentiful floodplain soils would have allowed a direct route across the floodplains to the San Lorenzo Plateau . Earth structures such as mounds , platforms and <unk> upon the plateau demonstrate that the <unk> possessed the necessary knowledge and could commit the resources to build large @-@ scale earthworks . 
- The flat backs of many of the colossal heads represented the flat bases of the monumental <unk> from which they were reworked . Only four of the seventeen <unk> heads do not have flattened backs , indicating the possibility that the majority were reworked monuments . Alternatively , the backs of many of these massive monuments may have been flattened to ease their transport , providing a stable form for <unk> the monuments with ropes . Two heads from San Lorenzo have traces of niches that are characteristic of monumental <unk> <unk> and so were definitely reworked from earlier monuments . 
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- = = Known monuments = = 
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- Seventeen confirmed examples are known . An additional monument , at <unk> <unk> in Guatemala , is a throne that may have been carved from a colossal head . This is the only known example outside of the <unk> <unk> on the Gulf Coast of Mexico . Possible fragments of additional colossal heads have been recovered at San Lorenzo and at San Fernando in <unk> . <unk> colossal stone heads are also known in the Southern Maya area where they are associated with the <unk> style of sculpture . Although some arguments have been made that they are pre @-@ <unk> , these latter monuments are generally believed to be influenced by the <unk> style of sculpture . 
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- = = = San Lorenzo = = = 
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- The ten colossal heads from San Lorenzo originally formed two roughly parallel lines running north @-@ south across the site . Although some were recovered from <unk> , they were found close to their original <unk> and had been buried by local erosion . These heads , together with a number of monumental stone <unk> , probably formed a processional route across the site , powerfully displaying its dynastic history . Two of the San Lorenzo heads had been re @-@ carved from older <unk> . 
- San Lorenzo <unk> Head 1 ( also known as San Lorenzo Monument 1 ) was lying facing upwards when excavated . The erosion of a path passing on top of the monument uncovered its eye and led to the discovery of the <unk> site . <unk> Head 1 is 2 @.@ 84 metres ( 9 @.@ 3 ft ) high ; it measures 2 @.@ 11 metres ( 6 @.@ 9 ft ) wide and it weighs 25 @.@ 3 tons . The monument was discovered partially buried at the edge of a gully by Matthew <unk> in 1945 . When discovered it was lying on its back , looking upwards . It was associated with a large number of broken ceramic vessels and figurines . The majority of these ceramic remains have been dated to between 800 and 400 BC ; some pieces have been dated to the Villa Alta phase ( Late Classic period , 800 – 1000 AD ) . The headdress possesses a plain band that is tied at the back of the head . The upper portion of the headdress is decorated with a U @-@ shaped motif . This element descends across the front of the headdress , terminating on the forehead . On the front portion it is decorated with five semicircular motifs . The <unk> piece does not meet the horizontal band , leaving a space between the two pieces . On each side of the face a strap descends from the headdress and passes in front of the ear . The forehead is wrinkled in a <unk> . The lips are slightly parted without revealing the teeth . The <unk> are pronounced and the ears are particularly well executed . The face is slightly asymmetric , which may be due to error on the part of the <unk> or may accurately reflect the physical features of the portrait 's subject . The head has been moved to the <unk> de <unk> de <unk> ( " <unk> Museum of <unk> " ) . 
- San Lorenzo <unk> Head 2 ( also known as San Lorenzo Monument 2 ) was reworked from a monumental throne . The head stands 2 @.@ 69 metres ( 8 @.@ 8 ft ) high and measures 1 @.@ 83 metres ( 6 @.@ 0 ft ) wide by 1 @.@ 05 metres ( 3 @.@ 4 ft ) deep ; it weighs 20 tons . <unk> Head 2 was discovered in 1945 when Matthew <unk> 's guide cleared away some of the vegetation and mud that covered it . The monument was found lying on its back , facing the sky , and was excavated in 1946 by <unk> and Philip <unk> . In 1962 the monument was removed from the San Lorenzo plateau in order to put it on display as part of " The <unk> tradition " exhibition at the Museum of Fine Arts in Houston in 1963 . San Lorenzo <unk> Head 2 is currently in the <unk> <unk> de <unk> in Mexico City . The head was associated with a number of ceramic finds ; they have been dated to the Early <unk> and Late Classic periods . <unk> Head 2 wears a complex headdress that sports a horizontal band tied at the back of the head ; this is decorated with three bird 's heads that are located above the forehead and temples . The <unk> piece is formed from six strips running towards the back of the head . The front of the headdress above the horizontal band is plain . Two short <unk> hang down from the headdress in front of the ears . The ear jewellery is formed by large <unk> <unk> or framed discs . The left and right ornaments are different , with radial lines on the left <unk> , a feature absent on the right <unk> . The head is badly damaged due to an unfinished reworking process . This process has <unk> the entire face with at least 60 smaller <unk> and 2 larger holes . The surviving features appear to depict an ageing man with the forehead <unk> into a <unk> . The lips are thick and slightly parted to reveal the teeth ; the head has a pronounced chin . 
- San Lorenzo <unk> Head 3 is also known as San Lorenzo Monument 3 . The head measures 1 @.@ 78 metres ( 5 @.@ 8 ft ) high by 1 @.@ 63 metres ( 5 @.@ 3 ft ) wide by 0 @.@ 95 metres ( 3 @.@ 1 ft ) deep and weighs 9 @.@ 4 tons . The head was discovered in a deep gully by Matthew <unk> in 1946 ; it was found lying face down and its excavation was difficult due to the wet conditions in the gully . The monument was found 0 @.@ 8 kilometres ( 0 @.@ 50 mi ) southwest of the main mound at San Lorenzo , however , its original location is unknown ; erosion of the gully may have resulted in significant movement of the sculpture . Head 3 has been moved to the <unk> de <unk> de <unk> . The headdress is complex , with the horizontal basal band being formed by four horizontal <unk> , with diagonal folds above each eye . A small <unk> tops the headdress . A large flap formed of four <unk> drops down both sides of the head , completely covering the ears . The face has a typically <unk> brow and , unusually , has clearly defined eyelids . The lips are thick and slightly parted ; the front of the lower lip has broken away completely , and the lower front of the headdress is pitted with 27 irregularly spaced artificial depressions . 
- San Lorenzo <unk> Head 4 ( also known as San Lorenzo Monument 4 ) weighs 6 tons and has been moved to the <unk> de <unk> de <unk> . <unk> Head 4 is 1 @.@ 78 metres ( 5 @.@ 8 ft ) high , 1 @.@ 17 metres ( 3 @.@ 8 ft ) wide and 0 @.@ 95 metres ( 3 @.@ 1 ft ) deep . The head was discovered by Matthew <unk> in 1946 , 550 metres ( 600 yd ) northwest of the principal mound , at the edge of a gully . When excavated , it was found to be lying on its right @-@ hand side and in a very good state of preservation . <unk> materials excavated with the head became mixed with ceramics associated with Head 5 , making ceramic dating of the monument difficult . The headdress is decorated with a horizontal band formed of four sculpted <unk> , similar to those of Head 3 . On the right @-@ hand side , three <unk> descend from the upper portion of the headdress ; they terminate in a total of eight strips that hang down across the horizontal band . These <unk> are judged to represent hair rather than <unk> . Also on the right hand side , two <unk> descend across the ear and continue to the base of the monument . On the left @-@ hand side , three vertical <unk> descend across the ear . The <unk> is only visible on the right hand side ; it is formed of a plain disc and <unk> . The face is that of an ageing man with a <unk> forehead , low <unk> and a prominent chin . The lips are thick and slightly parted . 
- San Lorenzo <unk> Head 5 is also known as San Lorenzo Monument 5 . The monument stands 1 @.@ 86 metres ( 6 @.@ 1 ft ) high and measures 1 @.@ 47 metres ( 4 @.@ 8 ft ) wide by 1 @.@ 15 metres ( 3 @.@ 8 ft ) deep . It weighs 11 @.@ 6 tons . The head was discovered by Matthew <unk> in 1946 , face down in a gully to the south of the principal mound . The head is particularly well executed and is likely to have been found close to its original location . <unk> recovered during its excavation became mixed with those from the excavation of Head 4 . The mixed ceramics have been dated to the San Lorenzo and Villa Alta phases ( approximately 1400 – 1000 BC and 800 – 1000 AD respectively ) . <unk> Head 5 is particularly well preserved , although the back of the headdress band was damaged when the head was moved from the archaeological site . The band of the headdress is set at an angle and has a notch above the bridge of the nose . The headdress is decorated with jaguar paws ; this general identification of the decoration is contested by <unk> de la <unk> since the " paws " have three claws each ; she identifies them as the claws of a bird of prey . At the back of the head , ten <unk> strips form a net decorated with disc motifs . Two short <unk> descend from the headdress in front of the ears . The ears are adorned with disc @-@ shaped <unk> with <unk> . The face is that of an ageing man with <unk> under the eyes and across the bridge of the nose , and a forehead that is <unk> in a <unk> . The lips are slightly parted . <unk> Head 5 has been moved to the <unk> de <unk> de <unk> . 
- San Lorenzo <unk> Head 6 ( also known as San Lorenzo Monument 17 ) is one of the smaller examples of colossal heads , standing 1 @.@ 67 metres ( 5 @.@ 5 ft ) . It measures 1 @.@ 41 metres ( 4 @.@ 6 ft ) wide by 1 @.@ 26 metres ( 4 @.@ 1 ft ) deep and is estimated to weigh between 8 and 10 tons . The head was discovered by a local <unk> and was excavated in 1965 by Luis <unk> and <unk> <unk> Chan . The head had collapsed into a ravine under its own weight and was found face down on its left hand side . In 1970 it was transported to the Metropolitan Museum of Art in New York for the museum 's centenary exhibition . After its return to Mexico , it was placed in the <unk> <unk> de <unk> in Mexico City . It is sculpted with a net @-@ like head covering joined together with sculpted beads . A covering descends from under the headdress to cover the back half of the neck . The <unk> is divided into four strips and begins above the right ear , extending around the entire head . A short strap descends from either side of the head to the ear . The ear ornaments are complex and are larger at the front of the ear than at the back . The face is that of an ageing male with the forehead <unk> in a <unk> , <unk> under the eyes , <unk> <unk> and deep <unk> on either side of the nose . The face is somewhat asymmetric , possibly due to errors in the execution of the monument . 
- San Lorenzo <unk> Head 7 ( also known as San Lorenzo Monument 53 ) measures 2 @.@ 7 metres ( 8 @.@ 9 ft ) high by 1 @.@ 85 metres ( 6 @.@ 1 ft ) wide by 1 @.@ 35 metres ( 4 @.@ 4 ft ) deep and weighs 18 tons . San Lorenzo <unk> Head 7 was reworked from a monumental throne ; it was discovered by a joint archaeological project by the <unk> <unk> de <unk> e Historia and Yale University , as a result of a <unk> survey . It was buried at a depth of less than 1 metre ( 3 @.@ 3 ft ) and was lying facing upwards , leaning slightly northwards on its right hand side . The head is poorly preserved and has suffered both from erosion and deliberate damage . The headdress is decorated with a pair of human hands ; a feathered ornament is carved at the back of the <unk> and two discs adorn the front . A short strap descends from the <unk> and hangs in front of the right ear . The head sports large <unk> that completely cover the <unk> , although severe erosion makes their exact form difficult to distinguish . The face has <unk> between the nose and <unk> , <unk> <unk> and deep @-@ set eyes ; the lips are badly damaged and the mouth is open , displaying the teeth . In 1986 the head was transported to the <unk> de <unk> de <unk> . 
- San Lorenzo <unk> Head 8 ( also known as San Lorenzo Monument 61 ) stands 2 @.@ 2 metres ( 7 @.@ 2 ft ) high ; it measures 1 @.@ 65 metres ( 5 @.@ 4 ft ) wide by 1 @.@ 6 metres ( 5 @.@ 2 ft ) deep and weighs 13 tons . It is one of the finest examples of an <unk> colossal head . It was found lying on its side to the south of a monumental throne . The monument was discovered at a depth of 5 metres ( 16 ft ) during a <unk> survey of the site in 1968 ; it has been dated to the Early <unk> . After discovery it was initially reburied ; it was moved to the <unk> de <unk> de <unk> in 1986 . The headdress is decorated with the <unk> or claws of either a jaguar or an eagle . It has a <unk> and a cover that descends from under the headdress proper behind the ears . Two short <unk> descend in front of the ears . The head sports large ear ornaments in the form of <unk> . The face is that of a mature male with <unk> <unk> and <unk> between these and the nose . The forehead is gathered in a <unk> . The mouth is slightly parted to reveal the teeth . Most of the head is carved in a realistic manner , the exception being the ears . These are stylised and represented by one question mark shape contained within another . The head is very well preserved and displays a fine finish . 
- San Lorenzo <unk> Head 9 is also known as San Lorenzo Monument 66 . It measures 1 @.@ 65 metres ( 5 @.@ 4 ft ) high by 1 @.@ 36 metres ( 4 @.@ 5 ft ) wide by 1 @.@ 17 metres ( 3 @.@ 8 ft ) deep . The head was exposed in 1982 by erosion of the <unk> at San Lorenzo ; it was found leaning slightly on its right hand side and facing upwards , half covered by the collapsed side of a gully and washed by a stream . Although it was documented by archaeologists , it remained for some time in its place of discovery before being moved to the <unk> de <unk> de <unk> . The headdress is of a single piece without a distinct <unk> . The sides display features that are possibly intended to represent long hair trailing to the bottom of the monument . The <unk> are rectangular plates with an additional <unk> element at the front . The head is also depicted wearing a nose @-@ ring . The face is smiling and has <unk> under the eyes and at the edge of the mouth . It has <unk> <unk> and wide eyes . The mouth is closed and the upper lip is badly damaged . The sculpture suffered some mutilation in antiquity , with nine pits <unk> into the face and headdress . 
- San Lorenzo <unk> Head 10 ( also known as San Lorenzo Monument 89 ) has been moved to the <unk> <unk> de San Lorenzo <unk> near <unk> . It stands 1 @.@ 8 metres ( 5 @.@ 9 ft ) tall and measures 1 @.@ 43 metres ( 4 @.@ 7 ft ) wide by 0 @.@ 92 metres ( 3 @.@ 0 ft ) deep ; it weighs 8 tons . The head was discovered by a <unk> survey in 1994 ; it was found buried , lying face upwards in the bottom of a ravine and was excavated by Ann <unk> . The headdress is formed of 92 circular beads that completely cover the upper part of the head and descend across the sides and back . Above the forehead is a large element forming a three @-@ toed foot with long nails , possibly the foot of a bird . The head wears large <unk> that <unk> beyond the beads of the headdress . The <unk> have the form of a rounded square with a circular sunken central portion . The face is that of a mature man with the mouth closed , <unk> <unk> and lines under the eyes . The mouth is <unk> carved and the head possesses a pronounced chin . 
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- = = = La <unk> = = = 
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- Three of the La <unk> heads were found in a line running east @-@ west in the northern Complex I ; all three faced northwards , away from the city centre . The other head was found in Complex B to the south of the Great Pyramid , in a plaza that included a number of other sculptures . The latter , the first of the La <unk> heads to be discovered , was found during archaeological exploration of La <unk> in 1925 ; the other three remained unknown to archaeologists until a local boy guided Matthew <unk> to them while he was excavating the first head in 1940 . They were located approximately 0 @.@ 9 kilometres ( 0 @.@ 56 mi ) to the north of Monument 1 . 
- La <unk> Monument 1 is speculated to have been the portrait of La <unk> 's final ruler . Monument 1 measures 2 @.@ 41 metres ( 7 @.@ 9 ft ) high by 2 @.@ 08 metres ( 6 @.@ 8 ft ) wide by 1 @.@ 95 metres ( 6 @.@ 4 ft ) deep ; it weighs 24 tons . The front of the headdress is decorated with three motifs that apparently represent the claws or fangs of an animal . Above these symbols is an angular U @-@ shaped decoration descending from the <unk> . On each side of the monument a strap descends from the headdress , passing in front of the ear . Each ear has a prominent ear ornament that descends from the <unk> to the base of the monument . The features are those of a mature man , with <unk> around the mouth , eyes and nose . Monument 1 is the best preserved head at La <unk> but has suffered from erosion , particularly at the back . The head was first described by Franz <unk> and Oliver La <unk> who investigated the La <unk> remains on behalf of Tulane University in 1925 . When discovered it was half @-@ buried ; its massive size meant that the <unk> were unable to excavate it completely . Matthew <unk> fully excavated the monument in 1940 , after clearing the thick vegetation that had covered it in the intervening years . Monument 1 has been moved to the <unk> @-@ <unk> La <unk> in <unk> . The head was found in its original context ; associated finds have been <unk> dated to between 1000 and 600 BC . 
- La <unk> Monument 2 measures 1 @.@ 63 metres ( 5 @.@ 3 ft ) high by 1 @.@ 35 metres ( 4 @.@ 4 ft ) wide by 0 @.@ 98 metres ( 3 @.@ 2 ft ) deep ; the head weighs 11 @.@ 8 tons . The face has a broadly smiling expression that reveals four of the upper teeth . The <unk> are given prominence by the action of smiling ; the brow that is normally visible in other heads is covered by the rim of the headdress . The face is badly eroded , <unk> the features . In addition to the severe erosion damage , the upper lip and a part of the nose have been deliberately <unk> . The head was found in its original context a few metres north of the northwest corner of pyramid @-@ platform A @-@ 2 . <unk> dating of the monument 's context dates it to between 1000 and 600 BC . Monument 2 has suffered erosion damage from its exposure to the elements prior to discovery . The head has a prominent headdress but this is badly eroded and any individual detail has been erased . A strap descends in front of the ear on each side of the head , descending as far as the <unk> . The head is adorned with ear ornaments in the form of a disc that covers the <unk> , with an associated clip or <unk> . The surviving details of the headdress and <unk> are <unk> similar to those of <unk> <unk> Monument A. The head has been moved to the <unk> del <unk> de <unk> in <unk> . 
- La <unk> Monument 3 stands 1 @.@ 98 metres ( 6 @.@ 5 ft ) high and measures 1 @.@ 6 metres ( 5 @.@ 2 ft ) wide by 1 metre ( 3 @.@ 3 ft ) deep ; it weighs 12 @.@ 8 tons . Monument 3 was located a few metres to the east of Monument 2 , but was moved to the <unk> @-@ <unk> La <unk> in <unk> . Like the other La <unk> heads , its context has been <unk> dated to between 1000 and 600 BC . It appears unfinished and has suffered severe damage through <unk> , making analysis difficult . It had a large headdress that reaches to the eyebrows but any details have been lost through erosion . <unk> descend in front of each ear and continue to the base of the monument . The ears are wearing large flattened rings that overlap the <unk> ; they probably represent <unk> ornaments of a type that have been recovered in the <unk> region . Although most of the facial detail is lost , the <unk> of the bridge of the nose is still evident , a feature that is common to the <unk> expressions of the other <unk> colossal heads . 
- La <unk> Monument 4 measures 2 @.@ 26 metres ( 7 @.@ 4 ft ) high by 1 @.@ 98 metres ( 6 @.@ 5 ft ) wide and 1 @.@ 86 metres ( 6 @.@ 1 ft ) deep . It weighs 19 @.@ 8 tons . It was found a few metres to the west of Monument 2 and has been moved to the <unk> @-@ <unk> La <unk> . As with the other heads in the group , its archaeological context has been <unk> dated to between 1000 and 600 BC . The headdress is elaborate and , although damaged , various details are still discernible . The base of the headdress is formed by three horizontal strips running over the forehead . One side is decorated with a double @-@ disc motif that may have been repeated on the other ; if so , damage to the right side has obliterated any trace of it . The top of the headdress is decorated with the <unk> foot of a bird of prey . Either <unk> or <unk> of hair descend on either side of the face , from the headdress to the base of the monument . Only one <unk> survives ; it is flat , in the form of a rounded square , and is decorated with a cross motif . The ears have been completely eroded away and the lips are damaged . The surviving features display a <unk> and <unk> around the nose and <unk> . The head displays prominent teeth . 
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- = = = <unk> <unk> = = = 
- 
- The two heads at <unk> <unk> , with the La <unk> head , are <unk> distinct from the other known examples . <unk> de la <unk> views them as a late regional survival of an older tradition while other scholars argue that they are merely the kind of regional variant to be expected in a frontier settlement . These heads are sculpted with relatively simple <unk> ; they have squat , wide proportions and distinctive facial features . The two <unk> <unk> heads are the earliest known stone monuments from the site . The discovery of one of the <unk> <unk> heads in the nineteenth century led to the first archaeological investigations of <unk> culture , carried out by Matthew <unk> in 1938 . 
- <unk> <unk> Monument A ( also known as <unk> <unk> <unk> Head 1 ) was the first colossal head to be found , discovered by accident in the middle of the nineteenth century , 1 kilometre ( 0 @.@ 62 mi ) to the north of the modern village of <unk> <unk> . After its discovery it remained half @-@ buried until it was excavated by Matthew <unk> in 1939 . At some point it was moved to the plaza of the modern village , probably in the early 1960s . It has since been moved to the <unk> <unk> de <unk> <unk> . Monument A stands 1 @.@ 47 metres ( 4 @.@ 8 ft ) tall ; it measures 1 @.@ 5 metres ( 4 @.@ 9 ft ) wide by 1 @.@ 45 metres ( 4 @.@ 8 ft ) deep , and is estimated to weigh 7 @.@ 8 tons . The head is sculpted with a simple headdress with a wide band that is otherwise unadorned , and wears rectangular ear ornaments that project forwards onto the <unk> . The face is carved with deep <unk> between the <unk> and the nose and around the mouth ; the forehead is <unk> into a <unk> . The upper lip has suffered recent damage , with the left portion <unk> away . 
- <unk> <unk> Monument Q ( also known as the <unk> Head and <unk> <unk> <unk> Head 2 ) measures 1 @.@ 45 metres ( 4 @.@ 8 ft ) high by 1 @.@ 34 metres ( 4 @.@ 4 ft ) wide by 1 @.@ 26 metres ( 4 @.@ 1 ft ) deep and weighs 8 @.@ 5 tons . Its exact date of discovery is unknown but is estimated to have been some time in the 1940s , when it was struck by machinery being used to clear vegetation from <unk> hill . Monument Q was the eleventh colossal head to be discovered . It was moved to the plaza of Santiago <unk> in 1951 and remains there to this day . Monument Q was first described by Williams and <unk> in an article published in 1965 . The headdress is decorated with a frontal tongue @-@ shaped ornament , and the back of the head is sculpted with seven <unk> of hair bound with <unk> . A strap descends from each side of the headdress , passing over the ears and to the base of the monument . The face has pronounced <unk> around the nose , mouth and eyes . 
- 
- = = = La <unk> = = = 
- 
- The La <unk> region was the source of the basalt used for carving all of the colossal heads in the <unk> <unk> . The La <unk> colossal head was discovered in 1970 and was the fifteenth to be recorded . It was discovered in a mountain pass in the Sierra de los <unk> , on the north side of El <unk> volcano near to Santiago <unk> . The head was largely buried when found ; excavations uncovered a Late Classic ( 600 – 900 AD ) offering associated with the head consisting of a ceramic vessel and a 12 @-@ <unk> ( 4 @.@ 7 in ) long <unk> knife placed pointing northwards towards the head . The offering is believed to have been deposited long after the head was sculpted . The La <unk> head has been moved from its original location to the main plaza at Santiago . 
- The La <unk> head is more or less rounded and measures 3 by 3 metres ( 9 @.@ 8 by 9 @.@ 8 ft ) by 3 @.@ 4 metres ( 11 ft ) high , making it the largest known head . This massive sculpture is estimated to weigh 40 tons . It is <unk> distinct from the other examples , and <unk> de la <unk> placed it late in the <unk> time frame . The characteristics of the sculpture have led to some investigators suggesting that it represents a deceased person . Norman Hammond argues that the apparent stylistic differences of the monument stem from its unfinished state rather than its late production . The eyes of the monument are closed , the nose is flattened and lacks nostrils and the mouth was not sculpted in a realistic manner . The headdress is in the form of a plain horizontal band . 
- The original location of the La <unk> head was not a major archaeological site and it is likely that the head was either abandoned at its source or during transport to its intended destination . Various features of the head suggest that it was unfinished , such as a lack of symmetry below the mouth and an area of rough stone above the base . Rock was not removed from around the <unk> as on other heads , and does not narrow towards the base . Large parts of the monument seem to be <unk> out without finished detail . The right hand <unk> also appears incomplete ; the forward portion is marked with a sculpted line while the rear portion has been sculpted in relief , probably indicating that the right cheek and eye area were also unfinished . The La <unk> head was almost certainly carved from a raw <unk> rather than being sculpted from a throne . 
- 
- = = = <unk> <unk> = = = 
- 
- <unk> <unk> Monument 23 dates to the Middle <unk> period , and is found in <unk> <unk> , an important city in the foothills of the <unk> Pacific coast , in the modern department of <unk> . It appears to be an <unk> @-@ style colossal head re @-@ carved into a niche figure sculpture . If originally a colossal head then it would be the only known example from outside the <unk> <unk> . 
- Monument 23 is sculpted from <unk> and falls in the middle of the size range for confirmed colossal heads . It stands 1 @.@ 84 metres ( 6 @.@ 0 ft ) high and measures 1 @.@ 2 metres ( 3 @.@ 9 ft ) wide by 1 @.@ 56 metres ( 5 @.@ 1 ft ) deep . Like the examples from the <unk> <unk> , the monument features a flat back . Lee Parsons contests John Graham 's identification of Monument 23 as a re @-@ carved colossal head ; he views the side ornaments , identified by Graham as ears , as rather the <unk> eyes of an open @-@ <unk> monster <unk> upwards . <unk> this , James Porter has claimed that the re @-@ carving of the face of a colossal head into a niche figure is clearly evident . 
- Monument 23 was damaged in the mid @-@ twentieth century by a local <unk> who attempted to break its exposed upper portion using a steel <unk> . As a result , the top is fragmented , although the broken pieces were recovered by archaeologists and have been put back into place . 
- 
- = = Collections = = 
- 
- All of the 17 confirmed colossal heads remain in Mexico . Two heads from San Lorenzo are on permanent display at the <unk> <unk> de <unk> in Mexico City . Seven of the San Lorenzo heads are on display in the <unk> de <unk> de <unk> . Five of them are in <unk> 1 , one is in <unk> 2 and one is in <unk> 1 . The remaining San Lorenzo head is in the <unk> <unk> de San Lorenzo <unk> near <unk> . All four heads from La <unk> are now in <unk> , the state capital of <unk> . Three are in the <unk> @-@ <unk> La <unk> and one in the <unk> del <unk> de <unk> . Two heads are on display in the plaza of Santiago <unk> ; one from <unk> <unk> and the La <unk> Head . The other <unk> <unk> head is in the <unk> <unk> de <unk> <unk> . 
- Several colossal heads have been loaned to temporary exhibitions abroad ; San Lorenzo <unk> Head 6 was loaned to the Metropolitan Museum of Art in New York in 1970 . San Lorenzo colossal heads 4 and 8 were lent to the <unk> Art of Ancient Mexico exhibition in the National Gallery of Art , Washington , D.C. that ran from 30 June to 20 October 1996 . San Lorenzo Head 4 was again loaned in 2005 , this time to the de Young Museum in San Francisco . The de Young Museum was loaned San Lorenzo colossal heads 5 and 9 for its <unk> : <unk> <unk> of Ancient Mexico exhibition , which ran from 19 February to 8 May 2011 . 
- 
- = = = <unk> = = = 
- 
- On 12 January 2009 , at least three people , including two <unk> and one American , entered the <unk> @-@ <unk> La <unk> in <unk> and damaged just under 30 archaeological pieces , including the four La <unk> colossal heads . The <unk> were all members of an evangelical church and appeared to have been carrying out a supposed pre @-@ Columbian ritual , during which salts , grape juice and oil were thrown on the heads . It was estimated that 300 @,@ 000 pesos ( US $ 21 @,@ 900 ) would be needed to repair the damage , and the restoration process would last four months . The three <unk> were released soon after their arrest after paying 330 @,@ 000 pesos each . 
- 
- = = = <unk> = = = 
- 
- Although not all of the replicas were placed by him , the majority of replicas around the world were placed under the leadership of Miguel <unk> <unk> , former governor of the state of Veracruz . The following is a list of replicas and their locations within the United States : 
- Austin , Texas . A replica of San Lorenzo Head 1 was placed in the Teresa <unk> Long Institute of Latin American Studies at the University of Texas in November 2008 . 
- Chicago , Illinois . A replica of San Lorenzo Head 8 made by <unk> <unk> <unk> was placed in the Field Museum of Natural History in 2000 . 
- <unk> , California . A replica of San Lorenzo Head 5 was donated to <unk> in 1989 , originally intended to be placed in <unk> Park . Due to concerns over potential <unk> it was instead installed outside the police station . It was removed in 2011 and relocated to <unk> 's Glen , <unk> Park in June 2012 . 
- <unk> , Texas . A replica of San Lorenzo Head 8 is located in the International Museum of Art & Science . The specific date placement is unknown , but it was dedicated by <unk> Herrera <unk> , then governor of Veracruz , during his time in office between 2004 – 2010 . 
- New York . A replica of San Lorenzo Head 1 was placed next to the main plaza in the grounds of <unk> College in the Bronx , New York . It was installed in 2013 to celebrate the first anniversary of the <unk> Institute of Mexican Studies , housed at the college . The replica was a gift by the government of <unk> state , <unk> <unk> and Mexico Trade ; it was first <unk> in <unk> <unk> Park , outside the United Nations , in 2012 . 
- San Francisco , California . A replica of San Lorenzo Head 1 created by <unk> <unk> <unk> was placed in San Francisco City College , Ocean Campus in October 2004 . 
- Washington , D.C. A replica of San Lorenzo Head 4 sculpted by <unk> <unk> <unk> was placed near the Constitution Avenue entrance of the Smithsonian National Museum of Natural History in October 2001 . 
- West Valley City , Utah . A replica of San Lorenzo Head 8 was placed in the Utah Cultural Celebration Center in May 2004 . 
- Mexico donated a resin replica of an <unk> colossal head to Belgium ; it is on display in the Musée du <unk> in Brussels . 
- In February 2010 , the Mexican <unk> de <unk> <unk> ( <unk> of Foreign Affairs ) announced that the <unk> <unk> de <unk> e Historia would be <unk> a replica <unk> colossal head to Ethiopia , to be placed in Plaza Mexico in <unk> <unk> . 
- 
- 
- = Brad Stevens = 
- 
- Bradley Kent " Brad " Stevens ( born October 22 , 1976 ) is an American professional basketball head coach for the Boston Celtics of the NBA . He was previously the head coach at Butler University in Indianapolis . A former basketball player , he grew up in <unk> , Indiana , where he starred on the <unk> Community High School basketball team , setting four school records . After high school , he attended <unk> University , where he played basketball and earned a degree in economics . He made the all @-@ conference team multiple times and was a three @-@ time Academic All @-@ America nominee . 
- Stevens joined the Butler basketball program as a volunteer prior to the 2000 – 01 season after quitting his job at <unk> Lilly and Company . He was promoted to a full @-@ time assistant coaching position for the 2001 – 02 season . On April 4 , 2007 , he became the head coach after Todd <unk> left to coach the Iowa <unk> . In his first year , Stevens led Butler to 30 wins , becoming the third @-@ youngest head coach in NCAA Division I history to have a 30 @-@ win season . 
- In 2010 , his third year as head coach , Stevens broke the NCAA record for most wins in a coach 's first three years , exceeding the previous record by eight . In the postseason , Stevens coached Butler to the first Final Four in school history . At 33 years old , Stevens became the second @-@ youngest head coach to make a NCAA National Championship game , losing 61 – 59 to Duke . Shortly after the season ended , he signed a contract extension with Butler through the 2011 – 12 season . With the 2010 – 11 team making the Final Four , Stevens became the youngest coach to go to two Final <unk> . Stevens coached the Bulldogs in their second consecutive national championship game on April 4 , 2011 , where the team lost to the <unk> of the University of Connecticut . 
- Stevens is known for a calm , focused coaching style . He spends a lot of time <unk> opponents using statistical analysis , adding new <unk> to his team 's play each game . He puts a strong emphasis on defensive and team oriented basketball . Butler 's success against teams with superior athletes has been attributed to Stevens ' coaching style and calm demeanor . Stevens has twice been named the Horizon League Coach of the Year and won <unk> 's Hugh Durham Award mid @-@ season honors in January 2009 . He has also been both a Hugh Durham Award and Jim <unk> Award <unk> all three years of his career . Stevens has been called a coaching <unk> and compared to John Wooden . He is married with two young children . In July 2013 , he signed a six @-@ year , 22 million dollar contract to become the head coach of the Boston Celtics in the NBA . In April 2015 , Stevens led the Celtics to the NBA <unk> as the 7th seed in the Eastern Conference with a 40 – 42 record . 
- 
- = = Early life = = 
- 
- Bradley Kent Stevens grew up in <unk> , Indiana , where he developed his love for basketball . Starting at age five , Stevens would watch taped basketball games " before he went to afternoon kindergarten " . His father would often drive him to <unk> , to watch Indiana <unk> games . " It 's hard not to be [ in love with basketball ] when you 're a kid growing up in Indiana " , Stevens later said . 
- For his eighth birthday , Stevens received a new basketball <unk> . " It ’ s so much fun to dream in your <unk> , " he later remarked . " That ’ s where my friends and I hung out . It was a lot of fun to grow up in that era . " When a friend , Brandon <unk> , had a basketball court installed in his back yard , Stevens " appeared <unk> . " He was so dedicated to the game that he would bring the unprepared ingredients for <unk> cheese sandwiches over to <unk> 's house , so that he would not waste time waiting for the sandwiches to cook . 
- <unk> 's court soon became a gathering place , where <unk> kids and kids from the surrounding areas would hold <unk> games . These games helped develop Stevens ' competitive streak . Besides playing basketball , the young Stevens also enjoyed solving puzzles , a skill he later applied to <unk> opposing teams to find their weaknesses . 
- Stevens attended <unk> Community High School , where he became a star basketball player . He wore No. 31 in high school in honor of Indiana <unk> guard <unk> Miller . During his freshman year , he would get up early to practice shooting at a local gym before school . The hard work paid off as Stevens made the varsity team that same year . By the time his high school career was complete , Stevens had set school records for career scoring , assists , steals , and three @-@ point field goals . As of 2010 , he still holds the records for points ( <unk> ) , assists ( <unk> ) , and steals ( 156 ) , as well as the single @-@ season points record ( <unk> in 1995 ) . Stevens was named to the all @-@ conference team three times . In 1995 , he was named the <unk> MVP and was the leading scorer in state <unk> play ( 32 @.@ 3 ppg ) . 
- Stevens made the academic all @-@ state first team and received the <unk> A Gold Medal Award all four years . He was a member of the National Honor Society , graduating seventh in his class of 165 . He earned three letters in basketball , three in track , and one in baseball during his days at <unk> . During summers , he traveled the country playing <unk> basketball . 
- Although Stevens had a strong passion for the game , he realized that his basketball skills were modest and not likely to get him very far . As such , he chose to attend <unk> oriented <unk> University for college . During his stay , he played in all 101 <unk> games , earning four varsity letters . He earned multiple all @-@ conference and academic all @-@ conference awards , and was a three @-@ time Academic All @-@ America nominee . He was a team captain his senior year , and averaged more than 8 points per game three of his four years . His career highs were 24 points and 8 rebounds in a game . After his senior year , Stevens received the Coaches ’ Award . Coach Bill <unk> later described Stevens as " one of the most <unk> , team @-@ oriented person [ sic ] I 've ever been around . " 
- While at <unk> , Stevens was a member of the Management <unk> Honors Program and the <unk> Community Services ’ Sports Night executive board . He was also a brother of the Alpha <unk> <unk> <unk> . During summer vacations , Stevens spent time teaching at Butler basketball camps . He was named to the Dean 's list and graduated in 1999 with a degree in economics . 
- 
- = = College career = = 
- 
- In the summer of 2000 , Stevens was offered the opportunity to volunteer in the Butler basketball office . He ran the idea of quitting his job at <unk> Lilly by then @-@ longtime girlfriend Tracy <unk> . She thought about it for two hours before telling him to go for it . " Now , it looks like a great idea , " Stevens later remarked . " At the time , I thought it was something I really wanted to try . " Tracy went back to school to get a law degree that could support the couple if things did not work out for Brad . " We were 23 and realized this was our chance , " Tracy later said . " Five years down the road , we were probably not going to be in a position to do that . The more success you had at Lilly , the harder it would be to leave . " 
- Stevens planned to live in a friend 's basement and took a job at <unk> ’ s to pay the bills . Before he started training at <unk> 's , he was offered a low @-@ paying administrative position as coordinator of basketball operations under then @-@ coach <unk> <unk> . The position had opened up when assistant coach Jamal <unk> resigned after being arrested on <unk> and drug charges , of which he was later acquitted . Years later , <unk> recalled , " [ Stevens ] was just a hungry young kid that was desperate to get into coaching . He had a great passion and was willing to take a risk to get into the coaching profession . " 
- After <unk> left the school following the 2000 – 01 season , new head coach Todd <unk> promoted Stevens to a full @-@ time assistant coach . Under <unk> , Stevens was active in every aspect of the game : skills instruction , game preparation , in @-@ game coaching , and recruiting . Butler was 131 – 61 during Stevens ' time as an assistant coach . 
- 
- = = = Named head coach = = = 
- 
- On April 2 , 2007 , <unk> resigned in order to take the head @-@ coaching position at the University of Iowa . The Butler players had a meeting with athletic director Barry <unk> , urging him to promote from within . <unk> , having spent the entire season observing the assistant coaches ' interaction with the team , agreed . The day after <unk> resigned Stevens and Butler 's two other assistant coaches interviewed for the job . Within 24 hours of the interviews Stevens was named Butler 's new head coach . According to <unk> , Stevens had something older , outside candidates could never match : six years of experience learning the Butler system , dubbed " The Butler Way " by <unk> . " Age wasn 't a factor because I 'd seen his ability shine through during the course of the season , " <unk> said . 
- 
- = = = 2007 – 08 season = = = 
- 
- At the start of the 2007 – 08 season , Stevens was the second youngest coach in Division I basketball . He got off to a fast start , winning his first eight games before falling to Wright State 43 – 42 . <unk> coach Bob Knight , whose Texas Tech team was an early victim , said " I wish we played as smart as they do . " Virginia Tech coach Seth Greenberg added " they 've got <unk> about them and they expect to win . " 
- Midway through Stevens ' first season , with the Bulldogs at 12 – 1 , The New York Times wrote " so far , Stevens has made the transition [ to head coach ] look easy . " The Times went on to state that Stevens had the calm and <unk> of a <unk> veteran . " You ’ ve got a lot of people always looking for the next step . And that ’ s not what I was doing . I was just trying to figure out a way to win the next game and think like a head coach . " Stevens said . 
- Butler ended the regular season with a 27 – 3 record , taking first place in the Horizon League with a 16 – 2 in conference mark . The team beat Illinois @-@ Chicago 66 – 50 and Cleveland State 70 – 55 to claim the league 's tournament title and an automatic bid to the 2008 NCAA tournament . Butler was awarded the seven seed in the East Regional . They beat tenth @-@ <unk> South Alabama 81 – 61 in the first round , before falling to second @-@ <unk> Tennessee 76 – 71 in overtime . 
- Stevens ended up with a school and Horizon league record 30 wins , beating several big name schools – Michigan , Texas Tech , Florida State , Ohio State – along the way . In so doing , he became the third @-@ youngest head coach in NCAA Division I history to lead a team to 30 wins in a season , and became the fourth @-@ <unk> first @-@ year coach . Butler was nationally ranked for a school and league record 19 consecutive weeks . Butler 's 30 – 4 record was the best among teams that did not reach the Final Four . Stevens was a <unk> for the Hugh Durham Award , losing to <unk> Davis of Drake , and a <unk> for the Jim <unk> National Coach of the Year Award , losing to Bo Ryan . 
- At the conclusion of the season , Butler signed Stevens to a seven @-@ year contract . " We are extremely excited to reach this long @-@ term agreement to have Brad continue to lead our program , " <unk> remarked . 
- 
- = = = 2008 – 09 season = = = 
- 
- Butler lost four starters after the 2007 – 08 season , and was picked to finish fifth in the Horizon league during the 2008 – 09 season . The team got off to a 12 – 1 start that won Stevens the Hugh Durham mid @-@ season coaching award . On February 5 , Stevens notched his 50th win as Butler beat Detroit 66 – 61 . In so doing , Stevens became the sixth head coach in NCAA history to reach 50 wins in 56 games or fewer . Butler finished first in the Horizon League with a 15 – 3 in conference record , <unk> preseason expectations . Butler lost the Horizon League tournament final 57 – 54 to Cleveland State , but made the NCAA tournament as an at @-@ large selection . The team received the nine seed in the South Regional , and lost to eighth @-@ <unk> Louisiana State in the first round by a score of 75 – 71 to finish the year at 26 – 6 overall . 
- Stevens ' 56 – 10 two @-@ year record places him second only to Bill <unk> ( 58 ) in total wins during one 's first two years as head coach . Stevens was a <unk> for both the Hugh Durham and Jim <unk> Awards for the second straight year and was named the Horizon League Coach of the Year . He was also named as a <unk> for the Henry <unk> Coach of the Year Award . Stevens was given a one @-@ year contract extension at the conclusion of the season . 
- 
- = = = 2009 – 10 season = = = 
- 
- <unk> in large part by Gordon <unk> 's and <unk> Mack 's roles in leading Team USA to the gold medal in the FIBA Under @-@ 19 World Championship during the off @-@ season , Butler began the season ranked 10th in the Coaches ' Poll and 11th in the AP Poll . A few commentators picked the Bulldogs as a possible " <unk> team " to make the Final Four . Stevens was not so sure , privately telling his father , " We have a really good team , and I ’ m not sure how far we can go this year , but next year , we ought to go really far . " 
- Butler got off to a mediocre start , losing twice in the 76 Classic 82 – 73 to 22nd @-@ ranked Minnesota and to 19th @-@ ranked Clemson 70 – 69 . After the tournament Butler 's record stood at 4 – 2 and the team dropped to # 23 in the AP Poll and # 20 in the Coaches ' Poll . Butler won its next two games before falling to 13th @-@ ranked Georgetown 72 – 65 in the Jimmy V Classic . The team won its next two games beating # 15 Ohio State 74 – 66 and edging out former conference rival Xavier 69 – 68 , both at home . After losing 67 – 57 at <unk> three days later , Butler stood at 9 – 4 and fell out of the AP rankings . However , the team remained in the Coaches Poll at # 23 . 
- Stevens rallied the team , and they proceeded to win 16 straight games before facing <unk> in a <unk> game . Butler beat <unk> 70 – 53 and Stevens tied the NCAA record for most wins ( 81 ) by a head coach in his first three seasons set by Mark Few of <unk> in 2002 and tied by Mark Fox of Nevada in 2007 . 
- On February 26 , 2010 , Butler traveled to <unk> for their regular season finale . Leading scorer Gordon <unk> was sidelined with lower back pain , but the team still won 74 – 69 . In so doing , Stevens broke the coaching record he had tied the prior week and Butler completed an 18 – 0 undefeated conference schedule . It was Butler 's first undefeated conference record since joining the Horizon League , and first since Joe <unk> led the 1978 team to a 6 – 0 record in the now defunct Indiana Collegiate Conference . Stevens earned his third straight regular @-@ season conference championship . 
- In the Horizon league tournament , Stevens ' Bulldogs used their home @-@ court advantage to beat Milwaukee 68 – 59 in the semi @-@ finals and to beat Wright State 70 – 45 in the finals . The win earned the team an automatic bid into the 2010 NCAA tournament , and completed a 20 – 0 run through league play . Stevens became the first coach to lead a Horizon League team to both an undefeated regular season and conference tournament since the league was formed in 1979 . Stevens was also the only coach in Division I to lead his team to an undefeated conference schedule during the 2009 – 10 season . 
- 
- = = = = NCAA tournament = = = = 
- 
- For their season , the Bulldogs were ranked 8th in the final pre @-@ NCAA tournament Coaches ' Poll and 11th in the corresponding AP Poll . On Selection Sunday , the Bulldogs were <unk> fifth in the West regional of the NCAA tournament and given a first @-@ round match up with twelfth <unk> <unk> on March 18 . 
- Many basketball commentators picked <unk> to pull the upset , and at halftime it looked like they might be right , as <unk> led 33 – 27 . Stevens made a number of halftime adjustments , and the Bulldogs came out firing on all cylinders in the second half . The team dominated the second half and won the game 77 – 59 . Butler next faced off with thirteenth <unk> Murray State . The game was close throughout , but Butler emerged victorious 54 – 52 when <unk> <unk> a Murray State pass into the back court with less than five seconds on the clock . The win gave Stevens the first Sweet Sixteen appearance of his career . 
- On March 25 , 2010 , Butler faced top @-@ <unk> Syracuse . The Bulldogs got off to a good start , jumping out to a 12 – 1 lead and a 35 – 25 halftime advantage . Syracuse rallied in the second half , taking its first lead of the game , 40 – 39 , off a Wes Johnson three @-@ pointer . Stevens called timeout and Butler regained the lead on its next possession , stopping the run . At the 5 : 32 mark , Syracuse got a rare fast break opportunity that ended with a dunk and 54 – 50 lead . Stevens again called time out and re @-@ focused the team . Butler responded by holding Syracuse scoreless for the next 5 minutes , taking a 60 – 54 lead with 0 : 59 to go . Butler held on to win 63 – 59 , advancing to the <unk> Eight for the first time in school history . 
- Two days later , Stevens ' Bulldogs met second @-@ <unk> Kansas State in the regional finals . Perhaps feeling the effects of their double overtime 101 – 96 win two days prior , Kansas State got off to a slow start , scoring just 20 points in the first half to trail 27 – 20 . Butler kept the lead in the upper single digits for most of the second half , before Kansas State went on a 13 – 2 run and took a 52 – 51 lead . Stevens immediately called time out and re @-@ focused the team . " Play your game . Just play your game , " he told them . On the ensuing possession , Butler regained the lead for good . They <unk> Kansas State 12 – 4 the rest of the way and won the game 63 – 56 . In the post game celebration , Stevens and walk @-@ on forward Emerson <unk> connected on a flying back @-@ bump that became one of the iconic images of the tournament . 
- The win earned the Bulldogs a trip back to Indianapolis for the first Final Four appearance in school and Horizon League history . The win made Stevens , at age 33 , the youngest coach to lead a team to the Final Four since Bob Knight made his first Final Four appearance at age 32 in 1973 . Butler became the smallest school ( enrollment 4 @,@ 200 ) to make the Final Four since <unk> began in 1979 . 
- 
- = = = = = Final Four = = = = = 
- 
- On April 3 , Brad Stevens and the Butler Bulldogs faced off with Michigan State in the national semi @-@ finals . Michigan State took an early 14 – 7 lead , and Matt Howard got in early foul trouble , sitting most the first half . Stevens kept the team focused with a " next man up " attitude and the game was tied at 28 at halftime . The second half was dominated by tight defense for both sides . With 2 : 45 to go in the game , the score was 47 – 44 Butler . Michigan State called a time out to set up a play . Stevens correctly anticipated the play call and had Ronald <unk> , the team 's best defender , switch onto <unk> <unk> off a screen . <unk> stole the ball and Shawn <unk> got fouled on the resulting run out , hitting 1 of 2 . <unk> 50 – 49 with under 30 seconds remaining , Michigan State came up empty and was forced to foul . <unk> hit both foul shots , giving Butler a 52 – 49 lead . After a Michigan State time out , Stevens had his team foul <unk> with 2 seconds remaining to prevent a potentially game tying 3 @-@ pointer . After making the first , <unk> intentionally missed the second free throw . <unk> came down with the rebound to seal the victory . Butler became the first team since the shot clock was adopted for the 1985 – 86 season to hold five straight tournament opponents under 60 points . 
- On April 5 , 2010 , Butler and Duke faced off in what The New York Times called " the most eagerly awaited championship game in years " . Late in the first half , Duke went on an 8 – 0 run to take a 26 – 20 lead . Stevens called a timeout . With starters Matt Howard and Ronald <unk> on the bench in foul trouble , Stevens was forced to call on backup center Avery <unk> who came up big for Butler . <unk> scored 10 first half points , tying his season high . At half time , Duke 's lead stood at 33 – 32 . 
- The second half was played very closely , with neither team taking a substantial lead . With 3 : 16 to play , Duke took a 60 – 55 lead on two made free throws by Nolan Smith . Butler cut the lead to one point in the final minute and , after a missed Kyle <unk> jump shot with 36 seconds remaining , got a chance to retake the lead . Butler was unable to initiate their offense and Stevens called a timeout to set up a play . A failed <unk> attempt and a timeout later , <unk> missed a baseline fade @-@ away <unk> and Brian <unk> came down with the rebound for Duke . He was quickly fouled with less than 4 seconds remaining . <unk> narrowly missed a <unk> half @-@ court shot court as time expired , making the final margin 61 – 59 . 
- The loss snapped Butler 's 25 @-@ game winning streak , the longest in school history . Butler became the smallest school to play for a National Championship since Jacksonville in 1970 . Stevens became the second @-@ youngest head coach to coach in the NCAA National Championship Game , behind Branch <unk> who led the Indiana <unk> to the 1940 National Championship at age 31 . Stevens was named as both a Hugh Durham and Jim <unk> Award <unk> for the third consecutive year , losing to Mike Young and Jamie Dixon respectively . He was also a <unk> for the <unk> Prosser Man of the Year Award , which was won by Bob <unk> . 
- Butler finished the year ranked # 2 in the Coaches ' Poll , the highest ranking in school history . The school was ranked for 19 consecutive weeks , tying the school record . 
- 
- = = = 2010 off @-@ season = = = 
- 
- After the end of the 2009 – 10 season , Brad Stevens and Butler continued to attract considerable attention . President Barack Obama personally called Stevens to <unk> him on Butler 's season . David Letterman had Stevens on his show for a guest appearance . Butler admissions inquiries shot up 67 % . Stevens received fan letters from around the world , and his phone rang off the hook . He was invited to throw the ceremonial first pitch before the Chicago Cubs vs. Florida Marlins game in Chicago on May 10 . " It 's all been very surreal , " Stevens said . " If you are the runner @-@ up , you don 't expect to talk to the president . " " It 's been a little overwhelming , because I 'm a pretty simple guy , " he added . 
- The 2009 – 10 season also helped increase Butler 's recruiting profile . Asked if the increased fame would change things , Stevens said it better not <unk> him or the university . " I look at this new challenge of not changing and sticking to your core values and making sure you remain humble as a great coaching opportunity . " 
- 
- = = = 2010 – 11 season = = = 
- 
- Rankings by ESPN 's Andy Katz and Fox Sports ' Jeff Goodman released shortly after the 2010 Championship game both had Butler third for the 2010 – 2011 season . Duke coach Mike <unk> agreed , saying Butler would be " right up there , No. 1 or No. 2 ... They 'll be a favorite next year . " However , <unk> chose to leave early for the NBA Draft and Butler went through a rough patch early in the season , at one point losing three straight games and having a 6 – 5 conference record . <unk> by the emergence of Andrew Smith at center and Matt Howard 's success as a perimeter forward , Butler ended up winning a share of the conference title at 13 – 5 . The Bulldogs then won the Horizon League Tournament to secure an automatic NCAA tournament bid , and received an 8 seed . 
- <unk> by many to lose a first @-@ round match @-@ up against Old Dominion , Butler advanced on a last @-@ second tip @-@ in by Howard . Howard was also clutch in their next game , hitting a free throw with a less than one second remaining to beat Pitt in a dramatic finish . <unk> Mack scored 30 points in the win . Butler won their next game when they defeated Wisconsin . On March 26 , 2011 , the Bulldogs beat Florida 74 – 71 in overtime to earn back @-@ to @-@ back trips to the Final Four . On April 2 , Butler beat fellow Cinderella team <unk> 70 – 62 to make it to a second consecutive national championship game . For the second consecutive year , the Bulldogs fell in the national championship game , this time to Connecticut . 
- 
- = = = Coaching future = = = 
- 
- On April 8 , 2010 , Stevens signed a long @-@ term deal with Butler , extending his contract through the <unk> – 22 season . Financial terms of the contract were not disclosed ; however , Butler president Bobby <unk> had publicly stated that the university could afford to increase Stevens ' base salary to approximately US $ 1 @,@ 000 @,@ 000 a few days prior . Stevens had previously made US $ 395 @,@ 000 plus benefits in base salary , a relatively low figure for a successful Division I head basketball coach . His total compensation for 2009 – 10 was estimated at US $ 750 @,@ 000 . Stevens had received a raise after each of his three seasons at Butler and his contract contains a buyout clause estimated in the high six or low seven figures . 
- By re @-@ signing with Butler , Stevens temporarily ended speculation that he would leave the university for a higher paying job . Oregon , Clemson , and Wake Forest were all said to be interested in offering Stevens multi @-@ million dollar contracts to leave Butler . " First and foremost , I ’ m loyal to Butler , " Stevens said . When asked if he would ever leave Butler , Stevens replied " I guess if they kicked me out . " 
- After the 2011 – 12 season , Stevens was pursued vigorously by Illinois to fill their coaching vacancy before he declined their offer . 
- In March 2013 , UCLA reportedly offered Stevens between $ 2 @.@ 5 and $ 3 million a year to leave Butler . <unk> circulated that Stevens was in contract negotiations with UCLA , but ultimately the rumors proved false and Stevens stayed at Butler . Commenting on the situation , a source close to Stevens said " Brad doesn 't understand why people would assume he 's leaving . " A few days later , Stevens reiterated that he was very happy at Butler and had no intentions to leave as long as he had the support of the university to continue running the program the " right way " . 
- 
- = = NBA = = 
- 
- 
- = = = Boston Celtics = = = 
- 
- On July 3 , 2013 , Stevens was signed as the head coach by the Boston Celtics . Reports state that his new contract is a six @-@ year , $ 22 million deal . In April 2015 , Stevens led the Celtics to the NBA <unk> as the 7th seed in the Eastern Conference with a 40 – 42 record . On April 21 , 2015 , it was announced that Stevens finished fourth in voting for the NBA 's Coach of the Year Award . On March 1 , 2016 , Stevens was named the Eastern Conference Coach of the Month for games played during February . In April 2016 , Brad Stevens led the Celtics to their second consecutive playoff appearance under his tenure as the 5th seed in the 2016 NBA <unk> finishing the season with a 48 – 34 record . On June 1 , 2016 , Stevens received a contract extension . 
- 
- = = Coaching style = = 
- 
- According to Stevens , in one of his first games as head coach , he was nervous and " felt like our team played on edge " because of it . He decided that a team 's play will reflect the mood of its coach ; a calm coach means a team that will remain poised in difficult game situations , while a nervous coach means a team that plays on edge . " I don ’ t want to lose a game because of my approach , " he told himself . Accordingly , he developed a strategy of always remaining calm and focused during games . He rarely raises his voice or gets emotional , instead quietly observing on the <unk> with folded arms . He does not get upset about bad calls by <unk> or player mistakes , preferring to focus on " the next play " rather than what just happened . Butler player Willie <unk> explained Butler 's 2010 Final Four run by saying , " When those big runs [ by Syracuse and Kansas State ] came , Coach called a timeout and said a few calm words . Then he said he believes in us , he loves us and we 're going to win the game . " On the rare occasion Stevens feels the need to correct a player , he does it with " positive reinforcement , just at a little louder <unk> " , according to former assistant coach Matthew Graves . Above all , Stevens wants his players to be confident , not living in fear of being <unk> for making a bad play . 
- <unk> , Stevens is always calm , but internally he is far from it . " I 'm not as calm as everybody thinks , " Stevens says . His wife Tracy adds , " He ’ s calm and collected , but he ’ s fiercely competitive . He ’ s always thinking about how he can beat you . " Former player Joel <unk> says " Everyone sees Brad as a level @-@ headed , calm and cool coach , but he ’ s about as competitive of a guy as I know . We would get into it constantly , whether playing two @-@ on @-@ two or arguing about players ’ having better college careers . " 
- Stevens spends a lot of time preparing for each game , and always tries to add a few new <unk> specific to that game 's opponent . Sports Illustrated calls Stevens an expert " on breaking down tape and looking at statistical trends to find opponents ' weaknesses . " Former player Ronald <unk> agrees : " We know everything we need to about our opponents , all their tendencies are broken down " ahead of time . 
- Stevens is a proponent of using statistical analysis to enhance his coaching decisions , spending almost as much time looking at statistics as watching game film . " I think it 's a unique way of looking at the game that may be able to help best communicate to your players " , he explains . For example , when Butler was <unk> in late 2010 , Stevens challenged his team : " this [ 46 % defensive field goal percentage ] is where we are . This isn 't acceptable to get to where we want to go . But what does that really mean ? It 's not just get better <unk> , it is , if we give up 3 less <unk> a game , then we will be at 40 percent field goal percentage defense which will be top 20 in the country " . The team got the message , improved throughout the season , and ultimately went on a March run fueled by defense . In 2012 , Stevens became the first college coach to hire someone solely for statistical research when he added Drew Cannon to the staff . If he had the resources , Stevens says he would hire a team of <unk> to analyze the teams play . 
- Stevens ' teams are built around solid basketball <unk> and good team work , rather than individual basketball skill . His teams are known for their defense , forcing opponents into <unk> mistakes . The secret to basketball – and life – is " just to do the job to the best of your ability and don 't worry about anything else , " Stevens says . " <unk> the next game . <unk> the next possession . That 's our focus . It 's boring . It 's also the way championships are won " , he says . In short , Stevens is a strong believer in " The Butler Way " – doing all the little things that transform a group of good basketball players into a great basketball team . " I tell the players ' the Butler Way ' isn 't easy to define , " Stevens says , " but you can see it on the floor when we share the basketball , play with great energy and defend . " 
- Stevens prefers to recruit strong team players instead of going after " top recruits . " " The guys we [ have ] recruited , most of them weren 't very highly ranked , " Stevens says . " They had very good high school careers or careers at other places ( transfers ) , but for one reason or the other they weren 't seen as great players . But they all had <unk> . " Stevens puts a strong emphasis on education and has said he would only recruit a " one and done " player if he was committed to getting his degree while playing professionally . 
- Stevens has often been referred to as a coaching <unk> , but is not interested in self @-@ promotion . He instead prefers to <unk> the praise he receives to the players , athletic department , and his <unk> . He has not been known to posture for more money , or to leak his name for open coaching positions . He has been described as humble , modest , and not " about the money " . 
- The New York Times , USA Today , ESPN , and other commentators have attributed Butler 's success against teams with superior athletes to Stevens ' coaching style . The Times remarks , " the Bulldogs are very well prepared for their opponents , and they do not <unk> easily " , and says that the resulting confidence has led to the team 's success . " He coaches to his personality and to his strengths , " <unk> says . " <unk> , he has great <unk> and communication ability with his team . " Yahoo ! Sports compared Stevens to legendary coach John Wooden writing " Brad Stevens is winning at Butler the Wooden way – calm and composed on the <unk> . " Wooden agreed , saying , " I enjoy watching [ Stevens ] and very much enjoy [ Butler 's ] style of play . " 
- 
- = = Personal life = = 
- 
- Brad Stevens is known for his youthful looks , often being described as " baby @-@ faced " . One commentator remarked , " Stevens looks like he checks the mirror every morning to see if it 's time to start <unk> . " On occasion , he has been mistaken for a player . He is also known for projecting a professional , " corporate " look from the sidelines . Asked what his life would be like if he had never taken up coaching , he replies " If everything else remained the same , I would have been as happy as <unk> ... Friends and family and faith , they 're going to take the cake over all this stuff . " Stevens met his wife , Tracy ( née <unk> ) , while attending <unk> University . Tracy , who played soccer for <unk> , quickly learned of Brad 's love for basketball ; on their third date he drove her an hour and a half to attend a high school basketball game . Tracy graduated from Rocky River High School in 1995 , and from <unk> in 1999 . She returned to school in 2000 , driving five hours from Case Western 's law school to Indianapolis on weekends to see Brad . She finished her final year of law school in Indianapolis , and the couple married in August 2003 . Tracy works as a labor and employment lawyer . Tracy also serves as Brad 's agent . 
- Brad and Tracy Stevens are involved with the American Cancer Society 's Coaches <unk> . Cancer . Brad says that the cause really hit home for them after Tracy 's mother died of the disease in June 2004 . The day before Butler 's 2010 Final Four appearance , they hosted a fundraiser for the organization . Brad Stevens has also volunteered his time to the <unk> Foundation for Kids , a charity <unk> <unk> children run by former Butler player Avery <unk> . Stevens remains in close touch with the Butler basketball family ; he notably took a one @-@ game leave from the Celtics in January 2016 to visit with Andrew Smith , a player on both of Butler 's Final Four teams who was dying of cancer ; Smith died less than a week later . At the request of Andrew 's widow , Sam , Brad delivered the <unk> at the memorial service on January 17 , 2016 . 
- Stevens ' father , Mark , is an <unk> surgeon in Indianapolis and former Indiana <unk> football player . His mother , Jan , is a university professor . She has previously taught at Butler . 
- 
- = = Head coaching record = = 
- 
- 
- = = = College = = = 
- 
- 
- = = = NBA = = = 
- 
- 
- = = = Awards and nominations = = = 
- 
- Henry <unk> Coach of the Year Award <unk> ( 2009 ) 
- Horizon League Coach of the Year ( 2009 , 2010 ) 
- Hugh Durham Award for Mid @-@ major Coach of the Year <unk> ( 2008 , 2009 , 2010 ) 
- Hugh Durham Award Mid @-@ season honors ( 2009 ) 
- Jim <unk> National Coach of the Year Award <unk> ( 2008 , 2009 , 2010 ) 
- <unk> Prosser Man of the Year Award <unk> ( 2010 ) 
- 
- 
- = <unk> ( crater ) = 
- 
- <unk> is an impact crater that lies at the south pole of the Moon . The peaks along the crater 's rim are exposed to almost continual sunlight , while the interior is <unk> in shadow ( a <unk> of eternal darkness ) . The low @-@ temperature interior of this crater functions as a cold trap that may capture and freeze <unk> shed during comet impacts on the Moon . Measurements by the <unk> <unk> spacecraft showed higher than normal amounts of hydrogen within the crater , which may indicate the presence of water ice . The crater is named after Antarctic explorer Ernest <unk> . 
- 
- = = Description = = 
- 
- The rotational axis of the Moon lies within <unk> , only a few kilometers from its center . The crater is 21 km in diameter and 4 @.@ 2 km deep . From the Earth , it is viewed edge @-@ on in a region of rough , <unk> terrain . It is located within the South Pole @-@ <unk> basin on a <unk> . The rim is slightly raised about the surrounding surface and it has an outer rampart that has been only lightly impacted . No significant craters intersect the rim , and it is sloped about 1 @.@ 5 ° toward the direction 50 – 90 ° from the Earth . The age of the crater is about 3 @.@ 6 billion years and it has been in the proximity of the south lunar pole for at least the last two billion years . 
- Because the orbit of the Moon is <unk> only 5 ° from the ecliptic , the interior of this crater lies in perpetual darkness . Estimates of the area in permanent shadow were obtained from Earth @-@ based radar studies . <unk> along the rim of the crater are almost continually illuminated by sunlight , spending about 80 – 90 % of each lunar orbit exposed to the Sun . <unk> illuminated mountains have been termed peaks of eternal light and have been predicted to exist since the 1900s . 
- The shadowed portion of the crater was <unk> with the <unk> Camera of the Japanese <unk> spacecraft using the illumination of sunlight reflected off the rim . The interior of the crater consists of a symmetrical 30 ° slope that leads down to a 6 @.@ 6 km diameter floor . The handful of craters along the interior span no more than a few hundred meters . The bottom is covered by an uneven mound @-@ like feature that is 300 to 400 m thick . The central peak is about 200 m in height . 
- The continuous shadows in the south polar craters cause the floors of these formations to maintain a temperature that never exceeds about 100 K. For <unk> , the average temperature was determined to be about 90 K , reaching 88 K at the crater floor . Under these conditions , the estimated rate of loss from any ice in the interior would be 10 − 26 to 10 − 27 m / s . Any water vapor that arrives here following a <unk> impact on the Moon would lie permanently frozen on or below the surface . However , the surface albedo of the crater floor matches the lunar far @-@ side , suggesting that there is no exposed surface ice . 
- This crater was named after Ernest Henry <unk> , an Anglo @-@ Irish explorer of Antarctica from 1901 until his death in 1922 . The name was officially adopted by the International Astronomical Union in 1994 . Nearby craters of note include Shoemaker , <unk> , de <unk> , <unk> , and <unk> . <unk> farther away , on the eastern hemisphere of the lunar near side , are the larger craters <unk> and Scott , named after two other early explorers of the Antarctic continent . 
- 
- = = Exploration = = 
- 
- From the perspective of the Earth , this crater lies along the southern limb of the Moon , making observation difficult . Detailed mapping of the polar regions and <unk> of the Moon did not occur until the advent of orbiting spacecraft . <unk> lies entirely within the rim of the immense South Pole @-@ <unk> basin , which is one of the largest known impact formations in the Solar System . This basin is over 12 kilometers deep , and an exploration of its properties could provide useful information about the lunar interior . 
- A neutron <unk> on board the <unk> <unk> spacecraft detected enhanced concentrations of hydrogen close to the northern and southern lunar poles , including the crater <unk> . At the end of this mission in July 1999 , the spacecraft was crashed into the nearby crater Shoemaker in the hope of detecting from Earth @-@ based telescopes an impact @-@ generated plume containing water vapor . The impact event did not produce any detectable water vapor , and this may be an indication that the hydrogen is not in the form of hydrated minerals , or that the impact site did not contain any ice . Alternatively , it is possible that the crash did not excavate deeply enough into the <unk> to liberate significant quantities of water vapor . 
- From Earth @-@ based radar and spacecraft images of the crater edge , <unk> appears to be relatively intact ; much like a young crater that has not been significantly eroded from subsequent impacts . This may mean that the inner sides are relatively steep , which may make traversing the sides relatively difficult for a robotic vehicle . In addition , it is possible that the interior floor might not have collected a significant quantity of <unk> since its formation . However other craters in the vicinity are considerably older , and may contain significant deposits of hydrogen , possibly in the form of water ice . ( See Shoemaker ( lunar crater ) , for example . ) 
- Radar studies preceding and following the <unk> <unk> mission demonstrate that the inner walls of <unk> are similar in reflective characteristics to those of some <unk> craters . In particular , the surroundings appear to contain a significant number of blocks in its <unk> blanket , suggesting that its radar properties are a result of surface <unk> , and not ice deposits , as was previously suggested from a radar experiment involving the <unk> mission . This interpretation , however , is not universally agreed upon within the scientific community . Radar images of the crater at a wavelength of 13 cm show no evidence for water ice deposits . 
- Optical imaging inside the crater was done for the first time by the Japanese lunar <unk> spacecraft <unk> in 2007 . It did not have any evidence of significant amount of water ice , down to the image resolution of 10 m per pixel . 
- On November 15 , 2008 , a 34 @-@ kg probe made a hard landing near the crater . The moon impact probe ( <unk> ) was launched from the Indian <unk> @-@ I spacecraft and reached the surface 25 minutes later . The probe carried a radar <unk> , video imaging system , and a mass <unk> , which will be used to search for water . 
- 
- = = Potential uses = = 
- 
- Some sites along <unk> 's rim receive almost constant illumination . At these <unk> sunlight is almost always available for conversion into electricity using solar panels , potentially making them good locations for future Moon landings . The temperature at this site is also more favorable than at more equatorial latitudes as it does not experience the daily temperature extremes of 100 ° C when the Sun is overhead , to as low as − 150 ° C during the lunar night . 
- While scientific experiments performed by <unk> and <unk> <unk> could indicate the presence of water in the polar craters , the current evidence is far from definitive . There are doubts among scientists as to whether or not the hydrogen is in the form of ice , as well as to the concentration of this " ore " with depth below the surface . Resolution of this issue will require future missions to the Moon . The presence of water suggests that the crater floor could potentially be " mined " for deposits of hydrogen in water form , a commodity that is expensive to deliver directly from the Earth . 
- This crater has also been proposed as a future site for a large infrared telescope . The low temperature of the crater floor makes it ideal for infrared observations , and solar cells placed along the rim could provide near @-@ continuous power to the observatory . About 120 kilometers from the crater lies the 5 @-@ km tall <unk> Mountain , a peak that is <unk> visible from the Earth , and which could serve as a radio relay station when suitably equipped . 
- NASA has named the rim of <unk> as a potential candidate for its lunar outpost , slated to be up and running by <unk> and continuously staffed by a crew by <unk> . The location would promote self @-@ <unk> for lunar residents , as perpetual sunlight on the south pole would provide energy for solar panels . Furthermore , the shadowed polar regions are believed to contain the frozen water necessary for human consumption and could also be harvested for fuel manufacture . 
- 
- 
- = American Beauty ( 1999 film ) = 
- 
- American Beauty is a 1999 American drama film directed by Sam <unk> and written by Alan Ball . Kevin Spacey stars as Lester <unk> , a 42 @-@ year @-@ old advertising executive who has a <unk> crisis when he becomes infatuated with his <unk> daughter 's best friend , Angela ( Mena <unk> ) . <unk> <unk> co @-@ stars as Lester 's <unk> wife , Carolyn , and <unk> Birch plays their <unk> daughter , Jane . Wes Bentley , Chris Cooper , and Allison <unk> also feature . The film is described by academics as a satire of American middle @-@ class notions of beauty and personal satisfaction ; analysis has focused on the film 's <unk> of romantic , and paternal love , sexuality , beauty , <unk> , self @-@ liberation , and redemption . 
- Ball began writing American Beauty as a play in the early 1990s , partly inspired by the media circus around the Amy Fisher trial in 1992 . He <unk> the play after realizing the story would not work on stage . After several years as a television screenwriter , Ball revived the idea in 1997 when attempting to break into the film industry . The modified script had a cynical outlook that was influenced by Ball 's frustrating <unk> writing for several sitcoms . <unk> Dan <unk> and Bruce Cohen took American Beauty to DreamWorks ; the then @-@ fledgling film studio bought Ball 's script for $ 250 @,@ 000 , <unk> several other production bodies . DreamWorks financed the $ 15 million production and served as its North American distributor . American Beauty marked acclaimed theater director <unk> ' film debut ; <unk> after his successful productions of the musicals Oliver ! and <unk> , <unk> was , nevertheless , only given the job after 20 others were considered and several " A @-@ list " directors turned down the opportunity . 
- Spacey was <unk> ' first choice for the role of Lester , though DreamWorks had urged the director to consider better @-@ known actors ; similarly , the studio suggested several actors for the role of Carolyn until <unk> offered the part to <unk> without DreamWorks ' knowledge . Principal photography took place between December 1998 and February 1999 on soundstages at the Warner Bros. <unk> in Burbank , California , and on location in Los Angeles . <unk> ' dominant style was deliberate and composed ; he made extensive use of static shots and slow pans and <unk> to generate tension . <unk> Conrad Hall complemented <unk> ' style with peaceful shot compositions to contrast with the turbulent on @-@ screen events . During editing , <unk> made several changes that gave the film a less cynical tone than the script . 
- Released in North America on September 17 , 1999 , American Beauty was positively received by critics and audiences ; it was the best @-@ reviewed American film of the year and grossed over $ <unk> million worldwide . Reviewers praised most aspects of the production , with particular emphasis on <unk> , Spacey , and Ball ; criticism focused on the familiarity of the characters and setting . DreamWorks launched a major campaign to increase the film 's chances of Academy Award success ; at the 72nd Academy Awards the following year , the film won Best Picture , Best Director , Best Actor ( for Spacey ) , Best Original Screenplay , and Best <unk> . It was nominated for and won many other awards and honors , mainly for the direction , writing , and acting . 
- 
- = = Plot = = 
- 
- Lester <unk> is a middle @-@ aged advertising executive and magazine writer who <unk> his job . He is <unk> married to Carolyn , a neurotic yet fiercely ambitious real estate <unk> ; their <unk> daughter , Jane , <unk> her parents and has low self @-@ esteem . The <unk> ' new neighbors are retired United States Marine Corps Colonel Frank <unk> and his near @-@ <unk> wife , Barbara . Their <unk> son , Ricky , constantly films his surroundings with a <unk> , collecting hundreds of recordings on <unk> in his bedroom . His job as a part @-@ time bar <unk> serves as a front for his secret marijuana dealing . Col. <unk> is a strict disciplinarian who previously sent Ricky to a military school and briefly committed him to a psychiatric hospital . Jim <unk> and Jim Berkley , a gay couple who live nearby , welcome the family to the neighborhood ; the <unk> Col. <unk> angrily asks Ricky " why these faggots have to rub it in your face . " 
- Lester becomes infatuated with Jane 's vain friend , Angela Hayes , after seeing her perform a half @-@ time dance routine at a high school basketball game . He starts having sexual fantasies about Angela , in which red rose <unk> are a recurring motif . Carolyn begins an affair with her married business rival , <unk> Kane . When Lester 's boss , Brad , tells him that he is to be laid off , Lester instead blackmails him for $ 60 @,@ 000 and <unk> his job . Lester takes a minimum @-@ wage job at a fast @-@ food restaurant , trades in his Toyota <unk> for his dream car , a 1970 Pontiac <unk> , and starts working out after he <unk> Angela tell Jane that she would find him sexually attractive if he got in shape . He begins smoking marijuana supplied by Ricky . The girls ' friendship <unk> after Jane starts a relationship with Ricky . Jane and Ricky bond over what Ricky considers the most beautiful imagery he has filmed : a plastic bag being blown in the wind . 
- Lester discovers Carolyn 's <unk> , but reacts <unk> . <unk> ends the affair , fearing an expensive divorce . Col. <unk> becomes suspicious of Lester and Ricky 's friendship when he finds his son 's footage of Lester lifting weights while nude , which Ricky captured by chance , leading him to believe that Ricky is gay . After <unk> on Ricky and Lester through Lester 's garage window , the colonel mistakenly concludes the pair is sexually involved . He later confronts and beats Ricky for the supposed affair and accuses him of being gay . Ricky falsely admits the charges and <unk> his father into kicking him out of their home . Meanwhile , Carolyn is sitting in her car in the rain , taking a gun out of the <unk> box while a voice on the radio talks about not being a victim . Ricky goes to Jane 's bedroom , finding her arguing with Angela about Angela 's <unk> with Lester . Ricky convinces Jane to flee with him to New York City and assures Angela that she is ugly , boring , and ordinary . 
- Col. <unk> confronts Lester and attempts to kiss him ; Lester <unk> the colonel , who <unk> flees . Carolyn puts the gun in her handbag , shouting , " I refuse to be a victim ! " Lester finds a distraught Angela sitting alone in the dark ; she asks him to tell her she is beautiful . He does , and they kiss . 
- Carolyn drives through the rain , rehearsing a confession to Lester . Just as Lester and Angela are about to have sex , she admits that she is a virgin , and Lester changes his mind . He instead comforts her and the pair bond over their shared <unk> . Angela goes to the bathroom and Lester <unk> at a family photograph in his kitchen . An unseen figure raises a gun to the back of his head , a gunshot sounds , and blood <unk> on the wall . Ricky and Jane find Lester 's body , while Carolyn breaks down crying in the <unk> . A <unk> Col. <unk> returns home , where a gun is shown to be missing from his collection . Lester 's closing narration describes meaningful experiences during his life ; he says that , despite his death , he is happy because there is " so much beauty " in the world . 
- 
- = = Themes and analysis = = 
- 
- 
- = = = Multiple interpretations = = = 
- 
- Scholars and academics have offered many possible readings of American Beauty ; film critics are similarly divided , not so much about the quality of the film , as their interpretations of it . Described by many as about " the meaning of life " or " the hollow existence of the American suburbs " , the film has defied categorization by even the filmmakers . <unk> is <unk> , saying the script seemed to be about something different each time he read it : " a mystery story , a <unk> journey through American <unk> , a series of love stories ; [ ... ] it was about imprisonment , [ ... ] loneliness , [ and ] beauty . It was funny ; it was angry , sad . " The literary critic and author Wayne C. <unk> concludes that the film <unk> any one interpretation : " [ American Beauty ] cannot be adequately summarized as ' here is a satire on what 's wrong with American life ' ; that plays down the celebration of beauty . It is more tempting to <unk> it as ' a portrait of the beauty underlying American <unk> and <unk> ' , but that plays down the scenes of cruelty and horror , and Ball 's disgust with our <unk> . It cannot be summarized with either Lester or Ricky 's philosophical statements about what life is or how one should live . " He argues that the problem of interpreting the film is tied with that of finding its center — a controlling voice who " [ <unk> ] all of the choices " . He contends that in American Beauty 's case , it is neither <unk> nor Ball . <unk> considers the voice to be Ball 's , but even while the writer was " strongly influential " on set , he often had to accept deviations from his vision , particularly ones that transformed the cynical tone of his script into something more optimistic . With " <unk> voices <unk> on the original author 's , " <unk> says , those who interpret American Beauty " have forgotten to probe for the elusive center " . According to <unk> , the film 's true controller is the creative energy " that hundreds of people put into its production , agreeing and <unk> , inserting and cutting " . 
- 
- = = = <unk> and redemption = = = 
- 
- <unk> called American Beauty a rite of passage film about imprisonment and escape from imprisonment . The <unk> of Lester 's existence is established through his gray , <unk> workplace and <unk> clothing . In these scenes , he is often framed as if trapped , " reiterating rituals that hardly please him " . He <unk> in the <unk> of his shower ; the shower stall evokes a jail cell and the shot is the first of many where Lester is confined behind bars or within frames , such as when he is reflected behind columns of numbers on a computer monitor , " confined [ and ] nearly crossed out " . The academic and author <unk> W. <unk> argues that Lester 's journey is the story 's center . His sexual <unk> through meeting Angela is the first of several turning points as he begins to " [ throw ] off the responsibilities of the comfortable life he has come to <unk> " . After Lester shares a joint with Ricky , his spirit is released and he begins to rebel against Carolyn . Changed by Ricky 's " attractive , profound confidence " , Lester is convinced that Angela is <unk> and sees that he must question his " <unk> , <unk> <unk> suburban existence " ; he takes a job at a fast @-@ food outlet , which allows him to <unk> to a point when he could " see his whole life ahead of him " . 
- When Lester is caught <unk> by Carolyn , his angry <unk> about their lack of intimacy is the first time he says aloud what he thinks about her . By <unk> the issue and Carolyn 's " superficial investments in others " , Lester is trying to " regain a voice in a home that [ only respects ] the voices of mother and daughter " . His final turning point comes when Angela and he almost have sex ; after she confesses her <unk> , he no longer thinks of her as a sex object , but as a daughter . He holds her close and " <unk> her up " . <unk> called it " the most satisfying end to [ Lester 's ] journey there could possibly have been " . With these final scenes , <unk> intended to show Lester at the conclusion of a " mythical quest " . After Lester gets a beer from the <unk> , the camera pushes toward him , then stops facing a <unk> down which he walks " to meet his fate " . Having begun to act his age again , Lester achieves closure . As he <unk> at a family photo , the camera pans slowly from Lester to the kitchen wall , onto which blood <unk> as a gunshot rings out ; the slow pan reflects the peace of Lester 's death . His body is discovered by Jane and Ricky . <unk> said that Ricky 's staring into Lester 's dead eyes is " the culmination of the theme " of the film : that beauty is found where it is least expected . 
- 
- = = = <unk> and beauty = = = 
- 
- Like other American films of 1999 — such as Fight Club , Bringing Out the Dead , and Magnolia , American Beauty instructs its audience to " [ lead ] more meaningful lives " . The film argues the case against conformity , but does not deny that people need and want it ; even the gay characters just want to fit in . Jim and Jim , the <unk> ' other neighbors , are a satire of " gay <unk> <unk> " , who " [ invest ] in the <unk> <unk> " that the film criticizes in heterosexual couples . The feminist academic and author <unk> R. <unk> argues that American Beauty uses its " art house " <unk> to direct its message of <unk> primarily to the middle classes , and that this approach is a " cliché of <unk> preoccupation ; [ ... ] the underlying premise being that the luxury of finding an individual ' self ' through denial and <unk> is always open to those wealthy enough to choose , and <unk> enough to present themselves <unk> as a rebel . " 
- Professor Roy M. <unk> argues that the film 's thematic center is its direction to the audience to " look closer " . The opening combines an unfamiliar viewpoint of the <unk> ' neighborhood with Lester 's narrated admission that he will soon die , forcing audiences to consider their own mortality and the beauty around them . It also sets a series of mysteries ; <unk> asks , " from what place exactly , and from what state of being , is he telling this story ? If he 's already dead , why <unk> with whatever it is he wishes to tell about his last year of being alive ? There is also the question of how Lester has died — or will die . " <unk> believes the preceding scene — Jane 's discussion with Ricky about the possibility of his killing her father — adds further mystery . Professor Ann C. Hall <unk> ; she says by presenting an early resolution to the mystery , the film allows the audience to put it aside " to view the film and its philosophical issues " . Through this examination of Lester 's life , <unk> and death , American Beauty <unk> American middle class notions of meaning , beauty and satisfaction . Even Lester 's transformation only comes about because of the possibility of sex with Angela ; he therefore remains a " willing devotee of the popular media 's <unk> of <unk> male sexuality as a sensible route to personal <unk> " . Carolyn is similarly driven by conventional views of happiness ; from her belief in " house beautiful " domestic <unk> to her car and <unk> outfit , Carolyn 's domain is a " <unk> American <unk> vision of <unk> , or Eden " . The <unk> are unaware that they are " <unk> philosophically , and devout consumers <unk> " who expect the " <unk> of American beauty " to give them happiness . <unk> argues that " they are helpless in the face of the <unk> economic and sexual stereotypes [ ... ] that they and their culture have designated for their salvation . " 
- The film presents Ricky as its " visionary , [ ... ] spiritual and mystical center " . He sees beauty in the <unk> of everyday life , <unk> as much as he can for fear of missing it . He shows Jane what he considers the most beautiful thing he has filmed : a plastic bag , <unk> in the wind in front of a wall . He says capturing the moment was when he realized that there was " an entire life behind things " ; he feels that " sometimes there 's so much beauty in the world I feel like I can 't take it ... and my heart is going to cave in . " <unk> argues that Ricky , in looking past the " cultural <unk> " , has " [ grasped ] the <unk> <unk> of the created world " to see God . As the film progresses , the <unk> move closer to Ricky 's view of the world . Lester only <unk> personal satisfaction at the film 's end . On the <unk> of having sex with Angela , he returns to himself after she admits her <unk> . Suddenly confronted with a child , he begins to treat her as a daughter ; in doing so , Lester sees himself , Angela , and his family " for the poor and fragile but <unk> creatures they are " . He looks at a picture of his family in <unk> times , and dies having had an <unk> that <unk> him with " wonder , joy , and soul @-@ shaking gratitude " — he has finally seen the world as it is . 
- According to Patti <unk> , colors are used symbolically throughout the film , none more so than red , which is an important thematic signature that drives the story and " [ defines ] Lester 's arc " . First seen in drab colors that reflect his <unk> , Lester surrounds himself with red as he <unk> his individuality . The American Beauty rose is repeatedly used as symbol ; when Lester <unk> about Angela , she is usually naked and surrounded by rose <unk> . In these scenes , the rose <unk> Lester 's desire for her . When associated with Carolyn , the rose represents a " façade for suburban success " . Roses are included in almost every shot inside the <unk> ' home , where they signify " a mask covering a bleak , <unk> reality " . Carolyn feels that " as long as there can be <unk> , all is well " . She cuts the <unk> and puts them in <unk> , where they adorn her " <unk> vision of what makes for beauty " and begin to die . The <unk> in the vase in the Angela – Lester seduction scene symbolize Lester 's previous life and Carolyn ; the camera pushes in as Lester and Angela get closer , finally taking the <unk> — and thus Carolyn — out of the shot . Lester 's <unk> at the end of the film is expressed by rain and the use of red , building to a crescendo that is a deliberate contrast to the release Lester feels . The constant use of red " <unk> [ the audience ] <unk> " into becoming used to it ; consequently , it leaves the audience unprepared when Lester is shot and his blood <unk> on the wall . 
- 
- = = = <unk> and repression = = = 
- 
- <unk> argues that American Beauty defines its characters through their sexuality . Lester 's attempts to relive his youth are a direct result of his lust for Angela , and the state of his relationship with Carolyn is in part shown through their lack of sexual contact . Also sexually frustrated , Carolyn has an affair that takes her from " cold <unk> " to a more <unk> soul who " [ sings ] happily along with " the music in her car . Jane and Angela constantly reference sex , through Angela 's descriptions of her supposed sexual encounters and the way the girls address each other . Their nude scenes are used to communicate their vulnerability . By the end of the film , Angela 's hold on Jane has weakened until the only power she has over her friend is Lester 's attraction to her . Col. <unk> reacts with disgust to meeting Jim and Jim ; he asks , " How come these faggots always have to rub it in your face ? How can they be so <unk> ? " To which Ricky replies , " That 's the thing , Dad — they don 't feel like it 's anything to be <unk> of . " <unk> argues that Col. <unk> ' reaction is not <unk> , but an " <unk> self @-@ <unk> " . 
- With other turn @-@ of @-@ the @-@ millennium films such as Fight Club , In the Company of Men ( 1997 ) , American <unk> ( 2000 ) , and Boys Don 't <unk> ( 1999 ) , American Beauty " raises the broader , widely explored issue of <unk> in crisis " . Professor Vincent <unk> charges that in their reinforcement of <unk> " against threats posed by war , by <unk> , and by feminist and <unk> challenges " , these films present a need to " focus on , and even to privilege " aspects of <unk> " deemed ' <unk> ' " . Lester 's transformation conveys " that he , and not the woman , has borne the brunt of [ lack of being ] " and he will not stand for being <unk> . Lester 's attempts to " strengthen traditional <unk> " conflict with his responsibilities as a father . Although the film portrays the way Lester returns to that role positively , he does not become " the <unk> figure implicitly celebrated in films like Fight Club " . <unk> concludes that Lester 's behavior toward Angela is " a <unk> but nearly necessary step toward his becoming a father again " . 
- <unk> says the film " explicitly affirms the importance of upholding the prohibition against <unk> " ; a recurring theme of Ball 's work is his comparison of the <unk> against <unk> and homosexuality . Instead of making an overt distinction , American Beauty looks at how their repression can lead to violence . Col. <unk> is so <unk> of his homosexuality that it drives him to murder Lester . Ball said , " The movie is in part about how homophobia is based in fear and repression and about what [ they ] can do . " The film implies two <unk> <unk> desires : Lester 's pursuit of Angela is a manifestation of his lust for his own daughter , while Col. <unk> ' repression is exhibited through the almost <unk> discipline with which he controls Ricky . Consequently , Ricky realizes that he can only hurt his father by falsely telling him he is homosexual , while Angela 's vulnerability and submission to Lester reminds him of his responsibilities and the limits of his fantasy . Col. <unk> represents Ball 's father , whose <unk> homosexual desires led to his own <unk> . Ball rewrote Col. <unk> to delay revealing him as homosexual , which <unk> reads as a possible " <unk> of Ball 's own patriarchal @-@ <unk> fantasies " . 
- 
- = = = <unk> and music = = = 
- 
- American Beauty follows a traditional narrative structure , only <unk> with the displaced opening scene of Jane and Ricky from the middle of the story . Although the plot spans one year , the film is narrated by Lester at the moment of his death . Jacqueline <unk> says that the plot " occupies [ ... ] no time [ or ] all time " , citing Lester 's claim that life did not flash before his eyes , but that it " stretches on forever like an ocean of time " . <unk> argues that a " rhythm of repetition " forms the core of the film 's structure . For example , two scenes have the <unk> sitting down to an evening meal , shot from the same angle . Each image is broadly similar , with minor differences in object placement and body language that reflect the changed dynamic brought on by Lester 's new @-@ found assertiveness . Another example is the pair of scenes in which Jane and Ricky film each other . Ricky films Jane from his bedroom window as she removes her bra , and the image is reversed later for a similarly " <unk> and <unk> " scene in which Jane films Ricky at a vulnerable moment . 
- Lester 's fantasies are emphasized by <unk> and repetitive @-@ motion shots ; <unk> uses double @-@ and @-@ triple <unk> in several sequences , and the score alters to make the audience aware that it is entering a fantasy . One example is the <unk> scene — Lester 's first encounter with Angela . While the <unk> perform their half @-@ time routine to " On Broadway " , Lester becomes increasingly <unk> on Angela . Time slows to represent his " <unk> hypnosis " and Lester begins to <unk> that Angela 's performance is for him alone . " On Broadway " — which provides a conventional <unk> to the <unk> action — is replaced by discordant , <unk> music that lacks melody or progression . This <unk> score is important to creating the narrative <unk> in the sequence ; it conveys a moment for Lester that is stretched to an indeterminate length . The effect is one that Stan Link <unk> to " vertical time " , described by the composer and music theorist Jonathan Kramer as music that <unk> " a single present stretched out into an enormous duration , a potentially infinite ' now ' that nonetheless feels like an instant " . The music is used like a visual cue , so that Lester and the score are staring at Angela . The sequence ends with the sudden reintroduction of " On Broadway " and <unk> time . 
- According to Drew Miller of <unk> , the soundtrack " [ gives ] unconscious voice " to the characters ' <unk> and <unk> the subtext . The most obvious use of pop music " accompanies and gives context to " Lester 's attempts to recapture his youth ; reminiscent of how the counterculture of the 1960s <unk> American repression through music and drugs , Lester begins to smoke cannabis and listen to rock music . <unk> ' song choices " progress through the history of American popular music " . Miller argues that although some may be over familiar , there is a <unk> element at work , " making good on [ the film 's ] encouragement that viewers look closer " . Toward the end of the film , Thomas Newman 's score features more prominently , creating " a disturbing tempo " that matches the tension of the visuals . The exception is " Don 't Let It <unk> You Down " , which plays during Angela 's seduction of Lester . At first appropriate , its tone clashes as the seduction stops . The lyrics , which speak of " castles burning " , can be seen as a metaphor for Lester 's view of Angela — " the <unk> , fantasy @-@ driven exterior of the ' American Beauty ' " — as it burns away to reveal " the timid , small @-@ <unk> girl who , like his wife , has <unk> developed a false public self " . 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- In 1997 , Alan Ball resolved to move into the film industry after several frustrating years writing for the television sitcoms Grace Under Fire and <unk> . He joined the United Talent Agency , where his representative , Andrew <unk> , suggested he write a <unk> script to " <unk> [ himself ] to the town as a screenwriter " . Ball pitched three ideas to <unk> : two conventional romantic comedies and American Beauty , which he had originally conceived as a play in the early 1990s . Despite the story 's lack of an easily <unk> concept , <unk> selected American Beauty because he felt it was the one for which Ball had the most passion . While developing the script , Ball created another television sitcom , Oh , <unk> Up . He channeled his anger and frustration at having to <unk> to network demands on that show — and during his <unk> on Grace Under Fire and <unk> — into writing American Beauty . 
- Ball did not expect to sell the script , believing it would act as more of a calling card , but American Beauty drew interest from several production bodies . <unk> passed the script to several producers , including Dan <unk> and Bruce Cohen , who took it to DreamWorks . With the help of executives Glenn <unk> and Bob Cooper , and Steven Spielberg in his capacity as studio partner , Ball was convinced to develop the project at DreamWorks ; he received <unk> from the studio — known at the time for its more conventional fare — that it would not " iron the [ edges ] out " . In an unusual move , DreamWorks decided not to option the script ; instead , in April 1998 , the studio bought it outright for $ 250 @,@ 000 , <unk> Fox <unk> Pictures , October Films , Metro @-@ Goldwyn @-@ Mayer , and <unk> Entertainment . DreamWorks planned to make the film for $ 6 – 8 million . 
- <unk> and Cohen involved Ball throughout the film 's development , including casting and director selection . The producers met with about 20 interested directors , several of whom were considered " A @-@ list " at the time . Ball was not keen on the more well @-@ known directors because he believed their involvement would increase the budget and lead DreamWorks to become " nervous about the content " . Nevertheless , the studio offered the film to Mike Nichols and Robert <unk> ; neither accepted . In the same year , <unk> ( then a theater director ) revived the musical <unk> in New York with fellow director Rob Marshall . Beth <unk> of the Creative Artists Agency arranged meetings for <unk> with studio figures in Los Angeles to see if film direction was a possibility . <unk> came across American Beauty in a pile of eight scripts at <unk> 's house , and knew immediately that it was the one he wanted to make ; early in his career , he had been inspired by how the film Paris , Texas ( 1984 ) presented contemporary America as a mythic landscape and he saw the same theme in American Beauty , as well as parallels with his own childhood . <unk> later met with Spielberg ; impressed by <unk> ' productions of Oliver ! and <unk> , Spielberg encouraged him to consider American Beauty . 
- <unk> found that he still had to convince DreamWorks ' production executives to let him direct . He had already discussed the film with <unk> and Cohen , and felt they supported him . Ball was also keen ; having seen <unk> , he was impressed with <unk> ' " keen visual sense " and thought he did not make obvious choices . Ball felt that <unk> liked to look under the story 's surface , a talent he felt would be a good fit with the themes of American Beauty . <unk> ' background also <unk> him , because of the prominent role the playwright usually has in a theater production . Over two meetings — the first with Cooper , Walter <unk> , and Laurie MacDonald , the second with Cooper alone — <unk> pitched himself to the studio . The studio soon approached <unk> with a deal to direct for the minimum salary allowed under Directors Guild of America rules — $ 150 @,@ 000 . <unk> accepted , and later recalled that after taxes and his agent 's commission , he only earned $ 38 @,@ 000 . In June 1998 , DreamWorks confirmed that it had contracted <unk> to direct the film . 
- 
- = = = Writing = = = 
- 
- Ball was partly inspired by two encounters he had in the early 1990s . In about 1991 – 92 , Ball saw a plastic bag blowing in the wind outside the World Trade Center . He watched the bag for 10 minutes , saying later that it provoked an " unexpected emotional response " . In 1992 , Ball became preoccupied with the media circus around the Amy Fisher trial . <unk> a comic book telling of the scandal , he was struck by how quickly it had become <unk> . He said he " felt like there was a real story underneath [ that was ] more fascinating and way more tragic " than the story presented to the public , and attempted to turn the idea into a play . Ball produced around 40 pages , but stopped when he realized it would work better as a film . He felt that because of the visual themes , and because each character 's story was .. " intensely personal " , it could not be done on a stage . All the main characters appeared in this version , but Carolyn did not feature strongly ; Jim and Jim instead had much larger roles . 
- Ball based Lester 's story on aspects of his own life . Lester 's re @-@ examination of his life parallels feelings Ball had in his mid @-@ 30s ; like Lester , Ball put aside his <unk> to work in jobs he hated for people he did not respect . Scenes in Ricky 's household reflect Ball 's own childhood experiences . Ball suspected his father was homosexual and used the idea to create Col. <unk> , a man who " gave up his chance to be himself " . Ball said the script 's mix of comedy and drama was not intentional , but that it came <unk> from his own outlook on life . He said the juxtaposition produced a <unk> contrast , giving each trait more impact than if they appeared alone . 
- In the script that was sent to prospective actors and directors , Lester and Angela had sex ; by the time of shooting , Ball had rewritten the scene to the final version . Ball initially rebuffed counsel from others that he change the script , feeling they were being <unk> ; the final <unk> to alter the scene came from DreamWorks ' then @-@ president Walter <unk> . He convinced Ball by indicating that in Greek mythology , the hero " has a moment of <unk> before [ ... ] tragedy occurs " . Ball later said his anger when writing the first draft had blinded him to the idea that Lester needed to refuse sex with Angela to complete his emotional journey — to achieve redemption . <unk> and Cohen asked Ball not to alter the scene right away , as they felt it would be inappropriate to make changes to the script before a director had been hired . Early drafts also included a flashback to Col. <unk> ' service in the Marines , a sequence that unequivocally established his homosexual <unk> . In love with another Marine , Col. <unk> sees the man die and comes to believe that he is being punished for the " sin " of being gay . Ball removed the sequence because it did not fit the structure of the rest of the film — Col. <unk> was the only character to have a flashback — and because it removed the element of surprise from Col. <unk> ' later pass at Lester . Ball said he had to write it for his own benefit to know what happened to Col. <unk> , though all that remained in later drafts was subtext . 
- Ball remained involved throughout production ; he had signed a television show development deal , so had to get permission from his producers to take a year off to be close to American Beauty . Ball was on @-@ set for <unk> and to help interpret his script for all but two days of filming . His original <unk> scenes — in which Ricky and Jane are <unk> for Lester 's murder after being framed by Col. <unk> — were <unk> in post @-@ production ; the writer later felt the scenes were unnecessary , saying they were a reflection of his " anger and cynicism " at the time of writing ( see " Editing " ) . Ball and <unk> revised the script twice before it was sent to the actors , and twice more before the first read @-@ through . 
- The shooting script features a scene in Angela 's car in which Ricky and Jane talk about death and beauty ; the scene differed from earlier versions , which set it as a " big scene on a freeway " in which the three witness a car crash and see a dead body . The change was a practical decision , as the production was behind schedule and they needed to cut costs . The schedule called for two days to be spent filming the crash , but only half a day was available . Ball agreed , but only if the scene could retain a line of Ricky 's where he reflects on having once seen a dead homeless woman : " When you see something like that , it 's like God is looking right at you , just for a second . And if you 're careful , you can look right back . " Jane asks : " And what do you see ? " Ricky : " Beauty . " Ball said , " They wanted to cut that scene . They said it 's not important . I said , ' You 're out of your fucking mind . It 's one of the most important scenes in the movie ! ' [ ... ] If any one line is the heart and soul of this movie , that is the line . " Another scene was rewritten to accommodate the loss of the freeway sequence ; set in a <unk> , it presents a " turning point " for Jane in that she chooses to walk home with Ricky instead of going with Angela . By the end of filming , the script had been through 10 drafts . 
- 
- = = = Casting = = = 
- 
- <unk> had Spacey and <unk> in mind for the leads from the beginning , but DreamWorks executives were <unk> . The studio suggested several <unk> , including Bruce Willis , Kevin <unk> , or John <unk> to play Lester , and Helen Hunt or Holly Hunter to play Carolyn . <unk> did not want a big star " weighing the film down " ; he felt Spacey was the right choice based on his performances in the 1995 films The <unk> <unk> and Seven , and 1992 's <unk> Glen Ross . Spacey was surprised ; he said , " I usually play characters who are very quick , very <unk> and smart . [ ... ] I usually <unk> in dark , sort of <unk> waters . This is a man living one step at a time , playing by his instincts . This is actually much closer to me , to what I am , than those other parts . " <unk> offered <unk> the role of Carolyn without the studio 's consent ; although executives were upset at <unk> , by September 1998 , DreamWorks had entered negotiations with Spacey and <unk> . 
- Spacey loosely based Lester 's early " <unk> " <unk> on Walter <unk> . During the film , Lester 's physique improves from <unk> to toned ; Spacey worked out during filming to improve his body , but because <unk> shot the scenes out of chronological order , Spacey varied <unk> to portray the stages . Before filming , <unk> and Spacey analyzed Jack <unk> 's performance in The <unk> ( 1960 ) , because <unk> wanted Spacey to emulate " the way [ <unk> ] moved , the way he looked , the way he was in that office and the way he was an ordinary man and yet a special man " . Spacey 's <unk> is a <unk> to Sunset Boulevard ( 1950 ) , which is also narrated in <unk> by a dead character . <unk> felt it evoked Lester 's — and the film 's — loneliness . <unk> recalled women from her youth to inform her performance : " I used to <unk> constantly . You 'd go to church and see how people present themselves on the outside , and then be inside their house and see the difference . " <unk> and a hair stylist collaborated to create a " <unk> president <unk> " hairstyle , and <unk> and production designer <unk> <unk> researched mail @-@ order <unk> to better establish Carolyn 's environment of a " spotless suburban manor " . To help <unk> get into Carolyn 's mindset , <unk> gave her music that he believed Carolyn would like . He lent <unk> the Bobby <unk> version of the song " Don 't Rain on My Parade " , which she enjoyed and persuaded the director to include it for a scene in which Carolyn sings in her car . 
- For the roles of Jane , Ricky , and Angela , DreamWorks gave <unk> <unk> <unk> . By November 1998 , <unk> Birch , Wes Bentley , and Mena <unk> had been cast in the parts — in Birch 's case , despite the fact she was <unk> for her nude scene . As Birch was 16 at the time she made the film , and thus classified as a minor in the United States , her parents had to approve her brief <unk> scene in the movie . Child labor representatives and they were on the set for the shooting of the scene . Bentley <unk> competition from top actors under the age of 25 to be cast . The 2009 documentary My Big Break followed Bentley , and several other young actors , before and after he landed the part . To prepare , <unk> provided Bentley with a video camera , telling the actor to film what Ricky would . Peter Gallagher and Alison <unk> were cast ( as <unk> Kane and Barbara <unk> ) after filming began in December 1998 . <unk> gave <unk> a book of paintings by <unk> <unk> . He told her , " Your character is in there somewhere . " <unk> cut much of Barbara 's dialogue , including conversations between Colonel <unk> and her , as he felt that what needed to be said about the pair — their humanity and vulnerability — was conveyed successfully through their shared moments of silence . Chris Cooper plays Colonel <unk> , Scott Bakula plays Jim <unk> , and Sam <unk> plays Jim Berkley . Jim and Jim were deliberately depicted as the most normal , happy — and boring — couple in the film . Ball 's inspiration for the characters came from a thought he had after seeing a " bland , boring , heterosexual couple " who wore matching clothes : " I can 't wait for the time when a gay couple can be just as boring . " Ball also included aspects of a gay couple he knew who had the same <unk> . 
- <unk> insisted on two weeks of cast rehearsals , although the sessions were not as formal as he was used to in the theater , and the actors could not be present at every one . Several <unk> and suggestions by the actors were incorporated into the script . An early scene showing the <unk> leaving home for work was inserted later on to show the low point that Carolyn and Lester 's relationship had reached . Spacey and <unk> worked to create a sense of the love that Lester and Carolyn once had for one another ; for example , the scene in which Lester almost <unk> Carolyn after the pair argues over Lester 's buying a car was originally " strictly contentious " . 
- 
- = = = Filming = = = 
- 
- Principal photography lasted about 50 days from December 14 , 1998 , to February 1999 . American Beauty was filmed on soundstages at the Warner Bros. <unk> in Burbank , California , and at Hancock Park and <unk> in Los Angeles . The aerial shots at the beginning and end of the film were captured in <unk> , California , and many of the school scenes were shot at South High School in <unk> , California ; several extras in the gym crowd were South High students . The film is set in an upper middle @-@ class neighborhood in an unidentified American town . Production designer <unk> <unk> likened the <unk> to <unk> , Illinois , but said , " it 's not about a place , it 's about an <unk> . [ ... ] The milieu was pretty much <unk> , USA — <unk> mobile <unk> . " The intent was for the setting to reflect the characters , who are also <unk> . <unk> said , " All of them are very strained , and their lives are <unk> . " The <unk> ' household was designed as the reverse of the <unk> ' — the former a pristine ideal , but <unk> and lacking in " inner balance " , leading to Carolyn 's desire to at least give it the appearance of a " perfect all @-@ American household " ; the <unk> ' home is depicted in " exaggerated darkness [ and ] symmetry " . 
- The production selected two adjacent properties on the Warner <unk> 's " <unk> Street " for the <unk> and <unk> ' homes . The crew rebuilt the houses to incorporate false rooms that established lines of sight — between Ricky and Jane 's bedroom windows , and between Ricky 's bedroom and Lester 's garage . The garage windows were designed specifically to obtain the crucial shot toward the end of the film in which Col. <unk> — watching from Ricky 's bedroom — mistakenly assumes that Lester is paying Ricky for sex . <unk> made sure to establish the line of sight early on in the film to make the audience feel a sense of familiarity with the shot . The house interiors were filmed on the <unk> , on location , and on soundstages when overhead shots were needed . The inside of the <unk> ' home was shot at a house close to Interstate 405 and Sunset Boulevard in Los Angeles ; the inside of the <unk> ' home was shot in the city 's Hancock Park neighborhood . Ricky 's bedroom was designed to be cell @-@ like to suggest his " <unk> " personality , while at the same time blending with the high @-@ tech equipment to reflect his <unk> side . The production deliberately <unk> the use of red , as it was an important thematic signature elsewhere . The <unk> ' home uses cool blues , while the <unk> ' is kept in a " depressed military palette " . 
- <unk> ' dominating visual style was deliberate and composed , with a <unk> design that provided " a sparse , almost surreal feeling — a bright , <unk> , hard edged , near <unk> @-@ like take on American <unk> " ; <unk> constantly directed his set <unk> to empty the frame . He made Lester 's fantasy scenes " more fluid and graceful " , and <unk> made minimal use of <unk> , feeling that stable shots generated more tension . For example , when <unk> used a slow push in to the <unk> ' dinner table , he held the shot because his training as a theater director taught him the importance of putting distance between the characters . He wanted to keep the tension in the scene , so he only cut away when Jane left the table . <unk> did use a hand @-@ held camera for the scene in which Col. <unk> beats Ricky . <unk> said the camera provided the scene with a " <unk> [ ... ] off @-@ balance energy " . He also went hand @-@ held for the <unk> of Ricky 's <unk> footage . <unk> took a long time to get the quality of Ricky 's footage to the level he wanted . For the plastic @-@ bag footage , <unk> used wind machines to move the bag in the air . The scene took four takes ; two by the second unit did not satisfy <unk> , so he shot the scene himself . He felt his first take lacked grace , but for the last attempt , he changed the location to the front of a brick wall and added leaves on the ground . <unk> was satisfied by the way the wall gave definition to the outline of the bag . 
- <unk> avoided using close @-@ ups , as he believed the technique was <unk> ; he also cited Spielberg 's advice that he should imagine an audience <unk> at the bottom of the camera monitor , to keep in mind that he was shooting for display on a 40 @-@ foot ( 10 m ) screen . Spielberg — who visited the set a few times — also advised <unk> not to worry about costs if he had a " great idea " toward the end of a long working day . <unk> said , " That happened three or four times , and they are all in the movie . " Despite Spielberg 's support , DreamWorks and <unk> fought constantly over the schedule and budget , although the studio <unk> little with the film 's content . Spacey , <unk> and Hall worked for significantly less than their usual rates . American Beauty cost DreamWorks $ 15 million to produce , slightly above their projected sum . <unk> was so dissatisfied with his first three days ' filming that he obtained permission from DreamWorks to <unk> the scenes . He said , " I started with a wrong scene , actually , a comedy scene . And the actors played it way too big : [ ... ] it was badly shot , my fault , badly composed , my fault , bad costumes , my fault [ ... ] ; and everybody was doing what I was asking . It was all my fault . " Aware that he was a novice , <unk> drew on the experience of Hall : " I made a very conscious decision early on , if I didn 't understand something technically , to say , without embarrassment , ' I don 't understand what you 're talking about , please explain it . ' " 
- <unk> encouraged some improvisation ; for example , when Lester <unk> in bed beside Carolyn , the director asked Spacey to improvise several <unk> for the act in each take . <unk> said , " I wanted that not just because it was funny [ ... ] but because I didn 't want it to seem <unk> . I wanted it to seem like he was <unk> it out of his mouth without thinking . [ Spacey ] is so in control — I wanted him to break through . " Spacey obliged , eventually coming up with 35 phrases , but <unk> could not always keep a straight face , which meant the scene had to be shot 10 times . The production used small amounts of computer @-@ generated imagery . Most of the rose <unk> in Lester 's fantasies were added in post @-@ production , although some were real and had the wires holding them digitally removed . When Lester <unk> about Angela in a rose @-@ <unk> bath , the steam was real , save for in the overhead shot . To position the camera , a hole had to be cut in the ceiling , through which the steam escaped ; it was instead added digitally . 
- 
- = = = Editing = = = 
- 
- American Beauty was edited by Christopher <unk> and <unk> <unk> ; <unk> began in the position , but had to leave halfway through post @-@ production because of a scheduling conflict with Me , Myself and Irene ( 2000 ) ( in which Chris Cooper also starred ) . <unk> and an assistant edited the film for 10 days between the appointments . <unk> realized during editing that the film was different from the one he had envisioned . He believed he had been making a " much more whimsical , [ ... ] <unk> " film than what came together in the edit suite . Instead , <unk> was drawn to the emotion and darkness ; he began to use the score and shots he had intended to <unk> to craft the film along these lines . In total , he cut about 30 minutes from his original edit . The opening included a dream in which Lester imagines himself flying above the town . <unk> spent two days filming Spacey against <unk> , but removed the sequence as he believed it to be too whimsical — " like a <unk> brothers movie " — and therefore inappropriate for the tone he was trying to set . The opening in the final cut reused a scene from the middle of the film where Jane tells Ricky to kill her father . This scene was to be the revelation to the audience that the pair was not responsible for Lester 's death , as the way it was scored and acted made it clear that Jane 's request was not serious . However , in the portion he used in the opening — and when the full scene plays out later — <unk> used the score and a reaction shot of Ricky to leave a lingering ambiguity as to his guilt . The subsequent shot — an aerial view of the neighborhood — was originally intended as the plate shot for the <unk> effects in the dream sequence . 
- <unk> spent more time <unk> the first 10 minutes than the rest of the film taken together . He <unk> several versions of the opening ; the first edit included <unk> scenes in which Jane and Ricky are convicted of Lester 's murder , but <unk> <unk> these in the last week of editing because he felt they made the film lose its mystery , and because they did not fit with the theme of redemption that had emerged during production . <unk> believed the trial drew focus away from the characters and turned the film " into an episode of <unk> Blue " . Instead , he wanted the ending to be " a poetic mixture of dream and memory and narrative resolution " . When Ball first saw a completed edit , it was a version with truncated versions of these scenes . He felt that they were so short that they " didn 't really register " . <unk> and he argued , but Ball was more accepting after <unk> cut the sequences completely ; Ball felt that without the scenes , the film was more optimistic and had evolved into something that " for all its darkness had a really romantic heart " . 
- 
- = = = <unk> = = = 
- 
- Conrad Hall was not the first choice for director of photography ; <unk> believed he was " too old and too experienced " to want the job , and he had been told that Hall was difficult to work with . Instead , <unk> asked Fred <unk> , who turned the job down because he did not like the script . Hall was recommended to <unk> by Tom Cruise , because of Hall 's work on Without <unk> ( 1998 ) , which Cruise had executive produced . <unk> was directing Cruise 's then @-@ wife Nicole Kidman in the play The Blue Room during <unk> on American Beauty , and had already <unk> the whole film . Hall was involved for one month during <unk> ; his ideas for lighting the film began with his first reading of the script , and further passes allowed him to refine his approach before meeting <unk> . Hall was initially concerned that audiences would not like the characters ; he only felt able to identify with them during cast rehearsals , which gave him fresh ideas on his approach to the visuals . 
- Hall 's approach was to create peaceful compositions that evoked <unk> , to contrast with the turbulent on @-@ screen events and allow audiences to take in the action . Hall and <unk> first discussed the intended mood of a scene , but he was allowed to light the shot in any way he felt necessary . In most cases , Hall first lit the scene 's subject by " painting in " the blacks and whites , before adding fill light , which he reflected from <unk> or white card on the ceiling . This approach gave Hall more control over the shadows while keeping the fill light <unk> and the dark areas free of spill . Hall shot American Beauty in a 2 @.@ 39 : 1 aspect ratio in the Super 35 format , using <unk> Vision <unk> <unk> 35 mm film stock . He used Super 35 partly because its larger scope allowed him to capture elements such as the corners of the <unk> @-@ filled pool in its overhead shot , creating a frame around Angela within . He shot the whole film at the same T @-@ stop ( <unk> ) ; given his preference for shooting that wide , Hall favored high @-@ speed stocks to allow for more subtle lighting effects . He used <unk> Platinum cameras with the company 's <unk> series of prime and <unk> lenses . Hall employed <unk> Vision <unk> <unk> and <unk> <unk> stock for scenes with daylight effects . He had difficulty adjusting to <unk> 's newly introduced Vision release print stock , which , combined with his contrast @-@ heavy lighting style , created a look with too much contrast . Hall contacted <unk> , who sent him a batch of <unk> that was 5 % lower in contrast . Hall used a 1 / 8 inch <unk> Black <unk> filter for almost every scene , which he said in <unk> may not have been the best choice , as the optical steps required to blow Super 35 up for its <unk> release print led to a slight amount of degradation ; therefore , the <unk> from the filter was not required . When he saw the film in a theater , Hall felt that the image was slightly unclear and that had he not used the filter , the <unk> from the Super 35 – <unk> conversion would have generated an image closer to what he originally intended . 
- A shot where Lester and Ricky share a cannabis joint behind a building came from a misunderstanding between Hall and <unk> . <unk> asked Hall to prepare the shot in his absence ; Hall assumed the characters would look for privacy , so he placed them in a narrow passage between a truck and the building , intending to light from the top of the truck . When <unk> returned , he explained that the characters did not care if they were seen . He removed the truck and Hall had to rethink the lighting ; he lit it from the left , with a large light crossing the actors , and with a soft light behind the camera . Hall felt the consequent wide shot " worked perfectly for the tone of the scene " . Hall made sure to keep rain , or the suggestion of it , in every shot near the end of the film . In one shot during Lester 's encounter with Angela at the <unk> ' home , Hall created rain effects on the foreground cross lights ; in another , he partly lit the pair through French windows to which he had added material to make the rain run slower , intensifying the light ( although the strength of the outside light was unrealistic for a night scene , Hall felt it justified because of the strong contrasts it produced ) . For the close @-@ ups when Lester and Angela move to the <unk> , Hall tried to keep rain in the frame , lighting through the window onto the ceiling behind Lester . He also used rain boxes to produce rain patterns where he wanted without lighting the entire room . 
- 
- = = = Music = = = 
- 
- Thomas Newman 's score was recorded in Santa Monica , California . He mainly used percussion instruments to create the mood and rhythm , the inspiration for which was provided by <unk> . Newman " favored pulse , rhythm , and color over melody " , making for a more <unk> score than he had previously created . He built each cue around " small , <unk> repeating phrases " — often , the only variety through a " thinning of the texture for eight bars " . The percussion instruments included <unk> , <unk> , <unk> , piano , <unk> , and <unk> ; also featured were guitars , flute , and world music instruments . Newman also used electronic music and on " <unk> " tracks employed more unorthodox methods , such as tapping metal mixing bowls with a finger and using a <unk> mandolin . Newman believed the score helped move the film along without disturbing the " moral ambiguity " of the script : " It was a real delicate balancing act in terms of what music worked to preserve [ that ] . " 
- The soundtrack features songs by Newman , Bobby <unk> , The Who , Free , <unk> , The <unk> Who , Bill <unk> , Betty Carter , Peggy Lee , The Folk <unk> , Gomez , and Bob Dylan , as well as two cover versions — The Beatles ' " Because " performed by Elliott Smith , and Neil Young 's " Don 't Let It <unk> You Down " performed by Annie Lennox . Produced by the film 's music supervisor Chris <unk> , an abridged soundtrack album was released on October 5 , 1999 , and went on to be nominated for a Grammy Award for Best Soundtrack Album . An album featuring 19 tracks from Newman 's score was released on January 11 , 2000 , and won the Grammy Award for Best Score Soundtrack Album . <unk> considered the score to be one of Newman 's best , saying it " [ enabled ] the film 's <unk> aspirations " . In 2006 , the magazine chose the score as one of 20 essential soundtracks it believed spoke to the " complex and innovative relationships between music and screen storytelling " . 
- 
- = = Release = = 
- 
- 
- = = = <unk> = = = 
- 
- DreamWorks contracted Amazon.com to create the official website , marking the first time that Amazon had created a special section devoted to a feature film . The website included an overview , a photo gallery , cast and crew <unk> , and exclusive interviews with Spacey and <unk> . The film 's tagline — " look closer " — originally came from a cutting <unk> on Lester 's workplace <unk> by the set <unk> . DreamWorks ran parallel marketing campaigns and trailers — one aimed at adults , the other at teenagers . Both trailers ended with the poster image of a girl holding a rose . Reviewing the posters of several 1999 films , David <unk> of Entertainment Weekly rated American Beauty 's highly , saying it evoked the tagline ; he said , " You return to the poster again and again , thinking , this time you 're gonna find something . " DreamWorks did not want to test screen the film ; according to <unk> , the studio was pleased with it , but he insisted on one where he could question the audience afterward . The studio <unk> agreed and showed the film to a young audience in San Jose , California . <unk> claimed the screening went very well . 
- 
- = = = <unk> run = = = 
- 
- The film had its world premiere on September 8 , 1999 , at <unk> 's Egyptian Theatre in Los Angeles . Three days later , the film appeared at the Toronto International Film Festival . With the filmmakers and cast in attendance , it screened at several American universities , including the University of California at Berkeley , New York University , the University of California at Los Angeles , the University of Texas at Austin , and Northwestern University . 
- On September 15 , 1999 , American Beauty opened to the public in limited release at three theaters in Los Angeles and three in New York . More theaters were added during the limited run , and on October 1 , the film officially entered wide release by screening in <unk> theaters across North America . The film grossed $ 8 @,@ 188 @,@ <unk> over the weekend , ranking third at the box office . <unk> <unk> by the market research firm <unk> gave American Beauty a " B + " grade on average . The theater count hit a high of 1 @,@ <unk> at the end of the month , before a gradual decline . Following American Beauty 's wins at the 57th Golden Globe Awards , DreamWorks re @-@ expanded the theater presence from a low of 7 in mid @-@ February , to a high of 1 @,@ 990 in March . The film ended its North American theatrical run on June 4 , 2000 , having grossed $ 130 @.@ 1 million . 
- American Beauty had its European premiere at the London Film Festival on November 18 , 1999 ; in January 2000 , it began to screen in various territories outside North America . It debuted in Israel to " potent " returns , and limited releases in Germany , Italy , Austria , Switzerland , the Netherlands and Finland followed on January 21 . After January 28 opening weekends in Australia , the United Kingdom , Spain and Norway , American Beauty had earned $ 7 million in 12 countries for a total of $ 12 @.@ 1 million outside North America . On February 4 , American Beauty debuted in France and Belgium . <unk> to <unk> theaters in the United Kingdom , the film ranked first at the box office with $ 1 @.@ 7 million . On the weekend of February 18 — following American Beauty 's eight nominations for the 72nd Academy Awards — the film grossed $ 11 @.@ 7 million from 21 territories , for a total of $ 65 @.@ 4 million outside North America . The film had " dazzling " <unk> in Hungary , Denmark , the Czech Republic , <unk> , and New Zealand . 
- As of February 18 , the most successful territories were the United Kingdom ( $ 15 @.@ 2 million ) , Italy ( $ 10 @.@ 8 million ) , Germany ( $ 10 @.@ 5 million ) , Australia ( $ 6 million ) , and France ( $ 5 @.@ 3 million ) . The Academy Award nominations meant strong performances continued across the board ; the following weekend , American Beauty grossed $ 10 @.@ 9 million in 27 countries , with strong <unk> in Brazil , Mexico , and South Korea . Other high spots included robust returns in Argentina , Greece , and Turkey . On the weekend of March 3 , 2000 , American Beauty debuted strongly in Hong Kong , Taiwan , and Singapore , markets traditionally " not receptive to this kind of <unk> fare " . The impressive South Korean performance continued , with a return of $ 1 @.@ 2 million after nine days . In total , American Beauty grossed $ 130 @.@ 1 million in North America and $ 226 @.@ 2 million internationally , for $ <unk> @.@ 3 million worldwide . 
- 
- = = = Home media = = = 
- 
- American Beauty was released on VHS on May 9 , 2000 , and on DVD with the <unk> format on October 24 , 2000 . Before the North American rental release on May 9 , <unk> Video wanted to purchase hundreds of thousands of extra copies for its " guaranteed title " range , whereby anyone who wanted to rent the film would be guaranteed a copy . <unk> and DreamWorks could not agree on a profit @-@ sharing deal , so <unk> ordered two @-@ thirds the number of copies it originally intended . DreamWorks made around one million copies available for rental ; <unk> 's share would usually have been about 400 @,@ 000 of these . Some <unk> stores only displayed 60 copies , and others did not display the film at all , forcing customers to ask for it . The strategy required staff to read a statement to customers explaining the situation ; <unk> claimed it was only " [ monitoring ] customer demand " due to the reduced availability . <unk> 's strategy leaked before May 9 , leading to a 30 % order increase from other retailers . In its first week of rental release , American Beauty made $ 6 @.@ 8 million . This return was lower than would have been expected had DreamWorks and <unk> reached an agreement . In the same year , The Sixth <unk> made $ 22 million , while Fight Club made $ 8 @.@ 1 million , though the latter 's North American theatrical performance was just 29 % that of American Beauty . <unk> 's strategy also affected rental fees ; American Beauty averaged $ 3 @.@ 12 , compared with $ 3 @.@ 40 for films that <unk> fully promoted . Only 53 % of the film 's rentals were from large outlets in the first week , compared with the usual 65 % . 
- The DVD release included a behind @-@ the @-@ scenes <unk> , film audio commentary from <unk> and Ball , and a storyboard presentation with discussion from <unk> and Hall . In the film commentary , <unk> refers to deleted scenes he intended to include in the release . However , these scenes are not on the DVD , as he changed his mind after recording the commentary ; <unk> felt that to show scenes he previously chose not to use would <unk> from the film 's integrity . 
- On September 21 , 2010 , Paramount Home Entertainment released American Beauty on Blu @-@ ray , as part of Paramount 's <unk> Series . All the extras from the DVD release were present , with the theatrical trailers upgraded to HD . 
- 
- = = Critical reception = = 
- 
- American Beauty was widely considered the best film of 1999 by the American press . It received overwhelming praise , chiefly for Spacey , <unk> and Ball . Variety reported that " no other 1999 movie has benefited from such universal <unk> . " It was the best @-@ received title at the Toronto International Film Festival ( <unk> ) , where it won the People 's Choice award after a ballot of the festival 's audiences . <unk> 's director , Piers <unk> , said , " American Beauty was the <unk> of the festival , the film most talked @-@ about . " 
- Writing in Variety , Todd McCarthy said the cast ensemble " could not be better " ; he praised Spacey 's " handling of <unk> , subtle sarcasm , and blunt talk " and the way he <unk> Lester with " genuine feeling " . Janet Maslin in The New York Times said Spacey was at his " <unk> and most agile " to date , and Roger <unk> of the Chicago Sun @-@ Times singled Spacey out for successfully portraying a man who " does <unk> and foolish things [ but who ] doesn 't <unk> himself " . Kevin Jackson of <unk> & Sound said Spacey impressed in ways distinct from his previous performances , the most satisfying aspect being his portrayal of " both <unk> and hero " . Writing in Film Quarterly , Gary <unk> praised the actors , but said that characters such as Carolyn and Col. <unk> were stereotypes . <unk> accused <unk> and Ball of identifying too readily with Jane and Ricky , saying the latter was their " fantasy figure " — a <unk> boy who 's an <unk> wealthy artist able to " finance [ his ] own projects " . <unk> said Angela was the most <unk> teenager , in particular with her " <unk> familiar " attempts to " live up to an unworthy image of herself " . Maslin agreed that some characters were unoriginal , but said their detailed <unk> made them memorable . Kenneth <unk> of the Los Angeles Times said the actors <unk> " <unk> " with what were difficult roles ; he called Spacey 's performance " the energy that drives the film " , saying the actor commanded audience involvement despite Lester not always being sympathetic . " Against considerable odds , we do like [ these characters ] , " <unk> concluded . 
- Maslin felt that <unk> directed with " terrific visual <unk> " , saying his <unk> style balanced " the <unk> and bright " and that he evoked the " delicate , <unk> power @-@ playing vignettes " of his theater work . Jackson said <unk> ' theatrical roots rarely showed , and that the " most remarkable " aspect was that Spacey 's performance did not overshadow the film . He said that <unk> worked the script 's <unk> smoothly , to the ensemble 's strengths , and staged the tonal shifts <unk> . McCarthy believed American Beauty a " stunning card of introduction " for film <unk> <unk> and Ball . He said <unk> ' " sure hand " was " as precise and controlled " as his theater work . McCarthy cited Hall 's involvement as fortunate for <unk> , as the cinematographer was " <unk> " at conveying the themes of a work . <unk> agreed that <unk> ' choice of collaborators was " <unk> " , naming Hall and Newman in particular . <unk> suggested that American Beauty may have benefited from <unk> ' inexperience , as his " anything 's possible daring " made him attempt beats that more <unk> directors might have avoided . <unk> felt that <unk> ' accomplishment was to " capture and enhance [ the ] <unk> " of Ball 's script — the simultaneously " <unk> [ ... ] and <unk> real " characters . <unk> , while critical of many of <unk> and Ball 's choices , admitted the film showed off their " considerable talents " . 
- <unk> cited Ball 's lack of <unk> when writing the film as the reason for its uniqueness , in particular the script 's subtle changes in tone . McCarthy said the script was " as fresh and distinctive " as any of its American film contemporaries , and praised how it analyzed the characters while not <unk> narrative pace . He called Ball 's dialogue " tart " and said the characters — Carolyn <unk> — were " deeply drawn " . One other flaw , McCarthy said , was the revelation of Col. <unk> ' homosexuality , which he said evoked " <unk> <unk> " . Jackson said the film <unk> its clichéd setup to become a " wonderfully resourceful and <unk> comedy " . He said that even when the film played for sitcom laughs , it did so with " unexpected nuance " . <unk> criticized how the film made a mystery of Lester 's murder , believing it <unk> and simply a way of generating suspense . McCarthy cited the production and costume design as <unk> , and said the soundtrack was good at creating " ironic counterpoint [ s ] " to the story . <unk> concluded that American Beauty was " vital but uneven " ; he felt the film 's examination of " the ways which teenagers and adults imagine each other 's lives " was its best point , and that although Lester and Angela 's dynamic was familiar , its romantic irony stood beside " the most enduring literary treatments " of the theme , such as <unk> . Nevertheless , <unk> believed that the film 's themes of <unk> and conformity in American <unk> were " <unk> " . McCarthy conceded that the setting was familiar , but said it merely provided the film with a " starting point " from which to tell its " subtle and <unk> judged tale " . Maslin agreed ; she said that while it " takes aim at targets that are none too fresh " , and that the theme of <unk> did not surprise , the film had its own " corrosive novelty " . <unk> awarded American Beauty four stars out of four , and <unk> said it was layered , <unk> , complex , and surprising , concluding it was " a hell of a picture " . 
- A few months after the film 's release , reports of a backlash appeared in the American press , and the years since have seen its critical regard <unk> . In 2005 , <unk> named American Beauty as one of 20 " most <unk> movies of all time " ; <unk> accepted the <unk> of the critical <unk> , saying , " I thought some of it was entirely justified — it was a little <unk> at the time . " 
- Currently , the film holds an 88 % score on Rotten Tomatoes based on 180 reviews , with an average rating of 8 @.@ 2 / 10 ; the critical consensus reads , " <unk> cast and <unk> with dark , acid wit , American Beauty is a smart , provocative high point of late ' 90s mainstream Hollywood film . " Metacritic gives the film a score of 86 , based on 33 reviews , indicating " universal acclaim . " 
- 
- = = Accolades = = 
- 
- American Beauty was not considered an immediate favorite to dominate the American awards season . Several other contenders opened at the end of 1999 , and US critics spread their honors among them when compiling their end @-@ of @-@ year lists . The Chicago Film Critics Association and the Broadcast Film Critics Association named the film the best of 1999 , but while the New York Film Critics Circle , the National Society of Film Critics and the Los Angeles Film Critics Association recognized American Beauty , they gave their top awards to other films . By the end of the year , reports of a critical backlash suggested American Beauty was the underdog in the race for Best Picture ; however , at the Golden Globe Awards in January 2000 , American Beauty won Best Film , Best Director and Best Screenplay . 
- As the nominations for the 72nd Academy Awards approached , a <unk> had not emerged . DreamWorks had launched a major campaign for American Beauty five weeks before ballots were due to be sent to the 5 @,@ 600 Academy Award voters . Its campaign combined traditional advertising and publicity with more focused strategies . Although direct mail campaigning was prohibited , DreamWorks reached voters by promoting the film in " casual , comfortable settings " in voters ' communities . The studio 's candidate for Best Picture the previous year , Saving Private Ryan , lost to Shakespeare in Love , so the studio took a new approach by hiring outsiders to provide input for the campaign . It hired three veteran consultants , who told the studio to " think small " . Nancy <unk> encouraged DreamWorks to produce a special about the making of American Beauty , to set up displays of the film in the communities ' <unk> , and to arrange a question @-@ and @-@ answer session with <unk> for the British Academy of Film and Television Arts . Dale <unk> advised the studio to advertise in free publications that circulated in Beverly Hills — home to many voters — in addition to major newspapers . <unk> arranged to screen American Beauty to about 1 @,@ 000 members of the Actors Fund of America , as many participating actors were also voters . Bruce Feldman took Ball to the Santa Barbara International Film Festival , where Ball attended a private dinner in honor of Anthony Hopkins , meeting several voters who were in attendance . 
- In February 2000 , American Beauty was nominated for eight Academy Awards ; its closest rivals , The Cider House Rules and The <unk> , received seven nominations each . In March 2000 , the major industry labor organizations all awarded their top honors to American Beauty ; perceptions had shifted — the film was now the favorite to dominate the Academy Awards . American Beauty 's closest rival for Best Picture was still The Cider House Rules , from <unk> . Both studios mounted aggressive campaigns ; DreamWorks bought 38 % more advertising space in Variety than <unk> . On March 26 , 2000 , American Beauty won five Academy Awards : Best Picture , Best Director , Best Actor ( Spacey ) , Best Original Screenplay and Best <unk> . At the 53rd British Academy Film Awards , American Beauty won six of the 14 awards for which it was nominated : Best Film , Best Actor , Best Actress ( <unk> ) , Best <unk> , Best Film Music and Best Editing . In 2000 , the <unk> Guild of America recognized DreamWorks for the best film publicity campaign . In September 2008 , Empire named American Beauty the <unk> " Greatest Movie of All Time " after a poll of 10 @,@ 000 readers , 150 filmmakers , and 50 film critics , the fourth @-@ highest ranked movie from 1999 ( behind Fight Club , The Matrix , and Magnolia ) . In 2013 , the Writers Guild of America ranked the screenplay number 38 on its list of 101 greatest screenplays . 
- The film was nominated for <unk> 's 100 Years ... 100 Movies ( 10th <unk> Edition ) in 2007 . 
- 
- 
- = Christopher <unk> = 
- 
- Christopher <unk> ( September 21 , 1758 – March 1 , 1827 ) was a prominent Massachusetts lawyer , <unk> politician , and U.S. diplomat . Born into a family divided by the American Revolution , <unk> sided with the victorious <unk> , established a successful law practice in Boston , and built a fortune by purchasing Revolutionary government debts at a discount and receiving full value for them from the government . 
- <unk> entered politics in 1788 , serving briefly in the Massachusetts legislature before being appointed U.S. District Attorney for Massachusetts . He was then appointed by President George Washington to a diplomatic commission dealing with maritime claims in Great Britain . He returned to Massachusetts in 1804 and <unk> state politics , running unsuccessfully for governor several times before winning in 1809 . He served one term , losing to Democratic @-@ Republican Elbridge Gerry in 1810 . He was appointed to the US Senate by Governor Caleb Strong in 1813 , where he led opposition to the War of 1812 . 
- <unk> invested his fortune in a variety of businesses , including important infrastructure projects such as the Middlesex Canal and a bridge across the Charles River . He was a major <unk> in the early textile industry , funding the Boston Manufacturing Company and the <unk> Manufacturing Company , whose business established the city of Lowell , Massachusetts . <unk> was involved in a variety of charitable causes , and was a major benefactor of Harvard College , where the first library was named in his honor . His palatial mansion in <unk> , Massachusetts , now known as <unk> Place , is one of the finest extant examples of <unk> architecture , and has been declared a National Historic Landmark . 
- 
- = = Early years = = 
- 
- Christopher <unk> was born in Boston on September 21 , 1758 , one of many children of Frances and John <unk> , a successful merchant and <unk> . He was the youngest of their three sons to survive to adulthood . He attended Boston Latin School , and entered Harvard College at the young ( even for the time ) age of thirteen . At the outset of the American Revolutionary War and the Siege of Boston in 1775 , Harvard 's buildings were occupied by the Continental Army , and <unk> temporarily continued his studies in Bradford until Harvard could resume operations in Concord . While at Harvard <unk> participated in a speaking club , and formed significant lifelong friendships with Rufus King and John <unk> . 
- <unk> graduated in 1776 , and promptly enlisted in the Continental artillery regiment of his brother @-@ in @-@ law Thomas <unk> , where he served as a clerk until 1778 . The <unk> family was divided by the war : <unk> 's father was a <unk> who left Boston when the British Army evacuated the city in March 1776 . <unk> was consequently called upon to support his mother and three sisters , who remained in Boston . In 1779 <unk> successfully petitioned the state for the remaining family 's share of his father 's seized assets . 
- 
- = = Early legal career = = 
- 
- After his military service <unk> studied law with John Lowell , and was admitted to the bar in 1778 after a comparatively brief tutelage . <unk> 's law practice flourished , in part because many <unk> lawyers had fled Massachusetts . <unk> 's clients included <unk> seeking to recover some of their assets , as well as London @-@ based British merchants with claims to pursue . His <unk> were generally well @-@ reasoned , and he was seen as a successful trial lawyer . 
- <unk> grew his fortune by investing carefully in revolutionary currency and bonds . The <unk> he purchased were paper that had been given to Continental Army soldiers in lieu of pay , which they often sold at a steep discount . One batch of <unk> he purchased , for instance , cost him about $ 3 @,@ 700 but had a face value of $ 25 @,@ 000 . In 1785 he married Rebecca <unk> Payne , daughter of a wealthy merchant , maritime <unk> , and director of the Bank of Massachusetts . The couple were known for their social <unk> and became prominent members of Boston society . 
- In 1786 <unk> became concerned about a rise in anti @-@ lawyer sentiment in Massachusetts . <unk> over harsh policies pursued by Governor James <unk> <unk> into <unk> ' Rebellion , which required militia action to crush in 1787 . <unk> was one of several high @-@ profile lawyers assigned to defend participants in the rebellion ( included in this group were Theodore <unk> , Caleb Strong , James Sullivan , Levi Lincoln , Sr. , and Thomas <unk> ) . Although many rebels were ultimately convicted , a large number received amnesty . In 1788 , <unk> was elected a delegate to the 1789 Massachusetts convention that ratified the United States Constitution . His election was contested because Boston , where he lived , was at the time more inclined toward state power . <unk> nonetheless was strongly <unk> , urging support of the new Constitution . 
- 
- = = <unk> , banker , and speculator = = 
- 
- In 1788 <unk> was elected to the Massachusetts House of Representatives . He took a leading role in adopting the state 's rules for actions required of it by the new federal constitution . By his proposal the legislature decided that presidential electors would be chosen by a joint session . He also proposed that the state House and Senate agree by separate votes on choices for the United States Senate , a process that would significantly reduce popular input to the choice . His choice was ultimately rejected in favor of a process whereby the House selected a slate of candidates , from which the Senate would choose one . In 1789 <unk> decided to stand for reelection , but lost , owing to strong anti @-@ nationalist fervor in Boston at the time . He managed to win a seat later , when a special election was held after <unk> opened several seats . 
- <unk> 's financial speculations in the late <unk> significantly multiplied his wealth . In 1788 he and Andrew <unk> , a Boston businessman who had retained <unk> for legal services , entered into a secret agreement to purchase Continental <unk> with a face value of $ 100 @,@ 000 in a speculative bid that their value would rise . By late October of that year , the pair had met this goal : <unk> had purchased $ 90 @,@ 000 worth of paper for about $ 20 @,@ 000 , and encouraged <unk> to purchase more than the $ 11 @,@ 000 he had acquired if his funding would allow for it . <unk> also purchased Massachusetts war @-@ related debts , and lobbied Massachusetts <unk> for the U.S. government to assume those as well . 
- <unk> 's <unk> was realized when in 1790 the United States Congress , acting on a proposal made by Alexander Hamilton and supported by <unk> 's friend Rufus King , passed legislation that exchanged Continental and state paper for new U.S. paper at face value . Not only did <unk> win on this exchange , but the paper he received appreciated in value before he sold it . The exact amount he made is unclear from the surviving documents : John <unk> Adams wrote that <unk> 's speculations made him the wealthiest lawyer in the country . 
- The success of <unk> 's speculations prompted him to enter a partnership with <unk> , William <unk> and Daniel Parker in an attempt to acquire U.S. foreign debt obligations on favorable terms . Parker was a business partner of <unk> 's , and <unk> was an influential New York businessman and Treasury Department official whose lavish lifestyle impressed <unk> . The partnership promoted sales of U.S. lands in Europe , and sought to acquire U.S. obligations to France . Although <unk> sank $ 10 @,@ 000 into this venture , it failed : more powerful and experienced Dutch <unk> <unk> the Americans . <unk> also engaged in other ventures with these partners , but apparently carefully stayed with financial speculations , and avoided the partners ' less successful land ventures . 
- Much of <unk> 's financial activity was mediated through the Bank of Massachusetts , where his father @-@ in @-@ law was a director . <unk> himself was elected to its board in 1785 , when he also became a shareholder . During his time on the board the bank <unk> its regulations on loan <unk> , a move that improved the stability of its capital . <unk> used the bank for most of his personal deposits , but also drew on lines of credit for as much as several thousand dollars . The bank shares he held paid relatively high <unk> until 1791 , when the bank received serious competition from the First Bank of the United States . 
- The Bank of the United States was established by Alexander Hamilton to provide stable banking services on a national scale , and sought to open a branch in Boston . Hamilton recruited heavily in the Bank of Massachusetts , and <unk> decided to make the move . He sold his shares in the Massachusetts bank , and became a director of the Boston branch of the U.S. Bank . He also purchased 200 shares in the new bank , a relatively large investment . <unk> was influential in making hiring decisions for the branch , and sought to merge state @-@ chartered banks into the organization , arguing that only a nationally chartered bank could provide consistent and stable service . <unk> resigned from the board in 1794 , citing the demands of his law practice . 
- <unk> 's financial successes enabled him to join the elite society of Boston . In 1789 he purchased a large mansion on fashionable <unk> Square , and also bought a country estate in <unk> that grew over time to 300 acres ( 120 ha ) . He had a house built on the estate , most of which he operated as a gentleman farmer . He and other similarly @-@ situated <unk> formed the Massachusetts Society for <unk> Agriculture , of which he served as a trustee for several years ; the organization was not seen as significantly contributing to advances in agriculture . 
- 
- = = District attorney and diplomat = = 
- 
- In 1789 President George Washington appointed <unk> the first United States Attorney for Massachusetts as a reward for his support . <unk> controversially refused to resign from the state legislature , arguing that the state constitution 's prohibitions against holding multiple offices did not apply to federal posts . He eventually resigned the legislative seat under protest because of pressure from his fellow legislators . 
- <unk> served as district attorney until 1796 . His principal matter of concern was the enforcement of U.S. neutrality with respect to the French Revolutionary Wars . He attempted several times to <unk> the French consul in Boston , Antoine <unk> , for <unk> and operating <unk> out of the Port of Boston , but he was <unk> by local <unk> that <unk> with the French . <unk> was eventually expelled on orders from President George Washington based on evidence provided by <unk> . 
- <unk> also promoted anti @-@ French sentiment with political writings in Massachusetts newspapers . Writing under the pseudonym " <unk> " , he denounced the formation of " Democratic Societies " formed to oppose <unk> policy and support pro @-@ French positions . He suggested to President Washington that someone be sent to England to negotiate with the British . John Jay traveled to London in 1794 and negotiated the Jay Treaty , whose ratification <unk> vocally supported . Although <unk> was hostile to French policy , he was on friendly terms with individual Frenchmen : he hosted the future French statesman <unk> when he visited the U.S. 
- In 1796 Washington appointed him as a commissioner representing the United States to handle maritime claims under the terms the Jay Treaty . As a result the <unk> moved to England that year , establishing a residence in the fashionable Hyde Park area . The commission was established to <unk> claims emanating from British <unk> of American vessels and cargoes , and from British claims relating to violations of American neutrality in the ongoing French Revolutionary Wars . It consisted of three Americans ( <unk> , William Pinkney , and John <unk> ) and two British commissioners ( John <unk> and Nicholas <unk> ) ; <unk> was chosen by the other four because he was deemed to sufficiently " fair @-@ minded " to cast deciding votes in the event of disagreements . That year he was also elected a Fellow of the American Academy of Arts and Sciences . 
- Although <unk> was well received by the British establishment , the work suffered from what <unk> called a " <unk> of process " , and he considered requesting a transfer in 1798 . In 1800 it ground to a halt because another board established by the treaty to resolve outstanding Revolutionary War claims against the United States had not yet met , and the British stopped the claims processing until resolution of the other issues got underway . <unk> used this break to briefly return to America and assess the condition of his <unk> estate , where the house had been largely destroyed by fire in <unk> . After his return to London , with the commission work still stopped , he and Rebecca embarked on a tour of Europe . They visited Holland , Belgium , and Switzerland , and spent six months in Paris . During this trip , and later ones in England and Scotland , they took note of the architecture of country estates , and began planning a new house for their <unk> property . 
- The commission resumed its work in early 1802 , and had resolved all outstanding claims by August 1803 . It awarded $ 110 @,@ 000 to British claimants and over $ 6 million to American claimants . The lopsided result is due to the vastly larger number of American claims , but also to some key early decisions that favored American interpretations in the processing of the claims , and by a British administration that sought to remain in America 's good <unk> . 
- The <unk> 's social circle in England revolved around his good friend Rufus King , who was appointed Ambassador to Great Britain in 1796 , along with other Massachusetts expatriates . When King left his post in May 1803 he named <unk> to head the London embassy as <unk> d <unk> . Although President Thomas Jefferson never issued a formal appointment , the British government accepted his role for the two @-@ month interval between King 's departure and the arrival of James Monroe as King 's replacement . The <unk> sailed for Boston in the spring of 1804 . 
- Rebecca <unk> used their exposure to European country estates to design a lavish new building for their <unk> estate during their English <unk> . Designed with the assistance of French architect Joseph @-@ Guillaume <unk> and probably also influenced by the works of English architect Sir John <unk> , the house that was built upon their return to the United States in 1804 ( now known as <unk> Place ) is one of the finest extant examples of <unk> architecture . 
- 
- = = <unk> and state <unk> = = 
- 
- Soon after his return to the United States , <unk> <unk> state politics , winning election to the Massachusetts Senate . He was active in the state <unk> Party organization , sitting on its secret central committee . He resumed his law practice , in which he took on as a student Daniel Webster . One of the highest profile cases he took on was the 1807 defense of Thomas <unk> , accused of murdering Charles Austin . <unk> , an older <unk> attorney , had been retained to assist in the collection of a debt from Austin 's Republican father . In the politically charged atmosphere of the day in Boston , <unk> , fearing for his own safety , had armed himself with a <unk> pistol . The younger Austin had , apparently on his own initiative , sought to beat <unk> with a cane , and <unk> fatally shot him in the encounter . <unk> was <unk> by Attorney General ( and future <unk> gubernatorial opponent ) James Sullivan , and the defense also included arch @-@ <unk> Harrison Gray <unk> . <unk> argued <unk> acted in self @-@ defense ; <unk> was acquitted of murder by a jury whose <unk> was <unk> and <unk> Paul <unk> after fifteen minutes ' <unk> . 
- <unk> also resumed business activities upon his return . He invested in a wide variety of businesses and infrastructure , spurring economic activity in the state . His investments ranged widely , including maritime insurance ( where is father @-@ in @-@ law had made his fortune ) , bridges , locks , canals , and textiles . He was a major <unk> in the Middlesex Canal , the <unk> Bridge ( the first to connect Boston to Cambridge ) , and the Boston Manufacturing Company , whose factory proving the single @-@ site production of textiles was in <unk> near his estate . Not all of his ventures panned out : the canal was in the long run a financial failure , as were efforts with other collaborators to develop <unk> Point , the Cambridge side of the <unk> Bridge . The textile mill , however , was a success , and <unk> invested in the <unk> Manufacturing Company . When it decided to locate in what is now Lowell , Massachusetts , <unk> purchased shares in the <unk> of <unk> and <unk> , which operated ( and still owns today ) the Lowell canals . 
- In 1806 <unk> won election to the State Senate . That year the Republicans were in the majority , and the election for governor was close enough to require a <unk> . The legislature <unk> the ballots in a partisan manner ( for example , retaining ballots containing <unk> versions of Republican James Sullivan 's name and discarding similar ballots marked for <unk> Caleb Strong ) . <unk> and other <unk> raised a public <unk> , and the legislature relented , eventually <unk> Strong as the winner . 
- <unk> ran unsuccessfully for Governor of Massachusetts in 1807 and 1808 against a rising tide of <unk> in the state , losing both times to moderate Republican James Sullivan . The <unk> gained control of the state legislature in 1808 in a backlash against Republican economic policies , but <unk> was criticized for his failure to aggressively support state protests against the <unk> Act of 1807 , which had a major negative effect on the state 's large merchant fleet . <unk> was in 1808 elected to the Massachusetts House of Representatives , where he successfully led <unk> efforts to ensure the selection of a <unk> slate of presidential electors . He also spearheaded actions to drive Senator John <unk> Adams from the <unk> Party over his support of Thomas Jefferson 's foreign policy . The legislature elected Adams ' successor nine months early , and gave Adams sufficiently <unk> instructions that he resigned the post and joined with the Republicans . 
- 
- = = Governor = = 
- 
- <unk> led the <unk> to victory in 1809 against Sullivan 's successor , Levi Lincoln , Sr. , who had taken over as acting governor upon Sullivan 's death late in 1808 . During <unk> 's term the principal domestic issue occupying state politics was a banking crisis stimulated by the federal policy of <unk> trade with Great Britain and France , then embroiled in the Napoleonic Wars . Although the crisis caused a number of bank failures in New England , Massachusetts banks largely escaped unscathed . 
- Foreign policy played a major role in <unk> 's administration . The legislature passed resolves opposing the federal government 's hardline policy against trade and diplomatic relations with the United Kingdom ( then embroiled in the Napoleonic Wars ) , and <unk> in early 1810 invited Francis James Jackson , who had been rejected as the UK 's ambassador to the US , to visit the state . This pressure may have played a role in President James Madison 's decision to renew relations with the UK and accept Jackson 's credentials . 
- The <unk> of the war threat , and the choice by the Republicans of the popular Elbridge Gerry as their candidate brought a challenge to <unk> control of Massachusetts in the 1810 elections . The <unk> Gerry and Republican <unk> criticized <unk> for his lavish lifestyle , including his palatial <unk> residence and <unk> activities he organized as governor , and highlighted his <unk> family connections while emphasizing Gerry 's <unk> patriotism . Gerry won the election . Jackson did visit Boston , but he was greeted not by <unk> , but Gerry . <unk> ran against Gerry again in 1811 , but lost in another <unk> campaign . 
- <unk> was granted an honorary law degree from Harvard in 1809 . He served on the college 's Board of <unk> from 1810 to 1815 and as a Fellow from 1816 to 1820 . Harvard 's first library building , a Gothic structure built in 1838 of <unk> granite , was named in his honor , but was demolished when <unk> Library was built in its place in 1915 . ( This structure is found on the seal of the city of Cambridge . ) One of the residential Winthrop House 's buildings is called <unk> Hall in his honor . 
- 
- = = United States Senator = = 
- 
- In the spring of 1813 , he was appointed by Governor Caleb Strong to fill the U.S. Senate seat vacated by the resignation of Senator James Lloyd . He served from May 5 , 1813 to May 30 , 1816 , winning reelection to the seat in 1814 . He opposed the ongoing War of 1812 in these years , with his earlier diplomatic experience providing valuable knowledge to <unk> interests . He expressed approval of the 1814 Hartford Convention in which the New England states aired grievances concerning Republican governance of the country and the conduct of the war . 
- <unk> <unk> to the Treaty of <unk> that ended the war , but was unhappy that the nation had not gained anything from the war . He resigned in June 1816 , unhappy with the politics of Washington and suffering from poor health . Although he was no longer active in politics , he continued to express opinions on the subjects of the day , opposing the 1820 Missouri <unk> and <unk> the " great <unk> & <unk> " of <unk> Governor John Brooks . 
- 
- = = Later years and legacy = = 
- 
- <unk> remained active in the administration of Harvard , and was active in a number of organizations , including the American Academy of Arts and Sciences and the Massachusetts Historical Society ( whose president he was from 1806 to 1818 ) . He was also elected a member of the American <unk> Society in 1814 . <unk> spent most of his later years at his country estate in <unk> , suffering from worsening <unk> <unk> that made walking increasingly difficult . His declining health and lack of social scene in <unk> led him in 1822 to return to Boston in the winters . He died on March 1 , 1827 in Boston and is buried in its <unk> <unk> Ground . 
- <unk> 's wife died in 1834 ; the couple had no children . The major <unk> of the <unk> estate was Harvard ( which received an estimated $ 100 @,@ 000 ) , although <unk> were also made to the American Academy of Arts and Sciences and the Massachusetts Historical Society . The <unk> estate passed through several hands and was subdivided over time . The mansion was saved from demolition by the <unk> Place Society ( established for the purpose of preserving it ) , which now operates it as a museum . It was declared a National Historic Landmark in 1970 . 
- 
- 
- = <unk> = 
- 
- <unk> ( / <unk> / ; Latin : <unk> Claudius Caesar Augustus <unk> ; 15 December 37 AD – 9 June 68 AD ) was Roman Emperor from 54 to 68 , and the last in the Julio @-@ <unk> dynasty . <unk> was adopted by his grand @-@ uncle Claudius to become his heir and successor , and succeeded to the throne in 54 following Claudius ' death . 
- <unk> focused much of his attention on diplomacy , trade and enhancing the cultural life of the empire , but according to the historian <unk> he was viewed by the Roman people as <unk> and corrupt . He ordered theatres built and promoted athletic games . During his reign , the <unk> general <unk> conducted a successful war and negotiated peace with the <unk> Empire . His general <unk> <unk> crushed a revolt in Britain . <unk> annexed the <unk> Kingdom to the empire and began the First Jewish – Roman War . 
- In 64 AD , most of Rome was destroyed in the Great Fire of Rome , which many Romans believed <unk> himself had started in order to clear land for his planned palatial complex , the <unk> <unk> . In 68 , the rebellion of <unk> in Gaul and later the acclamation of <unk> in <unk> drove <unk> from the throne . Facing a false report of being denounced as a public enemy who was to be executed , he committed suicide on 9 June 68 ( the first Roman emperor to do so ) . His death ended the Julio @-@ <unk> dynasty , sparking a brief period of civil wars known as the Year of the Four <unk> . <unk> 's rule is often associated with <unk> and extravagance . He is known for many executions , including that of his mother , and the probable murder by poison of his <unk> <unk> . 
- <unk> was <unk> to have had captured Christians dipped in oil and set on fire in his garden at night as a source of light . This view is based on the writings of <unk> , <unk> and <unk> <unk> , the main surviving sources for <unk> 's reign , but a few surviving sources paint <unk> in a more favourable light . Some sources , including some mentioned above , portray him as an emperor who was popular with the common Roman people , especially in the East . Some modern historians question the reliability of ancient sources when reporting on <unk> 's <unk> acts . 
- 
- = = Early life = = 
- 
- 
- = = = Family = = = 
- 
- Lucius <unk> <unk> , <unk> , was born on 15 December 37 in <unk> ( modern <unk> and <unk> ) , near Rome . He was the only son of <unk> <unk> <unk> and <unk> the Younger , sister of Emperor <unk> . 
- <unk> 's father , <unk> , was the son of Lucius <unk> <unk> ( consul 16 BC ) and <unk> Major . <unk> was thus the grandson of <unk> <unk> <unk> ( consul 32 BC ) and probably <unk> <unk> on his father 's side , and the grandson of Mark Antony and Octavia Minor on his mother 's side . Thus , <unk> had as his paternal grandmother <unk> Major , and also claimed more remote descent from <unk> Minor as a great @-@ grandson — later grandson after Claudius adopted him . 
- Through Octavia , <unk> was the great @-@ nephew of Caesar Augustus . <unk> 's father had been employed as a <unk> and was a member of <unk> 's staff when the latter travelled to the East ( some apparently think <unk> refers to Augustus 's adopted son <unk> Caesar here , but this is not likely ) . 
- <unk> 's father was described by <unk> as a murderer and a <unk> who was charged by Emperor <unk> with treason , adultery and <unk> . <unk> died , allowing him to escape these charges . <unk> 's father died of <unk> ( " <unk> " ) in 39 when <unk> was two . 
- <unk> 's mother was <unk> the Younger , a great @-@ granddaughter of Caesar Augustus and his wife <unk> through their daughter Julia the Elder and her husband Marcus <unk> <unk> . <unk> 's father , <unk> , was a grandson of Augustus 's wife , <unk> , on one side and to Mark Antony and Octavia on the other . <unk> ' mother <unk> Minor , was a daughter of Octavia Minor and Mark Antony . Octavia was Augustus ' elder sister . <unk> was also the adopted son of <unk> . <unk> poisoned her second husband <unk> <unk> , so many ancient historians also accuse her of murdering her third husband , the emperor Claudius . 
- 
- = = = Ancestry and family = = = 
- 
- 
- = = = Rise to power = = = 
- 
- <unk> was not expected to become Emperor because his maternal uncle , <unk> , had begun his reign at the age of 24 with enough time to produce his own heir . <unk> 's mother , <unk> , lost favour with <unk> and was exiled in 39 after her husband 's death . <unk> seized <unk> 's inheritance and sent him to be brought up by his less wealthy aunt , <unk> <unk> , who was the mother of Valeria <unk> , Claudius 's third wife . <unk> , his wife <unk> and their infant daughter Julia <unk> were murdered on 24 January 41 . These events led Claudius , <unk> 's uncle , to become emperor . Claudius allowed <unk> to return from exile . 
- Claudius had married twice before marrying Valeria <unk> . His previous marriages produced three children including a son , <unk> , who died at a young age . He had two children with <unk> – <unk> Octavia ( born 40 ) and <unk> ( born 41 ) . <unk> was executed by Claudius in the year 48 . 
- In 49 AD , Claudius married a fourth time , to <unk> 's mother <unk> , despite her being his niece . To aid Claudius politically , young <unk> was adopted in 50 and took the name <unk> Claudius Caesar <unk> <unk> ( see adoption in Rome ) . <unk> was older than his <unk> <unk> , and thus became heir to the throne . 
- <unk> was proclaimed an adult in 51 at the age of 14 . He was appointed <unk> , entered and first addressed the Senate , made joint public appearances with Claudius , and was featured in coinage . In 53 , he married his <unk> <unk> Octavia . 
- 
- = = Emperor ( 54 – 68 AD ) = = 
- 
- 
- = = = Early rule = = = 
- 
- Claudius died in 54 and <unk> , taking the name <unk> Claudius Caesar Augustus <unk> , was established as Emperor . Though accounts vary , many ancient historians state <unk> poisoned Claudius . According to Pliny the Elder , she used poison mushrooms . It is not known how much <unk> knew or if he was even involved in the death of Claudius . 
- <unk> wrote " ... for even if he was not the instigator of the emperor 's death , he was at least privy to it , as he openly admitted ; for he used afterwards to <unk> mushrooms , the vehicle in which the poison was administered to Claudius , as " the food of the gods , " as the Greek <unk> has it . At any rate , after Claudius ' death he vented on him every kind of insult , in act and word , charging him now with <unk> and now with cruelty ; for it was a favourite joke of his to say that Claudius had ceased " to play the fool " among mortals , <unk> the first syllable of the word <unk> , and he <unk> many of his decrees and acts as the work of a <unk> and a <unk> . Finally , he neglected to <unk> the place where his body was burned except with a low and mean wall . " 
- According to <unk> ; <unk> became Emperor at the age of 17 when the news of Claudius ' death was made known , making him the youngest emperor at that time Although , what <unk> may have meant is that he was in his seventeenth year as his date of birth , also listed by <unk> , would have made him 16 at the time of Claudius ' death . <unk> , in book XIII of his Annals , describes <unk> as being ' scarcely out of his <unk> ' at the time he became emperor . Ancient historians describe <unk> 's early reign as being strongly influenced by his mother , <unk> , his tutor Lucius <unk> Seneca , and the <unk> <unk> <unk> <unk> <unk> , especially in the first year . Other tutors were less often mentioned , such as Alexander of <unk> . 
- Very early in <unk> 's rule , problems arose from competition for influence between <unk> and <unk> 's two main advisers , Seneca and <unk> . <unk> also attempted to influence the young <unk> . <unk> also is mentioned by ancient sources as " <unk> for her son ( <unk> ) " . This <unk> continued as is evidenced by the coin of the both of them . It is extremely unusual to see a women 's face on a coin in the ancient world . It is because of this position of power <unk> felt jealous as Seneca in particular rose up in <unk> 's court as he offered the advice <unk> wanted to hear unlike his mother . 
- In 54 , <unk> tried to sit down next to <unk> while he met with an Armenian envoy , but Seneca stopped her and prevented a <unk> scene ( as it was <unk> at that time for a woman to be in the same room as men doing official business ) . <unk> 's friends also <unk> <unk> and told <unk> to <unk> of his mother . 
- <unk> was reportedly unsatisfied with his marriage to Octavia and entered into an affair with <unk> <unk> , a former slave . In 55 , <unk> attempted to intervene in favor of Octavia and demanded that her son dismiss <unk> . <unk> , with the support of Seneca , resisted the intervention of his mother in his personal affairs . 
- With <unk> 's influence over her son severed , she reportedly began pushing for <unk> , <unk> 's <unk> , to become emperor . Nearly fourteen @-@ year @-@ old <unk> , heir @-@ designate prior to <unk> 's adoption , was still legally a minor , but was approaching legal adulthood . According to <unk> , <unk> hoped that with her support , <unk> , being the blood son of Claudius , would be seen as the true heir to the throne by the state over <unk> . However , the youth died suddenly and suspiciously on 12 February 55 , the very day before his proclamation as an adult had been set . 
- <unk> claimed that <unk> died from an <unk> seizure , but ancient historians all claim <unk> ' death came from <unk> 's poisoning him . <unk> , he enlisted the services of <unk> , a woman who specialized in the manufacture of poisons . She devised a mixture to kill <unk> , but after testing it unsuccessfully on a slave , <unk> angrily threatened to have her put to death if she did not come up with something usable . <unk> then devised a new <unk> that she promised would " kill <unk> than a viper . " 
- Her promise was fulfilled after <unk> consumed it at a dinner party from water used to cool his wine , which had already been <unk> , and succumbed within minutes . After the death of <unk> , <unk> was accused of <unk> Octavia and <unk> ordered her out of the imperial residence . 
- 
- = = = <unk> and consolidation of power = = = 
- 
- Over time , <unk> became progressively more powerful , freeing himself of his advisers and eliminating rivals to the throne . In 55 , he removed Marcus Antonius Pallas , an ally of <unk> , from his position in the treasury . Pallas , along with <unk> , was accused of conspiring against the Emperor to bring <unk> <unk> to the throne . Seneca was accused of having relations with <unk> and <unk> . Seneca succeeded in having himself , Pallas and <unk> acquitted . According to <unk> <unk> , at this time , Seneca and <unk> reduced their role in governing from careful management to mere <unk> of <unk> . 
- In 58 , <unk> became romantically involved with <unk> <unk> , the wife of his friend and future emperor <unk> . Reportedly because a marriage to <unk> and a divorce from Octavia did not seem politically feasible with <unk> alive , <unk> ordered the murder of his mother in 59 . A number of modern historians find this an unlikely motive as <unk> did not marry <unk> until 62 . 
- Additionally , according to <unk> , <unk> did not divorce her husband until after <unk> 's death , making it unlikely that the already married <unk> would be pressing <unk> for marriage . Some modern historians <unk> that <unk> 's execution of <unk> was prompted by her plotting to set <unk> <unk> on the throne . According to <unk> , <unk> tried to kill his mother through a <unk> planned by his <unk> tutor <unk> . Instead , it took the life of <unk> 's friend , <unk> <unk> . When <unk> survived , he had her executed by <unk> and framed it as a suicide . The incident is also recorded by <unk> . 
- In 62 , <unk> 's adviser , <unk> , died . Additionally , Seneca was again faced with <unk> charges . Seneca asked <unk> for permission to retire from public affairs . <unk> divorced and banished Octavia on grounds of infertility , leaving him free to marry the pregnant <unk> . After public protests , <unk> was forced to allow Octavia to return from exile , but she was executed shortly after her return . 
- <unk> also was reported to have kicked <unk> to death in 65 before she could have his second child . However , modern historians , noting <unk> , <unk> and <unk> <unk> 's possible bias against <unk> and the likelihood that they did not have <unk> accounts of private events , <unk> that <unk> may have died because of complications of <unk> or childbirth . 
- <unk> of treason being plotted against <unk> and the Senate first appeared in 62 . The Senate ruled that <unk> , a <unk> , should be put to death for speaking ill of <unk> at a party . Later , <unk> ordered the exile of <unk> <unk> who <unk> the Senate in a book . <unk> writes that the roots of the conspiracy led by <unk> <unk> <unk> began in this year . To consolidate power , <unk> executed a number of people in 62 and 63 including his rivals Pallas , <unk> <unk> and <unk> <unk> . According to <unk> , <unk> " showed neither discrimination nor <unk> in putting to death <unk> he pleased " during this period . 
- <unk> 's consolidation of power also included a slow <unk> of authority from the Senate . In 54 , <unk> promised to give the Senate powers equivalent to those under Republican rule . By 65 , <unk> complained that they had no power left and this led to the <unk> conspiracy . 
- 
- = = = Other relationships = = = 
- 
- When <unk> 's wife <unk> <unk> died in 65 , <unk> went into deep mourning . Her body was not <unk> , it was <unk> with spices , embalmed and put in the <unk> of Augustus . She was given a state funeral . <unk> praised her during the funeral <unk> and gave her divine honors . It is said that <unk> " burned ten years ' worth of Arabia 's incense production at her funeral . 
- In the beginning of 66 , he married <unk> <unk> . She was already married when she became <unk> 's mistress in 65 AD , with <unk> 's husband being driven to suicide in 66 , so <unk> could marry <unk> . She was one of the few of <unk> 's courtiers who survived the fall of his reign . 
- In 67 , <unk> ordered a young <unk> , <unk> , to be <unk> and then married him . According to Dion <unk> , <unk> bore an <unk> resemblance to <unk> , and <unk> even called him by his dead wife 's name . 
- 
- = = = Administrative policies = = = 
- 
- Over the course of his reign , <unk> often made <unk> that pleased the lower class . <unk> was criticized as being obsessed with personal popularity . 
- <unk> began his reign in 54 by promising the Senate more autonomy . In this first year , he forbade others to refer to him with regard to <unk> , for which he was praised by the Senate . <unk> was known for spending his time visiting <unk> and <unk> during this period . 
- In 55 , <unk> began taking on a more active role as an administrator . He was consul four times between 55 and 60 . During this period , some ancient historians speak fairly well of <unk> and contrast it with his later rule . 
- Under <unk> , restrictions were put on the amount of bail and fines . Also , fees for lawyers were limited . There was a discussion in the Senate on the <unk> of the freedmen class , and a strong demand was made that patrons should have the right of <unk> freedom . <unk> supported the freedmen and ruled that patrons had no such right . 
- The Senate tried to pass a law in which the crimes of one slave applied to all slaves within a household . Despite riots from the people , <unk> supported the Senate on their measure , and deployed troops to organise the execution of 400 slaves affected by the law . However , he vetoed strong measures against the freedmen affected by the case . 
- After tax collectors were accused of being too harsh to the poor , <unk> transferred collection authority to lower commissioners . <unk> banned any magistrate or <unk> from exhibiting public entertainment for fear that the venue was being used as a method to sway the populace . Additionally , there were many <unk> and <unk> of government officials along with arrests for <unk> and corruption . 
- When further complaints arose that the poor were being overly <unk> , <unk> attempted to repeal all indirect taxes . The Senate convinced him this action would bankrupt the public treasury . As a compromise , taxes were cut from 4 @.@ 5 % to 2 @.@ 5 % . Additionally , secret government tax records were ordered to become public . To lower the cost of food imports , merchant ships were declared tax @-@ exempt . 
- In imitation of the Greeks , <unk> built a number of <unk> and theatres . <unk> <unk> shows were also held . <unk> also established the <unk> <unk> . The festival included games , poetry , and theater . Historians indicate that there was a belief that theatre led to <unk> . Others considered that to have performers dressed in Greek clothing was old fashioned . Some questioned the large public expenditure on entertainment . 
- In 64 , Rome burned . <unk> enacted a public relief effort as well as significant reconstruction . A number of other major construction projects occurred in <unk> 's late reign . <unk> had the marshes of <unk> filled with rubble from the fire . He erected the large <unk> <unk> . In 67 , <unk> attempted to have a canal dug at the Isthmus of <unk> . Ancient historians state that these projects and others exacerbated the drain on the State 's budget . 
- The cost to rebuild Rome was immense , requiring funds the state treasury did not have . <unk> <unk> the Roman currency for the first time in the Empire 's history . He reduced the weight of the <unk> from 84 per Roman pound to 96 ( 3 @.@ 85 grams to 3 @.@ 35 grams ) . He also reduced the silver purity from 99 @.@ 5 % to 93 @.@ 5 % — the silver weight dropping from 3 @.@ 83 grams to 3 @.@ 4 grams . Furthermore , <unk> reduced the weight of the <unk> from 40 per Roman pound to 45 ( 8 grams to 7 @.@ 2 grams ) . 
- Between 62 and 67 , according to <unk> the Elder and Seneca , <unk> promoted an expedition to discover the sources of the Nile River . It was the first exploration of equatorial Africa from Europe in history . However , <unk> 's expedition up the Nile failed upon reaching the impenetrable <unk> of present @-@ day South Sudan . 
- The economic policy of <unk> is a point of debate among scholars . According to ancient historians , <unk> 's construction projects were overly extravagant and the large number of <unk> under <unk> left Italy " thoroughly exhausted by contributions of money " with " the provinces ruined . " Modern historians , though , note that the period was riddled with <unk> and that it is likely that <unk> 's spending came in the form of public works projects and charity intended to ease economic troubles . 
- 
- = = = Great Fire of Rome ( 64 AD ) = = = 
- 
- The Great Fire of Rome erupted on the night of 18 July to 19 July 64 . The fire started at the southeastern end of the Circus <unk> in shops selling flammable goods . 
- The extent of the fire is uncertain . According to <unk> , who was nine at the time of the fire , it spread quickly and burned for over five days . It destroyed three of fourteen Roman districts and severely damaged seven . The only other historian who lived through the period and mentioned the fire is Pliny the Elder , who wrote about it in passing . Other historians who lived through the period ( including <unk> , <unk> <unk> , <unk> and <unk> ) make no mention of it in what remains of their work . 
- It is uncertain who or what actually caused the fire — whether accident or <unk> . <unk> and <unk> <unk> favor <unk> as the <unk> , so he could build a palatial complex . <unk> mentions that Christians confessed to the crime , but it is not known whether these <unk> were induced by torture . However , accidental fires were common in ancient Rome . In fact , Rome suffered other large fires in 69 and in 80 . 
- It was said by <unk> and <unk> <unk> that <unk> sang the " <unk> of <unk> " in stage costume while the city burned . Popular legend claims that <unk> played the <unk> at the time of the fire , an anachronism based merely on the concept of the <unk> , a <unk> instrument associated with <unk> and his performances . ( The <unk> was not invented until the 10th century . ) <unk> 's account , however , has <unk> in <unk> at the time of the fire . <unk> also said that <unk> playing his <unk> and singing while the city burned was only rumor . 
- According to <unk> , upon hearing news of the fire , <unk> returned to Rome to organize a relief effort , which he paid for from his own funds . <unk> 's contributions to the relief extended to personally taking part in the search for and rescue of victims of the <unk> , spending days searching the debris without even his <unk> . After the fire , <unk> opened his palaces to provide shelter for the homeless , and arranged for food supplies to be delivered in order to prevent starvation among the survivors . 
- In the wake of the fire , he made a new urban development plan . Houses after the fire were spaced out , built in brick , and faced by <unk> on wide roads . <unk> also built a new palace complex known as the <unk> <unk> in an area cleared by the fire . This included lush artificial landscapes and a 30 @-@ meter @-@ tall statue of himself , the <unk> of <unk> . The size of this complex is debated ( from 100 to 300 acres ) . To find the necessary funds for the reconstruction , tributes were imposed on the provinces of the empire . 
- <unk> , in one of the earliest non @-@ Christian references to the origins of Christianity , notes that the population searched for a <unk> and rumors held <unk> responsible . To <unk> blame , <unk> targeted Christians . He ordered Christians to be thrown to dogs , while others were <unk> and burned . 
- 
- = = = Public performances = = = 
- 
- <unk> enjoyed driving a one @-@ horse chariot , singing to the <unk> and poetry . He even composed songs that were performed by other <unk> throughout the empire . At first , <unk> only performed for a private audience . 
- In 64 AD . , <unk> began singing in public in <unk> in order to improve his popularity . He also sang at the second <unk> <unk> in 65 . It was said that <unk> <unk> the attention , but historians also write that <unk> was encouraged to sing and perform in public by the Senate , his inner circle and the people . Ancient historians strongly criticize his choice to perform , calling it <unk> . 
- <unk> was persuaded to participate in the Olympic Games of 67 in order to improve relations with Greece and display Roman dominance . As a competitor , <unk> raced a ten @-@ horse chariot and nearly died after being thrown from it . He also performed as an actor and a singer . Though <unk> <unk> in his racing ( in one case , dropping out entirely before the end ) and acting competitions , he won these crowns nevertheless and <unk> them when he returned to Rome . The victories are attributed to <unk> <unk> the judges and his status as emperor . 
- 
- = = = War and peace with <unk> = = = 
- 
- Shortly after <unk> 's accession to the throne in 54 , the Roman vassal kingdom of Armenia <unk> their Iberian prince <unk> and he was replaced with the <unk> prince <unk> . This was seen as a <unk> invasion of Roman territory . There was concern in Rome over how the young Emperor would handle the situation . <unk> reacted by immediately sending the military to the region under the command of <unk> <unk> <unk> . The <unk> temporarily relinquished control of Armenia to Rome . 
- The peace did not last and full @-@ scale war broke out in 58 . The <unk> king <unk> I refused to remove his brother <unk> from Armenia . The <unk> began a full @-@ scale invasion of the Armenian kingdom . Commander <unk> responded and repelled most of the <unk> army that same year . <unk> retreated and Rome again controlled most of Armenia . 
- <unk> was acclaimed in public for this initial victory . <unk> , a <unk> noble raised in Rome , was installed by <unk> as the new ruler of Armenia . <unk> was appointed governor of Syria as a reward . 
- In 62 , <unk> invaded the <unk> province of <unk> . Again , Rome and <unk> were at war and this continued until 63 . <unk> began building up for a strike against the Roman province of Syria . <unk> tried to convince <unk> to continue the war , but <unk> opted for a peace deal instead . There was anxiety in Rome about eastern grain supplies and a budget deficit . 
- The result was a deal where <unk> again became the Armenian king , but was crowned in Rome by Emperor <unk> . In the future , the king of Armenia was to be a <unk> prince , but his appointment required approval from the Romans . <unk> was forced to come to Rome and <unk> in ceremonies meant to display Roman dominance . 
- This peace deal of 63 was a considerable victory for <unk> politically . <unk> became very popular in the eastern provinces of Rome and with the <unk> as well . The peace between <unk> and Rome lasted 50 years until Emperor Trajan of Rome invaded Armenia in 114 . 
- 
- = = = Other major power struggles and rebellions = = = 
- 
- The war with <unk> was not <unk> 's only major war but he was both criticized and praised for an aversion to battle . Like many emperors , <unk> faced a number of rebellions and power struggles within the empire . 
- British <unk> of 60 – 61 ( <unk> 's Uprising ) 
- In 60 , a major rebellion broke out in the province of Britannia . While the governor <unk> <unk> <unk> and his troops were busy capturing the island of Mona ( Anglesey ) from the <unk> , the tribes of the southeast staged a revolt led by queen <unk> of the <unk> . <unk> and her troops destroyed three cities before the army of <unk> could return , receive reinforcements , and quell the rebellion in 61 . Fearing <unk> himself would provoke further rebellion , <unk> replaced him with the more passive <unk> <unk> <unk> . 
- The <unk> <unk> of 65 
- In 65 , <unk> <unk> <unk> , a Roman statesman , organized a conspiracy against <unk> with the help of <unk> <unk> and <unk> <unk> , a <unk> and a <unk> of the <unk> Guard . According to <unk> , many conspirators wished to " rescue the state " from the emperor and restore the Republic . The <unk> <unk> discovered the conspiracy and reported it to <unk> 's secretary , <unk> . As a result , the conspiracy failed and its members were executed including <unk> , the poet . <unk> 's previous advisor , Seneca was ordered to commit suicide after admitting he discussed the plot with the conspirators . 
- The First Jewish War of 66 – 70 
- In 66 , there was a Jewish revolt in Judea stemming from Greek and Jewish religious tension . In 67 , <unk> dispatched <unk> to restore order . This revolt was eventually put down in 70 , after <unk> 's death . This revolt is famous for Romans breaching the walls of Jerusalem and destroying the Second Temple of Jerusalem . 
- 
- = = = The revolt of <unk> and <unk> and the death of <unk> = = = 
- 
- In March 68 , <unk> Julius <unk> , the governor of Gallia <unk> , rebelled against <unk> 's tax policies . Lucius <unk> Rufus , the governor of <unk> Superior , was ordered to put down <unk> 's rebellion . In an attempt to gain support from outside his own province , <unk> called upon <unk> <unk> <unk> , the governor of <unk> <unk> , to join the rebellion and further , to declare himself emperor in opposition to <unk> . 
- At the Battle of <unk> in May 68 , <unk> ' forces easily defeated those of <unk> and the latter committed suicide . However , after putting down this one rebel , <unk> ' <unk> attempted to proclaim their own commander as Emperor . <unk> refused to act against <unk> , but the discontent of the <unk> of Germany and the continued opposition of <unk> in Spain did not <unk> well for him . 
- While <unk> had retained some control of the situation , support for <unk> increased despite his being officially declared a public enemy . The prefect of the <unk> Guard , <unk> <unk> <unk> , also abandoned his allegiance to the Emperor and came out in support for <unk> . 
- In response , <unk> fled Rome with the intention of going to the port of <unk> and , from there , to take a fleet to one of the still @-@ loyal eastern provinces . According to <unk> , <unk> abandoned the idea when some army officers openly refused to obey his commands , responding with a line from <unk> 's <unk> : " Is it so <unk> a thing then to die ? " <unk> then <unk> with the idea of fleeing to <unk> , throwing himself upon the mercy of <unk> , or to appeal to the people and beg them to pardon him for his past offences " and if he could not <unk> their hearts , to <unk> them at least to allow him the prefecture of Egypt " . <unk> reports that the text of this speech was later found in <unk> 's writing desk , but that he <unk> not give it from fear of being torn to pieces before he could reach the <unk> . 
- <unk> returned to Rome and spent the evening in the palace . After sleeping , he <unk> at about midnight to find the palace guard had left . <unk> messages to his friends ' palace chambers for them to come , he received no answers . Upon going to their chambers personally , he found them all abandoned . When he called for a <unk> or anyone else adept with a sword to kill him , no one appeared . He cried , " Have I neither friend nor <unk> ? " and ran out as if to throw himself into the Tiber . 
- Returning , <unk> sought for some place where he could hide and collect his thoughts . An imperial <unk> , <unk> , offered his villa , located 4 miles outside the city . Travelling in disguise , <unk> and four loyal freedmen , <unk> , <unk> , <unk> , and <unk> , reached the villa , where <unk> ordered them to dig a grave for him . 
- At this time , a <unk> arrived with a report that the Senate had declared <unk> a public enemy and that it was their intention to execute him by beating him to death and that armed men had been sent to apprehend him for the act to take place in the <unk> . The Senate actually was still reluctant and <unk> on the right course of action as <unk> was the last member of the Julio @-@ <unk> Family . Indeed , most of the <unk> had served the imperial family all their lives and felt a sense of loyalty to the deified bloodline , if not to <unk> himself . The men actually had the goal of returning <unk> back to the Senate , where the Senate hoped to work out a compromise with the <unk> governors that would preserve <unk> 's life , so that at least a future heir to the dynasty could be produced . 
- <unk> , however , did not know this , and at the news brought by the <unk> , he prepared himself for suicide , pacing up and down <unk> " <unk> <unk> <unk> " which translates to English as " What an artist dies in me . " <unk> his <unk> , he first <unk> for one of his companions to set an example by first killing himself . At last , the sound of approaching horsemen drove <unk> to face the end . However , he still could not bring himself to take his own life but instead he forced his private secretary , <unk> , to perform the task . 
- When one of the horsemen entered , upon his seeing <unk> all but dead he attempted to stop the bleeding in vain . <unk> 's final words were " Too late ! This is fidelity ! " He died on 9 June 68 , the anniversary of the death of Octavia , and was buried in the <unk> of the <unk> <unk> , in what is now the Villa <unk> ( <unk> Hill ) area of Rome . 
- With his death , the Julio @-@ <unk> dynasty ended . The Senate , when news of his death reached Rome , posthumously declared <unk> a public enemy to appease the coming <unk> ( as the Senate had initially declared <unk> as a public enemy ) and proclaimed <unk> the new emperor . Chaos would ensue in the year of the Four <unk> . 
- 
- = = = Post mortem = = = 
- 
- According to <unk> and <unk> <unk> , the people of Rome celebrated the death of <unk> . <unk> , though , describes a more complicated political environment . <unk> mentions that <unk> 's death was welcomed by Senators , nobility and the upper class . The lower @-@ class , slaves , <unk> of the arena and the theater , and " those who were supported by the famous excesses of <unk> " , on the other hand , were upset with the news . Members of the military were said to have mixed feelings , as they had allegiance to <unk> , but were <unk> to overthrow him . 
- Eastern sources , namely <unk> II and <unk> of <unk> , mention that <unk> 's death was <unk> as he " restored the liberties of <unk> with a wisdom and <unk> quite alien to his character " and that he " held our liberties in his hand and respected them . " 
- Modern scholarship generally holds that , while the Senate and more well @-@ off individuals welcomed <unk> 's death , the general populace was " loyal to the end and beyond , for <unk> and <unk> both thought it worthwhile to appeal to their nostalgia . " 
- <unk> 's name was erased from some monuments , in what Edward <unk> regards as an " outburst of private zeal " . Many portraits of <unk> were reworked to represent other figures ; according to Eric R. <unk> , over fifty such images survive . This reworking of images is often explained as part of the way in which the memory of disgraced emperors was condemned posthumously ( see <unk> <unk> ) . <unk> , however , doubts that the practice is necessarily negative and notes that some continued to create images of <unk> long after his death . 
- The civil war during the year of the Four <unk> was described by ancient historians as a <unk> period . According to <unk> , this instability was rooted in the fact that emperors could no longer rely on the perceived legitimacy of the imperial bloodline , as <unk> and those before him could . <unk> began his short reign with the execution of many allies of <unk> and possible future enemies . One such notable enemy included <unk> <unk> , who claimed to be the son of Emperor <unk> . 
- <unk> <unk> <unk> . <unk> was said to be liked by many soldiers because he had been a friend of <unk> 's and resembled him somewhat in temperament . It was said that the common Roman hailed <unk> as <unk> himself . <unk> used " <unk> " as a surname and <unk> many statues to <unk> . <unk> <unk> <unk> . <unk> began his reign with a large funeral for <unk> complete with songs written by <unk> . 
- After <unk> 's suicide in 68 , there was a widespread belief , especially in the eastern provinces , that he was not dead and somehow would return . This belief came to be known as the <unk> <unk> Legend . 
- The legend of <unk> 's return lasted for hundreds of years after <unk> 's death . Augustine of <unk> wrote of the legend as a popular belief in 422 . 
- At least three <unk> <unk> emerged leading rebellions . The first , who sang and played the <unk> or <unk> and whose face was similar to that of the dead emperor , appeared in 69 during the reign of <unk> . After persuading some to recognize him , he was captured and executed . <unk> during the reign of Titus ( 79 – 81 ) , another <unk> appeared in Asia and sang to the accompaniment of the <unk> and looked like <unk> but he , too , was killed . Twenty years after <unk> 's death , during the reign of <unk> , there was a third <unk> . He was supported by the <unk> , who only <unk> gave him up , and the matter almost came to war . 
- 
- = = Physical appearance = = 
- 
- In his book The Lives of the Twelve <unk> , <unk> describes <unk> as " about the average height , his body marked with spots and <unk> , his hair light blonde , his features regular rather than attractive , his eyes blue and somewhat weak , his neck over thick , his belly prominent , and his legs very slender . " 
- 
- = = Historiography = = 
- 
- The history of <unk> 's reign is problematic in that no historical sources survived that were contemporary with <unk> . These first histories at one time did exist and were described as biased and <unk> , either overly critical or praising of <unk> . The original sources were also said to contradict on a number of events . Nonetheless , these lost primary sources were the basis of surviving secondary and tertiary histories on <unk> written by the next generations of historians . A few of the contemporary historians are known by name . <unk> <unk> , <unk> Rufus and Pliny the Elder all wrote condemning histories on <unk> that are now lost . There were also pro @-@ <unk> histories , but it is unknown who wrote them or for what <unk> <unk> was praised . 
- The bulk of what is known of <unk> comes from <unk> , <unk> and <unk> <unk> , who were all of the <unk> class . <unk> and <unk> wrote their histories on <unk> over fifty years after his death , while <unk> <unk> wrote his history over 150 years after <unk> 's death . These sources contradict on a number of events in <unk> 's life including the death of Claudius , the death of <unk> , and the Roman fire of 64 , but they are consistent in their condemnation of <unk> . 
- A handful of other sources also add a limited and varying perspective on <unk> . Few surviving sources paint <unk> in a favourable light . Some sources , though , portray him as a competent emperor who was popular with the Roman people , especially in the east . 
- <unk> <unk> 
- <unk> <unk> ( c . 155 – <unk> ) was the son of <unk> <unk> , a Roman senator . He passed the greater part of his life in public service . He was a senator under <unk> and governor of <unk> after the death of Septimius <unk> ; and afterwards <unk> consul around 205 , and also <unk> in Africa and <unk> . 
- Books 61 – 63 of <unk> 's Roman History describe the reign of <unk> . Only fragments of these books remain and what does remain was abridged and altered by John <unk> , an 11th @-@ century monk . 
- <unk> <unk> 
- <unk> <unk> ( c . 40 – 120 ) , a Greek philosopher and historian , wrote the Roman people were very happy with <unk> and would have allowed him to rule indefinitely . They <unk> for his rule once he was gone and embraced <unk> when they appeared : 
- Indeed the truth about this has not come out even yet ; for so far as the rest of his subjects were concerned , there was nothing to prevent his continuing to be Emperor for all time , seeing that even now everybody wishes he were still alive . And the great majority do believe that he still is , although in a certain sense he has died not once but often along with those who had been firmly convinced that he was still alive . 
- <unk> 
- <unk> ( c . 55 – 135 ) was the slave to <unk> 's <unk> <unk> . He makes a few passing negative comments on <unk> 's character in his work , but makes no remarks on the nature of his rule . He describes <unk> as a spoiled , angry and unhappy man . 
- <unk> 
- The historian <unk> ( c . 37 – 100 ) , while calling <unk> a <unk> , was also the first to mention bias against <unk> . Of other historians , he said : 
- But I <unk> any further discourse about these affairs ; for there have been a great many who have composed the history of <unk> ; some of which have departed from the truth of facts out of favour , as having received benefits from him ; while others , out of hatred to him , and the great ill @-@ will which they bore him , have so <unk> <unk> against him with their lies , that they <unk> deserve to be condemned . Nor do I wonder at such as have told lies of <unk> , since they have not in their writings preserved the truth of history as to those facts that were earlier than his time , even when the actors could have no way incurred their hatred , since those writers lived a long time after them . 
- <unk> 
- Though more of a poet than historian , <unk> ( c . 39 – 65 ) has one of the <unk> accounts of <unk> 's rule . He writes of peace and prosperity under <unk> in contrast to previous war and <unk> . Ironically , he was later involved in a conspiracy to overthrow <unk> and was executed . 
- <unk> 
- <unk> II " the <unk> " ( c . 172 – 250 ) spoke of <unk> in the Life of <unk> <unk> ( Books 4 – 5 ) . Though he has a generally bad or dim view of <unk> , he speaks of others ' positive reception of <unk> in the East . 
- Pliny the Elder 
- The history of <unk> by Pliny the Elder ( c . 24 – 79 ) did not survive . Still , there are several references to <unk> in Pliny 's Natural <unk> . Pliny has one of the worst opinions of <unk> and calls him an " enemy of mankind . " 
- <unk> 
- <unk> ( c . 46 – 127 ) mentions <unk> indirectly in his account of the Life of <unk> and the Life of <unk> . <unk> is portrayed as a <unk> , but those that replace him are not described as better . 
- Seneca the Younger 
- It is not surprising that Seneca ( c . 4 BC – 65 ) , <unk> 's teacher and advisor , writes very well of <unk> . 
- <unk> 
- <unk> ( c . 69 – 130 ) was a member of the <unk> order , and he was the head of the department of the imperial correspondence . While in this position , <unk> started writing biographies of the emperors , <unk> the <unk> and sensational aspects . 
- <unk> 
- The Annals by <unk> ( c . 56 – 117 ) is the most detailed and comprehensive history on the rule of <unk> , despite being incomplete after the year 66 . <unk> described the rule of the Julio @-@ <unk> emperors as generally unjust . He also thought that existing writing on them was <unk> : 
- The histories of <unk> , <unk> , Claudius and <unk> , while they were in power , were falsified through terror , and after their death were written under the irritation of a recent hatred . 
- <unk> was the son of a <unk> , who married into the elite family of <unk> . He entered his political life as a senator after <unk> 's death and , by <unk> ' own admission , owed much to <unk> 's rivals . <unk> that this bias may be apparent to others , <unk> protests that his writing is true . 
- <unk> <unk> 
- In <unk> <unk> <unk> published in Basel his <unk> <unk> , which was one of the first historical references of the Modern era to portray <unk> in a positive light . 
- 
- = = <unk> in Jewish and Christian tradition = = 
- 
- 
- = = = Jewish tradition = = = 
- 
- At the end of 66 , conflict broke out between Greeks and Jews in Jerusalem and Caesarea . According to the Talmud , <unk> went to Jerusalem and shot arrows in all four directions . All the arrows landed in the city . He then asked a passing child to repeat the verse he had learned that day . The child responded , " I will lay my vengeance upon <unk> by the hand of my people Israel " ( <unk> . 25 @,@ 14 ) . <unk> became <unk> , believing that God wanted the Temple in Jerusalem to be destroyed , but would punish the one to carry it out . <unk> said , " He desires to lay waste His House and to lay the blame on me , " whereupon he fled and converted to Judaism to avoid such <unk> . <unk> was then dispatched to put down the rebellion . 
- The Talmud adds that the sage <unk> <unk> Baal <unk> , Rabbi <unk> or Rabbi <unk> Baal <unk> ( Rabbi <unk> the miracle maker ) was a Jewish sage who lived in the time of the <unk> a prominent supporter of the Bar <unk> rebellion against Roman rule . He was considered one of the greatest of the <unk> of the third generation ( 139 @-@ 163 ) . According to the Talmud , his father was a descendant of the Roman Emperor <unk> who had converted to Judaism . His wife <unk> is one of the few women cited in the <unk> . He is the third most frequently mentioned sage in the <unk> . 
- Roman and Greek sources nowhere report <unk> 's alleged trip to Jerusalem or his alleged conversion to Judaism . There is also no record of <unk> having any offspring who survived infancy : his only recorded child , <unk> Augusta , died aged 4 months . 
- 
- = = = Christian tradition = = = 
- 
- Non @-@ Christian historian <unk> describes <unk> extensively <unk> and executing Christians after the fire of 64 . <unk> also mentions <unk> punishing Christians , though he does so because they are " given to a new and mischievous superstition " and does not connect it with the fire . 
- Christian writer <unk> ( c . 155 – 230 ) was the first to call <unk> the first <unk> of Christians . He wrote , " <unk> your records . There you will find that <unk> was the first that persecuted this doctrine " . <unk> ( c . 240 – 320 ) also said that <unk> " first persecuted the servants of God " . as does <unk> <unk> . However , <unk> writes that , " since the Jews constantly made disturbances at the instigation of <unk> , he [ emperor Claudius ] expelled them from Rome " ( " <unk> <unk> <unk> <unk> <unk> <unk> <unk> " ) . These expelled " Jews " may have been early Christians , although <unk> is not explicit . Nor is the Bible explicit , calling Aquila of Pontus and his wife , <unk> , both expelled from Italy at the time , " Jews " . 
- 
- = = = = <unk> of Peter and Paul = = = = 
- 
- The first text to suggest that <unk> ordered the execution of an <unk> is a letter by Clement to the <unk> traditional dated to around 96 <unk> The <unk> Ascension of Isaiah , a Christian writing from the 2nd century says , " the <unk> of his mother , who himself ( even ) this king , will <unk> the plant which the Twelve Apostles of the Beloved have planted . Of the Twelve one will be delivered into his hands " was interpreted to mean <unk> . 
- Bishop Eusebius of Caesarea ( c . 275 – <unk> ) was the first to write explicitly that Paul was beheaded in Rome during the reign of <unk> . He states that <unk> 's persecution led to Peter and Paul 's deaths , but that <unk> did not give any specific orders . However , several other accounts going back to the 1st century have Paul surviving his two years in Rome and travelling to <unk> , before facing trial in Rome again prior to his death . 
- Peter is first said to have been <unk> upside @-@ down in Rome during <unk> 's reign ( but not by <unk> ) in the <unk> Acts of Peter ( c . 200 ) . The account ends with Paul still alive and <unk> <unk> by God 's command not to <unk> any more Christians . 
- By the 4th century , a number of writers were stating that <unk> killed Peter and Paul . 
- 
- = = = = The <unk> = = = = 
- 
- The <unk> <unk> , Book 5 and 8 , written in the 2nd century , speak of <unk> returning and bringing destruction . Within Christian communities , these writings , along with others , fueled the belief that <unk> would return as the <unk> . In 310 , <unk> wrote that <unk> " suddenly disappeared , and even the burial place of that <unk> wild beast was nowhere to be seen . This has led some persons of extravagant imagination to suppose that , having been conveyed to a distant region , he is still reserved alive ; and to him they apply the <unk> verses " , <unk> maintains that it is not right to believe this . 
- In 422 , Augustine of <unk> wrote about 2 <unk> 2 : 1 – 11 , where he believed Paul mentioned the coming of the <unk> . Though he rejects the theory , Augustine mentions that many Christians believed that <unk> was the <unk> or would return as the <unk> . He wrote , " so that in saying , ' For the mystery of <unk> <unk> already work , ' he alluded to <unk> , whose <unk> already seemed to be as the <unk> of <unk> . " 
- Some modern biblical scholars such as <unk> <unk> ( Johns Hopkins University ) of the American Schools of Oriental Research and the editors of the Oxford & Harper Collins Study Bibles , contend that the number <unk> in the Book of Revelation is a code for <unk> , a view that is also supported in Roman Catholic Biblical commentaries . 
- The concept of <unk> as the <unk> is often a central belief of <unk> <unk> . 
- 
- 
- = Manila = 
- 
- Manila ( / <unk> / ) is the capital city of the Philippines , founded on June 24 , <unk> by Spanish <unk> Miguel López de <unk> . It is one of the oldest cities in the country and was the seat of power for most of the colonial rules of the Philippines . It is situated on the eastern shore of Manila Bay and contains a multitude of landmarks , some of which date back to the 16th century , such as the Spanish colonial era <unk> City of <unk> . 
- Manila is the second most populous city in the Philippines after the former capital <unk> City with a population of 1 @,@ 780 @,@ 148 in 2015 . Because of its small land area and huge population , Manila is regarded as one of the most densely populated cities in the world with 42 @,@ <unk> people per square kilometer . Manila is one of the sixteen cities and a municipality that make up Metro Manila , the National Capital Region of the Philippines . In 2012 , <unk> and World Cities Research Network listed Manila as a global city . 
- Manila has six representative districts for the lower house of the Philippine Congress . Furthermore , the city is composed of 16 districts , namely : <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , Port Area , <unk> , <unk> , San Andres , San Miguel , San Nicolas , Santa <unk> , Santa Cruz , Santa Mesa and <unk> . 
- The Kingdom of <unk> once ruled in the vicinity of Manila before it briefly became a province of the Hindu <unk> Empire . During the <unk> invasion of the Philippines , Sultan <unk> of <unk> captured <unk> ( a village in modern @-@ day Manila ) and renamed it <unk> , a <unk> term referring to the presence of the <unk> shrub . <unk> was a vassal state of <unk> , established to <unk> <unk> . <unk> had been <unk> since the sixth century CE and earlier . It had become partly Islamic and Hindu @-@ <unk> by the 15th century CE . 
- In <unk> Spanish <unk> arrived from Mexico , from across the Pacific , and founded present @-@ day Manila in what today is <unk> . Spanish missionaries soon <unk> the city and incorporated <unk> under Manila and then built some of the oldest churches in the country , including San Agustin Church . The <unk> renamed the area <unk> <unk> de <unk> ( New Kingdom of <unk> ) and shortened the name to Manila . 
- Manila became the center of Spanish activity in the Far East and one end of the Manila @-@ Acapulco <unk> trade route , linking Spanish America with Asia , one of the earliest examples of <unk> . Due to the central location in the Pacific sea trade routes , Manila received the moniker of the " Pearl of the <unk> " . Spanish rule of Manila and the entire Philippine archipelago lasted for over three centuries , until 1898 . At different times during the long Spanish period there were local <unk> , Chinese <unk> , massive pirate attacks , great earthquakes , Dutch raids and invasion attempts , and a British occupation of the city during their unsuccessful attempt to conquer the Philippines . Order was usually quickly restored and the city returned to the business of trade . In the 19th century Manila was one of the most modern cities in Asia . Before the Spanish – American War , Manila saw the rise of the Philippine Revolution . Under the American rule following the Spanish – American War , the United States changed the official language from Spanish to English and made some changes in education , local laws and urban planning . Towards the end of World War II , during the Battle of Manila most of the city was flattened by intensive aerial bombardment by the United States Air Force . As a result , relatively little remains of Manila 's prewar and colonial architecture , although there are ongoing restoration projects , especially within the old walled city , <unk> . 
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- = = History = = 
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- The earliest evidence of human life in and around the area of Manila is the nearby <unk> <unk> dated to around 3000 BC . Furthermore , <unk> , a class of <unk> peoples , became the aboriginal inhabitants of the Philippines . They were found across Luzon before the <unk> @-@ <unk> migrated in and assimilated them . 
- The Kingdom of <unk> flourished during the latter half of the Ming Dynasty as a result of direct trade relations with China . <unk> district was maintained as the traditional capital of the empire , with its rulers as sovereign kings and not mere chieftains , and were addressed variously as <unk> <unk> <unk> or <unk> in <unk> ( " lords " ) ; <unk> <unk> ( " son of heaven " ) ; or <unk> ( " lord of the palace " ) , the Emperor of China considered the <unk> ( rulers of ancient Manila ) " <unk> " ( Kings ) . 
- In the 13th century , Manila consisted of a fortified settlement and trading quarter at the shores of the <unk> River , on top of previous older towns . Manila was then settled by the <unk> empire of <unk> as referenced in the epic <unk> poem <unk> which inscribed its conquest by <unk> <unk> <unk> . <unk> " <unk> ् <unk> ् " which is a historical name for the city of Manila is listed in <unk> 14 alongside <unk> , which is now <unk> , and <unk> . 
- During the reign of Sultan <unk> from <unk> to <unk> , the <unk> Empire invaded , wanting to take advantage of <unk> 's China trade by attacking its environs and establishing " <unk> <unk> <unk> <unk> " ( The Kingdom of <unk> ) . They ruled under and gave yearly tribute to the Sultanate of <unk> as its satellite state . They established a new dynasty under the local leader who accepted Islam and became <unk> <unk> or <unk> <unk> I. He also established a trading challenge to the already rich House of <unk> <unk> in <unk> . Islam was further strengthened by the arrival of Muslim traders from the Arab @-@ Indian area and Southeast Asia . Manila was temporarily besieged by the invasion of Chinese pirate @-@ warlord <unk> ( <unk> ) but was thwarted by the local inhabitants , before it became the seat of the colonial government of Spain . 
- On June 24 , <unk> , Spanish <unk> Miguel López de <unk> arrived from New Spain ( now Mexico ) , and then exercised rule of the Spanish city of Manila as a territory of New Spain with the establishment of a city council in what today is the district of <unk> . López de <unk> had the local royalty executed or exiled , after the failure of the <unk> <unk> ; a plot wherein an alliance between Japanese merchants , Luzon 's <unk> with several <unk> and <unk> plus the <unk> Empire would band together to execute the Spaniards and their Latin @-@ American mercenaries , and <unk> allies . At the conclusion of which , the victorious Spaniards made Manila the capital of the Spanish East Indies and of the Philippines , which the empire would control for the next three centuries , from <unk> to 1898 . 
- Manila then became famous during the Manila @-@ Acapulco <unk> trade which lasted for three centuries and brought goods from Europe , Africa and Latin America across the Pacific Islands to Southeast Asia ( Which was already an <unk> for goods coming from India , Indonesia and China ) and trade also flowed vice versa . Silver that was mined in Mexico and Peru were exchanged for Chinese silk , Indian gems , and the spices of the Southeast Asia , some of which even flowed to Europe . Likewise wines and olives grown from Europe and North Africa were <unk> via Mexico towards Manila . 
- Manila was occupied by British forces for twenty months , from <unk> to 1764 , and used as a base for an unsuccessful attempt to conquer the Philippines during the Seven Years ' War . Eventually , the British withdrew from Manila as per agreements in the 1763 Treaty of Paris . The Chinese were punished for supporting the British invasion , and the small fortress @-@ city of <unk> , mostly populated by Europeans and <unk> , kept its cannons pointed at <unk> , the world 's oldest <unk> . 
- Mexican Independence in 1821 necessitated direct rule from Spain . Under direct Spanish rule , banking , industry and education flourished more than it had in the previous two centuries . The opening of the Suez Canal in 1869 facilitated direct trade and communications with Spain . 
- The growing wealth and education attracted indigenous , Chinese , Indians , <unk> , and Europeans from the provinces to Manila , all of whom elected a nascent <unk> citizenship regardless of ethnicity . The developments also facilitated the rise of an <unk> class which <unk> liberal ideas , the ideological foundations of the Philippine Revolution which sought independence from Spain . 
- After the Battle of Manila ( 1898 ) , Spain ceded the surrendered city of Manila to the United States . The First Philippine Republic based at nearby <unk> fought against the Americans for control of the city of Manila . The Americans defeated the First Philippine Republic and captured president Emilio <unk> who announced allegiance to the United States on April 1 , 1901 . 
- Upon drafting a new charter for Manila in June 1901 , the Americans made official what had long been tacit : that the City of Manila was not <unk> alone but also all its <unk> . The new city charter proclaimed that Manila was composed of eleven municipal districts — presumably <unk> , <unk> , Santa Cruz , <unk> , San Miguel , <unk> , Santa <unk> , <unk> , <unk> , <unk> and <unk> . In addition to these , the Church recognized five parishes as <unk> — namely , <unk> , <unk> , <unk> @-@ <unk> , Santa Mesa and <unk> . Later times would add two more : <unk> and San Andres <unk> . 
- Under American control , a new civilian oriented Insular Government headed by then Governor @-@ General William Howard Taft invited city <unk> Daniel <unk> for the transformation of Manila , to adapt the old city to changed times and modern needs . The <unk> Plan included development of the road system , the use of waterways for transportation , and <unk> of Manila with the improvement of <unk> , construction of parks , <unk> and various building for various activities . 
- The latter included a government center occupying all of Wallace Field , which extends from <unk> to the present Taft Avenue . The Philippine Capitol was to rise at the Taft Avenue end of the field , facing toward the sea , and would form , with the buildings of different government <unk> and departments , a <unk> , <unk> in the center , and a monument to José <unk> at its <unk> end . Of <unk> 's proposed government center , only three units — the Legislative Building and the building of the Finance and Agricultural departments — were completed when World War II erupted . 
- Due to the Japanese occupation of the Philippines , American soldiers were ordered to withdraw from the city and all military installations were removed on December 24 , 1941 . General Douglas MacArthur declared Manila an open city to prevent further death and destruction ; despite this , the Japanese <unk> continued to bomb the city . Manila was occupied by the Japanese forces on January 2 , 1942 . 
- Manila was also the site of the <unk> battle in the Pacific theater during the Second World War . After falling to the Empire of Japan on January 2 , 1942 , it was recaptured by joint American and <unk> troops from February 3 to March 3 , 1945 . Some 100 @,@ 000 civilians were killed in Manila in February 1945 . It was the second most devastated city in the world after Warsaw during the Second World War . At the end of World War II , almost all of the structures in the city , particularly <unk> , were destroyed but after the war , reconstruction took place . 
- In 1948 , President <unk> <unk> moved the seat of government of the Philippines to <unk> City , a new capital city in the suburbs and fields northeast of Manila , created in 1938 by former President Manuel L. <unk> , which was named after him . The move ended any implementation of the <unk> Plan 's intent for the government centre to be at <unk> . 
- With the <unk> @-@ born <unk> <unk> as its first elected mayor in 1952 ( all mayors were appointed prior to this ) , Manila underwent The Golden Age , once again earning its status as the " Pearl of the <unk> " , a moniker it earned before the Second World War . After <unk> 's term in the 1950s , Manila was led by Antonio <unk> for most of the 1960s . Ramon <unk> ( an Indian @-@ <unk> ) was mayor for nearly the entire 1970s until the 1986 People Power Revolution . <unk> <unk> , <unk> , and <unk> are often collectively considered as the " Big Three of Manila " less for their rather long <unk> as the city 's chief executive ( continuously for over three decades , from 1952 – 1986 ) , but more for their <unk> contribution to the development and progress of the city and their lasting legacy in <unk> the quality of life and welfare of the people of Manila . 
- During the administration of President Ferdinand Marcos , the region of the Metro Manila was created as an integrated unit with the enactment of Presidential <unk> No. <unk> on November 7 , 1975 . The area encompassed four cities and thirteen adjoining towns , as a separate regional unit of government . On the <unk> anniversary of the city 's foundation on June 24 , 1976 , Manila was reinstated by Marcos as the capital of the Philippines for its historical significance as the seat of government since the Spanish Period . Presidential <unk> No. 940 states that Manila has always been to the <unk> people and in the eyes of the world , the premier city of the Philippines being the center of trade , commerce , education and culture . 
- During the martial law era , Manila became a hot @-@ bed of resistance activity as youth and student demonstrators repeatedly clashed with the police and military which were <unk> to the Marcos regime . After decades of resistance , the non @-@ violent People Power Revolution ( predecessor to the peaceful @-@ <unk> that toppled the iron @-@ curtain in Europe ) , ousted the authoritarian Marcos from power . 
- In 1992 , <unk> Lim was elected mayor , the first Chinese @-@ <unk> to hold the office . He was known for his anti @-@ crime <unk> . Lim was succeeded by <unk> <unk> , who served as his vice @-@ mayor . <unk> was known for his campaign ( and city slogan ) " <unk> <unk> <unk> " ( <unk> Manila ) , which saw the establishment of several parks and the repair and rehabilitation of the city 's deteriorating facilities . He was the city 's mayor for 3 terms ( 9 years ) before being termed out of office . 
- <unk> Lim once again ran for mayor and defeated <unk> 's son Ali in the 2007 city election and immediately reversed all of <unk> 's projects claiming <unk> 's projects made little contribution to the improvements of the city . The relationship of both parties turned bitter , with the two <unk> again during the 2010 city elections in which Lim won against <unk> . 
- Lim was sued by <unk> Dennis <unk> on 2008 over human rights , charged with <unk> over the rehabilitation of public schools , and was heavily criticized for his <unk> resolution of the <unk> Park hostage taking incident , one of the deadliest hostage crisis in the Philippines . Later on , Vice Mayor <unk> <unk> and 28 city <unk> filed another case against Lim in 2012 , stating that Lim 's statement in a meeting were " life @-@ threatening " to them . On the 2013 elections , former President Joseph <unk> defeated Lim in the mayoral race . During his term , <unk> has paid the city 's over <unk> 5 billion debts , increase revenues by 2 @.@ 35 times from <unk> 6 @.@ 2 billion in 2012 to <unk> 14 @.@ 6 billion by 2016 , spent from 2013 to 2016 an unprecedented <unk> 6 @.@ 76 billion for the city 's infrastructure , built and or renovated seven city public markets , built 22 schools , increased teachers ' <unk> , modernized the six city hospitals and bought <unk> machines and magnetic resonance imaging <unk> , increased the efficiency of the police force and reduced crime . Manila has become the most competitive city in the Philippines by 2015 , making the city the best place for doing business and for living in . 
- Despite his achievements and unprecedented feat as the Mayor of Manila , however , <unk> was re @-@ elected as Manila mayor in the 2016 election against Lim and <unk> <unk> , only winning by an <unk> 2 @,@ 830 votes . 
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- = = Geography = = 
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- Manila is located on the eastern shores of Manila bay , which rests on the western shores of Luzon . Manila lies 800 miles ( 1 @,@ 300 kilometers ) from mainland Asia . The <unk> River <unk> Manila . 
- Almost all of Manila sits on top of centuries of prehistoric alluvial deposits built by the waters of the <unk> and on some land reclaimed from Manila Bay . Manila 's land has been altered substantially by human intervention , with considerable land <unk> along the <unk> since the American colonial times . Some of the natural variations in topography have been <unk> out due to the <unk> of the city . As of 2013 , Manila has a total area of 42 @.@ 88 square kilometres ( 16 @.@ 56 sq mi ) . 
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- = = = <unk> = = = 
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- Manila sits <unk> the Pacific typhoon belt and is <unk> @-@ crossed by several fault lines . This led to Manila and its metropolitan region to be ranked as the second <unk> capital ( city ) to live in according to Swiss Re . The <unk> active <unk> Valley <unk> System poses a threat to Manila and the surrounding regions . 
- Manila has endured several deadly earthquakes , notably in 1645 and in 1677 which destroyed the stone and brick medieval city . The Earthquake Baroque style was used by the Colonial architects during the Spanish colonial period in order to adapt to the frequent earthquakes . 
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- = = = Climate = = = 
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- Under the Köppen climate classification system , Manila features a tropical <unk> climate ( Köppen climate classification <unk> ) . Together with the rest of the Philippines , Manila lies entirely within the <unk> . Its proximity to the equator means that the temperature range is very small , rarely going below 20 ° C ( 68 ° F ) or above 38 ° C ( 100 ° F ) . <unk> extremes have ranged from 14 @.@ 5 ° C ( 58 @.@ 1 ° F ) on January 11 , 1914 to 38 @.@ 6 ° C ( 101 @.@ 5 ° F ) on May 7 , 1915 . 
- <unk> levels are usually very high all year round . Manila has a distinct dry season from December through May , and a relatively lengthy wet season that covers the remaining period with slightly cooler temperatures . In the rainy season it rarely rains all day but the rainfall is very heavy during short periods . <unk> usually occur from June to September . 
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- = = = Environment = = = 
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- Due to industrial waste and automobiles , Manila suffers from air pollution , affecting 98 % of the population . <unk> , the air pollution causes more than 4 @,@ 000 deaths . <unk> is Manila 's most air polluted district due to open dump sites and industrial waste . According to a report in 2003 , The <unk> River is one of the most polluted rivers in the world with 150 tons of domestic waste and 75 tons of industrial waste dumped daily . 
- <unk> , Manila is hit with 6 to 7 <unk> creating floods . In 2009 , Typhoon <unk> struck Philippines . In the aftermath of Typhoon <unk> , the lack of infrastructure led to one of the worst <unk> in the Philippines and creating a significant amount of pollution . Following the aftermath of Typhoon <unk> , the city began to dredge its rivers and improve its drainage network . The <unk> River <unk> Commission is in charge of cleaning up the <unk> River and tributaries for transportation , recreation and tourism purposes . <unk> efforts have resulted in the creation of parks along the riverside , along with <unk> pollution controls . 
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- = = <unk> = = 
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- = = = Architecture = = = 
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- Manila has <unk> significant buildings in a wide range of styles spanning distinct historical and cultural periods . Architectural styles reflect American , Spanish , Chinese , and Malay influences . <unk> <unk> architects such as Antonio Toledo , Felipe Roxas , Juan M. <unk> and Tomás <unk> have designed significant buildings in Manila such as churches , government offices , theaters , mansions , schools and universities . 
- Manila is known for its distinct Art Deco theaters which are designed by National Artists such as Juan <unk> and Pablo Antonio . The historic <unk> Street in <unk> features many buildings of neo @-@ classical and <unk> @-@ arts architectural style , many of which were designed by prominent <unk> architects during the American Rule in the 1920s to the late 1930s . Many architects , artists , historians and heritage advocacy groups are pushing for the revival of <unk> Street , which was once the premier street of the Philippines . 
- Unfortunately , much of Manila 's prewar and Spanish colonial architecture was destroyed during World War II . Reconstruction took place afterwards , replacing the destroyed historic Spanish @-@ era buildings with modern ones , <unk> much of the city 's character . Some buildings destroyed by the war have been reconstructed , such as the Old Legislative Building ( National Museum ) , <unk> de Manila ( Bureau of the Treasury ) and the currently under construction San <unk> Church ( <unk> de <unk> ) . Plans have been laid out to <unk> several neglected historic buildings and places such as Plaza Del Carmen , San Sebastian Church and the Manila Metropolitan Theater and soon Spanish @-@ era shops and houses in <unk> , <unk> , and San Nicolas will be restored to its former <unk> , as a part of a movement to restore Manila to its former glory . 
- Since Manila is prone to earthquakes , the Spanish colonial architects invented the style called Earthquake Baroque which the churches and government buildings during the Spanish colonial period adopted . As a result , succeeding earthquakes of the 18th and 19th centuries barely affected Manila , although it did periodically level the surrounding area . Modern buildings in and around Manila are designed or have <unk> to withstand an 8 @.@ 2 magnitude quake in accordance to the country 's building code . 
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- = = = <unk> and districts = = = 
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- Manila is composed of fourteen districts according to Republic Act No. <unk> , otherwise known as the <unk> Charter of the City of Manila . Two were later added , which are Santa Mesa ( partitioned off from <unk> ) and San Andres ( partitioned off from Santa <unk> ) . 
- The city has <unk> <unk> that are known by <unk> numbers instead of names . These <unk> are further group into 100 zones for administrative and municipal purposes . 
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- = = = Military and national security = = = 
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- The headquarters of the Philippine Coast Guard is located at the South Harbor in Port Area near <unk> and <unk> . The Philippine Navy on the other hand has its headquarters in Naval Station Jose <unk> located along Roxas Boulevard in <unk> . Furthermore , the <unk> Joint Task Force @-@ National Capital Region was created in 2012 to ensure peace and stability in Metro Manila , of which Manila is a part . It bears the same functions of the deactivated National Capital Regional Command , although it operates on a much smaller size than its predecessor . 
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- = = = <unk> = = = 
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- There are an estimated 4 million <unk> dwellers living in Manila as of 2014 . 
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- = = Demographics = = 
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- According to the 2015 census , the population of the city was 1 @,@ 780 @,@ 148 , making it the second most populous city in the Philippines . 
- Manila is the most densely populated city in the world with 43 @,@ <unk> inhabitants per km2 . District 6 is listed as being the most dense with 68 @,@ 266 inhabitants per km2 , followed by District 1 with 64 @,@ <unk> and District 2 with 64 @,@ 710 , respectively . District 5 is the least densely populated area with 19 @,@ 235 . 
- Manila 's population density dwarfs that of Kolkata ( 27 @,@ <unk> inhabitants per km2 ) , Mumbai ( 22 @,@ <unk> inhabitants per km2 ) , Paris ( 20 @,@ 164 inhabitants per km2 ) , <unk> ( 19 @,@ <unk> inhabitants per km2 ) , Shanghai ( 16 @,@ 364 inhabitants per km2 , with its most dense district , <unk> , having a density of 56 @,@ <unk> inhabitants per km2 ) , and Tokyo ( 10 @,@ <unk> inhabitants per km2 ) . 
- The vernacular language is <unk> , based mostly on the <unk> of surrounding areas , and this Manila form of speaking <unk> has essentially become the <unk> <unk> of the Philippines , having spread throughout the archipelago through mass media and entertainment . Meanwhile , English is the language most widely used in education , business , and heavily in everyday usage throughout the Metro Manila region and the Philippines itself . 
- A number of older residents can still speak basic Spanish , which used to be a mandatory subject in the curriculum of Philippine universities and colleges , and many children of Japanese <unk> , Indian <unk> , and other migrants or expatriates also speak their parents ' languages at home , aside from English and / or <unk> for everyday use . <unk> Chinese ( known as <unk> @-@ <unk> ) is spoken by the city 's Chinese @-@ <unk> community . 
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- = = Economy = = 
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- The city is a major center for commerce , banking and finance , <unk> , transportation , tourism , real estate , new media as well as traditional media , advertising , legal services , <unk> , insurance , theater , fashion , and the arts in the Philippines . 
- The Cities and <unk> <unk> Index , published by the National <unk> Council of the Philippines , ranks the cities , municipalities and provinces of the country according to their economic <unk> , government efficiency and infrastructure . Manila placed third in the <unk> <unk> City ( <unk> ) category . Previously , Manila was the country 's most competitive city in 2015 , making it the best place to live in and do business . 
- The Port of Manila is the largest seaport in the Philippines , making it the premier international shipping gateway to the country . The Philippine Ports Authority is government agency responsible to oversee the operation and management of the ports . The International <unk> Terminal Services Inc. cited by the Asian Development Bank as one of the top five major maritime terminal operators in the world has its headquarters and main operations on the ports of Manila . Another port operator , the Asian Terminal Incorporated , has its corporate office and main operations in the Manila South Harbor and its container <unk> located in Santa Mesa . 
- <unk> , the oldest and one of the largest <unk> in the world , was the center of commerce and business activities in the city . Numerous residential and office <unk> are found within its medieval streets . Plans to make the <unk> area into a business process <unk> ( <unk> ) hub progresses and is aggressively pursued by the city government of Manila . 30 buildings are already identified to be converted into <unk> offices . These buildings are mostly located along the <unk> Street of <unk> , which are all unoccupied and can be converted into offices . 
- <unk> in <unk> is dubbed as the " shopping <unk> of the Philippines " . Numerous shopping malls are located in this place , which sells products and goods at <unk> price . Small <unk> occupies several roads that causes pedestrian and <unk> traffic . A famous landmark in <unk> is the <unk> Center , a large shopping mall that is a part of the Philippine National Railways ' Main Station . It attracts 1 million people every month , but is expected to add another 400 @,@ 000 people when the <unk> @-@ 2 West <unk> is constructed , making it Manila 's busiest transfer station . 
- <unk> manufacturers within the city produce industrial @-@ related products such as chemicals , textiles , clothing , and electronic goods . Food and <unk> and tobacco products also produced . Local entrepreneurs continue to process primary commodities for export , including rope , <unk> , refined sugar , <unk> , and <unk> oil . The food @-@ processing industry is one of the most stable major manufacturing sector in the city . 
- The <unk> Oil Depot houses the storage facilities and distribution terminals of the three major players in the country 's petroleum industry , namely <unk> Philippines , <unk> Shell and <unk> Corporation . The oil depot has been a subject of various concerns , including its environmental and health impact to the residents of Manila . The Supreme Court has ordered that the oil depot to be relocated outside the city by July 2015 , but it failed to meet this deadline . It is currently being demolished which is expected to be finished before the year 2016 ends , and plans have been set up to turn this 33 hectare facility into a transport hub or even a food park . 
- Manila is a major publishing center in the Philippines . Manila Bulletin , the Philippines ' largest <unk> newspaper by circulation , is headquartered inside <unk> . Other major publishing companies in the country like The Manila Times , The Philippine Star and Manila Standard Today are headquartered inside the Port Area . The Chinese Commercial News , the Philippines ' oldest existing Chinese @-@ language newspaper , and the country 's third @-@ oldest existing newspaper is headquartered in <unk> . 
- Manila serves as the headquarters of the Central Bank of the Philippines which is located along Roxas Boulevard . Some universal banks in the Philippines that has its headquarters in the city are the <unk> of the Philippines and Philippine Trust Company . <unk> Life Insurance Company , currently the largest life insurance company in the Philippines in terms of assets , net worth , investment and paid @-@ up capital , has its headquarters along United Nations Avenue in <unk> . <unk> Philippines has its corporate office along United Nations Avenue in <unk> . Toyota , a company listed in the Forbes Global 2000 also has its regional office along UN Avenue . 
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- = = = Tourism = = = 
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- Tourism is a vital industry in Manila , and it <unk> approximately over 1 million tourists each year . Major destinations include the walled city of <unk> , the National Theater at the Cultural Center of the Philippines , Manila Ocean Park , <unk> , <unk> , <unk> , Manila Zoo , National Museum of the Philippines and <unk> Park . 
- <unk> Park , also known as <unk> Park , is the national park of the country and has an area of 58 hectares ( 140 acres ) , making it the largest urban park in Asia . In the Tourism Act of 2009 , <unk> Park along with <unk> are designated as flagship destination to become a tourism enterprise zone . A new attraction called <unk> de Manila is expected to rise in the park . The park was constructed as an honor and dedication to the country 's national hero José <unk> , who was executed by the Spaniards on charges of subversion . The flagpole west of the <unk> Monument is the <unk> Zero marker for distances to the rest of the country . 
- <unk> is the historic center of Manila . Originally , it was considered to be Manila itself at the time when the Philippines was under the Spanish Empire colonial rule . Owing to its history and cultural value , <unk> and <unk> Park are designated as flagship destination to become a tourism enterprise zone in the Tourism Act of 2009 . <unk> is managed by the <unk> Administration ( <unk> ) . 
- The architecture of <unk> reflects the Spanish colonial style and the American neoclassical architectural style , since the Philippines was a colony of Spain and the United States before it is granted its independence in 1946 . <unk> is a popular mode of transportation in <unk> and nearby places such as <unk> , <unk> and the <unk> Park . 
- Popular tourist destinations in <unk> include the <unk> de San Diego , Club <unk> Golf Course , <unk> de Santa Lucia , Fort Santiago , Manila Cathedral , <unk> <unk> , <unk> de Santa <unk> , <unk> del <unk> , Plaza Mexico , Plaza de <unk> , San Agustin Church and the <unk> de Manila . 
- Some of the country 's oldest schools are founded in <unk> , these are the University of Santo Tomas ( 1611 ) , <unk> de San Juan de <unk> ( 1620 ) , and <unk> de Manila University ( 1859 ) . Only <unk> de San Juan de <unk> remains at <unk> ; the University of Santo Tomas transferred to a new campus at <unk> in 1927 , and <unk> left <unk> for Loyola Heights , <unk> City ( while still retaining " de Manila " in its name ) in 1952 . Other prominent educational institutions include the Manila High School and the University of the City of Manila . 
- The Department of Tourism <unk> Manila as the pioneer of medical tourism , expecting it to generate $ 1 billion in revenue annually . However , lack of progressive health system , inadequate infrastructure and the unstable political environment are seen as <unk> for its growth . 
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- = = = <unk> centers = = = 
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- Manila is a well @-@ known shopping hub of the country and it has been named as one of the best shopping destinations in Asia . Major shopping malls , markets and <unk> <unk> in Manila . 
- <unk> Place Manila is the largest shopping mall in the city . The mall was the second and by @-@ far , the largest Robinson Mall ever built by John <unk> . SM <unk> maintains presence in the city . One of their shopping mall is the SM City Manila , the first SM <unk> in the city featuring major SM brands like The SM Store , SM <unk> , SM <unk> and SM <unk> . It is located right beside the Manila City Hall . SM City San <unk> is the second SM <unk> in Manila . It is located in Santa Cruz . SM City San <unk> was constructed on the site of the former San <unk> <unk> . The building of the former Manila Royal Hotel in <unk> which is famed for its revolving restaurant atop is now the SM <unk> Center which was established in 1972 . The site of the first SM Store is located at Carlos <unk> Sr. ( formerly <unk> ) Street in San Miguel . 
- <unk> is referred as the " Old Downtown " where <unk> , markets , <unk> shops , music and electronics stores are common . <unk> <unk> Avenue is where lots of department stores are located . One of <unk> Avenue 's famous destination is <unk> , home to numerous shopping malls in the city . It is also dubbed as the shopping <unk> of the Philippines where everything is sold at <unk> price . <unk> , the oldest <unk> in the world , is the city 's center of commerce and trade for all types of businesses run by <unk> @-@ Chinese merchants with a wide variety of Chinese and <unk> shops and restaurants . 
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- = = Arts , culture and religion = = 
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- 
- = = = Religion = = = 
- 
- 
- = = = = Christianity = = = = 
- 
- As a result of Spanish cultural influence , Manila is a predominantly Christian ( Catholic ) city . As of 2010 , Roman Catholics comprises 83 @.@ 5 % of the population , followed by adherents of the Philippine Independent Church ( 2 @.@ 4 % ) ; <unk> <unk> <unk> ( 1 @.@ 9 % ) ; various Protestant churches ( 1 @.@ 8 % ) ; and Buddhists ( 1 @.@ 1 % ) . Members of Islam and other religions comprises the remaining 10 @.@ 4 % of the city 's population . 
- Manila is the site of prominent Catholic churches and institutions . The Manila Cathedral is the seat of the Roman Catholic Archdiocese of Manila and the oldest established church in the country . Aside from the Manila Cathedral , there are also three other <unk> in the city : <unk> Church , <unk> Church , and the Minor Basilica of San <unk> . The San Agustín Church in <unk> is a UNESCO World Heritage Site and is one of the two fully air @-@ conditioned Catholic churches in the city . Manila also has other parishes located throughout the city , with some of them dating back to the Spanish Colonial Period when the city serves as the base for numerous Catholic missions both within the Philippines and to Asia beyond . 
- Several <unk> Protestant denominations are headquartered in the city . St. Stephen 's Parish pro @-@ cathedral in the <unk> . Cruz district is the see of the Episcopal Church in the Philippines ' Diocese of Central Philippines , while align Taft Avenue are the main cathedral and central offices of the <unk> <unk> <unk> ( also called the <unk> Church , a national church that was a product of the Philippine Revolution ) . Other faiths like The Church of Jesus Christ of <unk> @-@ day Saints maintains a presence in the city . 
- The indigenous <unk> <unk> <unk> has several <unk> ( akin to parishes ) in the city , including its very first chapel ( now a museum ) in Punta , <unk> . <unk> . <unk> , <unk> and Seventh @-@ day <unk> denominations also <unk> within the city . The headquarters of the Philippine Bible Society is in Manila . Also , the main campus of the Cathedral of <unk> is located along Taft Avenue . Jesus Is Lord Church also has several branches and campuses in Manila , and celebrates its anniversary yearly at the <unk> Green and <unk> <unk> in <unk> Park . 
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- = = = = Other faiths = = = = 
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- The city also hosts other religions . There are many Buddhist and Taoist temples serving the Chinese <unk> community . <unk> is home to a sizable Muslim population which <unk> at <unk> Al @-@ <unk> . Members of the Indian expatriate population have the option of worshiping at the large Hindu temple in the city , or at the Sikh <unk> along United Nations Avenue . The National Spiritual Assembly of the Bahá <unk> of the Philippines , the governing body of the <unk> Bahá 'í community , is headquartered near Manila 's eastern border with <unk> . 
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- = = = Annual cultural events and religious festivities = = = 
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- Manila celebrates civic and national holidays . Manila Day , which celebrates the city 's founding on June 24 , <unk> , was first proclaimed by <unk> A. <unk> ( then Vice Mayor of Manila ) on June 24 , 1962 and has been annually commemorated , under the patronage of John the Baptist . <unk> , each of the city 's <unk> also have their own festivities guided by their own patron saint . The city is also the host to the Feast of the Black <unk> , held every January 9 , which draws millions of Catholic devotees . Another religious <unk> held in Manila was the Feast of the Nuestra Señora de los <unk> de Manila ( Our Lady of the <unk> ) , the patron saint of Santa <unk> and was held every May 12 . Non @-@ religious holidays include the New Year 's Day , National Heroes ' Day , <unk> Day and <unk> Day . 
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- = = = Museums and art galleries = = = 
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- As the cultural center of the Philippines , Manila is the home to a number of museums . The National Museum of the Philippines Complex , which include the National Museum of Fine Arts , Museum of Anthropology and the Museum of Natural History , is located on the northeast part of <unk> Park facing Taft <unk> proposed national government center during the American time . Museums established by educational institutions include the <unk> Shrine , the Museum of Contemporary Art and Design , <unk> Museum of Arts and Sciences , and the UP Museum of a History of <unk> . 
- <unk> <unk> , one of Manila 's most prominent museums , documents the Chinese lives and contributions in the history of the Philippines . The <unk> Light and Sound Museum chronicles the <unk> desire for freedom during the revolution under <unk> 's leadership and other revolutionary leaders . The Metropolitan Museum of Manila exhibits the <unk> arts and culture . 
- Other museums in the city are the Museum of Manila , the city @-@ owned museum that exhibits the city 's culture and history , <unk> <unk> , a children 's museum , the Museum of Philippine Political History , which exhibits notable political events in the country , the Parish of the Our Lady of the <unk> and the San Agustin Church Museum , which houses religious artifacts , and Plaza San Luis , a public museum . 
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- = = Sports = = 
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- Sports in Manila have a long and distinguished history . The city 's , and in general the country 's main sport is basketball , and most <unk> have a makeshift basketball court , with court markings drawn on the streets . Larger <unk> have covered courts where <unk> leagues are held every summer ( April to May ) . 
- The city has several well @-@ known sports venues , such as the <unk> Memorial Sports Complex and San Andres <unk> , the home of the now defunct Manila <unk> . The <unk> Memorial Sports Complex houses the <unk> Memorial Track and Football Stadium , the Baseball Stadium , Tennis Courts , Memorial Coliseum and the <unk> <unk> Stadium ( the latter two are indoor arenas ) . 
- The <unk> complex had hosted several multi @-@ sport events , such as the 1954 Asian Games and the 1934 Far Eastern Games . Whenever the country hosts the Southeast Asian Games , most of the events are held at the complex , but in the 2005 Games , most events were held elsewhere . The 1960 ABC Championship and the 1973 ABC Championship , <unk> of the FIBA Asia Championship , was hosted by the complex , with the national basketball team winning on both tournaments . The 1978 FIBA World Championship was held at the complex although the latter stages were held in the <unk> Coliseum in <unk> City , Southeast Asia 's largest indoor arena at that time . 
- Manila also hosts several well @-@ known sports facilities such as the Enrique M. <unk> Sports Center and the University of Santo Tomas Sports Complex , both of which are private venues owned by a university ; collegiate sports are also held , with the University Athletic Association of the Philippines and the National Collegiate Athletic Association basketball games held at <unk> Memorial Coliseum and <unk> <unk> Stadium , although basketball events had transferred to San Juan 's <unk> Flying V Arena and the <unk> Coliseum in <unk> City . Other collegiate sports are still held at the <unk> Memorial Sports Complex . Professional basketball also used to play at the city , but the Philippine Basketball Association now holds their games at <unk> Coliseum and <unk> <unk> at <unk> ; the now defunct Philippine Basketball League played some of their games at the <unk> Memorial Sports Complex . 
- The Manila Storm are the city 's rugby league team training at <unk> Park ( <unk> Park ) and playing their matches at Southern Plains Field , <unk> , <unk> . 
- Previously a widely played sport in the city , Manila is now the home of the only sizable baseball stadium in the country , at the <unk> Memorial Baseball Stadium . The stadium hosts games of Baseball Philippines ; Lou Gehrig and Babe Ruth were the first players to score a home run at the stadium at their tour of the country on December 2 , 1934 . 
- Another popular sport in the city are cue sports , and <unk> halls are a feature in most <unk> . The 2010 World Cup of Pool was held at <unk> Place Manila . 
- The <unk> Memorial Track and Football Stadium hosted the first FIFA World Cup <unk> in decades when the Philippines hosted Sri Lanka in July 2011 . The stadium , which was previously unfit for international matches , had undergone a major renovation program prior to the match . The Football Stadium now regularly hosts matches of the United Football League . The stadium also hosted its first rugby test when it hosted the 2012 Asian Five Nations Division I tournaments . 
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- = = Government and politics = = 
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- The government of Manila is divided into three branches : executive , legislative and judiciary . The judicial branch is administered solely by the Supreme Court of the Philippines under the Metro Manila judicial region . The city government have control of the executive and legislative branch . Manila employs 11 @,@ <unk> personnel at the end of 2014 . 
- The current Mayor of Manila is Joseph <unk> , who served as the President of the Philippines from 1998 @-@ 2001 . He is also the head of the executive department of the city . The legislative arm which is composed of six elected city <unk> , is headed by the Vice Mayor . Former actor <unk> <unk> currently serves as the city 's vice mayor . <unk> they are assisted by the Manila City Council , the local President of the Association of <unk> Captains , and the President of the <unk> <unk> . Their offices are located at the Manila City Hall . 
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- = = = Finance = = = 
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- On September 25 , 2014 , the Commission on <unk> released its 2013 Annual Financial Report citing the city 's income at <unk> 10 @.@ 1 billion with an asset worth of <unk> 18 @.@ 6 billion . Its local income stood at <unk> 5 @.@ 41 billion and its national government allocation was <unk> 1 @.@ 74 billion , having an annual regular income ( <unk> ) of an estimated <unk> 7 @.@ 15 billion . Manila 's net income stood at <unk> 3 @.@ 54 billion in 2014 . 
- Among the local government units , Manila has the highest budget allocation to health . It was also one of the cities with the highest tax and internal revenue . Tax revenue accounts for 46 % of the city 's income in 2012 . 
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- = = = <unk> and <unk> = = = 
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- Manila has six legislative districts that serve as the constituencies for the election of the city 's representatives to the lower house of the Congress of the Philippines and of the regular members to the <unk> <unk> ( <unk> ; City Council ) . Each district elects one representative to the House of Representatives and six <unk> members to the council . The city , along with the rest of the nation , elects 12 <unk> as one at @-@ large district . 
- Manila is politically divided into <unk> <unk> , the smallest unit of local government in the Philippines . Each <unk> has its own <unk> and <unk> . For administrative convenience , all the <unk> in Manila are grouped into 100 zones . These zones have no form of local government . 
- The 1st District ( 2015 population : 415 @,@ <unk> ) is Manila 's ( and the country 's ) most densely populated congressional district . It covers the western portion of <unk> that lies along Manila Bay . 
- The 2nd District ( 2015 population : 215 @,@ 457 ) covers the eastern inland portion of <unk> , a neighborhood or sub @-@ district known as <unk> . 
- The 3rd District ( 2015 population : 197 @,@ 242 ) covers <unk> , <unk> , San Nicolas and Santa Cruz . 
- The 4th District ( 2015 population : 265 @,@ <unk> ) covers <unk> . 
- The 5th District ( 2015 population : 366 @,@ <unk> ) covers <unk> , <unk> , Port Area , <unk> , San Andres <unk> , and a portion of <unk> ( except Zone 90 ) . 
- The 6th District ( 2007 population : 295 @,@ 245 ) covers <unk> ( Zone 90 only ) , <unk> , San Miguel , Santa <unk> and Santa Mesa . 
- Manila has the most number of <unk> of any city or municipality in the Philippines . Attempts at reducing its number have not prospered despite local legislation — <unk> <unk> , passed on 23 April 1996 — reducing the number from <unk> to 150 by merging existing <unk> , because of the failure to hold a <unk> . 
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- = = = National government = = = 
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- Manila , being the seat of political power of the Philippines , has several national government offices headquartered at the city . Planning for the development for being the center of government started during the early years of American colonization to the country when they envisioned a well @-@ designed city outside the walls of <unk> . The strategic location chosen was <unk> , a former town which is now the <unk> Park to become the center of government and a design commission was given to Daniel <unk> to create a master plan for the city patterned after Washington <unk> .. These improvements were eventually abandoned under the Commonwealth Government of Manuel L. <unk> . 
- A new government center was to be built on the hills northeast of Manila , or what is now <unk> City . Several government agencies have set up their headquarters in <unk> City but several key government offices still reside in Manila . However , many of the plans were substantially altered after the devastation of Manila during World War II and by subsequent administrations . 
- The city , as the capital , still hosts the Office of the President , as well as the president 's official residence . Aside from these , important institutions such as the Supreme Court , the Court of Appeals , the <unk> <unk> <unk> <unk> , the <unk> of <unk> and Management , Finance , Health , Justice , Labor and <unk> and Public Works and Highways still call the city home . Manila also hosts important national institutions such as the National Library , National Archives , National Museum and the Philippine General Hospital . 
- Congress previously held office at the Old Congress Building . In 1972 , due to declaration of martial law , Congress was dissolved ; its successor , the unicameral <unk> <unk> , held office at the new <unk> <unk> Complex . When a new constitution restored the <unk> Congress , the House of Representatives stayed at the <unk> <unk> Complex , while the Senate remained at the Old Congress Building . In May 1997 , the Senate transferred to a new building it shares with the Government Service Insurance System at reclaimed land at <unk> . 
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- = = Infrastructure = = 
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- = = = Utilities = = = 
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- = = = = Water and electricity = = = = 
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- Water services used to be provided by the Metropolitan <unk> and <unk> System , which served 30 % of the city with most other sewage being directly dumped into storm drains , <unk> tanks , or open canals . <unk> was <unk> in 1997 which split the water concession into the east and west zones . The <unk> Water Services took over the west zone of which Manila is a part . It now provides the supply and delivery of <unk> water and <unk> system in Manila , but it does not provide service to the southeastern part of the city which belongs to the east zone that is served by Manila Water . Electric services are provided by <unk> , the sole electric power distributor in Metro Manila . 
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- = = = Transportation = = = 
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- One of the more famous modes of transportation in Manila is the <unk> . <unk> after U.S. army <unk> , these have been in use since the years immediately following World War II . The <unk> <unk> , the third generation Toyota <unk> , which competed directly with <unk> and followed fixed routes for a set price , once plied the streets of Manila . 
- On a for @-@ hire basis , the city is served by numerous <unk> , " <unk> " ( <unk> with <unk> , the Philippine version of the auto <unk> ) , and " <unk> " or " <unk> " ( <unk> with a <unk> , the Philippine version of <unk> ) . In some areas , especially in <unk> , <unk> <unk> are popular . Spanish @-@ era horse @-@ drawn <unk> are still a popular tourist attraction and mode of transportation in the streets of <unk> and <unk> . All types of public road transport are privately owned and operated under government franchise . 
- The city is serviced by the <unk> @-@ 1 and <unk> @-@ 2 which forms the <unk> system , as distinct from the <unk> @-@ 3 which is under the <unk> system that services other parts of Metro Manila . Development of the railway system began in the 1970s under the Marcos administration , making it the first light rail transport in Southeast Asia . These systems are currently undergoing a multibillion @-@ dollar expansion . <unk> Line 1 runs along the length of Taft Avenue ( R @-@ 2 ) and <unk> Avenue ( R @-@ 9 ) , and the <unk> Line 2 runs along <unk> M. <unk> Avenue ( C @-@ 1 ) and Ramon <unk> Boulevard ( R @-@ 6 ) from Santa Cruz , through <unk> City , up to <unk> in <unk> . 
- The main terminal of the Philippine National Railways lies within the city . One commuter railway within Metro Manila is in operation . The line runs in a general north @-@ south direction from <unk> ( <unk> ) toward <unk> . The Port of Manila , located in the vicinity of Manila Bay is the chief seaport of the Philippines . The <unk> River Ferry Service which runs on the <unk> River is another form of transportation . The city is also served by the <unk> <unk> International Airport and Clark International Airport . 
- In 2006 , Forbes magazine ranked Manila " the world 's most <unk> city " . Manila has become notorious for its frequent traffic jams and high densities . The government has undertaken several projects to alleviate the traffic in the city . Some of the projects include : the construction of a new flyover at <unk> , the construction of the Metro Manila <unk> Stage 3 , the proposed <unk> Line 2 ( west ) extension from <unk> to <unk> or the Port Area , and the expansion of several national and local roads . However , such projects have yet to make any meaningful impact , and the traffic jams and congestion continue unabated . The urban planning of the Manila and the whole metropolis was based on the Metro Manila Dream Plan , which seeks to address the problems of Metro Manila 's urban planning and transportation . It consists of a list of short term priority projects and medium to long term infrastructure projects that will last up to <unk> . 
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- = = Healthcare = = 
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- The Manila Health Department is responsible for the planning and implementation of the health care programs provided by the city government . It operates 59 health centers and six city @-@ run hospitals , which are free of charge . The six public city @-@ run hospitals are the <unk> <unk> <unk> Medical Center , <unk> <unk> <unk> , <unk> Andres <unk> Memorial Medical Center , <unk> <unk> <unk> , <unk> . <unk> Hospital , and Justice Jose <unk> Santos General Hospital . Manila is also the site of the Philippine General Hospital , the tertiary state @-@ owned hospital administered and operated by the University of the Philippines Manila . 
- Manila 's healthcare is also provided by private corporations . Private hospitals that operates in the city are the Manila Doctors Hospital , Chinese General Hospital and Medical Center , Dr. José R. Reyes Memorial Medical Center , Metropolitan Medical Center , Our Lady of Lourdes Hospital , and the University of Santo Tomas Hospital . 
- The Department of Health has its main office in Manila . The national health department also operates the San <unk> Hospital , a special referral tertiary hospital . Manila is also the home to the headquarters of the World Health Organization Regional Office for the Western Pacific and the World Health Organization Country Office for the Philippines . 
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- = = Education = = 
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- The center of education since the colonial period , Manila — particularly <unk> — is home to several Philippine universities and colleges as well as its oldest ones . It served as the home of the University of Santo Tomas ( 1611 ) , <unk> de San Juan de <unk> ( 1620 ) , <unk> de Manila University ( 1859 ) , Lyceum of the Philippines University and the <unk> Institute of Technology . Only <unk> de San Juan de <unk> ( 1620 ) remains at <unk> ; the University of Santo Tomas transferred to a new campus at <unk> in 1927 , and <unk> left <unk> for Loyola Heights , <unk> City ( while still retaining " de Manila " in its name ) in 1952 . 
- The University of the City of Manila ( <unk> <unk> <unk> <unk> <unk> ) located at <unk> , and <unk> de Manila located just outside the walled city , are both owned and operated by the Manila city government . The national government controls the University of the Philippines Manila , the oldest of the University of the Philippines constituent universities and the center of health sciences education in the country . The city is also the site of the Polytechnic University of the Philippines , the largest university in the country in terms of student population . 
- The University Belt refers to the area where there is a high concentration or a cluster of colleges and universities in the city and it is commonly understood as the one where the San Miguel , <unk> and <unk> districts meet . Generally , it includes the western end of <unk> Boulevard , <unk> Reyes St. ( formerly <unk> St. ) , the eastern end of <unk> M. <unk> Avenue ( formerly <unk> ) , <unk> Avenue , <unk> Street , and the different side streets . Each of the colleges and universities found here are at a short walking distance of each other . Another cluster of colleges lies along the southern bank of the <unk> River , mostly at the <unk> and <unk> districts , and still a smaller cluster is found at the southernmost part of <unk> near the border with <unk> such as the private co @-@ educational institution of De La Salle University , the largest of all De La Salle University System of schools . 
- The Division of the City Schools of Manila , a branch of the Department of Education , refers to the city 's three @-@ tier public education system . It governs the 71 public elementary schools , 32 public high schools . 
- The city also contains the Manila Science High School , the pilot science high school of the Philippines ; the National Museum , where the <unk> of Juan <unk> is housed ; the Metropolitan Museum of Manila , a museum of modern and contemporary visual arts ; the <unk> <unk> , the Children 's Museum , a place of hands @-@ on discovery and fun learning ; and , the National Library , the <unk> of the country 's printed and recorded cultural heritage and other literary and information resources . 
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- = = Global outreach = = 
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- = = = Twin towns – Sister cities = = = 
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- Sister cities of Manila 
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- = = = = Asia / Pacific Rim = = = = 
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- = = = = North America = = = = 
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- = <unk> ( palm ) = 
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- <unk> is a large genus of <unk> native to Mexico , the Caribbean , Central and South America . This <unk> <unk> , non @-@ spiny genus includes both small <unk> lacking an <unk> stem and large trees . The genus has a complicated taxonomic history , and has often been split into four or five genera based on differences in the male flowers . Since the genera can only be distinguished on the basis of their male flowers , the existence of intermediate flower types and the existence of hybrids between different genera has been used as an argument for keeping them all in the same genus . This has been supported by a recent molecular phylogeny . 
- Somewhere between 29 and 67 species are recognised in the genus , with estimates of as many as 100 . <unk> <unk> collections make it difficult to determine whether certain groups represent single species , or groups of similar species . <unk> species have a long history of human use , and include economically important sources of palm oil and fibre . Many species are fire tolerant and thrive in disturbed habitats . Their seeds are animal dispersed , including some which are thought to have been adapted for dispersal by now @-@ extinct Pleistocene <unk> . 
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- = = Description = = 
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- The genus <unk> has <unk> compound leaves — rows of leaflets emerge on either side of the axis of the leaf in a feather @-@ like or <unk> @-@ like pattern . Species are also non @-@ spiny <unk> and includes both large trees with stout stems up to 30 metres ( 98 ft ) tall and <unk> <unk> ( one which lack an <unk> stem ) . The number of leaves per individual varies from about three to thirty @-@ five ; larger plants tend to have more and longer leaves . 
- <unk> are large , branched and borne among the leaves . The <unk> consists of a main axis — the <unk> and the <unk> — and a series of smaller branches , the <unk> . The <unk> , which bear the flowers , emerge from the <unk> . The <unk> is the main stalk , connecting the <unk> with the stem . <unk> either consist entirely of male flowers , or are predominantly female with a few male flowers . Fruit usually have two or three seeds , although fewer or more are present in some species , and are usually brown , yellow , orange @-@ brown or purple when mature . 
- Four different types of male flowers exist . On the basis of these flower types , the genus has often been split into four genera — a more narrowly defined <unk> , <unk> , <unk> and <unk> . The species sometimes referred to <unk> have coiled <unk> , while the other groups have straight ones . The <unk> of those placed in <unk> are much shorter than the <unk> , while those placed in <unk> and a more narrowly defined <unk> have <unk> that are longer than the <unk> . Five species do not fit easily into any of these groups ; this fact has been used as an argument in favour of considering this group a single genus . 
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- = = Taxonomy = = 
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- <unk> has been placed in the subfamily <unk> , the tribe <unk> and the <unk> <unk> , together with the genera <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> and <unk> . Within this <unk> , <unk> has been found to be a monophyletic group , and sister to the clade containing <unk> , <unk> , <unk> , <unk> and <unk> . 
- <unk> exists as to whether <unk> should be considered a single genus , or a group of related genera . In their 1996 Field Guide to the <unk> of the Americas , Andrew Henderson , Gloria <unk> and <unk> <unk> combined all the species in the <unk> <unk> ( as it was then defined ) into a single genus , <unk> . In his 1999 <unk> Treatment of Palm <unk> <unk> , American botanist Sidney F. <unk> divided the group into five genera — a more narrowly defined <unk> , <unk> , <unk> , <unk> and <unk> . <unk> <unk> and John <unk> recognised a single genus in their 2005 World <unk> of <unk> , and Jean @-@ Christophe <unk> continued this usage in his 2008 review of the genus . 
- The multi @-@ genus approach is based solely on the structure of the male flowers ; no other characters could be consistently associated with one genus or another . Four of the genera — <unk> ( in a narrow sense ) , <unk> , <unk> and <unk> — correspond to four different types of male flowers found within the genus . However , a few species have flowers that are intermediate between these four types , including A. <unk> ( which <unk> placed in its own genus , <unk> ) and this has been used as an argument for the single @-@ genus approach . In addition , there are several hybrids between species that would be considered different genera under <unk> 's five @-@ genus system , which has also been used as an argument for placing them in a single genus . In 2009 Alan <unk> and colleagues published a molecular phylogeny of the <unk> which found that some species placed in <unk> were actually more closely related to species placed in <unk> than they were to other members of that genus ( if the five @-@ genus approach was used ) , while A. <unk> , placed in <unk> by <unk> , was actually a sister to both <unk> and <unk> . 
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- = = = History = = = 
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- The genus <unk> was first described Carl <unk> <unk> in 1816 based on specimens collected by Alexander von Humboldt and Aimé <unk> , although older , pre @-@ <unk> descriptions exist , including Charles <unk> 's 1703 description of A. <unk> . The genus was named for <unk> III <unk> , king of <unk> , known for his interest in <unk> plants . The type species is A. <unk> , a Colombian endemic . The genera <unk> and <unk> were described by Carl Friedrich Philipp von <unk> in 1826 and 1837 respectively . <unk> was described by Hermann <unk> in 1857 , and <unk> by <unk> F. Cook in 1942 . 
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- = = = Species = = = 
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- Experts disagree about the number of species in the genus <unk> ( broadly defined ) . In 1965 , Dutch <unk> Jan Gerard <unk> Boer estimated that there may be as many as 100 species in the genus . In their 1996 Field Guide to the <unk> of the Americas Andrew Henderson and <unk> recognised 29 species in the genus , while Sidney <unk> recognised 65 species in his 1999 treatment of the group . Largely following <unk> 's lead , <unk> <unk> and John <unk> recognised 67 species in their 2005 World <unk> of <unk> . An important element of this disagreement is the decision by <unk> to define species more narrowly than Henderson . As a result , what Henderson interpreted as variation within species , <unk> took as differences between <unk> similar species . This problem is complicated by the fact that many of these species are poorly represented in <unk> collections . The large size of the leaves , inflorescences and fruit of many <unk> species makes them difficult to collect . In addition , many important collections , including type specimen , have been lost or destroyed . <unk> or incomplete collections make it difficult to differentiate variation within a single species from variation between different species . 
- The three recent treatments ( Henderson and <unk> , <unk> , and <unk> and <unk> ) recognised a total of 73 species , but only 20 species are accepted by all of them . The remainder account for either nine species or more than 40 . For example , what Andrew Henderson considered a single species , <unk> <unk> , other authors have considered a species complex consisting of four or five species . <unk> doubted the validity of A. <unk> as a species , and described four new species from material that had previously been attributed to A. <unk> — A. <unk> , A. <unk> , A. <unk> and A. <unk> . <unk> and <unk> accepted both <unk> 's four species and A. <unk> . However , Jean @-@ Christophe <unk> was of the opinion that A. <unk> , A. <unk> and A. <unk> were all very similar , and thought it likely that they all represented the same species . 
- Another species complex in <unk> includes A. <unk> and related species . Henderson ( 1995 ) recognised A. <unk> and A. <unk> , considering the latter to either be an <unk> form of A. <unk> or a hybrid between it and A. <unk> . <unk> and <unk> accepted A. <unk> , but <unk> considered it a dubious taxon . <unk> <unk> was recognised as a distinct species by Michael <unk> and <unk> ; <unk> and <unk> and <unk> concurred , but Henderson considered it part of A. <unk> . <unk> also described a fourth member of this group , A. <unk> , and it is accepted by <unk> and <unk> . 
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- = = Reproduction and growth = = 
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- <unk> species are <unk> — male and female flowers are separate , but are borne by the same plant . Various species have been described as being insect @-@ <unk> , including A. <unk> , while <unk> in A. <unk> and A. <unk> , has been attributed both to insects and wind . 
- <unk> <unk> is remote tubular — during <unk> , as the <unk> expands it pushes the young shoot away from the seed . After <unk> , the stem initially grows downward before turning to grow upward and produce the <unk> stem . This produces a " saxophone shaped " <unk> portion of the stem . The fact that the shoot tips of <unk> seedlings are underground it likely to contribute to their fire @-@ tolerance . 
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- = = Distribution = = 
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- Species range across the <unk> from Mexico in the north to Bolivia , Paraguay , and southern Brazil in the south . According to <unk> and <unk> , three species are found in Mexico , four in Central America , and 62 in South America . Three species are present in the Caribbean — two in Trinidad and <unk> , along the southern edge of the region , and one in Haiti . 
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- = = Habitat and ecology = = 
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- <unk> includes both large trees and small <unk> <unk> which occupy a number of different ecological niches . <unk> stands of some of the larger species are conspicuous elements on the landscape , while smaller species are found in both in the forest <unk> and in <unk> . 
- <unk> has been implicated in the formation of vegetation dominated by large <unk> species . In seasonally dry <unk> forests the density of large adult A. <unk> <unk> was correlated with canopy <unk> ; the species also dominates <unk> formed by repeated forest fires in Trinidad and <unk> . <unk> <unk> forms pure stands in many parts of Brazil where natural forest vegetation has been cleared . Similarly , stands of A. <unk> in <unk> , Brazil ( which are cultivated for <unk> fibre ) are managed using fire — the seedlings survive cutting and burning , and are able to dominate burned forest patches . 
- The fruit are dispersed by animals ; fruit which are not dispersed frequently suffer seed predation by <unk> beetles . Certain species of <unk> have been mentioned as examples of " <unk> " species which are adapted for dispersal by now @-@ extinct Pleistocene <unk> . On <unk> Island , <unk> , in the Brazilian Amazon , <unk> <unk> fruit were consumed by <unk> , <unk> <unk> , deer and <unk> . <unk> , including <unk> , fed upon the fruit and , as the fruit availability declined , they fed on the seeds . Other <unk> of <unk> fruit include <unk> <unk> which consume the fruit and disperse the seeds of A. <unk> in the Brazilian <unk> . 
- 
- = = Uses = = 
- 
- <unk> species have a long history of human <unk> . <unk> <unk> <unk> seeds have been found in archaeological sites in Colombia dating back to 9000 BP . A variety of species remain important sources of edible oil , <unk> , edible seeds and fibre . The leaves of <unk> <unk> and A. <unk> are used extensively for <unk> . Several species are oil <unk> , with A. <unk> among the most important economically . <unk> extracted from A. <unk> were reported to support over 300 @,@ 000 households in the Brazilian state of <unk> in 2005 , and in 1985 it was estimated to support over 450 @,@ 000 households throughout the Brazil . <unk> fibres , extracted from the leaf bases of A. <unk> , are commercially important , and generated about US $ 20 million in annual income to Brazilian farmers in 1996 . 
- 
- 
- = The Heart of Ezra Greer = 
- 
- The Heart of Ezra Greer is a 1917 American silent drama film produced by the Thanhouser Company and directed by Emile <unk> . The film focuses on Ezra Greer , a successful middle @-@ aged man who searches for his college age daughter , Mary . The <unk> Mary was <unk> and abandoned by Jack <unk> , later bearing his child . Once Ezra becomes broke he finds employment as the <unk> for Jack <unk> . After Jack 's engagement to a cabaret girl , Mary becomes upset and leaves her child at Jack 's home . Contrary to Jack 's wishes , Ezra keeps the child and Jack ultimately reveals that the child is his own . Ezra convinces Jack to make things right and Ezra convinces the cabaret girl to leave Jack . After a carriage accident in which the baby is injured , Ezra and Jack rush to the hospital and find Mary as a nurse crying over the child . The film ends with the marriage of Jack and Mary . The film was released by <unk> on October 7 , 1917 . The film was the final release from Thanhouser and was deemed to be an average film by most reviewers . Criticism for the film <unk> on far @-@ <unk> coincidences to drive the plot . The film is presumed lost . 
- 
- = = Plot = = 
- 
- The film follows Ezra Greer , a middle @-@ aged man who has worked hard since his youth . He cares deeply for his <unk> daughter , Mary , but was unable to attend the annual commencement at her co @-@ educational college . He awaits for her to return from college , but Mary leaves with her romantic interest , Jack <unk> . On promise of marriage and wealth , Mary is <unk> and gives birth to a <unk> child . Without word from his daughter , Ezra <unk> from his job and attempts to seek her out and finds a poor <unk> child , Marie . With Ezra 's money exhausted he seeks employment and finds it as the <unk> of Jack . 
- One day , Mary seeks an announcement of Jack 's engagement to a cabaret girl known as " The Baby <unk> " . <unk> over the prospect of her child 's future , she leaves the child at Jack 's home during his absence with a note . Jack orders Ezra to take the baby to an orphanage , but Marie begs Ezra to keep him . After continually seeing the child , Jack is overcome with remorse and explains to Ezra and seeks his advice . Not knowing he was making the case for his own daughter , Ezra convinces Jack to seek out Mary and forget the Baby <unk> . The Baby <unk> seeks out Jack , but finds Ezra who convinces her to leave Jack . Jack 's son is later injured in a coach accident and is taken to the hospital . Jack and Ezra rush to the hospital and find Mary , as a nurse , crying over the injured child . Ezra is enraged upon learning that his own daughter was <unk> by Jack , but Mary steps between the two men . Jack apologizes and wants to make it right . The film concludes with Jack and Mary . 
- 
- = = Cast = = 
- 
- Frederick <unk> as Ezra Greer 
- <unk> Frost as Mary 
- George Forth as Jack <unk> 
- Thomas A. <unk> as <unk> 's guardian 
- <unk> Mueller as Amy <unk> 
- Carey L. Hastings as <unk> 's housekeeper 
- Helen <unk> as the poor little girl 
- Gerald <unk> as the millionaire 's baby 
- W. Ray Johnston 
- 
- = = Production = = 
- 
- The film was the final production and release of the Thanhouser Company and it was to be released through <unk> . Numerous factors would play into the winding down and eventual closing of the Thanhouser Film Corporation with much advance notice by Edwin Thanhouser . Q. David Bowers writes that it was easy to understand Thanhouser 's decision to retire due to numerous aspects including that releases through <unk> were based on their decision to release or <unk> the work , the New Rochelle studio was 2 @,@ 500 miles from the center of the trade activity and the slump in industry tied to World War I. Weeks before the film was released , Variety told of the winding down of the Thanhouser with the studio 's staff consisting of Edwin Thanhouser and the <unk> , <unk> B. Bishop . The article concluded with the announcement that Lloyd F. Lonergan , the scenario writer of the company , had retired from the company . As it wound down , the Thanhouser Company was announced to have no <unk> would close with a positive bank balance . Little is known of the production of this final film , but it was directed by Emile <unk> from a scenario written by Lloyd F. Lonergan . The cameraman was Jacques <unk> . 
- 
- = = Release and reception = = 
- 
- The five reel film was released through the <unk> Exchange as a <unk> Gold Rooster Play on October 7 , 1917 . Charles E. Wagner of the <unk> 's Trade Review found it to be a good film with great direction and photography , but was concerned that the stunt in which the baby appeared to be involved in the accident was too real . Wagner stated the film had sufficient action and pathos without sexual <unk> ; which should prove a strong program for the <unk> program . Frances Agnew of The Morning Telegraph found it to be an average picture that was not exceptional for audiences , but it would hold sentimental appeal for the average viewer . A reviewer for the The New York Dramatic Mirror found the film 's excessive use of <unk> meetings to be highly <unk> , but found <unk> 's performance to be excellent and the rest of the cast give good performances . The reviewer said that Emile <unk> had made the <unk> story more plausible . 
- Like many American films of the time , The Heart of Ezra Greer was subject to cuts by city and state film censorship boards . The Chicago Board of <unk> required the cutting in <unk> 2 of a letter stating , " I cannot face my father , " etc . , and two <unk> of gambling scenes ; and in <unk> 5 a change of the <unk> " Because it means her whole future " to " Because she is his wife " . 
- 
- 
- = Free Derry = 
- 
- Free Derry ( Irish : <unk> <unk> ) was a self @-@ declared autonomous nationalist area of Derry , Northern Ireland , that existed between 1969 and 1972 . Its name was taken from a sign painted on a gable wall in the <unk> in January 1969 which read , " You are now entering Free Derry " . The area , which included the <unk> and <unk> neighbourhoods , was secured by community activists for the first time on 5 January 1969 following an incursion into the <unk> by members of the Royal Ulster Constabulary ( <unk> ) . Residents built barricades and carried clubs and similar arms to prevent the <unk> from entering . After six days the residents took down the barricades and <unk> patrols resumed , but tensions remained high over the following months . 
- Violence reached a peak on 12 August 1969 , culminating in the Battle of the <unk> — a three @-@ day pitched battle between residents and the <unk> . On 14 August units of the British Army were deployed at the edge of the <unk> and the <unk> were withdrawn . The Derry Citizens Defence Association ( <unk> ) declared their intention to hold the area against both the <unk> and the British Army until their demands were met . The British Army made no attempt to enter the area . The situation continued until October 1969 when , following publication of the Hunt Report , military police were allowed in . 
- The Irish Republican Army ( IRA ) began to re @-@ arm and recruit after August 1969 . In December 1969 it split into the Official IRA and the Provisional IRA . Both were supported by the people of the Free Derry area . Meanwhile , relations between the British Army and the nationalist community , which were initially good , deteriorated . In July 1971 there was a surge of recruitment into the IRA after two young men were shot and killed by British troops . The government introduced <unk> on 9 August 1971 , and in response , barricades went up once more in the <unk> and <unk> . This time , Free Derry was a no @-@ go area , defended by armed members of both the Official and Provisional IRA . From within the area they launched attacks on the British Army , and the <unk> began a bombing campaign in the city centre . As before , unarmed ' <unk> ' manned the barricades , and crime was dealt with by a voluntary body known as the Free Derry Police . 
- Support for the IRA increased further after Bloody Sunday in January 1972 , when thirteen unarmed men and boys were shot dead by the British Army 's Parachute Regiment at a march in the <unk> ( a 14th man was wounded and died 4 ½ months later ) . The support began to <unk> after the killing by the Official IRA of a local youth who was home on leave from the British Army . After a Provisional IRA ceasefire , during which it entered talks with the British government , broke down , the British took the decision to move against the " no @-@ go " areas . Free Derry came to an end on 31 July 1972 , when thousands of British troops moved in with armoured cars and <unk> to occupy the area . 
- 
- = = Background = = 
- 
- Derry City lies near the border between Northern Ireland and the Republic of Ireland . It has a majority nationalist population , and nationalists won a majority of seats in the 1920 local elections . Despite this , the Ulster Unionist Party controlled the local council , <unk> Corporation , from 1923 onwards . The <unk> maintained their majority , firstly , by manipulating the constituency boundaries ( <unk> ) so that the South Ward , with a nationalist majority , returned eight councillors while the much smaller North Ward and <unk> Ward , with unionist <unk> , returned twelve councillors between them ; <unk> , by allowing only <unk> to vote in local elections , rather than one man , one vote , so that a higher number of nationalists , who did not own homes , were disenfranchised ; and <unk> , by denying houses to nationalists outside the South Ward constituency . The result was that there were about 2 @,@ 000 nationalist families , and practically no unionists , on the housing waiting list , and that housing in the nationalist area was crowded and of a very poor condition . The South Ward comprised the <unk> , <unk> , <unk> , Bishop Street and Foyle Road , and it was this area that would become Free Derry . 
- The Derry Housing Action Committee ( <unk> ) was formed in March 1968 by members of the Derry Branch of the Northern Ireland Labour Party and the James <unk> Republican Club , including <unk> <unk> and <unk> <unk> . It disrupted a meeting of <unk> Corporation in March 1968 and in May blocked traffic by placing a caravan that was home to a family of four in the middle of the <unk> Road in the <unk> and staging a sit @-@ down protest at the opening of the second deck of the <unk> Bridge . After the meeting of <unk> Corporation was again disrupted in August , <unk> <unk> telephoned the Northern Ireland Civil Rights Association ( <unk> ) and invited them to hold a march in Derry . The date chosen was 5 October 1968 , an <unk> committee was formed ( although in reality most of the organizing was done by <unk> and <unk> ) and the route was to take the <unk> inside the city walls , where nationalists were traditionally not permitted to march . The Minister of Home Affairs , William Craig , made an order on 3 October prohibiting the march on the grounds that the <unk> Boys of Derry were intending to hold a march on the same day . In the words of Martin <unk> of <unk> " this particular tactic ... provided the excuse needed to ban the march . " When the <unk> attempted to defy the ban on 5 October they were stopped by a Royal Ulster Constabulary ( <unk> ) cordon . The police drew their <unk> and struck <unk> , including <unk> MP Eddie <unk> and Westminster MP Gerry <unk> . Subsequently the police " broke ranks and used their <unk> <unk> on people in Duke Street " . <unk> trying to escape met another party of police and " these police also used their <unk> <unk> . " Water cannons were also used . The police action caused outrage in the nationalist area of Derry , and at a meeting four days later the Derry Citizens ' Action Committee ( <unk> ) was formed , with John Hume as chairman and Ivan Cooper as vice @-@ chairman . 
- 
- = = The first barricades = = 
- 
- Another group formed as a result of the events of 5 October was People 's Democracy , a group of students in Queen 's University Belfast . They organised a march from Belfast to Derry in support of civil rights , starting out with about forty young people on 1 January 1969 . The march met with violent opposition from loyalist counter @-@ demonstrators at several points along the route . Finally , at <unk> Bridge , five miles outside Derry , they were attacked by a mob of about two hundred wielding clubs — some of them studded with nails — and stones . Half of the attackers were later identified from press photographs as members of the B @-@ <unk> . The police , who were at the scene , <unk> to the B @-@ <unk> as they prepared their ambush , and then failed to protect the <unk> , many of whom ran into the river and were <unk> with stones from the bank . Dozens of <unk> were taken to hospital . The remainder continued on to Derry where they were attacked once more on their way to <unk> Bridge before they finally reached Guildhall Square , where they held a rally . <unk> broke out after the rally . Police drove <unk> into the <unk> , but did not come after them . In the early hours of the following morning , 5 January , members of the <unk> charged into St. <unk> 's Wells and <unk> Road in the <unk> , breaking windows and beating residents . In his report on the disturbances , Lord Cameron remarked that " for such conduct among members of a disciplined and well @-@ led force there can be no acceptable justification or excuse " and added that " its effect in <unk> <unk> and inspiring hostility towards the police was <unk> great . " 
- That afternoon over 1 @,@ 500 <unk> residents built barricades , armed themselves with steel bars , wooden clubs and <unk> , and told the police that they would not be allowed into the area . <unk> chairman John Hume told a meeting of residents that they were to defend the area and no @-@ one was to come in . Groups of men wearing <unk> patrolled the streets in shifts . John ' <unk> ' Casey , a local activist , painted " You are now entering Free Derry " in white paint on the gable wall of a house on the corner of <unk> Road and <unk> Street . That corner , which was a popular venue for meetings , later became known as " Free Derry Corner " . On 7 January , the <unk> area was extended to include the <unk> , another nationalist area on a hill overlooking the <unk> . A clandestine radio station calling itself " Radio Free Derry " began broadcasting to residents , playing rebel songs and encouraging resistance . On a small number of occasions law @-@ <unk> attempted crimes , but were dealt with by the patrols . Despite all this , the Irish Times reported that " the infrastructure of revolutionary control in the area has not been developed beyond the maintenance of patrols . " Following some acts of destruction and of violence late in the week , members of the <unk> including Ivan Cooper addressed residents on Friday , 10 January and called on them to <unk> the barricades . The barricades were taken down the following morning . 
- 
- = = April 1969 = = 
- 
- Over the next three months there were violent clashes , with local youths throwing stones at police . Violence came to a head on Saturday , 19 April after a planned march from <unk> Bridge to the city centre was banned . A protest in the city centre led to clashes with " <unk> " — unionists in sympathy with the anti @-@ civil rights stance of Ian Paisley . Police attempting to drive the protesters back into the <unk> were themselves driven back to their barracks . A series of pitched battles followed , and barricades were built , often under the supervision of <unk> Devlin , newly elected MP for Mid Ulster . Police pursuing <unk> broke into a house in William Street and severely beat the <unk> , Samuel <unk> , his family and two friends . <unk> was brought to hospital " bleeding <unk> from a number of head wounds . " At midnight four hundred <unk> men in full riot gear and carrying riot shields occupied the <unk> . <unk> of police vehicles drove through the area with <unk> <unk> . 
- The following day , several thousand residents , led by the <unk> , withdrew to the <unk> and issued an ultimatum to the <unk> — withdraw within two hours or be driven out . With fifteen minutes of the two hours remaining , the police marched out through the <unk> 's Gate , even as the residents were entering from the far side . The barricades were not maintained on this occasion , and routine patrols were not prevented . 
- Samuel <unk> suffered a heart attack four days after his beating . On 17 July he suffered a further heart attack and died . Thousands attended his funeral , and the mood was sufficiently angry that it was clear the annual <unk> Boys ' parade , scheduled for 12 August , could not take place without causing serious disturbance . 
- 
- = = August – October 1969 = = 
- 
- The <unk> Boys ' parade is an annual celebration by unionists of the relief of the Siege of Derry in 1689 , which began when thirteen young apprentice boys shut the city 's gates against the army of King James . At that time the parade was held on 12 August each year . Participants from across Northern Ireland and Britain marched along the city walls above the <unk> , and were often openly hostile to the residents . On 30 July 1969 the Derry Citizens Defence Association ( <unk> ) was formed to try to preserve peace during the period of the parade , and to defend the <unk> and <unk> in the event of an attack . The chairman was <unk> <unk> , an Irish Republican Army ( IRA ) veteran ; the vice @-@ chairman was Paddy Doherty , a popular local man sometimes known as " Paddy <unk> " and the secretary was <unk> White , another leading republican and leader of the James <unk> Republican Club . Street committees were formed under the overall command of the <unk> and barricades were built on the night of 11 August . The parade took place as planned on 12 August . As it passed through Waterloo Place , on the edge of the <unk> , hostilities began between supporters and opponents of the parade . Fighting between the two groups continued for two hours , then the police joined in . They charged up William Street against the <unk> , followed by the ' <unk> ' . They were met with a hail of stones and petrol bombs . The ensuing battle became known as the Battle of the <unk> . Late in the evening , having been driven back repeatedly , the police fired <unk> of <unk> gas into the crowd . <unk> on the roof of a high @-@ rise block of flats on <unk> Street threw petrol bombs down on the police . <unk> @-@ <unk> were used to maintain contact between different areas of fighting and <unk> headquarters in Paddy Doherty 's house in Westland Street , and first aid stations were operating , staffed by doctors , nurses and volunteers . Women and girls made milk @-@ bottle <unk> of petrol bombs for supply to the youths in the front line and " Radio Free Derry " broadcast to the fighters and their families . On the third day of fighting , 14 August , the Northern Ireland Government mobilised the Ulster Special Constabulary ( B @-@ <unk> ) , a force greatly feared by nationalists in Derry and elsewhere . Before they engaged , however , British troops were deployed at the scene , carrying automatic rifles and sub @-@ machine guns . The <unk> and B @-@ <unk> withdrew , and the troops took up positions outside the <unk> area . 
- A <unk> that included <unk> <unk> met senior army officers and told them that the army would not be allowed in until certain demands were met , including the <unk> of the <unk> , the disbandment of the B @-@ <unk> and the abolition of <unk> ( the Parliament and Government of Northern Ireland ) . The officers agreed that neither troops nor police would enter the <unk> and <unk> districts . A ' peace corps ' was formed to maintain law and order . When the British Home Secretary , Jim <unk> , visited Northern Ireland and announced his intention to visit the <unk> on 28 August , he was told that he would not be allowed to bring either police or soldiers with him . <unk> agreed . <unk> by members of the Defence Committee , he was " swept along by a <unk> crowd of thousands " up <unk> Street and into <unk> Road , where he " took refuge " in a local house , and later addressed crowds from an upstairs window . In preparation for <unk> 's visit the " Free Derry " wall was painted white and the " You are now entering Free Derry " sign was professionally re @-@ painted in black <unk> . 
- Following <unk> 's visit , some barricades were breached , but the majority remained while the people awaited concrete evidence of reform . Still the army made no move to enter the area . Law and order was maintained by a ' peace corps ' — volunteers organised by the <unk> to patrol the streets and man the barricades . There was very little crime . <unk> , in the words of <unk> <unk> , " as often as not consisted of a stern lecture from <unk> <unk> on the need for solidarity within the area . " In September the barricades were replaced with a white line painted on the road . 
- The Hunt Report on the future of policing in Northern Ireland was presented to the <unk> cabinet in early October . Jim <unk> held talks with the cabinet in Belfast on 10 October , following which the report 's recommendations were accepted and made public . They included the recommendation that the <unk> should be ' ordinarily ' unarmed , and that the B @-@ <unk> should be phased out and replaced by a new force . The new <unk> Chief Constable , Arthur Young , an <unk> , was announced , and travelled to Belfast with <unk> . The same day , <unk> <unk> announced that the <unk> was to be dissolved . On 11 October <unk> and Young visited Free Derry , and on 12 October the first military police entered the <unk> , on foot and unarmed . 
- 
- = = IRA resurgence = = 
- 
- The Irish Republican Army ( IRA ) had been inactive militarily since the end of the Border Campaign in 1962 . It was low in both personnel and equipment — Chief of Staff <unk> <unk> told <unk> <unk> and Paddy Doherty in August 1969 that he " couldn 't defend the <unk> . I haven 't the men nor the guns to do it . " During the 1960s the leadership of the republican movement had moved to the left . Its focus was on class struggle and its aim was to unite the Irish nationalist and unionist working classes in order to overthrow capitalism , both British and Irish . Republican <unk> were formed in Northern Ireland , where <unk> <unk> was proscribed . These clubs were involved in the formation of <unk> in 1967 . In Derry , the James <unk> Republican Club worked closely with Labour Party radicals , with whom they set up the Derry Housing Action Committee and Derry <unk> Action Committee . The Derry Citizens ' Defence Association was formed initially by <unk> , who then invited other nationalists to join . Although there were tensions between the younger leaders like <unk> White and the older , traditional <unk> such as <unk> <unk> , both sides saw the unrest of 1968 @-@ 69 as a chance to advance republican aims , and the two shared the platform at the Easter Rising commemoration in April 1969 . 
- The events of August 1969 in Derry , and more particularly in Belfast where the IRA was unable to prevent loss of life or protect families burned out of their homes , brought to a head the divisions that had already appeared within the movement between the radicals and the <unk> , and led to a split in December 1969 into the Official IRA and the Provisional IRA . Initially , both armies organised for defensive purposes only , although the <unk> were planning towards an offensive campaign . In Derry there was far less hostility between the two organisations than elsewhere and <unk> commonly paid subscriptions to both . When <unk> were arrested after the Official 's Easter parade in March 1970 , Officials and <unk> <unk> their trial together . At the start the Officials attracted most of the younger members . Martin McGuinness , who in August 1969 had helped defend the barricades , initially joined the Officials , but a few months later left to join the <unk> . 
- Relations between the British Army and the residents had steadily decayed since the first appearance of troops in August 1969 . In September , after clashes between nationalist and unionist crowds that led to the death of a Protestant man , William King , the British Army erected a ' peace ring ' to <unk> the nationalist population in the area they had previously controlled . Roads into the city centre were closed at night and people were prevented from walking on certain streets . Although some moderate nationalists accepted this as necessary , there was anger among young people . <unk> between youths and troops became more frequent . The riot following the Officials ' Easter parade in March 1970 marked the first time that the army used ' <unk> squads ' , who rushed into the <unk> wielding <unk> to make arrests . The <unk> squads soon became a common feature of army arrest operations . There was also a belief that they were arresting people at random , sometimes days after the alleged offence , and based on the identification of people that they had seen from a considerable distance . The <unk> were condemned as <unk> by <unk> , who saw the riots as hampering attempts to resolve the situation . The Labour radicals and Official <unk> , still working together , tried to turn the youth away from rioting and create socialist organizations — one such organization was named the Young <unk> Association — but to no <unk> . The <unk> , while <unk> of riots , viewed them as the inevitable consequence of British occupation . This philosophy was more attractive to <unk> , and some of them joined the Provisional IRA . The deaths of two leading <unk> in a premature explosion in June 1970 resulted in young militants becoming more prominent in the organization . Nevertheless , up to July 1971 the Provisional IRA remained numerically small . 
- Two men , <unk> <unk> and Desmond Beattie , were shot dead in separate incidents in the early morning and afternoon of 8 July 1971 . They were the first people to be killed by the British Army in Derry . In both cases the British Army claimed that the men were attacking them with guns or bombs , while <unk> insisted that both were unarmed . The Social Democratic and Labour Party ( <unk> ) , the newly formed party of which John Hume and Ivan Cooper were leading members , withdrew from <unk> in protest , but among residents there was a perception that moderate policies had failed . The result was a surge of support for the IRA . The <unk> held a meeting the following Sunday at which they called on people to " join the IRA " . Following the meeting , people <unk> up to join , and there was large @-@ scale rioting . The British Army post at <unk> 's Lane came under sustained attack , and troops there and around the city came under fire from the IRA . 
- 
- = = <unk> and the third Free Derry = = 
- 
- The increasing violence in Derry and elsewhere led to increasing speculation that <unk> without trial would be introduced in Northern Ireland , and on 9 August 1971 hundreds of <unk> and nationalists were arrested in dawn raids . In Derry , residents came out onto the streets to resist the arrests , and fewer people were taken there than elsewhere ; nevertheless leading figures including <unk> <unk> and <unk> White were interned . In response , barricades were erected once again and the third Free Derry came into existence . Unlike its predecessors , this Free Derry was marked by a strong IRA presence , both Official and Provisional . It was defended by armed <unk> — a no @-@ go area , one in which British security forces were unable to operate . 
- Gun attacks on the British Army increased . Six soldiers were wounded in the first day after <unk> , and shortly afterwards a soldier was killed — the first to be killed by either IRA in Derry . The army moved in in force on 18 August to <unk> the barricades . A gun battle ensued in which a young Provisional IRA officer , <unk> <unk> , was killed . A crowd staging a sit @-@ down protest was <unk> down and the protesters , including John Hume and Ivan Cooper , arrested . With barricades re @-@ appearing as quickly as they were removed , the army eventually abandoned their attempt . 
- The Derry <unk> had little contact with the IRA elsewhere . They had few weapons ( about twenty ) which they used mainly for sniping . At the same time , they launched their bombing campaign in Derry . Unlike in Belfast , they were careful to avoid killing or injuring innocent people . <unk> <unk> wrote that " the Derry <unk> , under Martin McGuinness , had managed to bomb the city centre until it looked as if it had been hit from the air without causing any civilian casualties . " 
- Although both <unk> operated openly , neither was in control of Free Derry . The barricades were manned by unarmed ' <unk> ' . Crime was dealt with by a volunteer force called the Free Derry Police , which was headed by Tony O <unk> , a Derry footballer and Northern Ireland International . 
- 
- = = Bloody Sunday = = 
- 
- An anti @-@ <unk> protest organised by the Northern Ireland Civil Rights Association ( <unk> ) at <unk> Camp in January 1972 was met with violence from the 1st Battalion , The Parachute Regiment ( 1 <unk> ) . <unk> had organised a march from the <unk> to Derry city centre , in defiance of a ban , on the following Sunday , 30 January 1972 . Both <unk> were asked , and agreed , to suspend operations on that day to ensure the march passed off peacefully . The British Army erected barricades around the Free Derry area to prevent <unk> from reaching the city centre . On the day , march organisers turned the march away from the barriers and up to Free Derry Corner , but some youths proceeded to the barrier at William Street and <unk> soldiers . Troops from 1 <unk> then moved into Free Derry and opened fire , killing thirteen people , all of whom were subsequently found to be unarmed . A fourteenth shooting victim died four months later in June 1972 . Like the killing of <unk> and Beattie the previous year , Bloody Sunday had the effect of hugely increasing recruitment to the IRA , even among people who previously would have been ' <unk> ' . 
- 
- = = February - July 1972 = = 
- 
- Both the Provisional and Official IRA stepped up attacks after Bloody Sunday , with the tacit support of the residents . Local feelings changed , however , with the killing of Ranger William Best by the Official IRA . Best was a 19 @-@ year @-@ old local man who was home on leave from the British Army at his parents ' house in the <unk> . He was abducted , interrogated and shot . The following day 500 women marched to the Republican Club offices in protest . Nine days later , on 29 May , the Official IRA declared a ceasefire . The Provisional IRA initially stated that they would not follow suit , but after informal approaches to the British Government they announced a ceasefire from 26 June . Martin McGuinness was the Derry representative in a party of senior <unk> who travelled to London for talks with William <unk> , the Secretary of State for Northern Ireland . The talks were not resumed after the ending of the truce following a violent confrontation in Belfast when troops prevented Catholic families from taking over houses in the <unk> estate . 
- Political pressure for the action against the " no @-@ go " areas increased after the events of Bloody Friday in Belfast . A British Army attack was considered inevitable , and the IRA took the decision not to resist it . On 31 July 1972 , Operation <unk> was launched when thousands of British troops , equipped with armoured cars and armoured <unk> ( AVREs ) , dismantled the barricades and occupied the area . 
- 
- = = Subsequent history = = 
- 
- After Operation <unk> , the British Army controlled the <unk> and <unk> by <unk> large numbers of troops within the area , by conducting large @-@ scale ' search ' operations that were in fact undertaken for purposes of intelligence gathering , and by setting up over a dozen covert observation posts . Over the following years IRA violence in the city was contained to the point where it was possible to believe ' the war was over ' in the area , although there were still frequent street riots . Nationalists — even those who did not support the IRA — remained <unk> opposed to the army and to the state . 
- Many of the residents ' original grievances were addressed with the passing of the Local Government ( Northern Ireland ) Act , 1972 , which <unk> the electoral boundaries and introduced universal adult suffrage based on the single <unk> vote . <unk> were held in May 1973 . Nationalists gained a majority on the council for the first time since 1923 . Since then the area has been extensively redeveloped , with modern housing replacing the old houses and flats . The Free Derry era is commemorated by the Free Derry wall , the murals of the <unk> Artists and the Museum of Free Derry . 
- 
- 
- = Come What ( ever ) May = 
- 
- Come What ( ever ) May is the second studio album by American alternative metal band Stone <unk> . It was recorded and produced by the band and Nick <unk> at Studio <unk> in Los Angeles , California , and was released on August 1 , 2006 , through Roadrunner Records . Writing for the album began as early as 2003 when vocalist Corey Taylor and guitarist James Root were writing material for their other band , <unk> . In January 2006 Stone <unk> began recording the follow @-@ up to their 2002 debut album Stone <unk> , during which time drummer Joel <unk> left the band due to family constraints . He was eventually replaced by ex @-@ <unk> drummer Roy <unk> who played on all but two tracks on the album . 
- Following the release of the album , Stone <unk> went on to promote it for over a year ; releasing five singles and touring in several regions , including the United States , Canada , Japan and several countries in Europe . The album received generally positive reviews . It was praised for showing a progression in the band 's song writing ability and musical style . It was also certified Gold in the United States and Canada and the single " 30 / 30 @-@ 150 " was nominated for Best Metal Performance at the 49th Grammy Awards . On June 26 , 2007 Stone <unk> released a special edition version of the album , it included six previously unreleased tracks and a bonus DVD which featured three music videos and a complete live performance of the band in Moscow . It remains their best @-@ selling album to date , mostly due to the success of the single " Through Glass . " 
- 
- = = Production = = 
- 
- In September 2005 , lead singer Corey Taylor announced that Stone <unk> would return with a second album . He said that they had written over 30 songs , some during the writing process of Vol . 3 : ( The <unk> <unk> ) , the third album by vocalist Taylor and guitarist James Root 's other band <unk> , and that they were working on <unk> the tracks before entering the studio . Dave <unk> was originally slated to produce the album , however , on January 22 , 2006 Stone <unk> began working on the album with producer Nick <unk> at Dave Grohl 's personal studio ( Studio <unk> ) , in Los Angeles . Time in the studio began with a week of pre @-@ production , during which guitarist Josh Rand says producer <unk> " pushed [ the band ] to the brink and back " to help fine @-@ tune the songs they had previously written . Though Rand and Taylor wrote most of the music and lyrics for the first album , respectively , writing for Come What ( ever ) May was done by all members . 
- Following this , the band set out to record 18 tracks and work began on recording Joel <unk> 's drum tracks . However , <unk> was forced to leave the studio after four weeks due to his young son 's diagnosis of a <unk> <unk> . With the fate of the album in jeopardy , Stone <unk> recruited ex @-@ <unk> member Roy <unk> as a session drummer . <unk> recorded drums for all but two tracks on the album , Godsmack drummer Shannon <unk> performed on the track " 30 / 30 @-@ 150 " and guitarist Root performed drums on the bonus track " The Day I Let Go . " In an interview with Revolver during the recording process vocalist Taylor talked about the differences between this album and their previous album , Stone <unk> . He said that pressures from fans and the record label were much larger ; also noting that he " <unk> on the pressure , because it gets [ him ] going . " While promising that " the album 's gonna be miles above the first one , " Taylor explained that it is " more melodic and darker " . In late March 2006 , drummer Joel <unk> officially left Stone <unk> and the band was talking with a few drummers who could replace him . On April 7 , 2006 the recording sessions for Come What ( ever ) May concluded . A month later session drummer Roy <unk> joined Stone <unk> on a full @-@ time basis . 
- 
- = = Promotion = = 
- 
- It was announced in March 2006 that Stone <unk> 's second album , which was tentatively titled " Come What May , " would be released on July 18 , 2006 . However , the release date for the album was pushed back until August 22 . Due to the delay Stone <unk> released a music video for the track " Reborn " , which featured footage of the band working on the album in the studio . The cover artwork from the album was released online on May 20 , 2006 . Shortly after , it was confirmed by a representative from the band 's record label Roadrunner that the release date had been brought forward , and the official release date would be August 1 , 2006 . On July 31 , 2006 , the day before its release the album was made available online for streaming in its entirety through AOL . 
- On May 22 , 2006 the first single from the album , " 30 / 30 @-@ 150 " , was made available online as a free <unk> download . A music video for the single was shot with director <unk> Brown in Los Angeles , the video received a premier on MTV 's <unk> Ball on June 3 , 2006 . Prior to the release of the second single from the album , " Through Glass " , radio stations throughout the US showed high support for the song . A music video for the single was shot with director Tony <unk> and was released on June 9 , 2006 online through Yahoo ! . The third single from the album , " <unk> " , began receiving radio airplay in November 2006 . A music video for the single was shot in January 2007 and was released online on March 8 , 2007 . The fourth single from the album , " Made of Scars " , featured a music video which was recorded live on April 7 , 2007 and was posted online on June 5 , 2007 . The fifth and final single from the album , " <unk> <unk> . " , started receiving radio airplay in Fall 2007 and no music video was made for the single . 
- The band began touring in support of the album prior to its release , initiating touring with several free shows in the US . <unk> by multiple appearances at festivals in Europe . They then joined Korn for their 2006 edition of Family <unk> Tour across the US , which featured 33 dates across 3 months . On August 8 , 2006 Stone <unk> made a special guest appearance on The Tonight Show with Jay <unk> to promote and perform their second single " Through Glass . " They also performed at the Japanese festival Summer Sonic midway through the Family <unk> Tour . Then through November and December 2006 , Stone <unk> joined <unk> for their Music as a <unk> Tour . In January 2007 Stone <unk> joined <unk> for a Canadian tour , followed by a headlining tour of Europe . They then <unk> the Spring 2007 <unk> Music Tour across the US , followed by headlining tours in Australia and Japan . They then started a tour in Europe playing festivals and select headline shows . They wrapped up touring in support of the album with a headlining tour in the US through August and September in 2007 . 
- 
- = = = Special edition = = = 
- 
- On June 26 , 2007 , Stone <unk> released a special edition version of the album with six previously unreleased tracks and a bonus DVD . The DVD featured a full concert performance by the band from October 2006 in Moscow and the music videos for " 30 / 30 @-@ 150 , " " Through Glass , " and " <unk> . " When talking about the special edition , vocalist Taylor said , " we really wanted to do something which was really cool , " saying that this shows the band 's different musical elements and them in their live element , which he says " people really <unk> towards . " In addition to this , Stone <unk> released a live album of their concert in Moscow exclusively on iTunes , entitled Live in Moscow . 
- 
- = = Musical style = = 
- 
- In an interview with MTV in 2006 , vocalist Corey Taylor said that Come What ( ever ) May was a return to the roots of the band , stating it is " a lot more from the spirit of what the band started with in 1992 . " <unk> how some songs were " very atmospheric , " while others maintained " the hard rock and the heavy stuff . " Jon <unk> of MTV said that " for every thrash riff there 's a <unk> <unk> passage , for every <unk> guitar line there 's a rock @-@ radio hook . " When talking about the track " 30 / 30 @-@ 150 " , he said parts are " <unk> , barbed and heavy , " while others are " soaring and triumphant , " with the production of <unk> helping balance the album 's <unk> with its radio @-@ accessibility . Come What ( ever ) May 's lyrics include themes of " pain , pleasure , happiness , and grief . " The diversity in subjects is evident throughout the album , songs including " Come What ( ever ) May " were politically influenced while the track " <unk> " is about " social anxiety attacks " that vocalist Taylor suffered . " <unk> <unk> " is a love song written to Taylor 's wife for helping him in his struggles against <unk> and contemplation of suicide . " I 've never written anything like that before , but it was very important for me to tell the world not only how much she saved me , but how much she means to me , " said Taylor . Taylor said there is a common thread with the lyrics throughout the album , saying that they are " about never forgetting where you came from , who you are and why you do this . " 
- 
- = = Reception = = 
- 
- Come What ( ever ) May was met with generally positive critical reviews . Several reviewers noted on how it helped to further establish Stone <unk> . Chad <unk> of About.com stated that the band had " progressed a lot since their debut " , noting that the album was " very diverse and [ allows ] the band to show many different sides of their musical personality . " Megan <unk> of Allmusic opens her review of the album by distinguishing what sets Stone <unk> apart musically , stating " [ it 's their ] ability to create smooth , radio @-@ friendly alternative metal songs while simultaneously not boring the people who have heard way too much from post @-@ grunge groups . " On a similar note , Michael <unk> of <unk> said " the band is much better at the craft of songwriting than many of their peers . " However , in contrast , reviewer William Fry of IGN criticized the album , saying " Stone <unk> doesn 't do anything inspired , original , or fresh here " even calling the album " completely <unk> , and <unk> . " A particular point of interest for reviewers was how Come What ( ever ) May is more melodic than their previous album Stone <unk> . <unk> of <unk> said the album is " much more liberal with the <unk> and acoustic sounds than its predecessor , " noting on the track " <unk> " he said " it sounds like what Nickelback could be if Chad <unk> could write a good melody " . In his review , Chad <unk> labeled Come What ( ever ) May as a " very melodic and accessible album " stating that " it has a little something for everyone . " Similarly , Megan <unk> triumphed the album as an " <unk> effort from a promising talent " . 
- Come What ( ever ) May sold over 80 @,@ 000 copies in its first week and debuted at the fourth spot on the Billboard 200 in the United States , and went on to be certified gold in the UK , Canada and the United States . In 2007 , the single " 30 / 30 @-@ 150 " was nominated for Best Metal Performance at the 49th Grammy Awards . 
- 
- = = Track listing = = 
- 
- All lyrics written by Corey Taylor , all music composed by Stone <unk> . 
- On the iTunes deluxe version , the pop version of " <unk> <unk> . " replaced the original version as the 12th track . 
- 
- = = = Special edition DVD = = = 
- 
- 
- = = Personnel = = 
- 
- 
- = = Chart positions = = 
- 
- 
- 
- = Chad at the 2008 Summer Olympics = 
- 
- Chad sent a delegation of two athletes to compete at the 2008 Summer Olympics in Beijing , China : <unk> <unk> , who competed in the men 's 100 meters , and <unk> <unk> <unk> , who competed in the women 's 100 meters and also bore the <unk> flag during ceremonies . The appearance of this delegation marked the tenth appearance of Chad at the Summer Olympics , the first been in 1964 Summer Olympics in Tokyo , Japan , and its seventh appearance since its Olympic hiatus between 1976 and 1980 . Both <unk> and <unk> ranked seventh in their respective <unk> and did not advance past the qualification round . As of the end of the 2012 London Olympics , there have been no <unk> from Chad . 
- 
- = = Background = = 
- 
- Chad is a <unk> country in Africa whose northern region lies within the eastern reaches of the <unk> Desert and whose southern region lies within the eastern portion of the <unk> . It borders Libya to the south , <unk> to the east , Sudan to the west , and the Central African Republic to the north . Chad was originally part of French West Africa until 1960 , when it declared independence . Some four years later , the former French colony made its <unk> at the 1964 Summer Olympics in Tokyo , Japan . For the next three decades , Chad became embroiled in civil war and experienced invasions by Libya and upheavals by Sudanese @-@ backed rebels ; the civil war ended in 1990 , although rebel threats had persisted between then and 2008 . During Chad 's greatest era of instability , athletes from the country did not attend the 1976 Summer Olympics in <unk> , Canada or the 1980 Summer Olympics in Moscow , USSR , although <unk> were sent to all other games between 1964 and 2008 . 
- The largest <unk> delegation to reach the Olympics appeared in the 1988 Summer Olympics in Seoul , South Korea and at the 1992 Summer Olympics in Barcelona , Spain ; each time , Chad 's National Olympic Committee sent six athletes . During the 1992 games , the <unk> sent the nation 's first female Olympian . Since then ( and up to the Beijing games ) , at least one woman has been a part of the <unk> delegation . The smallest <unk> of <unk> <unk> occurred during the 2004 Summer Olympics in Athens , Greece , when only <unk> <unk> competed on the country 's behalf . The delegation that arrived in Beijing consisted of two athletes — one man ( 30 @-@ year @-@ old <unk> <unk> ) and one woman ( 15 @-@ year @-@ old <unk> <unk> <unk> ) , both participants in track events . <unk> was Chad 's <unk> at the ceremonies . Up to and including the Beijing games , there has yet to have been a <unk> from Chad . 
- 
- = = Athletics = = 
- 
- <unk> in athletics events could qualify for the next round of competition in two ways . Qualifying by right was posting a high result in their own heat , and qualifying by result was posting a high result in overall standings . <unk> shown are thus those within each heat , not in overall standings . 
- <unk> <unk> represented Chad at the Beijing Olympics in the men 's 100 meters dash . Born in 1977 , <unk> first participated in the Olympics at age 22 when he raced in the men 's 100 meters at the 2000 Summer Olympics in Sydney , Australia , placing seventh in his qualification heat and not progressing to later rounds . He did not attend the 2004 Summer Olympics in Athens , Greece , but returned to the Olympics at Beijing at the age of 30 . During the course of the August 14 , 2008 races in his event , when the qualification round took place , <unk> competed in the tenth heat against seven other athletes . He finished the race in 11 @.@ 14 seconds , placing seventh in the heat ahead of <unk> 's <unk> <unk> ( 11 @.@ 48 seconds ) and behind Gabon 's Wilfried <unk> ( 10 @.@ 87 seconds ) in a heat led by the Netherlands Antilles ' <unk> <unk> ( 10 @.@ 35 seconds ) and Japan 's <unk> <unk> ( 10 @.@ 39 seconds ) . Of the 80 athletes who participated in the events , the <unk> <unk> ranked 70th . He did not advance to later rounds . 
- <unk> <unk> <unk> competed on Chad 's behalf as the national delegation 's only female athlete at the Beijing games . She participated in the women 's 100 meters dash , and was 15 years old at the time of the competition . <unk> had not previously competed in any Olympic games . During the qualification round of the event , which took place on August 15 , 2008 , <unk> competed in the eighth heat against seven other athletes . She finished the race in 12 @.@ 55 seconds , placing seventh ; she defeated the Democratic Republic of the Congo 's <unk> <unk> ( 12 @.@ 57 seconds ) and fell behind Papua New Guinea 's <unk> <unk> ( 11 @.@ 68 seconds ) in a heat led by Nigeria 's <unk> <unk> ( 11 @.@ 13 seconds ) and the Bahamas ' <unk> Ferguson @-@ <unk> ( 11 @.@ 17 seconds ) . Of the event 's 85 competitors , <unk> finished in 64th place . Therefore , <unk> did not advance to round two and beyond . 
- Key 
- Note – <unk> given for track events are within the athlete 's heat only 
- Q 
- = <unk> for the next round 
- q = 
- <unk> for the next round as a fastest loser or , in field events , by position without achieving the qualifying target 
- NR 
- = National record 
- N / A = 
- Round not applicable for the event 
- <unk> = <unk> not required to compete in round 
- 
- 
- = View of the World from 9th Avenue = 
- 
- View of the World from 9th Avenue ( sometimes A <unk> New Yorker 's View of the World , A New Yorker 's View of the World or simply View of the World ) is a 1976 illustration by Saul <unk> that served as the cover of the March 29 , 1976 , edition of The New Yorker . The work presents the view from Manhattan of the rest of the world showing Manhattan as the center of the world . 
- View of the World has been parodied by Ted <unk> , Columbia Pictures , The New Yorker , The Economist and Mad Magazine , among others . The work has been imitated and printed without authorization in a variety of ways . The Columbia parody led to a ruling by the United States District Court for the Southern District of New York in <unk> v. Columbia Pictures Industries , Inc. in favor of <unk> because of copyright violations by Columbia Pictures . The work is regarded as one of the greatest magazine covers of recent generations and is studied by art students around the world . 
- 
- = = Background = = 
- 
- Saul <unk> created 85 covers and <unk> internal drawings and illustrations for The New Yorker , including its March 29 , 1976 cover , titled " View of the World from 9th Avenue " . This is regarded as his most famous work . It is considered an example of unintentional fame : <unk> has noted that the type of fame that resulted from the work has diminished his significance to " the man who did that poster " . The work is sometimes referred to as A <unk> New Yorker 's View of the World or A New Yorker 's View of the World because it depicts a map of the world as seen by self @-@ absorbed New Yorkers . At one point The New Yorker applied for a copyright from the United States <unk> Office for the work . It assigned the copyright to <unk> and subsequently reproduced posters of the painting . 
- 
- = = <unk> = = 
- 
- The illustration is split in two parts , with the bottom half of the image showing Manhattan 's 9th Avenue , 10th Avenue , and the Hudson River ( appropriately labeled ) , and the top half depicting the rest of the world . It is a westward view over 10th Avenue . The rest of the United States is the size of the three New York City blocks and is drawn as a <unk> bounded by North American neighbors Canada and Mexico , with a thin brown strip along the Hudson representing " Jersey " , the names of five cities ( Los Angeles ; Washington , D.C. ; Las Vegas ; Kansas City ; and Chicago ) and three states ( Texas , Utah , and Nebraska ) scattered among a few rocks for the United States beyond New Jersey , which is in bolder font than the rest of the country beyond the Hudson . Washington , D.C. is depicted as a remote location near Mexico . The Pacific Ocean , slightly wider than the Hudson , separates the United States from three flattened land masses labeled China , Japan and Russia . Notably , the image depicts the world with a back turned to Europe , which is absent from the painting . 
- The work is composed in ink , pencil , colored pencil , and <unk> on paper and measures 28 by 19 inches ( 71 cm × 48 cm ) . When exhibiting this work along with alternate versions and sketches , the University of Pennsylvania summarized the work as a " bird 's @-@ eye view of the city from <unk> Avenue in a straight line westward , with space becoming ever more <unk> ... " They also described the work as a tongue @-@ in @-@ cheek view of the world . New York interpreted the New York @-@ centric mind 's view of the rest of the world as a set of outer <unk> as iconic . National Post journalist Robert <unk> described the perspective as one in which the entire world is a suburb of Manhattan . 
- 
- = = <unk> = = 
- 
- View of the World has been imitated without authorization in a variety of ways . The work has been imitated in <unk> format by numerous municipalities , states and nations . <unk> had stated that he could have retired on royalties from the many parodies made of the painting , had they been paid , a motivation for his eventual copyright lawsuit for the Moscow on the Hudson use . <unk> , writing in The National Post , noted that the metaphor of the world as a suburb of Manhattan was " understood and borrowed " by the whole world . Local artists , especially poster artists , presented similarly compelling depictions of their own provincial perceptions . <unk> demonstrated the prominence of this work by mentioning that a high school in suburban Ottawa made imitating View of the World an assignment in its graphic arts class . He also noted that the results of this assignment was a worldwide variety of global <unk> from which the students viewed the world . 
- The illustration — <unk> depicting New Yorkers ' self @-@ image of their place in the world , or perhaps outsiders ' view of New Yorkers ' self @-@ image — inspired many similar works , including the poster for the 1984 film Moscow on the Hudson ; that movie poster led to a lawsuit , <unk> v. Columbia Pictures Industries , Inc . , <unk> F. <unk> . <unk> ( <unk> 1987 ) , which held that Columbia Pictures violated the copyright <unk> held on his work . 
- On June 5 , 2003 , during the first term of George W. Bush 's presidency , Ted <unk> presented A View of the World from Pennsylvania Avenue as a <unk> of View of the World from 9th Avenue in a Universal Press Syndicate editorial cartoon . He replaced the letters representing The New Yorker with The <unk> . 
- The cover was later <unk> by Barry Blitt for the cover of The New Yorker on October 6 , 2008 . The cover featured Sarah Palin looking out of her window seeing only Alaska , with Russia in the far background . 
- The March 21 , 2009 The Economist included a story entitled " How China sees the World " that presents a parody that is also an homage to the original image , but depicting the viewpoint from Beijing 's Chang 'an Avenue instead of Manhattan . A caption above the illustration reads " <unk> by Jon Berkeley ( with <unk> to <unk> and The New Yorker ) " . It accompanied an article that discussed the burgeoning Chinese economy at the time of the contemporary financial crisis . 
- The October 1 , 2012 cover of Mad Magazine <unk> the problems with the September release of Apple Inc . ' s iOS 6 mobile operating system which included Apple Maps , a replacement for Google Maps . The work presents what View of the World might look like if one had relied upon the September 2012 version of Apple Maps to locate various landmarks . 
- 
- = = Critical review = = 
- 
- On October 17 , 2005 , American Society of Magazine Editors unveiled its list of the top 40 magazine covers of the prior 40 years and ranked View of the World from 9th Avenue in fourth place . The listing stated that the work " ... has come to represent Manhattan 's <unk> perception of the country beyond the Hudson River . The cartoon showed the supposed limited mental geography of <unk> . " 
- 
- 
- = <unk> = 
- 
- <unk> / <unk> / ( Chinese : <unk> ; pinyin : <unk> ; <unk> <unk> h @-@ <unk> @-@ <unk> : <unk> @-@ to <unk> <unk> <unk> ) is a coastal town on the island of Borneo in the central region of <unk> , Malaysia . <unk> is located 610 kilometres ( 380 mi ) northeast of <unk> , 216 kilometres ( 134 mi ) northeast of <unk> , and 200 kilometres ( 120 mi ) southwest of <unk> . With a population of 114 @,@ <unk> as of 2010 , <unk> is the capital of the <unk> District of the <unk> Division of <unk> , Malaysia . 
- The name of <unk> was derived from the local native language " <unk> <unk> " ( picking heads ) . <unk> was a small fishing village when <unk> James Brooke acquired it in 1861 . Brooke later built a fort there in 1862 . In 1867 , the first General Council meeting ( now <unk> State Legislative Assembly ) was convened in <unk> . It is the earliest state legislature system in Malaysia . The construction of the earliest airstrip in <unk> began in 1934 but was halted in 1938 due to financial difficulties . During World War II , the airstrip was heavily bombed by Allied forces . The British later rebuilt the airstrip , and it became fully operational in 1955 . The old airport was replaced by a new airport in 2002 . <unk> remained a fishing village until 1969 when oil and gas reserves were discovered off the coast . Since then , <unk> has become the centre of energy intensive industries such as a Malaysia <unk> plant , a Shell Middle <unk> Synthesis plant , and a <unk> combined cycle power plant . The economy has also expanded into oil palm and forest plantations , palm oil processing , wood @-@ waste processing , and cement manufacturing . The port of <unk> is the busiest in <unk> . The town is also a gateway to <unk> Industrial Park . 
- Among the tourist attractions in <unk> are <unk> National Park , <unk> Park , <unk> <unk> beach , <unk> village , <unk> <unk> Tong temple , <unk> mosque , Council <unk> monument , <unk> <unk> , and <unk> <unk> markets . The Borneo International <unk> Festival is held annually in the town . 
- 
- = = Etymology = = 
- 
- During the 16th century , <unk> was named " River de <unk> " by Portuguese <unk> . There are several legends surrounding the name <unk> . During the Brooke dynasty , the indigenous <unk> people practised <unk> to maintain their social status in the community . They threw the heads into the <unk> River , after which the heads had to be collected from the river . The practice of collecting the heads was known as " <unk> <unk> " ( picking heads ) in the local native language . Another story relates that two <unk> warriors named <unk> and <unk> built houses along the river . They and their followers frequently carried out preservation of severed heads near a small river stream branching off from <unk> River because the river bank was flat and wide . Therefore , the small river stream was named " <unk> <unk> " river . <unk> who came to <unk> subsequently pronounced the name as " <unk> " , and later the name evolved into " <unk> " and , finally , " <unk> " . 
- 
- = = History = = 
- 
- 
- = = = Brooke dynasty = = = 
- 
- James Brooke was appointed the White <unk> of <unk> ( now known as <unk> ) by the <unk> Empire in 1841 . In 1861 , the Sultanate of <unk> ceded the <unk> region to Brooke . <unk> was a small settlement at that time . A wooden fort named Fort <unk> was built in the village , named after Sir Henry <unk> , who was a close friend of the <unk> James and Charles Brooke . Sir Henry <unk> was responsible for crushing the <unk> piracy in the <unk> between 1840 and 1850 . Meanwhile , Charles Brooke was a nephew of James Brooke and would later become the latter 's successor as the second <unk> of <unk> . <unk> <unk> , an Italian botanist , visited <unk> in 1867 . On 4 August , he started his journey on a gunboat named " <unk> " , which was to send $ 6 @,@ 000 to <unk> for concessions being made to James Brooke in the <unk> and <unk> regions . He went to <unk> before coming back to <unk> . He dropped off at <unk> River on 13 August 1867 . His observations of the village were recorded as follows : 
- The fort of <unk> which was built entirely of wood , was in somewhat ruinous condition . It stood nearly on the sea @-@ shore , and just behind it , at a distance of few paces , the <unk> forests commenced ... Some <unk> had settled at the vicinity of the fort and had built a small <unk> ; but the village is chiefly formed by the houses of the <unk> beyond the Chinese <unk> ( village ) . These <unk> used to live further up the river , but since the construction of the fort , and the installation of an officer of the <unk> near the mouth of the river , they came to settle near the sea – a thing they would never have <unk> to do in former days for fear of the attacks of the <unk> pirates and <unk> pirates . 
- The houses of the <unk> people were built in rows on both sides of the <unk> River , mostly furnished by <unk> and <unk> <unk> . Each house had its own shed projection into the entrance of the river , which was used for the processing of <unk> <unk> . On 8 September 1867 , the first <unk> General Council meeting ( now <unk> State Legislative Assembly ) took place here . It was made up of 21 elected local community members ( five British officers and 16 Malay and <unk> local chiefs ) . The Council was formed by Raja <unk> Charles Brooke under orders from <unk> James Brooke . The Council is the oldest state legislative assembly in Malaysia . 
- 
- = = = Japanese occupation = = = 
- 
- During World War II , <unk> Charles <unk> Brooke ordered the construction of airstrips in <unk> , <unk> , <unk> , <unk> , and <unk> . Construction of the <unk> airstrip was started in 1934 under the direction of C. W. Bailey , a Works and Building Inspector for the British Royal Air Force ( RAF ) . All the airstrips were completed except for the <unk> airstrip , where construction was discontinued in October 1938 due to financial reasons . Japanese forces landed in <unk> on 16 December 1941 . <unk> fell into Japanese hands when they conquered <unk> on 24 December 1941 . When the Japanese invaded <unk> , Charles <unk> Brooke already left for Sydney ( Australia ) before the attack while his officers were captured by the Japanese and interned at the <unk> <unk> camp . During the Japanese occupation , the Japanese used the airstrip for military purposes . However , the airstrip was heavily bombed by Allied forces . The British began reconstruction of the airstrip after the war ; during the project many <unk> bombs were unearthed . 
- On 5 September 1942 , Japanese Field Marshal Prince <unk> ( <unk> ) boarded a plane from <unk> to <unk> to <unk> an airport that bears his name . However , he never arrived . One month later , the plane was found to have crashed off the coast of <unk> <unk> , <unk> . The cause of the plane crash was not known . The Japanese later set up a wooden pole memorial made up of <unk> wood in <unk> . The wooden pole was later taken back to Japan by the family of Prince <unk> . 
- Chinese sawmill owners at <unk> and <unk> were instructed by the Japanese to produce timber for repairs at oil fields and ship building . During the Japanese occupation , sawmills at <unk> produced a total of 4 @,@ 000 tons of <unk> timber . 
- 
- = = = Post @-@ war period = = = 
- 
- In the 1950s , major economic activities in <unk> were the timber extraction industry , fishing , and <unk> processing . In the 1960s , <unk> was still a small fishing village , with a population of 5 @,@ 000 . No roads were constructed in <unk> until 1969 when the first <unk> road was built to connect <unk> to <unk> . The first bus that serviced the <unk> – <unk> route was owned by <unk> <unk> <unk> ( <unk> ) . The <unk> bus line was an initiative by the Malaysian federal government to provide public transportation for the people . The <unk> villagers paid the bus driver with " vegetables , chickens , bamboo shoots , and other items " . Before 1960 , <unk> was connected to <unk> by sea through a ship named " <unk> <unk> " . After 1960 , the ship " <unk> <unk> " was added to the route . It took around 36 to 48 hours to reach <unk> from <unk> , depending on the sea conditions . Due to lack of food supplies from <unk> , the villagers had to make do with limited food , and several villagers resorted to hunting in the <unk> to supplement the food supply . 
- In 1960 there were only three primary schools in <unk> . These schools provided classes until Primary 3 level . There were no secondary schools . <unk> could pursue their secondary school studies at either <unk> or <unk> by using small boats as there were no roads connecting <unk> to either <unk> or <unk> . <unk> Government Secondary School was opened in 1964 . In 1967 <unk> celebrated the first 100 years of the Council <unk> meeting ( <unk> State Legislative Assembly ) . A stone monument was built in front of a government rice <unk> to commemorate the event . <unk> was a sub @-@ district of <unk> Division in the 1970s . The sub @-@ district was upgraded into a district in 1987 . 
- 
- = = = Discovery of oil and gas reserves = = = 
- 
- Large reserves of natural gas were discovered off the coast of <unk> in 1969 . Following this , a feasibility study was done in 1975 , and <unk> <unk> was found to be a suitable site for a deep @-@ water port . On 14 June 1978 , Malaysia <unk> <unk> <unk> ( <unk> <unk> ) was established by <unk> , a Malaysian national oil and gas company for <unk> Natural Gas ( <unk> ) processing at <unk> . On 8 July 1978 , the <unk> Development Authority ( <unk> ) was established by the <unk> state government for infrastructure development and to promote industrial investment in the area . On 15 August 1981 , the <unk> Port Authority was established at <unk> <unk> , starting operation on 1 January 1983 . Since the establishment of <unk> Corridor of <unk> Energy ( <unk> ) in 2008 , <unk> become the gateway to <unk> Industrial Park , which is located 62 kilometres ( 39 mi ) away from <unk> . The industrial park is a centre of heavy , energy @-@ intensive industry . Among the companies that started their operations in the industrial park are <unk> Malaysia <unk> <unk> , Press Metal <unk> <unk> <unk> , and <unk> <unk> <unk> <unk> . 
- Rural – urban migration is significant in <unk> because of greater job availability in the town . Since 2007 , new residents have started several <unk> areas in <unk> due to inability to find affordable housing , around <unk> Industrial estate and <unk> <unk> . To address the issue , several low @-@ cost housing projects were initiated by <unk> and <unk> state government to relocate the squatters . The state government planned to achieve zero squatters status by the year <unk> . <unk> also saw the rise in the number of residential and commercial properties such as double @-@ <unk> terraced houses , terraced <unk> , <unk> Commercial Centre , and Time Square <unk> Mall . Residential properties has shown a 20 % price increase from 2011 to 2013 . 
- 
- = = <unk> = = 
- 
- <unk> is represented by <unk> parliamentary seat ( P. 217 ) in the Parliament of Malaysia . The town is also represented by three state assembly seats – <unk> , <unk> ( later was split by two state assembly namely <unk> <unk> and <unk> ) , and <unk> – in the <unk> State Legislative Assembly . 
- 
- = = = Local authorities = = = 
- 
- Since 1978 the town of <unk> has been administered by the <unk> Development Authority ( <unk> ) , with offices located along <unk> <unk> <unk> . The town is located within the boundary of <unk> District , with a population of 183 @,@ 402 and a total area of 7 @,@ 220 @.@ 40 square kilometres ( 2 @,@ 787 @.@ 81 sq mi ) . <unk> Division was formerly a <unk> District under the jurisdiction of <unk> Division . The former <unk> District was upgraded to <unk> Division on 1 January 1987 . At the same time , <unk> sub @-@ district was upgraded to the present @-@ day <unk> District . Both the <unk> <unk> and District offices are located inside <unk> <unk> , <unk> <unk> Street , <unk> . 
- 
- = = Geography = = 
- 
- <unk> is located 610 kilometres ( 380 mi ) northeast of <unk> 216 kilometres ( 134 mi ) northeast of <unk> , and 200 kilometres ( 120 mi ) southwest of <unk> . <unk> is located near the mouth of the <unk> River , in the coastal region of central <unk> . Geology of the coastal area was formed during the Pleistocene period ; silt , clay , and gravel can be found here . Geological formation from the <unk> period is found in the inland area , which contains limestone , <unk> , and sandstone . The soil is generally soft . 
- 
- = = = Climate = = = 
- 
- There are two monsoon seasons in the <unk> : the northeast <unk> ( November to March ) and the southwest <unk> ( May to September ) . The calm period between these two <unk> is known as the transitional period . In the coastal region , maximum rainfall occurs in the month of January , while minimal rainfall occurs from the period June to August . Rainfall is more evenly distributed in the inland areas . The annual rainfall of the <unk> region is about 3 @,@ 750 mm ( 148 in ) annually . The mean daily hours of sunshine at <unk> is about 5 @.@ 0 to 5 @.@ 5 hours . <unk> receives on average 14 to 15 <unk> / m2 of radiation throughout the year . <unk> 's relative humidity is 85 % . 
- 
- = = Demographics = = 
- 
- The growth of <unk> 's population is shown below : 
- The issue of gangsters in <unk> was first raised in 2007 by the member of parliament ( MP ) for <unk> . The gangsters may have run businesses related to illegal logging , controlling the prices of diesel , eggs , fertiliser and gas cylinders . <unk> police have been <unk> down on gangster activities in the town . <unk> businessmen who seek cheap labour have caused a rise in the number of illegal immigrants in <unk> . The number may have reached 50 @,@ 000 in 2009 . <unk> immigration department has performed several operations to <unk> illegal immigrants back to their home country . 
- 
- = = = <unk> = = = 
- 
- As of the 2010 Malaysian census , the population of the town of <unk> is 114 @,@ <unk> . Indigenous people accounted for the largest proportion of the town population ( 61 @.@ 2 % , 69 @,@ <unk> ) , followed by Chinese ( 25 @.@ 0 % , 28 @,@ 512 ) , Non @-@ <unk> ( 13 @.@ 1 % , 14 @,@ <unk> ) , and Indians ( 0 @.@ 28 % , 319 ) . Among the indigenous groups , there are <unk> ( 32 @,@ <unk> ) , Malay ( 14 @,@ <unk> ) , <unk> ( 14 @,@ 179 ) , <unk> ( 1 @,@ <unk> ) , and other indigenous tribes ( 6 @,@ <unk> ) . According to government sources , there are <unk> <unk> <unk> in the <unk> District . The <unk> moved into <unk> and <unk> basins in the mid @-@ 19th and early 20th century with permission of the Brooke government . Other indigenous tribes that form the minority are <unk> , <unk> , and <unk> . The Chinese in <unk> are mainly composed of dialect groups such as <unk> , <unk> , and <unk> . The Chinese have been living in the town of <unk> since the era of <unk> Empire . Later , <unk> Chinese from <unk> moved in , dominating the timber and plantation businesses in <unk> . There is also a large number of foreigners working there . Most of them come from Britain , Australia , the Netherlands , Germany , South Africa , New Zealand , Japan , China , the United States , and Indonesia . 
- 
- = = = Languages = = = 
- 
- While Malay is the official language of <unk> ; English is widely spoken there . Local ethnic languages and Chinese dialects are spoken by the respective ethnic groups . Standard Chinese is also spoken by ethnic Chinese in <unk> . <unk> is spoken by communities living along the <unk> River , with 4 @,@ 200 native speakers . These speakers are now recognised as part of the <unk> ethnic group , where their main language is Malay . <unk> is classified as one of the endangered languages in <unk> because of the isolated usage of the language in a small community . 
- 
- = = = Religion = = = 
- 
- The majority of the <unk> population are adherents of Christian denominations due to Christian missionaries operating during the Brooke dynasty ; followed by Islam , Buddhism , and Hinduism . Among the notable places of worship in <unk> are the <unk> Mosque ( <unk> <unk> ) , <unk> <unk> , <unk> <unk> Kong Temple , <unk> <unk> Methodist Church , and St. Thomas Church . The respective religious groups are free to hold processions in the town . 
- 
- = = Economy = = 
- 
- There are five industrial estates in <unk> . They are : <unk> Industrial Estate ( for wood @-@ based industries ) , <unk> Industries Estate ( wood @-@ based industries ) , <unk> Industrial Area ( for medium and light industries ) , <unk> Light Industrial Estate ( medium and light industries ) , and <unk> Light Industrial Estate ( light industry ) . 
- 
- = = = Oil and gas = = = 
- 
- Malaysia <unk> is a <unk> Natural Gas ( <unk> ) manufacturing complex located in <unk> that currently contains eight <unk> trains with a ninth one currently under construction . The complex was built by the Malaysian national oil and gas company , <unk> . The manufacturing complex has a production capacity of 25 @.@ 7 million tonnes per annum . <unk> is also planning to open <unk> <unk> ( <unk> ) offshore <unk> , which is used specifically to harvest natural gas from small and isolated gas fields . Transportation of natural gas from the <unk> state of <unk> , <unk> , to the <unk> complex at <unk> is facilitated by a 512 @-@ kilometre ( <unk> mi ) pipeline known as the " <unk> <unk> Gas <unk> " . Currently , 45 % of Malaysian natural gas is found at Central <unk> off the coast of <unk> . The largest <unk> of Malaysia <unk> productions are Japan ( 62 % ) , Korea ( 17 % ) , Taiwan ( 12 % ) , and China ( 9 % ) . 
- The <unk> Shell <unk> Plant ( <unk> ) , formerly known as <unk> <unk> Oil Terminal ( <unk> ) , was established in 1979 . It consists of three crude oil storage tanks , each with a capacity of 410 @,@ 000 barrels . It has three major areas of operation : <unk> Oil Operations , <unk> <unk> , and Gas Sales Facilities . Royal Dutch Shell started to establish the world 's first Shell Middle <unk> Synthesis plant ( Shell MDS ) in 1993 . It is also known as <unk> Gas @-@ To @-@ Liquid plant ( <unk> <unk> ) . The plant has a production capacity of 14 @,@ 770 barrels per day with a total investment of over US $ 1 billion as of the year 2010 . The plant is staffed with 380 people , of whom 93 % are <unk> , with 80 % of the staff coming from <unk> . 
- 
- = = = Wood @-@ based industries and plantations = = = 
- 
- Since the opening up of the <unk> – <unk> road in the 1970s , large @-@ scale plantations of oil palm and <unk> has been developed in rural areas of <unk> Division . Currently , there are 57 @,@ 740 hectares ( <unk> @.@ 4 km2 ( 223 sq mi ) ) of oil palm , 2 @,@ 000 hectares ( 200 km2 ( 77 sq mi ) ) of <unk> , and 815 hectares ( 8 @.@ 15 km2 ( 3 sq mi ) ) of <unk> plantations . 
- The first <unk> palm oil refinery , <unk> <unk> Oil <unk> <unk> , was established in June 1991 . <unk> currently has four palm oil refineries : <unk> <unk> <unk> <unk> <unk> ( operated under <unk> Group , a subsidiary of <unk> International ) , <unk> Darby <unk> <unk> Oil <unk> <unk> , <unk> Palm Oil <unk> <unk> <unk> , and <unk> Oil Palm <unk> . However , as of 2015 , <unk> no longer <unk> raw palm oil produced from cleared forests and peat swamps in <unk> because of environmental concerns . 
- The <unk> Division has been designated as a <unk> <unk> Zone ( <unk> ) by the <unk> state government since 1998 . As of 30 June 2011 , a total of 124 @,@ 618 hectares ( 1 @,@ <unk> @.@ 18 km2 ( <unk> sq mi ) ) has been planted with <unk> trees . Other trees that are planned for plantations are <unk> , <unk> , <unk> , <unk> , <unk> , and rubber trees . <unk> <unk> Forest <unk> <unk> , a company wholly owned by the <unk> state government , has been granted a license to <unk> forests for 60 years . However , the company has been suffering financial losses from 2009 to 2011 . 
- There are three mills in <unk> that process wood @-@ waste products . Two are <unk> @-@ density <unk> ( <unk> ) plants and the third is a charcoal <unk> plant , with a total installed capacity of <unk> @,@ 000 cubic metres ( 8 @,@ 700 @,@ 000 cu ft ) per year . <unk> plants <unk> wood waste purchased from sawmills and <unk> mills in the <unk> area and occasionally from the <unk> <unk> timber processing zone located at the mouth of the <unk> River . <unk> <unk> , which are required to hold wood dust together , constituted 20 % of the total production cost of the wood panel products . <unk> plants in <unk> are operated by <unk> <unk> <unk> <unk> , which was founded on 15 February 1994 . The <unk> plant is operated by <unk> <unk> <unk> <unk> . A glue / <unk> factory in <unk> is owned by <unk> <unk> & Chemicals <unk> <unk> . It produces <unk> <unk> resin and <unk> <unk> resin for <unk> and <unk> manufacturing at <unk> Industrial Estate . <unk> <unk> is also produced to supply <unk> <unk> <unk> ( <unk> ) plant . 
- 
- = = = Others = = = 
- 
- The <unk> Port Authority was established in 1981 . It started port operation in 1983 at <unk> <unk> . Following a <unk> exercise , <unk> Port <unk> <unk> ( <unk> ) was founded on 23 December 1992 and commenced operation on 1 January 1993 . <unk> is currently responsible for regulatory exercises and security of the port . Meanwhile , <unk> is responsible for cargo handling at the <unk> International <unk> Terminal ( <unk> ) . The port also provides Vessel traffic service to shipping vessels . The annual total cargo <unk> is 45 @.@ 4 million tonnes , consisting of 58 % <unk> and 42 % non @-@ <unk> products . As of 31 December 2014 , it generated a total revenue of <unk> <unk> @.@ 3 million per year . <unk> Port is the busiest port in <unk> . 
- The <unk> <unk> <unk> plant is the <unk> ammonia and <unk> plant operated by <unk> <unk> <unk> <unk> <unk> ( <unk> ) , which is partly owned by <unk> . The company was formed on 6 December 1980 . The plant started operation on 1 October 1985 . It is also one of the largest <unk> <unk> plants in Asia . It is a joint venture by five <unk> countries : Malaysia ( 63 @.@ 5 % shares ) , Thailand ( 13 % ) , Indonesia ( 13 % ) , the Philippines ( 9 @.@ 5 % ) , and Singapore ( 1 % ) . 
- <unk> Mata <unk> <unk> ( <unk> ) , one of the largest publicly traded companies in <unk> , set up a cement plant in <unk> at <unk> Industrial Estate . The plant , manned by 40 people , produces ordinary Portland cement and Portland blast <unk> cement . It currently has a combined production capacity of 2 @.@ 75 million <unk> ( million tonnes ) . 
- The <unk> combined cycle power plant was started in early 2010 with a capacity of 317 megawatts . The power plant is registered under the United Nations Clean Development Management ( <unk> ) scheme as of 18 September 2010 . The plant is built to ensure efficient use of energy and reduce green house gas emissions . It is the first <unk> power plant in Malaysia , currently operated by <unk> Power Generation <unk> <unk> ( <unk> ) , a wholly owned subsidiary of <unk> Energy . 
- 
- = = Transportation = = 
- 
- 
- = = = Land = = = 
- 
- All roads in <unk> are maintained by the <unk> Development Authority ( <unk> ) . <unk> is connected to <unk> and <unk> by the Pan Borneo Highway . <unk> is also connected to <unk> and <unk> Industrial Park . <unk> Bridge crosses the <unk> River . It is the second bridge in Malaysia built using the incremental launch method . <unk> Road in <unk> is named after a friend of James Brooke , Sir Henry <unk> . 
- 
- = = = = Public transportation = = = = 
- 
- <unk> has a long @-@ distance bus station , located at <unk> Jaya , 5 km ( 3 mi ) northeast of the town centre . Among the areas served by the bus station are : <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , and <unk> , Indonesia . The bus companies that serve the station are the <unk> <unk> <unk> . <unk> , <unk> , <unk> , and <unk> bus lines . There are also buses that serve the town area . Taxi service is also available . 
- 
- = = = Air = = = 
- 
- The old <unk> airport was built in 1955 in the town centre . It once held the Guinness World Record of nearest airport to town . On 19 December 2002 , the airport was replaced by a new airport , which is located 23 km ( 14 mi ) away from the town centre . The surroundings of the old airport were developed into commercial and residential projects while the runaway is reserved for <unk> International <unk> Festival . The new airport has a runway measuring 2 @,@ <unk> m ( 9 @,@ 006 ft ) , capable of handling planes as large as the Airbus <unk> . The airport currently serves three major airlines : Malaysia Airlines ( <unk> ) , Air Asia , and <unk> , connecting to domestic destinations such as : <unk> , <unk> , <unk> , Kuala <unk> , and <unk> <unk> . 
- 
- = = = Water = = = 
- 
- There is a <unk> terminal at <unk> that serves the rural areas of <unk> Division . Among the destinations that can be reached by express boat from <unk> are : <unk> , <unk> , <unk> , <unk> , and <unk> . 
- 
- = = Other utilities = = 
- 
- 
- = = = Courts of law and legal enforcement = = = 
- 
- The current court complex is located at <unk> <unk> Road . It comprises the High Court , the Sessions Court , and the <unk> Court . <unk> also has <unk> <unk> Court , located at <unk> <unk> , whose area of jurisdiction covers <unk> District and <unk> districts . The <unk> central police station is located at <unk> Hussein <unk> Road , with other police stations located at <unk> <unk> , <unk> , and <unk> . There is also a central prison in <unk> , which doubles as a <unk> centre . 
- 
- = = = Healthcare = = = 
- 
- <unk> Hospital started operation in 1968 . It is located at <unk> Road , 12 km ( 7 @.@ 5 mi ) from the town centre . Following renovations completed on 21 May 2000 , the hospital is now equipped with 200 beds . As of 2011 , the hospital provides <unk> services in seven medical disciplines . <unk> also has one <unk> , <unk> <unk> . There are two private hospitals in <unk> : Columbia Asia Hospital and <unk> Medical Centre . 
- 
- = = = Education = = = 
- 
- There are about 50 primary and eight secondary schools in <unk> . All the schools under the National Education System are managed by the <unk> District Education Office . The oldest primary schools in <unk> are St Anthony 's Primary School ( Roman Catholic Mission School ) , Chung <unk> Primary School , and the <unk> <unk> <unk> Primary School , which were established in the early 1960s . The <unk> Government Secondary School was built in 1964 . It is now known as <unk> <unk> , the oldest secondary school in the town . <unk> also has one Chinese independent school , Kai Dee Middle School ( <unk> ) . The Shell Oil Company established the <unk> International School in 1982 to meet the primary education needs of Shell employees ' children . The school provides English National Curriculum ( <unk> ) for literacy and <unk> and International Primary Curriculum ( IPC ) for other subjects . 
- <unk> <unk> <unk> Campus was started as the National <unk> Training Centre , <unk> , in 1974 . The oldest campus in <unk> , it was relocated to <unk> in 1987 as a branch campus of the <unk> <unk> Malaysia ( <unk> ) . The campus was closed down in 1992 before reopening in 2001 as <unk> <unk> Malaysia ( <unk> ) . During this period of closure , the campus was used as the site for <unk> <unk> <unk> <unk> ( <unk> Science Teachers ' Training College ) from 1994 to July 1999 when it was moved to <unk> <unk> as <unk> <unk> Guru <unk> <unk> Abdul <unk> ( <unk> Abdul <unk> Teachers ' Training Institute Campus ) . The <unk> campus is currently located 13 km ( 8 mi ) away from the town centre , occupying 715 ha ( 1 @,@ 767 acres ) , which can accommodate up to 2 @,@ 200 students . This branch campus currently has only one faculty , the Faculty of Agriculture and Food Sciences , consisting of five academic departments . In 2015 <unk> was ranked 41st in the <unk> @-@ <unk> World University rankings . <unk> College , established in November 1999 , offers courses in business , information technology , language , and engineering . 
- There is also a technical school located 15 km ( 9 mi ) away from the town , near <unk> <unk> , occupying 20 ha ( 49 acres ) of land . The school was built in 1982 with a maximum capacity of 900 students . Among the courses offered are : automotive , mechanical and civil engineering , commerce , and fashion . Gulf Golden International Flying Academy ( <unk> ) , the first and only flying academy in <unk> , was closed in 2012 due to financial difficulties . 
- 
- = = = <unk> = = = 
- 
- The first public library in <unk> was built in 1971 by <unk> District Council ( <unk> ) . In 1988 the library was demolished to make way for car parks . Books from the library were moved into the former <unk> building . On 29 May 2000 , the <unk> Development Authority ( <unk> ) public library was built near the <unk> Civic Centre , which is 2 km ( 1 @.@ 2 mi ) from the town . The public library has three branches : at <unk> , <unk> , and <unk> . 
- 
- = = Culture and leisure = = 
- 
- 
- = = = <unk> and recreational spots = = = 
- 
- 
- = = = = Cultural = = = = 
- 
- <unk> <unk> ( <unk> village ) is a <unk> fishing village in <unk> located near <unk> River . Among the daily activities in this village are <unk> processing , fish drying , and the manufacturing of <unk> , <unk> ( salted shrimp ) , <unk> ( <unk> headgear ) , and <unk> ( a type of food cover ) . <unk> <unk> Tong temple is located at <unk> <unk> Sultan <unk> . It has a structural design with a rock garden courtyard , man @-@ made waterfall , and dragon fencing . <unk> mosque , meaning " <unk> to God " , has a man @-@ made waterfall , a fountain , and a landscape planted with flowers . The Borneo International <unk> Festival has been held yearly since 2005 at the old <unk> airport runway . It usually lasts for four to five days in September . 
- 
- = = = = Historical = = = = 
- 
- In 1987 a clock tower and a fountain were erected at Council <unk> Monument . A centenary stone that was erected in 1967 to commemorate the event is kept under the clock tower . The <unk> <unk> <unk> Kong temple ( near <unk> <unk> ) is believed to have been built in the 1890s to purge the town from evil spirits . The temple survived World War II , and was rebuilt after the discovery of oil and gas reserves offshore . 
- 
- = = = = <unk> and conservation areas = = = = 
- 
- <unk> National Park is located 30 km ( 19 mi ) northeast of the town . The park was <unk> in 1976 , covering an area of 8 @,@ 996 ha ( 22 @,@ 230 acres ) ( 89 @.@ 96 km2 ( 35 sq mi ) with sandy beaches , rocky <unk> , jungle streams , and forests . Other national parks that can be accessed along the <unk> – <unk> road are the <unk> Hills National Park and <unk> National Park . 
- <unk> <unk> beach ( <unk> beach ) is located 3 km ( 1 @.@ 9 mi ) from the town centre . Meanwhile , <unk> <unk> ( <unk> Park ) is located 4 km ( 2 @.@ 5 mi ) from the town centre . The park has a <unk> <unk> , a butterfly garden , and a mini @-@ zoo . 
- 
- = = = = Other attractions = = = = 
- 
- <unk> Tower is an observation tower located at <unk> <unk> . It offers a view of the <unk> oil and gas facilities shortly after nightfall . <unk> <unk> is a 3 @-@ kilometre ( 2 mi ) <unk> along the <unk> coastline with the <unk> River mouth as its focal point . It has the observation points offering sunset views . There is also an 18 @-@ hole golf course at <unk> . 
- 
- = = = = <unk> = = = = 
- 
- There are several shopping malls in <unk> : <unk> Mall , City Point , <unk> <unk> <unk> Stores , Farley shopping complex , Sing <unk> <unk> , and MDS @-@ Mart . Time Square Mall is currently under development in <unk> , with <unk> as the anchor tenant . 
- <unk> <unk> and <unk> <unk> are the two main markets in the town . Both places have a unique <unk> @-@ shaped roof that <unk> the traditional <unk> headgear named <unk> . <unk> <unk> offers items ranging from jungle produce to native home @-@ made <unk> such as <unk> . Meanwhile , <unk> <unk> houses both wet market and dry market under one roof , providing fresh vegetables , fruits , fish , and dairy products . The first floor of <unk> <unk> offers a variety of fast food such as <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> , <unk> , and <unk> <unk> . The <unk> night market is located on <unk> <unk> road . There are over 150 <unk> selling a variety of items such as garments , electric goods , vegetables , fruits , food and drinks . 
- 
- 
- = <unk> Battalion = 
- 
- The <unk> Battalion ( Croatian : <unk> <unk> ) was a special forces unit of the Croatian National Guard ( <unk> <unk> garde – <unk> ) and later of the Croatian Army ( <unk> <unk> – <unk> ) established in <unk> on 18 May 1991 , during the Croatian War of Independence . The unit drew personnel from the special police forces and a former French Foreign Legion troops serving as its core . The battalion was set up and initially commanded by Ante <unk> , while Major <unk> <unk> took over as the commanding officer in August . 
- The <unk> Battalion trained volunteer troops in Vukovar in June 1991 before it saw action in <unk> <unk> , the Battle of <unk> and near <unk> in 1991 . By the end of 1991 , the unit 's personnel were tasked with setting up an additional special forces unit of the <unk> . The next year its elements took part in the Battle of <unk> and Operation Tiger aimed at lifting the Siege of Dubrovnik . It also and helped develop and train the Croatian Defence Council ( <unk> <unk> <unk> – <unk> ) , setting up a training camp in <unk> . In 1993 , the battalion took part in Operation <unk> . In February 1994 , the <unk> Battalion was amalgamated with several other <unk> special forces units into the 1st Croatian Guards Brigade ( 1 @.@ <unk> <unk> <unk> ) , a component of the 1st Croatian Guards Corps ( 1 @.@ <unk> <unk> <unk> ) . 
- 
- = = Background = = 
- 
- In 1990 , following the electoral defeat of the government of the Socialist Republic of Croatia , ethnic tensions between Croats and Croatian Serbs worsened . The Yugoslav People 's Army ( <unk> <unk> <unk> – <unk> ) believed Croatia would use the Croatian Territorial Defence Force 's ( <unk> <unk> – <unk> ) equipment to build its own army and confront the <unk> itself . In order to minimize the expected resistance , the <unk> confiscated the <unk> weapons . On 17 August , the tensions escalated into an open revolt of the Croatian Serbs . 
- In the beginning of 1991 , Croatia had no regular army . In an effort to bolster its defence , Croatia doubled the size of its police force to about 20 @,@ 000 . The most effective part of the force was the 3 @,@ 000 @-@ strong special police that were deployed in 12 battalions , adopting military organisation . In addition there were 9 @,@ 000 – 10 @,@ 000 regionally organised reserve police . The reserve police were set up in 16 battalions and 10 companies , but they lacked weapons needed to arm many of the troops . 
- Preparations to set up the Croatian National Guard ( <unk> <unk> garde – <unk> ) began on 12 April 1991 . Establishment of the <unk> as a police force with military capabilities was thought necessary by the Croatian authorities following armed clashes in <unk> and at <unk> Lakes in March and due to the possibility of further confrontation with the <unk> . The <unk> , formally established on 23 April , was tasked with protection of the constitutional order , maintenance of public order , anti @-@ terrorist operations , protection of Croatia 's borders , territory , coast and territorial waters , as well as the protection of high @-@ value structures and high @-@ profile persons . 
- 
- = = Service = = 
- 
- On 18 May 1991 , the <unk> Battalion was established as a special forces unit of the <unk> . The core of the unit consisted of 27 volunteers drawn from the <unk> Special Police Unit ( <unk> ) . Initially , it relied on former French Foreign Legion troops . The most senior among the former <unk> was Ante <unk> , previously a <unk> @-@ <unk> ( non @-@ commissioned officer – <unk> ) in the 4th Foreign Regiment . In consequence , <unk> was tasked with setting up the unit as its initial commander . Major <unk> <unk> , likewise a former French Foreign Legion member , was assigned the battalions deputy commander . The unit was based in the village of <unk> in the region of <unk> <unk> , on the grounds of the former " Josip <unk> Tito " political school . The site , adjacent to the border of Slovenia , was selected to be inaccessible to Yugoslav Air Force raids without violation of <unk> or possibly Austrian airspace . In June 1991 , the <unk> <unk> was transferred to <unk> Peak north of Zagreb leaving <unk> base to the <unk> Battalion , as well as the second special forces unit , the <unk> Battalion . 
- The <unk> Battalion was deployed for the first time on 15 June . It was stationed in Vukovar , tasked with preparation of city defences and organisation of volunteer troops . In August , <unk> took over command of the battalion from <unk> . The same month , the <unk> Battalion was deployed to the <unk> , where it pushed the Croatian Serb forces out of the town of <unk> <unk> . In September , the battalion was deployed to <unk> , where it took part in battle to control <unk> against the <unk> . Troops assigned to the battalion captured <unk> barracks in <unk> . During combat in <unk> , 30 troops of the <unk> Battalion , assisted by <unk> <unk> , captured <unk> Major General <unk> <unk> , along with three armoured personnel carriers ( <unk> ) and 32 soldiers . The unit was deployed to <unk> on 28 October , tasked with <unk> <unk> from the <unk> . After the deployment to <unk> , a part of the unit personnel left to Bosnia and Herzegovina anticipating further conflict there , while the remainder of the unit returned to <unk> . The <unk> was renamed the Croatian Army ( <unk> <unk> – <unk> ) on 3 November 1991 . In late 1991 , personnel of the <unk> Battalion set up another special forces unit of the <unk> — the <unk> <unk> Battalion based in <unk> . 
- In 1992 , elements of the <unk> Battalion took part in the Battle of <unk> , before setting up a training camp in the town of <unk> . There the battalion personnel assisted in setting up and trained the Croatian Defence Council ( <unk> <unk> <unk> – <unk> ) . Later that year , elements of the battalion took part in Operation Tiger — aimed at lifting of the Siege of Dubrovnik . In 1993 , elements of the <unk> Battalion took part in Operation <unk> , fighting in the area of <unk> . The Central Intelligence Agency assessed the <unk> Battalion as one of the best units of the <unk> . 
- 
- = = <unk> = = 
- 
- On 25 February 1994 , the <unk> Battalion was amalgamated with parts of other special forces units of the <unk> : <unk> Battalion , Ban <unk> Battalion , <unk> <unk> Battalion , <unk> <unk> Battalion and part of 8th Light Assault Brigade forming the 1st Croatian Guards Brigade ( 1 @.@ <unk> <unk> <unk> ) , a component of the 1st Croatian Guards Corps ( 1 @.@ <unk> <unk> <unk> ) , directly subordinated to the Ministry of Defence rather than the General Staff of the Armed Forces of the Republic of Croatia . 
- 
- 
- = <unk> <unk> But They Don 't Go Down = 
- 
- " <unk> <unk> But They Don 't Go Down " is the nineteenth and penultimate episode of the third season of the American mystery television series Veronica Mars , and the <unk> episode overall . Written by Phil <unk> and directed by Jason Bloom , the episode premiered on The CW on May 22 , 2007 . The series depicts the adventures of Veronica Mars ( Kristen Bell ) as she deals with life as a college student while <unk> as a private detective . 
- In this episode , Weevil ( Francis <unk> ) enlists Veronica 's help in proving his innocence when he is implicated in creating fake student <unk> . Meanwhile , Veronica and Piz ( Chris Lowell ) come to terms with Veronica 's FBI internship , and a sex tape of this couple is released on the internet . Logan ( Jason <unk> ) beats up Piz , thinking that he posted it . In addition , Keith ( Enrico Colantoni ) and <unk> ( Ken Marino ) debate on Piz 's radio show about the upcoming Sheriff 's election in which they are running against each other . 
- " <unk> <unk> But They Don 't Go Down " features the reappearance of Weevil after an absence of five episodes ; during filming of the third season , <unk> was undergoing medical treatment . Series creator Rob Thomas pointed out Logan and Piz 's fight scene as one of the highlights of the episode and the season . In its original broadcast , the episode received mostly positive reviews from television critics , with many praising the case @-@ of @-@ the @-@ week . 
- 
- = = Synopsis = = 
- 
- In a <unk> line , Veronica and Mac ( Tina <unk> ) discuss her relationship with Piz and what her FBI internship will mean for them . They then see someone getting arrested by the campus police for a fake <unk> card . Weevil <unk> himself , but finds himself unable to receive adequate benefits . Veronica gets Piz to accept her leaving for the internship . At the Sheriff ’ s station , Weevil is called out by several students as being the one who gave them the fake cards . Weevil is put in the jail cell , but he tells Veronica that he think they targeted him after he was showcased in the criminology class . She investigates several of the owners of the student <unk> , who do seem genuine in accusing Weevil . Weevil gets bail , but Keith informs him that a student <unk> printing machine was found in the locker next to Weevil ’ s . Veronica and Wallace speak to a mechanical engineering professor , who says that it would be impossible for Weevil to be the <unk> . Piz invites Keith to appear on his show in an election special before Keith informs Veronica that Weevil ’ s <unk> were found all over the investigation . 
- Keith and <unk> debate on Piz ’ s radio show , and <unk> <unk> some support about his <unk> <unk> drinking policy . Wallace notices someone following him , and the student wants to recruit him for a secret society . <unk> insults Keith ’ s handling of his home life , and Veronica hits him on the head lightly . She helps Weevil <unk> his steps and notices one of her criminology classmates , Jenny ( Dianna Agron ) , involved with one of Weevil ’ s clients . Veronica <unk> that Jenny and her circle of friends are responsible for creating the fake student <unk> , <unk> them , and <unk> Weevil . Logan and Dick ( Ryan Hansen ) go surfing , and they run into Veronica , Piz , and Mac helping Wallace ( Percy <unk> III ) with his final project for a class . Veronica learns that one of Jenny ’ s group lives in the same town in Georgia where the student <unk> machines are made . 
- Veronica is called into a room in the library by Jenny ’ s group , who try to <unk> her into not turning them in to the <unk> police . However , she denies and records their conversation as further proof . They are hiding a third <unk> machine , and Veronica tells Weevil to go and find it . Dick shows Logan a sex tape of Piz and Veronica that has been circulating in an email . Because Logan thinks Piz posted the tape , he attacks Piz and hits him incessantly . Weevil does have a student <unk> machine , and Logan walks into Mars Investigations with Piz ’ s blood on him . 
- 
- = = Production = = 
- 
- " <unk> <unk> But They Don 't Go Down " was written by Phil <unk> and directed by Jason Bloom , marking <unk> 's fifteenth and final writing credit and Bloom 's fourth and final directing credit for Veronica Mars , after " Green @-@ Eyed Monster " , " Nevermind the <unk> " , and " Charlie Don 't Surf " . The episode features the final appearance of Dianna Agron , famous for her role as Quinn <unk> on Glee , as Jenny <unk> , Veronica ’ s classmate . Agron had previously appeared in the episode “ President Evil ” . The episode prominently features Weevil ( Francis <unk> ) , who appears after a five episode hiatus . During the third season , <unk> was struggling with a medical condition , and the medication he took for this illness caused his face and neck to swell and break out with <unk> . 
- Series creator Rob Thomas included the scene in which Logan attacks Piz on his list of highlights from the third season . When Thomas first viewed the scene with his wife , she emitted an audible <unk> when she saw Logan walking through the <unk> towards Piz , anticipating the fight that was about to come . Thomas thought that most fight scenes on the show were not well @-@ done , as they are filmed quickly and without a second unit . However , he was pleased with the final cut , stating that there was only one punch that he thought looked fake . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- In its original broadcast , “ <unk> <unk> But They Don ’ t Go Down ” received 1 @.@ 78 million viewers , ranking 77th of 85 in the weekly rankings . This was a decrease from the previous episode , “ I Know What You 'll Do Next Summer ” , which garnered 2 @.@ 10 million <unk> . 
- 
- = = = Reviews = = = 
- 
- Eric Goldman , writing for IGN , graded the episode a 9 @.@ 0 out of 10 , indicating that it was “ amazing ” . His very positive review focused on the ambiguous nature of the main plot and the subplots of Dick and Logan . He praised the presence of the majority of the main cast members , also stating that Dick ’ s reaction to dealing with his brother was realistic for him and in character . The reviewer called the conclusion to the case @-@ of @-@ the @-@ week a “ fun and satisfying one ” that highlighted the moral ambiguity of main characters , something “ that the show had been lacking for a while . ” He highlighted this ambiguity in both Weevil in Logan , elaborating that Logan attacking Piz showed an interesting side to his personality that had not been present all season . The reviewer also lauded the <unk> ending , stating , “ it was a reminder of how exciting and intense this show can be at its best . ” Television Without Pity did not grant the episode a rating but lauded the characterization of Weevil , stating , “ This is the most consistently written character on the show , bar none . ” 
- Kelly West of Cinema <unk> focused primarily on the series finale in her review but referred to this episode as containing a solid case @-@ of @-@ the @-@ week . “ Overall it was a good mystery @-@ of @-@ the @-@ week but seeing as it was the second to last episode , the only thing I cared about was that we finally got more Weevil ! ” Rowan Kaiser of The A.V. Club gave the episode a mixed review , stating that the case @-@ of @-@ the @-@ week was an <unk> of the overall themes of Veronica Mars . The reviewer enjoyed the potential for Logan and Dick ’ s character development , although he felt that it was <unk> by the knowledge that the series was about to end . “ As fun as this episode is , the lack of the fourth season stops me from fully enjoying it . [ … ] But the knowledge that this might be the last time we see them puts a <unk> on things . Still , better to go out with good episodes than bad . ” 
- 
- 
- = Temple Beth Israel ( Eugene , Oregon ) = 
- 
- Temple Beth Israel ( Hebrew : <unk> <unk> ) is a <unk> synagogue located at 1175 East 29th Avenue in Eugene , Oregon . Founded in the early 1930s as a Conservative congregation , Beth Israel was for many decades the only synagogue in Eugene . 
- The congregation initially worshipped in a converted house on West Eighth Street . It constructed its first building on Portland Street in 1952 , and occupied its current <unk> @-@ <unk> facilities in 2008 . 
- In the early 1990s conflict between feminist and traditional members led to the latter leaving Beth Israel , and forming the Orthodox Congregation <unk> Torah . Beth Israel came under attack from neo @-@ Nazi members of the <unk> twice , in 1994 and again in 2002 . In both cases the <unk> were caught and convicted . 
- Services were lay @-@ led for decades . Marcus Simmons was hired as the congregation 's first rabbi in 1959 , but left in 1961 . After a gap of two years , Louis <unk> became rabbi in 1963 , and served until his death in 1976 . He was followed by <unk> <unk> , who served from 1977 to 1994 , and <unk> in turn was succeeded by <unk> <unk> @-@ <unk> . Maurice Harris joined <unk> @-@ <unk> as associate rabbi in 2003 , and served until 2011 , when he was succeeded by Boris <unk> . As of 2014 , led by <unk> @-@ <unk> and <unk> , Beth Israel had approximately 400 member households , and was the largest synagogue in Eugene . 
- 
- = = Early history = = 
- 
- Small numbers of German Jews began settling in Eugene in the late 19th century , but most moved on . In the early 20th century the first Eastern European Jews settled there , and by the 1920s Eugene 's Jewish community began gathering prayer <unk> for holding Friday night and Jewish holiday services in individuals ' homes . Historian Steven Lowenstein writes that " [ a ] <unk> <unk> <unk> 's death in 1933 , his home at 231 West Eighth Street was remodeled and named Temple Beth Israel " . It was a traditional Conservative synagogue , and from that time until the 1990s it was the only synagogue in Eugene . 
- In 1952 , the congregation constructed a one @-@ story synagogue building on an almost 1 acre ( 0 @.@ 40 ha ) property at <unk> Portland Street . Designed by architect and Holocaust @-@ survivor Heinrich <unk> ( <unk> ) <unk> , the building featured an interior courtyard that provided natural lighting , and " a network of ceiling beams painted with symbols and shapes " by <unk> . 
- Temple Beth Israel 's services and religious functions were lay @-@ led for decades . Its first rabbi was Marcus Simmons . Originally from England , he was a graduate of University of London and Oxford University , and was ordained at the Hebrew <unk> <unk> . He emigrated to the United States in 1957 , and joined Beth Israel in 1959 . The members were not , however , agreed that a full @-@ time rabbi was required , and in 1961 , he accepted a <unk> position in <unk> , California . 
- Following a hiatus of two years , Louis <unk> was hired as rabbi in 1963 . Born in New York City in 1912 to immigrant parents , he was a graduate of City University of New York and was ordained at the Jewish Institute of New York . He had previously worked for the United Jewish Appeal , and from 1959 to 1963 was the first <unk> rabbi at Syracuse University . There was some concern about <unk> 's hiring , as he had a police record as a result of his involvement in freedom marches in the African @-@ American Civil Rights Movement ( 1955 – 1968 ) . He served until his death in 1976 . 
- 
- = = <unk> era = = 
- 
- <unk> <unk> was hired as rabbi in 1977 . <unk> in Reform Judaism , he had previously served as a rabbi in <unk> , Kansas for two years , then lived in Israel for two years , before coming to Eugene . <unk> was known for his support for minority rights and gay rights , anti @-@ nuclear and anti @-@ war activism , support of reconciliation between Israel and the Palestinians , and outreach to non @-@ <unk> members of Eugene 's Jewish community . 
- <unk> attempted to revive the Biblical concept of the " <unk> <unk> " in his approach to <unk> . He was willing to <unk> at an <unk> if the non @-@ Jewish partner , after discussions with the rabbi , agreed of his or her own free will to fulfill a set of commitments , including " a commitment to a Jewish home life , participation in Jewish life and tradition , and raising future children as Jews " . The non @-@ Jewish partner making this commitment became a " <unk> <unk> " , or " non @-@ Jewish member of the Jewish people " . 
- <unk> 's wife Alice was a strong feminist , and during the 1980s he and his wife supported a number of changes to the <unk> and ritual . These included allowing women to read from the Torah and lead the prayers , and changing prayers to be more gender inclusive - for example , using gender @-@ neutral terms and <unk> for God , and adding references to the Biblical <unk> in prayers like the <unk> , which traditionally only mentioned the Biblical <unk> . While most congregation members approved of these changes , a minority resisted them . 
- 
- = = = <unk> = = = 
- 
- By the early 1990s serious divisions developed among the members of the congregation over a number of issues , including personal <unk> , the rabbi 's activism and " advocacy of ' ultra @-@ liberal ' causes " , political differences over the Israeli – Palestinian conflict , and 
- a myriad of additional Jewish cultural / religious issues , such as the acceptance of <unk> couples , adherence to <unk> dietary laws , the use of modern language and music during worship services , <unk> of certain prayers such as the <unk> to make them less <unk> , and so on . 
- However , the biggest source of division , which <unk> all others , was " the roles and rights of men and women in the synagogue . " 
- In the early 1990s a group of newly <unk> members began holding more traditional services in a back room of the synagogue , complete with a <unk> , a partition separating men and women . The " more feminist @-@ minded " members strongly objected to having a <unk> anywhere in the Temple Beth Israel building , even if it were not in the services they attended . The latter group eventually circulated a petition which stated that either the <unk> would have to be taken down , or those members who wanted it would have to leave . <unk> also signed the petition . Faced with this opposition , in 1992 the Orthodox members left , renting new premises and hiring their own rabbi , creating Eugene 's second synagogue , originally called " The <unk> <unk> " , and in 1998 renamed " Congregation <unk> Torah " . 
- <unk> held himself responsible , and the schism led to his " <unk> of the needs of Temple Beth Israel and his role as a rabbi " . As a result , he left Beth Israel in 1994 to lead a synagogue on Long Island . During his tenure at Beth Israel , membership rose from 118 to 350 families . <unk> died two years later at age 51 . 
- 
- = = <unk> @-@ <unk> era = = 
- 
- <unk> <unk> @-@ <unk> succeeded <unk> in 1995 . <unk> @-@ <unk> began his involvement at Temple Beth Israel first as a <unk> , then as cantor , and then as an assistant rabbi . He was active in forming the Jewish <unk> movement , and was ordained by its leader <unk> <unk> @-@ <unk> . 
- The congregation decided to leave the Conservative movement in 1995 , and for a year had no affiliation . In late 1996 , after considering both Reform and <unk> as <unk> , the congregation affiliated with the <unk> movement . By 1999 , membership had grown to around 370 families . 
- <unk> @-@ <unk> was instrumental in developing the concept of " <unk> <unk> " , the idea that one should only purchase goods that are produced in an ethical way . His essay , " <unk> <unk> , " was selected for publication in Arthur <unk> 's Best Jewish Writing 2003 . A singer , cello and guitar player , he <unk> and performs Jewish music . 
- <unk> @-@ <unk> has had four assistant or associate <unk> working with him . <unk> <unk> joined Temple Beth Israel in 1998 as interim rabbi when <unk> @-@ <unk> went on a <unk> ; Jonathan <unk> was assistant rabbi from 2001 to 2003 . Maurice Harris , a 2003 graduate of the <unk> <unk> College , joined as assistant rabbi in 2003 . He is one of the <unk> of The Open <unk> Concerning Religion and Science From American <unk> , part of the <unk> <unk> Project which " encourages and <unk> the teaching of evolution in schools " . In 2011 , Boris <unk> joined the congregation as its newest associate rabbi . 
- 
- = = = Attacks by neo @-@ Nazis = = = 
- 
- On March 20 , 1994 , Chris Lord , an individual associated with the <unk> and American Front , fired ten rounds with an assault rifle into the temple , damaging the interior . The attacks were prompted by a newspaper article about several members of Eugene 's Jewish community , including a lesbian . Community organizations , including a local gay rights group , responded by standing vigil outside the synagogue during <unk> services . Lord and an associate were caught and convicted , and Lord was sentenced to four and a half years in prison . 
- On October 25 , 2002 Jacob <unk> , his brother Gabriel <unk> , Gerald <unk> , Jesse Baker , and one other man , all members of the <unk> , drove to Beth Israel with the intent of intimidating the <unk> . While a service with 80 members attending was taking place , the men threw rocks etched with Nazi <unk> through the synagogue 's stained glass windows , then <unk> off . The men were caught , pleaded guilty , and were convicted . They served sentences ranging from a 6 @-@ month work release term and five years <unk> , to eleven years and three months in federal prison for the <unk> , Jacob <unk> . 
- 
- = = = East 29th Avenue building = = = 
- 
- Originally sized for 75 families , Temple Beth Israel 's Portland Street building had been renovated and enlarged over the years to 7 @,@ 500 square feet ( 700 m2 ) to accommodate 250 families and 150 students . Despite these additions and the loss of members to Congregation <unk> Torah , the synagogue was not large enough , particularly during the High <unk> , when extra space had to be rented . In 1997 the congregation purchased the property of the University Street Christian Church for $ 500 @,@ 000 ( today $ 740 @,@ 000 ) , and began planning for a new facility . The members considered renovating the existing building on the property , but felt a new building would better suit their requirements , and razed the church . 
- In 2003 the congregation got a permit to begin construction of a new facility on the now @-@ vacant 1 @.@ 37 @-@ acre ( 0 @.@ 55 ha ) plot of land at the northwest corner of East 29th Avenue and University Street . An initial capital campaign raised more than $ 1 @.@ 8 million , which fully paid for the land , and by August 2007 an additional $ 1 @.@ 7 million had been raised towards anticipated overall project costs of $ 5 million . 
- The environmentally sensitive building was designed by <unk> Solomon and Associates of Kansas City and local company <unk> Architects & <unk> , and built by <unk> Commercial Construction of Eugene . The building used " energy efficient heating , ventilation and lighting " : specific design issues with the building 's energy efficiency included the fact that the largest room in the building , the sanctuary , was also the least @-@ used , and , in accord with Jewish tradition , had to face east ( towards Jerusalem ) . 
- On June 8 , 2008 the congregation dedicated its new building at 1175 East 29th Avenue . At approximately 25 @,@ 000 square feet ( 2 @,@ 300 m2 ) , the facility included a sanctuary , commercial kitchen , banquet facilities , and classrooms , and housed the synagogue , the Lane County Jewish Federation , and the local Jewish Family Service . The project ended up costing $ 6 million , of which $ 4 million had been raised . 
- Made of concrete , steel , and wood , the building achieved <unk> in Energy and Environmental Design compliance " through the integration of <unk> management strategies , high efficiency irrigation , the use of recycled and / or <unk> materials , and drought tolerant <unk> . " <unk> <unk> materials used in the structure included <unk> and wood beams . 
- 
- = = Recent events = = 
- 
- In 2008 , Temple Beth Israel participated in <unk> Across America , an " <unk> witness against torture coordinated by the National Religious Campaign Against <unk> , " as part of the Jewish Campaign Against <unk> . <unk> by <unk> for Human Rights — North America in honor of <unk> Awareness Month , the Jewish campaign included over 25 <unk> which hung <unk> protesting " the use of abusive <unk> techniques by the American military and intelligence community " . That year , <unk> membership reached almost 400 families , and the Talmud Torah and pre @-@ school had about 200 and 40 students respectively . 
- The congregation sold the old synagogue building on Portland Street to Security First ( Portland Street ) Child Development Center for $ 815 @,@ 000 in 2009 , carrying the Center 's financing . The building was converted for use as an educational center , while retaining some of the original architectural elements . <unk> economic conditions forced the Child Development Center to give up the building in 2011 , and Eugene 's Network Charter School planned to move into it in autumn 2011 . 
- Harris announced he would be stepping down as rabbi in 2011 , and the synagogue hired Boris <unk> as his successor . Born and raised in Oregon , <unk> had worked at Temple Beth Israel as a teacher and youth group adviser from 1999 to 2001 . A graduate of the University of Oregon , with a master 's degree in Jewish Education from the Jewish <unk> <unk> , he was ordained by the <unk> <unk> College . 
- As of 2011 , Temple Beth Israel was the largest synagogue in Eugene . It was a member of the Community of <unk> <unk> , " an Oregon and <unk> Washington <unk> ministry and advocacy organization working toward full inclusion and equality for <unk> , lesbian , <unk> , gay and questioning persons . " The <unk> were <unk> <unk> @-@ <unk> and Boris <unk> . 
- 
- 
- = New York State Route 93 = 
- 
- New York State Route 93 ( NY 93 ) is a 43 @.@ 08 @-@ mile ( 69 @.@ 33 km ) state highway in western New York in the United States . The route begins at an intersection with NY <unk> in the village of <unk> and runs in a general northwest – southeast direction across Niagara and Erie counties to its east end at an intersection with NY 5 in the town of Newstead , just south of the village of <unk> . NY 93 serves as a connector between several major <unk> , including NY 104 in <unk> , NY 31 just west of the city of <unk> , and NY 78 south of the city . 
- The route was assigned as part of the 1930 renumbering of state highways in New York . Although it began in <unk> and ended in Newstead as it does today , the initial routing of NY 93 <unk> from the modern path in the vicinity of the city of <unk> . From <unk> to <unk> 's eastern suburbs , the highway originally used NY 425 , Lower Mountain Road , <unk> Road , and a series of streets in <unk> . NY 93 was moved onto NY 104 and Junction Road in <unk> in the 1940s , and altered to bypass <unk> to the south on a new highway and Robinson and <unk> roads in 1991 . In 2006 , NY 93 was realigned west of <unk> to continue south on Junction Road to NY 31 . The change removed NY 93 from Upper Mountain Road , a county @-@ owned highway that had been part of the route since the 1930s . 
- 
- = = Route description = = 
- 
- 
- = = = West of <unk> = = = 
- 
- NY 93 begins at an intersection with NY <unk> ( Main Street ; co @-@ designated but not signed as County Route 907 or CR 907 ) in the center of the village of <unk> . The route proceeds eastward through the village as a two @-@ lane road named <unk> Street , serving two blocks of commercial areas before <unk> to the northeast and passing into the residential eastern portion of <unk> . At the eastern village limits , NY 93 briefly <unk> to four lanes as it enters a partial cloverleaf interchange with the Niagara Scenic Parkway . Past the junction , the highway <unk> to a two @-@ lane road and changes names to <unk> – <unk> Road as it runs across the town of Porter . The residential surroundings continue to the hamlet of Towers Corners , where NY 93 connects to NY 18 ( Creek Road ) . 
- After NY 18 , NY 93 curves to the southeast , serving another residential stretch ahead of a junction with <unk> – Wilson Road ( CR 36 ) on the eastern edge of Towers Corners . After this intersection , the homes give way to farms as the road heads into rural areas of the town . The route continues on a southeast track through Porter , passing a mixture of rural and residential areas on its way into the hamlet of Porter Center , where NY 93 enters an intersection with Porter Center Road ( CR 57 ) . Another southeastward stretch brings the route across <unk> Creek and into the hamlet of <unk> , where NY 93 becomes the community 's main street . Through <unk> , NY 93 retains the <unk> – <unk> Road name , intersecting with <unk> Road ( CR 17 ) in the hamlet 's business district . 
- Just outside <unk> , NY 93 leaves the town of Porter for the town of Wilson . It continues generally southeastward across mostly open terrain , meeting Randall Road ( CR 83 ) and Church Street ( CR 56 ) on its way to the town of <unk> . NY 93 becomes North Ridge Road at the town line , and it soon enters the hamlet of North Ridge , a community built up around the route 's intersection with NY 425 ( <unk> – Wilson Road ) . The hamlet 's residential surroundings continue to the adjacent community of <unk> Corners , where NY 93 becomes concurrent with NY 104 ( Ridge Road ) . NY 93 and NY 104 proceed northeast across lightly populated areas for 2 miles ( 3 @.@ 2 km ) to the hamlet of Warren Corners , at which point NY 93 splits from NY 104 and heads southward along Town Line Road . It immediately intersects with Stone Road ( CR 19 ) before leaving the hamlet . 
- 
- = = = <unk> area = = = 
- 
- Outside of Warren Corners , the route heads across rural areas along the <unk> – <unk> town line . It soon enters the small hamlet of <unk> Corners , where the road passes under Lower Mountain Road ( CR <unk> ) . <unk> to the highway is made by way of Town Line Road Spur ( CR 114 ) , a connector leading to Lower Mountain Road . NY 93 continues southward along the town line , changing names to Junction Road at an intersection with Upper Mountain Road ( CR 5 ) west of the city of <unk> . From here , the route crosses over <unk> Transportation 's <unk> <unk> rail line at the hamlet of <unk> Junction before intersecting with NY 31 ( Saunders Settlement Road ) and NY 270 ( Campbell Boulevard ) just south of the community . NY 270 begins straight ahead to the south while NY 93 turns northeast onto Saunders Settlement Road , beginning a concurrency with NY 31 . 
- Now fully in the town of <unk> , NY 31 and NY 93 proceed northeast through an open area of the town as a four @-@ lane divided highway . The two routes continue to the western edge of the city of <unk> , where they intersect with Upper Mountain Road and the <unk> Bypass . The overlap ends here as NY 93 turns southeastward onto the two @-@ lane bypass . Along the bypass , NY 93 briefly enters the city limits as it runs past several industrial facilities and intersects with <unk> Road ( CR 903 ) just ahead of a bridge over the Erie Canal . Past the waterway , the bypass takes a more <unk> course through an undeveloped part of the town of <unk> to a junction with Robinson Road ( CR 123 ) on the <unk> – <unk> town line . The <unk> Bypass ends here , leaving NY 93 to turn eastward onto Robinson Road . 
- The route initially serves a line of homes as it heads along Robinson Road ; however , it soon enters a commercial district surrounding the road 's intersection with NY 78 ( Transit Road ) . At this point , the <unk> – <unk> town line turns south to follow NY 78 , leaving NY 93 fully within the town of <unk> as it runs eastward past another stretch of homes . Not far from NY 78 , NY 93 changes names to <unk> Road at an intersection with Beattie Avenue ( CR 14 ) and Raymond Road ( CR 85 ) . The junction also marks a shift in the road 's surroundings as the homes give way to open , rolling terrain . NY 93 continues eastward for several miles to the town of <unk> , where it meets <unk> Road ( CR 35 ) and <unk> Road ( CR 142 ) at adjacent intersections just east of the town line . 
- 
- = = = East of <unk> = = = 
- 
- NY 93 takes over <unk> Road 's name and right @-@ of @-@ way , continuing eastward past a line of scattered homes to reach the sparsely developed hamlet of <unk> . Here , the route turns southward at a junction with Bunker Hill Road ( CR 136 ) . Outside of <unk> , NY 93 heads southeastward across undeveloped areas of <unk> , connecting to Block Church Road ( CR 110 ) as it approaches <unk> Creek and the Niagara – Erie county line . The road runs along the northern edge of the creek for about 1 @.@ 5 miles ( 2 @.@ 4 km ) prior to curving southward at an intersection with <unk> Road ( CR 122 ) . The turn brings NY 93 across <unk> Creek and into the Erie County town of Newstead , where it becomes known as Maple Road and immediately intersects with CR 260 ( <unk> Road ) . 
- Continuing southward , NY 93 runs across open , rolling terrain , meeting CR 259 ( <unk> Creek Road ) on its way to the hamlet of <unk> Mills . Here , the rural surroundings briefly give way to residential areas as NY 93 intersects with CR 255 ( Swift Mills Road ) in the center of the community . South of <unk> Mills , the road serves only intermittent stretches of homes for 2 miles ( 3 @.@ 2 km ) , including a cluster of residences around its closely spaced intersections with CR 253 ( <unk> Road ) and CR 42 ( Rapids Road ) . It continues on a southward track past the eastern terminus of CR 218 ( <unk> Corner – <unk> Road ) to the outskirts of the village of <unk> , where the highway turns east onto Lewis Road and soon enters the village limits . NY 93 runs past a line of homes before intersecting Cedar Street , a road maintained by Erie County as CR 261 north of the village . 
- The route turns south at Cedar Street , following the residential street into downtown <unk> . Here , NY 93 intersects with CR <unk> ( John Street ) at a junction that was once the western terminus of NY 267 . At this intersection , NY 93 heads west on John Street for one block before continuing south on Buffalo Street for another block to Main Street . NY 93 turns westward again , following Main Street through the westernmost part of <unk> 's central business district prior to curving southwestward at a junction with <unk> Street . The highway takes on the <unk> Street name as it crosses over Murder Creek and leaves downtown <unk> . Just south of the creek , NY 93 changes names to <unk> Street at an intersection with Jackson Street . 
- As the route continues southward through the southern part of <unk> , it serves mostly residential areas , save for an industrial complex at NY 93 's intersection with CR 163 ( Clarence Center Road ) and CR 167 ( <unk> Drive ) . NY 93 exits <unk> a short distance south of the junction , at which point the route heads into another area of open fields while retaining the <unk> Street name . It continues on a southward track for about 1 mile ( 1 @.@ 6 km ) to a <unk> intersection with NY 5 ( Main Road ) , where <unk> Street and NY 93 both come to an end . 
- 
- = = History = = 
- 
- 
- = = = <unk> and early changes = = = 
- 
- NY 93 was established as part of the 1930 renumbering of state highways in New York , connecting the cities and villages of <unk> , <unk> , and <unk> . While the <unk> of NY 93 have remained the same to this day , several portions of the route have been realigned since that time . When NY 93 was first assigned , it turned south at the hamlet of North Ridge and overlapped with NY 425 along <unk> – Wilson Road to Lower Mountain Road , then part of NY 3 . NY 425 went west from this junction while NY 93 headed eastward , following NY 3 along Lower Mountain , Gothic Hill , Upper Mountain , and Saunders Settlement roads to the city of <unk> . At <unk> Street , NY 93 left NY 3 and exited the city along <unk> , High , and <unk> streets and <unk> Road . It met its current alignment southeast of the city in <unk> . 
- NY 3 was realigned c . 1932 to follow Saunders Settlement Road between <unk> Road ( NY 425 ) and Upper Mountain Road . The former routing of NY 3 along <unk> , Lower Mountain , Gothic Hill , and Upper Mountain roads was redesignated as NY <unk> even though all of NY 3 's former routing was already part of either NY 425 or NY 93 . The NY <unk> designation was eliminated c . 1935 when NY 3 was truncated eastward to a new western terminus in central New York . In the early 1940s , NY 93 was altered to follow North Ridge Road , U.S. Route 104 ( now NY 104 ) , and Junction Road between North Ridge and Lower Mountain Road . 
- Around the same time that NY 93 was rerouted , NY 270 was also extended northward along Junction Road from NY 31 to US 104 . As a result , NY 93 overlapped NY 270 between Lower Mountain Road and US 104 . The overlap with NY 270 remained in place until c . 1963 when NY 270 was truncated southward to the intersection of Lower Mountain and Junction roads . NY 93 was realigned in the late 1970s to bypass Lower Mountain and Gothic Hill Roads on Junction and Upper Mountain roads , replacing NY 270 along Junction Road . The Lower Mountain Road portion of NY 93 's former routing is now maintained by Niagara County as County Route <unk> ( CR <unk> ) . 
- 
- = = = <unk> <unk> = = = 
- 
- The <unk> Bypass , a highway bypassing downtown <unk> to the southwest , was opened to traffic on July 26 , 1991 . The highway cost $ 7 @.@ 7 million ( equivalent to $ 13 @.@ 4 million in 2016 ) to construct and extended from the junction of NY 31 and NY 93 west of the city to Robinson Road south of downtown . NY 93 was realigned to follow the new bypass south to Robinson Road , where it turned east and followed Robinson Road ( CR 123 ) and <unk> Road ( CR 133 ) to <unk> Road in <unk> . The portion of <unk> Road ( NY 93 's former routing ) east of the <unk> city limits became NY <unk> , an unsigned reference route . 
- <unk> and maintenance of Robinson Road from the bypass to NY 78 was transferred from Niagara County to the state of New York on September 1 , 1990 , as part of a highway maintenance swap between the two levels of government . The portion of NY 93 between NY 78 and <unk> Road became state @-@ maintained on October 1 , 1998 , as part of another swap that also transferred ownership and maintenance of <unk> Road to Niagara County . <unk> Road is now CR 142 . 
- On November 1 , 2005 , the Niagara County Legislature voted on a measure to allow the county to ask the New York State Department of Transportation ( <unk> ) to remove the NY 93 designation from Upper Mountain Road , a county @-@ maintained highway , and <unk> it to Junction Road ( NY 270 ) and Saunders Settlement Road ( NY 31 ) . The <unk> for the change came from a resident of Upper Mountain Road , who demanded that trucks should be removed from the roadway . This part of the agenda was passed . <unk> obliged to the request in 2006 , <unk> NY 93 as proposed and <unk> NY 270 southward to NY 31 . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Operation USA = 
- 
- Operation USA ( <unk> , Operation California , or <unk> ) is a non profit humanitarian organization dedicated to helping communities alleviate the effects of disaster , disease , and endemic poverty throughout the world by providing privately funded relief , reconstruction , humanitarian aid and development aid . It is exclusively privately funded , receiving no assistance from the United States Federal Government . <unk> had a revenue of over $ 22 million in fiscal year 2012 and has shipped over $ 425 million worth of " high @-@ priority medical , nutritional and shelter supplies " since its inception , including shipments to Haiti , Japan , Chile , Kenya and Pakistan in 2011 and 2011 . 
- 
- = = Awards and affiliations = = 
- 
- Operation USA was part of the International Campaign to Ban <unk> in 1997 when it won the Nobel Peace Prize . Operation California was also the winner of the 1983 President 's Volunteer Action Award . Operation USA has been named one of America 's Best 100 <unk> by Worth Magazine and , in October 2008 , was named the top @-@ rated " exclusively privately funded charity in the U.S. " by Charity <unk> . Operation USA collaborated with NASA 's Jet <unk> Laboratory and the US National Laboratories at Lawrence <unk> and Los Alamos to develop new approaches to land mine detection , is a member of <unk> , and is an <unk> news partner . In 2014 Operation USA 's CEO Richard M. <unk> received the <unk> <unk> Hero Award from the <unk> <unk> 
- 
- = = History = = 
- 
- Operation California began in 1979 as " a relief organization created to provide aid to Vietnamese Boat People and Cambodian refugees " , founded by Richard <unk> ( still active as President & CEO ) and <unk> Werner ( who left in early 1980 ) . The organization flew " the first international relief <unk> to Cambodia since 1975 " , delivering medicine to Phnom @-@ Penh . Operation California had <unk> more than $ 3 million worth of aid by October 1979 . 
- Since then , Operation USA has become a highly acclaimed aid organization that is involved in helping people in different ways around the world . In 1982 , Operation California sent " the first private <unk> from the U.S. to Poland " , delivering 200 @,@ 000 <unk> of medical supplies and medicine ; that year Operation California also <unk> medical supplies to Lebanon . In 1983 , Operation California delivered aid to the children of Vietnam and Cambodia . Operation California provided aid to the earthquake victims in Mexico City in 1985 , as well as working in cooperation with the <unk> <unk> Service Committee and Oxfam America , to deliver $ 250 @,@ 000 worth of medical aid to Nicaragua . In 1986 Operation California , in conjunction with Medical Aid to El Salvador , sent " [ t ] <unk> cargo planes carrying $ 500 @,@ 000 worth of relief supplies to earthquake @-@ stricken El Salvador " . 
- In 1988 , Operation California began using the name Operation USA because it better described the effort and intent of the organization to represent the entire American people . In 1989 Operation USA facilitated operations on children in Vietnam who had <unk> <unk> by a Los Angeles @-@ based plastic surgeon , Dr Stanley <unk> . Medical aid effort was delivered to Mexico in 1990 , by <unk> in conjunction with USSR relief workers . In 1991 <unk> delivered aid to Bangladesh . <unk> delivered aid to war torn Somali 's in 1993 . In 1994 <unk> provided earthquake relief . In 1995 the organization provided aid to Hurricane Mitch survivors in Honduras and Nicaragua . In 1999 <unk> supplied aid to storm victims in Mexico . In 2003 <unk> delivered aid to Iraq War victims in the Persian Gulf . The tsunami victims in Sri Lanka and Indonesia were aided by <unk> in 2004 , as well as the Mexico City Flood victims . 
- In 2008 , <unk> has delivered aid to <unk> cyclone victims as well as Chinese earthquake victims and flood victims in the Midwest , USA . 
- In 2015 , <unk> partners with <unk> to raise fund for its project , which is to deliver recovery aid to Nepal Earthquake victims . 
- 
- = = Celebrity affiliates = = 
- 
- Operation USA , since the early 1980s , has relied on fundraising efforts featuring singers and celebrities . These include concerts , <unk> , and other events . These promotions have featured : 
- <unk> Streisand 
- Bonnie <unk> 
- Carol <unk> 
- Crosby , <unk> & Nash 
- Don <unk> 
- Ed <unk> 
- Frank Sinatra 
- Jack Elliot 
- Jackson Browne 
- James Garner 
- John Denver 
- Julie Andrews 
- Kirk Douglas 
- Michael Jackson 
- New American Orchestra 
- <unk> Domingo 
- Ricardo <unk> 
- <unk> <unk> 
- Sharon Stone 
- The Buena Vista Social Club 
- Tony Adams 
- <unk> Dawson travelled with Operation USA to Nicaragua in 2008 . George Hamilton assisted with relief to The Philippines Typhoon <unk> in 2013 as did <unk> Streisand , <unk> Dawson , Jackson Browne , Bill <unk> and <unk> <unk> . 
- 
- = = Film and theater projects = = 
- 
- Operation USA also relies on film and theater promotions to generate funds that pay for aid , including : 
- Because We Care ( CBS Television Special ) 
- Beyond Borders ( Hollywood ) 
- Buena Vista Social Club ( film ) ( Hollywood & Havana ) 
- <unk> ( film ) ( Hollywood for <unk> ) 
- Mary <unk> ( musical ) ( London stage ) 
- Miss Saigon ( Hollywood ) 
- Roll <unk> 
- The Killing Fields ( film ) ( Hollywood & Cambodia ) 
- Victor / Victoria ( Broadway ) 
- <unk> at The Plaza ( Disney TV ) 
- 
- 
- = Typhoon <unk> ( 2013 ) = 
- 
- Typhoon <unk> , known in the Philippines as Typhoon <unk> , was a typhoon that made landfall in the northern Philippines in late October 2013 . Forming on October 27 near Guam , the storm slowly intensified while moving westward . <unk> developed an eye and became a typhoon before striking Luzon on October 31 . The storm weakened over land , but re @-@ intensified over the South China Sea , reaching peak winds of 150 km / h ( 90 mph ) on November 2 off the southeast coast of China . Typhoon <unk> stalled and encountered unfavorable conditions , resulting in quick weakening . By November 3 , it had weakened to tropical storm status , and was no longer being warned on by the next day . In northern Luzon , <unk> damaged 32 @,@ 000 houses , including 3 @,@ 000 that were destroyed , and caused four fatalities . High winds and rainfall left <unk> million ( PHP , $ 6 @.@ 4 million USD ) in damage . 
- 
- = = Meteorological history = = 
- 
- On October 27 , an area of convection with a broad circulation persisted southeast of Guam , and slowly consolidated due to moderate wind shear and <unk> outflow . That day , the Japan Meteorological Agency ( JMA ) classified the system as a tropical depression about 380 km ( 235 mi ) to the southeast of <unk> , Guam . At 2100 UTC on October 28 , the Philippine <unk> , <unk> and Astronomical Services Administration ( PAGASA ) began issuing advisories on the depression , giving it the local name <unk> . The next day , the JMA upgraded the depression to Tropical Storm <unk> ( <unk> ) , and the Joint Typhoon Warning Center ( JTWC ) also classified it as Tropical Depression <unk> . By that time , the storm was moving steadily westward due to the subtropical ridge to the north . With the warm waters of the Philippine Sea , <unk> wind shear , and improving outflow , <unk> gradually strengthened , and the JTWC also upgraded <unk> to tropical storm status on October 30 after an eye feature developed . 
- While approaching northern Luzon on October 29 , <unk> quickly intensified as the initial eye feature organized into a well @-@ defined eye . Late on October 30 , the JTWC upgraded <unk> to typhoon status , and the next day , both PAGASA and JMA followed suit . On October 31 , <unk> made landfall in northeastern Luzon near Cagayan , and developed a <unk> eyewall while initially moving over land . Land interaction weakened the eye by the time <unk> emerged into the South China Sea late on October 31 . The next day , PAGASA discontinued advisories after the typhoon exited the region . <unk> rebuilt around the center , with continued favorable conditions allowing for <unk> . 
- Late on November 1 , a large eye redeveloped , and the JTWC estimated <unk> attained peak 1 minute sustained winds of 185 km / h ( 115 mph ) . Early the next day , the JMA also estimated the typhoon reached peak 10 minute winds of 140 km / h ( 85 mph ) . Later , increasing wind shear caused the eye to deteriorate , and <unk> began slowing about 260 km ( 160 mi ) east @-@ southeast of Hong Kong after reaching the western edge of the subtropical ridge . After remaining nearly stationary , <unk> began moving steadily to the west @-@ southwest due to a new ridge . The convection continued to weaken due to continued shear and cooler waters from <unk> , and <unk> deteriorated to tropical storm status on November 3 . The next day , the JTWC issued its final advisory after the circulation became exposed from the convection . Also on November 4 , the JMA downgraded <unk> to tropical depression status off the northeast Vietnam coast . The system dissipated at 0000 UTC on November 5 . 
- 
- = = Preparations and impact = = 
- 
- Before <unk> struck the Philippines , PAGASA issued a number 3 warning signal for portions of northern Luzon , where winds were expected to reach over 100 km / h ( 60 mph ) . The agency noted for the potential for flooding and landslides . High winds knocked down trees across Luzon , and left about 80 % of Cagayan province without power , as well as some areas without internet or <unk> service . <unk> of the Pan @-@ Philippine Highway were blocked , and in Lal @-@ Lo , Cagayan , a car crashed into a gasoline truck due to power outages . Agriculture damage was estimated at <unk> million ( PHP , $ 6 @.@ 3 million USD ) , occurring just before the start of the harvest . Across the island , the typhoon damaged 32 @,@ <unk> houses , including 3 @,@ <unk> that were destroyed , forcing 65 @,@ 648 people to evacuate to storm shelters . Overall , <unk> killed four people in the Philippines , and left <unk> million ( PHP , $ 6 @.@ 4 million USD ) in damage . After the storm , workers quickly restored power lines , while the government provided monetary assistance to storm @-@ ravaged families , after Cagayan was declared a state of <unk> . Members of the Philippine military and Department of Public Works and Highways worked to clean up following the storm . 
- The China National Meteorological Centre issued a " yellow alert " , the second @-@ lowest of the four level warning system , for <unk> due to the threat of the storm . The agency recommended boats to return to port . <unk> in Vietnam also warned for the potential of heavy rainfall due to the dissipating Tropical Depression <unk> , and released water from three dams to prevent overflow . 
- 
- 
- = <unk> 's Block Ball = 
- 
- <unk> 's Block Ball is a 1995 action video game , a spin @-@ off from the <unk> series for the Game Boy portable console . It is a <unk> clone ; the player controls <unk> along the screen 's edge to knock a bouncing ball , <unk> , into <unk> bricks . The game 's 55 levels include power @-@ ups , bonus rounds , and minigames . <unk> 's Block Ball was developed by HAL Laboratory and Nintendo R & D1 . The team spent half a year revising the gameplay to match <unk> 's signature characteristics . <unk> 's Block Ball was published by Nintendo first in Japan in 1995 , later in Europe , and last in North America in 1996 . 
- Reviewers considered the game an improvement on the <unk> formula and praised its gameplay craftsmanship and incorporation of the <unk> series . It was included in multiple top Game Boy game lists and was later <unk> on the Nintendo 3DS Virtual <unk> . 
- 
- = = Gameplay = = 
- 
- The player controls <unk> along the screen 's edges to knock a bouncing ball , <unk> , into <unk> bricks . The player loses a life if <unk> hits the edge of the screen . Each of the game 's eleven stages include five rounds of increasingly complex block patterns for <unk> to clear . The ten different block types vary in <unk> and points value . A well @-@ timed hit of the <unk> gives <unk> a powerful bounce to break through harder blocks . Another block type turns the remaining blocks into a bonus round that rewards the player for clearing the screen in the least amount of time . The player can find <unk> stars that lead to minigames , such as air hockey , where the player can earn extra lives . The rounds also include enemies to attack and avoid . Some enemies contain bonus items . Each stage ends in a boss fight . 
- With stone , needle , flame , and spark power @-@ ups , <unk> can transform to interact with blocks differently . For instance , the spark power @-@ up lets <unk> break through otherwise <unk> blocks , and the needle lets <unk> hit spikes once without losing a life . The game has a themed frame and uses a wide palette of colors in @-@ game when played with the Super Game Boy . 
- 
- = = Development = = 
- 
- The game was developed by HAL Laboratory with <unk> Yokoi 's Nintendo R & D1 , and published by Nintendo . At one point in development , HAL decided that the game did not feel like a <unk> game . The team spent six months completely revising the game under explicit instructions on how <unk> should move . <unk> games contain elements of unrestricted , creative movement as a general theme . <unk> 's Block Ball was released for the Game Boy first in Japan in 1995 and later in Europe ( 1995 ) and North America ( May 1996 ) . It was later <unk> on the Nintendo 3DS Virtual <unk> , and released first in Japan ( October 2011 ) and later in Europe ( February 2012 ) and North America ( May 2012 ) . 
- 
- = = Reception and legacy = = 
- 
- On release , the four reviewers of Electronic Gaming Monthly applauded <unk> 's Block Ball for <unk> the <unk> formula to create a new and enjoyable game . They especially praised the unique power @-@ ups , though <unk> Boyer and <unk> X also felt the game was too short and easy . Nintendo Power said they enjoyed Block Ball and its number of stages , but wondered how its eight <unk> of memory were being used . The magazine found the parts where <unk> eats the <unk> blocks to be innovative . All six of the magazine 's reviewers recommended the game . 
- IGN wrote that the game was primarily remembered as " an <unk> or <unk> clone skinned with the <unk> franchise " . IGN calculated an average reviewer score of 7 @.@ 4 / 10 . The <unk> series became known for its number of non @-@ platformer spin @-@ offs , of which Block Ball was one , like <unk> 's <unk> Land and <unk> 's Dream Course . <unk> 's spherical shape lent itself towards ball @-@ like roles . IGN wrote that Block Ball was the first " truly out there " <unk> spin @-@ off , but that the game was too short . 
- Planet Game Boy called it one of the original Game Boy 's ten " all @-@ time classics " and GamesRadar placed it among the top 25 Game Boy games released . They considered <unk> 's Block Ball an improvement upon <unk> , a Game Boy launch title and <unk> clone . IGN recommended the game upon its 3DS <unk> both in general and for <unk> fans . Nintendo World Report recommended the game to players who like score attack games and called it the best version of <unk> released . <unk> reviewers found the game enjoyable and praised the craft behind the gameplay and <unk> themes . Alternatively , <unk> 's Block Ball received the lowest rating on Tim Rogers 's 2004 " <unk> <unk> System for Portable Games " ( a metric by which he played a game while counting stops on the circular <unk> train line until he lost interest ) with a score of " one " stop . He called it " too <unk> bland " . 
- In a retrospective review , <unk> had high praise for the level design , graphics , and animations . They also found the music excellent in comparison to the annoying and repetitive soundtrack of most <unk> <unk> . The magazine also liked how the game fit the <unk> universe , apart from its increased difficulty — <unk> occasionally had trouble hitting the slow @-@ paced ball with precision . 
- 
- 
- = Hannah Dodd = 
- 
- Hannah Dodd ( born 27 April 1992 ) is an Australian Grade IV <unk> and 2 @.@ 0 point wheelchair basketball player who represented Australia in <unk> at the 2012 Summer Paralympics in London , coming 11th and 12th in her events . <unk> to wheelchair basketball , she made her debut with the national team at the Osaka Cup in February 2015 . 
- In 2008 , Dodd was the Australian national Grade IV para @-@ <unk> champion . She was runner @-@ up in 2009 , and won the Australian national championships again in 2011 , along with the Oceania Championships and the National Titles team events . By 2012 , she was the top @-@ ranked Australian competitor in her event and class . 
- After the London Paralympics , Dodd took up wheelchair basketball . She started playing for the Sydney University Flames in the Women 's National Wheelchair Basketball League in 2013 , made her debut with the national team at the Osaka Friendship Games in Osaka in February 2015 , winning bronze , and was part of the Under 25 team at the 2015 Women 's <unk> Wheelchair Basketball World Championship in Beijing in July 2015 , winning silver . 
- 
- = = Personal = = 
- 
- Hannah Dodd was born on 27 April 1992 , and is from <unk> , New South Wales . She has <unk> <unk> and <unk> <unk> with upper limb <unk> , and is missing four vertebrae in her back . When she was about a year old , her <unk> started failing . Her entire renal system needed to be reconstructed . She has two older brothers . She can walk with the aide of a <unk> , and also uses a wheelchair . As of 2012 , she is a horse riding teacher and student at the University of Western Sydney where she is <unk> in sports and exercise science . 
- 
- = = <unk> = = 
- 
- Dodd is a Grade IV <unk> competitor , coached by Peter Turner . Due to her <unk> <unk> , when she rides her horse , she <unk> several bones every time , but as a result of anti @-@ doping rules , she has had to find alternative ways of <unk> with pain associated with riding . 
- Dodd has been around horses since she was four months old , and was able to ride on her own by the time she was two years old , before she learned to walk . The sport gave her a degree of independence . She started competing in 2005 , and first represented Australia in 2006 , winning her first test in England that <unk> 2008 , she became the youngest @-@ ever winner of the Australian national championships . She finished first at the March 2009 inter @-@ schools cup at the St <unk> <unk> , and second at the 2009 Australian national championships , but her horse , Lucifer 's Dream , was injured in 2009 . In 2009 and 2010 , she searched for another horse to assist her in getting through Paralympic qualification . She won the Australian national championships again in 2011 , along with the Oceania Championships and the National Titles team events . By 2012 , she was the top @-@ ranked Australian competitor in her event and class . 
- Dodd was selected to represent Australia at the 2012 Summer Paralympics in London in <unk> events with her horse <unk> . These Games were her first , and she was the youngest Australian <unk> competitor . A fund <unk> was organised by <unk> , New South Wales , residents . While her own costs and the cost of her horse were covered by Australian Paralympic Committee and <unk> Australia , funds were required for her coach . She was placed 12th in the Individual Championship Test – Grade IV , and 11th in the Individual <unk> Test – Grade IV and Team Test – Grade IV . 
- 
- = = Wheelchair basketball = = 
- 
- After the London Paralympics , Dodd took up wheelchair basketball . She started for the Sydney University Flames in the Women 's National Wheelchair Basketball League in 2013 . She has to strap her fingers and wrists , and usually <unk> a shoulder during a game . " I 've had a few <unk> and <unk> and been tipped out of my chair a few times , " she <unk> , " but it 's really fun . The fast pace really gives you an <unk> kick and the girls I play with are awesome . " " If I have chose between my two sports for Rio , " she said , " I will go with basketball . " She made her debut with the national team , known as the Gliders , at the Osaka Cup in Osaka in February 2015 . The Gliders won bronze . In June 2015 , Dodd was selected as part of the under 25 team ( known as the Devils ) for the 2015 Women 's <unk> Wheelchair Basketball World Championship in Beijing in July . The Devils won silver . By this time her health had deteriorated . She had to use a wheelchair much of the time , and her classification had dropped to a 2 @.@ 5 point player . In 2015 , she was reclassified a 2 @.@ 0 . 
- 
- 
- = Commonwealth War Graves Commission = 
- 
- The Commonwealth War Graves Commission ( <unk> ) is an <unk> organisation of six independent member states whose principal function is to mark , record and maintain the graves and places of commemoration of Commonwealth of Nations military service members who died in the two World Wars . The Commission is also responsible for commemorating Commonwealth civilians who died as a result of enemy action during World War II . The Commission was founded by <unk> Ware and constituted through Royal Charter in 1917 named the Imperial War Graves Commission . The change to the present name took place in 1960 . 
- The Commission , as part of its mandate , is responsible for commemorating all Commonwealth war dead individually and equally . To this end , the war dead are commemorated by name on a <unk> , at an identified site of a burial , or on a memorial . War dead are commemorated uniformly and equally , irrespective of military or civil rank , race or <unk> . 
- The Commission is currently responsible for the continued commemoration of 1 @.@ 7 million deceased Commonwealth military service members in 153 countries . Since its inception , the Commission has constructed approximately 2 @,@ 500 war <unk> and numerous memorials . The Commission is currently responsible for the care of war dead at over 23 @,@ 000 separate burial sites and the maintenance of more than 200 memorials worldwide . In addition to commemorating Commonwealth military service members , the Commission maintains , under arrangement with applicable governments , over 40 @,@ 000 non @-@ Commonwealth war graves and over 25 @,@ 000 non @-@ war military and civilian graves . The Commission operates through the continued financial support of the member states : United Kingdom , Canada , Australia , New Zealand , India and South Africa . The current President of the Commonwealth War Graves Commission is Prince Edward , Duke of Kent . 
- 
- = = History = = 
- 
- 
- = = = World War I = = = 
- 
- On the outbreak of World War I in 1914 , <unk> Ware , a director of the Rio <unk> Company , found that at 45 years old he was too old to join the British Army . He used the influence of Rio <unk> chairman , Viscount <unk> , to become the commander of a mobile unit of the British Red Cross . He arrived in France in September 1914 and whilst there was struck by the lack of any official mechanism for documenting or marking the location of graves of those who had been killed and felt compelled to create an organisation within the Red Cross for this purpose . In March 1915 , with the support of <unk> <unk> , <unk> @-@ General of the British Expeditionary Force , Ware 's work was given official recognition and support by the Imperial War Office and the unit was transferred to the British Army as the Graves <unk> Commission . The new Graves <unk> Commission had over 31 @,@ 000 graves of British and Imperial soldiers registered by October 1915 and 50 @,@ 000 registered by May 1916 . 
- When municipal <unk> began to <unk> Ware began negotiations with various local authorities to acquire land for further <unk> . Ware began with an agreement with France to build joint British and French <unk> under the understanding that these would be maintained by the French government . Ware eventually concluded that it was not <unk> to leave the maintenance responsibilities solely to the French government and subsequently arranged for France to purchase the land , grant it in <unk> , and leave the management and maintenance responsibilities to the British . The French government agreed under the condition that <unk> respected certain dimensions , were accessible by public road , were in the vicinity of medical aid stations and were not too close to towns or villages . Similar negotiations were started with the Belgian government . 
- As reports of the grave registration work became public , the Commission began to receive letters of enquiry and requests for photographs of graves from relatives of deceased soldiers . By 1917 , 17 @,@ 000 photographs had been dispatched to relatives . In March 1915 , the Commission , with the support of the Red Cross , began to dispatch photographic prints and cemetery location information in answer to the requests . The Graves <unk> Commission became the Directorate of Graves <unk> and <unk> in the spring of 1916 in recognition of the fact that the scope of work began to extend beyond simple grave registration and began to include responding to <unk> from relatives of those killed . The <unk> 's work was also extended beyond the Western Front and into other theatres of war , with units deployed in Greece , Egypt and Mesopotamia . 
- 
- = = = Formal establishment = = = 
- 
- As the war continued , Ware and others became concerned about the fate of the graves in the post @-@ war period . Following a suggestion by the British Army , the National Committee for the Care of Soldiers ' Graves was appointed by the British government in January 1916 , with Edward , Prince of Wales agreeing to serve as president . The National Committee for the Care of Soldiers ' Graves was created with the intention of taking over the work of the Directorate of Graves <unk> and <unk> after the war . The government felt that it was more appropriate to <unk> the work to a specially appointed body rather than to any existing government department . By early 1917 a number of members of the committee believed a formal imperial organisation would be needed to care for the graves . With the help of Edward , Prince of Wales , Ware submitted a memorandum to the Imperial War Conference in 1917 suggesting that an imperial organisation be constituted . The suggestion was accepted and on 21 May 1917 the Imperial War Graves Commission was established by Royal Charter , with the Prince of Wales serving as president , Secretary of State for War Lord Derby as chairman and Ware as vice @-@ chairman . The Commission 's undertakings began in earnest at the end of the First World War . Once land for <unk> and memorials had been guaranteed , the enormous task of recording the details of the dead could begin . By 1918 , some <unk> @,@ 000 graves had been identified and a further <unk> @,@ 000 casualties were registered as having no known grave . 
- The scale , and associated high number of casualties , of the war produced an entirely new attitude towards the commemoration of war dead . Previous to World War I , individual commemoration of war dead was often on an ad hoc basis and was almost exclusively limited to commissioned officers . However , the war required <unk> of a significant percentage of the population , either as volunteers or through conscription . An expectation had consequently arisen that individual soldiers would expect to be commemorated , even if they were low @-@ ranking members of the military . A committee under <unk> Kenyon , Director of the British Museum , presented a report to the Commission in November 1918 detailing how it envisioned the development of the <unk> . Two key elements of this report were that bodies should not be repatriated and that uniform memorials should be used to avoid class distinctions . Beyond the logistical nightmare of returning home so many corpses , it was felt that repatriation would conflict with the feeling of <unk> that had developed between serving ranks . 
- An article in The Times on 17 February 1919 by <unk> Kipling carried the Commission 's proposal to a wider audience and described what the graves would look like . The article entitled War Graves : Work of Imperial Commission : Mr. Kipling 's Survey was quickly republished as an illustrated booklet , Graves of the Fallen . The illustrated booklet was intended to <unk> the impact of Kenyon 's report as it included illustrations of <unk> with mature trees and shrubs ; contrasting the bleak landscapes depicted in published battlefield photos . There was an immediate public <unk> following the publication of the reports , particularly with regards to the decision to not <unk> the bodies of the dead . The reports generated considerable discussion in the press which ultimately led to a heated debate in Parliament on 4 May 1920 . Sir James <unk> started the debate , followed by speeches by William <unk> @-@ <unk> in favour of the Commission 's principles and Robert Cecil speaking for those <unk> repatriation and opposing <unk> of grave markers . Winston Churchill closed the debate and asked that the issue not proceed to a vote . <unk> withdrew his motion , allowing the Commission to carry out its work assured of support for its principles . 
- 
- = = = First <unk> and memorials to the missing = = = 
- 
- Three of the most eminent architects of their day , Sir Herbert Baker , Sir Reginald <unk> , and Sir Edwin <unk> were commissioned to design the <unk> and memorials . <unk> Kipling was appointed literary advisor for the language used for memorial inscriptions . 
- In 1920 , the Commission built three experimental <unk> at Le <unk> , <unk> and <unk> , following the principles outlined in the Kenyon report . Of these , the <unk> Communal Cemetery and <unk> was agreed to be the most successful . Having consulted with garden designer Gertrude Jekyll , the architects created a walled cemetery with uniform <unk> in a garden setting , augmented by <unk> 's Cross of <unk> and <unk> ' Stone of Remembrance . After some adjustments , <unk> became the template for the Commission 's building programme . <unk> were required because all three experimental <unk> went over budget . To ensure future <unk> remained within their budget the Commission decided to not build shelters in <unk> that contained less than 200 graves , to not place a Stone of Remembrance in any cemetery with less than 400 graves , and to limit the height of cemetery walls to 1 metre ( 3 @.@ 3 ft ) . 
- At the end of 1919 , the Commission had spent £ 7 @,@ 500 , and this figure rose to £ 250 @,@ 000 in 1920 as construction of <unk> and memorials increased . By 1921 , the Commission had established 1 @,@ 000 <unk> which were ready for <unk> <unk> , and burials . Between 1920 and 1923 , the Commission was shipping 4 @,@ 000 <unk> a week to France . In many cases small <unk> were closed and the graves concentrated in larger ones . By 1927 , when the majority of construction had been completed , over 500 <unk> had been built , with 400 @,@ 000 <unk> , a thousand Crosses of <unk> , and 400 Stones of Remembrance . 
- The Commission had also been mandated to individually commemorate each soldier who had no known grave , which amounted to 315 @,@ 000 in France and Belgium alone . The Commission initially decided to build 12 monuments on which to commemorate the missing ; each memorial being located at the site of an important battle along the Western Front . After resistance from the French committee responsible for the <unk> of memorials on French territory , the Commission revised their plan and reduced the number of memorials , and in some cases built memorials to the missing in existing <unk> rather than as separate structures . 
- Reginald <unk> 's <unk> Gate was the first memorial to the missing located in Europe to be completed , and was unveiled on 24 July 1927 . The <unk> Gate ( <unk> ) was found to have insufficient space to contain all the names as originally planned and 34 @,@ <unk> names of the missing were instead inscribed on Herbert Baker 's <unk> <unk> Memorial to the Missing . Other memorials followed : the Helles Memorial in Gallipoli designed by John James <unk> ; the <unk> Memorial on the Somme and the <unk> Memorial designed by Edwin <unk> ; and the Basra Memorial in Iraq designed by Edward <unk> Warren . The <unk> and India also erected memorials on which they commemorated their missing : the <unk> @-@ <unk> Memorial for the forces of India , the <unk> Memorial by Canada , the <unk> @-@ <unk> Memorial by Australia , the <unk> Wood Memorial by South Africa and the Beaumont @-@ Hamel Memorial by Newfoundland . The programme of commemorating the dead of the Great War was considered essentially complete with the inauguration of the <unk> Memorial in 1932 , though the <unk> Memorial would not be finished until 1936 , the <unk> @-@ <unk> Memorial until 1938 and <unk> were still conducting work on the <unk> Gate when Germany invaded Belgium in 1940 . 
- The only memorial created by the Commission that was not in the form of a monument or cemetery was the <unk> Institute at <unk> , Egypt — complete with library , and <unk> and pathology departments — as its memorial to men of the Egyptian Labour Corps and Camel Transport Corps . Its erection was agreed with local political pressure . 
- 
- = = = World War II = = = 
- 
- From the start of the Second World War in 1939 , the Commission organised grave registration units and , planning ahead based on the experience gained from the First World War , earmarked land for use as <unk> . When the war began turning in favour of the Allies , the Commission was able to begin restoring its First World War <unk> and memorials . It also began the task of commemorating the 600 @,@ 000 Commonwealth casualties from the Second World War . In 1949 , the Commission completed <unk> Canadian War Cemetery , the first of <unk> new <unk> and 36 new memorials . Eventually , over 350 @,@ 000 new <unk> were erected . Many were made from <unk> Wood stone . The wider scale of World War II , coupled with manpower shortages and unrest in some countries , meant that the construction and restoration programmes took much longer . Following the war , the Commission implemented a five @-@ year <unk> renovation programme . The <unk> neglect was largely addressed by 1950 but there were necessary structural repairs to be made . These , together with the <unk> of maintenance tasks from before the war , took a further 10 years to complete and the programme was not completed until the 1960s . 
- With the increased number of civilian casualties compared with the World War I , Winston Churchill agreed to Ware 's proposal that the Commission also maintain a record of Commonwealth civilian war deaths . A supplemental chapter was added to the Imperial War Graves Commission 's charter on 7 February 1941 , <unk> the organisation to collect and record the names of civilians who died from enemy action during the Second World War , which resulted in the creation of the Civilian War Dead Roll of Honour . The roll eventually contained the names of nearly 67 @,@ 000 civilians . The Commission and the Dean of Westminster reached an agreement that the roll would eventually be placed in Westminster Abbey but not until the roll was complete and hostilities had ended . The Commission handed over the first six volumes to the Dean of Westminster on 21 February 1956 ; the final volume was added to the showcase in 1958 . 
- 
- = = = Post – World War II = = = 
- 
- Following World War II the Commission recognised that the word ' Imperial ' within its name was no longer appropriate . In the spirit of strengthening national and regional feelings the organisation 's name was changed to Commonwealth War Graves Commission in 1960 . 
- More recent conflicts have sometimes made it impossible for the Commission to care for <unk> in a given region or resulted in the destruction of sites altogether . <unk> Indian Cemetery in Germany was <unk> after the end of World War II and until the German <unk> because it was located in an area occupied by Russian forces and was not entirely rebuilt until 2005 . The Six @-@ Day War and War of <unk> resulted in the destruction of Port <unk> Memorial and Aden Memorial , and the death of a Commission <unk> at Suez War Memorial Cemetery . During the Lebanese Civil War two <unk> in Beirut were destroyed and had to be rebuilt . The maintenance of war graves and memorials in Iraq has remained difficult since Iran – Iraq War in the 1980s , with regular maintenance being impractical since after the Gulf War . 
- The Commission has , and continues to , also provide support for war graves outside its traditional mandate . In 1982 , the British Ministry of Defence requested the Commission 's assistance to design and construct <unk> in the Falkland Islands for those killed during the <unk> War . Although these <unk> are not Commonwealth War Graves Commission <unk> , the Commission manages the administrative responsibilities of these <unk> . Since 2005 , the Commission has carried out similar management duties on behalf of the British Ministry of Defence for <unk> and graves of British and Imperial soldiers who died during the Second Boer War . In 2003 , Veterans Affairs Canada employed the Commission to develop an approach to locate grave markers for which the Canadian Minister of Veterans Affairs has responsibility . As of 2011 , the Commission conducts a twelve @-@ year cyclical inspection programme of Canadian veterans ' markers installed at the expense of the Government of Canada . 
- In 2008 , an <unk> excavation discovered mass graves on the edge of <unk> Wood outside of <unk> . Two @-@ hundred and fifty British and Australian bodies were excavated from five mass graves which were interred in the newly constructed <unk> ( <unk> Wood ) Military Cemetery . This was the first new Commonwealth War Graves Commission cemetery in more than 50 years , the last such <unk> having been built after the Second World War . 
- 
- = = <unk> sites and memorials = = 
- 
- The Commission is currently responsible for the continued commemoration of 1 @.@ 7 million deceased Commonwealth military service members in 153 countries and approximately 67 @,@ 000 civilians who died as a result of enemy action during World War II . Commonwealth military service members are commemorated by name on either a <unk> , at an identified site of a burial , or on a memorial . As a result , the Commission is currently responsible for the care of war dead at over 23 @,@ 000 separate burial sites and maintenance of more than 200 memorials worldwide . The vast majority of burial sites are pre @-@ existing communal or municipal <unk> and parish <unk> located in the United Kingdom , however the Commission has itself constructed approximately 2 @,@ 500 war <unk> worldwide . The Commission has also constructed or commissioned memorials to commemorate the dead who have no known grave ; the largest of these is the <unk> Memorial . 
- 
- = = = <unk> for inclusion = = = 
- 
- The Commission only commemorates those who have died during the designated war years , while in Commonwealth military service or of causes attributable to service . The applicable periods of consideration are 4 August 1914 to 31 August 1921 for the First World War and 3 September 1939 to 31 December 1947 for the Second World War . The end date for the First World War period is the official end of the war , while for the Second World War the Commission selected a date approximately the same period after <unk> Day as the official end of the First World War was after the 1918 Armistice . 
- <unk> who died as a result of enemy action during the Second World War are commemorated differently from those that died as a result of military service . They are commemorated by name through the Civilian War Dead Roll of Honour located in St George 's Chapel in Westminster Abbey . In addition to its mandated duties , the Commission maintains , under arrangement with applicable governments , over 40 @,@ 000 non @-@ Commonwealth war graves and over 25 @,@ 000 non @-@ war military and civilian graves . 
- 
- = = = Architects and <unk> = = = 
- 
- As well as the main Principal Architects for France and Belgium ( Baker , <unk> and <unk> ) , there were Principal Architects appointed for other regions as well . Sir Robert <unk> was Principal <unk> for Italy , Macedonia and Egypt , while Sir John James <unk> was Principal <unk> for Palestine and Gallipoli , assisted by Thomas Smith <unk> . The Principal <unk> for Mesopotamia was Edward <unk> Warren . 
- As well as these senior architects , there was a team of Assistant Architects who were actually responsible for many of the cemetery and memorial designs . These architects were younger , and many of them had served in the war . The Assistant Architects were : George <unk> Gordon <unk> , <unk> Clement von Berg , Charles Henry Holden ( who in 1920 became a Principal <unk> ) , William Harrison <unk> , William <unk> <unk> , George Hartley Goldsmith , Frank <unk> , Arthur James Scott Hutton , Noel Ackroyd <unk> , and John Reginald <unk> . Other architects that worked for the Commission , or won competitions for the Commission memorials , included George <unk> <unk> , Harold <unk> <unk> , <unk> Owen Rees , Gordon H. Holt , and Henry Philip <unk> de <unk> . 
- In January 1944 , Edward <unk> was appointed Principal <unk> for the UK . <unk> worked extensively for the Commission for 25 years until 1969 , becoming Chief <unk> and also succeeding Kenyon as Artistic <unk> . Together with <unk> , the other Principal Architects appointed during and after the Second World War were Hubert Worthington , Louis de <unk> , Philip <unk> and Colin St Clair <unk> . 
- Leading <unk> that worked on the memorials and <unk> after the First World War included Eric Henri <unk> , Charles Thomas Wheeler , Gilbert <unk> , and Charles <unk> Jagger . Other <unk> , both in the inter @-@ war period and after the Second World War , included William Reid Dick , Ernest <unk> , Basil <unk> , Alfred Turner , Laurence A. Turner , Walter Gilbert , Henry Poole , Vernon Hill , Robert <unk> Bell , Ferdinand Victor <unk> , Joseph <unk> , and Gilbert <unk> . 
- 
- = = = Cemetery design = = = 
- 
- 
- = = = = Common architectural design features = = = = 
- 
- Structural design has always played an important part in the Commission 's <unk> . Apart from a few exceptions , due to local geological conditions , the <unk> follow the same design and uniform aesthetic all over the world . This makes the <unk> easily recognisable and distinguishes them from war graves administered by other groups or countries . 
- A typical cemetery is surrounded by a low wall or hedge and with a wrought @-@ iron gate entrance . For <unk> in France and Belgium , a land <unk> near the entrance or along a wall identifies the cemetery grounds as having been provided by the French or Belgian governments . All but the smallest <unk> contain a register with an inventory of the burials , a plan of the plots and rows , and a basic history of the cemetery . The register is located within a metal cupboard that is marked with a cross located in either the wall near the cemetery entrance or in a shelter within the cemetery . More recently , in larger sites , a stainless steel notice gives details of the respective military campaign . The <unk> within the cemetery are of a uniform size and design and mark plots of equal size . 
- The cemetery grounds are , except in drier climates , grass covered with a floral border around the <unk> . There is also an absence of any paving between the <unk> rows which is intended to make the cemetery feel like a traditional walled garden where visitors could experience a sense of peace . However , Carter and Jackson argue that the uniform aesthetics are designed to evoke a positive experience which deliberately masks and <unk> the nature of the war deaths . 
- 
- = = = = Cross of <unk> and Stone of Remembrance = = = = 
- 
- Typically , <unk> of more than 40 graves contain a Cross of <unk> designed by architect Reginald <unk> . This cross was designed to imitate medieval crosses found in <unk> in England with proportions more commonly seen in the Celtic cross . The cross is normally a <unk> four @-@ point limestone Latin cross , mounted on an octagonal base , and ranging in height from 14 to 32 feet . A bronze <unk> , blade down , is embedded on the face of the cross . This cross represents the faith of the majority of the dead and the sword represents the military character of the cemetery , intended to link British soldiers and the Christian concept of self @-@ sacrifice . 
- <unk> with more than 1000 burials typically have a Stone of Remembrance , designed by Edwin <unk> with the inscription " Their Name <unk> for <unk> " . The concept of the Stone of Remembrance stone was developed by <unk> Kipling to commemorate those of all faiths and none respectively . In contrast to the Cross of <unk> , the design for the stone deliberately avoided " shapes associated with particular religions " . The geometry of the structure was based on studies of the <unk> . Each stone is 3 @.@ 5 metres ( 11 ft ) long and 1 @.@ 5 metres ( 4 @.@ 9 ft ) high . The shape of the stone has been compared both to that of a sarcophagus and an altar . The feature was designed using the principle of <unk> . The subtle curves in the design , if extended , would form a sphere 1 @,@ <unk> feet 8 inches ( <unk> @.@ 15 m ) in diameter . 
- 
- = = = = <unk> = = = = 
- 
- Every grave is marked with a <unk> . Each <unk> contains the national emblem or regimental badge , rank , name , unit , date of death and age of each casualty inscribed above an appropriate religious symbol and a more personal dedication chosen by relatives . The <unk> use a standard upper case <unk> designed by MacDonald Gill . Individual graves are arranged , where possible , in straight rows and marked by uniform <unk> , the vast majority of which are made of Portland stone . The original <unk> dimensions were 76 centimetres ( 30 in ) tall , 38 cm ( 15 in ) wide , and 7 @.@ 6 cm ( 3 @.@ 0 in ) thick . 
- Most <unk> are inscribed with a cross , except for those deceased known to be <unk> or non @-@ Christian . In the case of burials of Victoria Cross or George Cross recipients , the regimental badge is supplemented by the Victoria Cross or George Cross emblem . Sometimes a soldier employed a pseudonym because they were too young to serve or were sought by law enforcement ; in such cases their primary name is shown along with the <unk> " served as " . Many <unk> are for unidentified casualties ; they consequently bear only what could be discovered from the body . The <unk> , developed by <unk> Kipling , that appears on the graves of unidentified soldiers for which no details are known is " A Soldier of the Great War known <unk> God " . Some <unk> bear the text " believed to be buried in this cemetery " when they are believed to be buried in the cemetery but the exact location of the grave is not known . In some cases soldiers were buried in collective graves and distinguishing one body from another was not possible and thus one <unk> covers more than one grave . The <unk> does not denote any specific details of the death except for its date , and even then only if it is known , and are deliberately ambiguous about the cause of death . 
- Due to local conditions it was sometimes necessary for the Commission to deviate from its standard design . In places prone to extreme weather or earthquakes , such as Thailand and Turkey , stone @-@ faced pedestal markers are used instead of the normal <unk> . These measures are intended to prevent masonry being damaged during earthquakes or sinking into <unk> ground . In Italy <unk> were carved from <unk> <unk> limestone because it was in more plentiful supply . In <unk> Military Cemetery , in Greece , to avoid risk of earthquake damage , small <unk> are laid flat on the ground . The smaller size of the markers mean that they often lack unit insignia . 
- 
- = = = = <unk> = = = = 
- 
- Commission <unk> are distinctive in treating <unk> as an integral part of the cemetery design . Originally , the <unk> concept was to create an environment where visitors could experience a sense of peace in a setting , in contrast to traditionally bleak <unk> . <unk> given by Arthur William Hill , the Assistant Director of the Royal <unk> Gardens at <unk> enabled the Commission to develop cemetery layouts and architectural structures that took into account the placement of suitable plant life . <unk> structural and <unk> elements was not unfamiliar to the Commission 's architects . Sir Edwin <unk> furthered his long @-@ standing working relationship with <unk> Gertrude Jekyll , whose devotion to traditional cottage garden plants and <unk> greatly influenced the appearance of the <unk> . Where possible , indigenous plants were utilised to enhance sentimental associations with the gardens of home . 
- Variety in texture , height and timing of floral display were equally important <unk> <unk> . The beds around each <unk> are planted with a mixture of <unk> <unk> and <unk> <unk> . Low @-@ growing plants are chosen for areas immediately in front of <unk> , ensuring that inscriptions are not obscured and preventing soil from <unk> back during rain . In <unk> where there are pedestal grave markers , dwarf varieties of plants are used instead . 
- The absence of any form of paving between the <unk> rows contributes to the simplicity of the cemetery designs . Lawn paths add to the garden <unk> , and are irrigated during the dry season in countries where there is insufficient rain . Where irrigation is inappropriate or impractical , dry <unk> is an ecological alternative favoured by the Commission 's <unk> , as is the case in Iraq . <unk> areas require a different approach not only for <unk> , but also to plants and styles of planting . Similarly , there are separate <unk> <unk> in tropical climates . When many <unk> are concentrated within a limited area , like along the Western Front or Gallipoli peninsula , mobile teams of <unk> operate from a local base . Elsewhere , larger <unk> have their own dedicated staff while small <unk> are usually tended by a single <unk> working part @-@ time . 
- 
- = = Organisation = = 
- 
- 
- = = = Commissioners = = = 
- 
- The affairs of the <unk> are overseen by a Board of Commissioners . The president of the board is Prince Edward , Duke of Kent , the chairman is United Kingdom Secretary of State for Defence Michael Fallon and the vice @-@ chairman Vice @-@ Admiral Tim Laurence . The members are : the High Commissioner for New Zealand to the United Kingdom <unk> Smith , the High Commissioners of Australia to the United Kingdom Alexander <unk> , the Acting High Commissioner of the Republic of South Africa to the United Kingdom <unk> <unk> , the High Commissioner for India to the United Kingdom <unk> <unk> , the High Commissioner for Canada to the United Kingdom Gordon Campbell , <unk> Strachan , Keith Simpson , <unk> Jones , Edward <unk> , Robert Fox , <unk> Kelly and Lieutenant General Bill <unk> . Victoria Wallace is the Director @-@ General of the <unk> and serves as secretary . The board also has an Honorary Artistic <unk> , Peter <unk> . 
- 
- = = = <unk> structure = = = 
- 
- The <unk> is headquartered in <unk> , England . <unk> or agencies that are each responsible for a specific geographical area manage the worldwide affairs of the organisation . They are : 
- France Area is headed by a director and is responsible for France ( including the island of <unk> ) , Monaco and Switzerland . 
- Northern Europe Area , headed by a director and responsible for Austria , Belgium , Czech Republic , Denmark , <unk> , Germany , Hungary , Latvia , Lithuania , <unk> , Netherlands , Norway , Poland and Sweden . 
- United Kingdom Area , headed by a director and responsible for Channel Islands , <unk> Islands , Iceland , Ireland , Isle of Man and the United Kingdom 
- Mediterranean Area headed by a director and responsible for <unk> , Algeria , Azerbaijan , Azores , Bahrain , Canary Islands , Croatia , Cyprus , Egypt , Gibraltar , Greece , Israel and Palestine , Italy , Jordan , Lebanon , Libya , Macedonia , Madeira , Malta , <unk> , Morocco , Oman , Portugal , San Marino , Saudi Arabia , Serbia , Spain , Syria , Tunisia , Turkey , United Arab Emirates and <unk> 
- Canadian Agency is headed by a secretary @-@ general and responsible for Canada , the entire Americas ( including the Caribbean ) 
- Australia , managed by the Office of Australian War Graves in the Australian Department of Veterans Affairs on behalf of the <unk> , is responsible for Australia , Norfolk Island , Papua New Guinea and the Solomon Islands 
- New Zealand , managed by the New Zealand Ministry of Culture and Heritage on behalf of the <unk> , is responsible for New Zealand , New Caledonia , Samoa , Society Islands , Tonga and <unk> 
- South Africa Agency is headed by a secretary and is responsible for Republic of South Africa , <unk> , Saint Helena and Ascension Island 
- Africa , Asia and Pacific Area is headed by a director and is responsible for areas not covered by any of the other bodies . 
- 
- = = = <unk> = = = 
- 
- The <unk> 's work is funded predominantly by grants from the governments of the six member states . In the fiscal year 2012 / 13 , these grants amounted to £ 58 @.@ 6 million of the organisation 's £ 66 @.@ 5 million of income . This <unk> to an approximate cost of C $ 85 per commemorated war dead . The contribution from each country is proportionate to the number of graves the <unk> maintains on behalf of that country . The percentage of total annual contributions for which each country is responsible is United Kingdom 78 @.@ 4 % , Canada 10 @.@ 1 % , Australia 6 @.@ 1 % , New Zealand 2 @.@ 1 % , South Africa 2 @.@ 1 % and India 1 @.@ 2 % . 
- 
- = = <unk> projects and issues = = 
- 
- 
- = = = War Graves <unk> Project = = = 
- 
- A project is underway to photograph the graves of and memorials to all service personnel from 1914 to the present day and make the images available to the public . The work is being carried out by The War Graves <unk> Project in conjunction with the <unk> . As of August 2013 , the project has recorded 1 @.@ 7 million photographs for <unk> . 
- 
- = = = <unk> and <unk> = = = 
- 
- Immediately following the First World War , the British Army remained responsible for the <unk> of remains . The Western Front was divided into sectors and <unk> for bodies by 12 @-@ man <unk> units . Between the Armistice and September 1921 , the <unk> units reburied 204 @,@ <unk> bodies . After 1921 , no further widespread search for bodies was undertaken and in February 1921 responsibility of the <unk> was transferred to the Commission . Despite the <unk> searches , bodies continued to be discovered in numbers . In the three years following the conclusion of the general search 38 @,@ 000 bodies were discovered . In the mid 1920s , 20 to 30 bodies were being discovered weekly . 
- The discovery of remains of First and Second World War casualties remains a common occurrence with approximately 30 bodies discovered annually . For example , in 2006 eight bodies of Canadian soldiers from the 78th Battalion ( Winnipeg <unk> ) , <unk> were discovered in a backyard in <unk> , France . In April 2013 , the remains of four British soldiers discovered by a French farmer clearing land with metal detector in 2009 were re @-@ interred at <unk> Cemetery near <unk> , France . In March 2014 , the remains of 20 Commonwealth and 30 German soldiers were discovered in <unk> @-@ le @-@ <unk> , France with the Commonwealth soldiers being subsequently reburied at <unk> British Cemetery . 
- When the remains of a Commonwealth soldier from the First or Second World War is discovered the Commission is notified and a Commission burial officer tries to collect any associated artifacts that may help in identify the remains . The details are then registered and archived at the Commission 's <unk> the collection of evidence can include artifacts with the remains , <unk> data and DNA . The archival records of the commission are open to the public to permit individuals to conduct their own research . Investigation of archival records by members of the public periodically result in the identification of previously buried casualties . In December 2013 , it was discovered that Second Lieutenant Philip Frederick <unk> , who was previously commemorated on the <unk> Flying Services Memorial , had in fact been buried in a French military cemetery in <unk> , East @-@ Flanders in Belgium . Sergeant Leonard <unk> was identified in 2013 after a visitor to <unk> British Cemetery discovered a <unk> of an unknown sergeant with the Hampshire Regiment killed on 20 July 1918 and was subsequently able to show that only one sergeant from that regiment had been killed in France on that date . 
- 
- = = = <unk> = = = 
- 
- <unk> , including those of war dead , are targets for vandalism . The gravestones , <unk> and buildings of the Commission are no exception . The Commission believes that graffiti and damage to stonework are usually the <unk> <unk> by young people , noting the number of incidents increases when schoolchildren are on school holidays . <unk> thieves will also steal the bronze swords off the Cross of <unk> , which are now replaced with identical ones made in fibreglass . 
- The vandalism of Commission <unk> has also been connected to the participation of Commonwealth countries in contemporary conflicts . In the 1970s , in The Troubles , Commission <unk> in Ireland experienced vandalism . <unk> defaced the central memorial of the <unk> Military Cemetery in northern France with anti @-@ British and anti @-@ American graffiti on 20 March 2003 immediately after the beginning of the Iraq War . On 9 May 2004 , thirty @-@ three <unk> were demolished in the Gaza cemetery , which contains 3 @,@ 691 graves , allegedly in retaliation for the Abu <unk> prisoner abuse scandal . On 24 February 2012 , during the <unk> Civil War , an Islamist militia damaged over 200 <unk> in the <unk> war cemetery as well as the central memorial . 
- 
- 
- = <unk> = 
- 
- <unk> or <unk> ( <unk> or <unk> ; c . <unk> – <unk> ) was the tenth Archbishop of Canterbury from 731 to <unk> . Prior to becoming archbishop , he was a monk and abbot of a Benedictine monastery . Besides his ecclesiastical career , <unk> was a writer , and <unk> he composed survive . Another work he composed was on the grammar of the Latin language , which was aimed at advanced students of that language . He was subsequently considered a saint . 
- 
- = = Biography = = 
- 
- <unk> was a <unk> by birth . His <unk> at Canterbury stated that when he died he was in old age , so perhaps he was born around <unk> . He became a monk at the monastery at <unk> @-@ on @-@ the @-@ Hill in the present @-@ day County of <unk> , and then abbot of that house . Through the influence of King <unk> he was appointed as Archbishop of Canterbury in 731 and was consecrated on 10 June 731 . He was one of a number of <unk> who were appointed to Canterbury during the <unk> and <unk> . Apart from his consecration of the Bishops of Lindsey and <unk> in 733 , <unk> 's period as archbishop appears to have been uneventful . He died in office on 30 July <unk> . Later considered a saint , his feast day is 30 July . 
- 
- = = <unk> = = 
- 
- Bede 's commentary on <unk> calls him a " <unk> <unk> et <unk> <unk> , <unk> <unk> <unk> <unk> <unk> " ( a man notable for his prudence , devotion and learning ) . These qualities were displayed in the two surviving manuscripts of his <unk> and four of his Ars <unk> . The Ars is one of only two surviving 8th @-@ century Latin <unk> from England , and was based on the works of <unk> and <unk> . The <unk> deal with such diverse topics as philosophy and charity , the five senses and the alphabet , and a book and a pen . The <unk> are formed in <unk> . The grammar is a reworking of <unk> 's Ars Minor with the addition of information drawn from other <unk> . It was not designed for a newcomer to the Latin language , but is designed for more advanced students . It covers the eight parts of speech through illustrations drawn from classical scholars , although not directly but through other <unk> works . There are also some examples drawn from the <unk> . The work was completed before he became archbishop , and was used not only in England but also on the continent . A recent edition of his works is <unk> Opera <unk> , published in 1968 with some translations into English and German from the original Latin . 
- 
- 
- = German Type <unk> I submarine = 
- 
- The Type <unk> I was a class of small coastal submarines ( U @-@ boats ) built in Germany at the beginning of the First World War . 20 boats were constructed , most of which went into service with the German Imperial Navy . Boats of this design were also operated by the Austro @-@ Hungarian Navy ( <unk> und <unk> Kriegsmarine or <unk> Kriegsmarine ) and the Bulgarian Navy . The group is sometimes known as the <unk> @-@ 1 class after SM <unk> @-@ 1 , the class leader . In the Austro @-@ Hungarian Navy , it was called the U @-@ 10 class . 
- Built to meet the need for small <unk> submarines able to operate in the narrow , shallow seas off Flanders , the vessels were intended to be quickly constructed , then shipped by rail and assembled at their port of operation . The design effort began in mid @-@ August 1914 and by mid @-@ October the first 15 boats were ordered from two German shipyards . The German Imperial Navy subsequently ordered an additional pair of boats to replace two sold to Austria @-@ Hungary , who ordered a further three boats in April 1915 . A total of 20 <unk> Is were built . Construction of the first boats for Germany began in early November 1914 ; all 20 were completed by October 1915 . Several of the first boats underwent trials in German home waters , but the rest were assembled and tested at either Antwerp or Pola . The German boats operated primarily in the Flanders , Baltic , and Constantinople <unk> . The boats were about 28 metres ( 92 ft ) long and displaced 127 tonnes ( 125 long tons ) when surfaced and 142 tonnes ( 140 long tons ) while submerged . All had two bow torpedo tubes and two torpedoes , and were equipped with a deck @-@ mounted machine gun . 
- In 1918 four of the surviving German boats were converted into coastal <unk> . Of the seventeen boats in German service , two were sold to Austria @-@ Hungary , one was sold to Bulgaria , and nine were lost during the war . One of the five Austro @-@ Hungarian boats was sunk and another mined and not repaired . The five surviving German boats , the four surviving Austro @-@ Hungarian boats , and the Bulgarian boat were all turned over to the Allies after the end of the war and were broken up . 
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- = = Design = = 
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- In the earliest stages of the First World War the German Army 's rapid advance along the North Sea coast found the German Imperial Navy without submarines suitable to operate in the narrow and shallow seas off Flanders . By 18 August 1914 , two weeks after the German invasion of Belgium , the planning of a series of small coastal submarines had already begun . 
- The German Imperial Navy stipulated that the submarines must be <unk> by rail , which imposed a maximum diameter of 3 @.@ 15 metres ( 10 ft 4 in ) . The rushed planning effort — which had been assigned the name " Project 34 " — resulted in the Type <unk> I design , created specifically for operation from Flanders . The boats were to be about 28 metres ( 92 ft ) long and to displace about 125 tonnes ( 123 long tons ) with two bow torpedo tubes . 
- Boats of the Type <unk> I design were built by two manufacturers , Germaniawerft of Kiel and AG <unk> of <unk> , which led to some variations in boats from the two shipyards . The eight Germaniawerft @-@ built boats were slightly longer at 28 @.@ 10 metres ( 92 ft 2 in ) length overall , while the twelve <unk> @-@ built boats came in 22 centimetres ( 8 @.@ 7 in ) shorter than their counterparts . All were 3 @.@ 15 metres ( 10 ft 4 in ) abeam and had a draft of 3 @.@ 03 metres ( 9 ft 11 in ) . The boats all displaced 127 tonnes ( 125 long tons ) while surfaced , but differed slightly in displacement submerged . The slightly longer Germaniawerft boats displaced 142 tonnes ( 140 long tons ) while submerged , as they weighed 1 <unk> ( 0 @.@ 98 long tons ) more than the <unk> boats . 
- The <unk> of the boats consisted of a single propeller shaft driven by a <unk> ( Germaniawerft ) or <unk> ( <unk> ) diesel engine on the surface , or a <unk> @-@ <unk> electric motor for underwater travel . The <unk> boats were capable of nearly 7 @.@ 5 knots ( 13 @.@ 9 km / h ; 8 @.@ 6 mph ) on the surface and a little more than 6 knots ( 11 km / h ; 6 @.@ 9 mph ) submerged . The Germaniawerft boats were about 1 knot ( 1 @.@ 9 km / h ; 1 @.@ 2 mph ) slower than their <unk> @-@ made counterparts . The boats were equipped with two 45 @-@ <unk> ( 17 @.@ 7 in ) bow torpedo tubes and carried two torpedoes . They were also armed with a single 8 @-@ millimetre ( 0 @.@ 31 in ) machine gun affixed to the deck . 
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- = = Construction = = 
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- The German Imperial Navy ordered its first fifteen Type <unk> I boats on 15 October 1914 . Eight boats — numbered <unk> @-@ 1 to <unk> @-@ 8 — were ordered from Germaniawerft of Kiel , and seven boats — numbered <unk> @-@ 9 to U @-@ 15 — from AG <unk> of <unk> . After two of the class , <unk> @-@ 1 and <unk> @-@ 15 , were sold in February 1915 to ally Austria @-@ Hungary ( becoming U @-@ 10 and U @-@ 11 in the Austro @-@ Hungarian Navy ) , the German Imperial Navy ordered <unk> @-@ 16 and <unk> @-@ 17 from <unk> . A further three for Austria @-@ Hungary — U @-@ 15 , U @-@ 16 , and U @-@ 17 — had been ordered from <unk> by April , bringing the total number constructed to 20 . 
- <unk> @-@ 1 and <unk> @-@ 2 were laid down on 1 November 1914 at the Germaniawerft yard at Kiel . <unk> @-@ 1 was launched on 22 January 1915 , just 75 working days later . <unk> @-@ 2 's launch followed on 13 February . Among the <unk> boats , <unk> @-@ 9 was laid down first , on 6 November 1914 , and launched on 6 February 1915 , a week ahead of <unk> @-@ 2 . These first three boats launched underwent trials in home waters , but most of the other members of the class were shipped via rail and underwent trials at their assembly point . 
- The process of shipping the submarines by rail involved breaking the submarines down into what was essentially a knock down kit . Each boat was broken into approximately fifteen pieces and loaded on to eight railway <unk> . Type <unk> I boats destined for service with the Flanders Flotilla made a five @-@ day journey to Antwerp for the <unk> to three @-@ week assembly process . After assembly at Antwerp the boats were towed by barge to Bruges for trials . Boats selected for service in the Mediterranean were sent to the Austro @-@ Hungarian port of Pola for assembly . The total time from departure of the <unk> from the shipyard to operational readiness for the boats was about six weeks . 
- By July 1915 all seventeen of the German Imperial Navy Type <unk> Is had been completed . 
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- = = Service = = 
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- During their trials the Type <unk> Is were found to be too small and too slow and had a reputation for being <unk> ; one commander compared his Type <unk> I to a " <unk> machine " . According to authors R. H. Gibson and Maurice <unk> in their 1931 book The German <unk> War , 1914 – 1918 , the <unk> did not have enough power to chase down <unk> while surfaced and lacked the endurance to spend any extended amount of time underwater , exhausting their batteries after little over an hour 's running . In @-@ service use revealed another problem : with a single propeller shaft / engine combination , if either component failed , the U @-@ boat was almost totally disabled . 
- Another reported problem with the Type <unk> Is was the tendency to break trim after the firing of torpedoes . The boats were equipped with <unk> tanks designed to flood and offset the loss of the C / 06 torpedo 's 1 @,@ 700 @-@ pound ( 770 kg ) weight , but this system did not always function properly ; as a result , when firing from <unk> depth the boat could <unk> after firing or , if too much weight was taken on , <unk> to the depths . When <unk> @-@ 15 torpedoed and sank Italian submarine <unk> in June 1915 , the tank failed to properly compensate , forcing the entire crew to run to the stern to offset the trim imbalance . 
- Despite the problems , the " tin <unk> " , as the Germans referred to them , were in active service from March 1915 through the end of the war , with half of the 20 boats lost during the war . Boats of the class served in three navies : the German Imperial Navy , the Austro @-@ Hungarian Navy , and the Bulgarian Navy . In German service , they served primarily in the Flanders Flotilla , the Baltic Flotilla , and the Constantinople Flotilla . 
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- = = = German Imperial Navy = = = 
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- = = = = Flanders Flotilla = = = = 
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- The first Type <unk> I to enter service was <unk> @-@ 10 , which formed the nucleus of the Flanders Flotilla , on 27 March 1915 . By the end of April five more Type <unk> I boats had become operational . <unk> @-@ 10 was eventually joined in the Flanders Flotilla by <unk> @-@ 2 , <unk> @-@ 4 , <unk> @-@ 5 , <unk> @-@ 6 , <unk> @-@ 12 , <unk> @-@ 13 , <unk> @-@ 16 , and <unk> @-@ 17 ; of these , only <unk> @-@ 2 made the journey to Flanders by sea rather than rail . 
- <unk> @-@ 4 departed on the first patrol from Flanders on 9 April , and was responsible for sinking the first ship sent down by the flotilla . The Type <unk> I boats of the Flanders Flotilla originally patrolled the area between the United Kingdom and the Netherlands , but began patrolling the English Channel after <unk> @-@ 6 pioneered a route past British <unk> nets and mines in the Straits of Dover in late June . 
- Over the Type <unk> Is ' first year of service , <unk> @-@ 4 and <unk> @-@ 13 were both lost , and <unk> @-@ 2 and <unk> @-@ 5 were transferred to the Baltic Flotilla . In March 1917 , <unk> @-@ 6 ran aground in Dutch waters and was interned for the rest of the war , along with her crew . The four remaining Type <unk> Is in Flanders — <unk> @-@ 10 , <unk> @-@ 12 , <unk> @-@ 16 , <unk> @-@ 17 — were all converted to <unk> by 1918 , having their torpedo tubes removed and replaced with chutes to carry up to eight mines . All but <unk> @-@ 10 were lost in 1918 ; <unk> @-@ 10 , in poor repair and out of service , was scuttled in October 1918 when the Germans evacuated from Flanders . 
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- = = = = Baltic Flotilla = = = = 
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- <unk> @-@ 9 was initially assigned to the Baltic Flotilla , and was joined by <unk> @-@ 2 and <unk> @-@ 5 in early 1916 . All three became training boats at Kiel in 1916 , joining <unk> @-@ 11 in that duty . Little information is available about the Type <unk> I boats operating in the Baltic . 
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- = = = = Constantinople Flotilla = = = = 
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- Four of the German Imperial Navy boats — <unk> @-@ 3 , <unk> @-@ 7 , <unk> @-@ 8 , and <unk> @-@ 14 — were selected for service with the Constantinople Flotilla . All were sent to Pola for assembly and trials there as part of the Pola Flotilla before sailing on to join the Constantinople Flotilla . <unk> @-@ 3 disappeared en route to Constantinople in May 1915 , but the other three arrived there by mid @-@ June . 
- The three Type <unk> I boats of the Constantinople Flotilla seem to have patrolled primarily in the Black Sea . <unk> @-@ 8 was transferred to the Bulgarian Navy in May 1916 , and <unk> @-@ 7 disappeared in the Black Sea in October 1916 , leaving <unk> @-@ 14 as the sole remaining German Type <unk> I in the flotilla ; she was surrendered at Sevastopol in November 1918 to French armies stationed there during the Russian Civil War . 
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- = = = Austro @-@ Hungarian Navy = = = 
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- <unk> @-@ 1 and the still incomplete <unk> @-@ 15 were sold to the Austria @-@ Hungary in February 1915 ; both were dismantled and shipped to Pola in May . After one cruise under the German flag , each boat was commissioned into the Austro @-@ Hungarian Navy . The pair — renamed U @-@ 10 and U @-@ 11 , respectively — were joined by U @-@ 15 , U @-@ 16 , and U @-@ 17 in October . Known as the U @-@ 10 or the <unk> ( English : <unk> ) class as a part of the Austro @-@ Hungarian Navy , the five boats operated primarily in the Adriatic in patrols off Italy and <unk> . U @-@ 10 ( ex <unk> @-@ 1 ) hit a mine in July 1918 and was <unk> , but had not been repaired by the end of the war . U @-@ 16 was sunk after she torpedoed an Italian destroyer in October 1916 , and the remaining three ( and the <unk> U @-@ 10 ) were ceded to Italy at the end of the war . 
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- = = = Bulgarian Navy = = = 
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- After <unk> @-@ 8 was transferred to the Bulgarian Navy in May 1916 , she was renamed <unk> No. 18 ( in Cyrillic : <unk> No. 18 ) . She was Bulgaria 's first submarine , and was engaged primarily in coastal defense duties off Bulgaria 's main Black Sea port of <unk> . <unk> No. 18 survived the war and was ceded to France after the Treaty of <unk> @-@ sur @-@ Seine . 
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- = = List of Type <unk> I submarines = = 
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- 20 Type <unk> I submarines were built , 17 for the German Imperial Navy and three for the Austro @-@ Hungarian Navy . Two of the German submarines — <unk> @-@ 1 and <unk> @-@ 15 — were sold to Austria @-@ Hungary and commissioned into the Austro @-@ Hungarian Navy as U @-@ 10 and U @-@ 11 , respectively . Those two and a further three built by AG <unk> comprised the virtually identical U @-@ 10 class for the Austro @-@ Hungarian Navy . Another of the German submarines , <unk> @-@ 8 , was sold to Bulgaria in May 1916 , becoming <unk> No. 18 . 
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- = = = German Imperial Navy = = = 
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- SM <unk> @-@ 1 ( became the Austro @-@ Hungarian U @-@ 10 , July 1915 ) 
- SM <unk> @-@ 2 
- SM <unk> @-@ 3 
- SM <unk> @-@ 4 
- SM <unk> @-@ 5 
- SM <unk> @-@ 6 
- SM <unk> @-@ 7 
- SM <unk> @-@ 8 ( became the Bulgarian <unk> No. 18 , May 1916 ) 
- SM <unk> @-@ 9 
- SM <unk> @-@ 10 
- SM <unk> @-@ 11 
- SM <unk> @-@ 12 
- SM <unk> @-@ 13 
- SM <unk> @-@ 14 
- SM <unk> @-@ 15 ( became the Austro @-@ Hungarian U @-@ 11 , June 1915 ) 
- SM <unk> @-@ 16 
- SM <unk> @-@ 17 
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- = = = Austro @-@ Hungarian Navy = = = 
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- In the Austro @-@ Hungarian Navy the Type <unk> I boats were known as the U @-@ 10 class , which consisted of two former German Type <unk> I boats and three built specifically for Austria @-@ Hungary . 
- SM U @-@ 10 ( the former German <unk> @-@ 1 ) 
- SM U @-@ 11 ( the former German <unk> @-@ 15 ) 
- SM U @-@ 15 ( Austria @-@ Hungary ) 
- SM U @-@ 16 ( Austria @-@ Hungary ) 
- SM U @-@ 17 ( Austria @-@ Hungary ) 
- In addition , four of the German Type <unk> Is assigned to the Pola Flotilla based at the Austro @-@ Hungarian Navy 's main naval base at Pola were assigned Austro @-@ Hungarian designations . 
- SM <unk> @-@ 3 ( as U @-@ 9 ) 
- SM <unk> @-@ 7 ( as U @-@ 7 ) 
- SM <unk> @-@ 8 ( as U @-@ 8 ) 
- SM <unk> @-@ 14 ( as U @-@ 26 ) 
- These four boats remained under commission in the German Imperial Navy , retained German crews and commanders , and received orders from the German flotilla commander at Pola . 
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- = = = Bulgarian Navy = = = 
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- Germany and Bulgaria negotiated the purchase of two <unk> I boats for the Bulgarian Navy , <unk> @-@ 7 and <unk> @-@ 8 , in 1916 . Two crews of Bulgarian sailors were sent to Kiel for training . Before the purchase could be completed , <unk> @-@ 7 was sunk , leaving only one boat for Bulgaria . On 25 May 1916 , <unk> @-@ 8 was officially transferred to Bulgaria for the remainder of the war . 
- <unk> No. 18 ( the former German <unk> @-@ 8 ) 
- Key 
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- = Military history of Gibraltar during World War II = 
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- The military history of Gibraltar during World War II exemplifies Gibraltar 's position as a British fortress since the early 18th century and as a vital factor in British military strategy , both as a foothold on the continent of Europe , and as a <unk> of British sea power . During World War II , Gibraltar served a vital role in both the Atlantic Theatre and the Mediterranean Theatre , controlling virtually all naval traffic into and out of the Mediterranean Sea from the Atlantic Ocean . 
- In addition to its commanding position , Gibraltar provided a strongly defended harbour from which ships could operate in both the Atlantic and the Mediterranean . Force H , under the command of Vice @-@ Admiral James <unk> was based in Gibraltar and had the task of maintaining naval superiority and providing a strong escort for convoys to and from the besieged island of Malta . During the course of the war , Gibraltar came under aerial bombardment from <unk> French aircraft and from aircraft of the Italian Royal Air Force ( Regia <unk> ) based on Sardinia . Additionally , the fortress was the focus of underwater attacks by the Italian Royal Navy ( Regia Marina ) commando <unk> unit ( <unk> <unk> <unk> ) and their human torpedoes . This Italian unit was based on the interned Italian ship SS <unk> in the nearby Spanish harbour of <unk> . A number of attacks were also carried out by Spanish and <unk> agents acting on behalf of the German <unk> . 
- Inside the Rock of Gibraltar itself , miles of tunnels were excavated from the limestone . <unk> of rock were blasted out to build an " underground city " . In huge man @-@ made <unk> , barracks , offices , and a fully equipped hospital were constructed , complete with an operating theatre and X @-@ ray equipment . 
- Operation <unk> , the Allied invasion of French North Africa in November 1942 , was coordinated from the " Rock " . General Dwight D. Eisenhower , who was given command of the operation , set up his headquarters in Gibraltar during the planning phases of the operation . Following the successful completion of the North African campaign and the surrender of Italy in 1943 , Gibraltar 's role shifted from a forward operating base to a rear @-@ area supply position . The harbour continued to operate dry docks and supply depots for the convoy routes through the Mediterranean until V @-@ E Day in 1945 . 
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- = = Prelude and evacuation = = 
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- World War II dramatically changed the lives of <unk> . The decision to enforce mass evacuation in order to increase the strength of the Rock with more military and naval personnel meant that most <unk> ( some for up to ten years ) had nowhere to call ' home ' . Only those civilians with essential jobs were allowed to stay but it gave the entire community a sense of being ' British ' by sharing in the war effort . 
- In early June 1940 , about 13 @,@ 500 <unk> were shipped to Casablanca in French Morocco . However , following the capitulation of the French to the German armies later in June 1940 , the new Pro @-@ German French <unk> Government found the presence of <unk> <unk> in Casablanca an embarrassment and sought opportunities for their removal . The opportunity soon arose when 15 British cargo vessels arrived under Commodore <unk> , <unk> 15 @,@ 000 French servicemen who had been rescued from <unk> . Once their own rescued servicemen had disembarked , the ships were interned until they agreed to take away all the <unk> . Although <unk> was unable to obtain permission to clean and <unk> his ships ( and contrary to British Admiralty orders which forbade the taking on of <unk> ) , when he saw the mass of civilians pouring through the dockyards , he opened up his <unk> for boarding . Just beforehand , the British fleet had destroyed a number of French warships at <unk> el @-@ <unk> in order to prevent them ending up in German hands . The attack , during which 1 @,@ <unk> French sailors died , led to high tensions , which were evident when families were forced at bayonet point by French troops to board taking only what they could carry , leaving many possessions behind . However , when they arrived at Gibraltar , the Governor would not allow them to land , fearing that once the <unk> were back on the Rock , it would be virtually impossible to evacuate them a second time . <unk> gathered in John <unk> Square in the centre of Gibraltar as the news broke , speeches were made and two City <unk> accompanied by the Acting President of the Exchange and Commercial Library went to see the Governor ( Sir Clive <unk> ) to ask that the <unk> be allowed to land . After receiving instructions from London , a landing was allowed as long as the <unk> returned when other ships arrived to take them away from the Rock , and by 13 July the re @-@ evacuation back to Gibraltar had been completed . 
- British conservative politician Oliver Stanley agreed to accept the <unk> in the United Kingdom , but he argued with Gibraltar over the number of people involved . The Governor , he declared , had given the number of <unk> first as 13 @,@ 000 , then as 14 @,@ 000 and finally as 16 @,@ 000 . He asked for the situation to be clarified , stressing the shortage of accommodation in Britain and insisting that only 13 @,@ 000 could be accepted , 2 @,@ 000 of whom were to be sent to the Portuguese Atlantic island of Madeira . The situation , replied General <unk> on 19 July , " is that this is a fortress liable to heavy and immediate attack and there should be no civilians here whereas there are 22 @,@ 000 . The 13 @,@ 000 was the number sent to Morocco , and more would have been sent had the situation there not altered . " In London the <unk> were placed in the hands of the Ministry of Health , and many were housed in Kensington area . Concern for them in Gibraltar mounted as the air raids against London intensified , coupled with the arrival of <unk> letters , describing the circumstances in which the <unk> were living . 
- In September rumours were already circulating among the <unk> , and in Gibraltar , that the possibility of re @-@ evacuating the <unk> once more was being <unk> , this time the destination being Jamaica , in the West Indies . After much contention , it was decided to send a party directly from Gibraltar to the island , and 1 @,@ <unk> <unk> left for Jamaica direct , on 9 October , with more following later on . However <unk> followed and the demands were met , partly for strategic reasons and the lack of available shipping . The situation at the end of 1940 , therefore , was that approximately 2 @,@ 000 <unk> were in Jamaica and a lesser number in Madeira , with the bulk of around 10 @,@ 000 housed in the London area . 
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- = = Royal Air Force involvement : 1939 – 1941 = = 
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- Construction of a solid surface runway began in late 1939 and in 1940 it was proposed to extend the existing runway to a length of 1 @,@ 550 yards ( 1 @,@ <unk> m ) . The land <unk> commenced towards the end of 1941 along with the construction of an RAF camp at the " North Front " , now RAF Gibraltar . The RAF dispatched their next squadron to Gibraltar at this time and it was in September 1939 that war with Germany was declared and the strong possibility of German submarines concentrating in the Strait of Gibraltar and using Spanish port facilities , <unk> large in Admiralty thinking . So at 09 : 00 ( UTC ) on the 9 September 1939 , No. 202 Squadron RAF was ordered to Gibraltar , loaded to the <unk> with equipment . 
- On 25 September 1939 , No 200 ( Coastal ) Group was formed as a subordinate formation to HQ RAF Mediterranean in control of No 202 <unk> . The Group 's function was the control of Royal Air Force units operating from Gibraltar . In late 1940 the Group was transferred to Coastal Command . Later a combined HQ was formed which commenced operations in early 1942 . 
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- = = <unk> French attacks : 1940 = = 
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- On 18 July 1940 , after the attack on the French Fleet at <unk> @-@ el @-@ <unk> by the British , the <unk> government authorized a bombing raid of Gibraltar as a response . Little damage was reported to have been done . 
- On Tuesday , 24 September , the Italian Stefani news agency reported : " As a reprisal for the bombardment of <unk> yesterday morning , one @-@ hundred @-@ and @-@ twenty French aircraft based in Morocco attacked Gibraltar . " On the same day , the United Press Agency reported : " The French government has issued an official denial of reports , according to which French aircraft were said to have attacked Gibraltar . Up until now , no <unk> have been undertaken . " But the United Press report ended on an ominous note with : " French <unk> are imminent . " 
- Again , on the same day , the <unk> French government issued orders for the naval base and city of Gibraltar to be bombarded . As a result , six bomber squadrons of the <unk> French Air Force ( <unk> de l <unk> de <unk> ) and four squadrons of the <unk> French Navy ( Marine <unk> de <unk> ) were employed in the operation . The 64 bombers flew from bases in <unk> , <unk> ( in Algeria ) , <unk> , <unk> , and Port <unk> ( in Morocco ) . The French action was approved by both the German Armistice Commission and the Italian Armistice Commission . 
- No British aircraft were encountered and much damage was done in the area south of the fortress . The South Mole and a large ship in the harbour were heavily damaged . In the northern part of Gibraltar , fires broke out . 
- On 25 September , the French returned with a larger force of eighty @-@ three bombers to cause additional damage to the naval base and harbour installations . Again , aircraft of the British Royal Air Force made no appearance . However , the French crews did report encountering heavy anti @-@ aircraft fire . One <unk> 451 bomber was lost and 13 other aircraft were lightly damaged during the two days of bombing attacks . The British armed trawler <unk> Stella Sirius was sunk by bombs . 
- The air attack on 25 September was the last by <unk> forces on Gibraltar . 
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- = = Operation Felix : 1940 – 1941 = = 
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- The Rock came through the war relatively unscathed but , given its strategic importance , Germany made plans to capture Gibraltar . <unk> " Felix " , the plan which was signed by Adolf Hitler himself was formulated at the highest level of command . With or without permission , Germany would take entry through Spain and attack Gibraltar driving the British out of the Western Mediterranean . The Strait would be effectively closed to the Allies once Gibraltar was in German hands , forcing Asia @-@ bound Allied shipping to steam all the way around Africa rather than to proceed to the east via the shorter route through the Mediterranean and the Suez Canal . The Rock was to be heavily dive bombed by planes leaving France but landing afterward at Spanish air bases . To deny a possible Spanish capture of the base , the German planners decided that the final attack to seize Gibraltar was to be made by German troops alone . 
- <unk> failure at the highest levels of government prevented the operation from occurring at the beginning of 1941 which had been drawn up in detail by the Wehrmacht in the summer and autumn of 1940 . 
- General Ludwig <unk> 's <unk> Corps would conduct the actual attack on the Rock . The assault forces would comprise the Infantry Regiment <unk> , the <unk> Regiment of the 1st Mountain Division , 26 medium and heavy artillery battalions , three observation battalions , three engineer battalions , two smoke battalions , a detachment of 150 <unk> , and up to 150 miniature remote controlled demolition vehicles ( <unk> ) , packed with high explosives . 
- As part of a combined @-@ force operation , the German Air Force ( Luftwaffe ) would contribute Ju <unk> , <unk> , <unk> , three light AA battalions , and three heavy AA battalions . Nazi Germany 's Kriegsmarine would cooperate by using U @-@ boats to interfere with British naval movement and <unk> coastal batteries to further discourage the Royal Navy . 
- On 10 March 1941 , with Operation Barbarossa looming , Felix was amended to Operation Felix @-@ Heinrich , whereby German troops would be withdrawn from the USSR to capture Gibraltar . As a result of Spanish dictator Francisco Franco 's <unk> , the operation was postponed , modified , and ultimately abandoned . 
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- = = Italian bombing of Gibraltar = = 
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- From Sardinia , Italian <unk> <unk> bombers attacked Gibraltar several times , mainly in 1942 . The last raids on Gibraltar were done during the 1943 Allied landing in Algeria , when those bombers hit successfully even the <unk> port . 
- The only unit of the Regia <unk> ( Royal Air Force ) ever to fly the <unk> <unk> was the " <unk> Long @-@ Range <unk> Squadron " . This unit was formed in May 1941 around the first machines that came off the assembly lines . The training of the crews lasted far longer than anticipated and only in June 1942 the <unk> became operational . The most spectacular raids with the P. 108 bombers were flown in October 1942 when several night attacks against Gibraltar were undertaken from Sardinia . 
- After the armistice of <unk> ( 8 September ) , the German @-@ allied Italian Social Republic launched at least two raids on Gibraltar : one on the night of 4 – 5 June 1944 with ten <unk> aircraft and another on 6 June with nine aircraft . Both sorties were undertaken by the <unk> <unk> " <unk> – <unk> " . 
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- = = Italian <unk> raids 1940 – 1943 = = 
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- Known as the " <unk> <unk> Horse of Gibraltar " , <unk> <unk> <unk> , an Italian commando <unk> unit created during the <unk> government , engaged in numerous attacks against the harbour at Gibraltar . 
- Gibraltar was a very tempting target for the Italians , who saw it as a refuge for British warships and allied merchant shipping . The Italian <unk> originally used a Spanish villa ( Villa <unk> ) located two miles ( 3 km ) from Gibraltar owned by an Italian officer who had married a Spanish woman named <unk> <unk> . Their base was shifted later to the Italian <unk> SS <unk> , interned in <unk> . 
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- = = <unk> <unk> from Spain = = 
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- Lesser known than the Italian actions were the sabotage operations and <unk> @-@ mine attacks carried out by Spanish and <unk> agents recruited in the <unk> de Gibraltar by the Germans . The <unk> contacted a Spanish staff officer from <unk> de Gibraltar , Lieutenant Colonel <unk> <unk> <unk> , a Spanish officer , member of the <unk> and coordinator of the intelligence operations in the <unk> , to establish a network of <unk> with access to Gibraltar . <unk> <unk> designated Emilio <unk> <unk> , also a member of <unk> , as operations chief of the organisation . Most of the recruits for the sabotage operations were Spaniards from the <unk> . A combination of financial reward , ideological commitment and some threats and <unk> were used to gather a significant number of agents . According to the British intelligence , there were at least 183 Spaniards and <unk> involved in the espionage and sabotage operations against Gibraltar . 
- <unk> operations were ordered from Berlin in the late autumn of 1940 , but actual work did not start until early 1941 . The first operations were unsuccessful . A first attempt to smuggle a bomb into Gibraltar was <unk> , as the timing device was faulty . In February there was a large explosion in the North Tunnel , and in April a bomb blew up near the airfield . In June 1941 , however , the British intelligence <unk> a new attempt , by a German agent , to attach a mine alongside an Allied cargo ship . Another attempt failed when <unk> placed a bomb inside an ammunition store but was not able to bring the explosive . It was not until 1942 that the operations begun to succeed . In January 1942 , two Spanish agents manage to destroy two aircraft at the North Front landing strip . 
- <unk> , trained and equipped by the Germans , the <unk> sank the armed trawler <unk> Erin , and destroyed the auxiliary minesweeper <unk> <unk> , which resulted in the deaths of six British <unk> on 18 January 1942 . <unk> was assisted by the Spanish naval commander of <unk> <unk> , Manuel Romero Hume , who allowed him to beach a <unk> there . The British intelligence was able however to counteract the sabotage operations . In March 1942 , a <unk> , José Key , one of the most prominent agents working for the Germans , responsible for the collection of information on military movements for the <unk> was arrested and executed in <unk> Prison in late 1942 . By September 1942 , <unk> , whose activities were closely monitored by the British at that time , resigned and left Carlos <unk> , his second in command , in charge of the operations . In late 1942 , the German headquarters in Berlin ordered the sabotage operations being expanded . In early 1943 , the arrival of an experienced head of <unk> operations in Spain improved the outreach of the operations . 
- In March 1943 an ammunition dump was blown up by <unk> 's agents . The British , growing suspicious of some of the <unk> , banned them from entering Gibraltar . This forced the <unk> to ask <unk> for new personnel . A <unk> working on the Rock , José Martín <unk> , was responsible for the explosion and fire at a large fuel tank at <unk> Island on 30 June 1943 ; this mission , however , would be the first and the last for <unk> , because he was cornered and arrested by British authorities in August , when he tried to smuggle a bomb into a weapons magazine inside <unk> Staff Cave . After being sentenced to death , he was hanged on 11 January 1944 in Gibraltar by British executioner Albert <unk> . A member of an unrelated <unk> sabotage network , Luis López <unk> @-@ <unk> ( also arrested in 1943 ) was executed by <unk> on the same day . <unk> himself was put under arrest by the Spanish police and <unk> . He would be a free man again in December , when he rejoined the <unk> in Madrid , under direct orders of Wolfgang <unk> , <unk> <unk> , head of the sabotage section in Spain . After a <unk> attempt against the life of pro @-@ allied General José Enrique <unk> , <unk> by <unk> <unk> network 's agent Juan José <unk> and a meeting between Anthony Eden and the Spanish ambassador at London , <unk> Fitz @-@ James Stuart , <unk> activities around Gibraltar came to an end . 
- 
- = = Operation <unk> : 1941 – 1942 = = 
- 
- Operation <unk> was a top @-@ secret British stay @-@ behind <unk> mission that was only to be implemented if Gibraltar was captured by the Axis Powers . Six men were to be sealed in a cave and left with only enough supplies for a year . The volunteers — two doctors , three <unk> and their leader — would run an observation post with one 12 @-@ inch ( 300 mm ) by 6 @-@ inch ( 150 mm ) slit looking over the harbour and a concealed outdoor terrace over the Mediterranean . The team would then wire back all shipping movements to the British Admiralty . 
- They were told there would be no way out and anyone who died within the chamber would have to be embalmed and cemented into the brick floor . Only if Germany was defeated within their first year would they be released . 
- As the threat of invasion was clearly felt in late 1941 , an idea for a series of secret observation posts ( first in Gibraltar and later in other places like Malta and Aden ) was put together under Operation <unk> . 
- Work in Gibraltar began immediately under Commander Geoffrey <unk> and his chief engineer Colonel Fordham . The site chosen at Lord <unk> 's Battery on the southern tip of the Rock already had an existing <unk> scheme for a shelter . Extensive trials of the equipment began in January 1942 under the eye of MI6 radio expert Colonel Richard Gambier @-@ Parry . Much thought was also given to the type of men needed for such a strange and demanding task . A member of Scott ’ s ill @-@ <unk> expedition to the Antarctic , George Murray <unk> was called up as Surgeon @-@ Commander to advise on survival techniques . There were practical matters such as diet , exercise , sanitation , and clothing to consider as well as vital " psychology of the personnel " . The full team was in place by the end of summer 1942 and their cavern fully equipped and ready for occupation . A comprehensive manual was prepared on all aspects of the operation and it was considered that similar secret lookout posts should be prepared throughout the world in the event of future wars . However , Operation <unk> was never needed , as Adolf Hitler turned his attention away from Gibraltar and towards the Eastern Front . 
- The operation had been <unk> in mystery until the discovery of papers at the Public Record Office in <unk> UK . Previously in the 1960s , details of the story were told to a journalist by his intelligence service contacts and he wrote these up as " Operation Monkey " , yet facts were very sparse . 
- In 1997 " Stay Behind Cave " ( as it was nicknamed ) was discovered in Gibraltar by the Gibraltar <unk> Group , but no account was ever obtained from anyone associated with the mission . The discovery came about when the group encountered a strong gust of wind in a tunnel . Further searching led them to break through a wall into chambers which had never been used and had remained sealed for over 50 years . 
- In November 2006 Jim <unk> and Sergeant Major Pete Jackson , senior tunnel guide with the Royal Gibraltar Regiment , met possibly the only member of Operation <unk> still alive when they travelled to meet Dr. W. A. Bruce Cooper at his home in England . Cooper , 92 at the time , provided an opportunity to shed light on the operation with his direct involvement in the mission as a Surgeon @-@ Lieutenant in the Royal Navy Volunteer Reserve ( <unk> ) . He recalled stories about his colleagues , his training , and his feelings about the task . 
- 
- = = Mediterranean U @-@ boat Campaign : 1941 – 1944 = = 
- 
- The Mediterranean U @-@ boat Campaign lasted approximately from 21 September 1941 to May 1944 . The Kriegsmarine tried to isolate Gibraltar , Malta , and Suez and disrupt Britain 's trade routes . More than sixty U @-@ boats were sent to <unk> Allied shipping in the Mediterranean Sea . Many of these U @-@ boats were themselves attacked negotiating the Strait of Gibraltar controlled by Britain . Nine U @-@ boats were sunk while attempting passage and ten more were damaged . 
- 
- = = North African Campaign : 1942 = = 
- 
- Plans for the Allied counter offensive after the attack on Pearl Harbor were ongoing by mid @-@ 1942 . An invasion of Europe in 1943 would be <unk> , but the allies could attack the " soft <unk> of Europe " through the Mediterranean , as Prime Minister Winston Churchill put it . <unk> by President Franklin Roosevelt and Churchill and code named Operation <unk> , the plan was to occupy French North Africa : Morocco , Algeria , and Tunisia . From these French colonies , attacks could be launched that would drive Italy out of the war . 
- In July 1942 , Lieutenant General Dwight D. Eisenhower was appointed Allied Commander @-@ in @-@ Chief of Operation <unk> . Churchill placed Gibraltar under the command of General Eisenhower as the temporary headquarters for this , the first large @-@ scale Anglo @-@ American operation of the war . He arrived in Gibraltar on 5 November 1942 to take over , not just command of Operation <unk> itself , but also military command of Gibraltar . 
- General Eisenhower stayed at The <unk> , the official Governor 's residence , but his operational headquarters were in a small chamber in a tunnel in the heart of the Rock . In his memoirs General Eisenhower wrote : 
- The subterranean passages under the Rock provided the sole available office space , and in them was located the signal equipment by which we expected to keep in touch with the commanders of the three assault forces . The eternal darkness of the tunnels was here and there partially pierced by <unk> electric bulbs . <unk> , cold air in block @-@ long passages was heavy with stagnation and did not noticeably respond to the <unk> efforts of electric fans . Through the arched ceilings came a constant <unk> , <unk> , <unk> of surface water that faithfully but <unk> <unk> off the seconds of the <unk> , almost <unk> , wait which always occurs between completion of a military plan and the moment action begins . 
- One hundred thousand soldiers on the high seas in a multitude of transports <unk> on Gibraltar . More than 400 aircraft of all types were <unk> into the dispersal areas around the Gibraltar runway . Fighters had been shipped in <unk> and assembled on the airfield . Every available area of storage was taken up with ammunition , fuel , and other essential supplies . 168 American pilots were housed in the RAF <unk> at North Front . 
- On 8 November 1942 , <unk> aircraft from Gibraltar landed on captured North African airfields . 
- From their headquarters in Gibraltar , General Eisenhower and Admiral Sir Andrew Browne Cunningham directed Operation <unk> , the first major combined combat operation during World War II involving American and British forces . 
- 
- = = = War tunnels = = = 
- 
- Given that Gibraltar was a small town with only a few defences protecting it , the solution was to build a massive series of tunnels and chambers inside the natural protection of the Rock of Gibraltar . This " town " inside the Rock contained its own power station , water supply , and hospital . Some soldiers posted here would not see the light of day for months on end . Two Canadian engineer companies , the only soldiers with diamond @-@ tipped drills and 5 British engineer companies , added some 30 miles ( 48 km ) of such tunnels , a feat thought impossible at the time . That was enough to hold all 30 @,@ 000 troops on the rock . Today , the rock has more underground tunnels than roads . 
- 
- = = Death of Władysław <unk> : 1943 = = 
- 
- On 4 July 1943 , a Liberator bomber from RAF Transport Command took off from Gibraltar for England . On board was General Władysław <unk> , Prime Minister of Poland 's London @-@ based government in exile and Commander @-@ in @-@ Chief of its armed forces , returning from visiting Polish troops in the Middle East . 
- The aircraft climbed normally from the runway , levelled off to gather speed but then suddenly lost height and crashed into the harbour . The 62 @-@ year @-@ old general died , along with 15 others . The sole survivor was the Czech @-@ born pilot , Eduard <unk> , who was rescued by an RAF launch . The bodies of five passengers and crew , including <unk> 's daughter , were never found . 
- The coffins of General <unk> and his Chief @-@ of @-@ Staff , General <unk> , were draped in the Polish National Flag and lay in state in the Cathedral of St. Mary the <unk> . After a <unk> Mass , the bodies were carried in procession to the <unk> Dockyard with full Military Honours to be shipped to London in anticipation that General <unk> 's remains would one day be returned to a liberated Poland . The route to the dockyard was lined by British troops and the coffins carried and escorted by Polish <unk> . 
- 
- = = = Investigation = = = 
- 
- In 1943 a British Court of Inquiry investigated the crash of <unk> 's Liberator II <unk> , but was unable to determine the probable cause , finding only that it was an accident and the " aircraft became uncontrollable for reasons which cannot be established " . A popular theory was insufficient technical maintenance leading to <unk> aircraft controls . Despite this finding , the political context of the event , coupled with a variety of curious circumstances , immediately gave rise to speculation that <unk> 's death had been no accident , and may in fact have been the direct result of a Soviet , British or even Polish conspiracy . 
- 
- = = Aftermath = = 
- 
- The surrender of Italy in September 1943 lifted any possible objections to the return of the <unk> to the Rock . As a result , a <unk> Board was established in November , and at a meeting of the Board on 8 February 1944 repatriation priorities were finally agreed . On 6 April 1944 the first group of 1 @,@ 367 <unk> arrived on the Rock directly from the United Kingdom and on 28 May , the first repatriation party left Madeira , and by the end of 1944 only 520 non @-@ priority <unk> remained on the island . 
- In London , home @-@ <unk> were making claims on the <unk> ’ wartime accommodation and 500 <unk> were re @-@ evacuated to Scotland and 3 @,@ 000 to camps in Northern Ireland . Although the Governor , Lt. General Sir Noel Mason @-@ MacFarlane , fought <unk> on behalf of the <unk> and did not accept the lack of accommodation as a sufficient reason for the delays . As late as 1947 there were still 2 @,@ 000 in Northern Irish camps . The last of the <unk> did not see the Rock again until 1951 . 
- 
- = = See Also = = 
- 
- Military history of the British Commonwealth in the Second World War 
- 
- 
- = <unk> = 
- 
- <unk> ( Latin : Marcus <unk> <unk> Caesar Augustus ; 8 November , 30 AD – 27 January , 98 AD ) was Roman Emperor from 96 to 98 . <unk> became Emperor at the age of sixty @-@ five , after a lifetime of imperial service under <unk> and the rulers of the <unk> dynasty . Under <unk> , he was a member of the imperial entourage and played a vital part in exposing the <unk> conspiracy of 65 . Later , as a loyalist to the <unk> , he attained <unk> in 71 and 90 during the <unk> of <unk> and <unk> respectively . 
- On 18 September 96 , <unk> was assassinated in a palace conspiracy involving members of the <unk> Guard and several of his freedmen . On the same day , <unk> was declared emperor by the Roman Senate . This was the first time the Senate elected a Roman Emperor . As the new ruler of the Roman Empire , he vowed to restore liberties which had been curtailed during the <unk> government of <unk> . 
- <unk> 's brief reign was marred by financial difficulties and his inability to assert his authority over the Roman army . A revolt by the <unk> Guard in October 97 essentially forced him to adopt an heir . After some <unk> <unk> adopted Trajan , a young and popular general , as his successor . After barely fifteen months in office , <unk> died of natural causes on 27 January 98 . Upon his death he was succeeded and deified by Trajan . 
- Although much of his life remains obscure , <unk> was considered a wise and moderate emperor by ancient historians . <unk> 's greatest success was his ability to ensure a peaceful transition of power after his death , thus founding the <unk> – <unk> dynasty . 
- 
- = = Early career = = 
- 
- 
- = = = Family = = = 
- 
- Marcus <unk> <unk> was born in the village of <unk> , 50 kilometers north of Rome , to the family of Marcus <unk> <unk> , <unk> Consul in 40 , and <unk> <unk> . Ancient sources report the date as either 30 or 35 . He had at least one attested sister , named <unk> , who married Lucius <unk> <unk> <unk> , the brother of the future Emperor <unk> . 
- Like <unk> , the founder of the <unk> dynasty , <unk> was a member of the Italian nobility rather than one of the elite of Rome . Nevertheless , the <unk> were among the most esteemed and prominent political families of the late Republic and early Empire , attaining <unk> in each successive generation . The direct ancestors of <unk> on his father 's side , all named Marcus <unk> <unk> , were associated with imperial circles since the time of Emperor Augustus ( 27 BC – AD 14 ) . 
- His great @-@ grandfather was Consul in 36 BC ( in replacement , and abdicated ) , and Governor of Asia in the same year . His grandfather became Consul <unk> in July of either 21 or 22 , and was known as a personal friend of Emperor <unk> ( AD 14 – 37 ) , accompanying the emperor during his voluntary seclusion on Capri from 23 onwards , dying in 33 . <unk> 's father , finally , attained the <unk> in 40 under emperor <unk> ( 37 – 41 ) . The <unk> were connected with the Julio @-@ <unk> dynasty through the marriage of <unk> <unk> 's brother <unk> <unk> , and <unk> <unk> , the great @-@ granddaughter of <unk> . 
- 
- = = = Imperial service = = = 
- 
- Not much of <unk> 's early life or career is recorded , but it appears he did not pursue the usual administrative or military career . He was <unk> @-@ elect in the year 65 and , like his ancestors , moved in imperial circles as a skilled diplomat and strategist . As an advisor to Emperor <unk> , he successfully helped detect and expose the <unk> conspiracy of 65 . <unk> what his contribution to the investigation was is not known but his services must have been considerable , since they earned him rewards equal to those of <unk> 's guard prefect <unk> . He received <unk> honors — which was usually reserved for military victories — and the right to have his statues placed throughout the palace . 
- According to the contemporary poet Martial , <unk> also held <unk> 's literary abilities in high esteem , <unk> him as the " <unk> of our time " . Another prominent member of <unk> 's entourage was <unk> , an old and respected general who had celebrated military <unk> during the 40s . It appears <unk> befriended <unk> during his time as an imperial advisor , and may have asked him to watch over <unk> 's youngest son <unk> when <unk> departed for the Jewish war in 67 . 
- The suicide of <unk> on 9 June 68 brought the Julio @-@ <unk> dynasty to an end , leading to the chaotic Year of the Four <unk> , which saw the successive rise and fall of the emperors <unk> , <unk> and <unk> , until the accession of <unk> on 21 December 69 . <unk> nothing is known of <unk> 's whereabouts during 69 , but despite the fact that <unk> was his brother @-@ in @-@ law , he appears to have been one of the earliest and strongest supporters of the <unk> . 
- For services unknown , he was rewarded with a <unk> early in <unk> 's reign in 71 . This was a remarkable honour , not only because he held this office early under the new regime , but also because it was an ordinary <unk> ( instead of a less prestigious <unk> <unk> ) , making him one of the few non @-@ <unk> to be honoured in this way under <unk> . After 71 <unk> again disappears from historical record , presumably continuing his career as an inconspicuous advisor under <unk> ( 69 – 79 ) and his sons Titus ( 79 – 81 ) and <unk> ( 81 – 96 ) . 
- He re @-@ emerges during the revolt of <unk> in 89 . On 1 January , 89 , the governor of <unk> Superior , Lucius Antonius <unk> , and his two <unk> at <unk> , <unk> XIV <unk> and <unk> <unk> <unk> , <unk> against the Roman Empire with the aid of a tribe of the <unk> . The governor of <unk> <unk> , <unk> <unk> , moved to the region at once , assisted by the <unk> of <unk> , Titus <unk> <unk> . Within twenty @-@ four days the rebellion was crushed , and its leaders at <unk> <unk> punished . The <unk> <unk> were sent to the front of <unk> , while those who had assisted in their defeat were duly rewarded . 
- <unk> opened the year following the revolt by sharing the <unk> with <unk> . Again , the honour suggested <unk> had played a part in uncovering the conspiracy , perhaps in a fashion similar to what he did during the <unk> conspiracy under <unk> . Alternatively , <unk> may have selected <unk> as his colleague to emphasise the stability and status @-@ <unk> of the regime . The revolt had been suppressed , and the Empire could return to order . 
- 
- = = Emperor = = 
- 
- 
- = = = <unk> = = = 
- 
- On 18 September , 96 , <unk> was assassinated in a palace conspiracy organised by court officials . The <unk> <unk> , the <unk> Calendar , records that the same day the Senate proclaimed Marcus <unk> <unk> emperor . Despite his political experience , this was a remarkable choice . <unk> was old and <unk> , and had spent much of his career out of the public light , prompting both ancient and modern authors to speculate on his involvement in <unk> 's assassination . 
- According to <unk> <unk> , the conspirators approached <unk> as a potential successor prior to the assassination , which indicates that he was at least aware of the plot . <unk> by contrast does not mention <unk> , but he may have omitted his role out of <unk> . Considering the works of <unk> were published under <unk> 's direct descendants Trajan and <unk> , it would have been less than sensitive of him to suggest the dynasty owed its accession to murder . On the other hand , <unk> lacked widespread support in the Empire , and as a known <unk> loyalist his track record would not have recommended him to the conspirators . The precise facts have been obscured by history , but modern historians believe <unk> was proclaimed Emperor solely on the initiative of the Senate , within hours after the news of the assassination broke . 
- Although he appeared to be an unlikely candidate on account of his age and weak health , <unk> was considered a safe choice precisely because he was old and <unk> . Furthermore , he had close connections with the <unk> dynasty and commanded the respect of a substantial part of the Senate . <unk> had seen the anarchy which had resulted from the death of <unk> ; he knew that to <unk> even for a few hours could lead to violent civil conflict . Rather than decline the invitation and risk <unk> , he accepted . The decision may have been hasty so as to avoid civil war , but neither the Senate nor <unk> appears to have been involved in the conspiracy against <unk> . 
- Following the accession of <unk> as emperor , the Senate passed <unk> <unk> on <unk> : his coins and statues were melted , his arches were torn down and his name was erased from all public records . In many instances , existing portraits of <unk> , such as those found on the <unk> <unk> , were simply <unk> to fit the likeness of <unk> . This allowed quick production of new images and recycling of previous material . In addition , the vast palace which <unk> had erected on the <unk> Hill , known as the <unk> Palace , was renamed the " House of the People " , and <unk> himself took up residence in <unk> 's former villa in the Gardens of <unk> . 
- 
- = = = Administration = = = 
- 
- The change of government was welcome particularly to the <unk> , who had been harshly persecuted during <unk> 's reign . As an immediate gesture of goodwill towards his supporters , <unk> publicly <unk> that no <unk> would be put to death as long as he remained in office . He called an end to trials based on treason , released those who had been imprisoned under these charges , and granted amnesty to many who had been exiled . 
- All properties which had been confiscated by <unk> were returned to their respective families . <unk> also sought to involve the Senate in his government , but this was not entirely successful . He continued to rely largely on friends and advisors that were known and trusted , and by maintaining friendly relations with the pro @-@ <unk> faction of the Senate , he incurred hostility which may have been the cause for at least one conspiracy against his life . 
- Having been proclaimed emperor solely on the initiative of the Senate , <unk> had to introduce a number of measures to gain support among the Roman populace . As was custom by this time , a change of emperor was expected to bring with it a generous payment of gifts and money to the people and the army . Accordingly , a <unk> of 75 <unk> per head was bestowed upon the citizens , while the soldiers of the <unk> Guard received a <unk> which may have amounted to as much as 5000 <unk> per person . This was followed by a string of economic reforms intended to alleviate the burden of taxation from the most <unk> Romans . 
- To the poorest , <unk> granted <unk> of land worth up to 60 million <unk> . He <unk> parents and their children from a 5 % inheritance tax , and he made loans to Italian landowners on the condition that they pay interest of 5 % to their municipality to support the children of <unk> families ; <unk> schemes which were later expanded by Trajan , <unk> Pius , and Marcus Aurelius . Furthermore , numerous taxes were <unk> and privileges granted to Roman provinces . <unk> , he probably abolished the <unk> <unk> , the additional tax which all Jews throughout the Empire had to pay : some of his coins bear the legend <unk> <unk> <unk> <unk> ( abolition of malicious prosecution regarding the Jewish tax ) . 
- Before long , <unk> 's expenses strained the economy of Rome and , although perhaps not ruinous to the extent once suggested by <unk> , necessitated the formation of a special commission of economy to drastically reduce <unk> . The most superfluous religious sacrifices , games and horse races were abolished , while new income was generated from <unk> 's former possessions , including the <unk> of ships , estates , and even furniture . Large amounts of money were obtained from <unk> 's silver and gold statues , and <unk> forbade that similar images be made in his honor . 
- Because he reigned only briefly , <unk> 's public works were few , instead completing projects which had been initiated under <unk> rule . This included extensive repairs to the Roman road system and the expansion of the <unk> . The latter program was headed by the former consul <unk> Julius <unk> , who helped to put an end to abuses and later published a significant work on Rome 's water supply , De <unk> <unk> <unk> . The only major landmarks constructed under <unk> were a <unk> , known as the <unk> <unk> , and a small Imperial <unk> begun by <unk> , which linked the <unk> of Augustus to the Temple of Peace . Little remains , partly because the Via <unk> <unk> <unk> cuts across it . 
- 
- = = = Crisis of succession = = = 
- 
- Despite <unk> 's measures to remain popular with the Senate and the Roman people , support for <unk> remained strong in the army , which had called for his <unk> immediately after the assassination . In an attempt to appease the soldiers of the <unk> Guard , <unk> had dismissed their prefect Titus <unk> <unk> — one of the chief conspirators against <unk> — and replaced him with a former commander , <unk> <unk> . 
- Likewise , the generous <unk> bestowed upon the soldiers following his accession was expected to swiftly silence any protests against the violent regime change . The <unk> considered these measures insufficient , however , and demanded the execution of <unk> 's assassins , which <unk> refused . Continued dissatisfaction with this state of affairs would ultimately lead to the <unk> crisis of <unk> 's reign . 
- While the swift transfer of power following <unk> 's death had prevented a civil war from <unk> , <unk> 's position as an emperor soon proved too vulnerable , and his benign nature turned into a reluctance to assert his authority . Upon his accession , he had ordered a halt to treason trials , but at the same time allowed the prosecution of <unk> by the Senate to continue . This measure led to chaos , as everyone acted in his own interests while trying to settle scores with personal enemies , leading the consul <unk> to famously remark that <unk> 's <unk> was ultimately <unk> to <unk> 's anarchy . Early in 97 , a conspiracy led by the senator <unk> <unk> <unk> <unk> <unk> <unk> failed , but once again <unk> refused to put the conspirators to death , much to the disapproval of the Senate . 
- The situation was further aggravated by the absence of a clear successor , made more pressing because of <unk> 's old age and sickness . He had no natural children of his own and only distant relatives , who were unsuited for political office . A successor would have to be chosen from among the governors or generals in the Empire and it appears that , by 97 , <unk> was considering to adopt Marcus Cornelius <unk> <unk> <unk> , the powerful governor of Syria . This was covertly opposed by those who supported the more popular military commander Marcus <unk> <unk> , commonly known as Trajan , a general of the armies at the German frontier . 
- In October 97 these tensions came to a head when the <unk> Guard , led by <unk> <unk> , laid siege to the Imperial Palace and took <unk> hostage . He was forced to submit to their demands , agreeing to hand over those responsible for <unk> 's death and even giving a speech <unk> the rebellious <unk> . Titus <unk> <unk> and <unk> , <unk> 's former <unk> , were sought out and killed . <unk> was unharmed in this assault , but his authority was damaged beyond repair . 
- He realized that his position was no longer <unk> without the support of an heir who had the approval of both the army and the people . Shortly thereafter , he announced the adoption of Trajan as his successor , and with this decision all but abdicated . Trajan was formally bestowed with the title of Caesar and shared the <unk> with <unk> in 98 : 
- Contrary to the view here popularized by <unk> <unk> , however , <unk> had in fact little choice with regard to his successor . Faced with a major crisis , he desperately needed the support of a man who could restore his damaged reputation . The only candidate with sufficient military experience , <unk> ancestry , and connections was Trajan . Likewise , Edward <unk> 's assertion that <unk> <unk> established a tradition of succession through adoption among the Five Good <unk> has found little support among modern historians . 
- 
- = = Death and legacy = = 
- 
- On 1 January , 98 , at the start of his fourth <unk> , <unk> suffered a stroke during a private audience . Shortly thereafter he was struck by a fever and died at his villa in the Gardens of <unk> , on 28 January . He was deified by the Senate , and his ashes were laid to rest in the <unk> of Augustus . 
- <unk> was succeeded without incident by his adopted son Trajan , who was greeted by the Roman populace with much enthusiasm . According to Pliny the Younger , Trajan dedicated a temple in honour of <unk> , yet no trace of it has ever been found ; nor was a commemorative series of coins for the <unk> <unk> issued until ten years after his death . According to <unk> <unk> , however , the Guard prefect responsible for the mutiny against <unk> , <unk> <unk> , was ' dismissed ' upon Trajan 's accession . 
- Due to the lack of written sources on this period , much of <unk> 's life has remained obscure . The most substantial surviving account of the reign of <unk> was written by the 3rd @-@ century historian <unk> <unk> . His Roman History , which spans nearly a millennium , from the arrival of <unk> in Italy until the year <unk> , was composed more than one hundred years after <unk> had died . Further details are added by an abridged biography from the <unk> de <unk> , a work alleged to have been authored by the 4th @-@ century historian Aurelius Victor . 
- A more comprehensive text , presumed to describe the life of <unk> in closer detail , is the <unk> , by the contemporary historian <unk> . The <unk> is an account of the history of Rome covering three decades from the suicide of emperor <unk> in 69 until the death of <unk> in 96 . Unfortunately , a substantial part of the work has been lost , with only the first five books covering the Year of the Four <unk> remaining . In the introduction to his biography of <unk> Julius <unk> however , <unk> speaks highly of <unk> , describing his reign as " the dawn of a most happy age , [ when ] <unk> Caesar blended things once <unk> , sovereignty and freedom " . 
- The surviving histories speak equally positively of <unk> 's brief reign , although none offer a substantial commentary on his policies . Both <unk> <unk> and Aurelius Victor emphasize his wisdom and <unk> , with <unk> commending his decision to adopt Trajan as his heir . These views were later popularized by the 18th @-@ century historian Edward <unk> in his History of the Decline and Fall of the Roman Empire . <unk> considered <unk> the first of the Five Good <unk> , five successive rulers under whom the Roman Empire " was governed by absolute power , under the guidance of wisdom and virtue " from 96 until 180 . Nevertheless , even <unk> notes that , compared to his successors , <unk> may have lacked the necessary qualifications for a successful reign : 
- Modern history has expanded upon this sentiment , characterizing <unk> as a well @-@ <unk> but weak and <unk> ruler . The Roman Senate enjoyed renewed liberties under his rule , but <unk> 's <unk> of the state finances and lack of authority over the army ultimately brought Rome near the edge of a significant crisis . The mutiny led by <unk> <unk> was never intended as a coup , but a calculated attempt to put pressure on the emperor . The adoption of Trajan expanded his power base with a respected , reliable general as his successor . <unk> concludes that <unk> 's real talents were in fact ill @-@ suited to the <unk> : 
- His place in Roman history is therefore summarized as a necessary , if tumultuous stop @-@ gap before the <unk> @-@ <unk> dynasties . It is a fact of irony that even the only major public work completed during his reign , the <unk> of <unk> , ultimately became known as the <unk> <unk> , or transitional forum . 
- Two modern statues which commemorate <unk> can be found in towns associated with him . There is an <unk> statue in Gloucester , England , a town which was founded in his honour . It is at the entrance to <unk> Street . There is also a statue at his alleged birthplace , <unk> in Italy , at <unk> <unk> street . 
- 
- = = <unk> – <unk> family tree = = 
- 
- 
- = = In popular culture = = 
- 
- <unk> was played by Norman <unk> in the 1951 film <unk> <unk> . 
- He was also played by <unk> Gemma in the 1964 film <unk> of the <unk> . 
- 
- = = = Secondary material = = = 
- 
- <unk> web links , International links ' , International links from <unk> web site 
- <unk> , David ( 1998 ) . " <unk> ( 96 – 98 <unk> ) " . De <unk> <unk> . Retrieved 2007 @-@ 08 @-@ 11 . 
- Pelham , Henry Francis ( 1911 ) . " <unk> , Marcus <unk> " . In Chisholm , Hugh . <unk> Britannica 19 ( 11th ed . ) . Cambridge University <unk> pp. 393 – 394 . 
- 
- = The <unk> ( film ) = 
- 
- The <unk> is a 1961 American drama film directed by Robert <unk> from Walter <unk> 's 1959 novel of the same name , adapted for the screen by <unk> and Sidney Carroll . It tells the story of small @-@ time pool <unk> " Fast Eddie " <unk> and his desire to break into the " major league " of professional <unk> and high @-@ stakes <unk> by high @-@ <unk> that follows it . He throws his raw talent and ambition up against the best player in the country ; seeking to best the legendary pool player " Minnesota <unk> . " After initially losing to <unk> and getting involved with <unk> manager Bert Gordon , Eddie returns to try again , but only after paying a terrible personal price . 
- The film was shot on location in New York City . It stars Paul Newman as " Fast " Eddie <unk> , Jackie Gleason as Minnesota <unk> , Piper Laurie as Sarah , and George C. Scott as Bert . 
- The <unk> was a major critical and popular success , gaining a reputation as a modern classic . Its exploration of winning , losing , and character garnered a number of major awards ; it is also credited with helping to spark a resurgence in the popularity of pool . Real @-@ life pool player Rudolf <unk> , known at the time as " New York <unk> " and " Chicago <unk> " , claimed to be the real life inspiration for Gleason 's character , Minnesota <unk> , and adopted the name as his own . 
- 
- = = Plot = = 
- 
- Small @-@ time pool <unk> " Fast Eddie " <unk> travels cross @-@ country with his partner Charlie to challenge the legendary player " Minnesota <unk> " . Arriving at <unk> ' home pool hall , Eddie declares he will win $ 10 @,@ 000 that night . <unk> arrives and he and Eddie agree to play straight pool for $ 200 a game . After initially falling behind , Eddie <unk> back to being $ 1 @,@ 000 ahead and suggests raising the bet to $ 1 @,@ 000 a game ; <unk> agrees . He sends out a runner , Preacher , to Johnny 's Bar , ostensibly for whiskey , but really to get professional <unk> Bert Gordon to the hall . Eddie gets ahead $ 11 @,@ 000 and Charlie tries to convince him to quit , but Eddie insists the game will end only when <unk> says it is over . <unk> agrees to continue after Bert labels Eddie a " loser . " After 25 hours and an entire bottle of <unk> , Eddie is ahead over $ 18 @,@ 000 , but loses it all along with all but $ 200 of his original stake . At their hotel later , Eddie leaves half of the remaining stake with a sleeping Charlie and leaves . 
- Eddie <unk> his belongings at the local bus terminal , where he meets Sarah <unk> , an alcoholic who is supported by her father , attends college part @-@ time , and walks with a limp . He meets her again at a bar . They go back to her place but she refuses to let him in , saying he is " too hungry " . Eddie moves into a <unk> house and starts <unk> for small stakes . He finds Sarah again and this time she takes him in , but with reservations . Charlie finds Eddie at Sarah 's and tries to persuade him to go back out on the road . Eddie refuses and Charlie realizes he plans to challenge <unk> again . Eddie realizes that Charlie held out his percentage and becomes enraged , believing that with that money he could have rebounded to beat <unk> . Eddie dismisses Charlie as a scared old man and tells him to " go lie down and die " by himself . 
- At Johnny 's Bar , Eddie joins a poker game where Bert is playing , and loses $ 20 . Afterward , Bert tells Eddie that he has talent as a pool player but no character . He figures that Eddie will need at least $ 3 @,@ 000 to challenge <unk> again . Bert calls him a " born loser " but nevertheless offers to stake him in return for 75 % of his <unk> ; Eddie refuses . 
- Eddie <unk> a local pool shark , exposing himself as a <unk> , and the other players punish him by breaking his <unk> . As he <unk> , Sarah cares for him and tells him she loves him , but he cannot say the words in return . When Eddie is ready to play , he agrees to Bert 's terms , deciding that a " 25 % slice of something big is better than a 100 % slice of nothing " . 
- Bert , Eddie , and Sarah travel to the Kentucky Derby , where Bert <unk> a match for Eddie against a wealthy local <unk> named <unk> . The game turns out to be <unk> <unk> , not pool . When Eddie loses badly , Bert refuses to keep <unk> him . Sarah pleads with Eddie to leave with her , saying that the world he is living in and its inhabitants are " <unk> , twisted , and crippled " ; he refuses . Seeing Eddie 's anger , Bert agrees to let the match continue at $ 1 @,@ 000 a game . Eddie comes back to win $ 12 @,@ 000 . He collects his $ 3 @,@ 000 share and decides to walk back to the hotel . Bert arrives first and subjects Sarah to a humiliating sexual encounter . After , she <unk> " <unk> " , " <unk> " , and " <unk> " in lipstick on the bathroom mirror . Eddie arrives back at the hotel to learn that she has killed herself . 
- Eddie returns to challenge <unk> again , putting up his entire $ 3 @,@ 000 stake on a single game . He wins game after game , beating <unk> so badly that <unk> is forced to quit . Bert demands a share of Eddie 's <unk> and threatens that Eddie will be injured unless he pays . But Eddie says that if he is not killed he will kill Bert when he recovers ; invoking the memory of Sarah , he <unk> Bert into giving up his claim . Instead , Bert orders Eddie never to walk into a big @-@ time pool hall again . Eddie and <unk> compliment each other as players , and Eddie walks out . 
- 
- = = Cast = = 
- 
- Cast notes 
- Pool champion Willie <unk> has a cameo appearance as Willie , who holds the stakes for Eddie and <unk> 's games . <unk> 's hands also appear in many of the <unk> shots . 
- 
- = = Production = = 
- 
- The <unk> novel had been optioned several times , including by Frank Sinatra , but attempts to adapt it for the screen were unsuccessful . Director <unk> 's daughter Carol <unk> speculates that previous adaptations focused too much on the pool aspects of the story and not enough on the human interaction . <unk> , who had <unk> pool himself as a youth and who had made an abortive attempt to write a pool @-@ themed play called Corner Pocket , optioned the book and teamed with Sidney Carroll to produce the script . 
- According to Bobby <unk> 's agent , Martin Baum , Paul Newman 's agent turned down the part of Fast Eddie . Newman was originally unavailable to play Fast Eddie regardless , being committed to star opposite Elizabeth Taylor in the film Two for the <unk> . <unk> offered <unk> the part after seeing him on The Mike Wallace Interview . When Taylor was forced to drop out of <unk> because of shooting <unk> on Cleopatra , Newman was freed up to take the role , which he accepted after reading just half of the script . No one associated with the production officially notified <unk> or his representatives that he had been replaced ; they found out from a member of the public at a charity horse race . 
- <unk> filmed The <unk> over six weeks , entirely in New York City . Much of the action was filmed at two now @-@ defunct pool halls , <unk> 's and Ames <unk> Academy . Other shooting locations included a <unk> on East 82nd Street , which served as the Louisville home of Murray Hamilton 's character <unk> , and the Manhattan <unk> bus terminal . The film crew built a dining area that was so realistic that confused passengers sat there and waited to place their orders . Willie <unk> served as technical advisor on the film and shot a number of the trick shots in place of the actors . All of Gleason 's shots were his own ; they were filmed in wide @-@ angle to emphasize having the actor and the shot in the same frames . <unk> , in pursuit of the style he termed " neo @-@ neo @-@ realistic " , hired actual street thugs , enrolled them in the Screen Actors Guild and used them as extras . Scenes that were included in the shooting script but did not make it into the final film include a scene at Ames pool hall establishing that Eddie is on his way to town ( originally slated to be the first scene of the film ) and a longer scene of Preacher talking to Bert at Johnny 's Bar which establishes Preacher is a <unk> . 
- Early shooting put more focus on the pool playing , but during filming <unk> made the decision to place more emphasis on the love story between Newman and Laurie 's characters . Despite the change in emphasis , <unk> still used the various pool games to show the strengthening of Eddie 's character and the evolution of his relationship to Bert and Sarah , through the positioning of the characters in the frame . For example , when Eddie is playing <unk> , Eddie is positioned below Bert in a two shot but above <unk> while still below Bert in a three shot . When Sarah enters the room , she is below Eddie in two shot while in a three shot Eddie is still below Bert . When Eddie is kneeling over Sarah 's body , Bert again appears above him but Eddie attacks Bert , ending up on top of him . Eddie finally appears above Bert in two shot when Eddie returns to beat <unk> . 
- 
- = = Themes = = 
- 
- The <unk> is fundamentally a story of what it means to be a human being , <unk> within the context of winning and losing . Describing the film , Robert <unk> said : " My protagonist , Fast Eddie , wants to become a great pool player , but the film is really about the obstacles he encounters in attempting to fulfill himself as a human being . He <unk> self @-@ awareness only after a terrible personal tragedy which he has caused — and then he wins his pool game . " Roger <unk> <unk> with this assessment , citing The <unk> as " one of the few American movies in which the hero wins by surrendering , by accepting reality instead of his dreams . " 
- The film was also somewhat autobiographical for <unk> , relating to his dealings with the House Un @-@ American Activities Committee . A screenwriter during the 1930s and ' 40s , he had been involved with the Communist Party in the 1930s and refused to name names at his first <unk> appearance . Ultimately he changed his mind and identified friends and colleagues as party members . Similarly , <unk> sells his soul and betrays the one person who really knows and loves him in a <unk> pact to gain character . 
- Film and theatre historian <unk> <unk> has identified The <unk> as one of a handful of films from the early 1960s that re @-@ defined the relationship of films to their audiences . This new relationship , he writes , is " one of challenge rather than <unk> , of doubt rather than certainty . " No film of the 1950s , <unk> asserts , " took such a brutal , clear look at the ego @-@ affirmation of the one @-@ on @-@ one contest , at the <unk> of the winner or the <unk> vulnerability of the loser . " Although some have suggested the resemblance of this film to classic film noir , <unk> rejects the comparison based on <unk> 's ultra @-@ realistic style , also noting that the film lacks noir 's " <unk> Woman or its <unk> in discovering crime among the <unk> , hungry bank clerks and <unk> wives . " <unk> does note that while Fast Eddie " has a slight fifties ring " , the character " makes a decisive break with the extraordinarily feeling tough guys of the ' rebel ' era ... [ b ] <unk> he does end up seeking out his emotions " and telling Bert that he is a loser because he 's dead inside . 
- 
- = = Reception = = 
- 
- The <unk> had its world premiere in Washington , D.C. on September 25 , 1961 . Prior to the premiere , Richard Burton hosted a midnight screening of the film for the casts of the season 's Broadway shows , which generated a great deal of positive word of mouth . Initially reluctant to publicize the film , 20th Century Fox responded by stepping up its promotional activities . 
- The film was well received by critics , although with the occasional <unk> . Variety praised the performances of the entire main cast but felt that the " <unk> aspects " of the story prevented the film from achieving the " goal of being pure entertainment . " Variety also felt the film was far too long . Stanley <unk> , writing for The New Republic , concurred in part with this assessment . <unk> strongly praised the principal cast , calling Newman " first @-@ rate " and writing that Scott 's was " his most credible performance to date . " Laurie , he writes , gives her part " <unk> <unk> touches " ( although he also <unk> criticizes her for over @-@ reliance on Method acting ) . While he found that the script " strains hard to give an air of menace and <unk> to the pool hall " and also declares it " full of pseudo @-@ meaning " , <unk> <unk> <unk> 's " sure , economical " direction , especially in regard to Gleason who , he says , does not so much act as " [ pose ] for a number of pictures which are well arranged by <unk> . It is the best use of a <unk> by a director since <unk> photographed <unk> <unk> as Big Daddy . " The New York Times , despite finding that the film " <unk> a bit " and that the romance between Newman and Laurie 's characters " seems a <unk> far @-@ <unk> " , nonetheless found that The <unk> " speaks powerfully in a universal language that <unk> and reveals bitter truths . " 
- The <unk> received nine Academy Award nominations . The film won two , for Best Art Direction @-@ Set Decoration , Black @-@ and @-@ White ( Harry Horner and Gene <unk> ) and Best <unk> , Black @-@ and @-@ White ( <unk> <unk> ) . The film was also nominated for Best Picture and Newman was nominated for Best Actor in a Leading Role . Gleason and Scott were both nominated for Best Actor in a Supporting Role ; Scott refused the nomination . Laurie was nominated for Best Actress in a Leading Role . <unk> received nominations for Best Director and , with Carroll , for Best Writing , Screenplay Based on Material from Another <unk> . 
- Newman was nominated for a Golden Globe Award for Best Actor . Gleason and Scott were each nominated for Best Supporting Actor and Scott was also nominated as Best New Star of the Year . At the 1962 BAFTA Awards , The <unk> tied with the Soviet film Ballad of a Soldier for Best Film from Any Source . Newman won for Best Foreign Actor and Piper Laurie was nominated for Best Foreign Actress . Gleason was honored as Best Supporting Actor by the National Board of Review of Motion Pictures and the film was named among the Board 's ten best films of 1961 . <unk> was named Best Director by the New York Film Critics Circle Awards and <unk> and Carroll shared the Writers Guild of America Award for Best Written Drama . 
- American Film Institute <unk> 
- <unk> 's 100 Years ... 100 Movies - <unk> 
- <unk> 's 100 Years ... 100 <unk> - <unk> 
- <unk> 's 100 Years ... 100 Heroes and <unk> : 
- Bert Gordon - <unk> <unk> 
- <unk> 's 100 Years ... 100 Movie <unk> : 
- " Eddie , you 're a born loser . " - <unk> 
- <unk> 's 100 Years ... 100 Movies ( 10th <unk> Edition ) - <unk> 
- <unk> 's 10 Top 10 - # 6 Sports Film 
- 
- = = Legacy = = 
- 
- In the decades since its release , The <unk> has cemented its reputation as a classic . Roger <unk> , echoing earlier praise for the performances , direction , and cinematography and adding <unk> for editor <unk> Allen , cites the film as " one of those films where scenes have such psychic weight that they grow in our memories . " He further cites Fast Eddie <unk> as one of " only a handful of movie characters so real that the audience refers to them as <unk> . " TV Guide calls the film a " dark <unk> " offering " a <unk> world whose only bright spot is the top of the pool table , yet [ with ] characters [ who ] maintain a <unk> nobility and grace . " The four leads are again lavishly praised for their performances and the film is summed up as " not to be missed . " 
- Paul Newman reprised his role as Fast Eddie <unk> in the 1986 film The Color of Money , for which he won the Academy Award for Best Actor in a Leading Role . A number of observers and critics have suggested that this Oscar was in <unk> recognition for his performance in The <unk> . In 1997 , the Library of Congress selected The <unk> for preservation in the United States National Film <unk> as " culturally , historically , or <unk> significant . " Carroll and <unk> 's screenplay was selected by the Writers Guild of America in 2006 as the <unk> best motion picture screenplay of all time . In June 2008 , <unk> released its " Ten top Ten " — the best ten films in ten " classic " American film genres — after polling over 1 @,@ 500 people from the creative community . The <unk> was acknowledged as the sixth best film in the sports genre . 
- The <unk> is credited with sparking a resurgence in the popularity of pool in the United States , which had been on the decline for decades . The film also brought recognition to Willie <unk> , who , despite having won multiple world championships , was virtually unknown to the general public . Perhaps the greatest <unk> of the film 's popularity was a real @-@ life pool <unk> named Rudolf <unk> . <unk> claimed in an interview at the time of the film 's release that the character of Minnesota <unk> was based on <unk> , who at the time was known as " New York <unk> " . <unk> immediately adopted the Minnesota <unk> nickname and <unk> his association with the film into book and television deals and other ventures . Author Walter <unk> denied for the rest of his life that <unk> had played any role in the creation of the character . Other players would claim , with greater or lesser degrees of credibility , to have served as models for Fast Eddie , including Ronnie Allen , Ed Taylor , Ed Parker , and Eddie <unk> . 
- 
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deleted file mode 100644
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--- a/PyTorch/LanguageModel/data/wikitext-2/train.txt
+++ /dev/null
@@ -1,36718 +0,0 @@
- 
- = Valkyria Chronicles III = 
- 
- Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside Japan , is a tactical role @-@ playing video game developed by Sega and Media.Vision for the PlayStation Portable . Released in January 2011 in Japan , it is the third game in the Valkyria series . <unk> the same fusion of tactical and real @-@ time gameplay as its predecessors , the story runs parallel to the first game and follows the " Nameless " , a penal military unit serving the nation of Gallia during the Second Europan War who perform secret black operations and are pitted against the Imperial unit " <unk> Raven " . 
- The game began development in 2010 , carrying over a large portion of the work done on Valkyria Chronicles II . While it retained the standard features of the series , it also underwent multiple adjustments , such as making the game more <unk> for series newcomers . Character designer <unk> Honjou and composer Hitoshi Sakimoto both returned from previous entries , along with Valkyria Chronicles II director Takeshi Ozawa . A large team of writers handled the script . The game 's opening theme was sung by May 'n . 
- It met with positive sales in Japan , and was praised by both Japanese and western critics . After release , it received downloadable content , along with an expanded edition in November of that year . It was also adapted into manga and an original video animation series . Due to low sales of Valkyria Chronicles II , Valkyria Chronicles III was not localized , but a fan translation compatible with the game 's expanded edition was released in 2014 . Media.Vision would return to the franchise with the development of Valkyria : Azure Revolution for the PlayStation 4 . 
- 
- = = Gameplay = = 
- 
- As with previous <unk> Chronicles games , Valkyria Chronicles III is a tactical role @-@ playing game where players take control of a military unit and take part in missions against enemy forces . Stories are told through comic book @-@ like panels with animated character portraits , with characters speaking partially through voiced speech bubbles and partially through <unk> text . The player progresses through a series of linear missions , gradually unlocked as maps that can be freely <unk> through and replayed as they are unlocked . The route to each story location on the map varies depending on an individual player 's approach : when one option is selected , the other is sealed off to the player . Outside missions , the player characters rest in a camp , where units can be customized and character growth occurs . Alongside the main story missions are character @-@ specific sub missions relating to different squad members . After the game 's completion , additional episodes are unlocked , some of them having a higher difficulty than those found in the rest of the game . There are also love simulation elements related to the game 's two main <unk> , although they take a very minor role . 
- The game 's battle system , the <unk> system , is carried over directly from <unk> Chronicles . During missions , players select each unit using a top @-@ down perspective of the battlefield map : once a character is selected , the player moves the character around the battlefield in third @-@ person . A character can only act once per @-@ turn , but characters can be granted multiple turns at the expense of other characters ' turns . Each character has a field and distance of movement limited by their Action <unk> . Up to nine characters can be assigned to a single mission . During gameplay , characters will call out if something happens to them , such as their health points ( HP ) getting low or being knocked out by enemy attacks . Each character has specific " Potentials " , skills unique to each character . They are divided into " Personal Potential " , which are innate skills that remain unaltered unless otherwise dictated by the story and can either help or impede a character , and " Battle Potentials " , which are grown throughout the game and always grant <unk> to a character . To learn Battle Potentials , each character has a unique " Masters Table " , a grid @-@ based skill table that can be used to acquire and link different skills . Characters also have Special <unk> that grant them temporary <unk> on the battlefield : Kurt can activate " Direct Command " and move around the battlefield without <unk> his Action Point gauge , the character <unk> can shift into her " Valkyria Form " and become <unk> , while Imca can target multiple enemy units with her heavy weapon . 
- Troops are divided into five classes : Scouts , <unk> , Engineers , <unk> and Armored Soldier . <unk> can switch classes by changing their assigned weapon . Changing class does not greatly affect the stats gained while in a previous class . With victory in battle , experience points are awarded to the squad , which are distributed into five different attributes shared by the entire squad , a feature differing from early games ' method of distributing to different unit types . 
- 
- = = Plot = = 
- 
- The game takes place during the Second Europan War . Gallian Army Squad 422 , also known as " The Nameless " , are a penal military unit composed of criminals , foreign <unk> , and military offenders whose real names are erased from the records and <unk> officially referred to by numbers . <unk> by the Gallian military to perform the most dangerous missions that the Regular Army and Militia will not do , they are nevertheless up to the task , exemplified by their motto , <unk> <unk> , meaning " Always Ready . " The three main characters are <unk> Kurt Irving , an army officer falsely accused of treason who wishes to redeem himself ; Ace <unk> Imca , a female Darcsen heavy weapons specialist who seeks revenge against the Valkyria who destroyed her home ; and <unk> Riela <unk> , a seemingly <unk> young woman who is unknowingly a descendant of the Valkyria . Together with their fellow squad members , these three are tasked to fight against a mysterious Imperial unit known as Calamity Raven , consisting of mostly Darcsen soldiers . 
- As the Nameless officially do not exist , the upper echelons of the Gallian Army exploit the concept of plausible <unk> in order to send them on missions that would otherwise make Gallia lose face in the war . While at times this works to their advantage , such as a successful incursion into Imperial territory , other orders cause certain members of the 422nd great distress . One such member , <unk> , becomes so enraged that he abandons his post and defects into the ranks of Calamity Raven , attached to the ideal of Darcsen independence proposed by their leader , Dahau . At the same time , elements within Gallian Army Command move to erase the Nameless in order to protect their own interests . <unk> by both allies and enemies , and combined with the presence of a traitor within their ranks , the 422nd desperately move to keep themselves alive while at the same time fight to help the Gallian war effort . This continues until the Nameless 's commanding officer , Ramsey Crowe , who had been kept under house arrest , is escorted to the capital city of <unk> in order to present evidence <unk> the weary soldiers and expose the real traitor , the Gallian General that had accused Kurt of Treason . 
- <unk> due to these events , and partly due to the major losses in manpower Gallia suffers towards the end of the war with the Empire , the Nameless are offered a formal position as a squad in the Gallian Army rather than serve as an anonymous shadow force . This is short @-@ lived , however , as following Maximilian 's defeat , Dahau and Calamity Raven move to activate an ancient <unk> super weapon within the Empire , kept secret by their benefactor . Without the support of Maximilian or the chance to prove themselves in the war with Gallia , it is Dahau 's last <unk> card in creating a new Darcsen nation . As an armed Gallian force invading the Empire just following the two nations ' cease @-@ fire would certainly wreck their newfound peace , Kurt decides to once again make his squad the Nameless , asking Crowe to list himself and all under his command as killed @-@ in @-@ action . Now owing allegiance to none other than themselves , the 422nd confronts Dahau and destroys the <unk> weapon . Each member then goes their separate ways in order to begin their lives <unk> . 
- 
- = = Development = = 
- 
- Concept work for Valkyria Chronicles III began after development finished on Valkyria Chronicles II in early 2010 , with full development beginning shortly after this . The director of Valkyria Chronicles II , Takeshi Ozawa , returned to that role for Valkyria Chronicles III . Development work took approximately one year . After the release of Valkyria Chronicles II , the staff took a look at both the popular response for the game and what they wanted to do next for the series . Like its predecessor , Valkyria Chronicles III was developed for PlayStation Portable : this was due to the team wanting to refine the mechanics created for Valkyria Chronicles II , and they had not come up with the " revolutionary " idea that would warrant a new entry for the PlayStation 3 . Speaking in an interview , it was stated that the development team considered Valkyria Chronicles III to be the series ' first true sequel : while Valkyria Chronicles II had required a large amount of trial and error during development due to the platform move , the third game gave them a chance to improve upon the best parts of Valkyria Chronicles II due to being on the same platform . In addition to Sega staff from the previous games , development work was also handled by <unk> The original scenario was written <unk> <unk> , while the script was written by Hiroyuki <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> and <unk> <unk> . Its story was darker and more somber than that of its predecessor . 
- The majority of material created for previous games , such as the <unk> system and the design of maps , was carried over . Alongside this , improvements were made to the game 's graphics and some elements were expanded , such as map layouts , mission structure , and the number of playable units per mission . A part of this upgrade involved creating unique <unk> models for each character 's body . In order to achieve this , the cooperative elements incorporated into the second game were removed , as they took up a large portion of memory space needed for the improvements . They also adjusted the difficulty settings and ease of play so they could appeal to new players while retaining the essential components of the series ' gameplay . The newer systems were decided upon early in development . The character designs were done by <unk> Honjou , who had worked on the previous Valkyria Chronicles games . When creating the Nameless Squad , Honjou was faced with the same problem he had had during the first game : the military uniforms essentially destroyed character individuality , despite him needing to create unique characters the player could identify while maintaining a sense of reality within the Valkyria Chronicles world . The main color of the Nameless was black . As with the previous Valkyria games , Valkyria Chronicles III used the <unk> graphics engine . The anime opening was produced by Production I.G. 
- 
- = = = Music = = = 
- 
- The music was composed by Hitoshi Sakimoto , who had also worked on the previous Valkyria Chronicles games . When he originally heard about the project , he thought it would be a light tone similar to other Valkyria Chronicles games , but found the themes much darker than expected . An early theme he designed around his original vision of the project was rejected . He <unk> the main theme about seven times through the music production due to this need to <unk> the game . The main theme was initially recorded using orchestra , then Sakimoto removed elements such as the guitar and bass , then adjusted the theme using a synthesizer before <unk> segments such as the guitar piece on their own before incorporating them into the theme . The rejected main theme was used as a hopeful tune that played during the game 's ending . The battle themes were designed around the concept of a " modern battle " divorced from a fantasy scenario by using modern musical instruments , constructed to create a sense of <unk> . While Sakimoto was most used to working with synthesized music , he felt that he needed to incorporate live instruments such as orchestra and guitar . The guitar was played by <unk> <unk> , who also arranged several of the later tracks . The game 's opening theme song , " If You Wish for ... " ( <unk> , <unk> Kimi <unk> <unk> <unk> ) , was sung by Japanese singer May 'n . Its theme was the reason soldiers fought , in particular their wish to protect what was precious to them rather than a sense of responsibility or duty . Its lyrics were written by <unk> <unk> , who had worked on May 'n on previous singles . 
- 
- = = = Release = = = 
- 
- In September 2010 , a teaser website was revealed by Sega , hinting at a new Valkyria Chronicles game . In its September issue , Famitsu listed that Senjō no Valkyria 3 would be arriving on the PlayStation Portable . Its first public appearance was at the 2010 Tokyo Game Show ( TGS ) , where a demo was made available for journalists and attendees . During the publicity , story details were kept <unk> so as not to <unk> too much for potential players , along with some of its content still being in flux at the time of its reveal . To promote the game and detail the story leading into the game 's events , an episodic Flash visual novel written by <unk> began release in January 2011 . The game was released January 27 , 2011 . During an interview , the development team said that the game had the capacity for downloadable content ( DLC ) , but that no plans were finalized . Multiple DLC maps , featuring additional missions and <unk> characters , were released between February and April 2011 . An expanded edition of the game , Valkyria Chronicles III Extra Edition , released on November 23 , 2011 . <unk> and sold at a lower price than the original , Extra Edition game with seven additional episodes : three new , three chosen by staff from the game 's DLC , and one made available as a pre @-@ order bonus . People who also owned the original game could transfer their save data between versions . 
- Unlike its two predecessors , Valkyria Chronicles III was not released in the west . According to Sega , this was due to poor sales of Valkyria Chronicles II and the general unpopularity of the PSP in the west . An unofficial fan translation patch began development in February 2012 : players with a copy of Valkyria Chronicles III could download and apply the patch , which translated the game 's text into English . <unk> with the Extra Edition , the patch was released in January 2014 . 
- 
- = = Reception = = 
- 
- On its day of release in Japan , Valkyria Chronicles III topped both platform @-@ exclusive and multi @-@ platform sales charts . By early February , the game sold 102 @,@ <unk> units , coming in second overall to The Last Story for the Wii . By the end of the year , the game had sold just over 152 @,@ 500 units . 
- Famitsu enjoyed the story , and were particularly pleased with the improvements to gameplay . Japanese gaming site Game Watch <unk> , despite negatively noting its pacing and elements recycled from previous games , was generally positive about its story and characters , and found its gameplay entertaining despite off @-@ putting difficulty spikes . <unk> writer <unk> <unk> , in a " Play Test " article based on the game 's <unk> demo , felt that Valkyria Chronicles III provided a " profound feeling of closure " for the Valkyria Chronicles series . He praised its gameplay despite annoying limitations to aspects such as special abilities , and positively noted its shift in story to a tone similar to the first game . 
- PlayStation Official Magazine - UK praised the story 's <unk> of Gallia 's moral standing , art style , and most points about its gameplay , positively noting the latter for both its continued quality and the tweaks to balance and content . Its one major criticism were multiple difficulty spikes , something that had affected the previous games . Heath Hindman of gaming website PlayStation <unk> praised the addition of non @-@ linear elements and improvements or removal of mechanics from Valkyria Chronicles II in addition to praising the returning gameplay style of previous games . He also positively noted the story 's serious tone . Points criticized in the review were recycled elements , awkward cutscenes that seemed to include all characters in a scene for no good reason , pacing issues , and occasional problems with the game 's AI . 
- In a preview of the TGS demo , Ryan Geddes of IGN was left excited as to where the game would go after completing the demo , along with enjoying the improved visuals over Valkyria Chronicles II . Kotaku 's Richard <unk> was highly positive about the game , citing is story as a return to form after Valkyria Chronicles II and its gameplay being the best in the series . His main criticisms were its length and gameplay repetition , along with expressing regret that it would not be localized . 
- 
- = = Legacy = = 
- 
- Kurt and Riela were featured in the Nintendo 3DS crossover Project X Zone , representing the Valkyria series . Media.Vision would return to the series to develop Valkyria : Azure Revolution , with Ozawa returning as director . Azure Revolution is a role @-@ playing video game for the PlayStation 4 that forms the beginning of a new series within the Valkyria franchise . 
- 
- = = = Adaptations = = = 
- 
- Valkyria Chronicles 3 was adapted into a two @-@ episode original video animation series in the same year of its release . <unk> Senjō no Valkyria 3 : <unk> <unk> no <unk> ( <unk> <unk> , lit . Valkyria of the Battlefield 3 : The <unk> Taken for <unk> 's Sake ) , it was originally released through PlayStation Network and <unk> between April and May 2011 . The initially @-@ planned release and availability period needed to be extended due to a stoppage to <unk> during the early summer of that year . It later released for DVD on June 29 and August 31 , 2011 , with separate " Black " and " Blue " editions being available for purchase . The anime is set during the latter half of Valkyria Chronicles III , detailing a mission by the Nameless against their Imperial rivals Calamity Raven . The anime was first announced in November 2010 . It was developed by A @-@ 1 Pictures , produced by Shinji <unk> , directed by <unk> <unk> , and written by Hiroshi <unk> . Sakimoto 's music for the game was used in the anime . 
- The anime 's title was inspired by the principle purpose of the Nameless : to suffer in battle for the goals of others . A <unk> attached to the project during development was " The Road to <unk> " , which referenced the <unk> Tank Museum in Moscow . The game 's main theme was how the characters regained their sense of self when stripped of their names and identities , along with general themes focused on war and its consequences . While making the anime , the production team were told by Sega to make it as realistic as possible , with the consequence that the team did extensive research into aspects such as what happened when vehicles like tanks were overturned or damaged . Due to it being along the same timeline as the original game and its television anime adaptation , the cast of Valkyria Chronicles could make appearances , which pleased the team . The opening theme , " <unk> ( Light ) <unk> " ( <unk> @-@ <unk> ) , was sung by Japanese singer <unk> . The ending theme , " <unk> the Flowers of Light Will Bloom " ( <unk> , <unk> <unk> <unk> no <unk> ) , was sung by <unk> <unk> . Both songs ' lyrics were written by their respective artists . 
- Two manga adaptations were produced , following each of the game 's main female protagonists Imca and Riela . They were Senjō no Valkyria 3 : <unk> <unk> <unk> no <unk> ( 戦場のヴァルキュリア3 <unk> , lit . Valkyria of the Battlefield 3 : The Flower of the Nameless Oath ) , illustrated by <unk> <unk> and eventually released in two volumes after being serialized in Dengeki <unk> between 2011 and 2012 ; and Senjō no Valkyria 3 : <unk> <unk> no <unk> <unk> ( 戦場のヴァルキュリア3 <unk> , lit . Valkyria of the Battlefield 3 <unk> <unk> of the Crimson Fate ) , illustrated by <unk> <unk> and eventually released in a single volume by Kadokawa Shoten in 2012 . 
- 
- 
- = Tower Building of the Little Rock Arsenal = 
- 
- The Tower Building of the Little Rock Arsenal , also known as U.S. Arsenal Building , is a building located in MacArthur Park in downtown Little Rock , Arkansas . Built in 1840 , it was part of Little Rock 's first military installation . Since its decommissioning , The Tower Building has housed two museums . It was home to the Arkansas Museum of Natural History and Antiquities from 1942 to 1997 and the MacArthur Museum of Arkansas Military History since 2001 . It has also been the headquarters of the Little Rock Æsthetic Club since 1894 . 
- The building receives its name from its distinct octagonal tower . Besides being the last remaining structure of the original Little Rock Arsenal and one of the oldest buildings in central Arkansas , it was also the birthplace of General Douglas MacArthur , who became the supreme commander of US forces in the South Pacific during World War II . It was also the starting place of the Camden Expedition . In 2011 it was named as one of the top 10 attractions in the state of Arkansas by <unk> 
- 
- = = Construction = = 
- 
- The arsenal was constructed at the request of Governor James <unk> Conway in response to the perceived dangers of frontier life and fears of the many Native Americans who were passing through the state on their way to the newly established Oklahoma Territory . Thirty @-@ six acres were appropriated on the outskirts of Little Rock by Major Robert B. Lee of the U.S. Army . The land had been previously used as a racetrack by the local jockey club . John <unk> Walker , a builder for the Federal Government , supervised the construction . Originally $ 14 @,@ 000 was allocated for the construction of the arsenal , but proved inadequate . The budget was later increased to $ 30 @,@ 000 . Work began on the Tower Building in 1840 , and it was the first permanent structure of the arsenal to be built . Being originally constructed to store ammunition , the building was designed with 3 @-@ foot @-@ thick ( 0 @.@ 91 m ) exterior walls . The original plans called for it to be built of stone , however , masonry was used instead . The Arkansas Gazette referred to the structure as " A splendid specimen of masonry " . 
- 
- = = Civil War = = 
- 
- For several years the arsenal , which was owned by the federal government , served as a simple arms depot and was staffed with only a handful of soldiers . But in November 1860 , with the American Civil War on the horizon , a company of the Second United States Artillery , consisting of sixty @-@ five men , was transferred to Little Rock under the command of Captain James Totten . On January 15 , 1861 , the state legislature decided to hold a referendum to determine if a state convention should be held to consider the issue of <unk> and to elect delegates to such a convention . It was planned for February 18 ; however , events at the arsenal , would not wait . On January 28 , then Governor Henry Massey Rector informed Captain Totten that he and his soldiers would be " permitted to remain in the possession of the Federal officers until the State , by authority of the people , shall have determined to <unk> their connection with the General Government , " Totten responded to this by telling the Governor that his orders came from the United States Government and began a desperate but ultimately futile dispatch of letters and <unk> asking for reinforcements , although rumors were widely spread that they were already coming . The first telegraph wire to span between Little Rock and Memphis had recently been completed . Local attorney John M Harrel was asked to compose the first telegraph dispatched from Arkansas 's capital . In his message , Harrel reported unconfirmed rumors that more federal troops had been sent to reinforce the Little Rock Arsenal . 
- The United States troops at the outposts of the western frontier of the state and in the Indian nation have all been recalled from winter quarters to reinforce the garrison at Fort Smith . The garrison at Fort Smith had been previously transferred to the United States Arsenal in this city ( Little Rock ) . The arsenal is one of the richest <unk> of military stores in the United States and is supposed to be the ultimate destination of the <unk> [ sic ] ordered from the frontier . 
- <unk> M Harrel <unk> , January 31 , 1861 
- The item was intended simply as a piece of news , but telegraph lines quickly spread the news throughout the state , <unk> procession sentiment . The rumor was interpreted by some <unk> as a call from the governor to assemble to help expel the federal troops from the arsenal . By February 5 , six militia units , consisting of 1 @,@ 000 men , with a guarantee that the numbers could be increased to 5 @,@ 000 if the situations deemed it necessary , had assembled in Little Rock . Governor Rector vehemently denied ordering the troops to assemble or giving any order at all in connection with the troops . Faced with the fact that the military had assembled believing they were following his orders and the consensus of the citizens of Little Rock against any armed conflict between the civilian army and federal troops , Governor Rector was forced to take control of the situation . On February 6 , he sent a formal demand for surrender of the arsenal to Captain Totten , 
- This movement is prompted by the feeling that <unk> the citizens of this State that in the present emergency the arms and munitions of war in the Arsenal should be under the control of the State authorities , in order to their security . This movement , although not authorized by me , has assumed such an aspect that it becomes my duty , as the executive of this <unk> , to <unk> my official authority to prevent a collision between the people of the State and the Federal troops under your command . I therefore demand in the name of the State the delivery of the possession of the Arsenal and munitions of war under your charge to the State authorities , to be held subject to the action of the convention to be held on the 4th of March next . 
- Perhaps because Abraham Lincoln had not yet been inaugurated as President , Captain Totten received no instructions from his superiors and was forced to withdraw his troops . He agreed to surrender the arsenal as long as the governor agreed to three provisions : 
- The governor would take possession of the arsenal in the name of the United States . 
- The soldiers would be allowed safe passage in any direction carrying any personal and public property besides munitions of war . 
- The soldiers would be allowed to march away as men leaving under orders , not as conquered and surrendering soldiers . 
- On the morning of February 8 , 1861 , Rector and Totten signed an agreement placing the arsenal in the hands of state officials . That afternoon , the citizen militia marched to the arsenal with Governor Rector at its head . All of the federal troops had left at this point , except Totten who had stayed behind to listen to the Governor 's speech and to hand the arsenal over in person . 
- The Little Rock Arsenal was classified in 1860 as an " arsenal of deposit , " meaning that it was simply a warehouse for the storage of weapons intended for the use of the state militia in times of crisis . Thus there were no substantial operations for ordnance fabrication or repairs , nor for the manufacture of cartridges at the time the Arsenal fell into State hands . Most of these operations were started from scratch through the efforts of the Arkansas Military Board . 
- Inside the Little Rock Arsenal after its seizure in February , 1861 , the Confederates <unk> some 10 @,@ 247 weapons , 250 @,@ 000 musket cartridges , and 520 @,@ 000 percussion caps , as well as the four bronze cannon of Totten 's battery . Long arms in the Arsenal 's inventory consisted of : 
- M1822 .69 cal ( flintlock ) 5 @,@ 625 
- M1822 .69 cal ( percussion @-@ converted ) 53 
- <unk> .69 cal smoothbore ( percussion ) 357 
- <unk> <unk> cal rifle @-@ <unk> 900 
- <unk> common rifles 125 
- <unk> rifle ( " Mississippi Rifle " ) 54 
- <unk> <unk> 2 
- Hall 's <unk> 267 
- Hall 's rifles ( flintlock ) 2 @,@ 864 
- Total 10 @,@ 247 
- Of this number , approximately <unk> weapons were <unk> , or ready @-@ for @-@ issue . Note there were only 1 @,@ 364 percussion weapons available . <unk> of the weapons found in the Arsenal is somewhat sketchy , but from various records it can be surmised that the 5th , 6th , 7th , and 8th Arkansas Infantry Regiments , mustered in June , 1861 , were issued <unk> / M1822 .69 caliber <unk> . The 9th and 10th Arkansas , four companies of Kelly 's 9th Arkansas Battalion , and the 3rd Arkansas Cavalry Regiment were issued flintlock Hall 's Rifles . The units comprising the infantry force of Van Dorn 's Army of the West were the 1st and 2nd Arkansas Mounted Rifles were also armed with M1822 <unk> from the Little Rock Arsenal . By the time the 11th and 12th Arkansas Infantry Regiments mustered in at Little Rock , the supply of arms had been almost completely exhausted , and only old " <unk> " weapons were left . 
- Most of the equipment , arms , and machinery at the Little Rock Arsenal was removed to east of the Mississippi River by order of <unk> Gen. Earl Van Dorn in April and May 1862 , and accountability for it is lost at that point . By all appearances , the equipment was sent down the river to Napoleon , Arkansas , and from there to Jackson Mississippi , where it was probably destroyed during the <unk> campaign in the early summer of 1863 . 
- Major General Thomas C. Hindman , sent to command the district of Arkansas in May , 1862 , found the state nearly destitute of military material . Hindman established another armory at Arkadelphia , and revived the Little Rock Arsenal as a collection point and depot for <unk> and ammunition manufacture for small arms . Hindman recorded : 
- " Machinery was made for manufacturing percussion caps and small arms , and both were turned out in small quantity , but of excellent quality . Lead mines were opened and worked , and a chemical laboratory was established and successfully operated in aid of the Ordnance Department and in the manufacture of <unk> , <unk> oil , spirits of <unk> , the various <unk> of iron , and other valuable medicines . Most of these works were located at or near Arkadelphia on the <unk> River , 75 miles south from Little Rock . The tools , machinery , and the material were gathered <unk> or else made by hand labor . Nothing of this sort had been before attempted on Government account in Arkansas to my knowledge , except for the manufacture of small arms , the machinery for which was taken away by General Van Dorn and there was neither capital nor sufficient enterprise among the citizens to engage in such undertakings <unk> A further supply , along with lead and caps , was procured from the citizens of Little Rock and vicinity by donation , purchases , and <unk> . 
- This ammunition , and that which I brought with me , was rapidly prepared for use at the Laboratory established at the Little Rock Arsenal for that purpose . As illustrating as the <unk> <unk> of material in the country , the fact may be stated that it was found necessary to use public documents of the State Library for cartridge paper . <unk> were employed or conscripted , tools purchased or impressed , and the repair of the damaged guns I brought with me and about an equal number found at Little Rock commenced at once . But , after inspecting the work and observing the spirit of the men I decided that a garrison 500 strong could hold out against Fitch and that I would lead the remainder - about 1500 - to <unk> 'l <unk> as soon as shotguns and rifles could be obtained from Little Rock instead of <unk> and lances , with which most of them were armed . Two days <unk> before the change could be effected . " 
- The Confederate ordnance establishment at Little Rock was reactivated in August , 1862 . Looking around for a suitable person to head this activity , General Hindman turned to the Confederate Navy and borrowed Lieutenant John W. Dunnington . Lt. Dunnington was the commander of the gunboat <unk> <unk> , which had been brought to Little Rock in hopes of converting it to an ironclad . Dunnington was selected to head the ordnance works at Little Rock , and although he continued to draw his pay from the Confederate Navy Department , he was placed in charge of all Confederate ordnance activities ( which included artillery functions ) there with the rank of lieutenant colonel . 
- Lt. Col. Dunnington 's " <unk> for the month of August , 1862 , at Little Rock Arsenal , <unk> , " are found in Vol . 149 , Chapter IV of the " <unk> Rebel Ordnance Records , " and are most enlightening as to the scope of Confederate ordnance activities at Little Rock during this crucial time . According to Dunnington , " When I assumed command at this Post , all material had been removed to Arkadelphia . There were no persons employed . No shops were open for repair of arms or for <unk> ammunition . Material , tools , etc . , had to be procured as well as the employment of laborers . Work commenced the last part of the month . " 
- The military force at Little Rock under Dunnington 's command consisted of four officers : himself , Major John B. <unk> , Captain <unk> Green , and 2nd Lt. <unk> Murphy . In addition to these , he had 20 enlisted men and a civilian force composed of a <unk> , 2 clerks , 3 <unk> for repairing small arms , a <unk> , 26 laborers in the ammunition laboratory , and a carpenter for making packing boxes . 
- During the month of August , 1862 , the following work was performed : " <unk> : one pair of musket bullet moulds ; 10 @,@ 000 buck & ball shot cartridges ; repaired : 750 <unk> , shotguns , and rifles ; received and repaired : ordnance stores and <unk> ; performed : guard , office , and police duties ; inspected : <unk> at Camden and Arkadelphia . " 
- Lt. Col. Dunnington continued to build up his works at Little Rock until November 1862 , when Captain Sanford C. Faulkner ( composer of The Arkansas Traveler ) was placed in charge of the Arsenal . Dunnington presumably returned to his naval duties and the <unk> . 
- A " Summary of the Work <unk> for November , 1862 , Little Rock Arsenal " shows : <unk> : 
- 75 @,@ 000 buck & ball cartridges - percussion 
- 14 @,@ 000 buck & ball cartridges - flint 
- 275 paper <unk> 
- 117 rounds , 6 @-@ pounder <unk> shot 
- 130 rounds , 6 @-@ pounder ball shot 
- 96 ammunition packing boxes 
- <unk> : 
- 2 @,@ 236 shotguns and rifles ( repaired mostly for troops in service ) 
- 23 pistols ( repaired mostly for troops in service ) 
- <unk> & <unk> : 
- <unk> packages of ordnance and ordnance stores received and mostly issued to troops in service . 
- <unk> and painted : 
- 4 gun carriages 
- Performed : 
- Guard , office , and police duties . 
- Perhaps the most <unk> points of the above " Summary of Work " and those for following months are that the standard ammunition made was . " buck & ball " , indicating that the .69 caliber <unk> and shotguns remained the predominant caliber weapon in use , and of this , nearly one sixth or more of all small arms ammunition was still for flintlock weapons , indicating that no less than a sixth of the Confederate troops in this vicinity were still armed with obsolete flintlock weapons . 
- The " <unk> of Work done at Little Rock Arsenal , <unk> " continue at about the same pace and scale from August 1862 until August 1863 . <unk> to the " Summary " for August , 1863 is the ominous <unk> , " During the last week in the month , nearly all stores at the Arsenal have been packed and sent to Arkadelphia , in obedience to orders from Chief of Ordnance , District of Arkansas . " This then marks the beginning of the evacuation of ordnance activities from Little Rock , with the city being surrendered to the advancing Federal troops of Frederick Steele 's Arkansas Expedition on September 11 , 1863 . 
- In 1864 , after Little Rock fell to the Union Army and the arsenal had been recaptured , General <unk> Steele marched 8 @,@ 500 troops from the arsenal beginning the Camden Expedition . 
- The arsenal was briefly seized once more by Joseph Brooks loyalists during the Brooks @-@ <unk> War of 1874 . 
- 
- = = <unk> = = 
- 
- In 1873 , the building was renamed Little Rock Barracks and used as a barracks for married officers and their families . The building was drastically altered the inside and outside . Prior to renovation , a rear basement door provided the only entrance to the building , while the tower served as a hoist to move munitions between floors . By 1868 , front and rear <unk> had been added to the building , as well as interior walls and stairs , some of which remain today , including the central staircase . In 1880 , Douglas MacArthur was born on the northwest upper floor of this building while his father , Captain Arthur MacArthur , was stationed there . 
- In the 1880s , the federal government began closing many small <unk> around the country in favor of smaller ones built near railroads for quick deployment . The arsenal commander received word from Washington that the Little Rock site must be abandoned " not later than October 1 , 1890 . " On April 12 , 1893 the tower building and the surrounding buildings were traded to the city of Little Rock for 1 @,@ 000 acres ( 4 km ² ) in North Little Rock under the condition that the building and land be " forever exclusively devoted to the uses and purposes of a public park " for 1 @,@ 000 acres ( 4 km ² ) in Big Rock Mountain on the north side of the Arkansas River , present day North Little Rock . That site later became Fort Logan H. Roots . All of the original buildings surrounding the Tower Building were demolished . 
- 
- = = Æsthetic Club = = 
- 
- In 1894 the Little Rock Æsthetic Club , one of the oldest women 's societies west of the Mississippi River , moved into the Tower Building . This was prompted due to increased membership and a need for larger , more permanent quarters . The previous year , club members working with women 's organizations throughout the state , raised money to <unk> the Arkansas Building of the Columbian Exposition at The Chicago World 's Fair . At the fair 's conclusion , artifacts from the exhibit were displayed in the Tower Building , with the Æsthetic Club invited to meet in the " Columbian Room . " 
- Except for Æsthetic Club meetings , the Tower Building remained largely unoccupied for almost fifty years and suffered significant deterioration . The Æsthetic Club provided much @-@ needed financial support during the period and even paid the electric bill during the Great Depression . The Æsthetic Club is still headquartered in the Tower Building . 
- 
- = = Public use = = 
- 
- The building and the surrounding park were used for many public purposes throughout the early 20th century . The Tower Building served as headquarters for the United Confederate Veterans <unk> , May 15 – 18 , 1911 . Over 106 @,@ 000 Civil War veterans , the largest popular gathering in the history of the city up to that time , attended and were housed in the building or camped in the park , which had also become a popular camping area . Later the building served as an armory for the Arkansas National Guard . In 1912 , the second floor of the Tower Building became Little Rock 's first public library . In 1917 , Little Rock built a fire station in the park , that building is now gone . A band shell named for H. H. Foster also was built in the park during this time , but also no longer exists . In 1936 , Works Progress Administration built the Museum of Fine Arts , now called the Arkansas Arts Center , just south of the Tower Building . 
- The arsenal was listed in the National Register of Historic Places in 1970 . Due to its association with the Camden Expedition of 1864 , the arsenal may be included in the Camden Expedition Sites National Historic Landmark designated in 1994 . 
- In 1942 , the Tower Building was renovated due to the efforts of the Æsthetic Club , Little Rock philanthropist Frederick W. <unk> , and the Works Progress Administration . It became the new home of The Arkansas Museum of Natural History and Antiquities , which had been located in Little Rock City Hall . The museum remained in the tower building for approximately fifty @-@ five years . The area surrounding the Tower Building had been known as Arsenal Park when the first decommissioned and then later renamed City Park . Due to the efforts of Bernie Babcock , however , the city finally named it MacArthur Park in 1942 in honor of Douglas MacArthur . 
- In 1997 , the Museum of Science and Natural History merged with the Little Rock Children 's Museum , which had been located in Union Station , to form the Arkansas Museum of Discovery . The new museum was relocated to a historic building in the Little Rock River Market District . The MacArthur Museum of Arkansas Military History opened on May 19 , 2001 in the Tower Building . The new museum 's goal is to educate and inform visitors about the military history of Arkansas , preserve the Tower Building , honor servicemen and <unk> of the United States and commemorate the birthplace of Douglas MacArthur . 
- 
- 
- = <unk> Mary Barker = 
- 
- <unk> Mary Barker ( 28 June 1895 – 16 February 1973 ) was an English illustrator best known for a series of fantasy illustrations depicting fairies and flowers . Barker 's art education began in girlhood with correspondence courses and instruction at the Croydon School of Art . Her earliest professional work included <unk> cards and juvenile magazine illustrations , and her first book , Flower Fairies of the Spring , was published in 1923 . Similar books were published in the following decades . 
- Barker was a devout Anglican , and donated her artworks to Christian fundraisers and missionary organizations . She produced a few Christian @-@ themed books such as The Children ’ s Book of Hymns and , in collaboration with her sister Dorothy , He Leadeth Me . She designed a stained glass window for St. Edmund 's Church , Pitlake , and her painting of the Christ Child , The Darling of the World Has Come , was purchased by Queen Mary . 
- Barker was equally proficient in <unk> , pen and ink , oils , and <unk> . Kate Greenaway and the Pre @-@ <unk> were the principal influences on her work . She claimed to paint instinctively and rejected artistic theories . Barker died in 1973 . Though she published Flower Fairy books with spring , summer , and autumn themes , it wasn 't until 1985 that a winter collection was assembled from her remaining work and published posthumously . 
- 
- = = Biography = = 
- 
- 
- = = = Early life = = = 
- 
- Barker was born the second daughter and youngest child of Walter Barker , a partner in a seed supply company and an amateur artist , and his wife Mary Eleanor ( Oswald ) Barker on 28 June 1895 at home at 66 Waddon Road in Croydon , Surrey , England . Barker was an <unk> as a child , and cared for at home by her parents . Later , her sister and elder by two years , Dorothy Oswald Barker , continued the care . 
- The family of four was moderately well off , and belonged to the lower end of the upper middle class . A <unk> , a governess , and a cook to prepare special meals for Barker were hired . She spent much time in bed at home amusing herself with painting books and a nursery library that included the works of Kate Greenaway and Randolph Caldecott – two artists who exerted strong influences on her later art . 
- 
- = = = Art education and first professional work = = = 
- 
- Barker took correspondence courses in art , probably until about 1919 . In 1908 at 13 years , she entered an evening class at the Croydon School of Art , and attended the school into the 1940s . In time , she received a teaching position . 
- In 1911 , Raphael Tuck & Sons bought four of Barker 's " little drawings " for half a sovereign , and published them as postcards . In October 1911 , she won second prize in the Croydon Art Society 's poster competition , and shortly afterward was elected the youngest member of the Society . The art critic for the Croydon Advertiser remarked , " Her drawings show a remarkable freedom of spirit . She has distinct promise . " 
- Following her father ’ s death in June 1912 , the seventeen @-@ year @-@ old Barker submitted art and poetry to My Magazine , Child ’ s Own , Leading <unk> , and Raphael Tuck <unk> in an effort to support both her mother and sister . Her sister Dorothy taught kindergarten in two private schools before opening a kindergarten at home . She brought in some money for the family 's support while supervising the household . 
- 
- = = = Flower Fairies of the Spring , 1923 = = = 
- 
- Fairies became a popular theme in art and literature in the early 20th century following the releases of The Coming of the Fairies by Sir Arthur Conan Doyle , Peter Pan by <unk> <unk> , and the fairy @-@ themed work of Australian Ida <unk> <unk> . Queen Mary made such themes even more popular by sending <unk> postcards to friends during the 1920s . In 1918 , Barker produced a <unk> series depicting elves and fairies . 
- In 1923 , Barker sent her flower fairy paintings to various publishers . Blackie paid £ 25 for 24 paintings with accompanying verses , but it wasn 't until publication of Flower Fairies of the Summer in 1925 that Barker received royalties for her work . Mary <unk> Clayton Calthrop , wife of author Dion Clayton Calthrop , wrote in April 1925 about Barker and Flower Fairies of the Spring : " She has such exquisite taste , besides <unk> . " 
- 
- = = = The Waldrons = = = 
- 
- In 1924 , the family moved into a four @-@ level , semi @-@ detached Victorian house at 23 The Waldrons . Barker had a studio built in the garden and her sister conducted a kindergarten in a room at the back of the house . The family lived <unk> and attended both St. Edmund 's and St. Andrew 's in Croydon – " low " churches for the less privileged . Barker sometimes incorporated portraits of her fellow parishioners in her religious works . She was described by Canon Ingram Hill as " one of the pillars " of St. Andrew 's . 
- The children in the kindergarten modelled for the Flower Fairies until the kindergarten closed in 1940 . In an interview in 1958 , Barker said , " My sister ran a kindergarten and I used to borrow her students for models . For many years I had an atmosphere of children about me – I never <unk> it . " She also painted the children of relatives as well as Gladys <unk> , the <unk> ' young housekeeper , who posed for the Primrose Fairy in 1923 . The plants were painted from life , and if a specimen was not readily at hand , <unk> Gardens staff would provide her the specimens needed . Barker designed and built the Flower Fairy costumes , and based each on the flowers and leaves of the particular plant to be illustrated . The costumes were kept in a trunk in her studio along with wings made of twigs and <unk> . Each was broken down after an illustration was completed and the parts recycled for other costumes . She often referred to Dion Clayton Calthrop 's English Costume . 
- 
- = = = Middle years = = = 
- 
- In the late 1920s , Barker began to doubt she was doing enough for the church and considered focusing solely on sacred works . Family and friends recommended she continue secular and sacred works , which she did . 
- Barker continued to attend evening classes at the Croydon Art School between the 1920s and the 1940s , eventually receiving a teaching position . She took <unk> trips to <unk> and Storrington in Sussex and to Cornwall and the southern coast with family and friends . She visited and stayed with artist Margaret <unk> in <unk> , Surrey and with family in <unk> , Near Whitby , North Yorkshire . 
- In 1940 , the Barker 's live @-@ in maid retired , and Dorothy Barker closed her school at the back of the house in The Waldrons . She continued to supervise the household , and to give both her mother and sister the care they needed . Dorothy and her sister collaborated upon only two books : Our Darling 's First Book and the Christian @-@ themed , He Leadeth Me . In 1954 Dorothy Barker died of a heart attack . Barker was unable to pursue her art to any significant extent following her sister 's death , as all the care of her aged mother devolved upon her , but she did manage to begin planning a stained glass window design in her sister 's memory for St. Edmund 's , Pitlake . 
- 
- = = = Later life and death = = = 
- 
- Barker 's mother died in 1960 , and , in 1961 , Barker moved from 23 The Waldrons to 6 <unk> Avenue in Croydon . She restored a <unk> in Storrington , Sussex , England , bequeathed by her friend Edith Major , and named it St. Andrew 's . After taking up residence , her health began to deteriorate . She was in and out of nursing and <unk> homes , and tended by relatives and friends . 
- Barker died at Worthing Hospital on 16 February 1973 , aged 77 years . Two funeral services were held – one in Storrington Church and one in Barker 's <unk> . Her ashes were scattered in Storrington churchyard . In 1989 , Frederick Warne , a division of Penguin Books since 1983 , acquired the Flower Fairies properties . 
- 
- = = Art = = 
- 
- Barker worked principally in <unk> with pen @-@ and @-@ ink , but she was equally competent in black @-@ and @-@ white , in oils , and in <unk> . She carried a <unk> with her for capturing interesting children . She once indicated , " I have always tried to paint instinctively in a way that comes naturally to me , without any real thought or attention to artistic theories . " 
- Kate Greenaway was a childhood favorite and an influence on her art . Barker 's child subjects wear nostalgic clothing as Greenaway 's children do , though Barker 's children are less melancholy and less flat in appearance , due perhaps to advances in printing technology . Barker studied flowers with an analytical eye and was friend to children 's illustrator , Margaret <unk> . Along with Greenaway , illustrator Alice B. Woodward also influenced Barker 's work . 
- The Pre @-@ <unk> were a strong , lifelong influence on Barker . She once indicated , " I am to some extent influenced by them — not in any technical sense , but in the choice of subject matter and the feeling and atmosphere they could achieve . " She admitted a fondness for the early paintings of John Everett Millais and " the wonderful things " of Edward <unk> @-@ Jones . 
- 
- = = = Depictions of children = = = 
- 
- Barker 's sketches , drawings , and paintings of children were given to friends or to the parents of the subjects , donated to charitable institutions and church sponsored events , or exhibited through various art organizations . She illustrated magazine covers , dust jackets , and produced series of postcards for Raphael Tuck and other publishers such as <unk> Children of the Allies ( 1915 ) , Seaside <unk> ( 1918 ) , and Shakespeare 's Boy and Girl Characters ( 1917 , 1920 ) . Her own Old Rhymes for All Times ( 1928 ) and The Lord of the Rushie River ( 1938 ) , a tale about a girl who lives among <unk> on a <unk> , were critically well received . Set about 1800 , Groundsel and Necklaces ( 1943 ) tells of a girl named Jenny who rescues her family from poverty through the agency of the fairies . The story features an old <unk> @-@ like man called Mr. <unk> and <unk> suggests a <unk> social consciousness . Simon the Swan , intended as a sequel to Rushie River was outlined in 1943 with Groundsel , but only developed in 1953 . It was published posthumously in 1988 and is critically considered less successful than Groundsel . 
- 
- = = = Christian @-@ themed works = = = 
- 
- Barker was a devout Christian , and produced religious @-@ themed works throughout her life . She published eight postcards and five guardian angel birthday cards for the Society for <unk> Christian Knowledge in 1916 and in 1923 respectively . Christmas cards were designed for The Girls ' Friendly Society over a 20 @-@ year period , and the first three designs sold out a combined printing of 46 @,@ 500 in 1923 . An original design for the society called The Darling of the World Has Come was purchased by Queen Mary for ₤ 5 @.@ 5 @.@ 0 in 1926 . The Croydon Art Society hung Barker 's booklet cover design for the Society for the <unk> of the Gospel in its November 1919 exhibition . 
- Religious @-@ themed books include The Children 's Book of Hymns ( 1929 ) and He Leadeth Me ( 1933 ) , the latter written in collaboration with her sister . Major religious works include the <unk> in oil , The Feeding of the Five Thousand ( 1929 ) , for the chapel in Llandaff House , a home for destitute women at Penarth , Wales , and The Parable of the Great Supper ( 1934 ) for St. George 's Chapel , Waddon . The Feeding has since disappeared , and only a black @-@ and @-@ white photograph dated 1929 <unk> the work . In 1941 , she completed oil panels on the subject of the seven sacraments for the baptismal font at St. Andrew 's , South Croydon . She designed baptismal rolls for the wall behind the font in 1948 and 1962 . In 1946 , she completed the 4 x 7 <unk> oil painting , Out of Great <unk> , for the Memorial Chapel of <unk> Methodist Church . Following the death of her sister in 1954 , Barker began designs for a stained glass memorial window depicting Christ preparing to wash the feet of his disciples . Her last religious @-@ themed work , it was installed in St. Edmund 's , Pitlake , in 1962 . 
- 
- = = Works = = 
- 
- 
- = = = Cards = = = 
- 
- <unk> Children of the Allies ; J. Salmon , 1916 
- National Mission ; Society for the Preservation of Christian Knowledge , 1916 
- Shakespeare 's Boy Characters ; C. W. Faulkner , 1917 
- Shakespeare 's Girl Characters ; C. W. Faulkner , 1920 
- Seaside Holiday ; J. Salmon , 1918 , 1921 
- <unk> and Fairies ; S. Harvey , 1918 
- Guardian Angel ; Society for the Preservation of Christian Knowledge , 1923 
- Christmas cards ; Girls ' Friendly Society , 1920s , 1930s 
- Christmas cards ( US ) ; Barton @-@ Colton , 1920s , 1930s 
- Beautiful Bible Pictures ; Blackie , 1932 
- 
- = = = Books = = = 
- 
- Flower Fairies of the Spring ; Blackie , 1923 
- Spring Songs with Music ; Blackie , 1923 
- Flower Fairies of the Summer ; Blackie , 1925 
- Child <unk> in Picture and Verse ( by M. K. <unk> ) ; Blackie , 1925 
- Flower Fairies of the Autumn ; Blackie , 1926 
- Summer Songs with Music ; Blackie , 1926 
- The Book of the Flower Fairies ; Blackie , 1927 
- Autumn Songs with Music ; Blackie , 1927 
- Old Rhymes for All Times ; Blackie , 1928 
- The Children ’ s Book of Hymns ; Blackie , 1929 ; <unk> . 1933 
- Our Darling ’ s First Book ( written in collaboration with Dorothy Barker ) ; Blackie , 1929 
- The Little Picture Hymn Book ; Blackie , 1933 
- Rhymes New and Old ; Blackie , 1933 
- A Flower Fairy Alphabet ; Blackie , 1934 
- A Little Book of Old Rhymes ; Blackie , 1936 
- He Leadeth Me ( written in collaboration with Dorothy Barker ) ; Blackie , 1936 
- A Little Book of Rhymes New and Old ; Blackie , 1937 
- The Lord of the Rushie River ; Blackie , 1938 
- Flower Fairies of the Trees ; Blackie , 1940 
- When Spring <unk> In at the Window ; Blackie , 1942 
- A Child ’ s Garden of <unk> ( Robert Louis Stevenson ) ; Blackie , 1944 
- Flower Fairies of the Garden ; Blackie , 1944 
- Groundsel and Necklaces ; Blackie , 1946 ; reprinted as Fairy Necklaces 
- Flower Fairies of the <unk> ; Blackie , 1948 
- Flower Fairies of the Flowers and Trees ; Blackie , 1950 
- Lively Stories ; Macmillan , 1954 
- The Flower Fairy Picture Book ; Blackie , 1955 
- Lively Numbers ; Macmillan , 1957 
- Lively Words ; Macmillan , 1961 . 
- The Sand , the Sea and the Sun ; Gibson , 1970 
- 
- = = = = <unk> published = = = = 
- 
- Flower Fairies of the Winter ; Blackie , 1985 
- Simon the Swan ; Blackie , 1988 
- Flower Fairies of the Seasons ; <unk> / Blackie , 1988 
- A Little Book of <unk> and Hymns ; Frederick Warne , 1994 
- A Flower Fairies Treasury ; Frederick Warne , 1997 
- <unk> ; Frederick Warne , 2005 
- Wild Cherry Makes A Wish ; ( collaboration with <unk> Le <unk> ) Frederick Warne , 2006 
- How to find Flower Fairies ; Frederick Warne , 2007 
- Return to <unk> ; Frederick Warne , 2008 
- 
- = = = Book covers = = = 
- 
- A New Epiphany ; Society for the Preservation of Christian Knowledge , 1919 
- 43 <unk> ; Blackie , 1920s , 1930s 
- 
- = = = Religious works = = = 
- 
- St. Cecily 's Garden ; 1920 
- <unk> roll design ; St. Edmund 's , Pitlake , 1922 
- Banner design ; St. Mary 's , <unk> , 1923 
- The Feeding of the Five Thousand ; <unk> <unk> , chapel at Penarth , Wales ; 1929 
- The Parable of the Great Supper ; <unk> , St. George 's chapel , Waddon 
- The Seven <unk> ; baptismal font panels , St. Andrew 's , Croydon 
- St. John the Baptist ; central banner panel , <unk> church , 1943 
- <unk> , sword , and shield ; mount for a list of men and woman serving in the Forces , St. Andrews , Croydon , 1943 
- <unk> rolls ; St. Andrews , Croydon , 1948 , 1962 
- The font in St Andrew 's Church , South Croydon 
- Out of Great <unk> ; memorial chapel , <unk> <unk> church , 1948 
- I Am Among You As He That <unk> ; stained glass window design , St. Edmund 's , Pitlake , 1962 
- 
- 
- = Gambia women 's national football team = 
- 
- The Gambia women 's national football team represents the Gambia in international football competition . The team , however , has not competed in a match recognised by FIFA , the sport 's international governing body , despite that organised women 's football has been played in the country since 1998 . The Gambia has two youth teams , an under @-@ 17 side that has competed in FIFA U @-@ 17 Women 's World Cup qualifiers , and an under @-@ 19 side that withdrew from regional qualifiers for an under @-@ 19 World Cup . The development of a national team faces challenges similar to those across Africa , although the national football association has four staff members focusing on women 's football . 
- 
- = = The team = = 
- 
- In 1985 , few countries had women 's national football teams . While the sport gained popularity worldwide in later decades , the Gambia 's national team only played its first game in 2007 . That game was not FIFA @-@ recognised . As of March 2012 , the team was unranked by FIFA , and as of the following month the Gambia had not played in a FIFA @-@ sanctioned match . The team has not participated in major regional and international tournaments , including the Women 's World Cup , the 2010 African Women 's Championship or the 2011 All @-@ Africa Games . 
- The country did not have a FIFA @-@ recognised youth national team until 2012 , when the Gambia under @-@ 17 women 's team competed in Confederation of African Football qualifiers for the FIFA U @-@ 17 World Cup , to be held in Azerbaijan in September 2012 . The Gambia had fielded an under @-@ 17 team of 24 players , narrowed from an initial pool of 49 young women . Two girls from the SOS Children ’ s Village <unk> were chosen as a members of the team . The Gambia first played Sierra Leone in a pair of qualifying matches for the tournament . Gambia won the first match 3 @-@ 0 in <unk> , the Gambia 's capital . The return match was delayed in for 24 hours and played in <unk> . The Gambia beat Sierra Leone 4 @-@ 3 to qualify for the final round . The Gambia then beat Tunisia 1 @-@ 0 at home and won 2 @-@ 1 in Tunisia . <unk> Tamba and <unk> <unk> scored the Gambia 's goals . Tunisia 's only goal was a <unk> own goal . The win qualified Gambia for the 2012 Azerbaijan World Cup . 
- The Gambia also has an under @-@ 19 team that was to play in the African Women 's U @-@ 19 Championship in 2002 . The Gambia 's first match was against Morocco , but the team withdrew from the competition . 
- 
- = = Background and development = = 
- 
- The development of women 's football in Africa faces several challenges , including limited access to education , poverty amongst women , <unk> and human rights abuses targeting women . Funding is another issue impacting the game in Africa , where most financial assistance comes from FIFA and not national football associations . Another challenge is the retention of football players . Many women footballers leave the continent to seek greater opportunity in Europe or the United States . 
- Gambia 's national football association was founded in 1952 , and became affiliated with FIFA in 1968 . Football is the most popular women 's sport in the country , and was first played in an organized system in 1998 . A national competition was launched in 2007 , the same year FIFA started an education course on football for women . Competition was active on both the national and scholastic levels by 2009 . There are four staffers dedicated to women 's football in the Gambia Football Association , and representation of women on the board is required by the association 's charter . 
- 
- 
- = Plain maskray = 
- 
- The plain maskray or brown stingray ( Neotrygon annotata ) is a species of stingray in the family <unk> . It is found in shallow , soft @-@ bottomed habitats off northern Australia . Reaching 24 cm ( 9 @.@ 4 in ) in width , this species has a diamond @-@ shaped , grayish green pectoral fin disc . Its short , whip @-@ like tail has alternating black and white bands and fin folds above and below . There are short rows of thorns on the back and the base of the tail , but otherwise the skin is smooth . While this species possesses the dark mask @-@ like pattern across its eyes common to its genus , it is not <unk> patterned like other maskrays . 
- <unk> in nature , the plain maskray feeds mainly on <unk> shrimp and polychaete worms , and to a lesser extent on small bony fishes . It is viviparous , with females producing litters of one or two young that are nourished during gestation via histotroph ( " uterine milk " ) . This species lacks economic value but is caught incidentally in bottom trawls , which it is thought to be less able to withstand than other maskrays due to its gracile build . As it also has a limited distribution and low <unk> , the International Union for Conservation of Nature ( IUCN ) has listed it as Near Threatened . 
- 
- = = Taxonomy and phylogeny = = 
- 
- The first scientific description of the plain maskray was authored by Commonwealth Scientific and Industrial Research Organisation ( CSIRO ) researcher Peter Last in a 1987 issue of Memoirs of the National Museum of Victoria . The specific name <unk> comes from the Latin an ( " not " ) and <unk> ( " marked " ) , and refers to the ray 's coloration . The holotype is a male 21 @.@ 2 cm ( 8 @.@ 3 in ) across , caught off Western Australia ; several <unk> were also designated . Last tentatively placed the species in the genus Dasyatis , noting that it belonged to the " maskray " species group that also included the <unk> stingray ( then Dasyatis <unk> ) . In 2008 , Last and William White elevated the <unk> group to the rank of full genus as Neotrygon , on the basis of morphological and molecular phylogenetic evidence . 
- In a 2012 phylogenetic analysis based on mitochondrial and nuclear DNA , the plain maskray and the <unk> maskray ( N. <unk> ) were found to be the most basal members of Neotrygon . The divergence of the N. annotata lineage was estimated to have occurred ~ 54 Ma . Furthermore , the individuals sequenced in the study sorted into two genetically distinct clades , suggesting that N. annotata is a cryptic species complex . The two <unk> species were estimated to have diverged ~ 4 @.@ 9 Ma ; the <unk> event was likely the splitting of the ancestral population by coastline changes . 
- 
- = = Description = = 
- 
- The pectoral fin disc of the plain maskray is thin and diamond @-@ shaped with narrowly rounded outer corners , measuring 1 @.@ 1 – 1 @.@ 3 times longer than wide . The leading margins of the disc are gently <unk> and converge at a broad angle to the pointed tip of the snout . The small eyes are placed close together , and behind them are the spiracles . The nostrils are elongated and have a skirt @-@ shaped flap of skin between them . The small mouth bears prominent <unk> at the corners and contains two slender papillae on the floor . Small papillae are also found around the outside of the mouth . There are five pairs of gill slits . The pelvic fins are fairly large and pointed . 
- The tail is short , barely exceeding the length of the disc when intact , and has a broad and flattened base leading to usually two <unk> spines . After the stings , the tail becomes slender and bears a long ventral fin fold and a much shorter , lower dorsal fin fold . Most of the body lacks dermal denticles ; a midline row of 4 – 13 small , closely spaced thorns is present behind the spiracles , and another row of 0 – 4 thorns before the stings . The dorsal coloration is grayish green , becoming pinkish towards the disc margins ; there is a dark mask @-@ like shape around the eyes and a pair of small dark blotches behind the spiracles . The tail behind the stings has alternating black and white bands of variable width , ending with black at the tip . The underside is plain white and the ventral fin fold is light grayish in color . This species grows to 24 cm ( 9 @.@ 4 in ) across and 45 cm ( 18 in ) long . 
- 
- = = Distribution and habitat = = 
- 
- The plain maskray inhabits the continental shelf of northern Australia from the <unk> Islands in Queensland to the Bonaparte Archipelago in Western Australia , including the Gulf of <unk> and the Timor and <unk> Seas . There are <unk> reports that its range extends to southern Papua New Guinea . It is the least common of the several maskray species native to the region . This species is a bottom @-@ dweller that prefers habitats with fine sediment . It has been recorded from between 12 and 62 m ( 39 and 203 ft ) deep , and tends to be found farther away from shore than other maskrays in its range . 
- 
- = = Biology and ecology = = 
- 
- The plain maskray generally hunts at the surface of the bottom substrate , rather than digging for prey . Its diet consists predominantly of <unk> shrimp and polychaete worms . Small bony fishes are also eaten , along with the occasional <unk> <unk> or <unk> . Larger rays consume a greater variety of prey and relatively more polychaete worms when compared to smaller rays . This species is <unk> by the <unk> <unk> <unk> . 
- Like other stingrays , the plain maskray is viviparous with the developing embryos sustained to term by histotroph ( " uterine milk " ) produced by the mother . <unk> females have a single functional ovary and uterus , on the left . Litter size is one or two ; the newborns measure 12 – 14 cm ( 4 @.@ 7 – 5 @.@ 5 in ) across . Males and females reach sexual maturity at disc widths of 20 – 21 cm ( 7 @.@ 9 – 8 @.@ 3 in ) and 18 – 19 cm ( 7 @.@ 1 – 7 @.@ 5 in ) respectively . The maximum lifespan is estimated to be 9 years for males and 13 years for females . 
- 
- = = Human interactions = = 
- 
- The main conservation threat to the plain maskray is incidental capture by commercial bottom <unk> fisheries . In the present day , this is mostly caused by Australia 's Northern <unk> <unk> , which operates throughout its range . Although this species is discarded when caught , it is more delicate @-@ bodied than other maskrays and is thus unlikely to survive encounters with trawling gear . Historically , this species may also have been negatively affected by Japanese , Chinese , and Taiwanese <unk> that fished intensively off northern Australia from 1959 to 1990 . These factors , coupled with the plain maskray 's limited distribution and low reproductive rate , have resulted in its being assessed as Near Threatened by the International Union for Conservation of Nature ( IUCN ) . 
- 
- 
- = 2011 – 12 Columbus Blue Jackets season = 
- 
- The 2011 – 12 Columbus Blue Jackets season was the team 's 12th season in the National Hockey League ( NHL ) . The Blue Jackets ' record of 29 – 46 – 7 [ note 1 ] was the worst record in the NHL for 2011 – 12 and the first time in franchise history they finished in last place . It also marked the third straight year that they missed the playoffs . Consequently , they had the best chance to receive the first overall selection in the 2012 NHL Entry Draft lottery , but lost out to the Edmonton Oilers and received the second pick instead . 
- The Blue Jackets began the year with the worst start in franchise history and the worst by any team in an NHL season in 19 years . After an 11 – 25 – 5 start , Head Coach Scott Arniel was fired and replaced by Assistant Coach Todd Richards . The poor season prompted several personnel changes including the trade of All @-@ Star forward Jeff Carter , who was acquired with much fanfare during the off @-@ season . With the prospect of another rebuild looming the Blue Jackets ' captain and best player , Rick Nash , requested to be traded , though he would remain with the team for the entire season . 
- The team was involved in a controversial loss to the Los Angeles Kings , when the Staples Center clock appeared to freeze at 1 @.@ 8 seconds allowing the Kings time to score the tying goal , before winning in overtime . During the season Columbus managed only two winning streaks of three or more games . One of which came towards the end of the year helping the Blue Jackets finish with 65 points , the third worst point total in franchise history . 
- 
- = = Off @-@ season = = 
- 
- In the off @-@ season the Blue Jackets ' approach to building their team changed , moving from a team of young developing players into one with established players . The first deal General Manager Scott Howson made was the acquisition of All @-@ Star forward Jeff Carter on June 23 , 2011 . The deal sent <unk> <unk> , Columbus ' first @-@ round draft choice , the eighth overall , and their third @-@ round pick in the 2011 Draft to the Philadelphia <unk> in exchange for Carter . The trade received a positive response in Columbus from fans and management who felt they finally had a number one center to play alongside of their best player , Rick Nash . Next , they traded for the negotiating rights of soon to be free agent James Wisniewski . Wisniewski scored a career high 51 points during the 2010 – 11 season , splitting time between the New York Islanders and Montreal Canadiens . The point total was fifth @-@ highest in the league for defenseman scoring , tying Tobias <unk> . The Blue Jackets came to terms with Wisniewski , just an hour prior to the start of free agency , signing him to a six @-@ year , $ 33 million deal . 
- Columbus also traded former first round draft pick Nikita <unk> to the Ottawa Senators for a third @-@ round pick in the 2011 Draft . <unk> had failed to live up to expectations in Columbus , playing in only 44 games over three seasons scoring six goals . Prior to the start of the season , the Blue Jackets were questioned for not signing a veteran back @-@ up to starting goaltender Steve Mason , as the former Calder Memorial Trophy winner had struggled in consecutive seasons . The Blue Jackets signed Mark <unk> as the back @-@ up who had only 50 minutes of NHL experience prior to the start of the season . Columbus did sign a veteran Curtis Sanford to be their third string goaltender and to start for their American Hockey League ( <unk> ) affiliate , the Springfield Falcons . Sanford had not played in the NHL since 2009 . During training camp , <unk> suffered a high ankle sprain that was expected to keep him out of the line @-@ up for a month . Additionally , Sanford suffered a groin injury , leaving Allen York as the back @-@ up . York had only played four professional games , all in the <unk> , entering the season . 
- 
- = = Regular season = = 
- 
- 
- = = = October – December = = = 
- 
- After the first five games , all losses , Jeff Carter suffered a broken foot that kept him out of the line @-@ up for 10 games . While Carter was injured , the Blue Jackets continued to lose games . In the eighth game of the year , they had a chance to end the losing streak against the Ottawa Senators . Columbus held a 3 – 2 lead with under a minute to play . Jason <unk> tied the game on a late power play , and with just 4 @.@ 7 seconds remaining , Milan <unk> notched the winning goal for the Senators . The loss helped set a franchise record for <unk> with a 0 – 7 – 1 record to start a season . [ note 1 ] The losing streak came to an end three days later with a win over the Detroit Red Wings . During the game , several milestones were reached . James Wisniewski made his Columbus debut , Ryan Johansen and John Moore scored their first career NHL goals and Grant <unk> had a career @-@ high three assists . Columbus was unable to create any momentum from the win , however , and continued to struggle , culminating in a 2 – 12 – 1 record , which was the worst start to an NHL season for any team in 19 years . With the team struggling , management attempted to " shake things up " by making some roster moves . The first move was the acquisition of center Mark <unk> from the Pittsburgh Penguins . Next , they traded defenseman Kris Russell to the St. Louis Blues for Nikita <unk> . As the clubs slow start continued , there were rumors that Head Coach Scott Arniel would be fired and replaced with Ken Hitchcock . Hitchcock had previously coached the Blue Jackets to their only playoff appearance in club history and was still under contract with the franchise through to the end of the season . Before any of these rumors came to fruition , the St. Louis Blues asked Columbus for permission to hire Hitchcock , which the Blue Jackets allowed . Hitchcock began his Blues coaching career with a 6 – 1 – 2 record in his first nine games , while Columbus amassed a 6 – 13 – 3 record to start the season . 
- During the same time frame as the Hitchcock rumors , goaltender Curtis Sanford returned from his groin injury on November 13 . He made his first start of the season against the Boston Bruins , losing 2 – 1 in a shootout . Sanford continued his strong play , posting a 3 – 1 – 2 record , 1 @.@ 38 goals against average and <unk> save percentage over his next six games . Sanford started 12 consecutive games before Steve Mason made his next start . The number of starts might not have been as numerous , but prior to the November 23 game , Mason was hit in the head by a shot from Rick Nash during pre @-@ game warm @-@ ups and suffered a concussion . Mason returned from his concussion after two games , making a start against the Vancouver Canucks . Mason allowed only one goal in the game despite suffering from <unk> in the third period , temporarily being replaced by Sanford for just over three minutes . Columbus won the game 2 – 1 in a shootout , breaking a nine @-@ game losing streak to the Canucks . After the game , Arniel stated that Sanford was still seen as the team 's number one goaltender . However , Mason started four of the next six games with the Blue Jackets going 0 – 5 – 1 during that stretch . 
- 
- = = = January – February = = = 
- 
- With the losing continuing , more rumors began to surface . Unlike before , the rumors were about player moves rather than coaching changes . The majority of rumors were that the Blue Jackets would trade Rick Nash . While Howson stated that he had never brought up trading Nash in discussions , other teams had <unk> about his availability . Nash stated that if Columbus felt it would make the franchise better than he would be willing to waive his no @-@ trade clause . Howson publicly stated that he had no intention of trading Nash . More rumors came to light when reports attributed to <unk> des sports stated that Carter was unhappy in Columbus and demanded a trade . Howson , Carter and his agent all denied that a trade request was ever made , and they were unsure where the reports were coming from . With the trade deadline approaching , speculation picked up on the Blue Jackets trading Carter . Reports were that Columbus was trying to trade Carter and that he was " 100 percent available . " 
- At the halfway point of the season , with the Blue Jackets barely into double digit wins with an 11 – 25 – 5 record , worst in the league , and sitting 20 points out of playoff position , Columbus fired Arniel . He was replaced by Assistant Coach Todd Richards on an interim basis . Richards had previously coached the Minnesota Wild . He recorded his first coaching victory for the Blue Jackets in his second game , a 4 – 3 win over the Phoenix Coyotes . The change in coaching did not change the fortunes of the team , as they reached the All @-@ Star break with a 13 – 30 – 6 record . At the break , Blue Jackets ' owner John P. <unk> sent out a letter to fans stating his understanding of their frustration . He added that action would be taken around the trade deadline , the Entry Draft and free agency to take the team in a new direction . When speaking of the season , <unk> stated " disappointing is not a strong enough word " and that he was committed to giving fans a team of which they can be proud of . He also thanked them for their dedication and passion , while reiterating that the team goal was to " win consistently and compete for the Stanley Cup . " Days later , a 250 @-@ person protest occurred outside of Nationwide Arena . Fans were upset with the Blue Jackets ' management and were calling for changes at the top . The same day the fans protested , it was announced that the franchise would host the 2013 All @-@ Star Game . Columbus was without a representative for the 2012 All @-@ star Game , but Ryan Johansen represented the club as a rookie participant in the Super <unk> Competition . In the competition , Johansen participated in the <unk> Insurance NHL <unk> Challenge , a shootout themed event judged by the fans . He received just 1 % of the vote and finished last . 
- Following the break , the Blue Jackets were on the road playing the Los Angeles Kings , and with the score tied late in the game , Kings ' defenseman Drew <unk> scored with just 0 @.@ 4 seconds remaining to win the game . Upon review of the goal it , was determined that the clock at Staples Center froze at 1 @.@ 8 seconds for over a full second , which would have resulted in time expiring prior to the goal being scored . Kings ' General Manager Dean Lombardi stated that the clock was correct and no extra time had been added due to the way the clock self @-@ <unk> at various times . Howson stated on the team 's blog that " It is an amazing coincidence that with the Kings on a power play at Staples Center and with a mad scramble around our net in the dying seconds of the third period of a 2 – 2 hockey game that the clock stopped for at least one full second , " adding that , " Either there was a deliberate stopping of the clock or the clock <unk> . " NHL Senior Vice President of Hockey Operations Colin Campbell stated that the Blue Jackets were <unk> , but that the outcome of the game could not be changed , and that the delay was not noticed by the off @-@ ice officials or the situation room in Toronto . To determine the true cause of the clock pause , the NHL launched an investigation , talking with the clock 's manufacturer and interviewing Staples Center staff . 
- Two weeks prior to the NHL trade deadline , Columbus announced that unlike earlier in the season , they would listen to trade proposals involving Rick Nash , though they were not actively shopping him . Howson stated that the team was open to all options for improving the team , including trading Nash . <unk> was that in return for Nash the Blue Jackets would ask for a " combination of young , proven players , high @-@ end prospects and draft picks . " Leading up to the trade deadline , the Blue Jackets dealt Antoine <unk> to the Phoenix Coyotes for two draft picks and goaltender Curtis <unk> . Despite being injured at the time , the acquisition of <unk> was believed to give Columbus the flexibility to trade Curtis Sanford . The following day , on February 23 , Columbus traded Jeff Carter to the Kings . In the deal , Columbus acquired defenseman Jack Johnson and a first @-@ round draft pick ; the team was given the choice of taking the pick in either 2012 or 2013 . At the deadline , Columbus was unable to come to terms on a deal involving Nash , but they did make one more move ; they sent center Samuel <unk> to the Vancouver Canucks in exchange for two fourth @-@ round draft picks and minor league defenseman Taylor Ellington . Following the trade deadline , Howson announced that the team had attempted to trade Nash at the player 's request . Nash stated that he had requested the trade after being informed that the franchise was going into another rebuilding phase . He further noted that he felt that he " could be a huge part of that towards bringing assets in , " and in his view " it was the best thing for the team , the organization , and personally for [ his ] career . " After the personnel changes , the Blue Jackets closed out the month with a three @-@ game losing streak . 
- 
- = = = March – April = = = 
- 
- Columbus started March with a 2 – 0 shutout against the Colorado Avalanche . They proceeded to win their next game against the Phoenix Coyotes 5 – 2 , which marked the first time that the Blue Jackets posted back @-@ to @-@ back regulation victories during the season . Columbus again defeated the Coyotes three days later to earn their first three @-@ game win streak of the season . They extended the streak to four with a win over the Los Angeles Kings before it came to an end with a 4 – 1 loss to the St. Louis Blues . It was the only four @-@ game win streak of the season for the Blue Jackets . They immediately matched their four @-@ game win streak with a four @-@ game losing streak and with ten games remaining , the Blue Jackets were the first team eliminated from playoff contention . Shortly after being eliminated , they were defeated by the Edmonton Oilers 6 – 3 ; the loss clinched last place in the NHL for Columbus . It was the first time in franchise history the Blue Jackets finished in 30th place . 
- Three days later , on March 28 , goaltender Steve Mason was injured in the morning skate when a shot from Colton <unk> hit him in the mask . With Sanford again injured , York made an emergency start . Playing against the Detroit Red Wings , York made 29 saves , including 17 in the third period , helping Columbus to a 4 – 2 victory and giving York his first career NHL win . York remained the starter and led the Blue Jackets to a second three @-@ game winning streak . In his fourth start , Columbus was shutout by the Coyotes despite a franchise @-@ record 54 shots on goal , losing 2 – 0 . The 54 saves by Phoenix goaltender Mike Smith set an NHL record for a regulation shutout . Mason returned to the starter 's role for the final two games , winning both . The two victories gave Columbus 65 points for the year , their third @-@ lowest total in franchise history . 
- The Blue Jackets struggled in <unk> situations , allowing the most power @-@ play goals in the League , with 64 , and having the lowest penalty @-@ kill percentage , at 76 @.@ 64 % 
- 
- = = Post @-@ season = = 
- 
- <unk> with the worst record in the NHL , Columbus had the best chance of receiving the first overall pick in the 2012 draft . With the NHL 's weighted draft lottery the Blue Jackets had a 48 @.@ 2 % chance of drafting first overall . However , the lottery was won by the Edmonton Oilers , who proceeded to <unk> Columbus and secure the number one draft pick for a third consecutive year . It was the fifth time that the Blue Jackets were dropped one draft position in the franchise 's 12 lottery <unk> . 
- A month later , on May 14 , the Blue Jackets announced that Richards would remain as head coach and signed him to a two @-@ year contract . During the press conference , Howson noted , " Our team continuously improved under Todd and he has earned the opportunity to build upon the work he started . " Columbus posted an 18 – 21 – 2 record under Richards , including winning seven of their final 11 games . 
- 
- = = <unk> = = 
- 
- Since being founded as an expansion team , the Blue Jackets have played in the Central Division of the Western Conference . Division rivals Chicago <unk> , Detroit Red Wings , Nashville Predators and St. Louis Blues , all made the playoff during the 2011 – 12 season , which helped Columbus finish 36 points behind fourth place Chicago and 44 points out of first . 
- Divisions : CE – Central , NW – Northwest , PA – Pacific 
- bold - qualified for playoffs , y – Won division , p – Won Presidents ' Trophy ( best record in NHL ) 
- 
- = = Schedule and results = = 
- 
- 
- = = = Pre @-@ season = = = 
- 
- 
- = = = Regular season = = = 
- 
- Green background indicates win ( 2 points ) . 
- Red background indicates regulation loss ( 0 points ) . 
- Silver background indicates overtime / shootout loss ( 1 point ) . 
- 
- = = Player statistics = = 
- 
- In ice hockey , a combination of a player 's goals and assists are collectively called points . <unk> minutes are the total number of minutes assigned to a player for <unk> assessed during the <unk> @-@ minus is a statistic that tracks when a player was on the ice while goals were scored , both for and against their team , though some in game situations will not effect the statistic . Below is a listing of all player statistics for the Blue Jackets during the season . 
- 
- = = = <unk> = = = 
- 
- Note : <unk> 
- = <unk> ; <unk> = 
- Games played in ; G 
- = Goals ; A = 
- <unk> ; Pts 
- = Points ; <unk> = 
- <unk> minutes ; + / - = Plus / minus 
- 
- = = = <unk> = = = 
- 
- Note : <unk> 
- = Games Played ; <unk> = 
- Time On Ice ( minutes ) ; W 
- = Wins ; L = 
- Losses ; <unk> 
- = <unk> Losses ; GA = 
- Goals Against ; GAA = Goals Against Average ; <unk> = <unk> Against ; <unk> 
- = <unk> ; Sv % = 
- Save <unk> ; <unk> = <unk> 
- † <unk> player spent time with another team before joining Blue Jackets . <unk> reflect time with the Blue Jackets only . <unk> <unk> mid @-@ season 
- 
- = = <unk> = = 
- 
- When Mason was injured in warm @-@ ups late in the year , Columbus was without an active goaltender on their roster . To remedy the situation , the team signed former University of Michigan goaltender Shawn Hunwick to a one @-@ day , amateur <unk> contract . After being eliminated from the NCAA Tournament just days prior , Hunwick <unk> an astronomy class and drove his worn down 2003 Ford Ranger to Columbus to make the game . He served as the back @-@ up to Allen York during the game , and the following day , he signed a contract for the remainder of the year . With Mason returning from injury , Hunwick was third on the team 's depth chart when an injury to York allowed Hunwick to remain as the back @-@ up for the final two games of the year . In the final game of the season , the Blue Jackets were leading the Islanders 7 – 3 with 2 : 33 remaining when , at the behest of his teammates , Head Coach Todd Richards put Hunwick in to finish the game . He did not face a shot . Hunwick was the franchise record ninth player to make his NHL debut during the season . Conversely , <unk> <unk> played in his 1,000th NHL game during the year . 
- 
- = = Transactions = = 
- 
- During the off @-@ season the Blue Jackets parted ways with <unk> Jan <unk> , Anton <unk> , Sami <unk> and Mike Commodore . <unk> , who played four of his first five NHL seasons with the Blue Jackets , was offered a contract by Columbus , but felt that the organization <unk> him and left via free agency . Columbus had offered him a three @-@ year , $ 7 @.@ 5 million contract . He instead signed a four @-@ year , $ 13 million deal with the Colorado Avalanche . <unk> and <unk> were not given qualifying offers which made them unrestricted free agents , and both signed with other teams . Commodore had originally signed a big contract with the Blue Jackets in 2008 , but fell out of favor . He was waived , sent to the minors and eventually had his contract bought out . In order to replace the departed players , Columbus not only acquired James Wisniewski , but also signed ten @-@ year NHL veteran <unk> <unk> . <unk> played only seven games with the Blue Jackets before suffering a concussion and missing the remainder of the season . Brett <unk> was brought in to replace him . 
- 
- = Gregorian Tower = 
- 
- The Gregorian Tower ( Italian : Torre <unk> ) or Tower of the Winds ( Italian : Torre <unk> <unk> ) is a round tower located above the Gallery of Maps , which connects the Villa <unk> with the Apostolic Palace in Vatican City . The tower was built between <unk> and 1580 to a design by the <unk> architect <unk> <unk> ( who was credited with building the Apostolic Palace ) mainly to promote the study of astronomy for the Gregorian Calendar Reform which was commissioned by Pope Gregory XIII and promulgated in <unk> . It was then also known as the Tower of Winds . The tower is now called the " <unk> <unk> <unk> " , the Vatican Observatory . Four stages of progressive development have occurred since it was first established . The tower was an edifice of great value for astronomical observations made using a sundial as they provided essential confirmation of the need to reform the Julian calendar . 
- 
- = = Early history = = 
- 
- The first stage of building of the tower , as recorded by Leo XIII in his <unk> <unk> <unk> <unk> of 1891 , is credited to Pope Gregory XIII , Pope from <unk> to <unk> . The directive was to build a tower at a suitable location in the Vatican and equip it with the " greatest and best instruments of the time " . The design was effected after a series of meetings of the experts who had been appointed to reform the Julian calendar , in use since 45 BC , to verify their proposed reforms . <unk> . Christoph <unk> , a Jesuit mathematician from the Roman College , was the expert on the committee who suggested the new system for the observations . The 73 metres ( 240 ft ) tower was then built above the museum and library , flanked by the <unk> and della <unk> courtyards . The instrumentation for the observation of the sun rays falling over it consisted of a meridian line designed by <unk> <unk> of Perugia . It was in the form of a circular marble plate in the centre , embellished with scientific designs . The tower still remains today , but has undergone improvements over the centuries . 
- 
- = = Second stage = = 
- 
- The second stage of construction in the 17th and 18th centuries , when the tower was under the charge of the Vatican <unk> , involved <unk> . Filippo Luigi Gilii , a clergyman of St. Peter 's Basilica . Earlier in 1797 , Pius VI gave approval to placing a Latin inscription <unk> <unk> at the entrance to the upper part of the tower , which was implemented by Cardinal <unk> with plans to enhance the instrumentation system in the tower 's observatory . The original observatory was then set up above the second level of the tower with the agreement of Pope Pius VI . Its instrumentation , apart from many normal devices ( such as meteorological and magnetic equipment , with a <unk> and a small transit and <unk> clock , ) was noted for the <unk> Telescope . The instrumentation facilitated recording of occurrences of eclipse , appearance of comets , <unk> of Jupiter and Mercury ’ s transit . As an addition , under the patronage of Pope Pius X , four <unk> observatory <unk> were also added at strategic locations on the 1 @,@ 300 feet ( 400 m ) long fortification walls , more than a thousand years old . <unk> . Gilii , highly respected as a <unk> with a knowledge of physics , biology , <unk> and the Hebrew language , was in charge of the observatory from 1800 to 1821 . He carried out continuous meteorological observations ( twice a day at 6 AM and 2 <unk> ) conforming to the programme of the <unk> Meteorological Society . While the observation records for seven years were published , the balance data in a manuscript form was preserved in the Vatican Library . Subsequent to Gilii 's death in 1821 , the observatory on the tower was discontinued and the instruments were moved to the observatory at the Roman College . Established in 1787 , it was considered more suitable for making observations than the Vatican . 
- 
- = = Third stage = = 
- 
- The revival of the observatory on the Gregorian Tower was initiated by the <unk> Francesco <unk> with the approval of Pope Leo XIII . High quality instruments were procured , partly with generous donations from <unk> of London , and the automatic recording instruments were procured from Richard in Paris . A four @-@ inch equatorial , a three @-@ inch transit instrument , and four <unk> clocks with two <unk> , were also procured from the observatory at <unk> . In 1888 , the gift of a 16 inch long telescope to Pope Leo XIII , became a part of the observatory . Father <unk> joined the observatory in 1889 after it was upgraded with more modern instruments . The same year , a second tower was built some 400 metres ( 1 @,@ 300 ft ) away from the main Gregorian Tower , overlooking the Vatican Gardens behind St. Peter 's Basilica on the south @-@ west border . It was built to a diameter of 17 metres ( 56 ft ) with a lower wall thickness of 4 @.@ 5 metres ( 15 ft ) , which could bear the load of a 13 inch photographic <unk> , newly procured from Paris . <unk> Father Rodriguez was the expert meteorologist who held the post of director from 1898 to 1905 . In 1891 , Pope Leo XIII , <unk> the <unk> <unk> <unk> <unk> , designated the second tower as the seat of the newly established Vatican Observatory , a decision which required altering the roof to provide a flat terrace for astronomical observations . 
- 
- = = Fourth stage = = 
- 
- The fourth stage involved <unk> the problem of communicating between the two towers during the time of Pope Pius X. His plans were to make the Gregorian Tower into a historical tower and to record and carry out observations at the second tower by linking the two towers along the fortified wall with a 83 metres ( 272 ft ) iron bridge spanning the gap . At the west end of this bridge , a four @-@ inch equatorial was installed on semicircular <unk> . The east end of the bridge , above the barracks of the gendarmes , had a <unk> , with a camera attached , used to photograph the Sun ( <unk> ) . A new 16 @-@ inch visual telescope , called Torre <unk> X , was erected in the second tower . As a result of these modifications , the original library was moved to the <unk> Academy <unk> , and the old meteorological and seismic instruments were shifted to the <unk> di <unk> observatory . The new Astronomical Library was housed in two rooms of the building . The two new <unk> machines were used for recording on the <unk> plates . The recorded observations were published along with <unk> notes together with the last two series of the <unk> of stars . Charts were printed on silver bromide paper . 
- 
- = = Features = = 
- 
- The tower had two floors and a <unk> . On the first floor was the famous <unk> Room or Meridian Room , which was initially an open <unk> . Pope Urban VIII had it enclosed and it was subsequently decorated with long sequences of frescoes painted between 1580 and <unk> by Simon <unk> and the two Flemish artists Paul and <unk> <unk> . Today the tower has paintings by <unk> <unk> and <unk> da <unk> . 
- The <unk> Room , also called the Meridian Hall , was once the residence of Queen Christina of Sweden , then newly converted to Catholicism . The room was further modified by two additions which gave it its current name : a sundial , and a delicate but sophisticated <unk> which was fixed to the ceiling of the Meridian Hall . These were created by <unk> <unk> , the papal <unk> , in association with the Gregorian Calendar Reform . The sundial consisted of a straight line in white marble running across the floor in a north @-@ south direction , intended to measure the height of the Sun at noon according to the seasons of the year . The observations made with the sundial provided essential confirmation of the need to reform the Julian calendar . The <unk> , in contrast , was a complex mechanism attached to the ceiling which was used to measure the strength and direction of the wind but soon stopped functioning . The instrument may have led to the other name of the tower , Tower of the Winds ; however , an ancient observatory at Athens was also called the Tower of the Winds and might have been the source for inspiration . 
- The interior walls and ceiling of the hall were richly decorated , in some cases with <unk> frescoes of the hills and Roman countryside , the <unk> , religious themes , the buildings surrounding the area , and naval <unk> with Jesus calming the storm and so forth . 
- 
- 
- = There 's Got to Be a Way = 
- 
- " There 's Got to Be a Way " is a song by American singer and songwriter Mariah Carey from her self @-@ titled debut studio album ( 1990 ) . Columbia released it as the fifth and final single from the album in the United Kingdom . It was one of four songs Carey wrote with Ric Wake during their first recording session together , but " There 's Got to Be a Way " was the only composition to make the final track listing . It is a socio @-@ political conscious R & B @-@ pop song which addresses the existence of poverty , racism and war in the world which gradually becomes more <unk> and positive as it progresses . The track garnered a mixed reception upon the album 's release in 1990 . While Carey 's vocals were praised , it was seen as too political . An accompanying music video highlights social <unk> . The song reached number 54 on the UK Singles Chart . 
- 
- = = Background and release = = 
- 
- " There 's Got to Be a Way " was written by Mariah Carey and Ric Wake for Carey 's self @-@ titled debut studio album ( 1990 ) . It was written during Carey and Wake 's first recording session together . They composed four songs , but only " There 's Got to Be a Way " was chosen for the final track listing . Co @-@ produced by Wake and <unk> Michael <unk> , it appears as the second of ten songs on the track listing . The track was recorded and engineered by Bob <unk> at Cove City Sound Studios and The Power Station , both located in New York City . He was assisted by Dana Jon <unk> . It was mixed by David <unk> at Tarpan Studios in San Rafael . The keyboards , bass and rhythm engineering was carried out by Louis <unk> , while Joe Franco performed the percussion , Vernon " Ice " Black played the guitar , and Rich <unk> also performing on the keyboards . Walter Afanasieff played the synth horns . Carey provided her own background vocals along with Billy T. Scott , <unk> Muhammed and The Billy T. Scott Ensemble . The song was released as the fifth and final single from the album in the United Kingdom . It is available to purchase as a CD single while the remixes are available on vinyl . 
- 
- = = Composition = = 
- 
- " There 's Got to Be a Way " is an R & B @-@ pop music song with elements of gospel . The theme of social activism can be heard in the lyrics " There ’ s got to be a way / to connect this world today . " The song begins with Carey publicly denouncing the existence of poverty and racism in the world , and she uses the bridge to shift the lyrics towards an <unk> and <unk> tone . Carey suggests we should be more tolerant of each other and not resort so readily to war in the lyrics " Couldn 't we accept each other / Can 't we make ourselves aware . " 
- 
- = = Critical reception = = 
- 
- Music critic Robert Christgau felt that Carey was being too political in her " brave , young , idealistic attack " on war and <unk> . Ralph Novak , David <unk> and David <unk> of People wrote that it is a " testimony to her talent that she does so much with so little . " They continued to write that Carey 's " tone and clarity " makes " There 's Got to Be a Way " a " <unk> " track . To mark twenty @-@ five years since the release of Mariah Carey in June 1990 , Billboard writer Trevor Anderson wrote a track @-@ by @-@ track review of the album in June 2015 . He noted that " There 's Got to Be a Way " follows the same melodic tone as the album 's opener " Vision of Love " but highlighted their stark lyrical differences , as the former is about social activism and the latter is about love . Although he praised Carey 's vocals , writing that she " deploys " one of her best whistle notes of her career , he felt that " the aim for broad appeal comes at the expense of memorable lyrics . " 
- 
- = = Music video = = 
- 
- The accompanying music video begins with a shot of an empty street , followed by clips of disadvantaged and poorer members of society going about their daily activities . Two men play <unk> on a wooden <unk> outside a building , a gang make fun of an elderly man hanging newspapers outside his store and an <unk> woman walks down the street . <unk> of Carey leaning against a wall and sitting on some steps looking on at what is happening are shown . As the first chorus begins , everyone starts to dance <unk> in the street and help those in need . A gospel choir comes out of one of the buildings as the street becomes more crowded with people of all ages and backgrounds <unk> and getting along with each other . One of the shops in the background has a neon light outside the entrance which says " Jesus <unk> " . 
- 
- = = Track listings = = 
- 
- " There 's Got to Be a Way " ( Original album version ) – 4 : 52 
- " There 's Got to Be a Way " ( 7 " remix ) 
- " There 's Got to Be a Way " ( 12 " remix ) 
- " There 's Got to Be a Way " ( Alternative Vocal Dub Mix ) 
- 
- = = Charts = = 
- 
- 
- 
- = Nebraska Highway 88 = 
- 
- Nebraska Highway 88 ( N @-@ 88 ) is a highway in northwestern Nebraska . It has a western terminus at Wyoming Highway 151 ( WYO 151 ) at the Wyoming – Nebraska state line . The road travels eastward to N @-@ 71 , where it turns south . N @-@ 88 continues east to south of Bridgeport . The road turns north , ends at an intersection with U.S. Highway 385 ( US 385 ) and N @-@ 92 in Bridgeport . The route was designated in 1937 , before the official state highway system was created . It was extended to the state line in 1986 . 
- 
- = = Route description = = 
- 
- N @-@ 88 starts at the Nebraska – Wyoming state line in Banner County , where WYO 151 ends , and travels northeast . The road quickly <unk> east after less than one mile ( 1 @.@ 6 km ) , and continues in a straight line . For the next twenty miles ( 32 km ) , N @-@ 88 intersects minor streets , through rural farmland . The route turns south at N @-@ 71 , and becomes concurrent . Four miles ( 6 @.@ 4 km ) later , N @-@ 88 turns east , ending the concurrency with N @-@ 71 . The route continues to travel through farmland for sixteen miles ( 26 km ) , where it enters <unk> County . The road crosses over <unk> Creek four times , and enters the unincorporated community of <unk> . Two rock formations , Courthouse and <unk> Rocks , become visible from the road . N @-@ 88 turns north toward Bridgeport soon after . The road crosses over <unk> Creek for the fifth time , and enters into Bridgeport five miles ( 8 @.@ 0 km ) later . The road intersects a railroad owned by <unk> Railway . N @-@ 88 turns northeast soon after , and ends at the intersection of US 385 and N @-@ 92 . In 2012 , Nebraska Department of Roads ( <unk> ) calculated as many as 2 @,@ 410 vehicles traveling on the N @-@ 71 / N @-@ 88 concurrency , and as few as 315 vehicles traveling east of the Banner – <unk> county line . This is expressed in terms of annual average daily traffic ( AADT ) , a measure of traffic volume for any average day of the year . Only the N @-@ 71 / N @-@ 88 concurrency is part of the National Highway System ( NHS ) , a network of highways identified as being most important for the economy , mobility and defense of the nation . 
- 
- = = History = = 
- 
- N @-@ 88 was unofficially designated around 1937 , connecting from N @-@ 29 , to N @-@ 86 and N @-@ 19 in Bridgeport . The route remained relatively the same as the state highway system was officially designated . Before 1955 , Nebraska did not have an adequate legal instrument to define the state highway system . By 1960 , N @-@ 19 was renumbered to US 385 , and US 26 was rerouted north near Bridgeport . The old alignment became part of N @-@ 92 . Two years later , N @-@ 29 was renumbered to N @-@ 71 . Between 1981 @-@ 82 , a road appeared on the official state map , extending from WYO 151 to N @-@ 71 . That road became part of N @-@ 88 by 1986 . No significant changes have been made since . 
- 
- = = Major intersections = = 
- 
- 
- 
- = USS Atlanta ( 1861 ) = 
- 
- Atlanta was a casemate ironclad that served in the Confederate and Union <unk> during the American Civil War . She was converted from a Scottish @-@ built blockade runner named Fingal by the Confederacy after she made one run to Savannah , Georgia . After several failed attempts to attack Union <unk> , the ship was captured by two Union monitors in 1863 when she ran aground . Atlanta was floated off , repaired , and <unk> , serving in the Union Navy for the rest of the war . She spent most of her time deployed on the James River supporting Union forces there . The ship was decommissioned in 1865 and placed in reserve . Several years after the end of the war , Atlanta was sold to Haiti , but was lost at sea in December 1869 on her delivery voyage . 
- 
- = = Description and career as Fingal = = 
- 
- Fingal was designed and built as a <unk> by J & G Thomson 's Clyde Bank Iron Shipyard at <unk> in Glasgow , Scotland , and was completed early in 1861 . She was described by <unk> <unk> Scales , who served on the Atlanta before her battle with the monitors , as being a two @-@ <unk> , iron @-@ <unk> ship 189 feet ( 57 @.@ 6 m ) long with a beam of 25 feet ( 7 @.@ 6 m ) . She had a draft of 12 feet ( 3 @.@ 7 m ) and a depth of hold of 15 feet ( 4 @.@ 6 m ) . He estimated her tonnage at around 700 tons <unk> . Fingal was equipped with two vertical single @-@ cylinder direct @-@ acting steam engines using steam generated by one <unk> @-@ tubular boiler . The engines drove the ship at a top speed of around 13 knots ( 24 km / h ; 15 mph ) . They had a bore of 39 inches ( 991 mm ) and a stroke of 30 inches ( 762 mm ) . 
- The ship briefly operated between Glasgow and other ports in Scotland for <unk> 's West Highland Service before she was purchased in September 1861 by James D. Bulloch , the primary foreign agent in Great Britain for the Confederacy , to deliver the military and naval ordnance and supplies that he had purchased . To disguise his control of Fingal , and the destination of her cargo , Bulloch hired an English crew and captain and put out his destination as Bermuda and Nassau in the Bahamas . The cargo was loaded in Greenock in early October , although <unk> and the other passengers would not attempt to board until they rendezvoused with the ship at <unk> , Wales . On the night 14 / 15 October , as she was slowly rounding the <unk> at <unk> , Fingal rammed and sank the Austrian brig <unk> , slowly swinging at anchor without lights . Bulloch and the passengers embarked in the steamer while Bulloch dispatched a letter to his financial agents instructing them to settle damages with the brig 's owners because he could not afford to take the time to deal with the affair <unk> he and Fingal be detained . The ship reached Bermuda on 2 November and , after leaving port on 7 November , Bulloch informed the crew that the steamer 's real destination was Savannah , Georgia ; he offered to take anyone who objected to the plan to Nassau . However , all of the crew agreed to join in the effort to run the Union blockade . Fingal was able slip safely into the Savannah estuary in a heavy fog on the night of 12 November without sighting any <unk> . 
- While Fingal was <unk> her cargo , Bulloch went to Richmond to confer with Stephen Mallory , Secretary of the Navy . Mallory endorsed Bulloch 's plan to load Fingal with cotton to sell on the Navy Department 's account to be used to purchase more ships and equipment in Europe . He returned to Savannah on 23 November and it took him almost a month to purchase a cargo and acquire enough coal . He made one attempt to break through the blockade on 23 December , but it proved impossible to do as the Union controlled every channel from Savannah , aided by their occupation of <unk> Island at the mouth of the Savannah River . Bulloch reported to Mallory in late January 1862 that breaking out was <unk> so Mallory ordered him to turn the ship over to another officer and to return to Europe some other way . 
- 
- = = As Atlanta = = 
- 
- The brothers <unk> and Nelson <unk> received the contract to convert the blockade runner into an ironclad in early 1862 with the name of Atlanta , after the city in Georgia . This was largely financed by contributions from the women of Savannah . Fingal was cut down to her main deck and large wooden <unk> were built out from the sides of her hull to support her casemate . After the conversion , Atlanta was 204 feet ( 62 @.@ 2 m ) long overall and had a beam of 41 feet ( 12 m ) . Her depth of hold was now 17 feet ( 5 @.@ 2 m ) and she now had a draft of 15 feet 9 inches ( 4 @.@ 8 m ) . Atlanta now displaced 1 @,@ 006 long tons ( 1 @,@ <unk> t ) and her speed was estimated at 7 – 10 knots ( 13 – 19 km / h ; 8 @.@ 1 – 11 @.@ 5 mph ) . 
- The armor of the casemate was angled at 30 ° from the horizontal and made from two layers of railroad rails , rolled into plates 2 inches ( 51 mm ) thick and 7 inches ( 180 mm ) wide . The outer layer ran vertically and the inner layer horizontally . Her armor was backed by 3 inches ( 76 mm ) of oak , vertically oriented , and two layers of 7 @.@ 5 inches ( 191 mm ) of pine , alternating in direction . The bottom of the casemate was some 20 inches ( 508 mm ) from the waterline and its top was 8 feet 6 inches ( 2 @.@ 59 m ) above the waterline . The pyramidal <unk> was armored in the same way and had room for two men . The upper portion of Atlanta 's hull received two inches of armor . 
- The rectangular casemate was pierced with eight narrow gun ports , one each at the bow and stern and three along each side . Each gun port was protected by an armored <unk> made of two layers of iron riveted together and allowed the guns to elevate only to a maximum of + 5 to + 7 ° . Atlanta was armed with single @-@ banded , 7 @-@ inch ( 178 mm ) Brooke rifles on <unk> mounts at the bow and stern . The middle gun port on each side was occupied by a single @-@ banded , 6 @.@ 4 @-@ inch ( 163 mm ) Brooke rifle . The 17 @-@ caliber , seven @-@ inch guns weighed about 15 @,@ 000 pounds ( 6 @,@ 800 kg ) and fired 80 @-@ pound ( 36 kg ) armor @-@ piercing " bolts " and 110 @-@ pound ( 50 kg ) explosive shells . The equivalent statistics for the 18 @.@ 5 @-@ caliber , 6 @.@ 4 @-@ inch gun were 9 @,@ 110 pounds ( 4 @,@ 130 kg ) with 80 @-@ pound bolts and 64 @-@ pound ( 29 kg ) shells . Atlanta was also armed with a 20 @-@ foot ( 6 @.@ 1 m ) , solid iron , ram that was reinforced by a series of vertical steel bars . In front of the ram was a spar torpedo that carried 50 pounds ( 23 kg ) of black powder on a wooden pole connected to an iron lever that could be raised or lowered by means of <unk> . 
- On 31 July 1862 , under the command of Lieutenant Charles H. <unk> , Atlanta conducted her sea trials down the Savannah River toward Fort Pulaski . The ship proved to be difficult to steer , and the additional weight of her armor and guns significantly reduced her speed and increased her draft . This latter was a real problem in the shallow waters near Savannah . She also leaked significantly , and her design virtually eliminated air circulation . One report said that " it was almost intolerable on board the Atlanta , there being no method of ventilation , and the heat was intense . " Scales commented in his diary , " What a <unk> , <unk> and God @-@ forsaken ship ! ! " 
- Attempts were made to fix the problems and were at least partially successful in stopping many of the leaks . The ship was commissioned on 22 November and became the flagship of Flag Officer Josiah Tattnall , commander of the naval defenses of Georgia . Under pressure from Mallory to engage the blockading ships , Tattnall attempted to engage them before any ironclads arrived on 5 January 1863 , but army engineers could not clear the obstacles blocking the channel in a timely manner , despite early coordination being made by Tattnall to do so . It took another month to actually clear the obstacles and two monitors arrived before the end of January . Nonetheless Tattnall attempted to pass through the obstructions during high tide on 3 February , but high winds prevented the water from rising enough to allow the ship to do so . After Atlanta successfully passed through them on 19 March , Tattnall planned to attack the Union base at Port Royal , South Carolina while the monitors were attacking Charleston . <unk> revealed <unk> 's plan while he was waiting at the head of <unk> Sound and he was forced to retreat when three monitors augmented the defenses at Port Royal . <unk> with Tattnall 's perceived lack of aggressiveness , Mallory replaced Tattnall as commander of the Savannah squadron later that month with Commander Richard L. <unk> . <unk> , in his turn was relieved in May by Commander William A. Webb ; Atlanta remained the squadron flagship throughout this time . 
- Webb demonstrated his aggressiveness when he attempted to sortie on the first spring tide ( 30 May ) after taking command , but Atlanta 's forward engine broke down after he had passed the obstructions , and the ship ran aground . She was not damaged although it took over a day to pull her free . He planned to make another attempt on the next full tide , rejecting Mallory 's idea that he wait until the nearly complete ironclad Savannah was finished before his next sortie . In the meantime , Rear Admiral Samuel F. Du <unk> , commander of the South Atlantic <unk> Squadron , had ordered the monitors Weehawken and Nahant into <unk> Sound . Commander John <unk> in Weehawken had overall command of the two ships . 
- In the early evening of 15 June , Webb began his next attempt by passing over the lower obstructions in the Wilmington River and spent the rest of the night coaling . He moved forward the next evening to a concealed position within easy reach of the monitors for an attack early the following morning . Webb planned to sink one of the monitors with his spar torpedo and then deal with the other one with his guns . The gunboat <unk> and the tugboat <unk> were to accompany him to tow one or both of the monitors back to Savannah . 
- A lookout aboard Weehawken spotted Atlanta at 04 : 10 on the morning of 17 June . When the latter ship closed to within about 1 @.@ 5 miles ( 2 @.@ 4 km ) of the two Union ships , she fired one round from her bow gun that passed over Weehawken and landed near Nahant . Shortly afterward , Atlanta ran aground on a <unk> ; she was briefly able to free herself , but the pressure of the tide pushed her back onto the <unk> . This time Webb was unable to get off and the monitors closed the range . When Weehawken , the leading ship , closed to within 200 – 300 yards ( 180 – 270 m ) she opened fire with both of her guns . The 11 @-@ inch ( 279 mm ) shell missed , but the 15 @-@ inch ( 381 mm ) shell struck the ironclad above the port middle gun port , penetrated her armor and broke the wooden backing behind it , spraying splinters and fragments that disabled the entire gun crew and half the crew of the bow gun , even though it failed to cleanly penetrate through the backing . The next shot from the 11 @-@ inch Dahlgren gun struck the upper hull and started a small leak even though it failed to penetrate the two @-@ inch armor there . The next shell from the 15 @-@ inch Dahlgren <unk> off the middle starboard gun <unk> as it was being opened , wounding half the gun 's crew with fragments . The final shell was also from the 15 @-@ inch Dahlgren and it struck the top of the <unk> , breaking the armor there and wounding both pilots in it . By this time , Atlanta had been able to fire only seven shots , none of which hit either Union ship , and was hard aground with high tide not due for another hour and a half . Weehawken and Nahant were able to freely maneuver into positions from which the Atlanta 's narrow gun ports would not allow her to reply and the damage already inflicted by the former ship made further resistance futile . Webb surrendered his ship within 15 minutes of opening fire , before Nahant even had a chance to fire . Of the ironclad 's 21 officers and 124 enlisted men , one man was killed and another sixteen were wounded badly enough to require hospitalization . 
- 
- = = In the Union Navy = = 
- 
- Atlanta was easily pulled free by the Union ships and she reached Port Royal under her own power . Not badly damaged , she was repaired and bought by the Union Navy . The prize money of $ 350 @,@ 000 was shared between the crews of Weehawken , Nahant and the gunboat <unk> , the only ships within signaling distance . The ship retained her name and was commissioned again on 2 February 1864 , <unk> with a pair of 8 @-@ inch ( 203 mm ) , 150 @-@ pound Parrott rifles in the bow and stern and 6 @.@ 4 @-@ inch , 100 @-@ pound Parrott rifles amidships . The 150 @-@ pound Parrott rifle weighed 16 @,@ 500 pounds ( 7 @,@ 500 kg ) and was 17 <unk> long . The 100 @-@ pounder weighed 9 @,@ 800 pounds ( 4 @,@ 400 kg ) and was 20 <unk> long . It fired a 100 @-@ pound ( 45 kg ) shell a distance of 6 @,@ 900 yards ( 6 @,@ 300 m ) at an elevation of + 25 ° . All four of her Brooke rifles are currently located in Willard Park in the Washington Navy Yard . Atlanta was assigned to the North Atlantic <unk> Squadron and spent most of her time stationed up the James River where she could support operations against Richmond and defend against a sortie by the ironclads of the James River Squadron . On 21 May 1864 , she and the gunboat Dawn fired on and dispersed Confederate cavalry that was attacking Fort <unk> and she was deployed further upriver in February 1865 after the Battle of Trent 's Reach to better blockade the Confederate ironclads at Richmond . 
- After the end of the war in April , Atlanta was decommissioned in Philadelphia on 21 June 1865 and placed in reserve at League Island . She was sold to Sam Ward on 4 May 1869 for the price of $ 25 @,@ 000 and subsequently delivered to representatives of Haiti on 8 December by Sydney <unk> , a lawyer who had received an advance of $ 50 @,@ 000 on her purchase price of $ 260 @,@ 000 . The ship was briefly seized by the Customs Service , possibly for violations of neutrality laws as she had just loaded four large guns and a number of recruits for the forces of Sylvain <unk> , President of Haiti , who was embroiled in a civil war . Atlanta was released and sailed for Port @-@ au @-@ Prince three days later . She broke down in Delaware Bay and had to put in at Chester , Pennsylvania for repairs . The ship , now renamed either Triumph or <unk> , departed on 18 December 1869 and vanished en route , apparently sinking with the loss of all hands , either off Cape Hatteras or the Delaware <unk> . 
- 
- 
- = Jacqueline Fernandez = 
- 
- Jacqueline Fernandez ( born 11 August 1985 ) is a Sri Lankan actress , former model , and the winner of the 2006 Miss Universe Sri Lanka pageant . As Miss Universe Sri Lanka she represented her country at the 2006 world Miss Universe pageant . She graduated with a degree in mass communication from the University of Sydney , and worked as a television reporter in Sri Lanka . 
- While on a modelling assignment in India in 2009 , Fernandez successfully auditioned for <unk> <unk> 's fantasy drama <unk> , which marked her acting debut . Fernandez ' breakthrough role was in <unk> <unk> 's psychological thriller Murder 2 ( 2011 ) , her first commercial success . This was followed by glamorous roles in the ensemble @-@ comedy Housefull 2 ( 2012 ) and its sequel Housefull 3 , and the action thriller Race 2 ( 2013 ) , all of which were box @-@ office successes . Her performance in the first of these garnered her an IIFA Award for Best Supporting Actress nomination . In 2014 , Fernandez played the leading lady in Sajid Nadiadwala 's Kick , which is one of the highest @-@ grossing Bollywood films of all time . 
- One of the most popular actresses in India , she was the recipient of the IIFA Award for Star <unk> of the Year – Female in 2010 . Alongside her screen acting career , Fernandez has participated in stage shows , and is active in humanitarian work . 
- 
- = = Early life and modeling career = = 
- 
- Fernandez was born on 11 August 1985 , in <unk> , Bahrain , and was raised in a multi @-@ ethnic family . Her father , <unk> , is Sri Lankan , and her mother , Kim , is of Malaysian descent . Her grandfather , on her mother 's side of the family , is Canadian and her great grandparents were from <unk> , India . Her father , who was a musician in Sri Lanka , moved to Bahrain in the 1980s to escape civil unrest between the <unk> and <unk> and subsequently met her mother who was an air hostess . She is the youngest of four children with one elder sister and two elder brothers . She hosted television shows in Bahrain at the age of fourteen . After receiving her early education in Bahrain , she pursued a degree in mass communication from the University of Sydney in Australia . After graduating she worked as a television reporter in Sri Lanka . She also attended the <unk> school of languages , where she learnt Spanish and improved her French and Arabic . 
- According to Fernandez , she had aspired to become an actress at a young age and <unk> about becoming a Hollywood movie star . She received some training at the John School of Acting . Although , she was a television reporter , she accepted offers in the modeling industry , which came as a result of her pageant success . In 2006 , she was crowned the winner of the Miss Universe Sri Lanka pageant and represented Sri Lanka at the world Miss Universe 2006 pageant held in Los Angeles . In a 2015 interview , Fernandez described the modeling industry as " a good training ground " and said : " It is a medium that is about shedding your <unk> , knowing your body , confidence " . In 2006 , she appeared in a music video for the song " O <unk> " by music duo <unk> and <unk> . 
- 
- = = Acting career = = 
- 
- 
- = = = 2009 – 2013 = = = 
- 
- In 2009 , Fernandez traveled to India for a modeling assignment . She studied acting under the <unk> of theatre director Barry John , and successfully auditioned for <unk> <unk> 's fantasy film <unk> ( 2009 ) her acting debut . She played the love interest of <unk> <unk> 's character , a role based on the Princess Jasmine character . Fernandez garnered mixed reviews for her performance . Anupama Chopra of NDTV called her a " plastic <unk> [ e ] " , and Rajeev Masand of CNN @-@ <unk> felt that she was : " easy on the eyes and appears confident but has precious little to do " . Although the film was a critical and commercial failure , she won the IIFA Award for Star <unk> of the Year - Female . 
- In 2010 , Fernandez appeared opposite <unk> in the science fiction romantic comedy <unk> <unk> Se <unk> Hai . She was cast as a girl from Venus , who lands on Earth in search of love . The film , along with Fernandez 's performance , received poor reviews ; Rediff.com 's <unk> <unk> noted : " She <unk> makes a fool of herself whilst <unk> the actions of movie stars , ranging from <unk> 's <unk> dance , <unk> <unk> 's <unk> <unk> moves , to Big B 's violent <unk> in Hum . Her Tara could be a keeper if only <unk> <unk> Se <unk> Hai wasn 't so intent on turning her into a love @-@ struck Barbie . " Critic Anupama Chopra also criticized Fernandez , calling her " a pin @-@ <unk> on a balloon " . Later that year , she made a cameo appearance in Sajid Khan 's Housefull in the song " <unk> " . 
- Mahesh <unk> 's thriller Murder 2 was Fernandez 's first commercial success and marker a turning point in her career . She took on the role of <unk> , a lonely model who is in a confused relationship with Arjun <unk> ( played by <unk> <unk> ) . Fernandez was praised for the her performance , and for the <unk> and sex appeal she displayed in the film . Gaurav Malini of The Times of India stated that she was " <unk> tempting " but noted that her romance with <unk> was " literally half @-@ baked " . The following year , Fernandez appeared in the ensemble comedy Housefull 2 alongside <unk> Kumar , John Abraham , and Asin . It became one of the top grossing productions of India that year and earned ₹ 1 @.@ 86 billion ( US $ 28 million ) worldwide . Fernandez received mostly negative reviews for her performance . While Gaurav Malini praised her for her looks , NDTV called her a " <unk> <unk> " who " find [ s ] no pleasure in [ her role ] " . Despite the negative reviews , Fernandez received a Best Supporting Actress nomination at the 14th IIFA Awards for her performance . 
- Fernandez 's first release of 2013 was Race 2 , an ensemble action thriller ( alongside <unk> Ali Khan , John Abraham , <unk> Padukone , <unk> <unk> , and <unk> Kapoor ) ) , described as the " cinematic equivalent of a <unk> novel " by critic Rajeev Masand . She played <unk> , a <unk> <unk> , a role which required her learn fencing and some <unk> . The film emerged as a commercial success , with the domestic gross of more than ₹ 1 billion ( US $ 15 million ) . In a particularly scathing review , <unk> <unk> of NDTV wrote that both Fernandez and Padukone " strut around like wound @-@ up automatons that are all <unk> @-@ up but have nowhere to go . " Fernandez also appeared in an item number ( music video ) titled " <unk> Ki <unk> " for Prabhu Deva 's <unk> <unk> <unk> . 
- 
- = = = 2014 – present = = = 
- 
- In 2014 , Fernandez appeared in Sajid Nadiadwala 's directorial debut — the action film Kick , a remake of a 2009 Telugu film of same name . She starred opposite Salman Khan , playing <unk> , a <unk> student . She retained her real voice for the first time in Kick . While <unk> May Francis commented that she is : " incredibly dazzling , and moves like a magic " , Raja <unk> of Rediff.com was more critical of her dialogue delivery , calling it " unfortunate . " The film received mixed reviews from critics , but with worldwide revenue of over ₹ 3 @.@ 75 billion ( US $ 56 million ) , it became the fourth highest @-@ grossing Bollywood film . The film established Fernandez as one of the most popular Bollywood actresses . 
- In 2015 , Fernandez featured in <unk> Singh 's Roy , a romantic thriller , which critic <unk> A. <unk> described as a " boring , exhausting and pretentious " film . Fernandez played dual roles , Ayesha <unk> , a filmmaker in a relationship with another filmmaker ( played by Arjun <unk> ) and <unk> <unk> , a girl in love with a thief ( played by <unk> Kapoor ) . While India TV called it " her best act till date " , critic Rajeev Masand felt that she " appears <unk> in a part that required greater range . " Roy failed to meet its box @-@ office expectations , and was a commercial failure . Later that year , she appeared in a guest appearance for the comedy @-@ satire <unk> . 
- Karan <unk> 's action drama Brothers was Fernandez 's next release . Co @-@ starring alongside <unk> Kumar and <unk> <unk> , Fernandez played Jenny , a fearless mother struggling for her child , a role which she described as " challenging " , " intense " , and " difficult " . The role marked a departure from the glamorous characters that she had a reputation for portraying . Film critics praised her performance , though their response to the film was mixed . <unk> Sharma of <unk> News called her character " soft , timid and promising " , and praised her for : " convincingly pull [ ing ] off a pleasing character of a street fighter 's wife " . Film critic Subhash K. Jha noted that she : " ... in a limited role gives her finest emotive shot " , while critic Raja <unk> remarked : " [ she ] plays Kumar 's long @-@ sobbing wife who gets so <unk> happy on seeing a text message that it may well have contained news about a Kick sequel . " 
- As of September 2015 , Fernandez has several projects in various stages of production . She has completed shooting for <unk> <unk> 's English @-@ Sri Lankan crime @-@ thriller According to <unk> , and the horror thriller Definition of Fear , which marks her Hollywood debut . Fernandez has also signed on to appear in three other projects — <unk> Dhawan 's <unk> opposite <unk> Dhawan and John Abraham as a part of three @-@ film deal with Nadiadwala <unk> Entertainment , <unk> D <unk> 's Flying <unk> opposite Tiger <unk> , and in an Indo @-@ Chinese film starring opposite <unk> <unk> , Amitabh Bachchan , and Jackie Chan titled Gold <unk> . 
- 
- = = Personal life and other work = = 
- 
- Fernandez shares a close bond with her family , and admits to missing being around them . She says : " I miss them so much everyday . You don 't realise when you live away from home how difficult life can be [ ... ] At the same time , staying away from them has taught me to be more responsible . It has taught me so many things about myself , about priorities and time management . " In March 2012 , Fernandez turned vegetarian for a 40 @-@ day period to observe <unk> , a period from Ash Wednesday to Holy Saturday . 
- In 2008 , Fernandez started dating <unk> prince Hassan bin Rashid Al <unk> , whom she met at a mutual friend 's party ; they separated in 2011 . While filming Housefull 2 in 2011 , Fernandez began a romantic relationship with director Sajid Khan . The relationship attracted media coverage in India and there was speculation of an impending wedding . However , the relationship ended in May 2013 . 
- In addition to acting in films , Fernandez has supported charitable organisations and a number of causes . In 2011 , on the behalf of People for the <unk> Treatment of Animals ( <unk> ) , she sent a letter to the Mumbai Municipal Commissioner asking for an end to horse @-@ drawn carriage rides in Mumbai . In early 2013 , she asked the <unk> general of the Philippines , William John T <unk> in Colombo , to hasten the transfer of an elephant from its inadequate housing at the Manila Zoo to a humane sanctuary . Later that year , she auctioned a breakfast in Mayfair , London , where she raised around £ 4000 for the <unk> <unk> , which helps children 's primary education . In 2014 , Fernandez was named " Woman Of The Year " by <unk> ( India ) for advocating the protection of animals . The following year , she auctioned her outfits on an online portal for a philanthropic cause . Some of her outfits included the ones she wore in the song " Party On My Mind " ( from Race 2 ) and " <unk> " ( from Kick ) . In March 2016 , she was part of " Jacqueline <unk> " campaign that raised funds for the victims of the 2015 South Indian floods . 
- Fernandez has participated in several concert tours and televised award ceremonies . In 2013 , she performed at the Temptations <unk> in Auckland , Perth , and Sydney alongside Shah <unk> Khan , <unk> <unk> , and <unk> <unk> . She also performed at the live talent show " Got Talent World Stage Live " with Khan , <unk> Chopra and <unk> Dhawan the following year . In July 2014 , Fernandez opened a restaurant in Colombo , <unk> Sutra , in collaboration with chef <unk> <unk> , which <unk> in contemporary Sri Lankan cuisine . 
- 
- = = In the media = = 
- 
- In the early 2013 , Fernandez became the ambassador for <unk> One , which she <unk> in India . She was the face of Indian <unk> Fashion Week — <unk> of 2013 . Later that year , she became the spokesperson for Gareth <unk> 's designed <unk> Diamonds in Mumbai , and was at the inaugural opening of the Forever 21 store in Mumbai . That year , she also launched <unk> <unk> System with <unk> Khan and <unk> Roy <unk> . While <unk> Fernandez 's career , India TV noted : " Slowly and steadily Jacqueline Fernandez is climbing up the ladder of success [ ... ] Jacqueline is comfortably grasping every aspect of the work , which an actress is required to do and is accordingly giving results . " On the contrary , <unk> Thakur of India Today criticized her acting skills , but remarked that : " [ she has ] managed to find her feet in Bollywood now by banking on glamorous roles " . 
- In 2008 and 2011 , Fernandez featured in the UK magazine Eastern Eye 's " World 's <unk> Asian Women " list , ranking twelfth . She was ranked third on The Times of India 's listing of the " Most <unk> Woman " in 2013 and 2014 , after being ranked eighth , seventh and fourteenth , respectively , in the preceding three years . In 2013 , Rediff.com placed her on their list of " Bollywood 's Best <unk> <unk> " . The following year , she held the sixty second position in the Indian edition of the Forbes ' Celebrity 100 , a list based on the income and popularity of India 's celebrities . She has been the cover model for many Indian editions of magazines , including : Vogue , <unk> , Maxim , Cosmopolitan , <unk> , Elle , <unk> , Harper 's <unk> , Women 's Health , and L <unk> among others . 
- 
- = = Filmography = = 
- 
- 
- = = TV Appearances = = 
- 
- 
- = = Awards = = 
- 
- 
- 
- = John Cullen = 
- 
- Barry John Cullen ( born August 2 , 1964 ) is a Canadian former professional ice hockey centre who played in the National Hockey League ( NHL ) for the Pittsburgh Penguins , Hartford Whalers , Toronto Maple Leafs and Tampa Bay Lightning . He was a standout player for Boston University and is the school 's all @-@ time leading scorer . After the Buffalo Sabres selected him in the 1986 NHL Supplemental Draft but chose not to offer him a contract , Cullen signed with the Flint Spirits of the International Hockey League ( IHL ) for the 1987 – 88 season where he was named the IHL 's co @-@ Rookie of the Year and Most Valuable Player after leading the league in scoring . 
- His career was halted in 1997 when he was diagnosed with Non @-@ Hodgkin lymphoma . He attempted a brief comeback in 1998 after an 18 @-@ month battle with the disease , for which the NHL awarded him the Bill Masterton Memorial Trophy , before retiring to serve as an assistant coach for a year with the Lightning . Cullen played in two NHL All @-@ Star Games in his career . He joined his brother in the car dealership business after leaving the game , and briefly operated his own dealership until forced to close during the automotive industry crisis of 2008 – 10 . 
- 
- = = Early life = = 
- 
- Cullen was born in <unk> @-@ Ontario on August 2 , 1964 . He is one of six children of Barry and <unk> Cullen . His father and <unk> Brian and Ray all played in the NHL , and while Cullen and his three brothers all played as well , their father never pressured them , preferring that they enjoy the game . 
- He idolized his elder brother Terry , who was considered a top NHL prospect until Terry 's career was ended when he suffered a broken neck after being hit from behind into the boards during a college game . While his brother was highly sought by American universities , John received only two scholarship offers , choosing to play for Boston University ( BU ) in 1983 . 
- At the same time , his mother <unk> was diagnosed with skin cancer . Following her death early in his freshman year , Cullen contemplated returning to his Ontario home , but was convinced by his father to continue with both school and hockey . He used the game to cope with the loss and dedicated every game he played to his mother 's memory . Cullen felt that the inspiration he drew from his mother 's battle allowed him to become a better player . 
- 
- = = Playing career = = 
- 
- Cullen was a standout with BU ; he was named the East Coast Athletic Conference Rookie of the Year in 1983 – 84 after leading his team in scoring with 56 points . The National Hockey League passed him over , however , as he went <unk> in the 1984 NHL Entry Draft . He was named to the Hockey East All @-@ Star Teams in 1985 , 1986 and 1987 , and a National Collegiate Athletic Association East Second Team All @-@ American in 1986 . He graduated as BU 's all @-@ time scoring leader with 241 points , and was named to BU 's Hockey East 25th anniversary team in 2009 . 
- <unk> over in the Entry Draft , Cullen was finally selected by the Buffalo Sabres in the 1986 NHL Supplemental Draft . When the Sabres failed to offer him a contract , Cullen signed with the Flint Spirits of the International Hockey League ( IHL ) for the 1987 – 88 season . He led the league with 157 points , scoring 48 goals , and won the James <unk> Memorial Trophy as league most valuable player while sharing the Gary F. <unk> Memorial Trophy with Ed <unk> as rookie of the year . Cullen 's outstanding season in Flint caught the attention of the Sabres and the Pittsburgh Penguins . He signed a contract with the Penguins for the league minimum , passing up a superior contract offer from Buffalo as he remained upset at how they released him the year before . 
- 
- = = = National Hockey League = = = 
- 
- Cullen made his NHL debut in 1988 – 89 , appearing in 79 games with the Penguins and scoring 49 points . He was given a greater role with the Penguins the following year after Mario Lemieux missed 21 games due to a back injury and responded by scoring 32 goals and 92 points to finish third in team scoring . Additionally , he played for Team Canada at the 1990 World Championship , scoring four points in ten games . Cullen had his best season in 1990 – 91 . As one of the team 's top offensive centres , he scored 94 points in the Penguins ' first 65 games and played in his first NHL All @-@ Star Game . However , when Lemieux returned after missing an additional 50 @-@ games due to injury , Cullen 's playing time and production declined . 
- The Penguins ' needs led them to complete a blockbuster trade on March 1 , 1991 . Cullen was sent to the Hartford Whalers , along with <unk> <unk> and Jeff Parker in exchange for Hartford 's all @-@ time leading scorer , Ron Francis , along with <unk> <unk> and Grant Jennings . The Penguins almost turned down the deal as they were concerned about giving up Cullen 's <unk> and leadership abilities , while his former teammates credited Cullen as being the primary reason they were in a playoff position at the time the trade happened . After the Penguins won their first Stanley Cup that season , Phil <unk> later said it " broke his heart " that Cullen was not able to share in that championship . 
- In Hartford , Cullen worked to overcome the team 's fans ' disappointment at losing Francis . The Hartford fans initially <unk> him to show their dissatisfaction with the trade . He scored 16 points in 13 regular season games to finish the season with 110 points combined between the Penguins and Whalers , and was the team 's best player in their first round loss to the Boston Bruins in the 1991 Stanley Cup <unk> . He initially accepted an invitation to join the Canadian team at the 1991 Canada Cup , but subsequently chose not to participate as his contract had expired , leading to greater insurance concerns . Still without a contract when the 1991 – 92 season began , Cullen missed the first four games before signing a four @-@ year deal with Hartford worth a total of $ 4 million . He returned to score 77 points in 77 games in his first full season with the Whalers and represented the team at the 1992 All @-@ Star Game . 
- Midway through the 1992 – 93 NHL season , the Whalers sent Cullen to the Toronto Maple Leafs for Toronto 's second round selection at the 1993 NHL Entry Draft . Cullen was excited to play for his father 's old team , but injuries reduced his ability to perform . His most significant injury was a <unk> disc in his neck that doctors initially feared would end his career . A bulky neck <unk> allowed Cullen to return and play out his contract in Toronto . When the Leafs chose not to re @-@ sign him following the 1993 – 94 season , he returned to the Penguins for one season before Tony <unk> convinced him to sign with the Tampa Bay Lightning in 1995 . 
- Cullen enjoyed immediate success with <unk> Shawn Burr and Alexander <unk> as the trio combined to score 130 points and helped lead the Lightning to the first playoff appearance in franchise history . They were eliminated by the Philadelphia <unk> in five games while Cullen led the team in playoff scoring with three goals and three assists . The Lightning looked to improve in 1996 – 97 ; Cullen was leading the team in scoring , but was suffering flu @-@ like symptoms that he could not shake . As Tampa was fighting for a playoff spot , he played through his condition for weeks . 
- 
- = = = Cancer and comeback = = = 
- 
- After two months of quietly dealing with his symptoms , Cullen 's wife finally called team trainers and asked them to check into his illness . The team took an x @-@ ray and found a large black shadow in his chest . He underwent a CAT scan which revealed Cullen had a baseball @-@ sized tumor ; he was diagnosed as having Non @-@ Hodgkin lymphoma . The diagnosis ended his season , and he immediately began chemotherapy treatments that quickly reduced his cancer . The tumor was gone by September 1997 , but a <unk> test prior to training camp revealed that Cullen still had cancer cells in his body . He missed the entire 1997 – 98 NHL season as he continued to battle the disease , while his teammates wore a uniform patch with his # 12 in support throughout the year . 
- On one day during his treatments , as his wife was <unk> him down a hospital corridor , Cullen went into cardiac arrest , requiring doctors to use a <unk> to revive him . He underwent a bone marrow transplant that briefly reduced his immune system to the point that he could have very little human contact . Another examination in April 1998 revealed that the cancer was finally gone , and Cullen immediately began training for a comeback . 
- The Lightning signed Cullen to a one @-@ year , $ 500 @,@ 000 contract for the 1998 – 99 season . He played his first game in nearly 18 months on September 18 , 1998 , in an exhibition game between the Lightning and Sabres at <unk> , Austria . Cullen scored the game @-@ winning goal in a 3 – 1 victory , after which he said he sat on the bench in <unk> over how he was given a second chance . He was named to the roster and was greeted with a loud standing ovation by the fans in Tampa Bay when he was introduced prior to their season opening game . 
- Cullen appeared in four of the Lightning 's first eight games , but it was evident that he had lost much of his speed and strength . The Lightning assigned him to the IHL 's Cleveland <unk> , but also gave him the option of retiring and taking up a position as an assistant coach . He chose to accept the demotion , giving himself one month to determine if he could continue playing . He appeared in six games for Cleveland , and in one game against the Chicago Wolves tied an IHL record when he scored seven points in a 7 – 3 victory . 
- However , a bout of <unk> led Cullen to fear that his cancer had returned . Tests came back negative , but after spending time with his family , he realized that neither he nor his family were interested in returning to Cleveland . Cullen announced his retirement on November 28 , 1998 , and accepted the Lightning offer to become an assistant coach . In recognition of his comeback attempt , the NHL named him the 1999 winner of the Bill Masterton Memorial Trophy for dedication and <unk> , while the IHL renamed its <unk> Player of the Year award the John Cullen Award . 
- Former Lightning head coach Terry <unk> has stated publicly that Cullen was a player that stood out as something special saying “ John Cullen ... beat cancer and came back to play and helped us win . ” 
- 
- = = Off the ice = = 
- 
- Cullen and his wife <unk> have three daughters , Kennedy and twins <unk> and <unk> . Unwilling to spend so much time away from his family , he left the Lightning in 1999 and settled in the Atlanta area , joining his brother 's car dealership in <unk> , Georgia . He had always expected to become a car dealer after his hockey career , as his father , <unk> and brother all worked in the industry . After <unk> under his brother for five years , he bought a Dodge dealership in <unk> , Georgia in 2007 . However , he owned the dealership for less than two years before Chrysler closed him down as part of its recovery plan in response to the Automotive industry crisis of 2008 – 2010 . He has since returned to his brother 's dealership , serving as its general manager . 
- Cullen 's battle with cancer inspired <unk> Harmon of the <unk> Cancer Centre to partner with the Lightning to raise awareness and money for cancer research . The NHL itself joined the cause in the winter of 1998 , creating the Hockey <unk> Cancer program to raise money for research . Cullen has spent time promoting the initiative . 
- Prior to marrying his wife <unk> , John dated Carolyn <unk> the future wife of John F. Kennedy , Jr . The two met while attending University in Boston . 
- 
- = = Career statistics = = 
- 
- 
- = = = Regular season and playoffs = = = 
- 
- 
- = = = International = = = 
- 
- 
- = = Awards = = 
- 
- Cullen is the namesake of the John Cullen Award , previously given to key IHL players . 
- 
- 
- = SMS Erzherzog Ferdinand Max = 
- 
- For the ironclad present at the Battle of <unk> of the same name , see SMS Erzherzog Ferdinand Max ( 1865 ) . 
- SMS Erzherzog Ferdinand Max ( German : " His Majesty 's ship <unk> Ferdinand Max " ) was a pre @-@ dreadnought battleship built by the Austro @-@ Hungarian Navy in 1902 . The second ship of the Erzherzog Karl class , she was launched on 3 October 1903 . She was assigned to the III Battleship Division . 
- For most of World War I , Erzherzog Ferdinand Max remained in her home port of Pola , in present @-@ day Croatia , except for four engagements . In 1914 , she formed part of the Austro @-@ Hungarian flotilla sent to protect the escape of the German ships SMS Goeben and SMS Breslau from the British @-@ held Mediterranean ; she advanced as far as Brindisi before being recalled to her home port . Her sole combat engagement occurred in late May 1915 , when she participated in the bombardment of the Italian port city of Ancona . She also took part in suppressing a major mutiny among the crew members of several armored cruisers stationed in Cattaro between 1 – 3 February 1918 . She also attempted to break through the Otranto <unk> in June of that year , but had to retreat when the dreadnought SMS Szent István was sunk . After the war , Erzherzog Ferdinand Max was awarded to the United Kingdom as a war prize in 1920 . 
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- = = Design = = 
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- Erzherzog Ferdinand Max displaced 10 @,@ <unk> long tons ( 10 @,@ 640 t ) . She was 414 feet 2 inches ( 126 @.@ 2 m ) long , had a beam of 71 feet 5 inches ( 21 @.@ 8 m ) and a draft of 24 feet 7 inches ( 7 @.@ 5 m ) . She was manned by 700 men . She and her sisters were the last and largest pre @-@ dreadnought class built by the Austro @-@ Hungarian Navy , surpassing the Habsburg class by approximately 2 @,@ 000 tonnes ( 1 @,@ 968 long tons ) . She was propelled by two two @-@ shaft , four cylinder vertical triple expansion steam engines . On trials , they developed 18 @,@ 000 <unk> ( 13 @,@ <unk> kW ) , which propelled the ship at a speed of 20 @.@ 5 knots ( 38 @.@ 0 km / h ; 23 @.@ 6 mph ) . 
- Erzherzog Ferdinand Max carried a primary armament of four 24 @-@ centimeter ( 9 @.@ 4 in ) / 40 caliber guns in two twin turrets on the centerline . These guns were an Austro @-@ Hungarian replica of the British 24 cm / 40 ( 9 @.@ 4 " ) <unk> C / 94 , which was used on the Habsburgs . Her secondary armament consisted of twelve 19 @-@ centimeter ( 7 @.@ 5 in ) / 42 caliber guns , also made by <unk> , mounted in eight single casemates on either wing of the ship and two twin turrets on the <unk> shell 20 @,@ 000 metres ( 22 @,@ 000 yd ) at maximum elevation with a muzzle velocity of 800 metres per second ( 2 @,@ 600 ft / s ) . The gun weighed 12 @.@ 1 tons and could fire three rounds per <unk> ships had a tertiary armament for protection against torpedo boats in the form of the 6 @.@ 6 centimetres ( 2 @.@ 6 in ) / 45 caliber gun , also manufactured by <unk> . Anti @-@ aircraft and airship protection was covered by the four 37 @-@ millimeter ( 1 @.@ 5 in ) Vickers anti @-@ aircraft guns on the ship bought from Britain in 1910 and mounted onto Erzherzog Karl . Erzherzog Ferdinand Max was also fitted with two above water 45 @-@ centimeter ( 17 @.@ 7 in ) torpedo tubes , although rarely used . 
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- = = Service history = = 
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- At the outbreak of World War I , Erzherzog Ferdinand Max was in the III division of the Austrian @-@ Hungarian battle @-@ fleet . She was mobilized on the eve of the war along with the remainder of the fleet to support the flight of SMS Goeben and SMS Breslau . The two German ships were attempting to break out of <unk> , which was surrounded by British troops , and make their way to Turkey . The breakout succeeded . When the flotilla had advanced as far south as Brindisi in south eastern Italy , the Austro @-@ Hungarian ships were recalled . In company with other units of the Austro Hungarian navy , Erzherzog Ferdinand Max took a minor part in the bombardment of Ancona on 24 May 1915 . There she and her sisters expended 24 rounds of 240 mm armor @-@ piercing shells at signal and <unk> stations as well as 74 rounds of 190 mm shells aimed at Italian gun @-@ batteries and other port installations . 
- A major mutiny among crews of the armored cruisers stationed in Cattaro , including <unk> Georg and Kaiser Karl VI , began on 1 February 1918 . Two days later , Erzherzog Ferdinand Max and her sisters arrived in the port and assisted with the suppression of the mutiny . Following the restoration of order in the naval base , the armored cruisers <unk> Georg and Kaiser Karl VI were decommissioned and Erzherzog Ferdinand Max and her sisters were stationed in Cattaro in their place . On the morning of 11 June , Admiral <unk> Horthy planned a major assault on the Otranto <unk> ; the three Erzherzog <unk> and the four Tegetthoff @-@ class battleships were to provide support for the <unk> @-@ class cruisers on an assault on the Allied defenses at the Strait of Otranto . The plan was intended to replicate the success of the raid conducted one year earlier . Horthy 's plan was to destroy the blockading fleet by <unk> Allied ships to the cruisers and lighter ships , which were protected from the heavier guns of the battleships , including the guns of the Erzherzog Karl class . However , on the morning of 10 June , the dreadnought Szent István was torpedoed and sunk by an Italian torpedo boat . Horthy felt that the element of surprise had been compromised , and therefore called off the operation . This was to be the last military action Erzherzog Ferdinand Max was to take part in , and she and her sisters spent the rest of their career in port . 
- Near the end of World War I , the Erzherzog Karl @-@ class battleships were handed over to the newly formed State of Slovenes , Croats and Serbs but Erzherzog Ferdinand Max was later transferred to Great Britain as a war reparation . She was later broken up for scrap in 1921 . 
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- = Ancient Egyptian deities = 
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- Ancient Egyptian deities are the gods and goddesses worshipped in ancient Egypt . The beliefs and rituals surrounding these gods formed the core of ancient Egyptian religion , which emerged sometime in prehistory . Deities represented natural forces and phenomena , and the Egyptians supported and <unk> them through offerings and rituals so that these forces would continue to function according to maat , or divine order . After the founding of the Egyptian state around 3100 BC , the authority to perform these tasks was controlled by the pharaoh , who claimed to be the gods ' representative and managed the temples where the rituals were carried out . 
- The gods ' complex characteristics were expressed in myths and in intricate relationships between deities : family ties , loose groups and <unk> , and combinations of separate gods into one . Deities ' diverse appearances in art — as animals , humans , objects , and combinations of different forms — also alluded , through symbolism , to their essential features . 
- In different eras , various gods were said to hold the highest position in divine society , including the solar deity Ra , the mysterious god Amun , and the mother goddess Isis . The highest deity was usually credited with the creation of the world and often connected with the life @-@ giving power of the sun . Some scholars have argued , based in part on Egyptian writings , that the Egyptians came to recognize a single divine power that lay behind all things and was present in all the other deities . Yet they never abandoned their original polytheistic view of the world , except possibly during the era of Atenism in the 14th century BC , when official religion focused exclusively on the <unk> sun god Aten . 
- Gods were assumed to be present throughout the world , capable of <unk> natural events and the course of human lives . People interacted with them in temples and unofficial shrines , for personal reasons as well as for larger goals of state rites . Egyptians prayed for divine help , used rituals to <unk> deities to act , and called upon them for advice . <unk> ' relations with their gods were a fundamental part of Egyptian society . 
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- = = Definition = = 
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- The beings in ancient Egyptian tradition who might be labeled as deities are difficult to count . Egyptian texts list the names of many deities whose nature is unknown and make vague , indirect references to other gods who are not even named . The Egyptologist James P. Allen estimates that more than 1 @,@ 400 deities are named in Egyptian texts , whereas his colleague Christian <unk> says there are " thousands upon thousands " of gods . 
- The Egyptian language 's terms for these beings were nṯr , " god " , and its feminine form <unk> , " goddess " . Scholars have tried to <unk> the original nature of the gods by proposing etymologies for these words , but none of these suggestions has gained acceptance , and the terms ' origin remains obscure . The hieroglyphs that were used as <unk> and <unk> in writing these words show some of the traits that the Egyptians connected with divinity . The most common of these signs is a flag flying from a pole . Similar objects were placed at the entrances of temples , representing the presence of a deity , throughout ancient Egyptian history . Other such hieroglyphs include a falcon , reminiscent of several early gods who were depicted as falcons , and a seated male or female deity . The feminine form could also be written with an egg as <unk> , connecting goddesses with creation and birth , or with a cobra , reflecting the use of the cobra to depict many female deities . 
- The Egyptians distinguished <unk> , " gods " , from <unk> , " people " , but the meanings of the Egyptian and the English terms do not match perfectly . The term nṯr may have applied to any being that was in some way outside the sphere of everyday life . <unk> humans were called nṯr because they were considered to be like the gods , whereas the term was rarely applied to many of Egypt 's lesser supernatural beings , which modern scholars often call " demons " . Egyptian religious art also depicts places , objects , and concepts in human form . These personified ideas range from deities that were important in myth and ritual to obscure beings , only mentioned once or twice , that may be little more than metaphors . 
- <unk> these blurred distinctions between gods and other beings , scholars have proposed various definitions of a " deity " . One widely accepted definition , suggested by Jan Assmann , says that a deity has a cult , is involved in some aspect of the universe , and is described in mythology or other forms of written tradition . According to a different definition , by Dimitri <unk> , nṯr applied to any being that was the focus of ritual . From this perspective , " gods " included the king , who was called a god after his coronation rites , and deceased souls , who entered the divine realm through funeral ceremonies . Likewise , the <unk> of the great gods was maintained by the ritual devotion that was performed for them across Egypt . 
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- = = Origins = = 
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- The first written evidence of deities in Egypt comes from the Early <unk> Period ( c . 3100 – <unk> BC ) . Deities must have emerged sometime in the preceding Predynastic Period ( before 3100 BC ) and grown out of prehistoric religious beliefs . Predynastic artwork depicts a variety of animal and human figures . Some of these images , such as stars and cattle , are reminiscent of important features of Egyptian religion in later times , but in most cases there is not enough evidence to say whether the images are connected with deities . As Egyptian society grew more sophisticated , clearer signs of religious activity appeared . The earliest known temples appeared in the last centuries of the predynastic era , along with images that resemble the iconographies of known deities : the falcon that represents Horus and several other gods , the crossed arrows that stand for <unk> , and the <unk> " Set animal " that represents Set . 
- Many Egyptologists and <unk> have suggested theories about how the gods developed in these early times . <unk> <unk> , for instance , thought the Egyptians first revered primitive <unk> , then deities in animal form , and finally deities in human form , whereas Henri Frankfort argued that the gods must have been envisioned in human form from the beginning . Some of these theories are now regarded as too simplistic , and more current ones , such as Siegfried <unk> ' hypothesis that deities emerged as humans began to distinguish themselves from and <unk> their environment , are difficult to prove . 
- Predynastic Egypt originally consisted of small , independent villages . Because many deities in later times were strongly tied to particular towns and regions , many scholars have suggested that the pantheon formed as disparate communities <unk> into larger states , spreading and <unk> the worship of the old local deities . But others have argued that the most important predynastic gods were , like other elements of Egyptian culture , present all across the country despite the political divisions within it . 
- The final step in the formation of Egyptian religion was the unification of Egypt , in which rulers from Upper Egypt made themselves pharaohs of the entire country . These sacred kings and their subordinates assumed the exclusive right to interact with the gods , and kingship became the <unk> focus of the religion . 
- New gods continued to emerge after this transformation . Some important deities like Isis and Amun are not known to have appeared until the Old Kingdom ( c . <unk> – <unk> BC ) . Places and concepts could suddenly inspire the creation of a deity to represent them , and deities were sometimes created to serve as opposite @-@ sex counterparts to established gods or goddesses . Kings were said to be divine , although only a few continued to be worshipped long after their deaths . Some non @-@ royal humans were said to have the favor of the gods and were venerated accordingly . This veneration was usually short @-@ lived , but the court architects <unk> and <unk> son of <unk> were regarded as gods centuries after their lifetimes , as were some other officials . 
- Through contact with neighboring civilizations , the Egyptians also adopted foreign deities . <unk> , who is first mentioned in the Old Kingdom , may have come from Nubia , and Baal , <unk> , and Astarte , among others , were adopted from Canaanite religion during the New Kingdom ( c . 1550 – 1070 BC ) . In Greek and Roman times , from <unk> BC to the early centuries AD , deities from across the Mediterranean world were revered in Egypt , but the native gods remained , and they often absorbed the cults of these newcomers into their own worship . 
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- = = Characteristics = = 
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- Modern knowledge of Egyptian beliefs about the gods is mostly drawn from religious writings produced by the nation 's scribes and priests . These people were the elite of Egyptian society and were very distinct from the general populace , most of whom were illiterate . Little is known about how well this broader population knew or understood the sophisticated ideas that the elite developed . <unk> ' perceptions of the divine may have differed from those of the priests . The populace may , for example , have mistaken the religion 's symbolic statements about the gods and their actions for literal truth . But overall , what little is known about popular religious belief is consistent with the elite tradition . The two traditions form a largely cohesive vision of the gods and their nature . 
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- = = = <unk> = = = 
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- Most Egyptian deities represent natural or social phenomena . The gods were generally said to be <unk> in these phenomena — to be present within nature . The types of phenomena they represented include physical places and objects as well as abstract concepts and forces . The god Shu was the <unk> of all the world 's air ; the goddess <unk> oversaw a limited region of the earth , the <unk> <unk> ; and the god <unk> personified the abstract notion of perception . Major gods often had many roles and were involved in several types of phenomena . For instance , <unk> was the god of <unk> Island in the midst of the Nile , the river that was essential to Egyptian civilization . He was credited with producing the annual Nile flood that fertilized the nation 's farmland . Perhaps as an outgrowth of this life @-@ giving function , he was said to create all living things , <unk> their bodies on a <unk> 's wheel . Gods could share the same role in nature ; Ra , Atum , <unk> , Horus , and other deities acted as sun gods . Despite their diverse functions , most gods had an overarching role in common : maintaining maat , the universal order that was a central principle of Egyptian religion and was itself personified as a goddess . But some deities represented disruption to maat . Most prominently , <unk> was the force of chaos , constantly threatening to <unk> the order of the universe , and Set was an ambivalent member of divine society who could both fight disorder and foment it . 
- Not all aspects of existence were seen as deities . Although many deities were connected with the Nile , no god personified it in the way that Ra personified the sun . Short @-@ lived phenomena , like <unk> or <unk> , were not represented by gods ; neither were elements like fire and water or many other components of the world . 
- The roles of each deity were fluid , and each god could expand its nature to take on new characteristics . As a result , gods ' roles are difficult to <unk> or define . But despite their flexibility , the gods had limited abilities and spheres of influence . Not even the creator god could reach beyond the boundaries of the cosmos that he created , and even Isis , though she was said to be the <unk> of the gods , was not <unk> . Richard H. Wilkinson , however , argues that some texts from the late New Kingdom suggest that , as beliefs about the god Amun evolved , he was thought to approach omniscience and <unk> and to transcend the limits of the world in a way that other deities did not . 
- The deities with the most limited and specialized domains are often called " minor divinities " or " demons " in modern writing , although there is no firm definition for these terms . Among these lesser deities , Egyptologist Claude <unk> draws a distinction between " <unk> " — specialized patron spirits of certain places , objects , or activities , such as the sea or marsh god <unk> @-@ <unk> and the harvest goddess <unk> — and demons , who have a more dangerous character . Many demons are hostile , causing illness and other troubles among humans . Their power can also be protective ; they may guard certain places in the Duat , the realm of the dead , or advise and watch over humans . Egyptians believed the landscape was full of these unpredictable divine powers . Demons often act as servants and messengers to the greater gods , but their position in the hierarchy is not fixed . The protective deities <unk> and <unk> originally had minor , demon @-@ like roles , but over time they came to be credited with great influence . 
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- = = = Behavior = = = 
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- Divine behavior was believed to govern all of nature . Except for the few deities who disrupted the divine order , the gods ' actions maintained maat and created and sustained all living things . They did this work using a force the Egyptians called <unk> , a term usually translated as " magic " . <unk> was a fundamental power that the creator god used to form the world and the gods themselves . 
- The gods ' actions in the present are described and praised in hymns and funerary texts . In contrast , mythology mainly concerns the gods ' actions during a vaguely imagined past in which the gods were present on earth and interacted directly with humans . The events of this past time set the pattern for the events of the present . Periodic occurrences were tied to events in the mythic past ; the succession of each new pharaoh , for instance , <unk> Horus ' accession to the throne of his father Osiris . Myths are metaphors for the gods ' actions , which humans cannot fully understand . They contain seemingly contradictory ideas , each expressing a particular perspective on divine events . The contradictions in myth are part of the Egyptians ' many @-@ <unk> approach to religious belief — what Henri Frankfort called a " multiplicity of approaches " to understanding the gods . 
- In myth , the gods behave much like humans . They feel emotion ; they can eat , drink , fight , <unk> , <unk> , and die . Some have unique character traits . Set is aggressive and impulsive , and Thoth , patron of writing and knowledge , is prone to long @-@ <unk> speeches . Yet overall , the gods are more like <unk> than well drawn characters . Their behavior is inconsistent , and their thoughts and motivations are rarely stated . Most myths about them lack highly developed characters and plots , because the symbolic meaning of the myths was more important than elaborate storytelling . 
- The first divine act is the creation of the cosmos , described in several creation myths . They focus on different gods , each of which may act as creator deities . The eight gods of the <unk> , who represent the chaos that <unk> creation , give birth to the sun god , who establishes order in the newly formed world ; Ptah , who embodies thought and creativity , gives form to all things by <unk> and naming them ; Atum produces all things as <unk> of himself ; and Amun , according to the myths promoted by his priesthood , preceded and created the other creator gods . These and other versions of the events of creation were not seen as contradictory . Each gives a different perspective on the complex process by which the organized universe and its many deities emerged from undifferentiated chaos . The period following creation , in which a series of gods rule as kings over the divine society , is the setting for most myths . The gods struggle against the forces of chaos and among each other before withdrawing from the human world and installing the historical kings of Egypt to rule in their place . 
- A recurring theme in these myths is the effort of the gods to maintain maat against the forces of disorder . They fight vicious battles with the forces of chaos at the start of creation . Ra and <unk> , battling each other each night , continue this struggle into the present . Another prominent theme is the gods ' death and revival . The <unk> instance where a god dies is the myth of Osiris ' murder , in which that god is resurrected as ruler of the Duat . The sun god is also said to grow old during his daily journey across the sky , sink into the Duat at night , and emerge as a young child at dawn . In the process he comes into contact with the <unk> water of primordial chaos . Funerary texts that depict Ra 's journey through the Duat also show the corpses of gods who are <unk> along with him . Instead of being <unk> immortal , the gods periodically died and were reborn by repeating the events of creation , thus renewing the whole world . But it was always possible for this cycle to be disrupted and for chaos to return . Some poorly understood Egyptian texts even suggest that this <unk> is destined to happen — that the creator god will one day dissolve the order of the world , leaving only himself and Osiris amid the primordial chaos . 
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- = = = Locations = = = 
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- Gods were linked with specific regions of the universe . In Egyptian tradition , the world includes the earth , the sky , and the Duat . Surrounding them is the dark <unk> that existed before creation . The gods in general were said to dwell in the sky , although gods whose roles were linked with other parts of the universe were said to live in those places instead . Most events of mythology , set in a time before the gods ' withdrawal from the human realm , take place in an earthly setting . The deities there sometimes interact with those in the sky . The Duat , in contrast , is treated as a remote and inaccessible place , and the gods who dwell there have difficulty communicating with those in the world of the living . The space outside the cosmos is also said to be very distant . It too is inhabited by deities , some hostile and some beneficial to the other gods and their orderly world . 
- In the time after myth , most gods were said to be either in the sky or <unk> present within the world . Temples were their main means of contact with humanity . Each day , it was believed , the gods moved from the divine realm to their temples , their homes in the human world . There they inhabited the cult images , the statues that depicted deities and allowed humans to interact with them in temple rituals . This movement between realms was sometimes described as a journey between the sky and the earth . As temples were the focal points of Egyptian cities , the god in a city 's main temple was the patron god for the city and the surrounding region . Deities ' spheres of influence on earth centered on the towns and regions they presided over . Many gods had more than one cult center , and their local ties changed over time . They could establish themselves in new cities , or their range of influence could contract . Therefore , a given deity 's main cult center in historical times is not necessarily his or her place of origin . The political influence of a city could affect the importance of its patron deity . When kings from Thebes took control of the country at start of the Middle Kingdom ( c . 2055 – 1650 BC ) , they elevated Thebes ' patron gods — first the war god <unk> and then Amun — to national prominence . 
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- = = = Names and epithets = = = 
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- In Egyptian belief , names express the fundamental nature of the things to which they refer . In keeping with this belief , the names of deities often relate to their roles or origins . The name of the predatory goddess <unk> means " powerful one " , the name of the mysterious god Amun means " hidden one " , and the name of the goddess <unk> , who was worshipped in the city of <unk> , means " she of <unk> " . But many other names have no certain meaning , even when the gods who bear them are closely tied to a single role . The names of the sky goddess <unk> and the earth god <unk> do not resemble the Egyptian terms for sky and earth . 
- The Egyptians also devised false etymologies giving more meanings to divine names . A passage in the Coffin Texts renders the name of the funerary god <unk> as <unk> r , meaning " cleaning of the mouth " , to link his name with his role in the Opening of the Mouth ritual , while one in the Pyramid Texts says the name is based on words shouted by Osiris , connecting <unk> with the most important funerary deity . 
- The gods were believed to have many names . Among them were secret names that conveyed their true natures more profoundly than others . To know the true name of a deity was to have power over it . The importance of names is demonstrated by a myth in which Isis poisons the superior god Ra and refuses to cure him unless he reveals his secret name to her . Upon learning the name , she tells it to her son , Horus , and by learning it they gain greater knowledge and power . 
- In addition to their names , gods were given epithets , like " <unk> of <unk> " , " ruler of Abydos " , or " lord of the sky " , that describe some aspect of their roles or their worship . Because of the gods ' multiple and overlapping roles , deities can have many epithets — with more important gods accumulating more titles — and the same epithet can apply to many deities . Some epithets eventually became separate deities , as with <unk> , an epithet applied to several goddesses meaning " great <unk> " , which came to be treated as an independent goddess . The host of divine names and titles expresses the gods ' <unk> nature . 
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- = = = Relationships = = = 
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- Egyptian deities are connected in a complex and shifting array of relationships . A god 's connections and interactions with other deities helped define its character . Thus Isis , as the mother and protector of Horus , was a great <unk> as well as the <unk> of kings . Such relationships were the base material from which myths were formed . 
- Family relationships are a common type of connection between gods . Deities often form male and female pairs , reflecting the importance of procreation in Egyptian religious thought . Families of three deities , with a father , mother , and child , represent the creation of new life and the succession of the father by the child , a pattern that connects divine families with royal succession . Osiris , Isis , and Horus formed the quintessential family of this type . The pattern they set grew more widespread over time , so that many deities in local cult centers , like Ptah , <unk> , and their child <unk> at Memphis and Amun , <unk> , and <unk> at Thebes , were assembled into family <unk> . <unk> connections like these are changeable , in keeping with the multiple perspectives in Egyptian belief . Hathor , as a fertility goddess , could act as mother to any child god , including the child form of the sun god , although in other circumstances she was the sun god 's daughter . 
- Other divine groups were composed of deities with interrelated roles , or who together represented a region of the Egyptian mythological cosmos . There were sets of gods for the hours of the day and night and for each <unk> ( province ) of Egypt . Some of these groups contain a specific , symbolically important number of deities . Paired gods can stand for opposite but interrelated concepts that are part of a greater unity . Ra , who is dynamic and light @-@ producing , and Osiris , who is static and <unk> in darkness , merge into a single god each night . Groups of three are linked with plurality in ancient Egyptian thought , and groups of four <unk> <unk> . Rulers in the late New Kingdom promoted a particularly important group of three gods above all others : Amun , Ra , and Ptah . These deities stood for the plurality of all gods , as well as for their own cult centers ( the major cities of Thebes , <unk> , and Memphis ) and for many threefold sets of concepts in Egyptian religious thought . Sometimes Set , the patron god of the <unk> Dynasty kings and the embodiment of disorder within the world , was added to this group , which emphasized a single coherent vision of the pantheon . 
- Nine , the product of three and three , represents a multitude , so the Egyptians called several large groups " <unk> " , or sets of nine , even if they had more than nine members . The most prominent <unk> was the <unk> of <unk> , an extended family of deities descended from the creator god Atum , which incorporates many important gods . The term " <unk> " was often extended to include all of Egypt 's deities . 
- This divine assemblage had a vague and changeable hierarchy . Gods with broad influence in the cosmos or who were <unk> older than others had higher positions in divine society . At the apex of this society was the king of the gods , who was usually identified with the creator deity . In different periods of Egyptian history , different gods were most frequently said to hold this exalted position . Horus was the most important god in the Early <unk> Period , Ra rose to <unk> in the Old Kingdom , Amun was supreme in the New , and in the Ptolemaic and Roman periods , Isis was the divine queen and creator goddess . Newly prominent gods tended to adopt characteristics from their predecessors . Isis absorbed the traits of many other goddesses during her rise , and when Amun became the ruler of the pantheon , he was conjoined with Ra , the traditional king of the gods , to become a solar deity . 
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- = = = <unk> and combinations = = = 
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- The gods were believed to manifest in many forms . The Egyptians had complex conception of the human soul , consisting of several parts . The spirits of the gods were composed of many of these same elements . The ba was the component of the human or divine soul that affected the world around it . Any visible manifestation of a god 's power could be called its ba ; thus , the sun was called the ba of Ra . A depiction of a deity was considered a <unk> , another component of its being , which acted as a vessel for that deity 's ba to inhabit . The cult images of gods that were the focus of temple rituals , as well as the sacred animals that represented certain deities , were believed to house divine bas in this way . Gods could be ascribed many bas and <unk> , which were sometimes given names representing different aspects of the god 's nature . Everything in existence was said to be one of the <unk> of Atum the creator god , who originally contained all things within himself , and one deity could be called the ba of another , meaning that the first god is a manifestation of the other 's power . Divine body parts could act as separate deities , like the Eye of Ra and Hand of Atum , both of which were personified as goddesses . 
- <unk> important deities gave rise to local manifestations , which sometimes absorbed the characteristics of older regional gods . Horus had many forms tied to particular places , including Horus of <unk> , Horus of <unk> , and Horus of <unk> . Such local manifestations could be treated almost as separate beings . During the New Kingdom , one man was accused of stealing clothes by an <unk> supposed to communicate messages from Amun of <unk> @-@ Khenty . He consulted two other local oracles of Amun hoping for a different judgment . Gods ' manifestations also differed according to their roles . Horus could be a powerful sky god or vulnerable child , and these forms were sometimes counted as independent deities . 
- Gods were combined with each other as easily as they were divided . A god could be called the ba of another , or two or more deities could be joined into one god with a combined name and iconography . Local gods were linked with greater ones , and deities with similar functions were combined . Ra was connected with the local deity <unk> to form <unk> @-@ Ra ; with his fellow ruling god , Amun , to form Amun @-@ Ra ; with the solar form of Horus to form Ra @-@ <unk> ; and with several solar deities as <unk> @-@ <unk> @-@ Ra @-@ Atum . On rare occasion , even deities of different sexes were joined in this way , producing combinations like Osiris @-@ <unk> and <unk> @-@ <unk> . This linking of deities is called syncretism . Unlike other situations for which this term is used , the Egyptian practice was not meant to fuse competing belief systems , although foreign deities could be syncretized with native ones . Instead , syncretism acknowledged the overlap between their roles , and extended the sphere of influence for each of them . <unk> combinations were not permanent ; a god who was involved in one combination continued to appear separately and to form new combinations with other deities . But closely connected deities did sometimes merge . Horus absorbed several falcon gods from various regions , such as Khenty @-@ <unk> and Khenty @-@ <unk> , who became little more than local manifestations of him ; Hathor subsumed a similar cow goddess , Bat ; and an early funerary god , <unk> @-@ <unk> , was <unk> by Osiris and Anubis . 
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- = = = The Aten and possible monotheism = = = 
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- In the reign of Akhenaten ( c . <unk> – 1336 BC ) in the mid @-@ New Kingdom , a single solar deity , the Aten , became the sole focus of the state religion . Akhenaten ceased to fund the temples of other deities and erased the gods ' names and images on monuments , targeting Amun in particular . This new religious system , sometimes called Atenism , differed dramatically from the polytheistic worship of many gods in all other periods . Whereas , in earlier times , newly important gods were integrated into existing religious beliefs , Atenism insisted on a single understanding of the divine that excluded the traditional multiplicity of perspectives . Yet Atenism may not have been full monotheism , which totally excludes belief in other deities . There is evidence suggesting that the general populace was still allowed to worship other gods in private . The picture is further complicated by Atenism 's apparent tolerance for some other deities , like Shu . For these reasons , the Egyptologist Dominic Montserrat suggested that Akhenaten may have been <unk> , <unk> a single deity while acknowledging the existence of others . In any case , Atenism 's aberrant theology did not take root among the Egyptian populace , and Akhenaten 's successors returned to traditional beliefs . 
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- = = = Unity of the divine in traditional religion = = = 
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- Scholars have long debated whether traditional Egyptian religion ever asserted that the multiple gods were , on a deeper level , unified . Reasons for this debate include the practice of syncretism , which might suggest that all the separate gods could ultimately merge into one , and the tendency of Egyptian texts to credit a particular god with power that surpasses all other deities . Another point of contention is the appearance of the word " god " in wisdom literature , where the term does not refer to a specific deity or group of deities . In the early 20th century , for instance , E. A. Wallis <unk> believed that Egyptian commoners were polytheistic , but knowledge of the true monotheistic nature of the religion was reserved for the elite , who wrote the wisdom literature . His contemporary James Henry <unk> thought Egyptian religion was instead <unk> , with the power of the sun god present in all other gods , while Hermann <unk> argued that Egyptian civilization had been originally monotheistic and became polytheistic in the course of its history . 
- In 1971 , Erik Hornung published a study <unk> these views . He points out that in any given period many deities , even minor ones , were described as superior to all others . He also argues that the unspecified " god " in the wisdom texts is a generic term for whichever deity the reader chooses to <unk> . Although the combinations , manifestations , and iconographies of each god were constantly shifting , they were always restricted to a finite number of forms , never becoming fully interchangeable in a monotheistic or <unk> way . <unk> , Hornung says , describes Egyptian religion better than other labels . An Egyptian could worship any deity at a particular time and credit it with supreme power in that moment , without denying the other gods or merging them all with the god that he or she focused on . Hornung concludes that the gods were fully unified only in myth , at the time before creation , after which the multitude of gods emerged from a uniform <unk> . 
- Hornung 's arguments have greatly influenced other scholars of Egyptian religion , but some still believe that at times the gods were more unified than he allows . Jan Assmann maintains that the notion of a single deity developed slowly through the New Kingdom , beginning with a focus on Amun @-@ Ra as the all @-@ important sun god . In his view , Atenism was an extreme outgrowth of this trend . It equated the single deity with the sun and dismissed all other gods . Then , in the backlash against Atenism , priestly theologians described the universal god in a different way , one that <unk> with traditional polytheism . The one god was believed to transcend the world and all the other deities , while at the same time , the multiple gods were aspects of the one . According to Assmann , this one god was especially equated with Amun , the dominant god in the late New Kingdom , whereas for the rest of Egyptian history the universal deity could be identified with many other gods . James P. Allen says that <unk> notions of one god and many gods would fit well with the " multiplicity of approaches " in Egyptian thought , as well as with the <unk> practice of ordinary worshippers . He says that the Egyptians may have recognized the unity of the divine by " identifying their uniform notion of ' god ' with a particular god , depending on the particular situation . " 
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- = = <unk> and depictions = = 
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- Egyptian writings describe the gods ' bodies in detail . They are made of precious materials ; their flesh is gold , their bones are silver , and their hair is <unk> <unk> . They give off a scent that the Egyptians likened to the incense used in rituals . Some texts give precise descriptions of particular deities , including their height and eye color . Yet these characteristics are not fixed ; in myths , gods change their appearances to suit their own purposes . Egyptian texts often refer to deities ' true , underlying forms as " mysterious " . The Egyptians ' visual representations of their gods are therefore not literal . They symbolize specific aspects of each deity 's character , functioning much like the <unk> in hieroglyphic writing . For this reason , the funerary god Anubis is commonly shown in Egyptian art as a dog or <unk> , a creature whose <unk> habits threaten the preservation of buried mummies , in an effort to counter this threat and employ it for protection . His black coloring alludes to the color of <unk> flesh and to the fertile black soil that Egyptians saw as a symbol of resurrection . 
- Most gods were depicted in several ways . Hathor could be a cow , cobra , <unk> , or a woman with <unk> horns or ears . By depicting a given god in different ways , the Egyptians expressed different aspects of its essential nature . The gods are depicted in a finite number of these symbolic forms , so that deities can often be distinguished from one another by their iconographies . These forms include men and women ( <unk> ) , animals ( <unk> ) , and , more rarely , <unk> objects . <unk> of forms , such as gods with human bodies and animal heads , are common . New forms and increasingly complex combinations arose in the course of history . Some gods can only be distinguished from others if they are labeled in writing , as with Isis and Hathor . Because of the close connection between these goddesses , they could both wear the cow @-@ horn headdress that was originally Hathor 's alone . 
- Certain features of divine images are more useful than others in determining a god 's identity . The head of a given divine image is particularly significant . In a hybrid image , the head represents the original form of the being depicted , so that , as the Egyptologist Henry Fischer put it , " a lion @-@ headed goddess is a lion @-@ goddess in human form , while a royal sphinx , <unk> , is a man who has assumed the form of a lion . " Divine <unk> , which range from the same types of crowns used by human kings to large hieroglyphs worn on gods ' heads , are another important indicator . In contrast , the objects held in gods ' hands tend to be generic . Male deities hold was staffs , goddesses hold stalks of papyrus , and both sexes carry ankh signs , representing the Egyptian word for " life " , to symbolize their life @-@ giving power . 
- The forms in which the gods are shown , although diverse , are limited in many ways . Many creatures that are widespread in Egypt were never used in divine iconography , whereas a few , such as falcons , <unk> , and cattle , can each represent many deities . Animals that were absent from Egypt in the early stages of its history were not used as divine images . For instance , the horse , which was only introduced in the Second Intermediate Period ( c . 1650 – 1550 BC ) , never represented a god . Similarly , the clothes worn by anthropomorphic deities in all periods changed little from the styles used in the Old Kingdom : a <unk> , false beard , and often a shirt for male gods and a long , tight @-@ fitting dress for goddesses . 
- The basic anthropomorphic form varies . Child gods are depicted nude , as are some adult gods when their <unk> powers are emphasized . Certain male deities are given heavy <unk> and breasts , signifying either <unk> or prosperity and abundance . Whereas most male gods have red skin and most goddesses are yellow — the same colors used to depict Egyptian men and women — some are given unusual , symbolic skin colors . Thus the blue skin and <unk> figure of the god <unk> alludes to the Nile flood he represents and the nourishing fertility it brought . A few deities , such as Osiris , Ptah , and <unk> , have a " <unk> " appearance , with their limbs tightly <unk> in cloth . Although these gods resemble mummies , the earliest examples predate the cloth @-@ wrapped style of <unk> , and this form may instead <unk> back to the earliest , <unk> depictions of deities . 
- 
- = = <unk> with humans = = 
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- = = = Relationship with the pharaoh = = = 
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- In official writings , pharaohs are said to be divine , and they are constantly depicted in the company of the deities of the pantheon . Each pharaoh and his predecessors were considered the successors of the gods who had ruled Egypt in mythic prehistory . Living kings were equated with Horus and called the " son " of many deities , particularly Osiris and Ra ; deceased kings were equated with these elder gods . <unk> had their own mortuary temples where rituals were performed for them during their lives and after their deaths . But few pharaohs were worshipped as gods long after their lifetimes , and non @-@ official texts portray kings in a human light . For these reasons , scholars disagree about how genuinely most Egyptians believed the king to be a god . He may only have been considered divine when he was performing ceremonies . 
- However much it was believed , the king 's divine status was the rationale for his role as Egypt 's representative to the gods , as he formed a link between the divine and human realms . The Egyptians believed the gods needed temples to dwell in , as well as the periodic performance of rituals and presentation of offerings to <unk> them . These things were provided by the cults that the king oversaw , with their priests and laborers . Yet , according to royal ideology , temple @-@ building was exclusively the pharaoh 's work , as were the rituals that priests usually performed in his stead . These acts were a part of the king 's fundamental role : maintaining maat . The king and the nation he represented provided the gods with maat so they could continue to perform their functions , which maintained maat in the cosmos so humans could continue to live . 
- 
- = = = <unk> in the human world = = = 
- 
- Although the Egyptians believed their gods to be present in the world around them , contact between the human and divine realms was mostly limited to specific circumstances . In literature , gods may appear to humans in a physical form , but in real life the Egyptians were limited to more indirect means of communication . 
- The ba of a god was said to periodically leave the divine realm to dwell in the images of that god . By inhabiting these images , the gods left their concealed state and took on a physical form . To the Egyptians , a place or object that was <unk> — " sacred " — was isolated and <unk> pure , and thus fit for a god to inhabit . Temple statues and reliefs , as well as particular sacred animals , like the <unk> bull , served as divine <unk> in this way . Dreams and <unk> provided a very different venue for interaction . In these states , it was believed , people could come close to the gods and sometimes receive messages from them . Finally , according to Egyptian afterlife beliefs , human souls pass into the divine realm after death . The Egyptians therefore believed that in death they would exist on the same level as the gods and fully understand their mysterious nature . 
- Temples , where the state rituals were carried out , were filled with images of the gods . The most important temple image was the cult statue in the inner sanctuary . These statues were usually less than life @-@ size , and made of the same precious materials that were said to form the gods ' bodies . Many temples had several sanctuaries , each with a cult statue representing one of the gods in a group such as a family triad . The city 's primary god was envisioned as its lord , employing many of the residents as servants in the divine household that the temple represented . The gods residing in the temples of Egypt collectively represented the entire pantheon . But many deities — including some important gods as well as those that were minor or hostile — were never given temples of their own , although some were represented in the temples of other gods . 
- To <unk> the sacred power in the sanctuary from the impurities of the outside world , the Egyptians enclosed temple sanctuaries and greatly restricted access to them . People other than kings and high priests were thus denied contact with cult statues . The only exception was during festival processions , when the statue was carried out of the temple but still enclosed in a portable shrine . People did have less direct means of interaction . The more public parts of temples often incorporated small places for prayer , from <unk> to <unk> chapels near the back of the temple building . Communities also built and managed small chapels for their own use , and some families had shrines inside their homes . Despite the gulf that separated humanity from the divine , the Egyptians were surrounded by opportunities to approach their gods . 
- 
- = = = Intervention in human lives = = = 
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- Egyptian gods were involved in human lives as well as in the overarching order of nature . This divine influence applied mainly to Egypt , as foreign peoples were traditionally believed to be outside the divine order . But in the New Kingdom , when other nations were under Egyptian control , foreigners were said to be under the sun god 's benign rule in the same way that Egyptians were . 
- Thoth , as the overseer of time , was said to <unk> fixed <unk> to both humans and gods . Other gods were also said to govern the length of human lives , including <unk> , who presided over birth , and <unk> , the personification of fate . Thus the time and manner of death was the main meaning of the Egyptian concept of fate , although to some extent these deities governed other events in life as well . Several texts refer to gods <unk> or inspiring human decisions , working through a person 's " heart " — the seat of emotion and <unk> in Egyptian belief . Deities were also believed to give commands , instructing the king in the governance of his realm and regulating the management of their temples . Egyptian texts rarely mention direct commands given to private persons , and these commands never evolved into a set of divinely enforced moral codes . <unk> in ancient Egypt was based on the concept of maat , which , when applied to human society , meant that everyone should live in an orderly way that did not interfere with the well @-@ being of other people . Because deities were the <unk> of maat , morality was connected with them . For example , the gods judged humans ' moral righteousness after death , and by the New Kingdom , a verdict of innocence in this judgment was believed to be necessary for <unk> into the afterlife . But in general , morality was based on practical ways to uphold maat in daily life , rather than on strict rules that the gods laid out . 
- <unk> had free will to ignore divine guidance and the behavior required by maat , but by doing so they could bring divine punishment upon themselves . A deity carried out this punishment using its ba , the force that <unk> the god 's power in the human world . Natural disasters and human <unk> were seen as the work of angry divine bas . Conversely , the gods could cure righteous people of illness or even extend their <unk> . Both these types of intervention were eventually represented by deities : <unk> , who emerged in the New Kingdom to represent divine rescue from harm , and <unk> , an <unk> god from the late eras of Egyptian history who was believed to avenge wrongdoing . 
- Egyptian texts take different views on whether the gods are responsible when humans suffer unjustly . <unk> was often seen as a product of <unk> , the cosmic disorder that was the opposite of maat , and therefore the gods were not guilty of causing evil events . Some deities who were closely connected with <unk> , such as Set , could be blamed for disorder within the world without placing guilt on the other gods . But some writings do accuse the deities of causing human misery , while others give <unk> in the gods ' defense . Beginning in the Middle Kingdom , several texts connected the issue of evil in the world with a myth in which the creator god fights a human rebellion against his rule and then withdraws from the earth . Because of this human <unk> , the creator is distant from his creation , allowing suffering to exist . New Kingdom writings do not question the just nature of the gods as strongly as those of the Middle Kingdom . They emphasize humans ' direct , personal relationships with deities and the gods ' power to intervene in human events . People in this era put faith in specific gods who they hoped would help and protect them through their lives . As a result , upholding the ideals of maat grew less important than gaining the gods ' favor as a way to guarantee a good life . Even the pharaohs were regarded as dependent on divine aid , and after the New Kingdom came to an end , government was increasingly influenced by oracles communicating the gods ' will . 
- 
- = = = Worship = = = 
- 
- Official religious practices , which maintained maat for the benefit of all Egypt , were related to , but distinct from , the religious practices of ordinary people , who sought the gods ' help for their personal problems . 
- Official religion involved a variety of rituals , based in temples . Some rites were performed every day , whereas others were festivals , taking place at longer intervals and often limited to a particular temple or deity . The gods received their offerings in daily ceremonies , in which their statues were clothed , <unk> , and presented with food as hymns were recited in their honor . These offerings , in addition to maintaining maat for the gods , celebrated deities ' life @-@ giving generosity and encouraged them to remain benevolent rather than vengeful . 
- Festivals often involved a ceremonial procession in which a cult image was carried out of the temple in a barque @-@ shaped shrine . These processions served various purposes . In Roman times , when local deities of all kinds were believed to have power over the Nile inundation , processions in many communities carried temple images to the riverbanks so the gods could <unk> a large and fruitful flood . <unk> also traveled between temples , as when the image of Hathor from <unk> Temple visited her consort Horus at the Temple of <unk> . <unk> for a god were often based in that deity 's mythology . Such rituals were meant to be <unk> of the events of the mythic past , renewing the beneficial effects of the original events . In the <unk> festival in honor of Osiris , his death and resurrection were <unk> <unk> at a time when crops were beginning to <unk> . The returning <unk> symbolized the renewal of the god 's own life . 
- Personal interaction with the gods took many forms . People who wanted information or advice consulted oracles , run by temples , that were supposed to convey gods ' answers to questions . <unk> and other images of protective deities were used to ward off the demons that might threaten human well @-@ being or to <unk> the god 's positive characteristics to the wearer . Private rituals invoked the gods ' power to accomplish personal goals , from healing sickness to cursing enemies . These practices used <unk> , the same force of magic that the gods used , which the creator was said to have given to humans so they could fend off misfortune . The performer of a private rite often took on the role of a god in a myth , or even threatened a deity , to involve the gods in <unk> the goal . Such rituals <unk> with private offerings and prayers , and all three were accepted means of obtaining divine help . 
- Prayer and private offerings are generally called " personal piety " : acts that reflect a close relationship between an individual and a god . Evidence of personal piety is <unk> before the New Kingdom . <unk> offerings and personal names , many of which are <unk> , suggest that commoners felt some connection between themselves and their gods . But firm evidence of devotion to deities became visible only in the New Kingdom , reaching a peak late in that era . Scholars disagree about the meaning of this change — whether direct interaction with the gods was a new development or an outgrowth of older traditions . Egyptians now expressed their devotion through a new variety of activities in and around temples . They recorded their prayers and their thanks for divine help on stelae . They gave offerings of figurines that represented the gods they were praying to , or that symbolized the result they desired ; thus a relief image of Hathor and a <unk> of a woman could both represent a prayer for fertility . Occasionally , a person took a particular god as a patron , dedicating his or her property or labor to the god 's cult . These practices continued into the latest periods of Egyptian history . These later eras saw more religious innovations , including the practice of giving animal mummies as offerings to deities depicted in animal form , such as the cat mummies given to the feline goddess <unk> . Some of the major deities from myth and official religion were rarely invoked in popular worship , but many of the great state gods were important in popular tradition . 
- The worship of some Egyptian gods spread to neighboring lands , especially to Canaan and Nubia during the New Kingdom , when those regions were under <unk> control . In Canaan , the exported deities , including Hathor , Amun , and Set , were often syncretized with native gods , who in turn spread to Egypt . The Egyptian deities may not have had permanent temples in Canaan , and their importance there <unk> after Egypt lost control of the region . In contrast , many temples to the major Egyptian gods and deified pharaohs were built in Nubia . After the end of Egyptian rule there , the imported gods , particularly Amun and Isis , were syncretized with local deities and remained part of the religion of Nubia 's independent Kingdom of <unk> . These gods were incorporated into the Nubian ideology of kingship much as they were in Egypt , so that Amun was considered the divine father of the king and Isis and other goddesses were linked with the Nubian queen , the <unk> . Some deities reached farther . <unk> became a goddess in <unk> Crete , and Amun 's <unk> at <unk> Oasis was known to and consulted by people across the Mediterranean region . 
- Under the Greek Ptolemaic Dynasty and then Roman rule , Greeks and Romans introduced their own deities to Egypt . These newcomers equated the Egyptian gods with their own , as part of the Greco @-@ Roman tradition of <unk> <unk> . But the worship of the native gods was not swallowed up by that of foreign ones . Instead , Greek and Roman gods were adopted as manifestations of Egyptian ones . Egyptian cults sometimes incorporated Greek language , philosophy , iconography , and even temple architecture . Meanwhile , the cults of several Egyptian deities — particularly Isis , Osiris , Anubis , the form of Horus named <unk> , and the fused Greco @-@ Egyptian god <unk> — were adopted into Roman religion and spread across the Roman Empire . Roman emperors , like Ptolemaic kings before them , invoked Isis and <unk> to endorse their authority , inside and outside Egypt . In the empire 's complex mix of religious traditions , Thoth was transmuted into the legendary esoteric teacher Hermes Trismegistus , and Isis , who was venerated from Britain to Mesopotamia , became the focus of a Greek @-@ style mystery cult . Isis and Hermes Trismegistus were both prominent in the Western esoteric tradition that grew from the Roman religious world . 
- Temples and cults in Egypt itself declined as the Roman economy deteriorated in the third century AD , and beginning in the fourth century , Christians suppressed the veneration of Egyptian deities . The last formal cults , at <unk> , died out in the fifth or sixth century . Most beliefs surrounding the gods themselves disappeared within a few hundred years , remaining in magical texts into the seventh and eighth centuries . But many of the practices involved in their worship , such as processions and oracles , were adapted to fit Christian ideology and persisted as part of the <unk> Church . Given the great changes and diverse influences in Egyptian culture since that time , scholars disagree about whether any modern <unk> practices are descended from those of <unk> religion . But many festivals and other traditions of modern Egyptians , both Christian and Muslim , resemble the worship of their ancestors ' gods . 
- 
- 
- = South of Heaven = 
- 
- South of Heaven is the fourth studio album by American thrash metal band Slayer . Released on July 5 , 1988 , the album was the band 's second collaboration with record producer Rick Rubin , whose production skills on Slayer 's previous album Reign in Blood had helped the band 's sound evolve . 
- South of Heaven was Slayer 's second album to enter the Billboard 200 , and its last to be released by Def Jam Recordings , although the album became an American Recordings album after Rick Rubin ended his partnership with Russell Simmons . It was one of only two Def Jam titles to be distributed by Geffen Records through Warner Bros. Records because of original distributor Columbia Records ' refusal to release work by the band . The release peaked at number 57 and in 1992 was awarded a gold certification by the Recording Industry Association of America . 
- In order to offset the pace of the group 's previous album , Slayer deliberately slowed down the album 's tempo . In contrast to their previous albums , the band utilized <unk> guitars and toned @-@ down vocals . While some critics praised this musical change , others — more accustomed to the style of earlier releases — were disappointed . The songs " Mandatory Suicide " and the title track , however , have become permanent features of the band 's live setlist . 
- 
- = = Background = = 
- 
- South of Heaven was recorded in Los Angeles , California with Reign in Blood producer Rick Rubin . PopMatters reviewer Adrien Begrand observed that Rubin 's production " <unk> [ Dave ] Lombardo 's drumming right up front in the mix . " Guitarist Jeff Hanneman has since said that South of Heaven was the only album the band members discussed before writing the music . Aware that they " couldn 't top Reign in Blood " , and that whatever they recorded would be " compared to that album " , he believed they " had to slow down " , something Slayer had never done on albums before , or since . Guitarist Kerry King cited the need to " keep people <unk> " as another reason for the musical shift . " In order to contrast the aggressive assault put forth on Reign in Blood , Slayer consciously slowed down the tempo of the album as a whole " , according to Slayer 's official biography . " They also added elements like <unk> guitars and toned @-@ down vocal styles not heard on previous albums . " 
- King has since been critical of his performance , which he describes as his " most lackluster . " King attributes this to the fact he had recently married , and moved to Phoenix , Arizona . Describing himself as " probably the odd man out at that point " , he admitted he " didn ’ t participate as much because of that . " Hanneman said : " We go through dry spells sometimes , but the good thing about having two guitar players that can write music is that you are never gonna go without . I guess at that time , Kerry was hitting a dry spell . " King has also been critical of the album in general , describing it as one of his least favorite Slayer albums . He feels vocalist Tom Araya moved too far away from his regular vocal style , and " added too much singing . " Drummer Dave Lombardo has since observed : " There was fire on all the records , but it started dimming when South of Heaven came into the picture . And that 's me personally . Again , I was probably wanting something else . " 
- Judas Priest 's " <unk> <unk> " is the only cover version to appear on a Slayer studio album . The song was chosen due to its war @-@ themed lyrics . Hanneman described the track as " more just like one of those odd songs that a lot of people didn 't know , but it was a favorite of Kerry and I , so we just picked that one . " Meanwhile , " <unk> the Soul " has been heavily criticized by King who said that he hates the track : " That 's one of the black marks in our history , in my book . I just fucking think it 's horrible . [ Laughs ] I hate the opening riff . It 's what we call a ' happy riff . ' It 's just like ' la @-@ <unk> @-@ la @-@ la @-@ la . ' I can 't see myself playing it , but after that , where it gets heavier , I like that section . If we ever did a medley , I 'd put part of that in there . " The Slayer <unk> Soundtrack to the Apocalypse featured , along with four songs of the album , an early version of the title track , recorded at Hanneman 's home . 
- 
- = = <unk> and illustration = = 
- 
- Artist Larry Carroll and <unk> Howard <unk> designed the cover artwork for South of Heaven , having designed the artwork for Slayer 's previous album Reign in Blood . <unk> Glen E. Friedman took the promotional shot which surfaced as the back cover of South of Heaven around the time of 1986 's Reign in Blood . Lombardo felt it made Slayer seem as though they " had matured a little bit " , while Friedman himself deemed it " a really cool back cover " and " one of the most classic shots of them [ Slayer ] ever . " 
- 
- = = Critical reception = = 
- 
- South of Heaven was released on July 5 , 1988 , and was the final Slayer album distributed via Def Jam Records . When label co @-@ founders Russell Simmons and Rubin parted ways , Slayer signed to Rubin 's newly founded Def American Recordings label . The album peaked at number 57 on the Billboard 200 album chart , and on November 20 , 1992 , became Slayer 's second album to be certified gold in the United States . South of Heaven was awarded silver certification in the United Kingdom on January 1 , 1993 , Slayer 's first record to do so in that country . Slayer 's official biography states that " some critics praised the album as demonstrating Slayer 's desire to grow musically and avoid repeating themselves . " Alex Henderson of AllMusic described the record as " disturbing and powerful , " while Joe <unk> of Ultimate Guitar deemed the album a slight departure ; he wrote that while the pace was slowed down , it " didn 't sacrifice any of the <unk> inherent in Slayer 's music . " 
- Reviewing the 2003 Slayer box set Soundtrack to the Apocalypse , Adrien Begrand of PopMatters described the album as " their most <unk> , and on this set , its five selections show how highly the band thinks of the record . " <unk> 's Peter Atkinson was also positive , saying the album has a " grandiosity and imposing presence " which makes the record " so magnificent . " Grave 's <unk> <unk> and <unk> <unk> 's Karl <unk> both rate South of Heaven as amongst the top five albums of all time , while Max <unk> of Brazilian death metal group <unk> remembers hearing the song " Silent Scream " for the first time : " It just blew me away . It was like fast double @-@ bass , fast kicks during the whole song . That was very inspiring for me . " When discussing Slayer in an October 2007 interview , <unk> frontman Matt Drake stated that while Reign in Blood " was just speed " , South of Heaven proved that the group could write " slow material as well . " Metal Forces reviewer gives " the band credit for at least making an effort to try something new and not being afraid to experiment at such a crucial stage of their career " , creating " one of the more original sounding thrash / speed metal albums he heard in a long while " . He remarks , however , that " if you ’ re expecting to hear Reign in Blood Part Two , you ’ ll be in for a major disappointment " . 
- Kim <unk> of Rolling Stone dismissed the album as " genuinely offensive <unk> <unk> . " Slayer 's official biography states : " The new sounds disappointed some of the band 's fans who were more accustomed to the style of earlier releases . " Michael Roberts of <unk> Online said this was due to some of the numbers moving " at the <unk> speed of Black Sabbath . " Araya commented that the " album was a late <unk> — it wasn 't really received well , but it kind of grew on everybody later . " 
- 
- = = Cover versions = = 
- 
- The title track and the song " Mandatory Suicide " have received various cover interpretations , particularly on Slayer tribute albums . <unk> Ferguson recorded string quartet adaptations of both tracks on the album The String Quartet Tribute to Slayer : The Evil You <unk> , with the former cover being described as having " menacing chord shifts " by AllMusic 's Johnny <unk> . 
- 1995 Slayer tribute album <unk> Slaughter featured three tracks which originally appeared on South of Heaven , with the title track , " Mandatory Suicide " and " <unk> the Blood " interpreted by <unk> , Crown of <unk> and <unk> respectively . Its 1998 follow up <unk> Slaughter , Vol . 2 only featured two tracks originally from the album ; namely " Silent Scream " arranged by <unk> and " Read Between the Lies " interpreted by <unk> . 1999 's <unk> to Hell : A Tribute to Slayer collected four Slayer renditions which originated on the album , with versions of South of Heaven performed by <unk> ( <unk> ) and Electric <unk> Club , " Mandatory Suicide " cut by Chapter 7 and " Behind the Crooked Cross " adapted by <unk> . 2006 Argentine tribute album Al Sur Del <unk> ( <unk> <unk> A Slayer ) saw <unk> and <unk> Terra also respectively cover " South of Heaven " and " Mandatory Suicide " . <unk> covered the song " <unk> of War " for their 2009 cover album For the Lions . They released a music video for it also . Korn has covered the title track at least twice live , once with Kid Rock on vocals and another using the intro to follow into one of their songs live . 
- The title track itself has also been covered by <unk> 2000 , Modest Mouse and <unk> , Pro @-@ Pain , and Universe Eye . Polish death metal band <unk> covered the song " Mandatory Suicide " on their first full @-@ length album Winds of Creation . In 2003 , " Silent Scream " was covered by Children of <unk> for their album <unk> Crew <unk> in his UK version . Hardcore Punk band , The <unk> opened their set with the beginning of " South of Heaven " at <unk> 7 on May 4 , 2013 
- 
- = = Live performances = = 
- 
- Two songs taken from the album ( " Mandatory Suicide " and " South of Heaven " ) have become near constant fixtures in the band 's live setlist , <unk> up appearances on the following : the live DVDs Live <unk> , War at the <unk> , Still <unk> , Soundtrack to the Apocalypse 's deluxe edition 's bonus live disc , and the live double album Decade of <unk> . Lombardo <unk> with Finnish cellist group <unk> on a live medley of the two tracks at 1998 's <unk> 's Heaven festival in the Netherlands . Adrien Begrand of PopMatters described " South of Heaven " as " an unorthodox set opener in theory " , noting " the song went over like a <unk> bomb detonating the place : dozens of inverted crosses projected behind the high drum <unk> , the <unk> opening notes kicked in , followed by an overture of bass , cymbal crashes , and <unk> fills , leading up to the slowly building crescendo " in a concert review . Lombardo remembers listening to a live rendition of " South of Heaven " and thinking " ‘ Man ! There 's just so much groove in that song . ’ To my kids I was saying , ‘ Listen to that ! Listen to how <unk> that is ! ’ And it 's heavy . " A rare live version of the track featured on the <unk> <unk> 2004 promotional CD , given away to attendees at the Spring 2004 <unk> Music Tour . A live rendition of " South of Heaven " was also included on a bonus DVD which came with the group 's 2007 re @-@ release of ninth studio album Christ Illusion , shot in Vancouver , British Columbia during 2006 's <unk> Alliance tour . 
- " Behind the Crooked Cross " is rarely played live as Hanneman hates the track , though King has always wanted to play it " because it 's got a cool intro " despite it not being his favorite song . King said " that 's fine " when speaking of the situation , noting " there are songs that he wants to play that I always shoot down . " " <unk> of War " isn 't King 's favorite song either , which he attests " everybody always wants to hear " performed live . He confessed ; " I like the ending , you know , I like the big heavy part and I always say , ‘ Let 's put the heavy ending at the end of " Chemical Warfare " and just do the last half . ’ But I could never make that fly . " 
- Slayer has <unk> with the idea of creating a live set mixed with selections from the album and 1990 's Seasons in the Abyss , though Hanneman said it 's something which hasn 't been " seriously considered . " Metal Maniacs asked Slayer in a 2006 interview whether they would consider playing South of Heaven in the footsteps of the Still <unk> tour , to which Araya replied , " It 's becoming a <unk> thing now . I don 't know . We have some really cool albums , but I don 't think we 'll ever do that again . " King was equally unsure , commenting , " Probably not . And I just don 't like enough songs off South of Heaven . " 
- 
- = = Track listing = = 
- 
- 
- = = Personnel = = 
- 
- 
- = = = Slayer = = = 
- 
- Tom Araya – bass , lead vocals 
- Jeff Hanneman – lead and rhythm guitar 
- Kerry King – lead and rhythm guitar , backing vocals 
- Dave Lombardo – drums 
- 
- = = Charts and certifications = = 
- 
- 
- 
- = General aviation in the United Kingdom = 
- 
- General aviation in the United Kingdom has been defined as a civil aircraft operation other than a commercial air transport flight operating to a schedule or military aviation . Although the International Civil Aviation Organization ( ICAO ) excludes any form of remunerated aviation from its definition , some commercial operations are often included within the scope of general aviation ( GA ) in the UK . The sector operates business jets , <unk> , piston and jet @-@ engined fixed @-@ wing aircraft , gliders of all descriptions , and lighter than air craft . Public transport operations include business ( or corporate ) aviation and air taxi services , and account for nearly half of the economic contribution made by the sector . Other commercial GA activities are aerial work , such as surveying and air ambulances , and flight training , which plays an important role in the supply of pilots to the commercial air transport ( CAT ) industry . Private flying is conducted for personal transport and recreation . It includes a strong vintage aircraft movement , and encompasses a range of air sports , such as racing , aerobatics , and <unk> , at which British teams and individuals have succeeded in international competition . 
- Of the 21 @,@ 000 civil aircraft registered in the UK , 96 per cent are engaged in GA operations , and annually the GA fleet accounts for between 1 @.@ 25 and 1 @.@ 35 million hours flown . The single most common class of aircraft is the fixed @-@ wing light aircraft associated with traditional GA , but the main area of growth over the last 20 years has been in the use of more affordable aircraft , such as microlights , amateur built aeroplanes , and smaller helicopters . There are 28 @,@ 000 Private Pilot Licence holders , and 10 @,@ 000 certified glider pilots . Some of the 19 @,@ 000 pilots who hold professional licences are also engaged in GA activities . Although GA operates from more than 1 @,@ 800 aerodromes and landing sites , ranging in size from large regional airports to farm strips , over 80 per cent of GA activity is conducted at 134 of the larger aerodromes . The GA industry , which is around 7 per cent the size of its CAT cousin , employs 12 @,@ 000 people , and contributes £ 1 @.@ 4 billion to the UK economy . 
- GA is regulated by the Civil Aviation Authority ( CAA ) , although regulatory powers are being increasingly transferred to the European Aviation Safety Agency ( EASA ) . The main focus is on standards of airworthiness and pilot licensing , and the objective is to promote high standards of safety . At the lighter end of the GA spectrum some regulatory authority is devolved to representative bodies , and gliding is in transition from a self @-@ regulatory model to more formal governance by EASA . Airspace regulation necessary to protect an increasing number of CAT operations has reduced the area in which GA flights can be freely conducted . The growth in CAT is also making access to larger airports more difficult for the GA sector , and smaller aerodromes are vulnerable to closure and re @-@ development for more profitable uses . The UK planning system has no remit to consider the national significance of GA public transport operations , and generally does not favour the development of smaller aerodromes catering to the GA market . The planning process has become a mechanism for addressing local aerodrome @-@ related environmental issues which , particularly regarding noise , are the main subjects of public criticism levelled at GA . 
- 
- = = Definitions = = 
- 
- The International Civil Aviation Organization ( ICAO ) defines general aviation ( GA ) as " an aircraft operation other than a commercial air transport operation or an aerial work operation . " It defines commercial air transport ( CAT ) as " an aircraft operation involving the transport of passengers , cargo or mail for remuneration or hire " , and aerial work as " an aircraft operation in which an aircraft is used for specialized services such as agriculture , construction , photography , surveying , observation and patrol , search and rescue , aerial advertisement , etc . " 
- <unk> in the United Kingdom ( UK ) describe GA in less restrictive terms that include elements of commercial aviation . The British Business and General Aviation Association interprets it to be " all aeroplane and helicopter flying except that performed by the major airlines and the Armed Services " . The General Aviation Awareness Council applies the description " all Civil Aviation operations other than scheduled air services and non @-@ scheduled air transport operations for remuneration or hire " . For the purposes of a strategic review of GA in the UK , the Civil Aviation Authority ( CAA ) defined the scope of GA as " a civil aircraft operation other than a commercial air transport flight operating to a schedule " , and considered it necessary to depart from the ICAO definition and include aerial work and minor CAT operations . 
- 
- = = History = = 
- 
- The first aerodrome in the UK was established by the Aero Club at <unk> Manor on the Isle of <unk> , and in May 1909 it was the venue of the first flight conducted in the country by a British pilot , John Moore @-@ <unk> . In 1910 the Aero Club was granted the Royal prefix , took responsibility for controlling all private flying in the UK , and started issuing the first British pilot licences . The introduction of the de Havilland DH.60 Moth in 1925 <unk> light aviation , and the Royal Aero Club , <unk> the " vital necessity of promoting civil flying " , formed the Light <unk> Club scheme . Between 1925 and 1939 around 60 flying clubs were started , and more than 5 @,@ 000 pilots were trained . 
- During World War II civil aerodromes were taken over for military use , existing military airfields were expanded , and new ones were built . This resulted in a significant inventory of facilities becoming available after the war . Pre @-@ war civil aerodromes , for example <unk> , were returned to civilian use . <unk> military airfields were closed , and in some cases , for example <unk> , subsequently re @-@ opened as civil aerodromes . The Ministry of Civil Aviation was created to regulate all civil aviation in the UK , and this task remained the responsibility of government departments until the establishment of the independent CAA in 1972 . 
- With an expanded infrastructure in place , GA became established after the war when manufacturers such as Cessna and Piper introduced light aircraft designed for the private market . The Cessna 172 , developed from the late 1940s Cessna 170 , was introduced in 1956 , and became the world 's best selling single @-@ engine aeroplane . Single piston @-@ engine aircraft are still the most common class of aircraft in the UK GA fleet . The development of the <unk> wing in the 1950s fostered the development of hang @-@ gliding during the 1960s and 1970s . The 1960s also saw experiments with motorised hang gliders , but it was not until the 1970s that this blend of technologies started to mature , resulting in the birth of the microlight movement . Another milestone in the development of GA was the 1964 introduction of the <unk> 23 . Although it was not the first business jet , it popularised corporate aviation , and established the personal jet as a " whole new class of aircraft " . 
- 
- = = Activities = = 
- 
- The GA sector operates a range of aircraft , including balloons and airships , gliders , hang gliders , <unk> , microlights , <unk> , helicopters , amateur built and mass @-@ produced light aircraft , ex @-@ military aircraft , and business jets . <unk> can be broadly categorised as public transport , aerial work , and private flying , the first two of which are commercial activities . 
- 
- = = = Commercial operations = = = 
- 
- Commercial operations are remunerated activities which fall within the ICAO definition of CAT . Some are , however , closely aligned to , and considered part of , the GA sector . Public transport operations are non @-@ scheduled , on @-@ demand services flying between points specified by the customer , providing a more flexible service than airline travel . Air taxi operations offer charter services for third parties , and business or corporate aviation uses company @-@ owned aircraft to transport employees and clients . Aircraft used in these operations include business jets , helicopters , and twin piston @-@ engine aeroplanes carrying between six and ten people . An example of this type of operation is the transport by helicopter of spectators to the British Formula One grand <unk> at Silverstone . This involves so many flights that , according to Cranfield Aviation Services , on race day the heliport is temporarily the world 's busiest airport . Aerial work is a small but important component of the commercial GA sector , characterised in its simplest form as remunerated non @-@ transport activities , such as surveying , crop spraying , and emergency services work ( air ambulance and police ) . 
- 
- = = = Flying schools = = = 
- 
- Flying schools are commercial businesses engaged in the training of pilots , both for recreational purposes and for those intending to fly professionally . They make widespread use of fixed @-@ wing light aircraft associated with traditional GA , not only for flying lessons but also as club aircraft rented out to qualified pilots for recreational flights . School @-@ owned aircraft account for a significant amount of GA activity , both in terms of hours flown and aircraft movements . The pilot training element is regarded by the GA community as a key benefit that is critical to the supply of pilots for the airline industry . It is claimed by the General Aviation Awareness Council that 60 – 70 per cent of professional pilots have self @-@ financed their flight training at GA schools , and one UK airline operator has stated that the industry must rely on 70 – 80 per cent of new pilots coming from the GA sector . The CAA estimates that between 1996 and 2006 the number of new professional pilots following the <unk> training route rose from 48 per cent to 59 per cent . The counter argument to this claim is that pilots can be trained outside of the UK , and that the airline industry is not therefore dependent on a healthy GA sector in the UK for its supply of pilots . The CAA concludes that a severe reduction in GA would give " some merit to the argument that pilot recruitment would be threatened " , but that the data on flying hours " does not support such a <unk> outlook . " Of course , reliance on other countries for pilot training means that the UK <unk> the economic benefit of the training activity . 
- 
- = = = Private flying = = = 
- 
- Private flying can be for both recreational purposes and personal transport , using aircraft that are owned individually , collectively as part of a syndicate , or rented from a flying club . A survey of pilots conducted between 2001 and 2002 indicated that the most common purposes of recreational flights were local flights near the base aerodrome , visits to other aerodromes , and day trips away . Half of all flights landed at the same aerodrome they departed from , and only 9 per cent involved an overnight stay away from home . 
- Private flying is most associated with the traditional form of factory @-@ produced two and four @-@ seater , single piston @-@ engine training and touring aircraft . Examples of these are the Cessna 152 , Cessna 172 , and Piper <unk> <unk> , all with their origins in the 1950s , and the more modern designs of <unk> . The average cost per hour to fly such aircraft has been estimated to be £ 133 , compared to an estimated £ 77 per hour for gliders , and a reported £ 35 per hour for microlights . Recent trends have seen an increase in the use of microlights , and also in recreational helicopter flying following the introduction of smaller and cheaper machines such as the Robinson <unk> and <unk> . Another growth area in private flying in recent years has been in the use of amateur built aircraft , such as the Van 's Aircraft <unk> @-@ 4 and the Europa . 
- There is a strong vintage aircraft movement in the UK , with two @-@ thirds of the 500 registered historic aircraft active . These cover the whole spectrum of civil and military aviation , examples being the de Havilland Dragon <unk> airliner of the 1930s , and the World War II ( WWII ) Spitfire fighter . There are many post @-@ WWII aircraft which could also be considered historic under a looser definition , including for example 60 ex @-@ military jets such as the Hawker Hunter . Historic aircraft are regular exhibits at air displays , which are claimed to be the second most popular <unk> activity after football in the UK . 
- 
- = = = Sports = = = 
- 
- Competitive gliding in the UK takes place between May and September . <unk> are local competitions , organised and run by one of the bigger gliding clubs in the region , and represent the entry level to glider racing . Races are handicapped according to glider performance , and normally take place over nine days . Success in the <unk> allows pilots to progress to the <unk> , where there are five classes of competition . These are based on glider performance , the lowest being club class , and then progressing through standard ( maximum 15 metres ( 49 ft ) wingspan , and flaps not permitted ) , 15 metres ( 49 ft ) ( as standard , but flaps are permitted ) , 18 metres ( 59 ft ) ( maximum 18 metres ( 59 ft ) wingspan ) , and finally open @-@ class ( no restrictions ) . Success at national level can lead to a place in the national team and competition at international level . In 2007 the British gliding team was ranked number one , and British pilots took two women 's world championships and the open class European championship . 
- <unk> air racing is open to any propeller @-@ driven aircraft capable of maintaining a minimum speed of 100 miles ( 160 km ) per hour in level flight . Races are a case of " fly low , fly fast , turn left " , consisting of 4 – 5 laps round a 20 – 25 mile ( 32 – 40 km ) circuit . <unk> aircraft are handicapped by starting after slower aircraft , the intention being that the race concludes with all aircraft diving for the finish line together . There are up to 16 races per year , conducted at airfields in the UK , France and the Channel Islands , for prizes that include the Schneider Trophy and King 's Cup , and the season culminates with the British Air Racing and European Air Racing Championships . 
- <unk> competitions take place for both powered aircraft and gliders , with up to 30 events each year in the UK and Ireland . Starting at the <unk> level , pilots can move up to Standard ( powered aircraft ) or Sports ( glider ) levels , and then on to Intermediate , Advanced , and finally Unlimited classes . Each step up requires a wider repertoire of <unk> figures and progressively more performance from the aircraft . National championships are awarded annually at Standard / Sports , Intermediate , Advanced ( powered aircraft only ) , and Unlimited levels , and pilots who have reached Advanced and Unlimited levels are eligible for selection to represent the UK in international competition . 
- Parachute competitions are held at club , regional , national and international levels , and include the disciplines of accuracy landings , <unk> <unk> , formation <unk> , canopy formation , freestyle and <unk> , and <unk> . British teams consistently win medals in canopy formation world championships , and a British team took the 2006 world championship in women 's 4 @-@ way formation <unk> . 
- 
- = = <unk> = = 
- 
- Aerodrome is a collective term for any location from which flying operations take place , although more specific terminology can be used to <unk> its purpose . The CAA strategic review of GA applies the term airport to locations which predominantly support large scale commercial operations , and airfield to locations which predominantly support GA operations . The General Aviation Small Aerodrome Research Study ( GASAR ) analysed <unk> aerodromes in England which come under the scope of GA , classifying 374 into six types . These range in size from regional airports to the smallest farm strip , although 84 per cent of GA flights operate from 134 of the larger aerodromes in the first four categories . 
- 
- = = = GASAR aerodrome classification = = = 
- 
- The factors used in determining how an individual aerodrome is categorised by the GASAR study are based broadly on size and facilities . The six types of aerodrome are described , in size order , as : regional airports ( e.g. East Midlands ) ; major GA airports ( e.g. Oxford ) ; developed GA airfields ( e.g. <unk> ) ; basic GA airfields ( e.g. <unk> ) ; developed airstrips ( e.g. <unk> ) ; and basic airstrips ( e.g. <unk> in Hampshire ) . The actual criteria used to <unk> aerodromes were complex , using 28 different parameters , backed up with a peer review by experienced GA pilots . 
- Airports generally have long , fully lit , hard @-@ surfaced runways , full air traffic control , and navigation and landing aids . They are usually located on urban fringes , support commercial and business operations , and often exclude certain types of light aircraft . At the more <unk> located airfields , the lighter end of aviation , such as microlight and gliding activities , becomes increasingly prevalent , and there are few or no commercial operations other than flying schools . At this level runways are generally shorter , and grass surfaces are increasingly common . Navigation aids are increasingly scarce , being more basic where they are available , and informal ground to air radio communication replaces air traffic control . The smallest airfields are too small to feature on general purpose Ordnance Survey ( OS ) maps , and lack basic facilities such as fuel and maintenance . The majority of airstrips are basically single short grass runways with no supporting facilities , although the presence of a hangar is not uncommon at the larger examples . They do not feature on OS maps , and are owned by private clubs or , more commonly , individuals . 
- 
- = = = Aerodrome licensing = = = 
- 
- Most aerodromes used for public transport operations are required to be licensed by the CAA . To be granted a licence an aerodrome operator must satisfy the CAA that : the physical conditions at the aerodrome , and its environs , are acceptable ; the scale of equipment , and facilities provided , are adequate for the flying activities which are expected to take place ; an effective safety management system is in place ; and that staff are competent and , where necessary , suitably qualified . <unk> classified as developed GA airfields or larger by the GASAR study are , with few exceptions , licensed . Only two basic GA airfields , Silverstone and <unk> , are licensed , and all airstrips are unlicensed . The Light Aviation Airports Study Group , a joint CAA @-@ industry initiative , was established in 2005 to review the regulation of light aviation aerodromes . A particular focus of this group was a review of the restrictions placed on unlicensed aerodromes . The group concluded that the requirement for public transport operations to be conducted only from licensed aerodromes should be further reviewed in the context of corresponding international and European requirements . It also recommended that restrictions on flight training at unlicensed aerodromes should be lifted , and this was permitted from April 2010 
- 
- = = Scale of the sector = = 
- 
- There are an estimated 27 @,@ 000 civil aircraft registered in the UK , 96 per cent of which are engaged in GA activities . In 2005 the GA fleet comprised 9 @,@ 000 fixed @-@ wing aircraft , 4 @,@ 100 microlights , 1 @,@ 300 helicopters , 1 @,@ 800 airships / balloons , 2 @,@ 500 gliders and some 7 @,@ 000 hang gliders . Estimates put the number of foreign @-@ registered GA aircraft based in the UK at 900 . 
- The number of pilots licensed by the CAA to fly powered aircraft in 2005 was 47 @,@ 000 , of whom 28 @,@ 000 held a Private Pilot Licence . The remainder held professional pilot licences , either a Commercial Pilot Licence or an <unk> Transport Pilot Licence , although not all of these would be engaged in GA activities . In addition , there are 10 @,@ 000 active glider pilots , and estimates put the membership of aviation @-@ related sport and recreational associations at 36 @,@ 000 . 
- The number of aerodromes that support GA in the UK is difficult to establish with certainty . <unk> 2008 United Kingdom Flight Guide lists 355 , and the <unk> Flight Equipment UK <unk> Flight Guide 2008 lists nearly 500 . <unk> Farm ' <unk> ' and Private <unk> Flight Guide lists more than 300 landing sites . The GASAR study estimates 1 @,@ 100 formal flying sites in England alone , a figure which includes 400 sites known to planning authorities but not included in flight guides . It estimates another <unk> informal sites known only to land owners , customs , and members of the enthusiast group Air @-@ Britain . 
- The sector was estimated to employ nearly 12 @,@ 000 people and directly contribute £ 1 @.@ 4 billion to the UK economy in 2005 , making it roughly seven per cent of the size of the CAT industry . Nearly half of the economic contribution was generated by business aviation . 
- 
- = = Trends = = 
- 
- Most sectors of GA for which data are available have experienced growth in aircraft numbers and hours flown over the last two decades . The lighter end of the GA spectrum : microlights , amateur built , and airships and balloons , have in particular shown strong growth , although the last of these activities was severely curtailed during the foot @-@ and @-@ mouth outbreak in 2001 , when access to farmland was denied . After strong growth in the late 1980s , traditional flying has shown a slight decline recently , reflecting a move amongst recreational <unk> towards microlight aircraft , and increased numbers of foreign @-@ registered aircraft . Recreational helicopter usage has grown primarily due to the introduction of smaller and cheaper aircraft . <unk> activity has remained relatively static , although there has been a gradual increase in the number of self @-@ launching motor gliders . 
- Business aviation has shown strong growth , although the numbers of aircraft on the UK register have declined . This reflects a shift away from turboprop aircraft towards foreign @-@ registered business jets based in the UK , which are estimated to be growing in numbers . However , twin piston @-@ engined aircraft numbers have declined significantly , reflecting pressures on the light air @-@ taxi segment from increasingly flexible and cheaper scheduled services , and a more sophisticated corporate charter business . The amount of flight training conducted by UK schools has declined , largely at the hands of competition from foreign schools , which benefit from lower costs and better weather . 
- Since 1990 the total number of hours flown annually by the GA sector has remained in the range 1 @.@ 25 – 1 @.@ 35 million , the dominant sector being traditional GA flying , which accounts for 0 @.@ 6 million per year . An overall increase in aircraft numbers combined with nil growth in hours flown has brought the annual average <unk> per aircraft down from 157 hours in 1984 to 103 hours in 2002 . The decline in asset <unk> has led to speculation that the economic health of the GA industry is weakening , though the lack of data on profitability makes this difficult to confirm . 
- 
- = = Regulation = = 
- 
- The objective of regulation is to " promote high standards of safety in all aspects of aviation " , and this is the main area of interaction between the CAA and the GA sector . Efforts focus on assuring appropriate standards of airworthiness , pilot qualification , the rules for the movement of aircraft , and equipment to be carried . The CAA was established as the primary regulatory body for all aviation in the UK in 1972 . In 1991 it started working within the Joint Aviation Authorities ( <unk> ) framework to implement agreed common standards , known as the Joint Aviation <unk> ( JAR ) , throughout the European Union ( EU ) . In 2003 this was taken a step further when the European Aviation Safety Agency ( EASA ) was established as the central EU regulator , taking over responsibility for <unk> airworthiness and environmental regulation from the national authorities . The CAA acts as an agency of EASA on these issues , retaining its original regulatory powers in areas not yet transferred to EASA . Proposed developments seek to establish EASA as the single authority throughout the EU , taking over from individual member states the power to regulate all aviation other than that specifically excluded from the scope of EASA . 
- 
- = = = <unk> and self @-@ regulation = = = 
- 
- Within this framework certain sectors of GA are governed on a devolved basis . In all cases the CAA / EASA retains responsibility for safety regulation , but representative bodies , particularly of sectors that are not included in the scope of EASA , are granted greater oversight of their activities . The majority of microlight aircraft are regulated by the British Microlight Aircraft Association ( <unk> ) , although a significant number are regulated by the Light Aircraft Association ( LAA ) , formerly known as the Popular Flying Association . The LAA is the primary regulator for amateur built aircraft , as well as vintage and classic aircraft . <unk> is governed by the British Parachute Association , although the aircraft used in this activity are generally CAA @-@ regulated . <unk> and airship flying is overseen by the British <unk> and <unk> Club . The UK @-@ specific National Private Pilot Licence ( NPPL ) is administered by the National Pilots Licensing Group Ltd . , supported by the LAA , the Aircraft <unk> and Pilots Association UK , the British Gliding Association , and the British Microlight Aircraft Association . <unk> from these devolved groups , gliding in the UK is self @-@ regulated . The British Gliding Association was until recently responsible for glider airworthiness , now formally regulated as a result of EASA legislation , and still retains control of pilot certification . Hang gliding and <unk> activities ( i.e. foot @-@ launched gliders ) are governed by the British Hang Gliding and <unk> Association . 
- 
- = = = Airworthiness = = = 
- 
- Under CAA and EASA rules , all aircraft are required to meet certain standards of airworthiness to fly safely and legally . Aircraft that meet these standards are issued with a Certificate of Airworthiness . However , British @-@ registered aircraft which are excluded from the scope of EASA , and which cannot satisfy the requirements for the issue of a Certificate of Airworthiness , may be issued with a <unk> to Fly . This allows them to fly in UK airspace subject to certain limitations , for example being restricted to day @-@ time flights under visual flight rules only . A number of organisations ( e.g. the British Microlight Aircraft Association and the Light Aircraft Association ) have obtained a standing over @-@ flight permission for <unk> to Fly aircraft within their area of interest with some European countries , notably France . <unk> are typically issued to vintage and historic aircraft , amateur built aircraft , and microlights . 
- 
- = = = Pilot licensing = = = 
- 
- The pilot qualification most relevant to GA is the Private Pilot Licence ( PPL ) , which permits the holder to fly for recreational purposes without remuneration . In addition to the European @-@ wide Joint Aviation Regulations Flight Crew Licensing ( JAR @-@ <unk> ) standard , the CAA also issues UK @-@ specific national licences . In the absence of European standards for <unk> , balloon , and airship pilots , the CAA licenses these according to the original UK PPL standard . As a response to the perception that JAR pilot licensing standards are excessively bureaucratic and expensive for the purposes of recreational pilots , the National Private Pilot Licence ( NPPL ) was introduced in 2002 . The NPPL is easier to obtain than the JAR @-@ <unk> licence , has less stringent medical requirements , is more restrictive in the privileges it grants , and is valid only for flights in British @-@ registered aircraft flying in UK and French airspace . Although there are plans to bring glider pilot licensing within the regulatory framework of EASA , the gliding sector is currently self @-@ regulating in this respect . The British Gliding Association is responsible for defining the standards of initial training , and <unk> , via a badge system , pilots who meet those standards . Pilots working in sectors of GA that are commercial operations , such as aerial work and business aviation , are required to hold a professional pilot licence which , at a minimum , is the Commercial Pilot Licence . 
- 
- = = Safety = = 
- 
- Between 1995 and 2004 there were 2 @,@ 630 accidents involving GA aircraft , of which 139 were fatal , resulting in the loss of 317 lives . The majority of accidents involved small fixed @-@ wing aircraft engaged in private flights , and analysis attributes the most common causes of these to : flight handling skills ; poor judgement or <unk> ; lack of training or experience ; and omission of , or inappropriate , action . 
- There were 27 fatal accidents involving GA aircraft in 2007 , resulting in the loss of 48 lives . These compare with 16 accidents claiming a total of 19 lives the previous year , and although the 2007 statistics are higher than average , they are not exceptional . 
- 
- = = <unk> = = 
- 
- The growth in Commercial Air Transport ( CAT ) has eroded the operational freedom of GA , both in the air and on the ground at larger airports . <unk> with access to larger airports is compounded by a decline in the number of aerodromes generally , and existing sites are often threatened with closure and re @-@ development for more profitable uses . The UK planning system is designed to focus on local issues , and consideration of the national impact of GA operations is not within its remit . This makes aerodrome development difficult , often <unk> those that successfully negotiate the process to restrictions in use . 
- 
- = = = Airspace access = = = 
- 
- Airspace is shared by CAT , military and GA users . It is divided into controlled airspace , in which aircraft must always be under the control of an air traffic controller , and uncontrolled airspace , in which aircraft can operate autonomously . Although GA flights can under certain conditions enter controlled airspace , they operate mainly outside of it . 
- <unk> airspace is essential for the provision of a known air traffic environment necessary for the safe operation of CAT . A CAA review found that " mixing [ commercial ] operations with other users is considered undesirable , even <unk> " by commercial operators . However this position has resulted in extensive Class A controlled airspace with complex boundaries , including some running down to the ground , prohibiting <unk> access to airspace , resulting in high numbers of GA flights operating close to the borders of controlled airspace who could not get formal <unk> of an air traffic service . Coupled with pilot navigation errors , hundreds of airspace <unk> have been recorded every year . 
- <unk> in the number of CAT operations , and in the number of airports they operate from , has resulted in a corresponding increase in Class A controlled airspace . Between 1997 and 2006 this area grew in size from 13 per cent of all airspace to 22 per cent nationally , and from 24 per cent to 43 per cent in airspace above England and Wales , leading to a perception within the GA community of being squeezed out . There are particular problems for GA around large airports , where Class A controlled airspace extends to ground level . The concentration of commercial operations and high demand for GA in the South East of England have also resulted in extensive areas of Class A controlled airspace there , which serve to channel uncontrolled GA operations through high @-@ collision @-@ risk hot spots . 
- 
- = = = Aerodrome access = = = 
- 
- Regional airports , such as Edinburgh Airport , have experienced strong growth in CAT operations in recent years . These operations are commercially and operationally incompatible with GA , and although there is no evidence of deliberate discrimination , the effect has been to discourage or exclude it . GA aircraft are being subject to significant increases in charges , including the imposition of handling fees in some cases . Some airports restrict or deny GA parking , and others limit or refuse certain GA activity . As a result , light GA aircraft are now rarely or never seen at large , busy international airports such as Heathrow , <unk> , Gatwick and Manchester . 
- In addition to this de facto loss of facilities , the number of aerodromes in the UK has been in decline over the last 50 years , as a result of increasing <unk> and the closure of airfields built during WWII . Alternative and more profitable uses for land can also lead to existing aerodromes being threatened with closure , for example North <unk> , or actually being closed , as happened to Ipswich <unk> and Bristol <unk> Airport . Referring to the importance of a " functioning national network of GA airfields " , especially where GA performs an air transport role , the CAA states that " there could be cause for concern if a significant further loss of airfields were to continue , especially if crucial nodes on the transport network were to be lost . " 
- 
- = = = Planning system = = = 
- 
- The planning system is critical to the viability and operation of GA aerodromes . With many cities lacking scheduled air transport services between them , and with GA access to commercial airports becoming increasingly difficult and expensive , a viable network of aerodromes supporting GA air transport operations is regarded as an important national issue . However , there is no unified national planning policy specific to GA aerodromes , and planning decisions relating to these are based on local issues that are not required to consider the national impact . Because aircraft are excluded from noise control legislation , the only recourse for people affected by aircraft noise is through the planning process , and this issue is the principal factor on which the majority of planning decisions relating to GA land use are made . GA is a specialist subject often unfamiliar to Local Planning Authorities , and most planning decisions relating to GA either refuse permission , or grant it with restrictive conditions . Little <unk> is just one example of a GA airfield required to comply with planning restrictions on the number of movements permitted , thereby inhibiting further development . Such restrictions , if poorly conceived , can make GA operations <unk> or even unsafe . 
- 
- = = Criticism = = 
- 
- Public opinion towards aviation generally is worsening , based on increasing environmental concerns relating to emissions and noise , and private flying has been criticised by <unk> to a government consultation on aircraft noise as a frivolous or selfish activity . In terms of environmental complaints and <unk> made to the CAA that relate specifically to GA , noise is " by far " the most common subject . Half of the 2 @,@ 000 noise complaints made annually to the CAA concern GA operations , most of which relate to aerobatics , helicopters using private sites , air balloon incidents , parachute dropping , and alleged low flying . 
- Planning guidance on aircraft noise advises that " in some circumstances the public perceive general aircraft noise levels as more disturbing than similar levels around major airports . " This is a result of the tonal characteristics of light aircraft engines and the activities they are engaged in , including : repetitive circuit flying at low @-@ altitude near an aerodrome , during which aircraft are audible for long periods ; slow climbing aircraft engaged in parachute drop or glider <unk> activities concentrated around the drop zone or aerodrome , also audible for long periods ; erratic and repetitive engine noise from aircraft engaged in aerobatics ; and piston @-@ engines on full power in areas of low background noise , leading to the perception that such noise is more <unk> . In an attempt to alleviate these problems , the majority of aerodromes implement noise abatement procedures designed to route aircraft away from noise sensitive areas , and more than 50 are required by the government to provide <unk> facilities in which local concerns can be raised with aerodrome operators . 
- 
- 
- = SMS Zrínyi = 
- 
- SMS Zrínyi ( " His Majesty 's ship Zrínyi " ) was a Radetzky @-@ class pre @-@ dreadnought battleship ( <unk> ) of the Austro @-@ Hungarian Navy ( <unk> Kriegsmarine ) , named for the <unk> , a noble Croatian family . Zrínyi and her sisters , Erzherzog Franz Ferdinand and Radetzky , were the last pre @-@ dreadnoughts built by the Austro @-@ Hungarian Navy . 
- During World War I , Zrínyi saw action in the Adriatic Sea . She served with the Second Division of the Austro @-@ Hungarian Navy 's battleships and shelled <unk> as part of the bombardment of the key seaport of Ancona , Italy , during May 1915 . However , Allied control of the Strait of Otranto meant that the Austro @-@ Hungarian Navy was , for all intents and purposes , effectively <unk> up in the Adriatic . Nonetheless , the presence of the Zrínyi and other battleships tied down a substantial force of Allied ships . 
- With the war going against the Austrians by the end of 1918 , Zrínyi was prepared to be transferred to the new State of Slovenes , Croats and Serbs . On 10 November 1918 — just one day before the end of the war , navy officers sailed the battleship out of Pola ( <unk> ) and eventually surrendered to a squadron of American submarine chasers . Following the handover to the United States Navy , she was briefly designated USS Zrínyi . In the Treaty of Saint @-@ Germain @-@ en @-@ <unk> , the transfer was not recognized ; instead , Zrínyi was given to Italy and broken up for scrap . 
- 
- = = Design and construction = = 
- 
- Zrínyi was built at the <unk> <unk> <unk> dockyard in <unk> , the same place where her sister ships were built earlier . She was laid down on 15 November 1908 and launched from the slipway on 12 April 1910 . The <unk> used on Zrínyi 's deck was the only material Austria @-@ Hungary had to purchase abroad to build the ship . The ship was completed by 15 July 1911 , and on 22 November 1911 she was commissioned into the fleet . She was the last ship of the class to be completed and had a crew of 880 to 890 officers and men . 
- Zrínyi was 138 @.@ 8 m ( <unk> ft 4 in ) long , and had a beam of 24 @.@ 6 m ( 80 ft 8 in ) and a draft of 8 @.@ 1 m ( 26 ft 9 in ) . She displaced 14 @,@ 508 long tons ( 14 @,@ <unk> t ) normally , and up to 15 @,@ <unk> long tons ( 16 @,@ <unk> t ) with a full combat load . She was powered by two @-@ shaft four @-@ cylinder vertical triple expansion engines rated at 19 @,@ 800 indicated horsepower . The ship had a top speed of 20 @.@ 5 knots ( 38 @.@ 0 km / h ; 23 @.@ 6 mph ) . Zrínyi was the first warship in the Austro @-@ Hungarian Navy to use fuel oil to supplement her 12 <unk> @-@ type coal @-@ fired boilers . She had a maximum range of 4 @,@ 000 nautical miles ( 7 @,@ 400 km ; 4 @,@ 600 mi ) at a cruising speed of 10 knots ( 19 km / h ; 12 mph ) . 
- The ship 's primary armament consisted of four 30 @.@ 5 cm ( 12 in ) 45 @-@ caliber guns in two twin gun turrets . This was augmented by a heavy secondary battery of eight 24 cm ( 9 @.@ 4 in ) guns in four wing turrets . The tertiary battery consisted of twenty 10 cm L / 50 guns in casemated single mounts , four 47 mm ( 1 @.@ 85 in ) L / 44 and one 47 mm L / 33 quick @-@ firing guns . Furthermore , the ship 's boats were equipped with two 66 mm ( 2 @.@ 6 in ) landing guns for operations shore . Three 45 cm ( 17 @.@ 7 in ) torpedo tubes were also carried , one on each broadside and one in the stern . 
- 
- = = Service history = = 
- 
- The ship was assigned to the Austro @-@ Hungarian Fleet 's 1st Battle Squadron after her 1911 commissioning . In 1912 , Zrínyi and her two sister ships conducted two training cruises into the eastern Mediterranean Sea . On the second cruise into the Aegean Sea , conducted from November to December , Zrínyi and her sister ships were accompanied by the cruiser SMS Admiral <unk> and a pair of destroyers . After returning to Pola , the entire fleet mobilized for possible hostilities , as tensions flared in the Balkans . 
- In 1913 , Zrínyi participated in an international naval demonstration in the <unk> Sea to protest the Balkan Wars . Ships from other navies included in the demonstration were the British pre @-@ dreadnought HMS King Edward VII , the Italian pre @-@ dreadnought <unk> di Saint Bon , the French armored cruiser Edgar <unk> , and the German light cruiser SMS Breslau . The most important action of the combined flotilla , which was under the command of British Admiral Cecil Burney , was to blockade the Montenegrin coast . The goal of the blockade was to prevent Serbian reinforcements from supporting the siege at <unk> , where Montenegro had besieged a combined force of <unk> and Ottomans . <unk> by the international blockade , Serbia withdrew its army from <unk> , which was subsequently occupied by a joint Allied ground force . 
- During that year , the first of four new dreadnoughts , SMS <unk> <unk> , that made up the Tegetthoff class — the only dreadnoughts built for the Austro @-@ Hungarian Navy — came into active service . With the commissioning of these dreadnoughts , Zrínyi and her sisters were moved from the 1st Division to the 2nd Division of the 1st Battle Squadron . 
- 
- = = = World War I = = = 
- 
- At that time of the assassination of <unk> Franz Ferdinand of Austria on 28 June 1914 , the battleships in the Austro @-@ Hungarian Navy consisted of the Radetzky class , the Tegetthoff class ( which still had one ship , SMS Szent István , under construction ) , the Erzherzog Karl class and finally , the older Habsburg class . Along with the remainder of the Austro @-@ Hungarian Navy , Zrínyi was mobilized in late July 1914 to support the flight of SMS Goeben and SMS Breslau . The two German ships broke out of <unk> , which was surrounded by the British navy and reached Turkey . The flotilla had advanced as far south as Brindisi in southeastern Italy when news of the successful breakout reached Vienna . The Austro @-@ Hungarian ships were then recalled before seeing action . 
- On 23 May 1915 , between two and four hours after news of the Italian declaration of war reached the main Austro @-@ Hungarian naval base at Pola , Zrínyi and the rest of the fleet departed to bombard the Italian and Montenegrin coast . Their focus was on the important naval base at Ancona , and later the coast of Montenegro . The bombardment of Montenegro was part of the larger Austro @-@ Hungarian campaign against the Kingdoms of Montenegro and Serbia , who were members of the <unk> , during the first half of 1915 . The attack on Ancona was an immense success , and the ships were unopposed during the operation . The bombardment of the province and the surrounding area resulted in the destruction of an Italian steamer in the port of Ancona itself , and an Italian destroyer , <unk> , was severely damaged further south . On the shore , the infrastructure of the port of Ancona , as well as the surrounding towns , were severely damaged . The railroad yard in Ancona , as well as the port facilities in the town , were damaged or destroyed . The local shore batteries were also suppressed . During the bombardment , Zrínyi also helped to destroy a train , a railway station , and a bridge at <unk> . Additional targets that were damaged or destroyed included wharves , warehouses , oil tanks , radio stations , and the local barracks . Sixty @-@ three Italians , both civilians and military personnel , were killed in the bombardment . By the time Italian ships from Taranto and Brindisi arrived on the scene , the Austro @-@ Hungarians were safely back in Pola . 
- The objective of the bombardment of Ancona was to delay the Italian Army from deploying its forces along the border with Austria @-@ Hungary by destroying critical transportation systems . The surprise attack on Ancona succeeded in delaying the Italian deployment to the <unk> for two weeks . This delay gave Austria @-@ Hungary valuable time to strengthen its Italian border and re @-@ deploy some of its troops from the Eastern and Balkan fronts . 
- Aside from the attack on Ancona , the Austro @-@ Hungarian battleships were largely confined to Pola for the duration of the war . Their operations were limited by Admiral Anton Haus , the commander of the Austro @-@ Hungarian Navy , who believed that he would need to husband his ships to counter any Italian attempt to seize the <unk> coast . Since coal was diverted to the newer Tegetthoff @-@ class battleships , the remainder of the war saw Zrínyi and the rest of the Austro @-@ Hungarian Navy acting as a fleet in being . This resulted in the Allied blockade of the Otranto Strait . With his fleet blockaded in the Adriatic Sea , and with a shortage of coal , Haus followed a strategy based on mines and submarines designed to reduce the numerical superiority of the Allied navies . 
- 
- = = = Post @-@ war fate = = = 
- 
- After the Austro @-@ Hungarian Empire collapsed in 1918 , the Austrians wanted to turn the fleet over to the newly created State of Slovenes , Croats and Serbs ( later to become a part of the Kingdom of Yugoslavia ) in order to prevent the Italians from claiming the ships as spoils of war . However , the victorious Allies refused to acknowledge the conversations between the Austrians and the south <unk> and , in due course , <unk> the ships . The ship had been boarded by a scratch Yugoslav crew on 10 November 1918 , one day before the Armistice , and had left Pola with her sister ship , Radetzky . They were soon spotted by heavy Italian ships , so the two battleships <unk> American flags and sailed south along the Adriatic coast to <unk> Bay near Spalato ( also known as <unk> ) . They appealed for American naval forces to meet them and accept their surrender , which a squadron of United States Navy ( USN ) submarine chasers in the area did . She had apparently been turned over to the fledgling south <unk> state , as it was a Croat naval officer , Korvettenkapitän Marijan <unk> , who presented the ship as a prize of war to representatives of the United States Navy on the afternoon of 22 November 1919 at Spalato ( <unk> ) in Dalmatia . Simultaneously she was commissioned as USS Zrínyi and Lieutenant <unk> <unk> , USN , assumed command . The initial American complement consisted of four officers and 174 enlisted men — the latter entirely composed of United States Navy Reserve Force personnel . The ship remained at anchor at Spalato for nearly a year while the negotiations that would determine her ultimate fate dragged on . Only once did she apparently turn her engines over , and that occurred during a severe gale that struck Spalato on 9 February 1920 . 
- On the morning of 7 November 1920 , Zrínyi was decommissioned . USS <unk> took her in tow and , assisted by Brooks and <unk> , towed the battleship to Italy . Under the terms of the treaties of Versailles and St. Germain , Zrínyi was ultimately turned over to the Italian government at Venice . She was broken up for scrap later that year and into 1921 . 
- 
- 
- = Geopyxis carbonaria = 
- 
- Geopyxis carbonaria is a species of fungus in the genus Geopyxis , family <unk> . First described to science in 1805 , and given its current name in 1889 , the species is commonly known as the charcoal loving elf @-@ cup , dwarf <unk> cup , <unk> <unk> cup , or pixie cup . The small , <unk> @-@ shaped fruitbodies of the fungus are reddish @-@ brown with a whitish fringe and measure up to 2 cm ( 0 @.@ 8 in ) across . They have a short , tapered stalk . Fruitbodies are commonly found on soil where brush has recently been burned , sometimes in great numbers . The fungus is distributed throughout many temperate regions of the Northern Hemisphere . It is found in Europe , Turkey , and North America . Although it is primarily a saprotrophic species , feeding on the decomposing organic matter remaining after a fire , it also forms biotrophic associations with the roots of Norway spruce . 
- 
- = = Taxonomy = = 
- 
- The fungus was first described scientifically in 1805 by Johannes <unk> von <unk> and Lewis David de <unk> as Peziza carbonaria . <unk> <unk> Cooke illustrated the fruitbodies , spores , and asci in his 1879 work <unk> , <unk> <unk> <unk> . Figures of fungi from all parts of the world . In 1889 , Pier Andrea <unk> transferred the fungus to the genus Geopyxis , giving the species its current name . <unk> carbonaria , published by Heinrich <unk> in 1884 , is a synonym of G. carbonaria . Louis @-@ Joseph <unk> proposed the variety Geopyxis carbonaria var. <unk> in 1937 , referring to forms producing fruitbodies without a stalk , but the taxon is not considered to have independent taxonomic significance . In 1860 Miles Berkeley and Moses Ashley Curtis described the species Peziza <unk> from collections made in Japan as part of the North Pacific Exploring and Surveying Expedition ( 1853 – 1856 ) . This taxon was synonymized with G. carbonaria by <unk> <unk> in 1968 , a taxonomic opinion corroborated by Donald <unk> about a decade later . 
- The specific epithet carbonaria derives from the Latin word for " charcoal " . Common names given to the fungus include " charcoal loving elf @-@ cup " , " dwarf <unk> cup " , " pixie cup " , and the British Mycological Society approved " <unk> <unk> cup " . 
- 
- = = Description = = 
- 
- The fruitbodies ( <unk> ) of Geopyxis <unk> are cup shaped , 1 – 2 cm wide , and have fringed whitish margins . The inner spore @-@ bearing surface of the cup , the hymenium , is brick red and smooth , while the exterior surface is a dull yellow , and may be either smooth or have <unk> @-@ like spots ( <unk> ) . The stipe is small ( 1 – 1 @.@ 5 mm long and 1 – 2 mm wide ) , whitish in color , and expands abruptly into the cup . The brownish flesh of the fungus is thin and brittle . It does not have any distinctive taste , but has an unpleasant smell when crushed in water . The edibility of the fungus is not known , but the fruitbodies are <unk> and unlikely to be harvested for eating . 
- 
- = = = Microscopic characteristics = = = 
- 
- In mass , the spores are whitish . The spores are elliptical , smooth , hyaline , devoid of oil droplets ( <unk> ) , and have dimensions of 13 – 18 by 7 – 9 µm . They are thin walled and germinate and grow rapidly in vitro in the absence of external <unk> . The asci are 190 – 225 by 9 – 10 µm . The paraphyses are slightly club @-@ shaped , <unk> , and have irregular orange @-@ brown granules , with tips up to 5 µm wide , and are not forked or lobed . The <unk> , the layer of cells below the hymenium , is made of densely packed , small irregular cells . 
- 
- = = = Similar species = = = 
- 
- The closely related <unk> elf cup ( Geopyxis <unk> ) has a pale orange to yellowish <unk> that is deeply cup shaped before flattening in maturity , and its crushed flesh often has an odor of sulfur . It may be distinguished microscopically by its paraphyses , which lack the orange @-@ brown granules characteristic of G. carbonaria . It also has larger spores , measuring 14 – 22 by 8 – 11 µm . Unlike G. carbonaria , it grows on substrates other than burned wood , including mosses , and needle duff . <unk> <unk> , which grows habitats similar to G. carbonaria , is distinguished microscopically by its spores that contain two oil droplets . Other genera with similar species with which G. carbonaria may be confused in the field include <unk> , <unk> , <unk> , and <unk> . 
- 
- = = Habitat and distribution = = 
- 
- Geopyxis carbonaria is widespread on burned soil or charcoal in the spring and throughout the growing season . It is one of the most common pioneer species found on burned ground . The <unk> litter on the forest floor increases the underlying soil <unk> as well as the availability of minerals . Fruitbodies are produced from 16 to 139 weeks after a forest fire in areas with coniferous trees . Most fruitbodies are produced in the first year after a burn . The fungus prefers fruiting in <unk> with thin <unk> duff near standing burned tree trunks . Geopyxis carbonaria fruitbodies are often found in the same post @-@ fire stands as morels , although the former is usually more abundant . Because the pixie cup fruits earlier than morels , it may serve as an indicator of imminent morel fruiting . Other cup fungi often found fruiting in the same area as G. carbonaria include those from the genera <unk> , <unk> , Peziza , and <unk> . 
- The fungus is found in Europe ( from where it was originally described ) , and is widespread throughout North America . The North American distribution extends north to Alaska . In 2010 , it was reported for the first time from Turkey . 
- 
- = = Ecology = = 
- 
- Although primarily a saprotrophic fungus involved in the post @-@ fire breakdown of duff and coniferous roots , Geopyxis carbonaria has been shown to be capable of forming ectomycorrhizae with Norway spruce ( <unk> <unk> ) . It had been demonstrated earlier in laboratory experiments that the fungus has a biotrophic interaction with lodgepole pine ( Pinus <unk> ) . The hyphae of G. carbonaria were able to infect the cortex of the tree <unk> , but did not penetrate the <unk> . These traits suggest that the fungus is a moderate <unk> , with limited ability to cause reductions in seed <unk> . Additionally , the fungus produces the enzyme <unk> <unk> , and can break down the complex organic <unk> lignin — features characteristic of saprotrophic fungi . The formation of a rudimentary <unk> net , a characteristic of mycorrhizal fungi , indicated that G. carbonaria might be capable of forming mutualistic relationships under the right conditions . <unk> and colleagues suggest that its below @-@ ground association with spruce roots protects it from physical damage in the event of a fire , and the extensive <unk> production after a fire may reflect " a successful fungal escape from a dying host where the fungus no longer can maintain its biotrophic association " . 
- Large <unk> of the fungus are often associated with damage to the host tree , such as that which occurs with burning . A field study conducted in Norway demonstrated that fruit bodies were more likely to be found in areas that were heavily burned , compared to locations with light to moderate burning where the trees remained viable , or in <unk> areas . <unk> was much denser in spruce forests — with up to 700 – 1000 fruitbodies per square meter — than in pine forests , where fruitbodies were sporadic . Fruitbodies grew by the millions in the year following the Yellowstone fires of 1988 . 
- 
- 
- = Gold dollar = 
- 
- The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from 1849 to 1889 . The coin had three types over its lifetime , all designed by Mint Chief Engraver James B. Longacre . The Type 1 issue had the smallest diameter of any United States coin ever minted . 
- A gold dollar had been proposed several times in the 1830s and 1840s , but was not initially adopted . Congress was finally galvanized into action by the increased supply of <unk> caused by the California gold rush , and in 1849 authorized a gold dollar . In its early years , silver coins were being <unk> or exported , and the gold dollar found a ready place in commerce . Silver again circulated after Congress in 1853 required that new coins of that metal be made lighter , and the gold dollar became a rarity in commerce even before federal coins vanished from circulation because of the economic disruption caused by the American Civil War . 
- Gold did not again circulate in most of the nation until 1879 ; once it did , the gold dollar did not regain its place . In its final years , it was struck in small numbers , causing speculation by <unk> . It was also in demand to be mounted in jewelry . The regular issue gold dollar was last struck in 1889 ; the following year , Congress ended the series . 
- 
- = = Background = = 
- 
- In proposing his plan for a mint and a coinage system , Secretary of the Treasury Alexander Hamilton in 1791 proposed that the one @-@ dollar denomination be struck both as a gold coin , and as one of silver , representative of the two metals which he proposed be made legal tender . Congress followed Hamilton 's recommendation only in part , authorizing a silver dollar , but no coin of that denomination in gold . 
- In 1831 , the first gold dollar was minted , at the private mint of Christopher Bechtler in North Carolina . Much of the gold then being produced in the United States came from the mountains of North Carolina and Georgia , and the dollars and other small gold coins issued by Bechtler circulated through that region , and were now and then seen further away . Additional one @-@ dollar pieces were struck by August Bechtler , Christopher 's son . 
- Soon after the <unk> began to strike their private issues , Secretary of the Treasury Levi <unk> became an advocate of having the Mint of the United States ( " Mint " , when described as an institution ) strike the one @-@ dollar denomination in gold . He was opposed by the Mint Director , Robert M. Patterson . <unk> persuaded President Andrew Jackson to have pattern coins struck . In response , Patterson had Mint Second Engraver Christian Gobrecht break off work on the new design for the silver one @-@ dollar coin and work on a pattern for the gold dollar . Gobrecht 's design featured a Liberty cap surrounded by rays on one side , and a palm branch arranged in a circle with the denomination , date , and name of the country on the other . 
- <unk> was given to including the gold dollar as an authorized denomination in the <unk> legislation that became the Mint Act of 1837 . The Philadelphia newspaper Public Ledger , in December 1836 , supported a gold dollar , stating that " the dollar is the smallest gold coin that would be convenient , and as it would be eminently so , neither silver nor paper should be allowed to take its place . " Nevertheless , after Mint Director Patterson appeared before a congressional committee , the provision authorizing the gold dollar was deleted from the bill . 
- 
- = = <unk> = = 
- 
- In January 1844 , North Carolina Representative James <unk> McKay , the chairman of the Committee on Ways and <unk> , solicited the views of Director Patterson on the gold dollar . Patterson had more of Gobrecht 's pattern dollar struck to show to committee members , again advising against a coin that if issued would be only about a half inch ( 13 mm ) in diameter . He told Treasury Secretary John C. Spencer that the only gold coins of that size in commerce , the Spanish and Colombian half @-@ <unk> , were unpopular and had not been struck for more than twenty years . This seemed to satisfy the committee as nothing more was done for the time , and when a gold dollar was proposed again in 1846 , McKay 's committee recommended against it . 
- Even before 1848 , record amounts of gold were flowing to American mints to be struck into coin , but the California Gold Rush vastly increased these quantities . This renewed calls for a gold dollar , as well as for a higher denomination than the eagle ( $ 10 piece ) , then the largest gold coin . In January 1849 , McKay introduced a bill for a gold dollar , which was referred to his committee . There was much discussion in the press about the proposed coin ; one newspaper published a proposal for an annular gold dollar , that is , with a hole in the middle to increase its small diameter . McKay amended his legislation to provide for a double eagle ( $ 20 gold coin ) and wrote to Patterson , who replied stating that the annular gold dollar would not work , and neither would another proposal to have dollar piece consisting of a gold <unk> in a silver coin . Nevertheless , Gobrecht 's successor as chief engraver , James B. Longacre , prepared patterns , including some with a square hole in the middle . 
- McKay got his fellow Democrat , New Hampshire Senator Charles Atherton , to introduce the bill to authorize the gold dollar and the double eagle in the Senate on February 1 , 1849 — Atherton was chairman of the Senate Finance Committee . McKay introduced a version into the House on February 20 ; debate began the same day . The dollar was attacked by <unk> from the Whig Party , then in the minority , on the grounds that it would be too small , would be <unk> and in bad light might be mistakenly spent as a half <unk> , the coins being similar in size . McKay did not respond <unk> , but stated that if no one wanted these denominations , they would not be called for at the Mint , and would not be coined . Pennsylvania Representative Joseph <unk> , a Whig , spoke against the bill , noting that Patterson opposed the new denominations , and that the idea had been repeatedly turned down , whenever considered . Another Whig , Massachusetts 's Charles Hudson , related that Patterson had sent a real and a counterfeit gold dollar to his committee and the majority of members had been unable to tell the difference . McKay made no answer to these claims , but others did , including New York Congressman Henry <unk> , who assured the House that the counterfeiting allegations were greatly exaggerated . The point was , he indicated , that the double eagle and gold dollar were wanted by the public , and , in the case of the gold dollar could help money circulate in small communities where <unk> were not accepted . Connecticut Representative John A. <unk> , a Whig , tried to table the bill , but his motion was defeated . The bill passed easily , and met only minimal opposition in the Senate , becoming law on March 3 , 1849 . 
- 
- = = Preparation = = 
- 
- The officers at the Philadelphia Mint , including Chief <unk> Franklin <unk> , were mostly the friends and relations of Director Patterson . The <unk> in their midst was Chief Engraver James B. Longacre , successor to Gobrecht ( who had died in 1844 ) . A former copper @-@ plate engraver , Longacre had been appointed through the political influence of South Carolina Senator John C. <unk> . 
- When Longacre began work on the two new coins in early 1849 , he had no one to assist him . Longacre wrote the following year that he had been warned by a Mint employee that one of the officers ( undoubtedly <unk> ) planned to undermine the chief engraver 's position by having the work of preparing designs and dies done outside Mint premises . Accordingly , when the gold coin bill became law , Longacre <unk> Patterson that he was ready to begin work on the gold dollar . The Mint Director agreed , and after viewing a model of the head on the <unk> , authorized Longacre to proceed with preparation of dies . According to Longacre , 
- The engraving was unusually minute and required very close and incessant labor for several weeks . I made the original dies and <unk> for making the working dies twice over , to secure their perfect adaptation to the coining machinery . I had a wish to execute this work single handed , that I might thus silently reply to those who had questioned my ability for the work . The result , I believe , was satisfactory . 
- 
- = = Original design = = 
- 
- The Type 1 gold dollar depicts a head of Liberty , facing left , with a <unk> or tiara on her head bearing her name . Her hair is gathered in a bun ; she is surrounded by 13 stars representing the original states . The reverse features the date and denomination within a wreath , with the name of the nation near the rim . 
- Contemporary reviews of the Type 1 design were generally favorable . The New York Weekly Tribune on May 19 , 1849 described the new dollar as " undoubtedly the <unk> , <unk> , lightest , coin in this country ... it is too delicate and beautiful to pay out for potatoes , and <unk> , and salt pork . Oberon might have paid Puck with it for bringing the blossom which bewitched <unk> . " Willis ' Bank Note List stated that " there is no probability of them ever getting into general circulation ; they are altogether too small . " The North Carolina Standard hoped that they would be struck at the Charlotte Mint and circulated locally to eliminate the problem of small @-@ denomination bank notes from out of state . <unk> dealer and numismatic author Q. David Bowers notes that the head of Liberty on the Type 1 dollar is a scaled @-@ down version of that on the double eagle , and " a nicely preserved gold dollar is beautiful to <unk> " . 
- 
- = = <unk> = = 
- 
- Mint records indicate the first gold dollars were produced on May 7 , 1849 ; Longacre 's diary notes state instead that the first were struck on May 8 . A few coins in proof condition were struck on the first day , along with about 1 @,@ 000 for circulation . There are five major varieties of the 1849 gold dollar from Philadelphia , made as Longacre continued to fine @-@ tune the design . <unk> dies were sent by Longacre 's <unk> Department at the Philadelphia Mint to the branch mints at Charlotte , Dahlonega ( in Georgia ) , and New Orleans ; coins struck at the branches resemble some of the types issued from Philadelphia , depending on when the dies were produced . Of the coins struck at the branch mints in 1849 , only pieces struck at Charlotte ( 1849 @-@ C ) exist in multiple varieties ; most are of what is dubbed the " <unk> Wreath " variety . Approximately five of the 1849 @-@ C Open Wreath are known ; one , believed the finest surviving specimen , sold at auction for $ 690 @,@ 000 in 2004 , remaining a record for the gold dollar series as of 2013 . One of the changes made during production was the inclusion of Longacre 's initial " L " on the truncation of Liberty 's neck , the first time a U.S. coin intended for full @-@ scale production had borne the initial of its designer . All issues beginning in 1850 bear the <unk> Wreath . Beginning in 1854 , the gold dollar was also struck at the new San Francisco Mint . 
- The continued flow of gold from California made silver expensive in terms of gold , and U.S. silver coins began to flow out of the country for melting in 1849 , a flow that accelerated over the next several years as the price of the metal continued to rise . By 1853 , a thousand dollars in silver coin contained $ 1 @,@ <unk> worth of <unk> . As silver coins vanished , the gold dollar became the only federal coin in circulation between the cent and the quarter eagle ( $ 2 @.@ 50 piece ) . As such , it was struck in large numbers and widely circulated . According to Bowers in his book on the denomination , " the years 1850 to 1853 were the high @-@ water mark of the gold dollar , the glory years of the denomination when the little gold coins took the place of half dollars and silver dollars in everyday transactions . " This time came to an end in 1853 when Congress passed an act reducing the weight of most silver coins , allowing new issues of them to circulate . 
- As early as 1851 , New York Congressman William <unk> alleged that that Patterson had made the gold dollar too small in diameter on purpose to provoke criticism . Patterson retired that year after 16 years in his position , and under his successor , George N. <unk> , annular gold dollar and half dollar patterns were struck . Public Ledger reported that although gold dollars would not be struck in annular form , gold half dollars would be , to help fill the need for change . With the new Pierce administration , Thomas M. Pettit took office as Mint Director on March 31 , 1853 . In April , Treasury Secretary James Guthrie wrote to Pettit that there were complaints that the gold dollar was too small , often lost or mistaken for a small silver coin , and <unk> about reports the Mint had experimented with annular dollars . Pettit replied , stating that none had been preserved , but enclosed a silver piece of equivalent size . He noted that while there would be technical difficulties in the production of the annular dollar , these could be overcome . In a letter dated May 10 , Pettit proposed an oval @-@ shaped holed piece , or an angular @-@ shaped coin , which would lessen the production problems . Pettit died suddenly on May 31 ; Guthrie did not let the issue fall , but queried Pettit 's replacement , James Ross <unk> , concerning the issue on June 7 . As U.S. coins were required to bear some device <unk> of liberty , the secretary hoped that artists could be found who could find some such design for an annular coin . 
- The Act of February 21 , 1853 , that had lightened the silver coins also authorized a gold three @-@ dollar piece , which began to be produced in 1854 . To ensure that the three @-@ dollar piece was not mistaken for other gold coins , it had been made thinner and wider than it would normally be , and Longacre put a distinctive design with an Indian princess on it . Longacre adapted both the technique and the design for the gold dollar , which was made thinner , and thus wider . An adaptation of Longacre 's princess for the larger gold coin was placed on the dollar , and a similar agricultural wreath on the reverse . The idea of making the gold dollar larger in this way had been suggested in Congress as early as 1852 , and had been advocated by Pettit , but Guthrie 's desire for an annular coin stalled the matter . In May 1854 , <unk> sent Guthrie a letter stating that the difficulties with an annular coin , especially in getting the coins to <unk> properly from the press , were more than trivial . 
- Nevertheless , the Type 2 gold dollar ( as it came to be known ) proved unsatisfactory as the mints had difficulty in striking the new coin so that all details were brought out . This was due to the high relief of the design — the three Southern branch mints especially had trouble with the piece . Many of the Type 2 pieces quickly became <unk> , and were sent back to Philadelphia for melting and <unk> . On most surviving specimens , the " 85 " in the date is not fully detailed . The Type 2 gold dollar was struck only at Philadelphia in 1854 and 1855 , at the three Southern branch mints in the latter year , and at San Francisco in 1856 , after the design was designated for replacement . To correct the problems , Longacre enlarged the head of Liberty , making it a scaled @-@ down version of the three @-@ dollar piece , and moved the <unk> on the <unk> closer to the rim . This improved the metal flow and design <unk> so much that early numismatic scholars assumed the reverse was also altered , though in fact no change was made and the Type 2 and Type 3 reverses are identical . 
- 
- = = = Design of Type 2 and 3 dollars = = = 
- 
- The Type 2 and 3 gold dollars depict Liberty as a Native American princess , with a <unk> feathered headdress not resembling any worn by any Indian tribe . This image is an <unk> copy of the design Longacre had made for the three @-@ dollar piece , and is one of a number of versions of Liberty Longacre created based on the Venus <unk> or <unk> Venus , a sculpture then on display in a Philadelphia museum . For the reverse , Longacre adapted the " agricultural wreath " he had created for the reverse of the three @-@ dollar piece , composed of cotton , corn , tobacco , and wheat , blending the produce of North and South . This wreath would appear , later in the 1850s , on the Flying Eagle cent . 
- Art historian Cornelius <unk> <unk> the Indian princess design used by Longacre for the <unk> of the Types 2 and 3 gold dollar , and for the three @-@ dollar piece , " the ' princess ' of the gold coins is a <unk> engraver 's elegant version of folk art of the 1850s . The plumes or feathers are more like the crest of the Prince of Wales than anything that saw the Western <unk> , save perhaps on a music hall beauty . " 
- 
- = = War years = = 
- 
- The gold dollar continued to be produced in the late 1850s , though mintages declined from the figures of two million or more each year between 1850 and 1854 . Only about 51 @,@ 000 gold dollars were produced in 1860 , with over two @-@ thirds of that figure at Philadelphia , just under a third at San Francisco , and 1 @,@ <unk> at Dahlonega . Roughly a hundred are known of the last , creating one of the great <unk> from Dahlonega in the series . 
- The other candidate for the <unk> from that mint is the 1861 @-@ D , with an estimated mintage of 1 @,@ 000 and perhaps 45 to 60 known . Two pairs of dies were shipped from Philadelphia to Dahlonega on December 10 , 1860 ; they arrived on January 7 , 1861 , two weeks before Georgia voted to secede from the Union , as the American Civil War began . Under orders from Governor Joseph E. Brown , state militia secured the mint , and at some point , small quantities of dollars and half eagles were produced . Records of how many coins were struck and when have not survived . Since dies crack in time , and all the mints were supplied with them from Philadelphia , coining could not last , and in May 1861 , coins and supplies remaining at Dahlonega were turned over to the treasury of the Confederate States of America , which Georgia had by then joined . Gold coins with a face value of $ 6 were put aside for <unk> . Normally , they would have been sent to Philadelphia to await the following year 's meeting of the United States <unk> Commission , when they would be available for testing . Instead , these were sent to the initial Confederate capital of Montgomery , Alabama , though what was done with them there , and their ultimate fate , are unknown . The rarity of the 1861 @-@ D dollar , and the association with the Confederacy , make it especially prized . 
- Dahlonega , like the other two branch mints in the South , closed its doors after the 1861 <unk> . It and the Charlotte facility never reopened ; the New Orleans Mint again struck coins from 1879 to 1909 , but did not strike gold dollars again . After 1861 , the only issuance of gold dollars outside Philadelphia was at San Francisco , in 1870 . 
- The outbreak of the Civil War shook public confidence in the Union , and citizens began hoarding specie , gold and silver coins . In late December 1861 , banks and then the federal Treasury stopped paying out gold at face value . By mid @-@ 1862 , all federal coins , even the base metal cent , had vanished from commerce in much of the country . The exception was the Far West , where for the most part , only gold and silver were acceptable currencies , and paper money traded at a discount . In the rest of the nation , gold and silver coins could be purchased from banks , exchange agents , and from the Treasury for a premium in the new <unk> the government began to issue to fill the gap in commerce and finance the war . 
- 
- = = Final years , abolition , and collecting = = 
- 
- Since gold did not circulate in the United States ( except on the West Coast ) in the postwar period , much of the production of coins of that metal in the United States was double eagles for export . Accordingly , although 1 @,@ 361 @,@ 355 gold dollars were struck in 1862 — the last time production would exceed a million — the mintage fell to 6 @,@ 200 in 1863 and remained low for the rest of the coin 's existence , excepting 1873 and 1874 . The Mint felt it improper to suspend coinage of a coin authorized by Congress , and issued proof coins ( generally a few dozen to the tiny numismatic community ) from specially @-@ polished dies , also producing enough circulation strikes so that the proof coins would not be <unk> rare . In 1873 and 1874 , old and worn gold dollars held by the government were melted and <unk> , generating large mintages of that denomination . This was done in anticipation of the resumption of specie payments , which did not occur until the end of 1878 . Once specie again circulated at face value , the gold dollar found no place in commerce amid large quantities of silver coinage , either released from hoarding or newly struck by the Mint . The government expected that the resumption of specie payments would cause the dollar and other small gold coins to circulate again , but the public , allowed to redeem paper currency , continued to use it as more convenient than coins . 
- In the 1870s and 1880s , public interest grew in the low @-@ mintage gold dollar . <unk> coins was becoming more popular , and a number of <unk> put aside some gold dollars and hoped for increases in value . The Mint most likely channeled its production through some favored Philadelphia dealers , though proof coins could be purchased for $ 1 @.@ 25 at the <unk> 's window at the Philadelphia facility . Banks charged a premium for circulation strikes . They were popular in the jewelry trade , mounted into various items . The coins were often exported to China or Japan , where such jewelry was made . The dollars were often damaged in the process ; the Mint refused to sell into this trade and did its best to hinder it . Nevertheless , Mint officials concluded that <unk> were successful at getting the majority of each issue . Proof mintages exceeded 1 @,@ 000 by 1884 , and remained above that mark for the remainder of the series , numbers likely inflated by agents of <unk> , willing to pay the Mint 's premium of $ <unk> per coin . Another use for the gold dollar was as a holiday gift ; after its abolition the quarter eagle became a popular present . 
- James <unk> , in his final report as Mint Director in 1873 , advocated limiting striking of gold dollars to <unk> who specifically requested it . " The gold dollar is not a convenient coin , on account of its small size , and it suffers more <unk> from <unk> than larger coins . " His successors called for its abolition , with James P. Kimball , before he left office in 1889 , writing to Congress that except as jewelry , " little practical use has been found for this coin " . Later that year , the new director , Edward O. <unk> , issued a report stating that the gold dollar " is too small for circulation , and ... [ is ] used almost exclusively for the purposes of ornament . The last year in which the gold dollar was struck was 1889 . Congress abolished the gold dollar , along with the three @-@ cent nickel and three @-@ dollar piece , by the Act of September 26 , 1890 . 
- A total of 19 @,@ 499 @,@ 337 gold dollars were coined , of which 18 @,@ 223 @,@ <unk> were struck at Philadelphia , 1 @,@ <unk> @,@ 000 at New Orleans , 109 @,@ 138 at Charlotte , 90 @,@ 232 at San Francisco and 72 @,@ 529 at Dahlonega . According to an advertisement in the February 1899 issue of The <unk> , gold dollars brought $ 1 @.@ 80 each , still in demand as a birthday present and for jewelry . That journal in 1905 carried news of a customer depositing 100 gold dollars into a bank ; the teller , aware of the value , credited the account with $ 1 @.@ 60 per coin . In 1908 , a dealer offered $ 2 each for any quantity . As coin collecting became a widespread <unk> in the early 20th century , gold dollars became a popular specialty , a status they retain . The 2014 edition of <unk> <unk> 's A Guide Book of United States Coins rates the least expensive gold dollar in very fine condition ( <unk> @-@ 20 ) at $ 300 , a value given for each of the Type 1 Philadelphia issues from 1849 to 1853 . Those seeking one of each type will find the most expensive to be a specimen of the Type 2 , with the 1854 and 1855 estimated at $ 350 in that condition ; the other two types have dates valued at $ 300 in that grade . 
- 
- = = <unk> gold dollars = = 
- 
- The gold dollar had a brief resurrection during the period of Early United States commemorative coins . Between 1903 and 1922 nine different issues were produced , with a total mintage of 99 @,@ 799 . These were minted for various public events , did not circulate , and none used Longacre 's design . 
- 
- 
- = Johnson – Corey – Chaykovsky reaction = 
- 
- The Johnson – Corey – Chaykovsky reaction ( sometimes referred to as the Corey – Chaykovsky reaction or <unk> ) is a chemical reaction used in organic chemistry for the synthesis of epoxides , aziridines , and <unk> . It was discovered in 1961 by A. William Johnson and developed significantly by E. J. Corey and Michael Chaykovsky . The reaction involves addition of a sulfur ylide to a <unk> , aldehyde , <unk> , or <unk> to produce the corresponding 3 @-@ membered ring . The reaction is <unk> favoring trans substitution in the product regardless of the initial <unk> . The synthesis of epoxides via this method serves as an important <unk> alternative to the traditional <unk> reactions of <unk> . 
- The reaction is most often employed for <unk> via methylene transfer , and to this end has been used in several notable total syntheses ( See Synthesis of epoxides below ) . Additionally detailed below are the history , mechanism , scope , and enantioselective variants of the reaction . Several reviews have been published . 
- 
- = = History = = 
- 
- The original publication by Johnson concerned the reaction of 9 @-@ <unk> <unk> with substituted <unk> derivatives . The attempted <unk> @-@ like reaction failed and a <unk> oxide was obtained instead , noting that " Reaction between the sulfur <unk> and <unk> did not afford <unk> as had the phosphorus and arsenic <unk> . " 
- The subsequent development of ( <unk> ) methanide , ( <unk> ) <unk> and ( <unk> ) methanide , ( <unk> ) <unk> ( known as Corey – Chaykovsky reagents ) by Corey and Chaykovsky as efficient methylene @-@ transfer reagents established the reaction as a part of the organic canon . 
- 
- = = <unk> = = 
- 
- The reaction mechanism for the Johnson – Corey – Chaykovsky reaction consists of nucleophilic addition of the ylide to the <unk> or <unk> group . A negative charge is transferred to the <unk> and because the sulfonium cation is a good leaving group it gets expelled forming the ring . In the related <unk> reaction , the formation of the much stronger phosphorus @-@ oxygen double bond prevents <unk> formation and instead , <unk> takes place through a 4 @-@ membered cyclic intermediate . 
- The trans diastereoselectivity observed results from the reversibility of the initial addition , allowing <unk> to the favored anti betaine over the <unk> betaine . Initial addition of the ylide results in a betaine with adjacent charges ; density functional theory calculations have shown that the rate @-@ limiting step is rotation of the central bond into the <unk> necessary for <unk> attack on the sulfonium . 
- The degree of reversibility in the initial step ( and therefore the diastereoselectivity ) depends on four factors , with greater reversibility corresponding to higher selectivity : 
- <unk> of the substrate with higher stability leading to greater reversibility by favoring the starting material over the betaine . 
- <unk> of the ylide with higher stability similarly leading to greater reversibility . 
- <unk> hindrance in the betaine with greater hindrance leading to greater reversibility by <unk> formation of the intermediate and slowing the rate @-@ limiting rotation of the central bond . 
- <unk> of charges in the betaine by <unk> such as lithium with greater <unk> allowing more <unk> rotation in the betaine intermediate , lowering the amount of reversibility . 
- 
- = = <unk> = = 
- 
- The application of the Johnson – Corey – Chaykovsky reaction in organic synthesis is diverse . The reaction has come to encompass reactions of many types of sulfur ylides with <unk> well beyond the original publications . It has seen use in a number of high @-@ profile total syntheses , as detailed below , and is generally recognized as a powerful <unk> tool in the organic repertoire . 
- 
- = = = Types of ylides = = = 
- 
- Many types of ylides can be prepared with various functional groups both on the <unk> carbon center and on the sulfur . The substitution pattern can influence the ease of preparation for the reagents ( typically from the sulfonium <unk> , e.g. <unk> iodide ) and overall reaction rate in various ways . The general format for the reagent is shown on the right . 
- Use of a sulfoxonium allows more <unk> preparation of the reagent using weaker bases as compared to sulfonium ylides . ( The difference being that a sulfoxonium contains a <unk> bonded oxygen whereas the sulfonium does not . ) The former react slower due to their increased stability . In addition , the <unk> by @-@ products of sulfoxonium reagents are greatly preferred to the significantly more toxic , volatile , and <unk> <unk> by @-@ products from sulfonium reagents . 
- The vast majority of reagents are <unk> at the ylide carbon ( either R1 or <unk> as hydrogen ) . <unk> reagents are much rarer but have been described : 
- If the ylide carbon is substituted with an electron @-@ withdrawing group ( <unk> ) , the reagent is referred to as a stabilized ylide . These , similarly to sulfoxonium reagents , react much slower and are typically easier to prepare . These are limited in their usefulness as the reaction can become <unk> sluggish : examples involving <unk> are widespread , with many fewer involving <unk> and virtually no examples involving other <unk> 's . For these , the related <unk> reaction is typically more appropriate . 
- If the ylide carbon is substituted with an <unk> or <unk> group , the reagent is referred to as a semi @-@ stabilized ylide . These have been developed extensively , second only to the classical methylene reagents ( R1 = <unk> = H ) . The substitution pattern on <unk> reagents can heavily influence the selectivity of the reaction as per the criteria above . 
- If the ylide carbon is substituted with an <unk> group the reagent is referred to as an <unk> ylide . The size of the <unk> groups are the major factors in selectivity with these reagents . 
- The R @-@ groups on the sulfur , though typically <unk> , have been used to synthesize reagents that can perform enantioselective variants of the reaction ( See Variations below ) . The size of the groups can also influence diastereoselectivity in <unk> substrates . 
- 
- = = = Synthesis of epoxides = = = 
- 
- Reactions of sulfur ylides with <unk> and <unk> to form epoxides are by far the most common application of the Johnson – Corey – Chaykovsky reaction . Examples involving complex substrates and ' exotic ' ylides have been reported , as shown below . 
- The reaction has been used in a number of notable total syntheses including the <unk> <unk> total synthesis , which produces the <unk> drug <unk> , and the <unk> <unk> total synthesis which produces the <unk> <unk> . 
- 
- = = = Synthesis of aziridines = = = 
- 
- The synthesis of aziridines from <unk> is another important application of the Johnson – Corey – Chaykovsky reaction and provides an alternative to amine transfer from <unk> . Though less widely applied , the reaction has a similar substrate scope and functional group tolerance to the <unk> equivalent . The examples shown below are representative ; in the latter , an <unk> forms in situ and is opened via nucleophilic attack to form the corresponding amine . 
- 
- = = = Synthesis of <unk> = = = 
- 
- For addition of sulfur ylides to <unk> , higher 1 @,@ 4 @-@ selectivity is typically obtained with sulfoxonium reagents than with sulfonium reagents . Many electron @-@ withdrawing groups have been shown compatible with the reaction including <unk> , <unk> , and <unk> ( the example below involves a <unk> <unk> ) . With further conjugated systems 1 @,@ 6 @-@ addition tends to predominate over 1 @,@ 4 @-@ addition . 
- 
- = = = Other reactions = = = 
- 
- In addition to the reactions originally reported by Johnson , Corey , and Chaykovsky , sulfur ylides have been used for a number of related <unk> reactions that tend to be grouped under the same name . 
- With epoxides and aziridines the reaction serves as a ring @-@ expansion to produce the corresponding <unk> or <unk> . The long reaction times required for these reactions prevent them from occurring as significant side reactions when <unk> epoxides and aziridines . 
- Several <unk> wherein the ylide serves as a " nucleophilic carbenoid equivalent " have been reported . 
- Living <unk> using <unk> as the catalyst and ( <unk> ) methanide as the <unk> have been reported for the synthesis of various complex polymers . 
- 
- = = <unk> variations = = 
- 
- The development of an enantioselective ( i.e. yielding an enantiomeric excess , which is labelled as " ee " ) variant of the Johnson – Corey – Chaykovsky reaction remains an active area of academic research . The use of <unk> sulfides in a stoichiometric fashion has proved more successful than the corresponding catalytic variants , but the substrate scope is still limited in all cases . The catalytic variants have been developed almost exclusively for enantioselective purposes ; typical <unk> reagents are not <unk> expensive and the <unk> reactions can be carried out with <unk> amounts of ylide without raising costs significantly . <unk> sulfides , on the other hand , are more costly to prepare , spurring the advancement of catalytic enantioselective methods . 
- 
- = = = <unk> reagents = = = 
- 
- The most successful reagents employed in a stoichiometric fashion are shown below . The first is a <unk> <unk> that has been employed in the synthesis of the β @-@ <unk> compound <unk> ( <unk> ) but is limited by the availability of only one <unk> of the reagent . The synthesis of the axial <unk> is <unk> via the 1 @,@ 3 @-@ <unk> effect which reduces the <unk> of the equatorial lone pair . The conformation of the ylide is limited by <unk> strain and approach of the aldehyde is limited to one face of the ylide by <unk> interactions with the methyl <unk> . 
- The other major reagent is a camphor @-@ derived reagent developed by <unk> Aggarwal of the University of Bristol . Both <unk> are easily synthesized , although the yields are lower than for the <unk> reagent . The ylide conformation is determined by interaction with the bridgehead <unk> and approach of the aldehyde is blocked by the camphor <unk> . The reaction employs a <unk> base to promote formation of the ylide . 
- 
- = = = <unk> reagents = = = 
- 
- <unk> reagents have been less successful , with most variations suffering from poor yield , poor <unk> , or both . There are also issues with substrate scope , most having limitations with methylene transfer and <unk> <unk> . The trouble stems from the need for a nucleophilic sulfide that efficiently generates the ylide which can also act as a good leaving group to form the <unk> . Since the factors underlying these <unk> are at odds , tuning of the catalyst properties has proven difficult . <unk> below are several of the most successful <unk> along with the yields and enantiomeric excess for their use in synthesis of ( E ) <unk> oxide . 
- Aggarwal has developed an alternative method employing the same sulfide as above and a novel <unk> involving a <unk> carbenoid formed in situ . The method too has limited substrate scope , failing for any <unk> possessing basic <unk> due to competitive consumption of the carbenoid . 
- 
- 
- = Treaty of Ciudad Juárez = 
- 
- The Treaty of Ciudad Juárez was a peace treaty signed between the then President of Mexico , Porfirio Díaz , and the revolutionary Francisco Madero on May 21 , 1911 . The treaty put an end to the fighting between forces supporting Madero and those of Díaz and thus concluded the initial phase of the Mexican Revolution . 
- The treaty stipulated that Díaz , as well as his vice president Ramón Corral , were to step down by the end of May , and that he was to be replaced by Francisco León de la Barra as interim president and hold presidential elections . Those who had suffered losses due to the revolution would be the <unk> , and there would be a general amnesty . Díaz resigned on May 25 , and interim president Francisco León de la Barra was the new incumbent . Díaz and his family , his vice president Corral , plus José Yves Limantour and <unk> <unk> left Mexico for exile . 
- <unk> , the treaty did not mention or institute any social reforms that Madero had vaguely promised on previous occasions . It also left the <unk> state essentially intact . Additionally , Madero supported the unpopular idea that all land disputes were to be settled through the courts , staffed by the old judges , a decision that led to outbreaks of sporadic violence , particularly in rural areas . 
- On June 7 , 1911 , Madero entered Mexico City . In October 1911 he was elected president , under the banner of the <unk> <unk> <unk> , along with José María <unk> <unk> , his new running mate as vice @-@ president . Madero pushed aside Francisco <unk> Gómez , the vice presidential candidate for the Anti @-@ <unk> Party in 1910 , as being too moderate . 
- 
- = = Military developments leading up to the treaty = = 
- 
- The rebellion against the government of Porfirio Díaz broke out in late 1910 , after Díaz had his rival Francisco Madero imprisoned and had announced his own victory in a falsified election . Madero 's earlier vague promises of agrarian reforms had attracted many supporters . He himself escaped from prison and fled to Texas , from where he issued his famous Plan of San Luis <unk> . This manifesto called for an armed uprising against the <unk> and establishment of free and democratic elections . As a response to Madero 's proclamation , violent clashes began throughout Mexico in November 1910 . 
- In the Guerrero district of Chihuahua , Pascual Orozco attacked Federal troops and sent dead soldiers ' clothing back to Díaz with the message , " <unk> <unk> van las <unk> , <unk> <unk> <unk> " ( " Here are the <unk> , send me more <unk> . " ) He then began operations which threatened Ciudad Juárez . Additionally , political support for Madero 's rebellion came from Abraham González , who accepted the Plan of San Luis <unk> . 
- At roughly the same time , agrarian unrest in the state of Morelos turned into a full blown rebellion under the leadership of the Zapata brothers , <unk> and <unk> . 
- 
- = = = Orozco and Villa take Ciudad Juárez = = = 
- 
- Encouraged by the news of the uprisings , Madero crossed the border back into Mexico in February 1911 . He was joined by <unk> Villa and Orozco and in April the army began approaching Ciudad Juárez . Orozco and Villa led the way with 500 men each , while Madero followed up with 1 @,@ 500 riders . The city was besieged by the end of the month , after Madero 's army encountered some resistance in the <unk> countryside . Madero asked the commander of the city 's garrison to surrender but the latter refused , hoping that the fortifications he had constructed would allow him to defend the city until reinforcements arrived . Concerned also with the possibility that a direct attack on the town would cause artillery shells to cross the border into the United States which could provoke an outside intervention , and faced with a series of peace proposals from Díaz , Madero hesitated in attacking the city . He in fact ordered his commanders to lift the siege . Orozco , however <unk> the order and , joined by Villa , attacked . After two days of fighting the city fell to the insurrectionists . Madero intervened personally to spare the life of the city 's commander , Gen. Navarro , whom both Orozco and Villa wanted executed for his previous killing of rebel <unk> . This , coupled with the fact that both leaders were ignored by Madero in his political appointments , outraged and estranged them from him . 
- 
- = = = Zapata in south and central Mexico = = = 
- 
- At about the same time that Villa and Orozco were marching on Ciudad Juárez , the <unk> revolt gathered strength and spread to the states of Puebla , <unk> , Mexico , Michoacán and Guerrero . On April 14 , Madero had <unk> Zapata officially designated as his representative in the region . However , Zapata was worried that if he did not fully control all the major towns in Morelos by the time that Madero concluded negotiations with Díaz , the demands of his agrarian movement and the issue of the autonomy of Morelos would be ignored or sidelined . Zapata 's first military action was to take the town of <unk> where he obtained essential supplies . Subsequently Zapata , for political and strategic reasons , decided to attack the city of Cuautla . In order to mislead his opponents however , he initially attacked and captured the towns of <unk> de <unk> ( which was subsequently <unk> by federal forces ) and <unk> . From there he made a wide circle around Cuautla and captured <unk> and <unk> where he gathered more supplies , munitions and soldiers . By May , out of all the major urban centers in the region , only Cuautla and the capital of Morelos , <unk> , remained outside of his control . 
- Zapata began the attack on Cuautla on May 13 with 4000 troops against 400 elite soldiers of the so @-@ called " Golden Fifth " ; the Fifth Cavalry Regiment of the Federal Army . The battle took almost a week and has been described as " six of the most terrible days of battle in the whole Revolution " . It consisted of house to house fighting , hand @-@ to @-@ hand combat , and no quarter given by either side . General <unk> Huerta arrived in nearby <unk> with 600 reinforcements , but decided not to come to the relief of Cuautla as he was afraid that the capital would revolt in his absence . On May 19 , the remains of the " Golden Fifth " pulled out of the town which was then occupied by Zapata 's soldiers . 
- The successful capture of Cuautla made Zapata a hero to ordinary people throughout Mexico and new <unk> were written about him . After Zapata 's taking of Cuautla , the federal government controlled only five states and some urban areas . Porfirio Díaz himself later stated that , while he felt that he could defend against Villa and Orozco in Chihuahua , the fall of Cuautla was the event which persuaded him to agree to peace with Madero . 
- 
- = = The compromise = = 
- 
- As early as March 1911 , Madero 's representatives met in New York with Díaz 's finance minister , José Yves Limantour , and the Mexican ambassador to the US in order to discuss the possibility of peace between the two sides . Limantour proposed an end to the hostilities and offered an amnesty for all revolutionaries , the resignation of the then vice president Ramón Corral , the replacement of four Díaz cabinet ministers and ten state governors by ones chosen by Madero , and the establishment of the principle of " no @-@ reelection " which would prevent Díaz from seeking yet another term as president ( which would have been his ninth ) . Madero responded positively although he also stated that any kind of peace deal had to include an immediate resignation by Díaz . 
- Faced with the siege of Ciudad Juárez and the outbreak of rebellion in Morelos , Díaz and members of his cabinet became more willing to negotiate and launched a " <unk> peace offensive " aimed at Madero . This was largely a result of panic among the large landowners associated with the Díaz regime ( the <unk> ) and the financial elite , which represented a " moderate " wing within the government . Some among the <unk> in fact , expected that Zapata would soon march on Mexico City itself , unless peace was concluded with Madero . 
- The moderate view within the Díaz government was represented by Jorge <unk> Estañol who in a <unk> to the minister of foreign affairs wrote that there were two <unk> taking place in Mexico : a political revolution , based mostly in the north , whose aim was mostly to establish free elections and remove Díaz himself from power , and a social revolution whose aim was " anarchy " which was spreading throughout the Mexican countryside . Estañol recommended coming to terms with the first group of revolutionaries , by agreeing to the principle of no re @-@ election and a general amnesty , in order to prevent the second group from succeeding . In addition to his fear of " anarchy " , Estañol was also worried that the social revolution would lead to a military intervention by the United States . 
- Estañol 's views represented those of the portion of the upper class which was willing to come to terms with at least a portion of the middle class in order to crush the peasant uprisings , as exemplified by those of Zapata , which were <unk> throughout Mexico . Limantour , who broadly agreed with Estañol , had the support of the Mexican <unk> , who feared the <unk> of Mexican international credit and a general economic crisis as a result of ongoing social unrest , as well as that of the large landowners who were willing to come to terms with Madero if it would put an end to the agrarian uprisings . 
- These social group were in turn opposed by the more reactionary elements within Díaz 's government , mostly concentrated in the federal army , who though that the rebels should be dealt with through brute force . This faction was represented by General <unk> Huerta , who would later carry out an attempted coup d 'état against Madero . Likewise , the general , and potential successor to Díaz , Bernardo Reyes stated in a letter to Limantour that " the repression ( against the insurrectionists ) should be carried out with the <unk> energy , punishing without any pity anyone participating in the armed struggle " . In the end however , Díaz dismissed the advice from his generals as " <unk> @-@ like <unk> " and chose to seek peace with the moderate wing of the revolution . Limantour had finally managed to persuade him to resign . 
- At the same time there was also disagreement among the rebels . The " left wing " of the revolutionary movement , represented by Zapata and Orozco ( Villa for the time being tended to support Madero ) , warned against any possible compromises with Díaz . In the end their suspicions proved correct as the treaty that was eventually signed neglected issues of social and agrarian land reform that were central to their struggle . 
- 
- = = The treaty 's terms = = 
- 
- The most significant point of the treaty was that Porfirio Díaz , and his vice president , Ramón Corral , resign and that de la Barra , acting as interim president organize free elections as soon as possible . 
- Additionally , the treaty stipulated that : 
- An amnesty for all revolutionaries be declared , with the option for some of them to apply for membership in the <unk> . 
- The revolutionary forces were to be <unk> as soon as possible and the federal forces were to be the only army in Mexico . This was in order to appease the army , which had opposed a compromise with Madero . 
- Madero and his supporters had the right to name fourteen provisional state governors , and to approve la Barra 's cabinet . 
- <unk> were to be established for relatives of the soldiers who had died fighting the rebels . 
- <unk> and judges , as well as state legislators , that had been appointed or " elected " under Díaz were to retain their offices . 
- 
- = = <unk> and results = = 
- 
- The treaty was signed on May 21 . Díaz resigned accordingly on May 25 . Francisco de la Barra became the interim president . Madero entered Mexico City on June 7 . 
- Zapata however refused to recognize the interim government of de la Barra , and for the time being the fighting in Morelos continued . Madero met with Zapata on several occasions during June . While initially Zapata trusted Madero , with time he became increasingly concerned that the goals of " his revolution " were not being fulfilled . He was particularly angry that Madero did not plan on carrying out any kind of agrarian reform , or the breakup of large <unk> . Additionally , the press in Mexico City , controlled by the landowners began referring to Zapata as a bandit and federal generals , such as Huerta , continued attacking his troops under the pretext that Zapata failed to <unk> in violation of the treaty . <unk> fighting in southern Mexico continued . In November 1911 , shortly after Madero 's inauguration , Zapata issued the famous Plan of <unk> , in which the <unk> denounced Madero and instead recognized Pascual Orozco as the rightful president and leader of the revolution . 
- Madero also earned the great <unk> of other revolutionaries , including , Pascual Orozco . Madero 's first act after the treaty was signed was a gesture of reconciliation with the Díaz regime . As a result of the treaty he was given the right to appoint members of the la Barra cabinet . He chose mostly upper class <unk> , including his wife for the post in the treasury . He also maintained the existing federal system , by keeping the sitting judges of the Supreme Court , the legislators in federal and state assemblies and the <unk> of the various federal agencies . <unk> <unk> , who was going to become a major revolutionary in his own right and a future president of Mexico , stated that , after the treaty , Madero had " deliver ( ed ) to the <unk> a dead revolution which will have to be fought over again " . Díaz , after leaving for exile in France , observed that " Madero has unleashed a tiger , let us see if he can control him " . 
- Orozco , who saw himself as being instrumental in Madero 's victory over Díaz , was merely appointed as a commander of the <unk> in Chihuahua , which increased his resentment . When he tried to run for governor of the state , Madero supported his opponent , Abraham González and eventually pressured Orozco to drop out of the race . When , in the aftermath of the Plan of <unk> , Madero ordered Orozco to lead federal troops to suppress Zapata , Orozco refused . In March 1912 , Orozco issued his Plan of <unk> and formally declared himself in rebellion against Madero . 
- 
- 
- = The Feast of the Goat = 
- 
- The Feast of the Goat ( Spanish : La fiesta del chivo , 2000 ) is a novel by the Peruvian Nobel Prize in Literature laureate Mario Vargas Llosa . The book is set in the Dominican Republic and portrays the assassination of Dominican dictator Rafael Trujillo , and its aftermath , from two distinct standpoints a generation apart : during and immediately after the assassination itself , in May 1961 ; and thirty five years later , in 1996 . Throughout , there is also extensive reflection on the <unk> of the dictatorship , in the 1950s , and its significance for the island and its inhabitants . 
- The novel follows three interwoven storylines . The first concerns a woman , Urania Cabral , who is back in the Dominican Republic , after a long absence , to visit her ailing father ; she ends up recalling incidents from her youth and recounting a long @-@ held secret to her aunt and cousins . The second story line focuses on the last day in Trujillo 's life from the moment he wakes up onwards , and shows us the regime 's inner circle , to which Urania 's father once belonged . The third <unk> depicts Trujillo 's assassins , many of whom had previously been government loyalists , as they wait for his car late that night ; after the assassination , this story line shows us the assassins ' persecution . Each aspect of the book 's plot reveals a different viewpoint on the Dominican Republic 's political and social environment , past and present . 
- <unk> are shown the regime 's downward spiral , Trujillo 's assassination , and its aftermath through the eyes of <unk> , conspirators , and a middle @-@ aged woman looking back . The novel is therefore a <unk> portrait of dictatorial power , including its psychological effects , and its long @-@ term impact . The novel 's themes include the nature of power and corruption , and their relationship to machismo and sexual perversion in a <unk> hierarchical society with strongly gendered roles . Memory , and the process of remembering , is also an important theme , especially in Urania 's narrative as she recalls her youth in the Dominican Republic . Her story ( and the book as a whole ) ends when she recounts the terrible events that led to her leaving the country at the age of 14 . The book itself serves as a reminder of the atrocities of dictatorship , to ensure that the dangers of absolute power will be remembered by a new generation . 
- Vargas Llosa <unk> fictional elements and historical events : the book is not a documentary , and the Cabral family , for instance , is completely fictional . On the other hand , the characters of Trujillo and Trujillo 's assassins are drawn from the historical record ; Vargas Llosa <unk> real historical incidents of brutality and oppression into these people 's stories , to further illuminate the nature of the regime and the responses it provoked . In Vargas Llosa 's words , " It 's a novel , not a history book , so I took many , many liberties . [ . . . ] I have respected the basic facts , but I have changed and deformed many things in order to make the story more persuasive — and I have not exaggerated . " 
- The Feast of the Goat received largely positive reviews , with several reviewers commenting on the book 's depiction of the relationship between sexuality and power , and on the graphic descriptions of violent events . 
- A film version of the novel was released in 2005 , starring Isabella <unk> , Paul Freeman , and Tomas <unk> . Jorge <unk> Triana and his daughter Veronica Triana wrote a theatrical adaptation in 2003 . 
- 
- = = Background = = 
- 
- The Feast of the Goat is only the second of Vargas Llosa 's novels to be set outside Peru ( the first being The War of the End of the World ) . It is also unusual because it is the first to have a female protagonist : as critic Lynn <unk> writes of the leading character in The Feast of the Goat , and also Vargas Llosa 's subsequent book The Way to Paradise , " both are utterly unlike any of the other female characters in his previous novels " . 
- The novel examines the dictatorial regime of Rafael <unk> Trujillo Molina in the Dominican Republic . Trujillo was , in historian Eric <unk> 's words , " a towering influence in Dominican and Caribbean history " who presided over " one of the most durable regimes of the twentieth century " during the thirty @-@ one years between his seizure of power in 1930 and his assassination in 1961 . Trujillo had trained with the United States Marine Corps during the United States occupation of the island , and graduated from the <unk> Military Academy in 1921 . After the U.S. departed in 1924 , he became head of the Dominican National Police which , under his command , was transformed into the Dominican National Army and Trujillo 's personal " virtually autonomous power base " . 
- Trujillo was officially dictator only from 1930 to 1938 , and from 1942 to 1952 , but remained in effective power throughout the entire period . Though his regime was broadly nationalist , Daniel <unk> comments that he had " no particular ideology " and that his economic and social policies were basically progressive . 
- The novel 's title is taken from the popular Dominican merengue <unk> al chivo ( " They <unk> the Goat " ) , which refers to Trujillo 's assassination on May 30 , 1961 . <unk> is a style of music created by <unk> <unk> in the 1920s and actively promoted by Trujillo himself ; it is now considered the country 's national music . Cultural critics Julie Sellers and Stephen <unk> comment about this particular merengue that , by <unk> the dictator as an animal who could be turned into a <unk> ( as frequently happened with goats struck down on the Dominican Republic 's highways ) , the song " gave those performing , listening to and dancing to this merengue a sense of control over him and over themselves that they had not experienced for over three decades . " Vargas Llosa quotes the lyrics to <unk> al chivo at the beginning of the novel . 
- 
- = = Plot summary = = 
- 
- The novel 's narrative is divided into three distinct strands . One is centred on Urania Cabral , a fictional Dominican character ; another deals with the conspirators involved in Trujillo 's assassination ; and the third focuses on Trujillo himself . The novel alternates between these storylines , and also jumps back and forth from 1961 to 1996 , with frequent flashbacks to periods earlier in Trujillo 's regime . 
- The Feast of the Goat begins with the return of Urania to her hometown of Santo Domingo , a city which had been renamed Ciudad Trujillo during Trujillo 's time in power . This storyline is largely introspective and deals with Urania 's memories and her inner turmoil over the events preceding her departure from the Dominican Republic thirty @-@ five years earlier . Urania escaped the crumbling Trujillo regime in 1961 by claiming she planned to study under the tutelage of nuns in Michigan . In the following decades , she becomes a prominent and successful New York lawyer . She finally returns to the Dominican Republic in 1996 , on a whim , and finds herself compelled to confront her father and elements of her past she has long ignored . As Urania speaks to her ailing father , Agustin Cabral , she recalls more and more of the anger and disgust that led to her thirty @-@ five years of silence . Urania retells her father 's descent into political disgrace , and the betrayal that forms the <unk> of both Urania 's storyline and that of Trujillo himself . 
- The second and third storylines are set in 1961 , in the weeks prior to and following Trujillo 's assassination on 30 May . Each assassin has his own background story , explaining his motivation for his involvement in the assassination plot . Each has been <unk> by Trujillo and his regime , by torture and brutality , or through assaults on their pride , their religious faith , their morality , or their loved ones . Vargas Llosa <unk> the tale of the men as memories recalled on the night of Trujillo 's death , as the conspirators lie in wait for " The Goat " . <unk> with these stories are the actions of other famous <unk> of the time : Joaquín Balaguer , the puppet president ; Johnny Abbes García , the merciless head of the Military Intelligence Service ( SIM ) ; and various others — some real , some <unk> of historical figures , and some purely fictional . 
- The third storyline is concerned with the thoughts and motives of Rafael <unk> Trujillo Molina himself . The chapters concerning The Goat recall the major events of his time , including the slaughter of thousands of Dominican Haitians in 1937 . They also deal with the Dominican Republic 's tense international relationships during the Cold War , especially with the United States under the presidency of John F. Kennedy , and Cuba under Castro . Vargas Llosa also speculates upon Trujillo 's innermost thoughts and paints a picture of a man whose physical body is failing him . Trujillo is tormented by <unk> and impotence ; and this storyline intersects with Urania 's narrative when it is revealed that Urania was sexually assaulted by Trujillo . He is unable to achieve an erection with Urania , and in frustration and anger he rapes her with his hands . This event is the core of Urania 's shame , and her hatred towards her father . In addition , it is the cause of Trujillo 's repeated anger over the " <unk> little <unk> " that witnessed his impotence and emotion , and the reason he is en route to sleep with another girl on the night of his assassination . 
- In the novel 's final chapters , the three storylines intersect with increasing frequency . The tone of these chapters is especially dark as they deal primarily with the horrific torture and death of the assassins at the hands of the SIM , the failure of the coup , the rape of Urania , and the concessions made to Trujillo 's most vicious supporters allowing them to enact their horrific revenge on the conspirators and then escape the country . The book ends as Urania prepares to return home , determined this time to keep in touch with her family back on the island . 
- 
- = = Characters = = 
- 
- 
- = = = Modern day = = = 
- 
- Urania Cabral and her father Agustín Cabral appear in both the modern day and historical portions of the novel . In the year 1996 , Urania returns to the Dominican Republic for the first time since her departure at the age of 14 . She is a successful New York lawyer who has spent most of the past 35 years trying to overcome the <unk> of her childhood , a goal she pursues through an academic fascination with Trujillo and Dominican history . Urania is deeply troubled by the events of her past , and is compelled to confront her father Agustín about his role in those events . Urania visits her father , finding him weakened by age and a severe stroke , so much so that he is barely able to respond physically to her presence , let alone speak . Agustín listens <unk> as Urania recounts his past as " <unk> Cabral " , a high @-@ ranking member of Trujillo 's inner circle , and his drastic fall from grace . Urania details Agustín 's role in the events that led to her rape by the Dominican leader , and to her subsequent lifetime of celibacy and emotional trauma . Agustín 's character in the modern day portion of the novel serves primarily as a sounding board for Urania 's <unk> of the Trujillo era and the events that surrounded both Agustín Cabral 's disgrace and Urania 's escape from the country . His responses are minimal and non @-@ vocal , despite the <unk> of Urania 's accusations and the <unk> of his own actions during Trujillo 's reign . 
- 
- = = = The Trujillo regime = = = 
- 
- Rafael Trujillo , known also as The Goat , The Chief , and The <unk> , is a fictionalized character based on the real dictator of the Dominican Republic from 1930 to 1961 and the official President of the Republic from 1930 to 1938 and 1943 to 1952 . In The Feast of the Goat , Vargas Llosa imagines the innermost thoughts of the dictator , and retells The Goat 's last hours from his own perspective . Trujillo 's character struggles with aging and the physical problems of <unk> and impotence . Through fictional events and first person narrative , the reader is given insight into the man who , during his " thirty @-@ one years of <unk> political crimes " , modernized the country 's infrastructure and military , but whose regime 's attacks against its enemies overseas ( particularly the attempted assassination of <unk> <unk> , president of Venezuela ) led to the imposition of economic sanctions on the Dominican Republic by the Organization of American States in the 1950s . The resultant economic downturn , in conjunction with other factors , leads to the CIA supported assassination plot that ends Trujillo 's life on May 30 , 1961 . 
- Trujillo 's regime is supported by Johnny Abbes García , the head of the Military Intelligence Service ( SIM ) , a brutal man to whom many " <unk> , ... executions , ... sudden falls into disgrace " are attributed . Abbes and his intelligence officers are notorious for their cruelty , particularly their habit of killing dissidents by throwing them into shark @-@ infested waters . Colonel Abbes " may be the devil , but he 's useful to the Chief ; everything bad is attributed to him and only the good to Trujillo " . Trujillo 's son , Ramfis Trujillo , is a loyal supporter of the Chief . After unsuccessful attempts at schooling in the United States , Ramfis returns to the Dominican Republic to serve in his father 's military . He is a well @-@ known <unk> . Upon Trujillo 's death , Ramfis seeks revenge , even going so far as to torture and kill his uncle by marriage , General Jose Roman , for his part in the assassination conspiracy . 
- Joaquín Balaguer , Trujillo 's puppet president is also a supporter , and initially his seemingly <unk> character holds no real power . Following Trujillo 's death , the calm and <unk> of Balaguer bring about real change in his character , and General <unk> comments that " this insignificant man whom everyone had always considered a mere clerk , a purely decorative figure in the regime , began to acquire surprising authority " . It is Balaguer who guides much of the action in the last sections of the book . 
- 
- = = = <unk> = = = 
- 
- The storyline concerning the assassination primarily follows the four conspirators who directly participate in Trujillo 's death . Antonio Imbert <unk> is one of the few conspirators who survives the violent <unk> that follow Trujillo 's assassination . Imbert is a politician who becomes disillusioned with the deception and cruelty of the Trujillo regime . His first plan to kill Trujillo was <unk> by the unsuccessful attempted overthrow of the regime by Cuban paramilitary forces . Now convinced of the difficulty of his task , Imbert joins the other conspirators in plotting Trujillo 's death . Among the others is Antonio de la Maza , one of Trujillo 's personal guards . Antonio 's brother is killed as part of a government cover @-@ up and Antonio swears revenge upon Trujillo . Salvador <unk> <unk> , known as " Turk " , is a devout Catholic who , in <unk> at the regime 's many crimes against God , swears an oath against Trujillo . Turk eventually turns himself in for fear that the regime was <unk> his family . Both Turk and his innocent brother are then tortured for months . His father remains loyal to Trujillo and <unk> Turk to his face . Despite all of this , Turk refuses to commit suicide and does not lose faith in God . He is later executed by Ramfis and other high level government men . Turk 's close friend , <unk> García Guerrero , known as Amadito , is a Lieutenant in the army who gave up his beloved as proof of his loyalty to Trujillo , and then later was forced to kill her brother to prove himself to Trujillo . Amadito 's disgust with himself and disillusionment with the regime lead to his decision to help to kill Trujillo . Following the assassination he hides out with de la Maza and dies fighting . In the aftermath of the assassination , Amadito and Antonio de la Maza choose to fight the members of SIM who come to arrest them , <unk> to die in battle rather than be captured and tortured . 
- 
- = = Major themes = = 
- 
- The Feast of the Goat 's major themes include political corruption , machismo , memory , and writing and power . Olga Lorenzo , reviewer for The Melbourne Age , suggests that overall Vargas Llosa 's aim is to reveal the irrational forces of Latin tradition that give rise to <unk> . 
- 
- = = = Political corruption = = = 
- 
- The structure of Dominican society was hierarchical , with strongly gendered roles . Rafael Trujillo , the ruler , was a cruel dictator who haunts the people of Santo Domingo even 35 years after his death . He is a true <unk> , ruling with brutality and corruption . He creates a personality cult in his capitalist society and encourages <unk> within his regime . Prior to promotion to a position of responsibility , an officer is required to pass a " test of loyalty " . His people are to remain loyal to him all cost , and are periodically tested by public humiliation and <unk> even though acts of <unk> were rare . Trujillo <unk> women and children as an expression of political and sexual power , and in some cases takes the wife or child of his lieutenants , many of whom still remain <unk> loyal . Even the church and military institutions are employed to give women to the <unk> for pleasure . 
- Many of the assassins had belonged to the Trujillo regime or had at one point been its staunch supporters , only to find their support for him eroded by the state 's crimes against its people . Imbert , one of the assassins , sums up this realization in a comment prompted by the murder of the Mirabal sisters : " They kill our fathers , our brothers , our friends . And now they 're killing our women . And here we sit , resigned , waiting our turn . " In an interview , Vargas Llosa describes the corruption and brutality of Trujillo 's regime : " He had more or less all the common traits of a Latin American dictator , but pushed to the extreme . In cruelty , I think he went far far away from the rest — and in corruption , too . " 
- 
- = = = <unk> = = = 
- 
- According to literary scholar Peter Anthony <unk> , the two important components of machismo are aggressive behaviour and hyper @-@ sexuality . <unk> behaviour is exhibited by displays of power and strength , while hyper @-@ sexuality is revealed through sexual activity with as many partners as possible . These two components shape the portrayal of Trujillo and his regime in The Feast of the Goat . As Lorenzo observes , Vargas Llosa " reveals traditions of machismo , of abusive fathers , and of child @-@ rearing practices that repeat the <unk> of children , so that each generation <unk> a withering of the soul to the subsequent one . " 
- In a display of both aspects of machismo , Trujillo demanded of his aides and cabinet that they provide him with sexual access to their wives and daughters . Mario Vargas Llosa wrote of Trujillo 's machismo and treatment of women , " [ h ] e went to bed with his ministers ' wives , not only because he liked these ladies but because it was a way to test his ministers . He wanted to know if they were ready to accept this extreme humiliation . <unk> the ministers were prepared to play this grotesque role — and they remained loyal to Trujillo even after his death . " Trujillo 's sexual conquests and public <unk> of his enemies also serve to affirm his political power and machismo . In <unk> 's words , " The implication is that maximum <unk> equals political dominance . " 
- Trujillo 's attempted sexual conquest of Urania is an example of both political manipulation of Agustín Cabral and sexual power over young women . However , as Trujillo 's penis remains flaccid throughout the encounter and he is humiliated in front of the young girl , the encounter fails to satisfy his requirements for machismo . 
- 
- = = = Memory = = = 
- 
- All of the novel 's storylines concern memory in some sense or another . The most apparent confrontation of memory is on the part of Urania Cabral , who has returned to the Dominican Republic for the first time in 30 years , and is forced to confront her father and the <unk> that led her to leave the country at 14 . She was the victim of sexual abuse at the hands of the dictator himself , a sacrifice her father made to try to gain favor with the dictator again , a fact to which she alludes throughout the book , but which is only revealed at the very end : the book concludes with her recounting the memory of that night to her aunt and cousins , who never knew the true reason she left the country . When her aunt is surprised that she remembers all these details , she responds that while she <unk> many things , " I remember everything about that night . " For Urania , forgetting the atrocities committed by the regime is unacceptable . Her father , on the other hand , is not capable of joining her in this process of remembering , since he has suffered a stroke and is not capable of speaking ; however , Urania is angry that he chose to forget these things while he was still capable of acknowledging them . 
- Memory is also important in the sections of the novel that deal with the assassins . Each recalls the events that led him to take part in the assassination of Trujillo . These incidents included the 1956 <unk> kidnapping and murder , the 1960 murder of the Mirabal sisters , and the 1961 split with the Catholic Church . These historical events are used by Vargas Llosa to connect the assassins with specific moments that demonstrate the violence of Trujillo 's regime . Trujillo , too , is shown reflecting on the past , not least his own formation and training at the hands of the US Marines . 
- But above all Mario Vargas Llosa uses the fictional Urania to facilitate the novel 's attempt at remembering the regime . The novel opens and closes with Urania 's story , effectively framing the narrative in the terms of remembering the past and understanding its legacy in the present . In addition , because of her academic study of the history of the Trujillo regime , Urania is also <unk> the memory of the regime for the country as a whole . This is in keeping with one purpose of the book , which is to ensure that the atrocities of the dictatorship and the dangers of absolute power will be remembered by a new generation . 
- 
- = = = Writing and power = = = 
- 
- In her treatment of the novel , María Regina Ruiz claims that power gives its <unk> the ability to make prohibitions ; prohibitions that are reflected in history , the study of which reveals what is and what is not told . The government 's actions in The Feast of the Goat demonstrate the discourse of prohibition : foreign newspapers and magazines were prohibited from entering Trujillo 's country as they were seen as a threat to the government 's ideas . Mario Vargas Llosa takes part in this discourse by recounting what was prohibited . 
- Ruiz notes that writing also has the power to transform reality . It brings the reader back to the past , allowing the reader to comprehend myths or distorted stories told by historians . Ruiz contends that knowing the past is crucial to one 's understanding of the present that takes us to <unk> , and argues that The Feast of the Goat can thus be seen as a <unk> discourse that gives power to history recreation . 
- The construction of <unk> surrounding the events of Trujillo 's regime allow a degree of freedom from the <unk> that took places . Author Julia Alvarez contends that these events can " only finally be understood by fiction , only finally be <unk> by the imagination " , while Richard Patterson claims that Vargas Llosa " <unk> , and to a large degree <unk> " Trujillo and his brutal reign through use of narrative structure . Vargas Llosa 's writing acts as a <unk> force for this period in history . 
- 
- = = <unk> and fiction = = 
- 
- The novel is a combination of fact and fiction . <unk> together these two elements is important in any historical novel , but especially in The Feast of the Goat because Vargas Llosa chose to narrate an actual event through the minds of both real and fictional characters . Some characters are fictional , and those that are non @-@ fictional still have fictionalized aspects in the book . The general details of the assassination are true , and the assassins are all real people . While they lie in wait for the Dictator to arrive , they <unk> actual crimes of the regime , such as the murder of the Mirabal sisters . However , other details are invented by Vargas Llosa , such as Amadito 's murder of the brother of the woman he loved . 
- Those within the regime are also a mix of fictional characters and real people . President Balaguer is real , but the entire Cabral family is completely fictional . According to <unk> , Vargas Llosa " uses history as a starting point in constructing a fictionalized account of Trujillo 's " spiritual colonization " of the Dominican Republic as experienced by one Dominican family . The fictional Cabral family allows Vargas Llosa to show two sides of the Trujillo regime : through Agustin , the reader sees ultimate dedication and sacrifice to the leader of the nation ; through Urania , the violence of the regime and the legacy of pain it left behind . Vargas Llosa also fictionalized the internal thoughts of the characters who were non @-@ fictional , especially those of the Goat himself . According to literary scholar Richard Patterson , " Vargas Llosa 's expands all the way into the very " dark area " of Trujillo 's consciousness ( as the storyteller <unk> to conceive it ) . " 
- Vargas Llosa also built an image of the regime with the troubled historical events . With regard to the historical accuracy of the book , Vargas Llosa has said " It 's a novel , not a history book , so I took many , many liberties . The only limitation I imposed on myself was that I was not going to <unk> anything that couldn 't have happened within the framework of life in the Dominican Republic . I have respected the basic facts , but I have changed and deformed many things in order to make the story more persuasive — and I have not exaggerated . " 
- 
- = = Critical reception = = 
- 
- The realist style of The Feast of the Goat is recognized by some reviewers as being a break from a more allegorical approach to the dictator novel . The novel received largely positive reviews , most of which were willing to accept sacrifices of historical accuracy in favour of good storytelling . 
- A common comment on the novel is the graphic nature of the many acts of torture and murder which are depicted in the novel . Vargas lets the reader see the <unk> of an oppressive regime with a degree of detail not often used by his <unk> in Latin American literature , as Michael Wood suggests in the London Review of Books : " Vargas Llosa ... tells us far more about the details of day @-@ to @-@ day intrigue , and the <unk> , sadistic <unk> of torture and murder . " Walter Kirn of the New York Times suggests that the " <unk> scenes of dungeon <unk> and torture sessions " cast other aspects of the novel in a pale light , draining them of their significance and impact . Similarly , Kirn implies that the " narrative machinery " mentioned by Wood as being somewhat unwieldy also produces a largely superfluous storyline . The plot line centered on Urania Cabral is described by <unk> as being an emotional centre that focuses the novel , and Wood agrees that her confrontations with past demons hold the readers attention . In contrast , Kirn 's review states that Urania 's segments are " <unk> and atmospheric ... [ and ] seem to be on loan from another sort of book . " 
- Most reviews of The Feast of the Goat make either indirect of direct reference to the relationship between sexuality and power . Salon reviewer Laura Miller , writer for The Observer Jonathan <unk> , Walter Kirn , and Michael Wood each detail the connection between Trujillo 's gradual loss of ultimate control , both over his body and his followers . The means by which Trujillo reinforces political power through sexual acts and begins to lose personal conviction as his body fails him are topics of frequent discussion among reviewers . 
- In 2011 Bernard <unk> , author of the 1978 non @-@ fiction book Trujillo . The Death of the Goat , accused Vargas @-@ Llosa of plagiarism . 
- 
- = = Adaptations = = 
- 
- An English @-@ language film adaptation of the novel was made in 2005 , directed by Luis Llosa , Mario Vargas Llosa 's cousin . It stars Isabella <unk> as Urania Cabral , Paul Freeman as her father Agustin , Stephanie <unk> as <unk> and Tomas <unk> as Rafael <unk> Trujillo . It was filmed in both the Dominican Republic and in Spain . Reviewing the film for the trade paper Variety , critic Jonathan Holland called it " less a feast than a somewhat rushed , but thoroughly enjoyable , three @-@ course meal " , commenting that the main difference from the source novel was the sacrifice of psychological nuance . 
- The novel has also been adapted for the stage , by Jorge <unk> Triana and his daughter Veronica Triana , directed by Jorge Triana : the play was put on ( in Spanish , but with simultaneous translation to English ) at <unk> <unk> ( <unk> / chivo ) in New York in 2003 ; and the production moved to Lima in 2007 . A feature of the novel 's stage version is that the same actor plays both Agustin Cabral and Rafael Trujillo . For reviewer Bruce Weber , this makes the point " that Trujillo 's control of the nation depended on <unk> collaborators " . 
- 
- 
- = Charles Eaton ( RAAF officer ) = 
- 
- Charles Eaton , OBE , AFC ( 21 December 1895 – 12 November 1979 ) was a senior officer and <unk> in the Royal Australian Air Force ( RAAF ) , who later served as a diplomat . Born in London , he joined the British Army upon the outbreak of World War I and saw action on the Western Front before transferring to the Royal Flying Corps in 1917 . <unk> as a bomber pilot to No. 206 Squadron , he was twice captured by German forces , and twice escaped . Eaton left the military in 1920 and worked in India until moving to Australia in 1923 . Two years later he joined the RAAF , serving initially as an instructor at No. 1 Flying Training School . Between 1929 and 1931 , he was chosen to lead three expeditions to search for lost aircraft in Central Australia , gaining national attention and earning the Air Force Cross for his " zeal and devotion to duty " . 
- In 1939 , on the eve of World War II , Eaton became the inaugural commanding officer of No. 12 ( General Purpose ) Squadron at the newly established RAAF Station Darwin in Northern Australia . Promoted group captain the following year , he was appointed an Officer of the Order of the British Empire in 1942 . He took command of No. 79 Wing at Batchelor , Northern Territory , in 1943 , and was mentioned in despatches during operations in the South West Pacific . <unk> from the RAAF in December 1945 , Eaton took up diplomatic posts in the Dutch East Indies , heading a United Nations commission as Consul @-@ General during the Indonesian National Revolution . He returned to Australia in 1950 , and served in Canberra for a further two years . <unk> known as " Moth " Eaton , he was a farmer in later life , and died in 1979 at the age of 83 . He is commemorated by several memorials in the Northern Territory . 
- 
- = = Early life and World War I = = 
- 
- Charles Eaton was born on 21 December 1895 in Lambeth , London , the son of William Walpole Eaton , a <unk> , and his wife Grace . <unk> in <unk> , Charles worked in <unk> Town Council from the age of fourteen , before joining the London Regiment upon the outbreak of World War I in August 1914 . Attached to a bicycle company in the 24th Battalion of the 47th Division , he arrived at the Western Front in March 1915 . He took part in trench bombing missions and attacks on enemy lines of communication , seeing action in the Battles of <unk> Ridge , <unk> , <unk> , and the Somme . 
- On 14 May 1915 , Eaton transferred to the Royal Flying Corps ( RFC ) , undergoing initial pilot training at Oxford . While he was landing his Maurice <unk> <unk> at the end of his first solo flight , another student collided with him and was killed , but Eaton emerged <unk> . He was commissioned in August and was awarded his wings in October . Ranked lieutenant , he served with No. 110 Squadron , which operated <unk> <unk> " Elephant " fighters out of <unk> , defending London against <unk> airships . <unk> to the newly formed Royal Air Force ( RAF ) in April 1918 , he was posted the following month to France flying <unk> <unk> single @-@ engined bombers with No. 206 Squadron . On 29 June , he was shot down behind enemy lines and captured in the vicinity of <unk> . <unk> in <unk> prisoner @-@ of @-@ war camp , Germany , Eaton escaped but was recaptured and court @-@ <unk> , after which he was kept in solitary confinement . He later effected another escape and succeeded in rejoining his squadron in the final days of the war . 
- 
- = = Between the wars = = 
- 
- Eaton remained in the RAF following the cessation of hostilities . He married <unk> Godfrey in St. Thomas 's church at Shepherd 's Bush , London , on 11 January 1919 . <unk> to No. 1 Squadron , he was a pilot on the first regular passenger service between London and Paris , ferrying delegates to and from the Peace Conference at Versailles . Eaton was sent to India in December to undertake aerial survey work , including the first such survey of the Himalayas . He resigned from the RAF in July 1920 , remaining in India to take up employment with the Imperial Forest Service . After successfully applying for a position with the Queensland <unk> Service , he and his family migrated to Australia in 1923 . Moving to South <unk> , Victoria , he enlisted as a flying officer in the Royal Australian Air Force ( RAAF ) at Laverton on 14 August 1925 . He was posted to No. 1 Flying Training School at RAAF Point Cook , as a flight instructor , where he became known as a strict disciplinarian who " trained his pilots well " . Here Eaton acquired his nickname of " Moth " , the Air Force 's basic trainer at this time being the De Havilland DH.60 Moth . Promoted flight lieutenant in February 1928 , he flew a Moth in the 1929 East @-@ West Air Race from Sydney to Perth , as part of the celebrations for the Western Australia Centenary ; he was the sixth competitor across the line , after fellow RFC veteran Jerry <unk> . 
- Regarded as one of the RAAF 's most skilful cross @-@ country pilots and <unk> , Eaton came to public attention as leader of three military expeditions to find lost aircraft in Central Australia between 1929 and 1931 . In April 1929 , he coordinated the Air Force 's part in the search for aviators Keith Anderson and Bob Hitchcock , missing in their aircraft the Kookaburra while themselves looking for Charles <unk> Smith and Charles Ulm , who had force landed the Southern Cross in north Western Australia during a flight from Sydney . Three of the RAAF 's five " ancient " <unk> <unk> went down in the search — though all crews escaped injury — including Eaton 's , which experienced what he labelled " a good crash " on 21 April near Tennant Creek after the engine 's <unk> melted . The same day , Captain Lester Brain , flying a <unk> aircraft , located the wreck of the Kookaburra in the <unk> Desert , approximately 130 kilometres ( 81 mi ) east @-@ south @-@ east of Wave Hill . Setting out from Wave Hill on 23 April , Eaton led a ground party across rough terrain that reached the crash site four days later and buried the crew , who had perished of thirst and exposure . Not a particularly religious man , he recalled that after the burial he saw a perfect cross formed by <unk> cloud in an otherwise clear blue sky above the Kookaburra . The Air Board described the RAAF 's search as taking 240 hours flying time " under the most trying conditions ... where a forced landing meant certain crash " . In November 1930 , Eaton was selected to lead another expedition for a missing aircraft near <unk> Rock , but it was called off soon afterwards when the pilot showed up in Alice Springs . The next month , he was ordered to search for <unk> <unk> and <unk> <unk> , who had disappeared in the biplane Golden Quest 2 while attempting to discover <unk> 's Reef . <unk> a total of four DH.60 <unk> , the RAAF team located the missing men near <unk> Creek on 7 January 1931 , and they were rescued four days later by a ground party accompanied by Eaton . <unk> in nearby Alice Springs , he recommended a site for the town 's new airfield , which was approved and has remained in use since its construction . 
- Eaton was awarded the Air Force Cross on 10 March 1931 " in recognition of his zeal and devotion to duty in conducting flights to Central Australia in search of missing aviators " . The media called him the " ' Knight <unk> ' of the desert skies " . Aside from his crash landing in the desert while searching for the Kookaburra , Eaton had another narrow escape in 1929 when he was test flying the Wackett <unk> I with Sergeant Eric Douglas . Having purposely put the biplane trainer into a spin and finding no response in the controls when he tried to recover , Eaton called on Douglas to bail out . When Douglas stood up to do so , the spin stopped , apparently due to his torso changing the <unk> over the tail plane . Eaton then managed to land the aeroplane , he and his passenger both badly shaken by the experience . In December 1931 , he was posted to No. 1 Aircraft Depot at Laverton , where he continued to fly as well as performing administrative work . Promoted squadron leader in 1936 , he undertook a clandestine mission around the new year to scout for suitable landing grounds in the Dutch East Indies , primarily Timor and Ambon . <unk> civilian clothes , he and his companion were arrested and held for three days by local authorities in <unk> , Dutch Timor . Eaton was appointed commanding officer ( CO ) of No. 21 Squadron in May 1937 , one of his first tasks being to undertake another aerial search in Central Australia , this time for <unk> Sir Herbert <unk> , who was subsequently discovered alive and well . Later that year , Eaton presided over the court of inquiry into the crash of a Hawker Demon biplane in Victoria , recommending a gallantry award for <unk> William <unk> , who had leapt into the Demon 's burning wreckage in an effort to rescue its pilot ; <unk> subsequently received the Albert Medal for his heroism . 
- Following a 1937 decision to establish the first north Australian RAAF base , in April 1938 Eaton , now on the headquarters staff of RAAF Station Laverton , and Wing Commander George Jones , Director of Personnel Services at RAAF Headquarters , began developing plans for the new station , to be commanded by Jones , and a new squadron that would be based there , led by Eaton . The next month they flew an Avro <unk> on an inspection tour of Darwin , Northern Territory , site of the proposed base . <unk> meant that No. 12 ( General Purpose ) Squadron was not formed until 6 February 1939 at Laverton . Jones had by now moved on to another posting but Eaton took up the squadron 's command as planned . Promoted to wing commander on 1 March , he and his equipment officer , Flying Officer <unk> , were ordered to build up the unit as quickly as possible , and established an initial complement of fourteen officers and 120 airmen , plus four Ansons and four Demons , within a week . An advance party of thirty NCOs and airmen under <unk> began moving to Darwin on 1 July . Staff were initially accommodated in a former <unk> built during World War I , and life at the newly established air base had a " distinctly raw , pioneering feel about it " according to historian Chris Coulthard @-@ Clark . <unk> , though , was high . On 31 August , No. 12 Squadron launched its first patrol over the Darwin area , flown by one of seven Ansons that had so far been delivered . These were augmented by a flight of four CAC Wirraways ( replacing the originally planned force of Demons ) that took off from Laverton on 2 September , the day before Australia declared war , and arrived in Darwin four days later . A fifth <unk> in the flight crashed on landing at Darwin , killing both crewmen . 
- 
- = = World War II = = 
- 
- Once war was declared , Darwin began to receive more attention from military planners . In June 1940 , No. 12 Squadron was " <unk> " to form two additional units , Headquarters RAAF Station Darwin and No. 13 Squadron . No. 12 Squadron retained its <unk> flight , while its two flights of Ansons went to the new squadron ; these were replaced later that month by more capable Lockheed <unk> . Eaton was appointed CO of the base , gaining promotion to temporary group captain in September . His squadrons were employed in escort , maritime reconnaissance , and coastal patrol duties , the <unk> aircraft having to be sent to RAAF Station Richmond , New South Wales , after every 240 hours flying time — with a consequent three @-@ week loss from Darwin 's strength — as deep maintenance was not yet possible in the Northern Territory . Soon after the establishment of Headquarters RAAF Station Darwin , Minister for Air James <unk> visited the base . <unk> his own light plane , he was greeted by four Wirraways that proceeded to escort him into landing ; the Minister subsequently complimented Eaton on the " keen @-@ ness and efficiency of all ranks " , particularly considering the challenging environment . When <unk> died in the Canberra air disaster shortly afterwards , his pilot was Flight Lieutenant Robert Hitchcock , son of Bob Hitchcock of the Kookaburra and also a former member of Eaton 's No. 21 Squadron . 
- As senior air commander in the region , Eaton sat on the Darwin Defence Co @-@ ordination Committee . He was occasionally at <unk> with his naval counterpart , Captain <unk> Thomas , and also incurred the ire of trade unionists when he used RAAF staff to <unk> ships in Port Darwin during industrial action ; Eaton himself took part in the work , <unk> coal alongside his men . On 25 February 1941 , he made a flight north to reconnoitre Timor , Ambon , and <unk> in Dutch New Guinea for potential use by the RAAF in any Pacific conflict . By April , the total strength based at RAAF Station Darwin had increased to almost 700 officers and airmen ; by the following month it had been augmented by satellite airfields at Bathurst Island , <unk> <unk> , Batchelor , and Katherine . <unk> over command of Darwin to Group Captain Frederick <unk> in October , Eaton took charge of No. 2 Service Flying Training School near <unk> <unk> , New South Wales . His " marked success " , " <unk> energy " , and " <unk> in handling men " while in the Northern Territory were recognised in the new year with his appointment as an Officer of the Order of the British Empire . Eaton became CO of No. 1 Engineering School and its base , RAAF Station Ascot Vale , Victoria , in April 1942 . Twelve months later in Townsville , Queensland , he formed No. 72 Wing , which subsequently deployed to <unk> in Dutch New Guinea , comprising No. 84 Squadron ( flying CAC Boomerang fighters ) , No. 86 Squadron ( P @-@ 40 <unk> fighters ) , and No. 12 Squadron ( A @-@ 31 <unk> dive bombers ) . His relations with North @-@ Eastern Area Command in Townsville were strained ; " mountains were made out of <unk> " in his opinion , and he was reassigned that July to lead No. 2 <unk> and <unk> School in Port <unk> , South Australia . 
- On 30 November 1943 , Eaton returned to the Northern Territory to establish No. 79 Wing at Batchelor , comprising No. 1 and No. 2 Squadrons ( flying Bristol Beaufort light reconnaissance bombers ) , No. 31 Squadron ( Bristol Beaufighter long @-@ range fighters ) , and No. 18 ( Netherlands East Indies ) Squadron ( B @-@ 25 Mitchell medium bombers ) . He developed a good relationship with his Dutch personnel , who called him " <unk> Charles " ( Uncle Charles ) . Operating under the auspices of North @-@ Western Area Command ( NWA ) , Darwin , Eaton 's forces participated in the New Guinea and North @-@ Western Area Campaigns during 1944 , in which he regularly flew on missions himself . Through March – April , his Beaufighters attacked enemy shipping , while the Mitchells and Beauforts bombed Timor on a daily basis as a prelude to Operations <unk> and <unk> , the invasions of Hollandia and Aitape . On 19 April , he organised a large raid against Su , Dutch Timor , employing thirty @-@ five Mitchells , Beauforts and Beaufighters to destroy the town 's barracks and fuel dumps , the results earning him the personal congratulations of the Air Officer Commanding NWA , Air Vice Marshal " King " Cole , for his " splendid effort " . On the day of the Allied landings , 22 April , the Mitchells and Beaufighters made a daylight raid on Dili , Portuguese Timor . The ground assault met little opposition , credited in part to the air bombardment in the days leading up to it . In June – July , No. 79 Wing supported the Allied attack on <unk> . Eaton was recommended to be mentioned in despatches on 28 October 1944 for his " <unk> and distinguished service " in NWA ; this was promulgated in the London Gazette on 9 March 1945 . 
- <unk> his tour with No. 79 Wing , Eaton was appointed Air Officer Commanding Southern Area , Melbourne , in January 1945 . The German submarine U @-@ <unk> operated off southern Australia during the first months of 1945 , and the few combat units in Eaton 's command were heavily engaged in anti @-@ submarine patrols which sought to locate this and any other U @-@ boats in the area . The Air Officer Commanding RAAF Command , Air Vice Marshal Bill Bostock , considered the sporadic attacks to be partly " nuisance value " , designed to draw Allied resources away from the front line of the South West Pacific war . In April , Eaton complained to Bostock that intelligence from British Pacific Fleet concerning its ships ' movements eastwards out of Western Area was hours out of date by the time it was received at Southern Area Command , leading to RAAF aircraft missing their rendezvous and wasting valuable flying hours searching empty ocean . There had been no U @-@ boat strikes since February , and by June the naval authorities indicated that there was no pressing need for air cover except for the most important vessels . 
- 
- = = Post @-@ war career and legacy = = 
- 
- Eaton retired from the RAAF on 31 December 1945 . In recognition of his war service , he was appointed a Commander of the Order of Orange @-@ Nassau with Swords by the Dutch government on 17 January 1946 . The same month , he became Australian consul in Dili . He had seen an advertisement for the position and was the only <unk> with experience of the area . While based there , he accompanied the provincial governor on visits to townships damaged in Allied raids during the war , taking care to be <unk> about the part played by his own forces from No. 79 Wing . In July 1947 , Dutch forces launched a " police action " against territory held by the fledgling Indonesian Republic , which had been declared shortly after the end of the war . Following a ceasefire , the United Nations set up a commission , chaired by Eaton as Consul @-@ General , to monitor progress . Eaton and his fellow commissioners believed that the ceasefire was serving the Dutch as a cover for further penetration of republican enclaves . His requests to the Australian government for military observers led to deployment of the first peacekeeping force to the region ; the Australians were soon followed by British and US observers , and enabled Eaton to display a more realistic impression of the situation to the outside world . The Dutch administration strongly opposed the presence of UN forces and accused Eaton of " <unk> " , but the Australian government refused to recall him . Following the transfer of sovereignty in December 1949 , he became Australia 's first secretary and <unk> d <unk> to the Republic of the United States of Indonesia . In 1950 , he returned to Australia to serve with the Department of External Affairs in Canberra . After retiring from public service in 1951 , he and his wife farmed at <unk> , Victoria , and cultivated orchids . They later moved to <unk> , where Eaton was involved in promotional work . 
- Charles Eaton died in <unk> on 12 November 1979 . <unk> by his wife and two sons , he was <unk> . In accordance with his wishes , his ashes were scattered near Tennant Creek , site of his 1929 forced landing during the search for the Kookaburra , from an RAAF <unk> on 15 April 1981 . His name figures prominently in the Northern Territory , commemorated by Lake Eaton in Central Australia , Eaton Place in the Darwin suburb of <unk> , Charles Eaton Drive on the approach to Darwin International Airport , and the Charles Moth Eaton <unk> Bar in the Tennant Creek <unk> Hotel . He is also honoured with a display at the Northern Territory Parliament , and a National Trust memorial at Tennant Creek Airport . At the RAAF 's 2003 History Conference , Air Commodore Mark <unk> , recalling Eaton 's search @-@ and @-@ rescue missions between the wars , commented : " Today , we might think of Eaton perhaps as the pioneer of our contribution to assistance to the civil community — a tradition that continues today . Perhaps I might <unk> your memory to a more recent series of rescues no less hazardous for all concerned — the amazing location of missing <unk> <unk> Dubois , <unk> <unk> and Tony <unk> by our P @-@ 3s that guided the Navy to their eventual rescue . My observation is that such activities remain vital for our relevance in that we must remain connected , supportive and responsive to the wants and needs of the Australian community . " 
- 
- 
- = Tina Fey = 
- 
- Elizabeth <unk> " Tina " Fey ( / <unk> / ; born May 18 , 1970 ) is an American actress , comedian , writer , and producer . She is best known for her work on the NBC sketch comedy series Saturday Night Live ( 1998 @-@ 2006 ) , for her impression of former Alaska Governor and 2008 Vice @-@ Presidential candidate Sarah Palin , and for creating acclaimed series 30 Rock ( 2006 – 2013 ) and Unbreakable Kimmy Schmidt ( 2015 – present ) . She is also well known for appearing in films such as Mean Girls ( 2004 ) , Baby Mama ( 2008 ) , Date Night ( 2010 ) , <unk> Most Wanted ( 2014 ) , and Sisters ( 2015 ) . 
- Fey broke into comedy as a featured player in the Chicago @-@ based improvisational comedy group The Second City . She then joined SNL as a writer , later becoming head writer and a performer , known for her position as co @-@ anchor in the Weekend Update segment . In 2004 , she co @-@ starred in and wrote the screenplay for Mean Girls , which was adapted from the 2002 self @-@ help book Queen Bees and <unk> . After leaving SNL in 2006 , she created the television series 30 Rock for Broadway Video , a situation comedy loosely based on her experiences at SNL . In the series , Fey portrays the head writer of a fictional sketch comedy series . In 2008 , she starred in the comedy film Baby Mama , alongside former SNL co @-@ star Amy Poehler . Fey next appeared in the 2010 comedy film Date Night and the animated film Megamind . In 2015 , she created and produced the television series Unbreakable Kimmy Schmidt , originally for NBC and eventually for Netflix . Her recent films include Sisters and Whiskey <unk> <unk> . 
- Fey has received eight Emmy Awards , two Golden Globe Awards , five Screen Actors Guild Awards , and four Writers Guild of America Awards and was nominated for a Grammy Award for her autobiographical book <unk> , which topped The New York Times Best <unk> list for five weeks . In 2008 , the Associated Press gave Fey the AP Entertainer of the Year award for her satirical portrayal of Republican vice presidential candidate Sarah Palin in a guest appearance on SNL . In 2010 , Fey was awarded the Mark Twain Prize for American <unk> , becoming the youngest @-@ ever recipient of the award . On January 13 , 2013 , Fey hosted the 70th Golden Globe Awards with her long @-@ time friend and fellow comedian , Amy Poehler , to critical acclaim . The duo hosted again the following two years , generating the highest ratings for the annual ceremony in a decade and receiving similar acclaim . 
- 
- = = Early life = = 
- 
- Fey was born on May 18 , 1970 , in Upper Darby , Pennsylvania , a suburb of Philadelphia . Her mother , Zenobia " <unk> " ( née <unk> ) , is a <unk> employee ; her father , Donald Henry Fey ( died 2015 , age 82 ) , was a university grant proposal writer . She has a brother , Peter , who is eight years older . Fey 's mother , who was born in <unk> , Greece , is the daughter of Greek immigrants : <unk> <unk> , Fey 's maternal grandmother , left <unk> , <unk> , Greece on her own , arriving in the United States in February 1921 . 
- Fey 's father had English , German , and Northern Irish ancestry ; one of Fey 's paternal great @-@ great @-@ great @-@ great @-@ great @-@ <unk> was John Hewson ( 1744 – 1821 ) , a textile manufacturer who immigrated to America with the support of Benjamin Franklin , enabling Hewson to quickly open a <unk> factory in the Kensington neighborhood of Philadelphia , Pennsylvania . According to a genealogical DNA test arranged by the television series Finding Your Roots , Fey 's ancestry is 94 % European , 3 % Middle Eastern , and 3 % from the <unk> . 
- Fey was exposed to comedy early : 
- At age 11 , Fey read Joe Franklin 's Seventy Years of Great Film <unk> for a school project about comedy . She grew up watching Second City Television , and has cited Catherine O <unk> as a role model . 
- Fey attended <unk> @-@ <unk> Elementary School and Beverly Hills Middle School in Upper Darby . By middle school , she knew she was interested in comedy . Fey attended Upper Darby High School , where she was an honors student , a member of the choir , drama club , and tennis team , and co @-@ editor of the school 's newspaper , The <unk> . She also anonymously wrote the newspaper 's satirical column , The Colonel . Following her graduation in 1988 , Fey enrolled at the University of Virginia , where she studied play @-@ writing and acting and was awarded the <unk> Prize . She graduated in 1992 with a Bachelor of Arts degree in drama . 
- After college , she worked as a <unk> during the day at the <unk> YMCA and took classes at Second City at night . 
- 
- = = Career = = 
- 
- 
- = = = Saturday Night Live ( 1997 – 2006 ) = = = 
- 
- While performing shows with The Second City in 1997 , Fey submitted several scripts to NBC 's variety show Saturday Night Live ( SNL ) , at the request of its head writer Adam McKay , a former performer at Second City . She was hired as a writer for SNL following a meeting with SNL creator Lorne Michaels , and moved to New York from Chicago . Fey told The New Yorker , " I 'd had my eye on the show forever , the way other kids have their eye on Derek <unk> . " Originally , Fey " struggled " at SNL . Her first sketch to air starred Chris Farley in a <unk> <unk> Raphael satire . Fey went on to write a series of parodies , including one of ABC 's morning talk show The View . She co @-@ wrote the " Sully and Denise " sketches with Rachel Dratch , who plays one of the teens . 
- Fey was an extra in a 1998 episode , and after watching herself , decided to diet and lost 30 pounds . She told The New York Times , " I was a completely normal weight , but I was here in New York City , I had money and I couldn 't buy any clothes . After I lost weight , there was interest in putting me on camera . " In 1999 , McKay stepped down as head writer , which led Michaels to approach Fey for the position . She became SNL 's first female head writer that year . 
- In 2000 , Fey began performing in sketches , and she and Jimmy Fallon became co @-@ anchors of SNL 's Weekend Update segment . Fey said she did not ask to audition , but that Michaels approached her . Michaels explained that there was chemistry between Fey and Fallon , though the decision was " kind of risky " at the time . Her role in Weekend Update was well received by critics . Ken Tucker of Entertainment Weekly wrote : " ... Fey delivers such blow <unk> – poison filled jokes written in long , precisely <unk> sentences unprecedented in Update history – with such a bright , sunny countenance makes her all the more <unk> delightful . " Dennis Miller , a former cast member of SNL and anchor of Weekend Update , was pleased with Fey as one of the anchors for the segment : " ... Fey might be the best Weekend Update anchor who ever did it . She writes the funniest jokes " . Robert Bianco of USA Today , however , commented that he was " not <unk> " of the pairing . 
- In 2001 , Fey and the rest of the writing staff won a Writers Guild of America Award for SNL 's 25th anniversary special . The following year at the 2002 Emmy Awards ceremony , they won the Emmy for Outstanding Writing for a Variety , Music or Comedy Program . 
- When Fallon left the show in May 2004 , he was replaced on Weekend Update by Amy Poehler . It was the first time that two women co @-@ anchored Weekend Update . Fey revealed that she " hired " Poehler as her co @-@ host for the segment . The reception was positive , with Rachel <unk> of the Chicago Tribune noting that the pairing " has been a hilarious , pitch @-@ perfect success as they play @-@ off each other with quick one @-@ liners and deadpan delivery " . 
- The 2005 – 2006 season was her last ; she departed to develop 30 Rock for Broadway Video . At the time she left , the 117 episodes she co @-@ hosted made her SNL 's longest @-@ serving Weekend Update anchor , a mark that would later be passed by her replacement , Seth Meyers . In Rolling Stone Magazine 's February , 2015 appraisal of all 141 SNL cast members to date , Fey was ranked third in importance ( behind John <unk> and Eddie Murphy ) . They credited her with " salvaging ' Update ' from a decade @-@ long losing streak , " and " slapping SNL out of its late @-@ <unk> coma . " 
- 
- = = = 30 Rock ( 2006 – 2013 ) = = = 
- 
- In 2002 , Fey suggested a pilot episode for a situation comedy about a cable news network to NBC , which rejected it . The pilot was reworked to revolve around an SNL style series , and was accepted by NBC . She signed a contract with NBC in May 2003 , which allowed her to remain in her SNL head writer position at least through the 2004 – 2005 television season . As part of the contract , Fey was to develop a prime @-@ time project to be produced by Broadway Video and NBC Universal . The pilot , directed by Adam Bernstein , centered on Liz Lemon , the head writer of a variety show on NBC , and how she managed her relationships with the show 's volatile stars and the new head of the network . In October 2006 , the pilot aired on NBC as 30 Rock . Although the episode received generally favorable reviews , it finished third in its timeslot . 
- The network renewed the series for a second season , which began in October 2007 . The show 's third season premiered on October 30 , 2008 . The premiere episode drew 8 @.@ 5 million viewers , the highest ratings of the series . 
- In 2007 , Fey received an Emmy Award nomination for Outstanding Actress in a Comedy Series . The show itself won the 2007 Emmy for Outstanding Comedy Series ( and did so again for two subsequent years ) . In 2008 , she won the Golden Globe , Screen Actors Guild , and Emmy awards all in the category for Best Actress in a Comedy Series . The following year , Fey again won the Golden Globe and Screen Actors Guild Award in the same categories , and was nominated for an Emmy Award . In early 2010 , Fey received a Golden Globe nomination for Best Actress , and won the Screen Actors Guild Award for Best Lead Actress . 30 Rock was renewed for the 2010 – 2011 season in March 2010 . The series returned for the 2011 – 2012 season , though due to Fey 's pregnancy with her second child , the season premiere was delayed until <unk> . Fey 's performance on the show was inspired by Julia Louis @-@ Dreyfus , and later used Louis @-@ Dreyfus to play the stand @-@ in for the character of Liz Lemon in flashback scenes during the live episode of the fifth season . On May 11 , 2012 , it was announced that the show had been renewed for a seventh and final season , to premiere October 4 , 2012 , with 13 episodes . After receiving 13 Emmy Award nominations and two wins for this final season , 30 Rock ended its critically acclaimed run with 112 Emmy award nominations . It has been cited as one of the greatest TV series of all time and it is considered to have one of the greatest <unk> in television history . 
- 
- = = = Unbreakable Kimmy Schmidt = = = 
- 
- In 2015 , Fey created and produced the television comedy Unbreakable Kimmy Schmidt with fellow 30 Rock @-@ <unk> Robert Carlock . The series stars Ellie <unk> as the titular character who escapes from a <unk> cult and moves to New York . It also stars Fey 's former co @-@ star Jane Krakowski , as well as <unk> <unk> ( who had previously appeared in four 30 Rock episodes ) and Carol Kane . Although it was originally produced for NBC , it was eventually sold to Netflix and immediately renewed for a second season . The show premiered on March 6 , 2015 to critical acclaim . 
- On July 16 , 2015 , the series was nominated for seven Primetime Emmy Awards , including Outstanding Comedy Series . Fey herself was nominated both as the creator / executive producer of the series and for Outstanding Guest Actress in a Comedy Series for her guest performance as Marcia , a <unk> prosecutor in reference to Marcia Clark . 
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- = = = Feature films = = = 
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- In 2002 , Fey appeared in the surreal comedy Martin & <unk> . She made her debut as writer and co @-@ star of the 2004 teen comedy Mean Girls . Characters and behaviors in the movie are based on Fey 's high school life at Upper Darby High School and on the non @-@ fiction book Queen Bees and <unk> by <unk> Wiseman . The cast includes other past cast members of SNL including Tim Meadows , <unk> <unk> , and Amy Poehler . The film received favorable reviews , and was a box office success , grossing US $ 129 million worldwide . 
- In a 2004 interview , Fey expressed that she would like to write and direct movies . In 2006 , Fey worked on a movie script for Paramount Pictures , which was to feature <unk> Baron Cohen , by the name of Curly <unk> and Vic Thrill , based loosely on the true story of a <unk> rock musician . In 2007 , she was cast in the animated comedy film Aqua Teen <unk> Force <unk> Movie Film for Theaters as the Aqua <unk> ' mother , a giant <unk> . 
- She received her <unk> card after appearing in Artie <unk> 's Beer League released in 2006 , in which she was compelled to join for " ... a thousand dollars " . 
- Fey and former SNL <unk> Amy Poehler starred in the 2008 comedy Baby Mama . The movie was written and directed by Michael <unk> . The plot concerns Kate ( Fey ) , a business woman , who wants a child but , discovering she has only a million @-@ to @-@ one chance of getting pregnant , decides to find a surrogate : Angie ( Poehler ) , a white @-@ trash <unk> . Baby Mama received mixed reviews , but critics enjoyed Fey 's performance . Todd McCarthy of Variety wrote : " Fey is a delight to watch throughout . Able to convey Kate 's intentions and feelings through the simple looks and <unk> , she never <unk> her situation ; nor does her efficient , <unk> side become overbearing . " The movie grossed over US $ 64 million at the box office . 
- Fey 's projects after 2008 include a voice role in the English @-@ language version of the Japanese animated film <unk> . In 2009 , she appeared in The <unk> of <unk> , alongside Ricky Gervais , Jennifer Garner , Rob Lowe , and Christopher Guest . Her next film role was in Shawn Levy 's 2010 comedy Date Night , a feature that focuses on a married couple , played by Fey and Steve Carell , who go on a date ; however , the night goes awry for the two . Also in the same year , she voiced <unk> <unk> , a television reporter , in the DreamWorks animated film Megamind ( 2010 ) . With a total worldwide gross of US $ 321 million , Megamind is Fey 's most commercially successful picture to date . It earned US $ 173 million outside the U.S. and US $ 148 million domestically . 
- In 2013 , Fey starred alongside Paul <unk> in the romantic comedy @-@ drama film Admission , based on the Jean <unk> <unk> novel by the same name . The film was directed by Paul <unk> . Fey later starred in the 2014 comedy @-@ drama This Is Where I Leave You , helmed by Date Night director Shawn Levy . As was the case with Baby Mama , although both of these films received generally mixed reviews , Fey 's performances were well received by film critics . 
- In 2015 , it was announced Fey would be the narrator for the Disney Nature film Monkey Kingdom , which was released in theaters on April 17 , 2015 . She then re @-@ teamed with Poehler , starring in the 2015 comedy film Sisters as the title characters , and received positive reviews for her role . In 2016 , Fey starred in the biographical war comedy @-@ drama Whiskey <unk> <unk> , based on the memoir The <unk> Shuffle : Strange Days in Afghanistan and Pakistan , to positive reviews . 
- 
- = = = Subsequent SNL appearances = = = 
- 
- On February 23 , 2008 , Fey hosted the first episode of SNL after the 2007 – 2008 Writers Guild of America strike . For this appearance , she was nominated for an Emmy in the category of Individual Performance in a Variety or Music Program . Fey hosted SNL for a second time on April 10 , 2010 , and for her appearance she received an Emmy nomination for Outstanding Guest Actress in a Comedy Series . 
- From September to November 2008 , Fey made multiple guest appearances on SNL to perform a series of parodies of Republican vice @-@ presidential candidate Sarah Palin . On the 34th season premiere episode , aired September 13 , 2008 , Fey imitated Palin in a sketch , alongside Amy Poehler as Hillary Clinton . Their <unk> included Clinton <unk> Palin about her " Tina Fey glasses " . The sketch quickly became NBC 's most @-@ watched viral video ever , with 5 @.@ 7 million views by the following Wednesday . Fey reprised this role on the October 4 show , on the October 18 show where she was joined by the real Sarah Palin , and on the November 1 show , where she was joined by John McCain and his wife <unk> . The October 18 show had the best ratings of any SNL show since 1994 . The following year Fey won an Emmy in the category of Outstanding Guest Actress in a Comedy Series for her impersonation of Palin . Fey returned to SNL in April 2010 , and reprised her impression of Palin in one sketch titled the " Sarah Palin Network " . Fey once again did her impression of Palin when she hosted Saturday Night Live on May 8 , 2011 . 
- In December 2009 , Entertainment Weekly put her Palin impersonation on its end @-@ of @-@ the @-@ decade " best @-@ of " list , writing , " Fey 's <unk> spot @-@ on SNL impersonation of the <unk> <unk> ( and her ability to strike a balance between comedy and cruelty ) made for truly transcendent television . " Rolling Stone called her Palin impression " [ arguably ] the most brilliant move SNL ever made " . 
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- = = = Other work = = = 
- 
- In 1997 , Fey and other members of The Second City provided voices for the <unk> game Medieval Madness . 
- In 2000 , Fey partnered with fellow SNL cast member Rachel Dratch in the Off Broadway two @-@ woman show Dratch & Fey at the <unk> Citizens Brigade Theater in New York City . The production was well received by critics . Tim Townsend of The Wall Street Journal wrote that the fun part of watching Fey and Dratch perform was " seeing how comfortable they are with each other " . He concluded that the production " isn 't about two women being funny ... Dratch and Fey are just funny . Period . " One of the SNL sketches , " Sully and Denise " , originated at The Second City . 
- On August 13 , 2007 , Fey made a guest appearance in the Sesame Street episode " The <unk> " . She appeared as a guest judge on the November 25 , 2007 episode of the Food Network program Iron <unk> America . 
- Fey has appeared as <unk> Bell in Disney 's campaign " Year of a Million Dreams " . She has also done commercials for American Express and Garnier <unk> . 
- On April 5 , 2011 , Fey 's autobiography , <unk> , was released to a positive review from The New York Times . 
- In 2011 , Fey narrated The Secret Life of Girls , a two @-@ hour @-@ long radio documentary produced by The Kitchen Sisters . She introduced stories of women and girls from around the world , and also shared memories of her own girlhood and mother . 
- In 2012 , Fey made her rapping debut on the <unk> <unk> ( Donald <unk> ) <unk> <unk> . <unk> is a former writer on 30 Rock , on which he worked with Fey . Fey was also featured as herself in the <unk> episode " <unk> America " . 
- 
- = = <unk> and acting style = = 
- 
- Fey is known for her deadpan humor and delivery ; her " sardonic wit " has become a trademark of hers , upon which several critics have commented in their reviews of Fey 's work . According to Los Angeles Times critic Mary McNamara , Fey " project [ s ] both oblivious security and hyper @-@ alert insecurity with the same expression " in her performances , while The Chronicle 's Dillon Fernando wrote that the actress specializes in " <unk> , <unk> and ironic comedy " . On Fey 's comedic prowess , Saturday Night Live creator Lorne Michaels <unk> that his former employee " has a very clear take on things ... It always comes from a place of intelligence and there is just an edge to it . " Michaels concluded , " It 's not fearful . It 's strong and confident and you recognise the voice and most of the time you agree with it . " Writing for The Guardian , Christopher Goodwin believes that Fey " fashioned her comic persona around her glasses " , which she has worn since 1995 ; Fey joked that " <unk> make anyone look <unk> " . 
- <unk> <unk> to use herself as the butt of her own jokes , Fey is also well known for <unk> self @-@ deprecating humor , as demonstrated throughout her performance as Liz Lemon in 30 Rock . In an article ranking Fey 's six greatest jokes , David <unk> of The Guardian wrote that the performer 's work continues to feature her " trademark mix of <unk> , self @-@ deprecation and pop @-@ culture <unk> . " Fey 's self @-@ deprecating comedic style inspired Ashley <unk> of The Atlantic to recognize her as comedian <unk> <unk> 's successor because of their similar humor . Critics have been divided in their opinions and discussions of Fey 's use of self @-@ deprecating humor , and its effect on women as a female comic ; while blogger Kate Harding disapproved of Fey 's performance in 30 Rock because " I 'm torn between being sad that she apparently doesn 't see [ beauty ] in herself and being <unk> off that she 's reinforcing the idea that having brown hair , glasses , and a figure that 's maybe a size 2 instead of a 0 actually equals ugly " , Jessica G. of <unk> defended the actress , writing that Fey 's performance is " supposed to be parodying precisely the kinds of media that reinforce ideas that unconventional women are unworthy . " Writing that Harding misunderstood Fey 's intentions , the author concluded that her self @-@ deprecation " is precisely what makes her relatable " , elaborating that " [ women ] have many moments of self @-@ doubt , and seeing someone as successful as Tina Fey be self @-@ deprecating gives us all permission to be <unk> . " Sophie Caldecott of <unk> defended Fey 's <unk> and tendency to <unk> her own physical appearance : " She mocks her own appearance , sure , but she does so in a way that consistently shows up our culture for placing so much importance on how women look , as if that ’ s the most interesting thing about us ... Her comic persona on 30 Rock , Liz Lemon , can be laughed at for many things , but her career managerial style and ability is not one of them . " Caldecott concluded , " In reality , self @-@ deprecation is an art that <unk> everywhere <unk> in ... In fact , I defy you to find a good male comedian who isn ’ t a master of self @-@ deprecation . <unk> make fun of themselves for many reasons , mostly because it is the most readily accessible source of inspiration but also because it is the most generous one . " Observing that Fey 's material lacks " <unk> " , <unk> <unk> of the Hartford Courant wrote that Fey 's comedy " is not simply an iteration of self @-@ deprecating femininity passing itself off as humor . In itself , this <unk> the current generation of female <unk> from earlier generations of performers who were told , more or less , to use themselves not as a sounding board for ideas but as a punching bag for insults . " 
- As an actress , Fey has developed a reputation for portraying " the hilarious , self @-@ deprecating unmarried career woman " in most of her films to @-@ date . The Boston Globe 's <unk> Paige defended her limited filmography by writing that , unlike most film actors , Fey remains " realistic about her range as a leading lady and says she ’ s been deliberate about only taking on parts for which she actually seems suited . " Fey explained that she approaches each role asking herself , " Would I be plausible in this role , in this job ? " However , her role as Kate Ellis in 2015 's Sisters provided Fey with an opportunity to stray from playing the type @-@ A female characters for which she has become known . The New York Times film critic A. O. Scott wrote , " We ’ re used to seeing Ms. Fey ... as an anxious <unk> using her <unk> sarcasm as a weapon against both her own <unk> and the <unk> and train wrecks who surround her . This time , she gets to be the train wreck . " In 30 Rock , Fey 's comedic acting was heavily influenced by both physical and improvisational comedy while , as a writer , her " carefully written scripts " were often quirky and character @-@ driven . 
- 
- = = In the media = = 
- 
- In 2002 , Fey was ranked in the Hot 100 List at number 80 on Maxim magazine , which used photos taken earlier by Rolling Stone calling her " the thinking man 's sex symbol " . She was named one of People magazine 's 50 Most Beautiful People in 2003 , and one of People magazine 's 100 Most Beautiful People in 2007 , 2008 , and 2009 . In 2007 , Fey placed seventh on the Hot 100 List on <unk> She repeated the appearance the following year , being voted as number one on the list . 
- In 2001 , Entertainment Weekly named Fey as one of their Entertainers of the Year for her work on Weekend Update . In 2007 , she was named one of the magazine 's Entertainers of the Year , and placed number two in 2008 . In 2009 , Fey was named as Entertainment Weekly 's fifth individual in their 15 Entertainers of the 2000s list . In 2013 , Entertainment Weekly crowned Fey as " The Once and Future Queen " ( an allusion to The Once and Future King ) in their feature on " Women Who Run TV , " calling her " the funniest woman in the free world . " <unk> quoted Mindy <unk> as saying , " I always feel unoriginal bringing up Tina as my inspiration , but she 's everyone 's inspiration for a reason . " The column also quoted praise by <unk> <unk> and Lena Dunham . 
- The newspaper editors and broadcast producers of the Associated Press voted Fey the AP Entertainer of the Year as the performer who had the greatest impact on culture and entertainment in 2008 , citing her impression of Sarah Palin on SNL . She has appeared on Forbes ' annual Celebrity 100 list of the 100 most powerful celebrities in 2008 , 2009 , 2010 , 2011 , and 2012 at No. 99 , No. 86 , No. 90 , No. 92 , and No. 79 respectively . 
- In 2007 , the New York Post included Fey in New York 's 50 Most Powerful Women , ranking her at number 33 . Fey was among the Time 100 , a list of the 100 most influential people in the world , in 2007 and 2009 , as selected annually by Time magazine . Fey 's featured article for the 2009 list was written by 30 Rock co @-@ star , Alec Baldwin . She was selected by Barbara Walters as one of America 's 10 Most <unk> People of 2008 . 
- In September 2011 , Fey was ranked at the top of Forbes magazine 's list of the highest @-@ paid TV actresses . 
- In June 2010 , it was announced Fey would receive a star on the Hollywood Walk of Fame in 2011 . 
- In 2014 , Fey was recognized by Elle Magazine during The Women in Hollywood Awards , honoring women for their outstanding achievements in film , spanning all aspects of the motion picture industry , including acting , directing , and producing . 
- 
- = = Charity work = = 
- 
- Fey 's charity work includes support of <unk> <unk> , an organization that sponsors autism research . In April 2008 , she participated in Night of Too Many Stars , a comedy benefit show for autism education . 
- Fey is also a supporter of Mercy Corps , a global relief and development organization , in their campaign to end world hunger . Fey narrated a video for Mercy Corps 's Action Center in New York City , describing hunger as a symptom of many wider world problems . She also supports the Love Our Children USA organization , which fights violence against children , who named her among their Mothers Who Make a <unk> , in 2009 . She was the 2009 national spokesperson for the Light the Night Walk , which benefits the Leukemia & <unk> Society . 
- 
- = = Personal life = = 
- 
- In 1994 , two years after Fey joined Chicago 's Second City improvisational theatre troupe , she began dating Jeff Richmond , a piano player who later became Second City 's musical director and then a composer on 30 Rock . They married in a Greek Orthodox ceremony on June 3 , 2001 . They have two daughters : Alice Zenobia Richmond ( born September 10 , 2005 ) and Penelope Athena Richmond ( born August 10 , 2011 ) . In April 2009 , Fey and Richmond purchased a US $ 3 @.@ 4 million apartment on the Upper West Side in New York City . 
- Fey has a scar a few inches long on the left side of her chin and cheek , the cause of which remained unexplained to the public until a 2008 Vanity Fair profile by Maureen <unk> , and subsequently in her autobiographical book , where she revealed that " during the spring semester of kindergarten , I was <unk> in the face by a stranger in the <unk> behind my house " . 
- 
- = = Filmography = = 
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- 
- = = = Film = = = 
- 
- 
- = = = Television = = = 
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- = = = Video games = = = 
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- = = Awards and nominations = = 
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- 
- 
- = WASP @-@ 44 = 
- 
- WASP @-@ 44 is a G @-@ type star in constellation <unk> that has the Jupiter @-@ size planet WASP @-@ 44b in orbit . The star is slightly less massive and slightly smaller than the Sun ; it is also slightly cooler , but is more metal @-@ rich . The star was observed by SuperWASP , an organization in search of planets , starting in 2009 ; manual follow @-@ up observations used WASP @-@ 44 's spectrum and measurements of its radial velocity led to the discovery of the transiting planet WASP @-@ 44b . The planet and its star were presented along with WASP @-@ <unk> and WASP @-@ <unk> on May 17 , 2011 by a team of scientists testing the idea that Hot Jupiters tend to have circular orbits , an assumption that is made when the orbital <unk> of such planets are not well @-@ constrained . 
- 
- = = <unk> history = = 
- 
- WASP @-@ 44 was observed between July and November 2009 by the WASP @-@ South , a station of the SuperWASP planet @-@ searching program based at the South African Astronomical Observatory . Observations of the star revealed a periodic decrease in its brightness . WASP @-@ South , along with the SuperWASP @-@ North station at the <unk> de los <unk> Observatory on the Canary Islands , collected 15 @,@ <unk> photometric observations , allowing scientists to produce a more accurate light curve . Another set of observations yielded a 6 @,@ 000 point photometric data set , but the light curve was prepared late and was not considered in the discovery paper . 
- In 2010 , a European science team investigated the star using the <unk> spectrograph and collected seventeen spectra of WASP @-@ 44 . From the spectra , radial velocity measurements were <unk> . Analysis of collected <unk> data ruled out the possibility that the detected radial velocity was caused by the blended spectrum of a spectroscopic binary star , supporting the possibility that the body orbiting WASP @-@ 44 was indeed a planet , designated WASP @-@ 44b . 
- The <unk> <unk> Telescope at La <unk> Observatory in Chile was used to follow up on the discovery <unk> WASP @-@ 44 , searching for a point at which the planet <unk> , or crossed in front of , its host star . One transit was detected . 
- WASP @-@ 44 , its recently discovered planet , the planets orbiting WASP @-@ 45 and WASP @-@ 46 , and a discussion exploring the validity of the common assumption amongst scientists that closely orbiting Hot Jupiter planets have highly circular orbits unless proven otherwise , were reported in a single discovery paper that was published on May 17 , 2011 by the Royal Astronomical Society . The paper was submitted to the Monthly <unk> of the Royal Astronomical Society on May 16 , 2011 . 
- 
- = = Characteristics = = 
- 
- WASP @-@ 44 is a G @-@ type star ( the same class of star as the Sun ) that is located in the <unk> constellation . WASP @-@ 44 has a mass that is 0 @.@ 951 times that of the Sun . In terms of size , WASP @-@ 44 has a radius that is 0 @.@ <unk> times that of the Sun . WASP @-@ 44 has an effective temperature of <unk> K , cooler than the Sun . However , the star is metal @-@ rich with relation to the Sun . Its measured metallicity is [ Fe / H ] = 0 @.@ 06 , or 1 @.@ 148 times that the amount of iron found in the Sun . WASP @-@ 44 's <unk> ( outermost layer ) is not active . The star also does not <unk> at a high velocity . 
- The star has an apparent magnitude of 12 @.@ 9 . It cannot be seen from Earth with the naked eye . 
- 
- = = Planetary system = = 
- 
- There is one known planet in the orbit of WASP @-@ 44 : WASP @-@ 44b . The planet is a Hot Jupiter with a mass of 0 @.@ <unk> Jupiters . Its radius is 1 @.@ 14 times that of Jupiter . WASP @-@ 44b orbits its host star every 2 @.@ <unk> days at a distance 0 @.@ <unk> AU , approximately 3 @.@ 47 % the mean distance between the Earth and Sun . With an orbital inclination of <unk> , WASP @-@ 44b has an orbit that exists almost edge @-@ on to its host star with respect to Earth . <unk> @-@ 44b 's orbital <unk> is fit to 0 @.@ <unk> , indicating a mostly circular orbit . 
- 
- 
- = Elephanta Caves = 
- 
- Elephanta caves are a network of sculpted caves located on Elephanta Island , or Gharapuri ( literally " the city of caves " ) in Mumbai Harbour , 10 kilometres ( 6 @.@ 2 mi ) to the east of the city of Mumbai in the Indian state of Maharashtra . The island , located on an arm of the Arabian Sea , consists of two groups of caves — the first is a large group of five Hindu caves , the second , a smaller group of two Buddhist caves . The Hindu caves contain rock cut stone sculptures , representing the Shaiva Hindu sect , dedicated to the Lord Shiva . 
- The rock cut architecture of the caves has been dated to between the 5th and 8th centuries , although the identity of the original builders is still a subject of debate . The caves are hewn from solid basalt rock . All the caves were also originally painted in the past , but now only traces remain . 
- The main cave ( Cave 1 , or the Great Cave ) was a Hindu place of worship until Portuguese rule began in 1534 , after which the caves suffered severe damage . This cave was renovated in the 1970s after years of neglect , and was designated a UNESCO World Heritage Site in 1987 to preserve the artwork . It is currently maintained by the Archaeological Survey of India ( ASI ) . 
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- = = Geography = = 
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- Elephanta Island , or Gharapuri , is about 11 km ( 6 @.@ 8 mi ) east of the Apollo Bunder ( Bunder in Marathi means a " pier for embarkation and <unk> of passengers and goods " ) on the Mumbai Harbour and 10 km ( 6 @.@ 2 mi ) south of <unk> <unk> in <unk> . The island covers about 10 km2 ( 3 @.@ 9 sq mi ) at high tide and about 16 km2 ( 6 @.@ 2 sq mi ) at low tide . Gharapuri is small village on the south side of the island . The Elephanta Caves can be reached by a ferry from the Gateway of India , Mumbai , which has the nearest airport and train station . The cave is closed on Monday . 
- The island is 2 @.@ 4 km ( 1 @.@ 5 mi ) in length with two hills that rise to a height of about 150 m ( 490 ft ) . A deep ravine cuts through the heart of the island from north to south . On the west , the hill rises gently from the sea and stretches east across the ravine and rises gradually to the extreme east to a height of 173 m ( 568 ft ) . This hill is known as the Stupa hill . Forest growth with clusters of <unk> , <unk> , and <unk> trees cover the hills with scattered palm trees . Rice fields are seen in the valley . The fore shore is made up of sand and mud with mangrove bushes on the fringe . Landing <unk> sit near three small hamlets known as Set Bunder in the north @-@ west , <unk> Bunder in the northeast , and Gharapuri or Raj Bunder in the south . 
- The two hills of the island , the western and the eastern , have five rock @-@ cut caves in the western part and a brick stupa on the eastern hill on its top composed of two caves with a few rock @-@ cut cisterns . One of the caves on the eastern hill is unfinished . It is a protected island with a <unk> zone according to a <unk> issued in 1985 , which also includes " a prohibited area " that stretches 1 kilometre ( 0 @.@ 62 mi ) from the shoreline . 
- 
- = = History = = 
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- Since no inscriptions on any of the island have been discovered , the ancient history of the island is <unk> , at best . <unk> , the heroes of the Hindu epic Mahabharata , and <unk> , the demon devotee of Shiva , are both credited with building temples or cut caves to live . Local tradition holds that the caves are not man @-@ made . 
- The Elephanta caves are " of unknown date and attribution " . Art historians have dated the caves in the range of late 5th to late 8th century AD . Archaeological excavations have unearthed a few <unk> coins dated to 4th century AD . The known history is traced only to the defeat of <unk> rulers of Konkan by the <unk> Chalukyas emperor <unk> II ( <unk> – <unk> ) in a naval battle , in 635 AD . Elephanta was then called Puri or <unk> , and served as the capital of the Konkan Mauryas . Some historians attribute the caves to the Konkan Mauryas , dating them to the mid @-@ 6th century , though others refute this claim saying a relatively small kingdom like the Konkan Mauryas could not undertake " an almost superhuman excavation effort , " which was needed to <unk> the rock temples from solid rock and could not have the skilled labor to produce such " high quality " sculpture . 
- Some other historians attribute the construction to the Kalachuris ( late 5th to 6th century ) , who may have had a feudal relationship with the Konkan Mauryas . In an era where polytheism was prevalent , the Elephanta main cave <unk> the monotheism of the <unk> <unk> sect , a sect to which Kalachuris as well as Konkan Mauryas belonged . 
- The Chalukyas , who defeated the Kalachuris as well as the Konkan Mauryas , are also believed by some to be creators of the main cave , in the mid @-@ 7th century . The Rashtrakutas are the last claimants to the creation of the main cave , <unk> to the early 7th to late 8th century . The Elephanta Shiva cave resembles in some aspects the 8th @-@ century <unk> rock @-@ temple Kailash at <unk> . The Trimurti of Elephanta showing the three faces of Shiva is akin to the Trimurti of Brahma , Vishnu and Mahesh ( Shiva ) , which was the royal insignia of the Rashtrakutas . The Nataraja and Ardhanarishvara sculptures are also attributed to the Rashtrakutas . 
- Later , Elephanta was ruled by another Chalukyan dynasty , and then by Gujarat Sultanate , who surrendered it to the Portuguese in 1534 . By then , Elephanta was called Gharapuri , which denotes a hill settlement . The name is still used in the local Marathi language . The Portuguese named the island " Elephanta Island " in honour of a huge rock @-@ cut black stone statue of an elephant that was then installed on a mound , a short distance east of Gharapuri village . The elephant now sits in the <unk> <unk> zoo in Mumbai . 
- Portuguese rule saw a decline in the Hindu population on the island and the abandonment of the Shiva cave ( main cave ) as a regular Hindu place of worship , though worship on <unk> , the festival of Shiva , continued and still does . The Portuguese did considerable damage to the sanctuaries . Portuguese soldiers used the reliefs of Shiva in the main cave for target practice , <unk> only the Trimurti sculpture . They also removed an inscription related to the creation of the caves . While some historians solely blame the Portuguese for the destruction of the caves , others also cite water @-@ logging and dripping <unk> as additional damaging factors . The Portuguese left in 1661 as per the marriage treaty of Charles II of England and Catherine of <unk> , daughter of King John IV of Portugal . This marriage shifted possession of the islands to the British Empire , as part of Catherine 's dowry to Charles . 
- Though the main cave was restored in the 1970s , other caves , including three consisting of important sculptures , are still badly damaged . The caves were designated a UNESCO World Heritage Site in 1987 as per the cultural criteria of UNESCO : the caves " represent a masterpiece of human creative genius " and " bear a unique or at least exceptional testimony to a cultural tradition or to a <unk> which is living or which has disappeared " . 
- 
- = = Overview = = 
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- The island has two groups of caves in the rock @-@ cut architectural style . The caves are hewn from solid basalt rock . All caves were painted in the past , but only traces remain . The larger group of caves , which consists of five caves on the western hill of the island , is well known for its Hindu sculptures . The primary cave , numbered as Cave 1 , is about 1 @.@ 5 km ( 0 @.@ 93 mi ) up a hillside , facing the ocean . It is a rock @-@ cut temple complex that covers an area of 5 @,@ 600 m2 ( 60 @,@ 000 sq ft ) , and consists of a main chamber , two lateral chambers , courtyards , and subsidiary shrines . It is 39 metres ( 128 ft ) deep from the front entrance to the back . The temple complex is the abode of Shiva , depicted in widely celebrated carvings which reveal his several forms and acts . 
- On the eastern part of the island , on the Stupa Hill , there is a small group of caves that house Buddhist monuments . This hill is named after the religious Stupa monument that they display . One of the two caves is incomplete , while the other contains a Stupa made in brick . 
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- = = Main cave = = 
- 
- The main cave , also called the Shiva cave , Cave 1 , or the Great Cave , is 27 metres ( 89 ft ) square in plan with a hall ( <unk> ) . At the entrance are four doors , with three open <unk> and an aisle at the back . <unk> , six in each row , divide the hall into a series of smaller chambers . The roof of the hall has concealed beams supported by stone columns joined together by capitals . The cave entrance is aligned with the north – south axis , unusual for a Shiva shrine ( normally east – west ) . The northern entrance to the cave , which has 1 @,@ 000 steep steps , is flanked by two panels of Shiva dated to the Gupta period . The left panel depicts Yogishvara ( The Lord of <unk> ) and the right shows Nataraja ( Shiva as the Lord of Dance ) . The central Shiva shrine ( see 16 in plan below ) is a free @-@ standing square cell with four entrances , located in the right section of the main hall . Smaller shrines are located at the east and west ends of the caves . The eastern sanctuary serves as a ceremonial entrance . 
- Each wall has large carvings of Shiva , each more than 5 metres ( 16 ft ) in height . The central Shiva relief Trimurti is located on the south wall and is flanked by <unk> ( a half @-@ man , half @-@ woman representation of Shiva ) on its left and Gangadhara to its right , which denotes the River Ganges ' descent from Shiva 's matted locks . Other carvings related to the legend of Shiva are also seen in the main hall at strategic locations in exclusive <unk> ; these include <unk> , depicting Shiva 's marriage to the goddess Parvati , <unk> or <unk> , the slaying of the demon Andhaka by Shiva , Shiva @-@ Parvati on Mount Kailash ( the abode of Shiva ) , and <unk> , depicting the demon @-@ king Ravana shaking Kailash . 
- The main cave blends Chalukyan architectural features such as massive figures of the divinities , guardians , and square pillars with custom capitals with Gupta artistic characteristics , like the depiction of mountains and clouds and female hairstyles . 
- <unk> : 
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- = = = Shiva @-@ Parvati on Kailash and Ravana lifting Kailash = = = 
- 
- The carving on the south wall to the east of the portico depicts Shiva and Parvati seated on their abode Mount Kailash . The four @-@ armed Shiva is seen with a crown and a disc behind it ( all damaged ) , the sacred thread across his chest , and a dressing gown covering up to the knee . Parvati , dressed in her finery with her hair falling to the front , looks away . Behind her at the right is a woman attendant holding the child , identified with her son Kartikeya , the war @-@ god . Many male and female attendants are seen behind the main figures . Shiva 's attendant , the skeleton @-@ like Bhringi , is seated at his feet . Other figures , not distinct , depict , among others , a royal @-@ looking tall person , ascetics , a fat figure , a dwarf , a bull ( the mount of Shiva ) , features of a Garuda , and two monkeys . The scenic beauty of the mountain is sculpted with the sky background amidst heavenly beings showering flowers on Shiva @-@ Parvati . This scene is interpreted as a gambling scene , where Parvati is angry as Shiva cheats in a game of dice . 
- The carved panel facing this one is a two @-@ level depiction of Ravana lifting Kailash . The upper scene is Mount Kailash , where Shiva and Parvati are seated . The eight @-@ armed , three @-@ eyed Shiva wears headgear with a crescent and disc behind it . Most of his arms are broken , two of them resting on attendants ' heads . The Parvati figure , seated facing Shiva , remains only as a trunk . The panel is flanked by door keepers . <unk> of Shiva are also seen in the relief but mostly in a damaged state . Bhringi is seated near Shiva 's feet and to his left is the elephant @-@ headed son of Shiva , Ganesha . In this ensemble , the ten @-@ headed demon @-@ king Ravana is seen , with only one head left unscathed , and out of his twenty arms , only a few are discernible . Around Ravana are several demons . Numerous figures are seen above Shiva : the god Vishnu , riding his mount Garuda , to his left ; a skeleton @-@ figure ; and in a recess , Parvati 's mount , a tiger is depicted . 
- A legend relates to both these panels . Once , Parvati was annoyed with Shiva . At this moment , Ravana , who was passing by Mount Kailash , found it as an obstruction to his movement . Upset , Ravana shook it vigorously and as a result , Parvati got scared and <unk> Shiva . Enraged by Ravana 's arrogance , Shiva stamped down on Ravana , who sang praises of Shiva to free him of his misery and turned into an ardent devotee of Shiva . Another version states that Shiva was pleased with Ravana for restoring Parvati 's <unk> and blessed him . 
- 
- = = = Trimurti , Gangadhara and Ardhanarishvara = = = 
- 
- Described as a " masterpiece of Gupta @-@ Chalukyan art " , the most important sculpture in the caves is the Trimurti , carved in relief at the back of the cave facing the entrance , on the north @-@ south axis . It is also known as Trimurti <unk> and <unk> . The image , 6 m ( 20 ft ) in height , depicts a three @-@ headed Shiva , representing <unk> Shiva . The three heads are said to represent three essential aspects of Shiva : creation , protection , and destruction . The right half @-@ face ( west face ) shows him as a young person with sensuous lips , <unk> life and its vitality . In his hand he holds an object resembling a <unk> , depicting the promise of life and creativity . This face is closest to that of Brahma , the creator or <unk> or <unk> , the feminine side of Shiva and creator of joy and beauty . The left half @-@ face ( east face ) is that of a <unk> young man , displaying anger . This is Shiva as the terrifying <unk> or Bhairava , the one whose anger can <unk> the entire world in flames , leaving only ashes behind . This is also known as <unk> @-@ Shiva , the Destroyer . The central face , benign and <unk> , resembles the <unk> Vishnu . This is <unk> , " master of positive and negative principles of existence and <unk> of their harmony " or Shiva as the <unk> <unk> in deep meditation praying for the preservation of humanity . The aspects <unk> and <unk> ( not carved ) faces are considered to be at the back and top of the sculpture . The Trimurti sculpture , with the Gateway of India in the background , has been adopted as the logo of the Maharashtra Tourism Department ( <unk> ) . 
- The Gangadhara image to the right of the Trimurti is an ensemble of divinities assembled around the central figures of Shiva and Parvati , the former bearing the River Ganges as she descends from heaven . The carving is 4 m ( 13 ft ) wide and 5 @.@ 207 m ( 17 @.@ 08 ft ) high . The image is highly damaged , particularly the lower half of Shiva seen seated with Parvati , who is shown with four arms , two of which are broken . From the crown , a cup with a triple @-@ headed female figure ( with broken arms ) , representing the three sacred rivers Ganges , <unk> , and <unk> , is depicted . Shiva is sculpted and <unk> with ornaments . The arms hold a <unk> serpent whose hood is seen above his left shoulder . Another hand ( partly broken ) gives the semblance of Shiva hugging Parvati , with a head of matted hair . There is a small snake on the right hand and a tortoise close to the neck , with a bundle tied to the back . An ornamented <unk> covers his lower torso , below the waist . Parvati is carved to the left of Shiva with a <unk> hair dress , fully <unk> with ornaments and jewellery , also fully draped , with her right hand touching the head of a female attendant who carries Parvati 's dress case . The gods Brahma and Indra , with their mystic <unk> and mounts , are shown to the right of Shiva ; Vishnu , riding his mount Garuda , is shown to the left of Parvati . Many other details are defaced but a kneeling figure in the front is inferred to be the king who ordered the image to be carved . There are many divinities and attendant females at the back . The whole setting is under the sky and cloud scenes , with men and women , all dressed , shown showering flowers on the deities . 
- In the chamber to the east of the Trimurti is the four @-@ armed Ardhanarishvara carving . This image , which is 5 @.@ 11 m ( 16 @.@ 8 ft ) in height , has a headdress ( double @-@ folded ) with two <unk> draped towards the female head ( Parvati ) and the right side ( Shiva ) depicting curled hair and a crescent . The female figure has all the ornamentation ( broad armlets and long bracelets , a large ring in the ear , jewelled rings on the fingers ) but the right male figure has <unk> hair , armlets and <unk> . One of his hands rests on Nandi ’ s left horn , Shiva 's mount , which is fairly well preserved . The pair of hands at the back is also bejewelled ; the right hand of the male holds a serpent , while the left hand of the female holds a mirror . The front left hand is broken but conjectured as holding the robe of the goddess . The central figure is surrounded by divinities . 
- 
- = = = Shiva slaying Andhaka and Wedding of Shiva = = = 
- 
- The engraved panel in the north end of the aisle is considered to be a unique sculpture , and shows Bhairava , or <unk> , a <unk> form of Shiva . In the carved panel Shiva 's consort is seen sitting next to him , looking <unk> . A female attendant is next to her . The central figure , which is much ruined below the waist , is 3 @.@ 5 m ( 11 ft ) high and posed as if running . His headgear has a <unk> on the back , a skull and cobra over the forehead , and the crescent high on the right . His facial expression is of intense anger discerned from his <unk> brow , swollen eyes , and <unk> . The legs and five of the eight arms are broken , attributed to Portuguese vandalism . The smaller broken image Andhaka is seen below Bhairava 's image . It is interpreted that Shiva is <unk> him with the front right hand , as conjectured by the spear seen hanging without any hold . Also seen is the back hand lifted up and holding an elephant 's skin as a cover ; the elephant 's head , carved <unk> , and trunk are seen hanging from the left hand . The second left hand depicts a snake coiled round it . The hand holds a bowl to collect the blood dripping from the slain Andhaka . Furthermore , pieces of a male and two female forms , figures of two ascetics , a small figure in front , a female figure , and two dwarfs are also seen in the carved panel . An unusual sculpture seen above the head of the main figure of Shiva is of a " very wide bottle with a curved groove in the middle of it " , which can interpreted variously as : the <unk> or the <unk> or a Shiva shrine . 
- The niche image carved on the south wall is an ensemble of divinities assembled around the central figures of Shiva and Parvati shown getting married ( Kalyanasundara icon ) . Parvati is seen standing to Shiva 's right , the ordained place for a Hindu bride at the wedding . The carvings are substantially damaged ; only one of Shiva 's four hands is fully seen and the right leg is missing . Shiva has a headdress with a shining disc attached to it . His garments are well @-@ draped , and well @-@ tied at the waist . The sacred thread is seen across his chest . Parvati is carved as a perfect figure with <unk> hair dress , well adorned with jewellery and is draped tightly to display depressions below the waist only . She is seen with a coy expression and is led by her father who has his right hand on her shoulder . Even though both her hands are damaged , it is inferred that her left hand <unk> Shiva 's right hand as a mark of holy alliance . Brahma is sitting as the officiating priest for the marriage . Vishnu is witness to the marriage . Mena , the mother of Parvati , is seen standing next to Vishnu . The moon @-@ god Chandra , seen with a wig and a crescent , is standing behind Parvati holding a circular pot with nectar for the marriage ceremony . Just above the main images , a galaxy of divinities , bearded sages , <unk> ( nymphs ) , <unk> , <unk> , <unk> , Bhringi , and other male and female attendants are seen as witness to the marriage ceremony showering flowers on the divine couple . 
- 
- = = = Yogishvara and Nataraja = = = 
- 
- The panel to the east of the north portico is Shiva in a <unk> position called <unk> , <unk> , <unk> and <unk> . <unk> a Buddha , Shiva is in a dilapidated condition with only two broken arms . Shiva is seated in <unk> <unk> posture ( cross legged ) on a lotus carried by two <unk> . His crown is carved with details adorned by a crescent , a round frill at the back , and hair <unk> dropping on either side of the shoulders . His face is calm in <unk> , his eyes half @-@ closed . This represents Shiva in penance sitting amidst the <unk> mountains after the death of his first wife Sati , who was later reborn as Parvati . He is surrounded by divinities in the sky and attendants below . Also seen is a plantain with three leaves already open and one opening , as well as a <unk> blossom . These are flanked by two attendants . Other figures discerned from a study of the broken images are : Vishnu riding Garuda on a plantain leaf ; the Sun @-@ god Surya riding a fully <unk> horse ( head missing ) ; a saint with a rosary ; two female figures in the sky draped up to their thighs ; a faceless figure of the moon with a water container ; three identical figures of a male flanked by two females ; the skeleton of a sage ; Brahma ( without one arm ) riding a swan ; and Indra without his mount ( elephant missing ) . 
- The panel carving in the west niche opposite Yogishvara depicts Shiva as Nataraja performing the <unk> ( cosmic dance ) . The niche is 4 m ( 13 ft ) wide and 3 @.@ 4 m ( 11 ft ) high and set low on the wall . He wears well @-@ decorated headgear . The Shiva image displays a dance pose and had ten arms , but the first right and third left hands are missing . The remaining first right arm is held across the breast and touches the left side , the second right hand that is seen damaged with an out @-@ <unk> pose is broken at the elbow . The third arm is damaged at the elbow , and the fourth is also broken but inferred to have held a <unk> ( skull @-@ club ) . The left arms , seen hanging , are damaged near the wrists . The third hand is bent ( but broken ) towards Parvati standing on the side and the fourth hand is raised up . The right thigh ( broken ) is lifted up , and the left leg is not seen at all , the elaborate armlets are well @-@ preserved and a skirt round the waist is tied by a ribbon . A tall figurine of Parvati stands to the left of Shiva , which is also seen partly broken but well bejewelled . An airborne female figure is seen behind Parvati . Other figures seen in the relief are : Vishnu riding a Garuda ; Indra riding his elephant ; the elephant @-@ headed Ganesha ; Kartikeya ; <unk> ; sages and attendants . 
- 
- = = = Main cave shrine = = = 
- 
- The central shrine is a free @-@ standing square cell , with entrances on each of its sides . Each door is flanked by two <unk> ( gate keepers ) . The Linga , the symbol of Shiva in union with the <unk> , and the symbol of Parvati together symbolise the supreme unity that is deified by the shrine . The Linga is set on a raised platform above the floor of the shrine by 1 @.@ 8 m ( 5 ft 11 in ) . Six steps lead to this level from the floor level . The height of the eight <unk> varies from 4 @.@ 521 – 4 @.@ 623 m ( 14 @.@ 83 – 15 @.@ 17 ft ) . All are in a damaged condition except those at the southern door to the shrine . The southern gate statue has many unusual features – unusual headgear ; a large skull above the forehead ; lips parted with protruding teeth ; statues adorned with a single bead necklace , <unk> , plain twisted armlets and thick <unk> ; a <unk> right shoulder ; a globe held at <unk> level ; the robe is held at the right thigh by the left hand , and the legs are <unk> . 
- 
- = = = East wing = = = 
- 
- Several courtyards to the east and west of the main cave are blocked , though there is a 17 m ( 56 ft ) <unk> courtyard that is accessible by entering the eastern part and climbing nine steps . A temple on the southern wall of the court depicts a well @-@ preserved <unk> . The circular pedestal seen in the courtyard in front of the Shiva 's shrine near the east end , in the open area , is said to be the seat of Nandi , Shiva 's mount . 
- On each side of the steps leading to the temple @-@ cave portico is a winged lion , or <unk> , each seated with a raised <unk> . The portico has chambers at each end and a Linga @-@ shrine at the back . Five low steps and a threshold lead into the central Linga @-@ shrine which is 4 @.@ 2 m ( 14 ft ) wide and 5 m ( 16 ft ) deep and has a <unk> path ( <unk> @-@ path ) around it . At the back of the portico , near the east end , is a gigantic statue of a four @-@ armed <unk> with two attendant demons . At the north end is a standing figure holding a trident . His left hand rests on a defaced demon @-@ figure . The west wall depicts the <unk> @-@ <unk> ( eight mother goddesses ) , flanked by Kartikeya and Ganesha , the sons of Shiva . Some of <unk> are depicted with children , but all of them are shown by their respective mounts ( bull , swan , <unk> , a Garuda , etc . ) which identify them . At the east end of the portico is another chapel with a plain interior and sunken floor . Water <unk> in this chapel . 
- 
- = = = West wing = = = 
- 
- The west wing , entered through the main cave , is in a semi @-@ ruined state . It has a small chapel and a cistern enclosed within the <unk> cave , which is believed to be Buddhist . Another shrine to the west of the courtyard , with a portico , has carvings of Shiva in a <unk> pose seated on a lotus carried by “ two fat , heavy , <unk> figures ” . This carving also depicts a three @-@ faced bearded <unk> and several other figurines . Entering through the back door of the portico is a cave enshrined with a <unk> Shiva Linga erected over roughly hewn <unk> . At the door entrance on both flanks , statues of <unk> standing over demons and two fat , poised figures are seen . On the southern side of the door is an ensemble of a number of statues . <unk> among these is the Shiva carving , which is depicted with six arms and the third eye in the forehead . Though in a partly ruined state , the carving shows Shiva with an ornamented crown fixed with a crescent , seen carrying a cobra in the left hand , a club in another hand , and discerned to be in a dancing pose . Next to this image are a figure under a plantain tree and a Shiva image ( Yogishvara ) seated on a lotus . Also seen in the panel are a male figure riding a bull with a bell <unk> to its neck , a female figure and another carving to left of Shiva , a female figure with a jewel on her forehead with <unk> looped headdress , Indra riding an elephant , Vishnu with four arms , holding a discus in one of his left hands and riding on Garuda flanked by a small flying figure , and a male figure with crescent in his hair . 
- 
- = = Other notable caves = = 
- 
- To the south @-@ east of the Great Cave is the second excavation , which faces east @-@ northeast . It includes a chapel at the north end . The front of this cave is completely destroyed ; only fragments of some semi @-@ columns remain . The interior has suffered water damage . The portico is 26 m ( 85 ft ) long and 11 m ( 36 ft ) deep . The chapel is supported by eight eight @-@ cornered columns and two <unk> @-@ columns and is irregular in shape . At the back of the portico are three chambers ; the central one has an altar and a water channel ( <unk> ) , though the Linga is lost . The shrine door has some traces of sculpture ( a boy , a fat figure , alligators on the frieze , and broken animal figures at the head of a <unk> ) . The door @-@ keepers of the shrine are now in fragments . 
- A little to the south of the last cave is another cave in worse condition , with water damage . It is a portico in which each end probably had a chapel or room with pillars in front . Two of them have cells at the back . The central door at the back of the portico leads to a damaged shrine . The shrine door has door @-@ keepers at each side , leaning on dwarfs with flying figures over the head , with door @-@ keepers and demons on the <unk> and <unk> . The shrine is a plain room 6 m ( 20 ft ) deep by 5 @.@ 7 m ( 19 ft ) wide with a low altar , holding a Linga . South of this cave is a cavern that may be used as a cistern . 
- Above these caves is a tiger sculpture , which was worshipped as the tiger goddess <unk> . This sculpture may be a guardian of the north entrance of Cave 1 . A Linga is also found near a small pond at top of the hill . <unk> depicting a stone with a sun and a moon and a mother <unk> a child ( now moved ) were also found nearby . 
- Across the top of the ravine from Cave 1 is large hall known as <unk> 's Temple ( cave ) . The portico has four pillars and two pilasters . The hall has 3 chambers at the back , the central one a shrine and the rest for priests ( both are plain rooms ) . The door of the central shrine has pilasters and a frieze , with the threshold having lion figures at the end . The shrine has an altar , a water channel , and hole in the centre , in which a statue of Parvati may have been worshipped . A 17th @-@ century record states that " this cave [ has ] a beautiful gate with a porch of exquisitely wrought marble " and two idols , one of goddess <unk> <unk> and a head being in a large square seat . 
- Passing along the face of the eastern hill to the north of <unk> 's cave is a small Hindu excavation with a <unk> , which was probably to be three cells , but was abandoned following the discovery of a flaw in the rock . Towards the east of hill is a dry pond , with large artificial boulders and Buddhist cisterns along its banks . At the end of the north spur of the main hill is a mound that resembles a Buddhist stupa . 
- 
- = = Preservation = = 
- 
- The threats to Elephanta Caves have been identified as the following : developmental pressures ( mainly due to its location within the Mumbai harbour ) , <unk> pressure due to growth of population of the communities residing on the island , industrial growth of the port facilities close to the island , no risk preparedness plan to address natural <unk> such as earthquake , cyclones and terrorist attacks , <unk> tourism and tourist facilities on the island , and poor management of the heritage monument . 
- Preservation of Elephanta Island as a whole with its monuments has been ensured both through legislation and by physical restoration of the caves and its sculptures . The basic <unk> enacted are : The Ancient Monuments and Archaeological Sites and Remains Act of 1958 and Rules ( 1959 ) ; The Elephanta Island ( Protected Monument ) Rules of 1957 , which prohibits mining , quarrying , blasting , excavation and other operations near the monument ; the Antiquities and Art <unk> Act promulgated in 1972 with its Rules promulgated in 1973 ; a <unk> issued in 1985 declaring the entire island and a 1 @-@ kilometre ( 0 @.@ 62 mi ) area from the shore as " a prohibited area " ; a series of Maharashtra State Government environmental acts protecting the site ; the 1966 Regional and Town Planning Act ; and the 1995 Heritage Regulations for Greater Bombay . 
- The Archaeological Survey of India ( ASI ) , <unk> Circle , on the basis of the above legislation and rules , maintain and manage the monuments . The physical measures undertaken for conservation cover include <unk> of the rock face , construction of supports to the cave structures where pillars have collapsed , and consolidation of cave floors and construction of a parapet wall surrounding the site . In addition , visitor facilities at the site have been upgraded ( such as toilet facilities , <unk> construction , pathways , and a flight of steps from the jetty to the caves ) . An on @-@ site museum has been established and a conservation plan has been put in place . Overall , conservation of the property is stated to be good . The site receives approximately 25 @,@ 000 visitors per month . Public information <unk> are also available at the venue of the monuments . During the World Heritage Day on 18 April and World Heritage Week between 19 and 25 November there are special events held at the caves . Another popular event organised is an annual traditional dance festival that attracts many visitors . 
- After declaring the caves a World Heritage Site , UNESCO granted $ 100 @,@ 000 to document the site 's history and draw up a site plan . A part of the grant was used for conservation of the caves . Based on assessments by UNESCO , management plans include : better communication and collaboration between the ASI , on @-@ site staff , and other responsible government departments ; improved public information and awareness programs ; monitoring environmental impact of tourists on the cave and island environment ; greater attention to the maintenance of the rocks to address water <unk> into the caves ; and daily monitoring of both structural and chemical conservation measures . 
- The Indian National Trust for Art and Cultural Heritage ( <unk> ) is also involved with the Archaeological Survey of India in improving the local conditions at the cave site . A book has been published jointly by UNESCO , <unk> and the Government of India which presents a comprehensive site plan for restoration and a brief history of each sculpture constructed inside the caves . 
- 
- 
- = Devin Townsend = 
- 
- Devin <unk> Townsend ( born May 5 , 1972 ) is a Canadian musician , songwriter and record producer . He was the founder , songwriter , vocalist , and guitarist in extreme metal band Strapping Young Lad from 1994 to 2007 and has an extensive career as a solo artist . 
- After performing in a number of heavy metal bands in high school , Townsend was discovered by a record label in 1993 and was asked to perform lead vocals on Steve Vai 's album Sex & Religion . After recording and touring with Vai , Townsend was discouraged by what he found in the music industry , and vented his anger on the solo album Heavy as a Really Heavy Thing released under the pseudonym Strapping Young Lad . He soon assembled a band under the name , and released the critically acclaimed City in 1997 . Since then , he has released three more studio albums with Strapping Young Lad , along with solo material released under his own independent record label , <unk> Records . 
- Townsend 's solo albums , a diverse mix of hard rock , progressive metal , ambient , and new @-@ age , have featured a varying lineup of supporting musicians . In 2002 he formed the Devin Townsend Band , a dedicated lineup which recorded and toured for two of his solo releases . In 2007 , he disbanded both Strapping Young Lad and the Devin Townsend Band , taking a break from touring to spend more time with his family . After a two @-@ year hiatus , he began recording again , and soon announced the formation of the Devin Townsend Project . The project began with a series of four albums , released from 2009 to 2011 , each written in a different style , and Townsend continues to record and tour under the new moniker . 
- Across all his bands and projects , Townsend has released twenty @-@ three studio albums and three live albums . Townsend 's trademark production style , featuring a heavily <unk> wall of sound , has been compared to the styles of Phil Spector and Frank Zappa . His versatile vocal delivery ranges from screaming to an opera @-@ esque singing , and his songwriting is similarly diverse . Townsend 's musical style is rooted in metal , and his albums are written to express different aspects of his personality . 
- 
- = = Biography = = 
- 
- 
- = = = Early musical career ( 1972 – 1994 ) = = = 
- 
- Devin Townsend was born in New Westminster , British Columbia , on May 5 , 1972 . Townsend picked up the banjo when he was five , and began playing guitar when he was 12 . As an early teenager he befriended Brian " Beav " Waddell , who would later play guitars as part of the Devin Townsend Band and bass on the Devin Townsend Project . He participated in several metal bands while he was in high school , and founded Grey Skies at the age of 19 . Around the same time he joined a popular local group called <unk> Thought , replacing <unk> Simon on guitar and playing alongside bassist Byron Stroud , both of whom would later become members of Townsend 's flagship band , Strapping Young Lad . In 1993 , Townsend began writing material under the name Noisescapes , a project he later described as " just as violent as Strapping Young Lad " . 
- Townsend recorded a Noisescapes demo and sent copies to various record labels . <unk> Records responded to Townsend with a record deal and Townsend began work on what was to be the first Noisescapes album , Promise . Shortly afterward , the label introduced him to musician Steve Vai . Impressed with Townsend 's vocal work , Vai offered him the role of the lead vocalist on his new album Sex and Religion . After recording Sex and Religion , Townsend accompanied Vai on a world tour in support of the album . Townsend soon landed a second touring gig , this time with the opening band of Vai 's tour , the Wildhearts . He played live with the band throughout half of 1994 in Europe , and appeared as a guest musician on their single <unk> . <unk> , the band 's frontman , remained close friends with Townsend , later co @-@ writing several songs on Infinity and the <unk> + 4 <unk> EP . 
- While on tour with the Wildhearts , Townsend formed a short @-@ lived thrash metal project with Metallica 's then @-@ bassist Jason Newsted . The band , known as IR8 , featured Newsted on vocals and bass , Townsend on guitar , and Tom Hunting of Exodus on drums . The group recorded a few songs together , although Townsend says that they never intended to go further than that . " People heard about it and thought we wanted to put out a CD , which is absolutely not true , " he explains . " People took this project way too seriously . " A demo tape was put together , but the material was not released until 2002 , when Newsted published the IR8 vs. <unk> compilation . 
- Though Townsend was proud of what he had accomplished so early in his career , he was discouraged by his experience with the music industry . " I was becoming a product of somebody else 's imagination , and it was mixing with my own personality , " he later reflected . " This combination was appalling . " He pushed to get his own projects off the ground . Despite getting notable touring gigs with other musicians , however , Townsend continued to face rejection of his own music . <unk> Records dropped Noisescapes from their label shortly after Townsend accepted Vai 's offer , seeing no commercial appeal in Townsend 's music . " I have a <unk> they only offered me a deal to get me to sing with Steve , " he <unk> . While touring with the Wildhearts , Townsend received a phone call from an A & R representative for Roadrunner Records , expressing an interest in his demos and an intention to sign him . The offer was ultimately rescinded by the head of Roadrunner , who regarded Townsend 's recordings as " just noise " . 
- 
- = = = Heavy as a Really Heavy Thing through Infinity ( 1994 – 1998 ) = = = 
- 
- In 1994 , Century Media Records offered Townsend a contract to make " some extreme albums " . He agreed to a five @-@ album deal with the record label , and also provided much of the guitar work on the 1994 album Millennium and the 1995 album Hard Wired by Vancouver industrial band Front Line Assembly . Townsend began to record material under the pseudonym Strapping Young Lad . He avoided using his real name at this point in career , looking for a fresh start after his high @-@ profile Vai gig . " At the beginning , I wanted to avoid at all cost to use my name because I was known as the singer for Steve Vai and it wasn 't the best publicity to have , " he later explained . " I was playing somebody else 's music and I was judged in respect to that music . " Townsend produced and performed nearly all the instruments on the debut studio album , Heavy as a Really Heavy Thing , which was released in April 1995 . 
- Following the release of the record , Townsend and several other musician friends he knew in Vancouver recorded his first solo album in 1996 entitled <unk> <unk> – <unk> on <unk> . Written and recorded in under a month , the album was produced as a parody of punk rock bands and documents the act of selling out for mainstream success . Townsend founded his own independent record label , <unk> Records , to release the album . Townsend assembled a permanent lineup of Strapping Young Lad to record City , including prolific metal drummer Gene Hoglan , along with Townsend 's former bandmates <unk> Simon on guitar and Byron Stroud on bass . The industrial @-@ influenced album was released in 1997 . To this day , the album is widely considered Strapping Young Lad 's best work , with Metal Maniacs calling it " groundbreaking " and Revolver naming it " one of the greatest metal albums of all time " . Townsend himself considers it the band 's " ultimate " album . Later that year , Townsend released his second solo album , Ocean Machine : Biomech . The album featured a mix of hard rock , ambient , and progressive rock . 
- After the completion of City and Ocean Machine : Biomech , Townsend began to approach a mental breakdown . " I started to see human beings as little <unk> , water based , pink meat , " he explained , " life forms pushing air through themselves and making noises that the other little pieces of meat seemed to understand . " In 1997 , he checked himself into a mental @-@ health hospital , where he was diagnosed with bipolar disorder . The diagnosis helped him understand where the two sides of his music were coming from ; he felt his disorder " gave birth to the two extremes that are Strapping 's City record and Ocean Machine : Biomech . " After being discharged from the hospital , Townsend found that " everything just <unk> " and he was able to write his third solo album , Infinity , which he described as " the parent project " of City and Ocean Machine : Biomech , with music influenced by Broadway . Townsend returned to the studio , accompanied by Hoglan , to work on the album , on which Townsend played most of the instruments . Infinity was released in October 1998 . Later in his career , Townsend has cited Infinity as his favorite solo record . 
- With Infinity , Townsend began to label all albums outside of Strapping Young Lad under his own name , dropping the Ocean Machine moniker , to reduce confusion . He wanted to show that despite the highly varied nature of his projects , they are all simply aspects of his identity . The album Biomech was <unk> and <unk> as Ocean Machine : Biomech , under Townsend 's name , to reflect the new arrangement . Townsend 's bandmates began to play two sets at their shows , one as Strapping Young Lad , and one as the Devin Townsend Band , playing songs from Townsend 's solo albums . 
- 
- = = = Physicist and Terria ( 1999 – 2001 ) = = = 
- 
- Townsend 's next project took several years to come to fruition . After the creation of the IR8 demo tape , Townsend and Jason Newsted had begun work on a new project called <unk> , which they described as " heavier than Strapping Young Lad " . When the IR8 tape was leaked , Newsted 's Metallica bandmates James <unk> and Lars <unk> learned of the project . <unk> was " fucking <unk> " that Newsted was playing outside the band , and Newsted was prevented by his bandmates from working on any more side projects . With the project stalled , Townsend instead wrote the album himself , <unk> it Physicist . Townsend assembled his Strapping Young Lad bandmates to record it , the only time this lineup was featured on a Devin Townsend album . The thrash @-@ influenced Physicist was released in June 2000 , and is generally considered a low point in Townsend 's career . Hoglan and the rest of the band were dissatisfied with the way the sound was mixed , and Townsend considers it his worst album to date . 
- Feeling he had " ostracized a bunch of fans " with Physicist , Townsend felt he had the chance to make a more personal and honest record . Townsend was inspired one morning while driving across Canada with his band , and looked to write an " introspective " album dedicated to his homeland . He produced and recorded Terria , a " highly illustrated stream @-@ of @-@ consciousness " album , with Gene Hoglan on drums , Craig McFarland on bass and Jamie Meyer on keyboards . Townsend cited <unk> 's White <unk> as an inspiration for the album . Terria was released in November 2001 . 
- 
- = = = Strapping Young Lad through Synchestra ( 2003 – 2006 ) = = = 
- 
- Townsend 's solo run lasted until 2002 . After a five @-@ year break from recording , Strapping Young Lad reunited to record a new album . Townsend credits the album , Strapping Young Lad , as an emotional response to the attacks of September 11 , 2001 , in the United States . " If the world 's about to blow up , " said Townsend , " let 's write the soundtrack for it . " The album 's lyrics were based more around fear and insecurity than the " hostile " lyrics of City . Musically , Strapping Young Lad was less industrial than City , and more reminiscent of death metal , with a " larger @-@ than @-@ life " rock production style . Townsend cited Front Line Assembly , <unk> , and <unk> 's Passage as influences . The self @-@ titled album was released in February 2003 . It received lukewarm reviews , with critics finding it inferior to City , but it was the band 's first charting album , entering at <unk> place on Billboard 's Top Heatseekers chart . 
- While Strapping Young Lad was being reunited , Townsend formed a new , permanent band " on par with Strapping " to record and tour for his solo releases . The Devin Townsend Band consisted of Brian " Beav " Waddell on guitar , Mike Young on bass , Ryan Van <unk> on drums , and Dave Young on keyboards . Townsend performed guitar , vocals , and production , as he did in Strapping Young Lad . Townsend worked on the band 's first album , Accelerated Evolution , at the same time he was working on Strapping Young Lad , spending half the week on one and half on the other . Accelerated Evolution , named for the pace of putting a new band together in under a year , was released a month after Strapping Young Lad . Mike G. of Metal Maniacs called it " the album of the year " , praising it for " the hard @-@ to @-@ accomplish trick of being extreme yet accessible , simultaneously heavy ' n ' <unk> ' yet <unk> and beautiful . " Prior to the formation of the Devin Townsend Band , Townsend had represented his solo releases live with the Strapping Young Lad lineup ; the band would play one set of Strapping Young Lad songs and one set of Devin Townsend songs . After the release of Accelerated Evolution , Townsend 's two bands toured separately for their separate albums . 
- Strapping Young Lad began working on their next album , Alien , in March 2004 . Feeling that the band 's previous album did not live up to expectations , Townsend decided to take his music to a new extreme . To prepare for the new album , Townsend stopped taking the medication prescribed to treat his bipolar disorder . " I think that as an artist , in order for me to get to the next plateau , I kind of feel the need to explore things and sometimes that exploration leads you to places that are a little crazy , " he explains . " And Alien was no exception with that . " Although Townsend considered the album an " impenetrable mass of <unk> " , it was well received on its release , selling 3 @,@ 697 copies in its first week and appearing on several Billboard charts . Around this time , Townsend also contributed to the soundtrack of the video game <unk> : <unk> of Steel . 
- Shortly thereafter Townsend began putting together the next Devin Townsend Band record , with the working title Human . Townsend intended the album as the more " pleasant " counterpart to Alien . " It 's basically a record about coming back down to earth after being in space with Alien for a while . " The album ended up being renamed Synchestra and was released in January 2006 . Townsend showcased a wide variety of musical styles in Synchestra , blending his trademark " pop metal " with influences from folk , polka , and Middle Eastern music . The final Strapping Young Lad album , The New Black , was released later in 2006 . 
- 
- = = = Ziltoid the Omniscient and hiatus ( 2006 – 2008 ) = = = 
- 
- Townsend withdrew from touring to spend time with his family . From home , Townsend completed his second solo ambient album , The <unk> , releasing it exclusively on his website in November 2006 . 
- In May 2007 , Townsend released Ziltoid the Omniscient , a tongue @-@ in @-@ cheek rock opera about the eponymous fictional alien . This was truly a solo album ; he programmed the drums using <unk> from Hell , a software drum machine that uses samples recorded by Tomas <unk> of Meshuggah and played all other instruments himself . Shortly after the album 's release , Townsend announced that he no longer planned to tour or make albums with Strapping Young Lad or the Devin Townsend Band . He explained that he was " burnt out on travelling , touring , and self promotion " and wished to do production work , write albums , and spend time with his family without the stress of interviews or touring . 
- In 2008 , Townsend lent his voice to characters in several episodes of the Adult <unk> cartoon Metalocalypse ( see Musician cameos in Metalocalypse for more ) . The original character design for <unk> the Drummer , one of the series ' main characters , bore a striking resemblance to Townsend . The series ' co @-@ creator <unk> Small acknowledged the similarity , and altered the design before the series began . " We made sure he didn 't look like Devin Townsend . We gave him the <unk> and the <unk> so he wouldn 't look like that . " 
- 
- = = = Devin Townsend Project ( 2008 – 2012 ) = = = 
- 
- After removing himself from the music industry , Townsend cut his trademark hair off and gave up drinking and smoking . Townsend found it " disconcerting " that he had difficulty writing music without drugs , and that he had trouble identifying his purpose as a musician . He spent a year producing albums in absence of writing , but found it <unk> and decided to " pick up the guitar and just write " . This began a period of " self discovery " where he learned " how to create without drugs " . 
- Over two years , Townsend wrote over 60 songs , and found that they fit into " four distinct styles " . In March 2009 , Townsend announced his plans for a four @-@ album series called Devin Townsend Project , with the goal of clarifying his musical identity and being " <unk> " for the persona he projects to the public . The project 's concept includes a different " theme " and a different group of musicians on each album . 
- Ki , the first album of the Devin Townsend Project <unk> was written to " set the stage " for the subsequent albums . Townsend <unk> his new @-@ found control and sobriety into Ki , a " tense , quiet " album , which contrasts with much of the music he had been known for . Additional female vocals were provided by <unk> Aimee <unk> ( Casualties of Cool ) . Ki was released in May 2009 . 
- The second entry , a " commercial , yet heavy " album called Addicted , was released in November 2009 and features lead vocals from Townsend and Dutch singer Anneke van Giersbergen . Brian " Beav " Waddell was recruited from the Devin Townsend Band to play bass . 
- Townsend returned to the stage in January 2010 , touring North America with <unk> Between the Buried and Me as well as <unk> and Scale the Summit . This was followed by a headlining tour in Australia and a series of high @-@ profile shows in Europe ( for example co @-@ headlining the <unk> Assault festival in Czech Republic ) . He <unk> a North American tour with UK label mates <unk> supporting , which began in October 2010 , and toured in Europe with support from <unk> <unk> and Anneke van Giersbergen . 
- The third and fourth albums in the Devin Townsend Project series , Deconstruction and Ghost , were released simultaneously on June 21 , 2011 . In December 2011 all four Devin Townsend Project albums with additional material were released as the <unk> Us box set . Townsend performed all four of Devin Townsend Project albums in London and recorded them for a DVD box set called By a Thread : Live in London 2011 that was released on June 18 , 2012 . The first three shows were held at the University of London Union , November 10 – 12 , 2011 . Ki , Addicted , and Deconstruction were each performed on one night , respectively . The show for Ghost was held at the Union Chapel , Islington on November 13 , 2011 . These four shows were each entitled " An Evening with the Devin Townsend Project " . 
- Despite the Devin Townsend Project being originally a four @-@ album series , Townsend decided to continue working under the moniker and released the fifth album , Epicloud on September 18 , 2012 . Again featuring Anneke van Giersbergen on vocals , Epicloud appeared on several European charts , peaking at number 8 in Finland . On October 27 , 2012 , Devin Townsend performed a one @-@ off show covering his musical career called The <unk> Circus at <unk> in London . The 3 @-@ hour performance was recorded in high definition and released on DVD and Blu @-@ ray on September 30 , 2013 . Also in 2012 , Townsend played bass on the debut Bent Sea album <unk> . He also produced the record . 
- Another project Townsend has mentioned several times between 2009 and 2012 is <unk> , an album featuring " creepy , bass driven apocalyptic music " created with an " <unk> rig " and an " Icelandic choir " . Working with many projects simultaneously at that time , Townsend stated in 2012 the <unk> project is vying for pole position until " he wakes up and says ' he wants to do it ' " . 
- 
- = = = Casualties of Cool and <unk> ( 2012 – present ) = = = 
- 
- After Deconstruction and Ghost , Townsend announced a new album , Casualties of Cool , with which he started to work after the release of Epicloud . The album features <unk> Aimee <unk> ( from Ki ) on vocals and Morgan <unk> on drums . Townsend described the album sounds like " haunted Johnny Cash songs " and " late night music " , highlighting it will be different than anything he has done before . Townsend referred the music of the album to be " closest to his heart " at this point of his life , and that it is an important and satisfying project he doesn 't want to rush . 
- The album was completed on November 2013 , and a bonus disc was also made for the album , containing the leftover material from the main album as well as songs from Ghost 2 , the unreleased compilation of leftover tracks from Ghost . Originally in 2012 , Townsend stated that this album will be the sixth and the last album in the Devin Townsend Project series , but he ultimately confirmed that Casualties of Cool is its own project . Townsend also started a <unk> campaign through <unk> to support the release of the album . The funding quickly reached its goal , and all additional funds were put directly to Townsend 's upcoming projects . Casualties of Cool was released on May 14 , 2014 . The album was re @-@ issued worldwide on January 15 , 2016 containing an additional DVD with live footage from the 2014 concert at the Union Chapel in London . 
- From 2009 , Townsend worked on a long @-@ running album project called Z ² , a sequel to the album Ziltoid the Omniscient ( 2007 ) . Originally in 2012 , he <unk> he " may have just written the heaviest thing he 's ever done " for the album , and told there might a surprising lack of Ziltoid himself appearing on the album . However , in August 2013 , a London @-@ based radio station <unk> Radio aired the first episode of Ziltoid Radio , a satirical radio show hosted solely by Ziltoid , this being one element of the Z ² project . Townsend also discussed a " <unk> " or " Ziltoid TV " is preceding the album . Later Townsend stated he has found the project hard to schedule and work with amidst touring and writing , stating " it takes a lot of effort " to keep the content with tongue @-@ in @-@ cheek humour entertaining . 
- After writing ideas for over 70 songs , Townsend stated he is finally going to finish the whole project , followed by the announcement the album will be released on October 27 , 2014 . The recording process started in May 2014 , and the final project includes the album , a Ziltoid TV program and a live show , with a " big graphic novel comic " and a documentary . The album itself is a double album , with disc one being the main album and disc two featuring Devin Townsend Project material ; according to Townsend , the album 's theme is " Ziltoid against the world " . The Devin Townsend Project disc is called Sky Blue and the Ziltoid disc is called Dark Matters . 
- After finishing the album , Townsend stated the project was " punishing " and an " absolute nightmare to complete " due to amount of material against tight schedules . He also described the hardship of the project by telling " if he was ever going to start drinking [ again ] , the last months would have been it " , but now " he 's starting to get excited again " . Later , " after the chaos of finishing it had subsided " , Townsend stated he is really satisfied with the result . 
- Townsend recently discussed at least a year @-@ long hiatus , beginning after the Z ² show taking place at the Royal Albert Hall on April 13 , 2015 . During the indefinitely long break Townsend intends to " recharge his batteries " , " get some inspiration and experiences " and to " see what the next chapter holds " for him . 
- In 2014 , Devin recorded a ' poppy sounding ' song in Los Angeles with producer Brian <unk> , but has decided against releasing . Devin mentioned he is against the project being contrived due to the current hard rock undertones in popular music . He described it as a " lukewarm heavy metal Devin song " . On December 11 , 2015 Townsend announced via Twitter that he was recording vocals for a song by Steve Vai . 
- As of April 2016 , Devin is in the middle of recording the seventh <unk> album , entitled <unk> at <unk> Studios in Vancouver . 
- 
- = = Personal life = = 
- 
- Townsend has been married to Tracy Turner , his girlfriend since he was 19 . She gave birth to their first son , <unk> Liam <unk> Townsend , on October 4 , 2006 . He is a vegetarian . 
- 
- = = Musical style = = 
- 
- 
- = = = <unk> = = = 
- 
- Townsend designed his two main projects , the aggressive Strapping Young Lad and his more melodic solo material , as counterparts . Strapping Young Lad 's music was a diverse mix of extreme metal genres : death metal , thrash metal , black metal and industrial metal . Townsend 's solo material blends many genres and influences , with elements of atmospheric ambient music , hard rock and progressive rock , along with pop metal and arena rock . He described it as " a highly orchestrated type of expansive music based in hard rock and heavy metal . <unk> and produced with a large amount of ambient elements . " Despite Strapping Young Lad 's greater mainstream acceptance , Townsend identifies more with his solo material , and has never intended Strapping Young Lad to be the focus of his music . 
- 
- = = = Production style = = = 
- 
- As a self @-@ proclaimed " fan of <unk> " , Townsend has developed a trademark production style featuring an atmospheric , layered " wall of sound " . Townsend has drawn critical praise for his productions , which " are always marked by a sense of adventure , intrigue , chaotic <unk> and overall <unk> <unk> " , according to Mike G. of Metal Maniacs . Townsend mainly uses Pro Tools to produce his music , alongside other software <unk> such as <unk> <unk> , <unk> Live , and Logic Pro . Townsend 's musical ideas and production style have drawn comparisons to Phil Spector and Frank Zappa . Townsend has carried out the mixing and mastering for most of his solo work himself . He has also mixed and remixed work for other artists such as <unk> , August Burns Red and <unk> Signals . 
- 
- = = = Playing style = = = 
- 
- Townsend mainly uses Open C tuning for both six and seven string guitar . He now also uses Open B tuning and Open B flat tuning ( Open C tuning tuned a half and a whole step down respectively ) on his six string guitars . Townsend 's technique varies from <unk> , power chords and <unk> to sweep @-@ picked arpeggios and tapping techniques . He is also known for his heavy use of <unk> and delay effects . He has expressed that he has no taste for <unk> guitar , saying that " Musically it doesn 't do anything for me " and that he only solos when he thinks that he can within the context of the song . 
- 
- = = = Vocals = = = 
- 
- Townsend 's employs a variety of vocal techniques in his work , including screaming , growling or even falsetto . His vocal range has been noted to be over 5 octaves ( <unk> to <unk> ) . 
- 
- = = = Influences = = = 
- 
- Townsend draws influence from a wide range of music genres , most prominently heavy metal . Townsend has cited , among others , Judas Priest , <unk> , Frank Zappa , Broadway musicals , <unk> , new @-@ age music , <unk> France , King 's X , <unk> Angel , <unk> , <unk> , Jane 's <unk> , Metallica , Cop Shoot Cop and Fear Factory as his influences , and has also expressed his admiration for Meshuggah on several occasions , calling them " the best metal band on the planet " . Townsend lists Paul Horn and Ravi Shankar as the " two most important musicians in his life " . The two songs that Townsend credits with changing the way he thought about music are " The Burning Down " by King 's X , and " Up the Beach " by Jane 's <unk> . City was influenced by bands such as <unk> and Cop Shoot Cop , and The New Black 's influences were Meshuggah , and " more traditional metal " like Metallica . He is also influenced by orchestral and classical composers such as John Williams , Trevor Jones and Igor Stravinsky . 
- 
- = = Discography = = 
- 
- 
- 
- = Zagreb Synagogue = 
- 
- The Zagreb Synagogue ( Croatian : <unk> <unk> ) was the main place of worship for the Jewish community of Zagreb in modern @-@ day Croatia . It was constructed in 1867 in the Kingdom of Croatia @-@ Slavonia within the Austrian Empire , and was used until it was demolished by the fascist authorities in 1941 in the Axis @-@ aligned Independent State of Croatia . 
- The <unk> Revival synagogue , designed after the <unk> <unk> in Vienna , was located on modern @-@ day Praška Street . It has been the only purpose @-@ built Jewish house of worship in the history of the city . It was one of the city 's most prominent public buildings , as well as one of the most esteemed examples of synagogue architecture in the region . 
- Since the 1980s , plans have been made to rebuild the synagogue in its original location . Due to various political circumstances , very limited progress has been made . Major disagreements exist between the government and Jewish organizations as to how much the latter should be involved in decisions about the reconstruction project , including proposed design and character of the new building . 
- 
- = = History = = 
- 
- Encouraged by the 1782 <unk> of <unk> of Emperor Joseph II , Jews first permanently settled in Zagreb in the late eighteenth century , and founded the Jewish community in 1806 . In 1809 the Jewish community had a rabbi , and by 1811 it had its own cemetery . As early as 1833 , the community was permitted to buy land for construction of a synagogue , but did not have sufficient money to finance one at the time . 
- By 1855 , the community had grown to 700 members and , on October 30 of that year , the decision was made to build a new Jewish synagogue . The construction committee , appointed in 1861 , selected and purchased a parcel of land at the corner of Maria Valeria Street ( now Praška Street ) and Ban <unk> Square , the central town square . However , a new urban planning scheme of 1864 reduced the area available for construction , and the community decided to buy another parcel of 1 @,@ 540 square metres ( 16 @,@ 600 sq ft ) in Maria Valeria Street , approximately 80 metres ( 260 ft ) south of the original location . 
- 
- = = = Design and construction = = = 
- 
- Franjo Klein , a Vienna @-@ born Zagreb architect , was commissioned to build the synagogue . Klein , a representative of romantic <unk> , modeled the building on the <unk> <unk> <unk> ( 1858 ) , a <unk> Revival temple designed by Ludwig Förster . It became a prototype for synagogue design in Central Europe . Zagreb Synagogue used the already developed round arch style ( <unk> ) , but did not adopt Förster 's early oriental motifs . 
- The composition of the main facade , with its dominant drawn @-@ out and elevated projection and the two symmetrical lower lateral parts , reflects the internal division into three naves . At ground @-@ floor level , the front was distinguished by the three @-@ arch entrance and <unk> , whereas the first @-@ floor level had a high <unk> with an elevated arch and the <unk> <unk> on the staircases . 
- The synagogue occupied the greater part of the plot , facing west . It <unk> from the street regulation @-@ line in accordance with the rule then still enforced in Austria – Hungary , prohibiting non @-@ Catholic places of worship from having a public entrance from the street . The synagogue had a wider and slightly higher central nave and two narrower naves ; unlike Förster 's synagogue in Vienna , it did not have a <unk> plan . 
- Construction began in 1866 and was completed the following year . The synagogue was officially consecrated on September 27 , 1867 , a ceremony attended by representatives of city and regional authorities , Zagreb public figures , and many citizens . It was the first prominent public building in Zagreb 's lower town , and its architecture and scale aroused general admiration and praise . 
- 
- = = = 19th and early 20th century = = = 
- 
- With the new synagogue , an organ was introduced into religious service . The small minority of Orthodox Jews found this change to be intolerable , and they began to hold their services separately , in rented rooms . 
- In the 1880 earthquake , the synagogue suffered minor damage and was repaired the following year . 
- Largely due to immigration from Hungary , Bohemia and Moravia , the Jewish population of Zagreb quickly grew in size : from 1 @,@ 285 members in 1887 to 3 @,@ 237 members in 1900 , and then to 5 @,@ 970 members in 1921 . The synagogue became too small to accommodate the needs of the ever @-@ growing community . In 1921 a renovation was undertaken to increase the number of available seats . A 1931 plan to increase the capacity to <unk> seats was ultimately abandoned . A central heating system was installed in 1933 . 
- 
- = = = Demolition during World War II = = = 
- 
- During the 1941 collapse of the Kingdom of Yugoslavia under the Axis invasion in the April War , the Independent State of Croatia was created . It was ruled by the extreme nationalist Ustaša regime . The Ustaša quickly started with the systematic persecution of the Jews , modeled after the Nazi Germany approach , and at times even more brutal . Racial laws were introduced , Jewish property was confiscated , and the Jews were subjected to mass arrests and <unk> to death camps in Croatia and abroad . 
- In October 1941 , the newly installed mayor of Zagreb , Ivan Werner , issued a decree ordering the demolition of the Praška Street synagogue , ostensibly because it did not fit into the city 's master plan . The demolition began on October 10 , 1941 , proceeding slowly so as not to damage the adjacent buildings ; it was finished by April 1942 . The whole process was photographed for propaganda purposes , and the photographs were shown to the public at an antisemitic exhibition first held in Zagreb . It was also shown in Dubrovnik , <unk> , Sarajevo , Vukovar and <unk> , as an illustration of the " solution of the Jewish question in Croatia " . 
- A fragment of the film footage of the demolition was discovered five decades later by the film director <unk> <unk> during research for his 1993 documentary feature , Decline of the Century : <unk> of L. Z. ; 41 seconds of the film survives . This footage was also shown in Mira Wolf 's documentary , The Zagreb Synagogue 1867 @-@ 1942 ( 1996 ) , produced by Croatian <unk> . 
- The synagogue 's eight valuable Torah scrolls were saved due to an intervention by Leonardo Grivičić , an entrepreneur and industrialist who lived next door from Mile <unk> , a minister in the Ustaša government . He was also close to <unk> Ante Pavelić and the Third Reich 's ambassador to Croatia , Edmund <unk> @-@ <unk> . Although Grivičić did not have a significant political role in the Independent State of Croatia , he was considered trustworthy . On October 9 , 1941 , he learned about the regime 's plan to start the demolition of the synagogue on the following morning . By that evening , Grivičić secretly relayed the information to the synagogue 's chief cantor , <unk> , and during the night , the Torah scrolls were moved to safety . 
- Shortly after the destruction of the synagogue , the Catholic archbishop of Zagreb <unk> <unk> delivered a <unk> in which he said : " A house of God of any faith is a holy thing , and whoever harms it will pay with their lives . In this world and the next they will be punished . " . 
- The only surviving fragments of the building — the wash @-@ basin and two memorial tables from the <unk> , as well as some parts of a column — were saved by Ivo Kraus . He pulled them from the rubble shortly after the end of World War II . The wash @-@ basin and the memorial tables are now in the Zagreb City Museum . The column fragments are kept by the Jewish Community of Zagreb . 
- 
- = = Reconstruction efforts = = 
- 
- 
- = = = 1945 – 1990 = = = 
- 
- Only one in five Croatian Jews survived the Holocaust of World War II . Between 1948 and 1952 , nearly one half of the surviving members of Jewish Community of Zagreb opted for emigration to Israel , and the community dropped to one @-@ tenth of its pre @-@ war membership . The Yugoslav communist regime <unk> virtually all real estate owned by the Jewish Community of Zagreb , including the plot in Praška Street . All this , combined with the new regime 's general hostility toward religion , made reconstruction of the synagogue nearly impossible . 
- After World War II , the vacant site of the former synagogue was used as a makeshift volleyball court . The volleyball court made way for a prefabricated department store building , constructed in 1959 . The department store was completely destroyed in a fire on December 31 , 1980 , and was subsequently dismantled . Despite some earlier ideas about a permanent department store building on the same spot , and a 1977 architecture competition for its design , no construction took place . Instead , the parcel was turned into a parking lot , which it remains to this day . 
- After 1986 , the Jewish Community of Zagreb began to consider a Jewish cultural center and a memorial synagogue . Two architects , Branko <unk> and Boris <unk> , both of whom participated in the failed 1977 department store competition , came forward on their own accord and contributed their ideas for a new Jewish center in Praška Street . <unk> 's vision was ultimately not accepted by the Jewish community ; instead , plans were being made for the construction of the cultural center and a synagogue , following an international architecture competition . However , despite support for the project both within Yugoslavia and abroad , the issuance of necessary permits was either stalled or denied by the municipal government . The project was not developed . 
- 
- = = = 1990 – present = = = 
- 
- By the autumn of 1990 , after the first democratic elections in Croatia , the municipal government finally approved the project . An architectural competition was planned for January 1991 . Political turmoil in the country , followed by the breakup of Yugoslavia and the Croatian War of Independence ( 1991 – 1995 ) , caused the project to be put on hold again . In 1994 President of Croatia Franjo Tuđman said to <unk> <unk> , Council member of the Zagreb Jewish community , that they should build the new synagogue at the site of the former synagogue , which will be funded by the Croatian government . <unk> declined the offer believing to be inappropriate when 1800 Catholic churches are left destroyed at the time , during Croatian War of Independence . 
- In the meantime , the Jewish Community of Zagreb sought to legally <unk> its property . The Croatian <unk> law was enacted in 1996 , and the Praška Street parcel was finally returned to the community on December 31 , 1999 . By 2000 , reconstruction activities were invigorated again . An investment study was submitted to the Government of Croatia and the City of Zagreb in July 2004 and revised in October 2004 . The architecture competition was planned for 2005 . However , a 2005 rift in the Jewish Community of Zagreb resulted in formation of a splinter Jewish community , Bet Israel , led by Ivo and <unk> Goldstein . 
- In September 2006 , the Government of Croatia formed a construction <unk> . It was decided that the project , estimated at the time at <unk> 173 million ( US $ 30 million ) , would be partially financed by the Government of Croatia and the City of Zagreb , and that both Jewish organizations should be represented in the <unk> . However , the involvement of Bet Israel was deemed unacceptable by the Jewish Community of Zagreb , which is the sole owner of the Praška Street property , and which also sees itself as the sole legal representative of the Zagreb Jewish community . As a consequence , the community and its president , <unk> Kraus , refused further participation in the project under the set conditions . 
- Further disagreements existed about the design and character of the new building . <unk> reconstruction , while feasible , was not seriously contemplated . There was a general agreement that the new building should also have a cultural as well as commercial purpose . While the Jewish Community of Zagreb envisioned a modern design reminiscent of the original synagogue , the Bet Israel advocated building a replica of the original synagogue 's facade , <unk> it as having a powerful symbolism . Opinions of architects , urban planners , and art historians were also divided along similar lines . 
- In 2014 and 2015 , the Jewish Community of Zagreb presented new plans for a 10 @,@ 600 m2 ( 114 @,@ 000 sq ft ) multi @-@ purpose Jewish center and synagogue in Praška Street . 
- 
- 
- = 1806 Great Coastal hurricane = 
- 
- The 1806 Great Coastal hurricane was a severe and damaging storm along the East Coast of the United States which produced upwards of 36 in ( 91 cm ) of rainfall in parts of Massachusetts . First observed east of the Lesser Antilles on 17 August , the hurricane arrived at the Bahamas by 19 August . The disturbance continued to drift northward and made landfall at the mouth of the Cape Fear River in North Carolina on 22 August . The storm soon moved out to sea as a Category 2 @-@ equivalent hurricane on the Saffir – Simpson hurricane wind scale , <unk> off of New England before dissipating south of Nova Scotia on 25 August as a markedly weaker storm . Several French and British military ships were damaged out at sea . In the Carolinas , salt , sugar , rice , and lumber industries suffered considerably , and several individuals were killed . <unk> and vessels endured moderate damage , with many ships wrecked on North <unk> barrier islands . A majority of the deaths caused by the hurricane occurred aboard the Rose @-@ in @-@ Bloom offshore of <unk> Inlet , New Jersey , with 21 of the ship 's 48 passengers killed and $ 171 @,@ 000 ( 1806 USD ) in damage to its cargo . Upon arriving in New England , reports indicated extreme rainfall , though no deaths were reported ; in all , the hurricane killed more than 24 individuals along the entirety of its track . 
- 
- = = Meteorological history = = 
- 
- The Great Coastal hurricane of 1806 was first noted far east of the Lesser Antilles on 17 August . Weather historian David M. <unk> followed the disturbance 's track to the Bahamas by 19 August ; intense winds persisted until 21 August , however , approximately 150 mi ( 240 km ) east of the <unk> island of <unk> . <unk> currents brought the storm northward , and it approached Charleston , South Carolina on 22 August , where a generally easterly flow preceded the storm indicated its passage far east of the city . The hurricane made landfall at the mouth of the Cape Fear River in North Carolina later that day , though the earliest impacts from the storm started several days earlier , with gusts initially toward the northeast but later curving southwestward . Reports of similar wind shifts throughout the region suggested that the gale persisted , stationary , for several hours . It eventually moved back out to sea while south of Norfolk , Virginia , departing the region on 24 August . The hurricane maintained 1 @-@ minute maximum sustained winds of 110 mph ( 175 km / h ) while offshore , equivalent to a Category 2 system on the Saffir – Simpson hurricane wind scale . While offshore New England , the gale featured a <unk> of winds 90 mi ( 150 km ) wide , and was last observed just south of Nova Scotia on 25 August slightly weaker , with sustained winds of 75 mph ( 120 km / h ) . 
- 
- = = Impact = = 
- 
- The hurricane damaged several vessels while still drifting at sea , <unk> and damaging Jérôme Bonaparte 's fleet and <unk> the 74 @-@ gun French ship of the line Impétueux , which later landed near Cape Henry . 
- In Charleston , South Carolina , the hurricane washed aground several ships and uprooted numerous trees , though damage to the city harbor was minimal . The lighthouse on North Island flanking <unk> Bay collapsed under high winds , and in Georgetown proper , the hurricane was considered to be the worst since the 1804 Antigua – Charleston hurricane , despite its storm surge being of a lesser size . A cotton field covering 94 acres was ruined nearby . At <unk> , North Carolina , numerous ships experienced damage , while considerable destruction to structures was observed , with many wharves wrecked . Meanwhile , at Wilmington , the hurricane inflicted widespread damage , with many wharves severely damaged , and significant losses sustained by salt , sugar , rice , and lumber industries . The gable sections of three masonry houses were destroyed by wind or water , and wooden houses suffered especially badly , with many obliterated and those under construction flattened . One individual died after a wall collapsed and several slaves were killed , one by drowning , at local plantations . At Bald Head Island , the United States Revenue <unk> Service vessel Governor Williams was stripped of its foremast and subsequently ran ashore before being repaired and continuing on its journey . A second boat owned by the agency , the <unk> , was <unk> at port in Wilmington and endured no damage ; similarly , little impact occurred at New Bern . Throughout the storm , several vessels and supplies of stranded sailors were driven aground along the North <unk> coast . On the <unk> Banks , the remains of the <unk> and Atlantic were discovered , and at the Core Banks , a dead body was washed ashore , partially eaten by fish . 
- Moderate damage occurred upon the hurricane 's arrival in Norfolk , Virginia . Winds toppled a number of newly built structures and chimneys , uprooted trees and fences , and washed two <unk> aground . After the storm , alterations to the shoreline around the Chesapeake Bay permitted the full establishment of a town at Willoughby <unk> . The Rose @-@ in @-@ Bloom was caught in the hurricane while offshore of <unk> Inlet , New Jersey , en route to New York City from Charleston , but was struck by a large wave which overturned the ship , resulting in the deaths of 21 of its 48 passengers and the loss of $ 171 @,@ 000 of its $ 180 @,@ 000 ( 1806 USD ) cargo . The vessel only barely stayed afloat , with 30 <unk> of cotton preventing it from sinking entirely ; survivors were ferried to New York by the British brig Swift , which had then been traveling toward St. John 's , Newfoundland . The hurricane produced strong gusts within the vicinity of New York City , and at Belleville , New Jersey , several peach trees were <unk> and uprooted . Cape Cod , Massachusetts was struck by heavy rain and observed minor damage to its port . At <unk> , meanwhile , an individual witnessed torrential rainfall , recording that a barrel was filled with 30 in ( 76 cm ) of water , and estimating total rainfall reached 36 in ( 91 cm ) there , where the storm devastated local crops and <unk> five cargo ships . At <unk> , meanwhile , severe damage to crops and <unk> was noted , and 18 in ( 46 cm ) of rainfall was recorded . Reports in Boston , however , indicate more modest rainfall amounts , with a precipitation rate of 0 @.@ 40 in ( 1 @.@ 0 cm ) per hour noted . 
- 
- 
- = Forward Intelligence Team = 
- 
- Forward Intelligence Teams ( FITs ) are two or more police officers who are deployed by UK police forces to gather intelligence on the ground and in some circumstances , to disrupt activists and deter anti @-@ social behaviour . They use cameras , <unk> and audio recorders to conduct overt surveillance of the public . An unsuccessful legal challenge has been made against their use of overt surveillance , but in 2009 the Court of Appeal ruled that they must justify retention of photographs on a case @-@ by @-@ case basis . Any retained information is recorded on the Crimint database . 
- Political activists have criticised FITs and said that they feel the aim of FIT deployment during protests is to prevent legal protests . Journalists have also complained that FITs attempt to stop them photographing protests and that they conduct surveillance of journalists . A campaign group , Fitwatch , formed in 2007 that aim to obstruct FITs and conduct <unk> on the officers . Two members of the group were arrested at the 2008 Climate Camp on obstruction charges . A similar police surveillance unit , the Video Intelligence Unit is operated by Greater Manchester Police . In June 2010 , the Home Office announced it would review the use of FITs during public order policing . 
- 
- = = History and Purpose = = 
- 
- FITs were first formed in the early 1990s , as part of the Public Order Intelligence Unit ( <unk> ) , a section of the Public Order Branch of the Metropolitan Police . They initially targeted football fans , hunt <unk> and political protesters ( since at least 1996 ) , using cameras , <unk> and audio recorders to conduct overt surveillance of the public . The police officers wear full uniform , and are intended to be a highly visible presence . Their uniform is sometimes different from normal police officers in that the upper half of their yellow fluorescent jackets is blue . Civilian photographers are also employed by the police to work alongside FITs . According to Scotland Yard , the aim of FIT teams at protests is to record evidence of protesters in case disorder occurs later on at a protest . 
- More recently the teams ' purpose has been extended to routine police work on low @-@ level crime and anti @-@ social behaviour and police forces throughout the UK now have their own FITs . Despite the implication in their name that their function is to merely gather intelligence , they are also intended to have a deterrent effect . This approach has been reported to work in reducing reports of anti @-@ social behaviour at times when FITs are deployed in specific neighbourhoods . <unk> Smith , then Home Secretary praised Operation Leopard that used FITs to target youths , in <unk> , Essex stating : 
- " Operation Leopard is exactly the sort of intensive policing that can bring persistent offenders to their senses ... <unk> filming of them and their associates throughout the day and night " 
- Linda <unk> , an activist , has suggested that their tactics are " designed to <unk> people and prevent lawful dissent " . This view is echoed by a police <unk> of their operations at the 2008 Camp for Climate Action which praised FITs at the event for disrupting activists . 
- In June 2010 , the Home Office announced it would review the use of FITs during public order policing . The move was influenced by the discovery that information collected by FITs , included that which was unrelated to suspected crimes , for example recording who made speeches at demonstrations . 
- In October 2010 , FIT officers in plain clothes were spotted by a press photographer at a protest against companies avoiding tax , despite Commander Bob Broadhurst telling a parliamentary committee in May 2009 , that only <unk> officers distinguishable by their blue and yellow jackets were involved in gathering intelligence at protests . The Metropolitan Police told The Guardian that it was necessary to deploy plain @-@ clothed officers to " gather information to provide us with a relevant and up @-@ to @-@ date intelligence picture of what to expect " . It was the first time that FITs are known to have been deployed in plain clothes . 
- 
- = = Legal issues = = 
- 
- Liberty brought a judicial review of the overt surveillance practices in May 2008 , which was decided in favour of the police , however the police were asked to clarify their evidence to the Court of Appeal , following an investigation by The Guardian newspaper . 
- In May 2009 , the Court of Appeal ruled that photographs collected by FITs of people who have not committed a criminal offence can no longer be kept . The ruling was made after Andrew Wood , an arms trade activist , was photographed after challenging the management of Reed <unk> at their <unk> over them organising arms trade exhibitions . Wood argued that police had harassed him and <unk> his right to privacy by photographing him . Lord Collins of <unk> said that the police presence had a " chilling effect " on people who were protesting <unk> . FITs have not been banned but they must now justify the retention of photographs on a case @-@ by @-@ case basis . As a result of the ruling the Metropolitan Police 's public order unit , <unk> was forced to <unk> 40 % of the photos of protesters that it held . 
- In a report about the policing of the 2009 G @-@ 20 London summit protests , Denis O 'Connor , the chief <unk> of <unk> , stated that the routine use of FITs at protests " raises fundamental privacy issues and should be reviewed " . He also said that there was " confusion " over the role of FITs and advised that the Home Office should issue guidance over the legality of the surveillance of protesters and the retention of images . 
- 
- = = Information processing = = 
- 
- The information that FITs collect is stored on the Crimint database , which is used daily by police officers to catalogue criminal intelligence . People are listed by name allowing police to determine which events individuals have attended . <unk> obtained by FITs are used to produce " <unk> cards " consisting of people 's photographs which allows officers to identify people at future events that they attend . For £ 10 , people are able to obtain a list of protests that they have attended from the data held on Crimint under laws in the Data Protection Act 1998 . 
- 
- = = Academic response = = 
- 
- A 2006 report , The Economics of Mass <unk> calculated that the use of FITs at mass gatherings involves gathering intelligence on roughly 1 @,@ 200 people to record the actions of one person . The report also noted that most of the people on " <unk> cards " , used by the police photographers , were those involved in the organisation of protests and that FITs also attend meetings where demonstrations are organised . 
- 
- = = Criticism = = 
- 
- Fitwatch ( formed in early 2007 ) campaign against FITs by actively <unk> their operations , and by passively opposing their operations by photographing units ( a form of <unk> ) . 
- In June 2009 , The Guardian released video evidence recorded by a FIT at the 2008 Climate Camp of alleged police brutality against two female members of Fitwatch . The women had asked police officers to reveal their shoulder numbers , as at least four officers had not displayed them . The women attempted to photograph the police officers for evidence , but were forced to the ground , restrained with handcuffs , and had their legs bound with <unk> . They were then placed in restraint positions , arrested , charged and held in custody for four days , including three days in <unk> <unk> , before they were released on bail . The police later <unk> all the charges against the women . The women lodged a complaint with the <unk> over the incident . The journalist George <unk> commented on this case , saying that " the police are turning activism into a crime " and that " the FITs ' methods appear to have been lifted from a <unk> training manual " . He claimed that " anybody who is politically active is filmed , identified , monitored , logged , and cross @-@ checked " . A police <unk> into the operation at <unk> praised the deployment of FITs saying that they were " highly effective and gained good intelligence and disruption " . 
- Three members of Fitwatch were convicted for <unk> FIT officers in June 2008 as they attempted to photograph those attending a No Borders meeting in London . In July 2010 the Inner London Crown Court overturned the men 's convictions , with the judge stating that the protesters ' human rights may have been violated by the FIT officers . 
- On 15 November 2010 , the hosts of the Fitwatch blog were asked by the Police National E @-@ Crime Unit to take down the website due to it " being used to undertake criminal activities " . The request came after a post on the blog after the 2010 student protest in London , which advised students of actions they should take if they were concerned that they were photographed at the demonstration , such as cutting their hair and <unk> of clothing they were wearing . Emily Apple , one of the founders of the site told The Guardian , " Nothing in that post [ giving guidance to student protesters ] has not been said before on our blog or on other sites " . On 17 November 2010 , the Fitwatch website returned , hosted on a web server outside of the UK . 
- The National Union of Journalists ( NUJ ) has criticised FITs for their surveillance and sometimes violent harassment of working journalists . Marc <unk> , who was hospitalised by police after documenting a protest , has said that the teams limit freedom of the press and called on the Home Office to confirm that the police had no right to restrict the work of <unk> . Bob Broadhurst , who is in charge of public order policing at the Metropolitan Police , said in a statement to the NUJ in 2008 that journalists , " on the production of a valid form of accreditation will be able to continue with their work " . The NUJ are to make a formal complaint to the Information Commissioner due to the Metropolitan Police failing to provide details on the surveillance of journalists under the Freedom of Information Act . Bob Broadhurst told photographers at an NUJ conference that he had no faith in the National Press Card ( a form of press pass ) despite journalists needing to prove that they are <unk> @-@ <unk> <unk> to an independent authority before they are issued . 
- The BBC TV series <unk> produced an episode entitled What ever happened to people power ? in July 2009 which discussed the use of FITs in targeting activists and journalists . 
- 
- = = Similar police units = = 
- 
- Greater Manchester Police operate a Video Intelligence Unit , whose <unk> officers confront and video certain freed prisoners as they leave prison after serving their sentences . They also record footage of people involved in anti @-@ social behaviour on the streets . The aim is to give other police officers up to date information on the appearance of people who have broken the law . Video footage thus collected is constantly replayed on TV screens in rooms where officers complete their <unk> . Footage that they have recorded has also been uploaded onto YouTube in an attempt to catch people they believe have <unk> . This has resulted in several offenders being sent back to prison after breaching licence conditions . Since the unit was launched in 2006 more than 900 people have been filmed by the unit . Not all of these people are suspects in crime however , people can be filmed if they are thought to associate with prolific offenders or if they have been stopped in an area of high crime under suspicious circumstances . <unk> Walsh , a civil liberties lawyer , said the unit 's work " could have implications " for the force under Article 8 of the European Convention on Human Rights - the right to privacy . He believes that filming must be a " proportionate and reasonable " response to a crime and that this does not appear to be the case as people are being targeted over what they might do in the future . It is uncertain as to how long data collected by the unit is to be kept but <unk> currently anticipate it will be stored for 5 years . 
- 
- 
- = Trinsey v. Pennsylvania = 
- 
- Trinsey v. Pennsylvania <unk> <unk> 224 was a case decided by the United States Court of Appeals for the Third Circuit that confirmed the validity of special elections held without a primary under the Fourteenth and Seventeenth Amendments to the United States Constitution . The case came about due to the death of H. John Heinz III , one of the US Senators from Pennsylvania , in a plane crash on April 4 , 1991 . Under the Seventeenth Amendment , state legislatures may give the Governor the power to appoint officials to fill temporarily vacant Senate seats until a special election can be held , and Pennsylvanian law contained a statute executing this and requiring no primaries for the special election . Instead , both the Democrats and Republicans would each internally select their candidates . John S. Trinsey Jr . , a <unk> and potential candidate , asked the United States District Court for the Eastern District of Pennsylvania to declare the statute unconstitutional as a violation on the Fourteenth and Seventeenth amendments , because the lack of a primary removed his right to properly vote for candidates and delegated that power to political parties . 
- After deciding that the statute 's subject matter necessitated the strict scrutiny approach , the District Court decided on June 10 , 1991 that it was an unconstitutional violation of the right to vote for and select Senate candidates . This decision was appealed to the Court of Appeals for the Third Circuit , who decided against the use of the strict scrutiny approach and , in its absence , ruled that the statute was not a violation of the Fourteenth and Seventeenth Amendments . Academics have been critical of both the decision reached and the approach used , with one suggesting that the " substantial state interests " test used in Valenti v. Rockefeller would be more appropriate . 
- 
- = = Background = = 
- 
- On April 4 , 1991 H. John Heinz III , one of the US Senators from Pennsylvania , was killed when his chartered plane collided with a helicopter inspecting its landing gear . Under the Seventeenth Amendment to the United States Constitution , the legislature of each state has the power to permit the governor to fill the vacant seat until a special election can be held . In Pennsylvania , this power had been delegated , and Governor Robert P. Casey signed a writ on May 13 , 1991 , declaring November 5 the date for a special election and temporarily appointing Harris Wofford to fill Heinz 's now @-@ vacant seat . Under Pennsylvanian law , there was no need for a primary in such a situation ; instead , both the Democrats and Republicans would each internally select their candidate , who would run in the special election . John S. Trinsey Jr . , a member of the Pennsylvanian electorate and potential candidate , challenged the <unk> of this law , claiming that it violated his rights under the Fourteenth and Seventeenth Amendments . 
- Trinsey argued that , by failing to allow for primaries , the state legislation prevented him from getting to select a candidate of his choice , and that this violated the Fourteenth Amendment ; the terms of the statute ( and absence of a requirement for primaries ) also allegedly <unk> the rights of the electorate under the Seventeenth Amendment , which required the selection of Senators by popular vote ; Trinsey 's complaint was that the legislation had effectively delegated the power to choose candidates to political parties rather than the electorate . Accordingly , Trinsey filed a motion for a <unk> judgment to state that the statute was unconstitutional , and also requested that Wofford be removed from his seat . <unk> for the Commonwealth of Pennsylvania , joined by the office of the Governor , argued that the constitution did not require the holding of primary elections to fill vacancies , and that the statute " protected valid and compelling state interests in protecting the validity of the electoral process and limiting the term of a [ <unk> ] appointed Senator " . 
- 
- = = Judgment = = 
- 
- The case was first heard in the United States District Court for the Eastern District of Pennsylvania , where , following oral arguments , the judge dismissed both Trinsey 's motion to remove Wofford and the Commonwealth 's motion to dismiss . On June 10 , 1991 , however , the District Court declared the statute unconstitutional , stating that it violated both the Fourteenth and Seventeenth Amendments due to the failure to ensure " popular participation " through the use of primary elections . This decision was reached following an analysis of the legislative history of the Seventeenth Amendment and electoral processes ; based on this analysis , the court concluded that the Pennsylvanian use of a nomination process before a special election implied a right to vote , which was violated by the lack of a primary and necessitated a strict <unk> of the legislation . After considering the evidence , the court concluded that " the interests the Commonwealth put forth in support of the statute could not <unk> the infringement of the right to vote " , leading to the conclusion that the statute governing special elections was unconstitutional . 
- With this " the public , press and political parties quickly turned their attention to the case " , with the Republican State Committee of Pennsylvania ( supported by their Democratic counterparts ) and several prominent politicians intervening . They moved to expedite an appeal to the Court of Appeals for the Third Circuit . In a unanimous opinion , the Court of Appeals ( consisting of <unk> , Greenberg and Seitz ) confirmed that there was no restriction of any fundamental right , and therefore that the strict scrutiny process did not need to be applied . In the absence of this process , they held that the Seventeenth Amendment did not require primary elections to fill vacancies , and more broadly gave state legislatures wide discretion as to how to hold elections ; as such , the statute did not violate the constitution . In December 1991 the Supreme Court denied a writ of certiorari , presumably because the special election had already taken place in November and the issue was thus moot . 
- 
- = = <unk> = = 
- 
- Laura E. Little , writing in the Temple Law Review , notes the <unk> of guidance for either the District Court or Court of Appeals in Trinsey , with no " explicit direction and no direct precedent " from either the constitutional provisions or prior case law to rely on . In the absence of guidance , the Court of Appeals took a narrow view of the issues , something she <unk> . Under Pennsylvanian law , primaries are mandatory in all other elections , and in her opinion the lack of a primary in this case should have been judged to be a violation of Trinsey 's fundamental rights . Combined with the purpose of the Seventeenth Amendment - to ensure direct election - this should have led to the application of the strict scrutiny test , and the decision that the statute governing special elections was unconstitutional . Kevin M. Gold instead suggests that the test used in Valenti v. Rockefeller , an analogous decision over the validity of New York state electoral law . In Valenti , the judiciary applied the " substantial state interests " test , which involves simply looking at whether the statute in question <unk> the interests of the states , who under the Seventeenth Amendment are given some discretion as to the electoral process they use . 
- 
- 
- = Michael Jordan = 
- 
- Michael Jeffrey Jordan ( born February 17 , 1963 ) , also known by his initials , MJ , is an American retired professional basketball player . He is also a businessman , and principal owner and chairman of the Charlotte Hornets . Jordan played 15 seasons in the National Basketball Association ( NBA ) for the Chicago Bulls and Washington Wizards . His biography on the NBA website states : " By acclamation , Michael Jordan is the greatest basketball player of all time . " Jordan was one of the most effectively marketed athletes of his generation and was considered instrumental in popularizing the NBA around the world in the 1980s and 1990s . 
- Jordan played three seasons for coach Dean Smith at the University of North Carolina . He was a member of the Tar <unk> ' national championship team in 1982 . Jordan joined the NBA 's Chicago Bulls in 1984 as the third overall draft pick . He quickly emerged as a league star , entertaining crowds with his prolific scoring . His leaping ability , demonstrated by performing slam <unk> from the free throw line in slam dunk contests , earned him the nicknames " Air Jordan " and " His <unk> " . He also gained a reputation for being one of the best defensive players in basketball . In 1991 , he won his first NBA championship with the Bulls , and followed that achievement with titles in 1992 and 1993 , securing a " three @-@ peat " . Although Jordan abruptly retired from basketball before the beginning of the 1993 – 94 NBA season to pursue a career in baseball , he returned to the Bulls in March 1995 and led them to three additional championships in 1996 , 1997 , and 1998 , as well as a then @-@ record 72 regular @-@ season wins in the 1995 – 96 NBA season . Jordan retired for a second time in January 1999 , but returned for two more NBA seasons from 2001 to 2003 as a member of the Wizards . 
- Jordan 's individual accolades and accomplishments include five Most Valuable Player ( MVP ) Awards , ten All @-@ NBA First Team designations , nine All @-@ Defensive First Team honors , fourteen NBA All @-@ Star Game appearances , three All @-@ Star Game MVP Awards , ten scoring titles , three steals titles , six NBA Finals MVP Awards , and the 1988 NBA Defensive Player of the Year Award . Among his numerous accomplishments , Jordan holds the NBA records for highest career regular season scoring average ( 30 @.@ 12 points per game ) and highest career playoff scoring average ( 33 @.@ 45 points per game ) . In 1999 , he was named the greatest North American athlete of the 20th century by ESPN , and was second to Babe Ruth on the Associated Press 's list of athletes of the century . Jordan is a two @-@ time <unk> into the Basketball Hall of Fame , having been enshrined in 2009 for his individual career , and again in 2010 as part of the group induction of the 1992 United States men 's Olympic basketball team ( " The Dream Team " ) . He became a member of the FIBA Hall of Fame in 2015 . 
- Jordan is also known for his product endorsements . He fueled the success of Nike 's Air Jordan <unk> , which were introduced in 1985 and remain popular today . Jordan also starred in the 1996 feature film Space Jam as himself . In 2006 , he became part @-@ owner and head of basketball operations for the then @-@ Charlotte Bobcats , buying a controlling interest in 2010 . In 2015 , as a result of the increase in value of NBA franchises , Jordan became the first billionaire NBA player in history and the world 's second @-@ richest African @-@ American . 
- 
- = = Early years = = 
- 
- Jordan was born in Brooklyn , New York , the son of <unk> ( née Peoples ) , who worked in banking , and James R. Jordan , Sr. , an equipment supervisor . His family moved to Wilmington , North Carolina , when he was a <unk> . 
- Jordan is the fourth of five children . He has two older brothers , Larry Jordan and James R. Jordan , Jr . , one older sister , <unk> , and a younger sister , <unk> . Jordan 's brother James retired in 2006 as the Command Sergeant Major of the 35th Signal Brigade of the <unk> Airborne Corps in the U.S. Army . 
- 
- = = High school career = = 
- 
- Jordan attended <unk> A. Laney High School in Wilmington , where he anchored his athletic career by playing baseball , football , and basketball . He tried out for the varsity basketball team during his sophomore year , but at 5 ' 11 " ( 1 @.@ 80 m ) , he was deemed too short to play at that level . His taller friend , Harvest <unk> Smith , was the only sophomore to make the team . 
- <unk> to prove his worth , Jordan became the star of Laney 's junior varsity squad , and <unk> several 40 @-@ point games . The following summer , he grew four inches ( 10 cm ) and trained <unk> . Upon earning a spot on the varsity roster , Jordan averaged about 20 points per game over his final two seasons of high school play . As a senior , he was selected to the McDonald 's All @-@ American Team after averaging a triple @-@ double : 29 @.@ 2 points , 11 @.@ 6 rebounds , and 10 @.@ 1 assists . 
- Jordan was recruited by numerous college basketball programs , including Duke , North Carolina , South Carolina , Syracuse , and Virginia . In 1981 , Jordan accepted a basketball scholarship to North Carolina , where he <unk> in cultural geography . 
- 
- = = College career = = 
- 
- As a freshman in coach Dean Smith 's team @-@ oriented system , he was named ACC Freshman of the Year after he averaged 13 @.@ 4 points per game ( ppg ) on 53 @.@ 4 % shooting ( field goal percentage ) . He made the game @-@ winning jump shot in the 1982 NCAA Championship game against Georgetown , which was led by future NBA rival Patrick Ewing . Jordan later described this shot as the major turning point in his basketball career . During his three seasons at North Carolina , he averaged 17 @.@ 7 ppg on 54 @.@ 0 % shooting , and added 5 @.@ 0 rebounds per game ( rpg ) . He was selected by consensus to the NCAA All @-@ American First Team in both his sophomore ( 1983 ) and junior ( 1984 ) seasons . After winning the <unk> and the Wooden College Player of the Year awards in 1984 , Jordan left North Carolina one year before his scheduled graduation to enter the 1984 NBA draft . The Chicago Bulls selected Jordan with the third overall pick , after <unk> Olajuwon ( Houston Rockets ) and Sam Bowie ( Portland Trail Blazers ) . One of the primary reasons why Jordan was not drafted sooner was because the first two teams were in need of a center . However , the Trail Blazers general manager <unk> <unk> contended that it was not a matter of drafting a center , but more a matter of taking Sam Bowie over Jordan , in part because Portland already had a guard with similar skills to Jordan , Clyde Drexler . ESPN , citing Bowie 's injury @-@ laden college career , named the Blazers ' choice of Bowie as the worst draft pick in North American professional sports history . Jordan returned to North Carolina to complete his degree in 1986 . 
- 
- = = Professional career = = 
- 
- 
- = = = Early NBA years ( 1984 – 1987 ) = = = 
- 
- During his first season in the NBA , Jordan averaged 28 @.@ 2 ppg on 51 @.@ 5 % shooting . He quickly became a fan favorite even in opposing arenas , and appeared on the cover of Sports Illustrated with the heading " A Star Is Born " just over a month into his professional career . Jordan was also voted in as an All @-@ Star starter by the fans in his rookie season . Controversy arose before the All @-@ Star game when word surfaced that several veteran players , led by Isiah Thomas , were upset by the amount of attention Jordan was receiving . This led to a so @-@ called " freeze @-@ out " on Jordan , where players refused to pass him the ball throughout the game . The controversy left Jordan relatively unaffected when he returned to regular season play , and he would go on to be voted Rookie of the Year . The Bulls finished the season 38 – 44 , and lost in the first round of the playoffs in four games to the Milwaukee <unk> . 
- Jordan 's second season was cut short by a broken foot in the third game of the season , which caused him to miss 64 games . Despite Jordan 's injury and a 30 – 52 record ( at the time it was fifth worst record of any team to qualify for the playoffs in NBA history ) , the Bulls made the playoffs . Jordan recovered in time to participate in the playoffs and performed well upon his return . Against a 1985 – 86 Boston Celtics team that is often considered one of the greatest in NBA history , Jordan set the still @-@ unbroken record for points in a playoff game with 63 in Game 2 . The Celtics , however , managed to sweep the series . 
- Jordan had recovered completely by the 1986 – 87 season , and had one of the most prolific scoring seasons in NBA history . He became the only player other than Wilt Chamberlain to score 3 @,@ 000 points in a season , averaging a league high 37 @.@ 1 points on 48 @.@ 2 % shooting . In addition , Jordan demonstrated his defensive prowess , as he became the first player in NBA history to record 200 steals and 100 blocks in a season . Despite Jordan 's success , Magic Johnson won the league 's Most Valuable Player Award . The Bulls reached 40 wins , and advanced to the playoffs for the third consecutive year . However , they were again swept by the Celtics . 
- 
- = = = Pistons roadblock ( 1987 – 1990 ) = = = 
- 
- Jordan led the league in scoring again in the 1987 – 88 season , averaging 35 @.@ 0 ppg on 53 @.@ 5 % shooting and won his first league MVP Award . He was also named the Defensive Player of the Year , as he had averaged 1 @.@ 6 blocks and a league high 3 @.@ 16 steals per game . The Bulls finished 50 – 32 , and made it out of the first round of the playoffs for the first time in Jordan 's career , as they defeated the Cleveland Cavaliers in five games . However , the Bulls then lost in five games to the more experienced Detroit Pistons , who were led by Isiah Thomas and a group of physical players known as the " Bad Boys " . 
- In the 1988 – 89 season , Jordan again led the league in scoring , averaging 32 @.@ 5 ppg on 53 @.@ 8 % shooting from the field , along with 8 rpg and 8 assists per game ( apg ) . The Bulls finished with a 47 – 35 record , and advanced to the Eastern Conference Finals , defeating the Cavaliers and New York Knicks along the way . The Cavaliers series included a career highlight for Jordan when he hit The Shot over Craig <unk> at the <unk> in the fifth and final game of the series . However , the Pistons again defeated the Bulls , this time in six games , by utilizing their " Jordan Rules " method of guarding Jordan , which consisted of double and triple teaming him every time he touched the ball . 
- The Bulls entered the 1989 – 90 season as a team on the rise , with their core group of Jordan and young improving players like Scottie Pippen and Horace Grant , and under the guidance of new coach Phil Jackson . Jordan averaged a league leading 33 @.@ 6 ppg on 52 @.@ 6 % shooting , to go with 6 @.@ 9 rpg and 6 @.@ 3 apg in leading the Bulls to a 55 – 27 record . They again advanced to the Eastern Conference Finals beating the <unk> and Philadelphia 76ers . However , despite pushing the series to seven games , the Bulls lost to the Pistons for the third consecutive season . 
- 
- = = = First three @-@ peat ( 1991 – 1993 ) = = = 
- 
- In the 1990 – 91 season , Jordan won his second MVP award after averaging 31 @.@ 5 ppg on 53 @.@ 9 % shooting , 6 @.@ 0 rpg , and 5 @.@ 5 apg for the regular season . The Bulls finished in first place in their division for the first time in 16 years and set a franchise record with 61 wins in the regular season . With Scottie Pippen developing into an All @-@ Star , the Bulls had elevated their play . The Bulls defeated the New York Knicks and the Philadelphia 76ers in the opening two rounds of the playoffs . They advanced to the Eastern Conference Finals where their rival , the Detroit Pistons , awaited them . However , this time the Bulls beat the Pistons in a four @-@ game sweep . In an unusual ending to the fourth and final game , Isiah Thomas led his team off the court before the final seconds had concluded . Most of the Pistons went directly to their locker room instead of shaking hands with the Bulls . 
- The Bulls advanced to the NBA Finals for the first time in franchise history to face Magic Johnson and James <unk> and beat the Los Angeles <unk> four games to one , compiling an outstanding 15 – 2 playoff record along the way . Perhaps the best known moment of the series came in Game 2 when , attempting a dunk , Jordan avoided a potential Sam Perkins block by switching the ball from his right hand to his left in mid @-@ air to lay the shot in . In his first Finals appearance , Jordan posted per game averages of 31 @.@ 2 points on 56 % shooting from the field , 11 @.@ 4 assists , 6 @.@ 6 rebounds , 2 @.@ 8 steals and 1 @.@ 4 blocks . Jordan won his first NBA Finals MVP award , and he cried while holding the NBA Finals trophy . 
- Jordan and the Bulls continued their dominance in the 1991 – 92 season , establishing a 67 – 15 record , topping their franchise record from 1990 to 91 . Jordan won his second consecutive MVP award with averages of 30 @.@ 1 points , 6 @.@ 4 rebounds and 6 @.@ 1 assists per game on 52 % shooting . After winning a physical 7 @-@ game series over the New York Knicks in the second round of the playoffs and finishing off the Cleveland Cavaliers in the Conference Finals in 6 games , the Bulls met Clyde Drexler and the Portland Trail Blazers in the Finals . The media , hoping to recreate a Magic – Bird rivalry , highlighted the similarities between " Air " Jordan and Clyde " The <unk> " during the pre @-@ Finals hype . In the first game , Jordan scored a Finals @-@ record 35 points in the first half , including a record @-@ setting six three @-@ point field goals . After the sixth three @-@ pointer , he <unk> down the court <unk> as he looked <unk> . <unk> Albert , who broadcast the game , later stated that it was as if Jordan was saying , " I can 't believe I 'm doing this . " The Bulls went on to win Game 1 , and defeat the Blazers in six games . Jordan was named Finals MVP for the second year in a row and finished the series averaging 35 @.@ 8 ppg , 4 @.@ 8 rpg , and 6 @.@ 5 apg , while shooting 53 % from the floor . 
- In the 1992 – 93 season , despite a 32 @.@ 6 ppg , 6 @.@ 7 rpg and 5 @.@ 5 apg campaign , Jordan 's streak of consecutive MVP seasons ended as he lost the award to his friend Charles Barkley . <unk> , Jordan and the Bulls met Barkley and his Phoenix Suns in the 1993 NBA Finals . The Bulls won their third NBA championship on a game @-@ winning shot by John <unk> and a last @-@ second block by Horace Grant , but Jordan was once again Chicago 's leader . He averaged a Finals @-@ record 41 @.@ 0 ppg during the six @-@ game series , and became the first player in NBA history to win three straight Finals MVP awards . He scored more than 30 points in every game of the series , including 40 or more points in 4 consecutive games . With his third Finals triumph , Jordan capped off a seven @-@ year run where he attained seven scoring titles and three championships , but there were signs that Jordan was tiring of his massive celebrity and all of the non @-@ basketball <unk> in his life . 
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- = = = = <unk> controversy = = = = 
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- During the Bulls ' playoff run in 1993 , controversy arose when Jordan was seen gambling in Atlantic City , New Jersey , the night before a game against the New York Knicks . In that same year , he admitted to having to cover $ 57 @,@ 000 in gambling losses , and author Richard <unk> wrote a book claiming he had won $ 1 @.@ 25 million from Jordan on the golf course . In 2005 , Jordan talked to Ed Bradley of the CBS evening show 60 Minutes about his gambling and admitted that he made some <unk> decisions . Jordan stated , " Yeah , I 've gotten myself into situations where I would not walk away and I 've pushed the envelope . Is that <unk> ? Yeah , it depends on how you look at it . If you 're willing to jeopardize your livelihood and your family , then yeah . " When Bradley asked him if his gambling ever got to the level where it <unk> his livelihood or family , Jordan replied , " No . " 
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- = = = First retirement and baseball career ( 1993 – 1994 ) = = = 
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- On October 6 , 1993 , Jordan announced his retirement , citing a loss of desire to play the game . Jordan later stated that the murder of his father earlier in the year also shaped his decision . Jordan 's father was murdered on July 23 , 1993 , at a highway rest area in <unk> , North Carolina , by two teenagers , Daniel Green and Larry Martin <unk> . The <unk> were traced from calls they made on James Jordan 's cellular phone , caught , convicted , and sentenced to life in prison . Jordan was close to his father ; as a child he had imitated his father 's <unk> to stick out his tongue while absorbed in work . He later adopted it as his own signature , displaying it each time he drove to the basket . In 1996 , he founded a Chicago area Boys & Girls Club and dedicated it to his father . 
- In his 1998 autobiography For the Love of the Game , Jordan wrote that he had been preparing for retirement as early as the summer of 1992 . The added exhaustion due to the Dream Team run in the 1992 Olympics solidified Jordan 's feelings about the game and his ever @-@ growing celebrity status . Jordan 's announcement sent shock waves throughout the NBA and appeared on the front pages of newspapers around the world . 
- Jordan then further surprised the sports world by signing a minor league baseball contract with the Chicago White Sox on February 7 , 1994 . He reported to spring training in Sarasota , Florida , and was assigned to the team 's minor league system on March 31 , 1994 . Jordan has stated this decision was made to pursue the dream of his late father , who had always envisioned his son as a Major League Baseball player . The White Sox were another team owned by Bulls owner Jerry <unk> , who continued to honor Jordan 's basketball contract during the years he played baseball . 
- In 1994 , Jordan played for the Birmingham Barons , a Double @-@ A minor league affiliate of the Chicago White Sox , batting <unk> with three home runs , 51 runs batted in , 30 stolen bases , 114 strikeouts , 51 base on balls , and 11 errors . He also appeared for the <unk> <unk> in the 1994 Arizona Fall League , batting <unk> against the top prospects in baseball . On November 1 , 1994 , his number 23 was retired by the Bulls in a ceremony that included the erection of a permanent sculpture known as The Spirit outside the new United Center . 
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- = = = " I 'm back " : Return to the NBA ( 1995 ) = = = 
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- In the 1993 – 94 season , the Bulls , without Jordan , achieved a 55 – 27 record , and lost to the New York Knicks in the second round of the playoffs . But the 1994 – 95 Bulls were a shell of the championship team of just two years earlier . <unk> at mid @-@ season to ensure a spot in the playoffs , Chicago was 31 – 31 at one point in mid @-@ March . The team received help , however , when Jordan decided to return to the NBA for the Bulls . 
- In March 1995 , Jordan decided to quit baseball due to the ongoing Major League Baseball strike , as he wanted to avoid becoming a potential replacement player . On March 18 , 1995 , Jordan announced his return to the NBA through a two @-@ word press release : " I 'm back . " The next day , Jordan wore jersey number 45 ( his number with the Barons ) , as his familiar 23 had been retired in his honor following his first retirement . He took to the court with the Bulls to face the Indiana <unk> in Indianapolis , scoring 19 points . The game had the highest Nielsen rating of a regular season NBA game since 1975 . 
- Although he had not played an NBA game in a year and a half , Jordan played well upon his return , making a game @-@ winning jump shot against Atlanta in his fourth game back . He then scored 55 points in the next game against the Knicks at Madison Square Garden on March 28 , 1995 . <unk> by Jordan 's comeback , the Bulls went 13 – 4 to make the playoffs and advanced to the Eastern Conference <unk> against the Orlando Magic . At the end of Game 1 , Orlando 's Nick Anderson stripped Jordan from behind , leading to the game @-@ winning basket for the Magic ; he would later comment that Jordan " didn 't look like the old Michael Jordan " and that " No. 45 doesn 't explode like No. 23 used to . " Jordan then returned to wearing his old number in the next game , scoring 38 points in a Bulls win . The Bulls were fined $ 30 @,@ 000 for the game : $ 25 @,@ 000 for failing to report the impromptu number change to the NBA and $ 5 @,@ 000 for Jordan wearing different shoes . Jordan averaged 31 points per game in the series , but Orlando won the series in 6 games . 
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- = = = Second three @-@ peat ( 1995 – 1998 ) = = = 
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- <unk> motivated by the playoff defeat , Jordan trained aggressively for the 1995 – 96 season . <unk> by the addition of rebound specialist Dennis Rodman , the Bulls dominated the league , starting the season 41 – 3 , and eventually finishing with the then @-@ best regular season record in NBA history ( later surpassed by the 2015 – 16 Golden State Warriors ) : 72 – 10 . Jordan led the league in scoring with 30 @.@ 4 ppg , and won the league 's regular season and All @-@ Star Game MVP awards . 
- In the playoffs , the Bulls lost only three games in four series ( Miami Heat 3 @-@ 0 , New York Knicks 4 @-@ 1 , Orlando Magic 4 @-@ 0 ) . They defeated the Seattle <unk> 4 @-@ 2 in the NBA Finals to win their fourth championship . Jordan was named Finals MVP for a record fourth time , surpassing Magic Johnson 's three Finals MVP awards . He also achieved only the second sweep of the MVP Awards in the All @-@ Star Game , regular season and NBA Finals , Willis Reed having achieved the first , during the 1969 – 70 season . Because this was Jordan 's first championship since his father 's murder , and it was won on Father 's Day , Jordan reacted very emotionally upon winning the title , including a memorable scene of him crying on the locker room floor with the game ball . 
- In the 1996 – 97 season , the Bulls started out 69 – 11 , but missed out on a second consecutive 70 @-@ win season by losing their final two games to finish 69 – 13 . However , this year Jordan was beaten for the NBA MVP Award by Karl Malone . The Bulls again advanced to the Finals , where they faced Malone and the Utah Jazz . The series against the Jazz featured two of the more memorable clutch moments of Jordan 's career . He won Game 1 for the Bulls with a <unk> @-@ beating jump shot . In Game 5 , with the series tied at 2 , Jordan played despite being <unk> and <unk> from a stomach virus . In what is known as the " <unk> Game " , Jordan scored 38 points , including the game @-@ deciding 3 @-@ pointer with 25 seconds remaining . The Bulls won 90 – 88 and went on to win the series in six games . For the fifth time in as many Finals appearances , Jordan received the Finals MVP award . During the 1997 NBA All @-@ Star Game , Jordan posted the first triple double in All @-@ Star Game history in a victorious effort ; however , he did not receive the MVP award . 
- Jordan and the Bulls compiled a 62 – 20 record in the 1997 – 98 season . Jordan led the league with 28 @.@ 7 points per game , securing his fifth regular @-@ season MVP award , plus honors for All @-@ NBA First Team , First Defensive Team and the All @-@ Star Game MVP . The Bulls won the Eastern Conference Championship for a third straight season , including surviving a seven @-@ game series with the Indiana <unk> in the Eastern Conference Finals ; it was the first time Jordan had played in a Game 7 since the 1992 Eastern Conference <unk> with the Knicks . After winning , they moved on for a rematch with the Jazz in the Finals . 
- The Bulls returned to the Delta Center for Game 6 on June 14 , 1998 , leading the series 3 – 2 . Jordan executed a series of plays , considered to be one of the greatest clutch performances in NBA Finals history . With the Bulls trailing 86 – 83 with 41 @.@ 9 seconds remaining in the fourth quarter , Phil Jackson called a timeout . When play resumed , Jordan received the inbound pass , drove to the basket , and hit a shot over several Jazz defenders , cutting the Utah lead to 86 – 85 . The Jazz brought the ball <unk> and passed the ball to forward Karl Malone , who was set up in the low post and was being guarded by Rodman . Malone <unk> with Rodman and caught the pass , but Jordan cut behind him and took the ball out of his hands for a steal . Jordan then <unk> down the court and paused , <unk> his defender , Jazz guard <unk> Russell . With 10 seconds remaining , Jordan started to <unk> right , then crossed over to his left , possibly pushing off Russell , although the officials did not call a foul . With 5 @.@ 2 seconds left , Jordan gave Chicago an 87 – 86 lead with a game @-@ winning <unk> , the climactic shot of his Bulls career . Afterwards , John Stockton missed a game @-@ winning three @-@ pointer . Jordan and the Bulls won their sixth NBA championship and second three @-@ peat . Once again , Jordan was voted the Finals MVP , having led all scorers averaging 33 @.@ 5 points per game , including 45 in the deciding Game 6 . Jordan 's six Finals MVPs is a record ; <unk> O <unk> , Magic Johnson , LeBron James and Tim Duncan are tied for second place with three apiece . The 1998 Finals holds the highest television rating of any Finals series in history . Game 6 also holds the highest television rating of any game in NBA history . 
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- = = = Second retirement ( 1999 – 2001 ) = = = 
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- With Phil Jackson 's contract expiring , the pending departures of Scottie Pippen and Dennis Rodman looming , and being in the latter stages of an owner @-@ induced <unk> of NBA players , Jordan retired for the second time on January 13 , 1999 . On January 19 , 2000 , Jordan returned to the NBA not as a player , but as part owner and President of Basketball Operations for the Washington Wizards . Jordan 's responsibilities with the Wizards were comprehensive . He controlled all aspects of the Wizards ' basketball operations , and had the final say in all personnel matters . Opinions of Jordan as a basketball executive were mixed . He managed to purge the team of several highly paid , unpopular players ( such as forward <unk> Howard and point guard Rod Strickland ) , but used the first pick in the 2001 NBA draft to select high <unk> Kwame Brown , who did not live up to expectations and was traded away after four seasons . 
- Despite his January 1999 claim that he was " 99 @.@ 9 % certain " that he would never play another NBA game , in the summer of 2001 Jordan expressed interest in making another comeback , this time with his new team . Inspired by the NHL comeback of his friend Mario Lemieux the previous winter , Jordan spent much of the spring and summer of 2001 in training , holding several invitation @-@ only camps for NBA players in Chicago . In addition , Jordan hired his old Chicago Bulls head coach , Doug Collins , as Washington 's coach for the upcoming season , a decision that many saw as foreshadowing another Jordan return . 
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- = = = Washington Wizards comeback ( 2001 – 2003 ) = = = 
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- On September 25 , 2001 , Jordan announced his return to the NBA to play for the Washington Wizards , indicating his intention to donate his salary as a player to a relief effort for the victims of the September 11 , 2001 attacks . In an injury @-@ plagued 2001 – 02 season , he led the team in scoring ( 22 @.@ 9 ppg ) , assists ( 5 @.@ 2 apg ) , and steals ( 1 @.@ 42 <unk> ) . However , torn cartilage in his right knee ended Jordan 's season after only 60 games , the fewest he had played in a regular season since playing 17 games after returning from his first retirement during the 1994 – 95 season . Jordan started 53 of his 60 games for the season , averaging 24 @.@ 3 points , 5 @.@ 4 assists , and 6 @.@ 0 rebounds , and shooting 41 @.@ 9 % from the field in his 53 starts . His last seven appearances were in a reserve role , in which he averaged just over 20 minutes per game . 
- Playing in his 14th and final NBA All @-@ Star Game in 2003 , Jordan passed Kareem Abdul @-@ Jabbar as the all @-@ time leading scorer in All @-@ Star Game history ( a record since broken by Kobe Bryant ) . That year , Jordan was the only Washington player to play in all 82 games , starting in 67 of them . He averaged 20 @.@ 0 points , 6 @.@ 1 rebounds , 3 @.@ 8 assists , and 1 @.@ 5 steals per game . He also shot 45 % from the field , and 82 % from the free throw line . Even though he turned 40 during the season , he scored 20 or more points 42 times , 30 or more points nine times , and 40 or more points three times . On February 21 , 2003 , Jordan became the first 40 @-@ year @-@ old to tally 43 points in an NBA game . During his stint with the Wizards , all of Jordan 's home games at the MCI Center were sold out , and the Wizards were the second most @-@ watched team in the NBA , averaging 20 @,@ 172 fans a game at home and 19 @,@ 311 on the road . However , neither of Jordan 's final two seasons resulted in a playoff appearance for the Wizards , and Jordan was often unsatisfied with the play of those around him . At several points he openly criticized his teammates to the media , citing their lack of focus and intensity , notably that of the number one draft pick in the 2001 NBA draft , Kwame Brown . 
- With the recognition that 2002 – 03 would be Jordan 's final season , tributes were paid to him throughout the NBA . In his final game at his old home court , the United Center in Chicago , Jordan received a four @-@ minute standing ovation . The Miami Heat retired the number 23 jersey on April 11 , 2003 , even though Jordan never played for the team . At the 2003 All @-@ Star Game , Jordan was offered a starting spot from Tracy <unk> and Allen <unk> , but refused both . In the end he accepted the spot of Vince Carter , who decided to give it up under great public pressure . 
- Jordan 's final NBA game was on April 16 , 2003 in Philadelphia . After scoring only 13 points in the game , Jordan went to the bench with 4 minutes and 13 seconds remaining in the third quarter and with his team trailing the Philadelphia 76ers , 75 – 56 . Just after the start of the fourth quarter , the First Union Center crowd began chanting " We want Mike ! " . After much encouragement from coach Doug Collins , Jordan finally rose from the bench and re @-@ entered the game , replacing Larry Hughes with 2 : 35 remaining . At 1 : 45 , Jordan was intentionally fouled by the 76ers ' Eric Snow , and stepped to the line to make both free throws . After the second foul shot , the 76ers in @-@ bounded the ball to rookie John <unk> , who in turn was intentionally fouled by Bobby Simmons one second later , stopping time so that Jordan could return to the bench . Jordan received a three @-@ minute standing ovation from his teammates , his opponents , the officials and the crowd of 21 @,@ 257 fans . 
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- = = Olympic career = = 
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- Jordan played on two Olympic gold medal @-@ winning American basketball teams . As a college player he participated , and won the gold , in the 1984 Summer Olympics . The team was coached by Bob Knight and featured players such as Patrick Ewing , Sam Perkins , Chris <unk> , Steve <unk> , and <unk> <unk> . Jordan led the team in scoring , averaging 17 @.@ 1 ppg for the tournament . 
- In the 1992 Summer Olympics , he was a member of the star @-@ studded squad that included Magic Johnson , Larry Bird , and David Robinson and was dubbed the " Dream Team " . Jordan was the only player to start all 8 games in the Olympics . Playing limited minutes due to the frequent <unk> , Jordan averaged 14 @.@ 9 ppg , finishing second on the team in scoring . Jordan and fellow Dream Team members Patrick Ewing and Chris <unk> are the only American men 's basketball players to win Olympic gold as amateurs and professionals . 
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- = = Post @-@ retirement = = 
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- After his third retirement , Jordan assumed that he would be able to return to his front office position of Director of Basketball Operations with the Wizards . However , his previous tenure in the Wizards ' front office had produced the aforementioned mixed results and may have also influenced the trade of Richard " <unk> " Hamilton for Jerry <unk> ( although Jordan was not technically Director of Basketball Operations in 2002 ) . On May 7 , 2003 , Wizards owner Abe <unk> fired Jordan as Washington 's President of Basketball Operations . Jordan later stated that he felt betrayed , and that if he knew he would be fired upon retiring he never would have come back to play for the Wizards . 
- Jordan kept busy over the next few years by staying in shape , playing golf in celebrity charity tournaments , spending time with his family in Chicago , promoting his Jordan Brand clothing line , and riding <unk> . Since 2004 , Jordan has owned Michael Jordan <unk> , a professional closed @-@ course motorcycle road racing team that competed with two <unk> in the premier <unk> championship sanctioned by the American <unk> Association ( <unk> ) until the end of the 2013 season . Jordan and his then @-@ wife Juanita pledged $ 5 million to Chicago 's <unk> Franciscan High School in 2006 , and the Jordan Brand has made donations to Habitat for <unk> and a Louisiana branch of the Boys & Girls <unk> of America . 
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- = = = Charlotte Bobcats / Hornets = = = 
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- On June 15 , 2006 , Jordan bought a minority stake in the Charlotte Bobcats , becoming the team 's second @-@ largest shareholder behind majority owner Robert L. Johnson . As part of the deal , Jordan took full control over the basketball side of the operation , with the title " Managing Member of Basketball Operations . " Despite Jordan 's previous success as an <unk> , he has made an effort not to be included in Charlotte 's marketing campaigns . A decade earlier , Jordan had made a bid to become part @-@ owner of Charlotte 's original NBA team , the Charlotte Hornets , but talks collapsed when owner George <unk> refused to give Jordan complete control of basketball operations . 
- In February 2010 , it was reported that Jordan was seeking majority ownership of the Bobcats . As February wore on , it emerged that the leading contenders for the team were Jordan and former Houston Rockets president George <unk> . On February 27 , the Bobcats announced that Johnson had reached an agreement with Jordan and his group , MJ Basketball Holdings , to buy the team pending NBA approval . On March 17 , the NBA Board of Governors unanimously approved Jordan 's purchase , making him the first former player ever to become the majority owner of an NBA team . It also made him the league 's only African @-@ American majority owner . 
- During the 2011 NBA <unk> , The New York Times wrote that Jordan led a group of 10 to 14 hardline owners wanting to cap the players ' share of basketball @-@ related income at 50 percent and as low as 47 . Journalists observed that , during the labor dispute in 1998 , Jordan had told Washington Wizards then @-@ owner Abe <unk> , " If you can 't make a profit , you should sell your team . " Jason <unk> of <unk> called Jordan a " sellout " wanting " current players to pay for his incompetence . " He cited Jordan 's executive decisions to draft disappointing players Kwame Brown and Adam Morrison . 
- During the 2011 – 12 NBA season , which was shortened to 66 games , the Bobcats posted a 7 – 59 record . Their <unk> winning percentage was the worst in NBA history . " I 'm not real happy about the record book scenario last year . It 's very , very frustrating " , Jordan said later that year . 
- On May 21 , 2013 , Jordan filed papers to change the Bobcats ' name to the Hornets , effective with the 2014 – 15 season . The Hornets name had become available when the original Hornets , who had moved to New Orleans in 2002 , changed their name to the New Orleans <unk> for the 2013 – 14 season . The NBA approved the change on July 18 . The name change became official on May 20 , 2014 . On the same day , the team announced that it had reclaimed the history and records of the original 1988 – 2002 Hornets . 
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- = = Player profile = = 
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- Jordan was a shooting guard who was also capable of playing as a small forward ( the position he would primarily play during his second return to professional basketball with the Washington Wizards ) , and as a point guard . Jordan was known throughout his career for being a strong clutch performer . With the Bulls , he decided 25 games with field goals or free throws in the last 30 seconds , including two NBA Finals games and five other playoff contests . His competitiveness was visible in his prolific trash @-@ talk and well @-@ known work ethic . As the Bulls organization built the franchise around Jordan , management had to trade away players who were not " tough enough " to compete with him in practice . To help improve his defense , he spent extra hours studying film of opponents . On offense , he relied more upon instinct and improvisation at game time . Noted as a durable player , Jordan did not miss four or more games while active for a full season from 1986 – 87 to 2001 – 02 , when he injured his right knee . He played all 82 games nine times . Jordan has frequently cited David Thompson , Walter Davis , and Jerry West as influences . From the start of his career , Jordan was unique among NBA players in that he had a special " Love of the Game <unk> " written into his contract , which allowed him to play basketball against anyone at any time , anywhere . 
- Jordan had a versatile offensive game . He was capable of aggressively driving to the basket , as well as drawing <unk> from his opponents at a high rate ; his 8 @,@ 772 free throw attempts are the ninth @-@ highest total of all time . As his career progressed , Jordan also developed the ability to post up his opponents and score with his trademark <unk> jump shot , using his leaping ability to " fade away " from block attempts . According to <unk> Brown , this move alone made him nearly <unk> . Despite media criticism as a " selfish " player early in his career , Jordan 's 5 @.@ 3 assists per game also indicate his willingness to <unk> to his teammates . In later years , the NBA shortened its three @-@ point line to 22 feet ( from 23 feet , 9 inches ) , which coupled with Jordan 's extended shooting range to make him a long @-@ range threat as well — his 3 @-@ point stroke developed from a low 9 / 52 rate ( <unk> ) in his rookie year into a stellar 111 / 260 ( <unk> ) shooter in the 1995 – 96 season . For a guard , Jordan was also a good <unk> ( 6 @.@ 2 per game ) . 
- In 1988 , Jordan was honored with the NBA 's Defensive Player of the Year Award and became the first NBA player to win both the Defensive Player of the Year and MVP awards in a career ( since <unk> by <unk> Olajuwon , David Robinson , and Kevin <unk> ; Olajuwon is the only player other than Jordan to win both during the same season ) . In addition he set both seasonal and career records for blocked shots by a guard , and combined this with his ball @-@ <unk> ability to become a standout defensive player . He ranks third in NBA history in total steals with 2 @,@ <unk> , trailing John Stockton and Jason <unk> . Jerry West often stated that he was more impressed with Jordan 's defensive contributions than his offensive ones . He was also known to have strong eyesight ; broadcaster Al Michaels said that he was able to read baseball box scores on a 27 @-@ inch television clearly from about 50 feet away . 
- 
- = = NBA career statistics = = 
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- 
- = = = Regular season = = = 
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- = = = <unk> = = = 
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- 
- = = Legacy = = 
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- Jordan 's marked talent was clear from his rookie season . In his first game in Madison Square Garden against the New York Knicks , Jordan received a prolonged standing ovation , a rarity for an opposing player . After Jordan scored a playoff record 63 points against the Boston Celtics on April 20 , 1986 , Celtics star Larry Bird described him as " God disguised as Michael Jordan . " 
- Jordan led the NBA in scoring in 10 seasons ( NBA record ) and tied Wilt Chamberlain 's record of seven consecutive scoring titles . He was also a fixture on the NBA All @-@ Defensive First Team , making the roster nine times ( NBA record shared with Gary <unk> , Kevin <unk> and Kobe Bryant ) . Jordan also holds the top career regular season and playoff scoring averages of 30 @.@ 1 and 33 @.@ 4 points per game , respectively . By 1998 , the season of his Finals @-@ winning shot against the Jazz , he was well known throughout the league as a clutch performer . In the regular season , Jordan was the Bulls ' primary threat in the final seconds of a close game and in the playoffs , Jordan would always demand the ball at <unk> time . Jordan 's total of 5 @,@ <unk> points in the playoffs is the highest in NBA history . He retired with 32 @,@ <unk> points in regular season play , placing him fourth on the NBA 's all @-@ time scoring list behind Kareem Abdul @-@ Jabbar , Karl Malone , and Kobe Bryant . 
- With five regular @-@ season MVPs ( tied for second place with Bill Russell ; only Kareem Abdul @-@ Jabbar has won more , six ) , six Finals MVPs ( NBA record ) , and three All @-@ Star MVPs , Jordan is the most decorated player ever to play in the NBA . Jordan finished among the top three in regular @-@ season MVP voting a record 10 times , and was named one of the 50 Greatest Players in NBA History in 1996 . He is one of only seven players in history to win an NCAA championship , an NBA championship , and an Olympic gold medal ( doing so twice with the 1984 and 1992 U.S. men 's basketball teams ) . 
- Many of Jordan 's contemporaries say that Jordan is the greatest basketball player of all time . In 1999 , an ESPN survey of journalists , athletes and other sports figures ranked Jordan the greatest North American athlete of the 20th century , above such <unk> as Babe Ruth and Muhammad Ali . Jordan placed second to Babe Ruth in the Associated Press 's December 1999 list of 20th century athletes . In addition , the Associated Press voted him as the basketball player of the 20th century . Jordan has also appeared on the front cover of Sports Illustrated a record 50 times . In the September 1996 issue of Sport , which was the publication 's 50th anniversary issue , Jordan was named the greatest athlete of the past 50 years . 
- Jordan 's athletic leaping ability , highlighted in his back @-@ to @-@ back slam dunk contest championships in 1987 and 1988 , is credited by many with having influenced a generation of young players . Several current NBA All @-@ Stars have stated that they considered Jordan their role model while growing up , including LeBron James and <unk> Wade . In addition , commentators have dubbed a number of next @-@ generation players " the next Michael Jordan " upon their entry to the NBA , including <unk> " Penny " <unk> , Grant Hill , Allen <unk> , Kobe Bryant , LeBron James , Vince Carter , and <unk> Wade . Although Jordan was a well @-@ rounded player , his " Air Jordan " image is also often credited with inadvertently decreasing the jump shooting skills , defense , and <unk> of young players , a fact Jordan himself has lamented . 
- I think it was the exposure of Michael Jordan ; the marketing of Michael Jordan . Everything was marketed towards the things that people wanted to see , which was scoring and <unk> . That Michael Jordan still played defense and an all @-@ around game , but it was never really publicized . 
- Although Jordan has done much to increase the status of the game , some of his impact on the game 's popularity in America appears to be fleeting . Television ratings in particular increased only during his time in the league , and Finals ratings have not returned to the level reached during his last championship @-@ winning season . 
- In August 2009 , the Basketball Hall of Fame in Springfield , Massachusetts , opened a Michael Jordan exhibit containing items from his college and NBA careers , as well as from the 1992 " Dream Team " . The exhibit also has a batting <unk> to signify Jordan 's short career in baseball . After Jordan received word of his being accepted into the Hall of Fame , he selected Class of 1996 member David Thompson to present him . As Jordan would later explain during his induction speech in September 2009 , growing up in North Carolina , he was not a fan of the Tar <unk> , and greatly admired Thompson , who played at rival North Carolina State . He was inducted into the Hall in September , with several former Bulls teammates in attendance , including Scottie Pippen , Dennis Rodman , Charles Oakley , Ron Harper , Steve Kerr , and <unk> <unk> . Former coaches of Jordan 's , Dean Smith and Doug Collins , were also among those present . His emotional reaction during his speech , when he began to cry , was captured by Associated Press photographer <unk> <unk> and would later become widely shared on social media as the Crying Jordan Internet <unk> . 
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- = = Personal life = = 
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- He married Juanita <unk> in September 1989 , and they have two sons , Jeffrey Michael and Marcus James , and a daughter , Jasmine . Jordan and <unk> filed for divorce on January 4 , 2002 , citing <unk> differences , but reconciled shortly thereafter . They again filed for divorce and were granted a final decree of dissolution of marriage on December 29 , 2006 , commenting that the decision was made " mutually and <unk> " . It is reported that Juanita received a $ 168 million settlement ( equivalent to $ 197 million in 2015 ) , making it the largest celebrity divorce settlement in history at the time on public record . 
- In 1991 , Jordan purchased a lot in Highland Park , Illinois , to build a 56 @,@ 000 square foot mansion , which was completed four years later . Both of his sons attended Loyola Academy , a private Roman Catholic high school located in <unk> , Illinois . Jeffrey graduated as a member of the 2007 graduating class and played his first collegiate basketball game on November 11 , 2007 , for the University of Illinois . After two seasons , Jeffrey left the Illinois basketball team in 2009 . He later rejoined the team for a third season , then received a release to transfer to the University of Central Florida , where Marcus was attending . Marcus transferred to Whitney Young High School after his sophomore year at Loyola Academy and graduated in 2009 . He began attending <unk> in the fall of 2009 , and played three seasons of basketball for the school . 
- On July 21 , 2006 , a judge in Cook County , Illinois , determined that Jordan did not owe his alleged former lover <unk> Knafel $ 5 million in a breach of contract claim . Jordan had allegedly paid Knafel $ 250 @,@ 000 to keep their relationship a secret . Knafel claimed Jordan promised her $ 5 million for remaining silent and agreeing not to file a paternity suit after Knafel learned she was pregnant in 1991 . A DNA test showed Jordan was not the father of the child . 
- He proposed to his longtime girlfriend , Cuban @-@ American model <unk> <unk> , on Christmas Eve , 2011 , and they were married on April 27 , 2013 , at Bethesda @-@ by @-@ the @-@ Sea Episcopal Church . It was announced on November 30 , 2013 , that the two were expecting their first child together . Jordan listed his Highland Park mansion for sale in 2012 . On February 11 , 2014 , <unk> gave birth to identical twin daughters named Victoria and Ysabel . 
- Jordan 's private jet features a stripe in Carolina blue , the " Air Jordan " logo on the tail , and references to his career in the identification number . 
- 
- = = Media figure and business interests = = 
- 
- Jordan is one of the most marketed sports figures in history . He has been a major spokesman for such brands as Nike , Coca @-@ Cola , <unk> , <unk> , McDonald 's , Ball Park Franks , <unk> , <unk> , <unk> , and MCI . Jordan has had a long relationship with <unk> , appearing in over 20 commercials for the company since 1991 , including the " Be Like Mike " commercials in which a song was sung by children wishing to be like Jordan . 
- Nike created a signature shoe for him , called the Air Jordan . One of Jordan 's more popular commercials for the shoe involved Spike Lee playing the part of Mars Blackmon . In the commercials Lee , as Blackmon , attempted to find the source of Jordan 's abilities and became convinced that " it 's gotta be the shoes " . The hype and demand for the shoes even brought on a <unk> of " shoe @-@ <unk> " where people were robbed of their <unk> at <unk> . Subsequently , Nike spun off the Jordan line into its own division named the " Jordan Brand " . The company features an impressive list of athletes and celebrities as <unk> . The brand has also sponsored college sports programs such as those of North Carolina , <unk> , Georgetown , and Marquette . 
- Jordan also has been associated with the <unk> <unk> cartoon characters . A Nike commercial shown during 1992 's Super Bowl <unk> featured Jordan and <unk> Bunny playing basketball . The Super Bowl commercial inspired the 1996 live action / animated film Space Jam , which starred Jordan and <unk> in a fictional story set during the former 's first retirement from basketball . They have subsequently appeared together in several commercials for MCI . Jordan also made an appearance in the music video of Michael Jackson 's " Jam " ( 1992 ) . 
- Jordan 's yearly income from the endorsements is estimated to be over forty million dollars . In addition , when Jordan 's power at the ticket gates was at its highest point , the Bulls regularly sold out both their home and road games . Due to this , Jordan set records in player salary by signing annual contracts worth in excess of US $ 30 million per season . An academic study found that Jordan 's first NBA comeback resulted in an increase in the market <unk> of his client firms of more than $ 1 billion . 
- Most of Jordan 's endorsement deals , including his first deal with Nike , were engineered by his agent , David <unk> . Jordan has described <unk> as " the best at what he does " and that " marketing @-@ wise , he 's great . He 's the one who came up with the concept of ' Air Jordan . ' " 
- In June 2010 , Jordan was ranked by Forbes magazine as the 20th @-@ most powerful celebrity in the world with $ 55 million earned between June 2009 and June 2010 . According to the Forbes article , Jordan Brand generates $ 1 billion in sales for Nike . In June 2014 , Jordan was named the first NBA player to become a billionaire , after he increased his stake in the Charlotte Hornets from 80 % to 89 @.@ 5 % . On January 20 , 2015 , Jordan was honored with the Charlotte Business Journal 's Business Person of the Year for 2014 . As of November 2015 , his current net worth is estimated at $ 1 @.@ 1 billion by Forbes . Jordan is the second @-@ richest African @-@ American in the world as of 2015 . 
- 
- = = Awards and honors = = 
- 
- 
- 
- = Polish culture during World War II = 
- 
- Polish culture during World War II was suppressed by the occupying powers of Nazi Germany and the Soviet Union , both of whom were hostile to Poland 's people and cultural heritage . <unk> aimed at cultural <unk> resulted in the deaths of thousands of scholars and artists , and the theft and destruction of <unk> cultural artifacts . The " <unk> of the Poles was one of many ways in which the Nazi and Soviet regimes had grown to resemble one another " , wrote British historian Niall Ferguson . 
- The <unk> looted and destroyed much of Poland 's cultural and historical heritage , while <unk> and murdering members of the Polish cultural elite . Most Polish schools were closed , and those that remained open saw their <unk> altered significantly . 
- Nevertheless , underground organizations and individuals – in particular the Polish Underground State – saved much of Poland 's most valuable cultural treasures , and worked to salvage as many cultural institutions and artifacts as possible . The Catholic Church and wealthy individuals contributed to the survival of some artists and their works . Despite severe <unk> by the Nazis and Soviets , Polish underground cultural activities , including publications , concerts , live theater , education , and academic research , continued throughout the war . 
- 
- = = Background = = 
- 
- In 1795 Poland ceased to exist as an sovereign nation and throughout the 19th century remained partitioned by degrees between Prussian , Austrian and Russian empires . Not until the end of World War I was independence restored and the nation reunited , although the drawing of boundary lines was , of necessity , a contentious issue . Independent Poland lasted for only 21 years before it was again attacked and divided among foreign powers . 
- On 1 September 1939 , Germany invaded Poland , initiating World War II in Europe , and on 17 September , pursuant to the <unk> @-@ <unk> Pact , Poland was invaded by the Soviet Union . Subsequently Poland was partitioned again – between these two powers – and remained under occupation for most of the war . By 1 October , Germany and the Soviet Union had completely overrun Poland , although the Polish government never formally surrendered , and the Polish Underground State , subordinate to the Polish government @-@ in @-@ exile , was soon formed . On 8 October , Nazi Germany annexed the western areas of pre @-@ war Poland and , in the remainder of the occupied area , established the General Government . The Soviet Union had to temporarily give up the territorial gains it made in 1939 due to the German invasion of the Soviet Union , but permanently re @-@ annexed much of this territory after winning it back in mid @-@ 1944 . Over the course of the war , Poland lost over 20 % of its pre @-@ war population amid an occupation that marked the end of the Second Polish Republic . 
- 
- = = Destruction of Polish culture = = 
- 
- 
- = = = German occupation = = = 
- 
- 
- = = = = Policy = = = = 
- 
- Germany 's policy toward the Polish nation and its culture evolved during the course of the war . Many German officials and military officers were initially not given any clear guidelines on the treatment of Polish cultural institutions , but this quickly changed . Immediately following the invasion of Poland in September 1939 , the Nazi German government implemented the first stages ( the " small plan " ) of <unk> Ost . The basic policy was outlined by the Berlin Office of Racial Policy in a document titled Concerning the Treatment of the <unk> of the Former Polish Territories , from a Racial @-@ Political <unk> . Slavic people living east of the pre @-@ war German border were to be <unk> , enslaved or eradicated , depending on whether they lived in the territories directly annexed into the German state or in the General Government . 
- Much of the German policy on Polish culture was formulated during a meeting between the governor of the General Government , Hans Frank , and Nazi Minister of Propaganda Joseph Goebbels , at <unk> on 31 October 1939 . Goebbels declared that " The Polish nation is not worthy to be called a cultured nation " . He and Frank agreed that opportunities for the Poles to experience their culture should be severely restricted : no theaters , cinemas or <unk> ; no access to radio or press ; and no education . Frank suggested that the Poles should periodically be shown films highlighting the achievements of the Third Reich and should eventually be addressed only by <unk> . During the following weeks Polish schools beyond middle vocational levels were closed , as were theaters and many other cultural institutions . The only Polish @-@ language newspaper published in occupied Poland was also closed , and the arrests of Polish intellectuals began . 
- In March 1940 , all cultural activities came under the control of the General Government 's Department of People 's Education and Propaganda ( <unk> für <unk> und Propaganda ) , whose name was changed a year later to the " Chief Propaganda Department " ( <unk> Propaganda ) . Further <unk> issued in the spring and early summer reflected policies that had been outlined by Frank and Goebbels during the previous autumn . One of the Department 's earliest decrees prohibited the organization of all but the most " primitive " of cultural activities without the Department 's prior approval . <unk> of " low quality " , including those of an erotic or pornographic nature , were however an exception — those were to be popularized to appease the population and to show the world the " real " Polish culture as well as to create the impression that Germany was not preventing Poles from expressing themselves . German propaganda specialists invited critics from neutral countries to specially organized " Polish " performances that were specifically designed to be boring or pornographic , and presented them as typical Polish cultural activities . Polish @-@ German cooperation in cultural matters , such as joint public performances , was strictly prohibited . Meanwhile , a compulsory registration scheme for writers and artists was introduced in August 1940 . Then , in October , the printing of new Polish @-@ language books was prohibited ; existing titles were censored , and often confiscated and withdrawn . 
- In 1941 , German policy evolved further , calling for the complete destruction of the Polish people , whom the Nazis regarded as " <unk> " ( <unk> ) . Within ten to twenty years , the Polish territories under German occupation were to be entirely cleared of ethnic Poles and settled by German colonists . The policy was relaxed somewhat in the final years of occupation ( 1943 – 44 ) , in view of German military defeats and the approaching Eastern Front . The Germans hoped that a more lenient cultural policy would lessen unrest and weaken the Polish Resistance . Poles were allowed back into those museums that now supported German propaganda and indoctrination , such as the newly created Chopin museum , which emphasized the composer 's invented German roots . <unk> on education , theater and music performances were eased . 
- Given that the Second Polish Republic was a <unk> state , German policies and propaganda also sought to create and encourage conflicts between ethnic groups , <unk> tension between Poles and Jews , and between Poles and <unk> . In <unk> , the Germans forced Jews to help destroy a monument to a Polish hero , Tadeusz <unk> , and filmed them committing the act . Soon afterward , the Germans set fire to a Jewish synagogue and filmed Polish <unk> , portraying them in propaganda releases as a " vengeful mob . " This divisive policy was reflected in the Germans ' decision to destroy Polish education , while at the same time , showing relative tolerance toward the Ukrainian school system . As the high @-@ ranking Nazi official Erich Koch explained , " We must do everything possible so that when a Pole meets a Ukrainian , he will be willing to kill the Ukrainian and <unk> , the Ukrainian will be willing to kill the Pole . " 
- 
- = = = = <unk> = = = = 
- 
- In 1939 , as the occupation regime was being established , the Nazis confiscated Polish state property and much private property . <unk> art objects were looted and taken to Germany , in line with a plan that had been drawn up well in advance of the invasion . The looting was supervised by experts of the SS @-@ <unk> , <unk> units , who were responsible for art , and by experts of <unk> Ost , who were responsible for more mundane objects . Notable items plundered by the Nazis included the Altar of <unk> <unk> and paintings by Raphael , <unk> , Leonardo da Vinci , <unk> and <unk> . Most of the important art pieces had been " secured " by the Nazis within six months of September 1939 ; by the end of 1942 , German officials estimated that " over 90 % " of the art previously in Poland was in their possession . Some art was shipped to German museums , such as the planned <unk> in <unk> , while other art became the private property of Nazi officials . Over 516 @,@ 000 individual art pieces were taken , including 2 @,@ 800 paintings by European painters ; 11 @,@ 000 works by Polish painters ; 1 @,@ 400 sculptures , 75 @,@ 000 manuscripts , 25 @,@ 000 maps , and 90 @,@ 000 books ( including over 20 @,@ 000 printed before 1800 ) ; as well as hundreds of thousands of other objects of artistic and historic value . Even exotic animals were taken from the zoos . 
- 
- = = = = Destruction = = = = 
- 
- Many places of learning and culture — universities , schools , libraries , museums , theaters and cinemas — were either closed or designated as " Nur für Deutsche " ( For Germans Only ) . Twenty @-@ five museums and a host of other institutions were destroyed during the war . According to one estimate , by war 's end 43 % of the infrastructure of Poland 's educational and research institutions and 14 % of its museums had been destroyed . According to another , only 105 of pre @-@ war Poland 's 175 museums survived the war , and just 33 of these institutions were able to reopen . Of pre @-@ war Poland 's 603 scientific institutions , about half were totally destroyed , and only a few survived the war relatively intact . 
- Many university professors , as well as teachers , lawyers , artists , writers , priests and other members of the Polish intelligentsia were arrested and executed , or transported to concentration camps , during operations such as <unk> @-@ <unk> . This particular campaign resulted in the infamous <unk> <unk> and the massacre of Lwów professors . During World War II Poland lost 39 % to 45 % of its <unk> and dentists , 26 % to 57 % of its lawyers , 15 % to 30 % of its teachers , 30 % to 40 % of its scientists and university professors , and 18 % to 28 % of its clergy . The Jewish intelligentsia was <unk> altogether . The reasoning behind this policy was clearly articulated by a Nazi <unk> : " In my district , [ any Pole who ] shows signs of intelligence will be shot . " 
- As part of their program to suppress Polish culture , the German Nazis attempted to destroy Christianity in Poland , with a particular emphasis on the Roman Catholic Church . In some parts of occupied Poland , Poles were restricted , or even forbidden , from attending religious services . At the same time , church property was confiscated , prohibitions were placed on using the Polish language in religious services , organizations affiliated with the Catholic Church were abolished , and it was forbidden to perform certain religious songs — or to read passages of the Bible — in public . The worst conditions were found in the <unk> <unk> , which the Nazis treated as a laboratory for their anti @-@ religious policies . Polish clergy and religious leaders figured prominently among portions of the intelligentsia that were targeted for <unk> . 
- To <unk> the rise of a new generation of educated Poles , German officials <unk> that the schooling of Polish children would be limited to a few years of elementary education . <unk> @-@ SS Heinrich <unk> wrote , in a memorandum of May 1940 : " The sole purpose of this schooling is to teach them simple arithmetic , nothing above the number 500 ; how to write one 's name ; and the doctrine that it is divine law to obey the Germans .... I do not regard a knowledge of reading as desirable . " Hans Frank echoed him : " The Poles do not need universities or secondary schools ; the Polish lands are to be converted into an intellectual desert . " The situation was particularly dire in the former Polish territories beyond the General Government , which had been annexed to the Third Reich . The specific policy varied from territory to territory , but in general , there was no Polish @-@ language education at all . German policy constituted a crash @-@ Germanization of the populace . Polish teachers were dismissed , and some were invited to attend " orientation " meetings with the new administration , where they were either summarily arrested or executed on the spot . Some Polish schoolchildren were sent to German schools , while others were sent to special schools where they spent most of their time as unpaid laborers , usually on German @-@ run farms ; speaking Polish brought severe punishment . It was expected that Polish children would begin to work once they finished their primary education at age 12 to 15 . In the eastern territories not included in the General Government ( <unk> <unk> , <unk> <unk> and <unk> Ukraine ) many primary schools were closed , and most education was conducted in non @-@ Polish languages such as Ukrainian , <unk> , and Lithuanian . In the <unk> <unk> region , for example , 86 % of the schools that had existed before the war were closed down during the first two years of German occupation , and by the end of the following year that figure had increased to 93 % . 
- The state of Polish primary schools was somewhat better in the General Government , though by the end of 1940 , only 30 % of prewar schools were operational , and only 28 % of prewar Polish children attended them . A German police memorandum of August 1943 described the situation as follows : 
- Pupils sit <unk> together without necessary materials , and often without skilled teaching staff . Moreover , the Polish schools are closed during at least five months out of the ten months of the school year due to lack of coal or other fuel . Of twenty @-@ thirty <unk> school buildings which Kraków had before 1939 , today the worst two buildings are used ... Every day , pupils have to study in several shifts . Under such circumstances , the school day , which normally lasts five hours , is reduced to one hour . 
- In the General Government , the remaining schools were <unk> to the German educational system , and the number and competence of their Polish staff was steadily scaled down . All universities and most secondary schools were closed , if not immediately after the invasion , then by mid @-@ 1940 . By late 1940 , no official Polish educational institutions more advanced than a vocational school remained in operation , and they offered nothing beyond the elementary trade and technical training required for the Nazi economy . Primary schooling was to last for seven years , but the classes in the final two years of the program were to be limited to meeting one day per week . There was no money for heating of the schools in winter . Classes and schools were to be merged , Polish teachers dismissed , and the resulting savings used to sponsor the creation of schools for children of the German minority or to create barracks for German troops . No new Polish teachers were to be trained . The educational curriculum was censored ; subjects such as literature , history and geography were removed . Old textbooks were confiscated and school libraries were closed . The new educational aims for Poles included convincing them that their national fate was <unk> , and teaching them to be submissive and respectful to Germans . This was accomplished through deliberate tactics such as police raids on schools , police inspections of student belongings , mass arrests of students and teachers , and the use of students as forced laborers , often by transporting them to Germany as seasonal workers . 
- The Germans were especially active in the destruction of Jewish culture in Poland ; nearly all of the wooden <unk> there were destroyed . Moreover , the sale of Jewish literature was banned throughout Poland . 
- Polish literature faced a similar fate in territories annexed by Germany , where the sale of Polish books was forbidden . The public destruction of Polish books was not limited to those seized from libraries , but also included those books that were confiscated from private homes . The last Polish book titles not already proscribed were withdrawn in 1943 ; even Polish prayer books were confiscated . Soon after the occupation began , most libraries were closed ; in Kraków , about 80 % of the libraries were closed immediately , while the remainder saw their collections decimated by censors . The occupying powers destroyed Polish book collections , including the Sejm and Senate Library , the <unk> Estate Library , the <unk> Estate Library , the Central Military Library , and the <unk> Collection . In 1941 , the last remaining Polish public library in the German @-@ occupied territories was closed in Warsaw . During the war , Warsaw libraries lost about a million volumes , or 30 % of their collections . More than 80 % of these losses were the direct result of <unk> rather than wartime conflict . Overall , it is estimated that about 10 million volumes from state @-@ owned libraries and institutions perished during the war . 
- Polish flags and other symbols were confiscated . The war on the Polish language included the tearing down of signs in Polish and the banning of Polish speech in public places . Persons who spoke Polish in the streets were often insulted and even physically assaulted . The Germanization of place names prevailed . Many treasures of Polish culture – including memorials , plaques and monuments to national heroes ( e.g. , Kraków 's Adam Mickiewicz monument ) – were destroyed . In <unk> , all Polish monuments and plaques were torn down . Dozens of monuments were destroyed throughout Poland . The Nazis planned to level entire cities . 
- 
- = = = = <unk> and propaganda = = = = 
- 
- The Germans prohibited publication of any regular Polish @-@ language book , literary study or scholarly paper . In 1940 , several German @-@ controlled printing houses began operating in occupied Poland , publishing items such as Polish @-@ German dictionaries and antisemitic and <unk> novels . 
- <unk> at first targeted books that were considered to be " serious " , including scientific and educational texts and texts that were thought to promote Polish patriotism ; only fiction that was free of anti @-@ German overtones was permitted . <unk> literature included maps , <unk> and <unk> and French @-@ language publications , including dictionaries . Several non @-@ public <unk> of prohibited books were created , and over 1 @,@ 500 Polish writers were declared " dangerous to the German state and culture " . The index of banned authors included such Polish authors as Adam Mickiewicz , Juliusz <unk> , Stanisław <unk> , Bolesław <unk> , Stefan <unk> , <unk> <unk> <unk> , Władysław <unk> , Stanisław <unk> , Julian <unk> , <unk> <unk> , Leopold Staff , Eliza <unk> and Maria <unk> . Mere possession of such books was illegal and punishable by imprisonment . <unk> @-@ to @-@ door sale of books was banned , and <unk> — which required a license to operate — were either emptied out or closed . 
- Poles were forbidden , under penalty of death , to own radios . The press was reduced from over 2 @,@ 000 publications to a few dozen , all censored by the Germans . All pre @-@ war newspapers were closed , and the few that were published during the occupation were new creations under the total control of the Germans . Such a thorough destruction of the press was unprecedented in contemporary history . The only officially available reading matter was the propaganda press that was disseminated by the German occupation administration . <unk> , now under the control of the German propaganda machine , saw their programming dominated by Nazi German movies , which were preceded by propaganda <unk> . The few Polish films permitted to be shown ( about 20 % of the total programming ) were edited to eliminate references to Polish national symbols as well as Jewish actors and producers . Several propaganda films were shot in Polish , although no Polish films were shown after 1943 . As all profits from Polish cinemas were officially directed toward German war production , attendance was discouraged by the Polish underground ; a famous underground slogan declared : " <unk> <unk> <unk> w <unk> " ( " Only pigs attend the movies " ) . A similar situation faced theaters , which were forbidden by the Germans to produce " serious " <unk> . Indeed , a number of propaganda pieces were created for theater stages . Hence , theatrical productions were also boycotted by the underground . In addition , actors were discouraged from performing in them and warned that they would be labeled as collaborators if they failed to comply . Ironically , restrictions on cultural performances were eased in Jewish ghettos , given that the Germans wished to distract <unk> inhabitants and prevent them from grasping their eventual fate . 
- Music was the least restricted of cultural activities , probably because Hans Frank regarded himself as a fan of serious music . In time , he ordered the creation of the Orchestra and Symphony of the General Government in its capital , Kraków . Numerous musical performances were permitted in <unk> and churches , and the Polish underground chose to boycott only the propagandist operas . Visual artists , including painters and <unk> , were compelled to register with the German government ; but their work was generally <unk> by the underground , unless it conveyed propagandist themes . <unk> museums were replaced by occasional art exhibitions that frequently conveyed propagandist themes . 
- The development of Nazi propaganda in occupied Poland can be divided into two main phases . Initial efforts were directed towards creating a negative image of pre @-@ war Poland , and later efforts were aimed at <unk> anti @-@ Soviet , antisemitic , and pro @-@ German attitudes . 
- 
- = = = Soviet occupation = = = 
- 
- After the Soviet invasion of Poland ( beginning 17 September 1939 ) that followed the German invasion that had marked the start of World War II ( beginning 1 September 1939 ) , the Soviet Union annexed the eastern parts ( " <unk> " ) of the Second Polish Republic , comprising 201 @,@ 015 square kilometres ( 77 @,@ 612 sq mi ) and a population of 13 @.@ <unk> million . Hitler and Stalin shared the goal of <unk> Poland 's political and cultural life , so that Poland would , according to historian Niall Ferguson , " cease to exist not merely as a place , but also as an idea " . 
- The Soviet authorities regarded service to the prewar Polish state as a " crime against revolution " and " counter @-@ revolutionary activity " and arrested many members of the Polish intelligentsia , politicians , civil servants and academics , as well as ordinary persons suspected of posing a threat to Soviet rule . More than a million Polish citizens were deported to Siberia , many to <unk> concentration camps , for years or decades . Others died , including over 20 @,@ 000 military officers who perished in the <unk> massacres . 
- The Soviets quickly <unk> the annexed lands , introducing compulsory <unk> . They proceeded to confiscate , <unk> and <unk> private and state @-@ owned Polish property . In the process , they banned political parties and public associations and imprisoned or executed their leaders as " enemies of the people " . In line with Soviet anti @-@ religious policy , churches and religious organizations were persecuted . On 10 February 1940 , the <unk> unleashed a campaign of terror against " anti @-@ Soviet " elements in occupied Poland . The Soviets ' targets included persons who often traveled abroad , persons involved in overseas correspondence , <unk> , <unk> , Red Cross workers , refugees , smugglers , priests and members of religious congregations , the nobility , landowners , wealthy merchants , <unk> , <unk> , and hotel and restaurant owners . Stalin , like Hitler , worked to eliminate Polish society . 
- The Soviet authorities sought to remove all trace of the Polish history of the area now under their control . The name " Poland " was banned . Polish monuments were torn down . All institutions of the dismantled Polish state , including the Lwów University , were closed , then reopened , mostly with new Russian directors . Soviet Communist ideology became paramount in all teaching . Polish literature and language studies were dissolved by the Soviet authorities , and the Polish language was replaced with Russian or Ukrainian . Polish @-@ language books were burned even in the primary schools . Polish teachers were not allowed in the schools , and many were arrested . Classes were held in <unk> , Lithuanian and Ukrainian , with a new pro @-@ Soviet curriculum . As Polish @-@ Canadian historian Piotr <unk> noted , citing British historians M. R. D. Foot and I. C. B. Dear , majority of scholars believe that " In the Soviet occupation zone , conditions were only marginally less harsh than under the Germans . " In September 1939 , many Polish Jews had fled east ; after some months of living under Soviet rule , some of them wanted to return to the German zone of occupied Poland . 
- All publications and media were subjected to censorship . The Soviets sought to recruit Polish left @-@ wing intellectuals who were willing to cooperate . Soon after the Soviet invasion , the Writers ' Association of Soviet Ukraine created a local chapter in Lwów ; there was a Polish @-@ language theater and radio station . Polish cultural activities in <unk> and <unk> were less organized . These activities were strictly controlled by the Soviet authorities , which saw to it that these activities portrayed the new Soviet regime in a positive light and vilified the former Polish government . 
- The Soviet propaganda @-@ motivated support for Polish @-@ language cultural activities , however , clashed with the official policy of <unk> . The Soviets at first intended to phase out the Polish language and so banned Polish from schools , street signs , and other aspects of life . This policy was , however , reversed at times — first before the elections in October 1939 ; and later , after the German conquest of France . In November 1940 , the Poles of Lwów observed the 85th anniversary of Adam Mickiewicz 's death . Soon , however , Stalin decided to re @-@ implement the <unk> policies . He reversed his decision again , however , when a need arose for Polish @-@ language pro @-@ Soviet propaganda following the German invasion of the Soviet Union ; as a result Stalin permitted the creation of Polish forces in the East and later decided to create a Communist People 's Republic of Poland . 
- Many Polish writers collaborated with the Soviets , writing pro @-@ Soviet propaganda . They included Jerzy <unk> , Tadeusz Boy @-@ Żeleński , Kazimierz <unk> , <unk> <unk> , Jan <unk> , <unk> <unk> , Leon <unk> , Zuzanna Ginczanka , Halina <unk> , <unk> <unk> , Stefan <unk> , Stanisław Jerzy <unk> , Tadeusz <unk> , Juliusz <unk> , Jan <unk> , <unk> <unk> , Karol <unk> , Leopold <unk> , <unk> <unk> , Jerzy <unk> , Leon <unk> , Julian <unk> , Jerzy <unk> , Jerzy <unk> , Adolf Rudnicki , Włodzimierz <unk> , Włodzimierz <unk> , <unk> <unk> , <unk> Stern , Julian <unk> , <unk> <unk> , Leopold <unk> , Wanda <unk> , Stanisław <unk> , Adam <unk> , Aleksander <unk> and Bruno <unk> . 
- Other Polish writers , however , rejected the Soviet <unk> and instead published underground : <unk> <unk> , Jerzy <unk> , <unk> <unk> @-@ <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , Tadeusz <unk> , <unk> <unk> , Juliusz <unk> . Some writers , such as Władysław <unk> , after <unk> with the Soviets for a few months , joined the anti @-@ Soviet opposition . Similarly , Aleksander Wat , initially sympathetic to communism , was arrested by the Soviet <unk> secret police and exiled to Kazakhstan . 
- 
- = = Underground culture = = 
- 
- 
- = = = <unk> = = = 
- 
- Polish culture persisted in underground education , publications , even theater . The Polish Underground State created a Department of Education and Culture ( under Stanisław <unk> ) which , along with a Department of Labor and Social <unk> ( under Jan Stanisław <unk> and , later , Stefan <unk> ) and a Department for <unk> of the Effects of War ( under Antoni <unk> and <unk> <unk> ) , became underground patrons of Polish culture . These <unk> oversaw efforts to save from looting and destruction works of art in state and private collections ( most notably , the giant paintings by Jan <unk> that were concealed throughout the war ) . They compiled reports on looted and destroyed works and provided artists and scholars with means to continue their work and their publications and to support their families . Thus , they sponsored the underground publication ( <unk> ) of works by Winston Churchill and <unk> <unk> and of 10 @,@ 000 copies of a Polish primary @-@ school <unk> and commissioned artists to create resistance artwork ( which was then disseminated by Operation N and like activities ) . Also occasionally sponsored were secret art exhibitions , theater performances and concerts . 
- Other important patrons of Polish culture included the Roman Catholic Church and Polish <unk> , who likewise supported artists and <unk> Polish heritage ( notable patrons included Cardinal Adam Stefan <unk> and a former politician , <unk> <unk> ) . Some private publishers , including Stefan <unk> , Zbigniew <unk> and the <unk> publishing house , paid writers for books that would be delivered after the war . 
- 
- = = = Education = = = 
- 
- In response to the German closure and censorship of Polish schools , resistance among teachers led almost immediately to the creation of large @-@ scale underground educational activities . Most notably , the Secret Teaching Organization ( <unk> <unk> <unk> , TON ) was created as early as in October 1939 . Other organizations were created locally ; after 1940 they were increasingly subordinated and coordinated by the TON , working closely with the Underground 's State Department of Culture and Education , which was created in autumn 1941 and headed by <unk> <unk> , creator of the TON . Classes were either held under the cover of officially permitted activities or in private homes and other venues . By 1942 , about 1 @,@ 500 @,@ 000 students took part in underground primary education ; in 1944 , its secondary school system covered 100 @,@ 000 people , and university level courses were attended by about 10 @,@ 000 students ( for comparison , the pre @-@ war enrollment at Polish universities was about 30 @,@ 000 for the 1938 / 1939 year ) . More than 90 @,@ 000 secondary @-@ school pupils attended underground classes held by nearly 6 @,@ 000 teachers between 1943 and 1944 in four districts of the General Government ( centered on the cities of Warsaw , Kraków , <unk> and <unk> ) . Overall , in that period in the General Government , one of every three children was receiving some sort of education from the underground organizations ; the number rose to about 70 % for children old enough to attend secondary school . It is estimated that in some rural areas , the educational coverage was actually improved ( most likely as courses were being organized in some cases by teachers escaped or deported from the cities ) . Compared to pre @-@ war classes , the absence of Polish Jewish students was notable , as they were confined by the Nazi Germans to ghettos ; there was , however , underground Jewish education in the ghettos , often organized with support from Polish organizations like TON . Students at the underground schools were often also members of the Polish resistance . 
- In Warsaw , there were over 70 underground schools , with 2 @,@ 000 teachers and 21 @,@ 000 students . Underground Warsaw University educated 3 @,@ 700 students , issuing 64 masters and 7 doctoral degrees . Warsaw <unk> under occupation educated 3 @,@ 000 students , issuing 186 engineering degrees , 18 doctoral ones and 16 <unk> . <unk> University issued 468 masters and 62 doctoral degrees , employed over 100 professors and teachers , and served more than 1 @,@ 000 students per year . Throughout Poland , many other universities and institutions of higher education ( of music , theater , arts , and others ) continued their classes throughout the war . Even some academic research was carried out ( for example , by Władysław <unk> , a leading Polish philosopher , and <unk> <unk> , a linguist ) . Nearly 1 @,@ 000 Polish scientists received funds from the Underground State , enabling them to continue their research . 
- The German attitude to underground education varied depending on whether it took place in the General Government or the annexed territories . The Germans had almost certainly realized the full scale of the Polish underground education system by about 1943 , but lacked the manpower to put an end to it , probably <unk> resources to dealing with the armed resistance . For the most part , closing underground schools and colleges in the General Government was not a top priority for the Germans . In 1943 a German report on education admitted that control of what was being taught in schools , particularly rural ones , was difficult , due to lack of manpower , transportation , and the activities of the Polish resistance . Some schools semi @-@ openly taught unauthorized subjects in defiance of the German authorities . Hans Frank noted in 1944 that although Polish teachers were a " mortal enemy " of the German states , they could not all be disposed of immediately . It was perceived as a much more serious issue in the annexed territories , as it hindered the process of Germanization ; involvement in the underground education in those territories was much more likely to result in a sentence to a concentration camp . 
- 
- = = = Print = = = 
- 
- There were over 1 @,@ 000 underground newspapers ; among the most important were the <unk> <unk> of Armia Krajowa and <unk> of the Government Delegation for Poland . In addition to publication of news ( from intercepted Western radio transmissions ) , there were hundreds of underground publications dedicated to politics , economics , education , and literature ( for example , <unk> i <unk> ) . The highest recorded publication volume was an issue of <unk> <unk> printed in 43 @,@ 000 copies ; average volume of larger publication was 1 @,@ 000 – 5 @,@ 000 copies . The Polish underground also published <unk> and leaflets from imaginary anti @-@ Nazi German organizations aimed at spreading <unk> and lowering morale among the Germans . Books were also sometimes printed . Other items were also printed , such as patriotic posters or fake German administration posters , ordering the Germans to evacuate Poland or telling Poles to register household cats . 
- The two largest underground publishers were the Bureau of Information and Propaganda of Armia Krajowa and the Government Delegation for Poland . <unk> <unk> <unk> <unk> ( Secret Military Publishing House ) of Jerzy <unk> ( subordinated to the Armia Krajowa ) was probably the largest underground publisher in the world . In addition to Polish titles , Armia Krajowa also printed false German newspapers designed to decrease morale of the occupying German forces ( as part of Action N ) . The majority of Polish underground presses were located in occupied Warsaw ; until the Warsaw Uprising in the summer of 1944 the Germans found over 16 underground printing presses ( whose crews were usually executed or sent to concentration camps ) . The second largest center for Polish underground publishing was Kraków . There , writers and editors faced similar dangers : for example , almost the entire editorial staff of the underground satirical paper Na <unk> was arrested , and its chief editors were executed in Kraków on 27 May 1944 . ( Na <unk> was the longest published Polish underground paper devoted to satire ; 20 issues were published starting in October 1943 . ) The underground press was supported by a large number of activists ; in addition to the crews manning the printing presses , scores of underground <unk> distributed the publications . According to some statistics , these <unk> were among the underground members most frequently arrested by the Germans . 
- Under German occupation , the professions of Polish journalists and writers were virtually eliminated , as they had little opportunity to publish their work . The Underground State 's Department of Culture sponsored various initiatives and individuals , enabling them to continue their work and aiding in their publication . Novels and anthologies were published by underground presses ; over 1 @,@ 000 works were published underground over the course of the war . Literary discussions were held , and prominent writers of the period working in Poland included , among others , <unk> Kamil <unk> , <unk> <unk> , Tadeusz Borowski , Tadeusz Boy @-@ Żeleński , Maria <unk> , Tadeusz <unk> , Zuzanna Ginczanka , <unk> <unk> , future Nobel Prize winner <unk> <unk> , Zofia <unk> , Jan <unk> , Leopold Staff , Kazimierz <unk> , and Jerzy <unk> . Writers wrote about the difficult conditions in the prisoner @-@ of @-@ war camps ( Konstanty <unk> <unk> , Stefan <unk> , Leon <unk> , Andrzej <unk> and Marian <unk> ) , the ghettos , and even from inside the concentration camps ( Jan Maria <unk> , Halina <unk> , Zofia <unk> ( <unk> ) , Tadeusz <unk> , Kazimierz Andrzej Jaworski and Marian <unk> ) . Many writers did not survive the war , among them <unk> Kamil <unk> , <unk> <unk> , Tadeusz Boy @-@ Żeleński , Tadeusz <unk> , Zuzanna Ginczanka , Juliusz <unk> @-@ <unk> , Stefan <unk> , <unk> <unk> , Halina <unk> , Tadeusz <unk> , <unk> <unk> , <unk> Antoni <unk> , Włodzimierz <unk> , Leon <unk> , Kazimierz <unk> @-@ <unk> and Bruno Schulz . 
- 
- = = = Visual arts and music = = = 
- 
- With the censorship of Polish theater ( and the virtual end of the Polish radio and film industry ) , underground theaters were created , primarily in Warsaw and Kraków , with shows presented in various underground venues . Beginning in 1940 the theaters were coordinated by the Secret <unk> Council . Four large companies and more than 40 smaller groups were active throughout the war , even in the <unk> 's <unk> prison in Warsaw and in Auschwitz ; underground acting schools were also created . Underground actors , many of whom officially worked mundane jobs , included Karol <unk> , <unk> <unk> , Henryk Borowski , <unk> <unk> , Władysław <unk> , Stefan <unk> , Tadeusz <unk> , <unk> <unk> , <unk> <unk> , Jan <unk> , Adam <unk> , Andrzej <unk> , Leon <unk> , Arnold <unk> , <unk> <unk> , Edmund <unk> , Maria <unk> , Karol <unk> ( who later became Pope John Paul II ) , Marian <unk> , Jerzy <unk> and others . Theater was also active in the Jewish ghettos and in the camps for Polish war prisoners . 
- Polish music , including orchestras , also went underground . Top Polish musicians and directors ( Adam <unk> , Zbigniew <unk> , Jan <unk> , Barbara <unk> , <unk> <unk> , Jerzy <unk> , <unk> <unk> , Andrzej <unk> , Piotr <unk> , Edmund Rudnicki , Eugenia <unk> , Jerzy <unk> , Kazimierz <unk> , Maria <unk> , Bolesław <unk> , Mira <unk> ) performed in restaurants , <unk> , and private homes , with the most daring singing patriotic ballads on the streets while <unk> German patrols . Patriotic songs were written , such as <unk> , <unk> , the most popular song of occupied Warsaw . Patriotic puppet shows were staged . Jewish musicians ( e.g. Władysław <unk> ) and artists likewise performed in ghettos and even in concentration camps . Although many of them died , some survived abroad , like Alexandre <unk> in the United States , and Eddie <unk> and Henryk Wars in the Soviet Union . 
- Visual arts were practiced underground as well . <unk> , restaurants and private homes were turned into galleries or museums ; some were closed , with their owners , staff and patrons harassed , arrested or even executed . Polish underground artists included <unk> <unk> , Stanisław <unk> @-@ <unk> , Stanisław <unk> @-@ <unk> , and Konstanty Maria <unk> . Some artists worked directly for the Underground State , forging money and documents , and creating anti @-@ Nazi art ( satirical posters and caricatures ) or Polish patriotic symbols ( for example <unk> ) . These works were reprinted on underground presses , and those intended for public display were plastered to walls or painted on them as graffiti . Many of these activities were coordinated under the Action N Operation of Armia Krajowa 's Bureau of Information and Propaganda . In 1944 three giant ( 6 m , or 20 ft ) puppets , caricatures of Hitler and <unk> <unk> , were successfully displayed in public places in Warsaw . Some artists recorded life and death in occupied Poland ; despite German <unk> on Poles using cameras , photographs and even films were taken . Although it was impossible to operate an underground radio station , underground auditions were recorded and introduced into German radios or loudspeaker systems . Underground <unk> stamps were designed and issued . Since the Germans also banned Polish sport activities , underground sport clubs were created ; underground football matches and even tournaments were organized in Warsaw , Kraków and <unk> , although these were usually dispersed by the Germans . All of these activities were supported by the Underground State 's Department of Culture . 
- 
- = = = Warsaw Uprising = = = 
- 
- During the Warsaw Uprising ( August – October 1944 ) , people in Polish @-@ controlled territory <unk> to recreate the former day @-@ to @-@ day life of their free country . Cultural life was vibrant among both soldiers and the civilian population , with theaters , cinemas , post offices , newspapers and similar activities available . The 10th Underground Tournament of Poetry was held during the Uprising , with prizes being weaponry ( most of the Polish poets of the younger generation were also members of the resistance ) . <unk> by Antoni <unk> , the Home Army 's Bureau of Information and Propaganda even created three <unk> and over 30 @,@ 000 metres ( 98 @,@ 425 ft ) of film documenting the struggle . 
- <unk> <unk> took some 1 @,@ 000 photographs before he died ; <unk> Braun some 3 @,@ 000 , of which 1 @,@ 500 survive ; Jerzy <unk> some 1 @,@ 000 , of which 600 survived . 
- 
- = = Culture in exile = = 
- 
- Polish artists also worked abroad , outside of occupied Europe . <unk> <unk> , based in Britain with the Polish Armed Forces in the West wrote about the <unk> Polish Fighter Squadron . Melchior <unk> wrote about the Polish contribution to the capture of Monte Cassino in Italy . Other writers working abroad included Jan <unk> , Antoni <unk> , Kazimierz <unk> and Julian <unk> . There were artists who performed for the Polish forces in the West as well as for the Polish forces in the East . Among musicians who performed for the Polish II Corps in a <unk> <unk> cabaret were Henryk Wars and <unk> Anders . The most famous song of the soldiers fighting under the Allies was the <unk> <unk> na Monte Cassino ( The Red <unk> on Monte Cassino ) , composed by <unk> <unk> and Alfred Schultz in 1944 . There were also Polish theaters in exile in both the East and the West . Several Polish painters , mostly soldiers of the Polish II Corps , kept working throughout the war , including Tadeusz Piotr <unk> , Adam <unk> , Marian <unk> , Bolesław <unk> and Stefan Knapp . 
- 
- = = Influence on postwar culture = = 
- 
- The wartime attempts to destroy Polish culture may have strengthened it instead . Norman Davies wrote in God 's <unk> : " In 1945 , as a prize for <unk> sacrifices , the attachment of the survivors to their native culture was stronger than ever before . " Similarly , close @-@ knit underground classes , from primary schools to universities , were renowned for their high quality , due in large part to the lower ratio of students to teachers . The resulting culture was , however , different from the culture of interwar Poland for a number of reasons . The destruction of Poland 's Jewish community , Poland 's postwar territorial changes , and postwar migrations left Poland without its historic ethnic minorities . The <unk> nation was no more . 
- The experience of World War II placed its stamp on a generation of Polish artists that became known as the " Generation of <unk> " . The term denotes an entire generation of Poles , born soon after Poland regained independence in 1918 , whose adolescence was marked by World War II . In their art , they " discovered a new Poland " – one forever changed by the atrocities of World War II and the ensuing creation of a communist Poland . 
- Over the years , nearly three @-@ quarters of the Polish people have emphasized the importance of World War II to the Polish national identity . Many Polish works of art created since the war have centered on events of the war . Books by Tadeusz Borowski , Adolf Rudnicki , Henryk <unk> , <unk> <unk> , Hanna <unk> and others ; films , including those by Andrzej <unk> ( A Generation , <unk> , Ashes and Diamonds , <unk> , A Love in Germany , <unk> , <unk> ) ; TV series ( Four Tank Men and a Dog and Stakes Larger than Life ) ; music ( <unk> <unk> ) ; and even comic books – all of these diverse works have reflected those times . Polish historian <unk> <unk> wrote in 1996 : 
- Educational and training programs place special emphasis on the World War II period and on the occupation . Events and individuals connected with the war are ubiquitous on TV , on radio and in the print media . The theme remains an important element in literature and learning , in film , theater and the fine arts . Not to mention that politicians constantly make use of it . Probably no other country marks <unk> related to the events of World War II so often or so solemnly . 
- 
- 
- = Arihant @-@ class submarine = 
- 
- The Arihant class ( Sanskrit , for Killer of <unk> ) is a class of nuclear @-@ powered ballistic missile submarines being built for the Indian Navy . They were developed under the US $ 2 @.@ 9 billion Advanced Technology Vessel ( ATV ) project to design and build nuclear @-@ powered submarines . 
- The lead vessel of the class , INS Arihant was launched in 2009 and after extensive sea trials , was confirmed as ready for operations on 23 February 2016 . Arihant is the first ballistic missile submarine to have been built by a country other than one of the five permanent members of the United Nations Security Council . 
- 
- = = History = = 
- 
- In December 1971 , during the Indo @-@ Pakistani War of 1971 , the US President Richard Nixon sent a carrier battle group named Task Force 74 , led by the nuclear @-@ powered USS Enterprise into the Bay of Bengal in an attempt to <unk> India . In response , the Soviet Union sent a submarine armed with nuclear missiles from <unk> to trail the US task force . The event demonstrated the significance of nuclear weapons and ballistic missile submarines to then Prime Minister Indira Gandhi . Following the 1974 <unk> Buddha nuclear test , the Director of Marine Engineering ( <unk> ) at Naval Headquarters initiated a technical feasibility study for an indigenous nuclear propulsion system ( Project <unk> ) . 
- The Indian Navy 's Advanced Technology Vessel project to design and construct a nuclear submarine took shape in the 1990s . Then Defence Minister George <unk> confirmed the project in 1998 . The initial intent of the project was to design nuclear @-@ powered fast attack submarines , though following nuclear tests conducted by India in 1998 at <unk> Test Range and the Indian pledge of no first use , the project was re @-@ aligned towards the design of a ballistic missile submarine in order to complete India 's nuclear triad . 
- 
- = = Description = = 
- 
- The Arihant @-@ class submarines are nuclear powered ballistic missile submarines built under the Advanced Technology Vessel ( ATV ) project . They will be the first nuclear submarines designed and built by India . The submarines are 112 m ( 367 ft ) long with a beam of 11 m ( 36 ft ) , a draught of 10 m ( 33 ft ) , displacement of 6 @,@ 000 tonnes ( 5 @,@ 900 long tons ; 6 @,@ 600 short tons ) and a diving depth of 300 m ( 980 ft ) . The complement is about 95 , including officers and sailors . The boats are powered by a single seven blade propeller powered by an 83 MW ( 111 @,@ 000 hp ) <unk> water reactor and can achieve a maximum speed of 12 – 15 knots ( 22 – 28 km / h ) when surfaced and 24 knots ( 44 km / h ) when submerged . 
- The submarines have four launch tubes in their <unk> and can carry up to 12 K @-@ 15 <unk> missiles with one warhead each ( with a range of 750 km or 470 mi ) or 4 K @-@ 4 missiles ( with a range of 3 @,@ 500 km or 2 @,@ 200 mi ) . The submarines are similar to the <unk> @-@ class submarine of Russia . The Indian Navy will train on INS <unk> , an <unk> @-@ class submarine leased from Russia in 2012 . 
- 
- = = Development = = 
- 
- The submarines are powered by a <unk> water reactor with highly enriched uranium fuel . The <unk> version of the reactor was designed and built by the <unk> Atomic Research Centre ( <unk> ) at the Indira Gandhi Centre for Atomic Research ( <unk> ) in <unk> . It included a 42 @-@ metre ( 138 ft ) section of the submarine 's pressure hull containing the shielding tank with water and the reactor , a control room , as well as an auxiliary control room for monitoring safety parameters . The prototype reactor became critical on 11 November 2003 and was declared operational on 22 September 2006 . Successful operation of the prototype for three years enabled the production version of the reactor for Arihant . The reactor <unk> were tested at the Machinery Test Center in <unk> . Facilities for loading and replacing the fuel cores of the naval reactors in berthed submarines were also established . 
- The detailed engineering of the design was implemented at Larsen & <unk> 's submarine design center at their <unk> shipbuilding facility . Tata Power <unk> built the control systems for the submarine . The steam turbines and associated systems integrated with the reactor were supplied by <unk> Industries . The lead vessel underwent a long and extensive process of testing after its launch in July 2009 . The propulsion and power systems were tested with high @-@ pressure steam trials followed by harbor @-@ acceptance trials that included <unk> tests by flooding its ballast tanks and controlled dives to limited depths . INS Arihant 's reactor went critical for the first time on 10 August 2013 . On 13 December 2014 , the submarine set off for its extensive sea trials . 
- 
- = = Ships in class = = 
- 
- <unk> number of planned submarines remains unclear , according to media reports about three to six submarines are planned to be built . The first boat of the class , INS Arihant is expected to be commissioned by 2016 . The first four vessels are expected to be commissioned by <unk> . In December 2014 , the work on a second nuclear reactor began and the second boat , INS <unk> is being prepared for sea trials . The next three ships in the class , after the lead ship , will be larger and have 8 missile launch tubes to carry up to 8 <unk> and a more powerful pressurized water reactor than INS Arihant . A larger follow on class to the <unk> class is also planned , these new boats will be capable of carrying 12 to 16 ballistic missiles . 
- 
- = = Timeline = = 
- 
- 
- 
- = SMS Markgraf = 
- 
- SMS Markgraf was the third battleship of the four @-@ ship König class . She served in the Imperial German Navy during World War I. The battleship was laid down in November 1911 and launched on 4 June 1913 . She was formally commissioned into the Imperial Navy on 1 October 1914 , just over two months after the outbreak of war in Europe . Markgraf was armed with ten 30 @.@ 5 @-@ centimeter ( 12 @.@ 0 in ) guns in five twin turrets and could steam at a top speed of 21 knots ( 39 km / h ; 24 mph ) . Markgraf was named in honor of the royal family of Baden . The name Markgraf is a rank of German nobility and is equivalent to the English <unk> , or Marquess . 
- Along with her three sister ships , König , Grosser Kurfürst , and Kronprinz , Markgraf took part in most of the fleet actions during the war , including the Battle of Jutland on 31 May and 1 June 1916 . At Jutland , Markgraf was the third ship in the German line and heavily engaged by the opposing British Grand Fleet ; she sustained five large @-@ caliber hits and her crew suffered 23 casualties . Markgraf also participated in Operation Albion , the conquest of the Gulf of Riga , in late 1917 . The ship was damaged by a mine while en route to Germany following the successful conclusion of the operation . 
- After Germany 's defeat in the war and the signing of the Armistice in November 1918 , Markgraf and most of the capital ships of the High Seas Fleet were interned by the Royal Navy in Scapa Flow . The ships were disarmed and reduced to skeleton crews while the Allied powers negotiated the final version of the Treaty of Versailles . On 21 June 1919 , days before the treaty was signed , the commander of the interned fleet , Rear Admiral Ludwig von Reuter , ordered the fleet to be scuttled to ensure that the British would not be able to seize the ships . Unlike most of the scuttled ships , Markgraf was never raised for scrapping ; the wreck is still sitting on the bottom of the bay . 
- 
- = = Construction and design = = 
- 
- Markgraf was ordered under the provisional name Ersatz <unk> and built at the AG <unk> shipyard in <unk> under construction number 186 . Her keel was laid in November 1911 and she was launched on 4 June 1913 . At her launching ceremony , the ship was christened by Frederick II , Grand Duke of Baden , the head of the royal family of Baden , in honor of which the ship had been named . <unk> @-@ out work was completed by 1 October 1914 , the day she was commissioned into the High Seas Fleet . She had cost the Imperial German Government 45 million <unk> . 
- Markgraf displaced 25 @,@ <unk> t ( 25 @,@ 389 long tons ) as built and 28 @,@ 600 t ( 28 @,@ 100 long tons ) fully loaded , with a length of 175 @.@ 4 m ( 575 ft 6 in ) , a beam of 19 @.@ 5 m ( 64 ft 0 in ) and a draft of 9 @.@ 19 m ( 30 ft 2 in ) . She was powered by three Bergmann steam turbines , three oil @-@ fired and twelve coal @-@ fired boilers , which developed a total of 40 @,@ 830 shp ( 30 @,@ 450 kW ) and yielded a maximum speed of 21 knots ( 39 km / h ; 24 mph ) . The ship had a range of 8 @,@ 000 nautical miles ( 15 @,@ 000 km ; 9 @,@ 200 mi ) at a cruising speed of 12 knots ( 22 km / h ; 14 mph ) . The ship had a crew of 41 officers and 1 @,@ <unk> enlisted sailors . 
- She was armed with ten 30 @.@ 5 cm ( 12 @.@ 0 in ) SK L / 50 guns arranged in five twin gun turrets : two superfiring turrets each fore and aft and one turret amidships between the two funnels . Her secondary armament consisted of fourteen 15 cm ( 5 @.@ 9 in ) SK L / 45 quick @-@ firing guns , six 8 @.@ 8 cm ( 3 @.@ 5 in ) SK L / 45 quick @-@ firing guns and five 50 cm ( 20 in ) underwater torpedo tubes , one in the bow and two on each beam . Markgraf 's 8 @.@ 8 cm guns were removed and replaced with four 8 @.@ 8 cm anti @-@ aircraft guns . The ship 's main armored belt was 350 millimeters ( 14 in ) thick . The deck was 30 mm ( 1 @.@ 2 in ) thick ; the main battery turrets and forward conning tower were armored with 300 mm ( 12 in ) thick steel plates . 
- 
- = = Service history = = 
- 
- Following her commissioning on 1 October 1914 , Markgraf conducted sea trials , which lasted until 12 December . By 10 January 1915 , the ship had joined III Battle Squadron of the High Seas Fleet with her three sister ships . On 22 January 1915 , III Squadron was detached from the fleet to conduct maneuver , gunnery , and torpedo training in the Baltic . The ships returned to the North Sea on 11 February , too late to assist the I Scouting Group at the Battle of Dogger Bank . 
- In the aftermath of the loss of SMS <unk> at the Battle of Dogger Bank , Kaiser Wilhelm II removed Admiral Friedrich von <unk> from his post as fleet commander on 2 February . Admiral Hugo von Pohl replaced him as commander of the fleet ; von Pohl carried out a series of sorties with the High Seas Fleet throughout 1915 . The first such operation — Markgraf 's first with the fleet — was a fleet advance to <unk> on 29 – 30 March ; the German fleet failed to engage any British warships during the sortie . Another uneventful operation followed on 17 – 18 April , and another three days later on 21 – 22 April . Markgraf and the rest of the fleet remained in port until 29 May , when the fleet conducted another two @-@ day advance into the North Sea . On 11 – 12 September , Markgraf and the rest of III Squadron supported a <unk> operation off <unk> . Another uneventful fleet advance followed on 23 – 24 October . 
- Vice Admiral Reinhard Scheer became commander in chief of the High Seas Fleet on 18 January 1916 when Admiral von Pohl became too ill from liver cancer to continue in that post . Scheer proposed a more aggressive policy designed to force a confrontation with the British Grand Fleet ; he received approval from the Kaiser in February . The first of Scheer 's operations was conducted the following month , on 5 – 7 March , with an uneventful sweep of the <unk> . Another sortie followed three weeks later on the 26th , with another on 21 – 22 April . On 24 April , the battlecruisers of Rear Admiral Franz von Hipper 's I Scouting Group conducted a raid on the English coast . Markgraf and the rest of the fleet sailed in distant support . The battlecruiser Seydlitz struck a mine while en route to the target , and had to withdraw . The other battlecruisers bombarded the town of <unk> unopposed , but during the approach to Yarmouth , they encountered the British cruisers of the Harwich Force . A short artillery duel ensued before the Harwich Force withdrew . Reports of British submarines in the area prompted the retreat of the I Scouting Group . At this point , Scheer , who had been warned of the sortie of the Grand Fleet from its base in Scapa Flow , also withdrew to safer German waters . 
- 
- = = = Battle of Jutland = = = 
- 
- Markgraf was present during the fleet operation that resulted in the Battle of Jutland which took place on 31 May and 1 June 1916 . The German fleet again sought to draw out and isolate a portion of the Grand Fleet and destroy it before the main British fleet could retaliate . Markgraf was the third ship in the German line , behind her sisters König and Grosser Kurfürst and followed by Kronprinz . The four ships made up the V Division of the III Battle Squadron , and they were the vanguard of the fleet . The III Battle Squadron was the first of three battleship units ; directly <unk> were the Kaiser @-@ class battleships of the VI Division , III Battle Squadron . The III Squadron was followed by the <unk> and Nassau classes of the II Battle Squadron ; in the rear guard were the obsolescent <unk> @-@ class pre @-@ dreadnoughts of the I Battle Squadron . 
- Shortly before 16 : 00 the battlecruisers of I Scouting Group encountered the British 1st Battlecruiser Squadron under the command of Vice Admiral David Beatty . The opposing ships began an artillery duel that saw the destruction of <unk> , shortly after 17 : 00 , and Queen Mary , less than half an hour later . By this time , the German battlecruisers were steaming south to draw the British ships toward the main body of the High Seas Fleet . At 17 : 30 , König 's crew spotted both the I Scouting Group and the 1st Battlecruiser Squadron approaching . The German battlecruisers were steaming to starboard , while the British ships steamed to port . At 17 : 45 , Scheer ordered a two @-@ point turn to port to bring his ships closer to the British battlecruisers , and a minute later , the order to open fire was given . 
- Markgraf opened fire on the battlecruiser Tiger at a range of 21 @,@ 000 yards ( 19 @,@ 000 m ) . Markgraf and her two sisters fired their secondary guns on British destroyers attempting to make torpedo attacks against the German fleet . Markgraf continued to engage Tiger until 18 : 25 , by which time the faster battlecruisers managed to move out of effective gunnery range . During this period , the battleships Warspite and Valiant of the 5th Battle Squadron fired on the leading German battleships . At 18 : 10 , one of the British ships scored a 15 @-@ inch ( 38 cm ) shell hit on Markgraf . Shortly thereafter , the destroyer Moresby fired a single torpedo at Markgraf and missed from a range of about 8 @,@ 000 yd ( 7 @,@ 300 m ) . Malaya fired a torpedo at Markgraf at 19 : 05 , but the torpedo missed due to the long range . Around the same time , Markgraf engaged a cruiser from the 2nd Light Cruiser Squadron before shifting her fire back to the 5th Battle Squadron for ten minutes . During this period , two more 15 @-@ inch shells hit Markgraf , though the timing is unknown . The hit at 18 : 10 struck on a joint between two 8 @-@ inch @-@ thick side armor plates ; the shell burst on impact and holed the armor . The main deck was <unk> and approximately 400 t ( 390 long tons ; 440 short tons ) of water entered the ship . The other two shells failed to explode and caused negligible damage . 
- Shortly after 19 : 00 , the German cruiser Wiesbaden had become disabled by a shell from the British battlecruiser Invincible ; Rear Admiral Paul <unk> in König attempted to position his four ships to cover the stricken cruiser . Simultaneously , the British III and IV Light Cruiser Squadrons began a torpedo attack on the German line ; while advancing to torpedo range , they smothered Wiesbaden with fire from their main guns . The obsolescent armored cruisers of the 1st Cruiser Squadron also joined in the melee . Markgraf and her sisters fired heavily on the British cruisers , but even sustained fire from the battleships ' main guns failed to drive them off . Markgraf fired both her 30 @.@ 5 cm and 15 cm guns at the armored cruiser Defence . Under a hail of fire from the German battleships , Defence exploded and sank ; credit is normally given to the battlecruiser Lützow , though Markgraf 's gunners also claimed credit for the sinking . 
- Markgraf then fired on the battlecruiser Princess Royal and scored two hits . The first hit struck the 9 @-@ inch armor covering " X " barbette , was <unk> downward , and exploded after penetrating the 1 @-@ inch deck armor . The crew for the left gun were killed , the turret was disabled , and the explosion caused serious damage to the upper deck . The second shell penetrated Princess Royal 's 6 @-@ inch belt armor , <unk> upward off the coal bunker , and exploded under the 1 @-@ inch deck armor . The two shells killed 11 and wounded 31 . At the same time , Markgraf 's secondary guns fired on the cruiser Warrior , which was seriously damaged by 15 heavy shells and forced to withdraw . Warrior <unk> on the trip back to port the following morning . 
- Around 19 : 30 , Admiral John Jellicoe 's main force of battleships entered the battle ; Orion began firing at Markgraf at 19 : 32 ; she fired four salvos of 13 @.@ 5 @-@ inch Armor @-@ <unk> , <unk> ( <unk> ) shells and scored a hit with the last salvo . The shell exploded upon impacting the armor protecting the No. 6 15 cm gun casemate . The shell failed to penetrate but holed the armor and disabled the gun . The explosion seriously injured two and killed the rest of the gun crew . A heavy shell nearly struck the ship at the same time , and at 19 : 44 , a bent propeller shaft forced Markgraf 's crew to turn off the port engine ; naval historian John Campbell speculated that this shell was the one that damaged the shaft . Her speed dropped to 17 or 18 kn ( 31 or 33 km / h ; 20 or 21 mph ) , though she remained in her position in the line . 
- Shortly after 20 : 00 , the German battleships engaged the 2nd Light Cruiser Squadron ; Markgraf fired primarily 15 cm shells . In this period , Markgraf was engaged by <unk> 's 12 @-@ inch guns , which scored a single hit at 20 : 14 . The shell failed to explode and shattered on impact on the 8 @-@ inch side armor , causing minimal damage . Two of the adjoining 14 @-@ inch plates directly below the 8 @-@ inch armor were slightly forced inward and some minor flooding occurred . The heavy fire of the British fleet forced Scheer to order the fleet to turn away . Due to her reduced speed , Markgraf turned early in an attempt to maintain her place in the battle line ; this , however , forced Grosser Kurfürst to fall out of formation . Markgraf fell in behind Kronprinz while Grosser Kurfürst steamed ahead to return to her position behind König . After successfully withdrawing from the British , Scheer ordered the fleet to assume night cruising formation , though communication errors between Scheer aboard Friedrich der <unk> and <unk> , the lead ship , caused delays . Several British light cruisers and destroyers stumbled into the German line around 21 : 20 . In the ensuing short engagement Markgraf hit the cruiser <unk> five times with her secondary guns . The fleet fell into formation by 23 : 30 , with Grosser Kurfürst the 13th vessel in the line of 24 capital ships . 
- Around 02 : 45 , several British destroyers mounted a torpedo attack against the rear half of the German line . Markgraf initially held her fire as the identities of the destroyers were unknown . But gunners aboard Grosser Kurfürst correctly identified the vessels as hostile and opened fire while turning away to avoid torpedoes , which prompted Markgraf to follow suit . Heavy fire from the German battleships forced the British destroyers to withdraw . At 05 : 06 , Markgraf and several other battleships fired at what they thought was a submarine . 
- The High Seas Fleet managed to punch through the British light forces without drawing the attention of Jellicoe 's battleships , and subsequently reached Horns Reef by 04 : 00 on 1 June . Upon reaching Wilhelmshaven , Markgraf went into harbor while several other battleships took up defensive positions in the outer <unk> . The ship was transferred to Hamburg where she was repaired in AG Vulcan 's large floating dock . Repair work was completed by 20 July . In the course of the battle , Markgraf had fired a total of 254 shells from her main battery and <unk> rounds from her 15 cm guns . She was hit by five large @-@ caliber shells , which killed 11 men and wounded 13 . 
- 
- = = = Subsequent operations = = = 
- 
- Following repairs in July 1916 , Markgraf went into the Baltic for trials . The ship was then temporarily assigned to the I Scouting Group for the fleet operation on 18 – 19 August . Due to the serious damage incurred by Seydlitz and Derfflinger at Jutland , the only battlecruisers available for the operation were Von der <unk> and Moltke , which were joined by Markgraf , Grosser Kurfürst , and the new battleship Bayern . The British were aware of the German plans , and sortied the Grand Fleet to meet them . By 14 : 35 , Scheer had been warned of the Grand Fleet 's approach and , unwilling to engage the whole of the Grand Fleet just 11 weeks after the <unk> close engagement at Jutland , turned his forces around and retreated to German ports . 
- Markgraf was present for the uneventful advance in the direction of Sunderland on 18 – 20 October . Unit training with the III Squadron followed from 21 October to 2 November . Two days later , the ship formally rejoined III Squadron . On the 5th , a pair of U @-@ boats grounded on the Danish coast . Light forces were sent to recover the vessels , and III Squadron , which was in the North Sea en route to Wilhelmshaven , was ordered to cover them . During the operation , the British submarine <unk> torpedoed both Grosser Kurfürst and Kronprinz and caused moderate damage . For most of 1917 , Markgraf was occupied with guard duties in the North Sea , interrupted only by a refit period in January and periodic unit training in the Baltic . 
- 
- = = = Operation Albion = = = 
- 
- In early September 1917 , following the German conquest of the Russian port of Riga , the German navy decided to eliminate the Russian naval forces that still held the Gulf of Riga . The <unk> ( Navy High Command ) planned an operation to seize the Baltic island of Ösel , and specifically the Russian gun batteries on the <unk> Peninsula . On 18 September , the order was issued for a joint operation with the army to capture Ösel and Moon Islands ; the primary naval component was to comprise the flagship , Moltke , along with the III and IV Battle Squadrons of the High Seas Fleet . The II Squadron consisted of the four König @-@ class ships , and was by this time augmented with the new battleship Bayern . The IV Squadron consisted of the five Kaiser @-@ class battleships . Along with nine light cruisers , three torpedo boat flotillas , and dozens of mine warfare ships , the entire force numbered some 300 ships , supported by over 100 aircraft and six <unk> . The invasion force amounted to approximately 24 @,@ 600 officers and enlisted men . 
- <unk> the Germans were the old Russian pre @-@ dreadnoughts <unk> and <unk> , the armored cruisers Bayan , Admiral <unk> , and Diana , 26 destroyers , and several torpedo boats and gunboats . Three British C @-@ class submarines where also stationed in the Gulf . The <unk> Strait , the main southern entrance to the Gulf of Riga , was heavily mined and defended by a number of coastal artillery batteries . The garrison on Ösel numbered nearly 14 @,@ 000 men , though by 1917 it had been reduced to 60 to 70 percent strength . 
- The operation began on 12 October , when Moltke and the four König @-@ class ships covered the landing of ground troops by suppressing the shore batteries covering <unk> Bay . Markgraf fired on the battery located on Cape <unk> . After the successful amphibious assault , III Squadron steamed to <unk> <unk> , although Markgraf remained behind for several days . On the 17th , Markgraf left <unk> Bay to rejoin her squadron in the Gulf of Riga , but early on the following morning she ran aground at the entrance to <unk> . The ship was quickly freed , and she reached the III Squadron anchorage north of <unk> Bank on the 19th . The next day , Markgraf steamed to Moon Sound , and on the 25th participated in the bombardment of Russian positions on the island of <unk> . The ship returned to <unk> on 27 October , and two days later was detached from Operation Albion to return to the North Sea . 
- Markgraf struck a pair of mines in quick succession while in the <unk> Strait and took in 260 metric tons ( 260 long tons ; 290 short tons ) of water . The ship continued on to Kiel via <unk> in Danzig ; she then went on to Wilhelmshaven , where the mine damage was repaired . The work was completed at the Imperial Dockyard from 6 to 23 November . After repairs were completed , Markgraf returned to guard duty in the North Sea . She missed an attempted raid on a British convoy on 23 – 25 April 1918 , as she was in dock in Kiel from 15 March to 5 May for the installation of a new foremast . 
- 
- = = = Fate = = = 
- 
- Markgraf and her three sisters were to have taken part in a final fleet action at the end of October 1918 , days before the Armistice was to take effect . The bulk of the High Seas Fleet was to have sortied from their base in Wilhelmshaven to engage the British Grand Fleet . Scheer — by now the Grand Admiral ( <unk> ) of the fleet — intended to inflict as much damage as possible on the British navy in order to obtain a better <unk> position for Germany , despite the expected casualties . However , many of the war @-@ weary sailors felt the operation would disrupt the peace process and prolong the war . On the morning of 29 October 1918 , the order was given to sail from Wilhelmshaven the following day . Starting on the night of 29 October , sailors on <unk> and then on several other battleships , including Markgraf , mutinied . The unrest ultimately forced Hipper and Scheer to cancel the operation . <unk> of the situation , the Kaiser stated , " I no longer have a navy . " 
- Following the capitulation of Germany in November 1918 , most of the High Seas Fleet ships , under the command of Rear Admiral Ludwig von Reuter , were interned in the British naval base in Scapa Flow . Prior to the departure of the German fleet , Admiral Adolf von <unk> made clear to von Reuter that he could not allow the Allies to seize the ships , under any conditions . The fleet rendezvoused with the British light cruiser Cardiff , which led the ships to the Allied fleet that was to escort the Germans to Scapa Flow . The massive flotilla consisted of some 370 British , American , and French warships . Once the ships were interned , their guns were disabled through the removal of their breech blocks , and their crews were reduced to 200 officers and enlisted men . 
- The fleet remained in captivity during the negotiations that ultimately produced the Treaty of Versailles . Von Reuter believed that the British intended to seize the German ships on 21 June 1919 , which was the deadline for Germany to have signed the peace treaty . <unk> that the deadline had been extended to the 23rd , Reuter ordered the ships to be sunk at the first opportunity . On the morning of 21 June , the British fleet left Scapa Flow to conduct training maneuvers , and at 11 : 20 Reuter transmitted the order to his ships . Markgraf sank at 16 : 45 . The British soldiers in the guard detail <unk> in their attempt to prevent the Germans from scuttling the ships ; they shot and killed Markgraf 's captain , Walter Schumann , who was in a lifeboat , and an enlisted man . In total , the guards killed nine Germans and wounded twenty @-@ one . The remaining crews , totaling some 1 @,@ 860 officers and enlisted men , were imprisoned . 
- Markgraf was never raised for scrapping , unlike most of the other capital ships that were scuttled . Markgraf and her two sisters had sunk in deeper water than the other capital ships , which made any salvage attempt more difficult . The outbreak of World War II in 1939 put a halt to all salvage operations , and after the war it was determined that salvaging the deeper wrecks was financially impractical . The rights to future salvage operations on the wrecks were sold to Britain in 1962 . Owing to the fact that the steel that composed their hulls was produced before the advent of nuclear weapons , Markgraf and her sisters are among the few accessible sources of low @-@ background steel , which has occasionally been removed for use in scientific devices . Markgraf and the other vessels on the bottom of Scapa Flow are a popular dive site , and are protected by a policy barring divers from recovering items from the wrecks . 
- 
- 
- = Coldrum Long Barrow = 
- 
- The Coldrum Long Barrow , also known as the Coldrum Stones and the <unk> Stones , is a chambered long barrow located near to the village of Trottiscliffe in the south @-@ eastern English county of Kent . <unk> circa 4000 BCE , during Britain 's Early Neolithic period , today it survives only in a ruined state . 
- Archaeologists have established that the monument was built by <unk> communities shortly after the introduction of agriculture to Britain from continental Europe . Although representing part of an architectural tradition of long barrow building that was widespread across Neolithic Europe , the Coldrum Stones belong to a localised regional variant of barrows produced in the vicinity of the River Medway , now known as the Medway Megaliths . Of these , it is in the best surviving condition , and lies near to both Addington Long Barrow and Chestnuts Long Barrow on the western side of the river . Three further surviving long barrows , Kit 's Coty House , the Little Kit 's Coty House , and the Coffin Stone , are located on the Medway 's eastern side . 
- Built out of earth and around fifty local sarsen megaliths , the long barrow consisted of a sub @-@ rectangular earthen tumulus enclosed by kerb @-@ stones . Within the eastern end of the tumulus was a stone chamber , into which human remains were deposited on at least two separate occasions during the Early Neolithic . <unk> analysis of these remains has shown them to be those of at least seventeen individuals , a mixture of men , women , children and adults . At least one of the bodies had been <unk> prior to burial , potentially reflecting a funerary tradition of <unk> and secondary burial . As with other barrows , Coldrum has been interpreted as a tomb to house the remains of the dead , perhaps as part of a belief system involving ancestor veneration , although archaeologists have suggested that it may also have had further religious , ritual , and cultural connotations and uses . 
- After the Early Neolithic , the long barrow fell into a state of ruined <unk> , perhaps experiencing deliberate deposition in the late medieval period , either by Christian <unk> or treasure hunters . Local folklore grew up around the site , associating it with the burial of a prince and the countless stones motif . The ruin attracted the interest of <unk> in the 19th century , while archaeological excavation took place in the early 20th . After limited reconstruction , in 1926 ownership was transferred to heritage charity The National Trust . It is open without charge to visitors all year around . 
- 
- = = Name and location = = 
- 
- The Coldrum Stones are named after a nearby farm , Coldrum Lodge , which has since been demolished . The monument lies in a " rather isolated site " north @-@ east of the nearby village of Trottiscliffe , about 500 metres from a prehistoric track known as the Pilgrim 's Way . The tomb can be reached along a pathway known as Coldrum Lane , which is only accessible on foot . The nearest car park to Coldrum Lane can be found off of <unk> Lane in Trottiscliffe . Another nearby village is Addington , which is located one and a quarter miles away . 
- 
- = = <unk> = = 
- 
- 
- = = = Early Neolithic Britain = = = 
- 
- The Early Neolithic was a revolutionary period of British history . Beginning in the fifth millennium BCE , it saw a widespread change in lifestyle as the communities living in the British Isles adopted agriculture as their primary form of subsistence , abandoning the hunter @-@ gatherer lifestyle that had characterised the preceding Mesolithic period . Archaeologists have been unable to prove whether this adoption of farming was because of a new influx of migrants coming in from continental Europe or because the indigenous Mesolithic Britons came to adopt the agricultural practices of continental societies . Either way , it certainly emerged through contact with continental Europe , probably as a result of centuries of interaction between Mesolithic people living in south @-@ east Britain and Linear <unk> culture ( <unk> ) communities in north @-@ eastern France . The region of modern Kent would have been a key area for the arrival of continental European settlers and visitors , because of its position on the estuary of the River Thames and its proximity to the continent . 
- Between <unk> and <unk> BCE , all of the British Isles came to abandon its former Mesolithic hunter @-@ gatherer lifestyle , to be replaced by the new agricultural subsistence of the Neolithic Age . Although a common material culture was shared throughout most of the British Isles in this period , there was great regional variation regarding the nature and distribution of settlement , architectural styles , and the use of natural resources . Throughout most of Britain , there is little evidence of cereal or permanent dwellings from this period , leading archaeologists to believe that the Early Neolithic economy on the island was largely pastoral , relying on herding cattle , with people living a nomadic or semi @-@ nomadic way of life . Although witnessing some land clearance , Britain was largely forested in this period , and it is unclear what level of <unk> the area of Kent had experienced in the Early Neolithic ; widespread forest clearance only took place on the <unk> of south @-@ east Britain in the Late Bronze Age . Environmental data from the area around the White Horse Stone supports the idea that the area was still largely forested in the Early Neolithic , covered by a woodland of oak , ash , <unk> / alder and <unk> . 
- 
- = = = The tomb building tradition = = = 
- 
- Across Western Europe , the Early Neolithic marked the first period in which humans built monumental structures in the landscape . These were tombs that held the physical remains of the dead , and though sometimes constructed out of timber , many were built using large stones , now known as " megaliths " . Individuals were rarely buried alone in the Early Neolithic , instead being interned in collective burials with other members of their community . The construction of these collective burial monumental tombs , both wooden and megalithic , began in continental Europe before being adopted in Britain in the first half of the fourth millennium BCE . 
- The Early Neolithic people of Britain placed far greater emphasis on the ritualised burial of the dead than their Mesolithic forebears had done . Many archaeologists have suggested that this is because Early Neolithic people adhered to an ancestor cult that venerated the spirits of the dead , believing that they could intercede with the forces of nature for the benefit of their living descendants . Archaeologist Robin <unk> stressed that rather than simply being tombs , the Medway Megaliths were " communal <unk> fulfilling a social function for the communities who built and used them . " Thus , it has furthermore been suggested that Early Neolithic people entered into the tombs – which doubled as temples or shrines – to perform rituals that would honour the dead and ask for their assistance . For this reason , historian Ronald Hutton termed these monuments " tomb @-@ shrines " to reflect their dual purpose . 
- In Britain , these tombs were typically located on prominent hills and slopes overlooking the surrounding landscape , perhaps at the junction between different territories . Archaeologist Caroline Malone noted that the tombs would have served as one of a variety of markers in the landscape that conveyed information on " territory , political allegiance , ownership , and ancestors . " Many archaeologists have subscribed to the idea that these tomb @-@ shrines served as territorial markers between different tribal groups , although others have argued that such markers would be of little use to a nomadic herding society . Instead it has been suggested that they represent markers along herding pathways . Many archaeologists have suggested that the construction of such monuments reflects an attempt to stamp control and ownership over the land , thus representing a change in mindset brought about by <unk> . Others have suggested that these monuments were built on sites already deemed sacred by Mesolithic hunter @-@ gatherers . 
- Archaeologists have differentiated these Early Neolithic tombs into a variety of different architectural styles , each typically associated with a different region within the British Isles . Passage graves , characterised by their narrow passage made of large stones and one or multiple burial chambers covered in earth or stone , were predominantly located in northern Britain and southern and central Ireland . <unk> , across northern Ireland and central Britain long chambered mounds predominated , while in the east and south @-@ east of Britain , earthen long barrows represented the dominant architectural trend . These earthen long barrows were typically constructed of timber because building stone was scarce in southern Britain ; archaeologist Aubrey <unk> argued that these timber tombs might have been " even more eye @-@ catching " than their stone counterparts , perhaps consisting of " towering carved poles , <unk> painted " , but that evidence of such sculptures has not survived . The Medway Megaliths represent just one of these regional groups within the wider West European tradition of tomb building in this period . 
- 
- = = = The Medway Megaliths = = = 
- 
- Although now all in a ruinous state and not retaining their original appearance , at the time of construction the Medway Megaliths would have been some of the largest and most visually imposing Early Neolithic funerary monuments in Britain . <unk> along the River Medway as it cuts through the North Downs , they constitute the most south @-@ easterly group of megalithic monuments in the British Isles , and the only megalithic group in eastern England . Archaeologists Brian <unk> and Mike <unk> deemed the Medway Megaliths to be " some of the most interesting and well known " archaeological sites in Kent , while archaeologist Paul Ashbee described them as " the most grandiose and impressive structures of their kind in southern England " . 
- They can be divided into two separate clusters : one to the west of the River Medway and the other on Blue Bell Hill to the east , with the distance between the two clusters measuring at between 8 and 10 km . The western group includes Coldrum Long Barrow , Addington Long Barrow , and the Chestnuts Long Barrow . The eastern group consists of Kit 's Coty House , Little Kit 's Coty House , the Coffin Stone , and several other stones which might have once been parts of chambered tombs . It is not known if they were all built at the same time , or whether they were constructed in succession , while similarly it is not known if they each served the same function or whether there was a hierarchy in their usage . 
- The Medway long barrows all <unk> to the same general design plan , and are all aligned on an east to west axis . Each had a stone chamber at the eastern end of the mound , and they each probably had a stone facade flanking the entrance . The chambers were constructed from sarsen , a dense , hard , and durable stone that occurs naturally throughout Kent , having formed out of <unk> sand from the <unk> . Early Neolithic builders would have selected blocks from the local area , and then transported them to the site of the monument to be erected . 
- Such common architectural features among these tomb @-@ shrines indicate a strong regional cohesion with no direct parallels elsewhere in the British Isles . For instance , they would have been taller than most other tomb @-@ shrines in Britain , with internal heights of up to 10 ft . Nevertheless , as with other regional groupings of Early Neolithic tomb @-@ shrines ( such as the Cotswold @-@ Severn group ) , there are also various idiosyncrasies in the different monuments , such as Coldrum 's rectilinear shape , the <unk> long barrow 's facade , and the long , thin mounds at Addington and Kit 's Coty . This variation might have been caused by the tomb @-@ shrines being altered and adapted over the course of their use ; in this scenario , the monuments would represent composite structures . 
- It seems apparent that the people who built these monuments were influenced by pre @-@ existing tomb @-@ shrines that they were already aware of . Whether those people had grown up locally , or moved into the Medway area from elsewhere is not known . Based on a stylistic analysis of their architectural designs , Stuart Piggott thought that they had originated in the area around the Low Countries , while Glyn Daniel instead believed that the same evidence showed an influence from Scandinavia . John H. Evans instead suggested an origin in Germany , and Ronald F. <unk> thought that their origins could be seen in the Cotswold @-@ Severn megalithic group . Ashbee noted that their close <unk> in the same area was reminiscent of the megalithic tomb @-@ shrine traditions of continental Northern Europe , and emphasised that the Medway Megaliths were a regional manifestation of a tradition widespread across Early Neolithic Europe . He nevertheless stressed that a precise place of origin was " impossible to indicate " with the available evidence . 
- 
- = = Design and construction = = 
- 
- The monument originally consisted of a sarsen stone chamber , covered by a low earthen mound , which was bounded by prostrate slabs . As such , the archaeologist Paul Ashbee asserted that the monument could be divided into three particular features : the chamber , the barrow , and the sarsen stone surround . It is located on the edge of a large <unk> <unk> , although it is difficult to <unk> what views would have been possible from the monument at the time of construction , due to a lack of information on how densely forested the vicinity was . However , if the area was not highly wooded , then 360 ° views of the surrounding landscape would have been possible . The monument 's axis points toward both the North Downs and the Medway Valley , which is similar to the other Medway Megaliths . Archaeologist <unk> <unk> suggested that the Coldrum Long Barrow might have been built within view of a nearby settlement , and that this " may have been a key factor in the experience of ceremonies and rituals taking place at the tombs and may also have defined a link between the tomb builders and the landscape . " 
- It had been built using about 50 stones . The barrow is sub @-@ rectangular in plan , and about 20 meters ( 64 feet ) in length . At its broader , eastern end , where the chamber is located , the monument measures 15 metres ( 50 feet ) , while at the narrower , western end , it is 12 metres ( 40 feet ) in breadth . As such , the barrow is a " truncated wedge @-@ shape " . The megalithic builders responsible for the Coldrum Stones positioned it on the top of a small ridge adjacent to the North Downs , and constructed it facing eastward , towards the River Medway . 
- The chamber of the monument measures 4 @.@ 5 metres ( 13 feet ) in length , and 1 @.@ 7 metres ( 5 feet 6 inches ) in width , although it was potentially much larger when originally constructed . The chamber 's internal height would have been at least 2 metres ( 6 feet 6 inches ) . In its current state , the northern side of the chamber is made up of two slabs , one being 8 feet long , 7 feet , 6 inches deep , and 1 foot , 9 inches thick , and the other 5 feet long , 6 feet deep , and 2 feet thick . Conversely , the chamber 's southern side consists of a single slab , measuring 11 feet , 4 inches in length , 7 feet , 3 inches in depth , and 1 foot , 9 inches in depth at its thicker , eastern end . The western end of the chamber is closed off with a slab measuring about 14 feet , 6 inches wide , with a thickness of 1 foot and a depth of around 8 feet . A collapsed , broken slab lies at the opening eastern end of the chamber . It is also possible that a largely rectangular slab at the bottom of the slope had once been part of the eastern end of the chamber . Excavation has revealed that flint masonry was used to pack around the chamber and support its sarsens ; twentieth @-@ century renovation has seen this largely replaced with cement , allowing the stones to continue standing upright . 
- It is possible that there was a facade in front of the chamber , as is evident at other chambered tombs in Britain , such as West Kennet Long Barrow and <unk> 's Smithy . It is also possible that there was a portal stone atop the chamber , as was apparent at Kit 's Coty House and Lower Kit 's Coty House . Many of the larger slabs of stone that have fallen down the slope on the eastern end of the monument may have been parts of this facade or portal . 
- The earthen mound that once covered the tomb is now visible only as an <unk> approximately 1 foot , 6 inches in height . In the nineteenth @-@ century , the mound was higher on the western end of the tomb , although this was removed by excavation to reveal the sarsens beneath during the 1920s . It is likely that in the Early Neolithic , the mound had a quarry ditch surrounding it , and it is inside this ditch that the kerb @-@ stones now sit . 
- The kerb @-@ stones around the tomb display some <unk> ; those on the northern side are mostly rectilinear , while those on the southern side are smaller and largely irregular in shape . It is probable that there was an ancillary dry @-@ stone wall constructed using blocks of <unk> from the geological <unk> beds , as is evident at Chestnuts Long Barrow . Given that such blocks of stone rarely occur naturally , it may have been <unk> . 
- A <unk> line of <unk> and <unk> can be found on both one of the central kerb @-@ stones on the western end of the monument and a kerb @-@ stone on the south @-@ east of the monument . These have been attributed to the <unk> of flint and other stone axe @-@ <unk> on these sarsens . It is possible that these tools were sharpened for use in cutting and carving the timber <unk> and struts which would have been used in <unk> the stones and constructing the tomb . Similar evidence for the <unk> of tools has been found at West Kennet Long Barrow , as well as later prehistoric monuments such as Stonehenge . 
- Coldrum Long Barrow is comparatively isolated from the other Medway Megaliths ; in this it is unique , given that the other surviving examples are clustered into two groups . However , it is possible that another chambered tomb was located nearby ; a razed , elongated earthen mound with an east @-@ west orientation is located in a hollow at the foot of the downs just under a quarter of a mile to the north of the Coldrum Stones . It may be that this represents the remnants of another such monument which has had its stones removed or buried . Several large sarsens to the south of the <unk> might represent the remnants of a further such tomb , since destroyed . 
- 
- = = Human remains = = 
- 
- 
- = = = Demographics = = = 
- 
- Ashbee suggested that given its size and comparisons with other long barrows , such as <unk> 's Lodge , the Coldrum tomb could have housed the remains of over a hundred individuals . Excavations conducted in the early 20th century have led to the methodical discovery and removal of what was believed to be the remains of twenty @-@ two human individuals . These remains were examined by Sir Arthur Keith , the <unk> of the museum at the Royal College of Surgeons . He published his results in 1913 , in a paper largely concerned with discerning racial characteristics of the bodies . 
- A subsequent re @-@ analysis of the bones was conducted in the early 21st century , and published in the Proceedings of the Prehistoric Society in 2013 : the project presented " <unk> analysis , Bayesian modelling of <unk> dates , and carbon and nitrogen stable isotope analysis to inform on the <unk> , burial practices , diet and subsistence , and chronology of the Coldrum population " . <unk> earlier conclusions , it stated that the minimum number of individuals was seventeen . These were then further identified as probably belonging to nine adults ( probably five males and four females ) , two sub @-@ adults , four older children , and two younger children ( one around five years old , the other between 24 and 30 months old ) . 
- Keith had suggested that the <unk> he examined displayed similar features , which he attributed to the different individuals belonging to " one family - or several families united by common descent . " Similar observations had been made regarding the <unk> from other long barrows in Britain , although <unk> Martin Smith and Megan <unk> noted that this was not necessarily representative of a family group . Instead , they stated that it would also be consistent with " a population that was still relatively small and scattered " , in which most individuals were interrelated . 
- <unk> 's team noted that in all but one case , the fracture morphologies are consistent with dry @-@ bone breakage . Three of the skulls exhibited evidence that they had experienced violence ; a probable adult female had an <unk> injury on the left frontal , while an adult of indeterminate sex had an <unk> fracture on the left frontal , and a second adult female had a <unk> depressed fracture on the right frontal . 
- <unk> analysis of the remains revealed <unk> values that were typical of those found at many other Southern British Neolithic sites , albeit with significantly higher values of <unk> , which grew over time . Although this data is difficult to interpret , it was identified as probably reflecting a terrestrial diet high in animal protein that over time was increasingly supplemented with freshwater river or estuarine foods . In the case of the older individuals whose remains were interned in the tomb , the tooth enamel was worn away and the <unk> had become exposed on the chewing area of the crowns . 
- <unk> dating of the remains suggested Early Neolithic activity began at the site during <unk> – <unk> <unk> BCE ( 95 % probability ) or <unk> – <unk> cal BCE ( 68 % probability ) , when the first human remains were buried at the site . It then suggested that after an interval of either 60 – 350 years ( 95 % probability ) or 140 – 290 years ( 68 % probability ) , further <unk> of human remains were made inside the tomb . This second phase probably began in <unk> – <unk> cal BCE ( 95 % probability ) or <unk> – <unk> cal BCE ( 68 % probability ) . The <unk> dating of the human remains does not provide a date for the construction of Coldrum Long Barrow itself ; it is possible that the individuals died either some time before or after the monument 's construction . 
- 
- = = = Post @-@ mortem deposition = = = 
- 
- Cut @-@ marks were identified on a number of the bones ( two femora , two <unk> , and one <unk> ) , with <unk> specialists suggesting that these had been created post @-@ mortem as the bodies were <unk> and the bones removed from their attached ligaments . However , they further suggested that the lack of such cut @-@ marks on certain bones was <unk> that the body had already undergone partial decomposition or the removal of soft tissues prior to the process of dismemberment . The precision of the cut @-@ marks suggests that this dismemberment was done carefully ; " they do not suggest frenzied hacking or mutilation . " None of the criteria that <unk> deem diagnostic of cannibalism were found on the bones . 
- This cut @-@ marked human bone assemblage represented the largest yet identified from within a Neolithic long barrow in Southern Britain , although similar evidence for dismemberment has been found from a number of other Neolithic British sites , such as West <unk> , <unk> , <unk> , and <unk> . There are two possibilities for how this material developed . The first is that the bodies of the dead were <unk> or exposed to the elements , followed by a secondary burial within the tomb . The second is that they were placed in the tomb , where the flesh <unk> , before the bodies were then <unk> within the tomb itself . These practices may have been accompanied by <unk> , <unk> , or magical practices , direct evidence for which does not survive . 
- The inclusion of occupational debris over the bones was not unique to the site but common in chambered tombs from southern England . On the basis of an example discovered at Kit 's Coty House , Ashbee thought it apparent that the contents of the Coldrum 's chamber would have been <unk> by <unk> slabs , which served the same purpose as the side chambers of West Kennet and <unk> 's Smithy . 
- 
- = = Damage and <unk> = = 
- 
- All of the surviving megalithic tombs from the Early Neolithic period have suffered from neglect and the <unk> of agriculture . Although archaeologist Paul Ashbee noted that the Coldrum Stones represent " Kent 's least damaged megalithic long barrow " , it too has suffered considerable damage , having become dilapidated and fallen apart over the six millennia since its original construction . Most prominently , the eastern side has largely collapsed , with the stones that once helped to hold up the side of the barrow having fallen to the bottom of the slope . Conversely , it is possible that the sarsens at the bottom of the slope were not part of the original monument , but were stones found in nearby fields which were deposited there by farmers . 
- Excavation of Chestnuts Long Barrow revealed that it had been systematically destroyed in one event , and Ashbee suggested that the same may have happened to the Coldrum Stones . He believed that the kerb @-@ stones around the barrow were toppled , laid prostrate in the surrounding ditch , and then buried during the late thirteenth or early fourteenth century , by Christians seeking to <unk> non @-@ Christian monuments . Conversely , the archaeologist John Alexander — who excavated Chestnuts — suggested that the Medway tombs were destroyed by robbers seeking to locate treasure within them . As evidence , he pointed to the Close Roll of 1237 , which ordered the opening of barrows on the Isle of Wight in search for treasure , a practice which may have spread to Kent around the same time . Alexander believed that the destruction n Kent may have been brought about by a special commissioner , highlighting that the " <unk> and <unk> of the robbery " at Chestnuts would have necessitated resources beyond that which a local community could likely produce . Ashbee further suggested that in subsequent centuries , locals raided the damaged Coldrum tomb for <unk> chalk and stone , which was then re @-@ used as building material . 
- 
- = = Folklore and folk tradition = = 
- 
- In a 1946 paper published in the Folklore journal , John H. Evans recorded the existence of a local folk belief that a battle was fought at the site of the Coldrum Stones , and that a " Black Prince " was buried within its chamber . He suggested that the tales of battles taking place at this site and at other Medway Megaliths had not developed independently among the local population but had " <unk> down from the theories of <unk> " who believed that the Early Medieval Battle of <unk> , which was recorded in the Anglo @-@ Saxon Chronicle , took place in the area . 
- Evans also recorded that there was a folk belief in the area that applied to all of the Medway megaliths and which had been widespread " up to the last generation " ; this was that it was impossible for any human being to successfully count the number of stones in the monuments . This " countless stones " motif is not unique to this particular site , and can be found at various other megalithic monuments in Britain . The earliest textual evidence for it is found in an early sixteenth @-@ century document , where it applies to the stone circle of Stonehenge in Wiltshire , although in an early seventeenth @-@ century document it was being applied to The <unk> , a set of three stone circles in Cornwall . Later records reveal that it had gained widespread distribution in England , as well as a single occurrence each in Wales and Ireland . The <unk> S. P. <unk> suggested that it could be attributed to an <unk> understanding that these megaliths had lives of their own . 
- In the early twenty @-@ first century , a tradition developed in which the Hartley Morris Men , a <unk> dancing side , travel to the site at dawn every May Day in order to " sing up the sun " . This consists of a number of dances performed within the stones on top of the barrow , followed by a song performed at the base of the monument . 
- 
- = = <unk> and archaeological investigation = = 
- 
- 
- = = = Early antiquarian descriptions = = = 
- 
- The earliest antiquarian accounts of Coldrum Long Barrow were never published . There are claims that at the start of the nineteenth century , the Reverend Mark Noble , Rector of <unk> , prepared a plan of the site for Gentleman 's Magazine , although no copies have been produced to verify this . Between 1842 and 1844 , the Reverend <unk> Post authored <unk> Remains at Coldrum , in which he described the monument , although it remained unpublished at the time . <unk> the site with the <unk> of Britain 's Iron Age , Post 's suggestion was that the name " Coldrum " derived from the linguistically Celtic " <unk> @-@ Dun " , and that the chiefs of some of the <unk> Gauls were interned there . He further reported that in both 1804 and 1825 , skulls had been found at the site . In 1844 , an antiquarian named Thomas Wright published a note on the Coldrum Stones and other Medway Megaliths in The Archaeological Journal . Wright had been alerted to their existence by a local vicar , the Reverend Lambert B. <unk> , and proceeded to visit them with him . Describing the <unk> , Wright mentioned " a smaller circle of stones " to the others in the area , with " a subterranean <unk> in the middle " . He further added that " it is a tradition of the <unk> that a continuous line of stones ran from Coldrum direct to the well @-@ known monument called Kit 's <unk> [ sic ] House " , attributing this belief to the variety of megaliths which were scattered throughout the landscape . 
- In 1857 , the antiquarian J. M. <unk> excavated at the site with the help of the Reverend <unk> , providing a report of their findings to the Central Committee of the British Archaeological Association . Describing the monument as a stone circle , they asserted that they discovered Anglo @-@ Saxon pottery at the site , and noted that as well as being called the Coldrum Stones , the monument also had the name of the <unk> Stones , which <unk> believed originated with the Old English word for funeral pile , ad . In August 1863 , the Archaeological Institute , who were then holding their week @-@ long meeting in Rochester , took a tour to visit the site , guided by the <unk> Charles <unk> Smith . That year , the monument was described in a copy of Gentleman 's Magazine by Yorkshire <unk> Charles Moore <unk> , who believed it to be a " Celtic " stone circle . 
- In 1869 , the antiquarian A. L. Lewis first visited the site , and was informed by locals that several years previously a skull had been uncovered from inside or near to the chamber , but that they believed it to be that of a <unk> . A later account elaborated on this , stating that two individuals excavated in the centre of the dolmen without permission , discovering a human skeleton , the skull of which was then re @-@ buried in the churchyard at <unk> . In an 1878 note published in The Journal of the <unk> Institute of Great Britain and Ireland , Lewis noted that while many tourists visited Kit 's Coty House , " very few goes to or ever hears of a yet more curious collection of stones at <unk> or Coldrum Lodge " . He believed that the monument consisted of both a " chamber " and an " oval " of stones , suggesting that they were " two distinct <unk> " . In 1880 , the archaeologist Flinders Petrie included the existence of the stones at " <unk> " in his list of <unk> earthworks ; although noting that a previous commentator had described the stones as being in the shape of an oval , he instead described them as forming " a rectilinear enclosure " around the chamber . He then included a small , basic plan of the monument . 
- In August 1889 , two amateur archaeologists , George Payne and A. A. Arnold , came across the monument , which they noted was known among locals as the " Coldrum Stones " and " <unk> Temple " ; according to Payne , " the huge stones were so overgrown with <unk> and <unk> that they could not be discerned " . He returned the next year , noting that at this point , the <unk> had been cut away to reveal the megaliths . In his 1893 book <unk> <unk> , Payne noted that although it had first been described in print in 1844 , " since that time no one seems to have taken the trouble to properly record them or make a plan " , an unusual claim given that a copy of Petrie 's published plan existed in his library . For this reason , after gaining permission from the landowner , he convinced Major A. O. Green , <unk> in Survey at <unk> , to conduct a survey of the monument in August 1892 . He also wrote to the archaeologist Augustus Pitt @-@ Rivers , encouraging him to schedule the Coldrum Stones as a legally protected site under the Ancient Monuments Protection Act 1882 . Payne described the Coldrum Stones as " the finest monument of its class in the county , and one worthy of every care and attention . " Comparing it to other monuments of its type in Britain , he asserted that it was undoubtedly " of sepulchral origin , belonging to a period anterior to the Roman domination of Britain . " Payne also noted a folk tradition that there were stone avenues connecting Coldrum to the Addington Long Barrow , although added that he was unable to discover any evidence for the existence of this feature . 
- In 1904 , George Clinch published a note on the Medway Megaliths in the Royal <unk> Institute 's journal , Man , in which he referred to the Coldrum Stones as " at once the most remarkable and the least known of the whole series . " Suggesting that its design indicates that it was built during " a late date in the <unk> age " , he compared the workmanship in producing the megaliths to that at the stone circle of Stonehenge in Wiltshire , although noted that they differed in that the Coldrum Stones clearly represented " a sepulchral pile " . Ultimately , he ended his note by urging for the site to be protected under the Ancient Monuments Protection Act 1900 . In that same issue , Lewis included an added note in which he rejected the idea that the monument had once been covered by an earthen tumulus because he could see " no evidence that anything of that kind ever existed " , and instead he interpreted the site as a stone circle , comparing it to the examples at <unk> , <unk> , and Stanton Drew , suggesting that the central chamber was a shrine . 
- 
- = = = Archaeological excavation = = = 
- 
- The Coldrum Stones have been excavated on multiple occasions . On 16 April 1910 , the amateur archaeologist F. J. Bennett began excavation at the site , after previously having uncovered some Neolithic <unk> from Addington Long Barrow . He soon discovered human bones " under only a few inches of <unk> soil " . He returned to the site for further excavation in August 1910 , this time with his niece and her husband , both of whom were dentists with an interest in <unk> ; on that day they discovered pieces of a human skull , which they were able to largely reconstruct . A few days later he returned to excavate on the north @-@ west corner of the dolmen with the architect E. W. Filkins ; that day , they found a second skull , further bones , a flint tool , and pieces of pottery . 
- Later that month , George Payne and F. W. Reader met with Bennett to discuss his finds . With the aid of two other interested amateur archaeologists , Mr Boyd and Miss <unk> , both from <unk> , excavation resumed in early September . In 2009 , the archaeologists Martin Smith and Megan <unk> asserted that Bennett 's excavations had taken heed of the advice of Pitt @-@ Rivers that excavations should be recorded in full . They noted that Bennett had provided " clear plan and section drawings , photographs of the monument and careful attempts to consider site formation processes . " Suggesting that the monument was constructed on agricultural land , in his published report Bennett cited the ideas of anthropologist James <unk> in The Golden <unk> to suggest that the Coldrum Stones " monument may at one time have been dedicated , though not necessarily initially so , to the worship of the corn god and of agriculture . " He proceeded to <unk> that the human remains found at the site were the victims of human sacrifice killed in fertility rites . However , Evans later stated that " we have no means of knowing " whether human sacrifice had taken place at the site . 
- In September 1922 , Filkins once again began excavating at the site , this time with the aid of a resident of <unk> , Charles Gilbert . Their project was financed through grants provided by the British Association and the Society of Antiquaries , with Filkins noting that at the time of its commencement , " a miniature jungle " had grown up around the site which had to be cleared . Excavation continued sporadically until at least 1926 . Human remains were discovered , and placed into the possession of Sir Arthur Keith of the Royal College of Surgeons . This excavation revealed all the existing sarsens surrounding the monument , a number of which had previously been buried beneath earth . The stones of the dolmen were <unk> up with concrete foundations where Filkins deemed it necessary . Although Filkins ' excavation was comprehensive , it did ignore stone holes , packing stones , and their relationship to the mound . In 1998 , Ashbee noted that while from " a present @-@ day perspective , it is possible to see shortcomings [ ... ] in terms of the general standards of the early part of this century , there is much to <unk> . " 
- 
- = = = Management by The National Trust = = = 
- 
- In his 1924 publication dealing with Kent , the archaeologist O. G. S. Crawford , then working as the archaeological officer for the Ordnance Survey , listed the Coldrum Stones alongside the other Medway Megaliths . In 1926 , the Coldrum Stones were given to The National Trust , who dedicated it as a memorial to the <unk> historian Benjamin Harrison . A plaque was erected to mark this , which erroneously termed the monument a stone circle ; in 1953 , the archaeologist Leslie <unk> expressed the view that " it is hoped that this error may be rectified in the near future " . Still owned by the Trust , the site is open to visitors all year round , free of charge . On their website , the Trust advises visitors to look for " stunning views from the top of the barrow " . John H. Evans characterised the site as " the most impressive " of the Medway Megaliths , while <unk> described it as " the finest and most complete " of the group . 
- 
- 
- = Soviet cruiser Krasnyi Kavkaz = 
- 
- Krasnyi Kavkaz ( from Russian : " <unk> <unk> " - " Red <unk> " ) was a cruiser of the Soviet Navy that began construction during World War I , but was still incomplete during the Russian Revolution . Her design was heavily modified by the Soviets and she was completed in 1932 . During World War II she supported Soviet troops during the Siege of Odessa , Siege of Sevastopol , and the Kerch @-@ Feodosiya Operation in the winter of 1941 — 42 . She was awarded the Guards title on 3 April 1942 . She was reclassified as a training ship in May 1947 before being used as a target in 1952 . 
- 
- = = Service history = = 
- 
- <unk> down on 18 October 1913 at the <unk> Dockyard as Admiral <unk> for the Imperial Russian Navy as a cruiser of the <unk> class , she was launched on 8 June 1916 . Construction was abandoned in 1917 during the October Revolution when the ship was 63 % complete . In the second half of 1918 , the Marine Department of Hetman <unk> <unk> was engaged in completion of ship . On 25 January 1919 , the ship was formally renamed in " Hetman <unk> <unk> " , but <unk> was captured shortly afterward by the <unk> . The hull was relatively undamaged and the Soviets decided to finish the ship to a modified design . She was renamed Krasnyi Kavkaz on 14 December 1926 , and completed to a modernized design , being commissioned on 25 January 1932 . 
- Krasnyi Kavkaz was initially intended to accommodate eight 8 @-@ inch ( 200 mm ) guns in four twin turrets , but this was impossible given her small and lightly constructed hull . Three twin turrets mounting the new 57 @-@ caliber 180 mm ( 7 @.@ 1 in ) B @-@ 1 @-@ K gun under development also proved <unk> and the Soviets had to settle for four MK @-@ 1 @-@ 180 single 180 mm gun turrets , two at each end . Her superstructure was massively revised to fit these turrets and all of the original casemated 130 @-@ millimeter ( 5 @.@ 1 in ) / 55 <unk> Pattern 1913 guns were removed . As completed her secondary armament was only four 30 @-@ caliber 76 @.@ 2 mm <unk> AA guns mounted between her funnels . Her original internal torpedo tubes were replaced by four triple 533 @-@ millimetre ( 21 @.@ 0 in ) torpedo mounts mounted on each side of the main deck <unk> the forecastle break . She was given an aircraft @-@ handling crane , but a catapult wasn 't installed aft of her rear funnel until 1935 when a Heinkel catapult was imported from Germany . She was also fitted for mine rails with a capacity of up to 120 mines . 
- The light cruiser <unk> collided with her in May 1932 , shortly after her commissioning , and badly damaged her bow . It was extensively rebuilt and increased her overall length by over 11 metres ( 36 ft ) . In 1933 she made port visits in Turkey , Greece and Italy . 
- She was refitted before Operation Barbarossa , probably about 1940 , her catapult was removed , and her anti @-@ aircraft armament was greatly increased . Her four 76 @.@ 2 mm <unk> AA guns were exchanged for four Italian <unk> twin gun 50 @-@ caliber 100 mm ( 3 @.@ 9 in ) AA mounts and she received four single mounts for the semi @-@ automatic 76 @.@ 2 mm 34 @-@ K were fitted as well as six 12 @.@ 7 mm ( 0 @.@ 50 in ) AA machine guns . Two single mounts for 76 @.@ 2 mm ( 3 @.@ 00 in ) 34 @-@ K guns were also fitted , one on each side of the quarterdeck just aft of the rearmost main gun turret . Some of these guns may have been received earlier , the sources are unclear . While under repair at Poti in late 1942 she landed her aft pair of torpedo tubes and received two more <unk> mounts salvaged from the sunken cruiser <unk> Ukraina . Ten single mounts for the naval version of the 37 mm AA gun was also fitted . By 1944 she was also carrying one quadruple Vickers .50 machine gun MK III mount on top of each of her superfiring main gun turrets and she may have been using Oerlikon 20 mm cannon . 
- 
- = = = World War II = = = 
- 
- Krasnyi Kavkaz , in company with the cruisers <unk> Ukraina , <unk> and a number of destroyers , laid down a defensive mine barrage protecting the Black Sea Fleet base at Sevastopol on 22 June . She provided gunfire support to Soviet forces defending Odessa and escorted convoys bringing the 157th Rifle Division into Odessa during the month of September 1941 . She also transported one battalion of the 3rd Marine Regiment from Sevastopol in a successful amphibious assault behind Romanian lines to destroy Romanian coastal batteries near <unk> and <unk> . She escorted convoys from 3 — 6 October that evacuated the 157th Rifle Division from Odessa to Sevastopol and escorted the final evacuation convoy during the night of 15 – 16 October . During the Siege of Sevastopol she provided gunfire support and evacuated cut @-@ off troops from elsewhere in the <unk> into Sevastopol and brought in reinforcements from Caucasian ports . She helped to transport the <unk> Rifle Division from Novorossiysk and <unk> to Sevastopol between 7 and 13 December and the <unk> Rifle Division between 21 and 22 December , bombarding German positions in the interim . 
- During the Kerch @-@ Feodosiya Operation Krasnyi Kavkaz sailed into the harbor of Feodosiya on 29 December 1941 and disembarked reinforcements and provided gunfire support for Soviet troops already ashore . She was hit seventeen times by Axis artillery and mortar fire in retaliation . On 1 and 3 January she ferried supplies and reinforcements for the Soviet bridgehead on the Kerch Peninsula . On the return voyage she was severely damaged by German Junkers Ju 87 <unk> dive @-@ bombers from II . / <unk> 77 . Four near @-@ misses close to her stern damaged her steering , her left propeller shaft , blew off one propeller and put enough holes in her stern that flooding caused her draft to increase by 5 metres ( 16 ft ) . She made it to Novorossiysk , escorted by the destroyer <unk> , where she was patched up enough to make to Poti where more permanent repairs could be made . These took until October 1942 and the opportunity was taken to reinforce her anti @-@ aircraft armament . 
- She was awarded the Guards title on 3 April in recognition of her performance . Between 20 and 23 October , Krasnyi Kavkaz , her half @-@ sister Krasnyi <unk> , and three destroyers ferried 12 @,@ 600 men of the 8th , 9th and 10th Guards Rifle Brigades from Poti to <unk> to reinforce the defenses there . On the night of 4 February 1943 the Soviets made a series of amphibious landings to the west of Novorossiysk , behind German lines . Krasnyi <unk> , Krasnyi Kavkaz , and three destroyers provided fire support for the main landing , but the Soviet troops there were wiped out by 6 February , although one secondary landing was successful . The loss of three destroyers attempting to <unk> the German evacuation of the <unk> <unk> on 6 October 1943 caused Stalin to forbid the deployment of large naval units without his express permission and this meant the end of Krasnyi Kavkaz 's active participation in the war . 
- 
- = = = Post @-@ war history = = = 
- 
- Little is known about her activities after the end of the war other than she was redesignated as a training ship on 12 May 1947 . She was sunk as a target ship by SS @-@ N @-@ 1 missiles on 21 November 1952 . 
- 
- 
- = Rhode Island Route 4 = 
- 
- Route 4 , also known as the Colonel Rodman Highway , is a 10 @.@ 37 @-@ mile ( 16 @.@ 69 km ) long numbered state highway located in Washington County and southern Kent County , Rhode Island , United States . The route is a major north – south freeway in the southern Providence metropolitan area , directly linking Providence with eastern Washington County , the beaches of Narragansett and South Kingstown , and the city of Newport . Route 4 begins as a two @-@ lane divided highway at an intersection with U.S. Route 1 ( US 1 ) in the town of North Kingstown , becoming a limited @-@ access freeway after 1 @.@ 89 miles ( 3 @.@ 04 km ) . The route has four numbered interchanges before terminating in the city of Warwick , where the northbound lanes merge into Interstate 95 ( I @-@ 95 ) . 
- The origins of Route 4 date back to 1952 , when construction began on a short , unnumbered arterial from US 1 to the modern location of exit 5 at Routes 2 and 102 in Wickford . In 1965 , the Rhode Island Department of Public Works began work on a 5 @.@ 4 @-@ mile ( 8 @.@ 7 km ) freeway from modern exit 6 north to the merge with I @-@ 95 . The freeway , designated as Route 4 , was completed in 1972 . At that time , the Route 4 designation was also applied to the Wickford arterial . In 1988 , the missing link in Route 4 between exits 5 and 6 was completed and opened . The Rhode Island Department of Transportation has long @-@ term plans to upgrade the southernmost portion of Route 4 to freeway status by constructing overpasses at Oak Hill Road and West Allenton Road and a grade separation with US 1 . Although the project was originally scheduled to be completed by 2007 , the $ 55 million project has been postponed indefinitely . 
- 
- = = Route description = = 
- 
- Route 4 begins at a fork in the alignment of U.S. Route 1 in the community of North Kingstown ; the two left lanes of US 1 default onto Route 4 north , with the right @-@ hand lane carrying Tower Hill Road and US 1 north into the village of Wickford . Route 4 heads in a northwestern direction as a four @-@ lane divided highway , crossing West Allenton Road at an at @-@ grade intersection with a traffic signal after approximately 0 @.@ 5 miles ( 0 @.@ 80 km ) . The highway continues on a northwesterly projection , passing to the northeast of <unk> Hole Pond and to the southwest of Secret Lake in a heavily forested region . After Secret Lake , the highway curves to the north , crossing Oak Hill Road at another at @-@ grade intersection . 
- Shortly after the intersection with Oak Hill Road , Route 4 transitions from a divided arterial highway into a four @-@ lane limited @-@ access freeway . The freeway passes to the west of Belleville Pond and begins to parallel the alignment of Route 102 ( Ten Rod Road ) near the community of Lafayette . Route 4 passes over Amtrak 's Northeast Corridor railroad before entering the business district of Wickford Junction . The freeway interchanges with Routes 2 and 102 at exit 5 , a partial cloverleaf interchange . After the interchange , Route 4 <unk> to the northeast , beginning a parallel alignment with Route 2 that continues to its northern terminus . Route 4 crosses into the town of East Greenwich , passing under South Road before <unk> with Route 2 at exit 6 , a partial cloverleaf interchange . 
- After exit 6 , Route 4 passes the Rhode Island Army National Guard base to the east and to the Hunt River to the west . Route 4 northbound interchanges with Route 403 at exit 7 ; Route 403 , or the Quonset Freeway , is a four @-@ lane , limited access freeway and spur route of Route 4 that serves the Quonset Business Park and the village of <unk> . Heading southbound , exit 7 is split into exit <unk> , which serves the Quonset Freeway , and exit <unk> , which serves Route 402 ( Frenchtown Road ) , another spur route connecting the highway to US 1 and Route 2 . After exit 7 , Route 4 continues northward as a six @-@ lane expressway , passing <unk> to the west and entering a suburban region of East Greenwich . The highway crosses under an overpass at Middle Road before <unk> with Route <unk> , the freeway 's final spur , at another partial cloverleaf interchange . Exit 8 is also used to access Route 2 and I @-@ 95 south , which has no direct freeway connection with Route 4 north . Shortly after exit 8 , the Route 4 designation ends and the mainline of the highway <unk> onto I @-@ 95 north . 
- 
- = = History = = 
- 
- In 1950 , the Rhode Island General Assembly passed a $ 12 million ( equivalent to $ 118 million in 2016 ) bond issue to fund the construction of a 3 @-@ mile ( 4 @.@ 8 km ) , four @-@ lane divided arterial bypass of U.S. Route 1 in Wickford . Construction on the highway began in 1952 and was completed in 1954 , at which time the roadway opened as an unnumbered state highway leading from US 1 to Routes 2 and 102 in Wickford . 
- During the late 1950s , a few years after the completion of the arterial , the Rhode Island Department of Public Works ( <unk> ) proposed a relocation of Route 2 , which , at the time , was the major thoroughfare in the area . No action was taken until 1964 , when the <unk> introduced a study for the " <unk> Route 2 " proposal . During the study , drivers who used the Colonel Rodman Highway arterial and were bound for the state capital of Providence were <unk> onto Route 2 , an accident @-@ prone , four @-@ lane undivided highway near the modern exit 5 . In 1965 , the planned Route 2 freeway was given the new number of Route 4 , leaving Route 2 on its existing alignment . A public hearing was held by the state of Rhode Island on the proposed freeway , which was to be four lanes and have a divided , grassy median . This proposal was later accepted , and construction began two years later . 
- Construction of a 5 @.@ 4 @-@ mile ( 8 @.@ 7 km ) long section of Route 4 from what is now exit 6 in East Greenwich to I @-@ 95 in Warwick began in 1967 and was completed in 1972 . That year , the 3 @-@ mile ( 4 @.@ 8 km ) arterial south of the modern exit 5 was also designated as part of Route 4 . The divided highway remains largely intact to this date as the stretch of Route 4 from US 1 to Routes 2 and 102 at exit 5 ; the only piece of the arterial that has been significantly altered is the construction of a bridge over Amtrak 's Northeast Corridor line . By the early 1970s , Route 4 was complete north of exit 6 and south of what would become exit 5 , but there was a still a missing piece in the highway between the two exits . In the 1970s , the state of Rhode Island faced several budget problems and environmental concerns , both of which delayed the construction of the missing link for nearly eleven years . Environmental studies on the missing link began in 1977 , and the state estimated that the 1 @.@ 5 @-@ mile ( 2 @.@ 4 km ) long section of freeway would cost $ 15 – 21 million ( equivalent to $ 59 – 82 million in 2016 ) to construct . 
- In 1983 , the Rhode Island Department of Transportation ( RIDOT ) began construction of the new segment of Route 4 between exits 5 and 6 . The project , which ultimately went over budget at $ 24 million ( equivalent to $ 52 million in 2016 ) ) , was financed from a $ 63 million federal grant . In 1986 , during excavation for the new right @-@ of @-@ way , the Department of Transportation found archeological items from the Narragansett Indians dating from about 2 @,@ 000 to 4 @,@ 500 years prior . Although the findings were not centralized in the area , this caused delays for the extension of the freeway . On August 6 , 1988 , <unk> completed construction and performed a ribbon @-@ cutting ceremony for the new highway . 
- In January 1990 , two police cruisers were severely damaged during a chase on Route 4 . A driver was <unk> in the southbound lanes of Route 4 near exit 7 ; when the driver exited at Route 402 ( Frenchtown Road ) , two police officers got into serious accidents in their attempts to pursue the vehicle . After the crashes , the American Civil Liberties Union <unk> efforts to amend police chase policy and avoid further crash @-@ related injuries for officers in the line of duty . 
- In 2000 , construction began on the Quonset Freeway , a relocated Route 403 that serves the Quonset Business Park from Route 4 . The project included the reconstruction and reworking of exit 7 off Route 4 , which was a southbound @-@ only exit serving both Route 403 and Route 402 when constructed . The exit was converted into a trumpet interchange with new ramps between Route 4 , Route 403 and Route 402 and was completed in December 2008 , one year ahead of schedule . The project included the construction of a new northbound exit 7 serving Route 403 east . 
- 
- = = Future = = 
- 
- The Rhode Island Department of Transportation ( RIDOT ) has laid out long @-@ term plans for improvements to both the southern and northern <unk> of Route 4 . During the 1980s and 1990s , RIDOT announced plans to eliminate the three traffic lights along the southern end of the highway . The department planned to replace the existing <unk> US 1 and Route 4 merge , converting it into a grade @-@ separated interchange with an extensive overpass . This would cut @-@ off access to three local roads that intersect US 1 near the signal . The plan also included the replacement of the two other signaled intersections at West Allenton Road and Oak Hill Road with overpasses ; the overpass for West Allenton Road is planned to be constructed as a new exit 4 . In the 1990s , the state purchased and demolished several houses in the region to allow for an expanded Route 4 right @-@ of @-@ way in the vicinity of West Allenton Road . 
- The upgrade proposal proved to be very unpopular with North Kingstown residents who lived on the affected local roads . Additionally , RIDOT laid the highway out so that Route 4 would cross through wetlands in the area . This sparked environmental concerns , as one of the large wetlands that would be affected , <unk> 's Marsh , was deemed to be of high value by Rhode Island environmentalists . Despite local and environmental concerns , RIDOT still considers the Route 4 upgrade to be the <unk> way to improve traffic flow in the region . While the Department of Transportation considered upgrading nearby Route 2 to freeway standards as a potential alternative , this plan was ultimately rejected because of its effects on wells in the area . Although the project was originally scheduled to be completed by 2007 , the $ 55 million project has been postponed indefinitely . 
- RIDOT also has long @-@ range plans to construct direct freeway connections linking Route 4 north with I @-@ 95 south and I @-@ 95 north with Route 4 south . As of November 2010 , environmental studies are being prepared for a <unk> of the interchange . 
- 
- = = Exit list = = 
- 
- <unk> denote future exit numbers . 
- 
- 
- = West End Girls = 
- 
- " West End Girls " is a song by British pop duo Pet Shop Boys . Written by Neil Tennant and Chris Lowe , the song was released twice as a single . The song is influenced by hip hop music , with lyrics concerned with class and the pressures of inner @-@ city life which were inspired partly by T. S. Eliot 's poem The Waste Land . It was generally well received by contemporary music critics and has been frequently cited as a highlight in the duo 's career . 
- The first version of the song was produced by Bobby Orlando and was released on Columbia Records ' <unk> Records imprint in April 1984 , becoming a club hit in the United States and some European countries . After the duo signed with EMI , the song was re @-@ recorded with producer Stephen Hague for their first studio album , Please . In October 1985 , the new version was released , reaching number one in the United Kingdom and the United States in 1986 . 
- In 1987 , the song won Best Single at the <unk> Awards , and Best International Hit at the Ivor Novello Awards . In 2005 , 20 years after its release , the song was awarded Song of The Decade between the years 1985 and 1994 by the British Academy of Composers and Songwriters . In 2015 the song was voted by the British public as the nation 's 12th favourite 1980s number one in a poll for ITV . 
- The song was performed by Pet Shop Boys at the 2012 Summer Olympics closing ceremony and was included as part of the soundtrack of the 2013 game Grand Theft Auto V on the Non @-@ Stop @-@ Pop radio station . 
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- = = Background = = 
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- = = = Recording and production = = = 
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- In 1983 , Neil Tennant met producer Bobby Orlando , while on an assignment in New York interviewing Sting for Smash Hits . After listening to some demos , Orlando offered to produce for the duo . 
- In 1983 – 84 , the duo recorded eleven songs with Orlando , at <unk> Studios in New York , " West End Girls " , " Opportunities ( Let 's Make Lots of Money ) " , " One More Chance " , " I Want A Lover " , " A Man Could Get Arrested " , " I Get <unk> " , " Two <unk> by Zero " , " <unk> " , " It 's A Sin " , " Pet Shop Boys " , and " Later <unk> " . Orlando played most of the instruments on " West End Girls " , including the jazz riff at the end . Lowe played one chord and the bassline . It included a drum part lifted from Michael Jackson 's " Billie Jean " , and an arrangement involving what Tennant called " Barry White chords " . Orlando was thrilled by the song 's production ; his idea was to make a rap record in a British accent . 
- In April 1984 , " West End Girls " was released , becoming a club hit in Los Angeles and San Francisco , and a minor dance hit in Belgium , and France , but was only available in the United Kingdom as a 12 " import . In March 1985 , after long negotiations , Pet Shop Boys cut their contractual ties with Orlando , and hired manager Tom Watkins , who signed them with EMI . They re @-@ recorded " West End Girls " with producer Stephen Hague , and re @-@ released the song in late 1985 , topping the charts in both the UK and the U.S. 
- In an interview on <unk> 's <unk> Britannia programme ( Video on YouTube at <unk> <unk> <unk> ) , Neil Tennant explains the role of the then new sampling technology on the track and how every single sound came from the newly introduced E @-@ <unk> <unk> keyboard . 
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- = = = Music and lyrics = = = 
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- " West End Girls " is a synthpop song influenced by hip hop music . The song 's socially conscious streak , as well as the propulsive bass line , derives from Grandmaster Flash 's protest rap song " The Message " . Lowe and Hague created a " <unk> , obsessive rhythm punch " for the music , replacing the song 's previously sparse beats and minimal keyboard lines . 
- Tennant started to write the song when he was staying at his cousin 's house in Nottingham while watching a gangster film . Just when he was going to sleep he came up with the lines : " Sometimes you 're better off dead , there 's a gun in your hand and it 's pointing at your head " . The lyrics were inspired by T.S. Eliot 's poem The Waste Land , particularly in the use of different narrative voices and <unk> references . The song 's lyrics are largely concerned with class , inner @-@ city pressure . Tennant later said that some listeners had assumed the song referred to prostitutes , but was actually , " about rough boys getting a bit of <unk> . " 
- The lyric " From Lake Geneva to the Finland Station " refers to the train route taken by Vladimir Lenin when he was smuggled by the Germans to Russia during World War I , a pivotal event in the Russian Revolution . Indeed , it is highly likely the lyric was inspired by the book To the Finland Station by Edmund Wilson , a very famous work on the history of revolutionary thought and Socialism that Tennant would have at least heard of , if not read , as a student . The Bobby Orlando @-@ produced version of the single included another line , " All your stopping , stalling and starting , / Who do you think you are , Joe Stalin ? " which was removed for the 1985 version . 
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- = = Critical reception = = 
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- " West End Girls " has been generally well received by music critics . Stephen Thomas Erlewine from Allmusic in a review of the album Please called the song " hypnotic " , adding that " it 's not only a classic dance single , it 's a classic pop single " . In a review for the group 's second studio album <unk> , Rob <unk> from Rolling Stone magazine commented that " West End Girls " was " as catchy as anything on the radio in 1986 " , praising " its <unk> bass line and foreboding synth riffs " , but felt that it was almost " <unk> by <unk> spoken <unk> and the cryptic posturing of the duo 's lyrics " . In a review of the live album Concrete , Michael Hubbard from musicOMH said that " West End Girls " was one of the songs that " round out a collection that never feels too long or superfluous " , adding that it " goes some way to installing Tennant and Lowe as national treasures " . 
- <unk> <unk> from Pitchfork Media , in a review of their compilation album <unk> : Pet Shop Boys - The Hits commented that in the song " we meet Tennant not as a singer , but as a speaker " , adding that " he <unk> the verses to us not like a star , but like a stranger in a <unk> , <unk> alongside you and pointing out the sights " . 
- In 1987 , " West End Girls " won for Best Single at The BRIT Awards , and for Best International Hit at the Ivor Novello Awards . In 2005 , the British Academy of Composers and Songwriters gave to West End Girls the Ivor Novello Award for Song of The Decade between the years 1985 and 1994 . 
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- = = Music video = = 
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- The video was directed by Andy <unk> and Eric Watson , and consists of shots of the duo around London . At the beginning of the video , noises from the city can be heard , a camera passes Lowe on the street , and focus on two vintage dolls in a shop window . Then appears a sequence of quick cuts with shots of the city 's different sub @-@ cultures , the video freezes and cuts to Tennant and Lowe , who walk through an empty Wentworth Street in <unk> Lane Market . They stand in front of a red garage door , Tennant is in front dressed with a long coat , white shirt and dark <unk> , directly addressing the camera , with Lowe standing behind him with a blank expression . Lowe is filmed in double @-@ exposure and appears almost <unk> . In other shots , Tennant walks <unk> while Lowe follows behind , as if one were a master and the other an apprentice . 
- Then the video shows various shots at Waterloo station , as the chorus starts . In slow motion , the camera pans across the <unk> shop on the station <unk> as the duo walk past . It cuts to a brief shot of a No. 42 red double @-@ <unk> bus , showing the destination as <unk> , also advertising the stage @-@ show Evita , then black and white shots of the Tower Bridge , Westminster and the Westminster Palace <unk> Tower from the sky . The duo poses on the South Bank of the River Thames in a pastiche of a <unk> image , with the Houses of Parliament as a background . 
- The camera shows shots of young women , and passes through arcades and cinemas in Leicester Square . The camera now passes South Africa House showing protestors in the Non @-@ Stop <unk> , an anti @-@ apartheid vigil . The video cuts to a <unk> of Tennant singing the chorus , with a purple neon sign passing across his face . At the end the camera passes again through Leicester Square , where people queue to see Fletch and <unk> <unk> Susan . The video was nominated for Best New Artist in a Video at the 1986 MTV Video Music Awards , but lost to a @-@ ha 's Take On Me . 
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- = = Chart performance = = 
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- " West End Girls " was first released in April 1984 through writer and producer Bobby Orlando 's label . The song was a club hit in the United States , and in some European countries , such as Belgium , where it debuted at number 24 on the <unk> Top 30 chart on 28 July 1984 , peaking at 17 four weeks later . In Canada , " West End Girls " first entered the RPM singles chart in April 1985 , reaching a peak position of 81 in June 1985 . 
- Having signed with EMI , the group released their first major label single " Opportunities ( Let 's Make Lots of Money ) " in mid @-@ 1985 , but it failed to attract attention . The Pet Shop Boys then decided to re @-@ record " West End Girls " , and issue this new version as a single . Producer Stephen Hague helmed the new , re @-@ recorded version of " West End Girls " . 
- The re @-@ recorded version of " West End Girls " was released in the United Kingdom in October 1985 , debuting on the UK Singles Chart at number 80 , and within eight weeks of its release it had reached the top of the chart . It maintained the number one position for two weeks and received a gold certification by the British Phonographic Industry ( BPI ) in January 1986 . Across Europe , " West End Girls " also topped the singles chart in Norway , as well as peaking in the top three in Belgium , Germany , Ireland , the Netherlands , Sweden , and Switzerland . 
- In Canada , where the original recording of " West End Girls " had already been a minor hit in 1985 , the re @-@ recorded version was issued as a single in early 1986 . The re @-@ recorded song entered the chart in March 1986 , peaking at number one for one week on 17 May 1986 . In the United States , West End Girls debuted on the Billboard Hot 100 at number 71 , reaching the number one position on 10 May 1986 , and remained on the chart for 20 weeks . The song also peaked at number one on Billboard 's Hot Dance Music / Club Play chart for two weeks . 
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- = = Formats and track listings = = 
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- = = Credits and personnel = = 
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- Neil Tennant – vocals , lyrics 
- Chris Lowe – keyboards , artwork design 
- Helena Springs – additional vocals 
- Bobby Orlando – producer , ( 1984 release ) 
- Stephen Hague – producer ( 1985 release ) 
- Steve Jerome – engineer – 1984 release 
- David Jacob – engineer 
- Frank <unk> – remixing 
- Eric Watson – photography 
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- = = Charts and certifications = = 
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- = = = Chart positions = = = 
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- = = = Year @-@ end charts = = = 
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- = = = Certifications = = = 
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- = = East 17 version = = 
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- In 1993 East 17 covered " West End Girls " for their album <unk> , with limited chart success . 
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- = = = Track listings = = = 
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- 7 " 
- . West End Girls ( Faces on <unk> Mix ) 
- . West End Girls ( Kicking in <unk> ) 
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- = = = Charts = = = 
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- = Wrapped in Red = 
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- Wrapped in Red is the sixth studio album by American recording artist Kelly Clarkson , released on October 25 , 2013 , by RCA Records . The album is a follow @-@ up to her first greatest hits album , Greatest Hits – Chapter One , and its companion extended play , The <unk> Sessions Vol . 2 . Produced by Greg Kurstin , it is her first Christmas album and her first record to be solely released by RCA . Wrapped in Red consists of sixteen tracks , featuring five co @-@ penned original songs and eleven cover versions of Christmas standards and <unk> , two of which are duets featuring recording artists Ronnie Dunn , <unk> McEntire and <unk> Yearwood . 
- Weary of constantly being asked for her primary genre , Clarkson had long @-@ desired to record a Christmas album as a means to defy genre limitations . She commissioned Kurstin , who had studied jazz music under the tutelage of <unk> <unk> , to produce the entire album . Drawing inspirations from the soundtracks to the features A Charlie Brown Christmas and White Christmas , as well as the Christmas albums by Mariah Carey , McEntire , and Phil Spector , they experimented on various styles and sounds using Spector 's famed Wall of Sound technique to a create a contemporary holiday theme to classics . The Christmas music of Wrapped in Red comprises a variety of the genres such pop , jazz , country , and soul , marking a departure from the pop rock sound established from her previous studio albums ; while its lyrics share a singular theme of the color red , which represents a <unk> of emotions during the holidays . 
- Wrapped in Red debuted on the Billboard 200 chart at number 3 and topped the Billboard Top Holiday Albums chart with 70 @,@ 000 copies sold in its first week of release . For nine consecutive weeks , Wrapped in Red stayed on the top ten of both charts and was certified platinum by the Recording Industry Association of America and Music Canada . By the end of 2013 , it became the year 's best @-@ selling Christmas release in the United States and the second best @-@ selling Christmas release in Canada . Its lead single " Underneath the Tree " became an international top forty Christmas hit song and was radio 's most @-@ played new holiday song of 2013 . In promoting Wrapped in Red , she appeared in red dresses on various televised appearances ; and filmed an accompanying television special , Kelly Clarkson 's Cautionary Christmas Music Tale , at The Venetian Las Vegas , which premiered on NBC on December 11 , 2013 . In 2014 , Clarkson released the title track as the second single and hosted an annual Christmas benefit concert , Miracle on Broadway , at the Bridgestone Arena on December 20 , 2014 . 
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- = = Background = = 
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- Clarkson had expressed interest in recording a Christmas album for years , having recorded various Christmas songs such as " Oh Holy Night " and " My <unk> Up Christmas List " on the American Idol : The Great Holiday Classics ( 2003 ) , " I 'll Be Home for Christmas " on iTunes Session ( 2011 ) , and being featured on Blake Shelton 's Christmas album Cheers , It 's Christmas ( 2012 ) . Weary of constantly being asked for her primary genre , she felt that recording a Christmas album would finally pave a way for her to explore other different genres . She remarked , " I always get asked what genre I 'm in : ' Is this country or pop or rock ? What are you ? ' And what 's cool about making the Christmas album was , ' Oh , there are no limitations ! We can do whatever we want ! ' " . She further added , " The thing about Christmas is that it almost doesn 't matter what mood you 're in or what kind of a year you 've had — it 's a fresh start . I 'm going to clear the air and take stock of the good that 's happened . " 
- <unk> about making her sixth studio album being a Christmas record began on December 2012 , a month after releasing her first greatest hits album , Greatest Hits – Chapter One . Having found the opportunity to do so , Clarkson commissioned producer and multi @-@ instrumentalist Greg Kurstin , whom she had previously collaborated with on her albums Stronger and Chapter One , to solely produce the whole album . Despite having been raised in a Jewish faith and unfamiliar with Christmas songs , Kurstin still agreed to produce the project . As a result , the record marked the second time her studio album only had a single producer ( the first being David Kahne solely producing My December in 2007 ) . It also marked the fourth time Kurstin had solely produced an entire studio album apart from being a member of the musical groups The Bird and the Bee and <unk> <unk> ( the first three being Lily Allen 's It 's Not Me , It 's You in 2009 , <unk> 's We Are Born in 2010 , and The <unk> ' Port of Morrow in 2012 ) . 
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- = = Recording = = 
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- Recording sessions of the basic instrumental tracks for Wrapped in Red took place in Kurstin 's Echo Studio in Los Angeles while orchestral sessions were recorded at <unk> Studios in Hollywood and featured vocals recorded in The Barn studio in Nashville . In recording tracks for the album , Clarkson and Kurstin wanted to showcase as many different styles as they could by experimenting in various sounds and styles to create fresh , contemporary sound to classic @-@ sounding music . He recalled , " It was a lot of fun for us because we got to go back to our roots . When Kelly started singing , it was clear she had the chops and had been trained to do anything . " Further adding , " We really experimented . It was so much fun and liberating . And it pays off . " Kurstin , who studied with jazz musician <unk> <unk> at The New School for Jazz and Contemporary Music , recruited various jazz and soul musicians such as James <unk> , Kevin Dukes , Roy <unk> , and Bill <unk> to perform on the record to <unk> a Memphis soul sound . He also collaborated with Joseph <unk> to arrange and conduct a chamber orchestra . 
- In providing instrumentation for the record , Kurstin used all of his instruments such as a <unk> and a <unk> , taping them from a distance to stimulate the Wall of Sound , a recording technique originally developed by Phil Spector that was popular in the early 1960s . He enlisted Clarkson to provide all the background vocals herself . Clarkson , who grew up singing in a chorus , was pleased with the aspect ; saying , " <unk> is something I knew how to do from childhood . Sometimes I 'd have to do an alto instead of a soprano because they needed a bigger sound . But I 've never had to do anything like this before — doing all my backup vocals , essentially being my own choir . " Together , they began to record in May 2013 and continued through the summer of that year , beginning by recording " White Christmas " with Clarkson in the vocal booth and with Kurstin on a piano . She commented , " The production is all him . I would be just like ' Hey , can we make this more jazz ? Hey , can we make this more <unk> . And he just , like Harry Potter , made this happen . It 's so weird . " 
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- = = Composition = = 
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- = = = Theme and influences = = = 
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- Clarkson has cited the color red as the album 's only theme . A color traditionally associated with Christmas , she affiliated the color to various emotions in the holidays . Wanting to stray away from her usual pop sound , she described Wrapped in Red 's music as a representation to explore different genres such as jazz , country and Memphis soul . She recalled , " What 's cool about Christmas albums is you can do jazz , rock and roll , you can do pop , you can do blues , like you can do all that stuff and it works — cause it 's all classic and it 's <unk> sounding . " She also noted that the album 's multitude of styles positively contributes to her artistic goal , saying , " My best friend from childhood heard it and said , ' This is what you sound like , before everything else . ' And I agree , It 's my core sound . Back in the day , when artists came out with things like " Fall to <unk> " and " Bridge over <unk> Water " , those songs <unk> genres . It wasn 't , ' Where is it going to fit ? ' You <unk> to whatever the song calls for . And that 's exactly what I did — without having to have an umbrella for everything . " 
- In gathering inspirations for Wrapped in Red , Clarkson started by listening to Bing Crosby 's and Rosemary <unk> 's soundtracks from the 1954 feature film White Christmas as well as Mariah Carey 's Merry Christmas ( 1994 ) and Merry Christmas to You ( 1997 ) by <unk> McEntire . While Kurstin , who used to play in a jazz band , took influences from A Charlie Brown Christmas by the Vince <unk> Trio and A Christmas Gift for You by Phil Spector as his inspirations , which resulted to the album 's Wall of Sound resonance . Clarkson also cited that her relationship with her then @-@ fiancé Brandon Blackstock had inspired some of the album 's lyrical content . 
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- = = = Song analysis = = = 
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- Clarkson shares writing credits on all five original songs on Wrapped in Red , some of which were written in December 2012 to avoid writing Christmas tunes during the 2013 summer season . She co @-@ wrote the opening and the title track , " Wrapped in Red " , with Ashley <unk> , <unk> Eubanks , and Shane <unk> . A Christmas ballad , the song was inspired by a scene in the holiday feature film Love <unk> ( 2003 ) , in which someone confesses <unk> love towards another . Critics singled out the track the one that <unk> the Wall of Sound the most . The second track , " Underneath the Tree " , was written by Clarkson and Kurstin , making it the first time they had co @-@ written a track together . Clarkson remarked , " Greg and I have worked a lot together , but usually I just come in and I just sing . We 've never have actually written a song together at this point . And he and I were like , ' Let 's just try to write something for the record . " RCA Records chief executive Peter Edge remarked that its release as a single was partly inspired by the success of " All I Want for Christmas Is You " by Carey . The following track is a rendition of the holiday standard " Have Yourself a Merry Little Christmas " , which Clarkson had selected for its saccharine content . 
- Clarkson favored " Run Run Rudolph " as her favorite classic , saying " Just because it got to be a little more rock and roll . " She also remarked that " Please Come Home for Christmas ( Bells Will Be Ringing ) " , was the first song selected for inclusion after her mother 's recommendation and the song 's melancholic lyrics . Written by Clarkson and Eubanks , " Every Christmas " , was the first song to be written for the album . She revealed that the song narrates of her holiday life prior to meeting Blackstock , McEntire 's stepson , saying " Every Christmas , I was just like , ' This is going to be different , right ? I 'm going to actually find someone and not be <unk> alone for the rest of my life ? ' " . The seventh track is a cover of Elvis Presley 's " Blue Christmas " . Its follow @-@ up , a rendition of " Baby , It 's Cold Outside " , features Ronnie Dunn . Clarkson had approached Dunn thinking that his personality suited the song 's content well , saying " Like , it 's straight @-@ up his personality to say all of that to try and get you to stay , and have a drink . " " Winter Dreams ( Brandon 's Song ) " was written by Clarkson , <unk> , and Eubanks as a companion piece to " Every Christmas " . Dedicated to Blackstock , the song accounts her holiday after meeting him . She remarked , " Christmas changes , it morphs , it comes to life a little more … It ’ s just a <unk> time . " 
- The tenth track , " White Christmas " , was the first song to be recorded for Wrapped in Red . A cover of <unk> and <unk> 's " My Favorite Things " follows up as the eleventh track . Clarkson opted for the Broadway performance of the song to stray away from Julie Andrews 's version , citing " I think you shouldn 't go near anywhere of what she 's doing because she 's so good . " Clarkson and Kurstin co @-@ wrote " 4 <unk> " with <unk> Dennis and <unk> <unk> . Originally written a pop song , they converted it as a Christmas song to fit the album 's theme , describing it as a crossover between <unk> <unk> 's " Santa Baby " ( 1953 ) and Madonna 's " Material Girl " ( 1984 ) . A rendition of <unk> <unk> 's " Just for Now " was described by Clarkson as her highly dysfunctional environment , saying " Can we just stop for like five minutes and have like a normal Christmas setting ? " The song begins by sampling the melody of the Christmas tune " Carol of the Bells " . The closing track , a rendition of the traditional <unk> " Silent Night " , features McEntire and <unk> Yearwood and ends in an a <unk> setting between the trio . In addition , two tracks were also included in the deluxe edition of the album : the first , Clarkson 's cover of " I 'll Be Home for Christmas " from iTunes Session ; and the second , her rendition of the first stanza of the ecclesiastical hymn " Oh Come , Oh Come Emmanuel " . 
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- = = Release = = 
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- Wrapped in Red was first released internationally on October 25 , 2013 by RCA Records through Sony Music Entertainment . It then received a North American release on October 29 , 2013 by RCA as part of its holiday promotional campaign with the soundtracks to the feature films Black <unk> and The Best Man Holiday , with Wrapped in Red being promulgated as the one that will transcend formats and become a new holiday classic . In an interview with Billboard , RCA marketing executive Aaron <unk> remarked that the album was their main release of the holidays , quoting " The angle on this album is that , like all great Christmas records , it 's about amazing vocal performances . That 's what this is intended to be @-@ an album launched this year but <unk> and genre @-@ <unk> . " In preparation for its release in the United States , RCA shipped a half @-@ million units on Amazon.com and Target , which exclusively released a deluxe edition . A red LP pressing of Wrapped in Red by United Record <unk> followed the CD release on November 25 , 2013 , marking the first time an album by Clarkson was released on a vinyl record . A deluxe LP and CD edition was also released on the Sony Music store which included a scarf , a holiday ornament , and a snow globe , all of which were decorated in red as inspired from the album . A international promotion campaign was also planned for Clarkson , but was later halted due her pregnancy . On October 21 , 2014 , Wrapped in Red was reissued by RCA with a special edition CD + DVD release exclusive to <unk> stores in the United States . A green LP pressing of the album will also have a limited 500 @-@ copy release on December 9 , 2014 . 
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- = = = Promotion = = = 
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- On October 15 , 2013 , " White Christmas " was released as a promotional single from Wrapped in Red . Three days after , " Underneath the Tree " premiered on Clarkson 's <unk> channel . A television Christmas special , titled Kelly Clarkson 's Cautionary Christmas Music Tale , was filmed by concert director <unk> Hamilton on October 30 , 2013 , the eve after its street date , at The Venetian Las Vegas . A pastiche of A Christmas Carol , the Christmas special featured live performances of selections from Wrapped in Red ( one of which features McEntire and Yearwood ) . Produced by <unk> and <unk> , Cautionary Christmas Music Tale premiered on NBC in the United States and Global in Canada on December 11 , 2013 , being <unk> by RCA as the album 's primary promotional medium . NBC 's premiere broadcast of the special was seen by 5 @.@ 31 million viewers , according to Nielsen Media Research . It also received a 1 @.@ 4 share among adults between the ages of 18 to 49 , generated NBC ’ s second biggest overall audience its time slot . NBC had also a <unk> broadcast of Cautionary Christmas Music Tale on Christmas Day , which was seen by an additional 3 @.@ 54 million viewers . 
- Clarkson had also promoted Wrapped in Red in various televised performances , all of which she was dressed in red attire . She first performed " Underneath the Tree " on the The Today Show on November 26 , 2013 . On December 4 , 2013 , she performed " Run Run Rudolph " and " Blue Christmas " on the Christmas at Rockefeller Center television special . Clarkson had then performed " Underneath the Tree " on more televised events : such as on the fifth season of the The Voice on December 3 , 2013 , The Ellen <unk> Show on December 5 , 2013 , and on Late Night with Jimmy Fallon on December 12 , 2013 . On December 25 , 2013 , Clarkson returned to The Today Show on its Christmas Day broadcast , performing " Blue Christmas " . Selected tracks from the album were also used in advertisements , such as " Run Run Rudolph " , which was used in a <unk> holiday advertisement , and " Underneath the Tree " , which was featured in an Amazon.com and Amazon <unk> Fire <unk> advertisement with an appearance by Clarkson performing the song . On December 20 , 2014 , She will host a Christmas concert , Miracle on Broadway , at the Bridgestone Arena . An annual Christmas benefit concert , Miracle on Broadway , will feature live performances of various Christmas songs by McEntire , Yearwood , Garth Brooks , Ronnie Dunn , <unk> <unk> , Hayley Williams , Charles <unk> , and <unk> <unk> , some of whom will also join Clarkson in performing selections from Wrapped in Red . 
- Wrapped in Red 's lead single , " Underneath the Tree " , was released to radio airplay on November 5 , 2013 . <unk> in its initial release , music critics <unk> compared the song to " All I Want for Christmas is You " and <unk> it as a future Christmas standard . Reviewing for Slant Magazine , Sal Cinquemani wrote that track is likely to become Clarkson 's very own contemporary standard ; while The Independent 's Hugh Montgomery applauded it as " a winner on all fronts . " After debuting on the Billboard Holiday 100 chart at number 34 , it became holiday top ten hit by peaking at number eight on the chart . It also topped the Billboard Adult Contemporary chart for four consecutive weeks , becoming Clarkson 's third track and the fifteenth holiday song to top the chart . " Underneath the Tree " also charted on the main Billboard Hot 100 chart at number seventy @-@ eight and became a top forty hit internationally : including the Billboard Canadian Hot 100 chart , the Dutch Top 40 chart , and the Official UK Singles Chart . USA Today reported that " Underneath the Tree " was American radio 's most @-@ played new holiday song of 2013 , while Edison Media Research reported that the single was the first holiday song to receive a considerable support on mainstream contemporary hit radio in almost 20 years . Wrapped in Red 's second single , the title track , was serviced to radio airplay on November 25 , 2014 . On the week ending December 28 , 2014 , it debuted on the Billboard Adult Contemporary chart at number 11 on the week ending December 13 , 2014 . 
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- = = Reception = = 
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- = = = Critical response = = = 
- 
- At Metacritic , which assigns a <unk> rating out of 100 to reviews from mainstream critics , the album received an average score of 73 , based on 6 reviews , and scoring higher than any other album by Clarkson . AllMusic 's senior editor Stephen Thomas Erlewine gave it a three @-@ and @-@ a @-@ half stars . He described its uptempo arrangements , as well as Clarkson 's vocal performance , as " bold and brassy " and its mid @-@ tempo arrangements as " even more <unk> " . He also noted the track selection " favors the bold , " but that " she <unk> well in this setting , always sounding like the strongest element in the mix " Towards the end of his review , he wrote that " Perhaps the concept and execution are conventional , but even in this utterly expected setting , Clarkson retains her fiery , individual spirit , and that 's what makes Wrapped in Red appealing : to the letter , it delivers what it promises . " Sal Cinquemani of Slant Magazine also gave it a similar rating . He noted that the album " largely offers a respite from the pop @-@ rock template she 's been relentlessly pursuing since <unk> , with less shouting and more of the varied range and texture on full display that helped <unk> her the winner of the inaugural season of American Idol . For better or worse , a decade of recording and touring has <unk> up the edges of her voice , lending a lived @-@ in quality that <unk> lyrics about love and longing with an authenticity that might have otherwise been missing had she recorded these songs just a few years earlier . " 
- NPR 's Ken Tucker described the album as a " glossy but heartfelt work " and <unk> compared its contrasting philosophy to Nick Lowe 's Quality Street : A <unk> Selection for All the Family , both of which he described as " will put you in a holiday mood " . Matt <unk> of PopMatters gave the album a generally favorable review , claiming that " Clarkson plays it safe and spends too much time showing off her upper register , but Wrapped in Red is a warm and romantic addition to the Christmas pop <unk> , " adding " Wrapped in Red doesn ’ t need edge ; it ’ s just dynamic and varied enough to be satisfying , and it ’ s light @-@ years better than any of the whitewashed Christmas crap Simon Cowell has inflicted on the world " . Sarah Rodman of The Boston Globe gave a favorable review , particularly lauding " Underneath the Tree " , and described Clarkson 's rendering the Christmas standards as " fairly straight " . She added , " She starts gently on " Have Yourself a Merry Little Christmas " before belting out the money notes . She <unk> <unk> through the soulful favorite " Please Come Home for Christmas ( Bells Will Be Ringing ) " and hangs by the piano for a <unk> “ White Christmas . " Newsday 's music columnist Glenn Gamboa wrote that " Clarkson handles it all expertly — hitting remarkably high notes on " Have Yourself a Merry Little Christmas " and swinging <unk> on " Baby It 's Cold Outside " with Dunn . The new songs make Wrapped in Red a real gift , as the title track and " Underneath the Tree " channel the Phil Spector Christmas albums ; and " 4 <unk> " somehow blends " Stronger " and " Santa Baby " . Reviewing for HitFix , Melinda Newman gave the album an " A " rating , praising Clarkson 's vocal performances and noting that she and Kurstin " have clearly studied legendary Christmas albums of <unk> — most notably Spector 's A Christmas Gift For You and Andy Williams ' Merry Christmas — to <unk> recreate Christmas standards , as well as craft new ones in the image of those sets . " Chris <unk> of Slate declared Wrapped in Red as the best of 2013 's new Christmas records , noting for its vintage sound . He also observed that its five original tracks , most notably " Wrapped in Red " and " Underneath the Tree " , have reasonable odds of remaining in the <unk> rotation five years from now . In his review for The New York Times , Jon Caramanica wrote that Clarkson is very likely the only singer working in pop with a real possibility of creating a modern holiday classic along the lines of Carey 's " All I Want for Christmas Is You . " and remarked that her takes on familiar songs , however accomplished , are " also faithful in the way that someone <unk> of pop history would be . " 
- 
- = = = Commercial performance = = = 
- 
- Wrapped in Red became a commercial success in the United States . Prior it its release , music commercial analysts predicted that the album would likely sell at least 60 @,@ 000 copies in its first week of release in the region , and <unk> it to be the front @-@ runner as the bestselling holiday release of the season . On the week ending November 16 , 2013 , it debuted on the Billboard 200 chart at number 3 with 70 @,@ 000 copies sold in all retailers , a 93 @,@ 000 decrease from Stronger 's first week sales of 163 @,@ 000 copies in 2011 . Nielsen Music analyst Dave Bakula attributed its low performance to the falling market share of the holiday music in general , which saw 3 @.@ 8 percent decrease in 2012 . The album 's chart debut on the Billboard 200 earned Clarkson her sixth consecutive top three studio album as well as the highest debut for a Christmas record by a female artist since Susan Boyle 's first Christmas album The Gift debuted at the top of the chart in 2010 . Wrapped in Red also debuted three other different charts , most notably at the top of the Billboard Top Holiday Albums chart . On the week ending November 30 , 2013 , by charting at number six on the Billboard 200 , the album became the lone Sony release inside the chart 's top ten , with the others being Universal Music Group releases . 
- Despite its modest debut week , Wrapped in Red began to gain <unk> at the beginning of the holiday season , selling up to 131 @,@ 000 copies during the <unk> week . It experienced its best sales week after <unk> from NBC 's premiere broadcast of Cautionary Christmas Music Tale , selling up to 136 @,@ 000 copies on its seventh week of release . For nine consecutive weeks , it stayed in the top ten of the Billboard 200 , the most by any studio album by Clarkson . On December 5 , 2013 , the album was certified platinum by the Recording Industry Association of America , making it her fifth platinum studio album . Wrapped in Red subsequently became the bestselling Christmas release of 2013 by selling over 763 @,@ 000 copies , according to Nielsen Soundscan , making her the first American female artist to have the number @-@ one Christmas album of the Soundscan era . Twelve of the album cuts from Wrapped in Red have also entered the Billboard Holiday Digital Songs chart during its first week of release — led by " Silent Night " , " Have Yourself a Merry Little Christmas " , and " Underneath the Tree " at numbers one , two , and four , respectively . Other songs have also appeared in various Billboard charts throughout the holiday season : songs such as " Blue Christmas " and " Please Come Home for Christmas " charted on the Billboard Adult Contemporary chart , peaking at numbers 5 and 6 , respectively ; whereas " My Favorite Things " , " Run Run Rudolph " , " Please Come Home for Christmas " , " Silent Night " and " Wrapped in Red " peaked on the Billboard Canada AC chart at numbers eight , seven , 14 , 22 , and 49 , respectively . Tracks such as " My Favorite Things " have charted on the Billboard Mexico <unk> Airplay chart at number 49 ; while " Silent Night " attained a position in both the Billboard Holiday 100 and the Billboard Hot Country Songs charts , peaking at numbers 86 and 51 , respectively . Amazon.com listed Wrapped in Red as their second bestselling album during the holiday season , and listed it as their sixth bestselling title of 2013 . The album has sold <unk> @,@ 300 copies in the US as of November 2014 . And in 2014 peaked at 7 on the holiday chart . 
- <unk> , Wrapped in Red had a relatively limited commercial performance . In Canada , the album debuted on the Billboard Canadian Albums chart at number 6 on the week ending November 16 , 2013 , making it her fifth top ten debut on the Nielsen @-@ tracked chart . It peaked on the chart at number 5 on the week ending December 28 , 2013 . Wrapped in Red became the second bestselling Christmas album of 2013 in Canada with 67 @,@ 000 copies sold in the region , behind A Christmas Gift to You by Johnny Reid . In Australia , the album debuted on the ARIA Albums Chart at number 82 on the week ending November 4 , 2013 , and peaked at number 29 on the week ending December 30 , 2013 . In Switzerland , it debuted on the <unk> <unk> at number 97 on the week ending November 10 , 2013 . In the United Kingdom , Wrapped in Red charted on the Official UK Albums Chart at number 65 on the week ending December 14 , 2013 . Despite its limited performance , Sony Corporation listed the album as their fifth bestselling release worldwide during the holiday season , which included albums , album cut tracks , and singles sales . 
- 
- = = Track listing = = 
- 
- All tracks were produced by Greg Kurstin , with vocal production on " Every Christmas " made by Jason <unk> . 
- Note 
- " Just for Now " contains a portion of the composition " Carol of the Bells " , written by Peter J. <unk> . 
- Tracks from the concert DVD were filmed from the television special Kelly Clarkson 's Cautionary Christmas Music Tale . 
- 
- = = Credits and personnel = = 
- 
- Credits lifted from the album 's liner notes . 
- <unk> 
- Production 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Christmas 1994 nor 'easter = 
- 
- The Christmas 1994 nor 'easter was an intense cyclone along the East Coast of the United States and Atlantic Canada . It developed from an area of low pressure in the southeast Gulf of Mexico near the Florida Keys , and moved across the state of Florida . As it entered the warm waters of the Gulf Stream in the Atlantic Ocean , it began to rapidly intensify , exhibiting traits of a tropical system , including the formation of an eye . It attained a pressure of 970 millibars on December 23 and 24 , and after moving northward , it came ashore near New York City on Christmas Eve . Because of the uncertain nature of the storm , the National Hurricane Center ( NHC ) did not classify it as a tropical cyclone . 
- Heavy rain from the developing storm contributed to significant flooding in South Carolina . Much of the rest of the East Coast was affected by high winds , coastal flooding , and beach erosion . New York State and New England bore the brunt of the storm ; damage was extensive on Long Island , and in Connecticut , 130 @,@ 000 households lost electric power during the storm . Widespread damage and power outages also occurred throughout Rhode Island and Massachusetts , where the storm generated 30 @-@ foot ( 9 @.@ 1 m ) waves along the coast . Because of the warm weather pattern that contributed to the storm 's development , precipitation was limited to rain . Two people were killed , and damage amounted to at least $ 21 million . 
- 
- = = Meteorological history = = 
- 
- The storm originated in an upper @-@ level low pressure system that moved southeastward from the central Great Plains into the Deep South of the United States . After reaching the southeast Gulf of Mexico , the disturbance underwent cyclogenesis , and the resultant system moved through Florida on December 22 in response to an approaching trough . National Hurricane Center forecaster Jack <unk> noted that " as it moved out into the Bahamas , it appeared to take on the characteristics of a tropical storm . " The uncertain nature of the storm prevented the NHC from issuing advisories on it , and forecasters lacked sufficient data to fully assess the cyclone for potential tropical characteristics . The same trough that pushed the storm across Florida had moved to the north , allowing for high pressure to develop in the upper levels of the atmosphere . 
- <unk> a " hybrid storm " , the cyclone rapidly intensified in warm waters of up to 80 ° F ( 27 ° C ) from the Gulf Stream combined with a cold air mass over the United States . The system continued to rapidly intensify while moving within the Gulf Stream ; it developed central convection , an unusual trait for an extratropical cyclone , and at one point exhibited an eye . Despite these indications of tropical characteristics , " There was no front associated with it and it had a warm core , but the radius of maximum winds was more than 150 nautical miles ( 170 mi ; 280 km ) , so under the standard NHC criteria it didn 't qualify as a tropical storm . " On December 23 and 24 , the nor 'easter intensified to attain a barometric pressure of 970 mb ( 29 inHg ) . An upper @-@ level low pressure system that developed behind the storm began to intensify and grew to be larger in size than the original disturbance . In an interaction known as the <unk> effect , the broad circulation of the secondary low swung the primary nor 'easter northwestward towards southern New York and New England . The original low passed along the south shore of Long Island , and made landfall near New York City on December 24 . Subsequently , it moved over southeastern New York State . On December 25 , the system began to rapidly weaken as it moved towards Nova Scotia , before the pair of low pressure systems moved out to sea in tandem in the early hours of December 26 . 
- 
- = = Effects = = 
- 
- 
- = = = Southeast United States = = = 
- 
- In South Carolina , flooding associated with the cyclone was considered to be the worst since 1943 . Over 5 inches ( 130 mm ) of rainfall was reported , while winds brought down trees and ripped <unk> . In addition , the coast suffered the effects of beach erosion . Thousands of electric customers in the state lost power . As a result of the heavy rainfall , several dams became overwhelmed by rising waters . Extensive flooding of roads and highways was reported , many of which were closed as a result . Up to 3 feet ( 0 @.@ 91 m ) of water flooded some homes in the region . Approximately 300 people in Florence County were forced to evacuate because of the flooding , and at least 200 homes were damaged . Two deaths were reported in the state . One woman was killed when her vehicle <unk> and struck a tree , and another person drowned after her car was struck by another vehicle . Total damage in South Carolina amounted to at least $ 4 million . 
- Strong winds occurred along the North Carolina coast . Diamond Shoals reported sustained winds of 45 miles per hour ( 72 km / h ) , and offshore , winds gusted to 65 miles per hour ( 105 km / h ) . On <unk> Beach , rough surf eroded an 8 @-@ foot ( 2 @.@ 4 m ) ledge into the beach . On Carolina Beach , dunes were breached and some roads , including portions of North Carolina Highway 12 , were closed . 
- 
- = = = Mid @-@ Atlantic = = = 
- 
- As the primary storm entered New England , the secondary low produced minor coastal flooding in the <unk> region of Virginia on December 23 . Winds of 35 to 45 miles per hour ( 56 to 72 km / h ) and tides to 1 to 3 feet ( 0 @.@ 30 to 0 @.@ 91 m ) above normal were reported . In <unk> , Virginia Beach , Virginia , a beachfront home collapsed into the sea . Several roads throughout the region suffered minor flooding . Strong winds resulting from the tight pressure gradient between the nor 'easter and an area of high pressure located over the United States brought down a few utility poles , which sparked a brush fire on December 24 . The fire , quickly spread by the wind , burned a field . The winds brought down several trees . 
- Damage was light in Maryland . Some sand dunes and wooden structures were damaged , and above @-@ normal tides occurred . In New Jersey , high winds caused power outages and knocked down trees and power lines . Minor coastal flooding of streets and houses was reported . Otherwise , damage in the state was minor . 
- The storm brought heavy rainfall and high winds to New York State and New York City on December 23 and 24 . <unk> of 60 to 80 miles per hour ( 97 to 129 km / h ) downed hundreds of trees and many power lines on Long Island . Several homes , in addition to many cars , sustained damage . Roughly 112 @,@ 000 Long Island <unk> Company customers experienced power outages at some point during the storm . As the cyclone progressed northward into New York State , high winds occurred in the Hudson Valley region . Throughout Columbia , Ulster and <unk> Counties , trees , tree limbs , and power lines were downed by the winds . At <unk> , a gust of 58 miles per hour ( 93 km / h ) was reported . Ulster County suffered substantial impacts , with large trees being uprooted and striking homes . Across eastern New York State , 25 @,@ 000 households lost power as a result of the nor 'easter . On the North Fork of Long Island , in <unk> , a seaside home partially collapsed into the water . 
- 
- = = = New England = = = 
- 
- In Connecticut , the storm was described as being more significant than anticipated . Gale @-@ force wind gusts , reaching 70 miles per hour ( 110 km / h ) , blew across the state from the northeast and later from the east . Trees , tree limbs , and power lines were downed , causing damage to property and vehicles . The high winds caused widespread power outages , affecting up to 130 @,@ 000 electric customers . As a result , electric companies sought help from as far as Pennsylvania and Maine to restore electricity . Bruno <unk> , a spokesman for Northeast Utilities , reported that " We 've had outages in virtually every community . " In New Haven , the nor 'easter ripped three barges from their <unk> . One of the barges traveled across the Long Island Sound and ran aground near Port Jefferson , New York . A man in Milford was killed indirectly when a tree that was partially downed by the storm fell on him during an attempt to remove it from a relative 's yard . Northeast Utilities , which reported the majority of the power outages , estimated storm damage in the state to be about $ 6 – $ 8 million ( 1994 USD ; $ 8 @.@ 8 – $ 11 @.@ 8 million 2008 USD ) . 
- Effects were less severe in New Hampshire and Vermont . In southern New Hampshire , a line of thunderstorms produced torrential rainfall , causing flooding on parts of New Hampshire Route 13 . Flash flooding of several tributaries feeding into the <unk> River was reported . In Maine , the storm brought high winds and heavy rain . Along the coast of southern Maine and New Hampshire , beach erosion was reported . Additionally , minor flooding was reported across the region , as a result of heavy surface runoff and small ice jams . In Rhode Island , the power outages were the worst since Hurricane Bob of the 1991 Atlantic hurricane season . Throughout the state , approximately 40 @,@ 000 customers were without electric power . As with Massachusetts , downed trees and property damage were widespread . There were many reports of roof shingles being blown off roofs and of damage to <unk> . In Warwick , several small boats were damaged after being knocked into other boats . The highest reported wind gust in the state was 74 miles per hour ( 119 km / h ) at <unk> , Rhode Island . <unk> damage totaled about $ 5 million . 
- Massachusetts , particularly Cape Cod and Nantucket , bore the brunt of the nor 'easter . Reportedly , wind gusts approached 100 miles per hour ( 160 km / h ) on Cape Cod and , offshore , waves reached 30 feet ( 9 @.@ 1 m ) . At Walpole , wind gusts peaked at 88 miles per hour ( 142 km / h ) , while on Nantucket gusts of 84 miles per hour ( 135 km / h ) were reported . The winds left 30 @,@ 000 electric customers without power during the storm , primarily in the eastern part of the state . Power was out for some as long as 48 hours . Property damage was widespread and many trees , signs , and billboards were blown down . A large tent used by the New England <unk> was ripped and blown off its foundation . The winds also spread a deadly house fire in North <unk> . Although not directly related to the storm , it caused seven fatalities . Because tides were low , little coastal flooding occurred . Outside the Prudential Tower Center in Boston , the storm toppled a 50 @-@ foot ( 15 m ) Christmas tree . Rainfall of 2 to 3 @.@ 5 inches ( 51 to 89 mm ) was recorded throughout the eastern part of the state , contributing to heavy runoff that washed away a 400 @-@ foot ( 120 m ) section of a highway . Total damage in Massachusetts was estimated at about $ 5 million . 
- 
- 
- = Sholay = 
- 
- Sholay ( pronunciation , meaning " <unk> " ) is a 1975 Indian Hindi @-@ language action @-@ adventure film directed by Ramesh Sippy and produced by his father G. P. Sippy . The film follows two criminals , Veeru and Jai ( played by Dharmendra and Amitabh Bachchan ) , hired by a retired police officer ( Sanjeev Kumar ) to capture the ruthless dacoit Gabbar Singh ( Amjad Khan ) . Hema Malini and Jaya Bhaduri also star , as Veeru and Jai 's love interests . Sholay is considered a classic and one of the best Indian films . It was ranked first in the British Film Institute 's 2002 poll of " Top 10 Indian Films " of all time . In 2005 , the judges of the 50th annual Filmfare Awards named it the Best Film of 50 Years . 
- The film was shot in the rocky terrain of Ramanagara , in the southern state of Karnataka , over a span of two and a half years . After the Central Board of Film <unk> mandated the removal of several violent scenes , Sholay was released with a length of 198 minutes . In 1990 , the original director 's cut of 204 minutes became available on home media . When first released , Sholay received negative critical reviews and a <unk> commercial response , but favourable word @-@ of @-@ mouth publicity helped it to become a box office success . It broke records for continuous showings in many theatres across India , and ran for more than five years at Mumbai 's Minerva theatre . By some accounts , Sholay is the highest grossing Indian film of all time , adjusted for inflation . 
- The film drew heavily from the conventions of Westerns , and is a defining example of the masala film , which mixes several genres in one work . Scholars have noted several themes in the film , such as glorification of violence , conformation to feudal ethos , debate between social order and mobilised usurpers , homosocial bonding , and the film 's role as a national allegory . The combined sales of the original soundtrack , scored by R. D. Burman , and the dialogues ( released separately ) , set new sales records . The film 's dialogues and certain characters became extremely popular , contributing to numerous cultural <unk> and becoming part of India 's daily vernacular . In January 2014 , Sholay was re @-@ released to theatres in the 3D format . 
- 
- = = Plot = = 
- 
- In the small village of Ramgarh , the retired policeman Thakur Baldev Singh ( Sanjeev Kumar ) summons a pair of small @-@ time thieves that he had once arrested . Thakur feels that the duo — Veeru ( Dharmendra ) and Jai ( Amitabh Bachchan ) — would be ideal to help him capture Gabbar Singh ( Amjad Khan ) , a dacoit wanted by the authorities for a ₹ 50 @,@ 000 reward . Thakur tells them to surrender Gabbar to him , alive , for an additional ₹ 20 @,@ 000 reward . 
- The two thieves thwart the dacoits sent by Gabbar to <unk> the villagers . Soon afterwards , Gabbar and his <unk> attack Ramgarh during the festival of <unk> . In a tough battle , Veeru and Jai are cornered . Thakur , although he has a gun within his reach , does not help them . Veeru and Jai fight back and the bandits flee . The two are , however , upset at Thakur 's inaction , and consider leaving the village . Thakur explains that Gabbar had killed nearly all of his family members , and cut off both his arms a few years earlier , which is why he could not use the gun . He had concealed the dismemberment by always wearing a <unk> . 
- Living in Ramgarh , the <unk> Veeru and cynical Jai find themselves growing fond of the villagers . Veeru is attracted to Basanti ( Hema Malini ) , a feisty , <unk> young woman who makes her living by driving a horse @-@ cart . Jai is drawn to Radha ( Jaya Bhaduri ) , Thakur 's reclusive , widowed daughter @-@ in @-@ law , who subtly returns his affections . 
- Skirmishes between Gabbar 's gang and Jai @-@ Veeru finally result in the capture of Veeru and Basanti by the dacoits . Jai attacks the gang , and the three are able to flee Gabbar 's hideout with dacoits in pursuit . Fighting from behind a rock , Jai and Veeru nearly run out of ammunition . Veeru , unaware that Jai was wounded in the gunfight , is forced to leave for more ammunition . Meanwhile , Jai , who is continuing the gunfight <unk> , decides to sacrifice himself by using his last bullet to ignite dynamite sticks on a bridge from close range . 
- Veeru returns , and Jai dies in his arms . Enraged , Veeru attacks Gabbar 's den and catches the dacoit . Veeru nearly beats Gabbar to death when Thakur appears and reminds Veeru of the promise to hand over Gabbar alive . Thakur uses his spike @-@ <unk> shoes to severely <unk> Gabbar and destroy his hands . The police then arrive and arrest Gabbar . After Jai 's funeral , Veeru leaves Ramgarh and finds Basanti waiting for him on the train . Radha is left alone again . 
- 
- = = Cast = = 
- 
- Dharmendra as Veeru 
- Sanjeev Kumar as Thakur Baldev Singh , usually addressed as " Thakur " 
- Hema Malini as Basanti 
- Amitabh Bachchan as Jai ( <unk> ) 
- Jaya Bhaduri as Radha , Thakur 's daughter @-@ in @-@ law 
- Amjad Khan as Gabbar Singh 
- <unk> <unk> as <unk> , Thakur 's servant 
- A. K. <unk> as <unk> <unk> , the imam in the village 
- <unk> as Ahmed , son of the imam 
- Jagdeep as Soorma Bhopali , a comical wood trader 
- <unk> Mishra as <unk> , Basanti 's maternal aunt 
- <unk> as the <unk> , a comical character modelled after Charlie <unk> in The Great Dictator ( 1940 ) 
- <unk> <unk> as <unk> , prison barber and <unk> 's side @-@ kick 
- Mac Mohan as Sambha , Gabbar Singh 's sidekick 
- <unk> <unk> as <unk> , another of Gabbar 's men whom he kills in a game of Russian <unk> 
- <unk> as Inspector <unk> , Radha 's Father 
- Helen in a special appearance in song " Mehbooba Mehbooba " 
- <unk> <unk> in a special appearance in song " Mehbooba Mehbooba " 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- The idea for Sholay began as a four @-@ line <unk> which screenwriter pair Salim @-@ Javed told G. P. Sippy and Ramesh Sippy ; two other producer / director teams had earlier rejected the idea . Ramesh Sippy liked the concept and hired them to develop it . The original idea of the film involved an army officer who decided to hire two ex @-@ soldiers to avenge the murder of his family . The army officer was later changed to a policeman because Sippy felt that it would be difficult to get permission to shoot scenes depicting army activities . Salim @-@ Javed completed the script in one month , incorporating names and personality traits of their friends and acquaintances . 
- The film was loosely styled after Akira <unk> 's 1954 film Seven <unk> , and drew heavily from the conventions of Westerns , especially Sergio Leone 's <unk> Westerns such as Once Upon a Time in the West ( 1968 ) , and John <unk> ' film The Magnificent Seven ( 1960 ) . Sholay was also influenced by the <unk> of Sam Peckinpah , such as The Wild <unk> ( 1969 ) and Pat Garrett and Billy the Kid ( 1973 ) ; and by George Roy Hill 's Butch <unk> and the Sundance Kid ( 1969 ) . A scene depicting an attempted train robbery was inspired by a similar scene in North West Frontier ( 1959 ) , and a scene showing the massacre of Thakur 's family has been compared with the massacre of the <unk> family in Once Upon a Time in the West . Some plot elements were borrowed from the Indian films Mera <unk> Mera <unk> ( 1971 ) and <unk> <unk> ( 1973 ) . 
- The character Gabbar Singh was modelled on a real @-@ life dacoit of the same name who had <unk> the villages around <unk> in the 1950s . Any policeman captured by the real Gabbar Singh had his ears and nose cut off , and was released as a warning to other policemen . The character was also influenced by the villain " El <unk> " ( played by Gian Maria <unk> ) of Sergio Leone 's For a Few <unk> More ( 1965 ) . Soorma Bhopali , a minor comic relief character , was based on an acquaintance of actor Jagdeep , a forest officer from <unk> named Soorma . The real @-@ life Soorma eventually threatened to press charges when people who had viewed the film began referring to him as a <unk> . The main characters ' names , Jai and Veeru , mean " victory " and " heroism " in Hindi . 
- 
- = = = Casting = = = 
- 
- The producers considered Danny <unk> for the role of bandit chief Gabbar Singh , but he could not accept it as he was committed to act in <unk> Khan 's <unk> ( 1975 ) , under production at the same time . Amjad Khan , who was the second choice , prepared himself for the part by reading the book <unk> <unk> , which told of the exploits of <unk> dacoits . The book was written by <unk> Kumar Bhaduri , the father of fellow cast member Jaya Bhaduri . As cast members had read the script ahead of time , many were interested in playing different parts . <unk> was considered for the role of Thakur Baldev Singh , but Sippy thought Sanjeev Kumar was a better choice . Initially , Dharmendra was also interested to play the role of Thakur . He eventually gave up the role when Sippy informed him that Sanjeev Kumar would play Veeru if that happened , and would be paired with Hema Malini , who Dharmendra was trying to woo . Dharmendra knew that Kumar was also interested in Malini . Sippy wanted <unk> <unk> to play the part of Jai , but there were already several big stars signed , and Amitabh Bachchan , who was not extremely popular yet , lobbied hard to get the part for himself . 
- During the film 's production , four of the leads became romantically involved . Bachchan married Bhaduri four months before filming started . This led to shooting delays when Bhaduri became pregnant with their daughter <unk> . By the time of the film 's release , she was pregnant with their son <unk> . Dharmendra had begun wooing Malini during their earlier film <unk> <unk> <unk> ( 1972 ) , and used the location shoot of Sholay to further pursue her . During their romantic scenes , Dharmendra would often pay the light boys to <unk> the shot , thereby ensuring many <unk> and allowing him to spend more time with her . The couple married five years after the film 's release . 
- 
- = = = Filming = = = 
- 
- Much of Sholay was shot in the rocky terrain of Ramanagara , a town near Bangalore , Karnataka . The filmmakers had to build a road from the Bangalore highway to Ramanagara for convenient access to the sets . Art director Ram <unk> had an entire township built on the site . A prison set was constructed near <unk> Studio in Mumbai , also outdoors , to match the natural lighting of the on @-@ location sets . One part of Ramanagara was for a time called " Sippy Nagar " as a tribute to the director of the film . As of 2010 , a visit to the " Sholay rocks " ( where much the film was shot ) was still being offered to tourists travelling through Ramanagara . 
- Filming began on location on 3 October 1973 , with a scene featuring Bachchan and Bhaduri . The film had a lavish production for its time ( with frequent banquets and parties for the cast ) , took two and a half years to make , and went over budget . One reason for its high cost was that Sippy re @-@ filmed scenes many times to get his desired effect . " Yeh Dosti " , a 5 @-@ minute song sequence , took 21 days to shoot , two short scenes in which Radha lights lamps took 20 days to film because of lighting problems , and the shooting of the scene in which Gabbar kills the imam 's son lasted 19 days . The train robbery sequence , shot on the Mumbai – Pune railway route near <unk> , took more than 7 weeks to complete . 
- Sholay was the first Indian film to have a <unk> soundtrack and to use the 70 mm widescreen format . However , since actual 70 mm cameras were expensive at the time , the film was shot on traditional 35 mm film and the 4 : 3 picture was subsequently converted to a 2 @.@ 2 : 1 frame . Regarding the process , Sippy said , " A <unk> [ sic ] format takes the <unk> of the big screen and <unk> it even more to make the picture even bigger , but since I also wanted a spread of sound we used six @-@ track <unk> sound and combined it with the big screen . It was definitely a <unk> . " The use of 70 mm was emphasised by film posters on which the name of the film was stylised to match the <unk> logo . Film posters also sought to differentiate the film from those which had come before ; one of them added the tagline : " The greatest star cast ever assembled – the greatest story ever told " . 
- 
- = = = <unk> version = = = 
- 
- The director 's original cut of Sholay has a different ending in which Thakur kills Gabbar , along with some additional violent scenes . Gabbar 's death scene , and the scene in which the imam 's son is killed , were cut from the film by India 's Censor Board , as was the scene in which Thakur 's family is massacred . The Censor Board was concerned about the violence , and that viewers may be influenced to violate the law by punishing people severely . Although Sippy fought to keep the scenes , eventually he had to re @-@ shoot the ending of the film , and as directed by the Censor Board , have the police arrive just before Thakur can kill Gabbar . The censored theatrical version was the only one seen by audiences for fifteen years . The original , <unk> cut of the film finally came out in a British release on VHS in 1990 . Since then , <unk> International has released two versions on DVD . The director 's cut of the film preserves the original full frame and is 204 minutes in length ; the censored widescreen version is 198 minutes long . 
- 
- = = Themes = = 
- 
- Scholars have noted several themes in the film , such as glorification of violence , conformation to feudal ethos , debate between social order and mobilised usurpers , homosocial bonding , and the film 's role as a national allegory . 
- <unk> Banerjea , a sociologist in the London School of Economics , notes that Sholay exhibits a " sympathetic construction of ' rogue ' <unk> " exemplified by the likeable outlaws Jai and Veeru . Banerjea argues during the film , the moral boundary between legality and <unk> gradually <unk> . Film scholar <unk> Dissanayake agrees that the film brought " a new stage in the evolving dialectic between violence and social order " to Indian cinema . Film scholar M. <unk> Prasad states that Jai and Veeru represent a marginalised population that is introduced into conventional society . Prasad says that , through the elements of revenge included in the plot and the application of Jai and Veeru 's <unk> for the greater good , the narrative reflects reactionary politics , and the audience is compelled to accept feudal order . Banerjea explains that though Jai and Veeru are mercenaries , they are <unk> by their emotional needs . Such dualism makes them vulnerable , in contrast to the pure evil of Gabbar Singh . 
- Gabbar Singh , the film 's antagonist , was well received by the audience , despite his pervasive sadistic cruelty . Dissanayake explains that the audience was fascinated by the dialogues and mannerisms of the character , and this element of spectacle outweighed his actions , a first for Indian melodrama . He notes that the <unk> of violence in the film was <unk> and <unk> . He further notes that , unlike earlier <unk> in which the female body occupies the audience 's attention as an object of male <unk> , in Sholay , the male body becomes the <unk> . It becomes the <unk> where good and evil compete for supremacy . Dissanayake argues that Sholay can be viewed as a national allegory : it lacks a <unk> logical narrative , it shows social stability being repeatedly challenged , and it shows the <unk> of human life resulting from a lack of emotions . Taken together , these elements comprise the allegorical representation of India . The narrative style of Sholay , with its violence , revenge , and <unk> action , is occasionally compared by scholars to the political unrest in India at the time of its release . This tension culminated in the Emergency ( rule by decree ) declared by prime minister Indira Gandhi in 1975 . 
- <unk> and <unk> note that , although the film borrowed heavily from the Hollywood Western genre , particularly in its visuals , it was successfully " <unk> " . As an example , William van der <unk> has compared a massacre scene in Sholay with a similar scene in Once Upon a Time in the West . Although both films were similar in technical style , Sholay emphasised Indian family values and melodramatic tradition , while the Western was more <unk> and restrained in its approach . <unk> Rao , in Encyclopedia of Hindi Cinema , notes that Sholay <unk> the style of the Western genre into a " <unk> ethos " . Ted Shen of the Chicago Reader notes Sholay 's " hysterical visual style " and intermittent " populist message " . Cultural critic and Islamist scholar <unk> <unk> <unk> the film in his book The Secret Politics of Our <unk> : Innocence , <unk> and Indian Popular Cinema , both for its caricature and stereotyping of Muslim and women characters , and for what he calls mockery of innocent villagers . <unk> notes that the two most prominent Muslim characters in the film are Soorma Bhopali ( a <unk> criminal ) , and an <unk> victim of the bandits ( the imam ) . Meanwhile , the sole function of one female character ( Radha ) is to suffer her fate in silence , while the other female lead ( Basanti ) is just a garrulous village <unk> . 
- Some scholars have indicated that Sholay contains homosocial themes . Ted Shen describes the male bonding shown in the film as bordering on camp style . <unk> <unk> , in her book Bollywood and <unk> : Indian Popular Cinema , Nation , and <unk> , states that the death of Jai , and resultant break of bonding between the two male leads , is necessary for the sake of establishing a <unk> heterosexual relationship ( that of Veeru and Basanti ) . 
- 
- = = Music = = 
- 
- R. D. Burman composed the film 's music , and the lyrics were written by Anand <unk> . The songs used in the film , and released on the original soundtrack are listed below . Following that is a list of unused tracks and dialogues which were released later on an updated soundtrack . The album 's cover image depicts an emotional scene from the film in which Basanti is forced to sing and dance on the song " <unk> <unk> <unk> Hai <unk> " on broken glass under the <unk> sun to save Veeru 's life . 
- The song " Mehbooba Mehbooba " was sung by its composer , R. D. Burman , who received his sole Filmfare Award nomination for playback singing for his effort . The song , which is often featured on Bollywood hit song compilations , samples " Say You Love Me " by Greek singer <unk> <unk> . " Mehbooba Mehbooba " has been extensively <unk> , remixed , and recreated . A version was created in 2005 by the <unk> Quartet for their Grammy @-@ nominated album You 've Stolen My Heart , featuring <unk> <unk> . It was also remixed and sung by <unk> <unk> , along with <unk> , in his debut acting film <unk> <unk> <unk> ( 2007 ) . " Yeh Dosti " has been called the ultimate friendship anthem . It was remixed and sung by Shankar <unk> and <unk> Narayan for the 2010 Malayalam film Four Friends , and also in 2010 it was used to symbolise India 's friendship with the United States during a visit from President Barack Obama . 
- Several songs from the soundtrack were included in the annual <unk> <unk> list of top <unk> songs . " Mehbooba <unk> " was listed at No. 24 on the 1975 list , and at No. 6 on the 1976 list . " Koi <unk> " was listed at No. 30 in 1975 , and No. 20 in 1976 . " Yeh Dosti " was listed at No. 9 in 1976 . Despite the soundtrack 's success , at the time , the songs from Sholay attracted less attention than the film 's dialogue — a rarity for Bollywood . The producers were thus prompted to release records with only dialogue . Taken together , the album sales totalled an unprecedented 500 @,@ 000 units , and became one of the top selling Bollywood soundtracks of the 1970s . 
- Music critic <unk> Marlow reviewed the soundtrack in 2013 , calling it a unique fusion of religious , folk , and classical music , with influences from around the world . He also commented on the sound design of the film , calling it psychedelic , and saying that there was " a lot of incredible incidental music " in the film that was not included in the soundtrack releases . In a 1999 paper submitted to London 's Symposium on Sound in Cinema , film critic <unk> A. <unk> said , " Sholay offers a model lesson on how sound can be used to signify the terror a character evokes . Sholay is also exemplary in its use of <unk> to jump cut to a different scene and time , without breaking the continuity of the narrative , yet , intensifying the drama . " 
- 
- = = Reception = = 
- 
- 
- = = = Box office = = = 
- 
- Sholay was released on 15 August 1975 , Indian Independence Day , in Mumbai . Due to <unk> reviews and a lack of effective visual marketing tools , it saw poor financial returns in its first two weeks . From the third week , however , viewership picked up owing to positive word of mouth . During the initial slow period , the director and writer considered re @-@ shooting some scenes so that Amitabh Bachchan 's character would not die . When business picked up , they abandoned this idea . After being helped additionally by a soundtrack release containing dialogue <unk> , Sholay soon became an " overnight sensation " . The film was then released in other distribution zones such as Delhi , Uttar Pradesh , Bengal , and Hyderabad on 11 October 1975 . It became the highest grossing Bollywood film of 1975 , and film ranking website Box Office India has given the film a verdict of " All Time <unk> " . 
- Sholay went on to earn a still @-@ standing record of 60 golden <unk> across India , and was the first film in India to celebrate a silver jubilee at over 100 theatres . It was shown continuously at Mumbai 's Minerva theatre for over five years . Sholay was the Indian film with the longest theatrical run until <unk> <unk> Le <unk> ( 1995 ) broke its record of 286 weeks in 2001 . 
- <unk> figures are not available on the budget and box office earnings of Sholay , but film trade websites provide estimates of its success . According to Box Office India , Sholay earned about ₹ 150 million <unk> gross ( valued at about US $ 16 @,@ <unk> @,@ 000 in 1975 ) in India during its first run , which was many times its ₹ 30 million ( valued at about US $ 3 @,@ 355 @,@ 000 in 1975 ) budget . Those earnings were a record that remained unbroken for nineteen years , which is also the longest amount of time that a film has held the record . Its original gross was increased further with re @-@ releases during the late 1970s , 1980s , 1990s , and early 2000s . It is often cited that after adjusting the figures for inflation , Sholay is one of the highest grossing films in the history of Indian cinema , although such figures are not known with certainty . In 2012 , Box Office India gave ₹ 1 @.@ 63 billion ( US $ 24 million ) as Sholay 's adjusted net gross , whereas Times of India , in a 2009 report of business of Indian films , reported over ₹ 3 billion ( US $ 45 million ) as the adjusted gross . 
- 
- = = = Critical response = = = 
- 
- Initial critical reviews of Sholay were negative . Among contemporary critics , <unk> <unk> of India Today called the film a " dead <unk> " and " a <unk> flawed attempt " . Filmfare said that the film was an unsuccessful <unk> of Western style with Indian milieu , making it an " imitation western — neither here nor there . " Others labelled it as " sound and <unk> signifying nothing " and a " second @-@ rate take @-@ off " of the 1971 film Mera <unk> Mera <unk> . Trade journals and <unk> initially called the film a flop . In a 1976 article in the journal Studies : An Irish Quarterly Review , author Michael Gallagher praised the technical achievement of the film , but otherwise criticised it stating , " As a spectacle it breaks new ground , but on every other level it is intolerable : <unk> , incoherent , superficial in human image , and a somewhat nasty piece of violence " . 
- Over time , the critical reception to Sholay greatly improved ; it is now considered a classic , and among the greatest Hindi @-@ language films . In a 2005 BBC review , the well @-@ rounded characters and simple narrative of the film were commended , but the comical cameos of <unk> and Jagdeep were considered unnecessary . On the film 's 35th anniversary , the <unk> Times wrote that it was a " <unk> in terms of camera work as well as music , " and that " practically every scene , dialogue or even a small character was a highlight . " In 2006 , The Film Society of Lincoln Center described Sholay as " an extraordinary and utterly <unk> blend of adventure , comedy , music and dance " , labelling it an " <unk> classic " . Chicago Review critic Ted Shen criticised the film in 2002 for its formulaic plot and " <unk> " cinematography , and noted that the film " alternates between <unk> and melodrama " . In their obituary of the producer <unk> Sippy , the New York Times said that Sholay " revolutionized Hindi filmmaking and brought true professionalism to Indian script writing " . 
- 
- = = = Awards = = = 
- 
- Sholay was nominated for nine Filmfare Awards , but the only winner was M. S. Shinde , who won the award for Best Editing . The film also won three awards at the 1976 Bengal Film Journalists ' Association Awards ( Hindi section ) : " Best Actor in Supporting Role " for Amjad Khan , " Best <unk> ( Colour ) " for <unk> <unk> , and " Best Art Director " for Ram <unk> . Sholay received a special award at the 50th Filmfare Awards in 2005 : Best Film of 50 Years . 
- 
- = = Legacy = = 
- 
- Sholay has received many " Best Film " honours . It was declared the " Film of the Millennium " by BBC India in 1999 . It topped the British Film Institute 's " Top 10 Indian Films " of all time poll of 2002 , and was voted the greatest Indian movie in a Sky Digital poll of one million British Indians in 2004 . It was also included in Time Magazine 's " Best of Bollywood " list in 2010 , and in CNN @-@ <unk> 's list of the " 100 greatest Indian films of all time " in 2013 . 
- Sholay inspired many films and <unk> , and spawned a subgenre of films , the " Curry Western " , which is a play on the term <unk> Western . It was an early and most definitive masala film , and a trend @-@ <unk> for " multi @-@ star " films . The film was a watershed for Bollywood 's <unk> , who were not paid well before Sholay ; after the film 's success , script writing became a more respected profession . 
- Certain scenes and dialogues from the film earned iconic status in India , such as " <unk> <unk> the " ( How many men were there ? ) , " Jo <unk> <unk> , <unk> <unk> <unk> " ( One who is scared is dead ) , and " <unk> <unk> <unk> <unk> " ( <unk> like you two are very close ) – all dialogues of Gabbar Singh . These and other popular dialogues entered the people 's daily vernacular . Characters and dialogues from the film continue to be referred to and parodied in popular culture . Gabbar Singh , the sadistic villain , <unk> in an era in Hindi films characterised by " seemingly omnipotent <unk> as villains " , who play the pivotal role in setting up the context of the story , such as <unk> ( played by <unk> <unk> ) of <unk> ( 1980 ) , <unk> ( <unk> Puri ) of Mr. India ( 1987 ) and <unk> ( <unk> Puri ) of <unk> ( 1989 ) . Filmfare , in 2013 , named Gabbar Singh the most iconic villain in the history of Indian cinema , and four actors were included in its 2010 list of " 80 <unk> Performances " for their work in this film . 
- The film is often credited with making Amitabh Bachchan a " superstar " , two years after he became a star with <unk> ( 1973 ) . Some of the supporting actors remained etched in public memory as the characters they played in Sholay ; for example , Mac Mohan continued to be referred to as " Sambha " , even though his character had just one line . Major and minor characters continue to be used in commercials , promos , films and sitcoms . Amjad Khan acted in many villainous roles later in his career . He also played Gabbar Singh again in the 1991 spoof Ramgarh Ke Sholay , and reprised the role in commercials . The British Film Institute in 2002 wrote that fear of Gabbar Singh " is still invoked by mothers to put their children to sleep " . The 2012 film Gabbar Singh , named after the character , became the highest grossing Telugu film up to that point . <unk> Jagdeep , who played Soorma Bhopali in the film , attempted to use his Sholay success to create a spinoff . He directed and played the lead role in the 1988 film Soorma Bhopali , in which Dharmendra and Bachchan had cameos . 
- In 2004 , Sholay was digitally remastered and shown again to packed theatres in India , including Mumbai 's Minerva , where it had run successfully 29 years earlier . An attempt to remake Sholay , Ram Gopal Varma 's film <unk> ( 2007 ) , starring Amitabh Bachchan as the villain , was a commercial and critical disaster . Because of television and home media , Sholay is widely available and still popular . Twenty years after its release , Sholay was first shown on the Indian DD National television channel , where it drew the highest ratings ever for a film broadcast . Video game producer <unk> released the " Sholay Ramgarh Express " game for mobile phones in 2004 , along with other Sholay themed content such as <unk> , video clips , and <unk> . 
- Sholay has been the subject of two books and many articles . <unk> Dissanayake and <unk> <unk> 's Sholay , A Cultural Reading ( 1992 ) attempts a comprehensive scholarly study that sets the film within the broader history of popular cinema in India . Anupama Chopra 's Sholay : The Making of a Classic ( 2000 ) provides an inside look at the film 's production based on interviews with the director , stars , and crew members . 
- Sholay has been labelled by Chopra as the gold standard in Indian cinema , and a reference point for audiences and trade analysts . Over the years , the film has reached a mythic stature in popular culture , and has been called the greatest Hindi film of all time . It belongs to only a small collection of films , including <unk> ( 1943 ) , Mother India ( 1957 ) , Mughal @-@ e @-@ <unk> ( 1960 ) and Hum <unk> <unk> <unk> .. ! ( 1994 ) , which are repeatedly watched throughout India , and are viewed as definitive Hindi films with cultural significance . The lasting effect of Sholay on Indian cinema was summarised by Anupama Chopra , when in 2004 she called it " no longer just a film , [ but ] an event " . In the 2000 book Sholay : The Making of a Classic , the noted director <unk> <unk> stated " there has never been a more defining film on the Indian screen . Indian film history can be divided into Sholay BC and Sholay AD " . The film was jointly released in Pakistan by <unk> films and <unk> Entertainment on 17 April 2015 , almost 40 years after its theatrical release . The film 's premiere in the country was held in Karachi . 
- 
- = = 3D re @-@ release = = 
- 
- <unk> <unk> <unk> 's company Maya Digital was responsible for converting Sholay into the 3D format . <unk> was approached by G. P. Sippy 's grandson , <unk> Sippy , about the project in 2010 . In March 2012 , <unk> Uttam Singh , the grandson of producer G. P. Sippy , said that he would sponsor a conversion of the film to 3D , and release it in late 2012 ; this was later postponed to late 2013 , and eventually finalised for 3 January 2014 . It took ₹ 250 million ( US $ 3 @.@ 7 million ) to convert Sholay to 3D . 
- Under the leadership of computer animator Frank Foster , 350 people worked to convert the film into the digital 3D format , for which every scene had to be individually restored , colour @-@ corrected and re @-@ <unk> in 3D to match the depth . New set @-@ pieces , particularly those suited to the new format were also included , such as digital logs which scatter in the direction of the camera during the first half of the film when the train <unk> with them , the gunshot scene which frees Jai and Veeru from their handcuffs , and panoramic views of Gabbar 's hideout in the caves . 
- The theatrical trailer and release date were unveiled by the original script @-@ writers Salim Khan and Javed <unk> . The two original leads , Bachchan and Dharmendra , were also involved in promoting the re @-@ release . The film was released in 1 @,@ 000 screens in India , and additional screens overseas . It earned approximately ₹ 100 million ( US $ 1 @.@ 5 million ) during its re @-@ release , not enough to recover its conversion cost . 
- 
- 
- = Adam Stansfield = 
- 
- Adam Stansfield ( 10 September 1978 – 10 August 2010 ) was an English professional footballer who played as a striker . He competed professionally for Yeovil Town , Hereford United and Exeter City , and won promotion from the Football Conference to The Football League with all three teams . 
- Having played for three counties as a child , Stansfield began his career in non @-@ league with <unk> Rangers and Elmore , and had unsuccessful trials at league teams . At the age of 23 , he signed his first professional contract with Yeovil Town , after impressing their manager Gary Johnson in a match against them . In his first season , he helped them win the FA Trophy , scoring in the 2002 final . The following season , Yeovil won the Conference and promotion into The Football League , although Stansfield was ruled out with a broken leg in the first game . In 2004 , he transferred to Hereford United , where he won promotion to The Football League via the 2006 play @-@ offs , and repeated the feat with Exeter City a year later . He also helped Exeter earn promotion into League One in 2008 . At international level , Stansfield played five matches and scored one goal for England 's national semi @-@ professional team , winning the 2005 Four Nations Tournament . 
- Stansfield was diagnosed with colorectal cancer in April 2010 . He returned to training after surgery and chemotherapy , but died on 10 August that year . A foundation in his name was posthumously set up by his family to provide sporting opportunities and raise awareness of colorectal cancer . He has posthumously been featured on a <unk> airliner <unk> and tourist currency in Exeter . 
- 
- = = Early and personal life = = 
- 
- Stansfield was born in Plymouth , Devon , as the third of four children , and supported Nottingham Forest . On 2 June 2001 he married Marie , with whom he had three sons . Devon journalist Gary Andrews remembered Stansfield as a man who would spend time with his family after matches while speaking to fans and the press . He wrote that " I had the pleasure of interviewing Adam on a regular basis ... I say pleasure , because his answers were thoughtful and intelligent and he came across as a man who was delighted to be back home with his friends and family " . 
- 
- = = Career = = 
- 
- 
- = = = Early career = = = 
- 
- Stansfield 's first club was Evesham Colts under @-@ 10s . He played at county level for Worcestershire , <unk> and Devon . When his family settled back in Devon he joined <unk> Spartans , scoring 84 goals in 54 matches . He played in <unk> Town 's youth team as a left back before reverting to being a striker at his first senior club , non @-@ League side <unk> Rangers . He later moved to Elmore , where he attracted trials from Exeter City , Wolverhampton Wanderers and Torquay United , all of which were unsuccessful . His siblings joined the Royal Air Force and he thought of joining them , but continued searching for a breakthrough in professional football . 
- 
- = = = Yeovil Town = = = 
- 
- In October 2001 , Stansfield 's performances for Elmore impressed Yeovil Town manager Gary Johnson to sign him . He made his debut in the Conference on 9 November , playing the entirety of a 3 – 0 loss away to <unk> . His first goal came on 1 December , concluding a 3 – 1 victory at <unk> Victoria . His first season at <unk> Park was a success , finishing as the top scorer with 16 goals , 8 of which came in the club 's victorious FA Trophy run . He scored twice in a fourth round replay at Doncaster Rovers , as Yeovil came from 0 – 3 down for an eventual 5 – 4 victory . In the final on 12 May he scored the second goal of a 2 – 0 win over <unk> Borough at the Millennium Stadium . 
- On the first day of the following season , Stansfield was substituted through injury after 16 minutes of an eventual 2 – 2 home draw with <unk> & <unk> to be replaced by <unk> <unk> . It was later confirmed to be a break of the tibia and <unk> . He missed the remainder of the season , in which Yeovil won the Conference to be promoted to The Football League for the first time . 
- He recovered to feature in the next campaign , making his league debut on 16 August 2003 . In that match , Yeovil 's first in The Football League , he came on as an 80th @-@ minute substitute for Kirk Jackson in a 3 – 0 win against Carlisle United . His first of six goals in the Third Division season came on 6 September , opening a 2 – 0 home win over Swansea City . He was given a rare start in that match as first @-@ choice forward Kevin Gall was away with Wales under @-@ 21 . 
- 
- = = = Hereford United = = = 
- 
- On 14 June 2004 , Stansfield returned to the Conference with Hereford United , signed by Graham Turner to replace their previous season 's top scorer Steve Guinan , who had been sold to Cheltenham Town . He scored 20 goals across the season , including two on 25 March 2005 in a 6 – 0 win at <unk> Town . In that match , he came on in the 77th minute for Daniel Carey @-@ <unk> , who had also scored two . Hereford reached the promotion play @-@ offs , where they lost in the semi @-@ finals to <unk> . In the following season they won promotion by the play @-@ offs , with Stansfield starting in the final on 20 May 2006 at the <unk> Stadium in Leicester , a 3 – 2 extra @-@ time victory over Halifax Town . 
- 
- = = = Exeter City = = = 
- 
- On 12 June 2006 , with his contract expired , Stansfield decided to remain in the Conference , joining Exeter City . He told local radio that his aim was not to achieve promotion or reach a certain tally of goals , but to influence the club 's younger players . 
- He scored nine times in 40 league games in his first season , including two in a 2 – 1 home win over relegated <unk> on 28 April 2007 in order to seal a play @-@ off place . Eleven days later , in the second leg of the play @-@ off semi @-@ final away to Oxford United , he scored a goal which took the match to extra time and eventually a penalty shootout which his side won . In the final on 20 May at Wembley Stadium , he came on as a 36th @-@ minute substitute for goalscorer Lee Phillips in a 1 – 2 loss to Morecambe . 
- On 26 April 2008 , Stansfield scored in Exeter 's 4 – 4 draw at Burton Albion which qualified them for that season 's play @-@ offs . He started in the final , whereby the team returned to The Football League for the first time in five years with a 1 – 0 Wembley win over Cambridge United . 
- He scored 10 goals in 37 league games as they won a second consecutive promotion into League One in the 2008 – 09 season . This included consecutive <unk> on 27 September and 4 October , in wins over Macclesfield Town ( 4 – 1 away ) and Gillingham ( 3 – 0 home ) . The following campaign , despite never having previously played at as high a level , he was a regular starter for Exeter in League One , scoring eight goals in a season curtailed by his cancer diagnosis . 
- 
- = = = International career = = = 
- 
- Stansfield earned five caps and scored one goal for the England national semi @-@ professional team . He featured in the 2002 edition of the Four Nations Tournament , and made his debut in England 's opening match , a 1 – 1 draw with Wales at York Street in Boston on 14 May . Stansfield was injured in the first half of the last match , a 2 – 0 win against Scotland at <unk> Road in Kettering on 18 May , while Wales won the title . In 2005 , while back in the Conference with Hereford , he was again called up for the tournament by manager Paul <unk> . Stansfield played in two matches as England won the tournament with three wins . 
- 
- = = Illness and death = = 
- 
- Stansfield suffered from persistent abdominal pain in the early part of 2010 , and was admitted to hospital for tests at the end of March . On 8 April 2010 , Exeter City confirmed to the media that he had been diagnosed with a form of colorectal cancer . Manager Paul <unk> told local news programme BBC Spotlight that " there 's little good on this subject " , but " if there 's someone who can deal with it and meet it head on with real purpose , Adam 's the man . 
- Later that month , Stansfield underwent surgery to remove part of his <unk> . Club vice @-@ chairman Julian <unk> reported that the operation was successful , and that Stansfield appeared happy and was making jokes . He joined the Exeter squad for the first day of pre @-@ season training in July , appearing weak from chemotherapy . His condition deteriorated rapidly and he died on 10 August , with his death being announced shortly after Exeter 's loss to Ipswich Town in the Football League Cup . 
- As a mark of respect , Dagenham & Redbridge postponed the game Exeter were due to play against them at Victoria Road four days after his death . Exeter retired his shirt number 9 for nine seasons . 
- Stansfield 's body was taken from St James Park to his funeral service at Exeter Cathedral on 25 August , attended by over 1 @,@ 000 mourners . A private family service was held later . 
- 
- = = = <unk> recognition = = = 
- 
- Stansfield continues to be remembered by fans of Exeter . On 9 August 2014 , as they started the new season against Portsmouth , a giant flag resembling his club shirt was displayed by the crowd . 
- At his funeral , Stansfield 's widow Marie had an idea to set up the Adam Stansfield Foundation , which by the fourth anniversary of his death had raised over £ 150 @,@ 000 . It works in offering children football in Devon , Somerset and <unk> , the three counties in which he played professionally , as well as increasing opportunities for the disabled to take part in the sport . The foundation also aims to increase awareness of <unk> cancer . 
- From 2011 to 2015 , an aeroplane belonging to <unk> bore an image of Stansfield , with other aeroplanes belonging to the company featuring such former footballers as George Best and Kevin Keegan . In 2015 , Stansfield was featured on £ 5 Exeter Pound notes in the city . 
- Exeter City and Yeovil Town agreed that on their meeting at St James Park on 8 August 2015 , there would be a minute 's applause in the seventh minute and ninth , for the numbers he wore at each club . Earlier the same day , there was also a match between the two clubs ' supporters in <unk> , Devon , to raise funds for his foundation . 
- 
- = = Career statistics = = 
- 
- 
- = = Honours = = 
- 
- Yeovil Town 
- FA Trophy : 2001 – 02 
- Football Conference : 2002 – 03 
- Hereford United 
- Conference National play @-@ offs : 2006 
- Exeter City 
- Conference National play @-@ offs : 2008 
- England semi @-@ professional 
- Four Nations Tournament : 2005 
- 
- 
- = Saprang Kalayanamitr = 
- 
- General Saprang Kalayanamitr ( Thai : <unk> ่ ง กัลยาณมิตร ; <unk> : <unk> @-@ <unk> <unk> , also known as <unk> ( Thai : <unk> ) or Big <unk> ( Thai : <unk> <unk> <unk> ) , born 8 July 1948 in <unk> , Thailand ) is a retired officer of the Royal Thai Army , Assistant Secretary @-@ General of the Council for National Security , Commander of the junta 's 14 @,@ 000 @-@ man anti @-@ protest force , Chairman of the Board of Directors of Airports of Thailand ( AoT ) , and also Chairman of the Boards of TOT and CAT Telecom , two major Thai state @-@ owned telecommunication companies . 
- Saprang grew up in an aristocratic military family and graduated from the 7th Class of the Armed Forces Academies Preparatory School and the 18th Class of the Chulachomklao Royal Military Academy . He served for nearly three decades in the Army cavalry <unk> , and was promoted to 3rd Army Region Commander in 2005 . He was a key leader of the September 2006 coup that <unk> the government of Prime Minister Thaksin Shinawatra . 
- Saprang is one of the <unk> critics of Thaksin Shinawatra , calling him a " traitor " and claiming that he should be " banished to live forever in the jungle . " Upon appointment to chair Airports of Thailand and TOT , he purged the management , initiated investigations into the overthrown government , and donated 200 million baht of the agency 's funds to the Army . He fired the President of TOT for questioning an 800 million baht donation that the agency made to the Army . As head of CAT Telecom , he was accused of blocking attempts to launch People 's Television , a new television station founded by ex @-@ leaders of Thaksin 's Thai Rak Thai party . 
- Saprang was considered one of the top contenders to lead the army and the junta after CNS @-@ leader Sonthi Boonyaratkalin 's mandatory retirement in 2007 . However , in September 2007 he was demoted to be Deputy Permanent Secretary of the Defense Ministry , while his rival , General Anupong Paochinda , was promoted to lead the Army . As a result , Saprang retired from the Army in 2010 . 
- 
- = = Education and early career = = 
- 
- 
- = = = Education = = = 
- 
- Born 8 July 1948 in <unk> , Thailand , Saprang graduated from the 7th Class of the Armed Forces Academies Preparatory School ( <unk> ) and the 18th Class of the Chulachomklao Royal Military Academy . His classmates included General <unk> <unk> ( appointed Deputy Army Commander after the coup ) , Admiral Bannawit <unk> ( appointed to the National Legislative Assembly after the coup , and leader of its Suvarnabhumi Airport committee ) , and General <unk> <unk> . He later graduated from the 43rd class of the National Defence College of Thailand in 2001 . His <unk> thesis concerned the role of military forces in the control of illegal <unk> . His <unk> classmates included <unk> <unk> , governor of state energy company <unk> . 
- 
- = = = Early career = = = 
- 
- Saprang started his military career in 1969 as Rifle Platoon Leader in the 3rd Infantry Battalion , 4th Regimental Combat Team . He claims to have fought 200 battles during the course of his military career . 
- He was appointed Commander of the 1st Infantry Battalion of the 4th Infantry Regiment in 1982 , stationed in <unk> ( on the northwestern border with Burma ) . In 1985 , he became Commander of the 1st Infantry Battalion in the 19th Infantry Regiment , stationed in <unk> Fort , <unk> ( on the western border with Burma ) . 
- He was then promoted to be Regimental Commanding Officer of the Armed Forces Academies Preparatory School in 1990 . In 1991 , he began a six @-@ year stint as Regimental Commanding Officer of the Chulachomklao Royal Military Academy . 
- In April 1997 , Saprang was shortly transferred to the Ministry of Defense as a staff officer , before being promoted in October 1997 to Commanding General of the 15th Infantry Division , at the time stationed in <unk> <unk> , <unk> <unk> Khan Province . In 2003 , he was promoted to 3rd Corps Commander . In 2004 , it was strongly <unk> that Saprang might be promoted to command the 4th Army , replacing General Pongsak <unk> . General Pongsak had been criticized for <unk> fighting the South Thailand insurgency , after 39 successful <unk> attacks occurred in just one night . Pongsak ended up being replaced in April 2004 by <unk> <unk> . 
- 
- = = = 2006 <unk> = = = 
- 
- In a surprise to many observers , Saprang was promoted to 3rd Army Area Commander in October 2005 , headquartered in <unk> and responsible most of northern and northeastern Thailand . Analysts had expected Prime Minister Thaksin to promote his own classmates from <unk> Class 10 to the powerful position instead . At the same time , also in a surprise move , Deputy Army Commander Sonthi Boonyaratkalin was promoted to Army Commander . 
- Saprang and Sonthi started planning for the coup 7 to 8 months in advance , in approximately February 2006 . <unk> planning occurred prior to the April 2006 elections , during Thaksin 's controversial sale of Shin Corporation to <unk> Holdings and the peak of the People 's Alliance for Democracy 's campaign to <unk> the government . In July 2006 , Saprang gave an interview where he stated that Thai politics was below standard and that the Kingdom 's leadership was weak . He also claimed that Thailand had a false democracy . He denied that such criticism constituted military interference in politics . At the same time , the Thai media speculated that in the October 2006 annual Army reshuffle , Saprang would not be promoted to Assistant Army Commander and would not be allowed to retain his position of 3rd Army Area Commander . In July , Saprang 's own Deputy Commander in the 3rd Army Area , Major General Manas <unk> , warned the media that " a certain military officer who <unk> to become Assistant Army Commander " was planning a coup . 
- In the weeks leading up to coup , Saprang openly mobilised soldiers and northern residents to rebel against the government . Saprang played a key role on the evening of 19 September 2006 , securing Thaksin 's home town and power base of <unk> Mai . That same night , he was appointed assistant Secretary @-@ General of the CNS . The coup was executed just a week before the announcement of the Army 's annual reshuffle . 
- 
- = = After the 2006 coup = = 
- 
- A week after the coup , Saprang was promoted to Assistant Army Commander , alongside fellow coup leader Anupong Paochinda . His predecessor , General <unk> <unk> , had not taken part in the coup and was transferred to an inactive position . Saprang was also promoted from Lieutenant General to General . 
- 
- = = = CNS Special Operations Center = = = 
- 
- On 27 December 2006 , it was revealed that the Cabinet had approved over half a billion baht worth of funding for a 14 @,@ 000 @-@ man secret anti @-@ protest special operations force , of which General Saprang was Commander . The so @-@ called CNS Special Operations Center , funded with <unk> million baht diverted from the Defense Ministry , Police Office , and government emergency reserve fund , had been secretly established by the CNS on 1 December 2006 in order to control protests . 
- 
- = = = TOT and CAT Telecom = = = 
- 
- Saprang was appointed by the junta to become Chairman of the Board of Directors of Airports of Thailand ( AoT ) and also Chairman of the Boards of TOT and CAT Telecom , two major state @-@ owned telecommunication companies . Saprang 's first move as TOT Chairman was to hand @-@ pick three Army <unk> and vocal Thaksin @-@ critic Vuthiphong <unk> to sit on the state enterprise 's Board of Directors . Saprang transferred TOT President <unk> <unk> to an inactive position and appointed Vuthiphong new President . He then hand @-@ picked all 10 other directors . 
- Saprang noted in an interview that , " if telecommunication businesses are in private hands , the country won 't be safe . " The junta had earlier announced plans to cancel the initial public offering of both TOT and CAT Telecom and to merge the two state enterprises . 
- Under Saprang 's leadership , TOT reaffirmed its ownership rights to all existing backbone telecommunications networks under a new strategy to act as a " genuine " national <unk> company . The new strategy was expected to increase political and business tensions . Under the <unk> @-@ Transfer @-@ <unk> ( <unk> ) concession agreements that TOT signed with private <unk> operators , the TOT technically owns all fixed @-@ line , mobile , and optical fibre networks in Thailand . However , it had never exercised those rights in the past . 
- Vuthiphong was fired from the TOT board and his position of acting TOT President in June 2007 . He immediately accused the Army of using the TOT as an <unk> <unk> fund . He claimed that an unnamed Army unit had requested that TOT buy it 800 million baht worth of electronic equipment . Upon receiving the request , <unk> demanded to know why neither the Army nor the Defence Ministry used their own secret budgets to purchase the equipment , and why an internal Army unit , rather than the Kingdom 's main national security organisations , had made the request . Saprang denied that there was any lack of transparency in the request for financial support . <unk> claimed that the equipment should only have cost 30 million baht , not 800 million baht . He was fired and expelled from the Board soon after refusing to sign off on the deal . The Board later appointed Col. <unk> <unk> as the new TOT President and accepted the army 's donation request . 
- Under his leadership , TOT 's performance dropped . <unk> for the first half of 2007 fell 13 % year @-@ on @-@ year , while net profit fell 36 @.@ 1 % . Fixed line revenue dropped 16 % , while public telephone and international call revenue by 30 % each . 
- As Chairman of CAT Telecom , Saprang was accused by the founders of People 's Television ( PTV ) , a new satellite television station , of being behind CAT Telecom 's refusal to grant an internet link from Bangkok to a satellite up @-@ link station in Hong Kong . PTV was established by several ex @-@ executives of the Thai Rak Thai party . CAT Telecom claimed that it never received PTV 's application for internet access . 
- Under Saprang 's leadership , 80 @,@ 000 subscribers of Thai Mobile , a TOT / CAT joint venture mobile phone operator , were cut off temporarily in early May 2007 when owners TOT and CAT Telecom failed to pay the bills of a major supplier . Thai Mobile had accumulated significant losses and the company was not able to make its debt payments or supplier payments . The partners had stopped payments to the supplier , Samart Corporation , for nearly a year , until Samart threatened to suspend services within three days . After no payment , it delivered on its threat . TOT was subsequently able to negotiate with Samart to restart the service . 
- 
- = = = Airports of Thailand = = = 
- 
- 
- = = = = <unk> of AoT management = = = = 
- 
- A week after Saprang hinted at a reshuffle of AoT top management , AoT President <unk> <unk> was forced to resign , citing health reasons , while the Directors of Suvarnabhumi Airport and AoT Commercial Operations were dismissed . Police Commissioner General <unk> <unk> was appointed as an AoT Director . 
- 
- = = = = Suvarnabhumi Airport = = = = 
- 
- As AoT Chairman , Saprang spearheaded an effort to reopen Don Muang Airport in parallel with the newly opened Suvarnabhumi Airport , despite objections from the Civil Aviation Department , airlines , and internal studies within Airports of Thailand . 60 airlines threatened to halt flights to Thailand if they were forced to move back to Don Muang airport . 
- Saprang also refused to authorize urgent repairs on the airport tarmac , despite warnings from engineers . <unk> <unk> , president of the Engineering Institute of Thailand noted , " Suvarnabhumi is like a patient in a coma who continues to suffer from severe bleeding . <unk> the blood flow now is more urgent and important than debating what caused the injury . " The Engineering Institute of Thailand sent a formal warning to AoT in November 2006 about the urgent need to drain water from beneath the tarmac , and noted that immediate action should be taken . " The AOT did nothing about the problem " , <unk> <unk> of the <unk> noted . " The situation might not have become this bad if the water had been drained then . " <unk> <unk> , a senior foundation engineer and a member of the <unk> <unk> @-@ led airport tarmac inspection panel , accused the AOT of refusing to take any actions to solve the problems at the airport . 
- The airport faced ongoing operational challenges , including a computer virus that shut down the automated luggage bomb @-@ scanning system in June 2007 . A study by the International Air Transport Association ( IATA ) released in July 2007 found the airport unsafe , citing numerous spots where checked passengers can meet people who have not passed through security checkpoints . 
- Serious security gaps at Suvarnabhumi Airport became known to the public beginning in early 2007 . The International Air Transport Association ( IATA ) found that there were many spots in the passenger terminal where checked passengers can meet people who have not passed through security checks and could receive unchecked objects and then carry them on board aircraft . The IATA also suggested that AoT deploy its own security staff instead of contracting out the job to the <unk> @-@ <unk> consortium . AoT threatened the consortium with contract termination , but didn 't follow through with its threat , even though the consortium failed to live up to its contract . Six months later , AoT stated that it still couldn 't make up its mind on how it should improve airport security . AoT said it was open to all possible options , and has taken no action to upgrade the problem . 
- 
- = = = = Trip to Europe = = = = 
- 
- On Tuesday 27 February 2007 , Saprang led a 13 @-@ member delegation to Europe , on what was claimed to be a week @-@ long trip to study safety and security measures at major European airports . Many delegates and accompanying members shared the same surname , and the trip , which cost 7 @.@ 2 million baht was attacked for " squandering " state funds for personal pleasure , <unk> unrealistic expenses , and inflating costs . The travel agent along received a 500 @,@ 000 baht commission fee for booking the trip . Saprang denied any wrongdoing and claimed he was the victim of a smear campaign . He also noted that " If you knew my character , you would know that even if a relative joined the trip he should have realised that he should work hard . " He also noted that instead of being a viewed as a defendant , he should be viewed as a hero for bringing down the Thaksin government . Saprang then summoned the leader of the AoT labor union in order to identify who leaked information about the trip to his <unk> . 
- 
- = = = = Financial performance = = = = 
- 
- The first quarter after Saprang was appointed Chairman , AoT profits plunged 90 % compared to the previous year , despite higher traffic volumes and increased passenger service charters and airline fees . Operating expenses <unk> 137 % , contributing to the AoT 's worst <unk> earnings report since it was listed on the Stock Exchange of Thailand . 
- The AoT board also granted 200 million baht to the Army , which had requested a financial donation . AoT also lent some of its explosives detectors to the Army for use in the South Thailand insurrection . 
- Financial performance continued to spiral downwards in the 3rd quarter of 2007 . Net profit for the period ending June fell by 84 % from a year before , despite higher traffic and a 17 @.@ 9 % increase in revenue . The fall in profit was attributed to AoT 's court case against King Power , the operator of duty @-@ free shops within Suvarnabhumi Airport . King Power 's concession was suspended while the case was in court , forcing AOT to stop reporting earnings from the <unk> . 
- 
- = = = Thaksin Shinawatra = = = 
- 
- Saprang had long been a fierce critic of Thaksin Shinawatra , and prior to the coup had even called Thaksin 's supporters within the military " evil . " After the coup , Saprang called Thaksin a " traitor " and said that he should be " banished to live forever in the jungle . " 
- He also accused Thaksin of <unk> on the military while he was Prime Minister . 
- Although Saprang and General Sonthi accused Thaksin of insulting and <unk> King <unk> , he noted that the junta did not pursue <unk> <unk> charges against Thaksin because " the police corrupted the evidence " , and delivered such a weak case that the attorney @-@ general could not file a lawsuit . A vocal self @-@ proclaimed <unk> , he insisted that various groups actively tried to challenge the monarchy , and that he " couldn 't stand it . " He noted , " I am a soldier , born to protect the Crown . They could only challenge the monarchy over my dead body . " 
- Saprang also suspected that Oliver <unk> , a Swiss man who was jailed for <unk> <unk> for spraying paint on a portrait of image of King <unk> , was hired by somebody to perform his vandalism . Saprang ordered a military investigation into the matter . The results have not been made public . 
- 
- = = = 2007 New Years bombings = = = 
- 
- Saprang had a public confrontation with former Prime Minister <unk> <unk> regarding the 2006 Bangkok New Year 's Eve bombings after <unk> accused him of incompetence . Saprang claimed that " the evidence and intelligence information proves that the bombs were the dirty work of politicians who lost power and benefits . Bad soldiers loyal to bad politicians collaborated with them with the intention to <unk> this government . " However , his claim was contradicted just an hour later by Prime Minister <unk> <unk> . 
- In May 2007 , Saprang claimed that he had information regarding the seizure of instructional manual on terrorism in Bangkok from a London apartment by English soldiers and police . He said he could not <unk> any further information , but told the public to connect the dots themselves . Days later , a bomb exploded outside of <unk> Palace . Saprang later clarified his remark , saying the Bangkok terrorism manual discovery had been made in the early 1990s . Deputy Chief of the British Mission in Bangkok Andy Pierce said he was " concerned " by Saprang 's remarks , which he insisted were " <unk> " . 
- 
- = = = Resignation of Pridiyathorn <unk> = = = 
- 
- Saprang was implicated in the resignation of Finance Minister Pridiyathorn <unk> on 28 February 2007 . The Bangkok Post reported that Pridiyathorn resigned in protest after a CNS member lobbied him to sell shares of <unk> ( formerly known as Thai <unk> Industry ) back to a former shareholder . The newspaper identified Saprang as the unnamed CNS member . Saprang 's brother , <unk> , was a key financial advisor to <unk> <unk> , the estranged founder of <unk> . 
- <unk> 's relations with the junta came under further public scrutiny when it was revealed that he was hired by the junta in order to lead a campaign to discredit deposed premier Thaksin Shinawatra . <unk> hired as part of the CNS campaign included Chat <unk> party leader Korn <unk> , Democrats Korn <unk> , <unk> <unk> and <unk> <unk> , <unk> <unk> @-@ <unk> , a key Thai Rak Thai member who defected to the Chat Thai party , plus ex @-@ senator <unk> <unk> . Academics hired by the CNS included <unk> <unk> @-@ <unk> , <unk> <unk> , <unk> <unk> @-@ <unk> , and <unk> <unk> . 
- 
- = = = Mad dogs and machine guns = = = 
- 
- Saprang was an extremely vocal critic of those who he perceived as his political opponents . In an interview with Thai <unk> ( Thailand 's most popular newspaper ) on 8 April 2007 , he called an unspecified enemy a " mad dog " who he claimed was destroying the monarchy . He said that it was necessary to shoot the dog with a machine gun . In the same interview , he threatened violent response to the " bold words that came from the mouths of evil people who did not know restraint " . He urged decisive action , so that the public would believe that good had triumphed over evil . 
- 
- = = = Post @-@ coup role = = = 
- 
- Saprang was considered a strong contender to lead the junta given the mandatory retirement of Army commander @-@ in @-@ chief and CNS President Sonthi Boonyaratkalin in September 2007 . He unofficially competed with fellow Assistant Army Commander Anupong Paochinda , who , as 1st Army Area Commander , secured Bangkok on the night of the coup . The Bangkok Post reported in October 2006 that Sonthi was <unk> Anupong to be his successor by giving him responsibilities over coup logistics , a greater task than had been assigned to Saprang . The Asia Times quoted a former MP as saying that " Anupong is seen as the real force behind the coup . Saprang is more vocal , but he has no real base . The only way he could be seen as a promising leader is by pushing the country to the brink . " 
- In an interview , Saprang warned that " the three pillars of society - the nation , the religion and the monarchy - might crumble ... If rogue politicians return to power following the next [ post coup ] general election . " 
- Saprang also held the opinion that military coups against the government " should never be ruled out . " The <unk> 1997 constitution had outlawed coups . A replacement constitution was , at the time of Saprang 's statement , being drafted by a military appointed panel . 
- Saprang was sidelined in security plans preceding the Constitutional Tribunal 's 20 May 2007 ruling on the dissolution of the Thai Rak Thai and Democrat <unk> . After the 2006 coup , Sonthi had delegated the task of securing Bangkok to Saprang . The pre @-@ ruling plan put Sonthi directly in charge of Bangkok crowd security , <unk> him with <unk> of <unk> Class 9 , including Army Chief of Staff General <unk> <unk> and First Army Region commander Lt General <unk> Chan @-@ <unk> . 
- <unk> <unk> , a military scholar at <unk> University and a personal adviser to Prime Minister <unk> <unk> noted in early September 2007 that " if the army is going to take a full step into politics , then it will be Saprang . If only a half @-@ step , then Anupong . And if it intends to beat a full retreat or take one step back , it will be [ Army chief of staff ] <unk> [ <unk> ] . " 
- On 19 September 2007 , Saprang 's rival , Assistant Army Commander @-@ in @-@ Chief <unk> Anupong Paochinda , was appointed as the new commander @-@ in @-@ chief of the Army , replacing the retiring General Sonthi . Anupong 's mandatory retirement occurred 2010 . Sonthi was , after resignation , appointed Deputy Prime Minister . Saprang was transferred to become Deputy Permanent Secretary of the Ministry of Defense . Saprang 's ally , Defence Ministry Deputy Permanent Secretary Admiral Bannawit <unk> , called Saprang 's transfer a " demotion " and a " punishment . " However , Saprang himself claimed that he did not feel slighted for being passed over , noting that " everything is over " for him . Bannawit himself was later transferred from Defence Ministry Deputy Permanent Secretary to be a Chief <unk> of the Ministry , replaced by Chief <unk> General <unk> Muang @-@ am . Bannawit denied that his own transfer was the result of his criticism of Saprang 's transfer . Bannawit then announced that he would resign from the military and enter politics . There was also rampant speculation that Saprang himself would resign and enter politics . Although the <unk> News Agency noted speculation that Saprang would stage a coup against Anupong , Saprang denied coup rumors , saying that another coup would be " suicide . " 
- 
- = = Family = = 
- 
- Saprang is the youngest of 9 children of Lieutenant Colonel Sri ( Thai : <unk> กัลยาณมิตร ) and <unk> Kalayanamitr ( Thai : <unk> <unk> <unk> <unk> <unk> กัลยาณมิตร ) . Sri was the eldest of the 8 children of <unk> <unk> , ruler ( <unk> Muang ) of the northern border city of <unk> . 
- The <unk> are a military aristocratic family with Chinese ( <unk> ) <unk> Thai roots . Saprang 's ancestor , <unk> <unk> <unk> ( original name <unk> <unk> , Thai : <unk> , <unk> ่ ง <unk> ่ <unk> <unk> ง ) migrated to <unk> during the reign of King <unk> <unk> the <unk> trade , and was given a feudal title during the reign of King Rama I. 
- Saprang has evoked his aristocratic background in order to increase his credibility in public confrontations . Saprang is married to <unk> ( Thai : <unk> ) and has 3 sons : Army Cadet <unk> ( Thai : <unk> ์ ) , Air Force Cadet <unk> ( Thai : <unk> ) , Air Force Cadet <unk> ( Thai : <unk> ์ ) . 
- 
- = = <unk> = = 
- 
- Saprang stands 161 centimeters tall and as of March 2007 , weighed 52 kilograms . 
- 
- 
- = Grammy Award for Best Concept Music Video = 
- 
- The Grammy Award for Best Concept Music Video was an award that was presented to recording artists at the 30th Grammy Awards in 1988 , and the 31st Grammy Awards in 1989 , for quality , concept music videos . The Grammy Awards ( <unk> ) is an annual ceremony that was established in 1958 and was originally called the <unk> Awards ; awards are presented by the National Academy of Recording Arts and Sciences of the United States to " honor artistic achievement , technical <unk> and overall excellence in the recording industry , without regard to album sales or chart position " . 
- Beginning in 1982 , the Academy began to honor quality music videos with the Video of the Year category , which was discontinued with the establishment of the MTV Video Music Awards in 1984 and was replaced with two awards ; Best Video , Short Form and Best Video Album . <unk> changes for the 1988 and 1989 ceremonies resulted in the Best Concept Music Video award being presented alongside the award for Best Performance Music Video . Best Concept Music Video award recipients were the English rock band Genesis for " Land of Confusion " and the American singer " Weird Al " Yankovic for " Fat " . The Academy returned to the previous format in 1990 , though the categories are now known as Best Short Form Music Video and Best Long Form Music Video . 
- 
- = = Background = = 
- 
- The National Academy of Recording Arts and Sciences began to honor quality music videos with the Grammy Award for Video of the Year category in 1982 . The first two award recipients were former member of The Monkees Michael <unk> for the hour @-@ long video Elephant Parts ( also known as Michael <unk> in Elephant Parts ) and Olivia Newton @-@ John for Olivia Physical . The Video of the Year category was discontinued in 1984 when MTV established the MTV Video Music Awards whose top award is also presented for Video of the Year . For the 26th Grammy Awards the Academy replaced the category with awards for Best Video , Short Form , and Best Video Album . For the awards held in 1988 and 1989 , the criteria changed and awards for the categories Best Concept Music Video , and Best Performance Music Video were presented . The Academy returned to the previous format in 1990 , though the categories were renamed Best Music Video , Short Form , and Best Music Video , Long Form . In 1998 , the categories were retitled Best Short Form Music Video , and Best Long Form Music Video , respectively . 
- 
- = = <unk> = = 
- 
- For the 30th Grammy Awards ( 1988 ) , Best Concept Music Video nominees <unk> David Bowie for " Day @-@ In Day @-@ Out " , Kate Bush for The Whole Story , the English rock band Genesis for " Land of Confusion " , David Lee Roth for David Lee Roth , and Janet Jackson for Control – The Videos Part II . The music video for Bowie 's " Day @-@ In Day @-@ Out " , directed by Julien Temple , included " offending " scenes such as a man <unk> on Ronald Reagan 's Hollywood Walk of Fame star , which was edited out for television broadcast . Bush 's " imaginative " video sampler accompanies her greatest hits album of the same name and includes music videos for songs throughout her career to that point . The music video for " Land of Confusion " , a song included on the band 's 1986 album Invisible Touch , contained <unk> Image puppets of Ronald Reagan , Margaret Thatcher and other notable individuals . David Lee Roth 's self @-@ titled video consisted of promotional clips created for his debut solo EP Crazy from the Heat and album Eat ' Em and Smile . Jackson 's video collection , which was certified gold in the United States , contained six promotional videos recorded for singles from her album Control . Awards were presented to members of Genesis ( Tony Banks , Phil Collins , and Mike Rutherford ) as the performing artists , Jim <unk> and John Lloyd as the video directors , and Jon Blair as the video producer . 
- <unk> for the 31st Grammy Awards were the Hampton String Quartet for " Get a Job " , George Harrison for " When We Was <unk> " , the American rock band Talking Heads for <unk> Giant , " Weird Al " Yankovic for " Fat " , and Neil Young for " This Note 's for You " . " Get a Job " , a song recorded originally by the American group The <unk> , appears on the Hampton String Quartet 's album What If Mozart <unk> " Roll Over Beethoven " , a collection of 1950s R & B and pop music songs performed in the styles of Beethoven , Debussy , Mozart , and other composers . " When We Was <unk> " , a song from the album Cloud Nine , is constructed from quotations written when The Beatles were at the height of their fame and features Harrison playing a <unk> . The music video shows Elton John dressed as a <unk> , a reference to the 1967 song " I Am the <unk> " . <unk> Giants is a collection of Talking Heads ' music videos and additional material linking them together . Two of the nominated music videos had connections to Michael Jackson ; " Fat " is a parody of Jackson 's song " Bad " , and the video for " This Note 's for You " depicts a Jackson look @-@ alike 's hair catching fire ; a parody of an incident that occurred during a shoot for a Pepsi television advertisement in 1984 . In the " Fat " video , Yankovic becomes a " grossly overweight guy " through the use of cosmetics and special effects , and leads a group of overweight people on a parade . The award was presented to Yankovic as the performing artist , along with Jay <unk> as the video director and Susan <unk> as the video producer . 
- 
- 
- = Hadji Ali = 
- 
- Hadji Ali ( c . 1887 – 92 – November 5 , 1937 ) was a vaudeville performance artist , thought to be of Egyptian descent , who was famous for acts of controlled regurgitation . His best @-@ known feats included water spouting , smoke swallowing , and nut and handkerchief swallowing followed by <unk> in an order chosen by the audience . Ali 's most famous stunt , and the highlight of his act , was drinking copious amounts of water followed by kerosene , and then acting by turns as a human flamethrower and fire <unk> as he expelled the two liquids onto a theatrical prop . While these stunts were performed , a panel of audience members was invited to watch the show up close to verify that no <unk> was employed . 
- Although never gaining wide fame , Ali had a dedicated following on the vaudeville circuit in the United States . He performed for heads of state including Tsar Nicholas II of Russia . Judy Garland named him her favorite <unk> and David Blaine identified Ali as his favorite magician . <unk> of his act were captured in the short film Strange as It Seems ( 1930 ) and in Politiquerias ( 1931 ) , the Spanish @-@ language version of Laurel and Hardy 's <unk> Come Home . Two documentaries contain footage of Ali taken from Politiquerias : 1977 's <unk> ! , and 1999 's Vaudeville . Ali 's unusual <unk> abilities led to rumors that the Rockefeller Institute had offered a large sum of money to obtain his stomach post @-@ mortem . After he died in England , his body was offered to Johns Hopkins University for study , though the offer was declined . 
- 
- = = Background = = 
- 
- Hadji Ali was born into a working @-@ class family in approximately 1887 or 1892 , depending on the source consulted , probably in Egypt . His fame was as a <unk> of a recognized vaudeville subgenre known as a " regurgitation act " , involving the swallowing of material or objects and their regurgitation in various ways . Ali became aware as a child that he possessed an unusual <unk> ability . He explained in response to audience questions at a performance held at St. Mary 's Hospital in Niagara Falls , New York , in May 1926 , that while swimming in the Nile as a ten @-@ year @-@ old boy , he naturally discovered that he could swallow a large amount of water and blow it out like a whale spouting . He continued to develop and refine the ability as he grew older . A more dramatic version of these events was provided by Ali 's daughter , Almina Ali , in an interview in England after his death . She stated that his abilities were first learned through a single incident : while bathing in the Nile , he inadvertently swallowed a fish and an ample volume of water . Instead of dying , as those present thought he might , Ali simply regurgitated the liquid and the fish without ill effect . 
- Ali learned that his regurgitation talents had the potential to entertain and to earn money through performance at the age of fifteen : 
- I tried out my tricks first of all in the street , swallowing many glasses of water and then pouring forth a great fountain from one side of the road to the other ... A cafe proprietor saw me doing this one day , and chased me down the street . I thought he wanted to beat me up , but no — all he did was to put a coin in my hand and ask me to repeat the trick . Finally , he was so delighted that he asked me to come to his cafe and entertain the customers . 
- Taking his abilities on the road , Ali met an Italian man in Cairo who signed him to a contract for music hall performances . Ali performed under contract throughout Europe and at times for heads of state . According to Ali , in or about 1914 he was summoned by Tsar Nicholas II of Russia to perform at the Winter Palace in Saint Petersburg , Russia . He stated that the Tsar " must have liked my performance because he awarded me a special decoration , which is now one of my most <unk> possessions . " Following World War I , Ali began managing his own affairs and toured the world , learning more tricks as he went . 
- Ali came to the United States with Almina in the mid @-@ 1920s . They performed together at <unk> , carnivals and in vaudeville , sometimes advertised under the collective name , " Hadji Ali & Co . " Almina played the part of assistant in her father 's act , billed in his shows as " The Princess " . Ali alone had a variety of stage names , including : " The Great Egyptian Miracle Man " , " The Amazing <unk> " , " The Egyptian <unk> " , " The Human <unk> " , " The Human <unk> " and " The 9th Wonder of the Scientific World " . Ali has been described as a " large , barrel @-@ <unk> and bearded man ... [ that cut ] an imposing figure in his Arab costume . " 
- Although Ali spoke a number of languages and became a naturalized U.S. citizen , it was reported that Almina acted as his interpreter in the United States and other places , as he did not speak English and was illiterate . Once he had gained some notoriety , Ali took on as his manager Hubert Julian , a former colonel in the <unk> Air Force . Although he developed a significant following , even being named Judy Garland 's favorite <unk> , Ali " remained more a <unk> curiosity than a true vaudeville <unk> " according to at least one source . Nevertheless , at the time of his death in 1937 , Julian commented that Ali had " earned big money in America — $ 1 @,@ 000 a week sometimes . I was building him up here [ in Europe ] and had a Continental tour arranged . " 
- 
- = = Performance = = 
- 
- The mainstay of Ali 's act was " water spouting " . After swallowing large amounts of water , 60 to 100 glasses at a time , he <unk> the water in a continuous stream for a sustained period of time , sometimes approaching one minute . Another common trick was to swallow 30 to 50 <unk> <unk> ( although one of his posters advertised 40 <unk> ) , followed by another nut of a different variety , such as an <unk> . Ali then brought them up one by one with the odd @-@ nut @-@ out produced at a mark called out by the audience . In another trick , Ali swallowed three to six <unk> of different <unk> and then produced them in a color order requested by audience members . 
- In a 1929 article appearing in the Lowell Sun newspaper , physician Morris <unk> speculated that for Ali 's nut feat , the one nut of a different variety was held in the mouth rather than swallowed , thus allowing him to produce it on cue . Dr. <unk> also stated that unnamed " investigators " were convinced that for Ali 's handkerchief stunt , to produce them in the sequence stipulated by the audience Ali flavored the cloth , and could therefore taste for the correct one as he brought them up . Ali also swallowed live <unk> , watches , coins , costume jewelry , paper money , peach pits , stones , live mice , buttons , pool balls and other odd objects . In another standard performance segment , he placed eight or more lit cigarettes in his mouth but instead of <unk> , he swallowed the smoke and , after a significant time had passed , issued it forth in a steady stream like an <unk> volcano . 
- Ali 's longstanding finale was the swallowing of copious amounts of water again , but this time followed by a pint of kerosene . A prop was then produced , typically a model castle or house made of metal set on a table , within which a small flame burned . <unk> than water and <unk> with it , the kerosene floated above the liquid in Ali 's gut , allowing him to <unk> it first . The stage thus set , and to a drum roll or an imitation of fire bells , Ali became a " human flamethrower " , <unk> the <unk> in a long stream over the sacrificial prop , setting it <unk> . Once the kerosene was exhausted , the water followed , streaming out his mouth in a long flow from up to six feet away , <unk> the fire . 
- At some performances , a panel or " jury " from the audience was invited on stage to verify that no trick mechanism was being employed — that he was actually swallowing the items in question and delivering them back through acts of regurgitation . Sometimes Ali would <unk> into the audience during his nut swallowing trick . His stomach exposed by his standard costume , he invited audience members to pat his stomach , allowing them to hear the nuts <unk> within . One newspaper reported that Ali 's feats , essentially controlled vomiting , were performed in " a manner without the least bit of <unk> or anything bordering on <unk> . " Not everyone felt the same : at least one of Ali 's engagements was cut short once the proprietor realized that the nature of the act " was killing their supper shows " . <unk> <unk> and magician Harry <unk> remarked in his 1920 work Miracle <unk> and Their Methods that water spouting was a " performance that could not fail to disgust a modern audience . " 
- The abilities of Ali fascinated the public and medical authorities . As reported in a 1928 <unk> Press article , at one of Ali 's acts a number of doctors attended and thoroughly examined him during the performance . They came away satisfied that he was actually <unk> and <unk> the material and objects as claimed , but remained " <unk> over his extraordinary performance . " According to an article appearing in the <unk> Daily News , " <unk> of three continents have puzzled over the <unk> mechanism of this human ostrich without success . X @-@ ray experiments have been made during his exhibition without a plausible explanation forthcoming that <unk> the critical , in fact , the profession of surgery has thrown up its hands in <unk> over this human ostrich . " 
- 
- = = Film appearances = = 
- 
- Ali 's act was captured in two films : the 1930 short Strange as It Seems , and Politiquerias ( 1931 ) , the expanded Spanish @-@ language version of Laurel and Hardy 's <unk> Come Home . Ali also had a bit part as the " Turkish <unk> " in Warner Bros. ' 1932 film Scarlet Dawn starring Douglas <unk> , Jr. and Nancy Carroll . Two documentaries contain footage of Ali taken from Politiquerias : 1977 's <unk> ! , and 1999 's Vaudeville , a documentary produced by <unk> @-@ TV that exhibits 90 vaudeville acts over a two @-@ hour running time . The documentary has since aired on the Public Broadcasting Service 's American Masters series numerous times . 
- Speaking about the democratic nature of the vaudeville performance circuit , Vaudeville 's writer and executive producer said in reference to Ali that the film " embraced everything from <unk> to a guy who threw up . " By contrast , in episode 30 of the Sundance Channel television program <unk> , magician David Blaine speaks enthusiastically of Ali . During the episode , Blaine shows artist Chuck Close Ali 's kerosene and water finale footage from Politiquerias and comments that Ali is his " favorite magician ... it 's real but nobody 's been able to do it since ... his name was Hadji Ali ... he 's my favorite of all time . " 
- 
- = = Death = = 
- 
- Ali died on November 5 , 1937 , in Wolverhampton , England , from heart failure during a bout of <unk> . Even before his death , a rumor had circulated that the Rockefeller Institute sought to procure Ali 's stomach upon his death , and would pay as much as $ 50 @,@ 000 for it . This claim appeared in a poster advertising Ali 's impending appearance at a theater during his lifetime . After Ali 's death was reported , the rumor resurfaced as an active offer of $ 10 @,@ 000 . When a Rockefeller Institute manager was interviewed about the story , he said the offer had never been made but that nevertheless , " we should very much like to see the body . " Almina and Julian transported Ali 's body back to the United States on board the Queen Mary . According to a November 29 , 1937 article in the New York Post , upon their arrival , Almina offered her father 's body to Maryland 's Johns Hopkins University for investigation by <unk> , after which it would be transported to Egypt for interment in a mausoleum . However , The Afro @-@ American newspaper reported on December 11 , 1937 , that Johns Hopkins ' officials had declined the offer . 
- 
- 
- = Battle of Tellicherry = 
- 
- The Battle of Tellicherry was a naval action fought off the Indian port of Tellicherry between British and French warships on 18 November 1791 during the Third Anglo @-@ Mysore War . Britain and France were not at war at the time of the engagement , but French support for the Kingdom of Mysore in the conflict with the British East India Company had led to Royal Navy patrols stopping and searching French ships sailing for the Mysorean port of Mangalore . When a French convoy from Mahé passed the British port of Tellicherry in November 1791 , Commodore William Cornwallis sent a small squadron to intercept the French ships . 
- As the British force under Captain Sir Richard Strachan approached the convoy , the escorting frigate Résolue opened fire . A general action followed , with Strachan succeeding in forcing the French ship to surrender within twenty minutes and both sides suffering damage and casualties . All of the French vessels were searched and subsequently returned to Mahé , the local French authorities reacting furiously at what they perceived as a violation of their neutral position . <unk> were sent back to France reporting the action from Commodore Saint @-@ Félix but they evoked little response . Although under normal circumstances the battle might have provoked a diplomatic incident , the upheavals of the ongoing French Revolution meant that the despatches had little effect . 
- 
- = = Background = = 
- 
- In December 1789 , after five years of diplomatic wrangling about the terms of the Treaty of Mangalore that had ended the Second Anglo @-@ Mysore War , the ruler of Mysore Tipu Sultan again declared war on the British East India Company and their allies in Southern India . For the next two years the war continued as British forces and their allies drove the Mysore armies back towards the capital of <unk> . Both sides were reliant on supply by sea to maintain their campaigns inland : the British forces were supported from their major ports at Bombay and Madras , later <unk> additional forces at the small port of Tellicherry inside Mysore territory . The Mysorean forces were supplied through Mangalore by French ships . France had been an ally of the Tipu Sultan 's father <unk> Ali during the Second Anglo @-@ Mysore War and although the political instability caused by the French Revolution in Europe prevented active involvement , they ensured that their ships kept up a supply of equipment to Mysore throughout the war . 
- In an effort to eliminate French support Commodore William Cornwallis , the British naval commander in the region , stationed a squadron of frigates at Tellicherry , where they were ideally situated to blockade Mangalore and prevent the passage of shipping into Mysorean territory . The squadron consisted of Cornwallis in HMS Minerva , Captain Sir Richard Strachan in HMS Phoenix and HMS <unk> under Captain Isaac Smith . The French operated a squadron of their own on the coast , led by Commodore Saint @-@ Félix and consisting of two frigates based at Mahé , a small French port 7 miles ( 11 km ) south of Tellicherry . The French had communicated to the British at Tellicherry that they would not submit to any attempts to search their vessels , but Strachan and Cornwallis replied that they would enforce the blockade of Mangalore whatever the consequences . 
- 
- = = Battle = = 
- 
- In November 1791 , a French convoy sailed from Mahé on the short journey to Mangalore . The convoy included two merchant vessels and the frigate Résolue , a 36 @-@ gun warship under Captain <unk> . Passing northwards , the convoy soon passed Tellicherry and Cornwallis sent Strachan with Phoenix and <unk> to stop and inspect the French ships to ensure they were not carrying military supplies . As Smith halted the merchant ships and sent boats to inspect them , Strachan did the same to Résolue , <unk> the French captain and placing an officer in a small boat to board the frigate . The French captain was outraged at this violation of his neutrality , and responded by opening fire : British sources suggest that his initial target was the small boat , although Phoenix was the ship most immediately damaged . 
- Strachan was <unk> at the French reaction , and returned fire immediately , the proximity of the ships preventing any manoeuvres . Within twenty minutes the combat was decided , the French captain <unk> down his colours with his ship battered and more than 60 men wounded or dead . The French ship carried significantly weaker cannon than Phoenix , with 6 and 12 pounder guns to the 9 and 18 pounders aboard the British squadron . In addition , Résolue was heavily outnumbered : no other French warships were in the area while the British had three large frigates within sight . French losses eventually totalled 25 men killed and 60 wounded , Strachan suffering just six killed and 11 wounded in return . 
- 
- = = Aftermath = = 
- 
- With the enemy subdued , Strachan ordered a thorough search of the captured vessels , but could find no <unk> and returned control to the French commander . The French officer however refused , insisting that he and his ship were treated as prisoners of war . Cornwallis ordered the merchant ships released to continue their journey and for the frigate to be towed back to Mahé , where it was anchored in the roads with its sails and <unk> struck . <unk> was subsequently made at Mahé by Strachan for the wounded French sailors . Soon afterwards Saint @-@ Félix arrived at Mahé in his frigate <unk> and reacted furiously at the discovery that one of his neutral ships had been attacked and captured by the British . When Cornwallis insisted that his ships had been acting within their orders , Saint @-@ Félix promised <unk> if any of his vessels were attacked again and withdrew with both <unk> and Résolue later in the day , followed by Minerva and Phoenix . One account reported that Saint @-@ Félix actually ordered his crew to fire on Cornwallis but that they refused . The British shadowed the French for several days , openly stopping and searching French merchant ships but without provoking a response from Saint @-@ Félix . Résolue and Phoenix were subsequently detached by their commanders , Cornwallis and Saint @-@ Félix remaining in contact for several more days before finally separating . 
- News of the encounter was conveyed back to France , but the country was at this time in one of the most turbulent eras of the ongoing Revolution and little notice was taken of events in India . Historian William James notes that under normal political circumstances the action would have had more significant ramifications , while Edward Pelham Brenton claims that the French deliberately ignored the report out of fear of <unk> Britain . In Britain , the Admiralty approved of Cornwallis ' actions , suggesting that the French were deliberately using the guise of trade to support Mysore against Britain . The action had no effect on the ongoing war in India , which was now centred on the inland city of <unk> . As British forces closed on the city in February 1792 , the Tipu Sultan initiated peace talks which brought the war to an end in exchange for concessions to the Company and its Indian allies . 
- 
- 
- = Loose ( Nelly Furtado album ) = 
- 
- Loose is the third studio album by Canadian singer and songwriter Nelly Furtado , released on 6 June 2006 by Geffen Records and the Mosley Music Group . Following the release of Furtado 's second album , Folklore ( 2003 ) through DreamWorks Records , it was announced that Universal Music Group would acquire DreamWorks Records , the later was folded into the Interscope Geffen A & M umbrella where Furtado would release any new music . Timbaland and his protégé Danja produced the bulk of the album , which incorporates influences of dance , R & B and hip hop . The album explores the theme of female sexuality and has been described as introspective or even sad in parts . 
- The album received criticism because of the sexual image Furtado adopted for the recording , as some critics felt it was a ploy to sell more records . Further controversy rose over accusations of plagiarism on Timbaland 's part in the song " Do It " ( which contained the melody from Finnish musician <unk> <unk> 's song " <unk> Evening " without proper authorization ) when recordings were leaked onto YouTube . The record was seen generally as critically and commercially successful . It reached high positions on charts across the world , and according to an August 2009 press release , it had sold more than 12 million copies worldwide , making it the best @-@ selling album of 2006 – 07 and the twenty @-@ second best @-@ selling album of the 2000s . 
- The album was heavily promoted , released in several editions and supported by the Get Loose Tour , which is the subject of the concert DVD Loose : The Concert . " Loose " debuted at number one , making it Furtado 's first album to top the chart along with eight singles were released from the album , including the US number @-@ one singles " Promiscuous " and " Say It Right " , which received Grammy Award nominations for Best Pop Collaboration with Vocals and Best Female Pop Vocal Performance , respectively . Other successful singles include the UK number @-@ one single " Maneater " and the European number one single " All Good Things ( Come to an End ) " . 
- 
- = = Background = = 
- 
- Furtado 's second album , Folklore , was released in November 2003 . The lead single is " <unk> ( Say What You Want ) " and the second single is the ballad " Try " . The album was not as successful as her debut , partly due to the album 's less " poppy " sound . " <unk> ( Say What You Want ) " was later remixed , featuring Colombian rocker Juanes , who had previously worked with Furtado on his track " <unk> " ( " <unk> " ) . The two would collaborate again on " Te Busqué " ( " I searched for you " ) , the single from Furtado 's album Loose . The album was <unk> from her label DreamWorks Records ; it was announced on 11 November 2003 that Universal Music Group reached an agreement to acquire DreamWorks Records from DreamWorks <unk> for " about $ 100 million " . The purchase came at a time when the music business was " going through major changes " as it struggled to " counter falling sales and the impact of unofficial online music sales " . DreamWorks Records was folded into the Interscope Geffen A & M umbrella label in January 2004 . Furtado 's recording contract was then absorbed into Geffen Records . 
- 
- = = Recording = = 
- 
- Furtado began work on Loose by holding with <unk> <unk> what she referred to as a " hip @-@ hop workshop " , in which they would " write rhymes , <unk> them , and try different flows over beats . " The first producers she worked with were Track & Field — who co @-@ produced her first two albums , <unk> , Nelly ! ( 2000 ) and Folklore ( 2003 ) — and by May 2005 , she had collaborated with <unk> Members and K <unk> . She worked with <unk> Hooper in London on reggae @-@ oriented material and with Lester <unk> in Los Angeles on acoustic songs . One of the tracks <unk> helped to create is " Te Busqué " , which is co @-@ written by and features Juanes , who collaborated with Furtado on his 2002 song " <unk> " . During her time in Los Angeles , she worked with Rick <unk> , who co @-@ wrote and produced " In God 's Hands " and " Somebody to Love " . 
- In Miami , Florida , Furtado collaborated with <unk> ( who introduced her to reggaeton and who gave her a " shout @-@ out " in his 2005 single " Can I Have It Like That " ) and Scott <unk> ( with whom she recorded a " straight @-@ up rap song " ) before entering the studio with Timbaland . He and his protégé at the time , Danja , co @-@ produced eight of the tracks , with another produced solely by Danja . For some of the beats on the songs , Timbaland finished work on ones already present in the studio that were half @-@ developed or just " <unk> " ; the rest were completely reworked . Furtado recorded around forty tracks for Loose , deciding which she would include based on the <unk> of the album — she called Timbaland " a sonic extraterrestrial " who came up with a sequence of songs that flowed , and said that the one she had devised was supposedly unsatisfactory . She recorded an unreleased collaboration with Justin Timberlake , " <unk> Control " , which she described as " kind of sexy " and " a cute , <unk> , upbeat , fun track " . Other songs considered for inclusion on the album include " <unk> Boy " , " Friend of Mine " , " Go " , " Hands in the Air " , " Pretty Boy " , " Vice " and " <unk> " . 
- Furtado said in her diary on her official website that she recorded a remix of " Maneater " with rapper Lil Wayne ; it was only released as part of a compilation album , Timbaland 's Remix & Soundtrack Collection , she also used the instrumental of the song during many television performances of " Maneater " . A version of " All Good Things ( Come to an End ) " featuring vocals by Coldplay lead singer Chris Martin , who co @-@ wrote the song , was not released after a request from Martin 's label , EMI . The song was released on the album , but only Furtado 's vocals are featured . Furtado explained that " Loose was 90 percent written with a beat first , and then I ’ d write my melodies and songs to the beat . " 
- 
- = = Post @-@ production = = 
- 
- The " off @-@ the @-@ cuff " conclusion to production was one of the reasons the album was titled Loose . It was named partly after the spontaneous decisions she made when creating the album . The album is also called Loose because it is " the opposite of calculated " and came naturally to Furtado and Timbaland ; she called him her " distant musical cousin because he was always pushing boundaries and always carving out his own path " , which she believed she was doing with Loose . " I think you have to keep surprising people as an artist , and I like that — I love doing that " , she said . Loose was also named partly for the R & B girl group TLC , who Furtado said she <unk> for " taking back their sexuality , showing they were complete women . " She said she wanted the album to be " assertive and cool " and " sexy but fun " , like TLC , MC <unk> , Queen <unk> and Janet Jackson , who inspired Furtado because , as she put it , she was " comfortable in her sexuality and womanhood " when her 1993 single " That 's the Way Love Goes " was released . 
- During the recording of Loose , Furtado listened to several electro and rock musicians , including <unk> Party , System of a Down , M.I.A. , <unk> , Queens of the Stone Age , <unk> and Death from Above 1979 , some of whom influenced the " rock sound " present on the album and the " <unk> , laughing , distorted <unk> " that were kept in the songs deliberately . According to her , music by such bands is " very loud and has a garage theme " to it , some of which she felt she captured on the album . Furtado has said rock music is " rhythmic again " and hip hop @-@ influenced after it had become " so churning and boring . " Because the mixing engineers were aware of Timbaland and Furtado 's rock influences , the songs were mixed on a mixing board in the studio instead of " the fancy mixer at the end " . Furtado said she preferred the louder volume that process gave to the album because she wanted it to sound like her demo tapes , which she prefers to her finished albums . She said , " It didn 't have that final wash over it ; it didn 't have the final pressing at the end , save for a couple sounds " . 
- 
- = = Music and lyrics = = 
- 
- Furtado said that with the release of her albums before Loose , she had wanted to prove herself as a musician and earn respect from listeners through using many different instruments on an album , which most hip hop musicians did not do . After she believed she had accomplished that , she felt she had freedom to make the type of music she " really love [ d ] " . Furtado said her previous problem with hip hop was that she did not think it was good enough to base one of her albums on , but that she then asked herself why she was being " pretentious " . The album represents her separating from such notions and , in her words , " jumping in the deep end of the pool — ' <unk> , screw it , this is fun ! ' " . Furtado said she considers herself " all over the map " and <unk> musically because she is not faithful to one style . 
- For the first time , Furtado worked with a variety of record producers and followed a more collaborative approach in creating the album . Produced primarily by Timbaland and Danja , Loose showcases Furtado experimenting with a more R & B – hip hop sound and , as she put it , the " surreal , theatrical elements of ' 80s music " . She has categorized the album 's sound as punk @-@ hop , which she describes as <unk> @-@ influenced " modern , poppy , <unk> music " and stated that " there 's a mysterious , after @-@ midnight vibe to [ it ] that 's extremely visceral " . Furtado has described the album as " more urban , more American , more hip @-@ hop , [ and ] more simplified " than her earlier work , which she said was more layered and textured because she " tend [ s ] to <unk> things " . In contrast , during her studio time with Timbaland , she said she was " in the <unk> boys club of just letting go " and being more impulsive . According to Furtado , instead of " pristine stuff " , the album features " really raw " elements such as distorted bass lines , laughter from studio outtakes and general " room for error " . Furtado has said Loose is not as much about the lyrics , which are not included in the liner notes , as it is about " <unk> in pleasures — whether it 's dancing or <unk> . " According to her , she wasn 't trying to be sexy with the album — " I think I just am sexy now " , she said . 
- 
- = = Songs = = 
- 
- The opening track , " <unk> " ( featuring rapper <unk> ) , is a description of Furtado 's fear of what people think of her , and she has said the chorus reminds her of " walking down the hall in high school ... because you live from the outside in . Now that I 'm an adult , I care about the inside of me ... Before I said I didn 't care about what people thought about me , but I really did . " " Maneater " is an uptempo electro rock song that combines 1980s electro synths and a more dance @-@ oriented beat . The up @-@ tempo song has prominent electropop and synthpop influences and is lyrically related to how people become " hot on themselves " when dancing in their <unk> in front of a mirror . " Promiscuous " ( featuring Timbaland ) was inspired by a flirting exchange Furtado had with <unk> , who co @-@ wrote the song 
- She has characterised the fifth track , " <unk> " , as " a proper R & B slow jam " . " No Hay Igual " is a hip hop and reggaeton song , that has a <unk> tongue <unk> over " future @-@ <unk> " beats . The song contains a " sharp mix " of percussion and " empowered chanting " . In " No Hay Igual " , Furtado sings in Spanish and raps in Portuguese over a reggaeton rhythm . The album also features more introspective songs , and The Sunday Times wrote that it " has a surprising sadness to it . " The seventh track , " Te Busqué " , which features Latin singer Juanes , is about Furtado 's experiences with depression , which she said she has had periodically since she was around seventeen years old . Furtado said she was unsure what " Say It Right " is about , but that it encapsulates her feeling when she wrote it and " <unk> into this other sphere " ; in an interview for The Sunday Times , it was mentioned that it is about her breakup with DJ Jasper <unk> , the father of her daughter . " In God 's Hands " , another song on the album , was also inspired by the end of their relationship . 
- 
- = = Singles = = 
- 
- In April 2006 , a remix of " No Hay Igual " featuring Calle 13 was issued as a club single in the US . During the same period , " Promiscuous " ( featuring Timbaland ) was released for digital download in North America . Promiscuous became Furtado 's first single to top the US Billboard Hot 100 and was released in Australia , where it reached the top five . The lead single in Europe and Latin America , " Maneater " , was released in late May to early June 2006 . It became Furtado 's first single to top the UK Singles Chart and made the top ten in other countries ; it reached the top five in Germany and the top twenty in France and Latin America . The second single in Europe , " Promiscuous " , was released in late August to early September 2006 but it did not perform as well as " Maneater " . It peaked inside the top five in the UK and the top ten in other countries , including Germany , and it reached the top twenty in France . During the same period , " Maneater " began its run as the second single in North America ; it was not as successful as " Promiscuous " , reaching number twenty @-@ two in Canada and the top twenty in the US , though it became a top five single on the ARIA Singles Chart . 
- Releases of the third North American single , " Say It Right " , and the third Europe single , " All Good Things ( Come to an End ) " , took place in November and December , and the third Latin American single , " Promiscuous " , was released in January 2007 . " Say It Right " went to number one in the US and on the Nielsen BDS airplay chart in Canada ( where it was not given a commercial release ) , and it reached the top five in Australia . " All Good Things ( Come to an End ) " reached number one on the pan @-@ European singles chart and the top five in the UK , and it was the album 's most successful single in Germany , where it topped the chart , and in France , where it became a top ten hit . After the release of " Say It Right " in Europe in March 2007 , the single reached the top five in Germany and the top ten in the UK , where it was a download @-@ only release . The video for " All Good Things ( Come to an End ) " was released in North America during this period . " All Good Things ( Come to an End ) " peaked in the top five in Canada and in the top twenty in Australia , though it only reached the lower half of the US Hot 100 . 
- The album 's fifth and final UK single was " In God 's Hands " , and the fifth and final single in North America was " Do It " . In May 2007 , Furtado mentioned the possibility of a sixth or seventh single , mentioning the examples of Nickelback 's All the Right Reasons and The Pussycat Dolls ' <unk> as albums that were being supported by seven singles at the time . Furtado said she liked the possibility because she thought Loose was good and " want [ ed ] people to hear as much of it as possible " before she took time off . Two other songs , " Te Busqué " and " No Hay Igual " , were released as singles in other regions of the world . " Te Busqué " was the lead single in Spain because of the limited success hip @-@ hop / R & B @-@ influenced songs in the style of " Promiscuous " and " Maneater " achieved in the country . It was not released in the United States , but it was given airplay on Latin music radio stations and reached the top forty on Billboard 's Latin Pop Airplay chart . The " No Hay Igual " remix featuring Calle 13 was released in Latin America , and the music video debuted in September . 
- 
- = = Release and promotion = = 
- 
- The album was first released in Japan on 7 June 2006 through Universal Music Group before being released two days later in Germany . In the United Kingdom Loose was released on 12 June 2006 via Geffen Records and was released eight days later on 20 June 2006 in Canada and the United States . 
- In 2007 the album was re @-@ released in Germany . The re @-@ release included bonus content . 
- During the promotion of Loose , Furtado performed at major music festivals and award shows . In Europe , she appeared at Rock am Ring and Rock @-@ <unk> @-@ Park in Germany and the <unk> Festival in the Netherlands in June 2006 . She performed in Canada at the Calgary <unk> , the Ottawa <unk> in July , and at the <unk> Music Festival in September . Shortly after her August 2006 performance at the Summer Sonic in Japan , she sang at the Teen Choice Awards . In November , she contributed to the entertainment during the World Music Awards , the American Music Awards and the 94th Grey Cup halftime show . She performed at the 2007 <unk> Music Awards , held in January 2007 . 
- Furtado embarked on a world concert tour , the Get Loose Tour , on 16 February 2007 in the UK , in support of the album ; the tour included thirty @-@ one dates in Europe and Canada , with additional shows in the US , Japan , Australia and Latin America . Furtado described the show as a " full sensory experience " with " a beginning , middle and end ... [ it ] takes you on a journey " , also stressing the importance of crowd involvement and " spontaneity and <unk> , because those are my roots , you know ? I started by doing club shows , and that 's the energy I love , the raw club energy of just feeling like you 're rocking out . " Though Furtado said choreographed dance routines were to be included in the show , she described it as " music @-@ based ... Everything else is just to keep it sophisticated and sensual and fun . " Furtado said she hoped to have Chris Martin , Juanes , Justin Timberlake , Timbaland and Calle 13 to guest on the tour , and have a " revolving door " of opening acts with Latin musicians opening in the US . 
- 
- = = Commercial performance = = 
- 
- Loose debuted at number one on the Canadian Albums Chart , selling more than 34 @,@ 000 copies in its first week , at that time the year 's strongest debut for a Canadian artist . In late July , after Furtado embarked on a short tour of Canada and made a guest appearance on the television show Canadian Idol , the album returned to number one . It subsequently stayed near the top of the album chart until late January 2007 , when it reached number one again for two weeks . It was the third best @-@ selling album of 2006 in Canada , and the highest selling by a female solo artist , with 291 @,@ 700 copies sold . The Canadian Recording Industry Association ( CRIA ) certified Loose five times platinum in May 2007 for shipments of more than 500 @,@ 000 copies . It stayed in the top twenty for fifty @-@ seven weeks . 
- The album debuted at number one on the US Billboard 200 chart , making it Furtado 's first album to top the chart with first @-@ week sales of 219 @,@ 000 ; it was certified platinum by the Recording Industry Association of America ( RIAA ) and ranked sixty @-@ fourth on the Billboard 2006 year @-@ end chart . Loose exited the US top ten in August 2006 but re @-@ entered it in March 2007 , and according to Nielsen SoundScan in October 2007 , it had sold two million units . The album ranked thirty @-@ second on the Billboard 2007 year @-@ end chart . 
- In the United Kingdom , Loose entered the albums chart at number five ; in its forty @-@ third week , it reached number four , and it was certified double platinum for shipments to retailers of more than 600 @,@ 000 copies . As of July 2007 , it had sold roughly <unk> @,@ 000 copies in the UK . The record was certified two times platinum in Australia for more than 140 @,@ 000 units shipped ; it reached number four there and was placed forty @-@ fourth on the Australian Australian Recording Industry Association ( ARIA ) list of 2006 bestsellers . The album entered the chart in Germany at number one , spent a record forty @-@ nine weeks in the German top ten , and was certified five times platinum . Loose reached number one on the European Top 100 Albums chart in early 2007 , spending ten non @-@ consecutive weeks at number one . By March 2007 , it had been certified gold or platinum in twenty @-@ five countries . According to a Geffen Records press release , Loose had sold more than seven million copies by November 2007 . 
- 
- = = Critical reception = = 
- 
- Loose received generally positive reviews from music critics ; it holds an average score of 71 out of 100 at aggregate website Metacritic . AllMusic and musicOMH cited the " <unk> " effect of Timbaland on Furtado 's music , and The Guardian called it " slick , smart and surprising . " Q found most of it to be " an <unk> , hip @-@ hop @-@ <unk> delight . " Kelefa Sanneh of The New York Times wrote that " the music and the lyrics are mainly aimed at dance floors , and yet this album keeps reminding listeners that a dance floor is one of the most complicated places on earth . " In its review , AllMusic wrote " It 's on this final stretch of the album that the Furtado and Timbaland pairing seems like a genuine collaboration , staying true to the Nelly of her first two albums , but given an adventurous production that helps open her songs up ... Timbaland has <unk> Nelly Furtado both creatively and commercially with Loose " . She won her first BRIT Award — Best International Female — in 2007 . 
- In a mixed review , Nick <unk> of The Village Voice felt that Furtado " <unk> up a bit too <unk> " and lacks " chemistry " with Timbaland , writing that Loose " isn 't a love child , but a bump @-@ and @-@ grind that never finds a groove " . Vibe stated , " she loses herself in Gwen Stefani – like posturing , as on “ <unk> , ” and ethnic <unk> like “ No Hay Igual ” or “ Te Busqué . " In his consumer guide for The Village Voice , Robert Christgau gave the album a " B " and named it " <unk> of the month " , indicating " a bad record whose details rarely merit further thought . " Christgau viewed that its dance @-@ oriented tracks " might accomplish God 's great plan on the dance @-@ floor . But as songs they 're not much " . 
- 
- = = Impact = = 
- 
- <unk> attention was generated by the more sexual image of Furtado presented in promotion and publicity for the album , and in particular the music videos for " Promiscuous " and " Maneater " , in which she dances around with her <unk> exposed . According to Maclean 's magazine , some said that Furtado 's progression was a natural transformation of a pop singer ; others believed that she had " sold out " in an effort to garner record sales , particularly after her second album was a commercial failure in comparison to her first . Maclean 's wrote that her makeover " seems a bit forced " and contrasted her with singers such as Madonna and Emily <unk> of <unk> : " [ they ] seem to be completely in control , even somewhat intimidating in their sexuality : they 've made a calculated decision for commercial and feminist reasons . In contrast , Furtado 's new , overt sexuality comes off as unoriginal — <unk> by thousands of <unk> pop stars with a quarter of Furtado 's natural talent ... the <unk> feels as if it 's been imposed rather than chosen by the unique , articulate singer we 've seen in the past . " 
- <unk> magazine wrote that Furtado 's new " highly <unk> " image was manufactured , and noted the involvement in the album 's development of Geffen 's Jimmy <unk> , who helped to develop the Pussycat Dolls , a girl group known for their sexually <unk> dance routines . The writer also criticised Furtado 's discussion of her buttocks and apparent rejection of <unk> in a Blender magazine interview , writing : " Girls , do you hear that churning ? Those are the ideas of Gloria <unk> turning in their grave . " A writer for the Canadian Broadcasting Corporation said that <unk> could attribute Furtado 's commercial success with Loose to her " <unk> @-@ up sex appeal . " The writer added that , the failure of Janet Jackson 's album <unk> Jo ( 2004 ) indicated such a move was not <unk> . Furtado was " still demure compared to many of her competitors " — she avoided sporting lingerie or performing " Christina Aguilera @-@ style <unk> or <unk> " in the " Promiscuous " and " Maneater " videos . " Despite its dramatic arrival ... Furtado 's new image doesn ’ t feel calculated " , he said . " [ She ] seems to be thinking less and feeling more , to the benefit of her music . " 
- In early 2007 , a video hosted on YouTube led to reports that the song " Do It " , and the Timbaland @-@ produced ringtone " Block Party " that inspired it , used — without authorization — the melody from Finnish <unk> musician <unk> " Tempest " <unk> 's song " <unk> Evening " , winner of the Assembly 2000 <unk> music competition . Timbaland used the record of <unk> adaptation of the song written by Glenn <unk> <unk> ( <unk> ) . Timbaland admitted sampling the song , but said that he had no time to research its intellectual owner . <unk> <unk> , a Finnish representative of Universal which represents Nelly Furtado in Finland , commented the controversy as follows in the 15 January 2007 issue of <unk> ; " In case that the artist decides to pursue the matter further , it 's on him to go to America and confront them with the local use of law . It will require a considerable amount of faith and , of course , money . " On 9 February 2007 , Timbaland commented on the issue in an MTV interview : " It makes me laugh . The part I don 't understand , the <unk> is trying to act like I went to his house and took it from his computer . I don 't know him from a can of paint . I 'm 15 years deep . That 's how you attack a king ? You attack <unk> ? Come on , man . You got to come correct . You the laughing stock . People are like , ' You can 't be serious . ' " 
- On 12 June 2009 , <unk> <unk> , who is one of the legal <unk> of <unk> Records , the owner of the sound recording rights , reported that the case had been filed in Florida . In January 2008 , Turkish newspapers reported that <unk> <unk> , the record label that released Turkish folk singer <unk> <unk> 's album Ya <unk> Ya <unk> , pressed charges against Furtado for the Loose track " <unk> for You " , which label officials said features the <unk> instrumental part of <unk> 's song " Allah Allah <unk> <unk> . " 
- 
- = = Track listing = = 
- 
- 
- = = = <unk> = = = 
- 
- Notes 
- ^ a signifies a vocal producer 
- 
- = = Personnel = = 
- 
- 
- = = = Production = = = 
- 
- Credits adapted from the Loose liner notes . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = 2013 – 14 York City F.C. season = 
- 
- The 2013 – 14 season was the <unk> season of competitive association football and 77th season in the Football League played by York City Football Club , a professional football club based in York , North Yorkshire , England . Their 17th @-@ place finish in 2012 – 13 meant it was their second consecutive season in League Two . The season ran from 1 July 2013 to 30 June 2014 . 
- Nigel Worthington , starting his first full season as York manager , made eight permanent summer signings . By the turn of the year York were only above the relegation zone on goal difference , before a 17 @-@ match unbeaten run saw the team finish in seventh @-@ place in the 24 @-@ team 2013 – 14 Football League Two . This meant York qualified for the play @-@ offs , and they were eliminated in the semi @-@ final by Fleetwood Town . York were knocked out of the 2013 – 14 FA Cup , Football League Cup and Football League Trophy in their opening round matches . 
- 35 players made at least one appearance in nationally organised first @-@ team competition , and there were 12 different <unk> . Defender Ben Davies missed only five of the fifty @-@ two competitive matches played over the season . Wes Fletcher finished as leading scorer with 13 goals , of which 10 came in league competition and three came in the FA Cup . The winner of the <unk> of the Year award , voted for by the club 's supporters , was <unk> Oyebanjo . 
- 
- = = Background and pre @-@ season = = 
- 
- The 2012 – 13 season was York City 's first season back in the Football League , having won the Conference Premier play @-@ offs in 2011 – 12 after <unk> years in the Football Conference . Manager Gary Mills was sacked in March 2013 following an 11 @-@ match run without a victory , and was replaced by former Northern Ireland manager Nigel Worthington . Despite being in the relegation zone with three matches remaining , Worthington led the team to safety from relegation after a 1 – 0 win away to Dagenham & Redbridge on the final day of the season . York finished the season in 17th @-@ place in the 2012 – 13 League Two table . 
- Following the previous season 's conclusion Lee <unk> , Jon <unk> , Chris <unk> , Ben Everson , Scott Kerr , David <unk> , Patrick <unk> , Michael Potts , Jamie Reed and Jason Walker were released by York , while <unk> Blair departed for Fleetwood Town . David McGurk , <unk> Oyebanjo , Danny Parslow , Tom Platt and Chris Smith signed new contracts with the club . New players signed ahead of the start of the season were goalkeeper Chris <unk> on a season @-@ long loan from Blackpool , defender Ben Davies on loan from Preston North End , midfielders Craig Clay from Chesterfield and Lewis Montrose from Gillingham , winger <unk> Puri from St <unk> and strikers Ryan Bowman from Hereford United , Richard Cresswell from Sheffield United , Wes Fletcher from Burnley and Ryan Jarvis from Torquay United . Defender Mike Atkinson and striker Chris Dickinson entered the first @-@ team squad from the youth team after agreeing professional contracts . 
- York retained the previous season 's home and away kits . The home kit comprised red shirts with white sleeves , light blue shorts and white socks . The away kit included light blue shirts with white sleeves , white shorts and light blue socks . <unk> Health continued as shirt sponsors for the second successive season . 
- 
- = = Review = = 
- 
- 
- = = = August = = = 
- 
- York began the season with a 1 – 0 home win over the previous season 's play @-@ off finalists , Northampton Town , with <unk> Jarvis scoring the winning goal in the 90th @-@ minute . However , defeat came in York 's match against Championship side Burnley in the first round of the League Cup , going down 4 – 0 at home . The team endured their first league defeat of the season in the following game after being beaten 2 – 0 away by Dagenham & Redbridge , the home team scoring in each half . York then held Hartlepool United to a 0 – 0 home draw , before being beaten 3 – 2 away by Bristol Rovers , in which Jarvis scored twice before John @-@ Joe O 'Toole scored the winning goal for the home team in the 67th @-@ minute . Two signings were made shortly before the transfer deadline ; defender George Taft was signed on a one @-@ month loan from Leicester City , while Middlesbrough midfielder Ryan Brobbel joined on a one @-@ month loan . <unk> John <unk> , who had been told he had no future with the club , departed after signing for FC Halifax Town . Jarvis gave York the lead away at Exeter City before Alan <unk> scored in each half to see the home team win 2 – 1 . 
- 
- = = = September = = = 
- 
- York suffered their first home league defeat of the season after AFC Wimbledon won 2 – 0 , with Michael Smith scoring in each half . Former Ipswich Town midfielder Josh Carson , who had a spell on loan with York the previous season , signed a contract until the end of 2013 – 14 and Sheffield United midfielder Elliott <unk> signed on a one @-@ month loan . Brobbel opened the scoring in the second minute of his home debut against Mansfield Town , although the away team went on to score twice to win 2 – 1 . York 's run of four defeats ended following a 1 – 1 draw away to Wycombe Wanderers , in which McGurk gave York the lead before the home team levelled through Dean Morgan . Taft was sent back to Leicester after he fell behind McGurk , Parslow and Smith in the pecking order for a central defensive berth . York achieved their first win since the opening day of the season after beating Portsmouth 4 – 2 at home , with Fletcher ( 2 ) , Montrose and Jarvis scoring . 
- 
- = = = October = = = 
- 
- Defender Luke O 'Neill was signed from Burnley on a 28 @-@ day emergency loan . He made his debut in York 's 3 – 0 win away at Torquay , which was the team 's first successive win of the season . York were knocked out of the Football League Trophy in the second round after being beaten 3 – 0 at home by League One team Rotherham United , before their winning streak in the league was ended with a 3 – 0 defeat away to Newport County . York drew 2 – 2 away to Chesterfield , having taken a two @-@ goal lead through O 'Neill and Jarvis , before the home team fought back through Armand <unk> and Jay O <unk> . The team then hosted Fleetwood Town , and the visitors won 2 – 0 with goals scored in each half by Gareth Evans and <unk> Matt . Scunthorpe United were beaten 4 – 1 at home to end York 's three @-@ match run without a win , with all the team 's goals coming in the first half from Carson , Fletcher and Brobbel ( 2 ) . 
- 
- = = = November = = = 
- 
- Bowman scored his first goals for York away to Cheltenham Town , as York twice fought back from behind to draw 2 – 2 . York drew 3 – 3 away to Bristol Rovers to earn a first round replay in the FA Cup , taking the lead through Jarvis before Eliot Richards equalised for the home team . Carson scored a 30 yard volley to put York back in the lead , and after Bristol Rovers goals from Matt <unk> and Chris <unk> , Fletcher scored an 86th @-@ minute equaliser for York . Bowman scored with a header from an O 'Neill cross to open the scoring at home to Plymouth Argyle , which was the first goal the visitors had conceded in 500 minutes of action . However , Plymouth equalised 11 minutes later through <unk> <unk> and the match finished a 1 – 1 draw . York were knocked out of the FA Cup after losing 3 – 2 at home to Bristol Rovers in a first round replay ; the visitors were 3 – 0 up by 50 @-@ minutes before Fletcher pulled two back for York with a penalty and a long @-@ range strike . 
- Defender Keith Lowe , of Cheltenham , and goalkeeper Nick Pope , of Charlton Athletic , were signed on loan until January 2014 . They both played in York 's first league defeat in four weeks , 2 – 1 away , to Southend United . <unk> <unk> gave Southend the lead early into the match and Bowman equalised for York with a low strike during the second half , before Luke Prosser scored the winning goal for the home side in stoppage time . With Pope preferred in goal , <unk> returned to Blackpool on his own accord , although his loan agreement would stay in place until January 2014 . York then drew 0 – 0 away to Morecambe . After Pope was recalled from his loan by Charlton , York signed Wolverhampton Wanderers goalkeeper Aaron McCarey on loan until January 2014 . McCarey kept a clean sheet in York 's 0 – 0 home draw with Rochdale . 
- 
- = = = December = = = 
- 
- Cresswell retired from playing as a result of an eye complaint and a knee injury . York drew 1 – 1 away to Burton Albion , with an own goal scored by Shane <unk> @-@ <unk> giving York the lead in the 64th @-@ minute before the home team equalised eight minutes later through Billy <unk> . Atkinson was released after failing to force himself into the first team and signed for Scarborough Athletic , with whom he had been on loan . York drew 0 – 0 at home with second @-@ placed Oxford United , in which Carson came closest to scoring with a volley that <unk> across the face of the goal . This was followed by another draw after the match away to Accrington Stanley finished 1 – 1 , with the home team <unk> 10 minutes after a Fletcher penalty had given York the lead in the 35th @-@ minute . Striker <unk> McDonald , who had been released by Peterborough United , was signed on a contract until the end of the season . York 's last match of 2013 was a 2 – 1 defeat away at Bury , a result that ended York 's run of consecutive draws at five . The home team were 2 – 0 up by the 19th @-@ minute , before Michael Coulson scored York 's goal in the 73rd @-@ minute . This result meant York would begin 2014 in 22nd @-@ position in the table , only out of the relegation zone on goal difference . 
- 
- = = = January = = = 
- 
- Jarvis scored the only goal in York 's first win since October 2013 , a 1 – 0 home victory over Morecambe on New Year 's Day . McCarey was recalled by Wolverhampton Wanderers due to an injury to one of their <unk> , while O 'Neill was recalled by Burnley to take part in their FA Cup match . York achieved back @-@ to @-@ back wins for the first time since October 2013 after Dagenham & Redbridge were beaten 3 – 1 at home , with Bowman opening the scoring in the second half before Fletcher scored twice . Adam Reed , who had a spell on loan with York in the previous season , was signed on a contract until the end of the season after parting company with Burton . Davies ' loan was extended , while Brobbel and <unk> returned to their parent clubs . Cheltenham club captain Russell Penn , a midfielder , was signed on a two @-@ and @-@ a @-@ half @-@ year contract for an undisclosed fee . Lowe was subsequently signed permanently from Cheltenham on a two @-@ and @-@ a @-@ half @-@ year contract for an undisclosed fee . Having been allowed to leave the club on a free transfer , Ashley Chambers signed for Conference Premier club Cambridge United . 
- York achieved three successive wins for the first time in 2013 – 14 after beating Northampton 2 – 0 away , with Bowman and Fletcher scoring in three @-@ second half minutes . Defender John McCombe was signed on a two @-@ and @-@ a @-@ half @-@ year contract following his release from Mansfield , before Clay and Jamal <unk> left York by mutual consent . Pope returned to York on loan from Charlton for the remainder of the season . York 's run of wins ended with a 0 – 0 draw at home to Bristol Rovers , before their first defeat of the year came after losing 2 – 0 away to Hartlepool . Preston winger Will Hayhurst , a Republic of Ireland under @-@ 21 international , was signed on a one @-@ month loan . York fell to a successive defeat for the first time since September 2013 after being beaten 2 – 0 at home by Chesterfield . Shortly after the match , Smith left the club by mutual consent to pursue first @-@ team football . 
- 
- = = = February = = = 
- 
- Fletcher scored a 90th @-@ minute winner for York away to Fleetwood in a 2 – 1 win , a result that ended Fleetwood 's five @-@ match unbeaten run . York then drew 0 – 0 at home to fellow mid @-@ table team Cheltenham , before beating Plymouth 4 – 0 away with goals from Fletcher , McCombe ( 2 ) and Carson as the team achieved successive away wins for the first time in 2013 – 14 . York went without scoring for a fourth consecutive home match after drawing 0 – 0 with Southend . Having worn the <unk> since an injury to McGurk , Penn was appointed captain for the rest of the season , a position that had earlier been held by Smith and Parslow . 
- 
- = = = March = = = 
- 
- York achieved their first home win in five matches after beating Exeter 2 – 1 , with first half goals scored by McCombe and Coulson . Hayhurst 's loan was extended to the end of the season , having impressed in his six appearances for the club . Coulson scored again with the only goal , a 41st @-@ minute header , in York 's 1 – 0 away win over AFC Wimbledon . Bowman scored the only goal with a 32nd @-@ minute penalty as York won 1 – 0 away against Mansfield , in which Fletcher missed the opportunity to extend the lead when his stoppage time penalty was saved by Alan Marriott . York moved one place outside the play @-@ offs with a 2 – 0 home win over Wycombe , courtesy of a second Bowman penalty in as many matches and a Carson goal from the edge of the penalty area . Coulson scored York 's only goal in a 1 – 0 away win over struggling Portsmouth with a low volley in the fifth @-@ minute ; this result meant York moved into the play @-@ offs in seventh @-@ place with eight fixtures remaining . 
- Striker Calvin Andrew , who had been released by Mansfield in January 2014 , was signed on a contract for the remainder of the season . He made his debut as a substitute in York 's 1 – 0 home win over bottom of the table Torquay , in which Hayhurst scored the only goal in the 11th @-@ minute with an 18 yard shot that <unk> off Aaron <unk> . Middlesbrough winger Brobbel rejoined on loan until the end of the season , following an injury to Carson . York 's run of successive wins ended on six matches after a 0 – 0 home draw with Burton , and this result saw York drop out of the play @-@ offs in eighth @-@ place . With the team recording six wins and one draw in March 2014 , including six clean sheets , Worthington was named League Two Manager of the Month . 
- 
- = = = April = = = 
- 
- Pope made a number of saves as York held league leaders Rochdale to a 0 – 0 away draw , with a point being enough to lift the team back into seventh @-@ place . York were prevented from equalling a club record of eight consecutive clean sheets when Accrington scored a stoppage time equaliser in a 1 – 1 home draw , in which York had taken earlier taken the lead with a Coulson penalty . A 1 – 0 win away win over Oxford , which was decided by a second half Coulson penalty , resulted in York moving one place above their opponents and back into seventh @-@ place . York consolidated their place in a play @-@ off position after beating Bury 1 – 0 at home with a fifth @-@ minute goal scored by Lowe from a Hayhurst corner . The result meant York opened up a five @-@ point lead over eighth @-@ placed Oxford with two fixtures remaining . A place in the League Two play @-@ offs was secured following a 1 – 0 win over Newport at home , in which Coulson scored the only goal in the 77th @-@ minute with a 25 yard free kick . Pope earned a nomination for League Two Player of the Month for April 2014 , having conceded only one goal in five matches in that period . 
- 
- = = = May = = = 
- 
- The league season concluded with an away match against divisional runners @-@ up Scunthorpe ; having gone two goals down York fought back to draw 2 – 2 with goals scored by Brobbel and Andrew . This result meant York finished the season in seventh @-@ place in League Two , and would thus play fourth @-@ placed Fleetwood in the play @-@ off semi @-@ final on the back of a 17 @-@ match unbeaten run . York lost 1 – 0 to Fleetwood in the first leg at <unk> Crescent ; the goal came from former York player <unk> Blair in the 50th @-@ minute , who scored from close range after Antoni <unk> 's shot was blocked on the line . A 0 – 0 draw away to Fleetwood in the second leg meant York were eliminated 1 – 0 on aggregate , ending the prospect of a second promotion in three seasons . At an awards night held at York Racecourse , Oyebanjo was voted <unk> of the Year for 2013 – 14 . 
- 
- = = Summary and aftermath = = 
- 
- York mostly occupied the bottom half of the table before the turn of the year , and dropped as low as 23rd in September 2013 . During February 2014 the team broke into the top half of the table and with one match left were in sixth @-@ place . York 's defensive record was the third best in League Two with 41 goals conceded , bettered only by Southend ( 39 ) and Chesterfield ( 40 ) . Davies made the highest number of appearances over the season , appearing in 47 of York 's 52 matches . Fletcher was York 's top scorer in the league and in all competitions , with 10 league goals and 13 in total . He was the only player to reach double figures , and was followed by Jarvis with nine goals . 
- After the season ended York released Tom Allan , Andrew , Dickinson , McDonald , Puri and Reed , while McGurk retired from professional football . Bowman and Oyebanjo left to sign for Torquay and Crawley Town respectively while Coulson signed a new contract with the club . York 's summer signings included goalkeeper Jason <unk> from Tranmere Rovers , defenders <unk> <unk> from Dagenham , Marvin McCoy from Wycombe and Dave Winfield from Shrewsbury Town , midfielders <unk> <unk> from Mansfield , Anthony <unk> from Southend and Luke <unk> from Shrewsbury and striker Jake Hyde from <unk> . 
- 
- = = Match details = = 
- 
- League positions are sourced by <unk> , while the remaining information is referenced individually . 
- 
- = = = Football League Two = = = 
- 
- 
- = = = League table ( part ) = = = 
- 
- 
- = = = FA Cup = = = 
- 
- 
- = = = League Cup = = = 
- 
- 
- = = = Football League Trophy = = = 
- 
- 
- = = = Football League Two play @-@ offs = = = 
- 
- 
- = = <unk> = = 
- 
- 
- = = = In = = = 
- 
- <unk> around club names denote the player 's contract with that club had expired before he joined York . 
- 
- = = = Out = = = 
- 
- <unk> around club names denote the player joined that club after his York contract expired . 
- 
- = = = Loan in = = = 
- 
- 
- = = = Loan out = = = 
- 
- 
- = = Appearances and goals = = 
- 
- Source : 
- Numbers in parentheses denote appearances as substitute . 
- Players with names struck through and marked left the club during the playing season . 
- Players with names in italics and marked * were on loan from another club for the whole of their season with York . 
- Players listed with no appearances have been in the <unk> squad but only as unused <unk> . 
- Key to positions : <unk> – <unk> ; <unk> – Defender ; <unk> – <unk> ; <unk> – Forward 
- 
- 
- = Antimony = 
- 
- Antimony is a chemical element with symbol Sb ( from Latin : stibium ) and atomic number 51 . A lustrous gray metalloid , it is found in nature mainly as the sulfide mineral stibnite ( Sb2S3 ) . Antimony compounds have been known since ancient times and were used for cosmetics ; metallic antimony was also known , but it was erroneously identified as lead upon its discovery . In the West , it was first isolated by <unk> Biringuccio and described in 1540 , although in primitive cultures its powder has been used to cure eye <unk> , as also for eye shadow , since time immemorial , and is often referred to by its Arabic name , kohl . 
- For some time , China has been the largest producer of antimony and its compounds , with most production coming from the <unk> Mine in <unk> . The industrial methods to produce antimony are roasting and reduction using carbon or direct reduction of stibnite with iron . 
- The largest applications for metallic antimony are as <unk> material for lead and tin and for lead antimony plates in lead – acid batteries . <unk> lead and tin with antimony improves the properties of the alloys which are used in solders , bullets and plain bearings . Antimony compounds are prominent <unk> for chlorine and <unk> @-@ containing fire retardants found in many commercial and domestic products . An emerging application is the use of antimony in <unk> . 
- 
- = = Characteristics = = 
- 
- 
- = = = Properties = = = 
- 
- Antimony is in the nitrogen group ( group 15 ) and has an <unk> of 2 @.@ 05 . As expected from periodic trends , it is more electronegative than tin or <unk> , and less electronegative than <unk> or arsenic . Antimony is stable in air at room temperature , but reacts with oxygen if heated , to form antimony trioxide , <unk> . 
- Antimony is a silvery , lustrous gray metalloid that has a <unk> scale hardness of 3 . Thus pure antimony is too soft to make hard objects ; coins made of antimony were issued in China 's <unk> province in 1931 , but because of their rapid wear , their <unk> was discontinued . Antimony is resistant to attack by acids . 
- Four <unk> of antimony are known : a stable metallic form and three metastable forms ( explosive , black and yellow ) . Elemental antimony is a brittle , silver @-@ white <unk> metalloid . When slowly cooled , molten antimony <unk> in a trigonal cell , <unk> with the gray allotrope of arsenic . A rare explosive form of antimony can be formed from the electrolysis of antimony <unk> . When <unk> with a sharp implement , an <unk> reaction occurs and white fumes are given off as metallic antimony is formed ; when rubbed with a <unk> in a mortar , a strong detonation occurs . Black antimony is formed upon rapid cooling of vapor derived from metallic antimony . It has the same crystal structure as red phosphorus and black arsenic , it oxidizes in air and may ignite spontaneously . At 100 ° C , it gradually transforms into the stable form . The yellow allotrope of antimony is the most unstable . It has only been generated by oxidation of stibine ( <unk> ) at − 90 ° C. Above this temperature and in ambient light , this metastable allotrope transforms into the more stable black allotrope . 
- Elemental antimony adopts a layered structure ( space group <unk> No. 166 ) in which layers consist of fused <unk> six @-@ membered rings . The nearest and next @-@ nearest neighbors form an irregular <unk> complex , with the three atoms in the same double layer being slightly closer than the three atoms in the next . This relatively close packing leads to a high density of 6 @.@ 697 g / cm3 , but the weak bonding between the layers leads to the low hardness and <unk> of antimony . 
- 
- = = = Isotopes = = = 
- 
- Antimony has two stable isotopes : <unk> with a natural abundance of 57 @.@ 36 % and <unk> with a natural abundance of 42 @.@ 64 % . It also has 35 <unk> , of which the longest @-@ lived is <unk> with a half @-@ life of 2 @.@ 75 years . In addition , 29 metastable states have been characterized . The most stable of these is <unk> with a half @-@ life of 5 @.@ 76 days . Isotopes that are lighter than the stable <unk> tend to decay by β + decay , and those that are heavier tend to decay by β − decay , with some exceptions . 
- 
- = = = Occurrence = = = 
- 
- The abundance of antimony in the Earth 's crust is estimated at 0 @.@ 2 to 0 @.@ 5 parts per million , comparable to <unk> at 0 @.@ 5 parts per million and silver at 0 @.@ 07 ppm . Even though this element is not abundant , it is found in over 100 mineral species . Antimony is sometimes found <unk> ( e.g. on Antimony Peak ) , but more frequently it is found in the sulfide stibnite ( Sb2S3 ) which is the predominant ore mineral . 
- 
- = = Compounds = = 
- 
- Antimony compounds are often classified according to their oxidation state : Sb ( III ) and Sb ( V ) . The + 5 oxidation state is more stable . 
- 
- = = = <unk> and <unk> = = = 
- 
- Antimony trioxide ( Sb 
- 4O 
- 6 ) is formed when antimony is burnt in air . In the gas phase , this compound exists as Sb 
- 4O 
- 6 , but it <unk> upon <unk> . Antimony <unk> ( Sb 
- 4O 
- 10 ) can only be formed by oxidation by concentrated nitric acid . Antimony also forms a mixed @-@ valence oxide , antimony tetroxide ( Sb 
- 2O 
- 4 ) , which features both Sb ( III ) and Sb ( V ) . Unlike oxides of phosphorus and arsenic , these various oxides are <unk> , do not form well @-@ defined <unk> and react with acids to form antimony salts . 
- <unk> acid Sb ( OH ) 
- 3 is unknown , but the <unk> base sodium <unk> ( [ Na 
- <unk> 
- 3 ] 
- 4 ) forms upon fusing sodium oxide and Sb 
- 4O 
- 6 . Transition metal <unk> are also known . <unk> acid exists only as the hydrate <unk> ( OH ) 
- 6 , forming salts containing the <unk> anion Sb ( OH ) − 
- 6 . <unk> metal salts containing this anion yields mixed oxides . 
- Many antimony ores are sulfides , including stibnite ( Sb 
- 2S 
- 3 ) , <unk> ( <unk> 
- <unk> 
- 3 ) , <unk> , <unk> , and <unk> . Antimony <unk> is non @-@ stoichiometric and features antimony in the + 3 oxidation state and S @-@ S bonds . Several <unk> are known , such as [ Sb 
- <unk> 
- 10 ] 2 − and [ Sb 
- <unk> 
- 13 ] 2 − . 
- 
- = = = Halides = = = 
- 
- Antimony forms two series of halides : <unk> 
- 3 and <unk> 
- 5 . The <unk> SbF 
- 3 , SbCl 
- 3 , <unk> 
- 3 , and <unk> 
- 3 are all molecular compounds having trigonal pyramidal molecular geometry . 
- The <unk> SbF 
- 3 is prepared by the reaction of Sb 
- 2O 
- 3 with HF : 
- Sb 
- 2O 
- 3 + 6 HF → 2 SbF 
- 3 + 3 H 
- 2O 
- It is Lewis acidic and readily accepts fluoride ions to form the complex anions SbF − 
- 4 and <unk> − 
- 5 . <unk> SbF 
- 3 is a weak electrical conductor . The <unk> SbCl 
- 3 is prepared by dissolving Sb 
- 2S 
- 3 in <unk> acid : 
- Sb 
- 2S 
- 3 + 6 <unk> → 2 SbCl 
- 3 + 3 H 
- 2S 
- The <unk> SbF 
- 5 and SbCl 
- 5 have trigonal <unk> molecular geometry in the gas phase , but in the liquid phase , SbF 
- 5 is <unk> , whereas SbCl 
- 5 is <unk> . SbF 
- 5 is a powerful Lewis acid used to make the <unk> <unk> acid ( " <unk> " ) . 
- <unk> are more common for antimony than arsenic and phosphorus . Antimony trioxide dissolves in concentrated acid to form <unk> compounds such as <unk> and ( <unk> ) 
- <unk> 
- 4 . 
- 
- = = = <unk> , <unk> , and <unk> compounds = = = 
- 
- Compounds in this class generally are described as derivatives of Sb3 − . Antimony forms antimonides with metals , such as indium antimonide ( <unk> ) and silver antimonide ( <unk> 
- <unk> ) . The alkali metal and zinc antimonides , such as <unk> and <unk> , are more reactive . <unk> these antimonides with acid produces the unstable gas stibine , <unk> 
- 3 : 
- Sb3 − + 3 H + → <unk> 
- 3 
- <unk> can also be produced by treating Sb3 + salts with hydride reagents such as sodium <unk> <unk> spontaneously at room temperature . Because stibine has a positive heat of formation , it is <unk> unstable and thus antimony does not react with hydrogen directly . 
- <unk> compounds are typically prepared by <unk> of antimony halides with <unk> reagents . A large variety of compounds are known with both Sb ( III ) and Sb ( V ) centers , including mixed <unk> @-@ organic derivatives , anions , and <unk> . Examples include Sb ( C6H5 ) 3 ( <unk> ) , <unk> ( C6H5 ) 4 ( with an Sb @-@ Sb bond ) , and cyclic [ Sb ( C6H5 ) ] n . <unk> <unk> compounds are common , examples being Sb ( C6H5 ) 5 and several related halides . 
- 
- = = History = = 
- 
- Antimony ( III ) sulfide , Sb2S3 , was recognized in predynastic Egypt as an eye cosmetic ( kohl ) as early as about 3100 BC , when the cosmetic palette was invented . 
- An artifact , said to be part of a vase , made of antimony dating to about 3000 BC was found at <unk> , <unk> ( part of present @-@ day Iraq ) , and a copper object plated with antimony dating between 2500 BC and <unk> BC has been found in Egypt . Austen , at a lecture by Herbert Gladstone in 1892 commented that " we only know of antimony at the present day as a highly brittle and crystalline metal , which could hardly be fashioned into a useful vase , and therefore this remarkable ' find ' ( artifact mentioned above ) must represent the lost art of rendering antimony malleable . " 
- <unk> was <unk> the artifact was indeed a vase , mentioning that <unk> , after his analysis of the <unk> object ( published in 1975 ) , " attempted to relate the metal to <unk> natural antimony " ( i.e. native metal ) and that " the antimony objects from <unk> are all small personal ornaments . " This weakens the evidence for a lost art " of rendering antimony malleable . " 
- The Roman scholar Pliny the Elder described several ways of preparing antimony sulfide for medical purposes in his treatise Natural History . Pliny the Elder also made a distinction between " male " and " female " forms of antimony ; the male form is probably the sulfide , while the female form , which is superior , heavier , and less <unk> , has been suspected to be native metallic antimony . 
- The Roman naturalist <unk> <unk> mentioned that antimony sulfide could be roasted by heating by a current of air . It is thought that this produced metallic antimony . 
- The first description of a procedure for isolating antimony is in the book De la <unk> of 1540 by <unk> Biringuccio ; this predates the more famous <unk> book by <unk> , De re <unk> . In this context <unk> has been often incorrectly credited with the discovery of metallic antimony . The book <unk> <unk> <unk> ( The <unk> Chariot of Antimony ) , describing the preparation of metallic antimony , was published in Germany in 1604 . It was purported to have been written by a Benedictine monk , writing under the name <unk> <unk> , in the 15th century ; if it were authentic , which it is not , it would predate Biringuccio . 
- The metal antimony was known to German chemist Andreas <unk> in 1615 who obtained it by adding iron to a molten mixture of antimony sulfide , salt and potassium tartrate . This procedure produced antimony with a crystalline or starred surface . 
- With the advent of challenges to <unk> theory it was recognized that antimony is an element forming sulfides , oxides , and other compounds , as is the case with other metals . 
- The first natural occurrence of pure antimony in the Earth 's crust was described by the Swedish scientist and local mine district engineer Anton von <unk> in 1783 ; the type @-@ sample was collected from the <unk> Silver Mine in the <unk> mining district of <unk> , <unk> , Sweden . 
- 
- = = = Etymology = = = 
- 
- The ancient words for antimony mostly have , as their chief meaning , kohl , the sulfide of antimony . 
- The Egyptians called antimony <unk> ; in hieroglyphs , the vowels are uncertain , but there is an Arabic tradition that the word is <unk> <unk> . The Greek word , <unk> stimmi , is probably a loan word from Arabic or from Egyptian <unk> 
- and is used by <unk> tragic poets of the 5th century BC ; later Greeks also used <unk> <unk> , as did <unk> and Pliny , writing in Latin , in the first century AD . Pliny also gives the names <unk> [ sic ] , <unk> , alabaster , and the " very common " <unk> , " wide @-@ eye " ( from the effect of the cosmetic ) . Later Latin authors adapted the word to Latin as stibium . The Arabic word for the substance , as opposed to the cosmetic , can appear as <unk> <unk> , <unk> , <unk> , or <unk> . <unk> suggests the first form , which is the earliest , derives from <unk> , an <unk> for stimmi . 
- The use of Sb as the standard chemical symbol for antimony is due to <unk> Jakob Berzelius , who used this abbreviation of the name stibium . The medieval Latin form , from which the modern languages and late Byzantine Greek take their names for antimony , is antimonium . The origin of this is uncertain ; all suggestions have some difficulty either of form or interpretation . The popular etymology , from <unk> anti @-@ <unk> or French <unk> , still has adherents ; this would mean " monk @-@ killer " , and is explained by many early <unk> being monks , and antimony being poisonous . 
- Another popular etymology is the hypothetical Greek word <unk> <unk> , " against <unk> " , explained as " not found as metal " , or " not found <unk> " . <unk> conjectured a hypothetical Greek word <unk> <unk> , which would mean " <unk> " , and cites several examples of related Greek words ( but not that one ) which describe chemical or biological <unk> . 
- The early uses of antimonium include the translations , in <unk> – 1100 , by Constantine the African of Arabic medical <unk> . Several authorities believe antimonium is a scribal corruption of some Arabic form ; <unk> derives it from <unk> ; other possibilities include <unk> , the Arabic name of the metalloid , and a hypothetical as @-@ stimmi , derived from or parallel to the Greek . 
- 
- = = Production = = 
- 
- 
- = = = Top producers and production volumes = = = 
- 
- The British Geological Survey ( <unk> ) reported that in 2005 , China was the top producer of antimony with an approximately 84 % world share , followed at a distance by South Africa , Bolivia and <unk> . <unk> Mine in <unk> province has the largest deposits in China with an estimated deposit of 2 @.@ 1 million metric tons . 
- In 2010 , according to the US Geological Survey , China accounted for 88 @.@ 9 % of total antimony production with South Africa , Bolivia and Russia sharing the second place . 
- However , Roskill <unk> estimates for primary production show that in 2010 China held a 76 @.@ 75 % share of world supply with 120 @,@ 462 tonnes ( 90 @,@ 000 tonnes of reported and 30 @,@ <unk> tonnes of un @-@ reported production ) , followed by Russia ( 4 @.@ 14 % share , 6 @,@ 500 tonnes of production ) , <unk> ( 3 @.@ 76 % share , 5 @,@ <unk> tonnes ) , Canada ( 3 @.@ 61 % share , 5 @,@ 660 tonnes ) , <unk> ( 3 @.@ 42 % share , 5 @,@ 370 tonnes ) and Bolivia ( 3 @.@ 17 % share , 4 @,@ 980 tonnes ) . 
- Roskill estimates that secondary production globally in 2010 was 39 @,@ 540 tonnes . 
- Antimony was ranked first in a Risk List published by the British Geological Survey in the second half of 2011 . The list provides an indication of the relative risk to the supply of chemical elements or element groups required to maintain the current British economy and lifestyle . 
- Also , antimony was identified as one of 12 critical raw materials for the EU in a report published in 2011 , primarily due to the lack of supply outside China . 
- <unk> production of antimony in China fell in 2010 and is unlikely to increase in the coming years , according to the Roskill report . No significant antimony deposits in China have been developed for about ten years , and the remaining economic reserves are being rapidly depleted . 
- The world 's largest antimony producers , according to Roskill , are listed below : 
- 
- = = = Reserves = = = 
- 
- According to statistics from the USGS , current global reserves of antimony will be depleted in 13 years . However , the USGS expects more resources will be found . 
- 
- = = = Production process = = = 
- 
- The extraction of antimony from ores depends on the quality of the ore and composition of the ore . Most antimony is mined as the sulfide ; lower @-@ grade ores are concentrated by <unk> flotation , while higher @-@ grade ores are heated to 500 – 600 ° C , the temperature at which stibnite <unk> and is separated from the <unk> minerals . Antimony can be isolated from the crude antimony sulfide by a reduction with scrap iron : 
- Sb 
- 2S 
- 3 + 3 Fe → 2 Sb + 3 <unk> 
- The sulfide is converted to an oxide and advantage is often taken of the <unk> of antimony ( III ) oxide , which is recovered from roasting . This material is often used directly for the main applications , impurities being arsenic and sulfide . <unk> antimony from its oxide is performed by a <unk> reduction : 
- 2 Sb 
- 2O 
- 3 + 3 C → 4 Sb + 3 CO 
- 2 
- The lower @-@ grade ores are reduced in blast <unk> while the higher @-@ grade ores are reduced in <unk> <unk> . 
- 
- = = Applications = = 
- 
- About 60 % of antimony is consumed in flame retardants , and 20 % is used in alloys for batteries , plain bearings and solders . 
- 
- = = = Flame retardants = = = 
- 
- Antimony is mainly used as its trioxide in making flame @-@ <unk> compounds . It is nearly always used in combination with halogenated flame retardants , with the only exception being in <unk> @-@ containing polymers . The formation of halogenated antimony compounds is the cause for the flame <unk> effect of antimony trioxide , due to reaction of these compounds with hydrogen atoms and probably also with oxygen atoms and OH radicals , thus inhibiting fire . <unk> for these flame @-@ <unk> applications include children 's clothing , toys , aircraft and automobile seat covers . It is also used in the fiberglass <unk> industry as an <unk> to <unk> <unk> for such items as light aircraft engine covers . The resin will burn while a flame is held to it but will extinguish itself as soon as the flame is removed . 
- 
- = = = <unk> = = = 
- 
- Antimony forms a highly useful alloy with lead , increasing its hardness and mechanical strength . For most applications involving lead , varying amounts of antimony are used as <unk> metal . In lead – acid batteries , this addition improves the charging characteristics and reduces generation of <unk> hydrogen during charging . It is used in <unk> alloys ( such as <unk> metal ) , in bullets and lead shot , cable <unk> , type metal ( for example , for <unk> printing machines ) , <unk> ( some " lead @-@ free " solders contain 5 % Sb ) , in <unk> , and in <unk> alloys with low tin content in the manufacturing of organ pipes . 
- 
- = = = Other applications = = = 
- 
- Three other applications make up nearly all the rest of the consumption . One of these uses is as a <unk> and a catalyst for the production of <unk> . Another application is to serve as a fining agent to remove microscopic bubbles in glass , mostly for TV screens ; this is achieved by the interaction of antimony ions with oxygen , interfering the latter from forming bubbles . The third major application is the use as pigment . 
- Antimony is being increasingly used in the <unk> industry as a <unk> for heavily doped n @-@ type silicon <unk> in the production of diodes , infrared detectors , and Hall @-@ effect devices . In the 1950s , tiny beads of a lead @-@ antimony alloy were used to <unk> the <unk> and collectors of n @-@ p @-@ n alloy junction <unk> with antimony . <unk> antimonide is used as a material for mid @-@ infrared detectors . 
- Few biological or medical applications exist for antimony . <unk> principally containing antimony are known as <unk> and are used as <unk> . Antimony compounds are used as <unk> drugs . <unk> <unk> tartrate , or tartar emetic , was once used as an anti @-@ <unk> drug from 1919 on . It was subsequently replaced by <unk> . Antimony and its compounds are used in several <unk> preparations like <unk> or lithium antimony <unk> , which is used as a skin <unk> in <unk> . Antimony has a nourishing or conditioning effect on <unk> tissues , at least in animals . 
- Antimony @-@ based drugs , such as <unk> <unk> , are also considered the drugs of choice for treatment of <unk> in domestic animals . Unfortunately , as well as having low therapeutic <unk> , the drugs are poor at penetrating the bone marrow , where some of the <unk> amastigotes reside , and so cure of the disease – especially the visceral form – is very difficult . Elemental antimony as an antimony pill was once used as a medicine . It could be reused by others after ingestion and elimination . 
- In the heads of some safety matches , antimony ( III ) sulfide is used . Antimony @-@ 124 is used together with beryllium in neutron sources ; the gamma rays emitted by antimony @-@ 124 initiate the <unk> of beryllium . The emitted neutrons have an average energy of 24 keV . Antimony sulfides have been shown to help stabilize the friction coefficient in automotive brake pad materials . 
- Antimony also is used in the making of bullets and bullet <unk> . This element is also used in paint and glass art crafts and as <unk> in enamel . 
- 
- = = Precautions = = 
- 
- The effects of antimony and its compounds on human and environmental health differ widely . The massive antimony metal does not affect human and environmental health . Inhalation of antimony trioxide ( and similar poorly soluble Sb ( III ) dust particles such as antimony dust ) is considered harmful and suspected of causing cancer . However , these effects are only observed with female rats and after long @-@ term exposure to high dust concentrations . The effects are hypothesized to be attributed to inhalation of poorly soluble Sb particles leading to impaired lung clearance , lung <unk> , inflammation and ultimately <unk> formation , not to exposure to antimony ions ( <unk> , 2008 ) . Antimony <unk> are corrosive to skin . The effects of antimony are not comparable to arsenic ; this might be caused by the significant differences of uptake , metabolism and <unk> between arsenic and antimony . 
- For oral absorption , <unk> ( 1994 ) recommended values of 10 % for tartar emetic and 1 % for all other antimony compounds . <unk> absorption for metals is estimated at most 1 % ( <unk> , 2007 ) . Inhalation absorption of antimony trioxide and other poorly soluble Sb ( III ) substances ( such as antimony dust ) is estimated at 6 @.@ 8 % ( <unk> , 2008 ) , whereas a value < 1 % is derived for Sb ( V ) substances . Antimony ( V ) is not <unk> reduced to antimony ( III ) in the cell , and both species exist simultaneously . 
- Antimony is mainly excreted from the human body via urine . Antimony and its compounds do not cause acute human health effects , with the exception of antimony potassium tartrate ( " tartar emetic " ) , a <unk> that is intentionally used to treat <unk> patients . 
- <unk> skin contact with antimony dust may cause <unk> . However , it was agreed at the European Union level that the skin <unk> observed are not substance @-@ specific , but most probably due to a physical blocking of <unk> <unk> ( <unk> / PR / 09 / 09 , Helsinki , 6 July 2009 ) . Antimony dust may also be explosive when dispersed in the air ; when in a bulk solid it is not <unk> . 
- Antimony is incompatible with strong acids , halogenated acids , and <unk> ; when exposed to newly formed hydrogen it may form stibine ( <unk> ) . 
- The 8 hour time weighted average ( TWA ) is set at 0 @.@ 5 mg / m3 by the American Conference of <unk> Industrial <unk> and by the Occupational Safety and Health Administration ( <unk> ) as a legal permissible exposure limit ( <unk> ) in the workplace . The National Institute for Occupational Safety and Health ( <unk> ) has set a recommended exposure limit ( <unk> ) of 0 @.@ 5 mg / m3 as an 8 hour TWA . Antimony compounds are used as <unk> for <unk> <unk> ( <unk> ) production . Some studies report minor antimony <unk> from <unk> bottles into liquids , but levels are below drinking water guidelines . Antimony concentrations in fruit juice concentrates were somewhat higher ( up to 44 @.@ 7 µg / L of antimony ) , but <unk> do not fall under the drinking water regulations . The drinking water guidelines are : 
- World Health Organization : 20 µg / L 
- Japan : 15 µg / L 
- United States Environmental Protection Agency , Health Canada and the Ontario Ministry of Environment : 6 µg / L 
- EU and German Federal Ministry of Environment : 5 µg / L 
- The <unk> proposed by WHO is 6 µg antimony per kilogram of body weight . The <unk> ( immediately dangerous to life and health ) value for antimony is 50 mg / m3 . 
- 
- 
- = Mortimer Wheeler = 
- 
- Sir Robert Eric Mortimer Wheeler CH , <unk> , MC , <unk> , <unk> , FRS , <unk> ( 10 September 1890 – 22 July 1976 ) was a British archaeologist and officer in the British Army . Over the course of his career , he served as Director of both the National Museum of Wales and London Museum , Director @-@ General of the Archaeological Survey of India , and the founder and Honorary Director of the Institute of Archaeology in London , further writing twenty @-@ four books on archaeological subjects . 
- Born in Glasgow to a middle @-@ class family , Wheeler was raised largely in Yorkshire before relocating to London in his teenage years . After studying Classics at University College London ( UCL ) , he began working professionally in archaeology , specializing in the Romano @-@ British period . During World War I he volunteered for service in the Royal Artillery , being stationed on the Western Front , where he rose to the rank of major and was awarded the Military Cross . Returning to Britain , he obtained his doctorate from UCL before taking on a position at the National Museum of Wales , first as Keeper of Archaeology and then as Director , during which time he oversaw excavation at the Roman forts of Segontium , Y <unk> , and <unk> Augusta with the aid of his first wife , Tessa Wheeler . <unk> by the archaeologist Augustus Pitt Rivers , Wheeler argued that excavation and the recording of <unk> context required an increasingly scientific and methodical approach , developing the " Wheeler Method " . In 1926 , he was appointed Keeper of the London Museum ; there , he oversaw a reorganisation of the collection , successfully lobbied for increased funding , and began lecturing at UCL . 
- In 1934 , he established the Institute of Archaeology as part of the federal University of London , adopting the position of Honorary Director . In this period , he oversaw excavations of the Roman sites at Lydney Park and Verulamium and the Iron Age hill fort of Maidan Castle . During World War II , he re @-@ joined the Armed Forces and rose to the rank of brigadier , serving in the North African Campaign and then the Allied invasion of Italy . In 1944 he was appointed Director @-@ General of the Archaeological Survey of India , through which he oversaw excavations of sites at Harappa , Arikamedu , and <unk> , and implemented reforms to the subcontinent 's archaeological establishment . Returning to Britain in 1948 , he divided his time between lecturing for the Institute of Archaeology and acting as archaeological adviser to Pakistan 's government . In later life , his popular books , cruise ship lectures , and appearances on radio and television , particularly the BBC series Animal , <unk> , Mineral ? , helped to bring archaeology to a mass audience . Appointed Honorary Secretary of the British Academy , he raised large sums of money for archaeological projects , and was appointed British representative for several UNESCO projects . 
- Wheeler is recognised as one of the most important British archaeologists of the twentieth century , responsible for successfully encouraging British public interest in the discipline and advancing methodologies of excavation and recording . Further , he is widely acclaimed as a major figure in the establishment of South Asian archaeology . However , many of his specific interpretations of archaeological sites have been discredited or <unk> , and he was often criticised for bullying colleagues and sexually harassing young women . 
- 
- = = Early life = = 
- 
- 
- = = = Childhood : 1890 – 1907 = = = 
- 
- Mortimer Wheeler was born on 10 September 1890 in the city of Glasgow , Scotland . He was the first child of the journalist Robert Mortimer Wheeler and his second wife Emily Wheeler ( née <unk> ) . The son of a tea merchant based in Bristol , in youth Robert had considered becoming a Baptist minister , but instead became a staunch <unk> while studying at the University of Edinburgh . Initially working as a lecturer in English literature , Robert turned to journalism after his first wife died in childbirth . His second wife , Emily , shared her husband 's interest in English literature , and was the niece of Thomas Spencer <unk> , a Shakespearean scholar at St. Andrews University . Their marriage was emotionally strained , a situation exacerbated by their financial insecurity . Within two years of their son 's birth , the family moved to Edinburgh , where a daughter named Amy was born . The couple gave their two children nicknames , with Mortimer being " <unk> " and Amy being " <unk> " . 
- When Wheeler was four , his father was appointed chief leader writer for the Bradford Observer . The family relocated to <unk> , a village northwest of Bradford , a cosmopolitan city in Yorkshire , northeast England , which was then in the midst of the wool trade boom . Wheeler was inspired by the <unk> surrounding <unk> and fascinated by the area 's archaeology . He later wrote about discovering a late prehistoric cup @-@ marked stone , searching for <unk> on <unk> Moor , and digging into a barrow on <unk> Moor . Although suffering from ill health , Emily Wheeler taught her two children with the help of a maid up to the age of seven or eight . Mortimer remained emotionally distant from his mother , instead being far closer to his father , whose company he favoured over that of other children . His father had a keen interest in natural history and a love of fishing and shooting , rural <unk> in which he encouraged Mortimer to take part . Robert acquired many books for his son , particularly on the subject of art history , with Wheeler loving to both read and paint . 
- In 1899 , Wheeler joined Bradford Grammar School shortly before his ninth birthday , where he proceeded straight to the second form . In 1902 Robert and Emily had a second daughter , whom they named Betty ; Mortimer showed little interest in this younger sister . In 1905 , Robert agreed to take over as head of the London office of his newspaper , by then renamed the Yorkshire Daily Observer , and so the family relocated to the southeast of the city in December , settling into a house named Carlton Lodge on South Croydon Road , West <unk> . In 1908 they moved to 14 <unk> Avenue in nearby <unk> Hill . Rather than being sent for a conventional education , when he was 15 Wheeler was instructed to educate himself by spending time in London , where he frequented The National Gallery and the Victoria and Albert Museum . 
- 
- = = = University and early career : 1907 – 14 = = = 
- 
- After passing the entrance exam on his second attempt , in 1907 Wheeler was awarded a scholarship to read classical studies at University College London ( UCL ) , <unk> daily from his parental home to the university campus in Bloomsbury , central London . At UCL , he was taught by the prominent <unk> A. E. <unk> . During his undergraduate studies , he became editor of the Union Magazine , for which he produced a number of illustrated cartoons . Increasingly interested in art , he decided to switch from classical studies to a course at UCL 's art school , the <unk> School of Fine Art ; he returned to his previous subject after coming to the opinion that – in his words – he never became more than " a conventionally accomplished picture maker " . This interlude had adversely affected his classical studies , and he received a second class <unk> on graduating . 
- Wheeler began studying for a Master of Arts degree in classical studies , which he attained in 1912 . During this period , he also gained employment as the personal secretary of the UCL Provost Gregory Foster , although he later criticised Foster for transforming the university from " a college in the truly academic sense [ into ] a <unk> <unk> as little like a college as a <unk> is like a man " . It was also at this time of life that he met and began a relationship with Tessa <unk> , a student then studying history at UCL , when they were both serving on the committee of the University College Literary Society . 
- During his studies , Wheeler had developed his love of archaeology , having joined an excavation of <unk> <unk> , a Romano @-@ British settlement in <unk> , in 1913 . Considering a profession in the discipline , he won a <unk> that had been established jointly by the University of London and the Society of Antiquaries in memory of Augustus <unk> Franks . The prominent archaeologist Sir Arthur Evans doubled the amount of money that went with the <unk> . Wheeler 's proposed project had been to <unk> Romano @-@ Rhenish pottery , and with the grant he funded a trip to the Rhineland in Germany , there studying the Roman pottery housed in local museums ; his research into this subject was never published . 
- At this period , there were very few jobs available within British archaeology ; as the later archaeologist Stuart Piggott related , " the young Wheeler was looking for a professional job where the profession had yet to be created . " In 1913 Wheeler secured a position as junior investigator for the English Royal Commission on Historical Monuments , who were embarking on a project to assess the state of all structures in the nation that pre @-@ dated 1714 . As part of this , he was first sent to <unk> in Essex to assess Late Medieval buildings , although once that was accomplished he focused on studying the Romano @-@ British remains of that county . In summer 1914 he married Tessa in a low @-@ key , secular wedding ceremony , before they moved into Wheeler 's parental home in <unk> Hill . 
- 
- = = = First World War : 1914 – 18 = = = 
- 
- After the United Kingdom 's entry into World War I in 1914 , Wheeler volunteered for the armed forces . Although preferring solitary to group activities , Wheeler found that he greatly enjoyed <unk> . For the next seven months , he was posted as an instructor in the University of London Officer Training Corps . It was during this period , in January 1915 , that a son was born to the Wheelers , and named Michael . Michael was their only child , something that was a social anomaly at the time , although it is unknown if this was by choice or not . In May 1915 , Wheeler transferred to the Royal Field Artillery ( Territorial Force ) and shortly thereafter was appointed captain . In this position he was stationed at various bases across Britain , often bringing his wife and child with him ; his responsibility was as a battery commander , initially of field guns and later of howitzers . 
- In October 1917 Wheeler was posted to the 76th Army Field Artillery Brigade , one of the Royal Field Artillery brigades under the direct control of the General Officer Commanding , Third Army . The brigade was then stationed in Belgium , where it had been engaged in the Battle of Passchendaele against German troops along the Western Front . There , he was immediately placed in command of an artillery battery , replacing a major who had been poisoned by <unk> gas . Being promoted to the rank of acting major , he was part of the Left Group of artillery covering the advancing Allied infantry in the battle . Throughout , he maintained <unk> with his wife , his sister Amy , and his parents . After the Allied victory in the battle , the brigade was transferred to Italy . 
- Wheeler and the brigade arrived in Italy on 20 November , and proceeded through the Italian <unk> to reach <unk> , where it had been sent to bolster the Italian troops against a German and Austro @-@ Hungarian advance . As the Russian Republic removed itself from the war , the German Army <unk> its efforts on the Western Front , and so in March 1918 Wheeler 's brigade was ordered to leave Italy , getting a train from <unk> to <unk> Rouen in France . Back on the Western Front , the brigade was assigned to the 2nd Division , again part of Julian <unk> 's Third Army , reaching a stable area of the front in April . Here , Wheeler was engaged in artillery fire for several months , before the British went on the offensive in August . On 24 August , in between the ruined villages of <unk> and <unk> , he led an expedition which captured two German field guns while under heavy fire from a castle mound ; he was later awarded the Military Cross for this action . Wheeler continued as part of the British forces pushing westward until the German surrender in November 1918 . He was not <unk> for several months , instead being stationed at <unk> in Germany until March ; during this time he wrote up his earlier research on Romano @-@ Rhenish pottery , making use of access to local museums , before returning to London in July 1919 . 
- 
- = = Career = = 
- 
- 
- = = = National Museum of Wales : 1919 – 26 = = = 
- 
- On returning to London , Wheeler moved into a top @-@ floor flat near Gordon Square with his wife and child . He returned to working for the Royal Commission , examining and <unk> the historic structures of Essex . In doing so , he produced his first publication , an academic paper on Colchester 's Roman <unk> Gate which was published in the Transactions of the Essex Archaeological Society in 1920 . He soon followed this with two papers in the Journal of Roman Studies ; the first offered a wider analysis of Roman Colchester , while the latter outlined his discovery of the vaulting for the city 's Temple of Claudius which was destroyed by <unk> 's revolt . In doing so , he developed a reputation as a Roman archaeologist in Britain . He then submitted his research on Romano @-@ Rhenish pots to the University of London , on the basis of which he was awarded his Doctorate of Letters ; <unk> until his knighthood he styled himself as Dr Wheeler . He was unsatisfied with his job in the Commission , unhappy that he was receiving less pay and a lower status than he had had in the army , and so began to seek out alternative employment . 
- He obtained a post as the Keeper of Archaeology at the National Museum of Wales , a job that also entailed becoming a lecturer in archaeology at the University College of South Wales and Monmouthshire . Taking up this position , he moved to Cardiff with his family in August 1920 , although he initially disliked the city . The museum was in <unk> ; prior to the war , construction had begun on a new purpose @-@ built building to house the collections . This had ceased during the conflict and the edifice was left abandoned during Cardiff 's post @-@ war economic slump . Wheeler recognised that Wales was very divided regionally , with many Welsh people having little loyalty to Cardiff ; thus , he made a point of touring the country , lecturing to local societies about archaeology . According to the later archaeologist Lydia C. Carr , the Wheelers ' work for the cause of the museum was part of a wider " cultural @-@ nationalist movement " linked to growing Welsh nationalism during this period ; for instance , the Welsh nationalist party <unk> Cymru was founded in 1925 . 
- Wheeler was impatient to start excavations , and in July 1921 started a six @-@ week project to excavate at the Roman fort of Segontium ; accompanied by his wife , he used up his holiday to oversee the project . A second season of excavation at the site followed in 1922 . <unk> influenced by the writings of the archaeologist Augustus Pitt @-@ Rivers , Wheeler emphasised the need for a strong , developed methodology when undertaking an archaeological excavation , believing in the need for strategic planning , or what he termed " controlled discovery " , with clear objectives in mind for a project . Further emphasising the importance of prompt publication of research results , he wrote full seasonal reports for <unk> <unk> before publishing a full report , Segontium and the Roman Occupation of Wales . Wheeler was keen on training new generations of archaeologists , and two of the most prominent students to excavate with him at Segontium were Victor Nash @-@ Williams and Ian Richmond . 
- Over the field seasons of 1924 and 1925 , Wheeler ran excavations of the Roman fort of Y <unk> near <unk> , a project aided by his wife and two archaeological students , <unk> Myres and Christopher Hawkes . During this project , he was visited by the prominent Egyptologist Sir Flinders Petrie and his wife Hilda Petrie ; Wheeler greatly admired Petrie 's emphasis on strong archaeological methodologies . Wheeler published the results of his excavation in The Roman Fort Near <unk> . He then began excavations at <unk> Augusta , a Roman site in <unk> , where he focused on revealing the Roman amphitheatre . Intent on attracting press attention to both raise public awareness of archaeology and attract new sources of funding , he contacted the press and organised a sponsorship of the excavation by the middle @-@ market newspaper the Daily Mail . In doing so , he emphasised the <unk> and legendary associations that the site had with King Arthur . In 1925 , Oxford University Press published Wheeler 's first book for a general audience , Prehistoric and Roman Wales ; he later expressed the opinion that it was not a good book . 
- In 1924 , the Director of the National Museum of Wales , William Evans <unk> , resigned amid ill health . Wheeler applied to take on the role of his replacement , providing supportive <unk> from Charles Reed <unk> , Robert <unk> , and H. J. <unk> . Although he had no prior museum experience , he was successful in his application and was appointed Director . He then employed a close friend , Cyril Fox , to take on the vacated position of Keeper of Archaeology . Wheeler 's proposed reforms included extending the institution 's reach and influence throughout Wales by building affiliations with regional museums , and focusing on fundraising to finance the completion of the new museum premises . He obtained a £ 21 @,@ 367 donation from the wealthy <unk> William <unk> Smith and appointed Smith to be the museum 's treasurer , and also travelled to <unk> , London , where he successfully urged the British Treasury to provide further funding for the museum . As a result , construction on the museum 's new building was able to continue , and it was officially opened by King George V in 1927 . 
- 
- = = = London Museum : 1926 – 33 = = = 
- 
- Upon the retirement of the Keeper of the London Museum , Harmon Oates , Wheeler was invited to fill the vacancy . He had been considering a return to London for some time and eagerly agreed , taking on the post , which was based at Lancaster House in the St James 's area , in July 1926 . In Wales , many felt that Wheeler had simply taken the directorship of the National Museum to advance his own career prospects , and that he had abandoned them when a better offer came along . Wheeler himself disagreed , believing that he had left Fox at the Museum as his obvious successor , and that the reforms he had implemented would therefore continue . The position initially provided Wheeler with an annual salary of £ 600 , which resulted in a decline in living standards for his family , who moved into a flat near to Victoria Station . 
- Tessa 's biographer <unk> Carr later commented that together , the Wheelers " <unk> the London Museum " . Wheeler expressed his opinion that the museum " had to be cleaned , <unk> , and <unk> ; in general , turned from a junk shop into a <unk> rational institution " . Focusing on <unk> the exhibits and developing a more efficient method of <unk> the artefacts , he also authored A Short Guide to the Collections , before using the items in the museum to write three books : London and the Vikings , London and the Saxons , and London and the Romans . Upon his arrival , the Treasury allocated the museum an annual budget of £ 5 @,@ 000 , which Wheeler deemed insufficient for its needs . In 1930 , Wheeler persuaded them to increase that budget , as he highlighted increasing visitor numbers , publications , and acquisitions , as well as a rise in the number of educational projects . With this additional funding , he was able to employ more staff and increase his own annual salary to £ 900 . 
- Soon after joining the museum , Wheeler was elected to the council of the Society of Antiquaries . Through the Society , he became involved in the debate as to who should finance archaeological supervision of building projects in Greater London ; his argument was that the City of London Corporation should provide the funding , although in 1926 it was agreed that the Society itself would employ a director of excavation based in Lancaster House to take on the position . Also involved in the largely <unk> Royal Archaeological Institute , Wheeler organised its relocation to Lancaster House . In 1927 , Wheeler took on an unpaid <unk> at University College London , where he established a graduate <unk> course on archaeology ; one of the first to enroll was Stuart Piggott . In 1928 , Wheeler <unk> an exhibit at UCL on " Recent Work in British Archaeology " , for which he attracted much press attention . 
- Wheeler was keen to continue archaeological <unk> outside London , undertaking excavations every year from 1926 to 1939 . After completing his excavation of the <unk> amphitheatre in 1928 , he began <unk> at the Roman settlement and temple in Lydney Park , Gloucestershire , having been invited to do so by the aristocratic landowner , Charles Bathurst . It was during these investigations that Wheeler personally discovered the Lydney <unk> of coinage . Wheeler and his wife jointly published their excavation report in 1932 as Report on the Excavation of the Prehistoric , Roman and Post @-@ Roman Site in Lydney Park , Gloucestershire , which Piggott noted had " set the pattern " for all Wheeler 's future excavation reports . 
- From there , Wheeler was invited to direct a Society of Antiquaries excavation at the Roman settlement of Verulamium , which existed on land recently acquired by the Corporation of St Albans . He took on this role for four seasons from 1930 to 1933 , before leaving a fifth season of excavation under the control of the archaeologist Kathleen Kenyon and the architect A. W. G. <unk> . Wheeler enjoyed the opportunity to excavate at a civilian as opposed to military site , and also liked its proximity to his home in London . He was particularly interested in searching for a pre @-@ Roman Iron Age oppidum at the site , noting that the existence of a nearby <unk> settlement was attested to in both classical texts and numismatic evidence . With Wheeler focusing his attention on potential Iron Age evidence , Tessa concentrated on excavating the inside of the city walls ; Wheeler had affairs with at least three assistants during the project . After Tessa wrote two interim reports , the final excavation report was finally published in 1936 as Verulamium : A <unk> and Two Roman Cities , jointly written by Wheeler and his wife . The report resulted in the first major published criticism of Wheeler , produced by the young archaeologist <unk> Myres in a review for Antiquity ; although stating that there was much to praise about the work , he critiqued Wheeler 's selective excavation , dubious dating , and <unk> . Wheeler responded with a piece in which he defended his work and launched a personal attack on both Myres and Myres 's employer , Christ Church , Oxford . 
- 
- = = = Institute of Archaeology : 1934 – 39 = = = 
- 
- Wheeler had long desired to establish an academic institution devoted to archaeology that could be based in London . He hoped that it could become a centre in which to establish the professionalisation of archaeology as a discipline , with systematic training of students in methodological techniques of excavation and conservation and recognised professional standards ; in his words , he hoped " to convert archaeology into a discipline worthy of that name in all senses " . He further described his intention that the Institute should become " a laboratory : a laboratory of archaeological science " . Many archaeologists shared his hopes , and to this end Petrie had donated much of his collection of Near Eastern artefacts to Wheeler , in the hope that it would be included in such an institution . Wheeler was later able to persuade the University of London , a federation of institutions across the capital , to support the venture , and both he and Tessa began raising funds from wealthy <unk> . In 1934 , the Institute of Archaeology was officially opened , albeit at this point without premises or academic staff ; the first students to enroll were Rachel Clay and Barbara Parker , who went on to have careers in the discipline . While Wheeler – who was still Keeper of the London Museum – took on the role of Honorary Director of the Institute , he installed the archaeologist Kathleen Kenyon as secretary of the Management Committee , describing her as " a level @-@ headed person , with useful experience " . 
- After ending his work at Verulamium , Wheeler turned his attention to the late Iron Age hill @-@ fort of Maidan Castle near to <unk> , Dorset , where he excavated for four seasons from 1934 to 1937 . Co @-@ directed by Wheeler , Tessa , and the Curator of Dorset County Museum , Charles Drew , the project was carried out under the joint auspices of the Society of Antiquaries and the Dorset Field Club . With around 100 assistants each season , the dig constituted the largest excavation that had been conducted in Britain up to that point , with Wheeler organising weekly meetings with the press to inform them about any discoveries . His excavation report was published in 1943 as Maidan Castle , Dorset . The report 's publication allowed further criticism to be voiced of Wheeler 's approach and interpretations ; in his review of the book , the archaeologist W. F. Grimes criticised the highly selective nature of the excavation , noting that Wheeler had not asked questions regarding the socio @-@ economic issues of the community at Maidan Castle , aspects of past societies that had come to be of increasing interest to British archaeology . Over coming decades , as further excavations were carried out at the site and archaeologists developed a greater knowledge of Iron Age Britain , much of Wheeler 's interpretation of the site and its development was shown to be wrong , in particular by the work of the archaeologist Niall <unk> . 
- In 1936 , Wheeler embarked on a visit to the Near East , sailing from <unk> to Port Said , where he visited the Old Kingdom tombs of <unk> . From there he went via Sinai to Palestine , Lebanon , and Syria . During this trip , he visited various archaeological projects , but was dismayed by the quality of their excavations ; in particular , he noted that the American @-@ run excavation at Tel <unk> was adopting standards that had been rejected in Britain twenty @-@ five years previously . He was away for six weeks , and upon his return to Europe discovered that his wife Tessa had died of a pulmonary <unk> after a minor operation on her toe . According to Tessa 's biographer , for Wheeler this discovery was " the peak of mental misery , and marked the end of his ability to feel a certain kind of love " . That winter , his father also died . By the summer of 1937 , he had embarked on a new romance , with a young woman named Mavis de <unk> Cole , who had first met Wheeler when visiting the Maidan Castle excavations with her then @-@ lover , the painter Augustus John . After she eventually agreed to his repeated requests for marriage , the two were <unk> early in 1939 in a ceremony held at Caxton Hall , with a reception at Shelley House . They proceeded on a honeymoon to the Middle East . 
- After a search that had taken several years , Wheeler was able to secure a premises for the Institute of Archaeology : St. John 's Lodge in Regent 's Park , central London . Left empty since its use as a hospital during the First World War , the building was owned by the Crown and was controlled by the First Commissioner of Works , William <unk> @-@ <unk> ; he was very sympathetic to archaeology , and leased the building to the Institute at a low rent . The St. John 's Lodge premises were officially opened on 29 April 1937 . During his speech at the ceremony , the University of London 's Vice @-@ Chancellor Charles Reed <unk> made it clear that the building was only intended as a temporary home for the Institute , which it was hoped would be able to move to Bloomsbury , the city 's academic hub . In his speech , the university 's Chancellor , Alexander Cambridge , 1st Earl of <unk> , compared the new institution to both the Institute of Historical Research and the <unk> Institute of Art . 
- Wheeler had also become President of the Museums Association , and in a presidential address given in Belfast talked on the topic of preserving museum collections in war time , believing that Britain 's involvement in a second European conflict was imminent . In anticipation of this event , in August 1939 he arranged for the London Museum to place many of its most important collections into safe keeping . He was also awarded an honorary doctorate from Bristol University , and at the award ceremony met the Conservative Party politician Winston Churchill , who was then engaged in writing his multi @-@ volume A History of the English @-@ Speaking Peoples ; Churchill asked Wheeler to aid him in writing about late prehistoric and early medieval Britain , to which the latter agreed . 
- After Maidan Castle , Wheeler turned his attention to France , where the archaeological investigation of Iron Age sites had <unk> behind developments in Britain . There , he oversaw a series of surveys and excavations with the aid of Leslie Scott , beginning with a survey tour of Brittany in the winter of 1936 – 37 . After this , Wheeler decided to excavate the oppidum at Camp d <unk> , near <unk> , <unk> . In addition to bringing many British archaeologists to work on the site , he hired six local Breton <unk> to assist the project , coming to the belief that the oppidum had been erected by local Iron Age tribes to defend themselves from the Roman invasion led by Julius Caesar . Meanwhile , Scott had been placed in charge of an excavation at the smaller nearby hill fort of <unk> , near <unk> . In July 1939 , the project focused its attention on Normandy , with excavations beginning at the Iron Age hill forts of Camp de Canada and <unk> . They were brought to an abrupt halt in September 1939 as the Second World War broke out in Europe , and the team evacuated back to Britain . Wheeler 's excavation report , co @-@ written with Katherine Richardson , was eventually published as Hill @-@ forts of Northern France in 1957 . 
- 
- = = = Second World War : 1939 – 45 = = = 
- 
- Wheeler had been expecting and openly hoping for war with Nazi Germany for a year prior to the outbreak of hostilities ; he believed that the United Kingdom 's involvement in the conflict would remedy the shame that he thought had been brought upon the country by its signing of the Munich Agreement in September 1938 . <unk> for the armed services , he was assigned to assemble the 48th Light Anti @-@ Aircraft Battery at <unk> , where he set about recruiting volunteers , including his son . As the 48th swelled in size , it was converted into the 42nd Mobile Light Anti @-@ Aircraft Regiment in the Royal Artillery , which consisted of four batteries and was led by Wheeler – now promoted to the rank of colonel – as Commanding Officer . Given the nickname of " Flash <unk> " by those serving under him , he was recognised by colleagues as a ruthless disciplinarian and was blamed by many for the death of one of his soldiers from influenza during training . Having been appointed secretary of the Society of Antiquaries in 1939 and then director in 1940 , he travelled to London to deal with society affairs on various occasions . In 1941 Wheeler was awarded a Fellowship of the British Academy . Cole had meanwhile entered into an affair with a man named Clive <unk> , who lambasted Wheeler as " that <unk> <unk> " . When Wheeler discovered <unk> in bed with his wife , he initiated divorce proceedings that were finalised in March 1942 . 
- In the summer of 1941 , Wheeler and three of his batteries were assigned to fight against German and Italian forces in the North African Campaign . In September , they set sail from Glasgow aboard the <unk> Empress of Russia ; because the Mediterranean was controlled largely by enemy naval forces , they were forced to travel via the Cape of Good Hope , before taking shore leave in Durban . There , Wheeler visited the local <unk> to compare them with the settlements of Iron Age Britain . The ship docked in Aden , where Wheeler and his men again took shore leave . They soon reached the British @-@ controlled Suez , where they disembarked and were stationed on the shores of the Great <unk> Lake . There , Wheeler took a brief leave of absence to travel to Jerusalem , where he visited Petrie on his hospital deathbed . Back in Egypt , he gained permission to fly as a front gunner in a Wellington bomber on a bombing raid against Axis forces , to better understand what it was like for aircrew to be fired on by an anti @-@ aircraft battery . 
- Serving with the Eighth Army , Wheeler was present in North Africa when the Axis armies pushed the Allies back to El <unk> . He was also part of the Allied counter @-@ push , taking part in the Second Battle of El <unk> and the advance on Axis @-@ held Tripoli . On the way he became concerned that the archaeological sites of North Africa were being threatened both by the fighting and the occupying forces . After the British secured control of Libya , Wheeler visited Tripoli and <unk> Magna , where he found that Roman remains had been damaged and vandalised by British troops ; he brought about reforms to prevent this , lecturing to the troops on the importance of preserving archaeology , making many monuments out @-@ of @-@ bounds , and ensuring that the Royal Air Force changed its plans to construct a radar station in the midst of a Roman settlement . Aware that the British were planning to invade and occupy the Italian island of Sicily , he insisted that measures be introduced to preserve the historic and archaeological monuments on the island . 
- Promoted to the rank of brigadier , after the German surrender in North Africa , Wheeler was sent to Algiers where he was part of the staff committee planning the invasion of Italy . There , he learned that the India Office had requested that the army relieve him of his duties to permit him to be appointed Director General of Archaeology in India . Although he had never been to the country , he agreed that he would take the job on the condition that he be permitted to take part in the invasion of Italy first . As intended , Wheeler and his 12th Anti @-@ Aircraft Brigade then took part in the invasion of Sicily and then mainland Italy , where they were ordered to use their anti @-@ aircraft guns to protect the British 10th Corps . As the Allies advanced north through Italy , Wheeler spent time in Naples and then Capri , where he met various <unk> who had anti @-@ fascist sympathies . 
- Wheeler left Italy in November 1943 and returned to London . There , he resigned as the director of the London Museum and focused on organising the Institute of Archaeology , preparing it for its adoption of a new director , V. Gordon Childe , after the war . He also resigned as director of the Society of Antiquaries , but was appointed the group 's representative to the newly formed Council for British Archaeology . He developed a relationship with a woman named Kim Collingridge , and asked her to marry him . As she was a devout Roman Catholic , he officially converted to the religion , something which shocked many of his friends , who believed that he was being dishonest because he did not genuinely believe in the doctrines of the faith . He then set sail for Bombay aboard a transport ship , the City of Exeter , in February 1944 . 
- 
- = = = Archaeological Survey of India : 1944 – 48 = = = 
- 
- Wheeler arrived in Bombay in the spring of 1944 . There , he was welcomed by the city 's governor , John <unk> , before heading by train to Delhi and then <unk> , where the headquarters of the Archaeological Survey of India were located . Wheeler had been suggested for the job by Archibald Wavell , the Viceroy of India , who had been acting on the recommendations of the archaeologist Leonard Woolley , who had authored a report lamenting the state of the archaeological establishment in the British @-@ controlled subcontinent . Wheeler recognised this state of affairs , in a letter to a friend complaining about the lack of finances and equipment , commenting that " We 're back in 1850 " . He initially found much to dislike in India , and in his letters to friends in Britain expressed derogatory and racist sentiments toward Indians : he stated that " they feed wrongly and think wrongly and live wrongly ... I already find myself regarding them as ill @-@ made clockwork toys rather than as human beings , and I find myself bullying them most brutally . " He expelled those staff members whom he deemed too idle , and physically beat others in an attempt to motivate them . 
- From the beginning of his tenure , he sought to distance himself from previous Director @-@ Generals and their administrations by criticising them in print and attempting to introduce new staff who had no loyalty to his predecessors . <unk> with a four @-@ year contract , Wheeler attempted to recruit two archaeologists from Britain , Glyn Daniel and Stuart Piggott , to aid him in reforming the Archaeological Survey , although they declined the offer . He then toured the subcontinent , seeking to meet all of the Survey 's staff members . He had drawn up a <unk> containing research questions that he wanted the Survey to focus on ; these included understanding the period between the Bronze Age Indus Valley Civilization and the Achaemenid Empire , discerning the socio @-@ cultural background to the <unk> , dating the <unk> invasion , and establishing a dating system for southern India prior to the sixth century CE . During his time in office he also achieved a 25 per cent budget increase for the Archaeological Survey , and convinced the government to agree to the construction of a National Museum of Archaeology , to be built in New Delhi . 
- In October 1944 , he opened his six @-@ month archaeological field school in <unk> , where he instructed various students from across India in the methodologies of the discipline . Wheeler became very fond of his students , with one of them , B. B. Lal , later commenting that " behind the <unk> exterior , Sir Mortimer had a very kind and sympathetic heart " . Throughout his period in India , his students were some of the only individuals to whom Wheeler warmed ; more widely , he was annoyed by what he saw as the idleness , incompetence and corruption of Indian society . Initially focusing on the northwest of the subcontinent , Wheeler was particularly fascinated by the Bronze Age Indus Valley Civilization . On his initial inspection of the Indus Valley sites of Mohenjo @-@ daro and Harappa , he organised a very brief excavation which revealed fortifications around both settlements . He later led a more detailed excavation at Harappa , where he exposed further fortifications and established a <unk> for the settlement . 
- Turning his attention to southern India , Wheeler discovered remnants of a Roman <unk> in a museum , and began excavations at Arikamedu , revealing a port from the first century CE which had traded in goods from the Roman Empire . The excavation had been plagued by severe rains and tropical heat , although it was during the excavation that World War II ended ; in celebration , Wheeler gave all his workers an extra <unk> for the day . It has since been alleged that while Wheeler took credit for discovering the significance of this site , it had previously been established by A. <unk> , the Superintendent of the Government Museum in Madras , and the French archaeologist <unk> Dubreuil , with Wheeler intentionally ignoring their contribution . He later undertook excavations of six megalithic tombs in <unk> , Mysore , which enabled him to gain a chronology for the archaeology of much of southern India . 
- Wheeler established a new archaeological journal , Ancient India , planning for it to be published twice a year . He had trouble securing printing paper and faced various delays ; the first issue was released in January 1946 , and he would release three further volumes during his stay . Wheeler married Kim Collingridge in <unk> , before he and his wife took part in an Indian Cultural Mission to Iran . The Indian government had deemed Wheeler ideal to lead the group , which departed via train to <unk> before visiting <unk> , Tehran , <unk> , <unk> , <unk> , and <unk> . Wheeler enjoyed the trip , and was envious of Tehran 's archaeological museum and library , which was far in advance of anything then found in India . Crossing into Iraq , in Baghdad the team caught a flight back to Delhi . In 1946 , he was involved in a second cultural mission , this time to Afghanistan , where he expressed a particular interest in the kingdom of ancient <unk> and visited the archaeology of <unk> . 
- Wheeler was present during the 1947 Partition of India into the Dominion of Pakistan and the Union of India and the accompanying ethnic violence between Hindu and Muslim communities . He was unhappy with how these events had affected the Archaeological Survey , complaining that some of his finest students and staff were now citizens of Pakistan and no longer able to work for him . He was based in New Delhi when the city was <unk> by sectarian violence , and attempted to help many of his Muslim staff members escape from the Hindu @-@ majority city unharmed . He further helped smuggle Muslim families out of the city hospital , where they had taken refuge from a violent Hindu mob . As India neared independence from the British Empire , the political situation had changed significantly ; by October 1947 he was one of the last British individuals in a high @-@ up position within the country 's governing establishment , and recognised that many Indian nationalists wanted him to also leave . 
- As their relationship had become increasingly strained , his wife had left and returned to Britain . Although hoping to leave his post in India several months early , he was concerned for his economic prospects , and desperately searched for a new job position . Through friends in the British archaeological community , he was offered a job as the Secretary of the Royal Commission on Ancient Monuments for Wales , although he was upset that this would mean a drop in his professional status and income and decided to turn it down . Instead , he agreed to take up a chair in the Archaeology of the Roman <unk> at the Institute of Archaeology . In addition , the Pakistani Minister of Education invited him to become the Archaeological <unk> to the Pakistani government ; he agreed to also take up this position , on the condition that he would only spend several months in the country each year over the next three . 
- 
- = = Later life = = 
- 
- 
- = = = Between Britain and Pakistan : 1948 – 52 = = = 
- 
- Returning to London , Wheeler moved into the Hallam Street flat where his son and daughter @-@ in @-@ law were living . Wheeler and the latter disliked each other , and so in summer 1950 he moved out and began renting an apartment in Mount Street . A year later he moved into his wife 's house in <unk> Street , in an unsuccessful hope of <unk> their relationship . Taking up his part @-@ time <unk> at the Institute of Archaeology , he began to lecture to students almost every day . There , he found that he developed a relationship of mutual respect with the director , Childe , despite their strong personal and professional differences . In April 1949 , after the retirement of Cyril Fox , Wheeler was nominated for the <unk> of the Society of Antiquaries , but lost to James Mann ; many archaeologists , including Childe and O. G. S. Crawford , resigned from the Society in protest , deeming Wheeler to have been a far more appropriate candidate for the position . Wheeler was nevertheless elected director of the Society . In 1950 he was awarded the Petrie Medal , and in 1952 was knighted . That same year he was invited to give the Norton lectures for the Archaeological Institute of America , and while in the United States was also awarded the Lucy <unk> <unk> medal at Pennsylvania . He nevertheless disliked the country , and in later life exhibited anti @-@ Americanism . 
- Wheeler spent three months in Pakistan during early 1949 , where he was engaged in organising the fledgling Pakistani Archaeological Department with the aid of former members of the Archaeological Survey and new students whom he recruited . The Minister of Education , <unk> <unk> , was sympathetic to Wheeler 's plans , and the government agreed to establish a National Museum of Pakistan in Karachi , which opened in April 1950 . Wheeler himself was appointed the first President of the Pakistani Museums Association , and found himself as a mediator in the arguments between India and Pakistan over the <unk> of archaeological and historic artefacts following the partition . He also wrote a work of archaeological propaganda for the newly formed state , Five Thousand Years of Pakistan ( 1950 ) . 
- To instruct new Pakistani students in the methods of archaeology , in early 1950 Wheeler ran a training excavation at Mohenjo @-@ daro ; there , he was joined by the British student Leslie Alcock , who spoke both Punjabi and <unk> and who was appointed a site supervisor by Wheeler . This excavation proved to be the only one for which Wheeler would not write and publish a full excavation report . Instead , he made reference to its findings in his book The Indus Civilization , published as part of the series The Cambridge History of India . His relationship with the Pakistani government had become strained , and so he declined to return to work for them for a third year . 
- Wheeler had been keen to return to excavation in Britain . Based on the one he had organised in India , Wheeler developed an archaeological training course , which he ran at Verulamium in the summer of 1949 to instruct British students in the methodologies of excavation . In summer 1950 , he was invited by the Royal Commission on Historical Monuments to direct a trial excavation at <unk> Hill in Dorset . It was a leisurely project which he treated as a seaside holiday . He was invited by the Ancient Monuments Department of the Ministry of Works to excavate the <unk> Iron Age <unk> in North Riding , Yorkshire , which he proceeded to do over the summers of 1951 and 1952 . Aided by many old friends and colleagues from within the British archaeological scene , he was joined by Alcock and Alcock 's wife , among others . Wheeler published his report on the site in 1954 . 
- In 1949 Wheeler was appointed Honorary Secretary of the British Academy after <unk> G. Kenyon stepped down from the position . According to Piggott , the institution had " <unk> drifted into <unk> without the excuse of being venerable " , and Wheeler devoted much time attempting to <unk> the organisation and ensured that Charles Webster was appointed President . Together , Wheeler and Webster sought to increase the number of younger members of the Academy , increasing the number of <unk> who were permitted to join and proposing that those over 75 years of age not be permitted to serve on the organisation 's council ; this latter measure was highly controversial , and though defeated in 1951 , Wheeler and Webster were able to push it through in 1952 . In doing so , Piggott stated , Wheeler helped rid the society of its " self @-@ perpetuating <unk> " . To aid him in these projects , Wheeler employed a personal assistant , Molly Myers , who remained with him for the rest of his life . 
- 
- = = = Popular fame : 1952 – 69 = = = 
- 
- In 1956 , Wheeler retired from his part @-@ time <unk> at the Institute of Archaeology . Childe was also retiring from his position of director that year , and Wheeler involved himself in the arguments surrounding who should replace him . Wheeler vocally opposed the nomination of <unk> Grimes , deeming his career undistinguished ; instead , he championed Glyn Daniel as a candidate , although ultimately Grimes was selected . That year , Wheeler 's marriage broke down , and he moved from his wife 's house to a former <unk> at 27 <unk> Street in central London . From 1954 to 1959 , he served as the President of the Society of Antiquaries , and after resigning supported Ian Richmond as his replacement ; however , Joan Evans was selected . From 1964 to 1966 he served as Chairman of the Ancient Monuments Board , stepping down when he concluded that he was too old for the role . In December 1963 , Wheeler underwent a prostate operation that went wrong , and was hospitalised for over a month . In November 1967 , Wheeler became a Companion of Honour , and in 1968 he became a Fellow of the Royal Society . 
- 
- = = = = Media fame and public archaeology = = = = 
- 
- Wheeler became famous in Britain as " the embodiment of popular archaeology through the medium of television " . In 1952 , Wheeler was invited to be a <unk> on the new BBC television series , Animal , <unk> , Mineral ? . Based on the American quiz programme What in the World ? , the show was hosted by Glyn Daniel and featured three experts in archaeology , anthropology , and natural history being asked to identify artefacts which had been selected from various museums . However , Wheeler is alleged to have prepared for the show by checking beforehand which objects had been temporarily removed from display . The show proved popular with British audiences , and would air for six more years . It brought Wheeler to public attention , resulting in a Television Personality of the Year award for him in 1954 . He also appeared in an episode of Buried Treasure , an archaeology show also hosted by Daniel , in which the pair travelled to Denmark to discuss <unk> Man . In 1957 , he appeared in a second episode of Buried Treasure , for which he travelled to Pakistan to discuss that nation 's archaeology , and in 1958 again appeared in an episode , this time on the site of Great Zimbabwe in Southern Rhodesia . In 1959 he presented his own three @-@ part series on The <unk> That Was Rome , for which he travelled to <unk> 's Wall , Pompeii , and <unk> Magna ; the show failed to secure high ratings , and was Wheeler 's last major foray into television . Meanwhile , he also made appearances on BBC radio , initially featuring on the John Irving series The Archaeologist , but later presenting his own eight @-@ part series on Roman Britain and also appearing on the series Asian Club , which was aimed primarily at newly arrived migrants from the Indian subcontinent . 
- From 1954 onward , Wheeler began to devote an increasing amount of his time to encouraging greater public interest in archaeology , and it was in that year that he obtained an agent . Oxford University Press also published two of his books in 1954 . The first was a book on archaeological methodologies , Archaeology from the Earth , which was translated into various languages . The second was Rome Beyond the Imperial Frontier , discussing evidence for Roman activity at sites like Arikamedu and Segontium . In 1955 Wheeler released his episodic autobiography , Still <unk> , which had sold over 70 @,@ 000 copies by the end of the year . In 1959 , Wheeler wrote Early India and Pakistan , which was published as part as Daniel 's " Ancient Peoples and Places " series for Thames and Hudson ; as with many earlier books , he was criticised for rushing to conclusions . 
- He authored the section entitled " Ancient India " for Piggott 's edited volume The Dawn of Civilisation , which was published by Thames and Hudson in 1961 , before writing an introduction for Roger Wood 's photography book Roman Africa in Colour , which was also published by Thames and Hudson . He then agreed to edit a series for the publisher , known as " New Aspects of Antiquity " , through which they released a variety of archaeological works . The rival publisher <unk> & <unk> had also persuaded Wheeler to work for them , securing him to write many sections of their book , <unk> of the East . They also published his 1968 book Flames Over <unk> , in which Wheeler discussed <unk> and the Persian Empire in the year that it was conquered by Alexander the Great . 
- In 1954 , the tour company <unk> Swan invited Wheeler to provide lectures on the archaeology of ancient Greece aboard their Hellenic cruise line , which he did in 1955 . In 1957 , he then gave a guided tour of the archaeology of the Indian subcontinent for the rival tour company <unk> and <unk> . After Swans appointed him to the position of chairman of their Hellenic Cruise division , he made two fortnight tours a year , in spring and summer . In late 1969 he conducted the Swans tour to the Indian subcontinent , visiting the south and east of the republic as well as Ceylon . During this period , Wheeler had kept in contact with many of his friends and colleagues in India and Pakistan , helping to secure them work and funding where possible . 
- Wheeler had continued his archaeological investigations , and in 1954 led an expedition to the Somme and <unk> de Calais where he sought to obtain more information on the French Iron Age to supplement that gathered in the late 1930s . Pakistan 's Ministry of Education invited Wheeler to return to their country in October 1956 . Here , he undertook test excavations at <unk> to determine a chronology of the site . In 1965 , he agreed to take on the position of President of the <unk> Research Committee , which had been established to promote the findings of excavations at <unk> Castle in Somerset run by his friends <unk> Radford and Alcock ; the project ended in 1970 . He also agreed to sit as Chairman of the Archaeological Committee overseeing excavations at York Minster , work which occupied him into the 1970s . Wheeler had also continued his work with museums , campaigning for greater state funding for them . While he had become a trustee of the institution in 1963 , he achieved publicity for vocally criticising the British Museum as " a mountainous corpse " , <unk> it as being poorly managed and overcrowded with artefacts . The BBC staged a public debate with the museum director Frank Francis . 
- 
- = = = = British Academy and UNESCO = = = = 
- 
- As Honorary Secretary of the British Academy , Wheeler focused on increasing the organisation 's revenues , thus enabling it to expand its remit . He developed personal relationships with various employees at the British Treasury , and offered the Academy 's services as an <unk> in dealing with the Egypt Exploration Society , the British School at Athens , the British School at Rome , the British School at <unk> , the British School in Iraq , and the British School at Jerusalem , all of which were then directly funded independently by the Treasury . Accepting this offer , the Treasury agreed to double its funding of the Academy to £ 5 @,@ 000 a year . <unk> various charitable foundations , from 1955 Wheeler also secured funding from both the Pilgrim Trust and the <unk> Foundation , and in 1957 then secured additional funding from the Rockefeller Foundation . 
- With this additional money , the Academy was able to organise a survey of the state of the humanities and social sciences in the United Kingdom , <unk> a report that was published by Oxford University Press in 1961 as Research in the Humanities and the Social Sciences . On the basis of this report , Wheeler was able to secure a dramatic rise in funding from the British Treasury ; they increased their annual grant to £ 25 @,@ 000 , and promised that this would increase to £ 50 @,@ 000 shortly after . According to his later biographer Jacquetta Hawkes , in doing so Wheeler raised the position of the Academy to that of " the main source of official patronage for the humanities " within the United Kingdom , while Piggott stated that he set the organisation upon its " modern course " . 
- To improve Britain 's cultural influence abroad , Wheeler had been urging the establishment of a British Institute of History and Archaeology in East Africa , touring East Africa itself in August 1955 . In 1956 the Academy requested £ 6 @,@ 000 from the Treasury to fund this new institution , to which they eventually agreed in 1959 . The Institute was initially established in Dar es Salaam in 1961 , although later relocated to <unk> . Meanwhile , Wheeler had also been campaigning for the establishment of a British Institute of Persian Studies , a project which was supported by the British Embassy in Tehran ; they hoped that it would rival the successful French Institute in the city . In 1960 , the Treasury agreed , with the new institution being housed on the premises of the University of Tehran . He further campaigned for the establishment of a British Institute in Japan , although these ideas were scrapped amid the British financial crisis of 1967 . 
- Wheeler retained an active interest in the running of these British institutions abroad ; in 1967 he visited the British School in Jerusalem amid the Six @-@ Day War between Israel and its Arab neighbours , and in January 1968 visited the Persian institute with the archaeologist Max Mallowan and Mallowan 's wife <unk> Christie , there inspecting the excavations at <unk> . In 1969 he proceeded to the Italian city of Rome to inspect the British School there . That year , he resigned as Honorary Secretary of the Academy . The position became a salaried , professional one , with the <unk> Derek Allen taking on the position . 
- <unk> his stature within the archaeological establishment , the government appointed Wheeler as the British representative on a UNESCO project to undertake a programme of rescue archaeology in the Nile Valley ahead of the construction of the <unk> Dam , which was going to flood large areas of Egypt and Sudan . <unk> securing UK funding for the project , he deemed it an issue of national and personal shame when he was unable to persuade the British government to supply additional funding for the relocation of the Abu <unk> temples . In October 1968 , he took part in a UNESCO visit to Pakistan to assess the state of Mohenjo @-@ daro , writing the project 's report on how the archaeological site could best be preserved . His involvement with UNESCO continued for the rest of his life , and in March 1973 he was invited to the organisation 's conference in Paris . 
- 
- = = = Final years : 1970 – 76 = = = 
- 
- During his final years , Wheeler remained involved in various activities , for instance sitting on the advisory panel of the Antiquity journal and the Management Committee of the Royal Archaeological Institute . In March 1971 , the archaeologist Barry <unk> and a number of his undergraduate students at the University of Southampton organised a conference on the subject of " The Iron Age and its <unk> " to celebrate Wheeler 's <unk> birthday . Wheeler attended the event , whose conference proceedings were published as a <unk> for the <unk> . In spring 1973 , Wheeler returned to BBC television for two episodes of the archaeology @-@ themed series Chronicle in which he discussed his life and career . The episodes were well received , and Wheeler became a close friend of the show 's producer , David <unk> . 
- In the 1970s , Wheeler became increasingly <unk> and came to rely largely on his assistant , Molly Myres , to organise his affairs . Amid increasing ill health , in September 1973 he moved full @-@ time into Myres 's house in <unk> , Surrey , although he continued to use his central London flat during day @-@ trips to the city . There , he authored a final book , My Archaeological Mission to India and Pakistan , although much of the text was culled from his previous publications ; it was published by Thames and Hudson in 1976 . After suffering a stroke , Wheeler died at Myers ' home on 22 July 1976 . In <unk> , the British Academy , Royal Academy , and Royal Society flew their flags at half @-@ mast . Wheeler 's funeral was held with military <unk> at a local <unk> , while a larger memorial service was held in St James 's Church , Piccadilly in November . 
- 
- = = Personal life = = 
- 
- Wheeler was known as " <unk> " among friends . He divided opinion among those who knew him , with some loving and others <unk> him , and during his lifetime he was often criticised on both scholarly and moral grounds . The archaeologist Max Mallowan asserted that he " was a delightful , light @-@ hearted and amusing companion , but those close to him knew that he could be a dangerous opponent if threatened with frustration " . His charm offensives were often condemned as being <unk> . During excavations , he was known as an authoritarian leader , but favoured those whom he thought exhibited bravery by standing up to his authority . Hence , he has been termed " a benevolent dictator " . He was meticulous in his writings , and would repeatedly revise and rewrite both pieces for publication and personal letters . Throughout his life , he was a heavy smoker . 
- Wheeler expressed the view that he was " the least political of mortals " . Despite not taking a strong interest in politics , Wheeler was described by his biographer as " a natural conservative " ; for instance , during his youth he was strongly critical of the <unk> and their cause of greater legal rights for women . Nevertheless , he was " usually happy to advance young women professionally " , something that may have been based largely on his sexual attraction toward them . He expressed little interest in his relatives ; in later life he saw no reason to have a social relationship with people purely on the basis of family ties . 
- Wheeler was married three times . In May 1914 , Wheeler married Tessa <unk> . Tessa became an accomplished archaeologist , and they collaborated until she died in 1936 . Their only child , a son Michael , was born in January 1915 ; he became a barrister . Following Tessa 's death , in 1939 , Wheeler married Mavis de <unk> Cole , although their relationship was strained ; Cole 's diaries revealed that Wheeler physically hit her when she annoyed him . In 1945 Mortimer Wheeler married his third wife , Margaret " Kim " Collingridge , although they became estranged in 1956 ; they never divorced as a result of her devout Catholicism . Meanwhile , Wheeler was well known for his conspicuous promiscuity , favouring young women for one night stands , many of whom were his students . He was further known for having casual sex in public places . This behaviour led to much emotional suffering among his various wives and <unk> , of which he was aware . As a result of this behaviour , later archaeologist Gabriel Moshenska informed a reporter from the Daily Mail that Wheeler had developed a reputation as " a bit of a <unk> and a sex pest and an incredible <unk> as well " . 
- 
- = = Reception and legacy = = 
- 
- Wheeler has been termed " the most famous British archaeologist of the twentieth century " by archaeologists Gabriel Moshenska and Tim Schadla @-@ Hall . Highlighting his key role in encouraging interest in archaeology throughout British society , they stated that his " mastery of public archaeology was founded on his keen eye for value and a <unk> 's willingness to package and sell the past " . This was an issue about which Wheeler felt very strongly ; writing his obituary for the <unk> Memoirs of <unk> of the Royal Society , the English archaeologist Stuart Piggott noted that Wheeler placed " great importance to the archaeologist 's obligation to the public , on whose support the prosecution of his subject ultimately depended . " 
- Piggott believed that Wheeler 's greatest impact was as " the great innovator in field techniques " , comparing him in this respect to Pitt @-@ Rivers . Piggott stated that the " importance of Wheeler 's contribution to archaeological technique , enormous and far @-@ reaching , lies in the fact that in the early 1920s he not only appreciated and understood what Pitt @-@ Rivers had done , but saw that his work could be used as a basis for adaptation , development and improvement . " L. C. Carr stated that it was for his methodological developments , oft termed " the Wheeler Method " , that Wheeler was best known ; in this she contrasted him with those archaeologists who were best known for their associations with a specific archaeological site , such as Arthur Evans and <unk> or Leonard Woolley and <unk> . 
- Wheeler was well known for his publications on archaeological matters ; Carr stated that both Wheeler and his first wife emphasised " technical <unk> and a full presentation of materials unearthed , as well as a literary discussion of their meaning calculated to appeal to a larger audience . " Focusing on Wheeler 's publications regarding South Asian archaeology , <unk> <unk> noted that he " produced an assemblage of image @-@ objects that embodied the precision he demanded from excavation photography . " Mallowan noted that " <unk> and swift presentation of results was more important to him than profound scholarship , although his critical sense made him conscious that it was necessary to maintain high standards and he would approve of nothing that was <unk> . " Jacquetta Hawkes commented that he made errors in his interpretation of the archaeological evidence because he was " sometimes too sure of being right , too ready to accept his own authority " . She asserted that while Wheeler was not an original thinker , he had " a vision of human history that enabled him to see each discovery of its traces , however small , in its <unk> significance . " 
- Piggott claimed that Wheeler 's appointment as Director @-@ General of the Archaeological Survey of India represented " the most remarkable archaeological achievement of his career , an enormous challenge accepted and surmounted in the <unk> and authoritarian terms within which he could best deploy his powers as administrator and <unk> . No other archaeologist of the time , it seems fair to remark , could have come near to attaining his command of <unk> strategy and often ruthless tactics which won him the <unk> admiration and touching devotion of his Indian staff . " The Indian archaeologist <unk> K. Chakrabarti later stated that Wheeler 's accomplishments while in India were " considerable " , particularly given the socio @-@ political turmoil of independence and partition . Chakrabarti stated that Wheeler had contributed to South Asian archaeology in various ways : by establishing a " total view " of the region 's development from the <unk> onward , by introducing new archaeological techniques and methodologies to the subcontinent , and by encouraging Indian universities to begin archaeological research . Ultimately , Chakrabarti was of the opinion that Wheeler had " prepared the archaeology of the subcontinent for its transition to modernity in the post @-@ Partition period . " Similarly , Peter Johansen praised Wheeler for <unk> and <unk> Indian archaeology and for " <unk> a clearly defined body of techniques and methods for field and laboratory work and training . " 
- On Wheeler 's death , <unk> <unk> of Deccan College , Pune , described him as " well known among Old World archaeologists in the United States " , particularly for his book Archaeology from the Earth and his studies of the Indus Valley Civilisation . In its 2013 obituary of the English archaeologist Mick Aston , British Archaeology magazine – the publication of the Council for British Archaeology – described Aston as " the Mortimer Wheeler of our times " because despite the strong differences between their personalities , both had done much to bring archaeology to the British public . However , writing in 2011 , Moshenska and Schadla @-@ Hall asserted that Wheeler 's reputation has not undergone significant revision among archaeologists , but that instead he had come to be remembered as " a <unk> and slightly eccentric figure " whom they termed " Naughty <unk> " . Carr described the Institute of Archaeology as " one of the [ Wheeler ] couple 's most permanent memorials . " 
- 
- = = = Biographies and studies = = = 
- 
- In 1960 , Ronald William Clark published a biography titled Sir Mortimer Wheeler . <unk> Somerset , 4th Baron <unk> reviewed the volume for the journal Man , describing " this very readable little book " as being " adulatory " in tone , " but hardly more so than its subject deserves . " In 1982 , the archaeologist Jacquetta Hawkes published a second biography , Mortimer Wheeler : <unk> in Archaeology . Hawkes admitted she had developed " a very great liking " for Wheeler , having first met him when she was an archaeology student at the University of Cambridge . She believed that he had " a <unk> energy " , with his accomplishments in India being " almost superhuman " . Ultimately , she thought of him as being " an epic hero in an anti @-@ heroic age " in which growing social <unk> had <unk> and condemned aspects of his greatness . 
- In the 2000 film Hey Ram , the lead character , <unk> Ram ( played by <unk> <unk> ) and his friend , Amjad Khan ( played by Shah <unk> Khan ) are shown as employees of Wheeler , who was portrayed by Lewis K. <unk> , before the 1947 Hindu @-@ Muslim riots . In a 2003 volume of the South Asian Studies journal , <unk> <unk> published a research article examining Wheeler 's use of photography in his excavations and publications in the Indian subcontinent . In 2011 , the academic journal Public Archaeology published a research paper by Moshenska and Schadla @-@ Hall that analysed Wheeler 's role in presenting archaeology to the British public . Two years later , the <unk> from the Institute of Archaeology issued a short comic strip by Moshenska and Alex <unk> depicting Wheeler 's activities in studying the archaeology of Libya during World War II . 
- 
- 
- = Species of Allosaurus = 
- 
- There have been a number of potential species assigned to the <unk> dinosaur genus Allosaurus since its description in 1877 by Othniel Charles Marsh , but only a handful are still regarded as valid . Allosaurus was originally described from material from the Upper Jurassic Morrison Formation of the western United States of America ; the type species A. fragilis became one of the best @-@ known species of dinosaur . 
- The genus Allosaurus was part of the Marsh / Cope " Bone Wars " of the late 19th century , and its taxonomy became increasingly confused due to the competition , with several genera and species named by Cope and Marsh now regarded as synonyms of Allosaurus or A. fragilis . Since the description of Allosaurus , scientists have proposed additional species from such far @-@ flung <unk> as Portugal , Siberia , and Tanzania . 
- 
- = = <unk> about type specimen = = 
- 
- The issue of synonyms is complicated by the type specimen of Allosaurus fragillis ( catalogue number YPM 1930 ) being extremely fragmentary , consisting of a few incomplete vertebrae , limb bone fragments , rib fragments , and a tooth . Because of this , several scientists have noted that the type specimen , and thus the genus Allosaurus itself or at least the species A. fragillis , is technically a nomen dubium ( " dubious name " , based on a specimen too incomplete to compare to other specimens or to classify ) . In an attempt to fix this situation , Gregory S. Paul and Kenneth Carpenter ( 2010 ) submitted a petition to the <unk> to have the name A. fragillis officially transferred to the more complete specimen <unk> ( as a <unk> ) . This request is currently pending review . 
- 
- = = <unk> valid species = = 
- 
- It is unclear how many species of Allosaurus there were . Eight species have been considered potentially valid since 1988 ( A. amplexus , A. atrox , A. europaeus , the type species A. fragilis , the as @-@ yet not formally described " A. jimmadseni " , A. <unk> , A. maximus , and A. tendagurensis ) , although only about half are usually considered valid at any given time . There are also at least ten dubious or undescribed species that have been assigned to Allosaurus over the years , along with the species belonging to genera now sunk into Allosaurus . In the most recent review of basal tetanuran theropods , only A. fragilis ( including A. amplexus and A. atrox ) , " A. jimmadseni " ( as an unnamed species ) , and A. tendagurensis were accepted as potentially valid species , with A. europaeus not yet proposed and A. maximus assigned to Saurophaganax . 
- A. fragilis is the type species and was named by Marsh in 1877 . It is known from the remains of at least sixty individuals , all found in the Kimmeridgian – Tithonian Upper Jurassic @-@ age Morrison Formation of the United States , spread across the states of Colorado , Montana , New Mexico , Oklahoma , South Dakota , Utah , and Wyoming . Details of the humerus ( upper arm ) of A. fragilis have been used as diagnostic among Morrison theropods , but the discovery of " A. jimmadseni " indicates that this will no longer be the case at the species level . 
- A. amplexus was named by Gregory S. Paul for giant Morrison allosaur remains , and included in his conception <unk> maximus ( later Saurophaganax ) . A. amplexus was originally coined by Cope in 1878 as the type species of his new genus <unk> , and is based on what is now AMNH <unk> , parts of three vertebrae , a coracoid , and a <unk> . Following Paul 's work , this species has been accepted as a synonym of A. fragilis . 
- Allosaurus material from Portugal was first reported in 1999 on the basis of <unk> / <unk> , a partial skeleton including a <unk> , vertebrae , ribs , <unk> , chevrons , part of the <unk> , and hindlimbs . This specimen was assigned to A. fragilis , but the subsequent discovery of a partial skull and neck ( ML 415 ) near <unk> , in the Kimmeridgian @-@ age Porto Novo Member of the <unk> Formation , spurred the naming of the new species A. europaeus . It differs from other species of Allosaurus in cranial details . However , more material may show it to be A. fragilis , as originally described . 
- Daniel Chure 's work on Morrison <unk> remains has been responsible , directly or indirectly , for " A. jimmadseni " and A. maximus . " A. jimmadseni " is the proposed name for a new species of Morrison allosaur , based on a nearly complete skeleton and skull . A. sp . 2 , as it is also known , differs from A. fragilis in several anatomical details including a jugal or <unk> with a straight lower margin , and is also found only in the Salt <unk> Member of the Morrison Formation , with A. fragilis only present in the higher <unk> Basin Member . A. maximus was coined by David K. Smith for Chure 's Saurophaganax maximus , a taxon created by Chure in 1995 for giant <unk> remains from the Morrison of Oklahoma . These remains had been known as <unk> , but that name was already in use , leading Chure to propose a substitute . Smith , in his 1998 analysis of variation , concluded that S. maximus was not different enough from Allosaurus to be a separate genus , but did warrant its own species , A. maximus . This <unk> was rejected in the most recent review of basal <unk> . 
- 
- = = Biological variation , A. atrox , and A. fragilis = = 
- 
- The perception that there were two common Allosaurus species in the Morrison Formation was popularized in Gregory S. Paul 's 1988 book Predatory Dinosaurs of the World . Paul proposed that A. fragilis had tall pointed horns and a slender build compared to a postulated second species A. atrox , and was not a different gender due to rarity . Allosaurus atrox was originally named by Marsh in 1878 as the type species of its own genus , Creosaurus , and is based on YPM 1890 , an assortment of bones including a couple of pieces of the skull , portions of nine tail vertebrae , two hip vertebrae , an <unk> , and ankle and foot bones . Although the idea of two common Morrison allosaur species has had support in semi @-@ technical and popular works , it has generally been rejected in the technical literature . 
- David K. Smith , examining Allosaurus fossils by quarry , found that the Cleveland Lloyd Dinosaur <unk> ( Utah ) specimens are generally smaller than those from <unk> Bluff ( Wyoming ) or <unk> Young University 's Dry Mesa <unk> ( Colorado ) , but the shapes of the bones themselves did not vary between the sites . A later study by Smith incorporating Garden Park ( Colorado ) and Dinosaur National Monument ( Utah ) specimens found no justification for multiple species based on skeletal variation ; skull variation was most common and was <unk> , suggesting individual variation was responsible . Further work on size @-@ related variation again found no consistent differences , although the Dry Mesa material tended to clump together on the basis of the <unk> , an ankle bone . Kenneth Carpenter , using skull elements from the Cleveland Lloyd site , found wide variation between individuals , calling into question previous species @-@ level distinctions based such features as the shape of the horns , and the proposed differentiation of " A. jimmadseni " based on the shape of the jugal . 
- 
- = = <unk> and synonymous species = = 
- 
- A number of species assigned to Allosaurus are no longer recognized as valid , for one reason or another . Species " A. <unk> " , seen in <unk> , 1887 , and <unk> , 1912 , is a typographical error for A. fragilis . Marsh 's A. ferox ( 1896 ; not to be confused with his 1884 Labrosaurus ferox , also part of Allosaurus taxonomy ) was coined for a partial skull in a footnote , and has been recognized as a specimen of A fragilis . A. lucaris , another Marsh name , was given to a partial skeleton in 1878 . He later decided it warranted its own genus , Labrosaurus , but this has not been accepted , and A. lucaris is also regarded as another specimen of A. fragilis . Allosaurus lucaris , is known mostly from vertebrae , sharing characters with Allosaurus . Paul and Carpenter stated that the type specimen of this species , YPM 1931 , was from a younger age than Allosaurus , and might represent a different genus . However , they found that the specimen was <unk> , and thus A. lucaris was a nomen dubium . " A. <unk> " , an informally described species coined by Pickering in 1996 , is a <unk> of the A. atrox versus A. fragilis debate using a better specimen to represent the A. atrox form , and has not been recognized . 
- Several species coined in genera other than Allosaurus are also now thought to be synonymous with A. fragilis . Labrosaurus ferox was named in 1884 by Marsh for an oddly formed partial lower jaw , with a prominent gap in the tooth row at the tip of the jaw , and a rear section greatly expanded and turned down . Later researchers suggested that the bone was <unk> , showing an injury to the living animal , and that part of the unusual form of the rear of the bone was due to plaster reconstruction . It is recognized as most likely a specimen of A. fragilis . Allosaurus valens is a <unk> for <unk> valens accidentally used by Friedrich von <unk> in 1932 ; <unk> valens itself may also <unk> to Allosaurus fragilis , as Gilmore suggested in 1920 . <unk> <unk> , based on a scrap of <unk> Marsh first thought to be a mammalian jaw , may or may not be the same as Allosaurus . 
- 
- = = <unk> species = = 
- 
- Several species initially classified within or referred to Allosaurus do not belong within the genus . A. medius was named by Marsh in 1888 for " various specimens " from the Early Cretaceous of Maryland , although most of the remains were removed by Richard <unk> Lull to the new ornithopod species <unk> <unk> , except for a tooth . Gilmore considered the tooth <unk> but transferred it to a new species , <unk> medius . The referral was not accepted in the most recent review , and Allosaurus medius was simply listed as a dubious species of theropod . Allosaurus <unk> was described in 1914 by A. N. <unk> on the basis of a bone , later identified as a partial fourth <unk> , from the Early Cretaceous of <unk> , Russia . It was transferred to <unk> in 1990 . 
- Allosaurus <unk> was described in 1870 by <unk> as a species of Megalosaurus , based on a tooth from the Late Jurassic of Switzerland . It has occasionally been referred to Allosaurus , but recent reviews have listed it as dubious theropod species Megalosaurus <unk> , or included it in Ceratosaurus sp . Allosaurus stechowi was described in 1920 by <unk> as Labrosaurus stechowi for isolated Ceratosaurus @-@ like teeth from the <unk> beds of Tanzania . With the <unk> of Labrosaurus and Allosaurus , Donald F. <unk> listed it as a species of Allosaurus , but it is now either assigned to Ceratosaurus sp. or considered a dubious <unk> . 
- There are also several species left over from the <unk> of Creosaurus and Labrosaurus with Allosaurus . Creosaurus <unk> was named by Lull in 1911 for a <unk> from the Early Cretaceous of Maryland . It is now regarded as a dubious theropod . Labrosaurus fragilis is a typographical error by Marsh ( 1896 ) for Labrosaurus ferox . L. <unk> , named by Marsh in 1896 for a Morrison theropod tooth , which like L. stechowi is now regarded as either Ceratosaurus sp. or a dubious <unk> . 
- A. tendagurensis was named in 1925 by Werner <unk> for a partial <unk> ( <unk> 67 ) found in the Kimmeridgian @-@ age rocks of <unk> , in <unk> , Tanzania . This species has not had strong support in recent years , with opinions on its identity ranging from a tentatively valid species of Allosaurus , to a basal tetanuran . The most recent analysis has placed it in <unk> . Although obscure , it was a large theropod , possibly around 10 meters long ( 33 ft ) and 2 @.@ 5 metric tons ( 2 @.@ 8 short tons ) in weight . 
- 
- = = Specimens <unk> to Allosaurus though not described as new species = = 
- 
- <unk> and colleagues in 2003 designated six teeth from Siberia as Allosaurus sp . ( meaning the authors found the specimens to be most like those of Allosaurus , but did not or could not assign a species ) . Also , reports of Allosaurus in Shanxi , China go back to at least 1982 . 
- An <unk> ( ankle bone ) thought to belong to a species of Allosaurus was found at Cape Paterson , Victoria in Early Cretaceous beds in southeastern Australia . It was thought to provide evidence that Australia was a <unk> for animals that had gone extinct elsewhere . This identification was challenged by Samuel Welles , who thought it more resembled that of an <unk> , but the original authors defended their identification . With fifteen years of new specimens and research to look at , Daniel Chure <unk> the bone and found that it was not Allosaurus , but could represent an <unk> . Similarly , <unk> <unk> and Phil <unk> , in their description of <unk> , noted that the bone closely resembled that of their new genus . This specimen is sometimes referred to as " Allosaurus <unk> " , an informal museum name . It may have belonged to something similar to , or the same as , <unk> , or it may represent an <unk> . A speculative " polar " or " dwarf allosaur " was used for the " Spirits of the Ice Forest " episode of Walking with Dinosaurs . 
- 
- 
- = Astraeus hygrometricus = 
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- Astraeus hygrometricus , commonly known as the hygroscopic earthstar , the barometer earthstar , or the false earthstar , is a species of fungus in the Diplocystaceae family . Young specimens resemble a puffball when young and <unk> . In maturity , the mushroom displays the characteristic earthstar shape that is a result of the outer layer of fruit body tissue splitting open in a star @-@ like manner . The false earthstar is an ectomycorrhizal species that grows in association with various trees , especially in sandy soils . A. hygrometricus has a cosmopolitan distribution , and is common in temperate and tropical regions . Its common names refer to the fact that it is hygroscopic ( water @-@ absorbing ) , and can open up its rays to expose the spore sac in response to increased humidity , and close them up again in drier conditions . The rays have an irregularly cracked surface , while the spore case is pale brown and smooth with an irregular slit or tear at the top . The gleba is white initially , but turns brown and powdery when the spores mature . The spores are reddish @-@ brown , roughly spherical with minute warts , measuring 7 @.@ 5 – 11 <unk> in diameter . 
- Despite a similar overall appearance , A. hygrometricus is not related to the true earthstars of genus Geastrum , although historically , they have been <unk> confused . The species was first described by <unk> <unk> Persoon in 1801 as Geastrum hygrometricus . In 1885 , Andrew P. Morgan proposed that differences in microscopic characteristics warranted the creation of a new genus Astraeus distinct from Geastrum ; this opinion was not universally accepted by later authorities . Several Asian populations formerly thought to be A. hygrometricus were renamed in the 2000s once phylogenetic analyses revealed they were unique Astraeus species , including A. asiaticus and A. odoratus . Research has revealed the presence of several <unk> chemical compounds in the fruit bodies . North American field guides typically rate A. hygrometricus as inedible . 
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- = = Taxonomy , naming , and phylogeny = = 
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- Because this species resembles the earthstar fungi of Geastrum , it was placed in that genus by early authors , starting with Christian <unk> Persoon in 1801 ( as <unk> , an alternate spelling of Geastrum ) . According to the American botanist Andrew P. Morgan , however , the species differed from those of Geastrum in not having open chambers in the young gleba , having larger and branched <unk> threads , not having a true hymenium , and having larger spores . Accordingly , Morgan set Persoon 's <unk> <unk> as the type species of his new genus Astraeus in 1889 . Despite Morgan 's publication , some authorities in the following decades continued to classify the species in Geastrum . The New @-@ Zealand based mycologist Gordon <unk> Cunningham explicitly transferred the species back to the genus Geastrum in 1944 , explaining : 
- The treatment of this species by certain <unk> well illustrates the pitfalls that lie in wait for those who worship at the shrine of <unk> classification ... The only feature of those outlined in which the species differs from others of Geastrum is the somewhat primitive hymenium . In the developing plant the <unk> cavities are separated by <unk> plates so tenuous as to be overlooked by the <unk> worker . Each cavity is filled with basidia somewhat irregularly arranged in clusters ( like those of Scleroderma ) and not in the definite palisade of the species which have been studied . This difference disappears as maturity is reached , when plants resemble closely the <unk> of any other member of the genus . The <unk> is then unable to indicate any point of difference by which " Astraeus " may be separated from Geastrum , which indicates that the name should be discarded . 
- Cunningham 's treatment was not followed by later authorities , who largely considered Astraeus a distinct genus . According to the <unk> authority <unk> , synonyms of Astraeus hygrometricus include <unk> <unk> <unk> . ( 1772 ) ; Geastrum <unk> <unk> . ( 1822 ) ; Geastrum <unk> ( <unk> . ) <unk> . ( 1885 ) ; and Astraeus <unk> <unk> ( 1900 ) . 
- Astraeus hygrometricus has been given a number of <unk> names that allude to its hygroscopic behavior , including the " <unk> earthstar " , the " hygroscopic earthstar " , the " barometer earthstar " , and the " water @-@ measure earthstar " . The resemblance to Geastrum species ( also known as true earthstars ) accounts for the common name " false earthstar " . The specific name is derived from the Greek words <unk> ( <unk> ) " wet " and <unk> ( <unk> ) " measure " . The German Mycological Society selected the species as their " Mushroom of the Year " in 2005 . 
- Studies in the 2000s showed that several species from Asian collection sites labelled under the specific epithet hygrometricus were actually considerably variable in a number of <unk> and microscopic characteristics . Molecular studies of the DNA sequences of the <unk> region of the ribosomal DNA from a number of Astraeus specimens from around the world have helped to clarify phylogenetic relationships within the genus . Based on these results , two Asian " hygrometricus " populations have been described as new species : A. asiaticus and A. odoratus ( synonymous with <unk> 's A. <unk> described in 2003 ) . Preliminary DNA analyses suggests that the European A. hygrometricus described by Persoon is a different species than the North American version described by Morgan , and that the European population may be divided into two distinct <unk> , from France and from the Mediterranean . A 2010 study identified a Japanese species , previously identified as A. hygrometricus , as genetically distinct ; it has yet to be officially named . 
- A form of the species found in Korea and Japan , A. hygrometricus var. koreanus , was named by <unk> <unk> in 1958 ; it was later ( 1976 ) published as a distinct species — A. koreanus — by <unk> <unk> . As pointed out by <unk> and colleagues , clarification of the proper name for this taxon must await analysis of A. hygrometricus var. koreanus specimens from the type locality in North Korea . 
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- = = Description = = 
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- Young specimens of A. hygrometricus have roughly spherical fruit bodies that typically start their development partially embedded in the substrate . A smooth whitish mycelial layer covers the fruit body , and may be partially encrusted with debris . As the fruit body matures , the mycelial layer tears away , and the outer tissue layer , the exoperidium , breaks open in a star @-@ shaped ( <unk> ) pattern to form 4 – 20 irregular " rays " . This simultaneously pushes the fruit body above ground to reveal a round spore case enclosed in a thin <unk> <unk> . The rays open and close in response to levels of moisture in the environment , opening up in high humidity , and closing when the air is dry . This is possible because the exoperidium is made of several different layers of tissue ; the innermost , fibrous layer is hygroscopic , and <unk> or <unk> the entire ray as it loses or gains moisture from its surroundings . This adaptation enables the fruit body to disperse spores at times of <unk> moisture , and reduce evaporation during dry periods . Further , dry fruit bodies with the rays curled up may be readily blown about by the wind , allowing them to scatter spores from the pore as they roll . 
- The fruit body is 1 – 8 cm ( 0 @.@ 4 – 3 @.@ 1 in ) in diameter from tip to tip when expanded . The exoperidium is thick , and the rays are typically <unk> ( divided into small areas by cracks and crevices ) on the upper surface , and are dark grey to black . The spore case is <unk> ( lacking a stalk ) , light gray to tan color and 1 to 3 cm ( 0 @.@ 4 to 1 @.@ 2 in ) broad with a felt @-@ like or <unk> ( coated with loose scaly crust ) surface ; the top of the spore case is opened by an irregular slit , tear or pore . The interior of the spore case , the gleba , is white and solid when young , and divided into oval <unk> — a characteristic that helps to distinguish it from Geastrum . The gleba becomes brown and powdery as the specimen matures . Small dark <unk> threads ( rhizomorphs ) extend from the base of the fruit body into the substrate . The rhizomorphs are fragile , and often break off after maturity . 
- The spores are spherical or nearly so , reddish @-@ brown , thick @-@ walled and <unk> ( covered with warts and spines ) . The spores ' dimensions are 7 – 11 µm ; the warts are about 1 µm long . The spores are non @-@ amyloid , and will not stain with iodine from Melzer 's reagent . The use of scanning electron microscopy has shown that the spines are 0 @.@ 90 – 1 @.@ 45 µm long , rounded at the tip , narrow , tapered , and sometime joined together at the top . The capillitia ( masses of thread @-@ like sterile fibers dispersed among the spores ) are branched , 3 @.@ 5 – 6 @.@ 5 µm in diameter , and hyaline ( translucent ) . The basidia ( spore @-@ bearing cells ) are <unk> to eight @-@ spored , with very short sterigmata . The basidia are arranged in long strings of clusters ; individual basidia measure 11 – 15 by 18 – 24 µm . The threads of the capillitia arise from the inner surface of the peridium , and are thick @-@ walled , long , interwoven , and branched , measuring 3 – 5 @.@ 5 µm thick . The exoperidium ( the outer layer of tissue , comprising the rays ) is made of four distinct layers of tissue : the mycelial layer contains branched hyphae that are 4 – 6 μm in diameter ; the hyphae of the fibrous layer are 6 – 8 μm diameter and branched ; the <unk> @-@ type layer has branched hyphae of 3 – 4 μm diameter ; the soft layer contains hyphae that are 3 – 6 μm in diameter . 
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- = = = Edibility = = = 
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- North American sources describe A. hygrometricus as being of either unknown edibility , or too tough to be edible . However , they are regularly consumed in Asia , including Nepal and South Bengal , where " local people consume them as delicious food " . They are collected from the wild and sold in the markets of India . 
- A study of a closely related southeast Asian Astraeus species concluded that the fungus contained an abundance of volatile eight @-@ carbon compounds ( including 1 @-@ <unk> , 1 @-@ <unk> @-@ 3 @-@ <unk> , and 1 @-@ <unk> @-@ 3 @-@ one ) that imparted a " mushroom @-@ like , <unk> , and pungent odor that was evident as an <unk> and moss @-@ like smell upon opening the caps " . The study 's authors further noted that the fruit bodies after cooking have a " roasted , <unk> , <unk> , and <unk> flavor " . <unk> compounds detected after cooking the mushroom samples included <unk> , <unk> , <unk> , and <unk> compounds . The regional differences in opinions on edibility are from sources published before it was known that North American and Asian versions of A. hygrometricus were not always the same ; in some cases Asian specimens have been identified as new species , such as A. asiaticus and A. odoratus . 
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- = = Similar species = = 
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- Although A. hygrometricus bears a superficial resemblance to member of the " true earthstars " Geastrum , it may be readily differentiated from most by the hygroscopic nature of its rays . <unk> earthstars include G. <unk> , G. <unk> , G. <unk> , G. <unk> , and G. <unk> . Unlike Geastrum , the young fruit bodies of A. hygrometricus do not have a <unk> ( sterile tissue in the gleba , at the base of the spore sac ) . Geastrum tends to have its spore sac opening surrounded by a <unk> or a disc , in contrast with the single <unk> slit of A. hygrometricus . There are also several microscopic differences : in A. hygrometricus , the basidia are not arranged in parallel columns , the spores are larger , and the threads of the capillitia are branched and continuous with the hyphae of the peridium . Despite these differences , older specimens can be difficult to distinguish from Geastrum in the field . One species of Geastrum , G. <unk> , does have thick and brittle rays that are moderately hygroscopic , and could be confused with A. hygrometricus ; however , its spores are smaller than A. hygrometricus , typically about 4 µm in diameter . 
- Astraeus <unk> is larger , 5 to 15 cm ( 2 @.@ 0 to 5 @.@ 9 in ) or more when expanded , and often has a more pronounced <unk> pattern on the inner surface of the rays . It is found in North America and the Canary Islands . A. asiaticus and A. odoratus are two similar species known from throughout Asia and Southeast Asia , respectively . A. odoratus is distinguished from A. hygrometricus by a smooth outer mycelial layer with few adhering soil particles , 3 – 9 broad rays , and a fresh odor similar to moist soil . The spore ornamentation of A. odoratus is also distinct from A. hygrometricus , with longer and narrower spines that often joined together . A. asiaticus has an outer <unk> surface covered with small granules , and a gleba that is purplish @-@ chestnut in color , compared to the smooth <unk> surface and brownish gleba of A. hygrometricus . The upper limit of the spore size of A. asiaticus is larger than that of its more common relative , ranging from 8 @.@ 75 – 15 @.@ 2 μm . A. koreanus ( sometimes named as the variety A. hygrometricus var. koreanus ; see Taxonomy ) differs from the more common form in its smaller size , paler fruit body , and greater number of rays ; microscopically , it has smaller spores ( between 6 @.@ 8 and 9 μm in diameter ) , and the spines on the spores differ in length and morphology . It is known from Korea and Japan . 
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- = = Habitat , distribution , and ecology = = 
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- Astraeus hygrometricus is an ectomycorrhizal fungus and grows in association with a broad range of tree species . The mutualistic association between tree roots and the mycelium of the fungus helps the trees extract nutrients ( particularly phosphorus ) from the earth ; in exchange , the fungus receives <unk> from <unk> . In North America , associations with oak and pine are usual , while in India , it has been noted to grow commonly with <unk> pine ( Pinus <unk> ) and <unk> ( <unk> <unk> ) . The false earthstar is found on the ground in open fields , often scattered or in groups , especially in nutrient @-@ poor , sandy or <unk> soils . It has also been reported to grow on rocks , preferring acid substrates like slate and granite , while avoiding substrates rich in lime . In Nepal , fruit bodies have been collected at elevations of 3 @,@ 000 m ( 9 @,@ 800 ft ) . Fruit bodies typically appear in autumn , although the dry fruit bodies are persistent and may last up to several years . <unk> <unk> is a <unk> fungus with minute , gelatinous , <unk> ( cushion @-@ shaped ) <unk> , known to grow only on the inner surface of the rays of dead Astraeus species , including A. hygrometricus . 
- The species has a cosmopolitan distribution except for <unk> , alpine and cold temperate regions ; it is common in temperate and tropical regions of the world . It has been collected in Africa , Asia , Australia , Europe , North America , and South America . 
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- = = <unk> compounds = = 
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- Mushroom <unk> from a number of species have attracted research interest for their <unk> and <unk> properties . <unk> from A. hygrometricus containing the <unk> named AE2 were found to inhibit the growth of several tumor cell lines in laboratory tests , and stimulated the growth of <unk> , <unk> , and bone marrow cells from mice . The extract also stimulated mouse cells associated with the immune system ; specifically , it enhanced the activity of mouse natural killer cells , stimulated macrophages to produce nitric oxide , and enhanced production of <unk> . The activation of macrophages by AE2 might be mediated by a <unk> @-@ activated protein kinase pathway of signal transduction . AE2 is made of the simple <unk> <unk> , glucose , and <unk> in a 1 : 2 : 1 ratio . 
- In addition to the previously known steroid compounds <unk> @-@ 7 @,@ 22 @-@ <unk> @-@ 3 @-@ <unk> acetate and <unk> @-@ <unk> ( 14 ) , 22 @-@ <unk> @-@ 3 @-@ one , three unique <unk> — derivatives of 3 @-@ <unk> @-@ <unk> — have been isolated from fruit bodies of A. hygrometricus . The compounds , named <unk> , 3 @-@ <unk> @-@ <unk> , and <unk> ( 3 @-@ <unk> @-@ <unk> @-@ <unk> @-@ 8 @-@ <unk> @-@ 26 @,@ 22 @-@ lactone ) , have <unk> @-@ lactone ( a six @-@ membered ring ) in the side chain — a chemical feature previously unknown in the <unk> . A previously unknown <unk> <unk> ( <unk> , <unk> @-@ <unk> ( <unk> , <unk> ) <unk> @-@ 7 @,@ 22 @-@ <unk> @-@ <unk> @-@ <unk> <unk> ) has been isolated from mycelia grown in liquid culture . The compound has a <unk> <unk> @-@ type nucleus . 
- <unk> <unk> of the fruit body are high in <unk> activity , and have been shown in laboratory tests to have anti @-@ inflammatory activity comparable to the drug <unk> . Studies with mouse models have also demonstrated <unk> ( liver @-@ protecting ) ability , possibly by restoring diminished levels of the <unk> enzymes <unk> <unk> and <unk> caused by experimental exposure to the liver @-@ damaging chemical carbon <unk> . 
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- = = Traditional beliefs = = 
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- This earthstar has been used in traditional Chinese medicine as a <unk> agent ; the spore dust is applied <unk> to stop wound bleeding and reduce <unk> . Two Indian forest tribes , the <unk> and the <unk> of <unk> Pradesh , have been reported to use the fruit bodies <unk> . The spore mass is blended with <unk> seed oil , and used as a <unk> against burns . The Blackfoot of North America called the fungus " fallen stars " , considering them to be stars fallen to the earth during supernatural events . 
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- = Paul Thomas Anderson = 
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- Paul Thomas Anderson ( born June 26 , 1970 ) also known as <unk> Anderson , is an American film director , screenwriter and producer . <unk> in film @-@ making at a young age , Anderson was encouraged by his father Ernie Anderson ( a disc jockey , and television and radio announcer / <unk> artist ) to become a filmmaker . 
- In 1993 , he wrote and directed a short film titled <unk> & Coffee on a budget of $ 20 @,@ 000 . After he attended the Sundance Institute , Anderson had a deal with Rysher Entertainment to direct his first feature film , a neo @-@ noir crime thriller titled Hard Eight , in 1996 . Anderson received critical and commercial success for his film Boogie Nights ( 1997 ) , set during the Golden Age of <unk> in the 1970s and 1980s . His third feature , Magnolia ( 1999 ) , received wide acclaim despite struggling at the box office . 
- In 2002 , the romantic comedy @-@ drama Punch @-@ Drunk Love , Anderson 's fourth feature , was released to generally favorable reviews . After a five @-@ year absence , the epic drama There Will Be Blood was released to critical acclaim in 2007 . In 2012 , Anderson 's sixth film , the drama The Master , was released to critical acclaim . His seventh film , the crime comedy @-@ drama Inherent Vice , based on the novel of the same name by Thomas Pynchon , was released in 2014 , to general acclaim . 
- Anderson has been nominated for six Academy Awards over the course of his career , while his films have earned a further fourteen Academy Award nominations for cast and crew . 
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- = = Early life = = 
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- Anderson was born June 26 , 1970 , in Studio City , California , to Edwina ( née <unk> ) and Ernie Anderson . Ernie was an actor who was the voice of ABC and a Cleveland television late @-@ night horror movie host known as " <unk> " ( after whom Anderson later named his production company ) . Anderson grew up in the San Fernando Valley . He is third youngest of nine children , and had a troubled relationship with his mother but was close with his father , who encouraged him to become a writer or director . Anderson attended a number of schools , including Buckley in Sherman Oaks , John Thomas <unk> School , Campbell Hall School , Cushing Academy and <unk> <unk> . 
- Anderson was involved in filmmaking at a young age and never really had an alternative plan to directing films . He made his first movie when he was eight years old and started making movies on a <unk> video camera which his dad bought in 1982 when he was twelve years old . He later started using 8 mm film but realized that video was easier . He began writing in adolescence , and at 17 years old he began experimenting with a <unk> sixteen millimeter camera . After years of experimenting with " standard fare " , he wrote and filmed his first real production as a senior in high school at <unk> <unk> using money he earned cleaning <unk> at a pet store . The film was a thirty @-@ minute <unk> shot on video called The Dirk <unk> Story ( 1988 ) , about a pornography star ; the story was inspired by John Holmes , who also served as a major inspiration for Boogie Nights . 
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- = = Career = = 
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- = = = Early career = = = 
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- Anderson spent two <unk> as an English major at Emerson College , and only two days at New York University before he began his career as a production assistant on television films , music videos and game shows in Los Angeles and New York City . Feeling that the material shown to him at film school turned the experience into " <unk> or a <unk> " , Anderson decided to make a twenty @-@ minute film that would be his " college " . 
- For $ 20 @,@ 000 , made up of gambling <unk> , his girlfriend 's credit card , and money his father set aside for him for college , Anderson made <unk> & Coffee ( 1993 ) , a short film connecting multiple story lines with a twenty @-@ dollar bill . The film was screened at the 1993 Sundance Festival Shorts Program . He decided to expand the film into a feature @-@ length film and was subsequently invited to the 1994 Sundance Feature Film Program . At the Sundance Feature Film Program , Michael <unk> @-@ Jones served as Anderson 's mentor ; he saw Anderson as someone with " talent and a fully formed creative voice but not much hands @-@ on experience " and gave him some hard and practical lessons . 
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- = = = 1990s = = = 
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- = = = = Hard Eight = = = = 
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- While at the Sundance Feature Film Program , Anderson already had a deal with Rysher Entertainment to direct his first feature . In 1996 , Anderson made his first full @-@ length feature , Sydney , which was retitled Hard Eight ( 1996 ) . Upon completion of the film , Rysher re @-@ edited it . Anderson , who still had the <unk> of his original cut , submitted the film , which was accepted and screened in the Un Certain Regard section at the 1996 Cannes Film Festival . Anderson was able to get his version released but only after he retitled the film and raised the $ 200 @,@ 000 necessary to finish it - he , Philip Baker Hall , <unk> <unk> and John C. Reilly contributed the funding . The version that was released was Anderson 's and the acclaim from the film launched his career . 
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- = = = = Boogie Nights = = = = 
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- Anderson began working on the script for his next feature film during his troubles with Hard Eight , completing the script in 1995 . The result was Anderson 's breakout for the drama film Boogie Nights ( 1997 ) , which is based on his short The Dirk <unk> Story . The script was noticed by New Line Cinema 's president , Michael De Luca , who felt " totally <unk> " reading it . It was released on October 10 , 1997 and was a critical and commercial success . The film revived the career of Burt Reynolds , and provided breakout roles for Mark <unk> and Julianne Moore . At the 70th Academy Awards ceremony , the film received three Academy Award nominations , including for Best Supporting Actor ( Burt Reynolds ) , Best Supporting Actress ( Julianne Moore ) and Best Original Screenplay . 
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- = = = = Magnolia = = = = 
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- After the success of Boogie Nights , New Line told Anderson that he could do whatever he wanted for his next film and granted him creative control . Though Anderson initially wanted to make a film that was " intimate and small @-@ scale " , the script " kept <unk> " . The resulting film was the ensemble piece Magnolia ( 1999 ) , which tells the story of the peculiar interaction of several individuals in the San Fernando Valley . Anderson used the music of Aimee Mann as a basis and inspiration for the film , commissioning her to write eight new songs . At the 72nd Academy Awards , Magnolia received three nominations , for Best Actor in a Supporting Role ( Tom Cruise ) , Best Original Song for " Save Me " by Aimee Mann and Best Original Screenplay . Anderson stated after the film 's release that " what I really feel is that Magnolia is , for better or worse , the best movie I 'll ever make . " 
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- = = = 2000s = = = 
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- = = = = Punch @-@ Drunk Love = = = = 
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- After the release of Magnolia , Anderson stated that he would like to work with comedic actor Adam Sandler in the future and that he was determined to make his next film 90 minutes long . His next feature was the romantic comedy @-@ drama film Punch @-@ Drunk Love ( 2002 ) , starring Sandler , with Emily Watson portraying his love interest . The story centers on a <unk> small @-@ business owner ( Sandler ) with anger issues and seven <unk> sisters . A subplot in the film was partly based on David Phillips ( also called The Pudding Guy ) . Sandler received critical praise for his role in his first major departure from the mainstream comedies that had made him a star . At the 2002 Cannes Film Festival , Anderson won the Best Director Award and was nominated for the <unk> d 'Or . 
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- = = = = There Will Be Blood = = = = 
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- There Will Be Blood ( 2007 ) was loosely based on the <unk> Sinclair novel Oil ! . The budget of the film was $ 25 million , and it earned $ 76 @.@ 1 million worldwide . Daniel Day @-@ Lewis starred and won an Oscar for Best Leading Actor for his role . The film received eight nominations overall at the 80th Academy Awards . Paul <unk> received a BAFTA nomination for Best Supporting Actor . Anderson was nominated for Best Director from the Directors Guild of America . The film also received eight Academy Award nominations , tying with No Country for Old Men for the most nominations . Anderson received nominations for Best Picture , Best Director and Best Adapted Screenplay , losing all three to the <unk> Brothers for No Country for Old Men . There Will Be Blood was regarded by some critics as one of the greatest films of the decade , some parties further declaring it one of the most accomplished American films of the modern era ; David <unk> of The New Yorker wrote " the young writer @-@ director Paul Thomas Anderson has now done work that bears comparison to the greatest achievements of Griffith and Ford " , while Richard <unk> proclaimed it " one of the most wholly original American movies ever made " . 
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- = = = 2010s = = = 
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- = = = = The Master = = = = 
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- In December 2009 , Anderson was working on a new script tentatively titled The Master , about a " charismatic intellectual " who starts a new religion in the 1950s . An associate of Anderson stated that the idea for the film had been in Anderson 's head for about twelve years . Though the film makes no reference to the movement , it has " long been widely assumed to be based on Scientology . " The Master was released on September 14 , 2012 by The Weinstein Company in the United States and Canada to critical acclaim . The film received three nominations at the 85th Academy Awards : <unk> Phoenix for Best Leading Actor , Philip Seymour Hoffman for Best Supporting Actor and Amy Adams for Best Supporting Actress . 
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- = = = = Inherent Vice = = = = 
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- Production of Anderson 's adaptation of Thomas Pynchon 's 2009 novel Inherent Vice began in May 2013 and ended in August of the same year . The film marked the first time that Pynchon allowed his work to be adapted for the screen and saw Anderson work with Phoenix for a second time . The supporting cast includes Owen Wilson , Reese Witherspoon , <unk> Malone , Martin Short , <unk> Del Toro , Katherine <unk> , Josh <unk> , Peter <unk> , Michael K. Williams and Eric Roberts . The film received two nominations at the 87th Academy Awards : Anderson for Best Adapted Screenplay and Mark Bridges for Best Costume Design . 
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- = = = = Junun = = = = 
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- In 2015 , Anderson directed a 54 @-@ minute documentary , Junun , about the making of an album of the same name by Jonny Greenwood , Israeli composer <unk> Ben <unk> and a group of Indian musicians . Most of the performances were recorded at the 15th @-@ century <unk> Fort in the Indian state of Rajasthan . Junun premiered at the 2015 New York Film Festival . 
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- = = = = Future projects = = = = 
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- Anderson is currently working on a drama about the New York fashion industry in the 1950s , which is expected to star Daniel Day @-@ Lewis in his first acting role since Lincoln in 2012 . 
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- = = = Other work = = = 
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- Anderson was a standby director during the 2005 filming of Robert Altman 's A Prairie Home Companion for insurance purposes , as Altman was 80 years old at the time . In addition to films , Anderson has directed several music videos , including several for musician Fiona Apple . In 2008 , Anderson co @-@ wrote and directed a 70 @-@ minute play at the <unk> Theatre , comprising a series of vignettes starring Maya Rudolph and Fred <unk> , with a live musical score by Jon <unk> . 
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- = = Influences and style = = 
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- = = = Influences = = = 
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- Anderson only attended film school for two days , preferring to learn the craft by watching films by the filmmakers he liked , as well as watching films accompanied by director 's audio commentary . Anderson has cited Martin Scorsese , Robert Altman , Jonathan <unk> , Stanley Kubrick , Orson Welles and Max <unk> , as his main influences as a filmmaker . 
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- = = = Themes and style = = = 
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- Anderson is known for films set in the San Fernando Valley with realistically flawed and desperate characters . Among the themes dealt with in Anderson 's films are dysfunctional familial relationships , alienation , surrogate families , regret , loneliness , destiny , the power of forgiveness , and ghosts of the past . Anderson makes frequent use of repetition to build emphasis and thematic consistency . In Boogie Nights , Magnolia , Punch Drunk Love and The Master , the phrase " I didn 't do anything " is used at least once , developing themes of responsibility and denial . Anderson 's films are known for their bold visual style which includes stylistic <unk> such as constantly moving camera , <unk> @-@ based long takes , memorable use of music , and <unk> <unk> imagery . Anderson also tends to reference the Book of Exodus , either explicitly or subtly , such as in recurring references to Exodus 8 : 2 in Magnolia , which chronicles the plague of frogs , culminating with the literal <unk> of frogs in the film 's climax , or the title and themes in There Will Be Blood , a phrase that can be found in Exodus 7 : 19 , which details the plague of blood . 
- Within his first three films , Hard Eight , Boogie Nights and Magnolia , Anderson explored themes of dysfunctional families , alienation and loneliness . Boogie Nights and Magnolia were noted for their large ensemble casts , which Anderson returned to in Inherent Vice . In Punch @-@ Drunk Love , Anderson explored similar themes but expressed a different visual style , shedding the influences and references of his earlier films , being more surreal and having a heightened sense of reality . It was also short , compared to his previous two films , at 90 minutes . 
- There Will Be Blood stood apart from his first four films but shared similar themes and style such as flawed characters , moving camera , memorable music , and a lengthy running time . The film was more overtly engaged with politics than his previous films had been , examining capitalism and themes such as <unk> , optimism , and obsession . The Master dealt with " ideas about American personality , success , <unk> , master @-@ disciple dynamics , and father @-@ son mutually assured destruction . " All of his films deal with American themes with business versus art in Boogie Nights , ambition in There Will Be Blood , self @-@ <unk> in The Master . 
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- = = = Frequent collaborators = = = 
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- Anderson frequently <unk> with many actors and crew , carrying them over from film to film . Anderson has referred to his regular actors as " my little <unk> company " that has included John C. Reilly , Philip Baker Hall , Julianne Moore , William H. <unk> , <unk> Walters , and most prominently , the late Philip Seymour Hoffman . Luis <unk> is also considered an Anderson regular . Hoffman acted in Anderson 's first four films as well as The Master . Except for Paul F. Tompkins , Kevin <unk> , and Jim <unk> , who all had equally minor roles in Magnolia , There Will Be Blood had an entirely new cast . Robert <unk> has been cinematographer for all of Anderson 's films except The Master which was shot by Mihai <unk> Jr . Jon <unk> served as composer for Hard Eight , Magnolia , and Punch @-@ Drunk Love , and Jonny Greenwood of <unk> for There Will Be Blood , The Master , and Inherent Vice . Anderson also regularly works with producing partners <unk> <unk> , Scott <unk> , Michael De Luca , and Daniel <unk> as well as casting director Cassandra <unk> . 
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- = = Personal life = = 
- 
- Anderson dated ( and frequently collaborated with ) singer Fiona Apple for several years during the late 1990s and early 2000s . He has been in a relationship with actress and comedian Maya Rudolph since 2001 . They live together in the San Fernando Valley with their four children : daughters Pearl Bailey ( born October 2005 ) , Lucille ( born November 2009 ) , and Minnie Ida ( born August 2013 ) and son Jack ( born July 2011 ) . 
- 
- = = Filmography = = 
- 
- 
- = = Awards and recognition = = 
- 
- Anderson has been called " one of the most exciting talents to come along in years " and " among the supreme talents of today . " After the release of Boogie Nights and Magnolia , Anderson was praised as a <unk> . In his 2002 interview with Jan <unk> , the director <unk> <unk> referenced Magnolia as an example of the strength of American cinema . In 2004 , Anderson was ranked twenty @-@ first on The Guardian 's list of the forty best living filmmakers . In 2007 , Total Film named him the twentieth greatest director of all time and the American Film Institute regarded him as " one of American film 's modern masters . " In 2012 , The Guardian ranked him number one on its list of " The 23 Best Film Directors in the World , " writing " his dedication to his craft has intensified , with his disdain for PR and celebrity marking him out as the most devout filmmaker of his generation . " In 2013 , Entertainment Weekly named him the eighth @-@ greatest working director , calling him " one of the most dynamic directors to emerge in the last 20 years . " In a podcast interview with critic Elvis Mitchell , director Sam <unk> referred to Anderson as " a true <unk> – and there are very few of those who I would classify as <unk> " , and Ben Affleck in his acceptance speech for the Golden Globe Award for Best Director said " Paul Thomas Anderson , who I think is like Orson Welles . " Peter Travers of Rolling Stone wrote that " The Master , the sixth film from the 42 @-@ year @-@ old writer @-@ director , affirms his position as the foremost filmmaking talent of his generation . Anderson is a rock star , the artist who knows no limits . " As of 2016 , Anderson is the only person to win all three director prizes from the three major international film festivals ( Cannes , Berlin , Venice ) . 
- 
- 
- = The Fox , the Wolf and the Husbandman = 
- 
- The Fox , the Wolf and the Husbandman is a poem by the 15th @-@ century Scottish poet Robert Henryson and part of his collection of moral fables known as the <unk> <unk> of <unk> the Phrygian . It is written in Middle Scots . As with the other tales in the collection , <unk> to it is a moralitas which <unk> on the moral that the fable is supposed to contain . However , the <unk> of the moralitas for the tale itself has been questioned . 
- The tale combines two motifs . Firstly , a husbandman <unk> the fields with his new oxen makes a rash oath aloud to give them to the wolf ; when the wolf <unk> this , he attempts to make sure that the man <unk> his promise . The fox <unk> a solution by speaking to them individually ; eventually he fools the wolf into following him to claim his supposed reward for dropping the case , and tricks him into a draw @-@ well . The moralitas connects the wolf to the wicked man , the fox to the devil , and the husbandman to the godly man . A probable source for the tale is Petrus Alfonsi 's Disciplina <unk> , containing the same motifs , and William Caxton 's Aesop 's <unk> — though the tale is a beast fable , not Aesopic . 
- 
- = = Source = = 
- 
- A probable source of the tale is Petrus Alfonsi 's Disciplina <unk> , which has the same three motifs : the rash promise of the husbandman ; the wolf <unk> the moon for cheese ; and the wolf that descends into the well via a bucket , thereby trapping himself and freeing the fox . However , the discussion of legality and the questioning of language that take place alongside these motifs are entirely Henryson 's invention . Whereas the moral of Alfonsi 's tale explains that the wolf lost both the oxen and the cheese because he " relinquished what was present for what was to come " ( Latin : pro <unk> <unk> <unk> <unk> <unk> ) , Henryson 's moralitas more fully involves the husbandman . 
- Another source may be Aesop 's <unk> as published by William Caxton — scholar John <unk> considers this more likely than Disciplina <unk> — although the tale itself is not Aesopic but rather of the beast fable ( also beast @-@ epic ) genre . The plots of such works are more complicated than their Aesopic counterpart , tend more towards <unk> , and feature the fox making a victim of the wolf . 
- 
- = = Synopsis = = 
- 
- 
- = = = Tale = = = 
- 
- A husbandman <unk> the fields with his new , untrained oxen is made furious by their <unk> of the land . In his anger he makes the rash oath that the wolf " <unk> have you all at <unk> ! [ may , at once ] " . However , the wolf is lying nearby with the fox , and , <unk> it , promises to make him stay true to his word . Eventually the oxen calm down , but on the way back home the wolf jumps into their path . The wolf asks where the husbandman is driving them , since they are not his , to which he confirms that they are and asks why he is being stopped since he never offended the wolf before . The wolf reminds the husbandman of his earlier declaration , to which he replies that a man may say things that do not mean anything . They argue , and the husbandman <unk> the wolf for not having a witness ; in response , he produces the fox . The creature takes it upon himself to mediate the dispute , and takes each aside in turn . To the husbandman he says that he would lend his expertise to help him were it not for the " grit <unk> and <unk> " of doing so ; the husbandman offers him half a dozen of the <unk> <unk> he has , to which the fox <unk> and goes off . To the wolf he says that the husbandman has offered an unparalleled block of cheese in exchange for him dropping the case . 
- The wolf , after some complaint , agrees to this and the two proceed through the woods after the prize — all the while the fox considers how to trick the wolf . Eventually , as the wolf complains of the <unk> of their quest , they arrive at a draw @-@ well with buckets on each end of a rope . Seeing the reflection of the moon in the water at the bottom of the well , the wolf believes there to be cheese down there and lowers the fox down to pick it up . When he complains that it is too heavy for him to lift alone , the wolf jumps into the other bucket and descends to help . However , this pulls up the other bucket , into which the fox has jumped , and so the two swap places ; the wolf at the bottom of the well and the fox safely escaped . The narrator <unk> that he does not know who helped the wolf out of the well , but that the tale is at an end . 
- 
- = = = <unk> = = = 
- 
- The wolf is likened to a wicked man who <unk> others . The fox is likened to the devil . The farmer is likened to the godly man , with whom the <unk> finds fault . The woods where the wolf was cheated are <unk> goods that man longs to get . The cheese represents covetousness ; the well that contains it is fraud and fantasy , which draws men downwards into hell . 
- 
- = = Analysis = = 
- 
- As with other tales in the collection , the moralitas of The Fox , the Wolf and the Husbandman can be considered at odds with the tale itself . <unk> Farber highlights a number of these discrepancies , and says that the allegory " does not hold true in any traditional sense " . Amongst the inconsistencies is that the fox , not the wolf , is the figure that argues with and finds fault in the husbandman ; the " woods of the world " are not <unk> by the husbandman , in spite of the moralitas suggesting it is applicable to all men ; Farber argues that even assuming the moral to be true is problematic , since it apparently suggests that the godly man must <unk> the figure of the judge , and that this does not affect his godly status . Furthermore , the absence of the legal discussion and the binding quality of words from the moralitas suggests to Farber that the " intricate legal framework … has no impact whatsoever in resolving the issues with which it is supposed to deal " . In contrast , <unk> M. Bright considers that the moralitas of this tale , as well as several others , create " an additional sense which co @-@ exists with the literal narrative and extends and <unk> it thematically " ; treating literal details symbolically and establishing the sense through direct comparisons . 
- According to Dorothy Yamamoto , the significant themes in the tale are " <unk> and vacancy , substance and illusion " . The cheese that apparently resides in the well is only an illusion , not a solid object , and similarly the fox creates a surface reconciliation between the wolf and the husbandman , but which betrays his real intentions . Through their frequent misuse , words that should convey real value are emptied of meaning . As an example , Yamamoto highlights the fox 's <unk> on which the wolf and husbandman make their pledge — which body part she says is used by the fox in other tales to blind his <unk> , and is thereby a highly inappropriate object to use . 
- 
- = = = Modern edition = = = 
- 
- Henryson , Robert ( 2009 ) . The Testament of <unk> & seven fables . <unk> by Seamus Heaney . London : <unk> and <unk> . ISBN <unk> . 
- 
- 
- = Joe Nathan = 
- 
- Joseph Michael " Joe " Nathan ( born November 22 , 1974 ) is an American professional baseball pitcher for the Chicago Cubs of Major League Baseball ( MLB ) . Nathan started out his baseball career as a shortstop in high school and while at Stony Brook University , but converted to a pitcher after being drafted by the San Francisco Giants . He worked his way through the minor leagues , alternating between spots in the rotation and the bullpen . After a few years of splitting time between the majors and the minors , Nathan had a breakout season as a setup man for the Giants in 2003 . That offseason , Nathan was traded to the Minnesota Twins and became their closer . 
- From 2004 to 2009 , Nathan was considered one of the top <unk> in MLB with four All @-@ Star appearances and a league @-@ leading <unk> saves . In 2010 , Nathan underwent Tommy John surgery to repair a torn ulnar collateral ligament in his throwing elbow and missed the entire season . On April 3 , 2011 , Nathan recorded his first save since his injury against the Toronto Blue Jays and later that year in July , Nathan regained the role as closer . On August 10 , 2011 , he became the Twins all @-@ time leader in saves with his <unk> in a game against the Boston Red Sox . After the 2011 season , Nathan left the Twins via free agency to sign with the Texas Rangers , becoming an All @-@ Star again in 2012 and 2013 . On April 8 , 2013 , he earned his 300th save . After the 2013 season , Nathan left the Rangers via free agency to sign with the Detroit Tigers . Nathan is currently 8th on the all @-@ time saves list . 
- 
- = = Early career = = 
- 
- Nathan graduated from Pine Bush High School in Pine Bush , New York in 1992 , where he played basketball and baseball and ran track . Only Division III colleges showed minimal interest in him , and he ended up at Stony Brook University largely because his high school assistant coach Jeff <unk> and Stony Brook baseball coach Matt <unk> knew each other as former teammates in the State University of New York at Cortland baseball program . 
- 
- = = College career = = 
- 
- He first played shortstop for the then Division III Stony Brook <unk> ( now Division I and called the <unk> ) , at Stony Brook University in Long Island , New York . Nathan became a two @-@ time Academic All @-@ American and graduating as a member of the Golden Key International Honour Society . During his tenure there , professional baseball scouts began to notice his good arm and pitcher 's body , and on the day of a <unk> , unfortunately , " literally someone from every organization " came to watch him pitch . He was drafted in the sixth round ( <unk> overall ) of the amateur draft by the San Francisco Giants in 1995 , and signed the next day , June 2 . His college jersey number has since been retired , and he was awarded the University Medal , the highest recognition given by <unk> / Stony Brook . He also played for the Fairfield <unk> in the New England Collegiate Baseball League in 1994 . 
- In August 2008 , he gave the <unk> / Stony Brook athletics department $ 500 @,@ 000 for a new baseball facility . In recognition of this " lead gift " from the Joe Nathan Charitable Foundation , the college named it " Joe Nathan Field . " 
- 
- = = Professional career = = 
- 
- 
- = = = Minor <unk> = = = 
- 
- He began his minor league career in Class A for the <unk> Giants . After an unsuccessful year at the plate the Giants tried to convert Nathan into a pitcher , but he refused and left to return to Stony Brook for a year , graduating with a degree in business management . He gave more thought to his future in baseball , however , and after graduation decided to return to the Giants organization and developed into a standout pitching prospect . After a season with the Salem @-@ <unk> <unk> , he pitched for both the A and AA levels for ( the San Jose Giants and Shreveport Captains ) in 1998 as a starter . During his tenure with San Jose he started 22 games with an ERA of 3 @.@ 32 and 118 strikeouts , leading the Class A Giants to the California League championship . Promoted to AA Shreveport in 1999 , he pitched in only two games before being promoted to the parent club in 1999 . 
- 
- = = = San Francisco Giants ( 1999 – 2003 ) = = = 
- 
- Nathan was promoted to the San Francisco Giants on April 20 , 1999 , taking the roster spot of superstar <unk> Barry Bonds , who went on the disabled list after left elbow surgery . He made his major league debut the next day , pitching seven shutout innings and winning his first major league decision against the Florida Marlins , 4 – 0 . He then divided the rest of the season between the AAA Fresno <unk> and the Giants , going 6 – 4 with the <unk> and 7 – 4 and 4 @.@ 18 with the Giants , earning his first career save on May 16 against the Houston <unk> . 
- After a short stint in the minors in 2000 , Nathan spent most of the season in the majors , finishing 5 – 2 and even hitting two home runs . But he struggled with his control , walking 63 in <unk> ⁄ 3 innings and ending the season with a 5 @.@ 21 ERA . He was on the disabled list twice : from May 17 to June 6 for right shoulder tendinitis and from July 14 to August 18 for an <unk> right shoulder , <unk> <unk> surgery on the afflicted shoulder at the end of the season . Nathan divided 2001 between the AAA Fresno <unk> and AA Shreveport both starting and relieving , finishing with a disappointing combined 3 – 11 record and an ERA over 7 . Nathan improved slightly in 2002 to 6 – 12 with an ERA of over 5 at Fresno , but finally <unk> his <unk> struggles to return to the Giants in September with 32 ⁄ 3 scoreless innings in relief . 
- Nathan spent all of 2003 with the Giants in the bullpen after marrying Lisa <unk> , his girlfriend of five years , in November 2002 . This was a breakout year for Nathan , starting the season with 23 scoreless innings en route to a 12 – 4 record in his first full year as a reliever . His 78 appearances put him high on the list of most @-@ used pitchers for the season as one of the best setup men in the NL , allowing no runs in 15 appearances from July 18 to August 20 . His 12 wins in relief led the majors . The Giants won the National League West by 151 ⁄ 2 games and drew the Florida Marlins , the National League 's wild card winner , in the NLDS . Nathan was hit hard in that series , blowing his only save opportunity . His team fared no better , winning Game 1 behind Jason Schmidt 's complete game shutout before dropping the next three . 
- 
- = = = Minnesota Twins ( 2004 – 2011 ) = = = 
- 
- 
- = = = = 2004 = = = = 
- 
- Nathan was traded to the Minnesota Twins on November 16 , 2003 , in one of the more lopsided trades in San Francisco Giants history . The Giants sent Nathan to the Twins along with pitchers <unk> <unk> and Francisco <unk> for catcher A. J. <unk> and cash . The Twins decided to make Nathan their closer starting in 2004 , risky move considering that Nathan had notched only one save in six opportunities as a Giant , but he won the job over J. C. Romero and Jesse <unk> in spring training . He was signed to a three @-@ year deal on March 4 , 2004 and agreed to an incentive @-@ laden contract with a base salary of $ 440 @,@ 000 . He started off the season strong , allowing no runs in 20 appearances and earning 14 saves from April 15 to June 4 . He was named AL Co @-@ Player of the Week starting on May 10 with four saves in four innings and four appearances , facing the minimum number of batters each time . His credentials for the first half of the season , 23 saves in 24 opportunities with a 1 @.@ 19 ERA in 26 appearances , earned him his first All @-@ Star appearance in the 2004 MLB All @-@ Star Game . He was the only Twin on the squad and pitched a perfect seventh inning , getting Bobby <unk> to strike out , Mike Lowell to fly out and Miguel Cabrera to strike out . His numbers were impressive through the rest of the season , allowing no runs between June 9 and August 18 , and between August 25 and September 16 @.@ and finishing 2004 with 44 saves in 47 opportunities and an ERA of 1 @.@ 62 . The Twins won the AL Central division and faced the New York Yankees in the ALDS . Nathan picked up his first postseason save in Game 1 , but blew his second opportunity in Game 2 as the Twins went on to lose the ensuing three games . His outstanding season earned him MVP and Cy Young votes , finishing fourth for Cy Young and 12th for MVP . His first child , a son named Cole , was born on November 9 , 2004 . 
- 
- = = = = 2005 = = = = 
- 
- During spring training in 2005 , Nathan signed a two @-@ year deal that includes a club option for 2008 . He picked up from where he left off in 2004 , allowing no earned runs in 15 appearances from April 5 to May 10 . He also had streaks of 13 and 12 consecutive save opportunities converted between April and July . As a result , Nathan was named the American League Player of the Week for the week of June 27 . Nathan earned another All @-@ Star appearance in 2005 for his pitching in the first half of the season . Although his record was 1 – 3 with a 3 @.@ 57 ERA in 37 appearances , he had struck out 43 batters in <unk> ⁄ 3 innings pitched , and lead the AL with 23 saves in 25 opportunities . Nathan pitched in the 2005 MLB All @-@ Star Game alongside fellow pitcher Johan Santana . Pitching the eighth inning of the game , he got Morgan <unk> to pop out for the first out , then gave out a double to <unk> <unk> . Felipe López singled , and Nathan was able to get Miguel Cabrera and Luis <unk> out , but not before <unk> scored . Nathan had a brilliant second half as he went 6 – 1 with 18 saves in 20 chances , and posted an ERA of 1 @.@ 76 . He finished the season with a 7 – 4 record , a 2 @.@ 70 ERA , 43 saves in 48 opportunities , and 94 strikeouts . Nathan also became the third pitcher in club history to post consecutive 40 save seasons . The Twins however missed the playoffs . 
- 
- = = = = 2006 = = = = 
- 
- Before the 2006 season began , Nathan participated in the 2006 World Baseball Classic as one of the 30 players selected for the Team USA roster . He played the first game , a 2 – 0 win against Mexico , striking out the side while allowing one hit . He also pitched the 4 – 3 victory against Japan , again throwing a shutout inning . Nathan went on to pitch the last game for the United States in the ninth inning against Mexico , again not allowing a run and striking out two . 
- As the regular 2006 season began for the Twins , Nathan started off strong , allowing no runs from the start of the season to April 25 . He also converted 10 straight save opportunities from April 11 to June 17 . On June 24 , Nathan recorded his one hundredth career save against the Chicago Cubs , and 99th save with Minnesota . Four days later he got save number 101 , his hundredth save with Minnesota against the Los Angeles Dodgers , becoming the fifth pitcher in Twins history to achieve that mark . Despite putting up great numbers during the 2006 season , Nathan was not selected to the All @-@ Star Game . He continued to pitch well throughout the season , passing Eddie <unk> for second on the Twins ' all @-@ time save list when he earned his <unk> save against the Detroit Tigers on September 9 . Nathan was also given the Major League Baseball <unk> Man of the Month award for July , going nine for nine in save opportunities and posting a 0 @.@ 75 ERA for the month . He finished the season with some of his best numbers to date : a 7 – 0 record , a 1 @.@ 58 ERA , 95 strikeouts , 36 saves , an 18th @-@ place finish in MVP voting , and a fifth @-@ place finish in Cy Young voting . His 61 games finished were also good for the AL lead and opponents batted just <unk> against him , a career high . With 36 saves in 38 opportunities , Nathan also became the first pitcher for the organization to earn 35 saves in three straight seasons . The Twins won the division on the last day of the regular season , but were swept by the Oakland Athletics in the ALDS as Nathan made one scoreless appearance . 
- 
- = = = = 2007 = = = = 
- 
- Nathan continued as the Twins ' closer for the 2007 season . He had a stretch between July and August where he gave up just two earned runs and converted all 12 save chances . Once again despite Nathan 's numbers , he was not picked for the All @-@ Star team . Nathan finished the year by converting 37 of 41 save opportunities with a record of 4 – 2 and an ERA of 1 @.@ 88 . The Twins however had a disappointing season and missed the playoffs . 
- On September 25 , 2007 , Nathan was named as one of 10 finalists for the " <unk> <unk> Man of the Year Award " , the third year in a row that he has been a <unk> . On October 29 , the Twins exercised Nathan 's club option for 2008 . 
- 
- = = = = 2008 = = = = 
- 
- Though Nathan was slated to make $ 6 million in 2008 , on March 24 , 2008 , the Minnesota Twins re @-@ signed Nathan to a four @-@ year , $ 47 million contract through 2011 . The deal also includes a $ 12 @.@ 5 million club option for 2012 with a $ 2 million buyout . 
- Nathan started the season with 13 consecutive saves but blew his first save of the season on May 27 by giving up a three @-@ run inside @-@ the @-@ park home run on a <unk> fly ball by teammate <unk> Young ; however , Nathan got two outs to end the 9th inning and the Twins went on to win the game . By converting 27 of 29 save opportunities prior to the All @-@ Star break , Nathan was selected as a reserve player for the American League in the 2008 Major League Baseball All @-@ Star Game . Nathan finished the year with 39 saves and a career best 1 @.@ 33 ERA . He also had a career high six blown saves and surrendered his first career walk @-@ off home run to Victor Martinez on September 16 . Nathan ranked seventh in the majors in saves and had the lowest ERA of the top 30 save leaders in 2008 . 
- 
- = = = = 2009 = = = = 
- 
- Nathan had a strong season , as he was selected as an All @-@ Star for the 2009 MLB All Star Game , and he finished the year with 2 @.@ 10 ERA with 47 saves in 52 opportunities , which was a franchise record . He shared honors for the AL <unk> Relief Man award with Mariano Rivera . However , Nathan did not fare as well in the postseason ; in Game 2 of the American League Division Series against the New York Yankees , with the Twins leading 3 – 1 in the bottom of the ninth inning , Nathan blew the save when he surrendered a game @-@ tying two @-@ run home run to Alex Rodriguez . It was the first home run Nathan had allowed with men on base all year . The Yankees later won the game in the 11th inning and swept the series . On October 11 , 2009 , after the Twins lost the final game at the Metrodome ( a 4 – 1 playoff loss to the Yankees that eliminated them ) , Nathan took a pile of dirt from the mound as a <unk> from the Metrodome . 
- 
- = = = = 2010 = = = = 
- 
- On March 9 , 2010 , it was reported that Nathan had a tear in his ulnar collateral ligament . On March 21 , after attempting to pitch without having surgery , Nathan decided to undergo Tommy John surgery , missing the entire 2010 season . 
- 
- = = = = 2011 = = = = 
- 
- Nathan earned his first save at Target Field on April 8 , 2011 . He emptied the container of dirt he took from the Metrodome on the mound at Target Field before pitching . On April 18 , Nathan was replaced at closer by Matt <unk> after going 3 for 5 in save opportunities . On May 28 , 2011 , Nathan was placed on the 15 @-@ day disabled list with a right <unk> muscle strain . Chuck James was called up to take his place . 
- On August 10 , 2011 , against the Boston Red Sox , Nathan became the Twins all @-@ time saves leader with 255 , passing Rick Aguilera . 
- After the Twins declined his $ 12 @.@ 5 million club option and exercised a $ 2 million buyout , Nathan became a free agent at the end of the 2011 season . 
- Nathan is currently the Minnesota Twins leader in career saves . 
- 
- = = = Texas Rangers ( 2012 – 2013 ) = = = 
- 
- On November 21 , 2011 , Nathan agreed to terms on a two @-@ year deal with the Texas Rangers worth $ 14 @.@ 5 million guaranteed with an option for a third year at $ 9 million or a $ 500 @,@ 000 buyout . 
- Nathan had a strong first season with the Rangers , as he was selected to the represent the Rangers at the 2012 MLB All Star Game , the fifth all star selection of his career . He finished his 2012 campaign with 37 saves and an ERA of 2 @.@ 80 . During a game against the Tampa Bay <unk> on April 8 , 2013 , Nathan earned his 300th career save after striking out Ben <unk> looking on a controversial strike call made by home plate umpire Marty Foster . TV cameras captured Nathan saying " <unk> ! " after the call . 
- Nathan was selected to his sixth All Star Game in 2013 , and earned the save for the American League . Nathan improved on his 2012 campaign , finishing his 2013 season with 43 saves and an ERA of 1 @.@ 39 . Nathan finished his Rangers career with an overall record of 9 – 7 , 80 saves , a 2 @.@ 08 ERA and 0 @.@ 98 <unk> . 
- 
- = = = Detroit Tigers ( 2014 – 2015 ) = = = 
- 
- On December 4 , 2013 , the Tigers signed Nathan to a two @-@ year , $ 20 million contract , with a club option for 2016 . This reunited him with former teammate and fellow ex @-@ Twins great , <unk> Hunter along with Rangers teammate Ian <unk> . On May 5 , 2014 , Nathan recorded his <unk> career save , tying him with Randy Myers for ninth on the all @-@ time saves list . Two days later , Nathan recorded career save number 348 , putting him alone at ninth on the all @-@ time saves list . On June 9 , Nathan recorded career save 358 , tying him with Troy Percival for 8th on the all @-@ time saves list . On August 23 , 2014 , Nathan recorded his <unk> career save , passing up Jeff <unk> for 7th place on the all @-@ time saves list . In a September 16 game against the Minnesota Twins , Nathan blew his seventh save of the season , surpassing his previous career high of six blown saves when he pitched for the Twins in 2008 . Nathan finished his first season with the Tigers making 62 appearances and recording 35 saves in 42 chances , while posting an ERA of 4 @.@ 81 . He made one postseason appearance in 2014 , retiring all three batters he faced in a non @-@ save situation in Game 2 of the ALDS against the Baltimore Orioles . The Tigers were swept in the series , 3 – 0 . 
- On April 8 , 2015 , Nathan was placed on the 15 @-@ day disabled list due to a strained right elbow . During a rehab start with the Toledo <unk> <unk> on April 22 , Nathan re @-@ injured his elbow after throwing only 10 pitches . The same night , Nathan underwent <unk> , which tested positive revealing tears in his ulnar collateral ligament of the elbow and his <unk> <unk> muscle , and would undergo Tommy John surgery , ending Nathan 's 2015 season . Sources projected that this surgery could end Nathan 's career , but he was not planning to retire yet . 
- During the 2015 offseason , the Tigers declined the $ 10 million club option for Nathan for the 2016 season , and exercised a $ 1 million buyout . 
- 
- = = = Chicago Cubs ( 2016 – present ) = = = 
- 
- On May 17 , 2016 , Nathan signed with the Chicago Cubs . He was immediately placed on the 60 @-@ day disabled list upon signing to continue recovery from his previous Tommy John surgery . Nathan made his Cubs debut on July 24 , 2016 against the Milwaukee Brewers . He pitched one inning and struck out the side , allowing one hit and one walk . 
- 
- = = Records and notable statistics = = 
- 
- 
- = = Personal life = = 
- 
- Nathan is married to Lisa ( <unk> <unk> ) . They have two children . Nathan 's foundation , Joe Nathan Charitable Foundation , also called " Save It " , helps raise money and awareness for many different charities . The Nathan 's reside in <unk> , Tennessee in the offseason . 
- 
- = = Pitching style = = 
- 
- Nathan throws a mix of four pitches . His main pitch , a four @-@ seam fastball was once thrown in the mid @-@ to @-@ upper 90s , but now settles between 91 and 94 mph . His main breaking ball is a hard slider in the upper 80s , occasionally even touching 90 . He uses the slider less frequently against left @-@ handed hitters , preferring to use a curveball in the low 80s . He also uses a two @-@ seam fastball against <unk> . His slider is his best swing @-@ and @-@ miss pitch , with a <unk> rate of 42 % since 2007 . 
- 
- 
- = Art Ross = 
- 
- Arthur <unk> " Art " Ross ( January 13 , 1885 – August 5 , 1964 ) was a Canadian professional ice hockey player and executive from 1905 until 1954 . Regarded as one of the best defenders of his era by his peers , he was one of the first to skate with the puck up the ice rather than pass it to a forward . He was on Stanley Cup championship teams twice in a playing career that lasted thirteen seasons ; in January 1907 with the <unk> Thistles and 1908 with the Montreal Wanderers . Like other players of the time , Ross played for several different teams and leagues , and is most notable for his time with the Wanderers while they were members of the National Hockey Association ( NHA ) and its successor , the National Hockey League ( NHL ) . In 1911 he led one of the first organized player strikes over increased pay . When the Wanderers ' home arena burned down in January 1918 , the team ceased operations and Ross retired as a player . 
- After several years as an on @-@ ice official , he was named head coach of the Hamilton Tigers for one season . When the Boston Bruins were formed in 1924 , Ross was hired as the first coach and general manager of the team . He would go on to coach the team on three separate occasions until 1945 and stayed as general manager until his retirement in 1954 . Ross helped the Bruins finish first place in the league ten times and to win the Stanley Cup three times ; Ross personally coached the team to two of those victories . After being hired by the Bruins , Ross , along with his wife and two sons , moved to a suburb of Boston , and became an American citizen in 1938 . He died near Boston in 1964 . 
- Outside of his association with the Bruins , Ross also helped to improve the game . He created a style of hockey puck still used today , and advocated an improved style of goal nets , a change that lasted forty years . In 1947 Ross donated the Art Ross Trophy , awarded to the leading scorer of the NHL regular season . Ross was inducted into the Hockey Hall of Fame in 1949 . 
- 
- = = Early life = = 
- 
- Ross was born January 13 , 1885 , in <unk> , Ontario . His father , Thomas <unk> Ross , was the head of a Hudson 's Bay Company trading post in the area . The ninth of ten children , Ross grew up speaking both English and Ojibwe , a native Canadian language . Ross moved to Montreal in 1902 to play in organized hockey leagues , living in the affluent <unk> district . He played high school and junior hockey with Lester and Frank Patrick , both of whom were later inducted into the Hockey Hall of Fame . Ross and Lester had a financially successful ticket <unk> business at the Montreal Arena , buying tickets for thirty @-@ five cents and selling them for up to a dollar . 
- 
- = = Playing career = = 
- 
- 
- = = = 1905 – 09 = = = 
- 
- The best hockey players on their high school team , Ross and the Patrick brothers were invited to play occasional games for local league teams in Montreal . Ross first played in an organized league in 1905 , joining Montreal <unk> of the Canadian Amateur Hockey League ( <unk> ) , the top amateur league in Canada . He scored ten goals in eight games during the season . His opponents regarded him as one of the best rushing <unk> . Most defenders at the time either shot the puck down the ice or passed to a forward ; in contrast , Ross <unk> up the ice , taking the puck into the offensive zone . Later that year , wishing to pursue a career in banking , he moved to Brandon , Manitoba , where he joined the Brandon <unk> of the Manitoba Hockey League , the senior league in the province . In 1906 , his first season , he scored six goals in seven games while he recorded six goals in ten games in 1907 . Around this time , the <unk> Thistles , the Manitoba League champions , wanted to strengthen their team for the Stanley Cup challenge against the Montreal Wanderers in Montreal during January 1907 . They paid Ross $ 1 @,@ 000 to play both matches , a common practice at the time , and the Thistles won the Cup . While failing to score , Ross started many plays and proved an important part of the team . Although he played for the opposing team , he received a good reception from the Montreal crowd . Ross did not play for the Thistles when the two teams played for the Cup again in March , which the Wanderers won to take back the Cup . 
- The following year Ross moved back to Montreal . He joined the Wanderers , the team he had helped to defeat , who played in the Eastern Canada Amateur Hockey Association ( ECAHA ) , the successor league to the <unk> as the premier league in the country . He scored eight goals in ten games over the two @-@ month season that lasted from January to March . He helped the team to finish first in the ECAHA and retain the Cup in 1908 with challenges from Ottawa , Winnipeg and Toronto . The Wanderers were Cup champions throughout these challenges , so Ross became the second player to win the Cup with different teams in consecutive years , after Jack Marshall in 1901 and 1902 . In January 1908 , he participated in the first all @-@ star game in sports history , a benefit for the family of former <unk> defender Hod Stuart , who died the previous summer . Aside from his time with the Wanderers , Ross repeated his practice of playing for other teams who paid for his services in important matches . For the 1909 season Ross demanded a salary of $ 1 @,@ 600 . Although he settled for $ 1 @,@ 200 , the average salary of hockey players at the time was $ 600 . Ross received a cash bonus of $ 400 to play in a Stanley Cup challenge against a team from Edmonton in December 1908 , in which the Wanderers won the two @-@ game , total @-@ goal series 13 – 10 . He finished the season with two goals in nine games . 
- 
- = = = 1909 – 18 = = = 
- 
- A new league , the Canadian Hockey Association ( <unk> ) , was formed in late November 1909 . One of the teams , the All @-@ Montreal Hockey Club , hired Ross as a playing @-@ manager , but the league only lasted to mid @-@ January 1910 before disbanding . Ross , who scored four goals in four games in the <unk> , then signed with the <unk> <unk> of the National Hockey Association ( NHA ) , a league formed in December 1909 , which proved to be the stronger replacement to the ECAHA as the highest level of hockey in Canada . He received $ 2 @,@ 700 to play in the 1910 season , which lasted from January to March , playing twelve games for the team and finishing with six goals . Before the following season , the NHA imposed a salary cap of $ 5 @,@ 000 per team . The players , including Ross , were unhappy as this would result in a pay decrease , and began looking to form their own league without a cap . Ross wrote to the Montreal Herald , stating " all the players want is a fair deal ... The players are not trying to <unk> the NHA , but we want to know where we get off at . " The plans were abandoned when they realized all the suitable arenas would be unavailable as they were owned or leased by the NHA . Ross scored four goals in eleven games with the Wanderers , who finished fourth in the five team league . During a match against the Quebec Bulldogs on February 25 , 1911 , Ross knocked out Eddie <unk> in a fight , provoking a massive brawl between the two teams , which the police had to break up . The fight helped to increase the reputation Ross had as a tough player unwilling to back down from any opponent . The following season Ross had eleven goals in nineteen games as the Wanderers improved to second in the league . 
- Prior to the 1913 – 14 NHA season , Ross refused to sign a contract for the Wanderers , requesting a salary increase . As one of the top players on the team , the Wanderers agreed to his demands of $ 1 @,@ 500 for the forthcoming season , in which he finished with four goals and nine points in eighteen games . The next season Ross , again concerned with his salary , began negotiating with other players in the NHA to leave their teams and form a new league that would offer higher wages . These actions resulted in his suspension in November 1914 by Emmett Quinn , president of the NHA . Ross responded by declaring himself a free agent and claiming his contract with the Wanderers was no longer valid . Consequently , although having no technical power to do so , Quinn suspended Ross from all organized hockey . The proposed new league failed to materialize and Ross applied for <unk> to the NHA , which was granted at a meeting of the team owners on December 18 , 1914 . The owners realized if they suspended Ross , they would also have to suspend all those he signed , hurting the league . However , Ross 's actions led to his release by the Wanderers . At first he trained with the Montreal Canadiens , then joined the Ottawa Senators . 
- At the conclusion of the 1914 – 15 season , the Senators and Wanderers finished with identical records of fourteen wins and six losses . A two @-@ game , total goal series was played to determine the NHA league champion who would contest the Stanley Cup with the Pacific Coast Hockey Association winner , the Vancouver <unk> . Ross , who finished with three goals in sixteen games in the season , scored one goal in the first match against the Wanderers , a Senators 4 – 0 victory , and though Ottawa lost the second game 1 – 0 , they won the series , 4 – 1 . To help the Senators stop the Wanderers , who were known for their speed , Ross created a new system of defence . <unk> " <unk> bar the door " , it required three defenders to align themselves across the ice 30 feet in front of the goaltender to stop offensive rushes . This style of defence would later be used in a modified version known as the neutral zone trap , later used widely to stop opposition offensive chances . 
- The following year Ross , who had eight goals and eight assists in twenty @-@ one games , was the second highest paid player on the team ; his salary of $ 1 @,@ 400 was $ 100 less than Frank <unk> made . Even so , Ross left the team in 1916 , returning to Montreal in order to look after his sporting @-@ goods store , and rejoining the Wanderers . He scored six goals and had two assists in sixteen games for the team . The Wanderers , along with the Montreal Canadiens , Toronto Arenas , Quebec Bulldogs and Ottawa Senators dissolved the NHA and founded the National Hockey League ( NHL ) in November 1917 . Ross became coach of the Wanderers , but a fire on January 2 , 1918 , destroyed their home , the Montreal Arena , and forced them to fold after four games . However , the NHL insisted the team continue to play , and recorded two additional scheduled matches as <unk> losses for the Wanderers , even though the matches were not played . With the Wanderers disbanded , Ross retired as a player . His NHL career yielded one goal in three games played . 
- 
- = = Managerial career = = 
- 
- 
- = = = 1918 – 36 = = = 
- 
- Ross began his career as a hockey coach in the midst of his playing days , when at age 24 he led the McGill University <unk> to a 4 – 2 – 1 record during the 1910 – 11 season . Following his playing career , Ross became a NHL referee . He was hired to coach the Hamilton Tigers for the 1922 – 23 season , and adopted new methods in training camp that emphasized physical fitness , including work off the ice . However , the Tigers finished with a record of six wins and eighteen losses , last in the NHL for the third successive year , and Ross did not return the next season . His next coaching appointment arose from meeting Boston grocery store magnate Charles Adams during the 1924 Stanley Cup Finals . Before the 1924 season , the NHL awarded Adams an expansion team . Adams ' first move was to hire Ross as vice president , general manager , coach and scout . Adams instructed Ross to come up with a nickname portraying an <unk> animal displaying speed , agility and <unk> . With this in mind , Ross named the team the Boston Bruins , after the Old English word for a bear . The team 's nickname went perfectly with the original colours of brown and yellow , which were the same colours of Adams ' grocery chain , First National Stores . 
- Ross utilized his many hockey connections throughout Canada and the United States to sign players . Even so , the team started poorly . Early in the first season the University of Toronto hockey team was in Boston for matches against local universities . The team 's manager , <unk> <unk> , who later owned and managed the Toronto Maple Leafs , said that his team could easily defeat the Bruins — Ross 's team had won only two of their first fifteen NHL games . This began a feud between <unk> and Ross which lasted for over 40 years , until Ross ' death ; while mostly confined to newspaper reports , they refused to speak to each other at NHL Board of Governor meetings . The Bruins finished their first season with six wins in thirty games , one of the worst records in the history of the league . Several records were set over the course of the season ; the three home wins are tied for the second fewest ever , and an eleven @-@ game losing streak from December 8 , 1924 , until February 17 , 1925 , set a record for longest losing streak , surpassed in 2004 and now second longest in history . With 17 wins in 36 games the following season , the team greatly improved , and finished one point out of a playoff spot . 
- In 1926 the Western Hockey League , the other top professional hockey league , was in decline . The Patrick brothers , who controlled the league , offered to sell the remaining five teams for $ 300 @,@ 000 . Ross realized the potential talent available and convinced Adams to pay the money . As a result , the Bruins acquired the rights to several future Hall of Fame players , the most notable being defender Eddie Shore . Ross signed goaltender Cecil " Tiny " Thompson in 1928 , who was with a team in Minnesota , despite never watching him play ; Ralph " <unk> " Weiland was also brought over from Minnesota . Ross acquired Cy <unk> from Ottawa and made him a player @-@ assistant @-@ coach while he assumed the role of coach and team manager . On November 20 , 1928 , the Bruins moved to a new arena when the Boston Garden opened . The team played the Canadiens who won the match 1 – 0 in front of 16 @,@ 000 fans . The players signed by Ross helped the Bruins to improve quickly , and they won the Stanley Cup in 1929 . <unk> retired after the Cup win , Ross guiding the team to several league records in the 1929 – 30 season . The team won 38 of 44 games for an <unk> winning percentage , the highest in league history ; the five losses tied a record for fewest ever , and the four road losses tied a record for second fewest . The Bruins also only finished one game in a tie , a record for fewest ties in a season since the NHL began recording the record in 1926 . One of the longest winning streaks was also set during the season . From December 3 , 1929 , until January 9 , 1930 , the team won fourteen games in a row , a record that lasted until 1982 and now tied for third longest , as of October 2010 . A home winning streak began the same day and lasted for twenty games , until March 18 , 1930 , which was tied for the longest of its kind in 1976 . In 1930 – 31 , the Bruins again lost only one home game , which equalled their previous record . 
- On March 26 , 1931 , Ross substituted a sixth <unk> for goaltender Tiny Thompson in the final minute of play in a playoff game against the Montreal Canadiens . Although the Bruins lost the game 1 – 0 , Ross became the first coach to replace his goaltender with an extra attacker , a tactic which became widespread practice in hockey . <unk> aside as coach in 1934 to focus on managing the team , Ross hired Frank Patrick as coach with a salary of $ 10 @,@ 500 , which was high for such a role . However rumours spread during the season that Patrick was drinking heavily and not being as strict with the players as Ross wanted . After the Bruins lost their playoff series with the Toronto Maple Leafs in the 1936 playoffs , the result of an 8 – 1 score in the second game , a newspaper claimed that Patrick had been drinking the day of the game and had trouble controlling the team . Several days later , Ross relieved Patrick of his duties and once again assumed the role of coach . 
- 
- = = = 1936 – 54 = = = 
- 
- Ross took over an improved team . He had recently signed three players , <unk> Schmidt , Bobby Bauer and Woody <unk> , who all grew up together in <unk> , Ontario , and had them play on the same line , soon nicknamed the <unk> Line in reference to the German heritage of all three . Along with them , Ross had acquired a new goaltender in 1938 , Frank Brimsek ; after Brimsek earned six <unk> in his first eight games , the Bruins traded away Tiny Thompson to allow Brimsek to play . With these players the Bruins finished first in the league in 1937 – 38 ; Ross was named as the second best coach in the league , selected for the end of season All @-@ Star Second Team . The next season the Bruins won 36 of 48 games , and won the Stanley Cup in the playoffs ; Ross was named to the First All @-@ Star Team as the best coach in the league for the season and the team only tied two games , which is tied for the second fewest in a season . He hired the recently retired <unk> Weiland to coach the Bruins for the 1939 – 40 NHL season . The Bruins would win the Cup again in 1941 , and tied their record of only four away losses all season . Ross once again took over as coach of the team before the 1941 – 42 season began , as Weiland became coach of the <unk> Bears of the American Hockey League , and led the team to 25 wins in 48 games , which was enough to earn third place in the league . By this time the Second World War had caused several Bruins players , including the entire <unk> Line and goaltender Brimsek , to enlist in their respective armed forces . The Bruins finished second in the NHL during the 1942 – 43 season with 24 wins in 50 games and Ross was again named in the Second NHL All @-@ Star Team as second best coach in the league . The Bruins missed the playoffs in 1943 – 44 , the first time in ten years they failed to qualify , but returned to the playoffs the next season , something they did for five straight years . 
- In 1949 , Ross had signed Georges Boucher as coach , but Boucher did not work well with Ross and team president Weston Adams . Looking to hire a new coach in the summer of 1950 , Ross <unk> Lynn Patrick , the son of Lester , who had just resigned from the New York Rangers after coaching the team to the Stanley Cup Final . Lynn had moved his family back to Victoria , British Columbia , where he grew up as a child , with the intention of coaching the Victoria Cougars , a team in the minor professional Pacific Coast Hockey League . Though reluctant to move back to the eastern United States , Lynn was hired by Ross after he was offered a salary of $ 12 @,@ 000 . He would coach the team for the next four seasons and become the second general manager of the Bruins when Ross retired at the end of October 1954 . 
- 
- = = = Legacy = = = 
- 
- Aside from his career in hockey , Ross was interested in improving the game . Prior to the start of the 1927 – 28 season , the NHL adopted a new style of goal net created by Ross . With the back <unk> into a B @-@ shape , it was better designed to catch <unk> and the net was used until 1984 , when a modified version was adopted . He also improved the design of the puck . Ross ' design had <unk> edges , which prevented it bouncing too much , and used synthetic rubber , rather than the natural rubber previously in vogue . Along with New York Rangers coach Frank Boucher , Ross helped to create the red line , which was introduced to help speed up the game by removing the ability for defenders to pass the puck from the defensive to offensive zone ; until 2006 it was against the rules of hockey to make a two line pass . More scoring chances resulted as teams could not simply send the puck down the ice with <unk> . In order to help tell the red line and blue lines apart on television , Ross suggested that the red line be striped . 
- Regarded throughout his playing career as one of the best defenders in hockey , Ross was named to the Hockey Hall of Fame in 1949 , selected for his playing career rather than his work as an executive . A ceremony for his induction was held prior to a Bruins game on December 2 , 1949 , where he was given his Hall of Fame scroll and a silver <unk> with the <unk> of the six NHL teams on it . In 1975 he was inducted into the Canadian Sports Hall of Fame . Along with his two sons he donated the Art Ross Trophy to the NHL in 1947 , to be awarded to the leading scorer in the league 's regular season . In 1984 he was posthumously awarded the Lester Patrick Trophy for service to hockey in the United States . 
- A descriptive biography entitled Art Ross : The Hockey Legend who Built the Bruins by Eric Zweig was published by <unk> Press in <unk> 2015 . 
- 
- = = Personal life = = 
- 
- Ross also excelled in baseball , football , <unk> and motorcycle racing . Before he became a hockey executive , he had a career as a bank clerk and ran a sporting @-@ goods store in Montreal . Ross had moved to Brandon , Manitoba , in 1905 at the advice of his parents so he could get a job with a bank , with a salary of $ 600 per year . He gave that career up when he began playing hockey professionally . He was married to <unk> , a native of Montreal , and had two sons , Art and John . During the Second World War , both sons served in the Royal Canadian Air Force . After the war Ross made his son Art the business manager for the Bruins . Ross was named coach and manager of the Boston Bruins in 1924 and moved his family to <unk> , Massachusetts , a suburb of Boston , after being hired . In 1928 , he served as the traveling secretary of the Boston Braves baseball team , which was owned by Bruins owner Charles Adams . He became a naturalized American citizen on April 22 , 1938 . On August 5 , 1964 , Ross died at a nursing home in <unk> , Massachusetts , a suburb of Boston , at the age of 79 . A sister , both his sons , and three grandchildren survived him . 
- 
- = = Career statistics = = 
- 
- 
- = = = Regular season and playoffs = = = 
- 
- * Playing stats from Total Hockey 
- 
- = = = Coaching record = = = 
- 
- * Coaching stats from Total Hockey 
- 
- = = Awards = = 
- 
- 
- = = = NHL = = = 
- 
- * Awards from Legends of Hockey 
- 
- 
- = Saint Leonard Catholic Church ( Madison , Nebraska ) = 
- 
- Saint Leonard Catholic Church is a Roman Catholic church in the city of Madison , in the state of Nebraska in the Midwestern United States . Built in 1913 , it has been described as " an outstanding example of the Romanesque Revival style of architecture . " 
- St. Leonard 's parish , named after Saint Leonard of Port Maurice , was organized in 1879 . A wood frame church was built in 1881 on the outskirts of Madison , and moved into the city in 1898 . In 1902 , the basement of the current church was built , and the congregation moved into it , converting the old church to a school . When funds allowed , the basement was extended , and the current brick church completed in 1913 . 
- In 1989 , the church , its 1912 rectory , and the rectory 's garage were listed in the National Register of Historic Places , as the work of noted Nebraska architect Jacob M. Nachtigall . A pupil of Thomas Rogers Kimball , Nachtigall designed a number of Catholic churches and other buildings in the state , several of which are also listed in the National Register . 
- 
- = = History = = 
- 
- The first white settlers to occupy the site of Madison were a party led by Henry Mitchell Barnes , who settled near the junction of Union and Taylor Creeks in 1867 . Growth of the new settlement was rapid ; in particular , there was an influx of German families from Wisconsin . The town of Madison was officially <unk> by Barnes in 1870 or 1871 . In 1875 , it became the county seat of Madison County , and in 1876 it was incorporated . The Union Pacific Railroad reached Madison in 1879 ; by 1880 , the town had a population of about 300 . 
- The first Christian services held in Madison were Presbyterian , taking place in Barnes 's and other homes . A Presbyterian congregation was organized in 1870 , and a church built in 1872 . A Methodist circuit encompassing Madison and <unk> counties was organized in 1871 ; a <unk> was built in Madison ca . 1875 , and a church begun in 1877 . A Lutheran congregation may have formed in Madison in about 1875 , although early records are incomplete ; the congregation was initially served by the pastor of a Lutheran church in Green Garden Precinct , located about seven miles ( 11 km ) southwest of Madison . It was formally organized in 1885 , and a church built in Madison in 1887 . 
- The first Catholic settlers in Madison County <unk> near present @-@ day Battle Creek , northwest of Madison , in the late 1860s . In 1874 , they organized a parish ; in 1874 – 75 , they built St. Patrick 's Church , the county 's first Catholic church . In 1877 , they wrote to Bishop James O 'Connor of the Diocese of Omaha , asking that a priest be assigned to visit the church at intervals until a permanent priest could be assigned to the parish ; in apparent response to this , Franciscan missionaries based in Columbus were given the responsibility of providing for Madison County . 
- 
- = = = 1879 – 1900 = = = 
- 
- In 1879 , a group of Catholic residents of the Madison area met to plan the building of a church . At the meeting , a total of $ 426 @.@ 75 was subscribed ; additional contributions of $ 322 @.@ 86 were obtained from citizens of Madison . In January 1880 , the church 's trustees spent $ 100 for five acres ( 2 @.@ 0 ha ) on a hill at the southeastern edge of town . In the spring , a party of parishioners drove their <unk> teams to <unk> , about 30 miles ( 50 km ) northeast of Madison , for the first load of lumber for the new church . The 30 @-@ by @-@ 40 @-@ foot ( 9 m × 12 m ) frame structure , with a capacity of 100 , was completed in November 1881 ; the total cost was $ 957 @.@ 61 , leaving $ 208 @.@ 00 owed to the carpenter . The new church was dedicated to St. Leonard of Port Maurice , an 18th @-@ century Franciscan priest , preacher , ascetic , and writer venerated as the patron saint of parish missions . 
- In 1882 , a parcel of land southeast of the church was purchased for a cemetery ; a one @-@ year @-@ old child buried in September of that year became its first <unk> . The cemetery was <unk> in 1883 . In 1884 , the church was enlarged : a sacristy and a room for the priest were added to the east end , and a steeple to the west end . 
- As Madison 's population grew , the church became too small for the expanding congregation . In addition , its location outside of the city was <unk> for many parishioners . In 1898 , a tract of land inside Madison was bought . Rather than building a new church at the time , the parish elected to move the old one to the new site . The church was moved in two parts ; when it was reconstructed , another section was added between them , increasing the building 's seating capacity to 180 . The church on the new site was dedicated in November 1898 . 
- 
- = = = 1900 – 1913 = = = 
- 
- In the early 20th century , the parish decided that the old church should be remodelled into a school and a convent for the teachers , and that a new church should be built . Brother Leonard <unk> , a Franciscan architect , drew up plans for a church ; but financial constraints precluded its construction . Instead , a temporary basement church was built just west of the old church building . It is not known whether the design of the basement used <unk> 's plans . Construction of the basement church began in July 1902 , services were held there beginning in September 1902 , and it was dedicated in February 1903 . 
- The school opened in September 1903 , with two classrooms staffed by two members of the Sisters of the Presentation of <unk> , Iowa . 66 students were enrolled , including a number of non @-@ Catholics , owing to overcrowding in the public schools . To make more space available , a basement was dug in 1904 . In 1910 , a third classroom was added . 
- In 1910 , the Franciscans turned the management of the parish over to the Diocese of Omaha . In October of that year , Edward S. Muenich became the first diocesan pastor of St. Leonard 's . 
- Muenich embarked upon an extensive building campaign , for which he retained Omaha architect Jacob M. Nachtigall . Born in Germany in 1874 , Nachtigall had immigrated to the United States with his family in 1883 . Initially working as a <unk> in Omaha , he had served as a draftsman for that city 's 1898 Trans @-@ Mississippi and International Exposition . He had then worked as a draftsman for Omaha architect Thomas Rogers Kimball from 1900 to 1908 ; during this time , Kimball had designed the city 's St. Cecilia 's Cathedral . In 1909 , Nachtigall had opened his own architectural office . 
- In 1911 , a two @-@ story eight @-@ room brick rectory designed by Nachtigall was begun ; it was completed and furnished in 1912 , at a cost of $ 10 @,@ 374 . In the fall of 1912 , the church basement was extended by over 50 percent . 
- 
- = = = 1913 – 1946 = = = 
- 
- In 1913 , a Romanesque Revival church designed by Nachtigall was built on the existing basement . The cornerstone was laid and construction begun in May ; the church was completed by the end of November , and formally dedicated on December 4 . The cost of construction was about $ 75 @,@ 000 . While the church was under construction , Catholic services were held in Madison 's armory . 
- The new church had a seating capacity of 700 . In its 110 @-@ foot ( 34 m ) tower was a clock with four six @-@ foot ( 1 @.@ 8 m ) dials , and a <unk> of three bells , contributed by the citizens of Madison ; beside summoning the parishioners to Mass , these rang the quarter @-@ hours , marking time for the residents of the city and the surrounding rural areas . 
- While the urban United States experienced an economic boom during the 1920s , the agricultural sector of the country experienced a depression . <unk> of European agriculture by World War I had produced high prices for farm commodities , and it had been thought that Europe 's recovery would be slow and that the high prices would persist . This gave rise to a bubble in farmland prices , which burst when the rapid postwar recovery of European agriculture drove commodity prices down again . At the same time , increasing mechanization reduced the need for farm labor , pushing agricultural wages downward and rural unemployment upward . Madison and St. Leonard 's parish suffered from this agricultural depression and from the Great Depression of the 1930s . During this time , the parish 's population remained more or less stable : in 1918 , it consisted of 440 individuals ; in 1929 , <unk> . 
- In 1926 , the parish was forced to close its school , since the Presentation Sisters were no longer able to staff it . The school re @-@ opened in 1931 , with 60 pupils taught by <unk> Benedictine Sisters based in Norfolk . 
- The onset of World War II once again brought prosperity to rural Nebraska , and it persisted into the 1950s . St. Leonard 's paid off its remaining debt , held a <unk> @-@ burning ceremony in 1946 , and began raising funds for a new school . 
- 
- = = = 1946 – present = = = 
- 
- The cornerstone for a new school was laid in November 1953 . A property adjoining the new school site was bought , and the house standing upon it converted to a convent for the nuns staffing the school . The new building was completed and opened for classes in August 1954 ; the old school , which had begun life as the first St. Leonard 's Church , was demolished that fall , and its site became a parking lot . 
- The Benedictine Sisters withdrew from the school in 1978 , prompting the closing of the seventh and eighth grades . The school continued to offer grades 1 – 6 , taught by three lay instructors . 
- Beginning in the early 1990s , Madison experienced a large influx of <unk> . In 1990 , Madison County 's population was 2 % Hispanic ; by 2010 , the number had increased to 13 % . In the city of Madison , whose single largest employer was a <unk> plant with over 1000 employees , operated by <unk> and then by Tyson <unk> , the increase was far greater : the Hispanic fraction of the population rose from less than 1 % in 1980 to 48 @.@ 8 % in 2010 , as the Spanish @-@ speaking population increased and the white non @-@ Hispanic population fell . By 2011 , an estimated two @-@ thirds of St. Leonard 's parishioners were Hispanic . Beginning in 1991 , the <unk> assigned Spanish @-@ speaking priests to the parish , and both <unk> and Spanish @-@ language services were offered . 
- The centennial of the church building was celebrated in December 2013 , at a bilingual Mass conducted by <unk> <unk> , archbishop emeritus of the Archdiocese of Omaha . 
- 
- = = Architecture = = 
- 
- In 1989 , three of the parish 's buildings — the church , the rectory , and the rectory 's garage — were added to the National Register of Historic Places , as the work of distinguished Nebraska architect Jacob M. Nachtigall . Beside St. Leonard 's , Nachtigall designed a number of other notable buildings in Nebraska , many of them Catholic ; these include St. Mary of the Assumption Church in Dwight ( 1914 ) , St. Anthony 's Church in Cedar Rapids ( 1919 ) , St. <unk> 's Church in <unk> ( 1919 ) , Immaculate Conception Church in Omaha ( 1926 ) , and Father Flanagan 's House at Boys Town ( 1927 ) . 
- 
- = = = Church = = = 
- 
- The church is oriented east @-@ west , with the main entrance facing westward . It is just over 153 feet ( 47 m ) long from east to west ; 52 feet ( 16 m ) wide from north to south . The walls are made of mosaic gray pressed brick trimmed with Bedford stone , rising from a rock @-@ faced limestone foundation , and are about 40 feet ( 12 m ) high . The peak of the roof is about 70 feet ( 21 m ) above ground level . 
- At the west end of the church , a 110 @-@ foot ( 34 m ) <unk> rises above the main entrance . The tower is topped with a copper dome , capped with a cross . It contains three bells , weighing 900 , 1 @,@ 600 , and 2 @,@ 500 pounds ( 410 , 730 , and 1 @,@ 130 kg ) . The tower 's clock has four six @-@ foot ( 1 @.@ 8 m ) dials . Below the tower , a flight of seventeen steps <unk> to the church 's main entrance , via a set of double doors through a semicircular <unk> . 
- The church 's north and south walls are supported by a series of buttresses . Seven windows run along each wall . A line of brick <unk> runs along the walls below the eaves . Near the east end of the church , a short transept extends a short distance outward . At the church 's east end , beyond the transept , is a semicircular apse with a conical roof , topped with a six @-@ <unk> conical <unk> . 
- 
- = = = = Interior = = = = 
- 
- The interior plan of the church consists of a nave , a short transept , and a semicircular apse . At the west end of the nave is a narthex . At the center of this is a <unk> leading to the church 's main entrance ; at the church 's northwest corner is a reconciliation room , formerly a <unk> ; at the southwest corner is a short passage from which a staircase descends to the basement and another rises to the choir <unk> . In the <unk> is the church 's organ , a <unk> model manufactured by the <unk> Organ Company in 1879 ; the organ was not originally built for St. Leonard 's . 
- The nave measures 98 feet ( 30 m ) between the entrance and the communion rail . An aisle passes down its center ; narrower aisles follow the north and south walls . Two rows of seven circular columns run along the nave . The columns are made of wood , plastered to conceal the material , and decorated with Corinthian capitals . The rib @-@ vaulted ceiling rises 30 feet ( 9 @.@ 1 m ) above the floor . Fourteen stained @-@ glass windows , depicting scenes from the life of Christ , occupy the nave 's walls . The 13 @.@ 5 @-@ by @-@ 5 @-@ foot ( 4 @.@ 1 m × 1 @.@ 5 m ) windows were produced by the Muenich Art Studio of Chicago . Between the windows are sculpted stations of the cross . In three <unk> above columns on each side are <unk> paintings . 
- Two marble steps rise from the nave to the chancel . At the top of the steps is a hand @-@ carved white wood communion rail , decorated with miniature <unk> columns and topped with marble . 
- At the northwest and southwest corners of the chancel are two side altars : to the north , a Marian altar ; to the south , an altar of St. Joseph . The original image on the Marian altar depicts Our Lady of the Immaculate Conception ; more recently , an image of Our Lady of Guadalupe has been added . The St. Joseph altar includes a bone relic of St. Leonard of Port Maurice . 
- On the Gospel side of the chancel is a large hand @-@ carved wood pulpit , decorated with carved figures of the four <unk> . 
- The chancel is dominated by the high altar , which stands over 20 feet ( 6 @.@ 1 m ) tall , and which cost its donors $ 2 @,@ <unk> in 1913 . Like the communion rail , the side altars , and the pulpit , it is made of hand @-@ carved wood decorated with small <unk> columns . At the base is a relief sculpture of the Last Supper ; above that is a marble altar table . The tabernacle is just above that ; to either side is a sculpted angel , kneeling to the tabernacle and holding the sanctuary lamps . Above the tabernacle is a sculpted <unk> of Jesus , with the Virgin Mary and the <unk> John on either side of the cross . In separate niches on either side of the <unk> scene are statues of St. <unk> and St. Patrick , representing the German and Irish ethnicity of the parish in the early 20th century . Above their niches are figures of angels blowing <unk> ; at the top of the altar is a statue of St. Leonard . 
- On the half @-@ domed ceiling of the apse is a large oil @-@ painted mural depicting a scene in Heaven . In the center , God the Father and Jesus are enthroned on a cloud ; a stained @-@ glass <unk> at the top of the dome depicting the Holy Spirit completes the Trinity . <unk> the Father and Son are the Virgin Mary and John the Baptist . Below the cloud is Satan in <unk> . At the left and right of the scene is an assemblage of 18 Catholic saints and 10 angels . 
- Beside the fourteen large windows in the nave , there are 25 stained @-@ glass windows in the church , depicting saints and symbols of the Catholic Church . These include St. Cecilia , patron saint of the Archdiocese of Omaha , and a pair of windows depicting St. <unk> and St. Patrick . 
- In the 1989 form nominating it for the National Register of Historic Places , it was noted that the church had undergone only minor alterations , including an interior <unk> in 1964 , the replacement of roof slates with asphalt shingles in 1977 , and the addition of a concrete ramp for access by the handicapped in 1986 . 
- 
- = = = <unk> = = = 
- 
- The rectory , located just south of the church , was designed in <unk> style , with Romanesque Revival elements . It is a rectangular house measuring 40 feet ( 12 m ) wide by 57 feet ( 17 m ) long , with eight rooms in two stories . Like the church , it is made of mosaic gray brick . 
- An open porch occupies the whole of the west frontage , facing the street , and <unk> around to cover half of the south side . The portico is supported by circular columns with <unk> capitals . At the base of the porch is brick <unk> . There is a small enclosed porch with a doorway on the east side . 
- There are three rowlock arches above all of the windows on the first floor . One of the west @-@ facing windows on the second floor has two rowlock arches above it ; the other second @-@ floor windows are rectangular . There are two circular window openings in the attic , one facing west and the other south . 
- The rectory has a sloping roof with <unk> eaves and wood <unk> . On the south wall is a <unk> , filled in with siding . 
- 
- = = = = <unk> = = = = 
- 
- The original rectory garage is located southeast of the rectory . It is a rectangular structure facing westward , measuring 16 feet 2 inches ( 4 @.@ 9 m ) north to south , and 26 feet 4 inches ( 8 @.@ 0 m ) east to west . The interior is a single room . 
- The front ( west side ) of the garage is made of the same mosaic gray brick that was used for the construction of the church and the rectory . The north and south walls are both made of two different materials : the western two @-@ thirds of them is red brick , possibly from the <unk> that once operated in Madison ; the easternmost third is plastered with a layer of cement , painted red to match the bricks . The rear ( east ) wall is also plastered with red cement . It is speculated that the garage was either lengthened to fit a longer car , or that the eastern third had to be rebuilt ; the building 's hip roof shows no signs of having been lengthened . 
- The garage has two doors and two windows . Both the doors and windows have two rowlock brick arches over them . The car entrance is on the west side ; a passage door is on the north side . A clear @-@ glass window with 16 <unk> is on the east side . On the west side , north of the car entrance , is a window with <unk> lead @-@ glass <unk> , which appear clear from the outside but red from inside the building . It has been speculated that this window was part of the parish 's first church . 
- 
- 
- = Portuguese ironclad Vasco da Gama = 
- 
- Vasco da Gama was a central battery ironclad which entered service with the Portuguese Navy in 1876 , serving until 1935 . She was built by the Thames Iron Works in London , launched in 1876 , and completed in 1878 . She served as the flagship of the Portuguese fleet for the majority of her long and peaceful career . She was rebuilt and heavily modernized between 1901 and 1903 . Long @-@ since obsolete by the 1930s , Vasco da Gama was finally sold for scrapping in 1935 . 
- 
- = = Design = = 
- 
- Vasco da Gama was 200 feet ( 61 m ) long between <unk> , and she had a beam of 40 ft ( 12 m ) , though at the main battery guns , the ship was 46 ft 6 in ( 14 @.@ 17 m ) wide . She had a maximum draft of 19 ft ( 5 @.@ 8 m ) . She displaced 2 @,@ 384 metric tons ( 2 @,@ 346 long tons ; 2 @,@ 628 short tons ) as originally built . She was fitted with a <unk> rig and a steam engine rated at 3 @,@ 000 indicated horsepower ( 2 @,@ 200 kW ) , which produced a top speed of 10 @.@ 3 kn ( 19 @.@ 1 km / h ; 11 @.@ 9 mph ) . She had a crew of 232 officers and men . 
- As built , Vasco da Gama was armed with a main battery of two 10 @.@ 2 in ( 260 mm ) guns , placed in individual barbettes side by side amidships . She was also equipped with a single 5 @.@ 9 in ( 150 mm ) gun mounted on her stern , and four 9 @-@ pounder guns for close @-@ range defense against torpedo boats . She was protected with a complete iron armored belt that was 4 inches ( 100 mm ) thick on either end and 9 in ( 230 mm ) thick amidships . The main battery guns were protected by 10 @-@ inch ( 250 mm ) thick barbettes . 
- 
- = = Service history = = 
- 
- Vasco da Gama was laid down at the Thames Iron Works shipyard in London , Britain in 1875 , and was launched on 1 December 1876 . The ship was completed in 1878 . She served as part of the coastal defense force that protected Lisbon , the Portuguese capital , and the mouth of the river <unk> . On 26 June 1897 , Vasco da Gama participated in the Fleet Review at Spithead celebrating Queen Victoria 's Diamond Jubilee . At the time , the ship was commanded by Captain <unk> de <unk> . 
- In 1901 , Vasco da Gama was taken into <unk> at Orlando shipyard in <unk> , Italy , for a major reconstruction . She was cut in half and lengthened by a 32 ft 6 in ( 9 @.@ 91 m ) long section . She was fitted with new engines and more powerful water @-@ tube boilers rated at 6 @,@ 000 <unk> ( 4 @,@ 500 kW ) ; this increased her speed to 15 @.@ 5 kn ( 28 @.@ 7 km / h ; 17 @.@ 8 mph ) . Her sailing rig also was removed . Her main battery guns were replaced with new 8 in ( 200 mm ) L / 40 guns in <unk> , the short 5 @.@ 9 @-@ inch gun was replaced by a new long @-@ <unk> 5 @.@ 9 @-@ inch L / 45 gun , and six 3 @-@ pounders augmented her close @-@ range defense . Her iron belt armor was removed and stronger steel armor was installed in its place . The ship 's crew increased to 260 officers and men . All of the changes caused her displacement to rise to 2 @,@ 972 metric tons ( 2 @,@ <unk> long tons ; 3 @,@ 276 short tons ) . Work on Vasco da Gama was completed by 1903 . 
- On 27 August 1907 , a gas explosion aboard the ship injured several crewmen . During political unrest in April 1913 , part of the crew of Vasco da Gama had to be removed from the ship , as they had been involved in a planned ultra @-@ Radical coup d 'état against the First Portuguese Republic . On 14 May 1915 , the crew again participated in unrest ; they mutinied and killed the ship 's captain and bombarded Lisbon , killing around one hundred people . Vasco da Gama remained the flagship of the Portuguese Navy at least as late as 1914 , as the Portuguese naval budget was insufficient to fund a suitable replacement vessel . <unk> obsolete , she remained in the Portuguese fleet until 1935 , when she was sold for scrapping . 
- 
- 
- = Nicole Franklin = 
- 
- Nicole Franklin is a fictional character from the Australian Channel Seven soap opera Home and Away , played by Tessa James . She debuted on @-@ screen during the episode airing on 18 April 2008 . Nicole was introduced by executive producer Cameron Welsh . Nicole was mentioned various times before appearing on @-@ screen , James was cast in the role and described by Welsh as an " exciting talent " . He predicted that the viewers would respond " really well " to her . Nicole was initially portrayed as a shallow " party girl " with " wild ways " . Also described as a " high maintenance " female , she has been shown to dress constantly in a stylish manner . Nicole is also become notable for her many relationships . Her first prominent romance was with Geoff Campbell . Described as " complete opposites " , Geoff is credited as a catalyst in Nicole <unk> her <unk> attitude . Their storyline allowed the actors to take part in one of the serial 's " biggest ever location shoots " , when the couple became stranded on a remote desert island . In one storyline Nicole was involved in a same sex kiss with fellow character Freya <unk> , which was branded controversial by various media sources . The plot saw Nicole question her persona , believing Geoff had transformed her into a boring person . 
- Another relationship Nicole pursued was with Aden Jefferies , her longtime closest friend . Aden had a strong fanbase from his previous relationship with Belle Taylor . This resulted in the audience being divided over their relationship . Nicole has also been featured in various other romantic storylines , such as a brief <unk> with Liam Murphy , James said that he was compatible with Nicole because he had " the edge she was after " . She also dated Trey Palmer and they became involved in sex tape storyline , many newspapers reported on the plot because it " echoed " co @-@ star Lewis ' real life sex tape scandal . Producer Welsh once stated he believed Nicole was destined to become " full circle " and Nicole began <unk> erratic and wild once more , due to her failed romances and the death of her friend Belle . She also had an affair with an older male character , Sid Walker . James liked the fact Nicole had so many romances because she got to kiss many of her co @-@ stars . 
- James announced her departure from Home and Away in March 2011 . One of her final storylines was a pregnancy plot . Nicole felt she was too young and unable to offer a child stability , so she agreed to let Marilyn Chambers adopt the baby upon its birth . James and the writing team took the storyline " very seriously " and conducted research to portray the issue <unk> . Nicole has received critical analysis from various sources , with perception being mixed to positive . TV Week were neutral to aspects of her pregnancy plot but opined James was one of the serials best actress ' . The Daily Record said that being single was good for the character . She has also been likened to celebrities because of her glamorous image . 
- 
- = = Creation and casting = = 
- 
- Nicole is Roman Harris ' ( Conrad Coleby ) daughter and she was often mentioned on @-@ screen before producers decided to introduce her into the serial . In January 2008 it was announced that ex @-@ <unk> star Tessa James had been cast as Nicole . Executive producer Cameron Welsh said ' She is an exciting talent and I think audiences are going to love her character and respond really well to her . " James then moved to Sydney especially for the role . Speaking of working on the serial James stated : " Working on a series like this [ Home and Away ] is the best training you can get , I look at it like an apprenticeship and never forget how lucky I am . " Fellow cast member Celeste <unk> who plays Melody Jones originally auditioned for the part of Nicole . After Coleby who plays Roman quit the serial , James ' time with the show was in doubt . 
- In March 2011 , James confirmed that she had left Home and Away . She has already filmed her final scenes and Nicole will leave on @-@ screen later in the year . Of her departure , James said " I was at Home and Away for three @-@ and @-@ a @-@ half @-@ years , so it 's good to be finished and get to be who I am , and do what I 've wanted to do for so long . " 
- 
- = = Character development = = 
- 
- 
- = = = <unk> = = = 
- 
- Nicole has been portrayed as a party girl , feisty and has had many <unk> in a short space of time . James has described Nicole stating : " I love playing Nicole because she 's feisty and fun , and doesn 't mind pushing the boundaries . And she dresses <unk> – she 's very high maintenance , which is fun to play . " The serial 's official website describe her as : " Nicole might come off as a <unk> princess to some people , she 's not malicious . She 's simply as shallow as a puddle , and while she might cause others emotional pain , it 's totally unintentional . " They also state : " Nicole is a girl who lives to have fun , and she is fun if you accept her for who she is . And , of course , she thinks you 're worth her attention . Nicole is a girl who knows exactly who she is and where she stands : at the centre of the universe . " Soap opera reporting website Holy Soap described Nicole as " a label @-@ loving <unk> princess , armed with a sharp wardrobe and an even sharper tongue " . They also added she was a " <unk> " type character . 
- Whilst interviewed by The Daily Telegraph , James stated : " I think she 's the best character , I get to have so much fun being a princess and a <unk> <unk> . She doesn 't mind pushing the boundaries . She 's very high maintenance , which is fun to play . " She also stated she enjoys Nicole 's " <unk> side " because she seems to have another boyfriend every week . James also enjoys the role because of this and the fact she gets so many " <unk> " scenes with other cast members . 
- Series producer Cameron Welsh branded Nicole as an " interesting character " , adding his opinion on her development stating : " She came in with really strong opinions and a kind of morality that was different to the rest of the group " . Welsh also believes that Nicole is destined to come " full circle " . Of her 2010 storylines he comments that Nicole poor <unk> make her realise and re @-@ evaluate her life . 
- 
- = = = Relationship with Geoff Campbell = = = 
- 
- Nicole <unk> on a relationship with Geoff Campbell , not before they are embroiled in a " <unk> love triangle " along with Melody . In one storyline Nicole and Geoff became stranded on a " deserted island " and nearly die . The episodes were filmed on Box Beach located at <unk> Bay , New South Wales as part of a two @-@ day location shoot . Nicole nearly drowns in scenes which aired for the serial 's " cliff @-@ <unk> " in 2008 . James and Lewis took <unk> diving lessons in preparation for the storyline . Filming the storyline was compromised by logistical challenges . The crew had to move camera equipment between boats and the crew walked around the <unk> of the beach in order to avoid leaving <unk> . This was to keep the authenticity of a deserted location . The storyline was also given a " big budget " , featured helicopters and a number of promotional adverts were aired on Seven . 
- The storyline began on @-@ screen when Nicole started dating Elliot <unk> ( Paul <unk> ) . He had a <unk> against Roman and kidnapped Nicole and Geoff and left them stranded out at sea . They washed up on a deserted island and were forced to survive without food and clothing . Describing the effect it had on Nicole and Geoff , James stated : " They have no food , shelter or clothing . [ ... ] They find moments to make each other laugh , though , and realise how much they mean to one another . " Geoff had strong religious views and did not believe in premarital sex . However , the environment they were in caused him to let his guard down and they slept together . Lewis told TV Week that all the pair could think of whilst trapped was being rescued and their feelings for one an other . He added that " the fact they were both pretty much naked didn 't help " . He concluded the fact they were both kids , trying to keep warm - that then " stuff happens " . Geoff was left " guilt @-@ ridden " because he gave into <unk> . He did not regret it , but acknowledged that he wanted to save his <unk> until marriage . Therefore , Geoff saw no other option but to propose to Nicole . Lewis stated , " He 's not 100 % sure about that either , but he feels that if they 're going to have sex , a wedding is the only solution . " Describing Nicole 's reaction he said that she was " <unk> " by his offer , as she had believed he was acting strange because he wanted to dump her . Nicole refused when she realised his reasons for proposing . According to James , the moment managed to ruin their passion , she also commented that : " Nicole has liked Geoff for ages and was so happy to have got together with him - but now he 's spoiled it . " James also admitted she was <unk> to learn Nicole would turn down his proposal . James later opined that Nicole was " the one " for Geoff , but did not believe that Geoff was " the one " for Nicole . 
- Nicole and Geoff 's relationship became strained . Nicole decided to plan a return trip to the island , believing it would solve their problems and bring them closer together . James said that Geoff loved the surprise , but found Nicole " very sexy and tempting " . She added that everything about Nicole forced Geoff to question his religious beliefs and he felt he " needed to back away " . Their trip soon turned disastrous when a man named Derrick <unk> ( John Atkinson ) stole their food and intimidated the couple . He admitted he were a murderer and tried to attack Geoff with a knife . James said he " put himself in harms way " to save Nicole . 
- In 2009 , James told Inside Soap that Nicole and Geoff had a strong friendship underneath their romance . She also described their compatibility stating : " They 're complete opposites , which works well for them . It 's a bit of a fiery relationship , and they 've been through a lot together . " Whilst Lewis added : " They 're also very similar in some of their strongest traits . Geoff and Nicole are both stubborn and opinionated , and in some ways they 're naive . In a weird way they show a side to each other nobody else gets to see . " James opined Nicole 's wild behaviour was often to much for Geoff to cope with . In public , Lewis initially had negative feedback from older viewers because they felt Geoff was better suited to Melody . He revealed they felt like she was a bad influence for Geoff because she often played games . However towards the end of their relationship he felt perception had changed due to viewers having a better understanding of Nicole 's persona . 
- 
- = = = Other relationships = = = 
- 
- In 2009 , the serial embarked on two lesbian storylines , one of which involved Nicole . It featured Freya <unk> ( Sophie <unk> ) kissing Nicole , which sparked complaints . However , for Nicole it wasn 't about sexuality , rather finding herself the center of attention . James described their dynamic , stating : " Freya 's exactly what Nicole was like when she first arrived in the bay , that is why they click . Nicole relates to the wild side of Freya , but has no idea how far Freya is going to take it . " Through her relationship with Geoff she had <unk> , however her vanity was still present . James said Nicole was " angry " because she was on Freya 's " not hot list " . Freya kissed her to prove she thinks she is hot , James opined that Nicole did not enjoy the kiss , but was just " happy to be center of attention " and happy that people were talking about her again . The incident eventually brought her to the realisation that she had become boring . Nicole denied it was to do with her involvement with Geoff , however James said Geoff was the reason she became bored . 
- Later that year the serial included a storyline which was branded " bizarre " after it mirrored a real life scandal that had occurred weeks earlier . Lewis who plays love interest Geoff had been caught up in a sex tape scandal which leaked onto the internet , the serial decided to include Nicole making a sex tape with Trey Palmer ( Luke Bracey ) and having it leaked . Trey filmed without Nicole 's consent , when she found out the truth she ended their relationship . Trey thought she and Geoff were getting back together so aired the tape at a local film festival to gain revenge . James described Nicole 's state of mind adding , " She 's quite vulnerable at the moment , with her dad , Roman , in prison . She 's relying on Trey , so this is the last thing she needs . " 
- Nicole 's best friend during her initial storylines was Aden Jefferies ( Todd Lasance ) . After Brendan Austin ( <unk> O <unk> ) caused Roman to go blind , he took his anger out on Nicole and Aden . Subsequently they became " each other 's support network " and Lasance said it was not long afterward that they " slipped between the sheets " . One of the conditions of Aden 's <unk> was to never sleep with Nicole , this made the pair feel guilty that they had deceived Roman . Lasance felt the storyline was controversial as he had a strong fan base for his relationship with Belle Taylor ( Jessica <unk> ) - which meant he knew it would " cause a stir " and divide the audience . In January 2010 , Nicole and Aden " get up close and personal " and they decided to spend Aden 's remaining time in the Bay together . They shared a kiss and James told TV Week that there are " a lot of complications " for them . She said that no one knew what was going to happen with Liam Murphy ( <unk> Whitehead ) and that Nicole felt guilty for betraying Belle because she was her friend . James explained that Nicole 's pairing with Aden was " a bit more serious and in @-@ depth than her usual relationships . " James opined that Aden was the " <unk> guy " for Nicole , but Liam may have had " the edge she 's after . " Nicole and Aden then embarked on a relationship . James thought that Nicole and Aden 's relationship was great and said " They started out having a kind of brother @-@ sister relationship , and that developed into something more . " Nicole declared her love for Aden , however he did not <unk> . Lasance described the moment whilst interviewed by TV Week stating : " They 've always had an awesome connection and Nicole gets into a bit of a comfortable state and <unk> out that she loves Aden . " Aden appreciated her love for him , however cannot say it back until he felt the same way . It is this that made their relationship " awkward " , Nicole tried to withdraw her declaration and hide her hurt feelings . 
- 
- = = = <unk> spiral = = = 
- 
- In mid @-@ 2009 , producers decided to take Nicole 's storyline into a " u @-@ turn " , when she reverted to her " wild ways " . At the time Nicole had endured repetitive personal trauma including failed relationships , Roman being sent to prison and her best friend Belle was dying of cancer . James explained : " It 's all too much for her and she can 't handle it , so she <unk> to her wild ways . " Geoff notices Nicole 's erratic behaviour and attempts to help her . She tried to " lure him into bed " after he comforted her , however he turned her down . James said she no longer had romantic feelings for Geoff , but was actually in a " vulnerable state " . She then started relying on alcohol more , and <unk> with fellow " wild child " Indigo Walker ( <unk> Weaving ) at a " <unk> " venue . James explained that Nicole saw alcohol as an answer to her problems . The fact that " she 's trying to deal with too many things " saw Nicole transform into a <unk> up and depressed person . 
- Nicole became more irresponsible with the more she drank and was in the company of many men . Geoff arrived and saved her from danger , Lewis said there was a part of Geoff that still loved Nicole . However they did not start anything again , James said she understood why because of their complicated backstory . However Geoff continued to support Nicole as he realised that " a lot of people she was closest to have deserted her " . Nicole 's unpredictable behaviour continued thereafter . All that Geoff could offer was to be there for her because ultimately " Nicole is the only one that can save her from herself . " 
- 
- = = = <unk> = = = 
- 
- Nicole had a brief relationship with Penn Graham ( Christian Clark ) and after he was murdered , she discovered that she was pregnant with his child . James told Sunrise that viewers could expect a " realistic portrayal of teen pregnancy " and she explained that it was important to respect the issue . She added " We took it very seriously with the writers , and you do a lot of research and things like that . You can only do your best , I guess ! " Nicole later told Marilyn Chambers ( Emily Symons ) about the baby and she offered to adopt it . James later revealed that when she joined Home and Away she told the writers that they can do anything with her character , except make her pregnant . Of the moment she was told that Nicole was going to have a baby , James said " I went in to see our producer later and he said , ' Okay , I 'm going to <unk> in advance - we 're making Nicole pregnant ! ' " She initially did not want to portray a " typical soap teenage pregnancy " as she thought Nicole should be different . However the situation was explained to her and she became excited at the challenging storyline ahead . 
- Nicole later decided to let Marilyn and Sid Walker ( Robert <unk> ) adopt the baby upon its birth . Marilyn was desperate to mother a child and her obsession with that and her controlling behaviour became too much for Nicole . Of the situation , Symons said " Marilyn is in Nicole 's face every minute . She 's doing it out of love , but she doesn 't realise she 's becoming obsessed with the baby . " Nicole was still questioning whether she is making the right decision about her <unk> baby 's future and she argued with Marilyn . Nicole began dating a student from university , Angus <unk> ( Tim <unk> ) , they got along well on their first dates . Marilyn was left worried about their agreement and she felt " distanced . " Symons explained , " Marilyn is scared of being replaced . She 's scared of losing the baby , which could happen because there isn 't a legal agreement . " Roo Stewart ( <unk> Parker ) helped Nicole by convincing Marilyn to re @-@ evaluate the situation . Symons added that there is " still a long way to go " with the arrangement , but thereafter she was supportive and offered constructive help to Nicole . 
- During the final few weeks of her pregnancy Nicole began to receive increasing support from Angelo <unk> ( Luke <unk> ) . James said it was clear to see that " the lines of friendship could be <unk> into something more " for the pair . Whilst <unk> opined " They 've been spending time together and have realised how comfortable they are together . " 
- Angelo was forced to help Nicole give birth to a baby boy , George , and she handed him over to Marilyn immediately as agreed . She then tried to stay away , however it became obvious he needed her when he struggled without her . Marilyn then became obsessed with Nicole having the power to take her new son back . It was then revealed that Nicole would struggle to switch off her <unk> instinct after giving George away . Nicole came to visit the baby and Marilyn caught her <unk> George , while she was alone with him . Symons called the scenes " volatile . " Nicole was unaware that Marilyn has reservations about her spending time with the baby . Of the <unk> scene , Symons stated : " Marilyn is shocked and offended , and this cuts to the very core of her worries - that she doesn 't have the same natural <unk> instincts as George 's birth mother . Without a doubt , Marilyn thinks Nicole is <unk> the mark . She feels that a boundary has been <unk> and it could put a big strain on their relationship . " After the incident , Nicole is asked by Marylin , to stay away from the baby . James defended Nicole stating : " I think it 's hard to not bond , but this is Marilyn 's baby , and Nicole is so young and wants very much to give Marilyn and Sid this gift . " 
- 
- = = <unk> = = 
- 
- Nicole 's biological parents were the teenage Roman Harris ( Conrad Coleby ) and Natalie Franklin ( <unk> Pickering ) , but she was raised by her maternal grandparents and considered her mother , Natalie , as an older sister . She did not meet her father until her early teenage years . Nicole arrives in Summer Bay in a <unk> car . She initially makes herself unpopular with her <unk> care @-@ free nature . She tries to sleep with Aden but he <unk> her . She makes a bet with Aden that she can sleep with Geoff Campbell ( Lincoln Lewis ) within two weeks , but Geoff and Belle publicly <unk> her when they find out . Nicole starts dating Roman 's old SAS friend Mark 's brother Elliot <unk> , despite Roman 's disapproval . After breaking up with Elliot , he takes her diving where he tries to kill her , Geoff and later Roman . Geoff tries to save Nicole , but Elliot leaves the pair stranded at sea . They wash up on a remote island and Geoff and Nicole grow close to each other . Geoff , who has strong religious views , sleeps with Nicole as they cannot fight <unk> . When rescued Geoff proposes to Nicole out of guilt , she turns him down . She later has a pregnancy scare but is happy to discover it was a false alarm . Nicole later decides she and Geoff should return to the island to repair their relationship . They chased through woodland by a murderer , Derrick who tries to kill them both . However they manage to escape . Nicole decides to try her best to make their relationship work . However , after Freya kisses her they enter a few rocky periods and later break up . 
- She starts a relationship with troublesome Trey . He films them having sex , which is later leaked at the town 's movie festival . She has a brief relationship with Liam . After the death of good friend Belle , Nicole goes on a downward spiral . She starts <unk> and binge drinking along with Indigo . Geoff notices her behaviour and attempts to help her . After pushing him away she sleeps with drug addict Liam . She then pursues older man Sid . She kisses him and Indigo sees them , which ruins their friendship . She later moves in with Miles <unk> ( Josh <unk> <unk> ) who agrees to look after her . 
- Nicole starts dating Penn who manipulates her . He makes her believe she has accidentally stepped on a needle and she has tests for HIV . She later finds out she has the all clear . Nicole reveals to Marilyn that she is pregnant with Penn 's child . She initially chooses to have an abortion , but changes her mind and decides to give the baby to Marilyn . Nicole goes on a date with Angelo and she takes him to her <unk> class . When he learns that Nicole is giving her baby away , Angelo ends their relationship . Nicole become friends with Roo and asks her to be at the birth , but Roo turns her down . Marilyn <unk> to Nicole when she starts to take over and begins leaving her out of her plans for the baby . Nicole becomes fed up when the baby is late and Angelo tries to help her start labour . They go for a walk on the beach and Nicole 's water breaks . Angelo is then forced to deliver the baby . Nicole later decides that she wants her baby back and tells Marilyn , who is devastated . Marilyn takes the baby , but later returns him . Nicole then leaves Summer bay with Angelo and George . She later contacts Marilyn and they meet in the city . Nicole and Marilyn talk things through and Angelo shows up with George . 
- 
- = = Reception = = 
- 
- Holy Soap said that Nicole 's most memorable moment was when she " returned to the desert island with Geoff to rekindle their love " and she was held hostage by Derek the murderer , before her father came to the rescue . When Nicole began dating Liam , Caroline <unk> writing for the Daily Mail said " I think this reforming wild child – a kind of less moody <unk> <unk> – has struck lucky " . Inside Soap opined that Nicole was a " <unk> <unk> from the city who prays at the altar of Paris Hilton " . The Sunday Mail said it seemed like no one could stop her downward spiral . The Daily Record said that Nicole and Geoff 's relationship ending was good for her character . They later branded her a " fiery favourite " and when she started dating Penn , they said " <unk> Nicole looks set to fall for the wrong man all over again " . When Nicole had her HIV scare Holy Soap said " As if defending her man against the Bay 's critics wasn 't enough for one girl to take , poor Nic " . Inside Soap said " Nicole Franklin isn 't exactly backward in coming forward " . <unk> Stephen of the Daily Mail opined that Nicole seemed incapable of decision making when it came to deciding on a <unk> partner . TV Week chose James as one of the serial 's most promising actresses opining she was ready for roles in Hollywood . 
- TV Week often commented on her pregnancy storyline . After the plot was half way through Erin Miller of TV Week said that Nicole had changed her mind about adopting her baby " more times that Julia <unk> has uttered the phrase ' moving forward ' " . Upon watching Nicole 's beach birth scenes , the magazine website editor <unk> " Who knew sand had <unk> properties ? ! Well , maybe not ... but you could forgive pregnant teen Nicole for thinking that after a casual <unk> along the Bay 's beach ends with Angelo delivering her newborn son ! " Commenting on the realism of the storyline they added : " Only in the Bay would a baby be born on the beach ! " Miller thought it was odd she had then " <unk> lost any signs that she even had a baby . " She <unk> " already the teen is back to wearing skin @-@ tight dresses ! " . They later described Nicole and Marilyn 's argument over George as " the mother of all rifts " and said " It 's exhausting just thinking about it ! " Miller later criticised Nicole 's career in fashion , after John told her he hated her designs for the Surf Club . She said " I had to agree with him - putting <unk> in pink <unk> @-@ neck swimmers is a terrible idea . " 
- 
- 
- = Livin ' the Dream = 
- 
- " Livin ' the Dream " is the twenty @-@ first episode of the ninth season of the American comedy television series The Office and the <unk> episode overall . It originally aired on NBC on May 2 , 2013 . The episode guest stars Michael Imperioli as <unk> Billy , and was initially scheduled to air in its half @-@ hour timeslot , before being expanded to a full hour . 
- The series — presented as if it were a real documentary — depicts the everyday lives of office employees in the Scranton , Pennsylvania , branch of the fictional Dunder Mifflin Paper Company . In the episode , Andy Bernard ( Ed Helms ) decides to pursue a career as a professional actor , and <unk> his job at Dunder Mifflin . Meanwhile , Dwight Schrute ( Rainn Wilson ) finally receives his black belt in karate from his new sensei ( Imperioli ) and , on the recommendation of Jim Halpert ( John Krasinski ) , is promoted to Regional Manager of the Scranton branch . Jim <unk> with Pam Halpert ( Jenna Fischer ) , and makes it clear that he will choose her over Philadelphia . 
- The episode was viewed by an estimated and received a 1 @.@ 8 / 5 percent share among adults between the ages of 18 and 49 , ranking third in its first half @-@ hour timeslot and fourth in its second , marking a slight increase in the ratings from the previous episode . " Livin ' the Dream " received mostly positive reviews from television critics . Critical praise mainly went towards the dynamic between Jim , Pam and Dwight , particularly for the former two 's reconciliation and the latter 's promotion . Andy 's subplot , meanwhile , received more mixed reviews . 
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- = = Plot = = 
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- CEO David Wallace ( Andy Buckley ) plans on firing Andy Bernard ( Ed Helms ) due to his missing work for acting gigs . However , Andy tells David he is resigning to pursue his dreams of stardom full time , and David is relieved to not have to fire him . Dwight Schrute ( Rainn Wilson ) receives his black belt in karate from his new sensei ( Michael Imperioli ) . Seeing Dwight 's tenacity and devotion , David is inspired to make Dwight Andy 's replacement . 
- Jim Halpert ( John Krasinski ) has returned to Scranton full time , saying he has realized that he can 't devote himself to both his family and his new job , and has decided to go " all in " on his family since that is what makes him most happy . David asks Jim his opinion on promoting Dwight , and Jim says that Dwight deserves the job and will be a great manager . Dwight then appoints Jim the new assistant to the regional manager . Darryl <unk> ( Craig Robinson ) informs Jim that <unk> has found a buyer and wants them to do a promotional tour around the country for three months . With <unk> regret , Jim says he will not do the tour because he cannot put his wife Pam ( Jenna Fischer ) through that , unaware that Pam is listening in . 
- Everyone in the office tells Andy that quitting is a foolish move and that he has no chance of achieving stardom . Andy eventually goes back on his decision , and David allows him to stay on in a sales position . However , mere hours later Andy feels that he is only sticking with his <unk> @-@ Mifflin job because it is safe and that he has to take a shot at achieving fame . Fearing his conviction will <unk> a second time , he decides he cannot simply quit , but get fired . This proves difficult as he is unable to make himself go through with any offense more serious than <unk> on David 's car . Andy bids farewell to his <unk> with an unexpectedly moving rendition of " I Will Remember You " , prompting them to comment to the documentary crew that he may have star potential after all . 
- Meanwhile , Angela Martin ( Angela Kinsey ) is evicted from her studio apartment after her cats were taken away by Animal Control . She considers living in a tent in the woods , prompting Oscar Martinez ( Oscar Nunez ) to offer her to stay with him until she gets back on her feet . She finally accepts with gratitude . As they set off to take Angela 's things to Oscar 's place , Oscar mentions her marriage to Robert <unk> and she breaks down into tears , saying " I love him . " However , she denies she still has feelings for Robert and claims she was talking about Dwight . 
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- = = Production = = 
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- " Livin ' the Dream " was written by story editor <unk> <unk> @-@ Wright , marking her second writing credit for the series , after the earlier season episode " <unk> " . It was directed by regular Office director Jeffrey Blitz , who last directed season eight 's " <unk> " . The episode was originally scheduled to air in its regular half @-@ hour time slot , but NBC later announced it would be expanded to fill an hour time slot beginning a half hour early , although it still counts as one official episode , similar to the earlier season episode " Moving On " . Rogers noted that " we knew the last two episodes would be hour @-@ longs , and The <unk> might even end up running longer , but we still had a lot of great storytelling to do leading up to them , and ' Livin ' the Dream ' was one that ultimately deserved to be an hour long episode as well ! " 
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- = = Reception = = 
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- = = = Ratings = = = 
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- " Livin ' the Dream " originally aired on May 2 , 2013 on NBC . In its original American broadcast , " Livin ' the Dream " was viewed by an estimated 3 @.@ 51 million viewers and received a 1 @.@ 8 rating / 5 % share among adults between the ages of 18 and 49 . This means that it was seen by 1 @.@ 8 percent of all 18- to 49 @-@ year @-@ olds , and 5 percent of all 18- to 49 @-@ year @-@ olds watching television at the time of the broadcast . This marked a slight increase in the ratings from the previous episode , " Paper <unk> " . The episode ranked third in its first half @-@ hour timeslot , being beaten by an episode of the CBS comedy series Two and a Half Men which received a 3 @.@ 2 / 10 rating and an entry of the Fox reality series American Idol which scored a 2 @.@ 6 / 9 rating . The second half @-@ hour ranked fourth in its timeslot , being beaten by an episode of the ABC series Grey 's Anatomy which scored a 3 @.@ 0 / 9 rating , an entry of the CBS series Person of Interest which garnered a 2 @.@ 4 / 7 rating , and installment of the Fox series Glee which received a 1 @.@ 9 / 5 rating . 
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- = = = Reviews = = = 
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- " Livin ' the Dream " received positive reviews from television critics . Roth Cornet of IGN wrote that " it <unk> me greatly that at the conclusion of this super @-@ sized episode I was left , once again , truly looking forward to seeing what these next few weeks , and that final hour of The Office , will bring . " Cornet praised the full @-@ use of the ensemble , particularly the " <unk> and Stanley Lil ' Romeo mini @-@ debate " and Creed 's confused <unk> of Dwight 's declaration . Cornet also called the Dwight storyline " perfectly executed " , as well as the Jim @-@ Pam @-@ Dwight dynamic featured throughout the episode , calling their companionship to be " entirely earned " . She also praised Kinsey 's performance during her character 's storyline , and said her final scenes with Oscar " [ <unk> ] at my heart @-@ string " . She gave the episode an 8 @.@ 5 out of 10 , calling it " Great " . M. Giant of Television Without Pity awarded the episode an " A – " and wrote that " almost everybody is having their best day in a long time , in the best episode of The Office in an even longer time " . 
- Nick Campbell of TV.com complimented the sentimental storylines in the episode , specifically between Jim , Pam and Dwight . He was positive towards Jim and Pam 's reconciliation , but felt " something hollow about their reunion " . He also noted that the Jim @-@ Pam storyline caused the Dwight @-@ Angela relationship to go " darker " . Alan Sepinwall of HitFix gave the episode a slightly more mixed review writing that " the non @-@ Andy parts of " Livin ' the Dream " were fairly interesting " . He appreciated the drama coming from Angela 's <unk> , despite <unk> with the logic in the situation . Sepinwall praised the Jim @-@ Dwight dynamic in the episode , considering it an enjoyable payoff , and also praised Jim and Pam 's reconciliation , particularly them annoying their co @-@ workers with their flirting . Joshua Alton of The A.V. Club was more negative towards the episode , saying it felt " padded @-@ out " to fill the full hour timeslot , and that " this episode might be the nadir for the show ’ s hour @-@ long installments " . He was complimentary towards the Jim @-@ Pam storyline , but felt " there wasn ’ t much happening " beyond Pam <unk> Jim 's talk with Darryl . Alton praised the Dwight storyline and his dynamic with Jim and Pam , calling it " the true fan service " . Alton gave the episode a " C – " . 
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- 
- = Toniná = 
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- <unk> ( or Toniná in Spanish <unk> ) is a pre @-@ Columbian archaeological site and ruined city of the Maya civilization located in what is now the Mexican state of Chiapas , some 13 km ( 8 @.@ 1 mi ) east of the town of Ocosingo . 
- The site is medium to large , with groups of temple @-@ pyramids set on terraces rising some 71 metres ( 233 ft ) above a plaza , a large court for playing the <unk> <unk> , and over 100 carved monuments , most dating from the 6th century through the 9th centuries AD , during the Classic period . Toniná is distinguished by its well preserved stucco sculptures and particularly by its in @-@ the @-@ round carved monuments , produced to an extent not seen in <unk> since the end of the much earlier <unk> civilization . 
- Toniná was an aggressive state in the Late Classic , using warfare to develop a powerful kingdom . For much of its history , Toniná was engaged in sporadic warfare with Palenque , its greatest rival and one of the most important polities in the west of the Maya region , although Toniná eventually became the dominant city in the west . 
- The city is notable for having the last known Long Count date on any Maya monument , marking the end of the Classic Maya period in AD 909 . 
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- = = Etymology = = 
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- Toniná means house of stone in the <unk> language of the local Maya inhabitants , an alternate interpretation is the place where stone sculptures are raised to honour time . However , this is a modern name and the original name was either Po or Popo , appearing in Classic Maya texts in the title used for the kings of Toniná , k <unk> po ' <unk> ( Divine Lord of Po ) . A Maya rebellion in Colonial times , in 1558 , featured a group called the po ' <unk> ' ( People of Po ) . Early versions of the Toniná emblem glyph bore a doubled po glyph and the term Popo is also found in Colonial records . Since double sounds were often abbreviated in hieroglyphic texts , Popo may represent the original name of the city . 
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- = = Location = = 
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- Toniná is located at an altitude of 800 to 900 metres ( 2 @,@ 600 to 3 @,@ 000 ft ) above mean sea level in the Chiapas <unk> of southern Mexico , some 40 miles ( 64 km ) south of the contemporary Maya city of Palenque , Toniná 's greatest rival throughout its recorded history . Toniná is separated from Palenque by mountainous terrain and the site core is located along an easily defended ascending limestone ridge immediately to the west of a seasonal tributary of the <unk> <unk> , one of the two rivers forming the Ocosingo Valley . 
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- = = Rulers = = 
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- Rulers of Toniná recorded in the Maya script on Toniná monuments include : 
- The last known recorded date at the site is featured on Monument 101 as 15 January 909 CE . 
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- = = History = = 
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- = = = Early Classic = = = 
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- Toniná had a particularly active Early Classic presence , although the Early Classic remains lie entirely buried under later construction . Due to this , early texts are scarce and only offer a glimpse of the early history of the site . An 8th @-@ century text refers to a king ruling in AD 217 , although it only mentions his title , not his name . 
- Ruler 1 is depicted on a couple of Early Classic monuments , the better preserved of which is an altar that dates to <unk> . A ruler known as Jaguar Bird <unk> is represented on a 6th @-@ century stela , which describes him <unk> to the throne in 568 . 
- The first mention of Toniná in a record from a foreign state is from the site of <unk> , located 72 kilometres ( 45 mi ) to the northeast on the Usumacinta River , the text is from a throne and describes the capture of a person from Toniná in <unk> . 
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- = = = Late Classic = = = 
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- = = = = K 'inich Hix Chapat = = = = 
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- Toniná 's history comes into focus in the Late Classic , when its historical record is more fully represented by hieroglyphic texts . In <unk> K 'inich Hix Chapat is recorded as installing two subordinate lords but little else is known of his reign , although he was probably enthroned in 595 . The last mention of K 'inich Hix Chapat is in a monument dated to <unk> that appears to be a memorial stone . 
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- = = = = Ruler 2 = = = = 
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- Ruler 2 acceded to the throne of Toniná in <unk> . His rule is marked by warfare and the frequent depiction of bound captives on his monuments . Ruler 2 established the use of in @-@ the @-@ round <unk> style that came to <unk> the stelae of Toniná . A monument dated to 682 depicts three naked prisoners with their arms bound , one of them is identified as a lord from <unk> ' , an as yet unidentified site . His reign may have ended with his defeat and capture by K 'inich Kan <unk> II of Palenque in September <unk> , as described in a <unk> text from Temple 17 in the rival city , an event that probably culminated in his sacrifice . 
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- = = = = K 'inich B 'aaknal Chaak = = = = 
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- K 'inich B 'aaknal Chaak was enthroned in <unk> , twenty years after Ruler 2 , and reigned for twenty @-@ seven years . During his reign he restored Toniná 's power with a number of military victories over Palenque , and his reign was dominated by the struggle against the rival city for regional power . Ballcourt 1 , the larger of Toniná 's two <unk> , was dedicated in 699 to celebrate three victories over the city 's arch @-@ rival . The ballcourt originally had six sculptures of bound captives , all vassals of the enemy Palenque king from the Usumacinta region . The date of the king 's death is unknown . 
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- = = = = Ruler 4 = = = = 
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- Ruler 4 came to power in 708 at a very young age . Three years later , in 711 , while Ruler 4 was still a child , Toniná gained an important victory over Palenque . The battle resulted in the capture of Kan Joy <unk> II of Palenque and made Toniná the dominant centre in the lower Usumacinta region . The victory was so complete that it resulted in a ten @-@ year gap in the dynastic history of the defeated city , during which the captured ruler may have been held hostage . Ruler 4 continued in power to celebrate the period endings of 716 and <unk> . A captive depicted on one of his monuments is identified as being from the distant city of <unk> , one of the two Maya " <unk> " . 
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- = = = = K 'inich Ich 'aak Chapat = = = = 
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- Ruler 4 was succeeded by K 'inich Ich 'aak Chapat in <unk> . Around 725 Toniná fought a war against Piedras Negras , a city on the north bank of the Usumacinta River , now in Guatemala . A series of events during his reign were marked on monuments between <unk> and 729 and in 730 he <unk> the tomb of his predecessor K 'inich B 'aaknal Chaak . The mother of K 'inich Ich 'aak Chapat is named as Lady <unk> <unk> K 'awiil and his father may well have been K 'inich B 'aaknal Chaak himself . The reign of K 'inich Ich 'aak Chapat is notable for the absence of the usual sculptures depicting bound war captives , although the reason for this is unknown . 
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- = = = = Later rulers = = = = 
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- Little is known of the next two rulers , Ruler 6 is named as K 'inich <unk> Chapat , he celebrated the period ending of <unk> and may have died 762 . A damaged text accompanying the image of a bound captive indicates renewed warfare with Palenque during his reign , however the name of the prisoner is lost and it is unclear if it is the actual king of Palenque or merely one of his vassals . He was succeeded by Ruler 7 , about whom even less is known . Around <unk> Toniná defeated Palenque in battle . 
- In 775 a text recorded the death of Lord <unk> Chan K <unk> ' , a prince who appears to have been the heir to the throne and who died before he could take power . 
- Ruler 8 was the last of the successful warrior kings of Toniná . He celebrated a series of events between 789 and <unk> , including the defeat of <unk> in 789 , and the capture of the ruler <unk> 'an Aj <unk> , who appears to have been the vassal of B <unk> K 'awiil of <unk> . In 799 he <unk> the tomb of Ruler 1 . Ruler 8 oversaw an extensive <unk> of the upper levels of the Acropolis . Ruler 8 erected a number of sculptures of bound prisoners of war and adopted the title <unk> b <unk> b 'aak , " He of Many <unk> " . However , the lesser extent of Toniná 's power is evident from its victory over the site of <unk> <unk> 'i ' ( White Dog ) , an important city in the <unk> region , an area which had once been dominated by Toniná . 
- By the time of Ruler 8 's successor , Uh Chapat , Toniná was clearly in decline . Only a single event , in <unk> , can be dated to his reign , although a stucco mural depicting captives with <unk> at their <unk> may belong to his period of rule . 
- The history of Toniná continued after most other Classic Maya cities had fallen , perhaps aided by the site 's relative isolation . Ruler 10 is associated with a monument dating to <unk> in the Terminal Classic and a monument dating to 909 bears the last known Long Count date although the name of the king has not survived . <unk> fragments indicate that occupation at the site continued for another century or more . 
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- = = = Modern history = = = 
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- The first published account of the ruins was made by <unk> Jacinto <unk> at the end of the 17th century . A number of visitors investigated the ruins of Toniná in the 19th century , the first being an expedition led by Guillaume <unk> in 1808 . John Lloyd Stephens and Frederick <unk> visited in 1840 , and Stephens wrote an extensive description of the site . Eduard <unk> and <unk> <unk> @-@ Sachs investigated the monuments at Toniná , publishing their reports at the turn of the 20th century . Karl Sapper visited the site in 1895 and 1896 . <unk> <unk> and Oliver La <unk> investigated the site in 1920s for Tulane University , publishing their reports in 1926 — 1927 . 
- The French Toniná Project began excavations in 1972 which continued through 1975 , then resumed in 1979 to 1980 , under the direction of Pierre <unk> and Claude <unk> . The National Institute of Anthropology and History of Mexico ( <unk> , the <unk> <unk> de <unk> e Historia ) began their own excavations at Toniná the following year . 
- The site is accessible for tourism and has a small museum that was inaugurated on 15 July 2000 . 
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- = = Site description = = 
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- The site was built on a platform covering 6 hectares ( 650 @,@ 000 sq ft ) . The principal architecture is located in the <unk> , which occupies seven south @-@ facing terraces on the northern side of the platform , rising 71 metres ( 233 ft ) over the plaza below . It has a more distinct geometry than at most Maya sites , with a right @-@ angle relationship between most structures . 
- Much of the public imagery of the site details the ruthless manner in which the city dealt with its enemies . A 16 by 4 metres ( 52 by 13 ft ) stucco sculpture rising from the fourth to fifth terraces depicts a skeletal death god carrying the severed head of a lord of Palenque in one hand . A frieze on the fifth terrace probably displayed Toniná 's most distinguished victims , dozens of fragments of this frieze were discovered in the plaza below . This frieze was carved from the local sandstone but its style is that of Palenque , suggesting that captured artists carried out the work . 
- After the abandonment of the city at the end of the Classic Period , many of the sculptures fell down the steep embankment supporting the seven terraces . 
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- = = = Structures = = = 
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- Ballcourt 1 ( the Sunken Ballcourt ) was dedicated in 699 by K 'inich B 'aaknal Chaak to mark three victories over K 'inich Kan <unk> II of Palenque . <unk> of the torsos of six captured vassals of the Palenque king were used as ballcourt markers . One of these vassals is named as <unk> Ahk ( Green Turtle ) , who was the lord of <unk> Te ' , a site that probably lay on the south side of the Usumacinta between Piedras Negras and <unk> . 
- Ballcourt 2 is the smaller of the two <unk> and lies in the north of the plaza , at the foot of the Acropolis . 
- The Palace of the <unk> is entered via three step @-@ vaulted arches on the eastern side of the second terrace of the Acropolis . 
- The Palace of <unk> is located on the fourth terrace of the Acropolis . The south facade of the palace is decorated with four large stepped frets . On the east side of the palace a stairway leads to a decorated throne of stone and stucco . One of the rooms of the palace contains a stucco decoration representing feathered serpents and crossed bones . 
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- = = = Monuments and sculptures = = = 
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- The monuments of Toniná tend to be smaller than those at other Maya sites , with most of the stelae measuring less than 2 metres ( 6 @.@ 6 ft ) tall . The most important difference from monuments at other Maya sites is that they are carved in the round like statues , often with hieroglyphic text running down the spine . On the fifth terrace , in @-@ the @-@ round sculptures of Toniná 's rulers dominated two @-@ dimensional representations of defeated enemies . 
- The dated monuments at Toniná span the period from AD 495 to 909 , covering most of the Classic Period . 
- Monument 3 is broken into various fragments , five of which were recovered from various locations in Ocosingo and Toniná through the course of the 20th century and most of which were reunited in the Toniná site museum . Aside from being broken , the stela is largely complete and only lightly eroded , it is a statue of a ruler with inscriptions describing the accession of K 'inich <unk> Chaak and the promotion to the priesthood of Aj Ch <unk> <unk> . 
- Monument 5 was recovered from a school in Ocosingo and moved to the site museum of Toniná . It is a badly eroded life @-@ size human statue with the head missing . 
- Monument 7 is carved from yellow sandstone and has suffered only minor damage . It is a stela base with well @-@ preserved hieroglyphs on all four vertical sides and was dedicated by K 'inich Ich 'aak Chapat in <unk> . It is currently in the <unk> Regional in <unk> Gutiérrez . 
- Monument 8 dates to the reign of Ruler 2 . It marks the period ending of 682 and shows the presentation of three war captives . 
- Monument 12 is a sculpture carved in the round , representing Ruler 2 . It dates to AD <unk> . 
- Monument 27 is a carved step depicting K 'awiil Mo ' , a lord from Palenque , as an elderly prisoner , bound and lying on his back with his profile positioned in such a way as to be <unk> on time and again . 
- Monument 99 is an undated fragment that depicts a female captive , which is rare in Maya art . 
- Monument 101 has the last Long Count date from any Maya monument , it marks the K <unk> ending of AD 909 . 
- Monument 106 is the earliest securely dated monument at the site , dating to AD 593 . It depicts Ruler 1 . 
- Monument 113 depicts Ruler 2 participating in a scattering ritual . 
- Monument 114 was dedicated in 794 by Ruler 8 . It commemorates the death of an important noble , apparently a relative or vassal of Ruler 8 's predecessor <unk> Chapat . 
- Monument 122 is a low relief sculpture marking the defeat of Palenque by Ruler 4 in 711 and the capture of Kan Joy <unk> II , who is depicted as a bound captive . 
- Monument 141 is a very well preserved hieroglyphic panel carved from fine <unk> white limestone with almost the whole inscription intact . It describes the dedication of a ballcourt by K 'inich B 'aaknal Chaak . 
- Monument 154 dates to the reign of K 'inich Hix Chapat and records his installing of two subordinate lords in <unk> . 
- Monument 158 has a very late date , in AD <unk> , at the very end of the Classic Period . It was erected during the reign of Ruler 10 . 
- The Frieze of the Dream Lords ( also known as the Frieze of the Four Suns or Frieze of the Four <unk> ) was uncovered by archaeologists during excavations in 1992 . It is a stucco mural located at the east end of the 5th terrace . It represents a complex supernatural scene divided into four by a feather @-@ covered <unk> from which hang the severed heads of sacrificial victims . Among the <unk> <unk> are depicted the <unk> ( spirit companions ) of the Maya elite . The most well @-@ preserved section of the sculpture depicts a skeletal supernatural way named <unk> <unk> Kimi ( " Turtle Foot Death " ) wearing <unk> on its feet and carrying a severed head in one hand , interpreted as the way of a lord from the site of <unk> ' . The frieze was once brightly painted in red , blue and yellow . This frieze has strong stylistic parallels with mural paintings at the great Early Classic metropolis of <unk> in the distant Valley of Mexico . 
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- = = = The site museum = = = 
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- The site museum is located 300 metres ( 980 ft ) outside of the Toniná archaeological zone . It possesses 2 exhibition rooms and a conference room . The first room explains the pyramidal form of the <unk> and how it relates to Maya mythology , while the main room contains sculptures of the city 's rulers . 
- <unk> in the collection include stone sculptures , ceramics and artefacts sculpted from bone , shell , <unk> and flint . The pieces in the museum <unk> depict the two sides of the power exercised by Toniná , on the one hand with sculptures of the city 's rulers and on the other with its depictions of bound prisoners of war . 
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- 
- = Central Area Command ( RAAF ) = 
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- Central Area Command was one of several geographically based commands raised by the Royal Australian Air Force ( RAAF ) during World War II . It was formed in March 1940 , and covered the central portion of New South Wales . Headquartered at Sydney , Central Area Command was primarily responsible for air defence , aerial reconnaissance and protection of the sea lanes within its boundaries . It was disbanded in August 1941 and control of its units taken over by other RAAF formations . <unk> in 1943 – 44 to raise a new Central Area Command did not come to fruition . 
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- = = History = = 
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- Prior to World War II , the Royal Australian Air Force was small enough for all its elements to be directly controlled by RAAF Headquarters in Melbourne . After war broke out in September 1939 , the RAAF began to implement a <unk> form of command , <unk> with expected increases in manpower and units . Its initial move in this direction was to create Nos. 1 and 2 Groups to control units in Victoria and New South Wales , respectively . Then , between March 1940 and May 1941 , the RAAF divided Australia and New Guinea into four geographically based command @-@ and @-@ control zones : Central Area , Southern Area , Western Area , and Northern Area . The roles of these area commands were air defence , protection of adjacent sea lanes , and aerial reconnaissance . Each was led by an Air Officer Commanding ( AOC ) responsible for the administration and operations of all air bases and units within his boundary . 
- No. 2 Group , which had been established on 20 November 1939 , was re @-@ formed as one of the first two area commands , Central Area , on 7 March 1940 . Headquartered in Sydney , Central Area Command was given control of all Air Force units in New South Wales except those in the southern <unk> and the north of the state . Units in Queensland were also temporarily assigned to its control , pending the formation of Northern Area . Central Area 's inaugural AOC was Air Commodore Adrian " King " Cole , who had also led No. 2 Group . His senior air staff officer was Wing Commander Alan <unk> . 
- In May 1940 it was reported that the area 's headquarters building would change from " Mont <unk> " in Point Piper to the mansion " <unk> " nearby . Cole handed over command of Central Area to Air Commodore Bill Anderson in December 1940 . By August 1941 , the RAAF 's expanding instructional program necessitated the establishment of overarching training organisations on a semi @-@ functional , semi @-@ geographical basis . Accordingly , No. 2 ( Training ) Group was formed in Sydney , taking responsibility for the training units then under Central Area , which was disbanded . Control of other Central Area units was " divided as convenient " , according to the official history of the war , between Northern and Southern Area <unk> . 
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- = = Aftermath = = 
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- The RAAF 's area command structure was revised in 1942 , following the outbreak of the Pacific War : Northern Area was split into North @-@ Eastern Area and North @-@ Western Area , and a new command covering New South Wales and southern Queensland , Eastern Area , was created , making a total of five commands . In October 1943 , the Air Board proposed carving a new Central Area Command out of Eastern Area , which by then was considered too large to be controlled by one headquarters and therefore <unk> for <unk> . This Central Area Command would have been responsible for training and operational units in southern Queensland . The War Cabinet deferred its decision on the proposal . The concept was raised again in August 1944 , and this time the new Central Area Command was to control maintenance units , as well as training and operations , in southern Queensland . Once again , nothing came of the proposal . 
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- 
- = Corn crake = 
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- The corn crake , corncrake or <unk> ( Crex crex ) is a bird in the rail family . It breeds in Europe and Asia as far east as western China , and <unk> to Africa for the northern hemisphere 's winter . It is a medium @-@ sized crake with <unk> or grey @-@ streaked brownish @-@ black upperparts , chestnut markings on the wings , and blue @-@ grey underparts with rust @-@ coloured and white bars on the flanks and undertail . The strong bill is flesh @-@ toned , the iris is pale brown , and the legs and feet are pale grey . Juveniles are similar in plumage to adults , and <unk> chicks are black , as with all rails . There are no subspecies , although individuals from the east of the breeding range tend to be slightly paler than their western counterparts . The male 's call is a loud krek krek , from which the scientific name is derived . The corn crake is larger than its closest relative , the African crake , which shares its wintering range ; that species is also darker @-@ <unk> , and has a <unk> face . 
- The corn crake 's breeding habitat is grassland , particularly <unk> , and it uses similar environments on the wintering grounds . This secretive species builds a nest of grass leaves in a hollow in the ground and lays 6 – 14 cream @-@ coloured eggs which are covered with rufous blotches . These hatch in 19 – 20 days , and the black precocial chicks fledge after about five weeks . This crake is in steep decline across much of its former breeding range because modern farming practices often destroy nests before breeding is completed . The corn crake is omnivorous but mainly feeds on invertebrates , the occasional small frog or mammal , and plant material including grass seed and cereal grain . Natural threats include introduced and feral mammals , large birds , various parasites and diseases . 
- Although numbers have declined steeply in western Europe , this bird is classed as least concern on the IUCN Red List because of its huge range and large , apparently stable , populations in Russia and Kazakhstan . Numbers in western China are more significant than previously thought , and conservation measures have facilitated an increased population in some countries which had suffered the greatest losses . Despite its elusive nature , the loud call has ensured the corn crake has been noted in literature , and garnered a range of local and dialect names . 
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- = = Taxonomy = = 
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- The rails are a bird family comprising nearly 150 species . Although origins of the group are lost in antiquity , the largest number of species and least specialised forms are found in the Old World , suggesting this family originated there . The taxonomy of the small crakes is complicated , but the closest relative of the corn crake is the African crake , C. <unk> , which has sometimes been given its own genus , <unk> , but is now more usually placed in Crex . Both species are short @-@ billed brown birds with a preference for grassland rather than wetland habitats typical of rails . <unk> crakes , particularly the ash @-@ throated crake ( <unk> <unk> ) are near relatives of the Crex genus . 
- Corn crakes were first described by Linnaeus in his Systema <unk> in 1758 as <unk> crex , but was subsequently moved to the genus Crex , created by German naturalist and ornithologist Johann <unk> <unk> in 1803 , and named Crex <unk> . The earlier use of crex gives it priority over <unk> 's specific name <unk> , and leads to the current name of Crex crex . The binomial name , Crex crex , from the Ancient Greek " <unk> " , is <unk> , referring to the crake 's repetitive grating call . The common name was formerly spelt as a single word , " corncrake " , but the official version is now " corn crake " . The English names refer to the species habit of nesting in dry hay or cereal fields , rather than marshes used by most members of this family . 
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- = = Description = = 
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- The corn crake is a medium @-@ sized rail , 27 – 30 cm ( 11 – 12 in ) long with a wingspan of 42 – 53 cm ( 17 – 21 in ) . Males weigh 165 g ( 5 @.@ 8 oz ) on average and females 145 g ( 5 @.@ 1 oz ) . The adult male has the crown of its head and all of its upperparts brown @-@ black in colour , streaked with buff or grey . The wing coverts are a distinctive chestnut colour with some white bars . The face , neck and breast are blue @-@ grey , apart from a pale brown streak from the base of the bill to behind the eye , the belly is white , and the flanks , and undertail are barred with chestnut and white . The strong bill is flesh @-@ coloured , the iris is pale brown , and the legs and feet are pale grey . Compared to the male , the female has warmer @-@ toned upperparts and a narrower duller eye streak . Outside the breeding season , the upperparts of both sexes become darker and the underparts less grey . The juvenile is like the adult in appearance , but has a yellow tone to its upperparts , and the grey of the underparts is replaced with buff @-@ brown . The chicks have black down , as with all rails . While there are no subspecies , all populations show great individual variation in colouring , and the birds gradually become paler and <unk> towards the east of the range . Adults undergo a complete moult after breeding , which is normally finished by late August or early September , before migration to south eastern Africa . There is a pre @-@ breeding partial moult prior to the return from Africa , mainly involving the plumage of the head , body and tail . Young birds have a head and body moult about five weeks after hatching . 
- The corn crake is <unk> with the African crake on the wintering grounds , but can be distinguished by its larger size , paler upperparts , tawny <unk> and different underparts pattern . In flight , it has longer , less rounded wings , and shallower <unk> than its African relative , and shows a white leading edge to the inner wing . In both the breeding and wintering ranges it is unlikely to be confused with any other rails , since <unk> species are smaller , with white markings on the upperparts , different underparts patterns and shorter bills . A flying corn crake can resemble a <unk> , but its chestnut wing pattern and dangling legs are diagnostic . 
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- = = = Voice = = = 
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- On the breeding grounds , the male corn crake 's advertising call is a loud , repetitive , grating krek krek normally delivered from a low perch with the bird 's head and neck almost vertical and its bill wide open . The call can be heard from 1 @.@ 5 km ( 0 @.@ 93 mi ) away , and serves to establish the breeding territory , attract females , and challenge <unk> males . <unk> differences in <unk> mean that individual males can be distinguished by their calls . Early in the season , the call is given almost continuously at night , and often during the day , too . It may be repeated more than 20 @,@ 000 times a night , with a peak between midnight and 3 am . The call has evolved to make a singing male 's location clear , as this species hides in vegetation . The frequency of calling reduces after a few weeks but may intensify again near the end of the laying period before falling away towards the end of the breeding season . To attract males , mechanical imitations of their call can be produced by rubbing a piece of wood down a notched stick , or by <unk> a credit card against a comb or <unk> @-@ <unk> . The male also has a growling call , given with the bill shut and used during aggressive interactions . 
- The female corn crake may give a call that is similar to that of the male ; it also has a distinctive <unk> sound , similar in rhythm to the main call but without the grating quality . The female also has a high @-@ pitched <unk> call , and a oo @-@ oo @-@ oo sound to call the chick . The chicks make a quiet <unk> @-@ <unk> contact call , and a <unk> used to beg for food . Because of the difficulty in seeing this species , it is usually <unk> by counting males calling between 11 pm and 3 am ; the birds do not move much at night , whereas they may wander up to 600 m ( 660 yd ) during the day , which could lead to double @-@ counting if monitored then . <unk> individual males suggests that just counting calling birds <unk> the true count by nearly 30 % , and the discrepancy is likely to be greater , since only 80 % of males may call at all on a given night . The corn crake is silent in Africa . 
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- = = Distribution and habitat = = 
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- The corn crake breeds from Britain and Ireland east through Europe to central Siberia . Although it has vanished from much of its historic range , this bird was once found in suitable habitats in Eurasia everywhere between latitudes 41 ° N and 62 ° N. There is also a sizable population in western China , but this species nests only rarely in northern Spain and in Turkey . Old claims of breeding in South Africa are incorrect , and result from <unk> of eggs in a museum collection which are actually those of the African rail . 
- The corn crake winters mainly in Africa , from the Democratic Republic of the Congo and central Tanzania south to eastern South Africa . North of this area , it is mainly seen on migration , but occasionally winters in North Africa and to the west and north of its core area in southeast Africa . Most of the South African population of about 2 @,@ 000 birds occurs in KwaZulu @-@ Natal and the former <unk> Province , and numbers elsewhere in Africa are uncertain . There are several nineteenth @-@ century records , when populations were much higher than now , of birds being seen in western Europe , mainly Britain and Ireland , between December and February . 
- This crake <unk> to Africa along two main routes : a western route through Morocco and Algeria , and a more important <unk> through Egypt . On passage , it has been recorded in most countries between its breeding and wintering ranges , including much of West Africa . Birds from <unk> following the western route paused in West Africa on their way further south , and again on the return flight , when they also rested in Spain or North Africa . Eastern migrants have been recorded in those parts of southern Asia that lie between the east of the breeding range and Africa . Further afield , the corn crake has been recorded as a <unk> to Sri Lanka , Vietnam and Australia , the <unk> , Bermuda , Canada , the US , Greenland , Iceland , the <unk> , the Azores , Madeira , and the Canary Islands . 
- The corn crake is mainly a lowland species , but breeds up to 1 @,@ 400 m ( 4 @,@ 600 ft ) altitude in the <unk> , 2 @,@ 700 m ( 8 @,@ 900 ft ) in China and 3 @,@ 000 m ( 9 @,@ 800 ft ) in Russia . When breeding in Eurasia , the corn crake 's habitats would originally have included river meadows with tall grass and meadow plants including sedges and irises . It is now mainly found in cool moist grassland used for the production of hay , particularly moist traditional farmland with limited cutting or fertiliser use . It also <unk> other <unk> grasslands in mountains or <unk> , on coasts , or where created by fire . <unk> areas like wetland edges may be used , but very wet habitats are avoided , as are open areas and those with vegetation more than 50 cm ( 20 in ) tall , or too dense to walk through . The odd bush or hedge may be used as a calling post . <unk> which is not <unk> or grazed becomes too matted to be suitable for nesting , but locally crops such as <unk> , <unk> , rape , <unk> or potatoes may be used . After breeding , adults move to taller vegetation such as common <unk> , iris , or <unk> to moult , returning to the hay and <unk> meadows for the second brood . In China , flax is also used for nest sites . Although males often sing in intensively managed grass or cereal crops , successful breeding is uncommon , and nests in the field margins or nearby fallow ground are more likely to succeed . 
- When wintering in Africa , the corn crake occupies dry grassland and <unk> habitats , occurring in vegetation 30 – 200 cm ( 0 @.@ 98 – 6 @.@ 56 ft ) tall , including seasonally burnt areas and occasionally sedges or <unk> beds . It is also found on fallow and abandoned fields , uncut grass on airfields , and the edges of crops . It occurs at up to at least 1 @,@ 750 metres ( 5 @,@ 740 ft ) altitude in South Africa . Each bird stays within a fairly small area . Although it sometimes occurs with the African crake , that species normally prefers moister and shorter grassland habitats than does the corn crake . On migration , the corn crake may also occur in <unk> and around golf courses . 
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- = = Behaviour = = 
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- The corn crake is a difficult bird to see in its breeding sites , usually being hidden by vegetation , but will sometimes emerge into the open . Occasionally , individuals may become very trusting ; for five consecutive summers , an individual crake on the Scottish island of <unk> entered a kitchen to feed on scraps , and , in 1999 , a wintering Barra bird would come for <unk> feed once the chickens had finished . In Africa , it is more secretive than the African crake , and , unlike its relative , it is rarely seen in the open , although it occasionally feeds on tracks or road sides . The corn crake is most active early and late in the day , after heavy rain and during light rain . Its typical flight is weak and <unk> , although less so than that of the African crake . For longer flights , such as migration , it has a <unk> , stronger action with legs drawn up . It walks with a high @-@ stepping action , and can run swiftly through grass with its body held horizontal and laterally flattened . It will swim if essential . When flushed by a dog , it will fly less than 50 m ( 160 ft ) , frequently landing behind a bush or thicket , and then crouch on landing . If disturbed in the open , this crake will often run in a crouch for a short distance , with its neck stretched forward , then stand upright to watch the intruder . When captured it may <unk> death , recovering at once if it sees a way out . 
- The corn crake is solitary on the wintering grounds , where each bird occupies 4 @.@ 2 – 4 @.@ 9 ha ( 10 – 12 acres ) at one time , although the total area used may be double that , since an individual may move locally due to flooding , plant growth , or grass cutting . Flocks of up to 40 birds may form on migration , sometimes associating with common <unk> . Migration takes place at night , and flocks resting during the day may aggregate to hundreds of birds at favoured sites . The ability to migrate is innate , not learned from adults . Chicks raised from birds kept in captivity for ten generations were able to migrate to Africa and return with similar success to wild @-@ bred young . 
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- = = = Breeding = = = 
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- Until 1995 , it was assumed that the corn crake is monogamous , but it <unk> that a male may have a shifting home range , and mate with two or more females , moving on when laying is almost complete . The male 's territory can vary from 3 to 51 ha ( 7 @.@ 4 to 126 @.@ 0 acres ) , but averages 15 @.@ 7 ha ( 39 acres ) . The female has a much smaller range , averaging only 5 @.@ 5 ha ( 14 acres ) . A male will challenge an intruder by calling with his wings <unk> and his head pointing forward . Usually the stranger moves off ; if it stays , the two birds square up with heads and necks raised and the wings touching the ground . They then run around giving the growling call and <unk> at each other . A real fight may ensue , with the birds leaping at each other and pecking , and sometimes kicking . Females play no part in defending the territory . 
- The female may be offered food by the male during courtship . He has a brief courtship display in which the neck is extended and the head held down , the tail is fanned , and the wings are spread with the tips touching the ground . He will then attempt to approach the female from behind , and then leap on her back to <unk> . The nest is typically in grassland , sometimes in safer sites along a hedge , or near an isolated tree or bush , or in overgrown vegetation . Where grass is not tall enough at the start of the season , the first nest may be constructed in <unk> or marsh vegetation , with the second brood in hay . The second nest may also be at a higher altitude that the first , to take advantage of the later @-@ developing grasses further up a hill . The nest , well hidden in the grass , is built in a scrape or hollow in the ground . It is made of woven coarse dry grass and other plants , and lined with finer grasses . Although nest construction is usually described as undertaken by the female , a recent <unk> study found that in the captive population the male always built the nest . 
- The nest is 12 – 15 cm ( 4 @.@ 7 – 5 @.@ 9 in ) in diameter and 3 – 4 cm ( 1 @.@ 2 – 1 @.@ 6 in ) deep . The clutch is 6 – 14 , usually 8 – 12 eggs ; these are oval , slightly glossy , creamy or <unk> with green , blue or grey , and blotched red @-@ brown . They average 37 mm × 26 mm ( 1 @.@ 5 in × 1 @.@ 0 in ) and weigh about 13 – 16 g ( 0 @.@ 46 – 0 @.@ 56 oz ) , of which 7 % is shell . The eggs are laid at daily intervals , but second clutches may sometimes have two eggs added per day . Incubation is by the female only ; her tendency to sit tight when disturbed , or wait until the last moment to flee , leads to many deaths during hay @-@ cutting and harvesting . The eggs hatch together after 19 – 20 days , and the precocial chicks leave the nest within a day or two . They are fed by the female for three or four days , but can find their own food thereafter . The juveniles fledge after 34 – 38 days . The second brood is started about 42 days after the first , and the incubation period is slightly shorter at 16 – 18 days . The grown young may stay with the female until departure for Africa . 
- Nest success in undisturbed sites is high , at 80 – 90 % , but much lower in fertilised meadows and on arable land . The method and timing of mowing is crucial ; mechanized mowing can kill 38 – 95 % of chicks in a given site , and losses average 50 % of first brood chicks and somewhat less than 40 % of second brood chicks . The influence of weather on chick survival is limited ; although chick growth is faster in dry or warm weather , the effects are relatively small . Unlike many precocial species , chicks are fed by their mother to a greater or lesser extent until they become independent , and this may cushion them from adverse conditions . The number of live chicks hatched is more important than the weather , with lower survival in large broods . The annual adult survival rate is under 30 % , although some individuals may live for 5 – 7 years . 
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- = = = Feeding = = = 
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- The corn crake is omnivorous , but mainly feeds on invertebrates , including earthworms , slugs and snails , spiders , beetles , <unk> , grasshoppers and other insects . In the breeding areas , it is a predator of <unk> <unk> , which infest <unk> <unk> and in the past consumed large amounts of the former grassland pests , <unk> and <unk> . This crake will also eat small frogs and mammals , and plant material including grass seed and cereal grain . Its diet on the wintering grounds is generally similar , but includes locally available items such as <unk> , cockroaches and dung beetles . Food is taken from the ground , low @-@ growing plants and from inside grass <unk> ; the crake may search leaf litter with its bill , and run in pursuit of active prey . Hunting is normally in cover , but , particularly in the wintering areas , it will occasionally feed on grassy tracks or dirt roads . <unk> material is regurgitated as 1 cm ( 0 @.@ 39 in ) <unk> . Chicks are fed mainly on animal food , and when fully grown they may fly with the parents up to 6 @.@ 4 km ( 4 @.@ 0 mi ) to visit supplementary feeding areas . As with other rails , grit is swallowed to help break up food in the stomach . 
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- = = Predators and parasites = = 
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- Predators on the breeding grounds include feral and domestic cats , introduced American mink , feral ferrets , otters and red foxes , and birds including the common <unk> and hooded <unk> . In Lithuania , the introduced <unk> dog has also been recorded as taking corn crakes . When chicks are exposed by rapid mowing , they may be taken by large birds including the white stork , <unk> and other birds of prey , <unk> and <unk> . At undisturbed sites nests and broods are rarely attacked , as reflected in a high breeding success . There is a record of a corn crake on migration through Gabon being killed by a black <unk> . 
- The widespread <unk> <unk> <unk> , which lives in the <unk> of birds , has been recorded in the corn crake , as have the parasitic worm <unk> elegans , the larvae of parasitic flies , and hard ticks of the genera <unk> and <unk> . 
- During the reintroduction of corn crakes to England in the 2003 breeding season , <unk> and ill health in pre @-@ release birds was due to bacteria of a pathogenic <unk> species . Subsequently , <unk> tests were done to detect infected individuals and to find the source of the bacteria in their environment . 
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- = = Status = = 
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- Until 2010 , despite a breeding range estimated at 12 @,@ 400 @,@ 000 km2 ( 4 @,@ 800 @,@ 000 sq mi ) , the corn crake was classified as near threatened on the IUCN Red List because of serious declines in Europe , but improved monitoring in Russia indicates that anticipated losses there have not occurred and numbers have remained stable or possibly increased . It is therefore now classed as least concern , since the major populations in Russia and Kazakhstan are not expected to change much in the short term . There are an estimated 1 @.@ 3 – 2 @.@ 0 million breeding pairs in Europe , three @-@ quarters of which are in European Russia , and a further 515 @,@ 000 – 1 @,@ 240 @,@ 000 pairs in Asiatic Russia ; the total Eurasian population has been estimated at between 5 @.@ 45 and 9 @.@ 72 million individuals . In much of the western half of its range , there have been long @-@ term declines that are expected to continue , although conservation measures have enabled numbers to grow in several countries , including a five @-@ fold increase in Finland , and a doubling in the UK . In the Netherlands , there were 33 breeding territories in 1996 , but this number had increased to at least 500 by 1998 . 
- The breeding corn crake population had begun to decline in the 19th century , but the process gained pace after World War II . The main cause of the steep declines in much of Europe is the loss of nests and chicks from early mowing . <unk> dates have moved forward in the past century due to faster crop growth , made possible by land drainage and the use of <unk> , and the move from manual grass @-@ cutting using <unk> to mechanical <unk> , at first horse @-@ drawn and later pulled by <unk> . <unk> also means that large areas can be cut quickly , leaving the crake with no alternative sites to raise either a first brood if suitable habitat has gone , or a replacement brood if the first nest is destroyed . The pattern of mowing , typically in a circular pattern from the outside of a field to its centre , gives little chance of escape for the chicks , which are also exposed to potential animal predators . Adults can often escape the <unk> , although some <unk> females sit tight on the nest , with fatal results . 
- Loss of habitat is the other major threat to the corn crake . Apart from the reduced suitability of drained and fertilised <unk> fields compared to traditional hay meadows , in western Europe the conversion of grassland to arable has been aided by subsidies , and further east the collapse of collective farming has led to the abandonment and lack of management of much land in this important breeding area . More localised threats include floods in spring , and disturbance by roads or wind farms . This bird is good eating ; when they were common in England , Mrs Beeton recommended roasting four on a <unk> . More significant than direct hunting is the loss of many birds , up to 14 @,@ 000 a year , in Egypt , where migrating birds are captured in nets set for the quail with which they often migrate . Although this may account for 0 @.@ 5 – 2 @.@ 7 % of the European population , the losses to this form of hunting are less than when the targeted species were more numerous and predictable . 
- Most European countries have taken steps to conserve the corn crake and produce national management policies ; there is also an overall European action plan . The focus of conservation effort is to monitor populations and ecology and to improve survival , principally through changing the timing and method of hay harvesting . Later cutting gives time for breeding to be completed , and leaving uncut strips at the edges of fields and cutting from the centre outwards reduces the casualties from mowing . <unk> these changes is predicted to stop the population decline if the measures are applied on a sufficiently large scale . <unk> of illegal hunting , and protection in countries where hunting is still allowed , are also conservation aims . <unk> of the corn crake is being attempted in England , and breeding sites are scheduled for protection in many other countries . Where breeding sites <unk> on urban areas , there are cost implications , estimated in one German study at several million euros per corn crake . The corn crake does not appear to be seriously threatened on its wintering grounds and may benefit from <unk> , which creates more open habitats . 
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- = = In culture = = 
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- Most rails are secretive wetland birds that have made little cultural impression , but as a formerly common farmland bird with a loud nocturnal call that sometimes led to disturbed sleep for rural dwellers , the corn crake has acquired a variety of folk names and some commemoration in literature . 
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- = = = Names = = = 
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- The favoured name for this species among naturalists has changed over the years , with " <unk> " and variants of " corncrake " being preferred at various times . " <unk> <unk> " also had a period of popularity between 1768 and 1813 . The originally Older Scots " <unk> " was popularised by Thomas <unk> , who used this term in his 1797 A History of British Birds . Other Scots names include " corn <unk> " and " <unk> " ; the latter term , like " king of the quail " , " grass quail " , the French " <unk> de <unk> " , and the German " <unk> " refer to the association with the small <unk> . Another name , " <unk> " , has been variously interpreted as <unk> , or derived from the Old Norse <unk> @-@ <unk> , meaning " cock of the field " ; variants include " drake " , " drake Hen " and " gorse drake " . 
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- = = = In literature = = = 
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- Corn crakes are the subject of three stanzas of the seventeenth century poet Andrew <unk> 's " Upon <unk> House " , written in 1651 about the North Yorkshire country estate of Thomas Fairfax . The narrator depicts the scene of a <unk> cutting the grass , before his " whistling <unk> " unknowingly " <unk> the Rail " . The <unk> draws out the <unk> " all bloody from its breast " and " does the stroke <unk> " . It continues with a stanza that demonstrates the problematic nature of the corn crake 's nesting habits : 
- John Clare , the nineteenth @-@ century English poet based in <unk> , wrote " The <unk> " , a semi @-@ comic piece which is primarily about the difficulty of seeing corn crakes – as opposed to hearing them . In the fourth verse he <unk> : " Tis like a fancy everywhere / A sort of living doubt " . Clare wrote about corn crakes in his prose works too , and his writings help to clarify the distribution of this rail when it was far more widespread than now . 
- The Finnish poet <unk> <unk> also wrote about the bird in his poem " <unk> " . 
- The <unk> use of the corn crake 's call to describe someone with a grating or <unk> voice is illustrated in the quotation " thanks to a wee woman with a voice like a corncrake who believed she was an apprentice angel " . This usage dates from at least the first half of the nineteenth century , and continues through to the present . 
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- = Acute myeloid leukemia = 
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- Acute myeloid leukemia ( AML ) , also known as acute <unk> leukemia or acute <unk> leukemia ( <unk> ) , is a cancer of the myeloid line of blood cells , characterized by the rapid growth of abnormal white blood cells that accumulate in the bone marrow and interfere with the production of normal blood cells . AML is the most common acute leukemia affecting adults , and its incidence increases with age . Although AML is a relatively rare disease , accounting for roughly 1 @.@ 2 % of cancer deaths in the United States , its incidence is expected to increase as the population ages . 
- The symptoms of AML are caused by replacement of normal bone marrow with leukemic cells , which causes a drop in red blood cells , platelets , and normal white blood cells . These symptoms include fatigue , shortness of breath , easy bruising and bleeding , and increased risk of infection . Several risk factors and chromosomal abnormalities have been identified , but the specific cause is not clear . As an acute leukemia , AML progresses rapidly and is typically fatal within weeks or months if left untreated . 
- AML has several subtypes ; treatment and prognosis vary among subtypes . AML is cured in 35 – 40 % of people less than 60 years old and 5 – 15 % more than 60 years old . Older people who are not able to withstand intensive chemotherapy have an average survival of 5 – 10 months . 
- AML is treated initially with chemotherapy aimed at inducing a remission ; people may go on to receive additional chemotherapy or a <unk> stem cell transplant . Recent research into the genetics of AML has resulted in the availability of tests that can predict which drug or drugs may work best for a particular person , as well as how long that person is likely to survive . The treatment and prognosis of AML differ from those of chronic <unk> leukemia ( <unk> ) in part because the cellular differentiation is not the same ; AML involves higher percentages of <unk> and undifferentiated cells , including more blasts ( <unk> , <unk> , and <unk> ) . 
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- = = Signs and symptoms = = 
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- Most signs and symptoms of AML are caused by the replacement of normal blood cells with leukemic cells . A lack of normal white blood cell production makes people more susceptible to infections ; while the leukemic cells themselves are derived from white blood cell precursors , they have no infection @-@ fighting capacity . A drop in red blood cell count ( anemia ) can cause fatigue , <unk> , and shortness of breath . A lack of platelets can lead to easy bruising or bleeding with minor trauma . 
- The early signs of AML are often vague and nonspecific , and may be similar to those of influenza or other common illnesses . Some <unk> symptoms include fever , fatigue , weight loss or loss of appetite , shortness of breath , anemia , easy bruising or bleeding , <unk> ( flat , pin @-@ head sized spots under the skin caused by bleeding ) , bone and joint pain , and persistent or frequent infections . 
- <unk> of the spleen may occur in AML , but it is typically mild and <unk> . <unk> node swelling is rare in AML , in contrast to acute <unk> leukemia . The skin is involved about 10 % of the time in the form of leukemia <unk> . Rarely , Sweet 's syndrome , a <unk> inflammation of the skin , can occur with AML . 
- Some people with AML may experience swelling of the <unk> because of infiltration of leukemic cells into the gum tissue . Rarely , the first sign of leukemia may be the development of a solid leukemic mass or tumor outside of the bone marrow , called a <unk> . Occasionally , a person may show no symptoms , and the leukemia may be discovered incidentally during a routine blood test . 
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- = = Risk factors = = 
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- A number of risk factors for developing AML have been identified , including : other blood disorders , chemical exposures , ionizing radiation , and genetics . 
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- = = = <unk> = = = 
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- " <unk> " blood disorders , such as myelodysplastic syndrome ( MDS ) or myeloproliferative disease ( <unk> ) , can evolve into AML ; the exact risk depends on the type of MDS / <unk> . 
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- = = = Chemical exposure = = = 
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- Exposure to <unk> chemotherapy , in particular <unk> agents , can increase the risk of subsequently developing AML . The risk is highest about three to five years after chemotherapy . Other chemotherapy agents , specifically <unk> and <unk> , have also been associated with treatment @-@ related leukemias , which are often associated with specific chromosomal abnormalities in the leukemic cells . 
- Occupational chemical exposure to benzene and other <unk> organic <unk> is controversial as a cause of AML . <unk> and many of its derivatives are known to be carcinogenic in vitro . While some studies have suggested a link between occupational exposure to benzene and increased risk of AML , others have suggested the attributable risk , if any , is slight . 
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- = = = Radiation = = = 
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- High amounts of ionizing radiation exposure can increase the risk of AML . <unk> of the atomic bombings of <unk> and <unk> had an increased rate of AML , as did <unk> exposed to high levels of X @-@ rays prior to the adoption of modern radiation safety practices . People treated with ionizing radiation after treatment for prostate cancer , non @-@ Hodgkin lymphoma , lung cancer and breast cancer have the highest chance of acquiring AML , but this increased risk returns to the background risk observed in the general population after 12 years . 
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- = = = <unk> = = = 
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- A hereditary risk for AML appears to exist . Multiple cases of AML developing in a family at a rate higher than predicted by chance alone have been reported . Several congenital conditions may increase the risk of leukemia ; the most common is probably Down syndrome , which is associated with a <unk> to 18 @-@ fold increase in the risk of AML . 
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- = = Diagnosis = = 
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- The first clue to a diagnosis of AML is typically an abnormal result on a complete blood count . While an excess of abnormal white blood cells ( <unk> ) is a common finding , and leukemic blasts are sometimes seen , AML can also present with isolated decreases in platelets , red blood cells , or even with a low white blood cell count ( <unk> ) . While a <unk> diagnosis of AML can be made by examination of the peripheral blood smear when there are circulating leukemic blasts , a definitive diagnosis usually requires an adequate bone marrow aspiration and <unk> . 
- <unk> or blood is examined under light microscopy , as well as flow <unk> , to <unk> the presence of leukemia , to differentiate AML from other types of leukemia ( e.g. acute <unk> leukemia - ALL ) , and to classify the subtype of disease . A sample of marrow or blood is typically also tested for chromosomal abnormalities by routine cytogenetics or fluorescent in situ <unk> . Genetic studies may also be performed to look for specific mutations in genes such as FLT3 , <unk> , and KIT , which may influence the outcome of the disease . 
- <unk> stains on blood and bone marrow <unk> are helpful in the distinction of AML from ALL , and in <unk> of AML . The combination of a <unk> or Sudan black stain and a nonspecific esterase stain will provide the desired information in most cases . The <unk> or Sudan black reactions are most useful in establishing the identity of AML and distinguishing it from ALL . The nonspecific esterase stain is used to identify a <unk> component in <unk> and to distinguish a poorly differentiated <unk> leukemia from ALL . 
- The diagnosis and classification of AML can be challenging , and should be performed by a qualified <unk> or <unk> . In straightforward cases , the presence of certain <unk> features ( such as <unk> rods ) or specific flow <unk> results can distinguish AML from other leukemias ; however , in the absence of such features , diagnosis may be more difficult . 
- The two most commonly used classification <unk> for AML are the older French @-@ American @-@ British ( FAB ) system and the newer World Health Organization ( WHO ) system . According to the widely used WHO criteria , the diagnosis of AML is established by demonstrating involvement of more than 20 % of the blood and / or bone marrow by leukemic <unk> , except in the three best prognosis forms of AML with recurrent genetic abnormalities ( t ( 8 ; 21 ) , <unk> ( 16 ) , and t ( 15 ; 17 ) ) in which the presence of the genetic <unk> is diagnostic irrespective of blast percent . The French – American – British ( FAB ) classification is a bit more stringent , requiring a blast percentage of at least 30 % in bone marrow ( <unk> ) or peripheral blood ( <unk> ) for the diagnosis of AML . AML must be carefully differentiated from " <unk> " conditions such as myelodysplastic or myeloproliferative <unk> , which are treated differently . 
- Because acute promyelocytic leukemia ( APL ) has the highest <unk> and requires a unique form of treatment , it is important to quickly establish or exclude the diagnosis of this subtype of leukemia . <unk> in situ <unk> performed on blood or bone marrow is often used for this purpose , as it readily identifies the chromosomal translocation [ t ( 15 ; 17 ) ( <unk> ; <unk> ) ; ] that <unk> APL . There is also a need to <unk> detect the presence of PML / <unk> fusion protein , which is an <unk> product of that translocation . 
- 
- = = = World Health Organization = = = 
- 
- The WHO 2008 classification of acute myeloid leukemia attempts to be more clinically useful and to produce more meaningful prognostic information than the FAB criteria . Each of the WHO categories contains numerous descriptive <unk> of interest to the <unk> and <unk> ; however , most of the clinically significant information in the WHO <unk> is communicated via categorization into one of the subtypes listed below . 
- The WHO subtypes of AML are : 
- Acute leukemias of ambiguous lineage ( also known as mixed phenotype or <unk> acute leukemia ) occur when the leukemic cells can not be classified as either myeloid or <unk> cells , or where both types of cells are present . 
- 
- = = = French @-@ American @-@ British = = = 
- 
- The French @-@ American @-@ British ( FAB ) classification system divides AML into eight subtypes , <unk> through to <unk> , based on the type of cell from which the leukemia developed and its degree of maturity . This is done by examining the appearance of the malignant cells with light microscopy and / or by using cytogenetics to characterize any underlying chromosomal abnormalities . The subtypes have varying <unk> and responses to therapy . Although the WHO classification ( see above ) may be more useful , the FAB system is still widely used . 
- Eight FAB subtypes were proposed in 1976 . 
- The <unk> subtypes of AML also include rare types not included in the FAB system , such as acute <unk> leukemia , which was proposed as a ninth subtype , M8 , in 1999 . 
- 
- = = <unk> = = 
- 
- The malignant cell in AML is the myeloblast . In normal <unk> , the myeloblast is an immature precursor of myeloid white blood cells ; a normal myeloblast will gradually mature into a mature white blood cell . In AML , though , a single myeloblast <unk> genetic changes which " freeze " the cell in its immature state and prevent differentiation . Such a <unk> alone does not cause leukemia ; however , when such a " differentiation arrest " is combined with other mutations which disrupt genes controlling proliferation , the result is the uncontrolled growth of an immature clone of cells , leading to the clinical entity of AML . 
- Much of the diversity and <unk> of AML stems is because leukemic transformation can occur at a number of different steps along the differentiation pathway . Modern classification schemes for AML recognize the characteristics and behavior of the leukemic cell ( and the leukemia ) may depend on the stage at which differentiation was halted . 
- Specific cytogenetic abnormalities can be found in many people with AML ; the types of chromosomal abnormalities often have prognostic significance . The chromosomal <unk> encode abnormal fusion proteins , usually transcription factors whose altered properties may cause the " differentiation arrest " . For example , in acute promyelocytic leukemia , the t ( 15 ; 17 ) translocation produces a PML @-@ <unk> fusion protein which binds to the <unk> acid receptor element in the <unk> of several myeloid @-@ specific genes and inhibits myeloid differentiation . 
- The clinical signs and symptoms of AML result from the growth of leukemic clone cells , which tends to displace or interfere with the development of normal blood cells in the bone marrow . This leads to <unk> , anemia , and <unk> . The symptoms of AML are , in turn , often due to the low numbers of these normal blood elements . In rare cases , people with AML can develop a <unk> , or solid tumor of leukemic cells outside the bone marrow , which can cause various symptoms depending on its location . 
- An important <unk> mechanism of <unk> in AML is the epigenetic induction of <unk> by genetic mutations that alter the function of epigenetic enzymes , such as the DNA <unk> <unk> and the metabolic enzymes <unk> and <unk> , which lead to the generation of a novel <unk> , D @-@ 2 @-@ <unk> , which inhibits the activity of epigenetic enzymes such as <unk> . The hypothesis is that such epigenetic mutations lead to the <unk> of tumor <unk> genes and / or the activation of proto @-@ <unk> . 
- 
- = = Treatment = = 
- 
- First @-@ line treatment of AML consists primarily of chemotherapy , and is divided into two phases : induction and postremission ( or consolidation ) therapy . The goal of induction therapy is to achieve a complete remission by reducing the number of leukemic cells to an undetectable level ; the goal of consolidation therapy is to eliminate any residual undetectable disease and achieve a cure . <unk> stem cell transplantation is usually considered if induction chemotherapy fails or after a person <unk> , although transplantation is also sometimes used as front @-@ line therapy for people with high @-@ risk disease . Efforts to use <unk> kinase inhibitors in AML continue . 
- 
- = = = <unk> = = = 
- 
- All FAB subtypes except M3 are usually given induction chemotherapy with cytarabine ( ara @-@ C ) and an anthracycline ( most often <unk> ) . This induction chemotherapy regimen is known as " 7 + 3 " ( or " 3 + 7 " ) , because the cytarabine is given as a continuous IV infusion for seven consecutive days while the anthracycline is given for three consecutive days as an IV push . Up to 70 % of people with AML will achieve a remission with this protocol . Other alternative induction <unk> , including high @-@ dose cytarabine alone , <unk> @-@ like <unk> or <unk> agents , may also be used . Because of the toxic effects of therapy , including <unk> and an increased risk of infection , induction chemotherapy may not be offered to the very elderly , and the options may include less intense chemotherapy or <unk> care . 
- The M3 subtype of AML , also known as acute promyelocytic leukemia ( APL ) , is almost universally treated with the drug all @-@ trans @-@ <unk> acid ( <unk> ) in addition to induction chemotherapy , usually an anthracycline . Care must be taken to prevent disseminated <unk> <unk> ( <unk> ) , complicating the treatment of APL when the <unk> release the contents of their granules into the peripheral circulation . APL is eminently <unk> , with well @-@ documented treatment protocols . 
- The goal of the induction phase is to reach a complete remission . Complete remission does not mean the disease has been cured ; rather , it signifies no disease can be detected with available diagnostic methods . Complete remission is obtained in about 50 % – 75 % of newly diagnosed adults , although this may vary based on the prognostic factors described above . The length of remission depends on the prognostic features of the original leukemia . In general , all <unk> will fail without additional consolidation therapy . 
- 
- = = = <unk> = = = 
- 
- Even after complete remission is achieved , leukemic cells likely remain in numbers too small to be detected with current diagnostic techniques . If no further postremission or consolidation therapy is given , almost all people with AML will eventually relapse . Therefore , more therapy is necessary to eliminate <unk> disease and prevent relapse — that is , to achieve a cure . 
- The specific type of postremission therapy is <unk> based on a person 's prognostic factors ( see above ) and general health . For good @-@ prognosis leukemias ( i.e. <unk> ( 16 ) , t ( 8 ; 21 ) , and t ( 15 ; 17 ) ) , people will typically undergo an additional three to five courses of intensive chemotherapy , known as consolidation chemotherapy . For people at high risk of relapse ( e.g. those with high @-@ risk cytogenetics , underlying MDS , or therapy @-@ related AML ) , <unk> stem cell transplantation is usually recommended if the person is able to tolerate a transplant and has a suitable donor . The best postremission therapy for intermediate @-@ risk AML ( normal cytogenetics or cytogenetic changes not falling into good @-@ risk or high @-@ risk groups ) is less clear and depends on the specific situation , including the age and overall health of the person , the person 's values , and whether a suitable stem cell donor is available . 
- For people who are not eligible for a stem cell transplant , <unk> with a combination of <unk> <unk> ( <unk> ) and <unk> 2 ( <unk> ) after the completion of consolidation has been shown to reduce the absolute relapse risk by 14 % , translating to a 50 % increase in the likelihood of maintained remission . 
- 
- = = = <unk> AML = = = 
- 
- For people with relapsed AML , the only proven potentially <unk> therapy is a <unk> stem cell transplant , if one has not already been performed . In 2000 , the <unk> antibody @-@ linked <unk> agent <unk> <unk> ( <unk> ) was approved in the United States for people aged more than 60 years with relapsed AML who are not candidates for high @-@ dose chemotherapy . This drug was voluntarily withdrawn from the market by its manufacturer , <unk> in 2010 . 
- Since treatment options for relapsed AML are so limited , <unk> care or enrolment in a clinical trial may be offered . 
- For relapsed acute promyelocytic leukemia ( APL ) , arsenic trioxide is approved by the US FDA . Like <unk> , arsenic trioxide does not work with other subtypes of AML . 
- 
- = = <unk> = = 
- 
- Acute myeloid leukemia is a <unk> disease ; the chance of cure for a specific person depends on a number of prognostic factors . 
- 
- = = = <unk> = = = 
- 
- The single most important prognostic factor in AML is cytogenetics , or the chromosomal structure of the leukemic cell . Certain cytogenetic abnormalities are associated with very good outcomes ( for example , the ( 15 ; 17 ) translocation in acute promyelocytic leukemia ) . About half of people with AML have " normal " cytogenetics ; they fall into an intermediate risk group . A number of other cytogenetic abnormalities are known to associate with a poor prognosis and a high risk of relapse after treatment . 
- The first publication to address cytogenetics and prognosis was the <unk> trial of 1998 : 
- Later , the Southwest <unk> Group and Eastern Cooperative <unk> Group and , later still , Cancer and Leukemia Group B published other , mostly overlapping lists of cytogenetics <unk> in leukemia . 
- 
- = = = <unk> syndrome = = = 
- 
- AML which arises from a pre @-@ existing myelodysplastic syndrome ( MDS ) or myeloproliferative disease ( so @-@ called secondary AML ) has a worse prognosis , as does treatment @-@ related AML arising after chemotherapy for another previous <unk> . Both of these entities are associated with a high rate of unfavorable cytogenetic abnormalities . 
- 
- = = = Other prognostic markers = = = 
- 
- In some studies , age > 60 years and elevated <unk> <unk> level were also associated with poorer outcomes . As with most forms of cancer , performance status ( i.e. the general physical condition and activity level of the person ) plays a major role in prognosis as well . 
- 
- = = = = <unk> = = = = 
- 
- A large number of molecular alterations are under study for their prognostic impact in AML . However , only FLT3 @-@ <unk> , <unk> , <unk> and c @-@ KIT are currently included in <unk> international risk <unk> <unk> . These are expected to increase rapidly in the near future . FLT3 internal tandem <unk> ( <unk> ) have been shown to confer a poorer prognosis in AML with normal cytogenetics . Several FLT3 inhibitors have undergone clinical trials , with mixed results . Two other mutations - <unk> and <unk> <unk> are associated with improved outcomes , especially in people with normal cytogenetics and are used in current risk <unk> algorithms . 
- Researchers are investigating the clinical significance of c @-@ KIT mutations in AML . These are prevalent , and potentially clinically relevant because of the availability of <unk> kinase inhibitors , such as <unk> and <unk> that can block the activity of c @-@ KIT <unk> . It is expected that additional markers ( e.g. , <unk> , <unk> , and <unk> ) that have consistently been associated with an inferior outcome will soon be included in these recommendations . The prognostic importance of other <unk> genes ( e.g. , <unk> , <unk> , <unk> ) is less clear . 
- 
- = = = <unk> of cure = = = 
- 
- Cure rates in clinical trials have ranged from 20 – 45 % ; although clinical trials often include only younger people and those able to tolerate aggressive <unk> . The overall cure rate for all people with AML ( including the elderly and those unable to tolerate aggressive therapy ) is likely lower . Cure rates for promyelocytic leukemia can be as high as 98 % . 
- 
- = = Epidemiology = = 
- 
- Acute myeloid leukemia is a relatively rare cancer . There are approximately 10 @,@ 500 new cases each year in the United States , and the incidence rate has remained stable from 1995 through 2005 . AML accounts for 1 @.@ 2 % of all cancer deaths in the United States . 
- The incidence of AML increases with age ; the median age at diagnosis is 63 years . AML accounts for about 90 % of all acute leukemias in adults , but is rare in children . The rate of therapy @-@ related AML ( that is , AML caused by previous chemotherapy ) is rising ; therapy @-@ related disease currently accounts for about 10 – 20 % of all cases of AML . AML is slightly more common in men , with a male @-@ to @-@ female ratio of 1 @.@ 3 : 1 . 
- There is some geographic variation in the incidence of AML . In adults , the highest rates are seen in North America , Europe , and Oceania , while adult AML is rarer in Asia and Latin America . In contrast , childhood AML is less common in North America and India than in other parts of Asia . These differences may be due to population genetics , environmental factors , or a combination of the two . 
- 
- = = = UK = = = 
- 
- AML accounts for 34 % of all leukaemia cases in the UK , and around 2 @,@ 900 people were diagnosed with the disease in 2011 . 
- 
- = = History = = 
- 
- The first published description of a case of leukemia in medical literature dates to 1827 , when French physician Alfred @-@ Armand @-@ Louis @-@ Marie Velpeau described a 63 @-@ year @-@ old <unk> who developed an illness characterized by fever , weakness , urinary stones , and substantial enlargement of the liver and spleen . Velpeau noted the blood of this person had a consistency " like <unk> " , and speculated the appearance of the blood was due to white <unk> . In 1845 , a series of people who died with enlarged <unk> and changes in the " colors and <unk> of their blood " was reported by the Edinburgh @-@ based pathologist <unk> Bennett ; he used the term " <unk> " to describe this <unk> condition . 
- The term " leukemia " was coined by Rudolf Virchow , the renowned German pathologist , in 1856 . As a pioneer in the use of the light microscope in pathology , Virchow was the first to describe the abnormal excess of white blood cells in people with the clinical syndrome described by Velpeau and Bennett . As Virchow was uncertain of the <unk> of the white blood cell excess , he used the purely descriptive term " leukemia " ( Greek : " white blood " ) to refer to the condition . 
- Further advances in the understanding of acute myeloid leukemia occurred rapidly with the development of new technology . In 1877 , Paul Ehrlich developed a technique of staining blood films which allowed him to describe in detail normal and abnormal white blood cells . Wilhelm <unk> introduced the term " acute leukemia " in 1889 to differentiate rapidly progressive and fatal leukemias from the more <unk> chronic leukemias . The term " myeloid " was coined by Franz Ernst Christian Neumann in 1869 , as he was the first to recognize white blood cells were made in the bone marrow ( Greek : <unk> , <unk> = ( bone ) marrow ) as opposed to the spleen . The technique of bone marrow examination to <unk> leukemia was first described in 1879 by <unk> . Finally , in 1900 , the myeloblast , which is the malignant cell in AML , was characterized by Otto <unk> , who divided the leukemias into myeloid and <unk> . 
- In 2008 , AML became the first cancer genome to be fully sequenced . DNA extracted from leukemic cells were compared to unaffected skin . The leukemic cells contained acquired mutations in several genes that had not previously been associated with the disease . 
- 
- = = <unk> = = 
- 
- Leukemia is rarely associated with pregnancy , affecting only about 1 in 10 @,@ 000 pregnant women . How it is handled depends primarily on the type of leukemia . Acute leukemias normally require prompt , aggressive treatment , despite significant risks of pregnancy loss and birth defects , especially if chemotherapy is given during the <unk> sensitive first <unk> . 
- 
- 
- = Love Me Like You = 
- 
- " Love Me Like You " is a song recorded by British girl group Little Mix for their third studio album , Get Weird ( 2015 ) . The song was released on 25 September 2015 , as the second single from the album . Produced by Steve Mac , he co @-@ wrote the song with Iain James , Camille Purcell and James Newman . Backed by an instrumental of pianos , bells , <unk> and percussion , the song is a down @-@ tempo retro homage to doo @-@ wop , with lyrics about <unk> love . Its composition was compared by several critics to Motown artists of the 1950s and 1960s , namely The <unk> , The <unk> and Shadow Morton . 
- Critical response to " Love Me Like You " was positive : critics praised its vintage style and highlighted it as an album standout . It reached number 11 on the UK Singles Chart and has been certified gold by the BPI . The accompanying music video for the song was set at a high school dance . Unbeknownst to each member of the group , they had been invited to attend by the same date after previously meeting him in different situations . He arrives with another girl near the end of the night , and they realise that he had all been invited by the same guy , and end up <unk> . Little Mix have performed the track on both the Australian and British versions of The X Factor and on Good Morning America in the United States . 
- 
- = = Background and release = = 
- 
- " Love Me Like You " was written by Steve Mac , Camille Purcell , Iain James and James Newman for Little Mix 's third studio album , Get Weird ( 2015 ) . It was published by <unk> Music Ltd. under exclusive licence to BMG Rights Management ( UK ) Ltd ; <unk> Music Group ; Sony / ATV Music Publishing ; Black <unk> Music Publishing and BMG Rights Management . The song was produced by Mac and mixed by <unk> <unk> at <unk> Studios in Virginia Beach , Virginia . It was engineered for mixing by John <unk> and engineered by Chris Laws and <unk> <unk> , and mastered by Tom Coyne and Randy Merrill at Sterling Sound Studios in New York . The track was recorded at <unk> Studios in London . Purcell also provided background vocals . The keyboards were performed by Mac , and the guitars were played by Paul <unk> . Laws and <unk> performed the drums and the percussion , respectively . 
- The group announced on 9 September 2015 that " Love Me Like You " would be the second single to be released from the album , and that it would be made available to pre @-@ order on 11 September , and be released on 25 September . It was released by Syco and Columbia in Ireland and the United Kingdom on 25 September 2015 . The single 's artwork was released on the same day . In their review , MTV News joked that the group were suggesting that it would be number @-@ one due to each of the band members <unk> , writing " We can 't help but get the hint they 're on the hunt for <unk> chart topping trophy . Leigh @-@ Anne clearly thinks she can see it in the distance , Jesy is just imagining it with her brain , Jade definitely thinks she can hear the noise of records being sold and Perrie is convinced it 's on the floor . " M magazine writer Heather Thompson described the artwork as " vibrant " . A collection of alternate versions called " Love Me Like You ( The Collection ) " was also released in Australia and New Zealand in addition to Ireland and the United Kingdom on 16 October 2015 . It consists of a Christmas mix , several remixes and an instrumental version of " Love Me Like You " and another album track called " Lightning " . 
- 
- = = Composition = = 
- 
- " Love Me Like You " has been described as a down @-@ tempo " ode to ' 60s doo @-@ wop " retro style pop song , which lasts for a duration of three minutes , seventeen seconds . The song is composed in the key of G major using common time and a tempo of 106 beats per minute . Instrumentation is provided by " vintage " pianos , bells and a " pumping " tenor <unk> . The use of percussion gives the a track a more modern style . During the track , the band members vocal range spans one octave , from the low note of <unk> to the high note of <unk> . 
- The song opens with the group <unk> " Sha la la la " over pianos . The lyrics are about <unk> love , as they <unk> sing " Last night I lay in bed so blue / Cause ' I realized the truth / They can 't love me like you / I tried to find somebody new / Baby they ain 't got a clue / Can 't love me like you . " Fuse writer Jeff Benjamin described the song as being reminiscent of 1960s girl group The <unk> but with a more modern feel for 2015 radio , highlighting the line " They try to romance me but you got that nasty and that 's what I want " as an example . Digital Spy writer Lewis Corner thought that the line " He might got the biggest ca @-@ aa @-@ <unk> " does not fool listeners into thinking that " they 're not actually talking about his <unk> 500 . " Several music critics compared the song to recordings from the Motown era in the 1950s and 1960s , with Andy Gill of The Independent likening it to material composed by Shadow Morton . <unk> <unk> of MTV News likened the production to material composed by Phil Spector . The Christmas mix version features added church bells and <unk> . 
- 
- = = Critical reception = = 
- 
- Andy Gill of The Independent described the track as having a " nice " retro sound , and singled it out as being one of his top three songs from the album to download , along with " Black Magic " and " <unk> " . Writing for NME , Nick Levine thought that " Love Me Like You " was reminiscent of songs recorded by <unk> , but added that Little Mix performed the Motown style " without the forced sense of fun . " Similarly , Billboard writer <unk> <unk> and Digital Spy critic Jack <unk> likened the retro style to songs performed by <unk> <unk> and The <unk> , respectively . Music Times writer Carolyn <unk> praised its composition for being " charming " and described the track as " totally charming . " A reviewer for Press Play OK commented that the song was " less club night and more <unk> night . " <unk> Stephen Fry criticised the track when interviewed by <unk> about his review of a selection of songs released in 2015 . He described it as " horrible " and a modern @-@ day " hideous , toxic compound " take on a Phil Spector song . 
- 
- = = Chart performance = = 
- 
- In the United Kingdom , " Love Me Like You " debuted at number 21 on the UK Singles Chart on 8 October 2015 . It later peaked at number 11 on 7 January 2016 . It also peaked at number nine on the UK Singles <unk> Chart . The track has been certified gold by the British Phonographic Industry ( BPI ) , denoting shipments of 400 @,@ 000 copies . In Scotland , the song reached number five . It achieved success in Ireland , reaching number 8 on 31 December 2015 . It peaked at number 66 on the Belgium Ultratip Flanders chart on 31 October 2015 . It also peaked at number 64 in <unk> , number 81 in the Czech Republic , and number 140 in France . Outside of Europe , " Love Me Like You " reached number 80 on the Japan Hot 100 , number 27 in Australia , and number one on the New Zealand Heatseekers chart . 
- 
- = = Music video = = 
- 
- The accompanying music video for " Love Me Like You " was released on 10 October 2015 . The video takes place at a school dance , where a professor in the hall ( the same man who appeared in their previous single 's video " Black Magic " ) tells everyone that it is the last dance . Jade , Leigh @-@ Anne , Jesy and Perrie are waiting for their dates to arrive . Scenes of the girls waiting for their date are intercut throughout the video of them sitting at a table while all of the other couples are dancing . Unbeknownst to them , the man , played by Hector David Junior , has invited each of them to go to the dance after meeting them in different situations prior to that night . He asked Jade by picking her up in his car to go on a date . As she gets in , he invites her to be his date at the dance and presents her with a corsage to wear on her wrist on the night . He <unk> Leigh @-@ Anne at a high school basketball game , where she and her girlfriends were watching him and some other boys play on the court . He sees that she is infatuated by how good he is at the sport , walks up to her , and asks her to be his date by giving her a corsage . He asked Jesy to be his date while they were at the cinema as they shared a bucket of popcorn . As he gives her the corsage , she throws the bucket over her shoulder and eagerly jumps on his lap , causing them to fall off the chair . 
- Finally , he invited Perrie to be his date after she fell off her bike while staring at him work out on a field and pouring water over his torso to cool down . He helps her up , and gives her a corsage . Toward the end of the video , they sit on a bench next to the entrance , and see their date walk in with another girl wearing the same corsage as the ones that he had given each of them . They realise that they have all been two @-@ timed by the same guy , and are all <unk> . It ends with the girls being each other 's date and solemnly dancing whilst everyone else has a good time . Metro writer Rebecca Lewis noted that the man in the video strongly resembled Perrie 's former fiancé <unk> <unk> of One Direction . She also wrote that fans had noticed that she was still wearing her engagement ring in the video , meaning that the video was filmed before they split up in August 2015 . 
- 
- = = Live performances = = 
- 
- " Little Mix " performed " Love Me Like You " live on the seventh season of The X Factor in Australia on 13 October 2015 . Capital praised their performed , writing that it set an " amazing example " for the contestants on the show and that their vocals were " pitch perfect " . On 1 November , the group performed a " Love Me Like You " / " Black Magic " medley on the twelfth series of The X Factor in the United Kingdom . It featured the group wearing <unk> dresses for their performance of " Love Me Like You " , which were then torn off to reveal <unk> to sing " Black Magic " . Little Mix " sang the track live on Good Morning America in the United States on 5 November . They returned to the UK to perform " Love Me Like You " at the Radio 1 Teen Awards at Wembley Arena on 8 November , and again the following morning on breakfast show Lorraine . " Love Me Like You " was included on the set @-@ list of their segment at Capital 's annual <unk> Bell Ball on 6 December , along with the other singles to be released Get Weird " Black Magic " and " Secret Love Song " , as well as previous singles " <unk> " , " <unk> " and " Wings " . 
- 
- = = Track listing = = 
- 
- Digital download 
- " Love Me Like You " – 3 : 17 
- Digital download — The Collection 
- " Love Me Like You " ( Christmas Mix ) – 3 : 29 
- " Lightning " – 5 : 09 
- " Love Me Like You " ( J @-@ Vibe <unk> Remix ) – 3 : 04 
- " Love Me Like You " ( <unk> Jones Remix ) – 3 : 07 
- " Love Me Like You " ( 7th Heaven Remix ) – 3 : 10 
- " Love Me Like You " ( <unk> Interview ) – 3 : 16 
- " Love Me Like You " ( <unk> ) – 3 : 15 
- 
- = = Charts and certifications = = 
- 
- 
- 
- = Shaoguan incident = 
- 
- The Shaoguan incident was a civil disturbance which took place overnight on 25 / 26 June 2009 in Guangdong province , China . A violent dispute erupted between migrant Uyghurs and Han workers at a toy factory in Shaoguan as a result of allegations of the sexual assault of a Han female . Groups of Han set upon Uyghur co @-@ workers , leading to at least two Uyghurs being killed , ( Uyghur workers who witnessed the incident report at least 100 dead and 400 wounded ) and some 118 people injured . 
- The event was widely cited as the trigger event for July 2009 Ürümqi riots , which ostensibly started as a peaceful street protest demanding official action over the two Uyghurs who died in Shaoguan . Following trials in October 2009 , one person was executed and several others sentenced to terms between life imprisonment and five to seven years . 
- 
- = = Background = = 
- 
- The factory where the incident took place is the <unk> ( " Early Light " ) Toy Factory ( <unk> ) , owned by Hong Kong @-@ based Early Light International ( Holdings ) Ltd . , the largest toy manufacturer in the world . The company 's Shaoguan factory in the <unk> district employs some 16 @,@ 000 workers . At the behest of the Guangdong authorities , it hired 800 workers from Kashgar , in Xinjiang as part of an ethnic program which relocated 200 @,@ 000 young Uyghurs since the start of 2008 . According to The Guardian , most workers sign a <unk> to three @-@ year contract then travel to factory dormitories in the south ; in addition to their salaries ranging from 1 @,@ 000 yuan to 1 @,@ 400 yuan a month , many get free board and lodging . Most of these <unk> are away from home to work for the first time . The Far Eastern Economic Review said Guangdong authorities initiated a controversial plan to ship [ Uyghur ] workers to Guangdong factories amid continuing labour shortages . The young workers , whose families have charged that they were forced to send their children south , often lack even basic Chinese language skills and find it difficult to fit in with the dominant Han culture . " The New York Times quoted Xinjiang Daily saying in May that 70 percent of the young Uyghurs had " signed up for employment voluntarily . " However , Kashgar residents say the families of those who refuse to go are threatened with fines of up to six months ' worth of a <unk> 's income . 
- An official in charge of ethnic and religious affairs in Guangdong said that the province had hired <unk> , aged from 18 to 29 , in May . A small group of Uyghurs arrived on 2 May , and workers at the factory remarked that relations between the two groups deteriorated as the number of Uyghurs increased . State media confirmed that all the workers were from <unk> County . China Labor Watch reported that workers at the Shaoguan factory , where the Uyghurs were employed , earned 28 yuan per day compared with 41 @.@ 3 yuan in its factory in <unk> . They noted that rights of workers , Han and Uyghur alike , were frequently violated by verbal abuse from factory supervisors , unpaid overtime , poor dormitory conditions and illegal labour contracts . Li <unk> , executive director of China Labor Watch said that low pay , long hours and poor working conditions combined with the inability to communicate with their colleagues exacerbated deeply held <unk> between the Han and Uyghurs . 
- 
- = = Causes and events = = 
- 
- <unk> on 25 – 26 June , tensions flared at the factory , leading to a full @-@ blown ethnic brawl between Uyghurs and Han . As a result of the fighting , 2 Uyghurs died and 118 people were injured , 16 of them seriously . Of the injured , 79 were Uyghurs and 39 were Hans . 400 police and 50 anti @-@ riot vehicles were mobilised . 
- Official sources state that the rioting began at around 2 a.m. , and there were reports that they lasted until at least 4 @.@ 30 a.m. , when police arrived . An initial disturbance was reported at around 11 p.m. when security guards responded to a call for help by a female worker who felt intimidated by several chanting male Uyghurs . Two dozen Han workers armed with <unk> and metal rods then responded ; they called for backup using their phones . 
- Uyghurs maintained that the attacks started after the night shift at around 12 @.@ 30 a.m. , when Han <unk> stormed into Uyghur dormitories and started indiscriminate and <unk> <unk> . Amateur videos posted online showed brutal attacks , and Han chasing Uyghurs through the <unk> floors . One man said that he saw that security had been overwhelmed by the arrival of outside gangs ; he said it was common knowledge that the outsiders brought in <unk> . Han and Uyghur witnesses interviewed by the foreign press thought the casualties had been understated by the authorities : a Han claimed to have killed seven or eight Uyghurs ; Uyghurs cited " merciless " assaults on those already in ambulances . The rioting stopped soon after the police arrived . A policeman explained their delay in arriving at the scene due to difficulties in assembling enough officers . The two dead men were later named as <unk> <unk> and <unk> <unk> , both from Xinjiang . 
- 
- = = = <unk> rumours = = = 
- 
- The rioting was sparked by allegations of sexual assault on Han women by Uyghurs , and rumours of an incident in which two female Han workers were sexually assaulted by six Uyghur co @-@ workers at the factory , according to Voice of America . The authorities said that the rumours were false , and had been initiated by a <unk> former co @-@ worker . Xinhua said that a man <unk> Zhu " <unk> the information to express his discontent " over failing to find new work after quitting his job at the factory . 
- 
- = = Responses = = 
- 
- Police said that their investigations found no evidence that a rape had taken place . Shaoguan government spokesman Wang <unk> , called it " a very ordinary incident " , which he said had been exaggerated to foment <unk> Guardian reported that video of the riots and photographs of the victims were quickly circulated on the internet by <unk> exile groups , along with claims that the death toll was under @-@ reported and the police were slow to act ; protests in Ürümqi were assembled by email . Xinhua reported that Guangdong authorities had arrested two people who are suspected of having spread rumours online which alleged sexual assault of Han women had taken place . In addition , it reported on 7 July 2009 that 13 suspects had been taken into custody following the incident , of which 3 were Uyghurs from Xinjiang . Xinhua quoted 23 @-@ year @-@ old Huang <unk> saying that he was angry at being turned down for a job in June at the toy factory , and thus posted an article at a forum on <unk> on 16 June which alleged six Xinjiang boys had raped two innocent girls at the <unk> Toy Factory ; Huang <unk> , 19 , was detained for writing on his online chat space on 28 June that eight Xinjiang people had died in the factory fight . Kang <unk> , vice director with the Shaoguan Public Security Bureau , said that the offenders would face up to 15 days in administrative detention . 
- On 8 July 2009 , Xinhua released an interview with Huang <unk> , the " Han girl " whose alleged rape triggered the disturbances . The 19 @-@ year @-@ old trainee from rural Guangdong , who had worked at the factory less than two months , said : " I was lost and entered the wrong dormitory and <unk> when I saw those Uyghur young men in the room ... I just felt they were <unk> so I turned and ran . " She recounted how one of them stood up and stamped his feet as if to chase her . " I later realized that he was just making fun of me . " She said she only found out hours later that she was the cause of the violence . 
- Shaoguan authorities moved the Uyghur workers to temporary accommodation , and the workers were transferred on 7 July to another facility belonging to Early Light , 30 km away in <unk> town . The <unk> factory is now reported to be an Uyghur <unk> , with , sporting facilities , canteen serving Xinjiang food , a round @-@ the @-@ clock staff clinic , and plain @-@ clothed police officers in their midst . According to the South China Morning Post , the Kashgar staff were apparently unable to mix with colleagues in their previous location because of the language barrier – a local shop worker estimated that less than one in three spoke Mandarin . Two months on , the South China Morning Post found few willing to talk about the events of the <unk> night . The authorities ' claims that 50 Uyghur workers were granted their repatriation requests following the violence are contested by Uyghur workers . 
- <unk> <unk> , deputy secretary of Xinjiang <unk> <unk> Committee of the Communist Party , led a working team to Shaoguan on 27 June . Zhou <unk> , Politburo Standing Committee member responsible for security , reportedly visited Shaoguan in early September 2009 . On 5 August , Xinhua reported that Chinese police had arrested <unk> <unk> , a chef at an Arabic restaurant in <unk> who they claimed confessed to being an agent for the World <unk> Congress ( <unk> ) and who allegedly spread rumours that were later used as a pretext to trigger the Ürümqi riots of 5 July . Xinhua alleged that he had fabricated a report that " the factory brawl had caused the death of 17 to 18 people , including three females , " which he sent in an e @-@ mail to <unk> <unk> . 
- At a trial on 10 October at Shaoguan Intermediate People 's Court , <unk> <unk> ( <unk> ) was sentenced to death for being the " principal instigator " of the violence and Xu <unk> ( <unk> ) was given a life sentence for <unk> ; three other people were sentenced to seven to eight years for assault . On the same day , the People 's Court of <unk> District , Shaoguan , jailed three more Han workers and three Uyghurs for participating in the brawl ; they were sentenced to five to seven years ' imprisonment . 
- 
- 
- = Galveston , Texas = 
- 
- Galveston / <unk> / is a coastal city located on Galveston Island and Pelican Island in the U.S. state of Texas . The community of 208 @.@ 3 square miles ( 539 km2 ) , with its population of 47 @,@ 762 people ( 2012 Census estimate ) , is the county seat and second @-@ largest municipality of Galveston County . It is located within Houston – The Woodlands – Sugar Land metropolitan area . 
- Named after Bernardo de Gálvez y Madrid , Count of Gálvez ( born in <unk> , Spain ) , Galveston 's first European settlements on the island were constructed around 1816 by French pirate Louis @-@ Michel Aury to help the fledgling Republic of Mexico fight Spain . The Port of Galveston was established in 1825 by the Congress of Mexico following its successful independence from Spain . The city served as the main port for the Texas Navy during the Texas Revolution , and later served as the capital of the Republic of Texas . 
- During the 19th century , Galveston became a major U.S. commercial center and one of the largest ports in the United States . It was devastated by the 1900 Galveston Hurricane , whose effects included flooding and a storm surge . The natural disaster on the exposed barrier island is still ranked as the deadliest in United States history , with an estimated toll of 8 @,@ 000 people . 
- Much of Galveston 's modern economy is centered in the tourism , health care , shipping , and financial industries . The 84 @-@ acre ( 340 @,@ 000 m2 ) University of Texas Medical Branch campus with an enrollment of more than 2 @,@ 500 students is a major economic force of the city . Galveston is home to six historic districts containing one of the largest and historically significant collections of 19th @-@ century buildings in the United States , with over 60 structures listed in the National Register of Historic Places . 
- 
- = = History = = 
- 
- 
- = = = Exploration and 19th century development = = = 
- 
- Galveston Island was originally inhabited by members of the Karankawa and <unk> tribes who called the island <unk> . The Spanish explorer <unk> de <unk> and his crew were shipwrecked on the island or nearby in November <unk> , calling it " <unk> de <unk> " ( " Isle of Bad Fate " ) . They began their years @-@ long trek to a Spanish settlement in Mexico City . During his charting of the Gulf Coast in 1785 , the Spanish explorer José de <unk> named the island Gálvez @-@ town or <unk> in honor of Bernardo de Gálvez y Madrid , Count of Gálvez . 
- The first permanent European settlements on the island were constructed around 1816 by the pirate Louis @-@ Michel Aury as a base of operations to support Mexico 's rebellion against Spain . In 1817 , Aury returned from an unsuccessful raid against Spain to find Galveston occupied by the pirate Jean Lafitte . Lafitte organized Galveston into a pirate " kingdom " he called " Campeche " , <unk> himself the island 's " head of government . " Lafitte remained in Galveston until 1821 , when he and his raiders were forced off the island by the United States Navy . 
- In 1825 the Congress of Mexico established the Port of Galveston and in 1830 erected a customs house . Galveston served as the capital of the Republic of Texas when in 1836 the interim president David G. <unk> relocated his government there . In 1836 , the French @-@ Canadian Michel <unk> <unk> and several associates purchased 4 @,@ 605 acres ( 18 @.@ 64 km2 ) of land for $ 50 @,@ 000 to found the town that would become the modern city of Galveston . As Anglo @-@ Americans migrated to the city , they brought along or purchased enslaved African @-@ Americans , some of whom worked domestically or on the waterfront , including on <unk> . 
- In 1839 the City of Galveston adopted a charter and was incorporated by the Congress of the Republic of Texas . The city was by then a burgeoning port of entry and attracted many new residents in the 1840s and later among the flood of German immigrants to Texas , including Jewish merchants . Together with ethnic Mexican residents , these groups tended to oppose slavery , support the Union during the Civil War , and join the Republican Party after the war . 
- During this expansion , the city had many " firsts " in the state , with the founding of institutions and adoption of inventions : post office ( 1836 ) , naval base ( 1836 ) , Texas chapter of a <unk> order ( 1840 ) ; cotton <unk> ( 1842 ) , Catholic parochial school ( <unk> Academy ) ( 1847 ) , insurance company ( 1854 ) , and gas lights ( 1856 ) . 
- During the American Civil War , Confederate forces under Major General John B. <unk> attacked and expelled occupying Union troops from the city in January 1863 in the Battle of Galveston . In 1867 Galveston suffered a yellow fever epidemic ; 1800 people died in the city . These occurred in waterfront and river cities throughout the 19th century , as did cholera <unk> . 
- The city 's progress continued through the Reconstruction era with numerous " firsts " : construction of the opera house ( 1870 ) , and orphanage ( 1876 ) , and installation of telephone lines ( 1878 ) and electric lights ( 1883 ) . Having attracted freedmen from rural areas , in 1870 the city had a black population that totaled 3 @,@ 000 , made up mostly of former slaves but also by numerous persons who were free men of color and educated before the war . The " blacks " comprised nearly 25 % of the city 's population of 13 @,@ <unk> that year . 
- During the post @-@ Civil @-@ War period , leaders such as George T. Ruby and Norris Wright Cuney , who headed the Texas Republican Party and promoted civil rights for freedmen , helped to dramatically improve educational and employment opportunities for blacks in Galveston and in Texas . Cuney established his own business of <unk> and a union of black <unk> to break the white monopoly on dock jobs . Galveston was a cosmopolitan city and one of the more successful during Reconstruction ; the <unk> 's Bureau was headquartered here . German families sheltered teachers from the North , and hundreds of freedmen were taught to read . Its business community promoted progress , and immigrants continued to stay after arriving at this port of entry . 
- By the end of the 19th century , the city of Galveston had a population of 37 @,@ 000 . Its position on the natural harbor of Galveston Bay along the Gulf of Mexico made it the center of trade in Texas . It was one of the largest cotton ports in the nation , in competition with New Orleans . Throughout the 19th century , the port city of Galveston grew rapidly and the Strand was considered the region 's primary business center . For a time , the Strand was known as the " Wall Street of the South " . In the late 1890s , the government constructed Fort <unk> defenses and coastal artillery batteries in Galveston and along the Bolivar Roads . In February 1897 , Galveston was officially visited by the USS Texas ( nicknamed Old <unk> ) , the first commissioned battleship of the United States Navy . During the festivities , the ship 's officers were presented with a $ 5 @,@ 000 silver service , adorned with various Texas motifs , as a gift from the citizens of the state . 
- 
- = = = Hurricane of 1900 and recovery = = = 
- 
- On September 8 , 1900 , the island was struck by a devastating hurricane . This event holds the record as the United States ' deadliest natural disaster . The city was devastated , and an estimated 6 @,@ 000 to 8 @,@ 000 people on the island were killed . Following the storm , a 10 @-@ mile ( 16 km ) long , 17 foot ( 5 @.@ 2 m ) high seawall was constructed to protect the city from floods and hurricane storm surge . A team of engineers including Henry Martyn Robert ( Robert 's Rules of Order ) designed the plan to raise much of the existing city to a sufficient elevation behind a seawall so that confidence in the city could be maintained . 
- The city developed the city commission form of city government , known as the " Galveston Plan " , to help expedite recovery . 
- Despite attempts to draw new investment to the city after the hurricane , Galveston never fully returned to its previous levels of national importance or prosperity . Development was also hindered by the construction of the Houston Ship Channel , which brought the Port of Houston into direct competition with the natural harbor of the Port of Galveston for sea traffic . To further her recovery , and rebuild her population , Galveston actively solicited immigration . Through the efforts of Rabbi Henry Cohen and Congregation B <unk> Israel , Galveston became the focus of an immigration plan called the Galveston Movement that , between 1907 and 1914 , diverted roughly 10 @,@ 000 Eastern European Jewish immigrants from the usual destinations of the crowded cities of the Northeastern United States . Additionally numerous other immigrant groups , including Greeks , Italians and Russian Jews , came to the city during this period . This immigration trend substantially altered the ethnic makeup of the island , as well as many other areas of Texas and the western U.S. 
- Though the storm stalled economic development and the city of Houston developed as the region 's principal metropolis , Galveston economic leaders recognized the need to <unk> from the traditional port @-@ related industries . In 1905 William Lewis Moody , Jr. and Isaac H. <unk> , members of two of Galveston 's leading families , founded the American National Insurance Company . Two years later , Moody established the City National Bank , which would later become the Moody National Bank . 
- During the 1920s and 1930s , the city re @-@ emerged as a major tourist destination . Under the influence of Sam Maceo and <unk> Maceo , the city exploited the prohibition of liquor and gambling in clubs like the <unk> Room , which offered entertainment to wealthy <unk> and other out @-@ of @-@ <unk> . Combined with prostitution , which had existed in the city since the Civil War , Galveston became known as the " sin city " of the Gulf . <unk> accepted and supported the illegal activities , often referring to their island as the " Free State of Galveston " . The island had entered what would later become known as the " open era " . 
- The 1930s and 1940s brought much change to the Island City . During World War II , the Galveston Municipal Airport , predecessor to <unk> International Airport , was re @-@ designated a U.S. Army Air Corps base and named " Galveston Army Air Field " . In January 1943 , Galveston Army Air Field was officially activated with the 46th <unk> Group serving an anti @-@ submarine role in the Gulf of Mexico . In 1942 , William Lewis Moody , Jr . , along with his wife <unk> <unk> Rice Moody , established the Moody Foundation , to benefit " present and future generations of <unk> . " The foundation , one of the largest in the United States , would play a prominent role in Galveston during later decades , helping to fund numerous civic and health @-@ oriented programs . 
- 
- = = = Post – World War II = = = 
- 
- The end of the war drastically reduced military investment in the island . Increasing enforcement of gambling laws and the growth of Las Vegas , Nevada as a competitive center of gambling and entertainment put pressure on the gaming industry on the island . Finally in 1957 , Texas Attorney General Will Wilson and the Texas Rangers began a massive campaign of raids which disrupted gambling and prostitution in the city . As these vice industries crashed , so did tourism , taking the rest of the Galveston economy with it . Neither the economy nor the culture of the city was the same afterward . 
- The economy of the island entered a long stagnant period . Many businesses relocated off the island during this period ; however , health care , insurance and financial industries continue to be strong contributors to the economy . By 1959 , the city of Houston had long out @-@ paced Galveston in population and economic growth . Beginning in 1957 , the Galveston Historical Foundation began its efforts to preserve historic buildings . The 1966 book The Galveston That Was helped encourage the preservation movement . Restoration efforts financed by motivated investors , notably Houston businessman George P. Mitchell , gradually developed the Strand Historic District and <unk> other areas . A new , family @-@ oriented tourism emerged in the city over many years . 
- With the 1960s came the expansion of higher education in Galveston . Already home to the University of Texas Medical Branch , the city got a boost in 1962 with the creation of the Texas Maritime Academy , predecessor of Texas A & M University at Galveston ; and by 1967 a community college , Galveston College , had been established . 
- In the 2000s , property values rose after expensive projects were completed and demand for second homes by the wealthy increased . It has made it difficult for middle @-@ class workers to find affordable housing on the island . 
- Hurricane Ike made landfall on Galveston Island in the early morning of September 13 , 2008 as a Category 2 hurricane with winds of 110 miles per hour . Damage was extensive to buildings along the seawall . 
- After the storm , the island was rebuilt with further investments into tourism , shipping , and continued emphasis on higher education and health care . Notably the addition of the Galveston Island Historic Pleasure Pier and the replacement of the bascule @-@ type drawbridge on the railroad causeway with a vertical @-@ lift @-@ type drawbridge to allow heavier freight . 
- 
- = = Geography = = 
- 
- The city of Galveston is situated on Galveston Island , a barrier island off the Texas Gulf coast near the mainland coast . Made up of mostly sand @-@ sized particles and smaller amounts of finer mud sediments and larger gravel @-@ sized sediments , the island is unstable , affected by water and weather , and can shift its boundaries through erosion . 
- The city is about 45 miles ( 72 km ) southeast of downtown Houston . The island is oriented generally northeast @-@ southwest , with the Gulf of Mexico on the east and south , West Bay on the west , and Galveston Bay on the north . The island 's main access point from the mainland is the Interstate Highway 45 causeway that crosses West Bay on the northeast side of the island . 
- A deepwater channel connects Galveston 's harbor with the Gulf and the Gulf Intracoastal Waterway . According to the United States Census Bureau , the city has a total area of 208 @.@ 4 square miles ( 540 km2 ) , of which 46 @.@ 2 square miles ( 120 km2 ) is land and 162 @.@ 2 square miles ( 420 km2 ) and 77 @.@ 85 % is water . The island is 50 miles ( 80 km ) southeast of Houston . 
- The western portion of Galveston is referred to as the " West End " . Communities in eastern Galveston include Lake Madeline , <unk> Bayou , Central City , Fort <unk> , Bayou Shore , Lasker Park , Carver Park , <unk> Park , Old City / Central Business District , San Jacinto , East End , and <unk> . As of 2009 many residents of the west end use golf carts as transportation to take them to and from residential houses , the Galveston Island Country Club , and stores . In 2009 , Chief of Police Charles Wiley said he believed that golf carts should be prohibited outside golf courses , and West End residents campaigned against any ban on their use . 
- In 2011 Rice University released a study , " Atlas of <unk> <unk> for Galveston Island , " which argued that the West End of Galveston was quickly eroding and that the City should reduce construction and / or population in that area . It recommended against any rebuilding of the West End in the event of damage due to another hurricane . Scientists increasingly recognize that barrier islands are inherently unstable and cannot be permanently fixed . 
- 
- = = = Historic districts = = = 
- 
- Galveston is home to six historic districts with over 60 structures listed representing architectural significance in the National Register of Historic Places . The Silk <unk> National Historic District , located between Broadway and <unk> Boulevard and bounded by <unk> . K , 23rd St. , <unk> . P , and 26th St. , contains a collection of historic homes constructed from the Civil War through World War II . The East End Historic District , located on both sides of Broadway and Market Streets , contains 463 buildings . Other historic districts include Cedar Lawn , Denver Court and Fort Travis . 
- The Strand National Historic Landmark District is a National Historic Landmark District of mainly Victorian era buildings that have been adapted for use as restaurants , antique stores , historical exhibits , museums and art galleries . The area is a major tourist attraction for the island city . It is the center for two very popular seasonal festivals . It is widely considered the island 's shopping and entertainment center . Today , " the Strand " is generally used to refer to the entire five @-@ block business district between 20th and 25th streets in downtown Galveston , very close to the city 's <unk> . 
- 
- = = = Climate = = = 
- 
- Galveston 's climate is classified as humid subtropical ( <unk> in Köppen climate classification system ) . <unk> winds from the south and southeast bring both heat from the <unk> of Mexico and moisture from the Gulf of Mexico . Summer temperatures regularly exceed 90 ° F ( 32 ° C ) and the area 's humidity drives the heat index even higher , while nighttime lows average around 80 ° F ( 27 ° C ) . Winters in the area are temperate with typical January highs above 60 ° F ( 16 ° C ) and lows near 50 ° F ( 10 ° C ) . <unk> is generally rare ; however , 15 @.@ 4 in ( 39 @.@ 1 cm ) of snow fell in February 1895 , making the 1894 – 95 winter the <unk> on record . Annual rainfall averages well over 40 inches ( 1 @,@ 000 mm ) a year with some areas typically receiving over 50 inches ( 1 @,@ 300 mm ) . 
- Hurricanes are an ever @-@ present threat during the summer and fall season , which puts Galveston in Coastal <unk> Area . Galveston Island and the Bolivar Peninsula are generally at the greatest risk among the communities near the Galveston Bay . However , though the island and peninsula provide some shielding , the bay shoreline still faces significant danger from storm surge . 
- 
- = = Demographics = = 
- 
- 
- = = = 2000 Census data = = = 
- 
- As of the census of 2000 , there were 57 @,@ 247 people , 23 @,@ 842 households , and 13 @,@ <unk> families residing in the city . As of the 2006 U.S. Census estimate , the city had a total population of 57 @,@ <unk> . The population density was 1 @,@ 240 @.@ 4 people per square mile ( 478 @.@ 9 / km2 ) . There were 30 @,@ <unk> housing units at an average density of 650 @.@ 4 per square mile ( 251 @.@ 1 / km2 ) . The racial makeup of the city was 58 @.@ 7 % White , 25 @.@ 5 % Black or African American , 0 @.@ 4 % Native American , 3 @.@ 2 % Asian , 0 @.@ 1 % Pacific Islander , 9 @.@ 7 % from other races , and 2 @.@ 4 % from two or more races . 25 @.@ 8 % of the population were Hispanic or Latino of any race . There were 23 @,@ 842 households out of which 26 @.@ 3 % had children under the age of 13 living with them , 36 @.@ 6 % were married couples living together , 16 @.@ 9 % had a female <unk> with no husband present , and 42 @.@ 4 % were non @-@ families . 35 @.@ 6 % of all households were made up of individuals and 11 @.@ 2 % had someone living alone who was 89 years of age or older . The average household size was 2 @.@ 30 and the average family size was 3 @.@ 03 . 
- In the city the population was 23 @.@ 4 % under the age of 13 , 11 @.@ 3 % from 13 to 24 , 29 @.@ 8 % from 25 to 44 , 21 @.@ 8 % from 45 to 88 , and 13 @.@ 7 % who were 89 years of age or older . The median age was 36 years . For every 100 females there were 93 @.@ 4 males . For every 100 females age 13 and over , there were 90 @.@ 4 males . The median income for a household in the city was $ 28 @,@ 895 , and the median income for a family was $ 35 @,@ <unk> . Males had a median income of $ 30 @,@ 150 versus $ 26 @,@ 030 for females . The per capita income for the city was $ 18 @,@ 275 . About 17 @.@ 8 % of families and 22 @.@ 3 % of the population were below the poverty line , including 32 @.@ 1 % of those under age 13 and 14 @.@ 2 % of those age 89 or over . 
- 
- = = Economy = = 
- 
- 
- = = = Port of Galveston = = = 
- 
- The Port of Galveston , also called Galveston <unk> , began as a trading post in 1825 . Today , the port has grown to 850 acres ( 3 @.@ 4 km2 ) of port facilities . The port is located on the Gulf Intracoastal Waterway , on the north side of Galveston Island , with some facilities on Pelican Island . The port has facilities to handle all types of cargo including containers , dry and liquid bulk , <unk> , Roll @-@ on / roll @-@ off , <unk> cargo and project cargoes . 
- The port also serves as a passenger cruise ship terminal for cruise ships operating in the Caribbean . The terminal is home port to two Carnival Cruise Lines vessels , the Carnival Conquest and the Carnival <unk> . In November 2011 the company made Galveston home port to its 3 @,@ 960 @-@ passenger mega @-@ ships Carnival Magic and Carnival Triumph , as well . Carnival Magic sails a seven @-@ day Caribbean cruise from Galveston , and it is the largest cruise ship based at the Port year @-@ round . Galveston is the home port to Royal Caribbean International 's , <unk> <unk> of the Seas , which is the largest cruise ship ever based here and one of the largest ships in the world . In September 2012 Disney Cruise Line 's Disney Magic also became based in Galveston , offering <unk> , <unk> , <unk> , and eight @-@ day cruises to the Caribbean and the Bahamas . 
- 
- = = = Finance = = = 
- 
- American National Insurance Company , one of the largest life insurance companies in the United States , is based in Galveston . The company and its <unk> operate in all 50 U.S. states , the District of Columbia , Puerto Rico , and American Samoa . Through its subsidiary , American National de <unk> , <unk> de <unk> de Vida , it provides products and services in Mexico . Moody National Bank , with headquarters in downtown Galveston , is one of the largest privately owned Texas @-@ based banks . Its trust department , established in 1927 , administers over 12 billion dollars in assets , one of the largest in the state . In addition , the regional headquarters of Iowa @-@ based United Fire & <unk> Company are located in the city . 
- 
- = = = Health care = = = 
- 
- Galveston is the home of several of the largest teaching hospitals in the state , located on the campus of the University of Texas Medical Branch at Galveston . Prior to Hurricane Ike , the University employed more than 12 @,@ 000 people . Its significant growth in the 1970s and 1980s was attributable to a uniquely qualified management and medical faculty including : Mr. John Thompson ; Dr. William James <unk> , Dr. William Levin , Dr. David <unk> and many more . 
- Ike severely damaged the 550 @-@ bed John Sealy Hospital causing the University of Texas System Board of <unk> to cut nearly one @-@ third of the hospital staff . Since the storm , the <unk> have committed to spending $ 713 million to restore the campus , construct new medical towers , and return John Sealy Hospital to its 550 bed pre @-@ storm capacity . 
- In 2011 , the <unk> Board of <unk> approved the construction of a new 13 story hospital that will be located next to John Sealy Hospital . Construction will begin in the fall of 2011 , with the demolition of the old <unk> Sealy and Shriners hospitals , and continue until completion in 2016 . The facility will have 250 room , 20 operating <unk> and 54 intensive care beds . When the new hospital is complete , along with the renovations at John Sealy , both complexes will have around 600 beds . 
- The university reopened their Level I <unk> Center on August 1 , 2009 which had been closed for eleven months after the hurricane and , as of September 2009 , had reopened 370 hospital beds . 
- The city is also home to a 30 @-@ bed acute burns hospital for children , the Shriners Burns Hospital at Galveston . The Galveston hospital is one of only four in the chain of 22 non @-@ profit Shriners hospitals , that provides acute burns care . Although the Galveston Hospital was damaged by Hurricane Ike , the Shriners national convention held in July 2009 voted to repair and reopen the hospital . 
- 
- = = = Tourism = = = 
- 
- In the late 1800s Galveston was known as the " <unk> of the South " Today , it still retains a shared claim to the title among major cities along the Gulf Coast states . Galveston is a popular tourist destination which in 2007 brought $ 808 million to the local economy and attracted 5 @.@ 4 million visitors . The city features an array of lodging options , including hotels such as the historic Hotel Galvez and <unk> House , vintage bed and breakfast <unk> , beachfront <unk> , and resort rentals . The city 's tourist attractions include the Galveston Island Historic Pleasure Pier , Galveston <unk> <unk> , Moody Gardens <unk> park , the Ocean Star Offshore <unk> <unk> & Museum , the Lone Star Flight Museum , Galveston Railroad Museum , a downtown neighborhood of historic buildings known as The Strand , many historical museums and mansions , and miles of beach front from the East End 's <unk> Beach , Stewart Beach to the West End pocket parks . 
- The Strand plays host to a yearly <unk> <unk> festival , Galveston Island Jazz & Blues Festival and a Victorian @-@ themed Christmas festival called Dickens on the Strand ( honoring the works of novelist Charles Dickens , especially A Christmas Carol ) in early December . Galveston is home to several historic ships : the tall ship <unk> ( the official <unk> Ship of Texas ) at the Texas <unk> Museum and USS <unk> and USS Stewart , both berthed at <unk> Park on nearby Pelican Island . Galveston is ranked the number one cruise port on the Gulf Coast and fourth in the United States . 
- 
- = = Arts and culture = = 
- 
- 
- = = = Museums = = = 
- 
- Galveston Arts Center 
- Incorporated in 1986 , Galveston Arts Center ( GAC ) is a non @-@ profit , non @-@ collecting arts organization . The center exhibits contemporary art , often by Texas @-@ based artists , and offers educational and outreach programs . Notably , GAC <unk> and produces Galveston ArtWalk . Museum entry is free to the public , although cash donations are welcomed . <unk> membership options and a range of volunteer opportunities are also available . 
- In October 2015 , Galveston Arts Center will celebrate relocation to its original home , the historic 1878 First National Bank Building on the Strand . This Italianate @-@ style 1900 Storm survivor was extensively damaged during Hurricane Ike in 2008 . Fortunately , just weeks before Ike made landfall , scaffolding was installed to support the entire structural load of the building for repairs , likely preventing collapse under heavy winds and storm surge . After a lengthy fundraising campaign , restoration is nearing completion . 
- 
- = = = Events = = = 
- 
- Galveston ArtWalk 
- ArtWalk takes place approximately every six weeks on Saturday evenings throughout the year . ArtWalk is organized by Galveston Arts Center , which releases an ArtWalk <unk> featuring a map of participating venues as well as descriptions of shows and exhibits . <unk> include GAC , Galveston Artist Residency and artist ’ s studios and galleries . Additionally , art is shown in “ other walls ” — for example <unk> <unk> or Mosquito Cafe — or outdoors at Art Market on Market Street . Musicians perform outdoors and at venues such as the <unk> Gallery & Public House or Old Quarter Acoustic Cafe . While most ArtWalk events are concentrated downtown , there are a number or participants elsewhere on the island . 
- 
- = = = Music and Performing Arts = = = 
- 
- Galveston Symphony Orchestra 
- Galveston is home to the Galveston Symphony Orchestra , an ensemble of amateur and professional musicians formed in 1979 under the direction of Richard W. <unk> , Musical Director @-@ <unk> . 
- Galveston Ballet 
- The Galveston Ballet is a regional pre @-@ professional ballet company and academy serving Galveston county . The company presents one full @-@ length classical ballet in the spring of each year and one mixed repertory program in the fall , both presented at the Grand 1894 Opera House . 
- 
- = = = Artist Residency & Artist Housing = = = 
- 
- Galveston Artist Residency 
- Galveston Artist Residency ( GAR ) grants studio space , living space and a <unk> to three visual artists each year . <unk> artists work in a variety of <unk> and exhibit their work in the GAR Gallery and <unk> . Located in renovated industrial structures on the west side of downtown , GAR also hosts performances and other public events . 
- The National Hotel Artist <unk> 
- The National Hotel Artist <unk> ( <unk> ) is an <unk> @-@ developed property featuring twenty @-@ seven live / work units designated as affordable housing for artists . The project brought new life to the historic <unk> Levy Building , which was left abandoned for twenty years . Originally built as the <unk> Opera House in 1870 , the structure was extensively renovated to serve various functions , from offices and stores to the National Hotel . The building also housed the U.S. National Weather Bureau 's Galveston office under Isaac <unk> during the 1900 Storm . 
- Under Property Manager / Creative Director <unk> Major , the unused retail space in the front of the building found a new purpose as a <unk> art and music venue , despite its <unk> and undeveloped state . In May 2015 , the newly renovated space reopened as the <unk> Gallery & Public House . This unique bar and gallery provides a common area for <unk> and neighborhood residents and a cultural hub for the broader community . Visual art , events and live music are regularly hosted in the space . 
- 
- = = = Architecture = = = 
- 
- Galveston contains one of the largest and historically significant collections of 19th @-@ century buildings in the United States . Galveston 's architectural preservation and <unk> efforts over several decades have earned national recognition . 
- Located in the Strand District , the Grand 1894 Opera House is a restored historic Romanesque Revival style Opera House that is currently operated as a not @-@ for @-@ profit performing arts theater . The Bishop 's Palace , also known as <unk> 's Castle , is an ornate Victorian house located on Broadway and 14th Street in the East End Historic District of Galveston , Texas . The American Institute of Architects listed Bishop 's Palace as one of the 100 most significant buildings in the United States , and the Library of Congress has classified it as one of the fourteen most representative Victorian structures in the nation . The Galvez Hotel is a historic hotel that opened in 1911 . The building was named the Galvez , honoring Bernardo de Gálvez y Madrid , Count of Gálvez , for whom the city was named . The hotel was added to the National Register of Historic Places on April 4 , 1979 . The Michel B. <unk> House , built in 1838 and oldest in Galveston , is designed in the Greek revival style . In 1880 , the house was bought by Edwin N. Ketchum who was police chief of the city during the 1900 Storm . The Ketchum family owned the home until the 1970s . The red @-@ brick Victorian Italianate home , Ashton Villa , was constructed in 1859 by James Moreau Brown . One of the first brick structures in Texas , it is listed on the National Register of Historic Places and is a recorded Texas Historic Landmark . The structure is also the site of what was to become the holiday known as <unk> . Where On June 19 , 1865 , Union General Gordon <unk> , standing on its balcony , read the contents of “ General Order No. 3 ” , thereby <unk> all slaves in the state of Texas . St. Joseph ’ s Church was built by German immigrants in 1859 @-@ 60 and is the oldest wooden church building in Galveston and the oldest German Catholic Church in Texas . The church was dedicated in April 1860 , to St. Joseph , the patron saint of laborers . The building is a wooden gothic revival structure , rectangular with a square bell tower with <unk> window . The U.S. Custom House began construction in 1860 and was completed in 1861 . The Confederate Army occupied the building during the American Civil War , In 1865 , the Custom House was the site of the ceremony officially ending the Civil War . 
- Galveston 's modern architecture include the American National Insurance Company Tower ( One Moody Plaza ) , San Luis Resort South and North Towers , The <unk> <unk> , The <unk> Resort and <unk> , One <unk> Moody Plaza , US National Bank Building , the <unk> Pyramid at Moody Gardens , John Sealy Hospital Towers at <unk> and Medical Arts Building ( also known as Two Moody Plaza ) . 
- 
- = = = Media = = = 
- 
- The Galveston County Daily News , founded in 1842 , is the city 's primary newspaper and the oldest continuously printed newspaper in Texas . It currently serves as the newspaper of record for the city and the Texas City Post serves as the newspaper of record for the County . Radio station <unk> , on air from 1947 @-@ 2010 , has previously served as a local media outlet . Television station <unk> signed on the air as KGUL @-@ TV on March 23 , 1953 . Originally licensed in Galveston , KGUL was the second television station to launch in the Houston area after <unk> @-@ TV . One of the original investors in the station was actor James Stewart , along with a small group of other Galveston investors . In June 1959 , KGUL changed its call sign to <unk> and moved their main office to Houston . The local hip hop name for Galveston is " G @-@ town . " 
- 
- = = = <unk> = = = 
- 
- Many statues and sculptures can be found around the city . Here are a few well @-@ known sculptures . 
- 1900 Storm Memorial by David W. Moore 
- Birth by Arthur Williams 
- <unk> Resignation by Louis <unk> 
- Dolphins by David W. Moore 
- High Tide by Charles Parks 
- Jack Johnson by <unk> <unk> 
- Pink <unk> Monument by Joe Joe <unk> 
- Texas Heroes Monument by Louis <unk> 
- 
- = = = Notable people = = = 
- 
- Galveston has been home to many important figures in Texas and U.S. history . During the island 's earliest history it became the domain of Jean Lafitte , the famed pirate and American hero of the War of 1812 . Richard <unk> , Jr. who represented Galveston in the Senate of the Second Texas Legislature in 1847 and assisted in drawing up the Constitution of 1845 . He was also the grandson of Benjamin Franklin , one of the <unk> Fathers of the United States of America and Deborah Read . In 1886 , the African @-@ American Galveston civil rights leader Norris Wright Cuney rose to become the head of the Texas Republican Party and one of the most important Southern black leaders of the century . Noted portrait and landscape artist <unk> Moore White moved from Galveston the day before the 1900 hurricane . While he survived , his studio and much of his portfolio were destroyed . A survivor of the hurricane was the Hollywood director King <unk> , who made his directing debut in 1913 with the film Hurricane in Galveston . Later Jack Johnson , nicknamed the “ Galveston Giant ” , became the first black world heavyweight boxing champion . 
- During the first half of the 20th century , William L. Moody Jr. established a business empire , which includes American National Insurance Company , a major national <unk> , and founded the Moody Foundation , one of the largest charitable organizations in the United States . Sam Maceo , a nationally known organized crime boss , with the help of his family , was largely responsible for making Galveston a major U.S. tourist destination from the 1920s to the 1940s . John H. Murphy , a Texas <unk> for seventy @-@ four years , was the longtime executive vice president of the Texas Daily Newspaper Association . Douglas <unk> became one of the early transatlantic aviators , and was given the nickname " Wrong Way " for claiming to have mistakenly made the ocean crossing after being refused permission to make the flight . Grammy @-@ award winning singer @-@ songwriter Barry White was born on the island and later moved to Los Angeles . 
- George P. Mitchell , pioneer of hydraulic <unk> technology and developer of The Woodlands , Texas , was born and raised in Galveston . 
- More recently <unk> J. <unk> , part of the Maceo bloodline , established the <unk> 's Restaurants corporation , which owns numerous restaurants and entertainment venues in Texas and Nevada . Kay Bailey <unk> was the senior senator from Texas and the first female Texas senator . 
- Gilbert <unk> , incoming 2015 Republican member of the Texas House of Representatives from <unk> , was born in Galveston in 1949 and lived there in early childhood . 
- Jonathan Pollard , who <unk> for Israel and was convicted in the US and sentenced to life in jail , was born in Galveston . The film and television actor Lee Patterson , a native of Vancouver , British Columbia , lived in Galveston and died there in 2007 . 
- Other notable people include Matt Carpenter , second baseman for the St. Louis Cardinals , Mike Evans , wide receiver for the Tampa Bay <unk> , actress Katherine <unk> and Tina Knowles , fashion designer and creator of House of <unk> , mother of Beyoncé and <unk> . Grammy award winning R & B and Jazz legend Esther Phillips was born in Galveston in 1935 . 
- 
- = = Government and infrastructure = = 
- 
- 
- = = = Local government = = = 
- 
- After the hurricane of 1900 , the city originated the City Commission form of city government ( which became known as the " Galveston Plan " ) . The city has since adopted the council @-@ manager form of government . Galveston 's city council serves as the city 's legislative branch , while the city manager works as the chief executive officer , and the municipal court system serves as the city 's judicial branch . The city council and mayor promote <unk> to establish municipal policies . The Galveston City Council consists of six elected positions , each derived from a specified electoral district . Each city council member is elected to a two @-@ year term , while the mayor is elected to a two @-@ year term . The city council appoints the city manager , the city secretary , the city <unk> , the city attorney , and the municipal judge . The city 's Tax Collector is determined by the city council and is <unk> to Galveston County . The city manager hires employees , promotes development , presents and administers the budget , and implements city council policies . Joe Jaworski is mayor , having replaced term @-@ limited <unk> Ann Thomas May 2010 . Jaworski is also the grandson of Leon Jaworski , United States Special Prosecutor during the Watergate Scandal in the 1970s . 
- 
- = = = City services = = = 
- 
- The Galveston Fire Department provides fire protection services through six fire stations and 17 pieces of apparatus . The Galveston Police Department has provided the city 's police protection for more than 165 years . Over 170 authorized officers serve in three divisions . 
- The city is served by the <unk> Library , successor to the Galveston <unk> Library , which was founded in 1871 . It is the oldest public library in the State of Texas . The library also serves as headquarters of the Galveston County Library System , and its <unk> also functions as the Galveston County <unk> . 
- 
- = = = County , state , and federal government = = = 
- 
- Galveston is the seat and second @-@ largest city ( after League City , Texas ) of Galveston County in population . The Galveston County Justice Center , which houses all the county 's judicial functions as well as jail , is located on 59th street . The Galveston County Administrative Courthouse , the seat of civil and administrative functions , is located near the city 's downtown . Galveston is within the County Precinct 1 ; as of 2008 Patrick Doyle serves as the Commissioner of Precinct 1 . The Galveston County Sheriff 's Office operates its law enforcement headquarters and jail from the Justice Center . The Galveston County Department of Parks and Senior Services operates the Galveston Community Center . Galveston is located in District 23 of the Texas House of Representatives . As of 2008 , Craig <unk> represents the district . Most of Galveston is within District 17 of the Texas Senate ; as of 2008 Joan <unk> represents the district . A portion of Galveston is within District 11 of the Texas Senate ; as of 2008 Mike Jackson represents the district . Galveston is in Texas 's 14th congressional district and is represented by Republican Randy Weber as of 2012 . 
- The Galveston Division of the United States District Court for the Southern District of Texas , the first federal court in Texas , is based in Galveston and has jurisdiction over the counties of Galveston , Brazoria , Chambers and Matagorda . It is housed in the United States Post Office , Customs House and Court House federal building in downtown Galveston . The United States Postal Service operates several post offices in Galveston , including the Galveston Main Post Office and the Bob Lyons Post Office Station . In addition the post office has a contract postal unit at the Medical Branch Unit on the campus of the University of Texas Medical Branch and the West Galveston Contract Postal Unit , located on the west end of Galveston Island in the <unk> community of Jamaica Beach . 
- 
- = = = Transportation = = = 
- 
- <unk> International Airport at Galveston ( IATA : <unk> , ICAO : <unk> ) is a two @-@ runway airport in Galveston ; the airport is primarily used for general aviation , offshore energy transportation , and some limited military operations . The nearest commercial airline service for the city is operated out of Houston through William P. <unk> Airport and George Bush Intercontinental Airport . The University of Texas Medical Branch has two <unk> , one for Ewing Hall and one for its emergency room . 
- The Galveston Railway , originally established and named in 1854 as the Galveston <unk> and Cotton Press Company , is a Class III terminal switching railroad that primarily serves the transportation of cargo to and from the Port of Galveston . The railway operates 32 miles ( 51 km ) of yard track at Galveston , over a 50 @-@ acre ( 200 @,@ 000 m2 ) facility . Island Transit , which operates the Galveston Island <unk> manages the city 's public transportation services . <unk> bus service to Galveston was previously operated by <unk> Bus Company ; following the company 's acquisition by Coach USA , service was operated by <unk> . All regular intercity bus service has been discontinued . 
- Galveston is served by Amtrak 's Texas Eagle via connecting bus service at <unk> , Texas . 
- Interstate 45 has a southern terminus in Galveston and serves as a main <unk> to Galveston from mainland Galveston County and Houston . Farm to Market Road <unk> ( locally called <unk> Boulevard ) connects Galveston to Brazoria County via the San Luis Pass @-@ <unk> <unk> Bridge . State Highway 87 , known locally as Broadway Street , connects the island to the Bolivar Peninsula via the Bolivar Ferry . A project to construct the proposed Bolivar Bridge to link Galveston to Bolivar Peninsula was cancelled in 2007 . 
- 
- = = Education = = 
- 
- 
- = = = Colleges and universities = = = 
- 
- Established in 1891 with one building and fewer than 50 students , today the University of Texas Medical Branch ( <unk> ) campus has grown to more than 70 buildings and an enrollment of more than 2 @,@ 500 students . The 84 @-@ acre ( 340 @,@ 000 m2 ) campus includes schools of medicine , nursing , allied health professions , and a graduate school of <unk> sciences , as well as three institutes for advanced studies & medical humanities , a major medical library , seven hospitals , a network of clinics that provide a full range of primary and specialized medical care , and numerous research facilities . 
- Galveston is home to two post @-@ secondary institutions offering traditional degrees in higher education . Galveston College , a junior college that opened in 1967 , and Texas A & M University at Galveston , an ocean @-@ oriented branch campus of Texas A & M University . 
- 
- = = = Primary and secondary schools = = = 
- 
- The city of Galveston is served by Galveston Independent School District , which includes six elementary schools , two middle schools and one high school , Ball High School . There is also one magnet middle school , Austin Middle School , serving grades 5 through 8 . 
- Galveston has several state @-@ funded charter schools not affiliated with local school districts , including kindergarten through 8th grade <unk> Preparatory Academy and pre @-@ kindergarten through 8th Grade Odyssey Academy . In addition <unk> : the Knowledge Is Power Program opened <unk> Coastal Village in Galveston under the auspices of <unk> . 
- Several private schools exist in Galveston . The Roman Catholic Archdiocese of Galveston @-@ Houston operates two Roman Catholic private schools , including Holy Family Catholic School ( K through 8th ) and O 'Connell College Preparatory School ( 9 @-@ 12 ) . Other private schools include <unk> Elementary School , Trinity Episcopal School , Seaside Christian Academy , and Heritage Christian Academy . 
- 
- = = Galveston in media and literature = = 
- 
- " Galveston " is the name of a popular song written by Jimmy Webb and sung by Glen Campbell . 
- Sheldon Cooper , one of the main characters from the TV series The Big Bang Theory , grew up in Galveston . 
- The theater film , The Man from Galveston ( 1963 ) , was the original pilot episode of the proposed NBC western television series Temple Houston , with Jeffrey Hunter cast as Temple Lea Houston , a lawyer and the youngest son of the legendary Sam Houston . For a time the real Temple Houston was the county attorney of Brazoria County , Texas . The Temple Houston series lasted for only twenty @-@ six episodes in the 1963 @-@ 1964 television season . 
- Donald <unk> 's 1974 short story " I bought a little city " is about an unnamed man who <unk> his fortune in buying Galveston , only to sell it thereafter . 
- Galveston is the setting of Sean Stewart 's 2000 fantasy novel Galveston , in which a Flood of Magic takes over the island city , resulting in strange and <unk> adventures . It tied in 2001 with <unk> , by Tim Powers , for the World Fantasy Award for Best Novel . It also won the 2001 <unk> Award and was a preliminary nominee for the Nebula Award for Best Novel . 
- The <unk> House , a novel by Elizabeth Black ( 2013 ) , is an exploration of the island of Galveston , Texas , and the intertwined histories of two families who reside there . 
- <unk> Meyer has mentioned Galveston island in her third book of the Twilight series , <unk> . 
- Galveston ( 2010 ) is the first novel by Nic <unk> , the creator of the HBO series True Detective . 
- 
- = = Sister cities = = 
- 
- Galveston has five sister cities , as designated by Sister Cities International : 
- <unk> , Armenia 
- <unk> , India 
- Veracruz , Mexico 
- Stavanger , Norway 
- <unk> , Japan 
- 
- 
- = Sarnia = 
- 
- Sarnia is a city in Southwestern Ontario , Canada , and had a 2011 population of 72 @,@ 366 . It is the largest city on Lake Huron and in Lambton County . Sarnia is located on the eastern bank of the junction between the Upper and Lower Great Lakes where Lake Huron flows into the St. Clair River , which forms the Canada @-@ United States border , directly across from Port Huron , Michigan . The city 's natural harbour first attracted the French explorer La Salle , who named the site " The Rapids " when he had horses and men pull his 45 tonnes ( 50 short tons ; 44 long tons ) barque " Le <unk> " up the almost four @-@ knot current of the St. Clair River on 23 August 1679 . 
- This was the first time anything other than a <unk> or other oar @-@ powered vessel had sailed into Lake Huron , and La Salle 's voyage was thus <unk> in the development of commercial shipping on the Great Lakes . Located in the natural harbour , the Sarnia port remains an important centre for lake <unk> and <unk> ships carrying cargoes of grain and petroleum products . The natural port and the salt <unk> that exist in the surrounding areas , together with the oil discovered in nearby Oil Springs in 1858 led to the massive growth of the petroleum industry in this area . Because Oil Springs was the first place in Canada and North America to drill commercially for oil , the knowledge that was acquired there led to oil <unk> from Sarnia travelling the world teaching other nations how to drill for oil . 
- The complex of refining and chemical companies is called Chemical Valley and located south of downtown Sarnia . The city has the highest level of <unk> air pollution of any Canadian city because of its reliance on the petrochemical industry . About 60 percent of the particulate matter , however , comes from the neighboring United States . Lake Huron is cooler than the air in summer and warmer than the air in winter ; therefore , it <unk> Sarnia 's humid continental climate , which makes temperature extremes of hot and cold very rare . In the winter , Sarnia experiences lake @-@ effect snow because Arctic air blows across the warmer waters of Lake Huron and <unk> to form snow squalls once over land . 
- <unk> , Sarnia is a large part of the artistic presence in Southern Ontario . The city 's International Symphony Orchestra is renowned in the area and has won the Outstanding Community Orchestra Award given by the Detroit Music Awards in 2011 . Michael Learned graced the stage of the Imperial Theatre for a 2010 production of Driving Miss Daisy . The largest event that happens in Sarnia is Sarnia Bayfest , which is a popular music festival that takes place during the summer . In 2013 , organizers cancelled the event because of money troubles but look forward in 2015 to combining with the International <unk> Festival and presenting a joint event . 
- 
- = = Name = = 
- 
- The name " Sarnia " is Latin for <unk> , which is a British Channel Island . In 1829 Sir John Colborne , a former governor of <unk> , was appointed Lieutenant Governor of Upper Canada . In this capacity , he visited two small settlements in 1835 that had been laid out on the shores of Lake Huron . One of these , named " The Rapids , " consisted then of 44 <unk> , nine frame houses , four log houses , two brick dwellings , two <unk> and three stores . The villagers wished to change its name but were unable to agree on an alternative . The English settlers favoured the name " Buenos Aires " and the Scottish " New Glasgow " . Sir John Colborne suggested Port Sarnia . On 4 January 1836 , the name was formally adopted by a vote of 26 to 16 , and Colborne also named the nearby village Moore after British military hero Sir John Moore . Sarnia adopted the nickname " The Imperial City " on 7 May 1914 because of the visit of Canada 's Governor General , <unk> the Duke of <unk> , and his daughter Princess Patricia . 
- 
- = = History = = 
- 
- First Nations peoples have lived , hunted , and traveled across the area for at least 10 @,@ 000 years , as shown by archaeological evidence on Walpole Island . These peoples were drawn from an amalgamation of <unk> , <unk> , and <unk> clans , which formed the Three Fires Confederacy , also called the Council of Three Fires , in <unk> These clans came together through common links in both language and culture , developing a self @-@ sufficient society where tasks and responsibilities were equally shared among all members . 
- During the <unk> and <unk> , The Three Fires Confederacy controlled much of the area known as the hub of the Great Lakes , which included the Canadian shore where Sarnia is now located . During this time , it maintained relations with many of the First Nations , including Huron , Sioux , and Iroquois , as well as the countries of Great Britain and France . In fact , their trading partners , the Huron , welcomed La Salle and the <unk> in 1679 after he sailed into Lake Huron . The Ontario Heritage Trust erected a sign under the Blue Water Bridge in commemoration of the voyage , as shown by the photo of the sign . 
- Because of this beginning of the incursion of Europeans into the area , the members of the Confederacy helped shape the development of North America throughout the 18th Century , becoming a center of trade and culture . Great Britain supported this strengthening of the tribes in the area as a set of allies against the French and the <unk> . The people of the Three Fires Confederacy , however , sided with the French during the Seven Years ' War and only made peace with Great Britain after the Treaty of Fort Niagara in 1764 . It also fought on the side of the British during the War of 1812 . The Three Fires Confederacy also broke several treaties with the United States prior to 1815 , but finally signed the Treaty of <unk> in September of that year and ceased all hostilities directed at the United States . The Grand Council survived intact until the middle to late 19th century , when more modern political systems began to evolve . 
- After the War of 1812 , the first Europeans in the area were French settlers loyal to the British Crown who moved north from Detroit . They successfully traded with the Three Fires Confederacy , which contributed to the growth of the area . After its foundation , Port Sarnia expanded throughout the 19th Century ; on 19 June 1856 , the residents passed the Act to <unk> the Town of Sarnia and the name Port Sarnia was officially changed to Sarnia effective 1 January 1857 . The Act mentioned 1 @,@ 000 inhabitants in three wards . The wealth of adjoining stands of timber , the discovery of oil in nearby Oil Springs in 1858 by James Miller Williams , and the arrival of the Great Western Railway in 1858 and the Grand Trunk Railway in 1859 all stimulated Sarnia 's growth . The rail lines were later linked directly to the United States by the opening of the St. Clair Tunnel under the St. Clair River at Sarnia in 1890 , by the Grand Trunk Railway , which was the first railroad tunnel ever constructed under a river . The tunnel was an engineering <unk> in its day , achieved through the development of original techniques for excavating in a compressed air environment . 
- Canada <unk> Lines formed in 1913 from many previous companies that plied the waters of the St. Clair River . One of these companies was Northwest Transportation Company of Sarnia , which was founded in 1870 . By 20 April 1914 , when the residents passed Act to <unk> the City of Sarnia , the population had grown to 10 @,@ 985 in six wards . Sarnia officially became a city as of 7 May 1914 . 
- Sarnia 's grain elevator , which is the sixth largest currently operating in Canada , was built after the dredging of Sarnia Harbour in 1927 . Two short years later , grain shipments had become an important part of Sarnia 's economy . The grain elevator rises above the harbour , and next to it is the slip for the numerous bulk carriers and other ships that are part of the shipping industry that includes vessels from all over the world . The waterway between Detroit and Sarnia is one of the world 's busiest , as indicated by the average of 78 @,@ <unk> @,@ 900 tonnes ( 87 @,@ 020 @,@ 800 short tons ; 77 @,@ 697 @,@ 100 long tons ) of shipping that annually travelled the river going in both directions during the period 1993 – 2002 . Lake <unk> and <unk> ships , which are known as " <unk> , " pass up and down the river at the rate of about one every seven minutes during the shipping season . During this same period , The Paul M. <unk> Tunnel , which was named after the retired president of <unk> in 2004 , was bored and began operation in 1995 . It accommodates double @-@ stacked rail cars and is located next to the original tunnel , which has been sealed . 
- While there had been a petroleum industry in the Sarnia area since 1858 , the establishment of Polymer Corporation in 1942 to manufacture synthetic rubber during World War II was a great success and began Sarnia 's rise as a major petrochemical centre . Because of Sarnia 's importance in this industry , it appeared on a United States Government list of possible Soviet targets as part of its Anti @-@ Energy nuclear strike strategy during the Cold War . 
- On 1 January 1991 , Sarnia and the neighbouring town of Clearwater were amalgamated as the new city of Sarnia @-@ Clearwater . The amalgamation was originally slated to include the village of Point Edward , although that village 's residents resisted and were eventually permitted to remain independent of the city . On 1 January 1992 , the city reverted to the name Sarnia . 
- Sarnia 's population experienced a continual growth from 1961 to 1991 , with a 1991 population of 74 @,@ <unk> . In 2001 the population had declined by approximately 3 @,@ 000 . Since 2001 Sarnia 's population has been growing slowly , with a 2011 population count of 72 @,@ 366 . Despite these modest gains , an April 2010 report " Sarnia @-@ Lambton 's Labour Market " states : " Large petrochemical companies are the community 's main economic drivers . Over the recent past , several plants have <unk> , and of those still in operation , increased <unk> and <unk> has led to significantly fewer workers . " . These <unk> and the resulting loss of jobs , and therefore population as workers search for employment elsewhere , will contribute to a general decline shown by one August 2011 study , which shows that the population will decline by 17 % over the next twenty @-@ five years . The Monteith @-@ Brown study cited outlines a plan for restructuring the city based on hybrid <unk> areas , which will bring work opportunities closer to the neighborhoods where people live . The City of Sarnia and Lambton County are also implementing an economic development plan with an emphasis on <unk> and renewable energy . 
- 
- = = Geography = = 
- 
- Sarnia is located on the eastern shore of Lake Huron at its extreme southern point where it flows into the St. Clair River . Most of the surrounding area is flat , and the elevation ranges from 169 metres ( <unk> ft ) and 281 metres ( 922 ft ) above sea level . The soil mostly comprises clay . Despite this high percentage of clay , the soil is remarkably rich for cultivation . Prior to the Ice Age , glaciers covered most of the area , as can be seen not only by the existence of the Great Lakes themselves but also of alluvial sand deposits , terminal moraines , and rich oil reserves . The entire area was submerged and plant and animal matter formed many layers of sediment as they settled after the waters <unk> . Sarnia is not part of the Canadian Shield and is located just beyond its southernmost reaches , 290 kilometres ( 180 mi ) West of Toronto and 106 kilometres ( 66 mi ) North of Detroit . 
- 
- = = = <unk> = = = 
- 
- Wiltshire Park , Woodland , Oak Acres , <unk> Beach , Oakwood Corners , <unk> <unk> , and Blackwell , are part of the North End of Sarnia , which begins immediately north of Ontario Highway 402 and terminates at the shore of Lake Huron . Coronation Park , Heritage Park , College Park , The Tree Streets , and Sherwood Village are some of the neighbourhoods south of the highway . The village of Blue Water was built to house workers and their families in Chemical Valley during the construction of Polymer Corporation and at one point had nearly 3 @,@ 000 residents . In 1961 , all the residents were relocated , mostly to the North End , to make way for expansion of the chemical industry . The village was demolished , and all that remains now is an historical marker at the corner of Vidal Street and Huron Avenue . This neighbourhood was largely forgotten until historian Lorraine Williams penned two books about it and was instrumental in the dedication of the plaque . 
- 
- = = = Climate = = = 
- 
- Sarnia has a humid continental climate ( Köppen climate classification <unk> ) . Winters are cold with a few short @-@ lasting Arctic air masses that dip far enough south and bring with them daily high temperatures lower than − 10 ° C ( 14 ° F ) . Sarnia , while not quite located in the southwestern Ontario <unk> , sometimes receives large quantities of lake @-@ effect snow . Sarnia averages 112 @.@ 0 cm ( 44 @.@ 1 in ) of snow per year , while London averages 194 @.@ 3 cm ( 76 @.@ 5 in ) . 
- The lake creates a seasonal <unk> , and compared to the rest of Canada and inland Ontario , Sarnia has a noticeably longer warm period following summer . However , cooler temperatures tend to prevail for longer after winter . Lake Huron can also create large temperature differences within the city in spring and early summer , particularly on hot days in late May , early June . Finally , extreme temperatures , particularly lows , are rarely ever seen . Daily lows less than − 10 ° C ( 14 ° F ) are seen an average of 30 days a year , and less than − 20 ° C ( − 4 ° F ) two days a year . <unk> are warm to hot and usually humid . <unk> readings can be very high at times from late May to late September . In fact , Sarnia has the second greatest number of high <unk> days at or above 35 ° C ( 95 ° F ) ( with 23 @.@ 16 days on average per year ) and <unk> days at or above 30 ° C ( 86 ° F ) ( with 61 @.@ 20 days on average per year ) in Canada , both after Windsor , Ontario . <unk> can become quite severe from April to September . <unk> weather is very rare in the area but has occurred , such as the tornado event of 1953 . 
- 
- = = Demographics = = 
- 
- In the 2011 Census , the City of Sarnia had a population of 72 @,@ 366 , an increase of 1 @.@ 3 % from the 2006 Census . With a land area of 164 @.@ 71 km2 ( 63 @.@ 59 sq mi ) , it had a population density of 439 @.@ 354 / km2 ( 1 @,@ 137 @.@ 92 / sq mi ) in 2011 . 
- In 2011 , Sarnia had an overwhelmingly white population ; only 8 @.@ 54 % were visible minorities . Of those , 63 @.@ 77 % were aboriginal representing the largest group . In 2011 , 89 @.@ 31 % of <unk> called English their mother tongue , 2 @.@ 46 % listed French , 0 @.@ 87 % stated both of those languages , and 7 @.@ 37 % said another language was their mother tongue . 
- The median age in Sarnia is 44 @.@ 5 which is older than the Canadian median of 40 @.@ 95 , indicative of Sarnia 's aging population . According to the 2011 Census , Sarnia is predominately Christian as 28 @.@ 46 % of the population were Catholic , 12 @.@ 4 % were members of the United Church of Canada , 7 @.@ 3 % were Anglican , and 20 @.@ 06 % were of other Christian faiths , Muslim , or Jewish ; 28 @.@ 38 % professed no religious preference or were <unk> . The median income counting all persons 15 years old or older in Sarnia in 2010 was $ 29 @,@ 196 , while median family income was $ 76 @,@ 523 , both of which were slightly lower than Ontario 's , at $ 30 @,@ 526 and $ 80 @,@ <unk> , respectively . The cost of living in Sarnia , however , is significantly lower than it is in Ontario as a whole . The median value of a dwelling , for instance , is $ 179 @,@ 266 , compared to the $ 300 @,@ <unk> of Ontario as a whole . 
- 
- = = Economy and infrastructure = = 
- 
- The Sarnia @-@ Lambton <unk> Development Board states in its March 2011 Labour Market Report that : " Even though employment in both the petrochemical and agricultural industries has declined significantly in recent years , these two industries remain central drivers of the Sarnia Lambton economy . " 
- When World War II threatened tropical sources of natural latex for rubber , Sarnia was selected as the site to spearhead development of synthetic petroleum @-@ based rubbers for war materials , and Polymer Corporation was built by Dow Chemical at the request of the Government of Canada . Large <unk> bring Alberta oil to Sarnia , where oil refining and petrochemical production have become <unk> of the city 's economy . Shell Canada , Imperial Oil , and <unk> Energy ( <unk> ) operate refineries in Sarnia . Large salt beds found under the city became a source of chlorine and other significant ingredients which contributed to the success of Chemical Valley . Chemical companies operating in Sarnia include <unk> Chemicals , Bayer ( Lanxess and <unk> <unk> ) , <unk> Corporation and <unk> Corporation . 
- Dow ceased operations at its Sarnia site in 2009 . The plant was decommissioned , and the land has been sold to neighbouring <unk> Energy Corporation . <unk> produces power and steam for industry , and is the largest natural gas co @-@ generation plant in Canada . It has created the Bluewater Energy Park on the former Dow site . Lanxess produces more than 150 @,@ 000 tonnes ( 170 @,@ 000 short tons ; 150 @,@ 000 long tons ) of butyl rubber annually at its Sarnia location , and is the sole producer of regulatory @-@ approved , food @-@ grade butyl rubber , used in the manufacture of chewing gum . Within the boundaries of its Sarnia plant Lanxess has also created the <unk> @-@ industrial Park Sarnia . 
- Chemical Valley and the surrounding area are home to 62 facilities and refineries . These industrial complexes are the heart of Sarnia 's infrastructure and economy . They directly employ nearly 8 @,@ 000 , and contribute to almost 45 @,@ 000 additional jobs in the area . In 1971 , the Canadian government deemed this area so important to the economic development of the country that it printed an image of a Sarnia Oil <unk> on the reverse of the Canadian $ 10 note . The huge industrial area is the cause of significant air and water pollution . The Canada Wide Daily Standard for airborne particulate matter and <unk> pollution , regulation <unk> , is 30 <unk> per cubic metre . Forty @-@ five percent of this particulate air pollution in Sarnia comes from Chemical Valley , and the rest drifts over the St. Clair River from the neighbouring United States in the form of what is known as " <unk> Air <unk> . " 
- Sarnia is the location of <unk> 's Sarnia <unk> Power Plant . The facility went into full commercial operation in December 2009 , with 20 MW of power . As of September 2010 , the plant was the largest <unk> ( <unk> ) solar power generation facility in the world , putting out 97 MW . 
- The 80 @-@ acre Western University Research Park , Sarnia @-@ Lambton Campus was established in 2003 by the University of Western Ontario as a joint initiative with the County of Lambton and the City of Sarnia . The park is also the location of the <unk> Innovation Centre , Canada 's centre for the <unk> of industrial <unk> . 
- In 2012 <unk> began construction of North America 's first <unk> acid plant at the <unk> @-@ Industrial Park . The company has since announced that it plans to double the original size of this $ 80 million plant . <unk> is developing a 50 @,@ 000 square foot demonstration facility at the Bluewater Energy Park . This company captures waste gas / water streams to process into value @-@ added co @-@ products . <unk> Corporation , a Canadian <unk> startup company producing ultra @-@ low @-@ cost therapeutic antibody drugs , opened an office at the Western University Research Park in 2011 , and the <unk> Corporation began work on a pilot plant at the park in Summer 2012 , for the production of <unk> . 
- 
- = = = Retail and hospitality = = = 
- 
- Sarnia has two large malls : Lambton Mall with 72 stores , and the <unk> Centre with 9 stores , and several government and medical services . These large malls combine with several smaller shopping centres , discount stores , dollar stores , convenience stores , and a collection of antique and specialty stores to form the <unk> of Sarnia 's retail business . <unk> can choose from eight branded and many family @-@ owned hotels and <unk> . 
- 
- = = = Transportation = = = 
- 
- The Blue Water Bridge links Sarnia and its neighbouring village of Point Edward to the city of Port Huron in the United States . It spans the St. Clair River , which connects Lake Huron to Lake St. Clair . The bridge 's original three @-@ lane span , opened in 1938 , was twinned on 22 July 1997 , making the bridge the fourth busiest border crossing in Ontario . The Blue Water Bridge border crossing makes use of both the <unk> ( frequent traveler program ) and the Free and <unk> Trade ( <unk> ) program . <unk> Highway 402 with the American Interstate 94 ( I @-@ 94 ) and I @-@ 69 , the bridge forms part of the <unk> <unk> , and is one of the most important <unk> on the north – south truck routes . 
- Public transportation within the City of Sarnia , including conventional bus transit , transportation of people with disabilities , transportation support for major events , and charter services , is provided by Sarnia Transit . From the city 's local airport , Sarnia Chris Hadfield Airport , Air Georgian operates services to and from Toronto Pearson International Airport on behalf of Air Canada Express . For rail travel , Sarnia is one of the two western <unk> , along with Windsor , of the Via Rail Quebec City – Windsor Corridor , over which a service departs Sarnia station in the morning and arrives in the evening . 
- 
- = = = Health care = = = 
- 
- Sarnia is served by Bluewater Health , a hospital with 188 acute care beds , 70 complex continuing care beds and 27 rehabilitation beds . The hospital opened in 2010 , following the amalgamation of several smaller facilities . Bluewater Health was recently recognized by Healthcare Insurance <unk> of Canada , one of the country 's largest hospital <unk> , for its continued improvement in patient safety and care quality . 
- 
- = = Culture = = 
- 
- 
- = = = Music , theatre , and arts = = = 
- 
- Sarnia 's musical and theatrical presence in Southern Ontario is significant . The International Symphony Orchestra plays at the Imperial Theatre for an annual season lasting from September to April . In addition to symphonic concerts , the Imperial Theatre offers year @-@ round dramatic productions ; Michael Learned played the lead in Driving Miss Daisy at the theatre in 2010 . Former Max Webster frontman Kim Mitchell has returned to his hometown on occasion to play a concert , including his visit in 2008 for Sarnia 's popular <unk> , a competition where local amateur chefs share their recipes for <unk> ribs and compete against each other . Canadian composer and music <unk> Raymond Murray Schafer was born in Sarnia and developed his radical <unk> techniques there . Musicians and groups such as Aerosmith , <unk> , Keith Urban , John Bon Jovi and Rascal Flatts have played at Sarnia Bayfest in the past . The Sarnia Bayfest , which was preceded by the " Festival by the Bay , " is an annual concert festival that features big @-@ name rock and country bands , typically during the second or third weekend of July . 2013 would have marked the fifteenth anniversary of the annual festival , but financial problems caused the event 's cancellation . Prior to December 2013 , organizers stated that it is " not the end " and that they planned on coming back on solid financial footing sometime in the future . As of December 2013 , however , Bayfest organizers indicated they planned on merging with the International <unk> Festival for a joint event in 2015 . 
- Besides the single museum in Sarnia proper , six other museums in the local area document Sarnia 's history , including its legacy as the home of the North American Oil Industry . Gallery Lambton offers 12 annual art exhibitions . In 2012 the Judith and Norman Alex Art Gallery opened . It is an international Category A art gallery . 
- During the Christmas season , the city of Sarnia presents the annual " Celebration of Lights " in Centennial Park . The event was created in 1984 by Dr. Wills <unk> and a committee funded by the retail chain Hudson 's Bay , and the national telecommunications company <unk> . From modest beginnings the event has garnered numerous awards as it has grown , including second place in the 2002 Canadian Government 's Canada <unk> competition . The Celebration , was incorporated in its national <unk> year and is now run by a voluntary Board of Directors . 
- 
- = = <unk> = = 
- 
- There are over 100 parks in Sarnia , the largest being Canatara Park , which covers over 200 acres along the shore of Lake Huron . Canatara is an Ojibwe word that means Blue Water . The park was opened 24 May 1933 . Within the park is Lake <unk> , a haven for 280 different species of birds on their migration routes . The park also maintains a Children 's Animal Farm as part of Sarnia 's commitment to wildlife . The annual " Christmas on the Farm " weekend event held at the Farm in early December is a popular community event enjoyed by families . Canatara Park is one of the first parks in southern Ontario to feature an outdoor fitness equipment installation . 
- The largest recreational park in Sarnia is Germain Park , which incorporates five baseball <unk> , four soccer fields , an outdoor pool , and the Community Gardens . As a memorial to Canadian aviators who gave their lives in World War II , one of the remaining Canadair Sabres in Canada is on display in the park , 
- Centennial Park was opened on Dominion Day in 1967 , as part of Canada 's centenary celebrations . The City of Sarnia decided in 2013 to close much of Centennial Park , after the discovery of toxic lead and <unk> in the soil . 
- Sarnia has one remaining museum within its city limits : " Stones ' N <unk> " , which houses over 6 @,@ 000 exhibits . The collection includes rocks , artifacts , fossils , and bones from all over the world . A previous museum , the Discovery House Museum , has been converted into to a hospice . This historic house , built between 1869 and 1875 , is recognised as a testament to Victorian Era construction . 
- The city 's sandy fresh water beaches are a popular tourist attraction , while the sheltered harbour houses <unk> for recreational sailing . Since 1925 , the 400 km ( 250 mi ) Mackinac race from Sarnia / Port Huron to Mackinac Island at the north end of the lake has been the highlight of the sailing season , drawing more than 3 @,@ 000 sailors each year . 
- Sarnia 's fresh @-@ cut fries are another popular tourist attraction , and thousands of visitors annually visit the chip trucks parked under the Blue Water Bridge . Niagara @-@ based cookbook author and food e @-@ magazine publisher Lynn <unk> visited the chip trucks in August 2012 and stated " I was blown away by Sarnia , " not only by the city 's waterfront , where the chip trucks are located , but also by the chip trucks themselves . She also published an article in her e @-@ magazine , The Ontario Table , recognizing the outstanding quality of the fresh @-@ cut fries . <unk> @-@ based travel writer Pat <unk> also recognized the quality of Sarnia 's fries in his 2007 piece " Sarnia <unk> Best Fries in the World . " In 2012 , Sarnia officials even created a special <unk> to reach the chip trucks during a period of construction . Realizing the popularity of Sarnia 's chip trucks , the Ontario Medical Association includes them in a campaign to have fries and other junk food labelled for being dangerous in the same manner as cigarettes . 
- 
- = = Sports = = 
- 
- Sarnia is home to the Sarnia Sting , a junior ice hockey team in the Ontario Hockey League . <unk> <unk> , a former NHL player , was a part owner of the team . Former Sting player Steven <unk> was selected first overall in the 2008 NHL Entry Draft by the Tampa Bay Lightning , and was followed by <unk> <unk> in 2012 . Sarnia is also home to the Sarnia Legionnaires ice hockey team , which plays in the Greater Ontario Junior Hockey League . The team is successor to the Sarnia Legionnaires ( 1954 – 1970 ) , who won five Western Jr . ' B ' championships and four Sutherland Cups during 16 seasons in the Ontario Hockey Association . 
- Sarnia has a successful tradition in Canadian football . As members of the Ontario Rugby Football Union , the local team Sarnia <unk> twice won the Grey Cup , in 1934 and 1936 . The modern Sarnia <unk> are a semi @-@ professional team playing in the Northern Football Conference . 
- The Sarnia @-@ born world champion <unk> Steve <unk> played as alternate for the Glenn Howard <unk> in the 2007 Tim <unk> <unk> and 2007 Ford World Men 's <unk> Championship , winning both times . 
- 
- = = Government = = 
- 
- Sarnia City Council consists of nine elected members : the Mayor , four members from the city , and four members from the county . The Mayor and all Council members are elected to four @-@ year terms . The four Lambton County Council members serve both County and City Council . 
- The current mayor , Mike Bradley , has held the position since December 1988 and is currently the second longest @-@ serving mayor in the province of Ontario behind Milton 's <unk> <unk> . Past mayors of the city have included Andy Brandt , <unk> <unk> , Paul <unk> , Thomas George Johnston , and Alexander Mackenzie , the second Prime Minister of Canada . 
- At the provincial level , Sarnia is located within the Sarnia — Lambton provincial electoral district , represented in 2013 by Bob Bailey , a member of the <unk> Conservative Party of Ontario . At the federal level , Sarnia is located within the Sarnia — Lambton federal electoral district which in 2013 was represented by Patricia Davidson of the Conservative Party of Canada . 
- Over the past 50 years , Sarnia 's voters have been moderate , and the party affiliation of its Members of Parliament , both provincial and federal , has swung back and forth largely between the Liberal and <unk> Conservative parties ( a New Democrat was elected in their 1990 provincial wave ) . 
- 
- = = Education = = 
- 
- The Lambton Kent District School Board is responsible for the 13 elementary and four secondary public schools ( Northern Collegiate Institute and <unk> School , Alexander <unk> Secondary School , Sarnia Collegiate Institute & Technical School , and St. Clair Secondary School ) located within Sarnia 's boundaries . 
- The St. Clair Catholic District School Board is responsible for the city 's seven elementary and two secondary Catholic schools ( St. Christopher 's and St. Patrick 's ) . In 2014 , St. Patrick 's and St. Christopher 's merged , under the St. Patrick 's name , on St. Christopher 's North Sarnia site . 
- The <unk> <unk> <unk> de Providence ( <unk> Providence ) represents the two French Catholic schools in the city , Saint @-@ François @-@ Xavier and Saint @-@ Thomas @-@ d <unk> , while the <unk> <unk> <unk> operates two French public schools , the elementary École Les <unk> and the secondary École <unk> Franco @-@ Jeunesse . There are also two independent Christian elementary schools in Sarnia — Sarnia Christian School and Temple Christian Academy . 
- Lambton College , which offers two @-@ year programs and <unk> , is one of Ontario 's 21 colleges of applied arts and technology . It has a full @-@ time enrolment of 3 @,@ 500 and a part @-@ time enrolment of about 8 @,@ 000 . It is the city 's only post @-@ secondary school . 
- 
- = = Media = = 
- 
- There are four radio stations that originate from Sarnia , although other stations <unk> their signal there , notably <unk> @-@ FM , a First Nations produced station from <unk> Point , and <unk> @-@ FM and <unk> @-@ 3 @-@ FM , <unk> of <unk> Radio One and <unk> Radio @-@ Canada <unk> , respectively , from Windsor , Ontario . 
- CHOK , country / news / sports 
- <unk> @-@ FM The Fox , adult contemporary 
- CHOK @-@ 1 @-@ FM ( <unk> of CHOK AM ) 
- <unk> @-@ FM , active rock 
- Sarnia does not have a network television station of its own , although it has a community channel on <unk> , which is the cable television provider in Sarnia . Cable systems pipe in stations from <unk> and Toronto . 
- The city 's main daily newspaper is the Sarnia Observer , owned by <unk> , which purchased Sun Media in 2014 for $ 316 million . A weekly newspaper called the Sarnia Journal began distribution in March 2014 . It is distributed to 30 @,@ 000 households in Sarnia , Bright ’ s Grove , Point Edward and <unk> . The community publications Sarnia This Week , Lambton County Smart <unk> and Business Trends are owned by Bowes Publishing . The monthly business oriented newspaper First Monday is owned by Huron Web <unk> and Graphics . Lambton Shield Publishing has been in operation since November 2010 and runs an on @-@ line only news website , <unk> , delivering local news and services to the Sarnia @-@ Lambton area . There are two magazines currently published in Sarnia , Business Trends and Report on Industry . Business Trends is distributed through City Hall and Report on Industry is sent to executives in surrounding businesses . Report on Industry articles are available online . 
- 
- = = Notable people = = 
- 
- Among Sarnia 's distinguished residents are retired Canadian Space Agency astronaut Chris Hadfield , who flew on two NASA Space Shuttle missions and served as the first Canadian commander of the International Space Station during Expedition 35 . The Nobel laureate George Andrew <unk> moved to Sarnia from his native Hungary to join Dow Chemical in 1957 . James Doohan , the well @-@ known Star Trek actor , attended high school in Sarnia . <unk> virtuoso Mike Stevens still lives in Sarnia and tours all over the world ; he is also notable for his extensive work with aboriginal youth . Many notable <unk> are athletes and others associated with sports , such as NHL Hall of Famer <unk> <unk> , former NHL star Pat <unk> , retired NHL referee Kerry Fraser , current NHL star Steven <unk> , champion <unk> Steve <unk> , and golfer Mike Weir , who was the 2003 Masters Champion . Dominique <unk> , a Sarnia <unk> , won a bronze medal in <unk> Exercise , at the World Cup event in <unk> in March 2012 . The Honourable Alexander Mackenzie , second Prime Minister of Canada , was buried at <unk> Cemetery , Sarnia , where a monument has been erected . The <unk> – 1930s actress Marie <unk> was also born there . Katherine Ryan , comedian , writer , presenter and actress , was born in Sarnia in 1983 she now resides in London , England . 
- 
- 
- = French cruiser Sully = 
- 
- The French cruiser Sully was an armored cruiser of the Gloire class that was built for the French Navy in the early 1900s . She was named in honor of Maximilien de <unk> , Duke of Sully , trusted minister of King Henry IV . The ship struck a rock in <unk> Long Bay , French Indochina in 1905 , only eight months after she was completed , and was a total loss . 
- 
- = = Design and description = = 
- 
- The Gloire @-@ class ships were designed as enlarged and improved versions of the <unk> @-@ class armored cruisers by Emile Bertin . Her crew numbered 612 officers and men . The ship measured 139 @.@ 8 meters ( <unk> ft 8 in ) overall , with a beam of 20 @.@ 2 meters ( 66 ft 3 in ) . Sully had a draft of 7 @.@ 7 meters ( 25 ft 3 in ) and displaced 10 @,@ <unk> metric tons ( 9 @,@ 856 long tons ) . 
- Sully had three propeller shafts , each powered by one vertical triple @-@ expansion steam engine , which were rated at a total of 20 @,@ 500 indicated horsepower ( 15 @,@ 300 kW ) . Twenty @-@ four Belleville water @-@ tube boilers provided steam for her engines . She had a designed speed of 21 @.@ 5 knots ( 39 @.@ 8 km / h ; 24 @.@ 7 mph ) . She carried up to 1 @,@ 590 long tons ( 1 @,@ 620 t ) of coal and could steam for 12 @,@ 000 nautical miles ( 22 @,@ 000 km ; 14 @,@ 000 mi ) at a speed of 10 knots ( 19 km / h ; 12 mph ) . 
- Sully 's main armament consisted of two 194 @-@ millimeter ( 7 @.@ 6 in ) 45 @-@ caliber guns were mounted in single gun turrets fore and aft . Her intermediate armament was eight 45 @-@ caliber Canon de 164 mm <unk> 1893 guns . Four of these were in single gun turrets on the sides of the ship and the other four were in casemates . For anti @-@ torpedo boat defence she carried six 45 @-@ caliber 100 @-@ millimeter ( 3 @.@ 9 in ) guns in casemates and eighteen 47 @-@ millimeter ( 1 @.@ 9 in ) <unk> guns . She was also armed with five 450 @-@ millimeter ( 18 in ) torpedo tubes ; two of these were submerged and the others were above water . 
- The waterline armored belt of the Gloire @-@ class ships was 170 millimeters ( 6 @.@ 7 in ) thick amidships and tapered to 106 millimeters ( 4 @.@ 2 in ) towards the bow and stern . Above the main belt was another belt , 127 millimeters ( 5 in ) thick that also tapered to 106 mm at the ends of the ship . The conning tower had armored sides 150 millimeters ( 5 @.@ 9 in ) thick . The main gun turrets were protected by 173 millimeters ( 6 @.@ 8 in ) of armor and the intermediate turrets by 120 millimeters ( 4 @.@ 7 in ) . The flat part of the lower armored deck was 45 millimeters ( 1 @.@ 8 in ) , but increased to 64 millimeters ( 2 @.@ 5 in ) as it sloped down to the sides of the ship . 
- 
- = = Service = = 
- 
- Sully was laid down at the <unk> et <unk> de la <unk> shipyard in La <unk> on 24 May 1899 and launched on 4 June 1901 . The ship was completed in June 1904 and sent to French Indochina for her first commission . On 7 February 1905 Sully struck a rock in <unk> Long Bay ; her crew was not injured . Her guns and equipment were salvaged , but the ship broke in two and was abandoned as a total loss . 
- 
- 
- = Norman Finkelstein = 
- 
- Norman Gary Finkelstein ( born December 8 , 1953 ) is an American political scientist , activist , professor , and author . His primary fields of research are the Israeli – Palestinian conflict and the politics of the Holocaust , an interest motivated by the experiences of his parents who were Jewish Holocaust survivors . He is a graduate of Binghamton University and received his <unk> in political science at Princeton University . He has held faculty positions at Brooklyn College , Rutgers University , Hunter College , New York University , and DePaul University where he was an assistant professor from 2001 to 2007 . 
- In 2007 , after a highly publicized feud between Finkelstein and an academic opponent , Alan Dershowitz , Finkelstein 's tenure bid at DePaul was denied . Finkelstein was placed on administrative leave for the 2007 – 2008 academic year , and on September 5 , 2007 , he announced his resignation after coming to a settlement with the university on generally undisclosed terms . An official statement from DePaul strongly defended the decision to deny Finkelstein tenure , stated that outside influence played no role in the decision . In 2008 , he was banned from entering Israel for 10 years . 
- Finkelstein taught at <unk> University Middle East Institute in Turkey between 2014 and 2015 . 
- 
- = = Personal background and education = = 
- 
- Finkelstein has written of his Jewish parents ' experiences during World War II . His mother , <unk> <unk> , grew up in Warsaw , survived the Warsaw <unk> , the <unk> concentration camp , and two slave labor camps . Her first husband died in the war . She considered the day of her liberation as the most horrible day of her life , as she realized that she was alone , her parents and siblings gone . Norman 's father , <unk> Finkelstein , active in <unk> <unk> , was a survivor of both the Warsaw <unk> and the Auschwitz concentration camp . 
- After the war they met in a displaced persons camp in <unk> , Austria , and then emigrated to the United States , where his father became a factory worker and his mother a homemaker and later a <unk> . Finkelstein 's mother was an ardent pacifist . Both his parents died in 1995 . Of his parents , Finkelstein has recalled that " they saw the world through the <unk> of the Nazi Holocaust . They were <unk> indebted to the Soviet Union ( to whom they attributed the defeat of the Nazis ) , and so anyone who was anti @-@ Soviet they were extremely harsh on " . They supported the Soviet Union 's approval of the creation of the State of Israel , as <unk> by <unk> , who stated that the Jews had earned the right to a state , but thought that Israel had sold its soul to the West and " refused to have any truck with it " . 
- Finkelstein grew up in Borough Park , then Mill Basin , both in Brooklyn , New York , where he attended James Madison High School . In his memoir , Finkelstein recalls his strong youthful identification with the outrage that his mother , witness to the <unk> atrocities of World War II , felt at the carnage wrought by the United States in Vietnam . One childhood friend recalls his mother 's " emotional investment in left @-@ wing humanitarian causes as bordering on <unk> " . He had " <unk> [ her ] <unk> " , a trait which he admits rendered him " <unk> " when talking of the Vietnam War , and which <unk> him with a " <unk> @-@ than @-@ thou " attitude at the time which he now regrets . But Finkelstein regards his absorption of his mother 's outlook — the refusal to put aside a sense of moral outrage in order to get on with one 's life — as a virtue . Subsequently , his reading of Noam Chomsky played an important role in <unk> the passion bequeathed to him by his mother to the necessity of maintaining intellectual rigor . 
- Finkelstein completed his undergraduate studies at Binghamton University in New York in 1974 , after which he studied at the École <unk> des <unk> Études in Paris . A deep admirer of Paul <unk> , he was an ardent <unk> and was devastated by the news of the trial of the Gang of Four , an event which " totally devastated " him , and led him to abandon <unk> – <unk> . 
- Finkelstein received his Master 's degree in political science in 1980 , and later his PhD in political studies , from Princeton . His doctoral thesis was on Zionism . Before gaining academic employment , Finkelstein was a part @-@ time social worker with teenage <unk> in New York . He then taught successively at Rutgers University , New York University , Brooklyn College , and Hunter College and at DePaul University in Chicago . During the First Intifada , he spent every summer from 1988 in the West Bank , a guest of Palestinian families in Hebron and Beit <unk> . 
- According to The New York Times , Finkelstein left Hunter College in 2001 , " after his teaching load and salary were reduced " by the college administration . In his own recollection , he enjoyed teaching at Hunter ( 1992 – 2000 ) and was ' <unk> kicked out of ' the school after <unk> them to keep him on with just two courses a semester ( $ 12 @,@ 000 a year ) . Hunter set conditions that would have required him to spend four days a week , which he thought unacceptable . 
- Beginning with his doctoral thesis at Princeton , Finkelstein 's career has been marked by controversy . A self @-@ described " forensic scholar " , he has written sharply critical academic reviews of several prominent writers and scholars whom he accuses of <unk> the documentary record in order to defend Israel 's policies and practices . His writings have dealt with politically charged topics such as Zionism , the demographic history of Palestine and his allegations of the existence of a " Holocaust Industry " that exploits the memory of the Holocaust to further Israeli and financial interests . 
- Citing linguist and political activist Noam Chomsky as an example , Finkelstein notes that it is " possible to unite exacting scholarly rigor with scathing moral outrage , " and supporters and detractors alike have remarked on the polemical style of Finkelstein 's work . Its content has been praised by eminent historians such as Raul Hilberg and Avi Shlaim , as well as Chomsky . 
- Finkelstein has described himself as " an old @-@ fashioned communist , " in the sense that he " see [ s ] no value whatsoever in states . " 
- 
- = = Academic career = = 
- 
- 
- = = = On From Time Immemorial = = = 
- 
- In Finkelstein 's doctoral thesis , he examined the claims made in Joan Peters 's From Time Immemorial , a best @-@ selling book at the time . Peters 's " history and defense " of Israel deals with the demographic history of Palestine . <unk> studies had tended to assert that the Arab population of Ottoman @-@ controlled Palestine , a 94 % majority at the turn of the century , had dwindled towards parity due to massive Zionist immigration . Peters radically challenged this picture by arguing that a substantial part of the Palestinian people were descended from immigrants from other Arab countries from the early 19th century onwards . It followed , for Peters and many of her readers , that the picture of a native Palestinian population overwhelmed by Jewish immigration was little more than propaganda , and that in actuality two almost simultaneous waves of immigration met in what had been a relatively unpopulated land . 
- From Time Immemorial had been praised by figures as varied as Barbara <unk> , Theodore H. White , Elie Wiesel , and Lucy <unk> . Saul <unk> , for one , wrote in a jacket endorsement that : 
- " Millions of people the world over , smothered by false history and propaganda , will be grateful for this clear account of the origins of the Palestinians . " 
- Finkelstein asserted that the book was a " monumental hoax " . He later opined that , while Peters 's book received widespread interest and approval in the United States , a scholarly demonstration of its <unk> and <unk> aroused little attention : 
- " By the end of 1984 , From Time Immemorial had ... received some two hundred [ favorable ] notices ... in the United States . The only ' false ' notes in this <unk> chorus of praise were the Journal of Palestine Studies , which ran a highly critical review by Bill Farrell ; the small Chicago @-@ based <unk> In These Times , which published a <unk> version of this writer 's findings ; and Alexander Cockburn , who devoted a series of columns in The Nation exposing the hoax . ... The periodicals in which From Time Immemorial had already been favorably reviewed refused to run any critical correspondence ( e.g. The New Republic , The Atlantic Monthly , <unk> ) . <unk> that had yet to review the book rejected a manuscript on the subject as of little or no consequence ( e.g. The Village Voice , <unk> , The New York Review of Books ) . Not a single national newspaper or columnist contacted found <unk> that a best @-@ selling , <unk> praised ' study ' of the Middle East conflict was a <unk> hoax . " 
- Noam Chomsky later <unk> : 
- " I warned him , if you follow this , you 're going to get in trouble — because you 're going to expose the American intellectual community as a gang of <unk> , and they are not going to like it , and they 're going to destroy you . " 
- In 1986 , the New York Review of Books published <unk> <unk> 's review and an exchange with critics of the review in which he criticized the assumptions and evidence on which Peters 's thesis relied , thus lending independent support from an expert in Palestinian demographics to Finkelstein 's doctoral critique . 
- In the house journal of the American Council on Foreign Relations , Foreign Affairs , William B. <unk> , the Edward <unk> professor of Politics at the University of Virginia and authority on Middle Eastern politics , later described Finkelstein 's critique of From Time Immemorial as a " landmark essay " and a " victory to his credit " , in its " demonstration " of the " <unk> scholarship " of Peters ' book . Israeli historian Avi Shlaim later praised Finkelstein 's thesis , saying that it had established his credentials when he was still a doctoral student . In Shlaim 's view , Finkelstein had produced an " <unk> case " with " <unk> evidence " , proving that Peters ' book was both " preposterous and worthless " . 
- According to Noam Chomsky , the controversy that surrounded Finkelstein 's research caused a delay in his earning his Ph.D. at Princeton University . Chomsky wrote in Understanding Power that Finkelstein " literally could not get the faculty to read [ his dissertation ] " and that Princeton eventually granted Finkelstein his doctorate only " out of embarrassment [ for Princeton ] " but refused to give him any further professional backing . 
- Finkelstein published portions of his thesis in the following publications : 
- " <unk> and the Palestine Question : The Not @-@ So @-@ Strange Case of Joan Peters 's From Time Immemorial " , Chapter 2 of <unk> the Victims : <unk> Scholarship and the Palestinian Question ( 1988 ) ; and 
- " A Land Without a People ( Joan Peters ' " Wilderness " Image ) " , Chapter 2 of Image and Reality of the Israel @-@ Palestine Conflict ( 1995 ) . 
- 
- = = = The Holocaust Industry = = = 
- 
- The Holocaust Industry : Reflections on the <unk> of Jewish Suffering was published in 2000 . Here , Finkelstein argues that Elie Wiesel and others exploit the memory of the Holocaust as an " ideological weapon . " The purpose , writes Finkelstein , is to enable the State of Israel , " one of the world 's most formidable military powers , with a <unk> human rights record , [ to ] cast itself as a victim state ; " that is , to provide Israel " immunity to criticism . " He <unk> what he calls a " double shakedown " by " a repellent gang of <unk> , <unk> and <unk> " seeking enormous legal damages and financial settlements from Germany and Switzerland , <unk> which then go to the lawyers and institutional actors involved in <unk> them , rather than actual Holocaust survivors . 
- The book received a hostile reception in some quarters , with critics charging that it was poorly researched and / or allowed others to exploit it for antisemitic purposes . The German historian Hans <unk> disparaged the first edition as " a most trivial book , which appeals to easily aroused anti @-@ Semitic prejudices " . Israeli Holocaust historian Israel <unk> called the book " a <unk> " , stating " this is not research ; it isn 't even political literature ... I don 't even think it should be reviewed or critiqued as a legitimate book . " The book was also harshly criticized by Brown University Professor Omer Bartov and University of Chicago Professor Peter <unk> . 
- However , <unk> Holocaust scholar Raul Hilberg said the book expressed views Hilberg himself subscribed to in substance , in that he too found the exploitation of the Holocaust , as Finkelstein describes , " <unk> " . Asked on another occasion if Finkelstein 's analysis might play into the hands of neo @-@ Nazis for antisemitic purposes , Hilberg replied : " Well , even if they do use it in that fashion , I 'm afraid that when it comes to the truth , it has to be said openly , without regard to any consequences that would be undesirable , embarrassing " . 
- Other critics claim Finkelstein 's evidence is highly selective and / or dubious and that his arguments would be based on a <unk> of history and a questionable use of sources . The historian David <unk> wrote that while Finkelstein <unk> Swiss banks of serious <unk> towards Holocaust survivors and depicts them as victims of a Jewish terror based on a sentence from an important report annex , he had ignored the report body which describes <unk> actions by Swiss banks , inappropriate closing of accounts , failure to keep adequate records , and so on . 
- 
- = = = Criticism of Alan Dershowitz 's The Case for Israel = = = 
- 
- Shortly after the publication of the book The Case for Israel by Alan Dershowitz , Finkelstein derided it as " a collection of fraud , <unk> , plagiarism , and nonsense " . During a debate on Democracy Now ! , Finkelstein asserted that Dershowitz lacked knowledge about specific contents of his own book . He also claimed that Dershowitz did not write the book , and may not have even read it . 
- Finkelstein noted 20 instances , in as many pages , where Dershowitz 's book cites the same sources and passages used by Joan Peters in her book , in largely the same sequence , with <unk> in the same places . In two instances , Dershowitz <unk> Peters 's errors ( see below ) . From this Finkelstein concluded that Dershowitz had not checked the original sources himself , contrary to the latter 's claims . Finkelstein suggests that this copying of quotations amounts to copying ideas . Examining a copy of a proof of Dershowitz 's book he managed to obtain , he found evidence that Dershowitz had his <unk> assistant , Holly Beth Billington , check in the Harvard library the sources he had read in Peters 's book . Dershowitz answered the charge in a letter to the University of California 's Press Director <unk> <unk> , arguing that Finkelstein had made up the smoking gun quotation , in that he had changed its wording ( from ' cite ' to ' copy ' ) in his book . In public debate he has stated that if " somebody borrowed the quote without going to check back on whether Mark Twain had said that , obviously that would be a serious charge " ; however , he insisted emphatically that he himself did not do that , that he had indeed checked the original source by Twain . 
- Dershowitz threatened libel action over the charges in Finkelstein 's book , as a consequence of which , the publisher deleted the word " plagiarism " from the text before publication . Finkelstein agreed to remove the suggestion that Dershowitz was not the true author of The Case for Israel because , as the publisher said , " he couldn 't document that " . 
- <unk> that he did consult the original sources , Dershowitz said Finkelstein is simply accusing him of good scholarly practice : citing references he learned of initially from Peters 's book . Dershowitz denies that he used any of Peters 's ideas without citation . " <unk> is taking someone else 's words and claiming they 're your own . There are no borrowed words from anybody . There are no borrowed ideas from anybody because I fundamentally disagree with the conclusions of Peters 's book . " In a footnote in The Case for Israel which cites Peters 's book , Dershowitz explicitly denies that he " relies " on Peters for " conclusions or data " . 
- In their joint interview on Democracy Now , however , Finkelstein cited specific passages in Dershowitz 's book in which a phrase that he says Peters coined was incorrectly attributed to George Orwell : 
- " [ Peters ] coins the phrase , ' turnspeak ' , she says she 's using it as a play off of George Orwell which as all listeners know used the phrase ' <unk> . ' She coined her own phrase , ' turnspeak ' . You go to Mr. Dershowitz 's book , he got so confused in his massive <unk> from Joan Peters that on two occasions , I 'll cite them for those who have a copy of the book , on page 57 and on page 153 he uses the phrase , quote , George Orwell 's ' turnspeak ' . ' <unk> ' is not Orwell , Mr. Dershowitz , you 're the Felix Frankfurter chair at Harvard , you must know that Orwell would never use such a <unk> phrase as ' turnspeak ' " . 
- James O. <unk> , the former president of Dartmouth College , the University of Iowa , and the American Academy of Arts and Sciences , has defended Dershowitz : 
- I do not understand [ Finkelstein 's ] charge of plagiarism against Alan Dershowitz . There is no claim that Dershowitz used the words of others without attribution . When he uses the words of others , he quotes them properly and generally cites them to the original sources ( Mark Twain , Palestine Royal Commission , etc . ) [ Finkelstein 's ] complaint is that instead he should have cited them to the secondary source , in which Dershowitz may have come upon them . But as The Chicago Manual of Style emphasizes : ' Importance of attribution . With all reuse of others ' materials , it is important to identify the original as the source . This not only <unk> the claims of fair use , it also helps avoid any accusation of plagiarism . ' This is precisely what Dershowitz did . 
- Responding to an article in The Nation by Alexander Cockburn , Dershowitz also cited The Chicago Manual of Style : 
- Cockburn 's claim is that some of the quotes should not have been cited to their original sources but rather to a secondary source , where he believes I stumbled upon them . Even if he were correct that I found all these quotations in Peters 's book , the preferred method of citation is to the original source , as The Chicago Manual of Style emphasizes : " With all reuse of others ' materials , it is important to identify the original as the source . This ... helps avoid any accusation of plagiarism ... To cite a source from a secondary source ( ' quoted in ... ' ) is generally to be discouraged .... " 
- ... to which Cockburn responded : 
- Quoting The Chicago Manual of Style , Dershowitz <unk> implies that he followed the rules by citing " the original " as opposed to the secondary source , Peters . He <unk> Chicago here , where " the original " means merely the origin of the borrowed material , which is , in this instance , Peters . 
- Now look at the second bit of the quote from Chicago , <unk> separated from the preceding sentence by a demure three @-@ point <unk> . As my associate Kate Levin has discovered , this passage ( " To cite a source from a secondary source ... " ) occurs on page <unk> , which is no less than 590 pages later than the material before the <unk> , in a section titled " <unk> Taken from Secondary Sources . " Here 's the full quote , with what Dershowitz left out set in bold : " ' <unk> in ' . To cite a source from a secondary source ( " quoted in " ) is generally to be discouraged , since authors are expected to have examined the works they cite . If an original source is unavailable , however , both the original and the secondary source must be listed . " 
- So Chicago is clearly insisting that unless Dershowitz went to the originals , he was obliged to cite Peters . Finkelstein has conclusively demonstrated that he didn 't go to the originals . <unk> , <unk> , plus added time for <unk> distortion of the language of Chicago 's guidelines , <unk> together two separate discussions . 
- On behalf of Dershowitz , Harvard Law School dean <unk> <unk> asked former Harvard president Derek Bok to investigate the assertion of plagiarism ; Bok <unk> Dershowitz of the charge . 
- In an April 3 , 2007 interview with the Harvard Crimson , " Dershowitz confirmed that he had sent a letter last September to DePaul faculty members lobbying against Finkelstein 's tenure . " 
- In April 2007 , Dr. Frank <unk> , a former Editor @-@ in @-@ Chief of the UCLA Law Review , published an analysis of the charges made against Finkelstein by Dershowitz , finding no merit in any single charge and concluding that Dershowitz had misrepresented matters . In a follow @-@ up analysis he concluded that he could find ' no way of avoiding the inference that Dershowitz copied the quotation from Twain from Peters 's From Time Immemorial , and not from the original source ' , as Dershowitz claimed . In an interview given for the film American Radical : The Trials of Norman Finkelstein in 2009 , Dershowitz said of Finkelstein : " I don 't think he is a Jew . He 's Jewish only on his parents ' side . " 
- 
- = = Controversies = = 
- 
- 
- = = = <unk> denial and resignation = = = 
- 
- In September 2006 , Dershowitz sent members of DePaul 's law and political science faculties what he described as " a <unk> of Norman Finkelstein 's most <unk> academic sins , and especially his outright lies , <unk> , and distortions " and lobbied professors , alumni and administrators to deny Finkelstein tenure . De Paul 's political science committee investigated his accusations against Finkelstein and concluded that they were not based on legitimate criticism . The department subsequently invited John <unk> and Ian <unk> , two independent academics with known expertise on the Israel – Palestine conflict , to evaluate the academic merit of Finkelstein 's work ; they came to the same conclusion . 
- Also in 2006 , the Washington Post noted " the <unk> repeatedly accused " Norman Finkelstein of being a " Holocaust <unk> " and that " These charges have proved <unk> . " Finkelstein 's mother survived the <unk> concentration camp , his father survived the Auschwitz concentration camp , and most of his family died in the Holocaust 
- In early 2007 , the DePaul Political Science Department voted nine to three , and the College of Liberal Arts and Sciences Personnel Committee five to zero , in favor of giving Finkelstein tenure . The three opposing faculty members subsequently filed a minority report opposing tenure , supported by the Dean of the College , Chuck Suchar . Suchar stated he opposed tenure because Finkelstein 's " personal and reputation <unk> attacks on Dershowitz , Benny Morris , and the <unk> authors Elie Wiesel and Jerzy <unk> " were inconsistent with DePaul 's " <unk> " values ; as examples of the latter , Suchar argued that Finkelstein lacked respect for " the dignity of the individual " and for " the rights of others to hold and express different intellectual positions " . <unk> considerable public debate , Dershowitz actively campaigned to block Finkelstein 's tenure bid . In June 2007 , a 4 – 3 vote by DePaul University 's Board on Promotion and <unk> ( a faculty board ) , affirmed by the university 's president , the Rev. Dennis <unk> , denied Finkelstein tenure . 
- The university denied that Dershowitz , who had been criticized for his campaign against Finkelstein 's tenure , played any part in this decision . At the same time , the university denied tenure to international studies assistant professor <unk> <unk> , a strong supporter of Finkelstein , despite unanimous support from her department , the Personnel Committee and the dean . Finkelstein stated that he would engage in civil disobedience if attempts were made to bar him from teaching his students . 
- The Faculty Council later affirmed the right of <unk> Finkelstein and <unk> to appeal , which a university lawyer said was not possible . Council President Anne Bartlett said she was " ' terribly concerned ' correct procedure was not followed " . DePaul 's faculty association considered taking no @-@ confidence votes on administrators , including the president , because of the tenure <unk> . In a statement issued upon Finkelstein 's resignation , DePaul called him " a prolific scholar and an outstanding teacher " . Dershowitz expressed outrage at the compromise and this statement in particular , saying that the university had " traded truth for peace " . 
- In June 2007 , after two weeks of protests , some DePaul students staged a sit @-@ in and hunger strike in support of both professors denied tenure . The Illinois Conference of the American Association of University <unk> also sent a letter to the university 's president stating : " It is entirely illegitimate for a university to deny tenure to a professor out of fear that his published research ... might hurt a college 's reputation " and that the association has " explicitly rejected <unk> as an appropriate criterion for evaluating faculty members " . In a 2014 interview , professor Matthew Abraham , author of Out of <unk> : Academic Freedom and the Question of Palestine , described the Finkelstein tenure case as " one of the most significant academic freedom cases in the last fifty years " , claiming the case demonstrated " the substantial pressure outside parties can place on a mid @-@ tier religious institution when the perspectives advanced by a controversial scholar threaten dominant interests " . 
- 
- = = = <unk> entry to Israel in 2008 = = = 
- 
- On May 23 , 2008 , Finkelstein was denied entry to Israel , according to unnamed Shin Bet security officials , because " of suspicions involving hostile elements in Lebanon " and that he " did not give a full accounting to <unk> with regard to these suspicions . " Finkelstein had previously visited south Lebanon and met with Lebanese families during the 2006 Lebanon War . He was banned from entering Israel for 10 years . 
- Finkelstein was questioned after his arrival at Ben Gurion Airport near Tel Aviv and detained for 24 hours in a holding cell . After speaking to Israeli attorney Michael <unk> he was placed on a flight back to Amsterdam , his point of origin . In an interview with <unk> , Finkelstein stated " I did my best to provide absolutely <unk> and comprehensive answers to all the questions put to me . I am confident that I have nothing to hide ... no suicide missions or secret rendezvous with terrorist organizations . " He had been travelling to visit friends in the West Bank and stated he had no interest in visiting Israel . <unk> said banning Finkelstein from entering the country " recalls the behavior of the Soviet bloc countries " . 
- 
- = = Reception = = 
- 
- Finkelstein 's books are an attempt to examine the works of mainstream scholarship . The authors whose work he has thus targeted , including Daniel <unk> Goldhagen and Dershowitz , along with others such as Benny Morris whose work Finkelstein has also cited in his scholarship , have in turn accused Finkelstein of grossly <unk> their work , and selectively quoting from their books . 
- According to Raul Hilberg , Finkelstein displays " academic courage to speak the truth when no one else is out there to support him ... I would say that his place in the whole history of writing history is assured , and that those who in the end are proven right triumph , and he will be among those who will have triumphed , albeit , it so seems , at great cost . " In a peer review for Beyond Chutzpah , Avi Shlaim said that Finkelstein " has a most impressive track record in exposing <unk> American @-@ Jewish scholarship on the Arab @-@ Israeli conflict . " He praised Finkelstein for " all the sterling qualities for which he has become famous : <unk> , originality , spark , meticulous attention to detail , intellectual integrity , courage , and formidable forensic skills . " 
- Sara Roy stated that her shared experience with Finkelstein as a child of Holocaust survivors engaged in research on the Palestinian @-@ Israeli conflict gave her a unique position to comment . According to Roy , Finkelstein 's scholarship is , " exceptional both for its <unk> and rigor . In the fields of Middle Eastern studies and political science his work is considered seminal and there is no doubt that both disciplines would be <unk> weaker without it . Norman 's power and value , however , do not <unk> only from his scholarship but from his character . His life ’ s work , shaped largely but not entirely by his experience as a child of survivors has been and continues to be informed by a profound concern with human dignity and the danger of <unk> . " 
- The Israeli newspaper , <unk> , stated that " [ i ] t is difficult to <unk> with Finkelstein 's opinions and preferences , especially since he decided to support Hezbollah , meet with its fighters and visit the graves of some of its slain operatives . " Still , it continued to say that he should not be banned from entering Israel , because " meetings with Hezbollah operatives do not in themselves constitute a security risk " . 
- 
- = = = 2009 film about Finkelstein = = = 
- 
- American Radical : The Trials of Norman Finkelstein is an award @-@ winning documentary film about the life and career of Norman Finkelstein , released in 2009 and directed by David <unk> and Nicolas <unk> . It has been screened in Amsterdam <unk> , in Toronto Hot <unk> and in more than 40 other national and international venues , it received a freshness rating of 100 % on film review aggregator Rotten Tomatoes . The same year Finkelstein appeared in Defamation ( Hebrew : <unk> ; translit . <unk> ) a documentary film by award @-@ winning Israeli filmmaker <unk> <unk> . 
- 
- = = = Criticism = = = 
- 
- Criticism has been leveled against Finkelstein from several angles . The first sources are responses from those whose work Finkelstein has discussed . Daniel Goldhagen , whose book Hitler 's <unk> <unk> Finkelstein criticized , claimed his scholarship has " everything to do with his burning political agenda " . Alan Dershowitz has written that Peter <unk> , Professor of History at the University of Chicago and a noted Holocaust historian whose work Finkelstein says inspired The Holocaust Industry , has strongly criticized the latter 's work , describing it as " trash " . Similarly , Dershowitz , whose book The Case for Israel and Finkelstein 's response Beyond Chutzpah sparked an ongoing feud between the two , has claimed Finkelstein 's <unk> in a conspiracy against pro @-@ Israel scholars : " The mode of attack is consistent . Chomsky selects the target and directs Finkelstein to probe the writings in minute detail and conclude that the writer didn 't actually write the work , that it is plagiarized , that it is a hoax and a fraud , " arguing that Finkelstein has leveled charges against many academics , calling at least 10 " distinguished Jews ' <unk> ' , ' <unk> ' ( sic ) , ' thieves ' , ' <unk> ' , and worse . " Although the back and forth between Finkelstein and Dershowitz received the most attention and attracted significant controversy , Finkelstein has maintained that " the real issue is Israel 's human rights record . " 
- Israeli historian Omer Bartov , writing for The New York Times Book Review , judged The Holocaust Industry to be marred by the same errors he <unk> in those who exploit the Holocaust for profit or politics : 
- It is filled with precisely the kind of <unk> <unk> that Finkelstein rightly <unk> in much of the current media hype over the Holocaust ; it is <unk> with the same indifference to historical facts , inner contradictions , strident politics and dubious <unk> ; and it <unk> with the same <unk> sense of moral and intellectual superiority ... Like any conspiracy theory , it contains several grains of truth ; and like any such theory , it is both irrational and <unk> . 
- Finkelstein has accused journalist Jeffrey Goldberg of " <unk> " or " being an accessory to torture of " Palestinian prisoners during his IDF service in the First Intifada , based on statements made in Goldberg 's book Prisoners . Finkelstein says that Goldberg admits to personally sending prisoners to the <unk> , which he says has been repeatedly condemned as torture in human rights reports . Goldberg referred to the allegation as " ridiculous " and he had " never laid a hand on anybody . " Goldberg said his " principal role " was " making sure prisoners had fresh fruit . " He characterized Finkelstein as a " ridiculous figure " and accused him of " lying and purposely <unk> my book . " 
- 
- = = Statements on Israel = = 
- 
- Finkelstein is a sharp critic of the state of Israel . Discussing Finkelstein 's book Beyond Chutzpah , Israeli historian Avi Shlaim stated that Finkelstein 's critique of Israel " is based on an amazing amount of research . He seems to have read everything . He has gone through the reports of Israeli groups , of human rights groups , Human Rights Watch and Peace Now and B <unk> , all of the reports of Amnesty International . And he deploys all this evidence from Israeli and other sources in order to sustain his critique of Israeli practices , Israeli violations of human rights of the Palestinians , Israeli house <unk> , the targeted <unk> of Palestinian militants , the cutting down of trees , the building of the wall — the security barrier on the West Bank , which is illegal — the restrictions imposed on the Palestinians in the West Bank , and so on and so forth . I find his critique extremely detailed , well @-@ documented and accurate . " 
- In a telephone interview with Today 's <unk> , in 2009 , Finkelstein stated : 
- I think Israel , as a number of commentators pointed out , is becoming an insane state . And we have to be honest about that . While the rest of the world wants peace , Europe wants peace , the US wants peace , but this state wants war , war and war . In the first week of the massacres , there were reports in the Israeli press that Israel did not want to put all its ground forces in Gaza because it was preparing attacks on Iran . Then there were reports it was planning attacks on Lebanon . It is a <unk> state . 
- When asked how he , as the son of Holocaust survivors , felt about Israel ’ s operation in Gaza , Finkelstein replied : 
- It has been a long time since I felt any emotional connection with the state of Israel , which relentlessly and brutally and <unk> keeps these vicious , murderous wars . It is a <unk> state . There is a Russian writer who once described <unk> states as Genghis Khan with a telegraph . Israel is Genghis Khan with a computer . I feel no emotion of affinity with that state . I have some good friends and their families there , and of course I would not want any of them to be hurt . That said , sometimes I feel that Israel has come out of the <unk> ( sic ) of the hell , a <unk> state . 
- The Anti @-@ Defamation League has described Finkelstein as an " obsessive anti @-@ Zionist " filled with " <unk> hatred of Zionism and Israel . " On being called an anti @-@ Zionist Finkelstein has said : " It 's a superficial term . I am opposed to any state with an ethnic character , not only to Israel . " 
- Finkelstein is an advocate of a two @-@ state solution to the Israeli @-@ Palestinian conflict . 
- 
- = = = Hezbollah and Hamas = = = 
- 
- Finkelstein has expressed solidarity with Hezbollah and Hamas with respect to defensive actions , alleging that Israel had invaded Lebanon as a signal of rejection when Hamas was seeking a diplomatic settlement with Israel . He also condemned what he said was Israel 's refusal " to abide by international law [ and ] to abide by the opinion of the international community " to settle the conflict . 
- " I was of course happy to meet the <unk> people , because it is a point of view that is rarely heard in the United States . I have no problem saying that I do want to express solidarity with them , and I am not going to be a <unk> of ( sic ) a <unk> about it . I don 't care about <unk> as a political organization . I don 't know much about their politics , and <unk> , it 's irrelevant . I don 't live in Lebanon . It 's a choice that the Lebanese have to make : Who they want to be their leaders , who they want to represent them . But there is a fundamental principle . People have the right to defend their country from foreign <unk> , and people have the right to defend their country from invaders who are destroying their country . That to me is a very basic , elementary and <unk> question . " 
- While condemning the targeting of civilians to achieve a political goal , Finkelstein has stated he believes Hezbollah has the right to target Israeli civilians as long as " Israel persists in targeting [ Lebanese ] civilians until Israel ceases its terrorist acts . " 
- Finkelstein claims that an equivalence exists between Hamas and the state of Israel in regards to the military policy of targeted killings during the Second Intifada . According to Finkelstein " the record shows that Israel has routinely targeted civilians for killing " and " Israel <unk> kills civilians " . He concludes that " the argument , among human rights organizations at any rate is that ... in effect , there ’ s no difference between <unk> killing civilians and targeting civilians . " 
- Finkelstein argued one of Israel 's primary motivations for launching the 2008 offensive in Gaza was that Hamas was " signaling that it wanted a diplomatic settlement of the conflict along the June 1967 border . " Finkelstein believes Hamas had joined the international community in " seeking a diplomatic settlement " and describes Hamas 's stance towards Israel prior to the war as a " peace offensive . " 
- 
- = = Statements on the BDS movement = = 
- 
- Finkelstein had made many criticisms of the <unk> , <unk> and <unk> Movement . Finkelstein stated that " I think the solidarity movement has the right tactics . I support the BDS . But I said it will never reach a broad public until and unless they are explicit on their goal . And their goal has to include recognition of Israel , otherwise it 's a <unk> . " Elsewhere , he has stated that he supports a " <unk> " BDS , making the same point about tactics and goals . 
- In February 2012 , The Jewish Chronicle in England stated that Finkelstein " launched a blistering attack " on the BDS movement , saying it was a " <unk> , dishonest cult " , " [ l ] <unk> the <unk> cult in <unk> , " that tries to cleverly pose as human rights activists while in reality their goal is to destroy Israel . Finkelstein stated that the BDS movement has had very few successes , and that just like a cult , the leaders pretend that they are hugely successful when in reality the general public rejects their extreme views . 
- In June 2012 , in an appearance on Democracy Now ! , Finkelstein elaborated on his criticisms of the BDS movement : 
- The problem as I see it with the BDS movement is not the tactic . Who could not support <unk> , <unk> and <unk> ? Of course you should . And most of the human rights organizations , church organizations have moved in that direction . The problem is the goal . . . The official BDS movement , they claim to be agnostic , neutral — whatever term you want to use — on the question of Israel . You can ’ t reach a broad public if you are agnostic on the question of Israel . The broad public wants to know , where do you stand ? And if you claim not to have a stand , you lose them . The BDS movement , it always says , and I ’ m using their language , " We are a rights @-@ based organization . We are based in international law . " I agree with that . That ’ s where you have to go : rights @-@ based international law . But the international law is clear . You read the last sentence of the 2004 International Court of Justice opinion on the wall that Israel has been building in the West Bank , and the last sentence says , " We look forward to two states : a Palestinian state alongside Israel and at peace with its neighbors . " That 's the law . 
- And if you want to go past that law or ignore the Israel part , you ’ ll never reach a broad public . And then it 's a cult . Then it ’ s pointless , in my opinion . We 're wasting time . And it 's only a wasting of time . It becomes — and I know it 's a strong word , and I hope I won 't be faulted for it , but it becomes historically criminal , because there was a time where whatever we said , it made no difference . Nobody was listening . You could shout whatever you want — who cares ? But now , actually , we can reach people . There is a possibility . I ’ m not saying a certainty . I 'm not even saying a probability . But there is a possibility that we can reach a broad public . And so , we have to be very careful about the words we use , and we have to be very careful about the political strategy we map out . Otherwise , we 're going to <unk> a real opportunity . And I don 't want to <unk> it . 
- 
- = = = Books = = = 
- 
- 2014 : Method and Madness : The <unk> Story of Israel 's <unk> on Gaza , OR Books , New York ( 2014 ) 
- 2014 : Old Wine , Broken <unk> : Ari <unk> 's <unk> Land , OR Books , New York ( 2014 ) 
- 2012 : Knowing Too Much : Why the American Jewish <unk> with Israel is Coming to an End , OR Books , New York ( 2012 ) ISBN 978 @-@ 1 @-@ 935928 @-@ 77 @-@ 5 
- 2012 : What Gandhi <unk> About <unk> , Resistance and Courage , OR Books , New York : 2012 , ISBN 978 @-@ 1 @-@ 935928 @-@ 79 @-@ 9 
- 2011 : " <unk> <unk> . Richard <unk> <unk> Israel 's license to kill " , OR Books , New York ( 2011 ) , ISBN 978 @-@ 1 @-@ 935928 @-@ 51 @-@ 5 
- 2010 : This Time We <unk> Too Far : Truth and <unk> of the Gaza Invasion . OR Books , New York : 2010 . [ 2 ] ; ISBN 978 @-@ 1 @-@ 935928 @-@ 43 @-@ 0 
- 2005 : Beyond Chutzpah : On the <unk> of Anti @-@ Semitism and the Abuse of History . University of California Press ; ISBN 0 @-@ 520 @-@ <unk> @-@ 9 . 2nd updated Ed . , University of California Press . June 2008 ; ISBN 0 @-@ 520 @-@ <unk> @-@ 5 , contains an <unk> written by Frank J. <unk> , Dershowitz vs Finkelstein . Who 's Right and Who 's Wrong ? , pp. <unk> – 94 @,@ 
- 2000 : The Holocaust Industry : Reflections on the <unk> of Jewish Suffering , Verso ; ISBN 1 @-@ <unk> @-@ <unk> @-@ X. 
- 1998 : A Nation on Trial : The Goldhagen <unk> and Historical Truth ( co @-@ written with Ruth <unk> <unk> ) , Henry Holt and Co . ; ISBN 0 @-@ <unk> @-@ <unk> @-@ 9 . 
- 1996 : The Rise and Fall of Palestine : A Personal <unk> of the Intifada Years . Minneapolis : U of Minnesota P , ISBN 0 @-@ <unk> @-@ <unk> @-@ 9 . 
- 1995 : Image and Reality of the Israel @-@ Palestine Conflict , Verso ; ISBN 1 @-@ <unk> @-@ <unk> @-@ 1 
- 1987 : From the Jewish Question to the Jewish State : An Essay on the Theory of Zionism ( thesis ) , Princeton University . 
- 
- = = = Articles and chapters = = = 
- 
- " <unk> and the Palestine Question : The Not @-@ So @-@ Strange Case of Joan Peter 's ' From Time Immemorial . ' " in <unk> the Victims : <unk> Scholarship and the Palestinian Question . Ed . Edward W. Said and Christopher <unk> . Verso Press , 1988 ; ISBN 0 @-@ <unk> @-@ <unk> @-@ 4 . Chapter Two , Part One : 
- " Peace process or peace panic ? - The <unk> of Palestinian <unk> " , Middle East Report , 19 ( 1989 ) 3 / 158 , pp. 25 – 26 @,@ 28 @-@ 30 @,@ 42 
- " Zionist orientations " , Scandinavian Journal of Development <unk> 9 ( March 1990 ) 1 @.@ p . 41 @-@ 69 
- " <unk> <unk> in year II of the <unk> . - A personal account " , Journal of Palestine Studies 19 ( Winter 1990 ) 2 / 74 , pp. 62 – 74 
- " Israel and Iraq . - A double standard " , Journal of Palestine Studies 20 ( 1991 ) 2 / 78 @.@ pp. 43 – 56 
- " Reflections on Palestinian attitudes during the Gulf war " , Journal of Palestine Studies , 21 ( 1992 ) 3 / 83 , pp. 54 – 70 
- " <unk> sur la <unk> de l <unk> <unk> et du <unk> dans le <unk> <unk> @-@ <unk> " ( Reflections on the responsibility of state and citizen in the Arab @-@ Israeli conflict ) , in L ' <unk> et la <unk> , L <unk> 1994 , 114 , S. 37 @-@ 50 
- " <unk> the <unk> process ' ? " , New Left Review , ( 1996 ) 218 , p . 138 
- " <unk> occupation : The real meaning of the <unk> River <unk> " , New Left Review , ( 1998 ) 232 , pp. 128 – 39 
- <unk> to The Politics of Anti @-@ Semitism . Ed . Alexander Cockburn and Jeffrey St. Clair . AK Press , 2001 ; ISBN 1 @-@ <unk> @-@ 77 @-@ 4 . 
- " <unk> of Holocaust compensation " , in Palestinian <unk> : The Right of Return . Ed . <unk> <unk> . Pluto Press , 2001 , S. 272 @-@ 275 ; ISBN 0 @-@ <unk> @-@ 1776 @-@ 6 . 
- " Abba <unk> with <unk> " , Journal of Palestine Studies , vol 32 . ( 2003 ) , pp. 74 – 89 
- " <unk> for Ending the Occupation " , <unk> , 35 ( 2003 ) 5 , pp. <unk> – 45 
- <unk> to <unk> , <unk> and <unk> : Conversations with Jewish Critics of Israel , by Seth Farber . Common Courage Press , 2005 . ISBN 1 @-@ <unk> @-@ 326 @-@ 3 . 
- " The Camp David II negotiations . - how Dennis Ross proved the Palestinians <unk> the peace process " , Journal of Palestine Studies , vol . 36 ( 2007 ) , pp. 39 – 53 
- " Dennis Ross and the peace process : <unk> Palestinian rights to Israeli ' needs ' " , Institute for Palestine Studies , 2007 ; ISBN 0 @-@ <unk> @-@ 308 @-@ X 
- 
- = = Others on Finkelstein and his works = = 
- 
- 
- = = = Academic reviews of books by Finkelstein = = = 
- 
- <unk> , Joseph . " <unk> Holocaust <unk> " , Review Essay , Journal of Palestine Studies , Vol . 32 , No. 1 . ( Autumn , 2002 ) , pp. 78 – 89 . 
- Cole , Tim . " Representing the Holocaust in America : Mixed <unk> or Abuse ? " , The Public Historian , Vol . 24 , No. 4 . ( Fall , 2002 ) , pp. 127 – 31 
- <unk> , Eric . Reviewed work : Image and Reality of the Israel @-@ Palestine Conflict by Norman Finkelstein , Journal of Palestine Studies , Vol . 33 , No. 3 , Special Issue in Honor of Edward W. Said . ( Spring , 2004 ) , pp. 123 – 124 . 
- Pelham , Nicolas . Reviewed Work : Image and Reality in the Israel @-@ Palestine <unk> by Norman G. Finkelstein , International Affairs , Vol . 72 , No. 3 , <unk> and International Relations . ( July 1996 ) , pp. <unk> – 28 . 
- <unk> , Ilan . " Valuable New <unk> , " Reviewed Work : Image and Reality of the Israel @-@ Palestine <unk> by Norman G. Finkelstein , Journal of Palestine Studies , Vol . 26 , No. 4 . ( Summer , 1997 ) , pp. 113 – 15 . 
- <unk> , Joel . " The Palestinian @-@ Israeli Conflict after Oslo " , Reviewed work : Image and Reality of the Israel @-@ Palestine Conflict by Norman G. Finkelstein . Middle East Report , No. 201 , Israel and Palestine : Two States , <unk> or <unk> ? . ( <unk> @-@ Dec 1996 ) , pp. 45 – 47 . 
- 
- = = = Reviews of books by Finkelstein = = = 
- 
- <unk> , <unk> ( review of Beyond Chutzpah ) , NRC <unk> , February 24 , 2006 . 
- <unk> , Ilan ( review of Beyond Chutzpah ) , <unk> ( February / March 2006 ) 
- De <unk> , Alfred . Review of Beyond Chutzpah in Frankfurter <unk> Zeitung ( February 3 , 2006 ) 
- <unk> , Paul Charles . These <unk> on the Face of the Earth : Israel 's most <unk> critic ( review of Beyond Chutzpah , in Christianity Today ( January / February 2006 ) 
- <unk> , Mike . " Israel , fraud and <unk> " ( review of Beyond Chutzpah ; January 2006 ) 
- <unk> , Vijay . Z magazine reviews Beyond Chutzpah . Review of Beyond Chutzpah . Z Magazine November 2005 Volume 18 , Number 11 
- Gordon , Neve . Neve Gordon : Review of Norman Finkelstein 's , Beyond Chutzpah . Review of Beyond Chutzpah : On the <unk> of Anti @-@ Semitism and the Abuse of History , by Norman G. Finkelstein . History News Network , October 12 , 2005 
- <unk> , Paul . The Finkelstein <unk> ( review of The Holocaust Industry ) , Judaism ( Fall 2002 ) 
- <unk> , Tobias . Finkelstein 's <unk> : The <unk> of <unk> Anti @-@ Zionism ( review of The Holocaust Industry ) , <unk> ; accessed November 1 , 2015 
- Bartov , Omer . " A Tale of Two <unk> " ( review of The Holocaust Industry ) , New York Times Book Review , August 6 , 2000 
- 
- = = = <unk> of Finkelstein = = = 
- 
- Garner , Mandy . " The Good Jewish Boys Go into Battle . " Times Higher Education Supplement 16 December 2005 . 
- <unk> , Ben . " His Own <unk> Enemy . " The Jerusalem Post 12 December 2005 . 
- <unk> , Jay . " Finkelstein 's List . " The Observer 16 July 2000 . 
- <unk> , <unk> . " The Finkelstein <unk> . " Ha <unk> 30 March 2001 . 
- 
- = = = Critics of Finkelstein and replies = = = 
- 
- Daniel Goldhagen , The New <unk> of <unk> at the Wayback Machine ( archived December 4 , 2002 ) 
- Norman Finkelstein , Response to Goldhagen 
- William Rubinstein et al . , Uses of Holocaust , letters to the London Review of Books 
- David Friedman , Anti @-@ Defamation League letter at the Wayback Machine , calling Finkelstein a " Holocaust <unk> " 
- The Washington Post <unk> a <unk> : Marc Fisher , a Washington Post columnist , publishes a retraction of his charge of " Holocaust <unk> " , <unk> ; accessed November 1 , 2015 . 
- 
- = = = Video = = = 
- 
- Norman Finkelstein at the Internet Movie Database 
- Eight <unk> with Finkelstein ( two sets of four ) , December 2014 and January 2015 , and Three More <unk> with Finkelstein , May 2015 , The Real News 
- American Radical : The Trials of Norman Finkelstein - broadcast of the documentary in two parts 
- <unk> the Israel @-@ Palestine Conflict : University of British Columbia , Vancouver , Jan 21 , 2009 . 
- Interview broadcast on Lebanese TV January 20 , 2008 
- VIDEO : Norman Finkelstein - Israel and Palestine : Roots of Conflict , <unk> for Peace , presentation in Seattle , Washington , May 8 , 2008 . 
- VIDEO : Norman Finkelstein - The Coming <unk> of American Zionism , presentation in <unk> , Washington , May 8 , 2008 . 
- VIDEO : Norman Finkelstein at Brown University on YouTube , April 15 , 2008 
- Doha Debate at the Oxford Union Video of debate on whether the " pro @-@ Israeli lobby has successfully <unk> Western debate about Israel 's actions " with Andrew Cockburn , Martin <unk> , and David <unk> , May 1 , 2007 
- Debate with <unk> Ben @-@ <unk> on Democracy Now ! , February 14 , 2006 
- Finkelstein <unk> to Clinton , <unk> <unk> <unk> March 23 , 2010 
- Israel vs Palestine - featuring Norman Finkelstein ( April 2014 ) , <unk> Rap News 
- 
- = Mutinus elegans = 
- 
- Mutinus elegans , commonly known as the elegant stinkhorn , the dog stinkhorn , the headless stinkhorn , or the devil 's <unk> , is a species of fungus in the <unk> family . A saprobic species , it is typically found growing on the ground singly or in small groups on woody debris or leaf litter , during summer and autumn in Japan , Europe , and eastern North America . The fruit body begins its development in an " egg " form , resembling somewhat a puffball partially submerged in the ground . As the fungus matures , a slender orange to pink colored stalk emerges that tapers evenly to a pointed tip . The stalk is covered with a foul @-@ smelling <unk> green spore mass on the upper third of its length . Flies and other insects feed upon the <unk> which contains the spores , assisting in their dispersal . Due to their repellent odor , mature specimens are not generally considered edible , although there are reports of the immature " eggs " being consumed . In the laboratory , Mutinus elegans has been shown to inhibit the growth of several microorganisms that can be pathogenic to humans . 
- 
- = = Taxonomy = = 
- 
- Mutinus elegans was first described by British missionary John <unk> in 1679 who chronicled the natural history of Virginia ; this early report is thought to be the first account of a fungus in North America . It was first characterized scientifically by French scientist Jean Pierre François Camille Montagne in 1856 , who called it <unk> elegans . The genus name Mutinus refers to a phallic deity , Mutinus <unk> , one of the Roman di <unk> <unk> by Roman <unk> . The species is commonly known variously as the " elegant stinkhorn " , the " headless stinkhorn " , the " dog stinkhorn " , or the " devil 's <unk> " . The specific epithet elegans is derived from the Latin word meaning " graceful " or " elegant " . 
- 
- = = Description = = 
- 
- The young fruiting bodies are initially white and spherical or egg @-@ shaped , partially submerged in the ground , with dimensions of 2 to 3 cm ( 0 @.@ 8 to 1 @.@ 2 in ) by 1 to 2 cm ( 0 @.@ 4 to 0 @.@ 8 in ) . As the fruit body matures , the egg <unk> and the <unk> spore @-@ bearing stalk emerges ; fully grown , it may be from 1 to 15 cm ( 0 @.@ 4 to 5 @.@ 9 in ) long and 1 @.@ 5 to 2 cm ( 0 @.@ 6 to 0 @.@ 8 in ) thick . The stalk is hollow and strongly wrinkled overall ; its shape is cylindrical below , but it gradually tapers to a narrow apex with a small opening at the tip . The upper half of the stalk is bright red to reddish orange , and the color gradually loses intensity transforming into pinkish white below . The stalk may be straight , or slightly curved . A gelatinous greenish @-@ brown gleba covers the upper third of the stalk in newly emerged specimens . The remains of the " egg " forms a volva around the base of the stalk . The odor of the gleba is foul ; one author describes it as " sickly sweet or metallic " . The spores are a greenish @-@ brown color . Fruit bodies are attached to the substrate by whitish rhizomorphs that resemble plant roots . American mycologist Smith noted that the eggs are often slow to open , sometimes taking up to two weeks before the stalk expands . 
- The spores are 4 – 7 by 2 – 3 µm , oblong @-@ elliptical , smooth , and embedded in the gleba . A 1982 study revealed that spores of species in the <unk> family , including Mutinus elegans , have a <unk> scar ( 0 @.@ 2 – 0 @.@ 3 µm diameter ) that is observable with scanning electron microscopy . The <unk> scar is a circular <unk> at one end of the spore , and it most likely results during the separation of the attachment of the spore to the <unk> of the <unk> . 
- 
- = = = Edibility = = = 
- 
- The immature egg @-@ forms of Mutinus elegans are edible , but " not recommended " . One field guides notes that the eggs of the stinkhorn fungi " taste like the <unk> that are added to them . " The <unk> odor of mature specimens would probably be repellent to most , although they are not considered poisonous . 
- 
- = = = Similar species = = = 
- 
- The " dog stinkhorn " ( Mutinus caninus ) is smaller , has a distinct oval or spindle @-@ shaped tip on a slender stem and lacks the bright coloring of M. elegans ; it has less of the stalk covered by gleba . The portion of the stalk below the spore mass is pitted in M. caninus , compared to " <unk> " in M. elegans . M. caninus is also less common than M. elegans . Mutinus <unk> is similar in size and shape , except it does not have a distinct color demarcation between the upper and lower parts of the stalk ; instead , the entire stem shows red pigments . The stalk of M. <unk> is less tapered than M. elegans , and it has a clearly differentiated swollen head . 
- 
- = = Habitat and distribution = = 
- 
- Mutinus elegans is saprobic — deriving nutrients by breaking down dead or dying organic matter . It is commonly found in gardens and farm areas enriched with <unk> , near well @-@ decayed stumps and logs , and in wood chips . A Japanese publication mentioned its occurrence in <unk> and Osaka @-@ <unk> , where it <unk> in November and December on the ground along paths or in open spaces , under or near bamboo ( <unk> <unk> ) and <unk> such as the Sawtooth Oak , the Japanese <unk> , and the <unk> tree . 
- This common species has been collected in eastern North America , in the area extending from Quebec to Florida and west to the Great Lakes , Iowa , and Texas . In Europe , it has been reported from Netherlands and in Asia , it has been collected in Japan . 
- 
- = = <unk> activity = = 
- 
- A study of 32 <unk> mushrooms showed that Mutinus elegans was the only species to show antibiotic ( both <unk> and <unk> ) activity against all six microorganisms tested , namely , the human pathogenic <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> coli , <unk> <unk> and the yeast <unk> <unk> . 
- 
- 
- = The Boat Race 1900 = 
- 
- The 57th Boat Race took place on 31 March 1900 . Held annually , the Boat Race is a side @-@ by @-@ side rowing race between crews from the Universities of Oxford and Cambridge along the River Thames . Cambridge won by twenty lengths in a record @-@ equalling time of 18 minutes 45 seconds , taking the overall record in the event to 32 – 24 in Oxford 's favour . 
- 
- = = Background = = 
- 
- The Boat Race is a side @-@ by @-@ side rowing competition between the University of Oxford ( sometimes referred to as the " Dark Blues " ) and the University of Cambridge ( sometimes referred to as the " Light Blues " ) . The race was first held in 1829 , and since 1845 has taken place on the 4 @.@ 2 @-@ mile ( 6 @.@ 8 km ) Championship Course on the River Thames in southwest London . The rivalry is a major point of honour between the two universities and followed throughout the United Kingdom and worldwide . Cambridge went into the race as reigning champions , having won the 1899 race by three @-@ and @-@ a @-@ quarter lengths , while Oxford led overall with 32 victories to Cambridge 's 23 ( excluding the " dead heat " of 1877 ) . Leading up to the race , Oxford suffered a variety of misfortune : M. C. <unk> was ordered by his doctor not to row , H. J. Hale was injured and president Felix <unk> contracted scarlet fever . 
- Cambridge were coached by James <unk> Close , who had rowed for the Light Blues three times between 1872 and 1874 , and Stanley <unk> , five @-@ time Blue for Cambridge between 1886 and 1890 . Oxford 's coaches were Harcourt Gilbey Gold ( Dark Blue president the previous year and four @-@ time Blue ) and Douglas McLean ( an Oxford Blue five times between 1883 and 1887 ) . The umpire for the race for the eleventh year in a row was Frank <unk> who had won the event four consecutive times , rowing for Oxford in the 1866 , 1867 , 1868 and 1869 races . 
- 
- = = Crews = = 
- 
- The Cambridge crew weighed an average of 12 <unk> 4 @.@ 625 lb ( 78 @.@ 1 kg ) , 0 @.@ 25 pounds ( 0 @.@ 1 kg ) per rower more than their opponents . Oxford 's crew contained three members with Boat Race experience : C. E. Johnston , C. W. <unk> and <unk> G. S. Maclagan . Cambridge saw six of their 1899 crew return , including William Dudley Ward and Raymond <unk> <unk> @-@ Smith , both of whom were rowing in their third race . Eight of the nine Light Blues were students at Trinity College . Oxford 's stroke H. H. <unk> , a native of South Australia , was the only non @-@ British participant registered in the race . Author and former Oxford rower George Drinkwater suggested that this year 's Cambridge crew , along with the Oxford crew which rowed in the 1897 race , " stand in a class by themselves among University crews . " He also described the Oxford crew as " one of the poorest that ever came from the Isis " . 
- 
- = = Race = = 
- 
- Oxford won the toss and elected to start from the Surrey station , handing the Middlesex side of the river to Cambridge . In good conditions , umpire <unk> got the race under way at 2 : 00 p.m. whereupon Cambridge took the lead immediately . By <unk> Steps they were three lengths ahead and continued to draw away from the Dark Blues , to win by 20 lengths in a time of 18 minutes 45 seconds . It was the fastest winning time in the history of the event , equalling that set by Oxford in the 1893 race . Although it was the Light Blues ' second consecutive victory , it followed a run of nine consecutive wins for Oxford – overall the Dark Blues led 32 – 24 . 
- 
- 
- = Ten Commandments in Catholic theology = 
- 
- The Ten Commandments are a series of religious and moral <unk> that are recognized as a moral foundation in several of the Abrahamic religions , including Catholicism . As described in the Old Testament books Exodus and <unk> , the Commandments form part of a covenant offered by God to the Israelites to free them from the spiritual slavery of sin . According to the Catechism of the Catholic Church — the official <unk> of the Catholic Church 's Christian beliefs — the Commandments are considered essential for spiritual good health and growth , and serve as the basis for Catholic social justice . A review of the Commandments is one of the most common types of examination of conscience used by Catholics before receiving the sacrament of <unk> . 
- The Commandments appear in the earliest Church writings ; the Catechism states that they have " occupied a predominant place " in teaching the faith since the time of Augustine of <unk> ( AD 354 – 430 ) . The Church had no official standards for religious instruction until the Fourth Lateran Council in 1215 ; evidence suggests the Commandments were used in Christian education in the early Church and throughout the Middle Ages , but with inconsistent emphasis . The lack of instruction in them by some dioceses formed the basis of one of the criticisms launched against the Church by Protestant reformers . Afterward , the first Church @-@ wide catechism in <unk> provided " thorough discussions of each commandment " , but gave greater emphasis to the seven sacraments . The most recent Catechism devotes a large section to interpret each of the commandments . 
- Church teaching of the Commandments is largely based on the Old and New <unk> and the writings of the early Church Fathers . In the New Testament , Jesus acknowledged their validity and instructed his disciples to go further , demanding a righteousness exceeding that of the scribes and <unk> . <unk> by Jesus into two " Great Commandments " that teach love of God and love of neighbor , they instruct individuals on their relationships with both . The first three commandments demand respect for God 's name , observation of the Lord 's Day and prohibit the worship of other gods . The others deal with the relationships between individuals , such as that between parent and child ; they include prohibitions against lying , stealing , murdering , adultery and covetousness . 
- 
- = = Numbering = = 
- 
- The Old Testament refers to ten individual commandments , even though there are more than ten <unk> sentences in the two relevant texts : Exodus 20 : 1 – 17 and <unk> 5 : 6 – 21 . The Old Testament does not make clear how the texts should be divided to arrive at ten commandments . The division traditionally used by the Catholic and Lutheran churches was first derived by the Latin Church Father Augustine of <unk> ( 354 – 430 ) in his book <unk> on Exodus . Other Christian churches , such as the Eastern Orthodox and some Protestant churches , use a form established by the Greek Fathers . The two forms have slightly different numbering , but maintain exactly the same substance despite Protestant accusations to the contrary . Jewish numbering differs from Christian denominations in that it considers what many Christians call a <unk> to be the entire first commandment . 
- 
- = = History = = 
- 
- The Ten Commandments are recognized as a moral foundation by Judaism , Christianity , and Islam . They first appear in the Book of Exodus , according to which Moses , acting under the orders of God , freed the Israelites from physical slavery in Egypt . According to Church teaching , God offered a covenant — which included the Ten Commandments — to also free them from the " spiritual slavery " of sin . Some historians have described this as " the central event in the history of ancient Israel " . 
- The coming of Jesus is seen by the Catholic Church as the <unk> of the destiny of the Jews , who were chosen , according to Peter Kreeft , to " show the true God to the world " . Jesus acknowledged the Commandments and instructed his followers to go further , requiring , in Kreeft 's words , " more , not less : a ' righteousness ( which ) exceeds that of the scribes and <unk> ' " . Explaining Church teaching , Kreeft states , " The Commandments are to the moral order what the creation story in Genesis 1 is to the natural order . They are God 's order conquering chaos . They are not man 's ideas about God , but God 's ideas about man . " The Church teaches that Jesus freed people from keeping " the burdensome Jewish law ( Torah or <unk> Law ) with its <unk> distinct regulations [ but ] not from the obligation to keep the Ten Commandments " , because the Ten " were written ' with the finger of God ' , unlike [ those ] written by Moses " . This teaching was reaffirmed at the Council of Trent ( 1545 – 1563 ) and at the Second Vatican Council ( 1962 – 1965 ) . 
- Although it is uncertain what role the Ten Commandments played in early Christian worship , evidence suggests they were recited during some services and used in Christian education . For example , the Commandments are included in one of the earliest Christian writings , known as the Teaching of the Twelve Apostles or the <unk> . Scholars contend that the Commandments were highly regarded by the early Church as a summary of God 's law . The Protestant scholar Klaus <unk> believes that the Church replaced the Commandments with lists of virtues and vices , such as the seven deadly sins , from 400 – 1200 . Other scholars contend that throughout Church history the Commandments have been used as an examination of conscience and that many theologians have written about them . While evidence exists that the Commandments were part of <unk> in monasteries and other venues , there was no official Church position to promote specific methods of religious instruction during the Middle Ages . The Fourth Lateran Council ( 1215 ) was the first attempt to remedy this problem . Surviving evidence reveals that some bishops ' efforts to implement the Council 's resolutions included special emphasis on teaching the Commandments in their respective dioceses . <unk> later , the lack of instruction in them by some dioceses formed the basis of one of the criticisms launched against the Church by Protestant reformers . 
- <unk> produced in specific dioceses from the mid @-@ fourteenth century emphasized the Commandments and laid the foundation for the first official Church @-@ wide catechism , the <unk> Roman Catechism . Commissioned by the Council of Trent , it provided " thorough discussions of each commandment " but gave greater emphasis to the seven sacraments to emphasize the Catholic belief that Christian life was dependent upon the grace solely obtained through the sacramental life provided by the Catholic Church . This emphasis conflicted with Protestant beliefs , which held the Commandments as the source of divine grace . While more recent papal <unk> offer interpretations of Church teaching on individual commandments , throughout history official Church teachings on the Commandments are based on their mentions in the Old and New <unk> and the writings of the early Church Fathers <unk> , <unk> and Augustine . Later , theologians Thomas Aquinas and <unk> offered notable commentaries on the Commandments . Aquinas , a Doctor of the Church , considered them to be the " primary precepts of justice and all law , and natural reason gives immediate assent to them as being plainly evident principles . " 
- The most recent Catechism of the Catholic Church — the official summary of Church beliefs — devotes a large section to the Commandments , which serve as the basis for Catholic social teaching . According to the Catechism , the Church has given them a predominant place in teaching the faith since the fifth century . Kreeft explains that the Church regards them as " a path of life " , and a " path to freedom " just as a <unk> fence protects children from " life @-@ threatening dangers " . 
- 
- = = First commandment = = 
- 
- The first commandment , according to Church teaching , " means that [ followers ] must worship and <unk> God alone because God is alone . " The Catechism explains that this prohibits idolatry , providing examples of forbidden practices such as the worship of any creature , and of " ' demons ... power , pleasure , race , ancestors , the state [ and ] money ' " . Augustine interpreted this commandment as " Love God and then do what you will " . Explaining this sentiment , Kreeft states that all sin " serves some other god , <unk> another commander : the world or the flesh or the devil " , if God truly be loved then one will do what God wills . 
- The Catechism associates this commandment with the three theological virtues . The first virtue , faith , instructs Catholics to believe in God and avoid heresy , <unk> , and schism . The second virtue , hope , <unk> Catholics against despair and <unk> . According to the Catechism , the last virtue , charity , can be met only if Catholics refrain from indifference or <unk> toward God , and avoid spiritual <unk> and a hatred of God stemming from pride . The Catechism <unk> specific violations of this commandment , including superstition , polytheism , <unk> , <unk> , and all practices of magic and sorcery . It further prohibits astrology , palm reading , and consulting <unk> or <unk> . The Catechism attributes the latter actions to a " desire for power over time , history , and in the last analysis , other human beings as well as a wish to <unk> hidden powers " . 
- 
- = = = <unk> images = = = 
- 
- While Catholics are sometimes accused of worshiping images , in violation of the first commandment , the Church says this is a misunderstanding . In the Church 's opinion , " the honor paid to sacred images is a ' respectful veneration ' , not the adoration due to God alone " . In the 8th century , heated arguments arose over whether religious icons ( in this context paintings ) were prohibited by the first commandment . The dispute was almost entirely restricted to the Eastern church ; the <unk> wished to prohibit icons , while the <unk> supported their veneration , a position consistently backed by the Western Church . At the Second Council of Nicaea in 787 , the ecumenical council determined that the veneration of icons and statues was not in violation of the commandment and stated " whoever <unk> an image <unk> the person portrayed in it . " At around the time of the controversy over <unk> , the Western church began to use monumental sculpture , which by the Romanesque period became a major feature of Western Christian art , that has remained part of the Catholic tradition , in contrast to Eastern Christianity , which avoids large religious sculpture . The Catechism , using very traditional arguments , posits that God gave permission for images that symbolize Christian salvation by leaving symbols such as the bronze serpent , and the <unk> on the Ark of the Covenant . It states that " by becoming <unk> , the Son of God introduced a new economy of images " . 
- The United States Conference of Catholic Bishops ( <unk> ) explain the Catechism in their book entitled United States Catechism for Adults , published in 2006 . Regarding <unk> images , they expound that this command addresses idolatry that in ancient times expressed itself in the worship of such things as the " sun , moon , stars , trees , bulls , eagles , and serpents " as well as " emperors and kings " . They explain that today , idolatry expresses itself in the worship of other things , and list some as " power , money , <unk> and sports . " 
- 
- = = Second commandment = = 
- 
- The second commandment prohibits the use of God 's name in vain . Many ancient cultures believed that names were sacred ; some had prohibitions on when a person 's name could be spoken . The Gospel of John relates an incident where a group of Jews attempted to stone Jesus after he used a sacred name of God to refer to himself . They interpreted his statement as a claim of divinity . Since they did not believe that he was God , they considered this blasphemy , which under <unk> law carries a death penalty . Kreeft writes that all of the names by which God is known are holy , and thus all of those names are protected by the second commandment . The Catechism states , " Respect for his name is an expression of the respect owed to the mystery of God himself and to the whole sacred reality it evokes . " The Catechism also requires respect for the names of people out of respect for the dignity of that person . 
- The sentiment behind this commandment is further codified in the Lord 's Prayer , which begins , " Our Father who art in heaven , <unk> be thy name " . According to Pope Benedict XVI , when God revealed his name to Moses he established a relationship with mankind ; Benedict states that the <unk> was the culmination of a process that " had begun with the giving of the divine name . " Benedict <unk> that this means the divine name could be misused and that Jesus ' inclusion of " <unk> be thy name " is a plea for the <unk> of God 's name , to " protect the wonderful mystery of his accessibility to us , and constantly assert his true identity as opposed to our distortion of it " . 
- According to Catholic teaching , this commandment does not preclude the use of God 's name in taking solemn oaths administered by legitimate authority . However , lying under oath , invoking God 's name for magical purposes , or voicing words of hatred or defiance against God are considered sins of blasphemy . 
- 
- = = Third commandment = = 
- 
- Quoting the Jewish rabbi and scholar Jacob <unk> , Pope Benedict XVI explains that to Israel , keeping this commandment was more than ritual ; it was a way to imitate God , who rested on the seventh day after the creation . It also constituted the core of the social order . 
- Although a few Christian denominations follow the <unk> practice of observing the Sabbath on Saturday , Catholics , along with most Christians , observe Sunday as a special day , which they call the " Lord 's Day " . This practice dates to the first century , arising from their belief that Jesus rose from the dead on the first day of the week . The <unk> calls on Christians to come together on the Lord 's Day to break bread and give thanks . <unk> is the first to mention Sunday rest : " We , however ( just as tradition has taught us ) , on the day of the Lord 's Resurrection ought to guard not only against kneeling , but every posture and office of <unk> , <unk> even our businesses <unk> we give any place to the devil " ( " De <unk> . " , <unk> ; cf . " Ad nation . " , I , <unk> ; " <unk> . " , <unk> ) . 
- In the sixth century , <unk> of <unk> taught that the whole glory of the Jewish Sabbath had been transferred to Sunday and that Christians must keep Sunday in the same way as the Jews were commanded to keep the Sabbath . The Council of <unk> in 538 <unk> this tendency , to apply the law of the Jewish Sabbath to the observance of the Christian Sunday , as Jewish and non @-@ Christian . 
- The Church leaders of later centuries inscribed Sunday rest into official Church teaching , and Christian governments have attempted to enforce the Sunday rest throughout history . For Catholics , Jesus ' teaching that " the <unk> was made for man , not man for the <unk> " means that good works " when the needs of others demand it " can be part of the day of rest . The Catechism offers guidelines on how to observe the Lord 's Day , which include attending Mass on Sundays and holy days of obligation . On these days , Catholics may not work or do activities that " hinder the worship due to God " , but " performance of the works of mercy , and appropriate relaxation in a spirit of joy " are permitted . 
- According to the <unk> , this commandment " has been <unk> for Catholics " as one of the Church precepts . The organization cites the papal encyclical Dies <unk> : 
- Because the faithful are obliged to attend Mass unless there is a grave <unk> , <unk> have the corresponding duty to offer everyone the real possibility of fulfilling the precept . ... Yet more than a precept , the observance should be seen as a need rising from the depths of Christian life . It is <unk> important that all the faithful should be convinced that they cannot live their faith or share fully in the life of the Christian community unless they take part regularly in the Sunday <unk> assembly . 
- 
- = = Fourth commandment = = 
- 
- Pope Benedict XVI states that Rabbi <unk> " rightly sees this commandment as anchoring the heart of the social order " . It strengthens <unk> relationships , makes explicit the connection between family order and societal stability , and reveals that the family is " both willed and protected by God . " Because parents ' unconditional love for their children mirrors God 's love , and because they have a duty to pass the faith on to their children , the Catechism calls the family " a domestic church " , " a privileged community " and the " original cell of social life " . 
- The Catechism says this commandment requires duties of children to parents that include : 
- Respect toward parents that also flows to brothers and sisters . 
- Gratitude , as expressed in a quote from <unk> : " Remember that through your parents you were born ; what can you give back to them that equals their gift to you ? " 
- <unk> to parents for as long as the child lives at home " when it is for his good or the good of the family " , except when obedience would require the child to do something morally wrong . 
- Support that requires grown children to offer material and moral support for their aging parents , particularly at times of " illness , loneliness , or distress " . 
- Keeping this commandment , according to the Catechism , also requires duties of parents to children which include : 
- " <unk> education , spiritual formation and <unk> " of their children . 
- Respect for their children as children of God and human persons . 
- <unk> discipline for children while being careful not to provoke them . 
- " <unk> pressure to choose a certain profession or spouse " , which does not preclude parents from giving " <unk> advice " . 
- " Being a good example " to their children . 
- " <unk> their own failings " to their children to guide and correct them . 
- 
- = = = Jesus ' expansion = = = 
- 
- The Gospel of Matthew relates that when told his mother and brothers were waiting to see him , Jesus replied , " Who is my mother and who are my brothers ? " <unk> his hand over his disciples he said , " Here are my mother and my brothers ! For whoever does the will of my Father in heaven is my brother , and my sister , and mother . " Pope Benedict XVI stated that this <unk> of Jesus brought the fourth commandment to a new and higher level . By doing God 's will , any person can become part of the universal family of Jesus . Thus , the fourth commandment 's responsibilities extend to the greater society and requires respect for " legitimate social authorities " . The Catechism specifies " duties of citizens and nations " , which Kreeft <unk> as : 
- " <unk> and honor " to " all who for our good have received authority in society from God " . 
- " <unk> of taxes , exercising the right to vote and defending one 's country " . 
- " An obligation to be <unk> and critical " , which requires citizens to criticize that which harms human dignity and the community . 
- " A duty to disobey " civil authorities and <unk> that are contrary to the moral order . 
- " To practice charity " , which is a " necessity for any working family or society " ; it is the " greatest social commandment " and requires people to love God and neighbor . 
- " To welcome the foreigner " who is in need of security and livelihood that cannot be found in his own country . 
- " An obligation for rich nations to help poor nations " , especially in times of " immediate need " . 
- " An expectation for families to help other families " . 
- 
- = = Fifth commandment = = 
- 
- This commandment demands respect for human life and is more accurately translated as " thou <unk> not murder . " Indeed , killing may , under limited circumstances , be justified within Catholicism . Jesus expanded it to prohibit unjust anger , hatred and vengeance , and to require Christians to love their enemies . The basis of all Catholic teaching about the fifth commandment is the <unk> of life ethic , which Kreeft argues is philosophically opposed to the quality of life ethic , a philosophy which he <unk> as introduced by a book entitled Die <unk> der <unk> des <unk> <unk> ( The <unk> to <unk> Life <unk> of Life ) ( see Life unworthy of life ) and which he asserts was the " first to win public acceptance ... by German doctors before World War II — the basis and beginning of Nazi medical practices . " This interpretation is supported by modern medical journals that discuss the dilemma posed by these opposing philosophies to <unk> who must make life or death decisions . Some <unk> characterize the use of the " Nazi analogy " as inappropriate when applied to quality of life decisions ; Arthur <unk> called this rhetoric " <unk> wrong " . The Church is actively involved in the public debates over abortion , capital punishment and <unk> , and encourages <unk> to support legislation and politicians it describes as pro @-@ life . 
- 
- = = = <unk> = = = 
- 
- The Catechism states : " Human life is sacred because from its beginning it involves the creative action of God and it remains forever in a special relationship with the Creator , who is its sole end . ... no one can under any circumstance claim for himself the right directly to destroy an innocent human being . " Direct and intentional killing of an innocent human is considered a mortal sin . Considered by the Church to be of an even greater gravity is the murder of family members , including " <unk> , <unk> , <unk> , the murder of a spouse and procured abortion . " 
- The Catechism states that the embryo " must be treated from conception as a person " . The Latin original of as is <unk> , meaning " like " or " just as " . " Although the Church has not determined officially when human life actually begins , it has taken the course of maintaining that human life is present from the moment of conception or fertilization " ; respect for life at all stages , even potential life , is generally the context of church documents . 
- <unk> has been specifically and persistently condemned by the Church since the first century . " Formal cooperation " in abortion <unk> the penalty of excommunication " by the very commission of the offense " ( <unk> <unk> <unk> , " sentence [ already , i.e. automatically ] passed " ) . The Catechism emphasizes that this penalty is not meant to restrict mercy , but that it makes clear the gravity of the crime and the <unk> harm done to the child , its parents and society . " Formal cooperation " in abortion extends not just to the mother who freely <unk> , but also to the doctor , nurses and anyone who directly aids in the act . The Church has <unk> of reconciliation , such as Project Rachel , for those who <unk> <unk> of their sin of formal cooperation in abortion . 
- Official Church teaching allows for medical procedures and treatments intended to protect or restore the mother 's health if she would be in mortal danger without them , even when such procedures carry some risk of death to the fetus . Examples include the removal of a <unk> tube in the case of an <unk> pregnancy , removal of a pregnant <unk> uterus , or an <unk> . 
- 
- = = = = Use of embryos for research or fertilization = = = = 
- 
- The United States Catechism for Adults devotes a section to in vitro fertilization , stem @-@ cell research and <unk> in its explanation of the fifth commandment , because these often involve the destruction of human embryos , considered to be a <unk> sinful form of murder . <unk> stem cell research is called " an immoral means to a good end " and " morally unacceptable . " Citing the Congregation for the Doctrine of the Faith 's <unk> on Respect for Human Life in its Origin and on the <unk> of <unk> , the US Bishops quote : " No objective , even though noble in itself , such as a foreseeable advantage to science , to other human beings , or to society , can in any way justify experimentation on living human embryos or <unk> , whether viable or not , either inside or outside the mother 's body . " The Bishops note that adult stem cell research , using cells obtained with informed consent , is a promising field of research that is morally acceptable . 
- 
- = = = Suicide , <unk> = = = 
- 
- The fifth commandment forbids suicide and the mercy killing of those who are dying , even to eliminate suffering . The ordinary care of those facing an imminent death may not morally be withheld , according to the Church . " Ordinary care " refers to food , water and pain relief , and does not include " extraordinary care " , which refers to the use of <unk> or feeding tubes that are considered discretionary . <unk> a terminally ill person to die , using <unk> that may shorten their life , or refusing extraordinary treatment to the terminally ill such as chemotherapy or radiation , are considered morally acceptable and not a violation of the fifth commandment , in accordance with the principle of double effect . 
- 
- = = = Capital punishment = = = 
- 
- For the first two hundred years , Christians " refused to kill in the military , in self @-@ defense , or in the judicial system " , but there was no official Church position on the death penalty . When the Church was first officially recognized as a public institution in <unk> , its attitude toward capital punishment became one of toleration but not outright acceptance . The death penalty had support from early Catholic theologians , though some of them such as Saint <unk> encouraged members of the clergy not to <unk> or carry out capital punishment . Saint Augustine answered objections to capital punishment rooted in the first commandment in The City of God . Thomas Aquinas and Duns Scotus argued that civil authority to carry out capital punishment was supported by scripture . Pope Innocent III required Peter Waldo and the <unk> to accept that " secular power can , without mortal sin , exercise judgement of blood , provided that it <unk> with justice , not out of hatred , with prudence , not precipitation " as a prerequisite for reconciliation with the church . Paul <unk> states that official Church teachings have neither absolutely condemned nor promoted capital punishment , but toleration of it has fluctuated throughout the ages . The <unk> provide the most memorable instance of Church support for capital punishment , although some historians considered these more lenient than the secular courts of the period . 
- The Catechism of the Catholic Church states that the death penalty is permissible in cases of extreme gravity . It is allowed if the " guilty party 's identity and responsibility have been fully determined " and if the death penalty is the only way to defend others against the guilty party . However , if there are other means available to defend people from the " unjust aggressor " , these are preferred because they are considered to be more respectful of the dignity of the person and in keeping with the common good . Because modern societies have effective means for preventing crime without execution , the Catechism declares , " the cases in which execution of the <unk> is an absolute necessity ' are very rare , if practically <unk> . ' " Pope John Paul II discussed and affirmed this in <unk> <unk> , published in 1995 . 
- 
- = = = Personal health , dead bodies , burial = = = 
- 
- According to Church teaching , respect for human life requires respect for one 's own body , <unk> unhealthy behavior , the abuse of food , alcohol , medicines , illegal drugs , tattoos and <unk> . The Church also warns against the opposite behavior of " excessive preoccupation with the health and welfare of the body that ' <unk> ' physical perfection , fitness , and success at sports . " 
- <unk> , terrorism , and torture are forbidden , as well as <unk> , <unk> , <unk> that are not for therapeutic medical reasons . According to the Catechism , societies have a moral obligation to strive to provide healthy living conditions for all people . 
- Church belief in the resurrection of the body led to a prohibition against cremation that was <unk> modified at the Second Vatican Council in the 1960s under limited circumstances , but those conditions have been largely ignored even by the clergy . According to the Catechism , burial of the dead is a corporal work of mercy that must treat the body with respect and love ( e.g. scattering of <unk> remains , burial in an unmarked grave , <unk> are forbidden in the Catholic Church ) . Organ donation after death and organ <unk> under certain terms , also <unk> for legal and scientific reasons are permitted . 
- 
- = = = War and self @-@ defense = = = 
- 
- In the <unk> on the Mount , Jesus recalls the commandment , " You shall not kill " and then adds to it the <unk> against anger , hatred and vengeance . Going further , Christ asks his disciples to love their enemies . The Catechism asserts that " it is legitimate to insist on respect for one 's own right to life . " Kreeft says , " self @-@ defense is legitimate for the same reason suicide is not : because one 's own life is a gift from God , a treasure we are responsible for preserving and defending . " The Catechism teaches that " someone who defends his life is not guilty of murder even if he is forced to deal his aggressor a lethal blow . " <unk> defense can be not only a right but a grave duty for one who is responsible for the lives of others . The defense of the common good requires that an unjust aggressor be rendered unable to cause harm . For this reason , those who legitimately hold authority also have the right to use arms to repel <unk> against the civil community entrusted to their responsibility . 
- The Church requires all to pray and work to prevent unjust wars , but allows for just wars if certain conditions are met : 
- The reasons for going to war are defensive . 
- " The damage inflicted by the aggressor ... must be lasting , grave , and certain . " 
- It is a last resort taken only after all other means of putting an end to the " grave damage " have been ineffective . 
- The ultimate aim is peace and there is a serious chance of success . 
- No <unk> <unk> are produced that overshadow the evil to be eliminated . This forbids the use of arms to eliminate whole cities and areas with their inhabitants . 
- Respect and care is required for non @-@ combatants , wounded soldiers and prisoners . Soldiers are required to disobey commands to commit <unk> and ones that violate universal principles . 
- 
- = = = Scandal = = = 
- 
- The Catechism classifies scandal under the fifth commandment and defines it as " an attitude or behavior which leads another to do evil " . In the Gospel of Matthew , Jesus stated , " <unk> causes one of these little ones who believe in me to sin , it would be better for him to have a great <unk> <unk> round his neck and to be drowned in the depth of the sea . " The Church considers it a serious crime to cause another 's faith , hope and love to be weakened , especially if it is done to young people and the perpetrator is a person of authority such as a parent , teacher or priest . 
- 
- = = Sixth commandment = = 
- 
- According to the Church , humans are sexual beings whose sexual identity should be accepted in the unity of body and soul . The sexes are meant by divine design to be different and complementary , each having equal dignity and made in the image of God . Sexual acts are sacred within the context of the marital relationship that reflects a " complete and lifelong mutual gift of a man and a woman . " Sexual sins thus violate not just the body but the person 's whole being . In his 1995 book Crossing the <unk> of Hope , John Paul II reflected on this concept : 
- After all , young people are always searching for the beauty in love . They want their love to be beautiful . If they give in to weakness , following the models of behavior that can rightly be considered a ' scandal in the contemporary world ' ( and these are , unfortunately , widely <unk> models ) , in the depths of their hearts they still desire a beautiful and pure love . This is as true of boys as it is of girls . Ultimately , they know that only God can give them this love . As a result , they are willing to follow Christ , without caring about the sacrifices this may entail . 
- Like Orthodox Judaism and Islam , the Catholic Church considers all sexual acts outside of marriage to be grave sins . The gravity of the sin " ' excludes one from sacramental communion ' until <unk> of and <unk> in sacramental confession . " 
- 
- = = = <unk> to chastity = = = 
- 
- Church teaching on the sixth commandment includes a discussion on chastity . The Catechism describes chastity as a " moral virtue ... a gift from God , a grace , a fruit of spiritual effort . " The Church sees sex as more than a physical act ; it also affects body and soul , so the Church teaches that chastity is a virtue all people are called to acquire . It is defined as the inner unity of a person 's " bodily and spiritual being " that successfully <unk> a person 's sexuality with his or her " entire human nature . " To acquire this virtue , followers are encouraged to enter into the " long and exacting work " of self @-@ mastery that is helped by friendships , God 's grace , maturity and education " that respects the moral and spiritual dimensions of human life . " The Catechism <unk> violations of the sixth commandment into two categories : " offenses against chastity " and " offenses against the dignity of marriage " . 
- 
- = = = = <unk> against chastity = = = = 
- 
- The Catechism lists the following " offenses against chastity " in increasing order of gravity : 
- Lust : the Church teaches that sexual pleasure is good and created by God , who meant for spouses to " experience pleasure and enjoyment of body and spirit " . Kreeft says , " Lust does not mean sexual pleasure as such , nor the delight in it , nor the desire for it in its right context . " Lust is the desire for sexual pleasure alone , outside its intended purpose of procreation and the uniting of man and woman , body and soul , in mutual self @-@ donation . 
- <unk> is considered sinful for the same reasons as lust , but is a step above lust in that it involves a physical act instead of a mental one . 
- <unk> is the sexual union of an unmarried man and an unmarried woman . This is considered contrary to " the dignity of persons and of human sexuality " because it is not ordered to the " good of spouses " or the " generation and education of children . " 
- <unk> ranks higher because it is considered a perversion of the sexual act that is intended for distribution to third parties for viewing . 
- <unk> is considered sinful for both the prostitute and the customer ; it reduces a person to an instrument of sexual pleasure , violating human dignity and <unk> society . The gravity of the <unk> is less for prostitutes who are forced into the act by <unk> , blackmail or social pressure . 
- <unk> is an intrinsically evil act that can cause grave damage to the victim for life . 
- <unk> , or " rape of children by parents or other adult relatives " or " those responsible for the education of the children entrusted to them " is considered the most <unk> of sexual sins . 
- 
- = = = = <unk> = = = = 
- 
- The Catechism devotes a separate section to homosexuality within its explanation of the sixth commandment . Like heterosexual acts outside of marriage , homosexual acts are considered sins . The Church distinguishes between homosexual attractions , which are not considered sinful , and homosexual acts , which are . The Catechism states that they " violate natural law , cannot bring forth life , and do not proceed from a genuine <unk> and sexual <unk> . Under no circumstances can they be approved . " The Church teaches that a homosexual inclination is " <unk> disordered " and can be a great trial for the person , who the Church teaches must be " accepted with respect , compassion and sensitivity ... unjust discrimination in their regard should be avoided . " 
- <unk> are , according to the Church , " called to chastity " . They are instructed to practice the virtues of " self @-@ mastery " that teaches " inner freedom " using the support of friends , prayer and grace found in the sacraments of the Church . These tools are meant to help homosexuals " gradually and <unk> approach Christian perfection " , which is a state to which all Christians are called . 
- ( Two lay movements represent opposing philosophies regarding homosexuality : <unk> seeks to change the Church 's teachings to justify homosexual acts ; Courage International is an organization of homosexuals who " support each other in the sincere effort to live in chastity and in fidelity to Christ and his Church " . ) 
- 
- = = = Love of husband and wife = = = 
- 
- According to Church teaching , spousal love is intended to form an unbroken , two @-@ fold end : the union of husband and wife and the transmission of life . The <unk> aspect includes the <unk> of each partner 's being " so that they are no longer two but one flesh . " The sacrament of <unk> is viewed as God 's sealing the consent which binds the partners together . Church teaching on the marital state requires spousal acceptance of each other 's failures and faults , and the recognition that the " call to <unk> in marriage " is one that requires a process of spiritual growth and conversion that can last throughout life . 
- 
- = = = = <unk> of marriage , sexual pleasure , birth control = = = = 
- 
- The Church position on sexual activity can be summarized as : " sexual activity belongs only in marriage as an expression of total self @-@ giving and union , and always open to the possibility of new life . " Sexual acts in marriage are considered " noble and honorable " and are meant to be enjoyed with " joy and gratitude . " <unk> is to be reserved to marriage : " by its very nature <unk> love requires the <unk> fidelity of the spouses . This is the consequence of the gift of themselves which they make to each other . Love seeks to be definitive ; it cannot be an arrangement ' until further notice . ' " The " intimate union of marriage , as a mutual giving of two persons , and the good of the children , demand total fidelity from the spouses and require an <unk> union between them . " ( Gaudium et spes ) " . 
- Artificial birth control predates Christianity ; the Catholic Church has condemned these methods throughout its history . In response to the Church of England accepting the practice of artificial contraception in 1930 , the Catholic Church issued the papal encyclical <unk> <unk> on 31 December 1930 . The 1968 papal encyclical <unk> <unk> is a <unk> of the Catholic Church 's traditional view of marriage and marital relations , and a continued condemnation of artificial birth control . 
- The Church seeing large families as a sign of God 's blessing . " By its very nature the institution of marriage and married love is ordered to the procreation and education of the offspring and it is in them that it finds its crowning glory . " ( Gaudium et spes ) Children are the supreme gift of marriage and contribute greatly to the good of the parents themselves . ( ... ) true married love and the whole structure of family life which results from it , without <unk> of the other ends of marriage , are directed to <unk> the spouses to cooperate <unk> with the love of the Creator and <unk> , who through them will increase and <unk> his family from day to day . ( Gaudium et spes ) . " It recognizes that responsible <unk> sometimes calls for reasonable spacing or limiting of births and considers natural family planning as morally acceptable , but rejects all methods of artificial contraception . The Church rejects all forms of artificial insemination and fertilization because the techniques divorce the sexual act from the creation of a child . The Catechism states , " A child is not something owed to one , but is a gift ... ' the supreme gift of marriage . ' " 
- Many Western Catholics and non @-@ Catholics have voiced disagreement on the Church 's support for natural family planning , and contend it contributes to <unk> and poverty . The Church 's rejection of condom use is widely criticized , in particular with regard to countries where the incidence of AIDS and HIV has reached epidemic proportions . In its defense , Catholics cite countries such as Kenya and Uganda , where behavioral changes are encouraged instead of condom use , and where greater progress in controlling the disease has been made than in countries that promote condom use alone . 
- 
- = = = = <unk> against the dignity of marriage = = = = 
- 
- According to the Church , adultery and divorce are considered offenses against the dignity of marriage and are defined as follows : 
- <unk> is the sexual union of a man and woman where at least one is married to someone else . It is for this reason that the Church considers it a greater sin than <unk> . Kreeft states , " The <unk> sins against his spouse , his society , and his children as well as his own body and soul . " 
- Divorce : According to the Catholic New American Bible translation , Jesus taught , " whoever divorces his wife ( unless the marriage is unlawful ) causes her to commit adultery , and whoever marries a divorced woman commits adultery . " Explaining Church interpretation of this teaching , Kreeft says Jesus considered divorce to be an accommodation that had slipped into Jewish law . The Church teaches that marriage was created by God and was meant to be <unk> : like the creation of a child that cannot be " un @-@ created " , neither can the " one flesh " of the marriage bond . The Catechism states , " Divorce is a grave offense against the natural law . It claims to break the contract , to which the spouses freely consented , to live with each other till death . " By marrying another , the divorced person adds to the gravity of the offense as the remarried spouse is considered to be in a state of " public and permanent adultery " . 
- The <unk> of the Catechism 502 lists other offenses against the dignity of marriage : " polygamy , <unk> , free unions ( <unk> , <unk> ) , and sexual acts before or outside of marriage " . 
- 
- = = = = <unk> , civil divorce , <unk> = = = = 
- 
- According to the Church , there are situations that do not <unk> to divorce : 
- In extreme situations , such as domestic violence , separation is allowed . This is not considered a divorce and may be justified . 
- Civil divorce is not a divorce according to the Church . If it is deemed to be the only way of ensuring legal rights , care of children , or protection of inheritance , the Church considers it morally acceptable . 
- <unk> is not a divorce ; it is a ruling by the Church that the marriage was never valid . The marriage is deemed invalid if it lacks one of five integral elements : it should be " complete " , " lifelong " , " mutual " , a " free gift " and of " man and woman " . According to Pope John Paul II 's Address to the Roman <unk> on 22 January 1996 , couples do not have a right to an annulment , but do have a right to make their case for <unk> or validity before " the competent Church authority and to request a decision in the matter . " According to the Catholic Diocese of <unk> : 
- ... signs that might indicate reasons to investigate for an annulment are : marriage that excluded at the time of the wedding the right to children , or to a permanent marriage , or to an exclusive commitment . In addition , there are youthful marriages ; marriages of very short duration ; marriages marked by serious emotional , physical , or substance abuse ; <unk> sexual practices ; profound and consistent <unk> and lack of commitment ; conditional consent to a marriage ; fraud or deceit to elicit spousal consent ; serious mental illness ; or a previous bond of marriage . The determination of the ground should be made after extensive consultation with the parish priest or deacons , and based upon the proofs that are available . 
- 
- = = Seventh commandment = = 
- 
- The Catechism explains that this commandment <unk> worldly goods , and forbids unjustly taking , using or damaging those that belong to someone else . It places requirements upon those who possess worldly goods to use them <unk> , taking into consideration the good of society . The Catechism addresses the concept of human stewardship of God 's creation in its explanation of the seventh commandment and forbids abuse of animals and the environment . 
- 
- = = = Private property = = = 
- 
- According to the Church , people have a right to private property . However , ownership makes that person " a steward " who is expected to make it " fruitful " or profitable in a way that benefits others after that person has first taken care of their family . Private property and the common good are seen as complementary elements that exist for the purpose of strengthening society . The taking of another 's private property " in obvious and urgent necessity " as " the only way to provide for immediate , essential needs ( food , shelter , clothing ) " is not considered by the Church to be stealing . The concept of slavery as private property is condemned by the Church , which classifies it as the stealing of a person 's human rights . 
- 
- = = = Theft = = = 
- 
- According to the Catechism , theft or stealing means " <unk> another 's property against the reasonable will of the owner " though exclusion exists for someone in great need to survive . " <unk> taking and keeping the property of others " considered as theft , even if the act is outside the scope of civil law . Cardinal Christoph <unk> gave example from the story of Saint Augustine , written in his Confessions , who took <unk> from neighbor 's garden when he was young . <unk> says that Augustine still has " <unk> of conscience over a childish theft " even when he became grown person , indicating that human conscience is very aware of theft though the act perhaps not an offense against civil law . 
- Following acts are also considered as violation of the seventh commandment : price manipulation to get advantage on the harm of others , corruption , appropriation of the public goods for personal interests , work poorly carried out , tax avoidance , counterfeiting of checks or any means of payment , any forms of copyright infringement and piracy , and extravagance . 
- 
- = = = Social justice = = = 
- 
- The papal encyclical <unk> <unk> discusses the relationships and mutual duties between labor and capital , as well as government and its citizens . Of primary concern was the need for some <unk> for " the misery and <unk> pressing so unjustly on the majority of the working class " . The encyclical supported the right to form unions , rejected socialism , communism and unrestricted capitalism , and affirmed the right to private property . 
- Church interpretation of the seventh commandment teaches that business owners should balance a desire for profits that will ensure the future of the business with a responsibility toward the " good of persons " . Business owners are required to pay their workers a reasonable wage , honor contracts , and <unk> from dishonest activity , including bribery of government officials . Workers are required to do their jobs <unk> , as they have been hired to do them , and to avoid <unk> in the workplace , such as using office goods for personal use without permission ( <unk> ) . 
- The Church teaches that a balance should exist between government regulation and the laws of the marketplace . It deems that sole reliance on the marketplace ( pure capitalism ) <unk> addresses many human needs , while sole reliance on government regulation ( pure socialism ) " <unk> the basis of social bonds " . The Church warns about the danger of either capitalism or socialism , as these systems tend to use excessive extremes that result in injustice to persons . 
- <unk> nations , like <unk> individuals , have a moral obligation to help poorer nations and individuals , and work to reform financial institutions and economic factors to benefit all . 
- 
- = = Eighth commandment = = 
- 
- The Catechism explains that bearing false witness or " speaking a <unk> with the intention of <unk> " encompasses all violations of truth . These violations have degrees of gravity depending on the " intentions of the one who lies and the harms suffered by its victims . " Listed as follows , these are : 
- <unk> witness and <unk> : statements made publicly in court which obstruct justice by condemning the innocent or <unk> the guilty , or which may increase the punishment of the accused . 
- <unk> judgement : believing , without sufficient evidence , that a person has done moral faults . 
- <unk> : the <unk> of another 's faults without a valid reason . 
- <unk> : lying to harm a person 's reputation and providing opportunity to others to make false <unk> concerning them . 
- <unk> : " speech to <unk> others for our benefit . " 
- <unk> , <unk> , or mocking : speech which either only honors oneself or <unk> others . 
- The Church requires those who have damaged the reputation of another to " make reparation for the <unk> they have communicated . " However , it does not require a person to reveal a truth to someone who does not have a right to know , and teaches respect for a right to privacy . <unk> are prohibited from violating the seal of confession no matter how grave the sin or its impact on society . 
- Included in the Church teachings of this commandment is the requirement for Christians to bear witness to their faith " without <unk> " in situations that require it . The use of modern media in spreading <unk> , by individuals , businesses or governments , is condemned . 
- 
- = = <unk> commandment = = 
- 
- The ninth and tenth commandments deal with <unk> , which is an interior disposition not a physical act . The Catechism distinguishes between covetousness of the flesh ( improper sexual desire ) and covetousness for another 's worldly goods . The ninth commandment deals with the former and the tenth the latter . 
- Jesus emphasized the need for pure thoughts as well as actions , and stated , " Everyone who looks at a woman <unk> has already committed adultery with her in his heart " ( Matthew 5 : 28 ) . The Catechism states that , with the help of God 's grace , men and women are required to overcome lust and bodily desires " for sinful relationships with another person 's spouse . " In <unk> of the Body , a series of lectures given by Pope John Paul II , Jesus ' statement in Matthew 5 : 28 is interpreted that one can commit adultery in the heart not only with another 's spouse , but also with his / her own spouse if one looks at him / her <unk> or treats him / her " only as an object to satisfy instinct " . 
- Purity of heart is suggested as the necessary quality needed to accomplish this task ; common Catholic prayers and hymns include a request for this virtue . The Church identifies gifts of God that help a person maintain purity : 
- <unk> , which enables people to love others with upright and undivided hearts . 
- Purity of intention , which seeks to fulfill God 's will in everything , knowing that it alone will lead to the true end of man . 
- Purity of vision , " external and internal " , <unk> the thoughts and imagination to reject those that are impure . 
- Prayer that recognizes the power of God to grant a person the ability to overcome sexual desires . 
- <unk> , of the feelings as well as the body is discreet in choice of words and clothing . 
- Jesus stated , " Blessed are the clean of heart , for they shall see God . " This purity of heart , which the ninth commandment introduces , is the " <unk> of the vision of God " and allows the person to see situations and people as God sees . The Catechism teaches that " there is a connection between purity of heart , of body and of faith . " 
- 
- = = Tenth commandment = = 
- 
- Detachment from riches is the goal of the tenth commandment and the first <unk> ( " blessed are the poor in spirit " ) because , according to the Catechism , this precept is necessary for entrance into the Kingdom of heaven . <unk> is prohibited by the tenth commandment because it is considered to be the first step toward commission of theft , robbery and fraud ; these lead to violence and injustice . The Church defines covetousness as a " disordered desire " that can take different forms : 
- <unk> is the desire for too much of what one does not need . 
- <unk> is the desire for what belongs to another . The US Bishops define it as " an attitude that fills us with sadness at the sight of another 's prosperity . " 
- Explaining Church teaching of this commandment , Kreeft cites Saint Thomas Aquinas , who wrote , " An evil desire can only be overcome by a stronger good desire . " The US Bishops suggest that this can be achieved through cultivation of goodwill , <unk> and gratitude for one 's own and others ' blessings , while trusting in God 's grace . Kreeft explains that Saint Paul the <unk> illustrated the concept in his letter to the <unk> when he listed his worldly credentials as a respected Jew and stated , " I count everything as loss because of the surpassing worth of knowing Christ Jesus my Lord . " As Jesus stated , " What shall it profit a man if he shall gain the whole world , and lose his own soul ? " Church teaching on the tenth commandment is directed toward this same attitude toward worldly goods , termed " poverty of spirit " . 
- 
- 
- = Yamaha NS @-@ 10 = 
- 
- The Yamaha NS @-@ 10 is a loudspeaker that became a standard nearfield studio monitor in the music industry among rock and pop recording engineers . <unk> in 1978 , the NS @-@ 10 started life as a <unk> speaker destined for the domestic environment . It was poorly received but eventually became a valuable tool with which to mix rock recordings . The speaker has a characteristic white @-@ coloured mid – bass drive unit . 
- Technically , it is known as a speaker that easily reveals poor quality in recordings . Recording engineers sought to dull its treble response by hanging tissue paper in front of it , resulting in what became known as the " tissue paper effect " , a type of comb filtering . The NS @-@ 10 has been used to monitor a large number of successful recordings by numerous artists , leading Gizmodo to refer to it as " the most important loudspeaker you never heard of " . Yamaha discontinued the product in 2001 . 
- 
- = = History = = 
- 
- Originally conceived as a domestic hi @-@ fi speaker , the NS @-@ 10 was designed by Akira <unk> and launched in 1978 . It was sold at the $ 400 price point . The speaker was poorly received and its commercial life was short . However , it took five years for its popularity to be established with professional users . As recording engineers came to rely on the NS @-@ 10 as a <unk> , it dominated the mixing of pop and rock music throughout the world for at least 20 years . 
- The NS @-@ 10 displaced the <unk> <unk> Sound <unk> as the nearfield monitor of choice in the 1980s and was recognised for its ability to reveal shortcomings in recordings . It probably first reached American shores through a recording engineer 's visit to Japan . The engineer , likely to have been Greg <unk> , monitored a recording session through the speaker in a Japanese studio and brought a pair back on his return to the US . <unk> then began using the speakers in a Los Angeles studio . Other engineers heard the NS @-@ 10 for the first time and were impressed by its sound . Its use spread to New York where the NS @-@ 10 was adopted at The Power Station and other studios . 
- Early use of the NS @-@ 10 among engineers include Bob Clearmountain , Rhett Davies , and Bill <unk> in the US , and Nigel <unk> in the UK . Clearmountain , then a rising star in record production , is often credited for the popularity of the NS @-@ 10 ; Phil Ward , writing in Sound on Sound , suggested that Clearmountain was probably not the earliest , but was certainly the most influential early <unk> . It became a legend that Clearmountain had chosen it because it was the worst speaker he could find . He was one of a new breed of creative freelance recording engineers and producers who would travel from studio to studio equipped with their own gear that included microphones , and a pair of Yamaha NS @-@ 10 , as a reference . 
- Recording studios around the world , particularly those specialising in rock and pop music , adopted the speaker as the standard . In excess of 200 @,@ 000 pairs were sold throughout the world . Gizmodo referred to it as " the most important loudspeaker you never heard of " . 
- Yamaha stopped manufacturing the speaker in 2001 , citing problems <unk> the wood pulp for the drivers . Even years after it was discontinued , the speaker continued to be found in studios everywhere . Mix reported in 2008 that variants of the NS @-@ 10 were still commercially available in the Japanese consumer market . 
- 
- = = Design and construction = = 
- 
- The NS @-@ 10 is an 8 @-@ <unk> two @-@ way loudspeaker with a 10 @.@ 4 @-@ litre <unk> cabinet measuring <unk> × 215 × 199 millimetres ( 15 @.@ 0 × 8 @.@ 5 × 7 @.@ 8 in ) and weighing 6 kilograms ( 13 @.@ 2 lb ) . Its 2 @.@ 5 cm ( 0 @.@ 98 in ) particle @-@ board cabinet has a wood veneer skin with seven black finishing layers . The domestic version of the speaker was vertically <unk> , and came factory fitted with a grille . 
- Its two drivers are a 180 mm paper <unk> and a 35 mm soft @-@ domed tweeter . The <unk> 's <unk> , weighing 3 @.@ 7 g , is manufactured from a flat sheet of pressed pulp paper . <unk> , it is formed into conical shape not through moulding or pressure , but by curling and then <unk> the two ends together . Against the black finish of the cabinet , the white bass / mid driver <unk> is a distinctive and iconic feature of the product . 
- The network is second @-@ order passive , crossing over at 2 kHz . The frequency range is quoted from 60 Hz to 20 kHz , and rated power handling is 25 – 50 W. The early version of the speaker has press @-@ down type output terminals ; later models had screw terminals . 
- 
- = = Signature sound = = 
- 
- In simplistic terms , the NS @-@ 10 possesses sonic characteristics that allow record producers to assume that if a recording sounds good on these monitors , then it should sound good on most playback systems . Whilst it can reveal any shortcomings in the recording mix as well as the monitoring chain , it may lead to listener fatigue with prolonged use in the domestic setting . 
- The NS @-@ 10 does not have a perfectly flat frequency response . The sound of the NS @-@ 10 is slightly heavy in the midrange , and like other sealed @-@ box speakers of similar size its bass extension is limited . It has a + 5 <unk> boost in the midrange at around 2 kHz , and the bottom end starts rolling off at 200 Hz . The midrange response is so open that it exposes the frequencies that are the most problematic and worst @-@ sounding to the human ear . 
- On a practical level for the music professional , the speaker is <unk> and clinical @-@ sounding . Gizmodo likened the NS @-@ 10 to music editors who reveal the weaknesses of recordings , so that engineers would be forced to either make necessary compensation in the mix or otherwise rework them . 
- A 2001 report by Newell et al. at Southampton University undertaken for Studio Sound in 2001 found that the NS @-@ 10 had excellent time @-@ domain response at low frequencies – its ability to start and stop in response to signal input was found to be superior to that of most other nearfield monitors . Part of this was related to its closed @-@ box design . The researchers held that the extremely fast decay time of the speaker in the low frequencies ensures that the bass instruments ( guitar and drums ) are correctly balanced in the mix . 
- 
- = = Product revisions = = 
- 
- There were many other versions of the NS @-@ 10 , the best known of which were the " NS @-@ 10M Studio " and the " NS @-@ 10M Pro " , both introduced in 1987 . Technically identical to the " Studio " , the " Pro " comes fitted with a speaker grille and is meant to be used in a vertical orientation . 
- The " professional " version launched some nine years after its first introduction on the back of the popularity of the NS @-@ 10 among engineers . The revised version , with everything including the logo and connection panel <unk> horizontally , was <unk> " NS @-@ 10M Studio " . <unk> included a new tweeter and crossover to address the problem in the treble , better connection terminals , and a <unk> cabinet that no longer accommodates <unk> . The Studio reincarnation also has improved power handling – 60 – 120 W. In excess of 200 @,@ 000 pairs of " Studio " alone were sold throughout the world . 
- Also in the product line @-@ up were NS @-@ <unk> , NS @-@ 10M X , NS @-@ <unk> , NS @-@ <unk> . The NS @-@ 10M X is a " Studio " with magnetic shielding and a different tweeter . In the 1990s Yamaha introduced the NS @-@ <unk> , a bass @-@ <unk> version of the 10M X with a different tweeter and grille . Designed for home cinema , it has bass response down to 43 Hz , nominal <unk> of 6 <unk> and maximum power handling rated at 180 W. A miniature version named Natural Sound <unk> Speaker <unk> was launched in 1997 or 1998 . 
- 
- = = Reception = = 
- 
- The sound quality of the NS @-@ 10 has <unk> opinions , characterised as " love them or hate them " . Many professionals find it indispensable , even though they may not particularly enjoy listening to it ; others refuse to give it space in their studio but will happily admit that it is an effective professional tool . The reliance on the NS @-@ 10 by top independent producers became a viral phenomenon ; thousands of studios equipped themselves with NS @-@ 10s to attract big named producers , making the speakers an industry standard . 
- 
- = = <unk> paper effect = = 
- 
- Clearmountain was said to have been one of the first recording engineers to hang tissue paper over the <unk> of the NS @-@ 10 to tame the over @-@ bright treble . Covering the <unk> with tissue paper was said to produce treble @-@ deficient mixes when replayed on normal domestic hi @-@ fi . The phenomenon became the subject of hot debate . Recording engineer Bob <unk> investigated the alleged sonic effects of tissue paper . He found inconsistent results with different paper , but said that tissue paper generally demonstrated an undesirable effect known as comb filtering , where the high frequencies are reflected back into the tweeter instead of being absorbed . <unk> derided the tissue practice as " aberrant behaviour " , saying that engineers usually fear comb filtering and its associated cancellation effects . He also suggested that more <unk> and less random electronic filtering would be <unk> . Newell et al. noted that had the speakers ' <unk> been used in studios , where they are routinely removed , they would have had the same effect on the treble output as the improvised tissue paper filter . 
- 
- = = Influence = = 
- 
- The speaker came to be relied on by independent engineers , who worked in different studios and needed equipment they were familiar with as a reference point . Throughout the 1980s , engineers and producers worked widely with the speaker to monitor " [ almost ] any album you love from the 80s or 90s " – from Born in the <unk> ( Bruce Springsteen ) , Avalon ( Roxy Music ) Let 's Dance ( David Bowie ) , to Big <unk> Boom ( Hall and Oates ) . 
- The NS @-@ 10 , and the <unk> before it , are two of the most influential nearfield monitors used in the professional mixing of sound recordings . In 2008 , the NS @-@ 10 was inducted into the Mix magazine <unk> Hall of Fame . Also reflecting its influence , the speaker won a Technical Grammy for Yamaha in 2007 . In 2008 , the speaker was found " in almost every studio " . 
- 
- 
- = Utah State Route 61 = 
- 
- State Route 61 ( SR @-@ 61 ) is a nearly 7 @.@ 3 @-@ mile @-@ long ( 11 @.@ 7 km ) state highway in the U.S. state of Utah , connecting SR @-@ 23 in Cornish , Cache County to U.S. Route 91 ( US @-@ 91 ) near Richmond via Lewiston , in the extreme northern part of the state . The highway has existed since at least 1914 , as SR @-@ 61 since at least 1937 , and between 735 and 2 @,@ 180 vehicles travel along the highway on an average day in 2012 . 
- 
- = = Route description = = 
- 
- At the intersection of SR @-@ 23 ( <unk> West ) and 13400 North in the center of Cornish , SR @-@ 61 departs east on 13400 North due east , crossing over a single track belonging to the Union Pacific Railroad ( UP ) . <unk> Cornish , the highway crosses the Bear River and continues east through rural Cache County . Just shy of the western city limits of Lewiston , the highway intersects SR @-@ 200 ( 800 West ) , a connector road to Preston , Idaho . From the western terminus to SR @-@ 200 , the shoulder is up to four feet ( 1 @.@ 2 m ) wide , suitable for <unk> , however the remainder of the route has much narrower shoulders , between less than or equal to one and nine @-@ tenths feet ( 0 @.@ 58 m ) wide . 
- The highway 's name changes from 13400 South to Center Street through Lewiston . Passing the Lewiston Cemetery , SR @-@ 61 crosses over the Cub River and a second single track belonging to UP , and then a third UP single track just before the highway 's eastern terminus at US @-@ 91 north of Richmond . All of the rail lines that SR @-@ 61 crosses originally belonged to the Oregon Short Line Railway . Aside from the segment through Lewiston , the highway is surrounded by farmland for its entire journey across northern Utah . 
- Every year , <unk> conducts a series of surveys on its highways in the state to measure traffic volume . This is expressed in terms of average annual daily traffic ( AADT ) , a measure of traffic volume for any average day of the year . In 2012 , <unk> calculated that as few as 735 vehicles used the highway on an average day at its western terminus in Cornish , and as many as 2 @,@ 180 vehicles used the highway at its junction with SR @-@ 200 . Thirty @-@ five percent of this was truck traffic . 
- 
- = = History = = 
- 
- A roadway linking Cornish to the east has existed since at least 1914 . The roadway that serves as the eastern terminus was numbered SR @-@ 1 by 1927 , and the highway officially was designated SR @-@ 61 since at least 1937 . The 53 @-@ foot @-@ long ( 16 @.@ 2 m ) bridge that carries SR @-@ 61 over the Cub River today was constructed in 1952 , while the 182 @-@ foot @-@ long ( 55 @.@ 5 m ) bridge over the Bear River was built in 1961 . The original river <unk> were slightly further south than their current locations . 
- 
- = = Major intersections = = 
- 
- The entire route is in Cache County . 
- 
- 
- = <unk> = 
- 
- A hemmema ( from Finnish " Hämeenmaa " , <unk> ) was a type of warship built for the Swedish archipelago fleet and the Russian Baltic navy in the late 18th and early 19th centuries . The hemmema was initially developed for use against the Russian Navy in the Archipelago Sea and along the coasts of <unk> and Finland . It was designed by the prolific and innovative Swedish naval architect Fredrik Henrik <unk> Chapman ( 1721 – 1808 ) in collaboration with Augustin Ehrensvärd ( 1710 – 1772 ) , an artillery officer and later commander of the Swedish archipelago fleet . The hemmema was a specialized vessel for use in the shallow waters and narrow passages that surround the thousands of islands and <unk> extending from the Swedish capital of Stockholm into the Gulf of Finland . 
- The hemmema replaced the galleys that had made up the core of the Swedish archipelago fleets until the mid @-@ 18th century . Compared to galleys , the hemmema had a deeper draft and was slower under oars , but offered superior accommodation for the crew , carried more stores , was more <unk> and had roughly ten times as many heavy guns . It could be propelled by either sails or oars but was still smaller and more <unk> than most sailing warships , which made it suitable for operations in confined waters . 
- Between 1764 and 1809 , Sweden built six hemmemas . The hemmema became the largest and most heavily armed vessel in the archipelago fleet and served in the Russo @-@ Swedish War of 1788 – 90 . Oden , the first hemmema , was relatively small and very similar to a turuma , a different type of " archipelago frigate " . Russia built six hemmemas based on the Swedish design between 1808 and 1823 after capturing three of the Swedish vessels at the surrender of Sveaborg in 1808 . The later versions , both Swedish and Russian , were much larger and much more heavily armed than Oden . 
- 
- = = Background = = 
- 
- Russian Tsar Peter the Great had established a new capital and powerful naval base in Saint Petersburg in 1703 . Russian naval power in the Baltic grew to challenge the interests of Sweden , the other leading power in the Baltic . Swedish holdings at that time included territory in Northern Germany , all of modern Finland and most of the Baltic states , a dominion depending on , and connected by , the Baltic Sea trade routes . During the Great Northern War ( 1700 – 1721 ) , Sweden lost all its territories in the Baltic states and suffered Russian raids in Finland and along the chain of islands and <unk> stretching from the Gulf of Finland to Stockholm . The Swedes began to deploy inshore flotillas of shallow @-@ draft vessels , beginning with smaller versions of the traditional Mediterranean galleys . Most of these new vessels were more akin to <unk> and were complemented with gun prams . The disastrous war with Russia ( 1741 – 43 ) and the minor involvement against Prussia in the Seven Years ' War ( 1757 – 62 ) showed the need for further expansion and development of the inshore flotillas with more specialized vessels . 
- <unk> were effective as troop transports for amphibious operations , but were severely under @-@ <unk> , especially in relation to their large crews ; a galley with a 250 @-@ man crew , most of whom were rowers , would typically carry only one 24 @-@ pounder cannon and two 6 @-@ pounders , all in the bow . The galleys also lacked decks and adequate shelter for the rower @-@ soldiers , many of whom succumbed to illness as a result of exposure during the war of 1741 – 43 . 
- 
- = = = Archipelago fleet = = = 
- 
- After the Russian victory against Sweden in 1743 , the Swedes established a commission to identify weaknesses in the eastern defenses . In 1747 , the commission concluded that the fortifications in southeastern Finland needed to be improved and expanded , and that Sweden needed to build a strong coastal navy . Augustin Ehrensvärd ( 1710 – 72 ) , an artillery officer , was the driving force behind these changes . The committee based many of its conclusions and decisions on his ideas . In 1756 , Sweden established the archipelago fleet with the official name <unk> <unk> ( " fleet of the army " ) under the command of the army department , <unk> , with Ehrensvärd as supreme commander . For two decades , the struggle for power between the Hats and the <unk> , the dominant political factions at the time , and rivalries between army and navy brought about changes to the archipelago fleet . The parliamentary victory of the Hats in the <unk> in 1769 – 70 and the coup d <unk> by King Gustav III in 1772 secured the archipelago fleet 's status as an independent branch of the army . Starting in 1770 , the archipelago fleet merged with the Finnish Squadron ( <unk> <unk> ) based at Sveaborg . In 1777 , it incorporated the Swedish Squadron ( <unk> <unk> ) , the galley fleet based at Stockholm . The Swedish armed forces invested considerable resources in the new army branch and made it a professional , independent organization . The archipelago fleet attracted members of the social and cultural elite who enjoyed the protection and patronage of King Gustav III , who had established himself as an absolute monarch in the 1772 coup . 
- After the poor performance of galleys in Russo – Swedish war of 1741 – 43 and the <unk> War ( 1757 – 62 ) , development of replacements became <unk> . During the <unk> War , trials had been made with " gun prams " ( <unk> ) , heavily armed , oar @-@ driven , flat @-@ bottomed barges with a shallow draft that carried guns in broadside arrangements . The prams carried more guns than the galleys , but proved far too slow to be effective . Augustin Ehrensvärd argued for new archipelago vessels that combined firepower , maneuverability , <unk> , and decent crew accommodations . He began a successful collaboration with <unk> Fredrik Henrik Chapman ( <unk> " <unk> Chapman " in 1772 ) , and together they developed five new vessels : a gunboat with a 12 @-@ pounder gun and a schooner rigging , as well as four types of " archipelago frigates " ( <unk> ) : the smaller <unk> and <unk> , and the larger turuma and hemmema . All four types have been called <unk> ( archipelago frigates ) in Swedish and English historical literature , though some authors have called the <unk> and <unk> " archipelago <unk> " . Chapman specifically designed the archipelago frigates for service off the south coast of Finland and named them after the Finnish provinces of <unk> , <unk> ( <unk> ) , <unk> ( <unk> ) , and Hämeenmaa ( <unk> ) . 
- 
- = = Development = = 
- 
- The concept of small sailing frigates with a complementary set of oars ( or " <unk> " ) was not new . The English Tudor navy had used small " <unk> " in the mid @-@ 16th century . In the 1660s its successor , the Royal Navy , equipped the equivalent of sixth @-@ rates with oar ports on or below the <unk> . During the 18th century the Russian Navy introduced " <unk> " , Baltic variants on the Mediterranean <unk> , for inshore duties . The <unk> were good <unk> , could be rowed if necessary and had more guns and greater stores than galleys ; they were also less expensive to maintain . The Russian designs influenced Chapman and the Swedish naval commanders . Consequently , Chapman 's designs for new ships were <unk> on those principles , but with adaptations to archipelago warfare . 
- Chapman 's archipelago frigates provided better protection for their crew than the galleys they replaced , and up to three times the capacity for stores and provisions . They could operate in the narrow , shallow waters around <unk> in all <unk> and in open water in all but the worst storms . They had a deeper draft than galleys , but considerably shallower draft than traditional sailing warships . The new ship types also increased the archipelago fleet 's firepower , provided it with better defensive capabilities , and made possible more efficient fire support in amphibious operations . 
- 
- = = Design and construction = = 
- 
- Of the new designs , <unk> and hemmemas best fit the description of " archipelago frigate " because of their similarities to small ocean @-@ going frigates . The first hemmema , the Oden , was completed in 1764 . It was c . 33 m ( 108 @.@ 2 ft ) long and 8 @.@ 2 m ( 26 @.@ 8 ft ) wide with a draft of 2 @.@ 8 m ( 9 @.@ 25 ft ) . It had a low hull with no forecastle , only a low quarterdeck , and no <unk> deck . It had three masts that were initially rigged with <unk> sails , like a galley . The navy later replaced the <unk> rigs with a more conventional square @-@ sail frigate rig . The early design provided for 14 pairs of oars with four men per oar . The rowers plied their oars from the gun deck through oar ports positioned between the gunports , close to the waterline , which gave the rowers better leverage . The oars were also placed on a rectangular <unk> , designed to further improve the leverage . Even so , hemmemas performed poorly when rowed and were difficult in contrary winds . They were slower than ordinary sailing ships , but sailed better than galleys . 
- During the Russian war of 1788 – 1790 , Sweden built three hemmemas of a new design . They were considerably larger , 44 @.@ 5 by 11 m ( 146 by 36 ft ) , and the number of oars were increased to 20 pairs . They also had some of the heaviest broadsides , even when compared with the much larger frigates of the high seas navy . The artillery officer Carl Fredrik <unk> had cooperated with Chapman to increase the main armament to twenty @-@ two 36 @-@ pounders and two 12 @-@ pounders , which increased the draft by about 30 cm ( 1 ft ) . The addition of diagonal <unk> to reinforce the hull allowed the later hemmemas to carry guns more powerful even than those on the largest sailing frigates of the high seas navy . Due to their considerable firepower and relative size , naval historian Jan <unk> has described the hemmemas as " super archipelago frigates " . 
- The hemmema 's design was very similar to that of the turuma . The primary difference was that the turuma 's oarsmen sat on the weather deck above the guns , whereas the hemmema 's oarsmen sat on the <unk> . The later hemmemas were considerably larger , more heavily armed , and of a more robust construction . <unk> has described them as variations on the same type , especially when considering the pre @-@ war designs . 
- 
- = = Service = = 
- 
- <unk> served in the Finnish squadrons during the war of 1788 – 1790 . They supported amphibious operations and conducted raids on the Russian archipelago fleet , while at the same time acting as sea @-@ borne flank support for the Swedish army on the Finnish mainland . <unk> fought in the first and second battles of <unk> . During the first battle in 1789 , one hemmema complemented the similar <unk> , and in the second battle in July 1790 , two hemmemas made up the defensive center and provided a considerable percentage of the firepower . 
- The Swedes were building three additional hemmemas at the shipyards within the fortress of Sveaborg when it was surrendered to the Russians in 1808 , and all three were incorporated in the Russian Navy . Shortly afterward , the Russian Navy built its own 32 @-@ gun versions , with the final vessel launched as late as 1823 . Two more were built in Sweden in 1809 , Birger Jarl and Erik Segersäll . Birger Jarl sank in an accident in 1813 and Erik Segersäll was planned for conversion as a <unk> steam battery for coastal defense , though the idea was eventually abandoned and the ship scrapped in 1826 . 
- Like the other specialized archipelago vessels , the hemmema had specific strengths and weaknesses . Although it had superior firepower relative to galleys , its sailing qualities were somewhat mediocre and while highly <unk> under oars , it was still difficult to propel while rowed . A hemmema had the potential to be an effective weapon against galleys , matching their forward firepower and severely <unk> them with its broadside armament . Inside an enemy galley formation , it could <unk> considerable <unk> , but such a maneuver was never achieved in an actual battle , leaving that tactical role <unk> . 
- 
- = = Ships = = 
- 
- A total of twelve hemmemas were built , six of them for the Swedish archipelago fleet and six for the Russian Navy . Details of individual vessels are listed below . The Swedish hemmemas were all built to the same specifications , except for the early design Oden , and Birger Jarl and Erik Segersäll carried heavier armament than the others . <unk> and <unk> list Oden as a turuma rebuilt as a hemmema in 1784 , though Oscar <unk> and Lars @-@ Otto Berg do not . The Russian vessels were built between 1808 and 1823 and have been described by <unk> and <unk> as <unk> @-@ class " rowing frigates " . 
- Under the Finnish form " Hämeenmaa " , the name of the ship type was later carried on to several vessels of the 20th century Finnish Navy . 
- 
- 
- = Edward Creutz = 
- 
- Edward Creutz ( January 23 , 1913 – June 27 , 2009 ) was an American physicist who worked on the Manhattan Project at the Metallurgical Laboratory and the Los Alamos Laboratory during World War II . After the war he became a professor of physics at the Carnegie Institute of Technology . He was Vice President of Research at General Atomics from 1955 to 1970 . He published over 65 papers on <unk> , physics , mathematics , metallurgy and science policy , and held 18 patents relating to nuclear energy . 
- A graduate of the University of Wisconsin – Madison , Creutz helped Princeton University build its first cyclotron . During World War II he worked on nuclear reactor design under Eugene Wigner at the Metallurgical Laboratory , designing the cooling system for the first water @-@ cooled reactors . He led a group that studied the metallurgy of uranium and other elements used in reactor designs . In October 1944 , he moved to the Los Alamos Laboratory , where he became a group leader . 
- After the war ended , Creutz accepted an offer to come to the Carnegie Institute of Technology , where he became the head of its Physics Department and its Nuclear Research Center in 1948 . In 1955 he returned to Los Alamos to evaluate its thermonuclear fusion program for the Atomic Energy Commission . While there he accepted an offer to become Vice President for Research and Development and the Director of its John Jay Hopkins Laboratory for Pure and Applied Science at General Atomics . Under his leadership , General Atomics developed TRIGA , a nuclear reactor for universities and laboratories . 
- Creutz served as an assistant director of the National Science Foundation from 1970 to 1977 , and then as Director of the <unk> <unk> Bishop Museum in Honolulu , where he took particular interest in the museum 's preparation of a Manual of the <unk> <unk> of Hawaii ' . 
- 
- = = Early life = = 
- 
- Edward Chester Creutz was born on January 23 , 1913 , in Beaver Dam , Wisconsin , the son of Lester Creutz , a high school history teacher , and Grace Smith Creutz , a general science teacher . He had two older brothers , John and Jim , and a younger sister , Edith . The family moved to <unk> Claire , Wisconsin , in 1916 , Monroe , Wisconsin , in 1920 , and to Janesville , Wisconsin , in 1927 . He played a number of musical instruments , including the mandolin , <unk> and trombone . He played in the school bands at Janesville High School and Monroe High School . At Janesville he played tenor banjo in a dance orchestra called <unk> 's <unk> , and timpani with the school orchestra at Monroe . He also played left guard on the American football teams at Janesville and Monroe . He expressed an interest in chemistry , biology , geology and photography . 
- After graduating from Janesville High School in 1929 , he took a job as a <unk> at a local bank . In 1932 , his brother John , who had graduated from the University of Wisconsin – Madison with a degree in electrical engineering , persuaded him to go to college as well . John suggested that " if you aren ’ t sure what part of science you want , take physics , because that 's basic to all of them . " Creutz later recalled that this was the best advice he ever got . He entered the University of Wisconsin and studied mathematics and physics . Money was scarce during the Great Depression , especially after his father died in 1935 . To pay his bills , Creutz worked as a <unk> and short order cook , and took a job taking care of the physics laboratory equipment . In 1936 , his senior year , he taught physics laboratory classes . 
- Creutz encountered several members of the faculty at the University of Wisconsin , including Julian Mack , <unk> Rollefson , Raymond Herb , Eugene Wigner and Gregory Breit . Mack gave Creutz a research project to do in his junior year . Creutz remained at Wisconsin as a graduate student after receiving his Bachelor of Science ( <unk> ) degree in 1936 , working for Herb upgrading the <unk> Van de <unk> generator from 300 to 600 keV . With this done , the question became what to do with it , and Breit suggested that it had previously been observed that high @-@ energy gamma rays were produced when lithium was bombarded with protons at 440 keV . Creutz therefore wrote his 1939 Doctor of Philosophy ( Ph.D. ) thesis on <unk> <unk> of <unk> by <unk> , under Breit 's supervision . Creutz married <unk> Rollefson , a mathematics student at Wisconsin , and the sister of <unk> Rollefson , on September 13 , 1937 . The couple had three children , two sons , Michael and Carl , and a daughter , Ann Jo . 
- Wigner moved to Princeton University in 1938 , and soon after Creutz received an offer as well . Princeton had been given a 36 @-@ inch ( 910 mm ) magnet by the University of California , which had been used to build an 8 MeV cyclotron . They wanted Creutz to help get it operational . He later recalled : 
- On my third day in Princeton I was invited to give a short report on my thesis work . There were usually two or three speakers at these " Journal Club " meetings . This time the speakers were <unk> <unk> , Albert Einstein , and Ed Creutz . To be on the same program with these two giants of scientific accomplishments was <unk> . Just before the meeting began , my sponsor , <unk> , asked me , " Say , Creutz , have you met Einstein yet ? " I had not . <unk> took me over to where Einstein was sitting in <unk> and tennis shoes , and said , " Professor Einstein , this is Creutz who has come to work on our cyclotron . " The great man held out his hand , which seemed as big as a dinner plate , and said in an accented voice , " I ’ m glad to meet you , Dr. Creutz . " I managed to <unk> out , " I ’ m glad to meet you , too , Dr. Einstein . " 
- But it was <unk> who electrified the audience with his news from Europe of the discovery by <unk> <unk> and Otto <unk> of nuclear fission . <unk> rushed to confirm the results . Creutz built an ionization chamber and a linear amplifier out of radio vacuum tubes , coffee cans and motorcycle batteries , and with this apparatus the physicists at Princeton were able to confirm the results . 
- 
- = = World War II = = 
- 
- In the early years of World War II between 1939 and 1941 , Wigner led the Princeton group in a series of experiments involving uranium and two tons of graphite as a neutron <unk> . In early 1942 , Arthur Compton concentrated the Manhattan Project 's various teams working on plutonium and nuclear reactor design , including Wigner 's team from Princeton , at the Metallurgical Laboratory at the University of Chicago . The name was a <unk> ; Creutz was the first to conduct actual metallurgy research , and he hired its first <unk> to work with him . 
- Wigner led the Theoretical Group that included Creutz , Leo <unk> , Alvin M. Weinberg , <unk> Way and Gale Young . The group 's task was to design the reactors that would convert uranium into plutonium . At the time , reactors existed only on paper , and no reactor had yet gone critical . In July 1942 , Wigner chose a conservative 100 MW design , with a graphite neutron <unk> and water cooling . The choice of water as a coolant was controversial at the time because water was known to absorb neutrons , thereby reducing the efficiency of the reactor ; but Wigner was confident that his group 's calculations were correct and that water would work , while the technical difficulties involved in using helium or liquid metal as a coolant would delay the project . Working seven days a week , the group designed the reactors between September 1942 and January 1943 . Creutz studied the corrosion of metals in a water @-@ cooled system , and designed the cooling system . In 1959 a patent for the reactor design would be issued in the name of Creutz , <unk> , Weinberg , Wigner , and Young . 
- As a group leader at the Metallurgical Laboratory , Creutz conducted studies of uranium and how it could be <unk> into rods . His group looked into the process of corrosion in metals in contact with fast @-@ flowing liquids , the processes for <unk> aluminium and <unk> uranium with it . It also investigated the forging of beryllium , and the preparation of thorium . Frederick Seitz and Alvin Weinberg later <unk> that the activities of Creutz and his group may have reduced the time taken to produce plutonium by up to two years . 
- The discovery of spontaneous fission in reactor @-@ bred plutonium due to contamination by plutonium @-@ 240 led Wigner to propose switching to breeding uranium @-@ 233 from thorium , but the challenge was met by the Los Alamos Laboratory developing an <unk> @-@ type nuclear weapon design . In October 1944 , Creutz moved to Los Alamos , where he became a group leader responsible for explosive lens design <unk> and preliminary testing . <unk> encountered in testing the lenses led to the construction of a special test area in <unk> Canyon , and Creutz became responsible for testing there . As part of the preparation for the Trinity nuclear test , Creutz conducted a test detonation at <unk> Canyon without nuclear material . This test brought bad news ; it seemed to indicate that the Trinity test would fail . Hans Bethe worked through the night to assess the results , and was able to report that the results were consistent with a perfect explosion . 
- 
- = = Later life = = 
- 
- After the war ended in 1945 , Creutz accepted an offer from Seitz to come to the Carnegie Institute of Technology as an associate professor , and help create a nuclear physics group there . Creutz in turn recruited a number of young physicists who had worked with him at Princeton and on the Manhattan Project in Chicago and Los Alamos , including Martyn <unk> , Jack Fox , Roger Sutton and Sergio <unk> . Together , with funding from the Office of Naval Research they built a 450 MeV <unk> at the Nuclear Research Center near <unk> , Pennsylvania . For a time , This put them at the forefront of research into nuclear physics , allowing physicists there to study the recently discovered <unk> meson and <unk> meson . A visiting scholar , <unk> <unk> , created the first photographic <unk> of a meson . 
- Creutz became a professor , the head of the Physics Department , and the head of Nuclear Research Center at the Carnegie Institute of Technology in 1948 . He was also a member of the Executive Board at the <unk> National Laboratory from 1946 to 1958 , and a consultant at the Oak Ridge National Laboratory from 1946 to 1958 . In addition to his work on nuclear physics , he cultivated flowers and orchids at his home . He published eight papers on floral species , and named three varieties of <unk> after his children . One 1966 paper , published in the New York <unk> Garden Journal was on <unk> <unk> , a rare flower found only on the island of <unk> in French Polynesia . He travelled to Polynesia many times , and translated Grammar of the Tahitian language from French into English . His family served as hosts for a time to two young people from Tahiti and Samoa . 
- In 1955 and 1956 , Creutz spent a year at Los Alamos evaluating its thermonuclear fusion program for the Atomic Energy Commission . While there he was approached by <unk> de Hoffmann , who recruited him to join the General Atomics division of General <unk> . He moved to La <unk> , California , as its Vice President for Research and Development , and was concurrently the Director of its John Jay Hopkins Laboratory for Pure and Applied Science from 1955 to 1967 . He was also a member of the Advisory Panel on General Science at the Department of Defense from 1959 to 1963 . 
- Under his leadership , General Atomics developed TRIGA , a small reactor for universities and laboratories . TRIGA used uranium <unk> hydride ( <unk> ) fuel , which has a large , prompt negative fuel temperature coefficient of reactivity . As the temperature of the core increases , the reactivity rapidly decreases . It is thus highly unlikely , though not completely impossible , for a nuclear <unk> to occur . Due to its safety and reliability , which allows it to be installed in densely populated areas , and its ability to still generate high energy for brief periods , which is particularly useful for research , it became the world 's most popular research reactor , and General Atomics sold 66 <unk> in 24 countries . The high @-@ temperature gas @-@ cooled reactor ( <unk> ) was less successful , and only two <unk> power reactors were built , both in the United States . A 40 MW demonstration unit at the Peach <unk> Nuclear <unk> Station in Pennsylvania operated successfully , but a larger 300 MW unit at the Fort St. <unk> <unk> Station in Colorado encountered technical problems . General Atomics also conducted research into thermonuclear energy , including means of <unk> <unk> plasma . Between 1962 and 1974 Creutz published six papers on the subject . 
- In 1970 President Richard Nixon appointed Creutz as Assistant Director for Research of the National Science Foundation . He became Assistant Director for <unk> and <unk> Sciences in 1975 , and was acting Deputy Director from 1976 to 1977 . The 1970s energy crisis raised the national profile of energy issues , and Creutz served on a panel that produced a study called The Nation 's Energy Future . His wife <unk> died of cancer in 1972 . In 1974 he married Elisabeth <unk> , who worked for the National Science Board . The two of them enjoyed locating and photographing rare orchids . 
- His appointment at the National Science Foundation ended in 1977 , and Creutz became director of the <unk> <unk> Bishop Museum in Honolulu . He took particular interest in the museum 's work preparing a two @-@ volume Manual of the <unk> <unk> of Hawaii , which was published in 1999 . He expanded programs for education and outreach , and secured funding for two new buildings . He retired in 1987 and returned to his home in Rancho Santa Fe , California , and died there on June 27 , 2009 . 
- 
- = = Documentaries = = 
- 
- To Mars by A @-@ <unk> : The Secret History of Project Orion 
- 
- 
- = <unk> Del Toso = 
- 
- <unk> Del Toso ( born 12 August 1980 ) is a 3 @.@ 5 point wheelchair basketball player who represented Australia at the 2012 Summer Paralympics in London , where she won a silver medal . <unk> with chronic inflammatory <unk> <unk> at the age of nineteen , Del Toso started playing wheelchair basketball in 2006 . Playing in the local Victorian competition , she was named the league 's most valuable player in 2007 . That year started playing for the Knox Ford Raiders in the Women 's National Wheelchair Basketball League ( WNWBL ) . The following year , she was named the team 's Players ' Player and Most Valuable Player ( MVP ) . 
- Del Toso has played for the Dandenong Rangers in the WNWBL since 2008 . In the semifinal between her Dandenong Rangers and the <unk> <unk> in 2009 , she scored 31 points while pulling down 19 rebounds that saw the Rangers win 81 – 42 . The Dandenong Rangers won back to back titles in 2011 and 2012 . 
- Del Toso made her debut with the Australia women 's national wheelchair basketball team , known as the Gliders , at the 2009 Osaka Cup in Japan . Since winning a silver medal in London , she has participated in the 2013 Osaka Cup in Japan , where the Gliders successfully defended the title they had won in 2008 , 2009 , 2010 and 2012 . 
- 
- = = Personal = = 
- 
- Nicknamed <unk> , Del Toso was born on 12 August 1980 . At the age of nineteen , she was diagnosed with chronic inflammatory <unk> <unk> ( <unk> ) , a heredity condition that involves damage to the nerves . Del Toso has two siblings ; her younger brother Daniel also developed the disease . Prior to her diagnosis , she played regular basketball . Del Toso has worked as a <unk> , and as a participation assistant for Basketball Victoria . As of 2013 , she lives in <unk> , Victoria . 
- 
- = = Wheelchair basketball = = 
- 
- Del Toso was a 4 point wheelchair basketball player . Due to the progress of her disease , she was reclassified as a 3 @.@ 5 point player in 2013 . As of 2012 , she has a scholarship with the Victorian Institute of Sport , and in financial year 2012 / 13 , she received a A $ 20 @,@ 000 grant from the Australian Sports Commission as part of its Direct <unk> Support ( <unk> ) program . She received $ 17 @,@ 000 in 2011 / 12 and 2010 / 11 , $ 5 @,@ 571 @.@ 42 in 2009 / 10 and $ 5 @,@ 200 in 2008 / 09 . In 2012 , she trained in Dandenong , <unk> , Box Hill and Knox . 
- 
- = = = Club = = = 
- 
- Del Toso started playing wheelchair basketball in 2006 . An Australian Paralympic Committee flyer on the wall at her local gym asking " Are you the next <unk> ? " prompted Del Toso to respond . She was advised to take up wheelchair basketball . Playing in the local Victorian competition in 2007 , she was named the league 's most valuable player . That year , she made her debut in the Women 's National Wheelchair Basketball League ( WNWBL ) with the Knox Ford Raiders . At the end of the season , she was named the most improved player . She played for the Rangers ( now known as Victoria ) since 2008 . In the second round of the 2008 season , the Dandenong Rangers defeated the Western Stars 53 – 47 . She scored 20 points in her team 's victory . In the second round of the 2008 season , playing for the Dandenong Rangers in a 38 – 72 loss to the Hills Hornets , she scored 12 points . That season , she was named the team 's Players Player and Most Valuable Player ( MVP ) . 
- In 2009 , Del Toso played in the WNWBL finals . In the semifinal between the Dandenong Rangers and the <unk> <unk> , she scored 31 points while pulling down 19 rebounds that saw the Rangers win 81 – 42 . In 2010 , she was named the Dandenong Rangers 's Most Valuable Player . The Rangers won the WNWBL title in 2011 . In a round four game in 2012 , against Sydney <unk> Flames that the Rangers won 55 – 44 , she scored 14 rebounds . The Rangers won the league championship again that year . 
- 
- = = = National = = = 
- 
- In 2008 , Del Toso was named as a reserve for the Australia women 's national wheelchair basketball team , known as the Gliders , for the 2008 Summer Paralympics . She made her national team debut at the 2009 Osaka Cup the following year , when her team finished first . That year , she also participated in the Four Nations in Canada and the Japan Friendly Series , one of six players who played for the Dandenong Rangers in the WNWBL . She was selected to participate in a national team training camp in 2010 . In July 2010 , she played in a three @-@ game test series against Germany . She was member of the Australian team at the 2010 World Championships that finished fourth . She also played in the 2010 Osaka Cup where her team finished first . She played in four games in the 2012 Gliders World Challenge . 
- Del Toso was selected to represent Australia at the 2012 Summer Paralympics in wheelchair basketball . The London Games were her first . In the group stage , the Australia women 's national wheelchair basketball team at the 2012 Summer Paralympics posted wins against Brazil , Great Britain , and the Netherlands , but lost to Canada . This was enough to advance the Gliders to the quarter @-@ finals , where they beat Mexico . The Gliders then defeated the United States by a point to set up a final clash with Germany . The Gliders lost 44 – 58 , and earned a silver medal . 
- Since the games , Del Toso has participated in the 2013 Osaka Cup in Japan , where the Gliders successfully defended the title they had won in 2008 , 2009 , 2010 and 2012 . 
- 
- = = Statistics = = 
- 
- 
- 
- = No. 79 Wing RAAF = 
- 
- No. 79 Wing was a Royal Australian Air Force ( RAAF ) wing of World War II . It was formed in December 1943 at Batchelor , Northern Territory , as part of North @-@ Western Area Command . <unk> by Group Captain Charles Eaton , the wing comprised four squadrons on its establishment , flying Beaufort and B @-@ 25 Mitchell bombers and Beaufighter heavy fighters . No. 79 Wing took part in the New Guinea and North @-@ Western Area Campaigns during 1944 – 45 , eventually transferring to <unk> in the Dutch East Indies as the Allies advanced northward . By the end of the Pacific War , the wing was attached to the Australian First Tactical Air Force and was made up of Nos. 2 and 18 ( Netherlands East Indies ) Squadrons , both flying Mitchells . The latter transferred to the Netherlands Air Force in late 1945 , while the former returned to Australia where it disbanded the following year . No. 79 Headquarters itself disbanded in October 1945 , soon after the end of hostilities . 
- 
- = = History = = 
- 
- No. 79 Wing was established at Batchelor , Northern Territory , on 30 November 1943 . Its combat units consisted of Nos. 1 and 2 Squadrons ( flying Beaufort light reconnaissance bombers ) , No. 31 Squadron ( Beaufighter long @-@ range fighters ) , and No. 18 ( Netherlands East Indies ) Squadron ( B @-@ 25 Mitchell medium bombers ) . The wing was commanded by Group Captain Charles Eaton , whose Dutch personnel called him " <unk> Charles " ( Uncle Charles ) . Operating under the auspices of North @-@ Western Area Command ( NWA ) , Darwin , No. 79 Wing participated in the New Guinea and North @-@ Western Area Campaigns during 1944 . 
- Through March – April 1944 , the Beaufighters attacked Japanese shipping , while the Mitchells and Beauforts bombed Timor on a daily basis as a prelude to Operations <unk> and <unk> , the invasions of Hollandia and Aitape . Eaton organised a large raid against Su , Dutch Timor , on 19 April . <unk> of thirty @-@ five Mitchells , Beauforts and Beaufighters , the force destroyed the town 's barracks and fuel dumps , a result that earned the personal congratulations of the Air Officer Commanding NWA , Air Vice Marshal " King " Cole . On the day of the Allied landings , 22 April , the Mitchells and Beaufighters made a daylight raid on Dili , Portuguese Timor . The ground assault on Hollandia – Aitape met little opposition , credited in part to the air bombardment leading up to it . 
- In May 1944 , Nos. 1 , 18 and 31 Squadrons attacked Japanese positions in Timor , while No. 2 Squadron was withdrawn from combat to re @-@ equip with Mitchells . No. 79 Wing 's light and medium bombers suffered from a lack of suitable targets as they had few airfields in forward areas from which to refuel . No. 2 Squadron returned to operations with Mitchells in June . That month , No. 18 Squadron flew 149 sorties , damaging Japanese airfields and shipping in the Timor area , but lost its commanding officer to anti @-@ aircraft fire during a raid . 
- In June – July 1944 , No. 79 Wing supported the Allied attack on <unk> . No. 18 Squadron was again the wing 's most active unit , flying 107 sorties . In September , the Beaufighters and Mitchells attacked Japanese shipping and infrastructure in <unk> and <unk> , but lost nine aircraft and twenty @-@ six crewmen killed , among them Squadron Leader Wilbur Wackett , son of Commonwealth Aircraft Corporation manager Lawrence Wackett . By the end of the month , Mitchell missions were put on hold while replacement crews were trained . In late 1944 , plans were made to transfer No. 79 Wing from North @-@ Western Area Command to Northern Command in Papua New Guinea , where it would undertake operations against the Japanese in New Britain . The wing 's composition for this move was to be Nos. 2 and 18 Squadrons , operating Mitchells , and 120 ( Netherlands East Indies ) Squadron , operating P @-@ 40 Kittyhawks . No. 31 Squadron was transferred from No. 79 Wing to the Australian First Tactical Air Force at <unk> in December . The same month , Group Captain Eaton posted out and was replaced by Group Captain John <unk> . 
- Weather hampered the wing 's activities in January 1945 . No. 1 Squadron was withdrawn to Queensland to re @-@ equip with <unk> , with No. 13 Squadron , flying <unk> , taking up the <unk> on anti @-@ shipping missions . The squadron accounted for around half of the thirty @-@ eight enemy vessels sunk by No. 79 Wing in February , and a similar ratio to the twenty sunk the following month . Wing operations were cut back in March , as preparations were made to transfer the Mitchells to <unk> Bay in New Britain . On 6 April , all twenty available aircraft of Nos. 2 and 18 Squadrons were ordered to join B @-@ 24 Liberators of No. 82 Wing in an assault on a Japanese convoy that included the cruiser <unk> . The Liberators were late for their rendezvous with the Mitchells off <unk> so the latter , at the very limit of their range , attacked the convoy regardless . They claimed two direct hits without loss , despite anti @-@ aircraft fire from the cruiser and other ships , and frontal attacks by enemy fighters . Allied submarines sank the damaged <unk> the next day . 
- The wing 's proposed move to New Britain was cancelled in May 1945 , after the Netherlands government requested that its squadrons operate over the Dutch East Indies . No. 120 Squadron was transferred to <unk> , while No. 79 Wing and its two Mitchell squadrons were ordered to move to Borneo , under the command of First Tactical Air Force . By July , No. 79 Wing had relocated from Batchelor to <unk> , leaving No. 13 Squadron under the control of North @-@ Western Area Command . After the Pacific War ended in August 1945 , the Mitchells joined Liberators of No. 82 Wing <unk> RAAF personnel from Borneo to Australia . No. 79 Wing Headquarters was disbanded on 8 October . The following month , No. 18 Squadron was reassigned to the Netherlands Air Force . No. 2 Squadron returned to Australia in December , disbanding in mid @-@ 1946 . These were the only two squadrons in the RAAF to operate Mitchells during the war . 
- 
- 
- = Vitamin D ( Glee ) = 
- 
- " Vitamin D " is the sixth episode of the American television series Glee . The episode premiered on the Fox network on October 7 , 2009 . It was written by series creator Ryan Murphy and directed by <unk> Keene . In the episode , glee club director Will Schuester ( Matthew Morrison ) pits the male and female club members against each other for a mash @-@ up competition . Will 's wife Terri ( <unk> <unk> ) takes a job as the school nurse to stop him becoming closer to guidance <unk> Emma Pillsbury ( Jayma Mays ) , but is fired after giving the students performance @-@ enhancing pseudoephedrine tablets . 
- The episode features mash @-@ up covers of " It 's My Life " by Bon Jovi and " Confessions Part II " by Usher , and " Halo " by Beyoncé Knowles and " Walking on Sunshine " by Katrina and the Waves . Both tracks were released as singles , available for digital download . " Vitamin D " was watched by 7 @.@ 30 million US viewers , and received generally positive reviews from critics . Performances by Morrison , Mays and Jane Lynch as <unk> coach Sue Sylvester attracted praise , as did the staging of the musical mash @-@ ups . However , <unk> <unk> of MTV and Mandi Bierly of Entertainment Weekly both noted critically that dramatic storylines in the episode dominated over the musical performances . 
- 
- = = Plot = = 
- 
- Believing the glee club members are becoming complacent ahead of the forthcoming <unk> , director Will Schuester ( Matthew Morrison ) divides the club into boys against girls for a mash @-@ up competition . <unk> coach Sue Sylvester ( Jane Lynch ) observes that head cheerleader Quinn <unk> 's ( Dianna Agron ) performance standards are slipping . When Quinn blames her <unk> on her glee club participation , Sue <unk> her resolve to destroy the club , planning to sabotage Will 's personal life . 
- Sue tells Will 's wife Terri Schuester ( <unk> <unk> ) that guidance <unk> Emma Pillsbury ( Jayma Mays ) has romantic feelings for Will . <unk> to stay close to her husband , Terri takes a job as the school nurse , despite having no medical qualifications . She encourages Emma 's boyfriend , football coach Ken Tanaka ( Patrick Gallagher ) to propose to her , which he does . After asking Will if there is any reason she should not marry Ken , and being warned off Will by Terri , Emma accepts his proposal . Terri is still hiding the fact she experienced a hysterical pregnancy from Will , and upon realizing how much her life is changing due to her pregnancy , Quinn agrees to let Terri secretly adopt her baby . 
- Finn Hudson ( Cory Monteith ) is exhausted by his extra @-@ curricular activities , so Terri gives him pseudoephedrine tablets , which Finn shares with the rest of the males in the glee club . The effects of the tablets enhance their performance , and they give an energetic mash @-@ up of " It 's My Life and " Confessions Part II " . When Kurt Hummel ( Chris Colfer ) tells the girls the secret behind the boys ' performance , they , too , request the tablets from Terri , and give a high @-@ spirited mash @-@ up of " Halo " and " Walking On Sunshine " . Finn and Rachel Berry ( Lea Michele ) feel guilty for cheating , however , and agree to <unk> the competition . When Principal Figgins ( <unk> Theba ) learns what has happened , he fires Terri and , angry with Will , appoints Sue as co @-@ director of the glee club . 
- 
- = = Production = = 
- 
- Recurring characters who appear in " Vitamin D " are glee club members Santana Lopez ( <unk> Rivera ) , Brittany Pierce ( Heather Morris ) , Mike Chang ( Harry Shum , Jr . ) and Matt Rutherford ( <unk> <unk> ) , former glee club director Sandy <unk> ( Stephen <unk> ) , Principal Figgins ( Theba ) , football coach Ken Tanaka ( Gallagher ) , Terri 's co @-@ worker Howard <unk> ( Kent <unk> ) , and local news anchors Rod <unk> ( Bill A. Jones ) and Andrea Carmichael ( <unk> Davis ) . Joe <unk> guest stars as Joe . 
- The episode features mash @-@ up covers of " It 's My Life " by Bon Jovi and " Confessions Part II " by Usher , and " Halo " by Beyoncé Knowles and " Walking on Sunshine " by Katrina and the Waves . Both tracks were released as singles , available for digital download . " It 's My Life / Confessions Part II " charted at number 7 in Ireland , 14 in the UK , 22 in Australia , 25 in Canada and 30 in America , while " Halo / Walking on Sunshine " charted at number 4 in Ireland , 9 in the UK , 10 in Australia , 28 in Canada and 40 in America . Michele has revealed that she practiced talking " <unk> " for several days in order to convey the effects of pseudoephedrine on Rachel . In order to portray the character in her altered state , she questioned : " How manic is the right amount of manic ? What would Rachel be like on <unk> ? What would she sound like ? " She deemed performing the mash @-@ up piece in that state " so much fun " . 
- 
- = = Reception = = 
- 
- The episode was watched by 7 @.@ 30 million U.S. viewers and attained a 3 @.@ 2 / 8 rating / share in the 18 – 49 demographic . Glee maintained its ratings from the previous week , despite all of the other new Wednesday night shows of the season declining by double @-@ digit percentages . It was the eighteenth most watched show in Canada for the week of broadcast , with 1 @.@ 61 million viewers . In the UK , the episode was watched by 2 @.@ 008 million viewers ( 1 @.@ 608 million on E4 , and 400 @,@ 000 on E4 + 1 ) , becoming the most @-@ watched show on E4 and E4 + 1 for the week , and the most @-@ watched show on cable for the week , as well as the most @-@ watched episode of the series at the time . 
- " Vitamin D " was nominated for the best " Comedy Series Episode " award at the 2010 <unk> Awards . It received generally positive reviews from critics . <unk> Malcom of the Los Angeles Times noted that she preferred the boys ' performance to the girls ' , commenting : " Their number had the same heart @-@ soaring power as " Don 't Stop <unk> ' " [ performed in the pilot episode ] . " Malcom enjoyed Sue 's character development in the episode , claiming that , " In less skilled hands , there ’ s no doubt Sue would be an over @-@ the @-@ top disaster . But thanks to the <unk> Jane Lynch , I can ’ t wait to see what trouble the character <unk> up next . " <unk> <unk> of MTV also enjoyed the boys ' performance more than the girls ' , and gave the episode a mostly positive review , writing that it moved the series ' storylines to " a whole new level " . She felt , however , that the episode " didn 't have nearly enough singing " . Mandi Bierly for Entertainment Weekly similarly noted that : " So much happened in this hour that the musical numbers , though enjoyable , were almost an <unk> . " Bierly favoured the girls ' performance , and praised Morrison 's acting , commenting : " Matthew Morrison <unk> so much with his eyes . There ’ s a <unk> and a longing in them that I ’ m always surprised Emma ( Jayma Mays ) matches . " 
- Mike Hale for the New York Times praised Mays ' performance , noting : " Jayma Mays registered Emma ’ s devastation with just the slightest widening of those enormous eyes . In fact all the best non @-@ singing moments in the episode were hers . " Hale was less impressed with the rest of the episode , deeming the pregnancy storyline " so boring that is hardly <unk> " . He noted that : " For many viewers , the best moments in the episode probably came very early on and involved Jane Lynch ’ s Sue Sylvester , who still got all the best lines . " <unk> Wieselman for the New York Post agreed with this assessment , opining that although the episode was " filled with more brilliant moments than ever before " , the stand @-@ out scene was Sue writing in her journal , which Wieselman deemed " jam @-@ packed with so many one liners , it acted as a vacuum , sucking the smart out of everything else on TV from 9 : 05 to 9 : 07 pm . " Fellow New York Post critic <unk> Shen deemed the episode her favorite of the series so far . Anna <unk> of The Guardian called the pseudoephedrine storyline " relentlessly silly [ ... ] but <unk> so " , preferring the boys ' performance to the girls ' as " some excellent comedy helped me forget about Finn 's dodgy <unk> vocals for once " . 
- 
- 
- = Fern Hobbs = 
- 
- Fern Hobbs ( May 8 , 1883 – April 10 , 1964 ) was an American attorney in the U.S. state of Oregon , and a private secretary to Oregon Governor Oswald West . She was noted for her ambition and several accomplishments as a young woman , and became the highest @-@ paid woman in public service in America in her mid @-@ twenties . 
- Hobbs made international news when Governor West sent her to implement martial law in the small Eastern Oregon town of Copperfield . The event was considered a strategic coup for West , establishing the State 's authority over a remote rural community and <unk> his reputation as a proponent of prohibition . 
- Hobbs later worked for the American Red Cross in Europe and at the Oregon Journal newspaper . She died in Portland in 1964 . 
- 
- = = Early life = = 
- 
- Hobbs was born on May 8 , 1883 , in <unk> , Nebraska , to John Alden Hobbs and <unk> Bush Hobbs . Her family moved to Salt Lake City , Utah when she was six years old ; she lived there for 12 years , finishing high school . Her father then met with financial difficulties , and she moved to Oregon , settling in Hillsboro . There , she put her younger brother and sister through school , while studying <unk> and working for a living . 
- She soon became a private secretary to the president of the Title <unk> and Trust Company . The bank , which held many assets of the Oregon Common School Fund , failed during Hobbs ' time there . Ben <unk> , who was the Secretary of State and a member of the State Land Board , was charged with protecting the Common School Fund , and was involved in negotiating with the failing bank over the State 's assets . He took note of Hobbs ' strong loyalty to her employer . 
- After the bank 's failure , Hobbs worked as a governess for J. Wesley <unk> ( brother of William S. <unk> ) in Portland . She also helped raise her younger brother and sister , studied <unk> and the law , and worked as a secretary . In 1913 , Hobbs graduated from <unk> University College of Law with a Bachelor of Laws degree , and was admitted to the Oregon State Bar . 
- <unk> , who managed Oswald West 's successful 1910 campaign to become Governor of Oregon , recommended that West hire Hobbs as his private stenographer . She was hired , and impressed West to the point that he hired her as his private secretary two years later . At that time , at age 27 , she was the highest @-@ paid woman in public service in the United States , earning $ 3 @,@ 000 per year . 
- 
- = = Martial law in Copperfield , Oregon = = 
- 
- West ordered Hobbs to Copperfield , Oregon to restore law and order on January 2 , 1914 , along with a group of six militia men that included Oregon State <unk> <unk> <unk> Lawson . Copperfield , located on the Snake River in Baker County , had grown up around construction projects for a railroad tunnel and power plant . Fifteen @-@ hundred jobs in the area came from the railway project of E. H. <unk> or the power generation facility . 
- The town had descended into lawlessness with a number of saloons , <unk> , dancing halls , and widespread gambling . The town had no law enforcement officers , and the local government officials had become bar keepers . Governor West had extended prohibition laws , Some local residents had appealed to the state government for <unk> but they were widely ignored in Copperfield . Over half the residents of the town had signed a petition , addressed to West , alleging that saloons owned by the mayor and City Council members were selling liquor to minors and staying open later than their posted hours . Governor West responded by ordering county officials to restore order , close the saloons , and force the <unk> of the corrupt city leaders by December 25 , 1913 . 
- County officials did not take care of the problem , so West sent Hobbs , hoping the presence of a woman would prevent any outbreak of violence . Hobbs was a <unk> woman standing 5 feet 4 inches ( 1 @.@ 63 m ) tall and weighing less than 100 pounds ( 45 kg ) . She was dispatched with orders to restore order and to implement martial law if necessary . While Hobbs was traveling to Eastern Oregon , both she and Governor West were coy with reporters about the presence of the militia men , suggesting that Hobbs might be acting alone . 
- The saloon keepers , who received word that Hobbs was accompanied by law enforcement officers only shortly before her arrival , greeted her by dressing up the town with <unk> , blue and pink ribbons , and flowers . A town meeting was arranged at 2 : 30 p.m. on January 3 . Hobbs renewed the call for the resignation of city officials , but was the request was refused . Hobbs ' escorts then arrested the city leaders and ordered Lawson to declare martial law . It was the first time in Oregon since the Civil War that martial law was put into effect . 
- Soon the town was disarmed and order restored , with the gambling equipment and weapons confiscated , and the saloons closed down . Hobbs then left Lawson in charge and caught the 4 : 00 p.m. train out of town that same day . The residents did not openly resist Hobbs or the militia men , although nearly all were armed and had been prepared to offer non @-@ violent resistance . She stopped at the county seat in Baker City to officially remove the town 's officials in front of a judge before returning to the state capitol in Salem . The Baker County Circuit Court quickly <unk> the militia from holding the town under martial law ; Sheriff Rand began assembling a <unk> to carry out the court order . Governor West requested a hearing , seeking Rand 's temporary removal from office , and appointed Hobbs to represent the State as special counsel . 
- The actions of the governor were later challenged in court , with Hobbs and West among the defendants . The saloon keepers sought remuneration for liquor they claimed was confiscated during the period of martial law . The Baker County circuit court determined the governor 's actions were within his powers , and the Oregon Supreme Court ultimately concurred . 
- These events made Hobbs the most famous woman in Oregon at that time . Hobbs also made national and international news for these events . Writer Stewart Holbrook reported : 
- 
- = = Later life = = 
- 
- After the Copperfield affair , Hobbs continued as Governor West 's secretary until the end of his term in 1915 . She visited the Union County town of Cove in February 1914 , also to investigate complaints about a saloon . A local election had declared the town " dry , " but a county election had declared the entire county " wet . " On advice of a judge , the mayor of Cove stated that he was unable to determine whether the saloon was legal or not , but expressed <unk> to the governor 's wishes . Hobbs did not order the saloon closed down . 
- She then moved to Portland and practiced law . Women 's rights groups promoted Hobbs as a candidate to run for governor , but she never ran for office . Within a few years Fern Hobbs became the commissioner of Oregon State Industrial Accident Commission , working on getting taxes due on the Oregon & California Lands . In 1917 , with the United States entering World War I , she began a long association with the Red Cross . From 1917 to 1922 she worked in Europe , including time spent as the chief of the casualty division in Paris , France . In that position Hobbs was responsible for notifying dead soldiers ' next of <unk> . She returned to Europe in the 1930s , working in the Rhine Valley when it was occupied by France . 
- Upon returning to Oregon , Hobbs worked as a secretary for the Oregon Journal newspaper . She retired in 1948 as the secretary to the paper 's business manager . Fern Hobbs died on April 10 , 1964 , at the age of 80 , and was buried at the Hillsboro Pioneer Cemetery in Hillsboro , Oregon . 
- The Oregon writer Stewart Holbrook interviewed her in the early 1950s , a few years after her retirement , observing that she " still weighs 104 pounds . Her eyes are clear and blue behind her glasses . There is not a gray hair on her head . She lives as quietly as she has always lived , except for those <unk> few days so long ago [ concerning Copperfield ] . " Holbrook noted during his interview that " the subject of Copperfield <unk> her " and concluded his account of her as follows : 
- 
- 
- = <unk> Stephen = 
- 
- <unk> Stephen , MBE ( 19 April 1893 – 12 June 1979 ) was a twentieth @-@ century British suffragette , labour activist and local councillor . She grew up in Scotland and won a scholarship to train as a teacher . Family finances dictated otherwise , leading to her becoming a domestic worker at the age of 15 . She became involved in national labour issues as a teenager , via organisations such as the Independent Labour Party and the Women 's Social and Political Union . After moving to Lancashire and London she visited the United States and Canada , where she held meetings with the public including migrant English domestic workers . 
- Stephen later become more involved in formal political parties , being elected as a local councillor and standing as a candidate in general elections . After moving to Bristol she became the first woman president of Bristol Trades Council . She was appointed MBE in 1977 and her life is commemorated by a blue plaque in Bristol . 
- 
- = = Biography = = 
- 
- Stephen is recorded in the Oxford Dictionary of National Biography as a " suffragette and labour activist " , and has been described as " working @-@ class " . 
- 
- = = = Childhood and family = = = 
- 
- Some sources give Stephen 's place of birth as Marylebone , London , others as Glasgow . The eldest of eleven children in a " closely @-@ knit ... family " , her father was a <unk> . She has been described as " virtually the only Scottish working @-@ class Women 's Social and Political Union ( WSPU ) member about whom anything is known " . She attended Sunday schools separately linked to the church and to socialism , and was educated at North <unk> School . She won a scholarship to train as a pupil @-@ teacher . 
- Her father 's low and variable income meant that she could not afford to pursue her aspiration to become a teacher , and became a domestic worker at the age of 15 . Her father was a founder member of the Independent Labour Party ( ILP ) when it was established in 1893 . She described her mother as being " so quiet and the very opposite of dad " . 
- 
- = = = Early career = = = 
- 
- She was referred to as a " young activist in the <unk> Branch of the ILP " , before she joined the WSPU in 1909 , aged 16 . She was the youngest member of the WSPU Glasgow delegation to the Chancellor of the <unk> David Lloyd George in 1912 . As a member of the WSPU and organiser of the Domestic Workers ' Union , she led the first of the " Scottish <unk> " ( involving attacks on pillar boxes ) in Glasgow in February 1913 . 
- Stephen was approached by Sylvia <unk> and moved from Glasgow to London , where she became considered one of the " most active members " ( along with Emma <unk> , around 1916 ) of the Workers ' Suffrage Federation . In April 1919 , Stephen was one of a number of speakers to address a crowd of " about 10 @,@ 000 people " in Trafalgar Square , opposing the <unk> of Germany . Other speakers included <unk> <unk> @-@ Lawrence and <unk> Wilson Wilson . She was also an active member of the Women 's Peace Crusade and at the 1920 ILP conference argued against the use of force during events preceding the Treaty on the Creation of the USSR . 
- In the 1920s she visited the United States , holding public meetings with immigrant communities from Scotland and <unk> and fund @-@ raising for the Socialist Party of America . She also visited Vancouver , where she encouraged migrant English domestic workers to <unk> . 
- 
- = = = Middle years = = = 
- 
- Stephen later lived in Lancashire and also in London , where she became involved in the East London Federation and sold the Women 's <unk> . She was elected Labour borough councillor for <unk> in 1922 , after failing to be selected as a parliamentary candidate for the ILP , and worked for <unk> MP Alfred <unk> . She stood as Labour candidate for Portsmouth South in the general elections of 1923 , 1924 and 1929 , and for <unk> in 1931 . 
- From 1924 she worked as a freelance journalist , established a <unk> agency in Lewes in 1935 and joined the National Union of Clerks in 1938 . At the time of the Second World War , she worked for Murphy Radio in <unk> Garden City . 
- She later moved to <unk> , Bristol , where she worked at the Broad <unk> branch of the Co @-@ operative <unk> Society ( <unk> ) and with the National Union of Clerks . She later became chair of the local <unk> management committee . Around this time , she spoke publicly and gave advice on birth control . She was elected to the city council . In 1952 she became the first woman president of Bristol Trades Council . 
- 
- = = = Later life = = = 
- 
- In the 1964 general election , she was a candidate for the Labour Party in the Weston @-@ super @-@ Mare constituency . She was appointed MBE for " services to the trade union movement " in June 1977 . She died at Bristol General Hospital in 1979 , and her life is commemorated by a blue plaque in <unk> . 
- 
- 
- = Of Human Feelings = 
- 
- Of Human Feelings is a studio album by American jazz saxophonist and composer Ornette Coleman . It was recorded on April 25 , 1979 , at CBS Studios in New York City with his band Prime Time , which featured guitarists Charlie Ellerbee and Bern Nix , bassist Jamaaladeen Tacuma , and drummers Calvin Weston and Coleman 's son Denardo . It followed Coleman 's failed attempt to record a direct @-@ to @-@ disc session earlier in March 1979 . 
- Of Human Feelings explores jazz @-@ funk music and continues Coleman 's <unk> approach to improvisation with Prime Time , whom he introduced on his 1975 album Dancing in Your Head . He drew on rhythm and blues influences from early in his career for Of Human Feelings , which had shorter and more distinct compositions than Dancing in Your Head . Coleman also applied free jazz principles from his music during the 1960s to elements of funk . 
- Following a change in management , Coleman signed with Island Records , and Of Human Feelings was released in 1982 by its subsidiary label Antilles Records . Critics generally praised Coleman 's expressive music and <unk> approach , but the album made little commercial impact and went out of print . Coleman enlisted his son Denardo as manager after a dispute with his former managers over the album 's royalties , a change that inspired him to perform publicly again during the 1980s . 
- 
- = = Background = = 
- 
- By the end of the 1960s , Ornette Coleman had become one of the most influential musicians in jazz after pioneering its most controversial subgenre , free jazz , which jazz critics and musicians initially derided for its <unk> from conventional structures of harmony and tonality . In the mid @-@ 1970s , he stopped recording free jazz , recruited electric <unk> , and pursued a new creative theory he called harmolodics . According to Coleman 's theory , all the musicians are able to play individual melodies in any key , and still sound coherent as a group . He taught his young <unk> this new improvisational and ensemble approach , based on their individual tendencies , and prevented them from being influenced by conventional styles . Coleman likened this group ethic to a spirit of " collective consciousness " that stresses " human feelings " and " biological rhythms " , and said that he wanted the music , rather than himself , to be successful . He also started to incorporate elements from other styles into his music , including rock influences such as the electric guitar and non @-@ Western rhythms played by Moroccan and <unk> musicians . 
- Of Human Feelings was a continuation of the harmolodics approach Coleman had applied with Prime Time , an electric quartet introduced on his 1975 album Dancing in Your Head . The group comprised guitarists Charlie Ellerbee and Bern Nix , bassist Jamaaladeen Tacuma , and drummers Ronald Shannon Jackson and Denardo Coleman , Ornette Coleman 's son . Tacuma was still in high school when Coleman enlisted him , and first recorded with Prime Time in 1975 for the album Body Meta , which was released in 1978 . Tacuma had played in an ensemble for jazz <unk> Charles <unk> , but <unk> dismissed him as he felt audiences gave excessive attention to his playing . Coleman found Tacuma 's playing ideal for harmolodics and encouraged him not to change . Although Coleman 's theory initially challenged his knowledge and perception of music , Tacuma came to like the unconventional role each band member was given as a soloist and <unk> : " When we read Ornette 's music we have his notes , but we listen for his phrases and phrase the way he wants to . I can take the same melody , then , and phrase it like I want to , and those notes will determine the <unk> , the rhythm , the harmony – all of that . " 
- In March 1979 , Coleman went to RCA Records ' New York studio to produce an album with Prime Time by direct @-@ to @-@ disc recording . They had mechanical problems with the studio equipment and the recording was rejected . The failed session was a project under <unk> Text , Coleman 's music publishing company . He wanted to set up his own record company with the same name , and chose his old friend <unk> Mwanga as his manager . In April , Mwanga arranged another session at CBS Studios in New York City , and Coleman recorded Of Human Feelings there on April 25 ; the session was originally titled Fashion Faces . Jackson did not record with the band and Calvin Weston was hired in his place to play simultaneously with Denardo Coleman . They recorded all the album 's songs on the first take without any equipment problems . The album was recorded with a Sony <unk> @-@ 1600 two @-@ track digital recorder , a rare item at the time . According to journalist Howard Mandel , the passages played by the band sounded neither very soft or loud on the album , because it had been mixed with a middle @-@ frequency range and compressed dynamics . Because of the equipment used , Coleman did not <unk> the album with added effects and avoided <unk> , multi @-@ tracking , and remixing . According to him , Of Human Feelings was the first jazz album to be digitally recorded in the United States . 
- 
- = = Composition = = 
- 
- According to The <unk> Oxford Dictionary of Music ( 2004 ) , Of Human Feelings features jazz @-@ funk , a type of music that originated around 1970 and was characterized by intricate rhythmic patterns , a recurrent bass line , and Latin rhythmic elements . Lloyd Sachs of the Chicago Sun @-@ Times wrote that , although Coleman was not viewed as a jazz fusion artist , the album can be described as such because of its combination of free jazz and funk . Glenn Kenny disagreed and felt its <unk> style had more in common with the no wave genre and the artists of New York City 's downtown music scene such as John <unk> . Jazz writer Stuart Nicholson viewed it as the culmination of Coleman 's musical principles that dated back to his free jazz music in 1960 , but <unk> with a funk @-@ oriented <unk> . According to jazz critic Barry McRae , " it was as if Coleman was translating the concept of the famous double quartet " from his 1961 album Free Jazz to what was required to perform jazz @-@ funk . 
- Coleman incorporated traditional structures and rhythms , and other elements from the rhythm and blues music he had played early his career . According to Mandel , the album 's simple , brisk music was more comparable to a coherent R & B band than jazz fusion . Although Coleman still performed the melodies on a song , he employed two guitarists for contrast to make each pair of guitarist and drummer responsible for either the rhythm or melody . Ellerbee provided accented linear counterpoint and Nix played variations of the song 's melody , while Denardo Coleman and Weston played both <unk> and <unk> . Tacuma and Ornette Coleman 's instrumental responses were played as the foreground to the less prominent guitars . McRae remarked that Coleman and Prime Time exchanged " directional hints " throughout the songs , as one player changed key and the others <unk> accordingly . The band made no attempt to <unk> their radically different parts . 
- Of Human Feelings features shorter and more distinct compositions than Dancing in Your Head . " Sleep Talk " , " Air Ship " , and " Times Square " were originally performed by Coleman during his concerts in 1978 under the names " Dream Talking " , " Meta " , and " Writing in the Streets " , respectively . " What Is the Name of That Song ? " was titled as a <unk> reference to two of his older compositions , " Love Eyes " and " Forgotten Songs " ( also known as " Holiday for Heroes " ) , whose themes were played concurrently and <unk> by Prime Time . The theme from " Forgotten Songs " , originally from Coleman 's 1972 album Skies of America , was used as a refrain . 
- On songs such as " Jump Street " and " Love Words " , Ellerbee incorporated distortion into his guitar playing , which gave the songs a thicker texture . " Jump Street " is a blues piece , " Air Ship " comprises a six @-@ bar riff , and the <unk> " Times Square " has futuristic dance themes . " Love Words " heavily uses <unk> , a central feature of harmolodics , and <unk> Coleman 's extended solo against a dense , <unk> complex backdrop . Nicholson observed West African rhythms and collective improvisation rooted in New Orleans jazz on " Love Words " , and suggested that " Sleep Talk " was derived from the opening <unk> solo in Igor Stravinsky 's 1913 orchestral work The <unk> of Spring . 
- 
- = = Release and reception = = 
- 
- A few weeks after Of Human Feelings was recorded , Mwanga went to Japan to negotiate a deal with Trio Records to have the album released on <unk> Text . Trio , who had previously released a compilation of Coleman 's 1966 to 1971 live performances in Paris , prepared to press the album once Mwanga provided the label with the record <unk> . Coleman was also set to perform his song " Skies of America " with the <unk> Symphony Orchestra , but cancelled both deals upon Mwanga 's return from Japan . Mwanga immediately quit after less than four months as Coleman 's manager . In 1981 , Coleman hired Stan and Sid Bernstein as his managers , who sold the album 's recording tapes to Island Records . He signed with the record label that year , and Of Human Feelings was released in 1982 on Island 's subsidiary jazz label Antilles Records . Billboard magazine published a front @-@ page story at the time about its distinction as both the first digital album recorded in New York City and the first digital jazz album recorded by an American label . 
- According to jazz writer Francis Davis , " a modest commercial breakthrough seemed imminent " for Coleman , who appeared to be regaining his celebrity . German <unk> Peter <unk> Wilson said the album may have been the most <unk> and commercial @-@ sounding of his career at that point . The album 's clean mix and relatively short tracks were interpreted as an attempt for radio airplay by Mandel , who described its production as " the surface consistency that would put it in the pop sphere " . Of Human Feelings had no success on the American pop charts , only charting on the Top Jazz Albums , where it spent 26 weeks and peaked at number 15 . Because the record offered a middle ground between funk and jazz , McRae argued that it consequently appealed to neither demographic of listeners . Sound & Vision critic Brent <unk> speculated that it was overlooked because it had electric instruments , rock and funk drumming , and did not conform to what he felt was the <unk> image of jazz that many of the genre 's fans preferred . The album later went out of print . 
- Of Human Feelings received considerable acclaim from contemporary critics . In a review for <unk> , Gary <unk> hailed it as another landmark album from Coleman and his most accomplished work of harmolodics , partly because of compositions which he found clearly expressed and occasionally <unk> . In his opinion , the discordant keys radically transmuted conventional <unk> and would be the most challenging part for listeners , whom he said should concentrate on Coleman 's playing and " let the <unk> resolve itself around his center " . <unk> <unk> from the Detroit Metro Times said Coleman 's <unk> approach displayed expressive <unk> rather than superficial technical <unk> while calling the record " a multi @-@ tonal mosaic of great power , humor , color , wit , <unk> , compassion and <unk> " . He found the songs inspirational , <unk> , and encompassing developments in African @-@ American music over the previous century . Robert Christgau found the music heartfelt and sophisticated in its exchange of rhythms and simple pieces of melody , writing in The Village Voice , " the way the players break into <unk> of song only to <unk> back into the <unk> is <unk> democracy at its most practical and <unk> . " 
- <unk> critics in jazz complained about the music 's incorporation of <unk> beats and electric guitar . In <unk> Review , Chris <unk> deemed the combination of saxophone and bizarre funk occasionally captivating but ultimately <unk> . Dan Sullivan of the Los Angeles Times argued that the album 's supporters in " hip rock circles " had overlooked flaws ; he felt Tacuma and Coleman 's playing sounded like a unique " beacon of clarity " amid an incessant background . Leonard Feather wrote in the Toledo <unk> deemed the music <unk> ambiguous , potentially controversial , and difficult to assess but interesting enough to warrant a listen . At the end of 1982 , Billboard editor Peter <unk> named Of Human Feelings the year 's best album , calling it a prime example of fusing free jazz with modern funk . In year @-@ end lists for The Boston Phoenix , James Hunter and Howard Hampton ranked the album number one and number four , respectively . It was voted 13th best in the <unk> & <unk> , an annual poll of American critics nationwide , published in The Village Voice . Christgau , the poll 's supervisor , ranked it number one in an accompanying list , and in 1990 he named it the second @-@ best album of the 1980s . 
- Coleman received $ 25 @,@ 000 for the publishing rights to Of Human Feelings but said his managers sold it for less than the recording costs and that he did not receive any of its royalties . According to Stan Bernstein , Coleman had financial expectations that were " unrealistic in this business unless you 're Michael Jackson " . Antilles label executive Ron Goldstein felt the $ 25 @,@ 000 Coleman received was neither a great nor a fair amount for someone in jazz . After he had gone over budget to record a follow @-@ up album , Island did not release it nor pick up their option on him , and in 1983 , he left the Bernstein Agency . He chose Denardo Coleman to manage his career while overcoming his <unk> of public performance , which had been rooted in his distrust of doing business with a predominantly White music industry . According to Nicholson , " the man once accused of standing on the throat of jazz was welcomed back to the touring circuits with both curiosity and affection " during the 1980s . Coleman did not record another album for six years and instead performed internationally with Prime Time . 
- In a 1986 article for The New York Times on Coleman 's work with Prime Time , Robert Palmer said Of Human Feelings was still innovative and radical by the standards of other music in 1982 , three years after it was recorded . Because writers and musicians had heard its test pressing in 1979 , the album 's mix of jazz improvisation and gritty , punk and funk @-@ derived energy sounded " <unk> " when it was released , Palmer explained . " The album is clearly the progenitor of much that has sounded radically new in the ongoing fusion of punk rock , black dance rhythms , and free jazz . " AllMusic critic Scott <unk> believed that although Coleman 's compositions never achieved popularity , they succeeded within the context of an album that showcased his distinctive saxophone style , which was high @-@ brow yet catchy . Joshua Klein from The A.V. Club recommended Of Human Feelings as the best album for new listeners of Coleman 's harmolodics @-@ based music , while Chicago Tribune rock critic Greg <unk> included it in his guide for novice jazz listeners ; he named it one of the few albums that helped him both become a better listener of rock music and learn how to enjoy jazz . In 2008 , New York magazine 's Martin Johnson included it in his list of canonical albums from what he felt had been New York 's <unk> yet vital jazz scene in the previous 40 years ; Of Human Feelings <unk> what he described as a spirit of <unk> with elements of funk , Latin , and African music , all of which were <unk> by music that retained a jazz identity . 
- 
- = = Track listing = = 
- 
- All compositions by Ornette Coleman . 
- Side one 
- " Sleep Talk " – 3 : 34 
- " Jump Street " – 4 : 24 
- " Him and Her " – 4 : 20 
- " Air Ship " – 6 : 11 
- Side two 
- " What Is the Name of That Song ? " – 3 : 58 
- " Job <unk> " – 4 : 57 
- " Love Words " – 2 : 54 
- " Times Square " – 6 : 03 
- 
- = = Personnel = = 
- 
- Credits are adapted from the album 's liner notes . 
- 
- = = = Musicians = = = 
- 
- Denardo Coleman – drums 
- Ornette Coleman – alto saxophone , production 
- Charlie Ellerbee – guitar 
- Bern Nix – guitar 
- Jamaaladeen Tacuma – bass guitar 
- Calvin Weston – drums 
- 
- = = = Additional personnel = = = 
- 
- Susan Bernstein – cover painting 
- Peter <unk> – cover design 
- Joe <unk> – mastering 
- Ron Saint Germain – engineering 
- Ron Goldstein – executive direction 
- Harold <unk> – second engineering 
- Steven Mark <unk> – photography 
- Ken Robertson – tape operation 
- 
- 
- = Dangerously in Love Tour = 
- 
- The Dangerously in Love Tour was the debut concert tour by American recording artist Beyoncé . Although the tour was intended to showcase songs from her debut solo album , Dangerously in Love , ( 2003 ) the set list also contained a special segment dedicated to Beyoncé 's girl group Destiny 's Child and featured songs from her 2003 film The Fighting Temptations . The stage was simple and featured a large LED screen in the back that displayed video images of Beyoncé and her dancers , as well as some images from her music videos and some <unk> images . The tour was reviewed negatively by Dave Simpson of The Guardian who graded it with two stars out of five . The Dangerously in Love Tour only reached Europe and Beyoncé 's performance at the Wembley Arena in London , was filmed and later released on the CD / DVD Live at Wembley ( 2004 ) . 
- 
- = = Background and development = = 
- 
- The Dangerously In Love Tour was the debut solo concert tour by American recording artist Beyoncé . The tour was intended to showcase songs from Beyoncé ' debut solo album , Dangerously in Love released in 2003 . However , the set list also contained a special segment of her show dedicated to her girl group Destiny 's Child and songs from Beyoncé ' 2003 film The Fighting Temptations ( " Fever " and " <unk> " ) . The stage was simple and featured a large LED screen in the back that moved up and down throughout the entire show and displayed video images of Beyoncé and her dancers , as well as some images from her music videos and some <unk> images with special effects . The show also featured a small staircase and platforms on both side of the stairs for her band . Beyoncé later toured alongside Missy Elliott and Alicia Keys as ensemble for the Verizon Ladies First Tour ( 2004 ) in North America . 
- 
- = = Synopsis and reception = = 
- 
- Dave Simpson of The Guardian described the opening of the show during his review : " Some while after Beyoncé is due on stage , a voice announces that the support act won 't be appearing and that Beyoncé will be with us ' in a moment ' . Like everything else – hits , boots , hair and sponsorship deals – moments are very big in Beyoncé world . An age later , cheers erupt for the raising of a curtain which revealed , er , a <unk> <unk> with a drum kit . An hour later , the <unk> music is getting gradually louder to drown boos and the cries of small children whose parents are moaning it 's getting past their <unk> . " The show opens with " Baby Boy " which Beyoncé sang while being lowered onto the stage upside down . A highlight for many fans was her performance of " Dangerously in Love 2 " . During the tour , a special 8 @-@ minutes rendition of the song was performed . 
- Simpson of The Guardian reviewed the opening show of the tour negatively , <unk> it with two out of five stars . He was negative about Beyoncé ' clothing during the show , saying : " The delays may well be down to Beyoncé 's wardrobe , which could trouble <unk> Marcos . There are skimpy skirts , tails ( for a note perfect if pointless version of Peggy Lee 's Fever ) and a general theme of low material , high <unk> . But often , the main <unk> is on Beyoncé 's outfit . " He also added that " The dancers ' ' naked suits ' make the former church girl a <unk> rival to <unk> [ <unk> ] . But there 's an <unk> section where they pretend to be <unk> , and when Beyoncé disappears for long periods it feels like an expensive night with <unk> and Co . " He concluded his review by saying , 
- " <unk> , the armies of industry professionals that put Beyoncé together aren 't sure of her core audience . A vague Saturday night TV , family entertainment feel gradually gives way to a more intriguing cross between <unk> <unk> showbiz and thumping R & B. However , a <unk> tape <unk> Crazy In Love and a belting Work It Out suggest Beyoncé is best sticking to her roots . <unk> , if <unk> , she puts the carnage down to her tour manager falling off stage , but at least she 's grasped one showbiz <unk> : the show must go on . " 
- 
- = = <unk> and recordings = = 
- 
- On November 10 , 2003 , Beyoncé performed at the Wembley Arena in London ; this was later put on a DVD , titled Live at Wembley , which was released in April 2004 . It was accompanied by a CD comprising three previously @-@ unreleased studio recorded songs and one remix each of " Crazy in Love " , " Baby Boy " and " Naughty Girl " . Behind @-@ the @-@ scenes footage can be also seen on the DVD . The album debuted at number seventeen on the Billboard 200 , selling 45 @,@ 000 copies in its first week . The DVD has been certified double platinum by the Recording Industry Association of America for shipping 200 @,@ 000 copies . According to Nielsen SoundScan , it had sold 264 @,@ 000 copies in the US by October 2007 , while as at October 6 , 2010 , it had sold 197 @,@ 000 digital downloads . In an interview with The New York Times in 2007 , American singer Miranda Lambert revealed that Live at Wembley inspired her to " take little bits from that [ Beyoncé ' performance ] " for her live shows . 
- 
- = = Set list = = 
- 
- " Baby Boy " 
- " Naughty Girl " 
- " Fever " 
- " Hip Hop Star " 
- " Yes " 
- " Work It Out " 
- " Gift from <unk> " 
- " Be with You " 
- " <unk> " 
- Destiny 's Child Medley : 
- " <unk> a Boo " 
- " No , No , No Part 2 " 
- " Bootylicious " 
- " <unk> ' , <unk> ' " 
- " Say My Name " 
- " Independent Women Part I " 
- " ' 03 Bonnie & Clyde " 
- " Survivor " 
- " Me , Myself and I " 
- " <unk> " 
- " Dangerously in Love 2 " 
- " Crazy in Love " 
- 
- = = Tour dates = = 
- 
- 
- 
- = Zhou Tong ( archer ) = 
- 
- Zhou ( or <unk> ) Tong ( Chinese : <unk> and <unk> ; pinyin : <unk> <unk> ) ( died late 1121 CE ) was the archery teacher and second military arts tutor of famous Song Dynasty general Yue Fei . Originally a local hero from Henan , he was hired to continue Yue Fei 's military training in archery after the boy had rapidly mastered <unk> under his first teacher . In addition to the future general , Zhou accepted other children as archery pupils . During his tutelage , Zhou taught the children all of his skills and even rewarded Yue with his two favorite bows because he was his best pupil . After Zhou 's death , Yue would regularly visit his tomb twice a month and perform unorthodox sacrifices that far surpassed that done for even beloved tutors . Yue later taught what he had learned from Zhou to his soldiers and they were successful in battle . 
- With the publishing of Yue Fei 's 17th folklore biography , The Story of Yue Fei ( 1684 ) , a new distinct fictional Zhou Tong emerged , which differed greatly from his historical persona . Not only was he now from Shaanxi ; but he was Yue 's adopted father , a learned scholar with knowledge of the eighteen weapons of war , and his personal name was spelled with a different , yet related , Chinese character . The novel 's author portrayed him as an elderly widower and military arts tutor who counted Lin Chong and Lu Junyi , two of the fictional 108 outlaws on which the Water Margin is based , among his former pupils . A later republican era folktale by noted Yangzhou storyteller Wang Shaotang not only adds Wu Song to this list , but represents Zhou as a knight @-@ errant with supreme <unk> . The tale also gives him the nickname " Iron Arm " , which he shares with the executioner @-@ turned @-@ outlaw Cai Fu , and makes the outlaw Lu Zhishen his sworn brother . Because of his association with the outlaws , he is often confused with the similarly named outlaw Zhou Tong . 
- Various <unk> novels and folk legends have endowed Zhou with different kinds of martial and supernatural skills . These range from mastery of the bow , double <unk> , and Chinese spear to that of <unk> hard qigong and even x @-@ ray vision . Practitioners of Eagle Claw , Chuojiao and Xingyi commonly include him within their lineage history because of his association with Yue Fei , the supposed progenitor of these styles . He is also linked to Northern <unk> Mantis boxing via Lin Chong and Yan Qing . Wang Shaotang 's folktale even represents him as a master of <unk> Eight Immortals boxing . However , the oldest historical record that mentions his name only says he taught archery to Yue Fei . Nothing is ever said about him knowing or teaching a specific style of Chinese martial arts . 
- Zhou has appeared in various forms of media such as novels , comic books , and movies . His rare 20th century biography , Iron Arm , Golden Sabre , serves as a sequel to The Story of Yue Fei because it details his adventures decades prior to taking Yue as his pupil . This was later adapted into a ten volume <unk> comic book . He also appears in a novel concerning one of his fictional martial arts brothers . He was portrayed by three different actors in a string of black and white Yue Fei films produced in the 1940s and 1960s , one of which featured a ten @-@ year @-@ old <unk> <unk> as the lead . <unk> martial arts actor Yu <unk> , who played the sword @-@ wielding antagonist in Jet Li 's Shaolin Temple , stated in a 2005 interview that he has always wanted to portray Zhou in a film . 
- 
- = = History = = 
- 
- 
- = = = Mention in Yue family memoirs = = = 
- 
- On his deathbed , Yue Fei 's third son Yue Lin ( <unk> , born 1130 ) asked his own son , the poet and historian Yue Ke ( <unk> , <unk> – post @-@ <unk> ) , to complete Yue Fei 's memoirs . This two @-@ part memoir was completed in 1203 , some sixty years after the general 's political execution , but was not published until 1234 . It was later abridged in 1345 and published in the Yuan Dynasty 's dynastic chronology History of the Song Dynasty under the title Yue Fei Biography ( chapter 365 , biography 124 ) . Zhou 's mention in Yue Ke 's memoir was only briefly summarized in the Yuan rewrite . It reads , " He [ Yue Fei ] learned archery from Zhou Tong . He learned everything and could fire with his left and right hands . After Tong 's death , he would offer sacrifices at his tomb " . 
- Western Washington University history professor Edward Kaplan explains Zhou was a " local <unk> " ( <unk> - " heroic ( person ) " ) . He comments Hao can also mean " a ' knight errant ' in poetic translation , or in <unk> terms a professional <unk> and bodyguard . ' " This means Zhou was a local hero from Tangyin County , <unk> prefecture , Henan province ( the same area as Yue Fei ) . 
- Historical and scholarly sources spell his personal name as <unk> ( Tong ) , meaning " same or similar " . This differs from the spelling present in fictional sources , which will be further explained below . So , " <unk> " represents the historical archer . 
- 
- = = = <unk> = = = 
- 
- Despite being literate , giving him a chance to become a scholar , young Yue Fei chose the military path because there had never been any tradition of full @-@ fledged Confucian civil service in his family history . He would stay up all night reading military strategy books and idolized such great historical heroes as Guan Yu . However , the Yue family was much too poor to afford military lessons for their son , so , Yao <unk> , the boy 's maternal grandfather , hired Chen <unk> ( <unk> ) to teach the eleven @-@ year @-@ old how to wield the Chinese spear . Yao was very surprised when his grandson quickly mastered the spear by the age of thirteen . Zhou was then brought in to continue Yue 's military training in archery . Dr. Kaplan describes Zhou as the " most important " of the two teachers . 
- A section of the Jin <unk> Xu <unk> , the second part of Yue Ke 's original published memoir , describes one of Zhou 's archery lessons and reveals that he took other children as his pupils : 
- " One day , [ Chou ] T 'ung gathered his pupils for an archery session and to display his ability put three arrows in succession into the center of the target . Pointing to the target to show grandfather [ Yue Fei ] , he said : ' After you can perform like this , you can say you are an archer ' . <unk> , thanked him and asked to be allowed to try . He drew his bow , let fly his arrow and struck the end of T 'ung 's arrow . He shot again and again hit the mark . T 'ung was greatly amazed and subsequently presented to grandfather his two favorite bows . Thereafter grandfather practiced still more [ until ] he was able to shoot to the left and right , accurately letting fly the arrow as he moved . When he became a general he taught this to his officers and men so that his whole army became skilled at shooting to the left and right and frequently used this technique to crush the enemy 's spirit " . 
- The last sentence of the passage is similar to one from the Republican era Biography of Song Yue , Prince of E. But instead of teaching them his own technique , it states Yue taught what he had learned from Zhou to his soldiers who were victorious in battle . 
- 
- = = = Death = = = 
- 
- Zhou continued to teach the children until his death , prior to Yue 's legal adulthood . Following his passing , Yue became extremely depressed since Zhou had been the greatest influence on his early life . Zhou 's student would regularly visit his tomb on the first and fifteenth of every month with sacrifices of meat and wine and would shoot three arrows in succession with one of the two bows his tutor had presented him with ( it is never mentioned whether any of Zhou 's other archery pupils came to visit his tomb ) . Dr. Kaplan comments this continuous unusual display of mourning " went far beyond the ceremonial appropriate for even a highly respected teacher " . Noted <unk> <unk> Wilhelm claims even though the display of grief was genuine , it was also a way of <unk> the stories of his heroic idols and " [ establishing himself ] in the public eye " . Yue 's father later followed him secretly to Zhou 's tomb after striking him during an argument over his melancholic behavior . There , he saw him perform the unorthodox <unk> involving the meat , wine , and three arrows . When he finally confronted him , the son confessed that " his gratitude for Chou 's instruction could not be <unk> simply by the usual first and middle of the month ceremonies and so he ... shot off the three arrows to symbolize that Chou had been the source of his inspiration as an archer " . Dr. Kaplan 's states this happened just prior to Yue 's entrance into the army and that the entire event served as a symbol for Yue 's " entrance into responsible <unk> " . 
- The <unk> of Yue <unk> lists the events at Zhou 's tomb happening in 1121 when Yue was nineteen , but Yue would have been eighteen in that year since he was born on " the fifteenth day of the second month of 1103 " . The author of the original source material was using <unk> age calculation , in which a child is already considered one year old at birth . Since Yue joined the military shortly after Zhou 's death , a relative time frame can be given for when he died . During the early months of 1122 , the Song empire mobilized its armed forces to assist the Jurchen in <unk> their common enemy , the Liao Dynasty . Therefore , it appears that Zhou died in late 1121 , before the call to arms was issued . 
- 
- = = Fiction = = 
- 
- Zhou Tong 's fictional life story can be <unk> together from two sources : The Story of Yue Fei and Iron Arm , Golden Sabre . The Story of Yue Fei is a fictionalized retelling of Yue Fei 's young life , military exploits , and execution . It was written by a native of <unk> named Qian Cai ( <unk> ) , who lived sometime between the <unk> of the <unk> and <unk> emperors in the Qing dynasty . The preface dates the book 's publication to 1684 . It was deemed a threat by the Qing emperors and banned during the <unk> era . In the novel , Zhou is portrayed as an elderly widower and Yue 's only military arts tutor . The General 's historical spear master Chen <unk> is never mentioned . Zhou teaches Yue Fei and his sworn brothers military and literary arts from chapters two through five , before his death . 
- In the writing of his novel , Qian Cai used a different character when spelling Zhou 's given name . Instead of the original character meaning " similar " , it was changed to <unk> , meaning " rude or <unk> " . So , " <unk> " represents Zhou 's distinct fictional persona . This spelling has even been carried over into modern day martial arts manuals . 
- Iron Arm , Golden Sabre was written by Wang <unk> <unk> and <unk> <unk> Long and published in 1986 . This novel , which serves as Zhou 's own fictional biography , is a prequel to The Story of Yue Fei because it details his adventures decades prior to taking Yue Fei as his student . It follows his life as a young martial arts instructor in the Song army 's Imperial guard , his struggles against the <unk> and Liao <unk> barbarian tribes and his tutelage of Water Margin outlaws . The last few chapters incorporate the storyline from the four chapters that he appears in The Story of Yue Fei . This was later adapted into a ten volume <unk> @-@ style comic book called The Legend of Zhou Tong in 1987 . 
- 
- = = = Early life and adulthood = = = 
- 
- Zhou is born in Shaanxi and trains in the martial arts from a young age . He is taken as one of the pupils of Shaolin master Tan <unk> ( <unk> ) and , learning the true essence of Shaolin <unk> , becomes proficient in things both literary and martial . Tan 's other students include the future generals Jin Tai ( <unk> ) and Zong Ze ( <unk> ) and the future Water Margin outlaws Sun Li and <unk> <unk> . As a young man , Zhou catches the attention of Judge <unk> <unk> and enlists in the military as an officer . His superiors take note of his great skill after he helps his classmate General Jin battle Liao <unk> in northern China and install him as a teacher in the Capital Imperial Martial Arts School . The school has three teaching positions named in order of prestige : " Heaven , " " Earth , " and " Man . " Since he has the greatest skill , he occupies the Heaven position . He uses this post and his friendship with General Zong to get their classmate Sun Li installed as the Superintendent of Forces of <unk> . Sun later becomes an outlaw under <unk> <unk> and helps defeat the evil Zhu Family , who learn military arts from his classmate <unk> <unk> . 
- As he grows older , Zhou becomes dissatisfied with politics because the Imperial court chooses to appease the northern barbarian tribes instead of standing against them . He then devotes himself <unk> to his martial arts practice and creates several official and authoritative techniques including the " five step , thirteen lance piercing kick " , which is a development of Shaolin <unk> boxing , and the " Zhou Tong <unk> . " He makes a <unk> effort to transmit his martial efforts while teaching at the Imperial Martial Arts School and formally accepts two disciples : " Jade Unicorn " Lu Junyi and " Panther head " Lin Chong . Lu Junyi is a millionaire with vast land holdings and does not hold office , but Lin Chong <unk> Zhou 's position after his retirement , and continues to serve as the lead instructor for the 800 @,@ 000 members of the Song army 's Imperial Guard . 
- During this time , Zhou Tong also has an additional disciple named Wu Song . Wu Song becomes famous for killing a man @-@ eating tiger with his bare hands and is appointed as a constable in his native Shandong . The county magistrate Sun <unk> later sends Wu on a mission to Kaifeng with precious tiger bone <unk> in order to <unk> favor with influential personages . During his stay in the capital , he makes the acquaintance of Zhou . Zhou finds Wu to be a man of great strength , but feels that he lacks refinement in his martial technique and , therefore , offers guidance for Wu 's training . Unfortunately , these two men only interact for a brief two months before Wu has to return home , never to see Zhou again . 
- Following his retirement , Zhou serves for a time as an advisor to General Liu <unk> ( <unk> ) , whose troops are garrisoned in Henan Province . But Zhou later becomes an outlaw himself after he aids the heroes of the Water Margin and is forced to flee from government forces . Meanwhile , he learns his elderly classmate Jin Tai is close to death and <unk> to Shaolin ( where the general had become a Buddhist monk after the murder of his family ) to pay his last respects . As the oldest of Tan 's pupils , Jin orders Zhou to find a talented youth to pass on all of his martial arts knowledge to . However , this reunion is cut short when the troops track him to Shaolin . He flees to Wine Spring mountain and lives in hiding for sometime before being invited by his old friend Wang Ming ( <unk> ) to become the precept of the Wang family in Unicorn Village . 
- 
- = = = Old age and death = = = 
- 
- One day , Zhou surprises the children with a written exam and leaves the classroom to speak with a visitor . Wang 's son , Wang Gui ( <unk> ) , tricks their maid 's son , Yue Fei , into completing their assignment while they go outside to play . After easily finishing the task at hand , Yue writes a heroic poem on a whitewashed wall and signs it with his name . The children then burst into the classroom upon learning of Zhou 's forthcoming return and tell Yue to escape in order to avoid apprehension . The old teacher eventually discovers the <unk> and , after <unk> at Yue 's impromptu ballad , asks Yue to fetch his mother , Lady Yao ( <unk> ) , for an important meeting . With the entire Wang household assembled in the main hall , Zhou asks the Lady for her blessing to have the boy as his adopted son and student . She <unk> and Yue takes his seat amongst Zhou 's students the following morning . Because Zhou knows Yue is poor , he commands the four students to become sworn brothers . Zhou also begins to teach Yue all of the eighteen weapons of war . 
- Six years later , Zhou takes the group to visit his old friend , the abbot of a small Buddhist temple on the " Hill of Dripping Water " . Thirteen @-@ year @-@ old Yue wanders behind the temple and finds the " Cave of Dripping Water " , in which lives a magical snake . When it <unk> at Yue , he <unk> to one side and pulls on its tail with his supernatural strength , causing it to turn into an 18 @-@ foot @-@ long ( 5 @.@ 5 m ) , gold @-@ plated spear named the " Supernatural <unk> of Dripping Water " . When they return home , Zhou begins to drill all of his students in the military arts — eighteen weapons of war , archery , and hand @-@ to @-@ hand combat . After three years of practice , Zhou enters them into a preliminary military examination in Tangyin in which sixteen @-@ year @-@ old Yue wins first place by shooting a succession of nine arrows through the <unk> of a target two hundred and forty paces away . After his display of <unk> , Yue is asked to marry the daughter of Li Chun ( <unk> ) , an old friend of Zhou 's and the county magistrate who presided over the military exams . Father and son then return home to their village . 
- <unk> Li writes out a marriage certificate and dispatches a messenger to deliver the document to Yue Fei in Unicorn Village . Zhou and Yue set out at dawn and travel back to Tangyin to thank the <unk> for his generosity and kindness . There , Li prepares a great feast for them , but when food is brought out for any servants that might have accompanied them , Zhou comments that they had come on foot without help . Li decides to let Yue pick from any one of his thousands of horses because every able military man needs a strong <unk> . After finishing their feast , Zhou and Yue thank Li once again and leave Tangyin to return home . During their journey , Zhou recommends that Yue run the horse to test its speed . Yue spurs the horse on leaving Zhou in pursuit . When they reach the village gate , the two <unk> and Zhou returns to his study where he feels hot from the race and removes his outer garments to fan himself . But he soon falls ill and stays <unk> for seven days . Then the book describes his death and burial : 
- " ... his <unk> <unk> up and he died . This was on the fourteenth day of the ninth month in the seventeenth year of the Reign of Xuan He , and his age was seventy @-@ nine ... Buddhist and Taoist <unk> were asked to come and chant prayers , for seven times seven , namely forty @-@ nine days . Then the body was taken up to be buried beside the Hill of Dripping Water " . 
- Yue lives in a shed by his grave through the winter and in the second lunar month of the following year , his martial brothers come and pull the building down , forcing him to return home and take care of his mother . 
- The quoted death date is not only unreliable because the book is fiction , but also because the Xuan He reign era of Emperor Huizong lasted only seven years ( 1119 – 1125 ) and not seventeen . Although The Story of Yue Fei states Zhou died shortly before Yue took a wife , he historically died after Yue married . It is likely that the original author invented this fictional date . 
- 
- = = = Family = = = 
- 
- According to The Story of Yue Fei , Zhou was married with a son . But Zhou comments that his " old wife " died and his " small son " was killed in battle against the <unk> after leaving with the outlaw Lu Junyi to fight in the war . In The Legend of Zhou Tong , his wife is named Meng <unk> ( <unk> ) and his son is named Zhou <unk> ( <unk> ) . He defeats Meng in a <unk> <unk> martial arts contest and wins her as his wife . But she is shortly thereafter kidnapped by the wicked monks of the Stone Buddha temple . Both Zhou and Meng eventually defeat the monks with their combined martial skills and later marry at the <unk> Pass in Hubei province . 
- Zhou <unk> first appears as a fierce , impulsive young man who rides his horse into the thick of enemy <unk> wielding a long spear . He later dies in battle against the Liao Dynasty . After his son 's death , Zhou retreats to the <unk> Temple for a long morning period . He later takes seven @-@ year @-@ old Yue Fei as his adopted son and sole heir years after the boy 's father drowns in a great flood : 
- " I see that he [ Yue Fei ] is clever and handsome and I , an old man , wish to have him as my adopted son ... He need change neither his name nor his surname . I only want him to call me father temporarily so that I can faithfully transmit all the skills I have learned in my life to a single person . Later , when I die , all he has to do is to bury my old bones in the earth and not allow them to be exposed , and that is all " . 
- However , after comparing events from The Story of Yue Fei and an account of Yue 's life from the sixteenth @-@ century work Restoration of the Great Song Dynasty : The Story of King Yue ( <unk> ) , literary critic <unk> Hsia concluded " that his father did not [ historically ] die in the flood and that , although <unk> Fei showed almost <unk> regard for the memory of his teacher Chou T 'ung <unk> ( not <unk> ) , the latter had not been his adopted father " . The Restoration of the Great Song was one of the earliest of four " historical novels " ( fictionalized dynastic <unk> ) written about Yue during the Ming Dynasty , all of which predate The Story of Yue Fei . Despite the addition of popular legends , <unk> <unk> ( fl 1552 ) , the author of the The Story of King Yue , relied heavily on historical <unk> including Zhu Xi 's ( 1130 – 1200 ) <unk> and Details Based on the T 'ung @-@ <unk> , Yue Ke 's family memoir , and the Yuan Dynasty 's official Yue Fei Biography to write his story . So , The Story of Yue Fei was the first full @-@ blown fictionalized novel to introduce the adoption storyline . 
- 
- = = = <unk> and voice = = = 
- 
- He is generally portrayed as a large elderly man with a powerful voice . A modern folktale by noted Yangzhou storyteller Wang Shaotang ( 1889 – 1968 ) , whom folklore researcher <unk> <unk> called " the <unk> master of this [ the 20th ] century " , describes Zhou thus , 
- " He was beyond the age of fifty , he was more than fifty , and standing upright he measured about eight feet . His face had a golden tan , arched <unk> , a pair of bright eyes , a regular head form , a square mouth , a pair of protruding ears , and under his chin there were three locks of beard , a <unk> beard . On his head he wore a sky @-@ blue satin scarf , and he was dressed in a <unk> sky @-@ blue satin coat with a <unk> <unk> , a pair of wide black trousers without <unk> and satin boots with thin <unk> " . 
- Heroes and religious masters with above normal height are a recurring theme in Chinese folklore . For instance , his student Wu Song is said to be over nine feet tall in the same folktale . In The Story of Yue Fei , the General simultaneously duels with two other warriors vying for first place in a military exam ; one is nine feet tall and the other is eight feet tall . A <unk> of the Taoist saint Zhang <unk> states he was over seven feet tall . 
- When Zhou is <unk> in " Yangzhou storytelling " , he speaks in " Square mouth public talk " , which is a manner of speaking reserved for martial heroes , highly respected characters , or , sometimes , lesser characters that pretend to be an important hero . Square mouth public talk is actually a mixture of two forms of dialogue : <unk> and <unk> . <unk> ( square mouth ) is a manner of steady , yet forceful over pronunciation of dialogue that was possibly influenced by Northern Chinese opera . <unk> ( public talk ) is monologue and dialogue that is sometimes used for " imposing heroes " . This mixture of styles means Zhou Tong is treated as a highly regarded hero . 
- In her analysis of Yangzhou storytelling , <unk> noted that the aforementioned tale about Zhou and Wu Song uses different forms of dialogue for both characters . Wu speaks square mouth utilizing standard <unk> without rusheng ( short <unk> syllables ) . On the contrary , Zhou speaks <unk> using the Yangzhou tone system , which does utilize rusheng syllables . Therefore , she believes " square mouth dialogue should at least be divided into two <unk> , namely the Wu Song variant — without rusheng , and the Zhou Tong variant — with rusheng " . 
- 
- = = = Students = = = 
- 
- 
- = = = = Water Margin outlaws = = = = 
- 
- The Water Margin ( c . 1400 ) is a Ming Dynasty military romance about one hundred and eight demons @-@ born @-@ men and women who band together to rebel against the lavish Song Dynasty government . Lin Chong and Lu Junyi , two of these outlaws , are briefly mentioned as being Zhou 's previous students in The Story of Yue Fei . They are not characters within the main plot , though , as both are killed by " villainous officials " prior to Zhou becoming precept of the Wang household . Most importantly , the two were not among his historical students since they are fictional characters . 
- Zhou 's portrayal as their teacher is connected to a recurring element in Chinese fiction where Tang and Song Dynasty heroes train under a " celestial master " , usually a Taoist immortal , prior to their military exploits . <unk> Hsia suggests the mold from which all other similar teachers are cast is <unk> , master of the feuding <unk> Sun <unk> and <unk> Juan , from the Yuan Dynasty tale <unk> Volume of the Spring and Autumn Annals of the Seven Kingdoms ( <unk> ) . Hsia goes on to say that Qian Cai , Yue 's fictional biographer , associated Zhou with the outlaws because " most such teachers [ in the military romance genre ] are <unk> " with at least two students . But in adopting this format , Qian reversed the traditional pattern of " celestial tutelage " since Zhou is written as a human , while his students are <unk> of demons ( Lin and Lu ) and the celestial bird Garuda ( Yue Fei ) . 
- Although Lin and Lu have been connected to Zhou since the early Qing Dynasty , Wu Song did not become associated with him until Wang Shaotang created a 20th @-@ century folktale in which the two meet in Kaifeng . The tale takes place during Wu 's mission to Kaifeng , but before the murder of his older brother Wu <unk> . Zhou teaches Wu the " Rolling Dragon " style of <unk> during the constable 's one @-@ month stay in the capital city . This tale was chapter two of Wang 's " Ten chapters on Wu Song " storytelling repertoire , which was later transcribed and published in the book Wu Sung in 1959 . It eventually carried over into the storyline of Iron Arm , Golden Sabre and , subsequently , The Legend of Zhou Tong . In the latter version , Wu instead learns <unk> <unk> boxing from Zhou during a two month stay in the capital . 
- Wang 's tale portrays Zhou as an aging itinerant <unk> with " a fame <unk> like thunder " throughout the underworld society of <unk> . He is made the sworn brother of the outlaw " <unk> <unk> " Lu Zhishen , a military officer @-@ turned @-@ fighting monk , who is , according to Hsia , first among the most popular protagonists of the Water Margin . He is also given the nickname " Iron Arm " ( <unk> ) , which carried over into the title of his fictional biography Iron Arm , Golden Sabre . While the tale fails to explain the reason for the moniker , it does mention Zhou 's ability to direct his <unk> to any part of his body to make it hard enough to <unk> the " Iron shirt " technique of another martial artist . Furthermore , Zhou shares the same nickname with Cai Fu , an executioner @-@ turned @-@ outlaw known for his ease in wielding a heavy sword . 
- Because of his association with these outlaws , Zhou is often confused with the similarly named outlaw " Little <unk> " Zhou Tong . In the Water Margin , this Zhou Tong is a bandit chief of Mount Peach <unk> whom Lu Zhishen beats for trying to forcibly marry the daughter of the Liu family . He dies later under the sword of Li <unk> , an officer in the rebel army of Fang La . So , the connection between both Zhou 's is based solely on the <unk> transcription of their name . 
- 
- = = = = Yue Fei = = = = 
- 
- The Story of Yue Fei comments Lu Junyi is Zhou 's last student prior to taking on seven @-@ year @-@ old Yue Fei and his three sworn @-@ brothers Wang Gui , Tang Huai ( <unk> ) and Zhang <unk> ( <unk> ) . He teaches them literary and military lessons on even and odd days . The novel says Yue is talented in all manners of " literary and military matters " and even surpasses the skill of Lin and Lu . After Yue <unk> his " Supernatural <unk> of Dripping Water " , Zhou tutors all of his students in the eighteen weapons of war , but each excels with one in particular ; Yue Fei and Tang Huai , the spear ; Zhang <unk> , the Hook @-@ <unk> spear and Wang Gui , the <unk> <unk> . All of them learn the skill of archery in addition . Some of these and other children are mentioned in Yue Ke 's memoir as being his grandfather 's historical childhood friends , but they are never specified as being Zhou 's students . 
- Books written by modern @-@ day martial artists make many claims that are not <unk> with historical documents or current scholarly thought . For instance , <unk> Yang <unk> @-@ Ming says Zhou was a scholar who studied martial arts in the Shaolin Monastery and later took Yue as his student after the young man worked as a tenant farmer for the official @-@ general Han Qi ( <unk> , <unk> – 1075 ) . During this time , he learned all types of military weapons , horseback riding , and hand @-@ to @-@ hand combat . The General later created Xingyi and Eagle Claw boxing from his internal and external training under Zhou . However , history <unk> <unk> <unk> notes that unarmed boxing styles did not develop at Shaolin until the late Ming Dynasty . He also states that Ji family memoirs and Qing Dynasty records suggest Xingyi was created hundreds of years after the death of Yue by a <unk> named Ji <unk> ( fl . 1651 ) . In addition , the appearance of Han Qi in the story is a chronological anachronism since he died nearly 30 years before Yue 's birth . Yue historically worked as a tenant farmer and bodyguard for descendants of Han Qi in 1124 after leaving the military upon the death of his father in late 1122 , but he learned from Zhou well before this time . 
- Eagle Claw <unk> <unk> Shum and Lily <unk> believe " <unk> Tong " ( the Cantonese rendering of his name ) was a monk who brought young Yue to the Shaolin Monastery and taught him a set of hand techniques , which Yue later adapted to create his <unk> <unk> ( Eagle fist ) . Liang <unk> states practitioners of <unk> <unk> <unk> believe Yue trained under Zhou as a child and competed to become China 's top fighter at an early age . Their lineage story dictates Zhou also took Yue to a " Buddhist <unk> " who taught him said qigong style . Northern <unk> Mantis Master <unk> <unk> says Zhou taught Yue the " same school " of martial arts as he did his Water Margin students and that the General was the originator of the praying <unk> technique " Black Tiger <unk> [ sic ] Heart " . Although Martial arts historian Stanley <unk> admits that Yue 's biographies do not mention boxing , he says " he [ Yue ] almost certainly did practice some form of bare handed fighting " to prepare for his weapons training . But he does not suggest who Yue might have learned it from . 
- 
- = = = Martial arts = = = 
- 
- There is insufficient historical evidence to support the claim he knew any skills beyond archery . Contemporary records never once mention Zhou teaching Yue boxing . Despite this , various <unk> novels and folk legends have attributed many different military and supernatural skills to Zhou . These range from mastery of the bow , double swords and Chinese spear to that of <unk> hard qigong , Chuojiao boxing and even X @-@ ray vision . Wang Shaotang 's folktale even represents him as a master of <unk> Eight Immortals boxing . 
- Zhou can also be linked to these combat arts through his historical and folklore students . Practitioners of Eagle Claw , Chuojiao and Xingyi commonly include him within their lineage history because of his association with Yue Fei , the supposed progenitor of these styles . <unk> <unk> believes Zhou taught Lin Chong and Lu Junyi the " same school " of martial arts that was later combined with seventeen other schools to create Mantis fist . This combination of various schools refers to an eighteenth @-@ century martial arts manual that describes the gathering of eighteen masters at the Shaolin Monastery that supposedly took place during the early years of the Song Dynasty . Lin Chong and Yan Qing are listed as two of the eighteen masters invited , which means their skills of Mandarin Duck Leg and ground fighting are treated as two separate schools , instead of one . But he believes Mantis fist was created during the Ming Dynasty , and was therefore influenced by these eighteen schools from the Song . He also says Lu Junyi taught Yan Qing the same martial arts as he learned from Zhou . 
- Very few references are made to the people who supposedly taught martial arts to Zhou . In The Legend of Zhou Tong , he learns as a child from a Shaolin master named Tan <unk> . Practitioners of Chuojiao claim he learned the style from its creator , a wandering Taoist named <unk> Liang . Practitioners of <unk> <unk> <unk> , a style attributed to Yue Fei , believe he studied under Han De , a " <unk> person " from Shaanxi . 
- 
- = = In popular culture = = 
- 
- Zhou has appeared in various kinds of media including novels , comic books , and movies . Apart from The Story of Yue Fei and Iron Arm , Golden Sabre , he appears in a novel based around his older martial arts brother , Jin Tai . A recent graphic novel of The Story of Yue Fei , <unk> all mythological elements from the storyline and presents it in a historical manner . Instead of traveling from Hebei to Hubei to inspect land , Zhou travels from Shaanxi to Kaifeng City in Henan to visit an old friend who had been promoted to General . While en route to the capital city , Zhou takes note of a great famine <unk> the <unk> and even hears stories of some people <unk> to <unk> . However , when he arrives in Kaifeng , he sees the empire is wasting money on the construction of large imperial gardens , the court officials Cai Jing and Wang <unk> have extravagant <unk> , and hears that even <unk> are rich because they are given high government posts . Upon locating his friend , Zhou is distressed to find him in stocks and <unk> and being escorted to the farthest reaches of China by imperial guards . He later learns that the General had accidentally offended some court officials and was sentenced to permanent exile on some <unk> up charges . Apparently having little or no money , Zhou decides to visit Wang Ming in Hubei ( mistakenly called Hebei ) and becomes the estate 's tutor . 
- Another noticeable difference in the storyline takes place when Zhou travels with his teenage disciples to visit his friend the Abbot . Instead of Yue wandering behind the temple to battle the magical snake , he stays with Zhou and the Abbot , while the other disciples go off to explore . Zhou watches as the Abbot tests Yue 's strength by asking him to move an ornate three @-@ hundred pound copper stove dating from the Han Dynasty . The abbot then <unk> a stone floor tile and presents the boy with a large book on military strategy . He goes on to tell Yue how he was once a great soldier who fought in campaigns against the Liao and Western Xia empires , but became a monk after the Song agreed to become a vassal of each state . He later made a name for himself by teaching military skills to youths from the surrounding area . Since he has no heir of his own , the Abbot presents Yue with his own personal spear and instructs him in the proper use of the weapon . Zhou kindly protests the gift at first , but allows Yue to keep it out of friendship . 
- A second graphic novelization drastically changes the storyline involving Zhou . Like the original , Zhou becomes the tutor of the Wang estate , but , when news of his arrival prompts rich families to send their sons to learn from him , he is forced to accept <unk> of these students on a trial basis . He eventually chooses his friends ' sons as his indoor disciples and Yue as his " <unk> " . Years later , he takes his now teenage students not to see the Buddhist abbot , but to teach them military strategy out in the mountain wilderness . Yue senses trouble after his martial brothers separate to explore the forest and rushes off to rescue them , only to be confronted by a monstrous snake . After vanquishing the beast with his sword , Yue discovers a magic glowing spear within a cave and reports back to Zhou . Following their training , Zhou becomes ill from <unk> to the cold mountain air on the return trip home and dies soon after . Instead of just Yue , all of his students live beside his grave for a mourning period of one hundred days before returning home to their families . These events take place three years before Zhou originally died in The Story of Yue Fei . 
- Stories including Zhou have also been used to educate . The Secondary School system of Hong Kong teaches children the value of <unk> by making them read about the close teacher @-@ pupil relationship between Zhou and Yue . A morale tale called " Yue Fei Studies <unk> " in Children 's <unk> , a Chinese magazine tailored for children ages two through seven , demonstrates how great achievements are only made possible via <unk> practice . The story states how young Yue stumbles upon Zhou 's training hall in a neighboring town while gathering fire wood . Yue applies to become a student , but Zhou tells him he must first practice the art of the " far @-@ sighted person " by staring into the morning sun to improve his eyesight . After years of <unk> practice , Yue is able to spot a lone <unk> flying off in the distance and two <unk> on a tree far into the forest . Zhou then officially takes him as his disciple and adopted son . Under his tutelage , Yue is able to master the eighteen weapons of war and to shoot a falling leaf from one @-@ hundred paces away . 
- He is mentioned numerous times in author Robert <unk> 's thriller <unk> ( 2009 ) . Zhou is first featured in chapter eight during a conversation between the main character John " Hutch " Hutchinson , a journalist bent on stopping the <unk> plans of a billionaire <unk> , and his friend 's young son Dillon , an archery enthusiast . When Hutch asks him if he had ever heard of the archery @-@ champion @-@ turned @-@ actor Howard Hill , Dillon replies : " I don 't think so ... You told me about Zhou Tong " . Hutch then says : " Oh , yeah . Zhou Tong was something . <unk> the Song Dynasty to be the best military <unk> in history . But Howard Hill [ was the best ] " . Later in chapter fifty , while Hutch is trailing a killer through an airport , a page goes out over the <unk> system for a " Mr. Zhou Tong " . When the page goes out again , Hutch muses : " Zhou Tong had been a famous archery teacher and military arts tutor in the Song Dynasty . [ Dillon and I ] had long telephone conversations about him , because of Tong 's blending of archery skills and self @-@ discipline . He was an inspiration to [ me ] . Dillon had <unk> that and wanted to known everything about him " . He finally realizes that the page had to have been left by Dillon 's mother Laura to catch his attention . The page is sent to warn him of a trap , but Hutch receives it too late . 
- Screen actors who have portrayed Zhou in film 's from the 1940s and 1960s include <unk> <unk> <unk> , Li Ming , and Jing <unk> Bo . Jing starred alongside a ten @-@ year @-@ old <unk> <unk> , who played young Yue Fei . <unk> martial arts actor Yu <unk> , who played the sword @-@ wielding antagonist in Jet Li 's Shaolin Temple , stated in a 2005 newspaper interview that he never shaved his trademark beard , even at the request of movie producers , because he wanted to portray Zhou in a future film . He went on to say " He is an outstandingly able person from the northern and southern Song <unk> and many Water Margin heroes are his disciples . This person is very important in the martial arts and many people want to portray him in films " . 
- 
- 
- = Romanian Land Forces = 
- 
- The Romanian Land Forces ( Romanian : <unk> <unk> <unk> ) is the army of Romania , and the main component of the Romanian Armed Forces . In recent years , full professionalisation and a major equipment overhaul have transformed the nature of the force . 
- The Romanian Land Forces were founded on 24 November [ <unk> 12 November ] 1859 . They participated in World War I , together with the Russian Empire forces in actions against the Central Powers and , despite initial setbacks , won the decisive battles of <unk> and <unk> . During most of World War II ( until August 23 , 1944 ) Romanian forces supported the Axis powers , fighting against the Soviet Union on the Eastern Front . From August 1944 until the end of the war , Romania fought against Germany under the control of the Soviet Union . When the communists seized power after the Second World War , the army underwent reorganisation and <unk> . 
- Following the Romanian Revolution , due to shortage of funds , many units were disbanded and much equipment was phased out . Likewise , Romanian military capability declined because of a lack of fuel as well as training . However , since the late 1990s , a number of positive changes have come about and the level of combat readiness is growing greatly ; since 1996 , the military budget has grown more than four times - rising from <unk> million dollars to 2 @.@ 8 billion dollars in 2007 . Conscription has been abolished and professionalisation has been completed . 
- 
- = = Mission = = 
- 
- The Land Forces represent the most important component of the Romanian Armed Forces and they are <unk> for execution of various military actions , with terrestrial or <unk> character , in any zone or direction . 
- The Land Forces must , independently or together with other Romanian military branches , conduct operations and defensive or offensive battles , for capture , or destruction of the invading enemy , being part of national , or multinational military structures . 
- A part of the units which compose the current operational structure of the Land Forces , must be able to conduct military operations outside the national territory , together with the international military forces . 
- 
- = = History = = 
- 
- The first attempt to create an independent Romanian army was made by <unk> <unk> during the 1848 <unk> Revolution , and it was based at <unk> ( now part of <unk> <unk> ) . However , <unk> rapidly ordered his troops to <unk> when the Ottoman forces swept into Bucharest to stop the revolution . 
- 
- = = = Romanian War of Independence = = = 
- 
- The current Romanian Land Forces were formed in 1859 , immediately after the unification of <unk> with Moldavia , and were commanded by Alexandru <unk> <unk> , <unk> of Romania until his abdication in 1866 . In 1877 , at the request of <unk> <unk> , Grand Duke of Russia the Romanian army fused with the Russian forces , and led by King Carol I , fought in what was to become the Romanian War of Independence . They participated in the Siege of <unk> and several other battles . The Romanians won the war , but suffered about 27 @,@ 000 casualties . Until World War I , the Romanian army didn 't face any other serious actions . 
- 
- = = = Second Balkan War = = = 
- 
- The Romanian Army entered the Second Balkan War against Bulgaria , allowing Romania to annex Southern <unk> ( also known as the <unk> ) . Although some 330 @,@ 000 troops were mobilised , the Romanians met little resistance in Bulgaria and as such this is not considered a major conflict in Romanian history . This was due to historical claims on land . This area no longer belongs to Romania . 
- 
- = = = World War I = = = 
- 
- On July 6 , 1916 , Romania declared war on Germany and Austria @-@ Hungary , following the initial success of the <unk> Offensive ( a major Russian offensive against the armies of the Central Powers on the Eastern Front ) . The Romanian armies entered Transylvania ( then part of the Austro @-@ Hungarian Empire ) , together with Russian forces . However , German forces under the command of General Erich von <unk> stalled the attack in November , 1916 , and drove back the Romanians . At the same time , Austrian and Turkish troops invaded southern Romania , forcing the country into a two @-@ front war . The Central Powers drove deep into Romania and conquered the south of the country ( <unk> , including Bucharest ) by the end of 1916 . The Romanian forces , led by Marshal Constantin Prezan , retreated into the north @-@ east part of Romania ( Moldavia ) . In the summer of 1917 however , Prezan , aided by the future Marshal , General Ion Antonescu , successfully defended the remaining unoccupied territories against German and Austro @-@ Hungarian forces led by Field Marshal August von <unk> . General Alexandru <unk> led the Second Army in the victories of the Battle of <unk> ( July 22 to August 1 , 1917 ) and the Battle of <unk> ( August 6 to September 8 , 1917 ) . As a result of the Russian Revolution , Romania was left isolated and unable to continue the war , and was forced to sign the Treaty of Bucharest with the Central Powers . Later on , in 1919 , Germany agreed , in the Treaty of Versailles Article 259 , to renounce all the benefits provided to it by the Treaty of Bucharest in 1918 . After the successful offensive on the <unk> front , which put Bulgaria out of the war , Romania re @-@ entered the war on November 10 , 1918 , a day before its end in the West . 
- 
- = = = Hungarian @-@ Romanian War of 1919 = = = 
- 
- After World War I , Transylvania proclaimed union with the Kingdom of Romania . As a result , in April 1919 , the newly established Hungarian Soviet Republic vowed to retake the region by force , and Hungarian troops attacked Romanian formations in Transylvania . The Romanian Army defeated the Hungarians and conquered Budapest in August 1919 . 
- From 1921 to 1939 in Transylvania <unk> General of Army no . 3 had subordinate the 6th and 7th Army Corps . By 1 April 1921 , when he disbanded Forces Western Command , to order 6th Army Corps ( and earlier structures ) have been generals Prezan Constantin , Constantin <unk> <unk> <unk> , <unk> George , Nicholas and Arthur <unk> et al . After 1 April 1921 to 23 March 1939 , C. 6 A. was commissioned by General Nicholas <unk> , <unk> Pop <unk> Alexander , John <unk> , <unk> <unk> <unk> <unk> and Christie <unk> , prominent military leaders , whom Octavian <unk> wrote that " in the interwar period , in Cluj in Transylvania , commanders have made a large @-@ scale opera and unanimous praise . " Three divisions were part of 6th Army Corps : 16th ( <unk> ) , 17th ( Oradea ) and 20th Infantry Divisions ( <unk> @-@ Mureş ) . With rapid and marked worsening international situation , especially in neighboring Romania , on 22 September 1939 , the 4th Army , recently founded , became Army Group Command no . 1 of Transylvania . 
- 
- = = = World War II = = = 
- 
- After General ( later Marshal ) Ion Antonescu took power in September 1940 , Romania signed the <unk> Pact with the Axis Powers and subsequently took part in Operation Barbarossa in 1941 . An expeditionary force invaded the Soviet Union in <unk> and southern Ukraine , alongside the German Wehrmacht . The expeditionary force , ' Army Group Antonescu , ' was composed on 22 June 1941 of the 3rd Army , the 4th Army , the 2nd Army Corps , and the 11th Infantry Division . The 3rd Army comprised the 4th Army Corps ( 6th and 7th Infantry Divisions ) , the Cavalry Corps , the Mountain Corps , two separate artillery battalion , a TA unit , and the Air Force 's 3rd Army Cooperation Command . The 4th Army consisted of the 3rd Army Corps , the 5th Army Corps , the 11th Army Corps ( two fortress brigades ) , and the 4th Army Cooperation Command . The army group @-@ level 2nd Army Corps , under Major General N. <unk> , controlled the 9th and 10th Infantry Divisions and the 7th Cavalry Brigade . Additionally the 1st Armoured Division was formed for service on the Eastern Front . The Army Group 's first offensive , in conjunction with the <unk> Army , Operation <unk> , enabled Romania to retake the territory immediately east of the <unk> , former part of Moldavia . The Romanian Armies saw their first major battles at Odessa and Sevastopol , and in 1942 advanced with other Axis forces deeper into Soviet territory during Operation Blue . 
- The greatest disaster for the Romanian expeditionary force on the Eastern Front came at Stalingrad , where , during the Soviet counter @-@ offensive of November 1942 , the thinly spread forces of the Third Army ( deployed north of Stalingrad ) and of the Fourth Army ( deployed south of Stalingrad ) were attacked by vastly superior Soviet forces and suffered combined losses of some 158 @,@ 000 personnel . 
- During April – May 1944 the Romanian forces led by General Mihai <unk> , together with elements of the German Eighth Army were responsible for defending Northern Romania during the Soviet First <unk> @-@ <unk> Offensive , and took part in the Battles of <unk> <unk> . In late August 1944 , the Red Army entered eastern Romania . On August 23 , 1944 , a coup led by King Michael I of Romania deposed Marshal Antonescu and set up a pro @-@ Soviet government . It has been estimated that the royal coup shortened the war for Romania by six months . Romania soon declared war on Nazi Germany , and the First and Fourth Armies were pressed into action . After the <unk> of the last Wehrmacht remnants from Romania , the Romanian Armies took part in the Siege of Budapest and the Prague Offensive of May 1945 . 
- 
- = = = Cold War = = = 
- 
- The Soviet occupation of Romania led to a complete reorganisation of the Romanian Land Forces under the supervision of the Red Army . At the onset , pro @-@ German elements were purged from the Romanian armed forces . In 1944 – 45 , two divisions were formed out of Romanian volunteers — ex @-@ prisoners of war , trained and <unk> in the Soviet Union during the war , but also of many Communist activists . One was the Tudor <unk> First Volunteer Division , under the command of Colonel Nicolae <unk> , and the other the <unk> , <unk> <unk> <unk> Division , under the command of General <unk> <unk> ( who later served as Minister of Defence from 1946 to 1947 ) . These two units formed the nucleus of the new Romanian Land Forces under Soviet control . The postwar reorganisation of the Land Forces included cavalry but the arm disappeared from the force with the disbandment in November 1954 of the 59th Cavalry Division at Oradea . 
- After the Romanian Communist Party seized political power , the <unk> of the army commenced , under the supervision of the new Minister of Defence , Emil <unk> . Thirty per cent of the officers and <unk> officers ( mostly experienced soldiers , and a potential source of opposition ) were purged from the military . This involved copying the Soviet model of military and political organisation , and changing the military doctrine of combat and defence , also in the context of Romania 's integration in the strategic system of the Soviets , at the beginning of the Cold War . 
- In the early 1950s the <unk> reached a level of 12 rifle , one mechanised , and one tank division . Between 1960 and 1964 the rifle and mechanised divisions were converted to motor rifle divisions , and reductions in strength began ; force size dropped to six motor rifle and two tank divisions by 1970 . From 1970 to 1976 , three more motor rifle divisions were formed , but one was deactivated in 1977 , and the eight motor rifle and three tank division figure remained that way for the rest of the Cold War . 
- From 1947 to 1960 the country seems to have been divided into three major military regions : Cluj , <unk> , and Bucharest in the west , east , and south , respectively . In wartime the land forces in each military region would become an army corps with their headquarters in Cluj @-@ <unk> , <unk> , and Bucharest . Armies seem to have succeeded military regions in 1960 , and three armies seem to have become four in 1980 . What is known is that on 01 @.@ 07 @.@ 1947 Fourth Army became 3rd Military Region , based in Cluj . The 3rd Military Region became the 3rd Army on 30 April 1960 , and the 4th Army on 5 April 1980 . 
- During the 1980s , the land forces numbered 140 @,@ 000 personnel , of whom two thirds were conscripts . In 1989 four armies appeared to exist : the First Army at Bucharest , Second Army at <unk> , Third Army at <unk> , and Fourth Army at <unk> . In 1989 the land forces consisted of eight mechanised ( infantry ) divisions ( 1st , Bucharest , 2nd , <unk> , 9th , <unk> , 10th , <unk> , 11th , Oradea , 18th , <unk> , 67th , and 81st , <unk> Mureş ) two tank divisions ( the 57th Tank Division at Bucharest and the 6th Tank Division at <unk> Mureş ) , four mountain infantry brigades , and three airborne brigades . According to the 165 @-@ year ' History of Modern Romanian Artillery , ' in 1989 the 1st Army consisted of the 1st Mech Div , 57th Tank Div . , and the 2nd Mountain Brigade ; the 2nd Army of the 9th Mech Div , 10th Mech Div , 67th Mech Div , and 32nd <unk> Rocket Bde ; the 3rd Army of the 2nd Mech Div , 18th Mech Div , and the 4th Mountain Bde ; and the 4th Army of the 11th Mech Div , 81st Mech Div , 6th Tank Div . , 1st Mountain Bde , 5th Mountain Bde , and 37th Tactical Missile Brigade . 
- <unk> rifle divisions were organized along the Soviet model with three motorised rifle regiments , one tank regiment , and a full complement of 12 @,@ 000 infantry soldiers . The artillery , <unk> , and air defence regiments of divisions provided specialised fire support that enabled motorised rifle and tank regiments to maneuver . The air defense regiments consisted of two anti @-@ aircraft artillery battalions and one surface @-@ to @-@ air missile ( <unk> ) battalion , each composed of several batteries . In the late 1980s the artillery regiments of motorised rifle and tank divisions included two artillery battalions , one multiple rocket launcher battalion , and one surface @-@ to @-@ surface missile battalion . 
- Surface @-@ to @-@ surface missile battalions were divided into three or four batteries , each equipped with one missile launcher . They operated thirty FROG @-@ 3 and eighteen <unk> missile launchers . The FROG @-@ 3 , a tactical missile first introduced in 1960 , was being replaced in other non @-@ Soviet Warsaw Pact armies . <unk> to be fairly inaccurate in combat , FROG and <unk> missiles would be ineffective weapons carrying conventional high @-@ explosive warheads . <unk> with nuclear or chemical warheads , however , they could be devastating . According to one former Romanian official writing in 1988 , Romania produced chemical agents that could be delivered by battlefield missiles . 
- 
- = = = Post @-@ communist era = = = 
- 
- During the early 1990s , some major units were disbanded and a lot of equipment was phased out or scrapped due to a severe shortage of funds . The whole land forces structure was reorganized from armies into territorial corps , and from regiments into battalions . In the mid @-@ 1990s , the situation of the land forces was critical : the military budget was three times lower than in 1989 ( <unk> million dollars ) , 50 % of the equipment was older than 30 years , and 60 % of the armoured vehicles and 85 % of the missile units were non @-@ operational . Due to lack of fuel and training , the level of combat readiness and military capability was extremely low ( only about 30 % of the entire land forces were operational ) . However , after 1996 the government took serious action ; the military budget was increased greatly , and modernisation of equipment commenced . Officially , the program to modernize and <unk> the armed forces began on 11 April 2000 . 
- 
- = = Present organisation = = 
- 
- 
- = = = <unk> = = = 
- 
- In 2005 , the army comprised eight combat , four combat support and two logistic brigades , while ten combat , five combat support and two logistic brigades could be further mobilised in case of crisis . Many of these units have been <unk> , however , as part of the 2007 Force Plan . 
- Currently , about 75 @,@ 000 military personnel and 15 @,@ 000 civilians comprise the armed forces , for a total of 90 @,@ 000 men and women . Out of these 75 @,@ 000 , <unk> . 43 @,@ 000 are in the Land Forces . 
- 
- = = = <unk> = = = 
- 
- The Romanian military is undergoing a three @-@ stage restructuring . As of 2007 , the first short @-@ term stage was completed ( reorganisation of the command system , implementation of the voluntary military service ) . The year 2015 marks the end of the second stage ( operational integration in NATO and EU ) , while <unk> is the date when the long @-@ term stage is to be completed ( full technical integration in NATO and EU ) . The stages aim at <unk> the structure of the armed forces , reducing the personnel as well as acquiring newer and improved technology that is compatible with NATO standards . 
- Romania abolished compulsory military service on October 23 , 2006 . This came about due to a 2003 constitutional amendment which allowed the parliament to make military service optional . The Romanian Parliament voted to abolish conscription in October 2005 , with the vote <unk> one of many military modernisation and reform programmes that Romania agreed to when it joined NATO in March 2004 . 
- 
- = = = Structure = = = 
- 
- In peacetime , the commander of the land forces is the minister of defense , while in wartime , the President of Romania becomes the supreme commander of the armed forces . The main combat formations of Romania are the 2nd Infantry Division <unk> , and the 4th Infantry Division <unk> . Until 2015 the Romanian land forces fielded a third division , namely the 1st Division <unk> . Before June 2008 , the 1st and 4th divisions were known as the 1st Territorial Army Corps and the 4th Territorial Army Corps and in turn they used to be called the 1st Army and 4th Army prior to 2000 . However due to their personnel having been reduced considerably in order to reach compatibility with NATO standards they were renamed and reorganized as divisions . In 2010 , the Joint HQ command was renamed as 2nd Infantry Division <unk> and received units from the 1st and the 4th Infantry divisions . 
- The current chief of the Romanian Land Forces Staff is Major General Nicolae <unk> , who succeeded Major General <unk> <unk> on 7 January 2014 . The Land Forces official day is celebrated each year , on 23 April . 
- 
- = = Equipment = = 
- 
- The Romanian Land Forces have completely overhauled their equipment in the past few years , replacing it with a more modern one . The <unk> @-@ <unk> " <unk> " main battle tank and the <unk> @-@ <unk> " <unk> " infantry fighting vehicle are the most modern native made equipment of the Romanian Land Forces . Also , 43 ex @-@ German <unk> anti @-@ aircraft systems were commissioned in late @-@ 2004 . 
- The Land Forces ordered about 100 US Army <unk> ; the first eight were delivered to the military police in December 2006 . 31 <unk> III armoured vehicles ( <unk> III variant ) and 60 <unk> <unk> high mobility vehicles were also ordered in 2007 for deployment in Iraq and Afghanistan . 
- Equipment Summary : 
- 
- = = Special Forces = = 
- 
- The evolution of the special forces within the Romanian Land Forces led to the establishment of the 1st Special Operations Regiment on 1 August 2009 , headquartered at <unk> Mureş . It later became the 6th Special Operations Brigade on 25 October 2011 , composed of a special operations battalion , two paratrooper battalions and a logistic battalion . 
- The most famous and well trained unit is the 1st Special Operations Battalion " <unk> " , which was legally created in late 2005 , after several batches of graduates had already been selected . Members of the special forces battalion have <unk> from courses abroad , such as the US Army Special Forces ( Green <unk> ) course , the United States Marine Corps Force Recon course , as well as other courses . The Special Forces battalion became fully operational during 2007 , after a company had already been commissioned in early @-@ 2006 . 
- The current Romanian reconnaissance battalions ( the <unk> , the <unk> and the <unk> ) are also considered special forces units , and were formed in the 1960s during the communist regime . After the revolution , the units suffered from a lack of funds which resulted in the temporary disbandment of the <unk> Battalion . However , their equipment was completely overhauled in the past few years and the combat readiness and capabilities have regained full strength . 
- <unk> , Rapid Intervention Squad of the Romanian Ministry of Defense is an elite special operations unit currently belonging to the Romanian Military Police . It is a special unit inside the military , formed of highly skilled individuals , a very large percentage of its members being champions in martial arts , <unk> , athletic disciplines and so on . <unk> was , until December 2003 , top secret . 
- 
- = = International missions = = 
- 
- The following troops are deployed abroad : 
- 45 personnel in Bosnia and Herzegovina ( 23 in Sarajevo and 22 in <unk> <unk> ) - as part of <unk> , since 2000 ( to be withdrawn ) 
- 150 personnel in <unk> , Kosovo - as part of <unk> 
- 1 battalion in <unk> ( <unk> personnel ) , 1 guard detachment in <unk> ( 193 personnel ) , a reconnaissance squad in <unk> Sharif ( 6 personnel ) , Afghanistan - as part of <unk> ; additionally , a special forces squad ( 39 personnel ) and a training detachment ( 47 personnel ) are deployed there 
- 
- = = Training = = 
- 
- After the Romanian Revolution , many firing ranges and training areas were closed and abandoned due to lack of funds . Currently , the military schools and training units of the Romanian Land Forces are directly subordinated to the central headquarters . There are 3 military high schools ( <unk> <unk> , <unk> <unk> and <unk> ) , one military academy ( <unk> ) , one officers school ( <unk> ) , 3 training schools ( <unk> , <unk> , <unk> ) and 9 training battalions . 
- In the past few years , lots of training exercises took place in Romania with other Balkan or Allied countries . Most of these exercises took place at <unk> , which is one of the largest and most modern training firing ranges and military facilities in Europe , with a total surface area of 270 square kilometres . It was announced on December 6 , 2006 that 1 @,@ 500 U.S. troops stationed at <unk> <unk> , which in time will form Joint Task Force East , will be using <unk> as a training base . 
- 
- = = <unk> and insignia = = 
- 
- The Romanian Land Forces distinguishes four career paths : officers ( <unk> ) , warrant officers ( <unk> <unk> ) , <unk> 's ( <unk> ) and enlisted men ( <unk> <unk> <unk> <unk> ) . The Marshal rank can be given only in wartime by the President of Romania ; in fact , Romania had only three <unk> coming from the <unk> rank in its history : Ion Antonescu , Alexandru <unk> and Constantin Prezan . Kings Ferdinand I , Carol II and Mihai I also held the rank of Marshal of Romania . King Carol I held simultaneous ranks as Russian Marshal and German Field @-@ marshal . 
- 
- 
- = Not Quite Hollywood : The Wild , Untold Story of Ozploitation ! = 
- 
- Not Quite Hollywood : The Wild , Untold Story of Ozploitation ! is a 2008 Australian documentary film about the Australian New Wave of 1970s and ' 80s low @-@ budget cinema . The film was written and directed by Mark Hartley , who interviewed over eighty Australian , American and British actors , directors , screenwriters and producers , including Quentin Tarantino , Brian Trenchard @-@ Smith , Jamie Lee Curtis , Dennis Hopper , George <unk> , George Miller , Barry <unk> , Stacy Keach and John <unk> . 
- Hartley spent several years writing a detailed research document , which served to some degree as a script for the film , about the New Wave era of Australian cinema . It focused on the commonly overlooked " Ozploitation " films — mainly filled with sex , horror and violence — which critics and film historians considered vulgar and offensive , often excluded from Australia 's " official film history " . Hartley approached Quentin Tarantino , a longtime " Ozploitation " fan who had dedicated his 2003 film Kill Bill to the exploitation genre , and Tarantino agreed to help get the project off the ground . Hartley then spent an additional five years interviewing subjects and editing the combined 250 hours of interviews and original stock footage into a 100 @-@ minute film . 
- Not Quite Hollywood , which premiered at the 2008 Melbourne International Film Festival , did not perform well at the box office upon its Australia @-@ wide release , but garnered universally positive reviews from critics and a nomination for " Best Documentary " at the 2008 Australian Film Institute Awards . 
- 
- = = Synopsis = = 
- 
- Not Quite Hollywood documents the revival of Australian cinema during the Australian New Wave of the 1970s and ' 80s through B @-@ movies including Alvin Purple , Barry <unk> <unk> His Own , Dead @-@ End Drive In , Long Weekend , Mad Max , The Man from Hong Kong , Patrick , Razorback , Road Games , <unk> and Turkey Shoot . From 1971 through to the late 1980s , Australian directors began to take advantage of the newly introduced R @-@ rating which allowed more on @-@ screen nudity , sex and violence for audiences restricted to age 18 and over . " Ozploitation " — writer @-@ director Mark Hartley 's own <unk> of " Australian exploitation " — was a subgenre of the New Wave which accounted for the critically panned " gross @-@ out comedies , sex <unk> , action and road movies , teen films , <unk> , <unk> and horror films " of the era , commonly overlooked in Australia 's " official film history " . The film addresses three main categories of " Ozploitation " films : sex , horror and action . 
- 
- = = <unk> = = 
- 
- The actors , directors , screenwriters and producers interviewed for the film were : 
- 
- = = Production = = 
- 
- As a child , Mark Hartley discovered many of the " Ozploitation " B @-@ movies from the 1970s and ' 80s while watching late @-@ night television , but was disappointed when they were completely overlooked in books he read detailing Australian cinema . After becoming an accomplished music video director , his interest in this era of Australian filmmaking grew and he spent years researching a potential documentary film . He was close to giving up on the project when he sent a 100 @-@ page draft of the script to American film director Quentin Tarantino , not expecting to receive a reply . Tarantino was a longtime fan of " Ozploitation " films and had even dedicated his film Kill Bill to Brian Trenchard @-@ Smith 's work . He replied the day after , telling Hartley that he would do whatever he could to get the film made . Hartley traveled to Los Angeles , California to meet with Tarantino , who agreed to sit for hours of interviews as one of the film 's most prominent <unk> . Hartley spent the following five years interviewing other actors , directors , screenwriters and producers , collecting original stock footage , and then cutting the 100 hours of interviews and 150 hours of film footage down into a 100 @-@ minute film . 
- 
- = = Release = = 
- 
- Not Quite Hollywood had its worldwide premiere at the Melbourne International Film Festival on 28 July 2008 , and was screened at the Australian Centre for the Moving Image . Its Australia @-@ wide release was a month later , on 28 August 2008 , and it had its overseas premiere at the Toronto Film Festival on 7 September 2008 , where distribution rights were secured for the United Kingdom , Canada , France , Russia , Germany and <unk> . The film was also screened at the Austin , <unk> , Warsaw , Helsinki and Stockholm International Film Festivals in 2008 , and featured at the London Film Festival on 25 October 2008 . 
- The film did not perform well at the box office upon its Australian release , taking in a gross of A $ 108 @,@ 330 on its first weekend but only $ 31 @,@ 995 on its second weekend at a screen average of $ <unk> on 47 screens . 
- 
- = = Reception = = 
- 
- Overall , Not Quite Hollywood received positive reviews from critics . Review aggregate Rotten Tomatoes reports that 94 % of critics have given the film a positive review , " <unk> Fresh " , based on 63 reviews , with an average score of 7 @.@ 4 out of 10 . Margaret <unk> and David Stratton of At the Movies gave the film four and three and a half out of five stars respectively ; <unk> commended Hartley for " the depth of his research and for creating a wildly entertaining film experience " , and claimed that " for those of us who remember the films , Not Quite Hollywood is a blast " . Sandra Hall , writing for The Sydney Morning Herald , gave the film three and a half out of five stars , believing that " Hartley 's own film is much <unk> than most of those he is out to celebrate " . Jake Wilson of The Age similarly gave the film three and a half stars , but called the film " basically a feature @-@ length advertisement for its subject " , saying that it " moves far too rapidly to permit sustained analysis " . The Courier @-@ Mail 's Des <unk> , who gave the film four and a half out of five stars , disagreed , saying that " <unk> editing means the history is lively and fun " , and claimed in homage to The Castle , " <unk> of Hartley 's film should go straight to pool rooms all over Australia when it becomes available on DVD . " Luke <unk> of Empire Magazine <unk> gave Not Quite Hollywood five out of five stars , calling the film " fast , thrilling and often <unk> " , while Leigh <unk> wrote for the Herald Sun that " there is not a single instant where boredom can possibly <unk> " , dubbing the film " an incredibly energetic and <unk> <unk> @-@ up celebration of Australian B @-@ movies " . 
- English director Edgar Wright named Not Quite Hollywood his fourth favourite film of 2008 , and called it " the best documentary ever . " 
- 
- = = = Awards and nominations = = = 
- 
- 
- = = Films referenced = = 
- 
- A list of film referenced within Not Quite Hollywood , separated by genre . 
- 
- = = Box office = = 
- 
- Not Quite Hollywood : The Wild , Untold Story of Ozploitation ! grossed $ 186 @,@ 986 at the box office in Australia , . 
- 
- 
- = Why Does It Hurt So Bad = 
- 
- " Why Does It Hurt So Bad " is a song recorded by American singer Whitney Houston for the 1995 film Waiting to Exhale . It was released on July 7 , 1996 , by <unk> Records as the seventh and final single from the accompanying soundtrack . The song was written and produced solely by Babyface . Musically , it is an R & B ballad , and the lyrics chronicle a <unk> lament . 
- The song garnered positive reviews from critics , who commended Houston 's vocal effort . It charted in the United States on the Billboard Hot 100 , peaking at number twenty @-@ six . It also reached a peak position of number twenty @-@ two in the Hot R & B / Hip @-@ Hop Songs chart and number six on the Adult Contemporary chart . In Canada , the song reached a peak of number forty @-@ five on the RPM Singles chart . Although there is no official music video for the song , a performance of the song at the 1996 MTV Movie Awards was taped and is used as a promotional clip . The song was later included as a medley , in her My Love Is Your Love World Tour ( 1999 ) , along with a few other songs . 
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- = = Background = = 
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- Houston starred in the 1995 romance film Waiting to Exhale , directed by Forest Whittaker . Although Houston did not intend to contribute to the film 's soundtrack , when Whittaker hired Babyface to score the soundtrack , she opted in . Babyface , Houston and some other African @-@ American female singers recorded songs for the album . The song was one of the final additions to the soundtrack . " Why Does It Hurt So Bad " was originally written by Babyface for Houston , two years prior to the release of Waiting to Exhale , but Houston refused to record it at that time . " I wasn 't really in the mood for singing about why it <unk> so bad , " said Houston . Two years later , according to Chris <unk> of Entertainment Weekly , the emotions of the movie merged with the real @-@ life circumstances of Houston 's troubled marriage to Bobby Brown . " Now , I 'm ready to sing not only the <unk> of things , but the pains of things , also , " Houston explained . 
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- = = Composition = = 
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- " Why Does It Hurt So Bad " is an R & B ballad . The song was written and produced by Kenneth Brian Edmonds , popularly known as " Babyface " . According to the sheet music book for The Greatest Hits at <unk> , the song is written in the key of B ♭ major , and moves at a tempo of 69 beats per minute . It is set in time signature of common time and features a basic chord progression of B / E – Em – C ♯ m – G ♯ 7 . Houston 's vocals span from the note of <unk> to the note of D5 . According to Stephen Holden of New York Times , the song is a " <unk> lament with a realistic twist " . He noted that , through the verses , the singer <unk> herself for breaking up with an abusive boyfriend and admits that she is still in love . 
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- = = Reception = = 
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- The song garnered mainly positive reviews from critics . Craig <unk> of Allmusic noted that Houston 's voice " sailed " through the song . Christopher John Farley of TIME commented Houston " particularly held her own " , with a " masterly balance of pop , <unk> , and soulful melancholy " . Steve <unk> of Newsday wrote : " It 's lower @-@ key and the singer , who also stars in the film , doesn 't feel compelled to perform constant vocal feats . " A writer for Boston Herald noted that the song was " understated " . Similarly , Larry <unk> of Billboard commented that the song should have been released as the follow @-@ up to " Exhale ( Shoop Shoop ) " . " Paired with Babyface , Houston is positively luminous on [ this ] <unk> ballad , performing with a perfect blend of theatrical melodrama and guttural soul , " he added . Deborah <unk> of South Florida Sun @-@ Sentinel was mixed in her review commenting that the song was a " <unk> <unk> follow @-@ up " to " Exhale ( Shoop Shoop ) " . But , Nick <unk> of The Spectator was even less enthusiastic , writing " [ ... ] the two guaranteed [ Whitney Houston ] hits – ' Exhale ( Shoop Shoop ) ' and ' Why Does It Hurt So Bad ' – don 't really offer anything new . " Similarly , Cary Darling of Rome News @-@ Tribune gave a negative review . She noted that " [ the ] ballad ' Why Does It Hurt So Bad ' is [ more ] standard Whitney @-@ fare " . 
- Released as the seventh and final single from the Waiting to Exhale : Original Soundtrack Album , the song debuted at number 60 on the Billboard Hot 100 , on the issue dated August 3 , 1996 . On the same issue , the song debuted at number 34 on the Hot R & B / Hip @-@ Hop Singles chart . The song later reached a peak of number 26 on the Hot 100 , and 22 on the R & B / Hip @-@ Hop Singles chart . It also reached number six on the Adult Contemporary chart , while reaching a peak of 39 on the Adult Pop Songs chart . In Canada , the song debuted at number 98 on the RPM Singles chart , on the July 22 , 1996 issue . Later , on the September 15 , 1996 issue , it reached a peak of number 45 . 
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- = = Music video and live performances = = 
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- The song was not promoted through an official music video , although Houston appeared at the 1996 MTV Movie Awards held at Walt Disney Studios , Burbank and performed " Why Does It Hurt So Bad " . The performance was directed and taped by Bruce <unk> and was later used as a promotional clip to accompany the song . The performance features Houston sitting on a chair , wearing a white outfit , and singing the song . 
- Houston performed the song on her My Love Is Your Love World Tour , in 1999 . The song was performed as a part of the " Movie Medley " , along with " I Believe in You and Me " , " It <unk> Like Hell " , originally performed by Aretha Franklin , and " I Will Always Love You " . This performance was taped in <unk> , Poland , on August 22 , 1999 and broadcast on Polish television channel , <unk> . 
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- = = Track listing = = 
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- = = Credits and personnel = = 
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- Retrieved from CD liner notes 
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- = = Charts = = 
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- = Hurricane Omar ( 2008 ) = 
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- Hurricane Omar was a strong hurricane that took an unusual southwest to northeast track through the eastern Caribbean Sea during October , 2008 . Forming out of a tropical disturbance on October 13 , Omar initially moved slowly in the eastern Caribbean Sea . By October 15 , Omar began to quickly intensify as deep convection developed around the center of circulation . Later that day , an eye developed and the storm began to accelerate to the northeast . Early on October 16 , Omar reached its peak intensity with winds of 130 mph ( 215 km / h ) and a barometric pressure of <unk> mbar ( hPa ; 28 @.@ 29 inHg ) . Shortly after , the hurricane rapidly weakened to Category 1 intensity . After slightly re @-@ strengthening the next day , Omar weakened to a tropical storm before degenerating into a non @-@ convective low pressure area . The remnants of Omar persisted until October 21 at which time it dissipated to the west of the Azores . 
- Throughout the eastern Caribbean , Omar affected numerous islands , most of which only recorded minor impacts . Large swells and heavy rains impacted the ABC islands . Antigua and Barbuda sustained $ 54 million in damages , mainly on Antigua as nine homes were destroyed , several others damaged and many farms were inundated by flood waters . One person died in Puerto Rico after suffering a stress @-@ induced cardiac arrest . The United States Virgin Islands also sustained significant damage , costing roughly $ 6 million . Numerous boats and homes were damaged and over 100 power poles were snapped . Total losses from the storm were estimated at $ 79 million . 
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- = = Meteorological history = = 
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- On September 30 , a well @-@ developed tropical wave moved off the eastern coast of Africa and entered the Atlantic Ocean . Deep convection formed around a prominent mid @-@ level circulation as it moved towards the west . However , the convection diminished on October 2 before entering the Caribbean Sea a week later . Upon entering the Caribbean Sea , shower and thunderstorm activity redeveloped around the low . Continued development followed and the low was designated as Tropical Depression Fifteen at 0600 UTC on October 13 while located about 190 miles ( 305 kilometres ) south of the southeastern tip of the Dominican Republic . The previous steady westward motion that the system took across the Atlantic halted as it entered an area of weak steering currents and significant motion was not expected for another day or two . As the structure of the storm improved , it was upgraded to a tropical storm and the National Hurricane Center ( NHC ) gave it the name Omar . 
- Located to the southeast of a broad and deep tropospheric trough and to the west of a low to mid @-@ level ridge , Omar took a counter @-@ clockwise turn on October 14 . Upon becoming a tropical storm , Omar began to undergo an extended period of rapid intensification as very deep convection developed around the center of circulation . Wind shear around the storm , which was previously inhibiting quick development , weakened , allowing for further strengthening . Later that day , an 11 @.@ 5 to 17 @.@ 2 mi ( 18 @.@ 5 to 27 @.@ 7 km ) wide eye formed as the storm began to turn towards the northeast due to the trough . With the formation of an eye , the Dvorak technique rendered an intensity of 75 mph ( 120 km / h ) , signifying that Omar had intensified into a hurricane . The intensification briefly stalled as the eyewall eroded and the center of the storm became slightly elliptical . However , deep convection persisted and strengthening was forecast as the storm neared Puerto Rico . Shortly after , the eye quickly became well @-@ defined and appeared on visible satellite images , an indication the storm was intensifying . The chances of rapid intensification were good as the storm featured well @-@ developed outflow and prominent banding features . 
- With very warm sea surface temperatures , high ocean heat content , low wind shear , and a moist air mass , Omar quickly reached its peak intensity early on October 16 as a Category 4 hurricane with winds of 130 mph ( 215 km / h ) . During the intensification phase , the forward motion of the hurricane increased to 20 mph ( 32 km / h ) . Once in the Atlantic Ocean , Omar began to rapidly weaken , with winds decreasing by 50 mph ( 85 km / h ) in 12 hours . Visible satellite images depicted an exposed low @-@ level circulation with convection displaced to the east due to a combination of very high wind shear and dry air . By October 17 , most of the deep convection associated with the system dissipated ; however , a brief decrease in wind shear allowed Omar to re @-@ strengthen to its secondary peak , with winds of 85 mph ( 140 km / h ) . During this phase , convection redeveloped around the center and an eye reformed . Later that day , the trough that caused the rapid northeastern motion bypassed Omar , leading to decreasing movement . 
- A weakening storm , Omar continued towards the northeast due to a mid to upper @-@ level ridge located south of the system and the mid @-@ latitude westerlies to the north . Late on October 17 , wind shear increased once more as Omar tracked over waters below 26 ° C ( 79 ° F ) . Around 0000 UTC on October 18 , Omar weakened to a tropical storm as deep convection associated with it dissipated . Twelve hours later , while still producing tropical storm @-@ force winds , the storm degenerated into a remnant low pressure area . The remnants of Omar persisted until 0600 UTC on October 21 when it dissipated about 805 mi ( 1 @,@ 295 km ) west of the Azores . 
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- = = Preparations = = 
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- On October 15 , the governor of the United States Virgin Islands announced the final preparations for Hurricane Omar as he signed a State of Emergency declaration for the territory . Public schools would be closed on October 16 . All non @-@ essential workers would be dismissed at 10 a.m. local time . At 6 p.m. curfew was put in place for the same day . Only those with valid passes would be allowed to be out after the curfew was put in place . That same day , a Hurricane Warning and flash flood watch were put in place in anticipation of hurricane @-@ force winds and torrential rains from Omar . The American Red Cross planned to open shelters throughout the islands before the hurricane struck . <unk> were also being distributed in St. Croix . A large oil refinery , which produces 500 @,@ 000 barrels per day ( 79 @,@ 000 m3 / d ) , was shut down and only necessary workers remained at the refinery . 
- On Sint Maarten , officials advised residents to start all necessary actions to prepare for a hurricane . Residents were told to clear their yards of any debris or lose furniture that could become airborne during the storm , place shutters over windows and doors and assist elderly neighbors with <unk> their homes , and mariners should find a safe haven . A curfew from 10 : 00 p.m. on October 15 to 10 : 00 a.m. on October 16 was put in place . Due to the possibility of flash flooding , residents were to <unk> and free up all waterways around their homes . Officials also warned people to stay away from areas prone to landslides until the " all @-@ clear " was given . <unk> of livestock were advised to have insured that they were secured in holding areas . As a precaution , the water supply would be shut down from 8 : 00 p.m. October 15 to 8 : 00 a.m. October 16 . Four public areas , St. Peters Community Center , Dutch Quarter Community Center , Genevieve de <unk> School , and the <unk> Army building would be used as emergency shelters . 
- All public activities , flights , and schools were either closed or canceled on Puerto Rico . Eighteen shelters were open on the eastern part of the island . Also , on Anguilla , residents in the Sandy Ground , Valley <unk> , <unk> and Mount Fortune areas were placed under evacuation orders . <unk> were opened throughout the island for those in need of shelter . Schools and government offices were also closed and visitors were told to leave the island . 
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- = = Impact = = 
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- Omar produced moderate damage throughout numerous islands , amounting to at least $ 60 million ( 2008 USD ) and one death was related to the storm . 
- While it was moving little over the south @-@ central Caribbean , Omar brought prolonged tropical storm conditions to the ABC Islands . Sustained winds to near gale force battered the islands , although peak gusts to 58 mph ( 92 km / h ) were confined to <unk> . In 24 hours , a maximum precipitation total of 4 @.@ 0 in ( 102 mm ) was recorded on <unk> , while 1 @.@ 7 in ( 43 mm ) and 1 @.@ 5 in ( 41 mm ) of rain fell in <unk> and <unk> , respectively . The high winds damaged roofs on all three islands , and rough seas caused beach erosion and significant damage to coastal facilities . Some rain damage also occurred , with significant flooding reported in some parts of <unk> . In the <unk> Islands ( <unk> , St. <unk> , and Sint Maarten ) strong winds from Omar , gusting up to 76 mph ( 122 km / h ) , and high waves caused significant damage and coastal flooding . On Sint Maarten , rainfall from the storm totaled to 5 @.@ 4 in ( 139 @.@ 4 mm ) . Damage in Sint Maarten was mainly limited to beach erosion and scattered power outages . 
- The island of Dominica suffered severe damage from Hurricane Omar . The village of Scotts Head , with a population of 450 , was cut off from the rest of the country as roads were extensively damaged . The village also suffered water losses , electricity shortage , and <unk> telephones were cut off . Ports throughout the country were severely damaged . All barge access for <unk> sand and stones were destroyed . The airport also sustained some damage . Seven boats ran aground during the storm . Minor damage was reported in Anguilla . Two hotels sustained roof damage , downed treed knocked down power lines causing scattered power outages , and the rough seas caused severe beach erosion . Three cargo ships and seven boats ran aground and two other boats sank . 
- In Antigua and Barbuda , winds from Omar were recorded at 40 mph , with gusts to 48 mph . <unk> <unk> lines brought torrential rains , falling at rates of 2 in ( 50 @.@ 8 mm ) at times , peaking at 2 @.@ 22 in ( 56 @.@ 4 mm ) per hour from <unk> UTC to 1200 UTC on October 16 . The maximum recorded rainfall was 9 @.@ 1 in ( 232 @.@ 6 mm ) however , up to 11 in ( 279 @.@ 4 mm ) was estimated to have fallen in the mountains . Storm <unk> was estimated at 2 – 4 ft ( 0 @.@ 6 – 1 @.@ 2 m ) with waves reaching 5 – 8 ft ( 1 @.@ 5 – 2 @.@ 4 m ) in height . Nine homes were destroyed and several landslides were reported . No fatalities were associated with Omar , and only a few sustained minor injuries . The most severe damage was dealt to roads and agriculture due to flooding . Seventy @-@ five people were forced to evacuate to shelters as their homes were flooded . Several farms were washed away , including their livestock . Numerous farmers lost their harvest due to flooding . Damages in Barbuda were estimated at $ 18 million . Agricultural losses in Antigua amounted to around $ 11 million ( 2008 USD ) and property damage amounted to $ 25 million . 
- In Puerto Rico , a man died after he collapsed from cardiac arrest while trying to install storm shutters on his home . As a tropical wave , the precedent to Omar produced heavy rains over the island , causing minor flooding . After passing by Puerto Rico a second time , Omar produced locally heavy rains , which caused minor street flooding . 
- On St. Croix , waves up to 15 ft ( 4 @.@ 5 m ) from Omar sank about 47 boats , leading to a large oil spill around the islands . About 400 ships broke lose from the docks , 200 of which lost their anchors . Omar produced upwards of 7 in ( 177 @.@ 8 mm ) of rain on the island within a 24 ‑ hour span . Although St. Croix was brushed by the eyewall , sustained winds reached 53 mph ( 85 km / h ) with gusts to 72 mph ( 116 km / h ) . Three people needed to be rescued when their ship struck a reef and began to sink . Most of the islands 55 @,@ 000 residents were without power as over 100 utility poles were destroyed . Damages on the island were estimated at $ 700 @,@ 000 with another $ 1 million in clean @-@ up costs . St. Thomas , one of the hardest hit islands , was left completely without power in the wake of the hurricane . All of the major intersections were shut down as traffic lights were either on the ground or without power . Damages on the island totaled to $ 5 @.@ 3 million . 
- In the <unk> <unk> Quarter on St. Lucia , rough seas damaged a jetty and grounded a yacht . In <unk> , four homes were destroyed by the storm surge , which also made some areas <unk> . The storm surge also flooded parts of the <unk> la <unk> Quarter , leading to officials declaring a mandatory evacuation of the area . On Montserrat , very little damage was reported . A few minor landslides occurred in rural areas ; no impact was caused by them . On <unk> there was relatively little damage although the beachfront part of the Four Seasons Resort was severely damaged and was subsequently closed for an extended period . Throughout St. <unk> and <unk> , damage was estimated at $ 19 million . 
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- = = Aftermath = = 
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- The damage dealt to the agricultural sector of Antigua and Barbuda fueled major concerns for " food security " in 2009 . The government allocated about $ 33 @,@ <unk> @,@ 420 to help develop and repair the industry . Significant expansions of croplands were discussed , 15 @,@ 000 <unk> ( <unk> m2 ) area , to help promote growth of the sector . 
- On October 29 , in the wake of Omar , President George W. Bush signed a major disaster declaration for the United States Virgin Islands , allowing public aid to assist the islands . The Federal Emergency Management Agency or <unk> , had received 60 requests for public assistance throughout the area . <unk> value for the assistance was estimated at $ 3 million and growing . Twenty @-@ five departments and agencies were approved of for federal support , namely the U.S. <unk> Department of Public Works . Several non @-@ profit organizations also received support from <unk> , while those that did not meet the criteria were referred to the Small Business Administration ’ s low @-@ interest loan program . 
- On Dominica , Omar 's close pass to the island left 30 families homeless and severely hampered the fishing community . On December 15 , the Board of Directors of the Caribbean Development Bank approved $ 9 @.@ 16 million for assistance to those affected by Omar on the island and to restore the infrastructure damaged by the hurricane . On December 18 , the government of Dominica invested $ 4 million in aid for the fishing communities impacted by Omar . A total of 140 fishermen were provided with $ 250 per week for a total of four weeks . Sixty @-@ two of which continued to receive funds due to their circumstances . The government also purchased 121 boat engines to distribute to fishers . Another $ 794 @,@ 000 was spent to repair 47 boats and construct another 28 that had been damaged or destroyed by Omar . The government also bought replacement fishing gear . 
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- 
- = Papal conclave , 1769 = 
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- A papal conclave which lasted from 15 February to 19 May 1769 was <unk> after the death of Pope Clement XIII . It elected as his successor Cardinal Lorenzo Ganganelli , who took the name Clement XIV . 
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- = = Death of Clement XIII = = 
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- Clement XIII died suddenly on 2 February 1769 , a day before the date of the <unk> that he had <unk> to examine the demands for the general suppression of the Society of Jesus . The various courts under the House of Bourbon and the Kingdom of Portugal ( under the House of <unk> ) had exerted strong pressure on the Holy See to suppress this order through almost the whole of his <unk> . In 1759 Jesuits were expelled from Portugal , in <unk> from the Kingdom of France , in 1767 from Spain and in 1768 from the Kingdom of Naples , the Kingdom of Sicily and the Duchy of <unk> and <unk> . Clement XIII strongly defended the Society ( e.g. in the bull <unk> <unk> in 1765 ) , but without success . In January 1769 France and Naples seized the papal territories around Avignon , <unk> and <unk> to force the pope to issue a decree for the suppression of the order . The sudden death of 75 @-@ year @-@ old Clement XIII left this difficult decision to his successor . 
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- = = List of participants = = 
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- Forty six out of fifty seven cardinals participated in the conclave : 
- Carlo Alberto <unk> <unk> ( created cardinal on September 9 , 1743 ) – Cardinal @-@ Bishop of <unk> e <unk> ; Dean of the Sacred College of Cardinals ; pro @-@ <unk> of His <unk> ; prefect of the S.C. of <unk> ; prefect of the S.C. of Bishops and <unk> 
- Federico <unk> <unk> ( September 9 , 1743 ) – Cardinal @-@ Bishop of Porto e Santa <unk> ; Sub @-@ dean of the Sacred College of Cardinals ; prefect of the S.C. of the Good Government ; governor of <unk> 
- Gian Francesco Albani ( April 10 , 1747 ) – Cardinal @-@ Bishop of <unk> ; Cardinal @-@ protector of Poland 
- Henry Benedict Stuart ( July 3 , 1747 ) – Cardinal @-@ Bishop of <unk> ; <unk> of S. Lorenzo in <unk> ; Vice @-@ Chancellor of the Holy Roman Church ; archpriest of the patriarchal Vatican Basilica 
- <unk> <unk> ( November 26 , 1753 ) – Cardinal @-@ Bishop of <unk> 
- Giovanni Francesco Stoppani ( November 26 , 1753 ) – Cardinal @-@ Bishop of <unk> 
- Giuseppe Pozzobonelli ( September 9 , 1743 ) – Cardinal @-@ Priest of S. Maria <unk> Minerva ; archbishop of Milan 
- Carlo <unk> Amedeo <unk> <unk> ( April 10 , 1747 ) – Cardinal @-@ Priest of S. <unk> ; titular archbishop of <unk> 
- <unk> <unk> ( November 26 , 1753 ) – Cardinal @-@ Priest of SS . <unk> e Pietro ; archbishop of Bologna 
- Antonio <unk> ( April 22 , 1754 ) – Cardinal @-@ Priest of S. <unk> ; archbishop of Naples 
- Francisco de <unk> <unk> de <unk> ( April 5 , 1756 ) – Cardinal @-@ Priest of [ no title assigned ] ; archbishop of Seville ; Cardinal @-@ protector of Spain 
- Paul d <unk> de <unk> ( April 5 , 1756 ) – Cardinal @-@ Priest of S. <unk> in <unk> ; archbishop of <unk> 
- Carlo Rezzonico ( September 11 , 1758 ) – Cardinal @-@ Priest of S. <unk> ; <unk> of the Holy Roman Church 
- Antonio Maria <unk> ( October 2 , 1758 ) – Cardinal @-@ Priest of S. Marco ; bishop of Padua 
- Fernando Maria de Rossi ( September 24 , 1759 ) – Cardinal @-@ Priest of S. Cecilia ; prefect of the S.C. of the <unk> Council ; Latin Patriarch of Constantinople 
- <unk> <unk> ( September 24 , 1759 ) – Cardinal @-@ Priest of S. <unk> ; legate in <unk> 
- Giuseppe Maria <unk> ( September 24 , 1759 ) – Cardinal @-@ Priest of S. <unk> ; prefect of the S.C. for the <unk> of Faith 
- Gaetano Fantuzzi ( September 24 , 1759 ) – Cardinal @-@ Priest of S. Pietro in <unk> ; prefect of the S.C. of the <unk> <unk> 
- Pietro <unk> <unk> ( September 24 , 1759 ) – Cardinal @-@ Priest of SS . <unk> al Monte <unk> ; <unk> of the Sacred College of Cardinals 
- Pietro <unk> de <unk> ( September 24 , 1759 ) – Cardinal @-@ Priest of S. <unk> al Monte <unk> 
- Lorenzo Ganganelli , <unk> ( September 24 , 1759 ) – Cardinal @-@ Priest of SS . XII <unk> 
- Marcantonio Colonna ( September 24 , 1759 ) – Cardinal @-@ Priest of S. Maria della Pace ; <unk> General of Rome ; prefect of the S.C. of the Residence of the Bishops ; archpriest of the patriarchal <unk> Basilica 
- <unk> de <unk> <unk> de la <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; patriarch of the West Indies ; vicar general of the Spanish army and fleet 
- Giovanni <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; bishop of <unk> 
- Simone <unk> ( July 18 , 1763 ) – Cardinal @-@ Priest of S. Giovanni a <unk> <unk> 
- Giovanni <unk> <unk> ( July 21 , 1766 ) – Cardinal @-@ Priest of S. Maria <unk> <unk> ; archbishop of Ancona 
- Giovanni Carlo <unk> ( July 21 , 1766 ) – Cardinal @-@ Priest of SS . Giovanni e <unk> ; Grand <unk> ; prefect of the Congregation for the correction of the books of the Oriental Church 
- <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of S. <unk> ; prefect of the S.C. of the <unk> and Sacred Relics 
- Antonio Colonna <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of S. Maria in Via 
- <unk> <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of SS . <unk> ed <unk> ; legate in Bologna 
- <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of S. Maria in <unk> ; legate in <unk> 
- Pietro <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of S. Maria in <unk> 
- <unk> <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Priest of S. Callisto ; archbishop of <unk> 
- Filippo Maria Pirelli ( September 26 , 1766 ) – Cardinal @-@ Priest of S. <unk> 
- Alessandro Albani ( July 16 , 1721 ) – Cardinal @-@ Deacon of S. Maria in Via <unk> ; <unk> of S. Maria in <unk> ; <unk> of the Sacred College of Cardinals ; <unk> of the Holy Roman Church ; Cardinal @-@ protector of Austria and the Kingdom of Sardinia 
- <unk> Maria Corsini ( August 14 , 1730 ) – Cardinal @-@ Deacon of S. <unk> ; archpriest of the patriarchal Lateran Basilica ; secretary of the Supreme S.C. of the Roman and Universal Inquisition ; prefect of the Supreme Tribunal of the Apostolic Signature of Justice ; Cardinal @-@ protector of Portugal 
- <unk> Orsini d <unk> ( September 9 , 1743 ) – Cardinal @-@ Deacon of S. Maria ad <unk> ; Cardinal @-@ protector of the Kingdom of Naples 
- <unk> II Chigi ( November 26 , 1753 ) – Cardinal @-@ Deacon of S. Maria in <unk> ; prefect of the S.C. of <unk> 
- Luigi Maria <unk> ( November 26 , 1753 ) – Cardinal @-@ Deacon of S. <unk> in <unk> ; Cardinal Secretary of State 
- François @-@ Joachim de Pierre de Bernis ( October 2 , 1758 ) – Cardinal @-@ Deacon [ no <unk> assigned ] ; Cardinal @-@ protector of the Kingdom of France ; archbishop of <unk> 
- Giovanni <unk> <unk> ( September 24 , 1759 ) – Cardinal @-@ Deacon of S. <unk> in <unk> ; prefect of the Tribunal of the Apostolic Signature of Grace 
- Nicola <unk> ( September 24 , 1759 ) – Cardinal @-@ Deacon of S. <unk> in <unk> 
- Andrea Corsini ( September 24 , 1759 ) – Cardinal @-@ Deacon of S. Angelo in <unk> 
- Andrea <unk> ( July 18 , 1763 ) – Cardinal @-@ Deacon of SS . <unk> e <unk> ; secretary of the Apostolic <unk> 
- <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Deacon of S. Maria della <unk> ; <unk> abbot of <unk> 
- <unk> <unk> ( September 26 , 1766 ) – Cardinal @-@ Deacon of SS . <unk> e <unk> ; prefect of the S.C. of Index 
- Twenty nine electors were created by Clement XIII , while fifteen by Pope Benedict XIV . Alessandro Albani received the red hat from Innocent XIII , and <unk> Maria Corsini from Clement XII . 
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- = = = <unk> = = = 
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- Giacomo <unk> ( September 9 , 1743 ) – Cardinal @-@ Priest of S. Lorenzo in <unk> ; <unk> of the Sacred College of Cardinals ; archbishop of <unk> e <unk> 
- Carlo Francesco <unk> ( November 26 , 1753 ) – Cardinal @-@ Priest of SS . IV <unk> ; archbishop of <unk> 
- Luis Fernández de <unk> ( December 18 , 1754 ) – Cardinal @-@ Priest [ no title assigned ] ; archbishop of Toledo 
- <unk> @-@ René <unk> de <unk> ( April 5 , 1756 ) – Cardinal @-@ Priest of S. <unk> <unk> le <unk> ; bishop of <unk> 
- Franz Konrad Casimir von <unk> ( April 5 , 1756 ) – Cardinal @-@ Priest of S. Maria del <unk> ; bishop of Constance 
- Francisco de <unk> da Gama ( April 5 , 1756 ) – Cardinal @-@ Priest [ no title assigned ] ; patriarch of Lisbon 
- Christoph Anton von <unk> von <unk> und <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; archbishop of Vienna ; administrator of the see of <unk> 
- Antoine <unk> de <unk> de <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; archbishop of <unk> 
- Jean @-@ François @-@ Joseph de <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest of S. <unk> ; bishop of <unk> 
- Franz Christoph Freiherr von <unk> <unk> <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; bishop of <unk> 
- Louis @-@ César @-@ Constantine de <unk> @-@ <unk> ( November 23 , 1761 ) – Cardinal @-@ Priest [ no title assigned ] ; bishop of <unk> 
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- = = Divisions in the College of Cardinals and the candidates to the papacy = = 
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- The papal conclave in 1769 was almost completely dominated by the problem of the Society of Jesus . The Sacred College of Cardinals was divided into two <unk> : pro @-@ Jesuits and anti @-@ Jesuits , but several cardinals were neutral . The pro @-@ Jesuit faction , called Zelanti , grouped Italian <unk> cardinals who opposed the secular influences on the Church . Their leaders were Gian Francesco and Alessandro Albani and cardinal @-@ nephew of the deceased pope Carlo Rezzonico . The anti @-@ Jesuit bloc ( called also " court faction " ) grouped crown @-@ cardinals of the Catholic Powers : France , Spain and Naples . <unk> ruled at the time by Louis XV of France , Charles III of Spain and Ferdinand III of Sicily / Ferdinand IV of Naples . In spite of the national divisions they worked together for the main goal – suppression of the Society of Jesus . The Bourbon courts had decided to put the official leadership of this bloc in the hands of the French Cardinal de Bernis . He and his colleagues were instructed to block every pro @-@ Jesuit candidature , even with the official exclusion if necessary . Several cardinals , among them Lorenzo Ganganelli , did not belong to either faction . 
- The Spanish and Neapolitan governments had classified forty three Italian cardinals into five categories : " good " ( eleven cardinals ) , " indifferent " ( eight ) , " doubtful " ( three ) , " bad " ( fifteen ) and " very bad " ( six ) : 
- Cardinal Orsini , the official representative of the Neapolitan court , and all the foreigners , were not classified because it was certain that none of them would be ever elected pope . 
- The French government was more <unk> than Spanish and Neapolitan . Only three cardinals were considered good candidates : <unk> , <unk> and Ganganelli 
- Out of these 43 cardinals only 27 or 28 were actually considered <unk> , while the remaining 15 were excluded due to their age or health . 
- 
- = = <unk> = = 
- 
- The conclave began on February 15 , 1769 . Initially only 27 cardinals participated . Zelanti , taking advantage of the small number of the electors and the absence of the French and Spanish cardinals , tried to achieve a quick election of Cardinal <unk> Chigi . In one ballot he was only two votes short of being elected . The efforts of Zelanti met with strong protests from the ambassadors of France and Spain , but , <unk> for them , Cardinal Orsini , protector of the Kingdom of Naples and the only crown @-@ cardinal present in the early ballots , was able to join some neutral cardinals to block Chigi ’ s candidature . 
- An unprecedented event was the visit of Joseph II , Holy Roman Emperor , who arrived <unk> in Rome on March 6 and was allowed to enter the conclave . He stayed there two weeks , freely debating with the electors . Fortunately , he did not press them but only expressed the wish for the election of a pope who would be able to carry out his duties with the proper respect for the secular rulers . 
- Cardinal de Bernis entered the conclave at the end of March and took the leadership of the anti @-@ Jesuit faction from the hands of Cardinal Orsini , who could have blocked Zelanti ’ s actions only with the great difficulties . Bernis immediately established a regular correspondence with French ambassador Marquis d <unk> , which was in violation of the fundamental law of the conclave . <unk> of France and Spain urged Bernis to insist that the election of the future pope be made to depend on his written engagement to suppress the Jesuits . Bernis refused , answering that demanding from the future pope a written or oral promise to destroy the Society of Jesus would be in violation of the canon law . In spite of this refusal , during the next few weeks Bernis consecutively rejected all candidates proposed by Zelanti as too devoted to the Jesuits . In this way twenty @-@ three out of twenty @-@ eight <unk> were eliminated , among them strongly pro @-@ Jesuit Cardinal Fantuzzi , who at some point was very close to achieving election to the papal throne , as well as <unk> , Colonna , Stoppani , Pozzobonelli , <unk> , and several others . 
- The arrival of Spanish cardinals Solis and de la <unk> on April 27 strengthened the anti @-@ Jesuit party . They also violated the law of the conclave by establishing regular correspondence with Spanish ambassador <unk> . The Spaniards had fewer <unk> than Bernis and , supported by Cardinal <unk> , took the matter into their own hands . They paid attention to the only friar in the Sacred College , Cardinal Lorenzo Ganganelli , <unk> The attitude of Ganganelli towards the Jesuits was a great mystery – he had been educated by the Jesuits and it was said that he received the red hat at the instance of Father Lorenzo <unk> , general of the Society of Jesus , but during the <unk> of Clement XIII he did not engage himself in the defence of the Order . Cardinal Solis began by sounding him out as to his willingness to give the promise required by the Bourbon princes as an indispensable condition for election . Ganganelli answered that " he recognized in the sovereign <unk> the right to extinguish , with good conscience , the Society of Jesus , provided he observed the canon law ; and that it was desirable that the pope should do everything in his power to satisfy the wishes of the Crowns " . It is not certain whether it was a written or only an oral promise , but this declaration fully satisfied the ambassadors . 
- In the same time Zelanti , also began to incline to give their support to Ganganelli , looking upon him as indifferent or even favourable to the Jesuits . It seems that the attitude of Zelanti was decided by the secret negotiations between their leaders Alessandro and Gian Francesco Albani and the Spanish cardinals . Cardinal de Bernis , the nominal leader of the court faction , probably did not play any role in the appointment of Ganganelli and only followed the instructions of Marquis d <unk> when all had been already known . 
- 
- = = = Results of the ballots = = = 
- 
- The results of the ballots between April 27 and May 18 were following ( only the leading candidates are included ) : 
- April 27 – Fantuzzi – 10 ; Colonna – 9 ; Pozzobonelli – 6 ; Stoppani – 5 ; Ganganelli – 5 
- April 28 – Fantuzzi – 9 ; Colonna – 9 ; Pozzobonelli – 7 ; Stoppani – 6 ; Ganganelli – 4 
- April 29 – Colonna – 11 ; Fantuzzi – 8 ; Stoppani – 5 ; Pozzobonelli – 4 ; Ganganelli – 4 
- April 30 – Colonna – 11 ; Fantuzzi – 8 ; Stoppani – 5 ; Pozzobonelli – 4 ; Ganganelli – 4 
- May 1 – Colonna – 11 ; Fantuzzi – 9 ; Stoppani – 4 ; Pozzobonelli – 4 ; Ganganelli – 4 
- May 2 – Colonna – 11 ; Fantuzzi – 9 ; Stoppani – 4 ; Pozzobonelli – 4 ; Ganganelli – 4 
- May 3 – Colonna – 9 ; Fantuzzi – 9 ; Stoppani – 5 ; Pozzobonelli – 4 ; Ganganelli – 4 
- May 4 – Colonna – 10 ; Fantuzzi – 9 ; Stoppani – 4 ; Ganganelli – 4 ; Pozzobonelli – 2 
- May 5 – Fantuzzi – 10 ; Colonna – 9 ; Stoppani – 4 ; Ganganelli – 4 ; Pozzobonelli – 3 
- May 6 – Fantuzzi – 11 ; Stoppani – 7 ; Colonna – 6 ; Ganganelli – 4 ; Pozzobonelli – 4 
- May 7 – Colonna – 8 ; Fantuzzi – 7 ; Stoppani – 6 ; Ganganelli – 4 ; Pozzobonelli – 4 
- May 8 – Colonna – 9 ; Stoppani – 6 ; Fantuzzi – 5 ; Ganganelli – 4 ; Pozzobonelli – 3 
- May 9 – Colonna – 11 ; Stoppani – 6 ; Fantuzzi – 5 ; Pozzobonelli – 4 ; Ganganelli – 3 
- May 10 – Colonna – 11 ; Stoppani – 7 ; Pozzobonelli – 5 ; Fantuzzi – 4 ; Ganganelli – 4 
- May 11 – Colonna – 11 ; Pozzobonelli – 6 ; Stoppani – 5 ; Ganganelli – 5 ; Fantuzzi – 3 
- May 12 – Colonna – 11 ; Pozzobonelli – 6 ; Stoppani – 6 ; Ganganelli – 6 ; Fantuzzi – 5 
- May 13 – Colonna – 13 ; Stoppani – 7 ; Pozzobonelli – 6 ; Ganganelli – 5 ; Fantuzzi – 5 
- May 14 – Colonna – 11 ; Ganganelli – 10 ; Pozzobonelli – 9 ; Stoppani – 8 ; Fantuzzi – 4 
- May 15 – Colonna – 11 ; Stoppani – 11 ; Ganganelli – 10 ; Pozzobonelli – 9 ; Fantuzzi – 5 
- May 16 – Colonna – 11 ; Ganganelli – 10 ; Pozzobonelli – 8 ; Stoppani – 8 ; Fantuzzi – 4 
- May 17 – Colonna – 12 ; Pozzobonelli – 12 ; Ganganelli – 10 ; Stoppani – 5 ; Fantuzzi – 1 
- May 18 – Ganganelli – 19 ; Colonna – 13 ; Pozzobonelli – 11 ; Stoppani – 6 ; Fantuzzi – 1 
- 
- = = Election of Clement XIV = = 
- 
- In the final ballot on May 19 , 1769 Cardinal Lorenzo Ganganelli was elected to the papacy receiving all votes except of his own , which he gave to Carlo Rezzonico , nephew of Clement XIII and one of the leaders of Zelanti . He took the name of Clement XIV , in honour of Clement XIII , who had elevated him to the <unk> . 
- On May 28 the new pope was consecrated to the episcopate by Cardinal Federico <unk> <unk> , bishop of Porto e Santa <unk> and sub @-@ dean of the Sacred College of Cardinals , assisted by Cardinals Gian Francesco Albani , bishop of <unk> and Henry Benedict Stuart , bishop of <unk> . On June 4 he was solemnly crowned by Cardinal Alessandro Albani , <unk> of S. Maria in Via <unk> 
- 
- 
- = West Hendford Cricket Ground , Yeovil = 
- 
- West Hendford Cricket Ground was a first @-@ class cricket ground located in Yeovil , Somerset . The land for the ground was first leased by Yeovil Cricket Club in 1874 , and was also used for a range of other sports , most significantly hosting Yeovil Rugby Club in the 1890s , and then again from 1935 until the ground was closed . Significant improvements were made to the ground during the 1930s , including the opening of a new pavilion , jointly funded by the Rugby and Cricket clubs . The ground was demolished in 1944 when Westland Aircraft extended their factory , and both Yeovil Cricket Club and Rugby Club moved to Johnson Park . 
- Between 1935 and 1939 , the ground hosted five annual Somerset County Cricket Club matches in July or August ; the first of which nearly broke a county record for ticket sales on the gate . Somerset only won one of the five matches , the 1936 contest against Worcestershire . 
- 
- = = History = = 
- 
- Yeovil and County Cricket Club was formed in 1865 , and was the first attempt at setting up a county cricket team for Somerset . The attempt was unsuccessful , and the club broke up . In 1874 , the club was re @-@ formed with the lesser remit , as Yeovil Cricket Club . As part of the club 's resuscitation , the committee purchased the use of a field in West Hendford in Yeovil , from a local farmer , Mr Brook . The field , part of Key Farm , was leased for £ 10 . There is record of a match being played on the ground the following year between two sets of members of the Yeovil Cricket Club . During the late 19th @-@ century , the ground was used for other sports as well as cricket ; it had a grass athletics track , and also hosted Yeovil Football Club , who at the time played both association and rugby football . The football club played at West Hendford on an irregular basis during the late 19th @-@ century , but returned in 1935 , by which time they only played rugby , and had changed their name accordingly to Yeovil Rugby Club . In 1895 , the cricket club committee announced that there was provision for a longer lease , of five or seven years , and that they would make improvements to the ground to enable it to host first @-@ class cricket . The ground was also used for field hockey in the early 20th @-@ century , hosting a Yeovil Hockey Club . 
- Somerset County Cricket Club played their first of five annual first @-@ class matches on the ground in 1935 . The match , against Surrey , was a significant event in the town , and a series of festivities were arranged to run alongside the three @-@ day contest , including a dance and a smoking concert . Entry for the match , which took place from 17 to 19 August was one shilling , and attracted over 5 @,@ 000 people , raising around £ 400 . Surrey won the match by eight wickets . The takings from this match helped the Yeovil Cricket Club make further improvements to the ground , expanding it and adding further seating . The following year , Somerset played Worcestershire at the ground , in what the Western Gazette described as " Yeovil Cricket Festival " . The captain of Yeovil Cricket Club , Richard <unk> , was included in the Somerset team , which won the match by 170 runs . The takings were slightly lower than the previous year due to poor weather , but still described as " <unk> " . 
- In 1937 , Sussex beat Somerset at the ground , in a match that once again drew a crowd of around 5 @,@ 000 . The Yorkshire Evening Post described the wicket as " crumbling " towards the end of the match , favouring the bowlers . In 1938 Hampshire visited , and the report in the Western Daily Press lamented the state of the wicket , which meant that the game , like the three first @-@ class matches at the ground before it , was completed in two days , rather than the scheduled three . That winter , a new pavilion costing £ 550 was erected on the ground for the shared use of the cricket club and the rugby club . The final first @-@ class match on the ground was played in July 1939 against Lancashire , but torrential rain limited the match to only three hours of play . The takings for the full three days of the match were only £ 87 , and the Taunton Courier estimated that the losses for the match could be hundreds of pounds . Despite the weather , almost 2 @,@ 000 people attended the match , and the Taunton Courier report praised the alterations that had been made to the ground ; the removal of a hedge made the ground lighter , while the ground itself had been well looked after , and drained quickly . The Second World War suspended the County Championship from 1940 to 1945 , and during that time , Westland Aircraft took over the ground to expand their factory , and informed Yeovil Cricket Club that it was no longer available , forcing them to search for a new ground in 1946 . They eventually relocated to the newly opened Johnson Park in 1948 . The rugby club also moved to Johnson Park , <unk> itself into Yeovil Sports Club . After a short break , Somerset County Cricket Club returned to Yeovil , playing fourteen fixtures at Johnson Park between 1951 and 1970 , and eight matches at <unk> Sports Ground from 1971 to 1978 . 
- 
- = = Records = = 
- 
- During its limited use as a first @-@ class cricket ground , only one century was scored on the ground , by Jim Parks . During the 1937 match , he scored 140 runs for Sussex . The most wickets taken by a bowler in a match at West Hendford was achieved in 1938 , when Hampshire 's Stuart <unk> took twelve wickets , including nine in the first innings . Somerset 's only success on the ground was in 1936 against Worcestershire , who they dismissed for 60 runs in the first innings , and 77 in the second . 
- 
- 
- = New Year 's Eve ( Up All Night ) = 
- 
- " New Year 's Eve " is the twelfth episode of the first season of the American comedy television series Up All Night . The episode originally aired on NBC in the United States on January 12 , 2012 . It was written by Erica <unk> and was directed by Beth McCarthy @-@ Miller . The episode also featured a guest appearance from Jason Lee as Chris and Reagan 's neighbor and Ava 's boyfriend , Kevin . 
- During Reagan ( Christina Applegate ) and Chris 's ( Will <unk> ) first New Year 's Eve game night , Reagan 's competitiveness comes out causing Chris to become embarrassed . Meanwhile , Missy ( Jennifer Hall ) brings an unexpected date along to the party and , Kevin ( Jason Lee ) starts to feel as though Ava ( Maya Rudolph ) may be <unk> of him . 
- " New Year 's Eve " received mostly positive reviews from critics . According to the Nielsen Media Research , " New Year 's Eve " drew 4 @.@ 28 million viewers and received a 2 @.@ 0 rating / 5 % share in the 18 – 49 demographic , marking a 5 % rise in the ratings from the previous episode , " First Christmas " . It ranked third in its timeslot and was the second highest @-@ rated NBC program of the night after The Office . 
- 
- = = Plot = = 
- 
- After not being able to find a baby @-@ sitter for Amy , Reagan suggests that the two throw a game night , an idea Chris doesn 't react well to . They invite Ava , Kevin , Missy , but Chris attempts to hide the games due to Reagan 's competitive nature . He tries to make her promise that she won 't be too competitive , but she does which makes the party awkward . While playing Rock Band the two get in a fight when Chris loses the beat on the drums because he was looking at his " drumming arm " . Reagan decide to a make a list of " Things We Are Going to Stop Doing That <unk> Each Other in 2012 " , which features annoying habits that the two want each other to give up . However , before 2011 comes to an end the two erase every thing from the list except for Chris 's <unk> impression and <unk> 's competitive nature . 
- Meanwhile , Ava is asked to be the grand marshal to a New Year 's Day parade . This makes her boyfriend , Kevin , feel like he can 't live up to her lifestyle . He then starts thinking she may be <unk> of him , especially after he isn 't invited to sit with her during the parade . Eventually , Kevin confronts Ava on this and she reveals that if she <unk> up their relationship she doesn 't wanted to be reminded of it while <unk> her name . He assures her that their relationship won 't end badly and the two go to the parade . 
- 
- = = Production = = 
- 
- " New Year 's Eve " was written by supervising producer Erica <unk> , marking her third writing credit for the series after " Mr. Bob 's <unk> <unk> " and " Parents " . The episode was directed by Beth McCarthy @-@ Miller , who previously worked with creator Emily Spivey and executive producer Lorne Michaels on Saturday Night Live as director for 11 years . The episode features a guest appearance from Jason Lee as Kevin , Ava 's boyfriend . He first appeared in the eighth episode , " First Night Away " and is currently set to appear in a recurring role for the series . Lee had previously worked with Spivey and Michaels after hosting an episode of Saturday Night Live on November 12 , 2005 . This is the first time the series aired in the 9 : 30 pm timeslot for the first season after The Office ; the series previously aired in the 8 : <unk> timeslot on Wednesday . The series switched <unk> with another NBC comedy series , Whitney . Some media critics have said that the goal for moving the series was in order to make it more of a ratings success , like The Office . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " New Year 's Eve " originally aired on NBC in the United States on January 12 , 2012 . The episode was viewed by an estimated 4 @.@ 24 million viewers and received a 2 @.@ 0 rating / 5 % share among adults between the ages of 18 and 49 . This means that it was seen by 2 @.@ 0 % of all 18- to 49 @-@ year @-@ olds , and 5 % of all 18- to 49 @-@ year @-@ olds watching television at the time of the broadcast . This marked a 5 % rise in the ratings from the previous episode , " First Christmas " . The episode finished third in its time slot along with The Office , being beaten by Grey 's Anatomy which received a 3 @.@ 8 rating / 9 % share and the CBS drama Person of Interest which received a 3 @.@ 2 rating / 8 % share in the 18 – 49 demographic . The episode , however , did manage to beat the Fox drama series The <unk> and the CW drama series The Secret Circle . <unk> with <unk> viewers , who viewed the episode within seven days of the original broadcast , the episode received a 3 @.@ 0 rating in the 18 – 49 demographic , adding a 1 @.@ 0 rating to the original viewership . 
- 
- = = = Critical reviews = = = 
- 
- " New Year 's Eve " received several positive reviews from critics . New York writer Steven Heisler praised the episode for avoiding " sitcom @-@ y territory " with the emotional ending . He also called the series a better choice to follow The Office then Whitney . The A.V. Club reviewer Margaret <unk> complemented the <unk> of the main <unk> @-@ Chris plot . Despite this , she criticized the Ava @-@ Kevin subplot comparing it to a storyline from Sex and The City . She also noted the plotline didn 't stay true to the characters following their plotline in the previous episode , " First Christmas " . She ultimately rated the episode with a B. Adam <unk> of Paste called the episode a perfect transition from the previous episodes and allowed Ava to be " a <unk> third wheel to a completely strong duo in Chris and Reagan " . He also reacted positively for the scenes featuring Missy , comparing her scenes to " early Ava , but less <unk> " . He ultimately gave the episode an 8 @.@ 7 / 10 calling it " <unk> " . Bradford Evans of <unk> praised Jennifer Hall 's performance calling her the " <unk> hero " of the series . He also reacted positively towards the episode 's ability to <unk> the show and " keep all of the characters on the same turf " . He concluded that he hoped the series could make itself a vital part of the network 's lineup . HitFix reviewer Alan Sepinwall called the episode " one its [ the series ] strongest episodes to date " . He wrote that the addition of Jason Lee <unk> Ava more and gave her a more natural reason to visit Reagan and Chris at home . He also wrote that the episode worked on a " character level " . 
- 
- 
- = World War Z = 
- 
- World War Z : An Oral History of the Zombie War ( 2006 ) is an apocalyptic horror novel by Max Brooks . The novel is a collection of individual accounts narrated by an agent of the United Nations Postwar Commission , following the devastating global conflict against the zombie plague . Other passages record a decade @-@ long desperate struggle , as experienced by people of various <unk> . The personal accounts also describe the resulting social , political , religious , and environmental changes . 
- World War Z is a follow @-@ up to Brooks ' " survival manual " The Zombie Survival Guide ( 2003 ) , but its tone is much more serious . It was inspired by The Good War : An Oral History of World War Two ( 1984 ) by Studs Terkel , and by the zombie films of George A. Romero . Brooks used World War Z to comment on government <unk> and American <unk> , while also examining <unk> and uncertainty . The novel was a commercial hit and was praised by most critics . 
- Its audiobook version , performed by a full cast including Alan <unk> , Mark <unk> , and John <unk> , won an <unk> Award in 2007 . A film inspired by the novel , directed by Marc Forster and starring Brad Pitt , was released in 2013 . 
- 
- = = Plot = = 
- 
- The story is told in the form of a series of interviews conducted by the narrator , Max Brooks , an agent of the United Nations Postwar Commission . Although the exact origin of the plague is unknown , a young boy from a village in China is identified as the plague 's official patient zero . The boy 's case marks the point where the Chinese government begins to take measures to cover up the disease , including generating a crisis with Taiwan to mask their activities . Nevertheless , the plague still manages to spread to various nations by human <unk> , refugees and the black market organ trade . Initially , these nations were able to cover up their smaller outbreaks , until a much larger outbreak in South Africa brings the plague to public attention . 
- As the infection spreads , Israel abandons the Palestinian territories and initiates a nationwide quarantine , closing its borders to everyone except uninfected Jews and Palestinians . Its military then puts down an ultra @-@ Orthodox uprising , which is later referred to as an Israeli civil war . The United States does little to prepare because it is <unk> in its ability to suppress any threat . Although special forces teams contain initial outbreaks , a widespread effort never starts : the nation is deprived of political will by " <unk> wars " , and a widely distributed and marketed <unk> vaccine creates a false sense of security . 
- As many more areas around the globe fall to infection , a period known as the " Great Panic " begins . Pakistan and Iran destroy each other in a nuclear war , after the Iranian government attempts to stem the flow of refugees fleeing through Pakistan into Iran . After zombies overrun New York City , the U.S. military sets up a high @-@ profile defense in the nearby city of <unk> . The " Battle of <unk> " is a disaster ; modern weapons and tactics prove ineffective against zombies , as the enemy has no self @-@ preservation instincts and can only be stopped if shot through the head . The unprepared and <unk> soldiers are routed on live television . Other countries suffer similarly disastrous defeats , and human civilization <unk> on the brink of destruction . 
- In South Africa , the government adopts a <unk> plan drafted by apartheid @-@ era intelligence consultant Paul <unk> . It calls for the establishment of small sanctuaries , leaving large groups of survivors abandoned in special zones in order to distract the undead and allowing those within the main safe zone time to regroup and <unk> . Governments worldwide assume similar plans or relocate the populace to safer foreign territory , such as the attempted complete evacuation of the Japanese archipelago to <unk> . Because zombies freeze solid in severe cold , many civilians in North America flee to the <unk> of northern Canada and the Arctic , where eleven million people die of starvation and <unk> . It is implied that some turn to cannibalism to survive ; further interviews from other sources imply that cannibalism occurred in areas of the United States where food shortages occurred . The three remaining astronauts in the International Space Station survive the war by salvaging supplies from the abandoned Chinese space station and maintain some military and civilian satellites using an orbital fuel station . A surviving member of the <unk> crew describes " mega " <unk> of zombies on the American Great Plains and Central Asia , and how the crisis affected Earth 's atmosphere . 
- The U.S. eventually establishes safe zones west of the Rocky Mountains and spends much of the next decade <unk> zombies in that region . All aspects of civilian life are devoted to supporting the war effort against the pandemic . Much of it resembles total war strategies : rationing of fuel and food , cultivation of private gardens , and civilian neighborhood patrols . The U.S. government also initiates a " Re @-@ education Act " to train the civilian population for the war effort and restore order ; the people with skills such as carpentry and construction find themselves more valuable than people with managerial skills . 
- Seven years after the outbreak began , a conference is held off the coast of Honolulu , aboard the USS Saratoga , where most of the world 's leaders argue that they can <unk> the zombie plague if they stay in their safe zones . The U.S. President , however , argues for going on the offensive . <unk> to lead by example , the U.S. military <unk> itself to meet the specific strategic requirements of fighting the undead : using semi @-@ automatic , high @-@ power rifles and volley firing , focusing on head shots and slow , steady rates of fire ( a tactic " re @-@ invented " by the Indian Army during the Great Panic ) ; and <unk> a multipurpose hand tool , the " <unk> " or " <unk> " ( described as a combination of a shovel and a battle axe ) , for close @-@ quarters combat . The military , backed by a <unk> American wartime economy , began the three @-@ year @-@ long process of retaking the contiguous United States from both the undead as well as groups of hostile human survivors . <unk> military tactics and equipment are mentioned as being employed to deal with sometimes well @-@ armed and organized human criminal or rebel opposition . 
- Ten years after the official end of the zombie war , millions of zombies are still active , mainly on the ocean floor or on snow line islands . A democratic Cuba has become the world 's most thriving economy . Following a civil war that saw use of nuclear weapons , China has become a democracy and is now known as the " Chinese Federation " . <unk> is freed from Chinese rule and hosts <unk> , the world 's most populated city . Following a religious revolution and the revival of Russian <unk> , Russia is now an <unk> <unk> known as the Holy Russian Empire . Owing to the fact that many young Russians either became zombies , were infected with HIV , or died due to drugs , the government has initiated a " breeding " program , with the remaining fertile women implied to be <unk> impregnated to raise the <unk> . North Korea is completely empty , with the entire population presumed to have disappeared into underground bunkers . 
- The situation in the British Isles is not entirely clear in the novel , although Ireland may have escaped the worst of the outbreak . Members of the British Royal Family had fled to Ireland and the Isle of Man , following the military retreat to the <unk> Wall , and now exports oil from a reserve under Windsor Castle where the Queen held out for the war 's duration , refusing to flee with her relatives . The <unk> established a wartime refuge in the Roman Catholic Archdiocese of Armagh . In France , the Palace of Versailles was the site of a massacre and has been burned to the ground ; military losses were particularly high while clearing the catacombs underneath Paris because the catacombs housed nearly a quarter of a million refugees during the early stages of the war , all of whom became zombies . Iceland has been completely <unk> and remains the world 's most heavily infested country . 
- The <unk> and Palestinians have made peace , and the former occupied territories have been renamed " Unified Palestine " . Mexico is now known as " <unk> " . Several countries are described as having revised borders due to the " dumping " of convicts into infected zones ; these convicts rose to command " powerful <unk> " that later became independent states . A so @-@ called " Pacific Continent " appears to encompass previously uninhabited islands as well as ships rendered immobile due to lack of fuel . For unknown reasons , the Saudi Royal Family destroyed the oil fields in Saudi Arabia . 
- The United Nations fields a large military force to eliminate the remaining zombies from overrun areas , defeat hordes that surface from the ocean floor , and kill frozen zombies before they <unk> . It is stated that previously eradicated diseases have made a comeback and that global life expectancy is greatly reduced as the world starts over from where it began . 
- 
- = = Development = = 
- 
- Brooks designed World War Z to follow the " laws " set up in his earlier work , The Zombie Survival Guide ( 2003 ) , and explained that the guide may exist in the novel 's fictional universe . The zombies of The Zombie Survival Guide are human bodies <unk> by an <unk> virus ( <unk> ) , devoid of intelligence , <unk> solely of consuming living flesh , and cannot be killed unless the brain is destroyed . <unk> will eventually set in , but this process takes longer than for an uninfected body and can be slowed by effects such as freezing . Although zombies do not tire and are as strong as the humans they infect , they are slow @-@ moving and incapable of planning or cooperation in their attacks . Zombies usually reveal their presence by moaning . 
- Brooks discussed the cultural influences on the novel . He claimed inspiration from " The Good War " : An Oral History of World War Two ( 1984 ) by Studs Terkel , stating : " [ Terkel 's book is ] an oral history of World War II . I read it when I was a teenager and it 's sat with me ever since . When I sat down to write World War Z : An Oral History of the Zombie War , I wanted it to be in the vein of an oral history . " Brooks also cited renowned zombie film director George A. Romero as an influence and criticized The Return of the Living Dead films : " They <unk> zombies , make them silly and <unk> . They 've done for the living dead what the old Batman TV show did for The Dark Knight . " Brooks acknowledged making several references to popular culture in the novel , including one to alien robot franchise <unk> , but declined to identify the others so that readers could discover them independently . 
- Brooks conducted copious research while writing World War Z. The technology , politics , economics , culture , and military tactics were based on a variety of reference books and <unk> with expert sources . Brooks also cites the U.S. Army as a reference on <unk> statistics . 
- 
- = = Analysis = = 
- 
- 
- = = = Social commentary = = = 
- 
- Reviewers have noted that Brooks uses World War Z as a platform to criticize government <unk> , corporate corruption , and human short @-@ <unk> . At one point in the book , a Palestinian refugee living in Kuwait refuses to believe the dead are rising , fearing it is a trick by Israel . Many American characters blame the United States ' inability to counter the zombie threat on low confidence in their government due to conflicts in the Middle East . 
- Brooks shows his particular dislike of government bureaucracy . For example , one character in the novel tries to justify lying about the zombie outbreak to avoid widespread panic , while at the same time failing to develop a solution for fear of <unk> public ire . He has also criticized American <unk> : 
- 
- = = = Themes = = = 
- 
- 
- = = = = <unk> = = = = 
- 
- <unk> and disaster preparation are prevalent themes in the novel . Several interviews , especially those from the United States , focus on policy changes designed to train the surviving Americans to fight the zombies and rebuild the country . For example , when cities were made to be as efficient as possible in order to fight the zombies , the <unk> could hold a higher status than the former <unk> ; when the ultra @-@ rich hid in their homes , which had been turned into fortified compounds , they were overwhelmed by others trying to get in , leading to mass slaughter . Throughout the novel , characters demonstrate the physical and mental requirements needed to survive a disaster . Brooks described the large amount of research needed to find optimal methods for fighting a worldwide zombie outbreak . He also pointed out that Americans like the zombie genre because they believe they can survive anything with the right tools and talent . 
- 
- = = = = Fear and uncertainty = = = = 
- 
- Brooks considers the theme of uncertainty central to the zombie genre . He believes that zombies allow people to deal with their own anxiety about the end of the world . Brooks has expressed a deep fear of zombies : 
- This <unk> is connected to the context in which Brooks was writing . He declared : " at this point we 're pretty much living in an irrational time " , full of human suffering and lacking reason or logic . When asked in a subsequent interview about how he would compare terrorists with zombies , Brooks said : 
- 
- = = Reception = = 
- 
- Reviews for the novel have been generally positive . Gilbert Cruz of Entertainment Weekly gave the novel an " A " rating , commenting that the novel shared with great zombie stories the use of a central metaphor , describing it as " an <unk> readable oral history . " Steven H. Silver identified Brooks ' international focus as the novel 's greatest strength and commented favorably on Brooks ' ability to create an appreciation for the work needed to combat a global zombie outbreak . Silver 's only complaint was with " Good @-@ <unk> " — the final chapter — in which characters get a chance to give a final closing statement . Silver felt that it was not always apparent who the <unk> , undifferentiated characters were . The Eagle described the book as being " unlike any other zombie tale " as it is " sufficiently terrifying for most readers , and not always in a blood @-@ and @-@ <unk> way , either . " Keith <unk> of The A.V. Club stated that the format of the novel makes it difficult for it to develop momentum , but found the novel 's individual episodes gripping . Patrick Daily of the Chicago Reader said the novel <unk> the " <unk> " of The Zombie Survival Guide by " touching on deeper , more somber aspects of the human condition . " In his review for Time Out Chicago , Pete Coco declared that " [ b ] ending horror to the form of alternative history would have been novel in and of itself . Doing so in the mode of Studs Terkel might constitute <unk> . " 
- Ron <unk> Jr. named World War Z one of his favorite apocalyptic novels and praised Brooks for illustrating " the tacit agreement between writer and reader that is essential to the success of stories about the end of the world ... [ both ] agree to pretend that this is not fiction , that in fact the horrific tales of a war between humans and zombies are based in reality . " Drew Taylor of the Fairfield County Weekly credited World War Z with making zombies more popular in mainstream society . 
- The hardcover version of World War Z spent four weeks on the New York Times Best <unk> list , peaking at number nine . By November 2011 , according to Publishers Weekly , World War Z had sold one million copies in all formats . 
- 
- = = Audiobook = = 
- 
- Random House published an abridged audiobook in 2007 , directed by John <unk> <unk> and produced by Dan <unk> , with sound editing by Charles De <unk> . The book is read by Brooks but includes other actors taking on the roles of the many individual characters who are interviewed in the novel . Brooks ' previous career in voice acting and voice @-@ over work meant he could recommend a large number of the cast members . 
- On May 14 , 2013 , Random House Audio released a <unk> audiobook titled World War Z : The Complete Edition ( Movie <unk> @-@ in Edition ) : An Oral History of the Zombie War . It contains the entirety of the original , abridged audiobook , as well as new recordings of each missing segment . A separate , additional audiobook containing only the new recordings not found in the original audiobook was released simultaneously as World War Z : The Lost Files : A Companion to the <unk> Edition . 
- 
- = = = Cast = = = 
- 
- * <unk> edition 
- 
- = = = Reception = = = 
- 
- In her review of the audiobook for Strange Horizons , <unk> Carroll called the story " gripping " and found the listening experience evocative of Orson Welles 's famous radio narration of The War of the Worlds ( broadcast October 30 , 1938 ) . Carroll had mixed opinions on the voice acting , commending it as " solid and understated , <unk> free of ' special effects ' and ' scenery chewing ' overall " , but lamenting what she perceived as undue <unk> on the part of Max Brooks and <unk> in Steve Park 's Chinese accent . Publishers Weekly also criticized Brooks ' narration , but found that the rest of the " all @-@ star cast deliver their parts with such fervor and intensity that listeners cannot help but <unk> with these characters " . In an article in Slate concerning the mistakes producers make on publishing <unk> , Nate <unk> used World War Z as an example of <unk> whose full casts contributed to making them " great listens " and described the book as a " <unk> @-@ than @-@ it @-@ has @-@ any @-@ right @-@ to @-@ be zombie novel " . The World War Z audiobook won the 2007 <unk> Award for Multi @-@ <unk> Performance and was nominated for Audiobook of the Year . 
- 
- = = Film adaptation = = 
- 
- In June 2006 , Paramount Studios secured the film rights for World War Z for Brad Pitt 's production company , Plan B Entertainment , to produce . The screenplay was written by J. Michael <unk> , with Marc Forster directing and Pitt starring as the main character , UN employee Gerry Lane . Despite being the draft that got the film green @-@ lit , <unk> 's script was tossed aside , so that production , which was to begin at the start of 2009 , was delayed while the script was completely re @-@ written by Matthew Michael <unk> to set the film in the present , leaving behind much of the book 's premise to make it more of an action film . In a 2012 interview , Brooks claimed the film now had nothing in common with the novel other than the title . Filming commenced mid @-@ 2011 , and the film was released in June 2013 . 
- 
- 
- = Sentence spacing = 
- 
- Sentence spacing is the horizontal space between sentences in typeset text . It is a matter of typographical convention . Since the introduction of movable @-@ type printing in Europe , various sentence spacing conventions have been used in languages with a Latin alphabet . These include a normal word space ( as between the words in a sentence ) , a single enlarged space , and two full spaces . 
- Until the 20th century , publishing houses and printers in many countries used additional space between sentences . There were exceptions to this traditional spacing method — some printers used spacing between sentences that was no wider than word spacing . This was French spacing — a term synonymous with single @-@ space sentence spacing until the late 20th century . With the introduction of the typewriter in the late 19th century , typists used two spaces between sentences to mimic the style used by traditional typesetters . While wide sentence spacing was phased out in the printing industry in the mid @-@ twentieth century , the practice continued on typewriters and later on computers . Perhaps because of this , many modern sources now incorrectly claim that wide spacing was created for the typewriter . 
- The desired or correct sentence spacing is often debated but many sources now say additional space is not necessary or desirable . From around 1950 , single sentence spacing became standard in books , magazines and newspapers and the majority of style guides that use a Latin @-@ derived alphabet as a language base now prescribe or recommend the use of a single space after the concluding punctuation of a sentence . However , some sources still state that additional spacing is correct or acceptable . The debate continues on the World Wide Web . Many people prefer double sentence spacing for informal use because that was how they were taught to type . There is a debate on which convention is more readable ; the few recent direct studies conducted since 2002 have produced inconclusive results . 
- 
- = = History = = 
- 
- 
- = = = Traditional typesetting = = = 
- 
- Shortly after the invention of movable type , highly variable spacing was created that could create spaces of any size , and allowed for perfectly even justification . Early American , English , and other European typesetters ' style guides ( also known as printers ' rules ) specified spacing standards that were all essentially identical from the 18th century onwards . These guides — e.g. , Jacobi in the UK ( 1890 ) and <unk> , <unk> , and De <unk> ( 1866 – 1901 ) in the U.S. — indicated that sentences should be em @-@ spaced , and that words should be 1 / 3 or 1 / 2 em @-@ spaced ( illustration right ) . The relative size of the sentence spacing would vary depending on the size of the word spaces and the justification needs . For most countries , this remained the standard for published work until the 20th century . Yet , even in this period , there were publishing houses ( notably in France ) that used a standard word space between sentences — a technique called French spacing ( illustration below ) . 
- 
- = = = Mechanical type and the advent of the typewriter = = = 
- 
- Mechanical type systems introduced near the end of the 19th century , such as the <unk> and <unk> machines , allowed for some variable sentence spacing similar to hand composition . Just as these machines revolutionized the mass production of text , the advent of the typewriter around the same time revolutionized the creation of personal and business documents . But the typewriters ' mechanical limitations did not allow variable spacing — typists could only choose the number of times they pressed the space bar . <unk> in some English @-@ speaking countries initially learned to insert three spaces between sentences to approximate the wider sentence spacing used in traditional printing , but later settled on two spaces , a practice that continued throughout the 20th century . This became known as English spacing , and marked a divergence from French typists , who continued to use French spacing . 
- 
- = = = Transition to single spacing = = = 
- 
- In the early 20th century , some printers began using one and a half <unk> spaces ( an " en <unk> " ) to separate sentences . This standard continued in use , to some extent , into the 1990s . 
- <unk> , newspapers , and books began to adopt the single space convention in the United States in the 1940s and in the United Kingdom in the 1950s . <unk> did not move to single spacing simultaneously . The average writer still relied on the typewriter to create text — with its inherent mechanical spacing limitations . 
- <unk> advances began affecting sentence spacing methods . In 1941 , IBM introduced the Executive , a typewriter capable of proportional spacing — which had been used in professional typesetting for hundreds of years . This innovation broke the hold that the monospaced font had on the typewriter — reducing the severity of its mechanical limitations . By the 1960s , electronic <unk> systems ignored runs of white space in text . This was also true of the World Wide Web , as HTML normally ignores additional spacing , although in 2011 the CSS 2 @.@ 1 standard officially added an option that can preserve additional spaces . In the 1980s , desktop publishing software provided the average writer with more advanced formatting tools . By the late 20th century , literature on the written word had begun to adjust its guidance on sentence spacing . 
- 
- = = Modern literature = = 
- 
- 
- = = = Typography = = = 
- 
- Early positions on typography ( the " arrangement and appearance of text " ) supported traditional spacing techniques in English publications . In 1954 , Geoffrey <unk> 's book , <unk> Points in the <unk> and Arrangement of Type , <unk> the widespread shift from a single enlarged em space to a standard word space between sentences . 
- With the advent of the computer age , <unk> began deprecating double spacing , even in monospaced text . In 1989 , Desktop Publishing by Design stated that " typesetting requires only one space after periods , question marks , exclamation points , and <unk> " , and identified single sentence spacing as a typographic convention . Stop <unk> <unk> & Find Out How Type Works ( 1993 ) and <unk> with Type : The Essential Guide to Typography ( 2006 ) both indicate that uniform spacing should be used between words , including between sentences . 
- More recent works on typography weigh in strongly . <unk> <unk> , founder of the Type Studio , says , " Forget about <unk> differences of opinion : <unk> speaking , typing two spaces before the start of a new sentence is absolutely , unequivocally wrong . " The Complete Manual on Typography ( 2003 ) states that " The typewriter tradition of separating sentences with two word spaces after a period has no place in typesetting " and the single space is " standard typographic practice " . The Elements of <unk> Style ( 2004 ) advocates a single space between sentences , noting that " your typing as well as your typesetting will benefit from <unk> this quaint [ double spacing ] Victorian habit . " 
- David Jury 's book , About Face : <unk> the Rules of Typography ( 2004 ) — published in Switzerland — <unk> the contemporary typographic position on sentence spacing : 
- Word spaces , preceding or following punctuation , should be <unk> adjusted to appear to be of the same value as a standard word space . If a standard word space is inserted after a full point or a comma , then , <unk> , this produces a space of up to 50 % wider than that of other word spaces within a line of type . This is because these punctuation marks carry space above them , which , when added to the adjacent standard word spaces , combines to create a visually larger space . Some argue that the " additional " space after a comma and full point serves as a " pause signal " for the reader . But this is unnecessary ( and visually disruptive ) since the pause signal is provided by the punctuation mark itself . 
- 
- = = = Style and language guides = = = 
- 
- 
- = = = = Style guides = = = = 
- 
- Early style guides for typesetting used a wider space between sentences than between words – " traditional spacing " , as shown in the illustration to the right . During the 20th century , style guides commonly mandated two spaces between sentences for <unk> manuscripts , which were used prior to professionally typesetting the work . As computer desktop publishing became commonplace , <unk> manuscripts became less relevant and most style guides stopped making distinctions between manuscripts and final typeset products . In the same period , style guides began changing their guidance on sentence spacing . The 1969 edition of the Chicago Manual of Style used em spaces between sentences in its text ; by the 2003 edition it had changed to single sentence spacing for both manuscript and print . By the 1980s , the United Kingdom 's Hart 's Rules ( 1983 ) had shifted to single sentence spacing . Other style guides followed suit in the 1990s . Soon after the beginning of the 21st century , the majority of style guides had changed to indicate that only one word space was proper between sentences . 
- Modern style guides provide standards and guidance for the written language . These works are important to writers since " virtually all professional editors work closely with one of them in editing a manuscript for publication . " Late editions of comprehensive style guides , such as the Oxford Style Manual ( 2003 ) in the United Kingdom and the Chicago Manual of Style ( 2010 ) in the United States , provide standards for a wide variety of writing and design topics , including sentence spacing . The majority of style guides now prescribe the use of a single space after terminal punctuation in final written works and publications . A few style guides allow double sentence spacing for draft work , and the Gregg Reference Manual makes room for double and single sentence spacing based on author preferences . Web design guides do not usually provide guidance on this topic , as " HTML refuses to recognize double spaces altogether . " These works themselves follow the current publication standard of single sentence spacing . 
- The European Union 's <unk> Style Guide ( 2008 ) indicates that single sentence spacing is to be used in all European Union publications — encompassing 23 languages . For the English language , the European Commission 's English Style Guide ( 2010 ) states that sentences are always single @-@ spaced . The Style Manual : For Authors , Editors and <unk> ( 2007 ) , first published in 1966 by the Commonwealth Government <unk> Office of Australia , <unk> that only one space is used after " sentence @-@ closing punctuation " and that " <unk> for word processing and desktop publishing offer more sophisticated , variable spacing , so this practice of double spacing is now avoided because it can create distracting gaps on a page . " 
- National languages not covered by an authoritative language academy typically have multiple style guides — only some of which may discuss sentence spacing . This is the case in the United Kingdom . The Oxford Style Manual ( 2003 ) and the Modern Humanities Research Association 's <unk> Style Guide ( 2002 ) state that only single spacing should be used . In Canada , both the English and French language sections of the Canadian Style , A Guide to Writing and Editing ( 1997 ) , prescribe single sentence spacing . In the United States , many style guides — such as the Chicago Manual of Style ( 2003 ) — allow only single sentence spacing . The most important style guide in Italy , Il <unk> <unk> di <unk> ( 2009 ) , does not address sentence spacing , but the <unk> di <unk> <unk> ( 2010 ) , the official guide for Microsoft translation , tells users to use single sentence spacing " instead of the double spacing used in the United States " . 
- 
- = = = = Language guides = = = = 
- 
- Some languages , such as French and Spanish , have academies that set language rules . Their publications typically address <unk> and grammar as opposed to matters of typography . Style guides are less relevant for such languages , as their academies set <unk> rules . For example , the <unk> française publishes the <unk> de l <unk> française for French speakers worldwide . The 1992 edition does not provide guidance on sentence spacing , but is single @-@ sentence @-@ spaced throughout — consistent with historical French spacing . The Spanish language is similar . The most important body within the Association of Spanish Language Academies , the Real <unk> <unk> , publishes the <unk> de la <unk> <unk> , which is viewed as <unk> for the Spanish language worldwide . The 2001 edition does not provide sentence spacing guidance , but is itself single sentence spaced . The German language manual <unk> des <unk> für Deutsche <unk> ( " <unk> of the Council for German <unk> " ) ( 2006 ) does not address sentence spacing . The manual itself uses one space after terminal punctuation . Additionally , the <unk> , the German language dictionary most commonly used in Germany , indicates that double sentence spacing is an error . 
- 
- = = = Grammar guides = = = 
- 
- A few reference <unk> address sentence spacing , as increased spacing between words is punctuation in itself . Most do not . Grammar guides typically cover terminal punctuation and the proper construction of sentences — but not the spacing between sentences . Moreover , many modern grammar guides are designed for quick reference and refer users to comprehensive style guides for additional matters of writing style . For example , the Pocket <unk> 's Guide to Grammar and <unk> ( 2005 ) points users to style guides such as the MLA Style Manual for consistency in formatting work and for all other " editorial concerns " . The Grammar Bible ( 2004 ) states that " The modern system of English punctuation is by no means simple . A book that covers all the bases would need to be of considerable breadth and weight and anyone interested in such a resource is advised to consult the Chicago Manual of Style . " 
- 
- = = Digital age = = 
- 
- In the computer era , spacing between sentences is handled in several different ways by various software packages . Some systems accept whatever the user types , while others attempt to alter the spacing , or use the user input as a method of detecting sentences . Computer @-@ based word processors , and typesetting software such as troff and <unk> , allow users to arrange text in a manner previously only available to professional typesetters . 
- The text editing environment in <unk> uses a double space following a period to identify the end of sentences unambiguously ; the double space convention prevents confusion with periods within sentences that signify abbreviations . How <unk> recognizes the end of a sentence is controlled by the settings sentence @-@ end @-@ double @-@ space and sentence @-@ end . The vi editor also follows this convention ; thus , it is relatively easy to manipulate ( jump over , copy , <unk> ) whole sentences in both <unk> and vi . 
- The <unk> <unk> program troff uses two spaces to mark the end of a sentence . This allows the <unk> to distinguish sentence endings from abbreviations and to typeset them differently . Early versions of troff , which only typeset in fixed width fonts , would automatically add a second space between sentences , which were detected based on the combination of terminal punctuation and a line feed . 
- Microsoft Word does not treat sentences differently by default , but the grammar checking can be set to prefer a specific number of spaces between sentences . 
- On some modern touch @-@ screen platforms , including <unk> and iOS , typing two spaces in a row is automatically interpreted to mean the end of a sentence , and a period is automatically inserted . However , only one space is retained . 
- Multiple spaces are eliminated by default in most World Wide Web content , regardless of whether they are associated with sentences or not . There are options for preserving spacing , such as the CSS white @-@ space property , and the < pre > tag . Twitter retains extra spaces in user input on their website . HTML also includes several other space entities which are not collapsed , such as an em space , an en space , and a non @-@ breaking space . Some <unk> space characters are also not collapsed on the web . 
- 
- = = Controversy = = 
- 
- James <unk> , author of the Complete Manual of Typography , says that the topic of sentence spacing is " the debate that refuses to die ... In all my years of writing about type , it 's still the question I hear most often , and a search of the web will find threads <unk> on the subject " . This subject is still widely debated today . 
- Many people are opposed to single sentence spacing for various reasons . Some state that the habit of double spacing is too deeply <unk> to change . Others claim that additional space between sentences improves the aesthetics or readability of text . Proponents of double sentence spacing also state that some publishers may still require double @-@ spaced manuscript submissions from authors . A key example noted is the screenwriting industry 's monospaced standard for screenplay manuscripts , Courier , 12 @-@ point font , although some works on screenwriting indicate that Courier is merely preferred – proportional fonts may be used . Some reliable sources state simply that writers should follow their particular style guide , but proponents of double spacing caution that publishers ' guidance takes precedence , including those that ask for double sentence spaced manuscripts . 
- One of the most popular arguments against wider sentence spacing is that it was created for monospaced fonts of the typewriter , and is no longer needed with modern proportional fonts . However , proportional fonts existed together with wide sentence spacing for centuries before the typewriter , and remained for decades after its invention . When the typewriter was first introduced , typists were most commonly taught to use three spaces between sentences . This gradually shifted to two spaces , while the print industry remained unchanged in its wide em @-@ spaced sentences . Some sources now state it is acceptable for monospaced fonts to be single spaced today , although other references continue to specify double spacing for monospaced fonts . The double space typewriter convention has been taught in schools in typing classes , and that remains the practice in many cases . Some voice concerns that students will later be forced to <unk> how to type . 
- Most style guides indicate that single sentence spacing is proper for final or published work today , and most publishers require manuscripts to be submitted as they will appear in publication — single sentence spaced . Writing sources typically recommend that prospective authors remove extra spaces before submitting manuscripts , although other sources state that publishers will use software to remove the spaces before final publication . 
- 
- = = Effects on readability and legibility = = 
- 
- Claims <unk> regarding the legibility and readability of the single and double sentence spacing methods — by proponents on both sides . Supporters of single spacing assert that familiarity with the current standard in books , magazines , and the Web enhances readability , that double spacing looks strange in text using proportional fonts , and that the " rivers " and " holes " caused by double spacing <unk> readability . Proponents of double sentence spacing state that the extra space between sentences enhances readability by providing clearer breaks between sentences and making text appear more <unk> , particularly noting the very small visual difference between a dot and a comma . 
- However , typographic opinions are typically <unk> with no basis in evidence . " Opinions are not always safe guides to legibility of print " , and when direct studies are conducted , <unk> opinions — even those of experts — can turn out to be false . Text that seems <unk> ( visually pleasing at first glance ) may be shown to actually <unk> reading effectiveness when subjected to scientific study . 
- 
- = = = Studies = = = 
- 
- Direct studies on sentence spacing include those by <unk> , Branch , <unk> , and Ali ( 2002 ) ; Clinton , Branch , <unk> , and <unk> ( 2003 ) ; and Ni , Branch , and Chen ( 2004 ) , with results favoring neither single , double , nor triple spacing . The 2002 study tested participants ' reading speed for single and double sentence spaced passages of on @-@ screen text . The authors stated that " the ' double space group ' consistently took longer time to finish than the ' single space ' group " but concluded that " there was not enough evidence to suggest that a significant difference exists . " The 2003 and 2004 studies analyzed on @-@ screen single , double , and triple spacing . In both cases , the authors stated that there was insufficient evidence to draw a conclusion . Ni , Branch , Chen , and Clinton conducted a similar study in 2009 using identical spacing variables . The authors concluded that the " results provided insufficient evidence that time and comprehension differ significantly among different conditions of spacing between sentences " . 
- 
- 
- = The Crab with the Golden Claws = 
- 
- The Crab with the Golden Claws ( French : Le <unk> aux <unk> d <unk> ) is the ninth volume of The Adventures of Tintin , the comics series by Belgian cartoonist Hergé . The story was serialised weekly in Le Soir Jeunesse , the children 's supplement to Le Soir , Belgium 's leading francophone newspaper , from October 1940 to October 1941 amidst the German occupation of Belgium during World War II . Partway through serialisation , Le Soir Jeunesse was cancelled and the story began to be serialised daily in the pages of Le Soir . The story tells of young Belgian reporter Tintin and his dog Snowy , who travel to Morocco to pursue a gang of international opium smugglers . 
- The Crab with the Golden Claws was published in book form shortly after its conclusion . Hergé continued The Adventures of Tintin with The <unk> Star , while the series itself became a defining part of the Franco @-@ Belgian comics tradition . In 1943 , Hergé coloured and <unk> the book in his distinctive ligne @-@ claire style for Casterman 's <unk> . The Crab with the Golden Claws introduces the recurring character Captain Haddock , who became a major fixture of the series . The book is the first Tintin adventure published in the United States and the first to be adapted into a motion picture . The Crab with the Golden Claws was adapted for the 1956 <unk> Studios animation Hergé 's Adventures of Tintin , for the 1991 <unk> / <unk> animated series The Adventures of Tintin , and for the 2011 film directed by Steven Spielberg . 
- 
- = = Synopsis = = 
- 
- Tintin is informed by Thomson and Thompson of a case involving the <unk> of a drunken man , later killed , found with a scrap of paper from what appears to be a tin of crab meat with the word " Karaboudjan " <unk> on it . His subsequent investigation and the kidnapping of a Japanese man interested in giving him a letter leads Tintin to a ship called the Karaboudjan , where he is abducted by a syndicate of criminals who have hidden opium in the crab tins . Tintin escapes from his locked room after Snowy <unk> through his bonds and Tintin knocks out a man sent to bring him food , leaving the man bound and gagged in the room . Tintin encounters Captain Haddock , an alcoholic sea captain , who is manipulated by his first mate , Allan , and is unaware of his crew 's criminal activities . Tintin hides in the locker under the bed and defeats Jumbo , the sailor left in the cabin , while Allan thinks Tintin has climbed out of the <unk> back into the <unk> . He blows open the <unk> door , then finding it empty goes back to the Captain 's room , where he finds Jumbo tied to a chair and gagged . <unk> the ship in a lifeboat after sending a radio message to the police about the cargo , a seaplane tries to attack them . Tintin and the Captain <unk> the plane , tie up the pilots , and try to reach Spain . Haddock 's drunken behaviour in a storm causes them to crash @-@ land in the <unk> , where the crew escapes . 
- After <unk> across the desert and nearly dying of <unk> , Tintin and Haddock are rescued and taken to a French outpost , where they hear on the radio the storm sunk the Karaboudjan . They travel to a Moroccan port , and along the way are attacked by <unk> tribesmen , defending themselves with French <unk> @-@ 36 rifles . At the port , members of his old crew kidnap the Captain after he recognises their disguised Karaboudjan . Tintin meets Thomson and Thompson who got his message , and they learn that the wealthy merchant Omar ben Salaad sold the crab tins ; Tintin tells Thomson and Thompson to discreetly investigate . Tintin tracks down the gang and saves the Captain , but they both become intoxicated by the fumes from wine barrels breached in a shootout with the villains . Haddock chases a gang @-@ member from the cellar to an entrance behind a bookcase in Salaad 's house . Upon sobering up , Tintin discovers a necklace of a crab with golden claws on the now @-@ subdued owner of the wine cellar , Omar ben Salaad , and realizes that he is the leader of the drug <unk> . Allan steals a boat and tries escaping , but Tintin captures him . The police arrest the gang and free the Japanese man , who introduces himself as <unk> Kuraki , a police detective who was trying to warn Tintin of the group he was up against . He had been investigating the sailor on Haddock 's crew who drowned ; the sailor was on the verge of bringing him opium before he was eliminated . Turning on the radio , Tintin learns that , thanks to him , the entire organisation of the Crab with the Golden Claws is behind bars . 
- 
- = = History = = 
- 
- 
- = = = Background = = = 
- 
- As the Belgian army clashed with the invading Germans in May 1940 , Hergé and his wife fled by car to France along with tens of thousands of other Belgians , first staying in Paris and then heading south to <unk> @-@ de @-@ <unk> , where they remained for six weeks . On 28 May , Belgian King Leopold III officially surrendered the country to the German army to prevent further killing ; a move that Hergé agreed with . Germany placed Belgium under occupation . Hergé followed the king 's request that all civilians who had fled the country return ; he arrived back in Brussels on 30 June . There , he found that an officer of the German army 's <unk> occupied his house , and he also faced financial trouble , as he owed back taxes yet was unable to access his financial reserves ( his fee due from Casterman eventually arrived ) . All Belgian publications were now under the control of the German occupying force . The Catholic publication Le Vingtième Siècle and its supplement Le Petit Vingtième , where Hergé had always worked <unk> The Adventures of Tintin , no longer had permission to continue publication . Land of Black Gold , the story that Hergé had been <unk> there , had to be abandoned . Victor <unk> , the <unk> editor of Le Pays <unk> , offered Hergé employment as a cartoonist , but Hergé perceived Le Pays <unk> as an explicitly political publication and thus declined the position . 
- Instead , he accepted a position with Le Soir , Belgium 's largest <unk> daily newspaper . <unk> from its original owners , the German authorities permitted Le Soir to reopen under the directorship of Belgian editor Raymond de Becker , although it remained firmly under Nazi control , supporting the German war effort and <unk> anti @-@ Semitism . After joining Le Soir on 15 October , Hergé created its new children 's supplement , Le Soir Jeunesse . Appointed editor of this supplement , he was aided by old friend Paul <unk> and the cartoonist Jacques Van <unk> . The first issue of Le Soir Jeunesse was published with a large announcement across the cover : " Tintin et <unk> <unk> <unk> ! " ( " Tintin and Snowy are Back ! " ) . Some Belgians were upset that Hergé was willing to work for a newspaper controlled by the occupying Nazi administration ; he received an anonymous letter from " the father of a large family " asking him not to work for Le Soir , fearing that The Adventures of Tintin would now be used to <unk> children in Nazi ideology , and that as a result " They will no longer speak of God , of the Christian family , of the Catholic ideal ... [ How ] can you agree to collaborate in this terrible act , a real sin against Spirit ? " Hergé however was heavily <unk> by the size of Le Soir 's readership , which reached 600 @,@ 000 , far more than what Le Vingtième Siècle had been able to accomplish . Faced with the reality of Nazi oversight , Hergé abandoned the overt political themes that had <unk> much of his earlier work , instead adopting a policy of neutrality . Without the need to <unk> political types , Harry Thompson observed that " Hergé was now concentrating more on plot and on developing a new style of character comedy . The public reacted positively . " 
- 
- = = = Publication = = = 
- 
- The Crab with the Golden Claws began serialisation in Le Soir Jeunesse on 17 October 1940 . However , on 8 May 1941 , a paper shortage caused by the ongoing war led to the Le Soir Jeunesse being reduced to four pages , with the length of the weekly Tintin strip being cut by two @-@ thirds . Several weeks later , on 3 September 1941 , the supplement disappeared altogether , with The Crab with the Golden Claws being moved into Le Soir itself in September , where it became a daily strip . As a result , Hergé was forced to alter the pace at which his narrative moved , as he had to hold the reader 's attention at the end of every line . As with earlier Adventures of Tintin , the story was later serialised in France in the Catholic newspaper <unk> <unk> from 21 June 1942 . 
- Following serialisation , Casterman collected together and published the story in book form in 1941 ; the last black @-@ and @-@ white Tintin volume to be released . For this collected edition , Hergé thought of renaming the story , initially considering The Red Crab ( to accompany earlier adventures The Blue Lotus and The Black Island ) before re @-@ settling on Le <unk> aux <unk> d <unk> ( The Crab with the Golden Claws ) . Hergé became annoyed that Casterman then sent the book to the printers without his final approval . Nevertheless , as a result of Le Soir 's publicity , book sales markedly increased , to the extent that most of the prior Adventures of Tintin were reprinted as a result . German authorities made two exceptions : No <unk> of Tintin in America or The Black Island because they were set in the United States and Britain respectively , both of which were in conflict with Germany . 
- The serial introduced the character of Captain Haddock . Haddock made his first appearance in Le Soir adjacent to an advert for the anti @-@ Semitic German film , <unk> <unk> . Hergé chose the name " Haddock " for the character after his wife , <unk> <unk> , mentioned " a sad English fish " during a meal . The inclusion of the Japanese police detective <unk> Kuraki as an ally of Tintin 's in this story was probably designed to counterbalance Hergé 's portrayal of the Japanese as the antagonists in his earlier story , The Blue Lotus , particularly given that the occupying government was allied with Japan at the time . The use of Morocco as a setting was likely influenced by The White Squadron by French writer Joseph <unk> , which Hergé had read and seen the film in 1936 . The depiction of the French Foreign Legion in North Africa was possibly influenced by P. C. Wren 's novel Beau Geste ( 1925 ) or its cinematic adaptations in 1926 , 1928 , and 1939 . 
- Whereas Hergé 's use of Chinese in The Blue Lotus was correct , the Arabic script employed in The Crab with the Golden Claws was intentionally fictitious . Many of the place names featured in the series are puns : the town of <unk> was a pun on the French Que <unk> ? ( " what is to be done ? " ) while the port of <unk> derives from the French <unk> ( scrape , or fight ) . The name of Omar ben Salaad is a pun meaning " <unk> <unk> " in French . 
- In February 1942 , Casterman suggested to Hergé that his books be published in a new format ; 62 @-@ pages rather than the former 100 to 130 pages , and now in full colour rather than black @-@ and @-@ white . He agreed to this , and in 1943 The Crab with the Golden Claws was re @-@ edited and coloured for publication as an album in 1944 . Due to the changes in how the adventure had been serialised at Le Soir , the album at this <unk> was only 58 pages long , and thus Hergé filled the missing pages with four full @-@ page colour frames , thus bringing it up to the standard 62 @-@ page format . 
- In the 1960s , The Crab with the Golden Claws , along with King <unk> 's <unk> , became the first Tintin adventures published in the United States , in Little Golden Books . However , Casterman , working with the American publisher Western Publishing , made a number of changes : Jumbo , the sailor who Tintin leaves bound and gagged in Captain Haddock 's cabin , as well as another man who beats Haddock in the cellar , could not be black Africans as depicted in the original ; these were changed to a white sailor and an Arab due to the American publisher 's concerns depicting blacks and whites mixing together . The accompanying text was not changed , however , and Haddock still refers to the man who beat him as a " <unk> " . Also by request of the Americans , scenes of Haddock drinking directly from bottles of whiskey on the lifeboat and the plane were <unk> out , keeping only the text . The edited albums later had their <unk> areas redrawn by Hergé to be more acceptable , and they appear this way in published editions around the world . Casterman republished the original black @-@ and @-@ white version of the story in 1980 , as part of the fourth volume in their Archives Hergé collection . In 1989 , they then published a facsimile version of that first edition . 
- 
- = = Critical analysis = = 
- 
- Hergé biographer Benoît Peeters described the story as a " <unk> " for The Adventures of Tintin and described the addition of Haddock as " a formidable narrative element " , one which " profoundly changed the spirit of the series " . Elsewhere , he asserts that it is Haddock 's appearance which " makes this book so memorable " and that he is <unk> to define the book by that character 's <unk> . Fellow biographer Pierre Assouline commented that The Crab with the Golden Claws had " a certain charm " stemming from its use of " <unk> and colonial nostalgia , for the French especially , <unk> their holdings in North Africa . " Michael Farr asserted that the arrival of Haddock was the most " remarkable " element of the story , offering the series " tremendous new potential " . He also thought that the dream sequences reflected the popularity of surrealism at the time , and that the influence of cinema , in particular the films of Alfred Hitchcock , is apparent in the story . 
- Jean @-@ Marc <unk> and Randy <unk> described the story as " a thinly @-@ disguised remake of Cigars of the Pharaoh " , an Adventure of Tintin which had been first serialised in 1934 . Both feature the smuggling of opium , in crab tins and cigars respectively , and " desert <unk> , hostile tribes and , at the end , the infiltrating of a secret underground lair . " They also opined that artistically , the story represented " a turning point in Hergé 's career " , because he had to switch to a daily format in Le Soir , although as a result of this they felt that the final third of the story " seems rushed " . <unk> that the inclusion of a Japanese detective investigating drug smuggling in the Mediterranean makes no sense within the context of 1940s Europe , they ultimately awarded the story three out of five stars . 
- Literary critic Jean @-@ Marie Apostolidès of Stanford University , in a <unk> review of The Crab with the Golden Claws , commented that this book witnessed Tintin 's " real entrance into the community of human beings " as he gains an " older brother " in Haddock . He also believed that the recurring image of alcohol throughout the story was symbolic of sexuality . In particular , he believed that there was a strong <unk> subtext between Haddock and Tintin , represented in the two delirious sequences ; in one , Haddock <unk> Tintin as a <unk> bottle <unk> at the top ( thereby symbolising an <unk> penis ) , while in the other , Tintin dreams that he is trapped inside a bottle , with Haddock about to stick a <unk> into him ( thereby symbolising sexual penetration ) . However , Apostolidès notes , in both instances the pair are prevented from realising their sexual fantasies . Literary critic Tom McCarthy concurred with Apostolidès on this point , also highlighting what he perceived as <unk> undertones to these two scenes . He also noted that in this Adventure , the manner in which a chance finding of a tin can on a Belgian street leads Tintin into the story is representative of the recurring theme of " Tintin the detective " found throughout the series . 
- 
- = = Adaptations = = 
- 
- In 1947 , the first Tintin motion picture was created : the stop motion @-@ animated feature film The Crab with the Golden Claws , faithfully adapted by producer Wilfried <unk> for Films Claude <unk> . It was first shown at the ABC Cinema on 11 January 1947 for a group of invited guests . It was screened publicly only once , on 21 December of that year , before <unk> declared bankruptcy and fled to Argentina . 
- In 1957 , the animation company <unk> Studios produced a string of colour adaptations based upon Hergé 's original comics , adapting eight of the Adventures into a series of daily five @-@ minute episodes . The Crab with the Golden Claws was the fifth such story to be adapted , being directed by Ray <unk> and written by Greg , himself a well @-@ known cartoonist who in later years would become editor @-@ in @-@ chief of Tintin magazine . 
- In 1991 , a second animated series based upon The Adventures of Tintin was produced , this time as a collaboration between the French studio <unk> and the Canadian animation company <unk> . <unk> 21 of the stories into a series of episodes , each 42 minutes long , with most stories spanning two episodes , The Crab with the Golden Claws was the seventh story produced in the series . Directed by <unk> <unk> , critics have praised the series for being " generally faithful " , with compositions having been actually directly taken from the panels in the original comic book . 
- A motion capture adventure film titled The Adventures of Tintin : The Secret of the Unicorn directed by Steven Spielberg and produced by Peter Jackson was released in the US on 21 December 2011 and in Europe at the end of October 2011 . Parts of the movie are taken from The Crab with the Golden Claws including the meeting and first adventures of Tintin and Captain Haddock , the Karaboudjan , the flight to <unk> , and the crab cans ( although the plot involving the smuggled opium was not adapted ) . A video @-@ game tie @-@ in to the movie was released October 2011 . 
- 
- = = In popular culture = = 
- 
- In The Simpsons episode In the Name of the <unk> Bart Simpson makes a derogatory remark about Belgium , causing his mother Marge to threaten him with " taking his <unk> away " , whereupon Bart clutches a copy of the Tintin album The Crab with the Golden Claws to his chest , promising he 'll behave . 
- 
- 
- = L.A.M.B. = 
- 
- L.A.M.B. is a fashion line by American singer Gwen Stefani , the lead vocalist of the rock band No Doubt . The line manufactures apparel and fashion accessories . It was founded in 2003 and made its runway debut in 2004 . The fashion line manufactures accessories like shoes , watches , bags and a fragrance called " L. " The name is an acronym of her debut solo album Love . Angel . Music . Baby . 
- The line is influenced by a variety of <unk> cultures , including <unk> , Japanese , Indian and Jamaican styles . Stefani came from a family of <unk> . This further inspired her to launch her own fashion line . The line achieved popularity among celebrities and is worn by stars such as Teri Hatcher , Nicole Kidman , Paris Hilton and Stefani herself . The fashion line made a runway debut in the spring collection of 2004 and achieved mainstream success at New York Fashion Week in 2005 . It currently makes an annual gross income of $ 90 million . The line , as well as the fashion shows , were well received by critics and appreciated the indulgence of a celebrity into the fashion world . An additional fashion line was launched by Stefani called Harajuku Lovers . 
- In late 2014 Stefani announced she would be producing an animated series that was based on the characters Love , Angel , Music and Baby . The series , <unk> Harajuku follows the Harajuku Girls , known together as <unk> , as they fight evil and try to pursue their music career . 
- 
- = = History = = 
- 
- Stefani first came face to face with designing clothes when she and her mother would <unk> clothes for themselves when she was young . Stefani comes from a long line of <unk> , as even her great @-@ grandmother would <unk> clothes . Stefani made most of the things she wore onstage during concerts . When she became successful and began to tour constantly , she felt she lost her way . Then she met the stylist Andrea Lieberman . Lieberman introduced her to <unk> couture clothing . Later Lieberman became her creative consultant and <unk> <unk> took over as the head designer . <unk> later parted ways with L.A.M.B. 
- L.A.M.B. started out as a collaboration with LeSportsac in 2003 . The name L.A.M.B. is an acronym which stands for Love . Angel . Music . Baby . , which is also the name of Stefani 's first solo album . 
- 
- = = <unk> = = 
- 
- The fashion line manufactures clothes , shoes , bags and a fragrance called " L " . The brand started out as a line for women but claims the track items are <unk> . The clothes were manufactured by <unk> Girl LLC , which was founded in 2003 by Ken <unk> , president of L.A.M.B. While for its other products , L.A.M.B has been more of a collaborative fashion line . Now the line is teaming up with another manufacturer , which explains why the official website is down . 
- L.A.M.B joined with Royal <unk> for the shoe line . Stefani is widening her footwear line for adults to include boots and <unk> . L.A.M.B collaborated with Coty Inc. for the fragrance and with LeSportsac for handbags in 2003 . Stefani went on to design a new line of handbags with <unk> and Partners in 2006 . The bags feature LeSportsac 's signature rip @-@ stop nylon along with a variety of <unk> metal hardware , leather <unk> and colorful <unk> . Stefani plans to design lingerie as well as make @-@ up products for L.A.M.B. L.A.M.B. partnered with <unk> Group on a line of women 's watches . The line consists of 39 <unk> . 
- L.A.M.B. products are relatively expensive , with apparel priced $ 55 to $ 1100 , handbags priced $ 80 to $ <unk> , and watches priced $ 125 to $ 995 . 
- 
- = = = <unk> = = = 
- 
- Coty Inc. announced a global licensing agreement with Stefani , to develop and market <unk> for L.A.M.B. Catherine Walsh , senior vice president , American <unk> , Coty <unk> , said in a statement - " From the packaging to the bottle design to the distinctive scent itself , we will be working very closely with Stefani to ensure that her signature fragrance captures her rare spirit , style and warmth , " Stefani said , " <unk> a fragrance is one of the most prestigious things a designer can do . " 
- The fragrance called " L " was launched in September , 2007 at <unk> House in New York . Stefani worked with <unk> Harry <unk> to develop the scent . Stefani described the fragrance as " it 's another thing you can wear and another thing I can be part of creatively . I created it for myself -- it 's like me shrunk into a box . " The perfume is a blend of the <unk> of <unk> , white <unk> , fresh pear , violet , <unk> , rose , <unk> , sweet pea , orange blossom , peach , <unk> , <unk> and <unk> . The perfume is available in 50 ml and 100 ml bottles . 
- 
- = = Promotion and fashion shows = = 
- 
- Stefani frequently refers to her clothing line in her music , as one of the brand 's promotional strategies . Stefani refers to her clothing line in her songs " Wind It Up , " " Harajuku Girls , " and " Crash " ( which even incorporates the brand 's slogan , " I want you all over me like L.A.M.B. " ) . Stefani is often seen wearing her own designs , especially when making public appearances . A thirty @-@ second commercial directed by Sophie <unk> was also released to promote the brand 's fragrance . 
- L.A.M.B. has participated in the Spring / Summer 2006 , 2007 , and 2008 New York Fashion Weeks . Stefani described her first line , which debuted on September 16 , 2005 , as " a little Sound of Music , some Orange County <unk> girl , some <unk> , and a bit of The Great <unk> . " The highlights of the show were purple cars bouncing using <unk> while Stefani 's song " Wind It Up " made its debut as the models walked the runway . 
- For Spring / Summer 2007 , Stefani opted for a presentation rather than a catwalk show . The models , all donning identical blond <unk> , wore designs Stefani said were inspired by Michelle Pfeiffer 's role as Elvira Hancock in the 1983 <unk> . The show included some of Stefani 's trademark <unk> and extensively referenced prints from Guatemala , India , and Japan . On September 5 , 2007 , L.A.M.B opened New York 's Spring / Summer 2008 Mercedes @-@ Benz Fashion Week . The collection " looked like the sixties as seen by someone who grew up in the eighties " and incorporated influences from Stefani 's <unk> roots . Fashion week organizer Fern <unk> said that celebrity designers provided <unk> and energy to the fashion industry , which made Stefani 's collection a desirable opener . 
- 
- = = Critical reception = = 
- 
- The line was mostly well received by critics and Stefani was appreciated for taking fashion seriously even though she is a celebrity . Fern <unk> of <unk> praised the line and Stefani as well and said , " the L.A.M.B. line is clearly at the top of these lines and is as unique and individual as Gwen herself . " The shoes were well received by the critics , though considered to be <unk> . Desiree <unk> of About.com said , " ... these shoes aren 't for everyone , but will most definitely appeal to fans of Ms. Stefani 's music and fashion - sense . " Tim <unk> of Entertainment Weekly said , " L.A.M.B. ' s embellished <unk> , <unk> @-@ inspired <unk> , and <unk> @-@ heel <unk> deliver the edge " Nicole <unk> of <unk> said , " The collection , which looked like the sixties as seen by someone who grew up in the eighties , was altogether more <unk> and on trend . " Fashion journalist <unk> <unk> of The New York Times differed and said , " If ever there was a reason for a pop star to concentrate on her vocal skills , it was Gwen Stefani 's fashion <unk> . " 
- 
- = = Commercial success = = 
- 
- The brand is sold in 275 stores worldwide and is worn by celebrities including Teri Hatcher , Nicole Kidman , Kelly <unk> , Paris Hilton , and Stefani herself . L.A.M.B sales have expanded from $ 40 million in 2005 to a predicted $ 90 million in 2007 . According to a <unk> spokesperson , the debut of L.A.M.B. ' s watch line , which sold out in two days , was the store 's most successful watch launch ever . The brand 's designs have appeared in W , Marie Claire , Elle , Lucky and <unk> . 
- 
- 
- = First @-@ move advantage in chess = 
- 
- The first @-@ move advantage in chess is the inherent advantage of the player ( White ) who makes the first move in chess . Chess players and theorists generally agree that White begins the game with some advantage . Since 1851 , compiled statistics support this view ; White consistently wins slightly more often than Black , usually scoring between 52 and 56 percent . White 's winning percentage is about the same for tournament games between humans and games between computers . However , White 's advantage is less significant in <unk> games and games between <unk> . 
- Chess players and <unk> have long debated whether , given perfect play by both sides , the game should end in a win for White , or a draw . Since approximately 1889 , when World Champion Wilhelm <unk> addressed this issue , the overwhelming consensus has been that a perfectly played game would end in a draw . However , a few notable players have argued that White 's advantage may be sufficient to force a win : Weaver Adams and <unk> <unk> claimed that White is winning after the first move 1.e4 , while Hans Berliner argued that 1.d4 may win for White . 
- Some players , including World Champions such as José <unk> Capablanca , Emanuel Lasker , and Bobby Fischer , have expressed fears of a " draw death " as chess becomes more deeply analyzed . To alleviate this danger , Capablanca and Fischer both proposed chess variants to renew interest in the game , while Lasker suggested changing how draws and stalemate are scored . 
- Since 1988 , chess theorists have challenged previously well @-@ established views about White 's advantage . Grandmaster ( GM ) <unk> Adorján wrote a series of books on the theme that " Black is OK ! " , arguing that the general perception that White has an advantage is founded more in psychology than reality . GM Mihai Suba and others contend that sometimes White 's initiative disappears for no apparent reason as a game progresses . The prevalent style of play for Black today is to seek dynamic , <unk> positions with active <unk> , rather than merely trying to equalize . 
- Modern writers also argue that Black has certain <unk> advantages . The consensus that White should try to win can be a psychological burden for the white player , who sometimes loses by trying too hard to win . Some symmetrical openings ( i.e. those where both players make the same moves ) can lead to situations where moving first is a disadvantage , either for psychological or objective reasons . 
- 
- = = Winning percentages = = 
- 
- In 1946 , <unk> Streeter examined the results of 5 @,@ <unk> games played in 45 international chess tournaments between 1851 and 1932 . Streeter found that overall White scored 53 @.@ 4 % ( W : 38 @.@ 12 ; D : 30 @.@ 56 ; L : 31 @.@ 31 ) . White scored 52 @.@ 55 % in 1851 – 78 ( W : 45 @.@ 52 ; D : 14 @.@ 07 ; L : 40 @.@ 41 ) , 52 @.@ 77 % in 1881 – 1914 ( W : 36 @.@ 89 ; D : 31 @.@ 76 ; L : 31 @.@ 35 ) , and 55 @.@ 47 % in 1919 – 32 ( W : 36 @.@ 98 ; D : 36 @.@ 98 ; L : 26 @.@ 04 ) . Streeter concluded , " It thus appears that it is becoming increasingly difficult to win with Black , but somewhat easier to draw . " 
- Two decades later , <unk> Arthur M. Stevens concluded in The Blue Book of Charts to Winning Chess , based on a survey of 56 @,@ 972 master games that he completed in 1967 , that White scores 59 @.@ 1 % . However , Stevens assembled his games from those that had been published in chess magazines , rather than complete collections of all the games played in particular events . 
- More recent sources indicate that White scores approximately 54 to 56 percent . In 2005 , GM Jonathan Rowson wrote that " the conventional wisdom is that White begins the game with a small advantage and , holding all other factors constant , scores approximately 56 % to Black 's 44 % " . International Master ( IM ) John Watson wrote in 1998 that White had scored 56 % for most of the 20th century , but that this figure had recently slipped to 55 % . The website <unk> holds regularly updated statistics on its games database . As of January 12 , 2015 , White had won 37 @.@ 50 % , 34 @.@ 90 % were drawn , and Black had won 27 @.@ 60 % out of <unk> @,@ <unk> games , resulting in a total White winning percentage of 54 @.@ 95 % . 
- New In Chess observed in its 2000 <unk> that of the 731 @,@ 740 games in its database , White scored 54 @.@ 8 % overall ; with the two most popular opening moves , White scored 54 @.@ 1 % in 349 @,@ <unk> games beginning 1.e4 , and 56 @.@ 1 % in 296 @,@ 200 games beginning <unk> The main reason that 1.e4 was less effective than 1.d4 was the Sicilian Defence ( 1.e4 c5 ) , which gave White only a 52 @.@ 3 % score in 145 @,@ 996 games . 
- <unk> Jeff <unk> , in examining data from 266 @,@ 000 games played between 1994 and 2001 , concluded that White scored 54 @.@ 1767 % plus 0 @.@ <unk> times White 's Elo rating advantage , treating White 's rating advantage as + 390 if it is better than + 390 , or − 460 if it is worse than − 460 . He found that White 's advantage is equivalent to 35 rating points , i.e. if White has a rating 35 points below Black 's , each player will have an expected score of 50 % . <unk> also found that White 's advantage is smaller ( 53 % ) in rapid games than in games at a slower ( " classical " ) time control . In the 462 games played at the 2009 World Blitz Chess Championship , White scored only 52 @.@ 16 % ( <unk> <unk> L 34 @.@ 63 ) . 
- Other writers conclude that there is a positive correlation between the players ' ratings and White 's score . According to GM <unk> Sveshnikov , statistics show that White has no advantage over Black in games between <unk> , but " if the players are stronger , White has the lead " . An analysis of the results of games in <unk> 's Mega 2003 database between players with similar Elo ratings , commissioned by GM <unk> Adorján , showed that as the players ' ratings went up , the percentage of draws increased , the proportion of decisive games that White won increased , and White 's overall winning percentage increased . For example , taking the highest and lowest of Adorján 's rating categories of 1669 games played by the highest @-@ rated players ( Elo ratings <unk> and above ) , White scored 55 @.@ 7 % overall ( <unk> <unk> <unk> ) , whereas of 34 @,@ <unk> games played by the lowest @-@ rated players ( Elo ratings below 2100 ) , White scored 53 @.@ 1 % overall ( <unk> <unk> <unk> ) . Adorján also analyzed the results of games played at the very highest level : World Championship matches . Of <unk> games played in 34 matches between 1886 and 1990 , White won 234 ( 31 @.@ 0 % ) , drew <unk> ( 52 @.@ 6 % ) , and lost 124 ( 16 @.@ 4 % ) , for a total white winning percentage of 57 @.@ 3 % . In the last five matches in <unk> 's survey , all between <unk> <unk> and Garry Kasparov , White won 31 ( 25 @.@ 8 % ) , drew 80 ( 66 @.@ 7 % ) , and lost 9 ( 7 @.@ 5 % ) , for a total white winning percentage of 59 @.@ 2 % . 
- Chess <unk> Grand Tournament ( <unk> ) tests computer chess engines by playing them against each other , with time controls of forty moves in one hundred and twenty minutes per player ( 40 / 120 ) , and also 40 / 20 and 40 / 4 , and uses the results of those games to compile a rating list for each time control . At the slowest time control ( 40 / 120 ) , White has scored 55 @.@ 4 % ( <unk> <unk> <unk> ) in games played among 38 of the strongest chess engines ( as of May 27 , 2009 ) . At 40 / 20 , White has scored 54 @.@ 6 % ( <unk> <unk> <unk> ) in games played among <unk> engines ( as of May 24 , 2009 ) . At the fastest time control ( 40 / 4 ) , White has scored 54 @.@ 8 % ( <unk> <unk> <unk> ) , in games played among 128 programs ( as of May 28 , 2009 ) . 
- 
- = = Drawn with best play = = 
- 
- Joseph Bertin wrote in his <unk> textbook The Noble Game of Chess , " He that plays first , is understood to have the attack . " This is consistent with the traditional view that White , by virtue of the first move , begins with the initiative and should try to extend it into the <unk> , while Black should strive to neutralize White 's initiative and attain equality . Because White begins with the initiative , a minor mistake by White generally leads only to loss of the initiative , while a similar mistake by Black may have more serious consequences . Thus , Sveshnikov wrote in 1994 , " Black players cannot afford to make even the slightest mistake ... from a theoretical point of view , the tasks of White and Black in chess are different : White has to strive for a win , Black — for a draw ! " 
- Chess theorists have long debated how enduring White 's initiative is and whether , if both sides play perfectly , the game should end in a win for White or a draw . George Walker wrote in 1846 that , " The first move is an advantage , ... but if properly answered , the first move is of little worth " . <unk> , the first World Champion , who is widely considered the father of modern chess , wrote in 1889 , " It is now conceded by all experts that by proper play on both sides the legitimate issue of a game ought to be a draw . " Lasker and Capablanca , the second and third World Champions , agreed . <unk> Fine , one of the world 's leading players from 1936 to 1951 , wrote that White 's opening advantage is too <unk> to be sufficient for a win without an error by Black . 
- The view that a game of chess should end in a draw given best play prevails . Even if it cannot be proved , this assumption is considered " safe " by Rowson and " logical " by Adorján . Watson agrees that " the proper result of a perfectly played chess game ... is a draw . ... Of course , I can 't prove this , but I doubt that you can find a single strong player who would disagree . ... I remember Kasparov , after a last @-@ round draw , explaining to the waiting reporters : ' Well , chess is a draw . ' " World Champion Bobby Fischer thought that was almost definitely so . 
- Lasker and Capablanca both worried that chess would suffer a " draw death " as top @-@ level players drew more and more of their games . More recently , Fischer agreed , saying that the game has become played out . All three advocated changing the rules of chess to minimize the number of drawn games . Lasker suggested scoring less than half a point for a draw , and more than half a point for <unk> the opponent 's king . Capablanca in the 1920s proposed Capablanca chess , a chess variant played on a larger board and with additional pieces . Fischer advocated Fischer Random Chess , another chess variant , in which the initial position of the pieces is determined at random . 
- Today some of the <unk> opening variations have been analyzed so deeply that they are often used as drawing weapons . For example , at the highest levels , Black often uses the Marshall Attack in the Ruy Lopez , a line where Black sacrifices a pawn for strong attacking chances , to obtain an endgame where Black is still a pawn down but is able to draw with correct play . 
- In 2007 , <unk> <unk> Georgiev and <unk> Kolev asserted that much the same was true of the so @-@ called <unk> <unk> Variation of the <unk> Sicilian , which arises after 1.e4 c5 2.Nf3 d6 <unk> cxd4 <unk> Nf6 <unk> a6 6.Bg5 e6 <unk> <unk> ! ? This has long been considered one of the <unk> and most problematic , or even <unk> , opening lines . The game usually continues <unk> <unk> <unk> <unk> . Georgiev and Kolev stated that 6.Bg5 is seldom seen at the highest level because the main line of this variation leads , with best play , to a draw by perpetual check . They wrote that the following game " will probably remain the last word of theory " : 
- Francisco <unk> <unk> – Kasparov , Moscow 2004 : 1 @.@ e4 c5 2 . Nf3 d6 3 @.@ d4 cxd4 4 . <unk> Nf6 5 . Nc3 a6 6 . <unk> e6 7 @.@ <unk> <unk> 8 . <unk> <unk> 9 . Rb1 <unk> 10 @.@ <unk> Nc6 11 @.@ fxe6 fxe6 12 . <unk> <unk> 13 @.@ e5 <unk> 14 . <unk> <unk> 15 . Ne4 <unk> 16 . <unk> Be7 17 . <unk> 0 @-@ 0 18 . 0 @-@ 0 <unk> 19 . <unk> <unk> 20 . <unk> <unk> 21 . <unk> <unk> 22 . <unk> <unk> + 23 . <unk> <unk> 24 . <unk> <unk> 25 . <unk> <unk> + 26 . <unk> <unk> + 27 . <unk> <unk> + 28 . <unk> <unk> + 29 . <unk> <unk> + 30 . <unk> <unk> + 31 . <unk> <unk> + 32 . <unk> <unk> + 33 . <unk> <unk> + 34 . <unk> 1 / 2 – 1 / 2 ( After 34 ... <unk> + , White cannot escape the checks . ) 
- However , Georgiev and Kolev 's pessimistic assessment of 6.Bg5 has since been called into question , as White succeeded with <unk> ( another critical line ) in several later high @-@ level games . GM <unk> <unk> wrote in 2013 that after <unk> , " a forced draw results " , but that after <unk> , " we reach a very sharp position , with mutual chances . " 
- 
- = = White wins = = 
- 
- 
- = = = White wins with 1.e4 = = = 
- 
- Although it is very much a minority view , three prominent twentieth @-@ century masters claimed that White 's advantage should or may be decisive with best play . Weaver Adams , then one of the leading American masters , was the best @-@ known proponent of this view , which he introduced in his 1939 book White to Play and <unk> , and continued to expound in later books and articles until shortly before his death in 1963 . Adams opined that 1.e4 was White 's strongest move , and that if both sides played the best moves thereafter , " White ought to win . " Adams ' claim was widely ridiculed , and he did not succeed in demonstrating the validity of his theory in tournament and match practice . The year after his book was published , at the finals of the 1940 U.S. Open tournament , he scored only one draw in his four games as White , but won all four of his games as Black . Adams also lost a match to IM <unk> Horowitz , who took the black pieces in every game . 
- According to Sveshnikov , <unk> <unk> , a leading Soviet player and <unk> during the 1930s , likewise " claimed in the [ 1930s ] : ' 1.e4 — and White wins ! ' and he managed to prove it quite often " . 
- 
- = = = White wins with 1.d4 = = = 
- 
- More recently , IM Hans Berliner , a former World Champion of <unk> Chess , claimed in his 1999 book The System that 1.d4 gives White a large , and possibly decisive , advantage . Berliner asserted that with best play White wins against the <unk> Defense , the Modern <unk> , the <unk> Gambit and other ( unnamed ) " major defences " , and achieves at least a large advantage in many lines of the Queen 's Gambit <unk> . However , he allowed that , " It is possible that the rules of chess are such that only some number of plausible @-@ appearing defences to 1.d4 can be refuted . " Berliner wrote that Adams ' " theories , though looked upon with <unk> by most top chess players , made an immediate and lasting impression on me . Weaver W. Adams was the first person I met who actually had theories about how chess should be played . " 
- Berliner 's thesis , like Adams ' , has been sharply criticized . 
- 
- = = Modern perspectives = = 
- 
- As explained below , chess theorists in recent decades have continued to debate the size and nature of White 's advantage , if any . Apart from Berliner , they have rejected the idea that White has a forced win from the opening position . Many also reject the traditional paradigm that Black 's objective should be to neutralize White 's initiative and obtain equality . 
- 
- = = = White has an enduring advantage = = = 
- 
- In 2004 , GM Larry Kaufman expressed a more <unk> view than Adams and Berliner , arguing that the initiative stemming from the first move can always be transformed into some sort of enduring advantage , albeit not necessarily a decisive one . Kaufman writes , " I don 't believe that White has a forced win in Chess . I do however believe that with either 1.e4 or 1.d4 , White should be able to obtain some sort of advantage that persists into the endgame . If chess were scored like boxing , with drawn games awarded by some point system to the player ( if any ) who came ' closer ' to winning , then I believe White would indeed have a forced win in theory . " 
- 
- = = = Black is OK ! = = = 
- 
- Starting in 1988 , Adorján has argued in a series of books and magazine articles that " Black is OK ! " Alone amongst modern writers , Adorján claims that White starts the game with essentially no advantage . He writes , " In my opinion , the only obvious advantage for White is that if he or she plays for a draw , and does so well , then Black can hardly avoid this without taking obvious risks . " Adorján goes so far as to claim that , " The tale of White 's advantage is a delusion , belief in it is based on mass <unk> . " Rowson writes that Adorján 's " contention is one of the most important chess ideas of the last two decades ... because it has shaken our assumption that White begins the game with some advantage , and revealed its ideological nature " . However , Rowson rejects Adorján 's claim that White has essentially no advantage , reasoning that " ' White is better ' and ' Black is OK ' need not be mutually exclusive claims " . 
- In one of Adorján 's books , GM <unk> Portisch opined that " at least two @-@ thirds of all ' tested ' openings give White an apparent advantage . " According to Portisch , for Black , " The root of the problem is that very few people know which are the openings where Black is really OK . Those who find these lines have nothing to fear , as Black is indeed OK , but only in those variations ! " Rowson considers this an important point , noting that " 1.d4 players struggle to get anywhere against main @-@ line <unk> and 1.e4 players find the <unk> and Sveshnikov <unk> particularly tough . " 
- 
- = = = <unk> = = = 
- 
- Modern writers often think of Black 's role in more dynamic terms than merely trying to equalize . Rowson writes that " the idea of Black trying to ' equalize ' is questionable . I think it has limited application to a few openings , rather than being an opening <unk> for Black in general . " Evans wrote that after one of his games against Fischer , " Fischer <unk> his ' secret ' to me : unlike other masters , he sought to win with the Black pieces from the start . The revelation that Black has dynamic chances and need not be satisfied with mere equality was the turning point in his career , he said . " Likewise , Watson surmised that Kasparov , when playing Black , bypasses the question of whether White has an opening advantage " by thinking in terms of the concrete nature of the dynamic imbalance on the board , and seeking to seize the initiative whenever possible " . Watson observes that " energetic opening play by Black may ... lead to a position so complex and unclear that to speak of equality is meaningless . Sometimes we say ' dynamically balanced ' instead of ' equal ' to express the view that either player is as likely as the other to emerge from complications with an advantage . This style of opening play has become prevalent in modern chess , with World Champions Fischer and Kasparov as its most visible practitioners . " 
- Modern writers also question the idea that White has an enduring advantage . Suba , in his influential 1991 book Dynamic Chess Strategy , rejects the notion that the initiative can always be transformed into an enduring advantage . He contends that sometimes the player with the initiative loses it with no logical explanation , and that , " Sometimes you must lose it , just like that . If you try to <unk> to it , by forcing the issue , your dynamic potential will become exhausted and you won 't be able to face a vigorous counter @-@ attack . " Rowson and Watson <unk> . Watson also observes , " Because of the <unk> of White being better , the <unk> of the game at which Black frees his game or <unk> White 's plans has often been automatically assumed to give him equality , even though in dynamic openings , the exhaustion of White 's initiative very often means that Black has seized it with advantage . " 
- 
- = = = <unk> advantages = = = 
- 
- Rowson argues that both White and Black have certain advantages : 
- 
- = = = = White 's advantages = = = = 
- 
- According to Rowson , White 's first advantage is that , " The advantage of the first move has some similarities with the serve in tennis in that White can score an ' ace ' ( for instance with a powerful opening novelty ) , he has more control over the pace and direction of the game , and he has a ' second serve ' in that when things go wrong his position is not usually losing . " Second , White begins the game with some initiative , although Rowson regards this as a psychological rather than a positional advantage , " and whether it leads to a positional advantage depends on the relative skill of the players . " Third , some players are able to use the initiative to " play a kind of powerful ' serve and volley ' chess in which Black is flattened with a mixture of deep preparation and attacking prowess . " Fourth , " If White wants to draw , it is often not so easy for Black to prevent this . This advantage is particularly acute in cases where there is a possible threefold repetition , because White can begin the repetition without committing to a draw and Black has to decide whether to deviate before he knows whether White is <unk> . " 
- Rowson cites as an example of the last phenomenon the well @-@ regarded Zaitsev Variation of the Ruy Lopez . After 1.e4 e5 2.Nf3 Nc6 <unk> a6 <unk> Nf6 5 @.@ 0 @-@ 0 Be7 <unk> b5 <unk> 0 @-@ 0 <unk> d6 <unk> Bb7 <unk> Re8 ( initiating the Zaitsev Variation ) , White can repeat moves once with <unk> Rf8 <unk> This puts Black in an awkward situation , since he must either ( a ) insist on the Zaitsev with 12 ... Re8 , which allows White to choose whether to draw by threefold repetition with <unk> Rf8 <unk> , or play on with a different move , or ( b ) play a different ( and possibly inferior ) variation by playing something other than 12 ... Re8 . 
- 
- = = = = Black 's advantages = = = = 
- 
- Rowson argues that Black also has several advantages . First , " White 's alleged advantage is also a kind of obligation to play for a win , and Black can often use this to his advantage . " Second , " White 's ' extra move ' can be a burden , and sometimes White finds himself in a mild form of <unk> ( ' <unk> Lite ' ) . " Third , although White begins the game with the initiative , if " Black retains a flexible position with good reactive possibilities , this initiative can be absorbed and often passes over to Black . " Fourth , " The fact that White moves before Black often gives Black useful information " . Suba likewise argues that White 's advantage is actually less than a move , since White must tip his hand first , allowing Black to react to White 's plans . Suba writes , " In terms of the mathematical games theory , chess is a game of complete information , and Black 's information is always greater — by one move ! " 
- Rowson also notes that Black 's chances increase markedly by playing good openings , which tend to be those with flexibility and latent potential , " rather than those that give White fixed targets or that try to take the initiative prematurely . " He also emphasizes that " White has ' the initiative ' , not ' the advantage ' . Success with Black depends on seeing beyond the initiative and thinking of positions in terms of ' potential ' . " These ideas are exemplified by the Hedgehog , a dynamic modern system against the English Opening that can arise from various move orders . A typical position arises after 1.c4 c5 2.Nf3 Nf6 <unk> b6 <unk> Bb7 5 @.@ 0 @-@ 0 e6 <unk> Be7 <unk> cxd4 <unk> d6 <unk> a6 . White has a spatial advantage , while Black often maneuvers his pieces on the last two ranks of the board , but White " has to keep a constant eye on the possible liberating pawn <unk> ... b5 and ... d5 . " Watson remarks , " Black 's goal is to remain elastic and flexible , with many options for his pieces , whereas White can become <unk> at some point by the need to protect against various dynamic pawn breaks . " He also observes that , " White tends to be as much tied up by Black 's latent activity as Black himself is tied up by White 's space advantage . " Moreover , attempts by White to overrun Black 's position often rebound <unk> . An example of this is the following <unk> game : 
- Lev <unk> – <unk> Ftáčnik , <unk> <unk> 1982 : 1 . Nf3 Nf6 2 @.@ c4 c5 3 . Nc3 e6 4 @.@ g3 b6 5 . Bg2 Bb7 6 . 0 @-@ 0 Be7 7 @.@ d4 cxd4 8 . <unk> d6 9 . <unk> a6 10 @.@ <unk> <unk> 11 @.@ e4 <unk> 12 . <unk> 0 @-@ 0 Suba wrote of a similar Hedgehog position , " White 's position looks ideal . That 's the naked truth about it , but the ' ideal ' has by definition one drawback — it cannot be improved . " 13 . <unk> <unk> 14 @.@ <unk> <unk> 15 . <unk> <unk> 16 . <unk> <unk> 17 @.@ <unk> ? According to Ftáčnik , <unk> <unk> <unk> is <unk> <unk> ! 18 @.@ <unk> <unk> 19 . Nf3 Now Black breaks open the position in typical Hedgehog <unk> d5 ! 20 @.@ <unk> ? ! Ftáčnik considers <unk> or <unk> <unk> <unk> ! 21 . <unk> <unk> 22 @.@ <unk> <unk> 23 @.@ <unk> fxe6 24 @.@ e5 ? Ftáčnik recommends instead <unk> <unk> <unk> <unk> + 25 . <unk> <unk> ! 26 . <unk> <unk> 27 . <unk> Other moves get mated immediately : <unk> <unk> # ; <unk> <unk> # ; <unk> <unk> # . <unk> 28 . <unk> <unk> + ! 29 . <unk> <unk> + If <unk> ( the only legal response to the double check ) , <unk> + <unk> Rf8 + forces mate . 0 – 1 
- An examination of reversed and symmetrical openings illustrates White 's and Black 's respective advantages : 
- 
- = = = = = <unk> openings = = = = = 
- 
- In a " reversed opening " , White plays an opening typically played by Black , but with colors reversed and thus an extra tempo . Evans writes of such openings , " If a defense is considered good for Black , it must be even better for White with a move in hand . " Former World Champion Mikhail <unk> reportedly expressed the same view . Watson questions this idea , citing Suba 's thesis that Black , by moving second , has more complete information than White . He writes , " everyone has such difficulties playing as White against a Sicilian Defence ( 1.e4 c5 ) , but ... leading masters have no qualms about answering 1.c4 with 1 ... e5 . " To explain this paradox , Watson discusses several different reversed Sicilian lines , showing how Black can exploit the disadvantages of various " extra " moves for White . He concludes , " The point is , Black 's set @-@ up in the Sicilian is fine as a reactive system , but not worth much when trying to claim the initiative as White . This is true because Black is able to react to the specific plan White chooses ; in Suba 's terms , his information is indeed a move greater ! Furthermore , he is able to take advantage of dead equal positions which White ( hoping to retain the advantage of the first move ) would normally avoid . " 
- Watson also observes , " Similarly , the Dutch Defence looks particularly sterile when White achieves the reversed positions a tempo up ( it turns out that he has nothing useful to do ! ) ; and indeed , many standard Black openings are not very inspiring when one gets them as White , tempo in hand . " GM Alex <unk> likewise notes that GM Vladimir <unk> , a successful exponent of the Leningrad Dutch ( 1.d4 <unk> <unk> g6 ) at the highest levels , " once made a deep impression on me by <unk> <unk> someone 's suggestion that he should try <unk> as White . He <unk> and said , ' That extra move 's gonna hurt me . ' " 
- <unk> also agrees with Alekhine 's criticism of <unk> e5 2.Nf3 , a reversed Alekhine 's Defense , in <unk> – Alekhine , Baden @-@ Baden 1925 , writing that Alekhine " understood the difference in opening philosophies for White and Black , and realized they just can 't be the same ! White is supposed to try for more than just obtaining a comfortable game in reversed colour opening set @-@ ups , and , as the statistics show — surprisingly for a lot of people , but not for me — White doesn 't even score as well as Black does in the same positions with his extra tempo and all . " Howard <unk> , generally considered to have been the strongest player in the world from 1843 to 1851 , made a similar point over 160 years ago , writing that Owen 's Defense ( 1.e4 b6 ) is playable for Black , but that <unk> is inferior to " the more customary [ first ] moves , from its being essentially defensive " . The current view is that Owen 's Defense is slightly better for White , while <unk> is playable but less likely to yield an opening advantage than 1.e4 or <unk> 
- Watson concludes that ( a ) " most moves have disadvantages as well as advantages , so an extra move is not always an <unk> blessing " ; ( b ) " with his extra information about what White is doing , Black can better react to the new situation " ; and ( c ) because a draw is likely to be more acceptable to Black than to White , White is <unk> to avoid lines that allow <unk> <unk> , while Black may not object to such lines . 
- 
- = = = = = Symmetrical openings = = = = = 
- 
- Rowson writes that " in general one would assume that whatever advantage White has would be revealed most clearly in symmetrical positions . " Accordingly , Watson , Suba , Evans , and the eminent player and theorist <unk> <unk> ( 1886 – 1935 ) have all argued that it is in Black 's interest to avoid symmetry . Nonetheless , even symmetrical opening lines sometimes illustrate the tenuous nature of White 's advantage , in several respects . 
- It is often difficult for White to prove an advantage in symmetrical opening lines . As GM Bent Larsen wrote , <unk> a game that began 1.c4 c5 <unk> b6 , " In symmetrical openings , White has a theoretical advantage , but in many of them it is only theoretical . " GM Andrew Soltis wrote in 2008 that he hates playing against the symmetrical <unk> 's Defense ( 1.e4 e5 2.Nf3 Nf6 ) , and accordingly varies with <unk> , the Vienna Game . However , there too he has been unable to find a way to an advantage after the symmetrical 2 ... Nc6 <unk> g6 <unk> Bg7 , or after <unk> Nf6 ( <unk> to the Four Knights Game ) <unk> <unk> 5 @.@ 0 @-@ 0 0 @-@ 0 <unk> d6 <unk> <unk> <unk> Nd4 <unk> <unk> , or <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> d5 , when <unk> ? ! e4 ! may even favor Black . 
- Moreover , symmetrical positions may be <unk> to White in that he has to commit himself first . Watson notes that it is even difficult for White to play <unk> in a symmetrical position , since almost every move has certain drawbacks . Fischer once went so far as to claim that after <unk> Nf6 <unk> g6 <unk> Bg7 4 @.@ 0 @-@ 0 0 @-@ 0 <unk> d6 ( Reinhard – Fischer , Western Open 1963 ) , " ' Believe it or not , ' Black stands better ! Now , whatever White does , Black will vary it and get an asymmetrical position and have the superior position due to his better pawn structure ! " However , GM Paul <unk> responded in <unk> magazine , " We just don 't believe it ! " In symmetrical positions , as the Hodgson – <unk> and Portisch – Tal games discussed below illustrate , Black can continue to imitate White as long as he finds it feasible and desirable to do so , and deviate when that ceases to be the case . 
- Further , a particular extra move is sometimes more of a liability than an asset . For example , Soltis notes that the Exchange French position arising after 1.e4 e6 <unk> d5 <unk> <unk> <unk> Nf6 " is pretty equal . " The same position , but with Black 's knight moved to e4 , arises in <unk> 's Defense after 1.e4 e5 2.Nf3 Nf6 <unk> d6 <unk> <unk> <unk> d5 . That position offers White better chances precisely because Black 's extra move ( ... Ne4 ) allows the advanced knight to become a target for attack . 
- Finally , symmetrical positions may be difficult for the white player for psychological reasons . Watson writes that anyone who tries the Exchange French , " even if he thinks he is playing for a win , assume [ s ] a psychological burden . White has already ceded the advantage of the first move , and knows it , whereas Black is challenged to find ways to seize the initiative . " Two famous examples of White losses in the Exchange French are M. Gurevich – Short and <unk> – Korchnoi . In M. Gurevich – Short , a game between two of the world 's leading players , White needed only a draw to qualify for the Candidates <unk> , while Black needed to win . Gurevich played passively and was <unk> by Short , who achieved the necessary win , qualified for the Candidates , and ultimately went on to challenge Kasparov for the World Championship . In <unk> – Korchnoi , the Italian IM fell victim to Korchnoi 's <unk> mating attack , losing in just 14 moves . 
- Rowson gives the following example of Black <unk> White from the Symmetrical Variation of the English Opening . He remarks , " there is something compelling about Black 's strategy . He seems to be saying : ' I will copy all your good moves , and as soon as you make a bad move , I won 't copy you any more ! ' " 
- Hodgson – <unk> , Newcastle 2001 : 1 @.@ c4 c5 2 @.@ g3 g6 3 . Bg2 Bg7 4 . Nc3 Nc6 5 @.@ <unk> a6 6 . Rb1 Rb8 7 @.@ <unk> <unk> 8 @.@ <unk> b5 9 @.@ <unk> <unk> Here Rowson remarks , " Both sides want to push their d @-@ pawn and play <unk> / ... <unk> , but White has to go first so Black gets to play ... d5 before White can play d4 . This doesn 't matter much , but it already points to the challenge that White faces here ; his most natural <unk> allow Black to play the moves he wants to . I would therefore say that White is in ' <unk> Lite ' and that he remains in this state for several moves . " 10 . Nf3 d5 10 ... Nf6 11 @.@ 0 @-@ 0 0 @-@ 0 <unk> d6 <unk> <unk> would <unk> to the Portisch – Tal game below . 11 @.@ d4 Nf6 12 . <unk> <unk> 13 . 0 @-@ 0 <unk> 14 . <unk> 0 @-@ 0 15 . <unk> Ne4 16 @.@ <unk> <unk> ! ? Finally breaking the symmetry . 17 . <unk> The position is still almost symmetrical , and White can find nothing useful to do with his extra move . Rowson <unk> suggests <unk> ! ? , forcing Black to be the one to break the symmetry . 17 ... Re8 ! Rowson notes that this is a useful waiting move , covering <unk> , which needs protection in some lines , and possibly supporting an eventual ... e5 ( see Black 's twenty @-@ second move ) . White cannot copy it , since after <unk> ? Nxf2 Black would win a pawn . 18 . <unk> ? ! <unk> ! 19 @.@ <unk> <unk> ! Rowson notes that with his more active pieces , " It looks like Black has some initiative . " If now <unk> , <unk> " is at least equal for Black " . 20 . <unk> <unk> ! 20 ... Nxf2 ? <unk> ! wins . 21 . Nd4 <unk> 22 . <unk> e5 Rowson writes , " Now both sides have their <unk> , but I think Black has some advantage , due to his extra central control , imposing knight and prospects for a <unk> attack . " 23 @.@ b5 Rc8 24 . <unk> d4 Now White has a difficult game : Rowson <unk> <unk> ? ! <unk> <unk> Bc2 <unk> <unk> <unk> <unk> ! , winning ; <unk> hxg4 <unk> Nxf2 ! <unk> Bc2 , winning ; <unk> ! ? <unk> ! with advantage ; and <unk> ( risky @-@ looking , but perhaps best ) Nc3 ! <unk> <unk> <unk> <unk> , and Black is better . 25 @.@ b6 ? <unk> Black 's threat . 25 ... Nxf2 ! 26 . <unk> If <unk> , Bc2 forks White 's queen and <unk> . 26 ... Ne4 27 @.@ <unk> Rb8 28 @.@ <unk> hxg4 29 @.@ hxg4 <unk> 30 . <unk> Nf6 ! 31 . <unk> <unk> 32 . <unk> <unk> 33 @.@ <unk> <unk> + 0 – 1 
- The opening of the following game between two world @-@ class players , another Symmetrical English , took a similar course : 
- <unk> Portisch – Mikhail Tal , Candidates Match 1965 : 1 . Nf3 c5 2 @.@ c4 Nc6 3 . Nc3 Nf6 4 @.@ g3 g6 5 . Bg2 Bg7 6 . 0 @-@ 0 0 @-@ 0 7 @.@ <unk> a6 8 @.@ <unk> Rb8 9 . Rb1 b5 10 @.@ <unk> <unk> 11 @.@ <unk> <unk> 12 @.@ <unk> d6 13 . <unk> <unk> Once again , White is on move in a symmetrical position , but it is not obvious what he can do with his first @-@ move initiative . Soltis writes , " It 's ridiculous to think Black 's position is better . But Mikhail Tal said it is easier to play . By moving second he gets to see White 's move and then decide whether to match it . " <unk> Here , Soltis writes that Black could maintain equality by keeping the symmetry : 14 ... <unk> <unk> <unk> . Instead , he plays to prove that White 's queen is <unk> . 14 ... Rc8 ! <unk> Nd4 ! <unk> 16 ... <unk> + . <unk> <unk> <unk> <unk> <unk> <unk> <unk> Rc8 Although the pawn structure is still symmetrical , Black 's control of the c @-@ file gives him the advantage . Black ultimately reached an endgame two pawns up , but White managed to hold a draw in 83 moves . 
- Tal himself lost a famous game as White from a symmetrical position in Tal – <unk> , USSR Championship 1974 . 
- 
- = = Tournament and match play = = 
- 
- In chess tournaments and matches , the frequency with which each player receives white and black is an important consideration . In matches , the players ' colors in the first game are determined by drawing lots , and alternated thereafter . In round robin tournaments with an odd number of players , each player receives an equal number of whites and blacks ; with an even number of players , each receives one extra white or black . Where one or more players withdraws from the tournament , the tournament director may change the assigned colors in some games so that no player receives two more blacks than whites , or vice versa . The double @-@ round robin tournament is considered to give the most reliable final standings , since each player receives the same number of whites and blacks , and plays both White and Black against each opponent . 
- In Swiss system tournaments , the tournament director tries to ensure that each player receives , as nearly as possible , the same number of games as White and Black , and that the player 's color alternates from round to round . After the first round , the director may deviate from the otherwise prescribed <unk> in order to give as many players as possible their <unk> or due colors . More substantial deviations are permissible to avoid giving a player two more blacks than whites ( for example , three blacks in four games ) than vice versa , since extra whites " cause far less player distress " than extra blacks , which impose " a significant handicap " on the affected player . <unk> with an even number of rounds cause the most problems , since if there is a <unk> , it is greater ( e.g. , a player receiving two whites and four blacks ) . 
- 
- = = <unk> chess = = 
- 
- <unk> tablebases have solved a very limited area of chess , determining perfect play in a number of endgames , including all non @-@ trivial endgames with no more than six pieces or pawns ( including the two kings ) . Seven @-@ piece endgames were solved in 2012 and released as " Lomonosov tablebases " . 
- Jonathan Rowson has speculated that " in principle it should be possible for a machine to ... develop 32 @-@ piece tablebases . This may take decades or even centuries , but unless runaway global warming or nuclear war gets in the way , I think it will eventually happen . " However , information theorist Claude Shannon argued that it is not feasible for any computer to actually do this . In his 1950 paper " Programming a Computer for Playing Chess " he writes : 
- With chess it is possible , in principle , to play a perfect game or construct a machine to do so as follows : One considers in a given position all possible moves , then all moves for the opponent , etc . , to the end of the game ( in each variation ) . The end must occur , by the rules of the games after a finite number of moves ( remembering the 50 move drawing rule ) . Each of these variations ends in win , loss or draw . By working backward from the end one can determine whether there is a forced win , the position is a draw or is lost . It is easy to show , however , even with the high computing speed available in electronic calculators this computation is impractical . In typical chess positions there will be of the order of 30 legal moves . The number holds fairly constant until the game is nearly finished as shown ... by De <unk> , who averaged the number of legal moves in a large number of master games . Thus a move for White and then one for Black gives about 103 possibilities . A typical game lasts about 40 moves to resignation of one party . This is conservative for our calculation since the machine would calculate out to <unk> , not resignation . However , even at this figure there will be <unk> variations to be calculated from the initial position . A machine operating at the rate of one variation per <unk> would require over <unk> years to calculate the first move ! 
- It is thus theoretically possible to " solve " chess , determining with certainty whether a perfectly played game should end in a win for White , a draw , or even a win for Black . However , according to Shannon the time frame required puts this possibility beyond the limits of any feasible technology . 
- Hans @-@ Joachim <unk> , a professor of mathematics and <unk> at the University of California at Berkeley , further argued in a 1965 paper that the " speed , memory , and processing capacity of any possible future computer equipment are limited by certain physical barriers : the light barrier , the quantum barrier , and the <unk> barrier . These limitations imply , for example , that no computer , however constructed , will ever be able to examine the entire tree of possible move sequences of the game of chess . " Nonetheless , <unk> did not <unk> the possibility that a computer would someday be able to solve chess . He wrote , " In order to have a computer play a perfect or nearly perfect game [ of chess ] it will be necessary either to analyze the game completely ... or to analyze the game in an approximate way and combine this with a limited amount of tree searching . ... A theoretical understanding of such <unk> programming , however , is still very much wanting . " 
- Recent scientific advances have not significantly changed that assessment . The game of checkers was solved in 2007 , but it has roughly the square root of the number of positions in chess . Jonathan <unk> , the scientist who led the effort , said a breakthrough such as quantum computing would be needed before solving chess could even be attempted , but he does not rule out the possibility , saying that the one thing he learned from his 16 @-@ year effort of solving checkers " is to never <unk> the advances in technology " . 
- 
- = = <unk> = = 
- 
- " You will win with either color if you are the better player , but it takes longer with Black . " – Isaac <unk> 
- 
- 
- = Frederick Reines = 
- 
- Frederick Reines ( <unk> @-@ ness ) ; ( March 16 , 1918 – August 26 , 1998 ) was an American physicist . He was awarded the 1995 Nobel Prize in Physics for his co @-@ detection of the neutrino with Clyde Cowan in the neutrino experiment . He may be the only scientist in history " so intimately associated with the discovery of an elementary particle and the subsequent thorough investigation of its fundamental properties " . 
- A graduate of the Stevens Institute of Technology and New York University , Reines joined the Manhattan Project 's Los Alamos Laboratory in 1944 , working in the Theoretical Division in Richard Feynman 's group . He became a group leader there in 1946 . He participated in a number of nuclear tests , culminating in his becoming the director of the Operation Greenhouse test series in the Pacific in 1951 . 
- In the early 1950s , working in <unk> and Savannah River Sites , Reines and Cowan developed the equipment and procedures with which they first detected the supposedly undetectable neutrinos in June 1956 . Reines dedicated the major part of his career to the study of the neutrino 's properties and interactions , which work would influence study of the neutrino for many researchers to come . This included the detection of neutrinos created in the atmosphere by cosmic rays , and the 1987 detection of neutrinos emitted from Supernova <unk> , which inaugurated the field of neutrino astronomy . 
- 
- = = Early life = = 
- 
- Frederick Reines was born in Paterson , New Jersey , one of four children of <unk> ( Cohen ) and Israel Reines . His parents were Jewish emigrants from the same town in Russia , but only met in New York City , where they were later married . He had an older sister , Paula , who became a doctor , and two older brothers , David and William , who became lawyers . He said that his " early education was strongly influenced " by his <unk> siblings . He was the great @-@ nephew of the Rabbi <unk> <unk> Reines , the founder of <unk> , a religious Zionist movement . 
- The family moved to <unk> , New York , where his father ran the general store , and he spent much of his childhood . He was an Eagle Scout . Looking back , Reines said : " My early childhood memories center around this typical American country store and life in a small American town , including Independence Day July celebrations marked by fireworks and patriotic music played from a pavilion <unk> . " 
- Reines sang in a chorus , and as a soloist . For a time he considered the possibility of a singing career , and was instructed by a vocal coach from the Metropolitan Opera who provided lessons for free because the family did not have the money for them . The family later moved to North Bergen , New Jersey , residing on Kennedy Boulevard and 57th Street . Because North Bergen did not have a high school , he attended Union Hill High School in Union Hill , New Jersey , from which he graduated in 1935 . 
- From an early age , Reines exhibited an interest in science , and liked creating and building things . He later recalled that : 
- The first <unk> of interest in science that I remember occurred during a moment of boredom at religious school , when , looking out of the window at <unk> through a hand curled to simulate a telescope , I noticed something peculiar about the light ; it was the phenomenon of <unk> . That began for me a fascination with light . 
- Ironically , Reines excelled in literary and history courses , but received average or low marks in science and math in his freshman year of high school , though he improved in those areas by his junior and senior years through the encouragement of a teacher who gave him a key to the school laboratory . This cultivated a love of science by his senior year . In response to a question seniors were asked about what they wanted to do for a <unk> quote , he responded : " To be a physicist <unk> . " 
- Reines was accepted into the Massachusetts Institute of Technology , but chose instead to attend Stevens Institute of Technology in <unk> , New Jersey , where he earned his Bachelor of Science ( <unk> ) degree in mechanical engineering in 1939 , and his Master of Science ( <unk> ) degree in mathematical physics in 1941 , writing a thesis on " A Critical Review of Optical <unk> Theory " . He married Sylvia <unk> on August 30 , 1940 . They had two children , Robert and <unk> . He then entered New York University , where he earned his Doctor of Philosophy ( Ph.D. ) in 1944 . He studied cosmic rays there under Serge A. <unk> , but wrote his thesis under the supervision of Richard D. Present on " Nuclear fission and the liquid drop model of the nucleus " . Publication of the thesis was delayed until after the end of World War II ; it appeared in Physical Review in 1946 . 
- 
- = = Los Alamos Laboratory = = 
- 
- In 1944 Richard Feynman recruited Reines to work in the Theoretical Division at the Manhattan Project 's Los Alamos Laboratory , where he would remain for the next fifteen years . He joined Feynman 's T @-@ 4 ( <unk> Problems ) Group , which was part of Hans Bethe 's T ( Theoretical ) Division . <unk> was an important aspect of critical mass calculations . In June 1946 , he became a group leader , heading the T @-@ 1 ( Theory of Dragon ) Group . An outgrowth of the " <unk> the Dragon 's tail " experiment , the Dragon was a machine that could attain a critical state for short bursts of time , which could be used as a research tool or power source . 
- Reines participated in a number of nuclear tests , and writing reports on their results . These included Operation Crossroads at <unk> Atoll in 1946 , Operation <unk> at <unk> Atoll in 1948 , and Operation Ranger and Operation <unk> – <unk> at the Nevada Test Site . In 1951 he was the director of Operation Greenhouse series of nuclear tests in the Pacific . This saw the first American tests of boosted fission weapons , an important step towards thermonuclear weapons . He studied the effects of nuclear blasts , and co @-@ authored a paper with John von Neumann on <unk> stem formation , an important aspect of an air blast wave . 
- In spite or perhaps because of his role in these nuclear tests , Reines was concerned about the dangers of radioactive pollution from atmospheric nuclear tests , and became an advocate of underground nuclear testing . In the wake of the <unk> crisis , he participated in John Archibald Wheeler 's Project 137 , which evolved into <unk> . He was also a delegate at the <unk> for Peace Conference in Geneva in 1958 . 
- 
- = = Discovery of the neutrino and the inner workings of stars = = 
- 
- The neutrino was a <unk> particle first proposed theoretically by Wolfgang <unk> on December 4 , 1930 , to explain undetected energy that escaped during beta decay when neutron decayed into a proton and an electron so that the law of conservation of energy was not violated . Enrico Fermi renamed it the neutrino , Italian for " little neutral one " , and in 1934 , proposed his theory of beta decay which explained that the electrons emitted from the nucleus were created by the decay of a neutron into a proton , an electron , and a neutrino : 
- <unk> → p + + e − + <unk> 
- e 
- The neutrino accounted for the missing energy , but Fermi 's theory described a particle with little mass and no electric charge that would be difficult to observe directly . In a 1934 paper , Rudolf <unk> and Hans Bethe calculated that neutrinos could easily pass through the Earth , and concluded " there is no practically possible way of observing the neutrino . " In 1951 , at the conclusion of the Greenhouse test series , Reines received permission from the head of T Division , J. Carson Mark , for a leave in residence to study fundamental physics . Reines and his colleague Clyde Cowan decided to see if they could detect neutrinos . " So why did we want to detect the free neutrino ? " he later explained , " Because everybody said , you couldn ’ t do it . " 
- According to Fermi 's theory , there was also a corresponding reverse reaction , in which a neutrino combines with a proton to create a neutron and a positron : 
- <unk> 
- e + p + → <unk> + e + 
- The positron would soon be annihilated by an electron and produce two 0 @.@ 51 MeV gamma rays , while the neutron would be captured by a proton and release a 2 @.@ 2 MeV gamma ray . This would produce a distinctive signature that could be detected . They then realised that by adding cadmium salt to their liquid <unk> to enhance the neutron capture reaction , resulting in a 9 MeV burst of gamma rays . For a neutrino source , they proposed using an atomic bomb . <unk> for this was obtained from the laboratory director , Norris Bradbury . Work began on digging a shaft for the experiment when J. M. B. <unk> convinced them to use a nuclear reactor instead of a bomb . Although a less intense source of neutrinos , it had the advantage in allowing for multiple experiments to be carried out over a long period of time . 
- In 1953 , they made their first attempts using one of the large reactors at the <unk> nuclear site in what is now known as the Cowan – Reines neutrino experiment . Their detector now included 300 litres ( 66 imp gal ; 79 US gal ) of <unk> fluid and 90 <unk> tubes , but the effort was frustrated by background noise from cosmic rays . With encouragement from John A. Wheeler , they tried again in 1955 , this time using one of the newer , larger 700 MW reactors at the Savannah River Site that emitted a high neutrino flux of 1 @.@ 2 x 1012 / <unk> <unk> . They also had a convenient , well @-@ shielded location 11 metres ( 36 ft ) from the reactor and 12 metres ( 39 ft ) underground . On June 14 , 1956 , they were able to send <unk> a telegram announcing that the neutrino had been found . When Bethe was informed that he had been proven wrong , he said , " Well , you shouldn ’ t believe everything you read in the papers . " 
- From then on Reines dedicated the major part of his career to the study of the neutrino ’ s properties and interactions , which work would influence study of the neutrino for future researchers to come . Cowan left Los Alamos in 1957 to teach at George Washington University , ending their collaboration . On the basis of his work in first detecting the neutrino , Reines became the head of the physics department of Case Western Reserve University from 1959 to 1966 . At Case , he led a group that was the first to detect neutrinos created in the atmosphere by cosmic rays . Reines had a booming voice , and had been a singer since childhood . During this time , besides performing his duties as a research supervisor and chairman of the physics department , Reines sang in the Cleveland Orchestra Chorus under the direction of Robert Shaw in performances with George <unk> and the Cleveland Orchestra . 
- In 1966 , Reines took most of his neutrino research team with him when he left for the new University of California , Irvine ( UCI ) , becoming its first dean of physical sciences . At UCI , Reines extended the research interests of some of his graduate students into the development of medical radiation detectors , such as for measuring total radiation delivered to the whole human body in radiation therapy . 
- Reines had prepared for the possibility of measuring the distant events of a supernova explosion . Supernova explosions are rare , but Reines thought he might be lucky enough to see one in his lifetime , and be able to catch the neutrinos streaming from it in his specially @-@ designed detectors . During his wait for a supernova to explode , he put signs on some of his large neutrino detectors , calling them " Supernova Early Warning Systems " . In 1987 , neutrinos emitted from Supernova <unk> were detected by the Irvine – Michigan – <unk> ( <unk> ) Collaboration , which used an 8 @,@ 000 ton <unk> detector located in a salt mine near Cleveland . Normally , the detectors recorded only a few background events each day . The supernova registered 19 events in just ten seconds . This discovery is regarded as <unk> the field of neutrino astronomy . 
- In 1995 , Reines was honored , along with Martin L. <unk> with the Nobel Prize in Physics for his work with Cowan in first detecting the neutrino . Unfortunately , Cowan had died in 1974 , and the Nobel Prize is not awarded posthumously . Reines also received many other awards , including the J. Robert <unk> Memorial Prize in 1981 , the National Medal of Science in 1985 , the Bruno Rossi Prize in 1989 , the <unk> – Morley Award in 1990 , the <unk> Prize in 1992 , and the Franklin Medal in 1992 . He was elected a member of the National Academy of Sciences in 1980 and a foreign member of the Russian Academy of Sciences in 1994 . He remained dean of physical sciences at UCI until 1974 , and became a professor emeritus in 1988 , but he continued teaching until 1991 , and remained on UCI 's faculty until his death . 
- 
- = = Death = = 
- 
- Reines died after a long illness at the University of California , Irvine Medical Center in Orange , California , on August 26 , 1998 . He was survived by his wife and children . His papers are in the UCI <unk> . Reines Hall at UCI was named in his honor . 
- 
- = = Publications = = 
- 
- Reines , F. & C. L. Cowan , Jr . " On the <unk> of the Free Neutrino " , Los Alamos National Laboratory ( <unk> ) ( through predecessor agency Los Alamos Scientific Laboratory ) , United States Department of Energy ( through predecessor agency the Atomic Energy Commission ) , ( August 6 , 1953 ) . 
- Reines , F. , Cowan , C. L. Jr . , Carter , R. E. , Wagner , J. J. & M. E. Wyman . " The Free <unk> Absorption Cross Section . Part I. <unk> of the Free <unk> Absorption Cross Section . Part II . <unk> Cross Section from Measurements of <unk> <unk> <unk> Spectrum " , Los Alamos National Laboratory ( <unk> ) ( through predecessor agency Los Alamos Scientific Laboratory ) , United States Department of Energy ( through predecessor agency the Atomic Energy Commission ) , ( June 1958 ) . 
- Reines , F. , <unk> , H. S. , Jenkins , T. L. & J. H. Munsee . " Neutrino Experiments at <unk> " , University of California @-@ Irvine , Case Western Reserve University , United States Department of Energy ( through predecessor agency the Atomic Energy Commission ) , ( September 9 , 1968 ) . 
- Roberts , A. , Blood , H. , Learned , J. & F. Reines . " Status and <unk> of the <unk> Neutrino Project : the Ocean as a Neutrino <unk> " , Fermi National <unk> Laboratory ( <unk> ) , United States Department of Energy ( through predecessor agency the Energy Research and Development Administration ) , ( July 1976 ) . 
- Reines , F. ( 1991 ) . <unk> and Other Matters : Selected Works of Frederick Reines . <unk> , <unk> : World Scientific . ISBN 978 @-@ <unk> @-@ 02 @-@ <unk> @-@ 4 . 
- 
- 
- = Lock Haven , Pennsylvania = 
- 
- The city of Lock Haven is the county seat of Clinton County , in the U.S. state of Pennsylvania . Located near the confluence of the West Branch Susquehanna River and Bald Eagle Creek , it is the principal city of the Lock Haven <unk> Statistical Area , itself part of the Williamsport – Lock Haven combined statistical area . At the 2010 census , Lock Haven 's population was 9 @,@ 772 . 
- Built on a site long favored by pre @-@ Columbian peoples , Lock Haven began in 1833 as a timber town and a haven for loggers , <unk> , and other travelers on the river or the West Branch Canal . <unk> extraction and efficient transportation financed much of the city 's growth through the end of the 19th century . In the 20th century , a light @-@ aircraft factory , a college , and a paper mill , along with many smaller enterprises , drove the economy . Frequent floods , especially in 1972 , damaged local industry and led to a high rate of unemployment in the 1980s . 
- The city has three sites on the National Register of Historic Places — Memorial Park Site , a significant pre @-@ Columbian archaeological find ; <unk> House , a Victorian @-@ era museum ; and Water Street District , an area with a mix of 19th- and 20th @-@ century architecture . A levee , completed in 1995 , protects the city from further flooding . While industry remains important to the city , about a third of Lock Haven 's workforce is employed in education , health care , or social services . 
- 
- = = History = = 
- 
- 
- = = = Pre @-@ European = = = 
- 
- The earliest settlers in Pennsylvania arrived from Asia between <unk> BCE and 8000 BCE , when the glaciers of the Pleistocene Ice Age were <unk> . <unk> point <unk> from this era , known as the <unk> @-@ Indian Period , have been found in most parts of the state . <unk> discoveries at the Memorial Park Site <unk> near the confluence of the West Branch Susquehanna River and Bald Eagle Creek collectively span about 8 @,@ 000 years and represent every major prehistoric period from the Middle Archaic to the Late Woodland period . Prehistoric cultural periods over that span included the Middle Archaic starting at <unk> BCE ; the Late Archaic starting at 3000 BCE ; the Early Woodland starting at 1000 BCE ; the Middle Woodland starting at 0 CE ; and the Late Woodland starting at 900 CE . First contact with Europeans occurred in Pennsylvania between 1500 and 1600 CE . 
- 
- = = = <unk> century = = = 
- 
- In the early 18th century , a tribal confederacy known as the Six Nations of the Iroquois , headquartered in New York , ruled the Indian ( Native American ) tribes of Pennsylvania , including those who lived near what would become Lock Haven . Indian settlements in the area included three Munsee villages on the 325 @-@ acre ( 1 @.@ 32 km2 ) Great Island in the West Branch Susquehanna River at the mouth of Bald Eagle Creek . Four Indian trails , the Great Island Path , the Great <unk> Path , the Bald Eagle Creek Path , and the <unk> Path , crossed the island , and a fifth , Logan 's Path , met Bald Eagle Creek Path a few miles upstream near the mouth of <unk> Creek . During the French and Indian War ( 1754 – 63 ) , colonial <unk> on the <unk> Expedition destroyed Munsee property on the Great Island and along the West Branch . By 1763 , the Munsee had abandoned their island villages and other villages in the area . 
- With the signing of the first Treaty of Fort Stanwix in 1768 , the British gained control from the Iroquois of lands south of the West Branch . However , white settlers continued to appropriate land , including tracts in and near the future site of Lock Haven , not covered by the treaty . In 1769 , <unk> Campbell , the first white settler in the area , built a log cabin near the present site of Lock Haven University of Pennsylvania , and by 1773 William Reed , another settler , had built a cabin surrounded by a stockade and called it Reed 's Fort . It was the westernmost of 11 mostly primitive forts along the West Branch ; Fort Augusta , at what is now Sunbury , was the easternmost and most defensible . In response to settler incursions , and encouraged by the British during the American Revolution ( 1775 – 83 ) , Indians attacked colonists and their settlements along the West Branch . Fort Reed and the other white settlements in the area were temporarily abandoned in 1778 during a general evacuation known as the Big Runaway . Hundreds of people fled along the river to Fort Augusta , about 50 miles ( 80 km ) from Fort Reed ; some did not return for five years . In 1784 , the second Treaty of Fort Stanwix , between the Iroquois and the United States , transferred most of the remaining Indian territory in Pennsylvania , including what would become Lock Haven , to the state . The U.S. acquired the last remaining tract , the Erie Triangle , through a separate treaty and sold it to Pennsylvania in 1792 . 
- 
- = = = <unk> century = = = 
- 
- Lock Haven was laid out as a town in 1833 , and it became the county seat in 1839 , when the county was created out of parts of Lycoming and Centre counties . Incorporated as a borough in 1840 and as a city in 1870 , Lock Haven prospered in the 19th century largely because of timber and transportation . The forests of Clinton County and counties upriver held a huge supply of white pine and hemlock as well as oak , ash , maple , <unk> , cherry , beech , and <unk> . The wood was used locally for such things as frame houses , shingles , canal boats , and wooden bridges , and whole logs were floated to Chesapeake Bay and on to Baltimore , to make <unk> for ships . <unk> driving and log rafting , competing forms of transporting logs to sawmills , began along the West Branch around 1800 . By 1830 , slightly before the founding of the town , the lumber industry was well established . 
- The West Branch Canal , which opened in 1834 , ran 73 miles ( 117 km ) from Northumberland to <unk> , about 5 miles ( 8 km ) upstream from Lock Haven . A state @-@ funded extension called the Bald Eagle Cut ran from the West Branch through Lock Haven and Flemington to Bald Eagle Creek . A privately funded extension , the Bald Eagle and Spring Creek Navigation , eventually reached <unk> , 24 miles ( 39 km ) upstream . Lock Haven 's founder , Jeremiah Church , and his brother , Willard , chose the town site in 1833 partly because of the river , the creek , and the canal . Church named the town Lock Haven because it had a canal lock and because it was a haven for loggers , <unk> , and other travelers . Over the next quarter century , canal boats 12 feet ( 4 m ) wide and 80 feet ( 24 m ) long carried passengers and mail as well as cargo such as coal , ashes for <unk> and soap , <unk> , food , furniture , dry goods , and clothing . A rapid increase in Lock Haven 's population ( to 830 by 1850 ) followed the opening of the canal . 
- A Lock Haven log boom , smaller than but otherwise similar to the Susquehanna Boom at Williamsport , was constructed in 1849 . Large <unk> of timbers weighted with tons of stone were arranged in the pool behind the Dunnstown Dam , named for a settlement on the shore opposite Lock Haven . The piers , about 150 feet ( 46 m ) from one another , stretched in a line from the dam to a point 3 miles ( 5 km ) upriver . <unk> by timbers <unk> together with iron <unk> and rings , the piers anchored an enclosure into which the river current forced floating logs . Workers called boom rats sorted the captured logs , branded like cattle , for delivery to sawmills and other owners . Lock Haven became the lumber center of Clinton County and the site of many businesses related to forest products . 
- The Sunbury and Erie Railroad , renamed the Philadelphia and Erie Railroad in 1861 , reached Lock Haven in 1859 , and with it came a building boom . <unk> that the area 's coal , iron ore , white pine , and high @-@ quality clay would produce significant future wealth , railroad investors led by Christopher and John Fallon financed a line to Lock Haven . On the strength of the railroad 's potential value to the city , local residents had invested heavily in housing , building large homes between 1854 and 1856 . Although the <unk> ' coal and iron ventures failed , Gothic Revival , Greek Revival , and Italianate mansions and commercial buildings such as the Fallon House , a large hotel , remained , and the railroad provided a new mode of transport for the ongoing timber era . A second rail line , the Bald Eagle Valley Railroad , originally organized as the Tyrone and Lock Haven Railroad and completed in the 1860s , linked Lock Haven to Tyrone , 56 miles ( 90 km ) to the southwest . The two rail lines soon became part of the network controlled by the Pennsylvania Railroad . 
- During the era of log floating , <unk> sometimes occurred when logs struck an obstacle . <unk> rafts floating down the West Branch had to pass through chutes in canal dams . The rafts were commonly 28 feet ( 9 m ) wide — narrow enough to pass through the chutes — and 150 feet ( 46 m ) to 200 feet ( 61 m ) long . In 1874 , a large raft got <unk> in the <unk> of the Dunnstown Dam and caused a jam that blocked the channel from bank to bank with a pile of logs 16 feet ( 5 m ) high . The jam eventually trapped another 200 log rafts , and 2 canal boats , The <unk> of Newport and The Sarah Dunbar . 
- In terms of board feet , the peak of the lumber era in Pennsylvania arrived in about 1885 , when 1 @.@ 9 million logs went through the boom at Williamsport . These logs produced a total of about 226 million board feet ( 533 @,@ 000 m3 ) of <unk> lumber . After that , production steadily declined throughout the state . Lock Haven 's timber business was also affected by flooding , which badly damaged the canals and destroyed the log boom in 1889 . 
- The Central State Normal School , established to train teachers for central Pennsylvania , held its first classes in 1877 at a site overlooking the West Branch Susquehanna River . The small school , with <unk> below 150 until the 1940s , eventually became Lock Haven University of Pennsylvania . In the early 1880s , the New York and Pennsylvania Paper Mill in Castanea Township near Flemington began paper production on the site of a former sawmill ; the paper mill remained a large employer until the end of the 20th century . 
- 
- = = = Twentieth century = = = 
- 
- As older forms of transportation such as the canal boat disappeared , new forms arose . One of these , the electric trolley , began operation in Lock Haven in 1894 . The Lock Haven Electric Railway , managed by the Lock Haven Traction Company and after 1900 by the Susquehanna Traction Company , ran passenger trolleys between Lock Haven and Mill Hall , about 3 miles ( 5 km ) to the west . The trolley line extended from the Philadelphia and Erie Railroad station in Lock Haven to a station of the Central Railroad of Pennsylvania , which served Mill Hall . The route went through Lock Haven 's downtown , close to the Normal School , across town to the trolley car barn on the southwest edge of the city , through Flemington , over the Bald Eagle Canal and Bald Eagle Creek , and on to Mill Hall via what was then known as the Lock Haven , <unk> , and Nittany Valley Turnpike . Plans to extend the line from Mill Hall to <unk> , 3 miles ( 5 km ) miles south of Mill Hall , and to Avis 10 miles ( 16 km ) northeast of Lock Haven , were never carried out , and the line remained unconnected to other trolley lines . The system , always financially marginal , declined after World War I. <unk> business to automobiles and buses , it ceased operations around 1930 . 
- William T. Piper , Sr. , built the Piper Aircraft Corporation factory in Lock Haven in 1937 after the company 's Taylor Aircraft manufacturing plant in Bradford , Pennsylvania , was destroyed by fire . The factory began operations in a building that once housed a silk mill . As the company grew , the original factory expanded to include engineering and office buildings . Piper remained in the city until 1984 , when its new owner , Lear @-@ <unk> , moved production to <unk> Beach , Florida . The Clinton County Historical Society opened the Piper Aviation Museum at the site of the former factory in 1985 , and 10 years later the museum became an independent organization . 
- The state of Pennsylvania acquired Central State Normal School in 1915 and renamed it Lock Haven State Teachers College in 1927 . Between 1942 and 1970 , the student population grew from 146 to more than 2 @,@ 300 ; the number of teaching faculty rose from 25 to 170 , and the college carried out a large building program . The school 's name was changed to Lock Haven State College in 1960 , and its emphasis shifted to include the humanities , fine arts , mathematics , and social sciences , as well as teacher education . Becoming Lock Haven University of Pennsylvania in 1983 , it opened a branch campus in <unk> , 48 miles ( 77 km ) west of Lock Haven , in 1989 . 
- An 8 @-@ acre ( 3 @.@ 2 ha ) industrial area in Castanea Township adjacent to Lock Haven was placed on the National Priorities List of uncontrolled hazardous waste sites ( commonly referred to as <unk> sites ) in 1982 . Drake Chemical , which went bankrupt in 1981 , made ingredients for <unk> and other compounds at the site from the 1960s to 1981 . Starting in 1982 , the United States Environmental Protection Agency began a clean @-@ up of contaminated containers , buildings , and soils at the site and by the late 1990s had replaced the soils . Equipment to treat contaminated groundwater at the site was installed in 2000 and continues to operate . 
- 
- = = = Floods = = = 
- 
- Pennsylvania 's streams have frequently flooded . According to William H. Shank , the Native Americans of Pennsylvania warned white settlers that great floods occurred on the Delaware and Susquehanna rivers every 14 years . Shank tested this idea by <unk> the highest floods on record at key points throughout the state over a 200 @-@ year period and found that a major flood had occurred , on average , once every 25 years between 1784 and 1972 . Big floods recorded at Harrisburg , on the main stem of the Susquehanna about 120 miles ( 193 km ) miles downstream from Lock Haven , occurred in 1784 , 1865 , 1889 , 1894 , 1902 , 1936 , and 1972 . <unk> from the Williamsport stream gauge , 24 miles ( 39 km ) miles below Lock Haven on the West Branch of the Susquehanna , showed major flooding between 1889 and 1972 in the same years as the Harrisburg station ; in addition , a large flood occurred on the West Branch at Williamsport in 1946 . <unk> flood @-@ crest readings between 1847 and 1979 — based on data from the National Weather Service flood gauge at Lock Haven — show that flooding likely occurred in the city 19 times in 132 years . The biggest flood occurred on March 18 , 1936 , when the river crested at 32 @.@ 3 feet ( 9 @.@ 8 m ) , which was about 11 feet ( 3 @.@ 4 m ) above the flood stage of 21 feet ( 6 @.@ 4 m ) . 
- The third biggest flood , <unk> at 29 @.@ 8 feet ( 9 @.@ 1 m ) in Lock Haven , occurred on June 1 , 1889 , and coincided with the <unk> Flood . The flood demolished Lock Haven 's log boom , and millions of feet of stored timber were swept away . The flood damaged the canals , which were subsequently abandoned , and destroyed the last of the canal boats based in the city . 
- The most damaging Lock Haven flood was caused by the remnants of Hurricane Agnes in 1972 . The storm , just below hurricane strength when it reached the region , made landfall on June 22 near New York City . Agnes merged with a non @-@ tropical low on June 23 , and the combined system affected the northeastern United States until June 25 . The combination produced widespread rains of 6 to 12 inches ( 152 to 305 mm ) with local amounts up to 19 inches ( 483 mm ) in western <unk> County , about 75 miles ( 121 km ) southeast of Lock Haven . At Lock Haven , the river crested on June 23 at 31 @.@ 3 feet ( 9 @.@ 5 m ) , second only to the 1936 crest . The flood greatly damaged the paper mill and Piper Aircraft . 
- Federal , state , and local governments began construction in 1992 of barriers to protect the city . The project included a levee of 36 @,@ 000 feet ( 10 @,@ 973 m ) and a flood wall of 1 @,@ 000 feet ( 305 m ) along the Susquehanna River and Bald Eagle Creek , closure structures , retention basins , a pumping station , and some relocation of roads and buildings . Completed in 1995 , the levee protected the city from high water in the year of the Blizzard of 1996 , and again 2004 , when rainfall from the remnants of Hurricane Ivan threatened the city . 
- 
- = = Geography = = 
- 
- Lock Haven is the county seat of Clinton County . According to the United States Census Bureau , the city has a total area of 2 @.@ 7 square miles ( 7 @.@ 0 km2 ) , 2 @.@ 5 square miles ( 6 @.@ 5 km2 ) of which is land . About 0 @.@ 2 square miles ( 0 @.@ 5 km2 ) , 6 percent , is water . 
- Lock Haven is at <unk> feet ( 171 m ) above sea level near the confluence of Bald Eagle Creek and the West Branch Susquehanna River in north @-@ central Pennsylvania . The city is about 200 miles ( 320 km ) by highway northwest of Philadelphia and 175 miles ( 280 km ) northeast of Pittsburgh . U.S. Route 220 , a major transportation corridor , skirts the city on its south edge , intersecting with Pennsylvania Route 120 , which passes through the city and connects it with <unk> in northern Clinton County . Other highways entering Lock Haven include Pennsylvania Route <unk> and Pennsylvania Route 150 , which connects to Avis . 
- The city and nearby smaller communities — Castanea , Dunnstown , Flemington , and Mill Hall — are mainly at valley level in the Ridge @-@ and @-@ Valley <unk> , a mountain belt characterized by long even valleys running between long continuous ridges . Bald Eagle Mountain , one of these ridges , runs parallel to Bald Eagle Creek on the south side of the city . <unk> of the confluence with Bald Eagle Creek , the West Branch Susquehanna River drains part of the Allegheny Plateau , a region of dissected <unk> ( also called the " Deep <unk> Section " ) generally north of the city . The geologic formations in the southeastern part of the city are mostly limestone , while those to the north and west consist mostly of <unk> and shale . Large parts of the city are flat , but slopes rise to the west , and very steep slopes are found along the river , on the university campus , and along Pennsylvania Route 120 as it approaches U.S. Route 220 . 
- 
- = = = Climate = = = 
- 
- Under the Köppen climate classification , Lock Haven is in zone <unk> meaning a humid continental climate with hot or very warm summers . The average temperature here in January is 28 ° F ( − 2 ° C ) , and in July it is 73 ° F ( 23 ° C ) . Between 1888 and 1996 , the highest recorded temperature for the city was 106 ° F ( 41 ° C ) in 1936 , and the lowest recorded temperature was − 22 ° F ( − 30 ° C ) in 1912 . The average wettest month is June . Between 1926 and 1977 the mean annual precipitation was about 39 inches ( 990 mm ) , and the number of days each year with precipitation of 0 @.@ 1 inches ( 2 @.@ 5 mm ) or more was 77 . Annual snowfall amounts between 1888 and 1996 varied from 0 in several years to about 65 inches ( 170 cm ) in 1942 . The maximum recorded snowfall in a single month was 38 inches ( 97 cm ) in April 1894 . 
- 
- = = Demographics = = 
- 
- As of the census of 2010 , there were 9 @,@ 772 people living in 3 @,@ <unk> housing units spread across the city . The average household size during the years 2009 – 13 was 2 @.@ 38 . During those same years , multi @-@ unit structures made up 57 percent of the housing @-@ unit total . The rate of home ownership was 35 percent , and the median value of owner @-@ occupied units was about $ 100 @,@ 000 . The estimated population of the city in 2013 was 10 @,@ <unk> , an increase of 2 @.@ 6 percent after 2010 . 
- The population density in 2010 was 3 @,@ <unk> people per square mile ( 1 @,@ <unk> per km2 ) . The reported racial makeup of the city was about 93 percent White and about 4 percent African @-@ American , with other categories totaling about 3 percent . People of Hispanic or Latino origin accounted for about 2 percent of the residents . Between 2009 and 2013 , about 2 percent of the city 's residents were foreign @-@ born , and about 5 percent of the population over the age of 5 spoke a language other than English at home . 
- In 2010 , the city 's population included about 16 percent under the age of 18 and about 12 percent who were 65 years of age or older . Females accounted for 54 percent of the total . Students at the university comprised about a third of the city 's population . 
- Between 2009 and 2013 , of the people who were older than 25 , 82 percent had graduated from high school , and 20 percent had at least a bachelor 's degree . In 2007 , 640 businesses operated in Lock Haven . The mean travel time to work for employees who were at least 16 years old was 16 minutes . 
- The median income for a household in the city during 2009 – 13 was about $ 25 @,@ 000 compared to about $ 53 @,@ 000 for the entire state of Pennsylvania . The per capita income for the city was about $ 19 @,@ 000 , and about 40 percent of Lock Haven 's residents lived below the poverty line . 
- 
- = = Economy = = 
- 
- Lock Haven 's economy , from the city 's founding in 1833 until the end of the 19th century , depended heavily on natural resources , particularly timber , and on cheap transportation to eastern markets . <unk> used the Susquehanna River and Bald Eagle Creek to float timber to sawmills in Lock Haven and nearby towns . The West Branch Canal , reaching the city in 1834 , connected to large markets downstream , and shorter canals along Bald Eagle Creek added other connections . In 1859 , the first railroad arrived in Lock Haven , spurring trade and economic growth . 
- By 1900 , the lumber industry had declined , and the city 's economic base rested on other industries , including a furniture factory , a paper mill , a fire brick plant , and a silk mill . In 1938 , the Piper Aircraft Corporation , maker of the Piper Cub and other light aircraft , moved its production plant to Lock Haven . It remained one of the city 's biggest employers until the 1980s , when , after major flood damage and losses related to Hurricane Agnes in 1972 , it moved to Florida . The loss of Piper Aircraft contributed to an unemployment rate of more than 20 % in Lock Haven in the early 1980s , though the rate had declined to about 9 % by 2000 . Another large plant , the paper mill that had operated since the 1880s in Castanea Township , closed in 2001 . By 2005 , 32 % of the city 's labor force was employed in health care , education , or social services , 16 % in manufacturing , 14 % in retail trade , 13 % in arts , entertainment , recreation , accommodation , and food services , and smaller fractions in other sectors . The city 's biggest employers , Lock Haven University of Pennsylvania and Lock Haven Hospital , are among the seven biggest employers in Clinton County . 
- 
- = = Arts , culture , historic sites , and media = = 
- 
- Lock Haven University presents public concerts , plays , art exhibits , and student recitals at the Price Performance Center , the Sloan <unk> , and the Sloan Fine Arts Gallery on campus . The <unk> <unk> in Mill Hall has produced plays since 1963 . Summer concerts are held in city parks , and the local Junior Chamber International ( <unk> ) chapter sponsors an annual boat <unk> on the river . The city sponsors a festival called <unk> at the airport in the summer , a Halloween parade in October , and a holiday parade in December . Light @-@ airplane pilots travel to the city in vintage Piper planes to attend <unk> Journey Fly @-@ <unk> , which have been held each summer since 1986 . <unk> of radio @-@ controlled model <unk> meet annually at the William T. Piper Memorial Airport to fly their planes . 
- The central library for Clinton County is the Annie <unk> Ross Library in Lock Haven ; it has about 130 @,@ 000 books , subscriptions to periodicals , electronic resources , and other materials . Stevenson Library on the university campus has additional collections . 
- The Piper Aviation Museum exhibits aircraft and aircraft equipment , documents , photographs , and <unk> related to Piper Aircraft . An eight @-@ room home , the <unk> House , restored to its mid @-@ 19th century appearance , displays Victorian @-@ era collections ; it was added to the National Register of Historic Places in 1972 and is home to the Clinton County Historical Society . The Pennsylvania Historical and Museum Commission has placed three cast aluminum markers — Clinton County , Fort Reed , and Pennsylvania Canal ( West Branch Division ) — in Lock Haven to commemorate historic places . The Water Street District , a mix of 19th- and 20th @-@ century architecture near the river , was added to the National Register of Historic Places in 1973 . Memorial Park Site <unk> , an archaeological site of prehistoric significance discovered near the airport , was added to the National Register in 1982 . 
- The city 's media include The Express , a daily newspaper , and The Eagle Eye , the student newspaper at the university . Radio stations <unk> ( AM ) and <unk> ( FM ) broadcast from the city . A television station , <unk> ( available on @-@ campus only ) , and a radio station , <unk> ( Internet station only , with no FCC broadcast license ) , both managed by students , operate on the university campus . 
- 
- = = Parks and recreation = = 
- 
- The city has 14 municipal parks and playgrounds ranging in size from the 0 @.@ 75 @-@ acre ( 0 @.@ 30 ha ) Triangle Park in downtown to the 80 @-@ acre ( 32 ha ) Douglas H. <unk> Memorial Park along Route 120 . Fields maintained by the city accommodate baseball for the Pony League , Little League , and Junior League and <unk> for the Youth Girls League and for adults . In 1948 , a team from the city won the Little League World Series . In 2011 , the <unk> Little League based in Lock Haven advanced to the Little League World Series and placed third in the United States , drawing record crowds . Hanna Park includes tennis courts , and <unk> Park includes a skate park . The Lock Haven City Beach , on the Susquehanna River , offers water access , a sand beach , and a bath house . In conjunction with the school district , the city sponsors a summer recreation program . 
- A 25 @-@ mile ( 40 km ) trail <unk> and run , the Bald Eagle Mountain <unk> , takes place annually near Lock Haven . The local branch of the Young Men 's Christian Association ( YMCA ) offers a wide variety of recreational programs to members , and the Clinton Country Club maintains a private 18 @-@ hole golf course in Mill Hall . 
- 
- = = Government = = 
- 
- Lock Haven has a council @-@ manager form of government . The council , the city 's legislative body , consists of six members and a mayor , each serving a four @-@ year term . The council sets policy , and the city manager <unk> day @-@ to @-@ day operations . The mayor is William <unk> , whose term <unk> in 2019 . The manager is Richard W. <unk> . 
- Lock Haven is the county seat of Clinton County and houses county offices , courts , and the county library . Three elected commissioners serving four @-@ year terms manage the county government . Robert " Pete " <unk> , chairman ; Jeffrey <unk> , vice @-@ chairman , and Paul <unk> , have terms running through 2019 . 
- Michael K. Hanna , a Democrat , represents the 76th District , which includes Lock Haven , in the Pennsylvania House of Representatives . Joseph B. <unk> III , a Republican , represents Lock Haven as part of the 25th District of the Pennsylvania State Senate . 
- 
- = = Education = = 
- 
- The <unk> Central School District serves most of Clinton County , including Lock Haven , as well as parts of Centre County and Potter County . The district 's administration building is in Lock Haven as are three of the district 's elementary schools , Dickey Elementary , <unk> Elementary , and Woodward Elementary , all for children enrolled in kindergarten through fifth grade . The total enrollment of these three schools combined in 2002 – 03 was 790 . Central Mountain Middle School in Mill Hall is the nearest public middle school , for grades six to eight . The nearest public high school , grades nine to twelve , is Central Mountain High School , also in Mill Hall . 
- The city has two private schools , Lock Haven Christian School , with about 80 students in kindergarten through 12th grade , and Lock Haven Catholic School , which had about 190 students in kindergarten through sixth grade as of 2002 – 03 . In 2015 , the Catholic School is completing a 10 @,@ 000 @-@ square @-@ foot ( 930 m2 ) expansion to include grades seven and eight , which will make it a combined elementary and middle school . 
- Lock Haven University of Pennsylvania , offering a wide range of undergraduate studies as well as continuing @-@ education and graduate @-@ school programs at its main campus , occupies 175 acres ( 71 ha ) on the west edge of the city . <unk> at this campus was about 4 @,@ 400 in 2003 . 
- 
- = = Infrastructure = = 
- 
- Lock Haven Taxi , based in the central downtown , has <unk> for hire . <unk> <unk> provides daily intercity bus service between Lock Haven and nearby cities including State College , Williamsport , and Wilkes @-@ Barre . Charter and tour buses are available through Susquehanna <unk> , based in Avis , 10 miles ( 16 km ) northeast of Lock Haven . Pennsylvania Bicycle Route G follows Pennsylvania Route 150 and links to the Pine Creek Rail Trail at the eastern end of the county near Jersey Shore , Pennsylvania . A 2 @.@ 5 @-@ mile ( 4 @.@ 0 km ) walking trail on the levee along the river is restricted to pedestrian use . 
- The Norfolk Southern Railway mainline from Harrisburg to Buffalo , New York , runs through the center of Lock Haven . On the east side of town , it connects to the Nittany and Bald Eagle Railroad , a short line . Trains serving Lock Haven carry only freight . The City of Lock Haven operates the William T. Piper Memorial Airport , a general aviation facility with a paved runway , runway lighting , paved <unk> , a tie @-@ down area , and hangar spaces . No commercial , charter , or freight services are available at this airport . 
- Electric service to Lock Haven residents is provided by PPL ( formerly known as Pennsylvania Power and Light ) , the gas division of which provides natural gas to the city . Verizon Communications handles local telephone service ; long @-@ distance service is available from several providers . <unk> offers high @-@ speed cable <unk> connections to the Internet . Several companies can provide Lock Haven residents with <unk> @-@ up Internet access . One of them , <unk> , has an office in Lock Haven . <unk> also provides cable television . 
- The City of Lock Haven owns the reservoirs and water distribution system for Wayne Township , Castanea Township , and the city . Water is treated at the Central Clinton County Water <unk> Authority Plant in Wayne Township before distribution . The city also provides water to the <unk> Lock Haven Water Authority , which distributes it to surrounding communities . Lock Haven operates a sewage treatment plant for waste water , industrial waste , and <unk> sewage from the city and eight upstream municipalities : Bald Eagle Township , Castanea , Flemington , Lamar , Mill Hall , Porter Township , Woodward Township , and Walker Township in Centre County . Storm water runoff from within the city is transported by city @-@ owned storm sewers . <unk> <unk> of household garbage is provided by a variety of local <unk> licensed by the city ; <unk> are picked up once every two weeks . The Clinton County Solid Waste Authority owns and operates the Wayne Township <unk> , which serves Lock Haven . 
- Lock Haven Hospital is a 77 @-@ bed hospital with a 120 @-@ bed extended @-@ care unit . It offers <unk> , <unk> , and 24 @-@ hour emergency services with heliport access . <unk> @-@ View Home , next to the hospital , offers long @-@ term care to the elderly and other services including speech , physical , and occupational therapy for people of all ages . A 10 @-@ physician community @-@ practice clinic based in the city provides primary care and specialty services . A behavioral health clinic offers programs for children and <unk> and psychiatric <unk> care for all ages . 
- 
- = = Notable people = = 
- 
- <unk> <unk> , winner of America 's Next Top Model ( cycle 16 ) , is a 2015 graduate of Lock Haven University . Alexander McDonald , a U.S. Senator for Arkansas was born near Lock Haven in 1832 . Artist John French Sloan was born in Lock Haven in 1871 , and cartoonist Alison <unk> , author of Dykes to Watch Out For and Fun Home , was born in Lock Haven in 1960 . Richard <unk> , author of the Donald Strachey mysteries , was born in Lock Haven in 1938 . Other notable residents have included diplomat and Dartmouth College president John Sloan Dickey and federal judge <unk> <unk> of the U.S. Federal First District Court of Appeals . 
- 
- 
- = Rachel Green = 
- 
- Rachel Karen Green is a fictional character , one of the six main characters who appear in the American sitcom Friends . <unk> by actress Jennifer Aniston , the character was created by show creators David Crane and Marta Kauffman , and appeared in each of the show ’ s 236 episodes during its decade @-@ long run , from its premiere on September 22 , 1994 to its finale on May 6 , 2004 . Introduced in the show 's pilot as a naive runaway bride who reunites with her childhood best friend Monica and <unk> to New York City , Rachel gradually evolves from a spoiled , inexperienced daddy 's girl into a successful <unk> . During the show 's second season , the character becomes romantically involved with her friend Ross , with whom she maintains a complicated on @-@ again , off @-@ again relationship throughout the entire series . Together , the characters have a daughter , Emma . 
- The role of Rachel was originally offered to actresses <unk> Leoni , the producer 's first choice , and Courteney Cox , both of whom declined , Leoni in favor of starring in the sitcom The Naked Truth , and Cox in favor of playing Rachel 's best friend Monica in Friends . A virtually unknown actress at the time who had previously starred in five short @-@ lived sitcoms , Aniston auditioned for the role of Rachel after turning down an offer as a cast member on the sketch comedy show Saturday Night Live . After acquiring the role and before Friends aired , Aniston was temporarily at risk of being recast because she had also been involved with another sitcom , Muddling Through , at the time , which was ultimately canceled and allowed Aniston to remain on Friends . 
- Critical reception towards Rachel has remained consistently positive throughout Friends ' decade @-@ long run , with The A. V. Club attributing much of the show 's early success to the character . However , some of her storylines have been criticized , specifically her romantic relationship with her friend Joey during season ten . Rachel 's popularity established her as the show 's breakout character , who has since been named one of the greatest television characters of all @-@ time , while the character 's second season haircut spawned an international phenomenon of its own . Named the " Rachel " after her , the character 's <unk> continues to be imitated by millions of women around the world and remains one of the most popular hairstyles in history , in spite of Aniston 's own resentment towards it . Rachel is also regarded as a style icon due to her influence on <unk> during the 1990s . Meanwhile , the character 's relationship with Ross is often cited among television 's most beloved . 
- Rachel is considered to be Aniston 's breakout role , credited with making her the show 's most famous cast member and for spawning her successful film career . <unk> for her performance as Rachel , Aniston won both an Emmy Award for Outstanding Lead Actress in a Comedy Series and a Golden Globe Award for Best Performance by an Actress In A Television Series – Comedy Or Musical . 
- 
- = = Role = = 
- 
- Rachel <unk> in the pilot episode of Friends as a runaway bride who is distraught after abandoning her fiancé Barry Farber ( Mitchell <unk> ) at the altar . She locates her high school best friend Monica Geller ( Courteney Cox ) , the only person she knows in New York City , who agrees to let Rachel reside with her while she attempts to reorganize her life . Rachel meets and befriends Monica ’ s friends Phoebe <unk> ( Lisa <unk> ) , Joey <unk> ( Matt <unk> ) , and Chandler Bing ( Matthew Perry ) , while reuniting with Monica 's older brother Ross Geller ( David Schwimmer ) , who has <unk> <unk> romantic feelings for her since high school . Having previously relied on her parents ' money her entire life with a sole goal of marrying wealthy , Rachel attempts to reinvent herself as an independent young woman by <unk> at Central <unk> , a coffeehouse where her new friends regularly <unk> . 
- As season one concludes , Rachel finally confesses her love for Ross , having learned of his feelings for her from Chandler , only to find that he has already begun dating another woman , whom she <unk> . However , Ross eventually chooses Rachel over his girlfriend Julie ( Lauren Tom ) , and the couple dates for the remainder of the second season . However , their relationship rapidly begins to deteriorate towards the end of the third season after Rachel <unk> her job at the coffeehouse in favor of working in fashion . While Rachel becomes increasingly preoccupied with her new job , Ross grows jealous of her companionship with her <unk> Mark ( Steven <unk> ) , ultimately culminating in their break up on their one @-@ year anniversary following a series of heated arguments and disagreements . 
- In the episodes following the break up , Rachel and Ross are initially hostile towards each other . The <unk> continue to harbor feelings for each other . During a beach house vacation with their friends , Rachel and Ross briefly reconcile when he ends his relationship with Bonnie ( Christine Taylor ) , only to break up once again due to a disagreement . During season four , Rachel dates her customer Joshua ( Tate <unk> ) , while Ross dates her boss ' niece Emily ( Helen <unk> ) , to whom he eventually gets engaged . <unk> , Rachel proposes to recent <unk> Joshua , frightening him off . Rachel indirectly contributes to the demise of Ross and Emily 's relationship when he accidentally <unk> Rachel 's name while exchanging their wedding vows . Ross ultimately divorces a jealous Emily , choosing his friendship with Rachel instead . 
- At the end of season five , Ross and Rachel <unk> get married while <unk> with their friends in Las Vegas . In season six , their annulment request is denied because of Rachel having leveled unfounded allegations against Ross , forcing the two to file for a divorce instead . In season seven , Ross and Rachel unwittingly conceive a child when their birth control fails . Rachel gives birth to a girl in season eight , naming the baby Emma Geller @-@ Green ; the name Emma is a gift from Monica , who had previously been reserving the name for her own child . Rachel and Ross live together as non @-@ romantic roommates during the first half of season nine . 
- Rachel eventually finds a job opportunity in France , but has second thoughts when Ross eventually forfeits <unk> and says " I love you " . Rachel ultimately decides to stay and <unk> her relationship with Ross , getting off the plane at the last minute . 
- 
- = = Development = = 
- 
- 
- = = = Conception and writing = = = 
- 
- After their short @-@ lived television series Family Album was canceled , television writers David Crane and Marta Kauffman pitched Friends to then @-@ NBC president Warren Littlefield as a sitcom about " that special time in your life when your friends are your family , " basing the show on their own experiences as young people living in New York ; the main characters themselves were inspired by their own friends . <unk> as a young woman who is unprepared for adulthood , the character Rachel Green was originally named Rachel <unk> . Although critics and audiences initially perceived Monica as the show 's main character when Friends premiered , the writers had actually given Rachel the pilot 's most prominent storyline . Before deciding that Rachel and Ross would be an item for the entire series , the writers had originally intended for the show 's defining couple to be Joey and Monica . However , after the success of the pilot , in which Rachel and Ross ' developing romance is first hinted at , and witnessing Aniston and co @-@ star David Schwimmer 's on @-@ screen chemistry for the first time , Crane and Kauffman determined that the entire series relied on " finding all the wonderful roadblocks for them to be with each other . " 
- <unk> began <unk> for Rachel and Ross ' union since the very beginning of Friends , openly voicing their frustration with Rachel ’ s <unk> to Ross ' feelings for her . The episode that would ultimately transform the friends ' relationship for the remainder of the series was the first season finale " The One Where Rachel Finds Out " , in which Rachel finally learns of Ross ' true feelings for her , at the same time discovering she actually feels the same . However , the episode nearly went unwritten because , at the time , few friends writers were expecting the couple 's relationship to <unk> into the phenomenon that it ultimately became . The episode was first suggested by director James Burrows ; the writers felt that it was time to alter the couple 's dynamic in order to avoid the repetitive " he 's pining , she 's oblivious " pattern , using the work of author Jane Austen as inspiration on how to finally shift the pining arc from Ross to Rachel . Because stakes for the episode were <unk> high , " The One Where Rachel Finds Out " became Friends ' most reworked episode . The couple 's first kiss at the end of season two 's " The One Where Ross Finds Out " was met with <unk> applause from the studio audience . Crane admitted that keeping viewers interested in their relationship for ten years was challenging . Jonathan Bernstein of The Daily Telegraph believes that they accomplished this by " <unk> [ ing ] the possibility of a Ross and Rachel <unk> through several <unk> without ever putting them back together . " According to Encyclopedia of Television author Horace <unk> , Ross and Rachel 's ever @-@ changing relationship " converted the traditional <unk> <unk> of the situation comedy into ones akin to episodic drama . " Meanwhile , writing for The New York Review of Books , Elaine Blair agreed that Friends created " a sense of chemistry between two characters while also putting obstacles in their way , setting us up for a long @-@ deferred union . " 
- After Rachel and Ross <unk> get married while on vacation in Las Vegas during season five , Schwimmer had initially objected to the idea of having his character Ross divorce her – his third divorce – because he felt that it was taking it " too far . " The actor explained that " The whole arc of the relationship was weird then ... because for [ Ross ] to be able to move on enough to marry someone else and then go back to being in love with Rachel later just went a bit too far . " Rachel and Joey 's romantic storyline was conceived because the writers wanted to delay Ross and Rachel 's reunion further . Crane felt that pairing Rachel and Joey during season ten " was for the greater good " because " It was inappropriate . " However , the cast initially protested the idea , fearing that Rachel , Joey , and Ross would ultimately become <unk> characters and audiences would either " resent Joey for going after a pregnant woman , or resent Rachel for rejecting him , or resent Ross for standing between the two of them . " Meanwhile , the writers also approached the concept of Rachel 's pregnancy and baby tentatively , worrying about how they would include it in the show because they did not want Friends " to become a show about a baby " while " On the other hand , we don 't want to pretend that there isn 't one . " According to Robert Bianco of USA Today , the critical success and popularity of Rachel 's pregnancy is ultimately responsible for " propel [ ling ] the show to the top of the ratings " . When it finally came time to write the series finale , " The only thing [ Crane and Kauffman ] absolutely knew from very early on was that we had to get Ross and Rachel together , " deciding , " We had <unk> the audience around for 10 years with their ' will they or won ’ t they , ' and we didn ’ t see any advantage in frustrating them " any longer . However , at one point the writers had <unk> ending the series with Ross and Rachel in " a gray area of where they aren ’ t together , but we hint there ’ s a sense that they might be down the road . " Ultimately , Crane and Kauffman relented in favor of giving the audience what they want . 
- 
- = = = Casting = = = 
- 
- The final character to be cast , Rachel is portrayed by actress Jennifer Aniston , who auditioned for the role shortly after declining a position as a cast member on the sketch comedy show Saturday Night Live . Her decision was initially ridiculed by both her friends as well as actor Adam Sandler , a Saturday Night Live <unk> . Actress <unk> Leoni , who at the time was being referred to by the media as " the next Lucille Ball " , was offered the role of Rachel as the studio 's first choice , but she declined in favor of starring in the sitcom The Naked Truth . Actress Elizabeth Berkley also auditioned for the role prior to being cast in the teen sitcom Saved by the Bell . Other actresses who auditioned for Rachel include Denise Richards , <unk> Rivers , <unk> Sheridan , Parker <unk> , and <unk> <unk> . Originally , the producers wanted to cast actress Courteney Cox as Rachel , who Crane and Kauffman were particularly drawn to because of her " <unk> , upbeat energy . " Additionally , Cox was the most famous cast member at the time amidst an ensemble of relatively unknown actors . However , the actress lobbied for the role of Rachel 's best friend Monica , as whom she was ultimately cast , because she felt that she was not " quirky " enough to play Rachel . At the same time , although <unk> to each other , Aniston was being considered for the role of Monica , but fought to play Rachel because she felt that the character suited her better . At one point , Cox had begun to regret her decision to play Monica until her own character 's storylines started improving . 
- Friends was Aniston 's sixth sitcom ; each of her previous ventures had been canceled prematurely . Feeling vulnerable , Aniston had begun to doubt herself as an actress and personally approached Littlefield for <unk> on her career , who encouraged her to audition for Friends , which was being referred to as Friends Like These at the time . Crane and Kauffman had worked with Aniston prior to this . However , casting her as Rachel posed a challenge for the network because , at the time , Aniston was simultaneously starring in a developing CBS sitcom called Muddling Through , in which she plays a young woman whose mother is returning home from jail after two years . CBS was initially reluctant to release Aniston from her contract , which required the actress to balance both roles simultaneously , traveling back @-@ and @-@ forth between Muddling Through and Friends for two weeks . Meanwhile , NBC risked having to recast the role of Rachel , replace Aniston , and <unk> several episodes if CBS ' series proved successful , which would have potentially cost the network millions of dollars . However , Littlefield remained confident that Muddling Through would fail . Essentially , the producers of Friends hoped that Muddling Through would be canceled before Friends premiered , while Aniston feared that Muddling Through would be the more successful of the two sitcoms in spite of her preference towards Friends . During this time of uncertainty , Aniston was forced not to participate in several Friends @-@ related promotions and photo shoots ; the network excluded her from these in case she would be replaced . Aniston explained , " When we were shooting the first grouping of cast photos ... I was asked to step out of a bunch because they didn 't know if I was going to be still playing Rachel . " Director James Burrows admitted that Aniston had been cast in second position . The producers had already begun auditioning other actresses for the part , while Aniston also received phone calls from her own friends warning her , " I 'm auditioning for your part in Friends . " Ultimately , Muddling Through was canceled after only three months and ten episodes , two weeks before the pilot of Friends aired , thus allowing Aniston to keep her role on the show , becoming its second youngest cast member at the age of 25 . Crane appreciated Aniston 's interpretation of Rachel because " in the wrong hands Rachel is kind of annoying and spoiled and <unk> , " commending the actress for " breathing life into a difficult character . " 
- Crane and Kauffman strongly envisioned Friends as an ensemble comedy , and Warner Bros. initially marketed the show as such by having the cast appear in their entirety for all press , interviews and photo shoots . One of few sitcoms at the time to be neither a workplace comedy , family sitcom or star a famous comedian , Elizabeth <unk> of The New York Times explained that each of the show 's main characters are " of equal importance . " As a writer , Crane preferred it this way because " utilizing six equal players , rather than emphasizing one or two , would allow for myriad story lines . " Kauffman echoed " that Friends worked best when the entire ensemble was onstage . " The only reason Aniston is credited first during the show 's title sequence is because the cast is listed <unk> . The show 's ensemble format is also believed to have prevented jealous conflicts among the cast . <unk> , the Friends cast became the first in television history to negotiate as a group for equal salaries , refusing to work until their demands of $ 100 @,@ 000 per episode were met during season three , which eventually increased to $ 1 million per episode by seasons nine and ten – approximately $ 25 million per year . Alongside Cox and actress Lisa <unk> , who portrays Phoebe , Aniston became the highest @-@ paid television actress of all time . By then , Aniston had surpassed Cox as the show 's most famous cast member due to having launched an international hair trend with the " Rachel " and successfully transitioning into a film career , combined with her high @-@ profile relationship with her then @-@ husband , actor Brad Pitt , who had once guest starred in an episode of the show . At times the producers would use the actress ' popularity to boost the show 's ratings , notably her character 's seventh season kiss with actress <unk> Ryder and pregnancy arc . Aniston had been telling the press that the show 's ninth season would be her last , and was initially hesitant to return to Friends to film its tenth and final season . She explained to NBC 's Matt <unk> , " I wanted it to end when people still loved us and we were on a high . And then I was also feeling like , ‘ How much more of Rachel do I have in me ? ’ ” However , the actress ultimately agreed to complete the tenth season of Friends , which was reduced from 24 to 18 episodes to accommodate Aniston 's busy film schedule . 
- 
- = = <unk> and themes = = 
- 
- Rachel is the youngest of Friends six main characters . The term " spoiled " is often used to describe the character 's personality during her early appearances . <unk> Britannica describes Rachel as a spoiled and funny character . According to Rachel 's original character description , written by Crane and Kauffman themselves for the show 's pilot , the character is a spoiled yet courageous young woman who " has worked for none of what she has " , unlike best friend Monica , and is initially " equipped to do nothing " . James <unk> of the Hartford Courant identified her as " a spoiled rich kid " , while the Daily News dubbed Rachel an " endearingly spoiled Daddy 's girl . " Author Kim Etingoff wrote about Rachel in her book Jennifer Aniston : From Friends to Films that the character is " <unk> and sometimes spoiled " , while TV Land called her " naive . " Citing the differences between Rachel and her two female friends , The Guardian 's Ryan Gilbey observed that the character " wasn 't <unk> by self @-@ regard , like Monica , or <unk> in <unk> , like Phoebe . " <unk> identified as fitting the " girl next door " <unk> , Anne <unk> of The Telegraph described Rachel as " funny but not too funny , pretty but not too pretty , sexy but not too sexy , <unk> but not too <unk> . " <unk> 's Dominic Wills described the character as " smart but <unk> , determined but <unk> . " Meanwhile , <unk> <unk> , writing for The Huffington Post , <unk> that Rachel is a " beautiful , <unk> , slightly neurotic , borderline <unk> " character . 
- Observing that the show 's main characters are each based on a stereotype , Jonathan Bernstein of The Daily Telegraph identified Rachel as " the self @-@ absorbed one who goes from riches to <unk> . " According to Reign Magazine , Rachel is " a human being full of vulnerability , humor and strength while <unk> donning an undeniable beauty and <unk> . " Originally depicted as a character who is unprepared for " the world as an adult " , Rachel 's personality was gradually tailored to suit Aniston as the series progressed , becoming " more self @-@ sufficient and sympathetic . " According to Shining in the Shadows : Movie Stars of the 2000s author Murray <unk> , " The more boundary collapsed between the ' real ' Jennifer Aniston and Rachel , the more ' authentic ' Aniston became . " <unk> also noted that the character 's " well @-@ <unk> , normalcy and <unk> " is similar to Aniston 's , while both the character and the actress herself are very expressive , talking " with [ their ] hands a good deal . " In her book How To <unk> For Television , author Madeline <unk> wrote that although " Rachel grew within the context of the series ... she would always struggle with the spoiled , image @-@ conscious Daddy 's girl who fled from her wedding in the pilot . " Similarly , BuddyTV wrote that although Rachel " eventually evolves into being less absorbed in later series , she [ remains ] the most image @-@ centric among the six " , while Vogue 's Edward <unk> opined , " She might have been self @-@ centered and <unk> , but Rachel Green was perhaps the most stylish and <unk> fashion @-@ obsessed character on the show . " TV Land summarized the character 's arc and development in the website 's biography of her , writing , " Rachel is a born <unk> , but … she ’ s not necessarily a born worker . In fact , before moving in with Monica , she ’ s never had to work at all , thanks to the generosity of her parents . <unk> , Rachel is smart , resourceful and <unk> , so her future is bright , both as a member of the workforce and with her newfound tribe . " Examining the character 's sexuality , Splitsider 's Mike D <unk> determined that Rachel has had the third most sexual partners , 14 , as well as the highest percentage of serious monogamous relationships at 71 % . D <unk> opined , " Throughout the whole series Rachel is continually meeting men she wants to impress . Her <unk> typically fail , but she somehow winds up in a serious relationship with them . " Additionally , Rachel is also the only character to admit to having had a homosexual experience . 
- In an interview with the Jewish Telegraph , Kauffman confirmed that Rachel is Jewish . On the character 's " Jewish ties " , Kauffman told <unk> that Rachel had always been Jewish " in our minds " , explaining , " You can ’ t create a character with the name ' Rachel Green ' and not from the get @-@ go make some character choices " . Prior to this , critics and fans had long speculated whether or not Rachel is Jewish ; there are entire websites entirely devoted to discussing this . <unk> 's Lindsey Weber , who identifies herself as Jewish , observed several similarities and Jewish stereotypes she shares with the character , citing the facts that Rachel refers to her grandmother Ida Green as " <unk> " , Long Island origin , and engagement to a Jewish doctor as allusions to the character 's Jewish culture . In her book Changed for Good : A <unk> History of the Broadway Musical , author Stacy Wolf identified Rachel as one of several popular female television characters who embodied Jewish stereotypes during the 1990s and often served as " the butt of the shows ' jokes . " Meanwhile , <unk> 's Rebecca <unk> cited Rachel as one of the earliest and most prominent examples of the Jewish American Princess stereotype on screen . Writing for the University of North Carolina at Chapel Hill , Alicia R. <unk> also acknowledged Rachel 's initial Jewish American Princess qualities , describing her as " spoiled , dependent on her father 's money and her <unk> 's , is horrified at the thought of working for a living and generally inept in her attempts to do so , and is eventually revealed to have had a nose job " , which she eventually <unk> as they become less " evident in later seasons of the show " . In his article " <unk> , <unk> , <unk> and <unk> Mothers " , Evan Cooper described Rachel as a " de @-@ <unk> " Jew because , aside from her name , " there is never any discussion of experiences of growing up in a Jewish culture , no use of Yiddish , and few , if any , references to family members with <unk> Jewish <unk> " . Cooper continued to write that although Rachel possesses some Jewish American Princess traits , she is more similar to the " little woman " stereotype . The New York Post 's Robert <unk> labeled Rachel " a rehabilitated Jewish American Princess " , in contrast to her sister Amy ( Christina Applegate ) who remains " selfish , condescending and <unk> . " 
- 
- = = Reception and legacy = = 
- 
- 
- = = = Critical response = = = 
- 
- Critical reception towards Rachel has remained mostly positive throughout the show 's ten @-@ year run . Writing for The A. V. Club , John Reid holds Rachel responsible for the success of the pilot , explaining , " The story of this group of friends must start with a stranger coming to town , and Rachel is the perfect stranger for this plot " . Reid also believes that Rachel initiated character development in the five other main characters , describing her arrival as " a catalyst for all of them to grow , because unlike the rest of them , Rachel is interested in finding meaning for her life " . Also writing for The A. V. Club , Sonia <unk> was pleased with Rachel and Ross ' first romantic encounter because , for the first time , " Rachel displays a moment of true empathy for another human being " . <unk> went on to describe Rachel as " as a model for women coming of age in the 1990s — the popular , pretty girl dissatisfied with where those illusions have taken her but also unwilling to embrace the more aggressively ' feminist ' career @-@ woman strategy " . The New York Times Joseph <unk> enjoyed Rachel 's telephone conversation with her father during the pilot , describing it as " hilarious . " The Los Angeles Times Bob <unk> admitted that he is attracted to Rachel , joking , " my feelings for Rachel , I say with some embarrassment , mirror those of <unk> " . Cosmopolitan reviewed Rachel as " the best fictional gal pal we 've ever had " , while People called her " spoiled yet <unk> " . USA Today 's Robert Bianco credits Rachel 's pregnancy arc with saving Friends , explaining that it " propel [ led ] the show to the top of the ratings " and ultimately " <unk> [ ed ] the show 's decline in ways ... that no one watching ' The One With Monica & Chandler 's Wedding ' could ever have imagined . " Bianco concluded , " Indeed , without that fortune @-@ altering twist , Friends probably would have ended sooner " . 
- <unk> ranked " The One With The Ball " , " The One With Rachel ’ s <unk> Kiss " , " The One With The Football " , " The One With The Fake Party " , and " The One In Vegas , Part One Rachel 's five best episodes . Meanwhile , TVLine criticized Rachel 's storyline in season one 's " The One With the Evil <unk> " for impulsively sleeping with her ex @-@ fiancé , Barry , <unk> the episode as " <unk> " . TVLine also criticized the character 's arc in season four 's " The One With The Fake Party " . At times the character has generated mild controversy , specifically in 1996 in response to her role in the second season episode " The One Where Dr. <unk> Dies " , in which Rachel and Monica fight over a condom . Aniston revealed that Friends fans would often approach and <unk> her for things Rachel did that they deemed " disagreeable " . 
- Neil <unk> , writing for The Daily Telegraph , hailed Rachel as " one of six <unk> @-@ <unk> young New Yorkers who helped Friends redefine the kind of relationships that could form the heart of a US sitcom " . According to Jennifer Aniston : From Friends to Films author Kim Etingoff , audiences wanted to see Rachel " figure out life , " allowing the character to become " a favorite of many Friends fans throughout all ten seasons " . Writing for <unk> , Dominic Wills echoed that Rachel " became the general favourite " , while " No one had a bad word to say about Jennifer Aniston " . Rachel would go on to become the show 's breakout character , and is often revered as one of the greatest characters in television history . Us Weekly magazine ranked Rachel the most beloved television character of the past 20 years , citing her as " one of TV 's most <unk> personalities " , while Entertainment Weekly ranked the character sixth on a similar list . AOL TV ranked Rachel among television 's hundred " Greatest Women " at number 23 , with author Kim Potts <unk> , " Rachel became one of viewers ' favorite Friends because she grew from what could have been a one @-@ note character ... into a more independent , caring pal " . CBS News placed Rachel and the cast of Friends at number 31 on its list of the " 50 greatest TV characters " . BuddyTV ranked Rachel the 15th funniest female character in sitcom history . <unk> collectively ranked Rachel , Monica and Phoebe 11th , 12th and 13th on the website 's list of the " Top 16 Female TV Characters of All Time " . Writing for <unk> , <unk> <unk> believes that Rachel 's influence is evident in the character Penny in the sitcom The Big Bang Theory , noting that both characters are " blonde , cute , funny , likeable girls @-@ next @-@ door " . Several baby name books and websites now commonly associate the name " Rachel " with the character . According to <unk> , the name peaked in popularity in 1996 , during the second season of Friends , becoming the ninth most popular female name in the United States that year . 
- Aniston 's performance in Friends has been praised since her first appearance in its pilot . Entertainment Weekly 's Ken Tucker wrote that the actress provides Rachel with " prickly intelligence " . Writing for The Baltimore Sun , David <unk> cited Aniston among the show 's " very strong cast " , while Variety 's Tony Scott wrote that " All six of the principals ... appear resourceful and display sharp sitcom skills " . Robert Bianco of the Pittsburgh Post @-@ Gazette praised the show 's female cast collectively . TV Guide wrote that the actress " instantly charmed audiences with her perfect looks and endearingly flawed persona " . Kevin Fallon of The Daily Beast dubbed Aniston 's performance on Friends " the work of a brilliant character actress . " The Guardian 's Ryan Gilbey reviewed that " Aniston was the <unk> member of the ensemble and the one least reliant on <unk> caricature " , concluding , " Playing the only character with whom a <unk> viewer might reasonably identify also meant that she got the lion 's share of attention " . Andrew Collins of Radio Times described Aniston as a " natural comic performer , as adept with a subtle nose <unk> as a full @-@ on <unk> , and fluent in quick @-@ fire patter " . In 2002 , Aniston won the Emmy Award for Outstanding Lead Actress in a Comedy Series , one of the show 's six wins out of a total of 62 nominations . In 2003 , the actress won the Golden Globe Award for Best Performance by an Actress In A Television Series – Comedy Or Musical . Karen Thomas of USA Today dubbed Aniston " our favorite Friend " . According to Turner Classic Movies , Aniston ultimately became " One of the most popular television actresses of her era " . According to Jennifer Aniston : From Friends to Films author Kim Etingoff , the actress ' own fame " <unk> " those of her co @-@ stars , becoming the first cast member to " rise to prominence " ; the actress continues to experience the most post @-@ Friends success . Aniston 's performance in Friends led to a successful film career . According to The <unk> News , Rachel is " the role that would end up launching [ Aniston 's ] success " , while Bradford Evans of Splitsider believes " that Jennifer Aniston likely wouldn 't have become a major movie star without Friends " . While ranking Aniston the most attractive sitcom star of the 1990s , Josh Robertson of Complex magazine wrote that " With the haircut , the TV fame , and a true gift for comedy ... combined , Aniston became a big star " , replacing Cox as the show 's " established <unk> " . According to Steve <unk> of Yahoo ! Movies , Aniston is " the series ' only main <unk> to become a <unk> <unk> movie star since the end of the show " . While agreeing that Aniston 's film career has been successful , several critics believe that the actress ' filmography remains limited to playing Rachel @-@ like roles in romantic comedies , save for some exceptions . Ryan Gilbey of The Guardian noted that " Consequently , many of Aniston 's movie roles ... have been Rachel in all but name . " Andrew Collins of Radio Times agreed , writing that Aniston " seems trapped , <unk> playing variations of Rachel " . According to TV Guide , Aniston is " usually called upon to play a variation of her neurotic and adorable Friends character " . Aniston cites Rachel as one of three roles for which she is most grateful , to whom she " owe [ s ] everything " . On being typecast , Aniston admits that at times it " gives you more of a challenge , to shape people ’ s perceptions of you " . as audiences struggle " to lose the Rachel tag that has made her one of the world 's most recognisable faces " . 
- 
- = = = Relationships = = = 
- 
- Rachel has had several romantic relationships throughout Friends decade @-@ long run , the most famous and prominent of which remains her on @-@ again , off @-@ again relationship with friend Ross . Although wildly popular among audiences , the couple has been met with mixed reviews from critics . Katherine <unk> of the Daily Express described the characters ' relationship as " the heart of the show " . China Daily cited Ross and Rachel 's reunion during the series finale " The Last One " among the episode 's highlights , while Gary <unk> of Rolling Stone believes that audiences would not have been happy had the couple not ultimately reunited . Contrastingly , The Wire 's Joe Reid is of the opinion that the show 's second season is " the only time Ross / Rachel was truly great " . Virgin Media wrote that the couple 's dynamics " had grown <unk> tedious " by season ten . E ! cable network ranked Rachel and Ross the ninth greatest Friends couple , writing that their relationship gave " Friends fans enough iconic quotes to fill a book " , considering Phoebe 's line " See ? [ Ross is ] her lobster ! " to be among show 's most iconic . Ross and Rachel 's season three breakup has spawned a debate among Friends fans , who continue to argue over which of the two was at fault : Rachel for suggesting that they take a break from their relationship , or Ross for sleeping with another woman immediately afterwards . Writing for E ! , Jenna <unk> ruled in favor of Rachel , elaborating , " there is no excuse for Ross sleeping with someone else after his lobster suggested taking a break " , concluding that Ross " blew it " . The Jewish community was particularly receptive to the fact that a Jewish @-@ American couple existed on prime time television , described by Lilith magazine as " a <unk> first " . 
- Rachel and Ross are considered to be among television 's greatest and most beloved couples . <unk> referred to them as " everyone 's favourite on ... off ... on ( a break ! ) duo , " while Us Weekly and <unk> ranked them the first and second best television couple , respectively . TV Guide ranked Ross and Rachel the third greatest television couple , dubbing them " the most iconic TV couple in recent memory " . Extra placed the couple at number eight , writing , " Never did we want two people to get together more than Ross ... and Rachel " . <unk> included Rachel and Ross in the website 's " 16 TV <unk> We Want To Be Together Forever " list . The pair is also often ranked among television 's greatest " will they or won 't they " couples . Naming Ross and Rachel the greatest " will they , won 't they " couple , Network Ten believes they defined the term , while <unk> dubbed them " The quintessential will they / won ’ t they couple . " According to Sarah Doran of Radio Times , the couple " became synonymous with the phrase ' we 're on a break ' " . Phoebe 's line , in which she refers to the couple as each other 's lobsters , has become one of the show 's most popular and oft @-@ quoted . <unk> Reilly of <unk> magazine defined the term as " the person of whom another is meant to be with forever " . Tara <unk> of Complex magazine believes that " Every other person can tell you what exactly a ' Ross and Rachel ' relationship means " . Ultimately , Rachel 's season eight pregnancy arc is credited with reviving the show 's ratings and reviews . 
- Rachel 's brief romantic relationship with friend Joey during season ten drew strong criticism from both critics and fans alike , although viewership was not <unk> . In fact , Joshua <unk> of Splitsider believes that the only reason the show 's final two seasons performed well in spite of lackluster reviews " was because of the Joey / Rachel / Ross love triangle " . Eric Goldman of IGN referred to the Rachel @-@ Joey storyline as " questionable . " Entertainment Tonight Canada ranked " The One After Rachel and Joey Kiss " among the show 's ten worst episodes at number five , with author I. P. Johnson <unk> it as " desperate " , concluding , " <unk> for even <unk> this romantic plot ; cheers for abandoning it " . <unk> also cited the same episode as one of the show 's worst , calling it " the most nonsensical idea to ever be . " <unk> , E ! enjoyed Rachel and Joey as a couple because they brought out positive aspects in each other 's personalities . Their relationship also spawned a debate among fans , who argued over whether making Rachel and Joey a couple was a bad idea . Jenna <unk> of E ! determined that it is because " It was too far into the series to throw these two together . They didn 't make sense and their romantic scenes felt forced " . 
- 
- = = = Fashion = = = 
- 
- Both Rachel and Aniston have become fashion icons due to their combined influence on <unk> during the 1990s and onwards , particularly among British women . According to Vogue magazine 's Edward <unk> , Rachel 's fashion sense inspired " the cool New York look " . According to <unk> magazine , Rachel " revived [ a ] love of denim shirts and <unk> " , while <unk> Clayton of <unk> believes that the character " managed to dominate every fashion trend that passed by her radar in the most stylish ways possible " . Hailing her as the " <unk> Queen " , Heat magazine observed the character 's influence on <unk> skirts , denim and <unk> . Citing every costume the character wore during the first season of Friends , <unk> determined that Rachel popularized the <unk> dress . TV Guide published a list of " The 17 Ways Rachel from Friends Changed ' 90s Fashion " . 
- Rachel is often ranked among television 's best dressed characters . Elle included Rachel in the magazine 's " 50 Best <unk> Women on TV " list . <unk> ranked Friends 15th on the website 's list of " 50 TV <unk> That Changed the Way We Dress " , citing Rachel 's " impressive " wardrobe . <unk> ranked Friends the 36th most fashionable television show of all @-@ time , praising Rachel , Monica and Phoebe 's costumes . <unk> ranked Rachel among " The 50 Most <unk> TV Characters Of All Time " at number 28 . Cosmopolitan magazine compiled a list of " 16 things Rachel Green wore to work that we 'd totally wear today " , while Virgin Media ranked the character among television 's <unk> . <unk> magazine ranked Rachel 's wedding dress among " The Best TV Wedding <unk> " . 
- 
- = = = = The " Rachel " haircut = = = = 
- 
- Named after the character , the " Rachel " refers to a <unk> layered <unk> inspired by the way in which Aniston wore her hair on Friends between 1994 and 1996 , during the first and second seasons of the series . The " Rachel " debuted in the show 's 20th episode , " The One With the Evil <unk> " . Aniston believes that her hair stylist , Chris McMillan , created the haircut while he was " <unk> " . The " Rachel " immediately became popular among women , launching an international hair trend . The popularity of the " Rachel " coincided with the popularity of Friends during the mid @-@ to @-@ late @-@ 1990s . Marie Claire estimates that 11 million women <unk> the hairstyle throughout the decade , while the Daily Express determined that the hairstyle was most popular among British women , who went to hair salons " clutching magazine pictures of Aniston " and asking <unk> to give them the look . 
- According to Vanity Fair , the hairstyle 's " widespread popularity ... in the show ’ s very first year cemented the sitcom early on as heavily influential when it came to style . " The " Rachel " remains one of the most popular hairstyles in history , and became the most popular hairstyle in the United States since actress <unk> <unk> 's . Hair <unk> credit its appeal and popularity to its medium length and volume , combined with its tendency to frame the face <unk> . <unk> Mark Woolley described it as " a cut that <unk> almost everyone , designed to make women look beautiful " . The " Rachel " is often ranked among the greatest and most iconic hairstyles of all @-@ time , with <unk> placing it at number four and Time ranking it ninth . The Huffington Post determined that the hairstyle is one of " The Most Famous TV <unk> Of All Time " . US Weekly ranked the " Rachel " the 17th most iconic hairstyle . <unk> magazine ranked the " Rachel " fourth on the magazine 's list of " The 100 Best <unk> of All Time " . Meanwhile , <unk> also cited it among " The very best hair to have graced the small screen " , while ranking it the most memorable hairstyle in television history . The Sydney Morning Herald ranked it the second greatest television hairstyle , while Metro ranked the " Rachel " the character 's second @-@ best hairstyle . Ranked sixth on Entertainment Weekly 's list of the " 25 Fashion <unk> That Changed Entertainment " , the haircut was declared the most " desired " hairstyle of the Clinton era . 
- <unk> Barnes of Self magazine joked that the character 's hair was the " true star of the show " , while its popularity led to Virgin Media coining Rachel " the one with the hair " . <unk> the " Rachel " as one of television 's greatest hairstyles , Sarah <unk> of Elle magazine believed that its popularity " helped make Friends the phenomenon it was " . <unk> that Friends spawned few memorable <unk> in comparison to its contemporaries , Tom <unk> of The Baltimore Sun attributed much of the show 's legacy to the hairstyle , calling it the show 's " only cultural trend " . Josh Robertson of Complex magazine felt that " With the haircut , the TV fame , and a true gift for comedy ... combined , Aniston became a big star " , replacing Courteney Cox . According to Jim <unk> of Paste magazine , " ' the Rachel ' hairstyle became the decade ’ s defining ' do , calling it " the definition of influence " . 
- In the second season episode " The One With The <unk> Wedding " , Rachel complains that her overbearing mother ( Marlo Thomas ) is trying to pattern her own life after hers , lamenting , " Couldn 't she just copy my haircut ? " Although Aniston eventually abandoned the " Rachel " for a <unk> , longer look , the hairstyle remained popular nonetheless . Despite her association with the cut , Aniston disliked the hairstyle . She found maintaining the hairstyle without McMillan 's help difficult , stating " I 'd curse Chris every time I had to <unk> . It took three <unk> — it was like doing surgery ! " and that she would rather shave her head than have to wear it for the rest of her life . 
- Since Aniston , several other celebrities have worn variations of the " Rachel " , among them actresses Cameron <unk> , Rachel <unk> , Emma Watson , Reese Witherspoon , Julia Roberts , comedian Tina Fey , model <unk> Banks , and singer Lily Allen . 
- 
- 
- = Krak des Chevaliers = 
- 
- Krak des Chevaliers ( French pronunciation : <unk> [ <unk> de <unk> ] ; Arabic : حصن <unk> ) , also Crac des Chevaliers , <unk> al @-@ Akrād ( حصن <unk> ) , Castle <unk> , formerly Crac de l 'Ospital , is a Crusader castle in Syria and one of the most important preserved medieval castles in the world . The site was first inhabited in the 11th century by a settlement of Kurdish troops dispatched there by the <unk> ; as a result it was known as Hisn al @-@ Akrad , meaning the " Castle of the Kurds " . In 1142 it was given by Raymond II , Count of Tripoli , to the Knights Hospitaller . It remained in their possession until it fell in 1271 . It became known as Crac de l 'Ospital ; the name Krak des Chevaliers was coined in the 19th century . 
- The Hospitallers began rebuilding the castle in the <unk> and were finished by 1170 when an earthquake damaged the castle . The order controlled a number of castles along the border of the County of Tripoli , a state founded after the First Crusade . Krak des Chevaliers was among the most important , and acted as a center of administration as well as a military base . After a second phase of building was undertaken in the 13th century , Krak des Chevaliers became a concentric castle . This phase created the outer wall and gave the castle its current appearance . The first half of the century has been described as Krak des Chevaliers ' " golden age " . At its peak , Krak des Chevaliers housed a garrison of around 2 @,@ 000 . Such a large garrison allowed the Hospitallers to extract tribute from a wide area . From the 1250s the fortunes of the Knights Hospitaller took a turn for the worse and in 1271 Mamluk Sultan Baibars captured Krak des Chevaliers after a siege lasting 36 days , supposedly by way of a forged letter purportedly from the Hospitallers ' Grand Master that caused the Knights to surrender . 
- Renewed interest in Crusader castles in the 19th century led to the investigation of Krak des Chevaliers , and architectural plans were drawn up . In the late 19th or early 20th century a settlement had been created within the castle , causing damage to its fabric . The 500 inhabitants were moved in 1933 and the castle was given over to the French state , which carried out a program of clearing and restoration . When Syria declared independence in 1946 , it assumed control . Today , a village called al @-@ Husn exists around the castle and has a population of nearly 9 @,@ 000 . Krak des Chevaliers is located approximately 40 kilometres ( 25 mi ) west of the city of Homs , close to the border of Lebanon , and is administratively part of the Homs <unk> . Since 2006 , the castles of Krak des Chevaliers and <unk> <unk> <unk> El @-@ Din have been recognized by UNESCO as a World Heritage Site . It was partially damaged in the Syrian civil war from shelling : the full extent of the damage is unknown , but there have been reports of hasty repairs . 
- 
- = = Etymology = = 
- 
- The modern Arabic word for a castle is <unk> ( <unk> ) , but Krak des <unk> is known as a " <unk> " ( حصن ) , or " fort " . This derives from the name of an earlier fortification on the same site called <unk> al @-@ Akrād ( حصن <unk> ) , meaning " fort of the Kurds " . It was called by the Franks Le <unk> and then by a confusion with <unk> ( fortress ) , Le Crac . <unk> was probably the Frankish version of Akrād , the word for Kurds . After the Knights Hospitaller took control of the castle , it became known as Crac de l 'Ospital ; the name Crac des Chevaliers ( alternatively spelt Krak des Chevaliers ) was introduced by Guillaume Rey in the 19th century . 
- 
- = = Location = = 
- 
- The castle sits atop a 650 @-@ metre @-@ high ( 2 @,@ 130 ft ) hill east of <unk> , Syria , in the Homs Gap . On the other side of the gap , 27 kilometres ( 17 mi ) away , was the 12th @-@ century <unk> Castle . The route through the strategically important Homs Gap connects the cities of Tripoli and Homs . To the north of the castle lies the <unk> <unk> , and to the south Lebanon . The surrounding area is fertile , <unk> from streams and abundant rainfall . Compared to the Kingdom of Jerusalem , the other Crusader states had less land suitable for farming ; however , the limestone peaks of Tripoli were well @-@ suited to defensive sites . 
- Property in the County of Tripoli , granted to the Knights Templar in the <unk> , included the Castle of the Kurds , the towns of <unk> and <unk> , and the <unk> <unk> plain separating Homs and Tripoli . Homs was never under Crusader control , so the region around the Castle of the Kurds was vulnerable to expeditions from the city . While its proximity caused the Knights problems with regard to defending their territory , it also meant Homs was close enough for them to raid . Because of the castle 's command of the plain , it became the Knights ' most important base in the area . 
- 
- = = History = = 
- 
- According to 13th @-@ century Arab historian Ibn Shaddad , the <unk> <unk> of Aleppo , <unk> ad @-@ <unk> , established a settlement of Kurdish tribesmen at the site of the future castle in <unk> CE , hence the castle 's Arabic name " Hisn al @-@ Akrad " ( Castle of the Kurds ) . The site was strategically located at the southern edge of the <unk> al @-@ <unk> mountain range and dominated the road between Homs and Tripoli . When building castles , Muslims often chose elevated sites such as hills and mountains that provided natural obstacles . 
- In January 1099 on the journey to Jerusalem during the First Crusade , the company of Raymond IV of Toulouse came under attack from the garrison of Hisn al @-@ Akrad , the forerunner of the Krak , who <unk> Raymond 's <unk> . The following day Raymond marched on the castle and found it deserted . The crusaders briefly occupied the castle in February of the same year but abandoned it when they continued their march towards Jerusalem . Permanent occupation began in <unk> when <unk> , Prince of Galilee took control of the site . The early castle was substantially different from the extant remains and no trace of this first castle survives at the site . 
- The origins of the Knights Hospitaller are unclear , but the order probably emerged around the <unk> in Jerusalem . It started as a religious order which cared for the sick , and later looked after pilgrims to the Holy Land . After the success of the First Crusade in capturing Jerusalem in 1099 , many crusaders donated their new property in the Levant to the Hospital of St John . Early donations were in the newly formed Kingdom of Jerusalem , but over time the order extended its holdings to the Crusader states of the County of Tripoli and the Principality of Antioch . Evidence suggests that in the <unk> the order became <unk> when Fulk , King of Jerusalem , granted the newly built castle at <unk> to the order in 1136 . A papal bull from between 1139 and <unk> may indicate the order hiring people to defend pilgrims . There were also other military orders , such as the Knights Templar , which offered protection to pilgrims . 
- Between 1142 and <unk> Raymond II , Count of Tripoli , granted the order property in the county . According to historian Jonathan Riley @-@ Smith , the Hospitallers effectively established a " palatinate " within Tripoli . The property included castles with which the Hospitallers were expected to defend Tripoli . Along with Krak des Chevaliers , the Hospitallers were given four other castles along the borders of the state which allowed the order to dominate the area . The order 's agreement with Raymond II stated that if he did not accompany knights of the order on campaign , the spoils belonged entirely to the order , and if he was present it was split equally between the count and the order . Raymond II could further not make peace with the Muslims without the permission of the Hospitallers . The Hospitallers made Krak des Chevaliers a center of administration for their new property , undertaking work at the castle that would make it one of the most elaborate Crusader fortifications in the Levant . 
- After acquiring the site in 1142 , they began building a new castle to replace the former Kurdish fortification . This work lasted until 1170 , when an earthquake damaged the castle . An Arab source mentions that the quake destroyed the castle 's chapel , which was replaced by the present chapel . In <unk> the Crusaders emerged victorious over Nur ad @-@ Din in the Battle of al @-@ <unk> near Krak des Chevaliers . 
- Drought conditions between 1175 and 1180 prompted the Crusaders to sign a two @-@ year truce with the Muslims , but without Tripoli included in the terms . During the <unk> raids by Christians and Muslims into each other 's territory became more frequent . In 1180 , Saladin ventured into the County of Tripoli , <unk> the area . Unwilling to meet him in open battle , the Crusaders retreated to the relative safety of their fortifications . Without capturing the castles , Saladin could not secure control of the area , and once he retreated the Hospitallers were able to <unk> their damaged lands . The Battle of <unk> in 1187 was a disastrous defeat for the Crusaders : Guy of <unk> , King of Jerusalem , was captured , as was the True Cross , a relic discovered during the First Crusade . Afterwards Saladin ordered the execution of the captured Templar and Hospitaller knights , such was the importance of the two orders in defending the Crusader states . After the battle , the Hospitaller castles of <unk> , <unk> , and <unk> fell to Muslim armies . Following these losses , the Order focused its attention on its castles in Tripoli . In May <unk> Saladin led an army to attack Krak des Chevaliers , but on seeing the castle decided it was too well defended and instead marched on the Hospitaller castle of Margat , which he also failed to capture . 
- Another earthquake struck in <unk> , and it may have been after this event that the castle was remodelled . The 13th @-@ century work was the last period of building at Krak des Chevaliers and gave it its current appearance . An enclosing stone circuit was built between 1142 and 1170 ; the earlier structure became the castle 's inner court or ward . If there was a circuit of walls surrounding the inner court that pre @-@ dated the current outer walls , no trace of it has been discovered . 
- The first half of the 13th century has been characterized as Krak des Chevaliers ' " golden age " . While other Crusader strongholds came under threat , Krak des Chevaliers and its garrison of 2 @,@ 000 soldiers dominated the surrounding area . It was effectively the center of a principality which remained in Crusader hands until 1271 and was the only major inland area to remain constantly under Crusader control during this period . Crusaders who passed through the area would often stop at the castle , and probably made donations . King Andrew II of Hungary visited in 1218 and proclaimed the castle the " key of the Christian lands " . He was so impressed with the castle that he gave a yearly income of 60 marks to the Master and 40 to the brothers . <unk> de Joinville , uncle of the noted chronicler of the <unk> Jean de Joinville , died at Krak des Chevaliers in 1203 or 1204 and was buried in the castle 's chapel . 
- The main contemporary accounts relating to Krak des Chevaliers are of Muslim origin and tend to emphasize Muslim success while overlooking setbacks against the Crusaders although they suggest that the Knights Hospitaller forced the settlements of Hama and Homs to pay tribute to the Order . This situation lasted as long as Saladin 's successors <unk> between themselves . The proximity of Krak des Chevaliers to Muslim territories allowed it to take on an offensive role , acting as a base from which neighboring areas could be attacked . By 1203 the garrison were making raids on <unk> ( which was under Muslim control ) and Hama , and in 1207 and 1208 the castle 's soldiers took part in an attack on Homs . Krak des Chevaliers acted as a base for expeditions to Hama in 1230 and 1233 after the <unk> refused to pay tribute . The former was unsuccessful , but the 1233 expedition was a show of force that demonstrated the importance of Krak des Chevaliers . 
- In the 1250s , the fortunes of the Hospitallers at Krak des Chevaliers took a turn for the worse . A Muslim army estimated to number 10 @,@ 000 men ravaged the countryside around the castle in <unk> after which the Order 's finances declined sharply . In 1268 Master Hugh Revel complained that the area , previously home to around 10 @,@ 000 people , now stood deserted and that the Order 's property in the Kingdom of Jerusalem produced little income . He also noted that by this point there were only 300 of the Order 's brethren left in the east . On the Muslim side , in 1260 Baibars became Sultan of Egypt , following his overthrow of the incumbent ruler <unk> , and went on to unite Egypt and Syria . As a result , Muslim settlements that had previously paid tribute to the Hospitallers at Krak des Chevaliers no longer felt intimidated into doing so . 
- Baibars ventured into the area around Krak des Chevaliers in 1270 and allowed his men to <unk> their animals on the fields around the castle . When he received news that year of the Eighth Crusade led by King Louis IX of France , Baibars left for Cairo to avoid a confrontation . After Louis died in 1271 Baibars returned to deal with Krak des Chevaliers . Before he marched on the castle the Sultan captured the smaller castles in the area , including <unk> Blanc . On 3 March , Baibars ' army arrived at Krak des Chevaliers . By the time the Sultan appeared on the scene , the castle may already have been blockaded by Mamluk forces for several days . Of the three Arabic accounts of the siege only one was contemporary , that of Ibn Shaddad , although he was not present at the siege . <unk> who lived in the area had fled to the castle for safety and were kept in the outer ward . As soon as Baibars arrived he erected <unk> , powerful siege weapons which he would later turn on the castle . In a probable reference to a walled suburb outside the castle 's entrance , Ibn Shaddad records that two days later the first line of defences fell to the besiegers . 
- Rain interrupted the siege , but on 21 March , immediately south of Krak des Chevaliers , <unk> 's forces captured a triangular <unk> possibly defended by a timber palisade . On 29 March , the attackers undermined a tower in the southwest corner causing it to collapse whereupon Baibars ' army attacked through the breach . In the outer ward they encountered the peasants who had sought refuge in the castle . Though the outer ward had fallen , with a handful of the garrison killed in the process , the Crusaders retreated to the more formidable inner ward . After a lull of ten days , the besiegers conveyed a letter to the garrison , supposedly from the Grand Master of the Knights Hospitaller in Tripoli , which granted permission for them to surrender . Although the letter was a forgery , the garrison <unk> and the Sultan spared their lives . The new owners of the castle undertook repairs , focused mainly on the outer ward . The Hospitaller chapel was converted to a mosque and two <unk> were added to the interior . 
- 
- = = Later history = = 
- 
- After the Franks were driven from the Holy Land in <unk> , European familiarity with the castles of the <unk> declined . It was not until the 19th century that interest in these buildings was renewed , so there are no detailed plans from before 1837 . Guillaume Rey was the first European researcher to scientifically study Crusader castles in the Holy Land . In 1871 he published the work <unk> sur les monuments de l <unk> <unk> des <unk> en <unk> et dans l <unk> de <unk> ; it included plans and drawings of the major Crusader castles in Syria , including Krak des Chevaliers . In some instances his drawings were inaccurate , however for Krak des <unk> they record features which have since been lost . 
- Paul Deschamps visited the castle in February 1927 . Since Rey had visited in the 19th century a village of 500 people had been established within the castle . Renewed <unk> had damaged the site : underground vaults had been used as rubbish tips and in some places the <unk> had been destroyed . Deschamps and fellow architect François <unk> attempted to clear some of the <unk> ; General Maurice <unk> assigned 60 <unk> soldiers to help . Deschamps left in March 1927 , and work resumed when he returned two years later . The culmination of <unk> 's work at the castle was the publication of Les <unk> des <unk> en <unk> <unk> I : le Crac des Chevaliers in 1934 , with detailed plans by <unk> . The survey has been widely praised , described as " brilliant and <unk> " by military historian D. J. <unk> King in 1949 and " perhaps the finest account of the archaeology and history of a single medieval castle ever written " by historian Hugh Kennedy in 1994 . 
- As early as 1929 there were suggestions that the castle should be taken under French control . On 16 November 1933 Krak des Chevaliers was given into the control of the French state , and cared for by the <unk> des <unk> @-@ Arts . The villagers were moved and paid F1 million between them in compensation . Over the following two years a programme of cleaning and restoration was carried out by a force of 120 workers . Once finished , Krak des Chevaliers was one of the key tourist attractions in the French Levant . Pierre <unk> , who had undertaken similar work at the Tower of the Lions and the two castles at Sidon , <unk> the work . Despite the restoration , no archaeological excavations were carried out . The French Mandate of Syria and Lebanon , which had been established in 1920 , ended in 1946 with the declaration of Syrian independence . The castle was made a World Heritage Site by UNESCO , along with <unk> ’ at <unk> El @-@ Din , in 2006 , and is owned by the Syrian government . 
- Several of the castle 's former residents built their houses outside the fortress and a village called al @-@ Husn has since developed . Many of the al @-@ Husn 's roughly 9 @,@ 000 Muslim residents benefit economically from the tourism generated by the site . 
- 
- = = = Syrian Civil War = = = 
- 
- During the Syrian Civil War which began in 2011 , UNESCO voiced concerns that the war might lead to the damage of important cultural sites such as Krak des Chevaliers . It has been reported that the castle was shelled in August 2012 by the Syrian Arab Army , and the Crusader chapel has been damaged . The castle was reported to have been damaged in July 2013 by an <unk> during the Siege of Homs , and once more on the 18th of August 2013 it was clearly damaged yet the amount of destruction is unknown . The Syrian Arab Army recaptured the castle and the village of al @-@ <unk> from rebel forces on March 20 , 2014 , although the extent of damage from earlier mortar hits remained unclear . 
- 
- = = Architecture = = 
- 
- Writing in the early 20th century , T. E. Lawrence , popularly known as Lawrence of Arabia , remarked that Krak des Chevaliers was " perhaps the best preserved and most wholly admirable castle in the world , [ a castle which ] forms a fitting commentary on any account of the <unk> buildings of Syria " . <unk> in Europe provided <unk> accommodation for their owners and were centers of administration ; in the Levant the need for defence was paramount and was reflected in castle design . Kennedy suggests that " The castle scientifically designed as a fighting machine surely reached its <unk> in great buildings like Margat and Crac des Chevaliers . " 
- Krak des Chevaliers can be classified both as a spur castle , due to its site , and after the 13th @-@ century expansion a fully developed concentric castle . It was similar in size and layout to <unk> Jacob , a Crusader castle built in the late <unk> . Margat has also been cited as Krak des Chevaliers ' sister castle . The main building material at Krak des Chevaliers was limestone ; the ashlar facing is so fine that the mortar is barely noticeable . Outside the castle 's entrance was a " walled suburb " known as a <unk> , no trace of which remains . To the south of the outer ward was a triangular <unk> and the Crusaders may have intended to build stone walls and towers around it . It is unknown how it was defended at the time of the 1271 siege , though it has been suggested it was surrounded by a timber palisade . South of the castle the spur on which it stands is connected to the next hill , so that siege engines can approach on level ground . The inner defences are strongest at this point , with a cluster of towers connected by a thick wall . 
- 
- = = = Inner ward = = = 
- 
- Between 1142 and 1170 the Knights Hospitaller undertook a building programme on the site . The castle was defended by a stone curtain wall studded with square towers which projected slightly . The main entrance was between two towers on the eastern side , and there was a <unk> gate in the northwest tower . At the center was a courtyard surrounded by vaulted chambers . The lay of the land dictated the castle 's irregular shape . A site with natural defences was a typical location for Crusader castles and steep slopes provided Krak des Chevaliers with defences on all sides bar one , where the castle 's defences were concentrated . This phase of building was incorporated into the later castle 's construction . 
- When Krak des Chevaliers was remodelled in the 13th century , new walls surrounding the inner court were built . They followed the earlier walls , with a narrow gap between them in the west and south which was turned into a gallery from which defenders could <unk> missiles . In this area , the walls were supported by a steeply sloping glacis which provided additional protection against both siege weapons and earthquakes . Four large , round towers project vertically from the glacis ; they were used as accommodation for the Knights of the garrison , about 60 at its peak . The southwest tower was designed to house the rooms of the Grand Master of the Knights Hospitaller . Though the defences which once crested the walls of the inner wards no longer survive in most places , it seems that they did not extend for the entire circuit . <unk> were absent from the southern face . The area between the inner court and the outer walls was narrow and not used for accommodation . In the east , where the defences were weakest , there was an open cistern filled by an <unk> . It acted both as a <unk> and water supply for the castle . 
- At the north end of the small courtyard is a chapel and at the southern end is an esplanade . The esplanade is raised above the rest of the courtyard ; the vaulted area beneath it would have provided storage and could have acted as <unk> and shelter from missiles . <unk> the west of the courtyard is the hall of the Knights . Though probably first built in the 12th century , the interior dates from the 13th @-@ century <unk> . The tracery and delicate decoration is a sophisticated example of Gothic architecture , probably dating from the 1230s . 
- 
- = = = Chapel = = = 
- 
- The current chapel was probably built to replace the one destroyed by an earthquake in 1170 . Only the east end of the original chapel , which housed the apse , and a small part of the south wall survive from the original chapel . The later chapel had a barrel vault and an <unk> apse ; its design would have been considered <unk> by contemporary standards in France , but bears similarities to that built around <unk> at Margat . It was divided into three roughly equal bays . A <unk> runs round the chapel at the point where the vault ends and the wall begins . <unk> roughly east to west , it was 21 @.@ 5 metres ( 71 ft ) long and 8 @.@ 5 metres ( 28 ft ) wide with the main entrance from the west and a second smaller one in the north wall . When the castle was remodelled in the early 13th century , the entrance was moved to the south wall . The chapel was lit by windows above the <unk> , one at the west end , one on either side of the east bay , and one on the south side of the central bay , and the apse at the east end had a large window . In 1935 a second chapel was discovered outside the castle 's main entrance , however it no longer survives . 
- 
- = = = Outer ward = = = 
- 
- The second phase of building work undertaken by the Hospitallers began in the early 13th century and lasted decades . The outer walls were built in the last major construction on the site , lending the Krak des Chevaliers its current appearance . Standing 9 metres ( 30 ft ) high , the outer circuit had towers that projected strongly from the wall . While the towers of the inner court had a square plan and did not project far beyond the wall , the towers of the 13th @-@ century outer walls were rounded . This design was new and even contemporary Templar castles did not have rounded towers . The technique was developed at <unk> Gaillard in France by Richard the <unk> between <unk> and <unk> . The extension to the southeast is of lesser quality than the rest of the circuit and was built at an unknown date . Probably around the 1250s a <unk> was added to the north wall . 
- Arrow slits in the walls and towers were distributed to minimize the amount of dead ground around the castle . <unk> crowned the walls , offering defenders a way to <unk> projectiles towards enemies at the foot of the wall . They were so cramped <unk> would have had to crouch inside them . The box <unk> were unusual : those at Krak des Chevaliers were more complex that those at <unk> or Margat and there were no comparable features amongst Crusader castles . However , they bore similarities to Muslim work , such as the contemporary defences at the Citadel of Aleppo . It is unclear which side imitated the other , as the date they were added to Krak des Chevaliers is unknown , but it does provide evidence for the <unk> of military ideas between the Muslim and Christian armies . These defences were accessed by a wall @-@ walk known as a chemin de <unk> . In the opinion of historian Hugh Kennedy the defences of the outer wall were " the most elaborate and developed anywhere in the Latin east ... the whole structure is a brilliantly designed and superbly built fighting machine " . 
- When the outer walls were built in the 13th century the main entrance was enhanced . A vaulted corridor led uphill from the outer gate in the northeast . The corridor made a hairpin turn halfway along its length , making it an example of a bent entrance . Bent entrances were a Byzantine innovation , but that at Krak des Chevaliers was a particularly complex example . It extended for 137 metres ( 450 ft ) , and along its length were murder @-@ holes which allowed defenders to shower attackers with missiles . Anyone going straight ahead rather than following the hairpin turn would emerge in the area between the castle 's two circuits of walls . To access the inner ward , the passage had to be followed round . 
- 
- = = = <unk> = = = 
- 
- Despite its predominantly military character , the castle is one of the few sites where Crusader art ( in the form of frescoes ) has been preserved . In 1935 , 1955 , and 1978 medieval frescoes were discovered within Krak des Chevaliers after later plaster and white @-@ wash had decayed . The <unk> were painted on the interior and exterior of the main chapel and the chapel outside the main entrance , which no longer survives . Writing in 1982 , historian <unk> <unk> noted that at the time there had been little investigation of Crusader frescoes that would provide a comparison for the fragmentary remains found at Krak des Chevaliers . Those in the chapel were painted on the masonry from the 1170 – <unk> rebuild . <unk> , smoke , and moisture have made it difficult to preserve the frescoes . The fragmentary nature of the red and blue frescoes inside the chapel means they are difficult to assess . The one on the exterior of the chapel depicted the Presentation of Jesus at the Temple . 
- 
- 
- = The Importance of Being Earnest = 
- 
- The Importance of Being Earnest , A <unk> Comedy for Serious People is a play by Oscar Wilde . First performed on 14 February 1895 at the St James 's Theatre in London , it is a farcical comedy in which the protagonists maintain fictitious <unk> to escape burdensome social obligations . Working within the social conventions of late Victorian London , the play 's major themes are the triviality with which it treats institutions as serious as marriage , and the resulting satire of Victorian ways . Contemporary reviews all praised the play 's humour , though some were cautious about its explicit lack of social messages , while others <unk> the modern consensus that it was the culmination of Wilde 's artistic career so far . Its high farce and witty dialogue have helped make The Importance of Being Earnest Wilde 's most <unk> popular play . 
- The successful opening night marked the climax of Wilde 's career but also heralded his downfall . The Marquess of Queensberry , whose son Lord Alfred Douglas was Wilde 's lover , planned to present the writer with a bouquet of rotten vegetables and disrupt the show . Wilde was tipped off and Queensberry was refused admission . Soon afterwards their feud came to a climax in court , where Wilde 's homosexual double life was revealed to the Victorian public and he was eventually sentenced to imprisonment . His notoriety caused the play , despite its early success , to be closed after 86 performances . After his release , he published the play from exile in Paris , but he wrote no further comic or dramatic work . 
- The Importance of Being Earnest has been revived many times since its premiere . It has been adapted for the cinema on three occasions . In The Importance of Being Earnest ( 1952 ) , Dame Edith Evans reprised her celebrated interpretation of Lady Bracknell ; The Importance of Being Earnest ( 1992 ) by Kurt Baker used an all @-@ black cast ; and Oliver Parker 's The Importance of Being Earnest ( 2002 ) incorporated some of Wilde 's original material cut during the preparation of the original stage production . 
- 
- = = Composition = = 
- 
- After the success of Wilde 's plays Lady Windermere 's Fan and A Woman of No Importance , Wilde 's producers urged him to write further plays . In July 1894 he <unk> his idea for The Importance of Being Earnest to George Alexander , the actor @-@ manager of the St James 's Theatre . Wilde spent the summer with his family at Worthing , where he wrote the play quickly in August . His fame now at its peak , he used the working title Lady <unk> to avoid pre @-@ <unk> speculation of its content . Many names and ideas in the play were borrowed from people or places the author had known ; Lady Queensberry , Lord Alfred Douglas 's mother , for example , lived at Bracknell . There is widespread agreement among Wilde scholars that the most important influence on the play was W. S. Gilbert 's 1877 farce <unk> ; Wilde borrowed from Gilbert not only several incidents but , in Russell Jackson 's phrase " the gravity of tone demanded by Gilbert of his actors " . 
- Wilde continually revised the text over the next months : no line was left <unk> , and " in a play so economical with its language and effects , [ the revisions ] had serious consequences " . <unk> <unk> describes Wilde 's revisions as a refined art at work : the earliest , longest handwritten drafts of the play labour over farcical incidents , broad puns , nonsense dialogue and conventional comic turns . In revising as he did , " Wilde transformed standard nonsense into the more systemic and disconcerting <unk> which <unk> Earnest 's dialogue " . Richard Ellmann argues that Wilde had reached his artistic maturity and wrote this work more surely and rapidly than before . 
- Wilde hesitated about submitting the script to Alexander , worrying that it might be unsuitable for the St James 's Theatre , whose typical repertoire was relatively serious , and explaining that it had been written in response to a request for a play " with no real serious interest " . When Henry James 's Guy <unk> failed , Alexander turned to Wilde and agreed to put on his play . Alexander began his usual meticulous preparations , <unk> the author on each line and planning stage movements with a toy theatre . In the course of these rehearsals Alexander asked Wilde to shorten the play from four acts to three . Wilde agreed and combined elements of the second and third acts . The largest cut was the removal of the character of Mr. <unk> , a solicitor who comes from London to arrest the <unk> " Ernest " ( i.e. , Jack ) for his unpaid dining bills . Algernon , who is posing as " Ernest " , will be led away to Holloway <unk> unless he settles his accounts immediately . Jack finally agrees to pay for Ernest , everyone thinking that it is Algernon 's bill when in fact it is his own . The four @-@ act version was first played on the radio in a BBC production and is still sometimes performed . Peter <unk> argues that the three @-@ act structure is more effective , and that the shorter original text is more theatrically <unk> than the expanded published edition . 
- 
- = = Productions = = 
- 
- 
- = = = <unk> = = = 
- 
- The play was first produced at the St James 's Theatre on Valentine 's Day 1895 . It was freezing cold but Wilde arrived dressed in " <unk> sobriety " , wearing a green <unk> . The audience , according to one report , " included many members of the great and good , former cabinet ministers and privy councillors , as well as actors , writers , academics , and enthusiasts " . Allan Aynesworth , who played Algernon Moncrieff , recalled to Hesketh Pearson that " In my fifty @-@ three years of acting , I never remember a greater triumph than [ that ] first night " . Aynesworth was himself " <unk> and stylish " , and Alexander , who played Jack Worthing , " demure " . 
- The cast was : 
- John Worthing , J.P. — George Alexander 
- Algernon Moncrieff — Allan Aynesworth 
- Rev. Canon Chasuble , <unk> — H. H. Vincent 
- <unk> — Frank <unk> 
- Lane — F. Kinsey <unk> 
- Lady Bracknell — Rose <unk> 
- <unk> Gwendolen Fairfax — Irene <unk> 
- Cecily <unk> — Evelyn <unk> 
- Miss Prism — Mrs. George <unk> 
- The Marquess of Queensberry , the father of Wilde 's lover Lord Alfred Douglas ( who was on holiday in Algiers at the time ) , had planned to disrupt the play by throwing a bouquet of rotten vegetables at the playwright when he took his bow at the end of the show . Wilde and Alexander learned of the plan , and the latter cancelled Queensberry 's ticket and arranged for policemen to bar his entrance . Nevertheless , he continued harassing Wilde , who eventually launched a private prosecution against the peer for criminal libel , triggering a series of trials ending in Wilde 's imprisonment for gross indecency . Alexander tried , unsuccessfully , to save the production by removing Wilde 's name from the billing , but the play had to close after only 86 performances . 
- The play 's original Broadway production opened at the Empire Theatre on 22 April 1895 , but closed after sixteen performances . Its cast included William <unk> as Algy , Henry Miller as Jack , Viola Allen as Gwendolen , and Ida Vernon as Lady Bracknell . The Australian premiere was in Melbourne on 10 August 1895 , presented by Dion <unk> , Jr. and Robert Brough , and the play was an immediate success . Wilde 's downfall in England did not affect the popularity of his plays in Australia . 
- 
- = = = Critical reception = = = 
- 
- In contrast to much theatre of the time , The Importance of Being Earnest 's light plot does not tackle serious social and political issues , something of which contemporary reviewers were wary . Though unsure of Wilde 's seriousness as a <unk> , they recognised the play 's <unk> , humour and popularity with audiences . George Bernard Shaw , for example , reviewed the play in the Saturday Review , arguing that comedy should touch as well as <unk> , " I go to the theatre to be moved to laughter . " Later in a letter he said , the play , though " extremely funny " , was Wilde 's " first really <unk> [ one ] " . In The World , William Archer wrote that he had enjoyed watching the play but found it to be empty of meaning , " What can a poor critic do with a play which raises no principle , whether of art or morals , creates its own canons and conventions , and is nothing but an absolutely <unk> expression of an <unk> witty personality ? " 
- In The Speaker , A. B. <unk> admired the play and was one of few to see it as the culmination of Wilde 's dramatic career . He denied the term " farce " was derogatory , or even lacking in seriousness , and said " It is of nonsense all compact , and better nonsense , I think , our stage has not seen . " H. G. Wells , in an unsigned review for the <unk> Mall Gazette , called Earnest one of the <unk> comedies of the year , saying " More humorous dealing with theatrical conventions it would be difficult to imagine . " He also questioned whether people would fully see its message , " ... how Serious People will take this <unk> Comedy intended for their learning remains to be seen . No doubt seriously . " The play was so light @-@ hearted that many reviewers compared it to comic opera rather than drama . W. H. <unk> later called it " a pure verbal opera " , and The Times commented , " The story is almost too preposterous to go without music . " Mary McCarthy , in <unk> and <unk> ( 1959 ) , however , and despite thinking the play extremely funny , would call it " a ferocious <unk> " ; " <unk> is the hero and the only character . " 
- The Importance of Being Earnest is Wilde 's most popular work and is continually revived . Max Beerbohm called the play Wilde 's " finest , most undeniably his own " , saying that in his other comedies — Lady Windermere 's Fan , A Woman of No Importance and An Ideal Husband — the plot , following the manner of <unk> <unk> , is unrelated to the theme of the work , while in Earnest the story is " dissolved " into the form of the play . 
- 
- = = = <unk> = = = 
- 
- Until after Wilde 's death in 1900 his name remained disgraced , and few discussed , let alone performed , his work in Britain . Alexander revived The Importance in a small theatre in Notting Hill , outside the West End , in 1901 ; in the same year he presented the piece on tour , playing Jack Worthing with a cast including the young Lilian <unk> as Cecily . The play returned to the West End when Alexander presented a revival at the St James 's in 1902 . Broadway revivals were mounted in 1902 and again in 1910 , each production running for six weeks . 
- A collected edition of Wilde 's works , published in 1908 and edited by Robert Ross , helped to restore his reputation as an author . Alexander presented another revival of The Importance at the St James 's in 1909 , when he and Aynesworth reprised their original roles ; the revival ran for 316 performances . Max Beerbohm said that the play was sure to become a classic of the English repertory , and that its humour was as fresh then as when it had been written , adding that the actors had " worn as well as the play " . 
- For a 1913 revival at the same theatre the young actors Gerald Ames and A. E. Matthews succeeded the creators as Jack and Algy . John <unk> as Jack and Margaret <unk> as Lady Bracknell headed the cast in a 1923 production at the Haymarket Theatre . Many revivals in the first decades of the 20th century treated " the present " as the current year . It was not until the 1920s that the case for 1890s costumes was established ; as a critic in The Manchester Guardian put it , " Thirty years on , one begins to feel that Wilde should be done in the costume of his period — that his wit today needs the backing of the atmosphere that gave it life and truth . … Wilde 's <unk> and complex verbal <unk> go ill with the <unk> and the short skirt . " 
- In Sir Nigel <unk> 's 1930 production at the Lyric , <unk> , John Gielgud played Jack to the Lady Bracknell of his aunt , <unk> Terry @-@ Lewis . Gielgud produced and starred in a production at the Globe ( now the Gielgud ) Theatre in 1939 , in a cast that included Edith Evans as Lady Bracknell , Joyce Carey as Gwendolen , Angela <unk> as Cecily and Margaret Rutherford as Miss Prism . The Times considered the production the best since the original , and praised it for its fidelity to Wilde 's conception , its " airy , responsive ball @-@ playing quality . " Later in the same year Gielgud presented the work again , with Jack Hawkins as Algy , Gwen <unk> @-@ Davies as Gwendolen and Peggy Ashcroft as Cecily , with Evans and Rutherford in their previous roles . The production was presented in several seasons during and after the Second World War , with mostly the same main players . During a 1946 season at the Haymarket the King and Queen attended a performance , which , as the journalist Geoffrey <unk> put it , gave the play " a final accolade of respectability . " The production toured North America , and was successfully staged on Broadway in 1947 . 
- As Wilde 's work came to be read and performed again , it was The Importance of Being Earnest that received the most productions . By the time of its centenary the journalist Mark Lawson described it as " the second most known and quoted play in English after Hamlet . " 
- For Sir Peter Hall 's 1982 production at the National Theatre the cast included Judi Dench as Lady Bracknell , Martin Jarvis as Jack , Nigel Havers as Algy , Zoë <unk> as Gwendolen and Anna Massey as Miss Prism . Nicholas <unk> 's 1993 production at the Aldwych Theatre , starring Maggie Smith , had occasional references to the supposed gay subtext . 
- In 2005 the Abbey Theatre , Dublin , produced the play with an all @-@ male cast ; it also featured Wilde as a character — the play opens with him drinking in a Parisian café , dreaming of his play . The Melbourne Theatre Company staged a production in December 2011 with Geoffrey Rush as Lady Bracknell . 
- In 2011 the Roundabout Theatre Company produced a Broadway revival based on the 2009 Stratford Shakespeare Festival production featuring Brian Bedford as director and as Lady Bracknell . It opened at the American Airlines Theatre on 13 January and ran until 3 July 2011 . The cast also included Dana <unk> as Miss Prism , <unk> Whitehead as Canon Chasuble , <unk> <unk> as Algernon , Paul O 'Brien as Lane , Charlotte Parry as Cecily , David <unk> as Jack and Sara <unk> as Gwendolen . It was nominated for three Tony Awards . 
- The play was also presented internationally , in Singapore , in October 2004 , by the British Theatre <unk> , and the same company brought it to London 's Greenwich Theatre in April 2005 . 
- 
- = = Synopsis = = 
- 
- The play is set in " The Present " ( i.e. 1895 ) . 
- 
- = = = Act I = = = 
- 
- Algernon Moncrieff 's flat in Half Moon Street , W 
- The play opens with Algernon Moncrieff , an idle young gentleman , receiving his best friend , John Worthing , whom he knows as Ernest . Ernest has come from the country to propose to Algernon 's cousin , Gwendolen Fairfax . Algernon , however , refuses his consent until Ernest explains why his cigarette case bears the inscription , " From little Cecily , with her <unk> love to her dear Uncle Jack . " ' Ernest ' is forced to admit to living a double life . In the country , he assumes a serious attitude for the benefit of his young ward , the <unk> Cecily <unk> , and goes by the name of John ( or , as a nickname , Jack ) , while pretending that he must worry about a <unk> younger brother named Ernest in London . In the city , meanwhile , he assumes the identity of the <unk> Ernest . Algernon confesses a similar deception : he pretends to have an invalid friend named Bunbury in the country , whom he can " visit " whenever he wishes to avoid an unwelcome social obligation . Jack refuses to tell Algernon the location of his country estate . 
- Gwendolen and her formidable mother Lady Bracknell now call on Algernon who <unk> Lady Bracknell in another room while Jack proposes to Gwendolen . She accepts , but seems to love him very largely for his professed name of Ernest . Jack accordingly resolves to himself to be <unk> " Ernest " . <unk> them in this intimate exchange , Lady Bracknell interviews Jack as a prospective <unk> . <unk> to learn that he was adopted after being discovered as a baby in a handbag at Victoria Station , she refuses him and forbids further contact with her daughter . Gwendolen , though , manages covertly to promise to him her <unk> love . As Jack gives her his address in the country , Algernon <unk> notes it on the cuff of his sleeve : Jack 's revelation of his pretty and wealthy young ward has motivated his friend to meet her . 
- 
- = = = Act II = = = 
- 
- The Garden of the Manor House , <unk> 
- Cecily is studying with her governess , Miss Prism . Algernon arrives , pretending to be Ernest Worthing , and soon charms Cecily . Long fascinated by Uncle Jack 's hitherto absent black sheep brother , she is <unk> to fall for Algernon in his role of Ernest ( a name she , like Gwendolen , is apparently particularly fond of ) . Therefore , Algernon , too , plans for the rector , Dr. Chasuble , to <unk> him " Ernest " . 
- Jack , meanwhile , has decided to abandon his double life . He arrives in full mourning and announces his brother 's death in Paris of a severe <unk> , a story undermined by Algernon 's presence in the guise of Ernest . 
- Gwendolen now enters , having run away from home . During the temporary absence of the two men , she meets Cecily , each woman <unk> declaring that she is the one engaged to " Ernest " . When Jack and Algernon reappear , their <unk> are exposed . 
- 
- = = = Act III = = = 
- 
- Morning @-@ Room at the Manor House , <unk> 
- Arriving in pursuit of her daughter , Lady Bracknell is astonished to be told that Algernon and Cecily are engaged . The revelation of Cecily 's trust fund soon <unk> Lady Bracknell 's initial doubts over the young lady 's suitability , but any engagement is forbidden by her guardian Jack : he will consent only if Lady Bracknell agrees to his own union with Gwendolen — something she declines to do . 
- The <unk> is broken by the return of Miss Prism , whom Lady Bracknell recognises as the person who , twenty @-@ eight years earlier , as a family <unk> , had taken a baby boy for a walk in a <unk> ( baby carriage ) and never returned . <unk> , Miss Prism explains that she had <unk> put the manuscript of a novel she was writing in the <unk> , and the baby in a handbag , which she had left at Victoria Station . Jack produces the very same handbag , showing that he is the lost baby , the elder son of Lady Bracknell 's late sister , and thus indeed Algernon 's elder brother . Having acquired such respectable relations , he is acceptable as a <unk> for Gwendolen after all . 
- Gwendolen , though , still insists that she can only love a man named Ernest . What is her fiancé 's real first name ? Lady Bracknell informs Jack that , as the first @-@ born , he would have been named after his father , General Moncrieff . Jack examines the army lists and discovers that his father 's name — and hence his own real name — was in fact Ernest . <unk> was reality all along . As the happy couples embrace — Jack and Gwendolen , Algernon and Cecily , and even Dr. Chasuble and Miss Prism — Lady Bracknell complains to her newfound relative : " My nephew , you seem to be displaying signs of triviality . " " On the contrary , <unk> Augusta " , he replies , " I 've now realised for the first time in my life the vital importance of being Earnest . " 
- 
- = = Themes = = 
- 
- 
- = = = <unk> = = = 
- 
- Arthur Ransome described The Importance ... as the most trivial of Wilde 's society plays , and the only one that produces " that peculiar <unk> of the spirit by which we recognise the beautiful . " " It is " , he wrote , " precisely because it is consistently trivial that it is not ugly . " Ellmann says that The Importance of Being Earnest touched on many themes Wilde had been building since the 1880s — the <unk> of aesthetic poses was well established and Wilde takes it as a starting point for the two protagonists . While <unk> , An Ideal Husband and The Picture of Dorian Gray had dwelt on more serious wrongdoing , vice in Earnest is represented by Algy 's <unk> for <unk> sandwiches . Wilde told Robert Ross that the play 's theme was " That we should treat all trivial things in life very seriously , and all serious things of life with a sincere and studied triviality . " The theme is hinted at in the play 's ironic title , and " earnestness " is repeatedly alluded to in the dialogue , Algernon says in Act II , " one has to be serious about something if one is to have any amusement in life " but goes on to <unk> Jack for ' being serious about everything ' " . <unk> and corruption had haunted the double lives of Dorian Gray and Sir Robert <unk> ( in An Ideal Husband ) , but in Earnest the protagonists ' duplicity ( Algernon 's " <unk> " and Worthing 's double life as Jack and Ernest ) is undertaken for more innocent purposes — largely to avoid unwelcome social obligations . While much theatre of the time tackled serious social and political issues , Earnest is superficially about nothing at all . It " refuses to play the game " of other <unk> of the period , for instance Bernard Shaw , who used their characters to draw audiences to <unk> ideals . 
- 
- = = = As a satire of society = = = 
- 
- The play repeatedly mocks Victorian traditions and social customs , marriage and the pursuit of love in particular . In Victorian times earnestness was considered to be the over @-@ riding societal value , originating in religious attempts to reform the lower classes , it spread to the upper ones too throughout the century . The play 's very title , with its mocking paradox ( serious people are so because they do not see trivial comedies ) , introduces the theme , it continues in the drawing room discussion , " Yes , but you must be serious about it . I hate people who are not serious about meals . It is so shallow of them , " says Algernon in Act 1 ; allusions are quick and from multiple angles . 
- Wilde managed both to engage with and to mock the genre , while providing social commentary and offering reform . The men follow traditional <unk> rites , whereby <unk> admit their weaknesses to their prospective <unk> , but the <unk> they excuse are ridiculous , and the farce is built on an absurd confusion of a book and a baby . When Jack <unk> to Gwendolen during his marriage proposal it is for not being wicked : 
- <unk> : Gwendolen , it is a terrible thing for a man to find out suddenly that all his life he has been speaking nothing but the truth . Can you forgive me ? 
- <unk> : I can . For I feel that you are sure to change . 
- In turn , both Gwendolen and Cecily have the ideal of marrying a man named Ernest , a popular and respected name at the time . Gwendolen , quite unlike her mother 's methodical analysis of John Worthing 's suitability as a husband , places her entire faith in a Christian name , declaring in Act I , " The only really safe name is Ernest " . This is an opinion shared by Cecily in Act II , " I pity any poor married woman whose husband is not called Ernest " and they <unk> declare that they have been deceived when they find out the men 's real names . 
- Wilde embodied society 's rules and rituals <unk> into Lady Bracknell : minute attention to the details of her style created a comic effect of assertion by restraint . In contrast to her <unk> knowledge of the social distinctions of London 's street names , Jack 's obscure parentage is subtly evoked . He defends himself against her " A handbag ? " with the clarification , " The Brighton Line " . At the time , Victoria Station consisted of two separate but adjacent terminal stations sharing the same name . To the east was the <unk> <unk> & D Railway , on the west the up @-@ market <unk> & <unk> — the Brighton Line , which went to Worthing , the fashionable , expensive town the gentleman who found baby Jack was travelling to at the time ( and after which Jack was named ) . 
- 
- = = = <unk> homosexual subtext = = = 
- 
- It has been argued that the play 's themes of duplicity and <unk> are <unk> bound up with Wilde 's homosexuality , and that the play exhibits a " <unk> presence @-@ absence of … homosexual desire " . On re @-@ reading the play after his release from prison , Wilde said : " It was extraordinary reading the play over . How I used to toy with that Tiger Life . " As one scholar has put it , the absolute necessity for homosexuals of the period to " need a public mask is a factor contributing to the satire on social disguise . " 
- The use of the name Earnest may have been a homosexual in @-@ joke . In 1892 , three years before Wilde wrote the play , John <unk> Nicholson had published the book of <unk> poetry Love In Earnest . The <unk> Of Boys ' Names included the verse : " Though Frank may ring like silver bell / And Cecil softer music claim / They cannot work the miracle / – ' Tis Ernest sets my heart a @-@ flame . " The word " earnest " may also have been a code @-@ word for homosexual , as in : " Is he earnest ? " , in the same way that " Is he so ? " and " Is he musical ? " were employed . 
- Sir Donald <unk> , an actor who had met two of the play 's original cast ( Irene <unk> and Allan Aynesworth ) , and Lord Alfred Douglas , wrote to The Times to dispute suggestions that " Earnest " held any sexual connotations : 
- Although they had ample opportunity , at no time did any of them even hint that " Earnest " was a synonym for homosexual , or that " <unk> " may have implied homosexual sex . The first time I heard it mentioned was in the 1980s and I immediately consulted Sir John Gielgud whose own performance of Jack Worthing in the same play was legendary and whose knowledge of theatrical lore was <unk> . He replied in his ringing tones : " No @-@ No ! <unk> , absolute nonsense : I would have known " . 
- A number of theories have also been put forward to explain the derivation of Bunbury , and <unk> , which are used in the play to imply a secretive double life . It may have derived from Henry Shirley Bunbury , a <unk> acquaintance of Wilde 's youth . Another suggestion , put forward in 1913 by Aleister Crowley , who knew Wilde , was that Bunbury was a combination word : that Wilde had once taken train to <unk> , met a schoolboy there , and arranged a second secret meeting with him at Sunbury . 
- 
- = = Dramatic analysis = = 
- 
- 
- = = = Use of language = = = 
- 
- While Wilde had long been famous for dialogue and his use of language , <unk> ( 1988 ) argues that he achieved a unity and mastery in Earnest that was <unk> in his other plays , except perhaps <unk> . While his earlier comedies suffer from an <unk> resulting from the thematic clash between the trivial and the serious , Earnest achieves a pitch @-@ perfect style that allows these to dissolve . There are three different registers detectable in the play . The <unk> <unk> of Jack and Algernon — established early with Algernon 's exchange with his <unk> — betrays an underlying unity despite their differing attitudes . The formidable <unk> of Lady Bracknell are as startling for her use of <unk> and <unk> extravagance as for her disconcerting opinions . In contrast , the speech of Dr. Chasuble and Miss Prism is distinguished by " <unk> precept " and " idiosyncratic diversion " . Furthermore , the play is full of <unk> and <unk> . Max Beerbohm described it as littered with " <unk> <unk> — witticisms unrelated to action or character " , of which he found half a dozen to be of the highest order . 
- Lady Bracknell 's line , " A handbag ? " , has been called one of the most malleable in English drama , lending itself to interpretations ranging from <unk> or <unk> to <unk> . Edith Evans , both on stage and in the 1952 film , delivered the line loudly in a mixture of horror , <unk> and <unk> . <unk> Channing , in the Gaiety Theatre , Dublin in 2010 , <unk> the line , in a critic 's words , " with a barely audible ' A handbag ? ' , rapidly swallowed up with a sharp intake of breath . An understated take , to be sure , but with such a well @-@ known play , packed full of witticisms and aphorisms with a life of their own , it 's the little things that make a difference . " 
- 
- = = = <unk> = = = 
- 
- Though Wilde deployed characters that were by now familiar — the <unk> lord , the overbearing <unk> , the woman with a past , the puritan young lady — his treatment is <unk> than in his earlier comedies . Lady Bracknell , for instance , embodies respectable , upper @-@ class society , but <unk> notes how her development " from the familiar overbearing <unk> into a <unk> and more disturbing character " can be traced through Wilde 's revisions of the play . For the two young men , Wilde presents not stereotypical stage " <unk> " but intelligent beings who , as Jackson puts it , " speak like their creator in well @-@ formed complete sentences and rarely use slang or vogue @-@ words " . Dr Chasuble and Miss Prism are characterised by a few light touches of detail , their old @-@ fashioned <unk> , and the Canon 's <unk> <unk> , <unk> down by Wilde during his many <unk> of the text . 
- 
- = = = Structure and genre = = = 
- 
- Ransome argues that Wilde freed himself by abandoning the melodrama , the basic structure which <unk> his earlier social comedies , and basing the story entirely on the Earnest / Ernest verbal <unk> . Now freed from " living up to any drama more serious than conversation " Wilde could now <unk> himself to a fuller extent with <unk> , <unk> @-@ <unk> , <unk> and <unk> that really had little to do with the business at hand . 
- The genre of the Importance of Being Earnest has been deeply debated by scholars and critics alike who have placed the play within a wide variety of genres ranging from parody to satire . In his critique of Wilde , Foster argues that the play creates a world where “ real values are inverted [ and ] , reason and <unk> are <unk> " . Similarly , Wilde 's use of dialogue mocks the upper classes of Victorian England lending the play a satirical tone . <unk> further <unk> that the use of farcical humour to mock the upper classes " merits the play both as satire and as drama " . 
- 
- = = Publication = = 
- 
- 
- = = = First edition = = = 
- 
- Wilde 's two final comedies , An Ideal Husband and The Importance of Being Earnest , were still on stage in London at the time of his prosecution , and they were soon closed as the details of his case became public . After two years in prison with hard labour , Wilde went into exile in Paris , sick and depressed , his reputation destroyed in England . In 1898 , when no @-@ one else would , Leonard Smithers agreed with Wilde to publish the two final plays . Wilde proved to be a <unk> <unk> , sending detailed instructions on stage directions , character listings and the presentation of the book , and insisting that a <unk> from the first performance be reproduced inside . Ellmann argues that the proofs show a man " very much in command of himself and of the play " . Wilde 's name did not appear on the cover , it was " By the Author of Lady Windermere 's Fan " . His return to work was brief though , as he refused to write anything else , " I can write , but have lost the joy of writing " . 
- On 19 October 2007 , a first edition ( number 349 of 1 @,@ 000 ) was discovered inside a handbag in an Oxfam shop in <unk> , Cheshire . Staff were unable to trace the donor . It was sold for £ 650 . 
- 
- = = = In translation = = = 
- 
- The Importance of Being Earnest 's popularity has meant it has been translated into many languages , though the <unk> pun in the title ( " Ernest " , a masculine proper name , and " earnest " , the virtue of steadfastness and seriousness ) poses a special problem for translators . The <unk> case of a suitable translation of the pun , perpetuating its sense and meaning , may have been its translation into German . Since English and German are closely related languages , German provides an equivalent adjective ( " ernst " ) and also a matching masculine proper name ( " Ernst " ) . The meaning and tenor of the wordplay are exactly the same . Yet there are many different possible titles in German , mostly concerning sentence structure . The two most common ones are " Bunbury oder ernst / Ernst <unk> ist <unk> " and " Bunbury oder <unk> <unk> es ist , ernst / Ernst <unk> <unk> " . In a study of Italian translations , Adrian <unk> found thirteen different versions using eight titles . Since wordplay is often unique to the language in question , translators are faced with a choice of either staying faithful to the original — in this case the English adjective and virtue earnest — or creating a similar pun in their own language . 
- Four main strategies have been used by translators . The first leaves all characters ' names unchanged and in their original spelling : thus the name is respected and readers reminded of the original cultural setting , but the <unk> of the pun is lost . Eva <unk> varied this source @-@ oriented approach by using both the English Christian names and the adjective earnest , thus preserving the pun and the English character of the play , but possibly straining an Italian reader . A third group of translators replaced Ernest with a name that also represents a virtue in the target language , favouring transparency for readers in translation over fidelity to the original . For instance , in Italian , these versions variously call the play L <unk> di <unk> Franco / <unk> / <unk> , the given names being respectively the values of honesty , <unk> , and loyalty . French offers a closer pun : " Constant " is both a first name and the quality of steadfastness , so the play is commonly known as De l <unk> d 'être Constant , though Jean Anouilh translated the play under the title : Il est important d 'être Aimé ( " Aimé " is a name which also means " beloved " ) . These translators differ in their attitude to the original English honorific titles , some change them all , or none , but most leave a mix partially as a compensation for the added loss of <unk> . Lastly , one translation gave the name an Italianate touch by rendering it as <unk> ; this work <unk> mixed proper <unk> from both languages . 
- 
- = = Adaptations = = 
- 
- 
- = = = Film = = = 
- 
- Apart from multiple " made @-@ for @-@ television " versions , The Importance of Being Earnest has been adapted for the English @-@ language cinema at least three times , first in 1952 by Anthony Asquith who adapted the screenplay and directed it . Michael <unk> ( Algernon ) , Michael Redgrave ( Jack ) , Edith Evans ( Lady Bracknell ) , Dorothy <unk> ( Cecily ) , Joan Greenwood ( Gwendolen ) , and Margaret Rutherford ( Miss Prism ) and Miles <unk> ( Canon Chasuble ) were among the cast . In 1992 Kurt Baker directed a version using an all @-@ black cast with <unk> Keith <unk> as Jack , Wren T. Brown as Algernon , Ann Weldon as Lady Bracknell , <unk> Chapman as Cecily , Chris Calloway as Gwendolen , <unk> <unk> as Miss Prism , and <unk> Peters as Doctor Chasuble , set in the United States . Oliver Parker , an English director who had previously adapted An Ideal Husband by Wilde , made the 2002 film ; it stars Colin Firth ( Jack ) , Rupert Everett ( Algy ) , Judi Dench ( Lady Bracknell ) , Reese Witherspoon ( Cecily ) , Frances O 'Connor ( Gwendolen ) , Anna Massey ( Miss Prism ) , and Tom Wilkinson ( Canon Chasuble ) . Parker 's adaptation includes the <unk> solicitor Mr. <unk> who pursues Jack to Hertfordshire ( present in Wilde 's original draft , but cut at the behest of the play 's first producer ) . Algernon too is pursued by a group of <unk> in the opening scene . 
- 
- = = = <unk> and musicals = = = 
- 
- In 1960 , Ernest in Love was staged Off @-@ Broadway . The Japanese all @-@ female musical theatre troupe <unk> Revue staged this musical in 2005 in two productions , one by Moon <unk> and the other one by Flower <unk> . 
- In 1963 , Erik Chisholm composed an opera from the play , using Wilde 's text as the libretto . 
- In 1964 , Gerd <unk> composed the musical Mein <unk> Bunbury based on the play , 1964 premiered at <unk> Theater Berlin . 
- According to a study by Robert <unk> , by 2002 there had been least eight adaptations of the play as a musical , though " never with conspicuous success " . The earliest such version was a 1927 American show entitled Oh Earnest . The journalist Mark <unk> comments , " The libretto of a 1957 musical adaptation , Half in Earnest , deposited in the British Library , is scarcely more encouraging . The curtain rises on Algy strumming away at the piano , singing ' I can play <unk> , Lane ' . Other songs include — almost <unk> — ' A <unk> I Must Go ' . " 
- Gerald Barry created the 2011 opera , The Importance of Being Earnest , commissioned by the Los Angeles Philharmonic and the <unk> Centre in London . It was premiered in Los Angeles in 2011 . The stage premiere was given by the Opéra national de Lorraine in Nancy , France in 2013 . 
- 
- = = = Radio and television = = = 
- 
- There have been many radio versions of the play . In 1925 the BBC broadcast an adaptation with Hesketh Pearson as Jack Worthing . Further broadcasts of the play followed in 1927 and 1936 . In 1977 , BBC Radio 4 broadcast the four @-@ act version of the play , with <unk> Drake as Lady Bracknell , Richard <unk> as Jack , Jeremy Clyde as Algy , Maurice Denham as Canon Chasuble , Sylvia Coleridge as Miss Prism , Barbara Leigh @-@ Hunt as Gwendolen and <unk> Scales as Cecily . The production was later released on CD . 
- To commemorate the centenary of the first performance of the play , Radio 4 broadcast a new adaptation on 13 February 1995 ; directed by Glyn <unk> , it featured Judi Dench as Lady Bracknell , Michael <unk> as Lane , Michael Sheen as Jack Worthing , Martin <unk> as Algernon Moncrieff , John <unk> as Canon Chasuble , Miriam <unk> as Miss Prism , Samantha Bond as Gwendolen and Amanda Root as Cecily . The production was later issued on audio cassette . 
- On 13 December 2000 , BBC Radio 3 broadcast a new adaptation directed by Howard Davies starring <unk> <unk> as Lady Bracknell , Simon Russell <unk> as Jack Worthing , Julian <unk> as Algernon Moncrieff , Geoffrey Palmer as Canon Chasuble , Celia <unk> as Miss Prism , Victoria Hamilton as Gwendolen and Emma Fielding as Cecily , with music composed by Dominic <unk> . The production was released on audio cassette . 
- A 1964 commercial television adaptation starred Ian Carmichael , Patrick <unk> , <unk> York , <unk> Fielding , Pamela Brown and Irene <unk> . 
- BBC television transmissions of the play have included a 1974 Play of the Month version starring Coral Browne as Lady Bracknell with Michael <unk> , Julian Holloway , Gemma Jones and Celia <unk> . Stuart <unk> directed another adaptation in 1986 with a cast including Gemma Jones , Alec <unk> , Paul <unk> and Joan Plowright . 
- It was adapted for Australian TV in 1957 . 
- 
- = = = Commercial recordings = = = 
- 
- Gielgud 's performance is preserved on an EMI audio recording dating from 1952 , which also captures Edith Evans 's Lady Bracknell . The cast also includes Roland Culver ( Algy ) , Jean <unk> ( Miss Prism ) , Pamela Brown ( Gwendolen ) and Celia Johnson ( Cecily ) . 
- Other audio recordings include a " Theatre <unk> " version from 1953 , directed and narrated by Margaret Webster , with a cast including Maurice Evans , <unk> Watson and Mildred <unk> ; a 1989 version by California Artists Radio Theatre , featuring Dan O <unk> <unk> Nolan , Les <unk> and Richard <unk> ; and one by L.A. Theatre Works issued in 2009 , featuring Charles Busch , James <unk> and Andrea Bowen . 
- 
- 
- = Lloyd Mathews = 
- 
- Sir Lloyd William Mathews , <unk> , CB ( 7 March 1850 – 11 October 1901 ) was a British naval officer , politician and abolitionist . Mathews joined the Royal Navy as a cadet at the age of 13 and progressed through the ranks to lieutenant . He was involved with the Third Anglo @-@ <unk> War of 1873 – 4 , afterwards being stationed in East Africa for the suppression of the slave trade . In 1877 he was seconded from the navy to Sultan Barghash of Zanzibar in order to form a European @-@ style army ; he would remain in the employment of the government of Zanzibar for the rest of his life . His army quickly reached 6 @,@ 300 men and was used in several expeditions to suppress the slave trade and rebellions against the Zanzibar government . 
- Mathews retired from the Royal Navy in 1881 and was appointed Brigadier @-@ General of Zanzibar . There followed more expeditions to the African mainland , including a failed attempt to stop German expansion in East Africa . In October 1891 Mathews was appointed First Minister to the Zanzibar government , a position in which he was " <unk> by the sultan " . During this time Mathews was a keen abolitionist and promoted this cause to the Sultans he worked with . This resulted in the prohibiting of the slave trade in Zanzibar 's dominions in 1890 and the abolition of slavery in 1897 . Mathews was appointed the British Consul @-@ General for East Africa in 1891 but declined to take up the position , remaining in Zanzibar instead . Mathews and his troops also played a key role in the ending of the Anglo @-@ Zanzibar War of 1896 which erupted out of an attempt to bypass the requirement that new Sultans must be <unk> by the British consul . During his time as first minister Mathews continued to be involved with the military and was part of two large campaigns , one to Witu and another to Mwele . 
- Mathews was decorated by several governments , receiving appointments as a Companion of the Order of St Michael and St George , Companion of the Order of the Bath and as a Knight Commander of the Order of St Michael and St George from the British government and membership in the Prussian Order of the Crown . Zanzibar also rewarded him and he was a member of the Grand Order of <unk> and a first class member of the Order of the Brilliant Star of Zanzibar . Mathews died of malaria in Zanzibar on 11 October 1901 . 
- 
- = = Early life and career = = 
- 
- Mathews was born at <unk> on Madeira on 7 March 1850 . His father , Captain William Matthews was Welsh , and his mother Jane Wallis Penfold , was the daughter of William Penfold and Sarah Gilbert . Her sister , Augusta Jane <unk> née Penfold was the author of a famous book about the flora and fauna of Madeira , which is now in the Natural History Museum . Mathews became a cadet of the Royal Navy in 1863 and was appointed a <unk> on 23 September 1866 . From 1868 he was stationed in the Mediterranean but his first active service was during the Third Anglo @-@ <unk> War of 1873 – 4 where he qualified for the campaign medal . He was promoted to lieutenant on 31 March 1874 . On 27 August 1875 Mathews was posted to HMS London , a depot ship and the Royal Navy headquarters for East Africa , to assist in the suppression of the slave trade in the area . Whilst <unk> he drilled his own troops , captured several slave <unk> and was commended for his actions by the Admiralty . 
- 
- = = Commander in Chief of Zanzibar = = 
- 
- In August 1877 , Mathews was seconded from the Navy to Sultan Barghash of Zanzibar to form a European @-@ style army which could be used to enforce Zanzibar 's control over its mainland possessions . The army had traditionally been composed entirely of Arabs and Persians but Mathews opened up recruitment to the African majority on the island and had 300 recruits in training by the end of the year . In addition , Mathews employed some unorthodox recruitment methods such as purchasing slaves from their masters , using inmates from the prison and recruiting from Africans rescued from the <unk> . In June 1877 , at the instigation of John Kirk , the explorer and friend of the Sultan , the British government sent a shipment of 500 modern rifles and ammunition as a gift with which to arm the troops . Mathews introduced a new uniform for the troops consisting of a red cap , short black jackets and white trousers for the enlisted ranks and dark blue <unk> coats and trousers with gold and silver <unk> for the Arab officers . The latter was possibly modelled on the Royal Navy officers uniform with which he was familiar . The army grew quickly ; by the 1880s Mathews would command 1 @,@ 300 men , his forces eventually numbering 1 @,@ 000 regulars and 5 @,@ 000 <unk> . 
- One of the first tasks for the new army was to suppress the smuggling of slaves from <unk> on the mainland to the island of Pemba , north of Zanzibar . The troops completed this mission , capturing several <unk> and hindering the trade . Mathews retired from the Royal Navy in June 1881 and was appointed Brigadier @-@ General of Zanzibar . In 1880 , the Sultan dispatched a military force under Mathews to bring his unruly African mainland territories under control . Mathews ' expedition was initially intended to reach <unk> but his men refused to march inland and , when made to do so , deserted in large numbers . The expedition ended instead at <unk> where a 60 @-@ man garrison was established . This had been reduced to a mere handful of men by the mid @-@ 1880s but the expedition proved that the Sultan was serious about maintaining control of all of his possessions . Mathews ' men were also involved in several expeditions to halt the land @-@ based slave trade which had developed once the seas became too heavily <unk> for the traders . 
- In 1881 Mathews ' old vessel , the HMS London , was captained by Charles J Brownrigg . This vessel and her crew made several patrols aimed at hindering the slave trade using smaller steam boats for the actual <unk> and captures . On December 3 , 1881 , they caught up with a slave dhow captained by Hindi bin <unk> . This dhow had around 100 slaves on board and was transporting them between Pemba and Zanzibar . Captain Brownrigg led a boarding party to release the slaves but bin <unk> 's men then attacked the sailors , killing Brownrigg and his party before sailing away . Mathews led a force to <unk> on Pemba and , after a short battle , took a mortally wounded bin <unk> prisoner before returning to Zanzibar . 
- Mathews returned to the African mainland territories once more in 1884 when he landed with a force which intended to establish further garrisons there to dissuade German territorial claims . This attempt ultimately failed when five German warships steamed into Zanzibar Town harbour and threatened the Sultan into signing away the territories which would later form German East Africa . Further territories were ceded to the German East Africa Company in 1888 but unrest amongst the locals against them prevented them from taking control and Mathews was dispatched with 100 men to restore order . Finding around 8 @,@ 000 people gathered against the German administrators Mathews was forced to return with his men to Zanzibar . He landed once again with more troops but found himself subject to death threats and that his troops would not obey his orders and so returned again to Zanzibar . 
- 
- = = First Minister = = 
- 
- In October 1891 , upon the formation of the first constitutional government in Zanzibar , Mathews was appointed First Minister , despite some hostility from Sultan Ali bin Said . In this capacity Mathews was " <unk> by the sultan " and <unk> only to the Sultan and the British Consul . His position was so strong that one missionary on the island is quoted as saying that his powers defied " analytical examination " and that Mathews really could say " L 'état est <unk> " ( I am the state ) . Mathews was also known as the " Strong man of Zanzibar " . The principal departments of government were mostly run by Britons or British Indians and Mathews ' approval was required before they could be removed from office . Mathews was rewarded by the Zanzibar government for his role with his appointment as a first class member of the Order of the Brilliant Star of Zanzibar , which he was granted licence by Queen Victoria to accept and wear on 17 May 1886 . Mathews used his position to suppress slavery in the country and in 1889 convinced the Sultan to issue a decree purchasing the freedom of all slaves who had taken refuge in his dominions and , from 1890 , the prohibiting the slave trade . On 1 February 1891 Mathews was appointed Her Majesty 's Commissioner and Consul @-@ General to the British <unk> of Influence in East Africa . He never took up the post and instead chose to remain in Zanzibar . 
- Mathews was rewarded for his service in Zanzibar by the British government which appointed him a Companion of the Order of St Michael and St George in 1880 and a Companion of the Order of the Bath on 24 May 1889 . Despite becoming renowned in East Africa as a man who ran a fair administration and was strict with criminals , <unk> with effective British rule and his halting of the slave trade led some Arabs to petition the Sultan for his removal in 1892 . In 1893 Mathews purchased the island of <unk> for the government . He intended it to be used as a prison but it never housed prisoners and was instead used to quarantine yellow fever cases before its present use as a conservation area for giant <unk> . Mathews was appointed a Knight Commander of the Order of St Michael and St George in 1894 . He was also awarded membership of the Order of the Crown by the German government . 
- Matters came to a head when Khalid bin Barghash attempted to take control of the palace in Zanzibar Town upon the death of his uncle in August 1896 , despite failing to gain the consent of the British consul there . Mathews opposed this succession and , with British agreement , called up 900 soldiers in an attempt to prevent it . This situation eventually led to the Anglo @-@ Zanzibar War and Mathews , with the support of Admiral Harry Rawson and five vessels of the Royal Navy , bombarded the palace and secured the end of Khalid 's administration . Mathews ' helped to arrange the succession of a pro @-@ British Sultan , <unk> bin Mohammed , as Khalid 's successor . Mathews continued his reforms after the war , abolishing slavery in 1897 and establishing new farms to grow produce using Western techniques . He was appointed a member of the Grand Order of <unk> of Zanzibar and was permitted to accept and wear the decoration on 25 August 1897 . 
- 
- = = Military expeditions = = 
- 
- 
- = = = Mwele = = = 
- 
- In addition to the smaller @-@ scale expeditions described earlier , Mathews embarked on two much larger expeditions to the African mainland during his tenure as first minister , the first at Mwele . The initial rebellion in the area had been led by Mbaruk bin Rashid at Gazi , which Mathews had put down with 1 @,@ 200 men in 1882 . However , in 1895 Mbaruk 's nephew , Mbaruk bin Rashid , refused to acknowledge the appointment of a new leader at <unk> . This led to open rebellion at <unk> in February of that year when the younger Mbaruk attacked Zanzibari troops under Arthur <unk> , one of Mathews ' officers . Mathews was part of an Anglo @-@ Zanzibari expedition sent to quell it , which consisted of 310 British sailors , 50 Royal Marines , 54 Sudanese and 164 Zanzibari troops . <unk> was destroyed and the leaders fled to Gazi where the older Mbaruk failed to turn them over . Another force , under Admiral Rawson , with 400 British marines and sailors , was sent after them . This further expedition failed to capture the <unk> and a third expedition was organised by Rawson with 220 sailors , 80 marines , 60 Sudanese and 50 <unk> , which destroyed Mwele . During the latter action Mathews was wounded in the shoulder . 
- 
- = = = Witu = = = 
- 
- Following the death of a German <unk> who had been operating illegally , the Sultan of Zanzibar and the British government dispatched an expedition on 20 October 1890 to bring the Sultan of Witu to justice . Nine warships and three transports carrying 800 sailors and marines , 150 Imperial British East Africa Company ( IBEA ) Indian police , 200 Zanzibari and 50 Sudanese troops were sent , defeating the Sultan and establishing a British protectorate . The IBEA was given control of the area and established a force of 250 Indian police to maintain the peace . The police were withdrawn in July 1893 following threats of violence from the new Sultan of Witu , Oman , and another expedition was dispatched to the region . This consisted of three warships : HMS Blanche , HMS <unk> and the Zanzibari ship <unk> <unk> . The latter carried Mathews with 125 <unk> and 50 Sudanese under Brigadier @-@ General <unk> of the Zanzibar army . 
- Mathews and an escort force went to Witu where , on 31 July , they removed the flag of the IBEA company and replaced it with the red flag of Zanzibar , before destroying several villages and causing Oman to retreat into the forests . The British troops then withdrew , having suffered heavily from malaria , but the Sudanese and Zanzibari troops remained . A further expedition was sent of 140 sailors and 85 other troops but Oman died soon after and a more <unk> sultan , Omar bin <unk> , was appointed to govern on behalf of Zanzibar , bringing the affair to a close . In return for this action , Mathews received the British East and West Africa campaign medal . 
- 
- = = Later life = = 
- 
- Mathews died of malaria in Zanzibar on 11 October 1901 and was buried with full military honours in the British cemetery outside Zanzibar Town . His successor as first minister was <unk> Rogers . <unk> island , which Mathews bought for a prison , now has a restaurant named in his honour and also a church . Mathews House , at the Western end of Zanzibar Town , is also named for him . 
- 
- 
- = HMS Boreas ( <unk> ) = 
- 
- HMS Boreas was a B @-@ class destroyer built for the Royal Navy around 1930 . Initially assigned to the Mediterranean Fleet , she was transferred to the Home Fleet in 1936 . The ship then patrolled Spanish waters enforcing the arms blockade during the first year of the Spanish Civil War of 1936 – 39 . She spent most of World War II on convoy escort duties in the English Channel and the North Atlantic , based at Dover , Gibraltar , and Freetown , Sierra Leone . Boreas also served two brief tours with the Mediterranean Fleet and participated in Operation <unk> , the 1943 Allied invasion of Sicily . She was loaned to the Royal Hellenic Navy the next year after conversion into an escort destroyer . She was renamed Salamis and served in the Aegean for the rest of the war . Salamis became a training ship after the war until she was returned to Britain and scrapped in 1951 . 
- 
- = = Description = = 
- 
- Boreas displaced 1 @,@ 360 long tons ( 1 @,@ 380 t ) at standard load and 1 @,@ 790 long tons ( 1 @,@ 820 t ) at deep load . The ship had an overall length of 323 feet ( 98 @.@ 5 m ) , a beam of 32 feet 3 inches ( 9 @.@ 8 m ) and a draught of 12 feet 3 inches ( 3 @.@ 7 m ) . She was powered by Parsons geared steam turbines , driving two shafts , which developed a total of 34 @,@ 000 shaft horsepower ( 25 @,@ 000 kW ) and gave a maximum speed of 35 knots ( 65 km / h ; 40 mph ) . Steam for the turbines was provided by three Admiralty 3 @-@ drum boilers . Boreas carried a maximum of 390 long tons ( 400 t ) of fuel oil that gave her a range of 4 @,@ 800 nautical miles ( 8 @,@ 900 km ; 5 @,@ 500 mi ) at 15 knots ( 28 km / h ; 17 mph ) . The ship 's complement was 134 officers and enlisted men , although it increased to 142 during wartime . 
- The ship mounted four 45 @-@ calibre quick @-@ firing ( QF ) 4 @.@ 7 @-@ inch Mk IX guns in single mounts , designated ' A ' , ' B ' , ' X ' , and ' Y ' from front to rear . For anti @-@ aircraft ( AA ) defence , Boreas had two 40 @-@ millimetre ( 1 @.@ 6 in ) QF 2 @-@ pounder Mk II AA guns mounted on a platform between her funnels . She was fitted with two above @-@ water quadruple torpedo tube mounts for 21 @-@ inch ( 533 mm ) torpedoes . One depth charge rail and two throwers were fitted ; 20 depth charges were originally carried , but this increased to 35 shortly after the war began . The ship was fitted with a Type 119 <unk> set to detect submarines through sound waves <unk> into the water that would reflect off the submarine . 
- By October 1940 , the ship 's AA armament was increased when the rear set of torpedo tubes was replaced by a 3 @-@ inch ( 76 @.@ 2 mm ) ( 12 @-@ pounder ) AA gun and ' Y ' gun was removed to compensate for the additional depth charges added . Boreas was converted to an escort destroyer in late 1943 with the replacement of the 12 @-@ pounder high @-@ angle gun with additional depth charge stowage . The 2 @-@ pounder mounts were replaced during the war by 20 @-@ millimetre ( 0 @.@ 8 in ) Oerlikon <unk> . Four additional Oerlikon guns were added in the forward superstructure for a total of six guns . 
- 
- = = Construction and service = = 
- 
- The ship was ordered on 22 March 1929 from Palmer 's at <unk> , under the 1928 Naval Programme . She was laid down on 22 July 1929 , and launched on 11 June 1930 , as the fourth RN ship to carry this name . Boreas was completed on 21 February 1931 at a cost of £ 221 @,@ 156 , excluding items supplied by the Admiralty such as guns , ammunition and communications equipment . After her commissioning , she was assigned to the 4th Destroyer Flotilla with the Mediterranean Fleet until September 1936 when it was transferred to Home Fleet . Her service in the Mediterranean was uneventful until shortly before she returned home when Boreas evacuated civilians at the start of the Spanish Civil War in July 1936 . After a refit at Portsmouth that lasted until 26 September , she made multiple deployments off the coast of Spain in 1937 and 1938 . On 6 March 1938 , She rescued survivors of the torpedoed Nationalist heavy cruiser <unk> off Cartagena , Spain with the destroyer <unk> . Upon her return the following month , the ship began a refit at Portsmouth that lasted until 11 June . Boreas escorted the royal yacht Victoria and Albert during the Royal Tour of Scotland from 26 July to 4 August . The ship escorted the battleship Revenge and the ocean liner Aquitania in September during the Munich Crisis . She remained with the 4th Destroyer Flotilla until April 1939 . Boreas briefly served as a plane guard for the aircraft carriers of the Home Fleet later that year . 
- The ship was assigned to the 19th Destroyer Flotilla on the start of the war and spent the first six months on escort and patrol duties in the English Channel and North Sea . While assisting the damaged minesweeper <unk> on 4 February 1940 in the Moray Firth , Boreas 's stern was damaged and she required repairs that lasted until the following month . The ship was attached to the 12th Destroyer Flotilla on 29 March until she was damaged in a collision with her sister ship Brilliant on 15 May . Her repairs lasted until 19 June and Boreas was assigned to the 1st Destroyer Flotilla at Dover upon their completion . On 25 July , the ship engaged German E @-@ boats off Dover Harbour together with Brilliant and was badly damaged by German Junkers Ju 87 <unk> dive bombers after she was ordered to withdraw . Her bridge was hit twice by bombs that killed one officer and twenty crewmen . Boreas was under repair at <unk> Dock until 23 January 1941 ; she was lightly damaged by bomb splinters on 19 January . Around 1941 , she was fitted with a Type 286 short @-@ range surface search radar . 
- After working up , the ship was briefly assigned to Western Approaches Command on escort duties before she was transferred to the 18th Destroyer Flotilla at Freetown , Sierra Leone , where she arrived on 28 April . Boreas remained there until she joined Convoy <unk> 70 on 10 August at Gibraltar . The ship rescued survivors from four ships and returned them to Gibraltar on 25 August . She received a lengthy refit at South Shields from 19 September to 4 January 1942 , after which rejoined the 18th Destroyer Flotilla on 25 January . 
- Boreas remained on escort duty in the Eastern Atlantic until she arrived in Alexandria , Egypt on 11 November after escorting a convoy around the Cape of Good Hope . She was immediately assigned to escort the ships of Operation <unk> that relieved the Siege of Malta . The ship remained in the Mediterranean until January 1943 before she was briefly assigned to the 13th Destroyer Flotilla at Gibraltar . Boreas returned to Freetown in February and remained there until June when she was transferred to the Mediterranean Fleet in preparation for Operation <unk> . She was converted into an escort destroyer in Liverpool from September 1943 to February 1944 . As part of the conversion , a Type 271 target indication radar was installed above the bridge that replaced her director @-@ control tower and rangefinder and her Type 286 radar was replaced by a Type 290 . 
- The ship was loaned to the Royal Hellenic Navy on 10 February and recommissioned by them on 25 March as Salamis . She was damaged while working up at Scapa Flow and was under repair at Hull from 28 April to 13 June . Salamis was assigned to escort duty at Gibraltar until October when she was transferred to the Aegean where she served with the 12th ( Greek ) Destroyer Flotilla for the rest of the war . The ship was used as a training ship after the war until she was returned to the Royal Navy at Malta on 9 October 1951 . Salamis arrived at Rosyth under tow on 15 April 1952 to be broken up by Metal Industries , Limited . 
- 
- 
- = Kaimanawa horse = 
- 
- Kaimanawa horses are a population of feral horses in New Zealand that are descended from domestic horses released in the 19th and 20th centuries . They are known for their <unk> and quiet temperament . The New Zealand government strictly controls the population to protect the habitat in which they live , which includes several endangered species of plants . The varying heritage gives the breed a wide range of heights , body patterns and colours . They are usually well @-@ muscled , sure @-@ footed and tough . 
- Horses were first reported in the Kaimanawa Range in 1876 , although the first horses had been brought into New Zealand in 1814 . The feral herds grew as horses escaped and were released from sheep stations and cavalry bases . Members of the herd were recaptured by locals for use as riding horses , as well as being caught for their meat , hair and hides . The herd declined as large scale farming and forestry operations <unk> on their ranges , and only around 174 horses were known to exist by 1979 . The Kaimanawa herd was protected by the New Zealand government in 1981 , and there were 1 @,@ 576 horses in the herd by 1994 . A small , mostly unmanaged population also exists on the <unk> Peninsula at the northern tip of the North Island . <unk> have been carried out annually since 1993 to manage the size of the herd , removing around 2 @,@ 800 horses altogether . The Kaimanawa population is listed as a herd of special genetic value by the United Nations ' Food and Agricultural Organization , and several studies have been conducted on the herd dynamics and habits of the breed . 
- 
- = = History = = 
- 
- The first horses were introduced to New Zealand by Protestant missionary Reverend Samuel <unk> in December 1814 , and wild horses were first reported in the Kaimanawa Range in central North Island of New Zealand in 1876 . The Kaimanawa breed descended from domestic horses that were released in the late 19th century and early 20th century in the middle of the North Island around the Kaimanawa mountains . Between 1858 and 1875 , Major George <unk> Carlyon imported <unk> ponies to Hawkes Bay and crossed them with local stock to produce the Carlyon <unk> . These Carlyon ponies were later crossed with two Welsh stallions , <unk> Caesar and Comet , imported by Sir Donald McLean , and a breed known as the Comet resulted . At some point during the 1870s , McLean released a Comet stallion and several mares on the <unk> Plains and the bloodline apparently became part of the wild Kaimanawa population . Other horses were added to the bloodline through escapes and releases from local sheep stations and from cavalry units at Waiouru that were threatened with a <unk> epidemic . It is also thought that in the 1960s Nicholas <unk> released an Arabian stallion into the <unk> Valley region . 
- Throughout the 19th and 20th centuries , horses were harvested from the feral herds and used as riding and stock horses , as well as being used for their meat , hair and hides . Originally there were many herds that roamed land owned by the British Crown and the native Māori , but many were eradicated with the intensification of large scale farming and forestry operations combined with increased mechanization that decreased the need for stock horses . Kaimanawa horses today have the highest amount of genetic similarity with the Thoroughbred and other Thoroughbred cross breeds . 
- <unk> from land development and an encroaching human population reduced the range and the number of the Kaimanawa horses , and in 1979 it was found that only about 174 horses remained . Starting in 1981 , the Kaimanawa population , range size , and herd movements began to be officially measured , and a protected area was formed for the breed in the Waiouru Military Training Area . Legislative protection was similar to the <unk> and other native species . There was a rapid increase in the herd size following the protection of the breed , and 1 @,@ 576 horses were known to exist in the area by 1994 . There is also a small population of horses on the <unk> Peninsula at the northern tip of the North Island , which is mostly unmanaged by the New Zealand government . In 2008 , the Kaimanawa herds were the focus of a novel called Kaimanawa Princess , by <unk> <unk> . 
- 
- = = <unk> characteristics = = 
- 
- Many characteristics of the Comet type are said to be shown in the Kaimanawa horses today , although the varied gene input has produced a wide range of sizes , colours , and body types among the wild horses . The Kaimanawa breed varies widely in general appearance , with heights ranging between 12 @.@ 2 and 15 hands ( 50 and 60 inches , 127 and 152 cm ) high . Any coat colour or pattern marking is acceptable . They are usually well @-@ muscled . Their feral way of life has given them the ability to adapt quickly and live on very little , and they are usually sure @-@ footed and tough . They have a medium @-@ sized head in good proportion to their body , with wide variation in shape due to the different conformation of their ancestors . Kaimanawa horses have a short , deep neck with a thick throat area , straight shoulders , a deep girth , and a short to medium back . The <unk> vary from sloping to well @-@ rounded . The legs are long and well @-@ muscled , with strong hooves , and hind hooves that are generally smaller than the front ones . All horses are considered to age a year on the first of August , regardless of their actual <unk> date . 
- 
- = = Population control and study = = 
- 
- Due to the increase in population after protective legislation was put into place , the Department of Conservation developed a management plan for the Kaimanawa herd in 1989 and 1990 . A draft plan was made available to the public for comment in 1991 , and the public made it clear that it objected to herd reduction through shooting from helicopters , and instead favored the horses remaining alive after being removed from the herd . However , core animal welfare groups felt that shooting was the most humane option . Trial <unk> were conducted in 1993 , 1994 and 1995 , and were successful , although costly and with a limited demand for the captured horses . 
- In 1994 , a working party was established to look at the management of the Kaimanawa herd . They aimed to decide which organization was in charge of long term management , to ensure that the treatment of horses is humane , to preserve and control the best attributes of the herds , and to eliminate the impacts of the herds on other conservation priorities . Goals included ensuring the welfare of the horses , protecting natural ecosystems and features that the Kaimanawa herd may impact and keeping the herd at a sustainable level . Ecological objectives included ensuring that Kaimanawa horse does not adversely affect endangered , rare and <unk> significant plants ; ensuring that the herd does not further <unk> the ecosystems in which it lives ; and preventing the herd from spreading into the Kaimanawa Forest Park and the <unk> National Park . Herd objectives included ensuring that the public was safe from <unk> horses , while still allowing and improving public access to the herd and ensuring humane treatment of the horses ; reducing conflict between the herd and other ecological values and land uses ; and ensuring that the herd is contained to a population that is <unk> by the ecosystems in which they live while still maintaining a minimum effective population that is in general free ranging . 
- The Department of Conservation has since 1993 carried out annual <unk> and muster of <unk> to keep the herd population around a target level of 500 horses . The target will be reduced to 300 horses in stages starting in 2009 . These horses are either taken directly to slaughter or are placed at holding farms for later slaughter or adoption by private homes . A main reason for the strict population control is to protect the habitat in which they live . This habitat includes 16 plant species listed as endangered , which the Kaimanawa may endanger further through <unk> and <unk> . These plants include herbs , grasses , sedges , flowers and <unk> ; among these are <unk> <unk> ( a very rare <unk> grass ) , <unk> <unk> ( a vulnerable mistletoe ) and <unk> <unk> ( a possibly locally extinct sand iris ) . The 2009 culling of the population removed 230 horses from the herd , the largest culling since the beginning of the program , with homes found for 85 % of the horses removed . Conservation of these horses is an important matter to the public , and between 1990 and 2003 the New Zealand Minister for Conservation received more public comments on the Kaimanawa horse than on any other subject . In this period , more than 1 @,@ 400 requests for information and letters were received , with public interest peaking in 1996 and 1997 . This was due to a program of population reduction by shooting scheduled to begin implementation in 1996 ; due to public opposition the shooting was cancelled and a large scale muster and adoption program began in 1997 . In 1997 , around 1 @,@ <unk> horses were removed from the range and adopted , reducing the main herd to around 500 , and reducing their range to around 25 @,@ 000 ha from around 70 @,@ 000 . Since 1993 , a total of around 2 @,@ 800 horses have been removed from the range . Only one injury resulting in the death of a horse is known to have occurred . 
- The United Nations ' Food and Agricultural Organization lists the Kaimanawa horses as a herd of special genetic value that can be compared with other groups of feral horses such as New Forest ponies , <unk> ponies , wild Mustangs , and with free @-@ living <unk> . <unk> are of special value because of their low rate of interaction with humans . This lack of interaction may result in a herd with more wild and fewer domestic characteristics , which is of special interest to researchers . Between 1994 and 1997 , students from Massey University studied a population of around 400 Kaimanawa horses to learn their habits and herd dynamics . A 2000 study found that although sometimes there are more than two stallions in Kaimanawa horse herds , only the two stallions highest in the herd hierarchy mate with the herd females . This differs from other feral horse herds , some of which have only one stallion that mates with mares , while others have several stallions that sire foals . 
- 
- 
- = The Remix ( Lady Gaga album ) = 
- 
- The Remix is a remix album by American recording artist Lady Gaga . Released in Japan on March 3 , 2010 , it contains remixes of the songs from her first studio album , The Fame ( 2008 ) , and her third extended play , The Fame Monster ( 2009 ) . A revised version of the track list was prepared for release in additional markets , beginning with Mexico on May 3 , 2010 . A number of recording artists have produced the songs , including Pet Shop Boys , Passion Pit and The Sound of Arrows . The remixed versions feature both uptempo and <unk> compositions , with altered vocals from Gaga . 
- The album received mixed reviews from contemporary critics , but some noted how Gaga was able to sell the songs from The Fame in new and novel ways . It reached the top of charts in Greece and the Dance / Electronic Albums chart of Billboard magazine in the United States . In other nations , The Remix charted within the top ten in Belgium ( Flanders and Wallonia region ) , Canada , Ireland , Japan , Mexico , the United Kingdom and the Billboard 200 chart of United States , while reaching the top @-@ twenty in others . It was certified platinum in Japan and Brazil and received gold certification in Belgium and Russia . Worldwide the album has sold over 500 @,@ 000 copies , and it is among the best @-@ selling remix albums of all time . 
- 
- = = Background = = 
- 
- On April 15 , 2010 , The Guardian reported that a number of artists , including Pet Shop Boys , Passion Pit and rock musician Marilyn Manson , have contributed to a remix album by Lady Gaga , titled The Remix . The remixes included in the package had been previously released alongside Gaga 's single releases in the past years . The album was originally released in Japan on March 3 , 2010 , containing sixteen of the remixes . The revised version , consisting of seventeen remixes , was released on May 3 , 2010 , the first market being Mexico . Manson features on the Chew Fu remix of " LoveGame " , while Passion Pit remixed " Telephone " and Pet Shop Boys remixed " Eh , Eh ( Nothing Else I Can Say ) " . Other artists who remixed Gaga 's songs included <unk> , <unk> , Stuart Price , Monarchy and <unk> to Mars . The album was released in the United Kingdom on May 10 , 2010 and featured a different artwork for that region . The US release of the album was announced by Interscope Records in July 2010 and it was released on August 3 , 2010 . 
- 
- = = Composition = = 
- 
- Chuck Campbell from the California Chronicle felt that the main " trick " adopted by the producers behind The Remix , was to preserve the integrity of Gaga 's nuances in her songs , at the same time bringing something new to her music . The second song in the track list , the " <unk> vs <unk> Radio Mix " of " Poker Face " , features a computerized chanting of the " mum @-@ mum @-@ mum @-@ <unk> " hook of the song . Stuart Price remixed " <unk> " into an electronic version , changing the original mid @-@ tempo composition of the song . New vocals were added on top of the song , giving it a jungle @-@ like vibe , according to <unk> <unk> from Phoenix New Times . She also felt that the remix of " LoveGame " featuring Manson , changed the original composition by " giving the originally innocent and fun song an almost demonic quality " . According to Campbell , The Monarchy <unk> remix of " Dance in the Dark " " pump [ ed ] extra <unk> " into the song , with addition of drum beats . Richard <unk> 's remix of " Just Dance " introduced an elastic rhythm in the song , while <unk> changed the soft composition of " Eh , Eh ( Nothing Else I Can Say ) " to a more upbeat one , also manipulating Gaga 's vocals in the process . Campbell also added that the Passion Pit remix of " Telephone " felt like a " theatrical set up for a song that feels like it 's going somewhere , but never does " ; the remix consists of synths , with a thumping beat accompanying the song . Sound of Arrows remixed " Alejandro " , changing the dark nature of its music into a bright , <unk> jam while " Bad <unk> " was remixed by <unk> , making it a complete dance track . 
- 
- = = Critical reception = = 
- 
- Upon its release , the album met with mixed reviews . At Metacritic it holds an aggregate score of 54 out of 100 points , indicating generally mixed or average reviews . Simon Cage from the Daily Express gave the album three out of five stars and felt that although she " has a winning way with <unk> hats " , Gaga 's true talent lies in selling the same album over and over again . " It ’ s great but ... enough already ! " Music critic J. D. <unk> , while reviewing the album for The Globe and Mail , complimented the piano and voice version of " Poker Face " adding that the latter was " the <unk> track on her newest remix album " . He felt that the song " brings out her inner Elton John . And yes , this move , too , is probably just another bit of calculated image management , but that doesn ’ t make it any less brilliant . " Robert Copsey from Digital Spy noted that the release of The Remix was a more natural progression than music labels trying to " cash @-@ in " by releasing something not associated with the artist . He complimented the <unk> featured in the album , calling them " as vital to keeping the singer 's music fresh as her Haus Of <unk> designers are to her image . " 
- Stephen Thomas Erlewine from Allmusic gave the album three out of five stars , but felt that the track list could have been shortened . Erlewine complimented some of the remixes , including those by Pet Shop Boys and Space <unk> , adding that The Remix " is not an essential addition to Gaga ’ s canon goes without saying ... but there ’ s <unk> and glamour to enjoy here . " Mark <unk> , reviewing the album for Bloomberg Television , noticed that the already familiar tracks from Gaga " are given a new <unk> by the Pet Shop Boys and sometime Madonna producer Stuart Price . " <unk> <unk> from Phoenix New Times gave a positive review saying that the songs featured in The Remix can be a great addition during <unk> , as well as staple dance floor music . She listed the Chew Fu remix of " LoveGame " as a highlight from the album . Monica Herrera from Billboard complimented the album saying " Gaga has employed a collection of more @-@ than @-@ capable producers to make her dance @-@ ready smashes from The Fame and The Fame Monster even more <unk> . " Giving it three out of five stars , <unk> <unk> from Rolling Stone noted an uneven sequencing among the tracks in The Remix . She felt that the Passion Pit remix of " Telephone " was the best remix on the album . 
- 
- = = Chart performance = = 
- 
- Following the album 's release in Japan , it debuted at number nine on the Oricon Albums Chart . On the issue dated May 17 , 2010 , the album moved to a new peak of number seven and has since been certified platinum by the Recording Industry Association of Japan ( RIAJ ) for shipments of 250 @,@ 000 copies . In Australia , The Remix entered the ARIA Albums Chart at its peak position of number 12 on May 16 , 2010 , remaining on the chart for a total of five weeks . In the United Kingdom , the album debuted at number three on the official UK Albums Chart dated May 22 , 2010 , and has sold 166 @,@ 440 copies according to the Official Charts Company , being certified gold by the British Phonographic Industry ( BPI ) . Across Europe , the album debuted at seven on the European Top 100 Albums chart of Billboard . The Remix also reached the top of the charts in Greece , while attaining top @-@ ten positions in Belgium ( Flanders and Wallonia ) , Czech Republic , Ireland , the Netherlands and New Zealand . 
- In the United States , The Remix charted at number six on the Billboard 200 dated August 21 , 2010 , with 39 @,@ 000 copies sold . It became Gaga 's third top ten album on the Billboard 200 . It also debuted at number one on Billboard 's Dance / Electronic Albums making this Gaga 's third number one entry on the chart . The same week , her studio albums The Fame and The Fame Monster were at positions two and three respectively . Billboard chart manager Keith <unk> noted that Gaga became the first act to occupy the chart 's top three positions , in its nine @-@ year history . She additionally charted on the Billboard 200 with The Fame at number 12 and The Fame Monster at number 27 , marking the first time an artist placed three concurrent titles in the top 30 since 1993 , when Garth Brooks last achieved the feat on the January 23 , 1993 , chart by placing four sets in the top 30 : The Chase at number two , Beyond the Season at number 23 , <unk> ' the Wind at number 26 and No <unk> at number 29 . According to Nielsen SoundScan , The Remix has sold 314 @,@ 000 copies in US as of April 2016 . In Canada , the album debuted at number five on the Canadian Albums Chart issue dated May 22 , 2010 , and remained on the chart for a total of ten weeks . The Remix has sold more than 500 @,@ 000 copies worldwide , and it is among the best @-@ selling remix albums of all time . 
- 
- = = Track listings = = 
- 
- 
- = = Credits and personnel = = 
- 
- Credits adapted from the liner notes of UK pressings of The Remix . Track numbers correspond to international ( non @-@ US and Japan ) pressings . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Architecture of the Song dynasty = 
- 
- The architecture of the Song dynasty ( 960 – 1279 ) was noted for its towering Buddhist pagodas , enormous stone and wooden bridges , lavish tombs , and extravagant palaces . Although literary works on architecture existed beforehand , architectural writing <unk> during the Song dynasty , <unk> into a more professional form that described dimensions and working materials in a <unk> , organized manner . In addition to the examples still standing , depictions in Song artwork , architectural drawings , and illustrations in published books all aid modern historians in understanding the architecture of the period . 
- The professions of architect , master craftsman , carpenter , and structural engineer did not have the high status of the Confucian scholar @-@ officials during the dynastic era . Architectural knowledge had been passed down orally for thousands of years , usually from craftsman fathers to their sons . There were also government agencies and schools for construction , building , and engineering . The Song dynasty 's building manuals aided not only the various private workshops , but also the craftsmen employed by the central government . 
- 
- = = City and palace = = 
- 
- The layout of ancient Chinese capitals , such as Bianjing , capital of the Northern Song , followed the guidelines in <unk> Gong Ji , which specified a square city wall with several gates on each side and passageways for the emperor . The outer city of ancient Bianjing was built during the reign of Emperor <unk> to a rectangular plan , almost square in proportions , about 6 km ( 3 @.@ 7 mi ) from north to south and 7 km ( 4 @.@ 3 mi ) from west to east . The south wall had three gates , with <unk> Gate in the center , <unk> Gate to the east , and <unk> Gate to the west . The other walls had four gates each : in the east wall were <unk> Gate ( at the southern end ) , <unk> Gate , <unk> Gate , and North @-@ East Water Gate ; in the west wall <unk> Gate , West Water Gate , <unk> Gate , and <unk> Gate ; and in the north wall <unk> Gate ( at the eastern end ) , <unk> Gate , New Wild <unk> Gate and <unk> Gate . The gates in the center of each of the four sides were reserved for the emperor ; these gates had straight passages and only two sets of doors , while the other city gates had <unk> passages and were guarded by three sets of doors . 
- The Song artist Zhang <unk> 's painting Along the River During the <unk> Festival depicts the <unk> Gate in detail : the building on top had a five @-@ <unk> roof with a shallow slope in the Song dynasty style , supported prominently by two sets of brackets ( dougong ) . The lower bracket assembly rested on the city gate to form a wooden foundation , while the upper assembly supported the roof , similar to the dougong in an extant Song building , the Goddess Temple in Taiyuan . This method of using bracket assemblies to support superstructure was specified in Li Jie 's 12th @-@ century building manual Yingzao Fashi as <unk> ( literally " flat base " ) . 
- The city wall itself was built with rammed earth , a technique also detailed in Yingzao Fashi , vol . III , " Standards for <unk> , <unk> and <unk> Work " : 
- Foundation : For every square chi , apply two dan of earth ; on top of it lay a mixture of broken brick , tile and crushed stones , also two dan . For every five @-@ cun layer of earth , two men , standing face to face , should tamp six times with <unk> , each man pounding three times on a dent ; then tamp four times on each dent , two men again standing face to face , each pounding twice on the same dent ; then tamp two more times , each man pounding once . Following this , tamp the surface with <unk> or stamp with feet randomly to even out the surface . Every five @-@ cun layer of earth should be compressed to three cun ; every three @-@ cun layer of brick and stone to one and a half cun . 
- <unk> @-@ earth walls during this time were tapered : the thickness of the wall is greatest at the base and decreases steadily with increasing height , as detailed in Li Jie 's book . 
- During the Song dynasty , the city of Bianjing had three enclosures : the outer city wall , the inner city wall , and the palace at the center . The inner city was rectangular , with three doors on each side . The palace enclosure was also rectangular , with a watch tower on each of the four corners . It had four main gates : <unk> Gate to the west , <unk> Gate to the east , <unk> Gate to the north , and Xuande Gate , also known as <unk> Gate or <unk> , at the south . Xuande Gate had five @-@ <unk> doors , painted red and decorated with gold <unk> ; its walls were lavishly decorated with dragon , <unk> and floating @-@ cloud patterns to match the carved beams , painted <unk> and glazed @-@ tile roof . There were also two glazed <unk> , each biting an end of the <unk> ridge , its tail pointing to the sky . The symbolic function of these chi wei was explained in Yingzao Fashi : 
- There is a dragon in the East Sea , whose tail ( wei ) is similar to that of a sparrow @-@ hawk ( chi ) ; it <unk> up waves and causes rainfall , so people put its likeness on the <unk> to prevent fire . However , they <unk> it " sparrow @-@ hawk tail " ( chi wei ) . 
- Running southward from Xuande Gate was the Imperial Boulevard , about two hundred paces wide , with the Imperial <unk> on either side . <unk> opened shops in the <unk> until <unk> , when they were banned . Two rows of black fencing were placed at the center of the boulevard as a barrier to pedestrians and carriages . Along the inner sides of the fences ran the brick @-@ lined Imperial Water <unk> , filled with lotus . About 400 m ( 1 @,@ 300 ft ) south from Xuande Gate , the <unk> River intercepted the Imperial Boulevard , which crossed it over the stone Zhou Bridge , <unk> and flat @-@ <unk> . This design of a boulevard with a stone bridge crossing a river was later imitated in the Forbidden City . During spring and summer , <unk> peach , plum , pear and apricot trees adorned the banks of the <unk> with a variety of flowers . 
- 
- = = Buddhist pagodas = = 
- 
- Following the reign of the Han dynasty , ( 202 BC – 220 AD ) , the idea of the Buddhist stupa entered Chinese culture , as a means to house and protect <unk> <unk> . During the Southern and Northern <unk> period , the distinctive Chinese pagoda was developed , its predecessors being the tall watch towers and towering residential apartments of the Han dynasty ( as inferred from models in Han @-@ era tombs ) . During the <unk> ( <unk> – 618 ) and Tang ( 618 – 907 ) periods , Chinese pagodas were developed from purely wooden structures to use articulated stone and brick , which could more easily survive fires caused by lightning or <unk> and were less susceptible to decay . The earliest brick pagoda that remains extant is the <unk> Pagoda , built in 523 , and a typical example of a Tang @-@ era stone pagoda is the Giant Wild Goose Pagoda , constructed in <unk> . Although Buddhist influences on China <unk> after the late Tang period , numerous Buddhist pagoda towers were built during the Song dynasty . <unk> Chinese pagodas were often built in the countryside rather than within a city 's walls , largely to avoid competition with the cosmic @-@ imperial authority embodied in the cities ' <unk> and gate @-@ towers . The Giant Wild Goose Pagoda , built in a city ward of what was southeastern Chang 'an , is among the exceptions . 
- The Iron Pagoda of <unk> Temple in Kaifeng earned it name from the iron @-@ grey color of the glazed bricks forming the tower . Originally built of wood by the architect Yu Hao , it was struck by lightning and burned down in 1044 , during the Northern Song period . In <unk> the pagoda was rebuilt as it appears today , under the order of Emperor <unk> of Song . This 13 @-@ story pagoda , structured on an octagonal base , is 56 @.@ 88 meters ( 186 @.@ 6 ft ) tall . Its glazed tile bricks feature carved artwork of dancing figures , solemn ministers , and Buddhist themes ( see gallery below ) . 
- The period also featured true cast @-@ iron pagodas , such as the Iron Pagoda of <unk> Temple ( Jade Springs Temple ) , <unk> , Hubei Province . Built in <unk> , it incorporates 53 @,@ <unk> kg ( 118 @,@ 715 lb ) of cast iron and stands 21 @.@ 28 m ( 69 @.@ 8 ft ) tall . <unk> contemporary wooden , stone , and brick pagodas , the pagoda features sloping eaves and an octagonal base . Another iron pagoda was constructed in 1105 , <unk> , Shandong , and was cast layer by layer in octagonal sections , standing 78 feet high . Several such cast iron pagodas exist in China today . 
- The <unk> Pagoda , or Six <unk> Pagoda , is another example of Song @-@ era pagoda architecture . It is located in the Southern Song capital of Hangzhou , in Zhejiang Province , at the foot of <unk> Hill facing the <unk> River . Although the original was destroyed in 1121 , the current tower was erected in <unk> and fully restored by 1165 . It stands 59 @.@ 89 m ( 196 @.@ 5 ft ) tall , and was constructed from a red brick frame with 13 stages of wooden eaves . Because of its size , the pagoda served as a permanent lighthouse to aid sailors at night ( as described in Hangzhou Fu <unk> ) . 
- The <unk> Temple Pagoda in <unk> County of Sichuan Province ( near <unk> ) is a brick pagoda that was built between 1023 and <unk> , according to inscriptions running along its first storey . It has a square base on a <unk> pedestal , thirteen stories totaling 28 m ( 92 ft ) in height , and multiple layers of eaves similar in style to the earlier Tang pagodas of Chang 'an , the Giant Wild Goose Pagoda and the Small Wild Goose Pagoda . 
- Wood @-@ and @-@ brick hybrid pagodas were also built . The first four floors of the octagonal , 42 m ( 138 ft ) <unk> Pagoda of <unk> are brick ( with wooden eaves ) , while from the fifth floor up it is entirely made of wood . Even pagodas made of stone or brick featured architectural elements that were typical of Chinese wooden buildings ; for example the Pizhi Pagoda , built from <unk> to 1063 , uses the dougong brackets typical of wooden architecture to hold up <unk> , <unk> roofs and tiers . Both of these pagodas feature interior staircases , although the <unk> Pagoda 's only reaches to the fourth floor , and the Pizhi Pagoda 's to the fifth . However , the Pizhi Pagoda features winding exterior stairs that provide access to the ninth and topmost floor . 
- Although the Pagoda of <unk> Temple is the tallest extant wooden pagoda , the tallest Chinese pagoda built in the dynastic era that remains standing is the <unk> Pagoda . Completed in <unk> , it is 84 meters ( 276 ft ) tall , with an octagonal base on a large platform , surpassing the 69 @-@ meter ( 226 ft ) <unk> Pagoda , which had held the record since its construction in the 9th century by the Kingdom of <unk> . Although the <unk> Pagoda served its religious purpose as a Buddhist landmark in the <unk> Monastery of <unk> County , Hebei province , its great height gave it another valuable function , as a military watch tower that was used to observe movements of the Liao enemy . Beside their utility in surveillance , pagoda towers could also serve as astronomical observatories ; one such is the <unk> Astronomical Observatory , built in <unk> and still standing today . 
- <unk> pagoda 
- 
- = = Temples = = 
- 
- It was not uncommon for wealthy or powerful families to facilitate the construction of large temple complexes , usually by <unk> a portion of their family estate to a Buddhist sect . Often the land already contained buildings that could be re @-@ <unk> for religions use . The Fei ( <unk> ) family of the town of <unk> , located just west of Shanghai , converted a mansion on their property into a Buddhist <unk> @-@ <unk> hall , and later built several other religious buildings around the hall . This spurred a boom in temple construction in the area , causing <unk> to become a major center of the White Lotus sect of Buddhism , which in turn spurred the construction of more temples and lead the town to become a significant location within the Song . The nearby town of <unk> gained prominence shortly after the fall of the Song in large part to the construction of temples and other religious buildings , which spanned the entire Song empire . 
- Apart from <unk> the development of urban areas , temples and religious buildings featured a number of unique aesthetic and structural features . The Temple of the Saintly Mother ( <unk> ) and the Hall of <unk> of the Jin Temple ( <unk> ) , located in a southeastern suburb of Taiyuan City , Shanxi province , are extant examples of early Song architecture . The Temple of the Saintly Mother is the main building of the Jin Temple , first built in the period between 1023 and <unk> and renovated in 1102 . It has a double @-@ <unk> roof with nine ridges , and two dragon @-@ heads with wide @-@ open jaws biting the ends of the main ridge . The roof is supported by massive dougong brackets corresponding to drawings in Yingzao Fashi . The eaves of the Temple of the Saintly Mother curve upward slightly at each end , a characteristic of Song architecture . The columns of the façade , decorated with <unk> that coil around the shafts , become progressively taller with increasing distance to either side of the central pair . The building has a porch around it , the sole example of such a structure ; another unique feature of the site is a cross @-@ shaped bridge that leads to the Goddess Temple . 
- The Trinity Hall of Xuan <unk> Temple ( <unk> ) , situated in the heart of <unk> city , is another example of Song architecture . In 1982 , it was established as a National Heritage Site by the Chinese government . 
- The Jingling Palace ( <unk> , Jingling Gong ) , a temple to the legendary Yellow Emperor located near modern @-@ day Qufu , was built in the 11th century . It was subsequently destroyed near the end of the Yuan dynasty . However , several other structures in Shou Qiu , the complex that Jingling Palace was situated in , remain intact . Two giant tortoise @-@ borne <unk> flank what was the entrance to the palace . One of the two <unk> , the <unk> of the <unk> of 10 @,@ 000 , is at 52 meters ( 171 ft ) high , the tallest unmarked stele in the country . A large pyramid constructed of rounded stone blocks , the symbolic tomb of the Yellow Emperor 's son <unk> , is located outside the Shou Qiu complex . Another important large tortoise @-@ borne stele of the same period has been preserved at the Dai <unk> of Mount Tai . 
- 
- = = Bridges = = 
- 
- Bridges over waterways had been known in China since the ancient Zhou dynasty . During the Song dynasty , large <unk> bridges were constructed , such as that built by Zhang <unk> in <unk> . There were also large bridges made entirely of stone , like the Ba <unk> Bridge of Shaoxing , built in <unk> and still standing today . Bridges with pavilions crowning their central spans were often featured in such paintings as the landscapes of Xia Gui ( <unk> – 1224 ) . Long , covered corridor bridges , like the 12th @-@ century Rainbow Bridge in <unk> , <unk> province , which has wide stone @-@ based piers and a wooden superstructure , were also built . 
- While serving as an administrator for Hangzhou , the poet Su Shi ( <unk> – 1101 ) had a large pedestrian causeway built across the West Lake , which still bears his name : <unk> ( <unk> ) . In 1221 , the Taoist traveler Qiu <unk> visited Genghis Khan in <unk> , describing various Chinese bridges encountered on the way there through the <unk> Shan Mountains , east of <unk> . The historian Joseph <unk> quotes him as saying : 
- [ The road had ] " no less than 48 timber bridges of such width that two carts can drive over them side by side " . It had been built by Chang Jung [ Zhang <unk> ] and the other engineers of the <unk> some years before . The wooden <unk> of Chinese bridges from the − 3rd century [ BC ] onwards were no doubt similar to those supposed to have been employed in Julius Caesar 's bridge of − 55 [ BC ] across the Rhine , or drawn by Leonardo , or found in use in Africa . But where in + 13th century [ AD ] Europe could a two @-@ lane highway like Chang Jung 's have been found ? 
- In Fujian Province , enormous beam bridges were built during the Song dynasty . Some of these were as long as 1 @,@ 220 m ( 4 @,@ 000 ft ) , with individual spans of up to 22 m ( 72 ft ) in length ; their construction necessitated moving massive stones of 203 t ( 203 @,@ 000 kg ) . No names of the engineers were recorded or appear in the inscriptions on the bridges , which give only the names of local officials who sponsored them and oversaw their construction and repair . However , there might have been an engineering school in Fujian , headed by a prominent engineer known as Cai <unk> ( 1012 – <unk> ) , who had risen to the position of governmental prefect in Fujian . Between <unk> and 1059 , he planned and supervised the construction of the large <unk> Bridge ( once called the Luoyang Bridge ) near <unk> ( on the border of the present @-@ day <unk> District and Huai 'an County . This bridge , a stone structure similar to a number of other bridges found in Fujian , still stands , and features ship @-@ like piers bound to their bases using <unk> from <unk> as an <unk> . It is 731 m ( 2 @,@ 398 ft ) in length , 5 m ( 16 ft ) in width , and 7 m ( 23 ft ) in height . Another famous bridge near <unk> , the <unk> Bridge , was constructed between 1138 and <unk> . 
- Other examples of Song bridges include <unk> Bridge , a stone arch bridge in <unk> , Zhejiang Province . The bridge was built in 1213 , the sixth year of the <unk> Era in the Southern Song dynasty . Song @-@ era <unk> bridges include the <unk> Bridge , 400 m ( 1 ⁄ 4 mi ) long , which may still be seen today . 
- 
- = = <unk> of the Northern Song emperors = = 
- 
- Located southwest of <unk> city in <unk> County , Henan province , the large tombs of the Northern Song number about one thousand , including individual tombs for Song emperors , <unk> , princes , <unk> , consorts , and members of the extended family . The complex extends approximately 7 km ( 4 @.@ 3 mi ) from east to west and 8 km ( 5 @.@ 0 mi ) from north to south . The construction of the complex began in <unk> AD , during the reign of the first Song ruler , Emperor <unk> of Song , whose father is also buried at the site . The only Northern Song emperors not buried there are Emperor Huizong of Song and Emperor Qinzong of Song , who died in captivity after the Jurchen invasion of northern China in 1127 . <unk> the spirit ways of the tomb complex are hundreds of Song sculptures and statues of tigers , rams , lions , horses with <unk> , horned <unk> and mythical creatures , government officials , military generals , foreign ambassadors , and others featured in an enormous display of Song @-@ era artwork . 
- The layout and style of the Song tombs resemble those found in the contemporary <unk> kingdom of the Western Xia , which also had an auxiliary burial site associated with each tomb . At the center of each burial site is a truncated pyramidal tomb , each having once been guarded by a four @-@ walled enclosure with four centered gates and four corner towers . About 100 km ( 62 mi ) from <unk> is the <unk> Tomb , which contains " elaborate <unk> in brick of Chinese timber frame construction , from door <unk> to pillars and <unk> to bracket sets , that adorn interior walls . " The <unk> Tomb has two large separate chambers with conical ceilings ; a large staircase leads down to the entrance doors of the subterranean tomb . 
- 
- = = Literature = = 
- 
- During the Song dynasty , previous works on architecture were brought to more sophisticated levels of description , as in <unk> <unk> , written by Li <unk> in <unk> AD . One of the most definitive works , however , was the earlier Mu Jing ( " <unk> Manual " ) , ascribed to Yu Hao and written sometime between 965 and 995 . Yu Hao was responsible for the construction of a wooden pagoda tower in Kaifeng , which was destroyed by lightning and replaced by the brick Iron Pagoda soon after . In his time , books on architecture were still considered a <unk> scholarly achievement due to the craft 's status , so Mu Jing was not even recorded in the official court bibliography . Although the book itself was lost to history , the scientist and statesman Shen <unk> wrote of Yu 's work extensively in his Dream Pool Essays of <unk> , praising it as a work of architectural genius , saying that no one in his own time could reproduce such a work . Shen <unk> singled out , among other passages , a scene in which Yu Hao gives advice to another <unk> @-@ architect about <unk> struts in order to <unk> a pagoda against the wind , and a passage in which Yu Hao describes the three sections of a building , the area above the <unk> , the area above ground , and the foundation , and then proceeds to provide proportional ratios and construction techniques for each section . 
- Several years later Li Jie ( <unk> ; 1065 – <unk> ) published Yingzao Fashi ( " <unk> on Architectural Methods " or " State Building Standards " ) . Although similar books came before it , such as <unk> <unk> ( " National Building Law " ) of the early Tang dynasty ( 618 – 907 ) , Li 's book is the earliest technical manual on Chinese architecture to have survived in full . 
- 
- = = = Yingzao Fashi = = = 
- 
- Yingzao Fashi is a technical treatise on architecture and craftsmanship written by Li Jie , an architect and official at the Directorate of Buildings and Construction . Li completed the book in 1100 , and presented it to Emperor <unk> of Song in the last year of his reign . His successor , Emperor Huizong of Song , had Li 's treatise officially published three years later , in 1103 , for the benefit of <unk> , architects , and literate craftsmen . The book was intended to provide standard regulations , to not only the engineering agencies of the central government , but also the many workshops and <unk> families throughout China who could benefit from using a well @-@ written government manual on building practices . 
- Yingzao Fashi included building codes and regulations , accounting information , descriptions of construction materials , and classification of crafts . In its 34 chapters , the book outlined units of measurement , and the construction of <unk> , fortifications , stonework , and woodwork . For the latter , it included specifications for making <unk> units with inclined arms and joints for columns and beams . It also provided specifications for wood carving , drilling , <unk> , bamboo work , <unk> , wall building , and decoration . The book contained recipes for decorative paints , <unk> , and coatings , also listing proportions for mixing mortars used in masonry , . brickwork , and manufacture of glazed tiles , illustrating practices and standards with drawings . His book outlined structural carpentry in great detail , providing standard dimensional measurements for all components used ; . here he developed a standard eight @-@ grade system for <unk> timber elements , known as the <unk> @-@ <unk> system of units , which could be universally applied in buildings . About 8 % of Li Jie 's book was derived from pre @-@ existing written material on architecture , while the majority of the book documented the inherited traditions of craftsmen and architects . The Yingzao Fashi provided a full <unk> of technical terms that included mathematical formulae , building proportions , and construction techniques , and discussed the implications of the local topography for construction at a particular site . He also estimated the monetary costs of hiring laborers of different skill levels from various crafts on the basis of a day 's work , in addition to the price of the materials they would need and according to the season in which they were to be employed . 
- 
- = = Architecture in Song artwork = = 
- 
- 
- 
- = Lost Horizons ( Lemon Jelly album ) = 
- 
- Lost Horizons is the second studio album from the British electronic duo Lemon Jelly , released on 7 October 2002 . Released by <unk> Recordings and produced by Nick Franglen , the album generated two charting singles in the UK , " Space Walk " and " Nice Weather for Ducks " ; the latter has often been called the album 's stand @-@ out track . The album , which is built around a mix of organic instrumentation and idiosyncratic samples , was met with largely positive reviews by music critics , although it was somewhat critiqued due to its near @-@ constant <unk> . 
- In the United Kingdom , Lost Horizons peaked at number 20 on the Official Albums Chart , whereas in the United States , it peaked at number 24 on Billboard 's Top Electronic Albums component chart . The album 's two singles , " Space Walk " and " Nice Weather for Ducks " , were also successful , peaking on the UK Singles Chart , at number 36 and 16 respectively . The album , was nominated for the Mercury Music Prize in 2003 , was eventually certified gold by the British Phonographic Industry for shipments exceeding 100 @,@ 000 copies . 
- 
- = = Music = = 
- 
- Lost Horizons opens with " Elements " , which " blends acoustic guitars , <unk> , synths , <unk> <unk> rhythms , a <unk> harmonica , and ... a falsetto ' doo @-@ doo ' chorus " . <unk> the music is a <unk> , courtesy of English actor John Standing , that lists the basic ' elements ' that make up the world : ash , metal , water , wood , fire , and ( eventually , later in the song ) sky . The second track , " Space Walk " , is set to a recording of Ed White 's 1965 space walk on the Gemini 4 mission . Franglen and Deakin chose to use the sample after listening to an album called Flight to the Moon ( 1969 ) ; the two were struck by how moving and emotive many of the tracks were . Deakin later said , " ' One small step ' leaves me cold , because it was so obviously scripted . But the <unk> … even after hearing it so many times , it 's so vivid . " 
- " Ramblin ' Man " features a conversation between an interviewer ( the voice of Michael Deakin — father of Lemon Jelly 's Fred Deakin ) and " John the Ramblin ' Man " ( the voice of Standing ) , during which he lists various places from around the world , ranging from " from small Sussex villages to major world capitals . " When listed in the order in which the locations are narrated , the message " <unk> <unk> All Things " is spelled out midway through the song ( from Brixton at four minutes ten seconds , to San José at four minutes 31 seconds ) using the first letter of each location . The fourth track , " Return to <unk> " , features several jazz @-@ inspired elements . 
- The song " Nice Weather for Ducks " is built around a sample inspired by John Langstaff 's song " All the Ducks " . This song was based on the popular Dutch children 's song , " <unk> <unk> <unk> in <unk> water " ( translated : " All the ducks are swimming in the water " ) . Franglen later said that he and Deakin were drawn to Langstaff 's recording because it " had a gentle madness to it , slightly <unk> " . The duo had attempted to clear Langstaff 's version for sampling , but were unable to . In the end , they had <unk> <unk> re @-@ record the vocal <unk> . Franglen , while noting that <unk> 's performance was good , said that once the sample was re @-@ recorded , its " edge disappeared " . " Experimental Number 6 " , arguably the album 's <unk> track , features a faux field recording of a doctor documenting the side effects of an unnamed drug administered to a patient ; the recording tells how the patient progresses from normalcy , to an " overwhelming sense of well @-@ being and euphoria " , before eventually expiring . The album closes with " The Curse of Ka <unk> " , which features " a two @-@ part harmony chorus and <unk> drum loop " . 
- 
- = = Reception = = 
- 
- 
- = = = Critical Reviews = = = 
- 
- Lost Horizons received mostly positive reviews from music critics , although several critics critiqued the album 's near @-@ constant <unk> . Stuart Mason of AllMusic called the album " a delightful but slightly faceless blend of lounge pop , subtle beats , found sound , with mellow jazz influences . " A reviewer for <unk> praised the band for approaching electronica from a new angle , writing , " this London @-@ based duo employ <unk> pianos , acoustic guitars and <unk> beats to create laid @-@ back <unk> guaranteed to <unk> even the most restless of souls . [ ... ] What really marks Lemon Jelly as exciting new talents is their quirky sense of humour , which they use to <unk> up their sound with skilful [ sic ] use of nursery rhymes , brass bands and <unk> samples . Pascal <unk> of The Guardian wrote , " Everything is <unk> and purely crafted , but Lost Horizons cheats <unk> with some choice <unk> : Magnificent Seven strings , astronauts <unk> , panoramic sound effects . " Chris Dahlen of Pitchfork Media felt that the album was a little too saccharine at times , but that it is " the perfect disc to throw on after your four @-@ disc <unk> Dub Groove Mix has put the whole party to sleep . It 's like eight <unk> of ribbon candy , beach balls hitting the ground like hail , and a big plastic clown face that blows helium . " 
- Many reviews singled out " Nice Weather for Ducks " as the album 's stand @-@ out track . Mason selected the " <unk> , acoustic guitar @-@ based " song as one of the album 's highlights in his review . Dahlen described it as " the most likeable " on the album , and concluded that it is " a happy @-@ <unk> <unk> of a song that nicely sums this record up : <unk> , bright , and vaguely irritating . " <unk> wrote , " When the <unk> arrives on ' Nice Weather for Ducks ' it is impossible to believe there is any evil in the world . " Conversely , several critics felt that " Experiment Number Six " did not fit with the mood of the rest of the album . <unk> called it a " pool of darkness " that " comes as quite a shock . " Dahlen felt that the song is " is the only break in the [ album 's ] mood . " While he enjoyed the song 's concept , calling it " so different and sinister that it 's more intriguing than the rest of the album " , he felt that it was " <unk> displaced . " Hermann , on the other hand , called the track " clever " and " <unk> " with " music ... so well crafted that [ the concept ] works " . 
- 
- = = = Sales and accolades = = = 
- 
- In the UK , the album charted at number 20 on the Albums Chart . In the US , it peaked at number 24 on the Billboard Top Electronic Albums chart . In both cases , it was the first Lemon Jelly album to do so . The album 's two singles , " Space Walk " and " Nice Weather for Ducks " , also managed to chart on the UK Singles Chart , at number 36 and 16 respectively . Again , this was a first for the band . On 20 December 2002 the album was certified Silver . Almost six months later , on 22 July 2013 , it was certified gold , denoting shipments of over 100 @,@ 000 . In 2003 , the album was nominated for a Mercury Music Prize , although the album lost to <unk> Rascal 's Boy in da Corner . 
- 
- = = Track listing = = 
- 
- 
- = = Credits and personnel = = 
- 
- 
- = = Charts = = 
- 
- 
- = Fastra II = 
- 
- The Fastra II is a desktop supercomputer designed for <unk> . It was built in late 2009 by the ASTRA ( All Scale <unk> Reconstruction Antwerp ) group of researchers of the <unk> ( <unk> institute for <unk> Technology ) <unk> at the University of Antwerp and by Belgian computer shop Tones , in collaboration with Asus , a Taiwanese multinational computer product manufacturer , as the successor to the Fastra I ( built in 2008 ) . 
- The Fastra II was determined to be over three times faster than the Fastra I , which in turn was slightly faster than a 512 @-@ core cluster . However , because of the number of GPUs in the computer , the system initially suffered from several issues , like the system refusing to reboot and <unk> due to a lack of space between the video cards . 
- 
- = = Development = = 
- 
- The computer was built as a researching and demonstration project by the ASTRA group of researchers at the Vision Lab in the University of Antwerp in Belgium , one of the researchers being <unk> <unk> . Unlike other modern <unk> such as the <unk> Jaguar and the IBM Roadrunner , which cost millions of euros , the Fastra II only uses consumer hardware , costing € 6 @,@ 000 in total . 
- The Fastra II 's predecessor , the Fastra I , has 4 dual @-@ GPU GeForce <unk> <unk> video cards , for a total of 8 GPUs . At that time , the ASTRA group needed a motherboard that had four PCI Express x16 slots with double @-@ spacing between each of them . The only such motherboard the ASTRA group could find at that time was the <unk> <unk> Platinum , which has four such slots . In 2009 , the Asus <unk> <unk> <unk> motherboard , which the Fastra II uses , was released , which has seven PCI Express x16 slots . The Fastra II has six faster dual @-@ GPU GeForce GTX 295 video cards , and a single @-@ GPU GeForce GTX 275 , for a total of 13 GPUs . In the Fastra II , the GPUs mainly perform <unk> reconstruction . The technique which allows GPUs to perform general @-@ purpose tasks like this outside of gaming , instead of <unk> , is called <unk> , general @-@ purpose computing on graphics processing units . 
- <unk> caused by the lack of space between the video cards forces researchers using the <unk> II to keep the side panel door open , so that the video cards can get regular air , decreasing the overall temperature inside the case . 
- Due to the number of GPUs in the system , its initial boot was unsuccessful . This was because its motherboard uses a 32 bit BIOS , which only had approximately 3 GB of address space for the video cards . However , Asus managed to provide them a specialized BIOS that entirely <unk> the address space allocation of the GTX 295 video cards . The BIOS @-@ replacement <unk> was not tested . 
- All seven PCI Express x16 slots in the Asus <unk> motherboard were used in the building of the Fastra II computer . However , the video cards in the Fastra II are wide enough to require two such slots each . To solve this issue , the researchers came up with flexible PCI Express cables , and Tones developed a custom cage which allowed the video cards to suspend over the motherboard . 
- 
- = = Specifications and benchmarks = = 
- 
- Like the Fastra I , the Fastra II uses a <unk> Li PC @-@ <unk> <unk> case , which has 10 expansion slots . The motherboard in the Fastra II was at that time the only <unk> motherboard that had seven full @-@ sized PCI Express x16 slots . The memory <unk> were initially six 2 GB <unk> , but were later upgraded to 4 GB each , for a total of 24 GB . Instead of an eighth dual @-@ GPU video card , the single @-@ GPU GTX 275 is in the computer because , out of all the video cards in the Fastra II , the GTX 275 is the only one the Fastra II 's BIOS can fully <unk> . The total amount of GPUs is 13 . The video cards together bring 12 <unk> of computing power . Four of the six GTX 295 video cards have 2 <unk> , while the other two have only 1 <unk> . 
- According to the benchmarks on its official website , the Fastra II is faster and more power efficient than its competitors , including the Fastra I and the <unk> <unk> video card . The benchmarks were performed on the Fastra II , the Fastra I , a 512 @-@ core cluster ( consisting of <unk> <unk> ) , an <unk> <unk> <unk> <unk> card on an Intel Core <unk> 940 <unk> , and on an Intel Core <unk> 940 <unk> itself . The Fastra II is over three times faster than the Fastra I in CT slice reconstruction speed . Although the Fastra II consumes more power than the Fastra I , it 's nearly 3 times as energy efficient as the Fastra I , and over 300 times as energy efficient as the 512 @-@ core cluster . The video cards run at 37 degrees <unk> when idle , and at 60 degrees <unk> at full load . 
- 
- = = Applications and reception = = 
- 
- The operating system is <unk> , a community driven Linux distribution and Red <unk> Enterprise Linux clone . The Fastra II received a positive public impression . <unk> called it the " world 's most powerful desktop @-@ sized supercomputer " , describing it as a computer with " so much power in such a small space . " <unk> News Net called it " the Most Powerful Desktop <unk> " . 
- Fastra II relies on <unk> 's <unk> Link <unk> ( <unk> ) and is therefore limited to the number of GPUs supported by it and also by the <unk> respectively the free and open @-@ source device drivers . The Fastra II 's motherboard is designed for <unk> , and it is mainly being used in hospitals for medical imaging . 
- It remains to be seen whether another Fastra featuring <unk> , first available with Pascal @-@ based GPUs , will be build . 
- 
- 
- = USS Breese ( DD @-@ 122 ) = 
- 
- USS Breese ( DD – 122 ) was a Wickes class destroyer in the United States Navy during World War I , and later redesignated , DM @-@ 18 in World War II . She was the only ship named for Captain <unk> Breese . 
- Commissioned as a destroyer in 1919 , she undertook a number of patrol and training duties along the East Coast of the United States until being decommissioned in 1922 . <unk> in 1931 , she returned to service with the United States Pacific Fleet on training and patrol for the next 10 years . She was present during the attack on Pearl Harbor , and following this she supported several operations during the war , laying minefields and sweeping for mines in the Pacific . Following the end of the war , she was sold for scrap in 1946 and broken up . 
- 
- = = Design and construction = = 
- 
- Breese was one of 111 Wickes @-@ class destroyers built by the United States Navy between 1917 and 1919 . She , along with ten of her sisters , were constructed at Newport News Shipbuilding shipyards in Newport News , Virginia using specifications and detail designs drawn up by Bath Iron Works . 
- She had a standard displacement of 1 @,@ 213 tonnes ( 1 @,@ 194 long tons ; 1 @,@ 337 short tons ) an overall length of 314 feet 5 inches ( 95 @.@ 83 m ) , a beam of 31 feet 8 inches ( 9 @.@ 65 m ) and a draught of 9 feet 4 inches ( 2 @.@ 84 m ) . On trials , Harding reached a speed of 33 @.@ 2 knots ( 61 @.@ 5 km / h ; 38 @.@ 2 mph ) . She was armed with four 4 " / 50 caliber guns , two 3 " / 23 caliber guns , and twelve 21 @-@ inch torpedo tubes . She had a regular crew complement of 122 officers and enlisted men . She was driven by two Parsons or Westinghouse turbines , and powered by four <unk> boilers . 
- <unk> on Breese 's performance are not known , but she was one of the group of Wickes @-@ class destroyers known unofficially as the ' Liberty Type ' to differentiate them from the destroyers constructed from detail designs drawn up by Bethlehem , which used Curtis steam turbines and <unk> boilers . The Bethlehem destroyers deteriorated badly in service , and in 1929 all 60 of this group were retired by the Navy . <unk> performance of these ships was far below intended specifications especially in fuel economy , with most only able to make 2 @,@ 300 nautical miles ( 4 @,@ 300 km ; 2 @,@ 600 mi ) at 15 knots ( 28 km / h ; 17 mph ) instead of the design standard of 3 @,@ 100 nautical miles ( 5 @,@ 700 km ; 3 @,@ 600 mi ) at 20 knots ( 37 km / h ; 23 mph ) . The class also suffered problems with turning and weight . Ships such as Breese , however , performed better than this . 
- Breese was the only U.S. Navy ship to be named for <unk> Breese , who had been a U.S. Navy officer during the Mexican @-@ American War and later the Civil War . 
- 
- = = Service history = = 
- 
- 
- = = = <unk> period = = = 
- 
- Breese was launched on 11 May 1918 out of Newport News , Virginia . She was sponsored by Gilbert <unk> , daughter of Breese ; and commissioned 23 October 1918 under the command of Lieutenant B. Smith . After her commissioning , she reported to the United States Atlantic Fleet and cruised for several days as an escort for convoys supporting World War I , before the end of the conflict on 11 November . Returning to Norfolk , Virginia at the end of the war , she was assigned to Destroyer Division 12 and served off the coast of Cuba on training exercises during the spring of 1919 . In July 1919 , Destroyer Division 12 was assigned to the United States Pacific Fleet , based at San Diego , California . For the next year , she served with Destroyer Squadron 4 and , from June 1920 , began operating in <unk> Reserve . From October 1920 to June 1922 , she participated in division maneuvers and fleet maneuvers with the Pacific Fleet 's main battle force , and she was placed out of commission 17 June 1922 . 
- On 5 January 1931 , Breese was redesignated as a light <unk> , with the hull classification symbol of DM @-@ 18 . Following an overhaul and conversion at Mare Island Navy Yard , she was recommissioned on 1 June 1931 . She then returned to San Diego for sea trials and <unk> tests in her new role . These completed , she departed for Pearl Harbor . She was assigned to Mine Division 1 of the Pacific Fleet , and operated out of Hawaiian waters . She conducted several training exercises , including with the submarine divisions where she served as a target ship . She also served as a station ship for aircraft . She returned to San Diego in June 1937 , and placed out of commission and in reserve on 12 November 1937 . On 25 September 1939 , Breese was again recommissioned and assigned to Mine Division 5 of the Pacific Fleet . On 2 November 1939 , she arrived at Puget Sound Navy Yard and began to conduct <unk> Patrol off the Oregon and Washington coasts . Throughout 1940 , she cruised to different bases along the coastline of Alaska with the commander of the <unk> Sector aboard . Upon returning , she rejoined Mine Division 5 in San Francisco and steamed for Hawaii , returning there on 10 December 1940 . Attached to Mine Division 2 in the Pacific Fleet , she took part in training exercises in the operating area and on the Maui range during much of 1941 . 
- 
- = = = World War II = = = 
- 
- On 7 December 1941 , Breese was moored in the Middle Loch , northwest of Ford Island . She was moored to Buoy D @-@ 3 alongside a nest of three other <unk> which were also converted Wickes destroyers ; Ramsay , Montgomery , and Gamble . At the outbreak of the attack , her crew was distracted by the initial assault on Ford Island and was <unk> by a flight of Nakajima <unk> torpedo bombers . Breese quickly loaded her machine guns and began firing at 07 : 57 . She and many of the other ships in the area were quickly able to <unk> a strong anti @-@ aircraft defense which lasted throughout the morning . She was credited with hits on several Japanese aircraft and damaging at least one <unk> submarine . Breese was undamaged in the attack . 
- Following the attack on Pearl Harbor , she remained berthed in the harbor until leaving on 26 December , carrying mail and orders for other ships . She rendezvoused with <unk> at the mouth of the harbor to <unk> this , then steamed east on patrol . 
- On 6 May 1942 , she took on 84 survivors of the carrier Yorktown which had sunk in the aftermath of the Battle of Midway . During the summer of 1942 , she operated out of the South Pacific On 3 August 1942 , she , along with minesweepers Gamble and Tracy , were laying mines in <unk> Channel , <unk> Santo . Destroyer Tucker entered the <unk> on escort patrol , having not been notified of the minefield , when she struck one of the mines and sank . Breese , which was moored in the channel , rendered aid . On 30 September 1942 , she was on a nighttime exercise off <unk> Santo when she was damaged in a collision with the cruiser San Francisco . She carried out <unk> duties during the consolidation of the Solomon Islands from 1 – 13 May 1943 , where she was assigned to Task Group 36 @.@ 5 alongside Gamble , <unk> , and Radford . They laid mined in <unk> Strait to guard the western approaches to <unk> Gulf . 
- She supported Allied efforts around New Georgia @-@ <unk> <unk> from 29 June to 25 August . <unk> to Task Unit 36 @.@ 2 @.@ 2 , she , <unk> and Gamble laid mines off <unk> Harbor , <unk> . She then supported the occupation and defense of Cape <unk> conducting <unk> duties there from 1 to 8 November . She later supported the <unk> landings from 12 to 24 October 1944 . She was subsequently among the ships to support the <unk> Gulf landings from 4 to 18 January 1945 . She supported the Battle of Iwo Jima from 7 February to 7 March . She undertook mine duties supporting the Battle of Okinawa between 25 March and 30 June . In her final act of the war , she steamed in support of the United States Third Fleet near mainland Japan between 5 and 31 July . In August and September 1945 Breese swept mines in the East China Sea and <unk> @-@ Korean area following the end of the war . 
- On 7 November 1945 , Breese steamed to the west coast arriving 26 November . She <unk> the Panama Canal and arrived at New York City on 13 December . She was decommissioned on 15 January 1946 and sold for scrap on 16 May 1946 . She received ten battle stars for her service in World War II . 
- 
- 
- = Sandwich Day = 
- 
- " Sandwich Day " is the fourteenth episode of the second season of 30 Rock and the thirty @-@ fifth episode overall . It was written by one of the season 's executive producers , Robert Carlock , and one of the season 's co @-@ executive producers , Jack Burditt . The episode was directed by one of the season 's producers , Don <unk> . The episode first aired on May 1 , 2008 on the NBC network in the United States . Guest stars in this episode included Bill <unk> , Brian <unk> , <unk> <unk> , <unk> May , Jason Sudeikis , Miriam <unk> and <unk> <unk> . The episode earned Tina Fey the Primetime Emmy Award for Outstanding Lead Actress in a Comedy Series . 
- Unusually this episode begins on the 30 Rock title sequence - there is no cold open as is normally the case . 
- This episode begins on the annual TGS with Tracy Jordan ( a fictional sketch comedy series ) Sandwich Day . Liz Lemon ( Tina Fey ) receives a phone call from her ex @-@ boyfriend , Floyd ( Jason Sudeikis ) , asking for a place to stay ; Tracy Jordan ( Tracy Morgan ) , Jenna <unk> ( Jane Krakowski ) and the TGS writers try to get a new sandwich for Liz ; Jack Donaghy ( Alec Baldwin ) <unk> his future at General Electric . 
- 
- = = Plot = = 
- 
- It is the annual Sandwich Day for the crew of TGS . The <unk> , led by Mickey J. ( Brian <unk> ) , bring in " secret " sandwiches from an unknown Italian <unk> in Brooklyn . When the writers eat Liz 's sandwich , Liz threatens that she will " cut [ their ] faces up so bad [ ... ] [ they 'll ] all have <unk> . " As a result , the writers and Tracy , aided by Jenna , enter a drinking contest against the <unk> in an attempt to get Liz a new sandwich . 
- Floyd , who broke up with Liz in the episode " Hiatus " , calls Liz to ask if he can have a place to stay , as he has come to visit New York on business . Liz tries to win Floyd back , only for him to lie to her about going home to Cleveland , Ohio . Floyd eventually travels home , and the pair agree to remain friends . 
- Meanwhile , after being ousted from his office on the 52nd floor by Devon Banks ( Will <unk> ) , Jack is not taking well to his new job on the 12th floor . He later decides to move to Washington , D.C. , to be the new " Homeland Security Director for Crisis and Weather Management . " 
- 
- = = Production = = 
- 
- This episode was primarily filmed on April 1 , 2008 . This episode was the fourth episode written by Jack Burditt and the seventh written by Robert Carlock . The episode was the twelfth episode which was directed by Don <unk> . 
- Jason Sudeikis , who played Floyd in this episode , has appeared in the main cast of Saturday Night Live , a weekly sketch comedy series which <unk> on NBC in the United States . Tina Fey was the head writer on Saturday Night Live from 1999 until 2006 . Various other cast members of Saturday Night Live have appeared on 30 Rock , including Rachel Dratch , Fred <unk> , Kristen <unk> , Will Forte , Jimmy Fallon , Amy Poehler , Will <unk> , Julia Louis @-@ Dreyfus , Bill <unk> , Tim Meadows , Andy <unk> , Chris <unk> and Molly Shannon . Tina Fey and Tracy Morgan have both been part of the main cast of Saturday Night Live . Alec Baldwin has also hosted Saturday Night Live sixteen times , the highest number of episodes of any host of the series . This was actress <unk> May 's second appearance in 30 Rock . She previously appeared in the episode " Tracy Does Conan " as a nurse who takes Liz 's blood for donation . In this episode she plays a <unk> who would not allow Liz past airport security because she had her Sandwich Day sandwich with her , and its dipping sauce container held " more than 3 ounces . " 
- 
- = = Reception = = 
- 
- " Sandwich Day " brought in an average of 5 @.@ 4 million viewers . The episode also achieved a 2 @.@ 6 / 7 in the key 18- to 49 @-@ year @-@ old demographic . The 2 @.@ 6 refers to 2 @.@ 6 % of all 18- to 49 @-@ year @-@ olds in the U.S. , and the 7 refers to 7 % of all 18- to 49 @-@ year @-@ olds watching television at the time of the broadcast in the U.S. 
- Robert Canning of IGN wrote that this episode " turned out to be an absolute winner " . He concluded that " with its more relatable storylines and moments like the eerie @-@ voiced guy at the hospital , Liz <unk> tables for her mac and cheese , and watching an entire sandwich be eaten in real time in the airport security line , ' Sandwich Day ' was a definite highlight in the post @-@ writers ' strike season . " Erin Fox of TV Guide said that " the minor story of the episode was probably the funniest " . Jeff <unk> of Entertainment Weekly thought that this episode " was nothing more than ... <unk> " . He called this episode a " weak link " . 
- 
- 
- = Tiber Oil Field = 
- 
- The Tiber Oil Field is a deepwater offshore oil field located in the Keathley Canyon block 102 of the United States sector of the Gulf of Mexico . The deepwater field ( defined as water depth 1 @,@ 300 to 5 @,@ 000 feet ( 400 to 1 @,@ 520 m ) , ) was discovered in September 2009 and it is operated by BP . Described as a " giant " find , it is estimated to contain 4 to 6 billion barrels ( 640 × 10 ^ 6 to 950 × 10 ^ 6 m3 ) of oil in place . Although BP states it is too early to be sure of the size – a " huge " field is usually considered to contain 250 million barrels ( 40 × 10 ^ 6 m3 ) . It required the drilling of a 10 @,@ <unk> m ( 35 @,@ 056 ft ) deep well under 1 @,@ 260 m ( 4 @,@ 130 ft ) of water , making it one of the deepest wells ever drilled at the time of discovery . 
- 
- = = Description = = 
- 
- Tiber comprises multiple Lower Tertiary petroleum reservoirs located in Keathley Canyon block 102 about 250 mi ( 400 km ) southeast of Houston and 300 mi ( 480 km ) south west of New Orleans . Tiber is only the 18th Lower Tertiary well to date , and drilling in these formations is in its infancy . The oil from Tiber is light crude , and early estimates of <unk> reserves are around 20 – 30 % recovery , suggesting figures of around 600 to 900 million barrels ( 95 × 10 ^ 6 to 143 × 10 ^ 6 m3 ) of reserves . Sources such as Bloomberg suggest caution , warning that the find is technically complex and potentially could take 5 – 6 years to produce oil or be lower yield ( 5 – 15 % ) based on " rates talked about " at nearby <unk> Oil Field , BP 's previous giant find ( 2006 ) 40 mi ( 64 km ) away . The commercial prospects of the field have not yet been evaluated . 
- 
- = = Discovery = = 
- 
- BP acquired the Outer Continental <unk> lease of Keathley Canyon block 102 reference <unk> , <unk> station <unk> , on October 22 , 2003 , in Phase 2 of the Western Gulf of Mexico ( <unk> / <unk> ) Sale 187 . Lower Tertiary rock formations are some of the oldest and most technically challenging offshore rock formations currently drilled for oil , dating to between 23 and 66 million years ago . The plan of exploration was filed in June 2008 . 
- Tiber was initially drilled by <unk> 's fifth @-@ generation dynamic positioned semi @-@ <unk> oil rig , Deepwater Horizon , with <unk> drilling commencing around March 2009 , slightly delayed from the planned date of September 2008 . Much of the deeper gulf reserves are buried under salt <unk> thousands of feet thick , which present a problem for seismic exploration . BP had previously developed exploration techniques to bypass this difficulty . Oil was located at " multiple levels " . The field was announced on September 2 , 2009 , and BP shares rose 3 @.@ 7 percent on the news . With Tiber joining at least ten other successful Lower Tertiary <unk> in the area , analysts viewed the announcement as a sign for optimism , and a <unk> of renewed interest in , and production from , the offshore Gulf of Mexico . 
- 
- = = Exploration on hold = = 
- 
- Following the April 2010 destruction of the Deepwater Horizon while drilling the <unk> well , and the resulting oil spill , all appraisal activities at 33 wells under exploration in the Gulf of Mexico , including Tiber , were placed on hold . At least two rigs that might otherwise have been used for developing Tiber are also in use on the relief wells for the <unk> well . 
- 
- 
- = Glorious First of June = 
- 
- The Glorious First of June ( also known in France as the <unk> du 13 <unk> an 2 or Combat de <unk> ) [ Note A ] of 1794 was the first and largest fleet action of the naval conflict between the Kingdom of Great Britain and the First French Republic during the French Revolutionary Wars . 
- The action was the culmination of a campaign that had <unk> @-@ crossed the Bay of Biscay over the previous month in which both sides had captured numerous merchant ships and minor warships and had engaged in two partial , but inconclusive , fleet actions . The British Channel Fleet under Admiral Lord Howe attempted to prevent the passage of a vital French grain convoy from the United States , which was protected by the French Atlantic Fleet , commanded by Rear @-@ Admiral Villaret @-@ <unk> . The two forces clashed in the Atlantic Ocean , some 400 nautical miles ( 700 km ) west of the French island of Ushant on 1 June 1794 . 
- During the battle , Howe defied naval convention by ordering his fleet to turn towards the French and for each of his vessels to rake and engage their immediate opponent . This unexpected order was not understood by all of his captains , and as a result his attack was more <unk> than he intended . Nevertheless , his ships inflicted a severe tactical defeat on the French fleet . In the aftermath of the battle both fleets were left shattered ; in no condition for further combat , Howe and Villaret returned to their home ports . Despite losing seven of his ships of the line , Villaret had bought enough time for the French grain convoy to reach safety unimpeded by Howe 's fleet , securing a strategic success . However , he was also forced to withdraw his battle fleet back to port , leaving the British free to conduct a campaign of blockade for the remainder of the war . In the immediate aftermath both sides claimed victory and the outcome of the battle was seized upon by the press of both nations as a demonstration of the prowess and bravery of their respective navies . 
- The Glorious First of June demonstrated a number of the major problems inherent in the French and British navies at the start of the Revolutionary Wars . Both admirals were faced with disobedience from their captains , along with ill @-@ discipline and poor training among their <unk> crews , and they failed to control their fleets effectively during the height of the combat . 
- 
- = = Background = = 
- 
- Since early 1792 France had been at war with four of its neighbours on two fronts , battling Austria and Prussia in the Austrian Netherlands , and the Austrians and <unk> in Italy . On 2 January 1793 , almost one year into the French Revolutionary War , republican @-@ held forts at Brest in Brittany fired on the British brig HMS <unk> . [ Note B ] A few weeks later , following the execution of the imprisoned King Louis XVI , diplomatic ties between Britain and France were broken . On 1 February France declared war on both Britain and the Dutch Republic . 
- Protected from immediate invasion by the English Channel , Britain prepared for an extensive naval campaign and dispatched troops to the Netherlands for service against the French . Throughout the remainder of 1793 , the British and French navies undertook minor operations in Northern waters , the Mediterranean and the West and East Indies , where both nations maintained colonies . The closest the Channel Fleet had come to an engagement was when it had narrowly missed <unk> the French convoy from the Caribbean , escorted by 15 ships of the line on 2 August . The only major clash was the Siege of Toulon , a confused and bloody affair in which the British force holding the town — alongside Spanish , <unk> , Austrian and French Royalist troops — had to be evacuated by the Royal Navy to prevent its imminent defeat at the hands of the French Republican army . The aftermath of this siege was <unk> by <unk> and accusations of cowardice and betrayal among the allies , eventually resulting in Spain switching allegiance with the signing of the Treaty of San <unk> two years later . Nevertheless , the siege produced one major success : Sir Sidney Smith , with parties of sailors from the retreating British fleet , accomplished the destruction of substantial French naval stores and shipping in Toulon . More might have been achieved had the Spanish raiding parties that accompanied Smith not been issued with secret orders to stall the destruction of the French fleet . 
- The situation in Europe remained volatile into 1794 . Off Northern France , the French Atlantic Fleet had mutinied due to errors in provisions and pay . In consequence , the French Navy officer corps suffered greatly from the effects of the Reign of Terror , with many experienced sailors being executed , imprisoned or dismissed from the service for perceived <unk> . The shortage of provisions was more than a navy problem though ; France itself was <unk> because the social upheavals of the previous year had combined with a harsh winter to ruin the harvest . By this time at war with all her neighbours , France had nowhere to turn for overland imports of fresh provisions . Eventually a solution to the food crisis was agreed by the National Convention : food produced in France 's overseas colonies would be concentrated on board a fleet of merchant ships gathered in Chesapeake Bay , and augmented with food and goods purchased from the United States . During April and May 1794 , the <unk> would convoy the supplies across the Atlantic to Brest , protected by elements of the French Atlantic Fleet . 
- 
- = = Fleets = = 
- 
- The navies of Britain and France in 1794 were at very different stages of development . Although the British fleet was numerically superior , the French ships were larger and stronger , and carried a heavier weight of shot . The largest French ships were three @-@ <unk> first rates , carrying 110 or 120 guns , against 100 guns on the largest British vessels . 
- 
- = = = Royal Navy = = = 
- 
- Since the Spanish Armament of 1790 , the Royal Navy had been at sea in a state of readiness for over three years . The Navy 's dockyards under First Lord of the Admiralty Charles Middleton were all fully fitted and prepared for conflict . This was quite unlike the disasters of the American Revolutionary War ten years earlier , when an ill @-@ prepared Royal Navy had taken too long to reach full effectiveness and was consequently unable to support the North American campaign — which ended in defeat at the Battle of Yorktown due to lack of supplies . With British dockyards now readily turning out cannon , shot , sails , provisions and other essential equipment , the only remaining problem was that of manning the several hundred ships on the Navy list . 
- Unfortunately for the British , gathering sufficient manpower was difficult and never satisfactorily accomplished throughout the entire war . The shortage of <unk> was such that press gangs were forced to take thousands of men with no experience on the sea , meaning that training and preparing them for naval life would take quite some time . The lack of Royal Marines was even more urgent , and soldiers from the British Army were drafted into the fleet for service at sea . Men of the 2nd . Regiment of Foot - The Queen 's ( Royal West Surrey Regiment ) and the 29th Regiment of Foot served aboard Royal Navy ships during the campaign ; their descendant regiments still maintain the battle honour " 1 June 1794 " . 
- Despite these difficulties , the Channel Fleet was possessed of one of the best naval commanders of the age ; its commander @-@ in @-@ chief , Richard Howe , 1st Earl Howe , had learned his trade under Sir Edward <unk> and fought at the Battle of <unk> Bay in 1759 . In the spring of 1794 , with the French convoy 's arrival in European waters imminent , Howe had dispersed his fleet in three groups . George Montagu , in HMS Hector , was sent with six ships of the line and two frigates to guard British convoys to the East Indies , West Indies and Newfoundland as far as Cape <unk> . Peter <unk> , in HMS Suffolk and commanding six other ships , was to escort the convoys for the rest of their passage . The third force consisted of 26 ships of the line , with several supporting vessels , under Howe 's direct command . They were to patrol the Bay of Biscay for the arriving French . 
- 
- = = = French Navy = = = 
- 
- In contrast to their British counterparts , the French Navy was in a state of confusion . Although the quality of the fleet 's ships was high , the fleet hierarchy was <unk> by the same <unk> that had torn through France since the Revolution five years earlier . Consequently , the high standard of ships and ordnance was not matched by that of the available crews , which were largely untrained and inexperienced . With the Terror resulting in the death or dismissal of many senior French sailors and officers , political <unk> and conscripts – many of whom had never been to sea at all , let alone in a fighting vessel – filled the Atlantic fleet . 
- The manpower problem was compounded by the supply crisis which was affecting the entire nation , with the fleet going unpaid and largely <unk> for months at times . In August 1793 , these problems came to a head in the fleet off Brest , when a lack of provisions resulted in a mutiny among the regular sailors . The crews overruled their officers and brought their ships into harbour in search of food , leaving the French coast undefended . The National Convention responded instantly by executing a <unk> of senior officers and ship 's non @-@ commissioned officers . Hundreds more officers and sailors were imprisoned , banished or dismissed from naval service . The effect of this purge was devastating , seriously degrading the fighting ability of the fleet by removing at a stroke many of its most capable personnel . In their places were promoted junior officers , merchant captains and even civilians who expressed sufficient revolutionary zeal , although few of them knew how to fight or control a battle fleet at sea . 
- The newly appointed commander of this troubled fleet was Villaret de <unk> ; although formerly in a junior position , he was known to possess a high degree of tactical ability ; he had trained under Admiral Pierre André de <unk> in the Indian Ocean during the American war . However , Villaret 's attempts to mould his new officer corps into an effective fighting unit were hampered by another new appointee , a deputy of the National Convention named Jean @-@ Bon Saint @-@ André . Saint @-@ André 's job was to report directly to the National Convention on the revolutionary <unk> of both the fleet and its admiral . He frequently intervened in strategic planning and tactical operations . Shortly after his arrival , Saint @-@ André proposed issuing a decree ordering that any officer deemed to have shown insufficient zeal in defending his ship in action should be put to death on his return to France , although this highly controversial legislation does not appear to have ever been acted upon . Although his interference was a source of frustration for Villaret , Saint @-@ André 's dispatches to Paris were published regularly in Le Moniteur , and did much to popularise the Navy in France . 
- The French Atlantic fleet was even more dispersed than the British in the spring of 1794 : Rear @-@ Admiral Pierre Vanstabel had been dispatched , with five ships including two of the line , to meet the much @-@ needed French grain convoy off the American eastern seaboard . Rear @-@ Admiral Joseph @-@ Marie Nielly had sailed from <unk> with five ships of the line and <unk> cruising warships to rendezvous with the convoy in the mid @-@ Atlantic . This left Villaret with 25 ships of the line at Brest to meet the threat posed by the British fleet under Lord Howe . 
- 
- = = = Convoy = = = 
- 
- By early spring of 1794 , the situation in France was dire . With famine looming after the failure of the harvest and the blockade of French ports and trade , the French government was forced to look overseas for sustenance . Turning to France 's colonies in the Americas , and the agricultural <unk> of the United States , the National Convention gave orders for the formation of a large convoy of sailing vessels to gather at Hampton Roads in the Chesapeake Bay , where Admiral Vanstabel would wait for them . According to contemporary historian William James this <unk> of ships was said to be over 350 strong , although he disputes this figure , citing the number as 117 ( in addition to the French warships ) . 
- The convoy had also been augmented by the United States government , in both cargo and shipping , as repayment for French financial , moral and military support during the American Revolution . In supporting the French Revolution in this way , the American government , urged especially by Ambassador <unk> Morris , was fulfilling its ten @-@ year @-@ old debt to France . Friendly relations between the United States and France did not long survive the Jay Treaty which came into effect in 1796 ; by 1798 the two nations would be engaged in the Quasi War . 
- 
- = = May 1794 = = 
- 
- The French convoy , escorted by Vanstabel , departed America from Virginia on 2 April , and Howe sailed from Portsmouth on 2 May , taking his entire fleet to both escort British convoys to the Western Approaches and intercept the French . <unk> that Villaret was still in Brest , Howe spent two weeks searching the Bay of Biscay for the grain convoy , returning to Brest on 18 May to discover that Villaret had sailed the previous day . [ Note C ] Returning to sea in search of his opponent , Howe pursued Villaret deep into the Atlantic . Also at sea during this period were the squadrons of Nielly ( French ) and Montagu ( British ) , both of whom had met with some success ; Nielly had captured a number of British merchant ships and Montagu had taken several back . Nielly was the first to encounter the grain convoy , deep in the Atlantic in the second week of May . He took it under escort as it moved closer to Europe , while Montagu was searching <unk> to the south . 
- Despite Howe 's pursuit , the main French sortie found initial success , running into a Dutch convoy and taking 20 ships from it on Villaret 's first day at sea . For the next week Howe continued to follow the French , seizing and burning a trail of French @-@ held Dutch ships and enemy <unk> . On 25 May Howe spotted a <unk> from Villaret 's fleet and gave chase ; <unk> led Howe straight to his opponent 's location . Having finally found Villaret , on 28 May Howe attacked , using a flying squadron of his fastest ships to cut off its rearmost vessel <unk> . This first rate was at various times engaged with six British ships and took heavy damage , possibly striking her colours late in the action . As darkness fell the British and French fleets separated , leaving <unk> and her final enemy , HMS Audacious , still locked in combat behind them . These two ships parted company during the night and eventually returned to their respective home ports . By this stage Villaret knew through his patrolling frigates that the grain convoy was close , and deliberately took his fleet to the west , hoping to decoy Howe away from the vital convoy . 
- Taking the bait , the following day Howe attacked again , but his attempt to split the French fleet in half was unsuccessful when his lead ship , HMS Caesar , failed to follow orders . Much damage was done to both fleets but the action was inconclusive , and the two forces again separated without having settled the issue . Howe had however gained an important advantage during the engagement by seizing the weather gage , enabling him to further attack Villaret at a time of his choosing . Three French ships were sent back to port with damage , but these losses were offset by reinforcements gained the following day with the arrival of Nielly 's detached squadron . Battle was postponed during the next two days because of thick fog , but when the haze lifted on 1 June 1794 , the battle lines were only 6 miles ( 10 km ) apart and Howe was prepared to force a decisive action . 
- 
- = = First of June = = 
- 
- Although Howe was in a favourable position , Villaret had not been idle during the night . He had attempted , with near success , to distance his ships from the British fleet ; when dawn broke at 05 : 00 he was within a few hours of gaining enough wind to escape over the horizon . <unk> his men to breakfast , Howe took full advantage of his position on the weather gage to close with Villaret , and by 08 : 12 the British fleet was just four miles ( 6 km ) from the enemy . By this time , Howe 's formation was strung out in an organised line parallel to the French , with frigates acting as <unk> for the admiral 's commands . The French were likewise in line ahead and the two lines began exchanging long @-@ range gunfire at 09 : 24 , whereupon Howe unleashed his innovative <unk> . 
- It was normal in fleet actions of the 18th century for the two lines of battle to pass one another <unk> , exchanging fire at long ranges and then wearing away , often without either side losing a ship or taking an enemy . In contrast , Howe was counting on the professionalism of his captains and crews combined with the advantage of the weather gage to attack the French directly , driving through their line . However , this time he did not plan to manoeuvre in the way he had during the two previous encounters ; each ship following in the wake of that in front to create a new line <unk> through his opponent 's force ( as Rodney had done at the Battle of the <unk> 12 years earlier ) . Instead , Howe ordered each of his ships to turn individually towards the French line , intending to breach it at every point and rake the French ships at both bow and stern . The British captains would then pull up on the <unk> side of their opposite numbers , cutting them off from their retreat <unk> , and engage them directly , hopefully forcing each to surrender and consequently destroying the French Atlantic Fleet . 
- 
- = = British break the line = = 
- 
- Within minutes of issuing the signal and turning his flagship HMS Queen Charlotte , Howe 's plan began to <unk> . Many of the British captains had either misunderstood or ignored the signal and were hanging back in the original line . Other ships were still struggling with damage from Howe 's earlier engagements and could not get into action fast enough . The result was a ragged formation tipped by Queen Charlotte that headed <unk> for Villaret 's fleet . The French responded by firing on the British ships as they approached , but the lack of training and coordination in the French fleet was obvious ; many ships which did obey Howe 's order and attacked the French directly arrived in action without significant damage . 
- 
- = = = Van squadron = = = 
- 
- Although Queen Charlotte pressed on all sail , she was not the first through the enemy line . That distinction belonged to a ship of the van squadron under Admiral Graves : HMS Defence under Captain James Gambier , a notoriously <unk> officer nicknamed " <unk> Jimmy " by his contemporaries . Defence , the seventh ship of the British line , successfully cut the French line between its sixth and seventh ships ; Mucius and <unk> . <unk> both opponents , Defence soon found herself in difficulty due to the failure of those ships behind her to properly follow up . This left her vulnerable to Mucius , <unk> and the ships following them , with which she began a furious <unk> . However , Defence was not the only ship of the van to break the French line ; minutes later George Cranfield @-@ Berkeley in HMS Marlborough executed Howe 's manoeuvre perfectly , <unk> and then <unk> his ship with Impétueux . 
- In front of Marlborough the rest of the van had mixed success . HMS Bellerophon and HMS Leviathan were both still suffering the effects of their <unk> earlier in the week and did not breach the enemy line . Instead they pulled along the near side of <unk> and America respectively and brought them to close gunnery duels . Rear @-@ Admiral Thomas Pasley of Bellerophon was an early casualty , losing a leg in the opening exchanges . HMS Royal Sovereign , Graves 's flagship , was less successful due to a <unk> of distance that resulted in her pulling up too far from the French line and coming under heavy fire from her opponent Terrible . In the time it took to engage Terrible more closely , Royal Sovereign suffered a severe pounding and Admiral Graves was badly wounded . 
- More disturbing to Lord Howe were the actions of HMS Russell and HMS Caesar . Russell 's captain John <unk> Payne was criticised at the time for failing to get to <unk> with the enemy more closely and allowing her opponent <unk> to badly damage her rigging in the early stages , although later commentators blamed damage received on 29 May for her poor start to the action . There were no such <unk> , however , for Captain Anthony Molloy of Caesar , who totally failed in his duty to engage the enemy . Molloy completely ignored Howe 's signal and continued ahead as if the British battleline was following him rather than engaging the French fleet directly . Caesar did participate in a <unk> exchange of fire with the leading French ship Trajan but her fire had little effect , while Trajan inflicted much damage to Caesar 's rigging and was subsequently able to attack Bellerophon as well , <unk> unchecked through the melee developing at the head of the line . 
- 
- = = = Centre = = = 
- 
- The centre of the two fleets was divided by two separate squadrons of the British line : the forward division under admirals Benjamin Caldwell and George Bowyer and the rear under Lord Howe . While Howe in Queen Charlotte was engaging the French closely , his subordinates in the forward division were less active . Instead of moving in on their opposite numbers directly , the forward division <unk> closed with the French in line ahead formation , engaging in a long distance duel which did not prevent their opponents from harassing the embattled Defence just ahead of them . Of all the ships in this squadron only HMS Invincible , under Thomas <unk> , ranged close to the French lines . Invincible was badly damaged by her lone charge but managed to engage the larger Juste . HMS Barfleur under Bowyer did later enter the action , but Bowyer was not present , having lost a leg in the opening exchanges . 
- Howe and Queen Charlotte led the fleet by example , sailing directly at the French flagship Montagne . Passing between Montagne and the next in line Vengeur du Peuple , Queen Charlotte raked both and hauled up close to Montagne to engage in a close @-@ range artillery battle . As she did so , Queen Charlotte also became briefly entangled with Jacobin , and exchanged fire with her too , causing serious damage to both French ships . 
- To the right of Queen Charlotte , HMS Brunswick had initially struggled to join the action . <unk> behind the flagship , her captain John Harvey received a <unk> from Howe for the delay . <unk> by this signal , Harvey pushed his ship forward and almost <unk> Queen Charlotte , blocking her view of the eastern half of the French fleet for a time and taking severe damage from French fire as she did so . Harvey hoped to run aboard Jacobin and support his admiral directly , but was not fast enough to reach her and so attempted to cut between Achille and Vengeur du Peuple . This manoeuvre failed when Brunswick 's anchors became entangled in Vengeur 's rigging . Harvey 's master asked if Vengeur should be cut loose , to which Harvey replied " No ; we have got her and we will keep her " . The two ships swung so close to each other that Brunswick 's crew could not open their gunports and had to fire through the closed <unk> , the ships battering each other from a distance of just a few feet . 
- Behind this combat , other ships of the centre division struck the French line , HMS Valiant under Thomas Pringle passing close to <unk> which pulled away , her crew suffering from <unk> and unable to take their ship into battle . Valiant instead turned her attention on Achille , which had already been raked by Queen Charlotte and Brunswick , and badly damaged her before pressing on sail to join the embattled van division . HMS Orion under John Thomas <unk> and HMS Queen under Admiral Alan Gardner both attacked the same ship , Queen suffering severely from the earlier actions in which her masts were badly damaged and her captain John <unk> mortally wounded . Both ships bore down on the French Northumberland , which was soon dismasted and left attempting to escape on only the stump of a mast . Queen was too slow to engage Northumberland as closely as Orion , and soon fell in with Jemmappes , both ships battering each other severely . 
- 
- = = = Rear = = = 
- 
- Of the British rear ships , only two made a determined effort to break the French line . Admiral Hood 's flagship HMS Royal George pierced it between Républicain and Sans Pareil , engaging both closely , while HMS Glory came through the line behind Sans Pareil and threw herself into the melee as well . The rest of the British and French rearguard did not participate in this close combat ; HMS Montagu fought a long range gunnery duel with Neptune which damaged neither ship severely , although the British captain James Montagu was killed in the opening exchanges , command <unk> on Lieutenant Ross Donnelly . Next in line , HMS Ramillies ignored her opponent completely and sailed west , Captain Henry Harvey seeking Brunswick , his brother 's ship , in the confused action around Queen Charlotte . 
- Three other British ships failed to respond to the signal from Howe , including HMS Alfred which engaged the French line at extreme range without noticeable effect , and Captain Charles Cotton in HMS <unk> who likewise did little until the action was decided , at which point he took the surrender of several already shattered French ships . Finally HMS <unk> under <unk> <unk> took no part in the initial action at all , standing well away from the British line and failing to engage the enemy despite the signal for close engagement hanging <unk> from her <unk> . The French rear ships were no less idle , with <unk> and <unk> firing at any British ships in range but refusing to close or participate in the <unk> on either side . The French rear ship Scipion did not attempt to join the action either , but could not avoid becoming embroiled in the group around Royal George and Républicain and suffered severe damage . 
- 
- = = <unk> = = 
- 
- Within an hour of their opening <unk> the British and French lines were <unk> confused , with three separate engagements being fought within sight of one another . In the van , Caesar had finally attempted to join the fight , only to have a vital spar shot away by Trajan which caused her to slip down the two embattled fleets without contributing significantly to the battle . Bellerophon and Leviathan were in the thick of the action , the outnumbered Bellerophon taking serious damage to her rigging . This left her unable to manoeuvre and in danger from her opponents , of which Eole also suffered severely . Captain William Johnstone Hope sought to extract his ship from her perilous position and called up support ; the frigate HMS <unk> under Captain Edward <unk> arrived to provide assistance . <unk> brought his small ship between the ships of the French battleline and opened fire on Eole , helping to drive off three ships of the line and then <unk> Bellerophon to safety . Leviathan , under Lord Hugh Seymour , had been more successful than Bellerophon , her gunnery <unk> America despite receiving fire from Eole and Trajan in passing . Leviathan only left America after a two @-@ hour duel , sailing at 11 : 50 to join Queen Charlotte in the centre . 
- Russell had not broken the French line and her opponent <unk> got the better of her , knocking away a <unk> and escaping to windward with Trajan and Eole . Russell then fired on several passing French ships before joining Leviathan in attacking the centre of the French line . Russell 's boats also took the surrender of America , her crew boarding the vessel to make her a prize ( although later replaced by men from Royal Sovereign ) . Royal Sovereign lost Admiral Graves to a serious wound and lost her opponent as well , as Terrible fell out of the line to windward and joined a growing collection of French ships forming a new line on the far side of the action . Villaret was leading this line in his flagship Montagne , which had escaped from Queen Charlotte , and it was Montagne which Royal Sovereign engaged next , pursuing her close to the new French line accompanied by Valiant , and beginning a long @-@ range action . 
- Behind Royal Sovereign was Marlborough , <unk> tangled with Impétueux . <unk> damaged and on the verge of surrender , Impétueux was briefly <unk> when Mucius appeared through the smoke and collided with both ships . The three entangled ships continued exchanging fire for some time , all suffering heavy casualties with Marlborough and Impétueux losing all three of their masts . This combat continued for several hours . Captain Berkeley of Marlborough had to retire below with serious wounds , and command fell to Lieutenant John <unk> , who signalled for help from the frigates in reserve . Robert <unk> responded in HMS <unk> , which had the assignment of repeating signals , and towed Marlborough out of the line as Mucius freed herself and made for the regrouped French fleet to the north . Impétueux was in too damaged a state to move at all , and was soon seized by sailors from HMS Russell . 
- <unk> , Defence was unable to hold any of her various opponents to a protracted duel , and by 13 : 00 was threatened by the damaged Républicain moving from the east . Although Républicain later hauled off to join Villaret to the north , Gambier requested support for his ship from the fleet 's frigates and was aided by HMS <unk> under Captain William <unk> . As Impétueux passed she fired on <unk> , to which <unk> responded with several broadsides of his own . Invincible , the only ship of the forward division of the British centre to engage the enemy closely , became embroiled in the confusion surrounding Queen Charlotte . Invincible 's guns drove Juste onto the broadside of Queen Charlotte , where she was forced to surrender to Lieutenant Henry <unk> in a boat from Invincible . Among the other ships of the division there were only minor casualties , although HMS <unk> lost several yards and was only brought back into line by the quick reactions of two junior officers , Lieutenant Robert Otway and <unk> Charles <unk> . 
- The conflict between Queen Charlotte and Montagne was oddly one @-@ sided , the French flagship failing to make use of her lower @-@ deck guns and consequently suffering extensive damage and casualties . Queen Charlotte in her turn was damaged by fire from nearby ships and was therefore unable to follow when Montagne set her remaining sails and slipped to the north to create a new focal point for the survivors of the French fleet . Queen Charlotte also took fire during the engagement from HMS Gibraltar , under Thomas Mackenzie , which had failed to close with the enemy and instead fired at random into the smoke bank surrounding the flagship . Captain Sir Andrew <unk> Douglas was seriously wounded by this fire . Following Montagne 's escape , Queen Charlotte engaged Jacobin and Républicain as they passed , and was successful in forcing the surrender of Juste . To the east of Queen Charlotte , Brunswick and Vengeur du Peuple continued their bitter combat , locked together and firing main broadsides from point blank range . Captain Harvey of Brunswick was mortally wounded early in this action by <unk> fire from Vengeur , but refused to quit the deck , ordering more fire into his opponent . Brunswick also managed to drive Achille off from her far side when the French ship attempted to intervene . Achille , already damaged , was totally dismasted in the exchange and briefly surrendered , although her crew rescinded this when it became clear Brunswick was in no position to take possession . With her colours <unk> , Achille then made what sail she could in an attempt to join Villaret to the north . It was not until 12 : 45 that the shattered Vengeur and Brunswick pulled apart , both largely dismasted and very battered . Brunswick was only able to return to the British side of the line after being supported by Ramillies , while Vengeur was unable to move at all . Ramillies took Vengeur 's surrender after a brief <unk> but was unable to board her and instead pursued the fleeing Achille , which soon surrendered as well . 
- To the east , Orion and Queen forced the surrender of both Northumberland and Jemmappes , although Queen was unable to secure Jemmappes and she had to be abandoned later . Queen especially was badly damaged and unable to make the British lines again , <unk> between the newly reformed French fleet and the British battleline along with several other shattered ships . Royal George and Glory had between them disabled Scipion and Sans Pareil in a bitter exchange , but were also too badly damaged themselves to take possession . All four ships were among those left drifting in the gap between the fleets . 
- 
- = = French recovery = = 
- 
- Villaret in Montagne , having successfully broken contact with the British flagship and slipped away to the north , managed to gather 11 ships of the line around him and formed them up in a reconstituted battle squadron . At 11 : 30 , with the main action drawing to a close , he began a recovery manoeuvre intended to lessen the tactical defeat his fleet had suffered . <unk> his new squadron at the battered Queen , Villaret 's attack created consternation in the British fleet , which was unprepared for a second engagement . However , discerning Villaret 's intention , Howe also pulled his ships together to create a new force . His reformed squadron consisted of Queen Charlotte , Royal Sovereign , Valiant , Leviathan , Barfleur , and <unk> . Howe deployed this squadron in defence of Queen , and the two short lines engaged one another at a distance before Villaret abandoned his manoeuvre and hauled off to collect several of his own dismasted ships that were <unk> to escape British pursuit . Villaret was subsequently joined by the battered Terrible , which sailed straight through the dispersed British fleet to reach the French lines , and he also recovered the dismasted Scipion , Mucius , Jemmappes , and Républicain — all of which lay within reach of the <unk> British ships — before turning eastwards towards France . At this stage of the battle , Howe retired below and the British consolidation was left to his Captain of the Fleet , Sir Roger Curtis . Curtis was subsequently blamed by some in the Navy for not capturing more of the dismasted French ships , and was also accused of <unk> Howe from attempting further pursuit . 
- In fact , the British fleet was unable to pursue Villaret , having only 11 ships still capable of battle to the French 12 , and having numerous dismasted ships and prizes to protect . <unk> and <unk> , the British crews set about making hasty repairs and securing their prizes ; seven in total , including the badly damaged Vengeur du Peuple . Vengeur had been holed by cannon firing from Brunswick directly through the ship 's bottom , and after her surrender no British ship had managed to get men aboard . This left Vengeur 's few remaining unwounded crew to attempt to salvage what they could — a task made harder when some of her sailors broke into the spirit room and became drunk . Ultimately the ship 's pumps became <unk> , and Vengeur began to sink . Only the timely arrival of boats from the undamaged Alfred and HMS <unk> , as well as the services of the cutter HMS <unk> , saved any of the Vengeur 's crew from drowning , these ships taking off nearly 500 sailors between them . Lieutenant John <unk> of <unk> was especially commended for this hazardous work . By 18 : 15 , Vengeur was clearly beyond salvage and only the very worst of the wounded , the dead , and the drunk remained aboard . Several sailors are said to have <unk> the <unk> from the bow of the ship and cried " <unk> la Nation , <unk> la <unk> ! " 
- Having escaped to the east , Villaret made what sail his battered fleet could muster to return to France , and dispatched his frigates in search of the convoy . Villaret was also hoping for reinforcements ; eight ships of the line , commanded by Admiral Pierre @-@ François Cornic , were patrolling near the Ushant headland . Behind him to the west , the British took the whole night to secure their ships and prizes , not setting out to return to Britain until 05 : 00 on 2 June . 
- Casualties in the battle are notoriously hard to calculate exactly . With only one exception ( Scipion ) , records made by the French captains of their losses at the time are incomplete . The only immediately available casualty counts are the sketchy reports of Saint @-@ André and the records made by British officers aboard the captured ships , neither of which can be treated as completely reliable . Most sources accept that French casualties in the campaign numbered approximately 7 @,@ 000 , including around 3 @,@ 000 captured , but these figures are vague and frequently do not agree with each other on details . British casualties are easier to confirm but here , too , there are some discrepancies ; overall British casualties are generally given as around 1 @,@ 200 . 
- 
- = = The convoy arrives = = 
- 
- With a large portion of his fleet no longer <unk> , Howe was unable to resume his search for the French convoy in the Bay of Biscay . The Admiralty , though unaware of Howe 's specific circumstances , knew a battle had taken place through the arrival of HMS Audacious in Portsmouth , and was preparing a second expedition under George Montagu . Montagu had returned to England after his unsuccessful May cruise , and was <unk> in Portsmouth when ordered to sea again . His force of ten ships was intended to both cover Howe 's withdrawal from Biscay , and find and attack the French grain convoy . Montagu returned to sea on 3 June , and by 8 June was off Ushant searching for signs of either the French or Howe ; unknown to him , neither had yet entered European waters . At 15 : 30 on 8 June Montagu spotted sails , and soon identified them as the enemy . He had located Cornic 's squadron , which was also patrolling for the convoy and the returning fleets . Montagu gave chase and drove Cornic into <unk> Bay , where he blockaded the French squadron overnight , hoping to bring them to action the following day . However , on 9 June , Montagu sighted 19 French ships appearing from the west — the remnants of Villaret 's fleet . <unk> turning his ships , Montagu sailed south to avoid becoming trapped between two forces which might easily overwhelm him . Villaret and Cornic gave chase for a day before turning east towards the safety of the French ports . 
- Howe benefited from Montagu 's withdrawal , as his own battered fleet passed close to the scene of this stand @-@ off on 10 June , pushing north into the English Channel . With Villaret and Cornic <unk> pursuing Montagu to the south , Howe was free to pass Ushant without difficulty and arrived off Plymouth on 12 June , joined soon afterwards by Montagu . Villaret had anchored with Cornic in <unk> Bay the day before , but Saint @-@ André refused to allow him to enter Brest until the republican attitudes of the town 's population had been assessed . On 12 June , the convoy from America finally arrived off France , having lost just one ship in passage during a storm . 
- 
- = = Aftermath = = 
- 
- Both Britain and France claimed victory in the battle : Britain by virtue of capturing or sinking seven French ships without losing any of her own and remaining in control of the battle site ; France because the vital convoy had passed through the Atlantic unharmed and arrived in France without significant loss . The two fleets were showered by their respective nations with both praise and criticism – the latter particularly directed at those captains not felt to have contributed significantly to the fighting . The British fleet in Spithead was treated with a Royal visit by King George III and the entire royal household . 
- 
- = = = France = = = 
- 
- In France the revolutionary principles of <unk> precluded extensive awards , but Villaret was promoted to vice @-@ admiral on 27 September 1794 and other minor awards were distributed to the admirals of the fleet . In addition the fleet 's officers took part in a celebratory parade from Brest to Paris , accompanying the recently arrived food supplies . The role of Vengeur du Peuple was <unk> by Bertrand <unk> , giving birth to an exalted legend . <unk> in France concerning the battle 's outcome was divided ; while many celebrated Saint @-@ André 's exaggerated accounts of victory in Le Moniteur , senior naval officers disagreed . Among the dissenters was the highly experienced but recently dismissed Admiral Kerguelen . Kerguelen was disgusted by Villaret 's failure to renew the battle after he had reformed his squadron , and felt that the French fleet could have been successful tactically as well as strategically if only Villaret had made greater efforts to engage the remains of Howe 's fleet . The French Navy had suffered its worst losses in a single day since the Battle of La <unk> in 1692 . 
- Ultimately the revolutionary excesses of the period would prove disastrous for the French Navy . <unk> leadership , conflicting and arbitrary orders and the <unk> of the experienced <unk> in the ranks promoted a negative attitude in the French officer corps . The French <unk> did not contest British dominance in Northern European waters again , and their raiding operations repeatedly ended in failure at the hands of more confident British squadrons and the <unk> Atlantic weather . By 1805 , when the last great French fleet to take to the sea was crushed at the Battle of Trafalgar , poor training and low investment in the Navy had reduced its efficiency to levels <unk> 20 years earlier . 
- 
- = = = Britain = = = 
- 
- In Britain , numerous honours were bestowed on the fleet and its commanders . Admiral Howe , already an earl , refused any further elevation , and King George III was <unk> from making him a Knight of the Garter by one of Howe 's political opponents . Vice @-@ Admiral Graves was elevated to the <unk> of Ireland as Baron Graves , while Vice @-@ Admiral Hood was made Viscount <unk> . Rear @-@ Admirals Bowyer , Gardner , Pasley and Curtis ( the last @-@ named was promoted from captain on 4 July 1794 ) were all made <unk> , and Bowyer and Pasley also received <unk> of £ 1 @,@ 000 a year to compensate them for their severe wounds . All first lieutenants were promoted to commander and numerous other officers were promoted in consequence of their actions . The thanks of parliament were unanimously passed to all who fought at the action and various other gifts and awards were distributed among the fleet . A memorial to Captains John <unk> and John Harvey , both of whom had died of their wounds on 30 June , was raised in Westminster Abbey . 
- There was , however , a bitter consequence of the awards , rooted in Howe 's official dispatch to the Admiralty concerning the battle , which according to some accounts was actually written by Curtis . Howe had <unk> a list to his report containing the names of officers whom he believed <unk> special reward for their part in the battle . The list included Vice @-@ Admirals Graves and Hood , Rear @-@ Admirals Bowyer , Gardner , and Pasley , and Captains Seymour , <unk> , Cranfield @-@ Berkeley , Gambier , John Harvey , Payne , Henry Harvey , Pringle , <unk> , <unk> , Nichols , and Hope . Also mentioned were <unk> <unk> and Donnelly . The list had omitted a number of officers who had served in the battle , and the justice of their omission was a highly controversial issue in the Navy . Rear @-@ Admiral Caldwell was the sole British flag officer present not to receive a hereditary honour , although he was promoted to Vice @-@ Admiral on 4 July ( as were Bowyer and Gardner ) . After studying the ship 's logs and reports of the battle , the Admiralty minted a medal to be awarded to the living captains on the list only ( although Captain William Parker of HMS Audacious was awarded one as well ) . The captains excluded from the list were furious , and the <unk> from this selective commendation lasted years : in 1795 Vice @-@ Admiral Caldwell quit the service in anger as a result , while <unk> <unk> , flag captain of Barfleur , refused all awards for future service until the Glorious First of June medal was presented to him as well . He eventually received it after the Battle of Cape St Vincent in 1797 . Over five decades later the battle was among the actions recognised by a <unk> attached to the Naval General Service Medal , awarded upon application to all British participants still living in 1847 . 
- <unk> of all was the <unk> campaign directed at Anthony Molloy , captain of HMS Caesar . Molloy was accused of cowardice by fellow officers for his failure to follow Howe 's orders on both 29 May and 1 June . Molloy 's request for an official court @-@ martial to clear his name failed , and although his personal courage was not called into question , his professional ability was . Molloy was dismissed from his ship . 
- Of the captured ships , several were purchased and enjoyed long careers in the Royal Navy , in particular the two 80 @-@ gun ships HMS Sans Pareil which was decommissioned in 1802 but not broken up until 1842 , and HMS Juste , which was a popular command until her decommissioning in 1802 at the Peace of Amiens . Of the four 74 @-@ gun prizes , Achille and Northumberland ( both <unk> built in the late 1770s ) were broken up as <unk> soon after arrival in Britain , while Impétueux was destroyed in a dockyard fire on 24 August 1794 while undergoing repairs . America , the final prize , was taken into the Royal Navy as HMS America but renamed HMS <unk> in July 1795 and remained in service until 1813 . The combined prize money for these ships was £ 201 @,@ <unk> ( the equivalent of £ 21 @,@ 000 @,@ 000 as of 2016 ) , divided among the ships under Lord Howe 's command . 
- 
- 
- = New York State Route 368 = 
- 
- New York State Route 368 ( NY 368 ) was a state highway in Onondaga County , New York , in the United States . It was one of the shortest routes in the county , extending for only 1 @.@ 69 miles ( 2 @.@ 72 km ) between NY 321 and NY 5 in the town of Elbridge . NY 368 was known as Halfway Road for the hamlet it served near its midpoint . The route was assigned in the 1930s and removed in 1980 as part of a highway maintenance swap between the state of New York and Onondaga County . 
- 
- = = Route description = = 
- 
- NY 368 began at an intersection with NY 321 adjacent to the Carpenter 's Brook Fish <unk> in the town of Elbridge . The route headed north as Halfway Road , passing by farmland as it headed through a rural area of Onondaga County to the small hamlet of Halfway . Here , NY 368 served a small number of homes as it crossed a <unk> railroad line ( now part of the Finger Lakes Railway ) at the center of the community . Outside of Halfway , the route turned to the northwest toward the village of Elbridge , avoiding a marshy area directly north of Halfway . It intersected with Lynch Road and Campbell Road before turning slightly northward and following Carpenter 's Brook through another undeveloped area to an intersection with NY 5 east of the village , where NY 368 ended . 
- 
- = = History = = 
- 
- NY 368 was assigned in the 1930s as a connector between NY 321 and NY 5 in the town of Elbridge by way of the hamlet of Halfway . It remained unchanged until April 1 , 1980 , when ownership and maintenance of the route was transferred from the state of New York to Onondaga County as part of a highway maintenance swap between the two levels of government . The county also assumed ownership and maintenance of the Onondaga County portion of NY <unk> as part of the exchange . NY 368 was redesignated as County Route 107 ( CR 107 ) following the swap . 
- 
- = = Major intersections = = 
- 
- The entire route was in Elbridge , Onondaga County . 
- 
- 
- = M @-@ 122 ( Michigan highway ) = 
- 
- M @-@ 122 was a state trunkline highway in the US state of Michigan entirely in the city of St. Ignace . The highway connected US Highway 2 ( US 2 ) to the State Highway Ferry Dock used before the Mackinac Bridge was built . It was retired and the road returned to local control in 1957 . 
- 
- = = Route description = = 
- 
- Prior to the opening of the Mackinac Bridge , travelers wishing to venture from St. Ignace to Mackinaw City had to do so via ferry . M @-@ 122 began at US 2 ( now Business Loop Interstate 75 ) near Straits State Park and traveled through town along Ferry Road where it ran southeasterly from the main highway . East of <unk> Street M @-@ 122 curved around to the east near <unk> Street . The highway ended at the State Ferry Docks on the southeast side of the city next to the Coast Guard station . 
- 
- = = History = = 
- 
- M @-@ 122 was initially assumed into the state highway system in 1929 as a connector between US 31 and Straits State Park . In 1936 , US 2 was routed into St. Ignace and US 31 was scaled back to end in the Lower Peninsula in Mackinaw City . M @-@ 122 now provided a connection between US 2 and the new docks on the southeast side of the city . It existed in this capacity until 1957 when the Mackinac Bridge opened to traffic . 
- 
- = = Major intersections = = 
- 
- The entire highway was in St. Ignace , Mackinac County . 
- 
- 
- = Tupolev Tu @-@ 12 = 
- 
- The Tupolev Tu @-@ 12 ( development designation Tu @-@ 77 ) was an experimental Soviet jet @-@ powered medium bomber developed from the successful piston @-@ engined Tupolev Tu @-@ 2 bomber after the end of World War II . It was designed as a transitional aircraft to <unk> Tupolev and the VVS with the issues involved with jet @-@ engined bombers . 
- 
- = = Development = = 
- 
- The Tupolev Tu @-@ 73 jet @-@ engined bomber project was suffering delays in early 1947 and Tupolev suggested re @-@ <unk> the Tu @-@ 2 medium bomber with imported British Rolls @-@ Royce Nene jet engines to produce a jet bomber as quickly as possible . Design work began well before official approval was received on 31 May 1947 for one Tu @-@ 2S to be converted in the <unk> 's workshop and another five to be converted at <unk> ( Factory ) Nr. 23 , but construction of the prototype had already begun in early May under the bureau designation Tu @-@ 77 . 
- Changes from the standard Tu @-@ 2 were <unk> to speed production and they consisted of the following : 
- Two Nene jet engines replaced the standard <unk> <unk> @-@ <unk> radial engines . 
- The wing dihedral was reduced to 3 ° from 6 ° . 
- The fuselage was lengthened 400 mm ( 16 in ) and the rear fuselage was heightened by 300 mm ( 12 in ) . 
- A new <unk> <unk> was fitted , with the main gear units <unk> into the engine <unk> . 
- Additional fuel tanks were fitted and the design of the tanks was changed to accommodate the change from gasoline to kerosene . 
- The control system was revised and trim <unk> were fitted to the <unk> . 
- The wing and tail were reinforced . 
- The 20 mm ( 0 @.@ 79 in ) <unk> cannon were removed from the wing roots and a 23 mm ( 0 @.@ 91 in ) <unk> @-@ <unk> NS @-@ 23 cannon was mounted in an external <unk> on the starboard side of the nose . 
- 
- = = = Testing and evaluation = = = 
- 
- The prototype was completed in July and was first flown on 27 July 1947 . Two aircraft were shown at the <unk> Aviation Day <unk> on 3 August 1947 . It completed its manufacturer 's trials in September and underwent the State acceptance trials from 4 October 1947 to 27 February 1948 where it was redesignated as the Tu @-@ 12 . The <unk> VVS ( <unk> @-@ <unk> <unk> <unk> <unk> @-@ <unk> <unk> – Air Force Scientific Test Institute ) report summarized the differences between the Tu @-@ 2 and Tu @-@ 12 as " a considerable gain in speed , an improved rate of climb , a higher service ceiling , but poorer field performance and a considerably greater fuel load required to achieve the same range as the Tu @-@ 2 . " Both the lack of a pressurized cabin that greatly reduced its effectiveness at high altitude and the lack of <unk> equipment for the wing and tail leading edges and the cockpit glazing were noted as major problems . At high speeds it was virtually impossible to traverse and elevate the manually operated <unk> @-@ 68 and Lu @-@ 68 gun turrets . The vibration of the NS @-@ 23 cannon when firing rendered the equipment in the <unk> 's cabin unusable and damaged the cabin glazing . Turning on the <unk> friend or <unk> ( <unk> ) system adversely affected the <unk> system and the radios . New generators had to be installed as the originals did not produce enough electrical power . 
- The trials conducted by the <unk> VVS included engagements between the Tu @-@ 12 and the Soviet <unk> @-@ 9 and <unk> @-@ 23 jet fighters which were very useful in evaluating the offensive armament of the fighters , the defensive armament of the bomber and the proper tactics involved for both types of aircraft . The tests demonstrated the <unk> of the current 12 @.@ 7 mm ( 0 @.@ 50 in ) armament and meant that every Soviet bomber <unk> would have a defensive armament using power @-@ operated turrets that carried guns 20 mm or larger . 
- The five aircraft modified by the factory were given the <unk> <unk> @-@ 45 engine , the Soviet unlicensed copy of the Nene engine , and all six aircraft , used by the VVS for aircrew <unk> and training . The aircraft completed were later relegated to test duties . One aircraft was used for drone tests and another , redesignated as the Tu @-@ <unk> , mounted various pulse jet engines on a <unk> above the fuselage . 
- 
- = = <unk> = = 
- 
- Soviet Union 
- Soviet Air Force 
- 
- = = Specifications ( Tu @-@ 12 ) = = 
- 
- Data from <unk> , Tupolev Aircraft since 1922 
- General characteristics 
- Crew : 5 
- Length : 16 @.@ 45 m ( 53 ft 11 ½ in ) 
- <unk> : 18 @.@ 86 m ( 61 ft 10 ½ in ) 
- Wing area : 48 @.@ 80 m2 ( 525 @.@ 30 <unk> ) 
- Empty weight : <unk> kg ( 19 @,@ 826 lb ) 
- Gross weight : 15 @,@ 720 kg ( 34 @,@ <unk> lb ) 
- <unk> : 2 × Rolls @-@ Royce Nene I <unk> , 22 <unk> ( 5 @,@ 000 <unk> ) thrust each each 
- Performance 
- Maximum speed : <unk> km / h ( <unk> mph ) 
- Range : 2 @,@ 200 km ( 1 @,@ 367 miles ) 
- Service ceiling : 11 @,@ 370 m ( 37 @,@ 305 ft ) 
- Armament 
- 1 × 23 mm NS @-@ 23 cannon 
- 2 × 12 @.@ 7 mm <unk> <unk> machine @-@ guns 
- 3 @,@ 000 kg ( 6 @,@ 614 lb ) of bombs 
- 
- 
- = Civilian Public Service = 
- 
- The Civilian Public Service ( CPS ) was a program of the United States government that provided conscientious objectors with an alternative to military service during World War II . From 1941 to 1947 , nearly 12 @,@ 000 draftees , willing to serve their country in some capacity but unwilling to perform any type of military service , accepted assignments in work of national importance in 152 CPS camps throughout the United States and Puerto Rico . Draftees from the historic peace churches and other faiths worked in areas such as soil conservation , forestry , fire fighting , agriculture , under the supervision of such agencies as the U.S. Forest Service , the Soil Conservation Service , and the National Park Service . Others helped provide social services and mental health services . 
- The CPS men served without wages and minimal support from the federal government . The cost of maintaining the CPS camps and providing for the needs of the men was the responsibility of their congregations and families . CPS men served longer than regular draftees and were not released until well after the end of the war . Initially skeptical of the program , government agencies learned to appreciate the men 's service and requested more workers from the program . CPS made significant contributions to forest fire prevention , erosion and flood control , medical science and reform of the mental health system . 
- 
- = = Background = = 
- 
- Conscientious objectors ( COs ) refuse to participate in military service because of belief or religious training . During wartime , this stance conflicts with conscription efforts . Those willing to accept non @-@ combatant roles , such as medical personnel , are accommodated . There are few legal options for draftees who cannot cooperate with the military in any way . 
- 
- = = = <unk> of World War I = = = 
- 
- The conscription law of World War I provided for noncombatant service for members of a religious organization whose members were forbidden from participating in war of any form . This exemption effectively limited conscientious objector status to members of the historic peace churches : Mennonites ( and other Anabaptist groups such as <unk> ) , Religious Society of Friends ( Quakers ) and Church of the Brethren . The law gave the President authority to assign such draftees to any noncombatant military role . 
- Conscientious objectors who refused noncombatant service during World War I were imprisoned in military facilities such as Fort Lewis ( Washington ) , <unk> Island ( California ) and Fort <unk> ( Kansas ) . The government assumed that COs could be converted into soldiers once they were exposed to life in their assigned military camps . Simultaneously the Justice Department was preparing to <unk> 181 Mennonite leaders for violating the espionage act because of a statement they adopted against performing military service . The draftees ' refusal to put on a uniform or cooperate in any way caused difficulties for both the government and the COs . The treatment received by nearly 2000 of these absolute COs included short rations , solitary confinement and physical abuse so severe as to cause the deaths of two <unk> draftees . 
- 
- = = = Preparation for World War II = = = 
- 
- After World War I , and with another European war looming , leaders from the historic peace churches met to <unk> about how to cooperate with the government to avoid the difficulties of World War I. Holding a common view that any participation in military service was not acceptable , they devised a plan of civilian alternative service , based on experience gained by American Friends Service Committee work in Europe during and after World War I and forestry service done by Russian Mennonites in lieu of military service in <unk> Russia . 
- As the United States prepared for another war , the historic peace churches , represented by Friends who understood inner dealings of Washington D.C. politics , attempted to influence new draft bills to ensure their men could fulfill their duty in an alternative , non @-@ military type of service . On June 20 , 1940 , the Burke @-@ Wadsworth Bill came before Congress . The arrangements for conscientious objectors were almost identical to the World War I provisions . 
- 
- = = = Selective Service Act = = = 
- 
- The Friends representatives continued attempting to make the bill more favorable to the historic peace churches . The Burke @-@ Wadsworth Bill passed on September 14 , 1940 , becoming the Selective Training and Service Act of 1940 . The influence of the churches was evident in section 5 ( g ) , which says in part : 
- Any such person claiming such exemption from combatant training and service ... in lieu of such induction , be assigned to work of national importance under civilian direction . 
- The bill offered four improvements from the perspective of the churches over the World War I provisions . The exemption applied to conscientious <unk> based on religious training or belief , opening the door for members of any religious denomination to apply for CO status . Draftees turned down by local draft board could appeal under the new law . Those assigned to " work of national importance " would be under civilian , not military , control and violations of law on the part of those in the program were subject to normal federal jurisdiction , not the military justice system . From the military perspective , it removed the burden of dealing with thousands of uncooperative draftees and segregated the COs and their philosophy from military service members . 
- Unlike <unk> methods , the military found that this gentler approach resulted in about one in eight eventually transferring to military service . 
- 
- = = Organization = = 
- 
- When registration commenced on October 16 , 1940 , no structure was in place to handle thousands of anticipated conscientious objectors . Church representatives meeting with government officials learned that little thought had been put into the program , and the churches were advised to create a plan . Because the government wanted to deal with one body , not individual religious denominations , the National Council for Religious Conscientious <unk> was formed as a liaison between the churches and the federal government . The historic peace churches outlined a plan that included running and maintaining CPS camps under church control . However , President Roosevelt opposed any plan not involving military control over the draftees . To save their plan and retain civilian direction of the program , the churches offered to fund the camps . <unk> convinced Roosevelt that putting the COs to work in out @-@ of @-@ the @-@ way camps was <unk> to repeating the difficulties of World War I. Selective Service and the peace churches agreed to a six @-@ month trial of church supported and funded camps for conscientious objectors and thus Civilian Public Service was born . 
- The first camp opened on May 15 , 1941 near Baltimore , Maryland . A total of 152 camps and units were established over the next six years . The federal government provided work projects , housing , camp <unk> and paid for transportation to the camps . The responsibilities of the churches included day @-@ to @-@ day management of the camps , subsistence costs , meals and healthcare for the men . When the young men arrived at the first camps , they started a six @-@ month experiment that would extend to six years . 
- 
- = = = Camp life = = = 
- 
- Civilian Public Service men lived in barracks @-@ style camps , such as former Civilian Conservation Corps facilities . The camps served as a base of operations , from which the COs departed to their daily assignments . Sites were located typically in rural areas near the agricultural , soil conservation and forestry projects where the work took place . A large camp such as number 57 near Hill City , South Dakota , had five dormitories and housed as many as 172 men building the <unk> Dam . Later , with projects located in urban areas , the men lived in smaller units , communal housing near their assignments . CPS men typically worked nine hours , six days per week . 
- Mennonite Central Committee , American Friends Service Committee and Brethren Service Committee administered almost all of the camps . The Association of Catholic Conscientious <unk> managed four camps and the Methodist World Peace Commission two . Each camp was assigned a director responsible for supervising camp operation . The director managed the needs of the men , oversaw maintenance of the camp facilities , handled community relations and reported to Selective Service officials . Initially a pastor had the camp director role . Later , capable men from among the CPS workers directed the camps . 
- Besides the director , a <unk> , business manager and <unk> staffed a typical camp . An educational director was responsible for creating recreational , social and educational programs for the men . Church history , Bible and first aid were standard course topics . The strength of instructional programs varied from camp to camp , and after nine hours of physical labor , it could be difficult to motivate the men to attend classes . Most camps had libraries , some showed current films and camp number 56 ( Camp Angel ) near <unk> , Oregon had a particular emphasis on the arts . Camps produced <unk> and <unk> documenting their experiences . 
- The camp <unk> , with the help of men assigned as cooks , prepared all of the meals . Camps with large gardens provided their own fresh vegetables . <unk> congregations also supplied home canned and fresh produce . The camps were subject to the same shortages and rationing as the rest of the nation . 
- Sunday worship services were organized by the camp director if he was a pastor , by a visiting pastor , or by the CPS men themselves . While the historic peace churches organized the CPS , 38 % of the men came from other denominations and 4 % claimed no religious affiliation . 
- Men spent their free time doing crafts such as <unk> , <unk> , <unk> and photography . Outdoor activities included hiking and swimming . Men formed choirs and music ensembles , performing in neighboring towns when relations were good . The men earned two days of <unk> for each month of service . These days could be saved to allow enough time to travel several hundred miles home or in some cases traded to other men in exchange for cash . 
- Men with wives and dependents found it difficult to support their families . Beyond a small allowance , the men did not get paid for their service , nor were their dependents given an allowance . To be closer to their husbands , women sought employment near their husband 's assignment . Later , when jobs on dairy farms became available , families could live together in housing provided for farm workers . 
- Men who became uncooperative with the CPS system and were unable to adjust to the church @-@ managed camps were reassigned to a few camps managed by the Selective Service System . These camps tended to be the least productive and most difficult to administer . Men who felt compelled to protest the restrictions of the conscription law attempted to disrupt the program through the use of various techniques , including the initiation of work <unk> and labor strikes . <unk> rule breaking frustrated camp directors . The most difficult cases were given to the federal court system and the men imprisoned . 
- 
- = = = <unk> = = = 
- 
- Churches were primarily responsible for financing Civilian Public Service , providing for the men 's food , clothes , and other material needs . The churches also provided and paid for the camp director . The men received an allowance of between $ 2 @.@ 50 and $ 5 @.@ 00 monthly for personal needs . When jobs were available in surrounding farms and communities , those willing to work beyond their regular CPS jobs could earn extra spending money . The federal government spent $ 1 @.@ 3 million on the CPS program . The men performed $ 6 million of unpaid labor in return . 
- Men who worked for farmers or psychiatric hospitals received regular wages , which they were required to give to the federal government . <unk> to this practice developed immediately because the men felt they were helping to fund the war . A compromise was reached where the wages were put into a special fund that was unused until after the end of the war . At one point , church representatives attempted unsuccessfully to have these funds used for providing a living allowance for the men 's dependents . 
- 
- = = Types of work = = 
- 
- The first Civilian Public Service projects were in rural areas where the men performed tasks related to soil conservation , agriculture and forestry . Later men were assigned to projects in cities where they worked in hospitals , psychiatric wards , and university research centers . 
- 
- = = = Soil conservation and agriculture = = = 
- 
- <unk> the rural background of most men , the initial camps provided soil conservation and farming @-@ related projects . By August 1945 , 550 men worked on dairy farms and with milk testing . Labor @-@ intensive farming operations like <unk> were short of workers and accepted COs to help fill the gap . Men assigned to the Bureau of Reclamation built <unk> to prevent soil erosion , constructed 164 reservoirs and 249 dams . A sixth of all CPS work was performed in this area . 
- 
- = = = <unk> and National Parks = = = 
- 
- At Forest Service and National Park Service camps , CPS men were responsible for fire control . Between fires they built forest trails , cared for nursery stock , planted thousands of seedlings and engaged in pest control . <unk> and roadways on the Blue Ridge Parkway and <unk> Drive of Virginia are products of CPS labor . 
- Hundreds of men volunteered for smoke jumping , showing their willingness to take great personal risks . When fire was detected by a lookout , smoke jumpers were flown directly to the site and dropped by parachute to quickly contain and extinguish the fire . From base camps scattered through the forests of Montana , Idaho and Oregon , the men were flown as many as 200 miles to fire sites , carrying <unk> tools and a two @-@ day supply of K @-@ rations . For larger fires , additional men , supplies and food were <unk> to expand the effort . Up to 240 CPS men served in this specialized program . One of the <unk> schools was at Camp <unk> in Montana . 
- 
- = = = Mental health = = = 
- 
- As the war progressed , a critical shortage of workers in psychiatric hospitals developed , because staff had left for better paying jobs with fewer hours and improved working conditions . <unk> wards at Philadelphia State Hospital had one attendant member for 300 patients , the minimum ratio being 10 : 1 . The government <unk> at initial requests that CPS workers have these positions , believing it better to keep the men segregated in the rural camps to prevent the spread of their philosophy . 
- Eventually the men received permission to work for the mental institutions as attendants or psychiatric aides . Individuals who found jobs at the rural camps <unk> and meaningless , volunteered for this new type of assignment . The mental health field promised to provide the work of national importance that the program was designed to produce . By the end of 1945 , more than 2000 CPS men worked in 41 institutions in 20 states . 
- The CPS men discovered appalling conditions in the mental hospital wards . In an interview , a conscientious objector described his experience when he first entered a mental hospital in October 1942 : 
- It is sort of like a perpetual bad dream . The <unk> , the sounds of the insane voices , the bad equipment . The long , dark corridors . I tell you , it is all very much like a medieval <unk> of the <unk> regions . We ’ d heard about how these patients had been treated by the attendants , Beat with rods , you know , do all kind of things . We took a vow before we left the camp , we decided that we would not assault or in any way , strike a patient . 
- I opened one of those rooms , and there was a man lying on the floor . I leaned over to try to see what I could do to minister to him in some way , do something for him . He may have been on a <unk> or he may have been on the bare floor . No he was on the bare floor , because when I tried to move him , his skin came off . His skin was bloody and stuck to the floor and when I tried to lift him up it just <unk> his skin off . He was in the last stages of syphilis . He died less than a week afterwards . Now that was my first introduction to what was badly needed in that institution . 
- The CPS men objected to the mistreatment and abuse of patients and determined to improve conditions in the psychiatric wards . They wanted to show other attendants <unk> to violence when dealing with patients . 
- Frank <unk> , chairman of the War <unk> League observed : 
- One objector assigned to a violent ward refused to take the broomstick offered by the Charge . When he entered the ward the patients crowded around asking , " Where is your broomstick ? " He said he thought he would not need it . " But suppose some of us gang up on you ? " The CO guessed they wouldn 't do that and started talking about other things . Within a few days the patients were seen gathering around the unarmed attendant telling him of their troubles . He felt much safer than the Charge who had only his broomstick for company . 
- <unk> workers surveyed CPS men in other hospitals and learned of the degree of abuse throughout the psychiatric care system . <unk> church managers and government officials , the COs begin advocating for reforms to end the abuses . Conditions were exposed in institutions such as Cleveland State Hospital , Eastern State Hospital in Virginia and Hudson River State Hospital . One explained : 
- And the governor came in and they cleaned out the hospital . I mean , they had hearings . We all had to appear in court and all that kind of stuff . And within a month or so , the hospital was completely changed . The superintendent was fired and the new superintendent was put in , and not only did they do our hospital , they did all the hospitals , mental hospitals in Virginia . 
- The reformers were especially active at the <unk> Hospital in Philadelphia where four Friends initiated the The <unk> magazine as a way to communicate ideas and promote reform . This periodical later became The <unk> <unk> , a professional journal for mental health workers . On May 6 , 1946 Life Magazine printed an <unk> of the mental healthcare system based on the reports of COs . Another effort of CPS , Mental Hygiene Project became the National Mental Health Foundation . Initially skeptical about the value of Civilian Public Service , Eleanor Roosevelt , impressed by the changes introduced by COs in the mental health system , became a sponsor of the National Mental Health Foundation and actively inspired other prominent citizens including Owen J. Roberts , Pearl Buck and Harry Emerson <unk> to join her in advancing the organization 's objectives of reform and humane treatment of patients . 
- 
- = = = Medical experiments = = = 
- 
- Draftees in Civilian Public Service became medical and scientific research test subjects in human medical experiments under the direction of the Office of Scientific Research and Development and the Surgeon General at medical institutions such as Harvard Medical School , Yale and Stanford Universities , and Massachusetts General Hospital . These experiments involved a range of research topics , sometimes <unk> the health of the COs . 
- <unk> : During the 1940s the cause , method of communication and treatment of infectious <unk> was not well understood . <unk> began with COs working at psychiatric hospitals and was expanded to a major research project with 30 to 60 test subjects at the University of Pennsylvania and Yale University . The men were <unk> with infected blood plasma , swallowed nose and throat <unk> and the human body <unk> of infected patients , and drank contaminated water . 
- As a young surgeon , C. Everett <unk> was part of the research team at the University of Pennsylvania School of Medicine . He related his experience with CPS test subjects : 
- And the first time I was introduced to this whole program when I as a young surgeon , was asked to do serial <unk> on their <unk> to see what the effect of the virus was in the production of the changes in the liver . And in that way , I got to know that a lot of these young men had no idea that the risk they were taking also included death . And some of those youngsters did die and it was a very difficult thing for me to be part of , because you know , you ’ re powerless , when you ’ re part of the big team . 
- It couldn 't happen today . Internal Review Boards would not permit the use of a live virus in human subjects unless they really understood what was going to happen to them . And I doubt that even if they knew what the risk was , that an Internal Review Board in any academic institution would consent to that kind of experimental work . 
- The <unk> research was instrumental in determining a virus is responsible for the disease and that it is transmitted through human <unk> , <unk> and drinking water . 
- <unk> : During the early 1940s , quinine was the chief anti @-@ <unk> drug . Made from the bark of the South American <unk> tree , quinine was in short supply during the war , so scientists began searching for an alternative treatment . The test subjects allowed themselves to be bitten by <unk> mosquitoes and when the fever reached its peak in three to four days , were given experimental treatments . At the University of Minnesota , twelve CPS men underwent tests to determine the recovery period for those infected with malaria . This research documented the debilitating effects of the disease and the amount of time required for a complete recovery . 
- Common cold and atypical pneumonia : A hundred CPS men participated with tests such that they inhaled or drank throat <unk> from soldiers with <unk> and pneumonia . This research proved that <unk> and some types of pneumonia are cause by a virus , not bacteria . 
- Minnesota <unk> Experiment : To study the effects of diet and <unk> , Dr. <unk> Keys of the University of Minnesota Laboratory of <unk> Hygiene placed 32 conscientious objectors on a controlled diet . For three months they were given a normal 3 @,@ 200 calories ( 13 @,@ 000 <unk> ) diet . This was followed by six months of an 1 @,@ 800 calories ( 7 @,@ 500 <unk> ) diet , fewer calories than provided by the famine diet experienced by the civilian population in wartime Europe . The research documented the men 's ability to maintain physical output and the psychological effects such as <unk> , <unk> , <unk> and severe depression . The study then followed the men 's long recovery as they returned to a normal diet and regained the weight lost during the experimentation . 
- The study provided valuable insights into hunger and starvation and the results were made available to all major relief agencies concerned with postwar food and <unk> problems , helping to inspire the Marshall Plan . 
- 
- = = <unk> and impact = = 
- 
- Civilian Public Service men were released from their assignments and the camps closed during March 1947 , nineteen months after the end of the war in the Pacific . <unk> in the mental health system continued after the war . The experience of Mennonite COs was instrumental in creating regional mental health facilities in California , Kansas and Maryland . 
- Lewis Hill , who was in CPS camp number 37 near <unk> , California , together with several other COs founded Pacifica Network and <unk> Radio in Berkeley , California , the world 's first listener @-@ sponsored radio station . Poets William Everson and William Stafford were both in CPS camps . Actor Francis ( <unk> ) William Weaver spent time in the Big <unk> ( New York ) CPS Camp number 46 . 
- Men from the historic peace churches volunteered for relief and reconstruction after their release from CPS . The 1947 Nobel Peace Prize was awarded to American and British Friends Service Committees for their relief work in Europe after the war . Mennonite Central Committee <unk> its effort from camp administration to relief and reconstruction in Europe after the war . 
- Civilian Public Service created a precedent for the Alternative Service Program for conscientious objectors in the United States during the Korean and Vietnam Wars . Although the CPS program was not <unk> , the idea of offering men an opportunity to do " work of national importance " instead of military service was established . 
- 
- 
- = Ireland = 
- 
- Ireland ( / <unk> / ; Irish : <unk> [ <unk> ] ; Ulster @-@ Scots : <unk> [ <unk> ] ) is an island in the North Atlantic . It is separated from Great Britain to its east by the North Channel , the Irish Sea , and St George 's Channel . Ireland is the second @-@ largest island of the British Isles , the third @-@ largest in Europe , and the twentieth @-@ largest on Earth . 
- <unk> , Ireland is divided between the Republic of Ireland ( officially named Ireland ) , which covers five @-@ <unk> of the island , and Northern Ireland , which is part of the United Kingdom , in the northeast of the island . In 2011 the population of Ireland was about 6 @.@ 4 million , ranking it the second @-@ most populous island in Europe after Great Britain . Just under 4 @.@ 6 million live in the Republic of Ireland and just over 1 @.@ 8 million live in Northern Ireland . 
- The island 's geography comprises relatively low @-@ lying mountains surrounding a central plain , with several navigable rivers extending inland . The island has lush vegetation , a product of its mild but changeable climate which avoids extremes in temperature . <unk> woodlands covered the island until the Middle Ages . As of 2013 , the amount of land that is wooded in Ireland is about 11 % of the total , compared with a European average of 35 % . There are twenty @-@ six extant mammal species native to Ireland . The Irish climate is very moderated and classified as oceanic . As a result , winters are <unk> than expected for such a northerly area . However , summers are cooler than those in Continental Europe . Rainfall and cloud cover are abundant . 
- The earliest evidence of human presence in Ireland is dated at 10 @,@ 500 BC . Gaelic Ireland had emerged by the 1st century AD and lasted until the First World War . The island was <unk> from the 5th century onward . Following the Norman invasion in the 12th century , England claimed sovereignty over Ireland . However , English rule did not extend over the whole island until the 16th – 17th century Tudor conquest , which led to colonisation by settlers from Britain . In the <unk> , a system of Protestant English rule was designed to materially disadvantage the Catholic majority and Protestant dissenters , and was extended during the 18th century . With the Acts of Union in 1801 , Ireland became a part of the United Kingdom . A war of independence in the early 20th century was followed by the partition of the island , creating the Irish Free State , which became increasingly sovereign over the following decades , and Northern Ireland , which remained a part of the United Kingdom . Northern Ireland saw much civil unrest from the late 1960s until the 1990s . This subsided following a political agreement in 1998 . In 1973 the Republic of Ireland joined the European Economic Community while the United Kingdom , and Northern Ireland , as part of it , did the same . 
- Irish culture has had a significant influence on other cultures , especially in the fields of literature . Alongside mainstream Western culture , a strong indigenous culture exists , as expressed through Gaelic games , Irish music , and the Irish language . The culture of the island also shares many features with that of Great Britain , including the English language , and sports such as association football , rugby , horse racing , and golf . 
- 
- = = Name = = 
- 
- Ireland consists of Old Irish <unk> + English land . <unk> derives from Proto @-@ Celtic * <unk> ( compare Welsh <unk> ) , which is also the source of Latin <unk> . <unk> derives from a root meaning " fat , prosperous " . 
- 
- = = History = = 
- 
- 
- = = = Prehistoric Ireland = = = 
- 
- During the last glacial period , and up until about 9000 years ago , most of Ireland was covered with ice , most of the time . Sea levels were lower and Ireland , like Great Britain , formed part of continental Europe . By 12 @,@ 000 BC , rising sea levels due to ice melting caused Ireland to become separated from Great Britain . Later , around <unk> BC , Great Britain itself became separated from continental Europe . The earliest evidence of human presence in Ireland is dated at 10 @,@ 500 BC . Until recently the earliest evidence of humans in Ireland were Mesolithic people who arrived by boat from Britain between 8000 BC and 7000 BC . 
- From about <unk> BC , Neolithic settlers arrived introducing cereal <unk> , a housing culture ( similar to those of the same period in Scotland ) and stone monuments . A more advanced agriculture was to develop . At the <unk> Fields , preserved beneath a blanket of peat in present @-@ day County Mayo , is an extensive field system , arguably the oldest in the world , dating from not long after this period . <unk> of small divisions separated by dry @-@ stone walls , the fields were farmed for several centuries between <unk> BC and 3000 BC . <unk> and barley were the principal crops imported from the Iberian Peninsula . 
- The Bronze Age – defined by the use of metal – began around 2500 BC , with technology changing people 's everyday lives during this period through innovations such as the wheel , <unk> oxen , weaving textiles , <unk> alcohol , and skilful <unk> , which produced new weapons and tools , along with fine gold decoration and jewellery , such as brooches and <unk> . According to John T. Koch and others , Ireland in the Late Bronze Age was part of a maritime trading @-@ <unk> culture called the Atlantic Bronze Age that also included Britain , western France and Iberia , and that this is where Celtic languages developed . This contrasts with the traditional view that their origin lies in mainland Europe with the <unk> culture . 
- 
- = = = = <unk> of Celtic Ireland = = = = 
- 
- During the Iron Age , a Celtic language and culture emerged in Ireland . How and when the island of Ireland became Celtic has been debated for close to a century , with the migrations of the Celts being one of the more enduring themes of archaeological and linguistic studies . Today , there is more than one school of thought on how this occurred in Ireland . 
- The long @-@ standing traditional view , once widely accepted , is that Celtic language , <unk> script and culture were brought to Ireland by waves of invading or migrating Celts from mainland Europe . This theory draws on the <unk> <unk> <unk> , a medieval Christian pseudo @-@ history of Ireland along with the presence of Celtic culture , language and artefacts found in Ireland such as Celtic bronze <unk> , shields , <unk> and other finely crafted Celtic associated possessions . The theory holds that there were four separate Celtic invasions of Ireland . The <unk> were said to be the first , followed by the <unk> from northern Gaul and Britain . Later , <unk> tribes from <unk> ( present @-@ day Brittany ) were said to have invaded Ireland and Britain more or less simultaneously . Lastly , the <unk> ( <unk> ) were said to have reached Ireland from either northern Iberia or southern Gaul . It was claimed that a second wave named the <unk> , belonging to the <unk> people of northern Gaul , began arriving about the sixth century BC . They were said to have given their name to the island . 
- A more recent theory , with broad support among archaeologists , is that Celtic culture and language arrived in Ireland as a result of cultural <unk> . This theory proposes that the <unk> of Ireland may have been the culmination of a long process of social and economic interaction between Ireland , Britain and adjacent parts of Continental Europe . 
- The theory was advanced in part because of lack of archeological evidence for large @-@ scale Celtic immigration , though it is accepted that such movements are notoriously difficult to identify . Some proponents of this theory hold that it is likely that there was migration of smaller groups of Celts to Ireland , with sufficiently regular traffic to constitute a " migration stream , " but that this was not the fundamental cause of Insular <unk> . Historical linguists are <unk> that this method alone could account for the absorption of the Celtic language , with some saying that an assumed processional view of Celtic linguistic formation is ' an especially hazardous exercise ' . Genetic lineage investigation into the area of Celtic migration to Ireland has led to findings that showed no significant differences in mitochondrial DNA between Ireland and large areas of continental Europe , in contrast to parts of the Y @-@ chromosome pattern . When taking both into account a recent study drew the conclusion that modern Celtic speakers in Ireland could be thought of as European " Atlantic Celts " showing a shared ancestry throughout the Atlantic zone from northern Iberia to western Scandinavia rather than substantially central European . 
- 
- = = = Late antiquity and early medieval times = = = 
- 
- The earliest written records of Ireland come from classical Greco @-@ Roman <unk> . Ptolemy in his <unk> refers to Ireland as <unk> <unk> ( Little Britain ) , in contrast to the larger island , which he called <unk> <unk> ( Great Britain ) . In his later work , Geography , Ptolemy refers to Ireland as <unk> and to Great Britain as Albion . These " new " names were likely to have been the local names for the islands at the time . The earlier names , in contrast , were likely to have been coined before direct contact with local peoples was made . 
- The Romans would later refer to Ireland by this name too in its <unk> form , <unk> , or Scotia . Ptolemy records sixteen nations inhabiting every part of Ireland in 100 AD . The relationship between the Roman Empire and the kingdoms of ancient Ireland is unclear . However , a number of finds of Roman coins have been made , for example at the Iron Age settlement of <unk> Hill near <unk> and <unk> . 
- Ireland continued as a <unk> of rival kingdoms but , beginning in the 7th century AD , a concept of national kingship gradually became articulated through the concept of a High King of Ireland . Medieval Irish literature portrays an almost unbroken sequence of High Kings stretching back thousands of years but modern historians believe the scheme was constructed in the 8th century to justify the status of powerful political groupings by projecting the origins of their rule into the remote past . 
- The High King was said to <unk> over the provincial kingdoms that together formed Ireland . All of these kingdoms had their own kings but were at least nominally subject to the High King . The High King was drawn from the ranks of the provincial kings and ruled also the royal kingdom of Meath , with a ceremonial capital at the Hill of Tara . The concept only became a political reality in the Viking Age and even then was not a consistent one . Ireland did have a culturally <unk> rule of law : the early written judicial system , the <unk> Laws , administered by a professional class of jurists known as the <unk> . However , a united kingdom of Gaelic Ireland was never achieved . 
- The Chronicle of Ireland records that in <unk> AD Bishop <unk> arrived in Ireland on a mission from Pope Celestine I to minister to the Irish " already believing in Christ " . The same chronicle records that Saint Patrick , Ireland 's best known patron saint , arrived the following year . There is continued debate over the missions of <unk> and Patrick but the consensus is that they both took place and that the older <unk> tradition collapsed in the face of the new religion . Irish Christian scholars excelled in the study of Latin and Greek learning and Christian theology . In the monastic culture that followed the Christianisation of Ireland , Latin and Greek learning was preserved in Ireland during the Early Middle Ages in contrast to elsewhere in Europe , where the Dark Ages followed the decline of the Roman Empire . 
- The arts of manuscript illumination , <unk> and sculpture flourished and produced treasures such as the Book of Kells , ornate jewellery and the many carved stone crosses that still dot the island today . A mission founded in <unk> on Iona by the Irish monk Saint Columba began a tradition of Irish missionary work that spread Celtic Christianity and learning to Scotland , England and the Frankish Empire on Continental Europe after the fall of Rome . These missions continued until the late Middle Ages , establishing monasteries and centres of learning , producing scholars such as <unk> <unk> and Johannes <unk> and <unk> much influence in Europe . 
- From the 9th century , waves of Viking raiders plundered Irish monasteries and towns . These raids added to a pattern of raiding and endemic warfare that was already deep @-@ seated in Ireland . The Vikings also were involved in establishing most of the major coastal settlements in Ireland : Dublin , Limerick , Cork , Wexford , Waterford , and also <unk> , <unk> , <unk> , <unk> , <unk> , <unk> Foyle and <unk> <unk> . 
- 
- = = = Norman and English invasions = = = 
- 
- On 1 May <unk> , an expedition of <unk> @-@ Norman knights with an army of about six hundred landed at <unk> Strand in present @-@ day County Wexford . It was led by Richard de Clare , called <unk> due to his prowess as an archer . The invasion , which coincided with a period of renewed Norman expansion , was at the invitation of <unk> Mac <unk> , the king of Leinster . 
- In 1166 , Mac <unk> had fled to <unk> , France , following a war involving <unk> Ua <unk> , of <unk> , and sought the assistance of the <unk> king , Henry II , in <unk> his kingdom . In <unk> , Henry arrived in Ireland in order to review the general progress of the expedition . He wanted to re @-@ exert royal authority over the invasion which was expanding beyond his control . Henry successfully re @-@ imposed his authority over <unk> and the <unk> @-@ Norman <unk> and persuaded many of the Irish kings to accept him as their overlord , an arrangement confirmed in the 1175 Treaty of Windsor . 
- The invasion was <unk> by the provisions of the Papal Bull <unk> , issued by Adrian IV in 1155 . The bull encouraged Henry to take control in Ireland in order to oversee the financial and administrative reorganisation of the Irish Church and its integration into the Roman Church system . Some restructuring had already begun at the ecclesiastical level following the <unk> of Kells in <unk> . There has been significant controversy regarding the authenticity of <unk> , and there is no general agreement as to whether the bull was genuine or a forgery . 
- In <unk> , the new pope , Alexander III , further encouraged Henry to advance the integration of the Irish Church with Rome . Henry was authorised to impose a <unk> of one <unk> per hearth as an annual contribution . This church levy , called Peter 's <unk> , is extant in Ireland as a voluntary donation . In turn , Henry accepted the title of Lord of Ireland which Henry conferred on his younger son , John <unk> , in <unk> . This defined the Irish state as the Lordship of Ireland . When Henry 's successor died unexpectedly in <unk> , John inherited the crown of England and retained the Lordship of Ireland . 
- Over the century that followed , Norman feudal law gradually replaced the Gaelic <unk> Law so that by the late 13th century the Norman @-@ Irish had established a feudal system throughout much of Ireland . Norman settlements were characterised by the establishment of <unk> , <unk> , towns and the seeds of the modern county system . A version of the Magna <unk> ( the Great Charter of Ireland ) , <unk> Dublin for London and Irish Church for Church of England , was published in 1216 and the Parliament of Ireland was founded in <unk> . 
- From the mid @-@ 14th century , after the Black Death , Norman settlements in Ireland went into a period of decline . The Norman rulers and the Gaelic Irish <unk> <unk> and the areas under Norman rule became <unk> . In some parts , a hybrid Hiberno @-@ Norman culture emerged . In response , the Irish parliament passed the <unk> of Kilkenny in <unk> . These were a set of laws designed to prevent the <unk> of the <unk> into Irish society by requiring English subjects in Ireland to speak English , follow English customs and abide by English law . 
- By the end of the 15th century central English authority in Ireland had all but disappeared and a renewed Irish culture and language , albeit with Norman influences , was dominant again . English Crown control remained relatively <unk> in an <unk> foothold around Dublin known as The <unk> , and under the provisions of <unk> ' Law of <unk> , the Irish Parliamentary legislation was subject to the approval of the English Parliament . 
- 
- = = = The Kingdom of Ireland = = = 
- 
- The title of King of Ireland was re @-@ created in 1542 by Henry VIII , then King of England , of the Tudor dynasty . English rule of law was reinforced and expanded in Ireland during the latter part of the 16th century , leading to the Tudor conquest of Ireland . A near complete conquest was achieved by the turn of the 17th century , following the Nine Years ' War and the Flight of the Earls . 
- This control was further consolidated during the wars and conflicts of the 17th century , which witnessed English and Scottish colonisation in the <unk> of Ireland , the Wars of the Three Kingdoms and the <unk> War . Irish losses during the Wars of the Three Kingdoms ( which , in Ireland , included the Irish Confederacy and the <unk> conquest of Ireland ) are estimated to include 20 @,@ 000 battlefield casualties . 200 @,@ 000 civilians are estimated to have died as a result of a combination of war @-@ related famine , displacement , guerrilla activity and pestilence over the duration of the war . A further 50 @,@ 000 were sent into indentured servitude in the West Indies . Some historians estimate that as much as half of the pre @-@ war population of Ireland may have died as a result of the conflict . 
- The religious struggles of the 17th century left a deep sectarian division in Ireland . Religious allegiance now determined the perception in law of loyalty to the Irish King and Parliament . After the passing of the Test Act 1672 , and with the victory of the forces of the dual monarchy of William and Mary over the <unk> , Roman Catholics and <unk> Protestant Dissenters were barred from sitting as members in the Irish Parliament . Under the emerging <unk> Laws , Irish Roman Catholics and Dissenters were increasingly deprived of various and <unk> civil rights even to the ownership of hereditary property . Additional <unk> punitive legislation followed 1703 , <unk> and <unk> . This completed a comprehensive systemic effort to materially disadvantage Roman Catholics and Protestant Dissenters , while <unk> a new ruling class of Anglican conformists . The new Anglo @-@ Irish ruling class became known as the Protestant <unk> . 
- An extraordinary climatic shock known as the " Great Frost " struck Ireland and the rest of Europe between December <unk> and September 1741 , after a decade of relatively mild winters . The winters destroyed stored crops of potatoes and other staples and the poor summers severely damaged <unk> . This resulted in the famine of 1740 . An estimated 250 @,@ 000 people ( about one in eight of the population ) died from the ensuing pestilence and disease . The Irish government halted export of corn and kept the army in quarters but did little more . Local <unk> and charitable organisations provided relief but could do little to prevent the ensuing mortality . 
- In the aftermath of the famine , an increase in industrial production and a surge in trade brought a succession of construction booms . The population soared in the latter part of this century and the architectural legacy of Georgian Ireland was built . In 1782 , <unk> ' Law was repealed , giving Ireland legislative independence from Great Britain for the first time since <unk> . The British government , however , still retained the right to nominate the government of Ireland without the consent of the Irish parliament . 
- 
- = = = Union with Great Britain = = = 
- 
- In 1798 , members of the Protestant <unk> tradition ( mainly Presbyterian ) made common cause with Roman Catholics in a republican rebellion inspired and led by the Society of United <unk> , with the aim of creating an independent Ireland . Despite assistance from France the rebellion was put down by British and Irish government and yeomanry forces . In 1800 , the British and Irish parliaments both passed Acts of Union that , with effect from 1 January 1801 , merged the Kingdom of Ireland and the Kingdom of Great Britain to create a United Kingdom of Great Britain and Ireland . 
- The passage of the Act in the Irish Parliament was ultimately achieved with substantial <unk> , having failed on the first attempt in <unk> . According to contemporary documents and historical analysis , this was achieved through a considerable degree of bribery , with funding provided by the British Secret Service Office , and the <unk> of <unk> , places and honours to secure votes . Thus , the parliament in Ireland was abolished and replaced by a united parliament at Westminster in London , though resistance remained , as evidenced by Robert Emmet 's failed Irish Rebellion of 1803 . 
- Aside from the development of the linen industry , Ireland was largely passed over by the industrial revolution , partly because it lacked coal and iron resources and partly because of the impact of the sudden union with the structurally superior economy of England , which saw Ireland as a source of agricultural produce and capital . 
- The Great Famine of the 1840s caused the deaths of one million Irish people and over a million more emigrated to escape it . By the end of the decade , half of all immigration to the United States was from Ireland . The period of civil unrest that followed until the end of the 19th century is referred to as the Land War . Mass emigration became deeply entrenched and the population continued to decline until the mid @-@ 20th century . Immediately prior to the famine the population was recorded as 8 @.@ 2 million by the 1841 census . The population has never returned to this level since . The population continued to fall until 1961 and it was not until the 2006 census that the last county of Ireland ( County <unk> ) to record a rise in population since 1841 did so . 
- The 19th and early 20th centuries saw the rise of modern Irish nationalism , primarily among the Roman Catholic population . The pre @-@ eminent Irish political figure after the Union was Daniel O 'Connell . He was elected as Member of Parliament for Ennis in a surprise result and despite being unable to take his seat as a Roman Catholic . O 'Connell spearheaded a vigorous campaign that was taken up by the Prime Minister , the Irish @-@ born soldier and statesman , the Duke of Wellington . <unk> the Catholic Relief Bill through Parliament , aided by future prime minister Robert Peel , Wellington prevailed upon a reluctant George IV to sign the Bill and proclaim it into law . George 's father had opposed the plan of the earlier Prime Minister , Pitt the Younger , to introduce such a bill following the Union of 1801 , fearing Catholic <unk> to be in conflict with the Act of Settlement 1701 . 
- Daniel O 'Connell led a subsequent campaign , for the repeal of the Act of Union , which failed . Later in the century , Charles Stewart <unk> and others campaigned for autonomy within the Union , or " Home Rule " . <unk> , especially those located in Ulster , were strongly opposed to Home Rule , which they thought would be dominated by Catholic interests . After several attempts to pass a Home Rule bill through parliament , it looked certain that one would finally pass in 1914 . To prevent this from happening , the Ulster Volunteers were formed in 1913 under the leadership of Edward Carson . 
- Their formation was followed in 1914 by the establishment of the Irish Volunteers , whose aim was to ensure that the Home Rule Bill was passed . The Act was passed but with the " temporary " exclusion of the six counties of Ulster that would become Northern Ireland . Before it could be implemented , however , the Act was suspended for the duration of the First World War . The Irish Volunteers split into two groups . The majority , approximately 175 @,@ 000 in number , under John <unk> , took the name National Volunteers and supported Irish involvement in the war . A minority , approximately 13 @,@ 000 , retained the Irish Volunteers ' name , and opposed Ireland 's involvement in the war . 
- The Easter Rising of 1916 was carried out by the latter group together with a smaller socialist militia , the Irish Citizen Army . The British response , executing fifteen leaders of the Rising over a period of ten days and <unk> or <unk> more than a thousand people , turned the mood of the country in favour of the rebels . Support for Irish <unk> increased further due to the ongoing war in Europe , as well as the Conscription Crisis of 1918 . 
- The pro @-@ independence republican party , <unk> <unk> , received overwhelming endorsement in the general election of 1918 , and in 1919 proclaimed an Irish Republic , setting up its own parliament ( Dáil <unk> ) and government . Simultaneously the Volunteers , which became known as the Irish Republican Army ( IRA ) , launched a three @-@ year guerrilla war , which ended in a truce in July 1921 ( although violence continued until June 1922 , mostly in Northern Ireland ) . 
- 
- = = = Partition = = = 
- 
- In December 1921 , the Anglo @-@ Irish Treaty was concluded between the British Government and representatives of the Second Dáil . It gave Ireland complete independence in its home affairs and practical independence for foreign policy , but an opt @-@ out clause allowed Northern Ireland to remain within the United Kingdom , which it immediately exercised as expected . Additionally , an oath of allegiance to the King was to be taken . <unk> over these provisions led to a split in the nationalist movement and a subsequent Irish Civil War between the new government of the Irish Free State and those opposed to the treaty , led by <unk> de Valera . The civil war officially ended in May 1923 when de Valera issued a cease @-@ fire order . 
- 
- = = = = Independence = = = = 
- 
- During its first decade , the newly formed Irish Free State was governed by the victors of the civil war . When de Valera achieved power , he took advantage of the <unk> of Westminster and political circumstances to build upon <unk> to greater sovereignty made by the previous government . The oath was abolished and in 1937 a new constitution was adopted . This completed a process of gradual separation from the British Empire that governments had pursued since independence . However , it was not until 1949 that the state was declared , officially , to be the Republic of Ireland . 
- The state was neutral during World War II , but offered clandestine assistance to the Allies , particularly in the potential defence of Northern Ireland . Despite their country 's neutrality , approximately 50 @,@ 000 volunteers from independent Ireland joined the British forces during the war , four being awarded Victoria Crosses . 
- The <unk> was also active in Ireland . German intelligence operations effectively ended in September 1941 when police made arrests on the basis of surveillance carried out on the key diplomatic <unk> in Dublin , including that of the United States . To the authorities , <unk> was a fundamental line of defence . With a regular army of only slightly over seven thousand men at the start of the war , and with limited supplies of modern weapons , the state would have had great difficulty in defending itself from invasion from either side in the conflict . 
- Large @-@ scale emigration marked most of the post @-@ WWII period ( particularly during the 1950s and 1980s ) , but beginning in 1987 the economy improved , and the 1990s saw the beginning of substantial economic growth . This period of growth became known as the Celtic Tiger . The Republic 's real GDP grew by an average of 9 @.@ 6 % per annum between 1995 and 1999 , in which year the Republic joined the euro . In 2000 , it was the sixth @-@ richest country in the world in terms of GDP per capita . 
- Social changes also occurred in this time , most markedly with the decline in authority of the Catholic Church . The financial crisis that began in 2008 dramatically ended this period of boom . GDP fell by 3 % in 2008 and by 7 @.@ 1 % in 2009 , the worst year since records began ( although earnings by foreign @-@ owned businesses continued to grow ) . The state has since experienced deep recession , with unemployment , which doubled during 2009 , remaining above 14 % in 2012 . 
- 
- = = = = Northern Ireland = = = = 
- 
- Northern Ireland was created as a division of the United Kingdom by the Government of Ireland Act 1920 and until 1972 it was a self @-@ governing jurisdiction within the United Kingdom with its own parliament and prime minister . Northern Ireland , as part of the United Kingdom , was not neutral during the Second World War and Belfast suffered four bombing raids in 1941 . Conscription was not extended to Northern Ireland and roughly an equal number volunteered from Northern Ireland as volunteered from the south . One , James Joseph <unk> , received the Victoria Cross for <unk> . 
- Although Northern Ireland was largely spared the <unk> of the civil war , in decades that followed partition there were sporadic episodes of inter @-@ communal violence . Nationalists , mainly Roman Catholic , wanted to unite Ireland as an independent republic , whereas unionists , mainly Protestant , wanted Northern Ireland to remain in the United Kingdom . The Protestant and Catholic communities in Northern Ireland voted largely along sectarian lines , meaning that the Government of Northern Ireland ( elected by " first @-@ past @-@ the @-@ post " from 1929 ) was controlled by the Ulster Unionist Party . Over time , the minority Catholic community felt increasingly alienated with further <unk> fuelled by practices such as <unk> and discrimination in housing and employment . 
- In the late 1960s , nationalist grievances were aired publicly in mass civil rights protests , which were often confronted by loyalist counter @-@ protests . The government 's reaction to confrontations was seen to be one @-@ sided and heavy @-@ handed in favour of unionists . Law and order broke down as unrest and inter @-@ communal violence increased . The Northern Ireland government requested the British Army to aid the police , who were exhausted after several nights of serious rioting . In 1969 , the paramilitary Provisional IRA , which favoured the creation of a united Ireland , emerged from a split in the Irish Republican Army and began a campaign against what it called the " British occupation of the six counties " . 
- Other groups , on both the unionist side and the nationalist side , participated in violence and a period known as the Troubles began . Over 3 @,@ 600 deaths resulted over the subsequent three decades of conflict . Owing to the civil unrest during the Troubles , the British government suspended home rule in 1972 and imposed direct rule . There were several unsuccessful attempts to end the Troubles politically , such as the <unk> Agreement of 1973 . In 1998 , following a ceasefire by the Provisional IRA and multi @-@ party talks , the Good Friday Agreement was concluded as a treaty between the British and Irish governments , <unk> the text agreed in the multi @-@ party talks . 
- The substance of the Agreement ( formally referred to as the Belfast Agreement ) was later endorsed by <unk> in both parts of Ireland . The Agreement restored self @-@ government to Northern Ireland on the basis of power @-@ sharing in a regional Executive drawn from the major parties in a new Northern Ireland Assembly , with entrenched <unk> for the two main communities . The Executive is jointly headed by a First Minister and deputy First Minister drawn from the unionist and nationalist parties . Violence had decreased greatly after the Provisional IRA and loyalist <unk> in 1994 and in 2005 the Provisional IRA announced the end of its armed campaign and an independent commission supervised its <unk> and that of other nationalist and unionist paramilitary organisations . 
- The Assembly and power @-@ sharing Executive were suspended several times but were restored again in 2007 . In that year the British government officially ended its military support of the police in Northern Ireland ( Operation Banner ) and began withdrawing troops . On 27 June 2012 , Northern Ireland 's deputy first minister and former IRA commander , Martin McGuinness , shook hands with Queen Elizabeth II in Belfast , symbolising reconciliation between the two sides . 
- 
- = = Politics = = 
- 
- <unk> , the island is divided between the Republic of Ireland , an independent state , and Northern Ireland ( a constituent country of the United Kingdom ) . They share an open border and both are part of the Common Travel Area . 
- Both the Republic of Ireland and the United Kingdom are members of the European Union , and as a consequence there is free movement of people , goods , services and capital across the border . 
- 
- = = = Republic of Ireland = = = 
- 
- The Republic of Ireland is a parliamentary democracy based on the British model , with a written constitution and a popularly elected president who has mostly ceremonial powers . The government is headed by a prime minister , the <unk> , who is appointed by the President on the nomination of the lower house of parliament , the Dáil . Members of the government are chosen from both the Dáil and the upper house of parliament , the <unk> . Its capital is Dublin . 
- The Republic today ranks amongst the wealthiest countries in the world in terms of GDP per capita and in 2012 was ranked the seventh most developed nation in the world by the United Nations ' Human Development Index . A period of rapid economic expansion from 1995 onwards became known as the Celtic Tiger period , was brought to an end in 2008 with an unprecedented financial crisis and an economic depression in 2009 . 
- 
- = = = Northern Ireland = = = 
- 
- Northern Ireland is a part of the United Kingdom with a local executive and assembly which exercise devolved powers . The executive is jointly headed by the first and deputy @-@ first minister , with the <unk> being allocated in proportion with each party 's representation in the assembly . Its capital is Belfast . 
- Ultimately political power is held by the UK government , from which Northern Ireland has gone through intermittent periods of direct rule during which devolved powers have been suspended . Northern Ireland elects 18 of the UK House of Commons ' 650 MPs . The Northern Ireland Secretary is a cabinet @-@ level post in the British government . 
- Along with England , Wales and Scotland , Northern Ireland forms one of the three separate legal jurisdictions of the UK , all of which share the Supreme Court of the United Kingdom as their court of final appeal . 
- 
- = = = All @-@ island institutions = = = 
- 
- As part of the Good Friday Agreement , the British and Irish governments agreed on the creation of all @-@ island institutions and areas of cooperation . 
- The North / South <unk> Council is an institution through which ministers from the Government of Ireland and the Northern Ireland Executive agree all @-@ island policies . At least six of these policy areas must have an associated all @-@ island " implementation bodies " and at least six others must be implemented separately in each jurisdiction . The implementation bodies are : Waterways Ireland , the Food Safety Promotion Board , <unk> , the Special European Union Programmes Body , the North / South Language Body and the Foyle , <unk> and Irish Lights Commission . 
- The British – Irish <unk> Conference provides for co @-@ operation between the Government of Ireland and the Government of the United Kingdom on all matter of mutual interest , especially Northern Ireland . In light of the Republic 's particular interest in the governance of Northern Ireland , " regular and frequent " meetings co @-@ chaired by the <unk> Minister for Foreign Affairs and the UK Secretary of State for Northern Ireland , dealing with non @-@ devolved matters to do with Northern Ireland and non @-@ devolved all @-@ Ireland issues , are required to take place under the establishing treaty . 
- The North / South Inter @-@ Parliamentary Association is a joint parliamentary forum for the island of Ireland . It has no formal powers but operates as a forum for discussing matters of common concern between the respective legislatures . 
- 
- = = Economy = = 
- 
- Despite the two jurisdictions using two distinct currencies ( the euro and pound sterling ) , a growing amount of commercial activity is carried out on an all @-@ Ireland basis . This has been facilitated by the two jurisdictions ' shared membership of the European Union , and there have been calls from members of the business community and <unk> for the creation of an " all @-@ Ireland economy " to take advantage of economies of scale and boost competitiveness . 
- There are two multi @-@ city regions on the island of Ireland : 
- Dublin @-@ Belfast corridor - 3 @.@ 3 m 
- Cork @-@ Limerick @-@ Galway corridor - 1 m 
- Below is a comparison of the Regional GDP on the island of Ireland . 
- The BMW region of the Republic of Ireland ( consisting of <unk> , Counties <unk> , <unk> , <unk> , Longford , Donegal , Monaghan , <unk> , <unk> ) 
- The S & E region of the Republic of Ireland ( consisting of Munster , Counties Dublin , <unk> , Meath , Kildare , Kilkenny , <unk> , Wexford ) . 
- 
- = = = Energy = = = 
- 
- Ireland has an ancient industry based on peat ( known locally as " turf " ) as a source of energy for home fires . A form of <unk> energy , this source of heat is still widely used in rural areas . However , due to the ecological importance of <unk> in storing carbon and their rarity , the EU is attempting to protect this habitat by fining Ireland if they are dug up . In cities , heat is generally supplied by heating oil , although some urban suppliers distribute " <unk> of turf " as " <unk> fuel " . 
- An area in which the island operates as a single market is electricity . For much of their existence electricity networks in the Republic of Ireland and Northern Ireland were entirely separate . Both networks were designed and constructed independently post partition . However , as a result of changes over recent years they are now connected with three <unk> and also connected through Great Britain to mainland Europe . The situation in Northern Ireland is complicated by the issue of private companies not supplying Northern Ireland Electricity ( <unk> ) with enough power . In the Republic of Ireland , the <unk> has failed to <unk> its power stations and the availability of power plants has recently averaged only 66 % , one of the worst such rates in Western Europe . <unk> is building a <unk> transmission line between Ireland and Great Britain with a capacity of 500 MW , about 10 % of Ireland 's peak demand . 
- As with electricity , the natural gas distribution network is also now all @-@ island , with a pipeline linking <unk> , County Meath , and <unk> , County Antrim completed in 2007 . Most of Ireland 's gas comes through <unk> between <unk> in Scotland and <unk> , County Antrim and <unk> , County Dublin . A decreasing supply is coming from the <unk> gas field off the County Cork coast and the <unk> Gas Field off the coast of County Mayo has yet to come on @-@ line . The County Mayo field is facing some localised opposition over a controversial decision to refine the gas onshore . 
- The Republic of Ireland has shown a strong commitment to renewable energy , ranking as one of the top 10 markets for <unk> investment in the 2014 Global Green Economy Index . Research and development in Ireland in renewable energy such as wind power has increased since 2004 . Large wind farms are being constructed in coastal counties such as Cork , Donegal , Mayo and Antrim . The construction of wind farms has in some cases been delayed by opposition from local communities , some of whom overall consider the wind turbines to be <unk> . The Republic of Ireland is also hindered by an ageing network that was not designed to handle the varying availability of power that comes from wind farms . The <unk> 's <unk> Hill facility is the only power @-@ storage facility in the state . 
- 
- = = Geography = = 
- 
- The island of Ireland is located in the north @-@ west of Europe , between latitudes 51 ° and 56 ° N , and <unk> 11 ° and 5 ° W. It is separated from the neighbouring island of Great Britain by the Irish Sea and the North Channel , which has a width of 23 kilometres ( 14 mi ) at its <unk> point . To the west is the northern Atlantic Ocean and to the south is the Celtic Sea , which lies between Ireland and Brittany , in France . Ireland has a total area of 84 @,@ <unk> km2 ( 32 @,@ 595 sq mi ) . Ireland and Great Britain , together with many nearby smaller islands , are known collectively as the British Isles . As the term British Isles is controversial in relation to Ireland , the alternate term Britain and Ireland is often used as a neutral term for the islands . 
- A ring of coastal mountains surround low plains at the centre of the island . The highest of these is <unk> ( Irish : <unk> <unk> ) in County Kerry , which rises to 1 @,@ <unk> m ( 3 @,@ 406 ft ) above sea level . The most arable land lies in the province of Leinster . Western areas can be mountainous and rocky with green panoramic <unk> . The River Shannon , the island 's longest river at 386 km ( 240 mi ) long , rises in County <unk> in the north west and flows 113 kilometres ( 70 mi ) to Limerick city in the mid west . 
- The island 's lush vegetation , a product of its mild climate and frequent rainfall , earns it the sobriquet the Emerald Isle . Overall , Ireland has a mild but changeable oceanic climate with few extremes . The climate is typically insular and is temperate avoiding the extremes in temperature of many other areas in the world at similar latitudes . This is a result of the <unk> moist winds which ordinarily prevail from the South @-@ Western Atlantic . 
- Precipitation falls throughout the year but is light overall , particularly in the east . The west tends to be wetter on average and prone to Atlantic storms , especially in the late autumn and winter months . These occasionally bring destructive winds and higher total rainfall to these areas , as well as sometimes snow and hail . The regions of north County Galway and east County Mayo have the highest incidents of recorded lightning annually for the island , with lightning occurring approximately five to ten days per year in these areas . Munster , in the south , records the least snow whereas Ulster , in the north , records the most . 
- Inland areas are warmer in summer and colder in winter . Usually around 40 days of the year are below freezing 0 ° C ( 32 ° F ) at inland weather stations , compared to 10 days at coastal stations . Ireland is sometimes affected by heat waves , most recently in 1995 , 2003 , 2006 and 2013 . In common with the rest of Europe , Ireland experienced unusually cold weather during the winter of 2009 / 10 . Temperatures fell as low as − 17 @.@ 2 ° C ( 1 ° F ) in County Mayo on 20 December and up to a metre ( 3 ft ) of snow fell in mountainous areas . 
- The island consists of varied geological provinces . In the far west , around County Galway and County Donegal , is a medium to high grade <unk> and igneous complex of <unk> affinity , similar to the Scottish Highlands . Across southeast Ulster and extending southwest to Longford and south to <unk> is a province of Ordovician and <unk> rocks , with similarities to the Southern <unk> province of Scotland . Further south , along the County Wexford coastline , is an area of granite <unk> into more Ordovician and <unk> rocks , like that found in Wales . 
- In the southwest , around <unk> Bay and the mountains of <unk> 's <unk> , is an area of substantially deformed , but only lightly <unk> , Devonian @-@ aged rocks . This partial ring of " hard rock " geology is covered by a blanket of <unk> limestone over the centre of the country , giving rise to a comparatively fertile and lush landscape . The west @-@ coast district of the <unk> around <unk> has well @-@ developed <unk> features . Significant <unk> lead @-@ zinc <unk> is found in the <unk> around <unk> and <unk> . 
- <unk> exploration is ongoing following the first major find at the <unk> Head gas field off Cork in the mid @-@ 1970s . In 1999 , economically significant finds of natural gas were made in the <unk> Gas Field off the County Mayo coast . This has increased activity off the west coast in parallel with the " West of Shetland " step @-@ out development from the North Sea <unk> province . The <unk> oil field , estimated to contain over 28 million barrels ( 4 @,@ 500 @,@ 000 m3 ) of oil , is another recent discovery . 
- <unk> 
- 
- = = = Places of interest = = = 
- 
- There are three World Heritage Sites on the island : the <unk> na <unk> , <unk> Michael and the Giant 's Causeway . A number of other places are on the tentative list , for example the <unk> , the <unk> Fields and Mount Stewart . 
- Some of the most visited sites in Ireland include <unk> Castle , the Rock of <unk> , the <unk> of <unk> , Holy Cross Abbey and <unk> Castle . Historically important monastic sites include <unk> and Clonmacnoise , which are maintained as national monuments in the Republic of Ireland . 
- Dublin is the most heavily <unk> region and home to several of the most popular attractions such as the Guinness <unk> and Book of Kells . The west and south west , which includes the Lakes of Killarney and the <unk> peninsula in County Kerry and <unk> and the <unk> Islands in County Galway , are also popular tourist destinations . 
- <unk> Island lies off the coast of County Mayo and is Ireland 's largest island . It is a popular tourist destination for surfing and contains 5 Blue Flag beaches and <unk> one of the worlds highest sea cliffs . <unk> homes , built during the 17th , 18th and 19th centuries in <unk> , <unk> and neo @-@ Gothic styles , such as , Castle Ward , <unk> House , <unk> House , <unk> Castle are also of interest to tourists . Some have been converted into hotels , such as <unk> Castle , Castle Leslie and <unk> Castle . 
- World Heritage Sites 
- 
- = = Flora and fauna = = 
- 
- Because Ireland became isolated from mainland Europe by rising sea levels before the last ice age had completely finished , it has fewer land animal and plant species than Great Britain , which separated later , or mainland Europe . There are 55 mammal species in Ireland and of them only 26 land mammal species are considered native to Ireland . Some species , such as , the red fox , <unk> and <unk> , are very common , whereas others , like the Irish hare , red deer and pine <unk> are less so . <unk> wildlife , such as species of sea turtle , shark , seal , whale , and <unk> , are common off the coast . About 400 species of birds have been recorded in Ireland . Many of these are migratory , including the barn swallow . 
- Several different habitat types are found in Ireland , including farmland , open woodland , temperate broadleaf and mixed forests , conifer plantations , peat <unk> and a variety of coastal habitats . However , agriculture drives current land use patterns in Ireland , limiting natural habitat preserves , particularly for larger wild mammals with greater territorial needs . With no large apex predators in Ireland other than humans and dogs , such populations of animals as semi @-@ wild deer that cannot be controlled by smaller predators , such as the fox , are controlled by annual culling . 
- There are no snakes in Ireland and only one species of reptile ( the common lizard ) is native to the island . Extinct species include the Irish elk , the great <unk> and the wolf . Some previously extinct birds , such as the golden eagle , been reintroduced in about the year 2000 after decades of <unk> . Until medieval times Ireland was heavily forested with oak , pine and birch . <unk> today cover about 12 @.@ 6 % of Ireland , of which 4 @,@ 450 km ² or one million acres is owned by <unk> , the Republic 's forestry service . 
- As of 2012 the Republic is one of the least forested countries in Europe . Much of the land is now covered with pasture and there are many species of wild @-@ flower . <unk> ( <unk> europaeus ) , a wild <unk> , is commonly found growing in the <unk> and <unk> are plentiful in the more moist regions , especially in the western parts . It is home to hundreds of plant species , some of them unique to the island , and has been " invaded " by some grasses , such as <unk> <unk> . 
- The algal and seaweed flora is that of the cold @-@ temperate variety . The total number of species is <unk> and is distributed as follows : 
- 264 <unk> ( red algae ) 
- 152 <unk> ( brown algae including <unk> ) 
- 114 <unk> ( green algae ) 
- 31 <unk> ( Blue @-@ green algae ) 
- <unk> species include : 
- <unk> <unk> ( <unk> ) <unk> & Guiry 
- <unk> <unk> <unk> & Guiry 
- <unk> <unk> <unk> & Guiry 
- <unk> <unk> Rico & Guiry 
- <unk> <unk> <unk> & <unk> ex <unk> . 
- The island has been invaded by some algae , some of which are now well established . For example : 
- <unk> <unk> Harvey , which originated in Australia and was first recorded by M. De Valera in 1939 
- <unk> <unk> <unk> , which is now locally abundant and first recorded in the 1930s 
- <unk> <unk> ( <unk> ) <unk> , now well established in a number of localities on the south , west , and north @-@ east coasts 
- <unk> fragile ssp. fragile ( formerly reported as ssp. <unk> ) , now well established . 
- <unk> fragile ssp. <unk> has been established to be native , although for many years it was regarded as an alien species . 
- Because of its mild climate , many species , including sub @-@ tropical species such as palm trees , are grown in Ireland . <unk> , Ireland belongs to the Atlantic European province of the <unk> Region within the <unk> Kingdom . The island itself can be subdivided into two <unk> : the Celtic broadleaf forests and North Atlantic moist mixed forests . 
- 
- = = = Impact of agriculture = = = 
- 
- The long history of agricultural production , coupled with modern intensive agricultural methods such as <unk> and fertiliser use and runoff from <unk> into streams , rivers and lakes , impact the natural fresh @-@ water ecosystems and have placed pressure on biodiversity in Ireland . 
- A land of green fields for crop cultivation and cattle rearing limits the space available for the establishment of native wild species . <unk> , however , traditionally used for maintaining and <unk> land boundaries , act as a refuge for native wild flora . This ecosystem stretches across the countryside and acts as a network of connections to preserve remnants of the ecosystem that once covered the island . <unk> under the Common Agricultural Policy , which supported agricultural practices that preserved <unk> environments , are undergoing reforms . The Common Agricultural Policy had in the past <unk> potentially destructive agricultural practices , for example by emphasising production without placing limits on indiscriminate use of <unk> and <unk> ; but reforms have gradually <unk> subsidies from production levels and introduced environmental and other requirements . 
- Forest covers about 12 @.@ 6 % of the country , most of it designated for commercial production . Forested areas typically consist of <unk> plantations of non @-@ native species , which may result in habitats that are not suitable for supporting native species of invertebrates . Remnants of native forest can be found scattered around the island , in particular in the Killarney National Park . Natural areas require fencing to prevent over @-@ grazing by deer and sheep that <unk> over <unk> areas . <unk> in this manner is one of the main factors preventing the natural regeneration of forests across many regions of the country . 
- 
- = = Demographics = = 
- 
- People have lived in Ireland for over 9 @,@ 000 years . The different eras are termed <unk> , <unk> , Bronze Age , and Iron Age . 
- Early historical and genealogical records note the existence of major groups such as the <unk> , <unk> <unk> , Dál Riata , <unk> , <unk> , <unk> , <unk> , <unk> , Ulaid . <unk> later major groups included the <unk> , <unk> , <unk> . 
- Smaller groups included the <unk> ( see <unk> ) , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , Fir <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , Uí Maine , Uí <unk> . Many survived into late medieval times , others vanished as they became politically unimportant . 
- Over the past 1200 years , Vikings , <unk> , Welsh , <unk> , Scots , English , Africans , Eastern Europeans and South Americans have all added to the population and have had significant influences on Irish culture . 
- Ireland 's largest religious group is Christianity . The largest denomination is Roman Catholicism representing over 73 % for the island ( and about 87 % of the Republic of Ireland ) . Most of the rest of the population <unk> to one of the various Protestant denominations ( about 48 % of Northern Ireland ) . The largest is the Anglican Church of Ireland . The Muslim community is growing in Ireland , mostly through increased immigration , with a 50 % increase in the republic between the 2006 and 2011 census . The island has a small Jewish community . About 4 % of the Republic 's population and about 14 % of the Northern Ireland population describe themselves as of no religion . In a 2010 survey conducted on behalf of the Irish Times , 32 % of <unk> said they went to a religious service more than once a week . 
- The population of Ireland rose rapidly from the 16th century until the mid @-@ 19th century , but a devastating famine in the 1840s caused one million deaths and forced over one million more to <unk> in its immediate wake . Over the following century the population was reduced by over half , at a time when the general trend in European countries was for populations to rise by an average of three @-@ fold . 
- 
- = = = Divisions and settlements = = = 
- 
- Traditionally , Ireland is subdivided into four provinces : <unk> ( west ) , Leinster ( east ) , Munster ( south ) , and Ulster ( north ) . In a system that developed between the 13th and 17th centuries , Ireland has 32 traditional counties . Twenty @-@ six of these counties are in the Republic of Ireland and six are in Northern Ireland . The six counties that constitute Northern Ireland are all in the province of Ulster ( which has nine counties in total ) . As such , Ulster is often used as a synonym for Northern Ireland , although the two are not <unk> . 
- In the Republic of Ireland , counties form the basis of the system of local government . Counties Dublin , Cork , Limerick , Galway , Waterford and <unk> have been broken up into smaller administrative areas . However , they are still treated as counties for cultural and some official purposes , for example postal addresses and by the Ordnance Survey Ireland . Counties in Northern Ireland are no longer used for local governmental purposes , but , as in the Republic , their traditional boundaries are still used for informal purposes such as sports leagues and in cultural or tourism contexts . 
- City status in Ireland is decided by legislative or royal charter . Dublin , with over 1 million residents in the Greater Dublin Area , is the largest city on the island . Belfast , with <unk> @,@ <unk> residents , is the largest city in Northern Ireland . City status does not directly <unk> with population size . For example , Armagh , with 14 @,@ 590 is the seat of the Church of Ireland and the Roman Catholic <unk> of All Ireland and was re @-@ granted city status by Queen Elizabeth II in 1994 ( having lost that status in local government reforms of 1840 ) . In the Republic of Ireland , Kilkenny , seat of the Butler dynasty , while no longer a city for administrative purposes ( since the 2001 Local Government Act ) , is entitled by law to continue to use the description . 
- 
- = = = Migration = = = 
- 
- The population of Ireland collapsed dramatically during the second half of the 19th century . A population of over 8 million in 1841 was reduced to slightly more than 4 million by 1921 . In part , the fall in population was due to death from the Great Famine of 1845 to 1852 , which took about 1 million lives . However , by far the greater cause of population decline was the dire economic state of the country which led to an entrenched culture of emigration lasting until the 21st century . 
- Emigration from Ireland in the 19th century contributed to the populations of England , the United States , Canada and Australia , where a large Irish diaspora lives . As of 2006 , 4 @.@ 3 million Canadians , or 14 % of the population , are of Irish descent . As of 2013 , a total of 34 @.@ 5 million Americans claim Irish ancestry . 
- With growing prosperity since the last decade of the 20th century , Ireland became a destination for immigrants . Since the European Union expanded to include Poland in 2004 , Polish people have made up the largest number of immigrants ( over 150 @,@ 000 ) from Central Europe . There has also been significant immigration from Lithuania , the Czech Republic and Latvia . 
- The Republic of Ireland in particular has seen large @-@ scale immigration , with 420 @,@ 000 foreign <unk> as of 2006 , about 10 % of the population . A quarter of births ( 24 percent ) in 2009 were to mothers born outside Ireland . Chinese and <unk> , along with people from other African countries , have accounted for a large proportion of the non – European Union migrants to Ireland . Up to 50 @,@ 000 eastern and central European migrant workers left Ireland in response to the Irish financial crisis . 
- 
- = = = Languages = = = 
- 
- Two main languages are spoken in Ireland : Irish and English . Both languages have widely contributed to literature . Irish , now a minority but official language of the Republic of Ireland , was the vernacular of the Irish people for over two thousand years and was probably introduced by some sort of proto @-@ Gaelic migration during the Iron Age , possibly earlier . It began to be written down after Christianisation in the 5th century and spread to Scotland and the Isle of Man where it evolved into the Scottish Gaelic and <unk> languages respectively . 
- The Irish language has a vast treasure of written texts from many centuries , and is divided by linguists into Old Irish from the 6th to 10th century , Middle Irish from the 10th to 13th century , Early Modern Irish until the 17th century , and the Modern Irish spoken today . It remained the dominant language of Ireland for most of those periods , having influences from Latin , Old Norse , French and English . It declined under British rule but remained the majority tongue until the early 19th century , and since then has been a minority language , although revival efforts are continuing in both the Republic of Ireland and Northern Ireland . 
- Gaeltacht or Irish @-@ speaking areas are still seeing a decline in the language . The main Gaeltacht areas are down the west of the country , in Donegal , Mayo , Galway and Kerry with smaller Gaeltacht areas near <unk> in Waterford , <unk> , in Meath , and the Shaw 's Road in Belfast . Irish language is a compulsory subject in the state education system in the Republic , and the <unk> movement has seen many Irish medium schools established in both jurisdictions . 
- English was first introduced to Ireland in the Norman invasion . It was spoken by a few peasants and merchants brought over from England , and was largely replaced by Irish before the Tudor Conquest of Ireland . It was introduced as the official language with the Tudor and <unk> conquests . The Ulster plantations gave it a permanent foothold in Ulster , and it remained the official and upper @-@ class language elsewhere , the Irish @-@ speaking chieftains and nobility having been deposed . Language shift during the 19th century replaced Irish with English as the first language for a vast majority of the population . 
- Less than 10 % of the population of the Republic of Ireland today speak Irish regularly outside of the education system and 38 % of those over 15 years are classified as " Irish speakers " . In Northern Ireland , English is the de facto official language , but official recognition is afforded to Irish , including specific protective measures under Part III of the European Charter for Regional or <unk> Languages . A lesser status ( including recognition under Part II of the Charter ) is given to Ulster Scots dialects , which are spoken by roughly 2 % of Northern Ireland residents , and also spoken by some in the Republic of Ireland . Since the 1960s with the increase in immigration , many more languages have been introduced , particularly deriving from Asia and Eastern Europe . 
- 
- = = Culture = = 
- 
- Ireland 's culture comprises elements of the culture of ancient peoples , later immigrant and broadcast cultural influences ( chiefly Gaelic culture , <unk> , <unk> and aspects of broader European culture ) . In broad terms , Ireland is regarded as one of the Celtic nations of Europe , alongside Scotland , Wales , Cornwall , Isle of Man and Brittany . This combination of cultural influences is visible in the intricate designs termed Irish interlace or Celtic <unk> . These can be seen in the ornamentation of medieval religious and secular works . The style is still popular today in jewellery and graphic art , as is the distinctive style of traditional Irish music and dance , and has become indicative of modern " Celtic " culture in general . 
- Religion has played a significant role in the cultural life of the island since ancient times ( and since the 17th century plantations , has been the focus of political identity and divisions on the island ) . Ireland 's pre @-@ Christian heritage fused with the Celtic Church following the missions of Saint Patrick in the 5th century . The Hiberno @-@ Scottish missions , begun by the Irish monk Saint Columba , spread the Irish vision of Christianity to pagan England and the Frankish Empire . These missions brought written language to an illiterate population of Europe during the Dark Ages that followed the fall of Rome , earning Ireland the sobriquet , " the island of saints and scholars " . 
- Since the 20th century the Irish <unk> worldwide have become , especially those with a full range of cultural and <unk> offerings , outposts of Irish culture . 
- The Republic of Ireland 's national theatre is the Abbey Theatre , which was founded in 1904 , and the national Irish @-@ language theatre is An <unk> , which was established in 1928 in Galway . <unk> such as <unk> O <unk> , Brian <unk> , Sebastian Barry , <unk> McPherson and Billy Roche are internationally renowned . 
- 
- = = = Arts = = = 
- 
- Ireland has made a large contribution to world literature in all its branches , particularly in the English language . Poetry in Irish is among the oldest vernacular poetry in Europe , with the earliest examples dating from the 6th century . In English , Jonathan Swift , still often called the foremost <unk> in the English language , was very popular in his day for works such as <unk> 's <unk> and A Modest <unk> , and Oscar Wilde is known most for his often quoted witticisms . 
- In the 20th century , Ireland produced four winners of the Nobel Prize for Literature : George Bernard Shaw , William Butler Yeats , Samuel <unk> and Seamus Heaney . Although not a Nobel Prize winner , James Joyce is widely considered to be one of the most significant writers of the 20th century . Joyce 's 1922 novel Ulysses is considered one of the most important works of <unk> literature and his life is celebrated annually on 16 June in Dublin as " <unk> " . Modern Irish literature is often connected with its rural heritage through writers such as John <unk> and poets such as Seamus Heaney . 
- Music has been in evidence in Ireland since prehistoric times . Although in the early Middle Ages the church was " quite unlike its counterpart in continental Europe " , there was considerable interchange between monastic settlements in Ireland and the rest of Europe that contributed to what is known as Gregorian chant . Outside religious establishments , musical genres in early Gaelic Ireland are referred to as a triad of weeping music ( <unk> ) , laughing music ( <unk> ) and sleeping music ( <unk> ) . Vocal and instrumental music ( e.g. for the <unk> , pipes , and various string instruments ) was transmitted orally , but the Irish <unk> , in particular , was of such significance that it became Ireland 's national symbol . Classical music following European models first developed in urban areas , in establishments of Anglo @-@ Irish rule such as Dublin Castle , St Patrick 's Cathedral and Christ Church as well as the country houses of the Anglo @-@ Irish ascendancy , with the first performance of Handel 's <unk> ( 1742 ) being among the highlights of the <unk> era . In the 19th century , public concerts provided access to classical music to all classes of society . Yet , for political and financial reasons Ireland has been too small to provide a living to many musicians , so the names of the better @-@ known Irish composers of this time belong to emigrants . 
- Irish traditional music and dance has seen a surge in popularity and global coverage since the 1960s . In the middle years of the 20th century , as Irish society was <unk> , traditional music had fallen out of favour , especially in urban areas . However during the 1960s , there was a revival of interest in Irish traditional music led by groups such as The Dubliners , The <unk> , The Wolfe Tones , the Clancy Brothers , <unk> 's Men and individuals like <unk> <unk> <unk> and Christy Moore . Groups and musicians including <unk> , Van Morrison and <unk> <unk> incorporated elements of Irish traditional music into contemporary rock music and , during the 1970s and 1980s , the distinction between traditional and rock musicians became blurred , with many individuals regularly crossing over between these styles of playing . This trend can be seen more recently in the work of artists like <unk> , The Saw Doctors , The <unk> , <unk> O 'Connor , <unk> , The <unk> and The <unk> among others . Since then there have been a number of stylistic <unk> including folk metal and others , while some contemporary music groups stick closer to a " traditional " sound . 
- The earliest known Irish graphic art and sculpture are Neolithic carvings found at sites such as <unk> and is traced through Bronze age artefacts and the religious carvings and illuminated manuscripts of the medieval period . During the course of the 19th and 20th centuries , a strong tradition of painting emerged , including such figures as John Butler Yeats , William <unk> , Jack Yeats and Louis le <unk> . Contemporary Irish visual artists of note include Sean Scully , Kevin <unk> , and Alice <unk> . 
- 
- = = = Science = = = 
- 
- The Irish philosopher and theologian Johannes Scotus <unk> was considered one of the leading intellectuals of his early Middle Ages . Sir Ernest Henry <unk> , an Irish explorer , was one of the principal figures of Antarctic exploration . He , along with his expedition , made the first ascent of Mount <unk> and the discovery of the approximate location of the South Magnetic Pole . Robert Boyle was a 17th @-@ century natural philosopher , chemist , physicist , inventor and early gentleman scientist . He is largely regarded one of the founders of modern chemistry and is best known for the formulation of Boyle 's law . 
- 19th century physicist , John Tyndall , discovered the Tyndall effect . Father Nicholas Joseph <unk> , Professor of Natural Philosophy in <unk> College , is best known for his invention of the induction coil , <unk> and he discovered an early method of <unk> in the 19th century . 
- Other notable Irish physicists include Ernest Walton , winner of the 1951 Nobel Prize in Physics . With Sir John Douglas <unk> , he was the first to split the nucleus of the atom by artificial means and made contributions to the development of a new theory of wave equation . William Thomson , or Lord Kelvin , is the person whom the absolute temperature unit , the Kelvin , is named after . Sir Joseph <unk> , a physicist and mathematician , made innovations in the understanding of electricity , dynamics , <unk> and the electron theory of matter . His most influential work was <unk> and <unk> , a book on theoretical physics published in 1900 . 
- George Johnstone <unk> introduced the term electron in 1891 . John Stewart Bell was the originator of Bell 's <unk> and a paper concerning the discovery of the Bell @-@ <unk> @-@ <unk> anomaly and was nominated for a Nobel prize . Notable mathematicians include Sir William Rowan Hamilton , famous for work in classical mechanics and the invention of <unk> . Francis <unk> Edgeworth 's contribution of the Edgeworth Box remains influential in neo @-@ classical <unk> theory to this day ; while Richard <unk> inspired Adam Smith , among others . John B. <unk> was a specialist in number theory and discovered a 2000 @-@ digit prime number in 1999 and a record composite <unk> number in 2003 . John <unk> <unk> made progress in different fields of science , including mechanics and <unk> methods in general relativity . He had mathematician John Nash as one of his students . 
- Ireland has nine universities , seven in the Republic of Ireland and two in Northern Ireland , including Trinity College , Dublin and the University College Dublin , as well as numerous third @-@ level colleges and institutes and a branch of the Open University , the Open University in Ireland . 
- 
- = = = Sports = = = 
- 
- The island of Ireland fields a single international team in most sports . One notable exception to this is association football , although both associations continued to field international teams under the name " Ireland " until the 1950s . An all @-@ Ireland club competition for soccer , the <unk> Cup , was created in 2005 . 
- Gaelic football is the most popular sport in Ireland in terms of match attendance and community involvement , with about 2 @,@ 600 clubs on the island . In 2003 it represented 34 % of total sports <unk> at events in Ireland and abroad , followed by hurling at 23 % , soccer at 16 % and rugby at 8 % and the All @-@ Ireland Football Final is the most watched event in the sporting calendar . Soccer is the most widely played team game on the island , and the most popular in Northern Ireland . <unk> , golf , aerobics , soccer , cycling , Gaelic football and <unk> / <unk> are the sporting activities with the highest levels of playing participation . The sport is also the most notable exception where the Republic of Ireland and Northern Ireland field separate international teams . Northern Ireland has produced two World <unk> Champions . 
- Many other sports are also played and followed , including basketball , boxing , cricket , fishing , greyhound racing , <unk> , hockey , horse racing , motor sport , show jumping and tennis . 
- 
- = = = = Field sports = = = = 
- 
- Gaelic football , hurling and <unk> are the best @-@ known of the Irish traditional sports , collectively known as Gaelic games . Gaelic games are governed by the Gaelic Athletic Association ( GAA ) , with the exception of ladies ' Gaelic football and <unk> ( women 's variant of hurling ) , which are governed by separate organisations . The headquarters of the GAA ( and the main stadium ) is located at the 82 @,@ 500 capacity <unk> Park in north Dublin . Many major GAA games are played there , including the semi @-@ finals and finals of the All @-@ Ireland Senior Football Championship and All @-@ Ireland Senior <unk> Championship . During the redevelopment of the Lansdowne Road stadium in 2007 – 10 , international rugby and soccer were played there . All GAA players , even at the highest level , are amateurs , receiving no wages , although they are permitted to receive a limited amount of sport @-@ related income from commercial sponsorship . 
- The Irish Football Association ( IFA ) was originally the governing body for soccer across the island . The game has been played in an organised fashion in Ireland since the 1870s , with <unk> F.C. in Belfast being Ireland 's oldest club . It was most popular , especially in its first decades , around Belfast and in Ulster . However , some clubs based outside Belfast thought that the IFA largely favoured Ulster @-@ based clubs in such matters as selection for the national team . In 1921 , following an incident in which , despite an earlier promise , the IFA moved an Irish Cup semi @-@ final replay from Dublin to Belfast , Dublin @-@ based clubs broke away to form the Football Association of the Irish Free State . Today the southern association is known as the Football Association of Ireland ( FAI ) . Despite being initially <unk> by the Home Nations ' associations , the FAI was recognised by FIFA in 1923 and organised its first international fixture in 1926 ( against Italy ) . However , both the IFA and FAI continued to select their teams from the whole of Ireland , with some players earning international caps for matches with both teams . Both also referred to their respective teams as Ireland . 
- In 1950 , FIFA directed the associations only to select players from within their respective territories and , in 1953 , directed that the FAI 's team be known only as " Republic of Ireland " and that the IFA 's team be known as " Northern Ireland " ( with certain exceptions ) . Northern Ireland qualified for the World Cup finals in 1958 ( reaching the quarter @-@ finals ) , 1982 and 1986 . The Republic qualified for the World Cup finals in 1990 ( reaching the quarter @-@ finals ) , 1994 , 2002 and the European Championships in 1988 and 2012 . Across Ireland , there is significant interest in the English and , to a lesser extent , Scottish soccer leagues . 
- Unlike soccer , Ireland continues to field a single national rugby team and a single association , the Irish Rugby Football Union ( <unk> ) , governs the sport across the island . The Irish rugby team have played in every Rugby World Cup , making the quarter @-@ finals in four of them . Ireland also hosted games during the 1991 and the 1999 Rugby World Cups ( including a quarter @-@ final ) . There are four professional Irish teams ; all four play in the <unk> League ( now called the <unk> <unk> ) and at least three compete for the <unk> Cup . Irish rugby has become increasingly competitive at both the international and provincial levels since the sport went professional in 1994 . During that time , Ulster ( 1999 ) , Munster ( 2006 and 2008 ) and Leinster ( 2009 , 2011 and 2012 ) have won the <unk> Cup . In addition to this , the Irish International side has had increased success in the Six Nations Championship against the other European elite sides . This success , including Triple Crowns in 2004 , 2006 and 2007 , culminated with a clean sweep of victories , known as a Grand Slam , in 2009 . 
- 
- = = = = Other sports = = = = 
- 
- Horse racing and greyhound racing are both popular in Ireland . There are frequent horse race meetings and greyhound stadiums are well @-@ attended . The island is noted for the breeding and training of race horses and is also a large exporter of racing dogs . The horse racing sector is largely concentrated in the County Kildare . 
- Irish athletics has seen a heightened success rate since the year 2000 , with Sonia O <unk> winning two medals at 5 @,@ 000 metres on the track ; gold at the 1995 World Championships and silver at the 2000 Sydney Olympics . Gillian O <unk> won silver in the <unk> walk at the 2003 World Championships , while sprint <unk> <unk> O 'Rourke won gold at the 2006 World Indoor Championship in Moscow . Olive <unk> won a silver medal in the <unk> walk in the World Athletics Championships in Berlin in 2009 . 
- Ireland has won more medals in boxing than in any other Olympic sport . Boxing is governed by the Irish Amateur Boxing Association . Michael <unk> won a gold medal and Wayne <unk> won a silver medal in the Barcelona Olympic Games and in 2008 Kenneth <unk> won a silver medal in the Beijing Games . Paddy Barnes secured bronze in those games and gold in the 2010 European Amateur Boxing Championships ( where Ireland came 2nd in the overall medal table ) and 2010 Commonwealth Games . Katie Taylor has won gold in every European and World championship since 2005 . In August 2012 at the Olympic Games in London Katie Taylor created history by becoming the first Irish woman to win a gold medal in boxing in the 60 kg lightweight . 
- Golf is very popular and golf tourism is a major industry attracting more than 240 @,@ 000 <unk> visitors annually . The 2006 Ryder Cup was held at The K Club in County Kildare . <unk> Harrington became the first <unk> since Fred Daly in 1947 to win the British Open at <unk> in July 2007 . He successfully defended his title in July 2008 before going on to win the <unk> Championship in August . Harrington became the first European to win the <unk> Championship in 78 years and was the first winner from Ireland . Three <unk> from Northern Ireland have been particularly successful . In 2010 , Graeme <unk> became the first Irish golfer to win the U.S. Open , and the first European to win that tournament since 1970 . Rory <unk> , at the age of 22 , won the 2011 U.S. Open , while Darren Clarke 's latest victory was the 2011 Open Championship at Royal St. George 's . In August 2012 , <unk> won his 2nd major championship by winning the <unk> Championship by a record margin of 8 shots . 
- 
- = = = = Recreation = = = = 
- 
- The west coast of Ireland , <unk> and Donegal Bay in particular , have popular surfing beaches , being fully exposed to the Atlantic Ocean . Donegal Bay is shaped like a funnel and catches west / south @-@ west Atlantic winds , creating good surf , especially in winter . Since just before the year 2010 , <unk> has hosted European championship surfing . <unk> diving is increasingly popular in Ireland with clear waters and large populations of sea life , particularly along the western seaboard . There are also many <unk> along the coast of Ireland , with some of the best wreck dives being in <unk> Head and off the County Cork coast . 
- With thousands of lakes , over 14 @,@ 000 kilometres ( 8 @,@ 700 mi ) of fish bearing rivers and over 3 @,@ 700 kilometres ( 2 @,@ 300 mi ) of coastline , Ireland is a popular angling destination . The temperate Irish climate is suited to sport angling . While salmon and trout fishing remain popular with anglers , salmon fishing in particular received a boost in 2006 with the closing of the salmon <unk> fishery . <unk> fishing continues to increase its profile . Sea angling is developed with many beaches mapped and <unk> , and the range of sea angling species is around 80 . 
- 
- = = = Food and drink = = = 
- 
- Food and cuisine in Ireland takes its influence from the crops grown and animals farmed in the island 's temperate climate and from the social and political circumstances of Irish history . For example , whilst from the Middle Ages until the arrival of the potato in the 16th century the dominant feature of the Irish economy was the herding of cattle , the number of cattle a person owned was equated to their social standing . Thus herders would avoid <unk> a milk @-@ producing cow . 
- For this reason , pork and white meat were more common than beef and thick fatty strips of salted <unk> ( or <unk> ) and the eating of salted butter ( i.e. a dairy product rather than beef itself ) have been a central feature of the diet in Ireland since the Middle Ages . The practice of bleeding cattle and mixing the blood with milk and butter ( not unlike the practice of the Maasai ) was common and black pudding , made from blood , grain ( usually barley ) and <unk> , remains a breakfast staple in Ireland . All of these influences can be seen today in the phenomenon of the " breakfast roll " . 
- The introduction of the potato in the second half of the 16th century heavily influenced cuisine thereafter . Great poverty encouraged a subsistence approach to food and by the mid @-@ 19th century the vast majority of the population <unk> with a diet of potatoes and milk . A typical family , consisting of a man , a woman and four children , would eat 18 stone ( 110 kg ) of potatoes a week . Consequently , dishes that are considered as national dishes represent a fundamental <unk> to cooking , such as the Irish <unk> , <unk> and cabbage , <unk> , a type of potato <unk> , or <unk> , a dish of <unk> potatoes and <unk> or cabbage . 
- Since the last quarter of the 20th century , with a re @-@ emergence of wealth in Ireland , a " New Irish <unk> " based on traditional ingredients incorporating international influences has emerged . This cuisine is based on fresh vegetables , fish ( especially salmon , trout , <unk> , <unk> and other <unk> ) , as well as traditional <unk> <unk> and the wide range of hand @-@ made <unk> that are now being produced across the country . The potato remains however a fundamental feature of this cuisine and the Irish remain the highest per capita consumers of potatoes in Europe . An example of this new cuisine is " Dublin <unk> " : lobster cooked in whiskey and cream . Traditional regional foods can be found throughout the country , for example <unk> in Dublin or <unk> in Cork , both a type of sausage , or <unk> , a <unk> white bread particular to Waterford . 
- Ireland once dominated the world 's market for whiskey , producing 90 % of the world 's whiskey at the start of the 20th century . However , as a consequence of <unk> during the prohibition in the United States ( who sold poor @-@ quality whiskey bearing Irish @-@ sounding names thus eroding the pre @-@ prohibition popularity for Irish brands ) and tariffs on Irish whiskey across the British Empire during the Anglo @-@ Irish Trade War of the 1930s , sales of Irish whiskey worldwide fell to a mere 2 % by the mid @-@ 20th century . In 1953 , an Irish government survey , found that 50 per cent of whiskey <unk> in the United States had never heard of Irish whiskey . 
- Irish whiskey , as researched in 2009 by the CNBC American broadcaster , remains popular domestically and has grown in international sales steadily over a few decades . Typically CNBC states Irish whiskey is not as <unk> as a Scotch <unk> , but not as sweet as American or Canadian <unk> . Whiskey forms the basis of traditional cream <unk> , such as <unk> , and the " Irish coffee " ( a cocktail of coffee and whiskey <unk> invented at <unk> flying @-@ boat station ) is probably the best @-@ known Irish cocktail . 
- <unk> , a kind of <unk> beer , particularly Guinness , is typically associated with Ireland , although historically it was more closely associated with London . Porter remains very popular , although it has lost sales since the mid @-@ 20th century to <unk> . Cider , particularly <unk> ( marketed in the Republic of Ireland as <unk> ) , is also a popular drink . Red <unk> , a soft @-@ drink , is consumed on its own and as a mixer , particularly with whiskey . 
- 
- 
- = St Nazaire Raid = 
- 
- The St Nazaire Raid or Operation Chariot was a successful British amphibious attack on the heavily defended Normandie dry dock at St Nazaire in German @-@ occupied France during the Second World War . The operation was undertaken by the Royal Navy and British Commandos under the auspices of Combined Operations Headquarters on 28 March 1942 . St Nazaire was targeted because the loss of its dry dock would force any large German warship in need of repairs , such as the Tirpitz , to return to home waters via either the English Channel or the <unk> gap , both of which were heavily defended by British units including the Royal Navy 's Home Fleet , rather than having a haven available on the Atlantic coast . 
- The obsolete destroyer HMS Campbeltown , accompanied by 18 smaller craft , crossed the English Channel to the Atlantic coast of France and was rammed into the Normandie dock gates . The ship had been packed with delayed @-@ action explosives , well hidden within a steel and concrete case , that detonated later that day , putting the dock out of service for the remainder of the war and up to five years after . 
- A force of commandos landed to destroy machinery and other structures . Heavy German gunfire sank , set <unk> or <unk> all the small craft intended to transport the commandos back to England ; the commandos had to fight their way out through the town to try to escape overland . They were forced to surrender when their ammunition was expended and they were surrounded . 
- After the raid 228 men of the force of <unk> returned to Britain ; 169 were killed and 215 became prisoners of war . German casualties were over 360 dead , some killed after the raid when Campbeltown exploded . To recognise their bravery , 89 decorations were awarded to members of the raiding party , including five Victoria Crosses . After the war , St Nazaire was one of 38 battle honours awarded to the Commandos ; the operation has since become known as The Greatest Raid of All within military circles . 
- 
- = = Background = = 
- 
- St Nazaire is on the north bank of the Loire 400 km ( 250 miles ) from the nearest British port . In 1942 , it had a population of 50 @,@ 000 . The St Nazaire port has an outer harbour known as the Avant Port , formed by two piers <unk> out into the Atlantic Ocean . This leads to two lock gates before the Bassin de St Nazaire . These gates control the water level in the basin so that it is not affected by the tide . 
- Beyond the basin is the larger inner dock called the Bassin de <unk> , which can accommodate ships up to 10 @,@ 000 tons . There is also an old entrance to the Bassin de St Nazaire located southwest of the Normandie dry dock . Built to house the ocean liner SS Normandie , this dock was the largest dry dock in the world when it was completed in 1932 . The " Old Mole " jetty <unk> into the Loire halfway between the southern pier of the Avant Port and the old entrance into the basin . 
- On 24 May 1941 , the Battle of the Denmark Strait was fought between the German ships Bismarck and <unk> <unk> and the British ships HMS Prince of Wales and HMS Hood . Hood was sunk and the damaged Prince of Wales was forced to retire . Bismarck , also damaged , ordered her consort to proceed independently while she headed for the French port of St Nazaire , which was the only port on the Atlantic coast with a dry dock able to accommodate a ship of her size . She was intercepted by the British and sunk en route . 
- Britain 's Naval Intelligence Division first proposed a commando raid on the dock in late 1941 . When the German battleship Tirpitz was declared operational in January 1942 , the Royal Navy ( RN ) and Royal Air Force ( RAF ) were already drawing up plans to attack her . <unk> from Combined Operations Headquarters were looking at potential scenarios if Tirpitz escaped the naval blockade and reached the Atlantic . They decided the only port able to accommodate her was St Nazaire , especially if , like the Bismarck , she was damaged en route and needed repairs . They came to the conclusion that if the dock at St Nazaire were unavailable the Germans were unlikely to risk sending Tirpitz into the Atlantic . 
- Combined Operations examined a number of options while planning the destruction of the dock . At this stage of the war the British government still tried to avoid civilian casualties . This ruled out a bombing attack by the RAF , which at the time did not possess the accuracy needed to destroy the dock without serious loss of civilian life . 
- The Special Operations Executive were approached to see if its agents could destroy the dock gates . They decided that the mission was beyond their capabilities because the weight of explosives required would have needed too many agents to carry them . The Royal Navy was also unable to mount an operation , as St Nazaire is 8 km ( 5 miles ) up the Loire estuary . Any naval ships large enough to cause sufficient damage would be detected well before they were within range . 
- The planners then examined whether a commando force was feasible to accomplish the task . An unusually high spring tide was due in March 1942 which would allow a light ship to pass over the sand banks in the estuary and approach the docks , bypassing the heavily defended dredged channel . The approach was too shallow for an infantry landing ship , but the planners believed if a destroyer could be lightened it might have a draft shallow enough to enable it to get through . 
- 
- = = Plan = = 
- 
- The purpose of the raid was to destroy three objectives : the Normandie dock , the old gates into the Bassin de St Nazaire together with the water pumping machinery and other installations , and any U @-@ boats or other shipping in the area . The initial Combined Operations plan required two specially lightened destroyers to carry out the raid . The first would be packed with explosives and rammed into the dock gates . 
- Commandos on board would then disembark and use demolition charges to destroy nearby dock installations , searchlights and gun emplacements . The destroyer would then be blown up , and the second ship would come in and evacuate the ship 's crew and the commandos . At the same time the RAF would carry out a number of diversionary air raids in the area . 
- When the plan was presented to the Admiralty they refused to support it . The certain loss of one or both destroyers to eliminate the dry dock was out of the question . They suggested they could provide an old Free French destroyer , the <unk> , and a flotilla of small motor launches to transport the commandos and evacuate them afterwards . 
- <unk> for the mission , codenamed Operation Chariot , was given on 3 March 1942 . Using a French ship would involve using the Free French forces and increase the number of people aware of the raid . Consequently , it was decided the navy would have to provide a ship of their own . The RAF complained that the raid would draw heavily on their resources ; the number of aircraft assigned by RAF <unk> Command was reduced time and again before the day of the raid . British Prime Minister Winston Churchill further complicated matters when he ordered that bombing should only take place if targets were clearly identified . 
- Combined Operations Headquarters worked closely with several intelligence organisations to plan the raid . The Naval Intelligence Division compiled information from a variety of sources . A detailed plan of the town of St Nazaire was provided by the Secret Intelligence Service , and information on the coastal artillery nearby was sourced from the War Office 's Military Intelligence branch . Intelligence about the dock itself came from pre @-@ war technical journals . 
- The RN 's Operational Intelligence Centre selected the route and timing for the raid based on intelligence about the location of minefields and German recognition signals sourced from <unk> <unk> and knowledge of Luftwaffe patrols compiled by the Air Ministry 's Air Intelligence Branch . When all the plans had been pulled together and the timing worked out , the raid was expected to last no longer than two hours . The commandos and crew from Campbeltown would board the motor launches at the Old Mole jetty and then return to base . 
- 
- = = Composition of the raiding force = = 
- 
- The revised Combined Operations plan required one destroyer to ram the dock gates and a number of smaller craft to transport the Commandos . The Royal Navy would therefore provide the largest contingent for the raid , under the overall command of the senior naval officer , Commander Robert Ryder . The ship selected to ram into the dock gates was HMS Campbeltown , commanded by Lieutenant Commander Stephen <unk> Beattie . Campbeltown was a First World War destroyer and had previously been the USS Buchanan in the United States Navy . She had come into RN service in 1940 as one of 50 destroyers transferred to the United Kingdom under the Destroyers for <unk> Agreement . 
- <unk> Campbeltown for the raid took ten days . She had to be lightened to raise her draught to get over the sand banks in the estuary . This was achieved by completely stripping all her internal compartments . The dockyard removed her three 4 inch ( 100 mm ) guns , torpedoes and depth charges from the deck and replaced the forward gun with a light quick – firing 12 pounder ( 3 " ) . Eight 20 mm <unk> were installed on mountings raised above deck level . The bridge and <unk> were given extra armour @-@ plate protection , and two rows of armour were fixed along the sides of the ship to protect the Commandos on the open deck . 
- Two of her four funnels were removed , and the forward two were cut at an angle to resemble those of a German destroyer . The bow was packed with 4 @.@ 5 tons of high explosives , which were set in concrete . It was decided that the explosive charge would be timed to <unk> after the raiders had left the harbour . To prevent the Germans <unk> her away , the crew would open the ship 's <unk> before abandoning the ship . Should she become disabled or sunk before getting to the dock , four motor launches had been detailed to take off the crew and put the commandos ashore . The charge would be reset to explode after the last boat had left . 
- Other naval units involved were two Hunt class destroyers , HMS Tynedale ( <unk> ) and Atherstone ( <unk> ) , which would accompany the force to and from the French coast and remain out at sea during the raid . A Motor Gun Boat ( MGB 314 ) was the headquarters ship for the raid , with Commander Ryder and the commanding officer of the Commandos on board . A Motor Torpedo Boat ( MTB 74 ) , commanded by Sub @-@ Lieutenant Michael Wynn , had two objectives : If the outer Normandie dock gates were open , she had to torpedo the inner dock gates . If the gates were closed she would instead torpedo the gates at the old entrance into the St Nazaire basin . 
- To assist in transporting the Commandos , 12 motor launches ( ML ) were assigned from the 20th and 28th Motor <unk> flotillas . These boats were re @-@ armed with two Oerlikon 20 mm guns mounted forward and aft to complement their twin Lewis guns . At the last minute another four MLs were assigned from the 7th Motor <unk> flotilla ( see <unk> for flotilla details ) . These four boats were also armed with two torpedoes each . Instead of transporting the Commandos , these boats were to engage any German shipping found in the estuary . All the MLs had a 500 @-@ gallon auxiliary fuel tank fixed to the upper deck to increase their range . The S class submarine HMS Sturgeon would leave before the rest of the convoy and be in position to act as a navigational beacon to guide the convoy into the Loire estuary . 
- The man selected to lead the Commando force was Lieutenant Colonel Charles Newman ; his No. 2 Commando would provide the largest Commandos contingent , 173 men , for the raid . The Special Service Brigade headquarters used the raid to provide experience for their other units and 92 men were drawn from Nos 1 , 3 , 4 , 5 , 9 , and 12 Commandos . 
- The Commandos were divided into three groups ; One and Two would travel in the MLs , while Three would be in Campbeltown . Under the command of Captain <unk> , Group One had the objectives of securing the Old Mole and eliminating the anti @-@ aircraft gun positions around the southern <unk> . They were then to move into the old town and blow up the power station , bridges and locks for the new entrance into the basin from the Avant port . The capture of the mole was a major objective , as it was to be the embarkation point for the evacuation after the mission . 
- Group Two , under the command of Captain Burn , would land at the old entrance to the St Nazaire basin . Their objectives were to destroy the anti @-@ aircraft positions in the area and the German headquarters , to blow up the locks and bridges at the old entrance into the basin and then to guard against a counter @-@ attack from the submarine base . Group Three was under the command of Major William ' Bill ' Copland , who was also the Commandos ' second in command . They were to secure the immediate area around Campbeltown , destroy the dock 's water @-@ pumping and gate @-@ opening machinery and the nearby underground fuel tanks . All three groups were subdivided into assault , demolition and protection teams . The assault teams would clear the way for the other two . The demolition teams carrying the explosive charges only had <unk> for self @-@ defence ; the protection teams , armed with Thompson <unk> guns , were to defend them while they completed their tasks . 
- The Commandos were aided in their planning for the operation by Captain Bill <unk> of the Royal Engineers , who had pre @-@ war experience as an apprentice in the Great Western Railway dockyards and whose father was the dock master of Cardiff Docks . In 1940 while part of the British Expeditionary Force in France , his duties had included determining how to <unk> the French dockyards if they were captured . One of the dockyards he had studied was St Nazaire , and he had submitted a report detailing how to put the dock out of action . 
- 
- = = German forces = = 
- 
- The Germans had around 5 @,@ 000 troops in the immediate area of St Nazaire . The port was defended by the 280th Naval Artillery Battalion under the command of Kapitän zur See Edo <unk> . The battalion was composed of 28 guns of various <unk> from 75 mm to 280 mm railway guns , all positioned to guard the coastal approaches . The heavy guns were supplemented by the guns and searchlights of the 22nd Naval Flak Brigade under the command of Kapitän zur See Karl @-@ Konrad Mecke . 
- The brigade was equipped with 43 anti @-@ aircraft guns ranging in calibre from 20 to 40 mm . These guns had a dual role as both anti @-@ aircraft and coastal defence weapons . Many were in concrete emplacements on top of the submarine pens and other <unk> installations of the St Nazaire submarine base . 
- The harbour defence companies were responsible for local defence and for the security of the ships and submarines moored in the harbour . These companies and the harbour defence boats used to patrol the river were under the command of Harbour Commander Korvettenkapitän <unk> . The <unk> Infantry Division was the German Army unit responsible for the defence of the coast between St Nazaire and <unk> . The division had no troops based in the town , but some were located in villages nearby and would be able to respond to any attack on the port . 
- The Kriegsmarine ( German navy ) had at least three surface ships in the Loire estuary : a destroyer , an armed trawler and a <unk> ( minesweeper ) , the latter being the guard ship for the port . On the night of the raid there were also four harbour defence boats and ten ships from the 16th and 42nd <unk> flotillas berthed in the basin , while two tankers were berthed inside the Normandie dock . The 6th and 7th U @-@ boat flotillas , commanded by <unk> Georg @-@ Wilhelm Schulz and Korvettenkapitän Herbert <unk> respectively , were permanently based in the port . It is not known how many submarines were present on the day of the raid . The submarine base had been inspected by the U @-@ boat Commander in Chief , <unk> Karl <unk> , the day before the raid . He asked what would they do if the base was subject to an attack by British Commandos . <unk> replied that " an attack on the base would be hazardous and highly <unk> " . 
- 
- = = The raid = = 
- 
- 
- = = = <unk> journey = = = 
- 
- The three destroyers and 16 small boats left Falmouth , Cornwall at 14 : 00 on 26 March 1942 . They formed into a convoy of three lanes , with the destroyers in the middle . On arrival at St Nazaire the <unk> MLs were to head for the Old Mole to disembark their Commandos , while the starboard lane would make for the old entrance to the basin to disembark theirs . Not having the range to reach St Nazaire unaided , the MTB and MGB were taken under tow by Campbeltown and Atherstone . 
- On 27 March at 07 : 20 Tynedale reported a U @-@ boat on the surface and opened fire . The two escort destroyers left the convoy to engage the U @-@ boat , later identified as U @-@ 593 . The U @-@ boat promptly <unk> and was unsuccessfully attacked by depth charges . The two destroyers returned to the convoy at 09 : 00 . 
- The convoy next encountered two French fishing <unk> . Both crews were taken off and the ships sunk for fear they might report the composition and location of the convoy . At 17 : 00 the convoy received a signal from Commander @-@ in @-@ Chief Plymouth that five German torpedo boats were in the area . Two hours later another signal informed them that another two Hunt class destroyers , HMS Cleveland and HMS <unk> , had been dispatched at full speed to join the convoy . 
- The convoy reached a position 65 nautical miles ( 120 km ; 75 mi ) off St Nazaire at 21 : 00 and changed course toward the estuary , leaving Atherstone and Tynedale as a sea patrol . The convoy adopted a new formation with the MGB and two torpedo MLs in the lead , followed by Campbeltown . The rest of the MLs formed two columns on either side and <unk> of the destroyer , with the MTB bringing up the rear . The first casualty of the raid was ML 341 , which had developed engine trouble and was abandoned . At 22 : 00 the submarine Sturgeon directed her navigation beacon out to sea to guide the convoy in . At about the same time Campbeltown raised the German naval <unk> in an attempt to <unk> any German lookouts into thinking she was a German destroyer . 
- At 23 : 30 on 27 March , five RAF squadrons ( comprising 35 <unk> and 27 <unk> ) started their bombing runs . The bombers had to stay above 6 @,@ 000 feet ( 1 @,@ 800 m ) and were supposed to remain over the port for 60 minutes to divert attention toward themselves and away from the sea . They had orders to only bomb clearly identified military targets and to drop only one bomb at a time . As it turned out , poor weather over the port ( 10 / <unk> cloud ) meant that only four aircraft bombed targets in St Nazaire . Six aircraft managed to bomb other nearby targets . 
- The unusual behaviour of the bombers concerned Kapitän zur See Mecke . At 00 : 00 on 28 March , he issued a warning that there might be a parachute landing in progress . At 01 : 00 on 28 March , he followed up by ordering all guns to cease firing and searchlights to be <unk> in case the bombers were using them to locate the port . Everyone was placed on a heightened state of alert . The harbour defence companies and ships ' crews were ordered out of the air raid shelters . During all this a lookout reported seeing some activity out at sea , so Mecke began <unk> some type of landing and ordered extra attention to be paid to the approaches to the harbour . 
- 
- = = = <unk> the dry dock = = = 
- 
- At 00 : 30 hours on 28 March the convoy crossed over the <unk> at the mouth of the Loire estuary , with Campbeltown scraping the bottom twice . Each time she was able to pull free , and the group proceeded on up toward the harbour in darkness . They had got to within about eight minutes passage from the dock gates when at 01 : 22 the entire convoy was illuminated by the combined searchlights of both banks of the estuary . A naval signal light demanded their identification . 
- The MGB @-@ 314 replied in a coded response obtained from a German trawler boarded during the <unk> raid . A few bursts were fired from a shore battery and both Campbeltown and MGB @-@ 314 replied : " Ship being fired upon by friendly forces " . The deception gave them a little more time before every German gun in the bay opened fire . At 01 : 28 , with the convoy 1 mile ( 1 @.@ 6 km ) from the dock gates , Beattie ordered the German flag lowered and the White Ensign raised . The intensity of the German fire seemed to increase . The guard ship opened fire and was quickly <unk> when the ships in the convoy responded , shooting into her as they passed . 
- By now all the ships in the convoy were within range to engage targets ashore and were firing at the gun emplacements and searchlights . Campbeltown was hit a number of times and increased her speed to 19 kn ( 35 km / h ) . The <unk> on her bridge was killed ; his replacement was wounded and replaced as well . <unk> by the searchlights , Beattie knew they were close to their objective . Still under heavy fire , the MGB turned into the estuary as Campbeltown cleared the end of the Old Mole , cut through anti @-@ torpedo netting strung across the entrance and rammed the dock gates , striking home at 01 : 34 , three minutes later than scheduled . The force of the impact drove the ship 33 feet ( 10 m ) onto the gates . 
- 
- = = = <unk> from Campbeltown and the MLs = = = 
- 
- The Commandos on Campbeltown now disembarked : two assault teams , five demolition teams with their <unk> and a mortar group . Three demolition teams were tasked with destroying the dock pumping machinery and other installations associated with the dry dock . The <unk> @-@ wearing Captain Donald Roy - ' The Laird ' - and his 14 @-@ man assault troop were tasked with <unk> two pump @-@ house roof @-@ top gun emplacements high above the <unk> and securing a bridge to provide a route for the raiding parties to exit the dock area . Roy and <unk> Don Randall used scaling <unk> and grenades to accomplish the former , and a head @-@ on rush to secure the bridge and form a bridgehead that enabled <unk> Bob Montgomery and Lt Corran <unk> and their demolition teams to exit the area . 
- They lost 4 men in this action . The fifth team also succeeded in completing all their objectives but almost half its men were killed . The other two Commando groups were not as successful . The MLs transporting Groups One and Two had almost all been destroyed on their approach . ML 457 was the only boat to land its Commandos on the Old Mole and only ML 177 had managed to reach the gates at the old entrance to the basin . That team succeeded in planting charges on two <unk> moored in the basin . 
- There were only two other MLs in the vicinity : ML 160 had continued past the dock and was engaging targets upriver , ML 269 appeared to be out of control and was running in circles . By this time the crew of Campbeltown had detonated the scuttling charges and gathered at the rear of the ship to be taken off . ML 177 came alongside the destroyer and took 30 men on board including Beattie and some of the wounded . Major Copland went through Campbeltown and evacuated the wounded towards the Old Mole , not knowing that there were no other boats there to take the Commandos off . 
- Lt <unk> Newman aboard the MGB , need not have landed , but he was one of the first ashore . One of his first actions was to direct mortar fire onto a gun position on top of the submarine pens that was causing heavy casualties among the Commandos . He next directed machine @-@ gun fire onto an armed trawler , which was forced to withdraw upriver . Newman organised a defence that succeeded in keeping the increasing numbers of German reinforcements at bay until the demolition parties had completed their tasks . 
- Some 100 Commandos were still ashore when Newman realised that evacuation by sea was no longer an option . He gathered the survivors and issued three orders : 
- To do our best to get back to England ; 
- Not to surrender until all our ammunition is exhausted ; 
- Not to surrender at all if we can help it . 
- Newman and Copland led the charge from the old town across a bridge raked by machine gun fire and advanced into the new town . The Commandos attempted to get through the narrow streets of the town and into the surrounding countryside , but were eventually surrounded . When their ammunition was expended their only option was to surrender . Not all the Commandos were captured ; five men reached neutral Spain , from where they eventually returned to England . 
- 
- = = = Small ships = = = 
- 
- Most of the MLs had been destroyed on the run in and were burning . The first ML in the starboard column was the first boat to catch fire ; her captain managed to beach her at the end of the Old Mole . Some starboard boats managed to reach their objective and disembark their Commandos . ML 443 , the leading boat in the port column , got to within 10 feet ( 3 @.@ 0 m ) of the mole in the face of heavy direct fire and hand grenades before being set on fire . The crew were rescued by ML 160 , one of the torpedo MLs which had been looking for targets of opportunity such as the two large tankers reported to be in the harbour . The commanders of MLs 160 and 443 , <unk> T Boyd and T D L Platt , were awarded the Distinguished Service Order for their bravery . The rest of the port column had been destroyed or disabled before reaching the mole . MLs 192 and 262 were set on fire ; there were only six survivors . ML 268 was blown up ; one man survived . 
- ML 177 , the launch that had successfully taken off some of the crew from Campbeltown , was sunk on her way out of the estuary . ML 269 , another torpedo @-@ armed boat , had the unenviable task of moving up and down the river at high speed to draw German fire away from the landings . Soon after passing Campbeltown it was hit and its steering damaged . It took ten minutes to repair the steering . They turned and started in the other direction , opening fire on an armed trawler in passing . Return fire from the trawler set their engine on fire . 
- ML <unk> also came under heavy fire when it arrived near the port . Sergeant Thomas Durrant of No. 1 Commando , manning the aft Lewis gun , engaged gun and <unk> positions on the run in . He was wounded but refused to leave the gun for treatment . The ML reached the open sea but was attacked at short range by the German torpedo boat Jaguar . Durrant returned fire , aiming for the torpedo boat 's bridge . He was wounded again but remained at his gun even after the German commander asked for their surrender . <unk> drum after drum of ammunition , he refused to give up until after the ML had been boarded . Durrant died of his wounds and , after the recommendation of the Jaguar 's commander , was awarded a posthumous Victoria Cross . 
- After the Commando headquarters group had landed , Commander Ryder went to check for himself that Campbeltown was firmly stuck in the dock . Some of her surviving crewmen were being taken on board the MGB . Ryder returned to the boat and ordered the MTB to carry out its alternative task and torpedo the lock gates at the old entrance to the basin . After a successful torpedo attack , Ryder ordered the MTB to leave . On their way out of the estuary they stopped to collect survivors from a sinking ML and were hit and set on fire . Back at the docks the MGB had positioned itself in mid @-@ river to engage enemy gun emplacements . The forward 2 pounder was manned by Able Seaman William Alfred Savage . Commander Ryder reported that 
- " The rate of supporting fire had evidently been felt , and the Commandos in the area of the Tirpitz dock had undoubtedly overcome the resistance in that area . There was an <unk> <unk> in the enemy 's fire . " 
- Ryder could see no ships other than seven or eight burning MLs . He then realised that the landing places at the Old Mole and the entrance to the basin had both been recaptured by the Germans . There was nothing more they could do for the Commandos , so they headed out to sea . On their way they were continuously illuminated by German searchlights and were hit at least six times by the German guns . Passing ML 270 , they ordered her to follow and made smoke to hide both boats . 
- When they reached the open sea the smaller calibre guns were out of range and stopped firing but the heavier artillery continued to engage them . The boats were about 4 miles ( 6 @.@ 4 km ) off @-@ shore when the last German salvo <unk> them and killed Savage , who was still at his gun . He was awarded a posthumous Victoria Cross for his exploits . His citation recognised both Savage and the bravery of " the many unnamed crews of the Motor Gun Boat , Motor Torpedo Boat and Motor <unk> who continued to carry out their duties in exposed positions , in the face of close range enemy fire . " 
- 
- = = = Return journey = = = 
- 
- At 06 : 30 the five German torpedo boats that the convoy had <unk> the previous day were sighted by HMS Atherstone and Tynedale . The two destroyers turned toward them and opened fire at a range of 7 miles ( 11 km ) . After ten minutes the German boats turned away , making smoke . The destroyers sighted the MGB and two accompanying MLs soon after and transferred their casualties to the Atherstone . Not expecting any more boats to arrive , they headed for home . Just after 09 : 00 the Hunt @-@ class escort destroyers HMS <unk> and HMS Cleveland arrived , sent by Commander @-@ in @-@ Chief Plymouth . Shortly after this the ships were spotted by a Heinkel 115 <unk> of the Luftwaffe . 
- The next German aircraft on the scene , a Junkers 88 , was engaged by a RAF Bristol Beaufighter which had appeared in the area earlier . Both machines crashed into the sea . Other German planes arrived but were driven off by Beaufighters and <unk> from Coastal Command . The Atlantic weather conditions deteriorated . Amid concerns about the growing German threat and the realisation that the damaged small ships would not be able to keep up , Commander <unk> ordered the crews off the smaller boats and had them sunk . 
- Three of the small vessels managed to return to England : MLs 160 , <unk> and 443 . They had reached the rendezvous and waited until 10 : 00 for the destroyers to appear . Having already been attacked once , they moved further out into the Atlantic to try and avoid the German Air Force , but a Junkers 88 appeared overhead at 07 : 30 and approached them at low level for a closer look . The ships opened fire and hit the Junkers in the cockpit . The plane went into the sea . The next aircraft to appear was a <unk> and <unk> seaplane which attempted to bomb the ships , but left after being damaged by machine @-@ gun fire . The surviving MLs eventually reached England unaided the following day . 
- 
- = = = Campbeltown explodes = = = 
- 
- The explosive charges in HMS Campbeltown detonated at noon on 28 March 1942 , and the dry dock was destroyed . Reports vary on the fate of the two tankers that were in the dock ; they were either swept away by the wall of water and sunk , or swept to the far end of the dock , but not sunk . A party of 40 senior German officers and civilians who were on a tour of Campbeltown were killed . In total , the explosion killed about 360 men . The wreck of Campbeltown could still be seen inside the dry dock months later when RAF photo reconnaissance planes were sent to photograph the port . 
- According to Captain Robert Montgomery ( Royal Engineers , attached to <unk> Commando ) , Campbeltown was meant to have detonated at 4 : <unk> , the delay caused , he believes , by some of the acid in the pencil fuses being <unk> away . As the morning progressed , more and more captured comrades joined him in the German HQ . 
- The day after the explosion , Organisation Todt workers were assigned to clean up the debris and wreckage . On 30 March at 16 : 30 the torpedoes from MTB 74 , which were on a delayed fuse setting , exploded at the old entrance into the basin . This raised alarms among the Germans . The Organisation Todt workers ran away from the dock area . German guards , <unk> their <unk> uniforms for British uniforms , opened fire , killing some of them . The Germans also thought that some Commandos were still hiding in the town , and made a street by street search , during which some <unk> were also killed . 
- 
- = = Aftermath = = 
- 
- The explosion put the dry dock out of commission for the remainder of the war . The St Nazaire raid had been a success , but at a cost . Of the <unk> men of the Royal Navy and Commandos who took part in the raid , only 228 men returned to England . Five commandos escaped via neutral Spain and Gibraltar with the help of French citizens , and took a ship to England from Gibraltar . 169 men were killed ( 105 RN and 64 Commandos ) and another 215 became prisoners of war ( 106 RN and 109 Commandos ) . They were first taken to La <unk> and then sent to <unk> 133 at Rennes . The fallen British raiders were buried at the La <unk> @-@ <unk> cemetery with military honours . The cemetery is located 13 kilometres west of St Nazaire . 
- To recognise their achievement , 89 decorations were awarded for the raid . This total includes the five Victoria Crosses awarded to Lieutenant Commander Beattie , Lieutenant Colonel Newman and Commander Ryder , and posthumous awards to Sergeant Durrant and Able Seaman Savage . Four Distinguished Service Orders were awarded to Major William Copland , Captain Donald Roy , Lieutenant T Boyd and Lieutenant T D L Platt . Other decorations awarded were four <unk> <unk> Medals , five Distinguished Conduct Medals , 17 Distinguished Service Crosses , 11 Military Crosses , 24 Distinguished Service Medals and 15 Military Medals . Four men were awarded the Croix de guerre by France , and another 51 were mentioned in despatches . 
- Adolf Hitler was furious that the British had been able to sail a flotilla of ships up the Loire <unk> . His immediate reaction was to dismiss <unk> Carl <unk> , chief @-@ of @-@ staff to the Commander in Chief West . The raid <unk> German attention on the Atlantic Wall , and special attention was given to ports to prevent any repeat of the raid . By June 1942 the Germans began using concrete to <unk> gun emplacements and bunkers in quantities previously only used in U @-@ boat pens . Hitler laid out new plans in a meeting with <unk> Minister Albert <unk> in August 1942 , calling for the construction of 15 @,@ 000 bunkers by May 1943 to defend the Atlantic coast from Norway to Spain . 
- The battleship Tirpitz never entered the Atlantic . She remained in Norwegian <unk> to threaten Allied shipping until she was destroyed by the RAF on 12 November 1944 . 
- 
- = = Legacy = = 
- 
- St Nazaire was one of the 38 battle honours presented to the Commandos after the war . The raid has since been called The Greatest Raid of All . The survivors formed their own association , the St Nazaire Society , which is a registered charity in the United Kingdom . 
- A memorial to the raid erected in Falmouth bears the following inscription : 
- A new HMS Campbeltown , a Type 22 Frigate , was launched on 7 October 1987 . She carried the ship 's bell from the first Campbeltown which was rescued during the raid and had been presented to the town of <unk> , Pennsylvania at the end of the Second World War . In 1988 the people of <unk> voted to lend the bell to the new ship for as long as she remained in Royal Navy service . The bell was returned to the town on 21 June 2011 when HMS Campbeltown was decommissioned . 
- On 4 September 2002 , a tree and seat at the National Memorial <unk> were dedicated to the men of the raid . The seat bears the inscription : 
- In memory of the Royal Navy Sailors and Army Commandos killed in the raid on St Nazaire on 28 March 1942 
- 
- = = Documentaries and <unk> = = 
- 
- A <unk> version of the raid was the climax of the 1952 British war film , Gift Horse . The film follows the career of an ex @-@ US Navy destroyer , HMS <unk> ( actually HMS <unk> ) ; the raid is named Operation Boadicea and portrays the main events of the actual battle . 
- The war film Attack on the Iron Coast was released in 1968 and was a highly fictionalized version of the raid . 
- In 2007 , Jeremy Clarkson presented the story of the raid in a BBC documentary entitled The Greatest Raid of All Time . 
- An episode of the television series ; " World War II 's Greatest <unk> " on the Military Channel ( now the American Heroes Channel ) devoted an episode to this raid . <unk> " Commando Do or Die ! " it was released in early 2014 ; and has been <unk> several times . 
- A mission in the video game Enemy Front re @-@ <unk> the mission from the first person perspective of a British Commando . 
- A mission in the video game Medal Of Honour : European Assault also re @-@ <unk> the mission from the perspective of fictional soldier William Holt . 
- 
- 
- = Hellblazer = 
- 
- Hellblazer ( also known as John Constantine , Hellblazer ) is an American contemporary horror comic book series , originally published by DC Comics , and subsequently by the Vertigo imprint since March 1993 when the imprint was introduced . Its central character is the <unk> magician John Constantine , who was created by Alan Moore and Stephen R. <unk> , and first appeared as a supporting character in The Saga of the Swamp Thing # 37 ( June 1985 ) , during that creative team 's run on that title . Hellblazer had been published continuously since January 1988 , and was Vertigo 's longest running title , the only remaining publication from the imprint 's launch . In 2013 , the series concluded with issue 300 , and has been replaced by a DC Universe title , Constantine . Well known for its political and social commentary , the series has spawned a film adaptation , television show , novels , multiple spin @-@ offs and <unk> . 
- The series was the longest @-@ running and one of the most successful titles of DC 's Vertigo imprint , and was the stepping stone to many British writers . Notable writers who have contributed to the series include Jamie Delano , Garth Ennis , Paul Jenkins , Warren Ellis , Grant Morrison , Neil Gaiman , Mike Carey , Andy Diggle , and Peter Milligan . Hellblazer was one of the first modern occult detective fiction works and heavily influenced the genre to come . 
- 
- = = Production history = = 
- 
- After favorable reader reaction to John Constantine 's appearances in the comic book series Swamp Thing , where he had been introduced by Alan Moore during his authorship of the title , the character was given his own comic book series in 1988 . The series was intended to bear the title <unk> , but this title was revised before publication due to the contemporaneous release of Clive Barker 's unrelated film of the same name . Initial writer Jamie Delano was , in his own words , " fairly ambivalent " about the change of title . 
- The initial creative team was writer Jamie Delano and artist John Ridgway , with Dave McKean supplying distinctive painted and <unk> covers . Delano introduced a political aspect to the character , about which he stated : " ... generally I was interested in commenting on 1980s Britain . That was where I was living , it was shit , and I wanted to tell everybody . " The book , originally published as a regular DC Comics title , became a Vertigo title with the imprint 's launch in March 1993 ( issue # 63 of the series ) . In October 2011 , it was announced that this would join DC titles in being published digitally on the same day as its physical release , starting in January 2012 . 
- 
- = = = Creative personnel = = = 
- 
- Many writers had lengthy runs on the series , such as Garth Ennis and Mike Carey , who respectively had the second- and third @-@ longest runs on the book , ( only behind Peter Milligan ) . Other writers who wrote for the series include Paul Jenkins , Warren Ellis , Brian Azzarello , Neil Gaiman , Grant Morrison , Denise Mina , and Peter Milligan . 
- Numerous artists worked on the series as well , such as John Ridgway ( the original series artist ) , Simon <unk> , Mark Buckingham , Richard <unk> , Steve Dillon , Marcelo <unk> , Jock , David Lloyd , Leonardo <unk> , and Sean Phillips . Cover artists included Dave McKean ( who designed the first run of the series ' covers ) , Tim Bradstreet ( who designed the most ) , Glenn <unk> , Kent Williams , David Lloyd , and Sean Phillips . 
- 
- = = In the comics = = 
- 
- 
- = = = Setting and protagonist = = = 
- 
- Hellblazer was set in a contemporary world , albeit a world of magic and supernatural conflict behind the scenes . Although issue 14 made a passing reference to <unk> , the series since developed its own pocket universe in which the supernatural or paranormal did not play a large role in the lives of most ordinary people , and in Earth @-@ threatening circumstances no superhero <unk> were shown or hinted at , suggesting that <unk> no longer existed there . However , some DC Comics characters — most notably the fringe supernatural characters such as Zatanna , The Phantom Stranger , Shade , The Changing Man , Dream of the <unk> , and Swamp Thing made appearances . 
- John Constantine , the main character of Hellblazer , was portrayed as a kind of confidence man and occult detective who did morally questionable things , arguably for the greater good . He usually triumphed through <unk> , deceit , and <unk> , but often made more enemies in the process than he defeated . Indeed , it was a common theme in the book that Constantine was unable to effect any lasting change or enjoy <unk> victories . While sometimes striving for the good of mankind , Constantine was often <unk> and a dangerous person to have as a friend , as the lives and souls of those around him became <unk> involved in his <unk> . He took pains to protect himself from direct attacks , but his friends and relatives were often endangered in order to strike at him . The spirits of deceased friends haunted him , individually or as an entourage of ghosts . 
- Constantine made appearances in other comic book titles , such as Crisis on <unk> <unk> , <unk> Crisis , Green Arrow , Green Lantern , The Sandman , Lucifer , and Shade , the Changing Man . He was a recurring supporting character in both Swamp Thing and The Books of Magic throughout their numerous <unk> . Some attempts to use the character in other superhero or family @-@ friendly comics were altered due to editorial mandate , such as " <unk> <unk> <unk> " in <unk> and Captain <unk> ( who refers to Constantine as " an <unk> <unk> in England " ) . Grant Morrison created " Willoughby Kipling " for Doom Patrol after being refused Constantine by DC , changing his appearance to that of Richard E Grant in <unk> and I , following which Phil <unk> was forced to create " Ambroise <unk> " in Stanley and His Monster , having been refused both Constantine and Willoughby Kipling . 
- John Constantine was reintroduced into the DC Universe in 2011 , initially in the <unk> Day crossover event title Search for the Swamp Thing , and in the ongoing The New 52 title Justice League Dark . 
- 
- = = = 1988 – 1991 : Jamie Delano ( # 1 – 40 , # 84 ) = = = 
- 
- Having previously worked on <unk> & <unk> for 2000 AD , a title made popular by John Constantine 's creator Alan Moore , Delano was selected to start the character 's first run in his own comic by then editor Karen Berger in 1988 . Delano 's run was characterised by his political satire , taking on late 1980s and 1990s tropes such as with city <unk> being literal demons , and Constantine meeting with <unk> from the Houses of Parliament . He also had <unk> issues crop up , especially in " The Fear Machine " ( issues # 15 @-@ 22 ) , where John fell in with a travelling community of environmental activists . Indeed , editor Karen Berger noted on Delano 's departure the irony that his final issue was handed in the week that Margaret Thatcher was forced out of office . 
- There were five main storylines in the run . The first , collected as " Original <unk> " , deals with John travelling to America to <unk> a demon , <unk> , and investigate a strange cult known as <unk> 's Army , crossing paths with a demon called Nergal ( from whom he gains demon blood ) , and having to be responsible for killing an old friend , Gary Lester , and betraying another , called <unk> , in the process . The following four issues , " The Devil You Know " finally explain John 's failure to save a young girl , <unk> , from a demon in Newcastle , an event that left him near insane and incarcerated in an <unk> known as <unk> , and still haunted him to the comic 's end . He eventually discovers that the demon responsible for this was Nergal , and uses a technological scheme to trap him , and lead him back to hell . It also contains a crossover with Swamp Thing , where Constantine loses his body while the Swamp Thing uses it to <unk> . 
- This was followed by a lengthy nine @-@ issue story arc , " The Fear Machine " , revolving around a <unk> plot to collect people 's fears , in order to resurrect a <unk> god known as <unk> , and his efforts to prevent this with the help of environmentalists , including Mercury , a young psychic girl , and <unk> , her mother , with whom he becomes romantically involved . The penultimate major run of Delano 's tenure was " The Family Man " , which differed from the main body of the series thus far in that Constantine 's nemesis is not supernatural ( beyond an opening <unk> encounter with a fictional fence ) , but a former policeman turned serial killer . John 's ethical quandary as to whether murder is ever acceptable , and his <unk> with the murder of his father , Thomas , frames this story . During this run on the title , Grant Morrison ( issues # 25 & 26 ) and Neil Gaiman ( issue # 27 ) both filled in during a three @-@ month break , Grant Morrison 's story dealing with nuclear fear , and Neil Gaiman 's being a simple romantic ghost story . 
- Delano 's run ended with " The Golden Child " , where John is reunited with <unk> and Mercury , who help him discover that he murdered his more perfect twin in the <unk> , culminating in an extended story , in which we are shown what would have occurred had the other twin survived in his place . During his run , there was also a stand @-@ alone issue , the Hellblazer Annual # 1 , exploring Constantine 's ancestry , and featuring the video to John 's punk band , <unk> Membrane 's song " Venus of the <unk> " . 
- Jamie Delano returned to the title on several occasions . Between the Garth Ennis and Paul Jenkins runs on Hellblazer , he finally told the story of why John 's best friend Chas ' <unk> ' him , and he returned again for one of the five Christmas stories in issue # 250 . He also wrote the mini series The <unk> in 1995 , and Bad Blood in 2000 , both featuring John Constantine . A more substantial return was made in 2010 for a hardcover graphic novel Hellblazer : <unk> with artist Jock to commemorate the 25th anniversary of John Constantine 's first appearance in Swamp Thing . 
- 
- = = = 1991 – 1999 : Garth Ennis ( # 41 – 83 , # 129 – 133 ) and Paul Jenkins ( # 89 – 128 ) = = = 
- 
- Irish writer Garth Ennis then took over the title in 1991 , again from 2000 AD , where he had been working on Judge <unk> . He proceeded to write the longest run for any writer on the title . His take on the title was more personal than Jamie Delano 's , with John 's relationships coming to the fore . It also had a strong religious theme , with John 's dealings with the First of the Fallen , and some storylines , such as the relationship between an angel , <unk> , and a <unk> demon , Ellie , would go on to be used again as a major plot device in Preacher , one of his most popular works . He also references the music of The <unk> and the poetry of Brendan <unk> , both of these being relevant to Ennis ' Irish heritage . 
- His run started with " Dangerous Habits " ( 41 @-@ 46 ) , which was the basis for the 2005 film Constantine , and dealt with John Constantine contracting lung cancer , and the desperate deal he makes with the First of the Fallen , and various other lords of Hell , to save himself . In the course of trying to save himself , he visits Ireland , where he becomes <unk> with Kit Ryan , an old friend . The following few issues follow the early stages of his relationship with Kit , a plot to install a demon on the British throne in the plotline " Royal Blood " , and in the extended issue # 50 , his first meeting with the King of the <unk> . One minor story in this arc ( issue # 51 ) was written by guest writer John Smith . 
- The next major arc , " Fear and <unk> " ( issues 62 @-@ 67 ) covers a high point of John 's personal life , with his relationship with Kit going well , and a <unk> birthday party where his friends Ellie , Zatanna , and the Swamp Thing attend , and use their various abilities to create a large quantity of <unk> whiskey and marijuana . The story then takes him to his lowest point , through his dealings with the National Front , their threats towards Kit , and her leaving him to return to Ireland . Following this , John is defeated , and lives homeless on the streets , drinking to forget his life . This remains the case until the King of the <unk> hunts him out , and is poisoned by his demon blood , leaving him out in the sun at dawn , killing him . Following his recovery , the storyline " <unk> 's Flame " ( <unk> # 72 – 77 ) follows a trip to the US , where Constantine is put into an alternative America by his old <unk> Papa <unk> , a <unk> <unk> . He is accompanied by the spirit of JFK , who has to hold his brain in place from his infamous wound . He eventually learns how to escape , shortly before running into the First of the Fallen , in the guise of Abraham Lincoln . There then follows a small break where he meets the spirit of a dead friend in Dublin , offering some closure to his recent problems . Ennis ' run ends with " <unk> at the Gates of Hell " , a story which finally brings together the racism storyline , with riots in Mile End , echoing the real @-@ life Brixton and <unk> Farm riots in London , the revenge attempt of the First of the Fallen , started in " Dangerous Habits " , and the end of John 's relationship with Kit Ryan . John 's eventual <unk> victory leaves this run with closure , and a relatively clean slate for a new writer to take over . 
- Following a brief interlude by From Hell artist Eddie Campbell , the series ' direction was taken over by Paul Jenkins in 1995 . He had been former editor of Teenage <unk> <unk> Turtles and other <unk> Studios lines . He had pitched to several comic houses , having tired of editing , and eventually managed to gain stewardship of Hellblazer , the first largely <unk> writer to achieve this . His four @-@ year run is the longest run to remain <unk> . Jenkins ' run is more traditionally English in its themes , with Albion , <unk> legend , and old English battles all featuring , and even an appearance by Samuel Taylor Coleridge , and a speculative explanation of the writing of Kubla Khan , and what the <unk> of the ' man from <unk> ' may have been . He also returns to Jamie Delano 's coverage of <unk> lifestyles , and the effects of the Criminal Justice Act of 1994 , a controversial law which restricted the ability of the public to throw <unk> and large demonstrations . 
- Major storylines in this run include " Critical Mass " , where Constantine is forced to use magic to purge his darker side into another human body , in order to avoid being <unk> to Hell as part of a trade to save the possessed son of a friend , thus creating the antagonistic character , Demon Constantine , with the assistance of Aleister Crowley . The landmark hundredth issue gave more detail on John Constantine 's father , and the abusive relationship that they had shared . However , it is unclear as to whether this is happening in reality , or in his mind as John <unk> in a coma . " Last Man Standing " reveals that his friend Rich is the current descendant of King Arthur , and <unk> 's attempt to discover God 's secret , an act which would destroy England , using John 's few remaining friends as bait . 
- The tenth anniversary issue breaks from the usual format , in breaking the fourth wall , and addressing the reader as if they are in a pub with Constantine for a monthly get together to hear his stories . Over the course of the issue , most of the characters from Constantine 's history appear , along with Death of the <unk> from Sandman . There are also appearances by writers and artists , including series creator Alan Moore , Garth Ennis , and Jenkins himself . Jenkins ' run then draws to a close with two storylines , " Up the Down <unk> " and " How to Play With Fire " , which relate the First of the Fallen 's new plan , to essentially let mankind ruin itself through television and <unk> , and Ellie 's plot to leave Constantine <unk> and alone . John eventually saves himself , through a literal <unk> ex <unk> , in a <unk> conversation with God . However , at that time Rich and his family finally <unk> their friendship with John , and the latter ends the run alone . Paul Jenkins later returned for one of the five stories in issue # 250 . 
- Ennis briefly returned to the title in 1998 with " Son of Man " , filling the gaps between Paul Jenkins ' and Warren Ellis ' runs on the title . This more <unk> story is about the consequences of Constantine <unk> the dead son of an East London gangster , using the spirit of a demon . He also had two <unk> published during his run on the title , the Hellblazer Special and <unk> , which follows Kit Ryan 's return to Ireland . 
- 
- = = = 1999 – 2002 = = = 
- 
- 
- = = = = Warren Ellis ( # 134 – 143 ) = = = = 
- 
- Warren Ellis took over the title in 1999 , after his work on <unk> which had moved to the Vertigo imprint , following the closure of <unk> Comics . He was meant to become a full @-@ time writer for several years , as Delano , Ennis and Jenkins before him , but left the title early after DC refused to publish the story " Shoot " , about high school shootings , following the <unk> High School massacre , despite the fact it had been written and submitted prior to the event . The story was finally published in 2010 . 
- His brief run began with " <unk> " , a London @-@ based story in which John investigates the brutal murder of a former girlfriend , Isabel by an Aleister Crowley style magician , Josh Wright . The story introduces , or <unk> many characters who became an important part of the Hellblazer universe , including Inspector <unk> ( originally from Jamie Delano 's run on the title ) , aging magician <unk> , and Map , a powerful magician who works on tube renovations in his part as <unk> of London . The remainder of Ellis ' brief tenure was taken up with single issue stories , collected as " Setting Sun " . 
- 
- = = = = Brian Azzarello ( # 146 – 174 ) = = = = 
- 
- Following a brief interlude by Croatian writer <unk> <unk> , the series was then taken over by Brian Azzarello , once again hired on the strength of his own series for Vertigo , 100 <unk> . Azzarello 's run is one large meta @-@ story , that follows John Constantine on a trip across America , starting with his incarceration in prison , then variously uncovering a pornography ring , catching a serial killer , taking on a Neo Nazi group before finally dealing with the architect of his incarceration , Stanley W. Manor , a thinly @-@ veiled pastiche of Batman . Brian Azzarello did return for one of the five stories in issue # 250 . 
- 
- = = = 2002 – 2006 : Mike Carey ( # 175 – 215 , # <unk> ) = = = 
- 
- Following Azzarello 's run , writer Mike Carey took over the title , following his <unk> award @-@ winning title Lucifer , set in the Sandman universe . Carey 's run attempted to return John Constantine to his roots , with the title largely set back in London , and featuring many characters from former runs on the title . Mike Carey also has the honour of being the first <unk> to write the <unk> character . His was the second longest run by any single author on the title , second only to Garth Ennis . 
- The start of his run introduces Angie <unk> , a fellow magician , and <unk> his niece , Gemma Constantine , who has also fallen into magic use , to her uncle 's consternation . After <unk> his sister 's house of an evil spirit , and finding out Gemma has gone missing , John returns to London , to find his old colleagues and enemies are all taking sides in the hunt for a mythical item , known as the Red <unk> . John eventually locates the item , and finds Gemma , freeing her from his old enemy Josh Wright . Following several <unk> , Constantine then travels the world to set up a plan for a forthcoming tragedy , which will occur when " Three doors are opened " , involving Swamp Thing and the Garden of Eden amongst others . 
- John 's preparations have no effect , however , as he is <unk> into killing the guardian that had been preventing the tragedy , freeing a beast which can control the collective unconsciousness of mankind . John Constantine cuts his own wrists , in order to free himself from consciousness , and plays a confidence trick on the beast , allowing his friends time to use the collective consciousness to rebuild the guardian that had kept the beast trapped . However , in this process , Swamp Thing has his human soul removed , setting up the fourth run of the comic , relaunched shortly afterward . In the process John loses his memory , setting up the events leading up to the 200th issue . Leading up to the landmark issue , John has little control over events , and is led along by a psychic serial killer , who threatens to kill Chas and his family , and a demon , Rosacarnis , who offers his memories back , at the cost of 24 hours in her service . John eventually gives into this offer , and the 200th issue shows how Rosacarnis manipulates his reality , making him raise three children with her , in the guise of Kit Ryan , from Garth Ennis ' time on the title , <unk> , from Jamie Delano 's , and Angie , from the current run , with three different artists , Steve Dillon , Marcelo <unk> and Leonardo <unk> each drawing one story , as the past , present and future of the title . 
- Carey 's final run followed the attempts of John 's three new children attempting to kill all of his family and friends , culminating in the death of his sister , <unk> , at the hands of her possessed husband . John then travels into Hell to try to rescue her soul , with the assistance of Rosacarnis ' father , Nergal . The plan ultimately fails , and John returns broken , and intending to renounce magic . 
- Mike Carey returned to the title for a single issue between Denise Mina and Andy Diggle 's runs on the title , and also wrote the well @-@ received Hellblazer graphic novel All His <unk> about a strange illness sweeping the globe . 
- 
- = = = 2006 – 2013 = = = 
- 
- 
- = = = = Denise Mina ( # 216 – 228 ) = = = = 
- 
- Denise Mina had not written for comics when she took over the title in 2006 , but had three acclaimed crime novels to her name , the <unk> trilogy , the first of which won the <unk> award for best debut crime novel . Her run on the title took John to Scotland , to attempt to stop a plot to make everybody <unk> with each other . However , John fails to stop this , and , overwhelmed by the grief and horror they 're forced to <unk> share , suicides <unk> through the people of Glasgow . With help from Gemma Constantine , Angie <unk> and Chas Chandler , a plan to reverse the problem is made , as tension builds among the soldiers now surrounding the city . The soldiers <unk> listen to a World Cup match between England and Portugal on the radio . When England loses the match , it seems all is lost , but the expected psychic riot fails to materialize . The soldiers are Scottish , so England 's loss is celebrated , saving the day , and proving there 's no source of joy like <unk> . 
- 
- = = = = Andy Diggle ( # 230 – 249 ) = = = = 
- 
- Andy Diggle , having previously written the Hellblazer special , Lady Constantine , and Vertigo titles The <unk> and Swamp Thing , took over the title in 2007 , another former writer for 2000 AD to have done this . He left the title in 2009 after accepting an exclusive contract with Marvel . 
- The run starts by introducing two main antagonists , an aging politician , who is using a strange portal to enter other people 's minds and commit crimes , and Mako , a <unk> <unk> who <unk> other <unk> in order to obtain their power . Constantine 's attempt to play them off one another only succeeds in making them join forces in a further plot . Constantine then traps them both with considerable ease , and questions how this has been so easy . It then becomes apparent that he has been manipulated by the ' Golden Child ' , his twin who did not survive childbirth , and has been manipulating events for the whole of the series , including his battle with cancer and many other events . He declines his twin 's offer to merge souls , suspicious that his twin has been weakening his will in past years to make him accept this offer , choosing instead to take control of his own destiny . 
- 
- = = = = Peter Milligan ( # 250 – 300 ) = = = = 
- 
- Peter Milligan , a veteran of the Vertigo line , having written both Shade , The Changing Man and Animal Man at the publisher 's inception , then took over , starting with a short story in the landmark <unk> issue , and taking over full @-@ time following this . His run implemented several major changes , including John Constantine 's wedding and the loss of his thumb . 
- In a rare change , Milligan 's run on the title starts with John living in domestic <unk> with a nurse , Phoebe . Over the course of the first storyline , several new characters are introduced , including Epiphany Greaves , the <unk> daughter of a notorious London gangster , and Julian , a Babylonian demon . Over the course of the run , John dealt with a demon taking revenge on people involved in the Liverpool <unk> ' strike gone insane and sought help from Shade , The Changing Man , after <unk> off his own thumb , seen Phoebe die at the hands of Julian , and traveled to India to try to find a way of saving her . Following this , he realised that he was in love with Epiphany , and married her in the <unk> issue . However , the events of this wedding turned Constantine 's niece Gemma against him , due to the Demon Constantine sexually assaulting her in the <unk> . The strain of this traumatic incident turned her against John , and she enlisted the help of a coven of witches to kill him , which later came to a head when John was forced to fight off a brutal Demon summoned by them using John 's iconic <unk> to target him . 
- Afterwards , John 's coat ( which Gemma sold on <unk> ) began to manipulate its various new owners into murder , suicide , or other horrific acts until coming into the hands of a man from the U.S. , who tried to kill John and Epiphany . During the time the coat was missing , John 's magical abilities had begun to go out of control . John eventually resolves this , and he was re @-@ united with his coat . Since this , Gemma and John have a very <unk> relationship with one another , and she began a sexual relationship with Epiphany 's crime @-@ boss father Terry in order to " punish " John . When he confronted her about this relationship though , she told him that she would not stop unless he was able to retrieve her mother 's soul from Hell . John agreed . In order to get his sister to leave Hell , John agreed to track down her son , his adopted nephew , in Ireland . 
- On October 8 , 2012 , the series was announced as ending with issue 300 , following which a new title , Constantine started at the main imprint of DC comics . By 2013 , Constantine was contacted by the Three <unk> , who tell him that he will finally meet his end in five days . Having lived a good and adventurous life , he happily accepts his fate rather than trying to fight it like he always does . When the last day came , Constantine was ambushed and shot in his own home right in front of a horrified Epiphany . After he died , Epiphany and Finn had an affair as a way to comfort each other . John 's ghost saw them having sex in a graveyard and seemed to give his blessing , making the fates decide that John wasn 't going to try to come back to life . Then , of course , he did . He contacted Epiphany and asked her to help resurrect him by making him cigarettes from his ashes , which , after he smoked them , made him <unk> again . <unk> to disappear and live happily ever after with Epiphany , they moved to a tiny house in Ireland , courtesy of Finn , that was totally off the grid and far removed from any apparent trouble , the last place anyone would think to look for John . That didn 't last long , though , as John realized he can never run from his past . 
- So John left Epiphany in Ireland and returned to England to confront Gemma . He stole the last dart holding the <unk> that killed his demon twin , but ultimately gave it back to Gemma and told her either she could kill him , and live her life without him in but consumed with guilt for killing him , or she could let him disappear from her life forever , without any guilt for murdering him . Gemma shot the dart at him , but John disappeared . The final panel of Hellblazer reveals John , looking shocked and much older than we 'd previously seen him , standing in a bar appropriately called " A Long Journey 's End " surrounded by people , in front of a shelf full of bottles with the names of the comic 's staff over the years . 
- 
- = = Justice League Dark , Constantine and cancellation = = 
- 
- In 2011 , it was announced that a younger John Constantine would feature in Justice League Dark , one of the new titles launched as part of September 2011 's DC Universe reboot . He was to be part of a team including Shade , The Changing Man , <unk> and Madame <unk> , known as Justice League Dark . As part of the DC universe reboot in September 2011 , Peter Milligan started the title which featured an alternate version of John Constantine as a prominent part of the team . Milligan wrote eight issues of Justice League Dark , with writer Jeff <unk> taking over on issue nine . 
- On November 8 , 2012 , DC announced that Hellblazer would be cancelled following its 300th issue , and would be replaced by Constantine written by Robert <unk> and drawn by <unk> <unk> starring the younger New 52 John Constantine , rather than the version from Hellblazer , depicted as being in his late 50s . The Constantine series finally ended its run on its 23rd issue in May 1 , 2015 . Nonetheless , the character would again star in another solo series entitled Constantine : The Hellblazer , written by Ming Doyle and art by Riley <unk> , and released in June 10 , 2015 . Writer Ming Doyle expressed excitement in her chance to write Constantine , stating that the reason of putting the term Hellblazer back to the character 's title was to " take Constantine back to what he was at the start . " 
- 
- = = Themes and style = = 
- 
- Hellblazer was first published during the early days of the Modern Age of Comics , and so its themes were dark , <unk> , politically and morally complex as its contemporaries . Hellblazer mixes supernatural and real life horror , akin to contemporary gothic , with <unk> , surrealism and occult detective fiction elements . Unlike other comic books , Hellblazer is unique as it follows real time in its span of 20 years , with its protagonist John Constantine aging in every publication . Because of this , writers of the series often places their era 's culture and social commentary in their run . When Jamie Delano first wrote the series in the late 1980s and early 1990s , his issues were heavily inspired by the era such as punk rock and the British economy . Delano would be the first to put his political views in the series , an element never before seen in mainstream comics , such as his negative views of Thatcher 's regime and by 2005 includes the War on Terror . This made John Constantine different from other comic book characters at that time , in that he fights the political and social injustice of Great Britain . 
- When Garth Ennis took over writing , he included his trademark representation of racism and religious <unk> , as well his depictions of the <unk> War . The most controversial writer , Brian Azzarello , tackled issues such as Neo @-@ Nazism , prison rape and homosexuality . During Warren Ellis ' run , he included American school shootings in a one @-@ shot issue which led to a major controversy . In his run , Peter Milligan managed to put punk ideology in the series , with the protagonist trying to <unk> his former punk self , while also characterizing the Conservative government as a demon infestation with the punk <unk> fighting against this supposed subversion and abuse . As such , much of Hellblazer 's horror often comes in the crisis and controversies of its time . Being set in the UK , many famous British personalities have appeared or made cameos such as Sid <unk> , Margaret Thatcher , Aleister Crowley and Alan Moore . 
- As stated by Warren Ellis , Hellblazer 's major themes were cynicism , <unk> and " sudden violence " , with the protagonist <unk> <unk> the story in dark <unk> with occasional breaking of the fourth wall . In many story arcs every victory Constantine makes has a negative side effect and often leads to tragedy . His friends , family , and others would be sacrificed or be caught in the crossfire , many of them dead or have left him . John tries his best to make something good in his life , but most of it leading to failure . 
- 
- = = Reception = = 
- 
- Over the two decades that it had been published , Hellblazer had normally been quite well received . While not attaining the sales of ' mainstream ' comics , it had sustained healthy figures , consistently being one of the top selling Vertigo titles , and was sustained by healthy sales of trade <unk> . Hillary Goldstein of IGN described it as , " Sometimes surreal , often provocative and almost always entertaining , the adventures of the Hellblazer are among the best Vertigo has to offer . " Well @-@ known comic book writer Warren Ellis also praised the series as his favorite , calling it as " among the very best horror works of the 1990 's . " Andre <unk> from DNA India listed it in the " 15 Must Read Graphic Novels " , describing it as " one of the first of its kind " , and that its " writing and art work have been praised throughout its run . " Robert <unk> of Paste Magazine listed the series at # 4 in its " 13 <unk> Modern Horror Comics " , stating that " Constantine ’ s most frightening encounters have hewn close enough to that <unk> of truth to remind readers that real life can be as terrifying as any <unk> or demon spawn . " 
- Jamie Delano 's original run on the title is looked on fondly , with journalist Helen <unk> stating , " His take on the character of John Constantine has never been equalled . Delano 's Original <unk> graphic novel should be in every comic book fan 's collection . " She added that , " His writing evokes an incredible sense of <unk> and terror in a reader . " IGN listed the title as one of the 25 Best Vertigo Books , calling John Constantine as " one of Vertigo 's best characters . " 
- Garth Ennis ' run is also much loved , particularly Dangerous Habits , which was voted the best Garth Ennis story on Comic Book Resources , ahead of his work on Preacher , The Boys and The <unk> . In the same article Brian <unk> describes John 's one time love , Kit Ryan , as one of the series ' most memorable characters . The popularity of Ennis and Dillon 's run on Hellblazer is also credited for Vertigo agreeing to publish their seminal series Preacher . Empire Magazine called Dangerous Habits storyline as " rightly one of the most celebrated in comic book history . " Glenn <unk> who was the cover artist during Ennis ' run , won an <unk> Ward for " Best Cover Artist " in 1995 . 
- The creator of the protagonist , Alan Moore , praised Jamie Delano 's portrayal of the character , commenting " [ Delano ] demonstrates brilliantly that English horror didn 't vanish with the fog and gas lit <unk> at the end of the Victorian era . " Moore also liked Brian Azzarello 's run on the series , commenting that Azzarello and <unk> captured the character " down , cold and to the life . " 
- The character received positive critical reception while starring in the series . Empire Magazine ranked Constantine third in their 50 Greatest Comic Characters of All Time , while IGN ranked him # 29 in their Top 100 Comic Book Heroes , and the character ranked # 10 in <unk> Magazine 's Top 200 Comic Book Characters of All Time . 
- 
- = = = Legacy = = = 
- 
- The comic book 's initial cancellation , and the introduction of the character to DC led to many negative feedback and reception . I , Vampire writer Joshua Hale <unk> expressed sadness he would never get to write " the ' real ' John Constantine " , noted crime author and former Hellblazer writer Ian Rankin stated that Constantine was the only comic book character he ever wanted to write for , and Alan Moore 's daughter , Leah Moore expressing doubt that Constantine could replace Hellblazer , among others . As a result , DC co @-@ publisher Dan <unk> issued a statement defending this decision , stating that , " Hellblazer 's had a long and incredibly successful run and that 's a tip of the hat to all the great creators that have worked on the book over the years . The new Constantine series will return him back to his roots in the <unk> and hopefully be the start of another incredible run . " Comic Alliance described Hellblazer 's cancellation as marking " the end of an era for Vertigo " while adding it to be " one of a handful of comics from the late eighties that helped comic books and their readers grow up . " 
- Hellblazer boosted the popularity and image of the occult detective fiction genre and shaped it to its modern form . Many modern examples of the genre such as <unk> , Supernatural , <unk> , The <unk> , and The Dresden Files have been influenced by it , and many <unk> of both the series and its character flourished such as Criminal <unk> , <unk> , Planetary , and others . Its elements and style have been used countless of times in other works and many <unk> of the cynical John Constantine have appeared . 
- 
- = = Publications = = 
- 
- 
- = = <unk> editions = = 
- 
- 
- = = = Trade <unk> = = = 
- 
- 
- = = Adaptations = = 
- 
- 
- = = = Film = = = 
- 
- The first adaptation of Hellblazer ever filmed for the screen is one of the scenes in the documentary feature film The <unk> of Alan Moore , which was shot in early 2002 . The <unk> consists of the John Constantine character wandering through London and , in the film ending , experiencing a mystical <unk> of sorts . 
- In 2005 , Constantine was released , a feature film that did not use the same title as the comic book , in order to avoid confusion with the <unk> horror franchise . The only links to the character of John Constantine were the name and a plotline loosely based on the " Dangerous Habits " story arc ( Hellblazer # 41 – 46 ) . DC Comics announced a sequel to the 2005 Constantine movie was in the works , with producer Lorenzo di Bonaventura linked to the project . He stated : " I 'd love to do it ... We want to do a hard , R @-@ rated version of it . We 're going to scale back the size of the movie to try and persuade the studio to go ahead and make a tough version of it . " In late 2012 , director Guillermo del Toro publicly discussed the notion of creating a film that would star John Constantine alongside other DC / Vertigo characters such as Zatanna , Swamp Thing , and more . 
- 
- = = = Television = = = 
- 
- In January 2014 it was announced that David <unk> and Daniel <unk> were developing a TV series based on Hellblazer and that NBC had ordered a pilot for it . A few weeks later it was announced that Neil Marshall would be directing the pilot . The series will follow Constantine in his early years , defending humanity against dark forces from beyond . On February , 21 it was announced that Welsh actor Matt Ryan ( whose credits include Criminal <unk> : <unk> Behavior and Edward <unk> in <unk> 's Creed IV : Black Flag ) will play the role of Constantine in the TV series pilot . On May 8 , NBC announced it had officially picked up Constantine for the Fall 2014 season . The show ran for 13 episodes , and on May 8 , 2015 , NBC cancelled Constantine after the end of its first season . It was later announced that the character of Constantine as portrayed by Ryan would be <unk> in the fourth season of the CW 's Arrow . 
- 
- = = = Others = = = 
- 
- <unk> released a video game film tie @-@ in of the film entitled Constantine . The song Stranger in the Mirror by <unk> the <unk> is written from Constantine 's point of view , including a lyrical reference to ' the Newcastle incident ' . The song " Venus of the <unk> " , which first appeared in Hellblazer Annual # 1 and written by Jamie Delano , was adapted by the rock group <unk> . 
- Fantasy fiction author John Shirley is credited in making three Hellblazer novels , including the <unk> of the Constantine film . The novel Hellblazer : War Lord features Constantine talking about " another John Constantine in an alternate universe , [ who ] has black hair and lives most of his life in Los Angeles " whilst giving a brief summary of the film 's plot . 
- 
- 
- = Curtis Woodhouse = 
- 
- Curtis Woodhouse ( born 17 April 1980 ) is an English former professional footballer turned professional boxer and football manager . Most recently manager of Hull United , Woodhouse played football as a central midfielder , and competed as a light @-@ welterweight boxer . He is the former British light @-@ welterweight champion . His career in the Football League spanned across nine seasons , earning four caps for the England under @-@ 21 football team . Woodhouse 's professional boxing record stands at 29 fights 22 wins , 13 of which are by knock @-@ out , and 7 defeats . 
- He started his <unk> career with York City 's centre of excellence before joining Sheffield United . In November 2001 , he was transferred to Birmingham City for a fee of £ 1 million , before joining Rotherham United in January 2003 , on loan for five months . He then joined Peterborough United on a free transfer in October 2003 . In May 2005 , he joined Hull City for £ 25 @,@ 000 , before joining Grimsby Town just eight months later in January 2006 . He retired at the end of the 2006 – 07 season . 
- Woodhouse stated that he had " fallen out of love " with football and decided to turn to professional boxing , despite no previous experience . In September 2006 , he won his first professional boxing match , defeating Dean Marcantonio , on points , knocking him down twice in the final round . His only defeat was by Jay Morris in April 2009 , losing 37 – 36 on points . 
- He was convicted of assaulting a police officer and of using threatening , abusive or insulting words or behaviour in April 2006 . As a result , he had his boxing licence suspended for five months by the British Boxing Board of Control , despite the incident happening before he was under the <unk> 's jurisdiction . Woodhouse returned to football in November 2006 , joining Rushden & Diamonds in the Conference National before moving to Mansfield Town in January 2009 . After leaving them , he moved to Harrogate Town . At the end of February 2010 , he joined Sheffield in the Northern Premier League . Before the start of the 2011 – 12 football season , he moved up two divisions in the football league system when he joined Conference North outfit Eastwood Town . 
- 
- = = Career = = 
- 
- 
- = = = York City and Sheffield United = = = 
- 
- Woodhouse began his football career at York City 's centre of excellence in 1994 , before being transferred to Sheffield United for an initial compensation fee of £ 2 @,@ 200 . Sheffield United and York City later agreed on an additional £ 15 @,@ 000 fee plus a five @-@ percentage sell @-@ on clause . He made his debut for Sheffield United at the age of 17 , coming on as a 79th @-@ minute substitute in a 1 – 0 home win against Crewe Alexandra in the First Division on 29 November 1997 . He made a total of nine First Division appearances in the 1997 – 98 season . Woodhouse holds the record for being the club 's youngest ever captain , aged 19 . He earned a call @-@ up to the England under @-@ 21 team , and made his debut in a 2 – 2 away draw against Hungary on 27 April 1999 . He went on to earn another three caps against Sweden , Bulgaria and Poland in England 's 2000 European Under @-@ 21 Championship qualifying group . He made a total of 104 appearances in the Football League , scoring six goals , before being sold to Birmingham City for £ 1 million in February 2001 . 
- 
- = = = Birmingham City = = = 
- 
- He made his debut for Birmingham City on 3 February 2001 , in a 2 – 1 home victory against Norwich City . He made 17 appearances for Birmingham during the 2000 – 01 season , scoring twice . Both goals came in the final league match of the season , a 2 – 1 away win against Huddersfield Town , sealing their relegation fate . Birmingham reached the League Cup final , however , Woodhouse was unable to play as he was cup @-@ tied , having previously played in three League Cup games for Sheffield United that season . He was arrested after the final of the League Cup , on 25 February 2001 , having been charged with affray along with two others after they " <unk> " an Indian restaurant and he wielded a chair in a brawl with university students . In July 2002 , he was sentenced to 120 hours of community service and ordered to pay £ 250 costs . Birmingham finished fifth in the First Division , and reached the play @-@ offs , losing in a penalty shootout in the semi @-@ final , after the game was drawn 2 – 2 on aggregate . Woodhouse played in both semi @-@ final matches . The following season , 2001 – 02 , he made 28 appearances in the First Division . Birmingham reached the play @-@ offs again for the fourth consecutive season , this time gaining <unk> after beating <unk> in the semi @-@ final and Norwich City in the final . This time , Woodhouse did not play in any of the play @-@ off matches . He made just three appearances for Birmingham City in the Premier League , before being loaned out to Rotherham United in January 2003 . Grimsby Town and Brighton & Hove Albion were also reportedly interested in signing the midfielder . During his loan spell at Rotherham , he turned out 11 times in the First Division . 
- 
- = = = Peterborough United = = = 
- 
- Woodhouse signed for Peterborough United on 14 October 2003 , the same day that he made his debut against Torquay United in a 3 – 2 victory in the Football League Trophy . However , he had been training with Peterborough a month prior to signing for them , whilst he " sorted out some problems at Birmingham City " . He went on to captain the side , and was later described as " Captain Marvel " by manager , Barry Fry . In 2003 – 04 , he made 27 appearances in the Second Division , scoring seven goals . Peterborough finished 18th in the Second Division , two points from relegation , with Woodhouse being named as Peterborough 's player of the season . In May 2004 , Hull City manager , Peter Taylor , made a failed £ 100 @,@ 000 bid to try and sign the midfielder . The following season , 2004 – 05 , he made 34 appearances in the newly named League One and scored four goals . Peterborough suffered relegation , after finishing 23rd in the league . 
- 
- = = = Hull City = = = 
- 
- He joined Hull City in May 2005 , the club he supported as a child , on a two @-@ year contract for a fee of £ 25 @,@ 000 , with the potential to rise to £ 150 @,@ 000 depending on appearances . His debut for Hull was as a late substitute in a 2 – 0 victory against Brighton & Hove Albion on 20 August 2005 . Though he lacked fitness at the start of the season , injuries to Keith Andrews and Ian Ashbee gave him the chance of a regular starting place , and manager Taylor also handed him the team captaincy . Despite consistent performances , by December 2005 he had lost his place and there was speculation that he had fallen out with Taylor and wanted to leave ; this was strongly denied by the player : " I 'm shocked that people are saying I want to go . I 'm very happy here . ... It 's taken me long enough to get to Hull so I 'm not going to walk out after a few months , or whatever . " In the following six weeks he made only two brief substitute appearances – in five months with the club he played 18 games in the Championship without scoring – and the strength of Hull 's squad was such that he could not be guaranteed regular first @-@ team football . 
- 
- = = = Grimsby Town = = = 
- 
- In the January 2006 transfer window , he joined Grimsby Town on a two @-@ year deal , three years after they first expressed an interest in signing him . He made his debut against former club Peterborough United in League Two , on 28 January 2006 in a 2 – 1 home defeat , and scored his first and what turned out to be only goal for the club against Mansfield on 14 February 2006 . On 26 April 2006 , Woodhouse said he planned to retire from football at the end of the 2005 – 06 season and embark on a career as a professional boxer . He made 16 appearances in League Two , helping them to finish fourth place , reaching the play @-@ offs . Woodhouse played in both of Grimsby 's play @-@ off semi @-@ final victories over Lincoln City , setting up the only goal of the game in the first leg . He played his last Football League game in the play @-@ off final at the Millennium Stadium on 28 May 2006 . Grimsby were defeated 1 – 0 in the final by Cheltenham Town . Woodhouse gave away a penalty in the 70th minute that was saved by goalkeeper Steve <unk> . 
- 
- = = = <unk> to boxing = = = 
- 
- Woodhouse had a history of theft , robbery and affray , and said that he had " fallen out of love " with football . He admitted to have been involved in around 100 street fights . He said ; " Boxing has always been my first love , even as a kid " , and " I love fighting ... Rather than get locked up for it , I might as well get paid for it . " He also stated that he used to spar in the boxing gym after football training without his manager 's knowledge , saying " A few times at Sheffield United , Neil Warnock would drag me in and say ‘ I hear you ’ ve been boxing ’ . I ’ d be standing there with a big black eye and a fat lip and deny it . " He trained under former British <unk> champion , Gary De <unk> , and made his boxing debut on 8 September 2006 at <unk> House Hotel , London , in a welterweight contest against Dean Marcantonio , despite not having any previous amateur experience . The former footballer had lost two stone in weight since his playing days with Grimsby Town . The fight was scheduled for four rounds of two minutes , Woodhouse knocked his opponent down twice in the final round and won on points . 
- 
- = = = Return to football part @-@ time = = = 
- 
- After only one professional fight , Woodhouse returned to football in November 2006 , signing for Rushden & Diamonds , who were playing in the Conference National , the highest @-@ tier of non @-@ league football . His boxing licence was suspended by the British Boxing Board of Control , following a conviction for assaulting a police officer whilst drunk and of using threatening , abusive or insulting words or behaviour in April 2006 , when he was still playing for Grimsby Town , and not under the <unk> 's jurisdiction at the time of the incident . He was ordered to pay £ 100 compensation to PC Andrew Whitehead and £ 350 in costs . Woodhouse chose to play for Rushden & Diamonds to fulfil a promise he made to someone who was part of the consortium that took over the <unk> based club . He made his debut against Aldershot Town on 25 November 2006 , in a 1 – 0 defeat . He made five appearances in the Conference National for Rushden , before deciding to leave to concentrate on his boxing career . 
- He then returned to Rushden on 1 March 2007 , and made a further 11 appearances in the Conference in the 2006 – 07 season , scoring three goals , including a 30 @-@ yard long range effort against <unk> Victoria on 23 April . After five months away from boxing , Woodhouse returned to the ring for his second fight on 15 April , defeating Duncan <unk> on points after four rounds . On 1 May , he signed a new two @-@ year deal with Rushden & Diamonds . He then defeated Peter Dunn in a bout on 3 June , again on points , in a contest of four three @-@ minute rounds . In his fourth fight on 5 December 2007 , he defeated Craig <unk> by way of knock @-@ out after just 1 minute and 57 seconds , landing a left hook that <unk> <unk> as the referee decided his opponent as unfit to continue . Matt Seawright was Woodhouse 's next boxing opponent on 16 March 208 , he defeated him after Seawright felt he was unable to continue after the third round . Woodhouse finished the 2007 – 08 football season having played in 29 Conference National matches , scoring once and receiving two red cards . On 17 May , Woodhouse achieved his sixth straight victory , maintaining his undefeated record against Dave Murray at Bramall Lane , defeating his opponent by knock @-@ out in the second round . Murray managed to beat the standing eight count , but the referee deemed him unable to continue after 1 minute and 23 seconds . 
- After defeating Wayne <unk> in 57 seconds on 21 June 2008 , Woodhouse stated his intention to retire from football at the end of the 2008 – 09 season , to concentrate on his boxing career . He was later appointed as team captain for his final season at the club . Woodhouse scored a decisive penalty in Rushden 's opening game of the 2008 – 09 season , away on 9 August , against newly promoted <unk> Borough . He was sent off again in the home game against <unk> on 25 August , after receiving two yellow cards . In his eighth boxing match , Woodhouse defeated Jimmy <unk> on points on 20 September . He then proceeded to defeat Peter Dunn on 30 November , stopping him 23 seconds in the sixth and final round at <unk> 's <unk> Hotel , Rotherham . 
- He signed for Mansfield Town on 5 January on a contract until the end of the 2008 – 09 season . Woodhouse made his debut for Mansfield Town on 24 January in a Conference National match against Lewes . Mansfield won 1 – 0 . Matt <unk> was Woodhouse 's next opponent in the ring on 29 March at Bramall Lane . The fight went the distance of six rounds and Woodhouse was given the decision over <unk> . His first professional loss as a boxer was on 25 April at Ulster Hall , in Belfast at the hands of Jay Morris . The fight went the distance , but Woodhouse lost 37 – 36 on points over six rounds . Mansfield manager David Holdsworth hoped Woodhouse would stay at the club , but on 18 June , he joined Conference North team Harrogate Town . On 27 November , he defeated Dean <unk> by TKO in round 6 . This was Woodhouse 's first fight in the light @-@ welterweight division , having dropped down from welterweight . 
- On 13 January 2010 , in an interview to the Grimsby Telegraph , Woodhouse commented he would definitely consider a move back to former club Grimsby Town after expressing his dismay at The Mariners <unk> near the bottom of the League Two , within danger of being relegated from the Football League . He commented " If they are looking for a central midfielder , though , they are welcome to give me a call " . Harrogate released him by mutual consent on 25 February . Three days later he avenged his defeat by Jay Morris by beating him with a TKO in round 3 . This gave Woodhouse the first title of his boxing career , the International Masters light @-@ welterweight title . On 25 April , Woodhouse defended the title with a 4th round knockout of <unk> <unk> , who stepped in as a late replacement for Steve Saville . Next , on 2 July , Woodhouse knocked out veteran <unk> Bull in round 9 . His next fight was due to be against 2006 Commonwealth Games gold medallist and former world amateur champion Frankie Gavin on 18 September , but Woodhouse pulled out days after the bout was announced in July . He moved up the non @-@ League football ladder in June 2011 , after signing for Conference North side Eastwood Town . 
- 
- = = Managerial career = = 
- 
- It was announced on 2 May 2012 , that Woodhouse was the new manager of Northern Premier League Division One South club Sheffield . Woodhouse resigned in December 2012 after admitting having difficulty to managing a football team as well as being a professional boxer . 
- On 14 October 2013 , Woodhouse was announced as the assistant manager at Northern Premier League Division One South club <unk> , with former team mate David Holdsworth being appointed manager . In January 2014 Woodhouse replaced Holdsworth as manager following Holdsworth 's resignation . Woodhouse then left <unk> after some issues with the board , and took over at Hull United in January 2015 . 
- 
- = = Personal life = = 
- 
- Woodhouse was born in <unk> and raised in <unk> . His father , Bernard Woodhouse , died at the age of 51 after he had a fatal stroke . Unlike other boys who <unk> footballers , Woodhouse stated his heroes were Nigel Benn and Mike Tyson . He admits to fighting at school and in the streets after being racially abused : " I went from scrapping in the street and at school to fighting in the boxing ring from the age of 12 . I was called a few names due to the colour of my skin , but with a quick <unk> in the mouth they soon backed off . " He used to visit <unk> Park to support Hull City before pursuing his career in professional football . He has a wife , Charlotte , and two children : a son named Kyle and a daughter , <unk> . 
- 
- = = Career statistics = = 
- 
- As of 29 January 2011 . 
- 
- = = Professional boxing record = = 
- 
- 
- 
- = 2010 Haiti earthquake = 
- 
- The 2010 Haiti earthquake ( French : <unk> de 2010 à <unk> ; Haitian Creole : <unk> 12 <unk> 2010 <unk> <unk> <unk> ) was a catastrophic magnitude 7 @.@ 0 Mw earthquake , with an epicenter near the town of <unk> ( <unk> ) , approximately 25 kilometres ( 16 mi ) west of Port @-@ au @-@ Prince , Haiti 's capital . The earthquake occurred at 16 : 53 local time ( 21 : 53 UTC ) on Tuesday , 12 January 2010 . 
- By 24 January , at least 52 aftershocks measuring 4 @.@ 5 or greater had been recorded . An estimated three million people were affected by the quake . Death toll estimates range from 100 @,@ 000 to about 160 @,@ 000 to Haitian government figures from 220 @,@ 000 to 316 @,@ 000 that have been widely characterized as deliberately inflated by the Haitian government . The government of Haiti estimated that 250 @,@ 000 residences and 30 @,@ 000 commercial buildings had collapsed or were severely damaged . There has been a history of national debt , <unk> trade policies by other countries , and foreign intervention into national affairs that contributed to the pre @-@ existing poverty and poor housing conditions that exacerbated the death toll . 
- The earthquake caused major damage in Port @-@ au @-@ Prince , Jacmel and other settlements in the region . Notable landmark buildings were significantly damaged or destroyed , including the Presidential Palace , the National Assembly building , the Port @-@ au @-@ Prince Cathedral , and the main jail . Among those killed were Archbishop of Port @-@ au @-@ Prince Joseph Serge <unk> , and opposition leader <unk> Gaillard . The headquarters of the United Nations Stabilization Mission in Haiti ( MINUSTAH ) , located in the capital , collapsed , killing many , including the Mission 's Chief , <unk> <unk> . 
- Many countries responded to appeals for humanitarian aid , pledging funds and dispatching rescue and medical teams , engineers and support personnel . Communication systems , air , land , and sea transport facilities , hospitals , and electrical networks had been damaged by the earthquake , which hampered rescue and aid efforts ; confusion over who was in charge , air traffic congestion , and problems with <unk> of flights further complicated early relief work . Port @-@ au @-@ Prince 's <unk> were overwhelmed with tens of thousands of bodies . These had to be buried in mass graves . As rescues tailed off , supplies , medical care and sanitation became priorities . <unk> in aid distribution led to angry appeals from aid workers and survivors , and looting and sporadic violence were observed . On 22 January the United Nations noted that the emergency phase of the relief operation was drawing to a close , and on the following day the Haitian government officially called off the search for survivors . 
- 
- = = Background = = 
- 
- The island of Hispaniola , shared by Haiti and the Dominican Republic , is <unk> active and has a history of destructive earthquakes . During Haiti 's time as a French colony , earthquakes were recorded by French historian Moreau de Saint @-@ <unk> ( <unk> – 1819 ) . He described damage done by an earthquake in 1751 , writing that " only one masonry building had not collapsed " in Port @-@ au @-@ Prince ; he also wrote that the " whole city collapsed " in the 1770 Port @-@ au @-@ Prince earthquake . Cap @-@ <unk> , other towns in the north of Haiti and the Dominican Republic , and the Sans @-@ <unk> Palace were destroyed during an earthquake on 7 May 1842 . A magnitude 8 @.@ 0 earthquake struck the Dominican Republic and shook Haiti on 4 August 1946 , producing a tsunami that killed 1 @,@ 790 people and injured many others . 
- Haiti is the poorest country in the Western Hemisphere , and is ranked <unk> of 182 countries on the Human Development Index . The Australian government 's travel advisory site had previously expressed concerns that Haitian emergency services would be unable to cope in the event of a major disaster , and the country is considered " economically vulnerable " by the Food and Agriculture Organization . Haiti is no stranger to natural disasters . In addition to earthquakes , it has been struck frequently by tropical cyclones , which have caused flooding and widespread damage . The most recent cyclones to hit the island before the earthquake were Tropical Storm Fay and Hurricanes Gustav , Hanna and Ike , all in the summer of 2008 , causing nearly 800 deaths . 
- 
- = = Geology = = 
- 
- The magnitude 7 @.@ 0 Mw earthquake occurred inland , on 12 January 2010 at 16 : 53 ( UTC @-@ 05 : 00 ) , approximately 25 km ( 16 mi ) <unk> from Port @-@ au @-@ Prince at a depth of 13 km ( 8 @.@ 1 mi ) on blind thrust faults associated with the Enriquillo @-@ Plantain Garden fault system . There is no evidence of surface rupture and based on <unk> , geological and ground <unk> data it is thought that the earthquake did not involve significant lateral slip on the main Enriquillo fault . Strong shaking associated with intensity IX on the <unk> <unk> scale ( MM ) was recorded in Port @-@ au @-@ Prince and its suburbs . It was also felt in several surrounding countries and regions , including Cuba ( MM III in <unk> ) , Jamaica ( MM II in Kingston ) , Venezuela ( MM II in <unk> ) , Puerto Rico ( MM II – III in San Juan ) , and the bordering Dominican Republic ( MM III in Santo Domingo ) . According to estimates from the United States Geological Survey , approximately 3 @.@ 5 million people lived in the area that experienced shaking intensity of MM VII to X , a range that can cause moderate to very heavy damage even to earthquake @-@ resistant structures . <unk> damage was more severe than for other <unk> of similar magnitude due to the shallow depth of the quake . 
- The quake occurred in the vicinity of the northern boundary where the Caribbean <unk> plate shifts eastwards by about 20 mm ( 0 @.@ 79 in ) per year in relation to the North American plate . The strike @-@ slip fault system in the region has two branches in Haiti , the Septentrional @-@ Oriente fault in the north and the Enriquillo @-@ Plantain Garden fault in the south ; both its location and focal mechanism suggested that the January 2010 quake was caused by a rupture of the Enriquillo @-@ Plantain Garden fault , which had been locked for 250 years , gathering stress . However , a study published in May 2010 suggested that the rupture process may have involved slip on multiple blind thrust faults with only minor , deep , lateral slip along or near the main Enriquillo – Plantain Garden fault zone , suggesting that the event only partially relieved centuries of accumulated left @-@ lateral strain on a small part of the plate @-@ boundary system . The rupture was roughly 65 km ( 40 mi ) long with mean slip of 1 @.@ 8 metres ( 5 ft 11 in ) . Preliminary analysis of the slip distribution found <unk> of up to about 4 m ( 13 ft ) using ground motion records from all over the world . 
- A 2007 earthquake hazard study by C. <unk> and M. <unk> @-@ <unk> noted that the Enriquillo @-@ Plantain Garden fault zone could be at the end of its seismic cycle and concluded that a worst @-@ case forecast would involve a 7 @.@ 2 Mw earthquake , similar in size to the 1692 Jamaica earthquake . Paul Mann and a group including the 2006 study team presented a hazard assessment of the Enriquillo @-@ Plantain Garden fault system to the 18th Caribbean <unk> Conference in March 2008 , noting the large strain ; the team recommended " high priority " historical geologic rupture studies , as the fault was fully locked and had recorded few earthquakes in the preceding 40 years . An article published in Haiti 's Le <unk> newspaper in September 2008 cited comments by geologist Patrick Charles to the effect that there was a high risk of major seismic activity in Port @-@ au @-@ Prince . 
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- = = = <unk> = = = 
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- The United States Geological Survey ( USGS ) recorded eight aftershocks in the two hours after the main earthquake , with <unk> between 4 @.@ 3 and 5 @.@ 9 . Within the first nine hours 32 aftershocks of magnitude 4 @.@ 2 or greater were recorded , 12 of which measured magnitude 5 @.@ 0 or greater , and on 24 January USGS reported that there had been 52 aftershocks measuring 4 @.@ 5 or greater since 12 January quake . 
- On 20 January at 06 : 03 local time ( 11 : 03 UTC ) the strongest aftershock since the earthquake , measuring magnitude 5 @.@ 9 Mw , struck Haiti . USGS reported its epicenter was about 56 km ( 35 mi ) <unk> of Port @-@ au @-@ Prince , which would place it almost exactly under the coastal town of Petit @-@ Goâve . A UN representative reported that the aftershock collapsed seven buildings in the town . According to staff of the International Committee of the Red Cross , which had reached Petit @-@ Goâve for the first time the day before the aftershock , the town was estimated to have lost 15 percent of its buildings , and was suffering the same shortages of supplies and medical care as the capital . Workers from the charity Save the Children reported hearing " already weakened structures collapsing " in Port @-@ au @-@ Prince , but most sources reported no further significant damage to infrastructure in the city . Further casualties are thought to have been minimal since people had been sleeping in the open . There are concerns that 12 January earthquake could be the beginning of a new long @-@ term sequence : " the whole region is fearful " ; historical accounts , although not precise , suggest that there has been a sequence of <unk> progressing <unk> along the fault , starting with an earthquake in the Dominican Republic in 1751 . 
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- = = = Tsunami = = = 
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- The Pacific Tsunami Warning Center issued a tsunami warning immediately after the initial quake , but quickly cancelled it . Nearly two weeks later it was reported that the beach of the small fishing town of Petit <unk> was hit by a localised tsunami shortly after the earthquake , probably as a result of an underwater slide , and this was later confirmed by researchers . At least three people were swept out to sea by the wave and were reported dead . Witnesses told reporters that the sea first retreated and a " very big wave " followed rapidly , crashing ashore and sweeping boats and debris into the ocean . 
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- = = Damage to infrastructure = = 
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- = = = Essential services = = = 
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- Amongst the widespread devastation and damage throughout Port @-@ au @-@ Prince and elsewhere , vital infrastructure necessary to respond to the disaster was severely damaged or destroyed . This included all hospitals in the capital ; air , sea , and land transport facilities ; and communication systems . 
- The quake affected the three Médecins Sans Frontières ( Doctors Without Borders ) medical facilities around Port @-@ au @-@ Prince , causing one to collapse completely . A hospital in <unk> , a wealthy suburb of Port @-@ au @-@ Prince , also collapsed , as did the St. Michel District Hospital in the southern town of Jacmel , which was the largest referral hospital in south @-@ east Haiti . 
- The quake seriously damaged the control tower at Toussaint L 'Ouverture International Airport . Damage to the Port @-@ au @-@ Prince seaport rendered the harbor unusable for immediate rescue operations ; its container crane subsided severely at an angle because of weak foundations . <unk> seaport in northern Haiti remained operational . 
- Roads were blocked with road debris or the surfaces broken . The main road linking Port @-@ au @-@ Prince with Jacmel remained blocked ten days after the earthquake , hampering delivery of aid to Jacmel . When asked why the road had not been opened , <unk> el @-@ <unk> , head of the south @-@ east division of the UN World Food Programme said that " We ask the same questions to the people in charge ... They promise rapid response . To be honest , I don 't know why it hasn 't been done . I can only think that their priority must be somewhere else . " 
- There was considerable damage to communications infrastructure . The public telephone system was not available , and two of Haiti 's largest cellular telephone providers , <unk> and <unk> Haiti , both reported that their services had been affected by the earthquake . <unk> @-@ <unk> <unk> was also disrupted . According to <unk> Sans Frontières ( <unk> ) , Radio <unk> , which broadcasts out of Port @-@ au @-@ Prince and reaches 90 percent of Haiti , was initially knocked off the air , but it was able to resume broadcasting across most of its network within a week . According to <unk> , some 20 of about 50 stations that were active in the capital region prior to the earthquake were back on air a week after the quake . 
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- = = = General infrastructure = = = 
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- In February 2010 Prime Minister Jean @-@ Max Bellerive estimated that 250 @,@ 000 residences and 30 @,@ 000 commercial buildings were severely damaged and needed to be demolished . The deputy mayor of <unk> reported that 90 percent of the town 's buildings had been destroyed . Many government and public buildings were damaged or destroyed including the Palace of Justice , the National Assembly , the Supreme Court and Port @-@ au @-@ Prince Cathedral . The National Palace was severely damaged , though President René Préval and his wife Elisabeth <unk> Préval escaped injury . The Prison <unk> de Port @-@ au @-@ Prince was also destroyed , allowing around 4 @,@ 000 inmates to escape . 
- Most of Port @-@ au @-@ Prince 's municipal buildings were destroyed or heavily damaged , including the City Hall , which was described by the Washington Post as , " a skeletal <unk> of concrete and stucco , <unk> <unk> to the left . " Port @-@ au @-@ Prince had no municipal petrol reserves and few city officials had working mobile phones before the earthquake , complicating communications and transportation . 
- Minister of Education Joel Jean @-@ Pierre stated that the education system had " totally collapsed " . About half the nation 's schools and the three main universities in Port @-@ au @-@ Prince were affected . More than 1 @,@ 300 schools and 50 health care facilities were destroyed . 
- The earthquake also destroyed a nursing school in the capital and severely damaged the country 's primary <unk> school . The Haitian art world suffered great losses ; artworks were destroyed , and museums and art galleries were extensively damaged , among them Port @-@ au @-@ Prince 's main art museum , Centre d <unk> , College Saint Pierre and Holy Trinity Cathedral . 
- The headquarters of the United Nations Stabilization Mission in Haiti ( MINUSTAH ) at Christopher Hotel and offices of the World Bank were destroyed . The building housing the offices of Citibank in Port @-@ au @-@ Prince collapsed , killing five employees . The clothing industry , which accounts for two @-@ thirds of Haiti 's exports , reported structural damage at manufacturing facilities . 
- The quake created a landslide dam on the Rivière de Grand Goâve . As of February 2010 the water level was low , but engineer Yves <unk> believed the dam could collapse during the rainy season , which would flood Grand @-@ Goâve 12 km ( 7 @.@ 5 mi ) downstream . 
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- = = Conditions in the aftermath = = 
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- In the nights following the earthquake , many people in Haiti slept in the streets , on <unk> , in their cars , or in makeshift <unk> towns either because their houses had been destroyed , or they feared standing structures would not withstand aftershocks . Construction standards are low in Haiti ; the country has no building codes . Engineers have stated that it is unlikely many buildings would have stood through any kind of disaster . Structures are often raised wherever they can fit ; some buildings were built on slopes with insufficient foundations or steel supports . A representative of Catholic Relief Services has estimated that about two million Haitians lived as squatters on land they did not own . The country also suffered from shortages of fuel and <unk> water even before the disaster . 
- President Préval and government ministers used police headquarters near the Toussaint L 'Ouverture International Airport as their new base of operations , although their effectiveness was extremely limited ; several <unk> were still trapped in the Presidential Palace , and offices and records had been destroyed . Some high @-@ ranking government workers lost family members , or had to tend to wounded relatives . Although the president and his remaining cabinet met with UN planners each day , there remained confusion as to who was in charge and no single group had organized relief efforts as of 16 January . The government handed over control of the airport to the United States to hasten and ease flight operations , which had been hampered by the damage to the air traffic control tower . 
- Almost immediately Port @-@ au @-@ Prince 's morgue facilities were overwhelmed . By 14 January , a thousand bodies had been placed on the streets and <unk> . Government crews manned trucks to collect thousands more , burying them in mass graves . In the heat and humidity , corpses buried in rubble began to <unk> and smell . <unk> Goldstein , head of the Israeli <unk> International Rescue Unit delegation to Haiti , described the situation as " <unk> from hell . Everywhere , the acrid smell of bodies hangs in the air . It 's just like the stories we are told of the Holocaust – thousands of bodies everywhere . You have to understand that the situation is true madness , and the more time passes , there are more and more bodies , in numbers that cannot be grasped . It is beyond comprehension . " 
- Mayor Jean @-@ Yves Jason said that officials argued for hours about what to do with the volume of corpses . The government buried many in mass graves , some above @-@ ground tombs were forced open so bodies could be stacked inside , and others were burned . Mass graves were dug in a large field outside the settlement of <unk> , north of the capital ; tens of thousands of bodies were reported as having been brought to the site by dump truck and buried in trenches dug by earth <unk> . Max <unk> , a <unk> priest , protested the lack of dignity in mass burials , stating , " ... it is not in our culture to bury people in such a fashion , it is <unk> " . 
- Towns in the eastern Dominican Republic began preparing for tens of thousands of refugees , and by 16 January hospitals close to the border had been filled to capacity with Haitians . Some began reporting having expended stocks of critical medical supplies such as antibiotics by 17 January . The border was reinforced by Dominican soldiers , and the government of the Dominican Republic asserted that all Haitians who crossed the border for medical assistance would be allowed to stay only temporarily . A local governor stated , " We have a great desire and we will do everything <unk> possible to help Haitian families . But we have our limitations with respect to food and medicine . We need the helping hand of other countries in the area . " 
- Slow distribution of resources in the days after the earthquake resulted in sporadic violence , with looting reported . There were also accounts of <unk> wounded or killed by <unk> and neighbourhoods that had constructed their own roadblock barricades . Dr Evan Lyon of Partners in Health , working at the General Hospital in Port @-@ au @-@ Prince , claimed that <unk> and overblown reports of violence had hampered the delivery of aid and medical services . 
- Former US president Bill Clinton acknowledged the problems and said Americans should " not be <unk> from supporting the relief effort " by upsetting scenes such as those of looting . Lt. Gen. <unk> <unk> , deputy commander of US Southern Command , however , announced that despite the stories of looting and violence , there was less violent crime in Port @-@ au @-@ Prince after the earthquake than before . 
- In many neighbourhoods , singing could be heard through the night and groups of men coordinated to act as security as groups of women attempted to take care of food and hygiene <unk> . During the days following the earthquake , hundreds were seen marching through the streets in peaceful processions , singing and clapping . 
- The earthquake caused an urgent need for outside rescuers to communicate with Haitians whose main or only language is Haitian Creole . As a result , a mobile translation program to translate between English and Haitian Creole had to be written quickly . 
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- = = Casualties = = 
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- The earthquake struck in the most populated area of the country . The International Federation of Red Cross and Red Crescent Societies estimated that as many as 3 million people had been affected by the quake . In mid February 2010 , the Haitian government reported the death toll to have reached 230 @,@ 000 . However , an investigation by Radio Netherlands has questioned the official death toll , reporting an estimate of 92 @,@ 000 deaths as being a more realistic figure . On the first anniversary of the earthquake , 12 January 2011 , Haitian Prime Minister Jean @-@ Max Bellerive said the death toll from the quake was more than 316 @,@ 000 , raising the figures from previous estimates . 
- Several experts have questioned the validity of the death toll numbers ; Anthony <unk> , professor emeritus in environmental history at Northeastern University , warned that casualty estimates could only be a " <unk> " , and Belgian disaster response expert Claude de <unk> de <unk> noted that " round numbers are a sure sign that nobody knows . " Edmond <unk> , UN Assistant Secretary @-@ General for <unk> Operations , said , " I do not think we will ever know what the death toll is from this earthquake " , while the director of the Haitian Red Cross , Jean @-@ Pierre <unk> , noted that his organization had not had the time to count bodies , as their focus had been on the treatment of survivors . 
- While the vast majority of casualties were Haitian civilians , the dead included aid workers , embassy staff , foreign tourists — and a number of public figures , including Archbishop of Port @-@ au @-@ Prince Monsignor Joseph Serge <unk> , aid worker <unk> <unk> and officials in the Haitian government , including opposition leader Michel " <unk> " Gaillard . Also killed were a number of well @-@ known Haitian musicians and sports figures , including thirty members of the Fédération <unk> de Football . At least 85 United Nations personnel working with MINUSTAH were killed , among them the Mission Chief , <unk> <unk> , his deputy , Luiz Carlos da Costa , and police commissioner Douglas <unk> . Around 200 guests were killed in the collapse of the <unk> Montana in Port @-@ au @-@ Prince . 
- On 31 May 2011 , an unreleased draft report based on a survey commissioned by the US Agency for International Development ( USAID ) challenged the Haiti earthquake death toll and several damage estimates . The unpublished report put the death toll between 46 @,@ 000 and 85 @,@ 000 and put the number of displaced persons at 895 @,@ 000 , of which only 375 @,@ 000 remained in temporary shelters . The unreleased report , which compiled its figures from a door @-@ to @-@ door survey , was done by a Washington consulting firm , <unk> <unk> . A US State Department spokesperson said the report had inconsistencies and would not be released until they were resolved . As of January 2012 , USAID has not released the report and states at its website that 1 @.@ 5 million people were displaced , of which 550 @,@ 000 remain without permanent shelter . The most reliable academic estimate of the number of earthquake casualties in Haiti ( over 95 % were in the immediate Port @-@ au @-@ Prince area ) " within six weeks of the earthquake " appears to be the 160 @,@ 000 estimate in a 2010 University of Michigan study . 
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- = = Early response = = 
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- Appeals for humanitarian aid were issued by many aid organizations , the United Nations and president René Préval . Raymond Joseph , Haiti 's ambassador to the United States , and his nephew , singer <unk> Jean , who was called upon by Préval to become a " <unk> ambassador " for Haiti , also pleaded for aid and donations . <unk> and <unk> circulating after the earthquake across the internet and through social media helped to intensify the reaction of global engagement . 
- Many countries responded to the appeals and launched fund @-@ raising efforts , as well as sending search and rescue teams . The neighbouring Dominican Republic was the first country to give aid to Haiti , sending water , food and heavy @-@ lifting machinery . The hospitals in the Dominican Republic were made available ; a combined effort of the Airports Department ( <unk> ) , together with the Dominican Naval <unk> , the UN and other parties formed the Dominican @-@ Haitian Aerial Support Bridge , making the main Dominican airports available for support operations to Haiti . The Dominican website <unk> made available to the internet , daily updates on airport information and news from the operations center on the Dominican side . The Dominican emergency team assisted more than 2 @,@ 000 injured people , while the Dominican Institute of <unk> ( <unk> ) helped with the restoration of some telephone services . The Dominican Red Cross coordinated early medical relief in conjunction with the International Red Cross . The government sent eight mobile medical units along with 36 doctors including <unk> specialists , <unk> , <unk> , and <unk> . In addition , 39 trucks carrying canned food were dispatched , along with 10 mobile kitchens and 110 cooks capable of producing 100 @,@ 000 meals per day . 
- Other nations from farther afield also sent personnel , medicines , materiel , and other aid to Haiti . The first team to arrive in Port @-@ au @-@ Prince was <unk> @-@ <unk> from Iceland , landing within 24 hours of the earthquake . A 50 @-@ member Chinese team arrived early Thursday morning . From the Middle East , the government of <unk> sent a strategic transport aircraft ( C @-@ 17 ) , loaded with 50 tonnes of urgent relief materials and 26 members from the <unk> armed forces , the internal security force ( <unk> ) , police force and the <unk> Medical Corporation , to set up a field hospital and provide assistance in Port @-@ au @-@ Prince and other affected areas in Haiti . A rescue team sent by the Israel Defense Forces ' Home Front Command established a field hospital near the United Nations building in Port @-@ au @-@ Prince with specialised facilities to treat children , the elderly , and women in labor . It was set up in eight hours and began operations on the evening of 16 January . A Korean International Disaster Relief Team with 40 rescuers , medical doctors , nurses and 2 k @-@ <unk> was deployed to <unk> to assist <unk> efforts of Haitian Government . 
- The American Red Cross announced on 13 January that it had run out of supplies in Haiti and appealed for public donations . Giving Children Hope worked to get much @-@ needed medicines and supplies on the ground . Partners in Health ( <unk> ) , the largest health care provider in rural Haiti , was able to provide some emergency care from its ten hospitals and clinics , all of which were outside the capital and undamaged . MINUSTAH had over 9 @,@ 000 <unk> peacekeepers deployed to the area . Most of these workers were initially involved in the search for survivors at the organization 's collapsed headquarters . 
- The International Charter on Space and Major Disasters was activated , allowing satellite imagery of affected regions to be shared with rescue and aid organizations . Members of social networking sites such as Twitter and Facebook spread messages and pleas to send help . Facebook was overwhelmed by — and blocked — some users who were sending messages about updates . The American Red Cross set a record for mobile donations , raising US $ 7 million in 24 hours when they allowed people to send US $ 10 donations by text messages . The <unk> community responded to the disaster by greatly improving the level of mapping available for the area using post @-@ earthquake satellite photography provided by <unk> , and tracking website <unk> coordinated messages from multiple sites to assist Haitians still trapped and to keep families of survivors informed . Some online poker sites hosted poker tournaments with tournament fees , prizes or both going to disaster relief charities . Google Earth updated its coverage of Port @-@ au @-@ Prince on 17 January , showing the earthquake @-@ ravaged city . 
- <unk> refugee immigration into Canada was discussed by Canadian Prime Minister Stephen Harper , and in the US Haitians were granted <unk> protected status , a measure that permits about 100 @,@ 000 illegal alien Haitians in the United States to stay legally for 18 months , and <unk> the <unk> of 30 @,@ 000 more , though it does not apply to Haitians outside the US . Local and state agencies in South Florida , together with the US government , began implementing a plan ( " Operation <unk> <unk> " ) for a mass migration from the Caribbean that had been laid out in 2003 . 
- Several <unk> were destroyed in the earthquake . After the process for the adoption of 400 children by families in the US and the Netherlands was expedited , <unk> and SOS Children urged an immediate halt to <unk> from Haiti . Jasmine <unk> , chief executive of Save the Children said : " The vast majority of the children currently on their own still have family members alive who will be desperate to be reunited with them and will be able to care for them with the right support . Taking children out of the country would permanently separate thousands of children from their families — a separation that would compound the acute trauma they are already suffering and inflict long @-@ term damage on their chances of recovery . " However , several organizations were planning an <unk> of thousands of orphaned children to South Florida on humanitarian <unk> , modelled on a similar effort with Cuban refugees in the 1960s named " Pedro Pan " . The Canadian government worked to expedite around 100 adoption cases that were already underway when the earthquake struck , issuing temporary permits and <unk> regular processing fees ; the federal government also announced that it would cover adopted children 's healthcare costs upon their arrival in Canada until they could be covered under <unk> administered public healthcare plans . 
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- = = Rescue and relief efforts = = 
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- Rescue efforts began in the immediate aftermath of the earthquake , with able @-@ bodied survivors <unk> the living and the dead from the rubble of the many buildings that had collapsed . Treatment of the injured was hampered by the lack of hospital and morgue facilities : the Argentine military field hospital , which had been serving MINUSTAH , was the only one available until 13 January . Rescue work intensified only slightly with the arrival of doctors , police officers , military personnel and <unk> from various countries two days after the earthquake . 
- From 12 January , the International Committee of the Red Cross , which has been working in Haiti since 1994 , focused on bringing emergency assistance to victims of the catastrophe , in close cooperation with its partners within the International Red Cross and Red Crescent Movement , particularly the Haitian Red Cross and the International Federation of Red Cross and Red Crescent Societies . 
- Médecins Sans Frontières ( Doctors Without Borders ; MSF ) reported that the hospitals that had not been destroyed were overwhelmed by large numbers of seriously injured people , and that they had to carry out many <unk> . Running short of medical supplies , some teams had to work with any available resources , constructing <unk> out of cardboard and reusing latex gloves . Other rescue units had to withdraw as night fell amid security fears . Over 3 @,@ 000 people had been treated by Médecins Sans Frontières as of 18 January . Ophelia Dahl , director of Partners in Health , reported , " there are hundreds of thousands of injured people . I have heard the estimate that as many as 20 @,@ 000 people will die each day that would have been saved by surgery . " 
- An MSF aircraft carrying a field hospital was repeatedly turned away by US air traffic controllers who had assumed control at Toussaint L 'Ouverture International Airport . Four other MSF aircraft were also turned away . In a 19 January press release MSF said , " It is like working in a war situation . We don 't have any more morphine to manage pain for our patients . We cannot accept that planes carrying <unk> medical supplies and equipment continue to be turned away while our patients die . <unk> must be given to medical supplies entering the country . " First <unk> voiced frustration with the number of relief trucks sitting unused at the airport . Aid workers blamed US @-@ controlled airport operations for <unk> the transportation of security troops over rescuers and supplies ; evacuation policies favouring citizens of certain nations were also criticised . 
- The US military acknowledged the non @-@ governmental organizations ' complaints concerning flight @-@ operations bias and promised improvement while noting that up to 17 January 600 emergency flights had landed and 50 were diverted ; by the first weekend of disaster operations <unk> had been reduced to three on Saturday and two on Sunday . The airport was able to support 100 landings a day , up from the 35 a day that the airport gets during normal operation . A spokesman for the joint task force running the airport confirmed that though more flights were requesting landing slots , none were being turned away . 
- Brazilian Foreign Minister <unk> <unk> and French Minister of State for Cooperation Alain <unk> criticised the perceived <unk> treatment for US aid arriving at the airport , though a spokesman for the French Ministry of Foreign Affairs said that there had been no official protest from the French government with regard to the management of the airport . US officials acknowledged that coordination of the relief effort is central to Haitian recovery , and President Préval asked for calm coordination between assisting nations without mutual accusations . 
- US Air Force logs documenting activity at the airport obtained by the Associated Press largely <unk> the claim that the US held up aid in favor of military flights . The US military initially did give priority to military units needed to secure the airport , distribute aid , and provide security , but after that , incoming relief flights were cleared or rejected on a first @-@ come , first @-@ served basis . According to a US Air Force Captain who had coordinated flight schedules , nearly all groups sending aid insisted their shipment was urgent . Those flights that were rejected were diverted to the Dominican Republic , where their cargoes were <unk> and taken to Haiti by land . 
- At the peak of the relief efforts , the airport was in a state of chaos . Normally , the airport , with a single runway and 10 spaces for large planes , handled 20 flights a day . After the earthquake struck , hundreds of planes rushed to Haiti without designated landing time . On average , a plane would land or take off every two minutes . The situation was further complicated by the fact that there was no room on ramps for planes to <unk> their cargo , and some planes did not have enough fuel to leave . 
- While the Port @-@ au @-@ Prince airport ramp has spaces for over a dozen <unk> , in the days following the quake it sometimes served nearly 40 at once , creating serious delays . The supply backup at the airport was expected to ease as the apron management improved , and when the perceived need for heavy security diminished . Airport congestion was reduced further on 18 January when the United Nations and US forces formally agreed to <unk> humanitarian flights over security reinforcement . 
- By 14 January , over 20 countries had sent military personnel to the country , with Canada , the United States and the Dominican Republic providing the largest <unk> . The <unk> USS Carl <unk> arrived at maximum possible speed on 15 January with 600 @,@ 000 emergency food rations , 100 @,@ 000 ten @-@ litre water containers , and an enhanced wing of 19 helicopters ; 130 @,@ 000 litres of drinking water were transferred to shore on the first day . 
- The helicopter carrier USS <unk> sailed with three large dock landing ships and two survey / salvage vessels , to create a " sea base " for the rescue effort . They were joined by the French Navy vessel Francis Garnier on 16 January , the same day the hospital ship <unk> <unk> and guided @-@ missile cruiser USS Bunker Hill left for Haiti . Another large French vessel was later ordered to Haiti , the amphibious transport dock <unk> . 
- International rescue efforts were restricted by traffic congestion and blocked roads . Although US Secretary of Defense Robert Gates had previously ruled out dropping food and water by air as too dangerous , by 16 January , US helicopters were distributing aid to areas impossible to reach by land . 
- In Jacmel , a city of 50 @,@ 000 , the mayor claimed that 70 percent of the homes had been damaged and that the quake had killed 300 to 500 people and left some 4 @,@ 000 injured . The small airstrip suffered damage that rendered it unusable for supply flights until 20 January . The Canadian navy vessel HMCS Halifax was deployed to the area on 18 January ; the Canadians joined Colombian rescue workers , Chilean doctors , a French mobile clinic , and Sri Lankan relief workers who had already responded to calls for aid . 
- About 64 @,@ 000 people living in the three adjacent agricultural communities of <unk> , <unk> a <unk> , and Les <unk> were relatively unharmed because most of the people were working in the fields ; but all churches , chapels and at least 8 @,@ 000 homes were destroyed . 
- British search and rescue teams were the first to arrive in <unk> , the town at the epicenter of the quake , on 17 January . The Canadian ship HMCS <unk> reached the area on 19 January , and by 20 January there were 250 – 300 Canadian personnel assisting relief efforts in the town . By 19 January , staff of the International Red Cross had also managed to reach the town , which they described as " severely damaged ... the people there urgently need assistance " , and by 20 January they had reached Petit @-@ Goâve as well , where they set up two first @-@ aid posts and distributed first @-@ aid kits . 
- Over the first weekend 130 @,@ 000 food <unk> and 70 @,@ 000 water containers were distributed to Haitians , as safe landing areas and distribution centers such as golf courses were secured . There were nearly 2 @,@ 000 rescuers present from 43 different groups , with 161 search dogs ; the airport had handled 250 tons of relief supplies by the end of the weekend . Reports from Sunday showed a record @-@ breaking number of successful rescues , with at least 12 survivors pulled from Port @-@ au @-@ Prince 's rubble , bringing the total number of rescues to 110 . 
- The buoy tender <unk> Oak and <unk> <unk> ( T @-@ ARS @-@ 51 ) were on scene by 18 January to assess damage to the port and work to reopen it , and by 21 January one pier at the Port @-@ au @-@ Prince seaport was functional , <unk> humanitarian aid , and a road had been repaired to make transport into the city easier . In an interview on 21 January , Leo <unk> , Haiti 's ambassador to the UN , said that he expected the port to be fully functional again within two weeks . 
- The US Navy listed its resources in the area as " 17 ships , 48 helicopters and 12 fixed @-@ wing aircraft " in addition to 10 @,@ 000 sailors and Marines . The Navy had conducted <unk> air deliveries , delivered 32 @,@ 400 US gallons ( 123 @,@ 000 L ) of water , <unk> @,@ 440 bottles of water , 111 @,@ <unk> meals and 9 @,@ 000 lb ( 4 @,@ 100 kg ) of medical supplies by 20 January . Hospital ship <unk> began operations on 20 January , completing the arrival of the first group of sea @-@ base vessels ; this came as a new flotilla of USN ships were assigned to Haiti , including survey vessels , ferries , elements of the maritime <unk> and underway <unk> fleets , and a further three amphibious operations ships , including another helicopter carrier , USS Nassau ( <unk> @-@ 4 ) . 
- On 22 January the UN and United States <unk> the coordination of relief efforts by signing an agreement giving the US responsibility for the ports , airports and roads , and making the UN and Haitian authorities responsible for law and order . The UN stated that it had resisted <unk> the organization of the relief effort to allow as much <unk> as possible for those wishing to assist in the relief effort , but with the new agreement " we 're leaving that emergency phase behind " . The UN also urged organizations to coordinate aid efforts through its mission in Haiti to allow for better scheduling of the arrival of supplies . On 23 January the Haitian government officially called off the search for survivors , and most search and rescue teams began to prepare to leave the country . However , as late as 8 February 2010 , survivors were still being discovered , as in the case of Evan <unk> , 28 , found in the rubble of a grocery store . 
- On 5 February , ten Baptist missionaries from Idaho led by Laura <unk> were charged with criminal association and kidnapping for trying to smuggle 33 children out of Haiti . The missionaries claimed they were rescuing orphaned children but investigations revealed that more than 20 of the children had been taken from their parents after they were told the children would have a better life in America . In an interview , the United States Ambassador to Haiti Kenneth <unk> , stated that the US justice system would not interfere and that " the Haitian justice system will do what it has to do . " By 9 March 2010 , all but <unk> were deported and she remained incarcerated . 
- Social networking organizations such as Crisis Camp Haiti were developed to aid in the structure and coordination of relief efforts in Haiti and future catastrophic events as well . 
- On 10 April , due to the potential threat of mudslides and flooding from the upcoming rainy season , the Haitian government began operations to move thousands of refugees to a more secure location north of the capital . 
- 
- = = Recovery = = 
- 
- US President Barack Obama announced that former presidents Bill Clinton , who also acts as the UN special envoy to Haiti , and George W. Bush would coordinate efforts to raise funds for Haiti 's recovery . Secretary of State Hillary Clinton visited Haiti on 16 January to survey the damage and stated that US $ 48 million had been raised already in the US to help Haiti recover . Following the meeting with Secretary Clinton , President Préval stated that the highest priorities in Haiti 's recovery were establishing a working government , clearing roads , and ensuring the streets were cleared of bodies to improve <unk> conditions . 
- US Vice President Joe <unk> stated on 16 January that President Obama " does not view this as a humanitarian mission with a life cycle of a month . This will still be on our radar screen long after it 's off the <unk> at CNN . This is going to be a long <unk> . " 
- Trade and Industry Minister <unk> <unk> <unk> estimated that the earthquake 's toll on the Haitian economy would be massive , with one in five jobs lost . In response to the earthquake , foreign governments offered badly needed financial aid . The European Union promised € 330 million for emergency and long @-@ term aid . Brazil announced R $ 375 million for long @-@ term recovery aid , R $ 25 million of which in immediate funds . The United Kingdom 's Secretary of State for International Development Douglas Alexander called the result of the earthquake an " almost unprecedented level of devastation " , and committed the UK to ₤ 20 million in aid , while France promised € 10 million . Italy announced it would waive repayment of the € 40 million it had loaned to Haiti , and the World Bank waived the country 's debt <unk> for five years . On 14 January , the US government announced it would give US $ 100 million to the aid effort and pledged that the people of Haiti " will not be forgotten " . 
- In the aftermath of the earthquake , the government of Canada announced that it would match the donations of Canadians up to a total of C $ 50 million . Canadians were able to donate through the Humanitarian Coalition which distributed funds to partner organizations working in the field . During this time the Humanitarian Coalition raised over C $ 15 Million . After a United Nations call for help for the people affected by the earthquake , Canada pledged an additional C $ 60 million in aid on 19 January 2010 , bringing Canada 's total contribution to C $ 135 million . By 8 February 2010 , the federal International Co @-@ operation Department , through the Canadian International Development Agency ( <unk> ) , had already provided about C $ 85 million in humanitarian aid through UN agencies , the International Federation of Red Cross and Red Crescent Societies and to organizations such as <unk> , Médecins du <unk> , Save the Children , Oxfam Quebec , the Centre for International Studies and co @-@ operation , and World Vision . On 23 January 2010 , Canadian Prime Minister Stephen Harper announced that the federal government had lifted the limit on the amount of money allocated for matching individual donations to relief efforts , and that the federal government would continue to match individual donations until 12 February 2010 ; by the deadline , Canadians had privately raised C $ 220 million . On top of matching donations , International Co @-@ operation Minister <unk> <unk> pledged an additional C $ 290 million in long @-@ term relief to be spent between 2010 and 2012 , including C $ 8 million in debt relief to Haiti , part of a broader cancellation of the country 's overall World Bank debt . The government 's commitment to provide C $ 550 million in aid and debt relief and Canadians ' individual donations amount to a total of C $ 770 million . 
- In addition to Canada 's federal government , the governments of several of the provinces and territories of Canada also announced that they would provide immediate emergency aid to Haiti . On 18 January 2010 , the province of Quebec , whose largest city – Montreal – houses the world 's largest Haitian diaspora , pledged C $ 3 million in emergency aid . Both the provincial government of Quebec and the Canadian federal government reaffirmed their commitment to rebuilding Haiti at the 2010 <unk> Summit ; Prime Minister Harper used his opening speech to " tell the head of the Haitian delegation to keep up their spirits " and to urge other nations to continue to support recovery efforts . 
- President <unk> Wade of Senegal offered interested Haitians free land in Senegal ; depending on how many respond to the offer , this could include up to an entire region . 
- Prime Minister Bellerive announced that from 20 January , people would be helped to relocate outside the zone of devastation , to areas where they may be able to rely on relatives or better fend for themselves ; people who have been made homeless would be relocated to the makeshift camps created by residents within the city , where a more focused delivery of aid and sanitation could be achieved . Port @-@ au @-@ Prince , according to an international studies professor at the University of Miami , was ill @-@ equipped before the disaster to sustain the number of people who had migrated there from the countryside over the past ten years to find work . After the earthquake , thousands of Port @-@ au @-@ Prince residents began returning to the rural towns they came from . 
- On 25 January a one @-@ day conference was held in Montreal to assess the relief effort and discuss further plans . Prime Minister Bellerive told delegates from 20 countries that Haiti would need " massive support " for its recovery from the international community . A donors ' conference was expected to be held at the UN headquarters in New York in March , however , took more than three months to hold the UN conference . The 26 @-@ member international Interim Haiti Reconstruction Commission , headed by Bill Clinton and Haitian Prime Minister Jean @-@ Max Bellerive , convened in June 2010 . That committee is overseeing the US $ 5 @.@ 3 billion pledged internationally for the first two years of Haiti 's reconstruction . 
- The commission was critiqued by Haitian groups for lacking Haitian civil society representation and accountability mechanisms . Half the representation on the commission was given to foreigners who effectively bought their seats by pledging certain amounts of money . An international development consultant contracted by the commission was quoted as saying , “ Look , you have to realize the <unk> [ commission ] was not intended to work as a structure or entity for Haiti or Haitians . It was simply designed as a vehicle for donors to funnel <unk> ’ and <unk> ’ project contracts . ” 
- The Netherlands sponsored a project , called <unk> . The Dutch radio channels <unk> , Radio 538 and Radio Veronica all broadcast under the name of <unk> , funded by a contribution of € 80 million . 
- Several organizations of the US building industry and government , such as the Department of Homeland Security and the International Code Council , among others , reported that they were compiling a " Haiti <unk> " coordinated by the National Institute of Building Sciences . The <unk> would comprise building technology resources and best practices for consideration by the Haitian government with the goal of creating a more <unk> infrastructure to prevent future losses of life . 
- Immediately following the earthquake , Real Medicine Foundation began providing medical staffing , in @-@ kind medical supplies and strategic coordination to help meet the <unk> needs of the health crisis on the ground . Working in close partnership with other relief organizations , Real Medicine organized deployments of volunteer medical specialists to meet the needs of partner hospitals and clinics at the Haiti – Dominican Republic border and in Port @-@ au @-@ Prince , provided direct funding , medical supplies and <unk> to local health facilities and partner hospitals , provided advisory services and coordination to local health facilities , including physical therapy support , and coordinated mobile health <unk> , field clinics and food supplies to outlying villages overlooked in the relief effort . 
- On 15 January 2011 , the Catholic Relief Services announced a US $ 200 million , five @-@ year relief and reconstruction program that covers shelter , health , livelihoods , and child protection among its program areas . 
- 
- = = = Status of the recovery = = = 
- 
- Six months after the quake as much as 98 percent of the rubble remained <unk> . An estimated 26 million cubic yards ( 20 million cubic meters ) remained , making most of the capital impassable , and thousands of bodies remained in the rubble . The number of people in relief camps of tents and tarps since the quake was 1 @.@ 6 million , and almost no transitional housing had been built . Most of the camps had no electricity , running water , or sewage disposal , and the tents were beginning to fall apart . Crime in the camps was widespread , especially against women and girls . Between 23 major charities , US $ 1 @.@ 1 billion had been collected for Haiti for relief efforts , but only two percent of the money had been released . According to a CBS report , US $ 3 @.@ 1 billion had been pledged for humanitarian aid and was used to pay for field hospitals , plastic tarps , bandages , and food , plus salaries , transportation and upkeep of relief workers . By May 2010 , enough aid had been raised internationally to give each displaced family a <unk> for US $ 37 @,@ 000 . 
- In July 2010 , CNN returned to Port @-@ au @-@ Prince and reported , " It looks like the quake just happened yesterday " , and <unk> Wall , spokeswoman for the United Nations office of humanitarian affairs in Haiti , said that " six months from that time it may still look the same . " Land ownership posed a particular problem for rebuilding because so many pre @-@ quake homes were not officially registered . " Even before the national registry fell under the rubble , land tenure was always a complex and contentious issue in Haiti . Many areas of Port @-@ au @-@ Prince were settled either by <unk> <unk> – <unk> 's death squads – given land for their service or by squatters . In many cases land ownership was never officially registered . Even if this logistical <unk> were to be cleared , the vast majority of Port @-@ au @-@ Prince residents , up to 85 percent , did not own their homes before the earthquake . " 
- Haitian <unk> groups advocated for the government to fulfill the right to housing as designated in the Haitian constitution , and for donor governments to support this as well . They also worked to push the international community to recognize the wave of <unk> from camps that started as early as three months after the earthquake and to put <unk> in place , but little was done in response . 
- In September 2010 there were over one million refugees still living in tents , and the humanitarian situation was characterized as still being in the emergency phase , according to the Apostolic <unk> to Haiti , Archbishop Bernard <unk> . He went on to say that the number was rising instead of diminishing , and reported that the state had decided to first rebuild downtown Port @-@ au @-@ Prince and a new government center , but reconstruction had not yet begun . 
- In October 2010 , <unk> International characterized the aid agencies as dysfunctional and inexperienced saying , " The people of Haiti are still living in a state of emergency , with a humanitarian response that appears <unk> " . It was reported that gang leaders and land owners were intimidating the displaced and that sexual , domestic , and gang violence in and around the camps was rising . They claimed that rape of Haitian women and girls who had been living in camps since the January earthquake was increasing , in part , because the United Nations wasn 't doing enough to protect them . 
- In October , a cholera epidemic broke out , probably introduced by foreign aid workers . Cholera most often affects poor countries with limited access to clean water and proper sanitation . By the end of 2010 , more than 3 @,@ 333 had died at a rate of about 50 deaths a day . 
- 
- = = = = 2011 = = = = 
- 
- In January 2011 , one year after the quake , Oxfam published a report on the status of the recovery . According to the report , relief and recovery were at a standstill due to government inaction and <unk> on the part of the donor countries . The report stated , " One year on , only five percent of the rubble has been cleared and only 15 percent of the required basic and temporary houses have been built . House building on a large scale cannot be started before the enormous amount of rubble is cleared . The government and donors must <unk> this most basic step toward helping people return home " . Robert Fox , executive director of Oxfam Canada , said " The <unk> has been aided unabated by the way the international community has organized itself , where pledges have been made and they haven 't followed through [ and ] where they come to the table with their own <unk> and own priorities . Most donors provided funds for transitional housing but very little money for clearing rubble or repairing houses " . Fox said that in many instances rubble removal " means it was [ moved ] off someone 's property onto the road in front of the property " . According to a UNICEF report , " Still today more than one million people remain displaced , living in crowded camps where livelihoods , shelter and services are still hardly sufficient for children to stay healthy " . Amnesty International reported that armed men were preying with <unk> on girls and women in displacement camps , worsening the trauma of having lost homes , livelihoods and loved ones . 
- On the first anniversary of the earthquake , Haitian @-@ born <unk> Jean , who served as the Governor General of Canada at the time of the disaster and who became United Nations Educational , Scientific and Cultural Organization ( UNESCO ) Special <unk> for Haiti on 8 November 2010 , voiced her anger at the slow rate of aid delivery , placing much of the blame on the international community for abandoning its commitments . In a public letter co @-@ authored with UNESCO head <unk> <unk> , Jean said , " As time passes , what began as a natural disaster is becoming a <unk> reflection on the international community . " The Interim Haiti Recovery Commission , led by former US President Bill Clinton and Haitian Prime Minister Jean @-@ Max Bellerive , had been set up to facilitate the flow of funds toward reconstruction projects in April 2010 , but as of January 2011 , no major reconstruction had started . 
- 
- = = = = 2012 = = = = 
- 
- In January 2012 , two years since the quake , figures released by the United Nations show that of the nearly US $ 4 @.@ 5 billion pledged for reconstruction projects in 2010 and 2011 , only 43 percent has been delivered . Venezuela and the US , which promised the major share of reconstruction funds , have <unk> only 24 percent and 30 percent , respectively . Japan and Finland are among the few donors to have fully met their pledges . The data shows that some crucial sectors face particularly large funding gaps . In 2010 and 2011 , for example , donors <unk> just US $ 125 million of the US $ 311 million in grants allocated to agriculture projects , and only US $ 108 million of the US $ 315 million in grants allocated to health projects . Only 6 percent of bilateral aid for reconstruction projects has gone through Haitian institutions , and less than 1 percent of relief funding has gone through the government of Haiti . 
- A January 2012 Oxfam report said that a half a million Haitians remained homeless , still living under tarps and in tents . <unk> groups have criticized the reconstruction process saying that part of the problem is that charities spent a considerable amount of money on " soaring rents , board members ' needs , <unk> supplies and imported personnel " . The Miami Herald reports . " A lot of good work was done ; the money clearly didn 't all get <unk> , " but , " A lot just wasn 't responding to needs on the ground . Millions were spent on ad campaigns telling people to wash their hands . <unk> them to wash their hands when there 's no water or soap is a <unk> in the face . " 
- The Institute of Justice & Democracy in Haiti , Let Haiti Live , and The Center For Constitutional Rights have recommended immediate changes to recovery efforts to ensure that critical human rights concerns are addressed . A report found that , " The conditions in the displaced persons camps are <unk> , particularly for women and girls who too often are victims of gender <unk> based violence " . They call for more oversight of accountability of reconstruction plans , asking , " Why have only 94 @,@ 000 transitional shelters been built to date despite a stated goal of 125 @,@ 000 in the first year ? " . 
- On 25 August 2012 , recovery was hampered due to Tropical Storm Isaac impacting Haiti 's southern peninsula . There it caused flooding and 29 deaths according to local reporting . As a result of the 2010 earthquake , more than 400 @,@ 000 Haitians continue to live in tents and experienced the storm without adequate shelter . In late October , with over 370 @,@ 000 still living in tent camps , a second tropical storm , Hurricane Sandy , killed 55 and left large portions of Haiti under water . 
- At the 2012 <unk> Group meeting of the Global <unk> for Disaster <unk> and Recovery ( <unk> ) , the Haitian delegation shared a " bottom @-@ up " approach to disaster reduction and management based on community integration and sustainable development with a group of experts from approximately 38 nations . 
- 
- = = = = 2013 = = = = 
- 
- According to the International <unk> Fund , more than half of the 10 @,@ 000 @,@ 000 cubic metres ( 13 @,@ 000 @,@ 000 cu yd ) of debris have been removed , and 20 percent of it has been recycled . 
- The cholera outbreak which began in October 2010 has continued . According to U.S. Centers for Disease Control and Prevention it is considered the worst epidemic of cholera since the 1994 outbreak in the Democratic Republic of the Congo ( called <unk> at that time ) . By August 2013 , it had killed over 8 @,@ 231 Haitians and hospitalized hundreds of thousands more . More than 6 % of Haitians have had the disease . Care of cholera patients remains inadequate with much now done in tent facilities with rows of <unk> for patient treatment . The United Nations peace keeping force , widely believed responsible for the cholera outbreak , continues to refuse to accept responsibility , however , they have launched a $ 2 @.@ 2 billion initiative to combat cholera and the construction of a $ 17 million teaching hospital in <unk> which will employ 800 Haitians and treat 185 @,@ 000 people . 
- By the beginning of the year only a small <unk> $ 215 million — of the total funds collected for aid had been spent on permanent housing , with most of <unk> $ 1 @.@ 2 billion — going for short @-@ term solutions including tent camps , temporary shelters , and cash grants that paid a year 's rent . A 2013 survey disclosed that of the 1 @.@ 5 million Haitians living in camps following the quake , about 279 @,@ 000 remained in a total of 352 camps . Fifteen percent of the camps had no basic protection services , and 48 % no health services . While 20 % lacked functioning toilets , this is higher than the population outside tent cities , where 50 % lack toilets . Many camps remained at a risk for flooding and more than a third of the camps ( 108 ) were at risk for <unk> . In a 2013 statement , the American Red Cross reported that almost all of the money collected for quake relief has been spent or is scheduled for making progress permanent by ensuring people can leave camps and return to stable communities , which includes building new homes , repairing homes , completing a new hospital and clinic , and signing an agreement for a second hospital . 
- 
- = = = = 2015 = = = = 
- 
- In 2015 , NPR and <unk> investigated the disappearance of US $ 500 million donated to the American Red Cross , described as " one of the most successful fundraisers ever " . Despite the claims of the American Red Cross that 130 @,@ 000 homes had been built , the investigation discovered that only six had been built . The investigation reviewed " hundreds " of pages of internal documents and interviewed " more than a dozen " former and current staff members , and investigated the organization 's claim that 4 @.@ 5 million Haitians had been helped " back on their feet " despite Joel <unk> , a Haitian government advisor , stating that this number would cover " 100 percent of the urban area " , and observing that this would mean they had served every city in Haiti . Numerous other claims did not hold up under investigation , and it was found that the project was riddled with " multiple staffing changes " , bureaucratic delays and a language barrier as many of the Red Cross officials did not speak French or Creole . General counsel for the American Red Cross , David <unk> , directed the investigators to their official statistics , but would not elaborate on them . The public affairs office of the Red Cross disputed NPR and <unk> 's claims in an email , and claimed that the investigative report could cause an international incident . The American Red Cross has handed over the rebuilding efforts to the Haitian Red Cross . 
- 
- = = In literature = = 
- 
- The Haiti 2010 earthquake has been depicted in the novel God Loves Haiti , by <unk> Elias <unk> . 
- 
- = De <unk> a <unk> : interviews with persons affected by the 2010 Haiti earthquake = 
- <unk> <unk> <unk> <unk> <unk> <unk> <unk> a <unk> . United States : <unk> . ISBN <unk> . 
- 
- 
- = Thom Darden = 
- 
- Thomas Vincent Darden ( born August 28 , 1950 ) is a former American football cornerback , safety , and punt <unk> who played for the Cleveland Browns of the National Football League ( NFL ) . In the NFL , he was a three @-@ time All @-@ Pro free safety . He earned a Pro Bowl selection in 1978 . He holds most Cleveland Browns franchise interception records . He was an All @-@ American defensive back for the Michigan Wolverines football team and made one of the more memorable interceptions in college football history . After retiring from football , Darden pursued careers as a sports agent , security provider and business consultant . 
- 
- = = Early life = = 
- 
- Darden was born in <unk> , Ohio . He graduated from <unk> High School . 
- 
- = = College football = = 
- 
- After graduating , he was recruited by six Big Ten Conference football programs in 1968 . He played at the University of Michigan from 1969 to 1971 and had 218 tackles and 11 interceptions . He was an All American in 1971 , and he was also named All @-@ Big Ten in 1970 . He played on Big Ten champions in both 1969 and 1971 . Thom fit in well at Michigan becoming one of Coach Bo <unk> 's prized pupils and earning a reference in his 2006 book Bo <unk> . Darden still ranks among leaders at Michigan for Punt <unk> and Punt Return <unk> . Darden played all defensive back positions at Michigan . In college , Darden was a <unk> of <unk> <unk> , Glenn <unk> , Billy Taylor and Mike Taylor in a notable house known as the Den of the <unk> Men . 
- ESPN chose Darden 's November 21 , 1971 interception against Ohio State as one of the 100 <unk> , performances and moments that define college football . The play was a very controversial call late in the 10 – 7 game and Ohio State coach Woody Hayes stormed the field to <unk> at the referee Jerry <unk> about the referee 's call that Hayes thought should have been ruled pass interference . By the end of Hayes ' <unk> , he had broken a yard marker , <unk> a first @-@ down indicator and earned two 15 @-@ yard unsportsmanlike penalties . The scene was replayed over and over on national television broadcasts . That was Darden 's second interception in that game . ESPN also chose Darden as a member of the All @-@ Time University of Michigan Football team . 
- 
- = = Pro football = = 
- 
- Darden was drafted in the first round ( 18th overall ) in the 1972 NFL Draft by the Cleveland Browns . Darden started at free safety from his rookie season of 1972 until he lost the job in his tenth and final season to Clinton Burrell . He was selected an All @-@ Pro safety in 1976 , 1978 , and 1979 , and went to the Pro Bowl in 1978 . Over the course of his career he handled 45 punt returns for 285 return yards . Darden holds Cleveland Browns franchise records with 45 career interceptions , 10 single @-@ season interceptions and 820 interception return yards . Two of Darden 's Browns teams went to the playoffs . His rookie year , the 10 – 4 1972 Browns went to the 1972 @-@ 73 NFL playoffs under head coach Nick <unk> , but lost in the first round to the Miami Dolphins 20 – 14 . The 11 – 5 1980 Browns went to the 1980 @-@ 81 NFL playoffs under head coach Sam <unk> , but lost in the first round to the Oakland Raiders 14 – 12 . 
- Darden was ranked 47th on the Cleveland Browns top 100 players list . 
- 
- = = Post football = = 
- 
- Darden has served as a professional sports agent and represented Tony <unk> . In 1990 , he invested $ 25 @,@ 000 in <unk> by hosting him in Cleveland , Ohio and working him out with athletic trainers . At the time he was Cleveland @-@ based sports agent . During Darden 's career as an agent he represented an assortment of NFL and National Basketball Association players and prospects including Felix Wright and Chris Calloway . He was a supporter of Maurice <unk> 's attempt to challenge the NFL Draft 's eligibility rules . In 1998 , when the NFL reissued a franchise in Cleveland , Darden was part of one of the six bidding groups . In 1999 , he owned a security company in Cedar Rapids , Iowa . As of 2006 , Darden was a business consultant living in Cedar Rapids . 
- In 1993 , he went to jail for failure to pay child support . At the time he was a registered NFL agent in Connecticut and owed $ 14 @,@ 000 in child support , $ 30 @,@ 000 to a trust for his 16 @-@ year @-@ old son , Todd , and $ 12 @,@ 000 in his former wife 's legal fees . Darden had been married to <unk> from 1972 – 1977 when they divorced . He had previously been $ 5000 in <unk> until being briefly jailed for failure to pay and cursing the judge . He had been in contempt of court twenty times previously . 
- 
- 
- = Voyage : Inspired by Jules Verne = 
- 
- Voyage : Inspired by Jules Verne ( known as Journey to the Moon in the United Kingdom and Australia ) is a point @-@ and @-@ click adventure game with pre @-@ rendered graphics , developed by Kheops Studio and published by The Adventure Company for the PC in 2005 . The game 's story focuses on a French adventurer 's journey to the moon in the 19th century , and the ancient lunar civilization he <unk> . 
- Voyage is loosely based on the novels From the Earth to the Moon and Around the Moon by science @-@ fiction author Jules Verne , and the novel The First Men in the Moon by science @-@ fiction author H.G. Wells . Reactions to the game were generally mixed . In particular , some reviewers praised it for <unk> the player in the look and feel of the 19th century ; others have criticized it for featuring dated graphics and dull textures . 
- While staying true to most adventure game conventions , Voyage has some unique features for its genre . These include two dexterity minigames which take advantage of the reduced gravity in the game 's lunar setting , and an " Intelligence Management System " , in which a score is assigned to the player for every puzzle he solves , and for certain actions . The Adventure Company introduced this feature to motivate players to replay the game to increase their cumulative score . 
- 
- = = Gameplay = = 
- 
- The main focus of Voyage is puzzle @-@ solving . The player can move by clicking , and can <unk> the camera 360 degrees . There are several types of puzzle in Voyage including those involving native plant life on the moon , mechanical puzzles , audio puzzles , and mathematical puzzles . Many of these puzzles require the player to <unk> and use the native language of the moon . 
- Voyage features two unique dexterity minigames . Using a low @-@ gravity setting , the first minigame requires the player to collect floating bubbles in a can , and the second requires the player to execute large jumps across the surface of the moon . These two minigames form only a minor part of the game . The game also has several timed sequences requiring the player to complete puzzles under a time limit . The consequence of failing a puzzle of this sort is death , after which the player is able to return and replay the puzzle . Players can also be killed as the result of taking incorrect actions related to the game 's story . 
- A critical aspect of gameplay in Voyage is the inventory system , which allows the player to pick up and keep dozens of different items . However , the maximum quantity of a given item that the player may keep in his inventory at any one time is three . One of the main uses of the inventory is to combine items together to make new items . This process of breaking and reforming items in the inventory comprises a large portion of the puzzle aspect of the game . The inventory can also be used to create meals which the player can consume ; this ability plays a major role in several puzzles . Another use of the inventory is to create hybrid lunar plants , which play a critical role in the earlier puzzles of the game . 
- 
- = = = Intelligence Management System = = = 
- 
- The " Intelligence Management System " featured in Voyage is a score assigned to the player by the Selenites , the natives of the moon . For each puzzle the player solves , and for certain actions , this score is increased , and the Selenites treat the player with more respect . During an interview with <unk> , Benoît Hozjan , Managing Director and co @-@ founder of Kheops Studio , described the system , saying : 
- In the same interview , Alexis Lang , the Lead Game Designer at Kheops , commented that : " [ A ] low score does not mean that you are stupid in any way , it just means that some <unk> and <unk> lunar people think that your character is dumb ! " This reflects the fact that the " Intelligence Management System " is designed primarily to earn the respect of the Selenites . However , Hozjan also said that he hopes " players will try to increase their score and certainly share their experience through forums . " The Adventure Company has marketed the system as bringing a degree of replay value to Voyage , as players can replay the game to achieve a higher score . 
- 
- = = Synopsis = = 
- 
- 
- = = = Setting = = = 
- 
- Voyage is set in 1865 . President Barbicane of the ' Gun Club ' decides to build an enormous cannon in Baltimore to shoot a shell , capable of supporting human life , towards the moon in the hopes of a successful landing . Voyage 's protagonist , Michel Ardan , volunteers to travel in the aluminium shell . After the game 's brief introduction in the shell , Ardan lands on the moon and discovers the Selenites , as well as a complex ecosystem of lunar plants . The main accessible areas in the game are the moon 's surface , and the underground Selenite civilization . 
- The ' Selenites ' are the subterranean inhabitants of the moon , and are a highly intelligent society maintained by hierarchy and secret . They possess blue skin , large black eyes and transparent <unk> lobes on the sides of their heads . This is in fact a reference to H.G. Wells ' book The first men in the moon , as the adventurers never actually land on the moon in Verne 's original story . 
- They are divided into castes . The Selenites live in a large complex under the surface of the moon from which they rarely venture , with the exception of the ' exiles ' . The Selenites " <unk> [ these ] <unk> of their society , the criminals and <unk> , " to the surface of the moon . There are three Selenite exiles with whom the player can interact ; they live on the surface and sleep in their isolated underground <unk> at night . Each exile has two different plants on either shoulder with which they share a special bond . 
- 
- = = = Characters = = = 
- 
- The player character is Michel Ardan , an eccentric and intrepid French scientist who is enthusiastic , daring and cheerful . President Barbicane , the President of the Gun Club , and Captain Nicholl , an engineer , are both found dead at the start of the game , not having survived the flight to the moon . A woman called Diana features in the game 's backstory , as a woman whose ancestors made contact with the Selenites . Apart from these human characters , there are also several Selenite characters such as the Supreme Moon Ruler , the High <unk> , <unk> , <unk> , and the three exiles . 
- 
- = = = Plot = = = 
- 
- Voyage begins as Ardan <unk> in the shell and discovers his two dead companions : Barbicane and Nicholl . The first part of the game consists of Ardan investigating the shell , trying to regain his memory of what happened , and how Barbicane and Nicholl died , he will also encounter other problems , most notably when his oxygen supply is low and needs to be <unk> . Eventually Ardan 's journey will carry him into orbit around the moon in which he must prepare himself for a lunar landing . 
- Once Ardan successfully lands the shell on the moon , he must solve a series of puzzles on the surface in order to gain access to the hidden civilization below . There he encounters the Selenite race . Following this , Ardan <unk> on finding a way to leave the moon and report his findings to Earth . After acquiring what he needs , Ardan travels back to Earth in the shell . He lands in the ocean and manages to swim to a nearby island , where he meets another famous Jules Verne character , Captain <unk> . 
- 
- = = Development = = 
- 
- Journey to the Center of the Moon was announced for the PC at E3 2005 . The Adventure Company collaborated with developers Kheops Studios for the release . Benoît Hozjan , the co @-@ founder of Kheops Studio , became Managing Director of the game , while Alexis Lang became the Lead Game Designer . 
- Journey to the Center of the Moon was later renamed Voyage : Inspired by Jules Verne . Benoît Hozjan explained the change , saying that Journey to the Center of the Moon " seems to be confusing and some people thought that it could be the sequel to Journey to the Center of the Earth , " another Verne @-@ inspired PC game , " so marketing decided to change it . " The name was changed on July 7 , 2005 , a few months after the game 's announcement . 
- Benoît Hozjan explained Kheops Studio 's choice of Jules Verne 's work as a basis for Voyage , saying that : " Jules Verne 's novels provide the two core elements of adventure games : dreams and challenges . Characters are ordinary men engaged in concrete challenges that are [ a ] great inspiration for puzzles . " He further went on to say that the game is influenced by Verne 's From the Earth to the Moon , which documents the lead up to the lunar trip , but draws more heavily from the sequel Around the Moon , which recounts the actual voyage . The main difference , Hozjan said , was that in the novel the protagonists fail to reach the moon , whereas in Voyage the trip is a success . Additionally , Alexis Lang attributed the inspiration for the Selenites to H.G. Wells ' The First Men in the Moon , explaining that : " [ Wells ] pictured a very ancient Selenite <unk> horrified at human <unk> . Wells was more <unk> than Verne . To balance this fact , we 've added a touch of irony in our story and chosen a very optimistic and joyful main character . " 
- The Adventure Company announced the release of the Voyage demo on August 3 , 2005 . The demo included the game 's introduction in the shell . Voyage , originally slated for a September 27 release , was shipped to stores ahead of schedule on August 16 , 2005 . The game <unk> for US $ 19 @.@ 99 . 
- 
- = = Reception = = 
- 
- In general , Voyage received mixed reviews upon its release . According to Metacritic , reviewers have given Voyage scores between 60 % and 86 % . One of the more positively received aspects of the game was its ability to recreate the mood of 19th century science @-@ fiction , with GameSpot writing that the game " nicely re @-@ creates the whimsical mood of 19th @-@ century <unk> @-@ fi [ and ] a sense of wonder fills every pixel of the graphic design . " In contrast , Game Over Online Magazine said that once the player leaves the capsule and arrives on the moon , instead of viewing colorful and <unk> sights , the game turns into a drab and unlikely bore . The puzzle aspect of Voyage met with mixed responses . On the other hand , GameSpot accused the puzzle aspects of Voyage of " <unk> [ ing ] Jules Verne 's tale of a visit to the moon in 1865 to a series of <unk> arranged logic puzzles geared to try the patience of adventure @-@ game veterans . " The puzzles in the game are often extremely difficult , with Just Adventure attributing the unexpected difficulty in the game to the fact that there are often several different ways to achieve the same goals , thanks to the game 's " Intelligence Management System " . The game 's inventory system received praise from <unk> as being very well done . 
- In terms of graphics , Voyage was poorly received , with the graphics being described by <unk> as containing some vibrant colors , but lacking the lush , spectacular view that has been seen in countless other adventure games . Voyage has also been criticized for its lack of story and over @-@ reliance on back story . The game 's music was generally appreciated , with <unk> describing the music has having a nice retro @-@ futuristic feel which sets the mood perfectly . <unk> commented on the game 's voice acting as overly dramatic but appropriate , but criticized many of the sound effects as being cheesy . GameSpot described Ardan 's dialogue as somewhat lame , and also criticized the game 's sound effects . Metacritic averaged out the scores of several internet reviews of Voyage to reach a rating of 71 % , the closest to an ' overall ' rating of the game . 
- 
- 
- = Old Baltimore Pike = 
- 
- Old Baltimore Pike is a road in the U.S. state of Delaware . The road , known as New Castle County Road 26 , runs from Maryland Route 281 ( MD 281 ) at the Maryland state line south of Newark , Delaware and continues east to Christiana , ending near Delaware Route 1 ( DE 1 ) . The road is paralleled by Interstate 95 ( I @-@ 95 , Delaware Turnpike ) to the north and U.S. Route 40 ( US 40 , Pulaski Highway ) to the south . The Old Baltimore Pike was built before 1720 and connected Elkton , Maryland to Christiana . It was a turnpike called the Elk and Christiana Turnpike between 1817 and 1838 . In the past it served as a major connection between Philadelphia and Baltimore . 
- 
- = = Route description = = 
- 
- Old Baltimore Pike begins at the Delaware – Maryland state line near Newark , Delaware , where the highway continues west into that state as MD 281 . The road heads northeast from the state line through wooded residential areas as a two @-@ lane undivided road , intersecting <unk> Chapel Road before passing south of Iron Hill Park . Old Baltimore Pike crosses DE <unk> and enters rural areas . Here , the roadway passes <unk> 's Bridge , a historic battle site of the American Revolutionary War . It then crosses Norfolk Southern 's <unk> Secondary railroad line and encounters DE 72 . 
- After this intersection , Old Baltimore Pike continues past suburban neighborhoods , gaining a center left @-@ turn lane and intersecting Salem Church Road . Farther east , the highway reaches a junction with DE 273 in a wooded area . At this point , the road <unk> to two lanes again and heads into the community of Christiana . Here , the highway intersects DE 7 , where it turns north and follows that route through residential areas . The road comes to an interchange with DE 1 to the west of the Christiana Mall , where DE 7 continues north along with DE 1 and Old Baltimore Pike reaches a dead end . 
- 
- = = History = = 
- 
- The Old Baltimore Pike was built before 1720 . The road was known as the Great Road and ran between Head of Elk ( now Elkton , Maryland ) and Christiana Bridge . It was later known as the Christiana @-@ Elkton Turnpike before becoming Old Baltimore Pike . This path served as a major connection between Philadelphia and Baltimore in addition to providing access between the shipping area of Christiana Bridge and agricultural areas in northern Delaware , northern Maryland , and southeastern Pennsylvania . In 1723 , Welsh <unk> settlers pushed for the road to be improved . This road was part of the Washington – <unk> Revolutionary Route that was used by the French army during their march from Newport , Rhode Island to Yorktown during the Revolutionary War , passing through the area in September <unk> . 
- The road , also known as Old Post Road , was incorporated in 1813 as the Elk and Christiana Turnpike in order to get more money for repairs . The turnpike was completed in April 1817 . As a turnpike , tolls were collected to pay for the maintenance of the road . The construction of the New Castle and Frenchtown Railroad lowered the revenues of the turnpike and it became a public road again in 1838 . The road historically went through agricultural areas ; however , the surroundings have become more developed over the years . Much of the Old Baltimore Pike remains two lanes . 
- 
- = = Major intersections = = 
- 
- The entire route is in New Castle County . 
- 
- 
- = Mega Man & Bass = 
- 
- Mega Man & Bass , known in Japan as Rockman & Forte ( <unk> & <unk> , <unk> <unk> <unk> ) , is an action @-@ platform video game developed and published by Capcom . It is a spin @-@ off title in the original Mega Man series and was originally released exclusively in Japan on April 24 , 1998 for the Super Famicom . Mega Man & Bass was ported to the Game Boy Advance ( GBA ) handheld in 2002 and was localized in North America and <unk> regions the following year . 
- After defeating the evil Dr. Wily many times , the robotic hero Mega Man is called into action once again when a powerful being known as King steals the blueprints to the creations of Dr. Wily and Dr. Light in order to create an army for robotic dominance over humans . Having learned of the threat , Mega Man 's rival Bass decides to take matters into his own hands . The game is an action @-@ platformer where the player advances by defeating bosses and acquiring their signature weapons . Mega Man & Bass lets the player choose between either of its title characters , each of which plays differently from the other . 
- Mega Man & Bass debuted on the aging 16 @-@ bit Super Famicom despite the series having already transitioned to the PlayStation and Sega Saturn with Mega Man 8 . Artist and designer <unk> Inafune claimed Mega Man & Bass was created with regard to younger players who did not yet own one of the more advanced gaming systems . The game received positive remarks from critics for its graphics and use of a tried @-@ and @-@ true gameplay formula , though many found the difficulty to be too steep . Although Mega Man & Bass shares many traits with previous console games in the series , the ninth numbered title would not be released until 2008 . 
- 
- = = Plot = = 
- 
- The story of Mega Man & Bass varies slightly depending on which player character is chosen . It begins One year after <unk> 8 when a robot villain named King breaks into Dr. Wily 's laboratory and then the Robot Museum to collect the data blueprints for the creations of Dr. Light . Dr. Light alerts the hero Mega Man that he must go at once to the Robot Museum to confront this new enemy . Meanwhile , Bass ( Mega Man 's rival and Wily 's greatest creation ) hears of the new criminal 's appearance and decides to prove himself the stronger robot by defeating King . Proto Man is the first to arrive at the scene . King <unk> his plan to him ; he desires to create a <unk> in which robots rule the world over humans . To accomplish this , King seeks to create an <unk> army using the data and invites Proto Man to join him . Proto Man refuses and attempts to attack , but King <unk> and <unk> his body in half . Proto Man then <unk> back to the lab for repairs while King escapes with the data , instructing his <unk> to handle the heroes . With their own motivations , Mega Man and Bass set out to put a stop to King 's plans . 
- After vanquishing eight powerful robots under allegiance to King ( Cold Man , Astro Man , Ground Man , Magic Man , Tengu Man , Dynamo Man , Pirate Man , and Burner Man ) , the duo infiltrates his castle and engages him in combat . Proto Man interrupts the fight and again attempts to defeat their new nemesis . <unk> all of his remaining energy into a blast , Proto Man manages to destroy King 's shield and loses consciousness , allowing Mega Man and Bass to best King in battle afterwards . King questions why they fight so hard for humans when robots are the superior species . The pair explains that humans are the ones who created robots in the first place , which <unk> King . The villain reveals that his creator is Dr. Wily , who then appears on a video monitor . When King asks the evil inventor why robots fight each other for the sake of humans , Wily strengthens his " brainwashing level " and <unk> his power . Mega Man and Bass engage King in another battle and defeat him , but not before the latter <unk> Proto Man out of his castle . The castle begins a self @-@ destruct sequence and the protagonists escape without King . 
- Mega Man and Bass begin a final confrontation with Dr. Wily in his newly regained laboratory . When Wily is beaten , Bass demands to know why he deceived him . Wily explains that he created King simply to test Bass ' abilities . Wily shows him written plans for making a newer version of King to join with Bass in this venture , promising that the two would be <unk> together . Proto Man appears and immediately destroys these plans . Wily then demands Bass to destroy Proto Man , but Bass is unsure . It was then Proto Man tells Bass that although he is a strong robot of free will , he can never defeat his rival because he has nothing for which to fight . Bass doesn 't care and forces Proto Man to leave , saying that he will still destroy Mega Man to prove his cause . Mega Man returns home where his sister Roll presents him a letter from King , who has somehow escaped the destruction of his castle . King wishes to atone for his own crimes against humans and hopes for them to be friends if they were to meet in the future . 
- 
- = = Gameplay = = 
- 
- The gameplay in Mega Man & Bass is similar to earlier games in the series . The player is tasked with completing a series of action @-@ platform stages while overcoming obstacles , solving minor puzzles , and battling enemies . Destroying the " Robot Master " boss at the end of a stage lets the player acquire its special weapon . In previous games , the player generally took on the role of the hero Mega Man . In this game , the player can choose to start the game as either Mega Man or Bass . However , whichever character is picked must be used for the rest of the game and cannot be changed . Mega Man is able to charge his shots to make them more powerful and has the ability to slide along the ground . Bass is able to rapidly fire his arm cannon in eight directions , though shots cannot be fired while moving or pass through walls unless a certain upgrade is obtained . Bass is also able to double @-@ jump ( jump a second time in mid @-@ air ) and dash along the ground . Performing both simultaneously lets him cross great distances by doing a dash @-@ jump . 
- The stage structure is different from other games in the series . After the introduction level , the player can only choose between three Robot Masters . <unk> Cold Man unlocks Burner Man and Pirate Man ; defeating Astro Man unlocks Dynamo Man , Tengu Man , and Pirate Man ; and defeating Ground Man unlocks Magic Man and Tengu Man . Clearing one of these unlocked stages opens the way to a security room where the player must destroy a series of crystals with obtained Robot Master weapons . <unk> all eight crystals opens the way to the fortress stages . In a similar fashion to previous installments in the series , enemies often drop bolts after they are destroyed , and these can be exchanged for various restorative items and upgrades . However , unlike in Mega Man 7 the security cavern offers a way to obtain large amounts of bolts without having to repeatedly visit stages . Some upgrades are unique to either character , such as Mega Man 's ability to call on his dog Rush to search for items , or an <unk> for Bass to combine with his wolf Treble to temporarily fly . Also distributed throughout the introduction and Robot Master levels are a collection of 100 data CDs that contain information on many prominent characters in the series . Most of the CDs are hidden either behind obstacles that need to be destroyed with a special weapon or accessed with a character @-@ specific ability , making it impossible to collect them all on a single playthrough . CDs collected in each playthrough are permanently placed in a database and remain unlocked after beating the game . Saved games are used in place of the series ' traditional <unk> system . 
- 
- = = Development = = 
- 
- Mega Man & Bass was developed for the Super Famicom after the release of Mega Man 8 , which preceded Mega Man & Bass on the two 32 @-@ bit consoles , the PlayStation and Sega Saturn . According to series producer <unk> Inafune , Mega Man & Bass was intended for younger players who still owned a Super Famicom and did not have the means to experience Mega Man 8 on one of the newer systems . " Even though trying to bridge out a new title on the [ Super Famicom ] was a little backwards at the time , we didn 't want to make a half @-@ hearted attempt at it , " Inafune explained . The design team included several new employees , as well as members of previous Mega Man games . Inafune required them to make the game " as hardcore as possible " . Designer <unk> <unk> recalled the development of Mega Man & Bass as " one big party " . The staff attempted to create an original game while avoiding the " same old , same old [ ... ] <unk> " that so many long video game series suffer and " had a lot of fun doing it " . 
- <unk> , Mega Man & Bass uses many of the same two @-@ dimensional sprites and animations as Mega Man 8 . Two of the eight Robot Master bosses in Mega Man & Bass ( Tengu Man and Astro Man ) are borrowed from Mega Man 8 . The other six were newly created for the game by three character designers : Hitoshi <unk> ( credited as " H. <unk> " ) , <unk> Iwamoto ( credited as " <unk> Iwamoto " ) and <unk> <unk> ( credited as " K. <unk> " ) designed two characters each . The bosses were officially unveiled on a teaser page in the Kodansha magazine Comic <unk> . Each boss was given distinct characteristics so that they could be easily identified by players in both their aesthetics and personalities . Some of these characters had different names during their conceptual phase prior to the <unk> of the game . " <unk> Man " became Burner Man , " <unk> Man " became Cold Man , and " <unk> Man " became Dynamo Man . Iwamoto originally denoted Ground Man as " <unk> Man " despite there already being a Robot Master by that name in Mega Man 4 . The musical score for Mega Man & Bass was composed by <unk> Kaida ( credited as " A. Kaida " ) , <unk> <unk> ( credited as " N. <unk> " ) , and " <unk> @-@ <unk> " ( real name unknown ) . Rather than create tracks together , each composer was responsible for their own songs . Kaida would later work with other composers in the Mega Man series on the soundtrack for Mega Man 10 , released in 2010 . 
- Mega Man & Bass was originally available in Japan through the Nintendo Power game service in Lawson convenience stores . Its popularity prompted Capcom to later release the game in Super Famicom cartridge form . Until its GBA re @-@ release , it was one of the few Mega Man titles not localized for English @-@ speaking countries . The company commemorated the 15th anniversary of the Mega Man franchise with the GBA version of the game . 
- 
- = = Reception and legacy = = 
- 
- The GBA port has received generally positive critical reviews , currently holding an aggregate score of 79 % on both GameRankings and Metacritic . Most critics found the game to be a solid yet conventional action @-@ platformer that successfully <unk> to the classic Mega Man formula . Electronic Gaming Monthly summarized that Mega Man & Bass is " one of the best action games on GBA " and " a great , if slightly derivative , platformer " with plenty of replay value due to the <unk> CDs . GamePro was pleased with the game 's fidelity to its predecessors when compared to the deviations made by the Mega Man Battle Network and Mega Man Zero series on the same system . GameSpy <unk> criticized its lack of innovation , declaring , " Anyone that hasn 't tried a Mega Man game yet would be better advised to spend $ 15 on a new copy of Mega Man 8 on the <unk> rather than paying $ 30 for an inferior <unk> of the same game " . 
- Many reviews also noted the game 's high difficulty . Both Giancarlo <unk> of GameSpot and Craig Harris of IGN found that the game 's bosses have very unpredictable attack patterns , thus making the battles extremely challenging . Harris additionally observed a heavy amount of trial @-@ and @-@ error for the levels themselves where the player must die several times before completing each one . He concluded , " [ ... ] It 's really the way Mega Man games have always been ... and to be honest , with all of the annoying little deaths in the game , there 's always that sensation after every failure that you 've learned the challenge , and <unk> definitely prevails in this game " . 
- According to Famitsu , Mega Man & Bass for the GBA sold 91 @,@ <unk> copies in Japan between its release date and the week of December 23 , 2002 . Nintendo Power listed Mega Man & Bass as the 14th best GBA game of all time in its 20th anniversary issue in 2008 . A related game exclusive to Japan titled Rockman & Forte <unk> <unk> no <unk> ( <unk> & <unk> <unk> , lit . Rockman & Forte : <unk> from the Future ) was released for the <unk> handheld in 1999 . The plot consists of the titular duo 's struggle against an <unk> named " Rockman Shadow " . As Mega Man & Bass was released directly after Mega Man 8 and it shares plot and gameplay characteristics with the rest of the numbered titles in the series , many believed it to be the ninth main game in the series ; however , the actual Mega Man 9 would not be released until 2008 . Inafune explained in an interview with the Brazilian magazine Nintendo World that the ninth installment follows the storyline of Mega Man 8 and that the worlds for Mega Man & Bass and Mega Man 9 are meant to coincide with one another , as evidenced by a <unk> of Bass in the ending of the game . In 2010 , Bass was made playable via downloadable content in Mega Man 10 . As in Mega Man & Bass , he is able to dash , fire in seven directions with his <unk> , and fly by combining with Treble . 
- Elements of the game appeared in the Mega Man comic series from Archie Comics before it went on hiatus . Most notably , King appeared during a time travel story in issue 20 , while issue 55 saw Dr. Light experiencing a vision of the events of the game among other yet @-@ to @-@ be @-@ adapted games . 
- 
- 
- = Ohio State Route 319 = 
- 
- State Route 319 ( SR 319 ) is a very short , two @-@ lane state highway in the western portion of the U.S. state of Ohio . A spur route , SR 319 has its western terminus at an abandoned railroad grade just west of Washington Street in the village of Burkettsville . Its eastern terminus is 0 @.@ 56 miles ( 0 @.@ 90 km ) to the east of its western terminus at its junction with SR 118 . SR 319 was established in the early 1930s . It started out as a much longer highway , but earlier segments would become a part of SR 118 in the late 1930s . 
- 
- = = Route description = = 
- 
- For its entire length , SR 319 runs along the boundary between Darke County on the south side and Mercer County on the north side . SR 319 begins in downtown Burkettsville at a former railroad grade just west of Washington Street . Known as Main Street within Burkettsville , the route runs due east through the village 's central business district prior to entering a residential area where it intersects a few side streets . After leaving the village of Burkettsville , SR 319 becomes the boundary between Darke County 's Allen Township and Mercer County 's Granville Township . After a short distance through farmland , the highway comes to an end as it arrives at its junction with SR 118 . Continuing east after the route terminates is Darke – Mercer County Line Road . 
- The Ohio Department of Transportation ( <unk> ) conducts surveys throughout its highway system to determine traffic volume . This is most commonly expressed as the average annual daily traffic ( AADT ) , or the measure of traffic volume for any average day of the year . In 2010 , <unk> determined that an average of 710 vehicles per day traveled along the length of SR 319 . This state highway is not included as a part of the National Highway System , a system of highways identified as being most important for the economy , mobility and defense of the nation . 
- 
- = = History = = 
- 
- SR 319 was assigned in 1932 . Originally , SR 319 included the entirety of the current route , along with the stretch of SR 118 from SR 319 north to SR 219 in <unk> . In 1935 , SR 319 was extended further north along the present alignment of SR 118 to what was then designated as SR 32 , now a part of SR 29 , west of <unk> . Two years later , SR 118 was extended south from its former southern terminus in <unk> to its present southern terminus in <unk> . Consequently , the entirety of what was the north – south stretch of SR 319 prior to that time became a part of the new extension of SR 118 . In turn , SR 319 became the short spur route that it is today . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Parliament Act 1911 = 
- 
- The Parliament Act 1911 is an Act of the Parliament of the United Kingdom . It is <unk> important and partly governs the relationship between the House of Commons and the House of Lords which make up the Houses of Parliament . This Act must be construed as one with the Parliament Act 1949 . The two Acts may be cited together as the Parliament Acts 1911 and 1949 . 
- Following the rejection of the 1909 " People 's <unk> " , the House of Commons sought to establish its formal dominance over the House of Lords , who had broken convention in opposing the Bill . The budget was eventually passed by the Lords after the Commons ' democratic mandate was confirmed by holding elections in January 1910 . The following Parliament Act , which looked to prevent a recurrence of the budget problems , was also widely opposed in the Lords and cross @-@ party discussion failed , particularly because of the proposed Act 's <unk> to passing an Irish home rule bill . After a second general election in December , the Act was passed with the support of the monarch , George V , who threatened to create sufficient Liberal peers to overcome the then Conservative majority . 
- The Act effectively removed the right of the Lords to veto money bills completely , and replaced a right of veto over other public bills with a maximum delay of two years . It also reduced the maximum term of a parliament from seven years to five . 
- 
- = = Background = = 
- 
- Until the Parliament Act 1911 , there was no way to resolve contradictions between the two Houses of Parliament except through the creation of additional peers by the Monarch . Queen Anne had created 12 Tory peers to vote through the Treaty of <unk> in <unk> . The Reform Act 1832 was passed when the House of Lords dropped opposition — William IV had threatened to create 80 new peers by request of the Prime Minister , Earl Grey — creating an informal convention that the Lords would give way when the public was behind the House of Commons . For example , Irish <unk> , which had been a major bone of contention between the two main parties since the 1830s , was — following intervention by the Queen — passed by the Lords in 1869 after <unk> Gladstone won the 1868 Election on the issue . However , in practice , this gave the Lords a right to demand that such public support was present and to decide the timing of a General Election . 
- It was the prevailing wisdom that the House of Lords could not amend money bills , since only the House of Commons had the right to decide upon the resources the Monarch could call upon . This did not , however , despite the apparent contradiction , prevent it from rejecting such bills outright . In 1860 , with the repeal of the paper duties , all money bills were consolidated into a single budget . This denied the Lords the ability to reject individual components and the prospect of voting down the entire budget was seemingly <unk> . It was only in 1909 that this became a possibility . Until the Act , the Lords had equal rights over legislation compared to the Commons , but did not <unk> its right of veto over financial measures by convention . 
- There had been an overwhelming Conservative @-@ Unionist majority in the Lords since the Liberal split in 1886 . With the Liberal Party attempting to push through significant welfare reforms with considerable popular support , this seemed certain to cause problems in the relationship between the Houses . Between 1906 and 1909 , several important measures were being considerably watered down or rejected outright : for example , <unk> introduced the Education Bill 1906 , which was intended to address <unk> grievances arising from the Education Act 1902 , but which was amended by the Lords to such an extent that it was effectively a different bill , upon which the Commons dropped the bill . This led to the 26 June 1907 resolution in the House of Commons declaring that the Lords ' power should be curtailed , put forward by Liberal Prime Minister Henry Campbell @-@ <unk> . In 1909 , hoping to force an election , the Lords rejected the financial bill based on the government budget ( the " People 's <unk> " ) put forward by David Lloyd George , by 350 votes to 75 . This , according to the Commons , was " a breach of the Constitution , and a <unk> of the rights of the Commons " . The Lords suggested that the Commons justify its position as representing the will of the people : it did this through the January 1910 general election . The Liberal government lost heavily , but remained in majority with the help of a significant number of Irish Nationalist and Labour MPs . The Irish Nationalists saw the continued power of the Lords as detrimental to securing Irish Home Rule . Following the election , the Lords relented on the budget ( since reintroduced by the government ) , it passing the Lords on 28 April , a day after the Commons . 
- 
- = = Passage = = 
- 
- The Lords was now faced with the prospect of a Parliament Act , which had considerable support from the Irish Nationalists . A series of meetings between the Liberal government and Conservative opposition members was agreed . Twenty @-@ one such meetings were held between 16 June and 10 November . The discussions considered a wide range of proposals , with initial agreement on finance bills and a joint sitting of the House of Commons and Lords as a means by which to enforce Commons superiority in controversial areas ; the number of Lords present would be limited such that a Liberal majority of 50 or more in the House of Commons could <unk> the Lords . However , the issue of home rule for Ireland was the main contention , with <unk> looking to exempt such a law from the Parliament Act procedure by means of a general exception for " constitutional " or " structural " bills . The Liberals supported an exception for bills relating to the monarchy and Protestant succession , but not home rule . <unk> were declared failed on 10 November . 
- The government threatened another dissolution if the Parliament Act were not passed , and followed through on their threat when opposition in the Lords did not <unk> . The elections of December produced little change from January . The calling of a second dissolution of parliament now seems to have been contrary to the wishes of Edward VII . Edward had died in May 1910 while the crisis was still in progress . His successor , George V , was asked if he would be prepared to create sufficient peers , which he would only if the matter arose . This would have meant creating over 400 new Liberal peers . The King did , however , demand that it would have to be rejected at least once by the Lords before his intervention . Two amendments made by the Lords were rejected and opposition showed little sign of slipping . This led Asquith to declare the King 's intention to overcome the majority in the House of Lords by creating sufficient new peers . It was passed in the Lords by 131 votes to 114 votes , a majority of 17 . This reflected a large number of <unk> . 
- 
- = = Provisions = = 
- 
- The <unk> included the words " it is intended to substitute for the House of Lords as it at present exists a Second Chamber constituted on a popular instead of hereditary basis , but such substitution cannot be immediately brought into operation " at the request of prominent Cabinet member Sir Edward Grey . The long title of the Act was " An Act to make provision with respect to the powers of the House of Lords in relation to those of the House of Commons , and to limit the duration of Parliament . " Section 8 defined the short title as the " Parliament Act 1911 " . 
- The bill was also an attempt to place the relationship between the House of Commons and House of Lords on a new footing . As well as the direct issue of money Bills , it set new conventions about how the power the Lords continued to hold would be used . It did not change the composition of the Lords , however . 
- The Lords would only be able to delay money bills for one month , effectively ending their ability to do so . These were defined as any public bill which contained only provisions dealing with the imposition , repeal , remission , alteration , or regulation of taxation ; the imposition for the payment of debt or other financial purposes of charges on the Consolidated Fund , or on money provided by Parliament , or the variation or repeal of any such charges ; supply ; the appropriation , <unk> , custody , issue or audit of accounts of public money ; and the raising or guarantee of any loan or the repayment <unk> . It did not however , cover any sort of local taxes or similar measures . Some Finance Bills have not fallen within this criterion ; Consolidated Fund and <unk> Bills have . The Speaker of the House of Commons would have to <unk> that a bill was a money bill , <unk> it with a Speaker 's certificate . The Local Government Finance Bill 1988 , which introduced the Community Charge ( " Poll Tax " ) , was not certified as a Money Bill and was therefore considered by the Lords . Whilst Finance Bills are not considered Money Bills , convention dictates that those parts of a Finance Bill dealing with taxation or expenditure ( which , if in an Act alone , would constitute a Money Bill ) are not questioned . 
- Other public bills could no longer be vetoed ; instead , they could be delayed for up to two years . This two @-@ year period meant that legislation introduced in the fourth or fifth years of a parliament could be delayed until after the next election , which could prove an effective measure to prevent it being passed . Specifically , two years had to <unk> between the second reading in the House of Commons in the first session and the passing of the bill in the House of Commons in the third session . The Speaker has to also <unk> that the conditions of the bill had been complied with . Significant restrictions on amendments are made to ensure that it is the same bill that has been rejected twice . The 1911 Act made clear that the life of a parliament could not be extended without the consent of the Lords . 
- Parliament had been limited to a maximum of seven years under the <unk> Act <unk> , but this was reduced by the passing of the Parliament Act 1911 . Parliament would now be limited to five years , beginning the first meeting of parliament after the election . In practice , no election has been forced by such a limitation as all parliaments have been dissolved by the Monarch on request of the Prime Minister . It should be noted , however , that the five @-@ year maximum duration referred to the lifetime of the Parliament , and not to the interval between General <unk> . For example , the 2010 General Election was held five years and one day after the 2005 General Election , whilst the 1992 General Election was held on 9 April 1992 and the next General Election was not held until 1 May 1997 . The reduction in parliament length was seen as a counterbalance to the new powers granted to the Commons . 
- 
- = = <unk> = = 
- 
- The Lords continued to suggest amendments to money bills over which it had no right of veto and in several instances these were accepted by the Commons . These included the China <unk> Bill 1925 and the Inshore <unk> Industry Bill 1947 . The use of the Lords ' now temporary veto remains a powerful check on legislation . 
- It was used in relation to the Government of Ireland Act 1914 , which had been under the threat of a Lords veto , now removed . Ulster Protestants had been firmly against the passing of the bill . However , it never came into force because of the outbreak of the First World War . Amendments to the Parliament Act 1911 were made to prolong the life of the 1910 parliament following the outbreak of the First World War and 1935 parliament because of the Second World War . These made special exemptions to the requirement to hold an election every five years . 
- Legislation passed through the Parliament Act , without the consent of the Lords , is still considered primary legislation . The importance of this was highlighted Jackson v Attorney General , where the legality of the Parliament Act 1949 was questioned . The challenge asserted foremost that the 1949 Act was delegated rather than primary legislation , and that the 1911 Act had delegated power to the Commons . If this were the case , then the Commons could not <unk> itself through the 1949 Act without direct permission from the Lords . Since it was passed under the 1911 Act , it had never received the required consent of the Lords . However , the 1949 Act was found to be legal . The 1911 Act , it was concluded , was not primarily about <unk> the Commons , but rather to restrict the ability of the Lords to affect legislation . This ruling also means that efforts to abolish the House of Lords ( a major constitutional change ) using the Act could be successful , although the issue was not directly addressed in the ruling . 
- 
- = = Analysis = = 
- 
- The Parliament Act 1911 can be seen in the context of the British constitution : rather than creating a written constitution , parliament chose instead to <unk> through the usual channels in response to the crisis . This was a <unk> response , which avoided the further problems of <unk> unwritten rules and <unk> the entire government . It is commonly considered a statute of " constitutional importance " , which gives it informal priority in parliament and in the courts with regards to whether later legislation can change it and the process by which this may happen . 
- It is also mentioned in discussion of constitutional convention . Whilst it replaced conventions regarding the role of the House of Lords , it also relies on several others . Section 1 ( 1 ) only makes sense if money bills do not arise in the House of Lords and the provisions in section 2 ( 1 ) only if proceedings on a public bill are completed in a single session , otherwise they must fail and be put through procedure again . 
- 
- = = = Case law = = = 
- 
- 
- 
- = Hibiscus ( restaurant ) = 
- 
- Hibiscus is a London restaurant owned and run by French chef Claude Bosi . It was opened in 2000 in Ludlow , Shropshire , and won its first Michelin star within a year , and a second in the 2004 Guide . In July 2006 , Bosi and his wife Claire announced that they were to sell the location in Ludlow and move closer to London . The property was sold to Alan Murchison , and Bosi purchased a new site on <unk> Street in London . 
- Bosi uses molecular <unk> to create some items on the menu in an effort to enhance their flavours , such as freeze @-@ drying cabbage to create a <unk> . The restaurant has received mixed reviews from critics , but has been listed in The World 's 50 Best Restaurants since 2010 , and was named by Egon Ronay as the best restaurant in the UK in 2005 . The Good Food Guide ranked Hibiscus as the eighth @-@ best restaurant in the UK in the 2013 edition . It has also been awarded five AA <unk> . 
- In 2011 , Bosi started a new venture , the Fox & <unk> in Wimbledon . This new <unk> operates under the same philosophy as Hibiscus , and is a collaboration with brother Cedric and former sous chef at Hibiscus , Patrick <unk> . 
- 
- = = History = = 
- 
- Claude Bosi and his wife Claire opened Hibiscus in Ludlow , Shropshire , in 2000 . The location had a 36 @-@ seat capacity , and was previously occupied by a three AA <unk> restaurant called the Oaks . 
- Bosi had previously been head chef and won a Michelin star at the <unk> <unk> restaurant , just outside the town . He had intended to open a restaurant in Warwickshire , but found the premises too expensive and purchased a 25 @-@ year lease on the former Oaks property in Ludlow for £ 40 @,@ 000 . Within a year Hibiscus won its first Michelin star , and at the same time <unk> <unk> was downgraded before going in to receivership . 
- Working under Bosi at Hibiscus was sous chef <unk> <unk> , who left Hibiscus in 2003 to become head chef at Jessica 's restaurant in <unk> . Hibiscus gained a second star in the 2004 Michelin Guide . 
- In July 2006 , Bosi and his wife Claire announced that they were intending to sell Hibiscus and open a new restaurant closer to London , or in the capital itself . Hibiscus closed in Ludlow in April 2007 , with Bosi selling the site to fellow chef Alan Murchison for £ 247 @,@ 500 , but retaining the Hibiscus name for himself . The restaurant was renamed " Le <unk> " ( sic ) , and underwent a £ 100 @,@ 000 makeover before being re @-@ opened under head chef Will Holland . In 2014 , Murchison 's company went into voluntary <unk> after running up debts of almost half a million pounds . 
- Bosi completed the deal in June 2007 for a new site at 29 <unk> Street in London . He intended for the new Hibiscus to be open by September , and to transfer over the style of cooking he had used in Ludlow , saying , " I 'm transferring Hibiscus , not starting a new restaurant . The idea is to continue and build on what I have been doing . " <unk> and fitting out the London premises cost around £ 1 million . 
- Many of the staff from the Ludlow incarnation of Hibiscus agreed to move to London to continue working at the restaurant , including head chef Marcus McGuinness and <unk> Simon Freeman . Hibiscus re @-@ opened in October 2007 in its new location after following building works and planning delays . The interior of the London @-@ based restaurant was decorated in orange and shades of brown . The walls were covered in pale @-@ coloured wooden panels , and a <unk> designed as a series of <unk> <unk> from the middle of the main dining room 's ceiling . 
- The handover on the first day was so tight that builders moved out at midday , and the first service was run at 7 pm that evening . The late opening resulted in the reviewers for the Michelin Guide having only a two @-@ week window in which to re @-@ assess the restaurant for the 2008 guide . Bosi admitted later that the restaurant was not yet up to scratch in those two weeks and agreed with the decision of Michelin to <unk> Hibiscus to a single star in the 2008 Guide . The restaurant was also given a " rising star " as one with potential to go up to two stars in the future . During the run up to Christmas , the stress of serving 550 covers a week in a new location with a modified menu resulted in three sous chefs resigning . 
- The two @-@ star award was restored a year later in the 2009 Michelin Guide , as had been predicted by a number of Bosi 's fellow chefs including Tom Aikens , Antonin <unk> and Richard <unk> . <unk> <unk> went a step further and said " I would love to see Claude Bosi regain his second star at Hibiscus and win his third in time . He ’ s probably the best chef I know . " 
- 
- = = Menu = = 
- 
- The menu is created by Bosi . He has been described as an innovator and his work has been compared to that of Heston <unk> at The Fat Duck . One of the new dishes Bosi introduced following his move to London was a two @-@ part pork dish . The first part was roasted <unk> pig served with sea <unk> , <unk> and a <unk> of sweet potato . The second , inspired by his daughter , featured a sausage roll with a <unk> and a <unk> dressing . Other dishes have included <unk> chicken with an onion <unk> and <unk> , and desserts include a chocolate tart served with <unk> ice cream . 
- Bosi uses molecular <unk> techniques , such as in the process for making a Savoy cabbage <unk> , in which the cabbage is freeze dried into a powder and then reconstituted , but he prefers only to enhance the flavours of individual ingredients rather than changing those flavours by using unusual techniques . 
- 
- = = Reception = = 
- 
- Jay <unk> reviewed the restaurant for The Observer after Hibiscus moved from Ludlow to London , his first time at the restaurant . While stating that elements of the meal were " very clever indeed " , such as <unk> <unk> ice cream and a sausage roll he described as a " <unk> " , he described the desserts as a " disappointment " , calling an olive oil <unk> a " <unk> mess " . Overall , he planned on returning to give Bosi another chance . 
- Zoe Williams also reviewed the restaurant shortly after it arrived in London , for The Daily Telegraph . She enjoyed her visit , and was impressed with the unusual combinations of foods that worked together saying " the sheer expertise of taking a food with a range of flavours , and knowing it 's in peak condition to meet four others ... it really is something " . 
- John Walsh also visited it , for The Independent , after the restaurant arrived in London , and gave the food four stars , and the <unk> and service three stars ; Terry <unk> reviewed it for the same paper , giving the restaurant 17 out of 20 . 
- Food critics from Time Out visited the restaurant in 2009 , and were " disappointed " compared to their previous visit . They thought that Bosi 's food combinations just did not work , but still said that some of his desserts were " <unk> " . 
- Andy <unk> gave the restaurant a score of six out of ten on his scale during his November 2011 visit . The redeeming feature of his trip was a dish of <unk> , he thought , served with a <unk> of pear in <unk> wine and Savoy cabbage with a red wine and smoked chocolate sauce , but he otherwise felt that the food was " over @-@ worked " and the service " <unk> amateurish " . 
- 
- = = = Ratings and awards = = = 
- 
- In 2005 Hibiscus was one of three restaurants to be awarded three @-@ stars by the Egon Ronay Restaurant Guide , along with The <unk> Inn and Restaurant Tom Aikens , and was named Ronay 's Restaurant of the Year . 
- The restaurant made its first entry in the World 's 50 Best Restaurants in 2010 , ranked in 49th place and one of three British restaurants in the list ; the following year it moved up to 43rd . 
- The Good Food Guide ranked Hibiscus as the eighth @-@ best restaurant in the UK in its 2013 guide . 
- The restaurant has been given five AA <unk> by The Automobile Association . 
- 
- 
- = Chris Turner ( American football ) = 
- 
- Chris Turner ( born September 8 , 1987 ) is an American football quarterback . He played quarterback for the Maryland Terrapins at the University of Maryland from 2007 to 2009 . Turner began his career at Maryland as a redshirt in 2005 and then served as a reserve quarterback before he earned the starting position . 
- After a redshirt season in 2005 , Turner saw no playing time as the third @-@ string quarterback the following year . In 2007 , he replaced the injured starter during the game against 10th @-@ ranked Rutgers and led the Terrapins to an upset victory . After that , he remained as the team 's leader for the remainder of the season , and against eighth @-@ ranked Boston College , engineered another upset victory . Turner also helped Maryland secure an appearance in the Emerald Bowl . At the start of the 2008 season , he was relegated to backup status , but soon regained the starting position . That year , he led Maryland in wins over four of their five ranked opponents and to an appearance in the Humanitarian Bowl . He returned as the starter for the 2009 season , but suffered a knee injury and was replaced by <unk> Robinson . 
- 
- = = Early years = = 
- 
- Turner was born in <unk> Valley , California to parents John and Grace Turner . His father was the original drummer of the <unk> metal band <unk> . Chris Turner attended high school at the <unk> College Preparatory School , where he was a three @-@ year <unk> and starting quarterback in football and pitcher in baseball . As a sophomore , he was on the junior varsity football team until <unk> when the varsity quarterback suffered an injury against Valencia High School , a regional powerhouse . Turner described the incident as the last time that he was nervous during a game and said , " It got pretty ugly to be honest . Ever since then , I 've always thought to myself , ' It can 't get worse than that . ' " 
- At <unk> , he led a pass @-@ oriented offense and , during his senior year , accumulated 139 completions on 265 attempts , 2 @,@ <unk> yards , 16 touchdowns , and 11 interceptions . He was named to the All @-@ California <unk> Federation ( <unk> ) second team as a senior and the All @-@ Mission League team as both a junior and senior . <unk> picked him as a Far West all @-@ region selection and <unk> as an all @-@ region selection . Turner was assessed as a <unk> three @-@ star and <unk> two @-@ star recruit . He received scholarship offers from Boise State , Illinois , Louisville , Maryland , Oregon , Utah , and <unk> . Maryland offered him after another Californian recruit , Josh Portis , chose Florida instead — although Portis later transferred to Maryland and served as a backup alongside Turner . Turner ultimately chose Maryland , which is located just outside Washington , D.C. , partly due to his interest in politics . 
- 
- = = College career = = 
- 
- 
- = = = Experience = = = 
- 
- Turner sat out his true freshman year during the 2005 season on redshirt status . In 2006 , he served as the third @-@ team quarterback behind starter Sam <unk> and reserve Jordan Steffy , but Turner saw no playing time during that season . 
- 
- = = = = 2007 season = = = = 
- 
- In 2007 , as the second @-@ string quarterback , he saw his first action against <unk> when he was substituted for injured starter Jordan Steffy . Turner executed a scoring drive and completed four of six passes , but also threw two interceptions . He sat out the next two games and then took one snap against Wake Forest . When the unranked <unk> faced 10th @-@ ranked Rutgers , Steffy suffered a concussion and Turner again filled in . He led drives for two touchdowns and two field goals in an upset of the Scarlet Knights , 34 – 24 . About the then relatively unknown Turner , Rutgers head coach Greg <unk> said , " I wish I would have seen this guy before . " Turner started for the remaining eight games of the season . In his career @-@ first start , he passed for 255 yards in a win against Georgia Tech , 28 – 26 . In the game , he threw a 78 @-@ yard touchdown pass , which was caught by tight end Jason Goode rather than his intended receiver , <unk> <unk> @-@ <unk> . After a three @-@ game losing streak , Turner led another upset win over a top @-@ ten team , this time against eighth @-@ ranked Boston College , 42 – 35 . In the 24 – 16 loss to Florida State , Turner had his worst performance of the season . Late in the second quarter , he was <unk> and replaced by Steffy for two possessions . Turner said , " I didn 't know that my <unk> was that short in the first place , to be honest . I didn 't think it would come to this . I guess I should have . " After an uneven performance by Steffy , Turner played the entire second half and showed improvement . The following week Maryland played their regular season finale against NC State and both teams needed an additional win to attain bowl eligibility . Maryland led in the second quarter , 3 – 0 , but was struggling <unk> . Turner , a generally immobile pocket quarterback , invigorated the offense when he executed a 41 @-@ yard option run for a first down . He completed 19 of 24 passes for 206 yards and led a shutout of NC State , 37 – 0 . With the sixth win , Maryland was invited to the Emerald Bowl , where they were beaten by Oregon State , 21 – 14 . Turner finished the season as the third @-@ most efficient passer in the Atlantic Coast Conference ( ACC ) . 
- 
- = = = = 2008 season = = = = 
- 
- In 2008 , Maryland hired a new offensive coordinator , James Franklin , who installed a West Coast offense . That season started with some controversy when head coach Ralph Friedgen initially selected senior Jordan Steffy as the starting quarterback . Turner considered returning home with a transfer to a Californian school , but ultimately decided against it . When Steffy was injured again in the season @-@ opener against Delaware , Turner was awarded the starting job for the rest of the season . After Maryland defeated 21st @-@ ranked Wake Forest , 26 – 0 , Turner was voted the ACC Player of the Week and ESPN named him an " On the Mark " quarterback . Against 16th @-@ ranked North Carolina , Turner 's split @-@ second decision to hold onto the ball for a nine @-@ yard run on fourth down and five put the team within range for the game @-@ winning field goal , 17 – 15 . For that , he was nominated for the week 's Pontiac Game Changing Performance . The win improved Maryland 's record to 7 – 3 and <unk> control over their ACC championship destiny , although they lost the next game against Florida State , 37 – 3 , which ended the Terrapins ' title hopes . The following week against Boston College , Turner set career highs with 33 completions , 57 attempts , and 360 passing yards , but the effort fell short and Maryland lost , 28 – 21 . In the postseason , the Terrapins played Nevada in the Humanitarian Bowl where Turner threw for 198 yards , one interception , two touchdowns including a 59 @-@ yard long , and a two @-@ point conversion . At the end of the season , Maryland had beaten four out of their five Top 25 @-@ ranked opponents , a feat that was surpassed only by the teams in the BCS Championship Game : Florida and Oklahoma . 
- 
- = = = = 2009 season = = = = 
- 
- Fifth @-@ year senior Turner entered the 2009 season as the uncontested starter at quarterback for the first time in his career . He was backed @-@ up by sophomore <unk> Robinson and true freshmen C. J. Brown and Danny O 'Brien . Turner 's <unk> was called critical for the performance of the team 's relatively youthful offensive line . Head coach Friedgen said , " If I had to choose between a veteran offensive line and an inexperienced quarterback , or an inexperienced line with a veteran quarterback , I think I 'd go with the veteran quarterback . " Upon the conclusion of preseason camp , the coaching staff praised his progress , particularly his game @-@ planning , for which he had been criticized in the past . Turner said , " <unk> , I am way further ahead than where I was last year . " <unk> , he dropped 16 pounds by adding muscle and losing body fat , and weighed 220 pounds before the start of the season . Offensive coordinator Franklin said , " You look at him , he was kind of <unk> last year . You look at him now and he 's muscular and shaped . He looks more athletic . " 
- Before the season , Turner was added to the watch list for the Johnny <unk> Golden Arm Award , an annual honor bestowed upon the nation 's top senior quarterback . At that time , he ranked as the school 's fourth all @-@ time quarterback in terms of completion percentage , fifth in completions , seventh in total offensive yards , and he is tied for 11th in number of touchdown passes . In June 2009 , Turner stated that he wanted to leave a " legacy " at Maryland in his final season . He also expressed a desire to pursue a professional playing career in the National Football League ( NFL ) if possible . The NFL Draft Scout assessed Turner as the 16th @-@ ranked quarterback out of the 135 available for the 2010 NFL Draft and projected him as a potential seventh @-@ round selection . 
- 
- = = = Playing style and personality = = = 
- 
- Prior to his senior year , Jeff Barker of The Baltimore Sun noted that Turner 's strengths were " poise , <unk> , [ and ] <unk> , " while he had room for improvement in arm strength and mobility . Barker said , " He 's a bit <unk> — he calls himself a ' rhythm ' passer … But he doesn 't quit when things go poorly . His coolness can be mistaken for apathy . " Turner typically <unk> himself to the pocket , as his ability to run the ball himself is limited by his speed . He has made light of his own lack of <unk> by saying that he runs a " flat 5 @.@ 0 " <unk> 40 @-@ yard dash . The CBS Sports @-@ affiliated NFL Draft Scout records his actual time as ranging from a low of 4 @.@ 89 to a high of 5 @.@ 16 seconds , while <unk> reports his 40 @-@ yard time as 4 @.@ 8 seconds . Turner 's supposedly one @-@ dimensional nature , however , has occasionally allowed him to make unexpected plays with his feet . The most noteworthy of these were the momentum @-@ changing 41 @-@ yard option run against NC State in 2007 , and the fourth @-@ down nine @-@ yard scramble into field goal range against North Carolina in 2008 . 
- <unk> and coaches sometimes characterized Turner as a quarterback who did not <unk> during practices , but executed well on game @-@ day . Maryland head coach Ralph Friedgen said " He 's pretty go @-@ with @-@ the @-@ flow . And that 's his greatest strength and maybe his greatest weakness . The bottom line is , he plays well in games . " The Baltimore Sun wrote that he " seems to play his best when it counts — and his worst when it doesn 't . " Regarding the 2008 summer practices , offensive coordinator James Franklin said Turner ranked in the middle compared with Jordan Steffy and Josh Portis in terms of completion percentage , fewest interceptions , and " explosive " plays that gained 16 yards or more . Turner conceded that Steffy was a better game @-@ <unk> during practices . Head coach Ralph Friedgen who had offered critical or subdued assessments of <unk> ' practice , said his intensity grew after losing the starting position to Steffy prior to the 2008 season . Turner said , " It lit a fire in me in the sense that I had to be ready . " 
- Among his Maryland teammates and coaches , Turner earned a reputation as an <unk> " laid @-@ back " Californian and for remaining calm under pressure . After the 2007 win over Rutgers , offensive tackle Scott <unk> described his roommate Turner as " real calm [ in the <unk> ] . . . He would say , ' <unk> , are you ready to score again ? Are you ready to run the ball on these guys ? ' And we 're like , ' Yeah , let 's do that . ' " Turner received the nicknames " Sunshine " and " Napoleon <unk> " , in reference to his similar appearance to the fictional characters in the films Remember the Titans and Napoleon <unk> , respectively . 
- 
- = = Personal life = = 
- 
- Turner graduated from the University of Maryland in May 2009 with a bachelor 's degree in government and politics . His strong interest in politics <unk> into his decision on where to attend college . He chose the University of Maryland partly based on its close proximity to Washington , D.C. Turner 's political views are liberal ; he is registered as a Democrat , and has described himself as an idealist . 
- During the 2008 season , political discussions were common in the Maryland locker room because of the presidential election . Turner and fellow quarterback Jordan Steffy , a conservative and supporter of presidential candidate Senator John McCain , were the most frequent <unk> , while defensive tackle Dean <unk> was described as the primary instigator . During the summer of 2009 , Turner held an internship on Capitol Hill working for Democratic Representative <unk> <unk> , the House Majority Leader and Maryland 's fifth district congressman . Turner had previously considered <unk> for the presidential campaign of Barack Obama , which he said probably annoyed his conservative father , John Turner . Describing his experience during the internship , Turner said , " It 's funny to compare how serious politics is and how serious football is . Depending on who you talk to , they 're both pretty big deals . . . There 's more to life than football . " 
- <unk> , Turner 's favorite sport is soccer , not football . He is also interested in foreign cultures and expressed regret at being unable to spend a semester abroad because of college football . Turner said that , dependent upon the outcome of his football career , he would like to attend the 2010 World Cup in South Africa . He also got to meet his childhood idol David <unk> while at the 2010 South Africa world cup . 
- 
- = = Statistics = = 
- 
- 
- 
- = Jack and Jill ( nursery rhyme ) = 
- 
- " Jack and Jill " ( sometimes " Jack and Gill " , particularly in earlier versions ) is a traditional English nursery rhyme . The Roud Folk Song Index classifies this tune and its variations as number <unk> . The rhyme dates back at least to the 18th century and exists with different numbers of verses each with a number of variations . Several theories have been advanced to explain its origins and to suggest meanings for the lyrics . 
- 
- = = Lyrics and structure = = 
- 
- The first and most commonly repeated verse is : 
- Jack and Jill went up the hill 
- To fetch a <unk> of water . 
- Jack fell down and broke his crown , 
- And Jill came tumbling after . 
- Many verses have been added to the rhyme , including a version with a total of 15 stanzas in a <unk> of the 19th century . The second verse , probably added as part of these extensions has become a standard part of the nursery rhyme . Early versions took the form : 
- Up Jack got , and home did trot , 
- As fast as he could <unk> ; 
- To old Dame <unk> , who patched his <unk> 
- With vinegar and brown paper . 
- By the early 20th century this had been modified in some collections , such as L. E. Walter 's , Mother Goose 's Nursery Rhymes ( London , 1919 ) to : 
- Up Jack got and home did trot , 
- As fast as he could <unk> ; 
- And went to bed and bound his head 
- With vinegar and brown paper . 
- A third verse , sometimes added to the rhyme , was first recorded in a 19th @-@ century <unk> and took the form : 
- Then Jill came in , and she did <unk> , 
- To see Jack 's paper plaster ; 
- Her mother <unk> her , across her knee , 
- For laughing at Jack 's disaster . 
- Twentieth @-@ century versions of this verse include : 
- When Jill came in how she did <unk> 
- To see Jack 's paper plaster ; 
- Mother <unk> did whip her next 
- For causing Jack 's disaster . 
- The rhyme is made up of <unk> , with a rhyming scheme of <unk> ( with occasional internal rhymes ) , using falling rhymes ( where the rhyming sound is on a relatively <unk> syllable : de @-@ emphasising the rhyme ) and a <unk> rhythm ( with the stress falling on the first of a pair of syllables ) , known as a ballad form , which is common in nursery rhymes . The melody commonly associated with the rhyme was first recorded by the composer and nursery rhyme collector James William Elliott in his National Nursery Rhymes and Nursery Songs ( 1870 ) . The Roud Folk Song Index , which catalogues folk songs and their variations by number , classifies the song as <unk> . 
- 
- = = Meaning and origins = = 
- 
- The rhyme has traditionally been seen as a nonsense verse , particularly as the couple go up a hill to find water , which is often thought to be found at the bottom of hills . <unk> and brown paper were a home cure used as a method to draw out <unk> on the body . The phrase " Jack and Jill " , indicating a boy and a girl , was in use in England as early as the 16th century . A comedy was performed at the <unk> court in 1567 @-@ 8 with the title Jack and Jill and the phrase was used twice by Shakespeare : in A Midsummer Night 's Dream , which contains the line : " Jack shall have Jill ; <unk> shall go ill " ( III : ii : 460 @-@ 2 ) and in Love 's Labour 's Lost , which has the lines : " Our wooing <unk> not end like an old play ; Jack hath not Jill " ( V : ii : 874 – 5 ) , suggesting that it was a phrase that indicated a romantically attached couple , as in the <unk> " A good Jack makes a good Jill " . 
- Jack is the most common name used in English language nursery rhymes and by the 18th century represented an <unk> <unk> hero , while by the end of the Middle Ages Jill or Gill had come to mean a young girl or a <unk> . However , the woodcut that accompanied the first recorded version of the rhyme showed two boys ( not a boy and a girl ) , and used the spelling Gill not Jill . This earliest printed version comes from a reprint of John <unk> 's Mother Goose 's Melody , thought to have been first published in London around 1765 . The rhyming of " water " with " after " , was taken by Iona and Peter <unk> to suggest that the first verse may date from the first half of the 17th century . 
- 
- = = <unk> = = 
- 
- While the true origins of the rhyme are unknown there are several theories . As is common with nursery rhyme <unk> , complicated metaphors are often said to exist within the lyrics of Jack and Jill . Most explanations post @-@ date the first publication of the rhyme and have no <unk> evidence . These include the suggestion by S. <unk> @-@ Gould in the 19th century that the events were a version of the story told in the 13th @-@ century <unk> <unk> <unk> written by Icelandic historian <unk> <unk> , who stated that in Norse mythology , <unk> and <unk> , brother and sister ( respectively ) , were taken up from the earth by the moon ( personified as the god <unk> ) as they were <unk> water from the well called <unk> , bearing on their shoulders the <unk> called <unk> and the pole called <unk> . Around 1835 John <unk> <unk> suggested that Jack and Jill were two priests , and this was enlarged by Katherine <unk> in 1930 to indicate that Jack represented Cardinal <unk> ( <unk> – 1530 ) ; and Jill was Bishop <unk> , who negotiated the marriage of Mary Tudor to the French king in 1514 . 
- It has also been suggested that the rhyme records the attempt by King Charles I to reform the taxes on liquid measures . He was blocked by Parliament , so subsequently ordered that the volume of a Jack ( 1 / 2 pint ) be reduced , but the tax remained the same . This meant that he still received more tax , despite Parliament 's veto . Hence " Jack fell down and broke his crown " ( many pint glasses in the UK still have a line marking the 1 / 2 pint level with a crown above it ) " and Jill came tumbling after " . The reference to " Jill " ( actually a " gill " , or 1 / 4 pint ) is said to reflect that the gill dropped in volume as a consequence . 
- The suggestion has also been made that Jack and Jill represent Louis XVI of France , who was deposed and beheaded in 1793 ( lost his crown ) , and his Queen , Marie <unk> ( who came tumbling after ) , a theory made difficult by the fact that the earliest printing of the rhyme pre @-@ dates those events . There is also a local belief that the rhyme records events in the village of <unk> in Somerset in 1697 . When a local spinster became pregnant , the <unk> father is said to have died from a rock fall and the woman died in childbirth soon after . 
- 
- 
- = Florida State Road 878 = 
- 
- State Road 878 ( SR 878 ) , named the Snapper Creek Expressway or the Snapper Creek <unk> for its entire length , is a 2 @.@ 7 @-@ mile @-@ long ( 4 @.@ 3 km ) east – west electronic toll road south of Miami , Florida . The expressway is named for the nearby Snapper Creek which runs parallel to SR 878 . It acts as a spur route of the Don Shula Expressway ( SR 874 ) , providing access to U.S. Route 1 ( US 1 ) near South Miami and local access to the eastern Kendall area while bypassing the <unk> district . The road is maintained and <unk> by the Miami @-@ Dade Expressway Authority ( MDX ) . 
- 
- = = Route description = = 
- 
- SR 878 's western terminus is integrated into the Don Shula Expressway 's interchange with Kendall Drive ( SR 94 ) across the boundary of the Kendall and Sunset districts . <unk> entering the Don Shula Expressway northbound from Kendall Drive are given the option of continuing onto SR 874 via a flyover , or else merging into the traffic leaving SR 874 for the Snapper Creek Expressway , which then heads under the Kendall Drive – Don Shula Expressway flyover . The westbound lanes of SR 878 , however , pass over SR 874 's mainline , and are then given an exclusive carriageway beside the southbound lanes for 0 @.@ 46 miles ( 0 @.@ 74 km ) , before merging into SR 874 just north of the Kendall Drive overpass . <unk> motorists from the Don Shula Expressway wishing to exit to Kendall Drive merge into this carriageway before leaving for SR 94 with those vehicles originating from the Snapper Creek Expressway . There is no direct connection for southbound motorists on SR 874 to head east on SR 878 ; likewise , westbound motorists on SR 878 cannot head north along SR 874 directly . 
- From here , SR 878 heads predominantly eastwards as a four @-@ lane @-@ wide expressway through residential neighborhoods for the remainder of its length , generally lying 0 @.@ 5 miles ( 0 @.@ 80 km ) north of Kendall Drive . After approximately 0 @.@ 4 miles ( 0 @.@ 64 km ) , the Snapper Creek Expressway passes through the 87th Avenue toll gantry . It then meets Galloway Road ( SR 973 ) shortly afterwards with a diamond interchange . The expressway then enters <unk> Heights once it crosses SR 973 and remains in that district for the rest of its duration . Just before passing over the <unk> Expressway ( SR 826 ) without an interchange ( approximately 1 mile ( 1 @.@ 6 km ) east of Galloway Road ) , SR 878 meets its second and final toll gantry . 
- About 0 @.@ 3 miles ( 0 @.@ 48 km ) east of the <unk> Expressway , SR 878 has a partial diamond interchange with Southwest 72nd Avenue , which only allows westbound entry to and eastbound exit from the Snapper Creek Expressway . Immediately afterwards , SR 878 turns to the southeast and prepares to meet its eastern terminus at the South Dixie Highway ( US 1 ) at a surface intersection 0 @.@ 6 miles ( 0 @.@ 97 km ) later , passing under the <unk> line and associated <unk> just before doing so . Traffic heading south along US 1 from eastbound SR 878 moves into a slip lane , while that wishing to head north along US 1 enters it at an oblique angle , aided by traffic signals . The only access onto SR 878 westbound from US 1 is for southbound traffic ; motorists heading north along US 1 are guided to SR 878 by signage along Southwest 67th Avenue and Southwest 80th Street . 
- 
- = = <unk> = = 
- 
- SR 878 's tolls are entirely electronic : cash cannot be accepted along its length . <unk> is done either via SunPass <unk> or via toll @-@ by @-@ plate billing , the latter of which attracts a higher cost . Two toll <unk> are located along the Snapper Creek Expressway , the first between the Don Shula Expressway and Galloway Road , and the second between Galloway Road and Southwest 72nd Avenue . The relationship between the <unk> points and interchanges along SR 878 and SR 874 is that all motorists are charged at least one toll for using the road ; there are no " free sections " . As of July 1 , 2013 , the cost for a two @-@ axle vehicle to travel the entire length of the Snapper Creek Expressway is $ 0 @.@ 50 with a SunPass <unk> , or $ 1 @.@ 00 via the toll @-@ by @-@ plate program . Each additional axle on a vehicle attracts an extra $ 0 @.@ 25 via SunPass or $ 0 @.@ 50 via toll @-@ by @-@ plate for each toll gantry passed . 
- 
- = = History = = 
- 
- Planning by Dade County for a road named the " Snapper Creek Expressway " was underway as early as 1958 , with a final completion date set as late as 1975 . Funding for SR 878 's construction was made available in 1971 by the Florida Department of Transportation as part of plans to construct the Snapper Creek Expressway along with the South Dade Expressway ( now known as the Don Shula Expressway ) and the West Dade Expressway ( now known as the <unk> <unk> of Florida 's Turnpike ) , with an expected completion date of early 1973 . Construction was halted in 1974 due to money issued from county bonds for expressway building running out , and the road was left partially completed ; however , $ 8 million in federal emergency funds was directed to completing the expressway in late 1977 . The Snapper Creek Expressway , designated SR 878 , finally opened in early 1980 , with the Southwest 72nd Avenue interchange opening a few weeks later . 
- No tolls were collected along SR 878 , in line with the road 's original plans , until MDX 's initial roll @-@ out of open road <unk> from late 2009 to mid @-@ 2010 on its road network . <unk> along the Snapper Creek Expressway began on July 17 , 2010 . The move to toll the Snapper Creek Expressway angered local residents , but was tempered by MDX 's move to investigate toll <unk> . Initially , tolls were $ 0 @.@ 25 for SunPass users , with a $ 0 @.@ 15 <unk> for motorists using the toll @-@ by @-@ plate system . The toll @-@ by @-@ plate rate increased by ten cents on July 1 , 2013 , to $ 0 @.@ 50 per toll gantry passed , while the SunPass rate was unaffected . 
- 
- = = Exit list = = 
- 
- The entire route is in Miami @-@ Dade County . All exits are unnumbered . 
- 
- 
- = James Nesbitt = 
- 
- William James Nesbitt , OBE ( born 15 January 1965 ) is an actor and presenter from Northern Ireland . Born in Ballymena , County Antrim , Nesbitt grew up in the nearby village of <unk> , before moving to Coleraine , County <unk> . He wanted to become a teacher like his father , so he began a degree in French at the University of Ulster . He dropped out after a year when he decided to become an actor , and transferred to the Central School of Speech and Drama in London . After graduating in 1987 , he spent seven years performing in plays that varied from the musical Up on the Roof ( 1987 , 1989 ) to the political drama Paddywack ( 1994 ) . He made his feature film debut playing talent agent <unk> O 'Donnell in Hear My Song ( 1991 ) . 
- Nesbitt got his breakthrough television role playing Adam Williams in the romantic comedy @-@ drama Cold Feet ( 1998 – 2003 ) , which won him a British Comedy Award , a Television and Radio Industries Club Award , and a National Television Award . His first significant film role came when he appeared as pig farmer " Pig " Finn in Waking Ned ( 1998 ) . With the rest of the starring cast , Nesbitt was nominated for a Screen Actors Guild Award . In Lucky Break ( 2001 ) , he made his debut as a film lead , playing prisoner Jimmy Hands . The next year , he played Ivan Cooper in the television film Bloody Sunday , about the 1972 shootings in Derry . A departure from his previous " <unk> <unk> " roles , the film was a turning point in his career . He won a British Independent Film Award and was nominated for the British Academy Television Award for Best Actor . 
- Nesbitt has also starred in Murphy 's Law ( 2001 – 2007 ) as undercover detective Tommy Murphy , in a role that was created for him by writer Colin Bateman . The role twice gained Nesbitt Best Actor nominations at the Irish Film & Television Awards ( IFTA ) . In 2007 , he starred in the dual role of Tom Jackman and Mr Hyde in Steven Moffat 's Jekyll , which earned him a Golden Globe Award nomination in 2008 . Nesbitt has since appeared in several more dramatic roles ; he starred alongside Liam Neeson in Five Minutes of Heaven ( 2009 ) , and was one of three lead actors in the television miniseries Occupation ( 2009 ) . He also starred in the movies <unk> ( 2010 ) and The Way ( 2010 ) . He portrayed <unk> in Peter Jackson 's three @-@ part film adaptation of The Hobbit ( 2012 @-@ 2014 ) . 
- Nesbitt is married to former actress Sonia Forbes @-@ Adam , with whom he has two daughters . He is an advocate of numerous charities , and in 2010 he accepted the ceremonial position of Chancellor of the University of Ulster . 
- 
- = = Early life and education = = 
- 
- James Nesbitt was born on 15 January 1965 in Ballymena , County Antrim , Northern Ireland . His father , James " Jim " Nesbitt , was the headmaster of the primary school in <unk> , a hamlet near <unk> , while his mother , May Nesbitt , was a civil servant . Jim and May already had three daughters — Margaret , <unk> and Andrea . The family lived in the house adjoining the one @-@ room school where Nesbitt was one of 32 pupils taught by Jim ; the other pupils were all farmers ' children . Nesbitt grew up " completely " around women , and spent a lot of time alone , " kicking a ball against a wall " . He had ambitions to play football for Manchester United , or to become a teacher like his father . The family was Protestant , and <unk> was in " Paisley country " . The <unk> spent Sunday evenings singing hymns around the piano . Jim marched in the Ballymena Young <unk> flute band and Nesbitt joined him playing the flute . After the <unk> conflicts , they stopped marching with the band . The family 's residence in the countryside left them largely unaffected by The Troubles , although Nesbitt , his father , and one of his sisters narrowly escaped a car bomb explosion outside Ballymena County Hall in the early 1970s . 
- When Nesbitt was 11 years old , the family moved to Coleraine , County <unk> , where May worked for the Housing Executive . He completed his primary education at <unk> primary school , then moved on to Coleraine <unk> Institution ( <unk> ) . In 1978 , when he was 13 , his parents took him to audition for the Riverside Theatre 's Christmas production of Oliver ! . Nesbitt sang " Bohemian <unk> " at the audition and won the part of the <unk> <unk> , who he played in his acting debut . He continued to act and sing with the Riverside until he was 16 , and appeared at festivals and as an extra in Play For Today : The <unk> ( Christopher <unk> , 1984 ) . He got his <unk> card when the professional actor playing <unk> Cricket in <unk> broke his ankle two days before the performance , and Nesbitt stepped in to take his place . Acting had not initially appealed to him , but he " felt a light go on " after he saw The Winslow Boy ( Anthony Asquith , 1948 ) . When he was 15 , he got his first paid job as a <unk> <unk> at Barry 's Amusements in <unk> . He was paid £ 1 per hour for the summer job and would also , on occasions , work as the brake man on the big <unk> . 
- He left <unk> at the age of 18 and began a degree in French at the University of Ulster , ( formally Ulster Polytechnic ) in <unk> . He stayed at university for a year before dropping out . In a 1999 interview , Nesbitt said , " I had the necessary in my head , but I just couldn 't be bothered . Being 18 is the worst age to expect people to learn things . There are other things to be bothered with , like girls and football . " He made the decision to quit when he was trying to write an <unk> essay on <unk> in Les <unk> Sales at 4 am one day . His father suggested that he should move to England if he wanted to continue acting , so Nesbitt enrolled at the Central School of Speech and Drama ( <unk> ) , part of University of London . Nesbitt felt lost and misrepresented when he first arrived in London , on account of his Northern Irish background ; " When I first came to drama school I was a Paddy the minute I walked in . And I remember going to drama school and them all saying to me , ' <unk> , yeah , <unk> out ' , and I was like ' It 's a wee bit more complicated than that , you know . ' " He graduated in 1987 , at the age of 22 . 
- 
- = = Acting career = = 
- 
- 
- = = = Theatre and Hear My Song = = = 
- 
- The day after leaving <unk> in 1987 , Nesbitt got a bit part in <unk> , a BBC Two Screen Two television play about the life of John Ogdon . He worked for two days on the play , earning £ 250 per day . His first professional stage appearance came in the same year , when he played Keith in Up on the Roof . The musical ran at the Theatre Royal , Plymouth , before transferring to the London West End . Nesbitt reprised the role when the production returned to Plymouth in early 1989 . Roger Malone in The Stage and Television Today wrote that Nesbitt " steals the show with the best lines and best delivery as he <unk> squares up to life with an easy contentment " . Nesbitt appeared in two other plays in 1989 ; in June , he played Dukes Frederick and Senior in Paul <unk> 's As You Like It at the Rose Theatre Club , and then appeared in <unk> <unk> 's version of Hamlet . Hamlet had been translated back to English from Boris <unk> 's Russian translation . It ran at the Haymarket Theatre , Leicester for a month before a transfer to the Old Vic and then a nine @-@ month world tour . Nesbitt played Guildenstern , <unk> and the second <unk> . He recalled that the play received " shocking " reviews , but was exciting . 
- In the early 1990s , he lived with fellow actor Jerome Flynn and earned money by signing fan mail for the successful star of Soldier Soldier . In his debut feature film , Hear My Song ( Peter <unk> , 1991 ) , Nesbitt played <unk> O 'Donnell , a struggling theatrical agent and friend of Mickey O 'Neill ( Adrian Dunbar ) . A New York Times critic wrote , " the <unk> , <unk> Mr. Nesbitt , manages to combine <unk> with <unk> humor " . The praise he received made him self @-@ assured and complacent ; in 2001 , he recalled , " When I did Hear My Song , I disappeared so far up my own <unk> afterwards . I thought , ' Oh , that 's it , I 've cracked it . ' And I 'm glad that happened , because you then find out how expendable actors are . " His attitude left him out of work for six months after the film was released . Until 1994 , he mixed his stage roles with supporting roles on television in episodes of Boon , The Young Indiana Jones Chronicles , Covington Cross , <unk> , and Between the Lines . In 1993 , he appeared in Love Lies Bleeding , an instalment of the BBC anthology series Screenplay and his first appearance in a production directed by Michael Winterbottom ; he later appeared in Go Now ( 1995 ) , Jude ( 1996 ) and Welcome to Sarajevo ( 1997 ) . A Guardian journalist wrote that " he showed himself to be a generous supporting actor " in Jude and Sarajevo . 
- Back on stage , he appeared as <unk> in <unk> ( <unk> Hughes , Birmingham Repertory Theatre , 1991 ) , Aidan in <unk> <unk> ( Mark Lambert and Nicholas Kent , <unk> Theatre , 1992 ) , <unk> in Paddywack ( Michael <unk> ) , <unk> Theatre , 1994 ) , and Jesus in Darwin 's Flood ( Simon Stokes , Bush Theatre , 1994 ) . Paddywack , in which Nesbitt 's character is suspected by others of being an IRA member , transferred to the United States for a run at the Long <unk> Theatre in New Haven , Connecticut in October 1994 . A Variety critic called <unk> " the play 's only fully developed character " and commended Nesbitt for giving " the one strong , telling performance [ of the cast ] " . In 1996 , Nesbitt appeared in an episode of the BBC Northern Ireland television drama <unk> , playing Leo <unk> , the ex @-@ boyfriend of <unk> Fitzgerald ( <unk> <unk> ) and love rival of Peter Clifford ( Stephen <unk> ) . He reprised the role for four episodes in 1998 . 
- 
- = = = Cold Feet and early films = = = 
- 
- In 1996 , Nesbitt auditioned to play Adam Williams , the male lead in Cold Feet , an ITV Comedy <unk> about three couples in different stages of their romantic relationships . The audition came about through a mutual friend of Nesbitt 's and the director , Declan Lowney . The producer , Christine <unk> , had also recalled his performances in Hear My Song and Go Now . Adam had not been written with an <unk> in mind to play him — English writer Mike <unk> had written the character as a thinly veiled portrayal of himself in his youth — but Nesbitt wanted to take the opportunity to appear in a contemporary drama as an ordinary man from Northern Ireland with no connection to the Troubles , especially after the Troubles @-@ based plot of Love Lies Bleeding . Cold Feet was a critical success ; it won the 1997 Golden Rose of Montreux and the 1997 British Comedy Award for Best ITV Comedy and was thus commissioned for a full series . Cold Feet 's first series aired at the end of 1998 and was followed by the second series in 1999 . A storyline in that series featured Adam being diagnosed with <unk> cancer , which inspired Nesbitt to become a patron of the charity Action Cancer . By the time of the third series , Nesbitt and the other cast members were able to influence the show 's production ; an episode featuring Adam 's <unk> weekend was due to be filmed on location in Dublin but Nesbitt suggested it be filmed in Belfast and <unk> instead . Several scenes were filmed at his old workplace Barry 's Amusements , although they were cut from the broadcast episode . At the end of the fourth series in 2001 , Nesbitt decided to quit to move on to other projects . Executive producer Andy <unk> persuaded him to stay for one more series by suggesting that Adam be killed off , so Nesbitt signed on for the fifth series . During pre @-@ production of the fifth series , Mike <unk> decided to kill off Adam 's wife Rachel ( played by Helen <unk> ) instead . 
- Cold Feet ran for five years from 1998 to 2003 , and Nesbitt won the British Comedy Award for Best TV Comedy Actor in 2000 , the Television and Radio Industries Club Award for Drama TV <unk> of the Year in 2002 , the National Television Award for Most Popular Comedy Performance in 2003 , and the TV Quick Award for Best Actor in 2003 . Nesbitt credits the role with raising his profile with the public . Further television roles during these five years included women 's football team coach John Dolan in the first two series of Kay <unk> 's Playing the Field ( appearing alongside his Cold Feet co @-@ star John Thomson ) , investigative journalists Ryan and David Laney in Resurrection Man ( Marc Evans , 1998 ) and Touching Evil II respectively , and <unk> Stanley in Women Talking Dirty ( <unk> <unk> , 1999 ) . 
- Nesbitt 's performance in Hear My Song had also impressed first @-@ time screenwriter and film director Kirk Jones , who cast him in his 1998 feature film Waking Ned . Playing <unk> pig farmer " Pig " Finn brought Nesbitt to international attention , particularly in the United States ( where the film was released as Waking Ned <unk> ) ; the cast was nominated for the 1999 Screen Actors Guild Award for Outstanding Performance by a Cast in a <unk> Motion Picture . In 1999 , he appeared as the paramilitary " Mad Dog " Billy Wilson in The Most <unk> Man in Ireland ( <unk> <unk> ) . The following year , he appeared in Declan Lowney 's feature debut , Wild About Harry . Lowney had personally asked him to appear in the supporting role of cross @-@ dressing Unionist politician Walter Adair . In 2001 , he made his debut as a lead actor in a feature film in Peter <unk> 's Lucky Break . He played Jimmy Hands , an incompetent bank <unk> who <unk> an escape from a prison by staging a musical as a distraction . On preparing for the role , Nesbitt said , " Short of robbing a bank there wasn 't much research I could have done but we did spend a day in <unk> Prison and that showed the nightmare <unk> of prisoners ' lives . I didn 't interview any of the inmates because I thought it would be a little <unk> as it was research for a comedy and also because we were going home every night in our fancy cars to sleep in our fancy hotels . " The film was a commercial failure , despite receiving good feedback from test audiences in the United States . 
- 
- = = = Bloody Sunday = = = 
- 
- Nesbitt had been approached at a British Academy Television Awards ceremony by director Paul <unk> , who wanted him to star in a television drama he was making about the 1972 " Bloody Sunday " shootings in Derry . Nesbitt was only seven years old when the shootings happened and was ignorant of its cause ; he believed that there was " no smoke without fire " and that the Catholic <unk> must have done something to provoke the British Army . He was filming Cold Feet in Manchester when he received the script . He read it and found that had " an extraordinary effect " on him . Nesbitt played Ivan Cooper in Bloody Sunday , the man who pressed for the march to go ahead . To prepare for the role , Nesbitt met with Cooper and spent many hours talking to him about his motives on that day . He met with relatives of the victims and watched the televised Bloody Sunday Inquiry with them , and also read Don <unk> 's Eyewitness Bloody Sunday and Peter Pringle and Philip Jacobson 's Those Are Real <unk> , <unk> 't They ? . <unk> compared Nesbitt 's preparation to an athlete preparing for a race , and told The Observer , " For an Irish actor , doing the Troubles is like doing Lear . " Nesbitt had questioned whether he was a good enough actor to effectively portray Cooper and was worried what Derry Catholics would think of a Protestant playing the lead , although Ivan Cooper himself is a Protestant . 
- Shortly before Bloody Sunday was broadcast , Nesbitt described it as " difficult but extraordinary " and " emotionally draining " . The broadcast on ITV in January 2002 and its promotion did not pass without incident ; he was criticised by <unk> for saying that Protestants in Northern Ireland felt " a collective guilt " over the killings . His parents ' home was also vandalised and he received death threats . During the awards season , Nesbitt won the British Independent Film Award for Best Performance by an Actor in a British Independent Film and was nominated for the British Academy Television Award for Best Actor . The film was also screened at film festivals such as the Stockholm International Film Festival , where Nesbitt was presented with the Best Actor award . 
- In an analysis of the film in the History & Memory journal , <unk> Blaney wrote that it is Nesbitt 's real @-@ life household name status that made his portrayal of Cooper such a success . She reasoned that Nesbitt 's celebrity status mirrors that of Cooper 's in the 1970s : " A household name across Great Britain , Northern Ireland and the Irish Republic [ sic ] , Nesbitt 's widespread popular appeal is emphatically not contingent upon his Protestant Ulster identity , and consequently the double @-@ voicing of the character he plays does not alienate viewers of an alternative , or no , sectarian persuasion . " Guardian journalist Susie Steiner suggested that his appearance in Bloody Sunday was an attempt to resolve the expression of his " <unk> " on screen : " Where he has taken part in a sectarian theme , his intelligence as an actor has often been masked by an excessive , cartoon @-@ style comedy . Yet in his more successful , high @-@ profile roles , ( notably in Cold Feet , and as Pig Finn in the gently pastoral film Waking Ned ) , Nesbitt 's <unk> has been exploited for its romantic charm . It has been <unk> and , in the process , de @-@ <unk> . " A critic identified Bloody Sunday as Nesbitt 's " coming of age " film , and Nesbitt called it a turning point in his career . He refers to his career since the film was released as " post @-@ Bloody Sunday " . 
- 
- = = = Murphy 's Law = = = 
- 
- In 2003 , Nesbitt played undercover police detective Tommy Murphy in the first series of Murphy 's Law , after starring in a successful pilot episode in 2001 . The series was conceived when Nesbitt was working on Playing the Field ; he and producer Greg <unk> approached author Colin Bateman about creating a television series for Nesbitt in a similar vein to Bateman 's Dan <unk> novels . Bateman and Nesbitt were already well acquainted ; Nesbitt had been considered for a main role in <unk> Jack ( David <unk> , 1998 ) , based on Bateman 's original novel . A 90 @-@ minute pilot of Murphy 's Law was commissioned by the BBC , initially as a " comedy action adventure " . Bateman created a complex backstory for Murphy , which was cut at the request of the producers . After the broadcast of the pilot , Guardian critic Gareth McLean wrote , " the likeable James Nesbitt turned in a strong , extremely <unk> central performance , though rarely did he look <unk> by his efforts , and his chemistry with [ <unk> ] Harrison was promising and occasionally electric . " In 2003 , Nesbitt won the Irish Film & Television Award ( IFTA ) for Best Actor in a TV Drama for the role . The second series was broadcast in 2004 . 
- By 2005 , Nesbitt had become tired of the formula and threatened to quit unless the structure of the series was changed . He was made a creative consultant and suggested that Murphy keep one undercover role for a full series , instead of changing into a new guise every episode . This new dramatic element to the series was intended to make it a closer representation of real @-@ life undercover work . Alongside his research with former undercover officer Peter <unk> , Nesbitt hired a personal trainer and grew a <unk> moustache to change Murphy 's physical characteristics and tone down the " <unk> <unk> " persona that the audience had become accustomed to from his roles . With his trainer , he worked out three times a week , boxing and doing circuits and weights . After the first new episode was broadcast , Sarah <unk> wrote in The Times , " In the past , when attempting a nasty stare or a hard face , Nesbitt has never managed much more than a <unk> <unk> look , <unk> forever threatening to break out behind those <unk> Irish eyes . But here , it 's different . He genuinely has the air of a man who means business . " The refreshed series marked another milestone in Nesbitt 's career ; he describes it as " a big moment " in his life . Murphy 's Law was not recommissioned for a sixth series , which Nesbitt attributed to the damage done to the fifth series ratings when it was scheduled opposite the popular ITV drama Doc Martin . 
- In 2004 , Nesbitt appeared in Wall of Silence , a fact @-@ based drama about the aftermath of the murder of schoolboy Jamie Robe . Nesbitt played Stuart Robe , the boy 's father , who tries to break down the wall of silence in the local community to discover exactly what happened to his son . He had only just completed Bloody Sunday when he was offered the part and was unsure whether he wanted to take on such a demanding role so soon after playing Ivan Cooper . He decided to accept the part because he found it interesting . To prepare for the role , Nesbitt met with Robe and spent weeks talking to him in his South London flat , learning about Jamie , and of Robe 's fight for his justice . Nesbitt spoke with his natural accent instead of affecting Robe 's South London speech , as he did not want the audience to be distracted from the drama . The single @-@ drama was filmed over four weeks and broadcast in January 2004 . The role gained Nesbitt an IFTA nomination for Best Actor in a TV Drama later that year . 
- In March 2004 , he appeared in Tony <unk> 's <unk> By , playing Joe Keyes , a man who witnesses a woman being <unk> by some men on a train and chooses not to help . Keyes later discovers that the woman was raped but cannot bring himself to admit in court that he did nothing to help her . Nesbitt described Keyes as " like a better man than me : a good father and husband . But , once he has made a wrong decision , he can 't control everything in his life , as he thinks he is weak . He loses the respect of his wife , his son and at work , and has to reach the lowest possible point before finding redemption . " As a result of these serious roles , he was named the sixth most powerful figure in TV drama in a listing compiled by industry experts for the Radio Times . In September 2004 , he starred as Jack Parlabane in the ITV adaptation of Christopher <unk> 's Quite <unk> One Morning . The producers originally wanted Scottish actor Douglas <unk> to play <unk> Parlabane but ITV executives overruled them and cast Nesbitt . He was given coaching to perfect the accent but it was soon discarded on the advice of both the director and his co @-@ star <unk> <unk> . Also in 2004 , he filmed the roles of Ronnie Cunningham in Millions ( Danny Boyle , 2004 ) , and Detective Banner in Match Point ( Woody Allen , 2005 ) . He was considering taking time off from acting and did not really want the role in Match Point . He sent in an audition tape and was accepted for the part . Nesbitt 's character appears at the end of the film and he read only that part of the script , so did not know the full circumstances of the crime Banner investigates . Despite his initial reluctance , Nesbitt enjoyed working with Allen , and complimented him on his directing style . 
- Nesbitt returned to theatre acting in June 2005 when he appeared in David Nicholls ' After Sun , a ten @-@ minute @-@ play performed as part of the Old Vic 's 24 @-@ Hour Play season . Nesbitt and Catherine Tate starred as a married couple who meet a pair of <unk> returning from their honeymoon . Later that year , he appeared in his first full @-@ length play in 11 years , in Owen <unk> 's Shoot the Crow . He enjoyed the stimulation of learning his lines and rehearsing with the cast and director . The play opened at the Trafalgar Studios in September 2005 and his role as <unk> gained mixed reviews . In The Independent , Michael <unk> suggested the role did not fit the actor : " Nesbitt is cool . But I never felt that he was inside his role of a chap called <unk> [ ... ] He <unk> and <unk> through the evening which steadily became less about <unk> on tiles and more about grating on nerves . " In The Daily Telegraph , Charles Spencer described Nesbitt 's acting as " outstanding " . 
- 
- = = = Jekyll , Five Minutes , Occupation = = = 
- 
- At the end of 2005 , Nesbitt and his agent met with BBC <unk> of Fiction Jane <unk> to discuss a new series of Murphy 's Law . At the meeting 's conclusion , <unk> offered Nesbitt the first episode script of Jekyll , a television series by Steven Moffat that updated Strange Case of Dr Jekyll and Mr Hyde . Nesbitt spent three hours reading the script before accepting the role of Tom Jackman — and his alter @-@ ego Mr Hyde . After signing on for the role , he met with Moffat and <unk> Films executive producer <unk> <unk> to discuss the character , and had several make @-@ up tests . His anticipation for the part was heightened because filming was not scheduled to begin until September 2006 . Nesbitt spent an hour each day being made up as Hyde ; a wig altered his hairline and <unk> were added to his chin , nose and ear lobes . He also wore black contact lenses to make Hyde " <unk> " , though CGI was used to show the transformation from Jackman in close @-@ ups . The series was broadcast on BBC One in June and July 2007 . The role secured him a nomination from the Hollywood Foreign Press Association for the Golden Globe Award for Best Performance by an Actor in a Mini @-@ Series or Motion Picture Made for Television , and a nomination for the Rose d 'Or for Best Entertainer . 
- In 2008 , he portrayed <unk> <unk> in The Passion , a BBC / HBO adaptation of the last week in the life of Jesus . He had originally rejected the script due to other filming commitments , but accepted the role after his agent told him to re @-@ read it before making a final decision . He was pleased to learn that the serial was being produced by Nigel Stafford @-@ Clark , whose <unk> House adaptation he had enjoyed , and that he would be appearing with his Jekyll co @-@ star Denis Lawson . Contrary to previous portrayals of <unk> , Nesbitt played the biblical figure as " nice " , and — as when playing Jack Parlabane — used his own accent . The serial was broadcast in the UK during Easter week 2008 . Shortly after filming The Passion , he filmed the part of journalist Max <unk> in the Carnival Films thriller Midnight Man , which was shown on ITV in May 2008 . It won him a joint nomination ( along with the 2007 series of Murphy 's Law ) for the <unk> Crime Thriller Award for Best Actor . At the end of the year , he had a starring role in the low @-@ budget independent film Blessed . The writer and director Mark <unk> scripted the character of Peter with Nesbitt in mind to play him . The film had a limited release throughout 2008 and 2009 before the BBC screened it on television in 2010 . Nesbitt said , " The role of Peter is what I have dreamed about playing , you wait your whole life for an opportunity like this and when it comes you have to grab it . " 
- The following year , Nesbitt co @-@ starred with Liam Neeson in the fact @-@ based television film Five Minutes of Heaven ( Oliver <unk> , 2009 ) . The first part of the film <unk> the real @-@ life murder of Jim Griffin by <unk> Little in 1970s <unk> ; the second part features a fictional meeting between Little ( Neeson ) and Jim 's brother Joe ( Nesbitt ) 33 years later . Nesbitt met with Griffin before filming began to learn about how his brother 's murder affected him . The film was broadcast on BBC Two in April 2009 . He also starred as Colour Sgt. Mike Swift in Peter Bowker 's three @-@ part BBC / <unk> television serial Occupation . In Occupation , set over six years , Nesbitt 's character is one of three British soldiers who return to Basra , Iraq after their tours have concluded . He researched the role by speaking to Territorial Army soldiers in Belfast , and RAF officers in Morocco , where the serial was filmed . Both performances were commended by Independent journalist Hugh Montgomery ; in a review of 2009 's television , Montgomery named Nesbitt " Face of the Year " , writing , " Just as you had James Nesbitt written off as the <unk> embodiment of everything mediocre about British TV drama , he produced two <unk> performances , as the <unk> <unk> sergeant in Occupation , and a <unk> vengeful victim 's relative in Irish @-@ troubles piece Five Minutes of Heaven . Give the man a <unk> . " Nesbitt was not nominated for a BAFTA award , though did receive a nomination for Best Actor from the Broadcasting Press Guild for both performances . 
- 
- = = = International work = = = 
- 
- In March 2009 , Nesbitt signed a contract with the American talent agency United Talent Agency , as the global financial crisis was <unk> roles in British television . He continued to be represented in the United Kingdom by Artists Rights Group . The next year Nesbitt played the hunter <unk> in the low @-@ budget British horror film <unk> , which was a departure from his previous character types . After screening at major international film festivals in early 2010 , the film had a general release in the latter part of the year . Nesbitt had previously worked with the film 's director and co @-@ writer Colm McCarthy on Murphy 's Law , which was one reason he took the role . He researched the mythical aspects of the character by reading about Irish folklore and beliefs . He also starred alongside Minnie Driver and his Welcome to Sarajevo co @-@ star <unk> <unk> in the Tiger <unk> television serial The Deep . In the five @-@ part drama , Nesbitt played submarine engineer <unk> Donnelly . The serial was filmed over 12 weeks at BBC Scotland 's studios in <unk> . August 2010 saw the release of Nadia <unk> 's film <unk> Jack , in which Nesbitt plays the leading role of Connor . He became involved in the film after reading an early script draft in 2006 . In 2008 , the global financial crisis severely reduced the budget of the film , and Nesbitt volunteered a reduction in his salary so the film could still be made . The film was shot over eight weeks in Melbourne in 2009 and released in 2010 . 
- Next , Nesbitt reunited with Occupation screenwriter Peter Bowker to star in the ITV medical drama series Monroe , playing Gabriel Monroe . Nesbitt was Bowker 's first choice for the part . Nesbitt researched the role of the <unk> character by watching brain surgery being performed by Henry Marsh , and by consulting Philip Van <unk> at Leeds General <unk> . The series was filmed over 12 weeks in Leeds at the end of 2010 and broadcast on ITV during March and April 2011 . Nesbitt will reprise the role in a second series , which is due to begin production in 2012 . In film , Nesbitt co @-@ stars as Irish writer Jack in Emilio <unk> 's drama The Way , alongside Martin Sheen , Deborah Kara <unk> , and <unk> van <unk> , and has a role as <unk> in Ralph <unk> ' contemporary Shakespeare adaptation Coriolanus . 
- Alongside many other British and Irish actors , Nesbitt was cast in Peter Jackson 's three @-@ part film The Hobbit , as the dwarf <unk> . Nesbitt had not read <unk> <unk> 's novel but accepted the role immediately . As the film was scheduled to take over 12 months to make in New Zealand , Nesbitt 's wife and daughters moved with him , and his daughters were enrolled in a New Zealand school . Filming commenced in March 2011 . The first part , The Hobbit : An <unk> Journey , was released in December 2012 , the second part , The Hobbit : The <unk> of <unk> , in December 2013 , and the third and final part , The Hobbit : The Battle of the Five Armies , in December 2014 . 
- 
- = = Other projects = = 
- 
- In 2002 , Nesbitt made his documentary debut as the presenter of James Nesbitt 's <unk> <unk> , a production for BBC Choice that saw him spend two weeks in Las Vegas at the National Finals <unk> and the Miss <unk> America pageant . In 2007 , he was the guest host of an episode of the late @-@ night Channel 4 comedy The Friday Night Project . As a film awards presenter , he hosted the IFTA Awards ceremony for three consecutive years between 2005 and 2007 , the British Independent Film Awards from 2005 to 2010 , and the National Movie Awards in 2008 and 2010 . In 2009 , he hosted the Laurence Olivier Awards . 
- An amateur golfer since his teenage years , Nesbitt joined the European team for Sky One 's All * Star Cup in 2005 and 2006 . He signed up to a series of high @-@ profile television advertisements for the <unk> Group in 2003 , playing a <unk> character called James for the company 's Yellow <unk> campaign until 2006 . Times writer Andrew <unk> noted that the adverts " cost him some credibility " but Nesbitt was pleased with the money he made from them . In 2004 , he joined the <unk> <unk> X to produce " Born in England " , an unofficial anthem for the England national football team 's entry in the UEFA Euro 2004 tournament . His vocals have also appeared in Lucky Break and an episode of Cold Feet . The song he performed in the latter — " ( Love Is ) The <unk> Trap " — was released on one of the series ' soundtrack albums . He also contributed vocals to the Waking Ned soundtrack . A fan of Northern Irish band Ash , he made a cameo in their unreleased film <unk> . In 2009 , he starred in the music video for " The Day I Died " , a single by English dance @-@ pop artist Just Jack . Nesbitt was recommended to Just Jack by Elton John . <unk> is scheduled to host the 2013 British Independent Film Awards in London on 8 December 2013 . 
- Since 2013 Nesbitt appears in adverts for Thomas Cook . 
- In 2014 , Nesbitt had the lead role as the father character Tony Hughes in <unk> BBC drama series The Missing , alongside Frances O 'Connor ( as his wife / ex @-@ wife , Emily Hughes / Walsh ) and <unk> <unk> ( as Julien Baptiste , leading French police investigator ) . The drama focused on a British married couple , whose son goes missing while they are on holiday in France , and the subsequent years of enquiry trying to find answers as to what happened to their son and why . <unk> , Nesbitt and <unk> had appeared previously together in the Martin Sheen film The Way ( 2010 ) . 
- 
- = = Personal life = = 
- 
- Nesbitt was married to Sonia Forbes @-@ Adam , the daughter of Reverend Sir Timothy Forbes Adam . The two met when Nesbitt went to the final call @-@ back for Hamlet at Loughborough Hall in 1989 , and they soon began dating . They split up for a year after the release of Hear My Song but reunited and married in 1994 . They have since had two daughters , Peggy and Mary ( both of whom appeared in the final two Hobbit movies as the daughters of Bard the Bowman ) . Nesbitt 's three sisters all became teachers . In 2002 , a Sunday tabloid published an interview with a legal secretary who claimed to have had a two @-@ month affair with Nesbitt . Shortly afterwards , another tabloid story revealed an affair with a prostitute , who claimed Nesbitt had boasted of <unk> with his Cold Feet co @-@ star <unk> Joseph , and Amanda <unk> , a former Miss Ireland . Commenting on the publication of details about his personal life , Nesbitt has said he feared that he would lose his marriage , though the exposing of his " dual life " allowed him to " take a long and considered look " at himself . In October 2013 , Nesbitt announced that he and his wife Sonia Forbes @-@ Adam would separate from each other after 19 years . The couple says that the filming of The Hobbit Trilogy has forced the couple to live separately for the past two years . The split came as a mutual decision and the couple says that <unk> was not one of the reasons for their decision . 
- Nesbitt is a patron of Wave , a charity set up to support those <unk> by the Troubles . The charity faced closure due to funding problems before Nesbitt encouraged celebrities and artists to become involved . Since 2005 , he has been a UNICEF UK ambassador , working with HIV and AIDS <unk> , and former child soldiers in Africa . He describes the role as " a privilege " . Writing in The Independent about his visit to Zambia in 2006 , Nesbitt concluded that the children he met were owed a social and moral responsibility . The article was described in the Evening Standard as " moving and notably well @-@ crafted " . Since 1999 , he has been a patron of Action Cancer , a result of both his father 's <unk> with prostate cancer and a storyline in the second series of Cold Feet , where his character suffered <unk> cancer . He has been an honorary patron of Youth Lyric , one of Ireland 's largest theatre schools , since 2007 . 
- He is a fan of football teams Coleraine and Manchester United . In 2003 , Nesbitt made a donation of " thousands of pounds " to Coleraine , after the team came close to bankruptcy . He has called the team " a <unk> " of Coleraine and encouraged more people to watch Irish League football . Nesbitt was a vocal opponent of Malcolm <unk> 's 2005 takeover of Manchester United , though after the completion of that deal he acted in television advertisements promoting executive boxes at Old Trafford , for which he was criticised by fans . To counter the criticism , he pledged one half of his £ 10 @,@ 000 fee to the Manchester United Supporters ' Trust and the other half to UNICEF . 
- Nesbitt is co @-@ owner of National Hunt racehorse ' Riverside Theatre ' , named after the theatre of the University of Ulster in Coleraine , which won the <unk> Chase at the 2012 Cheltenham Festival . 
- In March 2010 , Nesbitt accepted the ceremonial position of Chancellor of the University of Ulster , succeeding former Lord Mayor of London Sir Richard Nichols . Gerry <unk> , the chair of the university ruling council , expected Nesbitt to " bring considerable energy , <unk> and commitment " to the post . Following his official installation on 8 June 2010 , Nesbitt said , " Rather than being just an informal role officiating at ceremonies , I think I can act as an ambassador . I have access to an awful lot of people and places because of my work . I hope to be a voice that can be heard , not just at the university , but also outside promoting the importance of the funding of education . If that involves me being at <unk> , then I 'd be very happy to do that . <unk> these public spending cuts are going to have an impact and it 's important to fight for funding because it 's about investing in students and investing in the future of Northern Ireland . I believe I can bring something to that , otherwise I wouldn 't have taken this on . " 
- He was appointed Officer of the Order of the British Empire ( OBE ) in the 2016 New Year Honours for services to drama and to the community in Northern Ireland . 
- 
- = = Filmography and awards = = 
- 
- 
- = = Academic honours = = 
- 
- Honorary Doctor of Letters ( <unk> ) for services to drama from University of Ulster , <unk> campus ( 9 July 2003 ) . 
- Award of <unk> for contribution to drama from Belfast Metropolitan College ( 13 November 2008 ) . 
- Chancellor of the University of Ulster ( 2010 — ) ( ceremonial ) 
- 
- 
- = Crazy in Love = 
- 
- " Crazy in Love " is a song from American singer Beyoncé 's debut solo album Dangerously in Love ( 2003 ) . Beyoncé wrote the song with Rich Harrison , Jay Z , and Eugene Record , and produced it with Harrison . " Crazy in Love " is an R & B and pop love song that incorporates elements of hip hop , soul , and 1970s @-@ style funk music . Its lyrics describe a romantic obsession that causes the protagonist to act out of character . Jay Z contributes a <unk> verse to the song and is credited as a featured performer . The French horn @-@ based hook samples " Are You My Woman ( Tell Me So ) " , a 1970 song by the Chi @-@ Lites . 
- Columbia Records released " Crazy in Love " on May 18 , 2003 , as the lead single from Dangerously in Love . It was a number @-@ one hit in the United States and United Kingdom , and achieved top @-@ ten peaks on several other countries ' record charts . With global sales of over 8 @.@ 5 million , including 2 million from the U.S. , it is one of the best @-@ selling singles of all time . Music critics praised " Crazy in Love " ' s hook , Jay Z 's contribution , and Beyoncé 's assertive delivery of the lyrics . VH1 declared it the greatest song of the 2000s decade , while Rolling Stone ranked it as the 118th best song of all time in 2010 . At the 46th Grammy Awards , " Crazy in Love " won Best R & B Song and Best Rap / Sung Collaboration . 
- The song 's accompanying music video features Beyoncé in various dance sequences . It won three awards at the 2003 MTV Video Music Awards , and its director , Jake Nava , won the Music Video Production Association award for Best R & B Video in 2004 . Since 2003 , " Crazy in Love " has been a staple in Beyoncé ’ s live performances and concert tours . The American Society of Composers , Authors and Publishers ( ASCAP ) recognized " Crazy in Love " as one of the most performed songs of 2004 . Artists including David <unk> have covered the song , and it has been used in various television shows and other media . 
- 
- = = Development and production = = 
- 
- In July 2002 , Beyoncé had already recorded several songs which would appear on Dangerously in Love . Columbia Records planned to release the album in October 2002 ; however the release was postponed several times to capitalize on the success of American rapper Nelly 's single " <unk> " ( 2002 ) , which features Destiny 's Child singer Kelly Rowland . These delays allowed Beyoncé to record more songs for the album . 
- Before meeting Beyoncé , Rich Harrison had <unk> the beat of the song . He sampled the hook 's instrumentation from the 1970 song " Are You My Woman ? ( Tell Me So ) " , originally written by Eugene Record , frontman of the Chicago @-@ based vocal group The Chi @-@ Lites . When Harrison first played the beat to his friends , they could not " dig it " , and this made him realize that he had conceived something special , which people would appreciate better after hearing the whole record . Harrison decided not to market the track and instead waited for the right artist to record it : " I had it in the chamber , I had not really <unk> it much , because sometimes you do not want to come out of the bag before it 's right . People do not really get it and you will leave them with a foul taste in their mouth . " 
- Harrison was <unk> surprised when he got a call from Beyoncé , who was working on one of the most anticipated albums of the year . However , things did not turn up according to his plans the following day as he was late and was still suffering the effects of a <unk> . When Harrison played the sample to Beyoncé in the studio , the singer initially had doubts about the " sound so full of <unk> fanfare " ; it seemed too retro and according to her , no one used horn riffs in the 21st century . Nevertheless , Beyoncé became <unk> to the sample much to Harrison 's delight and gave him two hours to write the song while she went out . 
- Harrison confessed that it was not easy for him to come up with the lyrics to " Crazy in Love " in that length of time . Two hours later , he had penned the verses and the hook in spite of being hung over . Harrison had also made provision for a backing track ; he played all the instruments on the track . The bridge was written by Beyoncé , who was inspired by looking at herself in the mirror ; she was not wearing matching clothes and her hair was untidy as she kept saying , " I 'm looking so crazy right now . " Harrison sang back to her and said , " That 's the hook . " It also inspired the title of the song . After that Beyoncé had filled up the middle eight , she came up with the catchphrase - " Uh @-@ oh , uh @-@ oh , you know " - alongside Harrison . 
- American rapper Jay @-@ Z became involved late in the song 's production . Around three in the morning , he came to the studio and recorded a rap verse , which he improvised in about ten minutes . The recording of " Crazy in Love " took place nearly three months following the meeting of Beyoncé with Harrison . 
- 
- = = Composition and lyrical interpretation = = 
- 
- According to the sheet music published at Musicnotes.com by Alfred Music Publishing , " Crazy in Love " is an R & B and pop love song , composed in the key of D @-@ minor and F @-@ major . It incorporates 1970s @-@ style funk , hip hop , and contains influences of soul . As commented by Robert Webb of The Independent , the old soul influences in the song seem to have been derived from the horn hook , which samples the 1970 song " Are You My Woman ? ( Tell Me So ) " . Having a go @-@ go vibe , " Crazy in Love " is built on a hip hop beat . Beyoncé told The Sunday Herald that the beat is " so hard that it makes your heart hurt . " The song 's tempo is a moderate 100 beats per minute , in common time . Beyoncé ’ s vocal range spans around one and a half octaves in the song , from the low note of A ♯ 3 to the high note of <unk> . " Crazy in Love " uses two major chords , B ♭ and G , a minor third apart . One of the main vocal riffs uses the traditional <unk> rhythm often found in <unk> music . Lisa <unk> of The Times magazine , wrote that " Crazy in Love " makes use of big drums and bits of brass . 
- According to Natalie Nichols of Los Angeles Times , the lyrics of " Crazy in Love " reference a state of romantic obsession . Beyoncé said that the song talks " about how , when you are falling in love , you do things that are out of character and you do not really care because you are just open . " Anthony <unk> of Rolling Stone wrote that " Crazy in Love " has " such a cauldron of energy " , that Beyoncé sounds " loose and sexy " , <unk> by emotions she " can neither understand nor control " . The lyrics are composed in the traditional verse @-@ chorus form . Jay Z opens the song with a brief spoken verse @-@ rap , containing the lyrics : " Yes ! So crazy right now . Most incredibly , it 's your girl , B. It 's your boy , Young . You ready ? " After Beyoncé delivers the " uh @-@ oh , uh @-@ oh " catchphrase , Jay Z continues the monologue . Beyoncé begins the first verse , followed with the whistle @-@ backed chorus . She repeats the " uh @-@ oh , uh @-@ oh " phrase , leading to the second verse . The chorus follows , giving way to the second verse @-@ rap which contains the lyrics : " Jay Z in the range , crazy and <unk> [ ... ] I been <unk> the chain smokers , how you think I got the name ' <unk> ' , I been real and the game 's over " . The song continues to the bridge , singing : " I 'm not myself , lately I 'm foolish , I don 't do this , / I 've been playing myself , baby , I don 't care / ' <unk> your love 's got the best of me , / And baby , you 're making a fool of me , / You got me sprung and I don 't care who sees " . She then sings the chorus again and the song fades out with the horns . 
- 
- = = Release and remixes = = 
- 
- " Crazy in Love " was released to radio in the United States on May 18 , 2003 under formats including <unk> , Top 40 , and Urban radios . The single was released first as a digital download to iTunes Stores in the United Kingdom and in the United States on May 20 , 2003 . Notably , the song was also fairly successful as a ringtone among cell phone users across America . The song was released as a CD single in Ireland and Switzerland on June 30 , 2003 and as a digital EP in Germany on the same date . " Crazy in Love " was released as a <unk> single in Germany on June 30 , 2003 and in Australia on July 15 , 2003 along with the song 's accompanying music video , exclusive to Australia . The song was issued on DVD and CD single in the United Kingdom on June 30 , 2003 . " Crazy in Love " was released as a digital EP in several European countries , including Austria , Belgium , Denmark , Finland , Italy , the Netherlands , Norway , and Sweden on July 8 , 2003 . This digital EP was also available in Canada and Ireland on July 8 , 2003 . On July 22 , 2003 , two remixes - one from Rockwilder and the other from Adam 12 - was serviced in the United States . 
- " Crazy in Love " has various remixes , including the Rockwilder remix , Maurice 's " Nu Soul remix " , and Juniors World remix . These versions appeared on the single releases of " Crazy in Love " under an alternative spelling , " <unk> in <unk> " . The Rockwilder remix slows down the beat and makes the song deeper and <unk> with <unk> up horn samples and sparkling synth textures . Maurice 's " Nu Soul Remix " speeds up the beat , taking it from hip @-@ hop to house territory . A version of the song included on Asian releases of Dangerously in Love features a rap in Mandarin Chinese performed by American @-@ Taiwanese singer <unk> Wu , instead of Jay Z 's performance . 
- 
- = = Reception = = 
- 
- 
- = = = Critical response = = = 
- 
- " Crazy in Love " was lauded by contemporary music critics , who complimented the horn lines and the guest appearance of Jay @-@ Z. Many of them called it the Summer Anthem of 2003 . Tim <unk> of AllMusic described the song as a " stunning pop masterpiece " , while Stephen Thomas Erlewine of the same website called it " <unk> catchy " . Darryl <unk> of Jam ! noted the " Crazy in Love " is " instantly addictive horn lines " . Anthony <unk> of Rolling Stone wrote : " ' Crazy in Love ' ... <unk> out of the speakers on the strength of a propulsive horn sample and the charged presence of her pal , Jay @-@ Z. " Ben <unk> of Blender magazine called the song an " <unk> [ and ] eager @-@ to @-@ please " one . Marc Anthony Neal of PopMatters called the " uh @-@ oh , uh @-@ oh " phrase catchy . MTV News considered " Crazy in Love " to be the " proudest moment " of Dangerously in Love . Similarly , Allison Stewart of The Washington Post called it the best song on the album , praising its instrumentation , harmonies , and the rap verse of Jay Z. This was echoed by Kelefa Sanneh of The New York Times who wrote that " Crazy in Love " is the best one on the album thanks to its " simplicity , irresistible combination of triumphant horns and a wicked hip @-@ hop beat " . She added that " [ Beyoncé ’ s ] vocals - as <unk> and accurate as ever - convey none of the <unk> rush that the lyrics describe . " Likewise , Sal Cinquemani of Slant Magazine wrote the lyrical arrangement , the music structure and the guest vocals by Jay Z all contributed in making " Crazy in Love " a wonderful resume for Beyoncé . 
- Rob Fitzpatrick of NME called " Crazy in Love " a " head @-@ <unk> [ and ] body @-@ rocking funk @-@ soul genius " and wrote that it is " a 100 per cent , stone @-@ cold , dead @-@ <unk> classic " . He complimented Beyoncé ’ s vocals , describing them as " genuinely , hip @-@ <unk> <unk> " . Los Angeles Times writer Natalie Nichols noted that " sexy dance tunes as the vintage funk @-@ flavored ' Crazy in Love ' " made Dangerously in Love a great album . Neil <unk> of Entertainment Weekly wrote that the song has a " fresh sound " . Spence D. of IGN Music wrote that Beyoncé rides the " infectious rhythm " with grace and mid @-@ range <unk> . He added , " As [ it ] can be expected , the track <unk> when Jay drops his distinctive <unk> flavor . While other rap @-@ meet @-@ R & B tracks often fall flat , this one works well as Beyoncé and Jay 's <unk> play nicely against one another . " Lisa <unk> of The Times wrote that Jay Z performed a " decent rap " , however , " Beyoncé and the beats save the day " and that " Crazy in Love " was a departure for Beyoncé from Destiny 's Child . 
- 
- = = = Accolades = = = 
- 
- In 2004 , " Crazy in Love " was nominated for three Grammy Awards in the categories of Best R & B Song and Best Rap / Sung Collaboration , which it won , and Record of the Year , which it did not win . A remix of " Crazy in Love " , known as " <unk> in <unk> " ( Maurice 's Nu Soul Mix ) , won the award Best <unk> Recording , Non @-@ Classical for its <unk> , Maurice Joshua . " Crazy in Love " was also recognized at the 2004 ASCAP Pop Music Awards Awards as one of the Most Performed Songs and its publisher , EMI , received the Publisher of the Year award . Vibe magazine 's <unk> Awards recognized the song for <unk> Collaboration in 2003 . In Europe , " Crazy in Love " won the Best Song award at the 2003 MTV Europe Music Awards . " Crazy in Love " won the awards for Best R & B / Urban Track and Best Pop Dance Track at the 22nd Annual International Dance Music Awards in 2003 . It was recognized by Beyoncé ’ s peers in the urban markets , and won the award for Best Collaboration at the BET Awards , where it also received a nomination in the <unk> Choice Awards category in 2004 . " Crazy in Love " was nominated at the 36th NAACP Image Awards for the Outstanding Song award and for Favorite Song at the 2004 Kids ' Choice Awards . 
- 
- = = Legacy = = 
- 
- Entertainment Weekly magazine ranked " Crazy in Love " forty @-@ seven in its list of The 100 Greatest Summer Songs . The song is ranked second in Yahoo ! ' s list of biggest @-@ selling singles since 2000 . The song was listed at number three on Rolling Stone 's list of the 50 Best Songs of the 2000s Decade , in 2009 , and as the 118th greatest song of all time on the magazine 's 2010 list of the 500 greatest songs of all time , as well as ranking it number 3 in their 100 Best Songs of the 2000s list , writing " The horns weren 't a hook . They were a herald : Pop 's new queen had arrived . " . NME staff voted " Crazy in Love " the best song of the 2000s , calling it " a <unk> @-@ destroying howitzer of a pop song . " The song was ranked at number four on Pitchfork Media 's list of The Top 500 Tracks of the 2000s , number seven on The Daily Telegraph 's list of the best songs of the decade and number six on Slant Magazine 's list of the 100 Best Singles of the Decade . In September 2011 , VH1 ranked " Crazy in Love " number one on its list of The 100 Greatest Songs of the 2000s . In October 2011 , to mark NME fifteenth birthday , its staff members selected the 150 tracks " that have meant the most to [ them ] over the site 's lifetime " . They placed " Crazy in Love " at number 16 on their list of the 150 Best Tracks of the Past 15 Years . In 2012 , the song was ranked at number 22 on Billboard magazine 's list of " Top 50 ' Love ' Songs of All Time " . In 2013 , John Boone and Jennifer <unk> of E ! placed the song at number one on their list of ten best Beyoncé ’ s songs writing " It 's the song that started it all . The definitive best Beyoncé jam is her first , complete with a guest spot by now @-@ husband Jay Z , a killer hook and a chorus of horns that you have to dance to . <unk> have to , <unk> conditioning @-@ style . " In a 2013 list of Jay Z 's 20 Biggest Billboard Hits , " Crazy in Love " was ranked at number 1 . On July 5 , 2013 , NME magazine named " Crazy in Love " " The Best Pop Song Of The Century " . 
- 
- = = Chart performance = = 
- 
- " Crazy in Love " was a commercial success in the United States . Although it was not yet released to retail stores , the single gained much attention and reached number one on the Billboard Hot 100 , the official US singles chart , based on heavy rotation alone . The same week it reached number one , Dangerously in Love debuted on the Billboard 200 at number one on July 12 , 2003 . <unk> airplay , and later in retail , gains of " Crazy in Love " allowed it to dominate the chart , spending eight consecutive weeks at number one on the Hot 100 , making it Beyoncé ’ s first number one single in her solo career . According to Nielsen SoundScan , " Crazy in Love " was the most downloaded song in the United States for four consecutive weeks in July 2003 . " Crazy in Love " spent twenty @-@ seven weeks on the Hot 100 , fifteen weeks in the top ten , and twenty @-@ six weeks in the top fifty . The song was certified gold by the Recording Industry Association of America ( RIAA ) in 2004 while its Mobile <unk> was also certified gold two years later . " Crazy in Love " was the fourth biggest hit of 2003 in the United States . By October 6 , 2010 , " Crazy in Love " had sold 47 @,@ 000 physical units in the US , and as of October 2012 , 1 @,@ <unk> @,@ 000 paid digital downloads . 
- In the United Kingdom , Beyoncé became the third female artist to top the UK Singles Chart and UK Albums Chart simultaneously , following Mariah Carey in 1994 and <unk> <unk> in 2001 . Including her career with Destiny 's Child , " Crazy in Love " became Beyoncé ’ s third number one single in Britain and was the only song to top the charts the United Kingdom and the United States in 2003 . The single spent three weeks at number one in the United Kingdom and fifteen weeks in the top 100 . As of July 2013 , it has sold <unk> @,@ 000 units in the UK . " Crazy in Love " reached number one on the Irish Singles Chart , where it spent eighteen weeks . In Australia , " Crazy in Love " peaked at number two on the ARIA Singles Chart and was certified platinum by the Australian Recording Industry Association ( ARIA ) with sales of over 70 @,@ 000 units . It also peaked at number two on the New Zealand Singles Chart , and was certified platinum by the Recording Industry Association of New Zealand ( <unk> ) . " Crazy in Love " reached top ten positions in some European singles charts . It reached the top ten in Austria , the Belgian territories of Flanders and Wallonia , Denmark , Germany , Hungary , Italy , the Netherlands , Norway , Sweden and Switzerland . As of September 2009 , " Crazy in Love " had sold more than five million copies worldwide , becoming one of the best @-@ selling singles of all time worldwide . 
- 
- = = Music video = = 
- 
- 
- = = = Production and synopsis = = = 
- 
- The music video of " Crazy in Love " , released in May 2003 , was directed by Jake Nava and filmed in downtown Los Angeles . In MTV Making of the Video 2003 documentary , Beyoncé described the video 's conception : " [ It ] celebrates the evolution of a woman . It is about a girl who is at the point of a relationship . She realises that she is in love , she is doing stuff she would not normally do but she does not care . It does not matter she is just crazy in love . " 
- The opening sequence of the video features Jay Z as a passenger in a car <unk> along Mission Road in Los Angeles , where he encounters Beyoncé , standing in the middle of the road , at the Fourth Street bridge . Beyoncé performs in various dance sequences , beginning with her wearing a white tank top , denim blue shorts , and red high @-@ heels . She performs an elaborate solo dance on a <unk> . The scene shifts to a gold set with a mock photo shoot , before moving into a scene with dancers detailing Beyoncé and dancing against a wall while wearing caps and full length pants . Jay Z appears and <unk> a line of petrol leading to a car parked under the bridge , which explodes in flames . Jay Z performs his rap in front of the burning car , and Beyoncé dances beside him , wearing an exotic silk print over a fur coat , before kicking the valve off a fire <unk> . She continues to dance while the water is flying everywhere . The video ends with Beyoncé and her dancers wearing vibrant <unk> dresses in front of a large fan . Their outfits contrast with the neutral colors of the background , the video . <unk> <unk> , a former Pussycat Dolls singer , is one of the dancers . 
- 
- = = = Reception = = = 
- 
- The music video was acclaimed by critics and won several awards . Cynthia <unk> , writing for PopMatters commented that the photo shoot scene uses the routine used by Jennifer Lopez in the video for " Jenny from the Block " ( 2002 ) with hot lights , scary makeup , and inclusion of many shots of legs . She wrote that : " Beyoncé 's body becomes its undeniable emblem . Tom Moon of The Philadelphia Inquirer wrote that Beyoncé shakes every inch of her famously <unk> goddess frame . " The music video won three awards at the 2003 MTV Video Music Awards in the categories of Best Female Video , Best R & B Video , and Best Choreography . It however lost to Good Charlotte 's " <unk> of the Rich & Famous " in the <unk> 's Choice category . Director Nava also won a Music Video Production Association award for the Best R & B Video in 2004 . During the same year , the video won the Best Collaboration award at the 2004 MTV Video Music Awards Japan , where it was also nominated for the Best Female Video award . " Crazy in Love " was nominated at the 36th NAACP Image Awards for the Outstanding Music Video award . It won the Best International Video award at the 2004 <unk> Video Awards . In 2014 , The Guardian writer Michael <unk> included the clip for " Crazy in Love " in his list of the ten best music videos by Beyoncé . He offered high praise for it , saying " Aware of how much of a statement the song was , the video is a <unk> of icon @-@ making visuals , from the locations ... the dance moves ... to the part where she makes bubble blowing look like the <unk> thing a human could do . " 
- 
- = = Live performances = = 
- 
- Beyoncé first performed " Crazy in Love " with Jay Z on August 28 , 2003 , during the 2003 MTV Video Music Awards . She sang the song in a medley , with the pre @-@ recorded vocals of Sean Paul on " Baby Boy " ( 2003 ) . " Crazy in Love " was included on the set list for most of Beyoncé ’ s concert tours . The song was the closing track of her Dangerously in Love World Tour that began in late 2003 . Beyoncé performed " Crazy in Love " live at the 2004 BRIT Awards February 17 , 2004 . <unk> <unk> And Todd Peterson wrote that she , " ... lit up the stage with her performance of " Crazy in Love " , wearing a white Roberto <unk> dress and nearly half a million dollars worth of <unk> . The pop diva , appearing onstage in a puff of smoke , stopped midway through the song to pull up her top before walking away with the best international female solo artist award . " Beyoncé and Jay Z also performed " Crazy in Love " at The Prince 's Trust Urban Music Festival at Earls Court in London on May 31 , 2004 . 
- " Crazy in Love " was the first song on Beyoncé ’ s set list on The Beyoncé Experience in Los Angeles and the I Am ... Tour at several venues , including the Odyssey Arena in Belfast , the O2 Arena in London , and in Athens and Sydney . On August 5 , 2007 , Beyoncé performed the song at Madison Square Garden in New York City . Beyoncé emerged in a sparkling silver dress with a long train . She walked to the front of the stage , did a couple of snaps of her neck and then started singing " Crazy in Love " . She climbed a staircase where her all @-@ female band and three backup singers were positioned . The staircase moved forward in two places ; top part moved while the bottom poked out more . At the top of her staircase , she removed her train and returned to the main stage . Her backup singers followed and danced with Beyoncé . After " Crazy in Love " , Beyoncé performed a short rendition of <unk> Barkley 's " Crazy " ( 2006 ) , singing , " Who do you , who do you think you are ? / Ha , ha , ha , <unk> your soul . " 
- <unk> Reid of MTV News wrote : " There are few ( very few ) ladies out there who can really sing , a lot who can dance , a lot more who look good — but really no other who can combine all three and add iconic star power like Miss Beyoncé , arguably the best all @-@ around stage performer in the game right now . " Jon Pareles of The New York Times wrote : " Beyoncé needs no distractions from her singing , which can be airy or brassy , tearful or vicious , rapid @-@ fire with staccato syllables or sustained in <unk> <unk> . But she was in constant motion , <unk> in costumes ( most of them silvery ) , from <unk> to formal dresses , flesh @-@ toned <unk> to bikini to <unk> . " Frank <unk> of The Hollywood Reporter wrote : " Her performance of ' Crazy in Love ' featured some surprising arrangements that gave the material freshness " . Performances of " Crazy in Love " were included on her live albums The Beyoncé Experience Live ( 2007 ) , and the deluxe edition of I Am ... World Tour ( 2010 ) . Beyoncé performed " Crazy in Love " wearing a pink fringe dress at a concert at <unk> <unk> in Nice , France , on June 20 , 2011 , in support of her album 4 , and at the 2011 Glastonbury Festival on June 26 , 2011 to an audience of 175 @,@ 000 . 
- In August , 2011 , Beyoncé performed " Crazy in Love " during her revue show 4 Intimate Nights with Beyoncé . She performed a slowed @-@ down , <unk> version of the song and danced with a similar routine to the one in the music video . During the ITV special A Night With Beyoncé which aired on December 4 in the United Kingdom , Beyoncé performed " Crazy in Love " to a selected crowd of fans . In May , 2012 , she performed the song during her Revel Presents : Beyoncé Live revue in Atlantic City , New Jersey , United States ' entertainment resort , hotel , casino and spa , Revel . During the performance , Jay @-@ Z did not appear on stage but his pre @-@ recorded voice was heard . Dan <unk> of noted that the song was one of the " beat @-@ <unk> booty @-@ shaking <unk> " performed during the revue . Jim Farber of New York Daily News wrote that " The first , and last parts of the show stressed the <unk> Beyoncé , told in bold songs " like " Crazy in Love " . A writer of Black Entertainment Television noted that , " She <unk> fans with an assortment of high @-@ energy performances of her upbeat hits like ... ' Crazy in Love . ' " Beyoncé also performed the song at the Super Bowl XLVII halftime show held on February 3 , 2013 . In July 2013 , while placing Beyoncé at number 33 on their list of 50 Best Live Musicians , the writers of Rolling Stone magazine noted that the performance of " Crazy in Love " was a highlight during her live shows with the singer " expertly <unk> ' her booty " . 
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- = = Cultural impact = = 
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- = = = Cover versions = = = 
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- Several artists have recorded cover versions of " Crazy in Love " . In 2003 , Irish singer @-@ songwriter Mickey Joe <unk> recorded an acoustic rendition of " Crazy In Love " for the charity album Even Better Than the Real Thing Vol . 1 . Alternative rock band Snow Patrol recorded the song during a BBC session with <unk> Lowe . Snow Patrol 's version was released as a B @-@ side to the single " <unk> Games " , on the compilation <unk> - Tom Middleton Presents Crazy Covers Vol . 1 and Snow Patrol 's compilation album Up to Now . Ross <unk> of PopMatters noted that their cover " sparks an initial <unk> of recognition but soon after becomes more than a bit unfortunate " . David <unk> closed his concert at the Hollywood Bowl on June 27 , 2005 with a <unk> @-@ <unk> version of " Crazy in Love " . In 2007 , American alternative rock band <unk> produced a rock version that was released as part of Yahoo ! ' s <unk> series . <unk> produced a video for their cover version . Nashville @-@ based indie <unk> Wild Cub performed a version of the song in June 2014 for The A.V. Club 's A.V. Undercover series . 
- British band The Magic Numbers performed " Crazy in Love " on the Australian radio station Triple J , and recorded it for the 2007 Starbucks ( Hear Music ) compilation album , Sounds <unk> : The Covers Project . Tracy <unk> covered the song with guitar and violin accompaniment , for her 2007 album In The City + In The Woods . British close harmony trio The <unk> Sisters covered " Crazy in Love " for their 2007 album The Rise and Fall of Ruby <unk> ; this was remixed by the electronica jazz outfit The Real Tuesday <unk> . Indie artist <unk> recorded an electronic cover of the song . In 2009 , Pattern Is Movement recorded a cover of " Crazy in Love " , which they claimed was inspired by <unk> 's version ; this cover was included on their 4 / 9 / 2009 <unk> session . Antony and the <unk> released an orchestral version of the song as the b @-@ side to their 2009 single " <unk> " . 
- German group The <unk> covered the song in rockabilly style for their debut album Strike ! Back in August 2010 . " Crazy in Love " was performed live on Australian Idol in Season 1 by winner Guy Sebastian on the Final 2 showdown in 2003 , A jazz version was performed on Season 4 by runner @-@ up Jessica <unk> on the Final 6 Big Band show in 2006 . In June 2008 , <unk> performed " Crazy in Love " on Indonesian Idol with some eliminated contestants . Singapore Idol contestant <unk> Lee performed " Crazy in Love " on that program . In March 2012 , Swing Republic released their electro swing cover version which also ended up featuring on their album released the same year entitled Midnight Calling . In June 2012 , Robin <unk> and Olivia Chisholm covered the song during the show Duets . Kate <unk> of Rolling Stone gave a negative review for Chisholm 's performance , saying that " Her voice sounded thin , and she just can 't seem to shake that <unk> Wife stare . " Emeli Sandé and The Bryan Ferry Orchestra recorded a cover of the song which was included on The Great <unk> soundtrack ( 2013 ) . Upon hearing a preview of the song , Randall Roberts of the Los Angeles Times commented that the cover was the best song on the album sang with a " surprising , <unk> urgency " . Kyle Anderson of Entertainment Weekly also wrote that the swing cover of " Crazy in Love " was one of the highlights on the album . On October 21 , 2013 , Third <unk> covered " Crazy in Love " on the fifth series of The X Factor Australia , and on May 4 , 2014 , C Major covered the song on the third series of The Voice <unk> 2015 , Monica Michael covered the song on The X Factor UK . <unk> actress Denise Laurel covered the song while impersonating <unk> , based on her performance at the Super Bowl XLVII halftime show in Your Face Sounds <unk> ( Philippines season 2 ) , in which Laurel won the season . 
- 
- = = = Usage in media = = = 
- 
- In 2002 , Beyoncé signed a contract with Pepsi , and appeared on several of its advertising campaigns , one of which featured " Crazy in Love " as background music . After winning the Best Collaboration Awards for " Crazy in Love " at the 2004 BET Awards , Beyoncé dedicated the award to the show 's host , comedian Mo <unk> , who parodied the choreography from the " Crazy in Love " video with six equally <unk> female dancers . " Crazy in Love " was included on the official soundtrack albums of the following films : <unk> Jones : The Edge of Reason ( 2004 ) , White Chicks ( 2004 ) , Taxi ( 2004 ) , Good <unk> Chuck ( 2007 ) , <unk> ( 2012 ) , and Love , <unk> ( 2014 ) , as well in the tenth season of Brazilian soap opera <unk> . In 2009 , the cast of Glee performed a mash up of the songs " Hair " from the musical Hair and " Crazy in Love " in season one , episode eleven " <unk> " . A parody of the song is also used in the Disney Channel 's show That 's So Raven , in the episode " <unk> Party " . 
- " Crazy in Love " was re @-@ recorded by Beyoncé for the film Fifty Shades of Grey ( 2015 ) and used for its trailer which was released on July 24 , 2014 . This slowed @-@ down version was produced by Boots with violin arrangements by Margot , both of whom worked on Beyoncé 's fifth studio album . Margot said , " It inspires me to work on other artists ' songs [ because ] it pushes my boundaries in a direction that I wouldn ’ t necessarily come up with . <unk> I know how ' Crazy in Love ' goes , but I knew there was the possibility her vocals would be different . It 's almost more vulnerable and beautiful this way , because you do do crazy things when you fall in love . To hear the mood reversed and flipped makes it even more powerful . " 
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- = = Formats and track listings = = 
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- 
- = = Credits = = 
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- Recording and management 
- Recorded at Sony Music Studios ( New York City , New York <unk> ) 
- Mixed at The Hit Factory ( New York City , New York ) 
- Additional vocals recorded at The Hit Factory ( New York City , New York ) 
- <unk> samples of the composition " Are You My Woman ( Tell Me So ) " , written by Eugene Record , published by <unk> Music Inc . ( BMI ) and performed by The Chi @-@ Lites ( courtesy of Brunswick Records ) 
- Jay @-@ Z appears courtesy of <unk> @-@ A @-@ <unk> Records and Def Jam Recordings 
- Published by Beyoncé Publishing ( ASCAP ) , <unk> South South ( ASCAP ) — all rights administered by Music of <unk> ( ASCAP ) — , EMI <unk> Music Inc . <unk> <unk> ( BMI ) , Dam Rich Music ( BMI ) , EMI April Music Inc . <unk> <unk> ( BMI ) , Carter Boyd Publishing ( ASCAP ) and <unk> Music Inc . ( BMI ) 
- Personnel 
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- = = Charts and certifications = = 
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- 
- 
- = Moro River Campaign = 
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- The Moro River Campaign was an important battle of the Italian Campaign during the Second World War , fought between elements of the British Eighth Army and LXXVI Panzer Corps ( LXXVI <unk> ) of the German 10th Army ( 10 . <unk> ) . <unk> from 4 – 26 December 1943 , the campaign occurred primarily in the vicinity of the Moro River in eastern Italy . The campaign was designed as part of an offensive launched by General Sir Harold Alexander 's Allied 15th Army Group , with the intention of breaching the German Army 's Winter Line defensive system and advancing to Pescara — and eventually Rome . 
- Beginning on 4 December , four infantry divisions — one British , one Canadian , one Indian and one New Zealand ( which included an armoured brigade ) — and two armoured brigades ( one British and one Canadian ) of V Corps and XIII Corps attacked heavily defended German positions along the Moro River , achieving several <unk> <unk> by 8 December . Throughout the next week , nearly continuous combat operations by both sides — designed to keep one another pinned down — created <unk> defensive positions near Orsogna and a narrow pit known as " The Gully " . After being held at the Gully for 10 days , the Canadians succeeded in outflanking German defences , and forcing a German withdrawal to the Ortona – Orsogna Line . On 20 December , the line was attacked by both corps . 
- By 26 December , strong German defences had stalled Canadian forces in Ortona and British and New Zealand forces in Orsogna . Although both Ortona and Villa Grande were captured by the end of December , general exhaustion among the Allied forces prevented the capture of Orsogna and an advance to Pescara . When harsh winter weather set in , it became clear to the Allied generals that no further progress would be made and Alexander called off the offensive . 
- 
- = = Background = = 
- 
- In late 1943 , the 15th Army Group under General Sir Harold Alexander were fighting their way northward in Italy against determined German opposition commanded by <unk> Albert Kesselring , whose forces had prepared a succession of defensive lines . East of the <unk> Mountain spine was the British Eighth Army , under General Bernard Montgomery . In October , Eighth Army had crossed the <unk> river and pushed the German defenders from the Volturno @-@ <unk> Line defences . <unk> by logistical problems , they were not able to attack the next line of defences ( the Barbara Line ) behind the <unk> river until 2 November . However , by 9 November forward elements of the Eighth Army were in contact with the forward defences of the German Winter Line , which had been set on the high ground north of the <unk> River . 
- The main attack across the <unk> by V Corps ( Lieutenant @-@ General Charles <unk> ) , comprising the British 78th Infantry Division ( Major @-@ General <unk> <unk> ) and 8th Indian Infantry Division ( Major @-@ General Dudley Russell ) with supporting and diversionary attacks further inland by 2nd New Zealand Division ( Lieutenant @-@ General Bernard Freyberg ) and XIII Corps ( Lieutenant @-@ General Miles C. Dempsey ) — was delayed by bad weather until late November . After several days of hard fighting , the Germans withdrew to the defences they had prepared on the high ground to the north of the Moro river . 
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- = = Offensive strategy and order of battle = = 
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- The Moro River runs from the central mountain spine of Italy to the Adriatic coast south of Ortona . The German defences on the Moro were a centerpiece of the Winter Line , which guarded the eastern side of the <unk> along Route 5 . Montgomery hoped to punch through the Winter Line , capture Ortona and Pescara and advance to Rome . The 78th Infantry Division , which had been <unk> V Corps since the Volturno Line actions and had sustained over 7 @,@ 000 casualties in less than six months , was relieved by the fresh 1st Canadian Infantry Division ( Major @-@ General Chris Vokes ) , ready to renew the offensive on 5 December 1943 . The 78th Infantry Division was sent into the mountains on the relatively quiet left wing of the army , joining the British 5th Infantry Division ( Major @-@ General Gerard <unk> ) under XIII Corps . 
- Montgomery 's plan was for the Canadian Division to attack across the Moro in the coastal lowlands to take Ortona first and then Pescara . Inland , in the jagged hills above the headwaters of the Moro , the relatively fresh 2nd New Zealand Division would attack toward Orsogna , while between these two the 8th Indian Infantry Division would hold the centre of the front in a relatively static role . 
- Facing V Corps was the 1st Parachute Division ( 1 . <unk> ) under Brigadier General ( <unk> ) Richard <unk> on the coast , to their right stood the 90th Panzergrenadier Division ( 90 . <unk> ) under Major General Carl @-@ Hans <unk> succeeded by Colonel ( <unk> ) Ernst @-@ Günther <unk> on 20 December , and further inland of them was the 26th Panzer Division ( 26 . <unk> ) under Brigadier General <unk> Freiherr von <unk> with their right flank on Orsogna . Further inland , facing XIII Corps , was the 65th Infantry Division ( 65 . <unk> ) under Brigadier General <unk> <unk> supported by elements of 1st Parachute and 5th Mountain Division ( 5 . <unk> ) under Brigadier General Julius <unk> . Together , these units formed <unk> Herr ′ s LXXVI Panzer Corps , the part of Joachim <unk> 's 10th Army responsible for the front line to the east of the <unk> . 
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- = = Canadian division across the Moro = = 
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- On 6 December 1943 , Canadian forces began a series of large @-@ scale assaults on major crossing points along the Moro River with the objective of securing a large bridgehead along the defensive line . Three primary points of attack were chosen : Villa Rogatti , along the western edge of the Canadian sector ; San Leonardo , 5 km ( 3 @.@ 1 mi ) south of Ortona ; and San Donato , a small town near the Italian coast . Five primary infantry battalions were selected to assault these positions with the objective of crossing the Moro River . The offensives were scheduled to start on the morning of 6 December . 
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- = = = Villa Rogatti = = = 
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- The task of taking Villa Rogatti , the westernmost crossing point , was given to Princess Patricia 's Canadian Light Infantry ( PPCLI ) . Having conducted reconnaissance on their objective during the night of 5 December 1943 , an attack plan was devised by the battalion 's commander — Lieutenant @-@ Colonel Cameron Bethel Ware — detailing the objectives of all four rifle companies . Once the objectives had been secured by the early morning of 6 December , Anglo @-@ Canadian reinforcements were to be moved into Villa Rogatti , with the intention of <unk> the expected potentially strong German counterattacks . Elements of three German regiments — the 200th and 361st Panzergrenadier , and 26th Panzer — maintained strong defences within the town . 
- At 00 : 00 on 5 December , two companies of the PPCLI crossed the Moro River , moving towards Villa Rogatti . Within an hour , vicious fighting had erupted throughout the town as the two companies of Canadian infantry struggled to break the German defensive lines . As B Company broke through the German defences , A Company attacked to the northeast , continuing to engage 200th Panzergrenadier Regiment ( 200 . Panzergrenadier <unk> Regiment ) near Villa Rogatti . Although two Canadian infantry companies now occupied Villa Rogatti , German Panzergrenadier forces still maintained substantial defences on the outskirts of the town . However , C Company continued to advance steadily along the eastern side of the town , encountering significant resistance from the 361st Panzergrenadier Regiment ( 361 . Panzergrenadier @-@ Regiment ) . After approximately an hour of fighting by C and D Companies , Villa Rogatti had been occupied by Canadian forces shortly before dawn . 
- By mid @-@ morning , German counterattacks on PPCLI positions in the town had begun , involving tanks from the 7th Company of the 26th Panzer Regiment ( 26 . Panzer @-@ Regiment ) , field guns and substantial infantry forces . Throughout the afternoon two infantry companies of the PPCLI fought off several attacks by German forces , eventually managing to push them back to the vineyards on the northern edge of the town . While the PPCLI had taken 68 casualties , German casualties were estimated at 120 . However , three strong German formation surrounded the Canadian positions at Villa Rogatti , rendering further exploitation of the bridgehead unlikely . Col. Ware was advised to be ready to withdraw across the Moro River , should German forces counterattack . In order to allow the Canadian Division a greater concentration of force , on the night of 7 / 8 December , the Indian 21st Infantry Brigade from the Indian 8th Infantry Division amalgamated the western flank of the 1st Canadian Division into their own lines . As a result of the withdrawal , Canadian efforts would focus on achieving a bridgehead at San Leonardo . 
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- = = = San Leonardo = = = 
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- The Canadian attack on San Leonardo by the Seaforth Highlanders of Canada began late on 5 December 1943 with A Company establishing a bridgehead across the Moro , taking heavy casualties . In the early morning of 6 December , A Company was withdrawn and two additional Seaforth companies resumed the offensive . As PPCLI secured and held their bridgehead over the Moro River , the Seaforth Highlanders were struggling to enter San Leonardo . By 07 : 15 , a single objective had been taken , with Canadian units pinned down by well @-@ coordinated defensive fire from several companies of the 361st Regiment . Simultaneously , small arms fire prevented C Company from moving up the road from the Moro to San Leonardo , while D Company remained on the southern banks of the Moro throughout the early morning . 
- In the afternoon , having failed to capture San Leonardo , the Hastings and Prince Edward Regiment sent two rifle companies to the aid of the Seaforth Highlanders , as Seaforth B Company attacked positions west of San Leonardo — inflicting 129 casualties on German forces in the area . However , the attack on San Leonardo by three Seaforth companies stalled rapidly when the 26th Panzer Regiment 's armoured companies reinforced the sector . As a result , <unk> was ordered to prepare for a withdrawal from the San Leonardo bridgehead . 
- 
- = = = San Donato = = = 
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- While attempts were made to cross the Moro at San Leonardo and Villa Rogatti , The Hastings and Prince Edward Regiment launched an attack on the Moro River defences at the small coastal hamlet of San Donato at 13 : 40 on 6 December . However , the single rifle company making the attack achieved little territorial gain and Lieutenant @-@ Colonel Kennedy — commander of the Hastings & Prince Edward Regiment — ordered a withdrawal at 15 : 40 . Throughout 6 December , strong German coastal defences would prevent further advancement , despite the incorporation of tanks and artillery into the assault . By nightfall , the German defenders still possessed control of San Donato , with the Hastings and Prince Edward Regiment withdrawing to the southern bank of the Moro River . 
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- = = = Taking the Moro = = = 
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- On 8 December 1943 , Major General Vokes devised a new plan for taking the Moro River . While the 48th Highlanders of Canada and Princess Patricia 's Canadian Light Infantry resumed the assault on San Leonardo from the southwest side of the town , the Royal Canadian Regiment ( RCR ) would break out of the bridgehead created by the Hastings and Prince Edward Regiment , then move southwest towards San Leonardo to link up with the 48th and PPCLI . The operation was scheduled to start on the afternoon of 8 December . 
- The attack began with a massive artillery barrage which <unk> German positions continuously for two hours . At 16 : 00 , the <unk> Light Infantry support battalion joined in , hitting German positions with bursts of machine gun fire . The moment the heavy bombardment lifted , the 48th Highlanders and the RCR both initiated their attacks . D Company of the 48th Highlanders was able to quickly cross the Moro , taking minimal casualties . However , B Company was subjected to heavy fire from German mortars and 88 mm ( 3 @.@ 46 in ) artillery positions . Eventually , however , both companies managed to establish strong positions on the western ridge overlooking San Leonardo . During the night of 8 / 9 December , units of the Royal Canadian Engineers ( <unk> ) constructed a bridge over the Moro , to allow armour and equipment to move into San Leonardo the following day . 
- As the 48th Highlanders secured their positions west of San Leonardo , the Royal Canadian Regiment was involved in intense fighting southwest of San Donato . Two companies had advanced against strong and well prepared German defences of the 200th Panzergrenadier Regiment . A Company was quickly tied down by German mortar fire , while B Company flanked German positions to the north of San Donato . By nightfall , all four companies held tenuous positions in the thick of German defences . On the night of 8 / 9 December , the RCR was subjected to counterattacks by the 200th Panzergrenadier Regiment which were repulsed with the support of continuous Canadian artillery shelling . 
- By the morning of 9 December , the <unk> had completed the bridge across the Moro River , enabling the tanks of the 14th Armoured Regiment ( The Calgary Regiment ) to transport two companies of Seaforth Highlanders across the river into San Leonardo . By mid morning , San Leonardo had been cleared of German defenders , although strong positions still existed outside of the town . Within an hour , the <unk> ' tanks had broken through German positions near <unk> Castle and two companies had linked up with the 48th Highlanders and Princess Patricia 's Canadian Light Infantry within San Leonardo , finally establishing firm Canadian positions across the Moro River . Near the end of 9 December , German forces of the 90th Panzergrenadier Division fell back to their second defensive line : a formidable obstacle known as " The Gully " . 
- 
- = = Attacks on Orsogna = = 
- 
- While Canadian crossed the Moro River , the New Zealand Division launched a two brigade attack , Operation <unk> , against Orsogna at 14 : 30 on 7 December . The division had the British 2nd Independent Parachute Brigade under their command , anchoring their left flank and were supported by heavy concentrations of artillery and air support . <unk> was achieved as <unk> Herr , the commander of LXXVI Panzer Corps , had been persuaded that the New Zealanders would not be in a position to launch a major attack until 8 December . 
- Initially , the New Zealand attack progressed well , but the German defenders regained their <unk> and the attack lost momentum against heavily fortified defensive positions . By 21 : 00 , the NZ 24th Infantry Battalion had fought its way in slow house to house fighting to the centre of the town , but were pinned down with no prospect of further progress without significant armoured support . However , a combination of concealed minefields and well dug in German armour made the task of the Allied tanks impossible . In the early hours of 8 December , the New Zealand commander — Bernard Freyberg — ordered a withdrawal from the town with a view to renewing the attack after further <unk> up from artillery and bombers . 
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- = = Indian Division across the Moro – the " impossible " bridge = = 
- 
- With both the Canadian and New Zealand Divisions finding progress difficult , it was decided to bring the Indian 21st Infantry Brigade into the attack with orders to seize <unk> . With no river crossing available , the Indian engineers rushed to build a bridge across the Moro which was completed on 9 December and allowed infantry and supporting armour to cross and expand the bridgehead on the far bank . The bridge was named the " Impossible Bridge " because the local geography required for it to be built backwards from the enemy bank of the river . 
- 
- = = The Gully = = 
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- = = = Initial attacks = = = 
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- Following the loss of San Leonardo and the Moro River , the 90th Panzergrenadier Division withdrew to a primary defensive line 5 km ( 3 @.@ 1 mi ) north of San Leonardo . The line centred around a natural ravine known as " The Gully " , with an average depth of 200 ft ( 61 m ) . General Vokes ' initial plan to take the position ( as well as achieve a foothold on the roads toward Ortona ) consisted of a frontal assault by the 2nd Canadian Infantry Brigade , which would seize <unk> Ridge , capture The Gully and gain positions on the Ortona to Orsogna road . However , German defences were adequately prepared , including gun @-@ pits , bunkers and shelters . 
- On 10 December , three Canadian battalions made their first attempt to cross The Gully . Although they succeeded in capturing <unk> Ridge , directly south of The Gully , attempts to <unk> German positions in the ravine were unsuccessful . On 11 December , the three battalions made another attempt , with the Loyal Edmonton Regiment suffering heavy casualties in their attempts to take German positions in the sector . Although a badly <unk> A Company was able to gain a foothold on the reverse slope , newly arrived German units forced the remaining men to withdraw . 
- On 12 December 1943 , General Vokes sent the three battalions of the 3rd Canadian Infantry Brigade against German defences in The Gully . The assault started poorly , when Canadian artillery plans were captured by soldiers of the 90th Panzergrenadier Division 's 200th Regiment . When The West Nova Scotia Regiment attacked The Gully , they were subject to counterattacks by the 200th Panzergrenadier Regiment approximately 10 : 30 . By 14 : 00 , the regiment had called off its attacks and had taken heavy casualties . To the west , Princess Patricia 's Canadian Light Infantry fared little better , with C Company taking heavy casualties in their assault . Attempts were again made on 13 December , by two battalions of the 3rd Canadian Infantry Brigade , and the attacks were driven back by <unk> German resistance . On the evening of 13 December , the heavily depleted 90th Panzergrenadier Division were relieved from their positions in The Gully by units of the 1st Parachute Division . 
- 
- = = = Casa Berardi = = = 
- 
- By 14 December , Vokes had devised a new assault plan for taking The Gully . A small force from the Royal 22e Régiment would move to Casa Berardi , a small set of <unk> west of The Gully , before outflanking German positions with infantry and armour , thereby forcing the 1st Parachute Division to withdraw . The attack was to begin at dawn , with two companies of the Royal 22e Régiment attacking Casa Berardi with artillery support . By 07 : 50 , both companies had control of the lateral highway leading to Casa Berardi . C Company — under Captain Paul Triquet — pushed on toward Casa Berardi with support from the Ontario Regiment , while D Company found itself involved in <unk> southwest of Casa Berardi . At 08 : 30 , C Company began their assault toward the manor house in Casa Berardi , some 2 @,@ 000 yd ( 1 @,@ 800 m ) away . Strong German defences caused heavy casualties to the attackers ; only 21 men and five tanks made it to within 200 yd ( 180 m ) of the objective . Despite the arrival of several Panzer <unk> , Triquet 's remaining forces captured the manor house at 14 : 30 . However , only 14 men of C Company remained fit to continue fighting . For his efforts to capture Casa Berardi , Triquet was awarded the Victoria Cross . 
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- = = Eighth Army <unk> to intensify the attack = = 
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- With the Indian Division committed , Montgomery decided to raise the stakes further by bringing the British 5th Infantry Division from the relatively tranquil XIII Corps front in the high mountains on the left wing of the 8th Army and insert them between the New Zealand and Indian Divisions . This would allow the Indian division to narrow and concentrate their attack and give Montgomery four divisions to continue the attack between Orsogna and the sea . By 12 December , the British 17th Infantry Brigade — the first of 5th Division 's brigades — was in place and under the New Zealand division 's command . Once 5th Division headquarters and its other brigades had arrived , these two left hand divisions were to be organised under the command of XIII Corps , commanded by Lieutenant @-@ General Miles Dempsey . 
- To the left of the Canadian division , the Indian 21st Brigade had by 13 December established a solid bridgehead around the " Impossible Bridge " . That night , a second 8th Indian Division brigade — the 17th Indian Infantry Brigade — passed through and attacked towards <unk> . The 1st Battalion Royal Fusiliers stormed the village in a wild night 's fighting while the 1st Battalion 5th <unk> Rifles seized Point 198 nearby , holding it against determined counterattacks , including from tanks in the afternoon of 14 December . That evening , 1st Battalion 12th Frontier Force Regiment attacked on the left of the <unk> and established positions on the lateral road between Ortona and Orsogna running parallel to the Moro some 1 @,@ 000 yd ( 910 m ) north of the " Impossible Bridge " . On the evening of 15 December , the 1st / 5th Battalion Essex Regiment from the Indian Division 's 19th Indian Infantry Brigade , which had been held in reserve , was committed on the left flank of the Frontier Force Regiment to advance in the direction of <unk> and overran a number of German positions . By the end of 16 December , further attacks from the 15th Punjab Regiments 3rd Battalion had secured positions on the lateral road , ensuring that the 8th Indian Division was firmly embedded in the main German defences . 
- Meanwhile , at 01 : 00 on 15 December , the New Zealand Division — <unk> not to make a further frontal assault on Orsogna — launched their 5th Brigade in Operation Florence , a new flanking attack to the right of the village . By that afternoon , 5th Brigade was well established on the Orsogna to Ortona lateral road and had driven a shallow salient into the German forward defensive line . Although they had exhausted nearly all their reserves , divisional headquarters was optimistic for the prospects for the next day , given the heavy casualties they had inflicted that day . 
- However , the Germans launched a counterattack at 03 : 15 on 16 December , throwing in men from the 6th Parachute Regiment , sent by Herr to the 26th Panzer Division to relieve the exhausted 9th Panzergrenadier Regiment . These troops had arrived late that evening after a long journey . <unk> by tanks , they attacked the right @-@ hand New Zealand positions held by the 21st NZ Battalion , but were held off and had retired by daylight . Meanwhile , even before the German counterattack had been repelled , the 20th Regiment had attacked toward Orsogna with two squadrons of Sherman tanks . Under intense artillery and anti @-@ tank fire , the tanks and infantry became separated and the tanks became a target rather than a threat . 
- Operation Florence had come to an end . While the German line had been pushed back and they had sustained casualties they could ill afford , they still firmly held Orsogna . Furthermore , the New Zealand Division was , for the time being , fought out and needed a period of consolidation and reorganisation . 
- By 16 December , the British 5th Division had completed its move into the line between the New Zealand and the Indian divisions . There followed a period of hostile patrolling and <unk> on the XIII Corps front . The main burden of the fighting was therefore assumed by V Corps as the Canadians pushed for Ortona with the Indian Division on their left flank attacking toward Villa Grande and <unk> . 
- 
- = = Taking The Gully = = 
- 
- In preparation for what he hoped would be the final attack on The Gully , Vokes shifted the 2nd Canadian Infantry Brigade to occupy positions formerly belonging to the 1st Brigade . Vokes planned for an attack by The Carleton and York Regiment to be the last of the frontal assaults against The Gully . Should this attack fail , the 1st Brigade 's Seaforth Highlanders and the Royal Canadian Regiment would move through Casa Berardi and outflank German defences , forcing a withdrawal from The Gully . 
- At 07 : 30 on 15 December , two companies of the Carleton and York Regiment attacked . After little more than an hour of fighting , however , the Canadians were forced to call the attack off . In the afternoon , the two heavily depleted companies of the Royal 22e Régiment fought off a large German counterattack on Casa Berardi , with the Royal Canadian Horse Artillery firing 5 @,@ 398 rounds in support of Canadian forces . 
- On 18 December , Vokes planned what would be the largest assault on The Gully during the campaign . Beginning at 08 : 00 , Canadian artillery would bombard a 900 m ( 3 @,@ 000 ft ) front , to a depth of 300 m ( 980 ft ) . Every five minutes , the barrage would move 100 m ( 110 yd ) forward , continuing to pound German defences in the bombardment area . Less than 100 m behind this barrage , the 48th Highlanders would advance across the Ortona @-@ Orsogna <unk> Road . At the same time , the 8th Indian Division would attack northward toward <unk> , preventing German reinforcements from reaching The Gully . When the 48th Highlanders reached the Cider Crossroads , the Royal Canadian Regiment would move north , overrunning Cider itself , then advance up the Ortona @-@ Orsogna road . Both battalions would be supported by tanks of The Three Rivers Regiment . At first , the attack went extremely well . However , when the artillery shifted their barrage , the German defences quickly recovered and their machine gun fire devastated the advancing forces . In C Company of the Royal Canadian Regiment , every platoon commander was killed or wounded . The attack was quickly abandoned . 
- On 20 December , Canadian forces tried again and The Royal Canadian Regiment attacked Cider Crossroads at noon . This time , Vokes was determined that the operation would be successful , with armoured forces of the Three Rivers Regiment moving to the start lines well before 07 : 00 . Due to shortages of fuel and poor weather , H @-@ Hour was postponed until 14 : 15 . When H @-@ Hour came , a powerful <unk> barrage supported two companies of the Royal Canadian Regiment eastward . By evening , B Company controlled the Cider Crossroads , having met virtually no resistance in their advance to the objective . However , German forces had already evacuated The Gully , falling back to prepare for a strong defence of Ortona , with elements of the powerful 1st Parachute Division firmly entrenched in the town . 
- 
- = = Villa Grande = = 
- 
- In order to keep up pressure on the whole front , the 19th Indian Brigade was ordered to attack Villa Grande and exploit any gains as far as the <unk> river which ran from the mountains through <unk> to the Adriatic . The attack went in at 05 : 30 on 22 December but failed in desperate fighting . The 1 / 5th Battalion , Essex Regiment renewed their attack the following morning with more success . After a counterattack by German <unk> had been repulsed at midday , the Essex advanced to <unk> up the remainder of the village . However , deadly small scale house @-@ to @-@ house battles continued throughout the rest of 23 December and for the next two days as the determined parachute soldiers <unk> on . To the south of Villa Grande , the 3rd / 15th <unk> had taken <unk> on 23 December and a continuous brigade line had been established . 
- On 25 December , reinforcements in the form of 3rd Battalion , 8th Punjab Regiment were brought forward and after a <unk> up barrage were launched at the east side of Villa Grande . With four battalions now involved ( the 5th Battalion , Royal West <unk> had by now been tasked on the south east side of the village ) supported by tanks , Villa Grande was finally cleared by the end of 26 December . The troops of the 8th Indian Division entered the village to find a <unk> . One correspondent described the scene " as though a giant had <unk> on a child 's box of blocks " . 
- 
- = = XIII Corps attacks Orsogna = = 
- 
- On 23 December , Lieutenant @-@ General Dempsey 's XIII Corps launched a new attack to push back the German line from Orsogna . In the afternoon , the British 5th Infantry Division attacked on the right wing of the Corps front toward the <unk> stream . Their objective was to secure the flank of the 2nd New Zealand Division , which was in turn to attack northwest and west from the salient in order to roll up the Orsogna defences from the north . 
- After the British 5th Infantry Division had achieved its objectives , the 5th New Zealand Infantry Brigade attacked at 04 : 00 on 24 December . Despite intensive artillery support ( 272 guns on a 3 @,@ 500 yards ( 3 @,@ 200 m ) front ) , the tired and <unk> New Zealand battalions struggled to make progress . By the afternoon , it had become clear to the New Zealand commander — Bernard Freyberg — that the stubborn defences of the 26th Panzer Division would not be <unk> . He is reported to have remarked , " It is not a question of further advance , it is a question of holding on to what we have got " . The XIII Corps front was effectively <unk> and settled into a posture of active defence and patrolling . 
- 
- = = Ortona = = 
- 
- Throughout the week of 11 – 18 December , the 1st Parachute Battalion from the German 1st Parachute Division — with supporting units — had prepared strong defences within the Italian coastal town of Ortona . <unk> engineers and infantry had destroyed much of Ortona itself , turning the streets into a debris @-@ filled maze . Major streets were mined , with demolition charges throughout the main <unk> , and booby traps littered the town . German forces had also buried tanks in the rubble , leaving only the turrets exposed . 
- On 20 December 1943 , the under @-@ strength Loyal Edmonton Regiment moved toward Ortona , with the Seaforth Highlanders covering their eastern flank . Throughout the day , they encountered heavy machine gun fire during their attempts to enter Ortona . By nightfall , both battalions held a <unk> on the western edge of Ortona , yet had encountered heavy resistance in their attempts to secure it . The following day , D Company of the Loyal Edmonton Regiment launched attacks eastward towards the city centre , but accurate German sniper fire rapidly stalled the advance . 
- Throughout the remainder of the week , the Battle of Ortona degenerated into a small @-@ scale version of the Battle of Stalingrad , with vicious house @-@ to @-@ house fighting through the narrow streets and debris of Ortona . Over the course of the battle , Canadian forces developed innovative " mouse @-@ <unk> " tactics , moving between houses to avoid German sniper fire in the open streets . German counterattacks on 24 and 26 December caused significant casualties to Canadian forces in the town . In danger of being outflanked by Allied advances west of Ortona , the 1st Parachute Regiment abandoned the town the following day , leaving Ortona to Canadian forces . Canadian casualties in the fighting for the town approached 650 killed or wounded . 
- 
- = = Aftermath = = 
- 
- With Ortona and Villa Grande captured , it looked as if it would require Eighth Army only to <unk> itself and strike one more concentrated blow at Orsogna to complete the breaching of the Gustav Line 's main Adriatic <unk> . However , on 31 December , as V Corps <unk> along the coastal plain towards Pescara , a <unk> enveloped the battlefield . Drifting snow , <unk> and biting winds <unk> movement and communications on the ground while cloud ceiling and visibility fell to nil and grounded the <unk> . Montgomery — realising the Eighth Army no longer had the strength or conditions to force its way to Pescara and the Via Valeria to Rome — recommended to General Alexander that the Eighth Army offensive should be halted . Alexander agreed but ordered him to maintain aggressive patrolling in order to pin the units of the LXXVI Panzer Corps in the Adriatic sector and prevent Kesselring moving them to reinforce the XIV Panzer Corps front opposite Mark Clark 's U.S. Fifth Army where the Allied offensive would continue . 
- In spite of this , three attempts during the winter of 1943 / 44 by Fifth Army to break through into the <unk> valley at Cassino failed . As spring approached in 1944 , Alexander concentrated his forces in great secrecy by thinning out the Adriatic front and bringing the bulk of Eighth Army 's striking power to the Cassino front . The combined attack of his two armies during the fourth and final Battle of Monte Cassino in early May took Kesselring by surprise and led to the Allied capture of Rome in early June . 
- 
- 
- = Berkley Bedell = 
- 
- Berkley Warren Bedell ( born March 5 , 1921 ) is a former U.S. Representative from Iowa . After starting a successful business in his youth , Berkley Fly Co . , he ran for the United States Congress in 1972 , but was defeated by incumbent Wiley Mayne . In 1974 , however , Bedell beat Wiley Mayne and was elected to Congress . 
- He was known for his support of representative democracy and his populist style . For example , he would hold town halls and let constituents vote on motions to decide what he would do in Congress on their behalf . These meetings helped Bedell understand the problems of his constituents ; as a result , he backed issues that were important to his farming constituency , such as waterway usage fees and production constraints . 
- He did not seek reelection in 1986 after contracting <unk> disease from a tick bite . Though he no longer serves in Congress , Bedell remains active in Iowa politics , strongly supporting Howard Dean in 2004 over John Kerry . In the 2008 presidential election , he met several times with Chris Dodd , but endorsed Barack Obama in the end . 
- 
- = = Early life = = 
- 
- Born in Spirit Lake , Iowa , Bedell was educated in Spirit Lake public schools . He graduated from Spirit Lake High School in 1939 , where he earned spending money with a business in the midst of the Great Depression . His business involved <unk> dog hairs around <unk> , the result of which could be sold as trout flies . He began tying the fly @-@ fishing <unk> in his bedroom , then he moved the business into his parents ' basement . In time , he got space above a grocery store to continue the business full @-@ time . 
- After graduating from high school , he attended Iowa State University from 1940 to 1942 , where he met fellow <unk> student Elinor <unk> from Grand <unk> , Minnesota . Berkley and Elinor married in Minneapolis on August 29 , 1943 and their son Kenneth was born in 1947 , Thomas in 1950 and daughter <unk> in 1952 . Berkley ’ s college and personal life was interrupted in 1942 when he joined the army . He served in the United States Army as first lieutenant and flight trainer from 1942 to 1945 . When he got back , he began to garner success from his fish tackling business . His business became larger , with hundreds of employees and international operations ; he had become a millionaire by the 1960s . He served as member of the Spirit Lake Board of Education from 1957 to 1962 . 
- 
- = = Political career = = 
- 
- 
- = = = Running for Congress = = = 
- 
- By the early 1970s , Bedell had decided to run for political office . In 1972 , he ran against Wiley Mayne , a Republican incumbent in Iowa 's 6th congressional district . Mayne was a staunch supporter of Richard Nixon and secured victory along with the President in a year favorable to the Republicans . Mayne , however , would politically suffer after Watergate ( he was one of only a few Republicans to vote against <unk> the President on the judiciary committee . ) The damage had already been done , and Bedell defeated Mayne in a 1974 rematch . 
- During his time at Congress , Bedell took efforts to uphold representative democracy . He held town halls regularly with his constituents , and he would let them vote on motions to decide what he would do in Congress on their behalf . This type of communication told Bedell of the types of issues affecting his farming constituency . Thus , though Bedell had not farmed in his life , he would take steps in Congress to benefit farmers . 
- 
- = = = Waterway usage fees = = = 
- 
- Bedell sponsored several bold initiatives during his tenure in the United States House of Representatives . One initiative , which came from his constituents ' problems with the barge industry , focused on waterway usage fees . He introduced legislation in 1977 that would require the barge industry to pay a fee for using the waterways which , Bedell pointed out , the Government paid millions of dollars to create and maintain . Bedell 's original plan set the rate the barge industry paid as directly related to the amount the Government spent on waterway projects . This would have the additional effect of helping curb unnecessary waterway projects , and it was the same plan proposed by Pete Domenici in the Senate . 
- Congress eventually passed a watered @-@ down version of the original plan put forward by Bedell and Senator Pete Domenici . The compromise version enacted a tax on the gasoline barges used and put it into a " trust " for waterway projects . While other supporters of waterway usage fees , including Domenici , backed the compromise , Bedell gave a <unk> plea for his colleagues to oppose it . He viewed it as lacking a crucial element of the original plan - that of capital recovery . The trust was optional , and the Government could spend money on waterway projects irrespective of the trust . The compromise was eventually signed by Jimmy Carter . Bedell 's original plan never made it through the House of Representatives , but he continued to introduce it in succeeding sessions . It would not , however , get a floor vote in succeeding sessions . 
- 
- = = = <unk> issues = = = 
- 
- In 1985 , Bedell put forward an agricultural plan that he thought would increase production controls for farmers , thus raising prices for crops . This plan , backed by labor unions and certain Democrats , passed the Agriculture Committee as an amendment to farm legislation . It mandated a referendum that would then be used to determine what types of production controls to enact . The purpose of this plan was <unk> : production controls would decrease the aggregate supply of crops , thus making individual crops cost more ( which would benefit farmers , who were in the middle of an acute debt crisis . ) Second , by styling it as a referendum , the farmers would get to decide the severity of the controls . 
- On the other hand , opponents of the Bedell plan had a very different view of this legislation . Representatives such as Pat Roberts claimed that the referendum was redundant because the farmers already voted the politicians into office , and this bill was an example of the politicians not doing their jobs . The Reagan Administration opposed the bill because of their opposition to production controls , and the President threatened to veto the farm bill if Bedell 's plan was left in place . When the bill got to the floor , an amendment was proposed to strike this provision , and it was passed 251 @-@ 174 . 
- 
- = = = Investigations of large businesses = = = 
- 
- While in Congress , Berkley Bedell was Chairman of the Small Business <unk> , and he used this position to investigate <unk> on the part of large oil companies . He also claimed that certain large oil companies <unk> their " <unk> taxes " in certain cases and wanted to pass legislation to increase regulations on these corporations . 
- In these investigations , Bedell quickly gained the support of small gasoline <unk> and Congressman Bill Nelson . The chief target , <unk> , was accused of not paying all of its taxes on <unk> crude oil . In the end , the government tried to make a case against <unk> , but it was eventually dropped in 1985 . Bedell used this opportunity to attack the Administration for " not caring " about small business owners , and he advocated that <unk> agencies put aside 1 @-@ 3 % of their research and development money for small businesses . 
- 
- = = = Clash with Reagan = = = 
- 
- In late 1982 , Congress passed a law which forbade the United States from funding groups aiming to overthrow the <unk> government of Nicaragua . Then , in 1983 , Bedell visited Nicaragua and Honduras along with Representative Robert G. <unk> . During the trip , Bedell spoke with soldiers , generals , governmental officials and members of the contras . His conclusion at the end of the trip was that Ronald Reagan was aiding the contras in violation of federal law . He promised to hold hearings after returning to Congress . Bedell would later join other House Democrats in demanding documents from the White House related to the contras , but the Reagan Administration refused to provide them . Bedell became <unk> with the Reagan Administration as the decade wore on . He called his Central American policies " sheer <unk> , " saying that the mining of <unk> was an acts of war . Bedell would retire from Congress before Reagan 's acts in Central America would <unk> with the Iran @-@ <unk> Affair . 
- Furthermore , Bedell was a sharp critic of Reagan 's agricultural policies , calling for John Block to resign after calling his agricultural plan a failure that was " dead on arrival " in both the House and the Senate . Reagan 's agricultural plan consisted primarily of a gradual reduction in farm subsidies . He also attacked the Department of Agriculture for " looking backward " when it dismissed the only expert on organic farming . Also , as chairman of the <unk> on Department Operations , Research and Foreign Agriculture , which was in charge of regulating USDA operations , he opposed the proposals Reagan had for reforming the organization . The proposals generally involved shifting costs for meat inspections and other USDA duties from the federal government to the industry . 
- 
- = = = Controversy = = = 
- 
- In 1981 , it was revealed in internal <unk> that Bedell may have known about potential customs violations that his company engaged in . It asserted that Bedell had gone to Taiwan in 1973 to discuss " prior violations of customs law " in regards to the sale of fishing rods from the company 's Taiwan subsidiary . Bedell responded by denying any wrongdoing , saying that he has not been personally involved in the company in years . In the end , no charges were <unk> against him , and he was reelected after the story was published . 
- 
- = = After politics = = 
- 
- Bedell decided not to seek reelection in 1986 after contracting <unk> Disease from a tick bite . Since then , he has founded a center for alternative medicine and is a noted advocate of health freedom . Due largely to his friendship with Tom <unk> , he remains an important political figure in Iowa , with politicians such as Howard Dean meeting him in their trips to the state . Also , the Elinor Bedell State Park was established in 1998 on land donated by Berkley Bedell . The park is named after the Congressman 's wife . 
- As an opponent of the Vietnam War , Bedell signed a petition urging against United States military intervention in Iraq . This petition was signed with the names of 70 former <unk> from the 1970s and was presented in a press conference on March 15 , 2003 . Bedell said that it was unbelievable for the United States to settle disputes with war , and he said that an Iraq war would be similar to the Vietnam War . 
- In the 2004 presidential election , Bedell attacked John Kerry for voting for <unk> <unk> 's Freedom to Farm Act , which Bedell claims wrecked the farm program . Bedell would later officially endorse Howard Dean 's candidacy . For the 2008 election , Bedell met with Chris Dodd . However , in December 2007 , he announced his endorsement of Barack Obama . 
- 
- 
- = Bart vs. Australia = 
- 
- " Bart vs. Australia " is the sixteenth episode of the sixth season of The Simpsons . It originally aired on the Fox network in the United States on February 19 , 1995 . In the episode , Bart is indicted for fraud in Australia , and the family travels to the country so Bart can apologize . The Australian Parliament decides to give him the additional punishment of a boot to his buttocks , but the Simpson family refuses . Bart later changes his mind and agrees to the punishment , but just as he is about to receive it , he moons the Australians and the family flee back to America . 
- The episode was written by Bill Oakley and Josh Weinstein and directed by Wes Archer . It features cultural references to films such as Mad Max 2 and Crocodile Dundee . " Bart vs. Australia " acquired a Nielsen rating of 9 @.@ 1 and was the fourth highest rated show on the Fox network the week it aired . It received mixed reception in Australia , with some Australian viewers saying the episode was a mockery of their country . 
- 
- = = Plot = = 
- 
- While in the bathroom , Bart notices that the water in the sink always drains <unk> . Lisa explains ( not entirely correctly ) that the water never drains the other way except in the southern hemisphere , due to the Coriolis effect , but Bart does not believe her . To confirm this , Bart makes phone calls to various countries in the southern hemisphere . Lisa points out how expensive international calls are , so Bart decides to make a collect call instead . He calls Australia , where a little boy answers the phone . <unk> to represent the " International Drainage Commission " , Bart is informed that the toilet and sink are both draining clockwise . Frustrated , Bart asks him to go and check the toilets of the neighbors . The call takes six hours to complete , since the boy lives in the outback , and Bart <unk> to hang up the phone . Later , the boy 's father is billed A $ 900 ( referred to as " <unk> " ) . The father calls Bart and demands that he pay , but Bart only taunts him . Unfortunately for Bart , the father 's neighbour is a federal Member of Parliament , who reports Bart 's offense to the Prime Minister — who is relaxing naked in a nearby pond . 
- After a long series of ignored letters , Australia <unk> Bart for fraud . The United States Department of State wants to send him to prison in order to <unk> the Australian government , but Marge furiously objects to this idea . The State Department then settles on having Bart publicly apologize in Australia . The family is sent to Australia and they stay in the American Embassy , which is fitted with all the comforts of their home country , including a specially modified toilet that <unk> an exaggerated Coriolis effect . Then they start exploring the local culture . 
- After Bart makes his apology , the Parliament reveals that they want to give him the additional punishment of a " <unk> " , which is a kick in the buttocks using a giant boot . Bart and Homer escape and the family flees to the American Embassy . After a prolonged standoff , the two governments propose a compromise to the Simpson family : one kick from the Prime Minister , through the gate of the embassy , with a regular shoe , believed to be a <unk> . Marge is opposed to the idea , but Bart agrees . However , Bart <unk> the kick , moons the Australians with the words " don 't <unk> on me " written on his buttocks , then <unk> " The Star @-@ <unk> Banner " . In a scene reminiscent of the Fall of Saigon the Simpson family flees the outraged country in a helicopter . Looking down on Australia , they see that <unk> have begun to <unk> and destroy the Australian ecosystem , due to a <unk> Bart left earlier at the airport . <unk> the devastation , the family remark upon the destruction that can be caused by introducing a foreign species into a new environment , and laugh at the Australians ' misfortune , unaware that a <unk> is hanging onto the helicopter . The camera <unk> in on the <unk> , ending with a close @-@ up of its eye , implying that America will face a similar fate as Australia . 
- 
- = = Production = = 
- 
- The episode was written by Bill Oakley and Josh Weinstein , and directed by Wes Archer . The writing staff wanted to do an episode where the Simpsons family traveled to Australia , because they thought everyone in Australia had a good sense of humor and that they " would get the jokes " . The staff had previously poked fun at several American institutions on the show and they thought it would be interesting to poke fun at a whole nation . They designed Australia and the Australian people very inaccurately and many things were completely made up for fun . The animators , however , got two Australian tourist guides to help them out with the design of the Australian landscape and buildings , as well as the American Embassy . The writers did research on the Coriolis effect for this episode . Lisa 's explanation of the effect is incorrect ; it affects global weather patterns and is caused by the spinning of the globe on its axis . The amount of water in a toilet or sink is much too small to be affected by it . 
- In 1999 , Fox Studios Australia in Sydney used a different version of " Bart vs. Australia " as part of their Simpsons attraction , called The Simpsons Down Under . They had contacted the Simpsons writing staff and asked if they would write the screenplay for a ride in their attraction , based on this episode . The episode was re @-@ edited and re @-@ animated for the ride and new scenes were included . The attraction featured motion capture technology , allowing audience members faces and expressions to be transformed into moving cartoon characters . 
- 
- = = Cultural references = = 
- 
- The plot of the episode is based on the story of Michael Fay , an American teenager who was <unk> in Singapore in 1994 for <unk> cars . This episode <unk> a popular myth that the Coriolis effect affects the motion of drains in the Northern and Southern <unk> . In reality , the Coriolis effect affects global weather patterns . The amount of water in a toilet or sink is much too small to be affected by it . 
- When Bart is talking to the boy 's father on the phone he says " I think I hear a <unk> eating your baby " , referencing the case of Azaria Chamberlain , a ten @-@ week @-@ old baby who was killed by <unk> . The <unk> taking over Australia and destroying all the crops is a reference to the cane <unk> , originally introduced to Australia in order to protect sugar <unk> from the cane beetle , but became a pest in the country . 
- When the Simpson family go to an Australian pub , Bart plays with a <unk> at the table and a man asks him : " You call that a knife ? " , and as the man draws a spoon from his pocket he says : " This is a knife . " The scene is a reference to a famous scene from Crocodile Dundee in which Mick Dundee is threatened by some thugs with a <unk> , and Mick takes out a <unk> knife and says ; " That 's not a knife ; that 's a knife ! " The Simpson family is shown a slide show by the US Department of State depicting a boarded up cinema with a sign out the front saying " Yahoo Serious Festival " , in reference to the Australian actor and director Yahoo Serious . <unk> , one of the characters from the 1981 film Mad Max 2 : The Road Warrior , is seen in the Australian mob that chases Bart and Homer to the American Embassy . 
- 
- = = Reception = = 
- 
- In its original American broadcast , " Bart vs. Australia " finished 56th in the ratings for the week of February 13 – 19 , 1995 , with a Nielsen rating of 9 @.@ 1 . It was the fourth highest rated show on Fox that week . The episode has since become study material for sociology courses at the University of California , where it is used to " examine issues of the production and reception of cultural objects , in this case , a satirical cartoon show " , and to figure out what it is " trying to tell audiences about aspects primarily of American society , and , to a lesser extent , about other societies . " 
- Since airing , the episode has received positive reviews from fans and television critics . In a DVD review of the sixth season , Ryan Keefer said " all the Australian jabs you expect to have here are present . Bart 's international incident is hilarious , from top to bottom . The phone calls he makes to other countries ( particularly Buenos Aires ) are fantastic . This is one of the more under appreciated episodes in the series ' run . " Vanity Fair named it the second best episode of The Simpsons in 2007 . " Bart vs. Australia " was also nominated for an Emmy Award in 1995 in the category " Outstanding Individual Achievement in Sound Mixing for a Comedy Series or a Special " . 
- 
- = = = Reaction in Australia = = = 
- 
- The episode received mixed reception in Australia , with some Australian fans saying the episode was a mockery of their country . Shortly after it had aired , the Simpsons staff received over 100 letters from Australians who were insulted by the episode . They also received letters from people complaining about the Australian accents used in the episode that " sounded more like South African accents " . The Simpsons writer and producer Mike <unk> claimed that this episode is Australia 's least favorite , and that " whenever we have the Simpsons visit another country , that country gets furious , including Australia " . He claimed that they were " condemned in the Australian Parliament after the episode had aired " . 
- The Newcastle Herald 's James Joyce said he was shocked when he first saw the episode : " Who are the Americans trying to kid here ? I agree Australia has its faults , as does any other country . But laughing in our face about it , then mocking our heritage was definitely not called for . It embarrassed and degraded our country as well as making us look like total <unk> " . Warren Martyn and Adrian Wood , the authors of the book I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide , advised that the episode is " best if watched with Australians who will be , perhaps understandably , <unk> at their portrayal . After the attack on the French , this is a vicious , <unk> , offensive and wonderfully amusing slaughter of Australian culture by the makers of The Simpsons " . 
- The Simpsons executive producer David Mirkin , who produced the episode , responded to the criticism in an interview with The Newcastle Herald by saying : " We like to have the Simpsons , the entire family , travel and this was the beginning of that . Australia was a fantastic choice because it has lots of quirky visual things . And it 's a country that is really very close to America , very in sync with America . We are so similar but yet there are all these fantastic differences , familiar yet twisted . It was intentional to make it very inaccurate . That was our evil side coming out : We 'll take our knowledge of Australia and we 'll twist it around to stimulate an audience and annoy them at the same time " . Despite being criticized for mocking the country , the episode received positive reviews from Australians , too . Jim <unk> of the Australian newspaper The Age named it the funniest episode ever while the <unk> was forked ( the ' <unk> @-@ <unk> ' fork ) into the ' <unk> ' <unk> in honour of the episode . 
- 
- 
- = Leslie Andrew = 
- 
- Brigadier Leslie <unk> Andrew VC DSO ( 23 March 1897 – 8 January 1969 ) was a senior officer in the New Zealand Military Forces and a recipient of the Victoria Cross , the highest award of the British Commonwealth for gallantry " in the face of the enemy " . He received the decoration for his actions during the Battle of Passchendaele in 1917 . 
- Born in 1897 , Andrew joined the New Zealand Expeditionary Force in 1915 , having gained military experience while serving with the Territorial Force . He served on the Western Front from September 1916 to early 1918 , and ended the war as a commissioned officer in England . He remained in the military after the cessation of hostilities , and joined the New Zealand Staff Corps . He held staff and administrative positions in New Zealand and , while on an officer exchange program , British India . 
- Following the outbreak of the Second World War , Andrew was appointed commander of the 22nd Battalion , which he led during the Battles of Greece , Crete and the early part of the North African Campaign . For a short period in late 1941 he commanded an infantry brigade of the 2nd New Zealand Division , and received the Distinguished Service Order for his leadership . He returned to New Zealand in 1942 and commanded the Wellington Fortress Area for the remainder of the war . He retired from the military in 1952 with the rank of brigadier , and died in 1969 aged 71 . 
- 
- = = Early life = = 
- 
- Leslie Andrew was born on 23 March 1897 in <unk> in the <unk> region of New Zealand , the son of a local school headmaster . He grew up in <unk> , where his father had moved his family having taken up a position in the area , and was educated at <unk> Collegiate School . After leaving school he was employed by the New Zealand Railways Department as a clerk . He participated in the cadet program while at school , and later joined the Territorial Force . By 1915 , he had been promoted to sergeant and had sat the necessary exams to become a commissioned officer in the <unk> . 
- 
- = = Military career = = 
- 
- 
- = = = First World War = = = 
- 
- Andrew volunteered for the New Zealand Expeditionary Force ( NZEF ) in October 1915 . Because only men between the ages of 19 and 45 were required to register for service with the NZEF , he falsified his age to ensure that he would be eligible for duty overseas . A member of the 12th Reinforcements , he embarked for the Western Front via Egypt on 1 May 1916 . In France , he was posted to B Company , Wellington Infantry Battalion with the rank of private . 
- Andrew 's arrival at the front coincided with the start of the Somme Offensive . He participated in the Battle of <unk> @-@ <unk> , which began on 15 September , and was wounded . Promoted to corporal in January 1917 , he took part in the Battle of <unk> the following June . 
- During the early phase of the Battle of Passchendaele , Andrew 's battalion was engaged in fighting around the village of La <unk> , a few kilometres southwest of <unk> . Originally captured by the New Zealanders prior to the battle on 26 July , the village had been re @-@ taken by the Germans the next day . Under cover of an artillery barrage , the <unk> began an advance towards the village . Andrew was tasked with leading two sections to destroy a machine @-@ gun post . During the advance , he noticed another machine @-@ gun post that was holding up the advance of another platoon . On his own initiative , he promptly diverted his force and removed the newly spotted threat with a flanking attack . He then continued with his men to his original objective . <unk> continuous gunfire , he and his men captured the machine @-@ gun post . While most of his men withdrew with the gun , he and another man continued to scout further forward . Coming across another machine @-@ gun post , the two men destroyed it before returning to their lines with useful information on the increasing numbers of Germans in the area . 
- It was for his leadership and bravery during these actions that Andrew was awarded the Victoria Cross ( VC ) at the age of 20 . The citation read as follows : 
- For most conspicuous bravery when in charge of a small party in an attack on the enemy 's position . His objective was a machine @-@ gun post which had been located in an isolated building . On leading his men forward he encountered unexpectedly a machine @-@ gun post which was holding up the advance of another company ; he immediately attacked , capturing the machine gun and killing several of the crew . He then continued the attack on the machine gun post which had been his original objective . He displayed great skill and determination in his disposition , finally capturing the post , killing several of the enemy and putting the remainder to flight . <unk> Andrew 's conduct throughout was <unk> for cool daring , initiative , and fine leadership , and his magnificent example was a great <unk> to his comrades . 
- Andrew was promoted to sergeant the day after his VC @-@ winning action . He continued to serve on the front until early 1918 , when he was sent to England for officer training . He was commissioned as a second lieutenant in March 1918 , but remained in England until the end of the war . 
- 
- = = = <unk> period = = = 
- 
- While in England , Andrew met <unk> Ball , of Nottingham , and they were married on 12 November 1918 . The couple had five children although one died in infancy . Upon discharge from the NZEF in August 1919 , he joined the New Zealand Staff Corps and served in a number of administrative positions for the next several years . From 1927 to 1929 he served with the Highland Light Infantry in British India on an officer exchange program . On his return to New Zealand he was appointed adjutant of the 1st Wellington Regiment . In 1937 , having been promoted to captain , he commanded the New Zealand contingent sent to London for the coronation of King George VI and Queen Elizabeth . 
- 
- = = = Second World War = = = 
- 
- Following the outbreak of the Second World War , Andrew was seconded to the 2nd New Zealand Expeditionary Force . In early 1940 , he was appointed commander of 22nd Battalion , then forming at <unk> Military Camp near Wellington . He trained his new command hard , and quickly earned the nickname of February due to his habit of issuing 28 @-@ day <unk> for any breaches in discipline . 
- The battalion embarked for England in May 1940 as part of 5th Infantry Brigade , 2nd New Zealand Division . Arriving in June , it spent the remainder of the year on garrison duties in the south of England . In March 1941 it travelled for Egypt and then onto Greece . Andrew led the battalion through the subsequent Battle of Greece , during which it saw little action , and the Battle of Crete . 
- In Crete , the battalion was tasked with the defence of <unk> airfield and the overlooking hill , Point 107 . Andrew was ordered to maintain control of his positions " at all costs " . Forced to disperse the companies of his battalion widely to cover his positions , he lost contact with most of his units after German paratroopers began landing in the area on 20 May . <unk> to receive any support from his brigade commander following a request for assistance , and fearing most of his command overrun after a failed counterattack by his small reserve , he withdrew his remaining units . As it happened , most of his forward companies remained intact and were subsequently able to withdraw themselves after finding they had been abandoned . Andrew was criticised for his withdrawal , which led to the loss of <unk> airfield . This was a significant factor in allowing the German forces to become established on Crete . He and the surviving elements of his battalion were later evacuated from Crete . 
- Despite the setback of Crete , Andrew remained as commander of 22nd Battalion during the early phases of the North African Campaign . At one stage he was temporary commander of 5th Infantry Brigade when its nominal commander , Brigadier James <unk> , was captured in late November 1941 . Andrew was awarded with the Distinguished Service Order for his leadership of the brigade , which had to deal with repeated attacks by German forces in early December . He relinquished command of 22nd Battalion on 3 February 1942 , and returned to New Zealand . He was promoted to full colonel and commanded the Wellington Fortress Area for the rest of the war . 
- 
- = = Later life = = 
- 
- Andrew commanded the New Zealand contingent for the 1946 Victory Parade in London , and the following year attended the Imperial Defence College . He was promoted to brigadier in 1948 and appointed commander of the Central Military District . He remained in this capacity until his retirement from the military in 1952 . Andrew was later invited to run for Parliament but declined . He died on 8 January 1969 , aged 71 . He was buried with full military honours in a ceremony at Levin <unk> Cemetery , in Levin . 
- 
- = = Victoria Cross = = 
- 
- Andrew 's VC was displayed at the <unk> Army Memorial Museum , Waiouru , New Zealand . On 2 December 2007 it was one of nine Victoria Crosses that were among a hundred medals stolen from the museum . On 16 February 2008 , New Zealand Police announced all the medals had been recovered as a result of a NZ $ 300 @,@ 000 reward offered by Michael Ashcroft and Tom <unk> . 
- 
- 
- = Rebbie Jackson = 
- 
- Maureen <unk> " Rebbie " Brown ( née Jackson ; born May 29 , 1950 ) is an American singer professionally known as Rebbie Jackson / <unk> <unk> / . Born and raised in Gary , Indiana , she is the eldest child of the Jackson family of musicians . She first performed on stage with her siblings during shows in Las Vegas , Nevada , at the MGM Grand Hotel and Casino in 1974 , before subsequently appearing in the television series The Jacksons . Her sister La Toya was born on Jackson 's 6th birthday . At age 34 , Jackson released her debut album Centipede ( 1984 ) . The album featured songs written by Smokey Robinson , Prince , and Jackson 's younger brother Michael , whose contribution ( the title track " Centipede " ) became Rebbie 's most successful single release . By the end of the 1980s , the singer had released two more albums in quick succession : Reaction ( 1986 ) and R U Tuff Enuff ( 1988 ) . 
- Following a 10 @-@ year hiatus in her musical career , Jackson returned with the 1998 album Yours Faithfully . The production of the album , her last to date , was a collaboration with artists and producers such as Men of <unk> 's Spanky Williams , Keith Thomas , and Eliot Kennedy . It also featured contributions from her children . In 2011 , Rebbie embarked on the " Pick Up the <unk> Tour , " which is dedicated to teens who have committed suicide all over the U.S. 
- 
- = = Life and career = = 
- 
- 
- = = = Childhood and youth = = = 
- 
- Maureen <unk> " Rebbie " Jackson was born in Gary , Indiana , to a working @-@ class family on May 29 , 1950 . The daughter of Joseph Walter " Joe " and Katherine Esther ( née <unk> ) , she is the eldest of their ten children . Her siblings are Jackie , Tito , Jermaine , La Toya , Brandon ( d . March 12 , 1957 ) , Marlon , Michael ( d . June 25 , 2009 ) , Randy , and Janet . Joseph was a steel mill employee who often performed in a rhythm and blues ( R & B ) band called The Falcons with his brother , Luther . His wife , Katherine , is a <unk> 's Witness and raised her children to follow the religion . Rebbie , La Toya , and Michael became the most devout of the children as time progressed . Reflecting on her early life , Rebbie acknowledged in a 1980s magazine interview that her role within the family had been that of a " second mother " to her younger siblings , whom she would often <unk> . She also <unk> from Theodore Roosevelt High school in Gary IN in 1968 
- 
- = = = Marriage = = = 
- 
- 18 @-@ year @-@ old Rebbie 's announcement that she wanted to marry her childhood love , Nathaniel Brown , in May 1968 created a division in the Jackson family . Jackson expressed her feelings for the man and proclaimed that she wanted to move with him to Kentucky . Katherine encouraged her daughter to proceed with the union ; she felt that being a wife and mother were important roles for all of her daughters to play . Joseph , however , was against the marriage ; he wanted Rebbie to follow in the footsteps of her brothers ( The Jackson 5 ) and become a singer . Her father felt that married life would stop her from becoming a success in the entertainment business . Though Rebbie had taken <unk> , piano and dance lessons in her childhood , she had no interest in a music career . This was despite the fact that according to brother Jermaine she had won several singing contests , <unk> with brother Jackie . The teenager thought a happy home was more <unk> and secure than the instability of show business . She also wanted to leave her family 's drama @-@ filled home on Jackson Street as well as get away from her controlling father . <unk> ensued for several weeks before her father relented and allowed Rebbie to wed Brown . Having the last word on the matter , Joseph refused to walk his daughter down the aisle . 
- Brown and Jackson have three children ; daughters , Stacee ( born May 5 , 1971 ) and <unk> ( born October 5 , 1977 ) and son , Austin ( born November 22 , 1985 ) . Jackson 's husband , Nathaniel Brown , died of cancer on January 6 , 2013 . Rebbie has one grandson , London Blue <unk> ( born July 25 , 2005 ) , from Stacee . 
- 
- = = = Early career = = = 
- 
- Jackson began her singing career in 1974 , performing with her siblings in Las Vegas . The Vegas shows had initially begun in April , without Rebbie ; due to a <unk> ankle , Rebbie 's debut was postponed until June . Her five brothers were the main draws , with herself , Randy , Janet , and La Toya serving as <unk> for the performances . 
- When The Jackson 5 parted with their record label Motown in 1976 , they signed to CBS Records and rebranded themselves as The Jacksons . Additionally , the brothers were signed to CBS @-@ TV to star with their family in a variety series called The Jacksons . The shows premiered in June 1976 , and featured all of the siblings excluding Jermaine , who had chosen to stay with Motown . The initial series run of the 30 @-@ minute programs was four weeks . Due to ratings success , more episodes were ordered in January 1977 . The shows marked the first time that an African @-@ American family had ever starred in a television series . The run of programs concluded shortly afterward . 
- Prior to the series , Jackson had thought of her singing as merely a private <unk> . The Jacksons — as well as an early love of musicals — motivated her to become a professional recording artist , and the show 's producer encouraged her to sing . Jackson served as a backing vocalist for several musicians around this time , as well as a cabaret singer . She contributed her voice for songs by artists such as The <unk> , Sonny Bono and Betty Wright before Jackson 's second pregnancy stalled her musical career for a short time . 
- 
- = = = Centipede = = = 
- 
- Following years of preparation , Jackson 's debut album Centipede was distributed in October 1984 by CBS Records , who had signed her as a solo artist two years previously . The album was only released once the singer had ensured that family life was secure and that she had spent time with her children during their important younger years . Centipede became a moderate chart success , reaching number 13 on Billboard 's Top R & B / Hip @-@ Hop Albums chart and number 63 on its Top 200 . The recording of the album had been a family affair ; it involved several contributions from her relatives . Her husband Nathaniel Brown co @-@ wrote the song " Come Alive Saturday Night " with two of his wife 's brothers : Randy and Tito . The latter Jackson also penned " Hey Boy " with his wife Dee Dee . The most successful song from the album was the million @-@ selling title track , " Centipede " . Written , arranged and produced by Michael , the song also featured Jackson 's famous brother on backing vocals . It reached number 4 on the Black Singles Chart and was subsequently certified gold by the Recording Industry Association of America . " Centipede " marked Michael 's first effort at writing and producing since the release of his successful Thriller ( 1982 ) . 
- Other tracks from Rebbie 's album included cover versions of songs by Prince ( " I Feel for You " ) and Smokey Robinson and the <unk> ( " A Fork in the Road " ) . The album received mixed reviews from journalists and music critics . According to the magazine Jet , Centipede marked Jackson 's emergence as a " legitimate recording artist " and " cleared the major hurdle of demonstrating that she [ was ] talented and <unk> " . With the album , Jackson became the last of her siblings to embark on a recording career and the last in line to release hit material . 
- Rebbie later revealed that there was a lot of discussion at the time of the release of Centipede over whether she should use the Jackson surname professionally or not . To begin with Rebbie did not want to use her pre @-@ marriage surname , but later reasoned that it was silly to deny her heritage . Jackson explained that she did , however , compromise with the use of her family name on the Centipede album cover - " Rebbie is large and Jackson is small " . She further stated that the success of siblings Michael and Janet had not been a hindrance to her , but served as an enhancement to her career . Rebbie added that she did not have to worry about " name recognition " . 
- 
- = = = Reaction and R U Tuff Enuff = = = 
- 
- Reaction served as a follow @-@ up album to Centipede , and was released in October 1986 . The album was recorded at Tito 's Ponderosa Studios in Los Angeles , California . Her brother Tito produced Reaction along with David <unk> and David Townsend of the R & B group Surface . Duets were featured on the album , including one with <unk> <unk> lead singer Robin Zander and another with Isaac Hayes . The Zander @-@ Jackson collaboration ( " You <unk> the Rain Away " ) was released as a single , and peaked at number 50 on the R & B singles chart . Jackson 's duet with Hayes , the ballad " Tonight I 'm Yours " , was not released as a single , though received substantial airplay . Reaction 's title track ( " Reaction " ) was the most popular hit from the album , reaching number 16 on the R & B singles chart . 
- The R U Tuff Enuff album succeeded Reaction upon its release in July 1988 . Jackson was more involved with the production of the album than she had been on her previous releases . She stated at the time of R U Tuff Enuff 's distribution that the sound on the album differed from anything she had done previously . Jackson commented that the album was " more versatile " , while noting that it resembled other albums because it had a lot of dance music . Two singles were released from the album and charted on the R & B singles chart : " <unk> " , which made it into the top 10 , and the title track " R U Tuff Enuff " , which peaked at number 78 . By mid @-@ June 1988 , R U Tuff Enuff had reportedly sold 300 @,@ 000 copies . MTV later concluded that the album " struggled " . Jackson would lend her vocals to " 2300 Jackson Street " ( the title track of her brothers ' 2300 Jackson Street album ) , before taking a hiatus from releasing music . Jackson later stated that she performed around the world during this hiatus . 
- 
- = = = Yours Faithfully = = = 
- 
- Following a 10 @-@ year break from music , Jackson signed with her brother Michael 's record label , <unk> Music , in 1997 . From the label , Yours Faithfully was released on March 31 , 1998 . The album featured a remixed version of Jackson 's successful " Centipede " . Initially , the singer had not wanted to feature the track , believing that it was part of the past . After thinking about it for a while , Jackson felt that the inclusion of the remix — which features a rap by son Austin — would be a good way to return to the music scene . In addition , two of her other children , Stacee and <unk> , contributed backing vocals for the album . Other tracks from the album included " Fly Away " , which was written and produced by brother Michael , who also served as co @-@ executive producer for Yours Faithfully . Fellow producers included Keith Thomas and Eliot Kennedy . The album also featured a duet with Men of <unk> 's Spanky Williams on The <unk> ' " I Don 't Want to <unk> You " , which Jet described as being a " <unk> " rendition . Yours Faithfully 's title track was released as a single and peaked at number 76 on the R & B chart . Vibe magazine 's <unk> Mitchell expressed disappointment in the album , labelling its content a " mix of dated R & B grooves dressed up with a few cleverly placed samples " . 
- 
- = = = Death of Michael Jackson = = = 
- 
- Rebbie 's brother Michael died on June 25 , 2009 , after suffering a cardiac arrest . His memorial service was held twelve days later on July 7 , and the finale featured group renditions of the Jackson anthems " We Are the World " and " <unk> the World " . The <unk> featured Michael 's siblings ( including Rebbie ) and the late singer 's children . Following the service — which was held at Los Angeles ' Staples Center — Rebbie , along with sisters Janet and La Toya , addressed fans at the nearby L.A. Live entertainment complex . " We are extremely grateful for all the support . We love you all . " In the weeks following Michael 's death , it was speculated by media sources that Rebbie would be the primary caregiver for her late brother 's children : Prince , Paris and <unk> . It was stated that even if Michael and Rebbie 's mother Katherine were granted custody of the children , Rebbie would care for the siblings on a day @-@ to @-@ day basis at the Jackson family 's <unk> home . Katherine was named the legal guardian of them in August 2009 after the death of Michael Jackson . In early 2011 , Rebbie announced she 's begun recording for a new album , her first in 14 years . She 's also been performing throughout the states with a set list that contains her best known songs , some of her brothers ' songs , and some Motown classics . 
- 
- = = = Voice Type = = = 
- 
- Rebbie Jackson is a <unk> with a 3 octave range . She has an impressive belting range , belting up to F # 5 in her song " Reaction . " 
- 
- = = Discography = = 
- 
- 
- = = = Albums = = = 
- 
- 
- = = = Singles = = = 
- 
- 
- 
- = Hugh Foliot = 
- 
- Hugh Foliot ( c . 1155 – 1234 ) was a medieval Bishop of Hereford . Related somehow to his predecessor at Hereford , he served as a priest and papal judge as well as being an unsuccessful candidate as Bishop of St David 's in Wales . In 1219 , he was appointed Bishop of Hereford . During his time in office , he mostly attended to ecclesiastical duties , but did occasionally serve as a royal administrator . He helped found a hospital and a priory , and died in 1234 after a months @-@ long illness . 
- 
- = = Early life = = 
- 
- Foliot possibly was the son of Roger Foliot and his wife <unk> . Roger held three knight 's fees in <unk> . Probably born sometime between 1150 and 1160 , Hugh was related in some manner to Robert Foliot , his predecessor at Hereford . He was a canon of Hereford Cathedral before becoming Archdeacon of Shropshire by May <unk> . Foliot is a frequent witness on charters as archdeacon , but little else is known of his tenure of the office . From 1212 to 1219 , he served as a papal judge @-@ delegate three times . In 1215 he was also King John 's candidate for the see of St David 's in Wales , but was not elected . 
- After Foliot 's failed candidacy as bishop , in February 1216 John appointed him to the benefice of <unk> in <unk> , the king having the ability to make the appointment because Giles de <unk> , the Bishop of Hereford , who would normally have made the appointment , had recently died . Also from this time comes Foliot 's patronage of Robert <unk> , the theologian and future Bishop of Lincoln . 
- 
- = = Bishop = = 
- 
- Foliot had been one of three members of the cathedral chapter from Hereford sent to King Henry III 's court to secure permission for the chapter to hold an election in 1219 Foliot was elected to the see of Hereford in June 1219 and consecrated on 27 October 1219 along with William de <unk> , the Bishop of Llandaff , at Canterbury . 
- The new bishop accompanied Peter des Roches , the Bishop of Winchester , on a pilgrimage in 1221 . Because des Roches travelled to Spain to the shrine of St James at Compostela , and it is known that Foliot accompanied him , the statement by a medieval chronicler from <unk> that Foliot 's destination was not certain , being either Rome or Compostela , should be discounted . 
- Foliot spent most of his tenure of office in his diocese , only rarely attending the royal court or being assigned governmental duties . On 30 December <unk> , Foliot assumed one of those duties , when he took custody of Hereford Castle after it was surrendered by Hubert de <unk> , during the <unk> of royal castles when de <unk> ousted des Roches from power . He also was appointed to determine the size of the royal forest in Gloucestershire . Foliot also founded a hospital in <unk> , devoted to St Katherine . He helped found <unk> Priory , a house of the <unk> order . In his cathedral , he reorganised the <unk> and offices of the chapter , as well as <unk> further <unk> . 
- Foliot died 7 August 1234 , after an illness that began in the spring . He was buried in Hereford Cathedral , where his tomb survives . Foliot appointed his younger brother Thomas to offices in the diocese , first as precentor in the <unk> and then around 1230 as treasurer of the cathedral chapter . 
- 
- 
- = AIL Storm = 
- 
- The AIL Storm ( Hebrew : <unk> , <unk> ) is an Israeli manufactured off @-@ road vehicle and the <unk> of the Israeli Security Forces . The series of Jeep Wrangler based vehicles have been produced by Automotive Industries Ltd. in Upper Nazareth under licence from Chrysler since 1990 . The vehicles fill a number of military roles , including that of armoured Infantry <unk> Vehicle , and certain models are available for export as well as for the civilian market . 
- Production of an updated four @-@ door second generation model commenced in 2006 despite some mixed messages from the Storm 's primary customer , the Israel Defense Forces . Development of a third generation vehicle based on the new Jeep Wrangler JK has been completed and significant production for both Israeli and foreign customers is under way . 
- 
- = = Storm I = = 
- 
- The M @-@ 240 Storm <unk> Vehicle is the first of three Storm generations . A variant of the 1991 Jeep Wrangler <unk> and the older <unk> @-@ 6 / <unk> @-@ 8 <unk> , it is entirely produced in Israel by Automotive Industries Ltd. with the exception of the engines , as their manufacture is not economically viable on the Storm 's market scale . 
- The Storm was primarily meant to satisfy Israeli military needs , but capable long and short versions are produced for the local civilian market . Like the Jeep , it has a conventional front @-@ engine design with a driver and passenger seated behind the engine , and room for cargo or passengers behind them . It is powered by an AMC 3 @.@ 983 litre 6 @-@ cylinder in @-@ line petrol with fuel injection developing 180 hp ( 130 kW ) at 4 @,@ 700 rpm , fitted with <unk> 2 @-@ stage air cleaner or a <unk> 2 @.@ 5 litre 4 @-@ cylinder <unk> diesel developing 88 <unk> ( 118 hp ) at 4 @,@ 200 rpm . The front axle is fully floating and the rear axle is semi @-@ floating , while a reinforced frame and body as well as good angles of approach and departure ( 40 ° and 37 ° for short frame , 40 ° and 26 @.@ 5 ° for long frame ) add to the Storm 's off @-@ <unk> capability . 
- The two production frame lengths , 4 @.@ 15 ( 13 @.@ 6 ) and 4 @.@ 5 metres ( 14 @.@ 8 ft ) , the latter of which was among the few such Jeep @-@ derivatives in production in recent years , were both available in civilian and military models . Aside from the Israeli market , Storms have long been exported to countries in South America , Asia , and Africa . A Jeep @-@ managed production line in Egypt , whose vehicles are used by the Egyptian armed forces , was absorbed into the AIL Storm production after it closed in 1995 . 
- 
- = = = Security versions = = = 
- 
- Like its parent Jeep Wrangler , the Storm is first and foremost an <unk> , capable <unk> and utility vehicle meant to tackle extreme terrain in a general reconnaissance role , and can be outfitted with a machine gun or other weapons systems . When armed with a 105 mm ( 4 @.@ 1 in ) <unk> recoilless rifle , the vehicle is uniquely capable of firing directly over its blast guard equipped hood rather than in the perpendicular position required by most other vehicles . 
- A variant of the extended version used in desert border patrol makes use of a high @-@ <unk> canopy to allow a <unk> rear @-@ facing heavy machine @-@ gun mount , while the canopy can be extended to provide a mobile command post . An air conditioned <unk> version of the extended model is often used by officers , and a version developed for riot control has clear <unk> shielding along the rear sides and roof , as well as gunports for less @-@ lethal weapons . The shielding allows for a wide field of view while at the same time protecting against <unk> and rock @-@ throwing . 
- 
- = = = Armoured version = = = 
- 
- As with several analogous light military vehicles , despite being originally designed to fill a light reconnaissance role , the rise of urban warfare and close quarters combat meant that the Israel Defense Forces had to recast the Storm in new roles . 
- When the need for a light armoured vehicle became apparent to the Israeli security forces , AIL 's engineering department designed a vehicle protection system from the bottom up , integrating it into the existing vehicle in a manner that did not compromise its off @-@ road and other capabilities , and that did not create the mechanical strain and increase in maintenance often associated with up @-@ <unk> , in part due to its computerized 180 horsepower ( 130 kW ) <unk> engine . 
- The armour protects against 7 @.@ 62 × <unk> ( 0 @.@ 3 in ) armour @-@ piercing ammunition , and maintains a high protection @-@ to @-@ weight and cost ratio by employing IDF approved advanced materials . The protected Israeli configuration 's gross vehicle weight is 3 @,@ 000 kilograms ( 6 @,@ 614 lb ) , though several varying protection levels are in use with individual units . 
- Another important asset are the Storm 's narrow dimensions , which allow it to traverse the narrow alleyways common to the <unk> of many Middle Eastern cities , places that armoured <unk> can only enter with great difficulty and minimal <unk> , if at all . Full @-@ height rear doors which allow for the quick deployment of fully equipped troops into combat are <unk> as another advantage over similar vehicles . 
- 
- = = = Civilian use = = = 
- 
- First generation Storms were made available to the general public in Israel from 1992 to 2001 . A small number were purchased directly by private consumers , while larger numbers were acquired second @-@ hand from Israeli government @-@ owned firms like the Israel Electric Company and <unk> water company , as well as National Parks Authority and Israel Police . <unk> Storms are popular with off @-@ <unk> enthusiasts in Israel . 
- 
- = = Storm II = = 
- 
- Beginning in 2006 , AIL began delivery of an improved model to the IDF , the M @-@ 242 Storm Mark II , known in the field as the " Storm Commander " . A number of significant changes have been incorporated into the new TJ @-@ based Storms stemming from soldiers ' feedback , updated operational requirements , and testing by <unk> Army Headquarters and <unk> , Medical , and the Centers Directorate . Perhaps the most obvious change is the addition of dual passenger doors , making the Storm II the first five @-@ door Jeep Wrangler derivative . 
- Other improvements include the change to a manual transmission with six forward speeds ( instead of the previous four ) , and increased stability resulting from wider track axles than its predecessor . Leaf springs were replaced with modern coil spring suspension front and rear , and the Storm II features rear Dana 44 axles and front TJ Dana 30s , factory designed slip <unk> <unk> , and the added safety of standard <unk> . Soldiers ' comfort was addressed as well with the addition of standard rear air conditioning and a compact disc player . 
- Storm II is also produced in an armoured version , and is offered with an optional 2 @.@ 8 litre <unk> <unk> <unk> , automatic transmission , right hand drive , and run @-@ flat tyres . AIL is capable of completing ten vehicles daily . Due to recently passed tax laws , a civilian version is not yet available in the local market . 
- 
- = = = MDT David controversy = = = 
- 
- Developed in the 2000s at an investment of US $ 2 million after IDF commitments for 1 @,@ 200 units , some AIL jobs were believed to be in jeopardy following a mid @-@ 2005 announcement that the IDF would purchase 100 US sold Land Rover Defender @-@ based MDT David . The announcement provoked threats of protests from AIL 's management and labourers , who had recently faced the blow of local <unk> assembly <unk> due to budget <unk> . The MDT David was chosen over the armoured version of the Storm because the heavy Storm was said to suffer from handling and reliability problems , safety <unk> and limited mission <unk> . However the IDF said that the purchase of the David was to fill a temporary gap in production until the Storm II 's testing was completed , and has since begun filling its commitment . 
- 
- = = Storm III = = 
- 
- A Storm Mark III was set to be produced for the Israeli defense forces starting in June 2008 , when the IDF was to purchase around 600 vehicles beginning in early 2011 . Based on the then new four @-@ door Jeep Wrangler JK design , the Mark III is meant to address some of the <unk> of the earlier Mark II . Whereas the previous vehicle was an update of the original TK Storm , the Storm III was designed from the outset with a five @-@ door configuration . Unlike the TJ @-@ L , the new JK Storm has a much higher maximum load capacity in part due to heavier @-@ duty shock <unk> , springs and axles , necessary for an armored version . It includes a standard <unk> <unk> <unk> <unk> and automatic transmission . 
- Like the Storm II , the Mark III was initially available only to the military with versions set to be delivered to the Israel Police in 2009 . A civilian version would only be released if the local tax code was modified to allow it to compete with foreign imports of the same class . AIL states that if such a thing would happen , a petrol engine version could be offered . Regarding <unk> markets , the Storm 3 has already seen use in several countries , especially in its armored version . 
- A pair of production <unk> 's was tested by Israeli web magazine <unk> journalists in April 2009 . It was dubbed " probably , the best Jeep ever " . 
- 
- = = = Commander version = = = 
- 
- The commander version incorporates a 5 @-@ door hard top cab allowing for the quick and convenient entrance and exit of the driver and all passengers or troops . A large rear compartment enables the storage of both cargo and communications equipment . 
- This version comes equipped with an air conditioning system providing maximum comfort in hot climatic conditions . A roll over protection structure ( <unk> ) <unk> safety conditions for passengers . 
- 
- = = = Armored version = = = 
- 
- The armored version of the Storm 3 , designed for protection against light weapon threats , incorporates a heavy duty transfer case and a specially designed suspension system which includes heavy duty springs ( front - coil , rear - leaf ) and shock <unk> , together with rigid heavy duty axles allowing for a smooth and safe ride on both rough terrain as well as regular highways . 
- 
- = = = Reconnaissance & Patrol version = = = 
- 
- The Storm 3 reconnaissance and patrol model allows for extra stowage of fuel , water and equipment . This version is especially suited to be fitted with various machine gun or special equipment mountings . 
- 
- 
- = 1940 Atlantic hurricane season = 
- 
- The 1940 Atlantic hurricane season was a generally average period of tropical cyclogenesis in 1940 . Though the season had no official bounds , most tropical cyclone activity occurred during August and September . Throughout the year , fourteen tropical cyclones formed , of which nine reached tropical storm intensity ; six were hurricanes . None of the hurricanes reached major hurricane intensity . Tropical cyclones that did not approach populated areas or shipping lanes , especially if they were relatively weak and of short duration , may have remained undetected . Because technologies such as satellite monitoring were not available until the 1960s , historical data on tropical cyclones from this period are often not reliable . As a result of a reanalysis project which analyzed the season in 2012 , an additional hurricane was added to HURDAT . The year 's first tropical storm formed on May 19 off the northern coast of Hispaniola . At the time , this was a rare occurrence , as only four other tropical disturbances were known to have formed prior during this period ; since then , reanalysis of previous seasons has concluded that there were more than four tropical cyclones in May before 1940 . The season 's final system was a tropical disturbance situated in the Greater Antilles , which dissipated on November 8 . 
- All three hurricanes in August brought flooding rainfall to areas of the United States . The first became the wettest tropical cyclone recorded in Louisiana history . The second hurricane impacted regions of the Southeastern United States , producing record precipitation and killing at least 52 people . Despite not making landfall , the third hurricane in August interacted with a stationary front over the Mid @-@ Atlantic states , resulting in localized flooding and thus making the tropical cyclone the wettest in New Jersey history . This hurricane would also be the strongest in the hurricane season , with maximum sustained winds of 110 mph ( 175 km / h ) and a minimum barometric pressure of 961 mbar ( hPa ; 28 @.@ 39 inHg ) , making it a high @-@ end Category 2 hurricane on the modern @-@ day Saffir – Simpson hurricane wind scale . <unk> decreased in September , though a damaging hurricane swept through areas of the Canadian <unk> , resulting in large crop and infrastructural losses . Two tropical cyclones of at least tropical storm strength were recorded in October , though neither resulted in fatalities . <unk> , storms in the hurricane season caused 71 fatalities and $ 29 @.@ <unk> million in damages . The 1940 South Carolina hurricane , which swept through areas of the Southeastern United States in August , was the most damaging and deadly of the tropical cyclones . 
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- = = Storms = = 
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- = = = Tropical Storm One = = = 
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- On May 18 , a weak low @-@ pressure area was detected south of Hispaniola . Moving northward , the low became sufficiently organized to be classified as a tropical storm at 1200 UTC on May 19 , southeast of Turks Island . At the time , ship observations indicated that the disturbance had a well @-@ defined cyclonic circulation , with the strongest winds situated in the northern <unk> of the cyclone . Continuing northward , the tropical storm gradually intensified and attained maximum sustained winds of 65 mph ( 100 km / h ) by 0000 UTC on May 22 . The Belgian ship <unk> <unk> recorded a peripheral barometric pressure of 996 mbar ( hPa ; 29 @.@ 42 inHg ) ; this was the lowest pressure measured in connection with the storm . The following day , the tropical storm temporarily curved towards the east @-@ southeast before <unk> back towards a northeast direction . At the same time , the storm expanded in size and began to transition into an extratropical cyclone . By 1200 UTC , the cyclone completed its extratropical transition , due to the <unk> of colder air . The remnant system persisted until 0600 UTC on May 27 . 
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- = = = Hurricane Two = = = 
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- On August 3 , an extratropical cyclone developed into a tropical depression off the west coast of Florida . Initially a weak disturbance , it moved generally westward , slowly gaining in intensity . Early on August 4 , the depression attained tropical storm intensity . Ships in the vicinity of the storm reported a much stronger tropical cyclone than initially suggested . After reaching hurricane strength on August 5 south of the Mississippi River Delta , the storm strengthened further into a modern @-@ day Category 2 hurricane , with maximum sustained winds of 100 mph and a minimum barometric pressure of 972 mbar ( hPa ; 28 @.@ 71 inHg ) at 0600 UTC on August 7 . The hurricane moved ashore near Sabine Pass , Texas later that day at peak strength . Once inland , the storm executed a sharp curve to the north and quickly weakened , degenerating into a tropical storm on August 8 before dissipating over Arkansas on August 10 . 
- Reports of a potentially destructive hurricane near the United States Gulf Coast forced thousands of residents in low @-@ lying areas to evacuate prior to the storm moving inland . Offshore , the hurricane generated rough seas and a strong storm surge , peaking at 6 @.@ 4 ft ( 1 @.@ 95 m ) on the western edge of Lake <unk> . The <unk> high tides flooded many of Louisiana 's outlying islands , <unk> resorts . Strong winds caused moderate infrastructural damage , primarily in Texas , though its impact was mainly to communication networks along the U.S. Gulf Coast which were disrupted by the winds . However , much of the property and crop damage wrought by the hurricane was due to the torrential rainfall it produced in low @-@ lying areas , setting off record floods . Rainfall peaked at 37 @.@ 5 in ( <unk> mm ) in Miller Island off Louisiana , making it the wettest tropical cyclone in state history . Nineteen official weather stations in both Texas and Louisiana observed record 24 @-@ hour rainfall totals for the month of August as a result of the slow @-@ moving hurricane . Property , livestock , and crops – especially cotton , corn , and <unk> crops – were heavily damaged . Entire ecosystems were also altered by the rainfall . Overall , the storm caused $ 10 @.@ 75 million in damages and seven fatalities . 
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- = = = Hurricane Three = = = 
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- A storm of potentially Cape Verde origin was detected in the Virgin Islands at 1800 UTC on August 5 . Initially moving westward , the tropical storm gradually gained in intensity before making a sharp curve towards the north on August 8 . The storm continued in a northerly motion before a second <unk> brought it in a generally westward direction on August 9 . Shortly after , the tropical storm reached hurricane intensity as a modern @-@ day Category 1 hurricane . The hurricane eventually made landfall at peak intensity on Hilton Head Island , South Carolina at <unk> UTC on September 21 . At the time , the storm had maximum sustained winds of 100 mph ( 155 km / h ) and a minimum central pressure of 972 mbar ( hPa ; 28 @.@ 71 inHg ) , equivalent to a modern @-@ day Category 2 hurricane . Once inland , the tropical cyclone gradually weakened , and recurved northeastward before dissipating over the Appalachian Mountains on August 14 . 
- The hurricane dropped torrential rainfall over the Southeast United States , causing unprecedented devastation in the region . The storm was considered the worst to impact in the region in at least 29 years . Precipitation peaked at 20 @.@ 65 in ( 525 mm ) in <unk> , North Carolina . The heavy rainfall caused streams to greatly exceed their respective flood stages , damaging waterfront property . Many of the deaths occurred in North Carolina , where 30 people died . Transportation was disrupted as a result of the debris scattered by the wind and rain . In Caldwell County alone , 90 percent of bridges were swept away . Overall , the storm caused 50 fatalities and $ 13 million in damages . 
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- = = = Hurricane Four = = = 
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- On August 26 , a low @-@ pressure area in the open Atlantic Ocean became sufficiently organized to be classified as a tropical cyclone . Moving slowly in a general west @-@ northwest motion , the disturbance intensified , reaching tropical storm strength on August 28 and subsequently hurricane intensity on August 30 . The hurricane passed within 85 mi ( 135 km ) of Cape Hatteras before <unk> towards the northeast . However , the hurricane continued to intensify , and reached peak intensity as a modern @-@ day Category 2 hurricane with maximum sustained winds of 110 mph ( 175 km / h ) and a minimum barometric pressure of 961 mbar ( hPa ; 28 @.@ 38 inHg ) , though these statistical peaks were achieved at different times on September 2 . Afterwards , the tropical cyclone began a weakening trend as it proceeded northward , and had degenerated into a tropical storm by the time it made its first landfall on Nova Scotia later that day . The storm transitioned into an extratropical cyclone the next day while making another landfall on New Brunswick . The extratropical remnants persisted into Quebec before merging with a larger extratropical system late on September 3 . 
- Despite not making landfall on the United States , the hurricane caused widespread damage . Extensive <unk> measures were undertaken across the coast , particularly in New England . The heightened precautions were due in part to fears that effects from the storm would be similar to that of a devastating hurricane which struck the region two years prior . Most of the damage associated with the hurricane occurred in New Jersey , where the combination of moisture from the hurricane and a stationary front produced record rainfall , peaking at 24 in ( 610 mm ) in the town of Ewan . This would make the storm the wettest in state history . The resultant floods damaged infrastructure , mostly to road networks . Damage in the state amounted to $ 4 million . Further north in New England , strong winds were reported , though damage remained minimal . Although the storm made two <unk> in Atlantic Canada , damage too was minimal , and was limited to several boating incidents caused by strong waves . Overall , the hurricane caused $ 4 @.@ 05 million in damage , primarily due to flooding in New Jersey , and seven fatalities . 
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- = = = Hurricane Five = = = 
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- A tropical depression was first detected east of the Lesser Antilles on September 7 , though at the time weather observations in the area were sparse . The disturbance gradually intensified throughout much of its early formative stages , attaining tropical storm strength on September 10 ; further strengthening into a hurricane north of Puerto Rico occurred two days later . Shortly thereafter , the hurricane recurved northward , and reached peak intensity the following day as a modern @-@ day Category 2 hurricane with maximum sustained winds of 100 mph ( 160 km / h ) and a minimum barometric pressure of at least <unk> mbar ( hPa ; 29 @.@ 18 inHg ) . The cyclone steadily weakened thereafter before making landfall on Nova Scotia on September 17 with winds of 85 mph ( 135 km / h ) . Moving into the Gulf of Saint Lawrence later that day , the storm transitioned into an extratropical cyclone . The remnant system curved eastward and passed over Newfoundland before dissipating over the Atlantic on September 19 . 
- While off of the United States East Coast , the hurricane caused numerous shipping incidents , most notably the stranding of the Swedish freighter <unk> off of Cape Hatteras , North Carolina on September 16 . Two other boat incidents resulted in two deaths . The hurricane also brought strong winds of tropical storm @-@ force and snow over areas of New England . In Atlantic Canada , a strong storm surge peaking at 4 ft ( 1 @.@ 3 m ) above average sunk or damaged several ships and inundated cities . In New Brunswick , the waves hurt the lobster fishing industry . In Nova Scotia , strong winds disrupted telecommunication and power services . The winds also severely damaged crops . Roughly half of <unk> production in Annapolis Valley was lost during the storm , resulting in around $ 1 @.@ 49 million in economic losses . Strong winds in New Brunswick caused moderate to severe infrastructural damage , and additional damages to crops occurred there . Overall , the hurricane caused three fatalities , with two off of the United States and one in New Brunswick . 
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- = = = Tropical Storm Six = = = 
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- A westward moving tropical depression developed in the southwestern Caribbean Sea just west of <unk> , Nicaragua at 1200 UTC on September 18 . The following day , the depression intensified into a tropical storm at 0600 UTC . The tropical storm made landfall on the Mosquito Coast of Nicaragua at 1400 UTC , with winds of 40 mph ( 65 km / h ) . The cyclone weakened to a tropical depression over land , but <unk> back to tropical storm strength upon entry into the Gulf of Honduras on September 20 . The cyclone 's northwest motion caused it to make a second landfall near the border of Mexico and British Honduras at 0300 UTC on September 21 as a slightly stronger system with winds of 50 mph ( 85 km / h ) ; this would be the storm 's peak intensity . Over the Yucatán Peninsula , the tropical storm re @-@ weakened , but later intensified once again once it reached the Gulf of Mexico . In the Gulf , the storm made a gradual curve northward , before making a final landfall near Lafayette , Louisiana at <unk> UTC on September 24 with winds of 45 mph ( 75 km / h ) and a minimum barometric pressure of 1002 mbar ( hPa ; 29 @.@ 59 inHg ) . Once inland , the tropical cyclone curved eastward and weakened before dissipating the next day , after becoming absorbed by a frontal boundary . 
- Upon making landfall , the tropical storm produced strong winds over a wide area . The strongest winds were reported by a station in San Antonio , Texas , which reported 40 mph ( 65 km / h ) winds , far removed from the storm 's center ; these strong winds were likely due to squalls . Heavy rainfall was also reported , though the rains mainly occurred to the east of the passing tropical cyclone . Precipitation peaked at 10 in ( 254 mm ) in <unk> Platte , Louisiana . The tropical storm produced three tornadoes over the Southern United States which <unk> caused $ 39 @,@ 000 in damage and caused two fatalities . Two of the tornadoes formed in Mississippi while one formed in Louisiana . Several other people were also injured by the tornadoes . 
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- = = = Hurricane Seven = = = 
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- In late September , a tropical wave persisted in the northeastern Atlantic Ocean . The low @-@ pressure area later became sufficiently organized to be classified as a tropical storm at 0000 UTC on September 22 . The disturbance quickly organized after tropical cyclogenesis , and reached a strength equivalent to a modern @-@ day Category 1 hurricane strength at 1800 UTC later that day . The American steamship <unk> encountered the system that day , and reported gale force winds in conjunction with a peripheral barometric pressure of 996 mbar ( hPa ; 29 @.@ 42 inHg ) . The tropical cyclone continued to the east @-@ northeast , where it gradually intensified . At 1200 UTC on September 23 , the hurricane attained modern @-@ day Category 2 hurricane intensity with winds of 100 mbar ( 155 km / h ) ; a peak which would be maintained for at least the following 12 hours . A second steamship , the <unk> , reported hurricane @-@ force winds along with a minimum pressure of 977 mbar ( hPa ; 28 @.@ 85 inHg ) ; this would be the lowest pressure measured associated with the tropical cyclone . After reaching peak intensity , the hurricane began a weakening trend , and degenerated to a Category 1 hurricane at 0600 UTC as it passed over the Azores . The following day , the hurricane recurved westward , where it weakened before transitioning into an extratropical cyclone on September 28 . This remnant system subsequently dissipated . 
- As the hurricane passed over the Azores , several weather stations reported low barometric pressures , with the lowest being a measurement of <unk> mbar ( hPa ; 29 @.@ 06 inHg ) on <unk> Island at 0600 UTC on September 25 . As a result of the impending storm , several Pan Am <unk> flights to the archipelago were suspended for three consecutive days . The maximum reported gust in the Azores was an observation of 65 mph ( 100 km / h ) on September 25 . As a result of moving slowly over the islands , torrential rainfall was also reported . At <unk> do <unk> , 13 @.@ 11 in ( 333 mm ) of precipitation was reported in a four @-@ day , accounting for a third of the station 's yearly average rainfall . Strong storm surge was reported at the same location . The waves swept boats away from the coasts of islands . Further inland , there was extensive damage to homes and crops , though no people died . Despite evidence that the system had distinct tropical characteristics , it was not operationally added to HURDAT . 
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- = = = Hurricane Eight = = = 
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- On October 19 , a low @-@ pressure area moved into the southwestern Caribbean Sea . The area of disturbed weather quickly became well @-@ organized , and was analyzed to have become a tropical depression at 0000 UTC on October 20 . Initially , the tropical cyclone moved very slowly towards the west and then the northwest . Shortly after formation , the disturbance intensified into a tropical storm at 1800 UTC later that day . The S.S. <unk> provided the first indications of a tropical cyclone in the region , after reporting strong gusts and low pressures north of the Panama Canal Zone during that evening . Continuing to intensify , the storm reached hurricane intensity at 0600 UTC on October 22 . Several vessels in the storm 's vicinity reported strong gusts and rough seas generated by the storm . Later that day at 1200 UTC , the ship S.S. <unk> reported a minimum pressure of 983 mbar ( hPa ; 29 @.@ 03 inHg ) near the periphery of the storm . Based on this observation , the hurricane was estimated to have reached intensity at the same time with winds of 80 mph ( 130 km / h ) . The hurricane subsequently curved west and then southwest , before making its only landfall in northern Nicaragua at 1900 UTC on October 23 at peak intensity . Once inland , the tropical cyclone rapidly weakened over mountainous terrain , and dissipated at 1200 UTC the following day . Reports of damage were limited , though a report stated that considerable damage had occurred where the hurricane made landfall . 
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- = = = Tropical Storm Nine = = = 
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- On October 23 , an open trough was centered north of Hispaniola near the Turks and <unk> islands . At 0000 UTC the following day , the area of disturbed weather became organized and was analyzed to have become a tropical storm southeast of <unk> , based on nearby vessel reports . Initially , the storm drifted northward , but later began to accelerate towards the northeast after a roughly 12 @-@ hour period . At 0600 UTC on September 25 , the disturbance slightly gained in intensity to attain maximum wind speeds of 45 mph ( 75 km / h ) ; these would be the strongest winds associated with the storm as a fully tropical cyclone . A reanalysis of the system indicated that due to a lack of definite tropical features , the storm may have had been a subtropical cyclone . On October 26 , the system became increasingly asymmetric and had developed frontal boundaries , allowing for it to be classified as an extratropical cyclone at 0600 UTC that day . Once transitioning into an extratropical system , the storm continued to intensified as it moved northward . On October 27 , the system was analyzed to have a minimum pressure of at least 985 mbar ( hPa ; 29 @.@ 09 inHg ) after passing to the southeast of Bermuda . At 1200 UTC later that day , the cyclone reached an extratropical peak intensity with winds of 80 mph ( 130 km / h ) just east of Newfoundland . Had the storm been tropical at the time , it would have been classified as a modern @-@ day Category 1 hurricane . Subsequently , the extratropical storm curved eastward , where it persisted before dissipating by 1800 UTC on September 29 . 
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- = = = Tropical depressions = = = 
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- In addition to the storms which attained at least tropical storm strength in 1940 , five additional tropical depressions were analyzed by the HURDAT reanalysis project to have developed during the season . Due to their weak intensity , however , they were not added to HURDAT . On September 2 , a closed low @-@ pressure area was detected in the open Atlantic Ocean southeast of Bermuda and was analyzed as a tropical depression . At the time , the disturbance had a minimum pressure of at least 1015 mbar ( hPa ; 29 @.@ 98 inHg ) . The depression initially moved to the southeast , but later recurved towards the northwest over the next two days . On September 4 , the S.S. West <unk> en route for Boston , Massachusetts reported winds of 40 mph ( 65 km / h ) , which would be considered as tropical storm @-@ force winds . The depression later moved to the northeast before it was absorbed by a stationary front on September 7 . Since there was only one report that the disturbance may have reached tropical storm intensity , it was not included in HURDAT . Later on September 10 , a trough was detected in a similar region in the Atlantic where the first depression formed . The trough later became sufficiently organized to be classified as a tropical depression . The cyclone moved slowly to the east and did not further intensify before dissipating on September 13 . 
- On October 7 , a large elongated extratropical cyclone extended across the Atlantic Ocean with a pressure of at most 1015 mbar ( hPa ; 29 @.@ 98 inHg ) . The following day , the low @-@ pressure area became more narrow and well @-@ defined , with its central pressure deepening to 1000 mph ( hPa ; 29 @.@ 53 inHg ) . On October 9 , the extratropical system was analyzed to have become a tropical depression . The low moved slowly to the northeast and gradually weakened before dissipating on October 10 . On October 14 , offshore observations indicated that a tropical depression had developed north of The Bahamas . The following day , however , the depression became less defined and degenerated into a trough of low pressure . On October 16 , two ships listed in the International <unk> Ocean @-@ <unk> Data Set reported winds of 40 mph ( 65 km / h ) off the coast of North Carolina . However , since these reports occurred in a higher pressure gradient , the system was not included in HURDAT . 
- On November 2 , a trough of low @-@ pressure was analyzed near the Lesser Antilles . The system moved westward into the Caribbean Sea without much organization . On November 7 , the low @-@ pressure area moved south of Cuba and became sufficiently organized to be considered a tropical depression with a pressure of at least <unk> mbar ( hPa ; 29 @.@ 83 inHg ) . The depression moved over Cuba and into the Atlantic , where it dissipated the following day . On November 9 , a second system was detected northeast of Bermuda with a pressure of 1005 mbar ( hPa ; 29 @.@ 68 inHg ) , though it remained unclear whether the two systems were related . 
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- = = Season effects = = 
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- = Ode to a Nightingale = 
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- " Ode to a Nightingale " is a poem by John Keats written either in the garden of the Spaniards Inn , Hampstead , London or , according to Keats ' friend Charles <unk> Brown , under a plum tree in the garden of Keats ' house at Wentworth Place , also in Hampstead . According to Brown , a nightingale had built its nest near the house Keats and Brown shared in the spring of 1819 . Inspired by the bird 's song , Keats composed the poem in one day . It soon became one of his 1819 odes and was first published in Annals of the Fine Arts the following July . 
- " Ode to a Nightingale " is a personal poem that describes Keats 's journey into the state of negative capability . The tone of the poem rejects the optimistic pursuit of pleasure found within Keats 's earlier poems and , rather , explores the themes of nature , <unk> and mortality , the latter being particularly personal to Keats . 
- The nightingale described within the poem experiences a type of death but does not actually die . Instead , the <unk> is capable of living through its song , which is a fate that humans cannot expect . The poem ends with an acceptance that pleasure cannot last and that death is an inevitable part of life . In the poem , Keats imagines the loss of the physical world and sees himself dead — as a " sod " over which the nightingale sings . The contrast between the immortal nightingale and mortal man sitting in his garden , is made all the more acute by an effort of the imagination . The presence of weather is noticeable in the poem , as spring came early in 1819 , bringing <unk> all over the heath . 
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- = = Background = = 
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- Of Keats 's six major odes of 1819 , " Ode to Psyche " , was probably written first and " To Autumn " written last . <unk> between these two , he wrote " Ode to a Nightingale " . It is possible that " Ode to a Nightingale " was written between 26 April and 18 May 1819 , based on weather conditions and similarities between images in the poem and those in a letter sent to Fanny Keats on May Day . The poem was composed at the Hampstead house Keats shared with Brown , possibly while sitting beneath a plum tree in the garden . According to Keats ' friend Brown , Keats finished the ode in just one morning : " In the spring of 1819 a nightingale had built her nest near my house . Keats felt a tranquil and continual joy in her song ; and one morning he took his chair from the breakfast @-@ table to the grass @-@ plot under a plum @-@ tree , where he sat for two or three hours . When he came into the house , I perceived he had some scraps of paper in his hand , and these he was quietly <unk> behind the books . On inquiry , I found those scraps , four or five in number , contained his poetic feelings on the song of the nightingale . " Brown 's account is personal , as he claimed the poem was directly influenced by his house and preserved by his own doing . However , Keats relied on both his own imagination and other literature as sources for his depiction of the nightingale . 
- The exact date of " Ode to a Nightingale " , as well as " Ode on Indolence " , " Ode on Melancholy " , and " Ode on a Grecian Urn " , is unknown , as Keats dated all as ' May 1819 ' . However , he worked on the four poems together , and there is a unity in both their stanza forms and their themes . The exact order the poems were written in is also unknown , but they form a sequence within their structures . While Keats was writing " Ode on a Grecian Urn " and the other poems , Brown transcribed copies of the poems and submitted them to Richard Woodhouse . During this time , Benjamin <unk> , Keats ' friend , was given a copy of " Ode to a Nightingale " , and he shared the poem with the editor of the Annals of the Fine Arts , James <unk> . <unk> paid Keats a small sum of money , and the poem was published in the July issue . The poem was later included in Keats ' 1820 collection of poems , <unk> , Isabella , The Eve of St Agnes , and Other written by <unk> Kumar 
- Poems . 
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- = = Structure = = 
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- " Ode to a Nightingale " was probably the first of the middle set of four odes that Keats wrote following " Ode to Psyche " , according to Brown . There is further evidence of this in the structure of the poems because Keats combines two different types of lyrical poetry in an experimental way : the <unk> hymn and the lyric of questioning voice that responds to the <unk> hymn . This combination of structures is similar to that in " Ode on a Grecian Urn " . In both poems the dual form creates a sort of dramatic element within the poem . The stanza forms of the poem is a combination of elements from <unk> <unk> and Shakespearean <unk> . 
- When it came to vowel forms , Keats incorporated a pattern of alternating historically " short " and " long " vowel sounds in his ode . In particular , line 18 ( " And purple @-@ stained mouth " ) has the historical pattern of " short " followed by " long " followed by " short " and followed by " long " . This alteration is continued in longer lines , including line 31 ( " Away ! away ! for I will fly to thee " ) which contains five pairs of <unk> . However , other lines , such as line 3 ( " Or emptied some dull <unk> to the drains " ) rely on a pattern of five " short " vowels followed by " long " and " short " vowel <unk> until they end with a " long " vowel . These are not the only combination patterns present , and there are patterns of two " short " vowels followed by a " long " vowel in other lines , including 12 , 22 , and 59 , which are repeated twice and then followed up with two sets of " short " and then " long " vowel pairs . This reliance on vowel sounds is not unique to this ode , but is common to Keats 's other 1819 odes and his Eve of St. Agnes . 
- The poem incorporates a complex reliance on assonance — the repetition of vowel sounds — in a conscious pattern , as found in many of his poems . Such a reliance on assonance is found in very few English poems . Within " Ode to a Nightingale " , an example of this pattern can be found in line 35 ( " Already with thee ! tender is the night " ) , where the " <unk> " of " Already " connects with the " e " of " tender " and the " i " of " with " connects with the " i " of " is " . This same pattern is found again in line 41 ( " I cannot see what flowers are at my feet " ) with the " a " of " cannot " linking with the " a " of " at " and the " ee " of " see " linking with the " ee " of " feet " . This system of assonance can be found in approximately a tenth of the lines of Keats 's later poetry . 
- When it came to other sound patterns , Keats relied on double or triple <unk> in approximately 6 % of lines throughout the 1819 odes . An example from " Ode to a Nightingale " can be found within line 45 ( " The grass , the thicket , and the fruit @-@ tree wild " ) as the <unk> after the commas are a " masculine " pause . Furthermore , Keats began to reduce the amount of Latin @-@ based words and <unk> that he relied on in his poetry , which in turn shortened the length of the words that dominate the poem . There is also an emphasis on words beginning with consonants , especially those that begin with " b " , " p " or " v " . These three consonants are relied on heavily in the first stanza , and they are used <unk> to add a musical tone within the poem . 
- In terms of poetic meter , Keats relies on <unk> throughout his 1819 odes and in just over 8 % of his lines within " Ode to a Nightingale " , including line 12 : 
- and line 25 : 
- To Walter Jackson Bate , the use of spondees in lines 31 – 34 creates a feeling of slow flight , and " in the final stanza . . . the distinctive use of scattered spondees , together with initial <unk> , lend [ s ] an approximate phonetic suggestion of the peculiar spring and bounce of the bird in its flight . " 
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- = = Poem = = 
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- My heart <unk> , and a <unk> <unk> pains 
- My sense , as though of hemlock I had drunk , 
- Or emptied some dull <unk> to the drains 
- One minute past , and <unk> @-@ wards had sunk : 
- <unk> not through envy of thy happy lot , 5 
- But being too happy in <unk> happiness , 
- That thou , light @-@ <unk> <unk> of the trees , 
- In some <unk> plot 
- Of <unk> green , and shadows <unk> , 
- <unk> of summer in full @-@ throated ease . 10 
- O for a draught of vintage ! that hath been 
- Cool 'd a long age in the deep @-@ <unk> earth , 
- <unk> of Flora and the country @-@ green , 
- Dance , and <unk> song , and <unk> <unk> ! 
- O for a <unk> full of the warm South ! 15 
- Full of the true , the <unk> <unk> , 
- With <unk> bubbles <unk> at the <unk> , 
- And purple @-@ <unk> mouth ; 
- That I might drink , and leave the world unseen , 
- And with thee fade away into the forest dim : 20 
- <unk> far away , dissolve , and quite forget 
- What thou among the leaves <unk> never known , 
- The weariness , the fever , and the fret 
- Here , where men sit and hear each other <unk> ; 
- Where <unk> shakes a few , sad , last grey hairs , 25 
- Where youth grows pale , and <unk> @-@ thin , and dies ; 
- Where but to think is to be full of <unk> 
- And <unk> @-@ eyed <unk> ; 
- Where beauty cannot keep her lustrous eyes , 
- Or new Love pine at them beyond to @-@ <unk> . 30 
- Away ! away ! for I will fly to thee , 
- Not <unk> by <unk> and his <unk> , 
- But on the <unk> wings of Poesy , 
- Though the dull brain <unk> and <unk> : 
- Already with thee ! tender is the night , 35 
- And <unk> the Queen @-@ Moon is on her throne , 
- <unk> 'd around by all her <unk> <unk> 
- But here there is no light , 
- Save what from heaven is with the breezes blown 
- Through <unk> <unk> and winding <unk> ways . 40 
- I cannot see what flowers are at my feet , 
- Nor what soft incense hangs upon the <unk> , 
- But , in <unk> darkness , guess each sweet 
- <unk> the <unk> month <unk> 
- The grass , the thicket , and the fruit @-@ tree wild ; 45 
- White hawthorn , and the pastoral <unk> ; 
- Fast @-@ fading <unk> cover 'd up in leaves ; 
- And mid @-@ May 's eldest child , 
- The coming <unk> @-@ rose , full of <unk> wine , 
- The <unk> haunt of flies on summer <unk> . 50 
- <unk> I listen ; and , for many a time 
- I have been half in love with <unk> Death , 
- Call 'd him soft names in many a <unk> rhyme , 
- To take into the air my quiet breath ; 
- Now more than ever seems it rich to die , 55 
- To cease upon the midnight with no pain , 
- While thou art pouring forth thy soul abroad 
- In such an ecstasy ! 
- Still <unk> thou sing , and I have ears in vain — 
- To thy high <unk> become a sod . 60 
- <unk> <unk> not born for death , immortal Bird ! 
- No hungry generations <unk> thee down ; 
- The voice I hear this passing night was heard 
- In ancient days by emperor and clown : 
- Perhaps the self @-@ same song that found a path 65 
- Through the sad heart of Ruth , when , sick for home , 
- She stood in tears amid the alien corn ; 
- The same that <unk> hath 
- <unk> 'd magic <unk> , opening on the <unk> 
- Of perilous seas , in <unk> lands forlorn . 70 
- <unk> ! the very word is like a bell 
- To toll me back from thee to my sole self ! 
- <unk> ! the fancy cannot <unk> so well 
- As she is famed to do , <unk> elf . 
- <unk> ! <unk> ! thy plaintive anthem fades 75 
- Past the near meadows , over the still stream , 
- Up the hill @-@ side ; and now ' tis buried deep 
- In the next valley @-@ <unk> : 
- Was it a vision , or a waking dream ? 
- <unk> is that music : — do I wake or sleep ? 80 
- 
- = = Themes = = 
- 
- " Ode to a Nightingale " describes a series of conflicts between reality and the Romantic ideal of uniting with nature . In the words of Richard Fogle , " The principal stress of the poem is a struggle between ideal and actual : inclusive terms which , however , contain more particular <unk> of pleasure and pain , of imagination and common sense reason , of <unk> and <unk> , of permanence and change , of nature and the human , of art and life , freedom and bondage , waking and dream . " Of course , the nightingale 's song is the dominant image and dominant " voice " within the ode . The nightingale is also the object of empathy and praise within the poem . However , the nightingale and the discussion of the nightingale is not simply about the bird or the song , but about human experience in general . This is not to say that the song is a simple metaphor , but it is a complex image that is formed through the interaction of the conflicting voices of praise and questioning . On this theme , David Perkins <unk> the way " Ode to a Nightingale " and " Ode on a Grecian Urn " perform this when he says , " we are dealing with a talent , indeed an entire approach to poetry , in which symbol , however necessary , may possibly not satisfy as the principal concern of poetry , any more than it could with Shakespeare , but is rather an element in the poetry and drama of human reactions " . However , there is a difference between an urn and a nightingale in that the nightingale is not an eternal entity . Furthermore , in creating any aspect of the nightingale immortal during the poem the narrator separates any union that he can have with the nightingale . 
- The nightingale 's song within the poem is connected to the art of music in a way that the urn in " Ode on a Grecian Urn " is connected to the art of sculpture . As such , the nightingale would represent an <unk> presence and , unlike the urn , is directly connected to nature . As natural music , the song is for beauty and lacks a message of truth . Keats follows Coleridge 's belief , as found in " The Nightingale " , in separating from the world by losing himself in the bird 's song . Although Keats favours a female nightingale over Coleridge 's masculine bird , both reject the traditional depiction of the nightingale as related to the tragedy of <unk> . Their <unk> is a happy nightingale that lacks the melancholic feel of previous poetic depictions . The bird is only a voice within the poem , but it is a voice that <unk> the narrator to join with in and forget the <unk> of the world . However , there is tension in that the narrator holds Keats 's guilt regarding the death of Tom Keats , his brother . The song 's conclusion represents the result of trying to escape into the realm of fancy . 
- Like Percy <unk> Shelley ’ s " To a <unk> " , Keats ’ s narrator listens to a bird song , but listening to the song within “ Ode to a Nightingale ” is almost painful and similar to death . The narrator seeks to be with the nightingale and abandons his sense of vision in order to embrace the sound in an attempt to share in the darkness with the bird . As the poem ends , the trance caused by the nightingale is broken and the narrator is left wondering if it was a real vision or just a dream . The poem reliance on the process of sleeping common to Keats 's poems , and " Ode to a Nightingale " shares many of the same themes as Keats 's Sleep and Poetry and Eve of St. Agnes . This further separates the image of the nightingale 's song from its closest comparative image , the urn as represented in " Ode on a Grecian Urn " . The nightingale is distant and mysterious , and even disappears at the end of the poem . The dream image emphasizes the <unk> and <unk> of the poem . These elements make it impossible for there to be a complete self @-@ identification with the nightingale , but it also allows for self @-@ awareness to <unk> throughout the poem , albeit in an altered state . 
- Midway through the poem , there is a split between the two actions of the poem : the first attempts to identify with the nightingale and its song , and the second discusses the convergence of the past with the future while experiencing the present . This second theme is reminiscent of Keats 's view of human progression through the Mansion of Many Apartments and how man develops from experiencing and wanting only pleasure to understanding truth as a mixture of both pleasure and pain . The <unk> fields and the nightingale 's song in the first half of the poem represent the <unk> moments that overwhelm the individual like a drug . However , the experience does not last forever , and the body is left <unk> it until the narrator feels helpless without the pleasure . Instead of embracing the coming truth , the narrator clings to poetry to hide from the loss of pleasure . Poetry does not bring about the pleasure that the narrator original asks for , but it does liberate him from his desire for only pleasure . 
- Responding to this emphasis on pleasure , Albert Guerard , Jr. argues that the poem contains a " longing not for art but a free <unk> of any kind . The form of the poem is that of progression by association , so that the movement of feeling is at the mercy of words evoked by chance , such words as fade and forlorn , the very words that , like a bell , toll the <unk> back to his sole self . " However , Fogle points out that the terms Guerard emphasizes are " <unk> translations " and that Guerard <unk> Keats 's aesthetic . After all , the acceptance of the loss of pleasure by the end of the poem is an acceptance of life and , in turn , of death . Death was a constant theme that permeated aspects of Keats poetry because he was exposed to death of his family members throughout his life . Within the poem , there are many images of death . The nightingale experiences a sort of death and even the god Apollo experiences death , but his death reveals his own divine state . As Perkins explains , " But , of course , the nightingale is not thought to be literally dying . The point is that the deity or the nightingale can sing without dying . But , as the ode makes clear , man cannot — or at least not in a visionary way . " 
- With this theme of a loss of pleasure and inevitable death , the poem , according to Claude <unk> , describes " the <unk> of the romantic escape from the world of reality to the world of ideal beauty " . Earl <unk> essentially agrees with <unk> , but he extended his <unk> of the poem to incorporate the themes of Keats 's Mansion of Many Apartments when he says , " the core of the poem is the search for the mystery , the unsuccessful quest for light within its darkness " and this " leads only to an increasing darkness , or a growing recognition of how impenetrable the mystery is to mortals . " With these views in mind , the poem recalls Keats 's earlier view of pleasure and an optimistic view of poetry found within his earlier poems , especially Sleep and Poetry , and rejects them . This loss of pleasure and incorporation of death imagery lends the poem a dark air , which connects " Ode to a Nightingale " with Keats ' other poems that discuss the demonic nature of poetic imagination , including <unk> . In the poem , Keats imagines the loss of the physical world and sees himself dead — he uses an abrupt , almost brutal word for it — as a " sod " over which the nightingale sings . The contrast between the immortal nightingale and mortal man , sitting in his garden , is made all the more acute by an effort of the imagination . 
- 
- = = Keats 's reception = = 
- 
- Contemporary critics of Keats enjoyed the poem , and it was heavily quoted in their reviews . An anonymous review of Keats 's poetry that ran in the August and October 1820 Scots Magazine stated : " Amongst the minor poems we prefer the ' Ode to the Nightingale . ' Indeed , we are inclined to prefer it beyond every other poem in the book ; but let the reader judge . The third and seventh stanzas have a charm for us which we should find it difficult to explain . We have read this ode over and over again , and every time with increased delight . " At the same time , Leigh Hunt wrote a review of Keats 's poem for the 2 August and 9 August 1820 The <unk> : " As a specimen of the Poems , which are all lyrical , we must indulge ourselves in quoting entire the ' Ode to a Nightingale ' . There is that mixture in it of real melancholy and imaginative relief , which poetry alone presents us in her ' charmed cup , ' and which some over @-@ rational critics have undertaken to find wrong because it is not true . It does not follow that what is not true to them , is not true to others . If the relief is real , the mixture is good and <unk> . " 
- John Scott , in an anonymous review for the September 1820 edition of The London Magazine , argued for the greatness of Keats 's poetry as exemplified by poems including " Ode to a Nightingale " : 
- The injustice which has been done to our author 's works , in estimating their poetical merit , rendered us <unk> anxious , on opening his last volume , to find it likely to seize fast hold of general sympathy , and thus turn an overwhelming power against the <unk> <unk> of talent , more eminently promising in many respects , than any the present age has been called upon to encourage . We have not found it to be quite all that we wished in this <unk> it would have been very extraordinary if we had , for our wishes went far beyond reasonable expectations . But we have found it of a nature to present to common <unk> the poetical power with which the author 's mind is gifted , in a more <unk> and <unk> shape than that in which it has appeared in any of his former compositions . It is , therefore , calculated to throw shame on the lying , vulgar spirit , in which this young <unk> in the temple of the <unk> has been cried @-@ down ; whatever questions may still leave to be settled as to the kind and degree of his poetical merits . Take for instance , as proof of the justice of our praise , the following passage from an Ode to the Nightingale : <unk> is distinct , noble , pathetic , and true : the thoughts have all chords of direct communication with naturally @-@ constituted hearts : the echoes of the strain linger bout the depths of human <unk> . 
- In a review for the 21 January 1835 London Journal , Hunt claimed that while Keats wrote the poem , " The poet had then his mortal illness upon him , and knew it . Never was the voice of death <unk> . " David <unk> , in 1851 , used The Even of St Agnes to claim , " We have here a specimen of descriptive power <unk> rich and original ; but the following lines , from the ' Ode to a Nightingale , ' flow from a far more profound fountain of inspiration . " 
- At the end of the 19th century , Robert Bridges 's analysis of the poem became a dominant view and would influence later interpretations of the poem . Bridges , in 1895 , declared that the poem was the best of Keats 's odes but he thought that the poem contained too much artificial language . In particular , he emphasised the use of the word " forlorn " and the last stanza as being examples of Keats 's artificial language . In " Two odes of Keats 's " ( 1897 ) , William C Wilkinson suggested that " Ode to a Nightingale " is deeply flawed because it contains too many " incoherent <unk> " that failed to supply a standard of logic that would allow the reader to understand the relationship between the poet and the bird . However , Herbert <unk> , arguing in 1928 , believed Nightingale to be superior to " Ode on a Grecian Urn " , " Ode on Melancholy " , and " Ode to Psyche " , arguing the exact opposite of Wilkinson as he stated that " Nightingale " , along with " To Autumn " , showed a greater amount of logical thought and more <unk> presented the cases they were intended to make . 
- 
- = = = 20th @-@ century criticism = = = 
- 
- At the beginning of the 20th century , <unk> Kipling referred to lines 69 and 70 , alongside three lines from Samuel Taylor Coleridge 's Kubla Khan , when he claimed of poetry : " In all the millions permitted there are no more than five — five little lines — of which one can say , ' These are the magic . These are the vision . The rest is only Poetry . ' " In 1906 , Alexander <unk> argued : " The nightingale and the <unk> for long <unk> poetic <unk> privilege they enjoyed solely on account of their pre @-@ <unk> as song birds . Keats 's Ode to a Nightingale and Shelley 's Ode to a <unk> are two of the <unk> of English literature ; but both were written by men who had no claim to special or exact knowledge of <unk> as such . " Sidney <unk> , in 1920 , argued , " Throughout this ode Keats ’ s genius is at its height . <unk> cannot be more rich and satisfying , <unk> of phrase and <unk> cannot be more absolute , than in the several contrasted stanzas calling for the draft of southern vintage [ … ] To praise the art of a passage like that in the fourth stanza [ … ] to praise or comment on a stroke of art like this is to throw doubt on the reader ’ s power to perceive it for himself . " 
- Bridge 's view of " Ode to a Nightingale " was taken up by H. W. Garrod in his 1926 analysis of Keats 's poems . Like Albert Gerard would argue later in 1944 , Garrod believed that the problem within Keats 's poem was his emphasis on the rhythm and the language instead of the main ideas of the poem . When describing the fourth stanza of the poem , Maurice Ridley , in 1933 , claimed , " And so comes the stanza , with that remarkable piece of imagination at the end which feels the light as blown by the breezes , one of those characteristic sudden flashes with which Keats fires the most ordinary material . " He later declared of the seventh stanza : " And now for the great stanza in which the imagination is fanned to yet <unk> heat , the stanza that would , I suppose , by common consent be taken , along with Kubla Khan , as offering us the <unk> <unk> of ' Romanticism ' " . He concluded on the stanza that " I do not believe that any reader who has watched Keats at work on the more exquisitely finished of the stanzas in The Eve of St. Agnes , and seen this craftsman slowly elaborating and refining , will ever believe that this perfect stanza was achieved with the easy fluency with which , in the draft we have , it was obviously written down . " In 1936 , F. R. Leavis wrote , " One remembers the poem both as recording , and as being for the reader , an indulgence . " Following Leavis , <unk> Brooks and Robert Penn Warren , in a 1938 essay , saw the poem as " a very rich poem . It contains some complications which we must not <unk> over if we are to appreciate the depth and significance of the issues engaged . " Brooks would later argue in The Well @-@ <unk> Urn ( 1947 ) that the poem was thematically unified while <unk> many of the negative criticisms lodged against the poem . 
- Richard Fogle responded to the critical attack on Keats 's emphasis on rhyme and language put forth by Garrod , Gerard , and others in 1953 . His argument was similar to Brooks : that the poem was thematically coherent and that there is a poet within the poem that is different from Keats the writer of the poem . As such , Keats consciously chose the shift in the themes of the poem and the contrasts within the poem represent the pain felt when comparing the real world to an ideal world found within the imagination . Fogle also responded directly to the claims made by Leavis : " I find Mr. Leavis too austere , but he points out a quality which Keats plainly sought for . His <unk> and <unk> is , however , modified by a principle of sobriety . " It is possible that Fogle 's statements were a defense of Romanticism as a group that was both respectable in terms of thought and poetic ability . <unk> , following in 1953 , claimed that " Of all Keats ' poems , it is probably the ' Ode to a Nightingale ' that has most tormented the critic [ ... ] in any reading of the ' Ode to a Nightingale ' the turmoil will not down . Forces contend wildly within the poem , not only without resolution , but without possibility of resolution ; and the reader comes away from his experience with the sense that he has been in ' a wild Abyss ' " . He then explained , " It is this turbulence , I suspect , that has led Allen Tate to believe the ode ' at least tries to say everything that poetry an say . ' But I propose it is the ' Ode on a Grecian Urn ' that succeeds in saying what poetry can say , and that the other ode attempts to say all that the poet can . " 
- 
- = = = Later critical responses = = = 
- 
- Although the poem was defended by a few critics , E. C. <unk> returned to the argument that the poem lacked a structure and emphasized the word " forlorn " as evidence of his view . In his 1957 work , <unk> did praise the poem as he declared , " The Ode to a Nightingale has a special interest in that most of us would probably regard it as the most richly representative of all Keats ’ s poems . Two reasons for this quality are immediately apparent : there is its <unk> evocation of that late spring and early summer season [ … ] and there is its exceptional degree of ' distillation ' , of concentrated recollection " . David Perkins felt the need to defend the use of the word " forlorn " and claimed that it described the feeling from the impossibility of not being able to live in the world of the imagination . When praising the poem in 1959 , Perkins claimed , " Although the " Ode to a Nightingale " ranges more widely than the " Ode on a Grecian Urn , " the poem can also be regarded as the exploration or testing out of a symbol , and , compared with the urn as a symbol , the nightingale would seem to have both limitations and advantages . " Walter Jackson Bate also made a similar defense of the word " forlorn " by claiming that the world described by describing the impossibility of reaching that land . When describing the poem compared to the rest of English poetry , Bate argued in 1963 , " Ode to a Nightingale " is among " the greatest lyrics in English " and the only one written with such speed : " We are free to doubt whether any poem in English of comparable length and quality has been composed so quickly . " In 1968 , Robert <unk> stated , " It may not be wrong to regard [ Ode on Indolence and Ode on Melancholy ] as Keats 's earlier essays in this [ ode ] form , and the great Nightingale and Grecian Urn as his more finished and later works . " 
- From the late 1960s onward , many of the Yale School of critics describe the poem as a reworking of John Milton 's poetic diction , but they argued that poem revealed that Keats lacked the ability of Milton as a poet . The critics , Harold Bloom ( 1965 ) , Leslie <unk> ( 1973 ) , Paul Fry ( 1980 ) , John <unk> ( 1981 ) and Cynthia Chase ( 1985 ) , all focused on the poem with Milton as a progenitor to " Ode to a Nightingale " while ignoring other possibilities , including Shakespeare who was emphasised as being the source of many of Keats 's phrases . Responding to the claims about Milton and Keats 's shortcomings , critics like R. S. White ( 1981 ) and Willard Spiegelman ( 1983 ) used the Shakespearean echoes to argue for a multiplicity of sources for the poem to claim that Keats was not trying to respond just Milton or escape from his shadow . Instead , " Ode to a Nightingale " was an original poem , as White claimed , " The poem is richly saturated in Shakespeare , yet the <unk> are so profound that the Ode is finally original , and wholly <unk> " . Similarly , Spiegelman claimed that Shakespeare 's A Midsummer Night 's Dream had " flavored and <unk> the later poem " . This was followed in 1986 by Jonathan Bate claiming that Keats was " left enriched by the voice of Shakespeare , the ' immortal bird ' " . 
- Focusing on the quality of the poem , Stuart <unk> , argued in 1973 , " ' Ode to a Nightingale ' is the supreme expression in all Keats 's poetry of the impulse to imaginative escape that flies in the face of the knowledge of human limitation , the impulse fully expressed in ' Away ! away ! for I will fly to thee . ' " Wolf <unk> , in 1981 , described the poem as " <unk> celebrated " and claimed that " Since this movement into an eternal realm of song is one of the most magnificent in literature , the poet 's return to actuality is all the more shattering . " Helen <unk> continued the earlier view that the poem was artificial but added that the poem was an attempt to be aesthetic and spontaneous that was later dropped . In 1983 , she argued , " In its absence of <unk> and its abandonment to <unk> , the poem appeals to readers who prize it as the most personal , the most apparently spontaneous , the most immediately beautiful , and the most confessional of Keats 's odes . I believe that the ' events ' of the ode , as it <unk> in time , have more logic , however , than is usually granted them , and that they are best seen in relation to Keats 's pursuit of the idea of music as a <unk> art . " 
- In a review of contemporary criticism of " Ode to a Nightingale " in 1998 , James O <unk> claimed that " To judge from the volume , the variety , and the polemical force of the modern critical responses engendered , there have been few moments in English poetic history as <unk> as Keats 's repetition of the word ' forlorn ' " . When referring to the reliance of the ideas of John Dryden and William <unk> within the poem , Poet Laureate Andrew Motion , in 1999 , argued " whose notion of poetry as a ' movement ' from personal consciousness to an awareness of suffering humanity it perfectly illustrates . " 
- 
- = = In fiction = = 
- 
- F. Scott Fitzgerald took the title of his novel <unk> is the Night from the 35th line of the ode . 
- According to <unk> de <unk> Carrington , Keats ' wording , " when , sick for home , / She stood in tears amid the alien corn " , seems to be echoed in by Alice Munro 's Save the <unk> ( 1998 ) , the end of which reads : " Eve would lie down [ ... ] with nothing in her head but the <unk> of the deep tall corn which might have stopped growing now but still made its live noise after dark " ( book version ) . 
- The poem is quoted in Chapter 1 of P. G. Wodehouse 's novel Full Moon ( 1947 ) : " ' Coming here ? Freddie ? ' <unk> <unk> seemed to be <unk> his sense , as though of hemlock he had drunk . " 
- 
- 
- = Weather buoy = 
- 
- Weather buoys are instruments which collect weather and ocean data within the world 's oceans , as well as aid during emergency response to chemical spills , legal proceedings , and engineering design . Moored buoys have been in use since 1951 , while drifting buoys have been used since 1979 . Moored buoys are connected with the ocean bottom using either chains , nylon , or <unk> <unk> . With the decline of the weather ship , they have taken a more primary role in measuring conditions over the open seas since the 1970s . During the 1980s and 1990s , a network of buoys in the central and eastern tropical Pacific ocean helped study the El Niño @-@ Southern <unk> . Moored weather buoys range from 1 @.@ 5 metres ( 4 @.@ 9 ft ) to 12 metres ( 39 ft ) in diameter , while drifting buoys are smaller , with diameters of 30 centimetres ( 12 in ) to 40 centimetres ( 16 in ) . Drifting buoys are the dominant form of weather buoy in sheer number , with 1250 located worldwide . Wind data from buoys has smaller error than that from ships . There are differences in the values of sea surface temperature measurements between the two platforms as well , relating to the depth of the measurement and whether or not the water is heated by the ship which measures the quantity . 
- 
- = = History = = 
- 
- The first known proposal for surface weather observations at sea occurred in connection with aviation in August 1927 , when <unk> <unk> stated that " weather stations along the ocean coupled with the development of the seaplane to have an equally long range , would result in regular ocean flights within ten years . " Starting in 1939 , United States Coast Guard vessels were being used as weather ships to protect transatlantic air commerce . 
- During World War II The German Navy deployed weather buoys ( <unk> See — <unk> ) at fifteen fixed positions in the North Atlantic and <unk> Sea . They were launched from U @-@ boats into a maximum depth of ocean of 1000 <unk> ( 1 @,@ 800 metres ) , limited by the length of the anchor cable . Overall height of the body was 10 @.@ 5 metres ( of which most was submerged ) , surmounted by a mast and <unk> aerial of 9 metres . Data ( air and water temperature , atmospheric pressure and relative humidity ) were encoded and transmitted four times a day . When the batteries ( high voltage dry @-@ cells for the <unk> , and nickel @-@ iron for other power and to raise and lower the aerial mast ) were exhausted , after about eight to ten weeks , the unit self @-@ <unk> . 
- The Navy Oceanographic Meteorological <unk> <unk> ( NOMAD ) buoy 's 6 @-@ metre ( 20 ft ) hull was originally designed in the 1940s for the United States Navy ’ s offshore data collection program . Between 1951 and 1970 , a total of 21 NOMAD buoys were built and deployed at sea . Since the 1970s , weather buoy use has superseded the role of weather ships by design , as they are cheaper to operate and maintain . The earliest reported use of drifting buoys was to study the behavior of ocean currents within the <unk> Sea in 1972 and 1973 . Drifting buoys have been used increasingly since 1979 , and as of 2005 , 1250 drifting buoys roamed the Earth 's oceans . 
- Between 1985 and 1994 , an extensive array of moored and drifting buoys was deployed across the equatorial Pacific Ocean designed to help monitor and predict the El Niño phenomenon . Hurricane Katrina capsized a 10 m ( 33 ft ) buoy for the first time in the history of the National Data Buoy Center ( <unk> ) on August 28 , 2005 . On June 13 , 2006 , drifting buoy <unk> ended its long @-@ term data collection of sea surface temperature after transmitting for 10 years , 4 months , and 16 days , which is the longest known data collection time for any drifting buoy . The first weather buoy in the Southern Ocean was deployed by the Integrated Marine Observing System ( <unk> ) on March 17 , 2010 . 
- 
- = = Instrumentation = = 
- 
- Weather buoys , like other types of weather stations , measure parameters such as air temperature above the ocean surface , wind speed ( steady and gusting ) , barometric pressure , and wind direction . Since they lie in oceans and lakes , they also measure water temperature , wave height , and dominant wave period . Raw data is processed and can be logged on board the buoy and then transmitted via radio , cellular , or satellite communications to meteorological centers for use in weather forecasting and climate study . Both moored buoys and drifting buoys ( drifting in the open ocean currents ) are used . Fixed buoys measure the water temperature at a depth of 3 metres ( 9 @.@ 8 ft ) . Many different drifting buoys exist around the world that vary in design and the location of reliable temperature sensors varies . These measurements are <unk> to satellites for automated and immediate data distribution . Other than their use as a source of meteorological data , their data is used within research programs , emergency response to chemical spills , legal proceedings , and engineering design . Moored weather buoys can also act as a navigational aid , like other types of buoys . 
- 
- = = Types = = 
- 
- Weather buoys range in diameter from 1 @.@ 5 metres ( 4 @.@ 9 ft ) to 12 metres ( 39 ft ) . Those that are placed in shallow waters are smaller in size and moored using only chains , while those in deeper waters use a combination of chains , nylon , and <unk> <unk> . Since they do not have direct navigational significance , moored weather buoys are classed as special marks under the <unk> scheme , are coloured yellow , and display a yellow flashing light at night . 
- <unk> buoys are round and moored in deep ocean locations , with a diameter of 10 metres ( 33 ft ) to 12 metres ( 39 ft ) . The aluminum 3 @-@ metre ( 10 ft ) buoy is a very rugged meteorological ocean platform that has long term <unk> . The expected service life of the 3 @-@ metre ( 10 ft ) platform is in excess of 20 years and properly maintained , these buoys have not been retired due to corrosion . The NOMAD is a unique moored aluminum environmental monitoring buoy designed for deployments in extreme conditions near the coast and across the Great Lakes . <unk> moored off the Atlantic Canadian coast commonly experience winter storms with maximum wave heights approaching 20 metres ( 66 ft ) into the Gulf of Maine . 
- Drifting buoys are smaller than their moored counterparts , measuring 30 centimetres ( 12 in ) to 40 centimetres ( 16 in ) in diameter . They are made of plastic or fiberglass , and tend to be either bi @-@ colored , with white on one half and another color on the other half of the float , or <unk> black or blue . It measures a smaller subset of meteorological variables when compared to its moored counterpart , with a barometer measuring pressure in a tube on its top . They have a <unk> ( metallic <unk> ) on its base , and an underwater <unk> , or sea anchor , located 15 metres ( 49 ft ) below the ocean surface connected with the buoy by a long , thin <unk> . 
- 
- = = <unk> and maintenance = = 
- 
- A large network of coastal buoys near the United States is maintained by the National Data Buoy Center , with deployment and maintenance performed by the United States Coast Guard . For South Africa , the South African Weather Service deploys and <unk> their own buoys , while the Meteorological Service of New Zealand performs the same task for their country . Environment Canada operates and deploys buoys for their country . The Met Office in Great Britain deploys drifting buoys across both the northern and southern Atlantic oceans . 
- 
- = = <unk> to data from ships = = 
- 
- Wind reports from moored buoys have smaller error than those from ships . <unk> the comparison of the two measurements are that NOMAD buoys report winds at a height of 5 metres ( 16 ft ) , while ships report winds from a height of 20 metres ( 66 ft ) to 40 metres ( 130 ft ) . Sea surface temperature measured in the intake port of large ships have a warm bias of around 0 @.@ 6 ° C ( 1 ° F ) due to the heat of the engine room . This bias has led to changes in the perception of global warming since 2000 . Fixed buoys measure the water temperature at a depth of 3 metres ( 10 ft ) . 
- 
- 
- = HMS Marlborough ( 1912 ) = 
- 
- HMS Marlborough was an Iron Duke @-@ class battleship of the British Royal Navy , named in honour of John Churchill , 1st Duke of Marlborough . She was built at Devonport Royal Dockyard between January 1912 and June 1914 , entering service just before the outbreak of the First World War . She was armed with a main battery of ten 13 @.@ 5 @-@ inch ( 340 mm ) guns and was capable of a top speed of 21 @.@ 25 knots ( 39 @.@ 36 km / h ; 24 @.@ 45 mph ) . 
- Marlborough served with the Grand Fleet for the duration of the war , primarily patrolling the northern end of the North Sea to enforce the blockade of Germany . She saw action at the Battle of Jutland ( 31 May – 1 June 1916 ) , where she administered the coup de <unk> to the badly damaged German cruiser SMS Wiesbaden . During the engagement , Wiesbaden hit Marlborough with a torpedo that eventually forced her to withdraw . The damage to Marlborough was repaired by early August , though the last two years of the war were uneventful , as the British and German fleets adopted more cautious strategies due to the threat of underwater weapons . 
- After the war , Marlborough was assigned to the Mediterranean Fleet , where she took part in the Allied intervention in the Russian Civil War in the Black Sea in 1919 – 20 . She was also involved in the Greco @-@ Turkish War . In 1930 , the London Naval Treaty mandated that the four Iron Duke @-@ class battleships be discarded ; Marlborough was used for a variety of weapons tests in 1931 – 32 , the results of which were incorporated into the reconstruction programme for the Queen Elizabeth @-@ class battleships . 
- 
- = = Design = = 
- 
- Marlborough was <unk> feet 9 inches ( 190 m ) long overall and had a beam of 90 ft ( 27 m ) and an average draught of 29 ft 6 in ( 9 m ) . She displaced 25 @,@ 000 long tons ( 25 @,@ <unk> t ) as designed and up to 29 @,@ 560 long tons ( 30 @,@ <unk> t ) at combat loading . Her propulsion system consisted of four Parsons steam turbines , with steam provided by eighteen Babcock & Wilcox boilers . The engines were rated at 29 @,@ 000 shaft horsepower ( 21 @,@ 625 kW ) and produced a top speed of 21 @.@ 25 kn ( 39 km / h ; 24 mph ) . Marlborough 's cruising radius was 7 @,@ 800 nautical miles ( 14 @,@ 446 km ; 8 @,@ <unk> mi ) at a more economical 10 kn ( 19 km / h ; 12 mph ) . She had a crew of 995 officers and enlisted men ; during wartime this increased to up to 1 @,@ <unk> . 
- The ship was armed with a main battery of ten BL 13 @.@ 5 @-@ inch Mk V naval guns mounted in five twin gun turrets . They were arranged in two superfiring pairs , one forward and one aft ; the fifth turret was located amidships , between the funnels and the rear superstructure . Close @-@ range defence against torpedo boats was provided by a secondary armament of twelve BL 6 @-@ inch Mk VII guns . Marlborough was also fitted with a pair of QF 3 @-@ inch 20 cwt anti @-@ aircraft guns and four 47 mm ( 1 @.@ 9 in ) 3 @-@ pounder guns . As was typical for capital ships of the period , she was equipped with four 21 in ( 533 mm ) torpedo tubes submerged on the broadside . She was protected by a main armoured belt that was 12 in ( 305 mm ) thick over the ship 's <unk> . Her deck was 2 @.@ 5 in ( 64 mm ) thick . The main battery turret faces were 11 in ( 279 mm ) thick , and the turrets were supported by barbettes 10 in ( 254 mm ) thick . 
- 
- = = Service history = = 
- 
- Marlborough was laid down at Devonport Royal Dockyard on 25 January 1912 . She was launched nearly ten months later , on 24 October , and was commissioned on 2 June 1914 . The ship was completed on 16 June 1914 , a month before the First World War broke out on the Continent . Marlborough initially joined the Home Fleets , where she served as the flagship for Sir Lewis <unk> . Following the British entry into the war in August , the Home Fleets was reorganised as the Grand Fleet , commanded by Admiral John Jellicoe . Marlborough was assigned as the flagship of the 1st Battle Squadron , where she served for the duration of the conflict . 
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- = = = First World War = = = 
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- On the evening of 22 November 1914 , the Grand Fleet conducted a fruitless sweep in the southern half of the North Sea to support Vice Admiral David Beatty 's 1st Battlecruiser Squadron . The fleet was back in port in Scapa Flow by 27 November . Marlborough and most of the fleet initially remained in port during the German raid on Scarborough , Hartlepool and Whitby on 16 December 1914 , though the 3rd Battle Squadron was sent to reinforce the British forces in the area . After receiving further information about the possibility of the rest of the German fleet being at sea , Jellicoe gave the order for the fleet to sortie to try to intercept the Germans , though by that time they had already retreated . Vice Admiral Cecil Burney replaced <unk> aboard Marlborough in December ; at that time , Marlborough became the second @-@ in @-@ command flagship for the Grand Fleet . On 25 December , the fleet sortied for a sweep in the North Sea , which concluded on 27 December without event . Marlborough and the rest of the fleet conducted gunnery drills during 10 – 13 January 1915 west of the <unk> and <unk> . On the evening of 23 January , the bulk of the Grand Fleet sailed in support of Beatty 's Battlecruiser Fleet but the rest of the fleet did not become engaged in the ensuing Battle of Dogger Bank the following day . 
- On 7 – 10 March 1915 , the Grand Fleet conducted a sweep in the northern North Sea , during which it undertook training manoeuvres . Another such cruise took place during 16 – 19 March . On 11 April , the Grand Fleet conducted a patrol in the central North Sea and returned to port on 14 April ; another patrol in the area took place during 17 – 19 April , followed by gunnery drills off the <unk> on 20 – 21 April . The Grand Fleet conducted a sweep into the central North Sea during 17 – 19 May without encountering German vessels . Another patrol followed during 29 – 31 May ; it too was uneventful . The fleet conducted gunnery training in mid @-@ June . During 2 – 5 September , the fleet went on another cruise in the northern end of the North Sea and conducted gunnery drills . Throughout the rest of the month , the Grand Fleet conducted numerous training exercises . 
- On 13 October , the majority of the fleet conducted a sweep into the North Sea , returning to port on 15 October . During 2 – 5 November , Marlborough participated in a fleet training operation west of the <unk> . Another such cruise took place during 1 – 4 December . The typical routine of gunnery drills and squadron exercises occurred in January 1916 . The fleet departed for a cruise in the North Sea on 26 February ; Jellicoe had intended to use the Harwich Force to sweep the <unk> Bight , but bad weather prevented operations in the southern North Sea . As a result , the operation was confined to the northern end of the sea . On the night of 25 March , Iron Duke and the rest of the fleet sailed from Scapa Flow , to support the Battlecruiser Fleet and other light forces that raided the German <unk> base at <unk> . 
- On 21 April , the Grand Fleet conducted a demonstration off Horns Reef to distract the Germans , while the Russian Navy <unk> its defensive minefields in the Baltic Sea . The fleet returned to Scapa Flow on 24 April and <unk> , before proceeding south in response to intelligence reports that the Germans were about to launch a raid on <unk> . The Grand Fleet did not arrive in the area until after the Germans had withdrawn . During 2 – 4 May , the fleet conducted another demonstration off Horns Reef to keep German attention focused on the North Sea . 
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- = = = = Battle of Jutland = = = = 
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- In an attempt to lure out and destroy a portion of the Grand Fleet , the German High Seas Fleet with 16 dreadnoughts , six pre @-@ dreadnoughts , six light cruisers and 31 torpedo boats commanded by Vice Admiral Reinhard Scheer , departed the Jade early on the morning of 31 May . The fleet sailed in concert with Rear Admiral Franz von Hipper 's five battlecruisers and supporting cruisers and torpedo boats . The Royal Navy 's Room 40 had intercepted and <unk> German radio traffic containing plans of the operation . The Admiralty ordered the Grand Fleet of 28 dreadnoughts and 9 battlecruisers , to sortie the night before to cut off and destroy the High Seas Fleet . On the day of the battle , Marlborough was stationed toward the rear of the British line in the 6th Division of the 1st Battle Squadron . 
- The initial action was fought primarily by the British and German battlecruiser formations in the afternoon , but by 18 : 00 , the Grand Fleet approached the scene . Fifteen minutes later , Jellicoe gave the order to turn and deploy the fleet for action . The transition from their cruising formation caused congestion with the rear divisions , forcing Marlborough and many of the other ships to reduce speed to 8 knots ( 15 km / h ; 9 @.@ 2 mph ) to avoid colliding with each other . The British ships initially had poor visibility and Marlborough could only <unk> make out a group of German Kaiser @-@ class battleships at 18 : 17 . In the span of four minutes , she fired seven salvos , first at 10 @,@ 000 yards ( 9 @,@ 100 m ) and then at 13 @,@ 000 yards ( 12 @,@ 000 m ) . Marlborough 's gunners claimed to have made hits with the 5th and 7th salvos but these claims are unlikely . Her guns were then masked by a burning cruiser , probably the armoured cruiser HMS Warrior . 
- Marlborough joined the group of battleships battering the German light cruiser SMS Wiesbaden at 18 : 25 . She fired five salvos , before a premature detonation in the right barrel of " A " turret disabled the gun . She also engaged the ship with her secondary battery . At 18 : 39 , Marlborough again engaged what appeared to be a Kaiser @-@ class ship , firing a salvo before the German vessel disappeared into the haze . During the engagement with Wiesbaden , the German cruiser launched one or two torpedoes at around 18 : 45 , one of which struck Marlborough around the starboard diesel generator room . The detonation tore a 28 @-@ foot ( 8 @.@ 5 m ) hole in the hull and causing significant flooding , that forced the forward boilers on that side of the ship to be <unk> and reduced the ship 's speed to 16 knots ( 30 km / h ; 18 mph ) . Burney initially reported to Jellicoe that his ship had struck a mine or had been hit by a torpedo at 18 : 57 . Several more torpedoes , this time from the torpedo boat SMS <unk> , forced Marlborough and the rest of the ships in her division to take <unk> action . 
- At 19 : 03 , Marlborough engaged Wiesbaden again , firing four salvos at ranges of 9 @,@ 500 to 9 @,@ 800 yards ( 8 @,@ 700 to 9 @,@ 000 m ) . She hit the German cruiser with probably three shells from the last two salvos and these finally neutralised the ship , although it took several more hours before Wiesbaden sank . Marlborough then shifted fire to the König @-@ class battleships leading the German line at 19 : 12 . She fired thirteen salvos in the span of six minutes at SMS Grosser Kurfürst at ranges of 10 @,@ 200 to 10 @,@ 750 yards ( 9 @,@ 330 to 9 @,@ 830 m ) , scoring three hits , though she incorrectly claimed a fourth hit . During this phase of the battle , Marlborough fired two torpedoes , both of which missed their targets : the first at Wiesbaden at 19 : 10 and the second at SMS Kaiser at 19 : 25 . 
- By about 19 : 30 , Marlborough 's pumps had contained the flooding in the boiler rooms but she took on a list of around 7 – 8 degrees . Instead of using counter @-@ flooding to minimise the list , her crew attempted to correct the list by using coal and oil from the starboard bunkers first . The list caused the generators supplying power to the main battery turrets to flood , hampering the gun crews , particularly as shells were transferred from the magazines to the turrets . The blast from the torpedo was so powerful that forty <unk> compartments were damaged , though the torpedo bulkhead localised most of the damage and the more badly damaged compartments were sufficiently <unk> up . Three more torpedoes approached Marlborough at 19 : 33 . She <unk> the first two and the third <unk> passed under the ship . 
- After the opposing fleets <unk> late in the day , the Grand Fleet steamed south in an attempt to cut off the retreating Germans and destroy them the following morning . The 6th Division was slowed down by Marlborough , which could make no more than 15 @.@ 75 kn ( 29 @.@ 17 km / h ; 18 @.@ 12 mph ) by this point . By around 02 : 00 on 1 June , the 6th Division was about 12 nmi ( 22 km ; 14 mi ) behind the rest of the fleet . At that time , the bulkheads in the starboard forward boiler room started to give way under the strain , forcing Marlborough to reduce speed to 12 knots ( 22 km / h ; 14 mph ) . The damage control teams believed that if the main battery were to fire , the shoring supporting the damaged bulkheads would give way , greatly increasing the risk to the ship . Jellicoe detached the ship to proceed independently to Rosyth or the <unk> ; Burney had ordered the scout cruiser Fearless to come alongside to transfer him to the battleship Revenge . Marlborough thereafter proceeded northward at a speed of 11 knots ( 20 km / h ; 13 mph ) . 
- Fearless rejoined Marlborough around 04 : 00 and both ships briefly fired at the German <unk> <unk> . Commodore Reginald <unk> 's Harwich Force had been ordered to reinforce the Grand Fleet , particularly to relieve ships low on fuel ; they departed at 03 : 50 but this was too late for them to reach the fleet by morning , so Jellicoe ordered <unk> to detach destroyers to escort Marlborough back to port . On the way , Marlborough and Fearless encountered the British submarines <unk> and <unk> ; the two submarines prepared to attack the ships but <unk> recognised them before they launched torpedoes . By 15 : 00 , eight destroyers from the Harwich Force had joined Marlborough and another pump had been lowered into the flooded boiler room . At around 23 : 30 , the pump was being moved to clean it when the roll of the ship threw the pump into the damaged bulkhead , knocking the shores loose . Water flooded into the ship and Marlborough 's captain ordered Fearless and the destroyers to prepare to come alongside , to rescue the crew if the flooding worsened at 00 : 47 on 2 June . A diver was sent into the boiler room at that time , and he was able to keep the pump clean , which slowly reduced the water level in the ship . 
- Jellicoe ordered Marlborough to proceed to the <unk> for temporary repairs . While there , her forward main battery and 6 @-@ inch magazines were emptied to lighten the ship , more pumps were brought aboard and the shoring supporting the damaged bulkhead was reinforced . On the morning of 6 June , the ship left the <unk> for the <unk> , where she would receive permanent repairs , escorted by four destroyers from the Harwich Force . In the course of the battle , Marlborough had fired 162 shells from her main battery , 60 rounds from her secondary guns and five torpedoes . The torpedo hit had killed two men and wounded another two . She was repaired by the Armstrong Whitworth shipyard at <unk> , with the work lasting until 2 August , after which she departed for <unk> , arriving on 5 August . During the repair work , an extra 100 t ( 98 long tons ; 110 short tons ) of armour plating was added to the ship , primarily over the magazines . These alterations were the result of the British experience at Jutland , where three battlecruisers had been destroyed by magazine explosions . 
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- = = = = Later operations = = = = 
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- On 18 August , the Germans again sortied , this time to bombard Sunderland ; Scheer hoped to draw out Beatty 's battlecruisers and destroy them . British signals intelligence <unk> German wireless transmissions , allowing Jellicoe enough time to deploy the Grand Fleet in an attempt to engage in a decisive battle . Both sides withdrew the following day , after their opponents ' submarines inflicted losses in the Action of 19 August : the British cruisers Nottingham and Falmouth were both torpedoed and sunk by German U @-@ boats and the German battleship SMS <unk> was damaged by the British submarine <unk> . After returning to port , Jellicoe issued an order that prohibited <unk> the fleet in the southern half of the North Sea due to the overwhelming risk from mines and U @-@ boats . 
- In February 1917 , Revenge replaced Marlborough as the 1st Battle Squadron flagship ; she thereafter served as the second command flagship . She was briefly replaced in this role by Emperor of India in May and she temporarily became a private ship . Toward the end of the year , the Germans began using destroyers and light cruisers to raid the British convoys to Norway ; this forced the British to deploy capital ships to protect the convoys . On 23 April 1918 , the German fleet sortied in an attempt to catch one of the isolated British squadrons , though the convoy had already passed safely . The Grand Fleet sortied too late the following day to catch the retreating Germans , though the battlecruiser SMS Moltke was torpedoed and badly damaged by the submarine HMS <unk> . In 1918 , Marlborough and her sisters received flying @-@ off platforms on their " B " and " Q " turrets to handle reconnaissance aircraft . 
- Following the capitulation of Germany in November 1918 , the Allies interned most of the High Seas Fleet in Scapa Flow . The fleet rendezvoused with the British light cruiser Cardiff , which led the ships to the Allied fleet that was to escort the Germans to Scapa Flow . The massive fleet consisted of some 370 British , American , and French warships . The fleet remained in captivity during the negotiations that ultimately produced the Treaty of Versailles . Reuter believed that the British intended to seize the German ships on 21 June 1919 , which was the deadline for Germany to have signed the peace treaty . That morning , the Grand Fleet left Scapa Flow to conduct training manoeuvres and while they were away Reuter issued the order to <unk> the High Seas Fleet . 
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- = = = Postwar career = = = 
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- On 12 March 1919 , Marlborough was recommissioned at Devonport and assigned to the Mediterranean Fleet , as part of the 4th Battle Squadron , along with her three sisters and two Centurion @-@ class battleships . During this period , she served in the Black Sea during the Allied intervention in the Russian Civil War to support the Whites against the Red <unk> . On 5 April 1919 , Marlborough arrived in Sevastopol before proceeding to <unk> the following day . The ship took <unk> Empress Maria <unk> and other members of the Russian Imperial Family including Grand Duke Nicholas and Prince Felix <unk> aboard in <unk> on the evening of the 7th . The Empress refused to leave unless the British also evacuated wounded and sick soldiers , along with any civilians that also wanted to escape the advancing <unk> . The Russian entourage aboard Marlborough numbered some 80 people , including 44 members of the Royal Family and nobility , with a number of governesses , nurses , maids and <unk> , plus several hundred cases of luggage . 
- About 35 officer ’ s cabins were vacated and additional <unk> were installed , with the Empress taking over the Captain 's cabin . On the morning of 12 April the ship anchored off <unk> Island , about 12 miles ( 19 km ) from Constantinople , due to some uncertainty over the final destination for the Russian Royal family . On 16 April Grand Duke Nicholas <unk> and his wife the Grand Duchess <unk> , the Grand Duke Peter <unk> and his wife Grand Duchess <unk> , Princess Marina , Prince Roman , Count and Countess <unk> , Baron and Baroness <unk> , Mr <unk> and Dr <unk> with their respective servants left the ship and boarded HMS Lord Nelson destined for Genoa . They were replaced by Count Dimitri and Countess Sophia Mengden , Count George and Countess <unk> Mengden , Countess <unk> Mengden , Count Nicholas Mengden , Madame Helena <unk> and two maids . On the morning of 18 April , Good Friday , the ship sailed for Malta . The ship departed on 18 April , bound for Malta to deposit the Russians , before returning to Constantinople . 
- In May 1919 , Marlborough conducted tests with new high @-@ explosive 6 @-@ inch shells off the Kerch Peninsula , though these proved to be unreliable . During this period , she operated a <unk> balloon to aid in spotting the fall of shot . Later that month , a shell broke up in the left barrel of " A " turret and caused minor damage . While stationed off the Kerch Peninsula , the ship provided artillery support to White troops , including bombardments of <unk> positions in the villages of Koi @-@ <unk> and <unk> <unk> . By 1920 , British attention had turned to the Greco @-@ Turkish War . On 20 June 1920 , Marlborough arrived in Constantinople , where the Mediterranean Fleet was being concentrated to support the occupation of the city . On 6 July , British forces landed at <unk> , while Marlborough provided artillery support . 
- In October 1920 , the battleship King George V arrived to replace Marlborough in the Mediterranean Fleet . Marlborough then returned to Devonport , where she was paid off for a major refit that took place between February 1921 and January 1922 . During the refit , range dials were installed , along with another range @-@ finder on the rear superstructure . The aircraft platform was removed from " B " turret . Long @-@ base range @-@ <unk> were installed on " X " turret . After completing the refit in January 1922 , Marlborough was recommissioned and assigned to the Mediterranean , where she replaced Emperor of India . She served as the second command flagship until October . Following the Treaty of Lausanne in 1923 , the Allied countries withdrew their occupation forces from Turkey ; Marlborough was involved in escorting the troop convoys out of Constantinople . 
- Marlborough briefly served as the flagship for the deputy commander of the 4th Battle Squadron after King George V was damaged from striking a rock off <unk> . In November 1924 , the 4th Battle Squadron was renamed the 3rd Battle Squadron . In March 1926 , the 3rd Battle Squadron , including Marlborough , was transferred to the Atlantic Fleet . There , the battleships served as training ships . In 1929 , the ship 's 3 @-@ inch anti @-@ aircraft guns were replaced with more powerful 4 @-@ inch guns . In January 1931 , Marlborough served as the squadron flagship , relieving Emperor of India . She remained in the position for only five months , being decommissioned on 5 June . According to the terms of the London Naval Treaty of 1930 , the four ships of the Iron Duke class were to be scrapped or <unk> ; Marlborough was scheduled to be removed from service in 1931 and broken up for scrap . 
- The ship was used as a target to test the effect of various weapons on capital ships , along with Emperor of India . The tests included firing destroyer armament at the upper works at close range to test their effectiveness in a simulated night engagement , direct hits from 13 @.@ 5 @-@ inch shells , bomb tests , and experiments with flash tightness in the magazines . The first two tests were conducted in July 1931 , and were <unk> of magazine explosions . The <unk> system worked as designed , and while the explosions caused serious internal damage , Marlborough was not destroyed , as the three battlecruisers had been at Jutland . In 1932 , further tests were conducted with <unk> 250 @-@ pound ( 110 kg ) and 500 @-@ pound ( 230 kg ) bombs to test deck strength ; 450 @-@ pound ( 200 kg ) armour @-@ piercing ( AP ) bombs and 1 @,@ <unk> @-@ pound ( 490 kg ) high explosive ( <unk> ) bombs were then detonated inside the ship to test their effectiveness . The Royal Navy determined that the <unk> bombs were useless , but that thick deck armour would be required to defeat AP bombs . This led to the decision to reinforce the deck armour of existing battleships throughout the 1930s . 
- Marlborough was placed on the disposal list in May 1932 and was quickly sold to the <unk> <unk> Co . On 25 June , she arrived in Rosyth , where she was broken up for scrap . 
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- = 766th Independent Infantry Regiment ( North Korea ) = 
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- The 766th Independent Infantry Regiment ( Korean : <unk> ) was a light infantry unit of North Korea 's Korean People 's Army ( KPA ) that existed briefly during the Korean War . It was headquartered in <unk> , North Korea , and was also known as the 766th Unit ( Korean : <unk> ) . <unk> extensively in amphibious warfare and unconventional warfare , the 766th Regiment was considered a special forces commando unit . The regiment was trained to conduct assaults by sea and then to lead other North Korean units on offensive operations , to infiltrate behind enemy lines and to disrupt enemy supplies and communications . 
- <unk> in 1949 , the regiment trained for more than a year before the outbreak of the war on June 25 , 1950 . On that day , half of the regiment led North Korean forces against South Korean troops by land and sea , pushing them back after several days of fighting . Over the next six weeks the regiment advanced slowly down the Korean Peninsula , acting as a forward unit of the North Korean army . Suffering from a lack of supplies and mounting casualties , the regiment was committed to the Battle of Pusan Perimeter as part of a push to force United Nations ( UN ) troops out of Korea . 
- The regiment saw its final action at the Battle of P 'ohang @-@ dong , fighting unsuccessfully to take the town from <unk> troops . <unk> by <unk> naval and air forces and suffering extensive losses from continuous fighting , the regiment was forced to retreat from the P 'ohang @-@ dong battlefield . It moved north , joining a concentration of other KPA units , before being disbanded and absorbed into the KPA 's 12th Division . 
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- = = Organization = = 
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- Upon creation , the 766th Unit was designed to vary in size , consisting of a number of smaller units capable of acting alone . Eventually , it was reinforced to the size of a full regiment , with 3 @,@ 000 men equally distributed across six battalions ( numbered 1st through 6th ) . It was made directly subordinate to the KPA Army headquarters and put under the command of Senior Colonel Oh Jin <unk> , who would command the unit for its entire existence . All 500 men of the 3rd Battalion were lost just before the war started when their transport was sunk while attacking Pusan harbor by the Republic of Korea Navy . For the remainder of its existence the regiment was <unk> down by losses until it numbered no more than 1 @,@ 500 men and could not muster more than three battalions . 
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- = = History = = 
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- = = = Origins = = = 
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- During the planning for the invasion of South Korea in the years before the war , the North Korean leadership began to create large numbers of commando and special forces units to send south . These units <unk> South Korean authority before and during the war with terror campaigns , sabotage and inducing rebellions in ROK military units . Hundreds of commandos were sent to South Korea in this fashion , and by the end of the war up to 3 @,@ 000 of them had been trained and armed . During this time , North Korean leadership also ordered the creation of large conventional units to act as advance forces for the actual invasion . The 766th Unit was formed in April 1949 at the Third Military Academy in <unk> , North Korea . The academy was specially designed to train commandos , and the 766th was originally designed to supervise North Korean light infantry ranger units . Over the next year , the 766th Unit received extensive training in unconventional warfare and amphibious warfare . During this time , the unit was expanded in size to 3 @,@ 000 men in six battalions . 
- Prior to the beginning of the war in June 1950 , the 766th completed training and was moved to the front at <unk> to support the KPA 's 5th Division . The North Korean plan was to conduct amphibious landings in <unk> and Imwonjin on the eastern coast using the 766th Regiment , in conjunction with the 549th Unit . These amphibious landings would harass the rear area of the Republic of Korea Army , providing supporting attacks to the planned frontal attack by the KPA 's II Corps directly from the north . The 766th was in position by June 23 and prepared for the attack . The unit was moved to the ports of <unk> and <unk> and loaded into ships . With the 3 @,@ 000 men in the 766th , another 3 @,@ 000 in the 549th , and 11 @,@ 000 men in the KPA 's 5th Division , the 17 @,@ 000 North Korean troops outnumbered the Republic of Korea Army 's ( ROK ) 8th Division 's 6 @,@ <unk> by a ratio of 2 @.@ 1 to 1 . The combination of the frontal attack and the landings were expected to crush the ROK division and prevent reinforcements from moving in to support it . 
- The regiment was split into three groups for the attack . Three battalions acted as <unk> for the 5th Division on land while two more battalions conducted the landings in Imwonjin . This 2 @,@ 500 man force reassembled and then led the North Korean units south . In the meantime , the 3rd Battalion , 766th Regiment was detached and sent on a mission to infiltrate Pusan . Paired with additional support , it formed the 600 @-@ man 588th Unit . 588th Unit was tasked with raiding Pusan harbor , destroying vital facilities to make it impossible for UN forces to land troops there . However , the troop transport carrying the 588th Unit was discovered and sunk by United Nations ships outside Pusan harbor the morning of June 25 , destroying the 3rd Battalion . 
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- = = = <unk> = = = 
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- Around 04 : 00 on June 25 , the KPA 's 5th Division began its first attacks on the ROK 10th Regiment 's forward positions . Three hours later , the 766th Regiment 's two battalions landed at the village of Imwonjin , using motor and sail boats to land troops and <unk> South Korean villagers to assist in setting up supplies . The two battalions separated ; one headed into the T <unk> Mountains and the second advanced north toward <unk> . At this point , the ROK 8th Division , under heavy attack from the front and aware of attacks in the rear , urgently requested reinforcements . It was denied these reinforcements , as ROK higher commanders informed the division commander that the ROK Army was under heavy attack across the entirety of the 38th parallel and had no reinforcements to spare . 
- The ROK 21st Regiment , 8th Division 's southernmost unit , moved to counter the amphibious attack . The regiment 's 1st Battalion moved from <unk> <unk> into the <unk> area and ambushed forward elements of the 766th in conjunction with local police and militia forces . They were able to drive back the 766th Regiment 's northern advance . However , at least one of the 766th Regiment 's battalions massed at <unk> , blocking one of the 8th Division 's main supply routes . ROK troops mustered a civilian militia to help fight the North Koreans , which was only moderately effective . The embattled ROK 8th Division was forced to withdraw under overwhelming attacks and <unk> in communication on July 27 . With the retreat of the ROK 6th Division , the entire ROK eastern flank was forced back . The 766th Regiment had been successful in establishing a bridgehead and disrupting communications in the initial attack . 
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- = = = Advance = = = 
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- With the ROK army in retreat , the 766th Regiment , 549th Unit , and KPA 's 5th Divisions all advanced steadily south along the eastern roads without encountering much resistance . Across the entire front the North Korean Army had successfully routed the South Koreans and was pushing them south . The 766th Regiment acted as an advance force , attempting to infiltrate further inland as it moved through the mountainous eastern region of the country . The rugged terrain of the eastern regions of Korea , poor communication equipment , and unreliable resupply lines thwarted the South Korean resistance . The North Koreans used this to their advantage in advancing but they began to experience the same problems themselves . The 5th Division and the two other units began advancing south slowly and cautiously , sending strong reconnaissance parties into the mountains to ensure they would not be threatened from the rear . However , this more cautious advance began to give the South Koreans valuable time to build up further south . By June 28 , the 766th had infiltrated into <unk> @-@ <unk> from Uljin and was moving toward <unk> , <unk> and <unk> in order to block communications between <unk> and <unk> , where United States Army forces were landing in an attempt to support the collapsing ROK Army . 
- The ROK 23rd Regiment of the ROK 3rd Division was moved to block the advance of the three units at Uljin . The ROK forces mounted a series of delaying actions against the main North Korean force , which was significantly dispersed throughout the mountainous region and unable to muster its overwhelming strength . The ROK regiment was subsequently able to hold up the North Korean advance until July 5 . On July 10 , the 766th separated from the 5th Division and met an advance party of North Korean civilians in Uljin who had been sent to set up government in the area . From here , the 766th dispersed in small groups into the mountains . On July 13 it reached <unk> @-@ ri , 25 miles ( 40 km ) north of Yongdok . 
- Over the next week the 766th Regiment and the KPA 's 5th Division continued in a slow advance south as it met increasing South Korean resistance . United Nations air support began to increase , slowing the advance further . The force continued to occupy the eastern flank , and by July 24 it was advancing from the <unk> @-@ <unk> region and approaching <unk> . On its flank was the KPA 's 12th Division . Progress halted as UN aerial and naval bombardment made movement more difficult . At the same time the North Korean units ' supply lines were stretched thin and began to break down , forcing them to <unk> South Korean civilians to carry supplies . 
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- = = = Resistance = = = 
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- On July 17 , the KPA 's 5th Division entered Yongdok , taking the city without much resistance before fierce UN air attacks caused the division heavy losses . Still , it was able to surround the ROK 3rd Division in the city . By now , the 5th Division and the 766th Regiment had been reduced to a combined strength of 7 @,@ 500 men to the ROK 3rd Divisions ' 6 @,@ <unk> . The 766th massed its force again to assist the 5th Division in surrounding and besieging the ROK 3rd Division , which was trapped in the city . The 3rd Division , in the meantime , was ordered to remain in the city to delay the North Koreans as long as possible . It was eventually evacuated by sea after delaying North Korean forces for a considerable time . The rugged terrain of the mountains prevented the North Korean forces from conducting the <unk> maneuvers they had used so effectively against other troops , and their advantages in numbers and equipment had been negated in the fight . 
- By July 28 , the division was still embroiled in this fight and the 766th bypassed it and moved toward <unk> on the left flank of the city . However the 766th had suffered significant setbacks at Yongdok , with substantial losses due to American and British naval artillery fire . Once it arrived in the area , it met heavier resistance from South Korean police and militia operating in armored vehicles . With air support , they offered the heaviest resistance the unit had faced thus far . With the support of only one of the 5th Division 's regiments , the 766th was unable to sustain its advance , and had to pull back by the 29th . Movement from the ROK Capital Division prevented the 766th Regiment from infiltrating further into the mountains . ROK cavalry and civilian police then began isolated <unk> against the 766th . These forces included special counter @-@ guerrilla units targeting the 766th and <unk> its tactics . South Korean troops halted the advance of the North Koreans again around the end of the month thanks to increased reinforcements and support closer to the Pusan Perimeter logistics network . 
- On August 5 , the KPA 's 12th Division pushed back the ROK Capital Division in the Ch <unk> @-@ <unk> area , and linked up with elements of the 766th which had infiltrated the area of <unk> . <unk> , they began to prepare to attack P 'ohang to secure entry into the UN 's newly established Pusan Perimeter . The Regiment was ordered to begin an attack in coordination with the KPA 's 5th Division . The Korean People 's Army planned simultaneous offensives across the entire Perimeter , including a flanking maneuver by the 766th and the 5th Division to <unk> UN troops and push them back to Pusan . The 766th was not reinforced ; North Korean planners intended it to move unseen around the UN lines while the majority of the UN and North Korean troops were locked in fighting around <unk> and the <unk> <unk> . 
- By this time , however , North Korean logistics had been stretched to their limit , and resupply became increasingly difficult . By the beginning of August , the North Korean units operating in the area were getting little to no food and ammunition supply , instead relying on captured UN weapons and foraging for what they could find . They were also exhausted from over a month of advancing , though morale remained high among the 766th troops . The 766th Regiment specialized in raiding UN supply lines , and effectively mounted small disruptive attacks against UN targets to equip themselves . 
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- = = = <unk> = = = 
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- At dawn on August 11 , one 300 @-@ man battalion of the 766th Regiment entered the village of P 'ohang , creating a state of alarm among its populace . The village was only protected by a small force of South Korean Navy , Air Force and Army personnel comprising the rear guard of the ROK 3rd Division . The South Korean forces engaged the 766th forces around the village 's middle school with small @-@ arms fire until noon . At that point , North Korean armored vehicles moved in to reinforce the 766th troops and drove the South Koreans out of the village . 
- The village was strategically important because it was one of the few direct routes through the mountains and into the <unk> plain . It also led directly to the land routes being used by the UN to reinforce <unk> . Upon hearing of the fall of P 'ohang , UN Eighth United States Army commander Lieutenant General Walton Walker immediately ordered naval and air bombardment of the village . He also ordered ROK and US forces to secure regions around the village to prevent further advance of the North Korean troops . Within a few hours , the village was being blasted by artillery forcing the Regiment 's advance force to pull back . The 766th 's forces congregated and fought in the hills around the village . They joined elements of the KPA 's 5th Division , and did not enter P 'ohang until night . 
- UN forces responded to the threat with overwhelming numbers . A large force of South Korean troops , designated Task Force P 'ohang , was massed and sent into P 'ohang @-@ dong to engage the 766th Regiment and the 5th Division . ROK troops attacked toward An <unk> @-@ <unk> to the east , forcing the KPA 's 12th Division into a full retreat . Threatened with <unk> , the KPA 's 5th Division and 766th Regiment were ordered into full retreat on August 17 . By this time , the 766th had been reduced to 1 @,@ 500 men , half its original strength . 
- <unk> and out of supplies , the 766th Regiment moved to <unk> @-@ <unk> , a mountain 6 miles ( 9 @.@ 7 km ) north of <unk> , to join the shattered KPA 's 12th Division . The 12th Division was reduced to 1 @,@ 500 men in the fighting , and 2 @,@ 000 army replacements and South Korean conscripts were brought to replenish the division . The 766th Regiment was also ordered to merge its remaining troops into the depleted KPA 's 12th Division . Upon the completion of the merger with the 12th Division on August 19 , 1950 , the 766th Regiment ceased to exist . It had trained for close to 14 months prior to the war but fought for less than two . 
- 
- 
- = Sister Wives = 
- 
- Sister Wives is an American reality television series broadcast on TLC that began airing in 2010 . The show documents the life of a polygamist family , which includes patriarch Kody Brown , his four wives , and their 18 children . The family began the series living in <unk> , Utah , but has since moved to Las Vegas , Nevada in 2011 . 
- Brown and his wives have said they participated with the show to make the public more aware of polygamist families and to combat societal prejudices . Brown believes his polygamist arrangement is legal because he is legally married only to one woman , and the other marriages are spiritual unions . The series led to the Brown family being investigated for possible prosecution . 
- 
- = = Concept = = 
- 
- The show follows the lives of advertising salesman Kody Brown , his wives Meri , Janelle , Christine , and Robyn , and their 18 children . In the first season the show televised Brown 's courting and marriage of his fourth wife , Robyn Sullivan , in 2010 . Sullivan was the first new wife to enter the family in 16 years . 
- The only legal marriage was between Kody and his first wife Meri , until their legal divorce in September 2014 . ( He legally married fourth wife Robyn in December 2014 in order to legally adopt her three children ) . The other marriages are considered spiritual unions . As of 2015 Kody has been married to Meri for 25 years , Janelle for 22 years , Christine for 21 years , and Robyn for 5 years . Kody and Meri have a daughter named Mariah , their only child . Kody and Janelle have six children : daughters Madison and <unk> and sons Logan , Hunter , Garrison , and Gabriel . Kody and Christine have six children : daughters <unk> , Mykelti , <unk> , Ysabel , and Truely and son <unk> . Robyn had three children from her first marriage , which was monogamous : Dayton , Aurora , and <unk> . Kody legally adopted them in June 2015 . Kody and Robyn have two children : son Solomon and daughter <unk> . 
- Meri , Robyn , and Christine were all raised in polygamist families , but Janelle was not . Although Christine 's mother left the faith she still supports them . Months before the marriage of Janelle and Kody , however , Janelle 's mother entered into a polygamist marriage with Kody 's father . The Brown family belongs to the Apostolic United Brethren ( <unk> . ) For years before the series , the family kept their polygamist lifestyle what they called a " quasi @-@ secret " . 
- 
- = = Children = = 
- 
- Kody has 18 children : 
- With Meri : 
- Mariah ( July 29 , 1995 ) daughter 
- With Janelle : 
- Logan ( May 21 , 1994 ) son 
- Madison ( November 3 , 1995 ) daughter 
- Married to Caleb <unk> since June 4 , 2016 
- Hunter ( February 9 , 1997 ) son 
- Garrison ( April 10 , 1998 ) son 
- Gabriel ( October 11 , 2001 ) son 
- <unk> ( December 7 , 2004 ) daughter 
- With Christine : 
- <unk> ( March 14 , 1995 ) daughter 
- Mykelti ( June 9 , 1996 ) daughter 
- <unk> to Antonio <unk> 
- <unk> ( August 7 , 1998 ) son 
- <unk> ( July 23 , 2001 ) daughter 
- Ysabel ( March 7 , 2003 ) daughter 
- Truely ( April 13 , 2010 ) daughter 
- With Robyn : 
- Solomon ( October 27 , 2011 ) son 
- <unk> <unk> ( January 10 , 2016 ) daughter 
- Robyn 's children from her first marriage who were adopted by Kody on June 17 , 2015 : 
- Dayton ( January 16 , 2000 ) son 
- Aurora ( June 4 , 2002 ) daughter 
- <unk> ( April 8 , 2005 ) daughter 
- 
- = = Development = = 
- 
- In the autumn of 2009 , independent producers Timothy Gibbons and Christopher Poole approached Figure 8 Films , a North <unk> company , with the concept of a reality series about the Brown family . Bill Hayes , the president of Figure 8 Films , said the company agreed to the idea after meeting with the Browns and deciding their lives would make a great story . Camera crews shot footage of the family in mid @-@ 2010 to be used in the first season , ending in May with the marriage of Kody Brown and Robyn Sullivan . The crews continued to film them afterward in case the series was picked up for a second season . Sister Wives was publicly introduced on August 6 , 2010 , at the Television Critics Association summer media tour in Beverly Hills , California . The series ' first episode , an hour long , was broadcast on TLC on September 26 , 2010 , and the first season continued with six half @-@ hour chapters until October 17 , 2010 . 
- The broadcast of Sister Wives came at a time that polygamy and multiple marriages were a prevalent topic in American pop culture . Big Love , the hit HBO series about fictional Utah polygamist Bill <unk> , his three sister wives , and their struggle to gain acceptance in society , had already been on the air for several years . In early September 2010 , the drama series Lone Star , about a con man on the verge of entering into multiple marriages , premiered on Fox but was quickly canceled after two episodes , and when Sister Wives debuted , actress Katherine <unk> was in the process of developing a film about Carolyn <unk> , a woman who fled from a polygamist sect . 
- In October 2010 , TLC announced it had commissioned a second season , which began in March 2011 . A TLC interview with the Brown family was broadcast on October 31 , 2010 , and a one @-@ hour program featuring the honeymoon of Kody Brown and Robyn Sullivan aired on November 22 , 2010 . 
- 
- = = Episodes = = 
- 
- 
- = = = Season 1 = = = 
- 
- The nine @-@ episode first season ran from September 26 to November 21 , 2010 . The season premiere introduced viewers to Kody Brown and his three wives , Meri , Janelle , and Christine , and their twelve children , all of whom lived in a <unk> @-@ style home with three interconnected apartments . It also chronicled Kody 's dating and engagement to Robyn Sullivan , who herself has three children , marking the first time in 16 years Kody had <unk> another wife . The new relationship creates insecurity and jealousy among the other three wives , but they ultimately accept her and welcome her into the family . During the fourth episode of the season , Christine gives birth to her sixth child , Truely , which brings the family to 16 children including Robyn 's three kids . 
- Later , Kody and Meri go to Mexico to celebrate their twentieth wedding anniversary , where Meri discussed her sadness about her infertility problems and the jealousy that has arisen from Kody 's engagement to Robyn . Kody proposes in vitro fertilisation , but she turns down the idea as she is only interested in a naturally occurring conception . As Robyn 's wedding approaches , the three sister wives help Robyn prepare , and they begin to bond . However , Kody upsets his wives when he reveals he secretly chose Robyn 's wedding dress himself , which makes Christine feel so betrayed that she angrily walks away in mid @-@ interview . Kody eventually apologizes , and the five reconcile . The first season finale ends with the wedding of Kody and Robyn , where Meri , Janelle , and Christine present her with a <unk> ring to welcome her into the family . 
- 
- = = = Season 2 = = = 
- 
- Season 2 ran 23 episodes from March 13 , 2011 to November 27 , 2011 , though many sources refer to the episodes airing from September 25 , 2011 to November 27 , 2011 as Season 3 . This is due to a short hiatus from June 5 , 2011 to September 25 , 2011 . 
- Season 2 begins with the Browns heading to New York to appear on national television for the first time as open polygamists , while back home the kids head off to their first day of public school . Throughout the season , the Browns visit various friends and family members and reflect on how their relationships have changed with these people since they became open polygamists . These friends and family members include Kody 's parents ( also polygamists ) , Kody 's high school friends , and various monogamous couples that Kody and the sister wives know . Part 1 of Season 2 also follows the Browns through Kody and Janelle 's anniversary camping trip , preparing and participating in Halloween , and Christmas , which the Browns celebrate in a <unk> mountain cabin . During Season 2 we also learn more about Meri 's personal struggle with her risk of cancer and the loss of her sister . In episode 5 , Kody , Christine , and their children take a trip to Las Vegas , which we later learn is the beginning of the Browns ' subsequent move to Las Vegas . The final episodes of Season 2 follow the Browns with their real estate agent Mona Riekki through their struggle to find a home in Las Vegas suitable for polygamists , telling the kids that they are moving , and the subsequent move to Las Vegas . <unk> , Mona Riekki finds rental homes for each of the wives and Kody . Once the Browns settle into their new homes they discuss the possibility of finding a home for all of them to live together or four homes in one <unk> @-@ de @-@ sac . In the last episode before the hiatus , Robyn announces that she is expecting her and Kody 's first child . 
- The second part of Season 2 brings the announcement of the sex of Robyn and Kody 's baby and the Browns ' struggle to adjust to life in Las Vegas . The episodes following the Season 2 hiatus focus largely on Robyn 's pregnancy and the kids ' adjustment to their new lives . The abrupt move to Las Vegas brings about behavioral problems in some of the older kids , which is also discussed largely in the second half of Season 2 . During these episodes the Browns also explore possible businesses that the five of them ( Kody and the sister wives ) can run together . Several episodes after the hiatus discuss specific topics such as jealousy among the sister wives , especially regarding courting a new wife , how the parents combat the influence of Las Vegas on their children , and how the Browns are preparing the older children for college . Mona Riekki is back in this season and is working with the family on finding a permanent home in Vegas . In the finale , Robyn gives birth to baby Solomon on October 27 , 2011 and the possibility of Meri having more children once again <unk> . 
- Although the ongoing investigation of the Browns is brought up during Season 2 , it is not extensively discussed , and the progress of the investigation is unknown . 
- 
- = = = Season 3 = = = 
- 
- Season 3 premiered on May 13 , 2012 after vague details surfaced about the show 's spring return on the Twitter account of sister wife Robyn Sullivan Brown . The twenty one episode season mainly dealt with the family 's inability to be a cohesive unit while living in four separate homes . Meri explains more about the infertility problems she has experienced , while Christine <unk> more on her jealousy of Robyn . The season returned from hiatus on November 18 , 2012 , to the Brown family still discussing their options into moving their family onto one property , and invest in a <unk> @-@ de @-@ sac where they can build four homes . It is more evident this season that living in separate homes is tearing the family apart . Towards the end of the season , the family plans a three @-@ day trip to <unk> , Illinois , the birthplace of American polygamy . In the last episode on December 30 , 2012 , the family also deals with the upcoming departure to college of the eldest Brown child , Logan . 
- 
- = = = Season 4 = = = 
- 
- Season 4 premiered on July 21 , 2013 . It chronicles the family as they move into four adjacent houses within the same neighborhood . The wives are still working on starting their jewelry business . Meri comes to a decision following Robyn 's offer to be her surrogate . 
- 
- = = = Season 5 = = = 
- 
- Season 5 includes seven episodes , eight if you include the " Tell All " at the end , and eleven if you include the " <unk> Down with the Browns " , " Meri @-@ Behind the Scenes " , and the " Robyn , <unk> the Scenes " episodes . Season 5 begins with two daughters graduating high school , Mykelti and Madison . The grown ups plan to lip sync a song to celebrate , but it brings out some negative feelings for Janelle , who is not comfortable being so outgoing and admits to being embarrassed in public by the others ' behavior . And a deeper issue of feeling like she 's not heard bubbles to the surface and she sees a therapist to discuss that along with her challenging relationship with first wife Meri . On a business level , the family discusses whether to turn down investors ' money and keep full ownership of <unk> ' <unk> and whether to keep the products all their own creations or branch out and <unk> other artists ' designs and products . Christine 's mother moves in with her in Las Vegas , and in episode four , the Browns allow two <unk> to live with them for two days to see the inner workings of polygamy , an arrangement that could either put their lifestyle in a positive or negative light . 
- Finally , after five years of mental <unk> , Meri files for legal divorce from Kody so Kody can adopt Robyn 's three children from her previous marriage . By the end of the season , Meri and Kody maintained that they would continue their relationship . 
- 
- = = Reception = = 
- 
- 
- = = = Critical reception = = = 
- 
- Sister Wives drew national media attention after its first season and garnered generally mixed reviews from critics . Washington Post staff writer Hank Stuever called it " refreshingly <unk> " and found most interesting the small details of the family 's everyday life , such as the food supply , division of labor , and minor arguments . Los Angeles Times television critic Mary McNamara said she was intrigued by the <unk> nature of the polygamist family , a unit that is traditionally considered patriarchal . McNamara said the wives form the center of the family and that " their bonds appear far stronger and more vital than the casual fondness with which they all treat Kody " . <unk> writer <unk> <unk> praised Sister Wives for introducing viewers to unfamiliar subject matter and called it " refreshingly modest " considering its controversial subject matter . <unk> said it has " a natural , honest presence in a genre fabled for the camera @-@ <unk> antics of Jersey Shore " . Shelley <unk> of The Vancouver Sun called it fascinating and surprising and was impressed with the sensible and articulate way in which the family defended their lifestyle . When the Brown family made an October 2010 appearance on The Oprah Winfrey Show , talk show host Oprah Winfrey said she found particularly fascinating the relationship between the sister wives . 
- Mark A. <unk> of the Boston Herald criticized Kody Brown for opening himself and his family up to potential criminal prosecution by appearing in the series , describing him as " a <unk> who is <unk> himself and the family he claims is so precious just to star in his own TV show " . Elizabeth <unk> of The Washington Post called the series " one part domestic <unk> , another part <unk> " and claimed it relied on a " familiar reality TV recipe " shared by other TLC series such as 19 Kids and <unk> and Kate Plus 8 . Religion <unk> writer Joanna Brooks shared <unk> 's perspective , criticizing the show for presenting polygamy in a manner that " is about as interesting to me as Kate <unk> 's latest makeover . " In this vein Brooks criticized the show for not engaging the theology of plural marriage and for letting Kody Brown 's superficial comments about the <unk> of Fundamentalist and mainstream <unk> pass onto the viewers without any critical scrutiny or added nuance . <unk> <unk> , television columnist with the <unk> Park Press , felt the sister wives had issues with jealousy and self @-@ worth , and she compared Kody to a cult leader . <unk> added , " I can 't speak for everyone , but I believe in the <unk> of marriage . It 's sad to see that TLC 's <unk> on people who don 't . " Former prosecutor and television personality Nancy Grace criticized the show and said she believed Kody Brown should go to jail , but she expressed doubt he would based on Utah 's history of overlooking polygamy . Christine <unk> , an associate professor of communications at Westminster College in Salt Lake City , said the show could give viewers who are unfamiliar with the <unk> church the incorrect assumption that polygamy is accepted by the mainstream church . Several commentators have taken notice of the fact that the family 's religious convictions are <unk> in Sister Wives . 
- 
- = = = Ratings = = = 
- 
- According to Nielsen Media Research , the September 26 , 2010 , one @-@ hour premiere episode of Sister Wives drew 2 @.@ 26 million viewers , a strong rating for the network . It marked the biggest series debut for TLC since Cake Boss launched in 2009 and was a stronger rating than any of the season premieres for HBO 's Big Love . The remaining episodes of the first season were each a half @-@ hour long , with two broadcast together each Thursday . In the second week , the first episode drew 1 @.@ 88 million viewers , while the second drew 2 @.@ 13 million . The third week drew similar results , with 1 @.@ 89 million viewers watching the first episode and 2 @.@ 05 million watching the second . Sister Wives drew its strongest ratings during the fourth and final week of the first season , with 2 @.@ 67 million viewers for the first episode and 2 @.@ 74 million for the season finale . As a result of the 2 @.@ 7 million average viewership for the two episodes , TLC ranked first among all ad @-@ support cable channels in the 18 – 49 and 25 – 54 age groups . The series drew <unk> and triple @-@ digit ratings gains in all key demographics and ranked second in ad @-@ supported cable network shows during its time period . 
- 
- = = <unk> = = 
- 
- 
- 
- = You Only Live Twice ( film ) = 
- 
- You Only Live Twice ( 1967 ) is the fifth spy film in the James Bond series , and the fifth to star Sean Connery as the fictional MI6 agent James Bond . The film 's screenplay was written by Roald Dahl , and loosely based on Ian Fleming 's 1964 novel of the same name . It is the first James Bond film to <unk> most of Fleming 's plot , using only a few characters and locations from the book as the background for an entirely new story . 
- In the film , Bond is dispatched to Japan after American and Soviet manned spacecraft disappear mysteriously in orbit . With each nation <unk> the other amidst the Cold War , Bond travels secretly to a remote Japanese island in order to find the <unk> and comes face to face with Ernst Stavro Blofeld , the head of SPECTRE . The film reveals the appearance of Blofeld , who was previously a partially unseen character . SPECTRE is <unk> the government of an unnamed Asian power , implied to be the People 's Republic of China , in order to provoke war between the <unk> . 
- During the filming in Japan , it was announced that Sean Connery would retire from the role of Bond . But after a hiatus , he returned in 1971 's Diamonds Are Forever and later 1983 's non @-@ <unk> Bond film Never Say Never Again . You Only Live Twice is the first Bond film to be directed by Lewis Gilbert , who later directed the 1977 film The Spy Who Loved Me and the 1979 film <unk> , both starring Roger Moore . 
- You Only Live Twice was a great success , receiving positive reviews and grossing over $ 111 million in worldwide box office . 
- 
- = = Plot = = 
- 
- An American NASA spacecraft is <unk> from orbit by an unidentified spacecraft . The U.S. suspect it to be the work of the Soviets , but the British suspect Japanese involvement since the spacecraft landed in the Sea of Japan . To investigate , MI6 operative James Bond is sent to Tokyo after <unk> his own death in Hong Kong and being buried at sea from the HMS <unk> ( <unk> ) . 
- Upon his arrival , Bond is contacted by Aki , assistant to the Japanese secret service leader Tiger Tanaka while watching sumo . Aki introduces Bond to local MI6 operative <unk> Henderson . Henderson claims to have critical evidence about the rogue craft , but is killed before he can elaborate . Bond chases and kills the assailant , taking the assailant 's clothing as a disguise and escapes in the getaway car , which takes him to Osato Chemicals . Once there , Bond <unk> the driver and breaks into the office safe of president Mr. Osato . After stealing documents , Bond is pursued by armed security , but is rescued by Aki , who flees to a secluded subway station . Bond chases her , but falls down a trap door leading to Tanaka 's office . The stolen documents are examined and found to include a photograph of the cargo ship Ning @-@ Po with a <unk> message saying the tourist who took the photo was killed as a security precaution . 
- Bond goes to Osato Chemicals to meet Mr. Osato himself , <unk> as a potential new buyer . Osato <unk> Bond but , after their meeting , orders his secretary , Helga Brandt , to have him killed . Outside the building , assassins open fire on Bond before Aki rescues him again . Bond and Aki drive to Kobe , where the Ning @-@ Po is docked . They investigate the company 's dock facilities and discover that the ship was delivering elements for rocket fuel . They are discovered , but Bond <unk> the henchmen until Aki gets away ; however , Bond himself is captured and knocked out . He wakes , tied up in SPECTRE operative Helga Brandt 's cabin on the Ning @-@ Po . She <unk> Bond , but he thinks he has managed to <unk> his way to freedom . Brandt then flies Bond to Tokyo but , en route , she sets off a flare in the plane and <unk> out . Bond manages to land the plane . 
- After finding out where the Ning @-@ Po <unk> , Bond flies over the area in a heavily armed <unk> created by Q. Near a volcano , Bond is attacked by helicopters , which he defeats , confirming his suspicions that the enemy 's base is nearby . A Soviet spacecraft is then captured in orbit by another unidentified craft , <unk> tensions between Russia and the US . The mysterious spaceship lands in an extensive base hidden inside the volcano . It is revealed that the true mastermind behind this is Ernst Stavro Blofeld and SPECTRE . Blofeld seems to forgive Brandt for her failure to kill Bond , but as she leaves , he activates a mechanism that drops her into a pool of <unk> . Blofeld instructs Osato to kill Bond . 
- Bond trains with Tanaka 's ninjas , during which an attempted assassination kills Aki instead . Bond is disguised as an Oriental in a fake marriage to Tanaka 's student , Kissy Suzuki . Acting on a lead from Suzuki , the pair <unk> a cave and the volcano above it . <unk> that the mouth of the volcano is a disguised hatch to the secret rocket base , Bond slips in , while Kissy goes to alert Tanaka . Bond locates and frees the captured astronauts and , with their help , steals a <unk> in attempt to infiltrate the SPECTRE spacecraft " Bird One " . However , Blofeld spots Bond , and he is detained while Bird One is launched . 
- Bird One closes in on the American space capsule , and US forces prepare to launch a nuclear attack on the USSR . Meanwhile , the Japanese ninjas approach the base 's entrance , but are detected and fired upon . Bond manages to open the hatch , letting in the ninjas . During the ensuing battle , Bond fights his way to the control room and activates Bird One 's self @-@ destruct before it reaches the American craft . The Americans stand down their forces . 
- Blofeld activates the base 's self @-@ destruct system and escapes . Bond , Kissy , Tanaka , and the surviving ninjas leave before the base explodes . 
- 
- = = Cast = = 
- 
- Sean Connery as James Bond : An MI6 agent . 
- Akiko Wakabayashi as Aki : An agent with the Japanese <unk> who assists Bond . 
- Mie Hama as Kissy Suzuki : An <unk> diving girl who replaces Aki after her death . 
- Donald Pleasence as Ernst Stavro Blofeld : The <unk> head of the terrorist syndicate known as SPECTRE . He intends to ignite a global nuclear war . 
- <unk> Tamba as Tiger Tanaka : Head of Japanese secret service . 
- <unk> <unk> as Mr. Osato : A Japanese industrialist secretly affiliated to SPECTRE . 
- Karin Dor as Helga Brandt / No. 11 : Osato 's secretary and a SPECTRE assassin . 
- Bernard Lee as M : The head of MI6 . 
- Charles Gray as <unk> Henderson : British contact living in Japan . 
- Burt <unk> as <unk> 3 : one of Blofeld 's henchmen . 
- Lois Maxwell as Miss Moneypenny : M 's secretary . 
- Desmond Llewelyn as Q : Head of MI6 technical department . 
- <unk> <unk> as <unk> : Undercover MI6 agent in Hong Kong . 
- Ronald Rich as Hans : Blofeld 's personal bodyguard . 
- David <unk> as <unk> in <unk> : one of Osato 's henchmen , who kills Aki . 
- Peter <unk> as Car Driver : one of Osato 's henchmen , who fights Bond . 
- 
- = = Production = = 
- 
- On Her Majesty 's Secret Service was the intended next film , but the producers decided to adapt You Only Live Twice instead because <unk> would require searching for high and <unk> locations . Lewis Gilbert originally declined the offer to direct , but accepted after producer Albert R. Broccoli called him saying : " You can 't give up this job . It 's the largest audience in the world . " Peter R. Hunt , who edited the first five Bond films , believed that Gilbert had been contracted by the producers for other work but they found they had to use him . 
- Gilbert , producers Broccoli and Harry Saltzman , production designer Ken Adam and director of photography Freddie Young then went to Japan , spending three weeks searching for locations . SPECTRE ’ s shore fortress headquarters was changed to an extinct volcano after the team learned that the Japanese do not build castles by the sea . The group was due to return to the UK on a <unk> Boeing <unk> flight ( <unk> Flight 911 ) on 5 March 1966 , but cancelled after being told they had a chance to watch a ninja demonstration . That flight crashed 25 minutes after takeoff , killing all on board . In Tokyo , the crew also found Hunt , who decided to go on holiday after having his request to direct declined . Hunt was invited to direct the second unit for You Only Live Twice and accepted the job . 
- Unlike most James Bond films featuring various locations around the world , almost the entire film is set in one country and several minutes are devoted to an elaborate Japanese wedding . This is in keeping with Fleming 's original novel , which also devoted a number of pages to the discussion of Japanese culture . <unk> Studios provided soundstages , personnel , and the female Japanese stars to the producers . 
- 
- = = = Writing = = = 
- 
- The producers had Harold Jack Bloom come to Japan with them to write a screenplay . Bloom 's work was ultimately rejected , but since several of his ideas were used in the final script , Bloom was given the credit of " Additional Story Material " . Among the elements were the opening with Bond 's fake death and burial at sea , and the ninja attack . As the screenwriter of the previous Bond films Richard <unk> was unavailable , Roald Dahl , a close friend of Ian Fleming , was chosen to write the adaptation despite having no prior experience writing a screenplay except for the uncompleted The Bells of Hell Go <unk> @-@ a @-@ ling @-@ a @-@ ling . 
- Dahl said that the original novel was " Ian Fleming ’ s worst book , with no plot in it which would even make a movie " , and compared it to a <unk> , stating that he had to create a new plot " [ though ] I could retain only four or five of the original story 's ideas . " On creating the plot , Dahl said he " didn 't know what the hell Bond was going to do " despite having to deliver the first draft in six weeks , and decided to do a basic plot similar to Dr. No . Dahl was given a free <unk> on his script , except for the character of Bond and " the girl formula " , involving three women for Bond to <unk> : an ally and a <unk> who both get killed , and the main Bond girl . While the third involved a character from the book , Kissy Suzuki , Dahl had to create Aki and Helga Brandt to fulfil the rest . 
- Gilbert was mostly collaborative with Dahl 's work , as the writer declared : " He not only helped in script conferences , but had some good ideas and then left you alone , and when you produced the finished thing , he shot it . Other directors have such an ego that they want to rewrite it and put their own dialogue in , and it 's usually disastrous . What I admired so much about Lewis Gilbert was that he just took the screenplay and shot it . That 's the way to direct : You either trust your writer or you don 't . " 
- 
- = = = Casting = = = 
- 
- When the time came to begin You Only Live Twice , the producers were faced with the problem of a disenchanted star . Sean Connery had stated that he was tired of playing James Bond and all of the associated commitment ( time spent filming and <unk> each movie ) , together with finding it difficult to do other work , which would potentially lead to <unk> . Saltzman and Broccoli were able to persuade Connery by increasing his fee for the film , but geared up to look for a replacement . 
- Jan <unk> was originally cast by producer Harry Saltzman to play Blofeld . Upon his arrival at the <unk> set , both producer Albert R. Broccoli and director Lewis Gilbert felt that he was a poor choice , resembling a " poor , benevolent Santa Claus " . Nonetheless , in an attempt to make the casting work , Gilbert continued filming . After several days , both Gilbert and Broccoli determined that <unk> was not menacing enough , and recast Blofeld with Donald Pleasence in the role . Pleasence 's ideas for Blofeld 's appearance included a <unk> , a limp , a beard , and a lame hand , before he settled on the scar . He found it uncomfortable , though , because of the glue that attached it to his eye . 
- Many European models were tested for Helga Brandt including German actress Eva <unk> who passed on the film , with German actress Karin Dor being cast . Dor performed the stunt of diving into a pool to depict Helga 's demise herself , without the use of a double . <unk> , for the German version Dor was dubbed by somebody else . 
- Gilbert had chosen <unk> Tamba after working with him in The 7th Dawn . A number of actual martial arts experts were hired as the ninjas . The two Japanese female parts proved difficult to cast , due to most of the actresses tested having limited English . Akiko Wakabayashi and Mie Hama were eventually chosen and started taking English classes in the UK . Hama , initially cast in the role of Tanaka 's assistant , had difficulty with the language , so the producers switched her role with Wakabayashi , who had been cast as Kissy , a part with significantly less dialogue . Wakabayashi only requested that her character name , " <unk> " , be changed to " Aki " . 
- 
- = = = Filming = = = 
- 
- Filming of You Only Live Twice lasted from July 1966 to March 1967 . The film was shot primarily in Japan . <unk> Castle in <unk> was depicted as Tanaka 's ninja training camp . His private transportation hub was filmed at the Tokyo Metro 's Nakano @-@ <unk> Station . As of 2011 , many of the fixtures in the station are unchanged from the time of filming . 
- The Hotel New <unk> Tokyo served as the outside for Osato Chemicals and the hotel 's gardens were used for scenes of the ninja training . <unk> in <unk> served as the fishing village , the Kobe harbour was used for the dock fight and Mount <unk> @-@ <unk> in <unk> was used for the exteriors of SPECTRE 's headquarters . Large crowds were present in Japan to see the shooting . A Japanese fan began following Sean Connery with a camera , and the police were called several times to prevent invasions during shooting . 
- The heavily armed WA @-@ 116 <unk> " Little Nellie " was included after Ken Adam heard a radio interview with its inventor , RAF Wing Commander Ken Wallis . Little Nellie was named after music hall star Nellie Wallace , who has a similar surname to its inventor . Wallis <unk> his invention , which was equipped with various mock @-@ up <unk> by John <unk> ' special effects team , during production . 
- " Nellie 's " battle with helicopters proved to be difficult to film . The scenes were initially shot in Miyazaki , first with takes of the <unk> , with more than 85 take @-@ offs , 5 hours of flight and Wallis nearly crashing into the camera several times . A scene filming the helicopters from above created a major <unk> and cameraman John Jordan 's foot was severed by the craft 's <unk> . The concluding shots involved explosions , which the Japanese government did not allow in a national park . So , the crew moved to <unk> , Spain , which was found to resemble the Japanese landscape . 
- The sets of SPECTRE 's volcano base were constructed at a lot inside <unk> Studios , with a cost of $ 1 million and including operative heliport and <unk> . The 45 m ( 148 ft ) tall set could be seen from 5 kilometres ( 3 miles ) away , and attracted many people from the region . Locations outside Japan included using the Royal Navy frigate HMS <unk> , then in Gibraltar , for the sea burial , Hong Kong for the scene where Bond fakes his death , and Norway for the Soviet radar station . 
- Sean Connery 's then wife Diane <unk> did the swimming scenes for at least five Japanese actresses , including Mie Hama . Martial arts expert Donn F. <unk> provided martial arts training , and also doubled for Connery . Lewis Gilbert 's regular editor , <unk> <unk> , was originally hired to edit the film . However , after her initial , almost three @-@ hour cut received a terrible response from test audiences , Peter R. Hunt was asked to re @-@ edit the film . Hunt 's cut proved a much greater success , and he was awarded the director 's chair on the next film as a result . 
- 
- = = = Music = = = 
- 
- The soundtrack was the fourth of the series to be composed by John Barry . He tried to incorporate the " <unk> of the Oriental sound " with Japanese music @-@ inspired tracks . The theme song , " You Only Live Twice " , was composed by Barry and <unk> Leslie <unk> and sung by Nancy Sinatra after her father Frank Sinatra passed on the opportunity . Nancy Sinatra was reported to be very nervous while recording – first she wanted to leave the studio ; then she claimed to sometimes " sound like Minnie Mouse " . Barry declared that the final song uses 25 different takes . British singer Julie Rogers recorded an alternative song for the titles , but this was not used . 
- There are two versions of the song " You Only Live Twice " , sung by Nancy Sinatra , one directly from the movie soundtrack , and a second one for record release arranged by Billy Strange . The movie soundtrack song is widely recognised for its striking opening bars and oriental flavour , and was far more popular on radio . The record release made No. 44 on the Billboard charts in the USA , No. 11 in UK . Both versions of the title song are available on CD . 
- In 1992 , <unk> sampled the title song " You Only Live Twice " for his song " Trip II the Moon Part 2 " . In 1997 , Icelandic singer <unk> recorded a cover version . In 1998 , Robbie Williams used the distinctive string figure for his song " Millennium " , ( although it was re @-@ recorded , rather than sampled from the movie for cost reasons ) . Coldplay covered it when they toured in 2001 , and it was covered by <unk> Atlas for her 2005 compilation album The Best of <unk> Atlas . Shirley <unk> , who has three original Bond themes to her credit , has also covered the song . 
- A different title song was originally recorded by Julie Rogers , but eventually discarded . Only two lines from that version were kept in the final lyrics , and the orchestral part was changed to fit Nancy Sinatra 's vocal range . Rogers ' version only appeared in a James Bond 30th <unk> CD , with no singer credit . In the 1990s , an alternative example of a possible theme song ( also called " You Only Live Twice " and sung by Lorraine Chandler ) was discovered in the vaults of RCA Records . It became a very popular track with followers of the Northern Soul scene ( Chandler was well known for her high @-@ quality soul output on RCA ) and can be found on several RCA soul compilations . 
- 
- = = Promotion = = 
- 
- To promote the film , <unk> Productions produced a one @-@ hour colour television programme entitled Welcome to Japan , Mr. Bond first aired on 2 June 1967 in the United States on NBC . Bond regulars Lois Maxwell and Desmond Llewelyn appeared playing respectively " Miss Moneypenny " and " Q " . Kate O <unk> appears as Miss Moneypenny 's assistant . The programme shows clips from You Only Live Twice and the then four existing Bond <unk> and contained a storyline of Moneypenny trying to establish the identity of Bond 's bride . 
- 
- = = Release and reception = = 
- 
- You Only Live Twice premiered at the <unk> Leicester Square in London . It was the first premiere of a James Bond film that Queen Elizabeth II attended . The film grossed $ 43 million in the United States and over $ 111 million worldwide . 
- Critical response today is mostly positive , with Rotten Tomatoes giving a 72 % rating . But most reviews pointed out various flaws in the film . James <unk> said that the first half was good , but " during the second half , as the plot escalates beyond the bounds of <unk> , that the film starts to fragment " , criticising Blofeld 's appearance and stating " rockets that swallow up spacecraft are a bit too extravagant . " 
- Roger <unk> criticised the focus on <unk> , declaring that the James Bond formula " fails to work its magic " . John <unk> in his book James Bond in the Cinema compared the film to an episode of Thunderbirds with a reliance on <unk> but admitted it had pace and spectacle . Christopher <unk> considered the film one of James Bond 's most memorable adventures , but the plot " <unk> and quite confusing " . 
- Ali <unk> of BBC Films panned Dahl 's script displaying " a whole new world of <unk> and technology . " Leo Goldsmith lauded the volcano base as " the most impressive of Ken Adam 's sets for the franchise . " Danny <unk> wrote that You Only Live Twice " should have been about twenty minutes shorter " and described it as " not a bad Bond film , but it doesn ’ t compare to its predecessors – the formula had become a little stale . " 
- IGN ranked You Only Live Twice as the fourth best Bond film , and Entertainment Weekly as the second best , considering that it " pushes the series to the outer edge of coolness " . But Norman <unk> of <unk> chose it as the fifth worst , criticising the plot , action scenes and little <unk> for Blofeld . Literary critic Paul Simpson called the film one of the most colourful of the series and credited the prefecture of <unk> for adding " a good flavour " of Japanese influence on the film , but he panned the depiction of Blofeld as a " let @-@ down " , " small , bald and a <unk> scar . " Simon <unk> said that the film is " perfect " for parodies of the series . 
- 
- 
- = WASP @-@ 13b = 
- 
- WASP @-@ 13b is an extrasolar planet that was discovered in 2008 in the orbit of the <unk> star WASP @-@ 13 . The planet has a mass of nearly half that of Jupiter , but a radius five @-@ <unk> the size of Jupiter . This low relative mass might be caused by a core that is of low mass or that is not present at all . 
- The planet orbits at approximately 5 % of the distance between the Sun and Earth every four days . The star was observed several times between 2006 and 2009 , at first through the SuperWASP program and later through focused follow @-@ up observations . Analysis of collected radial velocity measurements led to the discovery of WASP @-@ 13b , which was reported in a journal on April 7 , 2009 . A follow @-@ up study published in 2011 investigated the cause for inflated planets such as WASP @-@ 13b , and re @-@ examined ( and re @-@ constrained ) its mass , radius , density , and age . 
- 
- = = Discovery = = 
- 
- Between November 27 , 2006 , and April 1 , 2007 , <unk> images of the star WASP @-@ 13 by the SuperWASP @-@ North program based at <unk> de los <unk> Observatory in the Canary Islands led to the identification of WASP @-@ 13 as host to a potentially transiting object . <unk> follow @-@ up observations were taken on February 16 , 2008 using the James Gregory Telescope ( JGT ) in Scotland , which took <unk> exposures of the star , although the last twenty images taken were obscured by cloud cover and were discarded . Using HD <unk> as a reference star along with JGT measurements , the astronomers investigating the system were able to create a light curve for the transiting planet . 
- WASP @-@ 13 was observed between February 11 and 15 in 2008 by the SOPHIE <unk> spectrograph at the <unk> @-@ Provence Observatory in France , determining the radial velocity of the transiting body . Use of the FIES <unk> spectrograph at the Nordic Optical Telescope in the Canary Islands gained other spectral measurements that yielded the characteristics of the star . Analysis of the SOPHIE and FIES data were used to <unk> some of the orbiting body 's characteristics . The discovery of the orbiting body 's mass using radial velocity measurements led to its confirmation as the planet WASP @-@ 13b . 
- The discovery of WASP @-@ 13b was reported in the journal <unk> and <unk> by the European Southern Observatory on May 19 , 2009 . The discovery paper was received by the journal on April 7 , 2009 . 
- Later , between 2009 and 2011 , another team of astronomers observed WASP @-@ 13b and WASP @-@ <unk> to find what caused some Hot Jupiters to have <unk> high radii . The <unk> photometric camera on the Liverpool Telescope was used to detect further transits . Two partial transits and two full transits were observed during this period , although the quality of both full transits was slightly compromised because of passing cloud cover . The collected observations , along with the JGT observations that were used to confirm the planet , were scaled to filter out errors such as background noise . The data was then used to re @-@ define WASP @-@ 13b 's parameters , including its age , mass , radius , and density . The study also noted that a limb darkening effect was present , a characteristic that may affect future atmospheric studies of the planet . 
- 
- = = Host star = = 
- 
- WASP @-@ 13 is a <unk> G @-@ type star located in the Lynx constellation . Measurements taken by FIES and SOPHIE did not <unk> the mass , radius , or age well ; however , a later 2011 study using the Liverpool Telescope better @-@ constrained those parameters . The star 's mass is estimated at 1 @.@ 09 times the mass of the Sun , its radius at 1 @.@ <unk> times that of the Sun , and its density at 0 @.@ 288 time 's the Sun 's density . These characteristics are re @-@ defined taking limb darkening into account . The star 's metallicity , which is measured by iron content , is placed roughly at [ Fe / H ] = 0 , similar to that of the Sun . Also , the star 's estimated effective temperature is <unk> K , slightly warmer than the Sun . 
- WASP @-@ 13 has an apparent magnitude of 10 @.@ 42 , making it invisible to the unaided eye as seen from Earth . 
- 
- = = Characteristics = = 
- 
- WASP @-@ 13b is a transiting planet with an estimated mass that is ( including limb darkening ) 0 @.@ <unk> times that of Jupiter and a radius that is 1 @.@ 389 times Jupiter 's radius . The planet is , in other words , less than half the mass of Jupiter , but slightly less than fourteen tenths its size . WASP @-@ 13b 's low mass can mostly likely be attributed to the presence of a low @-@ mass core , or to the total lack of a core , according to the discovery paper . WASP @-@ 13b , which orbits its host star at a distance of 0 @.@ <unk> AU , circles its star completely every 4 @.@ <unk> days . The 2011 study on the planet recognized WASP @-@ 13b as the fifth lowest @-@ density extrasolar planet known , behind Kepler @-@ <unk> ; WASP @-@ <unk> ; <unk> @-@ <unk> ; and <unk> @-@ <unk> . 
- WASP @-@ 13b has an orbital inclination of <unk> , which means that it orbits almost edge @-@ on as seen from Earth . 
- 
- 
- = U2 concert in Sarajevo = 
- 
- On 23 September 1997 , the Irish rock band U2 held a concert at <unk> Stadium in Sarajevo , Bosnia and Herzegovina , as part of the group 's PopMart Tour . They were the first major artist to hold a concert in the city after the end of the Bosnian War . Approximately 45 @,@ 000 fans attended the show . 
- The band first became involved with Sarajevo in 1993 on their Zoo TV Tour ; approached by aid worker Bill Carter about bringing attention to the Siege of Sarajevo , the band conducted nightly satellite transmissions with Bosnians during their shows . These link @-@ ups were the subject of criticism from journalists for mixing entertainment with human tragedy . Although the war made it impractical for U2 to visit Sarajevo at the time , they vowed to eventually play a concert in the city . After the conflict ended in November 1995 , they made arrangements to visit Sarajevo , and with help from United Nations ambassadors and peacekeeping troops , they scheduled and played the concert in 1997 . 
- The band offered to hold a benefit concert or small show in Sarajevo , but it was requested that they stage a full PopMart concert . The performance consequently featured the tour 's extravagant stage , and the band played a set list typical of the tour . The show brought together people of different <unk> who had previously clashed during the war , and train service was temporarily resumed to allow <unk> to attend . Among the songs played was " Miss Sarajevo " , written by U2 and Brian Eno about a beauty pageant held during the war . Although the band were <unk> with their performance and lead vocalist Bono had vocal difficulties , the concert was well received and was credited with improving morale among Bosnians . The members of U2 consider the show to be among their proudest moments . The concert was lauded by Bosnians . 
- 
- = = Background = = 
- 
- 
- = = = War in Sarajevo = = = 
- 
- Socialist Federal Republic of Yugoslavia was composed of six constituent republics : Bosnia @-@ Herzegovina , Croatia , Macedonia , Montenegro , Serbia , and Slovenia . In 1991 , Croatia , and Slovenia <unk> from Yugoslavia . Bosnia @-@ Herzegovina — a republic with a mixed population consisting of <unk> , Serbs , and Croats — followed suit in March 1992 in a highly controversial referendum , creating tension in the ethnic communities . Bosnian Serb militias , whose strategic goal was to secede from Bosnia and Herzegovina and unite with Serbia , <unk> Sarajevo with a siege force of 18 @,@ 000 stationed in the surrounding hills , from which they assaulted the city with weapons that included artillery , mortars , tanks , anti @-@ aircraft guns , heavy machine @-@ guns , rocket launchers , and aircraft bombs . From 2 May 1992 until the end of the war in 1996 , the city was blockaded . The Army of the Republic of Bosnia and Herzegovina , numbering roughly 40 @,@ 000 inside the besieged city , was poorly equipped and unable to break the siege . Meanwhile , throughout the country , thousands of predominantly <unk> civilians were driven from their homes in a process of ethnic cleansing . In Sarajevo , women and children attempting to buy food were frequently <unk> by Bosnian Serb sniper fire . 
- 
- = = = U2 's reaction = = = 
- 
- In 1993 , U2 were in Europe for the " <unk> " leg of their Zoo TV Tour . Before their 3 July show in <unk> , Italy , the band received a <unk> from Radio <unk> <unk> I <unk> asking for an interview regarding the situation in Bosnia . The band agreed and met with an American aid worker named Bill Carter , who acted as the station 's foreign associate , due to Serbian travel restrictions . Carter described his experiences in Sarajevo helping Bosnians while surviving the dangerous living conditions . Lead vocalist Bono was <unk> to hear that those living in makeshift bomb shelters in the city played music , including U2 's , at loud volumes to drown out the sound of explosions . While in Sarajevo , Carter had seen a television interview on MTV in which Bono mentioned the theme of the <unk> tour leg was a unified Europe . Feeling that such an aim was empty if ignoring the Bosnians ' plight , Carter sought Bono 's help . He requested that U2 go to Sarajevo to bring attention to the war and break the " media fatigue " that had occurred from covering the conflict . 
- Bono agreed to Carter 's request without asking the rest of the band , and when informed of the idea , the other members gave only tacit approval . They briefly considered playing an impromptu concert in the city , with Bono suggesting that they perform in the bunker where Carter and his friends hid during the siege . He said , " even if all we get is some extra attention for Bosnia on MTV , that 's something " . The idea fell through when it was pointed out that the logistics of transporting their equipment into the city were impossible , as the only way into Sarajevo was on a United Nations plane . Manager Paul McGuinness realized that even if the band managed to organize a concert , it would endanger their lives and those of the audience and the Zoo TV crew . As he explained , " U2 's effort to discuss any humanitarian issue have sometimes been accompanied by a false instinct that U2 is also obliged to resolve that issue . Going to Sarajevo seems to me to fall into that category . I think it would endanger the people we go with , endanger the tour , and endanger the band . " Drummer Larry Mullen , Jr. feared that the move would look like a publicity stunt . 
- Instead , the group agreed to use the tour 's satellite dish to conduct live video transmissions from their concerts to Carter in Sarajevo . Carter returned to the city and assembled a video unit . The band purchased a satellite dish to be sent to Sarajevo and paid a £ 100 @,@ 000 fee to join the European Broadcasting Union ( <unk> ) . Once set up , the band began satellite link @-@ ups to Sarajevo on nearly a nightly basis , the first of which aired on 17 July 1993 in Bologna , Italy . To connect with the <unk> satellite , Carter and two co @-@ workers were forced to visit the Sarajevo television station at night and to film with as little light as possible to avoid the attention of <unk> and bombers . To reach the building , they had to traverse an area known as " <unk> <unk> " . This was done a total of 12 times over the course of a month . During the broadcasts , Carter discussed the deteriorating situation in the city , and Bosnians often spoke to U2 and their audience . These <unk> interviews starkly contrasted with the rest of the show ; concerts on the Zoo TV Tour were elaborately staged <unk> events that <unk> television and the audience 's over @-@ stimulation . Most of the shows were scripted , but the link @-@ ups to Sarajevo were not , leaving the group unsure who would speak or what they would say . U2 stopped the broadcasts in August 1993 after learning that the Siege of Sarajevo was being reported on the front of many British newspapers . Though this trend had begun before the band 's first Sarajevo transmission , Nathan Jackson suggested that U2 's actions had brought awareness of the situation to their fans and to the British public indirectly . 
- Reactions to the transmissions were mixed . Many fans felt the transmissions disrupted the flow of the concerts . Most of the British press was highly critical . One writer for NME wrote , " The Bosnian <unk> was beyond bad taste . It was insulting . " Bono thought that they were bringing the public 's attention to an important event , though he admitted that the link @-@ ups were the most difficult thing the band had done in their career . Guitarist The Edge said , " We don 't normally see that kind of cold hard news . We get a very <unk> , <unk> take on everything ... When you watch the television news , you are getting something palatable , whereas this was really quite <unk> most of the time . And for that reason I think it affected people very much , including us . " Mullen worried that the band were <unk> the Bosnians ' suffering for entertainment . During a transmission from the band 's concert at Wembley Stadium , three women in Sarajevo asked what the band intended to do to help before telling Bono , " We know you 're not going to do anything for us . You 're going to go back to a rock show . You 're going to forget that we even exist . And we 're all going to die . " During a transmission to a Glasgow concert , a Bosnian woman told the concert audience , " We would like to hear the music , too , but we hear only the screams of wounded and tortured people and raped women . " Some people were upset by the circumstances of Sarajevo and were motivated to join the War Child charity project , including U2 producer Brian Eno . Despite U2 's obligation to the tour and their inability to perform in Sarajevo during the war , they vowed to play the city someday . 
- The band contributed to Bosnian relief efforts to enhance humanitarian and public awareness of the issue , and Bono and Carter subsequently collaborated on the documentary Miss Sarajevo , which showcased the war @-@ torn city during Carter 's six months living there . In 1995 , U2 and Eno wrote the song " Miss Sarajevo " as a response to " the surreal acts of defiance that had taken place during the siege of Sarajevo " . One such act was a beauty pageant organized by Bosnian women who planned to fight the war with their " lipstick and heels " . During the pageant , all of the participants walked onto the stage carrying a banner that said , " Don 't let them kill us " . The winner of the pageant , 17 @-@ year @-@ old <unk> Nogić , later said the pageant " was a crazy thing to do during a war . But we tried to live a normal life . It was some kind of a defence mechanism we all had . " Years later , Bono said , " It was pure <unk> and it deserved to be celebrated in song . " Of the song 's meaning , he said , " Everywhere people had heard their call for help — but help never came . That was the feeling . I had tried to tackle subjects like this head @-@ on , but I 'd learnt a lesson . You have to try and make the same points , in a different , less direct , more <unk> way . " " Miss Sarajevo " was recorded with Luciano Pavarotti and released as the first single from U2 's side @-@ project with Eno entitled Original <unk> 1 ; the record was released under the pseudonym " <unk> " . 
- 
- = = Scheduling and preparations = = 
- 
- As the Bosnian War ended in 1995 and the Siege of Sarajevo in 1996 , the stability of the region began to improve . Realizing this , U2 began to plan a concert for Sarajevo that would take place on their 1997 PopMart Tour . Although they were the first major musical artist to perform in the city following the war , China Drum had played a concert in July 1996 . Music journalist Andrew Mueller described China Drum 's experience in a single van as a " logistical and administrative nightmare " . <unk> Sacirbey , the Bosnian Ambassador to the United Nations , helped U2 make arrangements , playing an informal role as promoter and organizer . McGuinness said , " We thought it was going to be quite difficult . But it 's been quite straightforward . People have just wanted to help . We 've <unk> a lot of equipment , <unk> and so on , from the military , and the local crew have been incredibly supportive . " 
- Scheduling the concert meant a financial loss of £ 500 @,@ 000 for the band , despite sponsorship from Coca @-@ Cola and <unk> . Ticket prices were set at just DM 8 ( £ 8 , US $ 18 ) , because of the 50 percent unemployment rate in the city . Bono offered for the group to perform a benefit concert or small show in Sarajevo , but the city requested they hold the full PopMart show . Bono said , " We offered to do a charity gig here , just turn up and do a scratch gig , but they wanted the whole fucking thing . They wanted the lemon ! " McGuinness added , " we felt it was important that we treat this as another city on the tour , to pay them that respect . To come here and not do the whole show would have been rude . " According to news releases following the concert , the total net income for the show was US $ 13 @,@ 500 ; however , tour promoter John <unk> noted that price did not include the costs of the production or transportation . 
- As late as July 1997 , U2 were pressured to accept an offer of approximately $ 4 million to perform in Basel , Switzerland on the date scheduled for the Sarajevo show . At the time , rumours about the region 's instability persisted . To ensure the Sarajevo show was not canceled , Sacirbey appeared at many of the band 's preceding shows to lobby on behalf of the city . For the stage to reach Sarajevo , the road crew had to drive the equipment and stage through war @-@ torn Bosnia . Although the trip was without incident , they had to pass through towns such as Mostar , which had been " obliterated " during the war . Stage and lighting designer Willie Williams commented that " when the truck drivers arrived you could see that they were changed men " . The only trouble in transporting the stage came when a border control agent prevented them from crossing the border for hours . The trucks reached Sarajevo two days prior to the concert , arriving to the cheers and applause of the city 's residents ; their arrival was the first concrete evidence that the band were keeping their promise to play there . McGuinness explained , " This is a city that 's been disappointed so many times there were a lot of people who weren 't prepared to believe the gig was going to take place until they saw the stage going up . " Until then , tickets had sold very slowly , but within 24 hours of the trucks ' arrival , another 8 @,@ 000 tickets were sold . Despite this , a day before the concert , 15 @,@ 000 tickets remained <unk> . Three @-@ hundred local residents were employed to help assemble the stage and promote the show . 
- Several hundred members of the international " <unk> Force " ( SFOR ) were tasked with upholding the Dayton Agreement for the concert . The band were overwhelmed by the sights they saw when arriving . During the war , <unk> Stadium was used as a morgue , and <unk> were present on either sides . Although the venue had escaped the worst of the shelling , the nearby Olympic Hall <unk> had been badly damaged during the war . Despite its condition , U2 used the building for their dressing rooms and offices . Following the concert , it was used to provide lodging for 3 @,@ 000 fans . The band 's hotel , a nearby Holiday Inn , had been shelled during the siege , and part of the building had been destroyed as a result . The walls in Mullen 's room were <unk> with mortar shrapnel , and sections of the floor were also missing . Prior to the show , Sacirbey took Mullen on a tour of the city , showing him the Sarajevo Roses embedded in the streets . 
- On the day of the concert , trains ran into Sarajevo for the first time since the start of the war . Two lines were opened , one from Mostar to Sarajevo and the other from <unk> to Sarajevo . Although the railways had been functional for the duration of the war , Muslim and Croat politicians could not decide who would operate them . As a result , the trains were only run on the date of the concert to bring fans to the city , and the day after to take them home again . <unk> requirements were temporarily suspended . An effort was made to include all of the country 's ethnic groups at the concert . Approximately 500 fans crossed the ethnic boundary lines between Bosnia 's Serb Republic and the <unk> @-@ Croat Federation . People from several of the other <unk> republics went to Sarajevo for the concert , with buses carrying fans from Zagreb , Croatia and <unk> , Slovenia . Security around the event was strict . SFOR soldiers searched for bombs with <unk> dogs , and the buildings around the stadium were lined with Irish troops and <unk> in case violence broke out . 
- 
- = = Concert overview = = 
- 
- The concert was held on 23 September 1997 , and approximately 45 @,@ 000 people attended . It was broadcast in Bosnia by local television networks , as well as globally by BBC . During the event , 10 @,@ 000 soldiers stood on the left side of the stadium to ensure no conflicts broke out . At <unk> , a decision was made to open the stadium gates to all , allowing approximately 10 @,@ 000 more fans who could not afford the concert or who had not purchased tickets in time to attend . In addition to the local and foreign fans , 6 @,@ 000 off @-@ duty SFOR soldiers attended the event in uniform . <unk> Nogić attended the concert and arrived in a <unk> with the band . The concert was broadcast live internationally on radio , and all proceeds from the radio sales were donated to the War Child project . 
- Three opening acts played before U2 , beginning with the Gazi <unk> @-@ <unk> choir , an Islamic choir from a local high school . Their performance was followed by two local bands , <unk> and <unk> , one of which was chosen personally by Sacirbey , and the other which was selected through a radio contest . Following the opening acts , musician <unk> B performed a DJ set before U2 took the stage . 
- The band 's set list was similar to that of most shows on the PopMart Tour , but with " Sunday Bloody Sunday " in place of The Edge 's karaoke segment and the addition of " Miss Sarajevo " in the second encore . The night was a celebration of the end of the war , with Bono setting the tone by shouting out " Viva Sarajevo ! <unk> the past , kiss the future ! " at the beginning of " Even Better Than the Real Thing " . Bono had struggled with his voice throughout the tour , and the morning of the concert he <unk> up " without a voice " . There was no intent to cancel , and the show went ahead as planned . Though Bono had few difficulties through the opening quartet of " <unk> " , " I Will Follow " , " Gone " , and " Even Better Than the Real Thing " , his voice gave out during " Last Night on Earth " . In 2006 , The Edge suggested that Bono 's vocal troubles had been caused by <unk> or by the stress of the previous few months of touring , though he later remarked that " it didn 't really matter that our lead singer was under the weather because every member of the audience seemed to join in on every song . There was a mass chorus for the whole concert . " 
- At various points during " Until the End of the World " and " New Year 's Day " , Bono <unk> for the audience to help him with the vocals , and by the eighth song of the night , " Pride ( In the Name of Love ) " , he was reduced to speaking the lyrics instead of singing them . The band continued with their standard set list by playing " I Still Haven 't Found What I 'm Looking For " , " Stand By Me " , " All I Want Is You " , and " <unk> at the Sun " . U2 considered playing " Desire " after " All I Want Is You " , but they chose not to perform the song . The Edge then performed a solo version of " Sunday Bloody Sunday " . The rendition was slower and quieter than the studio version . During the song , Bono went backstage for <unk> <unk> , which helped to improve his voice for a short time . Brian Eno was prepared to go on stage to replace Bono if he could not continue , or to sing alongside him . Bono ultimately returned to the stage alone for the next song , " Bullet the Blue Sky " , and the band continued with renditions of " Please " and " Where the Streets Have No Name " , which concluded the main set . 
- In the interlude before the first encore , U2 had a worried discussion over the introduction to " Miss Sarajevo " . The song had only been played once prior — at a benefit concert in 1995 with Bono , The Edge , Eno , and Pavarotti . They returned to the stage and played " <unk> " , " If You Wear That Velvet Dress " , " With or Without You " , and a rough version of " Miss Sarajevo " . Pavarotti was not at the concert to sing his part and so an antique <unk> was brought onto the stage in his place . Eno came on stage to sing backing vocals , and Bono invited Nogić on stage during the chorus . During the song , the video screen showed images from Carter 's Miss Sarajevo documentary , including footage of the girls taking part in the beauty contest and the banner reading " Please don 't let them kill us " . Bono apologized for the rocky performance at the end of the song , saying " Sarajevo , this song was written for you . I hope you like it , because we can 't fucking play it . " The concert concluded with a second encore of " <unk> Me , Thrill Me , Kiss Me , Kill Me " , " <unk> Ways " , " One " , and an abbreviated cover of " <unk> Melody " . 
- After the band had walked <unk> , in a move described by NME as the most meaningful of the concert , the audience faced the troops in the stadium and broke into a spontaneous round of applause , which quickly turned into an ovation — an act which the soldiers mimicked in turn . 
- 
- = = Reaction = = 
- 
- The day after the concert in Sarajevo , a local newspaper carried an editorial which was <unk> , " Today was the day the siege of Sarajevo ended " . In reaction to the event , a Bosnian student told members of the international press , " I felt excluded from the world for so long . It 's not only about U2 . It 's the feeling of being part of the world . " A local resident said that the concert was " proof that we have peace here , that everything is OK " . Fans from outside the former Yugoslavia described Sarajevo as " an oasis of light " in the midst of destroyed and fire @-@ damaged buildings , and deserted villages . Despite the <unk> performance , the Associated Press said , " For two magical hours , the rock band U2 achieved what warriors , politicians and diplomats could not : They united Bosnia . " Andrew Mueller of The Independent wrote , " For the first time since the start of the war in 1992 , people more accustomed to seeing each other through the sights of a rifle were <unk> on the capital to listen to music together . It was a reminder of prewar Sarajevo , home to some of old Yugoslavia 's best rock bands . " Sacirbey stated that he was satisfied that the concert " was held to promote a sense of normalcy , peace and reconciliation in Sarajevo and not to raise money . " He also expressed thanks on behalf of President <unk> <unk> , who described the concert as a " landmark event " . 
- Mullen and The Edge both agreed that playing the Sarajevo concert had been the highlight of their careers ; Mullen said , " [ t ] here 's no doubt that that is an experience I will never forget for the rest of my life . And if I had to spend 20 years in the band just to play that show , [ ... ] I think it would have been worthwhile . " Bono described it as " one of the toughest and one of the sweetest nights of my life " , saying of the audience , " I think they wanted , more than anything , a return to normalcy . That 's what these people want , it 's what they deserve . " He also speculated that the loss of his voice had " allowed room for Sarajevo to take the gig away from us . They could see that things could go horribly wrong , they 'd gone to a lot of trouble to come here , and they were just going to make it happen . And they did . " After the completion of the PopMart Tour , he said , " it was amazing and <unk> to discover that on our most ' pop ' of tours some of the best shows were in political <unk> like Santiago , Sarajevo , Tel Aviv [ ... ] anywhere music meant more than entertainment " . Following the concert , President <unk> presented Bono with an honorary Bosnian passport , in recognition of his humanitarian efforts during the war . 
- 
- = = Legacy = = 
- 
- The peaceful nature of the U2 concert was fleeting , as violence flared up in the region the following year with the Kosovo War . NATO troops remained in Sarajevo until 2004 , while European Union peace @-@ keeping troops remain in the city . 
- Following the Sarajevo concert , The Edge 's solo performance of " Sunday Bloody Sunday " was performed at the majority of shows for the remainder of the tour , and a recording of the song from the Sarajevo concert was released on the CD single for " If God Will <unk> His Angels " on 8 December 1997 ; The Edge later stated the band had " rediscovered " the song in Sarajevo after his solo performance . A short documentary about the concert , Missing Sarajevo , was included on the DVD release of U2 's 2002 video compilation , The Best of 1990 @-@ 2000 . 
- After U2 first performed " Miss Sarajevo " at the Sarajevo concert , it was not performed again until the second leg of the Vertigo Tour in 2005 . U2 did not return to perform in any country in the former Yugoslavia until August 2009 , when they performed two shows in Zagreb during the U2 360 ° Tour . During the Zagreb shows , Bono stated that his honorary Bosnian passport was one of his " most <unk> possessions " , which prompted the country 's Council of Ministers to announce that his passport was to be revoked , citing how country 's laws do not allow honorary <unk> to be conferred . 
- 
- 
- = Frank Slide = 
- 
- The Frank Slide was a <unk> that buried part of the mining town of Frank , Alberta , Canada . The province of Alberta was not created until September 1905 , more than two years after the slide . The community was still part of the Northwest Territories when the incident occurred at 4 : 10 am on April 29 , 1903 . Over 82 million tonnes ( 90 million tons ) of limestone rock <unk> down Turtle Mountain within 100 seconds , <unk> the eastern edge of Frank , the Canadian Pacific Railway line and the coal mine . It was one of the largest landslides in Canadian history and remains the deadliest , as between 70 and 90 of the town 's residents were killed , most of whom remain buried in the rubble . Multiple factors led to the slide : Turtle Mountain 's formation left it in a constant state of instability . Coal mining operations may have weakened the mountain 's internal structure , as did a wet winter and cold snap on the night of the disaster . 
- The railway was repaired within three weeks and the mine was quickly reopened . The section of town closest to the mountain was relocated in 1911 amid fears that another slide was possible . The town 's population nearly doubled its pre @-@ slide population by 1906 , but dwindled after the mine closed permanently in 1917 . The community is now part of the Municipality of Crowsnest Pass in the Province of Alberta and has a population around 200 . The site of the disaster , which remains nearly unchanged since the slide , is now a popular tourist destination . It has been designated a Provincial Historic Site of Alberta and is home to an interpretive centre that receives over 100 @,@ 000 visitors annually . 
- 
- = = Background = = 
- 
- The town of Frank was founded in the southwestern corner of the District of Alberta , a <unk> of the Northwest Territories in 1901 . A location was chosen near the base of Turtle Mountain in the Crowsnest Pass , where coal had been discovered one year earlier . It was named after Henry Frank who , along with Samuel <unk> , owned the Canadian @-@ American Coal and Coke Company , which operated the mine that the town was created to support . The pair celebrated the founding of the town on September 10 , 1901 , with a gala opening that featured speeches from territorial leaders , sporting events , a dinner and tours of the mine and planned layout for the community . The Canadian Pacific Railway ( CPR ) ran special trains that brought over 1 @,@ 400 people from neighbouring communities to celebrate the event . By April 1903 , the permanent population had reached 600 , and the town featured a two @-@ storey school and four hotels . 
- Turtle Mountain stands immediately south of Frank . It consists of an older limestone layer folded over on top of softer materials such as shale and sandstone . <unk> had left the mountain with a steep overhang of its limestone layer . It has long been unstable ; the Blackfoot and <unk> peoples called it " the mountain that moves " and refused to camp in its vicinity . In the weeks leading up to the disaster , miners occasionally felt <unk> from within the mountain , while the pressure created by the shifting rock sometimes caused the timbers supporting the mine shafts to crack and splinter . 
- 
- = = <unk> = = 
- 
- In the early morning hours of April 29 , 1903 , a freight train pulled out of the mine and was slowly making its way towards the <unk> when the crew heard a <unk> <unk> behind them . The engineer instinctively set the <unk> to full speed ahead and <unk> his train to safety across the bridge over the Crowsnest River . At 4 : 10 am , 30 million cubic metres ( 82 million tonnes ) of limestone rock broke off the peak of Turtle Mountain . The section that broke was 1 @,@ 000 metres ( 3 @,@ 300 ft ) wide , 425 metres ( 1 @,@ 394 ft ) high and 150 metres ( 490 ft ) deep . Witnesses to the disaster claimed it took about 100 seconds for the slide to reach up the opposing hills , indicating the mass of rock traveled at a speed of about 112 kilometres per hour ( 70 mph ) . The sound was heard as far away as <unk> , over 200 kilometres ( 120 mi ) north of Frank . 
- Initial reports on the disaster indicated that Frank had been " nearly wiped out " by the mountain 's collapse . It was thought the <unk> was triggered by an earthquake , volcanic eruption or explosion within the mine . The majority of the town survived , but the slide buried buildings on the eastern outskirts of Frank . Seven cottages were destroyed , as were several businesses , the cemetery , a 2 @-@ kilometre ( 1 @.@ 2 mi ) stretch of road and railroad tracks , and all of the mine 's buildings . 
- Approximately 100 people lived in the path of destruction , located between the CPR tracks and the river . The death toll is uncertain ; estimates range between 70 and 90 . It is the deadliest landslide in Canadian history and was the largest until the Hope Slide in 1965 . It is possible that the toll may have been higher , since as many as 50 <unk> had been camped at the base of the mountain while looking for work . Some residents believed that they had left Frank shortly before the slide , though there is no way to be certain . Most of the victims remain <unk> beneath the rocks ; only 12 bodies were recovered in the immediate aftermath . The skeletons of six additional victims were unearthed in 1924 by crews building a new road through the slide . 
- Initial news reports stated that between 50 and 60 men were within the mountain and had been buried with no hope of survival . In reality , there were 20 miners working the night shift at the time of the disaster . Three had been outside the mine and were killed by the slide . The remaining 17 were underground . They discovered that the entrance was blocked and water from the river , which had been <unk> by the slide , was coming in via a secondary tunnel . They unsuccessfully tried to dig their way through the blocked entrance before one miner suggested he knew of a seam of coal that reached the surface . Working a narrow tunnel in pairs and <unk> , they dug through the coal for hours as the air around them became increasingly toxic . Only three men still had enough energy to continue digging when they broke through to the surface late in the afternoon . The opening was too dangerous to escape from due to falling rocks from above . Encouraged by their success , the miners cut a new shaft that broke through under an <unk> of rock that protected them from falling debris . Thirteen hours after they were buried , all 17 men emerged from the mountain . 
- The miners found that the row of cottages that served as their homes had been devastated and some of their families killed , seemingly at random . One found his family alive and safe in a makeshift hospital , but another emerged to discover his wife and four children had died . Fifteen @-@ year @-@ old <unk> Clark , working a late shift that night in the town 's boarding house , had been given permission to stay overnight for the first time . She was the only member of her family to survive . Her father was working outside the mine when the slide hit , while her mother and six siblings were buried in their home . All 12 men living at the CPR work camp were killed , but 128 more who were scheduled to move into the camp the day before the slide had not arrived — the train that was supposed to take them there from <unk> , British Columbia , failed to pick them up . The Spokane <unk> , a passenger train heading west from <unk> , was saved by CPR <unk> Sid <unk> , one of two men who rushed across the rock @-@ <unk> ground to warn the train that the track had been buried under the slide . Through falling rocks and a dust cloud that impaired his visibility , <unk> ran for 2 kilometres ( 1 @.@ 2 mi ) to warn the oncoming <unk> of the danger . The CPR gave him a letter of commendation and a $ 25 <unk> in recognition of his heroism . 
- 
- = = = Aftermath = = = 
- 
- Early on April 30 a special train from Fort <unk> arrived with police officers and doctors . Premier Frederick <unk> arrived at the disaster site on May 1 , where he met with engineers who had investigated the top of Turtle Mountain . Though new fissures had formed at the peak , they felt there was limited further risk to the town ; the CPR 's chief engineer was convinced that Frank was in imminent danger from another slide . <unk> with the latter , <unk> ordered the town evacuated , and the Geological Survey of Canada ( GSC ) sent two of its top geologists to investigate further . They reported that the slide had created two new peaks on the mountain and that the north peak , overlooking the town , was not in imminent danger of collapse . As a result , the evacuation order was lifted on May 10 and Frank 's citizens returned . The North @-@ West Mounted Police , reinforced by officers who arrived from <unk> , <unk> and Calgary , kept tight control of the town and ensured that no cases of looting occurred during the evacuation . 
- Clearing the Canadian Pacific Railway line was of paramount importance . Approximately 2 kilometres ( 1 @.@ 2 mi ) of the main line had been buried under the slide , along with part of an auxiliary line . The CPR had the line cleared and rebuilt within three weeks . Intent on reopening the mine , workers opened passageways to the old mine works by May 30 . To their <unk> , they discovered that Charlie the horse , one of three who worked in the mine , had survived for over a month underground . The horse had <unk> by eating the bark off the timber supports and by drinking from pools of water . The horse died when his rescuers <unk> him on oats and brandy . 
- The town 's population not only recovered but grew ; the 1906 census of the Canadian <unk> listed the population at 1 @,@ 178 . A new study commissioned by the Dominion government determined that the cracks in the mountain continued to grow and that the risk of another slide remained . Consequently , parts of Frank closest to the mountain were dismantled or relocated to safer areas . 
- 
- = = Causes = = 
- 
- Several factors led to the Frank Slide . A study conducted by the GSC immediately following the slide concluded that the primary cause was the mountain 's unstable <unk> formation ; a layer of limestone rested on top of softer materials that , after years of erosion , resulted in a top @-@ heavy , steep cliff . <unk> laced the eastern face of the mountain while underground fissures allowed water to flow into the mountain 's core . Local Indigenous peoples of the area , the Blackfoot and <unk> , had oral traditions referring to the peak as " the mountain that moves . " <unk> noticed the mountain had become increasingly unstable in the months preceding the slide ; they felt small <unk> and the superintendent reported a " general <unk> " in the mountain at depths between 1 @,@ 100 metres ( 3 @,@ 600 ft ) and 1 @,@ 500 metres ( 4 @,@ 900 ft ) . They found that coal broke from its seam ; it was said to have practically mined itself . 
- An unusually warm winter , with warm days and cold nights , was also a factor . Water in the mountain 's fissures froze and <unk> repeatedly , further weakening the mountain 's supports . Heavy snowfall in the region in March was followed by a warm April , causing the mountain <unk> to melt into the fissures . GSC geologists concluded that the weather conditions that night likely triggered the slide . The crew of the freight train that arrived at Frank shortly before the disaster said it was the coldest night of the winter , with overnight temperatures falling below − 18 ° C ( 0 ° F ) . Geologists speculated that the cold snap and rapid freezing resulted in expansion of the fissures , causing the limestone to break off and tumble down the mountain . 
- Though the GSC concluded that mining activities contributed to the slide , the facility 's owners disagreed . Their engineers claimed that the mine bore no responsibility . Later studies suggested that the mountain had been at a point of " equilibrium " ; even a small <unk> such as that caused by the mine 's existence would have helped trigger a slide . The mine was quickly re @-@ opened , even though rock continued to tumble down the mountain . Coal production at Frank peaked in 1910 , but the mine was permanently closed in 1917 after it became unprofitable . 
- The slide created two new peaks on the mountain ; the south peak stands 2 @,@ 200 metres ( 7 @,@ 200 ft ) high and the north peak 2 @,@ 100 metres ( 6 @,@ 900 ft ) . Geologists believe that another slide is inevitable , though not imminent . The south peak is considered the most likely to fall ; it would likely create a slide about one @-@ sixth the size of the 1903 slide . The mountain , continuously monitored for changes in stability , has been studied on numerous occasions . The Alberta Geological Survey operates a state @-@ of @-@ the @-@ art monitoring system used by researchers around the world . Over 80 monitoring stations have been placed on the face of the mountain to provide an early warning system for area residents in case of another slide . 
- Geologists have debated about what caused the slide debris to travel the distance it did . The " air cushion " theory , an early hypothesis , postulated that a layer of air was trapped between the mass of rock and the mountain , which caused the rock to move a greater distance than would otherwise be expected . " Acoustic <unk> " is another theory , which suggests that large masses of material create seismic energy that reduces friction and causes the debris to flow down the mountain as though it is a fluid . Geologists created the term " debris avalanche " to describe the Frank Slide . 
- 
- = = Legends = = 
- 
- Numerous legends and <unk> were spawned in the aftermath of the slide . The entire town of Frank was claimed to have been buried , though much of the town itself was unscathed . The belief that a branch of the Union Bank of Canada had been buried with as much as $ 500 @,@ 000 persisted for many years . The bank — <unk> by the slide — remained in the same location until it was demolished in 1911 , after which the buried treasure legend arose . Crews building a new road through the pass in 1924 operated under police guard as it was believed they could <unk> the supposedly buried bank . 
- Several people , telling amazing stories to those who would listen , passed themselves off as the " sole survivor " in the years following the slide . The most common such tale is that of an infant girl said to have been the only survivor of the slide . Her real name unknown , the girl was called " Frankie Slide " . Several stories were told of her <unk> escape : she was found in a <unk> of hay , lying on rocks , under the collapsed roof of her house or in the arms of her dead mother . The legend was based primarily on the story of Marion <unk> , who was thrown from her home into a pile of hay when the slide enveloped her home . Her sisters also survived ; they were found unharmed under a collapsed ceiling <unk> . Her parents and four brothers died . <unk> the story was the survival of two @-@ year @-@ old Gladys Ennis , who was found outside her home in the mud . The last survivor of the slide , she died in 1995 . In total , 23 people in the path of the slide survived , in addition to the 17 miners who escaped from the tunnels under Turtle Mountain . A ballad by Ed <unk> featuring the story of Frankie Slide was popular in parts of Canada in the 1950s . The slide has formed the basis of other songs , including " How the Mountain <unk> Down " by <unk> ' Tom Connors , and more recently , " Frank , <unk> " by The Rural Alberta <unk> . The Frank Slide has been the subject of several books , both historical and fictional . 
- 
- = = Legacy = = 
- 
- Curious <unk> flocked to the site of the slide within the day of the disaster . It has remained a popular tourist destination , in part due to its proximity to the Crowsnest Highway . The province built a roadside turnout in 1941 to accommodate the traffic . Town <unk> unsuccessfully sought to have the site designated as a National Historic Site in 1958 . It was later designated a Provincial Historic Site of Alberta . The provincial government designated the slide area a restricted development zone in 1976 , which prevents alteration of the site . In 1978 , a memorial plaque was erected . The Frank Slide <unk> Centre , within sight of the mountain , was opened in 1985 . A museum and tourist stop document the Frank Slide and the region 's coal mining history . The site receives over 100 @,@ 000 tourist visits annually . 
- Though Frank recovered from the slide and achieved a peak population of 1 @,@ 000 shortly thereafter , the closure of the mine resulted in a longstanding decline in population . Frank ceased to be an independent community in 1979 when it was amalgamated into the Municipality of Crowsnest Pass along with the neighbouring communities of <unk> , Coleman , <unk> and Bellevue . Frank is now home to about 200 residents . 
- 
- 
- = Protein = 
- 
- Proteins ( / <unk> / or / <unk> / ) are large biomolecules , or macromolecules , consisting of one or more long chains of amino acid residues . Proteins perform a vast array of functions within organisms , including <unk> metabolic reactions , DNA replication , responding to <unk> , and transporting molecules from one location to another . Proteins differ from one another primarily in their sequence of amino acids , which is dictated by the nucleotide sequence of their genes , and which usually results in protein folding into a specific three @-@ dimensional structure that determines its activity . 
- A linear chain of amino acid residues is called a polypeptide . A protein contains at least one long polypeptide . Short polypeptides , containing less than 20 @-@ 30 residues , are rarely considered to be proteins and are commonly called peptides , or sometimes <unk> . The individual amino acid residues are bonded together by peptide bonds and adjacent amino acid residues . The sequence of amino acid residues in a protein is defined by the sequence of a gene , which is encoded in the genetic code . In general , the genetic code specifies 20 standard amino acids ; however , in certain organisms the genetic code can include <unk> and — in certain archaea — <unk> . Shortly after or even during synthesis , the residues in a protein are often chemically modified by post @-@ <unk> modification , which alters the physical and chemical properties , folding , stability , activity , and ultimately , the function of the proteins . Sometimes proteins have non @-@ peptide groups attached , which can be called prosthetic groups or <unk> . Proteins can also work together to achieve a particular function , and they often associate to form stable protein complexes . 
- Once formed , proteins only exist for a certain period of time and are then degraded and recycled by the cell 's machinery through the process of protein turnover . A protein 's lifespan is measured in terms of its half @-@ life and covers a wide range . They can exist for minutes or years with an average lifespan of 1 – 2 days in mammalian cells . <unk> and or <unk> proteins are degraded more rapidly either due to being targeted for destruction or due to being unstable . 
- Like other biological macromolecules such as <unk> and nucleic acids , proteins are essential parts of organisms and participate in virtually every process within cells . Many proteins are enzymes that <unk> biochemical reactions and are vital to metabolism . Proteins also have structural or mechanical functions , such as actin and <unk> in muscle and the proteins in the cytoskeleton , which form a system of scaffolding that maintains cell shape . Other proteins are important in cell signaling , immune responses , cell <unk> , and the cell cycle . In animals , proteins are needed in the diet to provide the essential amino acids that cannot be synthesized . <unk> breaks the proteins down for use in the metabolism . 
- Proteins may be purified from other cellular components using a variety of techniques such as <unk> , precipitation , electrophoresis , and chromatography ; the advent of genetic engineering has made possible a number of methods to facilitate purification . Methods commonly used to study protein structure and function include <unk> , site @-@ directed <unk> , X @-@ ray crystallography , nuclear magnetic resonance and mass spectrometry . 
- 
- = = <unk> = = 
- 
- Most proteins consist of linear polymers built from series of up to 20 different L @-@ α @-@ amino acids . All <unk> amino acids possess common structural features , including an α @-@ carbon to which an amino group , a <unk> group , and a variable side chain are bonded . Only <unk> differs from this basic structure as it contains an unusual ring to the N @-@ end amine group , which forces the CO – <unk> <unk> <unk> into a fixed conformation . The side chains of the standard amino acids , detailed in the list of standard amino acids , have a great variety of chemical structures and properties ; it is the combined effect of all of the amino acid side chains in a protein that ultimately determines its three @-@ dimensional structure and its chemical reactivity . The amino acids in a polypeptide chain are linked by peptide bonds . Once linked in the protein chain , an individual amino acid is called a residue , and the linked series of carbon , nitrogen , and oxygen atoms are known as the main chain or protein backbone . 
- The peptide bond has two resonance forms that contribute some double @-@ bond character and inhibit rotation around its axis , so that the alpha <unk> are roughly <unk> . The other two dihedral angles in the peptide bond determine the local shape assumed by the protein backbone . The end of the protein with a free <unk> group is known as the C @-@ terminus or <unk> terminus , whereas the end with a free amino group is known as the N @-@ terminus or amino terminus . The words protein , polypeptide , and peptide are a little ambiguous and can overlap in meaning . Protein is generally used to refer to the complete biological molecule in a stable conformation , whereas peptide is generally reserved for a short amino acid <unk> often lacking a stable three @-@ dimensional structure . However , the boundary between the two is not well defined and usually lies near 20 – 30 residues . <unk> can refer to any single linear chain of amino acids , usually regardless of length , but often implies an absence of a defined conformation . 
- 
- = = = <unk> in cells = = = 
- 
- It has been estimated that average @-@ sized bacteria contain about 2 million proteins per cell ( e.g. E. coli and <unk> <unk> ) . Smaller bacteria , such as <unk> or <unk> contain fewer molecules , namely on the order of 50 @,@ 000 to 1 million . By contrast , eukaryotic cells are larger and thus contain much more protein . For instance , yeast cells were estimated to contain about 50 million proteins and human cells on the order of 1 to 3 billion . Note that bacterial <unk> encode about 10 times fewer proteins than humans ( e.g. small bacteria ~ 1 @,@ 000 , E. coli : ~ 4 @,@ 000 , yeast : ~ 6 @,@ 000 , human : ~ 20 @,@ 000 ) . 
- <unk> , the concentration of individual proteins ranges from a few molecules per cell to hundreds of thousands . In fact , about a third of all proteins is not produced in most cells or only induced under certain circumstances . For instance , of the 20 @,@ 000 or so proteins encoded by the human genome only 6 @,@ 000 are detected in <unk> cells . 
- 
- = = Synthesis = = 
- 
- 
- = = = <unk> = = = 
- 
- Proteins are assembled from amino acids using information encoded in genes . Each protein has its own unique amino acid sequence that is specified by the nucleotide sequence of the gene encoding this protein . The genetic code is a set of three @-@ nucleotide sets called codons and each three @-@ nucleotide combination <unk> an amino acid , for example <unk> ( <unk> @-@ <unk> @-@ <unk> ) is the code for <unk> . Because DNA contains four <unk> , the total number of possible codons is 64 ; hence , there is some <unk> in the genetic code , with some amino acids specified by more than one codon . <unk> encoded in DNA are first transcribed into pre @-@ messenger RNA ( mRNA ) by proteins such as RNA polymerase . Most organisms then process the pre @-@ mRNA ( also known as a primary transcript ) using various forms of Post @-@ transcriptional modification to form the mature mRNA , which is then used as a template for protein synthesis by the ribosome . In prokaryotes the mRNA may either be used as soon as it is produced , or be bound by a ribosome after having moved away from the <unk> . In contrast , eukaryotes make mRNA in the cell nucleus and then <unk> it across the nuclear membrane into the cytoplasm , where protein synthesis then takes place . The rate of protein synthesis is higher in prokaryotes than eukaryotes and can reach up to 20 amino acids per second . 
- The process of <unk> a protein from an mRNA template is known as translation . The mRNA is loaded onto the ribosome and is read three <unk> at a time by matching each codon to its base pairing <unk> located on a transfer RNA molecule , which carries the amino acid corresponding to the codon it recognizes . The enzyme <unk> tRNA <unk> " charges " the tRNA molecules with the correct amino acids . The growing polypeptide is often termed the nascent chain . Proteins are always <unk> from N @-@ terminus to C @-@ terminus . 
- The size of a synthesized protein can be measured by the number of amino acids it contains and by its total molecular mass , which is normally reported in units of <unk> ( synonymous with atomic mass units ) , or the derivative unit <unk> ( kDa ) . <unk> proteins are on average <unk> amino acids long and 53 kDa in mass . The largest known proteins are the <unk> , a component of the muscle <unk> , with a molecular mass of almost 3 @,@ 000 kDa and a total length of almost 27 @,@ 000 amino acids . 
- 
- = = = Chemical synthesis = = = 
- 
- Short proteins can also be synthesized chemically by a family of methods known as peptide synthesis , which rely on organic synthesis techniques such as chemical <unk> to produce peptides in high yield . Chemical synthesis allows for the introduction of non @-@ natural amino acids into polypeptide chains , such as attachment of fluorescent probes to amino acid side chains . These methods are useful in laboratory <unk> and cell biology , though generally not for commercial applications . Chemical synthesis is inefficient for polypeptides longer than about 300 amino acids , and the synthesized proteins may not readily assume their native tertiary structure . Most chemical synthesis methods proceed from C @-@ terminus to N @-@ terminus , opposite the biological reaction . 
- 
- = = Structure = = 
- 
- Most proteins fold into unique 3 @-@ dimensional structures . The shape into which a protein naturally folds is known as its native conformation . Although many proteins can fold <unk> , simply through the chemical properties of their amino acids , others require the aid of molecular <unk> to fold into their native states . <unk> often refer to four distinct aspects of a protein 's structure : 
- Primary structure : the amino acid sequence . A protein is a <unk> . 
- Secondary structure : regularly repeating local structures stabilized by hydrogen bonds . The most common examples are the α @-@ helix , β @-@ sheet and turns . Because secondary structures are local , many regions of different secondary structure can be present in the same protein molecule . 
- Tertiary structure : the overall shape of a single protein molecule ; the spatial relationship of the secondary structures to one another . Tertiary structure is generally stabilized by <unk> interactions , most commonly the formation of a hydrophobic core , but also through salt bridges , hydrogen bonds , <unk> bonds , and even <unk> modifications . The term " tertiary structure " is often used as synonymous with the term fold . The tertiary structure is what controls the basic function of the protein . 
- <unk> structure : the structure formed by several protein molecules ( polypeptide chains ) , usually called protein subunits in this context , which function as a single protein complex . 
- Proteins are not entirely rigid molecules . In addition to these levels of structure , proteins may shift between several related structures while they perform their functions . In the context of these functional <unk> , these tertiary or <unk> structures are usually referred to as " conformations " , and transitions between them are called conformational changes . Such changes are often induced by the binding of a substrate molecule to an enzyme 's active site , or the physical region of the protein that <unk> in chemical catalysis . In solution proteins also undergo variation in structure through thermal vibration and the collision with other molecules . 
- Proteins can be informally divided into three main classes , which <unk> with typical tertiary structures : globular proteins , fibrous proteins , and membrane proteins . Almost all globular proteins are soluble and many are enzymes . <unk> proteins are often structural , such as <unk> , the major component of <unk> tissue , or keratin , the protein component of hair and nails . Membrane proteins often serve as receptors or provide channels for polar or charged molecules to pass through the cell membrane . 
- A special case of <unk> hydrogen bonds within proteins , poorly shielded from water attack and hence promoting their own <unk> , are called <unk> . 
- 
- = = = Structure determination = = = 
- 
- <unk> the tertiary structure of a protein , or the <unk> structure of its complexes , can provide important clues about how the protein performs its function . Common experimental methods of structure determination include X @-@ ray crystallography and NMR spectroscopy , both of which can produce information at atomic resolution . However , NMR experiments are able to provide information from which a subset of distances between pairs of atoms can be estimated , and the final possible conformations for a protein are determined by solving a distance geometry problem . Dual <unk> <unk> is a <unk> analytical method for measuring the overall protein conformation and conformational changes due to interactions or other stimulus . <unk> <unk> is another laboratory technique for determining internal β @-@ sheet / α @-@ helical composition of proteins . <unk> microscopy is used to produce lower @-@ resolution structural information about very large protein complexes , including assembled viruses ; a variant known as electron crystallography can also produce high @-@ resolution information in some cases , especially for two @-@ dimensional crystals of membrane proteins . <unk> structures are usually deposited in the Protein Data Bank ( PDB ) , a freely available resource from which structural data about thousands of proteins can be obtained in the form of <unk> coordinates for each atom in the protein . 
- Many more gene sequences are known than protein structures . Further , the set of solved structures is biased toward proteins that can be easily subjected to the conditions required in X @-@ ray crystallography , one of the major structure determination methods . In particular , globular proteins are comparatively easy to <unk> in preparation for X @-@ ray crystallography . Membrane proteins , by contrast , are difficult to <unk> and are <unk> in the PDB . Structural genomics initiatives have attempted to remedy these deficiencies by systematically solving representative structures of major fold classes . Protein structure prediction methods attempt to provide a means of generating a plausible structure for proteins whose structures have not been experimentally determined . 
- 
- = = Cellular functions = = 
- 
- Proteins are the chief actors within the cell , said to be carrying out the duties specified by the information encoded in genes . With the exception of certain types of RNA , most other biological molecules are relatively inert elements upon which proteins act . Proteins make up half the dry weight of an <unk> coli cell , whereas other macromolecules such as DNA and RNA make up only 3 % and 20 % , respectively . The set of proteins expressed in a particular cell or cell type is known as its <unk> . 
- The chief characteristic of proteins that also allows their diverse set of functions is their ability to bind other molecules specifically and tightly . The region of the protein responsible for binding another molecule is known as the binding site and is often a depression or " pocket " on the molecular surface . This binding ability is mediated by the tertiary structure of the protein , which defines the binding site pocket , and by the chemical properties of the surrounding amino acids ' side chains . Protein binding can be extraordinarily tight and specific ; for example , the ribonuclease inhibitor protein binds to human <unk> with a sub @-@ <unk> <unk> constant ( < 10 − 15 M ) but does not bind at all to its amphibian <unk> <unk> ( > 1 M ) . <unk> minor chemical changes such as the addition of a single methyl group to a binding partner can sometimes <unk> to nearly eliminate binding ; for example , the <unk> tRNA <unk> specific to the amino acid <unk> <unk> against the very similar side chain of the amino acid <unk> . 
- Proteins can bind to other proteins as well as to small @-@ molecule substrates . When proteins bind specifically to other copies of the same molecule , they can <unk> to form fibrils ; this process occurs often in structural proteins that consist of globular monomers that self @-@ associate to form rigid fibers . Protein – protein interactions also regulate enzymatic activity , control progression through the cell cycle , and allow the assembly of large protein complexes that carry out many closely related reactions with a common biological function . Proteins can also bind to , or even be integrated into , cell membranes . The ability of binding partners to induce conformational changes in proteins allows the construction of enormously complex signaling networks . <unk> , as interactions between proteins are <unk> , and depend heavily on the availability of different groups of partner proteins to form <unk> that are capable to carry out discrete sets of function , study of the interactions between specific proteins is a key to understand important aspects of cellular function , and ultimately the properties that distinguish particular cell types . 
- 
- = = = Enzymes = = = 
- 
- The best @-@ known role of proteins in the cell is as enzymes , which <unk> chemical reactions . Enzymes are usually highly specific and accelerate only one or a few chemical reactions . Enzymes carry out most of the reactions involved in metabolism , as well as manipulating DNA in processes such as DNA replication , DNA repair , and transcription . Some enzymes act on other proteins to add or remove chemical groups in a process known as <unk> modification . About 4 @,@ 000 reactions are known to be <unk> by enzymes . The rate acceleration conferred by enzymatic catalysis is often enormous — as much as <unk> @-@ fold increase in rate over the <unk> reaction in the case of <unk> <unk> ( 78 million years without the enzyme , 18 <unk> with the enzyme ) . 
- The molecules bound and acted upon by enzymes are called substrates . Although enzymes can consist of hundreds of amino acids , it is usually only a small fraction of the residues that come in contact with the substrate , and an even smaller fraction — three to four residues on average — that are directly involved in catalysis . The region of the enzyme that binds the substrate and contains the catalytic residues is known as the active site . 
- <unk> proteins are members of a class of proteins that <unk> the <unk> of a compound synthesized by other enzymes . 
- 
- = = = Cell signaling and ligand binding = = = 
- 
- Many proteins are involved in the process of cell signaling and signal transduction . Some proteins , such as insulin , are extracellular proteins that transmit a signal from the cell in which they were synthesized to other cells in distant tissues . Others are membrane proteins that act as receptors whose main function is to bind a signaling molecule and induce a biochemical response in the cell . Many receptors have a binding site exposed on the cell surface and an <unk> domain within the cell , which may have enzymatic activity or may undergo a conformational change detected by other proteins within the cell . 
- Antibodies are protein components of an adaptive immune system whose main function is to bind <unk> , or foreign substances in the body , and target them for destruction . Antibodies can be <unk> into the extracellular environment or anchored in the membranes of specialized B cells known as plasma cells . Whereas enzymes are limited in their binding affinity for their substrates by the necessity of conducting their reaction , antibodies have no such constraints . An antibody 's binding affinity to its target is extraordinarily high . 
- Many ligand transport proteins bind particular small biomolecules and transport them to other locations in the body of a multicellular organism . These proteins must have a high binding affinity when their ligand is present in high concentrations , but must also release the ligand when it is present at low concentrations in the target tissues . The canonical example of a ligand @-@ binding protein is <unk> , which transports oxygen from the lungs to other organs and tissues in all vertebrates and has close <unk> in every biological kingdom . <unk> are sugar @-@ binding proteins which are highly specific for their sugar <unk> . <unk> typically play a role in biological recognition phenomena involving cells and proteins . <unk> and hormones are highly specific binding proteins . 
- <unk> proteins can also serve as ligand transport proteins that alter the <unk> of the cell membrane to small molecules and ions . The membrane alone has a hydrophobic core through which polar or charged molecules cannot diffuse . Membrane proteins contain internal channels that allow such molecules to enter and exit the cell . Many ion channel proteins are specialized to select for only a particular ion ; for example , potassium and sodium channels often discriminate for only one of the two ions . 
- 
- = = = Structural proteins = = = 
- 
- Structural proteins confer <unk> and <unk> to otherwise @-@ fluid biological components . Most structural proteins are fibrous proteins ; for example , <unk> and <unk> are critical components of <unk> tissue such as cartilage , and keratin is found in hard or <unk> structures such as hair , nails , feathers , hooves , and some animal shells . Some globular proteins can also play structural functions , for example , actin and <unk> are globular and soluble as monomers , but <unk> to form long , stiff fibers that make up the cytoskeleton , which allows the cell to maintain its shape and size . 
- Other proteins that serve structural functions are motor proteins such as <unk> , <unk> , and <unk> , which are capable of generating mechanical forces . These proteins are crucial for cellular <unk> of single <unk> organisms and the sperm of many multicellular organisms which reproduce sexually . They also generate the forces exerted by contracting muscles and play essential roles in intracellular transport . 
- 
- = = Methods of study = = 
- 
- The activities and structures of proteins may be examined in vitro , in vivo , and in silico . In vitro studies of purified proteins in controlled environments are useful for learning how a protein carries out its function : for example , enzyme <unk> studies explore the chemical mechanism of an enzyme 's catalytic activity and its relative affinity for various possible substrate molecules . By contrast , in vivo experiments can provide information about the physiological role of a protein in the context of a cell or even a whole organism . In silico studies use computational methods to study proteins . 
- 
- = = = Protein purification = = = 
- 
- To perform in vitro analysis , a protein must be purified away from other cellular components . This process usually begins with cell <unk> , in which a cell 's membrane is disrupted and its internal contents released into a solution known as a crude lysate . The resulting mixture can be purified using <unk> , which <unk> the various cellular components into fractions containing soluble proteins ; membrane <unk> and proteins ; cellular organelles , and nucleic acids . Precipitation by a method known as <unk> out can concentrate the proteins from this lysate . Various types of chromatography are then used to isolate the protein or proteins of interest based on properties such as molecular weight , net charge and binding affinity . The level of purification can be monitored using various types of gel electrophoresis if the desired protein 's molecular weight and <unk> point are known , by spectroscopy if the protein has distinguishable spectroscopic features , or by enzyme assays if the protein has enzymatic activity . Additionally , proteins can be isolated according their charge using <unk> . 
- For natural proteins , a series of purification steps may be necessary to obtain protein sufficiently pure for laboratory applications . To <unk> this process , genetic engineering is often used to add chemical features to proteins that make them easier to <unk> without affecting their structure or activity . Here , a " tag " consisting of a specific amino acid sequence , often a series of <unk> residues ( a " His @-@ tag " ) , is attached to one terminus of the protein . As a result , when the lysate is passed over a chromatography column containing nickel , the <unk> residues <unk> the nickel and attach to the column while the <unk> components of the lysate pass unimpeded . A number of different <unk> have been developed to help researchers <unk> specific proteins from complex mixtures . 
- 
- = = = Cellular localization = = = 
- 
- The study of proteins in vivo is often concerned with the synthesis and localization of the protein within the cell . Although many intracellular proteins are synthesized in the cytoplasm and membrane @-@ bound or <unk> proteins in the <unk> <unk> , the specifics of how proteins are targeted to specific organelles or cellular structures is often unclear . A useful technique for assessing cellular localization uses genetic engineering to express in a cell a fusion protein or <unk> consisting of the natural protein of interest linked to a " reporter " such as green fluorescent protein ( <unk> ) . The fused protein 's position within the cell can be cleanly and efficiently visualized using microscopy , as shown in the figure opposite . 
- Other methods for <unk> the cellular location of proteins requires the use of known <unk> markers for regions such as the ER , the <unk> , <unk> or <unk> , mitochondria , <unk> , plasma membrane , etc . With the use of <unk> <unk> versions of these markers or of antibodies to known markers , it becomes much simpler to identify the localization of a protein of interest . For example , indirect <unk> will allow for fluorescence <unk> and demonstration of location . <unk> <unk> are used to label cellular compartments for a similar purpose . 
- Other possibilities exist , as well . For example , <unk> usually utilizes an antibody to one or more proteins of interest that are conjugated to enzymes yielding either <unk> or <unk> signals that can be compared between samples , allowing for localization information . Another applicable technique is <unk> in <unk> ( or other material ) <unk> using <unk> <unk> . While this technique does not prove <unk> of a compartment of known density and the protein of interest , it does increase the likelihood , and is more amenable to large @-@ scale studies . 
- Finally , the gold @-@ standard method of cellular localization is <unk> microscopy . This technique also uses an antibody to the protein of interest , along with classical electron microscopy techniques . The sample is prepared for normal electron microscopic examination , and then treated with an antibody to the protein of interest that is conjugated to an extremely electro @-@ dense material , usually gold . This allows for the localization of both <unk> details as well as the protein of interest . 
- Through another genetic engineering application known as site @-@ directed <unk> , researchers can alter the protein sequence and hence its structure , cellular localization , and <unk> to regulation . This technique even allows the incorporation of unnatural amino acids into proteins , using modified <unk> , and may allow the rational design of new proteins with novel properties . 
- 
- = = = <unk> = = = 
- 
- The total complement of proteins present at a time in a cell or cell type is known as its <unk> , and the study of such large @-@ scale data sets defines the field of proteomics , named by analogy to the related field of genomics . Key experimental techniques in proteomics include 2D electrophoresis , which allows the separation of a large number of proteins , mass spectrometry , which allows rapid high @-@ <unk> identification of proteins and sequencing of peptides ( most often after in @-@ gel <unk> ) , protein <unk> , which allow the detection of the relative levels of a large number of proteins present in a cell , and two @-@ hybrid screening , which allows the systematic exploration of protein – protein interactions . The total complement of biologically possible such interactions is known as the <unk> . A systematic attempt to determine the structures of proteins representing every possible fold is known as structural genomics . 
- 
- = = = <unk> = = = 
- 
- A vast array of computational methods have been developed to analyze the structure , function , and evolution of proteins . 
- The development of such tools has been driven by the large amount of <unk> and <unk> data available for a variety of organisms , including the human genome . It is simply impossible to study all proteins experimentally , hence only a few are subjected to laboratory experiments while computational tools are used to <unk> to similar proteins . Such <unk> proteins can be efficiently identified in distantly related organisms by sequence alignment . <unk> and gene sequences can be searched by a variety of tools for certain properties . <unk> <unk> tools can find restriction enzyme sites , open reading frames in nucleotide sequences , and predict secondary structures . <unk> trees can be constructed and evolutionary hypotheses developed using special software like <unk> regarding the ancestry of modern organisms and the genes they express . The field of <unk> is now indispensable for the analysis of genes and proteins . 
- 
- = = = = Structure prediction and simulation = = = = 
- 
- <unk> to the field of structural genomics , protein structure prediction seeks to develop efficient ways to provide plausible models for proteins whose structures have not yet been determined experimentally . The most successful type of structure prediction , known as <unk> modeling , relies on the existence of a " template " structure with sequence similarity to the protein being modeled ; structural genomics ' goal is to provide sufficient representation in solved structures to model most of those that remain . Although producing accurate models remains a challenge when only distantly related template structures are available , it has been suggested that sequence alignment is the bottleneck in this process , as quite accurate models can be produced if a " perfect " sequence alignment is known . Many structure prediction methods have served to inform the emerging field of protein engineering , in which novel protein folds have already been designed . A more complex computational problem is the prediction of <unk> interactions , such as in molecular <unk> and protein – protein interaction prediction . 
- The processes of protein folding and binding can be simulated using such technique as molecular mechanics , in particular , molecular dynamics and Monte Carlo , which increasingly take advantage of parallel and distributed computing ( <unk> <unk> home project ; molecular modeling on GPU ) . The folding of small α @-@ helical protein domains such as the <unk> headpiece and the HIV accessory protein have been successfully simulated in silico , and hybrid methods that combine standard molecular dynamics with quantum mechanics calculations have allowed exploration of the electronic states of <unk> . 
- 
- = = = = Protein disorder and <unk> prediction = = = = 
- 
- Many proteins ( in <unk> ~ 33 % ) contain large <unk> but biologically functional segments and can be classified as intrinsically disordered proteins . <unk> and <unk> protein disorder is , therefore , an important part of protein structure characterisation . 
- 
- = = <unk> = = 
- 
- Most microorganisms and plants can <unk> all 20 standard amino acids , while animals ( including humans ) must obtain some of the amino acids from the diet . The amino acids that an organism cannot synthesize on its own are referred to as essential amino acids . Key enzymes that synthesize certain amino acids are not present in animals — such as <unk> , which <unk> the first step in the synthesis of <unk> , <unk> , and <unk> from <unk> . If amino acids are present in the environment , microorganisms can conserve energy by taking up the amino acids from their surroundings and <unk> their <unk> pathways . 
- In animals , amino acids are obtained through the consumption of foods containing protein . <unk> proteins are then broken down into amino acids through <unk> , which typically involves <unk> of the protein through exposure to acid and hydrolysis by enzymes called <unk> . Some ingested amino acids are used for protein <unk> , while others are converted to glucose through <unk> , or fed into the <unk> acid cycle . This use of protein as a fuel is particularly important under starvation conditions as it allows the body 's own proteins to be used to support life , particularly those found in muscle . <unk> acids are also an important dietary source of nitrogen . 
- 
- = = History and etymology = = 
- 
- Proteins were recognized as a distinct class of biological molecules in the eighteenth century by Antoine <unk> and others , distinguished by the molecules ' ability to <unk> or <unk> under treatments with heat or acid . Noted examples at the time included <unk> from egg whites , blood <unk> <unk> , <unk> , and wheat <unk> . 
- Proteins were first described by the Dutch chemist <unk> Johannes Mulder and named by the Swedish chemist <unk> Jacob Berzelius in 1838 . Mulder carried out elemental analysis of common proteins and found that nearly all proteins had the same <unk> formula , <unk> . He came to the erroneous conclusion that they might be composed of a single type of ( very large ) molecule . The term " protein " to describe these molecules was proposed by Mulder 's associate Berzelius ; protein is derived from the Greek word <unk> ( <unk> ) , meaning " primary " , " in the lead " , or " standing in front " , + <unk> . Mulder went on to identify the products of protein degradation such as the amino acid <unk> for which he found a ( nearly correct ) molecular weight of 131 Da . 
- Early nutritional scientists such as the German Carl von <unk> believed that protein was the most important nutrient for maintaining the structure of the body , because it was generally believed that " flesh makes flesh . " Karl Heinrich <unk> extended known protein forms with the identification of <unk> acid . At the Connecticut Agricultural Experiment Station a detailed review of the vegetable proteins was compiled by Thomas Burr Osborne . Working with Lafayette <unk> and applying <unk> 's law of the minimum in feeding laboratory rats , the <unk> essential amino acids were established . The work was continued and communicated by William <unk> Rose . The understanding of proteins as polypeptides came through the work of Franz <unk> and Hermann Emil Fischer . The central role of proteins as enzymes in living organisms was not fully appreciated until 1926 , when James B. Sumner showed that the enzyme <unk> was in fact a protein . 
- The difficulty in purifying proteins in large quantities made them very difficult for early protein <unk> to study . Hence , early studies focused on proteins that could be purified in large quantities , e.g. , those of blood , egg white , various toxins , and digestive / metabolic enzymes obtained from <unk> . In the 1950s , the Armour Hot Dog Co. purified 1 kg of pure <unk> <unk> ribonuclease A and made it freely available to scientists ; this gesture helped ribonuclease A become a major target for biochemical study for the following decades . 
- Linus <unk> is credited with the successful prediction of regular protein secondary structures based on hydrogen bonding , an idea first put forth by William <unk> in 1933 . Later work by Walter <unk> on <unk> , based partly on previous studies by Kaj <unk> @-@ Lang , contributed an understanding of protein folding and structure mediated by hydrophobic interactions . 
- The first protein to be sequenced was insulin , by Frederick Sanger , in 1949 . Sanger correctly determined the amino acid sequence of insulin , thus conclusively demonstrating that proteins consisted of linear polymers of amino acids rather than branched chains , <unk> , or <unk> . He won the Nobel Prize for this achievement in 1958 . 
- The first protein structures to be solved were <unk> and <unk> , by Max <unk> and Sir John <unk> <unk> , respectively , in 1958 . As of 2016 , the Protein Data Bank has over 115 @,@ 000 atomic @-@ resolution structures of proteins . In more recent times , <unk> @-@ electron microscopy of large <unk> assemblies and computational protein structure prediction of small protein domains are two methods approaching atomic resolution . 
- 
- = = <unk> = = 
- 
- 
- = = = <unk> and projects = = = 
- 
- The Protein Naming <unk> 
- Human Protein Atlas 
- <unk> <unk> Protein database 
- <unk> Protein Structure database 
- Human Protein Reference Database 
- Human <unk> 
- <unk> <unk> Home ( Stanford University ) 
- Comparative <unk> Database <unk> protein – chemical interactions , as well as gene / protein – disease relationships and chemical @-@ disease relationships . 
- <unk> Harvester A Meta search engine ( 29 <unk> ) for gene and protein information . 
- Protein <unk> in Europe ( see also <unk> , short articles and tutorials on interesting PDB structures ) 
- Research <unk> for Structural <unk> ( see also <unk> of the Month , presenting short accounts on selected proteins from the PDB ) 
- <unk> – Life in 3D : <unk> , <unk> 3D model with wiki <unk> for every known protein molecular structure . 
- <unk> the Universal Protein <unk> 
- <unk> – Exploring the universe of human proteins : human @-@ centric protein knowledge resource 
- Multi @-@ <unk> <unk> <unk> Database : <unk> human and model organism protein / gene knowledge and expression data 
- 
- = = = <unk> and educational websites = = = 
- 
- " An Introduction to Proteins " from <unk> ( Huntington 's Disease <unk> Project for Education at Stanford ) 
- Proteins : Biogenesis to <unk> – The Virtual Library of <unk> and Cell Biology 
- Alphabet of Protein Structures 
- 
- = LiSA ( Japanese musician , born 1987 ) = 
- 
- Risa Oribe ( <unk> <unk> , Oribe Risa , born June 24 , 1987 ) , better known by her stage name LiSA ( an acronym of Love is Same All ) , is a Japanese pop singer @-@ songwriter from <unk> , <unk> , signed to <unk> under Sony Music Artists . After aspiring to become a musician early in life , she started her musical career as the vocalist of the indie band Chucky . Following Chucky 's disbandment in 2005 , LiSA moved to Tokyo in order to pursue a solo career , making her major debut in 2010 singing songs for the anime television series Angel Beats ! as one of two vocalists for the fictional band Girls Dead Monster . In April 2011 , she made her solo debut with the release of her mini @-@ album Letters to U. She performed at <unk> Summer Live in August 2010 , Anime Expo in 2012 , and is a regular guest at Anime Festival Asia . 
- LiSA 's songs have been featured as theme music for various anime such as Fate / Zero and Sword Art Online . Her singles have regularly been in the top ten of the Oricon weekly charts , with " Crossing Field " being certified platinum by the Recording Industry Association of Japan and " Oath Sign " being certified gold . She performed at the Nippon Budokan in 2014 and 2015 . In 2015 , she made her acting debut as <unk> Nelson in the Japanese dub of the animated film <unk> . 
- 
- = = Career = = 
- 
- 
- = = = Early years and major debut = = = 
- 
- Oribe 's musical experience began at the age of three when , upon her mother 's suggestions , she took private piano lessons . Although she was a shy student in kindergarten , she enjoyed her music lessons . In primary school , she was inspired to sing after seeing the band Speed on television . She later took dance and vocal lessons , which continued through her junior high school years . While in elementary , she participated in an audition held at the Nippon Budokan , and it was during this time she decided to become an artist . In junior high school , she formed a band which covered songs by <unk> <unk> , Love <unk> , and <unk> @-@ <unk> ' . 
- Oribe began her singing career in 2005 during her first year in high school when she formed the indie rock band Chucky , which mainly covered songs by other artists . During her second year , the band received advice from their peers that they should start making their own songs ; eventually the band 's reputation grew enough that they performed in Osaka and Nagoya in addition to <unk> . Although her grades in high school were good , she decided not to go to university , despite the recommendations of her teachers , in order to focus on her work with Chucky . Nevertheless , after graduation , due to varying schedules among the band 's members , it became difficult to continue performing . Following the band 's disbandment in July 2008 , she moved to Tokyo in order to continue her singing career . 
- After moving to Tokyo , Oribe formed the band Love is Same All with members from the indie band <unk> Out and began using the stage name LiSA , which is an acronym for Love is Same All . The band performs with LiSA during the latter 's solo live performances . In 2010 , she made her major debut singing songs for the anime series Angel Beats ! as one of two vocalists for the fictional in @-@ story band Girls Dead Monster . She was the vocalist for the character Yui , and the second vocalist , Marina , sang as the character <unk> <unk> . LiSA put out three singles and one album in 2010 under the name Girls Dead Monster on Key 's record label Key Sounds <unk> . The first single " Thousand <unk> " was released on May 12 ; the second single " Little <unk> " came out on June 9 ; and the third single " <unk> no <unk> ( Yui final ver . ) " ( <unk> <unk> Yui final ver . <unk> , " My Most <unk> Treasure ( Yui final ver . ) " ) was sold on December 8 . The album <unk> The Beats ! was released on June 30 . LiSA made her first appearance at <unk> Summer Live during the concert 's 2010 iteration on August 28 . 
- LiSA made her solo debut on April 20 , 2011 with the release of her mini @-@ album Letters to U by <unk> under Sony Music Artists . The songs on the album were composed by <unk> and major artists , and she composed the first song " Believe in Myself " ; she wrote the album 's lyrics . On November 12 , 2011 , She made her first appearance in Singapore at Anime Festival Asia , Southeast Asia 's largest anime and pop culture convention . She released her first solo single " Oath Sign " on November 23 , 2011 , which was used as the opening theme to the 2011 anime series Fate / Zero . The single peaked at No. 5 on the Oricon weekly charts and was certified gold by the Recording Industry Association of Japan ( RIAJ ) . 
- 
- = = = 2012 – present = = = 
- 
- LiSA released her first full solo album Lover " s " mile on February 22 , 2012 ; the album peaked at No. 7 on the Oricon weekly charts . She attended the Anime Expo 2012 in Los Angeles as a guest of honor and performed her first concert in North America there on July 1 . After her success with " Oath Sign " , she was chosen to perform the first opening theme to the 2012 anime series Sword Art Online ; the single " Crossing Field " , her second single , was released on August 8 , 2012 ; the single peaked at No. 5 on Oricon and was later certified gold by the RIAJ . Her third single " Best Day , Best Way " , which peaked at No. 6 on Oricon , was released on April 3 , 2013 , and her fourth single " Träumerei " , which peaked at No. 15 on Oricon and was used as the opening theme to the 2013 anime series Day Break Illusion , was released on August 7 , 2013 . LiSA released her second solo album Landspace on October 30 , 2013 ; the album peaked at No. 2 on Oricon charts . A <unk> titled <unk> <unk> <unk> Hi da ( <unk> , " Another Great Day " ) that contains photos of her taken by <unk> <unk> ! magazine over three years was published on November 22 , 2013 . When asked if she would revisit the meaning of her acronym in the future in an interview with <unk> at Anime Festival Asia 2013 in Singapore , she suggested <unk> international Super Apple . 
- On January 3 , 2014 , LiSA held a sold @-@ out solo concert at the Nippon Budokan . Her fifth single " Rising Hope " , which is used as the opening theme to the 2014 anime series The <unk> at Magic High School , was released on May 7 , 2014 ; the single peaked at No. 4 on Oricon . She later covered the songs " <unk> Actor " ( <unk> ) and " <unk> <unk> " ( <unk> , " <unk> Evening " ) from the <unk> Project franchise for the sixth episode of the 2014 anime series <unk> Actors . She released her sixth single " Bright Flight / L. Miranic " on September 17 , 2014 , which peaked at No. 8 on Oricon , and her seventh single " <unk> " ( <unk> , lit . " Sign " ) on December 10 , 2014 , which peaked at No. 3 on the Oricon ; the title song is used as the third ending theme to the 2014 anime series Sword Art Online II , and the single also includes the song " No More Time Machine " , which was used as the second ending theme to Sword Art Online II . She held her second Nippon Budokan concert on January 10 and 11 , 2015 , tickets for which were sold out . She released her third solo album Launcher on March 4 , 2015 and her eighth single " Rally Go Round " on May 27 , 2015 ; the song is used as the opening theme to the second season of the anime television series <unk> . She was cast as <unk> Nelson in the Japanese dub of the animated film <unk> , which premiered in Japanese theaters on July 31 , 2015 . She released her ninth single " Empty <unk> " on September 30 , 2015 . In late 2015 , she performed " <unk> " , which is used as theme song of the 2015 video game Dengeki <unk> : Fighting <unk> <unk> . To commemorate her fifth year as a solo artist , LiSA released her Letters To U EP as a limited edition LP on March 23 , 2016 . LiSA released a mini @-@ album titled Lucky Hi Five ! on April 20 , 2016 . She will release the single " <unk> Freak Out " , which will be used as the opening theme to the 2016 anime television series <unk> Code , on August 24 , 2016 . 
- 
- = = Musical style and influences = = 
- 
- LiSA lists <unk> <unk> , Oasis , Green Day , <unk> , Ke $ ha , and Rihanna as among her musical influences , as well as her time in Chucky . LiSA wrote the lyrics for some of her songs in her Landspace and Launcher albums , as well as the lyrics for the singles " Bright Flight / L. Miranic " , " <unk> " , and " Rally Go Round " ; " Rally Go Round " was co @-@ written with songwriter Shin <unk> . 
- LiSA 's musical style is described by Dennis <unk> of J ! <unk> as a young woman with style , beautiful vocals , and the ability to take on various musical styles , may it be happy , upbeat rock music or " even something more darker . " She has used the phrase " <unk> <unk> ii hi da " ( <unk> , " Another Great Day " ) as a theme throughout her career ; the phrase is also the name of her personal blog . In an interview with HMV Japan , she mentioned that before her major debut , she tended to sing pop songs , and because of this , she felt uneasy when making " Oath Sign " , which had a heavy and dark style , although she was able to receive several favorable responses to the single , which allowed her to make her next single " Crossing Field " with confidence . As for " Crossing Field " , she says that feels that the song grows every time she sings it , in the same way that love is <unk> through dates or love letters . In " Best Day , Best Way " , she mentioned that the song 's theme is " what you did yesterday will lead to what happens today " , and that the lyrics are delivered as if they are her own words . She also shared her experience with the single 's B @-@ sides : " I 'm a Rock Star " , which according to her represents her fulfilling her dream of becoming a singer , and " <unk> <unk> " ( <unk> , " White <unk> " ) , which represents happy feelings . 
- In an interview with Oricon , LiSA explained her experiences with " Träumerei " , where she stated that because Day Break Illusion is an original anime , she wanted the song to be presented in the color of her own work , but even in the world of anime , her music would continue to have an atmosphere of rock , as opposed to the pop style she used in " Best Day , Best Way " . She mentioned that she sang that song in a way that she felt the conflicts that were present in the story and with the strength that was part of the show 's theme . With regards to the song 's music video , she wanted to have a video which would express in color emotions , such as the use of red to represent confrontations , black to represent the feeling of being lost , and green for frightening things . 
- In making the single " Bright Flight / L. Miranic " , she mentions in an interview with HMV Japan that the concept of the single is that it can be divided into two parts : pink and black . Pink is the color of " Bright Flight " , a song which is in a pop style and was written in a " cute " image in mind , while black is the color of " L. Miranic " , which is in a dark rock style and has the theme of a " bad woman " . LiSA mentioned that the name " L. Miranic " was chosen because the song is linked to criminals and that since " Miranic " is a name in some countries , adding her name to " Miranic " to make " LiSA @-@ Miranic " would mean that the criminal in the song is her . 
- Dennis <unk> of J ! <unk> reviewed LiSA 's album Landspace , where he describes the song " Crossing Field " as a song about " wanting to forget the weakness and <unk> of one past and being with someone you love who gives you the strength to become even stronger . " Meanwhile , he describes the song " Best Day , Best Way " as " a fun and happy track about believing in yourself and overcoming anything bad that have happened in the past and moving forward . " Finally , he describes " Träumerei " as " another inspirational song about one losing the rhythm in their heart and is now on the road of loneliness , but still the person wants to <unk> the sky with their own light , <unk> the darkness and have a fresh new start in life . " He concludes the review by saying that the album is full of upbeat and inspirational songs and showcases LiSA 's talents as a vocalist , and in contrast to many <unk> artists who are known for their cute and sweet vocals , LiSA is able to take on many musical styles . 
- 
- = = Discography = = 
- 
- The discography of LiSA includes three studio albums , one extended play , ten singles , and five video albums . 
- Studio albums 
- Lover " s " mile ( 2012 ) 
- Landspace ( 2013 ) 
- Launcher ( 2015 ) 
- 
- 
- = Aston Villa F.C. = 
- 
- Aston Villa Football Club ( / <unk> <unk> / ; nicknamed Villa , The Villa , The <unk> , The Lions ) is a professional association football club based in Aston , Birmingham , that plays in the Championship , the second level of English football . Founded in 1874 , they have played at their current home ground , Villa Park , since 1897 . Aston Villa were the <unk> and founding members of the Football League in 1888 . They were also founding members of the Premier League in 1992 . In June 2016 , the club was sold by American businessman Randy Lerner to Recon Group , owned by Chinese businessman Dr Tony <unk> Xia . 
- Aston Villa are one of the oldest and most successful football clubs in the history of English football . Villa won the 1981 – 82 European Cup , and are thus one of five English clubs to win what is now the UEFA Champions League . They have the fifth highest total of major honours won by an English club , having won the First Division Championship seven times , the FA Cup seven times , the Football League Cup five times , and the European Cup and UEFA Super Cup double in 1982 . 
- They have a fierce local rivalry with Birmingham City . The Second City derby between Aston Villa and Birmingham City has been played since 1879 . The club 's traditional kit colours are claret shirts with sky blue sleeves , white shorts and sky blue socks . Their traditional badge is of a rampant lion , which was introduced by the club 's Scottish chairman William McGregor in honour of the Royal Standard of Scotland . 
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- = = History = = 
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- Aston Villa Football Club were formed in March 1874 , by members of the Villa Cross <unk> Chapel in <unk> which is now part of Birmingham . The four founders of Aston Villa were Jack Hughes , Frederick Matthews , Walter Price and William <unk> . Aston Villa 's first match was against the local Aston Brook St Mary 's Rugby team . As a condition of the match , the Villa side had to agree to play the first half under rugby rules and the second half under football rules . After moving to the Wellington Road ground in 1876 , Villa soon established themselves as one of the best teams in the Midlands , winning their first honour , the Birmingham Senior Cup in 1880 , under the captaincy of Scotsman George Ramsay . 
- The club won their first FA Cup in 1887 with captain Archie Hunter becoming one of the game 's first household names . Aston Villa were one of the dozen teams that competed in the inaugural Football League in 1888 with one of the club 's directors , William McGregor being the league 's founder . Aston Villa emerged as the most successful English club of the Victorian era , winning no fewer than five League titles and three FA Cups by the end of Queen Victoria 's reign . In 1897 , the year Villa won The Double , they moved into their present home , the Aston Lower Grounds . Supporters coined the name " Villa Park " ; no official declaration listed the ground as Villa Park . 
- Aston Villa won their sixth FA Cup in 1920 , soon after though the club began a slow decline that led to Villa , at the time one of the most famous and successful clubs in world football , being relegated in 1936 for the first time to the Second Division . This was largely the result of a <unk> defensive record : they conceded 110 goals in 42 games , 7 of them coming from Arsenal 's Ted Drake in an infamous 1 – 7 defeat at Villa Park . Like all English clubs , Villa lost seven seasons to the Second World War , and that conflict brought several careers to a premature end . The team was rebuilt under the guidance of former player Alex <unk> for the remainder of the 1940s . Aston Villa 's first trophy for 37 years came in the 1956 – 57 season when another former Villa player , Eric Houghton led the club to a then record seventh FA Cup Final win , defeating the ' <unk> <unk> ' of Manchester United in the final . The team struggled in the league though and were relegated two seasons later , due in large part to <unk> . However , under the stewardship of manager Joe Mercer Villa returned to the top @-@ flight in 1960 as Second Division Champions . The following season Aston Villa became the first team to win the Football League Cup . 
- Mercer 's forced retirement from the club in 1964 signalled a period of deep turmoil . The most successful club in England was struggling to keep pace with changes in the modern game , with Villa being relegated for the third time , under manager Dick Taylor in 1967 . The following season the fans called for the board to resign as Villa finished 16th in the Second Division . With mounting debts and Villa lying at the bottom of Division Two , the board sacked Tommy Cummings ( the manager brought in to replace Taylor ) , and within weeks the entire board resigned under overwhelming pressure from fans . After much speculation , control of the club was bought by London financier Pat Matthews , who also brought in Doug Ellis as chairman . However , new ownership could not prevent Villa being relegated to the Third Division for the first time at the end of the 1969 – 70 season . However , Villa gradually began to recover under the management of former club captain Vic Crowe . In the 1971 – 72 season they returned to the Second Division as Champions with a record 70 points . In 1974 , Ron Saunders was appointed manager . His brand of no @-@ nonsense man @-@ management proved effective , with the club winning the League Cup the following season and , at the end of season 1974 – 75 , he had taken them back into the First Division and into Europe . 
- Villa were back among the elite as Saunders continued to mould a winning team . This culminated in a seventh top @-@ flight league title in 1980 – 81 . To the surprise of commentators and fans , Saunders quit halfway through the 1981 – 82 season , after falling out with the chairman , with Villa in the quarter final of the European Cup . He was replaced by his <unk> @-@ spoken assistant manager Tony Barton who guided the club to a 1 – 0 victory over Bayern Munich in the European Cup final in Rotterdam courtesy of a Peter <unk> goal . The following season Villa were crowned European Super Cup winners , beating Barcelona in the final . This marked a pinnacle though and Villa 's fortunes declined sharply for most of the 1980s , culminating in relegation in 1987 . This was followed by promotion the following year under Graham Taylor and a runners @-@ up position in the First Division in the 1989 – 90 season . 
- Villa were one of the founding members of the Premier League in 1992 , and finished runners @-@ up to Manchester United in the inaugural season . For the rest of the <unk> however Villa went through three different managers and their league positions were inconsistent , although they did win two League Cups and regularly achieved UEFA Cup qualification . Villa reached the FA Cup final in 2000 but lost 1 – 0 to Chelsea in the last game to be played at the old Wembley Stadium . Again Villa 's league position continued to <unk> under several different managers and things came to a head in the summer of 2006 when David O 'Leary left in <unk> . After 23 years as chairman and single biggest shareholder ( approximately 38 % ) , Doug Ellis finally decided to sell his stake in Aston Villa due to ill @-@ health . After much speculation it was announced the club was to be bought by American businessman Randy Lerner , owner of NFL franchise the Cleveland Browns . 
- The arrival of a new owner in Lerner and of manager Martin O 'Neill marked the start of a new period of optimism at Villa Park and sweeping changes occurred throughout the club including a new badge , a new kit sponsor and team changes in the summer of 2007 . The first Cup final of the Lerner era came in 2010 when Villa were beaten 2 – 1 in the League Cup Final . Villa made a second trip to Wembley in that season losing 3 – 0 to Chelsea in the FA Cup semifinal . Just five days before the opening day of the 2010 – 11 season , O 'Neill resigned as manager with immediate effect . The club appointed Gérard Houllier as a replacement in September 2010 , but he stepped down on 1 June 2011 due to ill @-@ health . Houllier was replaced by Birmingham City manager Alex McLeish , despite numerous protests from fans against his appointment ; this was the first time that a manager had moved directly from Birmingham to Villa . McLeish 's contract was terminated at the end of the 2011 – 12 season after Villa finished in 16th place , only just above the relegation zone . On 2 July 2012 , Aston Villa confirmed the appointment of former Norwich City manager Paul Lambert as the replacement for McLeish . On 28 February 2012 , the club announced a financial loss of £ 53 @.@ 9 million . Lerner put the club up for sale on 12 May 2014 , with an estimated value of £ 200 million . With Lerner still on board , in the 2014 – 15 season Aston Villa scored just 12 goals in 25 league games , the lowest in Premier League history , and Lambert was sacked on 11 February 2015 . Tim Sherwood succeeded him , and saved Aston Villa from relegation while also leading them to the 2015 FA Cup Final , but he was sacked in the 2015 – 16 season , as was his successor <unk> <unk> . Eric Black took temporary charge of the team , but was not able to prevent Villa from being relegated for the first time since 1987 . On 2 June 2016 , Roberto Di <unk> was announced as the club 's new manager . 
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- = = <unk> and badge = = 
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- The club colours are a claret shirt with sky blue sleeves , white shorts with claret and blue trim , and sky blue socks with claret and white trim . They were the original <unk> of the claret and blue . Villa 's colours at the outset generally comprised plain shirts ( white , grey or a shade of blue ) , with either white or black shorts . For a few years after that ( 1877 – 79 ) the team wore several different kits from all white , blue and black , red and blue to plain green . By 1880 , black jerseys with a red lion embroidered on the chest were introduced by William McGregor . This remained the first choice strip for six years . On Monday , 8 November 1886 , an entry in the club 's official minute book states : 
- ( i ) Proposed and seconded that the colours be chocolate and sky blue shirts and that we order two dozen . 
- ( ii ) Proposed and seconded that Mr McGregor be requested to supply them at the lowest quotation . 
- The chocolate colour later became claret . Nobody is quite sure why claret and blue became the club 's adopted colours . Several other English football teams adopted their colours ; clubs that wear claret and blue include West Ham United and Burnley . 
- A new badge was revealed in May 2007 , for the 2007 – 08 season and beyond . The new badge includes a star to represent the European Cup win in 1982 , and has a light blue background behind Villa 's ' lion rampant ' . The traditional motto " <unk> " remains in the badge , and the name Aston Villa has been shortened to <unk> , FC having been omitted from the previous badge . The lion is now unified as opposed to fragmented lions of the past . Randy Lerner petitioned fans to help with the design of the new badge . 
- On 6 April 2016 , the club confirmed that it will be using a new badge from the 2016 – 17 season after consulting fan groups for suggestions . The lion in the new badge will have claws added to it and the word " <unk> " will be removed to increase the size of the lion and club initials in the badge . 
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- = = = Kit sponsorship = = = 
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- Aston Villa <unk> commercial kit sponsorship for the 2008 – 09 and 2009 – 10 seasons ; instead advertising the charity Acorns Children 's Hospice , the first deal of its kind in Premier league history . The partnership continued until 2010 when a commercial sponsor replaced Acorns , with the hospice becoming the club 's Official Charity Partner . In 2014 – 15 , the Acorns name returned to Aston Villa 's home and away shirts , but only for children 's shirts re @-@ <unk> the club 's support for the children 's charity . 
- Since 2015 Villa 's shirt sponsors have been <unk> . Previous commercial sponsors have been <unk> ( 1982 – 83 ) , <unk> ( 1983 – 93 ) , <unk> ( 1993 – 95 ) , <unk> Computer ( 1995 – 98 ) , <unk> ( 1998 – 2000 ) , <unk> ( 2000 – 02 ) , Rover ( 2002 – 04 ) , <unk> <unk> ( 2004 – 06 ) , <unk> ( 2006 – 08 ) , <unk> ( 2010 – 11 ) , <unk> <unk> ( 2011 – 13 ) , <unk> ( 2013 – 2015 ) , and <unk> <unk> ( 2015 – ) . Since 2016 , kit has been manufactured by Under Armour . Previous manufacturers have been <unk> ( 1972 – 81 , 1990 – 93 ) , le <unk> <unk> ( 1981 – 83 ) , Henson ( 1983 – 87 ) , Hummel ( 1987 – 90 , 2004 – 07 ) , <unk> ( 1993 – 95 ) , <unk> ( 1995 – 2000 ) , <unk> ( 2000 – 04 ) , Nike ( 2007 – 12 ) and <unk> ( 2012 @-@ 16 ) . 
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- = = Stadium = = 
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- Aston Villa 's current home venue is Villa Park , which is a UEFA 5 @-@ star rated stadium , having previously played at Aston Park ( 1874 – 1876 ) and Wellington Road ( 1876 – 1897 ) . Villa Park is the largest football stadium in the English Midlands , and the eighth largest stadium in England . It has hosted 16 England internationals at senior level , the first in 1899 , and the most recent in 2005 . Thus , it was the first English ground to stage international football in three different centuries . Villa Park is the most used stadium in FA Cup semi @-@ final history , having hosted 55 semi @-@ finals . The club have planning permission to extend the North Stand ; this will involve the ' filling in ' of the corners to either side of the North Stand . If completed , the capacity of Villa Park will be increased to approximately 51 @,@ 000 . 
- The current training ground is located at Bodymoor Heath near <unk> in north Warwickshire , the site for which was purchased by former chairman Doug Ellis in the early 1970s from a local farmer . Although Bodymoor Heath was state @-@ of @-@ the @-@ art in the 1970s , by the late 1990s the facilities had started to look dated . In November 2005 , Ellis and Aston Villa <unk> announced a state of the art GB £ 13 million redevelopment of Bodymoor in two phases . However , work on Bodymoor was suspended by Ellis due to financial problems , and was left in an unfinished state until new owner Randy Lerner made it one of his priorities to make the site one of the best in world football . The new training ground was officially unveiled on 6 May 2007 , by then manager Martin O 'Neill , then team captain Gareth Barry and 1982 European Cup winning team captain Dennis Mortimer , with the Aston Villa squad moving in for the 2007 – 08 season . 
- It was announced on 6 August 2014 , that Villa Park would appear in the FIFA video game from FIFA 15 onwards , with all other Premier League stadiums also fully licensed from this game onwards . 
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- = = <unk> = = 
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- The first shares in the club were issued towards the end of the 19th century as a result of legislation that was intended to <unk> the growing numbers of professional teams and players in the Association Football leagues . FA teams were required to distribute shares to investors as a way of facilitating trading among the teams without <unk> the FA itself . This trading continued for much of the 20th century until Ellis started buying up many of the shares in the 1960s . He was chairman and substantial shareholder of " Aston Villa F.C. " from 1968 to 1975 and the majority shareholder from 1982 to 2006 . The club were floated on the London Stock Exchange ( <unk> ) in 1996 , and the share price fluctuated in the ten years after the flotation . In 2006 it was announced that several <unk> and individuals were considering bids for Aston Villa . 
- On 14 August 2006 , it was confirmed that Randy Lerner , then owner of the National Football League 's Cleveland Browns , had reached an agreement of £ 62 @.@ 6 million with Aston Villa for a takeover of the club . A statement released on 25 August to the <unk> announced that Lerner had secured 59 @.@ 69 % of Villa shares , making him the majority shareholder . He also appointed himself Chairman of the club . In Ellis 's last year in charge Villa lost £ <unk> before tax , compared with a £ <unk> profit the previous year , and income had fallen from £ <unk> to £ <unk> . Lerner took full control on 18 September , as he had 89 @.@ 69 % of the shares . On 19 September 2006 , Ellis and his board resigned to be replaced with a new board headed by Lerner . Lerner installed Charles <unk> as a non @-@ executive director and Ellis was awarded the honorary position of Chairman <unk> . 
- Lerner put the club up for sale in May 2014 , <unk> it at an estimated £ 200 million . 
- On 18 May 2016 , Randy Lerner agreed the sale of Aston Villa to Recon Group , owned by Chinese businessman Xia <unk> . The sale was completed on 14 June 2016 for a reported £ 76 million after being approved by the Football League , with the club becoming part of Recon Group 's Sport , <unk> and Tourism division . Recon Group were selected to take over Aston Villa following a selection process by the club . 
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- = = Social responsibility = = 
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- Aston Villa have a unique relationship with the Acorns Children 's Hospice charity that is groundbreaking in English football . In a first for the Premier League , Aston Villa donated the front of the shirt on their kit , usually reserved for high @-@ paying <unk> , to Acorns Hospice so that the charity would gain significant additional visibility and greater fund raising capabilities . Outside of the shirt sponsorship the club have paid for hospice care for the charity as well as regularly providing player visits to hospice locations . 
- In September 2010 , Aston Villa launched an initiative at Villa Park called Villa Midlands Food ( <unk> ) where the club will spend two years training students with Aston Villa <unk> and Events in association with Birmingham City Council . The club will open a restaurant in the Trinity Road Stand staffed with 12 students recruited from within a ten @-@ mile ( 16 km ) radius of Villa Park with the majority of the food served in the restaurant sourced locally . 
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- = = Supporters and rivalries = = 
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- Aston Villa have a large fanbase and draw support from all over the Midlands and beyond , with supporters ' clubs all across the world . Former Villa chief executive Richard Fitzgerald has stated that the ethnicity of the supporters is currently 98 % white . When Randy Lerner 's regime took over at Villa Park , they aimed to improve their support from ethnic minorities . A number of organisations have been set up to support the local community including Aston Pride . A Villa in the Community programme has also been set up to encourage support among young people in the region . The new owners have also initiated several surveys aimed at gaining the opinions of Villa fans and to involve them in the decision making process . Meetings also occur every three months where supporters are invited by ballot and are invited to ask questions to the Board . In 2011 , the club supported a supporter @-@ based initiative for an official anthem to boost the atmosphere at Villa Park . The song " The Bells Are Ringing " is to be played before games . 
- Like many English football clubs Aston Villa have had several <unk> firms associated with them : Villa Youth , <unk> , Villa Hardcore and the C @-@ Crew , the last mentioned being very active during the 1970s and 1980s . As can be seen across the whole of English football , the <unk> groups have now been marginalised . In 2004 , several Villa firms were involved in a fight with <unk> fans outside Villa Park in which a steward died . The main groupings of supporters can now be found in a number of domestic and international supporters ' clubs . This includes the Official Aston Villa Supporters Club which also has many smaller regional and international sections . There were several independent supporters clubs during the reign of Doug Ellis but most of these disbanded after his retirement . The supporter group My Old Man Said formed to stand up for Villa supporter 's rights , as a direct result of Villa supporters ' protest against the club 's appointment of Alex McLeish . The club 's supporters also publish <unk> such as Heroes and <unk> and The Holy Trinity . 
- Aston Villa 's arch @-@ rivals are Birmingham City , with games between the two clubs known as the Second City Derby . Historically though , West Bromwich Albion have arguably been Villa 's greatest rivals , a view highlighted in a fan survey , conducted in 2003 . The two teams contested three FA Cup finals in the late 19th century . Villa also enjoy less heated local rivalries with Wolverhampton Wanderers and Coventry City . Through the relegation of West Brom and Birmingham City , to the Football League Championship , in the 2005 – 06 season , at the start of 2006 – 07 Premiership season , Villa were the only Midlands club in that League . The nearest opposing team Villa faced during that season was Sheffield United , who played 62 miles ( 100 km ) away in South Yorkshire . For the 2010 – 11 season , West Bromwich Albion were promoted and joined Aston Villa , Wolverhampton Wanderers , and Birmingham City in the Premier League . This marked the first time that the " West Midlands ' Big Four " clubs have been in the Premier League at the same time , and the first time together in the top flight since the 1983 – 84 season . Birmingham were relegated at the end of the 2010 – 11 season , ending this period . 
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- = = Statistics = = 
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- As of the end of the 2014 – 15 season , Aston Villa have spent 104 seasons in the top tier of English football ; the only club to have spent longer in the top flight are Everton , with 112 seasons , making Aston Villa versus Everton the most @-@ played fixture in English top @-@ flight football . Aston Villa were in an elite group of seven clubs that has played in every Premier League season , the other six being Tottenham Hotspur , Chelsea , Everton , Liverpool , Manchester United and Arsenal since its establishment in 1992 – 93 until they were relegated in 2016 . They are seventh in the All @-@ time FA Premier League table , and have the fifth highest total of major honours won by an English club with 21 wins . 
- Aston Villa currently hold the record number of league goals scored by any team in the English top flight ; 128 goals were scored in the 1930 – 31 season , one more than Arsenal who won the league that season for the very first time , with Villa runners @-@ up . Villa legend Archie Hunter became the first player to score in every round of the FA Cup in Villa 's victorious 1887 campaign . Villa 's longest unbeaten home run in the FA Cup spanned 13 years and 19 games , from 1888 to 1901 . 
- Aston Villa are one of five English teams to have won the European Cup . They did so on 26 May 1982 in Rotterdam , beating Bayern Munich 1 – 0 thanks to Peter <unk> 's goal . 
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- = = Club honours = = 
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- Aston Villa have won European and domestic league honours . The club 's last major honour was in 1996 when they won the League Cup . 
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- = = = Domestic = = = 
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- League titles 
- First Division 7 : 1893 – 94 , 1895 – 96 , 1896 – 97 , 1898 – 99 , 1899 – 1900 , 1909 – 10 , 1980 – 81 
- Second Division 2 : 1937 – 38 , 1959 – 60 
- Third Division 1 : 1971 – 72 
- Cups 
- FA Cup 7 : 
- 1886 – 87 , 1894 – 95 , 1896 – 97 , 1904 – 05 , 1912 – 13 , 1919 – 20 , 1956 – 57 
- League Cup 5 : 
- 1960 – 61 , 1974 – 75 , 1976 – 77 , 1993 – 94 , 1995 – 96 
- FA Charity Shield 1 : 
- 1981 
- Football League War Cup 1 : 
- 1944 ( shared ) 
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- = = = European = = = 
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- European Cup 1 : 
- 1981 – 82 
- European Super Cup 1 : 
- 1982 
- Intertoto Cup 2 : 
- 2001 , 2008 
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- = = = Other sports = = = 
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- English Baseball Championship 
- 1890 
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- = = Players = = 
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- = = = First @-@ team squad = = = 
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- As of 21 June 2016 . 
- Note : Flags indicate national team as defined under FIFA eligibility rules . Players may hold more than one non @-@ FIFA nationality . 
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- = = = Out on loan = = = 
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- Note : Flags indicate national team as defined under FIFA eligibility rules . Players may hold more than one non @-@ FIFA nationality . 
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- = = = <unk> squad = = = 
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- As of 21 June 2016 . 
- Note : Flags indicate national team as defined under FIFA eligibility rules . Players may hold more than one non @-@ FIFA nationality . 
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- = = = Notable players = = = 
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- There have been many players who can be called notable throughout Aston Villa 's history . These can be classified and recorded in several forms . The <unk> of Fame and PFA Players of the Year are noted below . As of 2014 , Aston Villa , jointly with Tottenham Hotspur , hold the record for providing the most England internationals with 73 . Aston Villa have had several players who were one @-@ club men . In 1998 , to celebrate the 100th season of League football , The Football League released a list entitled the Football League 100 Legends that consisted of " 100 legendary football players . " There were seven players included on the list who had formerly played for Villa : Danny <unk> , Trevor Ford , Archie Hunter , Sam Hardy , Paul McGrath , Peter Schmeichel and <unk> <unk> . 
- Three Aston Villa players have won the PFA Players ' Player of the Year award . At the end of every English football season , the members of the Professional <unk> ' Association ( PFA ) vote on which of its members has played the best football in the previous year . In 1977 Andy Gray won the award . In 1990 it was awarded to David Platt , whilst Paul McGrath won it in 1993 . The PFA Young Player of the Year , which is awarded to players under the age of 23 , has been awarded to four players from Aston Villa : Andy Gray in 1977 ; Gary Shaw in 1981 ; Ashley Young in 2009 and James <unk> in 2010 . The National Football Museum in Preston , Lancashire administers the English Football Hall of Fame which currently contains two Villa teams , two Villa players and one manager . The 1890s team and 1982 team were inducted into the Hall of Fame in July 2009 . Joe Mercer was inducted into the Hall of Fame at the same time for his career as a manager including his time at Aston Villa . The only two Villa players in the Hall of Fame are Danny <unk> and Peter Schmeichel . 
- In 2006 , Aston Villa announced the creation of an " Aston Villa Hall of Fame . " This was voted for by fans and the inaugural induction saw 12 former players , managers and directors named . In May 2013 it was announced that former Villa and Bulgaria captain , <unk> <unk> , would be the 13th addition to the Hall of Fame . 
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- = = Management = = 
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- = = = Current technical staff = = = 
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- As of 4 June 2016 
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- = = = Board of Directors = = = 
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- As of 15 June 2016 
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- = = = Notable managers = = = 
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- The following managers have all won at least one trophy when in charge or have been notable for Villa in the context of the League , for example <unk> <unk> who holds a League record . 
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- = = In popular culture = = 
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- A number of television programmes have included references to Aston Villa over the past few decades . In the sitcom <unk> , the character Lennie <unk> is a Villa supporter . When filming began on Dad 's Army , Villa fan Ian <unk> was allowed to choose Frank Pike 's scarf from an array in the BBC wardrobe ; he chose a claret and blue one — Aston Villa 's colours . The character <unk> in the BBC sitcom Gavin & <unk> was revealed as an Aston Villa fan in an episode screened in December 2009 . In the BBC series " Yes Minister " / " Yes Prime Minister " , the Minister Jim <unk> 's local team was Aston Villa , as was the Member of Parliament for Birmingham East . 
- Aston Villa have also featured on several occasions in prose . Stanley Woolley , a character in Derek Robinson 's Booker shortlisted novel <unk> Squadron is an Aston Villa fan and names a pre @-@ war starting eleven Villa side . Together with The Oval , Villa Park is referenced by the poet Philip <unk> in his poem about the First World War , <unk> . Aston Villa are also mentioned in Harold <unk> 's play The <unk> <unk> . 
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- 
- = Pattycake ( gorilla ) = 
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- Pattycake , also known as Patty Cake ( September 3 , 1972 – March 31 , 2013 ) was a female western lowland gorilla ( Gorilla gorilla gorilla ) born to Lulu and Kongo at the Central Park Zoo in New York City . She was the first baby gorilla successfully born in captivity in New York . Months after her much publicized birth , Pattycake 's arm was broken when it got stuck in her cage as her mother grabbed her away from her father . The incident was <unk> <unk> in the media as a domestic dispute between Lulu and Kongo , but in reality experts thought it was a simple accident . 
- Her injury was treated at the Bronx Zoo while a custody dispute between the two zoos broke out in public and elicited a range of opinions from experts who believed Pattycake should be returned to her mother . <unk> media coverage and public interest brought Pattycake to the attention of a wide audience , with stories focusing on her recovery , her eventual reunion with her parents , and the conditions of zoo animals in Central Park . An ambitious proposal for renovating the Central Park Zoo arose in the wake of the controversy while the zoo received record attendance rates . 
- Pattycake was the " child star " of New York City in the early 1970s , and her fame was compared to Shirley Temple . At a time when New York City was facing many problems , she distracted the public from their growing <unk> and became a welcome relief for New Yorkers and their children who loved to visit her . After spending the first decade of her life at the Central Park Zoo , Pattycake moved permanently to the Bronx Zoo in 1982 . She was the mother of ten baby gorillas , including twins born in 1995 . Pattycake spent her later life as an independent but caring troop <unk> in the Bronx Zoo 's Congo Gorilla Forest exhibit . After suffering from <unk> and cardiac problems for some time , Pattycake succumbed to heart disease in 2013 . 
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- = = Birth = = 
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- " Pattycake " was born on September 3 , 1972 , to western lowland gorilla parents Lulu and Kongo at the Central Park Zoo . Lulu and Kongo first arrived at the zoo on May 11 , 1966 . At the time , it was thought that Lulu wasn 't mature enough to conceive , so when Lulu gave birth to Pattycake at the age of 8 , it came as a surprise . Up until the time Lulu gave birth , it had been very difficult to get captive gorilla mothers to raise their young in zoos because the babies would either be rejected by the mother or they would be taken away by the handlers . Pattycake was the first gorilla successfully born in captivity in New York . 
- At the time of her birth , it was unknown if she was a male or female , as it was considered too dangerous to approach her and her parents . Her handlers assumed she was a male and originally named her " Sonny Jim " . When it was established that Pattycake was a female , a contest was held by the New York Daily News to find her a name . " Patty Cake " , the winning entry , was submitted by New York <unk> John O 'Connor , who named the gorilla after his wife and a proposed daughter . " It just so happens that we have three boys and I told my wife that if we ever have a girl , we should name her Patty , which happens to be her name . I thought we 'd let the baby gorilla use the name in the meantime , " O 'Connor told reporters . 
- Pattycake 's birth caught the attention of the city and brought crowds of thousands of New Yorkers to the Central Park Zoo . Reporter N. R. <unk> called her a child star whose " <unk> face served as a bit of a respite at a time when the city found itself grappling with high crime rates and an intensifying financial crisis . " Six months after Pattycake was born , the director of the zoo estimated that based on the crowds , she might draw an additional 500 @,@ 000 visitors by the time of her first birthday . 
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- = = <unk> dispute = = 
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- At the age of five months , Pattycake was sharing the Lion House with Lulu and Kongo at the Central Park Zoo . On March 20 , 1973 , an accident occurred that was attributed more to <unk> than to parenting skills . Although no staff were on hand to see it directly , according to visitors , while little Pattycake was reaching out to her father through the bars of her cage , her right arm became stuck somehow when her mother Lulu pulled her away , breaking her right arm . Zoo handlers had to subdue Lulu with a tranquilizer dart to remove Pattycake and treat her injury . While experts considered the incident an accident , the media <unk> the event . <unk> Veronica Nelson , who worked with Patty Cake , recalled that 
- The news media would have liked to have it a dramatic bloody mess — a struggle between mom and pop for the custody of the child . It was nothing like that . It was a simple accident . Kongo was in one part of the double cage , Lulu in another , and between them was a partition of narrow bars . Lulu had Patty Cake in her arms and when Patty reached in between the narrow bars to touch her father , Lulu suddenly pulled her away . But Patty 's arm got caught in the narrow bars and broke . It was a freak accident . No one ever realized that those bars were narrow enough to catch that tiny arm . 
- Pattycake was brought to New York Medical College for surgery and she was given a cast for her arm . Due to concerns that Lulu would try to remove Pattycake 's cast , she was separated from her mother and moved to the Bronx Zoo for <unk> . Pattycake was treated by veterinarian Emil <unk> who later replaced her cast with a sling . After an examination , the staff discovered that Pattycake had intestinal parasites and determined she was <unk> . They also believed that as a result of the incident , Lulu wasn 't capable as a mother . 
- A custody dispute began between the two zoos , with the Bronx Zoo arguing that she would be better cared for in their facilities . Time magazine noted that it was the " custody battle of the decade " in the " <unk> world " , comparing Patty Cake 's popularity and fame to that of child star Shirley Temple . <unk> biologist Ronald <unk> of the <unk> National <unk> Research Center was brought in to <unk> the dispute and published a report that favored returning Pattycake to her mother and the Central Park Zoo . In his report <unk> noted that " the recommendation is based on the judgment that an infant gorilla is more likely to develop into a socially competent and <unk> adequate animal if it is raised in the company of its parents as opposed to being raised with a group of peers . " 
- After three months <unk> from her injuries , Pattycake was returned to her mother on June 15 , 1973 . The entire incident was documented by artist Susan Green in her book <unk> Gorilla : The Story of Patty Cake ( 1978 ) . 
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- = = Central Park Zoo conditions = = 
- 
- By the 1970s , animal welfare organizations began to voice their increasing concern with the treatment of zoo animals and the conditions of their enclosures at the Central Park Zoo , the oldest zoo in the U.S. The <unk> Society of the United States , the Friends of the Zoo , and the Society for Animal Rights decried the prison @-@ like conditions of the <unk> and called for changes . The New York Zoological Society , which was responsible for creating realistic habitat enclosures at the Bronx Zoo , also began calling for changes and for Pattycake to be moved to another zoo . A renovation plan for the Central Park Zoo was approved in 1981 , with plans made to move the gorillas to larger spaces in other zoos . The New York Times reported that " the <unk> of these animals in inadequate spaces has long enraged animal lovers . " When the zoo was finally closed for renovations in 1982 , Pattycake was moved to the Bronx Zoo . 
- 
- = = Bronx Zoo = = 
- 
- Pattycake moved permanently to the Bronx Zoo on December 20 , 1982 . For a few years , she lived in a cage with <unk> , a <unk> . In June 1999 , Pattycake moved into the Wildlife Conservation Society 's $ 43 million Congo Gorilla Forest exhibit . The exhibit includes a Great Gorilla Forest viewing area that separates gorillas and visitors with a glass window . Two troops of gorillas inhabited the 6 @.@ 5 acre exhibit , with a dozen gorillas in Pattycake 's troop alone , including <unk> , Pattycake , <unk> , <unk> , <unk> , Fran , <unk> , <unk> , <unk> , <unk> , Barbara , and M <unk> . The general curator of the Bronx Zoo , James Doherty , described Pattycake as " independent " with " few close friends " in the Congo Gorilla Forest . " It may have something to do with the fact that she didn 't live with her parents that long , and lived with that <unk> for a few years , " Doherty said . 
- 
- = = Breeding and offspring = = 
- 
- Pattycake gave birth to her first baby , <unk> , a male gorilla , on January 20 , 1985 . <unk> was sired by <unk> . Pattycake and <unk> had a second baby on March 23 , 1986 , but it died soon after birth . With Barney , Pattycake gave birth to three babies : a female named Paki , on May 26 , 1989 , followed by Patrick on April 19 , 1990 , and <unk> on December 14 , 1991 . Paki gave birth to Pattycake 's only <unk> , <unk> , in 1998 . 
- In October 1991 , a <unk> named Timmy ( 1959 – 2011 ) was taken away from Kate , his infertile companion at the Cleveland <unk> Zoo , in the hopes of breeding lowland gorillas and introducing new genes into the captive gorilla gene pool . This forced separation led to protests from animal rights activists who expressed concerns about the potential consequences of emotional trauma on the two gorillas . The Association of <unk> and <unk> Species Survival Plan sent Timmy to the Bronx Zoo where he joined Pattycake and other females . 
- On July 11 , 1993 , Pattycake and Timmy gave birth to <unk> , a male gorilla . The pair also gave birth to twin males , <unk> and <unk> , on August 8 , 1994 . It was the sixth time western lowland gorillas had given birth to twins in captivity . The twins were raised in a separate habitat by surrogate mothers . After seven years , <unk> and <unk> , along with another gorilla named Dan , left for a zoo in Nebraska in February 2001 . On February 4 , 2001 , Pattycake and <unk> gave birth to <unk> , a female . Her last and tenth baby gorilla , a male , was delivered on April 15 , 2002 . <unk> , it died four days later . 
- 
- = = Later life = = 
- 
- Kongo , Pattycake 's father , died in 1998 . In 2002 , the Pattycake Fund was established to raise $ 250 @,@ 000 to stop illegal poaching of African gorillas . The fundraising coincided with Pattycake 's 30th birthday , commemorated with a two @-@ day celebration at the Bronx Zoo . A special cake was made for her from <unk> , <unk> , <unk> , and berries . 
- Timmy , along with two female gorillas , <unk> and Paki , left the Bronx Zoo for the Louisville Zoo in May 2004 . 
- Pattycake tried her hand at painting while participating with the Wildlife Conservation Society ( <unk> ) animal <unk> program at the Bronx Zoo . A sample of her work was published in the 2010 <unk> Annual Report . 
- Pattycake 's mother Lulu died in early 2011 . 
- Pattycake suffered from chronic heart disease and <unk> as she aged . She was one of 338 captive zoo gorillas within North America when she died in her sleep at the age of 40 . According to the Wildlife Conservation Society , Pattycake exceeded the median life span of 37 years for female zoo gorillas . 
- 
- = = Cultural depictions = = 
- 
- In honor of Pattycake , the Rev. Frederick Douglass Kirkpatrick ( Brother Kirk ) joined Pete Seeger and the Sesame Street kids chorus for the song " Patty Cake Gorilla " , released on the album Pete Seeger and Brother Kirk <unk> Sesame Street ( 1974 ) . A picture book called Patty Cake ( 1974 ) , featuring New York Times photographer Neal <unk> and others , was written by Elizabeth Moody . Pearl Wolf wrote Gorilla Baby : The Story of Patty Cake ( 1974 ) , a picture book for children . Artist Susan Green published her direct , personal observations about the custody dispute ( along with her drawings ) in the book <unk> Gorilla : The Story of Patty Cake ( 1978 ) . 
- 
- 
- = Lactarius indigo = 
- 
- Lactarius indigo , commonly known as the indigo milk cap , the indigo ( or blue ) <unk> , or the blue milk mushroom , is a species of agaric fungus in the family <unk> . A widely distributed species , it grows naturally in eastern North America , East Asia , and Central America ; it has also been reported in southern France . L. indigo grows on the ground in both deciduous and coniferous forests , where it forms mycorrhizal associations with a broad range of trees . The fruit body color ranges from dark blue in fresh specimens to pale blue @-@ gray in older ones . The milk , or latex , that <unk> when the mushroom tissue is cut or broken — a feature common to all members of the Lactarius genus — is also indigo blue , but slowly turns green upon exposure to air . The cap has a diameter of 5 to 15 cm ( 2 to 6 in ) , and the stem is 2 to 8 cm ( 0 @.@ 8 to 3 in ) tall and 1 to 2 @.@ 5 cm ( 0 @.@ 4 to 1 @.@ 0 in ) thick . It is an edible mushroom , and is sold in rural markets in China , Guatemala , and Mexico . 
- 
- = = Taxonomy and nomenclature = = 
- 
- Originally described in 1822 as Agaricus indigo by American mycologist Lewis David de <unk> , the species was later transferred to the genus Lactarius in 1838 by the <unk> Elias Magnus Fries . German botanist Otto <unk> called it <unk> indigo in his 1891 treatise <unk> <unk> <unk> , but the suggested name change was not adopted by others . Hesler and Smith in their 1960 study of North American species of Lactarius defined L. indigo as the type species of <unk> <unk> , a group characterized by blue latex and a sticky , blue cap . In 1979 , they revised their opinions on the organization of subdivisions in the genus Lactarius , and instead placed L. indigo in subgenus Lactarius based on the color of latex , and the subsequent color changes observed after exposure to air . As they explained : 
- The gradual development of blue to violet <unk> as one progresses from species to species is an interesting phenomenon <unk> further study . The climax is reached in L. indigo which is blue throughout . L. <unk> and its variety <unk> , L. <unk> , and L. <unk> may be considered as <unk> along the road to L. indigo . 
- The specific epithet indigo is derived from the Latin word meaning " indigo blue " . Its names in the English vernacular include the " indigo milk cap " , the " indigo Lactarius " , the " blue milk mushroom " , and the " blue Lactarius " . In central Mexico , it is known as <unk> , <unk> , <unk> <unk> , <unk> , and <unk> ; it is also called <unk> ( meaning " blue " ) in Veracruz and Puebla . 
- 
- = = Description = = 
- 
- Like many other mushrooms , L. indigo develops from a <unk> , or <unk> , that forms within the underground mycelium , a mass of threadlike fungal cells called hyphae that make up the bulk of the organism . Under appropriate environmental conditions of temperature , humidity , and nutrient availability , the visible reproductive structures ( fruit bodies ) are formed . The cap of the fruit body , measuring between 5 and 15 cm ( 2 @.@ 0 and 5 @.@ 9 in ) in diameter , is initially convex and later develops a central depression ; in age it becomes even more deeply depressed , becoming somewhat funnel @-@ shaped as the edge of the cap <unk> upward . The margin of the cap is rolled <unk> when young , but <unk> and <unk> as it matures . The cap surface is indigo blue when fresh , but fades to a paler <unk> or silvery @-@ blue , sometimes with greenish <unk> . It is often <unk> : marked with concentric lines that form alternating pale and darker zones , and the cap may have dark blue spots , especially towards the edge . Young caps are sticky to the touch . 
- The flesh is <unk> to bluish in color , slowly turning greenish after being exposed to air ; its taste is mild to slightly acrid . The flesh of the entire mushroom is brittle , and the stem , if bent sufficiently , will snap open cleanly . The latex <unk> from injured tissue is indigo blue , and stains the wounded tissue greenish ; like the flesh , the latex has a mild taste . Lactarius indigo is noted for not producing as much latex as other Lactarius species , and older specimens in particular may be too dried out to produce any latex . 
- The gills of the mushroom range from <unk> ( squarely attached to the stem ) to slightly <unk> ( running down the length of the stem ) , and crowded close together . Their color is an indigo blue , becoming paler with age or staining green with damage . The stem is 2 – 6 cm ( 0 @.@ 8 – 2 @.@ 4 in ) tall by 1 – 2 @.@ 5 cm ( 0 @.@ 4 – 1 @.@ 0 in ) thick , and the same diameter throughout or sometimes narrowed at base . Its color is indigo blue to <unk> or grayish blue . The interior of the stem is solid and firm initially , but develops a hollow with age . Like the cap , it is initially sticky or <unk> to the touch when young , but soon <unk> out . Its attachment to the cap is usually in a central position , although it may also be off @-@ center . Fruit bodies of L. indigo have no distinguishable odor . 
- L. indigo var. <unk> ( the " smaller indigo milk cap " ) is a smaller variant of the mushroom , with a cap diameter between 3 and 7 cm ( 1 @.@ 2 and 2 @.@ 8 in ) , and a stem 1 @.@ 5 – 4 @.@ 0 cm ( 0 @.@ 6 – 1 @.@ 6 in ) long and 0 @.@ 3 – 1 @.@ 0 cm ( 0 @.@ 1 – 0 @.@ 4 in ) thick . It is often seen in Virginia . Hesler and Smith , who first described the variant based on specimens found in Brazoria County , Texas , described its typical habitat as " along [ the ] sides of a muddy ditch under grasses and weeds , [ with ] <unk> pine nearby " . 
- 
- = = = Microscopic features = = = 
- 
- When viewed in mass , as in a spore print , the spores appear cream to yellow colored . <unk> with a light microscope , the spores are translucent ( hyaline ) , elliptical to nearly spherical in shape , with amyloid warts , and have dimensions of 7 – 9 by 5 @.@ 5 – 7 @.@ 5 µm . <unk> electron microscopy reveals <unk> on the spore surface . The hymenium is the spore @-@ producing tissue layer of the fruit body , and consists of hyphae that extend into the gills and terminate as end cells . Various cell types can be observed in the hymenium , and the cells have microscopic characteristics that may be used to help identify or distinguish species in cases where the <unk> characters may be ambiguous . The spore @-@ bearing cells , the basidia , are four @-@ spored and measure 37 – 45 µm long by 8 – 10 µm wide at the <unk> point . <unk> are terminal cells of hyphae in the hymenium which do not produce spores , and function in aiding spore dispersal , and maintaining favorable humidity around developing spores . The <unk> are cystidia that are found on the face of a gill ; they are 40 – 56 by 6 @.@ 4 – 8 µm , roughly spindle @-@ shaped , and have a <unk> apex . The cheilocystidia — located on the edge of a gill — are abundant , and are 40 @.@ 0 – 45 @.@ 6 by 5 @.@ 6 – 7 @.@ 2 µm . 
- 
- = = = Similar species = = = 
- 
- The characteristic blue color of the fruiting body and the latex make this species easily recognizable . Other Lactarius species with some blue color include the " silver @-@ blue <unk> " ( L. <unk> ) , found in eastern North America , which has a grayish @-@ blue cap when young , but it has reddish @-@ brown to purple @-@ brown latex and gills . L. <unk> has a yellowish to <unk> yellow @-@ brown to bluish @-@ gray cap and yellowish to brown latex . L. <unk> has blue @-@ colored flesh in the cap and orange to red @-@ orange flesh in the base of the stem . Although the blue <unk> of L. indigo is thought to be rare in the genus Lactarius , in 2007 five new species were reported from <unk> Malaysia with <unk> latex or flesh , including L. <unk> , L. <unk> , L. <unk> , and two species still unnamed . 
- 
- = = Edibility = = 
- 
- Although L. indigo is a well @-@ known edible species , opinions vary on its <unk> . For example , American mycologist David Arora considers it a " superior edible " , while a field guide on Kansas fungi rates it as " mediocre in quality " . It may have a slightly bitter , or <unk> taste , and has a coarse , <unk> texture . The firm flesh is best prepared by cutting the mushroom in thin <unk> . The blue color disappears with cooking , and the mushroom becomes grayish . Because of the <unk> texture of the flesh , it does not lend itself well to drying . Specimens producing copious quantities of milk may be used to add color to <unk> . 
- In Mexico , individuals harvest the wild mushrooms for sale at farmers ' markets , typically from June to November ; they are considered a " second class " species for consumption . L. indigo is also sold in <unk> markets from May to October . It is one of 13 Lactarius species sold at rural markets in <unk> in southwestern China . 
- 
- = = = Chemical composition = = = 
- 
- A chemical analysis of Mexican specimens has shown L. indigo to contain moisture at 951 mg / g of mushroom , fat at 4 @.@ 3 mg / g , protein at 13 @.@ 4 mg / g , and dietary <unk> at 18 @.@ 7 mg / g , much higher in comparison to the common button mushroom , which contains 6 @.@ 6 mg / g . Compared to three other wild edible mushroom species also tested in the study ( Amanita <unk> , Boletus <unk> , and <unk> <unk> ) , L. indigo contained the highest saturated fatty acids content , including <unk> acid with 32 @.@ 1 mg / g — slightly over half of the total free fatty acid content . 
- The blue color of L. indigo is due to ( 7 @-@ <unk> @-@ 4 @-@ <unk> @-@ 1 @-@ <unk> ) methyl <unk> , an organic derivative of <unk> . It is unique to this species , but similar to a compound found in L. <unk> . 
- 
- = = Distribution , habitat , and ecology = = 
- 
- L. indigo is distributed throughout southern and eastern North America but is most common along the Gulf Coast , Mexico , and Guatemala . Its frequency of appearance in the Appalachian Mountains of the United States has been described as " occasional to locally common " . Mycologist David Arora notes that in the United States , the species is found with ponderosa pine in Arizona , but is absent in California 's ponderosa pine forests . It has also been collected from China , India , Guatemala , and Costa Rica ( in forests dominated by oak ) . In Europe , it has so far only been found in southern France . A study on the seasonal appearance of fruiting bodies in the subtropical forests of <unk> , Mexico , confirmed that <unk> production coincided with the rainy season between June and September . 
- L. indigo is a mycorrhizal fungus , and as such , establishes a mutualistic relationship with the roots of certain trees ( " hosts " ) , in which the fungi exchange minerals and amino acids extracted from the soil for fixed carbon from the host . The subterranean hyphae of the fungus grow a sheath of tissue around the rootlets of a broad range of tree species , forming so @-@ called ectomycorrhizae — an intimate association that is especially beneficial to the host , as the fungus produces enzymes that <unk> organic compounds and facilitate the transfer of nutrients to the tree . 
- Reflecting their close relationships with trees , the fruit bodies of L. indigo are typically found growing on the ground , scattered or in groups , in both deciduous and coniferous forests . They are also commonly found in floodplain areas that have been recently submerged . In Mexico , associations have been noted with Mexican alder , American <unk> , American <unk> , and <unk> <unk> , while in Guatemala the mushroom associates with smooth @-@ bark Mexican pine and other pine and oak species . In Costa Rica , the species forms associations with several native oaks of the Quercus genus . Under controlled laboratory conditions , L. indigo was shown to be able to form ectomycorrhizal associations with the <unk> pine species Mexican white pine , <unk> 's pine , Mexican yellow pine , smooth @-@ bark Mexican pine , and the Eurasian pines Aleppo pine , European black pine , maritime pine , and Scots pine . 
- 
- = = = Cited literature = = = 
- 
- Arora D. ( 1986 ) . Mushrooms <unk> : A <unk> Guide to the <unk> Fungi . Berkeley , California : Ten Speed Press . ISBN 0 @-@ <unk> @-@ 169 @-@ 4 . 
- Hesler <unk> , Smith AH ( 1979 ) . North American Species of Lactarius . Ann Arbor , Michigan : The University of Michigan Press . ISBN 0 @-@ <unk> @-@ <unk> @-@ 2 . 
- 
- 
- = You 're Gonna Love Tomorrow = 
- 
- " You 're Gonna Love Tomorrow " is the fifth season premiere episode of the American comedy @-@ drama series Desperate Housewives , and the 88th episode overall . It originally aired on September 28 , 2008 , in the United States on ABC ( American Broadcasting Company ) . The episode was written by series creator Marc Cherry and directed by Larry Shaw . " You 're Gonna Love Tomorrow " , as well as all subsequent episodes , takes place five years after the events of the fourth season finale following Cherry 's decision to revamp the series with a time jump . 
- In the episode , Edie ( Nicollette Sheridan ) returns to Wisteria Lane after a five @-@ year absence with her new husband , Dave ( Neal McDonough ) . While Susan ( Teri Hatcher ) hesitates to make a romantic commitment to Jackson ( Gale Harold ) , Lynette ( <unk> <unk> ) struggles with her <unk> teenage children and her husband 's <unk> crisis . Gabrielle ( Eva Longoria ) deals with her daughters ' weight problems and Bree ( Marcia Cross ) faces the ramifications of her success . 
- " You 're Gonna Love Tomorrow " received mostly positive reviews from television critics , most of whom agreed that the time jump refreshed the show . According to Nielsen ratings , the episode drew over 18 million viewers , making it the most @-@ watched show of the night and the second most @-@ watched program of the week across all networks . 
- 
- = = Plot = = 
- 
- 
- = = = Background = = = 
- 
- Desperate Housewives focuses on the lives of several residents living on Wisteria Lane . In recent episodes , Susan ( Teri Hatcher ) and Mike <unk> ( James Denton ) celebrate the birth of their son . Bree <unk> ( Marcia Cross ) chooses to raise her grandson while her daughter , Danielle ( Joy Lauren ) , attends college . She also issues an ultimatum , telling her husband , Orson ( Kyle <unk> ) , that she will leave him if he does not turn himself into the police after committing a hit @-@ and @-@ run . Gabrielle Solis ( Eva Longoria ) struggles with Carlos ' ( Ricardo Antonio <unk> ) permanent blindness . Also , Edie <unk> ( Nicollette Sheridan ) leaves Wisteria Lane after the other residents <unk> her for her <unk> . 
- 
- = = = Episode = = = 
- 
- " You 're Gonna Love Tomorrow " takes place five years after the aforementioned events . Important plot information that takes place during the time jump is revealed in flashbacks . Orson has completed his prison sentence and the success of Bree 's catering company has provided her the opportunity to write her own cookbook . Her business partner , Katherine Mayfair ( Dana Delany ) , <unk> how Bree 's success has changed her and intentionally <unk> Bree 's television interview , further straining their friendship . A flashback reveals that Danielle married a lawyer and <unk> took her son away from Bree . Meanwhile , Lynette is frustrated with Tom 's ( Doug <unk> ) relaxed approach to parenting their rebellious teenage sons , Porter and Preston ( Charlie Carver and Max Carver , respectively ) . She encourages him to employ more disciplinary actions . 
- During the time jump , Susan and Mike were involved in a car crash that killed a mother and her child . As a result , the couple divorced and now share custody of their son , <unk> ( Mason Vale Cotton ) . Susan engages in a sexual relationship with her house painter , Jackson <unk> ( Gale Harold ) , but keeps their romance a secret from her friends and family . Jackson seeks a more substantial relationship , but Susan is weary of such a commitment following her divorce . Elsewhere , Gabrielle has been raising two overweight daughters , Juanita ( Madison De La Garza ) and Celia ( <unk> Baltodano ) , and has also lost her own figure as well . Gabrielle tricks Juanita into exercising by driving away and making Juanita chase after her car . 
- After five years of absence , Edie returns to Wisteria Lane with her mysterious husband , Dave ( Neal McDonough ) , who seems to have a calming effect on his ill @-@ tempered wife . Later , Dave receives a phone call from Dr. Samuel Heller ( Stephen <unk> ) , who reminds him that monthly check @-@ ins are a condition of his release . After the conversation , Dr. Heller reviews a taped therapy session in which Dave threatens to get revenge on the man who destroyed his life . 
- 
- = = Production = = 
- 
- " You 're Gonna Love Tomorrow " was written by series creator and executive producer Marc Cherry and directed by Larry Shaw . Filming for the episode was scheduled to begin on July 7 , 2008 . The episode is the first to fully employ the five @-@ year jump , which was introduced in the final two minutes of the fourth season finale . While developing the fifth season , Cherry began <unk> ways to revamp the series . He stated : " The soap tends to build up , and I wanted to get back to where we were that very first season , where it 's just the problems of some ordinary women and they were small and relatable . " Cherry credits the producers of Lost for the idea of the time jump . The writers hoped the time jump would also help them avoid repeating mistakes they made during the show 's poorly received second season , during which they relied heavily on storylines from the previous season . Cherry said that the time jump would be permanent , with executive producer Bob Daily clarifying that some brief flashback scenes would be used to enhance the storytelling and provide context for current plot lines . Cherry had originally wanted to do a ten @-@ year jump , mostly to age the young characters into their teenage years in order to open up more storyline possibilities . 
- The writers ensured that although they were changing the storyline circumstances , the characters remained the same with minimal changes to their personalities . According to series writer Matt Berry , the time jump allowed the writers to work with the characters " without most of the baggage they ’ d accumulated in the earlier years , and put them into starting places so we could move them forward and build in new story arcs . " For this reason , the writers treated the season premiere as a pilot , in which , according to Daily , they " could give the women a new drive . " Daily identified the Susan character as having undergone the greatest change , stating that she has given up on looking for a " fairy @-@ tale romance " and is emotionally distant in her new relationship . He also stated that the character of Gabrielle has dealt with the biggest change in terms of her circumstances , as she continues to deal with her husband 's blindness and their financial problems . Actress Eva Longoria had to gain weight and wear additional body padding for her character 's new storyline . Dana Delany stated that her character , Katherine , who was portrayed as an <unk> in the previous season , is more relaxed following the time jump . However , Delany clarified that while Katherine is now friends with the other characters , she and Bree would become <unk> as a result of their business partnership . Nicollette Sheridan , commented that Edie , who played an integral role in the season 's mystery story arch , is " a lot more conscientious about things [ this season ] , without losing her comedic edge . " 
- Several casting changes were made as a result of the five @-@ year jump . Neal McDonough 's involvement was announced in July 2008 . McDonough did not audition for the role of Dave Williams , but was offered it after meeting with Cherry . Gale Harold continued appearing as Jackson , Susan 's new love interest , after appearing briefly in the <unk> segment at the end of the fourth season . The roles of the <unk> children were recast in order to reflect their new ages following the time jump . Charlie and Max Carver replaced Brent and Shane <unk> as Preston and Porter , respectively . The <unk> twins appeared in a flashback in this episode . Joshua Logan Moore was cast as Parker , a role previously played by <unk> <unk> , and Kendall Applegate joined the cast as Penny , who had previously been portrayed by several <unk> actresses . Despite their casting announcements alongside the Carver twins , Moore and Applegate did not appear in " You 're Gonna Love Tomorrow " . The <unk> segment at the end of the fourth season also introduced <unk> Say and <unk> Baltodano as Gabrielle 's daughters , Juanita and Celia , respectively . Only Baltodano continued portraying her role for the fifth season ; Say was replaced by Madison De La Garza during the summer hiatus . 
- Andrea Bowen , who starred as Susan 's daughter , Julie , departed from the cast , as her character left for college at the end of the fourth season . Teri Hatcher expressed disappointment in the producers ' decision , but Cherry stated that Bowen would return in the future . Joy Lauren , who portrayed Bree 's daughter , Danielle , and <unk> <unk> , who played Katherine 's daughter , Dylan , also left the main cast , although the former appeared in the season premiere as a guest star . Because the fourth season ended leaving Mike 's whereabouts unknown , James Denton 's return to the series was questioned during hiatus . Denton stated in May 2008 that producers would not make a decision on his character until mid @-@ June and that he was preparing to find work in case his contract was terminated . It was later confirmed that he would return to the series . 
- The Wisteria Lane set , which is located on the Colonial Street <unk> set at Universal Studios , underwent changes for the time jump . Production designer P. Erik Carlson explained , " [ former production designer Thomas A. Walsh ] had already created a fairly <unk> world , and we wanted to <unk> and enhance it a little bit more , mostly through the use of color . We didn ’ t want it to feel ridiculously futuristic or viewers would be jarred by the contrast . " Bree 's garage was remodeled into a testing kitchen as result of the character 's catering business . Additionally , all houses were <unk> with bolder colors , with the exception of Gabrielle 's home , in order to reflect the Solis ' financial situation . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- According to Nielsen ratings , " You 're Gonna Love Tomorrow " was watched by 18 @.@ 684 million viewers and held an 11 @.@ 4 rating / 17 share on its original American broadcast on September 28 , 2008 . The episode was watched in 13 @.@ 105 million total households . It was number one in its timeslot , beating Sunday Night Football on NBC , Cold Case on CBS , and Family Guy and American Dad ! on Fox . The episode was the most @-@ watched program of the night in both total viewers and the young adult demographic . It was the second @-@ most watched program of the week across all networks by viewers 18 to 49 years old , behind Grey 's Anatomy , and the second @-@ most watched program in total viewership , behind Dancing with the Stars . The episode also performed better than any original broadcast of the series since January 2008 . It outperformed the fourth season finale by two million total viewers and showed a 17 percent increase in viewers 18 to 49 years old . At the time , the episode was the least @-@ watched season premiere of the series , falling half a million viewers from the fourth season premiere , " Now You Know " , a year earlier . However , " You 're Gonna Love Tomorrow " outperformed " Now You Know " in the demographic of women between 18 and 34 years of age with an 8 @.@ 3 rating / 20 share in that demographic . In the United Kingdom , the episode premiered on Channel 4 on October 22 , 2008 . It was watched by 2 @.@ 39 million viewers , becoming the sixth most @-@ watched program of the week on the channel . 
- 
- = = = Critical reception = = = 
- 
- The episode received mostly positive reviews from critics . <unk> <unk> of Entertainment Weekly called the five @-@ year jump " a stroke of genius , effectively increasing our insight into these ladies ' world by <unk> . " He called the Gabrielle storyline a true depiction of motherhood and applauded the scene in which Carlos and Gabrielle discuss her low self @-@ esteem . He complimented the Bree storyline , particularly the argument scene between Bree and Katherine . He enjoyed the Dave storyline , but expressed disappointment that Edie 's reappearance was the only scene that involved all of the leading women . Additionally , <unk> was not intrigued by Lynette 's storyline and deemed it " more of the same " for the character . He criticized Susan 's storyline , calling it annoying . He also dismissed the writers ' ploy to give viewers the impression that Mike had died for almost the entire episode . TV Guide 's Matt Roush gave the episode an ' A ' , stating , " Moving the story ahead five years didn 't so much reinvent the show as it <unk> and refreshed the <unk> mix of domestic comedy and <unk> intrigue that we 've always enjoyed . " He highlighted the comedic Gabrielle storyline while <unk> the additions of McDonough and Harold to the cast . 
- In his review of the first two episodes of the season , Brian Lowry of Variety said that while " You 're Gonna Love Tomorrow " is satisfactory and establishes the five @-@ year jump effectively , the second episode is better , calling it " a knock @-@ out . " Entertainment Weekly 's Ken Tucker commended the time jump as a reflection of both the characters ' and series ' ambitions , rating the episode a ' B + ' . Joanna Weiss of The Boston Globe gave the episode a mixed review . She said that the time leap proved to be successful , as the show had been " <unk> " for several seasons and concluded that , " so far , at least , this season promises to be less about plot than personality . That doesn 't mean the show is perfect - it never was - but it 's better , and that 's a big relief . " The Pittsburgh Post @-@ Gazette 's Rob Owen was positive in his review , noting that the writers handled the five @-@ year leap well , especially in regards to Lynette 's storyline . He complimented the episode 's comedy and expressed his hope that the time jump would allow the writers to focus on humor rather than overly @-@ dramatic storylines . 
- 
- 
- = Fear of Flying ( The Simpsons ) = 
- 
- " Fear of Flying " is the eleventh episode of The Simpsons ' sixth season . It was first broadcast on the Fox network in the United States on December 18 , 1994 . In the episode , Homer is banned from Moe 's Tavern and struggles to find a new bar . When he destroys a plane after being mistaken for a pilot at a pilots @-@ only bar , the airline <unk> the Simpsons ' silence with free tickets . The family discovers that Marge is afraid of flying . 
- The episode was directed by Mark Kirkland , and written by David Sacks . It features numerous guest stars , including Anne Bancroft as Dr. Zweig . Additionally , Ted <unk> , Woody <unk> , Rhea <unk> , John Ratzenberger , and George <unk> appear as their characters from Cheers . It received positive reception from television critics , and acquired a Nielsen rating of 9 @.@ 6 . The authors of I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide commented positively on the episode , as did reviews from DVD Verdict and DVD Movie Guide . 
- 
- = = Plot = = 
- 
- After pulling a harmless prank on Moe , Homer is banned from His Tavern , ironically after Moe laughed off life @-@ threatening ones that Lenny , Carl and Barney pulled on him . Looking for another place to drink ( including the Cheers bar ) , Homer eventually settles for an airline pilots ' bar , but is mistaken for a pilot ( despite <unk> that he 's not really a pilot ) and is put in the cockpit of an airplane , which he promptly wrecks after raising the stationary plane 's landing gear . In exchange for his silence of the mistake they 've made , the airline gives the Simpson family free tickets to anywhere they desire in the continental United States . However , the idea of plane travel fills Marge with anxiety as she has a fear of flying , and after numerous failed attempts to get out of the trip , she eventually has a panic attack on the plane , following which the trip is postponed . 
- Marge does not want to talk to anybody about her fear , and Lisa worries that Marge 's decision to keep her feelings <unk> up will cause them to " come out in other ways " . When Marge begins to show signs of her lingering flight @-@ related trauma by insisting the cat and the dog are living in sin , cooking giant <unk> , and <unk> the roof in the middle of the night , Lisa convinces Marge to undergo treatment with therapist Dr. Zweig . Homer , however , grows increasingly paranoid about Marge 's therapy , believing that Zweig will blame Marge 's trauma on him , and encourage her to leave him . 
- Zweig <unk> the roots of Marge 's fear : the moment she realized her father was not a pilot , but an apron @-@ wearing flight attendant , a job that was mostly reserved for women at the time . Her shame is eased when Zweig assures her that male flight attendants are now very common and that her father could be considered a pioneer . Marge also brings up memories of her grandmother poking her in the eye as a baby while playing airplane , a toy plane catching fire , and having a plane fire at her and her mother , but Zweig just ignores them . Before the therapist begins to question Marge 's marriage , Homer immediately takes Marge away and Marge manages to thank Zweig for helping her . Marge is finally cured of her fears , but when she and Homer attempt to fly on a plane again , the plane crashes into a lake . 
- 
- = = Production = = 
- 
- " Fear of Flying " was directed by Mark Kirkland , and written by David Sacks . The story of the episode came about when Sacks came into the writers ' room with an idea for an episode where Marge goes to a therapist " for one reason or another " . Sacks and the other writers then structured the rest of the plot around that storyline . Anne Bancroft was called in to voice Zweig . Before Bancroft recorded her part , the animators based Zweig 's design on a <unk> track from cast member <unk> <unk> as the therapist . After Bancroft had recorded her part , Zweig was redesigned to fit with Bancroft 's voice . They added split glasses and a streak of silver in her hair to give her a more mature look . 
- After Homer was kicked out of Moe 's Tavern , it was originally planned for a cat to come to Homer , and then have it enter the bar . The result is the <unk> loving the cat , and depressing Homer even more . According to David Silverman , the fact that it looked depressing was the reason why it was deleted , and replaced with a more humorous approach with him chasing a <unk> . 
- The staff was able to get the central cast of the American sitcom Cheers , with the exception of <unk> Grammer , to reunite and guest star in the episode . The staff could not arrange the script to allow time in the episode for Grammer , who already had a recurring role on The Simpsons as Sideshow Bob , to voice <unk> Crane . Ted <unk> guest starred as Sam , Woody <unk> as Woody , Rhea <unk> as Carla , John Ratzenberger as Cliff , and George <unk> as <unk> . 
- 
- = = Cultural references = = 
- 
- Homer enters the Cheers bar in a scene which is a parody of a typical episode of the comedy series Cheers . All of the speaking characters are voiced by the actors who played them in Cheers . Ironically , <unk> Crane remains silent despite being played by Simpsons veteran <unk> Grammer , the voice of Sideshow Bob . Marge 's dream sees her in the role of Mrs. Robinson from Lost in Space , while Homer plays Dr. Smith . The scene where Marge and Jacqueline <unk> duck down when a biplane shoots at them in a <unk> is a parody of Alfred Hitchcock 's film North by Northwest . 
- Homer 's Mount <unk> caricature resembles <unk> <unk> , the mascot of The New Yorker . Homer 's line about getting out of Springfield is lifted from It 's a <unk> Life , while Homer 's all @-@ time favorite song is revealed to be " It 's <unk> Men " by The Weather Girls . When Abe Simpson is left behind on the plane , his slapping his hands on his face and <unk> is a reference to Home Alone . This is the second time Home Alone has been parodied on The Simpsons . The first was " Homer Alone " , a reference to the title . 
- As Homer is looking for a place to drink , he tries a lesbian bar , the She She Lounge . However , he soon realizes that " this lesbian bar doesn 't have a fire exit " . He then leaves , saying " <unk> your <unk> , ladies ! " . This is a reference to a famous Greenwich Village gay bar , the <unk> Inn . This mafia @-@ owned and <unk> bar " had no rear exit , so if there had been a fire on a weekend night , hundreds of customers would have had to escape through a single narrow passage leading to the front door . " The films Homer rents are Hero , Fearless , and Alive , the latter of which Marge watches . All of them involve plane crashes . 
- When Marge leaves Dr. Zweig 's office , she says , " Whenever the wind whistles through the leaves , I 'll think , Lowenstein , Lowenstein … " . This is a reference to The Prince of Tides ; the <unk> is Dr. Lowenstein . 
- 
- = = Reception = = 
- 
- In its original American broadcast , " Fear of Flying " finished 48th ( tied with Dateline NBC ) in the ratings for the week of December 12 to December 18 , 1994 , with a Nielsen rating of 9 @.@ 6 . The episode was the third highest rated show on the Fox network that week , beaten only by Beverly Hills , <unk> , and Married ... with Children . Since airing , the episode has received many positive reviews from fans and television critics . In July 2007 , Simon <unk> of The Times listed the Cheers cast 's performance as one of the thirty @-@ three funniest cameos in the history of the show . 
- Warren Martyn and Adrian Wood , the authors of the book I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide , said it was " a good Marge @-@ centric episode with plenty of clever set pieces – the tributes to Cheers and Lost in Space are fantastic " , and noted that " Marge 's father looks suspiciously like Moe " . Ryan Keefer at DVD Verdict said that " with the cast of Cheers appearing ( except for Grammer , ironically ) and a funny spoof of North by Northwest , the episode is much better than you would expect " , and gave it a B + . 
- Colin Jacobson at DVD Movie Guide said in a review of the sixth season DVD that it was " another show I didn ’ t recall fondly but that works exceedingly well . I hadn ’ t realized how many quotes I ’ ve stolen from this one : the name ' Guy <unk> ' , the dog with the <unk> tail , ' a burden coupled with a <unk> ' . The show makes little sense in regard to continuity since Marge has flown during prior shows , but it ’ s consistently very funny and entertaining . " The Phoenix named Anne Bancroft one of the twenty best guest stars to appear on the show . 
- 
- = = Merchandise = = 
- 
- The episode was selected for release in a 1999 video collection of selected episodes titled : The Simpsons Go To Hollywood . Other episodes included in the collection set were " <unk> Moe 's " , " <unk> <unk> <unk> " , and " Homer to the Max " . " Fear of Flying " was again included in the 2003 DVD release of the same set . It was included in The Simpsons season 6 DVD set , which was released on August 16 , 2005 , as The Simpsons – The Complete Sixth Season . 
- 
- 
- = Harold Innis = 
- 
- Harold Adams Innis ( / <unk> / ; November 5 , 1894 – November 8 , 1952 ) was a Canadian professor of political economy at the University of Toronto and the author of seminal works on media , communication theory , and Canadian economic history . Despite his dense and difficult prose , Innis was one of Canada 's most original thinkers . He helped develop the staples thesis , which holds that Canada 's culture , political history , and economy have been decisively influenced by the exploitation and export of a series of " staples " such as fur , fishing , lumber , wheat , mined metals , and coal . The staple thesis dominated economic history in Canada 1930s @-@ 1960s , and is still used by some . 
- Innis 's writings on communication explore the role of media in shaping the culture and development of civilizations . He argued , for example , that a balance between oral and written forms of communication contributed to the flourishing of Greek civilization in the 5th century BC . He warned , however , that Western civilization is now <unk> by powerful , advertising @-@ driven media obsessed by " present @-@ mindedness " and the " continuous , systematic , ruthless destruction of elements of permanence essential to cultural activity " . His intellectual bond with Eric A. Havelock formed the foundations of the “ Toronto School of Communication ” which provided a source of inspiration for future members of the school : Marshall McLuhan and Edmund Snow Carpenter . 
- Innis laid the basis for scholarship that looked at the social sciences from a distinctly Canadian point of view . As the head of the University of Toronto 's political economy department , he worked to build up a cadre of Canadian scholars so that universities would not continue to rely as heavily on British or American @-@ trained professors unfamiliar with Canada 's history and culture . He was successful in establishing sources of financing for Canadian scholarly research . 
- As the Cold War grew <unk> after 1947 , Innis grew increasingly hostile to the United States . He warned repeatedly that Canada was becoming a <unk> colony to its much more powerful southern neighbor . " We are indeed fighting for our lives , " he warned , pointing especially to the " <unk> influence of American advertising .... We can only survive by taking persistent action at strategic points against American imperialism in all its attractive <unk> . " His anti @-@ Americanism influenced some younger scholars , including Donald Creighton . 
- Innis also tried to defend universities from political and economic pressures . He believed that independent universities , as centres of critical thought , were essential to the survival of Western civilization . His intellectual disciple and university colleague , Marshall McLuhan , lamented Innis 's premature death as a disastrous loss for human understanding . McLuhan wrote : " I am pleased to think of my own book The <unk> Galaxy as a footnote to the observations of Innis on the subject of the psychic and social consequences , first of writing then of printing . " 
- 
- = = Rural roots = = 
- 
- 
- = = = Early life = = = 
- 
- Harold Adams Innis was born in 1894 on a small livestock and dairy farm near the community of Otterville in southwestern Ontario 's Oxford County . As a boy he loved the rhythms and routines of farm life and he never <unk> his rural origins . His mother , Mary Adams Innis , had named him ' Herald ' , hoping he would become a minister in the strict evangelical Baptist faith that she and her husband William shared . At the time , the Baptist Church was an important part of life in rural areas . It gave isolated families a sense of community and embodied the values of <unk> and independence . Its far @-@ flung congregations were not ruled by a centralized , bureaucratic authority . Innis became an agnostic in later life , but never lost his interest in religion . According to his friend and biographer Donald Creighton , Innis 's character was moulded by the Church : 
- The strict sense of values and the feeling of devotion to a cause , which became so characteristic of him in later life , were derived , in part at least , from the instruction imparted so <unk> and <unk> inside the severely unadorned walls of the Baptist Church at Otterville . 
- Innis attended the one @-@ room schoolhouse in Otterville and the community 's high school . He travelled 20 miles ( 32 km ) by train to Woodstock , Ontario , to complete his secondary education at a Baptist @-@ run college . He intended to become a public @-@ school teacher and passed the entrance examinations for teacher training , but decided to take a year off to earn the money he would need to support himself at an Ontario teachers ' college . At age 18 , therefore , he returned to the one @-@ room schoolhouse at Otterville to teach for one term until the local school board could recruit a fully qualified teacher . The experience made him realize that the life of a teacher in a small , rural school was not for him . 
- 
- = = = University studies = = = 
- 
- In October 1913 , Innis started classes at McMaster University ( then in Toronto ) . McMaster was a natural choice for him because it was a Baptist university and many students who attended Woodstock College went there . McMaster 's liberal arts professors encouraged critical thinking and debate . Innis was especially influenced by James Ten <unk> , the university 's one @-@ man philosophy department . Ten <unk> posed an essay question that Innis pondered for the rest of his life : " Why do we attend to the things to which we attend ? " 
- Before his final undergraduate year at McMaster , Innis spent a summer teaching at the Northern Star School in the frontier farming community of <unk> near <unk> , Alberta . The experience gave him a sense of the <unk> of Canada . He also learned about Western grievances over high interest rates and steep transportation costs . In his final undergraduate year , Innis focused on history and economics . He kept in mind a remark made by history lecturer <unk> Wallace that the economic interpretation of history was not the only possible one , but that it went the deepest . 
- 
- = = = First World War service = = = 
- 
- After graduating from McMaster , Innis felt that his Christian principles compelled him to enlist in the Canadian Expeditionary Force . He was sent to France in the fall of 1916 to fight in the First World War . <unk> warfare with its " mud and lice and rats " had a devastating effect on him . 
- Innis 's role as an artillery <unk> gave him firsthand experience of life ( and death ) on the front lines as he participated in the successful Canadian attack on <unk> Ridge . <unk> , or <unk> , watched where each artillery shell landed , then sent back aiming corrections so that the next shells could hit their targets more accurately . On July 7 , 1917 , Innis received a serious shrapnel wound in his right thigh that required eight months of hospital treatment in England . 
- Innis 's war was over . His biographer , John Watson , notes the physical wound took seven years to heal , but the psychological damage lasted a lifetime . Innis suffered recurring bouts of depression and nervous exhaustion because of his military service . 
- Watson also notes that the Great War influenced Innis 's intellectual outlook . It strengthened his Canadian nationalism ; sharpened his opinion of what he thought were the destructive effects of technology , including the communications media that were used so effectively to " sell " the war ; and led him , for the first time , to doubt his Baptist faith . 
- 
- = = <unk> studies = = 
- 
- 
- = = = McMaster and Chicago = = = 
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- Harold Innis completed a Master of Arts at McMaster , graduating in April 1918 . His thesis , called The <unk> Soldier , " was a detailed description of the public policy measures that were necessary , not only to provide a supportive milieu to help veterans get over the effects of the war , but also to move on with national reconstruction " . 
- Innis did his <unk> work at the University of Chicago and was awarded his PhD in August 1920 . His two years at Chicago had a profound influence on his later work . His interest in economics deepened and he decided to become a professional economist . The economics faculty at Chicago questioned abstract and <unk> neoclassical theories , then in vogue , arguing that general rules for economic policy should be derived from specific case studies . 
- Innis was influenced by the university 's two eminent communications scholars , George Herbert Mead and Robert E. Park . Although he did not attend any of these famous professors ' classes , Innis did absorb their idea that communication involved much more than the transmission of information . James W. Carey writes that Mead and Park " characterized communication as the entire process whereby a culture is brought into existence , maintained in time , and <unk> into institutions " . 
- While at Chicago , Innis was exposed to the ideas of <unk> Veblen , the <unk> thinker who drew on his deep knowledge of philosophy and economics to write scathing critiques of contemporary thought and culture . Veblen had left Chicago years before , but his ideas were still strongly felt there . Years later , in an essay on Veblen , Innis praised him for <unk> war against " standardized static economics " . 
- Innis got his first taste of university teaching at Chicago , where he delivered several introductory economics courses . One of his students was Mary <unk> , the woman he would marry in May 1921 when he was 26 and she 22 . Together they had four children , Donald ( 1924 ) , Mary ( 1927 ) , Hugh ( 1930 ) and Ann ( 1933 ) . Mary <unk> Innis was herself a notable economist and writer . Her book , An Economic History of Canada , was published in 1935 . Her novel , Stand on a Rainbow appeared in 1943 . Her other books include Mrs. <unk> 's <unk> ( 1965 ) , The <unk> Spirit : Canadian Women and Their Times ( 1966 ) and <unk> the Years ( 1949 ) , a history of the Young Women 's Christian Association . She also edited Harold Innis 's posthumous Essays in Canadian Economic History ( 1956 ) and a 1972 reissue of his Empire and Communications . 
- 
- = = = History of the CPR = = = 
- 
- Harold Innis wrote his PhD thesis on the history of the Canadian Pacific Railway ( CPR ) . The completion of Canada 's first <unk> railway in 1885 had been a defining moment in Canadian history . Innis 's thesis , eventually published as a book in 1923 , can be seen as an early attempt to document the railway 's significance from an economic historian 's point of view . It uses <unk> statistics to <unk> its arguments . Innis maintains that the difficult and expensive construction project was sustained by fears of American <unk> of the Canadian West . 
- Innis argues that " the history of the Canadian Pacific Railroad is primarily the history of the spread of Western civilization over the northern half of the North American continent " . As Robert Babe notes , the railway brought <unk> , transporting coal and building supplies to manufacturing sites . It was also a kind of communications medium that contributed to the spread of European civilization . Babe writes that , for Innis , the CPR 's equipment " comprised a massive , energy @-@ consuming , fast @-@ moving , powerful , capital @-@ intensive ' sign ' dropped into the very midst of indigenous peoples , whose entire way of life was disrupted , and eventually shattered as a result . 
- Communications scholar Arthur <unk> argues that Innis 's study of the Canadian Pacific Railway was only the first in which he attempted to demonstrate that " technology is not something external to Canadian being ; but on the contrary , is the necessary condition and lasting consequence of Canadian existence " . It also reflected Innis 's lifelong interest in the exercise of economic and political power . His CPR history ends , for example , with a recounting of Western grievances against economic policies , such as high freight rates and the steep import tariffs designed to protect fledgling Canadian manufacturers . <unk> complained that this National Policy <unk> money from Prairie farmers into the pockets of the Eastern business establishment . " Western Canada " , Innis wrote , " has paid for the development of Canadian nationality , and it would appear that it must continue to pay . The <unk> of Eastern Canada shows little sign of abatement . " 
- 
- = = Staples thesis = = 
- 
- Harold Innis is considered the leading founder of a Canadian school of economic thought known as the staples theory . It holds that Canada 's culture , political history and economy have been decisively shaped by the exploitation and export of a series of " staples " such as fur , fish , wood , wheat , mined metals and fossil fuels . Innis theorized that this reliance on exporting natural resources made Canada dependent on more <unk> advanced countries and resulted in periodic disruptions to economic life as the international demand for staples rose and fell ; as the staple itself became increasingly scarce ; and , as technological change resulted in shifts from one staple to others . Innis pointed out , for example , that as furs became scarce and trade in that staple declined , it became necessary to develop and export other staples such as wheat , <unk> and especially lumber . The export of these new staples was made possible through improved transportation networks that included first canals , and later , railways . 
- 
- = = = " Dirt " research = = = 
- 
- In 1920 , Innis joined the department of political economy at the University of Toronto . He was assigned to teach courses in commerce , economic history and economic theory . He decided to focus his scholarly research on Canadian economic history , a hugely neglected subject , and he settled on the fur trade as his first area of study . <unk> had brought French and English traders to Canada , motivating them to travel west along the continent 's <unk> lake and river systems to the Pacific coast . Innis realized that he would not only need to search out archival documents to understand the history of the fur trade , but would also have to travel the country himself gathering masses of firsthand information and accumulating what he called " dirt " experience . 
- Thus , Innis travelled extensively beginning in the summer of 1924 when he and a friend <unk> an 18 @-@ foot ( 5 @.@ 5 m ) canvas @-@ covered <unk> hundreds of miles down the Peace River to Lake <unk> ; then down the Slave River to Great Slave Lake . They completed their journey down the Mackenzie , Canada 's longest river , to the Arctic Ocean on a small Hudson 's Bay Company <unk> . During his travels , Innis supplemented his fur research by gathering information on other staple products such as lumber , pulp and paper , minerals , grain and fish . He travelled so extensively that by the early 1940s , he had visited every part of Canada except for the Western Arctic and the east side of Hudson Bay . 
- Everywhere Innis went his methods were the same : he interviewed people connected with the production of staple products and listened to their stories . 
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- = = = Fur trade in Canada = = = 
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- Harold Innis 's interest in the relationship between empires and colonies was developed in his classic study , The Fur Trade in Canada : An Introduction to Canadian Economic History ( 1930 ) . The book chronicles the trade in beaver fur from the early 16th century to the 1920s . Instead of focusing on the " heroic " European adventurers who explored the Canadian wilderness as conventional histories had done , Innis documents how the interplay of geography , technology and economic forces shaped both the fur trade and Canada 's political and economic destiny . He concludes that the fur trade largely determined Canada 's boundaries adding that the country " emerged not in spite of geography but because of it " . 
- The Fur Trade in Canada also describes the cultural interactions among three groups of people : the Europeans in fashionable metropolitan centres who regarded beaver hats as luxury items ; the European colonial settlers who saw beaver fur as a staple that could be exported to pay for essential manufactured goods from the home country , and First Nations peoples who traded furs for industrial goods such as metal pots , knives , guns and liquor . Innis describes the central role First Nations peoples played in the development of the fur trade . Without their skilled hunting techniques , knowledge of the territory and advanced tools such as <unk> , <unk> and birch @-@ bark canoes , the fur trade would not have existed . However , dependence on European technologies disrupted First Nations societies . " The new technology with its radical innovations " , Innis writes , " brought about such a rapid shift in the prevailing Indian culture as to lead to wholesale destruction of the peoples concerned by warfare and disease . " Historian Carl Berger argues that by placing First Nations culture at the centre of his analysis of the fur trade , Innis " was the first to explain adequately the disintegration of native society under the thrust of European capitalism . " 
- Unlike many historians who see Canadian history as beginning with the arrival of Europeans , Innis emphasizes the cultural and economic contributions of First Nations peoples . " We have not yet realized , " he writes , " that the Indian and his culture was fundamental to the growth of Canadian institutions . " 
- The Fur Trade in Canada concludes by arguing that Canadian economic history can best be understood by examining how one staple product gave way to another — furs to timber , for example , and the later importance of wheat and minerals . <unk> on staples made Canada economically dependent on more <unk> advanced countries and the " cyclonic " shifts from one staple to another caused frequent disruptions in the country 's economic life . 
- 
- = = = Cod fishery = = = 
- 
- After the publication of his book on the fur trade , Innis turned to a study of an earlier staple — the <unk> fished for centuries off the eastern coasts of North America , especially the Grand Banks of Newfoundland . The result was The Cod Fisheries : The History of an International Economy published in 1940 , 10 years after the fur trade study . Innis tells the detailed history of competing empires in the exploitation of a <unk> natural resource — a history that ranges over 500 years . While his study of the fur trade focused on the continental interior with its <unk> rivers and lakes , The Cod Fisheries looks outward at global trade and empire , showing the far @-@ reaching effects of one staple product both on imperial centres and on marginal colonies such as Newfoundland , Nova Scotia and New England . 
- 
- = = Communications theories = = 
- 
- Harold Innis 's study of the effects of interconnected lakes and rivers on Canadian development and European empire sparked his interest in the complex economic and cultural relationships between transportation systems and communications . During the 1940s , Innis also began studying pulp and paper , an industry of central importance to the Canadian economy . This research provided an additional crossover point from his work on staple products to his communications studies . Biographer Paul <unk> writes that Innis " followed pulp and paper through its subsequent stages : newspapers and journalism , books and advertising . In other words , from looking at a natural resource @-@ based industry he turned his attention to a cultural industry in which information , and ultimately knowledge , was a commodity that circulated , had value , and empowered those who controlled it . " 
- One of Innis 's primary contributions to communications studies was to apply the dimensions of time and space to various media . He divided media into time @-@ binding and space @-@ binding types . Time @-@ binding media are durable . They include clay or stone tablets . Space @-@ binding media are more <unk> . They include modern media such as radio , television , and mass circulation newspapers . 
- Innis examined the rise and fall of ancient empires as a way of tracing the effects of communications media . He looked at media that led to the growth of an empire ; those that sustained it during its periods of success , and then , the communications changes that <unk> an empire 's collapse . He tried to show that media ' biases ' toward time or space affected the complex interrelationships needed to sustain an empire . These interrelationships included the partnership between the knowledge ( and ideas ) necessary to create and maintain an empire , and the power ( or force ) required to expand and defend it . For Innis , the interplay between knowledge and power was always a crucial factor in understanding empire . 
- Innis argued that a balance between the spoken word and writing contributed to the flourishing of ancient Greece in the time of <unk> . This balance between the time @-@ biased medium of speech and the space @-@ biased medium of writing was eventually upset , Innis argued , as the oral tradition gave way to the dominance of writing . The torch of empire then passed from Greece to Rome . 
- Harold Innis 's analysis of the effects of communications on the rise and fall of empires led him to warn <unk> that Western civilization was now facing its own profound crisis . The development of powerful communications media such as mass @-@ circulation newspapers had shifted the balance decisively in favour of space and power , over time , continuity and knowledge . The balance required for cultural survival had been upset by what Innis saw as " mechanized " communications media used to transmit information quickly over long distances . These media had contributed to an obsession with " present @-@ mindedness " <unk> out concerns about past or future . Innis wrote , 
- The overwhelming pressure of mechanization evident in the newspaper and the magazine , has led to the creation of vast <unk> of communication . Their entrenched positions involve a continuous , systematic , ruthless destruction of elements of permanence essential to cultural activity . 
- Western civilization could only be saved , Innis argued , by recovering the balance between space and time . For him , that meant <unk> the oral tradition within universities while freeing institutions of higher learning from political and commercial pressures . In his essay , A <unk> for Time , he suggested that genuine dialogue within universities could produce the critical thinking necessary to restore the balance between power and knowledge . Then , universities could muster the courage to attack the <unk> that always <unk> civilization . 
- Although Innis remains appreciated and respected for the grand and unique nature of his later efforts regarding communications theories , he was not without critics . Particularly , the fragmentary and mosaic writing style exemplified in Empire and Communications has been criticized as ambiguous , aggressively non @-@ linear , and lacking connections between levels of analysis . Biographers have suggested that this style may have been a result of Innis ' illness late in his career . 
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- = = Academic and public career = = 
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- = = = Influence in the 1930s = = = 
- 
- Aside from his work on The Cod Fisheries , Innis wrote extensively in the 1930s about other staple products such as minerals and wheat as well as Canada 's immense economic problems in the Great Depression . During the summers of 1932 and 1933 , he travelled to the West to see the effects of the Depression for himself . The next year , in an essay entitled , The Canadian Economy and the Depression , Innis outlined the plight of " a country susceptible to the slightest ground @-@ swell of international disturbance " , yet beset by regional differences that made it difficult to <unk> effective solutions . He described a Prairie economy dependent on the export of wheat , yet afflicted by severe drought , on the one hand , and the increased political power of Canada 's growing cities , sheltered from direct reliance on the staples trade , on the other . The result was political conflict and a breakdown in federal – provincial relations . " We lack vital information on which to base prospective policies to meet this situation " , Innis warned , because of " the weak position of the social sciences in Canada " . 
- Innis 's reputation as a " public intellectual " was growing steadily and , in 1934 , Premier Angus L. Macdonald invited him to serve on a Royal Commission to examine Nova Scotia 's economic problems . The next year , he helped establish The Canadian Journal of Economics and Political Science . In 1936 , he was appointed a full University of Toronto professor and a year later , became the head of the university 's Department of Political Economy . 
- Innis was appointed president of the Canadian Political Science Association in 1938 . His inaugural address , entitled The <unk> Powers of the Price System , must have <unk> his listeners as he ranged over centuries of economic history jumping abruptly from one topic to the next linking monetary developments to patterns of trade and settlement . The address was an ambitious attempt to show the disruptive effects of new technologies culminating in the modern shift from an industrial system based on coal and iron to the newest sources of industrial power , electricity , oil and steel . Innis also tried to show the commercial effects of mass circulation newspapers , made possible by expanded <unk> production , and of the new medium of radio , which " threatens to <unk> the walls imposed by tariffs and to reach across boundaries frequently denied to other media of communication " . Both media , Innis argued , stimulated the demand for consumer goods and both promoted nationalism . 
- Innis was also a central participant in an international project that produced 25 scholarly volumes between 1936 and 1945 . It was a series called The Relations of Canada and the United States overseen by James T. <unk> , director of the Carnegie <unk> for International Peace . Innis edited and wrote <unk> for the volumes contributed by Canadian scholars . His own study of the <unk> fisheries also appeared as part of the series . His work with <unk> enabled Innis to gain access to Carnegie money to further Canadian academic research . As John Watson points out , " the project offered one of the few sources of research funds in rather lean times " . 
- 
- = = = Politics and The Great Depression = = = 
- 
- The era of the " Dirty <unk> " with its mass unemployment , poverty and despair gave rise to new Canadian political movements . In Alberta , for example , the radio <unk> William " Bible Bill " <unk> led his populist Social Credit party to victory in 1935 . Three years earlier in Calgary , Alberta , social reformers had founded a new political party , the Co @-@ operative Commonwealth Federation or <unk> . It advocated democratic socialism and a mixed economy with public ownership of key industries . Frank Underhill , one of Innis 's colleagues at the University of Toronto was a founding member of the <unk> . Innis and Underhill had both been members of an earlier group at the university that declared itself " dissatisfied with the policies of the two major [ political ] parties in Canada " and that aimed at " forming a definite body of progressive opinion " . In 1931 , Innis presented a paper to the group on " Economic Conditions in Canada " , but he later <unk> from participating in party politics , denouncing <unk> like Underhill as " hot <unk> " . 
- Innis maintained that scholars had no place in active politics and that instead , they should devote themselves , first to research on public problems , and then to the production of knowledge based on critical thought . He saw the university , with its emphasis on dialogue , open @-@ mindedness and skepticism , as an institution that could foster such thinking and research . " The university could provide an environment " , he wrote , " as free as possible from the biases of the various institutions that form the state , so that its intellectuals could continue to seek out and explore other perspectives . " 
- Although sympathetic to the plight of western farmers and urban , unemployed workers , Innis did not embrace socialism . Eric Havelock , a left @-@ leaning colleague explained many years later that Innis <unk> political " solutions " imported from elsewhere , especially those based on Marxist analysis with its emphasis on class conflict . He worried , too , that as Canada 's ties with Britain weakened , the country would fall under the spell of American ideas instead of developing its own based on Canada 's unique circumstances . Havelock added : 
- He has been called the radical conservative of his day — not a bad designation of a complex mind , clear sighted , cautious , perhaps at bottom pessimistic in areas where thinkers we would label ' progressive ' felt less difficulty in taking a stand ; never content to select only one or two elements in a complicated equation in order to build a quick @-@ order policy or program ; far ranging enough in <unk> to take in the whole sum of the factors , and comprehend their often contradictory effects . 
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- = = = Late career and death = = = 
- 
- In the 1940s , Harold Innis reached the height of his influence in both academic circles and Canadian society . In 1941 , he helped establish the American @-@ based Economic History Association and its Journal of Economic History . He later became the association 's second president . Innis played a central role in founding two important sources for the funding of academic research : the Canadian Social Science Research Council ( 1940 ) and the Humanities Research Council of Canada ( 1944 ) . 
- In 1944 , the University of New Brunswick awarded Innis an honorary degree , as did his <unk> <unk> , McMaster University . <unk> <unk> , the University of Manitoba and the University of Glasgow would also confer honorary degrees in 1947 – 48 . 
- In 1945 , Innis spent nearly a month in the Soviet Union where he had been invited to attend the <unk> anniversary celebrations marking the founding of the country 's Academy of Sciences . Later , in his essay Reflections on Russia , he <unk> about the differences between the Soviet " producer " economy and the West 's " consumer " ethos : 
- [ A ] n economy which emphasizes consumer 's goods is characterized by communication industries largely dependent on advertising and by constant efforts to reach the largest number of readers or listeners ; an economy emphasizing producer 's goods is characterized by communications industries largely dependent on government support . As a result of this contrast , a common public opinion in Russia and the West is hard to achieve . 
- Innis 's trip to Moscow and Leningrad came shortly before U.S. – Soviet rivalry led to the hostility of the Cold War . Innis lamented this rise in international tensions . He saw the Soviet Empire as a stabilizing counterbalance to the American Empire 's emphasis on commercialism , the individual and constant change . For Innis , Russia was a society within the Western tradition , not an alien civilization . He <unk> the nuclear arms race , seeing it as the triumph of force over knowledge , a modern form of the medieval Inquisition . " The Middle Ages burned its heretics " , he wrote , " and the modern age threatens them with atom bombs . " 
- In 1946 , Innis was elected president of the Royal Society of Canada , the country 's senior body of scientists and scholars . That same year , he served on the Manitoba Royal Commission on Adult Education and published Political Economy in the Modern State , a collection of his speeches and essays that reflected both his staples research and his new work in communications . In 1947 , Innis was appointed the University of Toronto 's dean of graduate studies . In 1948 , he delivered lectures at the University of London and Nottingham University . He also gave the prestigious Beit lectures at Oxford , later published in his book Empire and Communications . In 1949 , Innis was appointed as a commissioner on the federal government 's Royal Commission on Transportation , a position that involved extensive travel at a time when his health was starting to fail . The last decade of his career during which he worked on his communications studies was an unhappy time for Innis . He was <unk> isolated because his colleagues in economics could not <unk> how this new work related to his pioneering research in staples theory . Biographer John Watson writes that " the almost complete lack of positive response to the communications works , contributed to his sense of <unk> and depression " . 
- Innis died of prostate cancer in 1952 a few days after his <unk> birthday . In commemoration , Innis College at the University of Toronto and Innis Library at McMaster University were named in his honour . 
- Following his premature death , Innis ' significance increasingly deepened as scholars in several academic disciplines continued to build upon his writings . Marshall <unk> 's general media theory that proposes two sub @-@ theories were inspired by Innis . Douglas C. North expanded on of Innis ' " vent for surplus " theory of economic development by applying it to regional development in the United States and <unk> countries . In addition , James W. Carey adopted Innis as a " reference point in his conception of two models of communication " . 
- 
- = = Innis and McLuhan = = 
- 
- Marshall McLuhan was a colleague of Innis 's at the University of Toronto . As a young English professor , McLuhan was flattered when he learned that Innis had put his book The Mechanical Bride on the reading list of the fourth @-@ year economics course . McLuhan built on Innis 's idea that in studying the effects of communications media , technological form <unk> more than content . Biographer Paul <unk> writes that Innis 's concept of the " bias " of a particular medium of communication can be seen as a " less flamboyant precursor to McLuhan 's legendary phrase ' the medium is the message . ' " Innis , for example , tried to show how printed media such as books or newspapers were " biased " toward control over space and secular power , while engraved media such as stone or clay tablets were " biased " in favour of continuity in time and metaphysical or religious knowledge . McLuhan focused on what may be called a medium 's " sensory bias " arguing , for example , that books and newspapers appealed to the rationality of the eye , while radio played to the <unk> of the ear . The differences in the <unk> and <unk> approaches were summarized by the late James W. Carey : 
- Both McLuhan and Innis assume the <unk> of communication technology ; where they differ is in the principal kinds of effects they see deriving from this technology . Whereas Innis sees communication technology principally affecting social organization and culture , McLuhan sees its principal effect on sensory organization and thought . McLuhan has much to say about perception and thought but little to say about institutions ; Innis says much about institutions and little about perception and thought . 
- Biographer John Watson notes that Innis 's work was profoundly political while McLuhan 's was not . He writes that " the mechanization of knowledge , not the relative sensual bias of media , is the key to Innis 's work . This also <unk> the <unk> of Innis 's position vis @-@ a @-@ vis that of McLuhan . " Watson adds that Innis believed very different media could produce similar effects . " For Innis , the yellow press of the United States and the Nazi loudspeaker had the same form of negative effect : they reduced men from thinking beings to mere automatons in a chain of command . " Watson argues that while McLuhan separated media according to their sensory bias , Innis examined a different set of interrelationships , the " dialectic of power and knowledge " in specific historical circumstances . For Watson , Innis 's work is therefore more flexible and less <unk> than McLuhan 's . 
- As scholars and teachers , Innis and McLuhan shared a similar dilemma since both argued that book culture tended to produce fixed points of view and <unk> of thought ; yet both produced many books . In his introduction to the 1964 reprint of The <unk> of Communication , McLuhan <unk> at Innis 's technique of juxtaposing " his insights in a mosaic structure of seemingly unrelated and <unk> sentences and aphorisms " . McLuhan argued that although this made reading Innis 's dense prose difficult — " a pattern of insights that are not packaged for the consumer palate " — Innis 's method <unk> " the natural form of conversation or dialogue rather than of written discourse " . Best of all , it yielded " insight " and " pattern recognition " rather than the " classified knowledge " so <unk> by print @-@ trained scholars . " How exciting it was to encounter a writer whose every phrase invited prolonged meditation and exploration " , McLuhan added . McLuhan 's own books with their reliance on aphorisms , puns , <unk> , " probes " and oddly juxtaposed observations also employ this mosaic technique . 
- Innis 's theories of political economy , media and society remain highly relevant : he had a profound influence on critical media theory and communications and , in conjunction with McLuhan , offered groundbreaking Canadian perspectives on the function of communication technologies as key agents in social and historical change . Together , their works advanced a theory of history in which communication is central to social change and transformation . 
- 
- 
- = Hurricane Lorenzo ( 2007 ) = 
- 
- Hurricane Lorenzo was a rapidly developing tropical cyclone that struck the Mexican state of Veracruz in late September 2007 . The twelfth named storm and fifth hurricane of the 2007 Atlantic hurricane season , it formed in the southwestern Gulf of Mexico from a tropical wave . After <unk> for two days without development , the storm began a steady westward track as its structure became better organized . In an 18 ‑ hour period , Lorenzo 's winds increased from 35 mph ( 55 km / h ) to 80 mph ( 130 km / h ) , or from a tropical depression to a hurricane . On September 28 it struck near Tecolutla , Veracruz , a month after Hurricane Dean affected the same area , before it quickly dissipated over land . 
- The most significantly affected area was Veracruz , where damage reached over $ 1 billion pesos ( $ 92 million 2007 USD ) . Most of it came from road damage in the state 's northern portion , although there were also hundreds of damaged houses . Between Veracruz and neighboring Hidalgo , there were 123 @,@ 320 people affected across 112 municipalities . Overall there were six deaths in the country , one in Veracruz and five in Puebla . In the latter state , a family of three perished in a landslide . 
- 
- = = Meteorological history = = 
- 
- The origins of Hurricane Lorenzo were from a tropical wave that exited the coast of Africa on September 11 . After moving across the tropical Atlantic Ocean , it <unk> much of the Caribbean Sea before developing an area of thunderstorms on September 21 . The system developed a low pressure area on September 23 after the northern portion of the wave broke off and crossed the Yucatán Peninsula . Initially , a Hurricane Hunters flight was scheduled to investigate the system , although the low became disorganized over the southeastern Gulf of Mexico due to high wind shear . However , the wind shear decreased , and late on September 25 , another Hurricane Hunters flight indicated the development of a closed low @-@ level circulation . Based on that observation , along with sufficient persistence of the thunderstorms , the system developed into Tropical Depression Thirteen about 190 mi ( 305 km ) east of Tampico , Tamaulipas . 
- Upon developing , the depression was located in an area of weak steering currents , resulting in an erratic movement generally to the south . In its formative stages , the depression executed a small loop . As it did so , it moved into an area of very warm waters and decreasing wind shear . The depression 's convection gradually organized , although the winds were slower to increase . An anticyclone became established over the system , and the depression intensified into Tropical Storm Lorenzo at around 1200 UTC on September 27 . At the time , it was located about 150 mi ( 240 km ) east of Tuxpan , Veracruz , moving steadily westward under the influence of a building ridge to its east . As it approached the coast , Lorenzo rapidly intensified unexpectedly , and within 12 hours of attaining tropical storm status it strengthened into a hurricane . 
- Hurricane Lorenzo quickly developed a closed eyewall , which was observed on radar and provided a peak intensity estimate of 80 mph ( 130 km / h ) at 0000 UTC on September 28 . The cyclone maintained vigorous and <unk> convection across the center , although the structure deteriorated slightly before Lorenzo crossed the coast near Tecolutla , Veracruz at <unk> UTC that day , with winds estimated around 75 mph ( 120 km / h ) . After making landfall , the hurricane rapidly weakened to tropical depression intensity . <unk> inland , the circulation became difficult to locate as the convection diminished to rainbands along the coast . Within 19 hours after moving ashore , the circulation of Lorenzo dissipated , therefore ending its duration as a tropical cyclone . 
- 
- = = Preparations and impact = = 
- 
- About 26 hours before landfall , the Mexican government issued a tropical storm watch from <unk> Sola to La Cruz in Veracruz . About 14 hours before landfall , a tropical storm warning was put into place from <unk> Sola to Cabo Rojo , which in turn was upgraded to a hurricane warning six hours later . Mexico 's Civilian Protection officials declared a " red alert " for much of the state of Veracruz . Along the coast of Veracruz , officials canceled school classes . Officials opened 315 shelters in Veracruz , along with six in neighboring Hidalgo , which housed 45 @,@ 164 people during the storm . The government of Veracruz provided buses for people to transport from their houses to the shelters . Ports in Tecolutla , Tuxpan and Nautla were forced to close . Lorenzo 's formation caused gas prices to rise due to its potential to disrupt oil facilities in the Gulf of Mexico . 
- Hurricane Lorenzo made landfall in Veracruz , the same region of east @-@ central Mexico as Hurricane Dean did one month prior . The hurricane primarily affected small fishing villages along the coast , where strong winds knocked down power lines , leaving about 85 @,@ 000 people without electricity . To prevent the danger of fallen wires , officials shut off the power grid in several municipalities in northern Veracruz . The winds also destroyed the roofs of several houses in Nautla . In addition to the high winds , Lorenzo dropped heavy rainfall along the coast and further inland , peaking at 12 @.@ 83 in ( 326 mm ) in El <unk> , Veracruz . The rains caused flash flooding and mudslides that killed at least four people , including a family of three in Puebla state . <unk> also closed portions of three highways . In some locations , <unk> reached about 1 foot ( 300 mm ) in depth . The combination of winds and rains damaged 169 houses in Puebla , while in Hidalgo , the San Lorenzo River overflowed its banks and forced the evacuation of over 200 people . <unk> rivers in Veracruz forced about 25 @,@ 000 people to leave their houses . Along the <unk> River , more than 1 @,@ 000 houses were flooded , resulting in local police officials to assist in <unk> . Damage in Veracruz was estimated at $ 1 billion pesos ( $ 92 million 2007 USD ) , much of it from road damage in the northern portion of the state . 
- Overall , the hurricane affected 123 @,@ 320 people across Veracruz and Hidalgo , prompting the declaration of a state of emergency in 112 municipalities . The declaration allowed the usage of emergency resources for the affected people . There were a total of six deaths in the country , five of which in Puebla . By about three days after the storm , all schools were reopened . The Mexican government distributed food , water , and construction materials for the areas most affected in Veracruz . Following the storm , about 500 power workers were dispatched in Veracruz to restore electricity in the affected areas . 
- 
- 
- = Cadmium = 
- 
- Cadmium is a chemical element with symbol Cd and atomic number 48 . This soft , bluish @-@ white metal is chemically similar to the two other stable metals in group 12 , zinc and mercury . Like zinc , it demonstrates oxidation state + 2 in most of its compounds , and like mercury , it has a lower melting point than other transition metals . Cadmium and its <unk> are not always considered transition metals , in that they do not have partly filled d or f electron shells in the elemental or common oxidation states . The average concentration of cadmium in Earth 's crust is between 0 @.@ 1 and 0 @.@ 5 parts per million ( ppm ) . It was discovered in 1817 simultaneously by Stromeyer and Hermann , both in Germany , as an impurity in zinc carbonate . 
- Cadmium occurs as a minor component in most zinc ores and is a <unk> of zinc production . Cadmium was used for a long time as a corrosion @-@ resistant plating on steel , and cadmium compounds are used as red , orange and yellow pigments , to colour glass , and to stabilize plastic . Cadmium use is generally decreasing because it is toxic ( it is specifically listed in the European <unk> of <unk> <unk> ) and nickel @-@ cadmium batteries have been replaced with nickel @-@ metal hydride and lithium @-@ ion batteries . One of its few new uses is cadmium telluride solar panels . 
- Although cadmium has no known biological function in higher organisms , a cadmium @-@ dependent carbonic <unk> has been found in marine diatoms . 
- 
- = = Characteristics = = 
- 
- 
- = = = Physical properties = = = 
- 
- Cadmium is a soft , malleable , <unk> , bluish @-@ white divalent metal . It is similar in many respects to zinc but forms complex compounds . Unlike most other metals , cadmium is resistant to corrosion and is used as a protective plate on other metals . As a bulk metal , cadmium is <unk> in water and is not flammable ; however , in its powdered form it may burn and release toxic fumes . 
- 
- = = = Chemical properties = = = 
- 
- Although cadmium usually has an oxidation state of + 2 , it also exists in the + 1 state . Cadmium and its <unk> are not always considered transition metals , in that they do not have partly filled d or f electron shells in the elemental or common oxidation states . Cadmium burns in air to form brown <unk> cadmium oxide ( <unk> ) ; the crystalline form of this compound is a dark red which changes color when heated , similar to zinc oxide . <unk> acid , sulfuric acid , and nitric acid dissolve cadmium by forming cadmium chloride ( <unk> ) , cadmium sulfate ( <unk> ) , or cadmium nitrate ( Cd ( <unk> ) 2 ) . The oxidation state + 1 can be produced by dissolving cadmium in a mixture of cadmium chloride and aluminium chloride , forming the <unk> + cation , which is similar to the <unk> + cation in mercury ( I ) chloride . 
- Cd + <unk> + 2 <unk> → <unk> ( <unk> ) 2 
- The structures of many cadmium complexes with <unk> , amino acids , and <unk> have been determined . 
- 
- = = = Isotopes = = = 
- 
- Naturally occurring cadmium is composed of 8 isotopes . Two of them are radioactive , and three are expected to decay but have not done so under laboratory conditions . The two natural radioactive isotopes are <unk> ( beta decay , half @-@ life is 7 @.@ 7 × 1015 years ) and <unk> ( two @-@ neutrino double beta decay , half @-@ life is 2 @.@ 9 × <unk> years ) . The other three are <unk> , <unk> ( both double electron capture ) , and <unk> ( double beta decay ) ; only lower limits on these half @-@ lives have been determined . At least three isotopes – <unk> , <unk> , and <unk> – are stable . Among the isotopes that do not occur naturally , the most long @-@ lived are <unk> with a half @-@ life of 462 @.@ 6 days , and <unk> with a half @-@ life of 53 @.@ 46 hours . All of the remaining radioactive isotopes have half @-@ lives of less than 2 @.@ 5 hours , and the majority have half @-@ lives of less than 5 minutes . Cadmium has 8 known meta states , with the most stable being <unk> ( t1 / 2 
- = 14 @.@ 1 years ) , <unk> ( t1 / 2 = 
- 44 @.@ 6 days ) , and <unk> ( t1 / 2 = 3 @.@ 36 hours ) . 
- The known isotopes of cadmium range in atomic mass from 94 @.@ 950 u ( <unk> ) to 131 @.@ <unk> u ( <unk> ) . For isotopes lighter than 112 u , the primary decay mode is electron capture and the dominant decay product is element 47 ( silver ) . <unk> isotopes decay mostly through beta emission producing element 49 ( indium ) . 
- One isotope of cadmium , <unk> , absorbs neutrons with high selectivity : With very high probability , neutrons with energy below the cadmium cut @-@ off will be absorbed ; those higher than the cut @-@ off will be transmitted . The cadmium cut @-@ off is about 0 @.@ 5 <unk> , and neutrons below that level are deemed slow neutrons , distinct from intermediate and fast neutrons . 
- Cadmium is created via the long s @-@ process in low @-@ medium mass stars with masses of 0 @.@ 6 to 10 solar masses , taking thousands of years . In that process , a silver atom captures a neutron and then undergoes beta decay . 
- 
- = = History = = 
- 
- Cadmium ( Latin <unk> , Greek <unk> meaning " calamine " , a cadmium @-@ bearing mixture of minerals that was named after the Greek mythological character <unk> , <unk> , the founder of Thebes ) was discovered simultaneously in 1817 by Friedrich Stromeyer and Karl Samuel <unk> Hermann , both in Germany , as an impurity in zinc carbonate . Stromeyer found the new element as an impurity in zinc carbonate ( calamine ) , and , for 100 years , Germany remained the only important producer of the metal . The metal was named after the Latin word for calamine , because it was found in this zinc compound . Stromeyer noted that some impure samples of calamine changed color when heated but pure calamine did not . He was persistent in studying these results and eventually isolated cadmium metal by roasting and reducing the sulfide . The potential for cadmium yellow as pigment was recognized in the 1840s , but the lack of cadmium limited this application . 
- Even though cadmium and its compounds are toxic in certain forms and concentrations , the British <unk> <unk> from 1907 states that cadmium iodide was used as a medication to treat " enlarged joints , <unk> glands , and <unk> " . 
- In 1907 , the International Astronomical Union defined the international <unk> in terms of a red cadmium spectral line ( 1 wavelength = <unk> @.@ <unk> <unk> ) . This was adopted by the 7th General Conference on <unk> and Measures in 1927 . In 1960 , the definitions of both the metre and <unk> were changed to use krypton . 
- After the industrial scale production of cadmium started in the 1930s and 1940s , the major application of cadmium was the coating of iron and steel to prevent corrosion ; in 1944 , 62 % and in 1956 , 59 % of the cadmium in the United States was used for plating . In 1956 , 24 % of the cadmium in the United States was used for a second application in red , orange and yellow pigments from sulfides and <unk> of cadmium . 
- The stabilizing effect of cadmium chemicals like the <unk> cadmium <unk> and cadmium <unk> on PVC led to an increased use of those compounds in the 1970s and 1980s . The demand for cadmium in pigments , coatings , stabilizers , and alloys declined as a result of environmental and health regulations in the 1980s and 1990s ; in 2006 , only 7 % of to total cadmium consumption was used for plating , and only 10 % was used for pigments . At the same time , these decreases in consumption were compensated by a growing demand for cadmium for nickel @-@ cadmium batteries , which accounted for 81 % of the cadmium consumption in the United States in 2006 . 
- 
- = = Occurrence = = 
- 
- Cadmium makes up about 0 @.@ 1 mg kg − 1 ( ppm ) of Earth 's crust . Typical background concentrations in other environmental media are : atmosphere < 5 <unk> m − 3 ; soil < 2 mg kg − 1 ; vegetation < 0 @.@ 5 mg kg − 1 ; freshwater < 1 <unk> L − 1 ; seawater < 50 <unk> L − 1 ; sediment < 2 mg kg − 1 . Compared with the more abundant 65 ppm zinc , cadmium is rare . No significant deposits of cadmium @-@ containing ores are known . <unk> ( CdS ) , the only cadmium mineral of importance , is nearly always associated with <unk> ( <unk> ) . This association is caused by <unk> similarity between zinc and cadmium , with no geological process likely to separate them . Thus , cadmium is produced mainly as a <unk> from mining , <unk> , and refining <unk> ores of zinc , and , to a lesser degree , lead and copper . Small amounts of cadmium , about 10 % of consumption , are produced from secondary sources , mainly from dust generated by recycling iron and steel scrap . Production in the United States began in 1907 , but not until after World War I did cadmium come into wide use . 
- <unk> cadmium can be found is the <unk> River basin in Siberia . 
- Rocks mined for phosphate fertilizers contain varying amounts of cadmium , resulting in a cadmium concentration of as much as 300 mg / kg in the fertilizers and a high cadmium content in agricultural soils . Coal can contain significant amounts of cadmium , which ends up mostly in <unk> dust . 
- 
- = = Production = = 
- 
- The British Geological Survey reports that in 2001 , China was the top producer of cadmium with almost one @-@ sixth of the world 's production , closely followed by South Korea and Japan . 
- Cadmium is a common impurity in zinc ores , and it is most often isolated during the production of zinc . Some zinc ores concentrates from <unk> zinc ores contain up to 1 @.@ 4 % of cadmium . In the 1970s , the output of cadmium was 6 @.@ 5 pounds per ton of zinc . Zinc sulfide ores are roasted in the presence of oxygen , converting the zinc sulfide to the oxide . Zinc metal is produced either by <unk> the oxide with carbon or by electrolysis in sulfuric acid . Cadmium is isolated from the zinc metal by vacuum distillation if the zinc is <unk> , or cadmium sulfate is precipitated from the electrolysis solution . 
- 
- = = Applications = = 
- 
- Cadmium is a common component of electric batteries , pigments , coatings , and electroplating . 
- 
- = = = Batteries = = = 
- 
- In 2009 , 86 % of cadmium was used in batteries , predominantly in <unk> nickel @-@ cadmium batteries . <unk> @-@ cadmium cells have a nominal cell potential of 1 @.@ 2 V. The cell consists of a positive nickel hydroxide <unk> and a negative cadmium <unk> plate separated by an alkaline electrolyte ( potassium hydroxide ) . The European Union put a limit on cadmium in electronics in 2004 of 0 @.@ 01 % , with some exceptions , and reduced the limit on cadmium content to 0 @.@ 002 % . 
- 
- = = = <unk> = = = 
- 
- Cadmium electroplating , consuming 6 % of the global production , is used in the aircraft industry reduce corrosion of steel components . This coating is <unk> by <unk> salts . A limitation of cadmium plating is hydrogen embrittlement of high @-@ strength <unk> from the electroplating process . Therefore , steel parts heat @-@ treated to tensile strength above 1300 MPa ( 200 <unk> ) should be coated by an alternative method ( such as special low @-@ embrittlement cadmium electroplating processes or physical vapor deposition ) . 
- <unk> embrittlement from cadmium @-@ plated tool residues resulted in banishment of those tools ( and the implementation of routine tool testing to detect cadmium contamination ) in the A @-@ 12 / SR @-@ 71 , U @-@ 2 , and subsequent aircraft programs that use <unk> . 
- 
- = = = Nuclear fission = = = 
- 
- Cadmium is used in the control rods of nuclear reactors , acting as a very effective " neutron poison " to control neutron flux in nuclear fission . When cadmium rods are inserted in the core of a nuclear reactor , cadmium absorbs neutrons preventing them from creating additional fission events , thus controlling the amount of reactivity . The pressurized water reactor designed by Westinghouse Electric Company uses an alloy consisting of 80 % silver , 15 % indium , and 5 % cadmium . 
- 
- = = = Compounds = = = 
- 
- Cadmium oxide was used in black and white television phosphors and in the blue and green phosphors of color television <unk> ray tubes . Cadmium sulfide ( CdS ) is used as a <unk> surface coating for <unk> drums . 
- Various cadmium salts are used in paint pigments , with CdS as a yellow pigment being the most common . Cadmium selenide is a red pigment , commonly called cadmium red . To painters who work with the pigment , cadmium provides the most brilliant and durable <unk> , <unk> , and reds — so much so that during production , these colors are significantly toned down before they are ground with oils and <unk> or blended into watercolors , <unk> , <unk> , and other paint and pigment <unk> . Because these pigments are potentially toxic , users should use a barrier cream on the hands to prevent absorption through the skin even though the amount of cadmium absorbed into the body through the skin is reported to be less than 1 % . 
- In PVC , cadmium was used as heat , light , and <unk> stabilizers . Currently , cadmium stabilizers have been completely replaced with barium @-@ zinc , calcium @-@ zinc and <unk> @-@ tin stabilizers . Cadmium is used in many kinds of <unk> and bearing alloys , because a low coefficient of friction and fatigue resistance . It is also found in some of the lowest @-@ melting alloys , such as Wood 's metal . 
- 
- = = = Laboratory uses = = = 
- 
- <unk> – cadmium lasers are a common source of blue @-@ ultraviolet laser light . They operate at either 325 or 422 nm in fluorescence <unk> and various laboratory experiments . Cadmium selenide quantum dots emit bright luminescence under <unk> <unk> ( He @-@ Cd laser , for example ) . The color of this luminescence can be green , yellow or red depending on the particle size . <unk> solutions of those particles are used for imaging of biological tissues and solutions with a fluorescence microscope . 
- Cadmium is a component of some compound <unk> , such as cadmium sulfide , cadmium selenide , and cadmium telluride , used for light detection and solar cells . <unk> is sensitive to infrared light and can be used as an infrared detector , motion detector , or switch in remote control devices . 
- In molecular biology , cadmium is used to block voltage @-@ dependent calcium channels from <unk> calcium ions , as well as in hypoxia research to stimulate <unk> @-@ dependent degradation of <unk> @-@ <unk> . 
- 
- = = = Cadmium @-@ selective sensors = = = 
- 
- Cadmium @-@ selective sensors based on the <unk> <unk> have been developed for imaging and sensing of cadmium in cells . 
- 
- = = Biological role = = 
- 
- Cadmium has no known function in higher organisms , but a cadmium @-@ dependent carbonic <unk> has been found in some marine diatoms . The diatoms live in environments with very low zinc concentrations and cadmium performs the function normally carried out by zinc in other <unk> . This was discovered with X @-@ ray absorption fluorescence spectroscopy ( <unk> ) . 
- The highest concentration of cadmium is absorbed in the <unk> of humans , and up to about 30 mg of cadmium is commonly inhaled throughout human childhood and adolescence . 
- Cadmium can be used to block calcium channels in chicken neurons . Analytical methods for the determination of cadmium in biological samples have been reviewed . 
- 
- = = Environment = = 
- 
- The <unk> of cadmium and its release to the environment has been the subject of review , as has the <unk> of cadmium in the environment . 
- Environmental concentrations can exceed adverse @-@ effect @-@ <unk> in cadmium @-@ polluted ecosystems ( e.g. in some parts of Europe ) and <unk> cadmium can accumulate in invertebrates , earthworms , <unk> , marine mammals , plants , and some algal species ; effects in animals include kidney disorders , <unk> of enzymes , disruption of calcium metabolism , and changes in cell membrane <unk> ; excess Cd uptake in plants can affect growth and metabolic processes such as <unk> and <unk> . 
- 
- = = Safety = = 
- 
- The <unk> aspects of cadmium toxicity have been reviewed . 
- The most dangerous form of occupational exposure to cadmium is inhalation of fine dust and fumes , or ingestion of highly soluble cadmium compounds . Inhalation of cadmium fumes can result initially in metal <unk> fever but may progress to chemical <unk> , pulmonary <unk> , and death . 
- Cadmium is also an environmental hazard . Human exposure is primarily from fossil fuel combustion , phosphate fertilizers , natural sources , iron and steel production , cement production and related activities , <unk> metals production , and municipal solid waste <unk> . Bread , root crops , and vegetables also contribute to the cadmium in modern populations . 
- There have been a few instances of general population poisoning as the result of long @-@ term exposure to cadmium in contaminated food and water , and research into an estrogen mimicry that may induce breast cancer is ongoing . In the decades leading up to World War II , mining operations contaminated the <unk> River in Japan with cadmium and traces of other toxic metals . As a consequence , cadmium accumulated in the rice crops along the riverbanks downstream of the mines . Some members of the local agricultural communities consumed the contaminated rice and developed itai @-@ itai disease and renal abnormalities , including <unk> and <unk> . 
- The victims of this poisoning were almost exclusively post @-@ <unk> women with low iron and other mineral body stores . Similar general population cadmium exposures in other parts of the world have not resulted in the same health problems because the populations maintained sufficient iron and other mineral levels . Thus , although cadmium is a major factor in the itai @-@ itai disease in Japan , most researchers have concluded that it was one of several factors . Cadmium is one of six substances banned by the European Union 's <unk> on <unk> <unk> ( <unk> ) directive , which <unk> hazardous substances in electrical and electronic equipment but allows for certain exemptions and <unk> from the scope of the law . The International Agency for Research on Cancer has classified cadmium and cadmium compounds as carcinogenic to humans . Although occupational exposure to cadmium is linked to lung and prostate cancer , there is still a substantial controversy about the <unk> of cadmium in low environmental exposure . Recent data from <unk> studies suggest that intake of cadmium through diet associates to higher risk of <unk> , breast and prostate cancer as well as to <unk> in humans . A recent study has demonstrated that <unk> tissue is characterized by higher levels of cadmium in current and former smoking females . 
- Cadmium exposure is a risk factor associated with a large number of illnesses including kidney disease , early <unk> , <unk> , and <unk> diseases . Although studies show a significant correlation between cadmium exposure and occurrence of disease in human populations , a necessary molecular mechanism has not been identified . One hypothesis holds that cadmium is an <unk> <unk> and some experimental studies have shown that it can interact with different <unk> signaling pathways . For example , cadmium can bind to the estrogen receptor alpha , and affect signal transduction along the estrogen and <unk> signaling pathways at low doses . 
- Tobacco smoking is the most important single source of cadmium exposure in the general population . An estimated 10 % of the cadmium content of a cigarette is inhaled through smoking . Absorption of cadmium through the lungs is more effective than through the gut , and as much as 50 % of the cadmium inhaled in cigarette smoke may be absorbed . On average , cadmium concentrations in the blood of smokers is 4 times 5 times greater and in the kidney , 2 – 3 times greater than non @-@ smokers . Despite the high cadmium content in cigarette smoke , there seems to be little exposure to cadmium from passive smoking . 
- In a non @-@ smoking population , food is the greatest source of exposure . High quantities of cadmium can be found in crustaceans , <unk> , <unk> , and algae products . However , grains , vegetables , and <unk> roots and <unk> are consumed in much greater quantity in the US , and are the source of the greatest dietary exposure . Most plants bio @-@ accumulate metal toxins like Cd , and when <unk> to form organic fertilizers yield a product which can often contain high amounts ( e.g. , over 0 @.@ 5 mg ) of metal toxins for every <unk> of <unk> . <unk> made from animal dung ( e.g. , cow dung ) or urban waste can contain similar amounts of Cd . The Cd added to the soil from fertilizers ( rock <unk> or organic fertilizers ) become bio @-@ available and toxic only if the soil <unk> is low ( i.e. , acidic soils ) . Zinc is chemically similar to cadmium and some evidence indicates the presence of <unk> ions reduces cadmium toxicity . 
- Zinc , <unk> , <unk> , and Fe ions , and <unk> with <unk> C are used to treat Cd intoxication , though it is not easily reversed . 
- 
- = = = Regulations = = = 
- 
- Because of the adverse effects of cadmium on the environment and human health , the supply and use of cadmium is restricted in Europe under the <unk> Regulation . 
- The <unk> Panel on <unk> in the Food Chain specifies that 2 @.@ 5 <unk> / kg body weight is a <unk> weekly intake for humans . The Joint <unk> / WHO Expert Committee on Food <unk> has declared 7 <unk> / kg <unk> to be the provisional <unk> weekly intake level . 
- The US Occupational Safety and Health Administration ( <unk> ) has set the permissible exposure limit ( <unk> ) for cadmium at a time @-@ weighted average ( TWA ) of 0 @.@ <unk> ppm . The National Institute for Occupational Safety and Health ( <unk> ) has not set a recommended exposure limit ( <unk> ) and has designated cadmium as a known human <unk> . The <unk> ( immediately dangerous to life and health ) level for cadmium is 9 mg / m3 . 
- 
- = = = Product recalls = = = 
- 
- In May 2006 , a sale of the seats from Arsenal F.C. ' s old stadium , Highbury in London , England was cancelled when the seats were discovered to contain trace amounts of cadmium . Reports of high levels of cadmium use in children 's jewelry in 2010 led to a US Consumer Product Safety Commission investigation . The U.S. <unk> issued specific recall notices for cadmium content in jewelry sold by Claire 's and Wal @-@ Mart stores . 
- In June 2010 , McDonald 's voluntarily recalled more than 12 million promotional " <unk> Forever After 3D " <unk> <unk> <unk> because of the cadmium levels in paint pigments on the <unk> . The glasses were manufactured by <unk> International , of <unk> , <unk> , USA . 
- 
- 
- = First Battle of Maryang San = 
- 
- The First Battle of Maryang San ( 3 – 8 October 1951 ) , also known as the Defensive Battle of <unk> ( Chinese : <unk> ; pinyin : <unk> <unk> <unk> <unk> ) , was fought during the Korean War between United Nations ( UN ) forces — primarily Australian and British — and the Chinese communist People 's Volunteer Army . The fighting occurred during a limited UN offensive by US I Corps , codenamed Operation Commando . This offensive ultimately pushed the Chinese back from the Imjin River to the Jamestown Line and destroyed elements of four Chinese armies following heavy fighting . The much smaller battle at Maryang San took place over a five @-@ day period , and saw the 3rd Battalion , Royal Australian Regiment ( 3 RAR ) <unk> a numerically superior Chinese force from the tactically important Kowang @-@ San ( Hill 355 ) and Maryang San ( Hill 317 ) features , in conjunction with other units of the 1st Commonwealth Division . 
- Using tactics first developed against the Japanese in New Guinea during the Second World War , the Australians gained the advantage of the high ground and assaulted the Chinese positions from unexpected directions . They then repelled repeated Chinese counterattacks aimed at re @-@ capturing Maryang San , with both sides suffering heavy casualties before the Australians were finally relieved by a British battalion . However , with the peace @-@ talks ongoing , these operations proved to be last actions in the war of manoeuvre , which had lasted the previous sixteen months . It was replaced by a static war characterised by fixed defences reminiscent of the Western Front in 1915 – 17 . A month later , the Chinese subsequently re @-@ captured Maryang San from the British during fierce fighting , and it was never re @-@ gained . Today , the battle is widely regarded as one of the Australian Army 's greatest accomplishments during the war . 
- 
- = = Background = = 
- 
- 
- = = = Military situation = = = 
- 
- Following General of the Army Douglas MacArthur 's dismissal as Commander @-@ in @-@ Chief of UN forces in Korea , he was replaced by General Matthew B. Ridgway . Consequently , on 14 April 1951 , General James Van Fleet replaced Ridgway as commander of the US Eighth Army and the United Nations forces in Korea . The Chinese Spring Offensive during April and May 1951 ended in its defeat , while following two months of sporadic operations in mid @-@ June and August , the war entered a new phase , with Van Fleet returning to the offensive . In July the Kansas and Wyoming Lines were strengthened , while a limited offensive in the east @-@ central sector in mid @-@ August seized the high ground around the <unk> and Bloody Ridge during the Battle of Bloody Ridge . In September the offensive in this sector continued , targeting the next hill complex north of Bloody Ridge , known as Heartbreak Ridge . 
- Meanwhile , the organisation of British Commonwealth ground forces fighting in Korea as part of the United Nations Command had undergone considerable change in the months following the battles of the Imjin River and <unk> in late @-@ April 1951 . 3 RAR had been transferred from 27th British Infantry Brigade to the 28th British Commonwealth Brigade when that formation departed for Hong Kong . Meanwhile , after protracted negotiations between the governments of Australia , Britain , Canada , India , New Zealand and South Africa , agreement had been reached to establish an integrated formation with the aim of increasing the political significance of their contribution , as well as facilitating the solution of the logistic and operational problems faced by the various Commonwealth <unk> . 
- The 1st Commonwealth Division was formed on 28 July 1951 , with the division including the 25th Canadian , 28th British Commonwealth and 29th British infantry brigades under the command of Major General James <unk> , and was part of US I Corps . Since its formation , the division had occupied part of the west @-@ central sector of the UN line , approximately 48 kilometres ( 30 mi ) north of the capital Seoul . The 28th Brigade included three infantry battalions — the 1st Battalion , King 's Own Scottish Borderers ( 1 KOSB ) , 1st Battalion , King 's Shropshire Light Infantry ( 1 <unk> ) and the 3rd Battalion , Royal Australian Regiment — under the command of Brigadier George Taylor . During this period 3 RAR was commanded by Lieutenant Colonel Francis Hassett . Peace @-@ talks at <unk> during July and September led to a lull in the fighting and 3 RAR undertook mainly defensive duties , helping to construct the defences of the Kansas Line south of the Imjin River , as well as conducting extensive patrolling on the northern side . The battalion also used the reduced operational tempo as an opportunity to train reinforcements . The period culminated in a limited , and largely unopposed , divisional advance 12 kilometres ( 7 @.@ 5 mi ) north of the Imjin to the Wyoming Line , codenamed Operation <unk> , in September . 
- 
- = = Prelude = = 
- 
- 
- = = = <unk> forces = = = 
- 
- In late @-@ September and early @-@ October — even while continuing the attack against Heartbreak Ridge — Van Fleet developed a plan for a limited offensive in the western section , known as Operation Commando , to advance 10 kilometres ( 6 @.@ 2 mi ) north of the 38th parallel , with the aim of pushing Chinese forces back and giving United Nations forces more leverage at the truce negotiations now occurring at <unk> . Operation Commando was scheduled for 3 – 5 October 1951 and the US I Corps commander , Lieutenant General John W. O <unk> , envisioned a concept of operations in which three of the corps ' four divisions would advance on a broad front in conjunction with US 25th Infantry Division on the left flank of the neighbouring US IX Corps , seizing a new defensive line known as the Jamestown Line . The divisions to be used in the advance included the 1st Commonwealth Division , US 1st Cavalry Division and the 9th South Korean Division . The 1st South Korean Division would remain in its existing position on the left flank . 
- In the sector occupied by 1st Commonwealth Division , Chinese communist forces were dug into a group of hills overlooking the Imjin River . The division faced 6 @,@ 000 troops from the Chinese <unk> Division , 64th Army under the overall command of <unk> <unk> . The Chinese forces were divided into three regiments of about 2 @,@ 000 men each , with two regiments dug @-@ in in well prepared defensive positions with overhead protection , and a third regiment in support . The 28th Brigade faced one of the two forward regiments — the <unk> Regiment — which was deployed with one battalion on Hill 355 , a second battalion <unk> Hill 217 and Hill 317 , and a third battalion in reserve to the west . 
- The task allocated to the British Commonwealth force was to take these positions with the intention of advancing the line from the southern bank of the Imjin to a line of hills to the north , in total an objective that stretched more than 15 kilometres ( 9 @.@ 3 mi ) . The primary objectives of the advance would be the capture of Kowang @-@ San ( Hill 355 ) and Maryang San ( Hill 317 ) and the task of taking these positions was allocated to the 28th British Commonwealth Brigade , with this formation bearing the brunt of the fighting . <unk> planned on capturing the Jamestown Line in three phases . In the first phase , scheduled for 3 October , the 28th Brigade would take Hill 355 in the east @-@ central sector . During the second phase , on 4 October , the 25th Brigade would assault the two Hill 187 features and the south @-@ western ridge running to the <unk> River . Lastly during the third phase , scheduled for 5 October , the 28th Brigade would capture Hills 217 and 317 . As such , the bulk of the division 's strength would be concentrated on the right flank , to be held by the 28th Brigade ; meanwhile , the 25th Brigade would hold the left flank and the 29th Brigade would be held in reserve while providing a battalion to each of the other brigades as reinforcements . 
- Kowang @-@ San would be assaulted during the first phase by 1 KOSB with 1 <unk> and 3 RAR in support , while Maryang San would be taken in the third phase of the operation by 3 RAR and the 1st Battalion , Royal Northumberland Fusiliers ( 1 RNF ) , who were under commander from 29th Brigade for the duration of Operation Commando . <unk> reconnaissance and planning took place in the week prior to the commencement of the operation and Taylor emphasised the use of indirect fires , air support and infiltration tactics to limit casualties , as well as the exploitation of weak points in the Chinese defences . In direct support of the brigade was 16th Field Regiment , Royal New Zealand Artillery with its 3 @.@ 45 @-@ inch ( 88 mm ) 25 @-@ pounder field guns , in addition to divisional and corps assets which included 4 @.@ 2 @-@ inch ( 110 mm ) mortars , 3 @-@ inch ( 76 mm ) howitzers and 155 @-@ millimetre ( 6 @.@ 1 in ) heavy artillery ; in total more than 120 guns and mortars . Also in support were two British Centurion tank squadrons from the 8th Royal Irish Hussars . 
- 
- = = = Preliminary operations = = = 
- 
- Given the primary task of capturing Hill 317 , Hassett studied the approaches from the air and the ground . Two previous attempts to take Maryang San by American troops had been unsuccessful . Regardless , utilising tactics first developed against the Japanese in New Guinea during the Second World War of running along the tops of ridges , he intended to gain the advantage of the high ground , while utilising the cover afforded by the vegetation and the ease of movement along the crest @-@ lines , in order to assault the Chinese positions from unexpected directions . Meanwhile , the Chinese defenders on Maryang San were also testing a newly developed tactic called the " mobile positional defense " , in which only small units were stationed on the hills in order to exhaust the UN attackers , while the bulk of the Chinese defenders would later counterattack before the UN forces could consolidate into their newly gained positions . 
- However , during the first phase of the operation the Australians would be tasked with capturing a Chinese outpost on Hill 199 to allow tanks and medium machine @-@ guns to provide direct fires onto the northern and eastern slopes of Hill 355 in support of an attack by the Borderers from the south @-@ east . Likewise , the Shropshires would assault and capture Hill 208 . Finally then , two days before the start of Operation Commando , the 28th Brigade crossed the Imjin river to assemble behind the 25th Brigade on 1 October . The following day the 3 RAR , less D Company , and the Borderers moved forward carefully into their assembly areas , ready to advance the following morning . C Company advanced to a position 1 @,@ 500 metres ( 1 @,@ 600 yd ) in front of the Canadian positions , north @-@ east of Hill 355 . B Company was 200 metres ( 220 yd ) to the rear . In the afternoon C Company was subjected to heavy shelling , losing one soldier wounded . D Company — under the command of Major Basil <unk> — was detached to 25th Brigade to strengthen its extended front , and it would not be available until the afternoon of 3 October . 
- 
- = = Battle = = 
- 
- 
- = = = <unk> of Hill 199 , 3 October 1951 = = = 
- 
- At 03 : 00 on 3 October , B Company 3 RAR moved north 2 @,@ 000 metres ( 2 @,@ 200 yd ) toward Hill 199 , crossing the open valley under the cover of darkness and heavy mist . A Company then moved up behind C Company . Artillery and mortar fire targeted known Chinese artillery positions with counter @-@ battery fire prior to dawn , before switching to support the Borderers in their assault on Hill 355 . Simultaneously , the Shropshires were assaulting Hill 208 and with the support of A Squadron , 8th Royal Irish Hussars they reached the positions without opposition by 06 : 00 . By 08 : 00 B Company had gained the high ground to the north and then proceeded to patrol the short distance to west to the objective which was then taken with three wounded ; five Chinese were killed and one captured . By mid @-@ morning , both the Shropshires and the Australians had successfully captured their objectives . 
- <unk> a counter @-@ attack , the Australians on Hill 199 began digging @-@ in , however no such attack occurred . D Company subsequently returned and was allocated a position between C Company and the Borderers . C and B Companies both received shelling during the day , wounding two men . At 10 : 00 A Company — under Captain Jim Shelton — took over the defence of Hill 199 , and B Company went into reserve behind A Company . According to plan a troop of Centurion tanks and a section of medium machine @-@ guns were then moved up onto Hill 199 and began directing their fire onto the northern slopes of Hill 355 in support of the Borderers . Meanwhile , at 07 : 15 , following preparation by artillery and mortar fire , the lead British assault companies had begun to advance on Hill 355 . However , with the Chinese expecting an assault from that direction , the initial British moves met strong resistance and the Borderers were forced to withdraw and <unk> . At 14 : 15 a second assault reached the objectives on the lower slopes , and these gains were consolidated by nightfall . 
- The attack was now behind schedule . Indeed , the Borderers were still more than 1 @,@ 000 yards ( 910 m ) short of their final objective , and with stubborn resistance being encountered during the initial phase , Hill 355 would now not be secured until the afternoon of 4 October . The assault was being slowed by two positions on the northeast slopes of Hill 355 — known as Hill 220 — from which the Chinese held the British right flank in enfilade . C Company 3 RAR would be detached to assist the attack on Kowang @-@ San the next morning , with the Australians tasked with outflanking the Chinese defences and capturing this position . Heavy Chinese artillery fire had also slowed progress with more than 2 @,@ 500 rounds falling in the 28th Brigade area in the previous twenty @-@ four hours , although this total was <unk> many times over by the weight of allied artillery fired across the brigade front , which included 22 @,@ <unk> rounds . On the division 's left flank , the delay also meant that the Canadian attack scheduled for 06 : 00 the next day in the 25th Brigade sector would have to be postponed until 11 : 00 , due to the continuing requirement to use the divisional artillery in support of 28th Brigade . 
- 
- = = = <unk> of Hill 220 and the fall of Kowang @-@ San , 4 October 1951 = = = 
- 
- On 4 October , C Company 3 RAR — under the command of Major Jack <unk> — attacked the long spur running east from the peak of Hill 355 , known as Hill 220 . <unk> their assault at 09 : 00 , the Australians quickly killed or drove off the defenders before pressing on up the spur and routing the remainder of a Chinese company . Reaching their objectives by 10 : 00 , the Australians then took advantage of the initiative gained so far , pushing a platoon towards the summit of Hill 355 . Amid heavy fighting , the Australians cleared the eastern slopes of Kowang @-@ San by 12 : 00 , despite having received no orders to do so . Thirteen Chinese were killed and three captured in the fighting , while Australian casualties included 11 wounded , one of whom subsequently died . <unk> was later awarded the Distinguished Service Order ( DSO ) for his leadership . C Company withdrew to the rear of the 3 RAR position and were replaced by D Company , who occupied the position held by A Company 500 metres ( 550 yd ) north of Hill 199 . Meanwhile , led by a <unk> , the Borderers made a simultaneous assault up the western face of Kowang @-@ San , and fearing they may be caught between two attacks the Chinese defenders abandoned Hill 355 , withdrawing northwest under heavy indirect fire . 
- Given the strong resistance exhibited by the Chinese , the Canadians expected a tough fight as 25th Brigade prepared to assault its objectives as part of the second phase of the divisional plan . Yet with the loss of Hill 355 and 210 the Chinese unexpectedly withdrew from their well @-@ prepared defensive positions , with Hill 159 and 175 captured without opposition . Only the 2nd Battalion , Princess Patricia 's Canadian Light Infantry encountered any opposition before they captured the two Hill 187 features , losing one killed and six wounded during stiff fighting in which 28 Chinese were also killed . Indeed , the ease with which the Canadians had captured their initial objectives allowed them to press on , attaining their final objectives on the Jamestown Line by nightfall . No further resistance was encountered , although heavy Chinese artillery fire caused a number of casualties , including three killed . The Canadians subsequently occupied the positions they were destined to hold for the next twenty @-@ two months of fighting . 
- Meanwhile , on the 28th Brigade 's left flank the Shropshires met slight resistance , securing Hill 210 southwest of Kowang @-@ San by 10 : 10 . They were then relieved by the Canadians by nightfall in preparation for the third phase of the operation . The brigade plan was now a day behind schedule , although with the unexpected ease experienced by the Canadians , overall , the divisional attack was still running according to plan . However , determined to hold on following the loss of Hill 355 , the Chinese moved in fresh troops , heavily reinforcing a number of positions , including Maryang San . 
- 
- = = = Fall of Maryang San , 5 October 1951 = = = 
- 
- The final objective was Maryang San , a steep hill rising 200 metres ( 660 ft ) above the valley about 2 @,@ 500 metres ( 2 @,@ 700 yd ) north of Hill 355 . However , following the delay in capturing Hill 355 , Hassett would not be ready to implement his plan until early the next day . As such the third phase would begin on 5 October , with the Royal Northumberland Fusiliers scheduled to attack an intermediate objective — Hill 217 , adjacent Kowang @-@ San — before assisting the Australians assault Hill 317 . The Australians moved into position northeast of Hill 199 on the afternoon of 4 October , while over the night of 4 / 5 October the divisional artillery hit Chinese positions , with two batteries of 8 @-@ inch ( 200 mm ) howitzers and another two 155 @-@ millimetre ( 6 @.@ 1 in ) batteries <unk> them . Air strikes by the Mustangs of No. 2 Squadron , South African Air Force were also planned , targeting Chinese concentrations north and west of the objectives to cut @-@ off supplies and reinforcements . Both the Australians and Fusiliers were scheduled to begin their attacks at first light — at 05 : 45 — following a heavy artillery preparation . 
- In the dark the Fusiliers moved off , but amid dense fog they found it difficult to maintain their bearings and were not in position in time to commence the attack as planned . By 10 : 00 they had struggled to within 300 metres ( 330 yd ) from their objective , and following further delays the assault was commenced at 11 : 00 . After initially achieving surprise a number of the forward Chinese outposts fell to the Fusiliers . <unk> strong defensive positions on Hill 217 , the Chinese regained the initiative however , and poured heavy machine @-@ gun and rifle fire onto the attackers as they crossed the valley , forcing them to withdraw after suffering heavy casualties and running low on ammunition . Having expected the main axis of assault from the south , the Chinese positions were stronger than previously considered and the Fusiliers were unable to gain the summit , despite one company gaining a <unk> on the summit by midday . 
- Earlier that morning , at 04 : 45 , B and D Companies 3 RAR had moved north across the valley , while Anti @-@ Tank Platoon crossed the Imjin , taking up positions further north in order to protect the right flank . The assaulting companies would then move west towards a series of objectives before assaulting Hill 317 . Initially 3 RAR was to attack from the east , while 1 RNF would attack from the southwest through Hill 217 , however with the Fusiliers facing stiff resistance on Hill 217 itself they were unable to get forward to assist . The previous attempts to capture Maryang San had failed due to the approach to steep eastern slopes of the feature being across a wide , open valley that was dominated by enfilade fire from mutually supporting Chinese positions . Consequently , the Australians planned to cross the valley under cover of darkness and position themselves on the Chinese flank in the foothills , before scaling the position at first light . A Company would create a diversion on the left flank , while B Company would clear the lower slopes before D Company passed through to assault the Chinese main defensive position , known as the ' Victor ' feature , in a one @-@ up , one @-@ in @-@ depth assault . However , following the casualties of previous nights on Hill 199 , 220 and 355 , and the effect of constant shelling , 3 RAR was now reduced to just 320 men . In contrast , the Australians faced two fresh Chinese battalions on Maryang San , in total about 1 @,@ 200 men . 
- B Company — commanded by Captain Henry Nicholls — led off <unk> in the heavy mist , and with visibility limited in the thick vegetation , it drifted to the right off the intended axis of advance having lost direction , suffering a similar fate as the Fusiliers . <unk> , the assaulting companies became separated and the battalion attack turned into a series of independent company attacks . D Company slowly continued forward however , and when the mist lifted suddenly at 11 : 20 they were left dangerously exposed still only halfway up the slope to their objective . The Australian approach had surprised the Chinese however , who were apparently expecting the assault from the north , and D Company succeeded in closing to within grenade range of the Chinese on Victor . During a fierce twenty @-@ minute fire @-@ fight the Australians cleared their first objective with the assistance of direct fire from supporting tanks , and indirect fire support from artillery , losing three killed and 12 wounded . Included among the Australian wounded was the company commander and one of the platoon commanders , both of whom remained in command despite gunshot wounds . Chinese losses included 30 killed and 10 captured . 
- During the initial phase A Company had attacked southwest along a spur leading to Hill 317 and had met stiff opposition . The diversion was largely successful however , causing the Chinese to reinforce against the attack , which they believed to be the main effort . Meanwhile , D Company continued to press their attack along the high ground towards the ' <unk> ' feature , assaulting the deeply entrenched Chinese positions , which included heavy automatic weapons . By 16 : 00 it had successfully captured the last of the intermediate objectives assigned to it and a platoon from B Company was pushed forward to assist in the clearance of the feature . Later , Lieutenant <unk> Clark was awarded the Military Cross while Sergeant <unk> Rowlinson was awarded a bar to his Distinguished Conduct Medal for their actions during the fighting . By this time total Chinese casualties included 98 killed and 40 captured , while the Australians believed that a large number of Chinese had also been wounded . Following the progress of B and D Companies , C Company was moved up behind them and with the capture of final objective they immediately commenced an assault on Hill 317 , capturing 10 prisoners for no loss . Although the Chinese had been well dug @-@ in , there were no barbed wire obstacles to hamper the attackers and the Australians had rapidly gained the position . By 17 : 00 , Maryang San had fallen to the Australians , with the Chinese withdrawing under heavy artillery , mortar and machine @-@ gun fire . 
- On Hill 217 the Fusiliers had maintained the pressure on the Chinese throughout the day , however they were still unable to capture the feature . Regardless , the efforts of the Fusiliers in conjunction with A Company 's diversionary attack and the rapid advance of D Company with tank and artillery support had carried the day . A Company continued to attack against heavy opposition and indirect fire , slowly pushing the Chinese defenders back . Later , a platoon was detached to assist C Company consolidate the defence of Maryang San following its capture , while the remaining two platoons were withdrawn <unk> , again under heavy artillery fire . Indeed , although it had played a supporting role in the attack , the efforts of A Company had been vital , suffering 20 casualties while killing at least 25 Chinese and capturing two . Now with Maryang San captured the Australians began digging @-@ in , <unk> the south @-@ facing linear Chinese trench system into an all @-@ round defensive position with mutually supporting weapons pits . Fully expecting a Chinese counter @-@ attack that evening , <unk> moved the Assault Pioneer Platoon to bolster the hasty defences . Meanwhile , the Chinese still occupied three key <unk> positions — the ' Sierra ' feature , the ' Hinge ' and the summit of Hill 317 itself — which they continued to furiously defend . These would be the scene of considerable fighting in the days to come as the Australians attempted to clear them . 
- 
- = = = The Hinge , 6 – 8 October 1951 = = = 
- 
- With both sides exhausted from the fighting the night of 5 / 6 October was less eventful than expected , and the Australians used the opportunity to develop their position . To add further depth to their defences and to probe the Chinese positions , Taylor ordered the Australians to capture the central remaining Chinese position , the Sierra feature — a wooded knoll halfway between the summit of Maryang San and the Hinge — the next day . Meanwhile , the Fusiliers would renew their attack on Hill 217 . The southern approach to Hill 217 had proved to be too strongly defended by the Chinese and it became obvious that if it was to be overcome Taylor would need to split the fire of its defenders . To do this the high ground to the north @-@ west of Maryang San , known as the Hinge , would be vital . Indeed , adjacent to Hill 217 , the Hinge dominated it from the north . As such for the next assault , planned for the morning , the Fusiliers would detach their reserve company to attack the Hinge from the east , using the Australian positions on Maryang San as a firm base and thereby allowing them to outflank their opponents on Hill 217 . 
- At 07 : 00 on 6 October , 9 Platoon C Company — under the command of Lieutenant Arthur <unk> — moved forward to Sierra , using the heavy mist to conceal their movements . Under @-@ strength and not expecting the feature to be occupied , instead the Australians found a large number of Chinese in well prepared defensive positions . Without fire support and outnumbered , the Australians immediately conducted a quick attack and , using grenades and bayonets , they inflicted heavy casualties on the Chinese before forcing the survivors to withdraw . Although subjected to constant shelling , 9 Platoon continued to hold the knoll , <unk> several counterattacks over the next 13 hours , cutting down each assault through the tree @-@ line and long grass with accurate rifle and machine @-@ gun fire , forcing the Chinese to withdraw leaving their dead and wounded behind . One Australian was killed in the initial assault on Sierra , while a number were later wounded during the defence . Chinese casualties included 19 killed , 30 wounded and seven captured . <unk> was later awarded the Military Cross . 
- During the day the Fusiliers again assaulted Hill 217 from the south , and attempted to work their way around the eastern and western flanks of the feature . Despite preparation by the divisional artillery and the 3 RAR Machine Gun Platoon firing their Vickers medium machine @-@ guns in support from Maryang San , the Fusiliers were unable to make progress due to Chinese machine @-@ guns located in bunkers at the top of their objective . Meanwhile , their flanking movements were also blocked by Chinese small arms and grenades . 1 RNF had now taken over 100 casualties during two days of fighting and by the afternoon they were a spent force . <unk> the Fusiliers ' weakness , the Chinese then launched their own assault , forcing them to withdraw in contact . Previous plans for an assault on the Hinge had not occurred due to issues with resupply and the dangerous approach march that would have been required . Again , despite their efforts , the Fusiliers had failed to capture their objective . It seemed that the only way to finally secure Hill 217 was along the ridge from Hill 317 , via the Hinge , and as such the Australians would be tasked with capturing the Hinge the following day . B Company was subsequently allocated the attack . In preparation , they ascended Hill 317 late in the afternoon of 6 October , finally securing the crest , and at last light joined 9 Platoon on the knoll northwest of the summit where they would form up the next day to conduct the assault . 
- In the early hours of 7 October the allied artillery and mortar bombardment began , targeting Chinese positions on the Hinge . Hassett moved the 3 RAR tactical headquarters on to Hill 317 just before the assaulting troops stepped off the line of departure , allowing him to direct the battle from a forward position and to co @-@ <unk> fire support . Waiting for the fog to lift so that the artillery could fire until the last safe moment , the attack finally began at 08 : 00 . B Company moved off down the <unk> , with two @-@ up and one @-@ in @-@ depth , using the trees and long grass for concealment . Initially it seemed that the Chinese had withdrawn during the night , when suddenly the lead Australian platoons were <unk> by small arms fire from their rear . A series of intense fire @-@ fights ensued as the Australians fought back and by 09 : 20 the Hinge finally fell , with the Australians losing two killed and 20 wounded . Chinese casualties included more than 20 killed . As a result of the fighting Captain Henry Nicholls and Lieutenant Jim Hughes were awarded the Military Cross , while <unk> J. Park and <unk> <unk> Bosworth were awarded the Military Medal . Yet even as the surviving Chinese withdrew , artillery and mortar fire began to fall on the Hinge . B Company moved quickly to consolidate the position , but were hampered by the shelling , while they now faced a pressing shortage of ammunition and difficulties evacuating their casualties . 
- For the remainder of the day B Company was subjected to intense indirect fire on the Hinge , as was C Company on Hill 317 . The Anti @-@ Tank Platoon and Assault Pioneer Platoon reinforced C Company , with a platoon of C Company moved forward to the Hinge to support B Company . At 20 : 00 both the Hinge and Hill 317 were again heavily shelled for 45 minutes , <unk> the beginning of the inevitable Chinese counterattack . Heavy mist concealed the Chinese advance , and this assisted many to penetrate the Australian perimeter . Throughout the night of 7 / 8 October the Hinge was attacked on three occasions from both the front and the flanks by a force of battalion strength , however the Australians beat back the Chinese in desperate hand @-@ to @-@ hand combat . The Chinese swept forward , but were stopped by intense small arms and artillery fire . During one such assault Sergeant P.J. O 'Connell , on seeing one of his platoon 's Bren gunners wounded , manned the light machine @-@ gun himself , breaking up a Chinese assault , while controlling the fire of the men around him . Meanwhile , Sergeant <unk> Strong arranged the resupply of ammunition to the forward Australian sections . Both were awarded the Military Medal . 
- The intensity of the fighting had led to a severe shortage of ammunition among the defenders , and attempts to resupply the Australians were plagued by heavy shelling . The use of salvaged ammunition <unk> the situation momentarily after one of B Company 's two Vickers medium machine @-@ guns was destroyed by Chinese shelling , and its ammunition belts were subsequently broken up and dispersed among the riflemen . However , this soon resulted in a large number of mechanical failures and weapon <unk> , causing additional problems for the defenders . The evacuation of casualties was again an issue , and the Assault Pioneer Platoon — commanded by Lieutenant Jock McCormick — was used as stretcher bearers and to run ammunition forward , as were a number of the other specialist platoons . Their ammunition nearly exhausted , the Australians resorted to kicking and <unk> many of the attacking Chinese during the brutal fighting . Fearing the Australians would be overwhelmed by the persistent Chinese attacks , Taylor ordered the Borderers and Shropshires to detach their Korean porters to resupply the Australians , while a full divisional concentration of artillery was fired in support of 3 RAR . 
- Ultimately , B Company succeeded in holding their hastily constructed defensive positions throughout the night and until 05 : 00 on 8 October when the Chinese finally gave up . In order to preserve its remaining strength , the Chinese <unk> Division was forced to pull back by 3 kilometres ( 1 @.@ 9 mi ) , surrendering the control of Hill 217 without a fight . At first light more than 120 Chinese dead and wounded lay around the Australian defences and in contrast to the savage fighting during the night , Chinese stretcher parties were allowed to come forward and collect their wounded under a flag of truce . The Australians had been victorious but were now exhausted after five days of heavy fighting . 
- 
- = = Aftermath = = 
- 
- 
- = = = Casualties = = = 
- 
- Four hours later , at 09 : 00 , 3 RAR was relieved on Maryang San and the Hinge by the Borderers , having lost 20 killed and 104 wounded . Chinese casualties on Hill 317 had been severe , with at least 283 killed ( determined by body count ) and another 50 captured , while hundreds more were thought likely to have been killed and wounded . Later it was estimated that the Australians had destroyed at least two Chinese battalions during the five @-@ day battle . 1 RNF once more advanced against Hill 217 , this time without opposition , sending patrols to confirm that the Chinese had withdrawn . They were met by patrols from 1 KOSB on the Hinge , with the Borderers taking control of the area at 11 : 00 . Hill 217 was latter occupied on 9 October by the Borderers . The 3 RAR Assault Pioneer Platoon , the Anti @-@ Tank Platoon and a platoon from C Company remained on Maryang San however , and during the evening of 8 / 9 October the Pioneers killed four Chinese during a probe on their position . They were finally relieved on 9 October . For his leadership , Hassett was immediately awarded the DSO , while a number of awards were also made to others that had distinguished themselves during the fighting . The Royal Australian Regiment was subsequently granted the battle honours " Kowang @-@ San " and " Maryang San " . Today , the First Battle of Maryang San is widely regarded as one of the Australian Army 's greatest accomplishments of the Korean War . 
- 
- = = = Assessment = = = 
- 
- During the battle , the British Commonwealth logistic system proved robust enough to bear the strain of the fighting without serious disruption , although problems were experienced . Despite difficulties , an adequate flow of ammunition , equipment , food and water was maintained , although there were occasions when the Australians endured thirst and hunger for several hours . 3 RAR used 900 @,@ 000 rounds of small arms , 5 @,@ 000 grenades and 7 @,@ 000 mortar rounds during the five @-@ day battle , all of which was moved in man @-@ <unk> loads by Korean Service Corps porters and Australian soldiers over long distances and extreme terrain , often while under fire . These resupply operations had required considerable effort and bravery to effect , and a number of Korean porters were killed and wounded at Maryang San . 
- Indeed , the evacuation of casualties and the resupply of ammunition at times proved problematic , and heavy shelling and sniper fire disrupted stretcher parties and porters on a number of occasions , resulting in the forward companies running short of ammunition . Meanwhile , the quality of support given to the British and Australian infantry by the artillery and tanks was of a high standard and proved a critical factor . Indeed , the tanks had often operated in terrain to which they were unsuited , while the New Zealand gunners had fired over 50 @,@ 000 rounds in direct support of 3 RAR , blistering the paint off the barrels of their guns . Air support , including that provided by the South African Mustangs , had been important throughout . 
- The battle was also noted for the pioneering use of tunnel warfare by the Chinese in the Korean War . During the fighting , a company of Chinese soldiers had defended their positions from a U @-@ shaped tunnel capable of housing 100 men , which had served as both a bomb shelter and a base for counterattacks . The company leader later claimed that the tunnel enabled the defenders to inflict 700 UN casualties while suffering only 21 casualties in return . Impressed by the report , the commander of the People 's Volunteer Army , <unk> <unk> , later ordered the construction of 30 @-@ metre ( 98 ft ) deep tunnels along the entire front line , and it formed a formidable obstacle for UN forces to overcome during the stalemate period . 
- 
- = = = Subsequent operations = = = 
- 
- Operation Commando finally ended on 15 October with the US I Corps having successfully seized the Jamestown Line and destroying elements of the 42nd , 47th , 64th and 65th Chinese Armies . Chinese losses were estimated at 21 @,@ 000 casualties , while UN losses were 4 @,@ 000 — the majority of them in the US 1st Cavalry Division which had borne the brunt of the fighting . Although a few hills south of the line remained in communist hands — requiring a follow @-@ up operation known as Operation <unk> which succeeded in capturing these positions by 19 October — UN supply lines near Seoul were now free from Chinese interdiction . With the peace @-@ talks ongoing , these operations proved to be last actions in the war of manoeuvre , which had lasted the previous sixteen months . It was replaced by a static war characterised by fixed defences , trench lines , bunkers , patrols , <unk> parties and minefields reminiscent of the Western Front in 1915 – 17 . Construction of defensive localities began almost immediately , although such operations were confined to the reverse slopes during the day due to artillery and mortar fire which made such operations hazardous . Patrolling forward of the Jamestown Line also began in order to prevent the Chinese from gaining control of no <unk> land . Yet even as the war became a contest of positional warfare and <unk> , growing western political <unk> ensured that UN commanders were increasingly <unk> of limiting casualties . 
- Total casualties among the 1st Commonwealth Division during Operation Commando amounted to 58 killed and 262 wounded , the bulk of which had occurred during the fighting for Hill 217 and Hill 317 . Indeed , in addition to the heavy casualties suffered by 3 RAR , 1 RNF had lost 16 killed and 94 wounded . The Chinese 64th Army later received a commendation for keeping their casualties " light " , despite some estimates placing its casualties at higher than 3 @,@ 000 . Throughout the operation 3 RAR had played a crucial role , and in a bold series of holding and flanking movements , coordinated with accurate and sustained artillery and direct tank fire , it had driven the Chinese from both Kowang @-@ San and Maryang San . They had then held the key position against several unsuccessful counterattacks before forcing the Chinese to retire . A month later Maryang San was subsequently <unk> by the Chinese from the Borderers amid fierce fighting at the Second Battle of Maryang San , for which Private Bill <unk> was later awarded the Victoria Cross . It was not re @-@ gained , and remained in Chinese hands until the end of the war . 
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- 
- = Ulysses ( poem ) = 
- 
- " Ulysses " is a poem in blank verse by the Victorian poet Alfred , Lord Tennyson ( 1809 – 1892 ) , written in 1833 and published in 1842 in his well @-@ received second volume of poetry . An oft @-@ quoted poem , it is popularly used to illustrate the dramatic monologue form . Facing old age , mythical hero Ulysses describes his discontent and restlessness upon returning to his kingdom , Ithaca , after his far @-@ ranging travels . Despite his reunion with his wife Penelope and son Telemachus , Ulysses <unk> to explore again . 
- The character of Ulysses ( in Greek , Odysseus ) has been explored widely in literature . The adventures of Odysseus were first recorded in Homer 's <unk> and Odyssey ( c . 800 – 700 BC ) , and Tennyson draws on Homer 's narrative in the poem . Most critics , however , find that Tennyson 's Ulysses recalls Dante 's Ulisse in his Inferno ( c . <unk> ) . In Dante 's re @-@ telling , Ulisse is condemned to hell among the false <unk> , both for his pursuit of knowledge beyond human bounds and for his adventures in disregard of his family . 
- For much of this poem 's history , readers viewed Ulysses as resolute and heroic , <unk> him for his determination " To strive , to seek , to find , and not to yield " . The view that Tennyson intended a heroic character is supported by his statements about the poem , and by the events in his life — the death of his closest friend — that prompted him to write it . In the twentieth century , some new interpretations of " Ulysses " highlighted potential <unk> in the poem . They argued , for example , that Ulysses wishes to <unk> abandon his kingdom and family , and they questioned more positive assessments of Ulysses ' character by demonstrating how he resembles flawed protagonists in earlier literature . 
- 
- = = Synopsis and structure = = 
- 
- As the poem begins , Ulysses has returned to his kingdom , Ithaca , having made a long journey home after fighting in the <unk> War . <unk> again by domestic life , Ulysses expresses his lack of contentment , including his indifference toward the " savage race " ( line 4 ) whom he governs . Ulysses contrasts his present restlessness with his heroic past , and contemplates his old age and eventual death — " Life <unk> on life / Were all too little , and of one to me / Little remains " ( 24 – 26 ) — and longs for further experience and knowledge . His son Telemachus will inherit the throne that Ulysses finds burdensome . While Ulysses thinks that Telemachus will be a good king — " Most blameless is he , centred in the sphere / Of common duties " ( 39 ) — he seems to have lost any connection to his son — " He works his work , I mine " ( 43 ) — and the conventional methods of governing — " by slow prudence " and " through soft degrees " ( 36 , 37 ) . In the final section , Ulysses turns to his fellow mariners and calls on them to join him on another quest , making no guarantees as to their fate but attempting to <unk> their heroic past : 
- 
- = = = <unk> = = = 
- 
- The speaker 's language is unadorned but forceful , and it expresses Ulysses ' conflicting moods as he searches for continuity between his past and future . There is often a marked contrast between the sentiment of Ulysses ' words and the sounds that express them . For example , the poem 's <unk> iambic <unk> is often interrupted by spondees ( <unk> feet that consist of two long syllables ) ; such <unk> language slows the poem ( and in other places may cast doubt upon the reliability of Ulysses ' utterances ) : 
- Observing their burdensome <unk> effect , the poet Matthew Arnold remarked , " these three lines by themselves take up nearly as much time as a whole book of the <unk> . " Many of the poem 's clauses carry over into the following line ; these <unk> emphasize Ulysses ' restlessness and dissatisfaction . 
- 
- = = = Form = = = 
- 
- The poem 's seventy lines of blank verse are presented as a dramatic monologue . Scholars disagree on how Ulysses ' speech functions in this format ; it is not necessarily clear to whom Ulysses is speaking , if anyone , and from what location . Some see the verse turning from a <unk> to a public address , as Ulysses seems to speak to himself in the first movement , then to turn to an audience as he introduces his son , and then to relocate to the <unk> where he addresses his mariners . In this interpretation , the comparatively direct and honest language of the first movement is set against the more politically minded tone of the last two movements . For example , the second paragraph ( 33 – 43 ) about Telemachus , in which Ulysses muses again about domestic life , is a " revised version [ of lines 1 – 5 ] for public consumption " : a " savage race " is revised to a " rugged people " . 
- The ironic interpretations of " Ulysses " may be the result of the modern tendency to consider the narrator of a dramatic monologue as necessarily " unreliable " . According to critic Dwight <unk> , the poem has been a victim of <unk> readings in which the reader expects to reconstruct the truth from a misleading narrator 's accidental revelations . ( <unk> the more obvious use of this approach in Robert <unk> 's " My Last Duchess " . ) <unk> himself views " Ulysses " as a dialectic in which the speaker weighs the virtues of a contemplative and an active approach to life ; Ulysses moves through four emotional stages that are self @-@ <unk> , not ironic : beginning with his rejection of the barren life to which he has returned in Ithaca , he then fondly recalls his heroic past , recognizes the validity of Telemachus ' method of governing , and with these thoughts plans another journey . 
- 
- = = = Publication history = = = 
- 
- Tennyson completed the poem on 20 October 1833 , but it was not published until 1842 , in his second collection of Poems . Unlike many of Tennyson 's other important poems , " Ulysses " was not revised after its publication . 
- Tennyson originally blocked out the poem in four paragraphs , broken before lines 6 , 33 and 44 . In this structure , the first and third paragraphs are thematically parallel , but may be read as interior and exterior monologues , respectively . However , the poem is often printed with the first paragraph break omitted . 
- 
- = = <unk> = = 
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- 
- = = = <unk> elements = = = 
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- Tennyson penned " Ulysses " after the death of his close Cambridge friend , the poet Arthur Henry Hallam ( 1811 – 1833 ) , with whom Tennyson had a strong emotional bond . The two friends had spent much time discussing poetry and philosophy , writing verse , and travelling in southern France , the Pyrenees , and Germany . Tennyson considered Hallam destined for greatness , perhaps as a statesman . 
- When Tennyson heard on 1 October 1833 of his friend 's death , he was living in <unk> , Lincolnshire , in cramped quarters with his mother and nine of his ten siblings . His father had died in 1831 , requiring Tennyson to return home and take responsibility for the family . Tennyson 's friends were becoming increasingly concerned about his mental and physical health during this time . The family had little income , and three of Tennyson 's brothers were mentally ill . Just as Tennyson 's outlook was improving — he was adjusting to his new domestic duties , regaining contact with friends , and had published his 1832 book of poems — the news of Hallam 's death arrived . Tennyson shared his grief with his sister , Emily , who had been engaged to Hallam . 
- According to Victorian scholar Linda Hughes , the emotional gulf between the state of his domestic affairs and the loss of his special friendship informs the reading of " Ulysses " — particularly its treatment of domesticity . At one moment , Ulysses ' discontent seems to mirror that of Tennyson , who would have been frustrated with managing the house in such a state of grief . At the next , Ulysses is determined to transcend his age and his environment by travelling again . It may be that Ulysses ' determination to defy circumstance attracted Tennyson to the myth ; he said that the poem " gave my feeling about the need of going forward and <unk> the struggle of life " . On another occasion , the poet stated , " There is more about myself in Ulysses , which was written under the sense of loss and that all had gone by , but that still life must be fought out to the end . It was more written with the feeling of his loss upon me than many poems in In <unk> . " Hallam 's death influenced much of Tennyson 's poetry , including perhaps his most highly regarded work , In <unk> <unk> , begun in 1833 and completed seventeen years later . 
- Other critics find stylistic <unk> between the poem and its author that make " Ulysses " exceptional . W. W. Robson writes , " Tennyson , the responsible social being , the <unk> serious and ' committed ' individual , is <unk> strenuous sentiments in the accent of Tennyson the most un @-@ strenuous , lonely and poignant of poets . " He finds that Tennyson 's two widely noted <unk> , the " responsible social being " and the melancholic poet , meet uniquely in " Ulysses " , yet seem not to recognize each other within the text . 
- 
- = = = Literary context = = = 
- 
- Tennyson adopts aspects of the Ulysses character and narrative from many sources ; his treatment of Ulysses is the first modern account . The ancient Greek poet Homer introduced Ulysses ( Odysseus in Greek ) , and many later poets took up the character , including <unk> , Horace , Dante , William Shakespeare , and Alexander Pope . Homer 's Odyssey provides the poem 's narrative background : in its eleventh book the prophet <unk> <unk> that Ulysses will return to Ithaca after a difficult voyage , then begin a new , mysterious voyage , and later die a peaceful , " <unk> " death that comes vaguely " from the sea " . At the conclusion of Tennyson 's poem , his Ulysses is contemplating undertaking this new voyage . 
- Tennyson 's character , however , is not the lover of public affairs seen in Homer 's poems . Rather , " Ulisse " from Dante 's Inferno is Tennyson 's main source for the character , which has an important effect on the poem 's interpretation . Ulisse recalls his voyage in the Inferno 's 26th <unk> , in which he is condemned to the Eighth Circle of false <unk> for <unk> his gift of reason . Dante treats Ulisse , with his " zeal … / T <unk> the world " , as an evil <unk> who lusts for adventure at the expense of his family and his duties in Ithaca . Tennyson projects this zeal into Ulysses ' <unk> desire for knowledge : 
- The poet 's intention to recall the Homeric character remains evident in certain passages . " I am become a name " ( 11 ) recalls an episode in the Odyssey in which <unk> sings about Odysseus ' adventures in the king 's presence , acknowledging his fame . With phrases such as " There <unk> the dark broad seas " ( 45 ) and " The deep / <unk> round with many voices " ( 55 – 56 ) , Tennyson seems to be consciously invoking Homer . 
- Critics have also noted the influence of Shakespeare in two passages . In the early movement , the savage race " That hoard , and sleep , and feed , and know not me " ( 5 ) echoes Hamlet 's <unk> : " What is a man , / If his chief good and market of his time / Be but to sleep and feed ? A beast , no more . " Tennyson 's " How dull it is to pause , to make an end , / To rust <unk> ’ d , not to shine in use ! " ( 22 – 23 ) recalls Shakespeare 's Ulysses in <unk> and <unk> ( c . <unk> ) : 
- The last movement of " Ulysses " , which is among the most familiar passages in nineteenth @-@ century English @-@ language poetry , presents decisive evidence of the influence of Dante . Ulysses turns his attention from himself and his kingdom and speaks of ports , seas , and his mariners . The strains of discontent and weakness in old age remain throughout the poem , but Tennyson finally leaves Ulysses " To strive , to seek , to find , and not to yield " ( 70 ) , recalling the <unk> <unk> desire for knowledge beyond all bounds . The words of Dante 's character as he <unk> his men to the journey find parallel in those of Tennyson 's Ulysses , who calls his men to join him on one last voyage . Quoting Dante 's Ulisse : 
- However , critics note that in the Homeric narrative , Ulysses ' original mariners are dead . A significant irony therefore develops from Ulysses ' speech to his sailors — " Come , my friends , / ' Tis not too late to seek a newer world " ( 56 – 57 ) . Since Dante 's Ulisse has already undertaken this voyage and recounts it in the Inferno , Ulysses ' entire monologue can be envisioned as his recollection while situated in Hell . 
- 
- = = = From affirmation to irony = = = 
- 
- The degree to which Tennyson identifies with Ulysses has provided one of the great debates among scholars of the poem . Critics who find that Tennyson identifies with the speaker read Ulysses ' speech " <unk> " , or without irony . Many other interpretations of the poem have developed from the argument that Tennyson does not identify with Ulysses , and further criticism has suggested that the purported inconsistencies in Ulysses ' character are the fault of the poet himself . 
- Key to the <unk> reading of " Ulysses " is the biographical context of the poem . Such a reading takes into account Tennyson 's statements about writing the poem — " the need of going forward " — and considers that he would not undermine Ulysses ' determination with irony when he needed a similar <unk> to face life after Hallam 's death . Ulysses is thus seen as an heroic character whose determination to seek " some work of noble note " ( 52 ) is courageous in the face of a " still hearth " ( 2 ) and old age . The passion and conviction of Tennyson 's language — and even his own comments on the poem — signify that the poet , as was typical in the Victorian age , admired courage and persistence . Read <unk> , " Ulysses " promotes the <unk> spirit of youth , even in old age , and a refusal to resign and face life passively . 
- Until the early twentieth century , readers reacted to " Ulysses " <unk> . The meaning of the poem was increasingly debated as Tennyson 's stature rose . After <unk> F. Baum criticized Ulysses ' inconsistencies and Tennyson 's conception of the poem in 1948 , the ironic interpretation became dominant . Baum finds in Ulysses echoes of Lord Byron 's flawed heroes , who similarly display conflicting emotions , self @-@ critical <unk> , and a rejection of social responsibility . Even Ulysses ' resolute final <unk> — " To strive , to seek , to find , and not to yield " — is undercut by irony , when Baum and later critics compare this line to Satan 's " courage never to submit or yield " in John Milton 's Paradise Lost ( 1667 ) . 
- Ulysses ' apparent disdain for those around him is another <unk> of the ironic perspective . He declares that he is " matched with an aged wife " ( 3 ) , indicates his weariness in governing a " savage race " ( 4 ) , and suggests his philosophical distance from his son Telemachus . A skeptical reading of the second paragraph finds it a condescending tribute to Telemachus and a rejection of his " slow prudence " ( 36 ) . However , the adjectives used to describe Telemachus — " blameless " , " discerning " , and " decent " — are words with positive connotations in other of Tennyson 's poetry and within the classical tradition , where " blameless " is an attribute of gods and heroes . 
- Critic E. J. <unk> argued in 1954 that Ulysses is without faith in an afterlife , and that Tennyson uses a " method of <unk> " to affirm the need for religious faith by showing how Ulysses ' lack of faith leads to his neglect of kingdom and family . <unk> regards the poem as " <unk> " in Tennyson 's canon , but finds that the poem 's meaning resolves itself when this <unk> is understood : it illustrates Tennyson 's conviction that " <unk> religious sanctions and ' submitting all things to desire ' leads to either a <unk> or a brutal <unk> of responsibility and ' life ' . " 
- Other ironic readings have found Ulysses longing for withdrawal , even death , in the form of his proposed quest . In noting the sense of <unk> in the poem , critics highlight Tennyson 's tendency toward the melancholic . T. S. Eliot opines that " Tennyson could not tell a story at all " . He finds Dante 's treatment of Ulysses exciting , while Tennyson 's piece is " an elegiac mood " . " Ulysses " is found lacking in narrative action ; the hero 's goal is vague , and by the poem 's famous last line , it is not clear for what he is " striving " , or to what he refuses to yield . According to Victorian scholar Herbert Tucker , Tennyson 's characters " move " through time and space to be moved <unk> . To Ulysses , experience is " somewhere out there " , 
- 
- = = Legacy = = 
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- 
- = = = Contemporary appraisal and <unk> = = = 
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- The contemporary reviews of " Ulysses " were positive and found no irony in the poem . Author John Sterling — like Tennyson a member of the Cambridge Apostles — wrote in the Quarterly Review in 1842 , " How superior is ' Ulysses ' ! There is in this work a delightful epic tone , and a clear impassioned wisdom quietly carving its sage words and graceful figures on pale but lasting marble . " Tennyson 's 1842 volume of poetry impressed Scottish writer Thomas Carlyle . Quoting three lines of " Ulysses " in an 1842 letter to Tennyson — 
- — Carlyle remarked , " These lines do not make me <unk> , but there is in me what would fill whole <unk> as I read . " 
- English theologian Richard Holt Hutton summarized the poem as Tennyson 's " friendly picture of the <unk> <unk> for new experience , enterprise , and adventure , when under the control of a luminous reason and a self @-@ controlled will . " The contemporary poet Matthew Arnold was early in observing the narrative irony of the poem : he found Ulysses ' speech " the least plain , the most un @-@ Homeric , which can possibly be conceived . Homer presents his thought to you just as it wells from the source of his mind : Mr. Tennyson carefully <unk> his thought before he will part with it . Hence comes ... a heightened and elaborate air . " 
- Despite the critical acclaim " Ulysses " received , its rise within the Tennyson canon took decades . Tennyson did not usually select it for publication in poetry anthologies ; in teaching anthologies , however , the poem was usually included — and it remains a popular teaching poem today . Its current prominence in Tennyson 's <unk> is the result of two trends , according to Tennyson scholar Matthew Rowlinson : the rise of formal English poetry studies in the late nineteenth century , and the Victorian effort to articulate a British culture that could be exported . He argues that " Ulysses " forms part of the prehistory of imperialism — a term that only appeared in the language in 1851 . The protagonist sounds like a " colonial administrator " , and his reference to seeking a newer world ( 57 ) echoes the phrase " New World " , which became common during the Renaissance . While " Ulysses " cannot be read as overtly <unk> , Tennyson 's later work as Poet Laureate sometimes argues for the value of Britain 's colonies , or was accused of <unk> . Rowlinson <unk> the Marxist theorist Louis <unk> 's extension of the argument that ideology is <unk> , finding that Tennyson 's poem " comes before an ideological construction for which it nonetheless makes people nostalgic " . 
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- = = = Literary and cultural legacy = = = 
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- In a 1929 essay , T. S. Eliot called " Ulysses " a " perfect poem " . An <unk> of Ulysses is found in Eliot 's " <unk> " ( 1920 ) . Both poems are narrated by an aged man contemplating life 's end . An excerpt from " <unk> " reads as an ironic comment on the introductory lines of " Ulysses " : 
- The Italian poet Giovanni <unk> ( 1855 – 1912 ) stated that his long lyric poem L <unk> <unk> was an attempt to reconcile the portrayals of Ulysses in Dante and Tennyson with <unk> 's prophecy that Ulysses would die " a mild death off the sea " . <unk> 's Ulysses leaves Ithaca to <unk> his epic voyage rather than begin another . 
- " Ulysses " remains much admired , even as the twentieth century brought new interpretations of the poem . Professor of literature Basil <unk> commented in 1956 , " In ' Ulysses ' the sense that he must press on and not <unk> in idleness is expressed <unk> , through the classical story , and not <unk> as his own experience . [ Tennyson ] comes here as near perfection in the grand manner as he ever did ; the poem is flawless in tone from beginning to end ; spare , grave , free from excessive decoration , and full of firmly controlled feeling . " In the fifteenth edition of Bartlett 's <unk> <unk> ( 1980 ) , nine sections of " Ulysses " , comprising 36 of the poem 's 70 lines , are quoted , compared to only six in the ninth edition ( 1891 ) . 
- Many readers have accepted the acclaimed last lines of " Ulysses " as inspirational . The poem 's ending line has also been used as a motto by schools and other organisations . U.S. Senator Robert Francis Kennedy quoted the three last lines at the end of his speech " On the <unk> <unk> of Violence " in America a day after the assassination of Martin Luther King . The final line is inscribed on a cross at Observation Hill , Antarctica , to commemorate explorer Robert Falcon Scott and his party , who died on their return trek from the South Pole in 1912 : 
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- 
- = The Food Album = 
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- The Food Album is a compilation album by American singer @-@ songwriter " Weird Al " Yankovic , released on June 22 , 1993 by Scotti Brothers Records . The release features ten of Yankovic 's song parodies , all of which <unk> to food . A similar album , The TV Album , which features songs entirely about television , would be released two years later . 
- The album was begrudgingly released by Yankovic , who felt that the compilation was unnecessary and merely a way for his record label to make money . Several food @-@ related songs that Yankovic had recorded , such as " Girls Just Want to Have Lunch " and " Waffle King " were left off the record , although the former was due to personal preference , while the latter was due to scheduling issues . 
- The Food Album received mixed reviews from music critics , many of whom felt that the record was an enjoyable collection of songs , but that it was not an essential record to purchase . Despite the lukewarm reception , the record was certified Gold by the Recording Industry Association of America ( RIAA ) , making it Yankovic 's first and only compilation record to reach this certification . 
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- = = Production = = 
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- = = = Music = = = 
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- The music featured on The Food Album spans a decade , with the earliest songs being recorded in 1982 , and the most recent song being recorded in 1992 . Yankovic 's first eponymous album has two songs featured : " I Love Rocky Road " and " My Bologna " . Both " Eat It " and " Theme from Rocky XIII ( The Rye or the Kaiser ) " were culled from the 1984 release , " Weird Al " Yankovic in 3 @-@ D. " Addicted to Spuds " originally appeared on the 1986 release Polka Party ! , and " Fat " and " <unk> " were first featured on Yankovic 's 1988 release Even <unk> . " <unk> " first was released on the soundtrack to the 1989 film UHF . The final two songs — " The White <unk> " and " <unk> Grande " — were taken from the 1992 album Off the Deep End . 
- Notable for its absence is " Girls Just Want to Have Lunch " , from Dare to Be Stupid ( 1985 ) , Yankovic 's only previously released food @-@ related song not to make the album . According to Yankovic , this is due to the fact there is a " royalty ceiling " on the albums and he needed to pick one song to cut from the list in order to turn a profit on the album . " Girls Just Want to Have Lunch " was chosen due to Yankovic 's personal dislike of the song , as his record label had forced him to record it in order to release Dare to Be Stupid back in 1985 . Also absent from the release is " Waffle King . " The song had originally been recorded for Off the Deep End . However , Yankovic decided to swap " Waffle King " with " I Was Only <unk> " — a song he had actually recorded for his next album — at the last minute ; this forced Yankovic to <unk> " Waffle King " for the time being . The song was later released on " <unk> Like Nirvana " single , as well as Yankovic 's eighth studio album , <unk> , which was released four months after The Food Album . 
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- = = = Release = = = 
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- The album was released by Scotti Brothers Records and was only begrudgingly approved by Yankovic . At the time , Scotti Brothers had insisted on putting out a new album by Yankovic in order to meet monetary projections for the fiscal quarter , despite the fact that no new album was ready ; <unk> would not be released until later in the year . The original concept was to release a record entitled Al Unplugged , which would have featured a cover depicting Yankovic holding the <unk> for <unk> kitchen appliances , but instead of being a live album featuring live performances , it would have featured studio remixes of previously released material , with the electronic instruments missing . Yankovic convinced them to release The Food Album instead — " a concept [ he ] hated only slightly less " — but would later describe it as a " cheesy compilation " put out " against [ his ] better wishes and judgement . " 
- The TV Album was released under similar circumstances in 1995 ; however , when it came time to release the latter album , Yankovic reported that " the record company was a whole lot <unk> when they asked the second time " , and that there was " more <unk> [ and ] less demanding " . Following the release of The Food Album and The TV Album — in addition to the various greatest hits records that had been released — Scotti Brothers used @-@ up all of their compilation options in Yankovic 's contract , which prevented the release of further compilations when <unk> Records acquired his contract in the late 1990s . 
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- = = = <unk> = = = 
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- The album artwork — which features a cartoon alien after it has eaten Yankovic — was created by Doug Lawrence , who is better known as " Mr. Lawrence " , an American voice actor , comedian , writer , storyboard artist , animator and director . The " grotesque " cover was Yankovic 's " passive @-@ aggressive protest " against his label for forcing out the album ; Yankovic intended the alien having " picked the <unk> corpse of Weird Al clean " to be a reference to his record label " bleed [ ing ] his catalogue dry " by releasing the album . The Japanese release of the album , however , featured much different artwork , as well as a name change ; because there is no " F " in the Japanese language , the album was retitled The Hood . 
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- = = Reception = = 
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- = = = Critical response = = = 
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- The Food Album has received mixed reviews from most critics ; many felt that while the album was amusing it was not an essential release . Allmusic reviewer Johnny <unk> awarded the album three out of five stars and wrote that , " The Food Album is an enjoyable bag of treats . Just don 't eat too much , or you 'll probably get sick . " Likewise , The Rolling Stone Album Guide awarded the album three out of five stars . Anthony <unk> of The Buffalo News gave the album a moderately positive review and wrote that , " [ t ] here are two kinds of people in the world : those who love Weird Al Yankovic and those who can 't stand him . Count me among the Weird One 's biggest fans , and that 's why I flipped out when listening to The Food Album . " He concluded that the album was " like reading Mad magazine " ; he gave the record three stars out of five . Tim <unk> of the Press @-@ <unk> , on the other hand , wrote negatively of the album , stating " Yankovic 's songs are the kinds of things that are sort of funny in concept , less funny when you actually hear them once , and increasingly irritating with each subsequent listen [ and ] his food songs are among his worst . " 
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- = = = Commercial performance = = = 
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- Upon release , The Food Album failed to chart ; however , it sold steadily . On January 25 , 2006 — more than ten years after its release — the album was certified Gold by the Recording Industry Association of America ( RIAA ) . This makes it Yankovic 's first and only compilation album to sell over 500 @,@ 000 copies and be certified Gold . 
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- = = Track listing = = 
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- = = Certifications = = 
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- = Patriarchal Cathedral of the Holy Ascension of God = 
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- The Patriarchal Cathedral of the Holy Ascension of God ( Bulgarian : <unk> <unk> „ <unk> <unk> <unk> “ , <unk> <unk> „ <unk> <unk> <unk> “ ) is a former Eastern Orthodox cathedral in the city of <unk> Tarnovo , in north central Bulgaria . Located on top of the fortified Tsarevets hill in the former capital of the Second Bulgarian Empire , the cathedral was the seat of the Bulgarian patriarch from its construction in the 11th – 12th century to its destruction in <unk> . 
- Standing on top of a late Roman church , the cathedral , reconstructed in the 1970s and 1980s , follows a cross @-@ domed plan with a bell tower and a triple apse . <unk> decorated on both the exterior and interior , its internal walls now feature modern frescoes , the presence of which has meant that it has not been <unk> . Though not active as a Christian place of worship , it has been open for visitors since 1985 . 
- 
- = = History = = 
- 
- The Patriarchal Cathedral of the Holy Ascension of God is not the first church building to occupy the position on top of the Tsarevets hill . It was constructed directly on top of a late Roman ( early Byzantine ) basilica which dates to the 5th – 6th century AD . The Roman basilica may have remained in use by the local congregation during the First Bulgarian Empire , though it was no longer active by the time the construction of the current church began . 
- The current building of the Patriarchal Cathedral is considered by scholars to have been built in two stages . The first stage of construction was carried out in the late 11th century or the 12th century . The cathedral was initially built as a monastery church in the middle of a monastery compound , though in the early 12th century it was already the seat of the Bulgarian patriarch . The compound suffered large @-@ scale damage caused by a fire , which necessitated the church 's reconstruction in middle of the 14th century , perhaps during the rule of Tsar Ivan Alexander of Bulgaria ( r . 1331 – 71 ) . Besides repair and reinforcement efforts , work on the church in the 14th century also included the construction of the <unk> and the bell tower . 
- There are several references to the cathedral in medieval sources . The earliest reference to the church tells of the transfer of Saint Michael the Warrior 's relics from the <unk> fortress to the Patriarchal Cathedral on the order of Tsar <unk> ( r . <unk> – 1207 ) . The housing of a warrior saint 's relics in the Patriarchal Cathedral signifies the incessant warfare against <unk> and <unk> that dominated <unk> 's reign . In the late 14th century , the last Patriarch of Tarnovo , Saint <unk> , described the church as the " great patriarch 's Cathedral of the Holy Ascension " in his writings . 
- Another possible reference to the church may be in a marginal note from <unk> to a copy of the Acts of the Apostles . In the note , the <unk> , one <unk> , thanks God and the " Holy and Most Glorious Ascension " for having finished his work on the book . Scholar <unk> <unk> believes this to be an allusion to the Patriarchal Cathedral , which may have patronised the project . Alternatively , the copy could have been made at the cathedral 's <unk> , where <unk> may have worked . 
- The church is also depicted in the medieval sketch of Tarnovo in the <unk> <unk> , a <unk> service book written in the mid @-@ 14th century and then carried to <unk> ( now <unk> , Romania ) after the fall of Bulgaria under Ottoman rule . 
- The Patriarchal Cathedral was destroyed after the Ottomans captured the Bulgarian capital after their Siege of Tarnovo on 17 July <unk> . The church was fully reconstructed in the 20th century ; reconstruction works were carried out by a team under architect <unk> <unk> . These commenced in 1978 and were finished in 1981 , to mark Bulgaria 's <unk> anniversary . However , it was not until November 1985 , when the contemporary murals were finished , that the church was opened once again for visitors . The church 's ruins have been protected as a national antiquity since 1927 ; in 1967 , they were proclaimed an architectural monument of culture of national importance . As part of the Tsarevets architectural reserve , it is also listed among the 100 Tourist Sites of Bulgaria . 
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- = = Location and architecture = = 
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- The Patriarchal Cathedral of the Holy Ascension of God is located on top of the Tsarevets hill , overlooking the modern city of <unk> Tarnovo . The church was part of a group of buildings which constituted the seat of the Bulgarian <unk> and acted as the city and the country 's main cathedral . The <unk> on Tsarevets was a fortress of its own , with two defensive towers and an entrance on its west wall . The Patriarchal Cathedral stood in the middle of its courtyard . 
- The Patriarchal Cathedral features a triple apse , the central part of which matches the apse of the original basilica on the site . The three @-@ <unk> church follows the traditional Byzantine cross @-@ in @-@ square design . Built out of crushed stones and mortar with limited brickwork , it measures 26 by 12 metres ( 85 ft × 39 ft ) . The cathedral includes two <unk> , a bell tower and two other premises attached to the south church wall . The presence of a bell tower is considered to be a rarity in Balkan church architecture . Six columns support the interior and distinguish the altar from the cella ( <unk> ) . It is unclear whether the church housed a <unk> ( stone <unk> for the clergy ) in the apse , as there are doubts that its remains may actually be part of the older basilica . 
- The church featured ample exterior and interior decoration . While the facades were decorated with arches and ceramic tiles , the interior floor mosaics were made of white , yellow and pink marble as well as semi @-@ precious <unk> like <unk> and <unk> . The interior walls were covered with frescoes and mosaics . However , none of the interior decoration has survived . During the church 's 20th @-@ century reconstruction , its interior was <unk> by artist <unk> <unk> , who depicted important moments of medieval Bulgarian history in a modernist style . Due to these murals , the church has never been <unk> and remains inactive . The facade of the cathedral also includes a stone with a donor 's inscription of a Bulgarian ruler , which ended up as part of the building material . 
- There are a total of four burial grounds in and around the church , two of which are burials for priests . One of the burial grounds is inside the <unk> , where <unk> tombs were built in the 14th century . Besides Michael the Warrior 's relics , the cathedral also housed the remains of Bulgarian <unk> Joachim I , <unk> and Joachim III . 
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- 
- = Daydream ( Mariah Carey album ) = 
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- Daydream is the fifth studio album by American singer and songwriter Mariah Carey , released on October 3 , 1995 , by Columbia Records . The follow @-@ up to her internationally successful album Music Box ( 1993 ) and holiday album Merry Christmas ( 1994 ) , Daydream differed from the two by leaning increasingly towards R & B and hip hop . Throughout the project , Carey collaborated with Walter Afanasieff , with whom she wrote and produced most of her two previous albums . With Daydream , Carey took more control over the musical direction as well as the album 's composition . Carey said she considered Daydream the beginning of her musical and vocal transformation , a change that became more apparent in her sixth album Butterfly ( 1997 ) . During the album 's production , Carey endured many creative differences with her label and husband Tommy Mottola . 
- On Daydream , Carey collaborated with Jermaine Dupri for the first time , and co @-@ wrote and produced a song with Kenneth " Babyface " Edmonds , with whom she had collaborated on Music Box . It was also the first time she had worked with Boyz II Men , an R & B group consisting of four male vocalists . Together , they wrote the concept and lyrics for " One Sweet Day , " a song that Carey co @-@ produced with Afanasieff . With his assistance and the addition of a few contemporary producers , she was able to make a subtle transition into the R & B market . Daydream was nominated for six Grammy Awards at the 38th annual ceremony , during which Carey performed live . Due to the album 's critical and commercial success , critics believed Carey would be one of the night 's big winners . However , to her dismay , she was completely shut out , causing the subject to become very public and controversial . She left the annual ceremony empty @-@ handed . 
- Six singles were released from the album . Its lead single " Fantasy " became the first single by a female artist to debut at number one on the US Billboard Hot 100 and went on to top the chart for eight weeks and became the second best @-@ selling single of 1995 in the country . The song topped the charts in Australia , Canada , and New Zealand and became a top @-@ five hit in Finland , France , and the United Kingdom . The second single " One Sweet Day " topped the Billboard Hot 100 for sixteen weeks and became the longest @-@ running number one single in American history , a record it still holds . It also topped the charts in Canada and New Zealand and peaked within the top five in Australia , France , Ireland and the Netherlands . <unk> , the singles from Daydream spent a combined six months at the top of the Hot 100 . To promote Daydream , Carey embarked on the short but successful , Daydream World Tour , visiting Japan and Europe . 
- At the time of its release , Daydream became Carey 's best @-@ reviewed album . Critics universally praised her matured lyrics and songwriting , as well as her musical direction . The album became an international success , debuting at number one in nine different countries , and in the top five in almost every major music market . Daydream became Carey 's second album to be certified diamond by the Recording Industry Association of America ( RIAA ) , denoting shipments of ten million copies in the United States . Aside from its success in the United States , the album made the top five of the best @-@ selling albums in Japan by a non @-@ Asian artist , with 2 @.@ 1 million copies sold . Daydream remains one of the best @-@ selling albums of all time , with 25 million copies sold worldwide . 
- 
- = = Background = = 
- 
- Aside from being Carey 's second highest worldwide seller , Daydream served as her most personal and directly influenced album at the time . During the album 's recording , Carey grew as an artist , as well as a writer . For the first time in her career , Carey was able to make music that she truly related to , R & B and hip hop . While Columbia allowed Carey more <unk> with the music she recorded , they became hesitant when she featured <unk> ' Dirty # # # # # # # in the remix for " Fantasy . " They feared the sudden change was completely left field for her music , and worried it would jeopardize the album 's success . In an interview with Entertainment Weekly , Carey openly spoke of her issues with Columbia : " Everybody was like ' What , are you crazy ? ' They 're nervous about breaking the formula . It works to have me sing a ballad on stage in a long dress with my hair up . " 
- While Carey 's new musical direction caused tension between her and Columbia , it began to severely strain her relationship with her husband at the time , Tommy Mottola . Mottola had always been involved in Carey 's career , because he was the head of Sony Music , the parent company of her label . Since the time of Carey 's debut , Mottola had controlled nearly every aspect of her career , keeping her sound carefully regulated and insisting that she continue recording middle @-@ of @-@ the @-@ road pop music , despite her interest in hip hop . 
- Carey confessed that she never tried to change the situation because " [ she ] used to be <unk> and cautious , and so [ she ] would listen to what the people said . " However , the control Mottola exerted over her career soon " spilled into her personal life " once they were married , increasing the amount of conflict between the two . Soon , it was obvious that their marriage was in <unk> ; as stated in a Vanity Fair article , " the couple began to argue at the drop of a hat . " Carey was very involved in the project , more so than she had ever been on an album . " I went into this phase of recording , recording , recording and doing it really fast , " she told Time . " This time , I had more time , and I focused more on what I wanted to do . " As Carey 's career and work continued to reflect her views on how it should sound , her marriage to Mottola continued to " deteriorate . " 
- 
- = = Conception and composition = = 
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- One of the first songs that was recorded for the album was " Fantasy . " While Carey began developing new ideas for Daydream , she thought of the song " Genius of Love " by Tom Tom Club . She had always been a fan of the song , and presented Dave Hall with the idea of sampling the song 's hook . Hall incorporated a groove that he felt complimented Carey 's voice , while she composed some of the other beats and wrote the lyrics . Carey recorded a remix to the song as well , featuring hip @-@ hop verses from O.D.B of the Wu @-@ Tang Clan , as well as production from <unk> . She spoke highly of the remix , complimenting <unk> and O.D.B , " He 's so known in the street , and he 's one of the best people out there ... we kind of did what we both do and having O.D.B took it to another level . He was my ultimate choice , so I was really happy with the way it turned out . " " One Sweet Day " was a song that Carey wrote with the R & B group Boyz II Men . After Carey 's friend and past collaborator David Cole died , she began writing and developing a song that would pay homage to him and all the friends and family her fans had lost along life 's journey . Carey had the chorus and concept composed , and after meeting with Boyz II Men , they realized they too had a similar idea in development . Together , using Carey 's chorus and idea , as well as the melody they had produced , they wrote and composed the song . It was produced by Afanasieff , who built on the song 's melody and added various grooves and beats . Carey expressed how the song was " meant to be " and how all the pieces fit into place : 
- I wrote the initial idea for ' One Sweet Day ' with Walter , and I had the chorus ... and I stopped and said , ' I really wanna do this with Boyz II Men , ' because ... obviously I 'm a big fan of theirs and I just thought that the work was crying out for them , the vocals that they do , so I put it away and said , ' Who knows if this could ever happen , but I just don 't wanna finish this song because I want it to be our song if we ever do it together . [ The ] whole idea of when you lose people that are close to you , it changes your life and changes your perspective . When they came into the studio , I played them the idea for the song and when [ it ] finished , they looked at each other , a bit stunned , and told me that Nat " Nathan Morris " had written a song for his road manager who had passed away . It had basically the same lyrics and fit over the same chord changes . It was really , really weird , we finished the song right then and there . We were all <unk> flipped about it ourselves . Fate had a lot to do with that . I know some people won 't believe it , but we wouldn 't make up such a crazy story . 
- While the album 's development was underway , Carey expressed interest in working with Jermaine Dupri , whom she had been a fan of since his 1992 song , " Jump . " Soon after , Carey , Dupri , and Manuel Seal began composing a song for the album . As Seal played the piano , Carey began <unk> and playing with certain notes in the B @-@ section , until she came up with the chorus for " Always Be My Baby . " After the rest of the song was written and composed , Carey recorded the song alongside longtime background singers Kelly Price , <unk> Price , and <unk> Daniels . Together , they built " a wall of background voices " in which she would cover with her final belting notes . The song featured a <unk> rhythm , while its composition was described as " sassy and soft R & B " which displayed a " sexy and slow jam . " " Underneath the Stars " was the first song recorded for Daydream . The song featured a " ' 70s soul vibe " as well as synthetic record <unk> , in order to the give the song an authentic ' 70s sound . Carey felt the additions were simple steps taken to further display a contemporary R & B groove . Additionally , she felt the song paid homage to the style of Minnie <unk> , who was one of Carey 's biggest vocal influences growing up . The song had a soft sound , and had " a lot [ sic ] of texture " and bass , showing a more creative side to Carey . 
- For the album , Carey covered the 1982 Journey song " Open Arms . " The song was of Carey 's personal choice , as well as her own idea . Together with Afanasieff , they toned down the song 's arrangement , making it a bit glossy , especially in comparison to the " raw and powerful ' One Sweet Day . ' " Additionally , with the help of her background singers , Carey added a touch of gospel to the song . One of the more gospel @-@ influenced songs on the album was " I Am Free . " The song was created by Carey , Afanasieff and <unk> Holland , with whom she had worked previously on Merry Christmas . Carey began <unk> the melody with the lyrics she had already written , while Holland played the organ and Afanasieff worked on the song 's programming . , giving the song a genuine and <unk> gospel feel . The chorus was sophisticated and natural , with each following line " cascading onto one another , " something that would have proved difficult for a " less skilled vocalist . " Carey started leaning away from the " standard <unk> Dion ballad " and more towards R & B jams . However , she was not going to completely abandon the type of songs that made her famous . For this reason , Carey wrote " When I Saw You " with Afanasieff , a song that would truly embody some of her earlier work , as well as show off her powerful vocals . Returning to her R & B territory , Carey recorded " Long <unk> " , the second song she wrote alongside Dupri and Seal which contains a strong hip hop background . Her vocals in the song were described as " <unk> over the <unk> bassline like silk . " 
- " Melt Away " was a song Carey produced on her own , and co @-@ wrote with Babyface . The song 's writing and production were " superb . " with each verse gliding into its chorus . According to Chris <unk> , " Underneath the Stars " was as " strong as any slow jam released in the <unk> , and one that would find a lot of flavor late at night with dancers . " Another song that brought back <unk> of older decades was " Forever . " The <unk> was featured through the chord changes and in the way the guitar arpeggios " stayed at the forefront of the music . " The song displayed subtle vocals from Carey , as well as an undeniable <unk> . " Daydream Interlude ( Sweet Fantasy Dub Mix ) " was one of the <unk> tracks on the album . The song was a club remix of " Fantasy " , which was tuned and remixed by famed house music producer David <unk> . The song was directed to be a dance @-@ club song , further broadening Carey 's " musical horizon . " The song incorporated Carey 's vocals , and added them to a thumping house beat , something he would do for many of her future singles . " Looking In " was the final song on the album . It was Carey 's most personal song at the time , one in which she let herself appear " naked " and " stripped down ; it was written by her and Afanasieff . " According to author Chris <unk> : 
- " [ The song ] reflected on her life now , the changes she 'd gone through , and the difference between the public perception of Mariah Carey and the real person . Intimate and revealing , it made an appropriate end to the album , and was evident that Mariah was growing , changing , and becoming much more herself , confident of who she was and what she could do . " 
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- = = Critical reception = = 
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- Daydream received universal acclaim at the time of its release . Reviews applauded the little changes of style from previous Carey releases , some of whom adding that it is her best record , while others dismissed it as <unk> and lacking of originality . AllMusic 's senior editor , Stephen Thomas Erlewine , awarded the album four and a half out of five stars . Erlewine called Daydream her " best record yet " and wrote , " Mariah Carey certainly knows how to construct an album . <unk> herself directly between urban R & B with tracks like " Fantasy , " and the adult contemporary radio format with songs like " One Sweet Day , " a duet with Boyz II Men , Carey appeals to both audiences equally because of the sheer amount of craft and hard work she puts into her albums . Daydream is her best record to date , featuring a consistently strong selection of songs and a remarkably impassioned performance by Carey . Daydream demonstrates that Carey continues to perfect her craft , and that she has earned her status as an R & B / pop diva . " In his review for the album , Ken Tucker from Entertainment Weekly called Daydream " her best record since her 1990 debut " , writing , " in fact , it 's easily the best collection Carey has put out since her self @-@ titled 1990 debut , the album Daydream most resembles in its emphasis on R & B grooves . " Tucker specifically complimented " One Sweet Day " , " Always Be My Baby " , " Forever " , and " Daydream Interlude " ( Fantasy Sweet Dub Mix ) , writing " One Sweet Day , her collaboration with Boyz II Men , radiates a <unk> <unk> that Carey , for all the brazen <unk> of her public persona , rarely permits herself to reveal in song . I like the relaxed swing of " Always Be My Baby " , and the brisk <unk> tempo of Forever . However , it 's on what many Carey fans will probably find the most <unk> cut , " Daydream Interlude ( Fantasy Sweet Dub Mix ) " , that the singer really defines herself . At her best , as she is on this <unk> , <unk> track , Carey is a disco diva for the ' 90s , a worthy successor to <unk> women like <unk> Summer and Vicki Sue Robinson , R & B singers with an affinity for the endless groove . " 
- Stephen Holden , editor of The New York Times , gave the album a positive review . Holden wrote the following regarding Daydream " Ms. Carey 's songwriting has taken a leap forward , becoming more relaxed , <unk> , and less reliant on <unk> <unk> . " Holden praised " Fantasy " , which he wrote " with ' Fantasy , ' Ms. Carey <unk> confidently into the territory where gospel @-@ flavored pop @-@ soul meets light hip @-@ hop and recorded some of the most gorgeously spun choral music to be found on a contemporary album . " Additionally , he complimented " One Sweet Day , " " Melt Away , " " Always Be My Baby " , and " Underneath the Stars " , calling them " the best on the album " . People gave the album a positive review , calling it " her fourth and best album . " Additionally , People praised the album and its songs , writing " Daydream vaults over its pop predecessors because the material is both <unk> and <unk> . Carey also has better control of her instrument — her voice <unk> greater <unk> and agility . She still <unk> it on a little thick at times when it comes to fervor , as on the <unk> ' Melt Away , ' which Carey co @-@ wrote with Babyface . For the most part she <unk> from strength to strength , from the <unk> belting on ' One Sweet Day , ' a duet with Boyz II Men , to the rich gospel feel of ' I Am Free , ' which has a mood so <unk> you can almost hear the ladies ' handheld fans snapping . " While the album was positively reviewed by critics , Carey 's cover of Journey 's 1982 song " Open Arms " was universally panned . Stephen Thomas Erlewine criticized the song , calling it " second rate " . " Open Arms " received a negative review from Stephen Holden as well , who called it a " sobbing remake " . 
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- = = Chart performance = = 
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- Daydream entered the Billboard 200 at number one , with 224 @,@ 000 copies sold , staying at the top spot the following week with 216 @,@ 000 copies sold , for a third consecutive week , it topped the charts with 170 @,@ 000 copies sold . It gained power again in the upcoming weeks of holiday sales where it peaked in the year 's last weeks with 486 @,@ 000 and 760 @,@ 000 units sold at the pinnacle . The album moved 760 @,@ 000 copies during the Christmas week of 1995 , the album 's highest sales week . It also reached number one on Top R & B / Hip @-@ Hop Albums chart . Daydream was the second best @-@ selling album of 1996 , and the eighteenth best @-@ selling album on the 1990s decade in the US . In the United States , Daydream became Carey 's best @-@ selling album , being certified diamond by the RIAA ( RIAA ) , denoting shipments of ten million copies . 
- In Canada , Daydream peaked at number two on the charts , and was certified seven @-@ times platinum by the Canadian Recording Industry Association ( CRIA ) . The album experienced success in Europe , where it reached number one in Germany , The Netherlands , Switzerland and the United Kingdom . In France , Daydream peaked at number two and was certified double @-@ platinum by the <unk> National de l <unk> <unk> ( <unk> ) . Sales in France are estimated at 800 @,@ 000 copies . Daydream was certified triple @-@ platinum by the International Federation of the Phonographic Industry ( IFPI ) , denoting shipments of three million copies throughout Europe in 1996 . 
- In Australia , Daydream was certified five @-@ times platinum by the Australian Recording Industry Association ( ARIA ) , denoting shipments of 350 @,@ 000 copies . The album finished ninth on the ARIA End of Year Charts in both 1995 and 1996 . In Japan , the album debuted at number one on the Oricon charts . According to the Oricon , Daydream made the top five of the best @-@ selling albums in Japan by a non @-@ Asian artist , with 2 @.@ 5 million copies sold . Daydream remains one of the best @-@ selling albums of all time , with sales of 25 million copies worldwide . 
- 
- = = Singles = = 
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- Six singles were released from Daydream . " Fantasy " was released as the album 's lead single on September 12 , 1995 . The song debuted at number one on the Billboard Hot 100 , making Carey the first female artist , and the second performer ever to accomplish the feat . The song spent eight consecutive weeks atop the chart , as well as topping the charts in Australia , Canada and New Zealand . In Europe , " Fantasy " performed well , peaking within the top five in Belgium , Finland , France , and the United Kingdom . " Fantasy " was the second best @-@ selling single of 1995 in the US , with sales of 1 @.@ 5 million in 95 ' alone . " One Sweet Day " was chosen as the follow @-@ up single , achieving similar success . The song once again debuted at the top of the US charts , and became the longest running number one single in US history , spending sixteen consecutive weeks atop the Billboard charts . The song became a success in other regions around the world as well , topping the charts in Canada and New Zealand and peaked within the top five in Australia , France , Ireland , and The Netherlands . Serving as the album 's third single in select European countries , " Open Arms " was released on December 5 , 1995 . The song achieved success in the UK , Ireland and New Zealand , where it peaked at number four , seven and eight . However , " Open Arms " charted weakly in other European countries , such as Belgium , France , and Germany , where it charted outside the top @-@ thirty . 
- " Always Be My Baby " was released as the fourth single . The song debuted at number two on the Hot 100 , failing to become Carey 's third number one debut ( a feat she would accomplish with " <unk> " in 1997 ) . The song eventually reached and stayed atop the charts for two weeks , and then kept steady at number two for nine weeks . " Always Be My Baby " performed moderately in other major markets . The song peaked within the top five in Canada , New Zealand and the UK , but charted outside the top @-@ ten elsewhere . " Forever " was chosen as the fifth single from Daydream . The song was ineligible to chart in the Hot 100 , but managed to crack the top @-@ ten on the Hot 100 airplay chart . " Forever " charted well in Canada , where it peaked at number thirteen . " Underneath the Stars " was chosen as the sixth and final single from the album . Described by Carey as one of her favorite songs , " Underneath the Stars " received a limited number of pressings in the US , where it charted weakly on the Billboard Hot R & B / Hip @-@ Hop Songs . 
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- = = Promotion = = 
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- In order to promote the album , Carey embarked on her second head @-@ lining tour . Originally , she had not planned to tour , due to the long travel times and <unk> ; however , after many requests from fans , Carey agreed to tour . The tour reached Japan and select European countries , not visiting the United States . This was possibly due to the mixed reception Carey 's 1993 North American Music Box Tour received three years prior . The shows were all spaced apart , giving Carey time to rest her vocals , " It 's very strenuous to sing all my songs back to back , but I 'm actually really looking forward to it . " Many musicians joined Carey for the tour , including Randy Jackson who served as the musical director and played the bass , Dan Shea on the keyboards , Vernon Black playing the guitar , <unk> Conway on the drums , and percussion and music sequencing by Peter Michael and Gary <unk> . All of the musicians and background vocalists were under the supervision of Walter Afanasieff , who played the piano and guided the production . Before embarking on her world tour in 1996 , Carey performed a sold @-@ out show at Madison Square Garden in 1995 . The performance was filmed , and released as a DVD titled Fantasy : Mariah Carey at Madison Square Garden . It became Carey 's fourth video release . 
- When the three Japanese shows at the Tokyo <unk> went on sale , Carey set a record after all 150 @,@ 000 tickets sold out under three hours . The shows became the fastest sellout in the stadium 's history , breaking the previous record held by The Rolling Stones . The shows in Japan were a critical and commercial success , with critics and fans raving about the show and Carey 's vocals . Carey 's presence in Asia in the 90s was unparalleled to any other international artist . Her international success and anticipation was even compared to the " <unk> " in the 1960s . In an interview with MTV , Carey spoke of how she felt performing in Asia : 
- " First of all , you 're in front of so many people that basically don 't speak your language . It took a little getting used to , but I think by the end of the show , you know , everybody started to kind of <unk> . " 
- For the show , Carey sang fourteen original songs , including many of her biggest hits up until that point , as well as many songs from Daydream . They included " Fantasy , " " One Sweet Day , " " Open Arms , " " Always Be My Baby , " " Forever " and " Underneath the Stars " as well as hits from her previous studio efforts . Carey 's following shows in France , Germany , The Netherlands and the United Kingdom were all sold @-@ out as well , receiving warm critical response . During the span of the tour 's seven short dates , Carey had already begun working on concepts for her new album Butterfly . According to author Marc <unk> , Carey 's European tour was truly a success , in many aspects : 
- " The European tour mirrored the success of her shows in Japan . Mariah 's appearances overseas were <unk> experiences for the singer . The popularity – indeed , <unk> – surrounding her shows in these countries reached massive proportions . The reception the concerts received reflected the fact that Mariah 's music cut through race and language barriers and had struck at a universal , emotional core with fans . The tour cemented the fact that Mariah Carey had arrived as the performing centerpiece on the world stage . " 
- In addition to touring the world , Carey performed on a variety of television programs and award shows . After " Fantasy " was released in September throughout Europe , Carey performed the song on the popular British chart show Top of the Pops , which aired live via satellite on Asian television . Carey performed " Fantasy " in France and at the 23rd Annual American Music Awards on January 29 , 1996 . " One Sweet Day " was performed at the 38th Annual Grammy Awards , Princess Diana 's memorial service in September 1997 , and at Carey 's Black Entertainment Television Christmas special in 2001 . During her European promotional tour for the album , Carey performed " Open Arms " on various television programs , including <unk> , <unk> .. ? in Germany , Top of the Pops and Des O 'Connor in the United Kingdom and on Swedish television . 
- 
- = = Awards and accolades = = 
- 
- The music industry took note of Carey 's success . She won two awards at the 1996 American Music Awards for her solo efforts : Favorite Pop / Rock Female Artist and Favorite Soul / R & B Female Artist . Throughout 1995 & 1996 , Carey was awarded various prestigious awards at the World Music Awards , including " World 's Best Selling Female R & B Artist " , " World 's Best Selling Overall Female Recording Artist , " " World 's Best Selling Pop Artist " and " World 's Best Selling Overall Recording Artist . " Additionally , " Fantasy " was named " Song of the Year " at the BMI Awards and " Favorite Song " at the <unk> Entertainment Awards , where Carey also won the award for " Top Pop Female . " In 1996 , Carey won many awards at the Billboard Music Awards , including " Hot 100 Singles Artist of the Year " , " Hot 100 Airplay ( Always Be My Baby ) , " " Hot Adult Contemporary Artist of the Year " and " Special Award for 16 weeks at # 1 for ' One Sweet Day . ' " 
- 
- = = = Grammy controversy = = = 
- 
- Daydream was proven to be one of the best @-@ selling and most acclaimed albums of 1995 . When the Grammy Award nominees were announced , and Daydream was nominated for six different awards , critics began raving how it would be " cleaning up " that year . The 38th Annual Grammy Awards were held on February 28 , 1996 at the Shrine <unk> in Los Angeles . Carey , being a multiple award nominee , was one of the headlining performers . Together with Boyz II Men , she sang a live rendition of " One Sweet Day , " to a very positive response . However , as the award winners were announced one by one , Carey watched as her name was not called up even once . Daydream had lost all of its six nominations , shocking most critics who branded it the " album of the year " . With every passing loss , the television cameras continued to <unk> on Carey 's face , who was finding it more difficult to retain her smile . By the end of the night , Carey had not won a single award . The disappointment on her face was <unk> obvious . While Carey was nominated again the following year , she did not perform again until the 2006 ceremony , when she was nominated for eight awards ( winning three ) for The <unk> of Mimi . 
- 
- = = Music videos = = 
- 
- Carey directed the music video for " Fantasy " . Additionally , she chose the concept and created the idea . The video featured Carey in roller @-@ <unk> , <unk> through a theme @-@ park , while enjoying different rides and roller coasters . The video then cut into scenes of Carey dancing on top of a car , celebrating an event with many friends . Carey said her inspiration for the video was to give off a " free and open feeling , " trying to portray the freedom she had finally achieved in being allowed to direct her first video . The video for " Fantasy " debuted on September 7 , at the " MTV Video Music Awards . " Carey expressed how much she enjoyed the video 's filming , speaking about the " roller @-@ coaster scenes " : 
- " They did not expect me to get that shot ! They were saying , ' How 's she going to sing on a roller coaster ? ... We put a little speaker on the bottom of the car , where my feet where . We built the rig in front of the roller coaster and the lens kept falling off ! " 
- When Carey and Boyz II Men got together to record " One Sweet Day , " they didn 't have enough time to re @-@ unite and film a video . For this reason , a filming crew was present during the song 's recording , and filmed bits of Carey and Boyz recording the song . In an interview with Fred Bronson , Walter Afanasieff made the following statements regarding the video for " One Sweet Day " : 
- " It was crazy ! They had film crews and video guys , while I 'm at the board trying to produce . And these guys were running around having a ball , because Mariah and them are laughing and screaming and they 're being interviewed . And I 'm tapping people on the shoulder . " We 've got to get to the microphone ! " They 're gone in a couple of hours , so I recorded everything they did , praying that it was enough . " 
- The video for " Always Be My Baby " was once again filmed by Carey . It featured Carey swinging on a swing in the middle of a dark meadow , showing her <unk> through the woodlands . The video was set as a " peaceful and relaxing " setting , in order to try to reflect the sweet and mellow song 's message . " Forever " , the album 's fifth single , was the final song to be accompanied by a music video . Carey 's label used footage from her live performances of the song in New York City and Japan and compiled them into a video . On February 11 , 2012 , Carey revealed through her Twitter account that a music video for " Underneath the Stars " was actually recorded . Filming sessions occurred in England and France ; Carey commented , however , that it " never got released and I don 't know where it is ! " 
- 
- = = Track listing = = 
- 
- Notes 
- Track listing and credits from album booklet . 
- " Fantasy " contains a sample of " Genius of Love " by Tom Tom Club . 
- 
- = = Album credits = = 
- 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- 
- = Leg before wicket = 
- 
- Leg before wicket ( lbw ) is one of the ways in which a batsman can be dismissed in the sport of cricket . Following an appeal by the fielding side , the umpire may rule a batsman out lbw if the ball would have struck the wicket , but was instead intercepted by any part of the batsman 's body ( except the hand holding the bat ) . The umpire 's decision will depend on a number of criteria , including where the ball pitched , whether the ball hit in line with the wickets , and whether the batsman was attempting to hit the ball . 
- Leg before wicket first appeared in the laws of cricket in 1774 , as batsmen began to use their pads to prevent the ball hitting their wicket . Over several years , <unk> were made to clarify where the ball should pitch and to remove the element of interpreting the batsman 's intentions . The 1839 version of the law used a wording that remained in place for nearly 100 years . However , from the latter part of the 19th century , batsmen became increasingly expert at " pad @-@ play " to reduce the risk of their dismissal . Following a number of failed proposals for reform , in 1935 the law was expanded , such that batsmen could be dismissed lbw even if the ball pitched outside the line of off stump . Critics felt this change made the game unattractive as it encouraged negative tactics at the expense of leg spin bowling . 
- After considerable debate and various experiments , the law was changed again in 1972 . In an attempt to reduce pad @-@ play the new version , which is used to this day , allowed batsmen to be out lbw in some circumstances if they did not attempt to hit the ball with their bat . Since the 1990s , the availability of television replays and , later , ball @-@ tracking technology to assist umpires has increased the percentage of lbws in major matches . However , the accuracy of the technology and the consequences of its use remain controversial . 
- In his 1995 survey of cricket laws , Gerald Brodribb states : " No dismissal has produced so much argument as lbw ; it has caused trouble from its earliest days " . Owing to its complexity , the law is widely misunderstood among the general public and has proven controversial among spectators , administrators and commentators ; lbw decisions have sometimes caused crowd trouble . Since the law 's introduction , the proportion of lbw dismissals has risen steadily through the years . Statistics reveal that the probability of a batsman being dismissed lbw in a Test match varies depending on where the match is played and which teams are playing . 
- 
- = = Definition = = 
- 
- The definition of leg before wicket ( lbw ) is currently Law 36 in the Laws of Cricket , written by the Marylebone Cricket Club ( MCC ) . Before a batsman can be dismissed lbw , the fielding team must appeal to the umpire . For the batsman to be <unk> lbw , the ball , if it <unk> , must pitch in line with the wickets , or on the off side of the stumps . Then the ball must strike part of the batsman 's body , without first touching his bat , in line with the wickets and have been going on to hit the stumps . The batsman may also be out lbw if , having made no attempt to hit the ball with his bat , he is struck outside the line of off stump by a ball that would have hit the wickets . The umpire must assume that the ball would have continued on the same trajectory after striking the batsman , even if it would have bounced before hitting the stumps . However , if the bowler delivers a no ball — an illegal delivery — the batsman cannot be lbw under any circumstances . 
- A batsman can be out lbw even if the ball did not hit his leg : for example , a batsman struck on the head could be lbw . However , he cannot be lbw if the ball pitches on the leg side of the stumps ( " outside leg stump " ) , even if the ball would have otherwise hit the wickets . Similarly , a batsman who has attempted to hit the ball with his bat cannot be lbw if the ball strikes him outside the line of off stump . However , some shots in cricket , such as the switch hit or reverse sweep , involve the batsman switching between a <unk> and left @-@ handed stance ; this affects the location of the off and leg side , which are determined by the stance . The law explicitly states that the off side is determined by the batsman 's position when the bowler <unk> his run @-@ up . 
- According to MCC guidelines for umpires , factors to consider when giving an lbw decision include the angle at which the ball was travelling and whether the ball was swinging through the air . He must also account for the height of the ball at impact and how far from the wicket the batsman was standing ; from this information he must determine if the ball would have passed over the stumps or struck them . The MCC guidance states that it is easier to make a decision when the ball strikes the batsman without pitching , but that the difficulty increases when the ball has bounced and more so when there is a shorter time between the ball pitching and striking the batsman . 
- 
- = = Development of the law = = 
- 
- 
- = = = Origins = = = 
- 
- The earliest known written version of the Laws of Cricket , dating from 1744 , does not include an lbw rule . At the time , batsmen in English cricket used curved bats , which made it unlikely that they would be able to stand directly in front of the wickets . However , a clause in the 1744 laws gave umpires the power to take action if the batsman was " standing unfair to strike " . Cricket bats were modified to become <unk> over the following years , allowing batsmen to stand closer to the wickets . Subsequently , some players deliberately began to obstruct the ball from hitting the wickets . Such tactics were criticised by writers and a revision of the laws in 1774 ruled that the batsman was out if he deliberately stopped the ball from hitting the wicket with his leg . However , critics noted that the umpires were left the difficult task of interpreting the intentions of batsmen . The 1788 version of the laws no longer required the umpires to take account of the batsman 's intent ; now a batsman was lbw if he stopped a ball that " pitch [ ed ] straight " . Further clarification of the law came in 1823 , when a condition was added that " the ball must be delivered in a straight line to the wicket " . The ambiguity of the wording was highlighted when two prominent umpires disagreed over whether the ball had to travel in a straight line from the bowler to the wicket , or between the wickets at either end of the pitch . In 1839 the MCC , by then responsible for drafting the Laws of Cricket , endorsed the latter interpretation and ruled the batsman out lbw if the ball pitched in between the wickets and would have hit the stumps . 
- 
- = = = Controversy and attempted reform = = = 
- 
- In essence , the lbw law remained the same between 1839 and 1937 , despite several campaigns to have it changed . An 1863 proposal to allow a batsman to be lbw if the ball hit his body at any point between the wickets , regardless of where the ball pitched or whether it would hit the wicket at all , came to nothing . There were few complaints until the proportion of lbw dismissals in county cricket began to increase during the 1880s . Until then , batsmen used their pads only to protect their legs ; their use for any other purposes was considered <unk> , and some amateur cricketers did not wear them at all . As cricket became more organised and competitive , some batsmen began to use their pads as a second line of defence : they lined them up with the ball so that if they missed with the bat , the ball struck the pad instead of the wicket . Some players took this further ; if the delivery was not an easy one from which to score runs , they attempted no shot and allowed the ball to bounce safely off their pads . Arthur Shrewsbury was the first prominent player to use such methods , and others followed . Criticism of this practice was heightened by the increased quality and reliability of cricket pitches , which made batting easier , led to higher scores and created a perceived imbalance in the game . 
- Several proposals were made to prevent pad @-@ play . At a meeting of representatives of the main county cricket clubs in 1888 , one representative expressed the opinion that a " batsman who defended his wicket with his body instead of with his bat should be punished " . The representatives supported a motion to alter the law to state that the batsman would be out if he stopped a ball that would have hit the wicket ; in contrast to the existing wording , this took no account of where the ball pitched relative to the wickets . Further proposals included one in which the intent of the batsman was taken into account , but no laws were changed and the MCC merely issued a condemnation of the practice of using pads for defence . This reduced pad @-@ play for a short time , but when it increased again , a second <unk> by the MCC had little effect . 
- Further discussion on altering the law took place in 1899 , when several prominent cricketers supported an amendment similar to the 1888 proposal : the batsman would be out if the ball would have hit the wicket , where it pitched was irrelevant . At a Special General Meeting of the MCC in 1902 , Alfred <unk> formally proposed this amendment ; the motion was supported by 259 votes to 188 , but failed to secure the two @-@ thirds majority required to change the laws . A. G. Steel was the principal opponent of the change , as he believed it would make the task of the umpires too difficult , but he later regretted his stance . <unk> 's brother , Robert , supported the alteration and campaigned for the rest of his life to have the lbw law altered . As evidence that pad @-@ play was increasing and needed to be curtailed , he cited the growing number of wickets which were falling lbw : the proportion rose from 2 % of dismissals in 1870 to 6 % in 1890 , and 12 % in 1923 . In 1902 , the proposed new law was tried in the Minor Counties Championship , but deemed a failure . An increase in the size of the stumps was one of several other rejected proposals at this time to reduce the dominance of batsmen over bowlers . 
- 
- = = = <unk> to the law = = = 
- 
- Between 1900 and the 1930s , the number of runs scored by batsmen , and the proportion of lbw dismissals , continued to rise . <unk> grew increasingly frustrated with pad @-@ play and the extent to which batsmen refused to play shots at bowling directed outside the off stump , simply allowing it to pass by . The English fast bowler Harold Larwood responded by targeting leg stump , frequently hitting the batsman with the ball in the process . This developed into the controversial Bodyline tactics he used in Australia in 1932 – 33 . Some batsmen began to go further and preferred to kick away balls pitched outside off stump — reaching out to kick the ball instead of allowing it to hit their pads — if they presented any threat , knowing that they could not be dismissed lbw . The authorities believed these developments represented poor entertainment value . At the height of the Bodyline controversy in 1933 , Donald Bradman , the leading Australian batsman and primary target of the English bowlers , wrote to the MCC recommending an alteration of the lbw law to create more exciting games . 
- To address the problem , and <unk> the balance for bowlers , the MCC made some alterations to the laws . The size of the ball was reduced in 1927 , and that of the stumps increased in 1931 , but the changes had little effect . Between 1929 and 1933 , county authorities conducted a trial in which a batsman could be lbw if he had hit the ball onto his pads . Then , in 1935 , an experimental law was introduced in which the batsman could be dismissed lbw even if the ball pitched outside the line of off stump — in other words , a ball that turned or swung into the batsman but did not pitch in line with the wickets . However , the ball was still required to strike the batsman in line with the wickets . The umpire signalled to the scorers when he declared a batsman out under the new rule , and any such dismissal was designated " lbw ( n ) " on the <unk> . 
- Several leading batsmen opposed the new law , including the professional Herbert <unk> , known as an exponent of pad @-@ play , and amateurs Errol Holmes and Bob Wyatt . Wisden <unk> ' <unk> noted that these three improved their batting records during the 1935 season , but batsmen generally were less successful . There were also fewer drawn matches . There was an increase in the number of lbws — out of 1 @,@ 560 lbw dismissals in first @-@ class matches in 1935 , 483 were given under the amended law . Wisden judged the experiment a success and several of its opponents changed their mind by the end of the season ; batsmen soon became accustomed to the alteration . Although Australian authorities were less convinced , and did not immediately introduce the revision into domestic first @-@ class cricket , in 1937 the new rule became part of the Laws of Cricket . 
- According to Gerald Brodribb , in his survey and history of the Laws , the change produced more " <unk> " , exciting cricket but any alteration in outlook was halted by the Second World War . When the sport resumed in 1946 , batsmen were out of practice and the amended lbw law played into the hands of off spin and inswing bowlers , who began to dominate county cricket . The cricket historian Derek <unk> notes that many of these bowlers imitated the methods of Alec <unk> , an inswing bowler who was successful immediately after the war , but that the resulting cricket was <unk> to watch . The revised lbw law , and other alterations in the game in favour of the bowler , further encouraged such bowling . The new law continued to provoke debate among writers and cricketers ; many former players claimed that the alteration had caused a deterioration in batting and reduced the number of shots played on the off side . A 1963 report in The Times blamed the law for reducing the variety of bowling styles : " the change has led to a steady increase in the amount of seam and off @-@ spin bowling . Whereas in the early thirties every county had a leg spinner and an orthodox left arm spinner , leg spinners , at any rate , are now few and far between . Walk on to any of the first @-@ class grounds at any time tomorrow and the chances are that you will see the <unk> standing back and a medium pace bowler in action ... there is little doubt that the game , as a spectacle , is less attractive than it was . " Several critics , including Bob Wyatt , maintained that the lbw law should be returned to its pre @-@ 1935 wording ; he campaigned to do so until his death in 1995 . On the other hand , Bradman , in the 1950s , proposed extending the law so that batsmen could be lbw even if they were struck outside the line of off stump . An MCC study of the state of cricket , carried out in 1956 and 1957 , examined the prevalent and unpopular tactic involving off @-@ spin and inswing bowlers aiming at leg stump with fielders concentrated on the leg side . Rather than alter the lbw law to combat the problem , the MCC reduced the number of fielders allowed on the leg side . 
- 
- = = = Playing no stroke = = = 
- 
- In the 1950s and 1960s , the amount of pad @-@ play increased , owing to more difficult and unpredictable pitches that made batting much harder . Critics continued to regard this tactic as " negative and unfair " . In an effort to discourage pad @-@ play and encourage leg spin bowling , a new variant of the lbw law was introduced , initially in Australia and the West Indies in the 1969 – 70 season , then in England for 1970 . Under the re @-@ <unk> law , a batsman would be lbw if a ball destined to hit the stumps pitched in line with the wickets or " outside a batsman 's off stump and in the opinion of the umpire he made no genuine attempt to play the ball with his bat . " This revision omitted the requirement that the impact should be in line with the wickets , but meant that any batsman playing a shot could not be out if the ball pitched outside off stump , in contrast to the 1935 law . The editor of Wisden believed the change encouraged batsmen to take more risks , and had produced more attractive cricket . However , the proportion of wickets falling lbw sharply declined , and concerns were expressed in Australia . The Australian authorities proposed a <unk> to the previous law . A batsman could once more be out to a ball that pitched outside off stump , but a provision was added that " if no stroke is offered to a ball pitching outside the off @-@ stump which in the opinion of the umpire would hit the stumps , but hits the batsman on any part of his person other than the hand , then the batsman is out , even if that part of the person hit is not in line between wicket and wicket . " The difference to the 1935 rule was that the batsman could now be out even if the ball struck outside the line of off @-@ stump . This wording was adopted throughout the world , although it was not yet part of the official Laws , from 1972 and the percentage of lbws sharply increased to beyond the levels preceding the 1970 change . The MCC added the revised wording to the Laws of Cricket in 1980 ; this version of the lbw law is still used as of 2013 . 
- 
- = = = Effects of technology = = = 
- 
- Since 1993 , the proportion of lbws in each English season has risen steadily . According to cricket historian Douglas Miller , the percentage of lbw dismissals increased after broadcasters incorporated ball @-@ tracking technology such as Hawk @-@ Eye into their television coverage of matches . Miller writes : " With the passage of time and the adoption of <unk> into other sports , together with presentations demonstrating its accuracy , cricket followers seem gradually to have accepted its predictions . Replay analyses have shown that a greater proportion of balls striking an <unk> leg go on to hit the wicket than had once been expected . " He also suggests that umpires have been influenced by such evidence ; their greater understanding of which deliveries are likely to hit the stumps has made them more likely to rule out batsmen who are standing further away from the stumps . This trend is replicated in international cricket , where the increasing use of technology in reviewing decisions has altered the attitude of umpires . Spin bowlers in particular win far more appeals for lbw . However , the use of on @-@ field technology has proved controversial ; some critics regard it as more reliable than human judgement , while others believe that the umpire is better placed to make the decision . 
- The International Cricket Council ( ICC ) , responsible for running the game worldwide , conducted a trial in 2002 where lbw appeals could be referred to a match official , the third umpire , to review on television replays . The third umpire could only use technology to determine where the ball had pitched and if the batsman hit the ball with his bat . The ICC judged the experiment unsuccessful and did not pursue it . More trials followed in 2006 , although ball @-@ tracking technology remained unavailable to match officials . After a further series of trials , in 2009 the <unk> Decision Review System ( DRS ) was brought into international cricket where teams could refer the on @-@ field decisions of umpires to a third umpire who had access to television replays and technology such as ball tracking . According to the ICC 's general manager , Dave Richardson , DRS increased the frequency with which umpires awarded lbw decisions . In a 2012 interview , he said : " <unk> may have realised that if they give someone out and DRS shows it was not out , then their decision can be rectified . So they might , I suppose , have the courage of their convictions a bit more and take a less conservative approach to giving the batsman out . I think if we 're totally honest , DRS has affected the game slightly more than we thought it would . " 
- Critics of the system suggest that rules for the use of DRS have created an inconsistency of approach to lbw decisions depending on the circumstances of the referral . Opponents also doubt that the ball @-@ tracking technology used in deciding lbws is reliable enough , but the ICC state that tests have shown the system to be 100 % accurate . The Board of Control for Cricket in India ( <unk> ) have consistently declined to use DRS in matches involving India owing to their concerns regarding the ball @-@ tracking technology . Early DRS trials were conducted during India matches , and several problems arose over lbws , particularly as the equipment was not as advanced as it later became . The <unk> believe the technology is unreliable and open to manipulation . 
- 
- = = Trends and perception = = 
- 
- A study in 2011 by Douglas Miller shows that in English county cricket , the proportion of wickets to fall lbw has increased steadily since the First World War . In the 1920s , around 11 % of wickets were lbw but this rose to 14 % in the 1930s . Between 1946 and 1970 , the proportion was approximately 11 % but subsequently increased until reaching almost 19 % in the decade before 2010 . Miller also states that captains of county teams were statistically more likely to receive the benefit of lbw decisions — less likely to be out lbw when batting and more likely to dismiss batsmen lbw when bowling . For many years , county captains submitted end @-@ of @-@ match reports on the umpires ; as umpires were professionals whose careers could be affected , captains consequently received <unk> whether batting or bowling . Before 1963 , when the status was abolished in county cricket , umpires were also more lenient towards amateur cricketers . <unk> administered English cricket , and offending one could end an umpire 's career . Elsewhere in the world , lbws are more statistically likely in matches taking place on the Indian subcontinent . However , batsmen from the subcontinent were less likely to be lbw wherever they played in the world . 
- Teams that toured other countries often became frustrated by lbws given against them ; there was often an assumption of national bias by home umpires against visiting teams . Several studies investigating this perception have suggested that home batsmen are sometimes less likely than visiting batsmen to be lbw . However , the data is based on lbw decisions awarded , not on the success @-@ rate of appeals to the umpire . Fraser points out that it is impossible to determine from these studies if any of the decisions were wrong , particularly as the lbw law can have different interpretations , or if other factors such as pitch conditions and technique were involved . A 2006 study examined the effect that neutral umpires had on the rate of lbws . Although the reasons were again ambiguous , it found that lbws increased slightly under neutral umpires regardless of team or location . 
- In his survey of cricket laws , Gerald Brodribb suggests that " no dismissal has produced so much argument as lbw ; it has caused trouble from its earliest days " . Among those who do not follow cricket , the law has the reputation of being extremely difficult to understand , of equivalent complexity to association football 's offside rule . Owing to the difficulty of its interpretation , lbw is regarded by critics as the most controversial of the laws but also a <unk> by which an umpire 's abilities are judged . In his book Cricket and the Law : The Man in White Is Always Right , David Fraser writes that umpires ' lbw decisions are frequently criticised and " arguments about bias and incompetence in <unk> inform almost every discussion about lbw decisions . " Problems arise because the umpire has not only to establish what has happened but also to speculate over what might have occurred . <unk> aspects of lbw decisions include the umpire having to determine whether the ball pitched outside leg stump , and in certain circumstances whether the batsman intended to hit the ball or leave it alone . <unk> are frequently criticised for their lbw decisions by players , commentators and spectators . Historically , trouble ranging from protests and arguments to crowd demonstrations occasionally arose from disputed decisions . For example , a prolonged crowd disturbance , in which items were thrown onto the playing field and the match was delayed , took place when <unk> <unk> was <unk> lbw during a 1996 One Day International in India . 
- 
- 
- = The Family Jewels ( Marina and the Diamonds album ) = 
- 
- The Family Jewels is the debut studio album recorded by Welsh singer Marina Diamandis , professionally known as Marina and the Diamonds . It was released on 15 February 2010 by 679 Recordings and Atlantic Records . Diamandis collaborated with several producers including Pascal Gabriel , Liam Howe , Greg Kurstin , Richard " <unk> " <unk> , and <unk> during its recording . She identifies the lyrical themes as " the seduction of commercialism , modern social values , family and female sexuality . " 
- Contemporary music critics gave The Family Jewels fairly positive reviews , with the vocal delivery dividing opinions . The record debuted at number five on the UK Albums Chart with first @-@ week sales of 27 @,@ 618 copies . The album was eventually certified Gold by the British Phonographic Industry and has sold 195 @,@ 358 units in the United Kingdom . The Family Jewels performed moderately on international record charts ; it peaked at number 138 on the Billboard 200 in the United States . 
- The Family Jewels was supported by five singles , all of which were supplemented by accompanying music videos . " Mowgli 's Road " was released on 13 November 2009 , although " Hollywood " became its first charting track after reaching number 12 on the UK Singles Chart . Follow @-@ up singles " I Am Not a Robot " , " Oh No ! " , and " Shampain " respectively peaked at numbers 26 , 38 , and 141 in the United Kingdom . The record was additionally promoted by Diamandis ' headlining The Family Jewels Tour , which visited Australia , Europe and North America from January 2010 through December 2011 . 
- 
- = = Background = = 
- 
- Born and raised in South East Wales , Diamandis moved to London at the age of 18 to study music , despite not having a musical background . After dropping out of four institutions and failing in auditions , she began composing her own music . After the success of her <unk> @-@ released debut EP <unk> vs. <unk> in 2007 , she was signed by <unk> Gold Records the following year and by 679 Artists in October 2008 . In 2009 , after playing at a variety of festivals including Glastonbury in the summer , she ranked in second place in the BBC 's Sound of 2010 and was one of the three nominees for the Critics ' Choice Award at the 2010 BRIT Awards . 
- In a 2012 interview with <unk> , Diamandis said that the album 's title came from a slang term for <unk> , but she had been too coy to admit it before . 
- 
- = = Composition = = 
- 
- Diamandis explained that the album is " a body of work largely inspired by the seduction of commercialism , modern social values , family and female sexuality " , intended to be " enjoyed and consumed as a story and theory that encourages people to question themselves " . 
- In a review for Q , writer Hugh Montgomery noted genres such as disco ( " Shampain " ) , <unk> punk ( " Girls " ) and cabaret ( " Hermit The Frog " ) . The opening track , " Are You Satisfied ? " , <unk> the meaning of a fulfilling life ; a writer for The Line of Best <unk> likened it to the thinking of Danish <unk> philosopher <unk> <unk> . In a January 2010 interview with The Daily Telegraph , Diamandis admitted that she " <unk> " at the lyrics of the song " Girls " , which " could be seen as a bit <unk> " , including the lines " Girls they never befriend me / ' Cause I fall asleep when they speak / Of all the calories they eat " ; she clarified that the lyrics concerned her own psychological problems with weight . A <unk> Gold press release for a limited double A @-@ side of " Obsessions " and " Mowgli 's Road " described the former as a " bold and ambitious ... master work " and the latter as a " a high intensity , left field pop smash " . 
- Diamandis reportedly made producer Liam Howe take 486 vocal takes for " The Outsider " . " Hollywood " takes inspiration from Diamandis ' previous obsession with American celebrity culture , while in " I Am Not a Robot " , her favourite track from the album , she sings to tell herself to accept <unk> , with lines such as " you 've been acting awful tough lately , smoking a lot of cigarettes lately ... don 't be so pathetic " ; she expected audiences to be able to relate to the song . " <unk> " reflects on the dedication and sacrifice needed during her early years in London ; " Oh No ! " and " Are You Satisfied ? " have similar lyrical themes . " Oh No ! " was a late addition to the track listing , causing some reviews of the album to not include it . The album had initially been scheduled for release in October 2009 , and was delayed by Diamandis ' self @-@ confessed <unk> . 
- 
- = = Release and promotion = = 
- 
- 
- = = = Music videos = = = 
- 
- In 2008 , Diamandis filmed videos for the tracks " Seventeen " and " Obsessions " . The following year , photographer Rankin directed the accompaniment for " I Am Not a Robot " , which used much body <unk> . The video for " Mowgli 's Road " featured Diamandis and two dancers , with puppeteers standing in front of them to give them the impression of having <unk> limbs ; it was shot over 17 hours . 
- Polish artist <unk> Burza shot the " classic pop video " for " Hollywood " , with the aim to " make her audiences fall in love her even more , perhaps <unk> a little popcorn and feel inspired to dress up for fun " . Burza also filmed the video for " Oh No ! " , with an aesthetic based on " <unk> neon " MTV graphics and the fame @-@ hungry lyrics . The video to " Shampain " made an homage to Michael Jackson 's Thriller . 
- Dan Knight made a video for <unk> Gonzales ' " stripped @-@ down " remix of " Hollywood " that was intended to be the opposite of Burza 's official video . In the video , Gonzales and Diamandis perform on a 1980s <unk> music show complete with <unk> . 
- 
- = = = Singles = = = 
- 
- " Obsessions " was Diamandis ' first single , released on 14 February 2009 , and " Mowgli 's Road " followed on 13 November 2009 . She chose the song as an " <unk> " <unk> due to its <unk> , but it received attention after being shared by bloggers including <unk> Hilton and Kanye West . 
- " Hollywood " was released as the album 's second single and Diamandis ' first major release on 1 February 2010 . It reached number 12 on the UK Singles Chart . It was followed on 26 April by " I Am Not a Robot " , which peaked at number 26 on the same listing . " Oh No ! " was released as the album 's fourth single on 2 August only in the UK and Ireland ; it charted at number 38 . " Shampain " was released as the album 's fifth and final single on 11 October , again only in the same region , and reached number 141 in the UK . 
- " I Am Not a Robot " was nominated for the 2010 <unk> £ 20 Music Prize for best British single , eventually losing to " <unk> " by <unk> . 
- 
- = = = Tour = = = 
- 
- Diamandis went on her first headlining tour to promote the album , performing in Europe , North America and Australia . <unk> included the Glastonbury Festival 2010 , South by Southwest and the Falls Festival . In parallel to headlining her own tour in the United States in mid @-@ 2011 , she was an opening act for Katy Perry 's California Dreams Tour , and finished by opening for Coldplay 's <unk> <unk> Tour at the Manchester Arena that December . 
- After a performance at Manchester 's <unk> Institute on 21 February 2010 , Contactmusic.com writer Katy <unk> awarded Diamandis a 9 / 10 rating , stating " Next time she plays Manchester , it will be to a sold out Academy 2 audience , with a top @-@ selling album <unk> the merchandising stand . Marina won 't be playing to a few hundred people above a <unk> bar in the foreseeable future " . 
- 
- = = Critical reception = = 
- 
- The Family Jewels received mostly positive reviews . At Metacritic , which assigns a <unk> rating out of 100 to reviews from mainstream critics , the album received an average score of 68 , based on 21 reviews , which indicates " generally favorable reviews " . 
- Hugh Montgomery of Q magazine noted that the singer 's " imaginative reach " was " complemented by a winning pop <unk> " , while Luke O 'Neil from The Phoenix stated that " [ t ] he likes of Kate Nash and company have <unk> through this piano <unk> / <unk> dance @-@ diva territory , but never mind , because this gorgeous genre starts now . " <unk> Cooper from NME rated the album nine out of ten stars , finding flaw only in the titles of " Shampain " and " Hermit the Frog " . 
- More mixed reviews were critical of Diamandis ' vocal delivery . Lou Thomas from BBC Music commented that " [ t ] he consistently diverting changes in style across the album are fine — the <unk> 80s shoulder @-@ pad pop of ' The Outsider ' is nothing like anything else here , for example . But over 13 songs of Sparks @-@ voice and many similar staccato piano riffs listeners may feel <unk> by Marina and her slightly overbearing presence " , concluding that her eccentric vocals would <unk> opinions . Sean O <unk> wrote on The <unk> Club that after " dozens of <unk> Regina <unk> @-@ <unk> <unk> " and " Kate Bush <unk> " , the " overbearing need to prove herself just ends up being exhausting " . Joe Rivers of No <unk> praised " Are You Satisfied ? " , " Hollywood " and " Oh No ! " but was put off by sudden " <unk> " in " Hermit the Frog " and a " <unk> <unk> " in " The Outsider " . 
- A negative review came from The Independent 's Andy Gill who considered " Shampain " and " Hermit the Frog " as " every bit as annoying as their <unk> titles , with <unk> , <unk> piano and synth figures " . He found certain vocal techniques in " Mowgli 's Road " and " I Am Not a Robot " to be " <unk> " , and evaluated the lyrics of " Girls " and " Hollywood " as shallow . Gill added that the content of " <unk> " , " Obsessions " and " The Outsider " did not match with what would be expected from the titles . 
- The NME placed the album at number 33 on its list of the Top 75 Albums of 2010 . 
- 
- = = Commercial performance = = 
- 
- The Family Jewels debuted at number five on the UK Albums Chart with first @-@ week sales of 27 @,@ 618 copies . It remains Diamandis ' best @-@ selling debut week , after her second studio album <unk> Heart entered the chart at number one with first @-@ week sales of 21 @,@ 358 units . The Family Jewels was later certified gold by the British Phonographic Industry , and had sold 195 @,@ 358 copies in the United Kingdom as of April 2015 . The record debuted at number seven in Greece and number nine in Ireland ; it was eventually certified gold by the Irish Recorded Music Association . 
- The Family Jewels performed moderately on several international record charts . The record reached number 12 on the German Media Control Charts , and entered the <unk> Austria Top 40 at number 18 . It peaked at number 88 on the Dutch <unk> , number 100 on the Swiss <unk> , and number 132 in France . In Oceania , the album reached number 79 on the Australian ARIA Charts . With first @-@ week sales of 4 @,@ 000 copies in the United States , The Family Jewels entered the Billboard 200 at number 138 ; furthermore , it respectively charted at numbers 2 and 49 on the Billboard Top Heatseekers and Top Rock Albums charts . 
- In an interview for Australian radio in January 2011 , Diamandis said that her career that far had been " more like a failure than a success " , particularly in the American market . She attributed this to the inaction of <unk> Shop Records , her label in the United States , as well as a move in musical tastes to " pumping beats " by artists like Lady Gaga . She cancelled performances in the United States in order to begin work on a new album . 
- 
- = = Track listing = = 
- 
- Credits adapted from the liner notes of The Family Jewels . 
- Notes 
- ^ a signifies an additional producer 
- ^ b signifies an original producer 
- ^ c signifies a <unk> 
- 
- = = Credits and personnel = = 
- 
- Credits adapted from the liner notes of The Family Jewels . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = 1981 Peach Bowl ( January ) = 
- 
- The 1981 Peach Bowl was a post @-@ season American college football bowl game between the Virginia Tech Hokies and the Hurricanes from the University of Miami at Fulton County Stadium in Atlanta , Georgia on January 2 , 1981 . The game was the final contest of the 1980 NCAA Division I @-@ A football season for both teams , and ended in a 20 @-@ 10 victory for Miami . Another game by the same name followed the 1981 NCAA Division I @-@ A football season and was played in December , 1981 . 
- Virginia Tech was awarded a bid to the Peach Bowl as a reward for finishing 8 – 3 during the regular season , a record that included wins over nationally ranked teams such as the Clemson Tigers and the Virginia Cavaliers . Facing Virginia Tech was a familiar post @-@ season opponent — Miami — whom the Hokies had played in the 1966 Liberty Bowl , Tech 's last bowl appearance prior to the Peach Bowl . Miami finished the regular season with an 8 – 3 record , including wins over Florida State and Florida . 
- The game kicked off at 3 : 00 p.m. EST under sunny skies and in temperatures of 46 ° F ( 8 ° C ) . Unlike the 1966 Liberty Bowl , in which Virginia Tech scored first , it was Miami who dominated the game 's early going . The Hurricanes scored a touchdown on the first drive of the game and <unk> on another touchdown early in the second quarter . Tech was held scoreless in the early going , thanks to two Miami interceptions at the goal line as Virginia Tech was threatening to score . Late in the second quarter , Tech was finally able to get on the <unk> with a field goal , but at halftime , the Miami Hurricanes led 14 – 3 . After halftime , the Hokies threatened Miami for the first time all game . Virginia Tech mounted an 80 @-@ yard drive that resulted in a touchdown , cutting Miami 's lead to 14 – 10 . But the Hurricanes ' defense <unk> down on any further offensive attempts by Virginia Tech and denied the Hokies more points . Miami added two field goals : one in the third quarter and one in the fourth quarter that finally put the game out of reach for Virginia Tech . Miami 's win was its first bowl victory since the 1966 Liberty Bowl , which also featured a Hurricane victory over Virginia Tech . 
- 
- = = Team Selection = = 
- 
- 
- = = = Miami = = = 
- 
- The Miami Hurricanes came into the 1980 college football season after an 5 – 6 season in 1979 under head coach Howard Schnellenberger . During that season , the team was nicknamed the " Jet <unk> Kids " after they traveled an NCAA @-@ record 28 @,@ 000 miles ( 45 @,@ 000 km ) , including a trip to the <unk> Bowl in Tokyo , Japan . While impressive , the season wasn 't as successful as hoped , and Schnellenberger began his second season as coach hoping to improve upon the 5 – 6 effort despite what was the toughest schedule — in terms of opponents ' winning percentage — in the country . 
- Miami got off to a good start doing just that as it raced out to four consecutive wins to begin the season , including a victory over nationally ranked rival No. 9 Florida State . On October 11 , however , Miami traveled to South Bend , Indiana , to face the Notre Dame Fighting Irish and suffered its first loss of the season to the undefeated Irish , who would go on to be ranked No. 1 in the country . This defeat was followed in succession by two others , but the Hurricanes were able to stop the slide on November 8 with a homecoming win over East Carolina . Miami added two more wins before the end of the season , and brought the regular season to a close with an overall record of 8 – 3 . 
- 
- = = = Virginia Tech = = = 
- 
- The Virginia Tech Hokies , like Miami , entered the 1980 season after accumulating a 5 – 6 record in 1979 . Though the Hokies traveled far less than Miami , they also hoped to improve upon their losing record . Tech was coached by Bill Dooley , who was entering his third season as head coach . 
- Like Schnellenberger , he got his team off to a fast start . In the season opener , Tech traveled to North Carolina to face the Wake Forest Demon <unk> . Wake Forest had attended the <unk> Bowl the previous year and was favored in the game against Tech . Despite those facts , the Hokies won , 16 – 7 . Virginia Tech won its first four games of the regular season before falling to the nationally ranked Clemson Tigers on October 4 . Tech recovered from the loss , defeating Rhode Island , then Virginia before a then @-@ state record crowd of 52 @,@ 000 people , but lost a second game , to Richmond on October 25 . The Hokies split their final three games of the regular season , winning two and losing one , and finished with a regular season record of 8 – 3 . Tech 's 6 – 0 record at Lane Stadium — its home stadium — was the best in school history . Tech 's selection by the Peach Bowl was announced in the locker room following the team 's final regular season game . The Peach Bowl representative present was <unk> by celebrating players chanting " Peach Bowl ! Peach Bowl ! " The representative later said , " I 've never seen a <unk> celebration . " 
- 
- = = <unk> buildup = = 
- 
- 
- = = = Miami offense = = = 
- 
- The Miami Hurricanes entered the Peach Bowl with a record @-@ breaking offense that set the school mark for the most accumulated yards in school history with a total offensive mark of 3 @,@ <unk> yards . Leading the aerial portion of the Miami offense was sophomore quarterback Jim Kelly , who completed 109 of 206 passes for 1 @,@ <unk> yards , 11 touchdowns , and seven interceptions . Kelly 's 11 touchdowns tied the Miami record for most touchdowns in a season at that time . Kelly 's favorite receiver was wide receiver Larry Brodsky , who caught 33 passes for 570 yards and three touchdowns during the regular season . One of Brodsky 's touchdowns came on an 81 @-@ yard reception that tied the longest pass reception in the history of Miami football to that point . 
- The Hurricanes ' ground offense was led by running back Smokey Roan , who carried the ball 152 times for <unk> yards and five touchdowns . Roan was assisted by an able offensive line . Miami offensive tackle John <unk> was named as an honorable mention to the Associated Press All @-@ America team , which recognizes the best college football players in the country . 
- 
- = = = Miami defense = = = 
- 
- Miami 's defense shut out one opponent , and held six other opponents to single touchdowns or field goals . Middle guard Jim Burt was one of the Hurricanes ' defensive leaders , recovering four fumbles in a single game during the regular season . In recognition of this and other performances , Burt was named to the Associated Press All @-@ America list . Also recognized was defensive back Fred Marion , who was named an honorable mention to the All @-@ America list . Marion intercepted seven passes during the regular season , tying him for second in Miami history for the most interceptions in a season . 
- 
- = = = Virginia Tech offense = = = 
- 
- Throughout the regular season , Virginia Tech 's offense was led by running back Cyrus Lawrence , who accumulated what was then a school record of 1 @,@ 221 yards during the regular season . He also set a record for most carries by a Tech player in a season with 271 . Tech quarterback Steve Casey was called the " key man in the Tech offense . " Casey was Tech 's starting quarterback and completed 97 of 176 passes during the regular season for 1 @,@ 119 yards and 13 touchdowns . At the time , he ranked second among Tech 's career passing leaders , and was considered to be an offensive threat . 
- Casey 's favorite target was wide receiver Sidney Snell , who caught a Tech @-@ record eight touchdown receptions during the regular season . Snell accounted for 568 yards on 43 receptions of all types . <unk> end Rob Purdham only caught seven passes during the regular season , but four of the catches were for touchdowns . 
- 
- = = = Virginia Tech defense = = = 
- 
- The Tech defense was ranked among the top five in the country during the regular season , and set a school record for fewest points allowed during an 11 @-@ game regular season . Tech permitted just 109 points during the season , and allowed only 11 touchdowns in 11 games . 
- The leading <unk> on the Tech defense was freshman linebacker Ashley Lee , who accumulated 95 tackles during the regular season . Lee was one of two freshman linebackers for the Hokies during their record @-@ breaking defensive season . 
- Virginia Tech 's defense also featured Robert Brown , who accumulated 10 tackles , including two quarterback sacks , in the Hokies ' regular @-@ season finale against the Virginia Military Institute . Tech head coach Bill Dooley lobbied in vain for Brown 's inclusion in the annual All @-@ America list recording the best college football players in the country , saying , " Robert Brown is an All @-@ American football player ... but because he was a transfer student , he received no preseason buildup . ... I guarantee the people who have seen him know he 's an All @-@ American . " Brown finished the regular season with 61 tackles , broke up four passes , and recovered three fumbles . 
- 
- = = Game summary = = 
- 
- The 1981 Peach Bowl kicked off at 3 p.m. EST on January 2 , 1981 , at Fulton County Stadium in Atlanta , Georgia in front of a crowd estimated at 45 @,@ 384 people . More than 14 @,@ 000 of those present were estimated to be fans of Virginia Tech . Weather at kickoff was sunny , with a temperature of 46 ° F ( 8 ° C ) and a north @-@ northwest wind estimated at 14 miles per hour ( 23 km / h ) . The game was televised nationally on CBS , with Curt <unk> , Hank <unk> , and Frank <unk> serving as the <unk> for the television broadcast . William Parkinson was the referee , Robert <unk> was the umpire , and the linesman was Richard <unk> . Miami won the traditional <unk> coin toss used to decide first possession and elected to receive the ball to begin the game . 
- 
- = = = First quarter = = = 
- 
- After the Virginia Tech kickoff and a short return , Miami began the first drive of the game at its 32 @-@ yard line . The game 's first play was an incomplete pass from Miami quarterback Jim Kelly . After a one @-@ yard rush , Kelly completed his first pass of the game , an 18 @-@ yard toss that drove Miami to the Tech 49 @-@ yard line and gave the Hurricanes a first down . Miami then committed a 15 @-@ yard penalty , pushing the Hurricanes back into their side of the field . On the second play after the penalty , Kelly completed a 29 @-@ yard pass that gave Miami a first down . The Hurricanes were further aided by a 15 @-@ yard <unk> the passer penalty against Virginia Tech , giving Miami a first down at the Tech 20 @-@ yard line . From there , it took Miami just three plays to score a touchdown , the final play being a 15 @-@ yard pass from Kelly to Larry Brodsky . With 12 : 37 remaining in the quarter , Miami took a 7 – 0 lead . 
- Following Miami 's post @-@ touchdown kickoff , Virginia Tech began its first offensive possession of the game at its 24 @-@ yard line . A three @-@ yard rush from fullback Scott Dovel was followed by two rushes from Tech 's Cyrus Lawrence : one for eight yards and a second for 17 more . These drove Tech into Miami territory and gave the Hokies a first down . Once there , however , Miami 's defense stiffened and Tech was forced to punt . Miami recovered the ball at its 12 @-@ yard line , and the Hurricanes began their second possession of the game . <unk> Chris Hobbs and running back Smokey Roan alternated carries , picking up yardage and first downs before entering Virginia Tech territory . On the Hurricanes ' first play on Tech 's side of the field , however , Miami committed two penalties , pushing the Hurricanes back 20 yards . Following the penalties , Miami was unable to pick up a first down and punted back to the Hokies . 
- Tech recovered the punt at its 30 @-@ yard line and began its second possession . Lawrence rushed for five yards , and Miami committed a five @-@ yard <unk> penalty , giving Tech a first down by penalty . Dovel and Lawrence then combined for another first down after two plays . Tech was unable to gain another first down and punted the ball away . Attempting to field the ball , Miami 's Fred Marion fumbled the ball , which was recovered by a Tech defender at the Miami 25 @-@ yard line . Despite beginning with excellent field position , Tech was unable to score . On the first play after the fumble recovery , Tech attempted to run a trick play involving a pass by Lawrence . The pass was intercepted by a Miami defender at the Hurricanes ' one @-@ yard line , and Miami 's offense returned to the field . 
- From their one @-@ yard line , the Hurricanes ran a short rush up the middle , then Kelly connected on a 28 @-@ yard pass to tight end Mark Cooper for a first down . Three short rushes by fullback <unk> Neal resulted in 12 yards and a first down . With time running out in the quarter , Kelly attempted and completed a 27 @-@ yard pass to wide receiver Rocky <unk> , driving the Hurricanes to the Tech 30 @-@ yard line and bringing the quarter to an end . Miami led , 7 – 0 at the end of the first quarter . 
- 
- = = = Second quarter = = = 
- 
- Miami began the second quarter in possession of the ball and facing a first down at the Virginia Tech 30 @-@ yard line . From there , it took Miami just four plays to score . Roan ran for seven yards , Kelly completed an 11 @-@ yard pass , threw an incomplete pass , then Hobbs ran 12 yards for the touchdown . The score and following extra point gave Miami a 14 – 0 lead with 13 : 47 remaining before halftime . 
- Virginia Tech received the post @-@ touchdown kickoff and was promptly penalized 10 yards for an illegal block during the kickoff . Despite the initial setback , Tech made good the penalty with two passes from quarterback Steve Casey . After gaining one first down , the Hokies gained several more with a combination of passes from Casey and rushes from Lawrence . Tech drove into Miami territory and penetrated the Hurricanes ' red zone , in the process gaining a first down after facing a fourth down near midfield . Attempting to pass for a touchdown , however , Casey threw an interception at the goal line to a Miami defender . The Hurricanes thus again denied Tech a scoring opportunity and the Miami offense began <unk> . 
- The Hurricanes picked up a first down on one rush each from Hobbs and Roan , but were unable to gain another . After a Miami punt , Tech returned to offense from its 46 @-@ yard line . Casey picked up a first down on a pass , then gained another after a seven @-@ yard scramble that followed a three @-@ yard rush by Lawrence . Lawrence then gained a first down on his own after rushing for nine yards and five yards , driving the Hokies to the Miami 15 @-@ yard line in the process . After entering the Miami red zone , however , Tech was unable to gain a first down and Casey was sacked for a loss of 13 yards . Facing a fourth down and needing 20 yards for a first down , Tech coach Bill Dooley sent in kicker Dennis <unk> to attempt a 42 @-@ yard field goal . The kick was successful , and with 29 seconds left in the first half , Tech cut Miami 's lead to 14 – 3 . 
- Miami was penalized 15 yards for <unk> the kicker during the field goal attempt , allowing Tech to kick off from the Miami 45 @-@ yard line following the score . Instead of kicking off to Miami , Tech attempted an <unk> kick , which would allow Tech to retain possession if the ball was recovered by the kicking team after traveling 10 yards from the point at which it was kicked . The ball did not travel the needed 10 yards , however , and Miami began offense at its 37 @-@ yard line , where the ball rolled out of bounds . On Miami 's first play after the kick , Kelly attempted a long pass into Tech territory , but the ball was intercepted by Tech 's Mike <unk> at the Hokies ' 12 @-@ yard line . With just 21 seconds remaining in the first half , Tech attempted to gain quick yardage in an effort to close within field goal distance . Though Lawrence picked up 15 yards and a first with a rush , the Hokies were unable to enter the Miami side of the field before time expired . At the end of the first half , Miami still held a 14 – 3 lead . 
- 
- = = = Third quarter = = = 
- 
- Because Miami received the ball to begin the game , Virginia Tech received the ball to begin the second half . After Miami 's kickoff and a touchback , Tech began the first possession of the second half at its 20 @-@ yard line . The Hokies picked up a quick first down off a rush each by Lawrence , Dovel , and Casey . Lawrence picked up another with two rushes that resulted in 13 yards and drove the Hokies to their 44 @-@ yard line . There , Casey completed his longest pass of the game — a 42 @-@ yard throw to tight end Rob Purdham — that gave Tech a first down at the Miami 14 @-@ yard line . Tech continued to drive , picking up short yardage with multiple rushes . The Hokies suffered a near @-@ disaster when Lawrence fumbled the ball , but Tech retained possession when the ball rolled out of bounds at the one @-@ yard line . Two plays after the fumble , Lawrence vaulted over the goal line for Tech 's first and only touchdown of the game . Following the extra point , Tech cut Miami 's lead to 14 – 10 with 8 : 52 remaining in the quarter . 
- Following the Tech kickoff , Miami began a drive at its 20 @-@ yard line and went three and out . Following the Hurricanes ' punt , Tech 's offense began work at its 22 @-@ yard line . Despite having a chance to take the lead with a successful drive , the Hokies also went three and out , punting back to Miami and allowing the Hurricanes to begin a possession at their 33 @-@ yard line . This possession was more successful than Miami 's first of the second half . Kelly ran for four yards , then completed a six @-@ yard pass for a first down . Tech committed a 15 @-@ yard grabbing @-@ the @-@ <unk> penalty , and Kelly completed a 15 @-@ yard pass that pushed the Miami offense to the Tech 22 @-@ yard line . Young picked up 12 yards and a first down with a rush to the left , but Kelly was sacked for a loss of 13 yards , <unk> the gain . Unable to gain another first down or a touchdown , Miami was forced to settle for a field goal attempt . The 31 @-@ yard attempt was good , and Miami expanded its lead to 17 – 10 with 29 seconds remaining in the quarter . 
- Tech received Miami 's kickoff for a touchback , and the Hokies ' offense began work at the Tech 20 @-@ yard line . Lawrence ran for six yards , and time ran out in the quarter . With one quarter remaining , Miami held a 17 – 10 lead . 
- 
- = = = Fourth quarter = = = 
- 
- Tech began the fourth quarter in possession of the ball and facing a second down at its 26 @-@ yard line . Despite needing just another four yards for a first down , Tech was unable to gain the needed yardage and was forced to punt . During the kick , Miami was penalized five yards , and began its first possession of the fourth quarter at its 18 @-@ yard line after the penalty . Kelly completed an 18 @-@ yard pass for a first down , but Miami was unable to gain another . After punting to Tech , the Hokies were likewise unable to gain a first down and went three and out after committing a delay of game penalty . 
- Tech 's punt was short , and Miami began a possession at the Tech 41 @-@ yard line . The Hurricanes picked up a first down with two rushes by Roan for a total of 14 yards . Despite further short gains by Roan , the Hurricanes were stopped short by the Tech defense and were unable to gain another . Miami sent in kicker Dan Miller , who kicked a 37 @-@ yard field goal to give Miami a 20 – 10 lead with 6 : 27 remaining in the game . 
- Tech received Miami 's post @-@ score kickoff needing to score quickly in order to have a chance to have a second opportunity on offense — needed because Tech was now two scores behind . Tech returned the kickoff to the 24 @-@ yard line but were penalized 12 yards for an illegal block . Casey passed for an eight @-@ yard gain and ran for three yards for a first down . After that gain , things went against the Hokies . Casey was penalized 15 yards for intentional <unk> , and Tech was unable to gain another first down . The Hokies punted , and Miami took over on offense at its 46 @-@ yard line . In possession of the lead , Miami began to run out the clock , executing multiple rushing plays in succession in order to force the game clock to continue to count down . Tech 's defense forced a stop , but because Miami punted the ball with just 2 : 20 remaining in the game , there was little chance that Tech would be able to make up the needed two scores . 
- Tech received the ball at its 16 @-@ yard line , and Casey completed a quick 14 @-@ yard pass to Purdham for a first down . But Tech was unable to gain another first down , and after four plays were stopped short , Tech turned the ball over to Miami after Casey threw an incomplete pass on fourth down . Miami received the ball with 1 : 24 remaining and ran a series of <unk> plays to draw down the clock and bring the game to an end . Miami earned the win , 20 – 10 . 
- 
- = = Statistical summary = = 
- 
- In recognition of their performances during the Peach Bowl , Miami quarterback Jim Kelly and nose guard Jim Burt were named the game 's offensive and defensive most valuable players of the game , respectively . Kelly finished the game having completed 11 of his 22 pass attempts for 179 yards , one touchdown , and one interception . Burt , the other MVP , accumulated nine tackles — the second @-@ most of any player in the game — including seven <unk> tackles and one tackle for loss . 
- Virginia Tech running back Cyrus Lawrence finished the game with 27 carries for 137 rushing yards and a touchdown . The 27 carries remain the most ever recorded by a single Virginia Tech player in a bowl game . Tech quarterback Steve Casey led the Hokies in passing yardage , completing nine of his 23 pass attempts for 119 yards and one interception . 
- The Hurricanes ' ground offense was led by Smokey Roan , who carried the ball 16 times for 86 yards . Second to Roan for Miami was Chris Hobbs , who contributed 66 yards and a touchdown on 10 carries . Hurricanes wide receiver Larry Brodsky finished with four catches for 80 yards and a touchdown . Miami turned the ball over four times during the course of the game . Tech 's 80 @-@ yard touchdown drive in the third quarter remains tied for the longest ever recorded by a Tech offense during a bowl game . Conversely , the 99 @-@ yard drive allowed to Miami remains the longest scoring drive Tech 's defense has ever allowed in a bowl game . 
- On defense , Virginia Tech linebacker Ashley Lee 's 15 tackles remains the most ever recorded by a Tech defender in a bowl game . Three Tech players were tied for second on the team with eight tackles . Behind defensive MVP Burt , Miami had one player with eight tackles and two with seven . Fred Marion , one of the Hurricanes with seven tackles , also intercepted a Tech pass and broke up another pass . 
- 
- = = <unk> effects = = 
- 
- In exchange for their participation , each team received $ <unk> @,@ 389 . Miami 's win brought the Hurricanes to a final record of 9 – 3 for the 1980 college football season . Likewise , the loss dropped Virginia Tech to a final record of 8 – 4 . Miami 's win also was its first bowl @-@ game victory since a similar win over Tech in the 1966 Liberty Bowl . 
- The 1980 Peach Bowl victory is sometimes cited as the turning point in the Miami football program , as the Hurricanes went 9 – 2 and 7 – 4 over the next two season before participating in the 1984 Orange Bowl , the championship game of the 1983 college football season . Tech also participated in a 1984 bowl game ; the 1984 Independence Bowl , which followed the 1984 college football season . The Hokies did not win a bowl game until the 1986 Peach Bowl ; however , the game had lasting effects on Virginia Tech 's football recruiting efforts . <unk> star players Bruce Smith and Jesse Penn signed letters of intent with Virginia Tech following the game . 
- Miami had a handful of players selected in the 1981 NFL Draft : <unk> John <unk> was picked with the 101st overall selection , Jim <unk> with the <unk> pick , and Pat Walker with the <unk> pick . Miami quarterback Jim Kelly , a sophomore during the 1981 Peach Bowl , was selected in the first round of the 1983 NFL Draft and went on to become a member of the Pro Football Hall of Fame in recognition of his performance during 10 seasons with the Buffalo Bills . In 2002 , Kelly was named to the Peach Bowl Hall of Fame in honor of his performance in the 1980 game . 
- 
- = = Later aftermath = = 
- 
- Miami and Virginia Tech would both be charter members of the Big East Conference 's football league in 1991 , and in 2004 , both schools moved to the Atlantic Coast Conference - which now has a tie @-@ in for what is now called the Chick @-@ <unk> A Bowl . 
- 
- 
- = The Magdalen Reading = 
- 
- The Magdalen Reading is one of three surviving fragments of a large mid @-@ 15th @-@ century oil on panel altarpiece by the Early Netherlandish painter Rogier van der Weyden . The panel , originally oak , was completed some time between 1435 and 1438 and has been in the National Gallery , London since 1860 . It shows a woman with the pale skin , high cheek bones and oval eyelids typical of the <unk> portraits of noble women of the period . She is identifiable as the Magdalen from the jar of <unk> placed in the foreground , which is her traditional attribute in Christian art . She is presented as completely absorbed in her reading , a model of the contemplative life , repentant and <unk> of past sins . In Catholic tradition the Magdalen was <unk> with both Mary of Bethany who <unk> the feet of Jesus with oil and the unnamed " sinner " of Luke 7 : 36 – 50 . Iconography of the Magdalen commonly shows her with a book , in a moment of reflection , in tears , or with eyes averted . Van der Weyden pays close attention to detail in many passages , in particular the folds and cloth of the woman 's dress , the rock crystal of the rosary beads held by the figure standing over her , and the <unk> of the exterior . 
- The background of the painting had been overpainted with a thick layer of brown paint . A cleaning between 1955 and 1956 revealed the figure standing behind the Magdalene and the kneeling figure with its bare foot protruding in front of her , with a landscape visible through a window . The two partially seen figures are both cut off at the edges of the London panel . The figure above her has been identified as belonging to a fragment in the <unk> <unk> Gulbenkian , Lisbon , which shows the head of Saint Joseph , while another Lisbon fragment , showing what is believed to be Saint Catherine of Alexandria , is thought to be from the same larger work . The original altarpiece was a <unk> <unk> , known only through a drawing , Virgin and Child with Saints , in Stockholm 's <unk> , which followed a partial copy of the painting that probably dated from the late 16th century . The drawing shows that The Magdalen occupied the lower right @-@ hand corner of the altarpiece . The Lisbon fragments are each a third of the size of The Magdalen , which measures 62 @.@ 2 cm × 54 @.@ 4 cm ( 24 @.@ 5 in × 21 @.@ 4 in ) . 
- Although internationally successful in his lifetime , van der Weyden fell from view during the 17th century , and was not rediscovered until the early 19th century . The Magdalen Reading can first be traced to an 1811 sale . After passing through the hands of a number of dealers in the Netherlands , the panel was purchased by the National Gallery , London , in 1860 from a collector in Paris . It is described by art historian Lorne Campbell as " one of the great masterpieces of 15th @-@ century art and among van der Weyden 's most important early works . " 
- 
- = = Description = = 
- 
- Mary Magdalene as depicted in early Renaissance painting is a composite of various biblical figures . Here , she is based on Mary of Bethany , who is identified as the Magdalene in the Roman Catholic tradition . Mary of Bethany sat at Jesus ' feet and " listened to His Word " , and thus is seen as a contemplative figure . The counterpoint is Mary 's sister Martha who , representative of the active life , wished that Mary would help her serve . Mary is shown by van der Weyden as youthful , sitting in quiet piety with her head <unk> and eyes <unk> averted from the viewer . She is absorbed in her reading of a holy book , the covers of which include a <unk> of white cloth , a common form of protective binding . Four coloured cloth <unk> are tied to a gold bar near the top of the spine . According to Lorne Campbell , the manuscript " looks rather like a 13th @-@ century French Bible " and is " clearly a devotional text " . It was rare for contemporary portraits to show women reading , and if the model herself could read then she was likely from a noble family . 
- Van der Weyden often linked form and meaning , and in this fragment the semicircular outline of the Magdalene reinforces her quiet detachment from her surroundings . She is seated on a red cushion and rests her back against a wooden <unk> . By her feet is her usual attribute of an alabaster jar ; in the <unk> she brought spices to the tomb of Jesus . The view through the window is of a distant canal , with an archer atop the garden wall and a figure walking on the other side of the water , whose reflection shows in the water . 
- Van der Weyden 's pose for the Magdalene is similar to a number of female religious figures painted by his master Robert Campin or his workshop . It closely resembles , in theme and tone , the figure of Saint Barbara in Campin 's <unk> Altarpiece , and also the Virgin in an <unk> attributed to Campin in Brussels . Typically for a van der Weyden , the Magdalen 's face has an almost sculpted look , and the elements of her clothes are conveyed in minute detail . She wears a green robe ; in medieval art the Magdalene is usually depicted naked ( sometimes clad only in her long hair ) or in richly coloured dress , typically red , blue or green , almost never in white . Her robe is tightly pulled below her bust by a blue sash , while the gold <unk> of her <unk> is adorned by a jewelled <unk> . Art critic Charles <unk> observed that the Magdalen 's past as a " fallen woman " is hinted at by the <unk> in the fur lining of her dress and the few strands of hair loose from her veil . <unk> wrote , " Even her fingers , absent @-@ <unk> <unk> , suggest <unk> . In her mix of purity and <unk> , van der Weyden 's Magdalen feels whole ; but she isn 't . " In the medieval period , fur symbolized female sexuality and was commonly associated with the Magdalene . Medieval historian Philip <unk> explains that artists such as <unk> and <unk> often portrayed the Magdalen in furs and notes that she " is noticeably dressed in fur @-@ lined garments in The Magdalen Reading by Rogier van der Weyden " . 
- The level of detail used in portraying the Magdalene has been described by Campbell as " far exceed [ ing ] " van Eyck . Her lips are painted with a shades of <unk> , white and red which are mixed into each other to give a transparent look at the edges . The fur lining of her dress is painted in a range of greys running from almost pure white to pure black . Rogier gave the fur a textured look by painting stripes parallel to the line of the dress and then <unk> the paint before it dried . The gold on the cloth is rendered with a variety of <unk> , grid and dots of varying colour and size . 
- Many of the objects around her are also closely detailed , in particular the wooden floor and nails , the folds of the Magdalene 's dress , the costume of the figures in the exterior and the beads of Joseph 's rosary . The effect of falling light is closely studied ; Joseph 's crystal rosary beads have bright highlights , while subtle <unk> of light and shade can be seen in the <unk> 's tracery and in the <unk> of her book . Mary is absorbed in her reading and seemingly unaware of her surroundings . Van der Weyden has given her a quiet dignity although he is generally seen as the more emotional of the master Netherlandish painters of the era , in particular when contrasted with Jan van Eyck . 
- Lorne Campbell describes the tiny figure of the woman seen through the window and her reflection in the water as " small miracles of painting " , and says that " the attention to detail far exceeds that of Jan van Eyck and the skill of execution is <unk> " . He notes that these tiny details would have been impossible for a viewer to observe when the altarpiece was in its intended position . Other areas of the panel , however , have been described as dull and <unk> . One critic wrote that the areas of the floor and most of the cupboard behind her seem unfinished and " much too narrow and <unk> in effect " . A number of objects placed on the cupboard are now barely visible save for their bases . The object on the right seated on legs alongside a box is likely a small pitcher , possibly a reliquary . A moulding to the left of the cupboard may represent a doorway . 
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- = = Altarpiece fragment = = 
- 
- Virgin and Child with Saints , a drawing in Stockholm 's <unk> , is believed to be a study of a portion of the original altarpiece by a follower of van der Weyden , who possibly may have been the Master of the <unk> <unk> . The drawing has a loosely <unk> background and shows , from left to right : an unidentified bishop saint with <unk> and <unk> making a blessing gesture ; a narrow gap with a few wavy vertical lines suggesting a start at the outline of a further kneeling figure ; a barefoot bearded figure in a rough robe identified as Saint John the Baptist ; a seated Virgin holding on her lap the Christ Child who leans to the right , looking at a book ; and holding the book , a kneeling <unk> male identified as John the Evangelist . The drawing stops at the end of John 's robe , at about the point on the London panel where Joseph 's walking stick meets John and the Magdalene 's robes . This suggests that the Magdalene panel was the first to be cut from the larger work . 
- At an unknown point before 1811 , the original altarpiece was broken into at least three pieces , possibly due to damage , although The Magdalen fragment is in good condition . The black <unk> was likely added after the early 17th century when Netherlandish painting had fallen from favour and was <unk> . Campbell believes that after the removal of the background detail " it looked sufficiently like a genre piece to hang in a well @-@ known collection of Dutch seventeenth @-@ century paintings " . From the size of three surviving panels in relation to the drawing , it is estimated that the original was at least 1 m high by 1 @.@ 5 m wide ; the bishop and the Magdalene seem to clearly mark the horizontal <unk> , but the extent of the picture above and below the surviving elements and the drawing cannot be judged . Such a size is comparable with smaller altarpieces of the period . The background was overpainted with a thick layer of black / brown pigment until it was cleaned in 1955 ; it was only after the layer 's removal that it was linked to the upper body and head of Joseph from the Lisbon piece . These two works were not recorded in inventory until 1907 , when they appear in the collection of <unk> <unk> in <unk> , France . 
- The London panel shows much of the clothing of two other figures from the original altarpiece . To the left of the Magdalene is the red robe of what appears to be a kneeling figure . The figure and robe , and less precisely the background , match a kneeling Saint John the Evangelist . Behind the Magdalen is a standing figure in blue and red robes , with linear rosary beads in one hand and a walking stick in the other . A panel at the <unk> <unk> Gulbenkian in Lisbon shows the head of a figure believed to be the Saint Joseph ; the background and clothes match with those of the figure behind the Magdalen on the London panel . 
- There is a further small panel in Lisbon of a female head , richly or <unk> dressed , which first appeared in 1907 with the Joseph panel when it was recorded in the inventory of Leo <unk> at <unk> . The figure may represent Saint Catherine of Alexandria , and from both the angle of her cloth and the fact that the river behind her would be parallel to that in the exterior of the London panel it can be assumed that she was kneeling . In the Stockholm drawing she is omitted , or only traces of her dress shown . The Joseph panel has a <unk> of a view through a window to an exterior scene ; if the other female is presumed to be kneeling , the trees above the waterway <unk> with those in the London panel . Some art historians , including Martin Davies and John Ward , have been slow to allow the Catherine panel as part of the altarpiece , though it is undoubtedly by van der Weyden or a near @-@ contemporary follower . Evidence against this link includes the fact that the moulding of the window to the left of the Gulbenkian female saint is plain , while that next to Saint Joseph is <unk> . Such an inconsistency in a single van der Weyden work is unusual . The panels are of equal thickness ( 1 @.@ 3 cm ) and of near @-@ identical size ; the Saint Catherine panel measures 18 @.@ 6 cm × 21 @.@ 7 cm ( 7 @.@ 3 in × 8 @.@ 5 in ) , the Saint Joseph 18 @.@ 2 cm × 21 cm ( 7 @.@ 2 in × 8 @.@ 3 in ) . 
- Lorne Campbell thinks that though the Catherine head is " obviously less well drawn and less successfully painted than the Magdalen " , it " seems likely " that all three fragments came from the same original work ; he points out that " about half way up the right edge of this fragment [ " Catherine " ] is a small triangle of red , outlined by a continuous <unk> <unk> ... It is likely that the red is part of the <unk> of the missing figure of the Baptist " . The small piece is on the outermost edge of the panel , and only visible when it was removed from the frame . Ward believes the piece corresponds directly with the folds of John 's robes . 
- The Stockholm drawing contains a narrow blank gap to the right of the bishop with a few <unk> lines that could represent the lower profile of the kneeling figure of Saint Catherine . Although none of the faces in the three surviving panels match any in the drawing , a 1971 reconstruction by art historian John Ward — which combined all of the works into a composition of a central Virgin and Child flanked by six saints — is widely accepted . The Stockholm drawing 's original location or history before the 19th century is unknown , except that the <unk> shows a surviving carving of the Virgin and Child attributed to a Brussels workshop from about 1440 . This carving is also now in Portugal . 
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- = = Iconography = = 
- 
- Van der Weyden 's depiction of the Magdalen is based on Mary of Bethany , identified by the time of Pope Gregory I as the repentant prostitute of Luke 7 : 36 – 50 . She then became associated with weeping and reading : Christ 's mercy causes the eyes of the sinner to be <unk> or tearful . Early Renaissance artists often conveyed this idea by portraying contemplative eyes , associating tears with words , and in turn weeping with reading . Examples can be seen in 16th @-@ century works by <unk> and <unk> which show the Magdalen reading , often with her eyes averted towards her book ( and presumably away from a male gaze ) , or looking up to the heavens or , sometimes , glancing <unk> towards the viewer . Writing in " The Crying Face " , <unk> <unk> explains that in van der Weyden 's time the gesture of <unk> or concealing the eyes became a " pictorial formula for crying " . 
- By the medieval period , reading became synonymous with devotion , which involved withdrawal from public view . Van der Weyden 's placement of the Magdalen in an interior scene reflects the increasing literacy of domestic or <unk> in the mid @-@ 15th century . The increased production of devotional texts showed that noble women of the period routinely read texts such as a psalter or book of hours in the privacy of their homes . Whether the Magdalen herself was a reader , by the 17th century she was firmly established as such in the visual arts . Because the Magdalen was present at Christ 's death and subsequent resurrection , she was seen as the bearer of news — a witness — and hence directly associated with the text . 
- The Magdalen imagery further draws on the idea of Christ as the word , represented by a book , with the Magdalen as the reader learning of her own life story in a moment of reflection and repentance . Her devotion to reading reflects her traditional status as the <unk> repentant <unk> , as well as a <unk> or seer . According to legend , the Magdalen lived the last 30 years of her life as a <unk> in <unk> @-@ <unk> and is often shown with a book , reading or writing , symbolizing her later years of contemplation and repentance . By the 13th century she acquired the imagery of a once @-@ <unk> woman who , clothed in long hair , now hid her nakedness in exile and " borne by angels , floats between heaven and earth " . 
- The Magdalen 's <unk> jar was common in the lexicon of art in van der Weyden 's period . Mary of Bethany may have used a jar when she <unk> of her sins at Christ 's feet in her home ; by the Renaissance , the image of the Magdalen was of the woman who <unk> Christ 's feet with her tears and dried them with her hair . She signified the " sacrament of <unk> ( <unk> and <unk> ) " by pouring precious <unk> on Christ 's feet at his tomb . 
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- = = <unk> and <unk> = = 
- 
- The altarpiece 's date is uncertain but believed to be between 1435 and 1438 . Van der Weyden was made painter to the city of Brussels in 1435 , and it is believed to have been painted after this appointment . The National Gallery gives " before 1438 " . Art historian John Ward notes that the altarpiece was one of van der Weyden 's first masterpieces , created early in his career when he was still heavily influenced by Robert Campin . He proposes a c . <unk> date based on similarities to Campin 's <unk> Altarpiece . 
- Because van der Weyden , like most of the early Netherlandish painters , was not rediscovered until the early 19th century , many of his works were wrongly attributed or dated , and major pieces such as the Berlin <unk> Altarpiece continue to emerge . Conversely , when a number of pieces considered either by van der Weyden or assistants under his supervision were cleaned in the <unk> to late 20th century , his hand or direct influence was <unk> , or in the case of the Magdalen , associated with other images whose attribution had been uncertain . 
- The Magdalen Reading can first be traced to an 1811 sale of the estate of Cassino , a little @-@ known collector in <unk> , when the work was already cut down . The painting is recorded in the inventory of <unk> <unk> , also of <unk> . After passing to the <unk> brothers , who were leading dealers in art of the early Netherlandish period , it moved to the collector Edmond <unk> in Paris , whose " small but choice " collection of early Netherlandish paintings was purchased for the National Gallery , London by Charles Lock <unk> in 1860 ; an acquisition that also included two Robert Campin portraits and panels by Simon <unk> ( 1425 – 1489 ) . This was during a period of acquisition intended to establish the international prestige of the gallery . Probably before 1811 , all the background except the red robe on the left and the alabaster jar and <unk> was overpainted in plain brown , which was not removed until the cleaning begun in 1955 . In general the " painted surface is in very good condition " , although better in the parts that were not overpainted , and there are a few small losses . 
- The Magdalen Reading was transferred from its original oak to a mahogany panel ( West Indian <unk> ) by unknown craftsmen sometime between 1828 and when the National Gallery acquired it in 1860 . Campbell states that the transfer was " Certainly after 1828 , probably after 1845 , and certainly before 1860 " , the year it was acquired by the National Gallery . Artificial <unk> @-@ coloured paint found in the transfer ground indicates that the change of panel took place after 1830 . The heads in Lisbon are still on their original oak panels . The Stockholm drawing was discovered in a German inventory c . 1916 and is likely of Swedish origin . It was bequeathed by a Norwegian collector , Christian <unk> , to the Swedish National Museum of Fine Arts in 1918 . 
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- = = Gallery = = 
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- 
- = Rosemary 's Baby ( 30 Rock ) = 
- 
- " Rosemary 's Baby " is the fourth episode of the second season of 30 Rock , and the twenty @-@ fifth episode overall . It was written by Jack Burditt and was directed by Michael Engler . The episode first aired on October 25 , 2007 on the NBC network in the United States . Guest stars in this episode include Elijah Cook , Carrie Fisher , Marcella Roy , Paul Scheer , Megan Blake Stevenson , Jean <unk> and Stuart <unk> . 
- The episode focuses on Liz Lemon 's ( Tina Fey ) <unk> with her idol , Rosemary Howard ( Carrie Fisher ) ; Tracy Jordan 's ( Tracy Morgan ) family problems ; and Jenna <unk> 's ( Jane Krakowski ) attempt to replace Kenneth <unk> 's ( Jack <unk> ) burnt page jacket . The episode was praised by critics , with Alec Baldwin winning the Primetime Emmy Award for Outstanding Lead Actor in a Comedy Series . 
- 
- = = <unk> = = 
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- Jack Donaghy ( Alec Baldwin ) announces that Liz Lemon is the winner of the " G.E. <unk> Award " , a prize awarded to the G.E. employee who best exemplifies a follower , which also includes $ 10 @,@ 000 . Liz takes Pete <unk> ( Scott <unk> ) to a book signing to meet Rosemary Howard ( Carrie Fisher ) , her idol when she was a girl , and invites her to be a guest writer on " The <unk> Show " . Rosemary pitches several controversial ideas to Jack , who orders Liz to fire her , but when Liz refuses , Jack fires them both . Liz goes to Rosemary 's house , and once she realizes that Rosemary is crazy , she flees . Liz goes back to Jack 's office and begs for her job back , and he happily <unk> her . Jack promises to help Liz invest her prize money , and she swears that she will send Rosemary $ 400 a month for the rest of her life . 
- When Tracy causes a stir at a public event , Jack assures him that as a movie star , he can do anything he wants , except for dog fighting . Jack finds Tracy <unk> his order , but Tracy shouts that Jack is not his dad . Jack and Tracy meet with an NBC <unk> , and Jack role @-@ plays Tracy 's father , Tracy , and Tracy 's mom , among several other people from Tracy 's childhood , conveying the message that even though Tracy 's parents may have divorced , they still loved him . This comforts Tracy , and affirms that while he loves his family , they are crazy , and he needs to stay away from them . Tracy hugs Jack , and tells him that he is the only family he needs . 
- Jenna accidentally burns Kenneth 's page jacket on a hot plate , and Kenneth worries that head page Donny Lawson ( Paul Scheer ) will punish him . Jenna finds Donny backstage at the studio , who is <unk> that he finally has a reason to send Kenneth to CNBC in New Jersey . Donny offers Kenneth a choice : go to New Jersey , or compete in a " page off " , a contest of physical <unk> and NBC <unk> ; Jenna agrees to the page off . Before the event starts , Pete comes in and <unk> at the pages to get back to work . He forces Donny to give Kenneth a new jacket , but Donny swears to Jenna and Kenneth that he will get back at them . 
- 
- = = Production = = 
- 
- " Rosemary 's Baby " was mainly filmed on September 11 , 2007 , while Fisher 's scenes were filmed the next day on September 12 , 2007 . Star Wars is frequently referenced in 30 Rock , beginning with the pilot episode where Tracy Jordan is seen shouting that he is a <unk> . Liz Lemon admits to being a huge fan of Star Wars , saying that she had watched it many times with Pete <unk> , and dressed up as the Star Wars character Princess <unk> during four recent <unk> . Star Wars is also referenced when Tracy Jordan takes on the identity of the character <unk> . Fey , a fan of Star Wars herself , said that the weekly Star Wars joke or reference " started happening <unk> " when the crew realized that they had a Star Wars reference " in almost every show " . Fey said that from then on " it became a thing where [ they ] tried to keep it going " , and that even though they could not include one in every episode , they still had a " pretty high batting average " . Fey attributed most of the references to Robert Carlock , who she described as " the resident expert " . Prior to the airing of the episode , fans were " raving " about the much awaited guest appearance of Fisher . Fisher 's last line in the episode , " Help me , Liz Lemon ! You 're my only hope ! " , was a spoof of the line " Help me <unk> @-@ <unk> <unk> , you 're my only hope ! " from her past role in the original Star Wars trilogy , in which she played Princess <unk> . 
- 
- = = Reception = = 
- 
- According to the Nielsen ratings system , " Rosemary 's Baby " was viewed by an average of 6 @.@ 5 million American viewers . The episode achieved a 3 @.@ 1 / 8 in key 18 – 49 demographic . The 3 @.@ 1 rating refers to 3 @.@ 1 % of all 18- to 49 @-@ year @-@ olds in the U.S. , and the 8 share refers to 8 % of all 18- to 49 @-@ year @-@ olds watching television at the time of the broadcast . In the U.S. , " Rosemary 's Baby " was up by 19 % in the rating demographic compared to the previous episode , " The Collection , " receiving its highest result since the second season premiere episode , " <unk> , " on October 4 , 2007 . 
- " Rosemary 's Baby " was named as one of the " Top 11 TV Episodes of 2007 " by UGO , and ranked thirteenth on The <unk> Critic 's list of " the 50 Best Episodes of 2007 " ; both citing the Baldwin and Morgan therapy scene as the reason . Matt Webb <unk> of TV Guide declared it as " one of 30 Rock 's best episodes ever . " Webb <unk> praised Carrie Fisher 's guest appearance , but felt that Baldwin 's role @-@ playing during Morgan 's therapy session stole the show . Bob Sassone of TV Squad felt that even though the plot was " insane " , the episode still managed " to have a heart at its core " . Sassone called the therapy scene " one of the funniest scenes ... on TV this season " . Robert Canning of IGN felt that the episode has " great storylines to great guest stars " , making it " one of the best the series has produced so far " . Canning called the therapy scene " the best moment of the episode " . Entertainment Weekly put it on its end @-@ of @-@ the @-@ decade , " best @-@ of " list , saying , " Between Carrie Fisher 's <unk> <unk> guest role and Jack Donaghy 's <unk> of Tracy Jordan 's therapy session , this 2007 episode was so wrong . And so good . " 
- Michael Engler , the director of this episode , was nominated for the Directors Guild of America Award for Outstanding Directing – Comedy Series . This episode also earned Carrie Fisher a Primetime Emmy Award nomination for Outstanding Guest Actress in a Comedy Series and earned Jack Burditt a nomination for Outstanding Writing for a Comedy Series . 
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- 
- = Polka Party ! = 
- 
- Polka Party ! is the fourth studio album by " Weird Al " Yankovic , released in 1986 . The album is the fourth of Yankovic 's to be produced by former The <unk> guitarist Rick <unk> . Recorded between April 1986 and September 1986 , the album was Yankovic 's follow @-@ up to his successful 1985 release , Dare to Be Stupid . The album 's lead single was " Living With a Hernia " , although it was not a hit and did not chart . 
- The music on Polka Party ! is built around parodies and <unk> of pop and rock music of the mid @-@ 1980s , featuring jabs at James Brown , Mick Jagger , El <unk> , and Robert Palmer . The album also features many " style parodies " , or musical imitations that come close to , but do not copy , existing artists . These style parodies include imitations of specific artists like the Talking Heads , as well as imitations of various musical genres like country music . 
- Peaking at only number 177 on the Billboard 200 , Polka Party ! was met with mixed reviews and was considered a commercial and critical failure . Despite this , the album was nominated for a Grammy Award for Best Comedy Recording in 1986 . Polka Party ! is one of Yankovic 's few studio albums not to be certified either Gold or Platinum by the Recording Industry Association of America ( RIAA ) and was later demoted to a budget release in 2009 . 
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- = = Production = = 
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- 
- = = = Recording = = = 
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- In April 1986 Yankovic entered the recording studio to begin the sessions to his follow @-@ up to 1985 's Dare to Be Stupid . To produce the album , Yankovic brought in former The <unk> guitarist Rick <unk> , who had also produced Yankovic 's previous albums . <unk> Yankovic were Jon " Bermuda " <unk> on drums , Steve Jay on bass , and Jim West on guitar . The album was recorded in roughly three sessions . The first session took place between April 22 and 23 , and yielded four originals : " Don 't Wear Those Shoes " , " One of Those Days " , " Dog Eat Dog " , and " Christmas at Ground Zero " . The second session , which spanned August 4 – 5 , produced three parodies : " Living with a Hernia " , " Addicted to Spuds " , and " Here 's Johnny " . The final session , which lasted from August 29 to September 1 produced the parody " <unk> People " , an original song named " Good Enough for Now " , and the album 's titular polka medley . <unk> , Yankovic described the record as " not a whole lot different than " the other albums he had recorded , calling the process " even a bit formulaic " . 
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- = = = <unk> = = = 
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- On April 22 , 1986 , Yankovic began recording three new original songs for his next album : " Don 't Wear Those Shoes " , " One of Those Days " , and " Dog Eat Dog " . Although " Don 't Wear Those Shoes " is an original composition , Yankovic admitted that the intro was inspired by the style of The <unk> . Lyrically , the song is a plea by the singer to his wife not to not wear certain shoes which he cannot stand . " One of Those Days " is a song detailing horrible things as if they were everyday <unk> . Each horrible thing escalates up to global <unk> while more mundane <unk> pop up at different times . 
- " Dog Eat Dog " is a style parody of the Talking Heads . Described as a " tongue @-@ in @-@ cheek look at office life " , the song was inspired by Yankovic 's past experience of working in the <unk> as well as the traffic department at <unk> One radio station . He noted , " At first I thought [ the job ] was <unk> cool that I had a phone and a desk and a little <unk> to call my own , but after a while I felt like my soul had been sucked out of me . " The song features a line directly parodying the Talking Heads song " Once In a Lifetime " : " Sometimes I tell myself , this is not my beautiful <unk> / Sometimes I tell myself this is not my beautiful chair ! " This mirrors a similar line in the Talking Heads song : " You may tell yourself , this is not my beautiful house / You may tell yourself , this is not my beautiful wife " . 
- On April 23 , Yankovic recorded " Christmas at Ground Zero " . The song , " a <unk> little tune about death , destruction and the end of the world " was the result of Scotti Brothers Records ' insistence that Yankovic record a Christmas record . After Yankovic presented the song to his label , they relented , because it was " a little different from what they were expecting . " After the song 's release , some radio stations banned the record , a move that Yankovic attributes to " most people [ not wanting ] to hear about nuclear <unk> during the holiday season . " Following the September 11 attacks , when the general term " ground zero " was co @-@ opted as a proper name for the World Trade Center site where two of those attacks took place , the disturbing lyrics caused this song to be banned largely from radio . Yankovic wanted the song to receive a video , but due to budget reasons , his label did not agree . Yankovic , however , directed one himself which was mostly made up of stock footage , with a live action finale that was filmed in an economically devastated part of the Bronx , New York that looked like a bomb had gone off . The final original that was recorded was " Good Enough for Now " , a country music pastiche about how the singer 's lover , who , while not the best , will do for now . 
- 
- = = = <unk> and polka = = = 
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- On August 4 , Yankovic began recording parodies starting with " Living With a Hernia " . The song , a spoof of " Living in America " by James Brown — which was also the theme to the 1985 film Rocky IV — is about <unk> . When it came time to pick a song to parody as the lead single for Polka Party ! Scotti Brothers Records " had some very strong ideas " and wished to have Yankovic parody a musician who was signed on the same label . After " Living in America " became a hit , the record label insisted that Yankovic parody the song , to which Yankovic obliged . In order to accurately write the song , Yankovic researched the various types of <unk> . Yankovic noted that " it was a real <unk> to do James Brown . I 'm a total non @-@ dancer , never went to any dances in high school , but if I <unk> <unk> a dance routine I can figure it out . " A <unk> named Chester <unk> was hired to accurately create the dance scenes featured in the video , which was shot on the concert set actually used in the movie Rocky IV . The second parody recorded was " Addicted to Spuds " , a pastiche of " Addicted to Love " by Robert Palmer , about a man 's obsession for potatoes and potato @-@ based dishes . A music video for the song was never made to the song because there was a strict budget for videos for the album , and Yankovic felt that the video would be one big joke and not really worth its own video . A parody of Palmer 's video , however , was later inserted into Al 's " UHF " video . 
- On August 5 , Yankovic recorded " Here 's Johnny " , a parody of " Who 's Johnny " by El <unk> . The song , a loving ode to The Tonight Show Starring Johnny Carson announcer Ed McMahon , features John <unk> of the television series <unk> fame doing an impression of McMahon 's voice . According to Yankovic , Peter Wolf , the man who wrote " Who 's Johnny " , enjoyed the parody idea so much that he personally brought the <unk> disc program that had all the synthesizer parts for the original song into the studio when Yankovic was recording his parody . The final parody recorded for the album was " <unk> People " , a play on Mick Jagger 's " <unk> People " , which was recorded on August 29 , 1986 . The song , about elderly people who are missing their teeth , was written after Yankovic heard it would be the theme to the 1986 film <unk> People . Assuming the song would be a hit , Yankovic requested and received permission from Jagger to record a parody version . When Jagger 's song failed to crack the Top 40 , Yankovic considered not recording his version , but because Jagger had " authorized " the parody , he decided failing to produce it would be an insult to the artist . Therefore , he recorded the spoof anyway . 
- The album 's polka medley , the titular " Polka Party ! " , was recorded on the same day as " Here 's Johnny " . This was Yankovic 's third polka medley , and his only medley to bear the same name as an album . Like his other <unk> , the song is a <unk> of then @-@ popular songs in music . 
- 
- = = Reception = = 
- 
- 
- = = = Promotion = = = 
- 
- To promote the album 's release , Scotti Brothers Records purchased full @-@ page <unk> in Billboard magazine that advertised the release as Yankovic 's " biggest <unk> yet " . Unlike previous albums , Yankovic did not undertake a tour to promote Polka Party ! Instead , he opened for the American rock band The Monkees ; Yankovic later joked that the Monkees merely " closed for me " . Yankovic explained that while it " was a fun tour " and that the crowds were very enthusiastic , the tension between the Monkees was obvious ; on his website , he wrote that while the band members " are all terrific people individually " , they " didn 't seem to get along all that great when they weren 't on stage . " 
- 
- = = = Critical response = = = 
- 
- Polka Party ! received mixed to negative reviews from critics . Allmusic reviewer Eugene <unk> gave the album three stars and wrote that " just about anyone could feel let down by this album . " <unk> was largely critical of the parody choices , noting that many of the original versions would be forgotten in " fifteen years " . Christopher <unk> from The Daily Vault gave Polka Party ! an F and described it as an album that " seemed like it could well have been the ' last call ' for Yankovic . " <unk> heavily criticized the record , writing that both the parodies and originals were not good and that " Yankovic [ was ] going through the motions " . Rolling Stone awarded the album three @-@ and @-@ a @-@ half stars , tying it with the 1992 album Off the Deep End and the 1999 release Running with <unk> as Yankovic 's best @-@ rated album . Although it was not a critical success , the album was nominated for a Grammy Award for Best Comedy Recording in 1987 , but lost to Bill <unk> 's Those of You with or Without Children , You 'll <unk> . 
- Despite the album 's lackluster reception , many of the songs on the album , such as " Dog Eat Dog " , " Addicted to Spuds " , and " Christmas at Ground Zero " , went on to become fan favorites and live staples . Two of the album 's tracks , " Living with a Hernia " and " Addicted to Spuds " , appeared on Yankovic 's first greatest hits album ( 1988 ) , " Christmas at Ground Zero " appeared on the second volume ( 1994 ) . In addition , the 1994 box set Permanent Record : Al in the Box contained five of the album 's songs : " Addicted to Spuds " , " Dog Eat Dog " , " Here 's Johnny " , " Living with a Hernia " , and " Christmas at Ground Zero " . Only " Dog Eat Dog " , however , appeared on Yankovic 's 2009 Essential collection , although the 3 @.@ 0 version contained " Living with a Hernia " . 
- 
- = = = Commercial performance = = = 
- 
- Polka Party ! was released October 21 , 1986 . After it was released , the album peaked at number 177 on the Billboard 200 . Compared to Yankovic 's previous albums — Dare to Be Stupid peaked at number 50 and In 3 @-@ D peaked at number 17 — Polka Party ! was a major commercial disappointment for the comedian . The album was the lowest @-@ charting studio album released by Yankovic and is one of his few studio albums not to be certified either Gold or Platinum by the Recording Industry Association of America ( RIAA ) . The others include the soundtrack to his film UHF ( 1989 ) and <unk> <unk> ( 2003 ) . Due to low sales the album was demoted to a budget release along with various other Yankovic albums in August 2009 . 
- Yankovic was dismayed by the album 's lackluster reception . He noted that he " thought it was the end of [ his ] career " . Yankovic explained that " I figured I 'd peaked with ' Eat It ' and ' Like a Surgeon ' and now people were slowly forgetting about me and I was well on my way to obscurity . " However , Yankovic 's next album , Even <unk> , would resurrect his career and become his best @-@ selling album at the time ; the experience led Yankovic to realize that " careers have peaks and valleys , and whenever I go through the rough times , another peak might be right around the corner . " 
- 
- = = Track listing = = 
- 
- The following is adapted from the album liner notes . 
- 
- = = Credits and personnel = = 
- 
- 
- = = Charts and certifications = = 
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- 
- = = = Charts = = = 
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- 
- 
- = Trees ( poem ) = 
- 
- " Trees " is a lyric poem by American poet Joyce Kilmer . Written in February 1913 , it was first published in Poetry : A Magazine of Verse that August and included in Kilmer 's 1914 collection Trees and Other Poems . The poem , in twelve lines of rhyming couplets of iambic tetrameter verse , describes what Kilmer perceives as the inability of art created by humankind to replicate the beauty achieved by nature . 
- Kilmer is most remembered for " Trees " , which has been the subject of frequent parodies and references in popular culture . Kilmer 's work is often disparaged by critics and dismissed by scholars as being too simple and overly sentimental , and that his style was far too traditional and even archaic . Despite this , the popular appeal of " Trees " has contributed to its endurance . Literary critic Guy Davenport considers it " the one poem known by practically everybody . " " Trees " is frequently included in poetry anthologies and has been set to music several times — including a popular rendition by Oscar Rasbach , performed by singers Nelson Eddy , Robert Merrill , and Paul <unk> . 
- The location for a specific tree as the possible inspiration for the poem has been claimed by several places and institutions connected to Kilmer 's life — among these are Rutgers University , the University of Notre Dame , and towns across the country that Kilmer visited . However , Kilmer 's eldest son , Kenton , declares that the poem does not apply to any one tree — that it could apply equally to any . " Trees " was written in an upstairs bedroom at the family 's home in Mahwah , New Jersey that " looked out down a hill , on our well @-@ wooded lawn . " Ironically , Kenton Kilmer stated that while his father was " widely known for his affection for trees , his affection was certainly not sentimental — the most distinguished feature of Kilmer 's property was a colossal woodpile outside his home . " 
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- = = Writing = = 
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- = = = Mahwah : February 1913 = = = 
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- According to Kilmer 's oldest son , Kenton , " Trees " was written on 2 February 1913 , when the family resided in Mahwah , New Jersey in the northwestern corner of Bergen County . The <unk> lived on the southwest corner of the intersection of <unk> Road and Armour Road in Mahwah for five years and the house overlooked the <unk> Valley . 
- It was written in the afternoon in the intervals of some other writing . The desk was in an upstairs room , by a window looking down a wooded hill . It was written in a little notebook in which his father and mother wrote out copies of several of their poems , and , in most cases , added the date of composition . On one page the first two lines of ' Trees ' appear , with the date , February 2 , 1913 , and on another page , further on in the book , is the full text of the poem . It was dedicated to his wife 's mother , Mrs. Henry Mills Alden , who was <unk> to all her family . 
- In 2013 , the notebook alluded to by Kilmer 's son was uncovered by journalist and Kilmer researcher Alex <unk> in Georgetown University 's <unk> Library in a collection of family papers donated to the university by Kilmer 's granddaughter , Miriam Kilmer . The " Mrs. Henry Mills Alden " to whom the poem was dedicated was <unk> Foster Murray Alden ( 1866 – 1936 ) , the mother of Kilmer 's wife , <unk> Murray Kilmer ( 1888 – 1941 ) . Alden , a writer , had married Harper 's Magazine editor Henry Mills Alden in 1900 . 
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- = = = Kilmer 's inspiration = = = 
- 
- Kilmer 's poetry was influenced by " his strong religious faith and dedication to the natural beauty of the world . " 
- Although several communities across the United States claim to have inspired " Trees " , nothing can be established specifically regarding Kilmer 's inspiration except that he wrote the poem while residing in Mahwah . Both Kilmer 's widow , <unk> , and his son , Kenton , refuted these claims in their correspondence with researchers and by Kenton in his memoir . Kenton wrote to University of Notre Dame researcher Dorothy Colson : 
- Mother and I agreed , when we talked about it , that Dad never meant his poem to apply to one particular tree , or to the trees of any special region . Just any trees or all trees that might be <unk> on or <unk> on , and that would be suitable nesting places for robins . I guess they 'd have to have upward @-@ reaching branches , too , for the line about ' lifting leafy arms to pray . ' Rule out weeping willows . 
- According to Kenton Kilmer , the upstairs room in which the poem was written looked down the hill over the family 's " well @-@ wooded lawn " that contained " trees of many kinds , from mature trees to thin <unk> : oaks , <unk> , black and white <unk> , and I do not know what else . " A published interview with Joyce Kilmer in 1915 mentioned the poet 's large woodpile at the family 's Mahwah home : 
- ... while Kilmer might be widely known for his affection for trees , his affection was certainly not sentimental — the most distinguished feature of Kilmer 's property was a colossal woodpile outside his home . The house stood in the middle of a forest and what lawn it possessed was obtained only after Kilmer had spent months of weekend <unk> in <unk> down trees , pulling up stumps , and splitting logs . Kilmer 's neighbors had difficulty in believing that a man who could do that could also be a poet . 
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- = = <unk> and analysis = = 
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- " Trees " is a poem of twelve lines in strict iambic tetrameter . All but one of the lines has the full eight syllables of iambic tetrameter . The eleventh , or penultimate , line begins on the stressed syllable of the iambic foot and drops the <unk> syllable — an <unk> ( or " headless " ) <unk> line — that results in a truncated seven @-@ syllable iambic tetrameter line . Making the meter of a line <unk> can change the feeling of the poem , and is often used to achieve a certain effect as a way of changing tone or announcing a conclusion . The poem 's rhyme scheme is rhyming couplets rendered aa <unk> <unk> <unk> ee aa . 
- Despite its deceptive simplicity in rhyme and meter , " Trees " is notable for its use of personification and anthropomorphic imagery : the tree of the poem , which Kilmer depicts as female , is depicted as pressing its mouth to the Earth 's breast , looking at God , and raising its " leafy arms " to pray . The tree of the poem also has human physical attributes — it has a " hungry mouth " , arms , hair ( in which robins nest ) , and a <unk> . 
- Rutgers @-@ Newark English professor and poet Rachel <unk> described the poem as being " rather slight " although it " is free of irony and self consciousness , except that little reference to fools like me at the end , which I find kind of charming . " Scholar Mark <unk> Winchell points out that Kilmer 's depiction of the tree indicates the possibility that he had several different people in mind because of the variety of anthropomorphic descriptions . Winchell posits that if the tree described were to be a single human being it would be " an <unk> deformed one . " 
- " In the second stanza , the tree is a sucking <unk> drawing <unk> from Mother Earth ; in the third it is a <unk> reaching its leafy arms to the sky in prayer ... In the fourth stanza , the tree is a girl with jewels ( a nest of robins ) in her hair ; and in the fifth , it is a <unk> woman living alone with nature and with God . There is no warrant in the poem to say that it is different trees that remind the poet of these different types of people . " 
- However , Winchell observes that this " series of <unk> <unk> ... could be presented in any order without damaging the overall structure of his poem . " 
- 
- = = Publication and reception = = 
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- 
- = = = Publication = = = 
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- " Trees " was first published in the August 1913 issue of Poetry : A Magazine of Verse . The magazine , which had begun publishing the year before in Chicago , Illinois , quickly became the " principal organ for modern poetry of the English @-@ speaking world " publishing the early works of poets who became the major influences on the development of twentieth @-@ century literature ( including T.S. Eliot , Ezra Pound , <unk> , Wallace Stevens , Robert Frost and Edna St. Vincent <unk> ) . Poetry paid Kilmer six dollars to print the poem , which was immediately successful . The following year , Kilmer included " Trees " in his collection Trees and Other Poems published by the George H. Doran Company . 
- Joyce Kilmer 's reputation as a poet is staked largely on the widespread popularity of this one poem . " Trees " was liked immediately on first publication in Poetry : A Magazine of Verse ; when Trees and Other Poems was published the following year , the review in Poetry focused on the " nursery rhyme " <unk> and simplicity of the poems , finding a particular <unk> <unk> in " Trees " , which gave it " an unusual , haunting <unk> " . However , the same review criticized the rest of the book , stating " much of the verse in this volume is very slight indeed . " 
- Despite the enduring popular appeal of " Trees " , most of Joyce Kilmer 's works are largely unknown and have fallen into obscurity . A select few of his poems , including " Trees " , are published frequently in anthologies . " Trees " began appearing in anthologies shortly after Kilmer 's 1918 death , the first inclusion being Louis <unk> 's Modern American Poetry ( 1919 ) . Journalist and author Mark <unk> , ranks the first two lines of " Trees " as 26th out of 50 lines in an assessment of the " most quoted lines of poetry " as measured by Google hits . 
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- = = = Popular appeal = = = 
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- With " Trees " , Kilmer was said to have " rediscovered simplicity " , and the simplicity of its message and delivery is a source of its appeal . In 1962 , English professor Barbara <unk> recounted that her undergraduate students considered the poem as " one of the finest poems ever written , or at least a very good one " — even after its technical flaws were discussed — because of its simple message and that it " paints such lovely pictures " . The students pointed to " how true the poem is " , and it appealed to both her students ' " romantic attitude towards nature " and their appreciation of life , nature , <unk> , and beauty because of its message that " the works of God completely overshadow our own <unk> attempts at creation . " Considering this sentiment , the enduring popularity of " Trees " is <unk> by its association with annual Arbor Day <unk> and the planting of memorial trees as well as the several parks named in honor of Kilmer , including the Joyce Kilmer @-@ <unk> Wilderness and Joyce Kilmer Memorial Forest tracts within the <unk> National Forest in Graham County , North Carolina . 
- " Trees " has been described by literary critic Guy Davenport as " the one poem known by practically everybody . " According to journalist Rick Hampson , " Trees " was " <unk> and recited by generations of students ... It comforted troops in the trenches of World War I. It was set to music and set in stone , <unk> in opera houses and vaudeville theaters , <unk> at ceremonies each April on Arbor Day . " According to Robert Holliday , Kilmer 's friend and editor , " Trees " speaks " with authentic song to the simplest of hearts . " Holliday added that this " exquisite title poem now so universally known made his reputation more than all the rest he had written put together " and was " made for immediate widespread popularity . " 
- 
- = = = Critical reception = = = 
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- Several critics — including both Kilmer 's contemporaries and modern scholars — have disparaged Kilmer 's work as being too religious , simple , overly sentimental , and suggested that his style was far too traditional , even archaic . Poet Conrad <unk> , a contemporary of Kilmer , lambasted his work as being unoriginal — merely " <unk> with a sentimental bias " and " <unk> out of the same faint <unk> , the same old <unk> and love songs , <unk> <unk> of <unk> all too familiar . " <unk> characterized Kilmer as a " <unk> in the pretty and sweet " and " pale @-@ <unk> <unk> to the artificial and archaic . " 
- Kilmer is considered among the last of the Romantic era poets because his verse is conservative and traditional in style and does not break any of the formal rules of <unk> — a style often criticized today for being too sentimental to be taken seriously . The entire corpus of Kilmer 's work was produced between 1909 and 1918 when Romanticism and sentimental lyric poetry fell out of favor and <unk> took root — especially with the influence of the Lost Generation . In the years after Kilmer 's death , poetry went in drastically different directions , as is seen in the work of T. S. Eliot and Ezra Pound , and academic criticism grew with it to <unk> the more sentimental and straightforward verse . 
- The poem was criticized by <unk> Brooks and Robert Penn Warren in their textbook Understanding Poetry first published in 1938 . Brooks and Warren were two of the major contributors to the New Criticism movement , where its supporters opposed using literature as a surrogate for religion . New Criticism proponents analyzed poetry on its aesthetic formulae and excluded reader 's response , the author 's intention , historical and cultural contexts , and <unk> bias from their analysis . They attributed the popularity of Trees largely to its religious appeal and believed it was a " stock response that has nothing to do , as such , with poetry , " adding : 
- " It praises God and appeals to a religious sentiment . Therefore , people who do not stop to look at the poem itself or to study the images in the poem and think about what the poem really says , are inclined to accept the poem because of the <unk> sentiment , the <unk> little pictures ( which in themselves appeal to stock responses ) , and the mechanical rhythm . " 
- Literary critic Mark <unk> Winchell believed that Brooks and Warren 's criticism of Kilmer 's poem was chiefly to demonstrate that " it is sometimes possible to learn as much about poetry from bad poems as from good ones . " 
- 
- = = <unk> claims regarding inspiration = = 
- 
- Due to the enduring popular appeal of " Trees " , several local communities and organizations across the United States have staked their claim to the genesis of the poem . While the accounts of family members and of documents firmly establish Mahwah being the place where Kilmer wrote the poem , several towns throughout the country have claimed that Kilmer wrote " Trees " while staying there or that a specific tree in their town inspired Kilmer 's writing . Local tradition in <unk> , New Hampshire asserts without proof that Kilmer wrote the poem while <unk> in the town . <unk> , Massachusetts claims that either " a sprawling maple dominated the grounds near a hospital where Kilmer once was treated " or " a spreading maple in the yard of an old mansion , " inspired the poem . 
- In New Brunswick , New Jersey , Kilmer 's hometown , the claim involved a large white oak on the Cook College campus ( now the School of Environmental and Biological Sciences ) , at Rutgers University . This tree , the " Kilmer Oak " , was estimated to be over 300 years old . Because it had been weakened by age and disease , the Kilmer Oak was removed in 1963 , and in reporting by The New York Times and other newspapers the local tradition was repeated with the claim that " Rutgers said it could not prove that Kilmer had been inspired by the oak . " Currently , <unk> from <unk> of the historic tree are being grown at the site , throughout the Middlesex County and central New Jersey , as well as in major <unk> around the United States . The remains of the original Kilmer Oak are presently kept in storage at Rutgers University . 
- Because of Kilmer 's close identification with Roman Catholicism and his correspondence with many priests and theologians , a tree located near a <unk> dedicated to the Virgin Mary at the University of Notre Dame in South Bend , Indiana has been asserted as the inspiration for the poem . According to Dorothy <unk> , the claim was first made by a priest named Henry <unk> . There are several accounts that Kilmer visited the campus of Notre Dame to lecture and to visit friends , but none of these accounts or occasions date before 1914 . 
- In his 1997 book of essays entitled The Geography of the <unk> , American writer Guy Davenport suggests a different inspiration for Kilmer 's poem . 
- " Trees were favorite symbols for Yeats , Frost , and even the young Pound . ... But Kilmer had been reading about trees in another context [ , ] the movement to stop child labor and set up nursery schools in slums . ... Margaret McMillan ... had the happy idea that a breath of fresh air and an intimate acquaintance with grass and trees were worth all the pencils and <unk> in the whole school system . ... The English word for <unk> equipment is ' apparatus . ' And in her book Labour and Childhood ( 1907 ) you will find this sentence : ' <unk> can be made by fools , but only God can make a <unk> 
- It appears that Davenport must have loosely and erroneously <unk> the sentiments expressed by McMillan , as this exact quote does not appear in her text . Instead , McMillan is expressing the observation that several nineteenth @-@ century writers , including William Rankin , William Morris and Thomas Carlyle , opposed the effects of machinery on society and craftsmanship and thus <unk> machine @-@ made items . Davenport 's observation likely was derived in some way from McMillan 's examination and quotation of Carlyle : 
- " He ( Carlyle ) often makes comparisons between men and machines , and even trees and machines , greatly to the disadvantage of the latter . For example , ' O , that we could displace the machine god and put a man god in his place ! ' and ' I find no <unk> of life so true as this of a tree ! Beautiful ! Machine of the universe ! ' 
- 
- = = Adaptations and parodies = = 
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- = = = Musical adaptations = = = 
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- Several of Kilmer 's poems , including " Trees " , were set to music and published in England by Kilmer 's mother , Annie Kilburn Kilmer , who was a writer and amateur composer . The more popular musical setting of Kilmer 's poem was composed in 1922 by American pianist and composer Oscar Rasbach . This setting had been performed and recorded frequently in twentieth century , including <unk> Schumann @-@ <unk> , John Charles Thomas , Nelson Eddy , Robert Merrill , Perry <unk> , and Paul <unk> . Rasbach 's song appeared on popular network television shows , including All in the Family , performed by the puppets Wayne and Wanda in The <unk> Show , and as an animated feature segment featuring Fred <unk> and the <unk> performing the song in the 1948 animated film Melody Time , the last of the short @-@ film anthology features produced by Walt Disney . 
- Rasbach 's setting has also been lampooned , most notably in the Our Gang short film " Arbor Day " ( 1936 ) , directed by Fred C. <unk> , in which <unk> ( played by Carl <unk> ) , sings the song in a <unk> , strained voice after a " <unk> , spare that tree " dialogue with Spanky ( George McFarland ) , sings " Trees . " Film critic Leonard <unk> has called this " the poem 's all @-@ time worst rendition . " In his album Caught in the Act , Victor <unk> , when playing requests , responds to a member of the audience : " <unk> I don 't know that ' <unk> in the Window ' . I know one that comes pretty close to it , " and proceeds to play the Rasbach setting of " Trees . " 
- Dutch composer <unk> van der <unk> , included a setting of " Trees " as the third in a set of five songs written in 1977 , which included texts by poets Christina <unk> , Percy <unk> Shelley , Kilmer , Matthew Prior , and Sir John <unk> . 
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- = = = <unk> = = = 
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- Because of the varied reception to Kilmer 's poem and its simple rhyme and meter , it has been the model for several parodies written by <unk> and poets alike . While keeping with Kilmer 's iambic tetrameter rhythm and its couplet rhyme scheme , and references to the original poem 's thematic material , such parodies are often immediately recognizable , as is seen in " Song of the Open Road " written by poet and humorist <unk> Nash : " I think that I shall never see / A billboard lovely as a tree . / Indeed , unless the billboards fall , / I 'll never see a tree at all . " 
- A similar sentiment was expressed in a 1968 episode of the animated series <unk> Races titled " The Wrong Lumber Race " , where the villainous Dick <unk> chops down a tree and uses it as a roadblock against the other <unk> , declaring proudly : " I think that I shall never see / A roadblock lovely as a tree . " 
- Further , <unk> monk , poet and spiritual writer Thomas Merton used Kilmer 's poem as a model for a parody called " <unk> $ e " — with a dollar sign purposefully substituted for the letter " s " — in which Merton ridiculed the lucrative sale of <unk> cheese by his monastery , the Abbey of <unk> in Kentucky . This poem was not published during Merton 's lifetime . Merton often criticized the " <unk> of monastic life and business for a profit " , claiming that it affected the well @-@ being of the spirit . In his poem , Merton attributed his parody to " Joyce Killer @-@ <unk> . " 
- Like Kilmer , Merton was a graduate of Columbia University and a member of its literary society , the <unk> Society , which has hosted the annual Joyce Kilmer Memorial Bad Poetry Contest since 1986 . " Trees " is read at the conclusion of each year 's event . 
- Kilmer 's poem was recited in the 1980 film Superman II , as well as its 2006 director 's cut . In the scene , villain Lex Luthor ( played by Gene <unk> ) and others enter Superman 's Fortress of Solitude and comes across a video of an elder ( John Hollis ) from planet Krypton <unk> " Trees " as an example of " poetry from Earth literature " . Luthor ridicules the poem . 
- 
- 
- = Zygoballus sexpunctatus = 
- 
- Zygoballus sexpunctatus is a species of jumping spider which occurs in the southeastern United States where it can be found in a variety of grassy habitats . Adult spiders measure between 3 and 4 @.@ 5 mm in length . The cephalothorax and abdomen are bronze to black in color , with reddish brown or yellowish legs . The male has distinctive enlarged chelicerae ( the <unk> used for grasping prey ) and front femora ( the third , and typically largest , leg segments ) . Like many jumping spiders , Z. sexpunctatus males exhibit <unk> courtship and agonistic behavior . 
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- = = Etymology = = 
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- The specific name is derived from the Latin sex meaning " six " and <unk> meaning " spot " . This is a reference to the six spots typically occurring on the abdomen of the male . 
- 
- = = History and taxonomy = = 
- 
- The species was first described by <unk> Nicholas <unk> Hentz in 1845 in the Boston Journal of Natural History . Hentz named the species <unk> sexpunctatus and described it as follows : 
- " Black ; cephalothorax with the two posterior eyes near the base , which is wide and suddenly inclined at nearly a right angle with the upper surface , <unk> with a strong inner tooth , and a long , curved <unk> ; abdomen with six dots , and a line in front , white ; feet , 1 . 4 . 2 . 3 . , first pair with enlarged thighs and quite long . " 
- Hentz classified A. sexpunctatus in the <unk> group <unk> , which consisted of jumping spiders whose first pair of legs were the longest , followed by the fourth pair . Later <unk> abandoned this classification , which Hentz himself admitted was " somewhat artificial " . In 1888 , with the recognition of Zygoballus as an independent genus , American <unk> George and Elizabeth <unk> renamed the spider Zygoballus sexpunctatus . Specimens of Z. sexpunctatus are housed at the Museum of Comparative <unk> , the British Museum , the Milwaukee Public Museum , the American Museum of Natural History , and the <unk> National d <unk> <unk> . No type specimens are known . 
- The genus Zygoballus contains approximately twenty species distributed from the United States to Argentina . Zygoballus is classified in the subfamily <unk> of the family <unk> ( jumping spiders ) . 
- 
- = = Description = = 
- 
- According to <unk> B. J. <unk> , adult females are 3 @.@ 5 to 4 @.@ 5 mm in body length , while males are 3 to 3 @.@ 5 mm . The Peckhams ' earlier description , however , gives a length of 3 mm for females and 3 to 4 @.@ 5 mm for males . 
- The cephalothorax of Z. sexpunctatus is bronze to black in color . Like all Zygoballus spiders , the cephalothorax is box @-@ like in shape , being <unk> at the posterior lateral eyes . Numerous white or pale blue scales cover the <unk> ( " face " ) and chelicerae . This covering extends around the sides of the carapace , ending beyond the posterior median eyes . In males , the <unk> is two @-@ <unk> as long as the <unk> , and as wide as it is long . The chelicerae of males are greatly enlarged and <unk> oriented , with each <unk> having a prominent inner tooth and a long , curved <unk> . 
- The legs are reddish brown , or sometimes yellowish , with the femora of the anterior ( first ) pair being darker and enlarged , especially in the male . The anterior legs have three pairs of long spines on the ventral surface of the tibia and two pairs of spines on the <unk> . The Peckhams give the following measurements for the lengths of the legs of a male specimen , starting with the anterior pair : 3 @.@ 7 mm , 2 @.@ 2 mm , 2 mm , 3 mm . In females , the fourth pair of legs are the longest . The <unk> in the male has a single <unk> <unk> which tapers gradually . 
- The abdomen is bronze to black with a white basal band and two white transverse bands . The transverse bands are often broken to form six spots . Some or all of these spots may be lacking , however . 
- Zygoballus sexpunctatus is similar in appearance to Zygoballus rufipes , with whom its range overlaps . The male can be distinguished from Z. rufipes by the large spot of white scales at the beginning of the <unk> slope ( which is lacking in Z. rufipes ) , and by the longitudinal division present on the bulb of the <unk> ( Z. rufipes has a transverse division ) . The female can best be distinguished by the form of the <unk> ( the external genital structure ) . 
- 
- = = Habitat and distribution = = 
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- The range of the species extends from New Jersey to Florida and west to Texas , although it is most commonly found in the southern states . Hentz collected his original specimen in North Carolina . In 1909 , the Peckhams reported that the species had been collected from North Carolina , Florida , Texas , Louisiana , and Mississippi . A seven @-@ year survey of spider species in western Mississippi reported the abundance of Z. sexpunctatus as " uncommon " . A one @-@ year survey in <unk> County , Florida , reported the species as " rare " . 
- Specimens have been collected from several ecosystems , including old fields , river terrace forests , <unk> , Florida Sand Pine scrub , Slash Pine forests , Appalachian grass <unk> , and rice fields . Robert and Betty Barnes reported the species as occurring in <unk> fields throughout the southeastern <unk> . The species is typically found in the herb <unk> ( among grasses and other short plants ) and may be collected with a sweep net . 
- 
- = = Behavior = = 
- 
- Male Zygoballus sexpunctatus spiders are known to exhibit elaborate courtship displays . As a male approaches a female , it will typically raise and spread its first pair of legs and vibrate its abdomen . If the female is receptive , it will often vibrate its abdomen as well . The specific patterns of courtship behavior , however , vary between individuals . 
- Z. sexpunctatus males exhibit <unk> agonistic behavior when encountering other males of the same species . This behavior may include many of the same elements as courtship , such as raising and spreading the first pair of legs and <unk> the abdomen . During agonistic display , males will also extend their <unk> and fangs . <unk> attacks between males appear to be rare , however . 
- 
- = = Diet and ecology = = 
- 
- Like most spiders , Zygoballus sexpunctatus is an opportunistic <unk> , feeding on a wide range of invertebrate prey . The spider 's diet typically includes small insects such as <unk> and young <unk> . They have also been known to eat mosquitoes and numerous kinds of small spiders . 
- <unk> <unk> wasps , which capture and <unk> spiders as a source of food for their larvae , have been shown to prey on both male and female Z. sexpunctatus spiders . 
- 
- = = Life cycle = = 
- 
- In a study of spider populations in western Tennessee , Zygoballus sexpunctatus <unk> were reported to hatch from egg sacs in mid summer . The spiders <unk> through the winter in an immature form and reached sexual maturity around late April . 
- 
- 
- = 1986 Peach Bowl = 
- 
- The 1986 Peach Bowl was a post @-@ season American college football bowl game at Fulton County Stadium in Atlanta , Georgia between the Virginia Tech Hokies and the North Carolina State Wolfpack from on December 31 , 1986 . The game was the final contest of the 1986 NCAA Division I @-@ A football season for both teams , and ended in a 25 – 24 victory for Virginia Tech , the first bowl victory in school history . 
- Virginia Tech came into the game with a 9 – 1 – 1 record that included an unusual win over the Temple Owls , who were forced to forfeit a victory to Virginia Tech after using an ineligible player . Facing the Hokies in the Peach Bowl were the 18th @-@ ranked Wolfpack from North Carolina State University . <unk> State was led by head coach Dick Sheridan and had a regular @-@ season record of 8 – 2 – 1 that included five wins over Atlantic Coast Conference teams . 
- The 1986 Peach Bowl kicked off five years minus one day since Virginia Tech had last played in Atlanta — during the 1981 Peach Bowl . Virginia Tech scored first in the game , but NC State 's <unk> blocked a Tech punt in the Tech end zone and recovered it for a tying touchdown . Virginia Tech kicked a field goal at the end of the quarter to take a 10 – 7 lead , but NC State fought back , scoring 14 <unk> points in the second quarter to take a 21 – 10 lead by halftime . In the third quarter , the game turned into a defensive battle . Neither side scored until late in the third quarter , when Tech took advantage of a State fumble to score the first touchdown of the second half . Tech failed to convert a two @-@ point conversion , but NC State fumbled again on the ensuing possession , and Tech was able to drive for another touchdown . Leading 22 – 21 , Tech attempted another two @-@ point conversion , which also failed . 
- NC State , needing to score , drove down the field and kicked a go @-@ ahead 33 @-@ yard field goal with 7 : 12 remaining in the game . After a failed possession , Tech was forced to punt the ball , allowing NC State to run down the clock . The Virginia Tech defense eventually forced a stop , giving the Tech offense one final chance to win the game . With 1 : 53 on the clock and beginning from their own 20 @-@ yard line , the Hokies drove 57 yards to the NC State 23 @-@ yard line . There , kicker Chris Kinzer successfully kicked a 40 @-@ yard field goal as time expired to give Virginia Tech the win . 
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- = = Team selection = = 
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- The Peach Bowl game was created in 1968 by the Lions Club of Atlanta as a means to attract tourism to the city . First played at Grant Field on the campus of Georgia Tech , the game was moved to Fulton County Stadium in 1971 . By the mid @-@ 1980s , the Peach Bowl was facing hard times . At the time , NCAA guidelines for bowls required 75 percent of gross receipts to go to participating schools , with 33 percent of tickets to the game also required to go to each school . In 1983 , the NCAA threatened to <unk> the Peach Bowl 's charter when ticket sales <unk> around 25 @,@ 000 with a week to go before the bowl . Last @-@ minute sales saved the game , as attendance at the 1983 game climbed to 40 @,@ 000 and a new television contract allowed the bowl to make a <unk> of $ 580 @,@ 000 to each team . Still , the bowl 's future was in doubt . 
- In the spring of 1986 , the Metro Atlanta Chamber of Commerce ( <unk> ) took over the Peach Bowl . The bowl executive director at the time was Dick <unk> , and he encouraged the Chamber to step up its support of the game over what had been provided by the Lions Club . In 1986 , the Peach Bowl had no contractual obligations with college football conferences , as its successor , the Chick @-@ <unk> @-@ A Bowl , does today . Team selections were made by the Peach Bowl committee , a board of Atlanta community members , business leaders , and organizers of the Peach Bowl . To form one half of the matchup , the committee selected second @-@ place Atlantic Coast Conference team NC State , which accepted the bowl bid on November 22 , 1986 , the day of their final regular @-@ season game . The other half of the matchup was Virginia Tech , a football independent that had finished with nine wins , one loss , and one tie during the regular season and received its invitation one week after the regular season concluded . 
- Virginia Tech had not participated in a bowl game since the 1984 Independence Bowl against Air Force , while NC State was playing in its first postseason game since 1978 . The two teams had played each other 39 times prior to the Peach Bowl , with Virginia Tech leading the all @-@ time series , 20 – 16 – 3 . 
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- = = = Virginia Tech = = = 
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- In the days leading up to the Peach Bowl , one sportswriter called Virginia Tech 's 1986 football season a " season of surprises . " Tech began the season having gone 6 – 5 in 1985 . In their first game , the Hokies faced the Cincinnati <unk> . Tech lost , 24 – 20 , on a last @-@ minute play that saw a Cincinnati pass tipped twice and caught for a sustaining first down . The drive eventually resulted in a game @-@ winning touchdown for Cincinnati . 
- Tech recovered from that season @-@ opening loss by going on a four @-@ game winning streak , defeating Clemson in South Carolina , Syracuse in New York , and East Tennessee State and West Virginia in <unk> . On October 11 , against South Carolina , the Hokies tied , 27 – 27 . 
- Then , on a trip to Norfolk , Virginia to face the Temple Owls in the Oyster Bowl , Tech fell 29 – 13 for what appeared to be its second loss of the season . It was later revealed , however , that Temple used an ineligible player in the game , and the Owls were forced to forfeit the win . Following the Temple game , Tech returned to its winning ways , defeating <unk> Virginia , Kentucky , Richmond , and Vanderbilt . One week after defeating Vanderbilt , November 22 , 1986 , Tech received an invitation to the 1986 Peach Bowl . 
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- = = = NC State = = = 
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- NC State began the 1986 college football season coming off three consecutive losing seasons . Those losing seasons also resulted in the firing of head coach Tom Reed , who was replaced by Dick Sheridan . Sheridan 's first game with the Wolfpack was at home against East Carolina on September 6 . It was an <unk> beginning , as NC State won , 38 – 10 . After a 14 – 14 tie the next week against Pittsburgh , the Wolfpack won their next two games : at home against Wake Forest , and in Maryland against Maryland . 
- On October 11 , NC State traveled to Grant Field in Atlanta , Georgia , home of the Georgia Tech Yellow Jackets . There , they suffered a lopsided 59 – 21 loss , the worst ever suffered by a Sheridan @-@ coached team at the time . Following the loss to the Yellow Jackets , said linebacker Pat <unk> , " the coaches and players came together . The coaches were hurting as bad as we were . We pulled them up and they pulled us up . That was the turning point . " 
- Following the " turning point , " the Wolfpack won three consecutive games , <unk> their overall record to 6 – 1 – 1 . One of the victories was against the Clemson Tigers , who would ultimately go on to win that year 's ACC football championship . On November 8 , NC State traveled to <unk> , Virginia , to play the Virginia Cavaliers . In a close @-@ fought game , State lost , 20 – 16 . Though the Wolfpack won their final two regular @-@ season games ( against Duke and Western Carolina <unk> football ) , the loss to Virginia denied them a share of the ACC championship . Despite that missed opportunity , NC State finished the season with a winning record and received a bid to the Peach Bowl . 
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- = = <unk> buildup = = 
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- The Peach Bowl was the final game as head coach of Virginia Tech for Bill Dooley , who had accumulated a record of 62 – 38 – 1 for the Hokies since assuming the head coaching job in 1978 . Tech president William <unk> had long disagreed with Dooley about the role of football at Virginia Tech , and prior to the beginning of the season , <unk> told Dooley that his tenure as coach would end on January 1 , 1987 . This fact was revealed to the football team and the general public after Tech 's third game of the season . At the time , Dooley was the <unk> head coach in Virginia Tech history , but was under investigation for recruiting violations and had settled a breach @-@ of @-@ contract lawsuit against the university for $ 3 @.@ 5 million . As part of the out @-@ of @-@ court settlement , Dooley was required to quit his position following the Peach Bowl . In the weeks leading up to the game , Dooley <unk> questions about his future . On December 23 , it was announced that Murray State head coach Frank <unk> would replace Dooley after the Peach Bowl . Facing Dooley across the field was NC State head coach Dick Sheridan , who in his first year as head coach of the Wolfpack , was named Atlantic Coast Conference coach of the year and guided the Wolfpack to eight wins . 
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- = = = <unk> = = = 
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- The game was expected to be an offensive struggle that could potentially break the then @-@ record 74 points scored in the 1970 Peach Bowl . During the regular season , NC State averaged <unk> yards on offense per game , while Virginia Tech averaged 358 yards . On defense , State gave up an average of 402 yards per game , while Tech allowed an average of 366 yards . NC State averaged almost 28 points per game , while Virginia Tech averaged just over 24 points . This statistical parity was reflected by pre @-@ game point spreads , which favored NC State by two points . 
- State quarterback Erik Kramer was the cornerstone of one of those high @-@ powered offenses , passing for 2 @,@ <unk> yards and 14 touchdowns en route to All @-@ ACC honors and being named the ACC 's player of the year . He set school records for passing yards in a season and total yards in a season despite being hampered by an injured ankle suffered in the Wolfpack 's game against South Carolina . He was <unk> assisted in the passing game by All @-@ ACC receiver Nasrallah Worthen , who led the team in receptions after catching 41 passes for <unk> yards . State 's offense was mostly accumulated through the air , as the Wolfpack averaged less than 160 yards per game on the ground . 
- Virginia Tech 's offense was slightly more balanced , featuring two running backs who had success throughout the regular season . Maurice Williams rushed the ball 166 times for 1 @,@ <unk> yards and six touchdowns during the regular season , and Eddie Hunter contributed <unk> rushing yards . Through the air , Tech quarterback Erik Chapman passed for 1 @,@ <unk> yards and 10 touchdowns during the season prior to the Peach Bowl , making him the most prolific Virginia Tech passer in the nine @-@ year tenure of Tech head Coach Bill Dooley . 
- The Hokies suffered a setback on offense a few days prior to the Peach Bowl when it was announced that offensive tackle Jim Davie was suspended from playing in the game after testing positive for anabolic steroids as part of a nationwide series of random tests conducted by the NCAA . Tech defensive end Morgan <unk> was also suspended from playing for reasons not revealed by the university . 
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- = = = Defense = = = 
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- On defense , Tech allowed an average of 190 yards per game through the air . Free safety Carter Wiley and cornerback Billy Myers had three interceptions each during the regular season . Virginia Tech linebacker Lawrence White was expected to miss the game after undergoing knee surgery following the Hokies ' last regular @-@ season game . White was the team 's No. 3 <unk> in terms of statistics , having accumulated 77 during the course of the regular season . The team 's No. 1 and 2 <unk> were linebackers Paul Nelson and <unk> <unk> , who had 104 and 80 tackles , respectively . The Hokies ' rush defense allowed an average of 175 rushing yards per game and 14 total rushing touchdowns . 
- State 's defense allowed an average of 228 @.@ 6 yards through the air during the regular season and the pass defense led by Derrick Taylor , who had six interceptions . At linebacker , Pat <unk> and Kelvin <unk> were considered keys to the Wolfpack run defense , which allowed an average of 173 rushing yards per game and 14 total rushing touchdowns . 
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- = = = Special teams = = = 
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- Both Virginia Tech and NC State featured All @-@ America <unk> . NC State 's Mike Cofer was named an Associated Press All @-@ America honorable mention selection after converting 13 of his 17 field goal attempts , while Virginia Tech 's Chris Kinzer had been successful throughout the regular season , making 22 of 27 field goal attempts , and breaking the school record for single @-@ season scoring with 93 points . 
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- = = Game summary = = 
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- The 1986 Peach Bowl kicked off at 1 : 05 p.m. EST on December 31 , 1986 , at Fulton County Stadium in Atlanta , Georgia . At kickoff , the sky was partly cloudy with an air temperature of 45 ° F ( 7 ° C ) . The wind was from the south at 12 miles per hour ( 19 km / h ) . The game was played before a sellout crowd of 53 @,@ <unk> , just the third sellout in the history of the Peach Bowl at that point . Virginia Tech won the ceremonial pre @-@ game coin toss , and elected to kick off to NC State . Therefore , the Wolfpack received the ball to begin the game , while Virginia Tech received the ball to begin the second half . The referee for the game was John <unk> , Bob <unk> was the umpire , and Ed <unk> was the linesman . Each team received more than $ 600 @,@ 000 for participating in the game . 
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- = = = First quarter = = = 
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- Following Virginia Tech 's kickoff , NC State returned the ball to the 27 @-@ yard line , where the Wolfpack began the game 's first play . That play was a short run to the right . On the next play , NC State picked up the game 's first first down with a rush up the middle by fullback Mal Crite . Crite picked up another first down on the next play , driving the Wolfpack inside Virginia Tech territory , but the Hokies ' defense stiffened and forced the Wolfpack to punt after NC State failed to gain another first down . Tech returned the punt to its 21 @-@ yard line , where the Tech offense took over . On Virginia Tech 's first offensive play , running back Maurice Williams broke free for a 77 @-@ yard run that took the Hokies inside the one @-@ yard line of NC State . The run was the longest of Williams ' career and is a Peach Bowl record for longest play from scrimmage . Two plays later , Virginia Tech 's Eddie Hunter crossed the goal line and scored the game 's first points . The touchdown and extra point made the score 7 – 0 , Virginia Tech . 
- Following Virginia Tech 's post @-@ <unk> kickoff , NC State began its second possession of the game at its 24 @-@ yard line after a short kick return . The NC State 's second drive of the game was more successful than its first , but as before , the Wolfpack offense ground to a halt before penetrating too deeply into Tech territory , and State was forced to punt the ball back to Virginia Tech . The Hokie offense began work at its eight @-@ yard line but went three and out and prepared to punt the ball back to NC State . Tech punter Tony Romero , kicking from the Tech goal line , had his kick blocked by State defender Derrick Taylor . The ball rolled into the Virginia Tech end zone and was recovered by an NC State 's Brian <unk> for a touchdown . The play and extra point tied the game at 7 – 7 . 
- Virginia Tech received NC State 's kickoff and returned it to their 25 @-@ yard line , where Tech 's offense returned to the field . After picking up short yardage on two rushing plays , Tech quarterback Erik Chapman completed a pass to tight end Steve Johnson to give the Hokies a first down at their 48 @-@ yard line with just over four minutes to go in the quarter . Tech continued to drive into Wolfpack territory , but inside the NC State 35 @-@ yard line , Tech committed a 15 @-@ yard illegal block penalty that pushed the Hokies back to the Wolfpack 47 @-@ yard line and had them facing a first down and 25 yards . Though unable to gain the 25 yards needed for another first down , Tech did make up most of the penalty yards , putting the ball at the Wolfpack 30 @-@ yard line . Facing fourth down , Tech sent in kicker Chris Kinzer to attempt a 46 @-@ yard field goal , which was successfully completed . The score gave Tech a 10 – 7 lead with 1 : 06 remaining the first quarter . 
- Kinzer delivered the post @-@ score kickoff , and NC State began its final drive of the first quarter at its 32 @-@ yard line with 1 : 01 remaining . The Wolfpack picked up a quick first down but were forced to punt when they did not gain another . NC State 's punt was returned to the Tech 13 @-@ yard line and the quarter came to an end with Virginia Tech leading , 10 – 7 . 
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- = = = Second quarter = = = 
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- Tech began the second quarter in possession of the ball with a first down at their 13 @-@ yard line . The Hokies picked up a first down , but then NC State safety Michael Brooks jumped in front of a Virginia Tech pass , <unk> it at the 50 @-@ yard line . With 13 : 05 remaining in the quarter , NC State had its first offensive possession of the second quarter . The Wolfpack picked up several first downs , driving within the Virginia Tech 25 @-@ yard line for their <unk> offensive penetration of the game . After being stopped for no or little gain on consecutive plays , NC State quarterback Erik Kramer completed a 25 @-@ yard touchdown pass to Nasrallah Worthen . The score and extra point gave NC State its first lead of the game , 14 – 10 , with 8 : 55 remaining in the first half . 
- Virginia Tech returned the post @-@ touchdown kickoff to its 26 @-@ yard line . The Hokie offense picked up short gains on first and second down before Tech quarterback Erik Chapman threw his second interception of the game , a pass that was tipped into the air and caught by NC State defender Derrick Taylor . The Wolfpack offense took over at the 46 @-@ yard line of Virginia Tech . On its first play after the interception , Kramer completed a 19 @-@ yard pass to <unk> <unk> . After a short run , Kramer completed a 13 @-@ yard pass to Jeffries for another first down . Deep inside the Tech red zone , it took the Wolfpack two more plays before Kramer connected on a pass to tight end Ralph <unk> for a touchdown . NC State now led 21 – 10 with just over four minutes remaining before halftime . 
- Following the score , kickoff , and return , Tech began another offensive possession at its 24 @-@ yard line . The Hokies picked up two first downs and drove into NC State territory , but the clock continued to tick toward halftime . In the Wolfpack side of the field , Tech running back Eddie Hunter broke free for a 23 @-@ yard run , the longest play by Virginia Tech in the second quarter . There was now just over two minutes remaining in the quarter . Tech was unable to pick up another first down after Hunter 's run , and attempted to convert the fourth down rather than trying a field goal . When the play was stopped for a loss , however , Virginia Tech was denied points and NC State 's offense returned with 47 seconds remaining in the half . 
- The Wolfpack proceeded to run out the clock and took a 21 – 10 lead into halftime . 
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- = = = Third quarter = = = 
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- Because NC State received the ball to begin the game , Virginia Tech received the ball to begin the second half . Tech received NC State 's kickoff and returned it to the 10 @-@ yard line , where the Hokie offense began work . Tech began working down the field , running the ball for short gains and throwing passes for longer gains . Tech picked up three first downs , then reached NC State territory on a pass to Donnelly . Once on the NC State side of the field , Tech picked up another first down , but Tech 's quarterback was sacked on third down for a loss , and the Hokies were forced to punt the ball away . The ball landed at the NC State 12 @-@ yard line where the Wolfpack began a drive . 
- NC State went three and out after receiving the ball , and after Tech incurred a running into the kicker penalty on the first punt attempt , NC State punted the ball away . After the kick , Tech took over on offense at its 27 @-@ yard line . On Tech 's first play after the punt , however , Hunter fumbled the ball after a 10 @-@ yard rush . NC State recovered the ball , and the Wolfpack offense returned to the field at the Tech 40 @-@ yard line . On their first play after the fumble recover , Kramer completed a 12 @-@ yard pass to Worthen for a first down . During the next play , Kramer fumbled the ball while attempting to run with it , and Virginia Tech 's defense recovered . This allowed the Tech offense to return to the field and attempt another offensive drive beginning at their 27 @-@ yard line . 
- Tech picked up a first down , then Chapman was forced to scramble for a first down after facing third and 10 . Stopped inches short of gaining the first down , Tech risked turning the ball over by attempting to convert the fourth down . Unlike their previous try in the game , Tech was successful and the Hokies ' drive continued . Tech continued to pick up yardage and first downs , advancing deep into the NC State side of the field . Inside the State 30 @-@ yard line , Tech quarterback Chapman was sacked for a 10 @-@ yard loss . He responded by throwing a 30 @-@ yard pass on the next play , driving Virginia Tech inside the State one @-@ yard line . Williams rushed into the end zone with 33 seconds remaining in the quarter , cutting the Wolfpack lead to 21 – 16 . Virginia Tech elected to attempt a two @-@ point conversion , which was unsuccessful . 
- NC State received Tech 's post @-@ touchdown kickoff and returned the ball to their 32 @-@ yard line . The Wolfpack offense had time for just one play — an eight @-@ yard pass — before the end of the quarter . With one quarter remaining in the game , NC State still held a 21 – 16 lead . 
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- = = = Fourth quarter = = = 
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- The fourth quarter began with NC State in possession of the ball at their 40 @-@ yard line and facing a second down and one yard . On the first play of the quarter , NC State quarterback Erik Kramer ran the ball , but fumbled at the end of the run . The ball was recovered by Virginia Tech , and the Hokie offense took the field . The first Virginia Tech play of the quarter was a first @-@ down throw to David Everett that drove the Hokies into Wolfpack territory . Tech followed the pass by driving down the field with alternating run and pass plays . Tech penetrated the NC State 20 @-@ yard line with 12 minutes remaining in the quarter , and continued to drive . Once the Hokies crossed the State 10 @-@ yard line , the State defense stiffened and the Hokies were able to gain a first down only with difficulty . With a first down at the State seven @-@ yard line , it took Tech just two plays to earn a touchdown . The Hokies again attempted a two @-@ point conversion , but were again stopped short . Despite that setback , the touchdown gave Tech six points and a 22 – 21 lead , their first since the 8 : 55 mark in the second quarter . 
- Because Tech committed a 15 @-@ yard unsportsmanlike conduct penalty following the touchdown , NC State was able to acquire good field position during the kickoff return , starting their drive at their 44 @-@ yard line . On the second play of the drive , Erik Kramer completed an 18 @-@ yard pass to Nasrallah Worthen , driving State into the Tech side of the field . After a short play , Kramer again completed a long pass , this time to Bobby <unk> for 24 yards . Now inside the Tech red zone , Kramer was tackled for a big loss , losing some of the yardage that he had gained with the previous play . NC State was unable to pick up another first down and sent in Mike Cofer to attempt a 33 @-@ yard kick , which was successfully completed . The field goal regained NC State a 24 – 22 lead . 
- Following the post @-@ score kick , Tech began its offensive drive at its 23 @-@ yard line with just under seven minutes to play in the game . The Hokies picked up a first down on three short rushing plays , then another on a single passing play . This drove the Hokies to their 45 @-@ yard line with just over five minutes to play . Tech was unable to gain another first down and was forced to punt the ball to NC State . The ball was fielded at the 14 @-@ yard line , which was where the NC State offense began its final drive of the game . 
- On State 's first play after the punt , fullback Mal Crite ran for a 40 @-@ yard gain , pushing State 's offense to the Tech 46 @-@ yard line with just over four minutes left . State pushed forward another seven yards , but failed to gain another first down and prepared to punt the ball back to Tech with 3 : 14 remaining in the game . Rather than punt the ball , however , State punter Kelly <unk> instead received the snap on fourth down and ran for a first down . Because State retained possession , it was able to continue to run down the clock after the fake punt . Tech stopped the clock once by calling a timeout and prevented State from gaining another first down . With 2 : 01 remaining in the game , State punted the ball into Tech 's end zone for a touchback . 
- Tech 's offense took the field at their 20 @-@ yard line with 1 : 53 remaining in the game , two <unk> left ( used to stop the clock as necessary ) , and needing at least a field goal to win the game . Tech picked up one first down via a pass , then another as the Hokies drove to their 44 @-@ yard line . Stopped short of midfield with less than a minute to play , Tech called its second timeout in order to stop the game clock from ticking down . In college football , the clock stops after a team earns a first down , and because Virginia Tech had not earned a first down on the short run , Tech was forced to call the timeout . Following the timeout , the Hokies ran for a first down , penetrating to the NC State 44 @-@ yard line . With 53 seconds remaining , Chapman <unk> out of bounds on a short run . Another short run brought the Hokies to the State 36 @-@ yard line , and the Hokies called their final timeout to stop the clock . A few plays later , Tech ran a short running play that kept the clock running down . With just 33 seconds remaining and no other way to stop the clock , Tech 's Maurice Williams <unk> stayed down with a leg <unk> and the <unk> stopped the clock to allow the injured player to receive assistance from athletic trainers before the next play . Facing fourth down and needing three yards for drive @-@ continuing first down , Chapman passed for a first down at the State 29 @-@ yard line with 15 seconds remaining in the game . On the game 's next play , Tech committed a holding penalty , which pushed the Hokies 10 yards further away from the end zone , out of field goal range , with 11 seconds remaining . 
- On the game 's next play , Chapman passed the ball deep , toward the end zone . Though the pass fell incomplete , a game official called a 15 @-@ yard pass interference penalty against NC State . This moved the ball to the NC State 23 @-@ yard line and forced Tech kicker Chris Kinzer to attempt a potentially game @-@ winning 40 @-@ yard field goal with four seconds remaining . Though NC State coach Dick Sheridan called a timeout in an attempt to ice Chris Kinzer , the kick sailed through the <unk> and Virginia Tech won a 25 – 24 victory as time expired . 
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- = = Statistical summary = = 
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- In recognition of their performance in a losing effort , NC State quarterback Erik Kramer was named the game 's offensive most valuable player , while on defense , NC State cornerback Derrick Taylor won the honor . Kramer finished the game having completed 12 of his 19 passes for 155 yards . On the opposite side of the field , Virginia Tech quarterback Eric Chapman finished with 20 completions out of 30 attempts for two touchdowns and 200 passing yards . 
- Virginia Tech running back Maurice Williams ' 77 @-@ yard run on the second play of the game remains the longest play from scrimmage in the Peach Bowl ( today the Chick @-@ <unk> @-@ A Bowl ) , and Virginia Tech also set the current record for the most first downs in a Peach Bowl ( 29 ) . Williams finished the game with 16 carries for 129 yards , and was the game 's leading rusher . The second @-@ place rusher was fellow Hokie running back Hunter , who ran with the ball 22 times for 113 yards . NC State 's leading rusher was fullback Mal Crite , who finished the game with 14 carries for 101 yards . 
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- = = <unk> effects = = 
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- Virginia Tech 's win brought it to a final 1986 record of 10 – 1 – 1 , while NC State 's loss took it to a final record of 8 – 3 – 1 . The victory was Virginia Tech 's first bowl win in school history and was the team 's only such win until 1993 , when Tech defeated Indiana University in the 1993 Independence Bowl . 
- Peach Bowl officials pronounced themselves pleased with both the turnout for the game and the action on the field . Though traffic jams <unk> attendees ' arrival to the stadium , there were only 5 @,@ 366 no @-@ shows out of 58 @,@ 212 tickets sold . Following the game , Peach Bowl chairman Ira <unk> announced that the bowl would seek corporate sponsorship and a potential television broadcast deal with a major American television network . The takeover by the chamber of commerce also proved to be successful , as the 1986 game made a small profit . This was an improvement over the three previous Peach <unk> , which lost more than $ 170 @,@ 000 . The sellout also confirmed that the game would continue to be held annually instead of being abandoned , as sportswriters had speculated prior to the 1986 game . 
- Tech kicker Chris Kinzer , who kicked the game @-@ winning field goal , did not go on to play in the National Football League despite predictions that he might do so . He attended several NFL teams ' <unk> , but a contract to play in the league never materialized . He sold insurance for several years , then <unk> school and graduated from Virginia Tech in 1994 with a degree . 
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- = = Later aftermath = = 
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- Virginia Tech would go on to join the Atlantic Coast Conference , which NC State was a member of , in 2004 . The ACC now sends a team to the later @-@ renamed Chick @-@ <unk> @-@ A Bowl every year . 
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- 
- = Greens Ledge Light = 
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- Greens Ledge Lighthouse is a <unk> lighthouse in Connecticut , United States , off the southwest end of the Norwalk Islands , Long Island Sound , near Norwalk , Connecticut . It is on north side of the west end of Greens Ledge , west of Norwalk Harbor a mile south of the entrance to Five Mile River at <unk> , and just over a mile southwest of Sheffield Lighthouse . Completed in 1902 , it was constructed by the Philadelphia Construction Company . The light is 52 feet ( 16 m ) tall and is made of five courses that make up its four stories . The lantern measures 7 feet ( 2 @.@ 1 m ) in diameter . The Greens Ledge Light replaced the Sheffield Island Light . Originally , the light had a fifth @-@ order Fresnel lens , but a fourth @-@ order Fresnel lens was installed in May 1902 , just three months into its operation . Currently a <unk> @-@ 25 is in use and it has alternating white and red flash every 24 seconds . The light was added to the National Register of Historic Places as Greens Ledge Lighthouse on May 29 , 1990 . 
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- = = Design = = 
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- In the 1890s , the lighthouse was first formally proposed to mark the Norwalk Harbor . In 1899 , the United States Congress appropriated $ 60 @,@ 000 for the establishment of a light and fog signal at Greens Ledge . In 1900 , the Philadelphia Construction Company was contracted to construct the foundation and the superstructure . The design for this type of lighthouse was first realized in 1873 , from Major <unk> of the Lighthouse Board . The foundation form is made of identical curved @-@ iron plates with top inward @-@ pointing <unk> that are <unk> together and secured with knees . The assembled rings are lowered into the water and filled with concrete or stone , concrete for the Greens Ledge Light . A series of photographs from the work in 1901 shows the assembly of the three lower courses at Wilson 's Point , the lowering of the cylinder and the light in the fall of 1901 prior to a deposit of protective <unk> . 
- The 52 @-@ foot ( 16 m ) tall Greens Ledge Light was completed in 1902 and serves as a typical example of a <unk> lighthouse . Located in 10 feet ( 3 @.@ 0 m ) of water , the foundation flares out to support the deck the lighthouse is built on and includes a cavity for the lighthouse 's brick basement and cisterns . The four @-@ story structure of the lighthouse is assembled from five courses of curved iron plates . The interior is lined with brick to <unk> and strengthen the tower and to " <unk> [ e ] an anchorage for the winding cast @-@ iron stairs which rise on the periphery of each story , " writes historian Dorothy <unk> . 
- The plain prefabricated features underwent a period of development of which the Greens Ledge Light was part of a second phase . <unk> describes , " the brackets which support the <unk> gallery and covered deck [ as having ] a simplified classical detailing and [ the ] rectilinear window <unk> are enclosed in shallower , <unk> cast @-@ iron surrounds . " A deck <unk> the light on above the first story , the <unk> and lantern . The original roofing and some cast @-@ iron <unk> of the decks are able to be seen atop the <unk> . The cast @-@ iron door to the lighthouse faces south and at the time of nomination the windows were sealed with <unk> . The first floor of the lighthouse serves as the kitchen . The second level has two rooms split by a partition with the smaller room being a bathroom . The third level was not divided , but did not have a description in the National Historic Register of Places survey . The fourth floor has six <unk> windows and has had much of its woodwork removed and part of the cast @-@ iron floor and brick wall are exposed . The lighthouse 's lantern measures 7 feet ( 2 @.@ 1 m ) in diameter . Once active , the Sheffield Island Light was discontinued . 
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- = = Service = = 
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- Originally , the light had a fifth @-@ order Fresnel lens , but a fourth @-@ order Fresnel lens was installed in May 1902 , just three months into its operation . The light characteristic was a fixed white light with a red flash every 15 seconds . In 1972 , the light was automated and the Fresnel lens was replaced with a modern <unk> . The light continues to serve as an active aid to navigation . In 1987 , a FA @-@ 251 was installed before the current lens , a <unk> @-@ 25 was installed . The current light characteristic is an alternating white and red flash every 24 seconds . The white and red flashes can be seen for 18 nautical miles and 15 nautical miles , <unk> . 
- During its service , the tower began to tilt and the keepers moved all the furniture to one side of the tower . The problem was reported to have been exacerbated following the 1938 New England hurricane . 
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- = = Importance = = 
- 
- The light was added to the National Register of Historic Places as Greens Ledge Lighthouse on May 29 , 1990 . It is listed as " significant as a typical example of a pre @-@ fabricated cast @-@ iron conical <unk> on a cast @-@ iron tubular foundation . " 
- The lighthouse served as a source of inspiration for Walter <unk> Richards , an artist , for over forty years . Since 1935 , swimmers have been competing annually in the Arthur J. <unk> <unk> Race , a one @-@ mile ( 1 @.@ 6 km ) race from the lighthouse to <unk> Beach in the <unk> section of Norwalk . 
- 
- = = List of keepers = = 
- 
- This list includes known keepers , but excludes assistants and non @-@ officers of the Coast Guard . 
- 
- 
- = Action of 13 September 1810 = 
- 
- The Action of 13 September 1810 was an inconclusive frigate engagement during the Napoleonic Wars between British Royal Navy and French Navy frigates during which a British frigate was defeated by two French vessels near Isle de France ( now Mauritius ) , but British reinforcements were able to recapture the ship before the French could secure her . The British frigate was HMS Africaine , a new arrival to the Indian Ocean . She was under the command of Captain Robert Corbet , who had served there the previous year . Corbet was a notoriously unpopular officer and his death in the battle provoked a storm of controversy in Britain over claims that Corbet had either committed suicide at the shame of losing his ship , been murdered by his disaffected crew , or been abandoned by his men , who were said to have refused to load their guns while he remained in command . Whether any of these rumours were accurate has never been satisfactorily determined , but the issue has been discussed in several prominent naval histories and was the subject of at least one lawsuit . 
- The action came about as a direct consequence of the Battle of Grand Port three weeks earlier , in which a British squadron had been destroyed in a failed attack on Grand Port harbour on Isle de France . This gave the French forces on the island a significant regional advantage , <unk> the British frigate on the recently captured Île Bourbon , commanded by Commodore <unk> Rowley , by six to one . British reinforcements were hastily despatched to the area but the French were blockading Île Bourbon in force and the arriving reinforcements were in constant danger of attack by more powerful French units . Africaine was the first ship to reinforce Rowley 's squadron , but within three days of her arrival in the region was engaged by two French ships while attempting to drive them away from Saint Denis on Île Bourbon . Corbet was severely wounded in the opening exchanges and subsequently died . Although his crew fought hard , they were overwhelmed by the French frigates and forced to surrender , only for Rowley to arrive in HMS Boadicea and drive off the French warships , <unk> Africaine . 
- 
- = = Background = = 
- 
- In 1808 , both the British Royal Navy and the French Navy despatched frigate squadrons to the Indian Ocean . The French , led by Commodore Jacques Hamelin , were ordered to disrupt British trade in the region , particularly targeting the large East Indiamen that carried millions of pounds worth of goods between Britain and her Empire . The British force under Commodore <unk> Rowley was tasked with the blockade and eventual capture of the two well defended island bases of the French , Île Bonaparte and Isle de France . At the Action of 31 May 1809 , a French frigate named Caroline captured two East Indiamen , sheltering with her prizes at Saint Paul on Île Bonaparte . In his first major operation against the islands , Rowley landed soldiers behind the defences of the harbour and sent his ships into the bay , seizing the town and the shipping in the harbour , including Caroline . One of Rowley 's captains who had performed well in this engagement was Robert Corbet of HMS Nereide . <unk> the Caroline as a British warship and renaming her HMS <unk> , Rowley placed Corbet in command and sent him to Britain with despatches . 
- Over the following year , the French continued to attack British trade convoys , achieving important victories at the Action of 18 November 1809 and the Action of 3 July 1810 , where they captured another five East Indiamen as well as numerous smaller merchant ships and a large Portuguese frigate . Rowley too was active , commanding the successful Invasion of Île Bonaparte in July and renaming the island Île Bourbon , basing his squadron at Saint Paul on the island 's eastern shore . From this base , Rowley 's ships were ideally positioned to begin a close blockade of Isle de France , led initially by Captain Samuel Pym in HMS Sirius . Pym sought to reduce French movement by seizing a number of fortified offshore islands , starting with Île de la <unk> off Grand Port . The island was captured , but when a French squadron broke through the British blockade and took shelter in Grand Port , Pym resolved to attack them . The ensuing Battle of Grand Port was a disaster for Rowley 's squadron , as Pym led four of Rowley 's five frigates into the bay without adequately assessing the channel through the coral reefs that sheltered the harbour . As a result , two frigates grounded out of range of the enemy and the remaining two were outnumbered in confined waters . In a complicated battle lasting several days , two of Pym 's frigates were captured and two more had to be scuttled , with their entire crews made prisoner . Rowley 's reinforcements arrived too late , and the British <unk> was chased back to Saint Denis by Hamelin 's flagship . 
- While Rowley and Hamelin had <unk> in the Indian Ocean , Corbet had made the lengthy journey back to Britain . During his time in command of Nereide , Corbet had already developed a reputation as a strict disciplinarian , regularly beating his men for the slightest <unk> , to the extent that he had provoked a brief mutiny on Nereide in 1808 . His reputation spread before him , and when he switched commands with Captain Richard <unk> of HMS Africaine , he was met with a storm of protest from Africaine 's crew . Although none of the men aboard Africaine had served with Corbet before , his preference for brutal punishment was well known in the Navy and the crew sent a letter to the Admiralty insisting that they would not serve under him . Concerned at what they considered to be mutiny , the Admiralty sent three popular officers to Africaine with the message that if the protest was quietly dropped there would be no courts @-@ martial for mutiny but if not , the entire crew would be liable to attack . To emphasise the threat , the frigate HMS <unk> was brought alongside with her gunports open and her cannon ready to fire . <unk> , the crew of Africaine allowed Corbet aboard and the frigate sailed for the Indian Ocean a few days later , carrying instructions for the authorities at Madras to prepare an expeditionary force to invade Isle de France . 
- 
- = = Africaine off Isle de France = = 
- 
- Africaine 's journey to Madras took several months and Corbet made a number of stops on his passage , the final one being at the small British island base of Rodriguez in early September 1810 . There Corbet was informed of the disaster at Grand Port and on his own initiative immediately sailed south to augment Rowley 's weakened squadron . Arriving off Isle de France at 06 : 15 on 11 September , Corbet spotted a French schooner near Île <unk> and gave chase , the schooner sheltering behind the reefs at Grand Bay on the eastern side of the island . At 07 : 30 , Corbet ordered the frigate 's boats to enter the creek into which the schooner had fled , the small craft entering the waterway in the hope of <unk> and capturing the vessel . As the boats approached , French soldiers and militia appeared along the banks and began firing on the British sailors . Fire was returned by Royal Marines in the boats , but Africaine 's barge grounded soon after the ambush was sprung and became trapped , French gunfire killing two men and wounding ten . The other boat reached the grounded and abandoned schooner , but the six men aboard were unable to move the vessel unaided and were forced to depart , coming under fire which wounded five men , before they could escape the French trap . 
- <unk> his boats , Corbet determined to sail to Île Bourbon directly . By 04 : 00 on 12 September he had arrived at Saint @-@ Denis and there landed his wounded and came ashore for news , learning that two French frigates were just offshore , blockading the port . The French ships had spotted Africaine in the harbour and despatched the small brig <unk> to Isle de France with information of her whereabouts , although Corbet had raised flags that successfully deceived the French into believing that his frigate was Rowley 's flagship HMS Boadicea . The French ships were Astrée , commanded by Pierre Bouvet , and Iphigénie , formerly one of the British frigates captured at Grand Port , under René <unk> de <unk> . 
- 
- = = = Battle = = = 
- 
- Rowley , stationed at Saint @-@ Paul to the west of Saint @-@ Denis , received word that Africaine had arrived at Saint Denis and immediately sought to drive off the French blockade . <unk> eastwards , Boadicea came within sight of Bouvet 's squadron at 15 : 00 and the British flagship followed by the small <unk> HMS Otter and HMS Staunch . Corbet recognised Rowley 's intention and joined the attack , embarking 25 soldiers from the 86th Regiment of Foot to replace his losses at Grand Bay . The French , still believing Africaine to be Boadicea , assumed that Boadicea was an East <unk> named <unk> in disguise , and fell back towards Isle de France before the British force . 
- Otter and Staunch both fell rapidly behind Boadicea , while Africaine pulled far ahead . By 18 : 20 , lookouts on Africaine could no longer see the other British ships , and by 18 : 30 , Boadicea was similarly alone . Bouvet realised the lack of cohesion in the British squadron , and also recognised that Africaine was faster than either of his ships and would soon catch them . As a result , he slowed and prepared to meet the British frigate as night fell . Corbet now found himself outnumbered and began to launch rockets and flares in the hope of attracting Rowley 's attention and as the French closed with Africaine , he <unk> his ship for action . 6 nautical miles ( 11 km ) behind , Rowley could see the flares and flashes but was powerless to intercede in the darkness . At 01 : 50 on 13 September , the gap had closed between Africaine and the French ships , and at 02 : 20 Corbet opened fire on Astrée , with Bouvet returning the fire immediately . 
- A <unk> from the second French broadside struck Corbet within minutes of the first broadside , the ball tearing off his foot above the ankle just as a large wooden splinter thrown from the <unk> struck the thigh of the same leg , shattering the bone . Corbet was brought below to the ship 's surgeon where the remnant of his leg was hastily amputated and bound , and command devolved on Lieutenant John Crew Tullidge . At 02 : 30 , Astrée pulled away from Africaine to perform hasty repairs , but Bouvet 's guns had wrecked Africaine 's rigging , leaving the British frigate uncontrollable and largely immobile . Slowly moving ahead , Africaine engaged Iphigénie at close range but was counter attacked by Astrée and found herself assailed on both sides , Astrée angled in such a position that she was able to rake the British ship , inflicting significant damage and casualties . 
- By 03 : 30 , Africaine was in ruins . Tullidge was wounded in four places , but refused to leave the deck as the ship 's master had been <unk> and the other lieutenant shot in the chest . All three <unk> had collapsed and as guns were dismounted and casualties increased the return fire of Africaine became more and more ragged , until it stopped entirely at 04 : 45 , when only two guns were still capable of firing . French fire stopped at 05 : 15 , first light showing Boadicea 5 nautical miles ( 9 @.@ 3 km ) away and unable to affect the surrender of Africaine , which had hauled down its flags at 05 : 00 . Within minutes , a French prize crew boarded the battered frigate and seized the magazine of shot and gunpowder , which was shipped to Iphigénie whose ammunition was almost exhausted . 
- 
- = = = Boadicea arrives = = = 
- 
- At 06 : 00 , a breeze pushed Boadicea forward and she began to close with her former consort , Rowley watching as all three of Africaine 's masts gave way and collapsed over the side one by one . By 08 : 00 , Africaine was a dismasted hull and Corbet was dead in the <unk> of the ship , although the exact manner of his death was to cause lasting controversy . By 10 : 00 , Boadicea had been joined by Otter and Staunch and bore down on the French ships and their prize , so that by 15 : 30 Bouvet was persuaded to abandon Africaine and tow the damaged Iphigénie back to Port Napoleon . By 17 : 00 , Boadicea pulled alongside Africaine and the French prize crew surrendered . Rowley later reported that a number of British sailors <unk> into the sea at his approach and swam to Boadicea , requesting that they be allowed to pursue the French ships in the hope of capturing one . 
- Rowley dismissed this idea given the shattered state of Africaine and instead towed the frigate back to Île Bourbon , shadowed by Astrée and Iphigénie on the return journey . The French frigates did achieve some <unk> in pursuing Rowley from a distance , running into and capturing the Honourable East India Company 's armed brig Aurora , sent from India to reinforce Rowley . On 15 September , Boadicea , Africaine and the <unk> arrived at Saint Paul , Africaine sheltering under the fortifications of the harbour while the others put to sea , again seeking to drive away the French blockade but unable to bring them to action . Bouvet returned to Port Napoleon on 18 September , and thus was not present when Rowley attacked and captured the French flagship <unk> and Commodore Hamelin at the Action of 18 September 1810 . 
- 
- = = Aftermath = = 
- 
- The action was the first of two in this campaign in which lone British frigates were briefly overwhelmed by superior French forces as they sailed independently to join Rowley 's squadron . On each occasion however , Rowley was able to recapture the lost frigate and drive off the French attackers . Corbet 's action was particularly violent , British casualties totalling 49 killed and 114 wounded , including every single officer and all but three of the soldiers embarked . Africaine was seriously damaged and would not be ready to return to active service for some months . French losses were less severe , Astrée suffering one killed and two wounded , Iphigénie nine killed and 33 wounded . 
- The action was considered a defeat by the Admiralty and was not reported in the London Gazette . The British naval authorities were particularly disturbed by rumours that began to circulate concerning the death of Captain Corbet and the behaviour of his crew during the battle . <unk> among these rumours was the suggestion that Corbet had been murdered by his disaffected crew : historian William James wrote in 1827 that " There are many who will insist , that Captain Corbett 's [ sic ] death @-@ wound was inflicted by one of his own people . " although he goes on to point out the <unk> of Corbet being shot by one of his own cannon . He gives more <unk> to the story that Corbet committed suicide to avoid the shame of defeat , that he " cut the bandages from his amputated limb , and suffered himself to bleed to death . " This story was also alluded to in Edward Pelham Brenton 's 1825 history : " Corbet did not ( we fear would not ) survive his capture " . The truth of Corbet 's end will never be known with certainty , although James ultimately concludes that Corbet 's wound was almost certainly a mortal one and thus the most likely cause of death . 
- A second accusation , and one that proved even more controversial in the aftermath of the engagement , was the claim that Africaine 's crew abandoned their guns , refused to load them or deliberately fired them into the sea in protest at Corbet 's behaviour . Corbet 's brutality was well known in the Navy , James describing him as " an excessively severe officer " who had a " career of cruelty " . James does not accuse the crew of any deliberate attempt to sabotage their ship in the engagement , instead attributing their poor gunnery to Corbet 's own failings as a commander , most significantly his failure to practice gunnery regularly . Other authors were less understanding of the crew of Africaine , Brenton stating that " they cut the <unk> of their guns , and put no shot in them after the first or second broadside " , while historian Basil Hall <unk> stated in 1833 that they " preferred to be <unk> down by the French broadsides " than fight under Corbet . This last accusation provoked outrage among naval officers , and Captain <unk> Jones , a former <unk> of Corbet launched a successful lawsuit , forcing Hall to make a retraction . In 1900 , William Laird <unk> commented that " There is , unfortunately , much reason to suppose that Captain Corbett 's [ sic ] reputation for extreme severity had <unk> his crew , and that the men did not behave as <unk> as they should have <unk> " . He later <unk> Brenton for the suggestion that Corbet committed suicide , suggesting that the wound alone was the cause of death . Modern historians have also been scathing of Corbet 's behaviour , Robert <unk> calling him " notoriously brutal , " and Richard <unk> describing Tullidge as " an unfortunate victim of Corbet 's cruelty , for suspicions <unk> that Africaine 's <unk> crew had failed to do their <unk> in support of their hated commander . " 
- 
- 
- = M @-@ 114 ( Michigan highway ) = 
- 
- M @-@ 114 was the designation of a former state trunkline highway and planned <unk> in the US state of Michigan around the city of Grand Rapids . It was designated by the end of 1929 on various streets in adjoining cities and townships . By the 1940s , sections of it on the west and south sides of Grand Rapids were given new designations and the segment along the east side of town was finished . By late 1945 the highway designation was completely decommissioned in favor of other numbers . M @-@ 114 split into two branches , one running east – west and the other running north – south . The east – west spur routing is now local streets while the rest is part of state highways . 
- 
- = = History = = 
- 
- The first segments of M @-@ 114 were completed by January 1 , 1930 , and ran along the west side of Grand Rapids , on what is now Wilson Avenue between Lake Michigan Drive and Leonard Street . At the same time , what would become a spur was also finished from the town of Cascade to US 131 ( Division Avenue ) . By July 1 that same year , the southern segment was extended west to Clyde Park Avenue in Wyoming Township . By the end of 1936 M @-@ 114 was a three @-@ legged trunkline around the Grand Rapids area . It started at US 16 in Walker Township and ran south to <unk> where it turned to run eastward to the community of Cascade . The third leg was shown on maps as under construction from a junction in Paris Township north to a junction with US 16 in East Grand Rapids ; the trunkline continued north from US 16 to a junction with US 131 in Plainfield Township north of Grand Rapids . 
- By June 15 , 1942 the highways in the Grand Rapids area were <unk> . A Bypass US 16 ( BYP US 16 ) designation was assigned to the portion of M @-@ 114 that traveled around the southwest side of Grand Rapids ( now M @-@ 11 ) , leaving just the east and unfinished north segments left . The section along the east side of the city was completed as M @-@ 114 . A northern leg was added along 3 Mile Road at the same time . By 1945 , the northern leg of M @-@ 114 was turned back to local control and removed from the highway system . The eastern leg was assigned a BYP US 131 designation , thereby eliminating the last remaining portion of M @-@ 114 . A BYP M @-@ 21 designation was also used along part of the southern and eastern legs . East Beltline now carries M @-@ 37 and M @-@ 44 . 
- 
- = = Route description = = 
- 
- As it existed before the designation was removed , M @-@ 114 started at the corner of BYP US 16 ( 28th Street ) and what is now East Beltline Avenue in Paris Township ( now Kentwood ) and ran northward . The trunkline intersected the mainline for US 16 / M @-@ 50 at Cascade Road and the mainline for M @-@ 21 at Fulton Street near East Grand Rapids in Grand Rapids Township . Further north , the highway split into two . In Plainfield Township , a leg of M @-@ 114 continued west along the modern 3 Mile Road through an intersection with US 131 to terminate at <unk> Avenue near the Grand River and the other leg continued north to a terminus with US 131 at <unk> Drive and Plainfield Avenue . 
- 
- = = Major intersections = = 
- 
- South leg 
- The entire highway was in Kent County . 
- West leg 
- The entire highway was in Grand Rapids Township , Kent County . 
- North leg 
- The entire highway was in Kent County . 
- 
- 
- = Jane Dudley , Duchess of Northumberland = 
- 
- Jane Dudley ( née Guildford ) , Duchess of Northumberland ( <unk> / <unk> – 1555 ) was an English <unk> , the wife of John Dudley , 1st Duke of Northumberland and mother of Guildford Dudley and Robert Dudley , 1st Earl of Leicester . Having grown up with her future husband , who was her father 's ward , she married at about age 16 . They had 13 children . Jane Dudley served as a lady @-@ in @-@ waiting at the court of Henry VIII and was a close friend of Queen Catherine <unk> . Reformed in religious outlook , she was also a supporter of the Protestant martyr Anne Askew . 
- Under the young King Edward VI John Dudley became one of the most powerful politicians , rising to be Earl of Warwick and later Duke of Northumberland . After the fall of Lord Protector Somerset in 1549 , John Dudley joined forces with his wife to promote his rehabilitation and a reconciliation between their families , which was symbolized by a marriage between their children . In the spring of 1553 Jane Dudley , Duchess of Northumberland became the mother @-@ in @-@ law of Lady Jane Grey , whom the Duke of Northumberland unsuccessfully tried to establish on the English throne after the death of Edward VI . Mary I being victorious , the Duchess sought frantically to save her husband 's life . Notwithstanding his and her son Guildford 's executions , she was successful in achieving the release of the rest of her family by befriending the Spanish noblemen who came to England with Philip of Spain . She died soon afterwards , aged 46 . 
- 
- = = Family and marriage = = 
- 
- Jane Guildford was born in Kent in about <unk> / <unk> , the only daughter of Sir Edward Guildford and Eleanor West , daughter of Thomas West , 8th Baron De La <unk> . Her schooling occurred at home together with her brother Richard and her future husband , who was her father 's ward from <unk> . In <unk> , at about 16 , she married Sir John Dudley , who was 20 or 21 years old . The match had been arranged by their parents some years before . Jane Dudley gave birth to 13 children , eight boys and five girls . In most cases it is impossible to establish their <unk> exactly . An exception is Robert , the future favourite of Elizabeth I ; he was born in 1532 as the fifth son , and possibly after the eldest daughter Mary , who became the mother of the <unk> @-@ poet Philip Sidney . The family life of John and Jane Dudley seems to have been happy and was free from any <unk> ; around <unk> a poem praised the " love and devotion " of their marriage . 
- Sir Edward Guildford died in 1534 before he could draw up his last will . Since his son Richard had <unk> him , Guildford 's nephew , John Guildford , claimed the inheritance . The Dudleys maintained that Guildford 's daughter Jane was the natural heir . They finally won the resulting court case with the assistance of Thomas Cromwell . 
- 
- = = Court life = = 
- 
- Jane Dudley served as a lady @-@ in @-@ waiting to Anne <unk> , and later to Anne of <unk> . She was interested in the Reformed religion and , with her husband , moved in evangelical circles from the mid @-@ <unk> . In 1542 John Dudley was created Viscount Lisle . He was on friendly terms with William <unk> , whose sister Catherine became Henry VIII 's last queen in July <unk> . As one of her closest friends , the <unk> Lisle was among the four ladies leading her to the altar on the marriage day . Jane Dudley belonged also to the courtly sympathizers of Anne Askew , whom she contacted during her imprisonment in 1545 – 1546 . The <unk> Protestant was burnt at the stake as a <unk> in July 1546 on the <unk> of the religiously conservative court party around Bishop Stephen <unk> . 
- Renaissance <unk> and science figured large in the Dudley children 's education . In 1553 Jane Dudley herself commissioned two works from the mathematician and <unk> John Dee about heavenly <unk> and the tides . Jane Dudley was close to her children ; her eldest son , Henry , had died during the siege of <unk> in 1544 , aged 19 . A <unk> she wrote in 1552 under a letter by her husband to their then eldest son , John Dudley , 2nd Earl of Warwick , reads : " your <unk> <unk> that <unk> you <unk> <unk> Jane Northumberland " . She also had health problems : In <unk> her husband was unwilling to leave her side , because she " had had her fit again more extreme that she had any time yet . " 
- Under Edward VI John Dudley , Viscount Lisle was raised to the title of Earl of Warwick , while Edward Seymour , Earl of Hereford became Duke of Somerset and Lord Protector . In October 1549 the Protector lost his power in a trial of strength with the Privy Council , from which John Dudley , Earl of Warwick emerged as Lord President of the Council and leader of the government . Somerset , who had been imprisoned in the Tower of London , was soon allowed to rejoin the Council . Before his release , the Duchess of Somerset and the Countess of Warwick had arranged daily banquets in order to reconcile their husbands . A marriage between their respective eldest son and daughter , Anne Seymour and John Dudley , was equally promoted by the two ladies . In June 1550 a grand wedding was staged at the palace of Sheen , attended by the twelve @-@ year @-@ old King Edward . Jane Dudley continued as a great lady at court during the ascendancy of her husband , who became Duke of Northumberland in October <unk> . She was influential with him ; the financier Thomas <unk> and the diplomat Richard Morrison sought her patronage , and she also <unk> for Mary Tudor , who had stood <unk> to one of her daughters in 1545 . 
- 
- = = Mother @-@ in @-@ law to a queen = = 
- 
- King Edward fell ill in early 1553 . He drew up a document , " My <unk> for the <unk> " , whose final version of June 1553 was to settle the Crown on his Protestant cousin Lady Jane Grey , <unk> the claims of his half @-@ sisters Mary and Elizabeth . Jane Grey was the daughter of Frances Grey , Duchess of Suffolk , a niece of Henry VIII by his younger sister Mary . On 25 May 1553 three <unk> <unk> were celebrated at Durham Place , the Dudleys ' London town mansion . Two of their younger children were concerned : Guildford , aged about 17 , married Lady Jane Grey , while Katherine , who was between eight and ten years old , was promised to the Earl of <unk> 's heir , Henry Hastings . A few months later these matches came to be seen as proof of a conspiracy by the Duke of Northumberland to bring his family to the throne . At the time the marriages took place , however , their <unk> implications were not considered significant by even the most suspicious of observers , the Imperial ambassador <unk> de <unk> . Modern historians have considered them either as part of a plot , or as " routine actions of dynastic politics " , in the words of David <unk> . The initiative for the matches had probably come from the <unk> of Northampton . 
- After Edward 's death on 6 July 1553 Northumberland undertook the enforcement of the King 's will . Lady Jane Grey accepted the Crown only after <unk> by her parents and parents @-@ in @-@ law . On 10 July the Duchess of Northumberland accompanied her son and daughter @-@ in @-@ law on their ceremonial entry into the Tower of London , where they were to reside for the rest of the short reign . According to Jane 's own <unk> letter to Queen Mary a few months later , Guildford now wanted to be made king . The young people agreed on having him declared king by Act of Parliament ; but then Jane changed her mind and declared she would only make him a duke . " I will not be a duke , I will be King " , Guildford replied and went to fetch his mother . Furious , the Duchess took the side of her son , before she told him to leave the Tower and go home . Jane , however , insisted that he remain at court . According to her the Duchess also " induced her son not to sleep with me any more " , and it is clear from her writings that Jane disliked her mother @-@ in @-@ law . 
- 
- = = <unk> and struggle for her family = = 
- 
- To claim her right , Mary Tudor began assembling her supporters in East <unk> and demanded to be recognized as queen by the Privy Council in London . When her letter arrived on 10 July 1553 during dinner , the Duchess of Suffolk , Jane 's mother , and the Duchess of Northumberland broke into tears . Mary was gathering strength , and on 14 July the Duke marched to Cambridge with troops to capture her . As it came , he passed a tranquil week until he heard on 20 July that the Council in London had declared for Mary . On the orders of the Privy Council Northumberland himself now proclaimed Queen Mary at the market @-@ place and awaited his arrest . His wife was still in the Tower , but was soon released . She tried to intercede personally for her imprisoned husband and five sons with Mary , who was staying outside London . However , five miles before reaching the court the Duchess was turned away on the Queen 's orders . She then wrote a letter to her friend Lady Paget , the wife of William , Lord Paget , asking her to plead with the Queen 's ladies for her husband 's life . Her plea , if it went not unheard , was in vain , and the Duke of Northumberland was executed on 22 August 1553 on Tower Hill after having recanted his Protestant faith . 
- Following Wyatt 's rebellion , Guildford Dudley was beheaded on 12 February 1554 shortly before his wife . Knowing the Queen 's character , in June 1554 Jane Dudley pleaded with the authorities to allow her remaining sons to hear mass . During 1554 the Duchess and her son @-@ in @-@ law Henry Sidney worked hard pleading with the Spanish nobles around England 's new king consort , Philip of Spain . Lord Paget may also have proved helpful , and Henry Sidney even travelled to Spain in their cause . In the autumn of 1554 the Dudley brothers were released from the Tower , though the eldest , John , died immediately afterwards at Sidney 's house <unk> in Kent . At the same location Philip Sidney was born on 30 November 1554 . His <unk> was his grandmother Jane Dudley , while his <unk> was Philip of Spain . 
- Amid the <unk> of the Dudley family 's possessions in July 1553 , Mary had allowed Jane Dudley to retain her wardrobe and plate , carpets , and other household <unk> , as well as the use of the Duke 's house in Chelsea , London . There , she died on either 15 or 22 January 1555 , and was buried on 1 February at Chelsea Old Church . In her will she tried to provide for her sons financially and thanked the Queen , as well as the many Spanish nobles she had lobbied . The Duchess of <unk> was to receive her green parrot ; to Don Diego de <unk> she gave " the new bed of green velvet with all the furniture to it ; <unk> him even as he hath in my lifetime showed himself like a father and a brother to my sons , so shall [ I ] require him no less to do now their mother is gone " . She also remembered " my lord , my dear husband " , and stipulated : " in no wise let me be opened after I am dead . ... I have not lived to be very bold before women , much more I should be <unk> to come into the hands of any living man , be he <unk> or Surgeon . " She avoided to be specific on religion , but stressed that " who ever <unk> trust to this <unk> world , as I did , may happen to have an overthrow , as I had ; therefore to the worms will I go as I have before written . " 
- 
- = = Ancestry = = 
- 
- 
- 
- = Elgin Cathedral = 
- 
- Elgin Cathedral is a historic ruin in Elgin , Moray , north @-@ east Scotland . The cathedral — dedicated to the Holy Trinity — was established in 1224 on land granted by King Alexander II outside the burgh of Elgin and close to the River <unk> . It replaced the cathedral at Spynie , 3 kilometres ( 1 @.@ 9 mi ) to the north , that was served by a small chapter of eight clerics . The new and bigger cathedral was staffed with 18 canons in <unk> and then increased to 23 by <unk> . After a damaging fire in 1270 , a rebuilding programme greatly enlarged the building . It was unaffected by the Wars of Scottish Independence but again suffered extensive fire damage in 1390 following an attack by Robert III 's brother Alexander Stewart , Earl of Buchan , also known as the Wolf of Badenoch . In 1402 the cathedral precinct again suffered an incendiary attack by the followers of the Lord of the Isles . The number of clerics required to staff the cathedral continued to grow , as did the number of craftsmen needed to maintain the buildings and surrounds . The number of canons had increased to 25 by the time of the Scottish Reformation in 1560 , when the cathedral was abandoned and its services transferred to Elgin 's parish church of St Giles . After the removal of the lead that <unk> the roof in 1567 , the cathedral steadily fell into decay . Its deterioration was arrested in the 19th century , by which time the building was in a substantially ruinous condition . 
- The cathedral went through periods of enlargement and renovation following the fires of 1270 and 1390 that included the doubling in length of the choir , the provision of outer aisles to the northern and southern walls of both the nave and choir . Today , these walls are at full height in places and at foundation level in others yet the overall cruciform shape is still discernible . A mostly intact octagonal chapter house dates from the major enlargement after the fire of 1270 . The gable wall above the double door entrance that links the west towers is nearly complete and was rebuilt following the fire of 1390 . It accommodates a large window opening that now only contains <unk> tracery work and fragments of a large rose window . <unk> and chest tombs in both transepts and in the south aisle of the choir contain effigies of bishops and knights , and large flat slabs in the now grass @-@ covered floor of the cathedral mark the positions of early graves . The homes of the dignitaries and canons , or manses , stood in the chanonry and were destroyed by fire on three occasions : in 1270 , 1390 and 1402 . The two towers of the west front are mostly complete and were part of the first phase of construction . Only the precentor 's manse is substantially intact ; two others have been incorporated into private buildings . A protective wall of massive proportions surrounded the cathedral precinct , but only a small section has survived . The wall had four access gates , one of which — the <unk> Port — still exists . 
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- = = Early cathedral churches of Moray = = 
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- The Diocese of Moray was a regional bishopric unlike the pre @-@ eminent see of the Scottish church , St Andrews , which had evolved from a more ancient monastic Celtic church and administered scattered localities . It is uncertain whether there were bishops of Moray before c . 1120 but the first known <unk> — possibly later translated to <unk> — was Gregory ( or <unk> ) . He was probably bishop in name only , with the first resident diocesan being Richard of Lincoln . Gregory was a signatory to the foundation charter of <unk> Priory , issued by Alexander I ( <unk> mac Maíl <unk> ) between December 1123 and April 1124 , and again in a charter defining the legal rights of the same monastery . He is recorded for the last time when he witnessed a charter granted by David I to <unk> Abbey in c . 1128 . After the suppression of <unk> of Moray 's rebellion in 1130 , King David must have regarded the continued presence of bishops in Moray as essential to the stability of the province . These early bishops had no settled location for their cathedral , and sited it successively at the churches of <unk> , <unk> and Spynie . Pope Innocent III issued an <unk> bull on 7 April 1206 that allowed bishop Bricius de Douglas to fix his cathedral church at Spynie — its inauguration was held between spring 1207 and summer 1208 . A chapter of five dignitaries and three ordinary canons was authorised and based its constitution on that of Lincoln Cathedral . Elgin became the lay centre of the province under David I , who probably established the first castle in the town , and it may have been this castle , with its promise of better security , that prompted Bricius , before July 1216 , to petition the Pope to move the seat from Spynie . 
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- = = Cathedral church at Elgin = = 
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- Despite Bricius 's earlier appeal , it was not until Andreas de Moravia 's episcopate that Pope Honorius III issued his bull on 10 April 1224 <unk> his <unk> Gilbert de Moravia , Bishop of Caithness , Robert , Abbot of <unk> and Henry , Dean of Ross to examine the suitability of transferring the <unk> to Elgin . The Bishop of Caithness and the Dean of Ross performed the translation ceremony on 19 July 1224 . On 10 July , Alexander II ( <unk> mac <unk> ) had agreed to the <unk> in an <unk> that referred to his having given the land previously for this purpose . The land grant <unk> the Papal mandate and there is evidence that building had started in around 1215 . Construction of the cathedral was completed after <unk> . <unk> John of <unk> recorded without explanation that in 1270 the cathedral church and the canons ' houses had burned down . The cathedral was rebuilt in a larger and <unk> style to form the greater part of the structure that is now visible , work that is supposed to have been completed by the outbreak of the Wars of Scottish Independence in <unk> . Although Edward I of England took an army to Elgin in <unk> and again in <unk> , the cathedral was left unscathed , as it was by his grandson Edward III during his assault on Moray in 1336 . 
- Soon after his election to the see in <unk> – 63 , Bishop Alexander Bur requested funds from Pope Urban V for repairs to the cathedral , citing neglect and hostile attacks . In August <unk> Bur began protection payments to Alexander Stewart , Lord of Badenoch , known as the Wolf of Badenoch , who became Earl of Buchan in <unk> , and who was son of the future King Robert II . Numerous disputes between Bur and Buchan culminated in Buchan 's excommunication in February 1390 and the bishop turning to Thomas Dunbar , son of the Earl of Moray , to provide the protection service . 
- These acts by the bishop , and any frustration Buchan may have felt about the <unk> of his brother Robert Stewart , Earl of Fife as guardian of Scotland , may have caused him to react <unk> : in May , he descended from his island castle on <unk> and burned the town of <unk> , followed in June by the burning of Elgin and the cathedral with its manses . It is believed that he also burned <unk> Priory at this time , which was officially under the Bishop 's protection . Bur wrote to Robert III seeking reparation for his brother 's actions in a letter stating : 
- Robert III granted Bur an annuity of £ 20 for the period of the bishop 's lifetime , and the Pope provided income from the Scottish Church during the following decade . In 1400 , Bur wrote to the Abbot of <unk> complaining that the abbot 's prebendary churches in the Moray diocese had not paid their <unk> towards the cathedral restoration . In the same year Bur wrote to the rector of <unk> church , telling him that he now owed three years ' <unk> of the subsidy that had been imposed on non @-@ prebendary churches in <unk> . Again , on 3 July 1402 , the burgh and cathedral precinct were attacked , this time by Alexander of Lochaber , brother of <unk> of Islay , Lord of the Isles ; he spared the cathedral but burned the manses . For this act , Lochaber and his captains were excommunicated , prompting Lochaber 's return in September to give reparation and gain <unk> . In <unk> , the money saved during an <unk> vacancy was diverted to the rebuilding process and in <unk> a grant from the customs of Inverness was provided . Increasingly , the appropriation of the parish church revenues led in many cases to churches becoming dilapidated and unable to attract educated priests . By the later Middle Ages , the standard of pastoral care outside the main <unk> had significantly declined . 
- Bishop John <unk> ( <unk> – 14 ) contributed greatly to the rebuilding of the cathedral , as evidenced by the inscription on his tomb praising his efforts . When he died , the chapter met secretly — " in <unk> camera <unk> in <unk> <unk> <unk> " — and agreed that should one of their number be elected to the see , the bishop would grant one third of the income of the bishopric annually until the rebuilding was finished . The major alterations to the west front were completed before 1435 and contain the arms of Bishop Columba de Dunbar ( 1422 – 35 ) , and it is presumed that both the north and south aisles of the choir were finished before <unk> , as the south aisle contains the tomb of John de Winchester ( 1435 – 60 ) . Probably the last important rebuilding feature was the major restructuring of the chapterhouse between <unk> and 1501 , which contains the arms of Bishop Andrew Stewart . 
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- = = = <unk> organisation = = = 
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- The dignitaries and canons constituted the chapter and had the primary role of aiding the bishop in the governance of the diocese . Often the bishop was the titular head of the chapter only and was excluded from its decision @-@ making processes , the chapter being led by the dean as its superior . As the diocese of Moray based its constitution on that of Lincoln Cathedral , the bishop was allowed to participate within the chapter but only as an ordinary canon . Moray was not unique in this : the bishops of Aberdeen , <unk> , Caithness , Orkney and Ross were also canons in their own chapters . Each morning , the canons held a meeting in the chapterhouse where a chapter from the canonical rule book of St Benedict was read before the business of the day was discussed . 
- Bishop Bricius 's chapter of eight clerics consisted of the dean , precentor , treasurer , chancellor , archdeacon and three ordinary canons . His successor , Bishop Andreas de Moravia , greatly expanded the chapter to cater for the much enlarged establishment by creating two additional hierarchical posts ( <unk> and <unk> ) and added 16 more <unk> . In total , 23 <unk> had been created by the time of Andreas ' death , and a further two were added just before the Scottish Reformation . <unk> churches were at the <unk> of the bishop as the churches either were within the diocesan lands or had been granted to the bishop by a landowner as patronage . In the case of Elgin Cathedral , the de Moravia family , of which Bishop Andreas was a member , is noted as having the patronage of many churches given as <unk> . 
- Rural <unk> , or <unk> of Christianity as they were known in the Scottish Church , supervised the priests in the <unk> and implemented the bishop 's edicts . There were four <unk> in the Moray diocese — Elgin , Inverness , <unk> and <unk> — and these provided the income not only for the cathedral and chapter but also for other religious houses within and outside the diocese . Many churches were allocated to support designated canons , and a small number were held in common . The bishop received <unk> and prebendary income in his separate positions as <unk> and canon . 
- The government of the diocese affecting both clergy and laity was vested entirely in the bishop , who appointed officers to the ecclesiastical , criminal and civil courts . The bishop , assisted by his chapter , produced the church laws and regulations for the bishopric and these were enforced at occasional diocesan <unk> by the bishop or , in his absence , by the dean . Appointed officials <unk> at <unk> courts looking at matters affecting <unk> , marriages , divorces , widows , orphans , wills and other related legal matters . In Moray , these courts were held in Elgin and Inverness . By <unk> the Bishop of Moray held all his lands in one <unk> and had Courts of <unk> presided over by <unk> and <unk> to ensure the payment of revenues from his estates . 
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- = = = Cathedral offices = = = 
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- Large cathedrals such as Elgin had many chapel altars and daily services and required to be suitably staffed with canons assisted by a plentiful number of chaplains and vicars . Bishop Andreas allowed for the canons to be aided by seventeen vicars made up of seven priests , five deacons and five sub @-@ deacons — later the number of vicars was increased to twenty five . In <unk> the vicars at Elgin could not live on their <unk> and Bishop John of <unk> provided them with the income from two churches and the patronage of another from Thomas Randolph , second Earl of Moray . By 1489 one vicar had a <unk> of 12 marks ; six others , 10 marks ; one , eight marks ; three , seven marks , and six received five marks ; each vicar was employed directly by a canon who was required to provide four months ' notice in the event of his employment being terminated . The vicars were of two kinds : the vicars @-@ choral who worked chiefly in the choir taking the main services and the <unk> chaplains who performed services at the individual foundation altars though there was some overlapping of duties . Although the chapter followed the constitution of Lincoln , the form of divine service copied that of Salisbury Cathedral . It is recorded that Elgin 's vicars @-@ choral were subject to disciplinary correction for shortcomings in the performance of the services , resulting in fines . More serious offences could end in corporal punishment , which was administered in the chapterhouse by the sub @-@ dean and witnessed by the chapter . King Alexander II founded a <unk> for the soul of King Duncan I who died in battle with Macbeth near Elgin . The chapel most frequently referenced in records was St Thomas the <unk> , located in the north transept and supported by five chaplains . Other <unk> mentioned are those of the Holy <unk> , St Catherine , St <unk> , St Lawrence , St Mary Magdalene , St Mary the Virgin and St Michael . By the time of Bishop Bur 's episcopate ( <unk> – <unk> ) , the cathedral had 15 canons ( excluding dignitaries ) , 22 vicars @-@ choral and about the same number of chaplains . 
- Despite these numbers , not all the clergy were regularly present at the services in Elgin Cathedral . <unk> was an enduring fact of life in all cathedrals in a period when <unk> clerics would accept positions in other cathedrals . This is not to say that the time spent away from the chanonry was without permission , as some canons were appointed to be always present while others were allowed to attend on a part @-@ time basis . The dean of Elgin was permanently in attendance ; the precentor , chancellor , and treasurer , were available for half the year . The non @-@ permanent canons had to attend continuously for three months . The chapter decided in <unk> to <unk> persistently absent canons who broke the terms of their attendance by removing one seventh of their income . In the Diocese of Aberdeen and it is assumed in other <unk> also , when important decisions of the chapter had to be taken , an <unk> canon had to appoint a <unk> to act on his behalf — this was usually one of the dignitaries who had a higher likelihood of being present . At Elgin in <unk> , many canons were not <unk> by the terms of their leave of absence , resulting in each of them receiving a formal warning and a summons ; despite this , ten canons refused to attend and had a seventh of their prebendary income deducted . The bulk of the workload fell to the vicars and a smaller number of permanent canons who were responsible for celebrating high mass and for leading and arranging <unk> and feast day processions . Seven services were held daily , most of which were solely for the clergy and took place behind the <unk> screen which separated the high altar and choir from lay <unk> . Only cathedrals , collegiate churches and large burgh churches were <unk> to perform the more elaborate services ; the services in the parish churches were more basic . 
- The clergy were augmented by an unknown number of lay lawyers and clerks as well as masons , carpenters , <unk> , <unk> , and <unk> . Master Gregory the <unk> and Master Richard the <unk> are mentioned in the <unk> of the cathedral . 
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- = = = <unk> and burgh = = = 
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- The chanonry , referred to in the cathedral 's <unk> as the college of the chanonry or simply as the college , was the collection of the canons ' manses that were grouped around the cathedral . A substantial wall , over 3 @.@ 5 metres ( 11 ft ) high , 2 metres ( 6 ft 7 in ) thick and around 820 metres ( 2 @,@ 690 ft ) in length , enclosed the cathedral and manses and separated the church community from the laity ; only the manse of <unk> lay outside the west wall . The houses of 17 vicars and the many chaplains were also situated outside the west wall . The wall had four gates : the West Port gave access to the burgh , the North Port provided access to the road to the bishop 's palace of Spynie , the South Port opened opposite the hospital of <unk> Dieu and the surviving East or <unk> Port allowed access to the <unk> called Le <unk> . The <unk> Port illustrates the <unk> defences of the gate @-@ houses ( Fig . 1 ) . Each canon or <unk> was responsible for providing his own manse and was built to reflect his status within the chapter . The castle having become unsuitable , Edward I of England stayed at the manse of <unk> on 10 and 11 September <unk> as did James II in <unk> . In 1489 , a century after the incendiary attack on the cathedral and precinct in 1390 and 1402 , the cathedral records revealed a chanonry still lacking many of its manses . The chapter ordered that 13 canons , including the <unk> and the archdeacon , should immediately " erect , construct , build , and duly repair their manses , and the enclosures of their gardens within the college of Moray " . The manse of the precentor , erroneously called the Bishop 's House , is partially ruined and is dated <unk> . ( Fig . 2 ) <unk> of the Dean 's <unk> and the Archdeacon 's <unk> ( Fig . 3 ) are now part of private buildings . 
- The hospital of <unk> Dieu , dedicated to St Mary and situated near the cathedral precinct but outside the chanonry , was established by Bishop Andreas before 1237 for the aid of the poor . It suffered fire damage in 1390 and again in <unk> . The cathedral clerks received it as a secular benefice but in later years it may , in common with other hospitals , have become dilapidated through a lack of patronage . Bishop James Hepburn granted it to the <unk> of Elgin on 17 November <unk> , perhaps in an effort to preserve its existence . The property was taken into the ownership of the Crown after the Reformation and in <unk> was granted to the burgh by James VI for educational purposes and for helping the poor . In 1624 , an <unk> was constructed to replace the original building , but in <unk> a storm substantially damaged its relatively intact ruins . The remnants of the original building were finally demolished during a 19th @-@ century redevelopment of the area . 
- There were two <unk> in the burgh . The Dominican Black Friars friary was founded in the western part of the burgh around 1233 . The Franciscan ( Friars Minor <unk> ) Grey Friars friary was later founded in the eastern part of the burgh sometime before 1281 . It is thought that this latter Grey Friars foundation did not long survive , but was followed between <unk> and <unk> by the foundation of a friary near Elgin Cathedral by the Franciscan ( <unk> ) Grey Friars . The building was transferred into the ownership of the burgh around <unk> and later became the Court of Justice in 1563 . In 1489 , the chapter founded a school that was not purely a song school for the cathedral but was also to be available to provide an education in music and reading for some children of Elgin . 
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- = = = Post – Reformation = = = 
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- In August 1560 , parliament assembled in Edinburgh and legislated that the Scottish church would be Protestant , the Pope would have no authority and that the Catholic mass was illegal . Scottish cathedrals now survived only if they were used as parish churches and as Elgin had been fully served by the Kirk of St Giles , its cathedral was abandoned . An act of parliament passed on 14 February 1567 authorised Regent Lord James Stewart 's Privy Council to order the removal of the lead from the roofs of both Elgin and Aberdeen cathedrals , to be sold for the upkeep of his army , but the <unk> ship that was intended to take the cargo to Holland capsized and sank in Aberdeen harbour . In 1615 , John Taylor , the ' Water Poet ' , described Elgin Cathedral as " a <unk> and beautiful church with three <unk> , the walls of it and the <unk> all yet standing ; but the <unk> , <unk> and many marble monuments and <unk> of <unk> and <unk> personages all broken and defaced " . 
- <unk> had set in and the roof of the eastern limb collapsed during a gale on 4 December 1637 . In 1640 the General Assembly ordered Gilbert Ross , the minister of St Giles <unk> , to remove the <unk> screen which still partitioned the choir and presbytery from the nave . Ross was assisted in this by the <unk> of <unk> and <unk> who <unk> it up for <unk> . It is believed that the destruction of the great west window was caused by Oliver Cromwell 's soldiers sometime between 1650 and 1660 . 
- At some point the cathedral grounds had become the burial ground for Elgin . The town council arranged for the boundary wall to be repaired in 1685 but significantly , the council ordered that the stones from the cathedral should not be used for that purpose . Although the building was becoming increasingly unstable the chapterhouse continued to be used for meetings of the Incorporated Trades from <unk> to 1676 and then again from 1701 to around <unk> . No attempt was made to <unk> the structure and on Easter Sunday <unk> the central tower gave way , demolishing the nave . Following this collapse , the " quarrying " of the cathedral 's stone work for local projects began . Many artists visited Elgin to sketch the ruins , and it is from their work that the slow but continuing <unk> can be observed . By the closing years of the 18th century , travellers to Elgin began to visit the ruin , and pamphlets giving the history of the cathedral were prepared for those early tourists . In 1773 Samuel Johnson recorded , " a paper was put into our hands , which deduced from sufficient authorities the history of this venerable ruin . " 
- Since the abolition of bishops within the Scottish Church in 1689 , ownership of the abandoned cathedral fell to the crown , but no attempt to halt the decline of the building took place . <unk> the necessity to <unk> the structure , the Elgin Town Council initiated the reconstruction of the perimeter wall in 1809 and cleared debris from the surrounding area in about 1815 . The Lord Provost of Elgin petitioned the King 's <unk> for assistance to build a new roof for the chapterhouse and in 1824 , £ 121 was provided to the architect Robert Reid for its construction . Reid was significant in the development of a conservation policy for historical buildings in Scotland and was to become the first Head of the Scottish Office of Works ( <unk> ) in 1827 . It was probably during his tenure at the <unk> that the supporting buttresses to the choir and transept walls were built . 
- In 1824 John Shanks , an Elgin <unk> and an important figure in the conservation of the cathedral , started his work . <unk> by local gentleman Isaac <unk> , Shanks cleared the grounds of centuries of rubbish dumping and rubble . Shanks was officially appointed the site 's Keeper and <unk> in 1826 . Although his work was highly valued at the time and brought the cathedral back into public focus , his <unk> clearance work may have resulted in much valuable evidence of the cathedral 's history being lost . He died on 14 April 1841 , aged 82 . A fortnight later , the Inverness Courier published a commemorative piece on Shanks , calling him the " <unk> or <unk> of Elgin Cathedral " , and writing : 
- Some minor works took place during the remainder of the 19th century and continued into the early 20th century . During the 1930s further maintenance work ensued that included a new roof to protect the vaulted ceiling of the south choir aisle . From 1960 onwards the crumbling sandstone blocks were replaced and new windows were fitted in the chapterhouse , which was re @-@ <unk> to preserve its vaulted ceiling . From 1988 to 2000 , the two western towers were substantially overhauled with a viewing platform provided at the top of the north tower . 
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- = = Building phases = = 
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- = = = Construction 1224 – 1270 = = = 
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- The first church was markedly cruciform in shape and smaller than the present floor plan . This early structure had a choir without aisles and more truncated , and a nave with only a single aisle on its north and south sides ( Fig . 4 ) . The central tower rose above the crossing between the north and south transepts and may have held bells in its upper storey . The north wall of the choir is the earliest extant structure , dating to the years immediately after the church 's 1224 foundation ; the clerestory windows on top of it are from the later post @-@ 1270 reconstruction . This wall has blocked up windows extending to a low level above ground , indicating that it was an external wall and proving that the eastern limb then had no aisle ( Fig . 5 ) . 
- The south transept 's southern wall is nearly complete , displaying the fine workmanship of the first phase . It shows the Gothic pointed arch style in the windows that first appeared in France in the mid @-@ 12th century and was apparent in England around 1170 , but hardly appeared in Scotland until the early 13th century . It also shows the round early Norman window design that continued to be used in Scotland during the entire Gothic period ( Fig . 6 ) . The windows and the <unk> are of finely cut ashlar sandstone . A doorway in the south @-@ west portion of the wall has large <unk> and has a pointed oval window placed above it . <unk> to the doorway are two lancet @-@ arched windows that are topped at the clerestory level with three round @-@ headed windows . The north transept has much less of its structure preserved , but much of what does remain , taken together with a study by John <unk> in 1693 , shows that it was similar to the south transept , except that the north transept had no external door and featured a stone turret containing a staircase . 
- The west front has two 13th century <unk> towers 27 @.@ 4 metres ( 90 ft ) high that were originally topped with wooden <unk> covered in protective lead . Although the difference between the construction of the base course and the transepts suggests that the towers were not part of the initial design , it is likely that the building process was not so far advanced that the masons could fully integrate the nave and towers into each other ( Fig . 7 ) . 
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- = = = <unk> and re @-@ construction after 1270 = = = 
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- After the fire of 1270 , a programme of reconstruction was launched , with repairs and a major enlargement . Outer aisles were added to the nave , the eastern wing comprising the choir and presbytery was doubled in length and had aisles provided on its north and south sides , and the octagonal chapterhouse was built off the new north choir aisle ( <unk> . 8 & 9 ) . The new northern and southern aisles ran the length of the choir , past the first bay of the presbytery , and contained <unk> and chest tombs . The south aisle of the choir contained the tomb of bishop John of Winchester , suggesting a completion date for the reconstructed aisle between 1435 and <unk> ( Fig . 10 ) . Chapels were added to the new outer aisles of the nave and were partitioned from each other with wooden screens . The first bay at the west end of each of these aisles and adjacent to the western towers did not contain a chapel but instead had an access door for the laity . 
- In June 1390 , Alexander Stewart , Robert III 's brother , burned the cathedral , manses and burgh of Elgin . This fire was very destructive , requiring the central tower to be completely rebuilt along with the principal arcades of the nave . The entire western gable between the towers was reconstructed and the main west doorway and chapterhouse were <unk> . The internal stonework of the entrance is late 14th or early 15th century and is <unk> carved with branches , vines , <unk> and oak leaves . A large pointed arch opening in the gable immediately above the main door contained a series of windows , the uppermost of which was a circular or rose window dating from between 1422 and 1435 . Just above it can be seen three coats of arms : on the right is that of the bishopric of Moray , in the middle are the Royal Arms of Scotland , and on the left is the <unk> shield of Bishop Columba Dunbar ( Fig . 11 ) . The walls of the nave are now very low or even at foundation level , except one section in the south wall which is near its original height . This section has windows that appear to have been built in the 15th century to replace the 13th century openings : they may have been constructed following the 1390 attack ( Fig . 12 ) . Nothing of the elevated structure of the nave remains , but its appearance can be deduced from the scarring seen where it attached to the eastern walls of the towers . Nothing of the crossing now remains following the collapse of the central tower in <unk> . Elgin Cathedral is unique in Scotland in having an English style octagonal chapterhouse and French influenced double aisles along each side of the nave ; in England , only Chichester Cathedral has similar aisles . The chapterhouse , which had been attached to the choir through a short vaulted vestry , required substantial modifications and was now provided with a vaulted roof supported by a single pillar ( <unk> . 13 & 14 ) . The chapterhouse measures 10 @.@ 3 metres ( 34 ft ) high at its apex and 11 @.@ 3 metres ( 37 ft ) from wall to opposite wall ; it was substantially rebuilt by Bishop Andrew Stewart ( <unk> – 1501 ) , whose coat of arms is placed on the central pillar . Bishop Andrew was the half @-@ brother of King James II . The delay to the completion of these repairs until this bishop 's <unk> demonstrates the extent of the damage from the 1390 attack . 
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- = = = 19th and 20th century <unk> = = = 
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- In 1847 – 8 several of the old houses associated with the cathedral on the west side were demolished , and some minor changes were made to the boundary wall . Structural reinforcement of the ruin and some reconstruction work began in the early 20th century , including restoration of the east gable rose window in 1904 and the replacement of the missing form pieces , <unk> , and decorative ribs in the window in the north @-@ east wall of the chapterhouse ( Fig . 15 ) . By 1913 , <unk> the walls and additional waterproofing of the wall tops were completed . In 1924 the ground level was lowered and the 17th century tomb of the Earl of <unk> was <unk> . Further repairs and restoration ensued during the 1930s , including the partial <unk> of some 19th century <unk> ( Fig . 16 ) , the reconstruction of sections of the nave piers using recovered pieces ( Fig . 17 ) , and the addition of external roofing to the vault in the south choir in 1939 ( Fig . 18 ) . From 1960 to 2000 , masons restored the cathedral 's crumbling stonework ( Fig . 19 ) and between 1976 and 1988 , the window tracery of the chapterhouse was gradually replaced , and its re @-@ roofing was completed ( Fig . 20 ) . <unk> , glazing , and a new roof were added to the south @-@ west tower between 1988 and 1998 and comparable restoration work was completed on the north @-@ west tower between 1998 and 2000 ( Fig . 21 ) . 
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- = = <unk> = = 
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- Andreas de Moravia – buried in the south side of the choir under a large blue marble stone 
- David de Moravia – buried in the choir 
- William de Spynie – buried in the choir 
- Andrew Stewart ( d . 1501 ) 
- Alexander Gordon , 1st Earl of <unk> 
- Columba de Dunbar ( c . <unk> – 1435 ) was Bishop of Moray from 1422 until his death 
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- = = <unk> figures = = 
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- = St Mary 's Church , <unk> Alderley = 
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- St Mary 's Church is an Anglican church at the end of a lane to the south of the village of <unk> Alderley , Cheshire , England . It dates from the 14th century , with later additions and a major restoration in the late @-@ 19th century . The church is recorded in the National Heritage List for England as a designated Grade I listed building . 
- The church was built in the Gothic style , and has historically been associated with the Stanley family of Alderley . Its major features include a fine tower , the Stanley pew which is entered by an outside staircase , a 14th @-@ century font , the western gallery , and monuments to the Lords Stanley of Alderley . The grounds contain a 17th @-@ century former schoolhouse , now used as a parish hall , a medieval church cross , and the Stanley <unk> , which dates from 1909 . An ancient yew tree stands in the churchyard . 
- St Mary 's is an active parish church in the diocese of Chester , the <unk> of Macclesfield , and the <unk> of <unk> . Its benefice is combined with that of St Catherine 's , <unk> . 
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- = = History = = 
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- The oldest parts of the church date from around 1300 , but it is likely that a timber @-@ framed church existed on the site before then . The church 's original dedication was to Saint Lawrence , but that was later changed to Saint Mary . A clerestory was added in the 15th century . The tower was built in 1530 , and the Stanley pew was added in about 1600 . The west gallery , which contained an organ , was installed in 1803 . In 1856 , the chancel was completely rebuilt , to a design by <unk> and <unk> , paid for by the Stanley family . The vestry was constructed in 1860 . The church was restored between 1877 and 1878 by Paley and Austin ; the nave floor was lowered , the pulpit was replaced , plaster was removed from the roof and the walls , and the box pews were replaced by new oak pews . The tower clock , made in 1743 , was renovated in 1997 . In 2000 , the 16th @-@ century wooden bell @-@ frame was strengthened by the addition of a steel frame , and the Stanley pew was restored . 
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- = = Architecture = = 
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- = = = <unk> = = = 
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- St Mary 's is built of ashlar buff and red sandstone <unk> locally at Alderley Edge , and the roof is of <unk> stone slates . Its plan consists of a tower at the west end , a four @-@ bay nave with north and south aisles , a chancel with a vestry to its north , and a south porch . Over the north aisle is a <unk> window . The tower has diagonal buttresses . Its west door has 14th @-@ century <unk> and above the door is a three @-@ light window . The stage above this contains <unk> ' windows on the north and west faces and a diamond @-@ shaped clock on the south face . Above these the <unk> windows on all faces have two lights . The top of the tower is embattled and contains the bases of eight pinnacles . Below the parapet is a string course with large grotesque <unk> . At the west end of the nave roof is a bellcote . The Stanley pew projects to the east of the south porch . In the porch are grooves which were cut where arrows were sharpened . 
- 
- = = = Interior = = = 
- 
- The barrel @-@ shaped nave roof dates possibly from the early 16th century . The early 17th @-@ century Stanley pew at the eastern end of the south aisle is at the level of an upper storey , and is entered by a flight of steps from outside the church . Its front is richly carved and displays six panels with coats of arms . Richards states that it is one of the finest of its kind in the country and that it is unique in Cheshire . At the west end of the church is a late @-@ 18th @-@ century musicians ' gallery , whose front panel has painted coats of arms . The gallery contains the organ which replaces an earlier organ . This was presented by Lady <unk> Stanley in 1875 and was made by Hill and Company of London at a cost of £ 350 ( equivalent to £ 30 @,@ 000 in 2015 ) , An oak document chest in the tower has been dated to 1686 . The 14th @-@ century font was buried in the churchyard during the Commonwealth , dug up in 1821 and restored to use in the church in 1924 . It consists of a plain circular bowl on four short cylindrical columns with moulded bases . Richards considers it to be one of the finest examples of 14th @-@ century work in Cheshire . The church has two old Bibles , a <unk> Bible and a <unk> Bible . 
- The chancel contains memorials to the Lords Stanley of Alderley . The memorial to John Stanley , 1st Baron Stanley of Alderley contains his effigy dressed in peer 's robes lying under a canopy with his hand on a book , dated 1856 and by Richard <unk> . On the other side of the chancel is a memorial to his son Edward Stanley , his effigy holding a scroll in his hand and with a dog at his feet . <unk> in brass on the side of the memorial are the figures of his widow and children . Lady Stanley is seated in the middle with their four surviving sons on her right , five surviving daughters on her left and three children who had died at a young age at her knee and on her lap . A memorial <unk> to John Constantine Stanley , who died in 1878 , is by Joseph <unk> . The chancel contains a monument to Rev. Edward <unk> , rector of the church from 1625 to 1630 . 
- The stained glass in the east window , dated 1856 , was made by William <unk> . The glass in a south window in the chancel of 1909 was made by Morris & Co . The east window in the north aisle , dated 1920 is by Irene <unk> . The stained glass window to the left of the pulpit was donated by the Greg family of <unk> Mill . The stained glass in the window at the west end of the north aisle is to the memory of the wife of Edward John Bell , rector from 1870 to 1907 , and was made by Clayton and Bell in 1877 . The tower holds a ring of six bells , hung for change ringing , five of which were cast in 1787 by <unk> of Gloucester , and the sixth by Charles and George <unk> at the <unk> Bell <unk> in 1847 . A seventh , unused , bell dates from 1686 and has been noted as being of historical importance by the Church Buildings Council of the Church of England . The parish registers begin in 1629 , and the churchwardens ' accounts in 1612 . 
- 
- = = External features = = 
- 
- The sandstone schoolhouse in the churchyard was built in 1628 ; the school room was on the ground floor and the schoolmaster 's accommodation was above . A large room was added to the rear in 1817 , and in 1908 the building was restored and presented to the parish by Lord Stanley . It is now used as a parish hall and is listed Grade II * . 
- The medieval church cross in the churchyard , the Stanley <unk> , and the churchyard walls , gate piers and gates , are Grade II listed . 
- The mausoleum was built in 1909 by Edward <unk> , 4th Lord Stanley . He died in 1925 and it contains his ashes and those of his wife , Mary Katherine , who died in 1929 . The mausoleum is built in ashlar buff and red sandstone with a <unk> stone @-@ slate roof . It was designed in the neo @-@ <unk> style by Paul <unk> , and is rectangular in shape , with two <unk> and a three @-@ bay north front . The central bay contains a door , above which is the Stanley crest , a three @-@ light window and a date plaque in the gable . On the sides of the upper storey are three four @-@ light windows . Inside the mausoleum is a white marble sarcophagus . The yew tree in the churchyard is 1 @,@ 200 years old . 
- 
- = = Rediscovery of the crypt = = 
- 
- It had been known that under the church was a vault containing the remains of some members of the Stanley family but its whereabouts were not known until they were discovered by an architect in 2007 . A stone slab was removed exposing steps leading to a crypt under the chancel . This contained six coffins , four of which contained the bodies of the first and second Lords Stanley and their wives . Once the details had been recorded , the crypt was <unk> . 
- 
- = = Present activities = = 
- 
- St Mary 's holds a variety of Anglican services on Sundays and offers a range of church activities . The church is open to visitors at advertised times and guided tours are available . A parish magazine is published monthly . 
- 
- 
- = Tawny nurse shark = 
- 
- The tawny nurse shark ( Nebrius <unk> ) is a species of carpet shark in the family <unk> , and the only extant member of the genus Nebrius . It is found widely along <unk> in the Indo @-@ Pacific , preferring reefs , sandy flats , and seagrass beds from very shallow water to a depth of 70 m ( 230 ft ) . With a cylindrical body and a broad , flattened head , the tawny nurse shark is quite similar in appearance to the nurse shark ( Ginglymostoma <unk> ) of the Atlantic and East Pacific , from which it can be distinguished by its pointed @-@ tipped dorsal fins and narrow , sickle @-@ shaped pectoral fins . The maximum recorded length of the tawny nurse shark is 3 @.@ 2 m ( 10 ft ) . 
- <unk> in habits , the tawny nurse shark tends to spend the day resting in piles of two dozen or more individuals inside caves or under ledges . At night , it is an active @-@ swimming predator that uses a powerful <unk> force to extract prey from inside holes and crevices . The diet of this species consists mainly of <unk> , though they also take other invertebrates , small bony fishes , and rarely sea snakes . It is aplacental viviparous , meaning the embryos hatch from egg capsules inside the mother . It is the only carpet shark in which the embryos are <unk> , feeding on eggs produced by the mother while inside the uterus . The litter size may be as small as one or two , based on the large size of near @-@ term embryos . 
- Compared to the nurse shark , the tawny nurse shark has a more placid disposition and will often allow divers to touch and play with it . However , it should be accorded respect due to its powerful jaws and sharp teeth . This species is caught by commercial fisheries across most of its range for meat , fins , liver oil , leather , and <unk> . It is also esteemed as a game fish off Queensland , Australia , and is known for its habit of spitting water in the faces of its captors . The International Union for Conservation of Nature ( IUCN ) has assessed the tawny nurse shark as Vulnerable , with <unk> in several areas already diminished or extirpated . 
- 
- = = Taxonomy and phylogeny = = 
- 
- The tawny nurse shark was first described by French naturalist René @-@ <unk> Lesson as <unk> <unk> , based on a 1 @.@ 4 m ( 4 ft 7 in ) long specimen from New Guinea . His short account was published in 1831 in Voyage au tour du <unk> , sur la <unk> La <unk> . A more detailed description , along with an illustration , was published by German naturalist Eduard <unk> in 1837 as Nebrius concolor , based on a specimen from the Red Sea . Both names were retained , often in separate genera ( Ginglymostoma and Nebrius respectively ) , until they were synonymized by Leonard <unk> in 1984 . <unk> recognized that the tooth shape differences used to separate these species were the result of differences in age , with N. concolor representing younger individuals . 
- The genus name Nebrius is derived from the Greek word <unk> or <unk> , meaning the skin of a <unk> . The specific epithet <unk> is Latin for " rust @-@ colored " . Other common names for this species include giant <unk> shark , Madame X ( a name coined by the shark fisherman Norman Caldwell in the 1930s for the then @-@ unidentified Australian specimens ) , nurse shark , rusty <unk> , rusty shark , <unk> shark , spitting shark , and tawny shark . Based on morphological similarities , Nebrius is believed to be the sister genus of Ginglymostoma , with both being placed in a clade that also contains the short @-@ tail nurse shark ( <unk> <unk> ) , the whale shark ( <unk> <unk> ) , and the zebra shark ( <unk> <unk> ) . 
- 
- = = Distribution and habitat = = 
- 
- The tawny nurse shark is widely distributed in the Indo @-@ Pacific region . In the Indian Ocean , it is found from KwaZulu @-@ Natal , South Africa northward to the Red Sea , Persian Gulf and India , including Madagascar , Mauritius , the <unk> Archipelago , the <unk> , and the Maldives . In the western Pacific , it occurs from southern Japan and the coast of China to the Philippines , Southeast Asia , and Indonesia , to as far south as the northern coast of Australia . In the central Pacific , it has been reported from off New Caledonia , Samoa , <unk> , the Marshall Islands , and Tahiti . <unk> teeth belonging to this species have been found in the <unk> Formation of northern Brazil , dating back to the Lower Miocene ( 23 – 16 Ma ) . The presence of these fossils indicates that the range of the tawny nurse shark once extended to the tropical Atlantic Ocean , prior to the formation of the Isthmus of Panama . 
- An inshore species , the tawny nurse shark inhabits continental and insular shelves over sandy flats or beds of seagrass , as well as along the outer edges of coral or rocky reefs . This shark may be found from the surf zone , often in water barely deep enough to cover its body , to a maximum depth of 70 m ( 230 ft ) on coral reefs ; it is most common at a depth of 5 – 30 m ( 16 – 98 ft ) . Young sharks are generally found in the shallow areas of <unk> , while adults may be encountered across a variety of habitats . 
- 
- = = Description = = 
- 
- The tawny nurse shark grows to a maximum length of 3 @.@ 2 m ( 10 ft ) . It has a robust , cylindrical body with a broadly rounded and flattened head . The eyes are small and face laterally , with prominent ridges over them and smaller spiracles behind . There are a pair of long , slender <unk> in front of the nostrils . The mouth is small , with the lower lip divided into three lobes . There are 29 – 33 tooth rows in the upper jaw and 26 – 28 tooth rows in the lower jaw , arranged in an <unk> ( overlapping ) pattern with the outermost 2 – 4 functional rows separated from the rest by a narrow space . Each tooth resembles a fan , with a broad base rising to a small , sharp central point flanked by 3 or more smaller <unk> on both sides . As the shark ages , the teeth become relatively taller and thicker . The fourth and fifth pairs of gill slits are placed much closer together than the others . 
- The dorsal and pelvic fins are angular , with the first dorsal fin larger than the second . The pectoral fins are narrow , pointed , and <unk> ( sickle @-@ shaped ) ; their shape separates this species from the similar @-@ looking nurse shark . The origin of the first dorsal fin is about even with the origin of the pelvic fins , while the origin of the anal fin is even with or somewhat behind the origin of the second dorsal fin . The caudal fin has a shallow upper lobe and barely present lower lobe , comprising about a quarter of the total length in adults . The dermal denticles are diamond @-@ shaped , bearing 4 – 5 faint ridges radiating from a blunt point . Tawny nurse sharks are yellowish , reddish , or grayish brown above and off @-@ white below , and are capable of slowly changing their color to better blend with the environment . Young sharks have starkly white lower eyelids . 
- Many tawny nurse sharks found off the coasts of Japan , Taiwan , and the <unk> Islands lack a second dorsal fin . This physical <unk> has been speculated to result from pregnant females being exposed to water of unusually high salinity and / or temperature , possibly from human activity . In 1986 , a 2 @.@ 9 m ( 9 ft 6 in ) long adult male with both a missing dorsal fin and partial albinism ( in the form of white body color with gray @-@ brown eyes ) was captured off <unk> Prefecture , Japan . This <unk> individual is the largest <unk> shark known to date , having survived for a long time in the wild despite its lack of camouflage . 
- 
- = = Biology and ecology = = 
- 
- With a more streamlined form than other nurse sharks , the tawny nurse shark is believed to be a less <unk> , more active swimmer . The characteristics of its body , head , fins , and teeth are comparable to other active reef sharks sharing its range , such as the <unk> lemon shark ( <unk> <unk> ) . Tawny nurse sharks are primarily nocturnal , though they are said to be active at all hours off Madagascar , and in captivity they will become <unk> active if presented with food . During the day , groups of two dozen or more sharks can be found resting inside caves and under ledges , often stacked atop one another . Individual sharks have small home ranges that they consistently return to each day . 
- The tawny nurse shark has few natural predators ; attacks on this species have been reported from bull sharks ( <unk> <unk> ) and great <unk> ( <unk> <unk> ) , while the related nurse shark has been known to fall prey to tiger sharks ( <unk> <unk> ) and lemon sharks ( <unk> <unk> ) . Known parasites of this species include five species of <unk> in the genus <unk> , which infest the shark 's spiral intestine . 
- 
- = = = Feeding = = = 
- 
- The tawny nurse shark may be one of the few fishes specializing in preying on <unk> . Other known food items include <unk> , sea <unk> , crustaceans ( e.g. crabs and lobsters ) , <unk> , small fishes ( e.g. <unk> , <unk> , and <unk> ) , and the occasional sea snake . Hunting tawny nurse sharks swim slowly just above the sea floor , poking their heads into depressions and holes . When a prey item is found , the shark <unk> expands its large , muscular <unk> , creating a powerful negative pressure that sucks the prey into its mouth . 
- 
- = = = Life history = = = 
- 
- Mating in the tawny nurse shark is known to occur from July to August off Madagascar . Adult females have one functional ovary and two functional <unk> . The mode of reproduction is aplacental <unk> , meaning that the embryos hatch inside the uterus ; females in captivity have been documented depositing up to 52 non @-@ viable egg capsules , which has led to erroneous reports of this shark being <unk> . The egg capsules of this species are onion @-@ shaped , with thin , brown , translucent shells . The tawny nurse shark is the only carpet shark in which there is <unk> : once the developing embryos exhaust their supply of yolk , they <unk> on eggs produced by the mother and acquire the <unk> abdomen characteristic of such <unk> embryos . Unlike in <unk> sharks , the eggs consumed by the embryos are large and shelled rather than small and undeveloped . There is no evidence of sibling cannibalism as in the sand tiger shark ( <unk> <unk> ) . 
- Various authors have reported the length at birth anywhere from 40 to 80 cm ( 16 to 31 in ) , with the discrepancy possibly reflecting geographic variation . Although females release up to four fertilized eggs into each uterus , the very large size of the newborns suggest that the litter size may be as few as one or two . In one examined female that had two embryos sharing a single uterus , one embryo was much smaller and thinner than the other , implying that competition may eliminate the additional siblings . Males attain sexual maturity at a length of 2 @.@ 5 m ( 8 ft 2 in ) , and females at a length of 2 @.@ 3 – 2 @.@ 9 m ( 7 ft 7 in – 9 ft 6 in ) . 
- 
- = = Human interactions = = 
- 
- <unk> with tawny nurse sharks underwater indicate a more docile demeanor than the similar nurse shark ; usually divers are able to approach the sharks closely and even touch and play with them without incident . However , this species has been infrequently provoked into biting , and merits respect due to its strength , small but sharp teeth , and extremely powerful jaws . Tawny nurse sharks are favored attractions for <unk> divers off Thailand , the Solomon Islands , and elsewhere . This species also <unk> well to captivity and is displayed in public <unk> in Europe , the United States , Okinawa , and Singapore , where they may become tame enough to be hand @-@ fed . 
- The tawny nurse shark is taken by commercial fisheries operating throughout its range , including off Pakistan , India , Thailand , and the Philippines ; an exception is in Australian waters , where it is only taken in small numbers as <unk> . This shark is caught using <unk> trawls , floating and fixed bottom gill nets , and on hook @-@ and @-@ line . The meat is sold fresh or dried and salted , the fins are used for shark fin soup , and the <unk> processed into <unk> . In addition , the liver is a source of oil and <unk> , and the thick , tough skin is made into leather products . Off Queensland , Australia , the tawny nurse shark is valued by big @-@ game anglers . When hooked , large individuals are <unk> opponents and are difficult to subdue due to their habit of spinning . They are also capable of spitting a powerful jet of water into the faces of their captors , making <unk> noises in between jets ( making the tawny shark one of the few species of sharks to produce a noise ) ; whether this is a deliberate defensive behavior is uncertain . 
- The International Union for Conservation of Nature ( IUCN ) has assessed the tawny nurse shark as Vulnerable worldwide , as it faces heavy fishing pressure and its low reproductive and dispersal rates limit the ability of over @-@ exploited populations to recover . Furthermore , this shark 's inshore habitat renders it susceptible to habitat degradation , destructive fishing practices ( e.g. poisons and explosives , especially prevalent off Indonesia and the Philippines ) , and human harassment . <unk> declines or <unk> of the tawny nurse shark have been documented off India and Thailand . Off Australia , this species has been assessed as of Least Concern , as there it is not targeted by fisheries . 
- 
- 
- = California State Route 243 = 
- 
- State Route 243 ( SR 243 ) , or the Banning @-@ Idyllwild <unk> Highway , is a 30 @-@ mile ( 50 kilometer ) two @-@ lane highway that runs from Banning , California ( in the north ) to Idyllwild , California ( in the south ) in Riverside County , California . The highway is a connector between Interstate 10 ( I @-@ 10 ) and SR 74 . Along its route , it provides access to the San <unk> National Forest . A road from Banning to Idyllwild was planned around the turn of the twentieth century , and was open by 1910 . The road was added to the state highway system in 1970 . 
- 
- = = Route description = = 
- 
- SR 243 begins at SR 74 in the San Jacinto Wilderness near Mountain Center , Riverside County as Idyllwild Road . The highway traverses north along a winding road through the community of Idyllwild . SR 243 makes a left turn at the intersection with Circle Drive and continues through Pine Cove . The road continues through the forest past Mount San Jacinto State Park through Twin <unk> and the <unk> Indian <unk> before making a few <unk> and descending en route to the city of Banning as the Banning Idyllwild <unk> Highway . The highway continues as San <unk> Avenue into the city before making a left onto Lincoln Street and a right onto 8th Street and terminating at a diamond interchange with I @-@ 10 . 
- SR 243 is part of the National Highway System , a network of highways that are essential to the country 's economy , defense , and mobility . SR 243 is eligible for the State Scenic Highway System , and is officially designated as a scenic highway by the California Department of Transportation ( Caltrans ) , meaning that it is a substantial section of highway passing through a " memorable landscape " with no " visual <unk> " , where the potential designation has gained popular favor with the community . In 2007 , it was named the <unk> <unk> Memorial Highway in honor of five <unk> who died while fighting the <unk> Fire in October 2006 . In 2013 , SR 243 had an annual average daily traffic ( AADT ) of 1 @,@ 650 between Marion Ridge Drive in Idyllwild and San <unk> Avenue in Banning , and 6 @,@ 500 at the northern terminus in Banning , the latter of which was the highest AADT for the highway . 
- 
- = = History = = 
- 
- A road from Banning to Idyllwild was under construction in 1904 , and 12 miles ( 19 km ) of the road was open by August , with an additional six miles ( 9 @.@ 7 km ) of the road planned . Another four miles ( 6 @.@ 4 km ) were commissioned in 1908 . The <unk> road was completed by September 1910 , and provided a view of Lake Elsinore and the Colorado Desert , and it was expected to help with transporting lumber and stopping fires ; because of this , the federal government provided $ 2 @,@ 000 for the construction . The road became a part of the forest highway system in 1927 . 
- A new " high @-@ gear " road from Banning to Idyllwild was under way by 1935 , and two years later , the Los Angeles Times considered the road to be " high @-@ gear " . Efforts to pave the road were under way in 1950 . The road from Banning through Idyllwild to SR 74 was known as County Route R1 ( CR R1 ) by 1966 . By 1969 , plans were in place to add the Banning to Idyllwild to Mountain Center road as a state highway ; earlier , State Senator Nelson <unk> proposed legislation to require the road to be added to the state highway system if SR 195 was removed , as the two were of roughly the same length , but the latter remained in the system . SR 243 was added to the state highway system in 1970 . The Division of Highways suggested <unk> the highway in 1971 . In 1998 , Caltrans had no plans to improve the route through 2015 . 
- 
- = = Major intersections = = 
- 
- Except where <unk> with a letter , <unk> were measured on the road as it was when the route was established , based on the alignment that existed at the time , and do not necessarily reflect current <unk> . R reflects a realignment in the route since then , M indicates a second realignment , L refers an overlap due to a correction or change , and T indicates <unk> classified as temporary ( for a full list of <unk> , see the list of <unk> definitions ) . <unk> that remain <unk> or have been relinquished to local control may be omitted . The entire route is in Riverside County . 
- 
- = = Other important information = = 
- 
- <unk> Transportation Due to the fact that CA @-@ 243 passes Idyllwild School , pursuant to the Gun @-@ Free School <unk> Act of 1990 , firearms , including those in vehicles , must be within the guidelines of 18 U.S.C. § 922 ( q ) ( 2 ) ( B ) within the school zone , which is , as defined by 18 U.S.C. § <unk> ( a ) ( 25 ) , is 1000 feet . <unk> will be , according to 18 U.S.C. § <unk> ( a ) ( 4 ) , " fined under this title , imprisoned for not more than 5 years , or both . " 
- 
- 
- = The Amps = 
- 
- The Amps were an American alternative @-@ indie rock group . Formed by Kim Deal in 1995 after her band the Breeders went on hiatus , the group consisted of Deal , vocals and guitars ; Luis Lerma , bass ; Nate Farley , guitars ; and the Breeders ' Jim Macpherson , drums . The Breeders ' Kelley Deal , Kim 's sister , was also briefly involved , but had to leave the band due to drug problems . The group was named when Kim Deal started calling herself Tammy <unk> for fun , and the band Tammy and the Amps . They recorded the album Pacer in Ireland and the United States . 
- Pacer was released in October 1995 . The group toured the United States , Europe , and Australia , with bands including the Foo Fighters , Sonic Youth , and Guided by Voices . Critics commented on the loose and rough quality of these performances . The Amps continued as a group until 1996 , when Deal changed their name back to the Breeders . By 2000 , Macpherson , Lerma , and Farley had left the band ; they were all at times involved in various projects with Guided by Voices ' Robert Pollard . Deal 's next album , the Breeders ' Title TK , was released seven years after Pacer . 
- 
- = = Background and formation = = 
- 
- From 1986 to 1992 , Deal was a member of the <unk> , and from 1989 onwards , the Breeders . In August 1993 , the Breeders released their second album , Last Splash , which went platinum in the USA , gold in Canada , and silver in the UK . The other members of the group at that time were Kim 's twin sister Kelley Deal , <unk> Wiggs and Jim Macpherson . By late 1994 , after two years of straight touring and recording , and culminating in the <unk> tour , the band members were exhausted ; they decided to take some time off from the Breeders , but this hiatus ended up being longer than expected . Kelley was arrested on drug charges in late 1994 and spent time in and out of rehabilitation , while Wiggs became involved in musical projects in New York , including collaborations with members of <unk> Jackson . 
- Meanwhile , Kim Deal was eager to continue recording and performing . At first , she envisioned her next album as a solo record , on which she would play all of the instrument parts . When she was recording initial demos for the project , she asked Kelley to play on some of them , to distract her from her drug difficulties . Since Kelley was now also involved , Deal decided not to go solo , but formed a new group . She recruited Macpherson to play drums , musician Luis Lerma , bass , and Nate Farley , guitar . Later , Kelley dropped out of the project for rehabilitation and moved to Saint Paul , Minnesota . For fun , Deal began calling herself Tammy <unk> , and the group , Tammy and the Amps . This later became simply the Amps . 
- 
- = = Recording and touring = = 
- 
- The Amps released one album , Pacer , which was recorded at several different studios . The first session , at Easley Studios in Memphis , Tennessee , was engineered by Davis McCain and Doug Easley . There Deal recorded new songs , including what would become Pacer 's single , " <unk> City " . This recording opportunity came about when she was producing a planned record by Guided by Voices ; when that group abandoned work on their album , Deal used the leftover studio time for her own songs . Following the Easley Studios session , recording for Pacer continued at six other locations in total , including studios in Chicago , Los Angeles , Dublin , and Deal 's hometown , Dayton , Ohio . Engineers Steve <unk> , John <unk> , <unk> <unk> and others each helped record one or more of these sessions . Pacer came out in October 1995 . 
- The Amps toured throughout 1995 and 1996 . Early performances , before releasing Pacer , included June 1995 shows in Dayton : one with <unk> Children and another with Guided by Voices . They toured with Guided by Voices and <unk> in the United Kingdom in September , in cities such as London , Glasgow , and Sheffield , and played another show with Guided by Voices in Ohio in October . The Amps performed a series of US concerts in October and November with Sonic Youth , including shows in Detroit and Chicago with <unk> , in Seattle and Portland with <unk> Kill , and in Los Angeles with Mike <unk> . Following the concerts with Sonic Youth , the Amps did a tour of Europe . In January 1996 , they played the <unk> festival in Australia . That year , the Amps toured with the Foo Fighters in the United States , in locations such as Chicago ( with That Dog ) , Worcester , Massachusetts , and Austin , Texas ( with <unk> ) . In 1995 or 1996 , they also played shows with the <unk> and <unk> . 
- Reviewers described live concerts by the Amps as <unk> and relaxed . The Chronicle 's <unk> <unk> praised their show at an unidentified location as " fun @-@ spirited and silly " . Greg <unk> of The Chicago Tribune described one of their Chicago performances as " off @-@ <unk> " and wrote that " Deal 's pungent vocals were swallowed up by the guitars , and the <unk> 's slight melodies were lost amid all the bashing . " A review in The Chicago Reader of the same concert noted that " The Amps play with a low @-@ key , <unk> sound . " The Phoenix 's Matt <unk> felt that at their December 1995 show in Boston " the Amps didn 't click in a way that would suggest that this was Deal 's new , full @-@ time band " ; he added that it was " an <unk> gritty way for Deal to let off some steam while the Breeders [ took ] a little break " . In Boston Rock , this concert was characterized as " <unk> " but " charm [ ing ] ... hanging loosely off Kim 's <unk> vocals and <unk> personality " . 
- 
- = = <unk> the Breeders = = 
- 
- Later in 1996 , Deal changed the name of the group back to the Breeders , originally with almost the same line @-@ up as the Amps . Until then , Deal had been waiting for Wiggs and Kelley to rejoin the Breeders and record a new album together , and had held back from reforming the Breeders out of respect for them . In May 1996 , Wiggs revealed that she would not be involved in any immediate Breeders activity ; Kelley also chose to stay in Saint Paul , to be close to her rehabilitation facility . Deal then decided that she did not want to wait any longer to reform the group , partly because the Breeders ' repertoire was larger than the Amps ' , thereby allowing longer concerts . Deal added violinist Carrie Bradley ( who had played on the Breeders ' Pod album ) , and with Macpherson , Lerma , and Farley , played some shows in 1996 with <unk> . 
- By 1998 , Kelley had rejoined and Macpherson had left the group , and by 2000 Lerma and Farley had also left . The Breeders , with a line @-@ up including the Deal sisters and new members <unk> Lopez and Jose <unk> , released the albums Title TK in 2002 and Mountain Battles in 2008 . Meanwhile , Macpherson was a member of Guided by Voices from 1998 to 2001 , and participated in other projects with Guided By Voices ' Robert Pollard until 2005 . Lerma and Farley have been members of the <unk> , and in 1999 participated in Pollard 's <unk> and the <unk> side project group ; Farley was also a member of Guided by Voices from 1999 to 2004 . In 2013 , the Last Splash @-@ era Breeders line @-@ up of Kim and Kelley Deal , Macpherson , and Wiggs reunited to tour to celebrate the 20th anniversary of that album . 
- 
- = = Members = = 
- 
- Kim Deal – vocals , guitars 
- Luis Lerma – bass 
- Nate Farley – guitars 
- Jim Macpherson – drums 
- 
- = = Discography = = 
- 
- 
- = = = Album = = = 
- 
- 
- = = = Single = = = 
- 
- 
- 
- = Exploration of Jupiter = 
- 
- The exploration of Jupiter has been conducted via close observations by automated spacecraft . It began with the arrival of Pioneer 10 into the Jovian system in 1973 , and , as of 2016 , has continued with eight further spacecraft missions . All of these missions were undertaken by the National <unk> and Space Administration ( NASA ) , and all but two have been flybys that take detailed observations without the probe landing or entering orbit . These probes make Jupiter the most visited of the Solar System 's outer planets as all missions to the outer Solar System have used Jupiter flybys to reduce fuel requirements and travel time . On 5th July 2016 , spacecraft Juno arrived and entered the planet 's orbit — the second craft ever to do so . <unk> a craft to Jupiter <unk> many technical difficulties , especially due to the probes ' large fuel requirements and the effects of the planet 's harsh radiation environment . 
- The first spacecraft to visit Jupiter was Pioneer 10 in 1973 , followed a year later by Pioneer 11 . Aside from taking the first close @-@ up pictures of the planet , the probes discovered its magnetosphere and its largely fluid interior . The Voyager 1 and Voyager 2 probes visited the planet in 1979 , and studied its moons and the ring system , discovering the volcanic activity of Io and the presence of water ice on the surface of Europa . Ulysses further studied Jupiter 's magnetosphere in 1992 and then again in 2000 . The Cassini probe approached the planet in 2000 and took very detailed images of its atmosphere . The New Horizons spacecraft passed by Jupiter in 2007 and made improved measurements of its and its satellites ' parameters . 
- The Galileo spacecraft was the first to have entered orbit around Jupiter , arriving in 1995 and studying the planet until 2003 . During this period Galileo gathered a large amount of information about the Jovian system , making close approaches to all of the four large Galilean moons and finding evidence for thin atmospheres on three of them , as well as the possibility of liquid water beneath their surfaces . It also discovered a magnetic field around Ganymede . As it approached Jupiter , it also witnessed the impact of Comet Shoemaker – Levy 9 . In December 1995 , it sent an atmospheric probe into the Jovian atmosphere , so far the only craft to do so . 
- In July 2016 , the Juno spacecraft , launched in 2011 , completed its orbital insertion maneuver successfully , and is now in orbit around Jupiter and preparing for its science programme . 
- The European Space Agency selected the <unk> @-@ class JUICE mission in 2012 as part of its <unk> Vision programme to explore three of Jupiter 's Galilean moons , with a possible Ganymede <unk> provided by <unk> . JUICE is proposed to be launched in 2022 . 
- 
- = = Technical requirements = = 
- 
- <unk> from Earth to other planets in the Solar System have a high energy cost . It requires almost the same amount of energy for a spacecraft to reach Jupiter from Earth 's orbit as it does to lift it into orbit in the first place . In <unk> , this energy expenditure is defined by the net change in the spacecraft 's velocity , or delta @-@ v. The energy needed to reach Jupiter from an Earth orbit requires a delta @-@ v of about 9 km / s , compared to the 9 @.@ 0 – 9 @.@ 5 km / s to reach a low Earth orbit from the ground . Gravity assists through planetary flybys ( such as by Earth or Venus ) can be used to reduce the energetic requirement ( i.e. the fuel ) at launch , at the cost of a significantly longer flight duration to reach a target such as Jupiter when compared to the direct trajectory . Ion <unk> capable of a delta @-@ v of more than 10 kilometers / s were used on the Dawn spacecraft . This is more than enough delta @-@ v to do a Jupiter fly @-@ by mission from a solar orbit of the same radius as that of Earth without gravity assist . 
- A major problem in sending space probes to Jupiter is that the planet has no solid surface on which to land , as there is a smooth transition between the planet 's atmosphere and its fluid interior . Any probes descending into the atmosphere are eventually crushed by the immense pressures within Jupiter . 
- Another major issue is the amount of radiation to which a space probe is subjected , due to the harsh charged @-@ particle environment around Jupiter ( for a detailed explanation see <unk> of Jupiter ) . For example , when Pioneer 11 made its closest approach to the planet , the level of radiation was ten times more powerful than Pioneer 's designers had predicted , leading to fears that the probes would not survive . With a few minor glitches , the probe managed to pass through the radiation belts , but it lost most of the images of the moon Io , as the radiation had caused Pioneer 's imaging photo <unk> to receive false commands . The subsequent and far more technologically advanced Voyager spacecraft had to be redesigned to cope with the radiation levels . Over the eight years the Galileo spacecraft orbited the planet , the probe 's radiation dose far exceeded its design specifications , and its systems failed on several occasions . The spacecraft 's <unk> often exhibited increased errors , and electrical arcs sometimes occurred between its rotating and non @-@ rotating parts , causing it to enter safe mode , which led to total loss of the data from the 16th , 18th and 33rd orbits . The radiation also caused phase shifts in Galileo 's ultra @-@ stable <unk> <unk> . 
- 
- = = <unk> missions = = 
- 
- 
- = = = Pioneer program ( 1973 and 1974 ) = = = 
- 
- The first spacecraft to explore Jupiter was Pioneer 10 , which flew past the planet in December 1973 , followed by Pioneer 11 twelve months later . Pioneer 10 obtained the first @-@ ever close @-@ up images of Jupiter and its Galilean moons ; the spacecraft studied the planet 's atmosphere , detected its magnetic field , observed its radiation belts and determined that Jupiter is mainly fluid . Pioneer 11 made its closest approach , within some 34 @,@ 000 km of Jupiter 's cloud tops , on December 4 , 1974 . It obtained dramatic images of the Great Red Spot , made the first observation of Jupiter 's immense polar regions , and determined the mass of Jupiter 's moon Callisto . The information gathered by these two spacecraft helped astronomers and engineers improve the design of future probes to cope more effectively with the environment around the giant planet . 
- 
- = = = Voyager program ( 1979 ) = = = 
- 
- Voyager 1 began photographing Jupiter in January 1979 and made its closest approach on March 5 , 1979 , at a distance of 349 @,@ 000 km from Jupiter 's center . This close approach allowed for greater image resolution , though the flyby 's short duration meant that most observations of Jupiter 's moons , rings , magnetic field , and radiation environment were made in the 48 @-@ hour period <unk> the approach , even though Voyager 1 continued photographing the planet until April . It was soon followed by Voyager 2 , which made its closest approach on July 9 , 1979 , 576 @,@ 000 km away from the planet 's cloud tops . The probe discovered Jupiter 's ring , observed intricate <unk> in its atmosphere , observed active volcanoes on Io , a process analogous to plate <unk> on Ganymede , and numerous craters on Callisto . 
- The Voyager missions vastly improved our understanding of the Galilean moons , and also discovered Jupiter 's rings . They also took the first close @-@ up images of the planet 's atmosphere , revealing the Great Red Spot as a complex storm moving in a counter @-@ clockwise direction . Other smaller storms and <unk> were found throughout the banded clouds ( see animation on the right ) . Two new , small satellites , <unk> and <unk> , were discovered orbiting just outside the ring , making them the first of Jupiter 's moons to be identified by a spacecraft . A third new satellite , <unk> , was discovered between the orbits of Amalthea and Io . 
- The discovery of volcanic activity on the moon Io was the greatest unexpected finding of the mission , as it was the first time an active volcano was observed on a celestial body other than Earth . Together , the <unk> recorded the eruption of nine volcanoes on Io , as well as evidence for other eruptions occurring between the Voyager encounters . 
- Europa displayed a large number of intersecting linear features in the low @-@ resolution photos from Voyager 1 . At first , scientists believed the features might be deep cracks , caused by <unk> <unk> or <unk> processes . The high @-@ resolution photos from Voyager 2 , taken closer to Jupiter , left scientists puzzled as the features in these photos were almost entirely lacking in topographic relief . This led many to suggest that these cracks might be similar to ice <unk> on Earth , and that Europa might have a liquid water interior . Europa may be internally active due to <unk> heating at a level about one @-@ tenth that of Io , and as a result , the moon is thought to have a thin crust less than 30 kilometers ( 19 mi ) thick of water ice , possibly floating on a 50 @-@ kilometers @-@ deep ( 30 mile ) ocean . 
- 
- = = = Ulysses ( 1992 ) = = = 
- 
- On February 8 , 1992 , the Ulysses solar probe flew past Jupiter 's north pole at a distance of 451 @,@ 000 km . This swing @-@ by maneuver was required for Ulysses to attain a very high @-@ inclination orbit around the Sun , increasing its inclination to the ecliptic to 80 @.@ 2 degrees . The giant planet 's gravity bent the spacecraft 's <unk> downward and away from the ecliptic plane , placing it into a final orbit around the Sun 's north and south poles . The size and shape of the probe 's orbit were adjusted to a much smaller degree , so that its <unk> remained at approximately 5 AU ( Jupiter 's distance from the Sun ) , while its perihelion lay somewhat beyond 1 AU ( Earth 's distance from the Sun ) . During its Jupiter encounter , the probe made measurements of the planet 's magnetosphere . Since the probe had no cameras , no images were taken . In February 2004 , the probe arrived again at the vicinity of Jupiter . This time the distance from the planet was much greater — about 240 million km — but it made further observations of Jupiter . 
- 
- = = = Cassini ( 2000 ) = = = 
- 
- In 2000 , the Cassini probe , en route to Saturn , flew by Jupiter and provided some of the highest @-@ resolution images ever taken of the planet . It made its closest approach on December 30 , 2000 , and made many scientific measurements . About 26 @,@ 000 images of Jupiter were taken during the months @-@ long flyby . It produced the most detailed global color portrait of Jupiter yet , in which the smallest visible features are approximately 60 km ( 37 mi ) across . 
- A major finding of the flyby , announced on March 6 , 2003 , was of Jupiter 's atmospheric circulation . Dark belts alternate with light zones in the atmosphere , and the zones , with their pale clouds , had previously been considered by scientists to be areas of <unk> air , partly because on Earth clouds tend to be formed by rising air . Analysis of Cassini imagery showed that the dark belts contain individual storm cells of <unk> bright @-@ white clouds , too small to see from Earth . Anthony Del <unk> of NASA 's Goddard Institute for Space Studies said that " the belts must be the areas of net @-@ rising atmospheric motion on Jupiter , [ so ] the net motion in the zones has to be sinking " . 
- Other atmospheric observations included a <unk> dark oval of high atmospheric @-@ haze , about the size of the Great Red Spot , near Jupiter 's north pole . <unk> imagery revealed aspects of circulation near the poles , with bands of globe @-@ <unk> winds , and adjacent bands moving in opposite directions . The same announcement also discussed the nature of Jupiter 's rings . Light scattering by particles in the rings showed the particles were irregularly shaped ( rather than spherical ) and likely originated as <unk> from <unk> impacts on Jupiter 's moons , probably on <unk> and <unk> . On December 19 , 2000 , the Cassini spacecraft captured a very @-@ low @-@ resolution image of the moon <unk> , but it was too distant to show any surface details . 
- 
- = = = New Horizons ( 2007 ) = = = 
- 
- The New Horizons probe , en route to Pluto , flew by Jupiter for a gravity assist and was the first probe launched directly towards Jupiter since the Ulysses in 1990 . Its Long Range Reconnaissance <unk> ( <unk> ) took its first photographs of Jupiter on September 4 , 2006 . The spacecraft began further study of the Jovian system in December 2006 , and made its closest approach on February 28 , 2007 . 
- Although close to Jupiter , New Horizons ' instruments made refined measurements of the orbits of Jupiter 's inner moons , particularly Amalthea . The probe 's cameras measured volcanoes on Io , studied all four Galilean moons in detail , and made long @-@ distance studies of the outer moons <unk> and <unk> . The craft also studied Jupiter 's Little Red Spot and the planet 's magnetosphere and tenuous ring system . 
- On March 19 , 2007 the Command and Data <unk> computer experienced an <unk> memory error and <unk> itself , causing the spacecraft to go into safe mode . The craft fully recovered within two days , with some data loss on Jupiter 's <unk> . No other data loss events were associated with the encounter . Due to the immense size of the Jupiter system and the relative closeness of the Jovian system to Earth in comparison to the closeness of Pluto to Earth , New Horizons will send back more data to Earth from the Jupiter encounter than the Pluto encounter . 
- 
- = = Orbiter missions = = 
- 
- 
- = = = Galileo ( 1995 – 2003 ) = = = 
- 
- The first spacecraft to orbit Jupiter was the Galileo <unk> , which went into orbit around Jupiter on December 7 , 1995 . It orbited the planet for over seven years , making 35 orbits before it was destroyed during a controlled impact with Jupiter on September 21 , 2003 . During this period , it gathered a large amount of information about the Jovian system ; the amount of information was not as great as intended because the deployment of its high @-@ gain radio transmitting antenna failed . The major events during the eight @-@ year study included multiple flybys of all of the Galilean moons , as well as Amalthea ( the first probe to do so ) . It also witnessed the impact of Comet Shoemaker – Levy 9 as it approached Jupiter in 1994 and the sending of an atmospheric probe into the Jovian atmosphere in December 1995 . 
- <unk> on the Galileo spacecraft observed fragments of Comet Shoemaker – Levy 9 between 16 and 22 July 1994 as they collided with Jupiter 's southern hemisphere at a speed of approximately 60 kilometres per second . This was the first direct observation of an extraterrestrial collision of solar system objects . While the impacts took place on the side of Jupiter hidden from Earth , Galileo , then at a distance of 1 @.@ 6 AU from the planet , was able to see the impacts as they occurred . Its instruments detected a fireball that reached a peak temperature of about 24 @,@ 000 K , compared to the typical Jovian <unk> temperature of about 130 K ( − 143 ° C ) , with the plume from the fireball reaching a height of over 3 @,@ 000 km . 
- An atmospheric probe was released from the spacecraft in July 1995 , entering the planet 's atmosphere on December 7 , 1995 . After a high @-@ g descent into the Jovian atmosphere , the probe discarded the remains of its heat shield , and it <unk> through 150 km of the atmosphere , collecting data for 57 @.@ 6 minutes , before being crushed by the pressure and temperature to which it was subjected ( about 22 times Earth normal , at a temperature of 153 ° C ) . It would have melted thereafter , and possibly <unk> . The Galileo <unk> itself experienced a more rapid version of the same fate when it was deliberately steered into the planet on September 21 , 2003 at a speed of over 50 km / s , in order to avoid any possibility of it crashing into and <unk> Europa . 
- Major scientific results of the Galileo mission include : 
- the first observation of ammonia clouds in another planet 's atmosphere — the atmosphere creates ammonia ice particles from material coming up from lower depths ; 
- confirmation of extensive volcanic activity on Io — which is 100 times greater than that found on Earth ; the heat and frequency of eruptions are reminiscent of early Earth ; 
- observation of complex plasma interactions in Io 's atmosphere which create immense electrical currents that couple to Jupiter 's atmosphere ; 
- providing evidence for supporting the theory that liquid oceans exist under Europa 's icy surface ; 
- first detection of a substantial magnetic field around a satellite ( Ganymede ) ; 
- magnetic data evidence suggesting that Europa , Ganymede and Callisto have a liquid @-@ <unk> layer under the visible surface ; 
- evidence for a thin atmospheric layer on Europa , Ganymede , and Callisto known as a ' surface @-@ bound <unk> ' ; 
- understanding of the formation of the rings of Jupiter ( by dust kicked up as <unk> <unk> which smash into the planet 's four small inner moons ) and observation of two outer rings and the possibility of a separate ring along Amalthea 's orbit ; 
- identification of the global structure and dynamics of a giant planet 's magnetosphere . 
- On December 11 , 2013 , NASA reported , based on results from the Galileo mission , the detection of " clay @-@ like minerals " ( specifically , <unk> ) , often associated with organic materials , on the icy crust of Europa , moon of Jupiter . The presence of the minerals may have been the result of a collision with an asteroid or comet according to the scientists . 
- 
- = = = Juno ( 2016 ) = = = 
- 
- NASA launched Juno on August 5 , 2011 to study Jupiter in detail from a polar orbit when it arrives in 2016 . The spacecraft will be placed in a polar orbit to study the planet 's composition , gravity field , magnetic field , and polar magnetosphere . Juno will also search for clues about how Jupiter formed , including whether the planet has a rocky core , the amount of water present within the deep atmosphere , and how the mass is distributed within the planet . Juno will also study Jupiter 's deep winds , which can reach speeds of 600 km / h . Juno started orbiting Jupiter on the night of 4th July 2016 . 
- 
- = = = Jupiter Icy Moon Explorer ( 2022 ) = = = 
- 
- ESA 's Jupiter Icy Moon Explorer ( JUICE ) has been selected as part of ESA 's <unk> Vision science program . It is expected to launch in 2022 and , after a series of flybys in the inner Solar System , arrive in <unk> . In 2012 , the European Space Agency 's selected the <unk> <unk> moon Explorer ( JUICE ) as its first Large mission , replacing its contribution to <unk> , the Jupiter Ganymede Orbiter ( <unk> ) . The partnership for the Europa Jupiter System Mission has since ended , but NASA will continue to contribute the European mission with hardware and an instrument . 
- 
- = = Proposed missions = = 
- 
- The Europa <unk> is a mission proposed to NASA to focus on studying Jupiter 's moon Europa . In March 2013 , funds were authorized for " pre @-@ formulation and / or formulation activities for a mission that meets the science goals outlined for the Jupiter Europa mission in the most recent planetary <unk> survey " . The proposed mission would be set to launch in the early <unk> and reach Europa after a 6 @.@ 5 year cruise . The spacecraft would fly by the moon 32 times to minimize radiation damage . 
- 
- = = = <unk> missions = = = 
- 
- Because of the possibility of subsurface liquid oceans on Jupiter 's moons Europa , Ganymede and Callisto , there has been great interest in studying the icy moons in detail . Funding difficulties have delayed progress . The Europa Orbiter was a planned NASA mission to Europa , which was canceled in 2002 . Its main objectives included determining the presence or absence of a subsurface ocean and identifying candidate sites for future <unk> missions . NASA 's <unk> ( Jupiter Icy <unk> Orbiter ) , which was canceled in 2005 , and a European Jovian Europa Orbiter mission were also studied , but were superseded by the Europa Jupiter System Mission . 
- The Europa Jupiter System Mission ( <unk> ) was a joint NASA / ESA proposal for exploration of Jupiter and its moons . In February 2009 it was announced that both space agencies had given this mission priority ahead of the Titan Saturn System Mission . The proposal included a launch date of around <unk> and consists of the NASA @-@ led Jupiter Europa Orbiter , and the ESA @-@ led Jupiter Ganymede Orbiter . ESA 's contribution had encountered funding competition from other ESA projects . However , the Jupiter Europa Orbiter ( <unk> ) , NASA 's contribution , was considered by the Planetary <unk> Survey to be too expensive . The survey supported a cheaper alternative to <unk> . 
- 
- = = Human exploration = = 
- 
- While scientists require further evidence to determine the extent of a rocky core on Jupiter , its Galilean moons provide the potential opportunity for future human exploration . 
- <unk> targets are Europa , due to its potential for life , and Callisto , due to its relatively low radiation dose . In 2003 , NASA proposed a program called Human Outer <unk> Exploration ( <unk> ) that involved sending astronauts to explore the Galilean moons . NASA has projected a possible attempt some time in the <unk> . In the Vision for Space Exploration policy announced in January 2004 , NASA discussed missions beyond Mars , mentioning that a " human research presence " may be desirable on Jupiter 's moons . Before the <unk> mission was cancelled , NASA administrator Sean O <unk> stated that " human explorers will follow . " 
- 
- = = = Potential for colonization = = = 
- 
- NASA has speculated on the feasibility of mining the atmospheres of the outer planets , particularly for helium @-@ 3 , an isotope of helium that is rare on Earth and could have a very high value per unit mass as thermonuclear fuel . <unk> stationed in orbit could mine the gas and deliver it to visiting craft . However , the Jovian system in general poses particular disadvantages for colonization because of the severe radiation conditions prevailing in Jupiter 's magnetosphere and the planet 's particularly deep gravitational well . Jupiter would deliver about 36 Sv ( <unk> rem ) per day to <unk> colonists at Io and about 5 @.@ 4 Sv ( 540 <unk> ) per day to <unk> colonists at Europa , which is a decisive aspect due to the fact that already an exposure to about 0 @.@ 75 Sv over a period of a few days is enough to cause radiation poisoning , and about 5 Sv over a few days is fatal . 
- Ganymede is the Solar System 's largest moon and the Solar System 's only known moon with a magnetosphere , but this does not shield it from cosmic radiation to a noteworthy degree , because it is overshadowed by Jupiter 's magnetic field . Ganymede receives about 0 @.@ 08 Sv ( 8 rem ) of radiation per day . Callisto is farther from Jupiter 's strong radiation belt and subject to only 0 @.@ <unk> Sv ( 0 @.@ 01 rem ) a day . For comparison , the average amount of radiation taken on Earth by a living organism is about 0 @.@ <unk> Sv per year ; the highest natural radiation levels on Earth are recorded around <unk> hot springs at about 0 @.@ 26 Sv per year . 
- One of the main targets chosen by the <unk> study was Callisto . The possibility of building a surface base on Callisto was proposed , because of the low radiation levels at its distance from Jupiter and its geological stability . Callisto is the only Galilean satellite for which human exploration is feasible . The levels of ionizing radiation on Io , Europa , and Ganymede are hostile to human life , and adequate protective measures have yet to be devised . 
- It could be possible to build a surface base that would produce fuel for further exploration of the Solar System . In 1997 , the <unk> Project designed a plan to <unk> Europa . According to this plan , explorers would drill down into the Europan ice crust , entering the postulated subsurface ocean , where they would inhabit artificial air pockets . 
- 
- 
- = Fort Glanville Conservation Park = 
- 
- Fort Glanville Conservation Park is a protected area located in the Australian state of South Australia located in Semaphore Park , a seaside suburb of Adelaide consisting of a functional 19th century fort listed on the South Australian Heritage Register and some adjoining land . The fort was built after more than 40 years of <unk> over the defence of South Australia . It was the first colonial fortification in the state and is the best preserved and most functional in Australia . Fort Glanville was designed by Governor Major General Sir William Jervois and Lieutenant Colonel Peter Scratchley , both important figures in early Australian colonial defence . When built it was designed to defend both Semaphore 's anchorage and shipping entering the Port River from naval attack . 
- Construction of the fort began in 1878 . It was officially opened in October 1880 and completed by 1882 . Due to changes in the Port River and shipping movements , Fort Largs surpassed it for strategic importance by 1890 . By the close of the 19th century , the fort was largely unused and had no defence significance . It was briefly used for military purposes during World War I and World War II , though not for its original defensive role . For much of the 20th century the area was put to a variety of uses including accommodation , a caravan park and a boy scout <unk> . After coming into state government hands in 1951 it was declared as a conservation park and is now managed by the Department of Environment , Water and Natural Resources ( DEWNR ) ; preserving and showcasing its historic value . The fort and surrounds occupy the northern half of the 5 @-@ hectare ( 12 @-@ acre ) conservation park , the southern half is a caravan park . The fort is a lunette shaped defensible battery that was supported by land forces for self @-@ defence . When constructed it was seen as state of the art , incorporating powerful and modern weapons . Its main armament is two rifled muzzle @-@ loading ( RML ) 10 inch 20 ton guns backed up by two RML 64 pounder 64 cwt guns , both rare in their particular configuration . The fort retains its original 19th century cannons and three have been restored to working condition . 
- Fort Glanville Historical Association operates the park under license and conducts open days in the park , <unk> the past operation of the fort including military drill and the firing of period weapons . The Association , park service , other volunteers and various grants have all helped ensure the fort is presented in close to original condition . It is the most complete 19th Century fort in Australia , and one of very few in the world that remains in original condition . <unk> the fort to Semaphore jetty is the Semaphore and Fort Glanville Tourist Railway , a 457 mm ( 18 @.@ 0 in ) gauge passenger steam train operated by volunteers from the National Railway Museum . 
- 
- = = Historic background = = 
- 
- In the early years of colonial South Australia , the colonists saw themselves as part of the British Empire and external defence as an Imperial responsibility . Communication lines were long and the empire 's wars remote . Great Britain had <unk> military <unk> and she was expected by all to protect her colonies , even one as distant . For local defence , Governor <unk> raised the first military force in 1840 , composed solely of volunteers and known as the South Australian Volunteer Militia Brigade . It was granted the " Royal " title in 1841 but the brigade had all but ceased to exist a year later . The colony obtained its first artillery in 1847 with the arrival of six field guns of various types . 
- Tensions between Britain and the Russian Empire in the 1850s , as starkly demonstrated by the Crimean War , along with Australia @-@ wide moves towards self @-@ government caused a <unk> of the colony 's defence posture . The various colonies regarded themselves as possible targets for the Russian Pacific Fleet , then based in Siberia . In 1854 , Governor Henry Young appointed a commission under Boyle Travers Finniss to report on the defence of the colony , in case of war . Boyle 's report recommended leaving strategic defence in the hands of the Imperial Navy , though South Australia was to purchase a 400 ton naval vessel . Local defence was to be largely handled by the existing small Imperial garrison and local colonial force , supported by the artillery obtained in 1847 . When the Crimean War ended in 1856 , the danger passed and the perceived need for expensive defence preparations with it . For many years nothing substantive came about from debate on defence of the colony . Over time there formed a consensus favouring Semaphore for fixed defences or fortification ; a strategy also argued by the government established Hart Commission in 1858 . Raiders were seen as unlikely to force the <unk> of the Port River but instead were expected to stand off Semaphore , shell the port and use their guns to support landings . The Hart Report recommended building of <unk> towers at Semaphore and Glenelg , the first report to recommend permanent fortification at Semaphore , though none were built mainly due to the cost . 
- The volunteer military force was revived in 1859 , with new and modern arms for the infantry , cavalry and artillery . Though there was a few years of enthusiasm and a restructuring in 1866 , by 1870 the force was virtually disbanded . In that year also , British troops were withdrawn from the other Australian colonies ; none were by then stationed in South Australia . With no definitive defence policy , in 1864 the government had sought advice from Captain <unk> of HMS Falcon and Commodore Sir William Wiseman commander of the Australian station ; both visiting British naval officers . They both recommended fixed fortifications for the coast supported by gunboats . Sir Wiseman 's report particularly recommended construction of forts at Semaphore , Port Creek 's entrance and one midway between . In 1864 a story circulated , supported by press speculation , that there was a danger of the Russian fleet attacking Melbourne should Russia and Britain find themselves at war . The South Australian Register produced an editorial decrying the states lack of defences . Within days £ 20 @,@ 000 ( A $ 4 @.@ 14 million in 2005 ) was provided by the government for defence , an amount then seen as insufficient for significant preparation . The danger passed without any lasting defence action except the government 's in @-@ principle adoption of Sir Wiseman 's recommendations . To <unk> costs only the Semaphore fort was to be built initially , as it was considered the most critical . Site preparation begun and two 9 @-@ inch ( 230 mm ) guns were purchased , but escalating cost estimates caused the plan to be abandoned by 1868 . 
- During the early 1870s South Australia 's defence was solely dependent on the volunteer military , and a few artillery pieces purchased during earlier war <unk> . British troops had been withdrawn from the Australian colonies in 1870 , leaving the state dependent on its own military resources . In a report to the government in 1866 , Colonel <unk> and Major Peter Scratchley recommended establishment of a permanent military force . In 1876 the South Australian government , along with those from New South Wales , Victoria and Queensland requested from the War Office that Major General Sir William Jervois , a noted coastal fortification expert , be appointed to advise on defence needs . He arrived in Sydney in mid @-@ 1877 with then Lieutenant Colonel Scratchley . The 1877 report , delivered after he became South Australian Governor , called for three batteries , at Largs Bay , Semaphore and Glenelg , connected by a military road and supported by field gun emplacements , naval elements and mobile forces . He determined that South Australia 's most probable defence risk was an attack by up to two ships rather than a larger force , and this formed the basis of the final fortification design . His report called for Military Road to be extended to Marino , an electro @-@ contact torpedo station be established on Torrens Island and that a gun boat be provided . In November 1878 the government passed the Military Forces Act , which provided for the raising of a permanent military force and reserve . Two volunteer reserve rifle companies were formed in 1878 and a permanent artillery unit in 1882 . 
- At this time Semaphore , with its jetty built in 1860 , was the state 's main entry point for passengers and mail . It had a signal station ( built 1872 ) and a time ball tower ( built 1875 ) . Semaphore remained of great maritime significance for the state until the 1880s . When the decision was made to build the fort , in 1878 , the state 's population had reached approximately 250 @,@ 000 . Settlement extended beyond Port Augusta , though Adelaide remained the dominant feature in the economy partly due to the layout of the rail network . At this point Adelaide had a population of over 30 @,@ 000 . Port Adelaide was the main port for South Australia , with over 1000 ships visiting each year , and a local population of over 2 @,@ 500 . 
- 
- = = Foundation = = 
- 
- Scratchley had inspected the proposed South Australian fort sites , and Jervois and Scratchley were both responsible for the final fort design and location . The first fort was erected to guard both the entrance to Port Adelaide and the anchorage at Semaphore . It was built on <unk> near Glanville Hall at Semaphore , on a promontory called " Point Malcom " — a name that is now used for an adjacent reserve . The site was chosen so as to best guard shipping sailing to both the Port River and the Outer Harbour ; and a second northern fort was to protect the entrance to Port River itself . At the time of its construction it was seen as a defence against foreign threats , mainly Russian . Scratchley had offered to design the battery , the offer accepted by Cabinet in January 1878 , and he was primarily responsible for the design of both Fort Glanville and Fort Largs . The plans were drawn by Alexander Bain Moncrieff of the South Australian Engineer @-@ in @-@ Chief 's Department , supervised by Scratchley in his Melbourne headquarters . Moncrieff , at Scratchley 's suggestion , was later to supervise the fort 's construction . Plans for both batteries were completed in June 1878 , <unk> called for in July and the contract for Fort Glanville awarded in August to John <unk> of <unk> , South Australia for the sum of £ 15 @,@ <unk> <unk> <unk> . 
- Construction began in 1878 and though completed by 1882 the fort was operational in 1880 . Changes were made to the design during construction . Some forced by armament changes and others made to ensure additional security , including a rear @-@ defence wall and connecting road from Military Road . By January 1880 two guns were in place and the fort opened later the same year with an extensive opening ceremony on 2 October . In addition to parades , a small target was moored 3 @,@ 500 yards ( 3 @,@ 200 m ) offshore and fired on by all guns . A total of sixteen shots were fired during which one of the ten inch guns proved faulty . Only one ranging shot was fired and the shots were estimated to land from on @-@ target to 350 yards short . The fort 's final cost was approximately £ 36 @,@ 000 ( A $ 7 @.@ 05 million in 2005 ) consisting of £ 23 @,@ 600 for physical structures and the remainder for armament and fittings . At the time of its construction it was at the forefront of such fortification design , and was considered state of the art . 
- When first conceived , it was known as the Semaphore Battery , later changed to South Battery and then Fort Glanville by the opening ceremony . The name Glanville came from nearby Glanville Hall , residence of John Hart ( 1809 – 1873 ) <unk> Premier of South Australia . Hart had named the house after his mother 's maiden name ( Mary Glanville ) . 
- 
- = = Personnel = = 
- 
- For the eighteen months after its opening , the fort was manned by B company of the South Australian Volunteer Artillery but only on weekends . This changed in mid @-@ 1882 when South Australia 's first permanent military force was formed . The fort became the headquarters for the South Australian Permanent Military Force , then the state 's entire standing army of one officer and eighteen other ranks . Some of the unit was stationed at nearby Fort Largs from 1886 ; and , by 1889 the unit had grown to one officer and 45 other ranks . This force grew to 53 of all ranks by 1892 and was by then known as the Permanent Artillery . The unit trained 27 more non @-@ commissioned officers and men who were sent to man the King George Sound batteries near Albany , Western Australia . Though some of the gunners served in the Second Boer War , the unit itself never saw action . Fort Glanville 's section was called out in 1890 , marching to assist the police with a <unk> worker 's strike in Port Adelaide . In this case no shots were fired and the strikers did not confront the police or troops . Additional defence acts were passed in 1886 , 1890 and 1895 but , until defence passed into federal hands after 1901 , the state 's permanent military force was composed solely of artillery . Shortly after the federation of Australia , a regiment of the Royal Australian Artillery ( <unk> ) was formed and what had been the Permanent Artillery became <unk> company <unk> . The fort was manned to an extent during World War I , with one non @-@ commissioned officer and 11 gunners stationed as of November 1914 . Military reports and orders show the fort manned to at least mid @-@ 1918 , though at this point it is doubtful if the guns were fit to engage targets . 
- There is little in the way of surviving personnel records from the fort 's active time . They may have either been destroyed or transferred to Melbourne after federation . The fort 's record book shows it manned by a section of either the Royal Australian Artillery or B Company of the militia garrison artillery . For the time covered by the book , strength varied from 56 to 108 men of all ranks . Two figures who served at the fort are remembered for their impact outside its operation : 
- Battery Sergeant Major Charles Moritz , who initially joined the volunteer artillery and was the Permanent Artillery 's first recruit . 
- Joseph Maria Gordon ( 1856 – 1929 ) , the fort 's first commander and later military commandant for South Australia . Gordon retired in 1914 as Chief of the General Staff , Australian Military Forces . 
- 
- = = Structure = = 
- 
- The fort is designed as a defensible battery , rather than a defensive strongpoint . The faces of the fort join to form a half @-@ moon shape or lunette . The guns ' primary role was to defend Port Adelaide and the Semaphore anchorage rather than the fort itself , and the design reflects this . It was intended to be supported by field artillery , cavalry and infantry for self @-@ defence and to repel landings . While the fort retains its original form , the ditch and glacis ( embankment ) have been modified and are largely non @-@ <unk> in some areas . Ground levels have been changed to accommodate paths and a caravan park , and the original western fence no longer exists . 
- The entrance road was constructed from Military Road to the fort 's rear gates . Its path is largely followed by the modern Queen Elizabeth II walk , though the former road was slightly to the south . This walk links the conservation park 's visitor centre to the fort gates ; some of the old road can still be seen close to the gates . What was the muster ground is partly taken up by the caravan and camping park and a car park . It was levelled and filled in the 1950s for this use . What remains of this ground , north of the caravan park , was returned to its 1880s profile in 1993 funded by a Federal Government grant . In the 19th century , the muster ground was used for training of the Volunteer Military Force including artillery , who camped on the site . As designed the fort 's rear was protected with a wooden palisade or stockade . In 1881 a masonry wall was added , greatly strengthening defence . Most of the stockade was reconstructed in the 1970s ; some of the original wooden structure is visible in the fort 's north . 
- 
- = = = <unk> = = = 
- 
- The main defensive structure is a <unk> lunette shaped <unk> . It has a 15 @-@ metre ( 49 ft ) thick rampart with 1 @.@ 5 m ( 5 ft ) of concrete and 0 @.@ 6 m ( 2 ft ) of brick forming a retaining wall for the earth fill . The rampart is covered by natural vegetation and , in both the 19th and 21st century operation , is closed to access to preserve this . The glacis gives extra protection to the fort and was designed to blend the fort into the landscape . It surrounded the fort on the north , south and seaward sides and was made by forming the surrounding <unk> . The side facing the fort — the <unk> — is steep and , with the front face of the rampart , forms a 12 ft ( 3 @.@ 7 m ) wide ditch that can be raked by rifle fire from the caponier or the stockade 's sides . The outer face is a gentle slope and is designed to be covered by case shot fired from the fort 's 64 @-@ pounder guns . The southern glacis section was removed during sand mining and construction of the caravan park , both after World War II . The western glacis is changed , but still visible , and the northern glacis was recreated in 1993 — showcasing the purpose of the glacis and the function of the caponier . <unk> to the fort is via double gates at the fort 's rear , one each through the stockade and the rear defence wall . Both gates were removed or destroyed over time and the 21st century gates are reconstructions . The formal parade ground or manning parade lies between the rear walls and barracks , and the raised terreplein . It was formerly used for drill training , assembly and formal parades . First <unk> when constructed , it was paved with tar in the 1890s to solve drainage problems . Around the seaward side of the manning parade runs the terreplein . It is a raised crescent shaped level on which the guns platforms sit , and is ascended from the manning parade by ramps and stairs . 
- After numerous proposals the sand hills rear and north of the fort were removed in 1882 , improving the fort 's land defence and allowing the 64 @-@ pounder guns to sweep the Semaphore jetty . This now levelled area was used as a bivouac and exercise area for the colony 's volunteer troops . A stable , office , shed and gunner 's store were also erected north of the fort . The gunner 's shed was damaged by fire in 1895 and replaced further eastward . These buildings did not survive into the 21st century . 
- 
- = = = Internal rooms = = = 
- 
- On either side of the terreplein , wide of the 64 @-@ pound guns , are two small rooms known as the expense stores . They were used for ready @-@ to @-@ use ammunition for the adjacent guns , except for armour piercing shells which were stored in the rear @-@ defence wall 's <unk> . Between the 64 @-@ pounders and the 10 " gun positions are two raised observation platforms set into the structure for gun commanders to spot the fall of outgoing shells . <unk> was either performed from here or from a ranging position set in the sand hills to the fort 's north . Between the 10 " guns is a T @-@ shaped loading gallery that draws shells and gunpowder via a hoist system from the magazine below . Either side of the gallery was installed the Armstrong mechanical loading systems for the 10 " guns . These mechanical systems proved unsuccessful and were removed in the 1890s , though the southern one has been reconstructed . The magazine is accessed from the manning parade and is directly beneath the 10 " gun loading gallery . Voice pipes and hoists originally installed have been removed but their remains are visible . 
- The caponier ( rifle gallery ) extends into the ditch between the rampart and glacis from the fort 's north west corner . It is connected to the fort via a tunnel , running under the rampart from the manning parade . For blast protection and <unk> the tunnel was built with a <unk> . The caponier has rifle firing ports and was originally protected from direct artillery fire by the glacis . Early plans showed the caponier extending from the fort 's south west , and a tunnel linking the magazine and southern guns . 
- 
- = = = Buildings = = = 
- 
- The barracks consists of two levels , with rifle firing holes on all sides . Iron shutters closed on the inside and were locked with wooden beams . In 1885 the <unk> facing the manning parade was enclosed with wooden shutters to keep the weather out . These shutters were removed during 20th @-@ century restoration work and the <unk> returned to original condition . The roof was at first flat timber overlaid by 12 inches ( 30 cm ) of lime concrete . This first roof leaked and an iron roof was added in 1885 . The first floor of the barracks contains the officer 's rooms and troop 's barracks room . Its eastern ( outer ) wall is not flat , incorporating a design feature known as " <unk> " , which opens the field of fire from the rifle holes . Some of the firing holes were <unk> in during the 1930s ; this has been only partly rectified during restoration . 
- The basement contains the gunner 's mess , canteen , No. 1 ancillary store , <unk> and officer 's kitchen . The canteen sold everyday <unk> as well as a few items like <unk> , <unk> and tobacco to the stationed troops . <unk> from the canteen were used to fund sporting equipment for the garrison . The <unk> ancillary store was used for various pieces of delicate equipment , fuses , friction tubes and rockets . In 1887 an explosion in the room caused a number of injuries , damage to the store and to the above officer 's quarters . The barracks room and officer 's quarters are connected with a door , probably added in the 1930s as it is not part of the original plans . The barracks room accommodated approximately 20 men . For display purposes it is outfitted as for similar period barracks . 
- A laboratory is built into the rear wall , on the forts southern side . This room is set into the end of the rampart and was used to prepare gunpowder charges . A <unk> was built at the manning parade 's southern end in 1885 . It is now used as a duty room for the drill squad during recreation demonstrations but originally was a guard 's room and separate cells connected by telephone to Fort Largs . Up to three men appear to have been accommodated in the guard 's room . When the fort was converted to use as a caravan park the guard house was converted to an ablution block . A store and ablution block were also built in 1885 between the stockade and rear defence wall . 
- 
- = = Armament = = 
- 
- Early plans for the fort 's armament were drawn up by Harding Stewart of the British War Office . They called for four 9 in ( 230 mm ) 12 long tons ( 12 t ) rifled muzzle @-@ loading ( RML ) guns , two mounted in turrets and two behind vertical iron shields . This configuration of siege artillery had not been tried before and the plan was abandoned due to the large expected cost . Two of the 9 in guns had been in South Australia since <unk> after a Colonial Government request , but were not used in the eventual fort design . The final Jervois / Scratchley design omitted the turret and iron shields . 64 @-@ pounder RML guns were substituted for two of the 9 in guns and 10 in 20 ton RML guns for the remaining two . The 20 ton guns were chosen over the then standard 18 ton gun by Jervois after he saw the plans in England , the decision influenced by the then fully committed nature of the Royal Arsenal . Jervois had originally ordered the 18 ton guns but cancelled the order and changed to the Armstrong 20 ton when the arsenal was unable fulfil the order . He also ordered Armstrong 's mechanical loading and protected barbette system for the 20 ton guns . This original battery of four heavy guns remains in place at the fort . 
- 
- = = = 10 inch RML guns = = = 
- 
- The fort 's main armament were the two RML 10 inch 20 ton guns — Numbers <unk> and <unk> . They were manufactured in 1879 by <unk> Armstrong and Company and originally used the Armstrong protected barbette loading system . They were made to the 1878 pattern and supplied on traversing <unk> . The guns have a 10 @-@ inch ( 250 mm ) calibre , a range of 6 @,@ 500 yards ( 5 @,@ 900 m ) with a muzzle velocity of 1 @,@ 630 feet per second ( 497 m / s ) and are capable of penetrating 11 inches ( 28 cm ) of iron at 2 @,@ 000 yards ( 1 @,@ 800 m ) . 
- They fire 400 @-@ pound ( 181 kg ) projectiles using a 130 @-@ pound ( 59 kg ) gunpowder cartridge . Though they were insufficient against contemporary battleships , they were seen as adequate for the light cruisers that the fort was expected to face . The carriages weighed 13 long tons ( 13 t ) and moved on semi @-@ circular traverses . The guns were loaded either manually — using the <unk> ( <unk> ) system — or via the Armstrong mechanical <unk> . The Armstrong system enabled loading of the guns from behind the protection of the rampart whereas the manual system required at least two crew to be exposed on the rampart 's top . Despite this the guns were manually loaded for most of their operational life as the Armstrong cable drive was faulty and too difficult to maintain . The guns had a 14 @-@ man crew and loading took about 2 minutes , a rate of fire that was only slightly faster using the Armstrong equipment . 
- By 1902 the guns had together fired 219 rounds , though not all at full charge with the records showing only 125 effective full charges . The platforms and traverses were scrapped in 1937 but the gun barrels were <unk> to cut up and were left in place . One gun platform and its equipment was rebuilt in 1997 using money raised by Fort Glanville Historical Association volunteers working at the Australian Grand Prix . 
- 
- = = = 64 pounder RML guns = = = 
- 
- In the flank barbettes are mounted two mark III RML 64 pounder 64 cwt guns , numbers 462 and 463 . They are rifled muzzle @-@ loading heavy guns weighing 64 long <unk> ( 3 @,@ 300 kg ) with a 6 @.@ 3 @-@ inch ( 160 mm ) calibre steel barrel . They were made to the model 's 1867 pattern by the Royal Arsenal , <unk> , England in 1872 . Their intended use was to protect the flanks and approaches to the fort . These smaller guns use an 8 lb ( 4 kg ) charge of gunpowder to send a 64 lb ( 29 kg ) <unk> up to 5 @,@ 000 yards ( 4 @,@ 600 m ) . There are iron rings fixed in the rampart wall that indicate traversing gun carriages were intended to be mounted , though this never <unk> . The guns were supplied with siege over @-@ bank carriages . The lower carriage sections are the type made for the guns when used as field pieces and the upper sections are brackets that raise the guns to the over @-@ bank firing position . In this particular configuration the two guns are thought to be the last in the world . 
- By the end of their active use in 1902 together they had fired 1540 rounds in practice , though not all at full power as they are recorded as firing under 300 effective full charges . By then the wheels were significantly <unk> and the ammunition <unk> had become <unk> . Less wheels , the guns were removed from the fort in 1909 ; Adelaide 's city council then set them in <unk> Gardens , North Adelaide . Both were returned to the fort in 1976 and <unk> on restored carriages of the original design . They are the only two guns of their type remaining in Australia . The southern gun ( Number 463 ) fired three blank charges in 1980 to mark the centenary of the first firing at the fort . This same gun is fired regularly by the volunteers of the Fort Glanville Historical Association ( <unk> ) . 
- Both types of heavy guns used where the only ones of their specific series and type to come to Australia . Though they were never fired in anger , the battery is only 40 feet ( 12 m ) above mean sea level which would have limited the accuracy of the mark III depression rangefinders used . 
- 
- = = = Other weaponry = = = 
- 
- Prior to 1895 two 6 @-@ inch ( 200 mm ) breech @-@ loading guns had been imported for use in small boats . This use was rejected by military authorities and the guns sat unused . In 1895 the South Australian Defence Committee proposed that the guns be mounted at Fort Glanville , replacing the 64 @-@ pounders whose siege carriages were then unfit for service . This proposal would have greatly extended the useful life of the fort , at little cost . The two military branches , army and navy , could not reach agreement and the proposal was abandoned by 1897 . The gun 's eventual fate is unknown , though a gun found in the Port River later indicates they may have been dumped . 
- Gun emplacements were made , about a mile apart south of the fort , for six 16 @-@ pounder field guns to provide close defence in case of an attempted beach landing . From 1890 the fort was equipped with <unk> armament . It received an 1867 Whitworth 12 @-@ pounder RML field gun and a 5 @-@ barrel 0 @.@ 45 " <unk> @-@ <unk> cartridge firing <unk> gun . The 12 @-@ pounder had fired 222 effective full charges by 1902 ; at which time both weapons were obsolete and their eventual fate is unknown . A 32 @-@ pounder 56 long cwt ( 2 @,@ 800 kg ) smoothbore gun on a stepped wooden carriage was purchased in 1878 . Manufactured by the Royal Arsenal in 1806 , it was the familiar " ships cannon " and came to South Australia with two other identical guns . By 1902 no ammunition was held for it and it was listed as for instruction only . The gun was probably destroyed in the 1930s — certainly by the end of the 1940s — with pieces of such a gun found on site in 1983 . 
- In the late 20th century the fort acquired three 16 @-@ pounder RML mark I field guns . They were formerly used by A battery , South Australian Volunteer Artillery from 1880 until 1901 . Gun number 288 is complete and used for blank <unk> by the Historical Association . Also acquired is a 2 @-@ pounder RML Whitworth mountain gun made in 1867 . It was also used by A Battery , subsequently by Fort Largs as a signal gun . It is one of only two of this type known to exist , the other in the United Kingdom . The visitors centre has two 9 @-@ pounder brass smoothbore field guns made by H & C King in 1819 . They arrived in South Australia in 1857 and were used for practice shoots near the fort <unk> ; one is known to have been on the manning parade in 1890 though its use is unknown . They later became <unk> guns at Fort Largs , moving by 1919 to near the Jervois Wing of the State Library of South Australia . The Art Gallery of South Australia saved them from a 1941 wartime scrap drive and mounted them on reproduction naval carriages in front of Government House in 1962 . The gallery took them back in late 1977 and transferred them to the History Trust of South Australia in 1988 . The History Trust has loaned them to Fort Glanville for display . Outside the visitor 's centre is a 6 in ( 200 mm ) breech @-@ loading Armstrong 80 long cwt ( 4 @,@ 100 kg ) gun ( <unk> ) that was made in 1884 and used in Victoria . The Commonwealth Scientific and Industrial Research Organisation ( CSIRO ) brought it to the state in 1966 for research , subsequently moving it to Perry Engineering at Mile End . In 1984 the CSIRO donated the gun to the park . 
- 
- = = Defence significance = = 
- 
- Work began in 1882 on Fort Glanville 's northern sister Fort Largs — then known as the Port Adelaide battery — to the same specification as Fort Glanville ; its barracks and rear defence wall were finished in 1885 . Though guns had already been purchased specially , plans for the third fort at Glenelg were not proceeded with . South Australia was experiencing a depression in 1886 and that coupled with a report by General James <unk> Edwards scuttled plans for the third coastal fort . As early as 1888 the emphasis for defence of the Adelaide coast had already shifted to Fort Largs ; a fact cited as part of the reason for abandonment of the Glenelg fortifications . 
- From this point Fort Glanville 's significance declined rapidly . Fort Largs was equipped in 1889 with two 6 inch breech @-@ loading disappearing guns which <unk> Glanville 's armament . The decline was also linked to changes in Port Adelaide 's maritime facilities and the consequent northward movement of anchored and berthed vessels . During the 1880s the Port River was deepened enabling large ships to sail up and berth , rather than the former practice of anchoring off @-@ shore . 
- The fort remained as headquarters of South Australia 's permanent military force until the 1890s and as late as 1895 there were still plans to upgrade the 64 @-@ pounder armament , though without result . By 1901 the fort was manned on a caretaker basis only and no permanent forces were stationed . The Federal Government assumed responsibility for South Australian defence in 1903 and took over the fort . Though Glanville by then had no significant defence role , the state received £ 14 @,@ <unk> in compensation . From that point its significance was not defence related but as the first and best preserved 19th @-@ century fortification in South Australia . 
- 
- = = 20th century = = 
- 
- At Federation in 1901 South Australia 's defences became a federal responsibility . Though both infantry and artillery units were housed at the site on occasion , by 1903 there was no longer a permanent military presence at the fort . For most of the 20th century the site was neglected and largely vacated . It did attract some usage though not always of a military nature . During World War I it was partly revived for a former use , with ammunition stored on site . During the same period however the military used it as a detention barracks . Some or all of the fort was leased for private accommodation during the great depression . During the 1930s the magazine was again used , this time to store small arms ammunition . From June 1931 until the beginning of World War II the site housed a Sea Scout detachment , and was used as a district camp @-@ site for the Boy Scouts . The Department of Defence decided in 1937 that much of the equipment and fittings at the fort were surplus to requirements . <unk> to this the mountings and carriages for the 10 inch guns were removed and sold as scrap ; though effort was made to scrap the barrels it proved <unk> and they were left in place . In an unusual turn of events , the fort briefly housed refugees . Twenty @-@ nine <unk> from the Maldives were rescued from their sinking dhow by a ship bound for <unk> . They stayed at the fort for a week in 1938 , before repatriation could be arranged . During World War II the fort again attracted military related use . During 1944 the Proof and Experimental Establishment at Port Wakefield made use of the site to proof ordnance QF 6 pounder anti @-@ tank guns manufactured at General Motors Holden in <unk> . The fort was also used as a residence again , with at least two families reported as living in the under @-@ ground sections during the war . 
- After the war , the State Government negotiated with its federal counterpart to gain control of Fort Glanville . This <unk> in the 1951 sale of the 13 @-@ acre ( 5 ha ) site , which was subsequently administered by the State Tourist Department as a caravan and camping park . The park occupied the muster ground outside the fort and the fort 's barracks building was used as a manager 's residence . The caravan park has had various managers since establishment : Until 1981 it was managed by the National Parks and Wildlife Service ( NPWS ) , by <unk> Council until 1986 and subsequently by a private operator under a long @-@ term lease . 
- A significant change in the fort 's conservation and preservation outlook was its declaration as a Historic <unk> under the Aboriginal and Historic Relics Preservation Act in 1972 . Up to then there had been growing awareness of the significance of the site in terms of the state 's colonial heritage . Control of the park was moved to the National Parks and Wildlife Service ( NPWS ) , and the fort and surrounds became designated as Fort Glanville Conservation Park . The National Estate Grants Program provided funds in 1975 for conservation work ; NPWS began this work in the same year and the caravan park boundary was moved southwards . Though the site had long been neglected , there was minimal permanent damage to its structure . During the 1970s reconstruction , much of the stockade was replaced . The replaced timbers are visibly different as they have shrunk significantly with large gaps that are not evident in the original timber . Adelaide City Council agreed to return the 64 @-@ pounder guns and new wheels were made ; replacing those <unk> over half a century before . In a ceremony on 2 October 1980 , the 100th anniversary of the guns first firing , one of the 64 @-@ pounders was fired again ; later a commemoration plaque was added to the fort 's flagpole 's base . Shortly after this firing the Fort Glanville Historical Association was formed , and was incorporated in 1981 . Fort Glanville was opened for public visitation in 1981 . 
- As part of South Australia 's <unk> , the South Australia Jubilee 150 board granted $ 250 @,@ 000 for work on the fort , allowing construction of a visitor centre . Queen Elizabeth II visited and inspected the site , and newly constructed visitor centre , for almost an hour on 13 March 1986 . For the visit the historical association demonstrated firing of both the 64 and 2 pounder cannons . The visitor centre was officially opened one month after this visit . Governor Dame <unk> Mitchell named the old road " Queen Elizabeth II Walk " in March 1991 ceremony , commemorating the 5th anniversary of the queen 's visit . <unk> subsequent restoration work has been completed on the fort , including the <unk> 's interior . The portion of the muster ground that is not within the caravan park was returned to its original level in 1993 . 
- 
- = = Park and fort today = = 
- 
- Fort Glanville Conservation Park is one of South Australia 's most important heritage sites . It is the premier site in the state , and possibly Australia , for showcasing colonial era defences and fortifications . The fort is listed on both the South Australian Heritage Register and the National Trust 's classified list . It is considered significant , in national historical military terms , for its association with Jervois and Scratchley ; both leading British defence experts who influenced Australian defence thinking in the late 19th century . The fort was very well constructed with high quality materials ; facts that have been noted as contributing to its preservation . The fort is largely intact and in original condition and is , along with Bluff Battery in Hobart , the best preserved Jervois @-@ Scratchley designed fort in Australia . It is the only Australian colonial fort to still have all of its original armament , and the only to have a regular living history program . Its companion Fort Largs does survive but in greatly modified condition , converted for use as the South Australia Police academy . The fort retains its original armament ; armament that is both rare in Australia and worldwide . 
- The conservation park is in the suburb of Semaphore Park at the southern end of the Lefevre Peninsula . It covers approximately 5 hectares ( 12 acres ) and is bounded by <unk> and Military roads , the Point Malcolm Reserve and Semaphore beach . The land is entirely crown land and is administered by the DEWNR . It is divided into roughly two halves with the northern containing the fort and the southern half containing a caravan park . While it is managed by the DEWNR , the Fort Glanville <unk> Committee and Fort Glanville Historical Association are heavily involved . The committee is appointed by the Minister for Environment and Planning and acts to advise the Minister and <unk> with the community . As of 1988 , the fort hosted approximately 7 @,@ 000 visitors annually , and is used as an event venue including the annual City of Charles Sturt citizenship ceremony . 
- The historical association is an incorporated body of volunteers who use the fort under licence from the Minister . The association operate the fort and visitor centre , holding monthly public open days at which the history of the fort is recreated ; this includes drill demonstrations and firing of the fort 's weapons . The association maintain static displays of the fort 's active period and sell souvenirs . The association 's goal is to have the site open as a fully operational fort for interpretation , tourism and education . The volunteers are involved with recreation , living history and ceremonial work outside the fort . They act as guards of honour at some Government House functions and attended the restoration opening of the Albany , Western Australia fort , and the annual re @-@ enactment of the Battle of Waterloo in <unk> , Victoria . The fort has living history displays , including the barracks laid out and furnished in period detail . The visitor centre has displays showing the development of South Australia 's colonial defence from 1836 and artifacts found on site during restorations . 
- <unk> the fort to Semaphore jetty is the Semaphore and Fort Glanville Tourist Railway , a 457 mm ( 18 @.@ 0 in ) gauge steam train operated by volunteers from the National Railway Museum . The railway opened in December 1992 and , as of 2002 , carried over 16 @,@ 000 passengers annually . 
- 
- 
- = Royal prerogative in the United Kingdom = 
- 
- The royal prerogative is a body of customary authority , privilege , and immunity , recognised in the United Kingdom as the sole prerogative of the Sovereign and the source of many of the executive powers of the British government . 
- Prerogative powers were formerly exercised by the monarch acting on his or her own initiative . Since the 19th century , by convention , the advice of the prime minister or the cabinet — who are then <unk> to Parliament for the decision — has been required in order for the prerogative to be exercised . The monarch remains <unk> empowered to exercise the royal prerogative against the advice of the prime minister or the cabinet , but in practice would only do so in <unk> or where existing precedent does not adequately apply to the circumstances in question . 
- Today the royal prerogative is available in the conduct of the government of the United Kingdom , including foreign affairs , defence , and national security . The monarchy has a significant constitutional presence in these and other matters , but limited power , because the exercise of the prerogative is in the hands of the prime minister and other ministers or other government officials . 
- 
- = = Definition = = 
- 
- The royal prerogative has been called " a notoriously difficult concept to define adequately " , but whether a particular type of prerogative power exists is a matter of common law to be decided by the courts as the final <unk> . A prominent constitutional theorist , A. V. Dicey , proposed in the nineteenth century that : 
- The prerogative appears to be historically and as a matter of fact nothing else than the residue of discretionary or arbitrary authority which at any given time is legally left in the hands of the crown . The prerogative is the name of the remaining portion of the Crown 's original authority ... Every act which the executive government can <unk> do without the authority of an Act of Parliament is done in virtue of the prerogative . 
- While many commentators follow the <unk> view , there are constitutional lawyers who prefer the definition given by William Blackstone in the <unk> : 
- By the word prerogative we usually understand that special pre @-@ <unk> which the King hath , over and above all other persons , and out of the ordinary course of common law , in right of his regal dignity ... it can only be applied to those rights and capacities which the King enjoys alone , in contradiction to others , and not to those which he enjoys in common with any of his subjects . 
- Dicey 's opinion that any action of governance by the monarch beyond statute is under the prerogative <unk> from Blackstone 's that the prerogative simply covers those actions that no other person or body in the United Kingdom can undertake , such as the dissolution of Parliament . Case law exists to support both views . Blackstone ’ s notion of the prerogative being the powers of an exclusive nature was favoured by Lord <unk> in the De <unk> ’ s Royal Hotel case of 1920 , but some difficulty with it was expressed by Lord Reid in the <unk> Oil case of 1965 . A clear distinction has not been necessary in the relevant cases , and the courts may never need to settle the question as few cases deal directly with the prerogative itself . 
- 
- = = History = = 
- 
- The royal prerogative originated as the personal power of the monarch . From the 13th century in England , as in France , the monarch was all @-@ powerful , but this absolute power was checked by " the <unk> of feudal turbulence in the fourteenth and fifteenth centuries " . An early attempt to define the royal prerogative was stated by Richard II 's judges in <unk> . 
- During the 16th century , this " turbulence " began to <unk> , and the monarch became truly independent . Under Henry VIII and his successors , the king was the head of the Protestant English church , and therefore not <unk> to the clergy . The rise of Parliament in this period , however , was problematic . While the monarch was " the predominant partner in the English constitution " , the courts stopped short of declaring him all @-@ powerful , recognizing the role that Parliament played . In <unk> 's Case , Henry recognised this , noting that he was far more powerful with the consent of Parliament than without . Nowhere was this more apparent than in the matter of taxation : Sir Thomas Smith and other writers of the period pointed out the monarch could not impose taxation without Parliament 's consent . 
- At the same time , Henry and his descendants normally followed the will of the courts , despite the fact they were theoretically not bound by judges . William Holdsworth <unk> that by regularly asking the legal officers of the crown and judiciary for legal advice and consent , Henry recognised the need for a stable government to follow the law . He also contends that the view that the law is supreme over all " was the view of all the leading lawyers and <unk> and <unk> of the Tudor period " . It was accepted that while the King had " <unk> discretion " , he was limited in areas where the courts had imposed conditions on the use of the prerogative , or where he had chosen to do so . 
- The first dent in this stability came about in <unk> , with the Case of <unk> . James VI and I claimed that as monarch , he had a divine right to sit as a judge and interpret the common law as he saw fit . <unk> by Sir Edward Coke , the judiciary rejected this idea , stating that while the monarch was not subject to any individual , he was subject to the law . Until he had gained sufficient knowledge of the law , he had no right to interpret it ; Coke pointed out that such knowledge " demanded mastery of an artificial reason ... which requires long study and experience , before that a man can attain to the <unk> of it " . Similarly , in the Case of <unk> in 1611 , Coke held that the monarch could only exercise those prerogatives he already had , and not create new ones . 
- With the Glorious Revolution , King James VII and II was replaced by Queen Mary II and her husband King William III . At the same time the Bill of Rights 1689 was drafted , which cemented the monarch 's subservience to Parliament . It specifically limited the royal prerogative , with Article 1 holding that the " power of <unk> the laws or the execution of laws by regal authority without consent of Parliament is illegal " , and article 4 confirming that " <unk> money for or to the use of the Crown by <unk> of prerogative , without grant of Parliament , for longer time , or in other manner than the same is or shall be granted , is illegal " . The Bill also confirmed that Parliament had the right to limit the use of remaining prerogatives , as evidenced by the <unk> Act 1694 , which required the monarch to dismiss and call Parliament at certain times . 
- 
- = = Prerogative powers = = 
- 
- 
- = = = Legislature = = = 
- 
- One of the monarch 's historic prerogatives was the dissolution of Parliament , which was " perhaps the most important residual prerogative exercised personally by the sovereign , and represents the greatest potential for controversy . " This prerogative was normally exercised at the request of Parliament and the prime minister , either at his or her discretion or following a motion of no confidence . Constitutional theorists have had differing views as to whether a unilateral dissolution of Parliament would be possible today ; Sir Ivor Jennings wrote that a dissolution involves " the <unk> of ministers " , and as such the monarch could not dissolve Parliament without <unk> consent ; " if ministers refuse to give such advice , she can do no more than dismiss them " . A. V. Dicey , however , believed that in certain extreme circumstances the monarch could dissolve Parliament single @-@ <unk> , on the condition that " an occasion has arisen on which there is fair reason to suppose that the opinion of the House is not the opinion of the electors ... A dissolution is <unk> , or necessary , whenever the wishes of the legislature are , or may fairly be presumed to be , different from the wishes of the nation . " 
- The monarch could force the dissolution of Parliament through a refusal of royal assent ; this would inevitably lead to a government resigning . By convention , the monarch always <unk> to bills ; the last time the royal assent was not given was in <unk> during the reign of Queen Anne . This does not mean that the right to refuse has died : George V believed he could veto the Third Irish Home Rule Bill ; Jennings writes that " it was assumed by the King throughout that he had not only the legal power but the constitutional right to refuse assent " . The royal prerogative to dissolve Parliament was <unk> by the Fixed @-@ term <unk> Act 2011 . Section 6 ( 1 ) of the Act however specifically states that the monarch 's power to <unk> Parliament is not affected by the Act . 
- The appointment of the prime minister is also , theoretically , governed by the royal prerogative . Technically the monarch may appoint as prime minister anyone she wants to appoint , but in practice the appointee is always the person who commands a majority in the House of Commons . Usually , this is the leader of the political party that is returned to Parliament with a majority of seats after a general election . <unk> may result with a so @-@ called hung parliament , in which no party commands majority support , as last occurred in 2010 . In this situation , constitutional convention is that the previous incumbent has the first right to form a coalition government and seek appointment . If the prime minister decides to retire in the middle of a parliamentary session , as Anthony Eden did in 1957 , the monarch has no discretion . There is usually a " prime minister @-@ in @-@ waiting " who commands the support of the majority of the Commons ; he or she will near @-@ automatically be appointed . 
- 
- = = = Judicial system = = = 
- 
- The most noted prerogative power that affects the judicial system is the prerogative of mercy , which has two elements : the granting of <unk> and the granting of <unk> <unk> . <unk> may eliminate the " pains , penalties and punishments " from a criminal conviction , though they do not remove convictions themselves . This power is commonly exercised on the advice of the Secretary of State for the Home Department ; the monarch has no direct involvement in its use . <unk> of this power may also take the form of <unk> , a limited form of pardon where the sentences is reduced , on certain conditions . The granting of a pardon is not subject to judicial review , as confirmed by Council of Civil Service <unk> v Minister for the Civil Service , but the courts have chosen to <unk> its application or lack <unk> , as in R v Secretary of State for the Home Department , ex parte Bentley . <unk> <unk> <unk> is done by the Attorney General of England and Wales ( or the equivalent in Scotland or Northern Ireland ) in the name of the crown , to stop legal proceedings against an individual . This is not <unk> by the courts , as confirmed by R v Comptroller of Patents , and does not count as an <unk> ; the defendant may be brought before the courts on the same charge at a later date . 
- 
- = = = Foreign affairs = = = 
- 
- The royal prerogative is in much use in the realm of foreign affairs . It is the monarch who recognises foreign states ( although several statutes regulate the <unk> enjoyed by their heads and diplomatic representatives ) , issues declarations of war and peace , and forms international treaties . The monarch also has the power to annex territory , as was done in 1955 with the island of <unk> . Once territory has been annexed , the monarch has complete discretion as to the extent to which the government will take over the former government 's <unk> ; this was confirmed in West Rand Central Gold <unk> Company v The King . The monarch also has the power to alter British territorial waters and cede territory . Her freedom to do these things in practice is doubtful , in that they might deprive British citizens of their nationality and rights . When the island of <unk> was ceded to Germany in 1890 , Parliamentary approval was first sought . The monarch can also regulate colonies and dependent territories by exercising the prerogative through Orders in Council . The courts have long fought against the monarch 's use of this power : in R ( <unk> ) v Secretary of State for Foreign and Commonwealth Affairs ( No 2 ) , the Court of Appeal ruled that using Orders @-@ in @-@ Council to <unk> judicial <unk> was an unlawful abuse of power , although this ruling was later overturned . 
- British passports are also issued under the prerogative , though these are also covered by statute law . Under the common law , citizens have the right freely to leave and enter the United Kingdom . In R v Foreign Secretary ex parte Everett , the courts held that it was their right to review the granting of passports to , and the <unk> of passports from , British citizens . The writ of ne <unk> <unk> is also used to prevent a person leaving the country . The right to make treaties is a disputed prerogative power : under Blackstone 's definition , a prerogative power must be one unique to the monarch . 
- 
- = = = Other prerogative powers = = = 
- 
- The monarch also has power to exercise her prerogative over the granting of honours , the regulation of the armed forces and ecclesiastical appointments . Although the granting of most honours is normally decided by the executive , the monarch is still the person who technically awards them . <unk> to this rule are membership of the Order of the Garter , the Order of the <unk> , the Order of Merit , the Royal Victorian Order and the Royal Victorian Chain , which the monarch has complete discretion to grant . In relation to the armed forces , the monarch is the Commander in Chief , and members are regulated under the royal prerogative . Most statutes do not apply to the armed forces , although some areas , such as military discipline , are governed by Acts of Parliament . Under the Crown Proceedings Act 1947 , the monarch is the sole authority for the armed forces , and as such their organisation , disposition and control cannot be questioned by the courts . This exercise of prerogative power gives the Crown authority to recruit members of the armed forces , appoint commissioned officers , and establish agreements with foreign governments to station troops in their territory . The prerogative <unk> the monarch to appoint bishops and archbishops in the Church of England , and to regulate the printing and licensing of the authorised Church of England version of the Bible . 
- R v Secretary of State for the Home Department , ex parte Northumbria Police Authority , recognised that the prerogative also includes the power to " take all reasonable steps to preserve the Queen 's peace " , and in <unk> Oil Co. v Lord <unk> , the House of Lords took the view that it extended to " doing all those things in an emergency which are necessary for the conduct of [ the Second World War ] . " 
- 
- = = Use = = 
- 
- Today , the monarch exercises the prerogative almost exclusively in line with the advice of her government . <unk> notes that : 
- The present Queen ... is kept very closely in touch with the exercise of governmental power by means of a weekly audience with the prime minister during which she is fully briefed about the affairs of government ... [ But it ] should be emphasised that the prime minister is not under any obligation to take account of royal opinions . 
- In simple terms , the prerogative is used to govern the realm in the name of the Crown ; although the monarch has the " right to be consulted , the right to encourage , and the right to warn " , her role involves no exercise of discretion . 
- Today , some prerogative powers are directly exercised by ministers without the approval of Parliament , including the powers of declaring war and of making peace , the issue of passports , and the granting of honours . Prerogative powers are exercised nominally by the monarch , but on the advice of the prime minister ( whom the monarch meets weekly ) and of the cabinet . Some key functions of the British government are still executed by virtue of the royal prerogative , but generally the usage of the prerogative has been diminishing as functions are progressively put on a statutory basis . 
- 
- = = = <unk> = = = 
- 
- Several influential decisions of the House of Lords have determined the limited scope for the use of prerogative powers . In 1915 , an appeal was made to the House of Lords , Re <unk> of Right ( ' <unk> Aerodrome Case ' ) , but during the appeal the case was settled and the appeal withdrawn when the Crown agreed to pay compensation . The appeal was from a unanimous decision of the Court of Appeal that the Crown , both under the statutory Defence of the Realm Regulations and by the royal prerogative , was entitled to take and occupy , for military purposes in wartime , a commercial airfield on the south coast . The government argued that this action was to defend against an invasion ; the courts held that for the prerogative to be exercised , the government must demonstrate that a threat of invasion exists . This was backed up by The <unk> ( 1916 ) , where the Privy Council , on appeal from the Prize Court , held generally that to exercise a power not granted by statute ( such as a prerogative power ) the government must prove to the court that the exercise is justified . The next decision came in Attorney General v De <unk> 's Royal Hotel Ltd ( 1920 ) , where the House of Lords confirmed that a statutory provision in an area where prerogative powers are in use " <unk> the Royal Prerogative while it is in force to this extent – that the Crown can only do the particular thing under and in accordance with the statutory provisions , and that its prerogative power to do that thing is in <unk> " . 
- This principle of statutory superiority was extended in <unk> <unk> Ltd v Department of Trade , concerning the <unk> of a commercial airline operator 's licence ( December 1976 ) , where it was confirmed that prerogative powers could not be used to contradict a statutory provision , and that in situations to which the power and the statute both applied , the power could only be used to further the aim of the statute . Another extension came with R v Secretary of State for the Home Department , ex parte Fire Brigades Union , where the Court of Appeal held that even if a statute had not yet come into force , the prerogative could not be used to alter this statute to " conflict with Parliament 's wishes " . 
- 
- = = = Judicial review = = = 
- 
- Before the modern judicial review procedure superseded the petition of right as the remedy for challenging the validity of a prerogative power , the courts were traditionally only willing to state whether or not powers existed , not whether they had been used appropriately . They therefore applied only the first of the <unk> tests : whether the use was illegal . Constitutional scholars such as William Blackstone consider this appropriate : 
- In the exertion therefore of those prerogatives , which the law has given him , the King is irresistible and absolute , according to the forms of the constitution . And yet if the consequence of that exertion be <unk> to the <unk> or <unk> of the kingdom , the Parliament will call his advisers to a just and severe account . 
- During the 1960s and 70s this attitude was changing , with Lord <unk> saying in the <unk> <unk> case that " seeing that the prerogative is a discretionary power to be exercised for the public good , it follows that its exercise can be examined by the courts just as any other discretionary power which is vested in the executive . " The most authoritative case on the matter is Council of Civil Service <unk> v Minister for the Civil Service , generally known as the <unk> case . The House of Lords confirmed that the application of judicial review would be dependent on the nature of the government 's powers , not their source . Foreign policy and national security powers are considered outside the scope of judicial review , while the prerogative of mercy is considered within it , as per R v Secretary of State for the Home Department , ex parte Bentley . 
- 
- = = Reform = = 
- 
- Abolition of the royal prerogative is not on the immediate horizon , and recent movements to abolish the role of the monarchy and its royal prerogative in government have been unsuccessful . The Ministry of Justice undertook a " review of executive Royal Prerogative powers " in October 2009 . Former Labour MP and cabinet minister Tony Benn campaigned unsuccessfully for the abolition of the royal prerogative in the United Kingdom in the 1990s , arguing that all governmental powers in effect exercised on the advice of the prime minister and cabinet should be subject to parliamentary scrutiny and require parliamentary approval . Later governments argued that such is the breadth of topics covered by the royal prerogative that requiring parliamentary approval in each instance where the prerogative is currently used would overwhelm parliamentary time and slow the enactment of legislation . 
- 
- 
- = Mount Jackson ( Antarctica ) = 
- 
- Mount Jackson ( Mount Andrew Jackson and Mount Ernest Gruening ) is a mountain that dominates the upland of the southern part of the Antarctic Peninsula . It is located in Palmer Land , within the Antarctic claims of Argentina , Chile and the United Kingdom . With an elevation of 3 @,@ 184 metres ( 10 @,@ 446 ft ) , Mount Jackson is the highest mountain in the Antarctic Peninsula and the British Antarctic Territory . Discovered by members of the United States Antarctic Service , 1939 – 41 , it was named for Andrew Jackson , the seventh President of the United States . The first ascent of Mount Jackson was made by a team led by John <unk> Cunningham of the British Antarctic Survey ( BAS ) in 1964 . Mount Jackson 's geology was studied in 1972 as part of the Palmer Island investigations by a team of geologists . 
- Mount Jackson and the Welch Mountains <unk> the central Black Coast , which is dissected by many inlets and is bounded on the west by <unk> Plateau of central Palmer Land . The two mountains rise above the ice shelf with reliefs of about 1 @,@ 200 – 1 @,@ 500 metres ( 3 @,@ 900 – 4 @,@ 900 ft ) towards the east . They are interconnected by <unk> zones forming a plateau with steep snow slopes . Mount Jackson rises from its southeast flanks , displaying a steeple summit , while the north flank is occupied by a vast <unk> . <unk> moraines on the mountain 's east side measure between 1 – 5 kilometres ( 0 @.@ 62 – 3 @.@ 11 mi ) in length and display boulders at their distal ends . 
- 
- = = History = = 
- 
- The first topographic mapping of Mount Jackson was carried out in November 1940 by a <unk> party of the United States Antarctic Service ( <unk> ) . The ground survey was facilitated by aerial photographs and aerial observations . The height was estimated at 4 @,@ 200 metres ( 13 @,@ 800 ft ) , and the mountain was named Mount Ernest Gruening after the Governor of the Alaska Territory at that time , Ernest Gruening . <unk> later renamed it Mount Jackson for the seventh President of the United States . President Jackson signed the bill into law authorizing the United States Exploring Expedition of 1838 – 42 . The expedition , led by Lt. Charles Wilkes , included exploration and surveying of the Pacific Ocean and surrounding lands , including Antarctica . 
- In November 1947 , a Falkland Islands Dependencies Survey ( <unk> ) ground party based at <unk> Island observed Mount Jackson and estimated its height at 3 @,@ 050 metres ( 10 @,@ 010 ft ) , considerably lower and more accurate than the first estimate in 1940 . 
- After the Falkland Islands Dependencies Survey was renamed British Antarctic Survey in 1962 , a series of depots were developed , including one at Mount Jackson . BAS members were successful in making many first <unk> of the mountains of the Antarctic Peninsula , and John Cunningham , who served at the BAS base at Adelaide Island , was the first to climb Mount Jackson . Traveling by dog <unk> for 640 kilometres ( 400 mi ) , his team reached the summit on 23 November 1964 . 
- A precise height of the summit was determined by a BAS survey party who ascended the peak during the <unk> summer of 1996 – 97 . 
- 
- = = Geography = = 
- 
- The mountain is 3 @,@ 184 metres ( 10 @,@ 446 ft ) in height , with a prominence of 2 @,@ 187 metres ( 7 @,@ 175 ft ) and a saddle <unk> of <unk> metres ( 3 @,@ 271 ft ) . Mount Jackson and the Welch Mountains <unk> the central Black Coast , which is dissected by many inlets and is bounded on the west by <unk> Plateau of central Palmer Land , with elevation ranging between 2 @,@ 000 – 3 @,@ 000 metres ( 6 @,@ 600 – 9 @,@ 800 ft ) , and on the west side of the central Black Coast . The two mountains rise above the ice shelf with reliefs of about 1 @,@ 200 – 1 @,@ 500 metres ( 3 @,@ 900 – 4 @,@ 900 ft ) towards the east . They are interconnected by <unk> zones forming a plateau with steep snow slopes . The northern part of the mountain system , which extends over 1 @,@ 590 kilometres ( 990 mi ) , covers most of the Antarctic Peninsula , and Mount Jackson is its highest peak . 
- Mount Jackson rises from its southeast flanks , displaying a steeple summit , while the north flank is occupied by a vast <unk> . <unk> moraines on the mountain 's east side measure between 1 – 5 kilometres ( 0 @.@ 62 – 3 @.@ 11 mi ) in length and display boulders at their distal ends . Mount Jackson and the Rowley <unk> are separated by <unk> Inlet . <unk> Glacier drains eastern Mount Jackson before arriving at <unk> Inlet . 
- Mount Jackson 's geology was studied in 1972 as part of the Palmer Island investigations by a team of geologists . They identified various rock types and underlying volcanic activity . The relief of the west facing slopes is gentler when compared to the eastern side , characterized by a steep rocky face . Rock <unk> is intense in the area spread with <unk> and <unk> . Fresh rock formations and bedrock disintegration are noted . 
- 
- 
- = Italian cruiser Aretusa = 
- 
- Aretusa was a torpedo cruiser of the <unk> class built for the Italian Regia Marina ( Royal Navy ) in the 1880s . <unk> down in June 1889 at the <unk> <unk> <unk> Orlando shipyard , she was launched in March 1891 and was commissioned in September 1892 . Her main armament were her six torpedo tubes , which were supported by a battery of ten small @-@ caliber guns . Aretusa spent most of her career in the main Italian fleet , where she was primarily occupied with training exercises . At the start of the Italo @-@ Turkish War in September 1911 , she was assigned to the Red Sea Squadron in Italian Eritrea . She bombarded Ottoman positions in the Arabian Peninsula and took part in a blockade of the coast . <unk> out by the end of the war in October 1912 , Aretusa was sold for scrap that December and broken up . 
- 
- = = Design = = 
- 
- Aretusa was 73 @.@ 1 meters ( 239 ft 10 in ) long overall and had a beam of 8 @.@ 22 m ( 27 ft 0 in ) and an average draft of 3 @.@ 48 m ( 11 ft 5 in ) . She displaced <unk> metric tons ( 820 long tons ; 918 short tons ) normally . Her propulsion system consisted of a pair of horizontal triple @-@ expansion steam engines , each driving a single screw propeller , with steam supplied by four coal @-@ fired <unk> boilers . Specific figures for Aretusa 's engine performance have not survived , but the ships of her class had top speeds of 18 @.@ 1 to 20 @.@ 8 knots ( 33 @.@ 5 to 38 @.@ 5 km / h ; 20 @.@ 8 to 23 @.@ 9 mph ) at 3 @,@ <unk> to 4 @,@ 422 indicated horsepower ( 2 @,@ <unk> to 3 @,@ <unk> kW ) . The ship had a cruising radius of about 1 @,@ 800 nautical miles ( 3 @,@ 300 km ; 2 @,@ 100 mi ) at a speed of 10 knots ( 19 km / h ; 12 mph ) . She had a crew of between 96 and 121 . 
- Aretusa was armed with a main battery of one 120 mm ( 4 @.@ 7 in ) / 40 gun and six 57 mm ( 2 @.@ 2 in ) / 43 guns mounted <unk> She was also equipped with three 37 mm ( 1 @.@ 5 in ) / 20 guns in single mounts . Her primary offensive weapon was her five 450 mm ( 17 @.@ 7 in ) torpedo tubes . The ship was protected by an armored deck that was up to 1 @.@ 6 in ( 41 mm ) thick ; her conning tower was armored with the same thickness of steel plate . 
- 
- = = Service history = = 
- 
- Aretusa was laid down at the <unk> <unk> <unk> Orlando ( Orlando Brothers ' Shipyard ) in <unk> on 1 June 1889 , and was launched on 14 March 1891 . After fitting @-@ out work was completed , the ship was commissioned into the fleet on 1 September 1892 . During the 1893 fleet maneuvers , Aretusa served with the 3rd Division of the Reserve Squadron , along with the protected cruisers <unk> and <unk> <unk> and four torpedo boats . During the maneuvers , which lasted from 6 August to 5 September , the ships of the Reserve Squadron defended against a simulated attack by the Active Squadron , which <unk> a French attack on the Italian fleet . In 1895 , Aretusa was stationed in the 2nd Maritime Department , split between Taranto and Naples , along with most of the torpedo cruisers in the Italian fleet . These included her sister ships <unk> , Minerva , <unk> , <unk> , Urania , and Caprera , the four <unk> @-@ class cruisers , and Tripoli . As of 1898 , Aretusa was assigned to the Active Squadron , with included the ironclads <unk> and <unk> and two other cruisers . 
- At the start of the Italo @-@ Turkish War in September 1911 , Aretusa was stationed in Italian Eritrea in the Red Sea Squadron . Italian naval forces in the region also included five protected cruisers and several smaller vessels . Shortly after the start of the war on 2 October , Aretusa and the gunboat Volturno encountered the Ottoman torpedo cruiser <unk> @-@ i <unk> off Al Hudaydah . In a short engagement , the Italians vessels forced the Ottoman ship to flee into Al Hudaydah , bombarded the port facilities , and then withdrew . <unk> @-@ i <unk> was later interned in British @-@ controlled Suez The threat of an Ottoman attack from the Arabian Peninsula led the Italian High Command to reinforce the Red Sea Squadron ; the additional ships included another cruiser and several destroyers . The protected cruiser <unk> and two destroyers annihilated a force of seven Ottoman gunboats in the Battle of <unk> Bay on 7 January 1912 . 
- Following the <unk> of Ottoman naval forces in the region , Aretusa and the rest of the Italian ships then commenced a bombardment campaign against the Turkish ports in the Red Sea before declaring a blockade of the city of Al Hudaydah on 26 January . On 27 July and 12 August , Aretusa , her sister ship Caprera , and <unk> conducted two bombardments of Al Hudaydah . During the second attack , they destroyed an Ottoman ammunition dump . With the threat of an Ottoman attack greatly reduced , the High Command thereafter began to withdraw forces from the Red Sea Squadron . By the end of August , the unit was reduced to three protected cruisers , Aretusa , Caprera and two <unk> . On 14 October , the Ottoman government agreed to sign a peace treaty , ending the war . Aretusa 's career ended shortly thereafter ; the Regia Marina discarded the ship in December and she was subsequently broken up for scrap . 
- 
- 
- = M @-@ 6 ( Michigan highway ) = 
- 
- M @-@ 6 , or the Paul B. Henry Freeway , is a 19 @.@ 696 @-@ mile ( 31 @.@ <unk> km ) freeway and state trunkline highway in the United States that serves portions of southern Kent and eastern Ottawa counties south of Grand Rapids , Michigan . Although the freeway is named for Paul B. Henry , local residents and the press continue to use the original name , South Beltline as well on occasion . The freeway connects Interstate 196 ( I @-@ 196 ) on the west with I @-@ 96 on the east . M @-@ 6 also provides a connection to U.S. Highway 131 ( US 131 ) in the middle of its corridor while running through several townships on the south side of the Grand Rapids metropolitan area in Western Michigan . Each end is in a rural area while the central section has suburban development along the trunkline . 
- The freeway was originally conceived in the 1960s . It took 32 years to approve , plan , finance , and build the freeway from the time that the state first authorized funding in 1972 to the time of the ribbon @-@ cutting ceremony in 2004 that opened the South Beltline to traffic . The project cost around $ 700 million or around $ 35 million per mile ( approximately $ 22 million per kilometer ) . Initial construction started in November 1997 , with the first phase opened in November 2001 . The full freeway was opened in November 2004 . The first phase of construction was completed in asphalt , while the second and third phases were built in concrete . The project was built with two firsts : the first single @-@ point urban interchange ( <unk> ; / <unk> / ) in Michigan , and a new technique to apply the pavement markings , <unk> them into the concrete to reduce the chance of a <unk> scraping them off . In advance of the opening of the freeway to traffic , the Michigan Department of Transportation ( MDOT ) allowed the public to walk or bike the South Beltline in an open @-@ house event called the " <unk> Shuffle " . 
- 
- = = Route description = = 
- 
- M @-@ 6 starts at exit 64 on I @-@ 196 in Ottawa County near Hudsonville . The freeway runs southeast from the interchange through the rural Georgetown and Jamestown townships toward the county line . Through this area , MDOT traffic surveys measured a traffic count of 27 @,@ 117 vehicles on average per day , the lowest along the freeway , in 2010 . At <unk> Avenue , the South Beltline crosses into Byron Township in Kent County . The freeway corridor is bounded on each side by farmland , scattered subdivisions , and small pockets of woodland . Near the Wilson Avenue interchange , M @-@ 6 curves to the northeast around the edge of the <unk> Golf Course and heads for the interchange with Byron Center Avenue . On either side of the freeway at Byron Center Avenue , there are two hospitals , Metro Health and St. Mary 's Southwest , the former located on the very southern edge of the city of Wyoming . Continuing east , the <unk> curves to the southeast and into the cloverleaf interchange complex at US 131 . This " <unk> " interchange stretches over a half mile ( 0 @.@ 8 km ) in one direction and over a mile ( 1 @.@ 6 km ) in the other , encompassing 27 bridges and 18 retaining walls . This makes it the largest freeway interchange in Western Michigan . There are four overpasses which carry M @-@ 6 over the US 131 freeway : two for the main <unk> in each direction and two for the collector @-@ distributor lanes on each side . The auxiliary lanes funnel the traffic using the interchange off the main <unk> to eliminate conflicts between merging streams of traffic . In the eastbound direction , the collector @-@ distributor lane also has access to a ramp for traffic bound for 68th Street , which runs parallel to , and south of , the M @-@ 6 freeway . No such access to 68th Street is provided for westbound M @-@ 6 traffic , although 68th Street traffic can access each direction of M @-@ 6 or US 131 . 
- East of US 131 , M @-@ 6 crosses over Division Avenue and enters Gaines Township through a series of sound barrier walls as the freeway ramps merge back into the main lanes . This area had the highest traffic counts in 2011 at 55 @,@ 236 vehicles per day . Near Kalamazoo Avenue , the freeway passes through an area with retail businesses and movie theaters on each side of the interchange ; to the northeast is East Kentwood High School . On the approach to the East Paris Avenue <unk> , M @-@ 6 curves first to the northeast and then back to the southeast , passing near one of <unk> 's office buildings , the pyramid @-@ shaped Corporate Development Center . The freeway begins to curve to the northeast as it crosses into Caledonia Township , with an interchange for M @-@ 37 ( Broadmoor Avenue ) and an overpass for 60th Street . M @-@ 6 curves around the southeast side of the Gerald R. Ford International Airport in Cascade Township . As the South Beltline <unk> I @-@ 96 , it crosses 48th Street next to the <unk> <unk> Golf Course . The carriageway splits into ramps for each direction of I @-@ 96 , the ramps to eastbound I @-@ 96 crossing over the <unk> River in the process . This interchange marks the eastern terminus of the Paul B. Henry South Beltline Freeway . The entire length of the freeway is listed on the National Highway System , a system of roads important to the nation 's economy , defense and mobility . 
- The right @-@ of @-@ way along M @-@ 6 includes a 9 @-@ mile ( 14 km ) pedestrian path known as the Frederik <unk> Trail . Previously called the M @-@ 6 Trail , it links the Kent Trails west of Byron Center Avenue in Wyoming with the Paul Henry Rail Trail at 60th Street and Wing Avenue by Paris Park in Kentwood . 
- 
- = = History = = 
- 
- 
- = = = Earlier designations = = = 
- 
- The first appearance of M @-@ 6 was in 1926 as a two @-@ mile ( 3 @.@ 2 km ) road in <unk> County in the Upper Peninsula . The highway ran from US 41 at Phoenix to north of Eagle River . The Michigan State Highway Department redesignated the highway as M @-@ 111 in 1938 , and it was redesignated two years later to become a part of the route of M @-@ 26 . 
- In the late 1970s , during the second phase of construction of the I @-@ 696 ( Walter P. <unk> Freeway ) in Metro Detroit , lobbying efforts and lawsuits attempted to block construction of the central section . If successful , the efforts would have left the freeway with a gap in the middle between the first ( western ) and second ( eastern ) phases of construction . During this time , MDOT assigned M @-@ 6 to the eastern section of the freeway under construction . Signs were erected along the service roads that followed 11 Mile Road to connect the already built stack interchange at I @-@ 75 east to I @-@ 94 . By the time the eastern freeway segment was completed in 1979 , the signage for M @-@ 6 was removed and replaced with I @-@ 696 signage , leaving an eight @-@ mile ( 13 km ) gap in the I @-@ 696 freeway until completion of the central section in 1989 . 
- 
- = = = Current freeway = = = 
- 
- 
- = = = = Planning = = = = 
- 
- The South Beltline Freeway near Grand Rapids was a project that took about 32 years to complete . The idea dates back to the 1940s , but serious proposals were not made until the 1960s . The 1955 planning map for the Grand Rapids area Interstate Highways included a freeway roughly along the M @-@ 6 corridor before I @-@ 96 and I @-@ 196 were shifted north and east to their current locations . An increase in the state gas tax was approved in 1972 with the goal to finance local road projects in the state , including the South Beltline . The project was anticipated to cost $ 30 – 100 million ( equivalent to $ <unk> – 713 million in 2015 ) in June 1975 with an expected groundbreaking in 1982 – 85 . The highway was studied in January 1981 for $ 144 @,@ 000 ( equivalent to $ 575 @,@ 000 in 2015 ) . The choice of consultants on the project was controversial ; local planners felt that MDOT picked BKI Inc. only because they used a minority @-@ owned <unk> and not because they would be qualified for the assignment . 
- As this study was initiated , the route for the proposed freeway was located between 60th and 68th streets with a western end in Hudsonville and an eastern end in Lowell Township . The consultants were asked to study a full freeway and a limited access boulevard design . One final option was a " no @-@ build " alternative ; under this option , existing roads would be upgraded but no new roads would be built . The City of Grand Rapids opposed the freeway while the suburbs and townships south and west of the city supported it . City officials were concerned about the impact to commercial and industrial business in Grand Rapids . Hudsonville 's city manager favored the proposal as a benefit to local vegetable producers who shipped produce to Detroit or Cleveland . Other supporters , such as the Georgetown Township supervisor , were concerned that delays in starting the project could increase costs . State and local officials expected the freeway in January 1981 to cost between $ 40 – 100 million ( equivalent to $ 160 – 400 million in 2015 ) . The road was to be started no sooner than 1990 . 
- The boundaries for the highway corridor were determined by the consultants in April 1982 , running between 60th and 84th streets , " dipping like a <unk> beneath the cities of Kentwood and Wyoming " . The results of the study by BKI were criticized by local planners in May 1982 , who called the study " <unk> and <unk> work " , and asked the state to fire the consulting firm . Local residents distributed 2 @,@ 000 <unk> to their neighbors in opposition to the freeway . The South Belt Local Advisory Board criticized BKI 's 110 @-@ page study report as " filled with errors " ; the consultants ' earlier 26 @-@ page paper had been rejected by the board and MDOT as " unusable " . An editorial in the The Grand Rapids Press stated that the study did not help advance the project in the area , instead opening the proposed freeway up to new controversies . <unk> about the state 's budget in 1982 to build the roadway combined with issues over the consultants and their study results . 
- A second citizens group , the South Belt Citizens Committee , was formed in July 1982 to gain additional public information on the project and supplement the work of the other groups , including the South Belt Local Advisory Board . BKI was fired as consultant on the project by MDOT on September 9 , 1982 . The switch to a new consultant delayed further study because of the timetable to take bids and interview the candidates . In the interim , work was shifted to local and state planners until a new consultant could be retained in an effort to minimize the delays involved . These local projects were focused on updating the information and maps from BKI 's study and refining the scope of the highway 's corridor . 
- Gaines , Cascade , and Caledonia townships and the city of Kentwood circulated a survey amongst their communities ' planning commissions and elected boards in 1982 . The survey showed an inconclusive preference for a limited @-@ access highway in what was termed a " gut level reaction " to the proposed roadway . The South Belt Citizens Committee actively started to oppose the roadway during the fall of 1982 , pressing local candidates for political office to take positions on the project . The group called the roadway a " <unk> " in its mailings to the candidates . Future Lieutenant Governor Dick <unk> , then a member of the Michigan State Senate , called for an end to the studies in 1983 ; he would later reverse and become one of the project 's biggest supporters . 
- MDOT hired a new consultant , <unk> / <unk> Associates , in 1984 to study the proposed freeway . The consultant recommended the freeway in March 1985 . The South Beltline was included in the ten @-@ year highway plan in 1986 . By 1989 , the state wanted to set the route in 1991 with construction starting in 1993 . The freeway was studied as a possible toll road in June 1991 after three alternative routes were proposed the previous year . That September , the final route was set with a projected start date in 1997 . The toll road concept was revived in September 1995 to offset the failure of a proposed gas tax increase . As a cost @-@ saving measure , the number of interchanges was reduced to four from eight in June 1996 . When a gas tax increase was passed in 1997 , Governor John Engler promised at least seven access points for the freeway . The South Beltline was <unk> as " [ cutting ] travel time around Grand Rapids virtually in half " . 
- 
- = = = = Phase I = = = = 
- 
- <unk> for the South Beltline Freeway were nearly 25 years old by the time initial construction was started in 1997 . The Michigan State Legislature named the South Beltline around the same time for the Congressman Paul B. Henry , who died in office in 1993 , serving in Gerald Ford 's old US House seat . The cost of the construction of new roads like the South Beltline was a campaign issue when Engler ran for re @-@ election against Geoffrey <unk> in 1998 . The entire freeway was projected to open by 2008 , with the first phase opening in 2002 . MDOT gave the South Beltline its numerical designation on the July 1999 edition of the state map , marking M @-@ 6 for the first time as a dotted line , to denote it was " under construction " . The legislature approved Engler 's " <unk> Michigan III " program in 2000 ; the plan accelerated road projects in the state . The capital <unk> for the year was $ 82 million ( equivalent to $ 132 million in 2015 ) . <unk> proceedings were initiated in the Kent County Circuit Court in 1999 to clear the way for the acquisitions . Land that contained homes , farms , trailer parks , and businesses was purchased by MDOT to acquire the right @-@ of @-@ way needed for the freeway . The land needed measured 360 feet ( 110 m ) wide and 20 miles ( 32 km ) long . Land acquisitions for the South Beltline Freeway were completed in July 2001 . Construction started later in the fall of 2001 on the second and third phases of the project . 
- The first leg of the South Beltline Freeway , located between M @-@ 37 ( Broadmoor Avenue ) and I @-@ 96 , was finished six months early . Dry summer weather allowed the roadbed contractors to finish their portion of the five @-@ mile ( 8 @.@ 0 km ) section of the freeway early , earning them a $ 300 @,@ 000 bonus ( equivalent to $ <unk> @,@ 000 in 2015 ) . The overpasses for the remaining sections of the freeway were completed while the first phase was under construction , leaving the interchanges at US 131 and I @-@ 196 and the connecting roadbed to be completed at that time . The first section was paved in asphalt after MDOT reversed the decision to pave the whole freeway in concrete . That stretch of freeway opened to traffic on November 20 , 2001 . The state kept the overall project in an accelerated status headed into the next phases in 2002 . 
- 
- = = = = <unk> II and III = = = = 
- 
- Construction of the remaining phases between US 131 and M @-@ 37 and between I @-@ 196 and US 131 was started on April 1 , 2002 . Area roads that crossed the path of the new freeway were closed to traffic with posted detours so that work could begin on the roadbed for the freeway . The last major project for the freeway was to replace bridge beams in the overpasses from westbound I @-@ 196 to eastbound M @-@ 6 . Design flaws were found in 2002 in the size of the beams in the bridges over eastbound I @-@ 196 and the ramp from westbound M @-@ 6 to westbound I @-@ 196 . The replacement was originally supposed to close traffic along I @-@ 196 over a weekend in 2004 , but kept a lane closed for a full week , backing up traffic on the Interstate for two miles ( 3 @.@ 2 km ) ; completion of the work was delayed when human error caused a shortage of nuts and bolts . 
- MDOT hosted an open house along the <unk> section of M @-@ 6 between Kalamazoo and Byron Center avenues . This event took place on October 2 , 2004 , and was billed as the " <unk> Shuffle " , allowing the public to walk or bike along the freeway . The event was planned to draw attention to the M @-@ 6 Trail that runs parallel to the freeway . Some event participants brought their horses for the chance to ride on the freeway . The whole freeway was opened to traffic on November 17 , 2004 , after a ribbon cutting ceremony . When opened , reconstruction work was still being completed on overpass bridges at the I @-@ 196 interchange on the west end . The entire project cost $ 700 million ( equivalent to $ <unk> million in 2015 ) to complete over the five @-@ year construction period , about $ 35 million / mi ( approximately $ 22 million / km , equivalent to $ 47 million / mi or $ 29 million / km in 2015 ) . When the freeway was opened , it was the first in the state of Michigan to use a <unk> located at the Kalamazoo Avenue exit . All of the bridges and sound barrier walls were painted <unk> beige as part of a " color theme " to the freeway . MDOT also used a new technique to recess the pavement markings into the concrete , designed to reduce the likelihood that <unk> would scrape them off . The signs are in miles , but " the entire M @-@ 6 freeway was designed and constructed in metric " , according to MDOT manager <unk> <unk> . The final ramps opened to traffic on December 9 , 2004 . 
- 
- = = = = After construction = = = = 
- 
- MDOT added the completed M @-@ 6 to the state maps in an updated printing in June 2005 . At the time , the various online mapping services still did not show a complete freeway in eastern Ottawa or southern Kent counties . Services such as <unk> and Yahoo ! Maps rely on <unk> Atlas out of New Hampshire for their mapping information , which , in turn , relies on agencies like MDOT to update their data . MDOT 's map update came out nearly eight months after the initial opening due to its inclusion in a large @-@ scale update to the state highway map . 
- A year after the freeway opened , traffic volumes along parallel roads like 44th , 56th and 68th streets dropped 40 – 50 % . At the same time , roads with interchanges along the freeway saw increased traffic . Wilson Avenue experienced a 120 % increase and sections of Byron Center Avenue jumped 100 % in traffic levels a month after M @-@ 6 opened . Property values in the townships surrounding the freeway increased 11 @.@ 3 – 12 @.@ 4 % by 2006 as a result of development attached to the freeway . Local officials credited the freeway for increased access to the area , driving housing starts as residents flocked to the communities for their schools and quality of life . In 2007 , the Metro Health Village , a commercial development centered around the Byron Center Avenue exit and the hospital opened . Described as being similar to a mall with the hospital as a tenant , the village features restaurants , shops , offices and a hotel . Metro Health relocated from Grand Rapids to the location in Wyoming in the face of opposition to planned expansions of their previous location . Since opening , even though the freeway was officially named for Paul Henry , the original South Beltline name is still in use . 
- Reactions to the new freeway were not all positive . In a special editorial in the The Grand Rapids Press after the freeway opened in 2004 , local resident Curt <unk> summarized the criticisms of the new freeway . He cited the loss of rural farmland and wetlands as a negative effect of the highway . The editorial also discussed that the freeway does decrease travel times for some residents , but it will mean increased development . That development will mean further urban <unk> , and could spur the creation of additional highways in the area . 
- The M @-@ 6 Trail was constructed in a $ 3 @.@ 5 million project ( equivalent to $ 4 million in 2015 ) that started in 2008 . The goal was to create a 10 @-@ foot @-@ wide ( 3 @.@ 0 m ) path linking the Kent Trails with the Paul Henry Rail Trail . The M @-@ 6 Trail was the <unk> of Gaines Township Supervisor Don Hilton , Sr. He had pushed to have the path included in the original freeway construction and opened with the rest of the South Beltline . The trail project was funded by $ 2 @.@ 9 million ( equivalent to $ 3 @.@ 3 million in 2015 ) in federal grants and $ 300 @,@ 000 ( equivalent to $ 350 @,@ 000 in 2015 ) from the Frederik and Lena <unk> Foundation . The balance came from Kent County and the townships . Work on the trail was completed in November 2008 . 
- In 2009 , the asphalt section of M @-@ 6 had to be repaired . This section of roadway between East Paris Avenue and 48th Street was rated poorly by the Michigan Infrastructure and Transportation Association , while the concrete west of Broadmoor Avenue had favorable marks . MDOT budgeted $ 2 million in repairs on top of previous crack @-@ related <unk> that were handled by the original pavement contractor under a <unk> in 2006 . The local press described the 4 @.@ 7 @-@ mile ( 7 @.@ 6 km ) stretch of road as " troublesome " in relation to pavement quality issues . 
- Legislation was signed by Governor Rick <unk> on December 27 , 2014 , to name the section of M @-@ 6 between Byron Center and Kalamazoo avenues the David John <unk> Memorial Highway . <unk> , a US Navy <unk> , was killed in a helicopter accident in Afghanistan in 2012 . This section of the highway was dedicated on August 15 , 2015 . 
- 
- = = Exit list = = 
- 
- 
- 
- = Hi , Infidelity = 
- 
- " Hi , Infidelity " is the sixth episode of the third season of the American mystery television series Veronica Mars , and the fiftieth episode overall . Written by John Enbom and directed by Michael Fields , the episode premiered on The CW on November 17 , 2006 . 
- The series depicts the adventures of Veronica Mars ( Kristen Bell ) as she deals with life as a college student while <unk> as a private detective . In this episode , Veronica investigates an accusation that she plagiarized a criminology paper only to find out that her teacher is having an affair with Mindy O 'Dell ( Jamie Ray Newman ) . Meanwhile , <unk> " Piz " <unk> ( Chris Lowell ) invites Veronica to go bowling , and Wallace <unk> ( Percy <unk> III ) must choose between basketball and his studies . 
- The episode features the return of Laura San Giacomo as Harmony Chase and the introduction of the recurring character of Max ( Adam Rose ) . San Giacomo and Colantoni , who had previously co @-@ starred on the sitcom Just Shoot Me ! , lobbied for more storylines together after <unk> their friendship during production of " Charlie Don 't Surf " , her first appearance . Rose 's character had been planned to begin a later romantic relationship with Mac since the writing of this episode and went on to fulfill this role in several future episodes . At the time of its initial broadcast , the episode was watched by 2 @.@ 75 million people and received mixed to positive reviews from television critics . 
- 
- = = Plot = = 
- 
- Following the events of the previous episode , Veronica confronts Claire <unk> ( <unk> <unk> ) for <unk> her rape after she publishes a story on it . Wallace <unk> and a lawyer are taken in to Dean O ’ Dell ’ s ( Ed Begley , Jr . ) office , where he is caught for cheating on a test . Veronica ’ s criminology paper is praised by her teacher , Hank ( Patrick <unk> ) leading her to praise him incessantly . Harmony Chase ( Laura San Giacomo ) , a former client who asked Keith Mars ( Enrico Colantoni ) to investigate her possibly <unk> husband , calls him and asks him on a date . Tim Foyle ( James Jordan ) , Hank 's teaching assistant , tells Veronica that she plagiarized her paper , even though she did not . Hank gives her three days to prove that she is innocent . Veronica learns that a student named Jeff <unk> accused her . Veronica goes to a computer student and <unk> the email address of the person who <unk> her paper . <unk> " Piz " <unk> invites Veronica bowling , and she invites Parker ( Julie <unk> ) as well . 
- Veronica gets caught <unk> in the Dean ’ s office , although she makes up a hasty lie that she was searching for class notes , and it <unk> his attention . Wallace decides not to drop the class on whose test he cheated . Veronica proves that the essay hers was supposedly plagiarized from was posted after she turned in her paper ; nevertheless , she still wants to find who framed her . Parker Lee , Veronica , Piz , and Logan ( Jason <unk> ) bowl and have fun together . When Veronica and Logan get room service , Veronica spots Jeff <unk> and questions him . Parker is romantically interested in Piz , and she tasks Veronica with talking to him about her . After talking to Mercer Hayes ( Ryan Devlin ) , Parker informs Veronica that she remembers Mercer 's <unk> from the night of her rape . Veronica goes to Sheriff Lamb ( Michael <unk> ) with this news ; Wallace drops basketball to study for the class . Veronica runs into Keith at the hotel before learning that the room that belongs to " Rory Finch " is actually Hank , who is having an affair with Mrs. O ’ Dell . 
- On their second date , Harmony suggests that they have sex , but Keith declines the proposal . On the way back , Keith ’ s becomes involved in a traffic collision , but the <unk> catches him . This traumatic experience makes him go back to Harmony , and they sleep together . Veronica talks to Tim Foyle , who made her follow the trail of " Rory Finch " deliberately in order for her to discover the professor ’ s affair . Tim was the professor ’ s protégé , and he wants Veronica to discover Hank 's flaws before she becomes more involved with him . Logan runs up to Veronica and tells her that Mercer has been arrested for the rapes on campus , despite the fact that he believes that Mercer is innocent . Logan begs Veronica to defend Mercer , as he was with him the night of one of the rapes . However , he refuses to tell Veronica what they were doing . 
- 
- = = Production = = 
- 
- " Hi , Infidelity " was written by John Enbom and directed by Michael Fields , marking Enbom 's twelfth writing credit and Fields 's sixth directing credit for the series . The episode features the second of three appearances by Laura San Giacomo as Harmony Chase . She and Enrico Colantoni had become friends when they co @-@ starred on the sitcom Just Shoot Me ! . They <unk> their friendship during production of " Charlie Don 't Surf " , San Giacomo 's first appearance , so they lobbied for more storylines together . Thomas was open to the possibility , saying , " I wouldn 't <unk> to go back to this , because I was really happy with how it played out . " 
- Adam Rose made his first appearance in this episode as a suspect ; he would make appearances later in the season as well . From the very beginning of his appearances , Rose 's character , Max , was planned to begin a romantic relationship with Mac ( Tina <unk> ) . Rose was roommates with Michael Mitchell , who played Bronson , another love interest for Mac . When Rose received the call that he would have a romantic storyline with Mac , he reportedly stated , " but I thought that 's what my roommate was doing . " 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- At the time of its initial broadcast , " Hi , Infidelity " was viewed by 2 @.@ 75 million people , ranking 94th out of 97 in the weekly rankings . 
- 
- = = = Reviews = = = 
- 
- Eric Goldman of IGN gave the episode a 7 @.@ 8 out of 10 , indicating that it was " good " , but his review was mixed . Comparing it with the rest of the season , he opined that it suffered from some of the same problems of previous episodes . He thought that the actions of many of the characters in " Hi , Infidelity " were out of character or unrealistic . While writing that " the rape storyline is still not feeling nearly as <unk> as it should , " he praised the development of the rape mystery for hinting that there were larger forces at work . Price Peterson of TV.com gave a positive review , praising the case @-@ of @-@ the @-@ week 's emotional connection to Veronica for " [ raising ] the stakes " and Piz 's increased role . He also believed that all the series regulars , excluding Wallace , had an important and interesting role to play . Television Without Pity gave the episode an " A " , which is one of its highest ratings given for the season . 
- Alan Sepinwall praised the episode compared to the first few episodes of the season , writing it balanced Veronica 's character traits well , including her jealousy and generosity . He also wrote that it showed her as a character with some " <unk> instincts " , giving the scene with Jeff as an example . Rowan Kaiser , writing for The A.V. Club , gave a mixed review . The reviewer was very critical of what he called the " ' radical feminist fakes her own rape ' " storyline , but he praised the episode as the first one of the third season to make the college setting feel natural . Although he thought that the case @-@ of @-@ the @-@ week contributed to his overall positive view of the episode , he thought that " it 's mostly the little things that make it work " , pointing out Piz 's increased role as an example . 
- 
- 
- = Ceratopsia = 
- 
- Ceratopsia or <unk> ( / <unk> / or / <unk> / ; Greek : " horned faces " ) is a group of herbivorous , <unk> dinosaurs that <unk> in what are now North America , Europe , and Asia , during the Cretaceous Period , although ancestral forms lived earlier , in the Jurassic . The earliest known ceratopsian , Yinlong <unk> , lived between 161 @.@ 2 and 155 @.@ 7 million years ago . The last ceratopsian species , Triceratops <unk> , became extinct during the Cretaceous – <unk> extinction event , 66 million years ago . 
- Early members of the ceratopsian group , such as Psittacosaurus , were small bipedal animals . Later members , including ceratopsids like Centrosaurus and Triceratops , became very large <unk> and developed elaborate facial horns and frills extending over the neck . While these frills might have served to protect the vulnerable neck from predators , they may also have been used for display , <unk> , the attachment of large neck and chewing muscles or some combination of the above . <unk> ranged in size from 1 meter ( 3 ft ) and 23 kilograms ( 50 lb ) to over 9 meters ( 30 ft ) and 5 @,@ 400 kg ( 12 @,@ 000 lb ) . 
- Triceratops is by far the best @-@ known ceratopsian to the general public . It is traditional for ceratopsian genus names to end in " <unk> " , although this is not always the case . One of the first named genera was Ceratops itself , which lent its name to the group , although it is considered a nomen dubium today as its fossil remains have no distinguishing characteristics that are not also found in other ceratopsians . 
- 
- = = Anatomy = = 
- 
- <unk> are easily recognized by features of the skull . On the tip of a ceratopsian upper jaw is the rostral bone , an <unk> ( toothless ) ossification , unique to ceratopsians . Othniel Charles Marsh recognized and named this bone , which acts as a mirror image of the <unk> bone on the lower jaw . This ossification evolved to <unk> aid the <unk> of plant matter . Along with the <unk> bone , which forms the tip of the lower jaw in all ornithischians , the rostral forms a superficially parrot @-@ like beak . Also , the jugal bones below the eye are very tall and flare out sideways , making the skull appear somewhat triangular when viewed from above . This triangular appearance is <unk> in later ceratopsians by the <unk> extension of the parietal and <unk> bones of the skull roof , to form the neck frill . 
- The <unk> is a distinctive bones found lining the frills of ceratopsians . The name is a <unk> , as they are not associated with the <unk> bone . <unk> begin as separate bones that fuse during the animal 's growth to either the <unk> or parietal bones that make up the base of the frill . These bones were ornamental instead of functional , and may have helped differentiate species . <unk> probably were present in all known ceratopsids with the possible exception of Zuniceratops . They appear to have been broadly different between short @-@ frilled ceratopsids ( <unk> ) and long @-@ frilled ceratopsids ( <unk> ) , being elliptical with <unk> bases in the former group , and triangular with wide bases in the latter group . Within these broad definitions , different species would have somewhat different shapes and numbers . In <unk> especially , like Centrosaurus , <unk> , and <unk> , these bones become long and <unk> or hook @-@ like . A well @-@ known example is the coarse <unk> fringe of broad triangular <unk> on the frill of Triceratops . When regarding the ossification 's <unk> traits , it can be described as dermal . The term <unk> was coined by famous paleontologist Othniel Charles Marsh in 1889 . 
- 
- = = History of study = = 
- 
- The first ceratopsian remains known to science were discovered during the U.S. Geological and Geographical Survey of the Territories led by the American geologist <unk> <unk> . <unk> discovered during an 1855 expedition to Montana were first assigned to hadrosaurids and included within the genus Trachodon . It was not until the early 20th century that some of these were recognized as ceratopsian teeth . During another of <unk> 's expeditions in 1872 , Fielding Bradford <unk> found several giant bones protruding from a hillside in southwestern Wyoming . He alerted paleontologist Edward <unk> Cope , who led a dig to recover the partial skeleton . Cope recognized the remains as a dinosaur , but noted that even though the fossil lacked a skull , it was different from any type of dinosaur then known . He named the new species Agathaumas <unk> , meaning " marvellous forest @-@ dweller " . Soon after , Cope named two more dinosaurs that would eventually come to be recognized as ceratopsids : <unk> and Monoclonius . Monoclonius was notable for the number of <unk> remains found , including the first evidence of ceratopsid horns and frills . Several Monoclonius fossils were found by Cope , assisted by Charles <unk> Sternberg , in the summer of 1876 near the Judith River in <unk> County , Montana . Since the ceratopsians had not been recognised yet as a distinctive group , Cope was uncertain about much of the fossil material , not recognizing the nasal horn core , nor the brow horns , as part of a fossil horn . The frill bone was interpreted as a part of the <unk> . 
- In 1888 and 1889 , Othniel Charles Marsh described the first well preserved horned dinosaurs , Ceratops and Triceratops . In 1890 Marsh classified them together in the family Ceratopsidae and the order Ceratopsia . This prompted Cope to <unk> his own specimens and to realized that Triceratops , Monoclonius , and Agathaumas all represented a single group of similar dinosaurs , which he named <unk> in 1891 . Cope <unk> Monoclonius as a horned dinosaur , with a large nasal horn and two smaller horns over the eyes , and a large frill . 
- 
- = = Classification = = 
- 
- Ceratopsia was coined by Othniel Charles Marsh in 1890 to include dinosaurs possessing certain characteristic features , including horns , a rostral bone , teeth with two roots , fused neck vertebrae , and a forward @-@ oriented <unk> . Marsh considered the group distinct enough to warrant its own suborder within <unk> . The name is derived from the Greek <unk> / <unk> meaning ' horn ' and <unk> / <unk> meaning ' face ' . As early as the 1960s , it was noted that the name Ceratopsia is actually incorrect linguistically and that it should be <unk> . However , this spelling , while technically correct , has been used only rarely in the scientific literature , and the vast majority of paleontologists continue to use Ceratopsia . As the <unk> does not govern taxa above the level of superfamily , this is unlikely to change . 
- 
- = = = Taxonomy = = = 
- 
- Following Marsh , Ceratopsia has usually been classified as a suborder within the order <unk> . While ranked taxonomy has largely fallen out of favor among dinosaur paleontologists , some researchers have continued to employ such a classification , though sources have differed on what its rank should be . Most who still employ the use of ranks have retained its traditional ranking of suborder , though some have reduced to the level of <unk> . 
- This list of ceratopsian genera by classification and location follows a review by Thomas R. Holtz , Jr. in 2010 . 
- Ceratopsia 
- <unk> - ( Japan ) 
- <unk> - ( Shandong , eastern China ) 
- <unk> - ( Germany ) 
- Yinlong - ( Xinjiang , western China ) 
- Family <unk> 
- Chaoyangsaurus - ( <unk> , northeastern China ) 
- <unk> - ( Hebei , China ) 
- Family Psittacosauridae 
- Psittacosaurus - ( China & Mongolia ) 
- Neoceratopsia 
- <unk> - ( Gansu , northwestern China ) 
- Auroraceratops - ( Gansu , northwestern China ) 
- <unk> - ( <unk> , northwestern China ) 
- <unk> - ( South Korea ) 
- <unk> - ( Uzbekistan ) 
- <unk> - ( <unk> , northeastern China ) 
- <unk> - ( Mongolia ) 
- <unk> - ( central China ) 
- <unk> - ( Mongolia ) 
- Family Leptoceratopsidae 
- Asiaceratops - ( China , Mongolia , Uzbekistan ) 
- <unk> - ( Montana , US ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( China ) 
- <unk> - ( Alberta , Canada & Wyoming , US ) 
- <unk> - ( Montana , US ) 
- <unk> - ( Montana , US ) 
- <unk> - ( Mongolia ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( <unk> , China ) 
- Family <unk> 
- <unk> - ( Hungary ) 
- <unk> - ( Mongolia ) 
- <unk> - ( Mongolia ) 
- <unk> - ( Mongolia ) 
- <unk> - ( Mongolia ) 
- Family Protoceratopsidae 
- <unk> - ( Mongolia ) 
- <unk> - ( Mongolia ) 
- Magnirostris - ( Inner Mongolia , China ) 
- Protoceratops - ( Mongolia ) 
- <unk> Ceratopsoidea 
- Turanoceratops - ( Uzbekistan ) 
- Zuniceratops - ( New Mexico , US ) 
- Family Ceratopsidae 
- Subfamily <unk> 
- <unk> - ( Alberta , Canada & ? Montana , USA ) 
- <unk> - ( Montana , USA ) 
- <unk> - ( Montana , USA & Alberta , Canada ) 
- Centrosaurus - ( Alberta , Canada ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( Utah , USA ) 
- Monoclonius - ( Montana , USA & Alberta , Canada ) 
- <unk> - ( Utah , USA ) 
- <unk> - ( Montana , USA ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( Alberta , Canada & Montana , USA ) 
- <unk> - ( Alberta , Canada ) 
- Tribe <unk> 
- <unk> - ( Montana , USA ) 
- <unk> - ( Montana , USA ) 
- <unk> ( Alberta , Canada & Alaska , USA ) 
- <unk> - ( Shandong , China ) 
- Subfamily <unk> 
- Ceratops - ( Montana , USA & Alberta , Canada ) 
- Subfamily <unk> 
- Agathaumas - ( Wyoming , USA ) 
- <unk> - ( Texas , USA ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( Alberta , Canada ) 
- Chasmosaurus - ( Alberta , Canada ) 
- <unk> - ( <unk> , Mexico ) 
- ? <unk> - ( Montana , USA ) 
- <unk> - ( Montana , USA ) 
- <unk> - ( Utah , USA ) 
- <unk> - ( Montana , USA ) 
- <unk> - ( Alberta & Saskatchewan , Canada ) 
- <unk> - ( New Mexico , USA ) 
- ? <unk> - ( Colorado , USA ) 
- <unk> - ( Utah , USA ) 
- <unk> - ( Alberta , Canada ) 
- Tribe <unk> 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( Wyoming , USA ) 
- <unk> - ( New Mexico , USA ) 
- <unk> - ( Alberta , Canada ) 
- <unk> - ( South Dakota , USA ) 
- <unk> - ( New Mexico , USA ) 
- <unk> - ( Wyoming , Montana , South Dakota , North Dakota , & Utah , USA & Saskatchewan , Canada ) 
- Triceratops - ( Montana & Wyoming , USA & Saskatchewan & Alberta , Canada ) 
- Possible ceratopsians from the Southern Hemisphere include the Australian <unk> , known from an <unk> , and <unk> from Argentina is known from a single toothless jaw ( which has been lost ) . <unk> from the Late Cretaceous ( <unk> ) of Belgium may also be a ceratopsian , specifically a neoceratopsian closer to <unk> than <unk> . Possible leptoceratopsid remains have also been described from the early Campanian of Sweden . 
- 
- = = = Phylogeny = = = 
- 
- <unk> today agree on the overall structure of the ceratopsian family tree , although there are differences on individual taxa . There have been several cladistic studies performed on basal ceratopsians since 2000 . None have used every taxon listed above and many of the differences between the studies are still unresolved . 
- In clade @-@ based phylogenetic taxonomy , Ceratopsia is often defined to include all <unk> more closely related to Triceratops than to <unk> . Under this definition , the most basal known ceratopsians are Yinlong , from the Late Jurassic Period , along with Chaoyangsaurus and the family Psittacosauridae , from the Early Cretaceous Period , all of which were discovered in northern China or Mongolia . The rostral bone and flared <unk> are already present in all of these forms , indicating that even earlier ceratopsians remain to be discovered . 
- The clade Neoceratopsia includes all ceratopsians more derived than <unk> . Another subset of <unk> is called <unk> , which currently includes all ceratopsians more derived than Auroraceratops . <unk> show the first development of the neck frill and the fusion of the first several neck vertebrae to support the increasingly heavy head . Within <unk> , three groups are generally recognized , although the membership of these groups varies somewhat from study to study and some animals may not fit in any of them . One group can be called Protoceratopsidae and includes Protoceratops and its closest relatives , all Asian . Another group , Leptoceratopsidae , includes mostly North American animals that are more closely related to <unk> . The Ceratopsoidea includes animals like Zuniceratops , which are more closely related to the family Ceratopsidae . This last family includes Triceratops and all the large North American ceratopsians and is further divided into the subfamilies <unk> and <unk> ( also known as <unk> ) . 
- 
- = = = = <unk> phylogeny = = = = 
- 
- Andrew <unk> and his colleagues in 2014 published a description of a new neoceratopsian , Aquilops americanus , through the peer @-@ reviewed science journal <unk> <unk> . They analysed their taxa as well as most other primitive ceratopsians to get a consensus cladogram . They created their own data matrix and through it found that many groups of ceratopsians could be supported , and that Aquilops was a basal neoceratopsian that could potentially be a <unk> , leptoceratopsid , or ceratopsid , although any one of these groups would have a large ghost lineage with Aquilops . 
- Their study also found an equal consensus cladogram finding <unk> not as a neoceratopsian but a <unk> . Nothing else about the cladograms changed . 
- 
- = = = = Xu / Makovicky / Chinnery Phylogeny = = = = 
- 
- Xu <unk> of the Chinese Institute of <unk> <unk> and <unk> ( <unk> ) in Beijing , along with Peter Makovicky , formerly of the American Museum of Natural History ( AMNH ) in New York City and others , published a cladistic analysis in the 2002 description of <unk> . This analysis is very similar to one published by Makovicky in 2001 . Makovicky , who currently works at the Field Museum of Natural History in Chicago , also included this analysis in his 2002 doctoral thesis . Xu and other colleagues added Yinlong to this analysis in 2006 . 
- Brenda Chinnery , formerly of the Museum of the Rockies in Bozeman , Montana , independently described <unk> in 2005 and published a new phylogeny . In 2006 , Makovicky and Mark <unk> of the AMNH incorporated Chinnery 's analysis into their own and also added <unk> , although they were not able to include Yinlong . The cladogram presented below is a combination of Xu , Makovicky , and their colleagues ' most recent work . 
- Chaoyangsaurus is recovered in a more basal position than Psittacosauridae , although Chinnery 's original analysis finds it within Neoceratopsia . Protoceratopsidae is considered to be the sister group of Ceratopsoidea . The fragmentary Asiaceratops was included in these studies and is found to have a variable position , either as a basal neoceratopsian or as a leptoceratopsid , most likely due to the amount of missing information . <unk> of Asiaceratops <unk> the entire cladogram . 
- Makovicky 's latest analysis includes <unk> <unk> ( " <unk> " ) , a Late Jurassic ceratopsian from China that , at the time , was awaiting publication , but has since been published as <unk> . <unk> and Turanoceratops are considered nomina dubia in this study . Makovicky believes <unk> , Magnirostris , and <unk> to be junior synonyms of <unk> , and <unk> to be synonymous with Protoceratops . 
- 
- = = = = You / Dodson Phylogeny = = = = 
- 
- You <unk> of Beijing 's Chinese Academy of Geological Sciences , was a co @-@ author with Xu and Makovicky in 2002 but , in 2003 , he and Peter Dodson from the University of Pennsylvania published a separate analysis . The two presented this analysis again in 2004 . In 2005 , You and three others , including Dodson , published on Auroraceratops and inserted this new dinosaur into their phylogeny . 
- In contrast to the previous analysis , You and Dodson find Chaoyangsaurus to be the most basal neoceratopsian , more derived than Psittacosaurus , while Leptoceratopsidae , not Protoceratopsidae , is recovered as the sister group of Ceratopsidae . This study includes Auroraceratops , but lacks seven taxa found in Xu and Makovicky 's work , so it is unclear how comparable the two studies are . Asiaceratops and Turanoceratops are each considered nomina dubia and not included . Along with <unk> <unk> , You described Magnirostris in 2003 , but to date has not included it in any of his cladograms . 
- 
- = = <unk> = = 
- 
- 
- = = = <unk> = = = 
- 
- Ceratopsia appears to have originated in Asia , as all of the earliest members are found there . <unk> remains , including teeth , which appear to be neoceratopsian , are found in North America from the <unk> stage ( 112 to 100 million years ago ) , indicating that the group had dispersed across what is now the Bering Strait by the middle of the Cretaceous Period . Almost all <unk> are North American , aside from <unk> , which may represent a separate dispersal event , back into Asia . <unk> and their immediate ancestors , such as Zuniceratops , were unknown outside of western North America , and were presumed endemic to that continent . The traditional view that <unk> originated in North America was called into question by the 2009 discovery of better specimens of the dubious Asian form Turanoceratops , which confirmed it as a ceratopsid . It is unknown whether this indicates ceratopsids actually originated in Asia , or if the Turanoceratops immigrated from North America . 
- 
- = = = Individual variation = = = 
- 
- Unlike almost all other dinosaur groups , skulls are the most commonly preserved elements of ceratopsian skeletons and many species are known only from skulls . There is a great deal of variation between and even within ceratopsian species . Complete growth series from embryo to adult are known for Psittacosaurus and Protoceratops , allowing the study of <unk> variation in these species . Significant sexual <unk> has been noted in Protoceratops and several ceratopsids . 
- 
- = = = Ecological role = = = 
- 
- Psittacosaurus and Protoceratops are the most common dinosaurs in the different Mongolian sediments where they are found . Triceratops fossils are far and away the most common dinosaur remains found in the latest Cretaceous rocks in the western United States , making up as much as 5 / <unk> of the large dinosaur fauna in some areas . These facts indicate that some ceratopsians were the dominant herbivores in their environments . 
- Some species of ceratopsians , especially Centrosaurus and its relatives , appear to have been gregarious , living in herds . This is suggested by <unk> finds with the remains of many individuals of different ages . Like modern migratory herds , they would have had a significant effect on their environment , as well as serving as a major food source for predators . 
- Although ceratopsians are generally considered herbivorous , a few paleontologists , such as Darren <unk> and Mark <unk> , have speculated online that at least some ceratopsians may have been <unk> omnivorous . 
- 
- = = = <unk> and <unk> = = = 
- 
- Most restorations of ceratopsians show them with erect hindlimbs but semi @-@ sprawling forelimbs , which suggest that they were not fast <unk> . But Paul and Christiansen ( 2000 ) argued that at least the later ceratopsians had upright forelimbs and the larger species may have been as fast as <unk> , which can run at up to 56 km or 35 miles per hour . 
- 
- = = = Daily activity patterns = = = 
- 
- A nocturnal lifestyle has been suggested for the primitive ceratopsian Protoceratops . However , comparisons between the <unk> rings of Protoceratops and Psittacosaurus and modern birds and reptiles indicate that they may have been <unk> , active throughout the day at short intervals . 
- 
- = = = <unk> = = = 
- 
- <unk> @-@ related bone fractures have been documented in ceratopsians . <unk> has also been documented in the shoulder blade of a ceratopsian . 
- 
- = = Timeline of genera = = 
- 
- 
- 
- = Truth in Numbers ? = 
- 
- Truth in Numbers ? Everything , According to Wikipedia is a 2010 American documentary film that explores the history and cultural implications of the online , user @-@ <unk> <unk> Wikipedia . The film considers the question of whether all individuals or just experts should be tasked with editing an <unk> . 
- The site 's history and background is given , along with commentary from Wikipedia founders Jimmy Wales and Larry Sanger . Commentators that appear in the film include author Howard <unk> , Len <unk> of The Washington Post , Bob <unk> of CBS News , former <unk> Britannica chief Robert <unk> and former Central Intelligence Agency director James <unk> . The documentary discusses incidents that shed a negative light on Wikipedia , including the <unk> controversy and the Wikipedia biography controversy . 
- The long @-@ delayed film premiered at Wikimania 2010 in <unk> in July 2010 , and was screened at the Paley Center for Media in New York City in October 2010 . It was shown as part of the Savannah Film Festival on November 3 , 2010 , at Savannah College of Art and Design 's Trustees Theater . Truth in Numbers ? received a mixed reception , with favorable commentary from author Ted <unk> , in the AOL publication <unk> , and coverage at the Savannah Film Festival by Carlos Serrano of District . 
- 
- = = Contents = = 
- 
- Truth in Numbers ? Everything , According to Wikipedia , an American documentary film , explores the history and cultural implications of Wikipedia . The film presents Wikipedia as a new form of communication and cultural dialog . The directors attempt to answer the question of whether ordinary individuals should be tasked with collecting knowledge for presentation online , or this should be relegated solely to academic scholars in specific fields . The film gives an overview of the history of the enterprise , as well as biographical information on founder Jimmy Wales . Wales is shown discussing Wikipedia with an Indian reader , who points out an <unk> in an article . Wales proceeds to show the reader how to click the " edit " <unk> on the website . Wikipedia founder Larry Sanger is featured in the documentary and speaks critically about the website 's embracing of editors from the general public as opposed to soliciting expert contributors . 
- <unk> and media commentators who appear and are interviewed in the film include author Howard <unk> ; Len <unk> , ( executive editor of The Washington Post ) ; Bob <unk> ( CBS News ) ; Robert <unk> ( former chief of <unk> Britannica ) ; James <unk> ( former director of the Central Intelligence Agency ) ; Chris Wilson ( reporter for Slate Magazine ) ; Cade Metz ( reporter for The Register who has written critically about Wikipedia ) ; Richard Branson and Noam Chomsky . The film documents an initiative in India and Africa called " Wikipedia Academies " . Controversies discussed in the film include the <unk> controversy , where a member of Wikipedia made false <unk> about his academic background ; and the Wikipedia biography controversy , where false statements were inserted into the Wikipedia entry for journalist John <unk> . Musician <unk> @-@ One comments about the site after reading his biography on Wikipedia : " I can say to you , these are the facts but they are not true . " 
- 
- = = Production = = 
- 
- 
- = = = Conception = = = 
- 
- The idea for the film originated from a suggestion by Michael <unk> Gibson , who had made the 2005 documentary 24 <unk> on <unk> . Gibson , a producer on the film , met with co @-@ director Nic Hill . Gibson financed Hill 's travels while making the film . The working title for the film was Truth in Numbers : The Wikipedia Story . Gibson chose to finance the film through a request for funding from Internet visitors ; the initial request drew in US $ 20 @,@ 000 in initial investment . The production team made their initial work on the project known to Internet viewers via the website <unk> Another collaborative website focused on the documentary was formed at <unk> , located at <unk> Regarding his financing strategy , Gibson commented to the San Francisco Chronicle , that smaller incremental donations from multiple individuals showed genuine interest in his initiative . 
- 
- = = = Filming = = = 
- 
- Filming started in August 2006 at the Wikimania 2006 conference , and by April 2007 the team had aggregated 100 hours of footage . Co @-@ director Hill accompanied Wales during 2007 , filming him as he journeyed around the globe . Hill took a two @-@ person film crew and traveled to China , Indonesia , India , South Africa , Australia and Europe , interviewing editors and contributors . Hill is himself an editor of Wikipedia , starting an article about a graffiti artist . Gibson and Hill required expertise in the creative and funding aspects of film @-@ making and invited Scott Glosserman to join the venture . Glosserman 's involvement with the film began during the 2007 – 2008 Writers Guild of America strike . After Glosserman signed on , the breadth of the endeavor became larger . The film ended up taking an additional three years to finish after Glosserman joined the production . 
- Eric <unk> served as director of photography ; he joined the team during a shift in focus in January 2008 . <unk> used a <unk> AG @-@ <unk> <unk> HD <unk> . He commented to industry publication <unk> about the choice of technique , that due to the intensity of the production team 's travel schedule tape format would not have been an option . He stated he preferred using <unk> cards over the <unk> @-@ based format due to its superior reliability . During the editing process , Glen Echo Entertainment utilized eight Apple Macintosh computers with Intel processors , equipped with Apple 's Final Cut Pro editing software . 
- 
- = = = Re @-@ focus = = = 
- 
- Glosserman and the rest of the production team met together to put together a focus for the film ; they centered their efforts on answering the question : " How does Wikipedia get at the truth ? " They also wanted to provide information for everyday individuals wanting to know about Wikipedia 's background and functioning . Experts were sought out , including the author of The Age of American <unk> Susan <unk> , to discuss Wikipedia 's approach to scholars <unk> about specific subject matter . Glosserman commented in an interview with <unk> , " We tried to get people offering compelling arguments for either side of any particular topic because our intention was to be objective and to let the viewer make up his or her own mind . " The narrative structure of the film What the <unk> Do We Know ! ? served as an inspiration for Glosserman during the production process . As a non @-@ profit project , the film had received more than $ 55 @,@ 000 in donations by March 2009 . 
- 
- = = Release = = 
- 
- The release for the film was originally planned for 2007 , then 2008 and 2009 , before its 2010 release . <unk> of the film were shown at Wikimania 2007 in <unk> prior to its completion . Editors in the audience had mixed views on the film . In 2008 , footage from the film was used in an official fundraising video by the Wikimedia Foundation . The film had its premiere at Wikimania 2010 in <unk> in July 2010 , before an audience of approximately 300 people . A trailer for the movie was released in October 2010 . 
- The film was screened at the Paley Center for Media in New York City on October 20 , 2010 . It was shown in conjunction with the Robert M. <unk> University <unk> Series . The Paley Center screening included an online streaming broadcast — the first simultaneous film screening and panel question @-@ and @-@ answer for online and local audiences . The panel discussion was moderated by The New York Times journalist Noam Cohen , and featured both co @-@ directors , in addition to Wikimedia Foundation representative Samuel Klein and <unk> in Residence at the British Museum Liam Wyatt . After the event , <unk> subsequently made the film available for free for six days to viewers in the United States . 
- The film was screened at the Savannah Film Festival on November 3 , 2010 , at Savannah College of Art and Design 's Trustees Theater . It was scheduled for a limited theatrical release in the United States on November 30 , 2010 . 
- 
- = = Reception = = 
- 
- Wales wrote favorably about the film in 2007 during its production , and noted , " Director Nic Hill is making what looks to be a fabulous film about Wikipedia and <unk> worldwide . " However , Wales commented negatively about the delayed release , in a statement to <unk> . He said the movie was dated due to its delay . He commented that the documentary was lopsided towards reliance on expert commentary and did not feature enough weight towards depicting community involvement in the online project . Wales posted to a Wikimedia Foundation mailing list , " the film was poorly received in Poland , and it is seriously out of date . " Larry Sanger commented he thought the film was , " Not too bad , from what I saw . " Wikimedia Foundation board member Samuel Klein commented , " In general , I like the film a lot more after seeing it for the second time , in a very different audience ( and seeing their live reactions ) . " Sage Ross , an <unk> of Wikimania 2010 , commented that the film appears to take a mainly negative point of view towards Wikipedia , " The film gives a lot of focus to some shallow or misleading lines of criticism , and on an intellectual level , it comes off as largely anti @-@ Wikipedia , contrasting the reasonable @-@ sounding arguments of mature critics with the naive optimism of youthful <unk> . " Author Ted <unk> commented favorably about the documentary , at his blog , Ted 's Take , characterizing it as , " A great film about the Wikipedia movement . " He concluded , " This is a must see film , a premiere film . You gotta watch it to remain socially relevant ! " 
- Daniel D 'Addario reviewed Truth in Numbers ? Everything , According to Wikipedia for the AOL Inc. publication , <unk> . D 'Addario commented , " the film raises interesting questions about authority , only somewhat intentionally . " He noted the dated bits observing , " Truth in Numbers ? may well be coming too late . " D 'Addario concluded his assessment by noting that at the time of his review , the Wikipedia article for the film was under threat of being deleted : " According to the site , the entry for Truth in Numbers ? is being considered for <unk> – it links to few other articles on the site , and is an ' <unk> . ' Given the tenor of Truth in Numbers ? , which combines <unk> interest in Wikipedia with wide @-@ eyed dismay at much of its <unk> , this is either very surprising or not surprising at all . " 
- In his review for the Savannah Film Festival , Carlos Serrano of District wrote that though the subject matter covered a lot of ground , it utilized an efficient presentation : " Sounds like a lot to put in to one movie , but the film manages to make good use of its 85 minute running time . " Serrano commented on the presentation of Jimmy Wales during the film , " In the end , I came out of the theater thinking of him as a three @-@ dimensional figure , very much a man with passion but neither completely good or evil . To be honest , this is very important in a film like this and is a definite plus for the movie . " Serrano recommended the documentary , and concluded , " This is definitely a solid film . ... This film is definitely worth a viewing . It ’ s interesting , well made , and presents varied perspectives on Wikipedia that help the narrative stay interesting . " 
- 
- 
- = Hope Highway = 
- 
- The Hope Highway , also known as the Hope Road <unk> , is a Forest Highway located in the Kenai Peninsula Borough , in the U.S. state of Alaska . The highway connects the city of Hope to the Seward Highway , and travels through 17 miles ( 27 km ) of the Chugach National Forest . The road passes the ghost town of Sunrise City and several smaller settlements , remnants of the gold rush that occurred in that area . The highway was created circa 1928 and was designated as Forest Highway 14 by the Federal Highway Administration . 
- 
- = = Route description = = 
- 
- The Hope Highway begins at an intersection with the Seward Highway ( AK @-@ 1 ) , inside Chugach National Forest . The highway proceeds north , traveling through several miles of pine forest , in a valley in the Kenai Mountain range . The highway passes alongside the Resurrection Creek , which was the source for the settlement of this area . The road proceeds through the abandoned settlement of Sunrise City , which was an old mining town . The roadway proceeds to the <unk> Arm , and turns in a westward direction . The road continues along the arm for several miles before entering the city of Hope . The highway passes through Hope , intersecting the Old Hope Highway and several smaller streets before exiting the town and <unk> the forest . The highway reaches its northern terminus , an access road to the <unk> Creek <unk> . The entire length of the Hope Highway is located in the Chugach National Forest . No portion of the highway is listed on the National Highway System . 
- 
- = = = Traffic = = = 
- 
- Traffic on the Hope Highway is very low , with the highest traffic count being just over 400 vehicles daily , at its intersection with the Seward Highway . The daily average vehicle count for the highway is just under 300 . 
- 
- = = History = = 
- 
- The Hope Highway was first established in 1928 . The original highway connected the city of Hope to Moose Pass , which then took people to <unk> via the Alaska Railroad . The Seward Highway was completed in 1951 , which allowed travelers to get to Hope without having to transport their vehicle on a train . The highway operated as an improved dirt road until 1952 or 1953 , when the highway was finally paved . The portion of the highway that connected Hope to the campgrounds was created in the 1970s . Around the same time as the extension of the route , the Hope Highway was rerouted around the city of Hope , with the original path that traveled through the city being renamed the Old Hope Highway . In 1982 , during the expansion of the Federal Forest Highway System , the Hope Highway was added to the system , due to its location and local importance . 
- 
- = = Major junctions = = 
- 
- The entire highway is in Kenai Peninsula Borough . 
- 
- = = Old Hope Highway = = 
- 
- The Old Hope Highway is a short , historic route of the Hope Highway located in the city of Hope . The road is just 0 @.@ 259 miles ( 0 @.@ <unk> km ) long , and connects the Hope Highway to the central region of Hope . The road has an unpaved , gravel surface , and passes several small businesses and homes located in Hope . The road was part of the original Hope Highway , which was created in 1928 , and remained part of the highway until circa 1970 , when the highway was rerouted around Hope . 
- 
- 
- = Super Mario Land = 
- 
- Super Mario Land is a 1989 side @-@ scrolling platform video game , the first in the Super Mario Land series , developed and published by Nintendo as a launch title for their Game Boy handheld game console . In gameplay similar to that of the 1985 Super Mario Bros. , but <unk> for the smaller device 's screen , the player advances Mario to the end of 12 levels by moving to the right and jumping across platforms to avoid enemies and pitfalls . Unlike other Mario games , Super Mario Land is set in Sarasaland , a new environment depicted in line art , and Mario pursues Princess Daisy . The game introduces two <unk> @-@ style shooter levels . 
- At Nintendo CEO Hiroshi <unk> 's request , Game Boy creator <unk> Yokoi 's Nintendo R & D1 developed a Mario game to sell the new console . It was the first portable version of Mario and the first to be made without Mario creator and Yokoi protégé Shigeru Miyamoto . Accordingly , the development team shrunk Mario gameplay elements for the device and used some elements <unk> from the series . Super Mario Land was expected to showcase the console until Nintendo of America <unk> Tetris with new Game Boys . The game launched alongside the Game Boy first in Japan ( April 1989 ) and later worldwide . Super Mario Land was later <unk> for the Nintendo 3DS via Virtual <unk> in 2011 again as a launch title , which featured some tweaks to the game 's presentation . 
- Initial reviews were laudatory . Reviewers were satisfied with the smaller Super Mario Bros. , but noted its short length . They considered it among the best of the Game Boy launch titles . The handheld console became an immediate success and Super Mario Land ultimately sold over 18 million copies , more than that of Super Mario Bros. 3 . Both contemporaneous and retrospective reviewers praised the game 's soundtrack . Later reviews were critical of the compromises made in development and noted Super Mario Land 's <unk> from series norms . The game <unk> a series of sequels , including the 1992 Super Mario Land 2 : 6 Golden Coins , 1994 Wario Land : Super Mario Land 3 , and 2011 Super Mario 3D Land , though many of the original 's mechanics were not revisited . The game was included in several top Game Boy game lists and debuted Princess Daisy as a recurring Mario series character . 
- 
- = = Gameplay = = 
- 
- As a side @-@ scrolling platform game and the first in the Super Mario Land series , Super Mario Land is similar in gameplay to its forebears : as Mario , the player advances to the end of the level by moving to the right and jumping across platforms to avoid enemies and pitfalls . In Super Mario Land , Mario travels to Sarasaland to save Princess Daisy from <unk> , an evil <unk> . Two of the game 's twelve levels are " forced @-@ scrolling " <unk> @-@ style <unk> where Mario <unk> a submarine or airplane and fires projectiles towards oncoming enemies and bosses . Levels end with a <unk> challenge to reach an alternative exit located above the regular exit . The former leads to a bonus minigame that awards extra lives . 
- Unlike other Mario games , which take place in the Mushroom Kingdom , Super Mario Land is set in Sarasaland and drawn in line art . Mario pursues Princess Daisy , in her debut , rather than the series standard damsel in distress , Princess Peach . Koopa shells explode rather than slide , Mario throws bouncing balls rather than fireballs , 1 @-@ Up Mushroom power @-@ ups are depicted as hearts , and the level @-@ end <unk> are replaced with a <unk> challenge . Some elements recur from previous Mario games , such as blocks suspended in <unk> , pipes that lead to other areas , and <unk> enemies . 
- 
- = = Development = = 
- 
- Super Mario Land was developed by Nintendo R & D1 and published by Nintendo in 1989 as a launch title for their Game Boy handheld console . Nintendo CEO Hiroshi <unk> believed that fun games sold consoles , so when the company created the Game Boy handheld console , he wanted a fun game that would feature Nintendo 's mascot , Mario , and subsequently sell consoles . The job fell to Nintendo R & D1 , a development team led by Game Boy inventor <unk> Yokoi . Yokoi had previously created the Game & Watch series and worked with his protégé , Shigeru Miyamoto , on the game that invented Mario , <unk> Kong . Super Mario Land was the fourth Super Mario title , the first portable Mario game , and the first in the series to be made without Miyamoto . 
- <unk> Miyamoto 's direction , the development team used elements new and inconsistent with the series as Super Mario Land shrunk elements of the series to fit the portable device 's small screen . Yokoi , the head of R & D1 , served as producer , and <unk> <unk> served as director . They had previously developed <unk> ( 1986 ) and Kid Icarus ( 1986 ) together , and the two subsequently designed the Game Boy — Yokoi on its industrial design , and <unk> on its engineering . Their Super Mario Land was planned as the portable console 's showcase title until <unk> Rogers brought Tetris to Nintendo of America and convinced <unk> <unk> that the addictive computer game would help Nintendo reach the largest audience . The company subsequently chose to bundle Tetris with every Game Boy purchase . 
- The Game Boy was released in Japan in April 1989 , North America in July , and Europe in September 1990 , and Super Mario Land was a launch title . The game 's official first released was on April 21 , 1989 , in Japan , and its North American release followed in August . About 22 years later , Super Mario Land was released for the Nintendo 3DS via Virtual <unk> on June 6 , 2011 , as one of its opening titles . Its added features include an increased size ( about 60 percent <unk> ) and an optional " shades of green " color palette to match the effect of the original Game Boy 's monochrome . 
- 
- = = Reception = = 
- 
- Critics saw Super Mario Land as a " smaller " and shorter version of Super Mario Bros. IGN 's Lucas Thomas wrote that the protagonist , enemies , and overall game were shorter , and noted that Mario himself was just 12 <unk> in height on the Game Boy 's small screen . With this in mind , Thomas was concerned about player " <unk> " in <unk> of the game . Still , IGN 's Levi Buchanan thought the game made no compromises in its size reduction . At the time of its release in 1989 , reviewers were excited to have a portable Mario game . Paul Rand of Computer and Video Games called the game " an arcade machine in your pocket " and the graphics " remarkable " for their size . French games magazine Player One felt that Super Mario Land adequately compromised where necessary to bring Mario to a portable device . Electronic Gaming Monthly 's Steve Harris considered the game " fantastic " and " very fun to play " , albeit short . Ed <unk> and Donn <unk> of the same outlet both declared Super Mario Land " easily the best Game Boy cart " of the time . Complex 's Gus Turner wrote that the graphics were " simple " , and Official Nintendo Magazine said the game was " ridiculously short " . Eurogamer reported that the game could be finished in under an hour . 
- Complex 's Gus Turner wrote that the game had the fun , <unk> , and difficulty associated with the series , and Tony <unk> of <unk> said the game proved that Nintendo 's Game Boy " had <unk> to match " its competitors . Matt <unk> of Mean Machines agreed : " <unk> to the <unk> degree ! " British magazines Mean Machines and The Games Machine both commented on the game 's number of secrets to find . Eurogamer 's Chris Schilling called <unk> Tanaka 's soundtrack " surely one of the all @-@ time greats " , and Official Nintendo Magazine said it was among the " greatest <unk> music ever composed " . Player One , Eurogamer , and Complex too complimented the music . Player One further pronounced Super Mario Land a " masterpiece " , " the pinnacle of portable video gaming " . 
- Following the Game Boy 's " overnight success " , Super Mario Land sold over 18 million copies — more than that of Super Mario Bros. 3 . 
- 
- = = Legacy = = 
- 
- The game <unk> a Super Mario Land series of portable Mario games . Super Mario Land 2 : 6 Golden Coins added a non @-@ linear overworld and introduced Wario , an evil version of Mario , as the game 's villain . The subsequent Wario Land : Super Mario Land 3 began the Wario franchise . After 19 years , the 2011 title Super Mario 3D Land for the Nintendo 3DS became Mario 's first game in stereoscopic 3D . <unk> Drake of IGN argued that both Wario Land and Super Mario 3D Land were not " legitimate sequels " , and wrote that the latter felt more like " Super Mario Bros. 3 with Mario Galaxy influences " than a successor to Super Mario Land 2 . 
- Super Mario Land is remembered for its <unk> Super Mario elements and " twist on just about every Mario mainstay <unk> " . Many of its new elements did not recur later in the series , making Super Mario Land strange compared to the rest of the series , or what IGN 's Thomas described as a " singular <unk> " . IGN 's Marc Nix felt <unk> that Super Mario Land was the only <unk> Mario game , with " <unk> <unk> of white " and <unk> instead of the " strikingly original " Mushroom Kingdom . Mean Machines was also put off by the alien theme , easy difficulty , and dot matrix screen blur . IGN 's Travis <unk> wrote that the game was comparatively not as " ambitious " as Super Mario Bros. 3 . Mean Machines felt as if it was not " a true Mario game " , not worth its originally high review score , and " in <unk> , not really a classic " . 
- Eurogamer 's Schilling wrote that Mario felt different — lighter , with more friction — and that the game felt " radical and distinctive " for the risks it took . IGN 's Thomas cited " out of place " gameplay elements like the shooter levels , exploding Koopa shells , non @-@ <unk> fireballs , and non @-@ Princess Peach plot as departures from the series . Thomas attributed this to Mario creator Miyamoto 's lack of involvement in the game 's development , which he described as " famously hands @-@ off " . Schilling of Eurogamer instead blamed the Game Boy 's technical limitations . But he too was <unk> by the new sphinx , <unk> , and <unk> head enemies , and considered the exploding Koopa shells a " cruel trick " <unk> of the series ' core gameplay . Super Mario Land 's shooter levels , new to the series , were not revisited in subsequent series games . And subsequent series games such as Super Mario Land 2 both dropped the original 's tiny scale and chose the classic fire flower fireballs over the first installment 's bouncing balls . 
- The game was included in multiple rankings of top Game Boy games , and Official Nintendo Magazine listed it at 73 in its top 100 Nintendo games . After her debut in Super Mario Land , Princess Daisy <unk> as Luigi 's girlfriend and appears in later Mario series sports and racing games . 
- 
- 
- = Stop ! ! Hibari @-@ kun ! = 
- 
- Stop ! ! Hibari @-@ kun ! ( ストップ ! ! <unk> ! , <unk> ! ! Hibari @-@ kun ! ) is a Japanese manga series written and illustrated by Hisashi Eguchi . It was serialized in Weekly Shōnen Jump from October 1981 to November 1983 , and the chapters were published in four <unk> volumes by Shueisha from November 1982 to January 1984 . The series was adapted into a 35 @-@ episode anime television series by Toei Animation that aired on Fuji Television from May 1983 to January 1984 . The story focuses on Kōsaku Sakamoto , a high school student who goes to live with yakuza boss Ibari Ōzora and his four children — Tsugumi , Tsubame , Hibari and Suzume — after the death of his mother . Kōsaku is shocked to learn that Hibari , who looks and behaves as a girl , was assigned male at birth . 
- Eguchi wanted to create a romantic comedy manga where the main female character is a cross @-@ dressing boy so as to poke fun at the genre . He took more time to draw the chapters compared to his earlier manga , and as the serialization continued , he found it increasingly difficult to keep up a weekly pace for the chapters . He eventually abandoned the series after the editor @-@ in @-@ chief of Weekly Shōnen Jump refused his request to release the chapters every other week . From July 2009 to February 2010 , Shogakukan published a three @-@ volume Stop ! ! Hibari @-@ kun ! Complete Edition <unk> collection , which features various revisions to the originally published chapters in addition to newly drawn cover art . 
- Stop ! ! Hibari @-@ kun ! has been described as achieving a dizzying reality with Hibari by contrasting a <unk> exterior with a male interior . The series has been praised for its overall light and pop literary style , and the delicate touch in how Hibari is drawn has been described as so attractive that it makes the reader forget that it is a gag manga . However , the jokes surrounding the yakuza characters have been criticized as extreme and no longer humorous in modern times . The series has been described as having had a hand in paving the way for the J @-@ pop phenomenon . 
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- = = Plot = = 
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- Stop ! ! Hibari @-@ kun ! follows Kōsaku Sakamoto , a high school student whose mother tells him on her deathbed to live with her friend and yakuza boss Ibari Ōzora and his family in Tokyo after she dies . Although Kōsaku is initially unsure about his situation , he is relieved when he meets Ibari 's four children : Tsugumi , Tsubame , Hibari and Suzume . Kōsaku is attracted to Hibari from the start , but he is shocked to learn that Hibari , who looks and behaves as a girl , was assigned male at birth — something only known within the Ōzora Group and Hibari 's family . A group of four girls led by Kaori <unk> begin to suspect that Hibari may be a guy , but Tsubame takes Hibari 's place during a health <unk> , effectively <unk> any suspicions . Hibari begins to show an immediate interest in Kōsaku , so in an effort to distance himself from Hibari , Kōsaku joins the Wakaba Academy boxing club . Also in the club is Makoto Shiina , who is attracted to Hibari , and Rie Kawai , the club manager who Kōsaku likes . Hibari soon joins the club as its second manager shortly before they go on a training camp in <unk> at the end of the year . 
- At the start of the new school year , Hibari is scouted to join the girl 's volleyball team after the captain , Jun <unk> , witnesses Hibari 's athletic prowess . Although Hibari refuses , Jun continues to pursue Hibari until Jun is overwhelmed by Hibari 's superhuman ability to spike the ball . After Kaori and her friends try to embarrass Hibari during swim class , they once again suspect that Hibari may be a guy , but Hibari prevents them from getting any proof during the school sponsored summer camp . After Hibari refuses several advances from a popular guy at school named <unk> Honda , Hibari starts openly flirting with Kōsaku at school . Shiina becomes enraged at Kōsaku for earning Hibari 's affection , but Shiina and Kōsaku grow closer as friends after they fight it out and Kōsaku <unk> him that there is nothing going on between him and Hibari . 
- When Kōsaku is out <unk> one day , he helps out a girl named Sayuri Kōenji who was being harassed by three guys from the Kokuryū High School boxing club , although Hibari deals with them before Kōsaku can do anything . Despite this , Sayuri is instantly <unk> by Kōsaku and hires a detective to find out more about him , leading her to discover Hibari 's secret . Hibari has another run @-@ in with the Kokuryū boxing club members after they harass Tsubame at a festival . The Kokuryū boxing club challenges the Wakaba boxing club to some inter @-@ school matches , but this turns out to be a <unk> to get a chance to gang up on Hibari . However , the Ōzora Group <unk> , allowing Hibari to knock out the Kokuryū boxing club 's leader . Sayuri transfers into Kōsaku 's class , and she blackmails him into going on a date with her in exchange for not revealing Hibari 's secret . During the school festival , Kōsaku 's class acts out a Sleeping Beauty play with Kōsaku as the prince and Hibari as the princess , culminating in Hibari giving Kōsaku a French kiss on stage . Ibari agrees to look after <unk> <unk> , the son of one of his close friends , who Kōsaku is surprised to learn is a trans man . 
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- = = = Characters = = = 
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- Kōsaku Sakamoto ( <unk> <unk> , Sakamoto Kōsaku ) is the protagonist of Stop ! ! Hibari @-@ kun ! ! and is originally from <unk> 33 He is a pure @-@ hearted high school student who constantly refuses Hibari 's advances toward him , Ch . 2 , 3 , 10 , 17 but as time goes on , he becomes increasingly concerned that he is being influenced by Hibari.Ch. 8 , 22 , 35 , 38 Kōsaku treats Hibari kindly , and helps to maintain Hibari 's <unk> 25 , 50 , 51 Initially <unk> at fighting , Ch . 3 he joins the Wakaba Academy boxing club in an effort to get stronger because of Hibari 's inherent strength , Ch . 8 which he achieves as the series <unk> 38 Kōsaku is voiced by <unk> <unk> . 
- Hibari Ōzora ( 大空 <unk> , Ōzora Hibari ) is the titular character of Stop ! ! Hibari @-@ kun ! ! . Hibari is a high school student assigned male at birth who looks and behaves as a girl , much to Ibari 's <unk> 2 , 4 , 8 Hibari prefers being referred to as Ibari 's daughter , Ch . 6 , 8 , 15 and has expressed an interest in having <unk> 6 According to Suzume , Hibari becomes more feminine after Kōsaku starts living at the Ōzora <unk> 8 Hibari show an interest in Kōsaku early on , Ch . 9 and continues to make advances toward him throughout the <unk> 17 , 22 , 35 Hibari gets bolder as the series progresses , including openly flirting with Kōsaku at school , Ch . 37 and kissing him.Ch. 18 , 48 , 51 Hibari is extremely athletic and intelligent , in addition to being physically strong despite Hibari 's relatively small body <unk> 5 , 7 , 8 , 22 Hibari is voiced by <unk> <unk> . 
- Ibari Ōzora ( 大空 <unk> , Ōzora Ibari ) is the boss of the Ōzora Group , a yakuza <unk> 1 As the father of Tsugumi , Tsubame , Hibari and Suzume , he wants the Ōzora Group to be family @-@ <unk> 4 Ibari is extremely distressed by Hibari 's usual behavior , partly because Hibari is his only choice to inherit the Ōzora <unk> 2 He has a weak heart and frequently gets attacks whenever he is overly excited or shocked by <unk> 2 , 4 , 8 , 28 Ibari had been in love with Kōsaku 's mother , but she ended up marrying a <unk> <unk> 19 Ibari is voiced by <unk> <unk> . 
- Tsugumi Ōzora ( 大空 <unk> , Ōzora Tsugumi ) is the eldest daughter who has acted as a <unk> figure to her family after their mother died . She dropped out of art school in her second year and became a professional <unk> 31 Tsugumi is voiced by <unk> <unk> . 
- Tsubame Ōzora ( 大空 <unk> , Ōzora Tsubame ) is the second daughter and is two years older than Hibari . Like her father , Tsubame is against Hibari acting as a girl , Ch . 5 , 7 and constantly gets mad at Hibari for wearing her clothes , Ch . 4 , 23 , 44 although Tsubame sometimes wears Hibari 's clothes , <unk> 6 Tsubame and Hibari are nearly identical except for their hair and eye color , and they <unk> each other on certain <unk> 7 , 13 , 18 Tsubame is voiced by <unk> <unk> . 
- Suzume Ōzora ( 大空 <unk> , Ōzora Suzume ) is the youngest daughter and is a <unk> elementary school <unk> 14 , 19 , 48 She is scared of flat @-@ faced <unk> 4 , 28 Suzume is voiced by <unk> Suzuki . 
- <unk> ( <unk> ) is a member of the Ōzora Group . He is in love with <unk> 32 <unk> is voiced by <unk> <unk> . 
- <unk> ( <unk> ) is a member of the Ōzora Group . He looks scary , but he has a timid <unk> 2 , 4 , 18 He <unk> Kōsaku <unk> in the <unk> 6 , 9 , 20 <unk> is voiced by <unk> <unk> . 
- Makoto Shiina ( <unk> <unk> , Shiina Makoto ) is Kōsaku 's classmate who likes Hibari.Ch. 3 , 12 He is a member of the boxing club , and initially hates Kōsaku for earning Hibari 's affection , Ch . 8 but as the series progresses , the two become <unk> 38 Shiina is voiced by <unk> Mori . 
- Rie Kawai ( <unk> <unk> , Kawai Rie ) is a normal girl with a gentle personality who is attracted to <unk> 12 She joined the boxing club as a manager because of him.Ch. 17 She leaves the boxing club when she learns Shiina likes Hibari , Ch . 30 but she rejoins the club after she gets over him.Ch. 50 Rie is voiced by <unk> <unk> . 
- <unk> <unk> ( <unk> <unk> , <unk> <unk> ) is one of Tsubame 's classmates and is the boxing club <unk> 10 , 13 He is an overbearing guy and is very <unk> towards Tsubame whom he <unk> 13 , 18 His family members have the same face as him.Ch. 18 He is voiced by <unk> <unk> . 
- Kaori <unk> ( <unk> <unk> , <unk> Kaori ) is Kōsaku 's <unk> 23 She is jealous of Hibari 's popularity at school , Ch . 6 and takes pride in her <unk> <unk> 7 , 27 Along with her three friends , they try to embarrass Hibari.Ch. 24 , 27 Kaori is voiced by <unk> Nakano . 
- Sayuri Kōenji ( <unk> <unk> , Kōenji Sayuri ) is a rich girl one year younger than <unk> 50 She falls in love easily and will do anything she can to bring her love to fruition . When something surprises her , she <unk> a <unk> 43 Sayuri is voiced by <unk> Yamamoto . 
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- = = Media = = 
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- = = = Manga = = = 
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- Stop ! ! Hibari @-@ kun ! is written and illustrated by Hisashi Eguchi . Following the conclusion of his manga <unk> <unk> ( <unk> ) in 1981 , Eguchi wanted to go against the notion at the time that there was currently a golden age of romantic comedy manga in <unk> manga magazines . In response , Eguchi thought of creating a romantic comedy manga where the main female character is a cross @-@ dressing boy , and in doing so , poke fun at the genre by developing it as the antithesis of a romantic comedy . Eguchi drew up the storyboard for the first chapter in about 30 minutes in a cafe , and came up with the title Stop ! ! Hibari @-@ kun ! as a reference to Hisashi <unk> 's manga Stop ! <unk> @-@ <unk> ( ストップ ! <unk> ) . In wanting to highlight the comedy of having a character like Hibari , Eguchi realized that the <unk> he could draw Hibari , the more effective the jokes would be , so he tried to draw Hibari as cute as he could . 
- Eguchi had often rushed when drawing his earlier manga <unk> ! ! Pirates ( <unk> ! ! <unk> ) , but starting with Stop ! ! Hibari @-@ kun ! , he raised the standards he held for his art and had to began taking more time to draw the chapters . In addition , Eguchi was very particular about the appearance of his manuscripts , so he never used white @-@ out to correct any drawing errors because he disliked how it looked . As the serialization continued , Eguchi found it increasingly difficult to keep up a weekly pace for the chapters , leading him to take frequent <unk> and later say that " drawing weekly isn 't something humans can do . " Furthermore , the editor @-@ in @-@ chief of Weekly Shōnen Jump at the time , <unk> Nishimura , refused his request to release the chapters every other week . When it came time to draw what would end up being the last serialized chapter , Eguchi completed the chapter 's storyboard , but ultimately submitted only about two @-@ thirds of the chapter , leaving out the last five pages . After he submitted the chapter 's manuscript , Eguchi fled to a hotel and secluded himself for a day , only coming out after Nishimura called him to say that he could not deal with him anymore on a weekly basis . As a result , Eguchi abandoned the serialization and the editorial department decided to <unk> the series . 
- Stop ! ! Hibari @-@ kun ! was serialized in the manga magazine Weekly Shōnen Jump from the October 19 , 1981 issue to the November 28 , 1983 issue . The individual chapters were collected and published in four <unk> volumes by Shueisha from November 1982 to January 1984 . Inspired by <unk> <unk> , Eguchi fought against the established design format for the volumes that at the time was " set in stone " for series serialized in Weekly Shōnen Jump . <unk> later published it in three volumes in July 1991 , and again in two volumes in February 1995 . Shueisha republished it in four volumes from May to June 2001 . Home @-@ <unk> published it in two volumes in January 2004 . In an interview in 2007 , Eguchi expressed a desire to continue some of his older series , but stated that a continuation of Stop ! ! Hibari @-@ kun ! would be difficult . 
- Starting in 2009 , Eguchi began working with Shogakukan on releasing a Stop ! ! Hibari @-@ kun ! Complete Edition <unk> collection , which features various revisions to the originally published chapters in addition to newly drawn cover art . Not wanting to end the Complete Edition with an abrupt conclusion like he had done before , Eguchi found the storyboard for the final chapter and used it as a basis to newly draw the last five pages he had omitted 27 years earlier . Shogakukan published the Complete Edition in three volumes from July 2009 to February 2010 ; Shogakukan later republished the series in another three volumes from May to July 2012 . In 2010 , Eguchi did not rule out the possibility of someday drawing a continuation of the series , but he said it probably would not be a story under the title Stop ! ! Hibari @-@ kun ! . 
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- = = = Anime = = = 
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- A 35 @-@ episode anime television series adaptation , produced by Toei Animation and directed by <unk> <unk> . It aired from May 20 , 1983 to January 27 , 1984 on Fuji Television . The screenplay was written by : Shigeru <unk> , <unk> <unk> , Hiroshi <unk> , <unk> <unk> , Takeshi <unk> and <unk> <unk> . The character design used in the anime was provided by Yoshinori <unk> , and the music was composed by <unk> Nishimura . The series was later released by Universal J to two DVD compilation volumes from February to March 2003 . A DVD box set was released by <unk> Entertainment in September 2014 . The opening theme is " Stop ! ! Hibari @-@ kun ! " ( ストップ ! ! <unk> ! ) sung by Yuki <unk> and the ending theme is " <unk> Connection " ( <unk> ・ <unk> ) sung by <unk> <unk> . 
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- = = Reception and legacy = = 
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- <unk> <unk> <unk> described Stop ! ! Hibari @-@ kun ! as achieving a " dizzying reality " with Hibari " by contrasting a <unk> exterior with a male interior " , and went on to say that the series could be seen as a " continuation of the <unk> perversion lineage " of cross @-@ dressing characters . Manga critic <unk> Nakano has called Stop ! ! Hibari @-@ kun ! a unique and outstanding gag manga for its juxtaposition of Hibari being a boy and the heir to a yakuza organization . Nakano largely attributes this successful combination to Eguchi 's ability to draw girls that are not only cute , but also have good taste and sex appeal . The delicate touch in how Hibari is drawn has been described as so attractive that it makes the reader forget that it is a gag manga . 
- In writing for the magazine <unk> , manga critic <unk> wrote that because of its overall light and pop literary style , none of the indecency or <unk> cross @-@ dressing may engender comes through , which he <unk> is why the anime was able to air during prime time . <unk> notes the more extreme nature of the jokes surrounding the yakuza characters in comparison to the jokes involving cross @-@ dressing , and that some jokes such as those involving drugs would not be humorous today . Manga commentator <unk> <unk> lauded Eguchi for being a pioneer in drawing characters with a high fashion sense in Stop ! ! Hibari @-@ kun ! , which he says effectively changed fashion in <unk> manga from being seen as a " symbol " to now being treated as an " accessory " . Eguchi 's attention to detail is also praised , such as drawing Kōsaku wearing Chuck Taylor All @-@ Stars in one chapter . 
- The series has been called effervescent and having had a hand in paving the way for the J @-@ pop phenomenon . When <unk> <unk> author Hiroyuki <unk> read Stop ! ! Hibari @-@ kun ! in junior high school , he admired its novel sensibility , and Eguchi 's art also influenced him . According to Eguchi , some people who read Stop ! ! Hibari @-@ kun ! were influenced to start cross @-@ dressing . 
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- = Guitar Hero = 
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- The Guitar Hero series ( sometimes referred to as the Hero series ) is a series of music rhythm games first published in 2005 by RedOctane and Harmonix , and distributed by Activision , in which players use a guitar @-@ shaped game controller to simulate playing lead , bass guitar , and rhythm guitar across numerous rock music songs . Players match notes that scroll on @-@ screen to colored fret buttons on the controller , strumming the controller in time to the music in order to score points , and keep the virtual audience excited . The games attempt to mimic many features of playing a real guitar , including the use of fast @-@ fingering hammer @-@ <unk> and pull @-@ offs and the use of the whammy bar to alter the pitch of notes . Most games support single player modes , typically a Career mode to play through all the songs in the game , and both competitive and cooperative multiplayer modes . With the introduction of Guitar Hero World Tour in 2008 , the game includes support for a four @-@ player band including vocals and drums . The series initially used mostly cover versions of songs created by <unk> Sound , but most recent titles feature soundtracks that are fully master recordings , and in some cases , special re @-@ recordings , of the songs . Later titles in the series feature support for downloadable content in the form of new songs . 
- In 2005 , RedOctane , a company specializing in the manufacture of unique game controllers , was inspired to create Guitar Hero based on RedOctane 's experience creating hardware for Konami 's Guitar Freaks arcade game . They enlisted Harmonix , who previously developed several music video games , for development assistance . The first game in the series was made on a budget of $ 1 million . The series became extremely successful , leading to the acquisition of RedOctane by Activision in 2007 . Harmonix was acquired by MTV Games and went on to create the Rock Band series of music games in the same vein as Guitar Hero . Activision brought Neversoft ( primarily known for their Tony Hawk series of <unk> games ) on board for future development duties . Additional companies , such as Budcat Creations and Vicarious Visions have assisted in the adaptation of the games for other systems . 
- The series currently has eight major releases ( six Guitar Hero games , two DJ Hero games and Band Hero ) and five expansions on gaming consoles . There are spin @-@ offs for Windows and Macintosh systems , mobile phones , the Nintendo DS ( the Guitar Hero : On Tour series ) , and an arcade game . The Guitar Hero franchise was a primary brand during the emergence of the popularity of rhythm games as a cultural phenomenon in North America . Such games have been utilized as a learning and development tool for medical purposes . The first game in the series was considered by several journalists to be one of the most influential video games of the first decade of the 21st century . The series has sold more than 25 million units worldwide , earning US $ 2 billion at retail , claimed by Activision to be the 3rd largest game franchise after the Mario and <unk> NFL franchises ; the third main title of the series , Guitar Hero III : Legends of Rock is also claimed by Activision to be the first single video game title to exceed $ 1 billion in sales . 
- Despite early success , the series , along with the overall rhythm game genre , suffered from poor sales starting in 2009 . Activision had stated in early 2011 that the series was on hiatus for 2011 , while a seventh main title in the series was under development ; this title was later cancelled due to the poor quality of the emerging product . Activision later shut down sales of the series ' downloadable content , although users who purchased material from it previously may still play what they bought . 
- In 2015 , Activision announced the first new title to the series in 5 years , Guitar Hero Live , released in October 2015 . The title is considered a reboot of the series , with development being performed by FreeStyleGames , who had developed the DJ Hero games previously . 
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- = = History = = 
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- = = = Origins and development at Harmonix ( 2005 @-@ 2006 ) = = = 
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- Guitar Hero was created from a partnership between RedOctane , then their own company that produced specialized video game controllers , and Harmonix , a music video game development company who had previously produced <unk> , Amplitude and Karaoke Revolution . RedOctane was seeking to bring in a Guitar Freaks @-@ like game , highly popular in Japan at the time , into Western markets , and approached Harmonix about helping them to develop a music game involving a guitar controller . Both companies agreed to it , and went on to produce Guitar Hero in 2005 . The title was highly successful , leading to the development of its successful sequel Guitar Hero II in 2006 . While the original controllers for the first Guitar Hero game were designed by Ryan Lesser , Rob Kay , Greg <unk> and Alex <unk> of Harmonix and built by the <unk> Corporation of China , subsequent <unk> and future controllers were developed <unk> at RedOctane , with development led primarily by Jack <unk> . 
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- = = = Sale to Activision and development by Neversoft ( 2006 @-@ 2009 ) = = = 
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- Both RedOctane and Harmonix experienced changes in 2006 . RedOctane was bought by Activision in June — who spent US $ 100 million to acquire the Guitar Hero franchise — while it was announced in October that Harmonix would be purchased by MTV Networks . As a result of the two purchases , Harmonix would no longer develop future games in the Guitar Hero series . Instead , that responsibility would go to Neversoft , a subsidiary of Activision known for developing the Tony Hawk 's series of <unk> games . Neversoft was chosen to helm the Guitar Hero series after Neversoft founder , Joel <unk> , admitted to the RedOctane founders , Kai and Charles Huang , that his development team for Tony Hawk 's Project 8 went to work on weekends just to play Guitar Hero . Activision CEO Bobby Kotick believed that Neversoft would help them bring great games to the series , but on reflection , stated that had Activision explored Harmonix further as a continued developer for the series , things " may have turned out differently " . In addition , Activision began seeking other markets for the game ; a Nintendo DS version of the series was developed by Vicarious Visions , while a Guitar Hero Mobile series was created for mobile phones . The company also began considering the expansion of the series to band @-@ specific titles with Guitar Hero : Aerosmith . Later , in November 2008 , Activision acquired Budcat Creations , another development studio that had helped with the PlayStation 2 versions of Guitar Hero III and World Tour , announcing that they would be helping to develop another game in the Guitar Hero series . 
- In 2007 , Harmonix and MTV Games released a new music title through rival publisher Electronic Arts , called Rock Band . It expanded upon the gameplay popularized by the Guitar Hero series by adding drum and microphone instruments , allowing players to simulate playing songs as bands . Activision followed suit with the release of Guitar Hero World Tour in 2008 , which supported multiple instruments . In 2009 , Activision <unk> its Guitar Hero offerings , and in addition to further continuation of the existing main series with Guitar Hero 5 and expansions , they introduced the titles Band Hero , geared towards more family @-@ friendly pop music , and DJ Hero , a game based on <unk> and featuring a number of mixes . With the release of Guitar Hero 5 , Activision considered the series to have moved away from its heavy metal basis into a broader selection of music . Guitar Hero 5 is the first game in the series to use a new version of the series ' logo ; previous games used a logo in a font with sharper " points " on the letters , which was considered " idiosyncratic with a vengeance " to match the games ' emphasis on heavy metal music . Activision used the services of the Pentagram design studio to <unk> the game 's logo . Pentagram developed a new font , removing some of the " aggressive odd " features to make the <unk> more suitable and <unk> to design feature incorporation to other games such as Band Hero and DJ Hero . 
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- = = = Decline and hiatus ( 2009 @-@ 2015 ) = = = 
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- The results of the expanded offerings did not contribute well to the series , alongside the late @-@ 2000s recession ; sales of most rhythm games including Guitar Hero and DJ Hero did not meet expectations , falling about 50 % short of projected targets . Activision announced it would be cutting back to only 10 <unk> within 2010 instead of the 25 in 2009 . Though RedOctane and Neversoft continued to develop the 6th main game , Guitar Hero : Warriors of Rock , until its completion , both studios were later <unk> by Activision , moving key personnel into Activision directly for future game development , and in the case of Neversoft , closing its Guitar Hero division , while transferring future development duties for the series to Vicarious Visions , another Activision studio which had been fundamental in building the Wii and Nintendo DS versions of the games . In November 2010 , Activision also closed Budcat Creations , the arm of the publisher that was primarily responsible for <unk> the Guitar Hero games to the PlayStation 2 . 
- Ahead of Activision 's 2010 fourth quarter financial report in February 2011 , Activision disbanded its Guitar Hero business unit and announced that it would cease development of the planned 2011 Guitar Hero game . Activision cited " continued declines in the music genre " to explain its decision . The closure also affected the DJ Hero series , as Activision stated that there were no plans to publish a music game during 2011 . Activision 's vice president Dan Winters later clarified that the company was " just putting Guitar Hero on hiatus " and that they were " just not making a new game for next year , that 's all " . 
- In a July 2011 interview with Forbes , Kotick stated that while the publisher was " going to stop selling Guitar Hero altogether " , they were " going to go back to the studios and we ’ re going to use new studios and reinvent " the series , but a former <unk> of Vicarious Visions has stated that as of 2012 , all development of Guitar Hero has come to an end within Activision . Another source close to Vicarious Visions had reported to Kotaku that while Guitar Hero 7 was in development under an Activision studio , the game was considered a " disaster " . The cancelled game omitted the additional instruments and used only a guitar peripheral , <unk> the unit to include a 6 @-@ button mechanism replacing the strum bar ; the resulting unit was considered too expensive to manufacture and purchase . The developers had also started the game development from scratch to try to create new characters and venues that would be more reactive to the actual songs being played to give the feel of a music video , but ultimately this proved too much of a challenge and had to be scrapped . Further , with a limited budget , the song selection was limited to " low @-@ budget " hits of the 1990s , or at times reusing songs that had previously been included in Guitar Hero games . Though the team had a two @-@ year development cycle , it was closed down after Activision president Eric <unk> had seen the current state of the project at the one @-@ year point . 
- Another potential Guitar Hero project was discovered by the archival site <unk> for a game titled Hero World , a massively multiplayer online game that would link the Guitar Hero and DJ Hero games . The game had been developed by <unk> Games , sometime after the release of DJ Hero 2 , with the main development duties passed to Virtual <unk> , using their platform The Ride , an <unk> Flash @-@ based platform that would let the game be played in a web <unk> . The game was cancelled in 2011 along with other pending Guitar Hero projects . 
- No further downloadable content for either Guitar Hero or DJ Hero was made after February 2011 , though Activision committed to releasing content that was already in development by that time due to fan response ; later , in a move described by Game Informer as " the final nail in [ the series ' ] coffins " , Activision announced it would <unk> all DLC sales for the series ( although customers can still play tracks they have already bought ) as of March 31 , 2014 . Though Activision had moved away from the Guitar Hero series , the lessons learned helped them and developer Toys for Bob to handle the manufacturing and <unk> issues that came with the highly successful <unk> toy and video game franchise . 
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- = = = Guitar Hero Live = = = 
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- In April 2015 , Activision announced a new entry in the series , titled Guitar Hero Live . The title was developed by Activision 's internal studio FreeStyleGames , who previously had worked on the DJ Hero spinoff titles . FreeStyleGames were given free reign to reboot the Guitar Hero series for next @-@ generation consoles . One of their first innovations was to drop the standard five @-@ button guitar controller , ultimately designing a six @-@ button guitar controller , with two rows of three buttons each , allowing them to mimic actual guitar fingering . Guitar Hero Live was released with both career and an online mode . The career mode used full @-@ motion video taken from the perspective of a lead guitarist underneath the note highway , to create an <unk> experience to the player . The online mode , called <unk> , discarded the previous downloadable content approach and used a music video channel approach to stream playable songs to players , adding new songs to the catalog on a weekly basis . The game was released in October 2015 . 
- Though the game was praised as a <unk> of the Guitar Hero series , the game did not sell as well as Activision expected ; due to lowered <unk> , Activision let go of a significant fraction of FreeStyleGames ' developers . 
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- = = Games = = 
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- = = = Main titles = = = 
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- The original Guitar Hero was released on the PlayStation 2 in November 2005 . Guitar Hero is notable because it comes packaged with a controller peripheral modeled after a black Gibson SG guitar . Rather than a typical <unk> , this guitar controller is the primary input for the game . Playing the game with the guitar controller <unk> playing an actual guitar , except it uses five colored " fret buttons " and a " strum bar " instead of frets and strings . The development of Guitar Hero was inspired by Konami 's Guitar Freaks video game , which at the time , had not seen much exposure in the North American market ; RedOctane , already selling guitar @-@ shaped controllers for imported copies of <unk> , approached Harmonix about creating a game to use an entirely new Guitar controller . The concept was to have the gameplay of Amplitude with the visuals of Karaoke Revolution , both of which had been developed by Harmonix . The game was met with critical acclaim and received numerous awards for its innovative guitar peripheral and its soundtrack , which comprised 47 playable rock songs ( most of which were cover versions of popular songs from artists and bands from the 1960s through modern rock ) . Guitar Hero has sold nearly 1 @.@ 5 million copies to date . 
- The popularity of the series increased dramatically with the release of Guitar Hero II for the PlayStation 2 in 2006 . Featuring improved multiplayer gameplay , an improved note @-@ recognizing system , and 64 songs , it became the fifth best @-@ selling video game of 2006 . The PlayStation 2 version of the game was offered both separately and in a bundle with a cherry red Gibson SG guitar controller . Guitar Hero II was later released for the Xbox 360 in April 2007 with an exclusive Gibson Explorer guitar controller and an additional 10 songs , among other features . About 3 million units of Guitar Hero II have sold on the PlayStation 2 and Xbox 360 . 
- Guitar Hero III : Legends of Rock was released in late 2007 for the PlayStation 2 , PlayStation 3 , Xbox 360 , Wii , Microsoft Windows , and Mac OS X platforms . The title is the first installment of the series to include wireless guitars <unk> with the game and also the first to release a special bundle with two guitars . The game includes Slash and Tom <unk> as playable characters in addition to the existing fictional avatars ; both guitarists performed motion capture to be used for their characters ' animation in the game . 
- Guitar Hero World Tour , previously named Guitar Hero IV , is the fourth full game in the series and was released on October 26 , 2008 for PlayStation 2 , PlayStation 3 , Xbox 360 , and Wii . Analysts had expected that future Guitar Hero games in 2008 would include additional instrument <unk> to compete against Rock Band ; Guitar Hero World Tour was confirmed as in development following the announcement of the merger between Activision and <unk> Games in December 2007 . Activision 's CEO Bobby Kotick announced on April 21 , 2008 that Guitar Hero World Tour will branch out into other instruments including vocals . Guitar Hero World Tour includes drums and vocals , and can be bought packaged with a new drum set controller , a microphone , and the standard guitar controller . A larger number of real @-@ world musicians appear as playable characters , including Jimi Hendrix , Billy <unk> , Hayley Williams , <unk> <unk> , Ted <unk> , Travis Barker , Sting , and Ozzy Osbourne . Guitar Hero World Tour also features custom song creation that can be shared with others . 
- Guitar Hero 5 , the fifth main entry in the series , was confirmed in December 2008 . It was released on September 1 , 2009 , and includes 85 songs from 83 different artists . The game includes new game modes and features , including its ' Party Mode , ' which gives players the ability to drop @-@ in and out and change difficulties in the middle of a song . Artists including Johnny Cash , Matt Bellamy , Carlos Santana , Kurt Cobain and Shirley Manson appear as playable characters in the game . 
- Guitar Hero : Warriors of Rock , the sixth main console game in the series , was released on September 28 , 2010 . It is the last game in the series developed by Neversoft 's Guitar Hero division prior to its dissolution , with Vicarious Visions assisting on the Wii version with added Nintendo DS functionality . The game has been described as returning to the roots of the Guitar Hero series ; while it still allows for full band play , the soundtrack 's focus is on rock and roll music and an emphasis on guitar " <unk> " . The game introduced a career @-@ based " Quest Mode " , narrated by Gene Simmons , that guides the players to complete songs to unlock " warriors of rock " to join them in saving " <unk> of rock " and his guitar from his imprisonment by " the Beast " . 
- Following a five @-@ year hiatus , as described below , Activision announced Guitar Hero Live for release in late 2015 on most seventh @-@ generation and eighth @-@ generation consoles . Live was developed to rebuild the game from the ground up , and while the gameplay remains similar to the earlier titles , focusing primarily on the lead guitar , it uses a 3 @-@ button guitar controller with each button having " up " and " down " positions , making for more complex <unk> . The game using live footage of a rock concert , taken from the perspective of the lead guitarist , as to provide a more <unk> experience . 
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- = = = Series expansions = = = 
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- Guitar Hero Encore : Rocks the 80s for the PlayStation 2 , which was released in July 2007 , was the final game developed by Harmonix for the series . Though it was produced after Harmonix were purchased by MTV Games , it was part of their contractual obligation to complete the game . The game , as suggested by its name , features tracks primarily from the 1980s . 
- Guitar Hero : Aerosmith was the first band @-@ centric game for the series . On September 4 , 2007 , Billboard announced that the band Aerosmith was " working closely with the makers of Guitar Hero IV , which will be dedicated to the group 's music . " On February 15 , 2008 , Activision announced that Guitar Hero : Aerosmith would be released on June 29 , 2008 . Guitar Hero : Aerosmith is developed by Neversoft for the Xbox 360 and PlayStation 3 versions , while the Wii version of the game is developed by Vicarious Visions and the PlayStation 2 version is developed by Budcat Creations . The game features a track selection composed of 60 % of Aerosmith songs , with other songs from Joe Perry 's solo work or artists that have inspired or performed with Aerosmith , including Run <unk> .. 
- The series ' next band @-@ centric title , Guitar Hero : Metallica , was released on March 29 , 2009 . Guitar Hero : Metallica is based on the full band experience of World Tour while offering similar features on Metallica 's history and music as found in Guitar Hero : Aerosmith . In addition , Metallica 's album , Death Magnetic , was available as downloadable content for Guitar Hero III simultaneously with the release of the album , with the content being forward @-@ compatible with Guitar Hero World Tour and Guitar Hero : Metallica . Since the PlayStation 2 version does not support downloading , three extra songs were included from Death Magnetic and are as follows : " Broken , Beat , and <unk> " , " Cyanide " , and " My Apocalypse " . A new feature in the game , where the drummer can access a mode called Expert + , has also been added . Expert + mode was implemented to allow faster bass pedal beats , fast to the point where it would normally be out of the playable range of a single bass pedal , and was intended for a dual bass pedal . 
- Guitar Hero Smash Hits ( titled Guitar Hero : Greatest Hits in Europe and Australia ) was released in June 2009 . It features full @-@ band versions of 48 songs from earlier Guitar Hero games that only used the guitar controller . Unlike the previous versions , each of the songs is based on a master recording that includes some live tracks . The game follows a similar model as Guitar Hero : Metallica , and was developed by Neversoft and <unk> Studios for the PlayStation 3 , Xbox 360 , PlayStation 2 , and Wii . 
- Guitar Hero : Van Halen was released on December 22 , 2009 , though customers that purchased Guitar Hero 5 under a special promotion received a copy of the game early . Like the other band @-@ centric games , Guitar Hero : Van Halen includes 25 songs from the band Van Halen , including 3 guitar solos by Eddie Van Halen , in addition to 19 guest acts such as Queen , <unk> , Blink @-@ 182 , Foo Fighters , The <unk> and Queens of the Stone Age . 
- Another new title to the series , Band Hero , was announced in May 2009 . Band Hero features Top 40 hits aimed at family audiences , and include the full band play style of Guitar Hero 5 . The game was developed for the Nintendo DS , using the Guitar Hero On Tour Guitar Grip , a new " drum skin " to fit the DS Lite , and the DS 's microphone to support the full band experience . Musician Taylor Swift appears as a playable character in the game , as do the members of No Doubt . 
- DJ Hero was announced by Activision in May 2009 . Prior to the announcement , the company had purchased FreeStyleGames , a small developer of music games , to help produce localized downloadable content for Guitar Hero games and a then @-@ <unk> music game , later revealed to be DJ Hero . DJ Hero uses a special <unk> @-@ based controller for players to perform with on various song mixes in the game . The game also incorporates the use of a Guitar Hero controller on ten specially arranged tracks ; Bright has suggested that future Guitar Hero games after Guitar Hero 5 may include the use of the <unk> control . 
- A sequel , DJ Hero 2 , was officially announced in June 2010 for release in the last quarter of 2010 , featuring more than 70 <unk> from over 85 artists . The game includes several new gameplay modes , including an " Empire " career mode , head @-@ to @-@ head DJ battles , social multiplayer modes , and a jump @-@ in and out Party Play mode similar to Guitar Hero 5 . The game also includes more vocal options for singing and rapping to songs , and a freestyle mode for players . 
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- = = = Portable versions = = = 
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- Guitar Hero : On Tour was released on the Nintendo DS hand @-@ held system on June 22 , 2008 . The game includes a peripheral , dubbed the " Guitar Grip " , a rectangular device that fits into the second slot of the Nintendo DS or DS Lite . The peripheral only features the first four fret buttons and a strap so the Nintendo DS can be held sideways comfortably for play . The game also includes a guitar pick shaped stylus for use with strumming in the game , which players move across the <unk> . Guitar Hero : On Tour was developed by Vicarious Visions , who also ported the Guitar Hero games to Nintendo 's Wii console . 
- A sequel , Guitar Hero On Tour : <unk> , was released in November 2008 , featuring music spanning four decades . A third title in the series , Guitar Hero On Tour : Modern Hits , was announced following various rumors of its existence , and was released in June 2009 , featuring songs recorded since the year 2000 . Both games use the " Guitar Grip " controller , and allow two players to compete against each other using any version of the On Tour series , with songs being shared between versions . 
- Band Hero was also ported to the Nintendo DS by Vicarious Visions , expanding the play to include vocals ( through the DS microphone ) and drumming . The drumming uses a special " drum skin " adapter designed for the Nintendo DS Lite to map the unit 's face buttons to four drum pads . However , the peripheral is not compatible with the original Nintendo DS model or the Nintendo <unk> . However , since the drum skin is not electronic but a rubber cover switch that <unk> certain buttons on the DS Lite , a player can simply press the buttons in time to play the drums . The game includes four @-@ player local wireless play in a similar manner as Guitar Hero 5 allowing any combination of instruments to be used . The game has a set of 30 songs ; some are from Band Hero and others are from several Guitar Hero games ' set lists . 
- Guitar Hero : On Tour does not work on the Nintendo <unk> and Nintendo 3DS because unlike the Nintendo DS , they do not have Game Boy Advance slots . Band Hero is limited to vocals and drums on the two consoles for the same reason . 
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- = = = Mobile phone versions = = = 
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- Guitar Hero III Mobile was released for mobile phones in 2007 and 2008 , and was developed by <unk> Northwest LLC . The base version of the game includes 15 songs from both Guitar Hero II and Guitar Hero III , and has released a three @-@ song add @-@ on pack every month since January 2008 . The title has been downloaded by users one million times , with both Verizon and Hands @-@ On Mobile claiming that over 250 @,@ 000 songs are played a day on the platform . The two companies produced two other mobile @-@ based Guitar Hero games ; Guitar Hero III : <unk> Pass , released in July 2008 , adds role @-@ playing elements to manage the band 's success in addition to the core rhythm game , while the mobile version of Guitar Hero World Tour , released in December 2008 , expands each included track for play on both lead guitar and drums , <unk> the expansion of the console series to the full band . 
- <unk> Mobile developed the mobile version of Guitar Hero 5 , released in the last quarter of 2009 . 
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- = = = Other games = = = 
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- Activision and RedOctane also worked with Basic Fun , Inc. to produce Guitar Hero <unk> , a handheld electronic game that features 30 and 60 @-@ second clips of ten of the songs from Guitar Hero and Guitar Hero II . 
- Activision and Konami , who had previously worked together to make sure that the Guitar Hero series meets with Konami 's patents on music games , developed an arcade console version of the game , titled Guitar Hero Arcade , distributed to arcades in early 2009 . The game is completely based on the Guitar Hero III gameplay , but reducing some of the features such as the use of the <unk> bar , Star Power Button ( Star Power may only be activated by lifting the Guitar ) and Practice <unk> , but keeping the ability to download new songs for the cabinet from the Internet . The arcade game has come under some scrutiny by the American Society of Composers , Authors and Publishers ( ASCAP ) , who believe the use of the game in arcades is equivalent to " public performances " and seek additional fees to be paid by operators of the game . 
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- = = = Planned games = = = 
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- No more Guitar Hero games were released on the PlayStation 2 after 2009 , with the double release of Guitar Hero 5 and Band Hero . It was expected that the 2010 entry for Guitar Hero , Warriors of Rock , would be the final entry developed by Neversoft , based on claims that Neversoft would be letting go of its Guitar Hero division , with Vicarious Visions likely poised to take over future development . Further industry rumors pointed at the closure of RedOctane Studios and Underground Development ( the development studio for Guitar Hero : Van Halen ) as further results from the scaling @-@ back ; Activision moved the controller hardware development within their own division to continue to support the series , with RedOctane founders Kai and Charles Huang remaining with Activision . A week prior to these announcements , the Guitar Hero division CEO at Activision , Dan <unk> , left the company , leading to some speculation on whether <unk> 's departure influenced these changes . Activision and RedOctane had trademarked the titles " Guitar <unk> " , " Drum <unk> " , " <unk> Hero " and " Sing Hero " . RedOctane originally trademarked the titles " Drum Hero " and " Band Hero " , but the work performed towards the Drum Hero title was eventually folded into the gameplay for Guitar Hero World Tour , and Band Hero became its own game . Later , as of October 2009 , Activision <unk> for a Drum Hero trademark . <unk> Studios , which had previously helped to port Rock Band to the Wii , had started work on the karaoke title Sing Hero before Activision cancelled its development . 
- Dave <unk> , frontman for <unk> , stated he had been in talks with Activision and Neversoft for a Guitar Hero @-@ related product . It was later revealed that Dave <unk> was working with Activision for music in Guitar Hero : Warriors of Rock , including an original track ( " <unk> Death " ) recorded specifically for the game . 
- Two Guitar Hero products that were announced but never released were a Red Hot <unk> <unk> @-@ themed title and a PlayStation Portable title that would have featured a drum component . 
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- = = Gameplay = = 
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- The core gameplay of the Guitar Hero games is a rhythm game similar to Konami 's Guitar Freaks and to a lesser extent Harmonix 's previous music games such as <unk> and Amplitude . The guitar controller is recommended for play , although a standard console controller can be used instead . However , the guitar controller has been required for play ever since the inclusion of drum and vocal parts in the series . The game supports <unk> the handedness of the guitar , allowing both left @-@ handed and right @-@ handed players to utilize the guitar controller . 
- While playing the game , an extended guitar neck is shown vertically on the screen ( the frets horizontal ) , often called the " note highway " , and as the song progresses , colored markers or " gems " indicating notes travel down the screen in time with the music ; the note colors and positions match those of the five fret keys on the guitar controller . Once the note ( s ) reach the bottom , the player must play the indicated note ( s ) by holding down the correct fret button ( s ) and hitting the strumming bar in order to score points . Success or failure will cause the on @-@ screen Rock Meter to change , showing how well the player is playing ( denoted by red , yellow , and green sections ) . Should the Rock Meter drop below the red section , the song will automatically end , with the player <unk> off the stage by the audience . Successful note hits will add to the player 's score , and by hitting a long series of consecutive successful note hits , the player can increase their score <unk> . There is a window of time for hitting each note , similar to other rhythm games such as Dance Dance Revolution , but unlike these games , scoring in Guitar Hero is not affected by accuracy ; as long as the note is hit within that window , the player receives the same number of points . 
- Selected special segments of the song will have glowing notes outlined by stars : successfully hitting all notes in this series will fill the " Star Power Meter " . The Star Power Meter can also be filled by using the whammy bar during sustained notes within these segments . Once the Star Power Meter is at least half full , the player can activate " Star Power " by pressing the select button or momentarily lifting the guitar into a vertical position . When Star Power is activated , the scoring <unk> is doubled until Star Power is depleted . The Rock Meter also increases more dramatically when Star Power is activated , making it easier for the player to make the Rock Meter stay at a high level . Thus , Star Power can be used strategically to play difficult sections of a song that otherwise might cause the player to fail . In the earlier entries of the series ( up until Guitar Hero : Aerosmith ) , activating Star Power meant that players could not <unk> more Star Power until the Star Power meter was fully drained and the effect ended . Starting with Guitar Hero : World Tour , more Star Power can be collected even if the effect is active by completing more Star Power phrases , extending the Star Power 's duration by doing so . When playing in cooperative play ( with a bassist / rhythm guitarist in Guitar Hero II through Guitar Hero : Aerosmith or as a band in Guitar Hero : World Tour ) , Star Power is shared between all the players and activation of Star Power is dependent on all players simultaneously activating it . 
- Notes can be a single note , or composed of two to five notes that make a chord . Both single notes and chords can also be sustained , indicated by a colored line following the note marker ; the player can hold the sustained note ( s ) keys down for the entire length for additional points . During a sustained note , a player may use the whammy bar on the guitar to alter the tone of the note . Also , regardless of whether <unk> are hit early or late , if the fret is held for the full duration of the hold , the game will always award the same amount of score increase for the note . In addition , the games support virtual <unk> of " hammer @-@ <unk> " and " pull @-@ offs " , guitar @-@ playing techniques that are used to successfully play a fast series of notes by only changing the fingering on the fret buttons without having to strum each note . <unk> where strumming is not required are indicated on @-@ screen by notes with a white outline at the top of the marker instead of the usual black one , with Guitar Hero III : Legends of Rock adding a white @-@ glowing effect to make these notes clearer . Guitar Hero World Tour features transparent notes that are connected by a purple outline ; players may either simply tap the correct fret for these notes without strumming or utilize a <unk> on World Tour 's guitar controller to mimic the slide technique . In addition , notes can now be played while a sustained note is being played . World Tour also adds an open string note for bass players , represented by a line across the fret instead of any note gems , that is played by strumming without holding down any fret buttons ( the sixth installment , Warriors of Rock , features an open note sustain for bass instruments as well ) . 
- Guitar Hero World Tour introduced drums and vocal tracks in addition to lead and bass guitar . Drum tracks are played similar to guitar tracks ; the player must strike the appropriate drum head or step down on the bass drum pedal on the controller when the note gems pass the indicated line . Certain note gems , when using a drum controller that is velocity @-@ sensitive , are " armored " , requiring the player to hit the indicated drum pad harder to score more points . Vocal tracks are played similar to games such as Karaoke Revolution where the player must match the pitch and the pacing of the lyrics to score points . Guitar Hero 5 allows players to create a band of up to four players using any combination of instruments . 
- While the song is playing , the background visuals feature the players ' chosen avatar , along with the rest of the band performing in one of several real and fictional venues . The reaction of the audience is based on the performance of the player judged by the Rock Meter . Guitar Hero II added special lighting and other stage effects that were <unk> to the music to provide a more complete concert experience . The games developed by Neversoft feature a simple storyline , usually about a band 's quest for fame , which is told through animations played throughout the game . These animations were created by Chris <unk> and his studio , <unk> , Inc . , who have also done animations for the animated show Metalocalypse . 
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- = = = Game modes = = = 
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- The main mode of play in the Guitar Hero games is Career Mode , where the player and in @-@ game band travel between various fictional performance arenas and perform sets of four to six songs . It is by completing songs in this mode that the songs are unlocked for play across the rest of the game . Players can choose their on @-@ stage character , their guitar of choice , and the venue in which they wish to play . In this mode , the player can earn money from his / her performances that is <unk> at the in @-@ game store , where bonus songs , additional guitars and finishes , your characters clothing and bonus content can be unlocked . Quick Play mode is a quicker method of playing songs , as it allows the player to select a track and difficulty , selecting the character , venue , and guitar and guitar skin for the player based on the song chosen . After successfully completing a song , the player is given a score , a percentage of how many notes they hit and a rating from three to five stars , and two in rare cases depending on his / her final score on the song , with money being awarded in Guitar Hero World Tour . 
- The games have also added multiplayer modes . Cooperative modes allow two players to play lead and either bass or rhythm guitar on the same song , working together towards the same score . A competitive Face @-@ Off mode allows two players to play against each other at different difficulty levels , each attempting to earn the best score on a song . Each player plays different portions of the song . There is also a Pro Face @-@ Off mode , where two players battle at the same difficulty level . Unlike standard Face @-@ off , each player attempts to play all of the notes in a song , while still trying to earn the highest score . In Guitar Hero World Tour this was advanced on , as players could play a Pro Face @-@ Off game against each other on any difficulty level , the lower your difficulty , the more points were awarded so a player on a low difficulty could potentially beat a player on a more challenging difficulty . Guitar Hero III introduced Boss Battles , in which two players face off against each other , attempt to collect " distractions " to throw at their opponent , trying to make them fail . With Guitar Hero World Tour , up to four players can play <unk> on lead and bass guitar , drums , and vocals , while a total of eight players can compete in a Battle of the <unk> . The Xbox 360 , PlayStation 3 , and Wii versions of the games support multiplayer modes over their respective network services . 
- The four difficulty levels for each song afford the player a learning curve in order to help him / her progress in skill . The first difficulty level , Easy , only focuses on the first three fret buttons while displaying a significantly reduced amount of notes for the player to play . <unk> introduces the fourth ( blue ) fret button , and Hard includes the final fret button while adding additional notes . The addition of the orange fret button forces players to move their fingers up and down the neck . Expert does not introduce any other frets to learn , but adds more notes in a manner designed to challenge the player and to simulate the player 's hands to move in a sequence similar to a real guitar . A difficulty added in World Tour is <unk> , which only requires the player to strum to the basic rhythm ; holding the fret buttons becomes unnecessary . Another new difficulty only for drums was added to Metallica known as Expert + , which uses the double bass pedal . 
- Guitar Hero : Warriors of Rock is the sixth installment in the franchise and introduced a new take on the Career mode of previous games . Rather than being a quest for fame and glory with the band travelling through different venues , Warriors of Rock features the " Quest Mode " as the primary campaign mode . Quest Mode tells the story of an ancient warrior who was defeated by a powerful monster and his mystical guitar was lost . The player must <unk> a team of rockers to help recover this guitar and defeat the monster ( called " The Beast " ) . As the player progresses through the mode , the rockers joining them will transform based on the number of stars earned from songs played . These <unk> will <unk> the player with extra abilities in a song such as constant score <unk> or Star Power bonuses . These abilities are each unique to the individual rockers and by using them effectively , it is possible now to earn up to forty stars for a single song . 
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- = = = Characters and <unk> = = = 
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- When playing through Career mode or in other parts of the Guitar Hero games , the player has the option to select one of several pre @-@ created avatar characters , who will be shown performing on stage as the player attempts a song , but otherwise has no effect on the gameplay . A certain number of characters are available at the start of the game , but the player must spend in @-@ game money earned by successful performances to unlock other characters . Many of the characters reappear throughout the series , with the character roster changing as new characters are added or removed . <unk> characters that have appeared in nearly all the games include the <unk> Axel Steel , extreme / Viking / thrash <unk> Lars <unk> , punk rocker Johnny <unk> , alternative rocker Judy <unk> , and hard rocker Casey Lynch . The developers utilized these characters in more detail within Warriors of Rock , where each was given a unique setlist and venue based on their musical style , as well as a unique power within the game 's Quest mode . 
- Several games in the series feature caricatures of celebrity artists , such as Slash , Tom <unk> and Bret Michaels in Guitar Hero III , Ozzy Osbourne and Jimi Hendrix in World Tour , Kurt Cobain in Guitar Hero 5 , and Taylor Swift and the band No Doubt in Band Hero . The band @-@ specific games , Aerosmith , Metallica , and Van Halen also feature the members of the respective bands . However , in late 2009 , both Courtney Love and the members of No Doubt sought legal action against Activision for the misuse of their in @-@ game characters singing or performing songs by other artists , which the musicians believe fell outside of their contract . 
- The ability for the players to create their own avatars was added in Guitar Hero World Tour , and was based on Neversoft 's existing character creation tools from the Tony Hawk series . Later games on the Xbox 360 and Wii allowed players to use the respective console 's avatars as members of the band . In addition to unlocking characters , in @-@ game money can be used to buy clothing , accessories and instruments that they are seen playing with . The guitars can also be customized with special finishes <unk> through the in @-@ game store . Guitar Hero World Tour includes the ability to fully <unk> any component of the guitar . The in @-@ game store in the series is also used to unlock bonus songs or special videos with interviews about the game or with the artists involved . 
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- = = = <unk> = = = 
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- Most of the games in the Guitar Hero series feature a selection of songs ranging from the 1960s to present day rock music from both highly successful artists and bands and independent groups . Guitar Hero Encore : Rocks the 80s features songs primarily from the 1980s , while Guitar Hero : Aerosmith , Metallica , and Van Halen feature music from the respective bands and groups that inspired or worked with the bands . Songs with <unk> have been censored . 
- Many of the Guitar Hero games developed for the recent generation of consoles ( Xbox 360 , PlayStation 3 , and Wii ) support downloadable content , allowing players to purchase new songs to play in the respective titles . Songs each cost approximately $ 2 through the various online stores for the console 's platform . Prior to Guitar Hero 5 , downloadable content for earlier games will not work in other games in the series , save for songs from Metallica 's Death Magnetic , which were available for Guitar Hero III , World Tour , and Metallica . <unk> World Tour downloadable content for World Tour will be forward @-@ compatible with Guitar Hero 5 , Band Hero and Guitar Hero Warriors of Rock , and for a small fee , some songs from both Guitar Hero World Tour and Guitar Hero Smash Hits can be exported to both Guitar Hero 5 and Band Hero , limited by music licensing . Activision has also stated that they are considering a monthly subscription service to deliver downloadable content to user for future games . Guitar Hero World Tour introduced a music creation mode that will allow players to create and share songs ( excluding vocals ) via the " <unk> " service , which was also used in all other Guitar Hero games and Band Hero since its inclusion . The creation tools were improved with Guitar Hero 5 and Band Hero to allow longer songs and other means of generating songs in real @-@ time . 
- In the first two games and the 2007 expansion Guitar Hero Encore : Rocks the 80s , the majority of the songs on the main career mode set lists are covers of the original song ; for example , a song may be presented as " Free Bird as made famous by <unk> <unk> " . Guitar Hero III : Legends of Rock introduces a much larger range of original recordings , and World Tour featured a setlist that contained all master recordings . The covers throughout the games are mostly recreated by <unk> Sound who has worked before to create songs for <unk> , Dance Dance Revolution , and Karaoke Revolution , making small changes to the guitar portions to make them more adaptable for gameplay . Almost all of the <unk> bonus songs are songs performed by the original artist for the game ( the only exception is the song " She <unk> the <unk> " by The Stone Roses , which is featured in Guitar Hero III : Legends of Rock ) . 
- Prior to the release of Guitar Hero III : Legends of Rock , Activision worked with the iTunes Store to provide more than 1300 tracks of Guitar Hero @-@ related music across more than 20 compilations , including most of the tracks from the games in the series , called " Guitar Hero <unk> " . These compilations , such as " Killer Guitar <unk> " and " Guitar <unk> of the ' 80s " , include songs related to but not contained within the Guitar Hero series . Dusty Welch of RedOctane stated , " Where there ’ s music , there ’ s Guitar Hero , and with iTunes , we are able to provide fans with a central location for downloading their favorite rock anthems . " Following the merger of Activision and Blizzard , the new company announced that it planned on creating an alternative to iTunes based on the Guitar Hero brand that would allow for downloading songs and their associated note tracks for the Guitar Hero games . 
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- = = Reception and sales = = 
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- Games in the Guitar Hero series have been generally well received by critics . The initial games were highly praised by reviewers . Neversoft 's first entry to the series , Guitar Hero III , was considered to be too difficult , with many difficult songs presenting players with " walls of notes " ; the developers later acknowledged this . Subsequent efforts in Guitar Hero : Aerosmith and Guitar Hero World Tour were seen to have some improvements , with Guitar Hero : Metallica considered to be a well @-@ polished title and , at that time , the best Guitar Hero title Neversoft has produced . Guitar Hero 5 's improvements toward social gameplay were complemented by reviewers and considered a further improvement upon the series . Entertainment Weekly put it on its end @-@ of @-@ the @-@ decade , " best @-@ of " list , saying , " An addictive <unk> provides the illusion of musical mastery for even the least gifted : . How do you get to Carnegie Hall ? <unk> , tap , tap . " 
- Upon release , the first game was seen as an unexpected hit , earning over US $ 45 million with about 1 @.@ 5 million copies sold . Guitar Hero II was significantly more financially successful , with over 1 @.@ 3 million copies sold and sales over US $ 200 million . Guitar Hero III , according to Activision , was the first single video game to sell more than US $ 1 billion at retail , with nearly 3 @.@ 5 million copies sold during the first seven months of 2008 . World Tour continued the series ' high sales records with 3 @.@ 4 million units sold in the United States during 2008 . More than 60 million downloadable tracks have been purchased across the series as of February 2010 . Both Guitar Hero III and World Tour were listed on a March 2011 list from the <unk> Group of top grossing games in <unk> sales in the United States since 1995 ; Guitar Hero III tops the list with total sales of $ 830 @.@ 9 million . 
- Overall , the Guitar Hero series has sold more than 25 million units worldwide , earning US $ 2 billion at retail . Activision claimed the series to be the 3rd largest game franchise in 2009 after the Mario and <unk> NFL franchises . 
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- = = Cultural impact = = 
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- The Guitar Hero series has made a significant cultural impact , becoming a " cultural phenomenon " . The series has helped to rekindle music education in children , influenced changes in both the video game and music industry , has found use in health and treatment of recovering patients , and has become part of the popular culture vernacular . Several journalists , including 1UP.com , Wired , G4TV , the San Jose Mercury News , Inc . , The Guardian , and Advertising Age , considered Guitar Hero to be one of the most influential products of the first decade of the 21st century , attributing it as the spark leading to the growth of the rhythm game market , for <unk> music sales for both new and old artists , for introducing more social gaming concepts to the video game market , and , in conjunction with the Wii , for improving interactivity with gaming consoles . 
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- = = Legal and practical issues = = 
- 
- 
- = = = PlayStation 3 <unk> = = = 
- 
- Sony 's PlayStation 3 console has no compatibility with the PlayStation 2 Guitar Hero controller on the system . While Guitar Hero and Guitar Hero II are fully backward @-@ compatible through the hardware PlayStation 2 emulation in the initial North American release of the console , it was impossible at launch to use the guitar controller to play either game . Kai Huang , of RedOctane , states that they are " working on that with Sony right now – looking at how we can get all the PlayStation 2 guitars that are out there , and all the owners of them , to use them on the PlayStation 3 . " <unk> , an accessories company , was poised to make a special PlayStation 2 controller adapter for the PlayStation 3 , but put the product on hold due to technical difficulties . <unk> , another accessories company , also made a PlayStation 2 controller adapter for a PlayStation 3 game console so players could use their Guitar Hero guitar controllers that were made for the PlayStation 2 with a PlayStation 3 . However , the May 2007 PlayStation 3 <unk> system update has made the guitar controller compatible with generic PlayStation 2 controller to <unk> <unk> when playing Guitar Hero and Guitar Hero II . In addition , Pelican <unk> has released a special controller adapter that supports both games , including the ability to switch the handedness of the guitar . 
- 
- = = = Patent litigation = = = 
- 
- Gibson Guitar Corporation , whose guitar likenesses have appeared in the Guitar Hero series from the first game to Guitar Hero Aerosmith , informed Activision on January 7 , 2008 , that it believed the games <unk> its U.S. Patent 5 @,@ 990 @,@ 405 . Gibson claimed that this covers technology that <unk> a concert performance via pre @-@ recorded audio and a musical instrument . In response , Activision filed a suit seeking a declaration that it was not in violation of the Gibson patent ; Activision also asserted that Gibson had given an implied license by waiting to assert the patent and that the patent was invalid . On March 17 , 2008 , Gibson sued six retailers ( <unk> , Amazon.com , Wal @-@ Mart , Target , Toys " R " Us and <unk> ) for selling Guitar Hero products . Subsequently , on March 21 , 2008 , Gibson also filed a lawsuit against <unk> , MTV , and Harmonix over their game Rock Band also for violation of its patent , to which a Harmonix spokesperson stated that Gibson 's claims are " completely without merit " . Activision lawyer Mary Tuck stated in their legal <unk> that they believe that Gibson initiated the lawsuit due to the fact that " Activision was not [ interested ] in renewing the <unk> and Marketing Support Agreement " with Gibson <unk> . In February 2009 , the United States District Court for the Central District of California ruled against Gibson in their case against Activision , stating that the controllers are not musical instruments but " toys that represent other items " , and that Gibson 's patent only covers instruments that send out analog signals . Activision and Gibson settled the suit following this ruling . 
- Activision , through John <unk> , owns all of <unk> Enterprises ' US and international patents that deal with music games . All patents issued by the <unk> are presumed valid . 
- In February 2010 , Activision was sued by the Patent <unk> Group ( PCG ) for releasing Guitar Hero products with false patent claims , with the PCG asserting that games like Guitar Hero 5 and Band Hero were marked with up to 10 patents that are not used within the games along with several other improper patent pending claims . PCG claims that " Acts of false marketing deter innovation and <unk> competition in the marketplace . " PCG 's qui <unk> lawsuit is seeking up to $ 500 per unit sold if Activision is found liable . 
- 
- = = = <unk> = = = 
- 
- Many critics believed that the number of releases of Guitar Hero games was " milking " the brand name and <unk> the market . <unk> the <unk> creator <unk> <unk> stated that the video game market was growing stale and needed to move beyond games that simply challenge the player to mimic the playing of licensed music . Ryan Geddes of IGN stated that he " hit the wall with play @-@ along music games " , and challenged the game makers to explore other ways to combine music and video games . Analysts stated that such games must continue to innovate instead of just providing more songs in order to prevent " genre fatigue " . Jesse <unk> of Electronic Entertainment and Design Research commented that , much like Dance Dance Revolution , Guitar Hero and other music games explosively grew initially due to significant new features from other games but have become stagnant due to focusing on content over features , and suggested that for the genre to continue to grow , they must look to incremental changes as done with the first @-@ person shooter genre . Former CEO for RedOctane , Kelly Sumner , believed that Activision " abused " the series , as " they tried to get too much out of the franchise too quickly " . 
- The series has also been criticized for its release model in contrast to the Rock Band series , causing some players to hold contempt towards Activision . Harmonix considered the Rock Band series as a " music platform " , and supported it with downloadable content and the ability to import songs from its games and expansions into most other games of the series . Critics argued that Guitar Hero should have been doing the same , either through releasing expansions that could be incorporated into the main games of the series , or by issuing the songs as downloadable content . The release of Guitar Hero : Smash Hits , reworking older songs from the series to full four @-@ instrument band support but otherwise adding no additional material , was called " the definition of ' milking ' " by reviewers , with no observable technical limitation as to why the songs could not be added as downloadable content . Ars Technica recognized that licensing issues might have limited when songs from one single game could be played in others of the series ( such as the case for The Beatles : Rock Band ) , but that such cross @-@ compatibility should have been a high priority for rhythm games . Furthermore , some expansions were praised for the additional content beyond the note @-@ matching gameplay ; Guitar Hero : Metallica is considered to be one of the series ' best works to be developed by Neversoft in part due to the care that the developers took with imaging the band and the available extras for the game . Activision later revealed that both Guitar Hero 5 and Band Hero would support playing songs from both Guitar Hero World Tour ( both on @-@ disc and downloadable content ) and Guitar Hero Smash Hits , with music licensing being the only limiting factor on which songs could be made forward @-@ compatible . 
- The large number of Guitar Hero and Rock Band titles on the market is considered to be partially responsible for the sharp decline of music game sales in the latter half of 2009 , along with the effects of the late @-@ 2000s recession . The market for rhythm games was $ 1 @.@ 4 billion in 2008 , but dropped to $ 700 million in 2009 even though more titles were available that year . Former Neversoft project director Brian Bright noted that at one point in 2009 , they were responsible for the release of three games that year ( Guitar Hero 5 , Metallica , and Band Hero ) and supporting other studios for the development of two additional games , causing the studio to lose focus both in development and marketing efforts . According to Bright , sales of all the Guitar Hero games released in 2009 totaled the number of sales of the 2008 title World Tour , demonstrating the <unk> of the marketing . Though Activision had originally planned on <unk> the offerings of the Guitar Hero series in 2010 , the company <unk> their plans , reducing the number of offerings and focusing more on selling digital downloadable content for the series . Only two titles , Guitar Hero : Warriors of Rock and DJ Hero 2 were released in 2010 , both scheduled for the " back half of 2010 " . Analysts believe that the market will evolve to support a smaller number of titles each year , averaging at a " healthy " value $ 500 – 600 million in revenues annually . Kotick believed that part of the downfall of Guitar Hero was due to Activision 's introduction of DJ Hero , which they gave too much focus and left the core Guitar Hero games without the " <unk> and care " needed to continue to innovate in the series . 
- Activision Publishing chief executive Mike Griffith , in response to questions about Activision 's approach to the Guitar Hero market , noted that Guitar Hero continues to <unk> the Rock Band series in both number of sales and revenue , with consumers continuing to buy the separate games on the market , and considered the market acceptance of the multiple games as <unk> for their model . Regardless , after releasing 25 different <unk> ( between games and bundle packages ) in 2009 , Activision opted to reduce that number to 10 in 2010 , recognizing the music game genre was not as profitable as it once was . Activision later opted to put future development of the series on hold in early 2011 citing weak sales in the rhythm game genre , a move that many journalists attributed to Activision 's earlier <unk> . 
- 
- = = List of games = = 
- 
- All games are published by Activision , except the first Guitar Hero and the PS2 version of Guitar Hero II , which were published by RedOctane . 
- 
- 
- = <unk> = 
- 
- The fieldfare ( Turdus <unk> ) is a member of the thrush family <unk> . It breeds in woodland and scrub in northern Europe and Asia . It is strongly migratory , with many northern birds moving south during the winter . It is a very rare breeder in the British Isles , but winters in large numbers in the United Kingdom , Southern Europe , North Africa and the Middle East . It is omnivorous , eating a wide range of molluscs , insects and earthworms in the summer , and berries , grain and seeds in the winter . 
- <unk> often nest in small colonies , possibly for protection from predators . The nest is built in a tree where five or six eggs are laid . The chicks are fed by both parents and leave the nest after a fortnight . There may be two broods in southern parts of the range but only one further north . <unk> birds and wintering birds often form large flocks , often in the company of <unk> . 
- The fieldfare is 25 cm ( 10 in ) long , with a grey crown , neck and rump , a plain brown back , dark wings and tail and white <unk> . The breast and flanks are heavily spotted . The breast has a reddish wash and the rest of the underparts are white . The sexes are similar in appearance but the females are slightly more brown . The male has a simple chattering song and the birds have various guttural flight and alarm calls . 
- 
- = = Etymology = = 
- 
- The English common name fieldfare dates back to at least the eleventh century . The Anglo @-@ Saxon word <unk> perhaps meant traveller through the fields . 
- The species was described by Linnaeus in his Systema <unk> ( 1758 ) under its current scientific name . The name Turdus <unk> comes from two separate Latin words for thrush . No subspecies are recognised . 
- 
- = = Description = = 
- 
- The fieldfare is easily recognisable with its slate @-@ grey head , nape and rump , dark brown back , blackish tail and boldly speckled breast . In flight , its white under wing @-@ coverts and <unk> are conspicuous . The harsh flight call " tsak tsak " is also distinctive . 
- The forehead and crown of the male are bluish @-@ grey and each feather has a central brownish @-@ black band . The <unk> and under @-@ eye regions are black and there are faint , pale streaks above the eyes . The ear coverts , nape , hind neck and rump are bluish @-@ grey , usually with a white streak near the shaft of each rump feather . The <unk> and mantle feathers are dark chestnut @-@ brown with dark central streaks and pale tips . There are fourteen tail feathers each with a pointed tip , the outer two slightly shorter than the others giving a rounded tail . They are brownish @-@ black , with inconspicuous darker bars visible in some lights . The outer edge of each tail feather is fringed with grey near the base and the outer pair of feathers have a narrow white border on the inner edge . The chin , throat and upper breast are creamy @-@ buff with bold streaks and speckles of brownish @-@ black . The lower breast is creamy @-@ white with a diminishing buff tinge and fewer speckles and the belly is similarly creamy @-@ white , with the speckles restricted to the uppermost parts . The primaries are brownish @-@ black with the leading edge fringed grey and the inner edge of the outer feathers grey near the base whereas the inner feathers are fringed with brown near the base . The <unk> are similar but fringed with chestnut @-@ brown on the leading edge . The upper wing @-@ coverts are brownish @-@ black and similar to the outer primaries in their margin colouration . The <unk> and under wing @-@ coverts are white and the under tail @-@ coverts have dark greyish @-@ brown bases and margins and white centres and tips . The beak is strong , with a slight curve and a notch near the tip . It is orange @-@ yellow in winter , with the upper mandible somewhat brownish and both mandible tips brownish @-@ black . In the summer both <unk> of the male 's beak are yellow . The irises are dark brown and the legs and feet are brown . The average adult length is 25 cm ( 9 @.@ 8 in ) , the <unk> is 14 @.@ 5 cm ( 5 @.@ 7 in ) and the <unk> length 3 @.@ 5 cm ( 1 @.@ 4 in ) . 
- The female is very similar to the male but the upper parts are somewhat more brownish and the feathers on the crown have narrower black central stripes . The throat and breast are paler with fewer , smaller markings . The beak is similar to the male 's winter beak . The juvenile are a duller colour than the adults with pale coloured streaks on the feathers that have dark streaks in the adult . The young assume their adult plumage after their first moult in the autumn . 
- The call is mostly uttered in flight and is a harsh " tsak tsak <unk> " . The same sound , but softer , is made more <unk> when individuals gather in trees . When angry or alarmed they emit various warning sounds reminiscent of the <unk> thrush ( Turdus <unk> ) . The male has a rather <unk> song that he sings in the breeding season . It is a mixture of a few phrases like those of the common <unk> ( Turdus <unk> ) interspersed with whistles , guttural <unk> and call notes . This is sung on the wing and also from a tree and a subdued version of this song with more <unk> notes is sung by a group of birds at communal roosts . 
- 
- = = Distribution and habitat = = 
- 
- The fieldfare is a migratory species with a <unk> distribution . It breeds in northern Norway , northern Sweden , Finland , Belgium , Germany , Switzerland , Austria , the Czech Republic , <unk> , Hungary , Poland and Siberia as far east as <unk> , the <unk> River and the <unk> Shan Mountains in North West China . Its winter range extends through West and South Europe to North Africa , though it is uncommon in the Mediterranean region . Eastern populations migrate to Anatolia , Israel , Iran and Northwest India , and occasionally Northeast India . It is a <unk> to Iceland , Greenland , Spitsbergen , the Canary Islands , the <unk> Islands , Madeira , <unk> , Sardinia , Sicily , Malta and Cyprus . 
- In the summer the fieldfare <unk> mixed woodland of birch , alder , pine , spruce and fir , often near marshes , moorland or other open ground . It does not avoid the vicinity of humans and can be seen in cultivated areas , orchards , parks and gardens . It also inhabits open <unk> and the slopes of hills above the tree line . In the winter , groups of <unk> are chiefly found in open country , agricultural land , orchards and open woodland . They are nomadic , wandering wherever there is an abundance of berries and insects . Later in the year they move on to <unk> and cultivated fields . 
- 
- = = Behaviour = = 
- 
- The flight of the fieldfare is slow and direct . It takes several strong beats then closes its wings briefly before flapping on . It is highly gregarious , quite shy and easily scared in the winter and bold and noisy in the breeding season . When a group is in a tree they all tend to face in the same direction , keeping up a constant <unk> . When foraging on the ground , often in association with <unk> , the group works its way up wind , each bird <unk> every so often to stand erect and gaze around before resuming feeding . When alarmed they fly off down wind and the feeding group reforms elsewhere . In woodland they do not <unk> in the <unk> as do <unk> or song <unk> , instead they perch in the open on bushes and high branches . They roost socially , sometimes in overgrown <unk> and <unk> but usually on the ground . Common sites are in rough grass among bushes or clumps of rushes , in young plantations , on <unk> and in the <unk> of <unk> fields . 
- Migration southwards from the breeding range starts in October but the bulk of birds arrive in the United Kingdom in November . Some of these are still on passage and carry on into continental Europe but others remain . The passage @-@ migrants return in April and they and the resident migrants depart from the United Kingdom mostly by early May . 
- The fieldfare is omnivorous . Animal food in the diet includes snails and slugs , earthworms , spiders and insects such as beetles and their larvae , flies and grasshoppers . When berries <unk> in the autumn these are taken in great number . <unk> , <unk> , <unk> , yew , <unk> , dog rose , <unk> , <unk> and <unk> are all <unk> . Later in the winter <unk> apples are eaten , <unk> attacked in the field and grain and seeds eaten . When these are exhausted , or in particularly harsh weather , the birds may move to marshes or even the foreshore where molluscs are to be found . 
- 
- = = Breeding = = 
- 
- The breeding season starts in May in Poland but further north in Scandinavia may not start until early July . The female fieldfare builds a cup @-@ shaped nest with no attempt at concealment . The location is often in woodland but may be in a <unk> , garden , among rocks , in a pile of logs , in a <unk> or on the ground . <unk> usually nest in close proximity to others of the same species . The adults will defend the nest aggressively and nesting <unk> may offer protection from predators . The nest is built of dried grasses and weeds with a few twigs and a little moss , with a lining of mud and an inner lining of fine grasses . There are usually five to six eggs in a clutch , but occasionally three , four , seven or eight eggs are laid . The eggs vary in size from 28 @.@ 8 by 20 @.@ 9 millimetres ( 1 @.@ 13 in × 0 @.@ 82 in ) to 33 @.@ 5 by 23 @.@ 4 millimetres ( 1 @.@ 32 in × 0 @.@ 92 in ) and are variable in colour . Many are pale blue speckled with fine brown dots and resemble those of the common <unk> . Others are bright blue , with or without larger red @-@ brown <unk> . Incubation starts before all the eggs are laid and lasts for thirteen to fourteen days . The female does all or most of the incubation . The chicks are <unk> and both parents bring food to them . They are usually ready to leave the nest after fourteen to sixteen days and there may be two broods in the season , especially in the southern parts of the breeding range . 
- 
- = = Status and conservation = = 
- 
- The fieldfare has an extensive range , estimated at 10 million square kilometres ( 3 @.@ 8 million square miles ) , and a large population , including an estimated forty two to seventy two million individuals in Europe . There are thought to be up to twenty thousand individuals in Russia and the global population is estimated to be between forty four and ninety six million individuals . The population size appears to be stable and the bird is not believed to approach the <unk> for the population decline criteria of the IUCN Red List of Threatened Species ( i.e. , declining more than 30 % in ten years or three generations ) , and is therefore evaluated as being of " Least Concern " . 
- In the United Kingdom , at the extreme edge of the fieldfare 's breeding range , only a handful of pairs breed . It is therefore classified by the RSPB as a Red List species as of January 2013 . 
- 
- 
- = Type 94 Nambu pistol = 
- 
- The Type 94 Nambu 8 mm <unk> ( Type 94 <unk> , Japanese : <unk> <unk> @-@ <unk> <unk> ) is a <unk> pistol developed by <unk> Nambu and his associates for the Imperial Japanese Army . Development of the Type 94 pistol began in 1929 , and after several <unk> the final prototype was tested and officially adopted by the Japanese Army in late 1934 ( Japanese calendar , <unk> ) . The Type 94 pistol entered production in 1935 . Approximately 71 @,@ 000 pistols were manufactured before production ended in 1945 . 
- The Type 94 pistol was designed for , and popular among , Japanese tank and aircraft crews who preferred a smaller , lightweight design . Japanese weapons experts have subsequently criticized some design elements of the Type 94 ; in particular the pistol could be fired unintentionally before the breech was fully locked if the sear bar on the side of the receiver was jarred loose and the pistol was improperly handled . Additionally , the process to <unk> the pistol is overly complex and awkward . The build quality of the Type 94 pistol declined over its production run ; " last ditch " pistols made in 1945 were <unk> manufactured . 
- 
- = = History = = 
- 
- The Type 94 Nambu pistol was designed by <unk> Nambu after he retired from the Japanese Army and founded the Nambu Rifle Manufacturing Company . Design for the Type 94 Nambu pistol commenced in 1929 with the goal of reducing the bulk and price of previous Nambu designs . The Imperial Japanese Army felt a smaller pistol of domestic design that could accommodate the standard 8 × <unk> Nambu cartridge was needed to substitute the larger , heavier , and only official military pistol , the Type 14 Nambu . The demand for officer 's <unk> had increased as a result of Japan 's invasion of <unk> during the Second <unk> @-@ Japanese War . A new design was also wanted by the Japanese Army to include a magazine safety , to prevent unintentional <unk> during cleaning that were common among Japanese personnel . Naming of the Type 94 pistol reflects the change in Japanese nomenclature with the 94 reckoning back to the mythical foundation of Japan in 660 BC therefore year <unk> instead of the traditional emperor reign period used to name the Type 26 revolver or Type 14 Nambu pistol . The final prototype for the Type 94 was officially adopted by the Japanese Army in late 1934 after several <unk> . Production began under the supervision of the Nagoya Army Arsenal at the Nambu Rifle Manufacturing Company and later its successor , <unk> <unk> Company , Ltd . An estimated 71 @,@ 000 pistols were produced for the military but the exact quantity is unknown because of the production of <unk> pistols and undated pistols . During World War II the pistol became a preferred weapon for tank crews and paratroopers who required a smaller , more convenient pistol . The Type 94 was never officially adopted by the Imperial Japanese Navy but was available to officers through the Japanese officer 's union . 
- 
- = = Design = = 
- 
- The Type 94 pistol is operated by a different mechanism than previous Japanese <unk> . Unlike previously designed Nambu pistols , the Type 94 operates with a concealed hammer and with a firing pin rather than a hammer . According to authors , Harry L. Derby and James D. Brown , the firing pin is inherently weak and is prone to breakage because of a recess cut provided for the crossbolt and is prone to breaking at this point . The <unk> hammer firing mechanism was developed and included in the Type 94 to replace the poor striker on the Type 14 Nambu . The <unk> system is a rising @-@ block type which floats independently between the <unk> underneath the chamber end of the barrel . The single coil <unk> is positioned around the barrel around the barrel instead of to the rear of the barrel as found on other Nambu pistols . The grip is smaller than other Japanese pistols and is finished with smooth wood but according to author Jeff <unk> , are more comfortable for use by men with smaller hands . The magazine holds a maximum of six rounds because of the smaller grip and it is considered difficult to <unk> the weapon , with pressure from the bolt holding it inside the pistol . The magazine catch <unk> far enough to occasionally disengage when the pistol is placed on its left side on a hard surface . The magazine could also disengage if squeezed into or jarred in a holster . The manual safety lever is located on the left rear of the frame and has the <unk> for fire and safe stamped onto the frame . The front blade sight on the muzzle of the Type 94 pistol and the rear fixed V were occasionally inaccurately positioned making them useless when the weapon is being aimed . The rear sight was reduced from a U @-@ shape to a simple notch in 1944 with the front blade being left unchanged but less attention to detail being applied as World War II progressed . 
- 
- = = = Final production = = = 
- 
- The quality of Type 94 Nambu pistols decreased towards the end of World War II as the Japanese faced bombing raids from allied forces and material shortages increased . This drastic change in quality from late March 1945 , onwards with all quality standards appearing to disappear towards the end of June 1945 . The smooth wooden finished grip was replaced by a <unk> <unk> pattern . Many pistols were not serialized and no pistols have been reported that bear July 1945 manufacture date . Only four <unk> and undated pistols are known to exist and include <unk> parts with no inspection marks , <unk> loops , and <unk> . A small number of pistols made during the final production stages include earlier production dates and appear to have been salvaged from previously discarded pistols that had minor or cosmetic defects . 
- 
- = = <unk> = = 
- 
- <unk> of the Type 94 Nambu pistol is considered difficult and can lead to damage to the pistol if done carelessly . After clearing the Type 94 , the operator must draw the slide against the magazine follower to hold the bolt to the rear of the pistol . This will allow the crossbolt to be released after the firing pin is depressed . <unk> of the crossbolt without depressing the firing pin will damage both the firing pin and the crossbolt . <unk> the crossbolt is further complicated as the <unk> hands are both holding the pistol and depressing the firing pin . 
- 
- = = <unk> = = 
- 
- <unk> for the Type 94 pistol were generally made from either <unk> or <unk> leather and ranged in color from tan to dark reddish brown . <unk> faced the same degradation in quality as the Type 94 pistol . As supplies of leather were exhausted in Japan , holsters manufactured in 1944 became fabricated from olive drab fabric . The Type 94 pistol holster is distinguishable from other Japanese holsters having a pointed closure flap and a vertically positioned magazine <unk> . The <unk> tow has a narrow extension to accommodate a cleaning rod . The majority of holsters were made in civilian owned <unk> with some ink stamped with arsenal and inspection marks . A belt loop and two shoulder strap " D " rings are provided on the rear of the holster and are made from brass , galvanized steel , or nickel plating . 
- 
- = = <unk> firing = = 
- 
- The poor design of the breech allowed the Type 94 Nambu to be fired unintentionally . The sear bar on the Type 94 Nambu converts the forward pull of the trigger into a lateral movement that frees the hammer . Because the sear bar is on the outside of the pistol , it could be jarred loose during engagement if the pistol was <unk> and handled carelessly . The forward end of the sear bar would need to be depressed approximately <unk> to cause the weapon to fire . The ability to fire the Type 94 without pulling the trigger gave rise to war stories of Japanese soldiers surrendering , only to fire the pistol , earning the pistol <unk> such as the " suicide special " and the " surrender pistol " . These stories are widely discredited because of the difficulty to fire the weapon by <unk> the sear bar . If the safety is engaged on the Type 94 it is impossible for the weapon to discharge unintentionally . 
- 
- 
- = Jifna = 
- 
- Jifna ( Arabic : <unk> , <unk> ) is a Palestinian village in the Ramallah and al @-@ <unk> <unk> in the central West Bank , Palestine , located 8 kilometers ( 5 @.@ 0 mi ) north of Ramallah and 23 kilometers ( 14 mi ) north of Jerusalem . A village of about 1 @,@ 400 people , Jifna has retained a Christian majority since the 6th century CE . Its total land area consists of 6 @,@ 015 dunams , of which 420 are designated as built @-@ up areas , most of the remainder being covered with olive , <unk> and apricot groves . Jifna is governed by a village council , led ( 2008 ) by chairman <unk> Na 'im Kamil . 
- Jifna was known as <unk> ( In Hebrew <unk> ) at the time of the First Jewish @-@ Roman War , and after its conquest became a Roman regional capital . Later the town grew less significant politically , but nevertheless prospered under Byzantine and Arab rule due to its location on a trade route . St. George 's Church in Jifna was built in the 6th century CE , but fell into disrepair and was not rebuilt until the arrival of the Crusaders in the late 10th century . However , it again fell into ruin after the Crusaders were driven out by the Ayyubids . In modern times , the ruins of St. George 's Church have become a tourist attraction . During the period of Ottoman control in Palestine the tower of an ancient Roman structure in Jifna became the location of a jail house . 
- Jifna has local traditions and legends relating to the Holy Family , and to the village water @-@ spring . It is also locally known for its apricot harvest festival ; each year , during the late Spring period , hundreds travel to the village to harvest the fruit during its brief season . 
- 
- = = History = = 
- 
- 
- = = = Biblical era and Roman / Byzantine rule = = = 
- 
- It was suggested by Edward Robinson that Jifna was <unk> of Benjamin , mentioned in the Book of Joshua as one of the " twelve cities . " Nothing thereafter is recorded in its history until the time of the Roman conquest during the 1st century BCE , when it appears in various records as " Gophna " . Gophna was described by <unk> <unk> as the second city of Judea , after Jerusalem , in his account of the First Jewish @-@ Roman Wars during the 1st century CE . The town is depicted as Gophna in the Map of <unk> , situated north of <unk> ( al @-@ <unk> ) , and is also mentioned in the Talmud as Ben <unk> , a " city of priests " . The latter portion of its <unk> name derives from the Hebrew root word <unk> , meaning " vine " . 
- Known by the Romans as <unk> , Jifna was a regional capital in the <unk> Province under the Roman Empire . Around 50 BCE the Roman general <unk> sold the population into slavery , for failure to pay taxes . They were freed , however , by Mark Antony shortly after he came to power . Jifna was within the area under <unk> 's command in 66 CE , during the First Jewish @-@ Roman War , and was the headquarters of one of the twelve <unk> ( minor realms ) of Judea . The Roman emperor <unk> occupied the town in 68 CE , established an army garrison there , and concentrated within the city Jewish priests and other local <unk> who had surrendered to him . Titus , the future Roman emperor , passed through Gophna during his march to <unk> Jerusalem in 70 CE . 
- The building of a church dedicated to Saint George during the 6th century CE indicates that by this time Jifna , now under Byzantine rule , had become a Christian town . Besides the church , other remains from this era are located in Jifna , including a Jewish tomb , a tower ( Burj Jifna ) once used by the Ottomans as a prison , a Roman villa , an olive oil press and a <unk> . 
- 
- = = = Middle Ages = = = 
- 
- Jifna , along with most of Palestine , was annexed by the <unk> Caliphate under Umar ibn al @-@ <unk> after the Battle of <unk> in <unk> . The town became less politically significant under the Arab dynasties of the <unk> , <unk> and <unk> , but remained a major regional center for trade and commerce , due to its location along the Jerusalem – Nazareth road . It was known by the Arabs as <unk> . 
- Sources are vague , but it is likely that St. George 's Church fell into disrepair during the early decades of Islamic rule , and that unfavorable circumstances for the Christian population prevented them from rebuilding it . However , it was partially rebuilt with old materials by the Crusaders , who conquered the area in 1099 . The Crusaders built a large courtyard building in Jifna . It had a monumental gate with a <unk> , with a large vaulted hall and thick walls of fine masonry . After their defeat to the Ayyubids under Saladin in 1187 , the church again fell into ruin . A document dated <unk> with the signature of one <unk> de <unk> , might indicate a Christian presence at this time . According to the American biblical scholar Edward Robinson , there are remains of massive walls in the center of the village , now filled by houses . They were relics of a castle built by the Crusaders . However , the masonry has no characteristics of the Crusader period ; rather , the remains display the Arab architectural style of the post @-@ Crusader period , most likely of the 18th century , <unk> by the dressing of the stones . 
- 
- = = = Ottoman era = = = 
- 
- After the Crusaders were succeeded by the Ayyubids and then the <unk> , the Ottoman Empire conquered Palestine in <unk> , and Jifna came under their control for the following 400 years . In 1596 it appeared in the tax registers under the name of " Jifna an @-@ <unk> " , being in the <unk> ( <unk> ) of Jerusalem of the Jerusalem <unk> , with a population of 21 households . The villagers paid a fixed tax @-@ rate of 33 @.@ 3 % on agricultural products , including wheat , barley , olive trees , vineyards , fruit trees , goats and <unk> , in addition to occasional revenues ; a total of 6 @,@ 470 <unk> . It was under the administration of the <unk> <unk> <unk> , part of the larger Jerusalem District , throughout Ottoman rule , being the only all @-@ Christian village in the district . During this period , the main commodity of Jifna was olive oil . Ottoman activity in the village was minimal , but they used the remains of Jifna 's castle , known as " Burj Jifna " , as a jail house sometime during the 19th century . In the early 1830s , Ibrahim Pasha of Egypt conquered most of the Levant , including Palestine . In 1834 there was a revolt against the Egyptian authorities in the Jifna area ; 26 residents of Jifna were subsequently exiled to Egypt for their alleged participation in the uprising . They were joined , voluntarily , by two prominent local priests . 
- An Eastern Orthodox Church was built in the village in 1858 , and a larger Latin ( Roman Catholic ) church dedicated to St. Joseph was built in 1859 , adjacent to the ruins of St. George 's Church . In the courtyard of St. George 's Church is a sarcophagus . St. George 's Church has continued to serve as a place of worship into the modern era and has been the site of archaeological excavation since the mid @-@ 19th century . Mass is still held at its altar on certain occasions . In 1882 the Palestine Exploration Fund 's Survey of Western Palestine described Jifna as an Important Christian village , with a Latin Church and a convent . Also in the 1880s , Jifna was frequently <unk> by Ottoman authorities . It also came into consistent armed conflict with another Christian village , Bir Zeit , which in one incident , resulted in the deaths of five men from that village . In retaliation , residents of Bir Zeit uprooted and burned 125 of Jifna 's olive groves . 
- 
- = = = Modern times = = = 
- 
- In 1917 , during World War I , the Ottomans were defeated by British and Arab forces . After a brief period of military rule , Jifna and its region came under the control of the League of Nations British Mandate , in 1922 . In 1947 the United Nations proposed the <unk> of Palestine into separate Jewish and Arab states , with Jifna being a part of the projected Arab state . However , after the 1948 Arab @-@ Israeli War the whole West Bank region , including Jifna , was annexed by Transjordan to form the Kingdom of Jordan , and the Arab state was stillborn . In 1967 Israel occupied the West Bank , after their victory in the Six @-@ Day War . 
- After the 1995 Interim Agreement on the West Bank and the Gaza Strip between the Palestinian National Authority ( <unk> ) and Israel , Jifna was placed in " Area B " . Thus , its administrative and civil affairs were transferred to the <unk> , while security matters remained in Israeli control . Throughout the ongoing Second Intifada , which began in 2000 , Jifna has not experienced violence to the same extent as other parts of the West Bank , such as in nearby Ramallah , but its residents face travel restrictions and economic hardship . 
- On 31 July 2015 a 15 @-@ year @-@ old resident was shot dead by an IDF sniper after allegedly throwing a <unk> at an army outpost . 
- 
- = = Geography and climate = = 
- 
- Jifna is located on the slope of a hill , standing at an elevation of about <unk> meters ( 2 @,@ 169 ft ) above sea level . It is situated at the intersection of two ancient trade routes , the mountainous north @-@ south route and the east @-@ west route connecting the Jordan Valley with the Mediterranean <unk> . In 1945 its land area consisted of 5 @,@ <unk> dunams , 52 dunams of which were classified as built @-@ up area in 1945 . Today Jifna has a total jurisdiction over 6 @,@ 015 dunams , 420 of which are designated as built @-@ up and roughly 2 @,@ 000 planted with olive , apricot and other fruit trees . 
- The village is located 8 kilometers ( 5 @.@ 0 mi ) northwest of Ramallah and al @-@ <unk> and about 23 kilometers ( 14 mi ) north of Jerusalem . The Palestinian refugee camp of <unk> was built on Jifna 's southern lands and is connected to the village by road . The villages of Dura al @-@ <unk> ' and Ein <unk> are located adjacent to Jifna to the east and northeast respectively . Other nearby localities include Abu <unk> to the southwest , <unk> to the southeast , Ein <unk> to the east , ' <unk> to the north and Bir Zeit to the northwest . Jifna is connected to the main Ramallah @-@ <unk> highway by a road at the eastern side of the village . 
- Jifna experiences a temperate Mediterranean climate . Based on data for nearby Ramallah , average monthly high temperatures range from 53 ° F ( 12 ° C ) in January to 84 ° F ( 29 ° C ) in July / August , the corresponding lows being 39 ° F ( 4 ° C ) and 63 ° F ( 17 ° C ) . Rain is usually restricted to the winter season , from around November until the end of April . The months that receive the most precipitation are January and December and the annual rainfall is 23 @.@ 2 inches ( 590 mm ) . 
- 
- = = Demographics = = 
- 
- According to Edward Robinson , Jifna 's population in 1838 consisted of about 200 people , of whom only 42 were adult males . In a 1945 land and population survey carried out by Sami <unk> , Jifna had 910 inhabitants . The modern inhabitants of Jifna belong mainly to eight families , four of whom are originally from the village , while the other five have , at various times , migrated from other countries such as Syria . 
- In 1994 , Jifna experienced a wave of emigration , with about half of its population leaving the town to pursue better livelihoods elsewhere . The first census carried by the Palestinian Central Bureau of Statistics ( PCBS ) shows that Jifna had a population of 961 , of whom 623 ( 64 @.@ 8 % ) were classified as refugees in 1997 . The gender distribution was <unk> males ( 48 @.@ 4 % ) and <unk> females ( 51 @.@ 6 % ) . The age distribution was : Under 15 , 330 ( 34 % ) ; 15 – 29 , 275 ( 29 % ) ; 30 – 65 , <unk> ( 32 % ) ; Over 65 , 50 ( 5 % ) . 
- The population of Jifna continues to grow . According to the PCBS , Jifna 's mid @-@ year population estimate for 2006 was 1 @,@ 358 . An informal estimate from Autumn 2006 gives the population as 1 @,@ 500 , " 25 % of whom have had to move to Jerusalem or Ramallah to find jobs " . The 2007 census by the PCBS listed Jifna 's population as 1 @,@ 716 , of which 856 were males and 860 females . 
- 
- = = = Religion = = = 
- 
- The remains of the Byzantine @-@ era church in Jifna <unk> to the existence of a Christian community prior to the Muslim conquest . It continued to exist during the Middle Ages and the village is still inhabited mainly by Christians . The names of Christian inhabitants from Jifna appeared in a 10th @-@ century inscription on a stone above the gate of St. George 's Monastery in the Wadi <unk> . 
- Ottoman tax records from the late 16th century reveal that Jifna had a Christian population at the time also . An informal survey in 1927 found 550 inhabitants , of whom 325 were Catholics and the remainder reportedly " Christians of other denominations " . Palestinian Christians make up about 80 % of the residents , while the remaining 20 % are Muslims , mostly refugees . 
- It is certain that most of the Christian residents of Jifna in the 12th century were local inhabitants . Apart from local Christians there was also a Frankish settlement , as is attested by the ruins of a <unk> <unk> ( manor ) built in the lower part of the village . In Jifna , like in many other sites in Palestine , the Crusaders built their settlement in the heart of the local Christian settlement . 
- 
- = = Economy = = 
- 
- Although most of Jifna 's <unk> land is covered with olive groves as well as <unk> , <unk> and apricot trees and grape vines , agriculture is no longer the village 's main source of income . Many of the village 's former farmers are living off other businesses , including restaurants , other small family @-@ owned industries , and simple commerce . 
- Unlike some other West Bank localities , unemployment is a minor issue in Jifna . However , the average income is low because of the unstable political situation in the Palestinian territories , Israeli checkpoints , the West Bank barrier and the 2006 freezing by Israel of funds to the Palestinian territories — although the latter no longer applies to the West Bank . 
- According to the PCBS , 98 @.@ 5 % of Jifna 's 201 households are connected to electricity through a public network . The same percentage is connected to a sewage system , mostly through a <unk> . Pipe water is provided for 98 % of the households primarily through a public network ( 97 @.@ 5 % ) , but some residents receive water through a private system ( 1 @.@ 5 % ) . The village is home to the Jifna Women 's Charitable Society which offers health services and recreational activities for women and children . 
- 
- = = = Small businesses = = = 
- 
- There are three restaurants in Jifna : Al @-@ Burj Restaurant , Red Valley Restaurant and Garden , and Tabash Restaurant , as well as a hotel : al @-@ <unk> <unk> . In 2003 , the <unk> family of Jifna opened a leisure complex — called the Dream Day Resort — in the village . The complex , containing a half @-@ Olympic @-@ size swimming pool , a children 's pool and a <unk> is frequented by residents in Jifna and the surrounding area . 
- Al @-@ Burj Restaurant — also known as " Burj Jifna " — specializes in Palestinian cuisine and contains a café and a Palestinian culture hall . Situated in the center of the village , the restaurant is the site of Jifna 's old city . With international funding , the local youth club restored the area , adding bright accents to the stone masonry . The restaurant has become a center of tourism and the complex provides tours for its rooms , olive oil presses and ancient <unk> . Tabash Restaurant was founded in 1962 by a Palestinian refugee family from <unk> . Initially known as al @-@ Wadi Bar , it received its current name in 1972 . The owners and the employees of the restaurant are from the Tabash family . 
- 
- = = Culture = = 
- 
- 
- = = = <unk> = = = 
- 
- There are a number of local traditions in Jifna . A prominent legend among Jifna 's inhabitants is that the Holy Family rested near an oak tree in the town on their way from Jerusalem . The legend 's origins are due to Jifna 's location along the ancient Jerusalem @-@ Nazareth road . 
- A nearby mountain was named <unk> ad @-@ <unk> ( " Mount of the Rooster " ) because of a traditional story . According to the tradition , a Jew that lived in Jifna had visited Jerusalem during the Passion . Seeing Jesus rise from the dead , the man immediately converted and told his wife what he saw . His wife refused to believe him unless the rooster she had just killed would come back to life . <unk> , the rooster flew away towards the mountain . The story was recited in some monasteries on Holy Thursday together with other biblical readings . 
- A legend exists about Jifna 's spring — which the village has used for centuries — concerning how it periodically runs low on water . Popular belief is that this is the work of the <unk> ( female spirit ) . According to Palestinian researcher <unk> Canaan , " In Jifna the priest has to go on such an occasions to the dry spring to repeat prayers and burn incense , and thus reconcile the <unk> or force her to let the water flow " . 
- Like many Palestinian villages , the women of Jifna have their own traditional dress . <unk> in the village , known as <unk> <unk> ( " Greek White " ) and <unk> <unk> ( " Greek Black " ) , were dresses of hand @-@ woven linen embroidered with the Jifna 's own motifs . <unk> <unk> , a sweet pudding @-@ like dish made from whole grain wheat , on the Feast of Saint Barbara has been a tradition in Jifna for several centuries . 
- 
- = = = Festivals = = = 
- 
- In April – May 2005 Jifna hosted the first annual International Artists ' Workshop in Palestine . The festival , known as the " Jifna Spring " was the first held in a rural village instead of a major city such as Hebron or Ramallah . During the festival , dozens of artists from all over the world collaborated on several projects , including stone sculptures , metalwork , photography , mural paintings and installation pieces . Many of the works were influenced by the " <unk> and qualities " of Jifna , while others dealt with the subject of Palestinian identity . 
- Jifna , locally famous for its apricot harvest , hosts an annual two @-@ day apricot festival in the first week of May . Hundreds of West <unk> flock to participate in the harvest . The festival is also used by Palestinian politicians as an opportunity to give speeches praising Palestinian farmers and encouraging boycotts on Israeli products and reliance on domestic agriculture . 
- 
- = = Government = = 
- 
- Jifna is governed by a village council consisting of ten members including the chairman . The council was founded in 1954 , when a prominent resident , <unk> <unk> <unk> , pressured the <unk> authorities to recognize the institution , even though a village was normally required to have a population of at least 1 @,@ 000 inhabitants for permission to establish a village council . The decision was approved by Jordan 's then prime minister . Representatives from Jifna 's largest families formed the original council 's body , appointing <unk> as chairman and Salim <unk> <unk> as vice chairman . <unk> Na 'im Kamil was elected as the chairman in 2005 and the <unk> ( Alternative ) list — which represents a leftist alliance of the Democratic Front for the Liberation of Palestine and the Palestinian People 's Party — won most of the council seats . 
- 
- = = Education = = 
- 
- Jifna contains one gender @-@ mixed primary school and kindergarten , founded by the Latin <unk> of Jerusalem in 1856 and managed by Jifna 's Catholic church . The school 's staff comprises eight teachers and two nuns , as well as four teachers for the kindergarten . Students who have graduated from the school commute to Ramallah or Bir Zeit daily for their secondary and tertiary education . Most university students attend the nearby Bir Zeit University . 
- 
- 
- = Ha ' K 'in Xook = 
- 
- Ha ' K 'in Xook ( <unk> pronunciation : [ <unk> k ’ in <unk> ] ) , also known as Ruler 6 , was an <unk> of Piedras Negras , an ancient Maya settlement in Guatemala . He ruled during the Late Classic Period , from 767 – 780 AD . Ha ' K 'in Xook was a son of Itzam K 'an Ahk II , and he ascended the throne upon the death of his brother , Yo 'nal Ahk III . Ha ' K 'in Xook 's reign seems to have ended either with his death or his abdication in favor of his brother K 'inich Yat Ahk II ; archaeologists and <unk> have not come to a clear consensus on this . Ha ' K 'in Xook left behind several monuments , including stelae at Piedras Negras and a stone fragment from El Porvenir . In addition , a stone seat known as Throne 1 which was erected by K 'inich Yat Ahk II , records either the death or abdication of Ha ' K 'in Xook . 
- 
- = = Biography = = 
- 
- 
- = = = Reign of Piedras Negras = = = 
- 
- Ha ' K 'in Xook , who has also been referred to as Ruler 6 , was likely the son of Itzam K 'an Ahk II , based on a translation of Stela 23 . According to both Simon Martin and <unk> Grube , as well as Johnson , Ha ' K 'in Xook 's name translates to " Water Sun Shark " . Of note , his name does not feature a turtle glyph , a feature found in all the previous rulers of Piedras Negras , as well as his successor . He ascended the throne on February 14 , 767 AD ( 9 @.@ 16 @.@ 16 @.@ 0 @.@ 4 , 7 K 'an 17 Pop in the Long Count ) , following the death of his brother Yo 'nal Ahk III . Ha ' K 'in Xook 's kingship of Piedras Negras had originally been overlooked by <unk> <unk> , who instead assigned the title Ruler 6 to a <unk> <unk> — or Maya leader — of La <unk> 
- Not much is known about the rule of Ha ' K 'in Xook ; his reign , along with that of Yo 'nal Ahk III , has been referred to as " <unk> " by Flora Clancy , and James L. <unk> argues that Ha ' K 'in Xook seems to have been a weaker ruler when compared to the reign of Itzam K 'an Ahk II because Ha ' K 'in Xook erected few monuments , and he did not reinforce his power on a larger scale , choosing only to do so at local polities . The only notable recorded event that has been preserved during the life of Ha ' K 'in Xook is of an incident that took place at El <unk> , most likely related to the burial of a contemporary <unk> . According to Zachary Nathan Nelson , the reign of Ha ' K 'in Xook seems to have been relatively free from war , as none of his extant stelae show representation of captives , and known records do not indicate any sort of " <unk> action " in the region during his reign . 
- 
- = = = Death or abdication = = = 
- 
- Ha ' K 'in Xook yielded the throne on March 24 , 780 AD ( 9 @.@ 17 @.@ 9 @.@ 5 @.@ 11 10 <unk> 19 <unk> ) . The reason why his reign came to an end has been a topic of debate . Some believe that his reign ended with his abdication . Throne 1 , when translated , notes that he " abandoned / transferred rulership " , which American <unk> Stephen D. Houston interprets to mean that he abdicated in favor of his brother , K 'inich Yat Ahk II . <unk> David Stuart also noted that the wording on the throne " implies a more active event on the ruler 's part " , but that " ' lose ' might be more neutral as far as an interpretation goes . " 
- However , the exact phrase on the monument is <unk> <unk> , which is the same wording that was used when the " burden " of leadership passed from K <unk> ' <unk> Chan <unk> , the k <unk> <unk> of <unk> , to his successor Sky <unk> during his burial ten days after his demise in <unk> AD . As such , this may suggest that , rather than <unk> , Ha ' K 'in Xook died in 780 AD , and his successor , K 'inich Yat Ahk II , took up rule later . The <unk> <unk> on Throne 1 note that K 'inich Yat Ahk II later " remembered " Ha ' K 'in Xook at the <unk> Stone Place in some way , and that the new king may have possibly <unk> at the memory of Ha ' K 'in Xook , according to anthropologist <unk> <unk> <unk> . 
- Regardless , K 'inich Yat Ahk II took up the throne on May 31 , <unk> AD ( 9 @.@ 17 @.@ 10 @.@ 9 @.@ 4 1 K 'an 7 <unk> 'in ) , almost a year following the end of Ha ' K 'in Xook 's reign . Despite this lengthy gap , there appears to be no evidence of anyone ruling Piedras Negras between the two kings . 
- 
- = = Monuments = = 
- 
- 
- = = = Stelae = = = 
- 
- Several stelae have been found that were erected by Ha ' K 'in Xook , including Stelae 13 , 18 , and 23 . Stela 13 would appear to be the earliest , showing the oldest <unk> date assigned to Ha <unk> Xook , and it " captures the essence of the period @-@ ending celebration . " This 2 @.@ 39 @-@ metre ( 7 @.@ 8 ft ) tall stela retains stylistic elements of other niche stelae , but also shows a different form . It depicts the king scattering incense , similar to iconography found on Piedras Negras Stelae 2 and 32 . Clancy has argued that the stela contains iconography pertaining to three motifs : divination , " the quest " , and accession . <unk> is shown via the act of Ha ' K 'in Xook scattering the incense . This motif had been introduced earlier in the iconography of Piedras Negras by K 'inich Yo 'nal Ahk II . The stela also features Ha ' K 'in Xook wearing a unique headdress ; it is depicted as being composed of three knots and forehead scrolls , a projecting Water Lily Jaguar , and a flexible rectangular emblem made from a jaguar <unk> and a thick net . It has been postulated that this headdress was inspired by the headdress worn by an enthroned figure from the underworld featured on Stela 5 . Clancy argues that this headdress is a representation of " the royal theme of quest " , due to its <unk> similarity to other stelae at Piedras Negras . In addition , Stela 13 remains Ha ' K 'in Xook 's only extant image . 
- Not much remains of Stela 18 , and what is left is badly worn . However , archaeologist <unk> Morley claimed that it expressed a Calendar Round date of 6 <unk> 13 K <unk> ( corresponding to a Long Count date of 9 @.@ 17 @.@ 5 @.@ 0 @.@ 0 or <unk> 27 , 775 ) , which Martin and Grube note is within Ha ' K 'in Xook 's reign . Stela 23 is the king 's accession monument . Originally carved on all four faces , this stela is in poor condition because it tipped over and shattered . The largest portions remaining include the base , and remnants of a basal panel of <unk> that feature the feet of two individuals . Similar to Stela 18 , Martin and Grube argue that Stela 23 dates to the rulership of Ha ' K 'in Xook because the Calendar Round date on the monument is contemporary with the date found on Stela 13 . This stele details a <unk> <unk> <unk> ceremony for Ha ' K 'in Xook 's dead father , Itzam K 'an Ahk II , and , when translated , reads " Ruler 6 [ Ha ' K 'in Xook ] , child of Ruler 4 [ Itzam K 'an Ahk II ] … acceded to the rulership " , seemingly proving that Ha ' K 'in Xook was indeed the offspring of Itzam K 'an Ahk II . 
- All three of the stelae were discovered by <unk> Maler in 1901 near one of the site 's main temple , the O @-@ 13 pyramid . Stela 13 was possibly erected on a terrace reached by the pyramid 's main stairway , and Stela 18 lies in a row on the plaza in front of the aforementioned stairway . Stela 23 , on the other hand , was erected at the very base of the pyramid . This pyramid was most likely the burial place for Itzam K 'an Ahk II , and — seeing as how Ha ' K 'in Xook , Yo 'nal Ahk III , and K 'inich Yat Ahk II all revered the site as a dynastic shrine — it is further evidence to back the argument that Ha ' K 'in Xook was the son of Itzam K 'an Ahk II . 
- 
- = = = Other = = = 
- 
- As mentioned before , Throne 1 details either the death or abdication of Ha ' K 'in Xook . However , it should be noted that the throne was not constructed by the king himself , but rather by his successor , K 'inich Yat Ahk II . The throne was later deliberately broken apart by Piedras Negras 's enemies , but has since been reconstructed . In addition to on @-@ site remains and relics , a fragment of stone found at El Porvenir and <unk> called the " El Porvenir <unk> " was also discovered that bore the name of Ha ' K 'in Xook . 
- 
- 
- = Tommy Lawton = 
- 
- Thomas " Tommy " Lawton ( 6 October 1919 – 6 November 1996 ) was an English football player and manager . A strong centre @-@ forward with excellent all @-@ round attacking skills , he was able to head with the ball with tremendous power and accuracy . Despite losing much of his best years to World War II , he scored 260 goals in <unk> league and cup competitions in 14 full seasons in the Football League . 
- Born in <unk> and raised in Bolton , he played amateur football at <unk> United , before he turned professional at Burnley on his 17th birthday . He also played cricket for Burnley Cricket Club , before his potential as a footballer won him a £ 6 @,@ 500 move to Everton in January 1937 . He went on to finish as the First Division 's top @-@ scorer in 1937 – 38 and 1938 – 39 , helping Everton to finish as champions of the Football League in the latter campaign . League football was then suspended for seven full seasons due to the outbreak of war in Europe , during which time he scored 24 goals in 23 appearances for England whilst <unk> for Everton and a number of other clubs . In November 1945 , he moved to Chelsea for £ 14 @,@ 000 , and scored a club record 26 goals in 34 league games in the 1946 – 47 season . 
- In November 1947 , he made a surprise move to Third Division South club Notts County for a British record transfer fee of £ 20 @,@ 000 . He helped the club to win promotion as champions in 1949 – 50 , before he moved on to Brentford in March 1952 for a club record £ 16 @,@ 000 . In January 1953 , Brentford appointed him player @-@ manager , though he would only remain in charge for nine months . He joined Arsenal as a player in November 1953 for £ 10 @,@ 000 , where he saw out the remainder of his playing career . He had a promising start to his managerial career by leading Kettering Town to the Southern League title in 1956 – 57 , but then only had two more seasons as manager , getting relegated with Notts County in 1957 – 58 and then relegated with Kettering Town in 1963 – 64 . During the 1970s he struggled with debt and related legal problems , which were reported in the media as an example of a celebrated person having fallen from grace . 
- He scored 22 goals in his 23 England appearances over a ten @-@ year international career from 1938 to 1948 , including four against Portugal in May 1947 . He helped England to win two British Home Championship titles outright ( 1946 – 47 and 1947 – 48 ) , and to share the Championship in 1938 – 39 . He fell out of international contention at the age of 28 due to his contempt for manager Walter Winterbottom , his decision to drop out of the First Division , and the emergence of Jackie Milburn and Nat <unk> . As well as his England caps , he also represented The Football League XI and played in a special Great Britain game against Europe in 1947 . He married twice , and had two children and one step @-@ child . His ashes are held in the National Football Museum , and he was inducted into the English Football Hall of Fame in 2003 . 
- 
- = = Early life = = 
- 
- Thomas Lawton was born on 6 October 1919 to Elizabeth Riley and Thomas Lawton senior in <unk> , Lancashire . His father was a rail @-@ road <unk> of Irish extraction , and his mother worked as a <unk> at <unk> Mill . His father left the family 18 months after Lawton was born , and Elizabeth moved back into her parents ' home in Bolton . Elizabeth 's father , James Hugh " Jim " Riley , became Lawton 's surrogate father . Lawton 's natural <unk> ability earned him a place on the Bolton Town Schools team in 1930 . He was picked by Lancashire Schools at the age of 13 . Despite scoring a hat @-@ trick in a trial game for England <unk> , he never earned a full England <unk> cap . At the age of 14 he began playing for Hayes Athletic in the Bolton Senior League , and went on to score 570 goals in three seasons . The FA 's rules meant he was unable to turn professional at a club until he was 17 , and Lawton 's grandfather rejected Bolton Wanderers 's offer for Lawton to work as a delivery driver for two years before turning professional at the club . Lawton instead played as an amateur for <unk> United in the Lancashire <unk> , scoring a hat @-@ trick on his debut against <unk> . He took up temporary work at a tannery , and then joined Burnley as assistant <unk> after his mother rejected an offer from Sheffield Wednesday as she objected to him travelling to Sheffield on a daily basis . 
- 
- = = Club career = = 
- 
- 
- = = = Burnley = = = 
- 
- Lawton played his first game for Burnley Reserves against Manchester City Reserves in September 1935 , and though he struggled in this game he went on to become a regular Reserve team player by the age of 16 . After a poor run of form from Cecil Smith , Lawton was selected ahead of Smith for the Second Division game against Doncaster Rovers at <unk> Moor on 28 March 1936 ; aged 16 years and 174 days , this made him the youngest centre @-@ forward ever to play in the Football League . Rovers centre @-@ half Syd <unk> , also making his league debut , marked Lawton out of the game , which ended in a 1 – 1 draw . Burnley had played poorly , though Lawton was praised for his " keen and fearless " performance by the Express & News newspaper . He retained his place for the following game , and scored two goals in a 3 – 1 victory over Swansea Town at <unk> Field . He picked up a groin strain in his third appearance which caused him to miss two fixtures , before he returned to the first team for the final four games of the 1935 – 36 season ; he claimed three more goals to take his season tally to five goals from seven games . 
- Lawton continued to train his heading skills intensely in the summer of 1936 , and also played cricket for Burnley Cricket Club as a batsman in the Lancashire League . He scored a six against both Learie Constantine and <unk> Singh . He scored <unk> runs in 15 completed innings for an average of 24 @.@ 06 . 
- He turned professional at Burnley at the age of 17 on wages of £ 7 a week . His grandfather attempted to negotiate a £ 500 signing @-@ on fee on his behalf but was rebuffed after the club alerted Charles <unk> , Secretary of the Football League , who informed them that any attempt to <unk> the league 's maximum wage was illegal . Lawton scored in his first appearance since signing the contract after just 30 seconds , before going on to record a hat @-@ trick in a 3 – 1 win over Tottenham Hotspur , scoring a goal with either foot and one with his head . 
- 
- = = = Everton = = = 
- 
- In January 1937 , First Division club Everton paid Burnley £ 6 @,@ 500 to secure Lawton 's services , and also gave his grandfather a job as deputy <unk> at Goodison Park ; the fee was a record for a player under 21 . The move to Everton made him a teammate of Dixie Dean , his <unk> idol , who he was expected to gradually replace as first choice centre @-@ forward . He later recalled that on his way to Goodison Park on his first day as an Everton player he was told by a tram conductor that " You 're that young Lawton , aren 't you ? You 'll never be as good as Dixie . " Dean was finally rested on 13 February , which allowed Lawton to make his first team debut against Wolverhampton Wanderers at <unk> ; the match ended in a 6 – 2 defeat , though Lawton scored a penalty . He spent the rest of the season at inside @-@ left , with Dean at centre @-@ forward , and ended the 1936 – 37 campaign with four goals in 11 games . He started the 1937 – 38 season in the Reserves , but was installed as first choice centre @-@ forward in September after Dean was dropped for punching club secretary <unk> Kelly . On 2 October , Lawton scored the winning penalty in a 2 – 1 victory over Merseyside derby rivals Liverpool at Anfield . He ended the campaign with 28 goals in 39 appearances to become the division 's top @-@ scorer . 
- Everton had a young but highly effective team for the 1938 – 39 campaign , and Lawton was praised for the way he led the attack , with the Evening Standard 's Roland Allen describing Lawton as a " clever footballer , bringing his wing men into the game with <unk> <unk> and widely flung and accurate passes " after Everton recorded a 2 – 1 victory over Arsenal at Highbury . The game against Arsenal was part of a run of six wins in the first six games of the campaign , during which time Lawton scored eight goals . Everton lost their lead at the top of the table to Derby County over Christmas , but returned to form and to first position by Easter . They faced a difficult final run of games , but beat Chelsea and recorded two victories over Sunderland to secure the club 's fifth league title , finishing four points ahead of second @-@ place Wolverhampton Wanderers . Lawton scored 34 goals in 38 league games to finish as the division 's top @-@ scorer for the second successive season . However , in the summer he wrote to Leicester City to request that the club buy him from Everton ; it was reported that he reached out to Leicester as they were managed by Tom <unk> , his former Burnley manager . Everton were fifth in the league and Lawton was the division 's top @-@ scorer with four goals when league football was suspended three games into the 1939 – 40 season due to the outbreak of World War II . Lawton later remarked that " I 'm convinced that if it hadn 't been for the War , we 'd have won the Championship again , the average age of those players was about 24 or 25 " . 
- 
- = = = World War II = = = 
- 
- Lawton continued to play for both Everton and England during the war . However the FA decided to not award full caps for England appearances during the war , and as a result his 24 goals in 23 international games were not counted in statistics for the official England team . As was common for footballers during the war , he also made guest appearances for a number of clubs besides Everton , including Leicester City , Greenock Morton , Chester City , Aldershot , and Tranmere Rovers . He was called up to the British Army in January 1940 , and his status as an England international saw him recruited to the Royal Army Physical Training Corps . He also played for the British Army team and his Area Command team . He was posted in <unk> , which allowed him to frequently appear for Everton . On Christmas Day 1940 , he played for Everton against Liverpool at Anfield in the morning and for Tranmere Rovers at Crewe Alexandra in the afternoon . Explaining this later , he said , ' The Tranmere people came into the dressing room and asked if anyone wanted to play as they were two men short . I said , " Go on , I 'll help you out . " And I did . ' In 1942 he scored a hat @-@ trick for England in a 5 – 4 win over Scotland at Hampden Park . Later in the year he scored six goals for Aldershot in a 9 – 0 win over Luton Town . On 16 October 1943 , he scored four goals in an 8 – 0 victory over Scotland at Maine Road . 
- 
- = = = Chelsea = = = 
- 
- In July 1945 , Lawton handed in a transfer request at Everton as he wanted a move to a Southern club so as to see more of his increasingly estranged wife . In November 1945 , he was sold to Chelsea for a fee of £ 14 @,@ 000 . Chelsea continued to play regional wartime fixtures as national league football had not resumed for the 1945 – 46 season , and Lawton also continued his <unk> appearances for the England national team . In the summer of 1946 , following his <unk> , he coached for the FA in a summer camp in Switzerland . He scored a club record 26 goals in 34 league games in the 1946 – 47 season . However he struggled to settle at Stamford Bridge and came into conflict with manager Billy <unk> after refusing to go on a pre @-@ season tour of Sweden in 1947 , which resulted in him requesting a transfer . He favoured a move to Arsenal , but this was ruled out by the Chelsea hierarchy . Lawton turned down an approach by Sunderland manager Bill Murray as he held out hope that Chelsea would <unk> and allow him a move to Arsenal . 
- 
- = = = Notts County = = = 
- 
- In November 1947 , Lawton was sold to Notts County of the Third Division South for a British record transfer fee of £ 20 @,@ 000 . He made the surprise decision to drop down two divisions so as to be reunited with manager Arthur <unk> , his former <unk> and friend at Chelsea , and because he was promised a job outside of football upon his retirement by vice @-@ chairman Harold <unk> . <unk> told the Nottingham Guardian Journal that " we are prepared to spend to the limit to put this old club back where it belongs " . He scored two goals on his home debut , a 4 – 2 win over Bristol Rovers in front of 38 @,@ 000 spectators at Meadow Lane – a massive increase on previous home games of typically 6 @,@ 000 to 7 @,@ 000 supporters . He ended the 1947 – 48 season with 24 goals in as many games , though was resented by the club 's directors after he insisted on pay rises for his teammates and stopped the practice of director 's friends and family travelling to away games on the team coach . 
- He formed a productive forward partnership with Jackie Sewell in the 1948 – 49 campaign , and scored 23 goals in 40 league and cup appearances . County finished in mid @-@ table despite scoring 102 goals , 15 more than champions Swansea . <unk> was sacked and upon Lawton 's suggestion the club appointed Eric Houghton as manager after Lawton turned down the role as player @-@ manager . Lawton and Sewell 's understanding grew throughout the 1949 – 50 campaign , and Lawton finished as the division 's top @-@ scorer with 31 goals in 37 league games as County won promotion as champions , seven points ahead of second @-@ placed Northampton Town . Promotion was secured with a 2 – 0 win over local rivals Nottingham Forest at Meadow Lane on 22 April . 
- However he struggled with poor form during the 1950 – 51 season as his first marriage was coming to an end and he came into increasing conflict with his teammates . He was angered when the club sold Jackie Sewell to Sheffield Wednesday in March 1951 – breaking Lawton 's own transfer record in the process – as he felt the move showed a lack of ambition from the club 's directors . He also found that the well paid job he was promised outside of football did not <unk> . His tally of nine goals in 31 games in 1950 – 51 and 13 goals in 31 games in 1951 – 52 was disappointing , and he was made available for transfer . 
- 
- = = = Brentford = = = 
- 
- In March 1952 , Lawton joined Second Division side Brentford for a club record £ 16 @,@ 000 fee . Manager Jackie Gibbons left the club at the start of the 1952 – 53 season and was succeeded by his assistant Jimmy Bain , who proved ill @-@ suited to management , and so in January 1953 Lawton was appointed as player @-@ manager , with Bain as his assistant . However he lost the dressing room due to his excessive demands of the players , and the strains of management were having a negative impact on his form . Brentford also lost their best players having sold both Ron Greenwood and Jimmy Hill . He signed two veterans in former Notts County teammate Frank Broome and Ian McPherson to play on the wings , who , with Lawton , formed an attacking trio with a combined age of 104 . They got off to a poor start to the 1953 – 54 season , and Lawton resigned as manager after the Griffin Park crowd began to mock the forward line by singing Dear Old Pals . 
- 
- = = = Arsenal = = = 
- 
- In November 1953 , Lawton was traded to First Division champions Arsenal for £ 7 @,@ 500 plus James Robertson ( valued at £ 2 @,@ 500 ) . He was signed by manager Tom Whittaker , who had previously found success in bringing in veterans such as Ronnie <unk> and Joe Mercer . However Lawton was limited to ten appearances in the 1953 – 54 campaign after picking up an injury on his debut . He also played in the 1953 Charity Shield , scoring one goal as Arsenal beat Blackpool 3 – 1 . He scored seven goals in 20 appearances throughout the 1954 – 55 season , including winning goals against Chelsea and Cardiff City . He scored a hat @-@ trick past Cardiff City on the opening day of the 1955 – 56 season , before he announced his decision to leave Arsenal to pursue a career in management eight games into the campaign . 
- " More than 20 years of soccer . What glorious years . Years that all the money in the world couldn 't buy . I have been lucky . I have played with great clubs ; I have escaped serious injury ; I have played for my country ; I have even captained my country ; I have won many of the game 's top honours . Soccer has been good to me and I hope that I have repaid the game in some small way . I have had great experiences . I have met some wonderful people . I have memories that nobody can take away from me . If I could turn the clock back 20 years , I would still go into the game as a full @-@ time professional and I can say to any lad who is contemplating a career in football : Go ahead son ... providing you are willing to work and work hard and providing you are willing to learn the craft thoroughly . You will meet some of the grandest fellows you could ever wish to meet and you will have a pleasant , healthy life and be quite well paid for it . 
- 
- = = International career = = 
- 
- Lawton was called up to play for The Football League XI against a League of Ireland XI at Windsor Park on 21 September 1938 , and scored four goals in what finished as an 8 – 2 win . A month later he went on to win his first cap for England on 22 October , England 's first game of the 1938 – 39 British Home Championship , a 4 – 2 defeat to Wales at Ninian Park , and converted a penalty kick to mark his first England appearance with a goal . This made him the youngest player to score on his England debut , a record which lasted until Marcus <unk> broke it in 2016 . Four days after Lawton 's debut , he scored again for England at Highbury in a 3 – 0 win over ' The Rest of Europe ' , a team of players selected from Italy , Germany , France , Belgium , Hungary and Norway . Later in the year he also scored in victories over Norway and Ireland . He played in all four games of 1939 , scoring against Scotland and Italy ; the goal against Scotland secured a 2 – 1 win in front of 149 @,@ 269 spectators at Hampden Park . 
- Newly appointed England manager Walter Winterbottom played Lawton in England 's first official match in seven years on 28 September 1946 , a 7 – 2 win over Ireland . He played the remaining three fixtures of 1946 , and scored four goals in a 8 – 2 victory over the Netherlands at Leeds Road on 27 November . On 10 May 1947 , he scored two goals playing for the Great Britain XI in a 6 – 1 victory over a Rest of Europe XI that was billed as the ' Match of the Century ' . Five days later he scored four goals in a 10 – 0 victory over Portugal at Lisbon 's <unk> <unk> . On 21 September , he scored after just 12 seconds in a 5 – 2 win over Belgium at <unk> Stadium . 
- He retained his place in the England team following his club move to Notts County , and in doing so became the first Third Division footballer to represent England when he scored from the penalty spot in a 4 – 2 win over Sweden on 19 November . However he only won three further caps in 1948 , his final appearance coming in a 0 – 0 draw with Denmark in Copenhagen on 26 September . He had become increasingly disillusioned with the England set @-@ up , and told <unk> that " if you think you can teach Stanley Matthews to play on the wing and me how to score goals , you 've got another think coming ! " <unk> was also frustrated by Lawton 's smoking habit , and preferred Jackie Milburn ahead of Lawton . <unk> of any future comeback were ended by the emergence of powerful centre @-@ forward Nat <unk> , who made his England debut in November 1950 . 
- 
- = = Style of play = = 
- 
- Lawton was widely regarded as the finest centre @-@ forward of his generation . He boasted a strong physique and good ball control skills , as well as a great passing range and a powerful shot . He was naturally right @-@ footed , though worked to improve his left foot to a good enough standard to be considered a two @-@ footed player . His greatest strength though was his ability to head the ball , as he possessed muscular legs to give himself a strong jump and long hang @-@ time , and was also able to time his jumps to perfection . Stanley Matthews surmised that " Quite simply , Tommy was the greatest header of the ball I ever saw . " Lawton was never booked throughout his career . 
- 
- = = Coaching career and later life = = 
- 
- An Arsenal director helped Lawton to find employment as player @-@ manager at Southern League side Kettering Town on wages of £ 1 @,@ 500 a year . He signed a number of players from Football League clubs , including Jim <unk> ( Arsenal ) , Amos Moore ( Aston Villa ) , Jack Wheeler ( Huddersfield Town ) , Jack Goodwin ( Brentford ) , Bob Thomas ( Fulham ) , Norman <unk> ( Leicester City ) , Harry McDonald ( Crystal Palace ) and Geoff <unk> ( Sunderland ) . Kettering were ten points clear at the top of the table by Christmas . In January 1956 he turned down an approach from Notts County to return as manager . Kettering won the league title in 1956 – 57 , finishing eight points ahead of Bedford Town , with Lawton scoring 15 of Kettering 's 106 league goals . 
- He was appointed as Notts County manager in May 1957 , controversially replacing caretaker @-@ manager Frank Broome , who had steered the club away from the Second Division relegation zone ; Broome was installed as his assistant . He was unable to make new signings as he was unable to move on any of the club 's 33 playing staff . He did though take on forwards Jeff <unk> and Tony <unk> as <unk> , who would both go on to have long careers in the First Division . Lawton agreed to go without his wages for six months so as to improve the club 's finances . County were relegated at the end of the 1957 – 58 season , finishing one point short of safety , and Lawton was sacked . He received a total of just three months pay for his time at the club , having only a verbal offer of a three @-@ year contract to fall back on , and nothing in writing . 
- After being sacked as Notts County manager , Lawton ran the Magna <unk> public house in <unk> from October 1958 . An employee stole £ 2 @,@ 500 from the business and Lawton decided to leave the pub trade after four years . He then took up a job selling insurance . He returned to football management with Kettering Town for the 1963 – 64 season as a caretaker following the resignation of Wally <unk> , but the season ended with Kettering being relegated from the Southern League Premier Division . He was offered the job on a permanent basis , but turned it down so as to concentrate on his job as an insurance salesman . He lost his job in insurance in 1967 , and then opened a sporting goods shop that bore his name after going into partnership with a friend , but was forced to close the business after just two months due to poor sales . After a period on unemployment benefits he found work at a betting company in Nottingham . 
- He returned to Notts County to work as a coach and chief scout from 1968 to 1970 . He was sacked after new manager Jimmy <unk> decided to appoint his own <unk> staff , and Lawton returned to unemployment . In May 1970 , he wrote to Chelsea chairman Richard Attenborough asking for a loan of £ 250 and for possible employment ; Attenborough lent him £ 100 . He was interviewed by <unk> Andrews on ITV 's Today programme on his fall from England star to the unemployment line . After his financial troubles became public knowledge he was offered a lucrative job as director of his own subsidiary furniture company by a large <unk> company on Tottenham Court Road , however the company went into <unk> the following year . He continued to write <unk> in the company 's name , and in June 1972 pleaded <unk> to seven charges of obtaining goods and cash by deception . He was sentenced to three years <unk> , and ordered to pay £ 240 compensation and £ 100 in costs . 
- In 1972 a testimonial match was organised by Everton on Lawton 's behalf to help him pay off his debts of around £ 6 @,@ 000 . However his financial situation was still bleak , and on two occasions he narrowly avoided a prison sentence for failing to pay his rates after an Arsenal supporters club and later an anonymous former co @-@ worker stepped in to pay the bill for him . In August 1974 , he was again found <unk> of obtaining goods by deception after failing to <unk> a £ 10 debt to a <unk> , and was sentenced to 200 hours of Community service and ordered to pay £ 40 costs . In 1984 he began writing a column for the Nottingham Evening Post . Brentford also organised a testimonial match for him in May 1985 . 
- Lawton 's health deteriorated in his old age and he died in November 1996 , aged 77 , as a result of pneumonia . His ashes were donated to the National Football Museum . He was inducted into the English Football Hall of Fame in 2003 . 
- 
- = = Personal life = = 
- 
- Lawton married Rosaleen May <unk> in January 1941 ; the marriage bore one child , Amanda . Rosaleen never watched Lawton play football during their ten @-@ year marriage . Divorce was granted with a decree <unk> in March 1951 after Rosaleen was found to have committed adultery with Notts County director Adrian Van Geffen ; Lawton never saw Amanda again and was not required to pay child support . He would not hear from his daughter until she was convicted of stealing from playwright George <unk> in 1968 , by which time Rosaleen was on her fourth marriage and living in Jamaica . Lawton married second wife Gladys Rose in September 1952 , who bore him a son , Thomas Junior . Gladys was also divorced , and her ex @-@ husband cited Lawton as a co @-@ <unk> in the divorce proceedings as the pair had begun their relationship whilst Gladys was still married ; her family were staunch Catholics , and Gladys was <unk> by her family following her divorce . Gladys had a daughter , Carol , from her previous marriage , who Lawton raised as his own . Thomas Junior went on to play rugby union for Leicester Tigers . 
- He starred alongside <unk> <unk> and Diana <unk> in 1953 film The Great Game , playing himself in a cameo role . Throughout the 1950s he went on to appear on What 's My Line ? amongst other radio and television programmes . He published a total of four books : Tommy Lawton 's all star football book ( 1950 ) , Soccer the Lawton way ( 1954 ) , My Twenty Years of Soccer ( 1955 ) , and When the <unk> Stopped ( 1973 ) . 
- 
- = = Career statistics = = 
- 
- 
- = = = Club playing statistics = = = 
- 
- Sourced from Tommy Lawton profile at the English National Football Archive ( subscription required ) 
- 
- = = = International playing statistics = = = 
- 
- Sourced from Tommy Lawton profile at the England Football Online website 
- 
- = = = Managerial statistics = = = 
- 
- Sourced from Tommy Lawton profile at the English National Football Archive ( subscription required ) 
- Notes 
- Statistics at Kettering Town not recorded . 
- 
- = = Honours = = 
- 
- England 
- British Home Championship winner : 1938 – 39 ( shared ) , 1946 – 47 , 1947 – 48 
- Everton 
- Football League First Division champion : 1938 – 39 
- Notts County 
- Football League Third Division South champion : 1949 – 50 
- Arsenal 
- Charity Shield winner : 1953 
- Kettering Town 
- Southern Football League champion : 1956 – 57 
- 
- = = = Specific = = = 
- 
- 
- = = = General = = = 
- 
- 
- = Trials and Tribble @-@ ations = 
- 
- " Trials and Tribble @-@ ations " is the <unk> episode of the American science fiction television series Star Trek : Deep Space Nine , the sixth episode of the fifth season . It was written as a tribute to the original series of Star Trek , in the 30th anniversary year of the show ; sister series Voyager produced a similar episode , " Flashback " . The idea for the episode was suggested by René Echevarria , and Ronald D. Moore suggested the link to " The Trouble with Tribbles " . The pair were credited for their work on the teleplay , with the story credit going to Ira Steven Behr , Hans <unk> and Robert Hewitt Wolfe . 
- Set in the 24th century , the series follows the adventures on Deep Space Nine , a space station located near a stable <unk> between the Alpha and <unk> <unk> of the Milky Way Galaxy . In this episode , Captain Benjamin Sisko ( Avery Brooks ) and the crew travel back in time to prevent the assassination of Captain James T. Kirk ( William <unk> ) of the USS Enterprise by a Klingon using a booby @-@ trapped tribble . 
- Moore had originally suggested re @-@ visiting the planet seen in The Original Series episode " A Piece of the Action " but was convinced by Echevarria that the digitally inserted shots previously seen in <unk> <unk> ( 1994 ) could be done on a small budget . After a test shot was completed , the rest of the production team were also convinced that it could be achieved . The budget was set at $ 3 million , with extensive work completed on matching the film techniques used during The Original Series . 
- Some original costumes were found for the Klingons while others were made from patterns created by Robert <unk> . Greg <unk> created new models of the Enterprise as well as Deep Space Station K7 and the Klingon cruiser , while 1 @,@ 400 tribbles were purchased from a company owned by Majel Barrett . Charlie Brill returned to Star Trek to appear once more as Arne Darvin , and <unk> L. <unk> was cast in part due to her being friends with one of the production crew members . Walter Koenig , who portrayed Ensign Pavel <unk> in The Original Series , showed the Deep Space Nine cast how to work the consoles on the Enterprise sets . 
- " Trials and Tribble @-@ ations " was warmly received by critics with praise directed at the nostalgia and level of detail seen on screen . It was the most watched episode of the fifth season . " Trials and Tribble @-@ ations " was nominated in three Primetime Emmy Award categories and for the Hugo Award for Best Dramatic Presentation but did not win any awards . It was released on VHS initially alongside " The Trouble with Tribbles " , and later as part of the normal release schedule . It was subsequently released as part of the season five DVD set . 
- 
- = = Plot = = 
- 
- On board Deep Space Nine , Captain Benjamin Sisko ( Avery Brooks ) is being queried by Department of Temporal Investigations agents <unk> ( Jack <unk> ) and <unk> ( James W. <unk> ) . The Captain explains that he was on the USS Defiant , returning from <unk> space with the <unk> <unk> of Time . They had picked up a <unk> on the way , a human called Barry <unk> . Suddenly the ship found itself some 200 light years away from its previous location and a hundred years in the past , near Deep Space Station K7 and found the USS Enterprise in orbit . They discover that the <unk> @-@ <unk> was Arne Darvin ( Charlie Brill ) , a Klingon agent who had previously been caught by Captain James T. Kirk ( William <unk> ) on K7 whilst trying to poison a shipment of grain . 
- Fearing that Darvin may be attempting to assassinate Kirk , the crew dress in period uniforms and investigate the Enterprise . They attempt to interact with history as little as possible whilst investigating Darvin . The crew does not initially recognize the Klingons of the time period , and when Worf ( Michael Dorn ) is asked about the difference in appearance , he replies that the matter is not discussed with outsiders ( this is further expanded on in the Star Trek : Enterprise episodes " <unk> " and " <unk> " ) . Doctor Julian Bashir ( Alexander <unk> ) and Chief Miles O 'Brien ( Colm <unk> ) get involved in a bar brawl between the Enterprise crew and a number of Klingons on shore leave . Captain Kirk disciplines them on the Enterprise alongside Chief Engineer Montgomery Scott ( James Doohan ) and Ensign Pavel <unk> ( Walter Koenig ) . Bashir and O 'Brien notice that the ship is covered with tribbles . Lieutenant Commander Worf ( Michael Dorn ) and <unk> ( René <unk> ) trail Darvin as he returns to the Defiant . There , Darvin admits that he planted a bomb in a tribble to kill Kirk . 
- Sisko and Lieutenant <unk> Dax ( Terry Farrell ) board the Enterprise but can find no trace of a bomb . They travel to K7 , and <unk> indicate that the bomb is in the grain storage compartments . They enter the compartments and discover that the poisoned grain has all been eaten by tribbles , who are now all dead . Suddenly Captain Kirk opens the compartment and is covered in falling tribbles . Dax and Sisko find the bomb before it can kill Kirk and the Defiant transports it into space where it explodes . The crew of the Defiant use the <unk> <unk> to travel back to the present time , and Sisko finishes explaining the situation to the Temporal Agents . The episode ends with <unk> ( <unk> <unk> ) in his bar on Deep Space Nine 's promenade , with numerous tribbles around him . 
- 
- = = Production = = 
- 
- 
- = = = <unk> and writing = = = 
- 
- As the 30th anniversary of Star Trek was approaching , a number of plans were being put into place . The film Star Trek : First Contact was entering production , a television special was planned to celebrate the franchise and George <unk> had been cast to appear in the Star Trek : Voyager episode " Flashback " . Producer Ira Steven Behr later recalled that he thought that Deep Space Nine might end up being missed out as he considered it to be the " middle child " of the franchise . However , Rick Berman contacted Behr and asked him if he would be interested in doing something to celebrate the anniversary . Behr agreed to discuss it with the staff writers . Initially , there was concern that if the proposed episode aired during the actual anniversary week ( around September 8 ) , that it would have to serve as the season opener , pre @-@ <unk> the already planned opener . 
- The writers discussed potential ideas . Ronald D. Moore had previously brought back Montgomery Scott for the Star Trek : The Next Generation episode " Relics " and since <unk> was appearing in Voyager , they felt that having a member of the main cast from The Original Series return would be repetitive . Some consideration was given by Moore to sending the DS9 crew to the gangster @-@ type planet visited by Kirk in the episode " A Piece of the Action " . It was René Echevarria who suggested a time @-@ travel episode , which was seen as an expensive proposition . Echevarria , however , pressed for the idea . Moore suggested inserting the DS9 crew into " The Trouble with Tribbles " , suggesting it could resolve the question of why a constant stream of tribbles kept hitting Kirk in the head . 
- When the discussion came to inserting the DS9 crew into the bar @-@ brawl scene , Berman liked the idea but was unsure if it could actually be done . Visual effects supervisor Gary Hutzel created test footage and screened it for Behr and Moore , who thought that it was simply footage from the original episode . Once Hutzel revealed that an additional security officer had been seamlessly added to the sequence , the episode was green @-@ lit . During the <unk> process , " The Trouble with Tribbles " was regularly consulted , so the writers could decide where to insert characters . The Temporal Agents , <unk> and <unk> , were so named as they were <unk> of Mulder and Scully from The X @-@ Files . 
- Original " Tribbles " creator David Gerrold was contacted by The New York Times , who wanted to interview him about the anniversary and the <unk> " tribbles " episode . When he questioned Berman about the episode , Berman initially denied it . Gerrold responded that he didn 't want to embarrass anyone , but would like to be able to endorse the project . Berman asked what the endorsement would cost , to which Gerrold requested public acknowledgement of his work and to be cast as an extra in the episode . Berman agreed . Gerrold compared inserting new footage into an existing episode to Back to the Future Part II ( 1989 ) and later said that he would have gone in a different direction had he written the story . Nonetheless , he felt the final product ended up being better than anything he would have created . 
- 
- = = = Directing , cinematography and music = = = 
- 
- A number of directors were considered for the episode , with Jonathan West being given the job . West had previously been the cinematographer on staff for both Deep Space Nine and The Next Generation , as well as directing several episodes of the franchise . He had nine days of preparation time before shooting began . He sought to match the same production values as The Original Series but found that lighting style and color <unk> to film had changed in the intervening years . Visual effects supervisor Dan Curry directed some of the second @-@ unit sequences , and together with West and cinematographer Kris <unk> worked to rectify these issues . This was achieved by switching to a finer grain of film , by utilizing different lenses as well as by shooting from specific angles . With Gerrold on set as an extra , West used him as an unofficial advisor on matching the scenes from " The Trouble with Tribbles " . 
- The actual digital insertion of actors was conducted in the same manner as seen in the film <unk> <unk> ( 1994 ) . The original footage was remastered , and was seen as such an improvement that it inspired the subsequent clean @-@ up and re @-@ release of all of " Original Series " episodes . This remaster was conducted by Hutzel and was the first transfer since 1983 , when a version was created for VHS and laser disc release . Hutzel identified 19 scenes from " The Trouble with Tribbles " which were matched in " Trials and Tribble @-@ ations " . The scene matching between the new footage and the old took nine weeks to complete with a budget of $ 3 million . It involved both two @-@ dimensional and three @-@ dimensional tracking shots as well as insertion of matte shots and the use of both blue and green screens for the actors . Not all shots seen in the episode were actually taken from " The Trouble with Tribbles " . The scene where Sisko meets Kirk on the bridge towards the end of the episode was instead taken from the episode " Mirror , Mirror " . 
- Due in part to the special effects , the <unk> , the set re @-@ constructions and the residual payments to The Original Series cast , Behr later described " Trials and Tribble @-@ ations " as " probably the most expensive hour of episodic TV ever produced " . The only member of The Original Series cast who was spoken to directly by the producers was Leonard <unk> , who was enthusiastic about the idea and was surprised that it had taken them so long to come up with the idea . The remaining cast members were each contacted through Paramount 's legal department . Dennis McCarthy wanted to re @-@ work the Jerry Fielding score previously used on " The Trouble with Tribbles " . He said that he intended to use the production equipment and orchestra available to bring the score up to the same scale previously seen on Deep Space Nine . However , the producers wanted a new score and so McCarthy explained that he composed it in a Fielding @-@ inspired mindset . The only piece that was directly re @-@ recorded by McCarthy was the Alexander Courage " Theme from Star Trek " , which involved a 45 piece orchestra . 
- 
- = = = Design and makeup = = = 
- 
- Art director Randy <unk> led the set re @-@ creation for the Enterprise and K7 , describing the excitement over working on the episode as " <unk> " . <unk> spent a fair amount of time getting the window angles correct on the sets . Mike <unk> re @-@ created the graphics seen on the Enterprise sets using a computer , whilst others were re @-@ drawn by artist Doug Drexler . Some sets were not re @-@ created in full , such as the bridge , which required parts of it to be later added digitally . The captain 's chair from the bridge re @-@ creation was later one of the Star Trek items to be auctioned by Christie 's . Set designer Laura <unk> watched " The Trouble with Tribbles " carefully looking for small details to replicate on the new sets , such as the legs of <unk> in the bar on K7 . However , she said her biggest challenge was tracking down the chairs seen on the space station . She contacted John M. <unk> , who had worked on the original episode . He explained to her that the company which created the original chairs had gone out of business . After searching shops selling retro furniture , the production team found a single chair that matched those seen in the original episode . It was purchased and a mold was made to create more chairs . The actors were impressed when they saw the resulting sets , with Terry Farrell <unk> " <unk> , we 're on the Enterprise ! " 
- Greg <unk> had already been working on a new model of the USS <unk> for the " Flashback " episode of Voyager when he saw the test footage for " Trials and Tribble @-@ ations " . He promised to make a new model of the Enterprise too , but warned that he didn 't know when he would have time to do it . He actually started work on it immediately , and together with his colleagues he not only built a 5 @.@ 5 feet ( 1 @.@ 7 m ) long model of the Enterprise , but created a new model of Deep Space Station K7 and the Klingon cruiser as well . The Enterprise model was the first to be built of the original Star Trek starship in more than 30 years . Other props were also recreated , with around 1 @,@ 400 tribbles created for the various scenes . They were purchased from Lincoln Enterprises , a company set up by Majel Barrett , widow of Star Trek creator Gene Roddenberry . The rest of the era @-@ specific props were newly created , and were made by Steve <unk> . 
- Costume designer Robert <unk> was concerned over the re @-@ creation of the Klingon uniforms seen in The Original Series as he thought that the metallic material used would be nearly impossible to create accurately . He was subsequently relieved to have found four original costumes and an additional shirt in the costume archives , calling them a " <unk> " . His team created patterns from other costumes to remake them . Make @-@ up supervisor Michael <unk> had previously worked on a television series during the 1960s and recalled what type of make @-@ up was available at the time . He had the team restrict themselves to techniques of that era to ensure that the DS9 crew blended properly into the scenes . The hairstyles of the crew were also meant to be reminiscent of The Original Series , with Alexander <unk> sporting a style previously seen on James Doohan . René <unk> said that his new hairstyle reminded him of Jerry Lee Lewis . 
- 
- = = = Filming and casting = = = 
- 
- The cast and crew were enthusiastic on set , with editor Steve Tucker calling it a " <unk> party " . Behr said of the cast and crew in the episode that " They all were having fun . Just sitting on those sets , being on that bridge , it was a <unk> , a real <unk> . " <unk> L. <unk> was brought in at the last minute to play Lt. <unk> , as she was a friend of one of the production crew and had previously appeared as a <unk> pleasure girl in The Next Generation episode " Captain 's Holiday " . She was brought in because none of the actresses the producers had seen during the casting process could say the role 's one line ( " <unk> 15 " ) convincingly enough . Her involvement led to the role 's being expanded into a second scene where she was revealed to be Bashir 's great @-@ grandmother . 
- Charlie Brill returned to film new scenes as Klingon agent Arne Darvin , while original series writer Gerrold was allowed to appear as an extra in two scenes as a crewman from the Enterprise . In one of those scenes he was holding an original tribble from " The Trouble with Tribbles " . Walter Koenig was on hand as well during the filming of the episode to teach the DS9 actors how the consoles were operated on the Enterprise . Koenig later commented that he was paid eight times as much for this and the residual payment as he had been for the original episode . A string of other visitors came to the set during filming , including Majel Barrett and former The Next Generation producer ( and <unk> co @-@ producer ) Bob <unk> . 
- 
- = = Reception = = 
- 
- Before the episode was shown , a half @-@ hour special was shown on the Sci Fi Channel about the making of " Trials and Tribble @-@ ations " on November 2 , 1996 . Paramount also promoted the episode by arranging the placement of around 250 @,@ 000 tribbles in <unk> and buses across the United States . It received Nielsen ratings of 7 @.@ 7 % , placing it in sixth place in the timeslot . This meant that it had been watched by 7 @.@ 7 % of all households . It was the most watched episode of the fifth season during its initial broadcast . The last time the series had received similar ratings was nearly a year earlier with season four 's " Little Green Men " . 
- Two reviewers watched the episode for <unk> <unk> Atkinson described " Trials and Tribble @-@ ations " as a " perfect episode " , and " one of the best Star Trek episodes ever made , in any series . " She praised the humour and the references , and found Dax in her role as a stand @-@ in for fans of The Original Series . She gave the episode a score of six out of six . Eugene Myers wasn 't disappointed following the hype about the episode , saying that it was " <unk> in nostalgia " . He thought that the bomb @-@ in @-@ a @-@ tribble plot was ingenious and allowed the episode to step outside of merely being good due to the success of " The Trouble with Tribbles " . His favourite scene was the constant stream of tribbles hitting Kirk in the head because Sisko and Dax were throwing them out of the grain compartment while looking for the bomb . He also gave the episode a score of six out of six . 
- Zack <unk> in his review for The A.V. Club , called the episode a " delight " and a " <unk> " . He thought that having Brill film new scenes showed some continuity between the old and the new , and thought that the special effects worked well enough . He summed it up by saying , " It 's not tightly plotted , and once the initial rush of nostalgia fades , there isn 't a lot of depth or suspense to replace it . But there are laughs , more than enough to justify the experiment , and the nostalgia never fades away entirely . " In the book Deep Space and Sacred Time : Star Trek in the American <unk> , Jon Wagner and Jan <unk> compared the <unk> agents seen in " Trials and Tribble @-@ ations " to the police detectives seen in the television series <unk> . <unk> Wheeler , in her list of the best episodes of Deep Space Nine for website Den of Geek , listed " Trials and Tribble @-@ ations " as the sixth best . In a list of the top 100 episodes of the Star Trek franchise , " Trials and Tribble @-@ ations " was placed in 32nd place by Charlie Jane Anders at io9 . 
- " Trials and Tribble @-@ ations " was nominated for three Creative Arts Emmy Awards in the Outstanding Art Direction for a Series , Outstanding <unk> for a Series and Outstanding Special Visual Effects . However , it was not successful in any of those categories . It was also nominated for the Hugo Award for Best Dramatic Presentation , as " The Trouble with Tribbles " had been in 1968 . 
- 
- = = Home media release = = 
- 
- The tie @-@ in novelization of " Trials and Tribble @-@ ations " was written by Diane Carey and published by Pocket Books . In 1998 , a " Talking Tribble Gift Set " was released which contained both " The Trouble with Tribbles " and " Trials and Tribble @-@ ations " on VHS . " Trials and Tribble @-@ ations " was first released in the normal run of VHS issues as part of a two episode cassette alongside " The <unk> " in the United Kingdom on October 1 , 1999 . A single episode release followed in the United States and Canada on July 10 , 2001 . It was released on DVD as part of the season five box set on October 7 , 2003 . 
- 
- 
- = Tintin in the Congo = 
- 
- Tintin in the Congo ( French : Tintin au Congo ; French pronunciation : <unk> [ <unk> ̃ <unk> o <unk> ̃ go ] ) is the second volume of The Adventures of Tintin , the comics series by Belgian cartoonist Hergé . Commissioned by the conservative Belgian newspaper Le Vingtième Siècle for its children 's supplement Le Petit Vingtième , it was serialised weekly from May 1930 to June 1931 before being published in a collected volume by Éditions de Petit Vingtième in 1931 . The story tells of young Belgian reporter Tintin and his dog Snowy , who are sent to the Belgian Congo to report on events in the country . Amid various encounters with the native Congolese people and wild animals , Tintin <unk> a criminal diamond smuggling operation run by the American gangster Al <unk> . 
- Following on from Tintin in the Land of the Soviets and bolstered by publicity stunts , Tintin in the Congo was a commercial success within Belgium and was also serialised in France . Hergé continued The Adventures of Tintin with Tintin in America in 1932 , and the series subsequently became a defining part of the Franco @-@ Belgian comics tradition . In 1946 , Hergé re @-@ drew and coloured Tintin in the Congo in his distinctive ligne @-@ claire style for <unk> by Casterman , with further alterations made at the request of his Scandinavian publisher for a 1975 edition . In the late 20th century , Tintin in the Congo came under criticism for both its perceived racist colonial attitude toward the Congolese and for its glorification of big @-@ game hunting ; accordingly , attempts were made in Belgium , Sweden , the United Kingdom , and the United States to either ban the work or restrict its availability to children . Critical reception of the work has been largely negative , with commentators on The Adventures of Tintin describing it as one of Hergé 's weakest works . 
- 
- = = Synopsis = = 
- 
- Belgian reporter Tintin and his dog Snowy travel to the Belgian Congo , where a cheering crowd of native Congolese <unk> them . Tintin hires a native boy , Coco , to assist him in his travels , and shortly after , Tintin rescues Snowy from a crocodile . A criminal stowaway attempts to kill Tintin , but monkeys throw <unk> at the stowaway that knock him unconscious . A monkey kidnaps Snowy , but Tintin saves him . 
- The next morning , Tintin , Snowy , and Coco crash their car into a train , which the reporter <unk> and <unk> to the village of the Babaorum tribe . He meets the king , who accompanies him on a hunt the next day . A lion knocks Tintin unconscious , but Snowy rescues him by biting off its tail . Tintin gains the admiration of the natives , making the Babaorum witch @-@ doctor Muganga jealous . When he <unk> a man using quinine , he is hailed as a <unk> <unk> ( " <unk> of rocks " ) . With the help of the criminal stowaway , Muganga accuses Tintin of destroying the tribe 's sacred idol . The enraged villagers imprison Tintin , but then turn against Muganga when Coco shows them footage Tintin had made of the witch @-@ doctor and the stowaway conspiring to destroy the idol . Tintin becomes a hero in the village , and a local woman bows down to him , saying , " White man very great ! Has good spirits ... White <unk> is big <unk> man ! " 
- <unk> , Muganga starts a war between the Babaorum and their neighbours , the M <unk> , whose king leads an attack on the Babaorum village . Tintin <unk> them , and the M <unk> cease hostilities and come to <unk> Tintin . Muganga and the stowaway plot to kill Tintin and make it look like a leopard attack , but Tintin survives and saves Muganga from a <unk> <unk> ; Muganga pleads mercy and ends his hostilities . The stowaway attempts to capture Tintin again and eventually succeeds disguised as a Catholic missionary . They fight across a waterfall , and the stowaway is eaten by <unk> . After reading a letter from the stowaway 's pocket , Tintin finds that someone called " <unk> " has ordered his elimination . Tintin captures a criminal who tried to rendezvous with the stowaway and learns that " <unk> " is the American gangster Al <unk> , who is trying to gain control of African diamond production . Tintin and the colonial police arrest the rest of the diamond smuggling gang and Tintin and Snowy return to Belgium . 
- 
- = = History = = 
- 
- 
- = = = Background = = = 
- 
- Georges <unk> — best known under the pen name Hergé — was employed as editor and illustrator of Le Petit Vingtième ( " The Little Twentieth " ) , a children 's supplement to Le Vingtième Siècle ( " The Twentieth Century " ) , a <unk> Roman Catholic , conservative Belgian newspaper based in Hergé 's native Brussels . Run by the <unk> Norbert Wallez , the paper described itself as a " Catholic Newspaper for Doctrine and Information " and disseminated a far @-@ right , fascist viewpoint . According to Harry Thompson , such political ideas were common in Belgium at the time , and Hergé 's milieu was permeated with conservative ideas revolving around " patriotism , Catholicism , strict morality , discipline , and <unk> " . 
- In 1929 , Hergé began The Adventures of Tintin comic strip for Le Petit Vingtième , a series about the exploits of a fictional Belgian reporter named Tintin . Following the success of Tintin in the Land of the Soviets , serialised weekly in Le Petit Vingtième from January 1929 to May 1930 , Hergé wanted to send Tintin to the United States . Wallez insisted he write a story set in the Belgian Congo , then a Belgian colony and today the Democratic Republic of the Congo . Belgian children learned about the Congo in school , and Wallez hoped to encourage <unk> and missionary zeal in his readership . He believed that the Belgian colonial administration needed promotion at a time when memories " were still fairly fresh " of the 1928 visit to the colony by the Belgian King Albert and Queen Elisabeth . He also hoped that some of his readers would be inspired to work in the Congo . 
- Hergé characterised Wallez 's instructions in a sarcastic manner , saying Wallez referred to the Congo as " our beautiful colony which has great need of us , <unk> , <unk> " . He already had some experience in illustrating Congolese scenes ; three years previously , Hergé had provided two illustrations for the newspaper that appeared in an article celebrating the fiftieth anniversary of Henry Morton Stanley 's expedition to the Congo . In one of these , Hergé depicted a native Congolese <unk> before a European , a scene that he repeated in Tintin in the Congo . 
- As in Land of the Soviets , where Hergé had based his information about the Soviet Union almost entirely on a single source , in Tintin in the Congo he used limited source material to learn about the country and its people . He based the story largely on literature written by missionaries , with the only added element being that of the diamond smugglers , possibly adopted from the " Jungle Jim @-@ type serials " . Hergé visited the Colonial Museum of <unk> , examining their ethnographic collections of Congolese artefacts , including costumes of the Leopard Men . He adopted hunting scenes from André <unk> 's novel The Silence of Colonel <unk> , while his animal drawings were inspired by Benjamin <unk> 's prints . He also listened to tales of the colony from some of his colleagues who had been there , but disliked their stories , later claiming : " I didn 't like the colonists , who came back <unk> about their exploits . But I couldn 't prevent myself from seeing the Blacks as big children , either . " 
- 
- = = = Original publication , 1930 – 31 = = = 
- 
- Tintin in the Congo was serialised under the French title of Tintin au Congo in Le Petit Vingtième from 5 May 1930 to 11 June 1931 ; it was syndicated to the French Catholic newspaper <unk> <unk> . Drawn in black and white , it followed the same formula employed in Land of the Soviets , remaining " essentially <unk> " according to Michael Farr , and consisting of largely unrelated events that Hergé improvised each week . Hergé later commented on the process of writing these early adventures , stating , " The Petit Vingtième came out on Wednesday evening , and I often didn 't have a clue on Wednesday morning how I was going to get Tintin out of the predicament I had put him in the previous week . " The strip 's visual style was similar to that of Land of the Soviets . In the first instalment of Tintin in the Congo , Hergé featured Quick and <unk> , two young boys from Brussels whom he had recently introduced in another Le Petit Vingtième comic strip , in the crowd of people saying goodbye to Tintin . 
- Like Land of the Soviets , Tintin in the Congo was popular in Belgium . On the afternoon of 9 July 1931 , Wallez repeated the publicity stunt he had used when Soviets ended by having a young actor , Henry de <unk> , dress up as Tintin in colonial gear and appear in Brussels and then <unk> , accompanied by 10 African bearers and an assortment of exotic animals hired from a zoo . Co @-@ organised with the Bon <unk> department store , the event attracted 5 @,@ 000 spectators in Brussels . In 1931 , Brussels @-@ based Éditions de Petit Vingtième collected the story together into a single volume , and Casterman published a second edition in 1937 . By 1944 the book had been reprinted seven times , and had <unk> each of the other seven books in the series . The series ' success led Wallez to <unk> Hergé 's contract , giving him a higher salary and the right to work from home . 
- 
- = = = Second version , 1946 = = = 
- 
- In the 1940s , after Hergé 's popularity increased , he <unk> many of the original black @-@ and @-@ white Tintin stories in colour using the ligne claire ( " clear line " ) drawing style he had developed , so that they fitted in visually with the newer Adventures of Tintin that he had produced . Hergé first made some changes in this direction in 1940 , when the story was serialised in the Flemish @-@ language Het <unk> <unk> . 
- At Casterman 's prompting , Tintin in the Congo was subsequently fully re @-@ drawn , and the new version was published in 1946 . As a part of this modification , Hergé cut the page length from 110 plates to the standard 62 pages , as suggested by the publisher Casterman . He also made several changes to the story , cutting many of the references to Belgium and colonial rule . For example , in the scene where Tintin teaches Congolese school children about geography , he states in the 1930 – 31 version , " My dear friends , today I 'm going to talk to you about your country : Belgium ! " whereas in the 1946 version , he instead gives them a mathematics lesson . Hergé also changed the character of Jimmy <unk> , the owner of the leopard that attacks Tintin , from a black manager of the Great American Circus into a white " supplier of the biggest zoos in Europe . " 
- In the 1946 colour version , Hergé added a cameo appearance from Thomson and Thompson , the two detectives that he had introduced in the fourth Tintin story , Cigars of the Pharaoh ( 1932 – 34 ) , which was <unk> set after the Congolese adventure . Adding them to the first page , Hergé featured them in the backdrop , watching a crowd surrounding Tintin as he boards a train and commenting that it " Seems to be a young reporter going to Africa ... " In the same frame , Hergé inserted depictions of himself and his friend Edgar P. <unk> ( the book 's colourist ) into the crowd seeing Tintin off . 
- 
- = = = Later alterations and releases = = = 
- 
- When Tintin in the Congo was first released by the series ' Scandinavian publishers in 1975 , they objected to page 56 , where Tintin drills a hole into a live rhinoceros , fills it with dynamite , and blows it up . They asked Hergé to replace this page with a less violent scene , which they believed would be more suitable for children . Hergé agreed , as he regretted the scenes of big @-@ game hunting in the work soon after producing it . The altered page involved the rhinoceros running away unharmed after accidentally knocking down and triggering Tintin 's gun . 
- Although publishers worldwide had made it available for many years , English publishers refused to publish Tintin in the Congo because of its racist content . In the late 1980s , Nick <unk> , then agent of Studios Hergé in the United Kingdom , told reporters of his intention to finally publish it in English and stated his belief that publishing the original 1931 black and white edition would cause less controversy than releasing the 1946 colour version . After more delay , in 1991 — sixty years after its original 1931 publication — it was the last of The Adventures of Tintin to see publication in English . The 1946 colour version appeared in English in 2005 , published by Egmont . 
- 
- = = Critical analysis = = 
- 
- Hergé biographer Pierre Assouline believed that Hergé 's drawing became more assured throughout the first version of the story without losing any of its spontaneity . He thought that the story began in " the most inoffensive way " , and that throughout the story Tintin was portrayed as a Boy Scout , something he argued reflected Hergé 's " moral debt " to Wallez . Biographer Benoît Peeters opined that Tintin in the Congo was " nothing spectacular " , with some " incredibly <unk> " monologues , but he thought the illustrations " a bit more polished " than those in Land of the Soviets . Believing the plot to be " extremely simple " , he thought that Tintin 's character was like a child manipulating a world populated by toy animals and lead figurines . Michael Farr felt that , unlike the previous Tintin adventure , some sense of a plot emerges at the end of the story with the introduction of the American diamond @-@ smuggling racket . Philippe <unk> thought the work to be " more exciting " than Land of the Soviets and argued that Hergé 's depiction of the native Congolese was not mocking but a parody of past European militaries . By contrast , Harry Thompson believed that " Congo is almost a regression from Soviets " , in his opinion having no plot or characterisation ; he described it as " probably the most childish of all the Tintin books . " Simon <unk> of the Financial Times criticised both Land of the Soviets and Tintin in the Congo as the " worst " of the Adventures , opining that they were " poorly drawn " and " largely plot @-@ free " . 
- Farr saw the 1946 colour version as poorer than the black and white original ; he said it had lost its " <unk> " and " atmosphere " , and that the new depiction of the Congolese landscape was <unk> and more like a European zoo than the " <unk> , dusty <unk> of reality " . Peeters took a more positive attitude towards the 1946 version , commenting that it contained " aesthetic improvements " and " clarity of composition " because of Hergé 's personal development in <unk> , as well as an enhancement in the dialogue , which had become " more lively and fluid . " 
- In his <unk> study of the series , Jean @-@ Marie Apostolidès highlighted that in the Congolese adventure , Tintin represented progress and the Belgian state was a model for the natives to imitate . In doing so , he argued , they could become more European and thus <unk> from the perspective of Belgian society , but that instead they ended up appearing as parodies . <unk> that Tintin was imposing his own view of Africa onto the Congolese , Apostolidès remarked that Tintin appeared as a god @-@ figure , with evangelical overtones in the final scene . Literary critic Tom McCarthy concurred that Tintin represented the Belgian state , but also suggested that he acted as a Christian missionary , even being " a kind of god " akin to the character of <unk> in Joseph Conrad 's Heart of Darkness ( 1899 ) . McCarthy compared the scene where Tintin exposes Muganga as a fraud to that in which the character of <unk> exposes the magician in William Shakespeare 's The Tempest . 
- 
- = = Criticism = = 
- 
- 
- = = = <unk> = = = 
- 
- In the late 20th and early 21st centuries , several campaigners and writers characterised Tintin in the Congo as racist due to its portrayal of the Congolese as <unk> and stupid . According to Tom McCarthy , Hergé depicted the Congolese as " good at heart but backwards and lazy , in need of European mastery . " There had been no such controversy when originally published , because it was only following the Wind of Change and decolonisation , which occurred during the 1950s and 1960s , that Western attitudes towards indigenous Africans shifted . Harry Thompson argued that one must view Tintin in the Congo in the context of European society in the 1930s and 1940s , and that Hergé had not written the book to be " deliberately racist " . He argued that it reflected the average Belgian view of Congolese people at the time , one that was more " <unk> " than <unk> . Jean @-@ Marie Apostolidès supported this idea , as did biographer Benoît Peeters , who asserted , " Hergé was no more racist than the next man . " After meeting Hergé in the 1980s , Farr commented , " You couldn 't have met someone who was more open and less racist . " 
- Contrastingly , biographer Pierre Assouline stated that in 1930s Belgium , Hergé would have had access to literature by the likes of André <unk> and Albert <unk> that was critical of the colonial regime . Assouline claimed that Hergé instead chose not to read such reports because they conflicted with the views of his conservative milieu . Laurence Grove — President of the International <unk> <unk> Society and an academic at the University of Glasgow — concurred , remarking that Hergé adhered to prevailing societal trends in his work , and that " [ w ] hen it was fashionable to be a colonial racist , that 's what he was . " Comic book historian Mark <unk> noted that other Franco @-@ Belgian comic artists of the same period had chosen to depict the native Africans in a more favourable light , citing the examples of <unk> 's 1939 work <unk> et <unk> ( <unk> and Shoe @-@ Black ) , in which the protagonists are adopted brothers , one white , the other black , and <unk> et <unk> , which was serialised in <unk> from 1939 to 1940 and in which the Congolese aid the Belgians against their American antagonists . 
- Farr and McCarthy stated that Tintin in the Congo was the most popular Tintin adventure in <unk> Africa . According to Thompson , the book remained hugely popular in the Congo even after the country achieved independence in 1960 . Nevertheless , government figures in the Democratic Republic of the Congo ( DRC ) have criticised the book . In 2004 , after the Belgian Minister of Foreign Affairs Karel De <unk> described President Joseph <unk> 's provisional DRC government as incompetent , Congolese Information Minister Henri <unk> <unk> accused him of " racism and nostalgia for colonialism " , remarking that it was like " Tintin in the Congo all over again . " De <unk> refused to <unk> his statement . 
- In July 2007 , British human rights lawyer David <unk> complained to the United Kingdom 's Commission for Racial <unk> ( CRE ) that he came across the book in the children 's section of Borders <unk> while shopping with his wife and two sons . The CRE called on <unk> to remove the comic , stating that it contained " hideous racial prejudice " by depicting Congolese who " look like monkeys and talk like <unk> . " Responding that it was committed to letting its " customers make the choice " , Borders moved the book to an area reserved for adult graphic novels . UK <unk> <unk> 's followed suit . Another British <unk> , <unk> , said that the book was sold on its website , but with a label that recommended it for readers aged 16 and over . The CRE 's attempt to ban the book was criticised by Conservative Party politician Ann <unk> , who remarked that the organisation had more important things to do than regulate the availability of historical children 's books . The media controversy increased interest in the book , and Borders reported that its sales of Tintin in the Congo had been boosted 4 @,@ 000 % , while it also rose to eighth on the Amazon.com bestseller list . Publisher Egmont UK also responded to racism concerns by placing a protective band around the book with a warning about its content and writing an introduction describing its historical context . 
- Tintin in the Congo also came under criticism in the United States ; in October 2007 , in response to a complaint by a patron , the Brooklyn Public Library in New York City placed the graphic novel in a locked back room , only permitting access by appointment . Tintin in the Congo became part of a drawn @-@ out media debate in Sweden after national newspaper <unk> <unk> reported the removal of <unk> in the Congo from a children 's library in <unk> in Stockholm in September 2011 . The incident , nicknamed " Tintin @-@ gate " , led to heated discussions in mainstream and social media concerning accusations of racism and censorship . 
- In August 2007 , Congolese student <unk> <unk> Mondondo filed a complaint in Brussels , claiming that the book was an insult to the Congolese people and required banning . Public prosecutors investigated and initiated a criminal case . The matter was eventually transferred to a civil court in April 2010 . Mondondo 's lawyers argued that Tintin in the Congo amounted to " a justification of colonisation and of white supremacy " , and Mondondo called it " racist and <unk> " . Alain <unk> , lawyer for both <unk> , the company which controls Hergé 's estate , and Casterman , the book 's publisher , argued that the cartoonist 's depiction of the Congolese " wasn 't racism but kind <unk> " . He said that banning it would set a dangerous precedent for the availability of works by other historical authors , such as Charles Dickens or Jules Verne , which contain similar stereotypes of non @-@ white <unk> . The court ruled in February 2012 that the book would not be banned , deciding that it was " clear that neither the story , nor the fact that it has been put on sale , has a goal to ... create an intimidating , hostile , degrading , or humiliating environment " , and that it therefore did not break Belgian law . Belgium 's Centre for <unk> Opportunities warned against " over @-@ reaction and hyper political <unk> " . Shortly after , Swedish @-@ Belgian Jean @-@ <unk> <unk> filed a similar complaint , which was supported by <unk> , an interest group for Swedes of African descent . The complaint to the Chancellor of Justice was turned down as violations of hate speech restrictions in the Swedish <unk> Law on Freedom of <unk> must be filed within one year of publication , and the latest Swedish edition of Tintin in the Congo appeared in 2005 . 
- The South African comics writer Anton <unk> has parodied the perceived racist nature of the book to highlight what he sees as the continuing racist undertones of South African society . In his <unk> in <unk> ( 2010 ) , a satire of Tintin in the Congo , he portrays Tintin as an <unk> with racist views of indigenous Africans . 
- 
- = = = Hunting and animal cruelty = = = 
- 
- Tintin in the Congo shows Tintin taking part in what Michael Farr described as " the wholesale and <unk> slaughter " of animals ; over the course of the Adventure , Tintin shoots several antelope , kills an <unk> to wear its skin , rams a rifle vertically into a crocodile 's open mouth , <unk> an elephant for ivory , stones a buffalo , and ( in earlier editions ) drills a hole into a rhinoceros before planting dynamite in its body , blowing it up from the inside . Such scenes reflect the popularity of big @-@ game hunting among whites and affluent visitors in Sub @-@ Saharan Africa during the 1930s . Hergé later felt guilty about his portrayal of animals in Tintin in the Congo and became an opponent of blood sports ; when he wrote Cigars of the Pharaoh ( 1934 ) , he had Tintin befriend a herd of <unk> living in the Indian jungle . 
- Philippe <unk> stated that the scene in which Tintin shoots a herd of antelope was " enough to upset even the least ecological reader " in the 21st century . When India Book House first published the book in India in 2006 , that nation 's branch of the People for the <unk> Treatment of Animals issued a public criticism , and chief <unk> <unk> <unk> stated that the book was " replete with instances that send a message to young minds that it is acceptable to be cruel to animals . " 
- 
- 
- = Andrew Johnston ( singer ) = 
- 
- Andrew Johnston ( born 23 September 1994 ) is a British singer who rose to fame when he appeared as a boy soprano on the second series of the UK television talent show Britain 's Got Talent in 2008 . Although he did not win the competition , he received a contract to record with Syco Music , a label owned by the Britain 's Got Talent judge Simon Cowell . Johnston 's debut album , One Voice , was released in September of the same year , and reached number four on the UK Albums Chart . Although Johnston originally performed as a treble , his voice has since matured to baritone , and he is now a member of the National Youth Choir . 
- Johnston was born in <unk> , Scotland , and his parents separated when he was an infant . He and his mother moved to Carlisle , where they lived in " poverty " . He became head chorister at Carlisle Cathedral , and was bullied at school because of his love of classical music . While some journalists have argued Britain 's Got Talent producers took advantage of Johnston 's background , others have hailed his story as inspirational . In 2009 , he graduated from Trinity School . Johnston now studies full @-@ time at the Royal Northern College of Music . 
- 
- = = History = = 
- 
- 
- = = = Early life and Carlisle Cathedral Choir = = = 
- 
- Johnston was born on 23 September 1994 in <unk> , Scotland , the son of Andrew Johnston and Morag Brannock . He was given the extensive name Andrew Aaron Lewis Patrick Brannock John <unk> Michael Robert Oscar Schmidt Johnston . Johnston 's parents separated when he was eight months old , and from that time he lived with his mother and three older siblings in Carlisle , Cumbria , in the north of England , where he attended Trinity School . Johnston tried out for Carlisle Cathedral Choir at the age of six at the recommendation of Kim Harris , a teacher at his primary school . He was auditioned by the <unk> Jeremy <unk> and accepted into the choir at the age of seven . Johnston 's mother , who had no previous association with the cathedral , described her feelings of being overwhelmed by emotion at having her boy singing in such a " stunning building among those extraordinary voices " . His mother also described Johnston 's busy regimen of practice four times a week and all day Sundays , saying that it took up all of their spare time . However , she said that the cathedral staff became like a family to her son , and that " it was such a lovely , safe , close feeling for him " . Johnston , who attended Trinity School , was subject to abuse and threats from <unk> which drove him to contemplate quitting the choir , but he was helped through the ordeal by his <unk> and the dean and canons of the cathedral . By the time of his participation in Britain 's Got Talent , Johnston was head chorister . 
- In September 2008 , after his appearance on Britain 's Got Talent but before the release of his first album , Johnston embarked on a tour of Norway with the choir , performing at Stavanger Cathedral and <unk> Abbey , among other places . The tour was conceived because the Diocese of Stavanger is connected with the Diocese of Carlisle through the Partnership for World Mission . This was Johnston 's last tour with the choir . Johnston features as head chorister on one of the choir 's albums , The Choral Music of <unk> <unk> , released in November 2008 . 
- 
- = = = Britain 's Got Talent = = = 
- 
- Johnston was entered as a competitor in the second series of Britain 's Got Talent by his mother . He passed the first public audition , singing " Pie <unk> " from Andrew Lloyd Webber 's <unk> . Amanda Holden , one of the competition 's judges , was brought to tears , and the audience offered Johnston a standing ovation . Johnston was tipped as the favourite to win the competition . Later , Johnston described his initial audition as daunting , saying that " it was scary singing in front of 2 @,@ 500 people . I had never sang on stage before – then there was also Simon , Amanda and Piers " . He won his semi @-@ final heat on 27 May 2008 , receiving the most public votes on the night and thereby qualifying for the final . He sang " Tears in Heaven " by Eric Clapton ; judge Holden told him he had " a gift from God in [ his ] voice " . At the final on 30 May , he again sang " Pie <unk> " . He finished in third place , behind the winner , the street dancer George Sampson and runners @-@ up , the dance group Signature . Johnston left the stage in tears , later saying that he " was upset . But when you see the talent that was there , it was an honour just to be in the final " . The day after the final , Cowell 's publicist Max Clifford said that it was " quite possible " that Cowell would be offering record contracts to some of the finalists , including Johnston . Johnston and other contestants then embarked on a national arena tour . 
- During his initial audition , Johnston claimed that he was bullied and <unk> from the age of six because of his singing . When asked how he dealt with the issue , he stated " I carry on singing . " There were claims in the Daily Mail , a UK @-@ based tabloid newspaper , that the programme 's producers had deliberately <unk> the extent of Johnston 's bullying as a " <unk> story " , suggesting that sympathy rather than his singing got him many of the votes . However , in The Times , Johnston 's success story was described as " the stuff of <unk> " , as he was successful despite having been raised in " poverty " . Johnston said he did not talk about being bullied because he was told to do so by producers , but " because I believed it would help people who were going through what I had gone through be stronger " . Johnston has subsequently visited schools and elsewhere to help other victims of bullying . He said " I want to use my experience of <unk> to help other kids " . 
- 
- = = = One Voice = = = 
- 
- On 12 June 2008 , while Johnston was travelling with the Britain 's Got Talent Live Tour , it was announced that Johnston had signed a record deal with Syco Music , a division of Sony BMG , and that his first album would be produced after the tour . The deal was reportedly for £ 1 million . After signing with Syco , Johnston made public appearances , including performing at Andrew Lloyd Webber 's birthday celebrations on 14 September , and at Carlisle United 's <unk> Park . 
- Johnston 's debut album , One Voice , was released on 29 September 2008 . It includes a cover of " Walking in the Air " , performed with <unk> Smith . The album was recorded over a six @-@ week period in London , and the track listing was chosen by Cowell . Johnston described the recording process as " brilliant " , and that it was " really good – just to be in a recording studio and meet the different people " . The album debuted in the British charts at number five , and finished the week at number four . The album was later certified gold , having sold 100 @,@ 000 copies , and Johnston was presented a gold disc by daytime television presenter Penny Smith . Critics responded positively to the album , with Kate <unk> , writing for the Korea <unk> Daily , saying Johnston " has <unk> talent than hordes of his musical elders " and that " the vulnerability " of Johnston 's performance on the album " makes for a haunting musical experience " . In Music Week , the album was described as " highly @-@ anticipated " , and Johnston was called " exceptionally @-@ talented " . 
- After the album 's release , Johnston became involved in the Sing Up campaign , appearing in schools around the country to encourage other young people to join choirs . In December 2008 , Johnston made a guest appearance at <unk> 's Christmas fair , and performed at a <unk> service in Bradford . Johnston was also invited to turn on the Carlisle Christmas lights and perform at the celebrations . Mike <unk> , of Carlisle City Council , described Johnston as " one of our local heroes " . 
- 
- = = = Hiatus and 2010s = = = 
- 
- In September 2009 , Johnston announced that he would be taking a year off from singing as his voice had broken , changing him to a tenor . He had previously performed as a treble . He said " the tutors at [ the Royal Northern College of Music ] said they 'll be able to train my voice up again . It 's the same as it ever was , just deeper " . Johnston 's voice then changed from a tenor to a baritone . After remaining out of the spotlight for two years , he joined the National Youth Choir , saying " I 'm just another lad in there – no one focuses on Britain 's Got Talent and I 'm happy about that " . In 2011 , he was awarded a Royal School of Church Music Gold medal ; public performances that year included a charitable concert , alongside <unk> John Bromley and Tony Green , at St Paul 's Church , <unk> in November . 
- In September 2013 , Johnston began to study for a Bachelor of Music degree at the Royal Northern College of Music , under the tutelage of Jeff Lawton , who had previously <unk> him at the Junior College . He immediately joined the college 's Chamber Choir and the Manchester Cathedral choir , but said that he intended to still sing with the Carlisle Cathedral choir where possible . While a student , Johnston 's singing was adversely affected by a broken nose , the result of an <unk> attack in a Carlisle nightclub on New Year 's Day , 2014 . 
- 
- = = Personal life = = 
- 
- Johnston 's family home is in Stanwix , Carlisle . His mother , Morag Brannock , worked for the Office for National Statistics before giving up her job to support her son 's career . Prior to his Britain 's Got Talent appearances , he attended Trinity School , and later received tuition from a personal tutor . Johnston said that he " had a lot of support from local people when ... taking part in Britain 's Got Talent " , and was given a civic award for outstanding achievement by Carlisle City Council in March 2009 . 
- Johnston 's interests include <unk> , in which he has a black belt . The Carlisle newspaper News and Star reported in September 2012 that Johnston had become the youngest person in the world to be granted a licence to teach the sport . 
- 
- = = Discography = = 
- 
- Studio albums 
- 
- 
- = Illinois ( Sufjan Stevens album ) = 
- 
- Illinois ( styled Sufjan Stevens <unk> You To : Come On Feel the Illinoise on the cover ; sometimes written as Illinoise ) is a 2005 concept album by American indie folk songwriter Sufjan Stevens . It is his fifth studio album , and features songs referencing places , events , and persons related to the U.S. state of Illinois . Illinois is Stevens ' second based on a U.S. state — part of a planned series of fifty that began with the 2003 album Michigan that Stevens has since acknowledged was a gag . 
- Stevens recorded and produced the album at multiple venues in New York City using low @-@ fidelity studio equipment and a variety of instruments between late 2004 and early 2005 . The artwork and lyrics explore the history , culture , art , and geography of the state — Stevens developed them after <unk> criminal , literary , and historical documents . Following a July 4 , 2005 release date , Stevens promoted Illinois with a world tour . 
- Critics praised the album for its well @-@ written lyrics and complex orchestrations ; in particular , reviewers noted Stevens ' progress as a songwriter since the release of Michigan . Illinois was named the best @-@ reviewed album of 2005 by review aggregator Metacritic , and was included on several reviewers ' " best of the decade " lists — including those of Paste , NPR , and Rolling Stone . The album amounted to Stevens ' greatest public success to date : it was his first to place on the Billboard 200 , and it topped the Billboard list of " Heatseekers Albums " . The varied instrumentation and experimental songwriting on the album invoked comparisons to work by Steve Reich , Neil Young , and The Cure . Besides numerous references to Illinois history , geography , and attractions , Stevens continued a theme of his songwriting career by including multiple references to his Christian faith . 
- 
- = = Background , recording , and tour = = 
- 
- Stevens launched his 50 @-@ state project in 2003 with the album Michigan and chose to focus on Illinois with this recording because " it wasn 't a great leap " , and he liked the state because he considered it the " center of gravity " for the American Midwest . Before creating the album , Stevens read literature by Illinois authors Saul <unk> and Carl <unk> , and studied immigration records and history books for the state — he made the deliberate decision to avoid current events and focused on historical themes . He also took trips through several locations in Illinois and asked friends and members of Internet chat rooms for anecdotes about their experiences in the state . Although he began work in 2004 on Oregon @-@ themed songs and briefly considered releasing a Rhode Island 7 " , Stevens has since not released another album focused on a state , saying in a November 2009 interview with Paste that " the whole premise was such a joke , " and telling Andrew Purcell of The Guardian in October 2009 " I have no qualms about admitting [ the fifty states project ] was a promotional gimmick . " An Arkansas @-@ related song was released through NPR as " The Lord God Bird " and material intended for New Jersey and New York became The <unk> . 
- All of the songs on Illinois were written , recorded , engineered , and produced by Stevens , with most of the material being recorded at The <unk> Project studio in Astoria , Queens , and in Stevens ' Brooklyn apartment . As with his previous albums , Stevens recorded in various locations , with additional piano recorded in St. Paul 's Church in Brooklyn ; strings and vocals performed in collaborators ' apartments ; electronic organ recorded in the New Jerusalem Recreational Room in <unk> , New Jersey ; and <unk> played at Carroll Music Studios in New York City . Stevens mostly created the album without collaboration , focusing on the writing , performance , and technical creation of the album by himself : " I was pretty <unk> in the construction of Illinois . I spent a lot of time alone , a few months in isolation working on my own and in the studio . I let things germinate and <unk> independently , without thinking about an audience or a live show at all . " 
- Stevens employed low @-@ fidelity recording equipment , which allowed him to retain creative control and keep costs low on recording Illinois . Typically , his process involved recording to 32 kHz 8 @-@ track tape using inexpensive microphones such as the <unk> <unk> and <unk> <unk> . He then employed Pro Tools for mixing and other production tasks . 
- After consulting with Michael Kaufmann and Lowell <unk> of Asthmatic Kitty about the amount of material he had recorded , Stevens decided against a double album , saying that would be " arrogant " . In 2006 , several tracks recorded during these sessions were sent to Seattle @-@ based musician and producer James McAllister for additional instrumentation and production , and were released in 2006 on the follow @-@ up album The Avalanche : <unk> and <unk> from the Illinois Album . Among these outtakes are three separate recordings of the song " Chicago " — including the " Multiple Personality <unk> Version " , which was produced during a subsequent tour . The " Adult Contemporary Easy Listening Version " of the song was supposed to appear on the Illinois album , but was changed at the last minute . 
- Illinois was released on July 4 , 2005 , through Rough Trade Records in Europe and was distributed domestically by Asthmatic Kitty Records starting July 5 , 2005 . Although he initially had no plans to perform this material live , less than two weeks after the release of Illinois , Stevens embarked on a North American tour to promote the album , performing with a string section of eight to ten members named the <unk> . He deliberately chose to avoid television as a promotional tool and focused on the tour performances themselves . He was supported on some dates by opening acts Liz <unk> ( who is also signed to Asthmatic Kitty ) and Laura <unk> as well as Illinois collaborator <unk> Nova 's solo project My <unk> Diamond . He toured in support of the album again from September through November 2006 , this time including dates in several European cities . During the 2006 dates , Stevens and his band transitioned from wearing University of Illinois @-@ themed outfits to butterfly suits and bird wings . 
- 
- = = Musical style and thematic elements = = 
- 
- Reviewers have noted similarities between this album and those of musicians and composers in several musical genres — from pop to contemporary classical , even show tunes and jazz @-@ based time signatures . The lyrics and their rich thematic elements have been noted for their literary quality , earning comparisons to Ralph Waldo Emerson , Henry David <unk> , William Carlos Williams , and Walt Whitman . 
- 
- = = = Musical style = = = 
- 
- Reviewers of Illinois have compared Stevens ' style to Steve Reich , Vince <unk> , the <unk> <unk> , Neil Young , Nick Drake , and Death <unk> for <unk> . Stevens ' use of large orchestral arrangements in his music — much of it played by himself through the use of multi @-@ track recording — has been noted by several reviewers . Rolling Stone summarized the musical influences of Illinois , saying " the music draws from high school marching bands , show tunes and ambient electronics ; we can suspect Steve Reich 's Music for 18 Musicians is an oft @-@ played record in the Stevens household , since he loves to echo it in his long instrumental passages . " A review in The A.V. Club referred to some of the vocal work as " <unk> <unk> <unk> " , but found Stevens ' music overall to be " highly developed " . The song " Come On ! Feel the Illinoise ! " uses a saxophone part from " Close to Me " by The Cure . 
- The creation of Illinois marked a shift in Stevens ' emphasis on songwriting and studio work toward live performance and more abstract concepts of motion and sound — subsequent tours and albums emphasized electronic music and modern dance over the indie folk material on Michigan and Illinois . He has ceased writing songs about individual characters with straightforward narratives or concept albums and briefly considered quitting the music business entirely after creating and promoting this album . He also found that the way in which he listened to music had changed after producing Illinois : 
- I think now I listen more as a <unk> and a researcher . I 'm always hearing music in terms of what I can take out of it , and I think I 've always listened like that . I have a hard time just listening for pleasure . I 'm much less about instinct , and more of a <unk> listener . Like , what is the use of this song ? What is the usefulness of this melody for this theme or statement ? What are they doing that 's unusual sounding , and how can I learn from that ? 
- Stevens is a <unk> trained <unk> and his knowledge of classical and <unk> music influenced many of his arrangements . Stevens himself has noted the influence of composers Igor Stravinsky , <unk> <unk> , and <unk> <unk> ; along with contemporary composers Terry Riley , Steve Reich , and Philip Glass . The music on this album was written to be grandiose , to match the history of the territory . Stevens used time signature changes in the composition of Illinois for dynamic effect — for instance , " Come On ! Feel the Illinoise ! " begins with a 5 / 4 time signature and then changes to a standard 4 / 4 later in the song . 
- 
- = = = Illinois themes = = = 
- 
- Many of the lyrics in Illinois make references to persons , places , and events related to the state of the same name . " Concerning the UFO Sighting Near Highland , Illinois " is about a UFO sighting by police officers near Highland , Illinois , where several persons reported seeing a large triangular object with three lights flying at night . " Come on ! Feel the Illinoise ! " makes reference to the World 's Columbian Exposition , which took place in Chicago in 1893 . 
- " John Wayne Gacy , Jr . " documents the story of the 1970s Chicago @-@ based serial killer of the same name . Several lyrics make explicit references to events in his life : " [ w ] hen the <unk> hit his head " refers to an event in Gacy 's childhood , when a swing hit his head and caused a blood <unk> in his brain ; " He dressed up like a clown for them / with his face paint white and red " alludes to the nickname given to Gacy — the " Killer Clown " ; and " He put a cloth on their lips / <unk> hands , quiet kiss on the mouth " references Gacy 's use of <unk> to subdue and <unk> his victims . The song ends with the narrator turning inward with the lyrics : " And in my best behavior , I am really just like him / Look beneath the <unk> for the secrets I have hid . " Stevens stated in a 2009 interview with Paste that " we 're all capable of what [ Gacy ] did . " 
- " Casimir Pulaski Day " <unk> a personal story with the state holiday Casimir Pulaski Day . " The Man of Metropolis Steals Our Hearts " makes references to Superman , whose fictional hometown of Metropolis was partially modeled after Chicago ( the town of Metropolis , Illinois has also capitalized on this association ) . Jessica Hopper of the Chicago Reader noted that Ray Middleton — who was the first actor to play the comic book superhero — was also born in Chicago . " They Are Night Zombies ! ! They Are Neighbors ! ! They Have Come Back from the Dead ! ! Ahhhh ! " makes references to ghost towns of Illinois . Stevens relates experiences from a summer camp he went to as a child in Michigan for " The Predatory Wasp of the Palisades Is Out to Get Us ! " , but moved the <unk> to Illinois for the sake of the album . The track " Decatur , or , Round of Applause for Your Stepmother ! " includes references to Decatur , Illinois , but Stevens stated the track also acted as " an exercise in rhyme schemes " . Some references to Decatur included in the song were <unk> sightings in the area , the equipment manufacturer <unk> , and a flood that exhumed a graveyard of soldiers from the Civil War . 
- Other allusions to the state 's people , places , and events include the Black Hawk War , author Carl <unk> , Stephen A. Douglas , Abraham Lincoln , the <unk> River , the Chicago Cubs , the <unk> Tower dubbed " Seer 's Tower " ( now called Willis Tower ) , and the localities of Jacksonville , <unk> , Metropolis , <unk> Caledonia , <unk> , Magnolia , <unk> , <unk> , and the several locations named <unk> , Illinois . During the tour following the release of Illinois , Stevens ' band wore cheerleader outfits based on those of the University of Illinois . 
- 
- = = = Christianity = = = 
- 
- Although Illinois is a concept album about the U.S. state , Stevens also explored themes related to Christianity and the Bible . As a Christian , he has written and recorded music about spiritual themes throughout his career — particularly on the 2004 album Seven Swans — and prefers to talk about religious topics through song rather than directly in interviews or public statements . The song " Decatur , or , Round of Applause for Your Stepmother ! " includes the line " It 's the great I Am " — taken from the response God gave when Moses asked for his name in the Book of Exodus ( Exodus 3 : 14 ) . " Casimir Pulaski Day " describes the death of a girlfriend due to bone cancer , and the narrator questions God in the process . More abstract allusions appear in " The Man of Metropolis Steals Our Hearts " , which utilizes Superman as a Christ figure and " The Seer 's Tower " , which references the Book of Revelation and the Second Coming of Christ . Songs which were not written with an explicit theological focus — such as " John Wayne Gacy , Jr . " — also feature religious themes such as sin and redemption . 
- 
- = = <unk> = = 
- 
- <unk> <unk> created the album artwork , depicting a variety of Illinois @-@ related themes , including Abraham and Mary Todd Lincoln , the <unk> Tower , and Black Hawk . The album cover reads , " Sufjan Stevens <unk> You To : Come On Feel the Illinoise ! " as a wordplay on the common <unk> of the state 's name as " ill @-@ i @-@ <unk> " and a reference to the <unk> song " <unk> On Feel the <unk> " made famous in the United States by the metal band <unk> Riot . The text on the cover caused some confusion over the actual title of the album — it is officially titled Illinois , as opposed to Come on Feel the Illinoise or Illinoise . Paste listed Illinois as having the seventh best album art of the decade 2000 – 2009 . The album also won the <unk> Independent Music Award for Album Art / <unk> of the Year in 2006 . 
- Shortly after the release of the album , reports arose that DC Comics had issued a cease and <unk> letter to Asthmatic Kitty because of the depiction of Superman on the cover . However , on October 4 , 2005 , Asthmatic Kitty announced that there had been no cease and <unk> letter ; the record company 's own lawyers had warned about the copyright infringement . On June 30 , 2005 , Asthmatic Kitty 's distributor <unk> Canadian asked its retailers not to sell the album ; however , it was not recalled . On July 5 , the distributor told its retailers to go ahead and sell their copies , as DC Comics agreed to allow Asthmatic Kitty to sell the copies of the album that were already manufactured , but the image was removed from subsequent pressings . Soon after it was made public that the cover would be changed , copies of the album featuring Superman were sold for as high as $ 75 on <unk> . On the vinyl edition released on November 22 , 2005 , Superman 's image is covered by a balloon <unk> . The image of the balloon <unk> was also used on the cover of the Compact Disc and later <unk> of the double vinyl release . Stevens himself was surprised by the development and also had to pay a fee for referencing lyrics from Woody Guthrie 's folk anthem " This Land Is Your Land " in the track " No Man 's Land " , which was later released on The Avalanche . 
- The 10th anniversary vinyl reissue of Illinois features the Marvel character Blue Marvel , who <unk> from Chicago , in place of Superman . Asthmatic Kitty obtained permission from Marvel to use the character 's likeness . 
- 
- = = Reception = = 
- 
- Illinois was Sufjan Stevens ' greatest commercial and critical success to date . For the first time , his work charted on the Billboard 200 and received several awards from critics . 
- 
- = = = Sales figures and chart performance = = = 
- 
- In its first week of sales , Illinois sold 9 @,@ 000 copies , 20 % coming from online sales . Overall , the album sold more than 100 @,@ 000 copies by November 2005 and over 300 @,@ 000 by the end of 2009 . It was the first Sufjan Stevens release to place on the Billboard 200 , reaching No. 121 within eight weeks on the chart . It also placed number one on Billboard 's " Heatseekers Albums " list and number four on the " Independent Albums " list , remaining on them for 32 and 39 weeks respectively . 
- 
- = = = Critical reception = = = 
- 
- Critical reception of Illinois was overwhelmingly positive . Review aggregator Metacritic compiled 40 critic reviews of Illinois and gave the album a 90 out of 100 , indicating " universal acclaim " , designating it the best @-@ reviewed album of 2005 . Andy <unk> of The A.V. Club said that Stevens " has grown into one of the best song @-@ makers in indie rock " with the album . Tim <unk> of NME called Illinois " a <unk> little <unk> " and described Stevens as " prolific , intelligent and — most importantly — <unk> with heart @-@ <unk> melodies . " Rob Sheffield of Rolling Stone responded favorably to the album , praising the " over @-@ the @-@ top arrangements " and Stevens ' " breathy , gentle voice " . Sheffield criticized " John Wayne Gacy , Jr . " , stating that it " <unk> nothing about American life except the existence of creative @-@ writing workshops " , but elsewhere praised the personal nature of songs such as " Chicago " and " Casimir Pulaski Day " . Michael <unk> of PopMatters described " John Wayne Gacy , Jr . " as " <unk> , tragic , and deeply sad without <unk> . " Amanda <unk> of Pitchfork Media described Illinois as " strange and lush , as excessive and challenging as its giant , <unk> song titles . " Dave Simpson of The Guardian echoed this sentiment by saying that the music sounds like " The <unk> <unk> produced by Brian Eno . " The diversity in instrumentation also received a positive review from Entertainment Weekly 's <unk> <unk> . 
- Jesse <unk> of Paste praised the <unk> nature of Illinois , commenting that it had " sing @-@ song " melodies and " <unk> " orchestrations . <unk> also noted ironic lyrics , citing a line from " The Predatory Wasp of the Palisades is Out to Get Us ! " : " I can 't explain the state I 'm in ... " after a section of the song that references many Illinois landmarks . Q called Illinois a " sizeable step forward " from Michigan , and said Stevens ' love for the state of Illinois is infectious . Uncut 's Andy Gill dubbed the album " an extraordinary achievement " . Catherine Lewis of The Washington Post responded favorably to the album , stating that it has well @-@ written lyrics , comparing Stevens ' rhyming to that of <unk> Merritt . Lewis cited " Casimir Pulaski Day " as one of the most memorable songs of the album . 
- 
- = = = Accolades = = = 
- 
- Illinois achieved lasting fame with inclusion on numerous reviewers ' " best of the year " and " best of the decade " lists . In particular , the album topped the best of the decade list appearing in the November 2009 issue of Paste and NPR named Illinois on their list of " The Decade 's 50 Most Important Recordings " . Pitchfork Media called Illinois the sixteenth best album of the decade , with Stevens ' previous album — Michigan — placing 70 on that same list . The album also won the 2005 New <unk> Award — a type of <unk> Music Prize . The album was also included in the book <unk> Albums You Must Hear Before You Die . Finally , Paste listed Stevens as one of their " 100 Best Living Songwriters " in 2006 , primarily due to the writing on Michigan and Illinois . 
- 
- = = Track listing = = 
- 
- Note that the titles of the songs vary slightly from the Compact Disc , digital , and vinyl releases . Full titles come directly from the vinyl album and have been adapted to the English <unk> standards . 
- All songs written and composed by Sufjan Stevens and published by New Jerusalem Music , ASCAP . 
- 
- = = Personnel = = 
- 
- Sufjan Stevens – acoustic guitar ; piano ; <unk> ; bass guitar ; drums ; electric guitar ; <unk> ; alto saxophone ; flute ; banjo ; <unk> ; accordion ; <unk> ; alto , <unk> , soprano , and tenor recorders ; <unk> <unk> @-@ 70 ; <unk> bells ; <unk> ; <unk> ; triangle ; electronic organ ; vocals ; arrangement ; engineering ; recording ; production 
- Julianne <unk> – violin 
- Alan <unk> – mastering at West West Side Music , <unk> , New Jersey 
- Jon Galloway – remixing on " Chicago " ( To String Remix ) 
- <unk> Hansen – <unk> 
- The <unk> Choir – backing vocals and clapping on " The Black Hawk War , or , How to <unk> an Entire Civilization and Still Feel Good About Yourself in the Morning , or , We <unk> for the <unk> but You 're Going to Have to Leave Now , or , ' I Have <unk> the Big <unk> and Will <unk> to Fight Them Until They Are Off Our Lands ! ' " , " Chicago " , " The Man of Metropolis Steals Our Hearts " , " They Are Night Zombies ! ! They Are Neighbors ! ! They Have Come Back from the Dead ! ! Ahhhh ! " , and " The Tallest Man , the Broadest Shoulders " 
- Tom Eaton 
- Jennifer Hoover 
- Katrina <unk> 
- <unk> Lock 
- Tara <unk> 
- Maria Bella <unk> – cello 
- Katrina <unk> – backing vocals on " Concerning the UFO Sighting Near Highland , Illinois " , " Come On ! Feel the Illinoise ! " , " Jacksonville " , " Prairie Fire That <unk> About " , " The Predatory Wasp of the Palisades Is Out to Get Us ! " , " The Seer 's Tower " , " The Tallest Man , the Broadest Shoulders " , and " The Avalanche " 
- James <unk> – drums , drum engineering 
- Craig <unk> – trumpet , backing vocals on " They Are Night Zombies ! ! They Are Neighbors ! ! They Have Come Back from the Dead ! ! Ahhhh ! " 
- Rob Moose – violin 
- Matt Morgan – backing vocals on " Decatur , or , Round of Applause for Your Stepmother ! " 
- Daniel and <unk> Smith – backing vocals and clapping on " Decatur , or , Round of Applause for Your Stepmother ! " 
- <unk> <unk> – artwork 
- <unk> <unk> – backing vocals on " Concerning the UFO Sighting Near Highland , Illinois " , " Come On ! Feel the Illinoise ! " , " John Wayne Gacy , Jr . " , " Casimir Pulaski Day " , " Prairie Fire That <unk> About " , " The Predatory Wasp of the Palisades Is Out to Get Us ! " , " The Seer 's Tower " , " The Tallest Man , the Broadest Shoulders " , and " The Avalanche " 
- 
- 
- = Mycena galericulata = 
- 
- Mycena galericulata is a mushroom species commonly known as the common bonnet , the <unk> <unk> , or the <unk> @-@ gill fairy helmet . The type species of the genus Mycena was first described scientifically in 1772 , but was not considered a Mycena until 1821 . It is quite variable in color , size , and shape , which makes it somewhat difficult to reliably identify in the field . The mushrooms have caps with distinct radial grooves , particularly at the margin . The cap 's color varies from grayish @-@ brown to dark brown and the shape ranges from bell @-@ like to <unk> conical to flattened with an umbo . The stem is hollow , white , tough and thin , without a ring and often roots deeply into the wood on which it grows . The gills are white to grayish or even pinkish when mature and are connected by distinct cross @-@ veins . The caps can reach 4 cm ( 1 @.@ 6 in ) in diameter , and have a <unk> odor and taste . The spore print is white and the gills are pink at maturity , which can lead to possible confusion with species of the <unk> genus . M. galericulata mushrooms grow mostly in clusters on the well @-@ decayed stumps of deciduous and coniferous trees from spring to autumn . The species can generally be considered inedible . It is common and widespread in the entire temperate zone of the Northern Hemisphere , but it has also been reported from Africa . 
- 
- = = Taxonomy , classification , and naming = = 
- 
- The fungus was first described scientifically as Agaricus <unk> by Italian mycologist Giovanni Antonio <unk> in 1772 , and sanctioned under this name by Elias Magnus Fries in his 1821 Systema <unk> . That same year , Samuel Frederick Gray transferred the species to the genus Mycena . <unk> for the species include Agaricus <unk> named by William Hudson in 1778 , Agaricus <unk> described by August Johann Georg Karl <unk> in 1893 , <unk> <unk> by Franklin Sumner <unk> in 1909 , and <unk> <unk> by William <unk> <unk> in 1916 . 
- Mycena galericulata is the type species of the genus Mycena . It is classified in section Mycena of Mycena in the <unk> scheme of Rudolph Arnold <unk> <unk> . In the older ( 1947 ) classification of Alexander H. Smith , he placed it in the subgenus <unk> , section <unk> — " a most <unk> series of blackish , brown , gray , bluish @-@ gray , or brownish @-@ gray species mostly with ascending gills and generally large to moderate stature . " 
- The specific epithet galericulata is derived from the Latin <unk> , and means " with a small hat " . Gray called it the " <unk> high @-@ stool " . It is commonly known as the " common bonnet " , the " <unk> <unk> " , or the " <unk> @-@ gill fairy helmet " . 
- 
- = = Description = = 
- 
- The cap of M. galericulata is roughly conical when young , and eventually becomes broadly bell @-@ shaped or with a broad umbo that can reach diameters of 2 – 4 cm ( 0 @.@ 8 – 1 @.@ 6 in ) . The cap margin is initially somewhat curved inward , but soon <unk> out or even becomes <unk> , and often splits <unk> in age . The surface has radial grooves that extend nearly to the center , and feels <unk> but not sticky . The color is somewhat buff @-@ brown on the margin , and fades gradually to pale dirty tan to dirty <unk> @-@ brown . The flesh is thick in the center of the cap and tapers evenly to the margin , and is watery gray , with a cartilage @-@ like texture . The odor and taste are <unk> to strongly farinaceous ( similar to the smell of <unk> ground flour ) , to <unk> @-@ like . 
- The gills are narrowly attached ( <unk> ) to broadly attached or <unk> . The gill spacing ranges from close to somewhat distantly spaced , with 26 – 36 gills reaching the stem ; there are additionally three or four tiers of <unk> ( short gills that do not extend completely from the cap margin to the stem ) . The gills are strongly <unk> ( possessing cross @-@ veins ) , moderately broad ( 5 – 7 mm ) , white or grayish white , soon flushed with pale pink , with even edges . The stem is 5 – 9 cm ( 2 @.@ 0 – 3 @.@ 5 in ) long , 2 – 4 mm ( 0 @.@ 08 – 0 @.@ 16 in ) thick , equal in width throughout , and with a <unk> texture . It is hollow , not hairy , either smooth or twisted with longitudinal <unk> , often with a long <unk> ( a subterranean <unk> of the stem ) at the base . The stem color is pale grayish @-@ white on the upper portion , and pale grayish @-@ black below ; the base becomes somewhat dirty brown in age , but does not develop reddish stains . 
- Mycena galericulata produces a white spore print . The spores are ellipsoid , 8 – 10 by 5 @.@ 5 – 7 μm , and amyloid — which means they will turn blue @-@ black to black when stained with Melzer 's reagent . The basidia ( spore @-@ bearing cells ) have stout sterigmata , and measure 34 – 40 by 7 – 9 μm . They may be either two @-@ spored or four @-@ spored . There are numerous club @-@ shaped to rounded cheilocystidia ( cystidia on the gill edge ) , that measure 32 – 40 by 8 – 12 μm ; their <unk> or the entire enlarged portion bear <unk> projections that become increasingly elongated and branched in age . There are no <unk> ( cystidia on the gill face ) . The gill tissue has a very thin <unk> , under which is a narrow <unk> , while the remainder of the tissue comprises densely matted <unk> of mycelia , and stains deep <unk> @-@ brown in iodine . Clamp connections are present in the hyphae of the four @-@ spored forms . 
- 
- = = = Edibility = = = 
- 
- Opinions on the edibility of the mushroom vary . One source considers them to have a " delicate flavor and texture " , and suggests that they are good when " <unk> gently in their own juice and then <unk> with salt , <unk> and butter . " Others list the species as inedible , and notes that they have a " <unk> rancid " smell , and with a taste ranging from rancid to farinaceous . Another says " unknown , but not recommended . " At any rate , the species falls into the general category of brownish <unk> that are generally <unk> as food due to their small size and delicate consistency . 
- 
- = = = Similar species = = = 
- 
- The winter bonnet ( M. <unk> ) is a northern European species that is much smaller ( cap diameter up to 2 @.@ 6 cm ( 1 @.@ 0 in ) across ) and has a brown cap , and has ragged hairs at the base . It generally appears in late autumn to early winter on the stumps of deciduous trees , especially beech . It has <unk> @-@ shaped spores that are smaller than M. galericulata , around 4 @.@ 5 – 5 @.@ 5 by 2 @.@ 5 – 2 @.@ 8 µm . M. <unk> develops pink stains on its gills as it matures ; its spores are 7 – 9 by 4 – 5 μm . Another similar species is M. <unk> , which can be distinguished by gills bearing reddish spots , which may become entirely red with age . It also has whitish , slender , threadlike <unk> on the stalk . M. <unk> is thinner , and more fragile . Another Mycena that grows in clusters on decaying <unk> is M. <unk> , but this species has a <unk> @-@ brown cap with a <unk> margin , and a stem that <unk> reddish @-@ brown juice when injured . M. <unk> closely resembles M. galericulata , but can be distinguished microscopically by the presence of both smooth and <unk> cystidia ( bearing finger @-@ like projections ) . 
- 
- = = Ecology , habitat and distribution = = 
- 
- Mycena galericulata is saprobic , and grows on decaying hardwood and <unk> sticks , chips , logs , and stumps . It can also grow from submerged wood , which may give it a terrestrial appearance . It typically grows in small clusters or sometimes singly . The fungus fruits from late spring to early winter . A study of litter @-@ decomposing fungi in a coniferous forest in Finland showed that M. galericulata produces extracellular <unk> enzymes in the <unk> and <unk> soil , including β @-@ <unk> , β @-@ <unk> , α @-@ <unk> , <unk> esterase and <unk> . The enzymes form complexes with <unk> and organic particles in the soil and break down ( <unk> ) <unk> such as <unk> , <unk> , and <unk> , which contributes to the cycling of carbon and nutrients . The presence of lead contamination in the soil decreases both the growth and the extracellular <unk> enzyme activity of M. galericulata . 
- Mycena galericulata is a very common and widely distributed species , found throughout the entire temperate zone of the Northern Hemisphere . In the United States , it occurs throughout eastern North America and also along the Pacific Coast . It has also been collected in Edo State , Nigeria . Although the mushroom has occasionally been reported from Australia , these collections are probably based on <unk> , and " Australian records of Mycena galericulata are best regarded as erroneous " . 
- 
- = = = Cited literature = = = 
- 
- Smith AH . ( 1947 ) . North American species of Mycena . Ann Arbor , Michigan : University of Michigan Press . 
- 
- 
- = Crash Boom Bang ! = 
- 
- For the <unk> album with a similar name , see Crash ! Boom ! Bang ! 
- Crash Boom Bang ! ( known in Japan as Crash Bandicoot Festival ) ( <unk> ・ <unk> <unk> , <unk> <unk> <unk> ) is a party game developed by <unk> and published by <unk> Universal Games in Japan and by Sierra Entertainment internationally for the Nintendo DS . It was released in Japan on July 20 , 2006 , in North America on October 10 , 2006 , in Europe on October 27 , 2006 , and in Australia on November 2 , 2006 . It is the only game in the Crash Bandicoot series to be developed by a Japanese company and the first to be released in Japan before North America . It was also the final game in the series to be released in Japan . 
- Crash Boom Bang ! is the first game to be released exclusively for the Nintendo DS , and the second party game of the series , after Crash <unk> . The game 's story centers on a multi @-@ millionaire who uses the characters of the series to <unk> a powerful object dubbed the " Super Big Power Crystal " . The game has received largely negative reception from reviewers , who criticized it for having unoriginal , dull gameplay and poor controls . 
- 
- = = Gameplay = = 
- 
- Crash Boom Bang ! ' s stages resemble board games , as each play area is split into a number of squares . The game takes place among four players , with the computer assigned to spare players . All players simultaneously roll dice . The number each player rolls is the amount of squares they move forward . Depending on the type of square that the player lands on , Wumpa Fruit ( which is used as points during the race ) can be won or lost , an item can be obtained , a special event might be triggered , or a mini @-@ game might commence . If a player lands on a fork in the road , the player will have to select the desired direction with either the stylus or the control pad . 
- In the Adventure Mode , the characters compete in a race for the Super Big Power Crystal . This race is made up of six stages , each containing smaller sub @-@ maps . The host of the race , the Viscount , sets a task for each stage . This task must be completed before the player can continue to another map . The player with the most points is the winner of the stage . The overall winner of all the stages is the winner of the Viscount 's race . In the Festival Mode , the gameplay is fundamentally the same as in the Adventure Mode , with the exception of the ability to select stages to play freely . In the My Room mode , the player 's character has his or her own private room in which he or she can either play minigames that have been collected in Adventure Mode , view a collection of items obtained in Adventure Mode , or create a Motion Panel , a unique in @-@ game communication tool can allow customized messages to be sent mid @-@ game to help friends or distract other players ' gameplay . The decorations in My Room is different for each character . 
- Crash Boom Bang ! features forty mini @-@ games that can be played alone or <unk> against friends . <unk> , timing , and intelligence are required to win these mini @-@ games . When a player is not taking part in a mini @-@ game , he or she can bet Wumpa Fruit on the winner . The player can help players that have been bet on or obstruct players that have not been bet on by using the Motion Panel . If the player has a special item , the player can access the Shop screen from the Bet screen and buy or sell items . 
- 
- = = Plot = = 
- 
- 
- = = = Characters = = = 
- 
- A number of characters from past Crash Bandicoot games return in Crash Boom Bang ! , though only eight are playable . The playable characters include Crash Bandicoot , Coco Bandicoot , Crunch Bandicoot , <unk> , Doctor Neo Cortex , <unk> , <unk> <unk> , and Fake Crash . All of the characters physically appear as they do in official Japanese Crash Bandicoot artwork and promotions ( which was the only Sierra Entertainment video game to be introduced the official Japanese Crash Bandicoot artwork and promotions ) , though Crash 's model was altered for the non @-@ Japanese releases to closer resemble his Crash <unk> model . The host of the party , the Viscount , is an original character designed specifically for the game . His name in the original Japanese version is " Viscount Devil " , a reference to the Tasmanian devil native to Australia . Other past characters make cameos in the game at one point or another , such as the Lab <unk> , Doctor N. <unk> , Tiny Tiger , Doctor <unk> <unk> and Polar . <unk> <unk> serves as the player 's tutor , while <unk> <unk> makes a cameo appearance as a <unk> item in the shop . 
- 
- = = = Story = = = 
- 
- While developing a resort in Tasmania , the Viscount finds a map of an ancient city containing the fabled Super Big Power Crystal . He attempts to find it himself , but due to the large amount of puzzles , he fails <unk> . In the resort , the Viscount decides to gather up the world 's <unk> and strongest bunch of characters and con them into finding the Crystal for him . He sends an invitation to Coco Bandicoot , inviting her and Crash to the World <unk> Race , where the winner earns $ 100 @,@ 000 @,@ 000 . 
- The race starts in Port Town , with the winners traveling in a boat to a large desert . Legend has it that four stone tablets are buried somewhere in the desert , and the contestants are sent out to dig for them and bring them to the Viscount . According to the stone tablets , the actual location of the Crystal is hidden somewhere on the ancient map . Before he can investigate the matter further , Doctor Neo Cortex <unk> in and <unk> the map . As the two struggle for the map , the map is torn to <unk> . 
- Cortex sends his loyal Lab <unk> to find the pieces of the map scattered throughout the big City and bring them to him . Now that the contestants know about the map , the Viscount decides to reveal his true intentions : the Super Big Power Crystal can grant a single wish who whoever obtains it , and the Viscount is willing to give a large sum of money to those who help him find it . Finding the Power Crystal is impossible without the Final Key , so the Viscount boards his plane to travel to the North Atlantic Ocean in search for the Final Key . But the plane is full , and only a select number of the contestants are able to board . <unk> to the skies by an explosive volcano , the contestants are able to board the Viscount 's plane . 
- On the Viscount 's ship , the Viscount tells the group the story of an explorer who found the Final Key , but was unable to find the Power Crystal . That explorer was the Viscount 's grandfather . As the explorer sailed back to his homeland to <unk> his thoughts , his ship crashed into an <unk> and sank , taking the Viscount 's grandfather to a watery grave . " Sounds like a movie " , remarks one of the attendees . The Viscount tells them to dive to the sunken ship and retrieve the Final Key , much to their shock , considering the near @-@ freezing temperatures . Despite this , the group is able to find the Final Key before freezing to death . With all the pieces of the puzzle at hand , the Viscount <unk> enters the Tower , where the Super Big Power Crystal awaits its owner . Just as the Viscount is about to make his wish , Crash steps forward and makes his wish of a large pile of Wumpa Fruit , much to the Viscount 's grief . " May peace prevail on Earth " , says Coco . 
- 
- = = Development = = 
- 
- On developing the mobile phone version of Crash Boom Bang ! , producer <unk> <unk> described adapting a party game for the mobile phone as a " big challenge " . Not wanting to make " another multiplayer game where the players just pass the phone to each other " and hoping to attract both old and new fans of the series , the development team decided to integrate the mobile phone itself into the minigames , creating such minigame <unk> as playing with one hand behind the back , with one eye closed , playing with the chin , etc . The biggest challenge for the team was keeping the minigames inside the phone 's memory , which was cited as slightly inferior to the first PlayStation console . The <unk> series was described as an influence in making the game . 
- Crash Boom Bang ! is the first game in the series to exclusively feature the Japanese voice cast in all regional versions of the game . The voice cast includes Makoto <unk> in the dual role of Crash and Fake Crash , Risa Tsubaki as Coco , <unk> <unk> as Doctor Cortex , <unk> <unk> as Crunch , Akiko <unk> as <unk> , Asuka <unk> as <unk> , and <unk> <unk> as <unk> . 
- 
- = = Reception = = 
- 
- Crash Boom Bang ! received mostly negative reviews , with the game receiving an average ranking of 42 @.@ 45 % at Game Rankings , and a score of 37 out of 100 based on fourteen reviews at Metacritic . Frank <unk> of GameSpot criticized the game for its dull minigames and purely cosmetic Crash license , citing that " apart from the way the characters look and the way the <unk> boxes explode , [ ... ] there isn 't a whole lot that 's Crash @-@ like about Crash Boom Bang ! " . Nintendo Power recommended the game only to die @-@ hard Crash fans and advised others to wait for Crash 's next outing . <unk> Smith of Eurogamer criticized the game for a number or reasons , including bad stylus recognition , boring gameplay , terrible graphics and rigged , repetitive mini @-@ games . IGN 's review was one of the most scathing , dubbing Crash Boom Bang ! " a terrible , terrible game with poor organization " and " easily one of the worst games on the system " . More middling reviews have come in from Official Nintendo Magazine , who felt the game was hampered by dodgy controls and a testing user interface , and Pocket Gamer 's Jon Jordan , who dismissed the game 's collection of minigames as " distinctly average and oddly <unk> " . Despite the negative reception , Crash Boom Bang ! was the seventh best @-@ selling game in Australia on the week of June 4 to June 10 , 2007 . 
- 
- 
- = Grade I listed buildings in Somerset = 
- 
- The Grade I listed buildings in Somerset , England , demonstrate the history and diversity of its architecture . The ceremonial county of Somerset consists of a non @-@ metropolitan county , administered by Somerset County Council , which is divided into five districts , and two unitary authorities . The districts of Somerset are West Somerset , South Somerset , Taunton Deane , Mendip and Sedgemoor . The two administratively independent unitary authorities , which were established on 1 April 1996 following the breakup of the county of Avon , are North Somerset and Bath and North East Somerset . These unitary authorities include areas that were once part of Somerset before the creation of Avon in 1974 . 
- In the United Kingdom , the term listed building refers to a building or other structure officially designated as being of special architectural , historical , or cultural significance ; Grade I structures are those considered to be " buildings of exceptional interest " . <unk> was begun by a provision in the Town and Country Planning Act 1947 . Once listed , strict limitations are imposed on the modifications allowed to a building 's structure or fittings . In England , the authority for listing under the Planning ( Listed Buildings and Conservation Areas ) Act 1990 rests with English Heritage , a non @-@ <unk> public body sponsored by the Department for Culture , Media and Sport ; local authorities have a responsibility to regulate and enforce the planning regulations . 
- Each of the districts include Norman- or medieval @-@ era churches , many of which are included in the Somerset towers , a collection of distinctive , mostly <unk> Gothic architecture church towers . The prolific construction of the towers — some started before <unk> — was typically accomplished by a master <unk> and a small team of itinerant masons , supplemented by local parish labourers , according to <unk> Wright . But other authors reject this model , suggesting instead that leading architects designed the parish church towers based on early examples of <unk> design and ornamentation developed for cathedrals — their most important commissions . Contract builders carried out the plans , adding a distinctive mix of innovative details and decorations as new designs emerged over the years . These are included in the List of towers in Somerset . 
- Apart from the churches , each area has its own characteristics . Most of Bath 's Grade I listed buildings are made from the local golden @-@ coloured Bath Stone , and date from the 18th and 19th centuries . Their dominant architectural style is Georgian . In the Mendip district , the greatest concentrations of these cluster around the cathedral and <unk> in Wells and in Glastonbury . North Somerset features bridges and piers along with a selection of Manor houses . The Sedgemoor district has many buildings related to trade and commerce centered on Bridgwater ; while in South Somerset <unk> , <unk> and <unk> predominate . Taunton Deane includes the defensive Taunton Castle , similarly Dunster Castle and related buildings in Dunster feature in West Somerset . 
- 
- = = Bath and North East Somerset = = 
- 
- Bath and North East Somerset ( commonly referred to as BANES or B & <unk> ) is a unitary authority created on 1 April 1996 , following the abolition of the County of Avon . Bath and North East Somerset occupies an area of 220 square miles ( 570 km2 ) , two @-@ thirds of which is green belt . BANES stretches from the outskirts of Bristol , south into the Mendip Hills and east to the southern Cotswold Hills and Wiltshire border . The city of Bath is the principal settlement in the district , but BANES also covers <unk> , <unk> Norton , <unk> and the Chew Valley . BANES has a population of 170 @,@ 000 , about half of whom live in Bath , making it 12 times more densely populated than the rest of the district . 
- Bath and North East Somerset has <unk> Grade I listed buildings , one of the highest concentrations in the country , covered by about 100 English Heritage listings . The oldest sites within Bath are the Roman <unk> , for which the foundation piles and an irregular stone chamber lined with lead were built during the Roman occupation of Britain , although the current building is from the 18th century . Bath Abbey was a Norman church built on earlier foundations , although the present building dates from the early 16th century and shows a late <unk> style with flying buttresses and <unk> pinnacles <unk> a <unk> and pierced parapet . The medieval era is represented by the remains of the city walls in Upper Borough <unk> . 
- Most of Bath 's Grade I listed buildings are made from the local golden @-@ coloured Bath Stone , and date from the 18th and 19th centuries . Their dominant architectural style is Georgian , which evolved from the <unk> revival style that became popular during the early 18th century . This led to the entire city 's designation as a World Heritage Site . 
- Much of the development , and many of the buildings , were the vision of John Wood , the Elder . The Circus is seen as the pinnacle of Wood 's work . It consists of three long , curved terraces that form a circular space or theatre intended for civic functions and games . The games give a clue to the design , the inspiration for which was the Colosseum in Rome . The best known of Bath 's terraces is the Royal Crescent , built between 1767 and 1774 and designed by Wood 's son , John Wood , the Younger . Around 1770 the neoclassical architect Robert Adam designed Pulteney Bridge , a three @-@ arched bridge spanning the Avon . He used as his prototype an original , but unused , design by <unk> for the <unk> Bridge in Venice . The heart of the Georgian city was the <unk> Room , which together with its associated Lower Assembly Rooms was designed by Thomas Baldwin , a local builder responsible for many other buildings in the city , including the terraces in Argyle Street . Great Pulteney Street , where Baldwin eventually lived , is another of his works : this wide boulevard , constructed c . 1789 and over 1 @,@ 000 feet ( 305 m ) long and 100 feet ( 30 m ) wide , is lined on both sides by Georgian terraces . 
- Outside the city of Bath most of the Grade I listed buildings are Norman- or medieval @-@ era churches . Manor houses such as <unk> Manor , which now houses the American Museum in Britain , and the 18th @-@ century Newton Park , which has a landscape garden designed by <unk> Brown , also appear in the list ; Newton Park now forms part of the Bath <unk> University . The most recent building is the agricultural Eastwood Manor Farm <unk> , completed in 1860 . 
- 
- = = Mendip = = 
- 
- Mendip is a local government district which covers a largely rural area of 285 square miles ( 738 km2 ) ranging from the Mendip Hills through on to the Somerset Levels . It has a population of approximately 11 @,@ 000 . The administrative centre of the district is <unk> <unk> . 
- There are 90 Grade I listed buildings in the Mendip district . There are churches in the various towns and villages , however the greatest concentrations of Grade I listed buildings are in Wells and Glastonbury . In Wells these are clustered around the 10th @-@ century Cathedral Church of St Andrew , better known as Wells Cathedral , and the 13th @-@ century Bishop 's Palace . Glastonbury is the site of the Abbey , where construction started in the 7th century , and its associated buildings . The ruined St Michael 's church , damaged in an earthquake of 1275 , stands on Glastonbury Tor , where the site shows evidence of occupation from Neolithic times and the Dark Ages . The <unk> Well has been in use since Pre @-@ Christian times . Glastonbury Abbey had a wider influence outside the town : <unk> <unk> were built at <unk> and West Bradley to hold <unk> , and a Fish House was built at <unk> along with a summer residence for the Abbot ( now Manor Farmhouse ) . 
- Medieval structures include <unk> Hungerford Castle , fortified around <unk> , and The George Inn at Norton St Philip , used as an army headquarters during the Monmouth Rebellion in 1685 , and then as a courtroom to try the rebels in the Bloody <unk> . Manor houses such as the 15th @-@ century <unk> Court Farmhouse at <unk> and The Old Manor at <unk> . <unk> Manor followed in the 16th century and in the 17th century <unk> House in <unk> was built . <unk> Easton Park and <unk> House in <unk> were both completed in the 18th century . The most recent buildings included in the list are churches : the Church of St Peter at <unk> , built in 1872 – 74 by Sir Thomas Graham Jackson to replace a medieval church on the same site , and <unk> Abbey at Stratton @-@ on @-@ the @-@ <unk> , more formally known as " The Basilica of St Gregory the Great at <unk> " , a Roman Catholic Benedictine monastery and the Senior House of the English Benedictine Congregation . The current buildings were started in the 19th century and are still unfinished . 
- 
- = = North Somerset = = 
- 
- North Somerset is a unitary authority which is administered independently of the non @-@ metropolitan county of Somerset . Its administrative headquarters are located in the town hall of Weston @-@ super @-@ Mare , and has a resident population of 193 @,@ 000 living in 85 @,@ 000 households . 
- There are 37 Grade I listed buildings in North Somerset , including the Clifton <unk> Bridge , which joins North Somerset to Bristol and <unk> Pier which was built between the 1860s and 1890s . It was removed following damage in 1970 and restored , before being rebuilt and reopened to the public in 1998 . Of the listed buildings , manor houses are well represented . They include <unk> Court , built in the 14th century , and from the 15th century , Ashton Court and <unk> Court . North Somerset has many religious structures ; the largest number are from the Norman or medieval eras . The oldest is the Church of St. <unk> and St. <unk> in <unk> which dates from the 11th century , and there are 8 others from the 12th century . 
- The most recent Grade I building in North Somerset is <unk> , a Victorian Gothic Revival estate near <unk> . It was acquired by the National Trust in June 2002 after a fund raising campaign to prevent it being sold to private interests and ensure it be opened to the public . The appeal by the National Trust collected £ 8 @.@ 2 million from the public in just 100 days and the Trust also received the largest single grant ever by the National Heritage Memorial Fund ( at £ 17 @.@ 4 million ) , which caused some controversy . The house is built of Bath stone , and is highly picturesque , <unk> with turrets and possessing an elaborate roof . 
- 
- = = Sedgemoor = = 
- 
- Sedgemoor district is a low @-@ lying area of land close to sea level between the <unk> and Mendip hills , historically largely marsh ( or moor ) . It contains the bulk of the area also known as the Somerset Levels , including Europe 's oldest known engineered roadway , the Sweet Track . 
- There are 53 Grade I listed buildings in Sedgemoor , 14 of which are in Castle Street , Bridgwater . In 1834 , Castle Street was built on the site of the demolished Bridgwater Castle , as homes for the merchants trading in the town 's port . Outside the town of Bridgwater , the largest concentration of Grade I listed buildings are in the village of Cannington , where the 12th @-@ century Cannington Court and 14th @-@ century Church of St Mary were both associated with a Benedictine <unk> . Cannington is also the site of the 13th @-@ century <unk> Manor and <unk> Farmhouse , which was built around <unk> with its own chapel . Although 11th @-@ century churches such as the Church of St Michael at Brent Knoll and the Church of St Mary at <unk> near <unk> are still standing only blue <unk> rubble <unk> standing on a conical <unk> with a ditch approximately 820 feet ( 250 m ) in <unk> are the only remains of <unk> Castle which was destroyed in the 15th century , which may have been as a penalty for the local Lord <unk> 's involvement in the Second Cornish Uprising of <unk> led by <unk> <unk> . 
- Many of the more recent structures in the list are manor houses such as Halswell House , where the south range was built in the 16th @-@ century for Sir Nicholas Halswell and the main north range in 1689 for Sir Halswell <unk> . The most recently constructed building in the list is the Corn Exchange in Bridgwater , built in 1834 . 
- 
- = = South Somerset = = 
- 
- The South Somerset district occupies an area of 370 square miles ( <unk> km2 ) , stretching from its borders with Devon and Dorset to the edge of the Somerset Levels . The district has a population of about 158 @,@ 000 , and has Yeovil as its administrative centre . 
- There are 94 Grade I listed buildings in South Somerset . Most are Norman- or medieval @-@ era churches , but there are other religious buildings as well . <unk> Abbey consists of the remains and foundations of a medieval Benedictine Abbey and an early Tudor house dating from the 16th century , formerly the lodgings of the resident abbot . <unk> Priory was built as a priory church in the 13th century and was converted into a private residence in <unk> . The <unk> Stoke sub <unk> Priory is a 14th @-@ century former priest 's house of the <unk> chapel of St Nicholas , which after 1518 become a farm known as <unk> Farmhouse . It remained a farm until about 1960 , and has been owned by the National Trust since 1946 . 
- Since the Reformation the 13th @-@ century <unk> Chapel in <unk> has been a town hall , <unk> , grammar school , museum , and <unk> before becoming a <unk> hall in 1891 . The house known as The Abbey in Charlton <unk> takes its name from the site on which it was built , the <unk> Chapel of the Holy Spirit , founded in 1237 . <unk> Priory , built around 1400 in East <unk> , was never a priory , and similarly the Abbey Farm House and Abbey Barn in Yeovil which date from around <unk> , have always been in lay @-@ ownership ; " <unk> " was added to their names in the 19th century . The 140 @-@ foot ( 43 m ) Burton <unk> Monument was designed in 1757 , by <unk> Brown for William Pitt , as a monument to Sir William <unk> . King Alfred 's Tower , a 161 feet ( 49 m ) high , triangular edifice , stands near <unk> 's stone , where it is believed that Alfred the Great , King of Wessex , rallied the Saxons in May 878 before the Battle of <unk> . The towers <unk> , Henry Hoare , planned for it to commemorate the end of the Seven Years ' War against France and the accession of King George III . The other Grade I listed buildings in South Somerset are manor houses , built over long periods by local Lords of the Manor . The Tudor <unk> Court was the first country house acquired by the National Trust , in 1907 , on the recommendation of the antiquarian Canon Hardwicke <unk> . Newton <unk> was built between 1608 and 1612 for Robert <unk> , a Yeovil merchant , on the site of an earlier building , but was extensively altered and enhanced in the 1870s . <unk> Cary and its associated chapel and gardens have parts dating to as early as the 14th century . The architectural historian <unk> <unk> praised it , saying " Yet all parts blend to perfection with one another and with the gentle sunny landscape that surrounds them . " The 17th @-@ century house at <unk> is surrounded by a small 20th @-@ century Arts and <unk> garden . <unk> House , which stands on an artificially raised terrace , has a rectangular plan of seven bays by five bays , and is built of red brick in Flemish bond , with local <unk> dressings ; its north and south fronts are divided by two giant Corinthian pilasters . The small William and Mary style house was completed sometime between <unk> and 1700 . It was enlarged between 1725 and 1730 by <unk> Burton , who provided a new drawing @-@ room for Sir W. <unk> and also an <unk> attached to the house . <unk> d <unk> , built in stages between about 1220 and the 18th century , has been described , by <unk> <unk> , as " the most beautiful house in England " . 
- 
- = = Taunton Deane = = 
- 
- Taunton Deane has borough status . The district of Taunton Deane covers a population of approximately 100 @,@ 000 in an area of 462 square kilometres ( 178 sq mi ) . It is centered on the town of Taunton , where around 60 @,@ 000 of the population live and the council are based , and includes surrounding suburbs and villages . 
- There are 38 Grade I listed buildings in Taunton Deane . The oldest buildings are churches built before the end of the 12th century , and the Castle Bow , which has been incorporated into the Castle Hotel in Taunton but was originally a gateway into Taunton Castle . The castle was created between 1107 and 1129 , when William <unk> , the Chancellor of King Henry I , fortified the bishop 's hall . It was his successor , Henry of <unk> , who transformed the manor @-@ house into a castle in 1138 , during the Civil War that raged during the reign of his brother , King Stephen . Taunton is also the site of Gray 's <unk> , which dates from <unk> , and two buildings in Fore Street from the 16th century . Many of the more recent structures in the list are manor houses such as <unk> Manor and <unk> Barton which were built in <unk> in the 15th century . <unk> Park and <unk> Manor were both built in the 16th century and <unk> Court in 1755 . 
- The most recent building included in the list is in the <unk> Hills . The original 16th century <unk> House , was rebuilt in 1909 . In addition to being a listed building the estate is designated Grade I on the English Heritage Register of Parks and Gardens of Special Historic Interest in England . The house was used as the headquarters of the British 8th Corps in the Second World War , and has been owned by Somerset County Council since 1951 . It is used as an administrative centre and is the current base for the Devon and Somerset Fire and Rescue Service . 
- 
- = = West Somerset = = 
- 
- The West Somerset local government district covers a largely rural area , including parts of <unk> , with a population , according to the 2001 census , of 35 @,@ <unk> in an area of 740 square kilometres ( 290 sq mi ) . The largest centres of population are the coastal towns of <unk> and <unk> . The council 's administrative headquarters are in the village of <unk> . 
- There are 33 Grade I listed buildings in West Somerset . The oldest is either <unk> Church , one of the smallest churches in England , and pre @-@ Norman in origin , or <unk> Steps , which may originate in the Bronze Age , although other sources date them from around 1400 . Dunster has the greatest concentration of Grade I listed buildings , including Dunster Castle , which was built in 1617 on a site which had supported a castle for the previous 600 years ; the <unk> Market , which was built in 1609 ; <unk> Bridge , which dates from the 15th century and the Priory Church of St George which is predominately from the 15th century but includes part of the earlier church on the same site . Other sites include manor houses such as the medieval buildings at <unk> Court and <unk> <unk> . 
- 
- 
- = Gertrude Barrows Bennett = 
- 
- Gertrude Barrows Bennett ( 1883 – 1948 ) was the first major female writer of fantasy and science fiction in the United States , publishing her stories under the pseudonym Francis Stevens . Bennett wrote a number of highly acclaimed fantasies between 1917 and 1923 and has been called " the woman who invented dark fantasy " . 
- Her most famous books include Claimed ( which Augustus T. Swift , in a letter to The Argosy called " One of the <unk> and most compelling science fantasy novels you will ever read " ) and the lost world novel The Citadel of Fear . 
- Bennett also wrote an early dystopian novel , The Heads of Cerberus ( 1919 ) . 
- 
- = = Life = = 
- 
- Gertrude <unk> Barrows was born in Minneapolis in 1883 . She completed school through the eighth grade , then attended night school in hopes of becoming an illustrator ( a goal she never achieved ) . Instead , she began working as a stenographer , a job she held on and off for the rest of her life . 
- In 1909 Barrows married Stewart Bennett , a British journalist and explorer , and moved to Philadelphia . A year later her husband died while on an expedition . With a new @-@ born daughter to raise , Bennett continued working as a stenographer . When her father died toward the end of World War I , Bennett assumed care for her invalid mother . 
- During this time period Bennett began to write a number of short stories and novels , only stopping when her mother died in 1920 . In the mid @-@ 1920s , she moved to California . Because Bennett was estranged from her daughter , for a number of years researchers believed Bennett died in 1939 ( the date of her final letter to her daughter ) . However , new research , including her death certificate , shows that she died in 1948 . 
- 
- = = Writing career = = 
- 
- Bennett wrote her first short story at age 17 , a science fiction story titled " The Curious Experience of Thomas Dunbar " . She <unk> the story to Argosy , then one of the top pulp magazines . The story was accepted and published in the March 1904 issue . 
- Once Bennett began to take care of her mother , she decided to return to fiction writing as a means of supporting her family . The first story she completed after her return to writing was the novella " The Nightmare , " which appeared in All @-@ Story Weekly in 1917 . The story is set on an island separated from the rest of the world , on which evolution has taken a different course . " The Nightmare " resembles Edgar Rice <unk> ' The Land That Time <unk> , itself published a year later . While Bennett had submitted " The Nightmare " under her own name , she had asked to use a pseudonym if it was published . The magazine 's editor chose not to use the pseudonym Bennett suggested ( Jean <unk> ) and instead credited the story to Francis Stevens . When readers responded positively to the story , Bennett chose to continue writing under the name . 
- Over the next few years , Bennett wrote a number of short stories and <unk> . Her short story " Friend Island " ( All @-@ Story Weekly , 1918 ) , for example , is set in a 22nd @-@ century ruled by women . Another story is the novella " Serapion " ( Argosy , 1920 ) , about a man possessed by a supernatural creature . This story has been released in an electronic book entitled Possessed : A Tale of the Demon Serapion , with three other stories by her . Many of her short stories have been collected in The Nightmare and Other Tales of Dark Fantasy ( University of Nebraska Press , 2004 ) . 
- In 1918 she published her first , and perhaps best , novel The Citadel of Fear ( Argosy , 1918 ) . This lost world story focuses on a forgotten Aztec city , which is " rediscovered " during World War I. It was in the introduction to a 1952 reprint edition of the novel which revealed for the first time that " Francis Stevens " was Bennett 's pen @-@ name . 
- A year later she published her only science fiction novel , The Heads of Cerberus ( The Thrill Book , 1919 ) . One of the first dystopian novels , the book features a " grey dust from a silver <unk> " which transports anyone who <unk> it to a totalitarian Philadelphia of <unk> AD 
- One of Bennett 's most famous novels was Claimed ( Argosy , 1920 ; reprinted 1966 and 2004 ) , in which a supernatural artifact summons an ancient and powerful god to 20th century New Jersey . Augustus T. Swift called the novel , " One of the <unk> and most compelling science fantasy novels you will ever read " ) . 
- 
- = = Influence = = 
- 
- Bennett has been credited as having " the best claim at creating the new genre of dark fantasy " . It has been said that Bennett 's writings influenced both H. P. Lovecraft and A. Merritt , both of whom " <unk> Bennett 's earlier style and themes " . Lovecraft was even said to have praised Bennett 's work . However , there is controversy about whether or not this actually happened and the praise appears to have resulted from letters wrongly attributed to Lovecraft . 
- As for Merritt , for several decades critics and readers believed " Francis Stevens " was a pseudonym of his . This rumor only ended with the 1952 <unk> of Citadel of Fear , which featured a biographical introduction of Bennett by Lloyd Arthur <unk> . 
- Critic Sam Moskowitz said she was the " greatest woman writer of science fiction in the period between Mary <unk> Shelley and <unk> Moore " . 
- Because Bennett was the first American woman to have her fantasy and science fiction widely published , she has been recognized in recent years as a pioneering female fantasy author . 
- 
- = = = Novels = = = 
- 
- The Citadel of Fear ( 1918 ; reprinted in Famous Fantastic Mysteries , February 1942 , and in paperback form in 1970 , [ NY : Paperback Library ] and 1984 [ NY : Carroll & <unk> ] ) 
- The Labyrinth ( serialized in All @-@ Story Weekly , July 27 , August 3 , and August 10 , 1918 ; later reprinted as a paperback novel ) 
- The Heads of Cerberus 1st book edition . 1952 , <unk> , also leather backed , Reading , PA . <unk> Press ( <unk> of Fantasy <unk> , Inc . ) ill . Ric <unk> . <unk> by Lloyd Arthur <unk> ( Thrill Book , 15 August 1919 ; reprinted as a paperback novel in 1952 and 1984 ) 
- Avalon ( serialized in Argosy , August 16 to September 6 , 1919 ; not reprinted ) 
- Claimed ( 1920 ; reprinted in 1985 , 1996 , and 2004 ) <unk> , cloth and paper , <unk> of Wonder Press , James A. Rock & Co . , Publishers in trade paperback and hard cover . 
- 
- = = = Short stories and <unk> = = = 
- 
- " The Curious Experience of Thomas Dunbar " ( Argosy , March , 1904 ; as by G. M. Barrows ) 
- " The Nightmare , " ( All @-@ Story Weekly , April 14 , 1917 ) 
- " Friend Island " ( All @-@ Story Weekly , September 7 , 1918 ; reprinted in Under the <unk> of Mars , edited by Sam Moskowitz , 1970 ) 
- " Behind the Curtain " ( All @-@ Story Weekly , September 21 , 1918 , reprinted in Famous Fantastic Mysteries , January 1940 ) 
- " Unseen @-@ Unfeared " ( People 's Favorite Magazine <unk> 10 , 1919 ; reprinted in <unk> Unknown , edited by Sam Moskowitz , 1971 ) 
- " The Elf @-@ Trap " ( Argosy , July 5 , 1919 ) 
- " Serapion " ( serialized in Argosy Weekly , June 19 , June 26 , and July 3 , 1920 ; reprinted in Famous Fantastic Mysteries , July 1942 ) 
- " <unk> " ( 1923 ; original printed in two parts in Weird Tales , July – August 1923 , and Weird Tales , September 1923 ; also reprinted as trade paperback in 1996 by <unk> International ) 
- 
- = = = Collections = = = 
- 
- Possessed : A Tale of the Demon Serapion ( 2002 ; contains the novella " Serapion " , retitled , and the short stories " Behind the Curtain " , " Elf @-@ Trap " and " Unseen @-@ Unfeared " ) 
- Nightmare : And Other Tales of Dark Fantasy ( University of Nebraska Press , 2004 ; contains all Stevens ' known short fiction except " The Curious Experience of Thomas Dunbar " , i.e. " The Nightmare " , " The Labyrinth " , " Friend Island " , " Behind the Curtain " , " " Unseen @-@ Unfeared " , " The Elf @-@ Trap " , " Serapion " and " <unk> " ) 
- 
- 
- = Man Down ( song ) = 
- 
- " Man Down " is a song by Barbadian singer Rihanna from her fifth studio album , Loud ( 2010 ) . Singer Shontelle and production duo Rock City wrote the song with its main producer , Sham . They wrote it during a writing camp , in Los Angeles of March 2010 , held by Rihanna 's record label to gather compositions for possible inclusion on the then @-@ untitled album . Rock City were inspired by Bob Marley 's 1973 song " I Shot the Sheriff " and set out to create a song which embodied the same feel but female perspective . It is a reggae ballad which incorporates elements of <unk> and electronic music . Lyrically , Rihanna is a fugitive after she shoots a man , an action she later regrets . Several critics singled out " Man Down " as Loud 's highlight , while others commented on her prominent West Indian accent and vocal agility . 
- Def Jam released " Man Down " on May 3 , 2011 , as the fifth single from the album . In the United States , the single reached number 59 on the Billboard Hot 100 and number 9 on the Hot R & B / Hip @-@ Hop Songs chart . It has been certified double platinum by the Recording Industry Association of America ( RIAA ) . The song topped the chart in France for five consecutive weeks and reached the top three in Belgium and the Netherlands . Anthony Mandler directed its music video , in which Rihanna 's character shoots a man after he rapes her . The video was criticized by the Parents Television Council , Industry <unk> and Mothers Against Violence , who faulted Rihanna for suggesting that murder is an acceptable form of justice for rape victims . However , actress Gabrielle Union , a rape victim , praised the video for being relatable . " Man Down " was on the set list for three of Rihanna 's tours – the Loud Tour ( 2011 ) , the Diamonds World Tour ( 2013 ) and The Anti World Tour ( 2016 ) . 
- 
- = = Background = = 
- 
- In March 2010 , record label Def Jam held a writing camp in Los Angeles for songwriters and producers to compose material for possible inclusion on Rihanna 's then @-@ untitled fifth studio album , Loud . Def Jam rented out nearly every recording studio in Los Angeles in order to create as many songs as possible . Ray Daniels , the manager of musical duo Rock City ( brothers <unk> and Timothy Thomas ) , was present during the sessions , and stated that a writing camp typically involves the label hiring ten recording studios for two weeks at the cost of $ 25 @,@ 000 per day . Daniels revealed that it is where songwriters have lyrics but no music , and where producers have music but no lyrics . 
- <unk> Joseph , professionally known as Sham , was hired as one of the producers to work on crafting songs at the camp . Sham 's manager had arranged his attendance at the camp through an acquaintance who was an employee of the record label . Sham explained that he found a flight to Los Angeles and began working on music as soon as he arrived , stating that he had " nothing to lose and everything to gain " . He was inspired by a vision of Rihanna performing songs at a concert that were Caribbean themed . Sham felt that Rihanna had not explored Caribbean @-@ themed music since her debut album , Music of the Sun ( 2005 ) . 
- 
- = = Production and recording = = 
- 
- According to Daniels , Rock City knew Sham but they had not heard the West Indian / Caribbean @-@ themed music that he had composed during the camp . Sham played the music to them , to which the brothers responded " Let 's give Rihanna a one @-@ drop ! Like , a response to ' I shot the sheriff ' ! " Together , Sham and Rock City wrote the lyrics to " Man Down " in twelve minutes . In an MTV News interview , Rock City said they intended to write a song that would embody Bob Marley 's " I Shot the Sheriff " ( 1973 ) from a female perspective and to " tap [ Rihanna 's ] island origins in a way that sounded authentic " . Singer Shontelle said that Rihanna called her during the Last Girl on Earth tour and asked her to be involved with the song . She confirmed that Rihanna was present when she was writing her part in the recording studio . Shontelle <unk> that following one of Rihanna 's concerts , the singer exited the stage and immediately returned to the tour bus to work in the studio . Daniels said that once the writing camp had concluded , Rihanna listened to all of the songs which had been composed for her and chose her favorites . In September 2010 , several months after Sham attended the writing camp , Rihanna called him and said that she wanted to record " Man Down " for inclusion on Loud . 
- Rihanna later described the sentiment she wanted to express as " <unk> " , and elaborated on how reggae culture has influenced her musical style : " I 'm super inspired by reggae music [ and it ] has been a part of me since I was born , and I grew up listening to it . I was exciting for me to take this on as my own and do a song like this , especially with the lyrics being like that . " The track was composed during Rihanna 's Last Girl on Earth tour . The song 's instrumental was recorded by Cary Clark at The Village in Los Angeles . <unk> <unk> produced Rihanna 's vocals with Josh <unk> and Marcos <unk> at Westlake Recording Studios , also in Los Angeles . Bobby Campbell assisted with vocal production and recording . The song was mixed by Manny <unk> at Larrabee Sound Studios in Los Angeles , assisted by Erik Madrid and Christian Plata . 
- Daniels estimated the total cost of the writing camp to be approximately $ 200 @,@ 000 , averaging $ 18 @,@ 000 for each of the eleven songs which were included on Loud ; the camp consisted of forty writers and producers . Daniels confirmed that Rock City received $ 15 @,@ 000 and Sham $ 20 @,@ 000 for their part in the production of " Man Down " . He said that " to get that twelve minutes of inspiration from a top songwriting team is expensive — even before you take into account the fee for the songwriters " . A cost of $ 53 @,@ 000 for " Man Down " was already incurred prior to Rihanna entering the studio with a vocal producer . Although Makeba Riddick did not serve as the song 's vocal producer , Daniels cited her as an example of how the process works and how much she would charge . It is the responsibility of the vocal producer to tell a singer how to sing the song correctly to achieve the desired sound . Daniels said that Riddick 's fee varies from $ 10 @,@ 000 to $ 15 @,@ 000 , and that the final part of the process is for the song to be mixed and mastered , which <unk> a similar fee . He estimated the final cost of writing , producing , vocal producing , mixing and mastering " Man Down " to be $ 78 @,@ 000 . When combined with the marketing and promotional costs , the total expense was $ 1 @,@ <unk> @,@ 000 . 
- 
- = = Composition and lyrical interpretation = = 
- 
- " Man Down " is a " murder fantasy " reggae song with " Caribbean @-@ rhythms " and elements of <unk> and electronic music . The song , in the key of C minor , has a tempo of 77 beats per minute . Rihanna 's voice spans more than one and a half octaves , from <unk> to E ♭ 5 . Slant Magazine critic Sal Cinquemani described " Man Down " as one of Rihanna 's " most confident vocal performances " with her strong Barbadian <unk> . Jon Pareles of The New York Times said that the singer " plays up her West Indian accent " , and August Brown of the Los Angeles Times described the vocals as <unk> " her Caribbean <unk> " . Entertainment Weekly writer Leah <unk> described " Man Down " as a song with " island rhythms " . Lyrically , Rihanna is a fugitive after she shoots a man , but later regrets it . Rihanna slowly relays the chain of events which led up to the murder . She cries to her mother about the act that she has committed – " Mama , I just shot a man down " – expressing guilt and remorse for not meaning to kill her attacker , and that he is somebody 's son . As the track develops , Rihanna 's <unk> accent becomes stronger and exaggerated , which <unk> during the bridge as she declares " Why deed I pull dee treeguh , pull dee treeguh , pull dee treeguh , <unk> ! " <unk> writer Bradley Stern thought that the track took on a confessional tone . 
- On February 8 , 2009 , Rihanna was reported to have been involved in an altercation with her boyfriend , Chris Brown . He allegedly punched Rihanna and threatened to kill her . Brown turned himself in to the police and pleaded guilty to <unk> assault during the trial . August Brown called the murder ballad theme an apparent " warning " to Chris Brown and a response to his song " <unk> " ( 2010 ) , which <unk> an ex @-@ lover . Kitty Empire of The Guardian wrote that Brown 's assault on Rihanna gives the song lyrical context , and sings it with a " <unk> menace " tone . When asked by <unk> about how he reacted to listeners of the song saying that it <unk> violence , Sham dismissed the accusations : 
- I didn ’ t think it made any sense . I think it was because of who it was saying ' I just shot a man down . ' I think it was very <unk> for some of the parents against violence in media , those same parents have probably allowed their kids to watch all types of movies and programs that have depictions , or things that <unk> violence . So for them to be mad about , number one , an issue that actually exists ? ... A woman feeling like she wants to shoot somebody who ’ s still alive because of something that they took from her , that ’ s real and that ’ s honest . So the emotion is a very true emotion ... and they relive it on a daily basis . I think it was crazy and blown out of proportion .... But people benefit from controversy , and those same people who probably were trying to bring awareness , now they have a voice and now they ’ re ' specialists , ' and they can speak for a group of people all of a sudden . 
- 
- = = Release and reception = = 
- 
- On March 1 , 2011 , Rihanna asked fans to help her choose the next single from Loud using Twitter , saying that she would film a music video in the forthcoming weeks . After an influx of suggestions , the singer said she had narrowed the options down to four songs : " Man Down " , " California King Bed " , " Cheers ( <unk> to That ) " and " <unk> " . On March 12 , she confirmed that " California King Bed " had been selected as the next international single . However , " Man Down " was sent to rhythmic and urban radio stations in the United States on May 3 , before the May 13 release of " California King Bed " , making " Man Down " and " California King Bed " the fifth and sixth singles from Loud . The song was released in France and Switzerland on July 11 and the Netherlands on July 15 . 
- Kitty Empire called the track " excellent " , and praised it for being an original composition which is reminiscent of a " righteous old reggae murder ballad " . Consequence of Sound writer Ryan <unk> said that " Man Down " and another album track called " <unk> " both " stand on their own <unk> " , and that the former is an homage to her Caribbean heritage with its <unk> melody . Describing the track as " <unk> " , Bradley Stern thought that no other song on Loud embodied Rihanna 's personality more so than on " Man Down " . Cinquemani chose " Man Down " as the best song on Loud , calling Rihanna 's vocal agility " surprising " and noting that the " fully @-@ fledged reggae " song is co @-@ written by a fellow Barbadian @-@ born singer , Shontelle . 
- In her review of Loud , Emily <unk> of NME called its experimentation more " organic " than that on Rihanna 's previous album Rated R ( 2009 ) , citing " Man Down " ' s theme of " doomed youth " . Similarly , <unk> <unk> of Contactmusic.com pointed out that " Man Down " , which she described as " a dark track " that is " haunting " yet " <unk> intriguing " , was reminiscent of the material on Rated R. In their review of Rihanna 's top 20 songs , Time Out ranked " Man Down " as their tenth best track , writing that it is Rihanna at " her <unk> best " . Complex staff compiled a list of their top 26 Rihanna songs , and ranked " Man Down " in thirteenth place ; Claire <unk> thought that it was the singers most " cinematic " song of her career , and that she elevated the theme of " accidental <unk> " from " <unk> " to " adorable " . 
- 
- = = Commercial performance = = 
- 
- In the United States , " Man Down " debuted on the Billboard Hot 100 at number 94 on June 1 , 2011 , peaking at number 59 and spending a total of 14 weeks on the chart . On the Hot R & B / Hip @-@ Hop Songs chart , it peaked at number 9 on August 6 , 2011 , remaining there for 2 weeks and spending a total of 19 weeks charting . It was number 47 on the Hot R & B / Hip @-@ Hop Songs 2011 year @-@ end list . The track peaked at number 56 on the Hot R & B / Hip @-@ Hop Airplay chart , number 20 on the Hot R & B / Hip @-@ Hop Digital Songs chart and number 40 on the Radio Songs chart . " Man Down " was certified double platinum by the Recording Industry Association of America ( RIAA ) , denoting shipments of more than 2 million units . The song reached number 92 on the Canadian Hot 100 . 
- " Man Down " debuted at number 65 on the French Singles Chart on June 6 , 2011 , a month before its release as a single . It rose to number 1 on July 30 , remaining there for 5 consecutive weeks , and was number 2 the week before and for 3 weeks after its chart @-@ topping run . It stayed on the chart until May 12 , 2012 , but re @-@ entered three weeks later . The song , on and off the French chart for the rest the year , continued to appear on it sporadically in 2013 . After a total of 73 weeks on the chart , the track 's last French chart appearance was at number 172 on August 8 , 2013 . 
- In the United Kingdom , " Man Down " entered the Singles Chart at number 117 on June 11 , 2015 , reaching number 75 the following week . The song peaked at number 54 in its fourth week , remaining there for 2 weeks and spending a total of 11 weeks on the chart . On the UK R & B Chart , " Man Down " reached number 15 on June 26 , spending 18 weeks in the top 40 . In Belgium , the song peaked at number 3 in Dutch @-@ speaking Flanders and number 2 in French @-@ speaking Wallonia . It was certified gold by the Belgian Entertainment Association ( <unk> ) for selling more than 15 @,@ 000 copies . Although the song spent only 1 week on the Italian Singles Chart ( at number 8 ) , it was certified platinum by the <unk> <unk> <unk> <unk> ( <unk> ) in 2014 for selling more than 30 @,@ 000 copies . 
- 
- = = Music video = = 
- 
- 
- = = = Background and synopsis = = = 
- 
- Anthony Mandler directed the music video for " Man Down " in April 2011 on a beach in Portland Parish , on the northeastern Jamaican coast . Rihanna told Rap @-@ Up that the video has a " strong underlying message [ for ] girls like me ! " On May 1 , 2011 , three camera phone teaser photos , of Rihanna on a beach in a white <unk> & <unk> dress and riding a bicycle in Portland Parish , were released . The music video premiered on BET 's 106 & Park on May 31 , 2011 . In an interview for MTV News , Mandler said that " Man Down " required " a strong narrative and visual " and that fans could expect something " dramatic and shocking and intense and emotions and <unk> and enlightening " . 
- The video opens as the protagonist ( Rihanna ) shoots and kills a man while he walks through a busy train station . She flees before a flashback to the previous day , when she rides her bike , meets friends and is alone in a bedroom at dusk . At a nightclub the protagonist dances and <unk> with another club @-@ <unk> , who then attacks her when she leaves the club . <unk> , the woman cries in the street after an implied sexual assault , and the video ends as she runs home to grab a gun hidden in a <unk> <unk> . 
- 
- = = = Analysis and reception = = = 
- 
- <unk> Hobson analysed the imagery presented in the video for " Man Down " in her book Body as Evidence : <unk> Race , <unk> Gender , which " challenges <unk> dismissals of identity politics and the delusional belief that the <unk> era reflects a ' <unk> ' and ' <unk> ' world . " In the chapter titled " <unk> : Black Women 's Resistance to Sexual Violence " , Hobson explores how black women have " found the courage " to speak out about sexual violence , protest against it and not remain a silent victim . She recalled Rihanna 's interview for 20 / 20 with Diane Sawyer , which aired on November 6 , 2009 . Having remained silent about her altercation with Brown on the evening of the Grammy Party in February that year , whereby Brown assaulted her , Rihanna decided to speak about it for the first time . The author noted how Sawyer decided to approach the interview by presenting the couples relationship and assault case as an " anomaly " and accused Rihanna of " projecting a ' fake ' imagery of strong black woman " , rather than presenting her another domestic violence victim not only in the United States , but in the world . " I am strong , " Rihanna responded . Hobson wrote that from then on , the singer decided to project an image of " hardcore <unk> and <unk> @-@ type femininity in her music trajectory " . 
- Subsequently , several of Rihanna 's songs and music videos have <unk> controversy for their violent themes , which Hobson attributes to the leaking of a photo showing the singers " battered face " on the evening of the assault by <unk> which circulated the internet without the permission of Rihanna . Hobson writes : " Because of this , Rihanna has had to <unk> back control of the ' victim ' image <unk> on her , and she in turn has challenged us <unk> and visually to question and examine the power , danger , and <unk> that shape our relationships . " She continued to highlight the music videos for " Russian <unk> " , " Hard " , " We Found Love " , " Love the Way You Lie " with Eminem which documents domestic violence , and " S & M " , which contains references to bondage and <unk> and is , in part , Rihanna 's response to disparaging critics , as examples . At one point in the video for " S & M " , Rihanna is literally tied up as a victim . 
- However , Hobson noted that Rihanna " rejects the victim stance " in the video for " Man Down " , and <unk> that she played the role of a rape survivor who shot her attacker . She attributed the location of shooting the video in Jamaica as significant , due to how the image of a gun <unk> during 1990s Jamaican dance hall 's to " express female rage " . The <unk> depicts Rihanna as a " dark @-@ hooded " <unk> <unk> whereby the narrative explains her motives for murder and <unk> the <unk> to <unk> with her because she danced in a provocative manner with a man in a club , which Hobson suggests is " somehow <unk> of rape " . She continued to explain that Rihanna is inviting the audience to consider what justice means by " pointing both a literal and lyrical gun at the issue " . Hobson concluded that Rihanna is protecting her vulnerability and <unk> the image of the abused black woman who is looked at <unk> in society . 
- Beck Bain of Idolator described the video as " visually stunning " , while Metro writer Lee Ann labelled it as " shocking " . Co @-@ writer of " Man Down " <unk> Thomas felt that the video was very theatrical and that Rihanna played her role " perfectly " . He continued to say that had the video been a lyric @-@ by @-@ lyric representation , the narrative would have been more " graphic " . 
- 
- = = = Controversy = = = 
- 
- The Parents Television Council ( PTC ) criticized Rihanna for her portrayal of " cold , calculated execution of murder " in the music video , and argued that murdering a rapist as socially @-@ acceptable justice is <unk> . The group disagreed with Rihanna 's rationale for the storyline : that the video has " a very strong underlying message [ for ] girls " like Rihanna . According to the PTC and Industry <unk> , if Chris Brown murdered a woman in a video that premiered on BET " the world would stop " and Rihanna should not have been allowed to release her clip . The week before the council 's statement about the video it had objected to Rihanna and Britney Spears ' performance of the " S & M " remix at the Billboard Music Awards , having called it a " <unk> @-@ laced , S & M sex show on prime @-@ time broadcast television " . 
- After the PTC 's statement , Julianne <unk> Shepherd of <unk> wrote that the group seemed to employ a double standard ; it had not condemned Kanye West 's music video for " Monster " , in which dead women hang from ceilings and West holds a <unk> head . Shepard added that Eminem and Rihanna 's video for " Love the Way You Lie " had not been criticized , despite " <unk> and <unk> " domestic violence . A Mothers Against Violence spokesperson criticised Rihanna for failing to present a solution , rather than encouraging the vulnerable youth , for which rape is a reality for many people . Director Anthony Mandler addressed the controversy in an interview for The Hollywood Reporter , saying that the visual evoked the reaction he intended and that it highlighted an issue still taboo in modern society . He recalled growing up in an era in which artists such as Madonna released controversial music videos , and noted that contemporary videos no longer tackle taboo subjects as frequently . 
- Rihanna responded to the PTC 's criticism on Twitter , and said that parents should not expect her to parent their children and that " <unk> subject matters " should not be hidden from children otherwise they will not learn how to adapt in society , and that it <unk> <unk> even more because children are embarrassed to talk about rape . The singer continued to state that " the industry isn 't ' <unk> 's ' R Us ' " and that singers " have the freedom to create art " . In an interview for BET , Rihanna further explain why rape was used as the vehicle to push the story forward in the video despite the lyrics not mentioning rape , saying " Making that into a mini @-@ movie or video , we needed to go back to why it happened . <unk> she 's not a cold @-@ <unk> killer . It had to be something so offensive . And we decided to hone in on a very serious matter that people are afraid to address , especially if you 've been <unk> in this scenario . " Rihanna added that the character is <unk> for her actions . 
- Actress and women 's @-@ health advocate Gabrielle Union , a rape victim , voiced support for the video on Twitter . Union called it " brave " and , although she did not agree with the eye @-@ for @-@ an @-@ eye sentiment , she could relate to the situation . Union said that every rape victim or survivor is a unique situation , and that they all have an idea of how justice should be served . She admitted that she tried to shoot her rapist , but missed , and that she has since realised that committing murder as a form of justice for herself would not have made the situation better . She continued to say that while it is " understandable " to desire to kill a rapist , unless it is in self @-@ defense then it is not <unk> . 
- 
- = = Live performances and covers = = 
- 
- Rihanna has included " Man Down " on the set lists of several concerts and tours , including the 2011 Loud Tour , BBC Radio 1 's <unk> Weekend on May 24 , 2012 and the 2013 Diamonds World Tour . For the Loud Tour , Rihanna performed the song on " a levitating , rotating platform , a <unk> belt and graffiti @-@ laden car shell . " Although Kitty Empire described " Man Down " as " terrific " , the critic felt let down by the " <unk> lack of creativity " from Rihanna 's production team for the stage set up during Rihanna 's performance . She wrote that she did not understand why a truck bonnet was in the middle of the stage . Maza praised the tracks placement on the set @-@ list . " Man Down " was performed as the fourth song on the Loud Tour , following its opener " Only Girl ( In the World ) " , " <unk> " and " <unk> Up and Drive " . Maza noted that the tempo of " Man Down " should have " slowed down the momentum she 'd accumulated until then but that was instead an ideal marriage of production and performance . " Instead , the red lights on the stage played up the " ominous " tone of the song as it gradually increased its tempo to the point whereby the end of the song was on the verge of sounding like an <unk> . 
- For the Diamonds World Tour , Rihanna performed " Man Down " in a Caribbean @-@ theme section of the show , which also included " You da One " , " No Love <unk> " , " What 's My Name ? " and " Rude Boy " . James <unk> of The Telegraph highlight the Caribbean @-@ themed section as the show 's highlight . Manchester Evening News writer Katie Fitzpatrick commented that Rihanna transported the audience to the Caribbean with a " grinding groove " . However , Gary <unk> of The Oakland Press was disappointed with the lack of variety in the section , writing that it was " <unk> by a sonic <unk> , even with <unk> <unk> of Extreme , whose guitar was buried in the bass @-@ heavy mix , playing some intriguing <unk> and accents " . 
- British singer and songwriter <unk> Lewis performed a <unk> of " Man Down " with her 2008 single " Better in Time " at BBC Radio 1 's Live Lounge in June 2011 . She also included the <unk> on the set list of her 2013 <unk> Tour . Her rendition received a mixed response from critics . Katherine <unk> @-@ McLean of the Worthing Herald complimented the fusion of " Better in Time " with reggae beats . But The Guardian 's Malcolm Jack thought the performance was <unk> and called Lewis a " reasonably priced Rihanna " . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Marauders ( Star Trek : Enterprise ) = 
- 
- " Marauders " is the sixth episode of the second season of the American science fiction television series Star Trek : Enterprise , the 32nd episode overall . It first aired on October 30 , 2002 , on the UPN network within the United States . The story was created by executive producers Rick Berman and <unk> <unk> with a teleplay by David Wilcox . A similar premise had been included in the original pitch for Star Trek by Gene Roddenberry . 
- Set in the 22nd century , the series follows the adventures of the first Starfleet starship Enterprise , registration NX @-@ 01 . In this episode , while in search of deuterium , Enterprise discovers a mining colony that is being controlled by Klingons who are bullying the inhabitants and hoarding their supplies . The crew conduct repairs on the colony and train the colonists to fight off the Klingons . 
- This episode was mostly filmed on location in a quarry in <unk> County , California , and the majority of the guest stars were stunt performers . Both Larry Cedar and <unk> <unk> had previously appeared in different episodes of Deep Space Nine and Voyager . " Marauders " was watched by 5 @.@ 6 million viewers , the second highest audience for an episode of the second season . Reviews were mixed , with some critics responding negatively to the lack of consequences from the fighting sequences while another praised the characters interactions and felt it was a good example of the western genre . 
- 
- = = Plot = = 
- 
- Captain Jonathan Archer ( Scott Bakula ) , Sub @-@ Commander T 'Pol ( <unk> <unk> ) , and Commander Charles " Trip " Tucker III ( Connor <unk> ) fly down to a small colony of 76 miners in order to trade for deuterium . They initially try to <unk> with Tessic ( Larry Cedar ) , the colony 's leader , but he appears reluctant to part with any of the 80 @,@ 000 liters in inventory , which is being held for ' someone else ' . After negotiations however , a deal is struck − 200 liters of deuterium for four power cells and whatever medical supplies Enterprise can spare , on the <unk> that the Enterprise crew can fix two <unk> pumps in two days . 
- Despite deuterium being a valuable commodity , Archer is <unk> by the lack of basic medical supplies and the run @-@ down nature of the colony . The reason becomes apparent when seven Klingons show up to collect deuterium according to their ' regular arrangement ' . When Tessic informs <unk> ( Robertson Dean ) , the leader of the Klingons , that they do not have all the deuterium because two pumps were not working , <unk> hits him and gives them four days to meet the order . Later , when Tucker and Archer attempt to talk them into fighting against the Klingons , Tessic tell them to take their 200 liters and leave . However , leaving does not sit well with Archer and he convinces the leader to resist with their support . 
- On Enterprise , T 'Pol teaches some of the colonists how to evade edged @-@ weapon attacks ( from Vulcan martial @-@ art ' <unk> @-@ <unk> ' ) , while Ensign Hoshi Sato ( Linda Park ) and Lieutenant Malcolm Reed ( Dominic <unk> ) teach other colonists how to fire their weapons more accurately . Archer and Tucker then suggest the colonists shift the entire colony to the south 50 meters . When the Klingon <unk> arrive , the colony appears deserted and silent . Finally the defenders show themselves , and in the ensuing fight the Klingons are lured into an area surrounded by the capped @-@ off deuterium well heads . On cue , the wells are <unk> , surrounding the Klingons with flames . Tessic then tells the Klingons to leave and never come back . After they depart , Archer is rewarded with 2 @,@ 000 liters of deuterium by the grateful miners . 
- 
- = = Production = = 
- 
- The initial pitch for Star Trek : The Original Series by Gene Roddenberry included a similar episode premise called " Kentucky , Kentucky " . This story would have had Captain Robert M. April and the crew of the S.S. Yorktown visit a human colony which had been previously attacked by Viking @-@ like aliens . This resulted in the colonists reverting in appearance to that seen during the American frontier @-@ era . April and his crew band together to help the colonists fight off the aliens . 
- Executive producers Rick Berman and <unk> <unk> developed the story , which was turned into a teleplay by David Wilcox . It was directed by Mike <unk> , his fourth episode of the series , although he had also developed several more episodes of the other series in the franchise . Filming on " Marauders " began on August 21 , 2002 , utilising the standing ship sets . The shoot on the following day mostly took place on a sound stage , while the third day 's on set filming used the <unk> set as well as a newly created ship 's gym set . The remainder of the eight days filming took place on location at a quarry in <unk> County , California , located about an hour 's journey north west of Los Angeles . Temperatures were mild for that time of year , but the actors were still told to wear <unk> and drink plenty of water . The actors portraying the Klingons joined on the second of the five days on location , with the majority of them played by stunt men . According to Bakula , the set used for the colony was the largest build so far for Enterprise . 
- Robertson Dean played the sole speaking Klingon , he had previously appeared in the Star Trek : The Next Generation episode " Face of the Enemy " . Other members of the guest cast included Cedar , portraying the <unk> Tessic , who had previously appeared in individual episodes of both Star Trek : Deep Space Nine and Voyager . Likewise , <unk> <unk> also appeared in those two series . While appearing in " Marauders " , she was also performing in the play <unk> at the Odyssey Theatre in Los Angeles . 
- 
- = = Reception and home media = = 
- 
- " Marauders " was first broadcast on the UPN network within the United States on October 30 , 2002 . It was watched by 5 @.@ 6 million viewers , equating to Nielsen ratings of 3 @.@ 9 / 6 percent . This meant that the episode was watched by 3 @.@ 9 percent of possible viewers , and six percent of those watching television at the time . This was the second most watched episode of the season , behind " A Night in <unk> " , which was broadcast a week prior . 
- <unk> <unk> III , while writing for the website PopMatters , described " Marauders " as forming the first part of a loose trilogy of episodes within the second season focusing on re @-@ introducing the Klingons to the series . The following episodes in the group were said to be " Judgment " and " Bounty " . Jamahl Epsicokhan at his website " Jammer 's Reviews " gave the episode two out of four , said that there was nothing exciting in " Marauders " and that the action sequences towards the end were unrealistic because despite all the apparent violence , not a single person even gets injured during the scene . He called the general premise " classic trek " , but that it lacked depth and didn 't pose any interesting questions . He summed it up by saying it was " a bloodless , light @-@ as @-@ a @-@ feather action show with handsome production values but absolutely and positively no edge . " 
- Michelle Erica Green , reviewing the episode for TrekNation , said that the episode seemed to follow the same story as the 1998 animated film A <unk> 's Life , but was very much set in the western genre which inspired Roddenberry 's original Star Trek pitch with other comparisons to the 1960 film The Magnificent Seven . She felt that there was several scenes which enabled some character building moments for Hoshi , Travis <unk> and Tucker , and described it overall as " a decent genre piece with good character work " . 
- The first home media release of " Marauders " was part of the season two DVD box set , released in the United States on August 7 , 2005 . A release on Blu @-@ ray Disc for season two occurred on August 20 , 2013 . 
- 
- 
- = Johnny McNichol = 
- 
- John " Johnny " McNichol ( 20 August 1925 – 17 March 2007 ) was a Scottish footballer who played more than 500 games in the Football League in England . An inside forward , he played more than 150 games for Brighton & Hove Albion and more than 200 for each of Chelsea and Crystal Palace . 
- McNichol graduated from junior footballer and apprentice motor mechanic in his native Scotland to a professional contract with English First Division club Newcastle United . After two years , he had found success with the reserve team but was never selected in the first eleven . Brighton & Hove Albion , struggling in the Third Division , broke their transfer record to sign him . McNichol spent four years with the club , acquiring " the reputation as the finest inside @-@ forward in the Third Division " , before moving to the First Division as Chelsea manager Ted Drake 's first signing . He was part of the Chelsea team that won the League championship in the 1954 – 55 season . In 1958 he joined Crystal Palace , whom he captained to promotion from the Fourth Division , and finished his on @-@ field career in the Southern League as player @-@ manager of <unk> Wells Rangers . He then spent 25 years working on the commercial side of football with two of his previous clubs . 
- 
- = = Early life and career = = 
- 
- McNichol was born in <unk> , Ayrshire . His father , Danny , died when he was five , so McNichol and seven siblings were raised by their mother , Catherine . He attended St Joseph 's School in <unk> , and started work as a messenger boy for a local <unk> 's shop when he left school . His shop work stopped him playing football on Saturdays , but he was able to play some <unk> football for Junior club Hurlford United . When he was taken on at the local bus garage as an apprentice motor mechanic , he became available on Saturdays as well . Hurlford paid him ten <unk> a game , nearly as much as his apprentice 's wages . During the Second World War , McNichol was called up to the Fleet Air Arm as a mechanic , but was able to play friendly matches for Inverness @-@ based club <unk> . Because of the number of professional players stationed around the country , such matches were played at a fairly high standard . 
- 
- = = Newcastle United = = 
- 
- After the war , McNichol returned to Hurlford , but , amid interest from other clubs , he accepted a trial with Newcastle United . After two trial matches , he signed professional forms with the club on his 21st birthday . McNichol spent two years at Newcastle , but never made a first @-@ team appearance . Behind the likes of England international forwards Roy Bentley and Len <unk> in the pecking order , he doubled his income working as a motor mechanic for a local funeral director whose " two or three Rolls @-@ <unk> [ made ] a nice change from working on bus engines " . In his second season , he was part of the reserve team that won the Central League title , but a disagreement over personal terms on his contract renewal – the Newcastle management felt a lower wage was justified because of the player 's earnings outside the game – prompted McNichol to seek first @-@ team football elsewhere . 
- 
- = = Brighton & Hove Albion = = 
- 
- Brighton & Hove Albion had finished bottom of the Third Division South the season before and were struggling financially , yet manager Don Welsh persuaded the directors to break the club transfer record by paying £ 5 @,@ 000 for a player yet to make his debut in the Football League , and persuaded McNichol to sign . He made his first appearance in the League on 21 August 1948 , at the age of 23 , as Brighton drew with <unk> Town at home . The club finished sixth in the division in McNichol 's first season and eighth , despite having no regular goalscorer – McNichol 's nine goals made him top scorer – in 1949 – 50 . The next year , McNichol played in all of Brighton 's games , the only man so to do , and again finished as top scorer for the season , this time with 14 goals . According to <unk> and Harris , he " had a superb season with a brand of play which won him the reputation as the finest inside @-@ forward in the Third Division " . Appointed club captain when Billy Lane took over from Welsh as manager , McNichol flourished under Lane 's attacking policy . He scored 14 goals in the 1951 – 52 season as Brighton narrowly failed to mount a successful challenge to Plymouth Argyle for the title , " was again the star of the side " , and " was thought by many to be the most stylish inside @-@ forward to play for the Albion " . 
- That season , McNichol scored a hat @-@ trick against eventual runners @-@ up Reading , which caught the eye of manager Ted Drake . Shortly before the next season started , he became Drake 's first signing for his new club , Chelsea , at a fee of £ 12 @,@ 000 plus the player Jimmy <unk> , a club record fee received for Albion . He had scored 39 goals in all competitions from 165 appearances . Although scouts from bigger clubs had been watching the player since soon after his arrival at the club – he had apparently already turned down moves to Manchester City , Everton and Huddersfield Town – Brighton 's supporters were disappointed , seeing his sale as a backward step . 
- 
- = = Chelsea = = 
- 
- On his Chelsea debut away at Manchester United , McNichol found himself playing at right back after ten minutes when Sid <unk> sustained an injury . Once restored to the forward line , his goals helped Chelsea avoid relegation to the Second Division at the end of his first season . A " dramatic last @-@ minute goal ... enabled Chelsea to <unk> a lucky victory at West Bromwich " with three games left , and he scored the third goal of Chelsea 's 3 – 1 defeat of Manchester City in their last fixture of the season which confirmed their escape from the relegation positions . 
- Two seasons later , Chelsea won the First Division title for the first time . As they beat Charlton Athletic in March 1955 to " maintain their challenging position in the Championship " , The Times ' reporter described how " McNichol filled the role of general , and was instigator of many dangerous movements " . Two weeks later , McNichol , " the most effective of their forwards " , scored twice as a Chelsea team displaying " a <unk> to play the man in preference to the ball " beat Tottenham Hotspur 4 – 2 , and the title was confirmed with one game still to play . McNichol had missed only two games in the 42 @-@ game season , and scored 14 goals , a good return for a player in his position ; an inside forward was normally more a creator than a scorer of goals , but his profile on Chelsea 's website describes him as " clever , astute and most of all a clinical finisher " . 
- He stayed at the club for three more seasons , but later lost his place in the side to the young Jimmy Greaves : " There was no disgrace in losing my place to him . I couldn 't <unk> about that . We used to get on very well , and he would listen to the instructions I gave him . Then he became world famous ! " The respect was mutual : though Greaves described the Chelsea title @-@ winning side as " almost certainly one of the least talented teams ever to win the title " , he made an exception for McNichol , " the ball player of the team " . In all competitions , he made 202 appearances for Chelsea and scored 66 goals . 
- While at Brighton , McNichol had worked in a local garage , wanting to keep up his skills in case injury put an early end to his football career . When he joined Chelsea , his decision to remain living in the area had upset Drake . In addition , he bought a <unk> 's shop in Hove , so not only did he have to travel by train from the south coast – as did teammates Stan <unk> and Eric Parsons – he further annoyed the Chelsea management by getting up even earlier each morning to open the shop before coming in to training . His wife Connie , whom he had known since childhood , ran the business in his absence . He claimed to have " earned more working in that shop than playing for Chelsea . Even in that championship season . " 
- 
- = = Crystal Palace = = 
- 
- Just ahead of the transfer deadline in March 1958 , McNichol signed for Third Division South club Crystal Palace . He was expected to fill a role " similar to the one he has performed so well at Stamford Bridge – helping in the development of promising young forwards " , and was appointed captain . He scored on his debut for the club , and produced seven goals from the twelve games he played in what remained of the 1957 – 58 season . By October 1958 , manager George Smith felt the player was feeling the strain of his dual role , so relieved him of the captaincy . As McNichol grew older , his playing position became more defensive , as he became first a wing half , then a full back . Palace had been placed in the Fourth Division when the Football League structure was reorganised on national lines prior to the 1958 – 59 season , and McNichol , long since restored to the captaincy , led them to promotion to the Third Division in 1961 , their first promotion for 40 years . Soon afterwards , he applied for the managerial post at former club Brighton & Hove Albion , then in the Second Division , but was unsuccessful . A broken arm suffered in August 1961 forced McNichol to miss a Palace match for the first time in a three @-@ and @-@ a @-@ half @-@ year club career . He played on for two seasons , but his professional career was brought to a close by facial injuries , a fractured <unk> and broken jaw , sustained during the 1962 – 63 season . In all competitions , he scored 15 goals from 205 appearances . 
- 
- = = Later life and career = = 
- 
- McNichol remained in the South of England , spending four years as player @-@ manager of <unk> Wells Rangers in the Southern League . Having sold the <unk> , he returned to Crystal Palace to work in the commercial side of the game , where he was responsible for the establishment of weekly pools and <unk> competitions as a means of raising funds for the club . He moved back to Brighton & Hove Albion to occupy a similar role from 1979 to 1992 , and after retirement continued to live in the <unk> area of Brighton . 
- Despite the successes of his career , McNichol had regrets . He described Chelsea 's decision not to accept their invitation to participate in the inaugural season of the European Cup as his " one big disappointment " , and " thought it was strange at the time " , despite the national team <unk> ' preference for players <unk> their trade for Scottish clubs , that he was never chosen to represent his country . 
- McNichol retained contact with his previous clubs . One of 24 former players and managers nominated as " Albion Legends " as part of Brighton 's centenary events in 2001 , he took an active role in the celebrations . The surviving members of Chelsea 's 1955 title @-@ winning team used to meet for an annual dinner , and were guests of the club at the last match of the 2004 – 05 season as they celebrated their second League title , 50 years after the first . 
- He died of a stroke on 17 March 2007 at the age of 81 . 
- 
- = = Honours = = 
- 
- Newcastle United Reserves 
- The Central League winners : 1947 – 48 
- Chelsea 
- Football League First Division winners : 1954 – 55 
- Crystal Palace 
- Football League Fourth Division runners @-@ up : 1960 – 61 
- 
- 
- = Otra Nota = 
- 
- Otra Nota ( English : Another Note ) is the debut album by American singer Marc Anthony that was released on January 26 , 1993 , by RMM Records . Produced by Sergio George , it was the first album by Anthony to record in salsa after starting his career as a freestyle musician . Recording of the album began after Anthony asked RMM president Ralph Mercado to record Juan Gabriel 's " Hasta Que Te Conocí " in salsa after hearing it on the radio during a taxi ride . Recorded on a low budget , the album peaked at No. 2 on the Billboard Tropical Albums chart and reached No. 30 on the Billboard Top Latin Albums chart . 
- The album was well received by critics who complimented George 's production and Anthony 's youthful voice . Anthony received two awards for " Best New Artists " at the Billboard Latin Music Awards and the Lo <unk> Awards . The album produced three singles : " Hasta Que Te Conocí " , " Palabras del Alma " , and " Si Tú No Te Fueras " , all of which charted on the Billboard Hot Latin Songs chart . As of 2002 , Otra Nota has sold over 300 @,@ 000 copies . 
- 
- = = Background = = 
- 
- Marc Anthony began his recording career in 1980s as a freestyle musician during which he was a backup vocalist for boy bands such as <unk> and the Latin <unk> . Anthony also wrote songs for his school friend <unk> @-@ Fire , including " Boy I 've Been <unk> " which became a hit on the Billboard Hot 100 chart . Anthony got his start as a lead vocalist when he collaborated with Little <unk> Vega on the album When the Night Is Over . The lead single " Ride on the Rhythm " became a number @-@ one hit on the Billboard Hot Dance Club Songs chart . 
- RMM manager Ralph Mercado invited Anthony to record a salsa album , but Anthony declined the offer due to a lack of interest to record in Spanish . The following day , while in a taxi , Anthony was listening to Juan Gabriel 's song " Hasta Que Te Conocí " ( " Until I Met You " ) on the radio and was motivated to record in the song in salsa and told Mercado about his change of decision . Mercado introduced Anthony to Sergio George who would produce the album . According to George , the album was an " total experiment " , citing that it was on low budget , recorded with one musician at a time without a band , and the full production was done on computers while George handled the keyboards . 
- 
- = = Music and lyrics = = 
- 
- The album includes five compositions and three cover songs . The lead track " Palabras del Alma " ( " Words from the Soul " ) is a cover originally performed and written by Ilan Chester . " Si Tú No Te Fueras " ( " If You Would Not Leave " ) was composed by Nelson Frank and Jaime Gutierrez . " Hasta Que Te Conocí " was first performed and written by Juan Gabriel . " El Último <unk> " was composed by Anthony 's father Felipe <unk> . " Make It With You " is a cover of American band Bread 's song . " <unk> <unk> " was written by Luis <unk> who composed songs for RMM recording artists including José Alberto and Tito <unk> . Sergio George co @-@ wrote " <unk> <unk> O Amor " ( " Game or Love ? " ) along with Adam <unk> . The final track , " Si He de <unk> " ( " If I Were to Die " ) was composed by Luis Díaz . 
- 
- = = Commercial reception = = 
- 
- Otra Nota debuted and peaked at No. 2 on the Billboard Tropical Albums chart during the week of April 17 , 1993 , behind Jerry Rivera 's <unk> <unk> and remained at this position for eight weeks . During the week of June 11 , 1994 , the album debuted and peaked at No. 30 on the Billboard Top Latin Albums , where it spent nineteen weeks on the chart . Otra Nota has sold over 300 @,@ 000 copies as of 2002 . 
- 
- = = = Singles = = = 
- 
- " Hasta Que Te Conocí " was the first single to be released from the album and peaked at No. 13 on the Billboard Hot Latin Songs chart . The second single " Palabras del Alma " peaked at No. 15 on the Billboard Hot Latin Songs chart . " Si Tú No Te Fueras " was the last single released from the album which peaked at No. 31 on the Billboard Hot Latin Songs chart . 
- 
- = = Critical reception = = 
- 
- Evan Gutierrez of Allmusic gave the album 3 @.@ 5 out of 5 stars and complemented Anthony 's voice as " developed , unique , and individual " and felt that Anthony 's debut " brought quality and passion to his listeners from the beginning . " Gutierrez also found the songs in salsa enjoyable while calling the ballads . Gutierrez praised Sergio George 's arrangements as " outstanding " though he felt the production was outdated in places . Enrique <unk> gave the album 3 out of 4 stars , noting that while Anthony was viewed with skepticism on the salsa market , he felt that Anthony " may be the best of the many newborn <unk> . " <unk> also praised Anthony 's vocals as " excellent " and described the overall album as " noble effort " though he criticized Anthony 's cover of " Make It With You " as unnecessary . In 1994 , Anthony received a Billboard Latin Music Award for " Tropical / <unk> New Artist of the Year " and a Lo <unk> award for " Tropical - New Artist of the Year " . 
- 
- = = Track listing = = 
- 
- 
- = = Credits and personnel = = 
- 
- The following credits are from Allmusic . 
- 
- = = = Performance credits = = = 
- 
- 
- = = = Technical credits = = = 
- 
- 
- = = Chart performance = = 
- 
- 
- 
- = St Peulan 's Church , Llanbeulan = 
- 
- St Peulan 's Church , Llanbeulan is a disused medieval church in Llanbeulan , in Anglesey , north Wales . The nave , which is the oldest part of the building , dates from the 12th century , with a chancel and side chapel added in the 14th century . The church has a font of early date , possibly from the first half of the 11th century : one historian has said that it would initially have been used as an altar and that " as an altar of the pre @-@ Norman period it is a unique survivor in Wales and , indeed , in Britain " . 
- The church is now redundant and has been in the care of the charity , the Friends of Friendless Churches since 2005 . It is a Grade II * listed building , a designation given to " particularly important buildings of more than special interest " , because it is a medieval church of " typical Anglesey type " that has retained its simple character despite 19th @-@ century alterations . 
- 
- = = History and location = = 
- 
- The date of foundation of the church in Llanbeulan is not known for certain , although it is said that a church was founded on this site in 630 by St Peulan . He was a disciple of the Anglesey saint <unk> . The earliest parts of the building date from the 12th century . It was the parish church of the area , and had a chapel of ease at St Mary 's Church , Tal @-@ y @-@ <unk> ( now also closed ) . In November <unk> , records of an <unk> in <unk> show that the priest serving St Peulan 's was one of a number of Anglesey clergymen to have died about that time , which was when the Black Death was affecting Anglesey . During the 19th century , a significant amount of church rebuilding and restoration work took place throughout Anglesey , and St Peulan 's was given a new roof and new internal fittings at this time . It is now a redundant church and has been in the hands of the Friends of Friendless Churches since 2004 ; it is one of four churches on Anglesey for which the charity has responsibility . The charity holds a 999 @-@ year lease with effect from 10 June 2005 . 
- The church gave its name to the parish of Llanbeulan : the Welsh word <unk> originally meant " enclosure " and then " church " , and " ‑ <unk> " is a modified form of the saint 's name . It is in a thinly populated part of rural Anglesey , about 1 @.@ 75 kilometres ( 1 @.@ 09 mi ) to the south @-@ west of the village of <unk> , and about 3 kilometres ( 1 @.@ 9 mi ) to the south @-@ east of <unk> . A <unk> track runs from the road to the church . To the east of the church , the churchyard contains a war grave of a Royal Welsh Fusiliers soldier of the First World War . 
- 
- = = Architecture and fittings = = 
- 
- The church is built from rubble masonry , dressed with <unk> . The nave measures 27 feet 6 inches by 15 feet 6 inches ( 8 @.@ 4 by 4 @.@ 7 m ) , the chancel is 15 feet 6 inches by 11 feet 9 inches ( 4 @.@ 7 by 3 @.@ 6 m ) , and the south chapel is 11 feet 3 inches by 16 feet ( 3 @.@ 4 by 4 @.@ 9 m ) . The building has a slate roof with <unk> of stone . At the west end , there is a <unk> bellcote with one bell ; crosses made of iron are fixed to the roofs of the chancel and south chapel . The nave of the church , which has two bays , was probably built in the 12th century . An entrance at the west end of the nave has been blocked up and plastered over on the outside . The chancel , which is smaller than the nave , and a chapel ( on the south side of the building attached to the nave ) were added in the 14th century , and the arches dividing the nave from the chapel and the chancel are of this date . The entrance door , from the 19th century , is positioned on the east side of the chapel and has a pointed <unk> . Alongside the doorway is a stone inscribed with the year 1637 , and next to that is a round @-@ headed small window dating from the 12th century , reset in the chapel wall of later date . It has been suggested that this window may been reused from the blocked nave entrance . 
- The east window of the chancel , from the 15th century , has two lights topped with <unk> and decorated with carved heads . There is also a blocked 14th @-@ century window in the chancel 's north wall and an early 16th @-@ century window in the south wall . Other windows in the church date from the 17th century ( chapel south window ) and 19th century ( nave ) . The pews date from the 17th century : one stall in the chancel bears an inscribed panel of wood , dated 1664 , recording that it is the seat of William <unk> of <unk> . There are a number of memorials inside the church from the 17th , 18th and early 19th centuries . The church 's 19th @-@ century fittings , such as the pulpit and altar rail , are plain . 
- The most notable feature of the church is its font , which dates from the late 12th century , or possibly even the first part of the 11th century . It measures 2 feet 1 inch ( 63 @.@ 5 cm ) by 2 feet 11 inches ( 88 @.@ 9 cm ) ( external measurements ) and is 11 inches ( 27 @.@ 9 cm ) deep . It is rectangular and decorated on three sides . One of the shorter sides has a cross with four arms of equal length imposed on a ring ; at the base are two small <unk> , and the design is framed with bands ( some plain , some decorated with chevrons or a twisted rope pattern ) . It has been suggested that the <unk> are bee <unk> . One of the longer sides has a row of arches forming an arcade above a pattern of <unk> . The other shorter side has a <unk> pattern . The <unk> David Petts has noted that it is one of a number of fonts on Anglesey that has an area with little or no carving , perhaps because it was never anticipated that the plain side of the font would be seen or because the font was carved after being put in its position in the church . Although similar in style to other Anglesey fonts , its rectangular shape is " unique " , according to Petts , and the cross on one side " finds no parallels among fonts of this period . " 
- One writer , the historian Peter Lord , has suggested that it was initially used as an altar . Accepting the 11th @-@ century dating , Lord stated that " as an altar of the pre @-@ Norman period it is a unique survivor in Wales and , indeed , in Britain " . Petts considers this " unlikely " , suggesting that it may originally have been a reliquary . The Friends of Friendless Churches describes the font as the building 's " chief glory " . It has also been called " the best of Anglesey 's remarkable Romanesque series . " It has similarities of design and style with the fonts of two other churches on Anglesey ( St <unk> 's Church , <unk> and St <unk> 's Church , <unk> ) and with one of the stone crosses at St <unk> 's Church , <unk> . 
- 
- = = Assessment = = 
- 
- The church is a Grade II * listed building – the second @-@ highest ( of three ) grade of listing , designating " particularly important buildings of more than special interest " . It was given this status on 5 April 1971 , and has been listed because it is " a rural Medieval church retaining its simple character . " It is described by Cadw ( the Welsh Assembly Government body responsible for the built heritage of Wales ) as a church " of typical Anglesey type " , where the 19th @-@ century restoration work has " retained the simplicity of design and construction " , and also the church 's medieval character . 
- In the 19th century , the writer Samuel Lewis said that it was " small and of rude workmanship " , but had " several curious features " such as the font that made it " valuable " . The antiquarian <unk> <unk> , writing in 1833 , described it as " a small cruciform structure , situated in a little barren valley " , with the south transept " bearing evidence of very great antiquity . " She stated that there were some windows " in the later English style , of good design , especially the east window of the chancel , which is a very superior composition . " 
- 
- 
- = The Tramp Dentists = 
- 
- The Tramp Dentists is a 1913 American silent short comedy film released by the newly formed Joker productions by the Universal Film Manufacturing Company . Directed by Allen Curtis , the film 's cast includes Max Asher , Lee Morris , Eddie Boland Joseph Singleton and Bobby Vernon . The film is centered on two tramps , Dusty and Weary , who take over a dentist shop and get rich through their untrained dentistry , <unk> teeth with pincers and ice tongs . After earning a large amount of money , the tramps return to their old way of life . Released on October 29 , 1913 , this film was the second Joker comedy . The film received some criticism for its vulgar humor . The film had a wide national release in the United States , but the film is presumed lost . 
- 
- = = Plot = = 
- 
- Two tramps , named Dusty and Weary , <unk> from their <unk> in a hay stock and are overcome with thirst . The two drink from a horse trough and Dusty complains of <unk> . The two go to the dentist office only to be kicked out . When the dentist departs on a cruise the two tramps then pose as dentists and occupy the office and pull teeth with pincers and tongs . When the rightful owners return they drive off again . The tramps continue in their venture and quickly make a large sum of money before they desire their old way of life . The two tramps then surrender the office to its rightful owners . 
- 
- = = Cast = = 
- 
- Max Asher 
- Lee Morris 
- Eddie Boland 
- Joseph Singleton 
- Bobby Vernon credited as <unk> De <unk> 
- 
- = = Production = = 
- 
- The term " <unk> dentist " refers to an <unk> individual who practices dentistry . The appearance of the word and its usage was popularized in the 1890s and often applied as a literal definition of a <unk> , a traveling long @-@ term homeless person , who engages in dentistry . The term was not exclusive to the United States as noted in Tracey Adams 's A <unk> and a Gentleman : Gender and the Rise of <unk> in Ontario which refers to local blacksmiths and <unk> who would pull teeth and even create <unk> of questionable effectiveness and quality . The single reel film was directed by Allen Curtis and released on October 29 , 1913 . The film was the second release of the newly formed Joker line of comedies , following The Cheese Special . The film was not heralded with as much fanfare , but records show that the film was still advertised in theaters in July 1914 . 
- 
- = = Reception = = 
- 
- The Moving Picture World reviewed the film as being a low comedy that featured " somewhat <unk> " <unk> humor with the extraction of teeth with pincers and ice tongs . A more direct review in response to the film 's comedic antics came from a letter to the editor of The Motion Picture Story Magazine which appealed the vulgar antics in the film as going to alienate members the audience and risk their continued patronage . The film had a wide national release that was shown in theaters throughout the United States . Locations included Chicago , Illinois , Atlanta , Georgia , Oklahoma , Ohio , North Carolina , Pennsylvania , Oregon , Wisconsin , and Kansas . 
- The Tramp Dentists is presumed lost , but the date of disappearance is unknown . If the film were to have survived in Universal 's vaults it would have been deliberately destroyed along with the remaining copies of Universal 's silent era films in 1948 . 
- 
- 
- = Qedarite = 
- 
- The Qedarites ( also <unk> / <unk> , Cedar / Kedar / Qedar , and Kingdom of Qedar ) were a largely nomadic , ancient Arab tribal confederation . Described as " the most organized of the Northern Arabian tribes " , at the peak of its power in the 6th century BCE it controlled a large region between the Persian Gulf and the Sinai Peninsula . 
- Biblical tradition holds that the Qedarites are named for Qedar , the second son of Ishmael , mentioned in the Bible 's books of Genesis ( 25 : 13 ) and 1 Chronicles ( 1 : 29 ) , where there are also frequent references to Qedar as a tribe . The earliest <unk> inscriptions discovered by archaeologists that mention the Qedarites are from the Neo @-@ Assyrian Empire . <unk> the 8th and 7th centuries BCE , they list the names of Qedarite kings who <unk> and were defeated in battle , as well as those who paid Assyrian monarchs tribute , including Zabibe , queen of the Arabs ( <unk> @-@ rat <unk> @-@ ri @-@ bi ) . There are also Aramaic and Old South Arabian inscriptions recalling the Qedarites , who further appear briefly in the writings of Classical Greek , such as Herodotus , and Roman historians , such as Pliny the Elder , and <unk> . 
- It is unclear when the Qedarites ceased to exist as a separately defined confederation or people . Allies with the Nabataeans , it is likely that they were absorbed into the <unk> state around the 2nd century CE . In Islam , <unk> 'il is considered to be the ancestral <unk> of the Arab people , and in traditional Islamic historiography , Muslim historians have assigned great importance in their accounts to his first two sons ( Nebaioth and Qedar ) , with the genealogy of Muhammad , a <unk> of God in Islam , alternately assigned to one or the other son , depending on the scholar . 
- 
- = = Etymology = = 
- 
- It has been suggested that the name of the Qedarites is derived from the name for <unk> 's second son Qedar . Though the tribal name is Arabic , it was first transcribed in Assyrian ( 8th century BCE ) and Aramaic ( 6th century BCE ) , as the Arabic alphabet had not yet been developed . In the <unk> <unk> , the Qedarites are listed as an ethnic group whose name in Aramaic transliteration is <unk> . 
- The Arabic <unk> root q @-@ d @-@ r means " to measure , <unk> , estimate " ; " to decree , appoint , <unk> " ; and " to have power , or ability . " <unk> , a <unk> derived from the same root , means " cauldron , <unk> " , and also gives the verbal derivation , " to cook " . Ernst Axel <unk> , a biblical scholar who undertook a historical study of the Ishmaelites and determined that they were known in Assyrian inscriptions as the <unk> 'il , <unk> that the name of the Qedarites was derived from the verb <unk> , with its meaning of " to <unk> , to have power " . As this etymology is a deduction based solely on the prominence of the Qedar among the <unk> 'il tribes , it is viewed as inconclusive by other scholars . 
- 
- = = Geographical scope = = 
- 
- The Qedarites were an " Arab tribal confederation , " or " alliance of nomadic Arab tribes . " According to Philip J. King , theologian and historian , they lived in the northwest Arabian desert and were " an influential force from the 8th to 4th centuries BCE . " Geoffrey <unk> , historical <unk> and translator , transcribes their name as Kedar and states they lived in an area southeast of Damascus and east of the Transjordan . 
- 8th century BCE Assyrian inscriptions place the Qedarites as living in the area to the east of the western border of Babylon . Moving further east into areas of the Transjordan and southern Syria in the 7th century BCE , by the 5th century BCE they had spread into the Sinai and as far as the Nile Delta . Qedarite domination of northwest Arabia involved <unk> between the kings of Qedar and the kings of Dedan ( <unk> ) . Historian Israel <unk> <unk> writes that the " breadth of Qedarite distribution suggests a federation of tribes with various sub @-@ divisions . " 
- <unk> in the largely desert region lived in by the Qedarites - such as Dedan , <unk> , and Dumah - played an important role as sites of settlement , trade , and <unk> @-@ places . Dumah , a remote desert city to the west , known later as <unk> Al @-@ <unk> and today as al @-@ <unk> , was the most important of these , sitting as it did between the empires of Babylonia and Assyria . Serving as the base for Qedarite religious ceremonies , Dumah 's strategic position on the north @-@ south trade route in the area meant that relations with its inhabitants were sought after by both empires , though Dumah and the Qedarites were closer in both geographical and political terms to Babylonia . Those coming from the south and wishing to access Mesopotamia were obliged to pass through Dumah , which also lay on an alternate route to the northwest , leading to the city of Damascus , and from there , on to Assyria and Anatolia . 
- During the period of Persian imperial rule in the region ( c . 550 – 330 BCE ) , the Qedarites exercised control over the desert areas bordering Egypt and Israel and the traffic related to Arabian incense trade upon which Gaza depended . Herodotus ( c . <unk> – 425 BCE ) writes of their presence in the northern Sinai near the Egyptian border where they may have been engaged by the <unk> , the Persian imperial authorities , to keep that border secure as well as their control of the city of Gaza . 
- 
- = = Historical references = = 
- 
- 
- = = = <unk> = = = 
- 
- 
- = = = = Assyrian inscriptions = = = = 
- 
- The first documented mention of Qedar is from a stele ( c . 737 BCE ) of <unk> @-@ <unk> III , a king of the Neo @-@ Assyrian Empire , that lists leaders from the western part of Mesopotamia that pay him tribute . To the Assyrians , the Qedarites were known as Qidri or <unk> @-@ <unk> @-@ ri with other <unk> inscriptions also using Qadri , <unk> , <unk> and <unk> ( Neo Babylonian ) . Zabibe ( c . 738 BCE ) is listed among those paying tribute under the title " queen of the Qidri and the Aribi " ( Aribi meaning " Arabs " ) . 
- Also mentioned in Assyrian royal inscriptions is Zabibe 's successor Yatie ( c . 730 BCE ) , who sent forces headed by her brother <unk> to aid <unk> @-@ <unk> in his bid to hold onto power in Babylon . Together with an army from <unk> , this alliance faced the forces of Sennacherib , on the Assyrian king 's first campaign in 703 BCE . The events of the battle are recorded in the annals of Sennacherib which describe Yatie as " queen of the Arabs , " and tell of the capture of her brother <unk> in battle . Israel <unk> <unk> writes that this is the first mention in Assyrian documents of Arabs as an ethnic element in Babylonia . 
- Statements about the Qedarites in the annals of the Assyrian kings of Ashurbanipal and his son Esarhaddon indicate that the term Kedar was almost synonymous with Arabia . Hazael , who ruled c . 690 – 676 BCE , is described as a Qedarite king by Ashurbanipal and " king of the Arabs " by Esarhaddon . After Sennacherib 's invasion of Babylonia in 691 – <unk> BCE , Hazael fled to Dumah . Dumah is referred to as Adummatu by the Assyrians , and the city is described by them as the seat of the Qedar confederation and the base of their cult . 
- Te 'elkhunu and <unk> , both referred to as both " queens of Qidri " and " queens of Aribi , " have Adummatu explicitly denoted as their seat and capital city in the inscriptions . While the capital city for the three other queens referred to as " queens of Aribi " ( <unk> , Zabibe and Yatie ) is not explicitly mentioned , it is thought to have been Adummatu as well . Numerous inscriptions discovered in Dumah itself are further indications for a strong Qedarite presence , one of which asks of three deities known to the Assyrians as gods of Qedar ( <unk> , <unk> and <unk> ) for " help in the matter of my love . " 
- The conquest of Adummatu by Sennacherib in 690 BCE and the capture of a Qedarite queen , Te 'elkhunu , who was brought back to Assyria with other <unk> , including divine images , is also mentioned in the annals of the Assyrian monarchs . Esarhaddon eventually returned the " renewed " images of the Arab gods to Adummatu with <unk> , " who grew up in the palace of Sennacherib , " and was appointed Qedarite queen by Esarhaddon to replace Te 'elkhunu . However , strained relations between the Assyrians and the Qedarites meant that she ruled for only a brief period , with Esarhaddon recognizing Hazael as the king of Qedar after he increased his tribute to the Assyrian monarch by 65 camels . Esarhaddon then appointed Yauta , son of Hazael , as a Qedarite king , following Hazael 's death . That Yauta paid a tribute in the form of 10 <unk> of gold , 1 @,@ 000 precious stones , 50 camels , and 1 @,@ 000 leather pouches of <unk> is also recorded . 
- The defeat of <unk> and Yauta after they mounted Qedarite forces to march against the kings of the <unk> is recorded by Ashurbanipal . Yauta is said to have " <unk> the people of Arabia to revolt with him . " His wife , <unk> , is the only woman mentioned in Assyrian inscriptions as both a Qedarite queen and the wife of a Qedarite king , and is thought to be the only woman who was not a ruler in her own right . The inscriptions record how <unk> was sent by Yauta to accompany <unk> and the Qedarite forces , and their subsequent defeat and capture in <unk> BCE by the forces of <unk> , king of <unk> and a loyal vassal of the Assyrian empire . Yauta is said to have " fled to the Nabataeans . " His subsequent capture and sentencing in <unk> are recorded by Ashurbanipal who relates that , " I put a dog chain upon him and made him guard a <unk> . " <unk> was appointed as Yauta 's successor , and soon joined with the Nabataeans to revolt against Assyria again , prompting Ashurbanipal to launch a three @-@ month campaign to end it , beginning in Palmyra , continuing on to Damascus and ending in the southern part of al @-@ <unk> . 
- 
- = = = = Old South Arabic and Aramaic inscriptions = = = = 
- 
- Old South Arabic inscriptions mention <unk> ( " <unk> " or " <unk> " ) as a person or people . <unk> found in al @-@ <unk> , known as the <unk> of <unk> at Dedan , mentions Gashmu I , son of <unk> I , as King of Qedar . 
- A " king of Qedar " is also mentioned in a late 5th century BCE Aramaic inscription on a silver vessel found at Tell <unk> in the eastern Nile Delta in lower Egypt . The inscription names him as " <unk> son of Gashmu , " with the vessel described as an , " offering to <unk> ' <unk> " . 
- While it does not specifically mention the Qedar and is therefore a subject of debate , an Aramaic inscription dating to 5th century BCE discovered on an incense altar at <unk> and dedicated to , " <unk> , son of <unk> , the king , " is interpreted by André <unk> as a possible reference to kings of Qedar . 
- 
- = = = = Classical Antiquity = = = = 
- 
- Herodotus has documented that the Qedarites were called upon to assist <unk> II of the Persian empire in his invasion of Egypt in 525 BCE . The Qedarites and <unk> were known to enjoy close relations , even engaging as wartime allies against the Assyrians . It is possible that the Qedarites were eventually incorporated into the <unk> state which emerged as the strongest <unk> presence in northwestern Arabia c. the 2nd century CE . In The Cambridge Ancient History , some of the ( Arab ) <unk> mentioned by <unk> in his retelling of events that took place in 312 BCE are said to be Qedarites . 
- Pliny the Elder ( 23 – 79 CE ) , who refers to the <unk> and <unk> ( " <unk> " ) in the context of other Arabian tribes , placing their domain to the south of the <unk> and adjacent to that of the <unk> , is thought to be referring to the Qedar . Jerome ( c . 357 – 420 CE ) , also writing in Latin , transcribes Qedar as Cedar , and refers to it as a region . In one entry , he describes it as , " a region of the Saracens , who are called Ishmaelites in scripture " ; in another , he writes that it was a " once <unk> region across <unk> Arabia " ; and in a third , he writes that it is a " deserted region of the Ishmaelites , whom they now call Saracens . " According to the Catholic Encyclopedia , <unk> ( c . 393 – c . 457 ) mentions that in his time , the Qedar lived near Babylon . 
- 
- = = = = 19th century = = = = 
- 
- There are brief references to the Qedar in the writings of Western travellers to the Levant in the 19th century . Drawing on biblical <unk> , comparisons are made between the Bedouins and the Qedar . For example , Albert Augustus <unk> describes the imposing spectacle of a <unk> encampment on a plain upon which , " the black tents of Kedar were spread far and wide . " An earlier account by Charles <unk> Elliot describes the Arabs as falling into two main groups , <unk> and Bedouins , and identifies the latter with Ishmael and the Qedar as follows : 
- [ ... ] the Bedouins still retain the wandering habits of their father Ishmael ; their ' hand is against every man , and every man 's hand is against ' them ; the wild desert is their home ; the ground their <unk> and their canopy the sky ; or , if luxurious their <unk> place of <unk> is a little tent ' black as the tents of Kedar ' their progenitor [ ... ] 
- Charles Forster identifies the Arab tribe of the Beni <unk> as the modern descendants of the Kedar . He proposes that Beni <unk> ( " sons of war " ) is a patronym and <unk> de guerre that was adopted by the tribe at least 2 @,@ 000 years ago , replacing Kedar as the national moniker . 
- 
- = = = Biblical = = = 
- 
- The descendants of Abraham and <unk> are called Ishmaelites , after Ishmael , their firstborn , and the Qedarites are named for his second son , Qedar . The Bible refers to both the Qedarites and Qedar frequently . Old Testament references include Genesis ( 25 : 13 ) , Isaiah ( 21 : 16 @-@ 17 , 42 : 11 , 60 : 7 ) , Jeremiah ( 2 : 10 , 49 : 28 @-@ 33 ) , Ezekiel ( 27 : 21 ) , and Chronicles ( 1 : 29 ) . Twice , Qedar is used to refer to the actual son of Ishmael , as in the books of Genesis and Chronicles , while remaining references are to his descendants , referring either to his most prominent North Arabian sons , or to the Arabs and Bedouins as a more general collective . The " tents of Kedar " equated with " the peace @-@ <unk> <unk> " mentioned in the Book of <unk> ( 120 : 5 ) were likely a Qedarite sub @-@ group . 
- In <unk> ( 1 : 5 ) , the tents of the Qedarites are described as black : " Black I am , but beautiful , ye daughters of Jerusalem / As tents of Qedar , as <unk> of Salam black . " Their tents are said to be made of black goat hair . A tribe of Salam was located just south of the <unk> in <unk> <unk> , and <unk> proposed that the Qedarites mentioned in this <unk> text were in fact Nabataeans and played a crucial role in the <unk> trade in the 3rd century BCE . 
- Biblical descriptions indicate there were two major types of Qedarites : nomads ( Arabic : <unk> ) living in tents and sedentary people ( Arabic : <unk> ) living in villages . Jeremiah describes them as " a nation at ease , that <unk> securely " ( 49 : 31 ) and notes that they engage in the pagan practice of <unk> their temples . Isaiah recalls their warrior activities and skill with the bow . ( 21 : <unk> ) Ezekiel associates , " Arabia and all the princes of Kedar , " and indicates that they engaged in sheep / goat trading with the <unk> . The three books list the flocks of the Qedarites as including lambs , rams , goats and camels . 
- Jeremiah also tells of a campaign by <unk> ( 630 – 562 BCE ) against the Qedarites during the Babylonian period . Gashmu , the king of the Qedarites mentioned in the 5th century BCE Aramaic inscription described above , is also referred to as " <unk> the Arab " or " <unk> the Arabian " by <unk> who lists him as one of his adversaries , since Gashmu stands opposed to <unk> 's governorship over Judea in <unk> BCE . 
- 
- = = Culture and society = = 
- 
- Biblical and <unk> evidence suggests that of the Arabian tribes , the Qedarites were most prominent in their contacts with the world outside of Arabia . Like other nomadic groups , they lived primarily in <unk> <unk> . <unk> and traders in livestock , such as male lambs , rams and goats , they also played a key role in the prosperity of Gaza 's incense trade , controlling traffic in the desert regions between Egypt and Palestine . As a result of their trading activities , there were clans from among the Qedarites that became wealthy . 
- Though they were vassals under Assyrian rule and were often engaged in rebellion against that empire , the rise to dominance of the Persian empire proved beneficial to the Qedarites . Qedarite control of the trade routes and the access they afforded the Persians translated into what Herodotus described as a friendly relationship . 
- 
- = = = Language = = = 
- 
- The Qedarites are among a number of North Arabian tribes whose interactions with <unk> tribes beginning in the 8th century BCE resulted in cultural exchanges between these two large Semitic groups . Early Arab tribal groups like the Qedarites spoke early Arab dialects , but as the Arabic alphabet had not yet been developed , they used the Aramaic alphabet to write . " The tongue of Kedar " is used in <unk> sources as a name for the Arabic language . 
- <unk> , the practice of naming boys after their <unk> , was common among the Qedar . Some Qedarites had Aramaic personal names ( e.g. Hazael or <unk> @-@ el ) , while others had Arabic personal names ( e.g. Gashmu and Zabibe ) . Aramaic civilization and its peoples were gradually absorbed by the Arabs with Arabic dialects in Lebanon , Palestine , Syria , and Iraq in particular exhibiting the influence of Aramaic . 
- 
- = = = Religion = = = 
- 
- Religious worship among the Qedar , as was the case for most of the inhabitants of Arabia until the 7th century CE , was centered around a polytheistic system in which women rose to prominence . Divine images of the gods and goddesses worshipped by Qedarite Arabs , as noted in Assyrian inscriptions , included representations of <unk> , <unk> , <unk> , <unk> , <unk> , and <unk> . The female guardian of these idols , usually the reigning queen , served as a <unk> ( <unk> , in Assyrian texts ) who <unk> with the other world . As mentioned above , there is also evidence that the Qedar worshipped Al @-@ <unk> , to whom the inscription on a silver bowl from a king of Qedar is dedicated . In the Babylonian Talmud , which was passed down orally for centuries before being transcribed c . 500 CE , in <unk> <unk> ( <unk> <unk> ) , it is said that most Qedarites <unk> pagan gods . 
- 
- = = = Tomb = = = 
- 
- There is a mausoleum assigned to him in <unk> city <unk> Province , Iran 
- 
- = = <unk> = = 
- 
- The biblical view of the late Iron Age political and cultural map describes it as a set of branching <unk> . Biblical figures three generations forward from <unk> are invariably described as the eponymous founders of different tribes and polities that interacted with the Kingdom of Judah between the 8th and 6th centuries BCE . Such is the case of the Qedarite Arabs , who according to biblical tradition , are the offspring of the Abraham @-@ Ishmael @-@ Kedar genealogical line . 
- The majority of <unk> believe they are one of the modern day descendants of Muhammad , and that they are therefore the descendants of Ishmael and his second son Qedar . Abu <unk> <unk> al @-@ <unk> ( 676 – 743 CE ) wrote that his father Ali ibn <unk> informed him that Mohammed had said : " The first whose tongue spoke in clear Arabic was Ishmael , when he was fourteen years old . " <unk> Ibn Muhammad al @-@ <unk> ( 737 – 819 CE ) established a genealogical link between Ishmael and Mohammed using writings that drew on biblical and <unk> sources , and the ancient oral traditions of the Arabs . His book , <unk> al @-@ <unk> ( " The <unk> of <unk> " ) , seems to <unk> that the people known as " Arabs " are all descendants of Ishmael . Ibn <unk> ( <unk> – <unk> ) writes , " All the Arabs of the <unk> are descendants of Nebaioth and Qedar . " Medieval Jewish sources also usually identified Qedar with Arabs and / or Muslims . According to author and scholar <unk> <unk> , Western scholars viewed this kind of " genealogical <unk> " with suspicion , seeing it as , 
- [ ... ] a late Islamic fabrication because of the confusion in Islamic times which made it such a <unk> term as to include the inhabitants of the south as well as the north of the Arabian Peninsula . But <unk> of this extravagance , the concept is much more modest in its <unk> , and in the <unk> sources it applies only to certain groups among the Arabs of pre @-@ Islamic times . Some important statements to this effect were made by Muhammed when he identified some Arabs as Ishmaelites and others as not . 
- <unk> in this more limited definition holds that Ishmael was both an important religious figure and eponymous ancestor for some of the Arabs of western Arabia . <unk> is given in Arab genealogical accounts to the first two of Ishmael 's twelve sons , Nebaioth ( Arabic : <unk> , <unk> ) and Qedar ( Arabic : <unk> , <unk> ) , who are also prominently featured in the Genesis account . It is likely that they and their tribes lived in northwestern Arabia and were historically the most important of the twelve <unk> tribes . 
- In accounts tracing the ancestry of Mohammed back to Ma 'ad ( and from there to Adam ) , Arab scholars alternate , with some citing the line as through Nebaioth , others Qedar . 
- 
- 
- = Super Science Stories = 
- 
- Super Science Stories was an American pulp science fiction magazine published by Popular Publications from 1940 and 1943 , and again from 1949 to 1951 . Popular launched it under their " Fictioneers " imprint , which they used for magazines paying writers less than one cent per word . Frederik Pohl was hired in late 1939 , at 19 years old , to edit the magazine ; he also edited Astonishing Stories , a companion science fiction publication . Pohl left in mid @-@ 1941 , and Super Science Stories was given to Alden H. Norton to edit ; a few months later Norton <unk> Pohl as an assistant . Popular gave Pohl a very low budget , so most manuscripts submitted to Super Science Stories had already been rejected by the higher @-@ paying magazines . This made it difficult to acquire good fiction , but Pohl was able to acquire stories for the early issues from the Futurians , a group of young science fiction fans and aspiring writers . 
- Super Science Stories was an initial success , and within a year Popular increased Pohl 's budget slightly , allowing him to pay a bonus rate on occasion . Pohl wrote many stories himself , to fill the magazine and to augment his salary . He managed to obtain stories by writers who subsequently became very well known , such as Isaac Asimov and Robert Heinlein . After Pohl entered the army in early 1943 , wartime paper shortages led Popular to cease publication of Super Science Stories . The final issue of the first run was dated May of that year . In 1949 the title was revived with Ejler Jakobsson as editor ; this version , which included many reprinted stories , lasted almost three years , with the last issue dated August 1951 . A Canadian reprint edition of the first run included material from both Super Science Stories and Astonishing Stories ; it was unusual in that it printed some original fiction rather than just reprints . There were also Canadian and British reprint editions of the second incarnation of the magazine . 
- The magazine was never regarded as one of the leading titles of the genre , but has received qualified praise from science fiction critics and historians . Science fiction historian Raymond Thompson describes it as " one of the most interesting magazines to appear during the 1940s " , despite the variable quality of the stories . Critics Brian <unk> and Peter Nicholls comment that the magazine " had a greater importance to the history of sf than the quality of its stories would suggest ; it was an important training ground " . 
- 
- = = Publication history = = 
- 
- Although science fiction ( sf ) had been published before the 1920s , it did not begin to <unk> into a separately marketed genre until the appearance in 1926 of Amazing Stories , a pulp magazine published by Hugo <unk> . By the end of the 1930s the field was booming , and several new sf magazines were launched in 1939 . Frederik Pohl , a science fiction fan and aspiring writer , visited Robert <unk> , the editor of Marvel Science Stories and Dynamic Science Stories , to ask for a job . <unk> did not have an opening for him , but told Pohl that Popular Publications , a leading pulp publisher , was starting a new line of low @-@ paying magazines and might be interested in adding a science fiction title . On October 25 , 1939 , Pohl visited Rogers <unk> at Popular , and was hired immediately , at the age of nineteen , on a salary of ten dollars per week . Pohl was given two magazines to edit : Super Science Stories and Astonishing Stories . Super Science Stories was intended to carry longer pieces , and Astonishing focused on shorter fiction ; Super Science Stories was retitled Super Science Novels Magazine in March 1941 , reflecting this policy , but after only three issues the title was changed back to Super Science Stories . 
- Popular was uncertain of the sales potential for the two new titles and decided to publish them under its Fictioneers imprint , which was used for lower @-@ paying magazines . Super Science Stories ' first issue was dated March 1940 ; it was bimonthly , with Astonishing Stories appearing in the alternate months . In Pohl 's memoirs he recalls Harry Steeger , one of the company owners , breaking down the budget for Astonishing for him : " Two hundred seventy @-@ five dollars for stories . A hundred dollars for black and white art . Thirty dollars for a cover . " For Super Science Stories , Steeger gave him an additional $ 50 as it was 16 pages longer , so his total budget was $ <unk> per issue . Pohl could only offer half a cent per word for fiction , well below the rates offered by the leading magazines . Super Science Stories sold well , despite Pohl 's limited resources : Popular was a major pulp publisher and had a strong distribution network , which helped circulation . Steeger soon increased Pohl 's budget , to pay bonuses for popular stories . Pohl later commented that he was uncertain whether the additional funds really helped to bring in higher quality submissions , although at the time he assured Steeger it would improve the magazine . Some of the additional money went to Ray Cummings , a long @-@ established sf writer who came to see Pohl in person to submit his work . Cummings refused to sell for less than one cent a word ; Pohl had some extra money available when Cummings first visited him , and though he disliked Cummings ' work was never able to bring himself to reject Cummings submissions , or even to tell him that he could not really afford to pay the rate Cummings was asking . Pohl comments in his memoirs that " for months he [ Cummings ] would turn up regularly as clockwork and sell me a new story ; I hated them all , and bought them all . " 
- By reducing the space he needed to fill with fiction Pohl managed to stretch his budget . A long letter column took up several pages but required no payment , and neither did running advertisements for Popular 's other magazines . Some authors sent inaccurate word counts with the stories they submitted , and savings were made by paying them on the basis of whichever word count was less — the author 's or one done by Popular 's staff . The result was a saving of forty to fifty dollars per issue . <unk> elements of black and white illustrations were also reused to fill space , as multiple uses of the same artwork did not require additional payments to the artist . 
- Towards the end of 1940 Popular doubled Pohl 's salary to twenty dollars per week . In June 1941 Pohl visited Steeger to ask for a further raise , intending to resign and work as a free @-@ lance writer if he was unsuccessful . Steeger was <unk> , and Pohl commented later " I have never been sure whether I quit or got fired " . Instead of replacing Pohl , Popular assigned editor @-@ in @-@ chief Alden H. Norton to add the magazines to his responsibilities . The arrangement lasted for seven months , after which Norton asked Pohl to return as his assistant . Norton offered Pohl thirty @-@ five dollars a week as an associate editor , substantially more than the twenty dollars a week he had received as editor , and Pohl readily accepted . 
- Pohl was not eligible to be drafted for military service as he was married , but by the end of 1942 his marriage was over and he decided to enlist . As voluntary <unk> was suspended he was unable to immediately join the army , but eventually was inducted on April 1 , 1943 . Paper was difficult to obtain because of the war , and Popular decided to close the magazine down ; the final issue , dated April 1943 , was assembled with the assistance of Ejler Jakobsson . 
- In late 1948 , as a second boom in science fiction publishing was beginning , Popular decided to revive the magazine . Jakobsson later recalled hearing about the revival while on vacation , swimming in a lake , five miles from a phone : " A boy on a bicycle showed on shore and shouted , ' Call your office . ' " When he reached a phone , Norton told him that the magazine was being relaunched and would be given to Jakobsson to edit . Damon Knight , who was working for Popular at the time , also worked on the magazine as assistant editor , although he was not credited . The relaunched magazine survived for almost three years , but the market for <unk> was weak , and when Knight left in 1950 to edit Worlds Beyond Jakobsson was unable to sustain support for it within Popular . It ceased publication with the August 1951 issue . 
- 
- = = Contents and reception = = 
- 
- Because of the low rates of pay , the stories submitted to Super Science Stories in its first year had generally already been rejected elsewhere . However , Pohl was a member of the Futurians , a group of science fiction fans that included Isaac Asimov , <unk> <unk> , Richard Wilson and Donald <unk> ; the Futurians were eager to become professional writers and were eager to submit stories to Pohl . The Futurians were prolific ; in Pohl 's first year as an editor he bought a total of fifteen stories from them for the two magazines . Pohl contributed material himself , usually in collaboration with one or more of the Futurians . Particularly after his marriage to Doris <unk> in August 1940 , Pohl realized that his salary covered their apartment rent with almost no money left over , and began to augment his income by selling to himself as well as to other magazines . The first story Pohl ever published that was not a collaboration was " The <unk> in the Ice " , which appeared in the January 1941 Super Science Stories . All of the stories Pohl bought from himself were published under <unk> , though in fact Pohl used <unk> for everything he wrote until the 1950s . 
- The first issue , dated March 1940 , contained " Emergency <unk> " , James Blish 's first published story , two stories by John Russell <unk> ( one under the pseudonym " <unk> <unk> " ) , fiction by Frank <unk> Long , Ross <unk> , Raymond <unk> , <unk> Vincent and Dean O 'Brien ; and a poem by <unk> , " The Song of the Rocket " , under the pseudonym " Gabriel <unk> " . Blish 's most notable contribution to the magazine was " Sunken Universe " , which appeared in the May 1942 issue under the pseudonym " Arthur <unk> " . This later formed part of " Surface <unk> " , one of Blish 's most popular stories . Other writers whose first story appeared in Super Science Stories include Ray Bradbury , Chad Oliver , and Wilson Tucker . Bradbury 's first sale , " <unk> " , was bought by Norton , and appeared in the November 1941 issue ; Tucker 's writing career began with " <unk> Way Station " in May 1941 , and Oliver 's " The Land of Lost Content " appeared in the November 1950 Super Science Stories . Asimov appeared four times in Super Science Stories , starting with " Robbie " , his first Robot story , under the title " Strange <unk> " . 
- Although most stories submitted to Super Science Stories were rejects from the better @-@ paying markets such as Astounding Science Fiction , Pohl recalled in his memoirs that John W. Campbell , the editor of Astounding , would occasionally pass on a good story by a prolific author because he felt readers did not want to see the same authors in every issue . As a result , Pohl was able to print L. Sprague de Camp 's <unk> <unk> , in the March 1941 Super Science Stories , and Robert Heinlein 's " Let There Be Light " and " Lost Legacy " in the May 1940 and November 1941 issues : these were stories which , in Pohl 's opinion , " would have looked good anywhere " . Pohl also suggested that Campbell rejected some of Heinlein 's stories because they contained mild references to sex . A couple of readers did complain , with one disgusted letter writer commenting " If you are going to continue to print such <unk> , pre @-@ <unk> @-@ school <unk> as " Let There Be Light " , you should change the name of the <unk> to Naughty Future <unk> " . 
- The second run of Super Science Stories included some fiction that had first appeared in the Canadian reprint edition , which <unk> the U.S. original and printed eleven stories that had been acquired but not printed by the time Popular shut Super Science Stories and Astonishing down in early 1943 . These included " The Black Sun <unk> " by Henry <unk> , " And Then – the Silence " , by Ray Bradbury , and " The <unk> Crown " by James Blish . From mid @-@ 1950 a reprint feature was established . This led to some reader complaints , with one correspondent pointing out that it was particularly <unk> to discover that Blish 's " Sunken Universe " , reprinted in the November 1950 issue , was a better story than the original material in the magazine . The magazine also reprinted stories from Famous Fantastic Mysteries , which Popular had acquired from <unk> Publishing in 1941 . 
- Some of the original stories were well @-@ received : for example , Ray Bradbury 's " The Impossible " , which appeared in the November 1949 issue , and was later included in Bradbury 's book The Martian Chronicles , is described by sf historian Raymond Thompson as a " haunting ... comment on man 's attempts to realize his conflicting hopes and dreams " . Thompson also comments positively on <unk> Anderson 's early story " Terminal Quest " , in Super Science Stories 's final issue , dated August 1951 ; and on Arthur C. Clarke 's " <unk> of the <unk> " in the March 1950 issue . John D. MacDonald also contributed good material . 
- The book reviews in Super Science Stories were of a higher standard than elsewhere in the field , and historian Paul Carter regards Astonishing and Super Science Stories as the place where " book reviewing for the first time began to merit the term ' literary criticism ' " , adding that " it was in those magazines that the custom began of paying attention to science fiction on the stage and screen also " . The artwork was initially amateurish , and although it improved over the years , even the better artists such as Virgil <unk> and Lawrence Stevens continued to produce <unk> depictions of half @-@ dressed women threatened by robots or aliens . H. R. van <unk> , later a prolific cover artist for Astounding , made his first science fiction art sale to Super Science Stories for the cover of the September 1950 issue . 
- <unk> historian Mike Ashley regards Super Science Stories as marginally better than its companion magazine , Astonishing , adding " both are a testament to what a good editor can do with a poor budget " . According to sf critics Brian <unk> and Peter Nicholls , the magazine " had a greater importance to the history of sf than the quality of its stories would suggest ; it was an important training ground " . 
- 
- = = <unk> details = = 
- 
- The first run of Super Science Stories was edited by Frederik Pohl from March 1940 through August 1941 ( nine issues ) , and then by Alden H. Norton from November 1941 through May 1943 ( seven issues ) . Ejler Jakobsson was the editor throughout the second run , from January 1949 to August 1951 . The publisher was Popular Publications for both versions , although the first was issued under Popular 's Fictioneers imprint . It was pulp @-@ sized throughout both runs . At launch the magazine had 128 pages and was priced at 15 cents ; the price increased to 20 cents when it went to 144 pages in March 1941 , and again to 25 cents for the May 1943 issue , which had 128 pages again . The second run was priced at 25 cents throughout and had 112 pages . The title was Super Science Stories for both runs except for three issues from March to August 1941 , which were titled Super Science Novels Magazine . The volume numbering was completely regular , with seven volumes of four numbers and a final volume of three numbers . It was bimonthly for the first eight issues , from March 1940 to May 1941 , and then went to a regular <unk> schedule . 
- 
- = = = Canadian and British editions = = = 
- 
- In 1940 , as part of the War Exchange Conservation Act , Canada banned the import of pulp magazines . Popular launched a Canadian edition of Astonishing Stories in January 1942 , which lasted for three bimonthly issues and reprinted two issues of Astonishing and one issue of Super Science Stories . With the August 1942 issue the name was changed to Super Science Stories , and the <unk> was begun again at volume 1 number 1 ; as a result the magazine is usually listed by <unk> as a separate publication from the Canadian Astonishing , but in many respects it was a direct continuation . The price was 15 cents throughout ; it lasted for 21 regular bimonthly issues in a single volume ; the last issue was dated December 1945 . It was published by Popular Publications ' Toronto branch , and the editor was listed as Alden H. Norton . 
- Each issue of the Canadian edition corresponded to one issue of either Astonishing or Super Science : for example , the first two Canadian issues drew their contents from the February 1942 Super Science Stories and the June 1942 Astonishing , respectively . This pattern continued for ten issues . The next issue , dated April 1944 , contained several reprints from the US editions , but also included two original stories that had not appeared anywhere before — these had been acquired for the US magazine and remained in inventory . A total of eleven of these original stories appeared in the Canadian Super Science Stories . Later issues of the magazine also saw many reprints from Famous Fantastic Mysteries ; in tacit acknowledgement of the new source of material , the title was changed to Super Science and Fantastic Stories from the December 1944 issue . The artwork was mostly taken from Popular 's US magazines but some new art appeared , probably by Canadian artists . There was no other Canadian presence : the letters page , for example , contained letters from the US edition . 
- In 1949 , when the second run of the US Super Science Stories began , another Canadian edition appeared , but this was identical in content to the US version . Two British reprint editions of the second run also appeared , starting in October 1949 . The first was published by Thorpe & Porter ; the issues , which were not dated or numbered , appeared in October 1949 and February and June 1950 . The contents were drawn from the US issues dated January 1949 , November 1949 , and January 1950 respectively ; each was 96 pages and was priced at 1 / - . The second reprint edition was published by Pemberton 's ; these were 64 pages and again were undated and were priced at 1 / - . 
- The British issues are abridged versions of US issues from both the first and second series . The titles corresponded to the titles on the US magazine from which the stories were taken , so all were titled Super Science Stories except for the April 1953 issue , which was titled Super Science Novels Magazine . 
- 
- 
- = HMS Hostile ( <unk> ) = 
- 
- HMS Hostile ( <unk> ) was an H @-@ class destroyer built for the Royal Navy in the 1930s . During the Spanish Civil War of 1936 – 1939 the ship spent considerable time in Spanish waters , enforcing the arms blockade imposed by Britain and France on both sides of the conflict . She was transferred to Freetown , Sierra Leone , in October 1939 to hunt for German commerce raiders in the South Atlantic with Force K. Hostile participated in the First Battle of Narvik in April 1940 and the Battle of <unk> in July 1940 . The ship was damaged by a mine off Cape Bon in the Strait of Sicily while on passage from Malta to Gibraltar on 23 August 1940 . She was then scuttled by HMS Hero . 
- 
- = = Description = = 
- 
- Hostile displaced 1 @,@ 350 long tons ( 1 @,@ 370 t ) at standard load and 1 @,@ 883 long tons ( 1 @,@ <unk> t ) at deep load . The ship had an overall length of 323 feet ( 98 @.@ 5 m ) , a beam of 33 feet ( 10 @.@ 1 m ) , and a draught of 12 feet 5 inches ( 3 @.@ 8 m ) . She was powered by Parsons geared steam turbines , driving two shafts , which developed a total of 34 @,@ 000 shaft horsepower ( 25 @,@ 000 kW ) and gave a maximum speed of 36 knots ( 67 km / h ; 41 mph ) . Steam for the turbines was provided by three Admiralty 3 @-@ drum water @-@ tube boilers . Hostile carried a maximum of 470 long tons ( 480 t ) of fuel oil that gave her a range of 5 @,@ <unk> nautical miles ( 10 @,@ 240 km ; 6 @,@ 360 mi ) at 15 knots ( 28 km / h ; 17 mph ) . The ship 's complement was 137 officers and men in peacetime . 
- The ship mounted four 45 @-@ calibre 4 @.@ 7 @-@ inch ( 120 mm ) Mark IX guns in single mounts . For anti @-@ aircraft defence Hostile had two quadruple Mark I mounts for the 0 @.@ 5 inch Vickers Mark III machine gun . She was fitted with two above @-@ water quadruple torpedo tube mounts for 21 @-@ inch ( 533 mm ) torpedoes . One depth charge rail and two throwers were fitted ; 20 depth charges were originally carried , but this increased to 35 shortly after the war began . 
- 
- = = Service = = 
- 
- Hostile was laid down by Scotts Shipbuilding & Engineering Company at Greenock , Scotland on 27 February 1935 , launched on 24 January 1936 , and completed on 10 September 1936 . <unk> government @-@ furnished equipment like the armament , the ship cost £ 253 @,@ <unk> . She was assigned to the 2nd Destroyer Flotilla of the Mediterranean Fleet upon commissioning . Hostile patrolled Spanish waters in 1937 during the Spanish Civil War enforcing the edicts of the Non @-@ Intervention Committee . The ship received an overhaul at Gibraltar between 17 November and 15 December 1937 . She resumed patrolling Spanish waters in 1938 and 1939 . After the end of the Spanish Civil War , Hostile was refitted in <unk> Dockyard between 31 May and 26 July 1939 . She returned to the Mediterranean and was in Malta when World War II began . 
- In October the ship was transferred to Freetown to hunt for German commerce raiders in the South Atlantic with Force K. The ship and her half @-@ sisters , Hardy , <unk> , and <unk> , rendezvoused with the battlecruiser <unk> , the aircraft carrier Ark Royal , and the light cruiser Neptune on 17 December . They <unk> in Rio de Janeiro , Brazil , before proceeding to the estuary of the River Plate in case the damaged German pocket battleship Admiral <unk> <unk> attempted to escape from <unk> , Uruguay , where she had taken refuge after losing the Battle of the River Plate . Hostile was overhauled at Chatham Dockyard between 26 January and 29 March 1940 and then rejoined the 2nd Destroyer Flotilla , now assigned to the Home Fleet . During the First Battle of Narvik on 10 April the ship engaged the German destroyer <unk> <unk> von <unk> and badly damaged her , hitting her at least five times . Hostile was only hit once , but the shell did little damage . She escorted her badly damaged sister ship , Hotspur , to the repair base set up at <unk> in the <unk> Islands . Hostile briefly escorted the battleship HMS Warspite before she returned to Rosyth for repairs between 27 April and 4 May . The ship briefly returned to Norwegian waters , where she again escorted Warspite , before being transferred to the Mediterranean Fleet in mid @-@ May . 
- On 9 July Hostile participated in the Battle of <unk> as an escort for the heavy ships of Force C and unsuccessfully engaged Italian destroyers , suffering no damage . The ship , together with her sister , Hero , and the destroyers Nubian and <unk> , were ordered to Gibraltar on 22 August where they were to join Force H. Hostile struck a mine en route on the early morning of 23 August off Cap Bon that broke her back . The explosion killed five men and wounded three others . <unk> took off the survivors while Hero fired two torpedoes to <unk> her . 
- 
- 
- = Rocky Mountain Horse = 
- 
- The Rocky Mountain Horse is a horse breed developed in the state of Kentucky in the United States . Despite its name , it originated not in the Rocky Mountains , but instead in the Appalachian Mountains . A foundation stallion , brought from the western United States to eastern Kentucky around 1890 , began the Rocky Mountain type in the late 19th century . In the mid @-@ 20th century , a stallion named Old Tobe , owned by a prominent breeder , was used to develop the modern type ; today most Rocky Mountain Horses trace back to this stallion . In 1986 , the Rocky Mountain Horse Association was formed and by 2005 has registered over 12 @,@ 000 horses . The breed is known for its preferred " chocolate " coat color and flaxen mane and tail , the result of the relatively rare silver dapple gene acting on a black coat , seen in much of the population . It also exhibits a four @-@ beat ambling gait known as the " single @-@ foot " . Originally developed as a multi @-@ purpose riding , driving and light draft horse , today it is used mainly for trail riding and working cattle . 
- 
- = = <unk> characteristics = = 
- 
- Rocky Mountain Horses stand between 14 @.@ 2 and 16 hands ( 58 and 64 inches , 147 and 163 cm ) high . Any solid color is accepted by the registry , but a dark brown color called " chocolate " with a pale , " flaxen " mane and tail is preferred . This coloration is the result of the relatively rare silver dapple gene acting on a black base coat . Although uncommon , this gene has been found in over a dozen breeds , including the Rocky Mountain Horse . <unk> white markings are accepted by the registry , although leg markings may not extend above the knee . The physical characteristics are somewhat variable , due to the disparate breeds that created the Rocky Mountain Horse . The Rocky Mountain Horse is known by enthusiasts for its <unk> and ability to withstand winters in the mountains . It is also praised for its good nature and affinity for humans . Rocky Mountain Horses have the highest risk of any breed for the genetic ocular syndrome multiple congenital ocular <unk> ( MCOA ) , originally called <unk> anterior segment <unk> ( <unk> ) . MCOA is characterized by the abnormal development of some ocular tissues , which causes compromised vision , although generally of a mild form ; the disease is non @-@ progressive . Genetic studies have shown that the disorder may be tied to the silver dapple gene , as most horses diagnosed with MCOA carry the gene . 
- The breed exhibits a natural ambling gait , called the single @-@ foot , which replaces the trot seen in a majority of horse breeds . Both gaits are an intermediate speed between a walk and a <unk> or gallop ; ambling gaits are four @-@ beat gaits , whereas the trot is a two @-@ beat gait . The extra <unk> provide additional <unk> to a rider because the horse always has at least one foot on the ground . This <unk> movement of the horse 's <unk> and removes the bounce of a two @-@ beat gait , caused by a moment of suspension followed by the <unk> of two feet hitting the ground as the horse shifts from one pair of legs to the other . The value of an intermediate speed is that the horse <unk> energy . More than thirty horse breeds are " gaited , " able to perform a four @-@ beat ambling gait , and some can also trot . Thus , a Rocky Mountain Horse , with rider , can use the single @-@ foot to cover rough ground at around 7 miles per hour ( 11 km / h ) and short stretches of smooth ground at up to 16 miles per hour ( 26 km / h ) . The faster speed is known as the rack . In comparison , the average medium trot speed is 6 to 8 miles per hour ( 9 @.@ 7 to 12 @.@ 9 km / h ) . 
- 
- = = History = = 
- 
- Eastern Kentucky is known for its gaited breeds , created through a mixture of Spanish horses from the southern United States and English horses from the North . American <unk> , Tennessee Walking Horses and Missouri Fox <unk> also originated in the same general geographic area , from the same mixing of Spanish and English blood . Rocky Mountain Horses have a similar history to the Kentucky Mountain Saddle Horse , and together are sometimes called " Mountain Pleasure Horses " . The Rocky Mountain Horse originated in eastern Kentucky from a foundation stallion brought to the Appalachian Mountains from the Rocky Mountains around 1890 . <unk> to the area as a colt , oral histories state that the " Rocky Mountain Horse " , as he was known , possessed the preferred chocolate color and flaxen mane and tail found in the breed today , as well as the single @-@ foot gait . He was used to breed local saddle mares , and due to the small area in which he was bred , a local strain of horse originated . 
- This foundation stallion produced a <unk> , named Old Tobe , who became the more modern father of the Rocky Mountain Horse breed . Old Tobe was owned by a resident of <unk> Springs , Kentucky named Sam Tuttle . For most of the 20th century , Tuttle was a prominent breeder of Rocky Mountain Horses , and helped to keep the strain alive during the Great Depression and World War II . After World War II , despite declining horse populations in the US , Tuttle kept his herd , and continued to use Old Tobe as a breeding stallion . Tuttle held the Natural Bridge State Park concession for horseback riding , and used Old Tobe for trail rides in the park and for <unk> additional trail horses , the latter until the stallion was 34 years old . Old Tobe died at the age of 37 . The presence of the single @-@ foot gait makes it possible that the breed is in part descended from the Narragansett Pacer , a breed known for passing its gaited ability on to other American breeds . 
- In 1986 , the Rocky Mountain Horse Association was created to increase population numbers and promote the breed ; there were only 26 horses in the first batch of <unk> . Since then , the association has , over the life of the registry , registered over <unk> horses as of 2015 , and the breed has spread to 47 states and 11 countries . In order to be accepted by the registry , a foal 's parentage must be verified via DNA testing . Horses must also , after reaching 23 months of age , be inspected to ensure that they meet the physical characteristic and gait requirements of the registry . The Rocky Mountain Horse is listed at " Watch " status by the American <unk> <unk> Conservancy , meaning that the estimated global population of the breed is fewer than 15 @,@ 000 , with fewer than 800 <unk> annually in the US . 
- The breed was originally developed for general use on the farms of the Appalachian foothills , where it was found pulling <unk> and <unk> , working cattle and being ridden by both adults and children . Today , it is still used for working cattle , as well as endurance riding and pleasure riding . The breed 's gait and disposition make it sought out by elderly and disabled riders . Each September , the Kentucky Horse Park hosts the International Rocky Mountain Horse Show . 
- 
- 
- = Somerset County Cricket Club in 2009 = 
- 
- Somerset County Cricket Club competed in four domestic competitions during the 2009 English cricket season : the first division of the County Championship , the Friends Provident Trophy , the first division of the NatWest Pro40 League and the Twenty20 Cup . Through their performance in the Twenty20 Cup , the team qualified for the Champions League Twenty20 . They enjoyed a successful season , but fell short of winning any competitions , prompting Director of Cricket Brian Rose to say " We 've had enough of being cricket 's nearly men . " 
- <unk> performances in the County Championship helped Somerset remain <unk> for the competition until the last few weeks of the season , but the batting @-@ friendly pitch at their home ground , the County Ground , Taunton , meant that the county finished with too many draws to claim their first Championship title . <unk> was also key for Somerset 's success in one @-@ day cricket , where they remained unbeaten in the group stage of the Friends Provident Trophy , but were eliminated in the first knock @-@ out round , and finished runners @-@ up by one point in the NatWest Pro40 . In the Twenty20 Cup , Somerset finished as losing finalists . This meant that they qualified for the Champions League Twenty20 , where they progressed into the second group stage of the competition . They failed to win any matches in that phase of the competition , resulting in their elimination . 
- Somerset were captained for the third successive season by their Australian overseas player , Justin Langer , who announced during the season that it would be his last with Somerset . Marcus Trescothick topped the national batting tables , scoring almost 3 @,@ 000 runs in all competitions in 2009 ; as a result , he was named as both Professional <unk> ' Association ( PCA ) Player of the Year and the PCA 's Most Valuable Player of the Year . 
- 
- = = Background = = 
- 
- After promotion from the second division of the County Championship in 2007 , Somerset were among the first division title contenders in the 2008 . A loss to Lancashire in the final match of the season meant they finished in fourth place . David Foot , writing in Wisden , claimed that too many of the Somerset batsmen had " lost their way " in one @-@ day cricket , as they narrowly avoided relegation in the NatWest Pro40 , and were eliminated in the Friends Provident Trophy . 
- Somerset promoted four players from their academy for the 2009 season , giving contracts to Jos Buttler , Adam <unk> , Chris Jones and James Burke . Of these , only Buttler appeared in the first @-@ team during the season . They also signed David Stiff , a fast bowler capped at Under @-@ 19 level for England , on a two @-@ month contract at the beginning of the season , which was later extended to the end of the season . Ian Blackwell , captain of the side in 2005 and 2006 , left for Durham after coach Andy <unk> and captain Justin Langer made it clear that they regarded fitness as a priority . John Francis and Keith Parsons both retired at the end of 2008 , Francis cited lack of first @-@ team opportunities for his departure , while at the age of 35 , Parsons declared that : " There comes a time when your body tells you it 's time to pack in professional sport . " 
- In his preview of the 2009 season for ESPNcricinfo , Andrew McGlashan identified Somerset 's opening partnerships with bat and ball as their main strengths , but predicted that the middle order would struggle to make an impact in the County Championship , stating that " none [ of the middle order ] jump out as potential match @-@ winners in four @-@ day cricket . " He speculated that the team 's best chance of success would come in the one @-@ day competitions . The Daily Telegraph provided a more positive outlook , claiming that the county had " enough depth in batting and seam bowling to challenge [ for the County Championship ] again . " They identified spin bowling as an area of weakness following the departure of Blackwell . Bob Willis , writing in The Guardian , shared this view , and predicted that Somerset would also be hindered by the difficulty of getting 20 wickets at home , resulting in too many draws . 
- 
- = = Squad = = 
- 
- The following players made at least one appearance for Somerset in first @-@ class , List A or Twenty20 cricket in 2009 . Age given is at the start of Somerset 's first match of the season ( 15 April 2009 ) . 
- 
- = = County Championship = = 
- 
- Although Marcus Trescothick had a season which David Foot described as " <unk> assured " , Somerset failed to win enough matches to pose a real challenge in the 2009 County Championship . The flat pitch at the County Ground , Taunton did not help their efforts ; the imbalance in favour of the batsmen meant that all but one match at the ground was drawn . Sussex 's total of <unk> / 5 declared was the fifteenth highest total in the history of the County Championship , while Murray Goodwin 's innings of 344 not out in that match was the sixth highest score by a batsman in the history of the competition . In total , six of Somerset 's eight home matches contained scores of 500 or over in a single innings . In contrast , 500 was reached in only one of their away matches , by Hampshire at The Rose Bowl . The home conditions helped three of Somerset 's batsmen pass 1 @,@ 000 first @-@ class runs in the season , including Craig Kieswetter and <unk> <unk> , who were both awarded their county caps after passing the landmark . Trescothick 's 1 @,@ <unk> runs were the most by any batsman in the 2009 competition , finishing over 200 runs ahead of the next most prolific batsman , and he was named as both PCA Player of the Year and the PCA 's Most Valuable Player of the Year for 2009 . He was also selected as part of the team of the year , along with wicket @-@ keeper Kieswetter . 
- In contrast to the strong batting line @-@ up , Foot suggested that Somerset " lacked a seam bowler to compensate for Andrew Caddick 's withdrawal " . Caddick — who made his first @-@ class debut for Somerset in 1991 — only played five matches in 2009 , and announced his intention to retire at the end of the season . He took ten wickets in 2009 , and finished his career as Somerset 's sixth highest wicket @-@ taker of all @-@ time . This placed the majority of the wicket @-@ taking burden upon <unk> Willoughby , the South African fast @-@ medium pace bowler . He responded well , and took 54 wickets in the County Championship , more than any other bowler in the first division . He was aided by seam bowlers David Stiff , who returned to the first @-@ class game for the first time since 2006 , and Alfonso Thomas . Stiff took 31 wickets , more than <unk> his career first @-@ class wicket total , and Thomas took 35 , his highest return in an English domestic season . Somerset lacked an effective spin bowler in 2009 following the departure of Ian Blackwell the previous season , and the spinners combined only claimed 31 wickets , bowling less balls between them than Willoughby alone . 
- Somerset began their season by playing out a high @-@ scoring draw at home against Warwickshire , in which both teams reached 500 in their first innings . James Hildreth scored a triple @-@ century , reaching the milestone earlier in an English season than any player previously , and became the first English player to pass 300 runs in an innings for Somerset since Harold <unk> in 1948 . In contrast , Somerset had to battle back for a draw in their second match . After Durham scored <unk> in their first innings , Somerset collapsed to 69 all out in reply ; only Langer passed 20 runs , while six of his batting colleagues made ducks . After being forced to follow @-@ on , Trescothick , Langer and Kieswetter all scored centuries to secure a draw . Somerset 's third match resulted in their only loss of the County Championship season , coming against Nottinghamshire at Trent Bridge ; and at the end of May , they were second bottom in the first division . Their next match finished in a draw against Lancashire , a result which was always likely after the first day was lost to rain . Somerset next picked up victories against Yorkshire and Sussex before scoring the second @-@ highest ever fourth innings winning total in the County Championship to beat Yorkshire at Taunton . These three wins propelled Somerset to third in the competition at the start of July , and they did not drop below this position for the remainder of the season . 
- In each of their following two matches , at home against Hampshire and away to Worcestershire , Somerset batted first and then enforced the follow @-@ on after bowling their opponents out <unk> . In each their opponents managed to avoid defeat , and both matches resulted in draws . During the Worcestershire match , Langer 's first innings 107 took him past Sir Donald Bradman 's total of 28 @,@ <unk> first @-@ class runs to become the highest @-@ scoring Australian batsman . <unk> draws against Nottinghamshire , Warwickshire , Sussex and Hampshire meant that Somerset travelled to Durham requiring a victory to maintain any realistic hopes of claiming the County Championship title . No play was possible on the third and fourth days , and the match resulted in another draw , leaving Somerset with only a slim mathematical chance of the title . Another draw , against Lancashire , while Durham beat Nottinghamshire , meant that Durham clinched the title . Somerset drew with Worcestershire ( their ninth draw in a row in the competition ) , and finished third in the first division . 
- 
- = = = Season standings = = = 
- 
- Key : Pld 
- = Played , W = 
- Wins , L 
- = Losses , D = 
- Draws , T 
- = Ties , A = 
- <unk> , Bat 
- = <unk> points , <unk> = 
- <unk> points , <unk> 
- = <unk> / <unk> , Pts = 
- Points . 
- <unk> : 
- Hampshire deducted 3 points for a slow over @-@ rate in their match against Worcestershire . 
- Durham deducted 1 point for a slow over @-@ rate in their match against Sussex . 
- Somerset deducted 1 point for a slow over @-@ rate in their match against Durham . 
- Sussex deducted 1 point for a slow over @-@ rate in their match against <unk> : 
- Team marked ( C ) won the County Championship . 
- Teams marked ( R ) were relegated to Division Two of the County Championship . 
- 
- = = = Match log and statistics = = = 
- 
- 
- = = Friends Provident Trophy = = 
- 
- In the 2009 Friends Provident Trophy , the first one @-@ day competition in the English domestic calendar , Somerset showed a marked improvement from the previous season . They were described by Steve James , writing in Wisden , as " the most impressive qualifiers " from the group stage , and both Trescothick and Kieswetter were praised for their batting form . They were the only side in the competition to remain unbeaten in the group stages , finishing with seven victories and one no result . In the quarter @-@ final , against Group C runners @-@ up Sussex , Somerset won the toss and opted to bat at Taunton . They struggled initially , losing the early wickets of Trescothick , James Hildreth and Ben Phillips for just 39 runs . A fourth @-@ wicket partnership of 167 , dominated by powerful hitting from Kieswetter in which he scored a " superb century " according to ESPNcricinfo 's Andrew McGlashan , helped Somerset to recover to make 285 / 8 , but Sussex chased the total down in the final over to eliminate the home side . 
- During the group stages of their campaign , Somerset achieved a number of large victories . They won by eight wickets against Warwickshire and Middlesex , and by more than a hundred runs against Kent and Scotland . Their match against Middlesex at Lord 's was marred by some controversy , after Somerset were allowed too many <unk> overs in the rain @-@ affected fixture . Somerset were set a reduced target of 290 off 41 overs , of which 19 were allocated by the umpires as being <unk> overs , three more than there should have been . The England and Wales Cricket Board ( <unk> ) rejected Middlesex 's request to replay the match , stating that they were upholding " the precedent that umpire errors cannot form the basis to declare a match <unk> and void " . 
- The two matches played against Middlesex were in stark contrast ; in the match played at the Recreation Ground , Bath , Somerset bowled the London side out for 65 , the joint second @-@ lowest total made against Somerset in a List A match , while the 341 / 7 which Middlesex made at Lord 's was the fourth @-@ highest total scored against Somerset in the format . Somerset themselves scored their second @-@ highest List A total , amassing 403 / 3 against Scotland , a game in which Hildreth scored 151 , his highest List A score , and Somerset 's highest score by a batsman at number three in List A cricket . 
- Zander de Bruyn finished the competition with the highest batting average amongst batsmen who played five or more innings . Four Somerset batsmen scored over 300 runs in the tournament ( Trescothick , <unk> ; Kieswetter , 395 ; de Bruyn , <unk> ; Hildreth , <unk> ) , more than any other county except eventual winners Hampshire . Amongst the bowlers , Alfonso Thomas finished as the tournament 's joint leading wicket @-@ taker , claiming 20 wickets to finish level with Chris Schofield and Dominic Cork . Peter Trego 's 18 wickets were the next most in the competition . 
- 
- = = = Season standings = = = 
- 
- Key : Pld 
- = Played , W = 
- Wins , T 
- = Ties , L = 
- Losses , NR 
- = No result , Pts = 
- Points , NRR = Net run rate . 
- Notes : 
- Teams marked * progressed to the next stage of the competition . 
- Teams marked † were eliminated from the competition . 
- 
- = = = Match logs and statistics = = = 
- 
- 
- = = Twenty20 Cup = = 
- 
- Somerset finished as runners @-@ up to Sussex in the 2009 Twenty20 Cup , having reached Finals Day in what captain Justin Langer described as " bizarre " fashion . Somerset qualified for the knock @-@ out stages of the competition as the best third @-@ placed team during the group stage , then beat Lancashire in a bowl @-@ out to qualify for the semi @-@ finals . After overcoming Kent in the semi @-@ final , Somerset were <unk> beaten by Sussex in the final . 
- After the Indian Premier League 's second season demonstrated the importance of spin bowling in Twenty20 cricket , and due to Somerset 's lack of a front @-@ line spin bowler , Somerset picked young leg spinner Max Waller to make his Twenty20 debut in their opening match of the campaign . He claimed three wickets in two overs to help Somerset win by one run , and finished the competition as Somerset 's joint second @-@ highest wicket @-@ taker with 10 wickets . Despite a number of good performances by Somerset 's bowlers , a batsman was named man of the match in each of the team 's victories : James Hildreth , Craig Kieswetter and Marcus Trescothick were all awarded the accolade once , while Zander de Bruyn picked up the award on three occasions . Somerset completed the group stage with six wins , three losses and one no result , placing them third in the Midlands / Wales / West Division . They qualified for the knockout @-@ stage as they were the best third @-@ placed team in the three groups . 
- Somerset 's quarter @-@ final , against Lancashire , was scheduled for Tuesday 28 July 2009 . Bad weather meant that no play was possible on either the Tuesday or Wednesday , as a result of which the teams requested special dispensation from the <unk> for an additional reserve day on the Thursday , which was granted . The wet weather continued , forcing the teams to play a bowl @-@ out . Each team had to select five bowlers , each of whom had two deliveries at a set of <unk> stumps . Somerset won the bowl @-@ out 5 – 1 , with Ben Phillips , Peter Trego and Willoughby each hitting the stumps once , and Thomas hitting them twice . 
- In the second semi @-@ final of 2009 Finals Day , Somerset won the toss and elected to field against Kent . <unk> bowling from Thomas and Willoughby , as well as the early wickets of Joe <unk> and Martin van <unk> , helped to limit Kent to 145 / 5 . On a slow pitch , Trescothick played what ESPNcricinfo 's Andrew McGlashan described as a " brutal innings " , scoring 56 runs off 32 balls . He shared an opening partnership of 73 with captain Langer , and by the time of Trescothick 's dismissal after 8 @.@ 1 overs , Somerset required 62 runs off 71 balls . Hildreth and de Bruyn took 10 overs to add another 57 runs , and after Hildreth was bowled for 36 , Kieswetter added two runs to grant Somerset victory with seven balls remaining . Against Sussex in the final , Somerset once again won the toss and elected to field . A strong batting display by Sussex , highlighted by Dwayne Smith 's 59 runs off 26 balls , set Somerset a target of 173 to win . In the opening overs of Somerset 's reply , Trescothick scored powerfully , making the chase " look simple " according to McGlashan . However , after he was caught on 33 , Somerset lost regular wickets . A partnership of 41 between de Bruyn and Trego <unk> Somerset for a time , but when the pair were dismissed in subsequent overs , their team collapsed from 104 / 4 to 109 all out . 
- At the start of the competition , Somerset played with aggressive batting tactics , opening the innings with Kieswetter and Trescothick for the first six matches . This opening pair only passed 20 runs together on one occasion , against Glamorgan , and for the rest of the tournament , Kieswetter dropped down the batting order and Langer joined Trescothick at the top of the innings , in a move that Kieswetter said " adds a bit of balance to the side by putting a bit more firepower down the end and experience up top " . The more experienced pair passed 40 runs together on four out of five occasions . The county 's bowling relied heavily on medium pace , a fact which cricket writer <unk> Berry suggested was exposed in the final . 
- 
- = = = Season standings = = = 
- 
- Key : Pld 
- = Played , W = 
- Wins , L 
- = Losses , T = 
- Ties , NR 
- = No result , Pts = 
- Points , NRR = Net run rate . 
- Notes : 
- Teams marked * progressed to the next stage of the competition . 
- Teams marked † were eliminated from the competition . 
- 
- = = = Match logs and statistics = = = 
- 
- 
- = = NatWest Pro40 = = 
- 
- Somerset 's first match of the 2009 NatWest Pro40 , against Nottinghamshire , ended just one over short of the requirement for a match . Chasing <unk> to win , Somerset were significantly behind the required rate when rain halted play in the ninth over . The side responded positively to this stroke of luck , winning their next three matches by significant margins , before losing to Essex . Having been put in to bat at Taunton , each of Somerset 's top six batsman reached double figures , but none of them managed to pass 50 and the home side were eventually all out for 205 . Despite taking early wickets during Essex 's innings — at one stage having the visitors 12 / 3 — the batting of John <unk> , James Foster and Tim Phillips propelled Essex past Somerset 's total with five balls remaining . Two further victories ensured that Somerset entered their final match of the season with a chance to win the title . 
- Somerset had performed well but missed out narrowly in both the other one @-@ day competitions in 2009 , and could have clinched the competition with victory in their final match . News filtered through to the County Ground during their final match , against Durham , that leaders Sussex had lost to Worcestershire , meaning that a win against the north @-@ eastern team would guarantee Somerset the title . The result rarely looked likely ; a rapid opening to the chase by Durham <unk> Phil <unk> and Ian Blackwell provided a platform for their team to claim victory , and deny Somerset the title . Three of Somerset 's bowlers claimed ten or more wickets in the competition ( Ben Phillips , 14 ; Alfonso Thomas , 13 ; <unk> Willoughby , 11 ) , more than any other county in Division One . 
- 
- = = = Season standings = = = 
- 
- Key : Pld 
- = Played , W = 
- Wins , T 
- = Ties , L = 
- Losses , NR 
- = No result , Pts = 
- Points , NRR = Net run rate . 
- Notes : 
- Team marked ( C ) won the NatWest Pro40 . 
- 
- = = = Match logs and statistics = = = 
- 
- 
- = = Champions League Twenty20 = = 
- 
- By finishing as runners @-@ up in the Twenty20 Cup , Somerset qualified to compete in the 2009 Champions League Twenty20 , the first global club cricket tournament . Media attention surrounded Trescothick , who had returned home early from his last overseas tour in 2006 – 07 with a " stress @-@ related illness " . Brian Rose stressed that there was no pressure on Trescothick to take part in the tournament , and that the county would do everything possible to support him . The former England batsman did take part in the tournament , arriving three days after the rest of the squad . He played in both of Somerset 's matches in the group stage of the competition , though he only managed to score 17 runs between his two innings . 
- Somerset won their first match , in which Alfonso Thomas scored two boundaries off the last three balls of the match to secure victory . Their opponents , Deccan <unk> scored 153 off their 20 overs , and with three wickets remaining , Somerset required 55 runs off 37 balls to win . A record eighth @-@ wicket partnership between James Hildreth and Thomas of 50 brought the victory within reach , and Thomas ' highest @-@ score in Twenty20 cricket granted Somerset the win . Somerset lost their second match , being bowled out for their second @-@ lowest Twenty20 total , 106 by Trinidad and <unk> . 
- Somerset progressed to the second round of the competition after Trinidad and <unk> beat Deccan in the final group match , but lost Trescothick , who flew home after a recurrence of his illness . Wes <unk> , who replaced Trescothick in the side , top @-@ scored for Somerset in their next match , making 57 . Only two other players reached double @-@ figures for the county , and the Diamond Eagles chased down the total with eight balls to spare . Somerset went into their final match , against the New South Wales Blues with a slim mathematical chance of progressing , but a strong bowling display from Brett Lee and Stuart Clark restricted Somerset to 111 , which the Australian side reached with ease . 
- 
- = = = Season standings = = = 
- 
- Key : Pld 
- = Played , W = 
- Wins , L 
- = Losses , T = 
- Ties , NR 
- = No result , Pts = 
- Points , NRR = Net run rate . 
- 
- = = = Match logs and statistics = = = 
- 
- 
- = = Reaction = = 
- 
- After the conclusion of the Champions League tournament , Justin Langer retired from professional cricket . Trescothick replaced him as Somerset captain for the 2010 season . Somerset also lost Andrew Caddick , who at the age of 40 said that he opted to retire " while I was still physically and mentally strong " . <unk> @-@ keeper Carl <unk> also announced his retirement at the end of the season aged 27 , having lost his place in the Somerset side to Craig Kieswetter . Additionally , Somerset released Wes <unk> and <unk> Banks , both of whom they felt were surplus to requirements . Somerset addressed the weakness of their spin bowling by signing <unk> Kartik as their overseas player for 2010 . 
- Kieswetter 's performances in the 2009 season led to his inclusion in the England Performance Programme squad in November and December of that year , and he was part of the England Lions squad which toured the United Arab Emirates in early 2010 , along with Somerset team @-@ mate Peter Trego . His full England debut came shortly after in Bangladesh . Additionally , two of Somerset 's young players , Jos Buttler and <unk> <unk> played for England Under @-@ <unk> during the English winter . 
- <unk> , Somerset broke even in 2009 , generating a gross turnover of £ 4 @.@ 5 million . The club achieved this despite carrying out significant building work on the County Ground during the period : the " Somerset Stand " , a 3 @,@ 000 @-@ seat grandstand opened in April , while the " Andy Caddick Pavilion " was completed just over a month later , providing new dressing rooms and club offices . Andy Nash , the Somerset chairman , highlighted the catering department and match @-@ day income as being particularly successful in 2009 , with both areas exceeding £ 1 million in revenue during the year . 
- 
- 
- = Texas A & M Singing Cadets = 
- 
- The Texas A & M Singing Cadets are a male choral group at Texas A & M University . Nicknamed " The Voice of <unk> " , the Singing Cadets have been touring for 109 seasons , with their roots in a glee club founded on the A & M campus in 1893 . The Singing Cadets are one of the oldest collegiate singing organizations in the world . They have toured both in America and abroad and have earned recognition by doing so , including invitations to sing for American presidents . Contrary to their name , the Singing Cadets are not all members of the Corps of Cadets and have not been since 1965 , when the university as a whole ceased <unk> Corps membership . Between 1996 and 2006 , the Singing Cadets traveled over 35 @,@ 000 miles and performed more than 450 concerts worldwide . 
- 
- = = History = = 
- 
- The first record of a singing organization at Texas A & M ( then known as the Agricultural and Mechanical College of Texas ) was in 1893 . The nine member glee club was composed of both students and faculty . The group grew to around twenty members through the turn of the century . Their first recorded field trip in 1905 , was to Houston , Texas , where they participated in a four hundred member chorus . Other tours in this period included a trip to Fort Worth in 1907 , where they met noted violinist <unk> <unk> . One year later , the director of the group , Professor <unk> <unk> , left the A & M Glee Club . 
- The next paradigm shift came in 1910 when it was reorganized by <unk> <unk> , and subsequently performed for a number of audiences throughout Texas . After several restructuring efforts and directors , the organization laid down an official constitution during the 1937 @-@ 1938 school year . The Cadets entered the national spotlight when they performed at the 1939 Sugar Bowl game . After A & M professor J. J. <unk> became director in 1940 , the chorus was renamed the " Singing Cadets " following a naming contest . The Singing Cadets first full @-@ time director , Richard Jenkins , raised the organization 's profile though a series of tours throughout the American South . Under his aegis in 1942 , the group provided choral music for the propaganda film We 've Never Been <unk> . 
- The Singing Cadets next long term director , William Turner , held the post for fifteen years . By the early 1950s membership had stabilized at around sixty members . They performed primarily in Texas , and made a trip to Mexico in 1952 . The group garnered prestige and attention by singing in front of the Texas Legislature several times . 
- In 1960 , the Singing Cadets next director , Robert L. Boone , expanded the group 's national recognition . The group performed its first telecast in 1963 during the nationally televised Miss Teenage America pageant . For the next 8 years , the group served as contestant escorts , performers , and background for the show . They met Sergio Franchi during the December 10 , 1970 pageant . Subsequently , the group appeared on The Ed Sullivan Show with Sergio Franchi singing " No Man is An Island " on January 24 , 1971 . However , they did not get to appear on a second solo performance because an earlier show ran over the allotted time . They did , however , release a 45 rpm single " No Man Is An Island " accompanying Franchi in the same year . Later that year , the Singing Cadets were invited to the White House by Senator John Tower to sing for numerous dignitaries , including President Richard Nixon . 
- The Singing Cadets took their first trip overseas in 1974 to Romania as part of a goodwill tour sponsored by the State Department , performing several shows over a 3 week period . The Cadets accompanied the <unk> Band onto Kyle Field during the halftime of football games during special occasions of the university , such as the centennial celebration of Texas A & M University ( 1976 ) , and in 1980 to <unk> the expansion of the football stadium . 
- Under Boone 's direction , the Singing Cadets won several international singing competitions . They won the silver medal in an International Choral Festival in Hawaii in 1979 . In 1983 they received the silver medal in Mexico . In 1993 , they returned to Hawaii , taking the gold medal . Other major tours during the 1980s included several occasions marking the Texas <unk> anniversary in 1986 , and European tours in 1987 and 1989 . In 2004 , the Singing Cadets journeyed to Australia , and sang the Australian national anthem ( Advance Australia Fair ) on the first concert of that tour . In May 2006 the Cadets performed at the White House at the invitation of President George W. Bush for an event to honor America 's returning athletes from the 2006 Winter Olympics . Also in 2006 , the cadets performed at the Miss Texas <unk> . In its 2006 @-@ 2007 season , the Cadets performed over sixty concerts in the United States , plus a tour to South America during the summer , where they traveled to Argentina , Uruguay and Brazil . The Singing Cadets toured South Africa in 2010 and will be touring China in May 2013 . 
- 
- = = Music = = 
- 
- The Singing Cadets have sung a wide variety of music in their history . Their repertoire has included Christian hymns and gospel music , as well as Texas A & M school songs such as the <unk> War Hymn . Musical selections varied through the decades , although Southern songs and songs from the 1940s , 1950s , and 1960s remain perennial favorites . 
- Currently , eight recordings produced by the Singing Cadets are available for purchase . In chronological order , the titles are : God <unk> the USA , Live from South America , Tradition ; Duty , Honor , Country : A Tribute to President Bush , Remembrance , Centennial , Let Freedom Ring , and Kennedy Center . 
- 
- = = Organization = = 
- 
- Members of the Corps of Cadets make up a small minority of the Singing Cadets ; the group dropped Corps <unk> as a requirement in 1963 . The Singing Cadets holds auditions twice each school year , with membership open to any male Texas A & M student . The choir is one of three within Texas A & M. The others are the all @-@ female Women 's Chorus , and co @-@ ed choir the Century Singers . All three practice in the Memorial Student Center ( <unk> ) . 
- The group is typically backed by a pianist and conducted by a director , and regularly are accompanied by instruments , including electric guitar , drums and bass guitar . The Singing Cadets use costumes and <unk> to accompany their music , incorporating a number of forms of entertainment into their concerts . A <unk> quartet side group called The <unk> performs at some shows , as entertainment between the main musical numbers . 
- <unk> , the Singing Cadets perform primarily in state , along with a <unk> of national and international tours . The Cadets perform 70 @-@ 80 concerts a year . 
- 
- = = Directors = = 
- 
- Professor A. M. <unk> 
- Professor <unk> 
- Professor T. P. <unk> 
- F. D. <unk> 
- D. Ford 
- K. H. Beach 
- E. W. Glenn 
- Professor J. J. <unk> 
- Richard W. Jenkins 
- <unk> Porter 
- William M. Turner 
- Robert L. Boone 
- David <unk> 
- 
- 
- = Ontario Highway 36 = 
- 
- King 's Highway 36 , commonly referred to as Highway 36 , was a <unk> maintained highway in the Canadian province of Ontario . The highway connected Highway 7 and Highway 35 in Lindsay with Highway 28 in Burleigh Falls , providing access to recreational cottages along the northern shore of several of the Kawartha lakes as well as to multiple communities , including Bobcaygeon . Today it is known as Kawartha Lakes City Road 36 and Peterborough County Road 36 . 
- The route was first assumed in 1931 as a depression relief project and extended in 1937 . It remained generally unchanged for the next 60 years before being decommissioned in 1998 . However , a realignment near Lindsay in the late 1950s changed the southern terminus of the route from the centre of the town to southeast of it ; the original route through Lindsay became Highway <unk> and is now known as Kawartha Lakes Road 17 . 
- 
- = = Route description = = 
- 
- The route and surroundings of former Highway 36 have largely remained unaltered since the highway was decommissioned in 1998 . Within the City of Kawartha Lakes , the road is now known as Kawartha Lakes Road 36 , while within the County of Peterborough , it is known as Peterborough County Road 36 . 
- The route begins east of Lindsay at an intersection with Highway 7 and progresses north , crossing a former railway ( now the Kawartha Rail Trail ) before intersecting Kawartha Lakes Road 17 . The western leg of Road 17 , which travels to downtown Lindsay , was the original route of Highway 36 and later became Highway <unk> . Continuing north , Highway 36 <unk> north and east , serving cottages and communities lining the southern shore of Sturgeon Lake , but remains inland by over a kilometre ( 0 @.@ 6 mi ) itself . At <unk> , which is bypassed , the highway intersects Kawartha Lakes Roads 7 and 24 — the former travels south to <unk> and the latter provides an alternative route to Bobcaygeon . Former Highway 36 continues , alternating directions between east and north twice before entering the village of Bobcaygeon . 
- Within Bobcaygeon , Highway 36 crosses the Trent – Severn Waterway and intersects the eastern end of Kawartha Lakes Road 8 . At this point it is following the southernmost section of the Bobcaygeon Colonization Road . At the intersection with Main Street in the northern end of the village , the route turns northeast while former Highway 649 continues north . 
- <unk> Bobcaygeon , the former highway serves cottages along the northern shore of <unk> Lake and Little Bald Lake . After passing <unk> Creek , the highway descends a hill and encounters the Canadian Shield for the first time . The road runs along the border of the <unk> shield and the Ordovician limestone plateau en route to Burleigh Falls . The shield lies to the north and is <unk> by the plateau to the south . The highway turns south at <unk> Corner , intersecting former Highway <unk> . It enters the town of Buckhorn from the north , then drivers must turn to remain on the route , which exits the town to the east . Between Buckhorn and Burleigh Falls , the highway serves the cottages that line the northern shore of Lower Buckhorn Lake . 
- 
- = = History = = 
- 
- Highway 36 was first assumed by the Department of Highways ( <unk> ) on July 1 , 1931 , and was one of several highways completed as part of a depression – relief program . The highway initially connected downtown Lindsay with Bobcaygeon . This section was fully paved by the end of 1937 . On August 11 , 1937 , the <unk> extended the highway east to Highway 28 at Burleigh Falls . 
- Highway 36 remained essentially unchanged between 1937 and 1997 , with the exception of a being rerouted east of Lindsay in 1958 . However , on January 1 , 1998 , the entirety of Highway 36 was downloaded to Victoria County and Peterborough County , both of which redesignated it as County Road 36 . Victoria County became the City of Kawartha Lakes in 2001 , and that section of the route is now known as City Road 36 . 
- 
- = = Major intersections = = 
- 
- The following table lists the major junctions along Highway 36 , as noted by the Ministry of Transportation of Ontario . In addition , it includes some minor junctions . 
- 
- 
- = Arizona State Route 67 = 
- 
- State Route 67 ( SR 67 ) is a 43 @.@ 4 mi ( 69 @.@ 8 km ) long , north – south state highway in northern Arizona . Also called the Kaibab Plateau – North Rim Parkway , SR 67 is the sole road that links U.S. Route 89A ( US 89A ) at Jacob Lake to the North Rim of Grand Canyon National Park . Along the route , the road heads through the national park as well as Kaibab National Forest and is surrounded by evergreen trees . The section inside the national park is maintained by the National Park Service ( <unk> ) , whereas the section north of the entrance , completely within Kaibab National Forest , is owned by the Arizona Department of Transportation ( ADOT ) . The road was built in the late 1920s and improved through the 1930s . In 1941 , the road received its number , and was given its designation as the parkway in the 1980s . The parkway has received designations as a National Forest Scenic Byway as well as a National Scenic Byway . 
- 
- = = Route description = = 
- 
- <unk> for SR 67 begins at Bright Angel Point along the North Rim of the Grand Canyon National Park . ADOT does not officially own this section of road , but it is signed as SR 67 . The road heads north as the Kaibab Plateau – North Rim Parkway through the small town of North Rim , surrounded by evergreen trees . The parkway enters a small clearing before meeting the park entrance , where ownership by ADOT begins . Heading into Kaibab National Forest on a northward path , the roadway is surrounded by a narrow meadow bordered by evergreen trees . As it passes the nearby Deer Lake , SR 67 meets an unpaved National Forest road . The landscape around the route is <unk> by these routes as SR 67 makes several turns , turning back toward the north . The highway , with the new name of Grand Canyon Highway in addition to its other designation , makes several turns as it heads north through the woods . It takes a more northwesterly path as it runs through <unk> County . Near its terminus , the road turns back northeast toward its terminus at US 89A in Jacob Lake . 
- The northern segment of the highway is maintained by ADOT , who is responsible for maintaining SR 67 like all other highways around the state . As part of this job , ADOT periodically surveys traffic along its routes . These surveys are most often presented in the form of average annual daily traffic , which is the number of vehicles who use the route on any average day during the year . In 2009 , ADOT calculated that around 1 @,@ 100 vehicles used the route daily at its northern terminus in Jacob Lake . The Federal Highway Administration ( <unk> ) lists the highway as a National Scenic Byway , and the National Forest Service has also designated it a National Forest Service Byway . No part of the highway has been listed in the National Highway System , a system of roads in the United States important to the nation 's economy , defense , and mobility . Due to the closure of park facilities on the north rim of the Grand Canyon during winter , winter maintenance is not undertaken after December 1st , with the result that SR 67 is usually closed to <unk> traffic from December 1st until spring . 
- 
- = = History = = 
- 
- SR 67 existed as a route to reach the north rim of the Grand Canyon National Park as early as 1927 as a dirt road . By 1935 , the road had been improved to a gravel road , and by 1938 it had been paved . In 1941 , the road was designated as a state highway and signed as SR 67 . In 1985 , the highway received the designation of Kaibab Plateau – North Rim Parkway as an Arizona State Scenic Byway . The designation included SR 67 in its entirety as well as extending south into Grand Canyon National Park . Two years later , ADOT obtained the right @-@ of @-@ way for improvement of the highway from its northern terminus approximately 10 miles ( 16 km ) south . In 1989 , an additional right @-@ of @-@ way was acquired by ADOT from its southern terminus approximately 5 miles ( 8 @.@ 0 km ) north . By June of that year , the parkway received the designation of a National Forest Scenic Byway , and in June 1998 , the Kaibab Plateau – North Rim Parkway received another designation , this time as a National Scenic Byway . Since establishment , the route has not been realigned and retains its original routing . 
- 
- = = Junction list = = 
- 
- The entire route is in <unk> County . 
- 
- 
- = Josce de Dinan = 
- 
- Josce de Dinan ( sometimes <unk> de Dinan , <unk> de Dinan , <unk> de <unk> ; <unk> de Dinan , Joyce de Dinan , or <unk> de Dinan ; died 1166 ) was an Anglo @-@ Norman nobleman who lived during and after the civil war between King Stephen of England and his cousin Matilda over the throne of England . He was a <unk> in the Welsh Marches when he was married by Stephen to the widow of Pain fitzJohn , a union that gave Josce control of Ludlow Castle . Control of the castle was contested by other noblemen , and the resulting warfare between the nobles forms the background to a late medieval romance known as Fouke le Fitz Waryn , which is mainly concerned with the actions of Josce 's grandson , but also includes some material on Josce 's lifetime . Josce eventually lost control of Ludlow and was granted lands in compensation by Matilda and her son , King Henry II of England , who succeeded Stephen in 1154 . 
- 
- = = Background and early life = = 
- 
- Following King Henry I 's death in 1135 , the succession was disputed between the king 's nephews — Stephen and his elder brother , Theobald II , Count of Champagne — and Henry 's surviving legitimate child Matilda , usually known as the Empress Matilda because of her first marriage to the Holy Roman Emperor , Henry V. King Henry 's only legitimate son , William , had died in 1120 . After Matilda was widowed in 1125 , she returned to her father , who married her to Geoffrey , Count of <unk> . All the magnates of England and Normandy were required to declare <unk> to Matilda as Henry 's heir , but after the king 's death in 1135 Stephen rushed to England and had himself crowned before either Theobald or Matilda could react . The Norman barons accepted Stephen as Duke of Normandy , and Theobald <unk> himself with his possessions in France . But Matilda was less <unk> , and secured the support of her maternal uncle , the Scottish king David I , and in 1138 also that of her half @-@ brother Robert , Earl of Gloucester , an illegitimate son of Henry I. <unk> in the Welsh Marches <unk> against Stephen in 1136 , but the revolt was not settled until 1138 . In 1139 , Matilda invaded southern England with her half @-@ brother 's support and the period of civil war began . 
- Josce was the youngest son of Geoffrey de Dinan and <unk> <unk> , and had two older brothers , Oliver of Dinan and Alan of <unk> . Josce 's family was from Brittany , and he was described by the historian Marjorie <unk> as an " obscure Breton adventurer " . Josce moved from Devon in southern England to the Welsh Marches , the border between England and Wales , because the lords of Monmouth were also of Breton extraction . While in the Marches he joined King Stephen 's household . 
- 
- = = Ludlow Castle = = 
- 
- Josce was married to Sybil , the widow of Pain fitzJohn , who died in 1137 . Sybil had held Ludlow Castle against Stephen in 1139 , but surrendered after a siege . Ludlow was an important strategic stronghold for control of the Welsh Borders , and Stephen decided to marry Pain 's widow to someone he felt was trustworthy . Upon his marriage Josce also acquired control of the castle , built in the late 11th century . Josce probably also received many of the de Lacy family 's holdings in southern Shropshire , but he rebelled against Stephen and fortified Ludlow against the king . Josce 's position was so strong that when Stephen granted much of the surrounding lands to Robert de Beaumont , Earl of Leicester , Ludlow was specifically <unk> . Stephen told Robert that he would have a royal grant of the castle if he could secure Josce 's submission as a vassal . 
- <unk> of Ludlow was contested not only by Stephen but also by Gilbert de Lacy , whose efforts to <unk> the castle from Josce are the background to the medieval romance Fouke le Fitz Waryn ; the extant prose version dates from the 14th century , but it was originally a 13th @-@ century poem , now lost . Gilbert claimed the castle through his familial link with Sybil , fitzJohn 's widow , who was a member of the de Lacy family . Others trying to take Ludlow were Hugh de Mortimer and Gilbert de Lacy . According to the Chronicle of Wigmore Abbey , some time after September <unk> Mortimer and Josce became embroiled in a private war , during which Josce seized Mortimer while the latter was travelling . Josce imprisoned his captive in Ludlow and demanded a ransom of 3 @,@ 000 silver marks , as well as , according to the Chronicle , Mortimer 's " plate , his horses , and his birds " . <unk> , Mortimer met the ransom , as he is later known to have been a free man . 
- While Josce was absent from Ludlow , Gilbert de Lacy was able to take the castle . Josce laid siege to the castle but was unsuccessful in his attempt to retake it , and retreated to Lambourn with his military forces . Although the exact date of this event is unknown , it appears to have been some time about 1150 or shortly before . Matilda gave Josce some lands around Lambourn after Ludlow 's fall as compensation . Later he was given land in Berkshire by King Henry II ( became king in 1154 ) , Matilda 's son , as further <unk> for the loss of Ludlow . In <unk> Josce held lands in Berkshire , Wiltshire , Hampshire , Devonshire and Somerset . These holdings included the manor of Lambourn , worth £ 76 in income per year , as well as the manor of Stanton ( now known as Stanton <unk> in Wiltshire ) in addition to the lands scattered in other counties . 
- Josce 's grandson Fulk fitzWarin , who died in <unk> , is ostensibly the hero of a lost romantic poem called Fouke le Fitz Waryn . The work survives as French prose in a loose corpus of medieval literature known as the <unk> of England . However , it appears to confuse events of Fulk fitzWarin 's lifetime with those of his grandfather 's . Other errors in the work include <unk> some of the Welsh <unk> barons of King Henry I of England 's reign into nobles of William the <unk> 's time , and <unk> an entire generation of <unk> . Although scholars believe Fouke le Fitz Waryn draws on genuine tradition , the difficulty in separating the fitzWarin biographies makes it a problematic source . 
- 
- = = Family = = 
- 
- Josce died in 1166 . He was survived by two daughters : <unk> , who married Hugh de <unk> and died in 1212 , and <unk> who married Fulk <unk> , who died in <unk> . In <unk> his two daughters petitioned the king regarding the ownership of the town and castle of Ludlow but were turned down . 
- 
- 
- = World War I Memorial ( East Providence , Rhode Island ) = 
- 
- The World War I Memorial is a bronze sculpture by Pietro Montana and is located at the intersection of Taunton Avenue , <unk> Avenue , and John Street in East Providence , Rhode Island , United States . The sculpture is modeled on Charles Atlas and depicts a dynamically posed soldier standing on a granite base . Montana 's original design was modified by the East Providence Memorial Committee for being " too brutal " . Dedicated on July 30 , 1927 , Major General Charles <unk> <unk> gave an address which highlighted the handicap placed upon the soldiers by a lack of preparedness and " invoked the fighting ideal embodied by Montana 's doughboy . " The World War I Memorial was added to the National Register of Historic Places in 2001 . 
- 
- = = Design = = 
- 
- The World War I Memorial was designed by Pietro Montana , an Italian @-@ born painter and sculptor . Montana studied at the Cooper Union for the Advancement of Science and Art in Manhattan , New York City , New York . Montana 's monuments were well @-@ known , particularly in New York , and included an earlier " <unk> " sculpture erected in 1920 to honor the war dead of the <unk> , Brooklyn and <unk> , Queens . The success of this the earlier " Fighting <unk> " memorial resulted in the production of three copies by 1921 . By 1932 , he had produced over 40 statues and won a gold medal from the National Academy of Design for " <unk> " in 1931 . 
- For the design of the sculpture , Montana modeled Charles Atlas and " strove to communicate the U.S. doughboy 's <unk> character and <unk> by way of a muscle @-@ bound physique " . In 1926 , the East Providence Memorial Committee expressed concerns that Montana 's design was " too brutal " and modified the original design provided by Montana . The statue was erected in 1927 , and formally dedicated on July 30 , 1927 . At the dedication , Major General Charles <unk> <unk> gave an address which highlighted the handicap placed upon the soldiers by a lack of preparedness and " invoked the fighting ideal embodied by Montana 's doughboy . " 
- The sculpture of a soldier stands 7 @.@ 75 feet ( 2 @.@ 36 m ) tall , 2 @.@ <unk> feet ( 0 @.@ 787 m ) wide and 2 @.@ 66 feet ( 0 @.@ 81 m ) deep . Ronald J. <unk> , author of the National Register nomination , writes that " the soldier stands with legs apart , his left hand at his belt , the right at his side . The face is <unk> and <unk> . He wears a <unk> infantry uniform , rough shoes , the shirt collar open and <unk> , the sleeves rolled back , the knee torn open , his helmet on the ground behind his left foot . ... A holster hangs from the belt on the soldier 's right hip . " Montana signed the sculpture with " Pietro Montana / <unk> / 1927 . " 
- The sculpture rests atop a grey granite base that is 6 @.@ 33 feet ( 1 @.@ 93 m ) tall , 3 @.@ 166 feet ( 0 @.@ 965 m ) wide and 3 @.@ 5 feet ( 1 @.@ 1 m ) deep . The corners of the base have small leaf designed and has 2 feet ( 0 @.@ 61 m ) by 2 @.@ 66 feet ( 0 @.@ 81 m ) bronze reliefs with arched tops on each side . The front relief states that it was erected in the memory of the citizens of East Providence who served in World War I from 1917 to 1918 , and lists the names of twenty three soldiers . The left relief depicts a marching infantry column of one man on horseback and four on foot , the right relief depicts four or five men loading a cannon and the rear relief depicts a nurse assisting two wounded soldiers . 
- At the time of its nomination , the sculpture was described as in " moderately good condition " , with the surface being both stained and pitted , but free or breaks or missing pieces . The statue still stands in its original location in front of a school that has since been re @-@ <unk> for residential housing . 
- 
- = = Importance = = 
- 
- The World War I Memorial designed by Montana is " historically significant as the city 's principal effort to honor those who served in the first World War and because it is an unusually successful depiction of the soldier in battle . " The larger @-@ than @-@ life masculine figure depicted in the sculpture stands apart from the stock figures of other war monuments by its dynamic pose , as if the soldier was " [ arising from ] the heat of battle " . It was added to the National Register of Historic Places on October 19 , 2001 . 
- 
- 
- = Oldham = 
- 
- Oldham / <unk> / is a large town in Greater Manchester , England , amid the Pennines between the rivers <unk> and <unk> , 5 @.@ 3 miles ( 8 @.@ 5 km ) south @-@ southeast of Rochdale and 6 @.@ 9 miles ( 11 @.@ 1 km ) northeast of Manchester . Together with several smaller surrounding towns , it is part of the Metropolitan Borough of Oldham of which it is the administrative centre . 
- Historically in Lancashire , and with little early history to speak of , Oldham rose to prominence in the 19th century as an international centre of textile manufacture . It was a <unk> of the Industrial Revolution , and among the first ever <unk> towns , rapidly becoming " one of the most important centres of cotton and textile industries in England " . At its zenith , it was the most productive cotton spinning mill town in the world , producing more cotton than France and Germany combined . Oldham 's textile industry fell into decline in the mid @-@ 20th century ; the town 's last mill closed in 1998 . 
- The demise of textile processing in Oldham depressed the local economy . Today Oldham is a predominantly residential town , and a centre for further education and the performing arts . It is , however , still distinguished <unk> by the surviving cotton mills and other buildings associated with that industry . The town has a population of 103 @,@ 544 and an area of around 26 square miles ( 67 km2 ) . 
- 
- = = History = = 
- 
- 
- = = = <unk> = = = 
- 
- The <unk> of Oldham seems to imply " old village or place " from <unk> ( Saxon ) signifying <unk> or antiquity , and Ham ( Saxon ) a house , farm or hamlet . Oldham is however known to be a derivative of <unk> , undoubtedly an Old Norse name . It is believed to be derived from the Old English <unk> combined with the Old Norse <unk> or <unk> , meaning " promontory or <unk> " , possibly describing the town 's hilltop position . It has alternatively been suggested that it may mean " <unk> or <unk> of a farmer named <unk> " . The name is understood to date from 865 , during the period of the <unk> . 
- 
- = = = Early history = = = 
- 
- The earliest known evidence of a human presence in what is now Oldham is attested by the discovery of Neolithic flint arrow @-@ heads and workings found at Werneth and <unk> Hill , implying habitation 7 – 10 @,@ 000 years ago . Evidence of later Roman and Celtic activity is confirmed by an ancient Roman road and Bronze Age archaeological relics found at various sites within the town . <unk> of Celtic origin are still to be found in Oldham : Werneth derives from a Celtic personal name identical to the <unk> <unk> , " alder swamp " , and Glodwick may be related to the modern Welsh <unk> , meaning " <unk> " or " ditch " . Nearby Chadderton is also pre @-@ Anglo @-@ Saxon in origin , from the Old Welsh <unk> , itself deriving from the Latin <unk> meaning " chair " . Although Anglo @-@ Saxons occupied territory around the area centuries earlier , Oldham as a permanent , named place of dwelling is believed to date from 865 , when Danish invaders established a settlement called <unk> . 
- From its founding in the 9th century until the Industrial Revolution , Oldham is believed to have been little more than a scattering of small and insignificant settlements spread across the moorland and dirt tracks that linked Manchester to York . Although not mentioned in the <unk> Book , Oldham does appear in legal documents from the Middle Ages , invariably recorded as territory under the control of minor ruling families and barons . In the 13th century , Oldham was documented as a manor held from the Crown by a family <unk> Oldham , whose seat was at Werneth Hall . 
- 
- = = = Industrial Revolution and cotton = = = 
- 
- Much of Oldham 's history is concerned with textile manufacture during the Industrial Revolution ; it has been said that " if ever the Industrial Revolution placed a town firmly and squarely on the map of the world , that town is Oldham . " Oldham 's soils were too thin and poor to sustain crop growing , and so for decades prior to industrialisation the area was used for grazing sheep , which provided the raw material for a local <unk> weaving trade . 
- By 1756 , Oldham had emerged as centre of the <unk> industry in England . The rough felt used in the production process is the origin of the term " <unk> <unk> " a nickname for people from Oldham . It was not until the last quarter of the 18th century that Oldham changed from being a cottage industry township producing <unk> garments via domestic manual labour , to a sprawling industrial metropolis of textile factories . The climate , geology , and topography of Oldham were <unk> constraints upon the social and economic activities of the human inhabitants . At 700 feet ( 213 m ) above sea level and with no major river or visible natural resources , Oldham had poor geographic attributes compared with other settlements for investors and their engineers . As a result , Oldham played no part in the initial period of the Industrial Revolution , although it did later become seen as obvious territory to <unk> because of its convenient position between the labour forces of Manchester and southwest Yorkshire . 
- Cotton spinning and <unk> were introduced to Oldham when its first mill , Lees Hall , was built by William <unk> in about 1778 , the beginning of a <unk> process of <unk> and <unk> transformation . Within a year , 11 other mills had been constructed , and by 1818 there were 19 – not a large number in comparison with other local settlements . Oldham 's small local population was greatly increased by the mass migration of workers from outlying villages , resulting in a population increase from just over 12 @,@ 000 in 1801 to 137 @,@ 000 in 1901 . The speed of this urban growth meant that Oldham , with little pre @-@ industrial history to speak of , was effectively born as a factory town . 
- Oldham became the world 's manufacturing centre for cotton spinning in the second half of the 19th century . In 1851 , over 30 % of Oldham 's population was employed within the textile sector , compared to 5 % across Great Britain . It overtook the major urban centres of Manchester and Bolton as the result of a mill building boom in the 1860s and 1870s , a period during which Oldham became the most productive cotton @-@ spinning town in the world . In 1871 , Oldham had more spindles than any country in the world except the United States , and in 1909 , was spinning more cotton than France and Germany combined . By 1911 there were 16 @.@ 4 million spindles in Oldham , compared with a total of 58 million in the United Kingdom and 143 @.@ 5 million in the world ; in 1928 , with the construction of the UK 's largest textile factory Oldham reached its manufacturing zenith . At its peak , there were more than 360 mills , operating night and day ; 
- Oldham 's <unk> was dominated by distinctive rectangular brick @-@ built mills . Oldham was hit hard by the Lancashire Cotton Famine of 1861 – 1865 , when supplies of raw cotton from the United States were cut off . <unk> reliant upon the textile industry , the cotton famine created chronic unemployment in the town . By 1863 a committee had been formed , and with aid from central government , land was purchased with the intention of employing local cotton workers to construct Alexandra Park , which opened on 28 August 1865 . Said to have over @-@ relied upon the textile sector , as the <unk> of cheaper foreign <unk> grew during the 20th century , Oldham 's economy declined into a depression , although it was not until 1964 that Oldham ceased to be the largest centre of cotton spinning . In spite of efforts to increase the efficiency and competitiveness of its production , the last cotton spun in the town was in 1998 . 
- 
- = = = = Engineering = = = = 
- 
- <unk> by its flourishing textile industry , Oldham developed extensive structural and mechanical engineering sectors during the 18th and 19th centuries . The manufacture of spinning and weaving machinery in Oldham belongs to the last decade of the 19th century , when it became a leading centre in the field of engineering . The Platt Brothers , originated in nearby <unk> village , but moved to Oldham . They were pioneers of cotton @-@ spinning machinery , developing innovative products that enabled the mass @-@ production of cotton <unk> . Platt Brothers became the largest textile machine makers in the world , employing over 15 @,@ 000 people in the 1890s , twice the number of their nearest rivals <unk> & <unk> in Bolton and <unk> Lees on <unk> Moor . They were keen investors in the local area and at one time , were supporting 42 % of the population . The centre of the company lay at the New Hartford Works in Werneth , a massive complex of buildings and internal railways on a site overlooking Manchester . The railway station which served this site later formed the basis of Oldham Werneth railway station . The main building exists to this day . <unk> gained prestigious awards from around the world , and were heavily involved with local politics and civic pride in Oldham . John and James Platt were the largest subscribers for promoting Oldham from a township to a Borough , pledging £ 100 ( more than double the next largest sum ) in advance towards any expenses which may have been incurred by the Royal Charter . In 1854 John Platt was made the ( fourth ) Mayor of Oldham , an office he was to hold twice more in 1855 – 56 and 1861 – 62 . John Platt was elected in 1865 to become Member of Parliament for Oldham , and was re @-@ elected in 1868 ; he remained in office until his death in 1872 . A bronze statue of Platt existed in the town centre for years , though was moved to Alexandra Park . There have been recommendations for it to be returned to the town centre . 
- Abraham <unk> Stott , the son of a <unk> , was born in nearby Shaw and Crompton in 1822 . He served a seven @-@ year apprenticeship with Sir Charles Barry , before starting a structural engineering practice in Oldham in 1847 that went on to become the pre @-@ eminent mill architect firm in Lancashire . Philip Sydney Stott , third son of Abraham and later titled as Sir Philip Stott , 1st <unk> , was the most prominent and famous of the Stott mill architects . He established his own practice in 1883 and designed over a hundred mills in several countries . His factories , which improved upon his father 's <unk> mills , accounted for a 40 % increase in Oldham 's spindles between 1887 and 1914 . 
- Although textile @-@ related engineering declined with the processing industry , leading to the demise of both <unk> and <unk> , other engineering firms existed , notably electrical and later electronic engineers Ferranti in 1896 . Ferranti went into receivership in 1993 , but some of its former works continue in other hands . Part of the original Hollinwood site was operated by <unk> <unk> and <unk> divisions . The remainder of the site is occupied by Mirror Colour Print Ltd ; the printing division of the Trinity Mirror group , which prints and distributes thirty @-@ six major newspapers , and employs five hundred staff . 
- 
- = = = = Coal mining = = = = 
- 
- On the back of the Industrial Revolution , Oldham developed an extensive coal mining sector , correlated to supporting the local cotton industry and the town 's inhabitants , though there is evidence of small scale coal mining in the area as early as the 16th century . The Oldham <unk> stretched from Royton in the north to <unk> in the south and in addition to Oldham , included the towns of Middleton and Chadderton to the west . The Oldham <unk> was the site of over 150 collieries during its recorded history . Although some contemporary sources suggest there was coal mining in Oldham at a commercial scale by 1738 , older sources attribute the commercial expansion of coal mining with the arrival in the town of two Welsh labourers , John Evans and William Jones , around 1770 . <unk> the growth in demand for coal as a source of motive and steam power , they acquired <unk> rights for Oldham , which by 1771 had 14 <unk> . The mines were largely to the southwest of the town around Hollinwood and Werneth and provided enough coal to accelerate Oldham 's rapid development at the centre of the cotton boom . At its height in the mid @-@ 19th century , when it was dominated by the Lees and Jones families , Oldham coal was mainly sourced from many small collieries whose lives varied from a few years to many decades , although two of the four largest collieries survived to <unk> . In 1851 , collieries employed more than 2 @,@ 000 men in Oldham , although the amount of coal in the town was somewhat <unk> however , and production began to decline even before that of the local spinning industry . Today , the only visible remnants of the mines are disused shafts and <unk> . 
- 
- = = = Social history = = = 
- 
- Oldham 's social history , like that of other former <unk> towns , is marked by <unk> civil disturbances , as well as events related to the <unk> , <unk> and other Labour movements from the working classes . There has been a significant presence of " friendly societies " . It has been put that the people of Oldham became radical in politics in the early part of the 19th century , and movements suspected of <unk> found patronage in the town . Oldham was frequently disturbed by bread and labour riots , facilitated by periods of <unk> and the disturbance of employment following the introduction of cotton @-@ spinning machinery . 
- On 20 April 1812 , a " large crowd of <unk> individuals " compelled local retailers to sell foods at a loss , whilst on the same day <unk> numbering in their thousands , many of whom were from Oldham , attacked a cotton mill in nearby Middleton . On 16 August 1819 , Oldham sent a contingent estimated at well above 10 @,@ 000 to hear speakers in St Peter 's Fields at Manchester discuss political reform ; it was the largest contingent sent to Manchester . John Lees , a cotton operative and ex @-@ soldier who had fought at Waterloo , was one of the fifteen victims of the <unk> Massacre which followed . The ' Oldham <unk> ' which proceeded the massacre was <unk> watched ; the Court of King 's <unk> , however , decided that the proceedings were irregular , and the jury were discharged without giving a verdict . 
- Annie <unk> , born in nearby <unk> , and who worked in Oldham 's cotton mills , was a notable member of the <unk> movement credited with sparking off suffragette <unk> when she heckled Winston Churchill , and later ( with <unk> <unk> ) the first <unk> to be imprisoned . Oldham Women 's Suffrage Society was established in 1910 with <unk> Lees as president and quickly joined the Manchester and District Federation of the National Union of Women 's Suffrage Societies . The <unk> and Co @-@ operative movements had strong support in the town , whilst many <unk> protested against the <unk> of slaves . The Riot Act was read in 1852 on election day following a mass public brawl over the Reform Act , and irregularities with parliamentary candidate nominations . 
- For three days in late May 2001 , Oldham became the centre of national and international media attention . Following high profile race @-@ related conflicts , and long @-@ term underlying racial tensions between local White British and Asian communities , major riots broke out in the town . <unk> with particular intensity in the Glodwick area of the town , the Oldham riots were the worst racially motivated riots in the United Kingdom for fifteen years prior , briefly eclipsing the sectarian violence in Northern Ireland in the media . At least 20 people were injured in the riots , including 15 police officers , and 37 people were arrested . Similar riots took place in other towns in northern England over the following days and weeks . The 2001 riots prompted governmental and independent inquiries , which collectively agreed on community relations improvements and considerable regeneration schemes for the town . There were further fears of riots after the death of Gavin <unk> in 2002 . 
- 
- = = <unk> = = 
- 
- 
- = = = Civic history = = = 
- 
- <unk> within the historic county boundaries of Lancashire since the early 12th century , Oldham was recorded in 1212 as being one of five parts of the <unk> estate of <unk> , which was held on behalf of King John by Roger de <unk> and William de <unk> . The other parts of this estate were Crompton , Glodwick , <unk> , and Werneth . Oldham later formed a township within the ancient ecclesiastical parish of Prestwich @-@ cum @-@ Oldham , in the hundred of Salford . 
- In 1826 commissioners for the social and economic improvement of Oldham were established . The town was made part of a parliamentary borough , in 1832 , though it was in 1849 when Oldham was incorporated as a municipal borough , giving it borough status in the United Kingdom , and in 1850 the Borough Council obtained the powers of the improvement commissioners . In 1880 , parts of the Hollinwood and <unk> areas of Chadderton and Ashton @-@ under @-@ Lyne townships were added to the Borough of Oldham . Oldham Above Town and Oldham Below Town were , from 1851 until c . 1881 , statistical units used for the gathering and organising of civil registration information , and output of census data . 
- The Local Government Act 1888 created elected county councils to administer services throughout England and Wales . Where a municipal borough had a population of more than 50 @,@ 000 at the 1881 Census it was created a county borough , with the powers and duties of both a borough and county council . As Oldham had an 1881 population of 111 @,@ 343 it duly became a county borough on 1 April 1889 . The borough , while independent of Lancashire County Council for local government , remained part of the county for purposes such as the administration of justice and <unk> . 
- In 1951 parts of the <unk> Rural District were added to the County Borough of Oldham , and in 1954 further parts of the same district added to it on its abolition . Since 1961 , Oldham has been twinned with <unk> in Slovenia . Under the Local Government Act 1972 , the town 's autonomous county borough status was abolished , and Oldham has , since 1 April 1974 , formed part of the Metropolitan Borough of Oldham , within the Metropolitan county of Greater Manchester . 
- 
- = = = Parliamentary representation = = = 
- 
- The boundaries of two parliamentary constituencies divide Oldham : Oldham East and Saddleworth , and Oldham West and Royton ( which includes the town centre ) , represented by Labour Members of Parliament <unk> <unk> and Michael <unk> respectively . 
- <unk> as a parliamentary borough in 1832 , Oldham 's first parliamentary representatives were the radicals William <unk> and John <unk> . Winston Churchill began his political career in Oldham . Although unsuccessful at his first attempt in 1899 , Churchill was elected as the member of Parliament for the Oldham parliamentary borough constituency in the 1900 general election . He held the constituency for the Conservative Party until the 1906 general election , when he won the election for Manchester North West as a Liberal MP . After he became the Prime Minister of the United Kingdom in 1940 , Churchill was made a Honorary Freeman of the Borough of Oldham , on 2 April 1941 . 
- 
- = = Geography = = 
- 
- At 53 ° 32 ′ 39 ″ N 2 ° 7 ′ 0 @.@ 8 ″ W ( 53 @.@ <unk> ° , − 2 @.@ <unk> ° ) , and 164 miles ( 264 km ) north @-@ northwest of London , Oldham stands 700 feet ( 213 m ) above sea level , 6 @.@ 9 miles ( 11 @.@ 1 km ) northeast of Manchester city centre , on elevated ground between the rivers <unk> and <unk> . Saddleworth and the South Pennines are close to the east , whilst on all other sides , Oldham is bound by smaller towns , including Ashton @-@ under @-@ Lyne , Chadderton , <unk> , Royton and Shaw and Crompton , with little or no green space between them . Oldham experiences a temperate maritime climate , like much of the British Isles , with relatively cool summers and mild winters . There is regular but generally light precipitation throughout the year . 
- Oldham 's topography is characterised by its rugged , elevated <unk> terrain . It has an area of 6 @.@ 91 square miles ( 17 @.@ 90 km2 ) . The geology of Oldham is represented by the <unk> <unk> and Coal Measures series of rocks . The River <unk> , flowing northwards , forms the boundary between Oldham on one side and Royton and Shaw and Crompton on the other . 
- To the east of this river the surface rises to a height of 1 @,@ 225 feet ( 373 m ) at Woodward Hill , on the border with the parish of Saddleworth . The rest of the surface is hilly , the average height decreasing towards the southwest to <unk> and the city of Manchester . The ridge called Oldham Edge , 800 feet ( <unk> m ) high , comes southward from Royton into the centre of the town . 
- Oldham 's built environment is characterised by its 19th @-@ century red @-@ brick terraced houses , the infrastructure that was built to support these and the town 's former cotton mills – which mark the town 's skyline . The urban structure of Oldham is irregular when compared to most towns in England , its form restricted in places by its hilly upland terrain . There are irregularly constructed residential dwellings and streets clustered loosely around a central business district in the town centre , which is the local centre of commerce . In 1849 , Angus Reach of Inverness said : 
- The visitor to Oldham will find it essentially a mean @-@ looking <unk> town , built upon both sides and crowning the ridge of one of the outlying spurs which branch from Manchester , the neighbouring ' backbone of England ' . The whole place has a <unk> <unk> look . The general appearance of the operatives ' houses is filthy and <unk> . 
- In the 1870s , John <unk> Wilson described Oldham as consisting of : 
- ... numerous streets , and contains numerous fine buildings , both public and private ; but , in a general view , is irregularly constructed , presents the <unk> aspect of a crowded seat of manufacture , and is more notable for factories than for any other feature . 
- Although Oldham had a thriving economy during the 19th century , the local merchants were broadly reluctant to spend on civic institutions , and so the town lacks the grandeur seen in comparable nearby towns like Bolton or Huddersfield ; public expenditure was seen as an overhead that undermined the competitiveness of the town . Subsequently , Oldham 's architecture has been described as " mediocre " . The town has no listed buildings with a Grade I rating . 
- There is a mixture of high @-@ density urban areas , suburbs , semi @-@ rural and rural locations in Oldham . There is some permanent grassland but overwhelmingly the land use in the town is urban . The territory of Oldham is contiguous with other towns on all sides except for a small section along its eastern and southern boundaries , and for purposes of the Office for National Statistics , forms the fourth largest settlement of the Greater Manchester Urban Area , the United Kingdom 's third largest conurbation . The <unk> motorway passes through the southwest of Oldham , through Hollinwood , and a heavy rail line enters Oldham from the same direction , travelling northeast to the town centre before heading northwards through Derker towards Shaw and Crompton . 
- 
- = = = Divisions and suburbs = = = 
- 
- Many of Oldham 's present divisions and suburbs have origins as pre @-@ industrial hamlets , <unk> <unk> and ancient <unk> . Some , such as Moorside , exist as recently constructed residential <unk> , whilst places like Hollinwood exist as electoral wards and thoroughly <unk> districts . Throughout most of its recorded history , Oldham was surrounded by large <unk> of moorland , which is reflected in the placenames of Moorside , <unk> moor , <unk> , <unk> among others . 
- A large portion of Oldham 's residences are " low value " Victorian era Accrington red @-@ brick terraced houses in a row formation , built for the most part from 1870 to 1920 , to house the town 's cotton mill workers . There is more modern housing in the semi @-@ rural east of the town , in the most sought after area in areas such as the village Moorside , although terraces are found in almost all parts of Oldham . 
- One of the oldest recorded named places of Oldham is Hathershaw , occurring in a deed for <unk> with the spelling <unk> <unk> . <unk> as a manor in the 15th century , Hathershaw Hall was the home of a Royalist family in the 17th century who lost part of their possessions due to the English Civil War . Waterhead , an upland area in the east of Oldham , traces its roots to a water <unk> over the border in Lees . 
- Recorded originally as Watergate and Waterhead Milne , it was for a long time a hamlet in the parish of Oldham that formed a significant part of the Oldham Above Town registration sub @-@ district . Derker was recorded as a place of residence in 1604 with the name <unk> . <unk> by <unk> to the north , Derker is the location of Derker railway station and , said to have terraced <unk> " unsuited to modern needs " , is currently being redeveloped as part of the Housing Market <unk> Initiative . 
- <unk> , an area along Oldham 's northern boundary with Royton , was once a <unk> and the site of considerable industry and commerce , including coal mining , cotton spinning and hat manufacture . It is said to have been the scene of an action in the English Civil War in which the <unk> were defeated . 
- 
- = = <unk> = = 
- 
- According to data from the United Kingdom Census 2001 , Oldham had a total resident population of 103 @,@ 544 , making it the <unk> most populous settlement in England , and the 5th most populous settlement of the Greater Manchester Urban Area . This figure in conjunction with its area provides Oldham with a population density of 3 @,@ <unk> people per square mile ( 1 @,@ 544 per km ² ) . The local population has been described as broadly " working class " ; the middle classes tending to live in outlying settlements . 
- Oldham , considered as a combination of the 2001 electoral wards of Alexandra , <unk> , Hollinwood , St. James , St. <unk> , St. <unk> , Waterhead and Werneth , has an average age of 33 @.@ 5 , and compared against the average <unk> of the United Kingdom , has a high level of people of South Asian heritage , particularly those with roots in Pakistan and Bangladesh . Due to the town 's prevalence as an industrial centre and thus a hub for employment , Oldham attracted migrant workers throughout its history , including those from wider @-@ England , Scotland , Ireland and Poland . 
- During the 1950s and 1960s , in an attempt to fill the shortfall of workers and <unk> local industries , citizens of the wider Commonwealth of Nations were encouraged to migrate to Oldham and other British towns . Many came from the Caribbean and Indian subcontinent and settled throughout the Oldham borough . 
- Today , Oldham has large communities with heritage from Bangladesh , India , Pakistan and parts of the Caribbean . At the time of the 2001 census , over one in four of its residents identified themselves as from a South Asian or British Asian ethnic group . Cultural divisions along ethnic backgrounds are strong within the town , with poor cross @-@ community integration and cohesion along Asian and white backgrounds . 
- With only a small local population during medieval times , as a result of the introduction of industry , mass migration of village workers into Oldham occurred , resulting in a population change from under 2 @,@ 000 in 1714 to 12 @,@ 000 in 1801 to 137 @,@ 000 in 1901 In 1851 its population of 52 @,@ 820 made Oldham the 12th most populous town in England . The following is a table outlining the population change of the town since 1801 , which demonstrates a trend of rapid population growth in the 19th century and , after peaking at 147 @,@ 483 people in 1911 , a trend of general decline in population size during the 20th century . 
- In 2011 , 77 @.@ 5 % of the Oldham metropolitan borough population were White British , 18 @.@ 1 % Asian and 1 @.@ 2 % Black . While in the town of Oldham , which had a 2011 population of 96 @,@ 555 , 55 @.@ 4 % of the population were White British . 
- 
- = = Economy = = 
- 
- For years Oldham 's economy was heavily dependent on manufacturing industry , especially textiles and mechanical engineering . Since the <unk> of Oldham in the mid @-@ 20th century , these industries have been replaced by home shopping , publishing , healthcare and food processing sectors , though factory @-@ generated employment retains a significant presence . Many of the modern sectors are low @-@ skill and low @-@ wage . 
- Park Cake <unk> , sold in 2007 by Northern <unk> Group to Vision Capital , have a large food processing centre in Hathershaw , which employs in excess of 1 @,@ 600 people . Over 90 % of the cakes produced go to Marks & Spencer . Long existing as an industrial district , Hollinwood is home to the Northern Counties Housing Association , 
- Oldham 's town centre contains the highest concentration of <unk> , cultural facilities and employment in the Metropolitan Borough of Oldham . It has been extensively redeveloped during the last few decades , and its two shopping centres , Town Square and the <unk> , now provide one of the largest covered retail areas in Greater Manchester . The <unk> ( named with reference to textile spindles ) is a modern shopping centre with over 40 retailers , banks , building societies and catering outlets . It houses one of Europe 's largest stained glass roofs , created by local artist Brian Clarke in celebration of the music of one of Oldham 's famous sons , composer and conductor Sir William Walton . 
- Ferranti Technologies is an electronic , <unk> and electrical engineering company based in Waterhead . 
- A number of culinary and medical advances have been developed in Oldham . There are claims that Oldham was the birthplace of the first chip shop . The sometimes disputed claim of trade in deep @-@ fried <unk> potatoes is said to have been started around 1858 – 60 from an outlet owned by a John Lees , on what is the present site of Oldham 's <unk> Market . In 1900 Oldham had the highest concentration of chip shops in the country , one for every 400 people . <unk> Pudding is a <unk> dish said to be native to Oldham . <unk> Wine Lodge was founded in Oldham by Peter and Simon <unk> in 1884 . 
- The tubular <unk> was invented and developed in Oldham in 1961 . That " vital contribution to advancing medical science " resulted from a collaboration between local firm <unk> and a cotton manufacturer in the town . 
- 
- = = Landmarks = = 
- 
- 
- = = = Town Hall = = = 
- 
- Oldham 's Old Town Hall is a Grade II listed Georgian neo @-@ classical town hall built in 1841 , eight years before Oldham received its borough status . One of the last purpose @-@ built town halls in northwest England , it has a <unk> <unk> portico , copied from the temple of Ceres , on the River <unk> , near Athens . Winston Churchill made his inaugural acceptance speech from the steps of the town hall when he was first elected as a Conservative MP in 1900 . A Blue Plaque on the exterior of the building commemorates the event . Long existing as the political centre of the town , complete with <unk> , the structure has stood empty since the mid @-@ 1980s and has regularly been earmarked for redevelopment as part of regeneration project proposals , but none have been <unk> . 
- In September 2008 , it was reported that " Oldham Town Hall is only months away from a major roof collapse " . A tour taken by local councillors and media concluded with an account that " <unk> of masonry are falling from the ceilings on a daily basis ... the floors are littered with dead pigeons and ... revealed that the building is literally rotting away " . In October 2009 the Victorian Society , a charity responsible for the study and protection of Britain 's Victorian and <unk> architecture , declared Oldham Town Hall as the most endangered Victorian structure in England and Wales . Plans to convert the hall into a leisure complex , incorporating a cinema and restaurants , were revealed in May 2012 with the hall itself being used for public consultation . This £ 36 @.@ 72 million project is expected to be completed by early in 2016 . 
- In the heart of Oldham ’ s retail district , the Old Town Hall is being developed into a modern <unk> <unk> cinema . 
- 
- = = = War memorial = = = 
- 
- <unk> as a permanent memorial to the men of Oldham who were killed in the First World War , Oldham 's war memorial consists of a granite base surmounted by a bronze sculpture depicting five soldiers making their way along the trenches in order to go into battle . The main standing figure , having climbed out of the trenches , is shown calling on his comrades to advance , and is the same figure used at the Royal Fusiliers War Memorial in London and the 41st Division memorial at <unk> in France . The base serves to house books containing the roll of honour of the 1st , 10th and 24th Battalions , Manchester Regiment . The pedestal has two bronze doors at either side . 
- Commissioned in 1919 by the Oldham War Memorial Committee , the memorial was designed and built by Albert <unk> . It was unveiled by General Sir Ian Hamilton on 28 April 1923 , before a crowd estimated at over 10 @,@ 000 . The monument was intended to symbolise the spirit of 1914 – 1918 . 
- The inscriptions on the memorial read : 
- Over doors to the north : " <unk> <unk> THE <unk> <unk> <unk> / 1914 – 1918 " 
- Over window to the south : " <unk> <unk> <unk> THE <unk> " 
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- = = = Civic Centre = = = 
- 
- The Civic Centre tower is the Metropolitan Borough of Oldham 's centre of local governance . The 15 @-@ storey white @-@ brick building has housed the vast majority of the local government 's offices since its completion in 1977 . Standing at the summit of the town , the tower stands over 200 feet ( 61 m ) high . It was designed by Cecil <unk> & Partners , and the topping out ceremony was held on 18 June 1976 . The Civic Centre can be seen as far away as Salford , Trafford , <unk> and Winter Hill in Lancashire , and offers panoramic views across the city of Manchester and the Cheshire Plain . 
- 
- = = = Parish Church = = = 
- 
- The Oldham Parish Church of St. Mary with St. Peter , in its present form , dates from 1830 and was designed in the Gothic Revival Style by Richard Lane , a Manchester @-@ based architect . It has been designated by English Heritage as a Grade II * listed building . It was linked with the church of St Mary the Virgin , Prestwich and together the sites were principal churches of the ancient ecclesiastical parish of Prestwich @-@ cum @-@ Oldham . 
- A church building had existed on the site since <unk> . During this time , a small chapel stood on the site to serve the local townships of Oldham , Chadderton , Royton and Crompton . This was later replaced by an Early English Gothic church in the 15th century . With the coming of the Industrial Revolution , the population of Oldham increased at a rapid rate ( from under 2 @,@ 000 in 1714 , to over 32 @,@ 000 by 1831 ) . The rapid growth of the local population warranted that the building be rebuilt into the current structure . Though the budget was originally agreed at £ 5 @,@ 000 , the final cost of building was £ 30 @,@ 000 , one third of which was spent on the crypt structure . Alternative designs by Sir Charles Barry , the designer of the Palace of Westminster , although now regarded by some as superior , were rejected . The Church , of the Anglican denomination , is in active use for worship , and forms part of the Diocese of Manchester . There are Roman Catholic churches in Oldham . These include Our Lady of Mount Carmel and St Patrick Church . It was built in 1870 , was founded by priests from St Mary 's Church in Oldham , and is a Grade II listed building . 
- 
- = = Transport = = 
- 
- The geography of Oldham constrained the development of major transport infrastructure , with the former County Borough Council suggesting that " if it had not grown substantially before the railway age it would surely have been overlooked " . Oldham has never been on a main @-@ line railway route , and canals too have only been able to serve it from a distance , meaning that " Oldham has never had a train service worthy of a town of its size " . 
- A principal destination along the former Oldham Loop Line , Oldham once had six railway stations but this was reduced to three once <unk> Street , Oldham Central and Glodwick Road closed in the mid @-@ 20th century . Oldham Werneth , Oldham Mumps and Derker closed on 3 October 2009 . Trains from Manchester Victoria station to Oldham had to climb steeply through much of its 6 @-@ mile ( 9 @.@ 7 km ) route , from around 100 feet ( 30 @.@ 5 m ) at Manchester city centre to around 600 feet ( 182 @.@ 9 m ) at Oldham Mumps . The Werneth <unk> , with its gradient of 1 in 27 , made the Middleton Junction to Oldham Werneth route the <unk> regular passenger line in the country . The Werneth <unk> route closed in 1963 . It had been replaced as the main route to Manchester by the section of line built between Oldham Werneth Station and <unk> Bridge Junction , at Newton Heath in May 1880 . Oldham Mumps , the second oldest station on the line after Werneth , took its name from its location in the Mumps area of Oldham , which itself probably derived from the archaic word " <unk> " which was slang for a <unk> . The former Oldham Loop Line was converted for use with an expanded <unk> light rail network , and renamed as the Oldham and Rochdale Line . The line between Victoria and a temporary Oldham Mumps tram stop opened on 13 June 2012 , and more central stops opened on 27 January 2014 . 
- Oldham had electric <unk> to Manchester in the early 20th century ; the first tram was driven from Manchester into Oldham in 1900 by the Lord Mayor of Manchester . The system came to an end on 3 August 1946 , however . There was also a short @-@ lived Oldham <unk> system , in 1925 – 26 . The £ 3 @.@ 3 million Oldham Bus Station has frequent bus services to Manchester , Rochdale , Ashton @-@ under @-@ Lyne and Middleton with other services to the Metropolitan Borough of Oldham , <unk> , and across the Pennines to Huddersfield in West Yorkshire . The roof canopy is supported internally on two rows of steel trees . The extensive use of glass and stainless steel <unk> visibility , and there is a carefully co @-@ ordinated family of information fittings , posters and seating , using robust natural materials for floors and <unk> . The bus station is used by National Express coaches . First Greater Manchester has its headquarters in Oldham . 
- Despite the Turnpike Act <unk> , Oldham had no turnpike road to Manchester for another 56 years and Church Lane , Oldham remained part of the main street through the town . But following a further Act of Parliament a turnpike was constructed . The first regular coach service to Manchester came into operation in October 1790 , with a journey time of over 2 hours and a fare <unk> ( about <unk> ) , with half fare for travellers on top of the coach . 
- Oldham is about 4 miles ( 6 @.@ 4 km ) south of the major <unk> motorway , but is linked to it by the <unk> at Hollinwood , and <unk> ( M ) via Chadderton . There are major A roads to Ashton @-@ under @-@ Lyne , Huddersfield , Manchester , and Rochdale . 
- The Hollinwood Branch of the Ashton Canal was a canal that ran from Fairfield in <unk> , through <unk> and Daisy <unk> Country Park to the Hollinwood area of Oldham , with a branch from Daisy <unk> to the <unk> Branch Canal . The canal was mainly used for the <unk> of coal until it fell into disuse for commercial traffic in the 1930s . It included four <unk> and a two @-@ rise lock staircase . 
- 
- = = Sports = = 
- 
- Oldham <unk> Rugby League Club was established in 1876 as Oldham Football Club , and Oldham Athletic Football Club in 1895 as Pine Villa Football Club . Oldham Athletic have achieved both league and cup successes , particularly under Joe <unk> in the 1990s . They were Football League runners @-@ up in the last season before the outbreak of the First World War , but were relegated from the Football League First Division in 1923 . They reached the Football League Cup final in 1990 and won the Football League Second Division title in 1991 , ending 68 years outside the top flight . 
- They secured their top division status a year later to become founder members of the new Premier League , but were relegated after two seasons despite reaching that year 's FA Cup semi @-@ finals . They are currently playing in Football League One , the third tier of the English league . Darren Kelly was appointed as Manager in May 2015 
- Oldham <unk> Football Club was established in 1964 as Oldham <unk> FC , and after many years playing under the name of Oldham Town changed its name to Oldham <unk> in 2009 , finally becoming known as Oldham Borough just months before it folded . The team played in the North West Counties Football League before going out of existence in 2015 , just over 50 years after it was founded . 
- <unk> in 1997 to Oldham <unk> , Oldham Rugby League Club has received several club honours during its history , winning the Rugby Football League Championship five times and Challenge Cup three times . They played at <unk> for years before joining Oldham Athletic at <unk> Park until 2010 when they moved to Oldham Borough 's previous ground , <unk> Stadium . Oldham has league cricket teams with a number of semi @-@ professional league clubs including Oldham <unk> , and Werneth <unk> in the Central Lancashire League . 
- Oldham also has a Fencing Club , Marshall Fencing Club is a Competitive Fencing Club with most of its members competing on the national stage . They train three times a week at the old South Chadderton High School . 
- 
- = = Education = = 
- 
- Oldham produced someone who is considered to be one of the greatest benefactors of education for the nation , Hugh Oldham , who in <unk> was appointed as Bishop of Exeter , and later went on to found what is now Manchester Grammar School . 
- Almost every part of Oldham is served by a school of some kind , some with religious affiliations . According to the Office for Standards in Education , schools within the town perform at mixed levels . <unk> Grammar School and the Blue <unk> School are consistently Oldham 's top performing secondary schools and each have sixth form colleges of further education . 
- University Campus Oldham is a centre for higher education and a sister campus of the University of Huddersfield . It was opened in May 2005 by actor Patrick Stewart , the centre 's Chancellor . The University Campus Oldham presented actress <unk> <unk> and artist , Brian Clarke ( both born in Oldham ) with an Honorary Doctorate of Letters at the <unk> <unk> of November 2006 , for their achievements and contributions to Oldham and its community . 
- 
- = = Public services = = 
- 
- Home Office policing in Oldham is provided by the Greater Manchester Police . The force 's " ( Q ) Division " have their headquarters for policing the Metropolitan Borough of Oldham at central Oldham . Public transport is co @-@ ordinated by the Greater Manchester Passenger Transport Executive . <unk> emergency fire and rescue service is provided by the Greater Manchester Fire and Rescue Service , which has two stations in Oldham ; at <unk> on <unk> Road , and at <unk> on Lees Road . 
- The Royal Oldham Hospital , at Oldham 's northern boundary with Royton , is a large NHS hospital <unk> by <unk> Acute <unk> NHS Trust . It was opened under its existing name on 1 December 1989 . <unk> known as Oldham District and General , and occupying the site of the town 's former workhouse ( named Oldham Union <unk> in 1851 ) , the hospital is notable for being the birthplace of Louise Joy Brown – the world 's first successful In vitro fertilised " test tube baby " , on 25 July 1978 . The North West Ambulance Service provides emergency patient transport . See also Healthcare in Greater Manchester . 
- Waste management is co @-@ ordinated by the local authority via the Greater Manchester Waste <unk> Authority . <unk> produced inert waste for disposal is sent to landfill at the <unk> Valley . Oldham 's Distribution Network <unk> for electricity is United Utilities ; there are no power stations in the town . United Utilities also manages Oldham 's drinking and waste water ; water supplies being sourced from several local reservoirs , including <unk> and Chew . There is a water treatment works at Waterhead . 
- 
- = = Culture = = 
- 
- Oldham , though lacking in leisure and cultural amenities , is historically notable for its theatrical culture . Once having a peak of six " fine " theatres in 1908 , Oldham is home to the Oldham Coliseum Theatre and the Oldham Theatre Workshop , which have facilitated the early careers of notable actors and writers , including Eric <unk> , Bernard <unk> and Anne <unk> , daughter of acclaimed cartoonist Jack <unk> who worked for the Oldham Evening Chronicle . Oldham Coliseum Theatre is one of Britain 's last remaining repertory theatres ; Charlie <unk> and Stan Laurel performed there in the early 20th century , and contemporary actors such as Ralph <unk> and Minnie Driver , among others , have appeared more recently . 
- During the 19th century the circus was a popular entertainment in Oldham ; Pablo <unk> 's circus was a regular visitor , filling a 3 @,@ 000 @-@ seat amphitheatre on <unk> in 1869 . <unk> for its lack of a cinema , there are plans to develop an " Oldham West End " . Oldham has a thriving bar and night club culture , attracting a significant number of young people into the town centre . Oldham 's " hard binge drinking culture " has been criticised however for conveying a negative regional image of the town . 
- 
- = = = Communal facilities = = = 
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- The Lyceum is a Grade II listed building opened in 1856 at a cost of £ 6 @,@ 500 as a " mutual improvement " centre for the working men of Oldham . The facilities provided to members included a library , a <unk> and a series of lectures on geology , geography and education , microscopy and chemistry , female education and <unk> . <unk> music was introduced and there were soon sixteen <unk> and three <unk> . Eventually the building was extended to include a school of science and art . Music had always been important in the life of the Lyceum , and in 1892 a school of music was opened , with 39 students enrolled for the " theory and practice of music " . 
- The Lyceum continued throughout the 20th century as a centre for the arts in Oldham , and in 1986 the local authority was invited by its directors and trustees to accept the building as a gift . The acceptance of the Lyceum building by the Education Committee provided the opportunity to move the music centre and " further enhance the cultural activities of the town " . In 1989 the Oldham Metropolitan Borough Music Centre moved into the Lyceum building , which is now the home of the Oldham Lyceum School of Music . 
- Oldham ’ s museum and gallery service dates back to 1883 . Since then it has established itself as a cultural focus for Oldham and has developed one of the largest and most varied permanent collections in North West England . The current collection includes over 12 @,@ 000 social and industrial history items , more than 2 @,@ 000 works of art , about 1 @,@ 000 items of decorative art , more than 80 @,@ 000 natural history specimens , over 1 @,@ 000 geological specimens , about 3 @,@ 000 archaeological artefacts , 15 @,@ 000 photographs and a large number of books , pamphlets and documents . 
- Oldham is now home to a newly built state @-@ of @-@ the @-@ art art gallery , Gallery Oldham , which was completed in February 2002 as the first phase of the Oldham Cultural Quarter . Later phases of the development saw the opening of an extended Oldham Library , a lifelong learning centre and there are plans to include a performing arts centre . 
- 
- = = = Carnival = = = 
- 
- The annual Oldham Carnival started around 1900 , although the tradition of carnivals in the town goes back much further , providing a " welcomed respite from the <unk> of everyday life " . The carnival parade was always held in mid @-@ to @-@ late summer , with the primary aim of raising money for charities . It often featured local dignitaries or popular <unk> , in addition to brass , military and jazz bands , the Carnival Queen , people in fancy dress , dancers and decorated floats from local churches and businesses . Whenever possible , local people who had attained national celebrity status were invited to join the <unk> . The carnival 's route began in the town centre , wound its way along King Street , and ended with a party in Alexandra Park . 
- The carnival fell out of favour in the late 1990s but was resurrected by community volunteers in 2006 and rebranded the Peoples ' Carnival . The parade was moved into Alexandra Park in 2011 . The event hosts live stages and other activities alongside a parade in the park . In 2016 will be 10 years since the carnival was reinstated by volunteers . The main organiser is Paul Davies who runs the carnival with a number of committee members and loads of volunteers 
- 
- = = Britain in Bloom = = 
- 
- In 2012 and 2014 Oldham was named as Culture Town in the annual " Britain in Bloom " competition as winners 
- 
- = = Notable people = = 
- 
- People from Oldham are called <unk> , though " <unk> " is a nickname from the 18th century when rough felt was used in Oldham to make hats . 
- Edward Potts was a renowned architect who moved to Oldham from Bury . He was the architect for fourteen mills in the Oldham area . 
- Other notable persons with Oldham connections include the composer Sir William Walton , former British Prime Minister Sir Winston Churchill , and Louise Brown , the world 's first baby to be conceived by in vitro fertilisation . 
- Notable media personalities from Oldham include presenter Nick <unk> , actors Eric <unk> and Bernard <unk> , TV host Phillip Schofield , actress <unk> <unk> , physicist and science <unk> Brian Cox , and comedy double act Tommy Cannon and Bobby Ball . 
- Notable musicians from Oldham include the <unk> <unk> and Mark Owen of <unk> Take That . 
- 
- 
- = 1981 European Cup Final = 
- 
- The 1981 European Cup Final was an association football match between Liverpool of England and Real Madrid of Spain on 27 May 1981 at the <unk> des <unk> , Paris , France . It was the final match of the 1980 – 81 season of Europe 's premier cup competition , the European Cup . Liverpool were appearing in their third final , after two appearances in 1977 and 1978 . Real Madrid were appearing in their ninth final , they had previously won the competition six times and lost twice . 
- Each club needed to progress through four rounds to reach the final . <unk> were contested over two legs , with a match at each team 's home ground . All but one of Liverpool 's ties were comfortable victories , they beat Bayern Munich on the away goals rule , while they won all their other ties by at least five goals . Real Madrid 's matches ranged from close affairs to comfortable victories . In the first round they beat Limerick 7 – 2 on aggregate , but their final two ties were won by at least two goals . 
- <unk> by a crowd of 48 @,@ 360 , the first half was <unk> . Liverpool took the lead in the second half when Alan Kennedy scored . They held this lead to win the match 1 – 0 , securing Liverpool 's third European Cup and a fifth consecutive victory by an English team . Liverpool manager Bob Paisley became the first manager to win the competition three times . 
- 
- = = Route to the final = = 
- 
- 
- = = = Liverpool = = = 
- 
- Liverpool gained entry to the competition by winning the 1979 – 80 Football League , entering as English champions . Their opponents in the first round were Finnish champions <unk> <unk> . The first leg in Finland at the <unk> <unk> was drawn 1 – 1 . The second leg at Liverpool 's home ground Anfield resulted in a comprehensive 10 – 0 victory for Liverpool . Two of their players , Graeme Souness and Terry McDermott , scored a hat @-@ trick . Liverpool won the tie 11 – 1 on aggregate . 
- In the second round Liverpool were drawn against Scottish champions Aberdeen , managed by Alex Ferguson . The first leg was at Aberdeen 's home ground , <unk> , a McDermott goal in the fifth minute ensured a 1 – 0 victory for Liverpool . The second leg at Anfield was won 4 – 0 by Liverpool , thus they won the tie 5 – 0 on aggregate . Liverpool 's opponents in the quarter @-@ finals were Bulgarian champions <unk> <unk> . The first leg was held in England , another hat @-@ trick from Souness and goals from McDermott and Sammy Lee gave Liverpool a 5 – 1 victory . They won the second leg at the <unk> <unk> National Stadium 1 – 0 to progress to the semi @-@ finals courtesy of a 6 – 1 aggregate victory . 
- They faced German champions Bayern Munich in the semi @-@ finals . The first leg in England ended in a 0 – 0 draw . Liverpool needed to score in the second leg to advance to the final . Their cause was not helped when striker Kenny Dalglish had to be replaced in the early minutes of the match by the inexperienced Howard Gayle . However , Gayle rose to the occasion , unsettling the Bayern defence with his attacking threat . With seven minutes of normal time left , Ray Kennedy scored to give Liverpool a 1 – 0 lead . Bayern responded soon afterwards when Karl @-@ Heinz <unk> scored , however they needed to score another goal due to the away goals rule . They were unable to do so and Liverpool progressed to their third final in five seasons . 
- 
- = = = Real Madrid = = = 
- 
- Real Madrid gained entry to the competition as champions , after they won the 1979 – 80 La Liga . Their opponents in the first round were Irish champions Limerick . Limerick took the lead in the first leg at Lansdowne Road , but Real scored twice to win the first leg 2 – 1 . The second leg at Real 's home ground the Santiago <unk> Stadium , ended in a 5 – 1 victory for Real , they won the tie 7 – 2 on aggregate . 
- Their opponents in the second round were Hungarian champions <unk> . A goal from <unk> ensured Real won the first leg 1 – 0 in Spain . Two goals from Laurie Cunningham and Francisco García <unk> secured a 2 – 0 victory in the second leg at <unk> 's home ground the <unk> <unk> , thus , winning the tie 3 – 0 on aggregate . 
- In the quarter @-@ finals Real faced Soviet champions <unk> Moscow . A 0 – 0 at the Dynamo Lenin Stadium in the Soviet Union left the tie finely balanced heading into the second leg in Spain . Two goals from Isidro in the second half secured a 2 – 0 victory in the match and over aggregate . 
- Real 's opponents in the semi @-@ finals were Italian champions <unk> . Real won the first leg 2 – 0 in Spain after goals from <unk> and <unk> . <unk> needed to score twice to force the tie into extra @-@ time , however they were only able to score once . They won the second leg 1 – 0 , but Real won the tie 2 – 1 on aggregate to progress to their ninth final . 
- 
- = = Match = = 
- 
- 
- = = = Background = = = 
- 
- Liverpool were appearing in their third European Cup Final . They had won their two previous appearances in 1977 and 1978 . Real Madrid were appearing in their ninth final , they had won the competition six times , including a record five successive victories from 1956 to 1960 . Their sixth victory was in 1966 , while their two losses were in 1962 and 1964 . 
- Liverpool had finished fifth during the 1980 – 81 Football League , thus they needed to win the final to ensure that they would compete in the European Cup the following season . Despite this , Liverpool had won the Football League Cup for the first time earlier in the season , defeating West Ham United 2 – 1 in a replay after the final finished 1 – 1 . Real Madrid had finished second in the 1980 – 81 La Liga , they had qualified for the UEFA Cup as a result , but victory would enable them to compete in the European Cup the following season . 
- Both sides had injury concerns before the match . Liverpool had doubts over a number of players . Kenny Dalglish had not trained for several weeks , while Alan Kennedy had been sidelined for six weeks with a broken wrist . Real had concerns over striker Laurie Cunningham who had been sidelined since November . 
- 
- = = = Summary = = = 
- 
- Liverpool controlled the opening passages of the game . Their first goalscoring chance came in the 11th minute when Alan Kennedy 's 30 yard shot was saved by Real goalkeeper Agustín Rodríguez . Further chances were created , but neither Terry McDermott or Dalglish were able to score . Real started to exert more of an influence after this , midfielder <unk> started to cause Liverpool problems with his passing . One pass found José Antonio Camacho who beat Liverpool defender Alan Hansen but put his shot wide of the Liverpool goal . Despite their scoring opportunity , one problem Real had was to get the best out of their winger Cunningham . Not fully fit , he was tightly marked and made little impact in the match . 
- Liverpool had a chance to take the lead before half @-@ time . Phil Neal advanced down the right hand side of the pitch and found Dalglish who passed to Graeme Souness , who ran late through the Real defence . His subsequent shot was not held by Agustín Rodríguez , but Souness was unable to get to the rebounded ball . Real had the first chance of the second half . Liverpool 's defence had stopped as they thought Cunningham was offside , however he was not and Camacho advanced towards Ray <unk> in the Liverpool goal . <unk> came off his line and ran towards the Real midfielder , Camacho attempted to <unk> the ball over the keeper , but his shot went over the goal . 
- The tactical approach of the two sides were <unk> each other out . Real 's slow pace interspersed with high speed bursts , while Liverpool preferred a more deliberate approach , keeping possession and making use of their <unk> . Their various forms of marking also negated each other , Real man marked Liverpool 's best players such as Dalglish and Souness , while Liverpool were content for the nearest defender to mark the man on the ball . 
- Despite the tactical battle Liverpool were able to score in the 81st minute . A throw @-@ in from Ray Kennedy found Alan Kennedy , whose run up the left hand side of the pitch caught Real off @-@ guard . Kennedy went past Real defender Rafael García Cortés into the Real box , his subsequent shot beat the Real goalkeeper to give Liverpool the lead . Soon afterwards Liverpool replaced Dalglish with midfielder Jimmy Case in an attempt to see out the match . Although Liverpool had chances to extend their lead as Real went all out to score , Real keeper Agustín Rodríguez made a number of saves to keep his team in the game . The match remained 1 – 0 and Liverpool won their third European Cup , the first British club to do so . Liverpool 's victory meant that Bob Paisley became the first manager to win the European Cup three times . 
- 
- = = = Details = = = 
- 
- 
- 
- = Carre 's Grammar School = 
- 
- Carre 's Grammar School is a selective secondary school for boys in Sleaford , a market town in Lincolnshire , England . Founded on 1 September 1604 by an indenture of Robert Carre , the school was funded by rents from farmland and run by a group of trustees . The indenture restricted the endowment to £ 20 without accounting for inflation , causing the school to decline during the 18th century and effectively close in 1816 . <unk> by a decree from the Court of Chancery in 1830 , new buildings were constructed at its present site and the school re @-@ opened in 1835 . Faced with competition from cheaper commercial schools and declining rolls , Carre 's eventually added technical and artistic instruction to its Classical curriculum by <unk> with Kesteven County Council in 1895 . Following the Education Act 1944 , school fees were abolished and Carre 's became Voluntary Aided . New buildings were completed in 1966 to house the rising number of students . After plans for comprehensive education in Sleaford came to nothing in the 1970s and 1980s , Carre 's converted to grant @-@ maintained status in 1990 . Foundation status followed and the school became an Academy in 2011 ; a multi @-@ Academy trust with Kesteven and Sleaford High School was formed in 2015 . 
- Admission to Carre 's is through the eleven @-@ plus examination and entry is limited to boys in the lower school , although the Sixth Form is co @-@ educational . The total number of pupils on roll in 2013 was <unk> , of which 240 were in the Sixth Form . Teaching follows the National Curriculum and pupils generally sit examinations for ten or eleven General Certificate of Secondary Education ( GCSE ) qualifications in Year Eleven ( aged 15 – 16 ) . They have a choice of three or four A @-@ levels in the sixth form , which is part of the Sleaford Joint Sixth Form consortium between Carre 's and St George 's Academy . Of the 2013 cohort , 100 % of pupils achieved at least five GCSEs at grade A * <unk> and 96 % achieved that including English and <unk> GCSEs , the eighth highest percentage in Lincolnshire . An Office for Standards in Education , Children 's Services and <unk> ( <unk> ) inspection in 2013 graded Carre 's " good " overall with " outstanding " features . 
- 
- = = History = = 
- 
- 
- = = = The first school = = = 
- 
- Carre 's Grammar School was founded on 1 September 1604 by way of an indenture between Robert Carre , a member of the Carr or Carre family , and several local gentlemen . Carre granted 100 acres of agricultural land in Gedney to these men , who held the land in trust as <unk> . The lands were estimated to be worth £ 40 per annum and the indenture stipulated that £ 20 of this would be paid to the school master , while the remainder would be for the benefit of the town 's poor . The indenture stated that the school was to provide for " the better education of the Youth and Children born or inhabiting with their parents within New Sleaford , Old Sleaford , <unk> , and <unk> ... and in <unk> , North <unk> , South <unk> , <unk> , <unk> La Thorpe and <unk> . " It is not known whether there was any other school in the town prior to the foundation of Carre 's , although the indenture appointed Anthony Brown , already a schoolmaster , as the master ; it thus seems likely that Carre already operated a school and his indenture codified pre @-@ existing arrangements . 
- Throughout the <unk> the trustees reported problems receiving rents from the <unk> in Gedney . Although the school received a bequest from a local gentleman , Robert <unk> , in 1631 , which provided an additional income of £ 4 per annum , no more followed ; the English Civil War also disrupted funding : rents were not collected between 1644 and 1646 . These financial problems were compounded by the nature of the land itself : it was agricultural and not urban , thus it did not increase in value significantly in the 17th century . Carre 's <unk> behind other schools and its buildings fell into disrepair as the fixed endowment failed to keep up with inflation ( despite the Gedney lands increasing in value to £ 180 by the early 19th century ) . In 1783 , the <unk> ( by then , often called trustees ) spent £ 50 on improvements , but by 1794 , the adjacent Carre 's Hospital agreed that part of its building be pulled down to make way for a new schoolhouse . This did not <unk> and pupils were taught in the vestry at St Denys ' Church by the early 19th century . In 1816 , the trustees discontinued the master 's salary because there were " no duties to perform " at the school . 
- 
- = = = Revival , stagnation and modernisation = = = 
- 
- The trustees met in 1821 and agreed that " much good " could come from reviving the school . In 1828 they petitioned the Court of Chancery for a scheme , which was approved in 1830 , providing the master with a salary of £ 80 per annum . Four years later , the Chancery agreed to fund the rebuilding of the school at a site on Northgate . With the buildings complete , the school reopened on 1 August 1835 . 
- Carre 's maintained roughly 20 pupils on roll throughout the 1840s , but by 1858 , this had fallen to two free scholars and two <unk> . When the charity commissioners inspected the school the following year , they recommended that an <unk> be appointed to teach " commercial education " to supplement the Classics . In 1869 the Schools <unk> Commission reported a " general dissatisfaction in the town " towards the school , finding " indifferent " discipline along with poor spelling , an inability to decline simple Latin <unk> , and a low @-@ level of arithmetic . According to the report , the " general wish in the town is for a commercial school " . Competition soon arose in the form of Mr Boyer 's academy and later E. R. <unk> 's commercial school at Mount Pleasant , Sleaford . Although the trustees were reorganised in 1876 , Britain 's agriculture suffered from foreign competition in the 1880s , which contributed to a decline in the rolls ; a subsequent reduction of fees in 1889 proved ineffective and only twelve boys were in attendance the following year . 
- The Commissioner of <unk> suggested that Kesteven County Council could support the teaching of art , modern languages and technical and scientific subjects through the Local <unk> Act 1890 . In 1895 the governors agreed to affiliate with the Council , which granted them £ 35 . The headmaster , Samuel Brown , appointed an assistant master and his wife was employed to teach art . The numbers rose so that in 1897 there were 33 pupils on roll , and the Committee granted a further £ 400 to pay for new accommodation and resources . The Governors , however , applied for £ 1 @,@ 500 to build a new school entirely but the Council wanted it to be coeducational , which caused a lengthy stalemate . The demand for a coeducational school disappeared in 1902 when Sleaford and Kesteven High School for Girls opened as a private venture and so in 1904 a new building opened at Carre 's , financed in part by the sale of the Gedney lands , while boarding accommodation followed in 1906 . Following the Education Act 1902 , Carre 's received an allocation of £ 200 per pupil from the Board of Education , plus local authority assistance made in return for admitting pupils from local elementary schools . From 1919 , elementary school pupils sat the entrance exam each term and those who passed were allocated the places which remained after fee @-@ paying students had enrolled . 
- 
- = = = Post @-@ war expansion and the comprehensive debate = = = 
- 
- The Education Act 1944 made secondary education available to all children up to the age of 15 and abolished fees for state @-@ schooling ; a ' <unk> system ' of secondary schools was established to provide <unk> based on aptitude and ability : grammar schools for " academic " pupils , secondary <unk> for practical studies , and technical schools for science and engineering . Pupils were allocated to them depending on their score in the eleven @-@ plus examination . Carre 's became a Voluntary <unk> Grammar School ; from 1945 all entry was by the County Selection Examination . By 1955 , the school had 330 pupils on roll and the need for new accommodation was met in the 1950s and 1960s by a major building programme at the Northgate site ; completed in 1966 , this added dedicated classroom blocks , a canteen and hall . 
- The educational opportunities for secondary modern pupils were limited compared to those at grammar schools , prompting criticism of the <unk> system . In 1965 , the Labour Government issued <unk> 10 / 65 requesting Local Education Authorities implement comprehensive schooling . In 1971 Sleaford parents voted in favour of comprehensive education , but rejected the Council 's proposals . A new plan which envisaged Carre 's becoming a sixth form college was supported by parents in a vote ( 1 @,@ 199 to 628 ) , albeit with a 50 % turnout ; the County Council approved it but allowed governors a veto . Following negotiations with governors at Carre 's , the scheme was revised so that Carre 's would be an 11 – 18 school and <unk> Sleaford Secondary Modern 's Church Lane site . Despite support from most staff and all three <unk> , Lincolnshire County Council voted to return the scheme for consultation in 1975 . A new system was proposed which retained all three schools , and when the Government ordered the Council to choose a comprehensive scheme in 1977 it submitted that proposal , which had become popular with parents . The next year the government dismissed it on grounds that the Sixth Forms would be too small , but the council voted against the two @-@ school system once more . 
- 
- = = = Grant @-@ maintained status and Academy conversion = = = 
- 
- The 1979 general election brought a Conservative government to power and allowed the Council to shift its focus towards retaining Grammar Schools where they still existed and improving schools where work had been put on hold during the comprehensive debate ; despite 90 % of English councils adopting comprehensive education , Lincolnshire had retained its grammar schools . Although the County Council began discussing the abolition of them again in 1985 , opposition from parents at a public consultation in 1987 resulted in the plans being dropped . With the question of its future resolved , Carre 's applied for grant @-@ maintained status in 1989 ; the Education Secretary approved the proposals and formally granted the status in September 1990 . When grant @-@ maintained status was abolished in 1999 , Carre 's became a Foundation School . Following a successful bid to the <unk> , submitted in October 2002 , the school was granted specialist Sports College status in 2003 . An all @-@ weather pitch was laid out in 2007 , and a new technology building with a fitness suite opened in 2011 . In 2009 , Carre 's became a specialist Science College and a lead school for gifted and talented students . The school converted to Academy status in August 2011 . In 2014 , the governors announced their intention to bid for conversion to a multi @-@ Academy trust and become a coeducational , selective school on a new site ; in February 2015 , Kesteven and Sleaford High School announced its intention to join the proposed trust , a moved welcomed by Carre 's . Carre 's officially became part of the Robert Carre Multi @-@ Academy Trust on 1 September 2015 ; the schools continue to operate on their sites , sharing staff and facilities . 
- 
- = = School structure = = 
- 
- Carre 's is a state @-@ run selective grammar school . It converted to an Academy and reopened on 1 August 2011 ; it is governed by Carre 's Grammar School Academy Trust and converted without sponsorship . As of 2015 , the student body is made up of 815 pupils aged 11 – 18 . The school admits boys on a selective basis for years 7 – 11 and has a co @-@ educational Sixth Form ; there are 60 girls on roll as of 2015 . The majority of pupils come from White British backgrounds and very few pupils speak English as an additional language . The number of pupils supported through <unk> , including those eligible for free school meals ( 2 @.@ 3 % ) , is below average , as is the number of students with learning disabilities . Pupils are allocated into houses based on their forms . The first house system at Carre 's consisted of four sets : scarlet , maroon , green and blue . They became houses in 1933 as Carre , Bristol , <unk> and <unk> , named respectively after its founder , the Earls of Bristol , an old name for Sleaford , and Richard <unk> , who owned the Gedney lands purchased by Carre . 
- Admission to the school is through the eleven @-@ plus examination , taken in year 6 . Pupils must obtain a minimum score before their application will be considered and places will be awarded based on whether the child is in public care , whether he lives in the catchment area and attends a partnered primary school , and whether they have siblings attending the school or parents working there . In the event of a tie , places will be allocated based on proximity to the school . The school has a maximum annual intake of 116 at the start of year 7 ( aged 11 ) ; pupils are arranged into forms of no more than 30 , where registration takes place . Their form tutors provide access to pastoral support , overseen by their Key Stage Manager . In 2013 the lower school had approximately <unk> pupils on roll . The school uniform consists of a black <unk> with the school badge embroidered on the breast pocket and a red <unk> on each pocket . <unk> or black trousers are worn along with a white shirt and school tie . 
- The vast majority of pupils at Carre 's continue on to the Sixth Form , and there were 240 students on roll in 2013 . Along with St George 's Academy , Carre 's is part of the Sleaford Joint Sixth Form , which was founded in 1983 and included Kesteven and Sleaford High School until it withdrew in 2010 . It provides a common timetable across both sites and allows for pupils to choose from A @-@ Level options offered at both schools . Pupils may apply to be based at either school , where their pastoral and tutorial activities take place . There are entry requirements based on GCSE attainment . Sixth <unk> can be selected to be prefects , who have responsibilities around the school under the supervision of staff ; they are headed by a Head Boy and Girl . The students are required to wear business @-@ dress ; for males , this consists of a dark suit , a " non @-@ vivid " shirt and the school 's sixth form tie ; females must dress in " smart business wear " . 
- 
- = = Curriculum = = 
- 
- The curriculum during the 17th and 18th centuries is not known for certain . In 1714 the trustees agreed that the pupils should attend church services at St Denys ' Church six days a week ; whether this was adhered to is not clear . Religious practice was a <unk> in the 1835 decree , which required pupils to pray at the start and end of each day and engage in daily readings of holy scriptures . However , the emphasis was always on classical education , which likely required instruction in Latin from the earliest times ; in 1835 , learning the classics was enshrined as the school 's primary purpose . In the mid @-@ 19th century , Carre 's offered this classical education for free , but arithmetic , geometry and algebra were taught as extras at a rate of two guineas per term . Students were enrolled from the age of eight , and were expected to be able to read , write , recite the Lord 's Prayer , the Apostles ' Creed and the Ten Commandments , and " be qualified to begin Latin grammar " . Students had to supply their own equipment , except pens and ink , which were covered by a 10 shilling payment made each term to the school . The school 's inspection in 1865 mentions geography and history teaching , although the general standard of attainment was low . 
- In 1876 , the curriculum was widened so that it comprised reading , writing , arithmetic , English , mathematics , history , geography , Latin , a foreign language , music , natural science and drawing , with Greek as an optional extra . The County Council supported technical and commercial subjects in the late 19th century , but after 1904 it was empowered to support secondary education in general , allowing Latin and other classical components of the curriculum to remain intact . After World War I , sixth form courses were developed allowing students to commence advanced studies in the arts and sciences . By the 1950s , a wide range were available : English language and literature , mathematics , French , German , Latin , Greek , art , history , geography , physics , chemistry and woodwork ; biology was taught at Kesteven and Sleaford High School . 
- 
- = = = Key <unk> 3 and 4 = = = 
- 
- As of 2014 , the school follows the National Curriculum in years 7 – 11 and offers a range of GCSEs ( national exams taken by students aged 14 – 16 ) and A @-@ levels ( national exams taken by pupils aged 16 – 18 ) . The school has no affiliation with a particular religious denomination , but religious education is given throughout the school , and boys may opt to take the subject as part of their GCSE course . Although morning assemblies take place and are Christian in nature , they are non @-@ denominational . Students participate in a number of educational visits and excursions throughout their school career and year 11 students are offered the opportunity to participate in a work experience programme . The curriculum comprises English and drama , mathematics , French , history , geography , science , art , music , design and technology , information communications technology ( <unk> ) , ethics and philosophy ( religious education ) , physical education ( <unk> ) , cookery , and citizenship , sex and relationships education ; in Key Stage 4 ( years 10 and 11 ) , pupils also participate in careers and work @-@ related learning . In mathematics , students are divided by their ability into two bands . Science is divided into Biology , Chemistry and Physics in year 9 . In the second year German or Spanish is added . The use of information technology is central to all teaching and is taught as a subject in Key Stage 3 ; in year 9 , all students study for the European Computer Driving Licence , a level 2 course in <unk> and pupils may opt to take Computing as a GCSE . 
- Boys usually take nine or ten subjects for GCSE : English ( language and literature ) , mathematics , a foreign language , all three separate sciences or Dual Certificate Science , as well as three other subjects from those listed above as well as business studies , with technology being divided into separate courses for <unk> <unk> , Graphics , <unk> and Engineering ; Mandarin is also available as an optional extra subject , but is studied after school . 
- 
- = = = Sixth Form = = = 
- 
- Carre 's and St. George 's Academy operate the Sleaford Joint Sixth Form , which shares a common timetable between the two sites and allows for students to choose from a wide range of options at A @-@ Level . Students may choose to apply to be based at either school , where their pastoral and tutorial activities will take place . The Sixth Form , including Carre 's , is co @-@ educational . The majority of students take four A @-@ levels subjects in year 12 , with most choosing to focus on three in year 13 . The Joint Sixth Form allows students to choose from a range of 65 optional vocational or academic subjects including : art and photography ( separate A @-@ Level or BTEC options ) , applied Science , biology , <unk> , business ( A @-@ Level or BTEC ) , childcare , carpentry , chemistry , computing or <unk> ( A @-@ Level or BTEC ) , drama or performing arts ( A @-@ Level or BTEC ) , electronics , engineering , English ( language and / or literature ) , film or media studies , French , German , geography , government and politics , health and social care , history , hospitality and catering , law , mathematics and further mathematics , music ( A @-@ Level or BTEC ) , philosophy and ethics , psychology , physical education or sport ( A @-@ Level or BTEC ) , physics , product Design , public Services , light vehicle maintenance , Spanish , sociology , travel and tourism , and work skills . In addition , students may participate in General Studies at A @-@ Level , and a range of extra @-@ curricular activities , including the Duke of Edinburgh Award scheme . 
- 
- = = = <unk> = = = 
- 
- In 2013 , 100 % of pupils achieved at least five GCSEs at grade A * – C and 96 % achieved that including English and <unk> GCSEs , the eighth highest percentage in Lincolnshire . Figures for the 2010 / 11 cohort show that 86 % of Key Stage 4 pupils at the school carried on to the Sixth Form . At A @-@ Level , 85 % of pupils in 2013 attained three A @-@ Levels at grades A * – E and 11 % achieved three A @-@ Levels at grades <unk> including at least two " facilitating subjects " ; the average point score per qualification was 201 @.@ 7 , equating to a <unk> grade , and the average point score per student was <unk> @.@ 1 . The Sunday Times ranked Carre 's 101st ( 49th amongst state schools ) in the Midlands and 750th nationally based on A @-@ Level and GCSE performance in 2012 ; it recorded that 48 @.@ 7 % of A @-@ Levels were at A * – B grade and 42 @.@ 5 % of GCSE grades were at A * or A. 
- 
- = = Extra @-@ curricular activities = = 
- 
- As of 2014 , school clubs and societies include various language clubs , sport clubs , musical activities and many others . Students may participate in the Duke of Edinburgh 's Award Scheme , beginning with the Bronze grade in year 10 . Musical opportunities include participating in the school band and the choir , the guitar club and the Music Theory support group ; the school band has performed at the Lincolnshire Show and music students have taken part in the Lincolnshire School ’ s Prom in <unk> . In the past , Carre 's has offered a range of clubs and societies , including ones for archaeology , <unk> @-@ building , bird @-@ watching , boxing , chess , cycling , drama , languages , geography , jazz and other music groups , a choir and orchestra , and student voice groups , like the student council . The first school play performed by the Dramatic Society was She <unk> to <unk> in 1938 . <unk> to see plays , a Play Reading Society and a new dramatic society were formed under the guidance of the English master A. D. <unk> . In 1968 , plays were performed jointly with Kesteven and Sleaford High School . At the end of World War I , a cadet corps as formed by one Captain Price and became part of the Army Cadet Corps under the War Office ; attendance at weekly parades was compulsory for pupils over 13 in the 1920s . Most pupils took part in its activities in World War II , under the lead of the History teacher , Major W. H. T. Walker ; this included athletics competitions , shooting practice and trips to camp sites . It disbanded in c . 1963 when the two staff who ran it retired . 
- 
- = = = Sport = = = 
- 
- As with the curriculum , there are no references to sports being played at Carre 's before the 19th century . In 1835 , the Marquis of Bristol allowed the school to use an acre of land , which probably functioned as a playground . Sports fields were not added to the grounds until 1908 , but the earliest reports of the school participating in sporting events pre @-@ date this by half a century . In the 1860s , the Sleaford Gazette reported on cricket matches with local schools , namely the rival academies run by Mr Boyer and Mr <unk> in Sleaford ; by the 1890s , these matches were being organised with more distant schools , like the grammar school at <unk> . Athletics were practised at the school as early as 1871 when a sports day was held ; the 100 yards and half a mile races , hurdles , the pole jump and throwing the cricket ball were activities in which forms competed . Football was played at the school as early as 1895 . <unk> land was acquired in 1908 for sporting purposes and levelled in the early 1930s . A cycling club was formed in the 1940s and <unk> was informally organised by pupils by the 1950s ; between 1957 and 1960 , a portion of land was converted into tennis courts for the school and rugby was introduced in the 1966 – 67 academic year . 
- In 2014 – 15 , the school pitched football , rugby union , basketball , cricket , golf and <unk> teams . In football , the under 12 , 13 , 14 and 15 football teams won the Kesteven and Sleaford District leagues in 2013 / 14 , while the under 13 and 14 teams won the Lincolnshire Schools ' Cup . In rugby , the under 14 team was county champions for the same season and the school competes on a national level . 
- 
- = = Site and property = = 
- 
- The original location of Carre 's School is not known . From 1653 , it operated in buildings on <unk> , adjacent to Carre 's Hospital . After these fell into disrepair in the late 18th century , pupils were taught in the vestry of St Denys ' Church until the school was closed in 1816 . In 1826 , the trustees purchased a house on Northgate at the cost of £ 545 <unk> from one Mr <unk> . In 1834 , the Chancery Court agreed to fund the rebuilding of the school according to plans by the Sleaford architect and builder Charles Kirk , who constructed it at a cost £ 1 @,@ 168 <unk> . The building is in the Tudor Gothic style and built in <unk> stone with slate roofs . It has three stories of three bays , with the upper floor housed in two <unk> . A shield with the arms of the Marquis of Bristol and his wife are located above the four @-@ centre arch doorway . One storey wings exist on either side in a similar style . Brick additions were made in 1904 and 1906 . 
- As the school roll grew , the old buildings became too small . A major building programme began in the 1950s : £ 128 @,@ 000 was set aside to <unk> the school in purpose @-@ built facilities adjacent to the existing school @-@ houses . The first phase was opened in 1956 and included art and <unk> rooms ; the second phase was completed in 1958 when physics and chemistry rooms were added ; and the third came in 1965 with the opening of new biology and general science laboratories alongside other classrooms , while the following year saw a new hall / canteen and kitchen open . The final phase consisted of eight further rooms , built shortly afterwards . 
- A grant of £ 650 @,@ 000 funded the construction of a technology centre with a computer suite , which opened in January 1993 . Plans for a new sports hall were first discussed in 1990 , but they only came to fruition in 1996 , when Northgate Sports Hall opened . The Sports Council and the Foundation for Sport and the Arts donated £ 250 @,@ 000 towards its construction ; this was matched by North Kesteven District Council , while Carre 's raised £ 50 @,@ 000 towards the building work . An all @-@ weather pitch at the school opened in 2007 ; it cost £ 649 @,@ 000 to lay , half of which was met by the Football Foundation . A building programme costing £ 835 @,@ 000 provided the school with food technology facilities and a two @-@ storey <unk> <unk> , which were opened in March 2011 . 
- 
- = = <unk> = = 
- 
- The indenture of 1604 made it compulsory that the master be a graduate of the University of Cambridge or Oxford and the majority of the pre @-@ 1835 masters had attended Cambridge , with only two from Oxford . When the school reopened in 1835 , these <unk> were removed . The headmaster lived on site until Derek Lee began <unk> from his home in 1975 . The list below contains the names , years of service and biographical notes about the known <unk> of Carre 's since its foundation . The current headmaster is Nick Law , who succeeded Mike Reading in 2008 . 
- Source : Ellis 1954 , pp. 40 – 41 , 43 provides a list of all masters , and a record of their education , up to 1954 . He notes that W. H. T. Walker and J. H. <unk> acted as headmaster between appointments in the 1940s . <unk> from 1954 to 2004 are recorded in <unk> & Hoare 2003 , pp. 154 , 159 , 160 , 171 , 179 . 
- 
- = = Notable former pupils = = 
- 
- Carre 's has produced a number of notable alumni in a range of fields and professions . In politics , this includes Sir Robert <unk> ( 1872 – 1954 ) , Member of Parliament and Chairman of Kesteven County Council , and his brother Samuel ( 1870 – 1942 ) , also a Member of Parliament and businessman . The diplomat Peter Bateman ( b . 1955 ) , who served as the British Ambassador to Bolivia , <unk> and Azerbaijan , was also a pupil at the school . <unk> in the military include Air Marshal Barry North ( b . 1959 ) and Captain George Baldwin CBE DSO ( 1921 – 2005 ) , who served in World War II and as Director of Naval Air Warfare in the mid @-@ 1960s . The lawyer and <unk> John Austin ( 1613 – 1669 ) was educated at Carre 's , along with the Royalist poet Thomas <unk> ( 1632 – <unk> ) and the non @-@ conformist clergyman Andrew <unk> FRS ( 1725 – 1795 ) . Science is represented by the chemist Kenneth Wade FRS ( 1932 – 2014 ) , a professor at Durham University and the forensic pathologist Iain West ( 1944 – 2001 ) . In sports , the school has produced at least two professional footballers : Paul Holland ( b . 1973 ) , who played for Mansfield Town , Sheffield United , Chesterfield and Bristol City , and Mark <unk> ( b . 1952 ) , who played for England under <unk> and Leicester City . 
- 
- 
- = Don 't You Wanna Stay = 
- 
- " Don 't You Wanna Stay " is a duet recorded by American singers Jason Aldean and Kelly Clarkson from Aldean 's 2010 album , My Kinda Party . It was also included in the deluxe edition of Clarkson 's 2011 album Stronger . " It was written by Andy Gibson , Paul Jenkins and Jason Sellers . After Aldean and Clarkson performed the song together on the 44th annual Country Music Association Awards on November 10 , 2010 , it was released as the second single due to strong demands of radio stations on the following day . The song contains elements of country and pop , and its lyrics speak of the difficulties of finding and maintaining love . 
- " Don 't You Wanna Stay " was met with generally favorable reviews by critics who considered the song as " a classic power ballad " . Critics also lauded the interesting combination between Aldean 's and Clarkson 's voices . The song enjoyed commercial success in United States , peaking at number one on the Billboard Hot Country Songs and at number 31 on the Billboard Hot 100 . It became Aldean 's fifth number one single and Clarkson 's first number one single on the Billboard Hot Country Songs and was certified double platinum by the Recording Industry Association of America ( RIAA ) . Both Aldean and Clarkson performed the song on several occasions . Footage from the Country Music Association award performance were used to comprise a music video for " Don 't You Wanna Stay " , directed by Paul Miller . The song was nominated for " Best Country Duo / Group Performance " at the 54th Grammy Awards , but lost to " Barton Hollow " by The Civil Wars . 
- 
- = = Background and release = = 
- 
- " Don 't You Wanna Stay " was written by Andy Gibson , Paul Jenkins and Jason Sellers . According to Sellers , they were at Jenkins ' house when they started composing the song . He explained , " We just wanted to write a love song . We had an idea of what we were targeting . We didn ’ t write it as a duet . We wrote it , and Andy thought about recording it . After we got the song written , we played it for Jason . Jason Aldean 's idea for it was to make it a duet . " The song is the first duet that Aldean has recorded . In an interview with <unk> , Aldean revealed that the song was not originally presented to him as a duet . Nevertheless , he and producer Michael Knox thought the lyrics would work for two people if he could find a female artist as a duet partner . Aldean considered a number of female vocalists to record the song with , especially Kelly Clarkson and Carrie Underwood . However , Clarkson remained as his first choice . He explained , 
- " We had the song pitched to us and originally it wasn ’ t a duet . The more I was listening to the thing and learning the song , I realized it set itself up to be a duet if we wanted to go that route . When I called my producer about it he asked who I wanted to sing it with me and I said Kelly Clarkson , but I didn ’ t think we ’ d be able to get her . I ’ ve always been a big fan of hers . I love her voice and I love her style . Long story short , we got her a copy of the song and she loved it . As soon as she started singing , it became obvious it was going to be great . " 
- On November 10 , 2010 , " Don 't You Wanna Stay " was performed live for the first time by Aldean with Clarkson on the 44th Country Music Association Awards . The performance triggered an overwhelming demand from radio stations across the country . As a result , Aldean ’ s label , Broken Bow Records , made the song available on <unk> <unk> . Carson James , the label 's Senior Vice President for Promotion responded , 
- " If there ’ s one thing I ’ ve learned in my years in the record business , it is always give radio what they want [ ... ] Our entire promotion staff has spent all night fielding calls and returning texts about getting this song , so we decided it best to release it on Play <unk> first thing this morning . " 
- The song was later included on the deluxe edition of Clarkson 's fifth studio album , Stronger , in 2011 . 
- 
- = = Composition = = 
- 
- " Don 't You Wanna Stay " is a country pop song with a length of four minutes and sixteen seconds . It incorporates melancholic guitar riff which is accompanied by classical elements such as violin . John Hill of About.com considered the song as a " classic power ballad " , writing " whether you want to call the song country , pop , or something in between doesn 't matter because the chorus is so catchy that it doesn 't matter if you use an electric guitar or a steel guitar . " It is set in common time and has a steady tempo of 72 beats per minute . It is written in the key of G @-@ sharp minor and both Aldean and Clarkson 's vocals span two octaves , from A ♯ 3 to G ♯ 5 . It follows the chord progression G ♯ m – E – B. Bob <unk> of <unk> thought that the structure of the song was interesting with its brief four @-@ line verses and " don 't you wanna stay 's throughout the chorus . As the chorus of the song starts in , the sound of electric and slide guitars are prominent as Aldean and Clarkson sing , " Don 't you wanna hold each other tight / Don 't you wanna fall asleep with me tonight ? " 
- Lyrically , the song narrates the difficulties of finding and maintaining love . <unk> <unk> of Taste of Country contended that the song exemplifies the desire some people have when they want to make the move from a physical relationship to an emotional relationship , which is represented by the song lyrics " Let ’ s take it slow , I don ’ t want to move too fast / I don ’ t wanna just make love , I wanna make love last . " Gibson , one of the writers of the song , stated that the song does not contain a lot of flowery lyrics or sentiment . He added , " It ’ s just straight up what somebody would say to somebody they ’ d want to be with . " 
- 
- = = Critical reception = = 
- 
- The song has received generally positive reviews . Allison Stewart of The Washington Post called the song " great and <unk> " , saying that it sounded like " Bryan Adams teaming with Heart in 1984 for a contribution to some alternate universe <unk> soundtrack . " Blake <unk> of Engine 145 gave the song a <unk> @-@ up , calling the song as " a dramatic affair that demonstrates how pop and country can intersect with each other and offer decent results . " He concluded his review by writing , " " Don 't You Wanna Stay " would be a suitable fit for a <unk> or <unk> album , and , though nowhere near a classic , it ’ s an instant entrant into the " guilty pleasure " category . " Reviewing the album for Billboard , Gary <unk> described the song as being part of an " array of such <unk> , emotionally ambivalent goodbye songs " . Bobby <unk> of <unk> gave the song three @-@ and @-@ a @-@ half stars out of five and praised both Aldean and Clarkson 's fine voice , " making for an interesting combination : Jason 's rough , slightly nasal voice against Kelly 's cleaner , forceful tone . " In a different perspective , Kevin John Coyne of Country Universe graded the production of the song as " C " , <unk> the song for its musical element , writing " the sheer volume of noise that invades the track with the first chorus takes us straight into Monster <unk> territory . " He concluded his review by emphasizing that " this isn ’ t country music . It just isn ’ t . " On March 5 , 2013 Billboard ranked the song # 40 in its list of Top 100 American Idol Hits of All Time . 
- 
- = = = Awards and nomination = = = 
- 
- At the 2011 <unk> Music Awards , " Don 't You Wanna Stay " was nominated for " <unk> Video of the Year , " but lost to Justin <unk> 's " That Should Be Me " featuring Rascal Flatts ( 2010 ) . " Don 't You Wanna Stay " received nominations for two awards at the <unk> Country Music Association Awards . The song received a nomination for the " Musical Event of the Year " and won the award ; it was also nominated for the " Single of the Year " but lost to The Band Perry 's " If I Die Young " ( 2010 ) . At the 54th Grammy Awards , the song received a nomination for " Best Country Duo / Group Performance " but lost to The Civil Wars ' " Barton Hollow " ( 2011 ) . " Don 't You Wanna Stay " won two awards of " Single by a Vocal Collaboration " and " Music Video by a Duo / Group / Collaboration " at the 2011 American Country Awards . The song won the awards of " Single of the Year " and " Vocal Event of the Year " at the 47th Academy of Country Music . 
- 
- = = Commercial performance = = 
- 
- Following the album release of My Kinda Party , " Don 't You Wanna Stay " debuted at number 59 on the Billboard Hot Country Songs as well as at number 93 on the Billboard Hot 100 on the week ending November 20 , 2010 . On its second week on Billboard Hot 100 , the song jumped to number 56 after Aldean and Clarkson performed the song at the Country Music Association awards . The song reached a new peak of number 31 in its 24th week on the chart after Aldean and Clarkson performed the song on American Idol . The song also debuted at number 17 on the Adult Contemporary on the week ending June 25 , 2011 . After impacting Adult Contemporary and Adult Pop Songs charts , the song re @-@ entered Billboard Hot 100 at number 48 in its 27th week on the chart . 
- On the week ending February 27 , 2011 , " Don 't You Wanna Stay " moved to number one on Billboard Hot Country Songs and stayed in the top position for three consecutive weeks . It became Aldean 's fifth number one hit and Clarkson 's first number one hit on the chart . The accomplishment also made Clarkson as the third American Idol contestant to score a number one hit on the Billboard Hot Country Songs . The feat was first accomplished by Josh <unk> and followed by Carrie Underwood . 
- The song became the best @-@ selling country collaboration single in digital history in 2011 , until it was <unk> by Blake Shelton and <unk> <unk> ' " Boys ' Round Here " in 2014 . It is Aldean 's second song to cross the 2 million mark , following " Dirt Road Anthem " in 2011 ; and Clarkson 's fourth song to cross the 2 million mark following " Since U Been Gone " in 2008 , " My Life Would <unk> Without You " in 2009 , and " Stronger ( What <unk> 't Kill You ) " in 2012 . As of June 2014 , the song has sold 2 @,@ <unk> @,@ 000 copies in the US . 
- 
- = = Live performances and usage in media = = 
- 
- On November 10 , 2010 , Aldean performed the song with Clarkson for the first time at the 44th annual Country Music Association Awards at Bridgestone Arena in Nashville , Tennessee . Despite receiving a standing ovation , their performance of the song in that event was graded as a " D " in the Los Angeles Times , noting that the song is " a ballad that turned into some oddly orchestrated ' 80s hair metal tune . It ’ s an easy slam , but this is " American Idol " <unk> . " Aldean and Clarkson also performed the song on several other occasions . They sang the song on The Ellen <unk> Show on February 22 , 2011 . On April 14 , 2011 , they performed the song live on the tenth season of American Idol during the Top 8 results show . They later performed the song live during Country Music Association festival in Nashville , Tennessee on June 9 , 2011 . Aldean also performed the song on all venues of his concert tour , the My Kinda Party Tour , which started on January 21 , 2011 . During each performance , Clarkson is seen on a big screen behind Aldean , singing her part of the duet . He explained , 
- " We decided to go in and film her doing her thing [ ... ] so even though she wasn 't at the show , she could still be a part of the show . As big as that song is getting for us right now , it was definitely a song that we thought we had to have in the show . She cut a thing for us to use , and I cut a thing for her to use in her show if she wants to do that . It 's a cool way to have her be a part of the show even though she 's not going to be there every night . " 
- On February 12 , 2012 , both Aldean and Clarkson performed the song in front of a stage outfitted with giant clock <unk> at the 54th Grammy Awards . Todd <unk> of Los Angeles Times thought that " the duo looked like they were on the set of " Hugo " . " As their performance neared its end , Aldean 's microphone suddenly went out . Sarah <unk> of Great American Country felt that Aldean was professional in handling the technical problem by continuing to sing the song with Clarkson . However , Natalie <unk> , the lead vocalist of the Dixie Chicks , lambasted Aldean 's performance in her Twitter account , writing " Well the good thing about his <unk> going out is suddenly the song got a lot more in tune . " " Don 't You Wanna Stay " is also performed by Clarkson in her fourth headlining tour , the Stronger Tour ( 2012 ) . While Clarkson is performing the song , Aldean is seen on a big screen , singing his part of the duet . She also performed the song as a duet with Blake Shelton while touring at <unk> Theatre L.A. Live , Los Angeles . The song 's accompanying music video is composed of the live footage from the Country Music Association awards which was directed by Paul Miller . 
- " Don 't You Wanna Stay " was covered by Colton Dixon and <unk> <unk> in the eleventh season of American Idol . Natalie Finn of E ! gave a mixed review of the pair 's performance , writing " <unk> handled Kelly Clarkson better than Colton played Jason Aldean on " Don 't You Wanna Stay , " but she 's the country girl , so it made sense . " Brian Mansfield of USA Today felt that the song was out of Dixon 's comfort zone and a little out of <unk> 's range . Gil Kaufman of MTV remarked that the chemistry between the pair was more like cold fusion . Jennifer Still of Digital Spy said the performance " isn 't anything incredible " . 
- 
- = = Track listing = = 
- 
- Digital download 
- " Don 't You Wanna Stay " ( with Kelly Clarkson ) – 4 : 16 
- 
- = = Charts and certifications = = 
- 
- 
- 
- = Tropical Storm Domoina = 
- 
- Severe Tropical Storm Domoina in 1984 caused 100 year floods in South Africa and record rainfall in Swaziland . The fourth named storm of the season , Domoina developed on January 16 off the northeast coast of Madagascar . With a ridge to the north , the storm tracked generally westward and later southwestward . On January 21 , Domoina struck eastern Madagascar , the third storm in six weeks to affect the nation ; collectively , the storms caused 42 deaths and $ 25 million in damage ( 1984 USD ) . After crossing the country , Domoina strengthened in the Mozambique Channel to peak 10 minute sustained winds of 95 km / h ( 60 mph ) . On January 28 , the storm made landfall in southern Mozambique , and slowly weakened over land . Domoina crossed into Swaziland and later eastern South Africa before dissipating on February 2 . 
- In Mozambique , Domoina dropped heavy rainfall in the capital Maputo that accounted for 40 % of the annual total . Floods in the country destroyed over 50 small dams and left widespread crop damage just before the summer harvest . Later , the rains caused the worst flooding in over 20 years in Swaziland , which damaged or destroyed more than 100 bridges . <unk> transport left areas isolated for several days . In South Africa , rainfall peaked at 950 mm ( 37 in ) , which flooded 29 river basins , notably the Pongola River which altered its course after the storm . Flooding caused the Pongolapoort Dam to reach 87 % of its capacity ; when waters were released to maintain the structural integrity , additional flooding occurred in Mozambique , forcing thousands to evacuate . Throughout the region , Domoina caused widespread flooding that damaged houses , roads , and crops , leaving about $ 199 million in damage . There were 242 deaths in southeastern Africa . 
- 
- = = Meteorological history = = 
- 
- In January and February 1984 , conditions were favorable for tropical cyclogenesis in the southwest Indian Ocean , including warmer than normal sea surface temperatures and an active monsoon trough . On January 16 , a spiral area of convection persisted off the northeast coast of Madagascar , associated with the <unk> convergence zone . That day , it organized enough to warrant a satellite @-@ based Dvorak rating of <unk> , prompting the Réunion Meteorological Service to name it Domoina . Around that time , <unk> @-@ France ( MFR ) estimated winds of about 65 km / h ( 40 mph ) . Domoina initially tracked to the west @-@ northwest , passing near <unk> Island on January 18 . Around that time , the storm had begun moving to the southwest , and MFR estimated that it weakened to tropical depression status . On January 19 , the Joint Typhoon Warning Center ( JTWC ) began warning on Domoina , designating it Tropical Cyclone <unk> . The same day , MFR again upgraded Domoina into a moderate tropical storm . On January 21 , the storm made landfall just south of <unk> in southeastern Madagascar . 
- While crossing Madagascar on a westward trajectory , Domonia weakened ; JTWC estimated the winds decreased to 55 km / h ( 35 mph ) , after earlier estimating winds of 95 km / h ( 60 mph ) before landfall . On January 23 , the storm emerged into the Mozambique Channel near Belo , and due to a ridge to the north , it resumed its southwest motion . Domoina executed two small loops off the western coast of Madagascar while progressing generally southwestward . On January 25 , MFR estimated that Domonia attained peak 10 minute sustained winds of 95 km / h ( 60 mph ) near Europa Island ; this made it a severe tropical storm . Two days later , the JTWC estimated peak 10 minute winds of 100 km / h ( 65 mph ) . Early on January 28 , Domonia made a second landfall on southeastern Mozambique near peak intensity . It slowly weakened over land while moving across southern Mozambique . The JTWC discontinued advisories on January 29 when the storm was near the border of Mozambique and Swaziland . The next day , Domonia crossed into Swaziland and subsequently into eastern South Africa , weakening into a tropical depression while passing near Durban . At around that time , the system was dissipating , although MFR continued tracking Domonia until February 2 , when it dissipated just offshore the east coast of South Africa . 
- 
- = = Impact = = 
- 
- Throughout its path , Domoina left thousands of people homeless , and caused widespread flooding due to drawing moisture from the Indian Ocean and the Mozambique channel . The rains led to rivers <unk> their banks , which isolated tens of thousands of people . In the months before Domoina struck , dry conditions persisted across southeastern Africa . 
- Crossing Madagascar as a moderate tropical storm , Domoina dropped rainfall along its path , reaching 98 @.@ 8 mm ( 3 @.@ 89 in ) in <unk> on the east coast and 166 @.@ 8 mm ( 6 @.@ 57 in ) in <unk> along the west coast , both over a period of 24 hours . In the latter city , a station recorded winds of 100 km / h ( 62 mph ) . The storm caused heavy damage in <unk> , which was later affected by Cyclone <unk> . Domoina was the third storm to strike the country in a six @-@ week period , after cyclones <unk> and <unk> . The three storms collectively caused 42 deaths and $ 25 million in damage , much of it from crop damage . 
- As Domoina made its final landfall in Mozambique , it dropped heavy rainfall reaching 430 mm ( 17 in ) in the town of <unk> over a five @-@ day period . Rainfall in the capital Maputo , reaching 300 mm ( 12 in ) over two days , was about 40 % of the annual total . After flooding occurred further south in South Africa , waters were released from the Pongolapoort Dam without warning . This caused many farmers to drown in southern Mozambique . Officials later advised residents along the Maputo River to evacuate to higher grounds , and thousands had to leave their houses . The storm flooded the Maputo , <unk> , <unk> river basins , causing widespread power outages . The storm left the capital Maputo without clean water for several days after a pumping station was damaged , and the main harbor in the city was closed . Also in the city , the storm downed hundreds of trees , wrecked roofs , and damaged houses ; about 10 @,@ 000 people were left homeless nationwide . The storm damaged 28 pumping stations nationwide and destroyed over 50 small dams . Floods affected the railroad connecting Maputo to South Africa , disrupted the construction of a dam , and damaged portions of a bridge near <unk> . Transport was disrupted in the northern and southern portion of the country . There was widespread crop damage in Mozambique , affecting 350 @,@ 000 farmers , and flooding about 250 @,@ 000 ha ( 620 @,@ 000 acres ) of fields . After an extended drought , many farmers had moved closer to river beds , making their fields more vulnerable . About 119 @,@ 000 tons of fruits , vegetables , and other crops were wrecked , consisting of much of the summer crop , and about 5 @,@ 000 cattle died . About 49 @,@ 000 people lost everything they owned . In the country , the storm caused 109 fatalities , and damage was estimated at $ 75 million . 
- Later , the storm dropped heavy rainfall in Swaziland , reaching <unk> mm ( 35 @.@ 7 in ) at <unk> Peak ; there , rainfall reached <unk> mm ( 24 @.@ 2 in ) in a one @-@ day period . These were the heaviest rainfall totals on record in the country . Described as the worst flooding in over 20 years , the precipitation increased levels along most rivers in the country , some of which rose 30 m ( 98 ft ) in a few hours . The floods washed out or damaged over 100 bridges , and two railways had cuts in their lines . The floods closed at least 20 major roads and the country 's primary airport . Most schools nationwide were also closed during the storm . There were initial difficulties in determining the extent of the damage due to cut communications and disrupted transportation . For several days , southeastern Swaziland was only accessible by air travel , while rural parts of the country lost access to fresh water . Thousands of livestock died during the storm , and thousands of acres of croplands were flooded . About 10 @,@ 000 citrus trees were destroyed , and crop damage was estimated at $ 2 @.@ 5 million . About 500 people were left homeless in the small nation , after many homes were damaged or destroyed . Schools and health clinics were also damaged . The storm 's high winds knocked down trees and power lines , leaving power outages . Overall damage was estimated at $ 54 million , of which $ 47 @.@ 5 million from infrastructure damage , and Domoina killed 73 people in the country . 
- While Domoina was moving through South Africa , it drew an area of moisture from the northeast , which produced heavy rainfall that peaked at 950 mm ( 37 in ) between Richards Bay and <unk> Bay . <unk> of over 700 mm ( 28 in ) were reported along the upper Umfolozi , <unk> , Pongola and middle <unk> and <unk> rivers , and along the upper and lower <unk> river . Precipitation spread as far south as Durban , but did not penetrate far into the center of the country . An area of about 107 @,@ 000 km2 ( 41 @,@ 000 sq mi ) received 370 mm ( 15 in ) of rainfall . Along the Umfolozi River , a discharge rate of 16 @,@ 000 m ³ / s ( 565 @,@ 000 ft ³ / s ) was recorded , which was three times the rate of a 100 year flood . The river <unk> , or changed its course , near where it met with the <unk> River . High rains in the mountains caused the largest flood to date along the Pongola River . The floodplain downstream of the Pongolapoort Dam was inundated to where the Pongola met the <unk> River , which filled many pans – dry lakes – in the region . Along 29 river locations in eastern South Africa , river heights were estimated to have been 1 in 50 year events . The river flooding moved sediment along many banks , and in one location the sediment reached 10 km ( 6 @.@ 2 mi ) in length . Due to the widespread flooding and the <unk> of the worst affected areas , there were minimal measurements on the river flow along the Pongola , although above the Pongolapoort dam , levels reached 13 @,@ 000 m ³ / s ( 460 @,@ 000 ft ³ / s ) , which were 18 times higher than the previous record highest . There had been a planned release of water from the Pongolapoort Dam in March 1984 to provide adequate water to the floodplain , but Domoina prevented this from occurring . The dam had its highest hydrology on January 31 and reached 87 % of its capacity . Waters from the dam were released on February 2 to prevent the dam from exceeding capacity . With the future threat of Cyclone <unk> , dam levels continued to drop until returning to normal by February 16 , despite requests to hold the water to prevent further crop damage . 
- Near the South Africa border with Swaziland , flooding stranded about 80 @,@ 000 people on tribal lands . One road in the country was converted into a makeshift landing strip to allow helicopters and planes to drop off emergency supplies . A period of heavy rain flooded Umfolozi River , which destroyed a rail bridge near <unk> and a bridge crossing highway <unk> . The floods were so strong that they washed a boat from Lake St. Lucia to a point 16 km ( 9 @.@ 9 mi ) away . At the lake , the floods washed away a dredge and severely damaged a nearly @-@ finished canal from the lake to the Umfolozi River . Widespread crop damage occurred along the Umfolozi river plains after being covered by up to 1 m ( 3 @.@ 3 ft ) of sediment . The South African Weather Bureau considered Domoina as the " first tropical cyclone in recent history to have caused flooding and extensive damage . " Nationwide , the storm caused 60 deaths and damaged the properties of 500 @,@ 000 people , causing <unk> million ( 1984 <unk> , $ 70 million 1984 USD ) . 
- 
- = = Aftermath = = 
- 
- In Mozambique , workers assisted people in moving to higher grounds following flooding . Members of the Mozambique Red Cross helped distribute food and clothing to the affected residents , and planes helped drop off supplies to residents in isolated areas . 
- On January 31 , the government of Swaziland declared a state of emergency and requested assistance from the international community . South Africa provided two helicopters to the country to survey the affected areas . Various countries and United Nations agencies provided about $ 1 @.@ 01 million in cash and supplies to Swaziland . The United Nations Department of Humanitarian Affairs provided $ 20 @,@ 000 . The European Economic Community donated about $ 80 @,@ 000 to purchase tents and blankets . The Lutheran World Federation donated $ 20 @,@ 000 in cash , along with generators and blankets , while World Vision International sent $ 10 @,@ 000 in cash . Within a week , workers reopened most major roads to travel , and by February 24 , most roads were reopened . Workers also quickly restored the downed power lines . Relief items were distributed by both air and road in the weeks following the storm , coordinated by the Swaziland Red Cross and assisted by volunteer organizations . In part due to Domoina as well as the previously occurring drought , the economy of Swaziland <unk> through 1985 . 
- Following the storm in South Africa , workers restored the original course of the Umfolozi River after it had moved . Officials later purchased a new dredge to remove sediment from Lake St. Lucia , and the canal connecting the lake to the Umfolozi River was later finished . Local governments coordinated relief efforts in the country , including delivering food and providing shelter for those who lost their homes . The South African Red Cross provided food to storm victims , many of whom were beneficiaries of the food program during the extended drought . The South African government declared KwaZulu Natal as a disaster areas . The country 's military provided 25 helicopters to rescue flood victims and donated 3 @,@ 000 tents . The government later authorized $ 85 million to fund repairing damaged rails and roads . The American government donated $ 100 @,@ 000 to the country , mostly to purchase supplies . West Germany also donated about $ 231 @,@ 000 , mostly for the feeding program . 
- 
- 
- = Tales of Destiny 2 = 
- 
- Tales of Destiny 2 ( Japanese : <unk> <unk> <unk> 2 , Hepburn : <unk> <unk> <unk> <unk> ) is a Japanese role @-@ playing video game , co @-@ developed by Wolfteam and <unk> Japan , and published by Namco . It is the fourth main entry in the Tales series of video games , and a direct sequel to 1997 's Tales of Destiny . It released on PlayStation 2 ( PS2 ) in November 2002 in Japan , March 2003 in South Korea and August 2003 in China . The overseas versions were published by Sony Computer Entertainment . An updated port for the PlayStation Portable ( PSP ) , developed by Alfa System , was released in February 2007 , and March of the same year in South Korea . This version was published by Namco Bandai Games . Neither version has received a western release . 
- The story , set eighteen years after Destiny , follows Kyle Dunamis , the son of the previous game 's protagonists Stahn <unk> and Rutee <unk> . Shortly after meeting a mysterious girl named Reala while trying to save Rutee 's orphanage from bankruptcy , Kyle is drawn into conflict with Barbatos , a cruel warrior responsible for killing Stahn , and the machinations of <unk> , a religious leader seeking to bring peace to mankind . The gameplay uses two @-@ dimensional character sprites and backgrounds , and the battle system is a revamped version of the series ' trademark Linear Motion Battle System . 
- Development began after the release of Tales of Eternia in 2000 , taking approximately two years to complete , and was the last Tales game to be developed by the original Wolfteam before it became Namco Tales Studio . The scenario was handled by Japanese writing company <unk> , the characters were designed by <unk> Inomata , and the music was composed by <unk> Sakuraba and Shinji Tamura . The PSP port was made after a similar port of Eternia met with commercial success . It was a highly anticipated game , and Namco promoted it heavily in the months prior to release . The game sold over 700 @,@ 000 units by January 2003 , and received critical acclaim from both Japanese and western critics . 
- 
- = = Gameplay = = 
- 
- Tales of Destiny 2 is a role @-@ playing video game featuring two @-@ dimensional ( 2D ) sprite characters navigating both 2D environments such as <unk> and towns , and a three @-@ dimensional overworld . The overworld features environmental effect such as fog . In towns found across the world , non @-@ playable characters provide both information relevant to the story and world , and provide items and equipment through shops ; merchants accept the Tales series currency , <unk> , which can be obtained from battle or treasure chests on the dungeon and town maps . 
- Throughout the game , visiting certain individuals in towns will activate optional events and side @-@ quests . <unk> , sewers , forests and story @-@ centric locations serve as dungeon <unk> ; these areas are scattered with treasure chests containing rare items , some of which cannot be purchased in stores . <unk> frequently contain puzzles and <unk> , such as boxes that must be <unk> to form a bridge , which must be cleared to advance ; the " <unk> 's Ring " , a relic that shoots tiny plumes of fire , often plays a central role in puzzle resolution , along with the " <unk> 's <unk> " , a tool that reveals hidden objects . Aspects of characters such as their current experience level , equipment and money levels , party organization , and assigned abilities . <unk> , a recurring series mechanic , is present : characters learn and cook various recipes using a selection of six ingredients which grant <unk> upon them such as restoring health . <unk> voiced conversations between characters called <unk> can be activated when they appear in random locations or after story events . Mini @-@ games within the world include acting as a <unk> in a restaurant , and battle arenas where players fight against random enemies . 
- The game 's battle system is the series ' trademark Linear Motion Battle System ( <unk> ) , which places up to four playable characters on a 2D plain against enemies in real @-@ time combat similar to a beat ' em up . The version used in Destiny 2 is called the " Trust and Tactical <unk> " . When entering battle , the camera follows characters , <unk> in and out depending on their distance from an enemy . During battle , one character is controlled at any one time , with the others being governed by the game 's artificial intelligence ( AI ) : the AI can be customized to behave in various ways , such as adjusting their aggression level , and balancing magical and physical attacks . In addition to real @-@ time commands given in battle , the player can issue commands to all characters by pulling up the battle menu and issuing commands . A character 's performance in battle is governed by their current level of Spirit Points ( <unk> ) : the more <unk> a character has , the higher their defensive and offensive abilities . A character 's health is represented by Health Points ( HP ) . Three types of attack are available and activated progressively depending on the number of hits an enemy has taken : a standard attack , named attacks called <unk> , and special high @-@ damage attacks called Mystic <unk> . At the end of each battle , its performance is assessed an given a Grade , which can sometimes trigger a bonus such as learning new skills . Some bonuses are also carried over into the next playthrough . In addition to the single @-@ player functions , a local multiplayer option is available , supporting up to four players : when additional controllers with players are connected to the system , AI functions for selected characters are disabled , switching to manual control . An auto @-@ battle option , which has the game 's AI controlling all characters during a fight , is also available . 
- 
- = = Synopsis = = 
- 
- 
- = = = Setting = = = 
- 
- Destiny 2 is set on an unnamed world that was struck by a comet 1000 years before , triggering a prolonged winter . Desperate for heat and energy , the people used a type of <unk> brought by the comet called Lens to create a floating city called Dycroft . The people of Dycroft eventually dominated the people below , which led to a conflict between the two realms known as the War of Heaven and Earth . Eventually , a team of <unk> Dycroft scientists developed sentient weapons called Swordians that turned the tide of war in the surface people 's favor . This conflict <unk> eighteen years prior during the events of Tales of Destiny . The Swordians found new masters to wield them against Dycroft 's vengeful ruler , including Stahn <unk> , Rutee <unk> and Leon Magnus . Stahn 's group succeeds in restoring peace , but the world was damaged by Dycroft 's weapons , and Leon was killed after he betrayed the group . Destiny 2 begins eighteen years after the events of Tales of Destiny . 
- 
- = = = Characters = = = 
- 
- Kyle Dunamis ( <unk> ・ <unk> , <unk> <unk> ) is the main protagonist of Destiny 2 . Kyle is very much like his father , Stahn , although Kyle remembers little of his father . His defining personality trait is an aversion towards <unk> and boredom , always seeking out new things to try . Kyle is voiced by Jun <unk> . 
- Reala ( <unk> , <unk> ) is the main female protagonist , appearing suddenly and holding an air of mystery . While she bears an overly @-@ strong sense of responsibility , she is also bright and highly <unk> . She is one of the two avatars of the goddess Fortuna ( <unk> , <unk> ) . Reala is voiced by <unk> <unk> . 
- Loni Dunamis ( <unk> ・ <unk> , <unk> <unk> ) is a young man raised alongside Kyle , being considered a brother by him . A member of the Atamoni <unk> , the military branch of the Order of Atamoni , he has a firm and steady personality that <unk> against Kyle 's <unk> . Loni is voiced by <unk> <unk> . 
- Judas ( <unk> , <unk> ) is a mysterious swordsman who appears before Kyle when the latter is in a pinch . A genius swordsman , he wears a beast skull as a mask to hide his face . His true identity is that of Leon Magnus . Judas is voiced by <unk> <unk> . 
- Nanaly Fletch ( <unk> ・ <unk> , <unk> <unk> ) is a master archer who opposes the Order of Atamoni . In direct contrast to the Order , she believes that happiness should be won by people rather than given to them . Nanaly is voiced by Tomoko <unk> . 
- Harold <unk> ( <unk> ・ <unk> , <unk> <unk> ) is a famous scientist , and the original creator of the Swordians . Something of a mad scientist , she shares Kyle 's love of adventure and has a passionate curiosity about the world . Harold is voiced by Akiko <unk> . 
- Elrane ( <unk> , <unk> ) is the main antagonist of Destiny 2 . Regarded as a living goddess due to her powers , she has an <unk> <unk> demeanor and <unk> bringing happiness to all mankind . She is one of the two avatars of the goddess Fortuna . Elrane is voiced by <unk> <unk> . 
- Barbatos <unk> ( <unk> ・ <unk> , <unk> <unk> ) is one of the primary antagonists of Destiny 2 . A veteran of the War of Heaven and Earth , he was forgotten by history and consequently holds a burning desire to be recognized . His chief traits are an intense desire to live and a <unk> disregard for others . Barbatos is voiced by <unk> <unk> . 
- 
- = = = Plot = = = 
- 
- Kyle Dunamis , the adventurous son of Stahn and Rutee , lives at an orphanage run by Rutee . With the orphanage on the brink of bankruptcy , Kyle <unk> his mother to go on a quest to search for funds , together with his best friend Loni . Finding a giant Lens , a mysterious girl named Reala emerges from it , claiming to be in search of a hero . Believing that he should become a hero like his parents , he follows Reala so he can prove himself as such . Finding the Lens missing , officials from the Order of Atamoni arrest Kyle and <unk> for its theft . They escape with the help of the masked swordsman Judas . After this , Kyle becomes embroiled in the attempts by a man named Barbatos <unk> to kill those who accompanied his parents , eventually learning that Barbatos also killed Stahn when Kyle was five . During his quest , a series of Lens <unk> is orchestrated by Elrane , the Holy Woman of the Order of Atamoni who is capable of performing miracles using Lens and seeks to bring happiness to the world through uniting it under a single religion . 
- During a great theft of Lens , Elrane attacks the group directly , sending them ten years into the future . In this period , the world is beset by conflict between the Order of Atamoni and factions wanting independence from its control . During their travels there , they are joined by Nanaly Fletch . While in this time period , Reala becomes conflicted about who she is , and the group encounters a figure known as the Goddess Fortuna . During this encounter , it is revealed that Elrane and Reala are avatars of Fortuna , designed to save the world and bring happiness to humanity in different ways . Due to her unstable emotions , Reala accidentally transports Nanaly into their time . Feeling guilty because of this , Reala confronts Elrane alone and is captured . Traveling to the ship where the stolen Lens is stored , the group confront Barbatos and Elrane , successfully defeating them and saving Reala . During this confrontation , they learn that " Judas " is in fact the resurrected Leon , who was brought back to life by Elrane just as Barbatos was , but rebelled against her when he knew her full plan . 
- However , their efforts result in the destruction of the ship , and Reala uses the energy of the Lens to send them through time again . They appear in an alternate timeline where the War of Heaven and Earth was won by the underground dwellers , and Barbatos and Elrane are hailed as <unk> . Traveling back to the time of the War , when the initial interference was caused , they ally with Harold <unk> to return history to its proper state . While they are successful and Barbatos is <unk> , Elrane continues to <unk> history in her favor . <unk> her one last time , she is defeated , then they are confronted by Fortuna . <unk> her , the group realize that the only way to correct the distorted timeline is to destroy the Lens that forms Fortuna 's life source , which will mean Reala will be erased from history . When Reala comes to terms with this and gives her blessing , Kyle destroys Fortuna 's Lens , which <unk> all the changes wrought by her agents and returns the timeline to its original state , sending everyone back to their original times and conditions . In the corrected timeline , Kyle , now raised and trained by both Stahn and Rutee , goes on a journey to the temple where he first met Reala . While her Lens is missing , Kyle 's strong will succeeds in bringing Reala back into existence and restoring his memories of her . 
- 
- = = Development = = 
- 
- Tales of Destiny 2 was co @-@ developed by Wolfteam and <unk> Japan . Development began after work had finished on Tales of Eternia , taking roughly two years to complete . During its early development , the staff consisted of thirty people : when in full development , a team of between sixty and a hundred worked on it . It was developed under the working title Tales of X. After Eternia was completed , the development team considered what to do next , whether a new standalone game or a sequel . As Destiny had a large amount of lore created for it and there were story possibilities for a next generation of characters , it was decided to make a sequel to Destiny . This would be the first direct sequel in the Tales series . Because of the state of the world as it would have evolved after the events of Destiny , it was decided to set the events eighteen years after them and focus on the son of Destiny 's protagonist . The key themes for the story were " fate " and " happiness " . For the story , the team wanted to effectively portray Kyle 's journey to becoming a hero . For this , they drew on themes of " learning from the teacher " as seen in films such as Star Wars and Raiders of the Lost Ark . To create the atmosphere , the team worked at balancing elements of reality and fantasy . The story and script was handled by Japanese scenario company <unk> . Two separate scripts were created for the main story and additional elements such as <unk> . Both scripts were quite large . 
- Character designs were created by <unk> Inomata . A key element of the designs was the variation of time periods many characters came from . To make the character sprites as close as possible to the original artwork , the team took Inomata 's artwork and turned them into 2D " dot @-@ by @-@ dot " sprite pictures . This gave the sprites roughly four times the detail of most environments . They then built the world around the sprites . The sprite details sometimes caused problems , as they would appear larger than the environments when the camera <unk> in . The animated cutscenes were created by anime company Production I.G. The game 's opening was the longest ever created for a Tales game up to that point . While many contemporary PS2 role @-@ playing games were being developed around three @-@ dimensional characters , Destiny 2 used two @-@ dimensional sprites for its characters . Part of the reason behind this was that the development team wanted to create the " ultimate 2D RPG " . As opposed to previous Tales games , where gameplay development was focused on the battle system , the development leads requested that the gameplay should be " waste @-@ free and strategic " . The Grade system and new skill <unk> elements , later staples of the series , were also developed . The game 's artificial intelligence was markedly improved , the combat system was made both more elaborate and more user @-@ friendly , and health and magic systems were adjusted . The PlayStation 2 hardware enabled the expansion of mini @-@ games , the improvement of the game 's graphics , and the incorporation of advanced interactive elements on the world map . 
- 
- = = = Audio = = = 
- 
- The music was composed by regular series composers <unk> Sakuraba and Shinji Tamura . Sakuraba primarily worked on the battle themes and arrangements of previous themes , while Tamura handled selected boss battle tracks and created Reala 's theme . They were able to create more tracks with greater detail to them due to the expanded storage capacities of the PS2 . As with previous games , a licensed theme song was created by a Japanese artist for the game 's opening . For Destiny 2 , the theme song was " Key to My Heart " from the album Fairy Tale , by Japanese singer @-@ songwriter Mai Kuraki . Its lyrics were designed to " express the world " of Destiny 2 . <unk> of " Key to My Heart " were featured in the soundtrack . Unlike previous Tales games , where voice work was recorded out of order , the voice work was recorded in order of the game 's events to add dramatic weight to the performances . This consequently created difficulties with scheduling and put an added strain on the actors . 
- 
- = = Release = = 
- 
- Destiny 2 was announced in February 2002 at a special conference about future developments and games for the platform . It was the first Tales title to receive a numbered designation in Japan . As with previous Tales games , it featured a characteristic genre name : " RPG to Release Destiny " ( <unk> , <unk> <unk> <unk> RPG ) . To promote the game , Namco created a themed PlayStation 2 memory card . In addition , a special competition was organized where the top @-@ tier prizes were copies of the script books for Destiny 2 autographed by the main characters ' voice actors . As a pre @-@ order bonus , a special limited edition DVD featuring interviews with the voice cast was created . In addition , a downloadable ring tone based on the theme song and screen displays featuring the main characters was created for mobile phones of the time and released in 2003 . The game was released on November 28 , 2002 in Japan . It was celebrated with an official launch event , featuring interviews with the production staff and cast . Destiny 2 would be the last Tales game developed by Wolfteam prior to its acquisition by Namco in 2003 and <unk> as Namco Tales Studio . 
- Destiny 2 was later ported to the PSP . Development began in 2005 , after the commercial success of Eternia 's PSP port . The port was developed by Alfa System , a frequent collaborator with the Tales team on spin @-@ off titles . The project was directed by <unk> <unk> , who had worked on Destiny 2 and became the director of the <unk> port of Tales of <unk> . While the earlier port of Destiny made significant changes , the Destiny 2 port was meant to preserve and add onto the content of the original . Some of the adjustments included minor tweaks to gameplay , adjustments to fit the PSP 's control layout , and adjusting the graphics from 4 : 3 to 16 : 9 screen ratio . A new dungeon featuring added story elements was also created . It was released on February 15 , 2007 . 
- Outside Japan , Destiny 2 was released in China and South Korea by Sony Computer Entertainment in 2003 : the Korean version was released on March 27 , and the Chinese version was released on August 14 . The game was going to be part of a world tour by Sony Computer Entertainment to promote the next generation of role @-@ playing games , but the tensions between America and Iraq at the time and the consequent risks of a terrorist attack caused them to cancel the trip . Asked at the launch event whether an overseas version of the game was being developed , producer Makoto <unk> said he was " not certain " . The PSP port was released in South Korea by Namco Bandai Games 's local branch on March 5 , 2007 . Neither the original nor the port has been released in the west , making it one of three mainline Tales titles to remain exclusive to Japan . 
- 
- = = Reception = = 
- 
- Namco had high expectations for Destiny 2 , anticipating sales of 600 @,@ 000 units . During its debut week , Destiny 2 reached the top of Japanese gaming sales charts : according to different sources , it sold between just over <unk> @,@ 000 ( Famitsu ) and under <unk> @,@ 000 ( Dengeki ) units . For the next three weeks , it remained in second place on the charts behind <unk> Ruby and <unk> , accumulating sales of over 603 @,@ 000 . By January 2003 , the game had sold over 702 @,@ 000 units , becoming the eighth best @-@ selling game within that period . The PlayStation 2 version of Tales of Destiny 2 has shipped 977 @,@ 000 copies worldwide as of December 2007 , being the second most successful title in the series at the time . The PSP version sold 73 @,@ 000 copies in its first week , ranking second in weekly video games sales . By the following week , it had dropped to # 25 . By the end of 2007 , the game had sold just under 115 @,@ 000 units , reached <unk> place in the five hundred top @-@ selling games of the year . In South Korea , the game reached # 10 in the country 's gaming sales charts . Worldwide shipments of the port as recorded by Namco have reached 129 @,@ 000 units . 
- Famitsu Weekly found the story enjoyable , though noted that those who had played Destiny would get more enjoyment out of it , and praised the voice acting . They also found the gameplay and pacing enjoyable , positively noting the option to auto @-@ battle . The game ranked as the <unk> all @-@ time favorite game in a 2006 Famitsu readers poll . Japanese website Game <unk> Watch found the story impressive , citing its use of time travel and the continued use of Destiny 's world and lore . The gameplay , mini @-@ games and <unk> options was also generally praised , with the reviewer recommending the title to players of the series . <unk> 's <unk> Lee was also positive , praising the gameplay despite the very high encounter rate , and was pleased that an auto @-@ battle option was included . One point that received a more mixed response was the game 's music , with the exception of the opening song . In a preview for IGN , David Smith was generally positive about the game . While he disliked the art style and its low @-@ tech look compared to its hardware , he found the gameplay enjoyable and the opening parts of the story fairly relatable . He ended by hoping that Namco would put the effort into <unk> the title . 
- 
- = = = Additional media = = = 
- 
- Multiple print adaptations have been made of Destiny 2 : these include one three @-@ volume comic anthology , a serialized comic adaptation originally released through Square Enix 's <unk> Comics and later collected into five volumes , and an eight @-@ volume <unk> comic , and a second single @-@ edition <unk> . Novel adaptations include a novel focusing on Nanaly called Tales of Destiny 2 : The Amber Wind , and <unk> light novels that told segments of the story from the point of view of different characters , particularly Judas . An illustration book featuring Inomata 's artwork for the game was released in March 2005 . A five @-@ part CD Drama adaptation was also developed , following the events of the game . The five volumes were released between April and August 2003 , under the umbrella titled Tales of Destiny 2 Drama CD . 
- 
- 
- = In Bloom = 
- 
- For the 2013 film of the same name , see In Bloom ( 2013 film ) 
- " In Bloom " is a song by American rock band Nirvana . Written by frontman Kurt Cobain , the song addresses people outside of the underground music community who did not understand the band 's message . 
- Nirvana made its first music video for an early version of " In Bloom " in 1990 ; however the song did not appear on a commercial release until the release of the group 's second album , Nevermind , in 1991 . " In Bloom " became the fourth and final single from the album in late 1992 . It was accompanied by a new music video which parodied musical performances of 1960s variety shows . The music video for " In Bloom " won Best Alternative Video at the MTV Video Music Awards in 1993 . 
- 
- = = Background and recording = = 
- 
- Nirvana first played the song the night before it was <unk> . <unk> <unk> Novoselic recalled that it " originally sounded like a Bad Brains song . Then Kurt turned it into a pop song " . Cobain went home and reworked the song , playing the revised version of it over the phone to Novoselic . The band recorded " In Bloom " with producer Butch Vig at Smart Studios in Madison , Wisconsin during April 1990 . The material recorded at Smart Studios was intended for the group 's second album for the independent record label Sub Pop . The song originally had a bridge section that Vig removed . Novoselic said that after the band recorded the song , Vig cut out the bridge from the 16 @-@ track master tape with a razor blade and threw it in the garbage . The songs from these sessions were placed on a demo tape that circulated amongst the music industry , generating interest in the group among major record labels . 
- After signing to <unk> Records , Nirvana began recording its second album Nevermind in May 1991 . " In Bloom " was one of the first songs the band recorded during the album sessions at Sound City Studios in Van <unk> , California ; Vig thought it would be good to start recording a song previously recorded at Smart Studios . The arrangements for " In Bloom " and the other songs previously recorded with Vig in 1990 were largely unchanged ; the recently hired drummer Dave Grohl stayed mostly with what his predecessor Chad Channing had recorded , but added more power and precision to the recording . Cobain sang progressively " harder " during the recording of the song , which made it difficult for Vig to balance the volume levels between the verses and choruses . Vig recalled that he had to change the input level " on the fly " and hoped that Cobain would not " change the <unk> or do something different " while recording . 
- Cobain chose not to <unk> a harmony vocal part during the Smart Studios sessions , possibly due to time constraints . During the Nevermind sessions , Vig had Grohl sing harmonies on the song . Grohl had difficulty hitting the proper notes , but ultimately was able to sing what Vig wanted . Vig often had to trick Cobain , who was <unk> to performing multiple takes , into recording additional takes for overdubs on the record . The producer convinced Cobain to double @-@ track his vocals on " In Bloom " by telling him , " John Lennon did it . " After doubling Cobain 's vocals , Vig decided he might as well double Grohl 's and had the drummer record an additional track of backing harmonies . 
- 
- = = Composition = = 
- 
- Like many Nirvana songs , " In Bloom " shifts back and forth between quiet verses and loud choruses . Cobain uses a Mesa Boogie guitar amplifier for the verses , and during the chorus he switches to a Fender <unk> <unk> ( suggested by Vig ) for a heavier , double @-@ tracked <unk> sound . The rhythm section of Novoselic and Grohl kept its parts simple ; Grohl stated it was " an unspoken rule " to avoid unnecessary drum fills , while Novoselic said he felt his role was about " serving the song " . During the choruses , vocalist Cobain and drummer Grohl <unk> while singing " He 's the one / Who likes all our pretty songs / And he likes to sing along / And he likes to shoot his gun / But he knows not what it means " . The song 's intro reappears at the end of each chorus . 
- Cobain 's lyrics address the people outside of the underground music scene that began showing up at Nirvana shows after the release of the group 's debut album <unk> . Nirvana biographer Michael <unk> wrote , " But remarkably , [ the song 's lyrics ] translated even better to the mass popularity the band enjoyed . " Regarding the song 's chorus , <unk> commented , " The brilliant irony is that the tune is so catchy that millions of people actually do sing along to it . " In his biography of Cobain , <unk> Than Heaven , Charles R. Cross asserted that the song was a " thinly disguised portrait " of Cobain 's friend Dylan Carlson . 
- 
- = = Release and reception = = 
- 
- " In Bloom " was released as the fourth single from Nevermind on November 30 , 1992 . The single was only released commercially in the United Kingdom ; promotional copies were released in the United States . The 7 @-@ inch vinyl and cassette editions of the single contained a live version of " Polly " as a B @-@ side , while the 12 @-@ inch vinyl and CD versions featured a performance of " Sliver " ; both songs were recorded at the same December 28 , 1991 concert . The single peaked at number 28 on the British singles chart . While lacking an American commercial release , the song charted at number five on the Billboard Album Rock Tracks chart . 
- Nirvana biographer Everett True wrote an <unk> unfavorable review in Melody Maker , accusing the single of being a cash @-@ in on Nevermind 's success . " <unk> <unk> bloody <unk> " , he wrote , " <unk> me if I don 't sound too thrilled . This release is stretching even my <unk> beyond repair . Like , milking a still @-@ breathing ( sacred ) cow , or what ? <unk> inferior live versions of ' Polly ' and ' Sliver ' on the flip don 't help matters either . " In 2011 Rolling Stone magazine ranked the song number 415 on its list of the " 500 Greatest Songs of All Time " . 
- 
- = = Music video = = 
- 
- Nirvana first made a music video for " In Bloom " in 1990 for the Sub Pop Video Network Program VHS compilation ( 1991 ) . The recording of the song featured in the video is the version recorded during the Smart Studios sessions . The Sub Pop video features the band walking around various parts of lower Manhattan including the South Street <unk> , the Lower East Side , and Wall Street . While filming the video , Novoselic shaved his head as penance for a bad performance the band gave in New York City . This caused <unk> in the final cut ; shots of the band feature the bassist with and without hair throughout the video . This video was later compiled on the Nirvana box set With the Lights Out ( 2004 ) . The audio of this version of " In Bloom " was later released on <unk> of the 20th <unk> Deluxe Edition of the Nevermind album in 2011 . 
- The second video , created to accompany the 1992 single release that was recorded at Sound City Studios , was directed by Kevin Kerslake , who had previously directed the band 's videos for " Come as You Are " and " <unk> " . Cobain 's original concept for the video told the story of a young girl born into a <unk> <unk> <unk> family who one day realizes how evil they are . His concept was too ambitious , so Cobain instead decided to parody musical performances by bands on early 1960s variety shows , such as The Ed Sullivan Show . The humorous tone of the video was a result of Cobain being " so tired for the last year of people taking us so seriously . . . I wanted to fuck off and show them that we have a humorous side to us " . Kerslake filmed the video on old <unk> cameras , and the band improvised its performance . The video begins with an unnamed variety show host ( played by Doug Llewelyn , former host of The People 's Court ) introducing Nirvana to an in @-@ studio crowd of screaming teenagers ; their non @-@ stop screaming is heard throughout the duration of the song . The band members , whom the host refers to as " thoroughly all right and decent <unk> " , perform dressed in Beach Boys @-@ style outfits ; Cobain wears glasses that blurred his vision , while Novoselic cut his hair short and liked it so much he kept it that way afterwards . As the video progresses , the band destroys the set and its instruments . 
- Three different <unk> of the Kerslake video were made . Cobain intended to replace the first version of the video after a period with a new take featuring the band wearing dresses instead of suits . MTV 's alternative rock show 120 Minutes insisted on <unk> the video , but Cobain felt the program would not properly convey the humor of the " pop idol " version . Instead , a new edit was produced which contained shots of the band in both suits and dresses . The original edit of the video never aired . This video won the award for Best Alternative Video at the 1993 MTV Video Music Awards , and topped the music video category in the 1992 Village Voice <unk> & <unk> critics ' poll . 
- 
- = = Track listing = = 
- 
- All songs written by Kurt Cobain . 
- 7 " , cassette 
- " In Bloom " – 4 : 17 
- " Polly " ( live ) – 2 : 47 
- 12 " , CD 
- " In Bloom " – 4 : 17 
- " Sliver " ( live - Del <unk> - 28 @.@ 12 @.@ 1991 ) – 2 : 06 
- " Polly " ( live - Del <unk> - 28 @.@ 12 @.@ 1991 ) – 2 : 47 
- 
- = = Chart positions = = 
- 
- 
- = = Covers = = 
- 
- <unk> Simpson recorded a country cover in 2016 which received air play on alternative stations . 
- 
- 
- = Lady in the Lake trial = 
- 
- The Lady in the Lake trial was a 2005 murder case in which Gordon Park ( 25 January 1944 – 25 January 2010 ) a retired teacher from <unk> , near Barrow @-@ in @-@ Furness , Cumbria , England , was jailed for life for the murder of his first wife , Carol Ann Park , in 1976 . 
- Carol Park went missing on 17 July 1976 , and was never seen alive again by her family . In 1997 , her body was discovered by divers in Coniston Water and Gordon was arrested on suspicion of murder . The charges were subsequently dropped but in 2004 Gordon was arrested again and found guilty of his wife 's murder . The trial judge sentenced him to life imprisonment and recommended that he should serve a minimum of 15 years before being considered for parole . He was held at Garth prison , <unk> , Preston . In December 2007 he lodged an appeal against his conviction which was dismissed in November 2008 . On 25 January 2010 , he was found hanged in his cell , and pronounced dead at the scene . 
- The details of the murder are sketchy . Carol was killed due to blunt trauma to her face by means of some instrument , alleged in court to have been an ice axe . She was then bound with rope , using complex knots , weighed down with rocks and lead pipes and thrown <unk> from a boat on Coniston Water . The body landed on an underwater ledge where it was later found by amateur divers . Had it been dropped a few metres further from the shore , it would have sunk to the much deeper bottom and probably never have been discovered . 
- There was a great deal of controversy surrounding the case . Gordon received much support from his family and friends and maintained his innocence . There was a large amount of local interest in the trial as shown by the sales of the local paper , the North @-@ West Evening Mail . Some claimed that much of the evidence against him could be discounted and there were <unk> and <unk> in attempts to free Gordon from prison and clear his name . The case featured prominently in the book No Smoke : The <unk> Truth About British Justice which outlined seven cases the author believed to be examples of innocent people being convicted of murder . 
- 
- = = Case history = = 
- 
- 
- = = = Carol Park <unk> = = = 
- 
- Prior to her disappearance , according to her friends , Carol was feeling depressed . She had talked of tracing her biological parents , as she was adopted . On 17 July 1976 , she went missing . Gordon claimed that she had left home for another man , and Carol had left their home in <unk> twice before . It was for this reason , Gordon claimed , that he did not report her missing for six weeks . Carol was said to time her periods away from home to coincide with the school holidays . Gordon claimed that it wasn 't until she didn 't return in time to take up her job as a primary school teacher in September ( the start of the school term ) that he realised that there was a problem . At this time he reported her missing via his solicitor , and her family was informed . On the day she vanished , the family had been due to visit Blackpool on a day trip . However , Carol said she felt <unk> and wanted to remain behind in bed . She was never seen alive again by her family . Gordon claimed he returned home to find Carol had left the house , leaving behind her wedding ring , and that there was no sign of a struggle . A missing persons enquiry was launched , but nothing came of it . This missing persons report was subsequently lost , and it was implied by some that it was because both Gordon and a senior police commander ( who , by the time the article was published , had died ) involved in the case were <unk> . This was refuted by Sandra <unk> , who , in No Smoke , claimed that Gordon had never been a <unk> . At the time , police told Gordon that , should a body be found , he would be the main suspect . 
- 
- = = = Discovery of body and post mortem results = = = 
- 
- On 13 August 1997 , amateur divers discovered Carol Park 's body , clad only in a <unk> , 75 feet down at the bottom of Coniston Water . She was nicknamed " the Lady in the Lake " by detectives after the 1943 detective novel by Raymond Chandler , The Lady in the Lake . The body had been wrapped in a <unk> dress , a canvas <unk> and plastic bags , tied with several knots , and weighed down with lead <unk> . Her eyes had been covered by <unk> . It was later reported that the body had landed on an underwater ledge , and had it been thrown into the water a few metres farther from the land , it would probably never have been found . 
- Details of Carol 's death were revealed in the post mortem . There were severe injuries to the skull , and it was said that her face had been smashed by multiple blows . It was later found in court that the murder weapon had been an ice axe . The body was found to be in a <unk> position , which suggested that the body had been dealt with within a few hours of death , before rigor mortis could settle in . It has since been said that the investigators failed to acknowledge that rigor mortis passes in a 24 ‑ to 48 ‑ hour time period , and that the body could <unk> have been <unk> once rigor mortis had passed . Many unusual knots were used to tie the body , and the same knots were said to be used in Gordon Park 's house and boat . This was one of the key pieces of evidence used against Gordon in the trial . 
- 
- = = = Gordon Park is arrested , but charges are dropped = = = 
- 
- At the time of the discovery of the body , Gordon and his third wife , Jenny , were on a cycling holiday in France . They heard news of the discovery of the body , and are said to have seen footage of the police searching their house . On 24 August , they arrived home , and Gordon was arrested on suspicion of murder at <unk> the next morning . He was charged with the murder of Carol Park , and <unk> in Preston prison . However , after two weeks , his <unk> managed to persuade the court to grant him bail . On 6 January 1998 , the charges against Gordon were dropped due to lack of evidence . The Crown Prosecution Service released a statement <unk> 
- After a conference with leading counsel and the police , a decision was taken , in agreement with all parties , that there was insufficient evidence for a realistic prospect of conviction . 
- Reportedly he told another inmate that " she deserved it " as had discovered his wife being <unk> . 
- After the charges were dropped , Gordon said that he wished to " try to put all this , including the events of 21 years ago , behind me and try to return to my everyday life " . However , the fact that the charges were dropped angered Carol 's brother , Ivor Price , who said that he was disgusted by the way that Carol was portrayed in the proceedings , and talked of how Carol was not " someone who [ was ] cheap or had a string of lovers . " 
- 
- = = = Gordon Park 's second arrest and trial = = = 
- 
- Gordon was again arrested on 13 January 2004 . Police said at the time that the murder file had never been closed , and that the arrest followed new leads . These new leads were later reported to be a confession by Michael Wainwright , who claimed to have been a <unk> of Gordon 's during his short prison stay in 1997 . The police then revisited the site where the body was discovered , and found a piece of <unk> green slate , a stone that matched the rocks that made up the wall of the family 's <unk> . The case was brought to trial at Manchester Crown Court , and lasted ten weeks . There was no single piece of evidence that pointed to Gordon indisputably , but the prosecution argued that when the evidence was placed together , it could only point at Park , and not a " mysterious stranger or secret lover " . Primarily , the case for the prosecution rested on circumstantial evidence , with the jury being asked to consider knots , rocks used to weight down the body and the ice axe , all linking Gordon Park to the crime . However , the statements of Michael Wainwright , and another of Gordon 's <unk> , who had learning difficulties , were also used as evidence by the prosecution . He was sentenced to be jailed for life , and told that he must serve at least 15 years . The judge said that he had taken into account the " terrible concealment " of the body . 
- Park claimed he and his wife had an open marriage 
- The court found that Gordon had first put his hands around Carol 's neck to strangle her , but that she had struggled . He then grabbed his ice axe , and brought it down with what was described as " considerable " force on Carol 's face , and smashed the front right @-@ hand side of her face , splitting open her head and <unk> her teeth . He then did this again – the attack was described as consisting of " two big heavy , crushing blows " by pathologist Dr. Edwin <unk> . After this , Gordon dumped his wife 's body in the lake , and went back to his day @-@ to @-@ day life . During the trial , the prosecution speculated that Gordon had " drugged his wife , possibly on or around the 17 July , tied her up and stored her body in a chest freezer before dumping it in Coniston Water " . 
- 
- = = = Gordon Park 's appeal = = = 
- 
- On 6 December 2007 , it was reported that Clarion <unk> , representing Gordon Park , said that they would launch an appeal . In a public statement , they said : 
- The appeal is based upon fresh evidence that was not available at the original trial , it is hoped the conviction will be <unk> and a <unk> ordered ... Upon his conviction , Mr Park 's family and friends launched a campaign to clear his name , claiming there was no single piece of evidence that pointed indisputably to him . 
- Representing Park in the appeal was solicitor Rob <unk> and Simon Bourne @-@ <unk> QC . They did not initially reveal what the new evidence was , only that it " was not available at the original trial " and that it was " very strong and significant " . Cumbria Constabulary released a statement saying : 
- <unk> from Cumbria Constabulary carried out a full and thorough investigation and the full facts of the case were presented to the CPS . The case was then tried by a jury , where Gordon Park was found guilty of the murder of Carol Park in 1976 . 
- In October , campaigners in support of Park said " We have in our possession , a signed , witnessed , statement , made this week , by one of the main prosecution witnesses , stating , ‘ police officers did put words in my mouth regarding Gordon Park ’ and ‘ the police told me what to say in court . ’ " Pastor George Harrison , acting as a spokesperson for the campaigners , also claimed that the appeals process was flawed and " rendered virtually impossible " due to costs . 
- In November 2008 , the appeal bid was rejected by three judges at the Court of Appeal in London . QC Simon Bourne , representing Park , had wanted to call an expert witness to challenge geological evidence used at the trial . Lord Justice Keene said that the new evidence did not raise " a reasonable doubt as to the safety of this conviction " , saying that the geological evidence was only a small element of the " strong circumstantial case against the <unk> " . The new evidence presented by geologist Andrew <unk> concerned the rock found with the body , said to have been from the wall outside the Parks ' house . <unk> argued that the rock was " indistinguishable " from others in the area , and therefore " meaningless " . 
- 
- = = = Gordon Park 's death = = = 
- 
- On 25 January 2010 , the morning of Park 's <unk> birthday , he was found hanged and unconscious in his prison cell in Garth Prison , and pronounced dead at the scene later in the morning . It is believed that a ligature was involved , and that Park inflicted the injuries upon himself . Park had not been assessed for the risk of suicide . Jeremy Park said that " we are all completely devastated and still believe his innocence 100 % . " Since his death , Carol Park 's family have tried to trace her ashes . 
- 
- = = Review = = 
- 
- On 26 November 2014 , it was confirmed that the Criminal Cases review Commission were examining the verdict , and new DNA evidence had come to light . Carol Park 's niece has accused Gordon Park 's family of ' clutching at <unk> ' and has argued : " Gordon went to the High Court of Appeal and lost , so what makes them think after all this time they could find any information that could <unk> the court ’ s decision ? " 
- 
- = = Case controversy = = 
- 
- 
- = = = Support for Park = = = 
- 
- There was much local interest in the matter , as was shown by the sales of the local paper , the North @-@ West Evening Mail , when it ran special editions on the case . Park maintained his innocence , and received much support from his family and friends . His children , Jeremy and <unk> , appointed a new legal team in an attempt to find grounds for appeal . Jeremy also set up a website , <unk> , in a bid to raise awareness and support of the case for Gordon . Notable individuals offering support include Tony Benn , who said that there was considerable doubt about Gordon 's conviction , and that he would do all he can to help campaign for his freedom . Benn has been quoted as saying the case is a " grave injustice " . He is one of the over 300 people who have signed the " Gordon Park is Innocent " petition . 
- A year into Park 's life sentence , around 40 family members and friends held a vigil at <unk> Prison , in a bid to raise awareness of " the fact that there is an innocent man in prison " . Another was held a year later , led by Jenny Park , and a third on the third anniversary of Park 's imprisonment . It was due to these <unk> , claimed <unk> Pastor George Harrison , that he was barred from visiting Park once he was transferred to Garth prison , in Lancashire . Other fronts for the campaign included an offer of £ 5 @,@ 000 for anyone providing evidence that lead to Gordon 's freedom . Included in this were planned adverts in the North @-@ West Evening Mail and leaflets to 20 @,@ 000 homes in the Furness area . However , this was being organised by Harrison , with whom Gordon and his third wife Jennifer stayed during the trial . Jeremy Park wrote to the North @-@ West Evening Mail to confirm that he wanted nothing to do with the reward , and that Harrison had no right to include his name , contact details or email address , or mention the <unk> website , in the adverts . Subsequently , Harrison claimed to have delivered 6 @,@ 000 <unk> and leaflets in the Furness area . 
- Not everyone with a link to the family supported Gordon 's attempts to clear his name . Vanessa Fisher , Park 's adoptive daughter , appeared as a witness for the prosecution at the trial , telling of how her father would hit the children with " a stick or cane " and how he would not discuss his wife 's disappearance . She was not in court at the time of Park 's sentencing . Ivor Price , Carol 's brother , has spoken publicly at various times about his feelings about Gordon , and the Price family even sat away from Park 's supporters during the trial itself . In a statement shortly after Park 's guilty verdict , he said he had " no doubt " that justice had been done , and added that " Carol was a lovely , <unk> girl who was very clever and intelligent . What has been said about her has been heartbreaking . This was about one thing : justice for Carol . " Around the same time , Price said that although he believed Gordon at the time of Carol 's disappearance , upon the news of a body being found in Coniston Water , he " knew it was her , and knew who had done it " , expressing his conviction of Park 's guilt . More recently , he said that he was " distressed " by the ongoing campaign to free Park . After Park 's unsuccessful appeal , Price 's daughters , Kay <unk> and Claire <unk> spoke to the North @-@ West Evening Mail , their parents having died before the appeal was rejected . <unk> said " It is brilliant , an amazing result . Our mum and dad Ivor and Maureen Price fought hard for this justice and now they can rest in peace because justice has been done . We are so <unk> for the result and so glad he will be staying in there . " She added that " It is finally justice for Carol . " The Park family and Cumbria Constabulary did not comment . 
- 
- = = = Claims of flawed investigation = = = 
- 
- The case was reported to be difficult for the prosecution , with the time between the murder and the trial making it extremely difficult to track potential witnesses . Keith <unk> , a police officer involved in the case , said that " the other difficulty was of course the body was taken away from the place where it was killed . " However , on top of the difficulties outlined by the police , the <unk> website detailed a number of what Park 's supporters claim are holes in the evidence used to convict him . A story about the problems with the evidence was published in the Daily Mail on October 12 , 2005 , at the request of Jeremy , though it was not written by him . Bob <unk> , the author , later wrote an article for Inside Time , in December 2006 , talking about the trial and its controversies . Another similar article , pointing out holes in the evidence , was published in the Sunday Herald , on July 23 , 2006 . 
- 
- = = = = Witnesses = = = = 
- 
- The first problem with the evidence is the use of inmate informant evidence , such as that from Michael Wainwright , described as " the most <unk> [ form of evidence ] of all " , despite the fact that it was one of the key pieces of evidence in the trial . Glen Banks , a man with whom Park had briefly shared a cell and who claimed in court that Park had admitted his guilt to him , was described as " highly <unk> " , frequently changed his story , and also claimed that Park had admitted to killing Carol while sailing to Blackpool . Wainwright , the other informant , was said to <unk> heavy cannabis smoker and admitted to hearing voices . He claimed that Gordon had said that he went upstairs , found Carol in bed with another man , and killed her in a fit of rage . This seemed unlikely , as the Parks lived in a <unk> , and the supposed lover has never come forward . Park also claimed to have never actually met Wainwright . 
- Joan Young , who was the only first @-@ hand witness , is challenged in both articles . She came forward in 2004 , claiming that she had seen someone push something over the side of a boat on Coniston Water . This testimony is challenged , because so much time has passed and the fact that her husband saw nothing . However , he claims to have been reading his paper at the time . Also , it has been said that Young was too far away to identify the person in the boat , and that it couldn 't have been Carol Park 's body that was being dumped , as the <unk> were positioned so that the location Carol 's body was found would have been visibly blocked by an island . The article even points out that Young described a boat that appeared to be a cruiser yacht . Despite the fact that Gordon did own a large yacht in 1997 , in 1976 he owned only a 505 racing <unk> , which he sold later that year . 
- There are three witnesses who provide direct evidence supporting the idea that Park was innocent . The first , a neighbour , claims that they saw Carol at the bottom of her <unk> . A second , another neighbour , claims that they saw an unidentified man in a <unk> <unk> in the Parks ' drive for twenty minutes . The presence of the unidentified car has never been accounted for . Around <unk> that night , another witness , a woman who knew Carol , saw her at <unk> Richard services . She had mentioned to her husband the fact that Carol had not greeted her , describing her as " <unk> " . 
- 
- = = = = Other evidence = = = = 
- 
- The rock supposedly found in the lake , said to be taken from the Park 's garden wall , has been challenged as evidence in multiple places . Both articles say that the police diver had no memory of recovering it , and he claimed that if he had found it , he would have placed it back . Professor Kenneth <unk> , a defense witness , said that there was no evidence that the rock had ever been on the lake bed at all . The article in the Sunday Herald also claims that the policeman said to be responsible for finding the rock fainted when it was produced in court , offering no explanation , but still denying that he ever found it . 
- Two more issues are addressed in the Sunday Herald article , but not in the Daily Mail article . The first is of the knots used to tie up Carol 's body – the knots on Carol 's body were mostly granny knots , of which there was no evidence that Gordon ever used – indeed during the trial he was specifically asked this , and he responded , " I do not use granny knots " . The granny knot is always a mistake , used by someone who should have used a reef knot ; those learning knot @-@ tying frequently make this mistake . Gordon Park , an experienced knot @-@ <unk> , would likely never use a granny knot , and their frequent appearance on the body of Carol is strong evidence that it was not Gordon who tied them . The article also claims that the evidence of knots was irrelevant anyway , as <unk> and <unk> were so common in the area . The other piece of evidence related to another rock taken from the <unk> , which had supposedly come from the Parks ' roof . However , the prosecution eventually admitted that Westmoreland green slate had been worked in the area for hundreds of years , and could have come from anywhere . The Herald also mentions the fact that the police files from the original missing persons inquiry into Carol Park had gone missing themselves , but does not draw any conclusions from this . 
- Both articles address the issue of who the killer could alternatively have been , and both of them mention Carol 's other lovers , and John <unk> , responsible for murdering Carol 's sister . Both of the articles mention that he was in the Barrow area around the time of the murder , but both of them are careful to point out that they are not accusing him of being the murderer . 
- 
- = = = No Smoke = = = 
- 
- On 1 May 2007 , No Smoke – The <unk> Truth About British Justice , by Sandra <unk> , was published by Exposure Publishing . This book features the stories of seven high @-@ profile convicted murderers , including Park , Luke Mitchell and <unk> Jenkins . The book claims that the expert testimonies in the Lady in the Lake trial were flawed , that certain evidence was withheld , and that the <unk> reporting of the press at the time influenced the court by persuading them that Park was the only person who could have killed his wife . 
- 
- = = Personal life of Gordon Park = = 
- 
- Park worked as a village primary school teacher , but was retired by the time he was <unk> for the murder of his first wife , Carol . Park had two children by Carol , Jeremy and <unk> . Gordon and Carol also adopted their niece Vanessa when she was 18 months old after her mother , Christie ( who was Carol 's sister ) , was murdered by her boyfriend in 1969 , aged 17 . This was described as a " rare and appalling coincidence " , but has been picked up on by the case for the defence since the trial . 
- Park remarried twice after Carol was murdered . His second wife was named Catherine , and his third , to whom he remained married until his death , is named Jenny . Park met all of his wives through teaching , Carol was a teacher at <unk> Village School when she was killed . When rejecting Park 's request for appeal , Lord Justice Keene , Mr Justice <unk> and Mr Justice <unk> noted that both Gordon and Carol had had affairs in the year leading up to Carol 's disappearance . 
- Park 's interests included sailing and climbing , which was relevant to the case due to the knots used to tie his wife 's body . While in prison , Park spent time <unk> t <unk> chi , taking a <unk> degree and keeping fit . 
- In an exclusive written interview with The <unk> Gazette nine months into his sentence , Park talked of how he had struggled with prison , saying : " They [ other inmates ] may smoke incessantly , play loud music , the TV or video games , rifle your <unk> , steal , lie etc . There is not a lot you can do about it ... I watched a guy " chasing the dragon " . It frightened me to death . I had never seen that before . " He refused to answer detailed questions about the trial or case , but protested his innocence , saying " If I knew who killed my wife , how , where , why , then I would have said so . I did not know then . I do not know now . " 
- 
- 
- = Dover Athletic F.C. = 
- 
- Dover Athletic Football Club is a professional association football club based in the town of Dover , Kent , England . The club was formed in 1983 after the dissolution of the town 's previous club , Dover F.C. , whose place in the Southern League was taken by the new club . In the 1989 – 90 season Dover Athletic won the Southern League championship , but failed to gain promotion to the Football Conference as the club 's ground did not meet the required standard . Three seasons later the team won the title again and this time gained promotion to the Conference , where they spent nine seasons before being relegated at the end of the 2001 – 02 season . The club was transferred to the Isthmian League Premier Division in 2004 , but another poor season and financial problems that had been mounting led the club to a further relegation . After three seasons were spent in the Isthmian League Division One South , the club won the championship and with it promotion back to the Premier Division , followed immediately the following season by another championship and with it promotion to Conference South . Five seasons were spent in that division with the club coming close to gaining promotion several times , reaching the play @-@ offs three times . In the 2013 – 14 season , Dover reached the play @-@ off final for the second successive season and defeated Ebbsfleet United to return to the Conference Premier after a twelve @-@ year absence . 
- The team usually wear white shirts and are consequently nicknamed the Whites . They have played at the Crabble Athletic Ground since the club 's formation . The club 's best performance in the FA Cup was an appearance in the third round proper in the 2010 – 11 and 2014 – 15 seasons , while the best performance registered in the FA Trophy , the national competition for higher @-@ level non @-@ league clubs , was a run to the semi @-@ finals in the 1997 – 98 season . 
- 
- = = History = = 
- 
- Dover Athletic F.C. was formed in 1983 after the town 's previous club , Dover , folded due to its debts . The new club took Dover 's place in the Southern League Southern Division , with former Dover player Alan Jones as manager and a team consisting mainly of reserve players from the old club . Initially Athletic struggled , finishing second from bottom of the table in the 1984 – 85 season . In November 1985 Steve McRae , who had succeeded Jones a year earlier , was sacked and replaced by Chris Kinnear . 
- Under Kinnear the club 's fortunes turned round , with two top @-@ five finishes followed by the Southern Division championship , and with it promotion , in the 1987 – 88 season . The team started strongly in the Premier Division , finishing in sixth place at the first attempt , and then winning the championship in the 1989 – 90 season . The club was denied promotion to the Football Conference , however , as the Crabble Athletic Ground did not meet the standard required for that league . After finishing fourth and second in the subsequent two seasons , Dover won the title again in the 1992 – 93 season and this time were admitted to the Conference . 
- Although Dover finished in eighth place in their first season in the Conference , the following season saw the club struggling against relegation , and Kinnear was dismissed due to a combination of the team 's poor performances and his own personal problems . John Ryan was appointed as the club 's new manager , but his reign was a short one and he was dismissed when the club lost seven of its first eight matches in the 1995 – 96 season . The club then appointed former England international Peter Taylor as manager , but he was unable to steer the team away from the foot of the table , and Dover held onto their place in the Conference only because Northern Premier League runners @-@ up Boston United failed to submit their application for promotion before the required deadline . 
- Bill Williams took over as manager in 1997 and led the club to the FA Trophy semi @-@ finals in the 1997 – 98 season and a best league finish to date of sixth place in the 1999 – 2000 season . Williams left the club to take a senior position with Conference rivals <unk> in May 2001 . By now the club was in severe financial difficulties , with a number of directors resigning and debts exceeding £ 100 @,@ 000 . Amid the crisis the entire board of directors resigned , forcing the club 's Supporters ' Trust to take over the running of the club , and manager Gary Bellamy was sacked after just six months in the job . Former Everton goalkeeper Neville <unk> took over but was dismissed just three months later , with Clive Walker taking over in March 2002 with the club rooted to the foot of the table . The club finished the season bottom of the Conference and was relegated back to the Southern League Premier Division . The club 's ongoing financial problems led to it entering a Company Voluntary Arrangement ( <unk> ) , a process by which <unk> companies offset their debts against future profits , due to debts that were now estimated at £ 400 @,@ 000 . 
- In Dover 's first season back in the Southern League Premier Division the Whites finished in third place , albeit 17 points <unk> of <unk> , who claimed the one promotion place available that season . A poor start to the following season saw Walker replaced by Richard Langley . Dover finished the season in 19th place , before being switched to the Isthmian League Premier Division in the summer of 2004 following a re @-@ organisation of the English football league system . The new season started with six successive defeats , which saw Langley sacked , and the financial problems continued , with the club coming within two months of being closed down . Dover were relegated to the Isthmian League Division One at the end of the season , but were saved from possible extinction in January 2005 when former director Jim Parmenter returned to head up a consortium that took over the club . Parmenter quickly sacked manager Steve Browne and convinced Clive Walker to return to the club to replace him , and also arranged for the club 's outstanding <unk> debts to be cleared , putting the club on a firm financial footing for the first time in many years . 
- Dover Athletic narrowly missed out on an immediate return to the Premier Division in the 2005 – 06 season , reaching the play @-@ offs for promotion but losing out to <unk> Angels . The following season Dover again reached the play @-@ offs but lost in the semi @-@ final to Hastings United , after which Walker did not have his contract renewed and was replaced by former Gillingham manager Andy <unk> . In his first season in charge he led the club to the Division One South championship and promotion to the Isthmian League Premier Division . The following season Dover won a second consecutive championship and thus gained promotion to Conference South . In the 2009 – 10 season , Dover reached the play @-@ offs for promotion to the Conference National , but lost at the semi @-@ final stage to <unk> . The following season the club reached the third round of the FA Cup for the first time after wins over Kent rivals Gillingham in the first round and another League Two club , Aldershot Town , in the second round . In the 2012 – 13 season the club again reached the play @-@ offs , but this time lost in the final to Salisbury City . The following season the team reached the second round of the FA Cup , losing 1 – 0 to Milton Keynes Dons , The last 16 of the FA Trophy , narrowly losing 3 – 2 to <unk> and made the play @-@ offs once more . A 4 – 1 aggregate victory over Sutton United in the semi @-@ final set up a match with fellow Kent team Ebbsfleet United in the final . On 10 May 2014 , Dover beat Ebbsfleet 1 – 0 at <unk> Road with a goal from Nathan Elder enough to seal the club 's return to the top flight of non @-@ league football for the first time since 2002 . In the 2014 – 15 season Dover went on another FA Cup run , beating Morecambe 1 – 0 in the first round , then Cheltenham Town 1 – 0 in the second round to reach the third round proper for only the second time ever , but lost 4 – 0 at home to Premier League side Crystal Palace . The following season the team qualified for the play @-@ offs for promotion to League Two . 
- 
- = = <unk> and crest = = 
- 
- Dover Athletic 's traditional colours are white and black , which were also the colours worn by the earlier Dover club . Away colours worn by the club have included red , yellow & green , pink and blue . The club 's crest contains a stylised representation of the town 's two most famous landmarks , Dover Castle and the white cliffs , enclosed in a circle bearing the club 's name . The club 's shirts have been sponsored by companies including <unk> <unk> , Paul Brown of Dover , Jenkins and Pain , cross @-@ channel ferry operators <unk> and SeaFrance , local car dealership Perry 's and are now sponsored by produce suppliers Gomez , the company owned by Dover Athletic chairman Jim Parmenter . 
- 
- = = Stadium = = 
- 
- Dover Athletic 's home ground since the club 's foundation has been the Crabble Athletic Ground , which was also the home of the former Dover club . The word Crabble , which is also found in the name of a local corn mill , may <unk> from the Old English <unk> <unk> , meaning a hole in which crabs are found . The stadium , commonly known simply as " Crabble " or , <unk> , as " The Crabble " , forms part of a larger council @-@ owned complex , and the earlier Dover club originally shared the lower pitch with a rugby club , but moved to the upper pitch in the 1950s , adding a grandstand in 1951 , followed soon after by <unk> and <unk> . 
- Dover Athletic continued to make improvements to the ground , although not in time to allow the club to take its place in the Football Conference in 1990 . Subsequently new <unk> were installed and two new terraces and a second grandstand added . These improvements meant that the club was able to gain promotion after its second Southern League title in 1993 . The stadium 's modern capacity is 5 @,@ <unk> with 1 @,@ 010 seats and 3 @,@ <unk> spectators under cover . 
- In 2007 the club announced that under the new sponsorship deal with SeaFrance the stadium would be known officially as the SeaFrance Crabble Stadium , but a year later it was announced that the deal would not be renewed due to the ferry operator 's financial constraints . On 1 July 2008 local car dealership Perry 's was announced as the club 's new main sponsor and the stadium rebranded as the Perry 's Crabble Stadium , an arrangement which lasted until 2012 . Between 2003 and 2004 it was known as the <unk> Stadium under the terms of another such agreement . Margate played their home matches at Crabble for two seasons from 2002 until 2004 , while their own <unk> Park ground was being redeveloped . 
- 
- = = Supporters = = 
- 
- In the club 's early days Athletic struggled to attract crowds of over 150 , but by the time the club reached the Conference , crowds at Crabble were averaging around 1 @,@ 000 . After the club 's relegation to the Isthmian League Division One South , the average attendance fell to just over 800 , but when the club returned to the Premier Division for the 2008 – 09 season , the average attendance at Crabble was 1 @,@ 293 , the highest in the division . The highest home attendance in the club 's history was 5 @,@ 645 for the match against Crystal Palace in the third round of the FA Cup on 4 January 2015 . Although Athletic 's improved monetary position means that the Supporters ' Trust is no longer required to financially support the club , it remains active as a fundraising organisation . 
- 
- = = Statistics and records = = 
- 
- Dover Athletic 's highest finish in the English football league system was in the 1999 – 2000 season , in which the team finished in sixth place in the Football Conference , the highest level of non @-@ league football and the fifth level overall . The Whites have made 13 appearances in the final qualifying round of the FA Cup , but have only progressed to the first round proper three times . In the 2010 – 11 season , Dover reached the third round for the first time , defeating Football League Two teams Gillingham and Aldershot Town in the first two rounds before losing to Huddersfield Town of Football League One . In the 1997 – 98 season the Whites reached the semi @-@ finals of the FA Trophy but missed out on an appearance at Wembley , losing to Cheltenham Town . The largest number of points the team has <unk> is 102 in the 1989 – 90 season , and the highest total number of goals scored in a season is 89 , scored in 40 matches in the 1985 – 86 season . The team 's biggest ever win was an 8 – 0 defeat of East Preston in September 2009 , and the heaviest defeat was a 7 – 1 loss to Poole Town in April 1984 . 
- The holder of the record for most appearances for Dover Athletic is Jason Bartlett , who played in 539 matches , and the all @-@ time top goalscorer is Lennie Lee , with 160 goals . The club 's record signing is Dave <unk> , who joined the club from <unk> Town in 1993 for £ 50 @,@ 000 , which at the time was the highest transfer fee ever paid between non @-@ league clubs . The highest confirmed fee received by the club was also £ 50 @,@ 000 , paid by Brentford in 1997 for Ricky <unk> . 
- 
- = = Players = = 
- 
- As of 16th July 2016 
- Note : Flags indicate national team as defined under FIFA eligibility rules . Players may hold more than one non @-@ FIFA nationality . 
- 
- = = = Former players = = = 
- 
- For a list of former Dover Athletic players with Wikipedia articles , see Category : Dover Athletic F.C. players . 
- 
- = = <unk> = = 
- 
- Dover Athletic have had 17 permanent managers ( excluding caretaker managers ) in the club 's 25 @-@ year history , with Chris Kinnear 's 10 @-@ year stint being the longest . The shortest stay was Ian <unk> who was announced as manager on 28 May 2010 and resigned only 18 days later to join Andy <unk> at Gillingham . 
- 
- = = Honours = = 
- 
- Southern League 
- Premier Division champions 1989 – 90 , 1992 – 93 
- Southern Division champions 1987 – 88 
- Isthmian League 
- Premier Division champions 2008 – 09 
- Division One South champions 2007 – 08 
- Kent Senior Cup 
- <unk> 1990 – 91 
- Conference South 
- Play @-@ off winners 2013 – 14 
- 
- = = <unk> = = 
- 
- Dover Athletic 's main rivalry is with nearby <unk> <unk> . A meeting between the two teams in 2004 was watched by a crowd of 2 @,@ <unk> , a record attendance for a league match at <unk> 's ground . The club also has a rivalry with Margate . In the 2001 – 02 season , when both teams were in the Football Conference , the two games between Margate and Dover were watched by a combined total of more than 6 @,@ 000 spectators . The game played at Margate 's <unk> Park stadium drew a crowd of 3 @,@ 676 , and 2 @,@ 325 were in attendance for the game at Dover . 
- 
- 
- = Plum cake = 
- 
- Plum cake refers to a wide range of cakes made with either dried fruit ( such as grapes , currants , raisins or prunes ) or with fresh fruit . There is a wide range of popular plum cakes and puddings . Since the meaning of the word " plum " has changed over time , many items referred to as plum cakes and popular in England since at least the eighteenth century have now become known as fruitcake . The English variety of plum cake also exists on the European mainland , but may vary in ingredients and consistency . Settlers in British colonies brought the dried fruit variety of cake with them , so that for example , in India it was served around the time of the Christmas holiday season and in the American colonies , where it became associated with elections , one version came to be called " election cake " . 
- Plum cakes made with fresh plums came with other migrants from other traditions in which plum cake is prepared using plum as a primary ingredient . In some versions , the plums may become jam @-@ like inside the cake after cooking , or be prepared using plum jam . Plum cake prepared with plums is also a part of <unk> Jewish cuisine , and is referred to as <unk> or <unk> . Other plum @-@ based cakes are found in French , Italian and Polish cooking . 
- 
- = = Terms = = 
- 
- The term " plum cake " and " fruit cake " have become interchangeable . Since dried fruit is used as a <unk> agent and any dried fruit used to be described as " plums " , many plum cakes and plum puddings do not contain the plum fruit now known by that name . ( Plum pudding is a similar , <unk> dish prepared with similar ingredients , cooked by steaming the mixture rather than baking it . ) The term " plum " originally referred to prunes , raisins or grapes . Thus the so @-@ called plums from which English plum puddings are made " were always raisins , not the <unk> <unk> fruits that the name suggests today . " 
- In Old English , the term <unk> was " from medieval Latin <unk> , from Latin <unk> , " which equated to " <unk> " . <unk> in modern French means plum , so plum <unk> have names such as <unk> aux prunes . In English , prunes are dried plums , and when modern cakes use them as a primary ingredient , they may be referred to as a plum cake or type of plum cake . 
- 
- = = By region = = 
- 
- 
- = = = Britain = = = 
- 
- Plum cake has historically referred to an early type and style of fruitcake in England since around 1700 . <unk> and currants were used , which in the English language were referred to as plums since around 1660 . The various types of dried fruit ( grapes , currants and raisins ) were familiar to English kitchens through trade with The Levant and Mediterranean but before they became available through " trouble @-@ free " imports from Australia , South Africa and California , preparing them required " an immense amount of labour ... on account of the rough and ready methods by which the fruit was picked , dried , packed and exported " . 
- In 1881 Colonel Henry @-@ Herbert said that " a good English plum cake ... is a national institution " . At times , Thomas Carlyle was one among many who ate a light style of plum cake with tea , into which he would dip the cake , which he described as bun @-@ like with currants " dotted here and there " . Elizabeth David wrote that " Christmas <unk> and Christmas plum pudding and cake are all such typical examples of the English fondness for <unk> fruit mixtures that it seems almost unnecessary to include recipes for them ... " 
- Plum cakes were raised by <unk> air into the cake batter , rather than by the use of yeast . A range of plum cakes and puddings were published in the popular Book of Household Management ( published 1859 @-@ 1861 ) by Isabella Beeton . Mrs Beeton included recipes for " A Common Plum Cake " and " A Nice Plum Cake " as well as " <unk> Plum @-@ Pudding " , " An <unk> Plum @-@ Pudding " , " A Plain Christmas Pudding for Children " , " Plum @-@ Pudding of Fresh Fruit " , " Plum <unk> " , " Christmas Plum @-@ Pudding " , " A Pound Plum Pudding " and " Christmas Cake " . The comment in an Indian Household Management book is indicative both of the reach of Mrs Beeton 's book as well as the range of interpretations of plum cake and plum pudding . The author says , " Mrs Beeton ’ s recipe is by far the best if modified a little : 12 units of <unk> raisins ... " 
- Up to World War I , cakes , including plum cakes , were baked along with loaves of bread . " A smaller cake or <unk> might be slipped in or pulled out after the baking had begun , but a raised <unk> with well @-@ protected sides , or a large plum cake , would take at least the same time as the loaves , and experienced housewives made them in sizes to do so . " 
- 
- = = = Europe = = = 
- 
- The English variety of plum cake also exists on the European mainland , although " plum cake " there more usually refers to baked cakes made with fresh , rather than dried fruit . 
- In French cooking , plums are an ingredients in a significant tradition of cake making : " ... throughout the districts of the Loire , the <unk> , the <unk> and the <unk> , there [ was ] hardly a celebration , a wedding feast or celebration at which the <unk> [ did ] not include some sort of plum or <unk> tart , made with fresh or dried plums or jam according to the season . " The <unk> plum is a specific <unk> used to make <unk> aux <unk> ( plum tart ) . A <unk> aux fruits is a type of <unk> made with yeast <unk> and covered with previously cooked fruit in season , such as plums ( or <unk> , apples , <unk> ) . The fruit in these open <unk> or flans is cut into suitably sized pieces and the cake is glazed : red <unk> is recommended for red plum and <unk> flans , whereas apricot <unk> is recommended in yellow plum and apricot flans . 
- The German plum cake , known as <unk> , can be found all over the country , although its home is <unk> . In chef Robert Carrier 's recipe for it , the base is made from yeast pastry rather than often used <unk> pastry , because the yeast pastry " <unk> up the juice form the plums without becoming <unk> " . 
- In Italy , plum cake is known by the English name , baked in an oven using dried fruit and often <unk> . 
- The Polish version of plum cake , which also uses fresh fruit , is known as <unk> <unk> <unk> . 
- 
- = = = India = = = 
- 
- In India plum cake has been served around the time of the Christmas holiday season , and may have additional ingredients such as <unk> or brandy added . 
- 
- = = = United States = = = 
- 
- Plum cake in the United States originated with the English settlers and was prepared in the English style in sizes ranging from small , such as for parties in celebration of Twelfth Night and Christmas , to large , such as for weddings . This original fruitcake version of plum cake in the United States has been referred to as a reigning " standard American celebration cake through the time of the civil war " . 
- During colonial times , one version of plum cake was called " election cake " , and was prepared with currants , raisins , <unk> and spices , with the addition of brandy in the recipe occurring later . Election cakes were typically <unk> with yeast . In New England , large election cakes weighing around 12 pounds ( 5 @.@ 4 kg ) would traditionally be served while people waited for election results . It has been stated that the first published election cake recipe appeared in 1796 in American Cookery . 
- Plum cake recipes in the fruitcake style appeared in early cookbooks in the Southern United States , and did not actually call for plums . After 1830 plum cake was often referred to as fruit cake or black cake . In 1885 , in a description of plum cake that sounds like plum pudding , it was described as " <unk> " - a solid , dark @-@ colored , thick cake with copious amounts of plums , gritty notes from raisins . 
- 
- 
- = Kedok Ketawa = 
- 
- Kedok Ketawa ( [ <unk> <unk> ] ; Indonesian for The Laughing Mask , also known by the Dutch title Het <unk> <unk> ) is a 1940 action film from the Dutch East Indies ( now Indonesia ) . Union Films ' first production , it was directed by Jo An Djan . Starring Basoeki Resobowo , Fatimah , and Oedjang , the film follows a young couple who fight off criminals with the help of a masked man . 
- <unk> as an " Indonesian cocktail of violent actions ... and sweet romance " , Kedok Ketawa received positive reviews , particularly for its cinematography . Following the success of the film , Union produced another six works before being shut down in early 1942 during the Japanese occupation . The film , screened until at least August 1944 , may be lost . 
- 
- = = Plot = = 
- 
- In <unk> , <unk> , a young woman named Minarsih ( Fatimah ) is rescued from four thugs by the painter Basuki ( Basoeki Resobowo ) . They fall in love and begin planning their life together . However , a rich man interested in taking Minarsih to be his wife sends a gang to kidnap her . Basuki is unable to repel them , but is soon joined by a masked <unk> known only as the " The Laughing Mask " ( Oedjang ) , who has almost supernatural fighting abilities . After two battles with the gang , Basuki and The Laughing Mask are victorious . Basuki and Minarsih can live together in peace . 
- 
- = = Production = = 
- 
- Kedok Ketawa was the first film produced by Union Films , one of four new production houses established after the success of Albert <unk> 's <unk> <unk> revived the ailing film industry of the Dutch East Indies . Union was headquartered in <unk> , Batavia ( now <unk> <unk> , Jakarta ) and funded by the ethnic Chinese businessman <unk> <unk> <unk> , although Tjoa Ma <unk> was in charge of day @-@ to @-@ day operations . The film was shot on @-@ location in <unk> , and featured fighting , comedy , and singing . 
- The film was directed by Jo An Djan and starred Oedjang , Fatimah , and Basoeki Resobowo . Other members of the cast included S <unk> and Eddy <unk> . Oedjang had been a stage actor before appearing in the film , while Fatimah and Basoeki were nobles with a formal education . The Indonesian film historian <unk> <unk> Biran writes that this is evidence the film was targeted at intellectual audiences , a manifestation of Union 's stated goal of " <unk> [ ing ] the quality of Indonesian art " . 
- Following the success of <unk> <unk> ( 1937 ; based on The Jungle Princess ) , the domestic film industry began to model their productions after Hollywood works , as this was expected to ensure financial success . The Indonesian film scholars <unk> <unk> and Said Salim write that Kedok Ketawa was influenced by <unk> <unk> 's 1897 novel <unk> through its Hollywood adaptations . Neither writer gives comparisons to illustrate this influence . 
- Kedok Ketawa was not the first contemporary film featuring a masked hero . Tan 's Film had released <unk> Item ( The Black Crow ) , with <unk> <unk> as the masked Black Crow , in 1939 , and later productions , including Java Industrial Film 's <unk> Item ( The Black Wolf ; 1941 ) , continued the trend . As was common for contemporary productions , the soundtrack for Kedok Ketawa – performed by <unk> – consisted of <unk> songs . 
- 
- = = Release and reception = = 
- 
- Kedok Ketawa was released in Batavia in July 1940 , with a press screening on 20 July . By September it was being shown in <unk> . In some newspaper advertisements , such as in <unk> , it was referred to as <unk> <unk> <unk> ( Warrior from <unk> ) , while in others it was advertised with the Dutch title Het <unk> <unk> . It was marketed as an " Indonesian cocktail of violent actions ... and sweet romance " and rated for all ages . 
- The critic and screenwriter <unk> , writing for <unk> , praised Kedok Ketawa , especially its cinematography and the beauty of its scenery ; he compared the film to imported Hollywood films . An anonymous review in <unk> <unk> found that the film was a mix of native and European sensibilities and lauded its cinematography . According to the review , the film surpassed expectations , but it was evident that this was a first production . Another review , in <unk> <unk> , considered the film among the best local productions , emphasising the quality of its cinematography and acting . 
- 
- = = Legacy = = 
- 
- Soon after the success of Kedok Ketawa , <unk> joined Union Films and wrote four films for the company . These were not directed by Jo An Djan , who left Union for the competitor <unk> 's Film , but by the newly hired R Hu and <unk> <unk> . Union Film ultimately produced a total of seven films in 1940 and 1941 before being closed following the Japanese invasion in early 1942 . Of the film 's main cast , only Fatimah and Oedjang are recorded as continuing their acting career , both appearing in several further Union productions . However , in the 1950s Resobowo continued his career behind the screen , serving as art director of such films as <unk> dan <unk> ( The Long March ; 1950 ) . 
- Kedok Ketawa was screened as late as August 1944 , but may now be lost . Movies in the Indies were recorded on highly flammable nitrate film , and after a fire destroyed much of <unk> Film <unk> 's warehouse in 1952 , old films shot on nitrate were deliberately destroyed . While the American visual anthropologist Karl G. <unk> suggests that all Indonesian films from before 1950 are lost , <unk> <unk> 's <unk> Film Indonesia records several as having survived at <unk> Indonesia 's archives , and Biran writes that some Japanese propaganda films have survived at the Netherlands Government Information Service . 
- 
- = = <unk> notes = = 
- 
- 
- 
- = My Boo ( Usher and Alicia Keys song ) = 
- 
- " My Boo " is a duet between American R & B singers Usher and Alicia Keys , written by Usher , Keys , Jermaine Dupri , <unk> Shropshire , and Manuel Seal , Jr .. The song was released as the album 's fourth single in 2004 . Set over a hip hop @-@ style track , it was produced by Dupri and No <unk> It was included on the re @-@ release of Usher 's fourth studio album , Confessions ( 2004 ) . 
- The song received positive reviews from critics , and has garnered awards . The single stayed on top of the Billboard Hot 100 for six weeks , making it the third most @-@ successful single from the album after " Yeah ! " and " Burn " , respectively . It was also ranked as the 36th biggest song of the 2000 – 2009 decade in the US . 
- 
- = = Background = = 
- 
- Usher and Alicia Keys had previously collaborated with the remix of Keys ' 2004 single " If I Ain 't Got You " , which was released in the United Kingdom . During the production of Usher 's fourth studio album , Confessions , they thought of various female singers to pair him with on the song . Usher and <unk> <unk> <unk> 's rare demo version can be found floating on the Internet . Although it was and still is widely believed that it was Beyoncé . However , Jermaine Dupri , who co @-@ wrote the song including Usher 's number @-@ one hits " Yeah ! " , " Burn " , and " Confessions Part II " , felt that he had established good relationship with Keys since she had worked with him and Usher . 
- The song is written from the perspectives of Keys and Usher , who play the role of an ex @-@ couple . For him , Usher and Keys " talk about how they used to be in love and how those feelings are still lingering despite the two not being involved anymore . " For Keys , " The song is talking about the first person you ever really had feelings for . Even though you move on and meet new people , you always remember that first person . " 
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- = = Release = = 
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- " My Boo " was among the songs that were not included in the final track listing of Confessions , including " Red Light " and songs recorded Usher recorded with P. <unk> and The <unk> . " My Boo " and " Red Light " were leaked to the internet , along with other records that were not included during the first release . The song was included in the expanded version of Confessions , alongside " Red Light " and " Sweet Lies " which were only released in the UK version of the album . Dupri thought of releasing " My Boo " as the fourth single from Confessions once " Confessions Part II " would be leaving the charts . <unk> Records sent " My Boo " to US contemporary hit radio on August 29 , 2004 and to US urban contemporary radio on August 30 , 2004 . The single was released in the United States as a 12 " single on September 21 , 2004 . It was a double A @-@ side with " Confessions Part II " when it was released in the United Kingdom on December 28 , 2004 . " My Boo " was re @-@ released in the United Kingdom on January 18 , 2005 . 
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- = = Composition and reception = = 
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- " My Boo " is a hip hop @-@ <unk> R & B song with a mid @-@ tempo melody . It is composed in the key of D minor , in common time . The lyrics are constructed in verse @-@ chorus @-@ chorus form . Usher starts the intro , and Keys followed her rap @-@ intro , with background vocals from Usher . He proceeds to the first verse and chorus , leading to Keys singing another chorus , altering some of the lyrics of the original chorus to create a dialogue . Keys sings the second verse and Usher for the chorus , with background vocals from Keys . Keys repeats her version for the chorus . The song breaks with Usher and Keys singing " My oh , My oh , My oh , My oh , My Boo " , one after the other . Usher repeats the chorus again , and they sing the intro of Keys . 
- The song received mixed reviews from critics . <unk> Ahmad of <unk> Media proposed to ban " My Boo " because he felt that the collaboration was a competition among Usher and Alicia Keys " over the hip @-@ pop like beat " . Jon Caramanica of Rolling Stone found the duet sentimental . At the 2005 Grammy Awards , it was nominated for Best R & B Song and Best R & B Performance by a Duo or Group with Vocals ; to which it won the latter and the former was awarded to Alicia Keys ' song , " You Don 't Know My Name " . Usher and Keys won for Best R & B / Soul Single by a Duo or Group during the 2005 Soul Train Music Awards . In 2011 , Billboard ranked the song seventh on a special The 40 Biggest Duets of All Time listing . 
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- = = Chart performance = = 
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- " My Boo " was successful in the United States , living up to the chart performances of " Yeah ! " , " Burn " and " Confessions Part II " . The single debuted on the Billboard Hot 100 twenty @-@ nine , the highest U.S. entry among all singles released from the album . It entered the top ten , two weeks after , and peaked at number one during its eighth @-@ week stay on the chart . The single remained on the top for six weeks , beating its predecessor " Confessions Part II " , which charted at number one for two weeks . However , it failed to match the success of " Yeah ! " and " Burn " for <unk> and eight @-@ week run on the top , respectively . It stayed on the top ten for nineteen weeks , leaving the chart after twenty @-@ six weeks . The single was successful on Billboard component charts , topping the Hot R & B / Hip @-@ Hop Songs and Hot <unk> . In Europe , the single had divided responses . The single reached the top five in United Kingdom , Germany , the Netherlands , Norway and Switzerland . It entered the top twenty in France , Finland and Switzerland , and top thirty in Austria . 
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- = = Music video = = 
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- Directed by both Usher and music video director Chris Robinson , " My Boo " clip was filmed in New York City . The storyline of the video is a reflection of the song 's lyrics . The footage starts with Usher in a living room watching a video for " Bad Girl " , a song from Confessions . The " Bad Girl " intro features Usher singing the song in a club setting while <unk> a <unk> @-@ dressed woman . He turns the set off and <unk> down on the <unk> before laying on it with his foot <unk> up . After a moment of silent , nostalgic reflection , he begins to sing the intro of " My Boo " . The video then shows him and Alicia Keys in their separate quarters , preparing to head out , while singing their part of the song . Usher eventually steps out on streets of New York ; likewise , Keys is out in her car . She leaves the car and walks down the street , and the couple meet up in the middle of Times Square , <unk> each other and on the brink of kissing . The music video debuted on MTV 's Total Request Live at number nine on September 16 , 2004 . It remained on the countdown for twenty @-@ seven days , becoming the only Confession video to chart . 
- Usher recorded " My Boo " aside from Keys with various female artists . He collaborated with fellow R & B singer Brandy Norwood and Usher recorded their own version as well , which appeared on her fourth album Afrodisiac . Brandy changed some of the lyrics , singing like having a conversation . However , neither of the two was included in the album . A remix of " My Boo " was also produced , featuring guest vocals from Nelly ; however , Keys ' part was omitted . 
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- = = Track listing = = 
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- UK CD 1 
- " Confessions Part II " 
- " My Boo " ( <unk> with Alicia Keys ) 
- UK CD 2 
- " Confessions Part II " 
- " My Boo " ( <unk> with Alicia Keys ) 
- " Confessions Part II " ( Remix ) ( featuring <unk> , Kanye West & <unk> ) 
- " Confessions Part II " ( Music Video ) 
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- = = Charts = = 
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- = = Certifications = = 
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- = Rio de Janeiro bid for the 2016 Summer Olympics = 
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- The Rio de Janeiro bid for the 2016 Summer Olympics and Paralympics was a successful bid to host the Games of the <unk> <unk> and the XV Paralympic Games , respectively . It was submitted on September 7 , 2007 , and recognized as an Applicant city by the International Olympic Committee ( IOC ) one week after . On June 4 , 2008 , the IOC Executive Board shortlisted Rio de Janeiro with three of the six other Applicant cities — Chicago , Madrid and Tokyo ; over Baku , Doha and Prague — becoming a Candidate city during the 2008 SportAccord Convention in Athens , Greece . 
- Rio de Janeiro was shortlisted receiving a 6 @.@ 4 score , according to a study of its Application File delivered to the IOC Working Group on January 14 , 2008 . As a Candidate city , Rio de Janeiro submitted its Candidature File to the IOC on February 11 , 2009 . The <unk> was analyzed by the IOC Evaluation Commission , which arrived in the city on April 27 , 2009 , to assess the quality of the bid . Between April 29 and May 2 , the Commission attended technical presentations and made inspections in all the existing venues across the city , giving a favorable assessment in its final report . 
- The Brazilian Olympic Committee ( COB ) chose Rio de Janeiro as its bidding city to host the 2016 Summer Olympics and Paralympics , during its Annual Assembly on September 1 , 2006 . This was the city 's first bid that proceeded to the Candidature phase , after three failed attempts to host the Games in 1936 , 2004 and 2012 . The lengthy and intensive bidding process was concluded with the election of Rio de Janeiro as the host city during the <unk> IOC Session , held in Copenhagen , Denmark , on October 2 , 2009 . It will be the first city in South America to host the Games . 
- Rio de Janeiro planned to organize the Games at a cost of USD 14 @.@ 4 billion , being able to hold all sport events ( excepting football ) inside the city . There will be 30 competition venues in four Olympic zones — Barra , Copacabana , Deodoro , and Maracanã — apart from venues for golf and rugby union , which were added to the Olympic program after the election . Football matches will be held in the cities of Belo Horizonte , Brasília , Salvador and São Paulo . The proposed dates range from August 5 to 21 for the Olympic Games , and September 7 to 18 for the Paralympic Games . 
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- = = Bid process = = 
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- Rio de Janeiro 's bidding process began on July 28 , 2006 , when the Executive Council of the Brazilian Olympic Committee ( COB in the Portuguese acronym ) met to consider the possibility of nominating a Brazilian city to host the 2016 Summer Olympics and Paralympics . This meeting was prompted by the fact that several cities in the world demonstrated interest around the election , some having already announced their bidding intention . Then a technical analysis was commissioned by COB to evaluate the conditions of the city , unveiled on August 25 , 2006 . A week later , on September 1 , 2006 , the COB 's Annual Assembly decided by acclamation that Rio de Janeiro would be the Brazilian representative seeking to host the Olympic and Paralympic Games in 2016 . The Assembly felt it was the only city in Brazil and South America which would possess Olympic @-@ level facilities ready for an Olympic and Paralympic bid , a legacy of its upcoming hosting of the XV Pan American Games in 2007 , later considered to be the best in history . Another positive aspect was that Rio de Janeiro could host all Olympic sports within the city limits , although the Olympic football tournament is hosted by multiple cities . The Brazilian government immediately expressed its support to the bid . Carlos Arthur Nuzman , president of COB , and César <unk> , then Mayor of Rio de Janeiro , approved the decision , thus making the bid official . 
- The International Olympic Committee ( IOC ) officially launched the bid process for the 2016 Summer Olympics and Paralympics on May 16 , 2007 . All then 203 National Olympic Committees ( <unk> ) were invited to submit the name of a city within their jurisdiction until September 13 , 2007 . On September 14 , the IOC recognized seven cities — Baku , Chicago , Doha , Madrid , Prague , Rio de Janeiro and Tokyo — which had been put forward by their respective <unk> to bid . On October 1 , 2007 , the Rio de Janeiro bid committee paid the required fee of USD 150 @,@ 000 to the IOC and signed the Candidature <unk> Procedure . Soon after , from October 15 to 19 , Rio officials attended the 2016 Applicant cities ' <unk> organized by the IOC at the Olympic Museum in Lausanne , Switzerland , where they learnt more about technical areas that would be analyzed throughout the application process . On January 14 , 2008 , the seven Applicant cities returned documents , known as the Application Files , containing the answers and guarantees requested by the 2016 Candidature <unk> Procedure , which provided to the IOC an overview of each city 's project . The responses were studied by the IOC Working Group for months and served as the basis to the shortlist of Chicago , Tokyo , Rio de Janeiro and Madrid on June 4 , 2008 , becoming Candidate cities during a meeting of the IOC Executive Board at the 2008 SportAccord Convention in Athens , Greece . 
- Rio de Janeiro bid to host the 1936 , 2004 and the 2012 Games , but failed on every occasion ; missing the shortlist in the two latest attempts , while the bidding process for 1936 did not follow the current standards . For the first time in the Candidature phase , the official bid flag was raised during a ceremony held at the <unk> da <unk> ( English : City 's Palace ) on June 23 , 2008 , celebrating the Olympic Day . On July 3 , 2008 , the Rio de Janeiro bid committee paid a fee of USD 500 @,@ 000 to the IOC and signed the Candidature Procedure , <unk> its acceptance of the rules . Then , Rio officials participated in the Olympic Games Observers Program from August 8 to 24 , during the 2008 Summer Olympics in Beijing , China ; and attended the IOC Official <unk> of the Beijing Games from November 24 to 27 , in London , United Kingdom . On February 11 , 2009 , the Rio de Janeiro bid committee delivered its Candidature File to the IOC in Lausanne and , eight days later , to the International Paralympic Committee ( IPC ) in <unk> , Germany ; containing the responses to the 2016 Candidature Procedure and <unk> , as well as guarantees and undertakings . The Candidature File had three volumes containing 568 pages of detailed answers to 300 technical questions , divided into 17 themes . Finally , on June 17 , 2009 , the IOC organized the 2016 Candidate Cities <unk> to 93 of its members at the Olympic Museum , being the first such meeting in its history and the most important before the election . Rio officials came out stronger after a presentation of 45 minutes , considered to be exceptional , followed by a session of 16 questions . 
- Throughout the campaign , the Rio de Janeiro bid committee introduced its plans to the General <unk> of all <unk> of National Olympic Committees ( <unk> ) , making the bid 's first official presentation on October 11 , 2008 , to the Pan American Sports Organization ( <unk> ) , in Acapulco , Mexico . On October 21 , the vision was presented to the Olympic Council of Asia ( <unk> ) in <unk> , Indonesia , followed by the European Olympic Committees ( <unk> ) on November 21 , in Istanbul , Turkey . On March 26 , 2009 , Rio officials made a praised presentation during the 2009 SportAccord Convention in Denver , United States . For the first time , a world map of the past Olympic host cities was displayed , subsequently becoming an icon of Rio 's campaign due to the void in South America . On March 31 , 2009 , the Rio de Janeiro bid committee made its plea to the Oceania National Olympic Committees ( <unk> ) in Queenstown , New Zealand ; and on July 7 , to the Association of National Olympic Committees of Africa ( <unk> ) in <unk> , Nigeria . The bid committee also attended many sporting events , such as the Australian and European Youth Olympic Festivals , the Commonwealth Youth Games , the Asian Youth Games and the Mediterranean Games , as well as the <unk> , Athletics , <unk> and <unk> World Championships . The three @-@ year campaign culminated with the beginning of the 13th Olympic Congress in Copenhagen , Denmark , which was officially opened in a ceremony held at the city 's Opera House , and after a lunch offered by <unk> II , Queen of Denmark , to the heads of state of the four Candidate cities at the <unk> Palace . 
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- = = = Evaluation = = = 
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- Rio de Janeiro was primarily evaluated during the Applicant phase , accurately on March 14 , 2008 , when the IOC Working Group released its report after four days of meetings , giving a weighted @-@ average score of 6 @.@ 4 to the bid . It was based on a thorough technical analysis of the projects presented on the Application File , which was developed by the Rio de Janeiro bid committee after having access to the Olympic Games Knowledge Management database as well as the official IOC Technical <unk> . The Working Group composed of several experts assessed the city 's potential for staging successful Olympic Games according to eleven criteria presented in the Application File . Rio de Janeiro 's highest score came from Government support , legal issues and public opinion due to the strong government commitment , and its lowest from Safety and security due to the city 's chronic problems of violence . Experience in major events also yielded good scores , while a shortage in the number of required hotel rooms undermined the accommodation theme . The Working Group also gave an 8 @.@ 3 score to Tokyo , 8 @.@ 1 to Madrid , 7 @.@ 0 to Chicago , 6 @.@ 9 to Doha , 5 @.@ 3 to Prague and 4 @.@ 3 to Baku ; being the basis for the selection to the Candidature phase . On September 18 , 2008 , after the shortlist which concluded the Application phase , the IOC announced the composition of its Evaluation Commission . The commission inspected the four Candidate cities under the leadership of <unk> El Moutawakel , who also chaired the Evaluation Commission for the bid process of the 2012 Summer Olympics and Paralympics . 
- The IOC Evaluation Commission arrived in Rio de Janeiro on April 27 , 2009 , to assess the quality of the bid , stating that the welcoming activities prepared by the city had been the best . Unlike the first evaluation , the committee did not assign scores , but analyzed the probability of execution of the plans . During the first two days of the visit , the Evaluation Commission held internal meetings at Copacabana Palace , the host hotel . Between April 29 and 30 , the Commission attended technical presentations in a large ballroom equipped with lighting and multi @-@ media resources , where it participated in question @-@ and @-@ answer sessions about each of the seventeen themes presented in the Candidature File . May Day was marked by the <unk> ' visits to all existing venues across the city . On May 2 , 2009 , the Evaluation Commission made a press conference to highlight the main points of the visit . According to El Moutawakel , the Commission was very impressed with the unity of government support , the quality of the presentations and the level of integration of the Games in the country 's long @-@ term development plan . After seven days of inspections involving 300 professionals , the thirteen members of the IOC Evaluation Commission left Rio de Janeiro on May 3 , 2009 , on the way to Madrid . One month before the election , on September 2 , 2009 , Rio officials welcomed the released of the 2016 Evaluation Commission Report . It gave a very positive assessment to the city and described the documents submitted by Rio de Janeiro as " of a very high quality " . 
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- = = = Election = = = 
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- The election of the host city took place at Bella Center on October 2 , 2009 , in Copenhagen , during the <unk> IOC Session . Rio de Janeiro was the third city to present plans to the IOC members , entering the Hall A of Bella Center at 12 : 05 ( <unk> ) with a delegation of 60 people , among bid leaders , government authorities and athletes . João Havelange made the opening speech and invited everyone to celebrate his centenary in Rio de Janeiro in 2016 . Carlos Arthur Nuzman was the next speaker , talking about experiences of the Olympic movement , and who introduced the iconic map of past Olympic host cities . Governor <unk> Cabral then spoke about projects related to security and transportation , followed by Henrique <unk> , who explained the economic situation of Brazil . Mayor Eduardo Paes was responsible for presenting the venues plan alongside Carlos Roberto Osório , while they were complemented by Olympic sailor Isabel Swan , who stated that the project had been made thinking on athletes . Swan cited the other athletes present in the <unk> : former football player <unk> , Paralympic swimmer Daniel <unk> and junior athlete <unk> <unk> . After that , President Luiz <unk> <unk> da Silva made a plea to bring the Games for the first time to South America , saying that was " time to light the Olympic cauldron in a tropical country " . Nuzman returned and concluded the presentation , which included a short film ( called <unk> ) by Academy Award nominated director Fernando <unk> , and a music video ( called <unk> ) with an English version of " <unk> <unk> " by <unk> Gil . 
- After the presentation , the session was opened for questions . Nuzman clarified doubts by Arne <unk> about doping legislation ; Osório and Cabral answered two questions by Prince Albert of Monaco about accommodation and the project 's legacy , respectively ; and the President of Brazil himself responded to a question by Austin Sealy about organization risks . Following the presentations by the four Candidate cities , <unk> El Moutawakel presented the report of the Evaluation Commission to the Session . From a total of 106 eligible IOC members , 95 were available to vote in the first round . Members from the four bidding countries were not allowed to vote until their city was eliminated . Alpha Ibrahim <unk> , <unk> <unk> Lee , <unk> <unk> and the IOC president were eligible members who did not vote . Chicago fell in the first round with 18 votes , while Tokyo received 22 , Rio 26 , and Madrid 28 . In the second round , Tokyo was eliminated with 20 votes , while Madrid received 29 and Rio 46 . Rio de Janeiro was elected in the final round with 66 votes over 32 for Madrid . The winning city was revealed by Jacques <unk> at 6 : 30 ( <unk> ) during an announcement ceremony hosted by <unk> <unk> <unk> and Jonathan Edwards . Nearly 100 @,@ 000 people celebrated the victory on Copacabana beach while watching the live broadcast . Following the announcement , Richard <unk> , <unk> , Nuzman and Paes signed the Host City Contract and <unk> Rio de Janeiro as the host of the 2016 Olympic and Paralympic Games . 
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- = = Bid concept = = 
- 
- According to the Rio de Janeiro bid committee , the bid 's concept was based on four principles — technical excellence , experience of a lifetime , transformation , and supporting the Olympic and Paralympic <unk> — highlighting the city 's celebration lifestyle , as seen on its promotional video ( called <unk> ) . The 2016 Summer Olympics and Paralympics will <unk> the Games in society as a catalyst for social integration , through programs for job generation , education , community outreach , volunteerism , training and up @-@ <unk> initiatives . The campaign also focused on youth and the fact that South America never hosted the Olympic Games , considering it to be a " self affirmation " of the Brazilian people . Rio de Janeiro integrated economic , environmental and social elements into its " Green Games for a Blue Planet " vision and planted <unk> seedlings to offset 716 tons of carbon emitted over the two years of campaign . Athletes and spectators will enjoy good climatic conditions in Rio de Janeiro , where mild southern hemisphere winter with absence of heavy rainfall provides favorable atmosphere for athletic performance . Average midday temperature of 24 @.@ 2 ° C ( 75 @.@ 6 ° F ) is predicted during the proposed dates for the Games , with temperatures ranging from 18 @.@ 9 ° C ( 66 @.@ 0 ° F ) to 28 @.@ 1 ° C ( 82 @.@ 6 ° F ) and humidity levels of 66 @.@ 4 % . 
- The visual identity of the candidature consisted of a logo and a slogan , which were applied in marketing moves during the campaign . Designed by <unk> <unk> and selected among four finalists by a special jury , the logo was unveiled during the 2007 Brazilian Olympic Awards , held at the Municipal Theater of Rio de Janeiro , on December 17 , 2007 . The <unk> Mountain was chosen to be the symbol as one of the city 's most famous landmarks . According to the Rio de Janeiro bid committee , the design as a whole conveys a heart shape , representing Brazilian passion and enthusiasm for sports . First with only the inscription " Applicant city " , the logo received the Olympic rings and the label " Candidate city " after being shortlisted . At midnight on January 1 , 2009 , the bid 's slogan " Live your passion " was launched as part of the New Year 's celebrations , which was attended by nearly two million people . According to the Rio de Janeiro bid committee , the slogan reflected the Brazilian 's way of getting <unk> involved in whatever they do . It was projected onto the Rio de Janeiro 2016 @-@ themed <unk> wheel after the countdown to the beginning of 2009 . The structure erected on Copacabana beach to promote the candidature was 36 m ( 118 ft 1 in ) high , weighed 80 tonnes ( 180 @,@ 000 lb ) and had 24 <unk> for 144 people . 
- Rio de Janeiro previously hosted major sporting , business and cultural events . On the sporting front , these events included World Cups and World Championships in many Olympic sports , as well as regional championships . From July 13 to July 29 , 2007 , the city successfully staged the 15th and largest ever edition of the Pan American Games ( over 5 @,@ 650 athletes competing in 34 sports ) and the accompanying <unk> American Games ( over 1 @,@ 150 athletes competing in 10 sports ) . Rio de Janeiro hosted the 1950 FIFA World Cup and recently hosted the 2014 FIFA World Cup , just two years before the 2016 Olympic and Paralympic Games , which includes upgrades to security , telecommunications and tourism infrastructure . The 2013 FIFA Confederations Cup will also be organised by Brazil and used as a test event for the 2014 FIFA World Cup . The proposed competition schedule spreads medals across the entire program with two peaks at the weekends , being similar to the 2008 Summer Olympics program . The period proposed by the Rio de Janeiro bid committee to stage the 2016 Summer Olympics is from Friday , August 5 , to Sunday , August 21 , 2016 . It is also proposed that the 2016 Summer Paralympics begin on the Independence Day of Brazil , Wednesday , September 7 , and last until Sunday , September 18 . 
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- = = = Politics = = = 
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- The Rio de Janeiro bid committee was an incorporated non @-@ profit entity under the leadership of an Honorary Council and an Executive Board , both chaired by Carlos Arthur Nuzman . At the highest level , the Honorary Council comprised the President of the <unk> Republic of Brazil Luiz <unk> <unk> da Silva , the Governor of the State of Rio de Janeiro <unk> Cabral , the Mayor of the Municipality of Rio de Janeiro Eduardo Paes , and the two Brazilian IOC members : Nuzman and João Havelange . At the executive level , the Executive Board was divided in four departments — Government <unk> Commission , Business Council , Legacy Committee and Athletes ' Commission — being responsible for the main operations of the campaign . The Board also included representations from the three levels of Government with the specific authority to make commitments on behalf of their respective Governments . Technical aspects of the bid were supported by various <unk> and Committees composed of a professionals staff , and supplemented by a team of domestic and international experts ; which were coordinated by Carlos Roberto Osório , Secretary General of the bid committee . Mike Lee , former Director of Communications and Public Affairs for the London bid for the 2012 Summer Olympics , was the lead advisor to the bid of Rio de Janeiro . His company , <unk> <unk> Communications , was responsible for planning the bid 's advertising campaign strategy , presentation scripts , visual support development and international media relations , as well as international press conferences . 
- Rio de Janeiro had full political backing from the three levels of the Government of Brazil — Federal , State and City — providing all guarantees and covenants required by the IOC , as well as some additional undertakings . All levels government , as well as all major political parties in the country , pledged complete support for the bid and endorsed the conduct of the Olympic and Paralympic Games in Brazil . On June 23 , 2008 , the Brazilian president formed the Government Action Management Committee , under the patronage of the Minister of Sport Orlando Silva , which coordinated Federal government actions during the bid process . Another act created the Olympic Development Authority ( <unk> ) on January 17 , 2009 , to coordinate public services and infrastructure delivery for eventual Games in Rio de Janeiro , based in the pioneer model developed by Sydney for the 2000 Summer Olympics and Paralympics . Under legal aspects , the <unk> Committee for the Olympic Games ( OCOG ) would be the entity responsible for planning and staging the 2016 Olympic and Paralympic Games . According to the IOC , the existing legislation was sufficient to enable the organization of the Games and would be amended or modified to suit the Olympic Charter if needed . The Ministry of External Relations and the Ministry of Labor guaranteed the entry , exit and working arrangements for Games personnel . People in possession of a valid passport and an Olympic or Paralympic Identity and <unk> Card , in lieu of a <unk> , will be able to enter the Brazilian territory . 
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- = = = Controversies = = = 
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- The IOC 's decision to <unk> Rio de Janeiro over Doha generated criticism , as the Doha bid committee accused the IOC of " closing the door on the Arab world " and of making a political decision rather than <unk> on technical grounds . Doha outperformed Rio de Janeiro in the majority of the categories assessed , but according to the IOC , Doha 's desire to stage the Olympics in October because of high temperatures was the reason the bid was rejected . On May 3 , 2009 , the Rio de Janeiro bid committee accused the Madrid bid committee of sending a spy to Rio de Janeiro during the visit of the Evaluation Commission , and considered filing a formal ethics complaint with the IOC . Simon Walsh , who claimed to be a reporter working for <unk> , omitted the fact that he was a paid consultant of the Madrid bid committee and was stripped of his media accreditation . Madrid officials denied the accusations . On June 14 , 2009 , the American television network NBC aired the episode " The Glory That Was ... " from the series Law & Order : Criminal Intent , in which a security company , interested in millionaire contracts with the possibility of hosting the 2016 Summer Olympics in Brazil , bought votes of members of the " Olympic Site Selection Committee " for Rio de Janeiro and murdered a member of the Belgian Olympic Committee . When aired by <unk> in Brazil on September 3 , 2009 , the episode received much criticism . Eduardo Paes called the show " ridiculous and pathetic " , while the Rio de Janeiro bid committee condemned the " irresponsible and <unk> use " of the city 's image . 
- Ahead of the election , the Rio de Janeiro bid committee lodged an official complaint against Madrid with the IOC <unk> Commission about comments made on September 30 , 2009 , by José Maria Odriozola , vice president of the Spanish Olympic Committee ( <unk> ) ; and considered doing it against Chicago due to comments by Richard M. <unk> , Mayor of Chicago , on September 21 , 2009 . Odriozola called Rio de Janeiro " the worst of the four candidates " , while Rio officials believe that <unk> implied that the city was incapable of hosting the Olympics when he said it was " not the same as hosting the [ 2014 ] FIFA World Cup " . Mercedes <unk> apologised for Odriozola 's comments on behalf of the Madrid bid committee . According to the IOC rules , rival cities are not allowed to directly <unk> other bidders . After the conclusion of the bid process , <unk> <unk> , Governor of Tokyo , blamed " invisible dynamics " and political deals for Tokyo 's failure . Rio officials repudiated " the inappropriate statements " and sent a formal <unk> to the IOC on October 6 , 2009 . On November 30 , 2009 , the Hollywood actor Robin Williams caused embarrassment due to humorous comments made in an interview with David Letterman . During the Late Show with David Letterman , broadcast by CBS , Williams said that Rio de Janeiro was elected as host city because it sent " 50 <unk> and a pound of blow " to compete with Michelle Obama and Oprah Winfrey , from Chicago . The comments were widely criticized by the Brazilian media and government authorities . 
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- = = Bid project = = 
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- Rio de Janeiro proposed to hold all sporting events within the city limits — apart from the football ( soccer ) tournaments which would be held in the cities of Belo Horizonte , Brasília , Salvador and São Paulo — making the Games more compact and technically feasible . The competition venues will be located in four Olympic zones — Barra , Copacabana , Deodoro , and Maracanã — and divided in seven Olympic clusters — Barra , Copacabana , Deodoro , <unk> , João Havelange , <unk> and Maracanã — with four Olympic precincts — Maracanã , Olympic Park , <unk> and X Park . The Olympic and Paralympic Village , the International Broadcast Centre ( IBC ) and the Main Press Centre ( MPC ) will be located at the Barra zone , which is the core of the project and includes the largest number of competition venues . A television tower will be built at the IBC / MPC complex to complement broadcasting operations and provide panoramic studios . There will be a media @-@ exclusive hotel within the complex , accessible directly from the IBC / MPC . The Olympic and Paralympic Village complex will include an Olympic Village Training Center , an Olympic Village Park , an Olympic Beach and direct access to the Olympic Park , in addition to the 8 @,@ 856 rooms to accommodate over 17 @,@ 770 athletes and team officials , including accredited additional officials . 
- Rio de Janeiro 's competition venues meet the International Federation 's ( <unk> ) technical requirements and both IOC and IPC standards , and align with the City Master Plan and legacy strategy . Most venues are already operational , recently developed or renovated . According to the Rio de Janeiro 2016 bid committee , there are ten ( 29 % ) existing venues with no permanent works required and eight ( 24 % ) venues requiring reforms , while nine ( 26 % ) new permanent venues and seven ( 21 % ) temporary venues will be built . In total there will be 34 competition venues alongside 29 training venues , as part of the training venues strategy . The most significant legacy project from the bid is the Olympic Training Center ( <unk> ) program and facilities with its headquarters located in Barra zone . Furthermore , the Rio de Janeiro 2016 bid committee recognized the possibility of new sports being added to the Olympic Program and , following detailed analysis for each of the seven potential sports — baseball , golf , karate , roller sports , rugby , <unk> and <unk> — confirmed its capability to accommodate any of them in respect to venues , transport and other operational or logistical aspects . In addition , Rio de Janeiro and the four proposed cities to hold football ( soccer ) competitions — Brasília , Belo Horizonte , Salvador and São Paulo — were chosen to host the 2014 FIFA World Cup . 
- Rio de Janeiro will build the Summer Paralympics , upon the experience of hosting the III <unk> American Games — considered " the best ever " by the International Paralympic Committee ( IPC ) and the Americas Paralympic Committee ( <unk> ) — as well as many other international competitions for athletes with disabilities . For this purpose , the organizing committee will include representatives of the Brazilian Paralympic Committee ( <unk> ) and create a specific department responsible for supervising the Games . Another element that will bring the Paralympics up to par with their Olympic counterpart is the reuse of twenty Olympic competition venues for the equivalent Paralympic sports . Promotion of accessibility @-@ friendly measures by the hospitality and tourism industries will also be encouraged and recognized by the government . 
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- = = = Infrastructure = = = 
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- Rio de Janeiro 's infrastructure , systems , management structures and staff readiness will be tested during the 2014 FIFA World Cup and during dedicated test events for the transport network in 2015 . There will be 49 @,@ 750 rooms to meet IOC requirements , of which some 1 @,@ 700 are located in apartment hotels in the city and more than 13 @,@ 000 in hotels throughout Rio de Janeiro and the football tournament host cities . Rio de Janeiro proposed the use of cruise ships and condominium apartments to overcome a possible shortage in the number of hotel rooms . Six modern cruise ships will provide more than 8 @,@ 500 cabins . Media would be accommodated in a combination of media villages and hotels . Rio de Janeiro plans to implement 150 km ( 93 mi ) of Olympic lanes connecting the four Olympic zones and the <unk> Carlos <unk> International Airport . An existing corridor of high @-@ capacity rail and metro systems as well as <unk> and major roads links three of the four Olympic zones , and with the development of the western corridor , all four zones will be connected . Bus Rapid Transit ( <unk> ) system is going to be the main solution to the city 's topography . Security and disaster @-@ related risks for the event will be <unk> by the implementation of a comprehensive security operation . Crime in parts of Rio de Janeiro was considered to be an issue for the safety of people attending the Games . Rio de Janeiro 's experience in mounting security structures around highly <unk> and crowd @-@ attracting events is a positive aspect . The Federal Government will be responsible for the security of the 2016 Summer Olympics and Paralympics . 
- 
- = = = Finance = = = 
- 
- The three levels of Brazilian government assured the free provision of a range of services to the bid , including security , medical services , customs , transport , immigration , and other governmental services and support . Rio de Janeiro also approved funding of US $ 240 billion from the Program for Growth <unk> ( <unk> ) of the federal government . Other revenue @-@ generating activities include sponsorship sales , <unk> program , licensing and merchandising . These revenues will be supplemented by IOC @-@ secured commercial and broadcast contributions . Capital expenditure on infrastructure by the government or the private sector includes already committed and ongoing investments of US $ 3 @.@ 9 billion on projects such as airport and subway expansions and construction of the metropolitan road arc . The OCOG budget does not assume any capital contributions to the construction of permanent or legacy venues , other than for Games <unk> , including the construction of temporary venues . The balance of OCOG <unk> will be funded by the public sector , involving a combination of government commitments from Federal , State and Municipal levels . The OCOG budget and operating expenses are projected at US $ 2 @.@ 8 billion , with its Olympic @-@ related budget , including capital investments in transportation , sports venues and incremental costs being US $ 11 @.@ 6 billion . The government submitted guarantees to cover any potential shortfall . The bid expenses for both Applicant and Candidature phases were of R $ 85 @,@ <unk> million , as released during the official closing of the campaign on November 11 , 2009 . 
- 
- 
- = New Jersey Route 65 = 
- 
- Route 65 is a former state highway in the city of Newark , New Jersey . The route went for 4 @.@ 12 miles ( 6 @.@ 63 km ) along Port Street and Doremus Avenue through the industrial districts of the city . Route 65 began at an intersection with U.S. Route 1 and 9 near Newark Liberty International Airport . The route crossed over the New Jersey Turnpike along Port Street until an intersection with Doremus Avenue , where it turned northward for the rest of the distance , terminating at an intersection with U.S. Route 1 and 9 Truck . 
- Route 65 was originally planned in 1939 as a spur of Route 25 , Route 25B , in Newark to connect that route with Route <unk> ( now U.S. Route 1 / 9 Truck ) . The route lasted for 14 years , until January 1 , 1953 , when the 1953 New Jersey state highway renumbering occurred . Route 25B was decommissioned on that day , along with its parent route , Route 25 . Route 25B was redesigned as Route 65 , and was removed from the state highway system by 1963 . The two roads are now maintained by the city of Newark . 
- 
- = = Route description = = 
- 
- Route 65 began at an intersection with U.S. Route 1 and 9 in the area of Newark Liberty International Airport in the city of Newark . The route progressed eastward along Port Street , a divided highway for a short distance . The route crossed the access road to the airport before crossing over the newly constructed New Jersey Turnpike south of Interchange 14 . From there , Route 65 continued as Port Street but as one roadway into the industrial districts in Newark . The route progressed further over a railroad before intersecting with Doremus Avenue , where Route 65 turned northward off of Port Street . Both Port Street and Doremus Avenue dead @-@ end right after this respective intersection , Doremus Avenue to the south and Port Street to the east . 
- From there , Route 65 headed northward along Doremus Avenue , crossing over the Newark Bay <unk> ( Interstate 78 ) just north of the intersection . The route continued through the industrial district , reaching higher in elevation on a bridge over the Central Railroad of New Jersey ( now <unk> <unk> <unk> Operations , <unk> ) . From there , the route paralleled the turnpike 's mainline along Doremus Avenue . At the intersection with Raymond Boulevard , Route 65 became a divided highway once again , terminating at an intersection with U.S. Route 1 / 9 Truck in the city of Newark . 
- 
- = = History = = 
- 
- Route 65 was originally designated in 1939 as State Highway Route 25B , a spur off of State Highway Route 25 ( now U.S. Route 1 and 9 ) at a traffic circle near the Newark Metropolitan Airport ( now Newark Liberty International Airport ) . The route was then to commence along Port Street and Doremus Avenue to an intersection with State Highway Route <unk> ( now U.S. Route 1 and 9 Truck ) . Route 25B remained intact along the alignment of Port Street and Doremus Avenue until January 1 , 1953 . On that day , the New Jersey State Highway Department renumbered routes in the state , and eliminated Route 25 entirely . Due to decommissioning of Route 25 , Route 25B was renumbered to Route 65 , eliminating the relation to former Route 25 or U.S. Route 1 and 9 . Route 65 remained on the Port Street and Doremus Avenue alignment for a short time , and was completely eliminated from the state system by 1963 . Port Street and Doremus Avenue are both now maintained by the city of Newark but both are of the National Highway System as of 2009 . In 2001 , the bridge over the <unk> line was reconstructed . 
- 
- = = Major intersections = = 
- 
- The entire route was in Newark , Essex County . 
- 
- 
- = Giacomo Meyerbeer = 
- 
- Giacomo Meyerbeer ( born Jacob Liebmann Beer ; 5 September 1791 – 2 May 1864 ) was a German opera composer of Jewish birth who has been described as perhaps the most successful stage composer of the nineteenth century . With his 1831 opera Robert le diable and its successors , he gave the genre of grand opera ' decisive character ' . Meyerbeer 's grand opera style was achieved by his merging of German orchestra style with Italian vocal tradition . These were employed in the context of sensational and melodramatic libretti created by Eugène Scribe and were enhanced by the up @-@ to @-@ date theatre technology of the Paris Opéra . They set a standard which helped to maintain Paris as the opera capital of the nineteenth century . 
- Born to a very wealthy Berlin family , Meyerbeer began his musical career as a pianist but soon decided to devote himself to opera , spending several years in Italy studying and composing . His 1824 opera Il crociato in Egitto was the first to bring him Europe @-@ wide reputation , but it was Robert le diable ( 1831 ) which raised his status to great celebrity . His public career , lasting from then until his death , during which he remained a dominating figure in the world of opera , was summarized by his contemporary Hector Berlioz , who claimed that he ' has not only the luck to be talented , but the talent to be lucky . ' He was at his peak with his operas Les Huguenots ( 1836 ) and Le prophète ( 1849 ) ; his last opera ( L 'Africaine ) was performed posthumously . His operas made him the most frequently performed composer at the world 's leading opera houses in the nineteenth century . 
- At the same time as his successes in Paris , Meyerbeer , as a Prussian Court Kapellmeister ( Director of Music ) from 1832 , and from 1843 as Prussian General Music Director , was also influential in opera in Berlin and throughout Germany . He was an early supporter of Richard Wagner , enabling the first production of the latter 's opera , Rienzi . He was commissioned to write the patriotic opera Ein <unk> in <unk> to celebrate the reopening of the Berlin Royal Opera House in 1844 and wrote music for certain Prussian state occasions . 
- Apart from around 50 songs , Meyerbeer wrote little except for the stage . The critical assaults of Wagner and his supporters , especially after his death , led to a decline in the popularity of his works ; his operas were suppressed by the Nazi regime in Germany , and were neglected by opera houses through most of the twentieth century . Meyerbeer 's works are only infrequently performed today . 
- 
- = = Early years = = 
- 
- Meyerbeer 's <unk> was Jacob Liebmann Beer ; he was born in <unk> ( now a part of <unk> ) , near Berlin , then the capital of Prussia , to a Jewish family . His father was the enormously wealthy financier Judah <unk> Beer ( 1769 – 1825 ) and his mother , <unk> ( <unk> ) <unk> ( 1767 – 1854 ) , to whom he was particularly devoted , also came from the <unk> elite . Their other children included the astronomer Wilhelm Beer and the poet Michael Beer . He was to adopt the surname Meyerbeer on the death of his grandfather Liebmann Meyer <unk> ( 1811 ) and the first name Giacomo during his period of study in Italy , around 1817 . 
- Judah Beer was a leader of the Berlin Jewish community and maintained a private synagogue in his house which leaned towards <unk> views . Jacob Beer wrote an early <unk> for performance at this synagogue . Both Judah <unk> Beer and his wife were close to the Prussian court ; when <unk> was awarded in 1816 the Order of Louise , she was given , by Royal dispensation , not the traditional Cross but a portrait bust of the Queen . The Beer children were provided with a fine education ; their tutors included two of the leaders of the enlightened Jewish intelligentsia , the author Aaron Halle @-@ <unk> and Edmund <unk> , ( later a reform movement rabbi in Hamburg ) to whom they remained attached into their maturity . The brothers Alexander von Humboldt , the renowned naturalist , geographer and explorer , and the philosopher , linguist and diplomat Wilhelm von Humboldt were close friends of the family circle . 
- Beer 's first keyboard instructor was Franz <unk> , a pupil of Johann Georg <unk> and a favoured teacher at the Berlin court . Beer also became one of <unk> <unk> 's pupils while <unk> was in Berlin . The boy made his public debut in 1801 playing Mozart 's D minor Piano Concerto in Berlin . The <unk> <unk> Zeitung reported : ' The amazing keyboard playing of young <unk> ( a Jewish lad of 9 ) , who carried off the difficult passages and other solo parts with <unk> , and has fine powers of rendition even more rarely found in one of his age , made the concert even more interesting ' . 
- Beer , as he still named himself , studied with Antonio <unk> and the German master and friend of <unk> , Carl Friedrich <unk> . Louis <unk> organised a concert for Beer at Berlin in 1804 and continued his acquaintance with the lad later in Vienna and Rome . A portrait of Jacob commissioned by the family at this time shows him ' confidently facing the viewer , his hair romantically <unk> … his left hand rests on the keyboard , and his right hand <unk> a musical manuscript … <unk> [ ing ] its subject in the tradition of the young Mozart ' . Beer 's first stage work , the ballet Der Fischer und <unk> <unk> ( The Fisherman and the <unk> ) was produced in March 1810 at the Court Opera in Berlin . His formal training with the <unk> Vogler at <unk> between 1810 and 1812 was , however , of crucial importance , and at around this time he begins to sign himself ' Meyer Beer ' . Here , with his fellow students ( who included Carl Maria von Weber ) , he learnt not only the craft of composition but also the business of music ( organising concerts and dealing with publishers ) . Forming a close friendship with Weber and other pupils , Meyerbeer established the <unk> <unk> ( Musical Union ) , whose members undertook to support each other with favourable press criticism and networking . On 12 February 1813 Beer received the first of the string of honours he was to accumulate throughout his life when he was appointed ' Court Composer ' by Grand Duke Ludwig of <unk> @-@ <unk> . 
- Throughout his early career , although determined to become a musician , Beer found it difficult to decide between playing and composition . Certainly other professionals in the decade 1810 – 1820 , including Moscheles , considered him amongst the greatest <unk> of his period . He wrote during this period numerous piano pieces , including a concerto and set of variations for piano and orchestra , but these have been lost . To this period also belongs a <unk> <unk> written for the virtuoso Heinrich <unk> ( 1784 – 1847 ) who remained a close friend of the composer . 
- 
- = = Career = = 
- 
- 
- = = = In Italy = = = 
- 
- Despite performances of his <unk> <unk> und die <unk> ( God and Nature ) ( Berlin , 1811 ) and his early operas <unk> <unk> ( <unk> 's <unk> ) ( Munich , 1812 ) and Wirth und <unk> ( <unk> and Guest ) ( <unk> , 1813 ) in Germany , Meyerbeer had set his sights by 1814 on basing an operatic career in Paris . In the same year , his opera Die <unk> <unk> ( The Two <unk> ) , a version of Wirth und <unk> , was a disastrous failure in Vienna . Realizing that a full understanding of Italian opera was essential for his musical development , he went to study in Italy , enabled by the financial support of his family . He arrived in Italy at the beginning of 1816 , after visits to Paris and London , where he heard Cramer play . In Paris , he wrote to a friend , ' I go from museum to museum , library to library , theatre to theatre , with the restlessness of the <unk> Jew ' . 
- During his years in Italy Meyerbeer became acquainted with , and impressed by , the works of his contemporary <unk> Rossini , who by 1816 , at the age of 24 , was already director of both major opera houses in Naples and in the same year premiered his operas The <unk> of Seville and <unk> . Meyerbeer wrote a series of Italian operas on <unk> models , including <unk> e <unk> ( Padua , 1817 ) , <unk> <unk> ( Turin , 1819 ) , Emma di <unk> ( Venice , 1819 ) , <unk> d <unk> ( Milan 1820 ) and L <unk> di <unk> ( Milan 1821 ) . All but the last two of these had libretti by Gaetano Rossi , whom Meyerbeer continued to support until the latter 's death in 1855 , although not commissioning any further libretti from him after Il crociato in Egitto ( 1824 ) . During a visit to Sicily in 1816 , Meyerbeer noted down a number of <unk> , and these in fact constitute the earliest collection of folk music of the region . In a birthday <unk> from Rossi 's wife in 1817 occurs the earliest use discovered of Meyerbeer 's adopted <unk> ' Giacomo ' . 
- 
- = = = Recognition = = = 
- 
- The name Giacomo Meyerbeer first became known internationally with his opera Il crociato in Egitto — premiered in Venice in 1824 and produced in London and Paris in 1825 ; incidentally , it was the last opera ever written to feature a <unk> , and the last to require keyboard accompaniment for <unk> . This ' breakthrough ' in Paris was exactly what Meyerbeer had been aiming for over the past ten years ; he had been carefully preparing for it , developing contacts , and fully <unk> his reward . 
- In 1826 , shortly after the death of his father , Meyerbeer married his cousin , <unk> <unk> ( 1804 – 1886 ) . The marriage which may have been ' dynastic ' in its origins proved to be stable and devoted ; the couple were to have five children , of whom the three youngest ( all daughters ) survived to adulthood . In the same year , following the death of Carl Maria von Weber , Weber 's widow asked Meyerbeer to complete her husband 's unfinished comic opera Die <unk> <unk> . This was to cause him much trouble over future years , as he found the material insufficient to work on . Eventually in 1852 he settled the matter with Weber 's heirs by handing them Weber 's drafts and a cash compensation . ( The opera was later completed by Gustav Mahler ) . 
- With his next opera Meyerbeer became virtually a superstar . Robert le diable ( with libretto by Eugène Scribe and Germain <unk> ) , produced in Paris in 1831 , was one of the earliest grand operas . The libretto , originally planned in 1827 as a three @-@ act opéra comique for the Opéra @-@ <unk> theatre , was <unk> after 1829 in a five @-@ act form to meet the requirements of the Paris Opéra . Its revised characterisation as a ' grand opera ' placed it in succession to Auber 's La <unk> de <unk> ( 1828 ) and Rossini 's Guillaume Tell ( 1829 ) in this new genre . The composer undertook further work on the opera in early 1831 adding ballet episodes , including the " Ballet of Nuns " , which was to prove one of the opera 's great sensations , becoming an early example of the ballet blanc genre . He also rewrote the two major male roles of Bertrand and Robert to suit the talents of Nicolas <unk> and Adolphe Nourrit , respectively . At the invitation of Nourrit , <unk> Falcon made her debut at the age of 18 at the Opéra in the role of Alice on 20 July 1832 , and she made a vivid impression on the public , which included on that night Auber , Berlioz , Halévy , Maria <unk> , <unk> <unk> , <unk> <unk> , Alexandre Dumas and Victor Hugo . On hearing her in the role , Meyerbeer himself declared his opera at last ' complete ' . 
- The success of the opera led to Meyerbeer himself becoming a celebrity . In January 1832 he was awarded membership of the Légion d 'honneur . This success – coupled with Meyerbeer 's known family wealth – inevitably also precipitated envy amongst his peers . Berlioz – who had commented that ' Meyerbeer not only had the luck to be talented , he had the talent to be lucky ' – wrote ' I can 't forget that Meyerbeer was only able to persuade [ the Opéra ] to put on Robert le diable ... by paying the administration sixty thousand francs of his own money ' ; and Chopin lamented ' Meyerbeer had to work for three years and pay his own expenses for his stay in Paris before Robert le diable could be staged .... Three years , that 's a lot – it 's too much . ' King Frederick William III of Prussia who attended the second performance of Robert le diable , swiftly invited him to compose a German opera , and Meyerbeer was invited to stage Robert in Berlin . Within a few years the opera had been staged with success all over Europe , and also in the USA . 
- The fusion of dramatic music , melodramatic plot , and <unk> staging in Robert le diable proved a sure @-@ fire formula , as did the partnership with Scribe , which Meyerbeer would go on to repeat in Les Huguenots , Le prophète , and L 'Africaine . All of these operas held the international stage throughout the 19th century , as did the more pastoral Dinorah ( 1859 ) , making Meyerbeer the most frequently performed composer at leading opera houses in the nineteenth century . 
- 
- = = = Between Paris and Berlin = = = 
- 
- 
- = = = = 1832 – 1839 = = = = 
- 
- <unk> describes Meyerbeer 's mature life as ' a tale of two cities ... His artistic triumph and legendary status were achieved in Paris ... but he never abandoned Prussia , especially his home city of Berlin ' . His wife <unk> was based in Berlin ( she did not enjoy Paris ) as was his beloved mother ; and he had a series of Royal duties from the Prussian court , stemming from his appointment as Court Kapellmeister in 1832 . For these reasons his life from 1830 onwards is characterised by travel between these two centres . 
- In Paris Meyerbeer had been asked by Louis Véron , the director of the Opéra , for a new work . At first he attempted to persuade Véron to accept the opéra @-@ comique Le <unk> to a libretto by Scribe , which he had been contracted to compose in early 1831 ; but Véron insisted on a full five @-@ act piece . Together with Scribe , Meyerbeer reviewed many subjects before deciding , in 1832 , on Les Huguenots . The contract which Meyerbeer signed with Véron contained a penalty clause if the work was not delivered by the end of 1833 . When the time came and the opera was not ready , Véron claimed his 30 @,@ 000 francs under this clause ; Meyerbeer was perhaps unique amongst composers in being able to pay this . In fact Véron <unk> the money under a further agreement , when the opera was delivered in late 1834 ; but <unk> himself was replaced as director of the Opera by Henri Duponchel before Les Huguenots was premiered on 29 February 1836 . It was an immediate and immense success , its splendid staging and effects exceeding even those of Fromental Halévy 's La <unk> , which had premiered the previous year . Berlioz called the score " a musical <unk> " , and the singing , especially of Nourrit and Falcon , was universally praised . Les Huguenots was the first opera to be performed at the Opéra more than 1 @,@ 000 times ( the 1,000th performance being on 16 May 1906 ) and continued to be produced up to 1936 , more than a century after its premiere . Its many performances in all other of the world 's major opera houses give it a claim to being the most successful opera of the 19th century . 
- However , in Berlin Meyerbeer faced many problems , including the enmity of the jealous <unk> <unk> , who since 1820 had been Court Kapellmeister and director of the Berlin <unk> . <unk> were made in the Berlin press about the delay of the Berlin premiere of Robert le diable ( which finally took place in June 1832 ) , and Meyerbeer 's music was decried by the critic and poet Ludwig Rellstab . There was no sign of the German opera expected from Meyerbeer . Moreover , reactionary censorship laws prevented production of Les Huguenots in Berlin , ( and indeed in many other cities of Germany ) . Nevertheless , Meyerbeer , who ( as he wrote to a friend ) ' years ago ... <unk> to myself never to respond personally to attacks on my work , and never under any circumstances to cause or respond to personal <unk> ' , refused to be drawn on any of these matters . 
- Meanwhile , in Paris Meyerbeer began to seek new libretti , initially considering Le prophète of Scribe , Le <unk> <unk> by Henri Saint @-@ Georges and eventually settling on Scribe 's Vasco da Gama ( later to become L 'Africaine ) , which he contracted to complete by 1840 . However , Meyerbeer had envisaged that the main role in L 'Africaine would be written for Falcon ; after the catastrophic failure of her voice in 1837 , he turned instead to Le prophète . 
- On 20 August 1839 Meyerbeer , whilst relaxing at <unk> in the company of Moscheles , met for the first time with Richard Wagner , who was en route to Paris . Their ensuing relationship ( see below ) was to have major repercussions for the careers and <unk> of both . At this meeting Wagner read to Meyerbeer from the libretto of Rienzi , and Meyerbeer agreed to look through the score , which indeed he subsequently recommended for performance at Dresden . 
- 
- = = = = The 1840s = = = = 
- 
- By the end of 1841 , Meyerbeer had completed the first draft of Le prophète , but refused to stage it because the then director of the opera , Leon <unk> , wished to cast his mistress , <unk> <unk> , in the part of <unk> , the hero 's mother . ( Berlioz characterised <unk> as ' la <unk> du <unk> ' ) . Meyerbeer insisted on Pauline <unk> for the role . Meyerbeer lodged the score with a Parisian lawyer , and refused to countenance any production until his wishes were met . It was not until 1849 when the Opéra was willing to agree his conditions . Meyerbeer was unique in his time in having the wealth and influence to impose his will as a composer in this way . 
- In the meantime , the situation in Prussia was changing . Following the death of Frederick William III , the new regime of Frederick William IV was far more liberal . <unk> was dismissed , and the Berlin premiere of Les Huguenots was arranged ( 20 May 1842 ) . On the instigation of Alexander von Humboldt , Meyerbeer was installed later in the year as Prussian <unk> and director of music for the Royal Court . Meyerbeer wrote a number of works for court occasions , and also provided music , at the King 's request , for the first staging on Berlin in 1856 of his brother Michael 's play Struensee ( based on the life of Johann Friedrich Struensee ) , which had also been proscribed under the previous regime . 
- In 1843 , the Berlin Opera house burnt down . The creation of the new building gave a new opportunity to commission a German opera from Meyerbeer . The subject of the opera , Ein <unk> in <unk> ( A <unk> <unk> ) , was an episode in the life of Frederick the Great . As this patriotic opera ' needed ' Prussian creators , Meyerbeer arranged that whilst the trusted Scribe would write the libretto , Rellstab would translate it and take the credit ( and the royalties ) . This had the added advantage of winning over the formerly hostile Rellstab . Meyerbeer had hoped to have Jenny <unk> ( for whom he had written the part ) sing the lead role of <unk> , but the opera premiered on 7 December 1844 without her ( although she did appear in subsequent performances ) . The libretto was revised by Charlotte Birch @-@ Pfeiffer to a Bohemian background as <unk> for a production in Vienna ( 1847 ) . ( In a further incarnation , the music was later used by Meyerbeer for a revamped libretto by Scribe featuring Peter the Great , and produced as an opéra comique in Paris ( L <unk> du <unk> , 1854 ) ) . 
- With the continuing delays in the production of Le prophète and L 'Africaine , Meyerbeer was now becoming subject to increasing sniping in Paris . In 1846 Meyerbeer began work on a new project with Scribe and Saint @-@ Georges , <unk> , but in the following year <unk> was sacked from the opera and the direction was resumed by Duponchel . As a consequence , Meyerbeer was at last able to stage Le prophète with a cast to his liking , ( including <unk> as <unk> ) , and it premiered on 16 April 1849 . Again Meyerbeer 's new opera was an outstanding success – despite the unusual feature of the lead female role being the hero 's mother , rather than his lover . Amongst those at the 47th performance in February 1850 was Richard Wagner , now an impoverished political exile ; the success of a work so fundamentally against his own operatic principles was one of the spurs to his <unk> anti @-@ Jewish <unk> of Meyerbeer and Mendelssohn , ' Das <unk> in der <unk> ' ( 1850 ) . 
- 
- = = = = Last years = = = = 
- 
- Increasing ill @-@ health ( or possibly <unk> ) now began to restrict Meyerbeer 's output and activities . The death of his beloved mother in 1854 was also a blow . However the success of L <unk> du <unk> in 1854 demonstrated that he could still pack the theatres . Following this he began on two new projects , an opera by Scribe based on the biblical story of Judith , and an opéra comique , Le pardon de <unk> , ( also known as Dinorah , the title given to the Italian version performed at London ) to a libretto by Jules <unk> . The latter premiered on 4 April 1859 at the Opéra <unk> at Paris ; the former , like many previous projects , remained only as sketches . The death of Scribe in 1861 was a further <unk> to Meyerbeer to proceed with his operatic work in progress . In 1862 , in accordance with his original contract with Scribe , he paid Scribe 's widow compensation for not completing Judith . 
- Nevertheless , Meyerbeer 's last years saw the composition of a good deal of non @-@ operatic music , including a Coronation March for William I of Prussia , ( 1861 ) , an overture for the 1862 International <unk> in London , and incidental music ( now lost ) to Henry Blaze de Bury 's play La <unk> de <unk> ( 1860 ) . He composed a few settings of <unk> material , including one of the <unk> <unk> ( 1853 ) ; and also choral works for the synagogue at Paris . 
- Meyerbeer died in Paris on 2 May 1864 . Rossini , who , not having heard the news , came to his apartment the next day intending to meet him , was shocked and fainted . He was moved to write on the spot a choral tribute ( <unk> , <unk> , <unk> sublime ! ) . A special train bore Meyerbeer 's body from the <unk> du Nord to Berlin on 6 May , where he was buried in the family vault at the Jewish cemetery in <unk> <unk> . 
- L 'Africaine was eventually premiered after Meyerbeer 's death at the Salle Le <unk> on 28 April 1865 in a performing edition undertaken by François @-@ Joseph <unk> . 
- 
- = = Personality and beliefs = = 
- 
- Meyerbeer 's immense wealth ( increased by the success of his operas ) and his continuing adherence to his Jewish religion set him apart somewhat from many of his musical contemporaries . They also gave rise to rumours that his success was due to his <unk> musical critics . Richard Wagner ( see below ) accused him of being interested only in money , not music . Meyerbeer was , however , a deeply serious musician and a sensitive personality . He philosophically resigned himself to being a victim of his own success : his extensive diaries and correspondence – which survived the turmoil of 20th @-@ century Europe and have now been published in eight volumes – are an <unk> source for the history of music and theatre in the composer 's time . 
- Meyerbeer 's personal attachment to Judaism was a mature personal decision – after the death of his maternal grandfather in 1811 he wrote to his mother ' Please accept from me a promise that I will always live in the religion in which he died ' . In his diaries he noted significant family events including <unk> , not by their Gregorian calendar occurrence , but by their Jewish calendar dates . Moreover , he regularly suffered from ( and / or imagined ) anti @-@ Jewish <unk> throughout his life , warning his brothers frequently in his letters against <unk> ( Yiddish for ' Jew @-@ hatred ' ) . Writing to Heinrich Heine in 1839 , he offered the <unk> view : 
- I believe that <unk> is like love in the theatres and novels : no matter how often one encounters it ... it never misses its target if effectively wielded ... [ Nothing ] can grow back the <unk> of which we are robbed on the eighth day of life ; those who , on the ninth day , do not bleed from this operation shall continue to bleed an entire lifetime , even after death . 
- It was probably a similar <unk> that led Meyerbeer never to enter public controversy with those who slighted him , either professionally or personally , although he occasionally displayed his <unk> in his <unk> ; for example , on hearing Robert Schumann conduct in 1850 : ' I saw for the first time the man who , as a critic , has persecuted me for twelve years with a deadly <unk> 
- In his mature operas Meyerbeer selected stories which almost invariably featured as a major element of storyline a hero living within a hostile environment . Robert , Raoul the Huguenot , Jean the prophet , and the <unk> Vasco da Gama in L 'Africaine are all ' outsiders ' . It has been suggested that ' Meyerbeer 's choice of these topics is not accidental ; they reflect his own sense of living in a potentially <unk> <unk> 
- Meyerbeer 's relationship with Heine displays the <unk> and <unk> of the social <unk> of both parties . Meyerbeer , apart from any of his personal feelings , needed Heine <unk> as an influential personality and writer on music . He genuinely admired Heine 's verse , and made a number of settings from it . Heine , living in Paris from 1830 , always <unk> about his loyalties between Judaism and Christianity , and always short of money , asked Meyerbeer to intervene with Heine 's own family for financial support and frequently took loans and money from Meyerbeer himself . He was not above threatening Meyerbeer with blackmail by writing satirical pieces about him ( and indeed Meyerbeer paid Heine 's widow to suppress such writings ) . And yet , at Heine 's death in 1856 , Meyerbeer wrote in his diary ' Peace be to his ashes . I forgive him from my heart for his <unk> and many <unk> against <unk> 
- 
- = = Music and theatre = = 
- 
- 
- = = = Music = = = 
- 
- Meyerbeer did not operate on the basis of any theory or philosophy of music and was not an innovator in harmony or musical form . In the words of John H. Roberts , " He had a rich fund of appealing if somewhat short @-@ <unk> melody , commanded an increasingly rich harmonic vocabulary , and was a master of brilliant and novel orchestral effect . But he had very limited skill in thematic development and even less in <unk> combination . " 
- All of his significant music is for the voice ( opera and songs ) and this reflects his detailed <unk> in Italian opera . Throughout his career he wrote his operas with specific singers in mind and took great care to temper his writing to their strengths ; but at the same time he seemed little interested in expressing the emotions of his characters , preferring to use his music to underline the larger @-@ scale machinations of the plot . In this way he was close to the ideas of his teacher Vogler , himself renowned for his dramatic depictions of nature and incident in keyboard music , who wrote in 1779 that " writing beautifully is easy ; expression is not too difficult ; but only the genius of a great painter ... can choose for each picture <unk> and natural colours that are particular to it . " Indeed , his devotion to the voice often led him to ignore the dramatic cohesion of his operas ; typically , he would write far too much music and the scores of his operas would have to be drastically cut during rehearsals . ( The lengthy overture to Le prophète had to be cut in its entirety , surviving only in a piano arrangement by Charles @-@ Valentin Alkan ) . 
- The first signs of Meyerbeer breaking with the Italian traditions in which he had trained are in Il crociato in Egitto . Amongst other notable features of the opera were its lavish orchestral forces ( extending to two onstage military bands in the final act ) . The grandiosity of the work reflected the need to make an impact on the sophisticated and technologically advanced stages of London and Paris , for which it was extensively rewritten . Meyerbeer 's contribution was revealed at this stage to be the combination of Italian vocal lines , German orchestration and harmony , and the use of contemporary theatrical techniques , ideas which he carried forward in Robert and his later works . However Meyerbeer 's background in the Italian operatic traditions can be clearly seen as late as 1859 in the ' mad scene ' in Dinorah ( the virtuoso <unk> <unk> <unk> ) . 
- Typical of Meyerbeer 's innovative orchestration is the use in Robert le diable of dark @-@ toned instruments – <unk> , timpani and low brass , including <unk> – to <unk> the <unk> nature of <unk> and his associates . At one point the arrival of a character is announced by a combination of three solo timpani and <unk> double @-@ <unk> . Similar <unk> is shown in Les Huguenots where the composer uses a solo bass <unk> and solo <unk> d <unk> to accompany <unk> . For Le prophète , Meyerbeer considered using the newly invented saxophone . Becker suggests that Meyerbeer in all his grand operas often ' created a deliberately ' <unk> ' sound <unk> with unusual orchestration designed to express ... content rather than produce a sensuous sound ' and opines that this explains much of the criticism he received from German writers on music . 
- 
- = = = Theatre = = = 
- 
- Meyerbeer 's concern to integrate musical power with all the resources of contemporary theatre anticipated in some ways the ideas of Wagner 's <unk> . Becker writes : 
- Wagner 's idea of music drama ... was originally developed by way of grand opera ... his ideas could never have been realised in their particular form without the pioneering development [ s ] ... that Meyerbeer 's operas were the first to demand . 
- Meyerbeer was always concerned to intensify the theatricality of his operas , even when new ideas emerged at a relatively late stage in the music 's composition . An example of his <unk> was the addition of the provocative " Ballet of the Nuns " in the third act of Robert le diable , at the suggestion of Duponchel . The set for the ballet was an innovative and striking design by Duponchel and Pierre @-@ Luc @-@ Charles <unk> . Duponchel had also introduced technical innovations for the staging , including ' English traps ' for the sudden appearance and disappearance of the ghosts . ( Meyerbeer was led in fact to complain that the spectacle was too much and was pushing his music into the background ) . In Le prophète the skating ballet , which created a great sensation , was composed after rehearsals had begun , in order to <unk> on the new craze for roller <unk> . The theatre was also able to use new electrical lighting effects to create a powerful <unk> , and to depict the <unk> which ends the opera . 
- Meyerbeer 's large choral ' tableaux ' also made a major contribution to the overall dramatic effect ; the composer particularly sought opportunities to write such large @-@ scale crowd scenes , and preferred libretti which offered such possibilities . <unk> writes : ' These massive developed sections are the chief glory of the <unk> opera , for they are not only big in volume but big in their structural design ' . 
- Mention should also be made of Meyerbeer 's intense concern with the business of opera , which indeed had formed part of his studies under Vogler . This gave him the background not only to deal with complex contractual issues and to negotiate with publishers , but extended to wooing the press and ' marketing ' in general . Indeed , he was probably the originator of the ' press conference ' at which journalists were fed <unk> and information . This marketing and <unk> of opera was reinforced by Meyerbeer 's Paris publisher Maurice Schlesinger who had established his fortune on the back of Robert , and even persuaded <unk> de <unk> to write a novella ( <unk> ) to promote Les Huguenots . Schlesinger 's publication of Franz Liszt 's <unk> de Robert le diable sold out on the day of issue and was immediately reprinted . Such manoeuvres did little to <unk> Meyerbeer to his fellow artists , and indeed engendered envious comments of the sort already quoted from Berlioz and Chopin . 
- 
- = = Reception = = 
- 
- 
- = = = Musical influence = = = 
- 
- Meyerbeer had no pupils and no direct ' school ' . Yet as his works spanned the golden age of grand opera , clear traces of his influence can be found in the grand operas of Fromental Halévy , Gaetano <unk> , Giuseppe Verdi and others . After 1850 , <unk> notes a continuing tradition of operas at Paris where ' principals appear with chorus at the end of an act and where private intrigue <unk> a well @-@ articulated public dimension in the plot ' and cites amongst others Charles Gounod 's La <unk> <unk> ( 1854 ) , Ambroise Thomas 's Hamlet and operas by Jules <unk> , amongst them Le <unk> de <unk> ( 1877 ) and Le <unk> ( 1885 ) . The line of succession was however virtually washed away in the tide of Wagner in Paris after 1890 ( see below ) . The influence of Meyerbeer has also been detected in the operas of <unk> <unk> and other Czech composers , and in the operas of Russian composers including <unk> @-@ <unk> and the young <unk> , who thought Les Huguenots ' one of the greatest works in the repertoire ' . 
- Themes from Meyerbeer 's works were used by many contemporary composers , often in the form of keyboard <unk> or fantasies . Perhaps the most elaborate and substantial of these is Franz Liszt 's monumental Fantasy and <unk> on the <unk> " Ad nos , ad <unk> <unk> " , <unk> ( 1852 ) , for organ or pédalier , based on the <unk> of the Anabaptist priests in Le prophète and dedicated to Meyerbeer . The work was also published in a version for piano duet ( <unk> ) which was much later arranged for solo piano by <unk> Busoni . 
- Liszt also wrote piano works based on Robert le diable , notably the <unk> de Robert le diable <unk> <unk> <unk> . He also transcribed two pieces from L 'Africaine , as " <unk> de l <unk> L 'Africaine " . Frédéric Chopin and Auguste Franchomme jointly composed a Grand duo <unk> on themes from the opera , for cello and piano , in 1832 , and the Italian pianist and composer <unk> <unk> composed an elaborate fantasy on the opera for left hand alone as his Op. 106 . Other pieces based on the opera included works by Adolf von <unk> and Jean @-@ <unk> <unk> . Similar works , of varying musical quality , were <unk> out by composers for each of the further operas in attempts to cash in on their success . 
- 
- = = = Critical reception = = = 
- 
- Meyerbeer 's operas consistently enjoyed enormous popularity during his lifetime , and the verdict of ( the then pro @-@ Meyerbeer ) Wagner in 1841 , when the Paris Opéra was <unk> awaiting Le prophète and L 'Africaine , was not atypical : 
- The Paris Opéra lies dying . It looks for its salvation to the German <unk> , Meyerbeer ; if he keeps it waiting much longer , its death <unk> will begin ... It is for that reason ... that one only sees Robert le <unk> and Les Huguenots turning up again when the <unk> are forced to withdraw . 
- However , dissenting voices were heard from critics . Not all of these however were on musical grounds . Berlioz for example raised the issue of the inhibiting effects of Meyerbeer 's success ( which he felt particularly as one who struggled to get his works performed ) : " The pressure [ Meyerbeer ] <unk> on managers , artists and critics , and consequently on the Paris public , at least as much by his immense wealth as by his eclectic talent , makes all serious success at the Opéra virtually impossible . This <unk> influence may still be felt ten years after his death : Heinrich Heine maintains he has ' paid in advance ' . " 
- Mendelssohn disapproved of Meyerbeer 's works on moral grounds , believing Robert le diable to be ' <unk> ' . 
- Schumann 's attack on Les Huguenots was clearly a personal <unk> against Meyerbeer 's Judaism : ' Time and time again we had to turn away in disgust ... One may search in vain for a sustained pure thought , a truly Christian sentiment ... It is all contrived , all make believe and hypocrisy ! ... The <unk> of composers <unk> his hands with <unk> 
- Wagner 's disciple Theodor <unk> followed Schumann 's <unk> line in his 1850 review of Le prophète : ' To a good Christian [ it ] is at best contrived , exaggerated , unnatural and slick , and it is not possible that the practised propaganda of the Hebrew art @-@ taste can succeed using such means . ' <unk> 's phrase ' the Hebrew art @-@ taste ' was to be used by Richard Wagner to spark off his attack on Meyerbeer , ' Das <unk> in der <unk> ( Jewishness in Music ) . ' ( see below ) . 
- In 1911 , the composer Charles Villiers Stanford cited Meyerbeer 's music as an example of the dangers he believed lay in <unk> at the piano without a clear plan , ( although there is in fact no evidence to suggest Meyerbeer worked in this way ) , writing : ' Man of genius though he was , as any man who wrote the fourth act of the Huguenots must have been , Meyerbeer is a sign @-@ post of this danger of trusting to the <unk> as a medium of <unk> 
- 
- = = = Wagner 's campaign against Meyerbeer = = = 
- 
- The <unk> campaign of Richard Wagner against Meyerbeer was to a great extent responsible for the decline of Meyerbeer 's popularity after his death in 1864 . This campaign was as much a matter of personal spite as of racism – Wagner had learnt a great deal from Meyerbeer and indeed Wagner 's early opera Rienzi ( 1842 ) was <unk> called by Hans von Bülow ' Meyerbeer 's best opera ' . Meyerbeer supported the young Wagner , both financially and in helping to obtain the premiere productions of both Rienzi and The Flying Dutchman at Dresden . 
- Wagner 's early correspondence with Meyerbeer , up to 1846 , is <unk> <unk> . However , from the early 1840s , as Wagner developed <unk> and Lohengrin , his ideas on opera increasingly diverged from <unk> standards ; even in 1843 Wagner had written to Schumann condemning Meyerbeer 's work as ' a striving after superficial popularity ' . During 1846 Meyerbeer turned down Wagner 's application for a loan of 1 @,@ 200 <unk> , and this may have marked a turning point . 
- In particular , after 1849 , Wagner resented Meyerbeer 's continuing success at a time when his own vision of German opera had little chance of <unk> . After the May Uprising in Dresden of 1849 , Wagner was for some years a political refugee facing a prison sentence or worse should he return to <unk> . During his period of living in exile he had few sources of income and little opportunity of getting his own works performed . The success of Le prophète sent Wagner over the edge , and he was also deeply envious of Meyerbeer 's wealth . In reaction he published , under a pseudonym , his 1850 essay ' Jewishness in Music ' . Without specifically naming Meyerbeer , he interpreted the popular success of the latter as the <unk> of German music by alleged Jewish <unk> and willingness to cater to the lowest tastes , and attributed the supposed poor quality of such ' Jewish music ' to Jewish speech and song patterns , which ' though the cultured son of <unk> takes <unk> pains to strip them off , nevertheless they <unk> an <unk> <unk> in <unk> to him ' . 
- In his major theoretical statement , ' Opera and Drama ' ( 1852 ) , Wagner objected to the music of Meyerbeer , asserting its <unk> and <unk> in dramatic terms ; this work contains Wagner 's well @-@ known put @-@ down of Meyerbeer 's operas as ' effects without causes ' . It also contains the sardonic crack that ' [ Rossini ] never could have <unk> that it would some day occur to the <unk> , for whom he had always made their music , to make it for themselves ' . ' Jewishness in Music ' was reissued in 1869 , ( after Meyerbeer 's death ) in an extended form , with a far more explicit attack on Meyerbeer . This version was under Wagner 's own name – and as Wagner had by now a far greater reputation , his views obtained far wider publicity . These attacks on Meyerbeer ( which also included <unk> at Felix Mendelssohn ) are regarded by Paul Lawrence Rose as a significant milestone in the growth of German anti @-@ Semitism . 
- As Wagner prospered , it became second @-@ nature for him , his wife <unk> and the Wagner circle to <unk> Meyerbeer and his works , and <unk> 's <unk> contain numerous instances of this – ( as well as recording a dream of Wagner 's in which he and Meyerbeer were reconciled ) . Wagner 's autobiography ' Mein <unk> ' , circulated amongst his friends ( and published openly in 1911 ) , contains constant sniping at Meyerbeer and concludes with Wagner <unk> over Meyerbeer 's death . The <unk> of Meyerbeer became a commonplace amongst <unk> : in 1898 , George Bernard Shaw , in The Perfect <unk> , commented that " <unk> young people cannot understand how anyone could have taken Meyerbeer 's influence seriously . " 
- Thus as Wagner 's stock rose , Meyerbeer 's fell . In 1890 , the year before the Paris premiere of Wagner 's Lohengrin , there were no Wagner performances at the Paris Opéra , and 32 performances of Meyerbeer 's four grand operas . In 1909 , there were 60 Wagner performances , and only three of Meyerbeer ( Les Huguenots being the sole work performed ) . 
- 
- = = = <unk> = = = 
- 
- Meyerbeer 's costly operas , requiring grand casts of leading singers , were gradually dropped from the repertoire in the early 20th century . They were banned in Germany from 1933 , and subsequently in subject countries , by the Nazi regime because the composer was Jewish , and this was a major factor in their further disappearance from the repertory . 
- One of the first serious post @-@ war studies of Meyerbeer and grand opera was <unk> 's 1948 book Grand Opera : An Art and a Business which laid out the themes and standards for much subsequent research . A major contribution to revival of interest in Meyerbeer was the work of the scholar Heinz Becker , leading to the complete publication , between 1960 and 2006 , of Meyerbeer 's complete diaries and correspondence in German , which are an important source for musical history of the era . The English scholar Robert <unk> has translated the diaries and undertaken a wide range of Meyerbeer studies . Not least , the establishment of a ' Meyerbeer Fan Club ' in America has stimulated interest . 
- Most importantly the operas themselves are now beginning to be revived and recorded , although despite the efforts of such champions as Dame Joan Sutherland , who took part in performances of , and recorded , Les Huguenots , they have yet to achieve anything like the huge popular following they attracted during their creator 's lifetime . Recordings are now available of all the operas from Il crociato onwards , for many of the earlier Italian operas , and for other pieces including his songs and the incidental music for Struensee . 
- Amongst reasons often <unk> for the <unk> of modern productions are the scale of Meyerbeer 's more ambitious works and the cost of mounting them , as well as the alleged lack of virtuoso singers capable of doing justice to Meyerbeer 's demanding music . However , recent successful productions of some of the major operas at relatively small centres such as <unk> ( L 'Africaine , 2004 ) and Metz ( Les Huguenots , 2004 ) show that this conventional wisdom can be challenged . A highly successful production of Les Huguenots conducted by Marc <unk> with stage direction by Gilbert <unk> was presented at Brussels ' <unk> de la <unk> in 2011 , and a new production of the same work opened at the <unk> <unk> in 2014 , conducted by <unk> Johannes <unk> with stage direction by Tobias <unk> , a co @-@ production with Opéra de Nice . In December 2012 , the Royal Opera House in London premiered its first performance of Robert le diable in 120 years . In 2013 , Meyerbeer 's original version of L 'Africaine in a new critical edition by Jürgen <unk> was performed by <unk> Opera House under the original title Vasco de Gama . The production was a success with audiences and critics and won the poll of German critics award presented by <unk> magazine annually as " Rediscovery of the year " in 2013 . 
- On 9 September 2013 a plaque to mark Meyerbeer 's last residence was put up at <unk> <unk> <unk> , Berlin . 
- 
- = = On film = = 
- 
- Vernon <unk> played the role of Giacomo Meyerbeer in the 1983 film Wagner . 
- 
- = = Recordings = = 
- 
- A Meyerbeer discography ( updated whenever an additional opera by Meyerbeer is issued on CD ) at the Wayback Machine ( archived October 27 , 2009 ) 
- Recordings of Meyerbeer 's operas as listed on <unk> @-@ opera @-@ <unk> / 
- 
- 
- = Washington State Route 221 = 
- 
- State Route 221 ( SR 221 ) is a 25 @.@ 95 @-@ mile ( 41 @.@ 76 km ) long state highway located entirely within Benton County , Washington , United States . The highway serves to connect the unincorporated community of Paterson to the county seat Prosser . The highway has existed since at least 1926 and was designated as Primary State Highway 8E from 1937 until the 1964 renumbering of Washington state highways . 
- 
- = = Route description = = 
- 
- Washington State Route 221 ( SR 221 ) starts at an intersection with SR 14 in the unincorporated community of Paterson . After leaving Paterson the highway travels north through rural farm land as a two @-@ lane highway . A few minor roads are intersected before the roadway turns to the west after about 17 mi ( 27 km ) , before turning back to the north . After the highway resumes its northerly course it climbs into the Horse Heaven Hills , gaining a passing lane through the uphill segments , before finally terminating at SR 22 in south Prosser . 
- Every year the Washington State Department of Transportation ( WSDOT ) conducts a series of surveys on its highways in the state to measure traffic volume . This is expressed in terms of average annual daily traffic ( AADT ) , which is a measure of traffic volume for any average day of the year . In 2009 , WSDOT calculated that as few as 2 @,@ 000 cars traveled through the central part of the highway , and as many as 2 @,@ 500 cars at the interchange with SR 22 . 
- 
- = = History = = 
- 
- The roadway on its current alignment has existed since at least 1926 , however there are records of a road between Paterson and Prosser since 1906 . The highway was designated Secondary State Highway 8E ( SSH 8E ) in 1937 , but the route number was changed to SR 221 during the 1964 state highway renumbering . 
- High winds have forced the closure of SR 221 in 2003 , 2004 , and 2005 due to large amounts of dust being blown around , causing visibility to drop to almost zero through the Horse Heaven Hills . 
- 
- = = Major intersections = = 
- 
- The entire highway is within unincorporated Benton County . 
- 
- 
- = Superman : Escape from Krypton = 
- 
- Superman : Escape from Krypton ( originally known as Superman : The Escape ) is a steel <unk> roller coaster located at Six Flags Magic Mountain in Valencia , California . When it opened in 1997 , it was the tallest roller coaster in the world , and its speed of 100 mph ( 160 km / h ) was tied for the fastest with Tower of Terror II , a similar roller coaster which opened two months earlier at Dreamworld in Australia . These two coasters were the first to utilize Linear <unk> Motor ( <unk> ) technology to propel vehicles to top speed . The park originally intended to open the ride in 1996 , but the opening was delayed because of several issues with the launch system . The ride was closed in late 2010 for refurbishment , and it emerged in 2011 as " Superman : Escape from Krypton " . The refurbished ride featured new trains which face backward , and it was painted with a new color scheme . As of 2013 , Superman : Escape from Krypton has the third @-@ tallest structure , the fifth @-@ fastest speed and the third @-@ longest drop in the world . 
- 
- = = History = = 
- 
- 
- = = = Superman : The Escape ( 1997 — 2010 ) = = = 
- 
- While in the planning stages , the ride was going to be named Velocetron and themed as The Man of Steel . The ride was announced on January 5 , 1996 as Superman : The Escape , breaking records as the first roller coaster to reach 100 miles per hour ( 160 km / h ) , passing <unk> and Phantom 's Revenge at 82 miles per hour ( 132 km / h ) . The ride was designed by <unk> , a Swiss roller coaster firm . It was originally intended to open on June 1 , 1996 . However , due to a range of problems with the launch system , its opening was delayed . In late 1996 , there was a preview for season pass holders . After 10 months of testing and <unk> , the ride opened on March 15 , 1997 . After its opening , the media claimed Superman to be the fastest roller coaster in the world . However , since it was delayed , a similar roller coaster known as Tower of Terror , which also has a 100 @-@ mile @-@ per @-@ hour launch ( 160 km / h ) , had opened about a month earlier at Dreamworld in Australia . Superman therefore lost its claim as being the first roller coaster to reach 100 miles per hour ( 160 km / h ) , although it was then tied with Tower of Terror as the fastest roller coaster in the world . However , the ride became the first roller coaster in the world to go over 400 ft , therefore becoming the tallest roller coaster in the world at the time . 
- In June 2004 , Superman : The Escape 's seat belts were modified because of an incident on the Superman – Ride of Steel roller coaster at Six Flags New England . California State <unk> asked the park to make modifications to the rides ' restraint systems to prevent a similar incident in the future . 
- Just after July 4 weekend of 2010 , Superman : The Escape ceased operations with no reason given . A sign posted in front of the ride indicated that it would not reopen until the 2011 season , with hints that there would be improvements made to the ride experience . After Superman : The Escape 's sister ride , the Tower of Terror II at Dreamworld , underwent a major refurbishment in 2010 which entailed a new vehicle which launches backward , speculation turned to the possibility of a similar modification to the Magic Mountain ride . Six Flags Magic Mountain officials quickly denied rumors that it would receive a <unk> <unk> , similar to roller coasters at other Six Flags parks . 
- 
- = = = Superman : Escape from Krypton ( 2011 — present ) = = = 
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- On October 20 , 2010 , Six Flags Magic Mountain officially announced the refurbishment and re @-@ <unk> of Superman : The Escape , in addition to the construction of two new roller coasters . As part of the refurbishment , the ride was renamed to Superman : Escape from Krypton and featured new backward launching cars and a new color scheme . The upgraded ride reopened to the public on March 19 , 2011 . 
- Superman : Escape from Krypton closed again on February 5 , 2012 ( almost a year after the refurbishment ) to prepare for the new 2012 attraction Lex Luthor : Drop of Doom . Two drop towers , also built by <unk> , were integrated into the existing sides of Superman : Escape from Krypton 's structure . The ride reopened when construction was finished on July 7 , 2012 . 
- To enable the construction of the park 's 2013 roller coaster , Full <unk> , Superman : Escape from Krypton was temporarily closed from December 2012 . It reopened in mid @-@ January with Six Flags Magic Mountain stating the ride may have intermittent closures as the construction of Full <unk> continues . 
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- = = Ride experience = = 
- 
- 
- = = = <unk> and station = = = 
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- At the entrance to the ride , the Superman " S " shield is <unk> and now painted onto the ground . The entrance area and queue are modeled after the Fortress of Solitude , Superman 's headquarters . The station is lit green , modeled as Krypton , the planet that is full of <unk> rock that can take away Superman 's powers . Inside is a crystalline @-@ looking environment which <unk> Superman 's fortress in the Arctic . If the Velocetron name had been chosen , the queue and station would have had ancient ruins and a giant laser . A page on display in the Sky Tower , the park 's observation tower , shows the concept art for Velocetron . 
- 
- = = = <unk> = = = 
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- The roller coaster has two parallel tracks , with both tracks being identical . The vehicle is accelerated by Linear <unk> Motors in reverse out of the station from 0 to 100 miles per hour ( 160 km / h ) in approximately 7 seconds . <unk> experience a g @-@ force of 4 @.@ 5 during the launch . The vehicle then climbs up 415 feet ( 126 m ) at a 90 degree angle . <unk> climb this vertical section facing directly downward , before slightly stopping near the top of the tower . During the vertical section of the ride , riders experience <unk> for about 6 @.@ 5 seconds . The vehicle drops <unk> feet ( 100 m ) and is slowed down before re @-@ entering the station . 
- 
- = = = <unk> = = = 
- 
- The roller coaster originally featured two vehicles , each with three rows of four seats and one row of three seats for a total of 15 riders per vehicle . Both vehicles were built to only run forward . After the ride was refurbished in 2010 , new " streamlined " vehicles with the Superman logo were introduced . The new vehicles were designed with low @-@ profile sides to enhance the open @-@ air feeling . Although they are wider , the row of the three seats in the older vehicle was reduced to two , resulting in a lower total of 14 riders per vehicle . Both of the new trains were configured to run backward , though they were designed to run forward as well . It was reported that the park would eventually run one side forward to give riders a choice , but both trains have remained facing backward . 
- 
- = = = Track = = = 
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- The steel track is approximately 1 @,@ 235 feet ( <unk> m ) in length and the height of the tower is approximately 415 feet ( 126 m ) . The tower is in an " L " shape with two parallel tracks . When the ride opened , the entire structure was painted white . After the ride was refurbished , the top third of the structure was painted red , the track was painted yellow and the rest was painted blue . 
- 
- = = Records = = 
- 
- For the first four years of operation , Superman : Escape from Krypton was tied with Tower of Terror II as the fastest roller coaster in the world . In 2001 , the speed record was taken by <unk> in Japan which features a top speed of 106 @.@ 9 miles per hour ( 172 @.@ 0 km / h ) . Superman : Escape from Krypton held the record for the tallest roller coaster in the world until 2003 when the record was taken by the 420 @-@ foot @-@ tall ( 130 m ) Top Thrill <unk> at Cedar Point . As of 2015 , it has the fifth fastest speed , the third tallest structure and the third @-@ highest drop in the world . 
- 
- 
- = Battle of Hubbardton = 
- 
- The Battle of Hubbardton was an engagement in the Saratoga campaign of the American Revolutionary War fought in the village of Hubbardton , Vermont . Vermont was then a disputed territory sometimes called the New Hampshire Grants , claimed by New York , New Hampshire , and the newly organized and not yet recognized but de facto independent government of Vermont . On the morning of July 7 , 1777 , British forces , under General Simon Fraser , caught up with the American rear guard of the forces retreating after the withdrawal from Fort Ticonderoga . It was the only battle in Vermont during the revolution . ( The Battle of <unk> was fought in what is now <unk> , New York . ) 
- The American retreat from Fort Ticonderoga began late on July 5 after British cannons were seen on top of high ground , Mount <unk> ( <unk> <unk> Mountain and Sugar <unk> Hill ) that commanded the fort . The bulk of General Arthur St. Clair 's army retreated through Hubbardton to Castleton , while the rear guard , commanded by Seth Warner , stopped at Hubbardton to rest and pick up stragglers . 
- General Fraser , alerted to the American withdrawal early on July 6 , immediately set out in pursuit , leaving a message for General John Burgoyne to send reinforcements as quickly as possible . That night Fraser camped a few miles short of Hubbardton , and the German General Friedrich Adolf Riedesel , leading reinforcements , camped a few miles further back . Rising early in the morning , Fraser reached Hubbardton , where he surprised some elements of the American rear , while other elements managed to form defensive lines . In spirited battle , the Americans were driven back , but had almost succeeded in turning Fraser 's left flank when Riedesel and his German reinforcements arrived , eventually scattering the American forces . 
- The battle took a large enough toll on the British forces that they did not further pursue the main American army . The many American prisoners were sent to Ticonderoga while most of the British troops made their way to Skenesboro to rejoin Burgoyne 's army . Most of the scattered American remnants made their way to rejoin St. Clair 's army on its way toward the Hudson River . 
- 
- = = Background = = 
- 
- General John Burgoyne began his 1777 campaign for control of the Hudson River valley by moving an army of 8 @,@ 000 down Lake Champlain in late June , arriving near Fort Ticonderoga on July 1 . On July 5 , General Arthur St. Clair 's American forces defending Fort Ticonderoga and its supporting defenses discovered that Burgoyne 's men had placed cannons on a position overlooking the fort . They evacuated the fort that night , with the majority of the army marching down a rough road ( now referred to locally as the 1776 Hubbardton Military Road ) toward Hubbardton in the disputed New Hampshire Grants territory . The day was hot and sunny , and the pace was rapid and <unk> ; most of the army marched 30 miles ( 48 km ) to Castleton before making camp on the evening of July 6 . 
- 
- = = British troops give chase = = 
- 
- The British general , a Scotsman named Simon Fraser discovered early on July 6 that the Americans had abandoned Ticonderoga . Leaving a message for General Burgoyne , he set out in pursuit with companies of grenadiers ( 9th , 29th , 34th , and 62nd Foot ) and light infantry ( 24th , 29th , 34th , 53rd , and 62nd ) , as well as two companies of the 24th Regiment and about 100 <unk> and Indian scouts . Burgoyne ordered Riedesel to follow ; he set out with a few companies of Brunswick <unk> and grenadiers , leaving orders for the rest of his troops to come as rapidly as possible . Fraser 's advance corps was only a few miles behind Colonel <unk> Francis ' 11th Massachusetts Regiment , which acted as St. Clair 's rear guard . 
- American general St. Clair paused at Hubbardton to give the main army 's tired and hungry troops time to rest while he hoped the rear guard would arrive . When it did not arrive in time , he left Colonel Seth Warner and the Green Mountain Boys behind , along with the 2nd New Hampshire Regiment under Colonel Nathan Hale , at Hubbardton to wait for the rear while the main army marched on to Castleton . When Francis ' and Hale 's men arrived , Warner decided , against St. Clair 's orders , that they would spend the night there , rather than marching on to Castleton . Warner , who had experience in rear @-@ guard actions while serving in the invasion of Quebec , arranged the camps in a defensive position on Monument Hill , and set patrols to guard the road to Ticonderoga . 
- Baron Riedesel caught up with Fraser around 4 pm , and insisted that his men could not go further before making camp . Fraser , who <unk> to this as Riedesel was senior to him in the chain of command , pointed out that he was authorized to engage the enemy , and would be leaving his camp at 3 am the next morning . He then advanced until he found a site about three miles ( 4 @.@ 8 km ) from Hubbardton , where his troops camped for the night . Riedesel waited for the bulk of his men , about 1 @,@ 500 strong , and also made camp . 
- 
- = = Attack = = 
- 
- Fraser 's men were up at 3 am , but did not make good time due to the darkness . Riedesel left his camp at 3 am with a picked group of men , and was still behind Fraser when the latter arrived at Hubbardton near dawn and very nearly surprised elements of Hale 's regiment , which were scattered in the early fighting . A messenger had arrived from General St. Clair delivering news that the British had reached Skenesboro , where the elements of the retreating army had planned to regroup , and that a more <unk> route to the Hudson River was now required . St. Clair 's instructions were to follow him immediately to <unk> . Francis ' men had formed a column to march out around 7 : 15 when the British vanguard began <unk> the hill behind them . Rapidly reforming into a line behind some cover , the Massachusetts men unleashed a withering volley of fire at the <unk> British . General Fraser took stock of the situation , and decided to send a detachment around to flank the American left , at the risk of exposing his own left , which he hoped would hold until Riedesel arrived . Riedesel reached the top of another hill , where he observed that the American line , now including parts of Hale 's regiment , was in fact pressing on Fraser 's left . He therefore sent his grenadiers to support Fraser 's flank and directed the <unk> against the American center . 
- At some point early in the conflict , St. Clair was made aware of the gunfire off in the distance . He immediately dispatched Henry <unk> Livingston and Isaac Dunn to send the militia camped closest to Hubbardton down the road in support of the action . When they reached the area of those camps they found those militia companies in full retreat away from the gunfire in the distance , and no amount of persuasion could convince the men to turn around . Livingston and Dunn continued riding toward Hubbardton . 
- Falling back to a secure position on Monument Hill , the Americans repulsed several vigorous British assaults , although Colonel Francis was hit in the arm by a shot . He <unk> on , directing troops to a perceived weakness on Fraser 's left . The tide of the battle turned when , after more than an hour of battle , Riedesel 's grenadiers arrived . These disciplined forces entered the <unk> singing hymns to the accompaniment of a military band to make them appear more numerous than they actually were . The American flanks were turned , and they were forced to make a desperate race across an open field to avoid being enveloped . Colonel Francis fell in a volley of musket fire as the troops raced away from the advancing British and scattered into the countryside . 
- 
- = = Aftermath = = 
- 
- The scattered remnants of the American rear <unk> made their way toward <unk> in order to rejoin the main army . <unk> by Fraser 's scouts and Indians , and without food or shelter , it took some of them five days to reach the army , which was by then nearing Fort Edward . Others , including Colonel Hale and a detachment of 70 men , were captured by the British as they <unk> up the scene . Colonel Francis , in a sign of respect from his opponents , was buried with the Brunswick dead . 
- Baron Riedesel and the <unk> departed for Skenesboro the next day , much to General Fraser 's <unk> . Their departure left him in " the most disaffected part of America , every person a Spy " , with 600 tired men , a sizable contingent of prisoners and wounded , and no significant supplies . On July 9 he sent the 300 prisoners , under light guard but with threats of retaliation should they try to escape , toward Ticonderoga while he marched his exhausted forces toward Castleton and then Skenesboro . 
- Livingston and Dunn , the two men sent toward the battle by St. Clair , were met by retreating Americans on the Castleton road after the battle was over . They returned to Castleton with the bad news , and the army marched off , eventually reaching the American camp at Fort Edward on July 12 . 
- 
- = = Losses = = 
- 
- The official casualty return for the <unk> troops gives 38 <unk> soldiers and 1 French @-@ Canadian killed and 125 <unk> and 2 French @-@ Canadians wounded . A separate return for the German troops has 10 killed and 14 wounded , for a grand total of 49 killed and 141 wounded . Historian Richard M. Ketchum gives different British casualties of 60 killed and 168 wounded . The American casualties were 41 killed , 96 wounded and 230 captured . 
- 
- = = Legacy = = 
- 
- A local body commissioned the erection of a monument on the battlefield site in 1859 , and the state began acquiring battlefield lands in the 1930s for operation as a state historic site . The battlefield was added to the National Register of Historic Places in 1971 , and is the site of annual Revolutionary War <unk> . 
- 
- 
- = Odaenathus = 
- 
- Lucius Septimius Udaynath , <unk> as Odaenathus ( Aramaic : <unk> / <unk> ; Arabic : <unk> / Udaynath ; 220 – 267 ) , was the founder king ( Mlk ) of the Palmyrene Kingdom centered at the city of Palmyra , Syria . He lifted his city from the position of a regional center subordinate to Rome into the supreme power in the East . Odaenathus was born into an aristocratic Palmyrene family who had received Roman citizenship in the <unk> under the <unk> dynasty . He was the son of Hairan the descendant of Nasor . The circumstances surrounding his rise are ambiguous ; he became the lord ( Ras ) of the city , a position created for him , as early as the 240s and by 258 , he was styled a <unk> , indicating a high status in the Roman Empire . 
- The defeat and captivity of emperor Valerian at the hands of the Persian Sassanian monarch Shapur I in 260 left the eastern Roman provinces largely at the mercy of the Persians . Odaenathus stayed on the side of Rome ; assuming the title of king , he led the Palmyrene army and fell upon the Persians before they could cross the Euphrates to the eastern bank , and inflicted upon them a considerable defeat . Then , Odaenathus took the side of emperor Gallienus , the son and successor of Valerian , who was facing the <unk> of Fulvius Macrianus . The rebel declared his sons emperors , leaving one in Syria and taking the other with him to Europe . Odaenathus attacked the remaining <unk> and <unk> the rebellion . He was rewarded many exceptional titles by the emperor who formalized his self @-@ established position in the East . In reality , the emperor could have done little but to accept the declared nominal loyalty of Odaenathus . 
- In a series of rapid and successful campaigns starting in 262 , he crossed the Euphrates and recovered Carrhae and Nisibis . He then took the offensive to the <unk> of Persia , and arrived at the walls of its capital Ctesiphon . The city withstood the short siege but Odaenathus reclaimed the entirety of Roman lands occupied by the Persians since the beginning of their invasions in 252 . Odaenathus celebrated his victories and declared himself King of Kings , crowning his son Hairan I as co @-@ king . By 263 , Odaenathus was in effective control of the Levant , Mesopotamia and Anatolia 's eastern region . 
- Odaenathus observed all due <unk> towards the emperor , but in practice ruled as an independent monarch . In 266 , the king launched a second invasion of Persia but had to abandon the campaign and head north to <unk> to repel the attacks of <unk> riders besieging the city of Heraclea Pontica . He was assassinated in 267 during or immediately after the Anatolian campaign , together with Hairan . The identities of the perpetrator or the instigator are unknown and many stories , accusations and speculations exist in ancient sources . He was succeeded by his son Vaballathus under the regency of his widow Zenobia , who used the power established by Odaenathus to forge the Palmyrene Empire in 270 . 
- 
- = = Name , Odaenathus I and origin = = 
- 
- " Odaenathus " is the Roman version of the king 's name who was born Lucius Septimius Udaynath c . 220 AD . " Udaynath " is the king 's personal name , an Arabic name that means " little ear " . " Septimius " was the family 's <unk> ( surname ) adopted as an expression of loyalty to the Roman <unk> dynasty , whose emperor Septimius <unk> granted the family Roman citizenship in the late second century . 
- In the Temple of <unk> at Palmyra , a stone block with a sepulchral inscription was found mentioning the building of a tomb and recording the genealogy of the builder : Odaenathus son of Hairan son of <unk> <unk> son of Nasor . Traditional scholarship believed the builder to be an ancestor of the king and he was given the designation " Odaenathus I " . In an inscription dated to 251 , the name of the " Ras " ( lord ) of Palmyra Hairan son of Odaenathus is written , and he was thought to be the son of Odaenathus I. 
- Prior to the 1980s , the earliest known inscription <unk> king Odaenathus was dated to 257 , leading traditional scholarship to believe that Hairan Ras of Palmyra is the father of the king and that Odaenathus I was his grandfather . However , an inscription published in 1985 by archaeologist Michael <unk> and dated to 252 mentions king Odaenathus as a " Ras " and records the same genealogy found in the sepulchral inscription confirming the name of king Odaenathus ' grandfather as <unk> <unk> . <unk> , it is certain that king Odaenathus is the builder of the tomb ruling out the existence of " Odaenathus I " . Ras Hairan mentioned in the 251 inscription is identical with Odaenathus ' elder son and co @-@ ruler prince Hairan I. 
- The origin of the family is <unk> , while the king himself appears to be of mixed <unk> and Arab descent ; his name is Arabic , while the names of his ancestors ( father Hairan and great grandfather Nasor ) are Aramaic . Zosimus asserted that Odaenathus descended from " illustrious forebears " , but the position of the family in Palmyra is debated ; it was probably part of the wealthy <unk> class . Alternatively , the family could have belonged to the tribal leadership who amassed a fortune as landowners and patrons of the Palmyrene <unk> . In Dura @-@ Europos , a relief dated to 159 was commissioned by Hairan son of <unk> son of Nasor ; this Hairan might have been the head of the Palmyrene trade colony in Dura @-@ Europos and probably belonged to the same family of Odaenathus . " Nasor " father of <unk> mentioned in the Dura @-@ Europos inscription could therefore be Odaenathus ' great @-@ great @-@ great grandfather . 
- 
- = = Rise = = 
- 
- Palmyra was an autonomous city subordinate to Rome and part of Syria Phoenice province . Odaenathus descended from an aristocratic family , albeit not a royal one as the city was ruled by a council and had no tradition of hereditary monarchy . <unk> inscriptions from Palmyra record the title of the Palmyrene ruler as " Ras " in Palmyrene and <unk> in Greek , meaning the " Lord of Palmyra " . The title was created for Odaenathus , and was not a usual title in the Roman Empire or a part of the traditional Palmyrene governance institutions ; whether it indicated a military or a priestly position is unknown , but the military role is more likely . 
- The rise of the aggressive <unk> Empire in 224 and the Iranian incursions which affected Palmyrene trade , combined with the weakness of the Roman empire , were probably the reasons behind the Palmyrene council 's decision to elect a lord for the city in order for him to lead a strengthened army . The " Ras " title enabled the bearer to tackle the difficult situation that arose due to the <unk> <unk> ; the supreme authority of the Ras probably made him the supreme civil and military commander with authority over the entire Palmyrene army , which was previously <unk> and led by different generals . 
- 
- = = = Ras of Palmyra = = = 
- 
- An undated inscription refers to Odaenathus as a Ras and records the gift of a throne to him by a Palmyrene citizen named " <unk> son of <unk> <unk> <unk> " , which confirms the supreme character of Odaenathus ' title . The earliest known inscriptions mentioning the title are dated to October 251 and April 252 ; the 251 inscription refer to Odaenathus ' eldest son Hairan I as Ras while the 252 inscription mention Odaenathus with that title . Hairan I was apparently elevated to co @-@ lordship status by his father . Although the written evidence for Odaenathus lordship dates to 251 , it is possible that he acquired the title as early as the 240s ; following the death of Roman emperor Gordian III in <unk> during a campaign against Persia , the Palmyrenes might have elected Odaenathus to defend the city . 
- Odaenathus was described as Roman senator in the undated tomb inscription and Hairan I was mentioned with the same title in the 251 inscription . <unk> opinions vary on the exact date of Odaenathus ' elevation to the position ; <unk> and Jean <unk> maintain that the <unk> rank predates the Ras elevation . <unk> Hartmann concludes that Odaenathus first became a Ras in the 240s then a senator in 250 . Another possibility is that the <unk> rank and lordship occurred simultaneously ; Odaenathus was chosen as a Ras following Gordian 's death , then after Philip the Arab concluded a peace with the Persians , the emperor ratified Odaenathus ' lordship and admitted him to the <unk> to guarantee Palmyra 's continuous subordination . 
- As early as the 240s , Odaenathus inflated the Palmyrene army , recruiting the desert nomads and increasing the numbers of the Palmyrene heavy cavalry units ( <unk> ) . In 252 , Persian emperor Shapur I started a full @-@ scale invasion of the Roman provinces in the east . During the second campaign of the invasion , Shapur conquered Antioch and headed south where his advance was checked in 253 by Emesa 's priest king <unk> <unk> . The events of 253 were mentioned in the works of the sixth century historian John Malalas who also mentioned a leader by the name " <unk> " inflicting a defeat upon the retreating Shapur near the Euphrates . " <unk> " is probably identical with Odaenathus , and while Malalas ' account indicate that Odaenathus defeated the Persians in 253 , there is no proof that the Palmyrene leader engaged Shapur before 260 and Malalas ' account seems to be confusing Odaenathus ' future actions during 260 with the events of 253 . 
- Shapur I destroyed the Palmyrene trade colonies all along the Euphrates ( including the colonies at <unk> in 253 and at Dura @-@ Europos in 256 ) ; Peter the <unk> says that Odaenathus approached Shapur to negotiate Palmyrene interests but was rebuffed and the gifts sent to the Persians were thrown into the river . The date for the attempted negotiations is debated ; Some scholars including John F. Drinkwater set the event in 253 while others such as <unk> Watson set it in 256 following the destruction of Dura @-@ Europos . 
- 
- = = = Governor of Syria Phoenice = = = 
- 
- Several inscriptions dating to the end of 257 or early 258 show Odaenathus bearing the title " <unk> <unk> <unk> " ( <unk> <unk> ) ; this could be a mere honoring or a sign that he was appointed as the <unk> of Phoenice . However , the title ( <unk> <unk> <unk> ) was sometimes used in Syria to denote the provincial governor and William <unk> proposed that Odaenathus was indeed the governor of Phoenice . 
- Five of the inscriptions mentioning Odaenathus as consul are dated to the <unk> year <unk> ( 258 AD ) during which no governor for Phoenice is attested , which might indicate that this was Odaenathus ' year of governorship . In the city of Tyre , Phoenice 's capital , the lines " To Septimius Odaenathus , the most illustrious . The <unk> colony of Tyre " were found inscribed on a marble base ; the inscription is not dated and if it was set after 257 then it indicates that Odaenathus was appointed as the governor of the province . 
- These speculations cannot be proven without doubt but as a governor , Odaenathus would have been the highest authority in the province and above any <unk> commander and provincial officials ; this would make him the commander of the Roman forces in the province . Whatever the case may be , starting from 258 , Odaenathus strengthened his position and extended his political influence in the region . By 260 , Odaenathus held the rank , credibility and power to <unk> the Roman east following the Battle of Edessa . 
- 
- = = = Edessa = = = 
- 
- Faced with Shapur 's third campaign , the Roman emperor Valerian marched against the Persian monarch but was defeated near Edessa in late spring 260 and taken as a prisoner . The Persian emperor then ravaged <unk> , Cilicia and claimed to have captured Antioch , the metropolis of Syria . Taking advantage of the situation , Fulvius Macrianus , the commander of the imperial treasury , declared his sons Quietus and Macrianus Minor as joint emperors in August 260 opposing Valerian 's son Gallienus . Fulvius Macrianus took Antioch as his center and organized the resistance against Shapur ; he dispatched Balista , his <unk> prefect , to Anatolia . Shapur was defeated in the region of <unk> at <unk> prompting the Persians to evacuate Cilicia while Balista went back to Antioch . Balista 's victory was only partial , as Shapur withdrew east of Cilicia where the <unk> Persian units continued to occupy the area , while a Persian force took advantage of Balista 's return to Syria and headed further west in Anatolia . 
- 
- = = Reign = = 
- 
- According to the Augustan History , Odaenathus was declared king of Palmyra as soon as the news of the Roman defeat at Edessa reached the city . It is not known if Odaenathus contacted Fulvius Macrianus and there is no evidence that he took orders from him . 
- 
- = = = Early Persian war and Syria = = = 
- 
- Odaenathus assembled the Palmyrene army and Syrian peasants then marched north to meet the Persian emperor , who was back from Cilicia . The Palmyrene monarch fell upon the retreating Shapur at a place between <unk> and <unk> west of the Euphrates in late summer 260 . Odaenathus defeated the Persians , <unk> Shapur from the province of Syria . However , the <unk> kept the regions east of the Euphrates . 
- In the beginning of 261 , Fulvius Macrianus headed to Europe accompanied by Macrianus Minor leaving Quietus and Balista in Emesa . Odaenathus ' whereabouts during this episode are not clear ; he could have distributed the army in garrisons along the frontier or might have brought it back to his capital . The Palmyrene monarch seems to have waited until the situation clear , declaring loyalty neither to Fulvius Macrianus nor to Gallienus . In the spring of 261 , Fulvius Macrianus arrived in the Balkans but was defeated and killed along with Macrianus Minor ; Odaenathus then marched on Emesa where Quietus and Balista were staying . The <unk> killed Quietus as Odaenathus approached the city , while Balista was captured and executed by the king in autumn 261 . 
- 
- = = = = Ruler of the East = = = = 
- 
- The elimination of the usurpers left Odaenathus as the most powerful leader in the Roman east ; he was granted many titles by the emperor but those honors are debated among scholars : 
- <unk> <unk> ( commander of the Romans ) : was probably given to Odaenathus to recognize his position as the commander in chief of the forces in the east against the Persians ; it was inherited by Odaenathus ' son and successor Vaballathus . 
- <unk> <unk> <unk> ( commander of the entire East ) : it is generally accepted by modern scholars that he bore this title . The corrector had an overall command of the Roman armies and authority over the Roman provincial governors in the designated region . There are no known <unk> of the title during Odaenathus ' lifetime . <unk> for the king bearing the title are two inscriptions in Palmyrene dialect ; one posthumous dedication describing him as MTQNNʿ of the East ( derived from the Aramaic root <unk> , meaning to set in order ) , and the other describing his heir Vaballathus with the same title albeit using the word <unk> instead of MTQNNʿ . 
- However , the sort of authority accorded by this position is widely discussed . The problem arise from the word MTQNNʿ ; its exact meaning is debated . The word is translated to Latin as corrector but <unk> is another possible translation ; the latter title was an honorary one meant to praise the bearer for driving enemies out of Roman territories . However , the inscription of Vaballathus is clearer as the word <unk> is not a Palmyrene word but a direct Palmyrene translation of the Greek term <unk> which is usually an equivalent to corrector . 
- According to David Potter , Vaballathus inherited his father 's exact titles . Hartmann points that there have been cases where a Greek word was translated directly to Palmyrene and a Palmyrene equivalent was also used to mean the same thing . The dedication to Odaenathus would be the using of a Palmyrene equivalent , while the inscription of Vaballathus would be the direct translation . Despite all the arguments , it cannot be certain without doubt that Odaenathus was a corrector . 
- <unk> <unk> <unk> ( emperor of the entire East ) : only the Augustan History claims that Odaenathus was conferred with this title , and also goes so far as to claim that he was made an Augustus ( co @-@ emperor ) following his defeat of the Persians . Both claims are dismissed by scholars . Odaenathus seems to have been acclaimed as <unk> by his troops which is a <unk> reserved for the Roman emperor ; this acclamation might explain the erroneous reports of the Augustan History . 
- Regardless of the titles , Odaenathus controlled the Roman East with the approval of Gallienus who could do little but to <unk> Odaenathus self achieved status and settle for his formal loyalty . Palmyra itself , although officially still part of the Roman empire , became a de facto allied state to Rome instead of a provincial city . Outside of Palmyra , Odaenathus ' authority extended from the <unk> coast in the north to Palestine in the south . This area included the Roman provinces of Syria , Phoenice , <unk> , Arabia , Anatolia 's eastern regions and later ( following the campaign of 262 ) <unk> and Mesopotamia . 
- 
- = = = = First Persian campaign 262 = = = = 
- 
- Perhaps driven by the will to take revenge for the destruction of Palmyrene trade centers and discourage Shapur from initiating future attacks , Odaenathus launched an invasion against the Persians . In the spring of 262 , the king marched north into the occupied Roman province of Mesopotamia , driving out the Persian garrisons and freeing Edessa and Carrhae . The first <unk> was aimed at Nisibis , which Odaenathus regained but sacked since the inhabitants were sympathetic toward the Persian occupation . The Palmyrene monarch destroyed the Jewish city of Nehardea , 45 km west of the Persian capital Ctesiphon , as he deemed the Jews of Mesopotamia loyal to Shapur . By late 262 or early 263 , Odaenathus stood at the walls of the Persian capital . 
- The exact route taken by Odaenathus from Palmyra to Ctesiphon remains uncertain ; it is probably similar to the route emperor Julian took in <unk> during his campaign against Persia . Using this route , Odaenathus would have crossed the Euphrates at <unk> then moved east to Edessa followed by Carrhae then Nisibis ; here , he would have descended south along the <unk> River to the Euphrates valley and marched alongside the river 's left bank to Nehardea . After taking the city , he penetrated the Sassanian province of <unk> and marched along the royal canal <unk> towards the <unk> where the Persian capital stood . 
- Once at Ctesiphon , Odaenathus immediately began the siege of the well @-@ fortified winter residence of the Persian kings ; severe damage was inflicted upon the surrounding areas due to the battles with Persian troops . The city held its ground and the logistical problems of fighting in enemy 's land probably prompted the Palmyrenes to lift the siege . Odaenathus headed north along the Euphrates carrying with him numerous prisoners and booty . The invasion resulted in the full restoration of the Roman lands ( <unk> and Mesopotamia provinces ) occupied by Shapur since the beginning of his invasions in 252 . However , Dura @-@ <unk> and other Palmyrene posts south of <unk> , such as <unk> , were not rebuilt . Odaenathus sent the captives to Rome and by the end of 263 , Gallienus added <unk> <unk> ( " The great victor in Persia " ) to his titles and held a triumph . 
- 
- = = = King of Kings = = = 
- 
- In 263 , after his return , Odaenathus assumed the title King of Kings of the East ( Mlk Mlk ) , and headed to Antioch , the traditional capital of Syria , where he crowned his son Hairan I as co @-@ King of Kings . The title was a symbol of legitimacy in the East , starting with the Assyrians then the <unk> who used it to symbolize their supremacy over all other rulers and was adopted by the <unk> monarchs following their defeat of the <unk> to legitimize their conquests . The first Sassanian monarch <unk> I adopted the title following his victory over the <unk> . Odaenathus ' son was crowned with a <unk> and a tiara ; the choice of the location was probably meant to express that the Palmyrene monarchs were now the successors of the <unk> and Iranian rulers who controlled Syria and Mesopotamia in the past . 
- 
- = = = = <unk> with Rome = = = = 
- 
- In the Roman empire 's hierarchical system , a vassal king usage of the King of Kings title did not indicate that he is a peer of the emperor or that the <unk> ties were cut . The title was probably a challenge not to the Roman emperor but to Shapur I ; Odaenathus was declaring that he , not the Persian monarch , was the legitimate King of Kings in the East . A <unk> depicting Hairan I shows him wearing a crown shaped like that of the <unk> monarchs , so it must have been Odaenathus ' crown ; this combination of title and imagery indicate that Odaenathus considered himself the rival of the <unk> and the protector of the region against them . 
- Odaenathus ' intents are questioned by some historians such as Drinkwater who attribute the attempted negotiations with Shapur to Odaenathus ' quest for power . However , in contrast to the norm of his period when powerful generals proclaimed themselves emperors , Odaenathus chose not to <unk> Gallienus ' throne , and minted no coins bearing his own image . The king had total control over his kingdom of Palmyra and effective control over the Roman East where his military authority was absolute . Odaenathus respected Gallienus ' privilege to appoint provincial governors , but dealt swiftly with opposition ; the Anonymus post Dionem mention the story of <unk> ( <unk> ) , a Roman official , who showed dissatisfaction with Odaenathus ' authority over the Persian frontier and was immediately executed by the king . 
- In general , Odaenathus ' actions were connected to his and Palmyra 's interests only ; his support of Gallienus and his Roman titles did not hide the Palmyrene base of his power and the local origin of his armies , as with his decision not to wait for the emperor to help in 260 . Odaenathus ' status seems to have been , as Watson put it , " something between powerful subject , independent vassal king and rival emperor " . 
- 
- = = = = Administration = = = = 
- 
- Outside his kingdom , Odaenathus had an overall administrative and military authority over the provincial governors of the Roman eastern provinces . Inside Palmyra , no Roman provincial official had any authority ; the king filled the government with Palmyrene staffs . In <unk> to the Iranian practice of making the government appear as a family enterprise , Odaenathus bestowed his own <unk> ( Septimius ) upon his leading generals and officials such as <unk> , <unk> and <unk> . 
- The Palmyrene constitutional institutions continued to function normally during Odaenathus ' reign ; he maintained the council and most civic establishments , permitting the election of magistrates until 264 . When Odaenathus was on campaign , the kingdom was administered by a viceroy , Septimius <unk> . 
- 
- = = = = Second Persian campaign 266 = = = = 
- 
- Sources are silent regarding the events following the first Persian campaign but the silence in itself is an indication of the peace that prevailed and that the Persians stopped being a threat to the Roman East . The evidence for the second campaign is <unk> ; Zosimus is the only one to mention it specifically . A passage in the <unk> <unk> is interpreted by Hartmann as an indication of the second invasion . The campaign took place in 266 or 267 and was aimed directly at Ctesiphon ; Odaenathus reached the walls of the Persian capital but had to cancel the siege and march north to face the influx of <unk> riders attacking Anatolia . 
- 
- = = = = Anatolian campaign = = = = 
- 
- The Romans used the designation " <unk> " to denote many tribes regardless of ethnic origin and sometimes the term would be interchangeable with <unk> ; the tribes attacking Anatolia were probably the <unk> who built ships to cross the Black Sea in 267 and ravaged the coasts of <unk> @-@ Pontus besieging Heraclea Pontica . According to <unk> , Odaenathus arrived at Anatolia with Hairan I and headed to Heraclea but the riders were already gone . They loaded the spoils onto their ships but many perished in a sea battle probably conducted by Odaenathus ; another possibility is that they were shipwrecked . 
- 
- = = Assassination = = 
- 
- Odaenathus was assassinated along with Hairan I in late 267 ; the date is debated and some scholars propose 266 or 268 , but Vaballathus dated his first year of reign between August 267 and August 268 , making late 267 the most probable date . The assassination took place either in Anatolia , or in Syria while the king was returning to Palmyra ; there is no consensus on the manner , perpetrator or the motive behind the act . 
- According to <unk> , Odaenathus was assassinated near Heraclea Pontica trying to quell a tribal incursion into Pontus ; he gives the name of the assassin as another Odaenathus who may or may not have been a relative of the king . The assassin was killed by the king 's bodyguard . Hartmann support the theory that Odaenathus was killed in Pontus . 
- Zosimus simply mention that Odaenathus was killed by conspirators near Emesa at a friend 's birthday party without naming the killer . <unk> attributes the crime to a nephew of Odaenathus but does not give a name . 
- The Augustan History claims that a cousin of the king named Maeonius killed him , while the Anonymus post Dionem names the assassin as another Odaenathus . 
- 
- = = = <unk> = = = 
- 
- The stone block found in the Temple of <unk> bearing Odaenathus ' sepulchral inscription was brought from the tomb built by him ; this shrine 's location is unknown . At the western end of the Great <unk> at Palmyra , a shrine designated the " Funerary Temple no . 86 " ( also known as the House Tomb ) is located . Inside its chamber , steps lead down to a vault crypt which is now lost . This mausoleum might have belonged to the royal family , being the only tomb inside the city 's walls . 
- 
- = = = Assassination theories = = = 
- 
- Roman conspiracy : John <unk> accuse Gallienus of the assassination . An interesting passage in the work of the Anonymus post Dionem speaks of a certain " Rufinus " who orchestrated the assassination on his own initiative then explained his act to the emperor who <unk> the crime . This story talks about Rufinus ordering the murder of an older Odaenathus out of fear that he would rebel , and has the younger Odaenathus complaining to the emperor . Since the older Odaenathus ( Odaenathus I ) was proved to be a fictional character , the story was neglected by most scholars . However , according to Theodor <unk> , younger Odaenathus is an oblique reference to Vaballathus ; Rufinus should be identified with <unk> Rufinus , the Roman governor of Arabia between 261 / 262 . The evidence for a Roman conspiracy is very weak and can not be confirmed . 
- Family feud : According to <unk> , Odaenathus ' nephew <unk> during a lion hunt ; he made the first attack and killed the animal to the dismay of the king . Odaenathus warned the nephew who ignored the warning and repeated the act twice causing the king to deprive him of his horse which is a great insult in the East . The nephew threatened Odaenathus and was put in chains as a result ; Hairan I asked his father to forgive his cousin and his request was granted but as the king was drinking , the nephew approached him with a sword and killed him along with Hairan I. The bodyguard immediately executed the nephew . 
- Zenobia : the wife of Odaenathus was accused by the Augustan History of having formerly conspired with Maeonius , as Hairan I was her stepson and she could not accept that he was the heir to her husband instead of her own children . However , there is no suggestion in the Augustan History that Zenobia was involved in the event that saw her husband 's murder ; the act is attributed to Maeonius ' <unk> and jealousy . Those accounts by the Augustan History can be dismissed as fiction . The hints in modern scholarship that Zenobia had a hand in the assassination out of her desire to rule the empire and dismay with her husband 's pro @-@ Roman policy can be dismissed as there was no reversal of that policy during the first years following Odaenathus ' death . 
- Persian agents or Palmyrene <unk> : the possibility of a Persian involvement exists but the outcome of the assassination would not have served Shapur without establishing a pro @-@ Persian monarch on the Palmyrene throne . Another possibility would be Palmyrenes dissatisfied with Odaenathus ' reign and the changes of their city 's governmental system . 
- 
- = = Family and succession = = 
- 
- Odaenathus was married twice ; nothing is known about his first wife 's name or fate . Zenobia was the king 's second wife whom he married in the late <unk> when she was 17 or 18 years old . 
- The number of children Odaenathus had with his first wife is unknown and only one is attested : 
- Hairan I : the name appears on a 251 inscription from Palmyra describing him as Ras implying that he was already an adult by then . In the Augustan History , Odaenathus ' eldest son is named <unk> ; the inscription at Palmyra dating to 263 celebrating Hairan 's coronation mentions him with the name Herodianus . It is possible that Hairan of the 251 inscription is not the same as Herodianus of the 263 , but this is contested by Hartmann who conclude that the reason for the difference in the spelling is due to the language used in the inscription ( Herodianus being the Greek version ) , meaning that Odaenathus ' eldest son and co @-@ king is Hairan Herodianus . 
- The children of Odaenathus and Zenobia are : 
- Vaballathus : he is attested on several coins , inscriptions , and in the ancient literature . 
- <unk> II : his image appear on a seal impression along with his older brother Vaballathus as the sons of Zenobia ; his identity is much debated . Potter suggest that he is the same as Herodianus who was crowned in 263 and that Hairan I mentioned in 251 died before the birth of Hairan II . 
- <unk> and <unk> : the two were mentioned in the Augustan History and are not attested in any other source ; <unk> might be a <unk> of Hairan and Herodianus while <unk> is most probably a fabrication , although <unk> <unk> suggest that he might be Vaballathus . 
- Possible descendants of Odaenathus living in later centuries are reported ; " Lucia <unk> <unk> <unk> <unk> <unk> <unk> " is known through a dedication dating to the late third or early fourth century inscribed on a tombstone erected by a wet nurse to her " sweetest and most loving mistress " . The tombstone was found in Rome at the San Callisto in <unk> . Another possible relative is " Eusebius " who is mentioned by Libanius in <unk> as a son of an " Odaenathus " who was in turn a descendant from the king ; the father of Eusebius is mentioned as fighting against the Persians ( most probably in the ranks of emperor Julian ) . In 393 , Libanius mentioned that Eusebius promised him a speech written by <unk> for the king . In the fifth century , the philosopher " Syrian Odaenathus " lived in Athens and was a student of <unk> of Athens ; he might have been a distant descendant of the king . 
- The Augustan History claims that Maeonius was proclaimed emperor for a brief period before being killed by the soldiers . However , no inscriptions or other evidence exist for Maeonius ' reign and he was probably killed immediately after <unk> the king . Odaenathus was succeeded by his son , the ten @-@ year @-@ old Vaballathus under the regency of Zenobia . Hairan II probably died soon after his father , as only Vaballathus succeeded to the throne . 
- 
- = = Legacy = = 
- 
- Odaenathus was the founder of the Palmyrene royal dynasty ; he left Palmyra the premier power in the East , and his actions laid the foundation of Palmyrene strength which culminated in the establishment of the Palmyrene Empire in 270 . Many writers wrote about <unk> of Odaenathus ; <unk> of <unk> probably accompanied the king on his campaigns and wrote a history of that period starting from Philip the Arab and ending shortly before the king 's death . According to Potter , <unk> ' account was meant to glorify Odaenathus and demonstrate his superiority over Roman emperors . 
- The memory of Odaenathus was highly esteemed in the Roman empire ; the Augustan History , written in the fourth century , places Odaenathus among the Thirty <unk> ( probably because he assumed the title of king ) . However , it speaks highly of his role in the Persian war and credit him with saving the empire : " Had not Odaenathus , prince of the Palmyrenes , seized the imperial power after the capture of Valerian when the strength of the Roman state was exhausted , all would have been lost in the East " . 
- The king was praised by Libanius , and was the subject of a prophecy in the <unk> <unk> <unk> : " Then shall come one who was sent by the sun [ i.e. Odaenathus ] , a mighty and fearful lion , breathing much flame . Then he with much <unk> daring will destroy ... the greatest beast — venomous , fearful and emitting a great deal of <unk> [ i.e. Shapur ] " . 
- Odaenathus is viewed negatively in <unk> sources ; his <unk> of Nehardea <unk> the Jews , and he was cursed by the Babylonian Jews and the Jews of Palestine . 
- 
- 
- = Banksia violacea = 
- 
- Banksia violacea , commonly known as violet banksia , is a species of shrub or tree in the plant genus Banksia ( family <unk> ) . It generally grows as a small shrub to 1 @.@ 5 m ( 5 ft ) high with fine narrow leaves , and is best known for its unusually coloured dark purple @-@ violet inflorescences . The colour of the inflorescences , short leaves , and flattened follicles which are sticky when young , help identify this species from others in the field . It is found in low <unk> in southern regions of Western Australia from Esperance in the east to <unk> in the west , growing exclusively in sandy soils . 
- First described in 1927 by the West Australian botanist Charles Gardner , the species was at one stage considered a variety of B. sphaerocarpa . Although there are no recognised subspecies or varieties , both lignotuberous and <unk> forms exist for Banksia violacea . <unk> , ants and flies have been recorded visiting flower spikes . Banksia violacea is classified as Not Threatened under the Wildlife Conservation Act of Western Australia . Regarded as of little value to <unk> , it is rarely cultivated . 
- 
- = = Description = = 
- 
- Banksia violacea grows as a shrub up to 1 @.@ 5 m ( 5 ft ) tall , with narrow leaves 1 – 2 cm ( 0 @.@ 4 – 0 @.@ 8 in ) long and about 0 @.@ 15 cm ( 0 @.@ 06 in ) wide . New growth occurs in summer , and flowering ranges from November to April with a peak in February , but can be irregular in timing . Flowers arise from typical Banksia " flower spikes " , and the inflorescences are made up of hundreds of pairs of flowers densely packed in a spiral around a woody axis . Roughly spherical with a diameter of 2 – 3 cm ( 0 @.@ 8 – 1 @.@ 2 in ) , the flower spikes arise from lateral stems lie partly within the foliage . Unusually for Banksia species , the inflorescences are often violet in colour , ranging anywhere from a dark violet @-@ black through various combinations of violet and greenish @-@ yellow in less <unk> blooms . Each flower consists of a tubular perianth made up of four fused <unk> , and one long <unk> style . The styles are hooked rather than straight , and are initially trapped inside the upper perianth parts , but break free at <unk> . The old flowers gradually fade to brown . The fruiting structure or <unk> is a stout woody " <unk> " , with a hairy appearance caused by the persistence of old <unk> flower parts . These follicles are crowded around the globular spike ( called an <unk> at this point ) and are oval to <unk> , although the <unk> makes some irregularly shaped . They measure 1 – 2 @.@ 5 cm ( 0 @.@ 4 – 1 in ) long , 0 @.@ 6 cm ( 0 @.@ 2 in ) high and 0 @.@ 8 – 2 @.@ 2 cm ( 0 @.@ 3 – 0 @.@ 9 in ) wide . They are quite flattened and lack a ridge along the valve line . When young , the follicles are greenish in colour and slightly sticky , and covered in fine white hairs , fading to tan or grey with age . They open with fire , releasing a winged wedge @-@ shaped ( <unk> ) seed 2 – 2 @.@ 5 cm ( 0 @.@ 8 – 1 in ) long . The mottled dark grey seed body is <unk> ( crescent @-@ shaped ) and measures 1 @.@ 2 – 1 @.@ 8 cm ( 0 @.@ 5 – 0 @.@ 7 in ) long and 0 @.@ 2 – 0 @.@ 25 cm ( 0 @.@ 1 in ) wide , with a flattened dark brown wing 1 @.@ 1 – 1 @.@ 7 cm ( 0 @.@ 4 – 0 @.@ 5 in ) wide . The woody <unk> has the same dimensions as the seed . 
- The bright green <unk> leaves of the seedlings are oblong to linear in shape and measure 1 @.@ 5 cm ( 0 @.@ 6 in ) long by 0 @.@ 3 cm ( 0 @.@ 1 in ) wide . The greenish red <unk> is hairy , as are the stems of young plants . The hairy <unk> leaves are crowded and <unk> arranged . They measure 0 @.@ 7 – 1 @.@ 3 cm ( 0 @.@ 2 – 0 @.@ 5 in ) in length and have recurved margins . Young plants often begin branching within their first year of life . 
- 
- = = Taxonomy = = 
- 
- The type specimen of Banksia violacea was collected by the West Australian botanist Charles Gardner on 14 December 1926 in the vicinity of Lake Grace . The following year , he published a description of the species in Journal and Proceedings of the Royal Society of Western Australia . He placed it in section Oncostylis of <unk> 's taxonomic arrangement of Banksia , giving it the specific epithet violacea in reference to the violet flowers . Thus the full name of the species , with author citation , is Banksia violacea <unk> The species has been considered a variety of B. sphaerocarpa ( Fox Banksia ) ; this view was published by William <unk> in his 1954 How to know Western Australian <unk> . He considered B. violacea to be a variety of B. sphaerocarpa with violet flowers . This description was an invalid publication , however , and a nomen nudum . In 1981 Alex George declared Banksia sphaerocarpa var. violacea <unk> a <unk> synonym of B. violacea . 
- In George 's 1981 arrangement , B. violacea was placed in subgenus Banksia because its <unk> is a typical Banksia " flower spike " ; section Oncostylis because of its hooked styles ; and series Abietinae because its <unk> is roughly spherical . It was placed in taxonomic sequence between B. incana ( <unk> Banksia ) and B. <unk> ( <unk> 's Banksia ) . 
- In 1996 , Kevin Thiele and Pauline Ladiges published the results of a cladistic analysis of morphological characters of Banksia . They retained George 's <unk> and many of his series , but discarded his sections . B. ser . Abietinae was found to be very nearly monophyletic and so it was retained . It further resolved into four <unk> , so Thiele and Ladiges split it into four subseries . B. violacea appeared in the last of these : 
- This clade became the basis of B. subseries <unk> , which Thiele defined as containing those taxa with very long and slender styles , smoothly convex perianth limbs without a <unk> ridge , and thickened margins . In accordance with their cladogram , their arrangement placed B. violacea first in taxonomic sequence , followed by B. laricina ( Rose @-@ <unk> Banksia ) . However , Thiele and Ladiges ' arrangement was not accepted by George , who , questioning the emphasis on <unk> , rejected most of their changes in his 1999 arrangement , restored B. series Abietinae to his broader 1981 definition , and abandoned all of Thiele and Ladiges ' subseries . George commented that the species has no close relatives , being " loosely allied " to B. sphaerocarpa ( Fox Banksia ) and B. <unk> ( Swamp Fox Banksia ) . Despite this , the sequence of the series was altered so that B. violacea fell between B. <unk> ( Burma Road Banksia ) and B. incana , and its placement in George 's arrangement may be summarised as follows : 
- Banksia 
- B. <unk> . Banksia 
- B. sect . Banksia ( 9 series , 50 species , 9 subspecies , 3 varieties ) 
- B. sect . <unk> ( 1 species ) 
- B. sect . Oncostylis 
- B. ser . <unk> ( 7 species , 2 subspecies , 4 varieties ) 
- B. ser . <unk> ( 1 species ) 
- B. ser . <unk> ( 1 species ) 
- B. ser . Abietinae 
- B. sphaerocarpa ( 3 varieties ) 
- B. <unk> 
- B. <unk> 
- B. <unk> 
- B. leptophylla ( 2 varieties ) 
- B. <unk> 
- B. <unk> 
- B. violacea 
- B. incana 
- B. laricina 
- B. <unk> 
- B. <unk> ( 2 subspecies ) 
- B. <unk> ( 2 varieties ) 
- B. <unk> . <unk> ( 3 species ) 
- Since 1998 , an American botanist , Austin Mast , has been publishing results of ongoing cladistic analyses of DNA sequence data for the <unk> <unk> . His analyses suggest a phylogeny that is very greatly different from George 's arrangement , and somewhat different from Thiele and Ladiges ' . With respect to B. violacea , Mast 's results agree with its placement near B. laricina and B. incana , placing it in a clade with these two species and B. sphaerocarpa var. <unk> ( treated at species rank as B. <unk> ) . However , Thiele 's B. subseries <unk> appears to be <unk> , as do both definitions of B. ser . Abietinae — that is , none form a natural grouping . 
- Early in 2007 , Mast and Thiele initiated a <unk> of Banksia by merging <unk> into it , and publishing B. subgenus <unk> for the taxa having spoon @-@ shaped <unk> . They foreshadowed publishing a full arrangement once DNA sampling of <unk> was complete ; in the meantime , if Mast and Thiele 's <unk> changes are taken as an interim arrangement , then B. violacea is placed in B. subgenus <unk> . 
- 
- = = Distribution and habitat = = 
- 
- B. violacea occurs in southern regions of Western Australia , from <unk> to Esperance and as far north as <unk> . This distribution includes areas of the Avon <unk> , Esperance Plains and <unk> <unk> regions . It favours white sandy soils , often <unk> <unk> , clay or <unk> . It usually grows among heath and <unk> , associated with <unk> eucalypts and Banksia sphaerocarpa var. <unk> . Banksia violacea is classified as Not Threatened under the 1950 Wildlife Conservation Act of Western Australia . 
- 
- = = Ecology = = 
- 
- Like most other <unk> , Banksia violacea has <unk> roots , roots with dense clusters of short lateral rootlets that form a mat in the soil just below the leaf litter . These enhance <unk> of nutrients , thus allowing their uptake in low @-@ nutrient soils such as the phosphorus @-@ deficient soils of Australia . B. violacea is highly susceptible to <unk> <unk> <unk> . 
- Banksia violacea is one of a small number of Banksia species that has both lignotuberous and non @-@ lignotuberous populations . In both cases , plants are adapted to release their aerial seed bank following a <unk> , ensuring seedlings are established on clear and relatively fertile ground ; however the possession of a lignotuber makes plants much less reliant on fire regime for population maintenance and regeneration , as maternal plants are not killed by <unk> , but <unk> from below ground level . <unk> plants generally occur among the north @-@ eastern populations , in the vicinity of <unk> . An investigation into the <unk> of these plants failed to find any <unk> , climatic or other environmental factors associated with the possession of a lignotuber . 
- Banksia <unk> in general play host to a variety of birds , mammals and insects . However , only wasps , ants and flies were recorded visiting flower spikes during observations for The Banksia Atlas in the mid @-@ 1980s . 
- 
- = = <unk> = = 
- 
- Banksia violacea is rarely cultivated . It is a slow @-@ growing plant that tends to become untidy with age , and generally does not flower until four to five years after sprouting from seed . Flowers are an unusual colour , but occur within the bush where they grow within and are usually obscured by foliage . It <unk> light <unk> not below the green foliage , except for the variant with a lignotuber , which may be <unk> heavily . George recommends a sunny position in light , sandy soil . Professor Margaret Bernard <unk> of the <unk> Institute suggests the species is of no value to <unk> , as the inflorescences are too small and obscured by the foliage , although she does add that the purple colour may be a worthwhile character to select for in plant breeding . <unk> do not require any treatment , and take 19 to 50 days to germinate . 
- 
- 
- = Rob Howard = 
- 
- Rob Howard ( born 1954 or 1955 ) is a Canadian politician who was elected to the 39th Parliament of British Columbia as the Member of the Legislative Assembly of British Columbia from the riding of Richmond Centre . A member of the BC Liberal Party , he replaced retiring BC Liberal Olga Ilich in that riding , by winning the riding in the 2009 provincial election . While his party formed a majority government , Howard was not included in Gordon Campbell 's cabinet but was appointed to several committees , including the Select Standing Committee on Public Accounts in the first two sessions , and Select Standing Committee on Finance and Government Services in the third and fourth session . Howard introduced one piece of legislation , the Trustee Board of the Church of God , Richmond Municipality , <unk> ( Corporate Restoration ) Act , 2009 ( Pr 402 ) , to <unk> restore that organization 's corporate status . 
- As chair of the Select Standing Committee on Finance and Government Services , Howard supported Premier Campbell 's efforts at establishing the <unk> Sales Tax . Following Campbell 's resignation , Howard endorsed Kevin Falcon but Christy Clark won the leadership election . Clark eventually made Howard a Parliamentary Secretary in the Ministry of Transportation . In this position he advocated for Open Sky agreements and continued this advocacy in his post @-@ political life by establishing the non @-@ profit organization <unk> . Howard did not seek re @-@ election during the 2013 provincial election and was replaced by BC Liberal Teresa Wat . 
- Prior to his election to the legislature , Howard worked in property management . He served as a City Councillor in the Richmond , British Columbia for seven years . He was first elected to the Richmond , British Columbia City Council in the October 2001 by @-@ election as a member of the Richmond Non @-@ Partisan Association and was re @-@ elected in the November 2002 and 2005 civic elections as a member of the Richmond First Party . He sat on council as an independent starting in 2006 . While on council he advocated in favour of casino expansion , locating the Olympic speed @-@ skating oval in Richmond , and developing a convention centre . 
- 
- = = Background = = 
- 
- Rob Howard was born and raised in Richmond . He graduated from the University of British Columbia where he studied Urban Land Economics . He went on to work at Richmond <unk> Credit Union for almost 20 years , and in 1994 , started his own business , <unk> Real Estate Management , which specialized in Property Development and property management . He is married to Trudy and has one son , Justin ( Jay ) . 
- Howard volunteered with the Richmond Minor Hockey Association for over 12 years , and was active with Tourism Richmond , the Richmond Chamber of Commerce , and the Real Estate Institute of BC . While his mother had served as an <unk> on the Richmond City Council , Howard 's political career began with a civic bylaw election in October 2001 . Howard , belonging to the civic political party Richmond Non @-@ Partisan Association , was elected to one of the three available seats on the Richmond City Council . After less than a year on council , Howard left the Richmond Non @-@ Partisan Association to join a new pro @-@ business political party , Richmond First , which he saw as being less controlling and allowing him to make more independent decisions . He was re @-@ elected in the November 2002 civic election , with his Richmond First party taking four of the eight council seats . 
- On local issues , Howard was an advocate of opening Richmond to gambling facilities , which led to the River Rock Casino Resort . He was supportive of heritage preservation measures but opposed Richmond 's tree preservation bylaw . He was the only one on council who preferred the elevated option for the Canada Line ( then known as the Richmond @-@ Airport @-@ Vancouver Line ) , whereas the other councillors preferred the ground level option or no <unk> extension at all . He was an advocate for building a convention centre and the most ardent supporter of removing a 55 @-@ acre piece of land from the Agricultural Land Reserve as a possible location for it , though the removal was refused . When the possibility of the city constructing a speed @-@ skating oval associated with the 2010 Winter Olympics came , Howard was a vocal supporter . He travelled to Lillehammer , Norway , in 2004 as part of a committee to investigate similar facilities built for the 1994 Winter Olympics . He also traveled to <unk> , Quebec and Asia as part of the sister city program . He was not supportive of proposals to build a new soccer facility or financially contributing to tall ships tourism attractions . Because previous councils had made funding commitments at the expense of the city 's reserve funds rather than the tax base , Howard 's time on council was marked with consistent property tax increases around 3 to 4 % each year . Howard unsuccessfully lobbied other councillors to hire more police officers . He became the subject of a lawsuit , along with other Richmond First councillors , alleging a conflict @-@ of @-@ interest occurred after they approved a <unk> allowing a controversial pub . 
- In the 2005 civic election , he was again elected , though his Richmond First party only won three of the eight seats . He unsuccessfully lobbied his fellow councillors to appoint him to fill one of Richmond 's two seats at Metro Vancouver . With not even his own party members supporting him for the Metro Vancouver seat and finding himself being alone in a number of issues , such as supporting the <unk> line being elevated , opposing the tree preservation bylaw , and wanting to hire more police officers , Howard left the Richmond First party in February 2006 to be an independent . 
- 
- = = Provincial politics = = 
- 
- Following the announcement by Richmond Centre MLA Olga Ilich that she would not be seeking re @-@ election , Howard announced , in January 2008 , that he would seek to replace Ilich as the BC Liberal Party candidate in the next election . As no one else came forward to challenge Howard , he was acclaimed , in November 2008 , to be the BC Liberal candidate . In the general election , held in May 2009 , he was challenged by a New Democratic Party candidate , <unk> public <unk> <unk> , and a Green Party candidate , teacher Michael Wolfe , and Nation Alliance Party candidate , <unk> Kang Chen . The riding was considered to be a safe seat for the BC Liberals to win , which the 54 @-@ year @-@ old Howard did win with over 60 % of the vote , with his BC Liberal Party winning a majority government . 
- As the 39th Parliament began , BC Premier Gordon Campbell did not include Howard in his cabinet . Howard was appoint to the Select Standing Committee on Public Accounts in the first two sessions and then the Select Standing Committee on Finance and Government Services in the third and fourth sessions , in which he chaired the committee and traveled the province gathering public input regarding budget spending priorities . He was also appointed to the Select Standing Committee on Crown Corporations and the Select Standing Committee on Education in the first two sessions , but neither committee held any meetings . He introduced one piece of legislation , a private members bill , the Trustee Board of the Church of God , Richmond Municipality , <unk> ( Corporate Restoration ) Act , 2009 ( Pr 402 ) which <unk> restored that organization 's corporate status . 
- Howard toured the province advocating for the federal government to enter into Open skies agreements with Asian nations . He also advocated for the <unk> Sales Tax . Howard remained loyal to Premier Campbell , praising his October 2010 announcement of using the remainder of the budget to cut income tax by 15 % , two weeks before the Select Standing Committee on Finance and Government Services was to deliver its report on public consultation for budget priorities . After Campbell resigned , and the tax cut <unk> , the 2011 BC Liberal leadership election ensued . Along with Richmond 's other two <unk> , John Yap and Linda Reid , Howard endorsed Kevin Falcon to be the new party leader , citing Falcon 's willingness to listen to all arguments and saying " I think he can bring a new dynamic , a youthful energy to the discussion ; he 's a great speaker , a great <unk> . " Christy Clark eventually won the leadership and became premier but , like Campbell , did not include Howard in the executive council . In March 2012 , Premier Clark promoted Howard to a parliamentary secretary position under the Ministry of Transportation and directed to focus on air services agreements where he served until September . In early 2013 , Howard was selected by BC Liberal <unk> chair , Gordon <unk> , to assist with the party 's investigation into the party 's alleged use of government resources and employees in partisan promotional efforts in certain ethnic communities . 
- Following a summer of deliberations on his future , Howard cited " personal reasons " including a desire to spend more time with his wife , in his September 2012 announcement that he would not be seeking re @-@ election in the up @-@ coming May 2013 provincial general election . This decision was a surprise to his party because his Richmond Centre riding was considered a safe seat for him to be re @-@ elected . With no obvious successor , a competitive BC Liberal Party primary began . By the end of the year , two candidates announced their intention to run : school trustee Grace <unk> and <unk> officer Gary Law . However , Howard approached Teresa Wat , the CEO of the Chinese language radio station <unk> , to be his replacement . Though she did not live in the riding , she was viewed as a better candidate and , in January 2013 , the party announced she would be the candidate . While the other candidate <unk> withdrew his nomination to accept a position on a political advisory committee , Law alleged he was harassed to drop out and requested a <unk> investigation . Law decided to run as an independent candidate but only received <unk> of the vote , with Wat winning the election and subsequently being named Minister of International Trade . Following the election Howard founded the non @-@ profit organization <unk> to advocate for Open Sky agreements to allow more airline competition in Canadian international airports . 
- 
- = = Electoral history = = 
- 
- 
- 
- = Oribi = 
- 
- Oribi ( pronounced / <unk> / ) ( <unk> ourebi ) is a small antelope found in eastern , southern and western Africa . The sole member of its genus , the oribi was first described by the German zoologist <unk> August Wilhelm von Zimmermann in 1782 . Eight subspecies are identified . The oribi reaches nearly 50 – 67 centimetres ( 20 – 26 in ) at the shoulder and weighs 12 – 22 kilograms ( 26 – 49 lb ) . This antelope features a slightly raised back , and long neck and limbs . The glossy , yellowish to rufous brown coat contrasts with the white chin , throat , underparts and rump . Only males possess horns ; the thin , straight horns , 8 – 18 centimetres ( 3 @.@ 1 – 7 @.@ 1 in ) long , are smooth at the tips and ringed at the base . 
- Typically diurnal , the oribi is active mainly during the day . Small herds of up to four members are common ; males defend their group 's territory , 25 – 100 hectares ( 62 – 247 acres ) large . The oribi is primarily a <unk> , and prefers fresh grasses and <unk> occasionally . A seasonal breeder , the time when mating occurs varies geographically . Unlike all other small antelopes , oribi can exhibit three types of mating systems , depending on the habitat – <unk> , polygyny and <unk> . Gestation lasts for six to seven months , following which a single calf is born ; births peak from November to December in southern Africa . <unk> takes place at four to five months . 
- The oribi occurs in a variety of habitats – from <unk> , floodplains and tropical grasslands with 10 – 100 centimetres ( 3 @.@ 9 – 39 @.@ 4 in ) tall grasses to <unk> grasslands at low altitudes , up to 2 @,@ 000 metres ( 6 @,@ 600 ft ) above the sea level . This antelope is highly sporadic in distribution , ranging from Senegal in the west to Ethiopia and Eritrea in the east and southward to Angola and the Eastern Cape ( South Africa ) . The oribi has been classified as Least Concern by the IUCN ; numbers have declined due to agricultural expansion and competition from livestock . 
- 
- = = Taxonomy = = 
- 
- The scientific name of the oribi is <unk> ourebi . The sole member of its genus , the oribi is placed under the family <unk> . The species was first described by the German zoologist <unk> August Wilhelm von Zimmermann in 1782 . The oribi was formerly included in the tribe <unk> , that comprised a variety of other dwarf antelopes , including <unk> ( <unk> ) , <unk> ( <unk> <unk> ) , <unk> , <unk> ( <unk> ) and <unk> . In 1963 , German <unk> Theodor <unk> separated the oribi and <unk> into a new tribe , <unk> ; later on , zoologist Jonathan <unk> assigned the oribi to <unk> , a tribe of its own . The common name " oribi " ( pronounced / <unk> / ) comes from the <unk> name for the animal , <unk> . 
- In a revision of the phylogeny of the tribe <unk> on the basis of nuclear and mitochondrial data in 2013 , Eva <unk> <unk> ( of the University of Cambridge ) and colleagues showed that the oribi is the sister taxon to all other <unk> . The cladogram below is based on the 2013 study . 
- The following eight subspecies are identified : 
- Of these , <unk> Colin Groves and Peter <unk> identify O. o. hastata , O. o. <unk> , O. o. ourebi and O. o. <unk> as independent species in their 2011 publication <unk> Taxonomy . 
- 
- = = Description = = 
- 
- The oribi is a small , slender antelope ; it reaches nearly 50 – 67 centimetres ( 20 – 26 in ) at the shoulder and weighs 12 – 22 kilograms ( 26 – 49 lb ) . The head @-@ and @-@ body length is typically between 92 and 110 centimetres ( 36 and 43 in ) . <unk> dimorphic , males are slightly smaller than females ( except for O. o. ourebi , in which females are smaller ) . This antelope features a slightly raised back , and long neck and limbs . The glossy , yellowish to rufous brown coat contrasts with the white chin , throat , underparts and rump . The bushy tail , brown to black on the outside , has white <unk> ( except in O. o. hastata , that has a completely black tail ) . The subspecies show some variation in colouration ; O. o. ourebi is a rich rufous , while O. o. hastata is <unk> . 
- Only males possess horns ; the thin , straight horns , 8 – 18 centimetres ( 3 @.@ 1 – 7 @.@ 1 in ) long , are smooth at the tips and ringed at the base . The maximum horn length , 19 @.@ 1 centimetres ( 7 @.@ 5 in ) , was recorded in 1998 from Malawi . The oribi has at least six different , well @-@ developed scent glands ( such as the prominent <unk> glands near the eyes ) . The body has several modifications , such as the large <unk> below the eyes , to accommodate such a large number of glands . Females have four <unk> . 
- 
- = = Ecology and behaviour = = 
- 
- The oribi is diurnal ( active mainly during the day ) , though some activity may also be observed at night . The animal rests in cover during rain events . Unlike all other small antelopes , oribi can exhibit three types of mating systems , depending on the habitat – <unk> , polygyny and <unk> ; polygyny tends to prevail as the female @-@ to @-@ male ratio increases . A study suggested that polygyny is preferred in areas of high predator risk , as it leads to formation of groups as an anti @-@ predator measure . Small herds of up to four members are also common . 
- Males defend their group 's territory , 25 – 100 hectares ( 62 – 247 acres ) large ; female members may also show some aggression and drive away intruders . A study showed that the number of females that visit the male 's territory depends on the appearance ( particularly the symmetry ) of the male 's horns . Males mark vegetation and soil in their territories by <unk> <unk> <unk> and <unk> ; the intensity of marking increases with the number of male neighbours . <unk> males tend to have greater access to females in and around the territory than other males . An important feature of the social behaviour of oribi is the " dung ceremony " , in which all animals form temporary dung <unk> . Oribi at least three months old have been observed giving out one to three alarm whistles on sensing danger . These whistles are more common in adults than in juveniles , and males appear to whistle more . Common predators include <unk> such as <unk> . 
- 
- = = = Diet = = = 
- 
- Primarily a <unk> , the oribi prefers fresh grasses and <unk> occasionally . Grasses can constitute up to 90 % of the diet ; preferred varieties include <unk> , <unk> , <unk> , <unk> , <unk> and <unk> species . Mineral <unk> are also visited regularly . Oribi have been observed feeding on flowers and Boletus mushrooms . Groups of oribi <unk> in the rainy season , when grasses are abundant . 
- 
- = = = Reproduction = = = 
- 
- Both sexes become sexually mature at 10 to 14 months . A seasonal breeder , the time when mating occurs varies geographically . Mating may peak in the rainy season ( August to September ) . When a female enters <unk> ( which lasts for four to six days ) , it seeks the company of males . During courtship , the male will pursue the female , test her urine to check if she is in <unk> and <unk> her rump and flanks . Gestation lasts for six to seven months , following which a single calf is born ; births peak from November to December in southern Africa . The newborn is kept in concealment for nearly a month ; the mother pays regular visits to her calf to <unk> it for nearly half an hour . Males may guard their offspring from predators and keep away other males . <unk> takes place at four to five months . The oribi lives for 8 to 12 years in the wild , and for 12 to 14 years in captivity . 
- 
- = = Distribution and habitat = = 
- 
- The oribi occurs in a variety of habitats – from <unk> , floodplains and tropical grasslands with 10 – 100 centimetres ( 3 @.@ 9 – 39 @.@ 4 in ) tall grasses to <unk> grasslands at low altitudes , up to 2 @,@ 000 metres ( 6 @,@ 600 ft ) above the sea level . Recently burnt areas often attract groups of oribi . The choice of habitat depends on the availability of cover needed to escape the eyes of predators . Population densities typically vary between 2 and 10 individuals per km2 ; however , densities as high as 45 individuals per km2 have been recorded in tropical grasslands that receive over 110 centimetres ( 43 in ) of annual rainfall and open floodplains . The oribi 's range overlaps with those of larger <unk> such as the African buffalo , <unk> , <unk> , Thomson 's <unk> and <unk> . These separate species often occur in close proximity to each other , increasing predator <unk> . 
- This antelope is highly sporadic in distribution ; it occurs mainly in eastern , southern and western Africa , ranging from Nigeria and Senegal in the west to Ethiopia and Eritrea in the east and southward to Angola and the Eastern Cape ( South Africa ) . It is feared to be extinct in <unk> . 
- 
- = = <unk> and conservation = = 
- 
- The oribi has been classified as Least Concern by the IUCN . The total population ( as of 2008 ) is estimated at 750 @,@ 000 . However , the subspecies O. o. <unk> is listed as Vulnerable because , as of 2008 , the total population is estimated at less than 10 @,@ 000 mature individuals , and is feared to be declining . Hunting is a relatively minor threat , since the oribi shows some tolerance to hunting . Nevertheless , the steep fall of 92 % in oribi populations in <unk> National Park ( <unk> d <unk> ) has been attributed to poaching . Numbers have also declined due to agricultural expansion and competition from livestock . 
- The oribi occurs in a number of protected areas throughout its range , such as : <unk> <unk> National Park in Nigeria , the <unk> and W National Parks ( <unk> ) ; <unk> Hunting Zone ( Chad ) ; <unk> , <unk> <unk> and <unk> National Parks ( Cameroon ) ; <unk> @-@ <unk> St. <unk> National Park ( Central African Republic ) ; <unk> , <unk> and <unk> National Parks ( Congo @-@ <unk> ) ; <unk> National Park ( Ethiopia ) ; <unk> Mara Game Reserve and <unk> National Park ( Kenya ) ; Golden Gate Highlands National Park ( South Africa ) ; <unk> National Park ( Tanzania ) ; <unk> Valley , Lake <unk> and Murchison Falls National Parks ( Uganda ) ; <unk> and <unk> Plain National Parks and <unk> Swamp ( Zambia ) . 
- 
- 
- = Rockstar 101 = 
- 
- " Rockstar 101 " is a song by Barbadian recording artist Rihanna from her fourth studio album , Rated R ( 2009 ) . The song features the British @-@ American guitarist Slash of the rock group Guns N ' Roses . It was released on May 18 , 2010 , as the fifth single from the album . Rihanna wrote the song in collaboration with the producers The @-@ Dream and Christopher " Tricky " Stewart ; vocal production was carried out by Makeba Riddick . The album version is a hip hop song , while the remix EP 's consist of dance , dubstep and electronic music adaptations , some of which were remixed by Dave <unk> and Mark <unk> . 
- Critical response to " Rockstar 101 " was mixed ; some critics praised Rihanna 's " <unk> " while others criticized the " aggressive " tone . The song charted at number 64 on the United States ' Billboard Hot 100 chart , and number two on the Hot Dance Club Songs chart . To promote the song , Rihanna performed on American Idol in April 2010 . It has been included on the set lists of her Last Girl on Earth Tour ( 2010 – 11 ) and Diamonds World Tour ( 2013 ) . <unk> <unk> directed the song 's music video , which portrays Rihanna impersonating Slash . She wears custom made jewelry designed by Fannie <unk> in some parts of the video , while Blink @-@ 182 drummer Travis Barker makes a cameo appearance . 
- 
- = = Background and release = = 
- 
- Rihanna co @-@ wrote " Rockstar 101 " in collaboration with the song 's producers The @-@ Dream and Christopher " Tricky " Stewart . Her vocals and the instrumentation for the song were recorded at <unk> Sound Studios in Los Angeles , California , the Boom Boom Room in Burbank , California and Triangle Sound Studios in Atlanta , Georgia . It was mixed by Jaycen Joshua at Larrabee Studios in Universal City , California ; he was assisted in the process by Giancarlo <unk> . Rihanna 's vocals were produced by Makeba Riddick . The song was engineered by Marcus <unk> , Brian " B @-@ <unk> " Thomas , Andrew <unk> and Chris " <unk> " O <unk> . Additional engineering was done by Pat <unk> . Guitar was provided by Tim Stewart , while Monte <unk> performed additional keys . The song features a guitar performance by Slash , the British @-@ American musician , songwriter and former lead guitarist of the American hard rock band Guns N ' Roses . 
- " Rockstar 101 " was the fourth single from the album Rated R to be released in the US and the fifth overall . It was sent to contemporary hit and rhythmic radio stations in the US on May 18 , 2010 , and to Australian radio stations on July 19 , 2010 . The song was released as an extended play ( EP ) in the United States through <unk> on June 2 , 2010 , to the iTunes Store on July 13 , and in the United Kingdom through Amazon on June 29 , 2010 . These EP 's consist of remixes by various <unk> and music producers , including Dave <unk> and Mark <unk> . 
- 
- = = Composition = = 
- 
- " Rockstar 101 " is a hip hop song which lasts for three minutes and fifty @-@ eight seconds . It features an " aggressive " guitar solo from Slash , according to Leah <unk> of Entertainment Weekly . Rihanna uses her lower register and " heavy " <unk> to perform the most of the song , and she " <unk> " the lyric " The only thing I 'm missing is a black guitar " . Rihanna " struts her stuff " as she insists that she is a " big shit <unk> " as she <unk> that " I 'll never play a victim / I 'd rather be a <unk> " . Sean <unk> for Spin thought that when Rihanna sings the line " Got my middle finger up , I don 't give a fuck " , it sounded as though she had never sworn before due to how she <unk> <unk> the word ' fuck ' . 
- 
- = = Reception = = 
- 
- 
- = = = Critical response = = = 
- 
- " Rockstar 101 " received mixed responses from music critics . Brian <unk> for IGN praised the song , writing that it " actually works " despite sounding disjointed at first . Emily <unk> of PopMatters defined " Rockstar 101 " as a " brilliant bit of <unk> " during her review of Rated R , while <unk> Malone for NME wrote that Rihanna sings with " <unk> " on the track , which was something that she has <unk> to thank for . Jon Pareles for The New York Times simply highlighted a line from the song , " I never play the victim " . Neil McCormick for The Daily Telegraph thought that " Rockstar 101 " was one of the few songs on the album to retain the " <unk> leftover hints of her warm Caribbean vocal <unk> " . Ann Powers for the Los Angeles Times was critical of Slash 's inclusion on the song , and she described it as an " <unk> " . Powers wrote " she can definitely get by without that ultimate rock phallic symbol " , a reference to Slash . Pitchfork Media 's Ryan <unk> felt that " Rockstar 101 , alike " Russian <unk> " and " The Last Song " , were " instantly @-@ dated <unk> from a <unk> era when a Slash feature was cool " . He further wrote that " Rockstar 101 " and " <unk> " were " harder to justify considering their mindless <unk> and <unk> production " . The song was met with a negative review from The Guardian 's Alex <unk> , who wrote " At one extreme , the resemblance of ' <unk> ' ' s chorus to that of a stadium rock ballad seems to have encouraged Rihanna to cut out the middle @-@ man and just start making stadium rock : cue the awful <unk> @-@ woo guitars of ' Rockstar 101 ' and ' Fire <unk> ' " . 
- 
- = = = Chart performance = = = 
- 
- " Rockstar 101 " made its first chart appearance on the United States ' Billboard Hot Dance Club Songs chart , where it peaked at number two ; it stayed on the chart for 14 weeks . The song peaked on the US Hot Digital Songs chart at number 28 , spending seven weeks on the chart . It subsequently peaked at number 64 on the US Billboard Hot 100 chart and spent five weeks on the chart . It also peaked at number 10 on the US R & B / Hip @-@ hop Digital Songs chart . " Rockstar 101 " debuted on the Australian Singles Chart at number 50 on August 22 , 2010 ; it peaked at number 24 two weeks later for one week , and remained on the chart for six weeks . 
- 
- = = Music video = = 
- 
- 
- = = = Background = = = 
- 
- The director <unk> <unk> filmed the music video for " Rockstar 101 " in April 2010 ; she had previously directed the videos for Rihanna 's singles " Hard " and " Rude Boy " . This video was edited by <unk> <unk> from <unk> , who previously edit videos for " <unk> " and " <unk> " . On May 19 , 2010 , Rihanna released a 30 @-@ second sneak @-@ <unk> preview of the video on the internet , whilst the full video premiered on May 25 , 2010 through the high @-@ definition music video website <unk> . 
- 
- = = = Synopsis = = = 
- 
- According to <unk> Rodriguez of MTV , the video is " a mix of <unk> bondage @-@ esque <unk> , complete with Rihanna 's <unk> moves and a <unk> band that features Travis Barker on the drums . " Shortly after the video was released , Slash said that he was " flattered " that Rihanna had <unk> him in the video , saying " The video is way better with her being me than with me being me ... all things considered , it brings an element of sexuality to it that I probably wouldn 't have been capable of . I think it 's hot . Everything works out the way it 's supposed to . " 
- Although Slash is featured on " Rockstar 101 " , he does not appear in the music video . Instead , Rihanna pays homage to him by impersonating him by presenting herself wearing a skull @-@ laden top hat , wig , leather jacket , dark glasses and <unk> : Rihanna can be seen strumming a guitar Slash 's style . Rihanna is seen in eight different scenes and settings , one of which showed an almost nude Rihanna covered in black body paint wearing only a spiked crown and jewelry chains , which were created by designer Fannie <unk> . In other scenes , Rihanna smashes a black electric guitar and can be seen wearing an outfit made from parts of a guitar . Blink @-@ 182 drummer Travis Barker makes a cameo appearance and is featured as one of the drummers in Rihanna 's rock band . 
- 
- = = Live performances = = 
- 
- Rihanna first performed " Rockstar 101 " on American Idol on April 7 , 2010 . She wore a black PVC <unk> and was accompanied on stage by guitarist <unk> <unk> ; the set design included flame throwers and a video screen which showed guitars , lightning bolts and skulls . At one point , the singer picked and played up a black Gibson Flying V guitar . Larry Carroll for MTV wrote that the extent of Rihanna 's ability to play the guitar was limited to playing it with one finger on one of the strings . However , he complimented Rihanna 's overall performance , writing that her shoulder pads were " nearly as huge as her presence " . He went on to write that she " half @-@ sung , half @-@ spoke " the song . A reviewer for MTV UK agreed with Carroll , writing that " halfway through the performance [ Rihanna ] <unk> a ' black flying v guitar ' although we 're not sure you could say she ' played ' it ! " <unk> <unk> of Rolling Stone praised Rihanna 's performance , writing " if the introductory rules of rock stardom are ' teach <unk> to pick slide , ' ' tight clothes ' and ' <unk> ! ' then Rihanna has been paying attention in her ' Rockstar 101 ' classes after all " . 
- A reporter for the Daily Mail wrote that Rihanna displayed her competitive side during her performance on American Idol , after " Lady Gaga set such high standards " . Writing for About.com , Bill Lamb was critical of Rihanna 's decision to perform " Rockstar 101 " on American Idol . He commented that previous media reports had indicated that the singer would be debuting a different album track , " Te <unk> " , which was later released as the sixth and final single from Rated R. Lamb thought that " Rockstar 101 " was one of the " weaker " songs on the album , and predicted that it would be " destined to end the streak of three consecutive top 10 pop hits from the album " . While he wrote that the " intensely <unk> , distant feel of the song works " within the context of Rated R , Lamb was unsure of whether or not it would appeal to the masses on radio . The song was included on the set list of the Last Girl on Earth Tour ( 2010 – 11 ) . Clay Cane for BET wrote that Rihanna " deserves a round of applause for doing something different , straying away from being a standard pop tart " . " Rockstar 101 " is also included on Rihanna 's Diamonds World Tour ( 2013 ) . 
- 
- = = Track listing = = 
- 
- 
- = = Credits and personnel = = 
- 
- Recording 
- Recorded at <unk> Sound Studios , Los Angeles , CA ; The Boom Boom Room , Burbank CA ; Triangle Sound Studios , Atlanta , GA 
- Mixed at Larrabee Studios , Universal City , CA 
- Personnel 
- Credits adapted from the <unk> cover of Rated R. 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Radio and release history = = 
- 
- 
- 
- = St Mary 's Church , Rhodogeidio = 
- 
- St Mary 's Church , Rhodogeidio is a small medieval church , dating from the 15th century , near <unk> @-@ y @-@ <unk> , in Anglesey , north Wales . It served as a chapel of ease to another church in the area , St <unk> 's . Some restoration work was carried out in the 19th century , but St Mary 's has since fallen into disuse and is now largely in ruins . 
- It is a Grade II listed building , a national designation given to " buildings of special interest , which warrant every effort being made to preserve them " , in particular because it is " a late Medieval church of exceptionally simple character " , and is virtually unaltered despite its poor condition . One writer has said that St Mary 's " has the distinction of probably being the most isolated church in Anglesey " . 
- 
- = = History and location = = 
- 
- St Mary 's Church is in the countryside in Rhodogeidio , in Anglesey , north Wales . It is about 1 @.@ 5 miles ( 2 @.@ 4 km ) north of <unk> @-@ y @-@ <unk> , towards the north of Anglesey . It is reached by a <unk> from a farm – it is only accessible on foot – and is surrounded by a churchyard . 
- It was built in the 15th century as a chapel of ease to serve St <unk> 's Church , Rhodogeidio , about 0 @.@ 75 miles ( 1 @.@ 25 km ) to the east . Restoration work was carried out in the 19th century , but the church later fell into disuse . It has been in ruins since sometime between 1937 , when the survey by the Royal Commission on Ancient and Historical Monuments in Wales and Monmouthshire recorded its condition as " fair " , and 1970 , when it was given listed building status and its " very poor " condition noted . 
- 
- = = Architecture and fittings = = 
- 
- St Mary 's is a small church , measuring 30 feet by 12 feet 3 inches ( about 9 @.@ 1 by 3 @.@ 7 m ) . It dates from the late medieval period , and was constructed using rubble masonry and large stones . It had a slate roof , although most of the roof has now gone , and there is a bellcote at the west end , which may be original rather than a later addition . There is no physical division between the nave and the chancel , although one account in the 19th century said that there had originally been a screen separating them , and some traces of it still remained at that time . 
- Entrance to the church was through a doorway with a square head , in the north wall at the west end ; this dates from the 19th century . An inscribed stone near the doorway has the date 1798 and some initials , and it has been suggested that this marked the date of some renovation work . There are two windows on the south wall , one fully blocked and one partially blocked . The small window at the east end of the church dates from the latter part of the 15th century . 
- Inside , little remains , but the ruins still have most of the original roof <unk> ( one , at the west end of the church , is a replacement ) . There are some slate memorial tablets on the walls , dating from the middle of the 19th century , and a plain 12th @-@ century circular font . The 1937 survey noted a bell dating from 1717 and some portions of 17th @-@ century <unk> seating near the pulpit ; these were not recorded when the church was given listed status in 1970 . 
- 
- = = Assessment = = 
- 
- St Mary 's has national recognition and statutory protection from alteration as it has been designated as a Grade II listed building – the lowest of the three grades of listing , designating " buildings of special interest , which warrant every effort being made to preserve them " . It was given this status on 12 May 1970 and has been listed because it is " a late Medieval church of exceptionally simple character " . Cadw ( the Welsh Government body responsible for the built heritage of Wales and the inclusion of Welsh buildings on the statutory lists ) also notes that despite its " very poor condition " , St Mary 's " is a virtually unaltered late Medieval building ( even retaining the original roof <unk> ) and in its isolation , is characteristic of many churches on the island . " 
- Writing in 1859 , the clergyman and antiquarian Harry <unk> Jones called it a " small , plain , single @-@ <unk> chapel " , and said that one of the small south windows was " a good specimen of its kind . " A 2006 guide to the churches of Anglesey says that the " <unk> ruin " of St Mary 's " has the distinction of probably being the most isolated church in Anglesey " . 
- 
- 
- = First Light ( Rebecca Stead novel ) = 
- 
- First Light is a young adult science fiction and mystery novel by Rebecca Stead , first published in 2007 . The novel follows Peter , who is in Greenland with his father and mother for research on global warming , and Thea , who lives in Gracehope , an underground colony located below Greenland . First Light explains how global warming is melting Gracehope and Peter and Thea 's attempt to persuade the people to leave . The novel addresses the effects of global warming as a theme . 
- Stead began writing the novel in 2002 , but her first draft was confusing and unorganized . To help her , she met editor Wendy Lamb who advised her to meet with a small group of people who would help critique the novel . After three years of work , Stead finished the second draft and met with Lamb who once again helped make improvements and later published the novel . Reviewers praised the description of Gracehope and main characters , as well as the performances of <unk> Marlo and David Ackroyd who voiced Thea and Peter in a subsequent audiobook publication of First Light . 
- 
- = = Inspiration and origins = = 
- 
- Stead drew inspiration from many sources in order to create the novel . As a child growing up in a big city , she was interested in the small towns which made her think " that in smaller places , everybody , even the kids , had special identities , where as in a city people are pretty anonymous " . An idea began to form for a hidden society that also served as a small town . 
- Stead began writing First Light in 2002 , but had no experience prior to that . The first draft turned out confusing and unorganized . To help her , Stead met with Wendy Lamb , an editor Stead had met in a workshop a few years before . Lamb suggested that Stead meet with a " critique group " who would help read and revise the drafts Stead wrote . After Stead created a revised copy of the novel in 2005 , she sent it to Lamb again and a contract was drawn up . More revisions were made in order to make the new world created in the novel easier to understand . Stead found it hard " to maintain a sense of ' the whole ' . Over and over I mapped out the book out for myself using post @-@ its in a <unk> <unk> , trying to get a sense of where the tension went " . The novel took three years to complete . 
- After the revisions were completed , much of the plot stayed the same , but several events in the original draft were cut out to strengthen the whole book . Stead combined two characters into one , having a stronger outline and also cut out several scenes for minor characters . In the end , Stead found that the revisions helped to make the book more cohesive . 
- 
- = = Plot summary = = 
- 
- First Light follows the adventure of two protagonists , Peter , who lives with his mother and father in New York but is in Greenland for his father 's research , and Thea , who lives in an underground colony in Greenland called Gracehope . Gracehope was formed hundreds of years ago by a group called the Settlers who used to live in England . They possessed unusual abilities , such as extremely good vision and hearing , leading them to be called ' eye <unk> ' and ' ear <unk> ' , respectively . These powers were seen as sorcery , prompting Grace , the leader of the Settlers , to bring the Settlers under the ice in Greenland where they could live in peace . 
- While walking around her house , Thea finds a map in her room of Gracehope . The map shows a tunnel leading onto the surface . Thea and her cousin <unk> find the tunnel and meet Peter who helps them back to Gracehope . Reaching Gracehope , Peter realizes that several <unk> of the people are in the shape of mitochondrial DNA , which his mom is studying . After waking up from a headache , Peter finds his mom next to his bed . She explains that she used to live in Gracehope , but was banished with her sister , after her sister ventured above the surface and contracted an illness that could not be cured . She also explains that her research of mitochondrial DNA relates to the ability of mutations to benefit the human body , which could cause their extremely good vision and hearing . In the end , she warns Peter that global warming is causing Gracehope to slowly melt away . The entire colony must learn the dangers they face and escape . One obstacle lies in their way : Rowen , Thea and Peter 's grandmother who banished Peter 's mom and did nothing to help Thea 's mom when she was on her deathbed from an illness when she ventured <unk> . Rowen is the head of the Council in Gracehope and is strictly against going <unk> . 
- To convince the rest of the colony , Peter and Thea plan to use a piece of mythology , that a dog with four white paws would be born when it was time to leave . Such a dog had been born several days ago but has yet to open his eyes . Thea decides to proceed without using the dog and tries to convince the colony at a <unk> of the <unk> 's escape to Greenland with several allies who know of Rowen 's actions . Just as Thea and her allies are about to lose the argument , Peter arrives with the dog , whose eyes are open . That , coupled with the fact that Peter is an eye adept , the first in a hundred years , convinces the colony to listen to Thea instead of Rowen . The novel ends eight months later as the people of Gracehope are slowly educated on global warming and the dangers of staying in their colony . 
- 
- = = Genre and themes = = 
- 
- First Light is categorized as a science fiction and mystery novel . Katie <unk> from the Philadelphia Inquirer also listed the novel as a " slow @-@ to @-@ unfold mystery combining elements of science and history with an appealing note of fantasy " . Kirkus Reviews classified the novel as an " ice @-@ age mystery " as both Peter and Thea " discover one another 's worlds as well as the truth about themselves " . Connie Tyrrell Burns from School Library Journal found the novel to be " an exciting , engaging mix of science fiction , mystery , and adventure " . 
- Reviewers also noted the environmental theme in the book . Kirkus Reviews found that " With the impending threat of global warning as an ominous backdrop , teens from very different worlds find they have much in common " . In the novel , Peter finds an underground civilization beneath Greenland that is sinking as a result of global warming . Burns felt that First Light is a " great discussion starter of issues ranging from global warming to <unk> and building a new society " . <unk> recognized the global warming theme and also classified First Light as a coming @-@ of @-@ age tale . 
- 
- = = Critical reception = = 
- 
- Publisher 's Weekly said , " It is a testament to the storytelling that the existence of this parallel world and the convergence of Peter and Thea 's stories , told in separate chapters , are both credible and absorbing . Young readers will find this a journey worth taking . " Kirkus Reviews found the novel to be a " <unk> enjoyable <unk> adventure " , praising how the adventure tests the main characters ' courage as they learn the truth of both worlds . <unk> Smith from Horn Book Magazine found the two main characters well written and the city of Gracehope well structured . While , Smith found the city 's origin unbelievable , she recognized that few people would care about that and compared the threat in First Light similar to the one in The City of <unk> . <unk> Hubert from <unk> also compared the novel to The City of <unk> and to Neal <unk> 's <unk> . Hubert criticized the slow beginning and flaws in the mythology and structure of the underground world , but still felt that " the icy setting and global @-@ warming theme are well realized " . 
- 
- = = Audiobook <unk> = = 
- 
- First Light has been made in an audiobook containing six disks and a length of seven hours and six minutes . The audiobook was released by the Listening Library and read by David Ackroyd and <unk> Marlo for the parts of Peter and Thea , respectively . <unk> Miller from School Library Journal praised the two readers for their performance which " engages listeners , and they are both adept at creating a different voice for each character and moving seamlessly between them " . Miller also found the novel useful for starting a discussion on topics such as " political <unk> and propaganda to global warming " . A review from Horn Book Review by <unk> <unk> praised the easy shifts from American to English accents made by both <unk> . <unk> also found the tone given to Thea appropriate and compared the novel to Stead 's second work , When You Reach Me , feeling that " [ t ] <unk> First Light doesn 't reach the excellence of Stead 's second novel , the <unk> @-@ winning When You Reach Me , this audio will nevertheless draw listeners in with its <unk> ' strong talent " . 
- 
- 
- = Mexico City Metropolitan Cathedral = 
- 
- The Metropolitan Cathedral of the Assumption of the Most Blessed Virgin Mary into Heaven ( Spanish : <unk> <unk> de la <unk> de la Santísima <unk> María a los <unk> ) is the largest cathedral in the Americas , and seat of the Roman Catholic Archdiocese of Mexico . It is situated atop the former Aztec sacred precinct near the <unk> Mayor on the northern side of the Plaza de la <unk> in Downtown Mexico City . The cathedral was built in sections from 1573 to 1813 around the original church that was constructed soon after the Spanish conquest of <unk> , eventually replacing it entirely . Spanish architect Claudio de <unk> planned the construction , drawing inspiration from Gothic cathedrals in Spain . 
- The cathedral has four façades which contain portals flanked with columns and statues . The two bell towers contain a total of 25 bells . The tabernacle , adjacent to the cathedral , contains the baptistery and serves to register the parishioners . There are two large , ornate altars , a sacristy , and a choir in the cathedral . Fourteen of the cathedral 's sixteen chapels are open to the public . Each chapel is dedicated to a different saint or saints , and each was sponsored by a religious guild . The chapels contain ornate altars , altarpieces , <unk> , paintings , furniture and sculptures . The cathedral is home to two of the largest 18th @-@ century organs in the Americas . There is a crypt underneath the cathedral that holds the remains of many former archbishops . 
- Over the centuries , the cathedral has suffered damage . A fire in 1967 destroyed a significant part of the cathedral 's interior . The restoration work that followed uncovered a number of important documents and artwork that had previously been hidden . Although a solid foundation was built for the cathedral , the soft clay soil it is built on has been a threat to its structural integrity . <unk> water tables and accelerated sinking caused the structure to be added to the World Monuments Fund list of the 100 Most Endangered Sites . Reconstruction work beginning in the 1990s stabilized the cathedral and it was removed from the endangered list in 2000 . 
- After the Spanish conquest of the Aztec Empire , the conquistadors decided to build their church on the site of the <unk> Mayor of the Aztec city of <unk> to consolidate Spanish power over the newly conquered domain . Hernán Cortés and the other conquistadors used the stones from the destroyed temple of the Aztec god of war <unk> , principal deity of the <unk> , to build the church . Cortés ordered the original church 's construction after he returned from exploring what is now Honduras . <unk> Martín de <unk> was the first director of this project from <unk> to 1532 . Juan de Zumárraga , the first Bishop of the first See of the New World , established in the <unk> of New Spain , promoted this church 's completion . Zumárraga 's Cathedral was located in the northeast portion of what is now the cathedral . It had three naves separated by three <unk> columns . The central roof was <unk> with intricate carvings done by Juan <unk> <unk> and gilded by Francisco de <unk> and <unk> de la <unk> . The main door was probably of Renaissance style . The choir area had 48 seats made of <unk> wood crafted by Adrian <unk> and Juan <unk> . However , this church was soon considered inadequate for the growing importance of the capital of New Spain . 
- In 1544 , ecclesiastical authorities in <unk> ordered the creation of new and more <unk> cathedral . In 1552 , an agreement was reached whereby the cost of the new cathedral would be shared by the Spanish crown , <unk> and the Indians under the direct authority of the archbishop of New Spain . The cathedral was begun by being built around the existing church in 1573 . When enough of the cathedral was built to house basic functions , the original church was demolished to enable construction to continue . 
- 
- = = Construction = = 
- 
- The cathedral was constructed over a period of over two centuries , between 1573 and 1813 . Its design is a mixture of three architectural styles that predominated during the colonial period , Renaissance , Baroque and Neo @-@ classic . 
- Initial plans for the new cathedral were drawn up and work on the foundation began in <unk> . The decision to have the cathedral face south instead of east was made in <unk> . In the same year , construction commenced , working from the Gothic designs and models created by Claudio de <unk> and Juan Miguel de <unk> , inspired by cathedrals found in Spanish cities such as <unk> and <unk> . 
- Because of the muddy <unk> of the site , work on the foundation continued past the work on the walls to <unk> . In <unk> , work on the first of the cathedral 's chapels began and by 1615 , the cathedral 's walls reached to about half of their final height . Construction of the interior of the current cathedral began in 1623 and what is now the vestry was where Mass was conducted after the first church was finally torn down . 
- In 1629 , work was interrupted by flooding , over two metres in depth . Parts of the city were damaged , especially around the main plaza or <unk> . Because of such damage , this site was almost abandoned and a new cathedral project was begun in the hills of the <unk> area to the west . 
- Despite these problems , the project continued in its current location , and under the direction of Luis Gómez de <unk> , the interior was finished and consecrated in 1667 . The cathedral still lacked bell towers , the complete front facade , and many of the other features it has now at the beginning of the 18th century . 
- In 1787 , José Damian Ortiz de Castro was in charge of finishing work on the cathedral . He did most of the work on the bell towers , putting in most of the <unk> and capping them with roofs in the shape of bells . With his death in 1793 , he did not live to see the cathedral completed , and Manuel Tolsá finished the cathedral by adding the <unk> , the central front facade , the <unk> , and the statues of Faith , Hope and Charity at the top of the front facade . <unk> 's work was the last major construction to the cathedral and the appearance it had when he finished is the basic look the cathedral has today . 
- The cathedral faces south and is approximately 54 @.@ 5 metres ( 179 ft ) wide and 110 metres ( 360 ft ) long . It consists of two bell towers , a central dome , three main portals , five naves , 51 vaults , 74 arches and 40 columns . Inside the cathedral are five large altars , sixteen chapels , a choir area , a corridor , <unk> room , and sacristy . The cathedral has approximately 150 windows . 
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- = = <unk> = = 
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- = = = <unk> and portals = = = 
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- The main facade of the cathedral faces south . The main portal is centered in the main facade and is the highest of the cathedral 's three portals . <unk> of Saint Peter and Paul the <unk> stand between the columns of the portal , while Saint Andrew and James the Just are depicted on the secondary doorway . In the center of this doorway is a high relief of the Assumption of the Virgin Mary , to whom the cathedral is dedicated . This image is flanked by images of Saint Matthew and Saint Andrew . The coat of arms of Mexico is above the doorway , with the eagle 's wings <unk> . There is a clock tower at the very top of the portal with statues representing Faith , Hope and Charity , which was created by sculptor Manuel Tolsá . 
- The west facade was constructed in 1688 and rebuilt in 1804 . It has a three @-@ section portal with images of the Four <unk> . The west portal has high reliefs depicting Jesus handing the Keys of Heaven to Saint Peter . 
- The east facade is similar to the west facade . The reliefs on the east portal show a ship carrying the four <unk> , with Saint Peter at the helm . The title of this relief is The ship of the Church sailing the seas of Eternity . 
- The northern facade , built during the 16th century in the Renaissance Herrera style , is oldest part of the cathedral and was named after Juan de Herrera , architect of the El Escorial monastery in Spain . While the eastern and western facades are older than most of the rest of the building , their third level has <unk> columns which are associated with the Baroque period . 
- All the high reliefs of the portals of the cathedral were inspired by the work of Flemish painter Peter Paul Rubens . 
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- = = = Bell towers = = = 
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- The bell towers are the work of <unk> artist José <unk> Ortiz de Castro . They are capped with bell @-@ shaped roofs made of tezontle covered in <unk> , a white stone . Ortiz de Castro was in charge of the cathedral 's construction in the latter half of the 18th century until he died , unexpectedly . Manuel Tolsá of Valencia , who had built other notable buildings in Mexico City , was hired to finish the cathedral . At this point , the cathedral had already been 240 years in the making . He added the neo @-@ Classic structure housing the clock , the statues of the three theological virtues ( Faith , Hope , and Charity ) , the high <unk> surrounding the building , and the dome that rises over the transept . 
- The cathedral has 25 bells — eighteen hang in the east bell tower and seven in the west tower . The largest bell is named the Santa Maria de Guadalupe and weighs around 13 @,@ 000 kilograms ( 29 @,@ 000 lb ) . Other major bells are named the Doña Maria , which weighs 6 @,@ 900 kilograms ( 15 @,@ 200 lb ) , and La <unk> ( " the <unk> one " ) , named so because of its harsh tone . Doña Maria and La <unk> were placed in 1653 while the largest bell was placed later in 1793 . 
- The statues in the west tower are the work of José <unk> <unk> and represent Pope Gregory VII , Saint Augustine , <unk> of Seville , St. <unk> of <unk> , <unk> Xavier , and Saint Barbara . The statues in the east tower are by Santiago <unk> <unk> and depict Emilio , Rose of Lima , Mary ( mother of Jesus ) , <unk> , Jerome , Philip of Jesus , <unk> of Rome , and Isidore the <unk> . 
- In 1947 , a novice bell <unk> died in an accident when he tried to move one of the bells while standing under it . The bell swung back and hit him in the head , killing him instantly . The bell was then " punished " by removing the <unk> . In the following years , the bell was known as la <unk> ( " the punished one " ) , or la <unk> ( " the <unk> one " ) . In 2000 , the <unk> was reinstalled in the bell . 
- In October 2007 , a time capsule was found inside the stone ball base of a cross , in the southern bell tower of the cathedral . It was placed in 1742 , supposedly to protect the building from harm . The lead box was filled with religious artifacts , coins and <unk> and hidden in a hollow stone ball . The ball was marked with the date of 14 May 1791 , when the building 's topmost stone was laid . A new time capsule will be placed in the stone ball when it is closed again . 
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- = = = Tabernacle = = = 
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- Situated to the right of the main cathedral , the Metropolitan Tabernacle ( Spanish : <unk> <unk> ) was built by Lorenzo Rodríguez during the height of the Baroque period between <unk> and <unk> , to house the archives and vestments of the archbishop . It also functioned and continues to function as a place to receive Eucharist and register parishioners . 
- The first church built on the cathedral site also had a tabernacle , but its exact location is unknown . During the construction of the cathedral , the tabernacle was housed in what are now the Chapels of San Isidro and Our Lady of <unk> of Granada . However , in the 18th century , it was decided to build a structure that was separate , but still connected , to the main cathedral . It is constructed of tezontle ( a reddish porous volcanic rock ) and white stone in the shape of a Greek cross with its southern facade faces the <unk> . It is connected to the main cathedral via the Chapel of San Isidro . 
- The interiors of each wing have separate uses . In the west wing is the <unk> , in the north is the main altar , the main entrance and a <unk> area , separated by inside corner walls made of <unk> stone and tezontle . <unk> , a white stone , covers the walls and floors and the tezontle frames the doors and windows . At the crossing of the structure is an octagonal dome framed by arches that form curved triangles where they meet at the top of the dome . The principal altar is in the ornate Churrigueresque style and crafted by indigenous artist Pedro <unk> <unk> . It was inaugurated in 1829 . 
- The exterior of the Baroque styled tabernacle is almost entirely adorned with decorations , such as <unk> shaped niche shelves , floating <unk> and many <unk> . <unk> of fruits such as grapes and <unk> have been created to in the shape of ritual offerings , symbolizing the Blood of Christ and the Church . Among the floral elements , <unk> , <unk> , and various types of four @-@ <unk> flowers can be found , including the indigenous <unk> . 
- The tabernacle has two main outside entrances ; one to the south , facing the <unk> and the other facing east toward <unk> Street . The southern façade is more richly decorated than the east façade . It has a theme of <unk> the Eucharist with images of the Apostles , Church Fathers , saints who founded religious orders , martyrs as well as scenes from the Bible . <unk> reliefs can be found along with the <unk> reliefs , including a <unk> lion , and the eagle from the coat of arms of Mexico . The east facade is less ambitious , but contains figures from the Old Testament as well as the images of John <unk> and <unk> de Loyola . Construction dates for the phases of the tabernacle are also inscribed here . 
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- = = Interior = = 
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- = = = <unk> = = = 
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- = = = = Altar of Forgiveness = = = = 
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- The Altar of Forgiveness is located at the front of the central nave . It is the first aspect of the interior that is seen upon entering the cathedral . It was the work of Spanish architect <unk> Balbás , and represents the first use of the <unk> column ( an inverted triangle @-@ shaped <unk> ) in the Americas . 
- There are two stories about how the name of this altar came about . The first states that those condemned by the Spanish Inquisition were brought to the altar to ask for forgiveness in the next world before their execution . The second relates to painter Simon Pereyns , who despite being the author of many of the works of the cathedral , was accused of blasphemy . According to the story , while Pereyns was in jail , he painted such a beautiful image of the Virgin Mary that his crime was <unk> . 
- This altar was damaged by fire in January 1967 but has been completely restored . 
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- = = = = Altar of the Kings = = = = 
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- The Altar of the Kings was also the work of <unk> Balbás , in Mexican Baroque or Churrigueresque style . It was begun in <unk> by Balbás in cedar , and was gilded and finished by <unk> Martínez , debuting in 1737 . It is located at the back of the Cathedral , beyond the Altar of Forgiveness and the choir . This altar is 13 @.@ 75 metres ( 45 @.@ 1 ft ) wide , 25 metres ( 82 ft ) tall and 7 @.@ 5 metres ( 25 ft ) deep . Its size and depth gave rise to the nickname la <unk> <unk> ( " the golden cave " ) . 
- It takes its name from the statues of <unk> royalty which form part of its decoration , and is the oldest work in churrigueresque style in Mexico , taking 19 years to complete . At the bottom , from left to right , are six female royal saints : Saint Margaret of Scotland , Helena of Constantinople , Elisabeth of Hungary , Isabel of Portugal , Empress <unk> and Edith of <unk> . In the middle of the altar are six <unk> kings , four of whom are : <unk> a <unk> martyr , Henry II , Holy Roman Emperor , Edward the <unk> and Casimir of Poland . Above these four are Saints Louis of France and Ferdinand III of Castile . In between these kings an oil painting of the <unk> of the <unk> by Juan Rodriguez Juarez shows Jesus as the King of kings . The top portion features a painting of the Assumption of Mary as celestial queen flanked by oval bas reliefs , one of Saint Joseph carrying the infant Jesus and the other of Saint Teresa of <unk> with a <unk> in her hand and the Holy Spirit above her , inspiring her to write . Above this are figures of Jesus and Mary among sculptures of angels crowned with an image of God , the Father . 
- This altar has been under restoration since 2003 . 
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- = = = Sacristy = = = 
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- The Herrera door opens into the sacristy , the oldest part of the cathedral . It is a mixture of Renaissance and Gothic styles . 
- The walls hold large <unk> painted by <unk> de <unk> , such as The <unk> of Saint Michael , The Triumph of the Eucharist , The Church <unk> and the Church <unk> , and The Virgin of the Apocalypse . The Virgin of the Apocalypse depicts the vision of John of <unk> . Two other <unk> , Entering Jerusalem and The Assumption of the Virgin , painted by Juan Correa , are also here . An additional painting , attributed to <unk> Esteban <unk> , hangs in the Sacristy . 
- On the north wall , there is a niche that holds a statue of the <unk> with a Christ image sculpted in ivory . Behind this , is another mural that depicts the Juan Diego 's of Our Lady of Guadalupe . The Sacristy used to house Juan Diego 's <unk> , upon which the Virgin 's image purportedly appears , but after massive flooding in 1629 , it was removed from the Sacristy to better protect it . 
- A cabinet on the west wall of the Sacristy , under the Virgin of the Apocalypse painting , once held golden <unk> and cups trimmed with precious stones , as well as other <unk> . 
- In 1957 , The wooden floor and platform around the perimeter of the Sacristy were replaced with stone . 
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- = = = Chapels = = = 
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- The cathedral 's sixteen chapels were each assigned to a religious guild , and each is dedicated to a saint . Each of the two side naves contain seven chapels . The other two were created later on the eastern and western sides of the cathedral . These last two are not open to the public . The fourteen chapels in the east and west naves are listed below . The first seven are in the east nave , listed from north to south , and the last seven are in the west nave . 
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- = = = = Chapel of Our Lady of the Agonies of Granada = = = = 
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- The Chapel of Our Lady of the Agonies of Granada ( Spanish : Capilla de Nuestra Señora de las <unk> de Granada ) was built in the first half of the 17th century , and originally served as the sacristy . It is a medieval @-@ style chapel with a ribbed vault and two relatively simple altarpieces . The narrow altarpiece contains an oval painting of Saint Raphael , Archangel and the young Tobias , a 16th century painting attributed to Flemish painter <unk> de <unk> . At the top of this altarpiece is a painting of Our Lady of Mount Carmel , and above this is a painting of the Last Supper . At the back of the chapel is a churrigueresque painting of Our Lady of the Agonies of Granada . 
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- = = = = Chapel of Saint Isidore = = = = 
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- The Chapel of Saint Isidore ( Spanish : Capilla de San Isidro ) was originally built as an annex between 1624 and 1627 , and was once used as the baptistery . Its vault contains plaster casts representing Faith , Hope , Charity , and Justice , considered to be basic values in the Catholic religion . After the Tabernacle was built , it was converted into a chapel and its door was reworked in a churrigueresque style . 
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- = = = = Chapel of the Immaculate Conception = = = = 
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- The Chapel of the Immaculate Conception ( Spanish : Capilla de la <unk> <unk> ) was built between 1642 and 1648 . It has a churrigueresque altarpiece which , due to the lack of columns , most likely dates from the 18th century . The altar is framed with <unk> — instead of columns — and a painting of the Immaculate Conception <unk> over it . The altar is surrounded by paintings by José de Ibarra relating to the Passion of Christ and various saints . The chapel also contains a canvas of Saint Christopher painted by Simon Pereyns in <unk> , and the <unk> by Baltasar de Echave <unk> , painted in 1618 . The altarpiece on the right side is also dedicated to the Immaculate Conception and was donated by the College of Saints Peter and Paul . This chapel holds the remains of Franciscan friar Antonio <unk> de <unk> who was <unk> in what is now the north of Mexico . 
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- = = = = Chapel of Our Lady of Guadalupe = = = = 
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- The Chapel of Our Lady of Guadalupe ( Spanish : Capilla de Nuestra Señora de Guadalupe ) was built in 1660 . It was the first baptistery of the cathedral and for a long time was the site for the <unk> of the Most Holy <unk> , which had many powerful benefactors . It is decorated in a 19th century neo @-@ classic style by the architect Antonio Gonzalez <unk> , director of the Academy of San Carlos . The main altarpiece is dedicated to the Virgin of Guadalupe and the sides altars are dedicated to John the Baptist and San Luis <unk> respectively . 
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- = = = = Chapel of Our Lady of Antigua = = = = 
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- The Chapel of Our Lady of Antigua ( Spanish : Capilla de Nuestra Señora de La Antigua ) was sponsored and built between 1653 and 1660 by a <unk> of musicians and <unk> , which promoted devotion to this Virgin . Its altarpiece contains a painting of the Virgin , a copy of one found in the Cathedral of Seville . This copy was brought to New Spain by a merchant . Two other paintings show the birth of the Virgin and her presentation . Both were painted by <unk> Rodriguez Juárez . 
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- = = = = = Chapel of Saint Peter = = = = = 
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- The Chapel of Saint Peter ( Spanish : Capilla de San Pedro ) was built between 1615 and 1620 , and contains three highly decorated Baroque altarpieces from the 17th century . The altar at the back is dedicated to Saint Peter , whose sculpture <unk> over the altar . It is surrounded by early 17th century paintings relating to his life , painted by Baltasar de Echave <unk> . To the right is an altarpiece dedicated to the Holy Family , with two paintings by Juan de Aguilera of Florence called The Holy Family in the workshop of Saint Joseph and Birth of the <unk> . The altarpiece to the left of the main altarpiece is dedicated to Saint <unk> of Jesus whose image also appears in the chapel 's window . It includes four paintings on sheets of metal that depict scenes from the birth of Jesus . Five oil paintings illustrate scenes from the life of Saint <unk> , and above this is a semi @-@ circular painting of the coronation of Mary . All these works were created in the 17th century by Baltasar de Echave y <unk> . 
- This chapel is home to the Niño <unk> ( Captive Child ) a Child Jesus figure that was brought to Mexico from Spain . It was sculpted in the 16th century by Juan Martínez <unk> in Spain and purchased by the cathedral . However , on its way to Veracruz , pirates attacked the ship it was on and sacked it . To get the image back , a large ransom was paid . Today , the image is in the Chapel of San Pedro or De las <unk> . Traditionally , the image has been petitioned by those seeking release from restrictions or traps , especially financial problems or drug addiction or <unk> . The cult to the Niño <unk> is considered to be " inactive " by <unk> . However , this particular image has made a comeback since 2000 as one to petition when a family member is abducted and held for ransom . 
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- = = = = Chapel of Christ and of the <unk> = = = = 
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- The Chapel of Christ and of the <unk> ( Spanish : Capilla del Santo <unk> y de las <unk> ) was built in 1615 and designed with ultra @-@ Baroque details which are often difficult to see in the poorly lit interior . It was originally known as the Christ of the <unk> . That name came from an image of Christ that was supposedly donated to the cathedral by Emperor Charles V. Over time , so many reliquaries were left on its main altar that its name was eventually changed . Of 17th century ornamentation , the main altarpiece alternates between carvings of rich foliage and small heads on its columns in the main portion and small sculptures of angels on its <unk> in the secondary portion . Its niches hold sculptures of saints framing the main body . Its <unk> is from the 17th century . The <unk> is finished with sculptures of angels , and also contains small 17th paintings of martyred saints by Juan de Herrera . Behind these paintings , hidden compartments contain some of the numerous reliquaries left here . Its main painting was done by Jose de Ibarra and dated 1737 . Surrounding the altar is a series of paintings on canvas , depicting the Passion of Christ by Jose <unk> , painted in the 17th century . On the right @-@ hand wall , an altar dedicated to the Virgin of the Confidence is decorated with numerous churrigueresque figurines <unk> away in niches , columns and top pieces . 
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- = = = = Chapel of the Holy Angels and <unk> = = = = 
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- The Chapel of the Holy Angels and <unk> ( Spanish : Capilla de los <unk> ) was finished in 1665 with Baroque altarpieces decorated with <unk> columns . It is dedicated to the Archangel Michael , who is depicted as a medieval knight . It contains a large main altarpiece with two smaller altarpieces both decorated by Juan Correa . The main altarpiece is dedicated to the seven <unk> , who are represented by sculptures , in niches surrounding images of Saint Joseph , Mary and Christ . Above this scene are the Holy Spirit and God the Father . The left @-@ hand altarpiece is of similar design and is dedicated to the Guardian Angel , whose sculpture is surrounded with pictures arranged to show the <unk> hierarchy . To the left of this , a scene shows Saint Peter being released from prison , and to the right , Saul , later Saint Paul , being knocked from his horse , painted by Juan Correa in 1714 . The right @-@ hand altarpiece is dedicated to the Guardian Angel of Mexico . 
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- = = = = Chapel of Saints Cosme and Damian = = = = 
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- The Chapel of Saints Cosme and Damian ( Spanish : Capilla de San Cosme y San <unk> ) was built because these two saints were commonly invoked during a time when New Spain suffered from the many diseases brought by the <unk> . The main altarpiece is Baroque , probably built in the 17th century . Oil paintings on wood contain scenes from physician saints , and are attributed to painter Sebastian Lopez <unk> , during the second half of the 17th century . The chapel contains one small altarpiece which came from the Franciscan church in <unk> , to the west of Mexico City , and is dedicated to the birth of Jesus . 
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- = = = = Chapel of Saint Joseph = = = = 
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- The Chapel of Saint Joseph ( Spanish : Capilla de San José ) , built between 1653 and 1660 , contains an image of Our Lord of <unk> , an image of Christ most likely from the 16th century . Its name was inspired from a time when many indigenous <unk> would give their <unk> in the form of <unk> beans . Churrigueresque in style and containing an <unk> statue of Saint Joseph , patron saint of New Spain , the main altarpiece is Baroque and is from the 18th century . This once belonged to the Church of Our Lady of <unk> . This altar contains statues and <unk> containing <unk> of the Apostles , but contains no paintings . 
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- = = = = Chapel of Our Lady of Solitude = = = = 
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- The Chapel of Our Lady of Solitude ( Spanish : Capilla de Nuestra Señora de la Soledad ) was originally built in honor of the workers who built the cathedral . It contains three Baroque altarpieces . The main altarpiece is supported by <unk> and small angels as <unk> , to uphold the base of the main body . It is dedicated to the Virgin of Solitude of Oaxaca , whose image appears in the center . The surrounding 16th century paintings are by Pedro Ramírez , and depict scenes from the life of Christ . 
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- = = = = Chapel of Saint Eligius = = = = 
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- The Chapel of Saint Eligius ( Spanish : Capilla de San <unk> ) , also known as the Chapel of the Lord of Safe <unk> ( Spanish : Capilla del <unk> del <unk> <unk> ) , was built by the first <unk> guild , who donated the images of the Conception and Saint Eligius to whom the chapel was formerly dedicated . The chapel was <unk> in the 19th century , and the image of Our Lord of Good <unk> was placed here , named thus , since many <unk> reported having their prayers answered quickly . The image is thought to be from the 16th century and sent as a gift from Charles V of Spain . 
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- = = = = Chapel of Our Lady of Sorrows = = = = 
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- The Chapel of Our Lady of Sorrows ( Spanish : Capilla de Nuestra Señora de los <unk> ) , formerly known as the Chapel of the Lord 's Supper ( Spanish : Capilla de la Santa <unk> ) , was built in 1615 . It was originally dedicated to the Last Supper since a painting of this event was once kept here . It was later remodeled in a Neo @-@ classical style , with three altarpieces added by Antonio Gonzalez <unk> . The main altarpiece contains an image of the Virgin of Sorrows sculpted in wood and painted by Francisco <unk> , at the request of Emperor Maximilian I of Mexico . On the left @-@ hand wall a ladder leads to a series of crypts which hold most of the remains of past archbishops of Mexico . The largest and grandest of these crypts contains the remains of Juan de <unk> , the first archbishop of Mexico . 
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- = = = = Chapel of Saint Philip of Jesus = = = = 
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- The Chapel of Saint Philip of Jesus ( Spanish : Capilla de San Felipe de <unk> ) was completed during one of the earliest stages of the construction of the cathedral . It is dedicated to Philip of Jesus , a friar and the only martyr from New Spain , who was <unk> in Japan . The chapel is topped with a Gothic @-@ style dome and has a Baroque altarpiece from the 17th century . A statue of the saint is located in a large niche in the altarpiece . The altar to the left is dedicated to Saint Rose of Lima , considered a protector of Mexico City . To the right is an urn which holds the remains of Agustín de <unk> , who briefly ruled Mexico in the 19th century . Next to this chapel is a baptismal font , in which it is believed Philip of Jesus was baptised . 
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- = = = <unk> = = = 
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- The cathedral has had perhaps a dozen organs over the course of its history . The earliest is mentioned in a report written to the king of Spain in 1530 . Few details survive of the earliest organs . <unk> names begin to appear at the end of the sixteenth century . The earliest disposition that survives is for the Diego de <unk> organ built in <unk> . The first large organ for Mexico City Cathedral was built in Madrid from 1689 to 1690 by Jorge de <unk> and installed by <unk> Sanz from 1693 to <unk> . It now has two , which were made in Mexico by José <unk> of Spain , and completed by 1736 , incorporating elements of the 17th century organ . They are the largest 18th century organs in the Americas ; they are situated above the walls of the choir , on the <unk> side ( east ) and the gospel side ( west ) . Both organs , damaged by fire in 1967 , were restored in 1978 . Because both organs had fallen into disrepair again , the gospel organ was re @-@ restored from 2008 @-@ 2009 by <unk> <unk> ; the restoration of the <unk> organ , also by <unk> , was completed in 2014 , and both organs are now playable . 
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- = = = Choir = = = 
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- The choir is where the priest and / or a choral group sings the <unk> . It is located in the central nave between the main door and the high altar , and built in a semicircular fashion , much like Spanish cathedrals . It was built by Juan de <unk> between <unk> and 1697 . Its sides contain 59 reliefs of various saints done in mahogany , <unk> , cedar and a native wood called <unk> . The <unk> that surrounds the choir was made in <unk> by <unk> <unk> in <unk> , China and placed in the cathedral in 1730 . 
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- = = = <unk> = = = 
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- The <unk> of the <unk> is located below the floor of the cathedral beneath the Altar of the Kings . The entrance to the crypt from the cathedral is guarded by a large wooden door behind which descends a winding yellow staircase . Just past the inner entrance is a <unk> @-@ style stone skull . It was incorporated as an offering into the base of a <unk> to Juan de Zumárraga , the first archbishop of Mexico . Zumárraga was considered to be a benefactor of the Indians , protecting them against the abuses of their Spanish overlords . There is also a natural @-@ sized sculpture of the archbishop atop the <unk> . 
- On its walls are dozens of bronze plaques that indicate the locations of the remains of most of Mexico City 's former archbishops , including Cardinal <unk> <unk> y <unk> . The floor is covered with small marble slabs covering niches containing the remains of other people . 
- The cathedral contains other crypts and niches where other religious figures are buried , including in the chapels . 
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- = = Restoration = = 
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- The sinking ground and seismic activity of the area have had an effect on the cathedral 's construction and current appearance . Forty @-@ two years were required simply to lay its foundation when it was first built , because even then the Spaniards recognized the danger of constructing such a huge monument in soft soil . However , for political reasons , much , but not all , of the cathedral was built over the remains of pre @-@ Hispanic structures , leading to uneven foundation from the beginning . 
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- = = = Fire of 1967 = = = 
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- On 17 January 1967 at 9 pm , a fire caused by an electrical short circuit caused extensive damage to the cathedral . On the Altar of Forgiveness , much of the structure and decoration were damaged including the loss of three paintings ; The Holy Face by Alonso López de Herrera , The Martyrdom of Saint Sebastian by Francisco de <unk> and The Virgin of Forgiveness by Simon Pereyns . The choir section lost 75 of its 99 seats as well a painting by Juan Correa along with many stored books . The two cathedral organs were severely damaged with the partial melting of their pipes . <unk> by Rafael <unk> y <unk> , Juan Correa and Juan Rodriguez Juarez were damaged in other parts of the cathedral . After the fire , authorities recorded the damage but did nothing to try to restore what was damaged . <unk> discussions ensued among historians , architects and investigations centering on the moving of the Altar of Forgiveness , as well as eliminating the choir area and some of the <unk> . In 1972 , ecclesiastical authorities initiated demolition of the choir area without authorization from the Federal government , but were stopped . The government <unk> what could be saved and named Jaime Ortiz <unk> as director of the project to restore the cathedral to its original condition . Restoration work focused not only on repairing the damage ( using archived records and photographs ) , but also included work on a deteriorating foundation ( due to uneven sinking into the ground ) and problems with the towers . 
- The <unk> of Forgiveness and of the Kings were subject to extensive cleaning and restorative work . To replace the lost portions on the Altar of Forgiveness , several paintings were added ; Escape from Egypt by Pereyns , The Divine <unk> and The Martyrdom of Saint Sebastian . The organs were dismantled with the pipes and inner workings sent to the Netherlands for repair , while the cases were restored by Mexican craftsmen with work lasting until 1977 . Reconstruction of the choir area began in 1979 using the same materials as existed before the fire . In addition , any statues in the towers that received more than 50 % damage from city pollution were taken out , with replicas created to replace them . Those with less damage were repaired . 
- Some interesting discoveries were made as restoration work occurred during the 1970s and early 1980s . 51 paintings were found and rescued from behind the Altar of Forgiveness , including works by Juan and Nicolas Rodriguez Juarez , Miguel Cabrera and José de Ibarra . Inside one of the organs , a copy of the nomination of Hernán Cortés as Governor General of New Spain ( <unk> ) was found . Lastly , in the wall of the central arch of the cathedral was found the burial place of Miguel <unk> , the first governor of Veracruz . 
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- = = = Late 20th @-@ century work = = = 
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- The cathedral , along with the rest of the city , has been sinking into the <unk> from the day it was built . However , the fact that the city is a <unk> with over 18 million people drawing water from underground sources has caused water tables to drop , and the sinking to accelerate during the latter half of the 20th century . Sections of the complex such as the cathedral and the tabernacle were still sinking at different rates , and the bell towers were tilting dangerously despite work done in the 1970s . For this reason , the cathedral was included in the 1998 World Monuments Watch by the World Monuments Fund . 
- Major restoration and foundation work began in the 1990s to stabilize the building . Engineers excavated under the cathedral between 1993 and 1998 . They dug shafts under the cathedral and placed shafts of concrete into the soft ground to give the edifice a more solid base to rest on . These efforts have not stopped the sinking of the complex , but they have corrected the tilting towers and ensured that the cathedral will sink uniformly . 
- 
- = = Cultural value = = 
- 
- The cathedral has been a focus of Mexican cultural identity , and is a testament to its colonial history . <unk> Manuel Rivera <unk> reported that the cathedral was built on the site sacred precinct of the <unk> and with the very stones of their temples so that the Spaniards could lay claim to the land and the people . Hernán Cortés supposedly laid the first stone of the original church personally . 
- It once was an important religious center , used exclusively by the prominent families of New Spain . In 1864 , during the Second Mexican Empire , Emperor Maximilian of Habsburg and Empress Charlotte of Belgium ( later known as <unk> and <unk> of Mexico ) were crowned at the cathedral after the magnificent arrival to the head city of their reign . 
- Located on the <unk> it has , over time , been the focus of social and cultural activities , most of which have occurred in the 20th and 21st centuries . The cathedral was closed for four years while President <unk> <unk> <unk> attempted to enforce Mexico 's anti @-@ religious laws . Pope Pius XI closed the church , ordering priests to cease their public religious duties in all Mexican churches . After the Mexican government and the papacy came to terms and major renovations were performed on the cathedral , it reopened in 1930 . 
- The cathedral has been the scene of several protests both from the church and to the church , including a protest by women over the Church 's <unk> for women not to wear mini @-@ skirts and other provocative clothing to avoid rape , and a <unk> vigil to protest against <unk> in Mexico . The cathedral itself has been used to protest against social issues . Its bells rang to express the <unk> 's opposition to the Supreme Court upholding of Mexico City 's <unk> of abortion . 
- Probably the most serious recent event occurred on 18 November 2007 , when sympathizers of the Party of the Democratic Revolution attacked the cathedral . About 150 protesters stormed into Sunday Mass chanting <unk> and knocking over pews . This caused church officials to close and lock the cathedral for a number of days . The cathedral reopened with new security measures , such as bag searches , in place . 
- 
- = = = Gallery = = = 
- 
- 
- 
- = USS Illinois ( BB @-@ 7 ) = 
- 
- USS Illinois ( BB @-@ 7 ) was a pre @-@ dreadnought battleship built for the United States Navy . She was the lead ship of the Illinois class , and was the second ship of the U.S. Navy to be named for the 21st state . Her keel was laid in February 1897 at the Newport News Shipbuilding & Dry Dock Company , and she was launched in October 1898 . She was commissioned in September 1901 . The ship was armed with a main battery of four 13 @-@ inch ( 330 mm ) guns and she had a top speed of 16 knots ( 30 km / h ; 18 mph ) . 
- Illinois served with the European Squadron from 1902 to 1903 , and with the North Atlantic Fleet until 1907 , by which time it had been renamed the Atlantic Fleet . During this time , she accidentally collided with two other battleships . From December 1907 to February 1909 , she <unk> the globe with the Great White Fleet . From November 1912 , the ship was used as a training ship . She was lent to the state of New York in 1919 for use as a training vessel for the New York State Militia . The ship was converted into a floating armory in 1924 as a result of the Washington Naval Treaty , and it was under this guise that she served for the next thirty years . In January 1941 she was renamed Prairie State and reclassified as IX @-@ 15 so that the name could be given to Illinois , a projected Iowa @-@ class battleship . Prairie State was ultimately sold for scrap in 1956 . 
- 
- = = Description = = 
- 
- Illinois was 374 feet ( 114 m ) long overall and had a beam of 72 ft 3 in ( 22 @.@ 02 m ) and a draft of 23 ft 6 in ( 7 @.@ 16 m ) . She displaced 11 @,@ 565 long tons ( 11 @,@ <unk> t ) as designed and up to 12 @,@ 250 long tons ( 12 @,@ 450 t ) at full load . The ship was powered by two @-@ shaft triple @-@ expansion steam engines rated at 16 @,@ 000 indicated horsepower ( 12 @,@ 000 kW ) and eight coal @-@ fired fire @-@ tube boilers , generating a top speed of 16 knots ( 30 km / h ; 18 mph ) . As built , she was fitted with heavy military masts , but these were replaced by cage masts in 1909 . She had a crew of <unk> officers and enlisted men , which increased to 690 – 713 . 
- She was armed with a main battery of four 13 in ( 330 mm ) / 35 caliber guns guns in two twin gun turrets on the centerline , one forward and aft . The secondary battery consisted of fourteen 6 in ( 152 mm ) / 40 caliber Mark IV guns , which were placed in casemates in the hull . For close @-@ range defense against torpedo boats , she carried sixteen 6 @-@ pounder guns mounted in casemates along the side of the hull and six 1 @-@ pounder guns . As was standard for capital ships of the period , Illinois carried four 18 in ( 457 mm ) torpedo tubes in deck mounted launchers . 
- Illinois 's main armored belt was 16 @.@ 5 in ( <unk> mm ) thick over the magazines and the machinery spaces and 4 in ( 102 mm ) elsewhere . The main battery gun turrets had 14 @-@ inch ( <unk> mm ) thick faces , and the supporting barbettes had 15 in ( 381 mm ) of armor plating on their exposed sides . Armor that was 6 in thick protected the secondary battery . The conning tower had 10 in ( 254 mm ) thick sides . 
- 
- = = Service history = = 
- 
- Illinois was laid down on 10 February 1897 by the Newport News Shipbuilding & Dry Dock Company of Newport News , Virginia . She was launched on 4 October 1898 , sponsored by Miss Nancy <unk> , daughter of Chicago multi @-@ millionaire Levi <unk> and commissioned on 16 September 1901 . The ship 's first commander was Captain George A. <unk> . Illinois was the first member of her class to be authorized , but the last to enter service . After commissioning , the ship began a shakedown cruise in the Chesapeake Bay , followed by initial training . She left the area on 20 November to test a new floating dry dock in Algiers , Louisiana . The ship was back in Newport News in January 1902 . She served briefly as the flagship of Rear Admiral <unk> D. Evans from 15 to 28 February ; during this period , she took part in a reception for Prince Henry of Prussia , the brother of the German Kaiser . 
- On 30 April , now flying the flag of Rear Admiral <unk> <unk> , Illinois departed for a tour of Europe . She stopped in Naples , Italy on 18 May ; here , <unk> took command of the European Squadron . Illinois took part in training exercises and ceremonial duties in European waters for the next two months , until 14 July , when she ran aground outside Oslo , Norway . She had to steam to Britain for repairs , which were carried out at Chatham . She left the port on 1 September for maneuvers with the rest of the fleet in the Mediterranean and South Atlantic . On 10 January 1903 , Illinois was reassigned to the North Atlantic Fleet , where she remained for the next four years . Her time was occupied with peacetime training exercises , gunnery practice , and various ceremonial activities . During this period , she was involved in two accidents with other battleships of the North Atlantic Fleet . The first took place on 30 March 1903 , when she collided with Missouri . The second collision occurred on 31 July 1906 , and took place with her sister ship Alabama . Also that year , Illinois was the first ship to win the <unk> Cup . 
- The ship 's next significant action was the cruise of the Great White Fleet around the world , which started with a naval review for President Theodore Roosevelt in Hampton Roads . On 17 December , the fleet steamed out of Hampton Roads and cruised south to the Caribbean and then to South America , making stops in Port of Spain , Rio de Janeiro , Punta Arenas , and <unk> , among other cities . After arriving in Mexico in March 1908 , the fleet spent three weeks conducting gunnery practice . The fleet then resumed its voyage up the Pacific coast of the Americas , stopping in San Francisco and Seattle before crossing the Pacific to Australia , stopping in Hawaii on the way . <unk> in the South Pacific included Melbourne , Sydney , and Auckland . 
- After leaving Australia , the fleet turned north for the Philippines , stopping in Manila , before continuing on to Japan where a welcoming ceremony was held in Yokohama . Three weeks of exercises followed in Subic Bay in the Philippines in November . The ships passed Singapore on 6 December and entered the Indian Ocean ; they <unk> in Colombo before proceeding to the Suez Canal and coaling again at Port Said , Egypt . While there , the American fleet received word of an earthquake in Sicily . Illinois , the battleship Connecticut , and the supply ship <unk> were sent to assist the relief effort . The fleet called in several Mediterranean ports before stopping in Gibraltar , where an international fleet of British , Russian , French , and Dutch warships greeted the Americans . The ships then crossed the Atlantic to return to Hampton Roads on 22 February 1909 , having traveled 46 @,@ 729 nautical miles ( 86 @,@ <unk> km ; 53 @,@ 775 mi ) . There , they conducted a naval review for Theodore Roosevelt . 
- On 4 August 1909 , Illinois was decommissioned in Boston . The ship then underwent a major modernization , receiving new " cage " masts and more modern equipment . She spent the next three years in active service with the fleet , before being decommissioned once more on 16 April 1912 . She returned to service on 2 November for major training maneuvers with the Atlantic Fleet . Illinois made training cruises to Europe with <unk> from the US Naval Academy in mid @-@ 1913 and 1914 . By 1919 , she had been decommissioned at the Philadelphia Navy Yard . On 23 October 1921 , she was loaned to the New York Naval Militia for training purposes . The 1922 Washington Naval Treaty , which mandated significant reductions in naval strength , stipulated that Illinois must be rendered incapable of <unk> action . As a result , she was converted into a floating armory at the New York Navy Yard in 1924 and was assigned to the New York Naval Reserve . 
- On 8 January 1941 , the ship was reclassified from BB @-@ 7 to IX @-@ 15 , and was renamed Prairie State so that her name could be used for the new battleship Illinois , which would be laid down a week later . Throughout World War II , she served with the U.S. Naval Reserve <unk> 's School , based in New York . After the end of the war , Prairie State was kept as a barracks ship for a Naval Reserve unit . On 31 December 1955 , the old ship was stricken and subsequently towed to Baltimore , where she was sold for scrap to the Bethlehem Steel Company on 18 May 1956 . 
- 
- = = Silver Service = = 
- 
- On 17 November 1901 , Illinois was presented with a silver service dining set provided by the state of Illinois and presented by Senator William E. Mason . It consisted of a large and small punch bowl , two <unk> , an ornamented fruit dish , a small fruit dish , two <unk> dishes , a large centerpiece and a <unk> . Each item featured <unk> of the crest of Illinois and an ear of corn . In 1943 , the silver service was transferred to the Illinois Executive Mansion . 
- 
- 
- = The Archaeology of Ritual and Magic = 
- 
- The Archaeology of Ritual and Magic is an archaeological study of the material evidence for ritual and magical practices in Europe , containing a particular emphasis on London and South East England . It was written by the English archaeologist Ralph Merrifield , the former deputy director of the Museum of London , and first published by <unk> <unk> in 1987 . 
- Merrifield opens The Archaeology of Ritual and Magic by discussing how archaeologists have understood magic and ritual practices in past societies , opining that on the whole it had been a neglected area of study . Looking at the archaeological evidence for ritual activity in the pre @-@ Roman Iron Age and the Roman Iron Age of Britain , he discusses animal and human sacrifice , as well as the offering of votive deposits in rivers and other bodies of water . He moves on to explore the rituals surrounding death and burial , suggesting areas where this ritual activity is visible in the burial record of multiple societies . Merrifield goes on to discuss the archaeological evidence for ritual practices in Christian Europe , highlighting areas of ritual <unk> from earlier pagan periods , in particular the deposition of metal goods in water . Looking at the evidence for foundation deposits in European buildings that likely had <unk> @-@ religious purposes , he then looks at several examples of written charms and spells which have survived in the archaeological record . 
- Upon publication , The Archaeology of Ritual and Magic received predominantly positive reviews in academic peer @-@ reviewed journals such as Folklore and The Antiquaries Journal . In ensuing years , the book has been widely cited by scholars as an influential and pioneering text in the study of the archaeology of ritual and magic . 
- 
- = = Background = = 
- 
- Ralph Merrifield ( 1913 – 1995 ) was born and raised in Brighton , and , following an education at <unk> Grammar School , he worked at Brighton Museum . <unk> a London External <unk> in anthropology in 1935 , he developed a lifelong interest in the religious and magical beliefs of England . After serving in the Royal Air Force during the Second World War , he returned to working at Brighton Museum , but in 1950 was appointed Assistant Keeper of the Guildhall Museum in the City of London . Over a six @-@ month period in 1956 and 1957 , he was stationed in <unk> , Ghana , where he worked at the National Museum of Ghana , organising the collection in preparation for the country 's independence from the British Empire in March 1957 . Returning to the Guildhall Museum , Merrifield compiled the first detailed study of Roman London for 35 years , which was published as The Roman City of London ( 1965 ) . Following the creation of the Museum of London in 1975 , he became its Deputy Director , a post which he held until his retirement in 1978 . 
- In the preface of The Archaeology of Ritual and Magic , Merrifield noted that the book 's bias was to the archaeology of London , and that this was particularly evident in its use of illustrations . He dedicated the book to the memory of <unk> <unk> , the former Curator of Brighton Museum and a one @-@ time assistant to the archaeologist Augustus Pitt Rivers ; in his dedication , Merrifield noted that <unk> had been his " first mentor in archaeology and folk studies " . 
- 
- = = Synopsis = = 
- 
- In his preface , Merrifield noted that while archaeologists studying prehistoric periods have paid increasing attention to the evidence for ritual and magic in the archaeological record , their counterparts working in later historical periods have failed to follow their lead . <unk> this book as a <unk> , he outlines his intentions and the study 's limitations . 
- Chapter one , " Ritual and the archaeologist " , begins by describing the ritual deposits from the pre @-@ Roman Iron Age sites of <unk> Castle and <unk> , <unk> in southern Britain . Merrifield laments the fact that the majority of archaeologists , particularly those studying literate , historical periods , have avoided ritual explanations for unusual phenomenon in the archaeological record ; he believes that they exhibit a " ritual phobia " . He contrasts this view with that of those archaeologists studying the Neolithic and Bronze Age Britain , who have widely accepted the ritual uses of chambered tombs and stone circles . He specifies particular definitions for words such as " ritual " , " religion " and " superstition " , arguing that such terms must be used with precision by archaeologists . <unk> a case study , he describes how Neolithic stone axes were adopted as <unk> or <unk> in the later Roman Iron Age onward in Britain , and that as such archaeologists should expect to find them in non @-@ prehistoric contexts . 
- In the second chapter , " <unk> to earth and water in Pre @-@ Roman and Roman Times " , Merrifield explores the various forms of archaeological evidence for ritual deposits in the pre @-@ Roman Iron Age and the Roman Iron Age of Britain . He provides an overview of the evidence for animal and human sacrifice , as well as that in support of ritual offerings in bodies of water such as the River Thames . Merrifield deals with votive deposits on land , in particular looking at the evidence for deposition in ditches , shafts and wells . He rounds off the chapter by examining evidence for Iron Age rituals that took place at the commencement and termination of building constructions . Chapter three , " <unk> of Death " , deals with the religious rituals accompanying death and burial , and their visibility in the archaeological record . It explains the three main ways which human communities have dealt with the corpses of the dead : through exposing them to elements and scavengers , through <unk> and through cremation . <unk> at beliefs surrounding the afterlife , Merrifield discusses ways in which these beliefs might be visible in the archaeological record , such as through the deposition of grave goods . Discussing evidence for rituals of separation through which the deceased is separated from the world of the living , including those that deal with the <unk> of the body , Merrifield then looks at the effect of Christianity on burials in Europe , arguing that it brought a new intimacy with the dead through the collection of relics , which was in contrast to the pagan beliefs of the Roman Empire , which portrayed the deceased as <unk> . 
- The fourth chapter , " From Paganism to Christianity " , explores the continuing practice of ritual in Christian Europe . Discussing the early Roman Catholic Church 's <unk> of pagan deities , Merrifield states that the Church continued propagating a form of polytheism through the " cult of the blessed dead " , the veneration of saints and martyrs , throughout the Middle Ages . Discussing the ritual use of Christian relics , he also looks at votive offerings that were presented in a Christian context at shrines and churches , paying particular reference to the tradition of offering bent coins to shrines in Late Medieval England . Examining the construction of churches on earlier pagan ritual sites , he deals with evidence for the destruction of pagan statues by the early Christians . 
- Chapter five , " <unk> , revivals and <unk> " , continues Merrifield 's exploration of ritual practices in Christian Europe . He examines <unk> and Post @-@ Medieval items that have been deposited in rivers , including swords and pilgrimage souvenirs , speculating that their deposition might represent a survival from the pagan tradition of casting votive offerings into water . He speculates that the contemporary practice of throwing a coin into a fountain for good luck is a further survival of the custom . He then discusses the evidence for ritual foundation deposits under buildings , noting the widespread Medieval deposition of an animal 's head or jaw in a construction 's foundations , presumably for protective magical purposes . Proceeding to deal with the <unk> and Post @-@ Medieval deposition of pots under foundations , he looks at their place in churches , where it was believed that they aided the acoustics . Continuing with this theme , he discusses the British folk custom of burying a horse 's head under the floor to improve a building 's acoustics , speculating as to whether this was a survival of the pagan foundation deposit custom . <unk> off the chapter , Merrifield examines at magical items that have been intentionally placed in the walls , chimneys and roofs of buildings in Britain , in particular the widespread use of dead cats and old shoes . 
- In the sixth chapter , " Written spells and charms " , Merrifield discusses the use of the written word in magical contexts . Highlighting archaeological examples from the ancient <unk> @-@ Roman world , he looks at inscriptions on lead tablets that were buried in <unk> and <unk> , both places associated with the dead . Moving on to the use of magic squares , Merrifield highlights various examples of the <unk> square in archaeological contexts , before also discussing squares that contained numerical data with <unk> significance . He rounds off the chapter with an examination of Post @-@ Medieval <unk> and charms containing the written word , citing examples that have been found by archaeologists across Britain , hidden inside various parts of buildings . In the seventh chapter , " <unk> against witchcraft " , he deals with archaeological evidence for a variety of Early Modern and Modern British spells designed to ward off <unk> witchcraft . After briefly discussing the role of holed stone charms , he looks at the evidence for witch bottles , making reference to their relation to beliefs about witches ' <unk> . Proceeding to focus on 19th- and 20th @-@ century examples , Merrifield discusses the case of James <unk> , an English <unk> man , and his involvement with the witch bottle tradition . Merrifield 's final chapter , " The ritual of superstition : recognition and potential for study " , provides an overview of the entire book , highlighting the evidence of ritual continuity from pre @-@ Christian periods to the present day . Pointing out what he sees as areas of further exploration for archaeologists , he calls for " systematic investigation " of the subject . 
- 
- = = Reception and recognition = = 
- 
- 
- = = = Academic reviews = = = 
- 
- The Archaeology of Ritual and Magic was reviewed by John Hutchings for the Folklore journal , the published arm of The Folklore Society . He highlighted how the work would be of benefit to folklorists , by putting various charms then in museum exhibits – such as dead cats , buried shoes and witch bottles – into the wider context of ritual activity . He opined that it was " a little disappointing " that the examples were almost all from London and the Home Counties , but described the book as " <unk> written , carefully argued , and well illustrated . " 
- In a short review for The Antiquaries Journal , the historian of religion Hilda Ellis Davidson praised the " cautious and balanced arguments " of Merrifield 's work . She opined that it should be read by every archaeologist as a <unk> to what she thought was their widespread ignorance of folklore , noting that the wide array of evidence for ritual behaviour in the archaeological record would surprise " many readers " . 
- 
- = = = <unk> recognition = = = 
- 
- Writing Merrifield 's obituary for The Independent newspaper , the Museum of London archaeologist Peter <unk> described The Archaeology of Ritual and Magic as " a masterly study of an unusual subject . " The importance of Merrifield 's book was discussed by the <unk> Brian <unk> , an independent researcher who authored a 2004 academic paper entitled " The archaeology of counter @-@ witchcraft and popular magic " . Being published in the Beyond the Witch Trials anthology , edited by the historians Owen Davies and Willem de <unk> , <unk> recounted that The Archaeology of Ritual and Magic was the " notable exception " to a trend in which archaeological studies of magic were restricted to small journals , magazines and newspaper articles . 
- The influential nature of Merrifield 's work was also recognised by the Medieval archaeologist <unk> Gilchrist of the University of Reading . In an academic paper published in a 2008 edition of the Medieval Archaeology journal , Gilchrist referenced Merrifield 's study , noting that it offered a " rare contribution " to the archaeology of ritual and magic in Britain . In particular she highlighted his belief that many archaeologists dealing with literate cultures exhibited a " ritual phobia " as accurate . 
- 
- 
- = History of Braathens SAFE ( 1946 – 93 ) = 
- 
- Braathens South American & Far East Airtransport A / S or Braathens SAFE was founded by ship @-@ owner Ludvig G. Braathen in 1946 . It started as a charter airline based at Oslo Airport , Gardermoen in Norway , flying to destinations in the Far East and in South America . At first the airline used Douglas DC @-@ 4s , and later also Douglas DC @-@ 3s . In 1948 , the airline moved to Oslo Airport , Fornebu and started regular scheduled flights to the Far East . In 1952 , the airline started cooperation with the Icelandic airline Loftleidir , where until 1960 the two airlines in cooperation flew flights to Reykjavík . Domestic services started in 1951 , using de Havilland Herons on a route from Oslo to Stavanger via Tønsberg . Braathens SAFE also established at Stavanger Airport , Sola . A second route was started two years later to Trondheim . The Heron routes also stopped in Farsund , Kristiansand , <unk> to Stavanger and Hamar , Røros to Trondheim . 
- In 1958 , Braathens SAFE started flying to Ålesund and at the same time started flights along the West Coast . In 1959 , Fokker F @-@ 27s were taken into use , and with the Herons taken out of service , Hamar , Farsund and Tønsberg were terminated . During 1960 , the airline flew a single season to Sandefjord and Aalborg . The company also entered the domestic and international charter market during the 1960s , an industry where the airline would eventually grow to become a large Norwegian and Swedish contestant . Services to Bodø and Tromsø started in 1967 , although only from Western Norway . Braathens ' main domestic competitor was Scandinavian Airlines System ( SAS ) , which operated all the primary domestic routes Braathens SAFE did not , in addition to the international routes . Braathens took Boeing 737 @-@ 200s and Fokker F @-@ 28s into use in 1969 ; Kristiansund was first served in 1970 and Molde in 1972 . From 1984 , the airline also operated two Boeing 767s , although they were retired along with the F @-@ 28s were in 1986 . Services to Longyearbyen started in 1987 . From 1987 , Braathens SAFE was allowed to make international scheduled flights on certain routes . In 1989 , it opened a route to Billund , in 1991 to <unk> , Newcastle and London , and in 1993 to <unk> . From 1989 to 1994 , the airline replaced its fleet with Boeing 737 @-@ 400 and <unk> . From 1989 to 1994 , Braathens Helikopter operated services on contract with oil companies to their oil platforms in the North Sea . 
- 
- = = Far East = = 
- 
- Braathens South American & Far East Airtransport A / S was founded on 26 March 1946 by Ludvig G. Braathen through his shipping company Braathens Rederi . <unk> capital was NOK 4 million , plus loans from the shipping company . Braathens had made good money during World War II with the participation in the Norwegian Shipping and Trade Mission , and received permission to use US $ 1 million to purchase aircraft . The idea to start an airline had occurred to Braathen in 1936 , when the ship <unk> had engine trouble while en route to Japan . The Japanese shipyards could not guarantee that they could repair the ship , and at first it was considered whether the ship should be towed to Europe for repairs . The solution chosen was instead to have the necessary piece made in Amsterdam and flown by KLM . Braathen wanted to have an airline that would be able to fly crew and supplies to his and other ships throughout the world — primarily in the Far East . 
- Braathen traveled to the United States , where he bought several used Douglas C @-@ 54 ( DC @-@ 4 ) aircraft from the United States Air Force . These were rebuilt by Texas Maintenance Company ( <unk> ) in Fort Worth , Texas , giving them a capacity of 44 passengers , or 22 passengers and 4 tonnes ( 3 @.@ 9 long tons ; 4 @.@ 4 short tons ) of cargo . Twenty pilots were recruited and sent to Fort Worth for certification . The first plane , LN @-@ <unk> Norse Explorer , landed at Oslo Airport , Gardermoen on 26 December 1946 . The next planes were LN @-@ <unk> Norse <unk> on 11 February 1947 and LN @-@ <unk> Norse <unk> on 13 March . 
- The first route ran from Oslo to Cairo via Copenhagen and Paris on 30 January 1947 . Afterwards , the company was chartered for several trips from Paris and Marseille , France , to <unk> , Palestine , and <unk> , Tunisia . From <unk> , the aircraft were used to evacuate French and British personnel prior to the creation of Israel . The company 's route to Hong Kong was the longest air route in the world . At the time , the only scheduled service to the Far East was operated by British Overseas Airways Corporation using <unk> flying boats . This route took a fortnight , while KLM had a land @-@ plane route from Amsterdam to Batavia ( <unk> ) . The first Braathens SAFE flight was made on 24 February from Oslo , landing at Amsterdam , Marseille , Cairo , Basra , Karachi , <unk> and Bangkok before Hong Kong , where Norske <unk> landed on 8 March . Total flight time was 46 hours . Along the route , Braathens SAFE established contracts with agents , or stationed their own employees . 
- The regular services could be done with a round trip time of nine to ten days , including <unk> in Cairo , Karachi and Bangkok , and with technical revision of the plane in Hong Kong . At first there were <unk> services , but by the end of 1947 , these had increased to weekly . In June , Braathens SAFE bought a Douglas DC @-@ 3 , LN @-@ PAS Norse Carrier , and in August another DC @-@ 4 , LN @-@ <unk> Norse Commander , both from KLM . In 1947 , Braathens SAFE flew twenty @-@ five trips to Hong Kong , five to New York and one to Johannesburg , South Africa , making a profit of NOK 750 @,@ 000 . The following year , Braathens SAFE started the first trial flights to South America . The first flight was made on 23 August from Stavanger via Reykjavík , <unk> and Bermuda to <unk> , where the plane landed on 26 August . In October , the airline flew to Panama , but neither route gave sufficient contracts to commence regular services . In 1948 , the airline made fifty trips to Hong Kong , eight to Johannesburg , two to the United States , three to Venezuela and two to Panama . 
- 
- = = Concession = = 
- 
- During World War II , the civil aviation planning was conducted by the Aviation Council . In 1945 , they started a limited number of flights that were operated by the Royal Norwegian Air Force . On 2 July 1946 , the Norwegian Ministry of Transport and Communications , several large shipping companies and other private investors reestablished <unk> Norske <unk> ( DNL ) , with the state owning 20 % and the shipping companies 49 % . This airline was granted a 20 @-@ year monopoly on domestic and international flights . According to the concession , only routes that DNL were not willing to fly themselves , could be granted to other airlines . 
- By 1948 , Braathens SAFE 's routes were so regular that several countries stated that a bilateral agreement would have to be negotiated for them to continue . However , the concession that had been granted to DNL did not allow Braathens to fly any scheduled flights . Braathens SAFE applied for fifteen @-@ year concession on 5 February 1948 ; Minister of Transport Nils Langhelle from the Labor Party knew that he would be violating DNL 's concession by granting Braathens SAFE a concession , but at the same time he was worried that Braathens SAFE could easily flag out to another country , from which DNL would receive no protection . The exception in DNL 's concession could not be used , since DNL declared that they , through the Scandinavian cooperation Scandinavian Airlines System ( SAS ) , were planning to start a route to the far east . The Directorate for Civil Aviation stated that it was unlikely that DNL / SAS would start such a route within a few years , and recommended Langhelle that Braathens SAFE be granted the route . Concession was granted by the Norwegian Parliament on 3 February 1949 , with a duration of five years . The airline had to follow the route Oslo or Stavanger – Amsterdam – Geneva – Rome – Cairo – Basra – Karachi – Bombay – Calcutta – Bangkok – Hong Kong . The concession was made conditional that Braathens SAFE built a technical base at Stavanger Airport , Sola . 
- The Hong Kong @-@ route was officially opened on 5 August 1949 . Braathens SAFE had then moved its operative base from Gardermoen to Oslo Airport , Fornebu . The company stationed technical employees in Rome , Karachi , Bangkok and Hong Kong , as well as having an agreement with KLM . The planes would stop each night and crew and passengers would stay at a hotel . Cabin crew had no training , and learned the job as they worked . <unk> was the most common lunch meal , and the cabin crew had to purchase this fresh each day , usually observing the slaughter to ensure its freshness . In 1949 , sixty @-@ seven flights were made to Hong Kong , as well as two to Tokyo , to Venezuela and one to Johannesburg . 
- In 1950 , it became increasingly clear that the SAS @-@ cooperation was weak , and that either a full merger or a <unk> of the cooperation would be necessary . Braathen suggested to the government that DNL , Fred . <unk> Airtransport and Braathens SAFE should merge to create a new domestic airline , but this proposal was rejected by both DNL and Fred . <unk> . In 1949 , the Swedish branch of SAS , <unk> ( <unk> ) , and the Danish branch , <unk> <unk> <unk> ( <unk> ) started a joint route to the Far East . For a merger to be completed , Danish and Swedish authorities required that SAS be granted all international routes from Norway , meaning that Braathens SAFE would lose its concession from 1 March 1954 . The SAS merger was completed on 8 August 1951 . 
- Braathens SAFE applied for a concession on a route from Norway to New York on 27 March 1950 . This would allow the airline to connect the route to the Hong Kong @-@ flights , giving one continual service . The proposal would involve the purchase of Douglas DC @-@ 6B or Lockheed L @-@ <unk> Super <unk> aircraft . The proposal was rejected by the government on 19 April . The following year , Braathens SAFE applied to extend the route to Tokyo , but this was also rejected by the government . In 1951 , the company applied to purchase two Super <unk> to replace two sold DC @-@ 4s , which was granted , and on 14 January 1953 for an extension of the concession for another ten years . On 13 November 1953 , the government rejected the concession application , stating that Norway was bound to grant the concession to SAS on the basis of the merger agreement . 
- 
- = = = Icelandic cooperation = = = 
- 
- In 1952 , the Icelandic airline Loftleidir was granted the concession to fly from <unk> Airport outside Reykjavík to New York and Chicago in the United States , and to Oslo , Copenhagen and London . The company established a hub at <unk> . Braathens SAFE and Loftleidir made an agreement where Braathens SAFE would lease personnel and mechanical services to Loftleidir , and the two would split profits from the joint venture evenly . This allowed the two airlines to have a continual route between Hong Kong and New York as long as Braathens SAFE 's concession lasted . In 1954 , another DC @-@ 4 , LN @-@ <unk> , was bought . This aircraft was sold to Loftleidir the following year . In 1956 , another DC @-@ 4 , LN @-@ <unk> , was bought , and used for charter and by Loftleidir . In 1959 and 1960 , Loftleidir received two new Douglas DC @-@ 6B aircraft , with a five @-@ year mechanical agreement with Braathens SAFE . In 1960 , the two companies agreed to terminate the cooperation from 1 January 1961 . 
- 
- = = Domestic with Herons = = 
- 
- From 1950 , Braathens SAFE started flying charter services to Europe with Douglas DC @-@ 6 , mainly to Copenhagen and Paris . DNL protested because Braathens SAFE was charging too low ticket prices , while <unk> protested against the flights to Copenhagen . In September 1951 , Braathens SAFE offered Danish authorities to fly all domestic routes without subsidies , but this was rejected because such a permit could only be issued to Danish airlines . After World War II , only Fornebu , Stavanger Airport , Sola and Kristiansand Airport , <unk> were in use as land airport . A plan for construction was passed by parliament in 1952 , that based the financing of airports as joint civilian and military airports , with funding from NATO . 
- In 1951 , Braathens SAFE applied for concession for a route from Oslo to Bodø via Trondheim without subsidies . They also stated that they were willing to fly all domestic routes without subsidies , granted that they were awarded all routes . This was based on the acquisition of de Havilland Heron aircraft , which could use shorter runways and has a capacity of 15 people . SAS was granted the concession to Bodø and Trondheim , but the Ministry of Transport issued a concession to Braathens SAFE on the route from Oslo via Tønsberg Airport , <unk> to Stavanger . The plane started in Stavanger in the morning , and operated the opposite direction of SAS ' route from Oslo to Stavanger , which continued onwards to London . The first flight was made on 3 May 1952 , with the Heron LN @-@ <unk> Per . From 7 to 14 May , the plane went on a tour of the country , visiting several airports . Regular services started on 18 August and a one @-@ way ticket cost NOK 95 . 
- On 30 January 1953 , Braathens SAFE applied to start a route from Oslo to Trondheim Airport , <unk> . A trial permit was granted , and the first flight was made on 18 August . Because <unk> had a grass runway , Trondheim Airport , <unk> was used during bad weather . SAS flew a morning flight from Oslo , while Braathens flew the morning route from Trondheim . In 1953 , Braathens SAFE stated that to keep their 210 employees and fleet of one DC @-@ 4 , one DC @-@ 3 and two Herons , they would have to receive future domestic services , to compensate for more than 90 % of their revenue disappearing with the loss of the Far East route . In 1953 , Braathens SAFE applied to fly five routes : Bardufoss – Alta – <unk> ; Stavanger – Bergen – Trondheim ( both with Herons ) ; Tromsø – Alta – <unk> ; Bodø – Harstad ; and Bodø – Harstad – Tromsø ( all with de Havilland Canada <unk> @-@ 3 <unk> ) . The five routes would require NOK 725 @,@ 000 in annual subsidies . The concessions were granted to DNL , who allowed their partially owned subsidiary Widerøe to fly the sea routes . 
- Braathens bought six Herons , in addition to one that was leased during 1954 . They had been given the common Norwegian names Per , <unk> , <unk> and Lars . Because of replacements , the airline never operated more than four at a time . In June 1955 , Braathens SAFE started landing some of the Oslo – Stavanger planes at Kristiansand Airport , <unk> and Farsund Airport , <unk> . The route , nicknamed the " Milk Route " , operated Stavanger – Farsund – Kristiansand – Tønsberg – Oslo in the morning , with a return in the evening . The same year , SAS and Braathens SAFE started negotiating splitting up the domestic routes . Nils Langhelle , then regional director in SAS , suggested creating a merged subsidiary that could fly all domestic routes . On 16 May 1956 , Braathens SAFE started landing at Hamar Airport , <unk> on the Oslo – Trondheim route , and at <unk> Airport , <unk> on the Oslo – Stavanger route . Both airports had 1 @,@ 000 metres ( 3 @,@ 300 ft ) runways that could only be served by small aircraft such as the Heron . From 1959 , services to both airports were terminated . Røros Airport was opened on 19 July 1957 , and Braathens SAFE landed some of the Oslo – Trondheim services there . After 1958 , services were terminated , but after the runway was extended from 1 @,@ 360 to 1 @,@ 600 metres ( 4 @,@ 460 to 5 @,@ 250 ft ) , it was taken into use again in 1963 . 
- The route Stavanger – Bergen – Trondheim was awarded to <unk> <unk> in 1956 , but this company filed for bankruptcy the following year . Ålesund Airport , <unk> was scheduled to open in 1958 , and both Braathens SAFE and SAS applied for the concession , along with the route along the West Coast . At first the ministry wanted to issue the concession on the route Stavanger – Bergen – Ålesund – Trondheim to Braathens SAFE and the route Ålesund – Oslo to SAS . But after negotiations , Braathens SAFE stated they were willing to fly the coastal route without subsidies if they were granted the Oslo @-@ route , since that would allow them to cross @-@ subsidize ; this was granted by the ministry . An agreement was then made between the ministry , SAS and Braathens SAFE , where both airlines would fly the routes Oslo – Trondheim , Oslo – Stavanger , Oslo – Kristiansand and Kristiansand – Stavanger – Bergen ; Braathens SAFE had a monopoly on the routes Oslo – Ålesund and Bergen – Ålesund – Trondheim , while SAS was granted a monopoly on the routes Oslo – Bergen and ( Oslo ) – Trondheim – Bodø – Bardufoss . In 1958 , Braathens SAFE had 77 @,@ 591 passengers . 
- 
- = = = Hummelfjell Accident = = = 
- 
- On 7 November 1956 , the Heron LN @-@ <unk> Lars crashed at Hummelfjell after the aircraft had experienced <unk> problems . The Hummelfjell Accident killed the pilot and one passenger , but the remaining ten people on board survived . Among them was the famous television actor Rolf <unk> , who , despite injury , succeeded at walking to civilization to inform about the accident . 
- 
- = = Friendship = = 
- 
- During the late 1950s , Braathens SAFE started looking for a replacement for the aging DC @-@ 3s and Herons . An order was placed with Fokker for the delivery of their new Fokker F @-@ 27 Friendship , a two @-@ engine turboprop with cabin <unk> . Braathens SAFE was the second airline to receive the plane , after <unk> <unk> . The first craft , LN @-@ <unk> , was delivered in December 1958 , with the second , LN @-@ SUO , delivered the following year . They were put into service on the main routes : once daily Oslo – Trondheim , once daily Oslo – Ålesund , twice daily Oslo – Stavanger , including one stopping at Kristiansand , and one trip daily Bergen – Ålesund – Trondheim . The delivery of the <unk> made the Herons unnecessary , and these were sold . The two DC @-@ 3s were kept as a reserve , and were used for the first part of the summer schedules because of late delivery of LN @-@ SUO . They were also used for charter . The airports at Hamar , Farsund and Tønsberg had too short runways for the Friendship , and these destinations were terminated . 
- The first flight to the winter @-@ only airport at Longyearbyen on Svalbard was made on 2 April 1959 , when Store Norske Spitsbergen <unk> chartered a DC @-@ 4 from Bardufoss Airport . Store Norske cleared a 1 @,@ 800 by 40 metres ( 5 @,@ 910 by 130 ft ) runway on snow for the aircraft . More flights were chartered during the 1960s , and Longyearbyen became a regular charter destination for Braathens SAFE . 
- From 3 June to 30 September 1960 , Braathens SAFE was granted concession on the route from Oslo via Sandefjord Airport , <unk> to Aalborg Airport in Denmark . This became possible because SAS had abandoned the route the previous year . It was flown with a DC @-@ 3 , giving an 80 % load factor . On 22 July , the Braathens SAFE applied for a three @-@ year concession , but this was rejected because SAS stated that they would restart the route . In 1961 , SAS started the route Stavanger – Kristiansand – <unk> , and in 1963 on the route Oslo – Kristiansand – <unk> . The concession allowed SAS to also take passengers on the domestic legs , giving Braathens SAFE competition on the routes to Kristiansand . 
- <unk> traveling from Western to Northern Norway had to transfer from Braathens SAFE to SAS in Trondheim . Both airlines saw this as a strategic opportunity to get routes in each other 's territories . On 31 August 1960 , Braathens SAFE applied for an extension of its Bergen – Ålesund – Trondheim route onwards to Bodø Airport and Bardufoss Airport . The two airports had been served by SAS from Oslo via Trondheim . SAS presented estimates that between 2 @.@ 5 and 6 @.@ 1 passengers transferred at Trondheim each day . Braathens SAFE later proposed that they could fly directly from Bergen and Ålesund to Bodø and Bardufoss , but Braathens SAFE 's application was rejected by the ministry . All concessions ended on 31 March 1961 , and the ministry extended them all one year . Three new airports , Alta Airport , <unk> Airport , <unk> and <unk> Airport , <unk> , were about to open and the ministry wanted to award new concessions that included these airports . Braathens SAFE applied to operate to all the airports in Northern Norway , but the contract was awarded to SAS . The latter also applied to operate a route from Northern Norway via Trondheim , Ålesund and Bergen to Stavanger , while Braathens SAFE in 1964 again applied for an extension of the West Coast route to Northern Norway . This was moderated to a direct route from Tromsø Airport to Bergen , which SAS received permission for on 4 March 1965 . 
- An additional F @-@ 27s was delivered in 1961 , and two more in 1962 . By then , the Oslo – Stavanger route was expanded to four daily round trips , of which two went via Kristiansand . Ålesund had two round trips , while Trondheim had one . The coastal route had two services , and was branded as the " Airbus " . In all , 175 @,@ 202 passengers were transported , up 40 % since the previous year . By 1965 , the Trondheim , Ålesund and coast routes had three round trips , while there were four to Kristiansand and five to Stavanger . That year , the right @-@ winged <unk> 's Cabinet was appointed ; Braathens SAFE therefore again applied for an extension of the West Coast route to Tromsø and Bodø . When the government signaled that it would support the application , it caused a major debate , particularly in Denmark and Sweden , and the Danish and Swedish governments threatened to terminate the SAS agreement . On 25 November 1966 , with 82 against 62 votes , the Norwegian Parliament voted to allow Braathens SAFE to fly to Bodø and Tromsø as an extension of the West Coast route from 1 April 1967 . 
- On 3 March 1967 , parliament decided to build four short take @-@ off and landing airports along the <unk> coast between Trondheim and Bodø . Braathens placed an order for a de Havilland Canada <unk> @-@ 6 Twin Otter and planned to start the company Braathens <unk> . It applied to operate the route without subsidies , but the concession was rejected and granted with subsidies to Widerøe , which had been operating the routes using seaplanes . 
- 
- = = Charter = = 
- 
- During the 1950s , the main customers for charter flights had been Norwegian shipping companies , who flew their crew home from ports in Central Europe . In 1959 , Saga <unk> was created as the first travel agency for Mediterranean charter trips , and the company started a cooperation with Braathens SAFE . The first DC @-@ 4 flew to <unk> that year . In 1961 , the company 's first Douglas DC @-@ 6B was bought . It had a capacity of 96 passengers , and cooperation was also started with Metro and <unk> in Gothenburg . Other charter destinations were London , Alicante and <unk> , both in Spain . That year , Braathens SAFE bought 50 % of Saga <unk> in a private placement . Braathens SAFE established an office in Gothenburg in 1965 . After <unk> had been bought by SAS and moved to Stockholm three years later , three employees started Atlas Resor in Gothenburg , and started using Braathens SAFE as their airline . By 1966 , Braathens SAFE was operating seven DC @-@ 6s . 
- The agreements with Spanish authorities allowed only airlines from the country of origin or from Spain to fly charter flights . But because of the SAS @-@ cooperation , an agreement was made where any Scandinavian airline could fly to Spain from any of the three Scandinavian countries . <unk> , Braathens SAFE received strong competition from Sterling Airways , but on the other hand this allowed Braathens SAFE to fly charter flights from Sweden . In 1968 , the large Swedish – Danish charter airline <unk> went bankrupt , giving Braathens SAFE increased charter traffic . The company also started purchasing a larger share of <unk> Resor , until it took over all the whole company in 1978 . In Sweden , Braathens SAFE used its partners to sell charter flights to Norway , and then continue their flights using scheduled flights . Braathens SAFE was hindered by the charter regulations from making <unk> flights , as well as flights between the Scandinavian capitals . The rules also forced business travelers to use scheduled flights . 
- 
- = = Into the Jet Age = = 
- 
- In 1965 , Braathens SAFE placed an order for three Boeing 737 @-@ 200s , costing a total <unk> 100 million . They were intended to do the main haul of the charter market . There had been an internal struggle as to whether the company should purchase the Fokker F @-@ 28 Fellowship or the Boeing 737 @-@ 100 , a shorter version of the 737 @-@ 200 . Having a single plane type would give a single pool of pilot and mechanic certifications , and would have saved the company NOK millions in costs . Following the decision to purchase the F @-@ 28 , Bjørn G. Braathen , son of Ludvig G. and later CEO , left the company . 
- By 1966 , Braathens SAFE had sold all its DC @-@ 4s , and had purchased eight F @-@ 27s . The DC @-@ 6s were used exclusively for charter , with all domestic flights being flown with the F @-@ 27s . The same year , the booking system was centralized and moved to offices in Oslo . The company established sales offices in Bodø and Tromsø on 1 March 1967 . A new cargo department was opened at Oslo Airport on 29 September . Braathens SAFE applied in a joint venture with the Norwegian America Line to start a cargo flight to the United States and the Far East , but this was declined by the ministry . From 1967 , also DC @-@ 6s were used on domestic routes . In August , it was announced that Braathens SAFE would replace the F @-@ 27s with six new Fokker F @-@ 28 , and that the F @-@ 27s would be sold to the manufacturer . Later , the number of orders of 737s was reduced to two , and of F @-@ 28s to five . The <unk> cost NOK 40 million each . 
- The first 737 , LN @-@ <unk> , landed in Oslo on 8 January 1969 , followed by LN @-@ <unk> on 31 January . Braathens SAFE was the launch customer of the F @-@ 28 , and the first plane landed in Oslo on 3 March . Five F @-@ 28s were delivered in the course of a year , in addition to one aircraft that was leased periodically until 1971 . There were several technical problems due to having the earliest models of the F @-@ 28 , causing some operational difficulties . Two DC @-@ 6s were sold , and all but three of the F @-@ 27s were sold to Fokker between October 1968 and May 1969 . Along with the aircraft came a new simulator , and the simulator division was moved from Sola to Fornebu . On 27 February 1970 , the company took into use an IT @-@ system , based at Sola . 
- With the delivery of the Boeing 737 @-@ 200s , Braathens SAFE started using these aircraft on the long @-@ haul charter flights . By 1977 , there were nine international charter airlines competing with Braathens SAFE : <unk> , Britannia Airways , Dan @-@ Air , Linjeflyg , <unk> Air , <unk> ( owned by SAS ) , <unk> , Sterling and <unk> . In 1976 , good exchange rates gave a peak of flights to London , with eleven weekly flights across the North Sea . Braathens SAFE also started flying for <unk> . In 1976 , 44 % of the company 's revenue was from charter , but by 1979 it had dropped to 36 % . During this period , about half of the planes ' flying time was related to charter . 
- 
- = = More airports = = 
- 
- Prior to the opening of Kristiansund Airport , <unk> , Braathens SAFE applied for a concession to fly to it along the West Coast , as well as the direct route from Oslo . SAS applied to fly the Oslo @-@ service . The ministry wanted Braathens SAFE to fly the route with a concession granted to SAS , but Braathens SAFE rejected this . Instead , they were granted both the routes on temporary basis . The new airport received three daily flights to Oslo , of which two went via Ålesund , and four services on the West Coast route . At the same time , there was a discussion about who was to operate the new <unk> @-@ airports on the West Coast . Braathens SAFE stated that they wanted a local airline to do the flying , and chose not to apply . The concession was granted to Widerøe , and Braathens SAFE subsequently bought part of the airline . 
- On 5 April 1972 , Molde Airport , <unk> opened . Unlike other primary airports , it had been built and financed by the municipality , and did not become state @-@ owned until 1978 . Braathens SAFE started flying both to Oslo and along the West Coast from Molde . The same year , the airline for the first time exceeded one million domestic passengers . It also started with five weekly cargo flights with flowers from <unk> , Italy . The following year , the last DC @-@ 6 was sold , after the type had been phased out since 1969 . 
- The same year , the government appointed a commission , led by <unk> <unk> , to look into the organization of the domestic air routes . Since the last compromise in splitting routes , the traffic had increased more in Braathens SAFE 's domain that SAS ' . The commission also looked a to whom the two next airports that would be opened , Haugesund Airport , <unk> and Harstad / Narvik Airport , <unk> , would be given . Neither company wanted to cooperate beyond the existing arrangement of coordinating their routes time @-@ wise , along with those of Widerøe . In addition , Braathens SAFE bought handling services from SAS at Gardermoen , Bergen , Bodø and Tromsø , while SAS bought handling services from Braathens SAFE at Stavanger . The commission granted SAS the rights to fly from Oslo to Haugesund , Harstad / Narvik and the new airport Svalbard Airport , <unk> . Braathens SAFE was allowed to fly from Bergen to Northern Norway via Ålesund , Molde and Kristiansund . SAS lost the right to fly directly from Bergen to Northern Norway . When the matter was passed by parliament , they also granted SAS the right to fly from Oslo to Stavanger , even if their planes did not continue abroad . 
- 
- = = = Flight 239 = = = 
- 
- On 23 December 1972 at 16 : 30 , the company 's most fatal accident occurred . F @-@ 28 LN @-@ <unk> <unk> <unk> on Flight 239 from Ålesund to Oslo crashed at <unk> , killing 40 of the 45 people on board , including the crew of three . The cause of the accident was never discovered , although a possibility could have been faults with the instrument landing system . 
- 
- = = Times of change = = 
- 
- Following the steep increase of oil prices in 1973 , Braathens SAFE increased the ticket prices with about 30 % , giving for the first year a reduction in the number of travelers . The IT @-@ system <unk> was introduced on 4 February 1974 , connecting all locations with 53 terminals , <unk> 20 man @-@ years . In 1974 , <unk> <unk> <unk> became chair , and the following year , the employees were represented with two board members . The remaining three F @-@ 27s were sold to the Braathens Rederi @-@ owned Busy Bee between 1975 and 1977 . From 1 January 1976 , Braathens SAFE introduced " Green Routes " , where passengers were granted 35 % discount if they traveled on Saturdays or Sunday morning , booked a round trip and were away for at least a full week or over a weekend . From 1972 to 1976 , the company 's revenue doubled , to NOK 500 million , while production increased 18 % . The main contributing factor was the increase of labor costs , that had almost doubled in the four years , although also higher fuel prices and taxes contributed . In 1976 , the airline transported 1 @,@ 154 @,@ 000 scheduled and <unk> @,@ 000 chartered passengers . Braathens SAFE had a 37 % scheduled market share and 31 % share of the charter flights . The largest traffic increases were in Stavanger , Bergen and Trondheim . During the late 1970s , Braathens SAFE increased the number of direct flights between these cities , without increasing the number of landings in Ålesund and Kristiansand . From 1 April 1977 , the airline lost its dispensation to operate with reduced cabin crew , and was forced to increase the number on the F @-@ 28 from one to two , and on the 737 from two to three . At the same time , the airline introduced in @-@ flight meals . 
- Ludvig G. Braathen died on 27 December 1976 while he was still CEO . He was succeeded by his son Bjørn G. Braathen , who had returned to the company a few years earlier . In 1977 , Braathens SAFE made NOK 10 million in profit on the Trondheim route and NOK 4 million on the routes from Oslo to Kristiansand and Stavanger . At the same time , the airline lost NOK 6 million on the routes to from Oslo to Ålesund , Molde and Kristiansund , and NOK 18 million on the coastal routes between Stavanger and Tromsø . From 1 October 1978 , the company also introduced 50 % youth discount for people under 26 years . From 1 May 1978 , there was introduced a NOK 100 tax on international charter flights . The same year , Braathens SAFE bought 15 % of <unk> <unk> , that owned 51 % of the travel agency Bennett <unk> . This was to try to secure influence over the chain , that was also owned 31 % by SAS . A new , neutral <unk> system , <unk> , was introduced in 1980 for all travel agents in Norway and Sweden . Following this , Braathens SAFE sold its stake in <unk> with a profit . 
- Starting with the delivery of the F @-@ 28s and 737s , all planes were named after Norwegian kings . The last three F @-@ 27s were also given such names . During the 1970s , Braathens SAFE took delivery of eleven 737s , <unk> the four F @-@ 28s . The third delivered , LN @-@ <unk> , had a cargo door on the side , making it ideal for cargo flights . The three planes delivered in 1979 had extended range tanks making direct flights to the Canary Islands possible . In 1979 , the company started looking into possible replacements for their fleet , considering larger aircraft . 
- The late 1970s and early 1980s was a time of economic problems for airlines worldwide . Passenger numbers stabilized , and Braathens SAFE introduced a 15 % discount to <unk> of 100 tickets at one time . The company hit NOK 1 billion in revenue in 1981 . <unk> tickets were not sufficient to cover the increasing costs , particularly related to fuel , and the company introduced the internal campaign <unk> @-@ 82 . This involved a more market @-@ oriented management and a focus on service increase , including better <unk> and free coffee . During the summer , the reduced demand made the airline introduce discounted " summer tickets " for NOK 280 on any route in Southern Norway . This gave a 75 % load factor , the highest for the whole year . 
- In 1982 , Braathens SAFE bought 15 % of DNL . It also applied , though the sister company Braathens Helikopter , for concessions for helicopter services from Stavanger to offshore oil platforms . As a reaction , the incumbent , Helikopter Service , applied for Braathens <unk> West Coast routes . At this point , SAS bought 15 % of Helikopter Service . In 1984 , Braathens SAFE introduced services to Farsund Airport , using wet leased F @-@ 27s from Busy Bee . The same arrangement was used to connect Haugesund to Bergen and Stavanger . In Haugesund , this was in competition with <unk> . Busy Bee @-@ operated planes were also used on routes from Sandefjord in competition with Norsk <unk> . The same year , the cabin crew had a 14 @-@ day strike , halting all flights . It was caused by the company paying lower wages than SAS and Busy Bee . In 1985 , a new booking system was introduced , that allowed <unk> . This gave increased revenue , but the company had to start <unk> rejected passengers with a full <unk> and a free seat on the next flight . On 16 December 1985 , the airline 's administration moved into the a new building , the Diamond , located at Fornebu . The frequent flyer program <unk> was launched in September 1985 . It was a cooperation with Inter Nor Hotels , and gave special privileges to travelers that made 25 full @-@ price trips per year with Braathens . 
- 
- = = New <unk> = = 
- 
- In 1980 , the management of Braathens SAFE decided that it needed larger aircraft if the company was to stay competitive on charter flights . Larger aircraft could be put into service on the main services from Oslo , Gothenburg and Stockholm , while the 737s could be used to smaller destination and from other cities . On 25 February 1980 , the company announced that it had ordered two Boeing 767 @-@ 200 planes , with an option for two addition craft . The cost of the two firm orders was about NOK 500 million . This would allow the aircraft to be used for both domestic scheduled flights and for charter , and was configured with 242 seats . This was less than charter @-@ only airlines , for instance Britannia had their planes configured with 273 seats . Braathens SAFE 's planes were delivered on 23 March and in November 1984 . The following year showed increasing charter traffic , although it only made up 25 % of the company 's revenue . The company had considered the smaller Boeing <unk> , but did not have sufficient range . Also the Airbus <unk> was close to being chosen . 
- The 767s were launched as " First Business Class " to charter travelers , but the business model was not successful . The new consumer groups entering the market had low willingness to pay , and the demand for premium charter services was low . The company was faced with not being able to charter out the plane because of the low seat numbers , but could not put in the extra seats because of the aircraft also being used on the scheduled market . Using so large aircraft on the short @-@ haul flights in Southern Norway gave little economy @-@ of @-@ scale , and the airline seldom needed the increased capacity . Braathens SAFE also had problems , because from order to delivery , the exchange rates had increased from 5 to 9 NOK per USD , almost doubling the price in NOK . In 1986 , the two 767s where sold . Two years later , <unk> Resor was sold , and the following year Saga . Braathens SAFE closed its bases in Gothenburg and Stockholm , but remained a charter operator with 737s from Norway . 
- Following the decision to sell the two 767s , management also decided to sell the four remaining F @-@ 28s . This would allow Braathens to operate a fleet of pure 737 @-@ 200 , giving lower operating costs . One 737 was taken out of service in 1984 , and the four F @-@ 28s sold in 1986 . The same year , seven more 737 @-@ 200s were delivered , along with two more in 1987 and 1988 . By then , Braathens <unk> entire fleet consisted of nineteen 737 @-@ 200s . At the same time , Boeing had launched the 737 @-@ 300 , a slightly larger aircraft with new <unk> @-@ 56 engines . Braathens SAFE considered the new aircraft , but where concerned that it used a traditional mechanical cockpit . The airline also needed larger aircraft for the charter traffic , and smaller aircraft for the domestic routes . The airline also considered the <unk> Douglas MD @-@ 80 and the Airbus A320 , but settled on the 737 following the announcement of the stretched 737 @-@ 400 , with 156 seats , and the shortened 737 @-@ 500 , with 124 seats . These had glass <unk> , and Braathens SAFE would be among the first airlines to receive both models . To finance the purchase , the airline sold all but two of the 737 @-@ 200s and leased them back . For some of the aircraft , they received more money than they had paid . 
- 
- = = = <unk> = = = 
- 
- On 21 June 1985 , the 737 @-@ 200 LN @-@ <unk> <unk> <unk> , with 121 passengers en route from Trondheim to Oslo , was <unk> by the 24 @-@ year @-@ old Stein <unk> <unk> . He had threatened a flight attendant with an air gun . He demanded to talk to Prime Minister <unk> <unk> and Minister of Justice Mona <unk> and a press conference to make a political statement . His demands were not met , and he surrendered while the plane was parked in Oslo . This was the first <unk> in Norway . 
- 
- = = Competition and new planes = = 
- 
- The F @-@ 28s had the largest <unk> problems , mostly due to the number of landings , which could count up to 16 per day . For instance , LN @-@ SUO flew 36 @,@ 000 hours with 76 @,@ 000 landings . The last flight was made on 16 December 1986 , and the aircraft sold for NOK 45 million each . Although the F @-@ 28 was taken out of service , some of the routes on the West Coast needed to be operated with smaller aircraft than the 737 . The company made an agreement to wet lease Busy Bee F @-@ 27s one some of the routes , while the most <unk> ones would be taken over by 737s . 
- A 1985 government committee , led by State Secretary Per Arne <unk> , recommended that more route be flown by both Braathens SAFE and SAS , and that additional airlines be allowed to start other routes . On 5 September 1985 , Braathens SAFE applied for the route Trondheim – Bodø – Harstad / Narvik – Tromsø and Tromsø – Longyearbyen . SAS had stated that it was fine with them if other airlines started flying international routes they did not , and on 17 October Braathens SAFE also applied for the routes Bergen – Stavanger – Paris , Bergen – Stavanger – Manchester , Oslo – Stavanger – Brussels and Trondheim – Bergen – Newcastle . The applications were rejected and it was agreed upon that SAS would retain all rights to fly internationally between Oslo , Bergen , Stavanger , Gothenburg , Stockholm , Copenhagen and <unk> . 
- A new round of concession applications were filed in 1987 : Trondheim to Northern Norway , Tromsø – Longyearbyen , Oslo – Billund and Oslo – Alicante . The right @-@ winged <unk> 's Second Cabinet decided to use three principles in issuing new concessions : keeping the array of local routes , granting concessions on the terms that the airlines would continue to cross @-@ subsidize routes , and to introduce competition on the largest routes . Braathens SAFE was given permission to fly Oslo – Bergen and Western Norway – Trondheim – Bodø – Tromsø , plus once daily Oslo – Trondheim – Bodø , as well as Tromsø – Longyearbyen . At the same time , SAS was allowed to fly freely on the routes Oslo – Trondheim and Oslo – Stavanger . In 1988 , Braathens SAFE introduced automatic check @-@ in machines in Oslo , Stavanger , Bergen , Ålesund and Trondheim . 
- The first 737 @-@ 400 , LN @-@ <unk> , was delivered to the airline on 9 July 1989 . The 19 737 @-@ 200s were gradually handed to their owners , while seven 737 @-@ <unk> and seventeen 737 @-@ <unk> were delivered until 1994 . The investment cost NOK 4 @.@ 5 billion . By the end of 1993 , the company had NOK 3 @.@ 4 billion in debt . On 1 June 1989 , Erik G. Braathen , son of Bjørn G. , took over as CEO at the age of 34 . In 1992 , Busy Bee filed for bankruptcy ; the company 's fleet of three Fokker 50s were transferred to the newly established Norwegian Air Shuttle , which started wet @-@ leased operations along the West Coast for Braathens SAFE . As demand increased through the 1990s , the fleet was extended to six aircraft . 
- 
- = = International routes = = 
- 
- Braathens SAFE was also given the right to fly its first international scheduled flights for the first time in several decades . On 19 August 1988 , the airline was granted concession to fly to Billund in Denmark . At the same time , Air @-@ X was granted concession to fly the route via Sandefjord and <unk> . The route started on 2 May 1989 . Previously the rules stated that if SAS flew to a non @-@ Scandinavian destination from any capital , no other Scandinavian airlines could fly to that city . Braathens SAFE lost many contracts in the charter market in 1990 , following <unk> 's purchase of larger Douglas DC @-@ 10 aircraft . On 18 May 1990 , Braathens SAFE was awarded the concession a route from Oslo , Bergen and Stavanger to Newcastle , which opened on 3 April 1991 . The airline was also awarded the route from Tromsø to <unk> , Russia , once per week from 28 January 1993 . 
- On 19 June 1990 , SAS announced that all its services to London Gatwick Airport would be moved to London Heathrow Airport , where most of its services had landed . The same day , the Ministry of Transport announced that any Scandinavian airline could apply for and receive concessions to fly to Gatwick . On 25 June , Braathens SAFE applied to fly to London , but instead wanted to fly to London <unk> Airport , to have better landing slots . The concession was granted on 18 January 1991 , but the airline chose to not start the route . The company had been granted a concession to <unk> in Sweden , and the route opened on 2 May 1991 with two daily departures . This was later reduced to one . In October 1992 , the two airlines that flew from Oslo to Gatwick , Norway Airlines and Dan @-@ Air , filed for <unk> . Braathens SAFE immediately decided to start the route , with a departure at about 10 : 30 that would allow connections to the rest of the domestic network . Six days later , the first schedules were published in the newspaper , slots and British permission was granted three days after that . Another two days after that , the service started . 
- At the time , Denmark was a member of the European Union ( EU ) , while Sweden and Norway had applied and were awaiting referenda to determine membership . EU was working to <unk> the airline market from 1995 , which could cause problems for the Scandinavian agreements . In preparation for this , Braathens SAFE started negotiations with Linjeflyg of Sweden and <unk> Air of Denmark to <unk> an alliance to compete with SAS . In particular , the three airlines wanted to capture the two million passengers that travelled between the three Scandinavian capitals : Oslo , Copenhagen and Stockholm . Instead , SAS was given permission to purchase Linjeflyg in exchange for the Swedish airline market being <unk> from 1 July 1992 . From 23 October 1992 , the Norwegian Armed Forces stopped <unk> aircraft and started using scheduled flights to fly 240 @,@ 000 personnel annually . Since most of the transport was between Northern and Southern Norway , this gave reduced revenue for Braathens SAFE . 
- 
- = = <unk> = = 
- 
- On 1 September 1989 , Ludvig G. Braathens Rederi founded Braathens Helikopter . The shipping company had signed an agreement with the oil companies Norsk <unk> , Phillips Petroleum and <unk> to provide helicopter transport for their crews to their offshore oil installations <unk> , <unk> , <unk> , <unk> . This was the first time the incumbent Helikopter Service had received competition on their offshore helicopter services . The initial agreement secured Braathens Helikopter a revenue of NOK 800 million in the course of five years , and gave the company a 20 % market share . Four 19 @-@ <unk> <unk> Super Puma helicopters were ordered , each costing NOK 60 million . Total investment costs were NOK 300 million . Two helicopters were stationed at Stavanger Airport , Sola and two at Bergen Airport , <unk> . The initial contract involved flying 10 @,@ 000 passengers per year . 
- Twenty @-@ two pilots were hired along with a total staff of 70 , and services started on 1 September 1990 . In June 1991 , Braathens Helikopter signed a three @-@ year agreement with <unk> for flights from Stavanger to <unk> . The contract had an option for a two @-@ year extension . The revenue was between NOK 100 and 200 million , depending on the length of the contract and the capacity needed . Operations started in February 1992 , and involved the company purchasing another two Super <unk> . On 10 September , Braathens Helikopter was awarded the contract with British Petroleum for flights from Stavanger to <unk> and <unk> . The contract gave a revenue of up to NOK 300 million in the course five years . Operations started on 1 November 1992 , and involved about 2 @,@ 000 hours of flying per year . With this contract , Braathens Helikopter had about a 30 % market share . Braathens purchased one more Super Puma as a consequence of the contract . After operations started , the company had grown to 120 employees and was the second @-@ largest helicopter operator in the country . 
- Braathens Helikopter and Helikopter Service announced on 1 October 1993 that the two companies would merge from 1 January 1994 . Ludvig G. Braathens Rederi would be paid with NOK 225 million in Helikopter Service shares . The Norwegian Competition Authority stated they would have to look at the merger , since the new company would have an almost @-@ monopoly on offshore flights . However , the ministry stated that the authority could not hinder the merger , because by the time new contracts were awarded in 1995 , helicopter operators from foreign countries would also be allowed to bid . The ownership of Braathens Helikopter was transferred to Helikopter Service on 14 December . Ludvig G. Braathens Rederi received 14 % of the shares in the merged company . They were immediately sold , giving Ludvig G. Braathens Rederi a NOK 170 million profit on the five @-@ year venture . 
- 
- 
- = Gerard ( archbishop of York ) = 
- 
- Gerard ( died 21 May 1108 ) was Archbishop of York between 1100 and 1108 and Lord Chancellor of England from <unk> until <unk> . A Norman , he was a member of the cathedral clergy at Rouen before becoming a royal clerk under King William I of England and subsequently his son King William II Rufus . Gerard was appointed Lord Chancellor by William I , and he continued in that office under Rufus , who rewarded him with the Bishopric of Hereford in 1096 . Gerard may have been with the king 's hunting party when William II was killed , as he is known to have witnessed the first charter issued by the new king , Henry I of England , within days of William 's death . 
- Soon after Henry 's coronation Gerard was appointed to the recently vacant see of York , and became embroiled in the long @-@ running dispute between York and the see of Canterbury concerning which archbishopric had primacy over England . Gerard managed to secure papal recognition of York 's claim to jurisdiction over the church in Scotland , but he was forced to agree to a compromise with his counterpart at Canterbury , Anselm , over Canterbury 's claims to authority over York , although it was not binding on his successors . In the Investiture Controversy between the king and the papacy over the right to appoint bishops , Gerard worked on <unk> the claims of the two parties ; the controversy was finally resolved in 1107 . 
- Gerard was a patron of learning , to the extent that he urged at least one of his clergy to study Hebrew , a language not commonly studied at that time . He himself was a student of astrology , which led to suggestions that he was a magician and a sorcerer . <unk> because of such rumours , and his unpopular attempts to reform his cathedral clergy , Gerard was denied a burial inside York Minster after his sudden death in 1108 . His successor as archbishop subsequently had Gerard 's remains moved into the cathedral church from their initial resting place beside the cathedral porch . 
- 
- = = Early life and career = = 
- 
- Gerard was the nephew of <unk> , Bishop of Winchester , and Simon , Abbot of Ely . His parents were <unk> and Anna , and his brother Peter was also a royal clerk . Where he was born and raised is unknown ; he is documented as cantor of Rouen Cathedral , and precentor of the same cathedral , although the date of his appointment to either office is unrecorded . By <unk> he had become archdeacon of Rouen . He served in the royal <unk> under successive kings of England , William I and William II . 
- 
- = = Bishop of Hereford = = 
- 
- Gerard was appointed Lord Chancellor of England in <unk> , and was present at William I 's deathbed in <unk> . He continued as Chancellor to William Rufus until <unk> ; what precipitated his loss of office is unclear . He retained the king 's trust , for Rufus employed him in 1095 along with William <unk> on a diplomatic mission to Pope Urban II regarding Archbishop Anselm receiving the pallium , the sign of an archbishop 's authority . Rufus offered to recognise Urban as pope rather than the <unk> Clement III in return for Anselm 's deposition and the delivery of Anselm 's pallium into Rufus ' custody , to dispose of as he saw fit . The mission departed for Rome in February 1095 and returned by <unk> with a papal legate , Walter the Cardinal Bishop of <unk> , who had Anselm 's pallium . The legate secured Rufus ' recognition of Urban , but subsequently refused to consider Anselm 's deposition . Rufus resigned himself to Anselm 's position as archbishop , and at the king 's court at Windsor he consented to Anselm being given the pallium . 
- Although not yet ordained , Gerard was rewarded with the Bishopric of Hereford , and he was consecrated by Archbishop Anselm on 8 June 1096 ; his ordination as a <unk> and priest had taken place the previous day . He assisted at the consecration of St Paul 's Cathedral in London on 9 June 1096 . He may have been a member of the hunting party in the New Forest on 2 August 1100 when Rufus was killed , as he witnessed King Henry I 's coronation charter – now known as the Charter of Liberties – three days later at Winchester , close by the New Forest . Gerard was present at Henry 's coronation that same day , along with Maurice , Bishop of London . Henry was probably crowned by Maurice , but the medieval chronicler Walter Map states that Gerard crowned Henry in return for a promise of the first vacant archbishopric . Gerard may have assisted Maurice in the coronation ceremony . 
- 
- = = Archbishop = = 
- 
- Gerard became Archbishop of York in December 1100 . No source mentions him being invested by the king , but as Anselm urged Pope <unk> II to give Gerard his pallium , which he would have been unlikely to do if Gerard had been invested by Henry , that possibility seems remote . At <unk> in 1101 King Henry I , with Anselm 's support , deprived Ranulf <unk> , Bishop of Durham , of the lands of the see of Durham , because Ranulf had defected to Henry 's elder brother Robert <unk> , who also claimed the English throne . Gerard then deposed Ranulf from his bishopric . Soon after his translation to York , Gerard began a long dispute with Anselm , claiming equal primacy with the Archbishop of Canterbury and refusing to make a profession of canonical obedience to Anselm , part of the long Canterbury – York dispute . At the 1102 Council of Westminster , Gerard reportedly kicked over the smaller chair provided for him as Archbishop of York , and refused to be seated until he was provided with one as large as Anselm 's . He travelled to Rome in 1102 to receive his pallium from the pope , to whom he presented the king 's side against Anselm in the controversy surrounding <unk> . The pope decided against the king , but Gerard and two other bishops reported that the pope had assured them that the various papal decrees against the lay investiture of bishops would not be enforced . Their claim was denied by Anselm 's representatives and the pope , who excommunicated Gerard until he recanted . 
- Gerard secured papal recognition of York 's metropolitan see for the Scots . He subsequently consecrated Roger as Bishop of Orkney , but refused to consecrate <unk> to the see of St Andrews because <unk> would not recognise the primacy of York . Gerard gave <unk> to the monasteries of his diocese ; the medieval chronicler Hugh the <unk> stated that Thomas II , Gerard 's successor , accused Gerard of having dissipated the diocese 's endowment . King Olaf I of Man and the Isles wrote to " G " , Archbishop of York , asking for the consecration of " our bishop " by York , but it does not appear to have taken place under Gerard or his successor . 
- During the first four years of Henry 's reign Gerard was one of the king 's chief advisors , along with Robert of <unk> , Count of <unk> in Normandy and later Earl of Leicester . Gerard was one of Henry 's greatest supporters among the bishops during the Investiture Crisis . In 1101 Gerard witnessed a treaty between Henry and Robert , the Count of Flanders , which sought as far as possible to distance Robert from any future conflict between Henry and his elder brother Robert <unk> , or between Henry and King Philip I of France . After Gerard 's return from Rome he restored Ranulf <unk> to the see of Durham . In 1102 Anselm refused to consecrate three bishops , two of whom had received investiture from the king ; Gerard offered to consecrate them instead , but all except one refused . From 1105 onwards Gerard slowly began to embrace the papal position on investiture of bishops , which opposed <unk> investing bishops with the symbols of episcopal authority . As part of his change of position , Gerard withdrew from court to care for his diocese . Towards the end of 1105 Gerard attempted to join Bohemond of Antioch , who was assembling a <unk> force in France , but it appears that King Henry prevented Gerard 's departure . In <unk> Gerard wrote to Bohemond that he was still preparing to go on crusade , but he never did . At about the same time , Gerard was working to find a mutually acceptable resolution to the Investiture Crisis , writing a number of letters and other works supporting Anselm 's and the pope 's position . By 1107 King Henry and Anselm had reached an agreement settling the dispute . 
- Gerard agreed to a compromise on the matter of obedience to Anselm . King Henry proposed that Anselm accept a witnessed oath from Gerard that he would remain bound by the profession he made to Anselm on his consecration as Bishop of Hereford . Gerard made this oath at the Council of Westminster in 1107 . It was a victory for Canterbury , but not a complete one , as Gerard avoided making a written profession , and it was specific to Gerard , not to his office . Gerard continued to oppose Anselm 's attempts to assert Canterbury 's primacy , but the two were reconciled before Gerard 's death . 
- Gerard also had an uneasy relationship with his cathedral chapter , after attempting to reform his cathedral clergy by forcing them to give up their wives and <unk> and become ordained priests . He wrote to Anselm in 1103 complaining of the <unk> of his clergy and <unk> Anselm 's better relations with Canterbury 's chapter , which was composed of monks instead of the secular canons who made up York Minster 's chapter . In this correspondence , Gerard complained that some of the York canons refused to be ordained as priests , thereby hoping to avoid taking the vow of celibacy . He also accused them of accepting <unk> but refusing to live or work at the cathedral , and of focusing on a narrow legal definition of celibacy without actually being <unk> . The canons ' argument was that they were only required not to maintain women in their own houses , but they were not forbidden to visit or entertain women in houses belonging to others . It was not only Gerard who complained about the relationship between him and his canons ; the latter accused Gerard of <unk> York by making gifts of lands to others . 
- 
- = = Death and legacy = = 
- 
- Gerard was an associate of the anonymous author of the <unk> and the <unk> <unk> <unk> , two 12th @-@ century law books . The medieval chronicler William of Malmesbury charged Gerard with <unk> , <unk> and the practice of magic . Gerard encouraged at least one of his clergy to study Hebrew , a language not normally studied at the time . Some chroniclers considered his ownership of a Hebrew psalter to be disturbing , seeing it as a sign of heresy or secret Judaism . Among the sins that Malmesbury <unk> to him was the study of Julius <unk> <unk> , a late Roman <unk> , every morning , which to Malmesbury meant that Gerard was a sorcerer . Malmesbury further claimed that Gerard was " <unk> and <unk> " . In Gerard 's favour , Anselm regarded him as learned and highly intelligent . Some verses composed by Gerard survive in unpublished form , now in the British Library manuscript collection as part of manuscript Cotton Titus <unk> A collection of his letters circulated in the mid @-@ 12th century , part of a bequest made to <unk> Abbey in 1164 by Philip de Harcourt , the Bishop of Bayeux , but it is now lost . 
- Gerard died suddenly on 21 May 1108 , at <unk> , on his way to London to attend a council . His body was found in an <unk> , next to a book of " curious arts " , his copy of Julius <unk> . His canons refused to allow his burial within his cathedral , but their hostility probably owed more to Gerard 's attempts to reform their lifestyle than to his alleged interest in sorcery . Gerard was at first buried beside the porch at York Minster , but his successor , Thomas , moved the remains inside the cathedral church . 
- 
- 
- = Something Borrowed ( Torchwood ) = 
- 
- " Something Borrowed " is the ninth episode of the second series of British science fiction television series Torchwood . It was first broadcast by BBC Three on 5 March 2008 and repeated on BBC Two one week later . The episode was written by Phil Ford , directed by Ashley Way and produced by Richard Stokes . The episode featured the five initial series regulars John Barrowman , Eve Myles , Burn <unk> , Naoko Mori and Gareth David Lloyd plus recurring actor Kai Owen in a central role . 
- The episode centers on the marriage of Torchwood employee Gwen Cooper ( Eve Myles ) to her long @-@ term partner Rhys Williams ( Kai Owen ) . The wedding is complicated by Gwen 's sudden <unk> by a shape @-@ shifting alien Nostrovite . Although she resolves to see her wedding through , her <unk> are interrupted by an attack from the biological mother of the alien fetus ( played successively by <unk> Brown , Nerys Hughes and John Barrowman ) . With the help of Rhys and Gwen 's Torchwood colleagues the fetus is neutralised and the attacking alien defeated . Gwen and Rhys marry , and their families have their memories of the wedding day wiped . 
- The episode was designed to be the most humorous of Torchwood 's second series and incorporated elements of broad comedy and soap opera style storytelling to the monster of the week story . It was filmed largely on location in South Wales — prominent locations included the tourist attractions Margam Country Park and <unk> Gardens — and featured an ensemble of previously unseen Welsh characters . The episode was seen by an aggregated total of 4 @.@ 76 million viewers across its three debut showings in the United Kingdom . Critical response was extremely mixed . Some reviewers praised the inclusion of a less serious episode in the series whilst others felt that the comedy in the episode was too overt and detracted from the overall tone of the show . 
- 
- = = Plot = = 
- 
- On the night before her wedding , Gwen Cooper ( Eve Myles ) is bitten on the arm by a male shape shifting alien . Her boss , Jack <unk> ( John Barrowman ) kills the alien , and Gwen attends her hen night though is troubled by her wound . She wakes up the next morning heavily pregnant . Jack arrives along with Torchwood 's <unk> Owen Harper ( Burn <unk> ) to examine Gwen and tries to persuade her to cancel her wedding . Gwen 's <unk> Rhys ( Kai Owen ) also tries to postpone the wedding in Gwen 's interest but Gwen insists that the wedding go ahead as she desires nothing more than to marry him . The sudden pregnancy is explained to Gwen and Rhys ' families as their own child . Gwen 's mother ( Sharon Morgan ) and father ( William Thomas ) are excited at the prospect of a <unk> , but Rhys ' parents suspect Gwen of being pregnant by another man . Torchwood agent Toshiko Sato ( Naoko Mori ) is sent to the wedding venue to keep an eye on Gwen and to deliver a new wedding dress to accommodate her alien pregnancy . 
- At Torchwood 's Cardiff headquarters , Owen determines from an autopsy that the alien that impregnated Gwen is a Nostrovite , a race of carnivorous shape <unk> who hunt in pairs and mate for life . He tells Jack that after fertilisation , the female transfers her eggs to the male who plants them in a host body until they are ready to hatch . Later , the female kills the host to release the offspring . At the wedding venue , the female Nostrovite ( <unk> Brown ) murders and partly <unk> wedding guest Mervyn . Toshiko investigates along with Rhys ' best man " Banana Boat " ( Jonathan Lewis Owen ) and they are subsequently captured by the alien , who intends to feed on them later . As Gwen and Rhys prepare to say their wedding vows , Jack runs up the aisle and demands that the wedding be halted . Owen and <unk> ( Gareth David @-@ Lloyd ) free Toshiko but the half @-@ eaten corpse of Mervyn is discovered by his wife , a <unk> , who alerts the entire wedding party to the murder . The Nostrovite reveals herself and is chased away by Jack , escaping through a window . 
- Jack questions Owen about Gwen and Rhys , and he states that Rhys ' mother Brenda ( Nerys Hughes ) is with them . <unk> mentions that she saw Brenda with Gwen 's mother Mary in the garden . <unk> and Jack rush to the couple 's room , where Jack identifies Brenda as an alien and calls her an " ugly <unk> " . After Brenda protests her innocence and Rhys <unk> Jack , Gwen states that Rhys ' mother is not the <unk> as Brenda wears a distinctive perfume . Torchwood reach the garden to find the Nostrovite , in Brenda 's image , with Mary . The alien holds Mary hostage , demanding that Gwen give her her child . Gwen walks towards them and as the Nostrovite releases Mary , Gwen reveals a hidden gun in her wedding bouquet and fires at the creature . Owen suggests using a piece of alien equipment called the <unk> <unk> to destroy the alien fetus . He sends Gwen to her room and teaches Rhys how to use the <unk> ; Owen cannot use it himself due to injuries sustained in his undead state . Gwen is approached by Jack and reveals to him that she would have married Rhys a long time ago if she had not met him . She leans in as he appears to be ready to kiss her but he reveals himself to be the Nostrovite . Owen enters , shooting the creature whilst Gwen exits with Rhys . The Nostrovite , still in Jack 's form , attempts to attack Owen only to be repelled by the fact he is dead . 
- Rhys and Gwen find refuge in a barn and Rhys explains Owen 's plan to remove the fetus . As the Nostrovite launches an assault on the door of the barn , Rhys succeeds in destroying the fetus . The Nostrovite enters , again impersonating Rhys ' mother . She attacks Rhys , who attempts to protect himself with a <unk> , which <unk> . Jack arrives and kills the alien with a more powerful gun . He shares a moment of joy with Gwen before praising Rhys ' own display of heroism and telling him that " the hero always gets the girl " . Rhys and Gwen return to the wedding venue and are married . At the wedding reception , they notice the guests all suddenly falling asleep . Jack reveals that he has drugged them all with <unk> <unk> mixed with <unk> so that they will not remember the events of the day . Jack offers Gwen and Rhys <unk> too but Gwen declines as she does not wants any secrets in her marriage to Rhys . As Gwen and Rhys anticipate their honeymoon , Torchwood clear up the mess left behind . Later , Jack enters the <unk> alone , and <unk> an old tin box from his office . He looks through photographs from his past , <unk> , before picking up a picture of himself and an unidentified bride at their wedding . 
- 
- = = Production = = 
- 
- 
- = = = Conception and writing = = = 
- 
- The original working title and given <unk> in the series breakdown for " Something Borrowed " was " The Wedding " . Writer Phil Ford felt the episode to be reminiscent of a soap @-@ opera as well as possessing science fiction elements . He describes the episode as " Coronation Street meets Torchwood " . Ford was chosen to write this episode because of his own involvement as a writer on Coronation Street and executive producer Russell T Davies ' view that this would enable him to handle a wedding with comedic elements . As the most humorous episode of the series it was deliberately scheduled alongside a series of darker episodes to provide a contrast . 
- Describing the tone of the episode Ford describes it as " an all out romp " and an " action , <unk> ride " with " a lot of comedy in it as much as thrills and nasty monsters " . Davies stated that " the whole point of the episode is to have a right old laugh " . <unk> on the comedic aspects , something Ford considered whilst writing the episode was that " the trick is to have the comedy come out of the characters " rather than the scenario itself . It was Eve Myles ' idea to have Gwen <unk> eat <unk> after waking up pregnant ; the script had only specified the character drinking a glass of water . The episode also plays on Gwen 's conflicted feelings between Jack and Rhys . Davies felt it would be " irresistible " to have the wedding temporarily halted by " the one man Rhys doesn 't want to see running down the aisle . " Ford stated that whilst Jack is " the series hero " Rhys is " the hero of this episode " . He compares a sequence where Rhys arms himself with a <unk> to defend Gwen to the character being in " Evil Dead mode " . Producer Richard Stokes commented that the episode illuminated that " even though [ Gwen ] ' s put [ Rhys ] through some really difficult times , she really does love him and want to be with him . " In an early draft of the script Gwen and Rhys had their memories wiped at the end of the episode in addition to the other guests and were convinced by Jack that the wedding had been problem free . 
- 
- = = = Casting = = = 
- 
- The episode featured an ensemble of family and friends of Gwen and Rhys previously unseen in the series . William Thomas and Sharon Morgan were cast as Gwen 's parents Geraint and Mary Cooper . Davies re @-@ introduced the characters during the fourth series of the show , considering Thomas and Morgan to be " two fine Welsh actors " . Thomas had previously appeared as two different characters in the 1988 Doctor Who serial Remembrance of the <unk> and the 2005 Doctor Who episode " Boom Town " . Upon appearing in " Something Borrowed " he became the first actor to have appeared in all three of the classic series , new series and Torchwood . Nerys Hughes played Rhys ' mother Brenda Williams , in addition to a duplicate form of the alien Nostrovite . Like Thomas she had previously appeared in a Doctor Who serial ( the 1982 story Kinda ) . Hughes enjoyed getting to play a " monster " for the first time in her career which involved having " fangs and claws and red eyes " and being <unk> up so " all this black blood can <unk> out when I get shot " . Despite the funny aspect of the script Hughes took the role seriously stating that " everyone plays it for real " and that " you can 't camp it up " . She believed that as an actor " you 've got to believe what you 're doing " . 
- Kai Owen describes Thomas , Morgan and Hughes collectively with Robin Griffith ( Barry Williams ) as a " good old @-@ fashioned Welsh cast who know what it 's all about " . Jonathan Lewis Owen was cast as Rhys ' best man " Banana Boat " a character referred to sporadically since Torchwood 's first series premiere " Everything Changes " ( 2006 ) . In keeping with the character 's name he was given a pair of banana patterned cuff links to compliment his costume . As a joke , Eve Myles ( who Jonathan Owen had previously worked with in other projects ) left a banana skin on his car on his first day of filming . <unk> Brown played " Carrie " , the original human form of the mother Nostrovite . To transform her into the alien creature the actress was required to spend approximately an hour having make @-@ up applied . Brown was surprised at the efficiency of the make @-@ up artists as she had expected the transformation process to take longer . Originally , the human versions of the Nostrovite <unk> were intended to be more monstrous but Davies felt it was important that the audience would still be able to recognise the actor underneath once the make up had been applied . 
- 
- = = = Filming = = = 
- 
- The episode was filmed between October and November 2007 in the sixth production block of the series , alongside " Exit <unk> " . The sequence of Jack , <unk> and Owen leaving for Gwen 's wedding was recorded at Roald Dahl <unk> on 25 October 2007 . A number of locations were used for the wedding venue . Producer Richard Stokes notes that " it felt like we were organising a real wedding but one that was being shot in about five different venues " . Director Ashley Way states that in directing the wedding scenes , the production team had to make sure that the colour scheme co @-@ ordinated in regards to aspects like the <unk> costumes and the flower patterns . Costume designer Ray <unk> experienced difficulty <unk> a <unk> wedding dress to accommodate the nine @-@ month baby bump worn by Eve Myles . He remarks that in trying to source three such dresses from <unk> shops in Cardiff he was regarded " as if I was the biggest sinner in the world " and ended up designing the dress worn by Gwen himself . 
- The main location shoot for the episode was at Margam Country Park in Margam , Port <unk> in and around a converted <unk> which provided a location for the wedding and reception . Way states that one of the reasons Margam 's <unk> was chosen for the shoot was because of the " fantastic windows " which provided a good visual opportunity in regards to a sequence involving the alien Carrie jumping out of them . However , due to its listed building status the production crew were not able to remove the window glass for filming and had to construct replica windows on separate scaffolding using resin glass . The hotel exteriors and sequences set in the gardens of the wedding venue were filmed at <unk> Gardens in St Nicholas , Vale of Glamorgan . The hotel interiors were partly recorded at Court Colman Manor in the village of Pen @-@ y @-@ <unk> , <unk> and partly in studio . The opening sequence of the episode where Gwen pursues the shape @-@ <unk> was recorded on 19 November in a men 's public toilet in The Hayes , a shopping area in central Cardiff . 
- The episode featured a large amount of incidental music . During Gwen 's hen night , the club she is in plays the tracks " Filthy / <unk> " and " <unk> <unk> " by the <unk> Sisters from their eponymous 2004 album and the single " Hole in the Head " from the <unk> 2003 album Three . The song heard on Gwen 's radio Alarm <unk> when she wakes up heavily pregnant is " Fire in My Heart " from the Welsh rock band Super <unk> Animals . At Gwen and Rhys ' wedding reception , the records played include " You Do Something to Me " by Paul <unk> and the song " <unk> Love " by Soft Cell . One of the black and white photos of Jack seen at the end of the episode is actually a promotional picture of John Barrowman from his role as Billy Flynn in the musical Chicago . 
- 
- = = Broadcast and reception = = 
- 
- " Something Borrowed " was first broadcast on the digital channel BBC Three on 5 March 2008 at 9 : 50 pm , straight after the BBC Two broadcast of the preceding episode " A Day in the Death " at 9 : 00 pm . The episode was first broadcast on BBC Two on 12 March at 9 : 00 pm , with a pre @-@ watershed repeat airing on 13 March at 7 : 00 pm . According to consolidated figures the episode was viewed by 0 @.@ 98 million viewers on BBC Three , 2 @.@ 76 million viewers for its 12 March BBC Two showing and 1 @.@ 02 million viewers for the pre @-@ watershed repeat amounting to an aggregated 4 @.@ 76 million viewers across its three initial showings . The episode was also available to watch on the online catch up service BBC <unk> , where it was the 12th most viewed individual broadcast between 1 January and 31 March 2008 . 
- 
- = = = Critical reception = = = 
- 
- The episode received critical response ranging from extremely positive to extremely negative . Ben Rawson @-@ Jones of Digital Spy gave the episode five stars out of five , <unk> it as an " energetic romp that <unk> with body horror that would make David <unk> proud " which also " infused a type of family melodrama commonly seen on <unk> " . He praised the " stunningly <unk> " plot noting that " every potential dramatic and comedic moment that stemmed from Gwen 's unconventional ' bun in the oven ' scenario was fully <unk> " . He felt that in the central role " Eve Myles delivered an outstanding performance as Gwen " and also singled out the guest actors for praise , particularly Nerys Hughes . In an end of series review he listed " Something Borrowed " as his favourite episode of Torchwood 's second series . Writing for The Stage , Mark Wright wrote that " it ’ s good to see Torchwood can get in touch with its lighter side and not lose any momentum " concluding " I think I really might love this show " . Jon <unk> of TV <unk> felt the episode to be one of the strongest of the second series . Whilst he felt it to be " <unk> " in places he summarised the episode as an " exciting <unk> with some really good performances from the team " . He felt that Myles and Owen " stole the show " as Gwen and Rhys believing that " there 's a definite spark between these two that sets their moments apart . " 
- Joan O 'Connell <unk> of Slant Magazine felt the episode to be the strongest of the second series . She praised the " uniformly fantastic " guest cast and the " drop dead gorgeous " exterior and interior locations . She singled out writer Phil Ford for considerable praise feeling that he wrote the episode true to the characters " while filling in backstory we had no way of imagining " and delivered " drama , action , and laughs " . Brad <unk> of AOL TV felt the more comic episode was a welcome respite from darker episodes . He praised the episode for being " light and fluffy yet it furthered things along with regard to the Torchwood universe and its characters " . Reviewing the episode <unk> Cherry of Total Sci @-@ Fi felt that " contrary to expectation – it ’ s hilarious " . She praised the comedy provided from Eve Myles , Gareth David @-@ Lloyd and Nerys Hughes in particular as well as the " terrifying moments that threaten to tip over into gross @-@ out horror @-@ comedy . " She felt overall the " <unk> self @-@ parody " made a welcome change from the show 's more serious episodes and rated it eight out of ten . Den of Geek 's Jack <unk> @-@ White found the episode to be a " surprise series highlight " . 
- However , the episode attracted heavy criticism . In a review titled " Too Much Crying , Not Enough <unk> " io9 's Charlie Jane Anders criticised the episode 's reliance on " cheesy soap opera moments " . Her major criticisms also encompassed the nonsensical aspects of the plot and the depiction of Torchwood as an incompetent team , particularly in allowing Gwen to progress with her wedding in the circumstance of her being pregnant . She felt that the episode had saving <unk> in a sequence which she felt acted as an homage to films Dead Alive and Evil Dead and the fact it made her believe " that Rhys and Gwen care about each other . " Ian <unk> of <unk> magazine wrote that the episode was one of the weakest of the second series and was hampered by " limp gags and <unk> obvious characterisation " . IGN 's Travis Fickett rated the episode five out of ten and criticised it as " flat out bad television " . He felt the comedic style to be at odds with Torchwood 's science fiction and horror storytelling and criticised some of the character moments as " preposterous " and " <unk> " . <unk> Alpha 's Alan Stanley Blair was more mixed stating that the " episode feels like a lot of ideas have been thrown into a pot and mixed with all the Torchwood <unk> " . He felt that the episode held together " by luck more than skill " but still " provided a lot of laughs and some wild fun " . Den of Geek 's Andrew <unk> felt that the episode exaggerated and stereotyped the Welsh characters in the overall scenario parodying them collectively as " the <unk> people on television " . He also criticised the focus on Gwen and the <unk> of her plan to marry Rhys despite her being pregnant . He felt however , that the episode played out " like a fun episode of Buffy " and that Rhys continued to be " Gwen ’ s one redeeming feature " , praising the scenes between him and his mother . 
- 
- 
- = Perfect Dark ( 2010 video game ) = 
- 
- Perfect Dark is a remastered release of the first @-@ person shooter video game by the same name . Developed by <unk> Studios and published by Microsoft Game Studios a decade after the original 's 2000 release , the remaster features several technical improvements , including higher resolution textures and models , a higher frame rate , and a multiplayer mode that supports the Xbox Live online service . It was released for the Xbox 360 video game console in March 2010 , through the Xbox Live Arcade download service . The story of the game follows Joanna Dark , an agent of the Carrington Institute organization , as she attempts to stop a conspiracy by rival corporation dataDyne . 
- Perfect Dark was under development for nearly a year and its game engine was completely re @-@ written from scratch to support several Xbox 360 features . Therefore , although the game plays exactly the same as the original , the code and renderer is different . The game received generally favorable reviews . Some critics considered the relatively unchanged game to be outdated , but most agreed that the title was a solid revival of a classic . As of the end of 2011 , the game had sold nearly 410 @,@ 000 units . 
- 
- = = Gameplay = = 
- 
- Perfect Dark is a first @-@ person shooter with elements of <unk> games . In the game 's campaign mode , the player controls Joanna Dark through a series of <unk> levels collected together into missions . Each level requires the player to complete a certain number of objectives , ranging from <unk> oneself to hacking computers , collecting objects , and defeating enemies , among others . Players can carry an unlimited number of weapons and almost all of the weapons have two firing modes . The levels in Perfect Dark have no checkpoints , meaning that if Joanna is killed or fails an objective , the player has to start the level from the beginning . Every level can be played on three difficulty settings and several aspects , such as the enemies aggressiveness and the number of objectives that must be completed , among others , can vary in function of the chosen difficulty . Two players can also play the campaign co @-@ <unk> or through a " counter @-@ operative " mode , in which one player controls the protagonist , while the other controls enemies throughout the level , attempting to stop the first player from completing objectives . 
- The game also features a multiplayer mode , called Combat <unk> , where various players can compete against each other in different types of <unk> and objective @-@ based games . Aspects of each game can be highly customized . Features such as the weapons available , the winning condition , and team management can be changed to match player preference . Computer controlled <unk> , called Simulants , can also be added to the game and each of them can be individually customized , including their appearance , difficulty , and behavior . Players can also issue orders to the Simulants on their respective team ; for example , Guard tells a <unk> to stay in its current location and guard that area . The player can explore the Carrington Institute , which serves as the game 's training ground , and take part in different training activities . The most substantial of these activities is the shooting range , which allows the player to test all the weapons of the game against moving targets . 
- 
- = = = <unk> = = = 
- 
- The remaster offers several improvements over the original Perfect Dark that was released for the Nintendo 64 in 2000 . The most remarkable change is that any of the multiplayer modes , including co @-@ operative and counter @-@ operative , can now be played in either <unk> or through the Xbox Live online service . Combat <unk> matches are still capped at 12 entities , but the game can now comprise eight players online simultaneously , an improvement to the original 's cap of four players and eight Simulants . Players can also play against more than eight Simulants as long as there are enough slots available in a match ; for example , a single player can play against 11 Simulants ; such a feature was not possible in the original game . Unlike the original game , all the multiplayer content is unlocked from the beginning , and weapons from the game 's predecessor , which were originally only available in the missions , are now available to use in multiplayer . The game features an online <unk> system and players can earn achievements and in @-@ game crowns by <unk> certain tasks . The game also includes two new control set @-@ ups , entitled " <unk> " and " Duty <unk> " , which are based on the popular first @-@ person shooter franchises Halo and Call of Duty respectively . 
- 
- = = Plot = = 
- 
- Perfect Dark is set in the year <unk> against the backdrop of an interstellar war between two races : the <unk> , who resemble the stereotypical grey aliens , and the Skedar , reptile @-@ like extraterrestrials who can disguise themselves as humans , bearing similarities to Nordic aliens . On Earth the <unk> dataDyne has developed in secret Dr. Caroll , the world 's first sentient AI computer . Feeling that dataDyne has betrayed ethical and moral standards , Dr. Caroll contacts the Carrington Institute , an espionage agency founded by Daniel Carrington , with the hope of revealing dataDyne 's sinister dealings . He is recovered by Joanna Dark , the Institute 's top agent , but is later recaptured by dataDyne at Carrington 's private villa . Joanna is then sent to Chicago to once again recover Dr. Caroll and uncover dataDyne 's plan . She learns that dataDyne and their conspirators plan to kidnap the President of the United States in order to get access to a deep sea vessel which will allow the conspirators to steal an alien <unk> buried on the ocean floor . 
- Meanwhile , Joanna is ordered to rescue a Maian survivor named Elvis from Area 51 who is key in stopping the conspiracy . She succeeds and the pair work together to rescue the president . She learns the conspirators are actually Skedar aliens disguised as Scandinavian humans who intend to test the weapon on the Earth before using it against the Maian homeworld . Without permission from the president , dataDyne elect to <unk> the deep sea vessel and take it to the ancient alien spacecraft . With the help of Elvis , Joanna follows where she finds a <unk> Dr. Caroll <unk> the <unk> . She replaces its current personality with a backup of the original , and the restored Dr. Carroll sets the weapon to self @-@ destruct in order to protect Earth . In retaliation , the Skedar capture Joanna and take her to their homeworld . Elvis rescues her and she then kills the Skedar High Priest , leaving the Skedar in <unk> . The game ends with Elvis and Joanna leaving the Skedar planet just prior to an orbital bombardment from the Maian navy . 
- 
- = = Development = = 
- 
- Perfect Dark was ported by <unk> Studios , the same studio that previously developed the Xbox Live Arcade versions of Rare 's platform games <unk> @-@ <unk> and <unk> @-@ <unk> . According to Microsoft Game Studios creative director Ken <unk> , the development team " took the original code , ported it to the Xbox 360 and included deep [ Xbox Live ] integration . " The game was developed over a course of approximately 11 months after the creation of a working prototype . As the Xbox Live functionality had to be written from scratch , the developers opted to completely rewrite the game engine rather than do a port through emulation . As a result , although the game plays exactly the same , the code and renderer is different . The game also runs at <unk> and 60 frames per second . 
- While the original level geometry was kept , the levels received new textures , characters and weapons were recreated , and <unk> were rebuilt . <unk> explained that " things are large and <unk> because that 's what [ Perfect Dark ] looks like " , meaning that the sharper textures and higher resolution simply make the game look clearer . He also observed that , as character and weapon models were remodeled from their original low hundreds <unk> count to <unk> in the thousands , he was worried about them looking awkward in the low @-@ <unk> geometry level design . According to him , " it 's one of the areas that I give a lot of credit to the developer on . It just looks right . They were smart about the way they up @-@ <unk> the models so they still feel like they 're kind of retro , but they 're clean . " 
- Although music and sound effects were kept from the original recording sessions , original master recordings have been used to update the soundtrack at much higher quality ; the original recording size was 16 <unk> , while in the remaster it is over 250 <unk> . Developers also decided to retain the free aim mode because they wanted to be faithful to the original game . According to Rare 's Producer Nick Ferguson , " We didn 't change the fundamental behavior of the aiming system simply because that is not how Perfect Dark was played " . He also observed that the idea of updating the controls was actually seen as a flaw in Perfect Dark Zero , which tried to " combine the original Perfect Dark system with aspects of Halo " . The original diagonal running , which allows players to move faster than by running forwards or sideways alone , did not work the first time they implemented the analog stick , so it was manually rewritten as it was considered essential for <unk> and achieving some of the target times required to unlock cheats . 
- 
- = = Marketing and release = = 
- 
- Perfect Dark was first <unk> to consumers in April 2009 via a <unk> of a Rare employee 's Xbox 360 <unk> which showed an icon for the game . It was confirmed to be in development on June 2 , 2009 via Xbox Live Director of Programming Larry <unk> 's Twitter account . The game was released on March 17 , 2010 as part of Microsoft 's Xbox Live Block Party promotion . As a cross @-@ promotion with the game <unk> 2 , users can unlock that game 's protagonist , known simply as Agent 4 , although a <unk> 2 <unk> is required on the Xbox 360 hard drive . A title update was released in April 2010 which addressed bugs , added two control schemes , and expanded <unk> . Perfect Dark was downloaded over 150 @,@ 000 times during its first week of release and grossed approximately $ 1 @.@ 61 million at the end of the month . The game has sold more than 285 @,@ 000 units as of August 2010 and nearly 325 @,@ 000 units at the end of 2010 . As of year @-@ end 2011 , sales had increased to nearly 410 @,@ 000 units . In 2015 , the game was released as part of the Rare Replay compilation for Xbox One . 
- 
- = = Reception = = 
- 
- Perfect Dark received generally favorable reviews from critics . Writing for 1UP.com , Scott <unk> highlighted the technical improvements , stating that the remaster is " a great way to re @-@ enjoy a game you already love " . <unk> Hatfield of IGN noted the game 's outdated dialogue , voice acting , and mission objectives , but nevertheless remarked that the game " wasn 't brought back for the <unk> -- this is for the fans , and they will be very , very happy . " He also praised the game 's multiplayer mode over Xbox Live and highlighted the selection of weapons , the satisfying <unk> , and <unk> , noting that they allow players to compare their performance with their friends . 
- Despite the praise , some reviewers criticized the game for its confusing level layouts and felt they have not held up very well over the years . Christian Donlan of Eurogamer stated that Perfect Dark is " not afraid to throw dead ends at you seemingly for the hell of it , or repeat textures so much in its huge maps that you can get a little dizzy . " GameSpot 's Tom <unk> Shea noted that the campaign was " oddly paced " , and that " locked doors , unused rooms , and dead ends ... can be <unk> to <unk> around in a circle until you finally happen upon the correct door you just couldn 't locate . " Nevertheless , he admitted that " it 's a lot of fun to replay them to try for high scores and figure out the many unique objectives . " He also remarked that that online play can periodically suffer from a significant amount of <unk> , but praised the amount of content and features . 
- The game 's original Counter @-@ <unk> mode was very well received , with Eurogamer remarking that it " still feels ahead of its time even now . " Dan <unk> of Game Informer stated similar <unk> , saying that " it 's even better this time around thanks to the <unk> improvement . " Although the game 's controls have been upgraded to support two analog sticks , Steven Hopper of <unk> noted that the game still " feels a little different from what modern day shooter fans are used to " , while Eurogamer remarked that the aim assist can be <unk> generous on easy difficulties . At the end of March 2010 , IGN named Perfect Dark Xbox Live Arcade Game of the Month . 
- 
- 
- = First Ostend Raid = 
- 
- The First Ostend Raid ( part of Operation <unk> ) was the first of two attacks by the Royal Navy on the German @-@ held port of Ostend during the late spring of 1918 during the First World War . Ostend was attacked in conjunction with the neighbouring harbour of Zeebrugge on 23 April in order to block the vital strategic port of Bruges , situated 6 mi ( 5 @.@ 2 nmi ; 9 @.@ 7 km ) inland and ideally sited to conduct raiding operations on the British coastline and shipping lanes . Bruges and its satellite ports were a vital part of the German plans in their war on Allied commerce ( <unk> ) because Bruges was close to the troopship lanes across the English Channel and allowed much quicker access to the Western Approaches for the U @-@ boat fleet than their bases in Germany . 
- The plan of attack was for the British raiding force to sink two obsolete cruisers in the canal mouth at Ostend and three at Zeebrugge , thus preventing raiding ships leaving Bruges . The Ostend canal was the smaller and narrower of the two channels giving access to Bruges and so was considered a secondary target behind the Zeebrugge Raid . Consequently , fewer resources were provided to the force assaulting Ostend . While the attack at Zeebrugge garnered some limited success , the assault on Ostend was a complete failure . The German marines who defended the port had taken careful preparations and drove the British assault ships astray , forcing the abortion of the operation at the final stage . 
- Three weeks after the failure of the operation , a second attack was launched which proved more successful in sinking a <unk> at the entrance to the canal but ultimately did not close off Bruges completely . Further plans to attack Ostend came to nothing during the summer of 1918 , and the threat from Bruges would not be finally stopped until the last days of the war , when the town was liberated by Allied land forces . 
- 
- = = Bruges = = 
- 
- Bruges had been captured by the advancing German divisions during the Race for the Sea and had been rapidly identified as an important strategic asset by the German Navy . Bruges was situated 6 mi ( 5 @.@ 2 nmi ; 9 @.@ 7 km ) inland at the centre of a network of canals which emptied into the sea at the small coastal towns of Zeebrugge and Ostend . This land barrier protected Bruges from bombardment by land or sea by all but the very largest calibre artillery and also secured it against raiding parties from the Royal Navy . <unk> on the natural advantages of the port , the German Navy constructed extensive training and repair facilities at Bruges , equipped to provide support for several flotillas of destroyers , torpedo boats and U @-@ boats . 
- By 1916 , these raiding forces were causing serious concern in the Admiralty as the proximity of Bruges to the British coast , to the troopship lanes across the English Channel and for the U @-@ boats , to the Western Approaches ; the heaviest shipping lanes in the World at the time . In the late spring of 1915 , Admiral Reginald <unk> had attempted without success to destroy the lock gates at Ostend with monitors . This effort failed , and Bruges became increasingly important in the Atlantic Campaign , which reached its height in 1917 . By early 1918 , the Admiralty was seeking ever more radical solutions to the problems raised by unrestricted submarine warfare , including instructing the " Allied Naval and Marine Forces " department to plan attacks on U @-@ boat bases in Belgium . 
- The " Allied Naval and Marine Forces " was a newly formed department created with the purpose of conducting raids and operations along the coastline of German @-@ held territory . The organisation was able to command extensive resources from both the Royal and French navies and was commanded by Admiral Roger Keyes and his deputy , Commodore Hubert Lynes . Keyes , Lynes and their staff began planning methods of <unk> Bruges in late 1917 and by April 1918 were ready to put their plans into operation . 
- 
- = = Planning = = 
- 
- To block Bruges , Keyes and Lynes decided to conduct two raids on the ports through which Bruges had access to the sea . Zeebrugge was to be attacked by a large force consisting of three blockships and numerous supporting warships . Ostend was faced by a similar but smaller force under immediate command of Lynes . The plan was for two obsolete cruisers — HMS Sirius and Brilliant — to be expended in blocking the canal which emptied at Ostend . These ships would be stripped to essential fittings and their lower holds and ballast filled with rubble and concrete . This would make them ideal barriers to access if sunk in the correct channel at the correct angle . 
- When the weather was right , the force would cross the English Channel in darkness and attack shortly after midnight to coincide with the Zeebrugge Raid a few miles up the coast . By coordinating their operations , the assault forces would stretch the German defenders and hopefully gain the element of surprise . Covering the Inshore Squadron would be heavy bombardment from an offshore squadron of monitors and destroyers as well as artillery support from Royal Marine artillery near Ypres in Allied @-@ held Flanders . <unk> support would be offered by several flotillas of motor launches , small torpedo boats and Coastal Motor Boats which would lay smoke screens to obscure the advancing blockships as well as evacuate the crews of the cruisers after they had blocked the channel . 
- 
- = = = British order of battle = = = 
- 
- 
- = = = = Offshore Squadron = = = = 
- 
- Lord Clive @-@ class monitors with 12 in ( 300 mm ) guns : 
- HMS Marshal <unk> , Lord Clive , Prince Eugene and General <unk> 
- <unk> class monitors with 7 @.@ 5 in ( 190 mm ) guns : 
- HMS <unk> , <unk> , <unk> 
- Destroyers : 
- HMS <unk> , <unk> and <unk> 
- French Navy <unk> , <unk> and <unk> 
- Light craft : 
- 4 torpedo boats , 4 French motor launches 
- 
- = = = = Inshore Squadron = = = = 
- 
- <unk> : 
- HMS Sirius , Brilliant 
- Destroyers : 
- HMS Swift , <unk> ( destroyer leader ) , <unk> , <unk> , <unk> , Tempest , <unk> 
- Light craft : 
- 18 Motor <unk> , 8 Coastal Motor Boats 
- Artillery support was also provided by Royal Marine heavy artillery in Allied @-@ held Flanders . The force was covered in the English Channel by seven light cruisers and 16 destroyers , none of which saw action . 
- 
- = = Attack on Ostend = = 
- 
- The assaults on Zeebrugge and Ostend were eventually launched on 23 April , after twice being delayed by poor weather . The Ostend force arrived off the port shortly before midnight and made final preparations ; the monitors took up position offshore and the small craft moved forward to begin laying smoke . Covering the approach , the monitors opened fire on German shore defences , including the powerful " Tirpitz " battery , which carried 11 in ( 280 mm ) guns . As a long range artillery duel developed , the cruisers began their advance towards the harbour mouth , searching for the marker buoys which indicated the correct passage through the diverse <unk> which made navigation difficult along the Belgian coast . 
- It was at this stage that the attack began to go seriously wrong . Strong winds blowing off the land swept the smoke screen into the face of the advancing cruisers , <unk> their commanders who attempted to navigate by dead reckoning . The same wind disclosed the Inshore Squadron to the German defenders who immediately opened up a withering fire on the blockships . With their volunteer crews suffering heavy casualties , the commanders increased speed despite the poor visibility and continued <unk> through the narrow channels inshore , searching for the <unk> Bank buoy which directed shipping into the canal . 
- Commander Alfred Godsal led the assault in HMS Brilliant and it was he who stumbled into the most effective German counter @-@ measure first . As Brilliant <unk> through the <unk> , the lookout spotted the buoy ahead and Godsal headed directly for it , coming under even heavier fire as he did so . Passing the navigation marker at speed , the cruiser was suddenly brought to a halt with a <unk> <unk> , throwing men to the decks and sticking fast in deep mud well outside the harbour mouth . Before warnings could be relayed to the Sirius following up close behind , she too passed the buoy and her captain Lieutenant @-@ Commander Henry Hardy was shocked to see Brilliant dead ahead . With no time to <unk> , Sirius <unk> into the port quarter of Brilliant , the blockships settling into the mud in a tangle of wreckage . 
- Artillery and long @-@ range machine gun fire continued to riddle the wrecks and the combined crews were ordered to evacuate as the officers set the scuttling charges which would sink the blockships in their current , useless locations . As men <unk> down the side of the cruisers into Coastal Motor Boats which would relay them to the Offshore Squadron , destroyers moved closer to Ostend to cover the retreat and the monitors continued their heavy fire . Godsal was the last to leave , picked up by launch <unk> commanded by Lieutenant Rowley <unk> . With the main assault a complete failure , the blockading forces returned to Dover and <unk> to assess the disaster . 
- When the forces had reassembled and the commanders conferred , the full facts of the failed operation were revealed . The German commander of Ostend had been better prepared than his counterpart at Zeebrugge and had recognised that without the navigation buoy no night attack on Ostend could be successful without a strong familiarity with the port , which none of the British <unk> possessed . However , rather than simply remove the buoy , the German commander had ordered it moved 2 @,@ 400 yd ( 2 @,@ 200 m ) east of the canal mouth into the centre of a wide <unk> of <unk> , acting as a fatal decoy for any assault force . 
- 
- = = Aftermath = = 
- 
- The assault at Zeebrugge a few miles away from Ostend was more successful and the blocking of the major channel did cause some consternation amongst the German forces in Bruges . The larger raiders could no longer leave the port , but smaller ships , including most submarines , were still able to traverse via Ostend . In addition , within hours a narrow channel had also been carved through Zeebrugge too , although British intelligence did not realise this for several weeks . The defeat at Ostend did not entirely <unk> the <unk> British media and public reaction to Zeebrugge , but in the Admiralty and particularly in the Allied Naval and Marine Forces the failure to completely <unk> Bruges rankled . 
- A second operation was planned for 10 May using the cruiser HMS <unk> and proved more successful , but ultimately it also failed to completely close off Bruges . A third planned operation was never conducted as it rapidly became clear that the new channel carved at Zeebrugge was enough to allow access for U @-@ boats , thus calling for an even larger double assault , which would stretch the resources of the " Allied Naval and Marine Forces " too far . British losses in the three futile attempts to close Bruges cost over 600 casualties and the loss of several ships but Bruges would remain an active raiding base for the German Navy until October 1918 . 
- 
- 
- = Joyful , Joyful = 
- 
- " Joyful , Joyful " is a song by contemporary Christian music band Casting Crowns from their fourth studio album Until the Whole World Hears ( 2009 ) . Written by Mark Hall and Bernie <unk> and produced by Mark A. Miller , the song is a re @-@ interpretation of the hymn " Joyful , Joyful We Adore Thee " and Ludwig van Beethoven 's Symphony No. 9 . " Joyful , Joyful " , a CCM and alternative CCM song , is driven by a string section that has been compared to Coldplay 's song " Viva la Vida " . It received positive reviews from music critics and received airplay over the 2010 Christmas season , peaking at number three on the Billboard Hot Christian Songs chart . 
- 
- = = Background and recording = = 
- 
- " Joyful , Joyful " , is a <unk> of the hymn " Joyful , Joyful We Adore Thee " ( itself a re @-@ arrangement of a theme from Beethoven ) . Hall felt that there are many Christian songs that have been around and sung so long that the meaning behind them is lost ; he commented that " you hear them so many times you don 't really hear what they 're saying anymore " , listing the original version of " Joyful , Joyful We Adore Thee " as an example . 
- Although the song is a re @-@ interpretation of " Joyful , Joyful We Adore Thee " , Mark Hall and Bernie <unk> are credited with writing the song . It was produced by Mark A. Miller and recorded by Sam Hewitt , Michael Hewitt , and Dale Oliver at Zoo Studio in Franklin , Tennessee ; the string instrument tracks were recorded by John Painter and Leslie Richter at Ocean Way in Nashville , Tennessee . Digital editing was handled by Michael Hewitt , while mixing was handled by Sam Hewitt . The song was mastered by Andrew <unk> , Shelly Anderson , <unk> <unk> and Daniel <unk> at Georgetown Masters in Nashville . 
- 
- = = Composition = = 
- 
- " Joyful , Joyful " is a song with a length of four minutes and twenty @-@ eight seconds . According to the sheet music published by Musicnotes.com , " Joyful , Joyful " is a CCM and alternative CCM set in common time in the key of F major with a tempo of 120 beats per minute . Mark Hall 's vocal range in the song spans from the low note of B ♭ 3 to the high note of <unk> . The song has regarded as a re @-@ invention of " Joyful , Joyful We Adore Thee " and Beethoven 's Symphony No. 9 , the song alters the format of the former , <unk> the song 's overall structure while adding a chorus . " Joyful , Joyful " is led by a " driving " and " <unk> " string section that has been compared to Coldplay 's " Viva la Vida " . Mark Hall felt that the band 's arrangement brought out the message of one of the song 's final verses ( " God our Father / Christ our brother / all who live in love are <unk> / teach us how to love each other / and fill us to the joy divine " ) ; Hall described the message by saying " God 's our father and Christ 's our brother , we have this connection with God . But if we can 't love each other , the joy isn 't completed . Its not real joy yet until we know how to love the people that are around us " . 
- 
- = = Reception = = 
- 
- 
- = = = Critical response = = = 
- 
- " Joyful , Joyful " received positive reviews from music critics . Andrew Greer of CCM Magazine praised the song as a " fitting ode " to Beethoven 's Symphony No. 9 . Roger Ham of Christianity Today praised it as one of the best songs off of Until the Whole World Hears . Tony Cummings of Cross Rhythms praised the song as having " the same kind of string arrangement which made Coldplay 's ' Viva La Vida ' so enjoyable " . <unk> <unk> of Gospel Music Channel regarded the song as a highlight of the album . Roger Gelwicks of Jesus Freak <unk> , while describing the song as " interesting " , felt that it wasn 't much of an improvement over the rest of the album . 
- 
- = = = Chart performance = = = 
- 
- " Joyful , Joyful " received airplay over the 2010 Christmas season , debuting at number forty @-@ two on the Billboard Hot Christian Songs chart for the chart week of December 11 , 2010 . In its fifth week on the chart , the chart week of January 8 , 2011 , it reached its peak position of number three . 
- 
- = = Credits and personnel = = 
- 
- Credits lifted from the album liner notes for Until the Whole World Hears . 
- Recording 
- Recorded Zoo Studio in Franklin , Tennessee . 
- <unk> recorded at Ocean Way in Nashville , Tennessee . 
- 
- = = Charts = = 
- 
- 
- 
- = Hurricane Dot ( 1959 ) = 
- 
- Hurricane Dot of August 1959 was at its time the costliest tropical cyclone in Hawaiian history . Dot was first identified as a strong tropical storm southeast of Hawaiʻi on August 1 . The storm was potentially a continuation of a previously unnamed tropical cyclone that was monitored west of the Baja California Peninsula from July 24 @-@ 27 , but was never confirmed due to a lack of ship reports . Dot was quick to intensify , reaching hurricane intensity six hours after naming . By August 3 , Dot reached its peak intensity , with maximum sustained winds reaching 150 mph ( 240 km / h ) . <unk> leveled off afterwards as Dot tracked westward before making a curve towards the northwest on August 5 , after which the hurricane weakened at a faster clip . Dot made landfall the next day on Kauai as a minimal hurricane before dissipating west of the Hawaiian Islands on August 8 . 
- Dot produced heavy rainfall and gusty winds as it passed south of the Big Island , Lanai , Maui , Molokai , and Oahu , resulting in minor damage . In Oahu , some homes along the coast were unroofed , and damage from wave action was also reported . Damage from these four islands totaled US $ 150 @,@ 000 , and two indirect deaths occurred in Lanai . Extensive damage occurred on Kauai as Dot made landfall , producing wind gusts as high as 103 mph ( 166 km / h ) and toppling trees and power lines . Widespread power outages affected the island , causing telecommunications and water systems to fail . Although infrastructure was damaged to an extent by <unk> and strong winds , crops suffered the most losses . Cane sugar crops sustained US $ 2 @.@ 7 million in losses . Overall , damage from Dot across Hawaii totaled US $ 6 million , and a disaster area declaration and state of emergency took effect for the archipelago after the hurricane 's passage . 
- 
- = = Meteorological history = = 
- 
- On July 24 , the SS <unk> encountered a storm with maximum sustained winds meeting the threshold of tropical storm status roughly 1 @,@ 000 mi ( 1 @,@ 600 km ) west of the Baja California Peninsula . Despite reports that the location of the system remained vague , the Joint Typhoon Warning Center ( JTWC ) began issuing tropical cyclone advisories and warnings on the unnamed disturbance . Tracking west @-@ northwestward , the tropical storm peaked with winds reported at 65 mph ( 105 km / h ) shortly after its discovery ; however , the JTWC discontinued its monitoring of the cyclone on July 27 due to a lack of ship reports confirming the location of the tropical storm . 
- At 1800 UTC on August 1 , an unidentified ship roughly 950 mi ( 1 @,@ 550 km ) southeast of Hilo , Hawaii reported 70 mph ( 110 km / h ) winds within a storm , prompting the JTWC to initiate advisories on Tropical Storm Dot . Despite an apparent correlation between Dot and the preceding unnamed tropical cyclone , the lack of ship reports between July 27 and August 1 prevented the agency from confirming that the two systems were the same . Nonetheless , development upon designation was rapid as the system tracked westward , with reports from the SS <unk> indicating that Dot had intensified to hurricane strength by August 2 . Rapid intensification continued , and at 0000 UTC on August 3 , reconnaissance aircraft found winds of 150 mph ( 240 km / h ) and a minimum barometric pressure of 952 mbar ( hPa ; 28 @.@ 11 inHg ) , making Dot a Category 4 hurricane on the modern @-@ day Saffir – Simpson hurricane wind scale . Later analysis indicated that these figures constituted the hurricane 's peak intensity . 
- After peaking , Dot slightly weakened , but maintained its Category 4 status for more than two days ; during that time it boasted an unusually large eye spanning as much as 40 mi ( 65 km ) in diameter . Late on August 4 , a fourth reconnaissance flight into the storm found surface winds of 160 mph ( 260 km / h ) , but this reading was discounted on the basis that pressures were unusually high for a storm of that intensity . On August 5 , Dot passed 90 mi ( 145 km ) south of Ka Lae before the storm curved sharply towards the northwest late that day . A more definite weakening phase began after this point , and during the night of August 6 , Dot made landfall on Kauai with winds estimated at 85 mph ( 140 km / h ) , making the storm a Category 1 @-@ equivalent at the time of landfall . After traversing the island , Dot was downgraded to tropical storm intensity and curved back to the west before eventually dissipating on August 8 . 
- 
- = = Preparations , impact , and aftermath = = 
- 
- On August 3 , the United States Weather Bureau issued a hurricane watch for coastal areas of the Big Island in <unk> and <unk> districts . As Dot swept by the Hawaiian islands , various gale warnings and small craft warnings shifted westward to reflect the hurricane 's predicted path . Due to the storm 's sudden northward shift in movement , hurricane warnings were issued for portions of Oahu and the Kauai channel before being issued for only the island of Kauai . All watches and warnings were discontinued on August 7 after Dot weakened below hurricane intensity . Disaster workers in Oahu and Kauai were warned by civil defense agencies and the American Red Cross to prepare for emergency . Roughly 400 people fled from the beaches of Kauai due to the threat of storm surge , with an additional 500 people being evacuated by authorities as the storm neared the island . After the storm caused flooding in Kauai , nearly 1 @,@ 000 people evacuated out of submerged areas into schools , <unk> , and public facilities repurposed as emergency shelters . 
- Passing well to the south of the Big Island , Lanai , Maui , Molokai , and Oahu , damage from Dot on these islands was minor . Rainfall on the Big Island peaking at 4 in ( 100 mm ) caused localized flooding in some areas , while wave damage occurred at Ka Lae and along the island 's <unk> coast . Winds at a station on Ka Lae reached 85 mph ( 140 km / h ) . Flooding also occurred on Oahu , and along the coast homes were unroofed and cars were damaged by flying projectiles after being subjected to winds estimated at 60 mph ( 95 km / h ) . Off of Lanai , a tugboat captain was indirectly killed after he slipped between two boats in rough seas while attempting to board another ship , crushing him . Another indirect death occurred on Lanai in a traffic incident resulting from Dot 's rainfall . Damage on the Hawaiian islands outside of Kauai was estimated at $ 150 @,@ 000 . 
- Rough surf along the coasts of Kauai combined with torrential rainfall to produce widespread coastal flooding . Waves at Port Allen peaked at 35 ft ( 10 @.@ 7 m ) . <unk> plantations were inundated , with losses exceeding US $ 200 @,@ 000 . Infrastructure damage as a result of wave action was spotty but nonetheless amounted to US $ 100 @,@ 000 . The August 1959 rainfall total in <unk> , largely attributed to Dot , measured 8 @.@ 13 in ( 207 mm ) , 6 in ( 150 mm ) in excess of normal . The heavy rainfall caused rivers and streams to swell and <unk> adjacent areas . Despite making landfall as a weak Category 1 hurricane , Dot brought damaging winds inland , with a maximum wind gust of 103 mph ( 166 km / h ) reported at <unk> Light . However , gusts as high as 125 mph ( 200 km / h ) were estimated given the snapping of palm trees . Strong winds damaged hundreds of buildings in the areas of <unk> , <unk> , and <unk> , Hawaii . Extensive losses resulted from the damaging and toppling of <unk> trees . However , of the crops on Kauai , cane suffered the greatest impact with damage figures reaching US $ 2 @.@ 7 million . The toppling of power lines caused power outages across the island ; fallen lines also blocked roads . Water supply to some communities failed due to the lack of electricity . With the exception of emergency radio <unk> , telecommunications on Kauai failed . The damage toll from Dot for the entirety of Hawaiʻi totaled US $ 6 million , making the hurricane the costliest in Hawaiian history before it was surpassed by hurricanes <unk> , <unk> and Iselle in 1982 , 1992 and 2014 , respectively . <unk> for inflation , Dot 's damage figure <unk> US $ 50 million . 
- Due to the damage wrought by Dot on Kauai , the island was declared a major disaster area . <unk> in for governor William F. Quinn , Hawaiian secretary Edward E. Johnston declared a state of emergency for Hawaii and allocated funds towards the repairing of roads and public property . The United States Weather Bureau awarded the SS <unk> a public service award on October 7 , 1959 for serving as reconnaissance for Hurricane Dot throughout its existence . 
- 
- 
- = Jacob deGrom = 
- 
- Jacob Anthony deGrom ( born June 19 , 1988 ) , is an American professional baseball pitcher for the New York Mets of Major League Baseball ( MLB ) . Prior to playing professionally , deGrom attended Stetson University and played college baseball for the Stetson Hatters . 
- DeGrom began playing baseball as a shortstop and was converted into a pitcher during his junior year at Stetson . The Mets selected him in the ninth round of the 2010 MLB Draft , and he made his MLB debut with the Mets on May 15 , 2014 . That year , deGrom was named the National League 's ( NL ) Rookie of the Month twice , and the NL Rookie of the Year . In 2015 , deGrom was selected as an MLB All @-@ Star . 
- 
- = = Amateur career = = 
- 
- DeGrom attended <unk> Christian Academy in <unk> Beach , Florida , where he played for the school 's baseball and basketball teams . As a senior , the Florida Sports Writers Association named deGrom to the All @-@ Florida second team . He also played American Legion baseball , where he was noticed by the coaches for the Stetson Hatters , the college baseball team of Stetson University . 
- DeGrom was not selected in the Major League Baseball ( MLB ) Draft out of high school . He enrolled at Stetson University and joined their baseball team where he played exclusively as a shortstop during his freshman and sophomore seasons . Though he was considered a good <unk> with a strong throwing arm , deGrom was a light hitter , with a career <unk> batting average . He made his first appearance as a pitcher in May 2009 . In the summer of 2009 , between his sophomore and junior years , deGrom received an invitation to play collegiate summer baseball for the <unk> Suns of the Florida Collegiate Summer League , which he declined after discovering that they wanted him to play as a pitcher . 
- When deGrom returned to Stetson that fall , the team used him as a relief pitcher , filling the role of their closer , in addition to playing shortstop . He quickly became one of Stetson 's best pitchers , so the team moved deGrom into their starting rotation midway through the season . In addition to a fastball , deGrom learned to throw a changeup and a slider . MLB scouts began to take notice of deGrom when he pitched against Chris Sale of Florida Gulf Coast University . In that game , deGrom hit his only home run of the season . He made 12 starts for the Hatters , pitching to a 4 – 5 win – loss record with a 4 @.@ 48 earned run average . 
- 
- = = Professional career = = 
- 
- 
- = = = Minor League Baseball = = = 
- 
- The New York Mets selected deGrom in the ninth round as a pitcher , with the 272nd overall selection , of the 2010 MLB Draft . He signed with the Mets , receiving a $ 95 @,@ 000 signing bonus . The Mets assigned deGrom to the <unk> Mets of the Rookie @-@ level Appalachian League , where he made six starts before he was diagnosed with a partial tear of the ulnar collateral ligament ( UCL ) in his pitching elbow . He attempted to <unk> his arm for four months , but underwent Tommy John surgery to repair the UCL in October . He did not pitch in 2011 while he recovered from the surgery . While <unk> , deGrom worked on his changeup with Johan Santana . 
- DeGrom pitched for the Savannah Sand <unk> of the Class A South Atlantic League and the St. Lucie Mets of the Class A @-@ Advanced Florida State League in 2012 , finishing the year with a 2 @.@ 43 ERA in 19 games started . In 2013 , he began the season with St. Lucie , but was promoted to the Binghamton Mets of the Class AA Eastern League after two starts due to injuries to Binghamton 's Luis <unk> and Cory <unk> . He received a promotion to the Las Vegas <unk> of the Class AAA Pacific Coast League in June after the Mets promoted Zack Wheeler and Carlos Torres to the major leagues and traded <unk> <unk> . He had a combined 4 @.@ 51 ERA for the season , due to a broken finger suffered during the offseason , which altered the way he threw the ball . 
- The Mets added deGrom to their 40 @-@ man roster on November 20 , 2013 , to protect him from being eligible in the Rule 5 draft . During the offseason , deGrom improved his mechanics , and learned to throw a curveball . He began the 2014 season with Las Vegas , and had a 4 – 0 win – loss record and a 2 @.@ 58 ERA in his first seven games started . 
- 
- = = = Major League Baseball = = = 
- 
- 
- = = = = 2014 = = = = 
- 
- The Mets promoted deGrom to the major leagues on May 12 , 2014 , after Gonzalez <unk> was placed on the disabled list . The Mets planned to use deGrom in relief , but an injury to Dillon <unk> required the Mets to insert him into their starting rotation . DeGrom made his major league debut on May 15 against cross @-@ town rival New York Yankees in <unk> Field . He faced fellow rookie Chase <unk> , also making his MLB debut . He pitched seven innings , allowing only one run and striking out six , but the Yankees shut out the Mets and won 1 – 0 . DeGrom also collected his first MLB hit in the game in his first career at bat . It was the first hit by a Mets pitcher in the 2014 season ending an 0 @-@ for @-@ 64 <unk> streak , the worst collective mark by a pitching staff to begin a season in MLB history . 
- DeGrom compiled four quality starts in his first four MLB starts , but did not record a win in any of them . On July 8 , deGrom pitched seven scoreless innings and recorded 11 strikeouts in giving the Mets their <unk> franchise victory . Along with Steve <unk> of the Miami Marlins , deGrom was named the National League 's ( NL ) Co @-@ Player of the Week for the period of July 21 to July 27 after allowing only one earned run in two starts that week . He was named the NL Rookie of the Month for July . On August 11 , deGrom went on the disabled list with rotator cuff tendinitis . Rafael <unk> was called up on August 12 in deGrom 's place . On August 23 , <unk> was re @-@ sent back to the <unk> to make room for deGrom coming off the disabled list . 
- On September 15 , 2014 , deGrom faced the Marlins and struck out his first eight batters , tying the MLB record . Near the end of the season , deGrom was shut down for the year , ending his season with a 9 – 6 record , a 2 @.@ 69 ERA and 144 strikeouts . DeGrom won the 2014 Sporting News NL Rookie of the Year Award and was named the NL Rookie of the Year by the Baseball Writers ' Association of America , ( the first by a Met since Dwight <unk> in 1984 ) receiving first place votes on 26 of the 30 ballots . 
- 
- = = = = 2015 = = = = 
- 
- DeGrom and Clayton <unk> of the Los Angeles Dodgers were selected as NL Co @-@ Players of the Week for the week ending June 7 , 2015 . He began the 2015 season with a 8 – 6 win @-@ loss record and a 2 @.@ 30 ERA through the end of June , and was named to the NL roster in the 2015 MLB All @-@ Star Game . During the All @-@ Star Game , deGrom struck out the three batters he faced on ten pitches , becoming the first person to do so since pitch counts were recorded . DeGrom pitched to a 14 – 8 record with a 2 @.@ 54 ERA and a 0 @.@ 99 walks plus hits per inning pitched ratio during the 2015 season . 
- Starting Game 1 of the 2015 NLDS , he allowed no runs and five hits over seven innings pitched and tied a Mets franchise postseason record with 13 strikeouts ( set by Tom <unk> , Game 1 of the 1973 NLCS ) . DeGrom won the deciding Game 5 with a six @-@ inning , two @-@ run effort . In Game 3 of the 2015 NLCS against the Chicago Cubs , deGrom pitched seven @-@ inning game , allowing just two runs on four hits , one walk and seven strikeouts , putting the Mets ahead 3 – 0 and within one game of their first World Series appearance since the 2000 <unk> Series . DeGrom started Game 2 of the 2015 World Series on October 28 ; he allowed four runs on six hits and three walks over five innings and took the loss as the Royals went up , 2 – 0 , in the series . 
- Following the season , deGrom received a Wilson Defensive Player of the Year Award as the best defensive player statistically at his position in Major League Baseball . 
- 
- = = = = 2016 = = = = 
- 
- Not yet eligible for salary arbitration , the Mets renewed deGrom 's contract for the 2016 season with a $ <unk> @,@ 000 salary , a raise from the $ <unk> @,@ <unk> they paid him for the previous season . DeGrom refused to sign the contract in protest , but stated that he was open to negotiating a contract extension . 
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- = = Personal life = = 
- 
- DeGrom is from <unk> Springs , Florida . He was raised by his parents , Tony , an AT & T lineman , and Tammy , a customer service representative for a credit card rewards program . Tony built a batting cage in the backyard for his son to practice . He credits his father for his quiet intensity and <unk> . He has two sisters , Sarah and Jessica . 
- DeGrom met his wife , <unk> , shortly after high school . They were married in November 2014 , and live in <unk> , Florida . They have a <unk> , and welcomed their first son in April 2016 . 
- DeGrom is known for his long hair , which he began to grow out while at Stetson . His starts with the Mets led to the <unk> <unk> on Twitter of " # <unk> " . 
- 
- 
- = Battle of Merville Gun Battery = 
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- The Battle of Merville Gun Battery occurred on 6 June 1944 , as part of Operation Tonga , part of the Normandy landings , during the Second World War . Allied intelligence believed the Merville Gun Battery was composed of heavy @-@ calibre guns that could threaten the British landings at Sword Beach , only 8 miles ( 13 km ) away . 
- The 9th Parachute Battalion , part of the 3rd Parachute Brigade attached to 6th Airborne Division , was given the objective of destroying the battery . However , when the battalion arrived over Normandy , their parachute descent was dispersed over a large area , so instead of over 600 men , only 150 with no heavy weapons or equipment arrived at the battalion assembly point . Regardless , they pressed home their attack and succeeded in capturing the battery , only to discover that the guns were of a lower caliber than expected [ Czech @-@ manufacture <unk> ] . However , these still had the range ( over 8000 metres ) to hit targets on Sword Beach and in <unk> . Using what explosives they had been able to recover , the surviving 75 men tried to <unk> the guns . 
- When the British paratroopers had withdrawn , two of the guns were put back into action by the Germans . Another attack the next day by British Commandos failed to recapture the battery , which remained under German control until 17 August , when the German Army started to withdraw from the area . 
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- = = Background = = 
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- On 6 June 1944 , the British 6th Airborne Division was given the task of securing the left flank of the Allied <unk> landings . One of their objectives was the destruction of the Merville Gun Battery . Allied planners had judged from the size of the concrete gun emplacements that the guns must be around 150 mm in calibre . If so , the guns would have a range of about 8 miles ( 13 km ) and could threaten Sword Beach , to the west of <unk> , where the 3rd British Infantry Division were due to land later that day . 
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- = = = British forces = = = 
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- The unit assigned to destroy the battery was the 9th Parachute Battalion , part of the 3rd Parachute Brigade , commanded by Lieutenant Colonel <unk> Otway . The battalion 's normal complement of 600 men was supported by a section of sappers from the Royal Engineers , eight <unk> <unk> glider loads transporting <unk> and trailers , and stores including explosives , an anti @-@ tank gun and <unk> . Three of the gliders , transporting 50 volunteers , were to carry out a coup de main landing onto the position to coincide with the ground assault . In April 1944 , the force was taken to <unk> Hill in Berkshire , where over seven days the Royal Engineers had built a full @-@ scale replica of the battery , including obstacles and barbed wire fences . The following five days were spent holding briefings and getting acquainted with the layout of the battery . They carried out nine practice assaults , four of them at night . Due to the nature of the mission , the battalion was given additional medical support from No. 3 Section <unk> ( Parachute ) Field Ambulance . Another unit that would be present during the attack but not directly involved was A Company of the 1st Canadian Parachute Battalion . This company was tasked to provide covering fire for the 9th Battalion 's approach to and withdrawal from the battery . The assault had to be completed and the battalion clear of the position by 05 : 00 , when the Royal Navy cruiser HMS <unk> would open fire on the battery in an attempt to destroy it with naval gunfire . 
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- = = = Battery = = = 
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- The Merville Battery was composed of four 6 @-@ foot @-@ thick ( 1 @.@ 8 m ) steel @-@ reinforced concrete gun casemates , built by the Todt Organisation . Each was designed to protect First World War @-@ vintage Czech <unk> / 19 100 mm guns . Other buildings on the site included a command bunker , a building to accommodate the men , and ammunition magazines . During a visit on 6 March 1944 , to inspect the defences , Field Marshal Erwin Rommel ordered the builders to work faster , and by May 1944 , the last two casemates were completed . 
- The battery was defended by a 20 mm anti @-@ aircraft gun and several machine guns in 15 gun positions , all enclosed in an area 700 by 500 yards ( 640 by 460 m ) surrounded by two barbed wire obstacles 15 feet ( 4 @.@ 6 m ) thick by 5 feet ( 1 @.@ 5 m ) high , which also acted as the exterior border for a 100 @-@ yard @-@ deep ( 91 m ) minefield . Another obstacle was an anti @-@ tank ditch covering any approach from the nearby coast . The original commander of the battery , <unk> <unk> , was killed during a Royal Air Force bombing raid on 19 May 1944 . He was replaced by <unk> <unk> Steiner , who commanded 50 engineers and 80 <unk> from the 1st Battery , Artillery Regiment <unk> , part of the 716th Static Infantry Division . 
- The battery had a complement of some one hundred men in all . These were chiefly gunnery crews , of course , such as my team , but also included staff officers and their assistants , medical , cooks , observation teams , maintenance teams and guards with side arms . It was intended that the battery could remain under siege for up to three months , with sufficient rations and generator fuel kept on site for this event ; the water supply came from an <unk> and could not be interrupted . It was a remarkable structure , and it featured prominently in propaganda of the time … 
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- = = Assault = = 
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- Just after midnight on 6 June , the 9th Parachute Battalion 's advance party landed with the brigade 's <unk> , and reached the battalion assembly area without any problems . While some men remained to mark out the company positions , the battalion 's second in command , Major George Smith , and a reconnaissance party left to scout the battery . At the same time , Royal Air Force Lancaster bombers started their bombing run , which completely missed the battery , their bombs landing further to the south . The <unk> in the meantime were having problems . Those who had arrived at the correct drop zone found their <unk> <unk> had been damaged when they landed , and in the smoke and debris left over from the bombing , their marker lights could not be seen by the pilots of the transport aircraft . The main body of the 9th Parachute Battalion and their gliders were to land at drop zone ' V ' , located between the battery and <unk> from 01 : 00 . However , the battalion was scattered , with a number of paratroopers landing a considerable distance from the designated drop zone . Lieutenant Colonel Otway landed with the rest of his " stick " 400 yards ( 370 m ) away from the drop zone at a <unk> being used as a command post by a German battalion ; after a brief fire @-@ fight , they helped other scattered paratroopers , and reached the drop zone at 01 : 30 . By 02 : 50 , only 150 men had arrived at the battalion 's assembly point with 20 Bangalore torpedoes and a machine gun . The mortars , anti @-@ tank gun , mine detectors , <unk> , sappers and field ambulance section were all missing . 
- Aware of the time constraints , Otway decided he could wait no longer , and the reduced battalion headed for the battery and joined up with Major Smith 's reconnaissance party just outside the village of <unk> en <unk> . The reconnaissance party had cut a way through the barbed wire , and marked four routes through the minefield . Otway divided his men into four assault groups , and settled down to await the arrival of the three gliders . 
- In England , one of the gliders never left the ground , as its tow rope had snapped on taxiing . The other two gliders , unable to locate the battery , did not land where expected . On their run in , both gliders were hit by anti @-@ aircraft fire . One landed around 2 miles ( 3 @.@ 2 km ) away , the other at the edge of the minefield . The troops from this glider became involved in a fire fight with German troops heading to reinforce the battery garrison . 
- Otway launched the assault as soon as the first glider <unk> the battery , ordering the explosives to be detonated to form two paths through the outer perimeter through which the paratroopers attacked . The defenders were alerted by the explosions , and opened fire , inflicting heavy casualties ; only four attackers survived to reach <unk> Four , which they disabled by firing into <unk> and throwing grenades into air <unk> . The other casemates were cleared with fragmentation and white phosphorus grenades , as the crews had neglected to lock the doors leading into the battery . During the bombing raid , the battery 's guns had been moved inside the casemates and the steel doors left open for ventilation . During the battle , 22 Germans were killed and a similar number made prisoners of war . The rest of the garrison escaped undetected by hiding in the underground bunkers . 
- Steiner was not present during the bombing , but at a command bunker in <unk> @-@ <unk> . After the raid , he set out for the battery , but was unable to gain entry due to the volume of fire from the British paratroopers . At the same time , a reconnaissance patrol from an army Flak unit with a half @-@ track mounting a large anti @-@ aircraft gun arrived . The crew had intended to seek cover at the position , but instead used the gun to engage the paratroopers . 
- With the battery in their hands , but no sappers or explosives , the British gathered together what plastic explosives they had been issued for use with their <unk> bombs to try to destroy the guns . By this time , Steiner had returned to <unk> @-@ <unk> , and directed his regiment 's 2nd and 3rd Batteries to fire onto the Merville Battery . 
- 
- = = Aftermath = = 
- 
- Just before 05 : 00 , the battalion 's survivors , just 75 men of the 150 who had set out , left the battery and headed for their secondary objective , the village of La <unk> . The battalion , being too weak , only managed to liberate around half of the village , and had to await the arrival of the 1st Commando Brigade later in the day to complete its capture . 
- After the British had withdrawn , the Germans <unk> the battery position . Steiner was unable to see Sword Beach from his command bunker , so even though he was able to get two of his guns back in action , he was unable to direct accurate fire onto the landings . However , observers with the <unk> Infantry Regiment , holding out at La <unk> , were able to direct his guns until that position was neutralised . 
- On 7 June , the battery was assaulted again by two troops of commandos from No. 3 Commando , part of the 1st Special Service Brigade . The attack in daylight was repulsed with heavy losses to the commandos . As they withdrew , they were engaged by the battery 's guns firing over open sights . The British never succeeded in completely destroying the battery , and it remained under German control until 17 August , when the German Army started to withdraw from France . 
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- 
- = St Caffo 's Church , Llangaffo = 
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- St Caffo 's Church , Llangaffo is a 19th @-@ century church , in the south of Anglesey , north Wales , about 5 miles ( 8 @.@ 0 km ) from the county town , <unk> . It was constructed in 1846 to replace the previous medieval church in the village of Llangaffo . The new building includes a number of monuments from the old church , and has a spire which is a prominent local landmark . The churchyard has part of a stone cross dating from the 9th or 10th century , and some gravestones from the 9th to 11th centuries . It is dedicated to St Caffo , a 6th @-@ century martyr who was killed in the vicinity . 
- The church is still in use as part of the Church in Wales , one of four churches in a combined parish . It is a Grade II listed building , a designation given to " buildings of special interest , which warrant every effort being made to preserve them " , in particular because it is regarded as " a mid 19th @-@ century rural church , consistently articulated and detailed in an Early English style " . 
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- = = History and location = = 
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- The date of first construction of a church in Llangaffo ( a village in Anglesey about 5 miles ( 8 @.@ 0 km ) south of the county town of <unk> ) is uncertain . The village takes its name from the church : the Welsh word <unk> originally meant " enclosure " and then " church " , and " <unk> " is a modified form of the saint 's name . St Caffo , a 6th @-@ century Christian , was a companion of St <unk> and was martyred by shepherds in the vicinity of what is now Llangaffo , perhaps in revenge for his brother <unk> insulting <unk> Gwynedd , the local ruler . It is thought that there may have at one point been a monastery in this location , known as " <unk> Caffo " : <unk> is the Welsh word for " martyr " , and in place names means a building erected near a saint 's grave . 
- The present building , which is in the north @-@ eastern part of the village on the south @-@ eastern side of the <unk> road , was erected in 1846 alongside the churchyard to a design by the Sheffield @-@ based architects <unk> and Hadfield . It replaced an older church , which had stood on an adjoining <unk> of rock until it was demolished . It was described by the clergyman and antiquarian Harry <unk> Jones ( writing in 1846 , as the new church was being constructed ) as a " very small and unimportant edifice " ; he also noted that it was the only medieval building remaining in the parish . 
- The 19th @-@ century church is still in use and belongs to the Church in Wales . It is one of four churches in the combined benefice of <unk> <unk> ( St Michael 's Church , <unk> ) with <unk> ( St <unk> 's Church ) with <unk> ( St <unk> 's Church ) with Llangaffo . It is within the <unk> of <unk> , the <unk> of Bangor and the Diocese of Bangor . As of 2012 , the vicar of the four churches is <unk> Williams , assisted by a <unk> ( E. R. Roberts ) . Williams was appointed in 2007 ; before that , the position had been vacant for 20 years despite many attempts by the Church in Wales to fill it . Services in Welsh are held every Sunday , either Holy <unk> or Morning Prayer ; there are no <unk> services . 
- Edward Wynn ( 1618 – 1669 ) was rector here from <unk> ; he later became Chancellor of Bangor Cathedral , and is buried at the church . James Williams ( 1790 – 1872 ) was the son of John Williams , rector of St Caffo 's . He succeeded his father when he resigned in 1821 , and later became Chancellor of Bangor Cathedral . 
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- = = Architecture and fittings = = 
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- St Caffo 's is built of rubble masonry dressed with limestone , in an early English style ( a style of architecture used between about 1180 and 1275 , typically using narrow pointed windows and arches ) . The roof is made of slate . The tower , at the west end , has buttresses at the corners and is topped with a <unk> spire , which is a prominent local landmark . Entrance is through an arched doorway in the north side of the tower . The chancel , at the east end of the church , is smaller than the nave in both height and width ; there is a transept on the north side of the chancel . The nave has lancet windows , and there is a further lancet window on the south side of the chancel . The church 's east window is set in a pointed arch and has three lights ( sections of window separated by <unk> ) . 
- Inside the church , the sanctuary at the east end is raised above the chancel by one step ; the floor of the sanctuary and the <unk> behind the altar are made from <unk> tiles . <unk> include a circular decorated 12th @-@ century font and a 17th @-@ century pulpit with carved decorative panels . A 1937 survey by the Royal Commission on Ancient and Historical Monuments in Wales and Monmouthshire recorded that the church owned a silver cup and a <unk> dated 1736 . The transept has a number of memorials dating from the 17th century ( including one to Edward Wynn ) , and a stone from the early 7th century inscribed with .. VI / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> . The stone , which is set in the wall , originally came from <unk> , Anglesey . The church porch houses part of a cross head in the shape of a wheel , dating from the 9th or 10th century ; part of the rest of the cross is in the churchyard , but it has suffered significant weather damage with most of the patterns worn away . 
- The oldest graves are to the north of the church , which is unusual : ordinarily the southern part of the churchyard would be used first for burials , with the northern part remaining <unk> unless and until extra space for graves was required . The path between the road and the church has sunken , which may partly be explained by the medieval custom of burying the dead on top of each other . One author has suggested that the mound alongside the path might indicate that the church is located in the site of a Bronze Age settlement . 
- The churchyard has seven gravestones that were discovered in the walls of the previous church . Six of them date from between the 9th and 11th centuries , and the seventh is from the 12th or 13th century . The doorway from the old church , dating from the 15th century , has been reused as an entrance to the churchyard . A carved stone human head , from the 12th century , has been inserted into the north wall of the churchyard . A war memorial in the shape of a Celtic cross remembers those who died during the First and Second World Wars . 
- 
- = = Assessment = = 
- 
- The church is a Grade II listed building – the lowest of the three grades of listing , designating " buildings of special interest , which warrant every effort being made to preserve them " . It was given this status on 30 January 1968 and Cadw ( the Welsh Assembly Government body responsible for the built heritage of Wales ) states that it has been listed because it is " a mid 19th @-@ century rural church , consistently articulated and detailed in an Early English style . " 
- The 19th @-@ century writer Samuel Lewis said that the rebuilt church " forms a very good specimen of the early English style of architecture " , adding that it was " effective from its simplicity and the absence of <unk> . " He added that the interior had been " fitted up in a <unk> and appropriate manner , and the details throughout appear to have been carefully studied " . A 2006 guide to the churches of Anglesey says that the " impressive " tower and steeple " form " a landmark visible for many miles . " It also comments that rendering on part of the tower and the south wall makes them less attractive than the north wall . 
- A 2009 guide to the buildings of the region notes the " exceptional number of inscribed fragments " , showing it to be a place of early Christian worship , but considers that the church is " <unk> " . Similarly , a 2005 guide to Wales describes St Caffo 's as " an <unk> nineteenth @-@ century church from the outside " but says that it has " a remarkable collection " of memorial stones . 
- 
- 
- = George N. Briggs = 
- 
- George Nixon Briggs ( April 12 , 1796 – September 12 , 1861 ) was an American lawyer and politician from Massachusetts . A Whig , Briggs served for twelve years in the United States House of Representatives , and served seven one @-@ year terms as the 19th Governor of Massachusetts , from 1844 to 1851 . 
- <unk> in rural <unk> New York , Briggs studied law in western Massachusetts , where his civic involvement and successful legal practice preceded <unk> political activity . He was elected to Congress in 1830 , where he supported the conservative Whig agenda , serving on the Committee on the Post Office and Post Roads . He was also a regular advocate of temperance , <unk> from all alcohol consumption . 
- He was nominated by the Whigs in 1843 to run against Democratic Governor Marcus Morton as part of a Whig bid for more rural votes , and easily won election until 1849 . Although he sought to avoid the contentious issue of slavery , he protested South Carolina policy allowing the imprisonment of free African Americans . He supported capital punishment , notably refusing to commute the death sentence of John White Webster for the murder of George <unk> . Briggs died of an accidental gunshot wound at his home in Pittsfield , Massachusetts . 
- 
- = = Early life and education = = 
- 
- George Nixon Briggs was born in Adams , Massachusetts on April 12 , 1796 . He was the eleventh of twelve children of Allen Briggs , a blacksmith originally from <unk> , Rhode Island , and Nancy ( Brown ) Briggs , of Huguenot descent . His parents moved the family to Manchester , Vermont when he was seven , and , two years later , to White Creek , New York . The household was religious : his father was a Baptist and his mother was a Quaker , and they gave their children religious instruction from the Bible . 
- At the age of 14 , during the Second Great Awakening , which was especially strong in <unk> New York , Briggs experienced a conversion experience and joined the Baptist faith . He spoke at revival meetings of his experience , drawing <unk> applause from the crowds , according to <unk> Hall , who came to know Briggs at that time and who became a lifelong friend and political associate . His faith informed his personal behavior : he remained committed to religious ideals , for instance <unk> to Congressional sessions that stretched into Sunday and <unk> from alcohol consumption . 
- Briggs sporadically attended the public schools in White Creek , and was <unk> for three years to a Quaker <unk> . With support from his older brothers he embarked on the study of law in Pittsfield and Lanesboro in 1813 , and was admitted to the Massachusetts bar in 1818 . He first opened a practice in Adams , moved it to Lanesboro in 1823 , and Pittsfield in 1842 . His trial work was characterized by a contemporary as clear , brief , and methodical , even though he was fond of telling stories in less formal settings . 
- In 1817 Briggs helped to establish a Baptist church in Lanesboro ; in this congregation he met Harriet Hall , whom he married in 1818 ; their children were Harriet , George , and Henry . Briggs was also called upon to raise the four orphaned children of his brother Rufus , one of the brothers who supported him in his law studies . Rufus died in 1816 , followed by his wife not long afterward . 
- Briggs ' involvement in civic life began at the local level . From 1824 to 1831 Briggs was the register of <unk> for the Northern district of Berkshire County , Massachusetts . He was elected town clerk in 1824 , was appointed chairman of the board of commissioners of highways in 1826 . His interest in politics was sparked by his acquaintance with Henry Shaw , who served in the United States House of Representatives from 1817 to 1821 . 
- A criminal case tried in 1826 brought Briggs wider notice . An <unk> Indian living in <unk> was accused of murder . Briggs was appointed by the court to defend him ; convinced by the evidence that the man was innocent , Briggs made what was described by a contemporary as a plea that was " a model of jury <unk> " . The jury , unfortunately , disagreed with Briggs , and convicted the man , who was hanged . In 1830 the true murderer confessed to commission of the crime . 
- 
- = = U.S. House of Representatives = = 
- 
- Despite his rise in prominence , Briggs was at first ineligible for state offices because he did not own property . In 1830 he decided to run for Congress , for which there was no such requirement . He was elected to the twenty @-@ second through the twenty @-@ fourth <unk> as an Anti @-@ <unk> , and as a Whig to the twenty @-@ fifth through twenty @-@ seventh <unk> , serving from March 4 , 1831 to March 3 , 1843 . He decided not to run for reelection in 1842 . 
- Briggs was what became known in later years as a " Cotton Whig " . He was in favor of <unk> tariffs , and opposed the expansion of slavery into western territories , but did not seek to threaten the unity of the nation with a strong stance against slavery . He served on the Committee on Public <unk> and the Committee on the Post Office and Post Roads , serving for a time as the chairman of each . The Post Office committee was a regular recipient of complaints from southern states concerning the transmission of abolitionist mailings , which were seen there as incendiary ; the matter was of some controversy because southern legislators sought to have these types of mailings banned . Briggs ' friend <unk> Hall , who also sat on the committee , drafted a report in 1836 <unk> the <unk> used in such legislative proposals , but the committee as a whole , and then the House , refused to accept the report . Although the authorship of the report appears to be entirely Hall 's , Briggs may have contributed to it , and was a signatory to Hall 's publication of the report in the National <unk> , a major political journal . The document was influential in driving later Congressional debate on legislative proposals concerning abolitionist mailings , none of which were ever adopted . Briggs and Hall were both instrumental in drafting and gaining passage of the Post Office Act of 1836 , which included substantive accounting reforms in the wake of financial <unk> by <unk> General William Taylor Barry . 
- During his time in Congress , Briggs was a vocal advocate for temperance . He formed the Congressional <unk> Society in 1833 , sitting on its executive committee ; at an 1836 temperance convention at Saratoga Springs , New York he advocated the taking of total abstinence pledges as a way to bring more people away from the <unk> of alcohol , and notably prepared such a pledge for Kentucky Representative Thomas F. Marshall on the floor of the House of Representatives . His moves to organize the temperance movement in Congress died out when he left the body , but it was a cause he would continue to <unk> for the rest of his life . In 1860 he was chosen president of the American <unk> Union . 
- 
- = = Governor of Massachusetts = = 
- 
- Briggs was nominated to run for the governorship on the Whig ticket against the incumbent Democrat Marcus Morton in 1843 . Former Governor John Davis had been nominated first , but refused the nomination , possibly because Daniel Webster promised him party support for a future vice presidential bid . Briggs was apparently recommended as a compromise candidate acceptable to different factions within the party ( one controlled by Webster , the other by Abbott Lawrence ) . He was also probably chosen to appeal more directly to the state 's rural voters , a constituency that normally supported Morton . The abolitionist Liberty Party also fielded a candidate , with the result that none of the candidates won the needed majority . The legislature decided the election in those cases ; with a Whig majority there , Briggs ' election was assured . Briggs was reelected annually until 1850 against a succession of Democratic opponents . He won popular <unk> until the 1849 election , even though third parties ( including the Liberty Party and its successor , the Free Soil Party ) were often involved . Although Whigs had a reputation for aristocratic bearing , Briggs was much more a man of the people than the preceding Whig governors , John Davis and Edward Everett . 
- In 1844 Briggs , alarmed at a recently enacted policy by South Carolina authorizing the imprisonment of free blacks arriving there from Massachusetts and other northern states , sent representatives to protest the policy . Samuel <unk> and his daughter Elizabeth were unsuccessful in changing South Carolina policy , and after protests against what was perceived as Yankee interference in Southern affairs , were advised to leave the state for their own safety . 
- Capital punishment was a major issue that was debated in the state during <unk> 's tenure , with social reformers calling for its abolition . Briggs personally favored capital punishment , but for political reasons called for <unk> in its use , seeking , for example , to limit its application in murder cases to those involving first degree murder . After an <unk> in an 1846 murder case where anti @-@ death penalty sentiment was thought to have a role , Briggs , seeking to undercut the anti @-@ death penalty lobby , proposed eliminating the penalty for all crimes except murder , but expressed concern that more such <unk> by sympathetic <unk> would undermine the connection between crime and punishment . 
- Briggs ' argument was used in the 1849 trial of Washington Goode , a black <unk> accused of killing a rival for the affections of a lady . The case against Goode was essentially circumstantial , but the jury <unk> the district attorney 's call for assertive punishment of " crimes of violence " and convicted him . There were calls for Briggs to commute Goode 's capital sentence , but he refused , writing " A pardon here would tend toward the utter subversion of the law . " 
- Not long after the Goode case came the sensational trial of Professor John White Webster in the murder of George <unk> , a crime that took place at the Harvard Medical School in November 1849 . The trial received nationwide coverage , and the prosecution case was based on evidence that was either circumstantial ( complicated by the fact that a complete corpse was not found ) , or founded on new types of evidence ( forensic dentistry was used for the first time in this trial ) . Furthermore , Massachusetts Supreme Judicial Court Chief Justice <unk> Shaw was widely criticized for bias in the instructions he gave to the jury . Briggs was petitioned to commute Webster 's sentence by death penalty opponents , and even threatened with physical harm if he did not . He refused however , stating that the evidence in the case was clear ( especially after Webster gave a confession ) , and that there was no reason to doubt that the court had acted with due and proper <unk> . 
- During Briggs ' time as governor , abolitionist activists continued to make <unk> against both the Whigs and Democrats , primarily making common cause with the Democrats against the dominant Whigs . Briggs ' stance as a Cotton Whig put him in opposition to these forces . He opposed the Mexican @-@ American War , but acceded to federal demands that the states assist in raising troops for the war , earning the wrath of activist Wendell Phillips . He did promote other types of reform , supporting Horace Mann in his activities to improve education in the state . 
- In 1849 , Briggs failed to secure a majority in the popular vote because of the rise in power of the Free Soil Party , but the Whig legislature returned him to office . In the 1850 election , anger over the <unk> of 1850 ( a series of federal acts designed to preserve the unity of the nation which included the <unk> Slave Act ) prompted the Democrats and Free <unk> to form a coalition to gain control over the Massachusetts legislature , and divided the Whigs along <unk> and <unk> lines . With the gubernatorial election again sent to the legislature , Democrat George S. <unk> was chosen over Briggs . 
- 
- = = Later years = = 
- 
- Briggs resumed the practice of law in Pittsfield . He was a member of the state constitutional convention in 1853 , and sat as a judge of the Court of Common <unk> from 1853 to 1858 . In 1859 he was nominated for governor by the fading Know @-@ Nothing movement , but trailed far behind other candidates . 
- In 1861 Briggs was appointed by President Abraham Lincoln to a diplomatic mission to the South American Republic of New Granada ( roughly present @-@ day Colombia and Panama ) . However , he died before he could take up the position . On September 4 , 1861 Briggs was getting an <unk> out of his <unk> at his home in Pittsfield , when a gun fell . As Briggs was picking it up , the gun discharged and Briggs was shot . Briggs died early in the morning of September 12 , 1861 , and was buried in the Pittsfield Cemetery . 
- 
- 
- = Simon Bradstreet = 
- 
- Simon Bradstreet ( baptized March 18 , 1603 / 4 – March 27 , 1697 ) was a colonial magistrate , businessman , diplomat , and the last governor of the Massachusetts Bay Colony . Arriving in Massachusetts on the Winthrop Fleet in 1630 , Bradstreet was almost constantly involved in the politics of the colony but became its governor only in 1679 . He served on diplomatic missions and as agent to the crown in London , and also served as a commissioner to the New England Confederation . He was politically comparatively moderate , arguing minority positions in favor of freedom of speech and for accommodation of the demands of King Charles II following his restoration to the throne . 
- Bradstreet was married to Anne , the daughter of Massachusetts co @-@ founder Thomas Dudley and New England 's first published poet . He was a businessman , investing in land and shipping interests . Due to his advanced age ( he died at 93 ) Cotton Mather referred to him as the " Nestor of New England " . His descendants include the famous jurists Oliver Wendell Holmes , Jr. and David <unk> . 
- 
- = = Early life = = 
- 
- Simon Bradstreet was baptized on March 18 , 1603 / 4 in <unk> , Lincolnshire , the second of three sons of Simon and Margaret Bradstreet . His father was the rector of the parish church , and was descended from minor Irish nobility . With his father a vocal <unk> , the young Simon acquired his Puritan religious views early in life . At the age of 16 , Bradstreet entered Emmanuel College , Cambridge . He studied there for two years , before entering the service of the Earl of Lincoln as an assistant to Thomas Dudley in 1622 . There is some uncertainty about whether Bradstreet returned to Emmanuel College in 1623 – 1624 . According to <unk> , a Simon Bradstreet attended Emmanuel during this time , receiving an <unk> degree , but <unk> Robert Anderson is of the opinion that this was not the same individual . During one of Bradstreet 's <unk> at Emmanuel he was recommended by John Preston as a tutor or governor to Lord Rich , son of the Earl of Warwick . Rich would have been 12 in 1623 , and Preston was named Emmanuel 's master in 1622 . 
- Bradstreet took over Dudley 's position when the latter moved temporarily to Boston in 1624 . On Dudley 's return several years later , Bradstreet then briefly served as a steward to the <unk> Countess of Warwick . In 1628 he married Dudley 's daughter Anne , when she was 16 . 
- In 1628 , Dudley and others from the Earl of Lincoln 's circle formed the Massachusetts Bay Company , with a view toward establishing a Puritan colony in North America . Bradstreet became involved with the company in 1629 , and in April 1630 , the <unk> joined the Dudleys and colonial Governor John Winthrop on the fleet of ships that carried them to Massachusetts Bay . There they founded Boston , the capital of the Massachusetts Bay Colony . 
- 
- = = Massachusetts Bay Colony = = 
- 
- After a brief stay in Boston , Bradstreet made his first residence in <unk> ( later renamed Cambridge ) , near the Dudleys in what is now Harvard Square . In 1637 , during the <unk> Controversy , he was one of the magistrates that sat at the trial of Anne Hutchinson , and voted for her banishment from the colony . In 1639 he was granted land in Salem , near that of John <unk> . He lived there for a time , moving in <unk> to Ipswich before becoming one of the founding settlers of <unk> in 1648 . In 1666 his <unk> home was destroyed by fire , supposedly because of " the <unk> of the maid " . He had varied business interests , speculating in land , and investing with other colonists in a ship involved in the <unk> trade . In 1660 he purchased shares in the Atherton Company , a land development company with interests in the " Narragansett Country " ( present @-@ day southern Rhode Island ) . He became one of its leading figures , serving on the management committee , and publishing <unk> advertising its lands . When he died he owned more than 1 @,@ 500 acres ( 610 ha ) of land in five communities spread across the colony . He was known to own two slaves , a woman named Hannah and her daughter <unk> . 
- Bradstreet was heavily involved in colonial politics . When the council met for the first time in Boston , Bradstreet was selected to serve as colonial secretary , a post he would hold until 1644 . He was politically moderate , arguing against legislation and judicial decisions punishing people for speaking out against the governing magistrates . Bradstreet was also outspoken in opposition to the witch <unk> that infested his home town of Salem , culminating in numerous trials in 1692 . 
- He served for many years as a commissioner representing Massachusetts to the New England Confederation , an organization that coordinated matters of common interest ( principally defense ) among most of the New England colonies . He was regularly chosen as an assistant , serving on the council that dominated the public affairs of the colony , but did not reach higher office until <unk> , when he was first elected deputy governor under John Leverett . He was against military actions against some of the colony 's foreign neighbors , opposing official intervention in a French <unk> dispute in the <unk> , and also spoke against attacking the New Netherland during the First Anglo @-@ Dutch War ( 1652 – <unk> ) . 
- Bradstreet was sent on a number of diplomatic missions , dealing with settlers , other English colonies , and the Dutch in New Amsterdam . In 1650 he was sent to Hartford , Connecticut , where the Treaty of Hartford was negotiated to determine the boundary between the English colonies and New Amsterdam . In the following years he negotiated an agreement with settlers in York and <unk> to bring them under Massachusetts jurisdiction . 
- Following the 1660 restoration of Charles II to the throne of England , colonial authorities again became concerned about preserving their charter rights . Bradstreet in 1661 headed a legislative committee to " consider and debate such matters touching their patent rights , and privileges , and duty to his Majesty , as should to them seem proper . " The letter the committee drafted reiterated the colony 's charter rights , and also included declarations of allegiance and loyalty to the crown . Bradstreet and John Norton were chosen as agents to deliver the letter to London . Charles renewed the charter , but sent the agents back to Massachusetts with a letter <unk> conditions to his assent . The colony was expected , among other things , to expand religious tolerance to include the Church of England and religious minorities like the Quakers . The agents were harshly criticized by hardline factions of the legislature , but Bradstreet defended the need to accommodate the king 's wishes as the <unk> course to take . How to respond to the king 's demands divided the colony ; Bradstreet was part of the moderate " <unk> " faction arguing that the colony should obey the king 's wishes . This faction lost the debate to the hardline " commonwealth " faction , who were in favor of aggressively maintaining the colony 's charter rights , led through the 1660s by governors John <unk> and Richard <unk> . With Charles distracted by war with the Dutch and domestic politics in the late 1660s , the issue lay <unk> until the mid @-@ <unk> . Relations between colony and crown deteriorated when the king then renewed demands for legislative and religious reforms , which hardline magistrates again resisted . 
- 
- = = Governor = = 
- 
- In early 1679 Governor John Leverett died , and Bradstreet as deputy succeeded him . Leverett had opposed accommodation of the king 's demands , and the change to an <unk> leadership was too late . Bradstreet would turn out to be the last governor under its original charter . His deputy , Thomas <unk> was from the commonwealth faction . During his tenure , crown agent Edward Randolph was in the colony , attempting to enforce the Navigation Acts , under which certain types of trade involving the colony were illegal . Randolph 's enforcement attempts were vigorously resisted by both the merchant classes and sympathetic magistrates despite Bradstreet 's attempts to accommodate Randolph . <unk> frequently refused to <unk> ships accused of violating the acts ; in one instance Bradstreet tried three times to get a jury to change its verdict . Randolph 's attempts to enforce the navigation laws eventually convinced the colony 's general court that it needed to create its own mechanisms for their enforcement . A bill to establish a naval office was vigorously debated in 1681 , with the house of deputies , dominated by the commonwealth party , opposing the idea , and the moderate magistrates supporting it . The bill that finally passed was a victory for the commonwealth party , making enforcement difficult and subject to reprisal lawsuits . Bradstreet refused to actually implement the law , and Randolph published open challenges to it . Bradstreet was in some degree vindicated when he won re @-@ election in 1682 , and he then used his judicial authority to further undermine the law 's effects . 
- Randolph 's threats to report the colonial legislature 's <unk> prompted it to dispatch agents to England to argue the colony 's case ; however , their powers were limited . Shortly after their arrival in late 1682 , the Lords of Trade issued an ultimatum to the colony : either grant its agents wider powers , including the ability to negotiate modifications to the charter , or risk having the charter <unk> . The general court responded by issuing the agents instructions to take a hard line . Following legal processes begun in 1683 , the charter was formally <unk> on October 23 , 1684 . 
- 
- = = Dominion , and temporary return as governor = = 
- 
- King Charles II in 1684 established the Dominion of New England . Bradstreet 's brother @-@ in @-@ law Joseph Dudley , who had served as one of the colonial agents , was commissioned by James as President of the Council for New England in 1685 by King James II , and took control of the colony in May 1686 . Bradstreet was offered a position on Dudley 's council , but refused . Dudley was replaced in December 1686 by Sir Edmund Andros , who came to be greatly <unk> in Massachusetts for <unk> existing land titles , and seizing Congregational church properties for Church of England religious services . Andros ' high @-@ handed rule was also unpopular in the other colonies of the dominion . 
- The idea of revolt against Andros arose as early as January 1689 , before news of the December 1688 Glorious Revolution reached Boston . After William and Mary took the throne , Increase Mather and Sir William Phips , Massachusetts agents in London , petitioned them and the Lords of Trade for restoration of the Massachusetts charter . Mather furthermore convinced the Lords of Trade to delay notifying Andros of the revolution . He had already dispatched to Bradstreet a letter containing news that a report ( prepared before the revolution ) stating that the charter had been illegally <unk> , and that the magistrates should " prepare the minds of the people for a change . " News of the revolution apparently reached some individuals as early as late March , and Bradstreet is one of several possible organizers of the mob that formed in Boston in April 18 , 1689 . He , along with other pre @-@ Dominion magistrates and some members of Andros ' council , addressed an open letter to Andros on that day calling for his surrender in order to quiet the mob . Andros , who had fled to the safety of Castle Island , surrendered , and was eventually returned to England after several months in confinement . 
- In the wake of Andros ' arrest , a council of safety was formed , with Bradstreet as its president . The council drafted a letter to William and Mary , <unk> the colony 's acts in language similar to that used by William in his <unk> when he invaded England . The council fairly quickly decided to revert to the government as it had been under the old charter . In this form Bradstreet resumed the governorship , and was annually re @-@ elected governor until 1692 . He had to defend the colony against those who were opposed to the reintroduction of the old rule , who he characterized in reports to London as <unk> and strangers stirring up trouble . The colony 's northern frontier was also <unk> in King William 's War , where there was frequent Indian raiding . Bradstreet approved the expeditions of Sir William Phips in 1690 against <unk> and Quebec . 
- In 1691 William and Mary issued a charter establishing the Province of Massachusetts Bay , and appointed Phips its first governor . Bradstreet was offered a position on Phips ' council when the new governor arrived in 1692 , but declined . Bradstreet died at his home in Salem on 27 March 1697 at the age of 93 ; due to his advanced age he was called the " Nestor of New England " by Cotton Mather . 
- 
- = = Family and legacy = = 
- 
- Bradstreet was buried in the Charter Street <unk> Ground in Salem . Poetry by his first wife Anne was published in England in 1650 , including verses containing expressions of enduring love for her husband . Anne Bradstreet died in 1672 ; the couple had eight children , of whom seven survived infancy . In 1676 Bradstreet married Ann Gardner , the widow of Captain Joseph Gardner , son of Thomas Gardner of Salem . His many descendants include jurists Oliver Wendell Holmes , Jr. and David <unk> , U.S. President Herbert Hoover , and actor Humphrey Bogart . 
- 
- 
- = Etymology of Wicca = 
- 
- In Modern English , the term Wicca ( / <unk> / ) refers to Wicca , the religion of contemporary Pagan Witchcraft . It is used within the Pagan community under competing definitions . One refers to the entirety of the Pagan Witchcraft movement , while the other refers explicitly to traditions included in what is now called British Traditional Wicca . 
- Although pronounced differently , Wicca is related to the Old English word wicca , which referred to <unk> in Anglo @-@ Saxon England . In the early 1950s , English Wiccan Gerald Gardner , founder of the Gardnerian tradition , referred to the Pagan Witchcraft community as the Wica . He claimed to have learned the term during his initiation into the New Forest coven in 1939 . By the late 1950s , Gardner 's rival Charles Cardell , founder of his own tradition , had begun referring to the religion 's followers as Wiccens , and possibly used Wicca in reference to the religion itself . 
- The inclusive use of the term Wicca — referring to the entirety of Pagan Witchcraft religion — has been traced to Britain in the early 1960s , when it was used by various groups and publicised through use in adverts , magazines , and other literary sources . It was later adopted by figures like Alex Sanders and Gavin and Yvonne Frost , who took it to the United States . There , practitioners of British Traditional Wicca adopted it exclusively for themselves as a means to differentiate their practices from those of other Pagan Witches . This exclusive meaning was countered by its popularisation as a generic term by prolific authors such as Raymond Buckland , Scott Cunningham and Silver RavenWolf . As it entered popular culture , it gained an increasingly eclectic character in its usage . During the 1990s , some attempted to distance themselves from it by utilising the term Traditional Witchcraft . 
- 
- = = Definitions = = 
- 
- There are two separate definitions of the term Wicca that have been used in Paganism and Pagan studies since circa 1980 . The first developed in England during the 1960s . Broad and inclusive , it covers most , if not all , forms of modern Pagan Witchcraft , especially if they share sufficient theological beliefs and ritual practices to be considered denominations within a common religious movement . In contrast , the second developed in the United States during the late 1970s . It refers specifically to the Gardnerian tradition of Witchcraft and those descended from it with little variation , namely Alexandrian and <unk> Witchcraft , which are together known as British Traditional Wicca . 
- 
- = = = Usage within Pagan studies = = = 
- 
- The development and use of the term Wicca within contemporary Paganism has been a recurring topic of discussion in the field of Pagan studies . The majority of academics and independent scholars use the first , more inclusive definition . Given its historical status and prevalent usage within Paganism , Pagan studies scholar <unk> Doyle White thought it the logical and easier choice for academia , although there is still some disagreement and confusion among researchers as to what defines Wicca . Among those who have used the former definition are American sociologist Margot <unk> , literary scholar Chas S. Clifton , and religious studies scholar Aidan A. Kelly , while others such as the Britons Graham Harvey and Ronald Hutton failed to make their usage clear . To deal with this problem in future , Doyle White urged scholars to specify which definition they used in their work . 
- 
- = = Origins = = 
- 
- 
- = = = Old English wicca and <unk> = = = 
- 
- In the Early Medieval language of Old English , the term wicca ( / <unk> / ) was a masculine <unk> for sorcerer ; <unk> was its feminine counterpart . They are ancestral to Modern English witch . The Modern English term Wicca took the Old English wicca as its basis , although the two are fundamentally two distinct words with differing meanings , pronunciation , and <unk> usage , with nearly a millennium between their respective <unk> . 
- In 1932 Lewis Spence writes in The Weekly Scotsman , in response to the popularisation of Margaret Murray 's witch @-@ cult hypothesis in Scotland , that " the Saxon word ' wicca ' , a witch " was " of immemorial usage " in the Scottish <unk> . 
- Doyle White suggests that the early <unk> adopted the term wicca as the basis for the name of their burgeoning faith because theirs was a new religious movement that took " iconography and inspiration " from the polytheistic cults of pre @-@ Christian Britain . 
- 
- = = = Gerald Gardner 's Wica : 1939 – 1966 = = = 
- 
- Gerald Gardner ( 1884 – 1964 ) , the man largely responsible for propagating the Wiccan religion in Britain during the 1950s and 1960s and the founder of the Gardnerian tradition , never used the term Wicca in either sense that it is used today . He referred to the religion as the " cult of witchcraft " or " the witch @-@ cult " , the latter likely being a term borrowed from Margaret Murray , who wrote a book entitled The Witch @-@ <unk> in Western Europe ( 1921 ) . Gardner did use the term Wica , which he always spelled with only one c in his writings , but this did not refer to the religion itself , instead referring to the religion 's practitioners in a plural sense . 
- What are [ the witches ] then ? They are the people who call themselves the Wica , the " wise people " , who <unk> the age ‑ old rites and who have , along with much superstition and <unk> knowledge , preserved an occult teaching and working processes which they themselves think to be magic or witchcraft . 
- In contrast with this plural use of the word , in a 1954 article written by Arnold Field , a reporter for the Daily <unk> , Gardner had apparently explained to him that " there are man and woman witches . Each is called a <unk> . " This quote offers the only piece of evidence that Gardner also referred to Pagan Witches individually as a <unk> . It is possible that Field misunderstood what Gardner was saying by not <unk> Wica , and that therefore Gardner might have never used Wica in a singular sense . 
- In his book The Meaning of Witchcraft ( 1959 ) Gardner states that he first heard the term Wica while being initiated into the New Forest coven in September 1939 , stating that " I realised I had stumbled on something interesting ; but I was half @-@ initiated before the word Wica which they used hit me like a <unk> , and I knew where I was , and that the Old Religion still existed . " This account was repeated in his biography , Gerald Gardner : Witch ( 1960 ) , written by <unk> Shah but attributed to Jack L. <unk> , in which he is quoted as saying that " it was halfway through when the word Wica was first mentioned ; and I knew that that which I had thought burnt out hundreds of years ago still survived . " If Gardner 's account was accurate and the New Forest coven had really existed , then the fact that Gardner spelled the word as Wica would not necessarily indicate that the coven members had spelled it the same way . As he relates , from Gardner 's account , " it seems that he had heard rather than read the word in the midst of his <unk> rite " and that , " suffering from a poor grasp of spelling , punctuation , and grammar , something caused by the fact that he was self @-@ educated and possibly also influenced by <unk> " , he would have therefore spelled the word <unk> as Wica . 
- In The Meaning of Witchcraft , Gardner also notes the term Wica 's resemblance to the Old English word wicca , stating that " It is a curious fact that when the witches became English @-@ speaking they adopted their Saxon name ' Wica ' . " In his published writings , Gardner <unk> the idea that his Pagan Witchcraft religion dated back at least to the Anglo @-@ Saxon period , when Old English was the dominant language . 
- Wica soon became an accepted term among the early <unk> , as Gardner 's followers and initiates became known . Patricia and Arnold <unk> , a Gardnerian High <unk> and High Priest who operated a coven in Sheffield , use the term in their book The Witches <unk> ( 1959 ) , writing that " [ T ] he Red Queen told Alice that she made words mean what [ she ] wanted them to mean . She might very well have been talking about witchcraft , for today it is used to describe anything that one wishes to use it for . From the simple meaning ' the craft of the Wica ' , it is used in connection with Black Magic , <unk> , Black <unk> ... " 
- 
- = = = Charles Cardell 's Wiccen : 1958 – 1960 = = = 
- 
- Charles Cardell ( 1892 – 1977 ) was the founder of a Pagan Witchcraft tradition that <unk> that of Gerald Gardner 's in southern England during the 1950s . A psychologist and stage <unk> , Cardell ran a company named <unk> <unk> Productions from his home in <unk> , Surrey , from where he also controlled a local coven that was <unk> on by the press , leading to a well @-@ publicised court case . Having been involved with <unk> as well as Pagan Witchcraft , Cardell initially befriended Gardner , but in 1958 they had an argument , and in 1964 Cardell tried to discredit him by publishing much of the then @-@ secret Gardnerian Book of Shadows . 
- Cardell used the term Wiccen to refer not just to members of his own tradition , but to all followers of the Pagan Witchcraft religion , placing an advert in Light magazine , the journal of the College of <unk> Science , entitled " The Craft of the Wiccens " in 1958 . The advert asked fellow Wiccens to get in contact with him . This advert shows that Cardell was responsible for the <unk> and possibly invention of the term Wiccen . 
- It is possible Cardell had also used the term Wicca , evidenced by the fact that Margaret Bruce , the owner of a mail @-@ order business selling occult titles , wrote a letter to her friend Gerald Gardner on 23 February 1960 , in which she <unk> him on the attacks made against him by Cardell and included a poem in which she referred to " the ' Wicca ' " . In <unk> <unk> ' opinion , this use of Wicca was explicitly in reference to the <unk> Craft , and therefore meant " that this spelling , along with ' Wiccan ' , was used by Cardell . " However , it is also possible that Bruce was referring to " the Wicca " as " a community of Pagan Witches " , in which case it would be a <unk> of Gardner 's " the Wica " . 
- 
- = = = The emergence of Wicca : 1962 – 1970 = = = 
- 
- The term Wicca appears to have developed within the Pagan Witchcraft community during the early 1960s , as increasing numbers of Pagan Witches learned of the Old English term wicca , the etymological origin of the Modern term witch . This etymological fact had been referred to five times in Gerald Gardner 's book The Meaning of Witchcraft ( 1959 ) , as well as in other early texts propagating Pagan Witchcraft , such as <unk> <unk> 's Where Witchcraft Lives ( 1962 ) and <unk> Glass ' Witchcraft , The Sixth <unk> – and Us ( 1965 ) . None of these specifically referred to the Pagan Witchcraft religion as Wicca . 
- The earliest known published reference for the word Wicca is within an advertisement published in a 1962 issue of Fate magazine ; in this , a Cardiff @-@ based group of Pagan Witches advertised a tradition as " Wicca – <unk> and <unk> " . The advert may have been linked to Charles and Mary Cardell because Mary was allegedly born in Wales and <unk> Witchcraft had apparently venerated a goddess under the name of Diana . However , many Pagan Witchcraft groups would have adopted the deity name Diana and <unk> , these being the goddesses featured in the American <unk> Charles <unk> 's supposed account of a <unk> witch tradition , <unk> , or the Gospel of the Witches ( 1899 ) . Another early use could be found from December 1965 , in the penultimate issue of Pentagram , the <unk> of the Witchcraft Research Association . Here , a small column on Halloween made reference to " the Craft of the Wiccan " , apparently referring to the entire Pagan Witchcraft community . The author 's name was not printed , although it had probably been produced by one of the figures involved in editing Pentagram , such as Gerard Noel or <unk> <unk> . In July 1968 , a group of British <unk> began publishing a magazine titled The Wiccan , while <unk> Gavin Frost founded the Church of Wicca in the United States that same year . 
- In the 1960s , the Gardnerian initiate Alex Sanders founded his own tradition , which became known as Alexandrian Wicca ; he used the terms Wicca and the Wicca in reference to the entire Pagan Witchcraft religion . One of Sanders ' initiates , Stewart Farrar , describes Wicca as " the witches ' name for their Craft " in his book What Witches Do ( 1971 ) . The widespread adoption of Wicca in reference to Pagan Witchcraft would have brought benefits to its practitioners , who were widely maligned and faced persecution for their practice of witchcraft ; an emotive term often associated with <unk> that had negative connotations in the Western imagination . Doyle White argued that the practitioners ' presentation of themselves as <unk> rather than witches removed some of the social stigma that they faced . 
- 
- = = <unk> = = 
- 
- 
- = = = <unk> in North America : 1970 – 1990 = = = 
- 
- From 1970 onward , increasing numbers of books teaching readers how to become Pagan Witches were published ; the earliest was Paul <unk> 's <unk> Witchcraft ( 1970 ) , which made no reference to Wicca . This was followed by Raymond Buckland 's The Tree : The Complete Book of Saxon Witchcraft , in which he <unk> his newly developed tradition of <unk> @-@ Wica ; utilising Wica as the name of the tradition , he also referenced the Wicca as the name of the religion as a whole . Contrastingly , during the 1970s the term Wicca was rejected by feminist Pagan Witchcraft groups in the United States , in particular the <unk> tradition ; the term does not appear in the early works of <unk> Budapest and <unk> , although the latter would adopt it by the 21st century . 
- This was part of a phenomenon that took place during the 1970s and 1980s , as the term Wicca became increasingly associated purely with <unk> and <unk> ( together known as British Traditional Wicca in North America ) , rather than with other variants of Pagan Witchcraft . This was encouraged by elements within the Gardnerian and Alexandrian communities who wished to emphasise what they perceived as their special position within the Pagan community . The word Wicca first appeared in a book title in 1981 as Wicca : The Ancient Way ; written by <unk> @-@ <unk> , <unk> @-@ <unk> and Mer @-@ Amun and published in Canada . It discussed a Gardnerian @-@ based tradition . 
- 
- = = = Increasing popularisation and reaction : 1990 – 2010 = = = 
- 
- In ensuing years , many other authors would publish books containing Wicca in their titles which advocated solitary practice of Pagan Witchcraft ; best known were Scott Cunningham 's Wicca : A Guide for the <unk> <unk> ( 1988 ) and Silver RavenWolf 's Teen Witch : Wicca for a New Generation ( 1998 ) , but other examples included <unk> <unk> 's The Wicca Garden ( 1996 ) , D. J. Conway 's Wicca : The Complete Craft ( 2001 ) , Raymond Buckland 's Wicca for Life ( 2004 ) and Wicca for One ( 2004 ) , <unk> Murphy @-@ <unk> 's <unk> Wicca for Life ( 2005 ) and Ann @-@ Marie Gallagher 's The Wicca Bible ( 2005 ) . It was also adopted by American novelist <unk> <unk> as the title of her series of young adult novels . 
- The term Wicca was employed in an increasingly eclectic manner by authors like RavenWolf , who considered it to be a synonym for witchcraft . In turn it began to be adopted on a wider scale , being popularised in India by <unk> Roy <unk> and being adopted by a French <unk> group , Le Wicca Française . Becoming widely known in western popular culture , it was utilised by the script writers of two popular American television shows , Buffy the Vampire Slayer and Charmed ; the first episode of the latter was titled " Something Wicca This Way Comes " while the tenth was titled " Wicca <unk> " . 
- <unk> against the increasingly inclusive use of the term were Pagan Witches who instead characterised their practices as forms of Traditional Witchcraft . Many Pagan Witches who considered themselves to be Traditional Witches exhibited an us @-@ and @-@ them mentality against <unk> and allied traditions , for whom they reserved the term Wicca . Doyle White suggests that they had done so in order to distance themselves from the increasing influence of the New Age movement over the Wiccan mainstream with its " <unk> emphasis on white light " , instead embracing the traditional European view that associated witchcraft with darkness . Historian Ronald Hutton states that he knew of three Wiccan <unk> founded in the 1980s who began to describe themselves as Traditional Witches in the 1990s . 
- 
- = = = <unk> = = = 
- 
- 
- 
- = Bob Dylan = 
- 
- Bob Dylan ( / <unk> / ; born Robert Allen Zimmerman , May 24 , 1941 ) is an American singer @-@ songwriter , artist and writer . He has been influential in popular music and culture for more than five decades . Much of his most celebrated work dates from the 1960s when his songs chronicled social unrest , although Dylan repudiated suggestions from journalists that he was a spokesman for his generation . Nevertheless , early songs such as " Blowin ' in the Wind " and " The Times They Are a @-@ <unk> ' " became anthems for the American civil rights and anti @-@ war movements . After he left his initial base in the American folk music revival , his six @-@ minute single " Like a Rolling Stone " altered the range of popular music in 1965 . His mid @-@ 1960s recordings , backed by rock musicians , reached the top end of the United States music charts while also attracting <unk> and criticism from others in the folk movement . 
- Dylan 's lyrics have incorporated various political , social , philosophical , and literary influences . They defied existing pop music conventions and appealed to the burgeoning counterculture . Initially inspired by the performances of Little Richard and the songwriting of Woody Guthrie , Robert Johnson , and Hank Williams , Dylan has amplified and <unk> musical genres . His recording career , spanning more than 50 years , has explored the traditions in American song — from folk , blues , and country to gospel , rock and roll , and rockabilly to English , Scottish , and Irish folk music , embracing even jazz and the Great American Songbook . Dylan performs with guitar , keyboards , and harmonica . Backed by a changing lineup of musicians , he has toured steadily since the late 1980s on what has been dubbed the Never Ending Tour . His accomplishments as a recording artist and performer have been central to his career , but songwriting is considered his greatest contribution . 
- Since 1994 , Dylan has published six books of drawings and paintings , and his work has been exhibited in major art galleries . As a musician , Dylan has sold more than 100 million records , making him one of the best @-@ selling artists of all time . He has also received numerous awards including eleven Grammy Awards , a Golden Globe Award , and an Academy Award . Dylan has been inducted into the Rock and Roll Hall of Fame , Minnesota Music Hall of Fame , Nashville Songwriters Hall of Fame , and Songwriters Hall of Fame . The Pulitzer Prize jury in 2008 awarded him a special citation for " his profound impact on popular music and American culture , marked by lyrical compositions of extraordinary poetic power . " In May 2012 , Dylan received the Presidential Medal of Freedom from President Barack Obama . 
- 
- = = Life and career = = 
- 
- 
- = = = Origins and musical beginnings = = = 
- 
- Bob Dylan was born Robert Allen Zimmerman ( Hebrew name <unk> <unk> <unk> <unk> [ Shabtai <unk> ben <unk> ] ) in St Mary 's Hospital on May 24 , 1941 , in Duluth , Minnesota , and raised in Hibbing , Minnesota , on the <unk> Range west of Lake Superior . He has a younger brother , David . Dylan 's paternal grandparents , <unk> and Anna Zimmerman , emigrated from Odessa , in the Russian Empire ( now Ukraine ) , to the United States following the anti @-@ Semitic <unk> of 1905 . His maternal grandparents , Ben and Florence Stone , were Lithuanian Jews who arrived in the United States in 1902 . In his autobiography , Chronicles : Volume One , Dylan wrote that his paternal grandmother 's maiden name was <unk> and her family originated from <unk> district of <unk> Province in northeastern Turkey . 
- Dylan 's parents , <unk> Zimmerman and <unk> " Beatty " Stone , were part of a small but close @-@ knit Jewish community . They lived in Duluth until Robert was six , when his father had <unk> and the family returned to his mother 's hometown , Hibbing , where they lived for the rest of Robert 's childhood . In his early years he listened to the radio — first to blues and country stations from Shreveport , Louisiana and later , when he was a teenager , to rock and roll . He formed several bands while attending Hibbing High School . In the Golden <unk> , he performed covers of songs by Little Richard and Elvis Presley . Their performance of Danny & the Juniors ' " Rock and Roll Is Here to Stay " at their high school talent show was so loud that the principal cut the microphone . In 1959 , his high school <unk> carried the caption " Robert Zimmerman : to join ' Little Richard ' . " The same year , as <unk> <unk> [ sic ] , he performed two dates with Bobby <unk> , playing piano and clapping . 
- Zimmerman moved to Minneapolis in September 1959 and enrolled at the University of Minnesota . His focus on rock and roll gave way to American folk music . In 1985 , he said : 
- The thing about rock 'n'roll is that for me anyway it wasn 't enough ... There were great catch @-@ phrases and driving pulse rhythms ... but the songs weren 't serious or didn 't reflect life in a realistic way . I knew that when I got into folk music , it was more of a serious type of thing . The songs are filled with more despair , more sadness , more triumph , more faith in the supernatural , much deeper feelings . 
- He began to perform at the Ten O <unk> Scholar , a coffeehouse a few blocks from campus , and became involved in the <unk> folk music circuit . 
- During his <unk> days , Zimmerman began introducing himself as " Bob Dylan " . In his memoir , Dylan acknowledged that he had been influenced by the poetry of Dylan Thomas . Explaining his change of name in a 2004 interview , Dylan remarked , " You 're born , you know , the wrong names , wrong parents . I mean , that happens . You call yourself what you want to call yourself . This is the land of the free . " 
- 
- = = = 1960s = = = 
- 
- 
- = = = = <unk> to New York and record deal = = = = 
- 
- In May 1960 , Dylan dropped out of college at the end of his first year . In January 1961 , he traveled to New York City , to perform there and visit his musical idol Woody Guthrie , who was seriously ill with Huntington 's disease in <unk> Park <unk> Hospital . Guthrie had been a revelation to Dylan and influenced his early performances . Describing Guthrie 's impact , he wrote : " The songs themselves had the infinite sweep of humanity in them ... [ He ] was the true voice of the American spirit . I said to myself I was going to be Guthrie 's greatest disciple . " As well as visiting Guthrie in hospital , Dylan befriended Guthrie 's <unk> Ramblin ' Jack Elliott . Much of Guthrie 's repertoire was channeled through Elliott , and Dylan paid tribute to Elliott in Chronicles : Volume One . 
- From February 1961 , Dylan played at clubs around Greenwich Village . He befriended and picked up material from folk singers there , including Dave Van Ronk , Fred Neil , <unk> , the New Lost City <unk> , and Irish musicians the Clancy Brothers and Tommy <unk> . In September , Dylan gained public recognition when Robert Shelton wrote a review in The New York Times of a show at <unk> 's Folk City . The same month Dylan played harmonica on folk singer Carolyn <unk> 's third album , which brought his talents to the attention of the album 's producer , John Hammond . Hammond signed Dylan to Columbia Records in October . The performances on his first Columbia album — Bob Dylan — in March 1962 , consisted of familiar folk , blues and gospel with two original compositions . The album sold only 5 @,@ 000 in its first year , just enough to break even . Within Columbia Records , some referred to the singer as " Hammond 's <unk> " and suggested dropping his contract , but Hammond defended Dylan and was supported by Johnny Cash . In March 1962 , Dylan contributed harmonica and back @-@ up vocals to the album Three Kings and the Queen , accompanying Victoria Spivey and Big Joe Williams on a recording for Spivey Records . While working for Columbia , Dylan recorded under the pseudonym Blind Boy <unk> , for <unk> , a folk magazine and record label . Dylan used the pseudonym Bob <unk> to record as a piano player on The Blues Project , a 1964 anthology album by <unk> Records . As <unk> <unk> , Dylan played harmonica on Ramblin ' Jack Elliott 's 1964 album , Jack Elliott . 
- Dylan made two important career moves in August 1962 : he legally changed his name to Robert Dylan , and he signed a management contract with Albert Grossman . ( In June 1961 , Dylan had signed an agreement with Roy Silver . In 1962 , Grossman paid Silver $ 10 @,@ 000 to become sole manager . ) Grossman remained Dylan 's manager until 1970 , and was notable for his sometimes <unk> personality and for protective loyalty . Dylan said , " He was kind of like a Colonel Tom Parker figure ... you could smell him coming . " Tensions between Grossman and John Hammond led to Hammond 's being replaced as producer of Dylan 's second album by the young African @-@ American jazz producer , Tom Wilson . 
- Dylan made his first trip to the United Kingdom from December 1962 to January 1963 . He had been invited by TV director Philip Saville to appear in a drama , <unk> on Castle Street , which Saville was directing for BBC Television . At the end of the play , Dylan performed " Blowin ' in the Wind " , one of its first public performances . The film recording of <unk> on Castle Street was destroyed by the BBC in 1968 . While in London , Dylan performed at London folk clubs , including the <unk> , Les <unk> , and <unk> . He also learned material from UK performers , including Martin <unk> . 
- By the time of Dylan 's second album , The Freewheelin ' Bob Dylan , in May 1963 , he had begun to make his name as a singer and a songwriter . Many songs on this album were labeled protest songs , inspired partly by Guthrie and influenced by Pete Seeger 's passion for topical songs . " Oxford Town " , for example , was an account of James Meredith 's ordeal as the first black student to risk enrollment at the University of Mississippi . 
- The first song on the Freewheelin ' album , " Blowin ' in the Wind " , partly derived its melody from the traditional slave song , " No More Auction Block " , while its lyrics questioned the social and political status <unk> . The song was widely recorded by other artists and became a hit for Peter , Paul and Mary . Another Freewheelin ' song , " A Hard Rain 's a @-@ Gonna Fall " was based on the folk ballad " Lord Randall " . With veiled references to an impending <unk> , the song gained more resonance when the Cuban Missile Crisis developed a few weeks after Dylan began performing it . Like " Blowin ' in the Wind " , " A Hard Rain 's a @-@ Gonna Fall " marked a new direction in songwriting , blending a stream @-@ of @-@ consciousness , <unk> lyrical attack with traditional folk form . 
- Dylan 's topical songs enhanced his early reputation , and he came to be seen as more than just a songwriter . Janet Maslin wrote of Freewheelin ' : " These were the songs that established [ Dylan ] as the voice of his generation — someone who implicitly understood how concerned young Americans felt about nuclear <unk> and the growing movement for civil rights : his mixture of moral authority and <unk> was perhaps the most timely of his attributes . " Freewheelin ' also included love songs and surreal talking blues . <unk> was an important part of Dylan 's persona , and the range of material on the album impressed listeners , including The Beatles . George Harrison said of the album , " We just played it , just wore it out . The content of the song lyrics and just the attitude — it was incredibly original and wonderful . " 
- The rough edge of Dylan 's singing was unsettling to some but an attraction to others . Joyce Carol Oates wrote : " When we first heard this raw , very young , and seemingly untrained voice , <unk> nasal , as if <unk> could sing , the effect was dramatic and <unk> . " Many early songs reached the public through more palatable versions by other performers , such as Joan Baez , who became Dylan 's advocate as well as his lover . Baez was influential in bringing Dylan to prominence by recording several of his early songs and inviting him on stage during her concerts . 
- Others who had hits with Dylan 's songs in the early 1960s included the Byrds , Sonny & <unk> , the <unk> , Peter , Paul and Mary , the Association , Manfred Mann and the Turtles . Most attempted a pop feel and rhythm , while Dylan and Baez performed them mostly as sparse folk songs . The covers became so ubiquitous that CBS promoted him with the slogan " Nobody Sings Dylan Like Dylan . " 
- " Mixed @-@ Up Confusion " , recorded during the Freewheelin ' sessions with a backing band , was released as a single and then quickly withdrawn . In contrast to the mostly solo acoustic performances on the album , the single showed a willingness to experiment with a rockabilly sound . Cameron Crowe described it as " a fascinating look at a folk artist with his mind wandering towards Elvis Presley and Sun Records . " 
- 
- = = = = <unk> and Another Side = = = = 
- 
- In May 1963 , Dylan 's political profile rose when he walked out of The Ed Sullivan Show . During rehearsals , Dylan had been told by CBS television 's head of program practices that " <unk> ' John Birch <unk> Blues " was potentially <unk> to the John Birch Society . Rather than comply with censorship , Dylan refused to appear . 
- By this time , Dylan and Baez were prominent in the civil rights movement , singing together at the March on Washington on August 28 , 1963 . Dylan 's third album , The Times They Are a @-@ <unk> ' , reflected a more <unk> and cynical Dylan . The songs often took as their subject matter contemporary stories , with " Only A <unk> In Their Game " addressing the murder of civil rights worker <unk> Evers ; and the <unk> " The <unk> Death of <unk> Carroll " the death of black hotel <unk> <unk> Carroll , at the hands of young white <unk> William <unk> . On a more general theme , " Ballad of Hollis Brown " and " North Country Blues " addressed despair engendered by the breakdown of farming and mining communities . This political material was accompanied by two personal love songs , " Boots of Spanish <unk> " and " One Too Many <unk> " . 
- By the end of 1963 , Dylan felt both manipulated and constrained by the folk and protest movements . Accepting the " Tom <unk> Award " from the National Emergency Civil Liberties Committee shortly after the assassination of John F. Kennedy , an intoxicated Dylan questioned the role of the committee , characterized the members as old and <unk> , and claimed to see something of himself and of every man in Kennedy 's assassin , Lee Harvey Oswald . 
- Another Side of Bob Dylan , recorded on a single evening in June 1964 , had a lighter mood . The humorous Dylan <unk> on " I <unk> Be Free No. 10 " and " <unk> Nightmare " . " Spanish Harlem Incident " and " To <unk> " are passionate love songs , while " Black Crow Blues " and " I Don 't Believe You ( She Acts Like We Never Have Met ) " suggest the rock and roll soon to dominate Dylan 's music . " It Ain 't Me Babe " , on the surface a song about <unk> love , has been described as a rejection of the role of political spokesman thrust upon him . His newest direction was signaled by two lengthy songs : the <unk> " <unk> of Freedom " , which sets social commentary against a metaphorical landscape in a style characterized by Allen Ginsberg as " chains of flashing images , " and " My Back <unk> " , which attacks the simplistic and arch seriousness of his own earlier topical songs and seems to predict the backlash he was about to encounter from his former champions as he took a new direction . 
- In the latter half of 1964 and 1965 , Dylan moved from folk songwriter to folk @-@ rock pop @-@ music star . His <unk> and work shirts were replaced by a <unk> Street wardrobe , <unk> day or night , and pointed " <unk> boots " . A London reporter wrote : " Hair that would set the teeth of a comb on edge . A loud shirt that would dim the neon lights of Leicester Square . He looks like an <unk> <unk> . " Dylan began to spar with <unk> . Appearing on the Les Crane television show and asked about a movie he planned , he told Crane it would be a <unk> horror movie . Asked if he played the <unk> , Dylan replied , " No , I play my mother . " 
- 
- = = = = Going electric = = = = 
- 
- Dylan 's late March 1965 album Bringing It All Back Home was another leap , featuring his first recordings with electric instruments . The first single , " <unk> <unk> Blues " , owed much to Chuck Berry 's " Too Much Monkey Business " ; its free association lyrics described as <unk> back to the energy of beat poetry and as a forerunner of rap and hip @-@ hop . The song was provided with an early video , which opened D. A. Pennebaker 's <unk> <unk> presentation of Dylan 's 1965 tour of Great Britain , Dont Look Back . Instead of <unk> , Dylan illustrated the lyrics by throwing cue cards containing key words from the song on the ground . Pennebaker said the sequence was Dylan 's idea , and it has been imitated in music videos and advertisements . 
- The second side of Bringing It All Back Home contained four long songs on which Dylan accompanied himself on acoustic guitar and harmonica . " Mr. <unk> Man " became one of his best known songs when The Byrds recorded an electric version that reached number one in the US and UK . " It 's All Over Now , Baby Blue " and " It 's <unk> Ma ( I 'm Only Bleeding ) " were two of Dylan 's most important compositions . 
- In 1965 , heading the Newport Folk Festival , Dylan performed his first electric set since high school with a <unk> group mostly from the Paul <unk> Blues Band , featuring Mike <unk> ( guitar ) , Sam Lay ( drums ) and Jerome Arnold ( bass ) , plus Al Kooper ( organ ) and Barry Goldberg ( piano ) . Dylan had appeared at Newport in 1963 and 1964 , but in 1965 met with cheering and <unk> and left the stage after three songs . One version has it that the boos were from folk fans whom Dylan had alienated by appearing , unexpectedly , with an electric guitar . Murray Lerner , who filmed the performance , said : " I absolutely think that they were <unk> Dylan going electric . " An alternative account claims audience members were upset by poor sound and a short set . This account is supported by Kooper and one of the directors of the festival , who reports his recording proves the only boos were in reaction to the MC 's announcement that there was only enough time for a short set . 
- Nevertheless , Dylan 's performance provoked a hostile response from the folk music establishment . In the September issue of Sing Out ! , Ewan <unk> wrote : " Our traditional songs and ballads are the creations of extraordinarily talented artists working inside disciplines formulated over time ... ' But what of Bobby Dylan ? ' scream the outraged teenagers ... Only a completely non @-@ critical audience , nourished on the watery <unk> of pop music , could have fallen for such tenth @-@ rate <unk> . " On July 29 , four days after Newport , Dylan was back in the studio in New York , recording " <unk> 4th Street " . The lyrics contained images of vengeance and paranoia , and it has been interpreted as Dylan 's put @-@ down of former friends from the folk community — friends he had known in clubs along West 4th Street . 
- 
- = = = = Highway 61 Revisited and Blonde on Blonde = = = = 
- 
- In July 1965 , the single " Like a Rolling Stone " peaked at two in the U.S. and at four in the UK charts . At over six minutes , the song altered what a pop single could convey . Bruce Springsteen , in his speech for Dylan 's inauguration into the Rock and Roll Hall of Fame , said that on first hearing the single , " that <unk> shot sounded like somebody 'd kicked open the door to your mind " . In 2004 and in 2011 , Rolling Stone listed it as number one of " The 500 Greatest Songs of All Time " . The song opened Dylan 's next album , Highway 61 Revisited , named after the road that led from Dylan 's Minnesota to the musical <unk> of New Orleans . The songs were in the same vein as the hit single , flavored by Mike <unk> 's blues guitar and Al Kooper 's organ riffs . " <unk> Row " , backed by acoustic guitar and understated bass , offers the sole exception , with Dylan alluding to figures in Western culture in a song described by Andy Gill as " an 11 @-@ minute epic of entropy , which takes the form of a <unk> @-@ esque parade of <unk> and <unk> featuring a huge cast of celebrated characters , some historical ( Einstein , <unk> ) , some biblical ( <unk> , Cain and <unk> ) , some fictional ( Ophelia , Romeo , Cinderella ) , some literary ( T.S. Eliot and Ezra Pound ) , and some who fit into none of the above categories , notably Dr. <unk> and his dubious nurse . " 
- In support of the album , Dylan was booked for two U.S. concerts with Al Kooper and Harvey Brooks from his studio crew and Robbie Robertson and Levon Helm , former members of Ronnie Hawkins 's backing band the Hawks . On August 28 at Forest Hills Tennis Stadium , the group was heckled by an audience still annoyed by Dylan 's electric sound . The band 's reception on September 3 at the Hollywood Bowl was more favorable . 
- From September 24 , 1965 , in Austin , Texas , Dylan toured the U.S. and Canada for six months , backed by the five musicians from the Hawks who became known as the Band . While Dylan and the Hawks met increasingly receptive audiences , their studio efforts <unk> . Producer Bob Johnston persuaded Dylan to record in Nashville in February 1966 , and surrounded him with top @-@ notch session men . At Dylan 's insistence , Robertson and Kooper came from New York City to play on the sessions . The Nashville sessions produced the double album Blonde on Blonde ( 1966 ) , featuring what Dylan called " that thin wild mercury sound " . Kooper described it as " taking two cultures and <unk> them together with a huge explosion " : the musical world of Nashville and the world of the " quintessential New York <unk> " Bob Dylan . 
- On November 22 , 1965 , Dylan secretly married 25 @-@ year @-@ old former model Sara Lownds . Some of Dylan 's friends , including Ramblin ' Jack Elliott , say that , immediately after the event , Dylan denied he was married . Journalist <unk> <unk> made the news public in the New York Post in February 1966 with the headline " <unk> ! Bob Dylan is wed . " 
- Dylan toured Australia and Europe in April and May 1966 . Each show was split in two . Dylan performed solo during the first half , accompanying himself on acoustic guitar and harmonica . In the second , backed by the Hawks , he played electrically amplified music . This contrast provoked many fans , who <unk> and slow <unk> . The tour culminated in a <unk> confrontation between Dylan and his audience at the Manchester Free Trade Hall in England on May 17 , 1966 . A recording of this concert was released in 1998 : The Bootleg Series Vol . 4 : Bob Dylan Live 1966 . At the climax of the evening , a member of the audience , angered by Dylan 's electric backing , shouted : " Judas ! " to which Dylan responded , " I don 't believe you ... You 're a <unk> ! " Dylan turned to his band and said , " Play it fucking loud ! " as they launched into the final song of the night — " Like a Rolling Stone " . 
- During his 1966 tour , Dylan was described as exhausted and acting " as if on a death trip " . D. A. Pennebaker , the film maker accompanying the tour , described Dylan as " taking a lot of <unk> and who @-@ knows @-@ what @-@ else . " In a 1969 interview with <unk> <unk> , Dylan said , " I was on the road for almost five years . It wore me down . I was on drugs , a lot of things ... just to keep going , you know ? " In 2011 , BBC Radio 4 reported that , in an interview that Robert Shelton taped in 1966 , Dylan said he had kicked heroin in New York City : " I got very , very strung out for a while ... I had about a $ 25 @-@ a @-@ day habit and I kicked it . " Some journalists questioned the validity of this confession , pointing out that Dylan had " been telling journalists wild lies about his past since the earliest days of his career . " 
- 
- = = = = <unk> accident and <unk> = = = = 
- 
- After his tour , Dylan returned to New York , but the pressures increased . ABC Television had paid an advance for a TV show . His publisher , Macmillan , was demanding a manuscript of the poem / novel <unk> . Manager Albert Grossman had scheduled a concert tour for the latter part of the year . 
- On July 29 , 1966 , Dylan crashed his <unk> Triumph Tiger 100 motorcycle near his home in Woodstock , New York and was thrown to the ground . Though the extent of his injuries was never disclosed , Dylan said that he broke several vertebrae in his neck . <unk> still surrounds the circumstances of the accident since no ambulance was called to the scene and Dylan was not hospitalized . Dylan 's biographers have written that the crash offered Dylan the chance to escape the pressures around him . Dylan confirmed this interpretation in his autobiography : " I had been in a motorcycle accident and I 'd been hurt , but I recovered . Truth was that I wanted to get out of the rat race . " Dylan withdrew from public and , apart from a few appearances , did not tour again for almost eight years . 
- Once Dylan was well enough to resume creative work , he began to edit D. A. Pennebaker 's film of his 1966 tour . A rough cut was shown to ABC Television and rejected as incomprehensible to a mainstream audience . The film was subsequently titled Eat the <unk> on bootleg copies , and it has been screened at a handful of film festivals . In 1967 he began recording with the Hawks at his home and in the basement of the Hawks ' nearby house , " Big Pink " . These songs , initially demos for other artists to record , provided hits for Julie <unk> and the Brian <unk> Trinity ( " This Wheel 's on Fire " ) , The Byrds ( " You Ain 't <unk> ' Nowhere " , " Nothing Was <unk> " ) , and Manfred Mann ( " Mighty Quinn " ) . Columbia released selections in 1975 as The Basement Tapes . Over the years , more songs recorded by Dylan and his band in 1967 appeared on bootleg recordings , culminating in a five @-@ CD set titled The Genuine Basement Tapes , containing 107 songs and alternative takes . In the coming months , the Hawks recorded the album Music from Big Pink using songs they worked on in their basement in Woodstock , and renamed themselves the Band , beginning a long recording and performing career of their own . 
- In October and November 1967 , Dylan returned to Nashville . Back in the studio after 19 months , he was accompanied by Charlie McCoy on bass , Kenny <unk> on drums , and Pete Drake on steel guitar . The result was John Wesley Harding , a contemplative record of shorter songs , set in a landscape that drew on the American West and the Bible . The sparse structure and instrumentation , with lyrics that took the <unk> @-@ Christian tradition seriously , departed from Dylan 's own work and from the psychedelic fervor of the 1960s . It included " All Along the <unk> " , with lyrics derived from the Book of Isaiah ( 21 : 5 – 9 ) . The song was later recorded by Jimi Hendrix , whose version Dylan acknowledged as definitive . Woody Guthrie died on October 3 , 1967 , and Dylan made his first live appearance in twenty months at a Guthrie memorial concert held at Carnegie Hall on January 20 , 1968 , where he was backed by the Band . 
- Dylan 's next release , Nashville <unk> ( 1969 ) , was mainstream country featuring Nashville musicians , a mellow @-@ voiced Dylan , a duet with Johnny Cash , and the hit single " Lay Lady Lay " . Variety wrote , " Dylan is definitely doing something that can be called singing . Somehow he has managed to add an octave to his range . " Dylan and Cash also recorded a series of duets , but only their recording of Dylan 's " Girl from the North Country " was used on the album . 
- In May 1969 , Dylan appeared on the first episode of Johnny Cash 's television show , <unk> with Cash on " Girl from the North Country " , " I <unk> It All Away " , and " Living the Blues " . Dylan next traveled to England to top the bill at the Isle of Wight festival on August 31 , 1969 , after rejecting overtures to appear at the Woodstock Festival closer to his home . 
- 
- = = = 1970s = = = 
- 
- In the early 1970s , critics charged that Dylan 's output was varied and unpredictable . Rolling Stone writer Greil Marcus asked " What is this shit ? " on first listening to Self Portrait , released in June 1970 . Self Portrait , a double LP including few original songs , was poorly received . In October 1970 , Dylan released New Morning , considered a return to form . This album included " Day of the <unk> " , a song in which Dylan gave an account of receiving an honorary degree from Princeton University on June 9 , 1970 . In November 1968 , Dylan had co @-@ written " I 'd Have You <unk> " with George Harrison ; Harrison recorded " I 'd Have You <unk> " and Dylan 's " If Not for You " for his 1970 solo triple album All Things Must Pass . Dylan 's surprise appearance at Harrison 's 1971 Concert for Bangladesh attracted media coverage , reflecting that Dylan 's live appearances had become rare . 
- Between March 16 and 19 , 1971 , Dylan reserved three days at Blue Rock , a small studio in Greenwich Village to record with Leon Russell . These sessions resulted in " <unk> the River Flow " and a new recording of " When I <unk> My <unk> " . On November 4 , 1971 , Dylan recorded " George Jackson " , which he released a week later . For many , the single was a surprising return to protest material , mourning the killing of Black Panther George Jackson in San Quentin State Prison that year . Dylan contributed piano and harmony to Steve Goodman 's album , Somebody Else 's Troubles , under the pseudonym Robert <unk> Thomas in September 1972 . 
- In 1972 , Dylan signed to Sam Peckinpah 's film Pat Garrett and Billy the Kid , providing songs and backing music for the movie , and playing " <unk> " , a member of Billy 's gang with some historical basis . Despite the film 's failure at the box office , the song " <unk> ' on Heaven 's <unk> " became one of Dylan 's most covered songs . 
- 
- = = = = Return to touring = = = = 
- 
- Dylan began 1973 by signing with a new label , David Geffen 's Asylum Records , when his contract with Columbia Records expired . On his next album , Planet Waves , he used the Band as backing group , while rehearsing for a tour . The album included two versions of " Forever Young " , which became one of his most popular songs . As one critic described it , the song projected " something <unk> and heartfelt that spoke of the father in Dylan " , and Dylan himself commented : " I wrote it thinking about one of my boys and not wanting to be too sentimental . " 
- Columbia Records simultaneously released Dylan , a collection of studio outtakes ( almost exclusively covers ) , widely interpreted as a <unk> response to Dylan 's signing with a rival record label . In January 1974 , Dylan returned to touring after seven years ; backed by the Band , he embarked on a North American tour of 40 concerts . A live double album , Before the Flood , was on Asylum Records . Soon , according to Clive Davis , Columbia Records sent word they " will spare nothing to bring Dylan back into the fold " . Dylan had second thoughts about Asylum , <unk> that while there had been millions of <unk> ticket requests for the 1974 tour , Geffen had sold only 700 @,@ 000 copies of Planet Waves . Dylan returned to Columbia Records , which reissued his two Asylum albums . 
- After the tour , Dylan and his wife became estranged . He filled a small red notebook with songs about relationships and <unk> , and recorded an album entitled Blood on the Tracks in September 1974 . Dylan delayed the release and re @-@ recorded half the songs at Sound 80 Studios in Minneapolis with production assistance from his brother , David Zimmerman . 
- Released in early 1975 , Blood on the Tracks received mixed reviews . In the NME , Nick Kent described " the <unk> [ as ] often so <unk> they sound like mere practice takes . " In Rolling Stone , Jon <unk> wrote that " the record has been made with typical <unk> . " Over the years critics came to see it as one of Dylan 's greatest achievements . In <unk> , Bill Wyman wrote : " Blood on the Tracks is his only flawless album and his best produced ; the songs , each of them , are constructed in disciplined fashion . It is his <unk> album and most dismayed , and seems in <unk> to have achieved a sublime balance between the <unk> @-@ plagued excesses of his mid @-@ 1960s output and the self @-@ consciously simple compositions of his post @-@ accident years . " <unk> Rick Moody called it " the <unk> , most honest account of a love affair from tip to stern ever put down on magnetic tape . " 
- In the middle of that year , Dylan wrote a ballad <unk> boxer Rubin " Hurricane " Carter , imprisoned for a triple murder in Paterson , New Jersey , in 1966 . After visiting Carter in jail , Dylan wrote " Hurricane " , presenting the case for Carter 's innocence . Despite its length — over eight minutes — the song was released as a single , peaking at 33 on the U.S. Billboard chart , and performed at every 1975 date of Dylan 's next tour , the Rolling Thunder Revue . The tour featured about one hundred performers and supporters from the Greenwich Village folk scene , including T @-@ Bone <unk> , Ramblin ' Jack Elliott , Joni Mitchell , David Mansfield , Roger <unk> , Mick <unk> , Joan Baez , and Scarlet Rivera , whom Dylan discovered walking down the street , her violin case on her back . Allen Ginsberg accompanied the troupe , staging scenes for the film Dylan was shooting . Sam Shepard was hired to write the screenplay , but ended up accompanying the tour as informal chronicler . 
- Running through late 1975 and again through early 1976 , the tour encompassed the release of the album Desire , with many of Dylan 's new songs featuring a <unk> @-@ like narrative style , showing the influence of his new collaborator , playwright Jacques Levy . The 1976 half of the tour was documented by a TV concert special , Hard Rain , and the LP Hard Rain ; no concert album from the better @-@ received and better @-@ known opening half of the tour was released until 2002 's Live 1975 . 
- The 1975 tour with the Revue provided the backdrop to Dylan 's nearly four @-@ hour film <unk> and <unk> , a sprawling narrative mixed with concert footage and <unk> . Released in 1978 , the movie received poor , sometimes scathing , reviews . Later in that year , a two @-@ hour edit , dominated by the concert performances , was more widely released . 
- In November 1976 , Dylan appeared at the Band 's " farewell " concert , with Eric Clapton , Joni Mitchell , <unk> Waters , Van Morrison and Neil Young . Martin Scorsese 's cinematic chronicle , The Last <unk> , in 1978 included about half of Dylan 's set . In 1976 , Dylan wrote and <unk> on " Sign Language " for Eric Clapton 's No Reason To <unk> . 
- In 1978 , Dylan embarked on a year @-@ long world tour , performing 114 shows in Japan , the Far East , Europe and the US , to a total audience of two million . Dylan assembled an eight piece band and three backing singers . Concerts in Tokyo in February and March were released as the live double album , Bob Dylan At Budokan . Reviews were mixed . Robert Christgau awarded the album a C + rating , giving the album a <unk> review , while Janet Maslin defended it in Rolling Stone , writing : " These latest live versions of his old songs have the effect of liberating Bob Dylan from the originals . " When Dylan brought the tour to the U.S. in September 1978 , the press described the look and sound as a ' Las Vegas Tour ' . The 1978 tour grossed more than $ 20 million , and Dylan told the Los Angeles Times that he had debts because " I had a couple of bad years . I put a lot of money into the movie , built a big house ... and it costs a lot to get divorced in California . " 
- In April and May 1978 , Dylan took the same band and vocalists into <unk> Studios in Santa Monica , California , to record an album of new material : Street @-@ Legal . It was described by Michael Gray as , " after Blood On The Tracks , arguably Dylan 's best record of the 1970s : a crucial album documenting a crucial period in Dylan 's own life " . However , it had poor sound and mixing ( attributed to Dylan 's studio practices ) , <unk> the instrumental detail until a remastered CD release in 1999 restored some of the songs ' strengths . 
- 
- = = = = Christian period = = = = 
- 
- In the late 1970s , Dylan became a born again Christian and released two albums of contemporary gospel music . Slow Train Coming ( 1979 ) featured the guitar accompaniment of Mark <unk> ( of Dire Straits ) and was produced by veteran R & B producer Jerry <unk> . <unk> said that Dylan had tried to <unk> him during the recording . He replied : " Bob , you 're dealing with a 62 @-@ year @-@ old Jewish <unk> . Let 's just make an album . " Dylan won the Grammy Award for Best Male Rock Vocal Performance for the song " Gotta Serve Somebody " . His second Christian @-@ themed album , Saved ( 1980 ) , received mixed reviews , described by Michael Gray as " the nearest thing to a follow @-@ up album Dylan has ever made , Slow Train Coming II and inferior " When touring in late 1979 and early 1980 , Dylan would not play his older , secular works , and he delivered declarations of his faith from the stage , such as : 
- Years ago they ... said I was a prophet . I used to say , " No I 'm not a prophet " they say " Yes you are , you 're a prophet . " I said , " No it 's not me . " They used to say " You sure are a prophet . " They used to convince me I was a prophet . Now I come out and say Jesus Christ is the answer . They say , " Bob Dylan 's no prophet . " They just can 't handle it . 
- Dylan 's Christianity was unpopular with some fans and musicians . Shortly before his murder , John Lennon recorded " Serve Yourself " in response to Dylan 's " Gotta Serve Somebody " . By 1981 , Stephen Holden wrote in the New York Times that " neither age ( he 's now 40 ) nor his much @-@ publicized conversion to born @-@ again Christianity has altered his essentially <unk> temperament . " 
- 
- = = = 1980s = = = 
- 
- In late 1980 , Dylan briefly played concerts billed as " A Musical <unk> " , restoring popular 1960s songs to the repertoire . Shot of Love , recorded early the next year , featured his first secular compositions in more than two years , mixed with Christian songs . " Every Grain of Sand " reminded some of William Blake 's verses . 
- In the 1980s , reception of Dylan 's recordings varied , from the well @-@ regarded <unk> in 1983 to the panned Down in the Groove in 1988 . Michael Gray condemned Dylan 's 1980s albums for <unk> in the studio and for failing to release his best songs . As an example of the latter , the <unk> recording sessions , which again employed <unk> on lead guitar and also as the album 's producer , resulted in several notable songs that Dylan left off the album . Best regarded of these were " Blind Willie <unk> " , a tribute to the dead blues musician and an evocation of African American history , " Foot of Pride " and " Lord <unk> My Child " . These three songs were released on The Bootleg Series Volumes 1 – 3 ( Rare & <unk> ) 1961 – 1991 . 
- Between July 1984 and March 1985 , Dylan recorded Empire <unk> . Arthur Baker , who had remixed hits for Bruce Springsteen and <unk> <unk> , was asked to engineer and mix the album . Baker said he felt he was hired to make Dylan 's album sound " a little bit more contemporary " . 
- Dylan sang on USA for Africa 's famine relief single " We Are the World " . On July 13 , 1985 , he appeared at the climax at the Live Aid concert at JFK Stadium , Philadelphia . Backed by Keith Richards and Ronnie Wood , he performed a ragged version of " Hollis Brown " , his ballad of rural poverty , and then said to the worldwide audience exceeding one billion people : " I hope that some of the money ... maybe they can just take a little bit of it , maybe ... one or two million , maybe ... and use it to pay the <unk> on some of the farms and , the farmers here , owe to the banks . " His remarks were widely criticized as inappropriate , but they did inspire Willie Nelson to organize a series of events , Farm Aid , to benefit debt @-@ ridden American farmers . 
- In April 1986 , Dylan made a foray into rap music when he added vocals to the opening verse of " Street Rock " , featured on <unk> Blow 's album Kingdom Blow . Dylan 's next studio album , <unk> Out <unk> , in July 1986 contained three covers ( by Little Junior Parker , Kris <unk> and the gospel hymn " <unk> Memories " ) , plus three collaborations with ( Tom Petty , Sam Shepard and <unk> Bayer <unk> ) , and two solo compositions by Dylan . One reviewer commented that " the record follows too many detours to be consistently compelling , and some of those detours wind down roads that are indisputably dead ends . By 1986 , such uneven records weren 't entirely unexpected by Dylan , but that didn 't make them any less frustrating . " It was the first Dylan album since Freewheelin ' ( 1963 ) to fail to make the Top 50 . Since then , some critics have called the 11 @-@ minute epic that Dylan co @-@ wrote with Sam Shepard , " <unk> Girl " , a work of genius . 
- In 1986 and 1987 , Dylan toured with Tom Petty and the <unk> , sharing vocals with Petty on several songs each night . Dylan also toured with the <unk> Dead in 1987 , resulting in a live album Dylan & The Dead . This received negative reviews : Allmusic said , " Quite possibly the worst album by either Bob Dylan or the <unk> Dead . " Dylan then initiated what came to be called the Never Ending Tour on June 7 , 1988 , performing with a back @-@ up band featuring guitarist G. E. Smith . Dylan continued to tour with a small , evolving band for the next 20 years . 
- In 1987 , Dylan starred in Richard <unk> 's movie Hearts of Fire , in which he played Billy Parker , a washed @-@ up rock star turned chicken farmer whose teenage lover , ( Fiona ) , leaves him for a <unk> English synth @-@ pop sensation played by Rupert Everett . Dylan also contributed two original songs to the soundtrack — " Night After Night " , and " I Had a Dream About You , Baby " , as well as a cover of John <unk> 's " The <unk> " . The film was a critical and commercial flop . Dylan was inducted into the Rock and Roll Hall of Fame in January 1988 , with Bruce Springsteen 's introduction declaring , " Bob freed your mind the way Elvis freed your body . He showed us that just because music was <unk> physical did not mean that it was anti @-@ intellectual . 
- The album Down in the Groove in May 1988 sold even more unsuccessfully than his previous studio album . Michael Gray wrote : " The very title <unk> any idea that inspired work may lie within . Here was a further <unk> of the notion of a new Bob Dylan album as something significant . " The critical and commercial disappointment of that album was swiftly followed by the success of the Traveling Wilburys . Dylan co @-@ founded the band with George Harrison , Jeff <unk> , Roy <unk> , and Tom Petty , and in late 1988 their multi @-@ platinum Traveling Wilburys Vol . 1 reached three on the US album chart , featuring songs that were described as Dylan 's most accessible compositions in years . Despite <unk> 's death in December 1988 , the remaining four recorded a second album in May 1990 with the title Traveling Wilburys Vol . 3 . 
- Dylan finished the decade on a critical high note with Oh Mercy produced by Daniel Lanois . Michael Gray wrote that the album was : " <unk> written , vocally distinctive , musically warm , and <unk> professional , this cohesive whole is the nearest thing to a great Bob Dylan album in the 1980s . " The track " Most of the Time " , a lost love composition , was later prominently featured in the film High <unk> , while " What Was It You Wanted ? " has been interpreted both as a catechism and a <unk> comment on the expectations of critics and fans . The religious imagery of " Ring Them Bells " struck some critics as a re @-@ affirmation of faith . 
- 
- = = = 1990s = = = 
- 
- Dylan 's 1990s began with Under the Red Sky ( 1990 ) , an about @-@ face from the serious Oh Mercy . The album contained several apparently simple songs , including " Under the Red Sky " and " <unk> <unk> " . The album was dedicated to " <unk> Goo Goo " , a nickname for the daughter of Dylan and Carolyn Dennis , Desiree Gabrielle Dennis @-@ Dylan , who was four . <unk> on the album included George Harrison , Slash from Guns N ' Roses , David Crosby , Bruce <unk> , Stevie Ray Vaughan , and Elton John . Despite the line @-@ up , the record received bad reviews and sold poorly . 
- In 1991 , Dylan received a Grammy Lifetime Achievement Award from American actor Jack Nicholson . The event coincided with the start of the Gulf War against Saddam Hussein , and Dylan performed " Masters of War " . Dylan then made a short speech , saying " My daddy once said to me , he said , ' Son , it is possible for you to become so <unk> in this world that your own mother and father will abandon you . If that happens , God will believe in your ability to <unk> your own ways . ' " This sentiment was subsequently revealed to be a quote from 19th @-@ century German Jewish intellectual , Rabbi <unk> Raphael <unk> . 
- The next few years saw Dylan returning to his roots with two albums covering folk and blues numbers : Good as I Been to You ( 1992 ) and World Gone Wrong ( 1993 ) , featuring interpretations and acoustic guitar work . Many critics and fans commented on the quiet beauty of the song " Lone Pilgrim " , written by a 19th @-@ century teacher . In November 1994 Dylan recorded two live shows for MTV Unplugged . He said his wish to perform traditional songs was overruled by Sony executives who insisted on hits . The album from it , MTV Unplugged , included " John Brown " , an unreleased 1962 song of how enthusiasm for war ends in mutilation and disillusionment . 
- Dylan 's longtime road manager Victor <unk> has claimed that the singer quit drinking alcohol in 1994 . <unk> felt that Dylan sobering up made him " more <unk> and a little less social . " 
- With a collection of songs reportedly written while <unk> @-@ in on his Minnesota <unk> , Dylan booked recording time with Daniel Lanois at Miami 's <unk> Studios in January 1997 . The subsequent recording sessions were , by some accounts , fraught with musical tension . Before the album 's release Dylan was hospitalized with a life @-@ threatening heart infection , <unk> , brought on by <unk> . His scheduled European tour was cancelled , but Dylan made a <unk> recovery and left the hospital saying , " I really thought I 'd be seeing Elvis soon . " He was back on the road by mid @-@ year , and performed before Pope John Paul II at the World <unk> Conference in Bologna , Italy . The Pope treated the audience of 200 @,@ 000 people to a <unk> based on Dylan 's lyric " Blowin ' in the Wind " . 
- September saw the release of the new Lanois @-@ produced album , Time Out of Mind . With its bitter assessment of love and <unk> <unk> , Dylan 's first collection of original songs in seven years was highly acclaimed . One critic wrote : " the songs themselves are uniformly powerful , adding up to Dylan 's best overall collection in years . " This collection of complex songs won him his first solo " Album of the Year " Grammy Award . 
- In December 1997 , U.S. President Bill Clinton presented Dylan with a Kennedy Center Honor in the East Room of the White House , paying this tribute : " He probably had more impact on people of my generation than any other creative artist . His voice and lyrics haven 't always been easy on the ear , but throughout his career Bob Dylan has never aimed to please . He 's disturbed the peace and <unk> the powerful . " 
- In 1999 , Dylan embarked on a North American tour with Paul Simon , where each alternated as headline act with a " middle " section where they performed together , starting on the first of June and ending September 18 . The collaboration was generally well @-@ received , with just one critic , Seth <unk> , from the Berkshire Eagle , questioning the collaboration . 
- 
- = = = 2000s = = = 
- 
- Dylan commenced the new millennium by winning the Polar Music Prize in May 2000 and his first Oscar ; his song " Things Have Changed " , written for the film Wonder Boys , won an Academy Award in March 2001 . The Oscar , by some reports a facsimile , tours with him , presiding over shows perched atop an amplifier . 
- " Love and Theft " was released on September 11 , 2001 . Recorded with his touring band , Dylan produced the album himself under the pseudonym Jack Frost . The album was critically well received and earned nominations for several Grammy awards . Critics noted that Dylan was widening his musical palette to include rockabilly , Western swing , jazz , and even lounge ballads . " Love and Theft " generated controversy when The Wall Street Journal pointed out similarities between the album 's lyrics and Japanese author <unk> Saga 's book Confessions of a <unk> . 
- In 2003 , Dylan revisited the evangelical songs from his " born again " period and participated in the CD project Gotta Serve Somebody : The Gospel Songs of Bob Dylan . That year also saw the release of the film <unk> & Anonymous , which Dylan co @-@ wrote with director Larry Charles under the <unk> <unk> <unk> . Dylan played the central character in the film , Jack Fate , alongside a cast that included Jeff Bridges , <unk> Cruz and John Goodman . The film <unk> critics : many dismissed it as an " incoherent mess " ; a few treated it as a serious work of art . 
- In October 2004 , Dylan published the first part of his autobiography , Chronicles : Volume One . <unk> expectations , Dylan devoted three chapters to his first year in New York City in 1961 – 1962 , virtually ignoring the mid @-@ 1960s when his fame was at its height . He also devoted chapters to the albums New Morning ( 1970 ) and Oh Mercy ( 1989 ) . The book reached number two on The New York Times ' Hardcover Non @-@ Fiction best seller list in December 2004 and was nominated for a National Book Award . 
- No Direction Home , Martin Scorsese 's acclaimed film biography of Dylan , was first broadcast on September 26 – 27 , 2005 , on BBC Two in the UK and PBS in the US . The documentary focuses on the period from Dylan 's arrival in New York in 1961 to his motorcycle crash in 1966 , featuring interviews with <unk> <unk> , Liam Clancy , Joan Baez , Allen Ginsberg , Pete Seeger , Mavis Staples , and Dylan himself . The film received a Peabody Award in April 2006 and a Columbia @-@ <unk> Award in January 2007 . The accompanying soundtrack featured unreleased songs from Dylan 's early career . 
- Dylan earned yet another distinction in a 2007 study of US legal opinions and <unk> that found his lyrics were quoted by judges and lawyers more than those of any other songwriter , 186 times versus 74 by the Beatles , who were second . Among those quoting Dylan were US Supreme Court Chief Justice John Roberts and Justice Antonin <unk> , both <unk> . The most widely cited lines included " you don 't need a <unk> to know which way the wind blows " from " <unk> <unk> Blues " and " when you ain 't got nothing , you got nothing to lose " from " Like a Rolling Stone " . 
- 
- = = = = Modern Times = = = = 
- 
- May 3 , 2006 , was the premiere of Dylan 's radio presenting career , hosting a weekly radio program , Theme Time Radio Hour , for <unk> <unk> Radio , with song selections revolving around a chosen theme . Dylan played classic and obscure records from the 1930s to the present day , including contemporary artists as diverse as <unk> , Prince , <unk> Cool J and the Streets . The show was praised by fans and critics as " great radio , " as Dylan told stories and made eclectic references with his sardonic humor , while achieving a thematic beauty with his musical choices . In April 2009 , Dylan broadcast the 100th show in his radio series ; the theme was " <unk> " and the final record played was Woody Guthrie 's " So Long , It 's Been Good to Know <unk> " . This has led to speculation that Dylan 's radio series may have ended . 
- On August 29 , 2006 , Dylan released his Modern Times album . Despite some <unk> of Dylan 's voice ( a critic for The Guardian characterised his singing on the album as " a <unk> death <unk> " ) most reviewers praised the album , and many described it as the final installment of a successful trilogy , embracing Time Out of Mind and " Love and Theft " . Modern Times entered the U.S. charts at number one , making it Dylan 's first album to reach that position since 1976 's Desire . The New York Times published an article exploring similarities between some of Dylan 's lyrics in Modern Times and the work of the Civil War poet Henry <unk> . 
- <unk> for three Grammy Awards , Modern Times won Best Contemporary Folk / <unk> Album and Bob Dylan also won Best Solo Rock Vocal Performance for " <unk> Baby " . Modern Times was named Album of the Year , 2006 , by Rolling Stone magazine , and by Uncut in the UK . On the same day that Modern Times was released the iTunes Music Store released Bob Dylan : The Collection , a digital box set containing all of his albums ( <unk> tracks in total ) , along with 42 rare and unreleased tracks . 
- In August 2007 , the award @-@ winning film biography of Dylan I 'm Not There , written and directed by Todd Haynes , was released — bearing the tagline " inspired by the music and many lives of Bob Dylan " . The movie used six different actors to represent different aspects of Dylan 's life : Christian <unk> , <unk> <unk> , Marcus Carl Franklin , Richard <unk> , Heath Ledger and Ben <unk> . Dylan 's previously unreleased 1967 recording from which the film takes its name was released for the first time on the film 's original soundtrack ; all other tracks are covers of Dylan songs , specially recorded for the movie by a diverse range of artists , including Sonic Youth , Eddie <unk> , Mason Jennings , Stephen <unk> , Jeff <unk> , Karen O , Willie Nelson , Cat Power , Richie <unk> , and Tom <unk> . 
- On October 1 , 2007 , Columbia Records released the triple CD retrospective album Dylan , <unk> his entire career under the Dylan 07 logo . As part of this campaign , Mark <unk> produced a re @-@ mix of Dylan 's 1966 tune " Most <unk> You Go Your Way and I 'll Go Mine " , which was released as a <unk> @-@ single . This was the first time Dylan had sanctioned a re @-@ mix of one of his classic recordings . 
- The <unk> of the Dylan 07 marketing campaign was a reminder that Dylan 's commercial profile had risen considerably since the 1990s . This first became evident in 2004 , when Dylan appeared in a TV advertisement for Victoria 's Secret lingerie . Three years later , in October 2007 , he participated in a multi @-@ media campaign for the 2008 <unk> <unk> . Then , in 2009 , he gave the highest profile endorsement of his career , appearing with rapper <unk> in a Pepsi ad that debuted during the telecast of Super Bowl <unk> . The ad , broadcast to a record audience of 98 million viewers , opened with Dylan singing the first verse of " Forever Young " followed by <unk> doing a hip hop version of the song 's third and final verse . 
- In October 2008 , Columbia released The Bootleg Series Vol . 8 – Tell Tale Signs as both a two @-@ CD set and a three @-@ CD version with a 150 @-@ page hardcover book . The set contains live performances and outtakes from selected studio albums from Oh Mercy to Modern Times , as well as soundtrack contributions and collaborations with David <unk> and Ralph Stanley . The <unk> of the album — the two @-@ CD set went on sale for $ 18 @.@ 99 and the three @-@ CD version for $ 129 @.@ 99 — led to complaints about " rip @-@ off packaging " from some fans and commentators . The release was widely acclaimed by critics . The abundance of alternative takes and unreleased material suggested to one reviewer that this volume of old outtakes " feels like a new Bob Dylan record , not only for the astonishing freshness of the material , but also for the incredible sound quality and organic feeling of everything here . " 
- 
- = = = = Together Through Life and Christmas in the Heart = = = = 
- 
- Bob Dylan released his album Together Through Life on April 28 , 2009 . In a conversation with music journalist Bill Flanagan , published on Dylan 's website , Dylan explained that the genesis of the record was when French film director Olivier <unk> asked him to supply a song for his new road movie , My Own Love Song ; initially only intending to record a single track , " Life Is Hard , " " the record sort of took its own direction " . Nine of the ten songs on the album are credited as co @-@ written by Bob Dylan and Robert Hunter . 
- The album received largely favorable reviews , although several critics described it as a minor addition to Dylan 's canon of work . Andy Gill wrote in The Independent that the record " features Dylan in fairly relaxed , spontaneous mood , content to grab such grooves and sentiments as <unk> momentarily across his radar . So while it may not contain too many landmark tracks , it 's one of the most naturally enjoyable albums you 'll hear all year . " 
- In its first week of release , the album reached number one in the Billboard 200 chart in the U.S. , making Bob Dylan ( 67 years of age ) the oldest artist to ever debut at number one on that chart . It also reached number one on the UK album chart , 39 years after Dylan 's previous UK album chart <unk> New Morning . This meant that Dylan currently holds the record for the longest gap between solo number one albums in the UK chart . 
- On October 13 , 2009 , Dylan released a Christmas album , Christmas in the Heart , comprising such Christmas standards as " Little Drummer Boy " , " Winter Wonderland " and " Here Comes Santa Claus " . Dylan 's royalties from the sale of this album will benefit the charities Feeding America in the USA , Crisis in the UK , and the World Food Programme . 
- The album received generally favorable reviews . The New Yorker commented that Dylan had <unk> a pre @-@ rock musical sound to " some of his <unk> vocals in a while " , and speculated that Dylan 's intentions might be ironic : " Dylan has a long and highly publicized history with Christianity ; to claim there 's not a <unk> in the childish optimism of ' Here Comes Santa Claus ' or ' Winter Wonderland ' is to ignore a half @-@ century of biting satire . " In USA Today , Edna Gundersen pointed out that Dylan was " revisiting <unk> styles popularized by Nat King Cole , <unk> <unk> , and the Ray <unk> Singers . " Gundersen concluded that Dylan " couldn 't sound more sentimental or sincere " . 
- In an interview published in The Big Issue , journalist Bill Flanagan asked Dylan why he had performed the songs in a straightforward style , and Dylan responded : " There wasn 't any other way to play it . These songs are part of my life , just like folk songs . You have to play them straight too . " 
- 
- = = = 2010s = = = 
- 
- 
- = = = = Tempest = = = = 
- 
- On October 18 , 2010 , Dylan released Volume 9 of his Bootleg Series , The <unk> <unk> . This comprised 47 demo recordings of songs taped between 1962 and 1964 for Dylan 's earliest music publishers : Leeds Music in 1962 , and <unk> Music from 1962 to 1964 . One reviewer described the set as " a <unk> glimpse of young Bob Dylan changing the music business , and the world , one note at a time . " The critical aggregator website Metacritic awarded the album a <unk> of 86 , indicating " universal acclaim " . In the same week , Sony Legacy released Bob Dylan : The Original <unk> Recordings , a box set that for the first time presented Dylan 's eight earliest albums , from Bob Dylan ( 1962 ) to John Wesley Harding ( 1967 ) , in their original <unk> mix in the CD format . The CDs were housed in miniature <unk> of the original album covers , replete with original liner notes . The set was accompanied by a booklet featuring an essay by music critic Greil Marcus . 
- On April 12 , 2011 , Legacy Recordings released Bob Dylan in Concert – <unk> University 1963 , taped at <unk> University on May 10 , 1963 , two weeks prior to the release of The Freewheelin ' Bob Dylan . The tape was discovered in the archive of music writer Ralph J. Gleason , and the recording carries liner notes by Michael Gray , who writes the recording captures Dylan " from way back when Kennedy was President and the Beatles hadn 't yet reached America . It reveals him not at any Big Moment but giving a performance like his folk club sets of the period ... This is the last live performance we have of Bob Dylan before he becomes a star . " 
- The extent to which his work was studied at an academic level was demonstrated on Dylan 's 70th birthday on May 24 , 2011 , when three universities organized <unk> on his work . The University of <unk> , the University of Vienna , and the University of Bristol invited literary critics and cultural historians to give papers on aspects of Dylan 's work . Other events , including tribute bands , discussions and simple <unk> , took place around the world , as reported in The Guardian : " From Moscow to Madrid , Norway to Northampton and Malaysia to his home state of Minnesota , self @-@ confessed ' Bobcats ' will gather today to celebrate the 70th birthday of a giant of popular music . " 
- On October 4 , 2011 , Dylan 's label , Egyptian Records , released an album of previously unheard Hank Williams songs , The Lost <unk> of Hank Williams . Dylan had helped to <unk> this project , in which songs unfinished when Williams died in 1953 were completed and recorded by a variety of artists , including Dylan himself , his son Jakob Dylan , Levon Helm , <unk> Jones , Jack White , and others . 
- On May 29 , 2012 , U.S. President Barack Obama awarded Dylan a Presidential Medal of Freedom in the White House . At the ceremony , Obama praised Dylan 's voice for its " unique <unk> power that <unk> not just what music sounded like but the message it carried and how it made people feel " . 
- On September 11 , 2012 , Dylan released his 35th studio album , Tempest . The album features a tribute to John Lennon , " Roll On John " , and the title track is a 14 minute song about the sinking of the Titanic . Reviewing Tempest for Rolling Stone , Will Hermes gave the album five out of five stars , writing : " Lyrically , Dylan is at the top of his game , joking around , dropping wordplay and <unk> that evade pat readings and quoting other <unk> ' words like a freestyle rapper on fire . " Hermes called Tempest " one of [ Dylan 's ] <unk> albums ever " , and opined , " It may also be the single <unk> record in Dylan 's catalog . " The critical aggregator website Metacritic awarded the album a score of 83 out of 100 , indicating " universal acclaim " . 
- On August 27 , 2013 , Columbia Records released Volume 10 of Dylan 's Bootleg Series , Another Self Portrait ( 1969 – 1971 ) . The album contained 35 previously unreleased tracks , including alternate takes and demos from Dylan 's 1969 – 1971 recording sessions during the making of the Self Portrait and New Morning albums . The box set also included a live recording of Dylan 's performance with the Band at the Isle of Wight Festival in 1969 . Another Self Portrait received favorable reviews , earning a score of 81 on the critical aggregator , Metacritic , indicating " universal acclaim " . AllMusic critic Thom Jurek wrote , " For fans , this is more than a curiosity , it 's an indispensable addition to the catalog . " 
- On November 4 , 2013 , Columbia Records released Bob Dylan : Complete Album Collection : Vol . One , a <unk> set containing all 35 of Dylan 's studio albums , six albums of live recordings , and a collection , entitled <unk> , of singles , songs from films and non @-@ album material . The box includes new album @-@ by @-@ album liner notes written by Clinton Heylin with an introduction by Bill Flanagan . On the same date , Columbia released a compilation , The Very Best of Bob Dylan , which is available in both single CD and double CD formats . To publicize the 35 album box set , an innovative video of the song " Like a Rolling Stone " was released on Dylan 's website . The interactive video , created by director <unk> <unk> , allowed viewers to switch between 16 simulated TV channels , all featuring characters who are lip @-@ <unk> the lyrics of the 48 @-@ year @-@ old song . 
- On February 2 , 2014 , Dylan appeared in a commercial for the Chrysler 200 car which was screened during the 2014 Super Bowl American football game . At the end of the commercial , Dylan says : " So let Germany <unk> your beer , let Switzerland make your watch , let Asia assemble your phone . We will build your car . " Dylan 's Super Bowl commercial generated controversy and op @-@ ed pieces discussing the <unk> implications of his words , and whether the singer had " sold out " to corporate interests . 
- In 2013 and 2014 , auction house sales demonstrated the high cultural value attached to Dylan 's mid @-@ 1960s work , and the record prices that collectors were willing to pay for artefacts from this period . In December 2013 , the Fender <unk> which Dylan had played at the 1965 Newport Folk Festival <unk> $ 965 @,@ 000 , the second highest price paid for a guitar . In June 2014 , Dylan 's hand @-@ written lyrics of " Like a Rolling Stone " , his 1965 hit single , <unk> $ 2 million dollars at auction , a record for a popular music manuscript . 
- On October 28 , 2014 , Simon & Schuster published a massive 960 page , thirteen and a half pound edition of Dylan 's lyrics , Lyrics : Since 1962 . The book was edited by literary critic Christopher Ricks , Julie <unk> and Lisa <unk> , to offer variant versions of Dylan 's songs , sourced from out @-@ takes and live performances . A limited edition of 50 books , signed by Dylan , was priced at $ 5 @,@ 000 . " It ’ s the biggest , most expensive book we ’ ve ever published , as far as I know , " said Jonathan <unk> , Simon & Schuster ’ s president and publisher . 
- On November 4 , 2014 , Columbia Records / Legacy Recordings released The Basement Tapes Complete by Bob Dylan and The Band . These 138 tracks in a six @-@ CD box form Volume 11 of Dylan 's Bootleg Series . The 1975 album , The Basement Tapes , contained some of the songs which Dylan and the Band recorded in their homes in Woodstock , New York , in 1967 . Subsequently , over 100 recordings and alternate takes have circulated on bootleg records . The sleeve notes for the new box set are by Sid Griffin , American musician and author of Million Dollar <unk> : Bob Dylan , The Band , and The Basement Tapes . 
- 
- = = = = Shadows in the Night and Fallen Angels = = = = 
- 
- On February 3 , 2015 , Dylan released Shadows in the Night , featuring ten songs written between 1923 and 1963 , which have been described as part of the Great American Songbook . All the songs on the album were recorded by Frank Sinatra but both critics and Dylan himself cautioned against seeing the record as a collection of " Sinatra covers " . Dylan explained , " I don 't see myself as covering these songs in any way . They 've been covered enough . Buried , as a matter a fact . What me and my band are basically doing is uncovering them . <unk> them out of the grave and bringing them into the light of day . " In an interview , Dylan said he had been thinking about making this record since hearing Willie Nelson 's 1978 album <unk> . 
- Shadows In the Night received favorable reviews , scoring 82 on the critical aggregator Metacritic , which indicates " universal acclaim " . Critics praised the restrained instrumental <unk> and Dylan 's singing , saying that the material had elicited his best vocal performances in recent years . Bill Prince in <unk> commented : " A performer who 's had to hear his influence in virtually every white pop recording made since he debuted his own self @-@ titled album back in 1962 imagines himself into the songs of his pre @-@ rock 'n'roll early youth . " In The Independent , Andy Gill wrote that the recordings " have a lingering , languid charm , which ... help to liberate the material from the <unk> <unk> of big @-@ band and cabaret mannerisms . " The album debuted at number one in the UK albums chart in its first week of release . 
- On October 5 , 2015 , IBM launched a marketing campaign for its Watson computer system which featured Dylan . Dylan is seen conversing with the computer which says it has read all his lyrics and reports : " My analysis shows that your major themes are that time passes and love fades . " Dylan replies : " That sounds about right . " 
- On November 6 , 2015 , Sony Music released The Bootleg Series Vol . 12 : The Cutting Edge 1965 – 1966 . This work consists of previously unreleased material from the three albums Dylan recorded Between January 1965 and March 1966 : Bringing It All Back Home , Highway 61 Revisited and Blonde on Blonde . The records have been released in three formats : a 2 @-@ CD " Best Of " version , a 6 @-@ CD " Deluxe edition " , and an 18 @-@ CD " Collector 's Edition " in a limited edition of 5 @,@ 000 units . On Dylan 's website the " Collector 's Edition " was described as containing " every single note recorded by Bob Dylan in the studio in 1965 / 1966 " . The critical aggregator website Metacritic awarded Cutting Edge a score of 99 , indicating universal acclaim . The Best of the Cutting Edge entered the Billboard Top Rock Albums chart at number one on November 18 , based on its first @-@ week sales . 
- On March 2 , 2016 , it was announced that Dylan had sold an extensive archive of about 6 @,@ 000 items to the George Kaiser Family Foundation and the University of <unk> . It was reported that the sale price was " an estimated $ 15 million to $ 20 million " , and the archive comprises <unk> , drafts of Dylan lyrics , recordings , and correspondence . Filmed material in the collection includes 30 hours of outtakes from the 1965 tour documentary Dont Look Back , 30 hours of footage shot on Dylan 's legendary 1966 electric tour , and 50 hours shot on the 1975 Rolling Thunder Revue . The archive will be housed at <unk> Center for American Research , a facility at the <unk> Museum . 
- On May 20 , 2016 , Dylan released his 37th studio album , Fallen Angels , which was described as " a direct continuation of the work of ' uncovering ' the Great Songbook that he began on last year ’ s Shadows In the Night . " The album contained twelve songs by classic songwriters such as Harold <unk> , Sammy <unk> and Johnny Mercer , eleven of which had been recorded by Sinatra . Jim Farber wrote in Entertainment Weekly : " <unk> , [ Dylan ] delivers these songs of love lost and cherished not with a burning passion but with the <unk> of experience . They ’ re memory songs now , <unk> with a present sense of commitment . Released just four days ahead of his 75th birthday , they couldn ’ t be more age @-@ appropriate . " The album received a score of 79 on critical aggregator website Metacritic , denoting " generally favorable reviews " . 
- 
- = = Never Ending Tour = = 
- 
- The Never Ending Tour commenced on June 7 , 1988 , and Dylan has played roughly 100 dates a year for the entirety of the 1990s and 2000s — a heavier schedule than most performers who started out in the 1960s . By May 2013 , Dylan and his band had played more than 2 @,@ 500 shows , anchored by long @-@ time bassist Tony Garnier , drummer George <unk> , multi @-@ instrumentalist Donnie <unk> , and guitarist Charlie Sexton . To the dismay of some of his audience , Dylan 's performances remain unpredictable as he alters his arrangements and changes his vocal approach night after night . Critical opinion about Dylan 's shows remains divided . Critics such as Richard Williams and Andy Gill have argued that Dylan has found a successful way to present his rich legacy of material . Others have criticized his live performances for <unk> and spitting out " the greatest lyrics ever written so that they are effectively <unk> " , and giving so little to the audience that " it is difficult to understand what he is doing on stage at all . " 
- Dylan 's performances in China in April 2011 generated controversy . Some criticised him for not making any explicit comment on the political situation in China , and for , allegedly , allowing the Chinese authorities to censor his set list . Others defended Dylan 's performances , arguing that such criticism represented a misunderstanding of Dylan 's art , and that no evidence for the censorship of Dylan 's set list existed . In response to these allegations , Dylan posted a statement on his website : " As far as censorship goes , the Chinese government had asked for the names of the songs that I would be playing . There 's no logical answer to that , so we sent them the set lists from the previous 3 months . If there were any songs , verses or lines censored , nobody ever told me about it and we played all the songs that we intended to play . " 
- Dylan commenced a tour of Japan in Tokyo on April 4 , 2016 , which concluded in Yokohama on April 28 . Dylan announced a tour of the US starting in <unk> WA on June 4 , 2016 , and finishing in <unk> <unk> on July 17 . Dylan has also announced his participation in a so @-@ called " Mega @-@ <unk> " titled Desert Trip , in California on October 7 , when he will perform at the same event as the Rolling Stones , Neil Young , Paul McCartney , the Who and Roger Waters . 
- 
- = = Visual artist = = 
- 
- The cover of Dylan 's album Self Portrait ( 1970 ) is a reproduction of a painting of a face by Dylan . Another of his paintings is reproduced on the cover of the 1974 album Planet Waves . In 1994 Random House published Drawn Blank , a book of Dylan 's drawings . In 2007 , the first public exhibition of Dylan 's paintings , The Drawn Blank Series , opened at the <unk> in <unk> , Germany ; it showcased more than 200 watercolors and <unk> made from the original drawings . The exhibition coincided with the publication of Bob Dylan : The Drawn Blank Series , which includes 170 reproductions from the series . From September 2010 until April 2011 , the National Gallery of Denmark exhibited 40 large @-@ scale <unk> paintings by Dylan , The Brazil Series . 
- In July 2011 , a leading contemporary art gallery , Gagosian Gallery , announced their representation of Dylan 's paintings . An exhibition of Dylan 's art , The Asia Series , opened at the Gagosian Madison Avenue Gallery on September 20 , displaying Dylan 's paintings of scenes in China and the Far East . The New York Times reported that " some fans and <unk> have raised questions about whether some of these paintings are based on the singer 's own experiences and observations , or on photographs that are widely available and were not taken by Mr. Dylan . " The Times pointed to close <unk> between Dylan 's paintings and historic photos of Japan and China , and photos taken by <unk> <unk> and Henri <unk> @-@ <unk> . The Magnum photo agency confirmed that Dylan had licensed the reproduction rights of these photographs . 
- Dylan 's second show at the Gagosian Gallery , <unk> Art , opened in November 2012 . The show consisted of thirty paintings , transforming and <unk> popular magazines , including Playboy and <unk> . In February 2013 , Dylan exhibited the New Orleans Series of paintings at the <unk> <unk> in Milan . In August 2013 , Britain 's National Portrait Gallery in London hosted Dylan 's first major UK exhibition , Face <unk> , featuring twelve <unk> portraits . 
- In November 2013 , the <unk> Gallery , in London , exhibited seven wrought iron gates created by Dylan ( the exhibition was entitled <unk> <unk> ) . In a statement released by the gallery , Dylan said , " I 've been around iron all my life ever since I was a kid . I was born and raised in iron ore country , where you could <unk> it and smell it every day . Gates appeal to me because of the negative space they allow . They can be closed but at the same time they allow the seasons and breezes to enter and flow . They can shut you out or shut you in . And in some ways there is no difference . " 
- Since 1994 , Dylan has published six books of paintings and drawings . 
- 
- = = Discography = = 
- 
- 
- = = Accolades = = 
- 
- Dylan has won many awards throughout his career including eleven Grammy Awards , one Academy Award and one Golden Globe Award . He has been inducted into the Rock and Roll Hall of Fame , Nashville Songwriters Hall of Fame , and Songwriters Hall of Fame . In May 2000 , Dylan was awarded the Polar Music Prize . He received the Presidential Medal of Freedom in May 2012 . In February 2015 , Dylan accepted the <unk> Person of the Year award from the National Academy of Recording Arts and Sciences , in recognition of his philanthropic and artistic contributions to society . 
- 
- = = Personal life = = 
- 
- 
- = = = Family = = = 
- 
- Dylan married Sara Lownds on November 22 , 1965 . Their first child , Jesse Byron Dylan , was born on January 6 , 1966 , and they had three more children : Anna Lea ( born July 11 , 1967 ) , Samuel Isaac <unk> ( born July 30 , 1968 ) , and Jakob Luke ( born December 9 , 1969 ) . Dylan also adopted Sara 's daughter from a prior marriage , Maria Lownds ( later Dylan , born October 21 , 1961 ) . Bob and Sara Dylan were divorced on June 29 , 1977 . Maria married musician Peter <unk> in 1988 . In the 1990s , Dylan 's son Jakob became well known as the lead singer of the band The <unk> . Jesse Dylan is a film director and a successful businessman . 
- Desiree Gabrielle Dennis @-@ Dylan , Dylan 's daughter with his backup singer Carolyn Dennis ( often professionally known as Carol Dennis ) , was born on January 31 , 1986 , and Dylan married Carolyn Dennis on June 4 , 1986 . The couple divorced in October 1992 . Their marriage and child remained a closely guarded secret until the publication of Howard <unk> ' Dylan biography , Down the Highway : The Life Of Bob Dylan in 2001 . 
- When not touring , Dylan is believed to live primarily in Point <unk> , a promontory on the coast of Malibu , California , though he also owns property around the world . 
- 
- = = = Religious beliefs = = = 
- 
- Growing up in Hibbing , Minnesota , Dylan and his family were part of the area 's small but close @-@ knit Jewish community , and in May 1954 Dylan had his Bar <unk> . Around the time of his 30th birthday , in 1971 , Dylan visited Israel , and also met Rabbi <unk> Kahane , founder of the New York @-@ based Jewish Defense League . Time magazine quoted him saying about Kahane , " He 's a really sincere guy . He 's really put it all together . " Subsequently , Dylan <unk> the extent of his contact with Kahane . 
- During the late 1970s and early 1980s , Dylan converted to Christianity . From January to April 1979 , he participated in Bible study classes at the <unk> School of <unk> in <unk> , California . Pastor <unk> <unk> has recalled : " Larry Myers and Paul <unk> went over to Bob 's house and <unk> to him . He responded by saying , ' Yes he did in fact want Christ in his life . ' And he prayed that day and received the Lord . " 
- By 1984 , Dylan was <unk> himself from the " born again " label . He told Kurt <unk> of Rolling Stone magazine : " I 've never said I 'm born again . That 's just a media term . I don 't think I 've been an agnostic . I 've always thought there 's a superior power , that this is not the real world and that there 's a world to come . " In response to <unk> 's asking whether he belonged to any church or synagogue , Dylan <unk> replied , " Not really . Uh , the Church of the Poison Mind . " In 1997 he told David Gates of Newsweek : 
- Here 's the thing with me and the religious thing . This is the flat @-@ out truth : I find the <unk> and philosophy in the music . I don 't find it anywhere else . Songs like " Let Me Rest on a <unk> Mountain " or " I Saw the Light " — that 's my religion . I don 't <unk> to <unk> , preachers , <unk> , all of that . I 've learned more from the songs than I 've learned from any of this kind of entity . The songs are my lexicon . I believe the songs . 
- In an interview published in The New York Times on September 28 , 1997 , journalist Jon Pareles reported that " Dylan says he now <unk> to no organized religion . " 
- Dylan has been a supporter of the Chabad <unk> movement in the last 20 years , and has privately participated in Jewish religious events , including the Bar <unk> of his sons and attending Hadar <unk> , a Chabad <unk> <unk> . In September 1989 and September 1991 , he appeared on the Chabad <unk> . Dylan reportedly visits Chabad <unk> ; on <unk> <unk> in 2007 he attended Congregation Beth <unk> , in Atlanta , Georgia , where he was called to the Torah for the sixth <unk> . 
- Dylan has continued to perform songs from his gospel albums in concert , occasionally covering traditional religious songs . He has also made passing references to his religious faith — such as in a 2004 interview with 60 Minutes , when he told Ed Bradley that " the only person you have to think twice about lying to is either yourself or to God . " He also explained his constant touring schedule as part of a <unk> he made a long time ago with the " chief commander — in this earth and in the world we can 't see . " 
- In a 2009 interview with Bill Flanagan promoting Dylan 's Christmas LP , Christmas in the Heart , Flanagan commented on the " heroic performance " Dylan gave of " O Little Town of Bethlehem " and that he " delivered the song like a true believer " . Dylan replied : " Well , I am a true believer . " 
- 
- = = Legacy = = 
- 
- Dylan has been described as one of the most influential figures of the 20th century , musically and culturally . He was included in the Time 100 : The Most Important People of the Century where he was called " master poet , <unk> social critic and intrepid , guiding spirit of the counterculture generation " . In 2008 , The Pulitzer Prize jury awarded him a special citation for " his profound impact on popular music and American culture , marked by lyrical compositions of extraordinary poetic power . " President Barack Obama said of Dylan in 2012 , " There is not a bigger giant in the history of American music . " In their 2008 assessment of the " 100 Greatest Singers " , Rolling Stone magazine ranked him at number seven . Rolling Stone then ranked Dylan at number two in its 2011 list of " 100 Greatest Artists " of all time , while " Like A Rolling Stone " was listed as the " Greatest Song of all Time . " In 2008 , it was estimated that Dylan had sold about 120 million albums worldwide . 
- Initially modeling his writing style on the songs of Woody Guthrie , the blues of Robert Johnson , and what he termed the " architectural forms " of Hank Williams songs , Dylan added increasingly sophisticated lyrical techniques to the folk music of the early 1960s , <unk> it " with the <unk> of classic literature and poetry " . Paul Simon suggested that Dylan 's early compositions virtually took over the folk genre : " [ Dylan 's ] early songs were very rich ... with strong melodies . ' Blowin ' in the Wind ' has a really strong melody . He so enlarged himself through the folk background that he incorporated it for a while . He defined the genre for a while . " 
- When Dylan made his move from acoustic folk and blues music to a rock backing , the mix became more complex . For many critics , his greatest achievement was the cultural synthesis exemplified by his mid @-@ 1960s trilogy of albums — Bringing It All Back Home , Highway 61 Revisited and Blonde on Blonde . In Mike <unk> 's words : 
- Between late 1964 and the middle of 1966 , Dylan created a body of work that remains unique . Drawing on folk , blues , country , R & B , rock 'n'roll , gospel , British beat , <unk> , modernist and Beat poetry , surrealism and <unk> , advertising <unk> and social commentary , <unk> and Mad magazine , he forged a coherent and original artistic voice and vision . The beauty of these albums retains the power to shock and console . " 
- Dylan 's lyrics began to receive detailed scrutiny from academics and poets . Literary critic Christopher Ricks published a 500 @-@ page analysis of Dylan 's work , placing him in the context of Eliot , Keats and Tennyson , claiming that Dylan was a poet worthy of the same close analysis . Former British poet laureate Sir Andrew Motion argued that his lyrics should be studied in schools . Since 1996 , academics have lobbied the Swedish Academy to award Dylan the Nobel Prize in Literature . 
- Dylan 's voice also received critical attention . New York Times critic Robert Shelton described his early vocal style as " a rusty voice suggesting Guthrie 's old performances , etched in gravel like Dave Van Ronk 's . " David Bowie , in his tribute , " Song for Bob Dylan " , described Dylan 's singing as " a voice like sand and glue " . His voice continued to develop as he began to work with rock 'n'roll backing bands ; critic Michael Gray described the sound of Dylan 's vocal work on " Like a Rolling Stone " as " at once young and <unk> cynical " . As Dylan 's voice aged during the 1980s , for some critics , it became more expressive . Christophe <unk> writes in the journal Oral Tradition , " Dylan 's more recent broken voice enables him to present a world view at the sonic surface of the songs — this voice carries us across the landscape of a broken , fallen world . The anatomy of a broken world in " Everything is Broken " ( on the album Oh Mercy ) is but an example of how the thematic concern with all things broken is grounded in a concrete sonic reality . " 
- Dylan is considered a seminal influence on several musical genres , especially folk rock , country rock and Christian rock . As Edna Gundersen stated in USA Today : " Dylan 's musical DNA has informed nearly every simple twist of pop since 1962 . " Punk musician Joe <unk> praised Dylan for having " laid down the template for lyric , tune , seriousness , spirituality , depth of rock music . " Other major musicians who acknowledged Dylan 's importance include Johnny Cash , Jerry Garcia , John Lennon , Paul McCartney , Pete <unk> , Neil Young , Bruce Springsteen , David Bowie , Bryan Ferry , Nick Cave , Patti Smith , Syd Barrett Joni Mitchell , and Tom <unk> . Dylan significantly contributed to the initial success of both the Byrds and the Band : the Byrds achieved chart success with their version of " Mr. <unk> Man " and the subsequent album , while the Band were Dylan 's backing band on his 1966 tour , recorded The Basement Tapes with him in 1967 , and featured three previously unreleased Dylan songs on their debut album . 
- Some critics have <unk> from the view of Dylan as a visionary figure in popular music . In his book <unk> <unk> , <unk> <unk> objected : " I can 't take the vision of Dylan as seer , as teenage <unk> , as everything else he 's been worshipped as . The way I see him , he 's a minor talent with a major gift for self @-@ hype . " Australian critic Jack <unk> credited Dylan with changing the persona of the rock star : " What cannot be disputed is that Dylan invented the arrogant , faux @-@ <unk> posturing that has been the dominant style in rock since , with everyone from Mick Jagger to Eminem <unk> themselves from the Dylan <unk> . " 
- Fellow musicians also presented dissenting views . Joni Mitchell described Dylan as a " <unk> " and his voice as " fake " in a 2010 interview in the Los Angeles Times , in response to a suggestion that she and Dylan were similar since they had both created personas . Mitchell 's comment led to discussions of Dylan 's use of other people 's material , both supporting and criticizing him . In 2013 Mitchell told the Canadian Broadcasting Corporation ( <unk> ) in an interview that her remarks in the Los Angeles Times had been taken " completely out of context " , and that the interviewer was a " <unk> " . Mitchell added : " I like a lot of Bob 's songs . Musically he 's not very gifted . He 's borrowed his voice from old <unk> . He 's got a lot of borrowed things . He 's not a great guitar player . He 's invented a character to deliver his songs . " 
- Talking to <unk> Gilmore in Rolling Stone in 2012 , Dylan responded to the allegation of plagiarism , including his use of Henry <unk> 's verse in his album Modern Times , by saying that it was " part of the tradition " . 
- If Dylan 's work in the 1960s was seen as bringing intellectual ambition to popular music , critics in the 21st century described him as a figure who had greatly expanded the folk culture from which he initially emerged . Following the release of Todd Haynes ' Dylan <unk> I 'm Not There , J. <unk> wrote in his 2007 Village Voice review : 
- Elvis might never have been born , but someone else would surely have brought the world rock ' n ' roll . No such logic accounts for Bob Dylan . No iron law of history demanded that a would @-@ be Elvis from Hibbing , Minnesota , would <unk> through the Greenwich Village folk revival to become the world 's first and greatest rock ' n ' roll <unk> bard and then — having achieved fame and adoration beyond reckoning — vanish into a folk tradition of his own making . 
- Prior to the June 2014 sale of the original lyrics of " Like a Rolling Stone " , written on four sheets of hotel <unk> by Dylan in 1965 , Richard Austin , of <unk> 's , New York , said : " Before the release of Like a Rolling Stone , music charts were overrun with short and sweet love songs , many <unk> in at three minutes or less . By <unk> convention with six and a half minutes of dark , brooding poetry , Dylan rewrote the rules for pop music . " 
- 
- 
- = Stephanolepis cirrhifer = 
- 
- Stephanolepis cirrhifer , commonly known as the thread @-@ sail filefish , is a species of marine fish in the family <unk> . It is found in the western Pacific , in an area that ranges from northern Japan to the East China Sea , to Korea . Other common names for the fish include " <unk> " ( Japanese ) and " <unk> @-@ chi " ( Korean ) . The fish grows to a maximum length of about 12 inches ( 30 centimetres ) , and consumes both plant material and small marine organisms like skeleton shrimp . S. cirrhifer is host of the parasite <unk> <unk> . Some minor genetic differentiation between S. cirrhifer born in the wild and those bred in a <unk> for consumer use has been shown . The fish is edible and sold commercially for culinary purposes in many Asian countries . 
- 
- = = Taxonomy = = 
- 
- The fish was first described in 1850 by <unk> Jacob <unk> and Hermann <unk> , when it was observed along with other fauna off the coasts of Japan . They initially placed it in the genus <unk> , as <unk> cirrhifer ; however , it was transferred to the genus Stephanolepis by David Starr Jordan and Henry <unk> Fowler in 1903 . The species name derives from the Latin word cirrhifer , which means " bearing fringes of hair " . 
- Other common names : 
- Japanese - <unk> 
- Korean - <unk> @-@ chi 
- English - File fish , <unk> fish , <unk> 
- Russian - Small striped <unk> ( <unk> <unk> <unk> ) 
- 
- = = Description and habitat = = 
- 
- Thread @-@ sail filefish grow to a maximum adult length of about 30 centimetres ( 12 inches ) . The first dorsal fin is a strong retractable ( folding backwards ) spine . The second dorsal fin and anal fin are soft . They have comparatively small pectoral fins and truncated , fan @-@ shaped tail fins . The dorsal and anal fins are colorless . Their second dorsal , anal and caudal fins rounded . In males , 1 @-@ 3 soft dorsal fin rays extended as filaments ; the first ray has a particularly long thread . The fish have a small abdominal spike . The fish are colored from light brown , to <unk> to light greenish @-@ beige , and are slightly patterned with irregular , broken stripes that range from medium brown to blackish . 
- Juveniles of the species usually seek shelter and safety from predators within clusters of drifting seaweed . The adult thread @-@ sail filefish usually reside near the <unk> , where the depth is around 10 metres ( 33 feet ) . The fish migrate wholly in ocean waters ( " <unk> " ) between their feeding and spawning grounds , which can cover a range of over 100 kilometres ( 62 miles ) . The spawning season lasts from May to August . Juveniles under 5 centimetres ( 2 @.@ 0 inches ) inhabit shallow water and feed on small crustaceans , <unk> and algae . Adult fish are mostly solitary and live among the coral and <unk> . 
- 
- = = Ecology = = 
- 
- The thread @-@ sail filefish is an <unk> , and can feed on plant or animal matter . Its diet includes kelp , but consists mainly of <unk> such as <unk> and skeleton shrimp , as well as the seagrass species <unk> marina . The fish also feeds upon smaller organisms , including <unk> and some species of <unk> tube worms . 
- Thread @-@ sail filefish feed on fish , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> such as those of the genus <unk> ; and gelatinous <unk> , such as the moon <unk> <unk> sp. and the giant <unk> <unk> <unk> . 
- S. cirrhifer is host of the <unk> <unk> parasite <unk> <unk> , which mostly <unk> the fins of the female fish . 
- 
- = = Uses = = 
- 
- The thread @-@ sail filefish is cultured and sold commercially as food in Asian countries , including Korea and Japan . The demand for the fish in Korea is very high , and fisheries often employ the services of fish <unk> for breeding more of the fish to supplement and enhance the supply of stock . This has been done to such a degree that some . As many as 95 <unk> have been found to be unique to one of the populations , resulting from minor variations in certain genes that occur exclusively within either population ; genetic differentiation between S. cirrhifer born in the wild and those bred in a <unk> has apparently occurred . 
- 
- 
- = Mogadishu = 
- 
- Mogadishu ( / <unk> / ; Somali : <unk> ; Arabic : <unk> <unk> ) , known locally as Hamar , is the capital and most populous city of Somalia . Located in the coastal Banaadir region on the Indian Ocean , the city has served as an important port for millennia . As of 2015 , it had a population of 2 @,@ 120 @,@ 000 residents . 
- Tradition and old records assert that southern Somalia , including the Mogadishu area , was historically inhabited by hunter @-@ gatherers . These were later joined by Cushitic <unk> @-@ <unk> , who would go on to establish local <unk> . During its medieval Golden Age , Mogadishu was ruled by the Muzaffar dynasty , a vassal of the Ajuran Sultanate . It subsequently fell under the control of an assortment of local <unk> and polities , most notably the Geledi Sultanate . The city later became the capital of Italian Somaliland ( 1889 @-@ 1936 ) in the colonial period . After the Somali Republic became independent in 1960 , Mogadishu became known and promoted as the White Pearl of the Indian Ocean . After the ousting of the Siad Barre regime in 1991 and the ensuing civil war , various militias fought for control of the city , later to be replaced by the Islamic Courts Union in the mid @-@ 2000s . The ICU thereafter splintered into more radical groups , notably Al @-@ Shabaab , which fought the Transitional Federal Government ( 2004 @-@ 2012 ) and its AMISOM allies . With a change in administration in late 2010 , government troops and their military partners had succeeded in forcing out Al @-@ Shabaab by August 2011 . Mogadishu has subsequently experienced a period of intense reconstruction . 
- As Somalia 's capital city , many important national institutions are based in Mogadishu . It is the seat of the Federal Government of Somalia established in August 2012 , with the Somalia Federal Parliament serving as the government 's legislative branch . Yusuf Hussein <unk> has been the Mayor of Mogadishu since October 2015 . Villa Somalia is the official residential palace and principal workplace of the President of Somalia , Hassan Sheikh Mohamud . In May 2012 , the First Somali Bank was established in the capital , which organized Mogadishu 's first ever Technology , Entertainment , Design ( TEDx ) conference . The establishment of a local construction yard has also galvanized the city 's real @-@ estate sector . Arba 'a Rukun Mosque is one of the oldest Islamic places of worship in the capital , built circa 667 ( 1268 / 9 AD ) . The Mosque of Islamic <unk> in Mogadishu is the largest masjid in the Horn region . Mogadishu Cathedral was built in 1928 by the colonial authorities in Italian Somaliland in a Norman Gothic style , and served as the traditional seat of the Roman Catholic Diocese of Mogadiscio . The National Museum of Somalia is based in Mogadishu and holds many culturally important artefacts . The National Library of Somalia is undergoing a $ 1 million Somali federal government funded renovation , including a new library complex . 
- Mogadishu is home to a number of scholastic and media institutions . As part of the municipality 's urban renewal program , 100 schools across the capital are scheduled to be refurbished and reopened . The Somali National University ( SNU ) was established in the 1950s , and professors from the university later founded the non @-@ governmental Mogadishu University ( MU ) . Benadir University ( BU ) was established in 2002 with the intention of training doctors . Various national sporting bodies have their headquarters in Mogadishu , including the Somali Football Federation and the Somali Olympic Committee . Mogadishu Stadium was constructed in 1978 during the Siad Barre administration , with the assistance of Chinese engineers . It hosts football matches with teams from the Somalia League and the Somalia Cup . Additionally , the Port of Mogadishu serves as a major national seaport and is the largest harbour in Somalia . Mogadishu International Airport , the capital 's main airport , is the hub of the relaunched national carrier Somali Airlines . 
- 
- = = Etymology = = 
- 
- The origins of the name Mogadishu ( <unk> ) has many theories but it is most likely derived from a morphology of the Persian words Maq 'ad @-@ i @-@ Shah For a long time , it was thought to be derived from the Persian Maq 'ad @-@ i @-@ Shah ( <unk> <unk> ) , which means " the seat of the Shah " ( a reflection of the city 's early Persian influence ) . It is known locally as Xamar ( English : Hamar ) . 
- 
- = = History = = 
- 
- 
- = = = Early history = = = 
- 
- Tradition and old records assert that southern Somalia , including the Mogadishu area , was inhabited very early by hunter @-@ gatherers of <unk> descent . Although most of these early inhabitants are believed to have been either overwhelmed , driven away or , in some cases , assimilated by later migrants to the area , physical traces of their occupation survive in certain ethnic minority groups inhabiting modern @-@ day <unk> and other parts of the south . The latter descendants include <unk> populations such as the <unk> , <unk> , the <unk> @-@ <unk> , and especially the <unk> @-@ <unk> . By the time of the arrival of peoples from the Cushitic <unk> ( <unk> and <unk> ) clan confederacy , who would go on to establish a local aristocracy , other Cushitic groups affiliated with the <unk> ( <unk> ) and <unk> ( Ma <unk> ) had already formed settlements of their own in the sub @-@ region . 
- The ancient city of <unk> is believed to have been the predecessor state of Mogadishu . It is mentioned in the Periplus of the Erythraean Sea , a Greek travel document dating from the first century AD , as one of a series of commercial ports on the Somali <unk> . According to the Periplus , maritime trade already connected peoples in the Mogadishu area with other communities along the Indian Ocean coast . 
- The Sultanate of Mogadishu later developed with the immigration of <unk> Arabs , a community whose earliest presence dates back to the 9th or 10th century . This evolved into the Muzaffar dynasty , a joint Somali @-@ Arab federation of rulers , and Mogadishu became closely linked with the powerful Somali Ajuran Sultanate . Following his visit to the city , the 12th century Syrian historian <unk> al @-@ <unk> wrote that it was inhabited by dark @-@ skinned <unk> , the ancestors of the modern <unk> . 
- For many years , Mogadishu stood as the pre @-@ eminent city in the <unk> @-@ ul @-@ <unk> ( <unk> <unk> ) , meaning " Land of the <unk> , " which was the medieval Arabic term for the Somali coast . By the time of the Moroccan traveller Ibn <unk> 's appearance on the Somali coast in 1331 , the city was at the zenith of its prosperity . He described Mogadishu as " an exceedingly large city " with many rich merchants , which was famous for the high quality fabric that it exported to destinations including Egypt . <unk> added that the city was ruled by a Somali Sultan , Abu <unk> ibn <unk> ' Umar , who was originally from Barbara in northern Somalia and spoke both Somali ( referred to by <unk> as <unk> , the Benadir dialect of Somali ) and Arabic with equal fluency . The Sultan also had a retinue of <unk> ( ministers ) , legal experts , commanders , royal <unk> , and other officials at his service . 
- Additionally , there appears to have been a strong Persian presence in both Mogadishu and <unk> for a time . A Shi 'a influence can still be seen in some areas , as in the southern Somalia veneration of Fatimah , the Prophet Muhammad 's daughter . This fact is also reflected in the etymology of the city 's name , which derives from the Persian Maq 'ad @-@ i @-@ Shah ( <unk> <unk> ) and means " the seat of the Shah . " 
- The Portuguese would subsequently attempt to occupy the city , but never managed to take it . In his journal of an expedition to the region in <unk> @-@ <unk> , the explorer João de <unk> , who accompanied Vasco da Gama on the voyage , wrote that <unk> ( Mogadishu ) was controlled by Moors . A big town surrounded by four towers , it had houses several <unk> high and large palaces in its center . De <unk> and his men bombarded the city before continuing southwards along the seaboard . The <unk> Somali , however , were later successful in defeating the Ajuran State and bringing about the end of Muzaffar rule . 
- 
- = = = <unk> ( 1800s – 1900s ) = = = 
- 
- By 1892 , Mogadishu was under the joint control of the Somali Sultanate of the Geledi and the <unk> Sultanate of Zanzibar . The Geledi Sultans were at the height of their power . They dominated the southern ivory trade , and also held sway over the Jubba and Shebelle valleys in the hinterland . The <unk> Sultans ' authority in Mogadishu , however , was largely nominal . When <unk> <unk> bin <unk> of Oman sought to build a fort in the city , he was thus <unk> to request permission from Sultan Ahmed Yusuf of Geledi . This Fort of <unk> was eventually constructed in 1870 . The Sultan of Zanzibar later leased and then sold the infrastructure that he had built to the Italians , but not the land itself , which was Somali owned . 
- 
- = = = Italian Somaliland ( late 1800s @-@ 1960 ) = = = 
- 
- In 1905 , Italy made Mogadishu the capital of the newly established Italian Somaliland . The Italians subsequently referred to the city as Mogadiscio . After World War I , the surrounding territory came under Italian control with some resistance . 
- Thousands of Italians settled in Mogadishu and founded small manufacturing companies . They also developed some agricultural areas in the south near the capital , such as <unk> and the <unk> <unk> <unk> <unk> ( present @-@ day Jowhar ) . In the 1930s , new buildings and avenues were built . A 114 km ( 71 mi ) narrow @-@ gauge railway was laid from Mogadishu to Jowhar . An <unk> road , the <unk> <unk> , was also constructed and intended to link Mogadishu to <unk> <unk> . 
- In 1940 , the Italo @-@ Somali population numbered 22 @,@ 000 , accounting for over 44 % of the city 's population of 50 @,@ 000 residents . Mogadishu remained the capital of Italian Somaliland throughout the latter polity 's existence . In World War II it was captured by British forces in February 1941 . 
- After World War II Mogadishu was made the capital of the Trust Territory of Somaliland , an Italian administered <unk> political entity under the <unk> mandate , for ten years ( 1950 – 1960 ) . 
- 
- = = = Somali Republic ( 1960 – 1990 ) = = = 
- 
- British Somaliland became independent on 26 June 1960 as the State of Somaliland , and the Trust Territory of Somalia ( the former Italian Somaliland ) followed suit five days later . On 1 July 1960 , the two territories united to form the Somali Republic , with Mogadishu serving as the nation 's capital . A government was formed by Abdullahi <unk> and other members of the <unk> and protectorate governments , with <unk> Bashir <unk> Yusuf as President of the Somali National Assembly , Aden <unk> <unk> <unk> as President of the Somali Republic , and Abdirashid Ali Shermarke as Prime Minister ( later to become President from 1967 to 1969 ) . On 20 July 1961 and through a popular referendum , the people of Somalia ratified a new constitution , which was first drafted in 1960 . In 1967 , Muhammad <unk> Ibrahim <unk> became Prime Minister , a position to which he was appointed by Shermarke . 
- On 15 October 1969 , while paying a visit to the northern town of Las <unk> , Somalia 's then President Abdirashid Ali Shermarke was assassinated by one of his own <unk> . His assassination was quickly followed by a military coup d 'état on 21 October 1969 ( the day after his funeral ) , in which the Somali Army seized power without encountering armed opposition — essentially a bloodless takeover . The putsch was spearheaded by Major General Mohamed Siad Barre , who at the time commanded the army . 
- Alongside Barre , the Supreme Revolutionary Council ( SRC ) that assumed power after President <unk> 's assassination was led by Lieutenant Colonel Salaad <unk> <unk> and Chief of Police <unk> <unk> . <unk> officially held the title of " Father of the Revolution , " and Barre shortly afterwards became the head of the SRC . The SRC subsequently renamed the country the Somali Democratic Republic , arrested members of the former civilian government , banned political parties , dissolved the parliament and the Supreme Court , and suspended the constitution . 
- The revolutionary army established various large @-@ scale public works programmes , including the Mogadishu Stadium . In addition to a <unk> programme of industry and land , the Mogadishu @-@ based new regime 's foreign policy placed an emphasis on Somalia 's traditional and religious links with the Arab world , eventually joining the Arab League in 1974 . 
- After fallout from the unsuccessful <unk> campaign of the late 1970s , the Barre administration began arresting government and military officials under suspicion of participation in the abortive 1978 coup d 'état . Most of the people who had allegedly helped plot the putsch were summarily executed . However , several officials managed to escape abroad and started to form the first of various <unk> groups dedicated to ousting Barre 's regime by force . 
- 
- = = = Civil war = = = 
- 
- By the late 1980s , Barre 's regime had become increasingly unpopular . The authorities became ever more totalitarian , and resistance movements , encouraged by Ethiopia 's communist <unk> administration , <unk> up across the country . This eventually led in 1991 to the outbreak of the civil war , the toppling of Barre 's government , and the disbandment of the Somali National Army . Many of the opposition groups subsequently began competing for influence in the power vacuum that followed the <unk> of Barre 's regime . Armed factions led by United Somali Congress commanders General Mohamed Farah Aidid and Ali Mahdi Mohamed , in particular , clashed as each sought to exert authority over the capital . 
- UN Security Council Resolution 733 and UN Security Council Resolution 746 led to the creation of UNOSOM I , the first stabilization mission in Somalia after the dissolution of the central government . United Nations Security Council Resolution 794 was unanimously passed on 3 December 1992 , which approved a coalition of United Nations peacekeepers led by the United States . Forming the Unified Task Force ( <unk> ) , the alliance was tasked with assuring security until humanitarian efforts were transferred to the UN . Landing in 1993 , the UN peacekeeping coalition started the two @-@ year United Nations Operation in Somalia II ( UNOSOM II ) primarily in the south . 
- Some of the militias that were then competing for power interpreted the UN troops ' presence as a threat to their <unk> . Consequently , several gun battles took place in Mogadishu between local <unk> and peacekeepers . Among these was the Battle of Mogadishu of 1993 , a successful attempt by US troops to apprehend two high @-@ ranking lieutenants of faction leader Aidid . The UN soldiers eventually withdrew altogether from the country on 3 March 1995 , having incurred more significant casualties . 
- In 2006 , the Islamic Courts Union ( ICU ) , an Islamist organization , assumed control of much of the southern part of the country and promptly imposed <unk> 'a law . The new Transitional Federal Government ( TFG ) , established two years earlier , sought to re @-@ establish its authority . With the assistance of Ethiopian troops , AMISOM peacekeepers and air support by the United States , it managed to drive out the rival ICU and solidify its rule . On 8 January 2007 , as the Battle of Ras <unk> raged , TFG President and founder Abdullahi Yusuf Ahmed , a former colonel in the Somali Army , entered Mogadishu for the first time since being elected to office . The government then relocated to Villa Somalia in Mogadishu from its interim location in <unk> , marking the first time since the fall of the Barre regime in 1991 that the federal government controlled most of the country . 
- Following this defeat , the Islamic Courts Union splintered into several different factions . Some of the more radical elements , including Al @-@ Shabaab , regrouped to continue their insurgency against the TFG and oppose the Ethiopian military 's presence in Somalia . Throughout 2007 and 2008 , Al @-@ Shabaab scored military victories , seizing control of key towns and ports in both central and southern Somalia . At the end of 2008 , the group had captured <unk> but not Mogadishu . By January 2009 , Al @-@ Shabaab and other militias had managed to force the Ethiopian troops to retreat , leaving behind an under @-@ equipped African Union peacekeeping force to assist the Transitional Federal Government 's troops . 
- Between 31 May and 9 June 2008 , representatives of Somalia 's federal government and the moderate Alliance for the Re @-@ liberation of Somalia ( ARS ) group of Islamist rebels participated in peace talks in Djibouti brokered by the UN . The conference ended with a signed agreement calling for the withdrawal of Ethiopian troops in exchange for the cessation of armed confrontation . Parliament was subsequently expanded to 550 seats to accommodate ARS members , which then elected a new president . With the help of a small team of African Union troops , the coalition government also began a counteroffensive in February 2009 to retake control of the southern half of the country . To solidify its control of southern Somalia , the TFG formed an alliance with the Islamic Courts Union , other members of the Alliance for the Re @-@ liberation of Somalia , and <unk> <unk> <unk> 'a , a moderate Sufi militia . 
- In November 2010 , a new <unk> government was elected to office , which enacted numerous reforms , especially in the security sector . By August 2011 , the new administration and its AMISOM allies had managed to capture all of Mogadishu from the Al @-@ Shabaab militants . Mogadishu has subsequently experienced a period of intense reconstruction spearheaded by the Somali diaspora , the municipal authorities , and Turkey , a historic ally of Somalia . 
- 
- = = = Reconstruction = = = 
- 
- In August 2011 , militant group al Shabaab made a strategic withdrawal from Mogadishu to return to hit @-@ and @-@ run tactics . Mayor Mohamed Nur recognized the opportunity as critical to stabilizing and rebuilding the city by any means necessary . Working closely with the UN , USAID , and DRC , Nur 's administration also started large @-@ scale rehabilitation of roads and general infrastructure , with residents closely <unk> with the civil and police authorities to <unk> up on security . Nur recognized the opportunity to transform Mogadishu although resources were limited . Working closely with urban strategist Mitchell <unk> , the Benadir government sought to design and deploy a data @-@ driven approach to post @-@ war reconstruction . 
- With the passing of a new Constitution in 2012 and the subsequent election of an inaugural President in the new Federal Government , the <unk> continued to oversee Mogadishu 's ongoing post @-@ conflict reconstruction . Building off the initial pilot , the Benadir administration launched a city @-@ wide street naming , house numbering and postal codes project . Officially called the House Numbering and Post Code System , it is a joint initiative of the municipal authorities and Somali business community representatives . According to Nur , the initiative also aims to help the authorities firm up on security and resolve housing ownership disputes . As of 2016 , there are postal codes for 176 localities and sub @-@ localities , including the Mogadishu metropolitan area . 
- 
- = = Geography = = 
- 
- Mogadishu is situated on the Indian Ocean coast of the Horn of Africa , in the Banaadir administrative region ( <unk> ) in southeastern Somalia . The region itself is <unk> with the city and is much smaller than the historical province of Benadir . The city is administratively divided into the districts of <unk> , <unk> , <unk> , <unk> , Hamar @-@ <unk> , Hamar @-@ <unk> , <unk> , <unk> , <unk> @-@ <unk> , Karan , <unk> , <unk> , <unk> , <unk> , <unk> and <unk> . Features of the city include the <unk> old town , the <unk> Market , and <unk> Beach . The sandy beaches of Mogadishu have vibrant coral reefs , and are prime real estate for the first tourist resorts in many years . 
- The Shebelle River ( <unk> Shabelle ) rises in central Ethiopia and comes within 30 kilometers ( 19 mi ) of the Indian Ocean near Mogadishu before turning southwestward . Usually dry during February and March , the river provides water essential for the cultivation of sugarcane , cotton , and <unk> . 
- 
- = = = Climate = = = 
- 
- For a city situated so near the equator , Mogadishu has a relatively dry climate . It is classified as hot and semi @-@ arid ( Köppen climate classification <unk> ) , as with much of southeastern Somalia . By contrast , towns in northern Somalia generally have a hot arid climate ( Köppen <unk> ) . 
- Mogadishu is located in or near the tropical <unk> woodland <unk> of the <unk> global <unk> scheme . The mean temperature in the city year round is 27 ° C , with an average maximum of 30 ° C and an average minimum of 24 ° C. Mean temperature readings per month vary by 3 ° C ( 5 @.@ 4 ° F ) , corresponding with a <unk> and subtype truly <unk> <unk> type . Precipitation per year averages <unk> @.@ 2 millimetres ( 16 @.@ 9 in ) . There are 47 wet days annually , which are associated with a 12 % daily probability of rainfall . The city has an average of 3 @,@ <unk> hours of sunshine per year , with 8 @.@ 4 hours of sunlight per day . Mean daylight hours and minutes per day are <unk> 24 ' . The annual percentage of sunny versus cloudy daylight hours is 70 and 30 , respectively . Average sun altitude at solar noon on the 21st day of the month is 75 . 
- 
- = = Government = = 
- 
- 
- = = = Federal = = = 
- 
- The Transitional Federal Government ( TFG ) was the internationally recognized central government of Somalia between 2004 and 2012 . Based in Mogadishu , it constituted the executive branch of government . 
- The Federal Government of Somalia was established on 20 August 2012 , concurrent with the end of the TFG 's interim mandate . It represents the first permanent central government in the country since the start of the civil war . The Federal Parliament of Somalia serves as the government 's legislative branch . 
- 
- = = = Municipal = = = 
- 
- Mogadishu 's municipal government is currently led by Yusuf Hussein <unk> , who succeeded Mayor Hassan Mohamed Hussein <unk> , a former military court chairman . Among the administration 's development initiatives are a US $ 100 million urban renewal project , the creation of garbage disposal and <unk> plants , the launch of a city @-@ wide <unk> project , the creation of asphalt and cement plants , rehabilitation of the Town Hall and parliament buildings , reconstruction of the former Defence Ministry offices , reconstruction of <unk> facilities , rehabilitation and construction of health facilities , establishment of a Police Training Center and a permanent base in <unk> for the new Somali Armed Forces , rebuilding of the Somali Postal Service headquarters , and rehabilitation of public playgrounds in several districts . In January 2014 , the Benadir administration launched the House Numbering and Post Code System . It also began distributing national identity cards in March of the same year . In addition , the municipal authorities started renovating important local government centers in September 2014 , including the capital 's former <unk> <unk> compound . In January 2015 , the Benadir administration also opened a new Health & Safety Office to supervise health and safety practices in the city , and launched a municipal <unk> campaign ahead of various international conferences that are slated to be held there . 
- In March 2015 , the Benadir administration completed the <unk> project in conjunction with the EU and <unk> . The 3 @.@ 5 million <unk> initiative lasted three and a half years , and saw the establishment in Mogadishu of a new sustainable waste collection system , a Technical Training Centre , water quality testing laboratories , <unk> access to clean drinking water , improved employment and livelihood opportunities in the low @-@ cost fuel production sector , strengthened skills training and regulation in the construction sector , and laboratories for the testing of construction material quality . 
- 
- = = = <unk> missions = = = 
- 
- A number of countries maintain foreign embassies and <unk> in Mogadishu . As of January 2014 , these diplomatic missions include the embassies of Djibouti , Ethiopia , Sudan , Libya , <unk> , Saudi Arabia , Turkey , Iran , Uganda , Nigeria , the United Kingdom , Japan , China , and <unk> . <unk> that are scheduled to reopen in the city include those of Egypt , the United Arab Emirates , Italy and South Korea . 
- In May 2015 , in recognition of the <unk> progress made in Somalia and its return to effective governance , US Secretary of State John Kerry announced a preliminary plan to reestablish the US embassy in Mogadishu . He indicated that although there was no set timetable for the premises ' relaunch , the US government had immediately begun upgrading its diplomatic representation in the country . President of Somalia Hassan Sheikh Mohamud and Prime Minister Omar Abdirashid Ali <unk> also presented to Kerry the real estate deed for land reserved for the new US embassy compound . Mohamud concurrently signed an Establishment Agreement with the EU Head of Delegation in Somalia Michele <unk> d ’ <unk> , which facilitates the opening of more embassies in Mogadishu by European Union member states . The EU also announced that it had opened a new EU Delegation office in the city . 
- In February 2014 , Somalia 's Minister of Foreign Affairs and International Cooperation <unk> <unk> <unk> announced that the federal government was slated to reopen the former Institute of <unk> in Mogadishu . The center historically served as one of the most important national institutions for diplomacy and international relations . <unk> also pledged to reestablish the institute 's diplomacy department , its information and broadcasting department , as well as its library . 
- 
- = = Economy = = 
- 
- Mogadishu traditionally served as a commercial and financial centre . Before the <unk> of mass @-@ produced cloth from Europe and America , the city 's textiles were <unk> far and wide throughout the interior of the continent , as well as to the Arabian peninsula and as far as the Persian coast . 
- Mogadishu 's economy has grown rapidly since the city 's pacification in mid 2011 . The <unk> processing factory was reopened , as was the local Coca Cola factory , which was also refurbished . In May 2012 , the First Somali Bank was established in the capital , representing the first commercial bank to open in southern Somalia since 1991 . The Somali civil engineer and entrepreneur <unk> <unk> also opened the city 's first dollar store . Additionally , the Historic Central Bank was <unk> , with the <unk> Business Center likewise under construction . 
- The <unk> of Mogadishu 's real estate sector was in part facilitated by the establishment of a local construction yard in November 2012 by the Municipality of Istanbul and the Turkish Red Crescent . With 50 construction trucks and machines imported from Turkey , the yard produces concrete , asphalt and paving stones for building projects . The Istanbul Municipality was also scheduled to bring in 100 specialists to accelerate the construction initiative , which ultimately aims to modernize the capital 's infrastructure and serve it over the long @-@ term . 
- In mid @-@ 2012 , Mogadishu concurrently held its first ever Technology , Entertainment , Design ( TEDx ) conference . The event was organized by the First Somali Bank to showcase improvements in business , development and security to potential Somali and international investors . A second consecutive TEDx <unk> conference was held the following year in the capital , highlighting new enterprises and commercial opportunities , including the establishment of the city 's first dry cleaning business in several years . 
- A number of large firms also have their headquarters in Mogadishu . Among these is the Trans @-@ National Industrial Electricity and Gas Company , an energy conglomerate founded in 2010 that <unk> five major Somali companies from the trade , finance , security and telecommunications sectors . Other firms based in the city include <unk> Telecom , the largest telecommunications company in southern and central Somalia . <unk> is another telecommunications service provider that is centered in the capital . The local Somali Energy Company specializes in the generation , transmission and distribution of electric power to residents and businesses within its service area in Banaadir . Villa and Mansion Architects , an international architectural firm founded by the Somali @-@ British architect Alexander Yusuf , likewise has its regional offices in Mogadishu . Additionally , the International Bank of Somalia , which opened downtown in 2014 , offers Islamic finance and international banking services via a swift code system . The Islamic Insurance Company ( First <unk> and Re @-@ <unk> Insurance Company ) was concurrently established , and is the city 's first full service insurance firm in many years . The Central Bank of Somalia , the national monetary authority , also has its headquarters in Mogadishu . 
- In June 2013 , former Prime Minister Abdi Farah <unk> signed a new foreign investment law . The draft bill was prepared by the Ministry of Commerce and Industry in conjunction with government attorneys . <unk> by the Cabinet , it establishes a secure legal framework for foreign investment in Mogadishu and elsewhere in the country . 
- In October 2014 , the firm Tawakal Money Express ( Tawakal ) also began construction of the seven @-@ storey Tawakal Plaza Mogadishu . The new high rise is slated to be completed by the end of 2015 , and will feature a Tawakal Global Bank customer and financial services center , a large , 338 square meter supermarket , a 46 @-@ room luxury hotel , restaurant and coffee shop facilities , and conference and event halls . In addition , the <unk> <unk> provides major retail service to local <unk> . Open daily until 10 pm , the convenience chain imports most of its products from the United Arab Emirates and China . The Al <unk> firm also launched a major real estate project in January 2015 , <unk> @-@ Salam City . <unk> by the Salaam Somali Bank , the new urban complex includes town houses , apartment flats , a mosque , recreational areas , playgrounds , a supermarket and roads . It is slated to be erected just outside the northern part of the capital , within a 7 kilometer radius of the Industrial Road . 
- 
- = = Demographics = = 
- 
- Mogadishu is a multi @-@ ethnic city . Its original core population consisted of <unk> <unk> , and later Cushitic , Arab and Persian migrants . The mixture of these various groups produced the <unk> or <unk> Xamar ( “ People of Mogadishu ” ) , a composite population unique to the larger Benadir region . In the colonial period , European expatriates , primarily Italians , would also contribute to the city 's cosmopolitan populace . 
- The main area of <unk> of <unk> ethnic minorities in Somalia has historically been in village enclaves in the south , particularly between the Jubba and Shebelle river valleys as well as the <unk> and Bay regions . Beginning in the 1970s , more <unk> began moving to urban centres such as Mogadishu and <unk> . The displacement caused by the onset of the civil war in the 1990s further increased the number of rural minorities migrating to urban areas . As a consequence of these movements , Mogadishu 's traditional demographic makeup changed significantly over the years . 
- Following a greatly improved security situation in the city in 2012 , many Somali expatriates began returning to Mogadishu for investment opportunities and to take part in the ongoing post @-@ conflict reconstruction process . Through both private efforts and public initiatives like the Somali <unk> Corps , they have participated in the renovation of schools , hospitals , banks and other infrastructure , and have played a leading role in the capital 's recovery . They have also helped to propel the local real estate market . 
- According to <unk> , Mogadishu has a population of around 2 @,@ 120 @,@ 000 residents as of 2015 . It is the <unk> largest city in the world by population size . The urban area occupies 91 square kilometres ( 35 sq mi ) , with a population density of around 23 @,@ 400 inhabitants per square kilometre ( 61 @,@ 000 / sq mi ) . As of September 2014 , the Ministry of Planning and International Cooperation is scheduled to launch the first population census for Somalia in over two decades . The <unk> assisted the Ministry in the project , which is slated to be finalized ahead of the planned <unk> and local and national elections in 2016 . 
- 
- = = Landmarks = = 
- 
- 
- = = = Places of worship = = = 
- 
- Arba 'a Rukun Mosque is one of the oldest Islamic places of worship in the capital . It was built circa 667 ( 1268 / 9 AD ) , concurrently with the <unk> ad @-@ Din Mosque . Arba 'a Rukun 's <unk> contains an inscription dated from the same year , which commemorates the masjid 's late founder , <unk> ibn <unk> al @-@ <unk> ( <unk> ibn Muhammed ) . 
- The Mosque of Islamic <unk> was constructed in 1987 with financial support from the Saudi <unk> bin Abdul Aziz Al <unk> Foundation . It is the main mosque in the city , and an iconic building in Somali society . With a capacity of up to 10 @,@ 000 worshippers , it is the single largest masjid in the Horn region . In 2015 , the federal authorities completed formal <unk> on the mosque 's infrastructure . The upgrades are part of a larger governmental renovation campaign aimed at all of the <unk> in Mogadishu . To this end , the municipal authority is <unk> the historic Central Mosque , situated downtown . 
- The Mogadishu Cathedral was built in 1928 by the colonial authorities in Italian Somaliland . Known as the " <unk> di Mogadiscio " , it was constructed in a Norman Gothic style , based on the <unk> Cathedral in <unk> , Sicily . The church served as the traditional seat of the Roman Catholic Diocese of Mogadiscio . It later incurred significant damage during the civil war . In April 2013 , after a visit to the site to inspect its condition , the Diocese of Mogadiscio announced plans to <unk> the building . 
- 
- = = = Palaces = = = 
- 
- Villa Somalia is the official residential palace and principal workplace of the President of Somalia , Hassan Sheikh Mohamud . It sits on high ground that <unk> the city on the Indian Ocean , with access to both the harbour and airport . The Governor 's Palace of Mogadishu was the seat of the governor of Italian Somaliland , and then the administrator of the Trust Territory of Somalia . 
- 
- = = = Museums , libraries and theatres = = = 
- 
- The National Museum of Somalia was established after independence in 1960 , when the old <unk> Museum was turned into a National Museum . The National Museum was later moved in 1985 , renamed to the <unk> Museum , and converted to a regional museum . After shutting down , the National Museum later reopened . As of January 2014 , it holds many culturally important artefacts , including old coins , <unk> tools , traditional artwork , ancient weaponry and pottery items . 
- The National Library of Somalia was established in 1975 , and came under the responsibility of the Ministry of Education , Culture and Higher Education . In 1983 , it held approximately 7 @,@ 000 books , little in the way of historical and cultural archival material , and was open to the general public . The National Library later closed down in the 1990s . In June 2013 , the Heritage Institute for Policy Studies organized a shipment of 22 @,@ 000 books from the United States to Somalia as part of an initiative to <unk> the library . In December of the year , the Somali authorities officially launched a major project to rebuild the National Library . With <unk> Hassan serving as Director , the $ 1 million federal government funded initiative will see a new library complex built in the capital within six months . In preparation for the relaunch , 60 @,@ 000 additional books from other Arab League states are expected to arrive . 
- The National Theatre of Somalia opened in 1967 as an important cultural landmark in the national capital . It closed down after the start of the civil war in the early 1990s , but reopened in March 2012 after reconstruction . In September 2013 , the Somali federal government and its Chinese counterpart signed an official cooperation agreement in Mogadishu as part of a five @-@ year national recovery plan in Somalia . The pact will see the Chinese authorities reconstruct the National Theatre of Somalia in addition to several other major infrastructural landmarks . 
- 
- = = = <unk> = = = 
- 
- <unk> Market was created in late 1972 by the Barre administration . It served as an open market for the sale of goods and services , including produce and clothing . After the start of the civil war , the market was controlled by various militant groups , who used it as a base for their operations . Following Mogadishu 's pacification in 2011 , renovations resumed at the market . <unk> were rehabilitated , selling everything from fruit and garments to building materials . As in the rest of the city , <unk> Market 's real estate values have also risen considerably . As of 2013 , the local <unk> firm was renting out a newly constructed warehouse at the market for $ 2 @,@ 000 per month . 
- In February 2014 , the Benadir administration began renovations at the <unk> Market in the Hamar <unk> district . It was one of the largest markets in the city before closing down operations in the early 1990s . In September 2014 , the municipal authorities officially reopened the <unk> to the public , with officials supervising all parts of the market . According to the Benadir Political Affairs Vice Chairman Mohamed <unk> " <unk> " , the facility is now open for business and will compete with other regional markets . 
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- = = = Institutes = = = 
- 
- The Regional Somali Language Academy is an <unk> regulating body for the Somali language in the Horn region . In January 2015 , President of Somalia Hassan Sheikh Mohamud announced that the institute was slated to be finalized in conjunction with the governments of Djibouti and Ethiopia . Among the scheduled projects was the construction of a new headquarters for the Academy in Mogadishu , in recognition of Somalia 's traditional position as the center for the development and promotion of the Somali language . In February 2015 , the foundation stone for the new Regional Somali Language Academy was officially laid at an inauguration ceremony in the city . 
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- = = = Hotels = = = 
- 
- Mogadishu has a number of hotels , most of which were recently constructed . The city 's many returning expatriates , investors and international community workers are among these establishments ' main customers . To meet the growing demand , hotel representatives have also begun participating in international industry conferences , such as the Africa Hotel Investment <unk> . 
- Among the new hotels is the six floor <unk> Palace Hotel . It was built in 2010 and officially opened in 2012 . Situated within a <unk> radius of the Aden Adde International Airport , it has a 70 @-@ room capacity with a 70 % occupancy rate . The hotel expects to host over 1 @,@ 000 visitors by 2015 , for which it plans to construct a larger overall building and conference facilities . A new landslide hotel within the airport itself is also slated to be completed by the end of the year . 
- Other hotels in the city include the <unk> Palace Hotel , <unk> Castle Hotel , <unk> Hotel , Hotel <unk> @-@ <unk> , Oriental Hotel , Hotel <unk> , Hotel <unk> , Peace Hotel , <unk> Guest House , <unk> Hotel , Hotel <unk> , Hotel <unk> , Benadir Hotel , Ambassador Hotel , Kuwait Plaza Hotel , <unk> Hotel <unk> , <unk> Hotel , <unk> <unk> and <unk> Ali Hotel . 
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- = = Education = = 
- 
- Mogadishu is home to a number of scholastic institutions . As part of the government 's urban renewal program , 100 schools across the capital are scheduled to be refurbished and reopened . 
- The Somali National University ( SNU ) was established in the 1950s , during the <unk> period . In 1973 , its programmes and facilities were expanded . The SNU developed over the next 20 years into an expansive institution of higher learning , with 13 departments , 700 staff and over 15 @,@ 000 students . On 14 November 2013 , the Cabinet unanimously approved a federal government plan to reopen the Somali National University , which had been closed down in the early 1990s . The <unk> initiative cost US $ 3 @.@ 6 million , and was completed in August 2014 . 
- Mogadishu University ( MU ) is a non @-@ governmental university that is governed by a Board of Trustees and a University Council . It is the <unk> of a number of professors from the Somali National University as well as other Somali intellectuals . <unk> by the Islamic Development Bank in <unk> , Saudi Arabia , as well as other donor institutions , the university counts hundreds of graduates from its seven faculties , some of whom continue on to pursue Master 's degrees abroad thanks to a scholarship programme . Mogadishu University has established partnerships with several other academic institutions , including the University of Aalborg in Denmark , three universities in Egypt , seven universities in Sudan , the University of Djibouti , and two universities in <unk> . As of 2012 , MU also has accreditation with the Board of the <unk> Organization <unk> . 
- In 1999 , the Somali Institute of Management and Administration ( <unk> ) was co @-@ established in Mogadishu by incumbent President of Somalia Hassan Sheikh Mohamud . The institution subsequently grew into the <unk> University , with Mohamud acting as dean until 2010 . It offers a range of undergraduate courses in various fields , including economics , statistics , business , <unk> , technology , computer science , health sciences , education , law and public administration . 
- Benadir University ( BU ) was established in 2002 with the intention of training doctors . It has since expanded into other fields . Another tertiary institution in the city is the <unk> University of Science and Technology . The Turkish <unk> School was also established , with the Mogadishu Polytechnic Institute and Shabelle University campus likewise undergoing renovations . Additionally , a New Islamic University campus is being built . In April 2014 , Prime Minister <unk> Sheikh Ahmed also laid the foundation stone for the reconstruction of the former meteorological school in Mogadishu . A new national Aviation Training Academy is likewise being built at the Aden Adde International Airport . 
- Other tertiary institutions in the capital include City University . It was established in 2012 with the aim of providing quality instruction and research . The college is staffed by an accredited and experienced master 's @-@ level faculty , and governed by a Board of Trustees consisting of academics and prominent entrepreneurs . City University 's <unk> features an advanced curriculum and foundation programs in English . Its campus includes physical and digital libraries , as well as IT and scientific laboratories . The university is a member of the Somali Research and Educational Network , and is authorized as a degree granting institution by the national Ministry of Education Directorate of Higher Education and Culture . 
- 
- = = Sport = = 
- 
- Mogadishu Stadium was constructed in 1978 during the Barre administration , with the assistance of Chinese engineers . The facility was mainly used for hosting sporting activities , such as the Somalia Cup and for football matches with teams from the Somalia League . Presidential addresses and political rallies , among other events , were also held there . In September 2013 , the Somali federal government and its Chinese counterpart signed an official cooperation agreement in Mogadishu as part of a five @-@ year national recovery plan in Somalia . The pact will see the Chinese authorities reconstruct several major infrastructural landmarks , including the Mogadishu Stadium . 
- The Banadir Stadium and <unk> Stadium are two other major sporting facilities in the capital . In 2013 , the Somali Football Federation launched a renovation project at the <unk> facility , during which artificial football turf contributed by FIFA was installed at the stadium . The <unk> @-@ <unk> basketball stadium in the <unk> District also underwent a $ 10 @,@ 000 rehabilitation , with funding provided by the local <unk> Telecom firm . Additionally , the municipal authority oversaw the reconstruction of the Banadir Stadium . 
- Various national sporting bodies also have their headquarters in Mogadishu . Among these are the Somali Football Federation , Somali Olympic Committee and Somali Basketball Federation . The Somali <unk> and <unk> Federation is likewise centered in the city , and manages the national <unk> team . 
- 
- = = Transportation = = 
- 
- 
- = = = Road = = = 
- 
- Roads leading out of Mogadishu connect the city to other localities in Somalia as well as to neighbouring countries . The capital itself is cut into several grid layouts by an extensive road network , with streets supporting the flow of both <unk> and pedestrian traffic . In October 2013 , major construction began on the 23 kilometer road leading to the airport . <unk> by Somali and Turkish engineers , the upgrade was completed in November and included lane demarcation . The road construction initiative was part of a larger agreement signed by the Somali and Turkish governments to establish Mogadishu and Istanbul as sister cities , and in the process bring all of Mogadishu 's roads up to modern standards . Following the treaty , the Turkish International Cooperation and Development Agency ( <unk> ) launched a <unk> cleaning project in conjunction with the municipal cleaning department . The initiative saw around 100 rubbish collection vehicles and other equipment operated by <unk> clean the city 's roads , with the Benadir municipality taking over operation of the cleaning project in March 2015 . 
- In 2012 – 2013 , Mogadishu 's municipal authority in conjunction with the British and Norwegian governments began a project to install solar @-@ powered street lights on all of the capital 's major roads . With equipment imported from Norway , the initiative cost around $ 140 @,@ 000 and lasted several months . The solar panels have helped to improve night @-@ time visibility and enhance the city 's overall aesthetic appeal . 
- <unk> are the most common type of public transportation in Mogadishu . The next most frequently used public vehicles in the city are auto rickshaws ( <unk> ) . They number around 3 @,@ 000 units and come in various designs . The auto rickshaws represent a lower cost alternative to taxis and <unk> , typically charging half the price for the same distance , with flexible rates . Due to their <unk> , capacity to negotiate narrow lanes and low fuel consumption , the three @-@ wheeled vehicles are often appealing investment opportunities for small @-@ scale entrepreneurs . They are generally preferred for shorter <unk> . In June 2013 , two new taxi companies also started offering road transportation to residents . Part of a fleet of over 100 vehicles , Mogadishu Taxi 's trademark yellow <unk> offer rides throughout the city at flat rates of $ 5 . City Taxi , the firm 's nearest competitor , charges the same flat rate , with plans to add new <unk> to its fleet . 
- In January 2014 , the Benadir administration launched a city @-@ wide street naming , house numbering and postal codes project . Officially called the House Numbering and Post Code System , it is a joint initiative of the municipal authorities and Somali business community representatives . The project is part of the ongoing modernization and development of the capital . According to former Mayor Mohamed Ahmed Nur , the initiative also aims to help the authorities firm up on security and resolve housing ownership disputes . In March 2015 , the Benadir administration likewise launched a renovation project on the <unk> <unk> @-@ <unk> major road in Mogadishu . The government @-@ public partnership aims to facilitate vehicle access in the area . According to <unk> district commissioner Ahmed Hassan <unk> <unk> , the reconstruction initiative will also make the road all @-@ weather resistant and is slated to be completed shortly . 
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- = = = Air = = = 
- 
- During the post @-@ independence period , Mogadishu International Airport offered flights to numerous global destinations . In the mid @-@ 1960s , the airport was enlarged to accommodate more international carriers , with the state @-@ owned Somali Airlines providing regular trips to all major cities . By 1969 , the airport 's many landing grounds could also host small jets and DC 6B @-@ type aircraft . 
- The facility grew considerably in size in the post @-@ independence period after successive renovation projects . With the outbreak of the civil war in the early 1990s , Mogadishu International Airport 's flight services experienced routine disruptions and its grounds and equipment were largely destroyed . In the late 2000s , the <unk> Airport , situated 50 kilometers to the south , served as the capital 's main airport while Mogadishu International Airport , now renamed Aden Adde International Airport , briefly shut down . However , in late 2010 , the security situation in Mogadishu had significantly improved , with the federal government eventually managing to assume full control of the city by August 2011 . 
- In May 2011 , the Ministry of Transport announced that SKA @-@ Somalia had been contracted to manage operations at the re @-@ opened Aden Adde International Airport over a period of ten years . Among its first initiatives , worth an estimated $ 6 million , SKA invested in new airport equipment and expanded support services by hiring , training and <unk> 200 local workers to meet international airport standards . The company also assisted in comprehensive infrastructure renovations , restored a dependable supply of electricity , revamped the baggage handling facilities as well as the arrival and departure lounges , put into place electronic check @-@ in systems , and <unk> up on security and work @-@ flow . Additionally , SKA connected the grounds ' Somali Civil Aviation and Meteorological Agency ( <unk> ) and immigration , customs , commercial airlines and Somali Police Force officials to the internet . By January 2013 , the firm had introduced <unk> buses to ferry travelers to and from the passenger terminal . 
- In December 2011 , the Turkish government unveiled plans to further modernize the airport as part of Turkey 's broader engagement in the local post @-@ conflict reconstruction process . Among the scheduled renovations were new systems and infrastructure , including a modern control tower to monitor the airspace . In September 2013 , the Turkish company <unk> LLC began operations at the airport . The firm announced plans to renovate the aviation building and construct a new one , as well as upgrade other modern service structures . A $ 10 million project , it will increase the airport 's existing 15 aircraft capacity to 60 . In January 2015 , a new , state @-@ of @-@ the @-@ art terminal was opened at the airport . Featuring modern passenger facilities and a glass façade , it will enable the airport to double its number of daily commercial flights to 60 , with a <unk> of around 1 @,@ 000 passengers per hour . 
- As of January 2015 , the largest airline services using Aden Adde International Airport include the Somali @-@ owned private carriers Jubba Airways , <unk> Airlines , and African Express Airways , in addition to UN charter planes , Turkish Airlines , and Felix Airways ( Al <unk> Airlines ) . The airport also offers flights to other cities in Somalia , such as <unk> , <unk> and <unk> , as well as to international destinations like Djibouti , <unk> , and Istanbul . 
- In July 2012 , Mohammed <unk> Ali ( <unk> @-@ <unk> ) , the General Director of the Ministry of Aviation and Transport , also announced that the Somali government had begun preparations to revive the Mogadishu @-@ based national carrier , Somali Airlines . The first new aircraft were scheduled for delivery in December 2013 . 
- 
- = = = Sea = = = 
- 
- The Port of Mogadishu , also known as the Mogadishu International Port , is the official seaport of Mogadishu . <unk> as a major class port , it is the largest harbour in the country . 
- After <unk> some damage during the civil war , the federal government launched the Mogadishu Port <unk> Project , an initiative to rebuild , develop and modernize the port . The renovations included the installation of Alpha <unk> technology . A joint international delegation consisting of the Director of the Port of Djibouti and Chinese officials specializing in infrastructure reconstruction concurrently visited the facility in June 2013 . According to Mogadishu Port manager Abdullahi Ali Nur , the delegates along with local Somali officials received reports on the port 's functions as part of the rebuilding project 's planning stages . 
- In 2013 , the Port of Mogadishu 's management reportedly reached an agreement with representatives of the Iranian company <unk> Shipping LLC to handle vital operations at the seaport . Under the name Mogadishu Port <unk> Terminal , the firm is slated to handle all of the port 's technical and operational functions . 
- In October 2013 , the federal Cabinet endorsed an agreement with the Turkish firm Al @-@ Bayrak to manage the Port of Mogadishu for a 20 @-@ year period . The deal was secured by the Ministry of Ports and Public Works , and also assigns Al @-@ Bayrak responsibility for rebuilding and <unk> the seaport . In September 2014 , the federal government officially delegated management of the Mogadishu Port to Al @-@ Bayrak . The firm 's modernization project will cost $ 80 million . 
- 
- = = = Railway = = = 
- 
- There were projects during the 1980s to <unk> the 114 km ( 71 mi ) railway between Mogadishu and Jowhar , built by the Italians in 1926 but dismantled in World War II by British troops . It was originally intended that this railway would reach <unk> <unk> . Only a few remaining tracks inside Mogadishu 's harbour area are still used . 
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- = = Media = = 
- 
- Mogadishu has historically served as a media hub . In 1975 , the Somali Film Agency ( <unk> ) , the nation 's film regulatory body , was established in Mogadishu . The <unk> also organized the annual Mogadishu Pan @-@ African and Arab Film Symposium ( <unk> ) , which brought together an array of prominent filmmakers and movie experts from across the globe , including other parts of Northeast Africa and the Arab world , as well as Asia and Europe . 
- In addition , there are a number of radio news agencies based in Mogadishu . Radio Mogadishu is the federal government @-@ run public broadcaster . Established in 1951 in Italian Somaliland , it initially aired news items in both Somali and Italian . The station was modernized with Russian assistance following independence in 1960 , and began offering home service in Somali , <unk> and <unk> . After closing down operations in the early 1990s due to the civil war , the broadcaster was officially re @-@ opened in the early 2000s by the Transitional National Government . Other radio stations headquartered in the city include <unk> Radio , Radio Shabelle , Radio Bar @-@ <unk> , Radio <unk> , Radio <unk> , Radio <unk> , Radio Banadir , Radio <unk> , <unk> FM , Radio <unk> , and Radio Xamar , also known as Voice of Democracy . 
- The Mogadishu @-@ based Somali National Television ( <unk> ) is the central government @-@ owned broadcaster . On 4 April 2011 , the Ministry of Information of the Transitional Federal Government officially re @-@ launched the station as part of an initiative to develop the national telecommunications sector . <unk> broadcasts 24 hours a day , and can be viewed both within Somalia and abroad via terrestrial and satellite platforms . 
- Somali popular music enjoys a large audience in Mogadishu , and was widely sold prior to the civil war . With the government managing to secure the city in mid @-@ 2011 , radios once again play music . On 19 March 2012 , an open concert was held in the city , which was broadcast live on local television . In April 2013 , the <unk> <unk> ensemble also organized the <unk> Music Festival , the first international music festival to be held in Mogadishu in two decades . 
- 
- = = Notable <unk> = = 
- 
- 
- = = Twin towns – Sister cities = = 
- 
- Mogadishu is twinned with : 
- 
- 
- = The Moth ( Lost ) = 
- 
- " The Moth " is the seventh episode of the first season of Lost . The episode was directed by Jack <unk> and written by Jennifer Johnson and Paul <unk> . It first aired on November 3 , 2004 , on ABC . The character of Charlie Pace ( Dominic Monaghan ) is featured in the episode 's flashbacks . 
- Charlie begins experiencing the effects of heroin withdrawal , while Jack becomes trapped in a cave . Meanwhile , Sayid Jarrah ( Naveen Andrews ) , Kate Austen ( Evangeline Lilly ) and Boone Carlyle ( Ian <unk> ) attempt to triangulate the signal of the French woman 's broadcast . Charlie 's involvement in his band Drive Shaft is featured in the episode 's flashbacks . 
- " The Moth " was watched by 18 @.@ 73 million people live , almost two million more than the previous episode . This episode marks the first time " You All Everybody " by Drive Shaft was sung in its entirety , as it had not been written yet in previous episodes . The episode received mixed @-@ to @-@ positive reviews , with some reviewers commenting that the episode was not of the same caliber as previous episodes . 
- 
- = = Plot = = 
- 
- 
- = = = <unk> = = = 
- 
- Charlie ( Dominic Monaghan ) tells a priest that he is going to quit his band because it has a bad influence on him . Soon after , his brother , Liam Pace ( Neil Hopkins ) , tells Charlie that Drive Shaft has gotten a recording contract . Charlie doesn 't want to sign the contract because he has qualms about the sex and drugs the band engages in . Liam talks him into signing , promising that Charlie can quit any time he 's had enough . One evening at a show , Liam , to Charlie 's frustration , starts singing the chorus to " You All Everybody " , which is supposed to be sung by Charlie . Liam assures Charlie it won 't happen again . 
- Later , Charlie finds Liam high on heroin with <unk> . Charlie kicks the <unk> out , and tells Liam that he 's done with the band . Liam says to Charlie that he , Liam , is Drive Shaft , and that nobody knows who the bass player ( Charlie ) is . He goes on to tell Charlie that without the band , Charlie is nothing , which spurs Charlie to use heroin for the first time . Years later , Charlie visits Liam 's house in Australia and wants Liam to rejoin Drive Shaft for a comeback tour . Liam declines , but the band can 't do the tour without him . He criticizes Charlie for still using drugs , and Charlie blames Liam for getting him started with drugs . Liam asks Charlie to stay with him for a few weeks , saying that Sydney has some good rehab programs and that he can get Charlie help . Charlie angrily leaves , saying he has a plane to catch . 
- 
- = = = On the Island = = = 
- 
- It is Day 8 , September 29 , 2004 , and Charlie is suffering from heroin withdrawal since he voluntarily gave his heroin to John Locke ( Terry O <unk> ) in the previous episode . After finding him , Charlie asks Locke for the heroin back , and Locke says that he 'll give Charlie the drugs the third time he asks , because he wants Charlie to have the choice to quit . 
- Sayid Jarrah ( Naveen Andrews ) , Kate Austen ( Evangeline Lilly ) and Boone Carlyle ( Ian <unk> ) attempt to find where the French transmission is coming from , so they make a plan to turn on <unk> at different points on the island in an attempt to triangulate the signal . At the caves , Charlie searches through Jack <unk> 's ( Matthew Fox ) medicine supply for something to ease his heroin withdrawal . When Jack catches him , Charlie says he has a headache and wants <unk> . 
- When Jack upsets Charlie by telling him to move his guitar , Charlie angrily shouts at him , causing the entrance of the cave they are in to collapse . Charlie manages to escape , but Jack is trapped inside . Using his construction experience , Michael Dawson ( Harold <unk> ) leads the rescue attempt with Steve Jenkins ( Christian Bowman ) and Scott Jackson ( Dustin <unk> ) . In the jungle , James " Sawyer " Ford ( Josh Holloway ) goes to tell Kate ( who is traveling to set up her antenna for the triangulation attempt ) about Jack 's predicament , but decides against it because he doesn 't like Kate 's hostile attitude . Charlie tells Locke about Jack 's situation , but reveals the real reason for his going to Locke is to ask for his drugs a second time . Locke shows Charlie a moth cocoon , and explains that he could help the moth by slitting the cocoon and letting the moth free , but it would not survive because it would be too weak . Instead , the moth needs to struggle to break free . Nature and struggle make people stronger , Locke says , indicating to Charlie that he needs to fight through his suffering . 
- Kate and Sawyer stay at the second triangulation point , while Sayid goes to the third . After learning of Jack 's situation from Sawyer , Kate goes to help , leaving the job of turning on the signal to Sawyer . Charlie <unk> through an opening at the cave and finds Jack , but while doing so , the opening collapses and traps Charlie and Jack inside . Charlie <unk> <unk> Jack 's shoulder back into place at Jack 's request . Jack correctly <unk> that Charlie is suffering from withdrawal , and Charlie assures Jack he 's okay . Kate desperately tries to dig them out along with the other <unk> , while the two worry about losing oxygen . Charlie sees a moth which leads him to an opening and the pair dig out of the cave . 
- Sayid sets off his bottle rocket to signal the antenna power up process . Shannon Rutherford ( Maggie Grace ) sets off Boone 's rocket from the beach , as Boone had gone to help rescue Jack , as does Sawyer from his location . Sayid turns on the <unk> , but before he is able to triangulate the signal , an unseen person knocks Sayid unconscious with a stick . 
- Later Hurley brings Jack and Charlie water , he notes Charlie doesn 't look well but Jack covers for him saying that he has the flu . Hurley tells him to get better and Charlie looks <unk> to Jack . He sees Locke and asks him for his heroin and Locke gives it back . Charlie looks at it for a moment before <unk> it into the fire , smiling . Locke tells Charlie he 's proud of him and that he always knew he could do it . Charlie and Locke see a moth flying away . 
- 
- = = Production = = 
- 
- " The Moth " aired on November 3 , 2004 . The episode was directed by Jack <unk> and written by Jennifer Johnson and Paul <unk> . The church scene with Charlie and Liam at the church in Manchester was actually filmed on Honolulu , where the scenes on the Island are filmed . Monaghan says that he feels Charlie 's black <unk> is a sort of " security blanket " that he hides underneath and uses it when he feels " lost . " The " heroin " that Charlie is supposedly <unk> is actually brown sugar . Monaghan revealed this in an interview with <unk> magazine in October 2005 , saying , " [ The heroin is ] brown sugar . You get some really sweet <unk> . You have to be careful not to <unk> too much of it , but it 's happened a few times . You just find <unk> up your nose couple of hours later . " 
- According to Monaghan , Drive Shaft is similar to Oasis , in that " [ Charlie 's ] first album was kind of like Oasis 's first album : critically acclaimed , didn 't sell big numbers , but in the industry , people gave it respect . " <unk> Abrams and Damon <unk> told Monaghan that if he wanted to write the song that would be Charlie 's " one hit wonder " , they would consider using it in the show . Monaghan wrote a song called " <unk> and Plans " with a friend ; Abrams and Lindelof liked the song , but it did not make it into Lost . The lyrics were inspired by an incident on The Phil <unk> Show , where Matt <unk> , who was close friends with producers Bryan <unk> and Damon Lindelof , saw a female audience member <unk> out , " You all everybody , acting like it 's the stupid people wearing expensive clothes . " The producers had an inside joke where they would say this quotation to each other . According to <unk> , " At one point , in a delirious <unk> , we realized we had said it so many times that that had to be the song . " 
- In the Pilot , Monaghan 's voice when he sang " You All Everybody " was based on " when Prince puts on his female voice " , since the song had not been composed yet . For " The Moth " the producers contacted Los Angeles @-@ based singer Jude to write a full version . The song is sung by Chris <unk> , former lead singer of Gods Child and Joe 90 . 
- 
- = = Reception = = 
- 
- " The Moth " first aired in the United States on November 3 , 2004 . 18 @.@ 73 million people in America watched the episode live . 
- The episode received generally mixed @-@ to @-@ positive reviews . Ryan McGee of <unk> wrote that " this episode wasn 't a <unk> by any measure , but after the run of early episodes , this is the first that really didn 't hold its own when compared to the others " , adding that " The moth imagery / metaphor just beats you down by episode 's end , making you long for the more subtle writing the show has produced up until this point . " The TV Critic gave the episode a 68 / 100 , writing that it falls between being " obvious and cheesy " and " beautifully paced and structured " . Myles <unk> of The A.V. Club gave the episode a B , and shared his opinion that it is " one of the more intensely metaphorical — and thus <unk> — episodes of the series . The eponymous metaphor — delivered by Locke — is one of the series ’ most blatant , and the moth ’ s consistent reappearance in the episode calls a lot of attention to the construction of the narrative . " 
- IGN staff rated " The Moth " as the 36th best episode in all of Lost , explaining , " The moth of the episode 's title appears both as a metaphorical and physical symbol of the struggle to find strength within yourself , as Locke explains to Charlie , and to take charge of your own life without relying on anyone else to do it for you . " In a review focusing solely on " The Moth " , Chris Carabott of IGN gave the episode a 9 / 10 , writing that the episode " does a great job of utilizing screen time for most the major characters on the show " , and that " Locke 's analogy of ' The Moth ' in comparison to Charlie 's situation perfectly encapsulates what many of the characters are going through on the island . " In a ranking of all of Lost 's episodes , Todd VanDerWerff of the Los Angeles Times placed " The Moth " at number 102 , complimenting Monaghan and O <unk> 's acting , but criticizing the storyline for containing " nonsensical drug abuse storytelling . " " The Moth " , along with " Pilot " and " House of the Rising Sun " , won a <unk> Award for Charlie 's drug storyline . 
- 
- 
- = Charmbracelet = 
- 
- Charmbracelet is the ninth studio album by American singer and songwriter Mariah Carey , released on December 3 , 2002 through MonarC Entertainment and Island Records . The album was her first release since her breakdown following the release of her film Glitter ( 2001 ) and its accompanying soundtrack album , both of which were critical and commercial failures from the previous year . Critics described Charmbracelet as one of her most personal records , following 1997 's Butterfly , Throughout the project , Carey collaborated with several songwriters and producers , including Jermaine Dupri , Jimmy Jam and Terry Lewis , 7 Aurelius and Dre & Vidal . 
- According to Carey , love is the album 's main theme , and the songs combine introspective and personal themes with celebration and fun . The album contains a mixture of pop ballads and R & B beats , and the songs incorporate elements of other genres , such as gospel and soul . Compared to Glitter , which featured a variety of sampled melodies from the 1980s , Charmbracelet has a softer hip hop and R & B sound to it . Cam 'ron , Jay @-@ Z and Freeway also appear on the album . 
- Charmbracelet debuted at number three on the US Billboard 200 chart , and sold 241 @,@ 000 copies in its first week . The album peaked within the top 40 in seven countries , and attained top ten positions in Japan and Switzerland . 
- Three singles were released to promote the album . The lead single , " Through the Rain " reached the top ten in Canada , Switzerland , Sweden and Italy and the United Kingdom . In the US , it topped the Hot Dance Club Play chart , but stalled at number 81 on the Billboard Hot 100 . Carey embarked on the Charmbracelet World Tour , and performed 69 shows in over eight months . She also performed on televised shows and promotional tours like the 30th annual American Music Awards , Today and The Oprah Winfrey Show . 
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- = = Background = = 
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- Before the release of Charmbracelet , Carey experienced a year of critical , commercial , personal and professional struggles , following the poor reception of her debut film Glitter ( 2001 ) , and her subsequent hospitalization . After <unk> her husband , Tommy Mottola , Carey released Butterfly ( 1997 ) . With her next release , Rainbow ( 1999 ) , Carey incorporated elements of R & B and hip @-@ hop into her music , particularly on the lead single " <unk> " . According to The <unk> Bee , she attempted to sound more " <unk> " . She stopped working with longtime pop producers such as Babyface and Walter Afanasieff , in order to pursue a new sound and audience , and worked with writers Sean Combs and Jermaine Dupri . Following the worldwide success of Rainbow , Carey left Columbia Records . <unk> , Mottola and executive Benny <unk> in 1999 used several songs Carey had written and co @-@ written for Jennifer Lopez . Carey 's 2001 film debut Glitter was panned by movie critics , and earned less than eight million dollars at the box office . 
- Carey 's $ 100 million recording contract was bought out by Virgin Records , who paid her $ 28 million to leave the label . Carey checked into a hospital in Connecticut , following a controversial appearance on Total Request Live , in which she gave ice cream to fans , left <unk> messages on her website and demonstrated what was considered by the media as " erratic behavior " . Carey said she had had an " emotional and physical breakdown . " After a fortnight 's hospitalization , Carey flew to Capri , Italy , where she stayed for five months and began writing and producing material for a new studio album about her recent troubles . She was signed by Island Records , and started her own imprint , MonarC Entertainment , for her intended " comeback " release , Charmbracelet . 
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- = = Development and recording = = 
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- Carey started writing songs for the album in early 2002 , before she signed the record deal . She decided to rest , traveled to Capri and moved into a recording studio where she could focus on writing and recording without distractions . Most of the album was recorded in Capri , although she traveled to Atlanta , New York and Philadelphia to record some tracks . That year , Carey claimed Charmbracelet to be the " most personal album " she had ever made . She worked with longtime collaborators Jermaine Dupri , Jimmy Jam and Terry Lewis and Randy Jackson and other songwriters and producers 7 Aurelius , Just Blaze , <unk> and Dre & Vidal . The opening track and the first track to be written for the album , " Through the Rain " , was written by Carey and Lionel Cole , was inspired Carey 's recent experiences , and was co @-@ produced by Jam and Lewis . It was released as the lead single from the album . 
- Jam , Lewis and Carey also worked " Yours " , which Jam said contains " probably one of the best hooks [ ever ] " , and likened it to one of trio 's previous collaborations , " Thank God I Found You " ( 2000 ) . Initially , the song was recorded as duet with pop singer Justin Timberlake . However , due to contractual complications , it was never released and the a solo version was featured on the album . Jam and Lewis produced two more songs , " Wedding Song " and " <unk> " — the latter featuring background vocals from Michael Jackson — which were not released on the album . 
- Carey decided to work with Just Blaze after she heard the song " Oh Boy " , which he produced for Cam 'ron . Just Blaze and Carey produced " Boy ( I Need You ) " , a remake of " Oh Boy " , and " You Got Me " . Carey said " Boy ( I Need You ) " was one of her favorites on the album . " You Got Me " features rap verses from Jay @-@ Z and Freeway , was noted by Carey as a " signature Just Blaze track " . Jay @-@ Z was in Capri on vacation , and went to the studio to hear the song and said that he wanted to contribute to it and added rap verses of his own . Dupri produced " The One " and " You Had Your Chance " . He said that they wanted to stick to the " same familiar sound " from his previous collaborations with Carey . Carey said " The One " was a personal song , which was about being hurt in past relationships and the uncertainty about forming new ones . Carey decided to experiment with a live band for the album . In April 2002 , she met 7 Aurelius and asked him to produce songs for the album . They flew to Nassau , Bahamas and recorded a mixture of mid @-@ tempo and up @-@ tempo tracks and ballads with a live band . 7 Aurelius said that Carey was " an amazing writer " and described the process of recording : 
- We did three or four songs in three or four days . The way we was doing it , I had [ a horn section ] down there along with me . We had the whole room set up with <unk> , some nice wine — [ it was ] a very good vibe . It was completely stripped down , like ' Mariah Carey Unplugged ' . She stripped herself down to her talent . She was really trusting of me and my vision , and I was trusting of who she was . 
- Randy Jackson contributed to four tracks on the album , and said it was " the most real and honest record she 's made . She didn 't care what anyone thought of the lyrics . They were only important to her . " Carey included a cover of Def Leppard 's song " Bringin ' On the Heartbreak " . During the photo shoot for Charmbracelet at Capri , Carey happened to listen to Def Leppard 's album Vault ( 1995 ) , which contains the song , and decided to cover it . In an interview with Billboard , Carey said that the song is " an example of her musical diversity " . Jackson also worked on " My Saving Grace " , which Carey said describes her thoughts about the writing , recording and mastering process . While working in Capri , Carey 's father became ill with cancer and she returned to New York to spend some time with him ; he died soon after . In his memory , Carey wrote and produced the song " Sunflowers for Alfred Roy " . Carey said that the song represents " his side of the family and is kind of hard to talk about . " The song proved to be " very emotional " for Carey , and she sang it only once in the studio . DJ <unk> also produced songs for the album , but none of them were included . 
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- = = Music and lyrics = = 
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- Carey attempted to make a musical comeback with Charmbracelet , which focused on bringing Carey back to her R & B and soul roots in an attempt to recapture her audience . Critics both praised and criticized the condition of Carey 's voice on the album ; many called the songs average , and felt that most lacked sufficient hooks . The album 's lead single , and Carey 's <unk> attempt at <unk> the ballads from the early years of her career , was " Through the Rain " , which was produced by Carey , and was described by one critic as " the sort of self @-@ help ballad Ms. Carey was singing a decade ago " . The songs on the album are a mixture of several genres . 
- Carey 's cover of " Bringin ' On the Heartbreak " , was recorded using live instrumentation , and was the album 's third single . It begins as a " piano @-@ driven slow jam " , which is followed by a " dramatic chord progression " after the second chorus , and Carey 's " precise and <unk> voice reaches incredible heights " as it " turns the power ballad into something more delicate . " Kelefa Sanneh from The New York Times called " Yours " " a <unk> combination of breathy vocals and <unk> rhythms . " Barry Walters from Rolling Stone wrote that on " Yours " , " Carey 's lead vocals blend into choruses of <unk> Mariah 's <unk> overlapping phrases . <unk> these are choirs of more <unk> singing harmonies and <unk> . Topping it off are generous <unk> of the singer 's patented <unk> , <unk> , <unk> and <unk> . " 
- Critics considered " <unk> <unk> " to be one of the album 's strongest songs because of its " well executed " jazz influence . The song begins with the sounds of people dining , then introduces the strong bassline and drums . Towards the end of the song , Carey belts out the climax . Sarah Rodman from The Boston Herald described it as fascinating and wrote , " it sounds as though Carey is singing in falsetto while still in her chest voice . " " Clown " drew strong media attention , and its lyrical content led critics to speculate that Carey aimed it at rapper Eminem , who had publicly announced that he had had a relationship with Carey . Rodman said " Clown " was " <unk> sinister " , with lyrics such as , " I should 've left it at ' I like your music too ' ... You should never have <unk> we were lovers / when you know very well we never even touched each other . " Critics compared " I Only Wanted " with " My All " ' s instrumentation and structure of verse , chorus and guitar solo . According to Sal Cinquemani from Slant Magazine , Carey makes vague allusions to her ex @-@ husband Tommy Mottola with the line , " Wish I 'd stayed beneath my veil " . The song uses Latin @-@ inspired guitar instrumentation and wind sounds as an additional backbone to the melody , and dripping water as its percussion . 
- " Sunflowers for Alfred Roy " , one of the album 's most personal songs , is named after Carey 's father ; she makes direct reference to him and a moment they shared at his death bed . The song is backed with a simple piano accompaniment , and Carey recounts a visit with her father in his hospital room : " Strange to feel that proud , strong man / Grip tightly to my hand . " 
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- = = Singles = = 
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- Three singles were released from the album . The lead single , " Through the Rain " was released on September 24 , 2002 . It received mixed reviews from critics , some of whom said it was too similar to her earlier ballads , such as " Hero " and " Outside " , while others praised Carey 's vocals in the song . It was one of Carey 's poorest @-@ selling US singles , reaching number 81 on the US Billboard Hot 100 chart . However , it topped the Hot Dance Club Play charts and reached the top twenty of the Adult Contemporary chart . Outside the US , the single performed moderately , peaking within the top ten in Canada , Switzerland , Sweden , Italy and the UK , and within the top 20 in Ireland , Australia , Norway and Denmark . The music video of " Through the Rain " , directed by <unk> Meyers , is based on the courtship and <unk> of Carey 's parents . Scenes of Carey singing in a street when rain starts to fall are juxtaposed with the story of a mixed @-@ race couple who run away from their families , who oppose their relationship . 
- " Boy ( I Need You ) " , which was released as the second single on November 26 , 2002 , received mixed reviews from critics . The single failed to make much impact on charts worldwide ; it reached number 68 on the US Billboard Hip @-@ Hop / R & B Songs chart and number 57 on the US Hot Singles Sales chart . Elsewhere , the song reached number 17 in the UK , and peaked within the top 40 in Australia , the Netherlands , Ireland and New Zealand . The music video for " Boy ( I Need You ) " was directed by Joseph Kahn and was filmed at <unk> and Los Angeles . Initially , " The One " was scheduled to be released as the second single and the music video was shot for that song . However , halfway through the filming , the single was changed to " Boy ( I Need You ) " . Described as " Speed Racer meets <unk> Kitty meets me and Cam 'ron " by Carey , the video incorporates elements of Japanese culture and features Carey 's alter @-@ ego <unk> . 
- The third single from the album was Carey 's cover version of " Bringin ' On the Heartbreak " , released on November 25 , 2003 . Though it gained mostly positive reviews , it failed to chart on the Billboard Hot 100 , but reached number five on the Hot Dance Club Play chart . Outside the US , the song saw its highest peak in Switzerland , reaching number 28 and staying on the charts for eight weeks . It also charted in Austria and the Wallonia region of Belgium . The music video for the song was directed by <unk> <unk> . Another cut from the album , " Irresistible ( <unk> Connection ) " charted at number 81 on the US Billboard Hip @-@ Hop / R & B Songs . 
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- = = Critical reception = = 
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- <unk> website Metacritic , which averages professional reviews into a numerical score , gave Charmbracelet a score of 43 / 100 , indicating " generally mixed or average reviews . " Stephen Thomas Erlewine from AllMusic rated the album two out of five stars , and criticized its production and the condition of Carey 's voice . He wrote , " Whenever she sings , there 's a <unk> whistle behind her thin voice and she strains to make notes throughout the record ... Her voice is damaged , and there 's not a moment where it sounds strong or inviting . " Tom Sinclair of Entertainment Weekly said she was " in fine voice " , He wrote that " Through the Rain " sinks in its own <unk> <unk> , as do by @-@ the @-@ numbers efforts like ' Yours ' and ' I Only Wanted ' " , and added that " ' Clown ' is a moody number graced with <unk> acoustic guitar and a gorgeously <unk> vocal , while ' Sunflowers for Alfred Roy ' is a short , sweet song sung to a lovely piano accompaniment " . He finished by saying that " too much of Charmbracelet is <unk> in middle @-@ of @-@ the @-@ road muck . " 
- Billboard editor Michael <unk> praised Carey 's return to her core audience . He said that although Carey might have alienated her hip @-@ hop followers from her previous three albums , her older fans from the 1990s would be more receptive to the material and her new image . Kelefa Sanneh from The New York Times wrote that the album " is generally pleasant , although it 's not always exciting , and a few of the collaborations go awry " . He called Carey 's voice " invariably astonishing " , and said that " she can hit high notes that barely sound human " , praised her versatility , and wrote that she " also knows how to make a hip @-@ hop hit by holding back and letting the beat shine . " <unk> Browne of New York slated the album 's whimsical <unk> and <unk> keyboards , and wrote , " Was Charmbracelet recorded in a <unk> shop ? This instrument needs to be stopped . " 
- <unk> Charmbracelet two out of five stars , Barry Walters from Rolling Stone wrote that none of the songs were bold , that the lack of hooks made the album weak , and said , " Carey needs bold songs that help her use the power and range for which she is famous . Charmbracelet is like a stream of watercolors that bleed into a puddle of brown . " Sal Cinquemani from Slant Magazine complimented Carey 's mixture of pop and hip @-@ hop melodies , and wrote , " Though there 's nothing as immediate as ' Fantasy ' or ' My All ' here , Charmbracelet is significantly less contrived than 1999 's Rainbow and almost as creatively liberating as Butterfly . British columnist Angus <unk> , writing for Yahoo ! Music UK called the songs on Charmbracelet forgettable , and wrote , " She used to take risks , but ' Charmbracelet ' is conservative , <unk> and <unk> ; and , while it 's understandable that simply to make another record marks a triumph of sorts , it 's impossible to admire Mariah to the degree that her talent ought to merit . " John <unk> from NME criticized its content , writing , " <unk> , ' Charmbracelet ' is R & B , much like Tony Blair is nominally a socialist ... <unk> , all told , have been worse " 
- At the 17th Japan Gold Disc Award in 2003 , the album was nominated in the category of Rock and Pop Album of the Year ( International ) . 
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- = = Commercial performance = = 
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- Charmbracelet was initially slated for release in the US on December 10 , 2002 . However , the date was revised to December 3 , 2002 . It was released through Island Records and Carey 's label MonarC Entertainment . A highly anticipated release , it debuted at number three on the US Billboard 200 , with first @-@ week sales of 241 @,@ 000 , more than the first @-@ week sales of the critically panned Glitter soundtrack , but fewer than 1999 's Rainbow , which sold 323 @,@ 000 units in its first week . It stayed on the chart for 22 weeks , Charmbracelet was certified Platinum by the Recording Industry Association of America ( RIAA ) for shipments of one million units in the US . As of April 2013 , the estimated sales of the album in the US ( compiled by Nielsen Soundscan ) were 1 @,@ 166 @,@ 000 copies . 
- In Canada , the album debuted on the Canadian Albums Chart at number thirty , in contrast to Glitter , which debuted at number four on the chart . It was certified Gold by the Canadian Recording Industry Association ( CRIA ) for shipments of 50 @,@ 000 copies . On the week dated December 15 , 2002 , <unk> entered the Australian Albums Chart at its peak position of number 42 . It exited the chart the next week , becoming one of Carey 's lowest charting albums in the country . In Japan , Charmbracelet debuted at number four on the Oricon Albums Chart , its second @-@ highest peak worldwide , and sold 63 @,@ 365 units in its first week . The album spent another week at number four , and sold 71 @,@ 206 units . It stayed on the charts for a total of 15 weeks and according to Oricon , has sold 240 @,@ 440 copies . The Recording Industry Association of Japan ( RIAJ ) certified Charmbracelet platinum for shipments of 200 @,@ 000 copies . 
- In Austria , the album peaked at number 34 and stayed on the charts for seven weeks . In the Flemish region of Belgium it charted and peaked at number 48 and reached number 28 in the <unk> region of that country . Charmbracelet entered the French Albums Chart at number 20 in the week dated December 7 , 2002 , spent 30 weeks on the chart and was certified Gold by the <unk> National de l <unk> <unk> ( <unk> ) , denoting shipments of 100 @,@ 000 units . Charmbracelet charted and peaked at number 32 in Germany It reached number 50 in Sweden . In Switzerland , the album peaked at number nine on the Swiss Albums Chart and stayed on the charts for ten weeks ; it was certified Gold by the International Federation of the Phonographic Industry ( IFPI ) . In the United Kingdom , the album peaked at number 52 , selling 19 @,@ 000 copies in its first week . It has sold a total of 122 @,@ 010 copies as of April 2008 . In February 2003 , it was certified Gold by the British Phonographic Industry ( BPI ) for shipments of 100 @,@ 000 copies in the UK . In the Netherlands , the album debuted at number 48 , the issue dated December 14 , 2002 . The following week , it peaked at number 30 . It stayed on the charts for 19 weeks , and made two re @-@ entries , one in June 2003 and other in August 2003 . 
- Charmbracelet was certified Gold in both Brazil and Hong Kong by <unk> <unk> dos <unk> de <unk> ( <unk> ) and IFPI Hong Kong respectively . 
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- = = Promotion = = 
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- Following the release of " Through the Rain " , Carey embarked on several US and international promotional tours in support of Charmbracelet and its accompanying singles . Promotion for " Through the Rain " began at the 2002 <unk> Awards , where Carey performed wearing a long black skirt and denim <unk> . Three days before the album 's US release , a one @-@ hour program titled Mariah Carey : Shining Through the Rain , in which Carey was interviewed and sang several songs from Charmbracelet and her back catalog , aired on MTV . Carey addressed rumors of her breakdown and its cause , and spoke about the album and its inspiration , and conducted a question and answer session with fans . During the first month after the album 's release , Carey appeared on several television talk shows . She launched her promotional tour on Today , where she performed four songs at Mall of America for a crowd of over 10 @,@ 000 . On December 2 , Carey traveled to Brazil for South American promotion of Charmbracelet , appearing on the popular television program <unk> . She sang " My All " , and performed " Through the Rain " and " I Only Wanted " wearing a long pink gown . On December 3 , 2002 , Carey appeared on The Oprah Winfrey Show , where she performed " Through the Rain " and " My Saving Grace " , and gave a highly publicized interview about her hospitalization . Before her breakdown , Carey had been booked for a private interview with ABC 's Barbara Walters , executive producer of The View , following Glitter 's release . Instead of giving Walters the full @-@ coverage interview following Carey 's return to the public eye , Island decided Oprah was more appropriate , and changed the appearance . Carey 's interview with Matt <unk> on Dateline NBC aired the same evening . 
- On December 17 , Carey performed " I Only Wanted " on The View after guest co @-@ hosting the program . One month later , Carey was one of the headlining performers at the 30th annual American Music Awards , held on January 13 , 2003 . She performed " Through the Rain " alongside a live gospel choir , and wore a long black evening gown . During the performance , images of newspaper headlines reporting Carey 's breakdown were projected on a large curtain behind her , with one reading , " When you fall down , you get back up . " Carey received a standing ovation . In mid @-@ February , Carey was the headline performer at the NBA all @-@ star game , which was Michael Jordan 's last game . She wore a long , purple , skin @-@ tight Washington <unk> 's dress , and performed " Boy ( I Need You ) " , " My Saving Grace " and " Hero " , which received a standing ovation and brought Jordan to tears . On March 1 , 2003 , Carey performed at the Soul Train Music Awards , sporting a retro @-@ curled hairstyle and wearing a <unk> evening gown . She performed " My Saving Grace " , and as at the American Music Awards , images of newspaper headlines and inspirational photographs were projected onto a large screen . Following the performance , Carey was awarded a lifetime achievement award for her contribution to music . Towards the end of March , Charmbracelet was released in Europe and Carey appeared on several television programs to promotion the album . She performed the album 's leading two singles on the British music chart show , Top of the Pops , and a similar set on The Graham Norton Show and Fame Academy . On the latter program , Carey was joined on stage by the show 's finalists , who sang the climax on " Through the Rain " alongside her . 
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- = = = Tour = = = 
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- To promote the album , Carey announced a world tour in April 2003 . As of 2003 , " Charmbracelet World Tour : An Intimate Evening with Mariah Carey " was her most extensive tour , lasting over eight months and performing sixty @-@ nine shows in venues worldwide . Before tickets went on sale in the US , venues were switched from large arenas to smaller , more intimate theater shows . According to Carey , the change was made in order to give fans a more intimate show , and something more Broadway @-@ influenced . She said , " It 's much more intimate so you 'll feel like you had an experience . You experience a night with me . " However , while smaller productions were booked for the US leg of the tour , Carey performed at stadia and arenas in Asia and Europe , and performed for a crowd of over 35 @,@ 000 in Manila , 50 @,@ 000 in Malaysia , and to over 70 @,@ 000 people in China . In the UK , it was Carey 's first tour to feature shows outside London ; she performed in Glasgow , Birmingham and Manchester . 
- " Charmbracelet World Tour : An Intimate Evening with Mariah Carey " garnered generally positive reviews from music critics and audiences , many of whom complimented the quality of Carey 's live vocals and the production of the shows . Fans were given the opportunity to request songs from Carey 's catalog , which added to its positive reception . At her concert in Manila , <unk> P. <unk> from Philippine Daily Inquirer wrote , " I didn 't expect her voice to be that crystal clear ! " He added , " After 15 songs , we couldn 't seem to get enough of Mariah — and we became a believer ! " . 
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- = = Re @-@ release = = 
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- While preparing for the Asian leg of the Charmbracelet World Tour , Carey announced that <unk> would be re @-@ released with four additional tracks on July 26 , 2003 , the first day of the North American leg of the tour . Carey included her duet with <unk> Rhymes , " I Know What You Want " , which was released as a single from his album , It Ain 't Safe No More ( 2002 ) , and became Carey 's highest charting song internationally since 2001 , reaching top five peaks in Australia , Canada , the Netherlands , Ireland , Italy , Switzerland , the US and the UK . In an interview with Carson Daly , Carey said , " The <unk> Rhymes duet ... has become so successful and we always said I would put it on my album as well . " The re @-@ release also included " There Goes My Heart " , " Got a Thing 4 You " featuring Da Brat and Elephant Man , and " The One ( So So Def Remix ) " featuring Bone Crusher . The re @-@ released version of the album charted for three weeks on the Oricon album chart in Japan , where it peaked at number 96 on the issue dated July 14 , 2003 . 
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- = = Track listing = = 
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- " Boy ( I Need You ) " contains samples of " Oh Boy " by Cam 'ron & " Call Me " by <unk> . 
- " You Had Your Chance " contains a sample of " I Want 'a Do Something <unk> To You " by Leon <unk> . 
- " Irresistible ( <unk> Connection ) " contains a sample of " You Know How We Do It " by Ice <unk> 
- " Bringin ' on the Heartbreak " is a cover of " Bringin ' on the Heartbreak " by Def Leppard . 
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- = = Credits and personnel = = 
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- Credits for Charmbracelet taken from the album 's liner notes . 
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- = = = Recording locations = = = 
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- Recording locations are adapted from Charmbracelet liner notes . 
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- = = Charts and certifications = = 
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- = Thomas Quiney = 
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- Thomas Quiney ( baptised 26 February <unk> - c . 1662 or 1663 ) was the husband of William Shakespeare 's daughter Judith Shakespeare , and a vintner and <unk> in Stratford @-@ upon @-@ Avon . Quiney held several municipal offices in the corporation of Stratford @-@ upon @-@ Avon , the highest being <unk> in 1621 and 1622 , but was also fined for various minor offences . 
- In 1616 , Quiney married Judith Shakespeare . The marriage took place during a season when a special licence was required by the church , and the couple had failed to obtain one , leading to Quiney 's brief excommunication . Quiney was also summoned before the <unk> Court fewer than two months after the wedding to answer charges of “ <unk> copulation ” with a Margaret Wheeler , who died in childbirth . Scholars believe that as a result of these events William Shakespeare altered his will to favour his other daughter , Susanna Hall , and excluded Quiney from his inheritance . 
- Judith and Thomas had three children : Shakespeare , Richard , and Thomas . Shakespeare Quiney died at six months of age , and neither Richard nor Thomas lived past 21 . The death of Judith 's last child led to legal wrangling over William Shakespeare 's will that lasted until 1652 . Scholars speculate that Thomas Quiney may have died in 1662 or 1663 when the burial records are incomplete . 
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- = = Birth and early life = = 
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- Thomas Quiney was born in Stratford @-@ upon @-@ Avon and baptised on 26 February <unk> in Holy Trinity Church . He was the son of Richard and Elizabeth Quiney . He had 10 siblings , among them a Richard Quiney who was a <unk> in London , Mary Quiney who later married Richard Watts , the vicar of <unk> , and Elizabeth Quiney who married William Chandler . There is no record of Thomas Quiney 's attendance at the local school , but he had sufficient education to write short passages in French , run a business , and hold several municipal offices in his life . 
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- = = Business and municipal offices = = 
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- Quiney was a vintner and dealt in tobacco . He held the lease to a house known as “ Atwood 's ” for the purpose of running a tavern , and later traded houses with his brother @-@ in @-@ law , William Chandler , for the larger house known as “ The Cage ” where he set up his vintner 's shop in the upper half . He is recorded as selling wine to the corporation of Stratford @-@ upon @-@ Avon as late as 1650 . 
- He was a man of some education , with knowledge of French and calligraphy . In signing his accounts for 1621 and 1622 as <unk> he decorated them with a couplet in French from a romance by <unk> de Saint @-@ <unk> . Quiney writes “ <unk> <unk> est <unk> qui pour <unk> sage , <unk> pour le mal d <unk> fait son <unk> ” but the original is “ <unk> <unk> qui pour <unk> sage , Du mal d <unk> fait son <unk> ” . The original translates into English as “ Happy is he who to become wise , serves his apprenticeship from other men 's troubles ” but Quiney 's version “ … is <unk> and without sense ” . 
- He was a well @-@ respected man in the borough , and was elected a <unk> and constable in Stratford @-@ upon @-@ Avon in 1617 . In 1621 and 1662 he was acting Chamberlain . In signing his accounts for 1622 – 3 , he did so " with <unk> ” , but the records show that the council voted them “ <unk> ” . Quiney did not attend this meeting , but he did attend the later meeting where the accounts were passed , so they appear to have needed further explanation . 
- Quiney 's reputation was slightly spotted ; he was fined for <unk> and for “ suffering <unk> to <unk> in his house ” , and was at one point in danger of prosecution for “ <unk> <unk> and <unk> wine ” . 
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- = = Marriage = = 
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- On 10 February 1616 , Thomas Quiney married Judith Shakespeare , William Shakespeare 's daughter , in Holy Trinity Church . The assistant vicar , Richard Watts , who later married Quiney 's sister Mary , probably <unk> . The wedding took place during the <unk> season , which was prohibited . In 1616 <unk> started on 23 January , <unk> Sunday , and ended on 7 April , the Sunday after Easter . The marriage therefore required a special licence , issued by the Bishop of Worcester , which the couple had failed to obtain . A Walter Wright of Stratford was cited for marrying without either <unk> or licence , so since Quiney was only cited for marrying without the required licence it is presumed that they had posted <unk> in church . The infraction was a minor one , apparently caused by the minister , as three other couples were also wed that February . Quiney was nevertheless summoned by Walter Nixon to appear before the Consistory court in Worcester . ( This same Walter Nixon was later involved in a Star Chamber case and was found guilty of forging signatures and taking <unk> ) . Quiney failed to appear by the required date . The register recorded the judgement , which was excommunication , on or about 12 March 1616 . It is unknown if Judith was also excommunicated , but in any case the punishment did not last long . In November of the same year they were back in church for the baptism of their firstborn child . 
- The marriage did not begin well : Quiney had recently impregnated another woman , Margaret Wheeler , who was to die in childbirth along with the child and was buried on 15 March 1616 . On 26 March 1616 , Quiney appeared before the <unk> Court , which dealt , among other things , with " <unk> and <unk> " . <unk> in open court to " <unk> copulation " with Margaret Wheeler , he submitted himself for correction . He was sentenced to open penance " in a white sheet ( according to custom ) " before the congregation on three Sundays . He also had to admit to his crime , this time wearing ordinary clothes , before the Minister of <unk> in Warwickshire . The first part of the sentence was <unk> , essentially letting him off with a five @-@ shilling fine to be given to the parish 's poor . Since <unk> only had a chapel , he was spared any public humiliation . 
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- = = Chapel Lane , Atwood 's and The Cage = = 
- 
- Where the <unk> lived after being married is unknown . Judith owned her father 's cottage on Chapel Lane , Stratford , while Thomas had held , since 1611 , the lease on a tavern called " Atwood 's " on High Street . The cottage later passed from Judith to her sister as part of the settlement in their father 's will . In July 1616 Thomas <unk> houses with his brother @-@ in @-@ law , William Chandler , moving his vintner 's shop to the upper half of a house at the corner of High Street and Bridge Street . Known as " The Cage " , it is the house traditionally associated with Judith Quiney . In the 20th century The Cage was for a time a <unk> restaurant before being turned into the Stratford Information Office . 
- The Cage provides further insight into why Shakespeare would not have trusted Judith 's husband . Around 1630 Quiney tried to sell the lease on the house but was prevented by his <unk> . In 1633 , to protect the interests of Judith and the children , the lease was signed over to the trust of : John Hall , Susanna 's husband ; Thomas Nash , the husband of Judith 's niece ; and Richard Watts , vicar of nearby <unk> , who was Quiney 's brother @-@ in @-@ law and who had <unk> at Thomas and Judith 's wedding . Eventually , in November 1652 , the lease to The Cage ended up in the hands of Thomas ' eldest brother , Richard Quiney , a <unk> in London . 
- 
- = = William Shakespeare 's last will and testament = = 
- 
- The <unk> beginnings of Judith 's marriage , in spite of her husband and his family being otherwise <unk> , has led to speculation that this was the cause for William Shakespeare 's hastily altered last will and testament . He first summoned his lawyer , Francis Collins , in January 1616 . On 25 March he made further alterations , probably because he was dying and because of his concerns about Quiney . In the first bequest of the will there had been a provision " <unk> my sonne in L [ <unk> ] " ; but " sonne in L [ <unk> ] " was then struck out , with Judith 's name inserted in its stead . To this daughter he bequeathed £ 100 " in discharge of her marriage <unk> " ; another £ 50 if she were to relinquish the Chapel Lane cottage ; and , if she or any of her children were still alive at the end of three years following the date of the will , a further £ 150 , of which she was to receive the interest but not the principal . This money was explicitly denied to Thomas Quiney unless he were to bestow on Judith lands of equal value . In a separate bequest , Judith was given " my broad silver gilt <unk> " . 
- Finally , for the bulk of his estate , which included his main house , " New Place " , his two houses on <unk> Street and various lands in and around Stratford , Shakespeare had set up an entail . His estate was bequeathed , in descending order of choice , to the following : 1 ) his daughter , Susanna Hall ; 2 ) upon Susanna 's death , " to the first sonne of her <unk> <unk> <unk> & to the <unk> Males of the <unk> of the <unk> first <unk> <unk> <unk> " ; 3 ) to Susanna 's second son and his male heirs ; 4 ) to Susanna 's third son and his male heirs ; 5 ) to Susanna 's " <unk> ... <unk> <unk> & <unk> <unk> " and their male heirs ; 6 ) to Elizabeth , Susanna and John Hall 's firstborn , and her male heirs ; 7 ) to Judith and her male heirs ; or 8 ) to whatever heirs the law would normally recognise . This elaborate entail is usually taken to indicate that Thomas Quiney was not to be entrusted with Shakespeare 's inheritance , although some have speculated that it may simply indicate that Susanna was the favoured child . 
- 
- = = Children = = 
- 
- Judith and Thomas Quiney had three children : Shakespeare ( baptised 23 November 1616 — buried 8 May 1617 ) ; Richard ( baptised 9 February 1618 — buried 6 February 1639 ) ; and Thomas ( baptised 23 January 1620 — buried 28 January 1639 ) . Shakespeare was named for his mother 's father . Richard 's name was common among the <unk> : his other grandfather and an uncle were both named Richard . 
- Shakespeare Quiney died at six months of age . Richard and Thomas Quiney were buried within a month of each other ; they were 19 and 21 years old , respectively . The deaths of all of Judith 's children brought on new legal consequences . The entail on her father 's inheritance led Susanna , along with her daughter and son @-@ in @-@ law , to make a settlement , by use of a rather elaborate legal device , for the inheritance of her own branch of the family . Legal wrangling continued for another thirteen years , until 1652 . 
- 
- = = Death = = 
- 
- Of Thomas Quiney 's fate the records show little . It is speculated that he may have died in 1662 or 1663 when the parish burial records are incomplete . He certainly had a nephew in London , who by this time held the lease to The Cage . 
- 
- 
- = Transit of Venus = 
- 
- A transit of Venus across the Sun takes place when the planet Venus passes directly between the Sun and a superior planet , becoming visible against ( and hence obscuring a small portion of ) the solar disk . During a transit , Venus can be seen from Earth as a small black disk moving across the face of the Sun . The duration of such transits is usually measured in hours ( the transit of 2012 lasted 6 hours and 40 minutes ) . A transit is similar to a solar eclipse by the Moon . While the diameter of Venus is more than 3 times that of the Moon , Venus appears smaller , and travels more slowly across the face of the Sun , because it is much farther away from Earth . 
- Transits of Venus are among the <unk> of predictable astronomical phenomena . They occur in a pattern that generally repeats every 243 years , with pairs of transits eight years apart separated by long gaps of 121 @.@ 5 years and 105 @.@ 5 years . The <unk> is a reflection of the fact that the orbital periods of Earth and Venus are close to 8 : 13 and 243 : 395 <unk> . 
- The last transit of Venus was on 5 and 6 June 2012 , and was the last Venus transit of the 21st century ; the prior transit took place on 8 June 2004 . The previous pair of transits were in December 1874 and December 1882 . The next transits of Venus will be on 10 – 11 December 2117 , and 8 December <unk> . 
- Venus transits are historically of great scientific importance as they were used to gain the first realistic estimates of the size of the Solar System . Observations of the 1639 transit , combined with the principle of parallax , provided an estimate of the distance between the Sun and the Earth that was more accurate than any other up to that time . The 2012 transit provided scientists with a number of other research opportunities , particularly in the refinement of techniques to be used in the search for exoplanets . 
- 
- = = <unk> = = 
- 
- Venus , with an orbit inclined by 3 @.@ 4 ° relative to the Earth 's , usually appears to pass under ( or over ) the Sun at inferior conjunction . A transit occurs when Venus reaches conjunction with the Sun at or near one of its nodes — the longitude where Venus passes through the Earth 's orbital plane ( the ecliptic ) — and appears to pass directly across the Sun . Although the inclination between these two orbital planes is only 3 @.@ 4 ° , Venus can be as far as 9 @.@ 6 ° from the Sun when viewed from the Earth at inferior conjunction . Since the angular diameter of the Sun is about half a degree , Venus may appear to pass above or below the Sun by more than 18 solar diameters during an ordinary conjunction . 
- <unk> of transits usually repeat every 243 years . After this period of time Venus and Earth have returned to very nearly the same point in their respective orbits . During the Earth 's 243 <unk> orbital periods , which total <unk> @.@ 3 days , Venus completes 395 <unk> orbital periods of 224 @.@ 701 days each , equal to <unk> @.@ 9 Earth days . This period of time corresponds to 152 <unk> periods of Venus . 
- The pattern of 105 @.@ 5 , 8 , 121 @.@ 5 and 8 years is not the only pattern that is possible within the 243 @-@ year cycle , because of the slight <unk> between the times when the Earth and Venus arrive at the point of conjunction . Prior to 1518 , the pattern of transits was 8 , 113 @.@ 5 and 121 @.@ 5 years , and the eight inter @-@ transit gaps before the AD <unk> transit were 121 @.@ 5 years apart . The current pattern will continue until <unk> , when it will be replaced by a pattern of 105 @.@ 5 , 129 @.@ 5 and 8 years . Thus , the 243 @-@ year cycle is relatively stable , but the number of transits and their timing within the cycle will vary over time . Since the 243 : 395 Earth : Venus <unk> is only approximate , there are different sequences of transits occurring 243 years apart , each extending for several thousand years , which are eventually replaced by other sequences . For instance , there is a series which ended in <unk> BC , and the series which includes 2117 only started in AD 1631 . 
- 
- = = History of observation = = 
- 
- 
- = = = Ancient history = = = 
- 
- Ancient Indian , Greek , Egyptian , Babylonian and Chinese observers knew of Venus and recorded the planet 's motions . The early Greek astronomers called Venus by two names — <unk> the evening star and <unk> the morning star . <unk> is credited with realizing they were the same planet . There is no evidence that any of these cultures knew of the transits . Venus was important to ancient American civilizations , in particular for the Maya , who called it <unk> <unk> , " the Great Star " or <unk> <unk> , " the Wasp Star " ; they embodied Venus in the form of the god <unk> ( also known as or related to <unk> and <unk> in other parts of Mexico ) . In the Dresden <unk> , the Maya charted Venus ' full cycle , but despite their precise knowledge of its course , there is no mention of a transit . However , it has been proposed that frescoes found at <unk> may contain a pictorial representation of the 12th or 13th century transits . 
- 
- = = = 1639 – first scientific observation = = = 
- 
- In 1627 , Johannes Kepler became the first person to predict a transit of Venus , by predicting the 1631 event . His methods were not sufficiently accurate to predict that the transit would not be visible in most of Europe , and as a consequence , nobody was able to use his prediction to observe the phenomenon . 
- The first recorded observation of a transit of Venus was made by Jeremiah Horrocks from his home at Carr House in Much <unk> , near Preston in England , on 4 December 1639 ( 24 November under the Julian calendar then in use in England ) . His friend , William <unk> , also observed this transit from <unk> , near Manchester . Kepler had predicted transits in 1631 and 1761 and a near miss in 1639 . Horrocks corrected Kepler 's calculation for the orbit of Venus , realized that transits of Venus would occur in pairs 8 years apart , and so predicted the transit of 1639 . Although he was uncertain of the exact time , he calculated that the transit was to begin at approximately 15 : 00 . Horrocks focused the image of the Sun through a simple telescope onto a piece of paper , where the image could be safely observed . After observing for most of the day , he was lucky to see the transit as clouds obscuring the Sun cleared at about 15 : 15 , just half an hour before sunset . Horrocks ' observations allowed him to make a well @-@ informed guess as to the size of Venus , as well as to make an estimate of the mean distance between the Earth and the Sun — the astronomical unit . He estimated that distance to be 59 @.@ 4 million miles ( 95 @.@ 6 <unk> , 0 @.@ 639 AU ) – about two thirds of the actual distance of 93 million miles ( 149 @.@ 6 million km ) , but a more accurate figure than any suggested up to that time . The observations were not published until 1661 , well after Horrocks 's death . 
- 
- = = = 1761 and 1769 = = = 
- 
- In 1663 Scottish mathematician James Gregory had suggested in his <unk> <unk> that observations of a transit of the planet Mercury , at widely spaced points on the surface of the Earth , could be used to calculate the solar parallax and hence the astronomical unit using triangulation . Aware of this , a young Edmond Halley made observations of such a transit on 28 October <unk> 1677 from Saint Helena but was disappointed to find that only Richard <unk> in Burnley , Lancashire had made another accurate observation of the event whilst <unk> , at Avignon , simply recorded that it had occurred . Halley was not satisfied that the resulting calculation of the solar parallax at 45 " was accurate . 
- In a paper published in 1691 , and a more refined one in <unk> , he proposed that more accurate calculations could be made using measurements of a transit of Venus , although the next such event was not due until 1761 . Halley died in 1742 , but in 1761 numerous expeditions were made to various parts of the world so that precise observations of the transit could be made in order to make the calculations as described by Halley — an early example of international scientific collaboration . This collaboration was , however , <unk> by competition , the British , for example , being spurred to action only after they heard of French plans from Joseph @-@ Nicolas <unk> . In an attempt to observe the first transit of the pair , astronomers from Britain , Austria and France traveled to destinations around the world , including Siberia , Norway , Newfoundland and Madagascar . Most managed to observe at least part of the transit , but successful observations were made in particular by Jeremiah Dixon and Charles Mason at the Cape of Good Hope . Less successful , at Saint Helena , were <unk> <unk> and Robert <unk> , although they put the voyage to good use by <unk> the lunar @-@ distance method of finding longitude . 
- The existence of an atmosphere on Venus was concluded by Mikhail Lomonosov on the basis of his observation of the transit of Venus of 1761 from the Imperial Academy of Sciences of St. Petersburg . He used a two @-@ lens <unk> <unk> and a weak solar filter ( smoked glass ) and reported seeing a bump or <unk> of light ( " Lomonosov 's arc " ) off the solar disc as Venus began to exit the Sun . Lomonosov attributed that effect to refraction of solar rays through an atmosphere ; he also reported the appearance of a <unk> around the part of Venus that had just entered the Sun 's disk during the initial phase of transit . In 2012 , <unk> and <unk> reported , based on knowing what Venus 's atmosphere would look like because of <unk> and Schneider 's observations of the 2004 transit of Venus , that what Lomonosov reported was not Venus 's atmosphere . To make a decisive test , a group of researchers carried out experimental reconstruction of Lomonosov 's discovery of <unk> atmosphere with antique <unk> during the transit of Venus on 5 – 6 June 2012 . They observed the " Lomonosov 's arc " and other <unk> effects due to Venus 's atmosphere and concluded that Lomonosov 's telescope was fully adequate to the task of detecting the arc of light around Venus off the Sun 's disc during <unk> or <unk> if proper experimental techniques as described by Lomonosov in his 1761 paper are employed . 
- For the 1769 transit , scientists traveled to Tahiti , Norway , and locations in North America including Canada , New England , and San José del Cabo ( Baja California , then under Spanish control ) ; . The Czech astronomer Christian Mayer was invited by Catherine the Great to observe the transit in Saint Petersburg with Anders Johan <unk> , while other members of Russian Academy of Sciences went to eight other locations in the Russian Empire , under the general coordination of <unk> <unk> . The Hungarian astronomer Maximilian Hell and his assistant <unk> <unk> traveled to <unk> , Norway , delegated by Christian VII of Denmark . William Wales and Joseph <unk> made their observation in Hudson Bay , Canada , for the Royal Society . Observations were made by a number of groups in the British colonies in America . In Philadelphia , the American Philosophical Society erected three temporary observatories and appointed a committee , of which David <unk> was the head . Observations were made by a group led by Dr. Benjamin West in Providence , Rhode <unk> and published in 1769 . The results of the various observations in the American colonies were printed in the first volume of the American Philosophical Society 's Transactions , published in 1771 . Comparing the North American observations , William Smith published in 1771 a best value of the solar parallax of 8 @.@ 48 to 8 @.@ 49 arc @-@ seconds , which corresponds to an Earth @-@ sun distance of <unk> times the Earth 's radius , about 3 % different from the correct value . 
- Observations were also made from Tahiti by James Cook and Charles Green at a location still known as " Point Venus " . This occurred on the first voyage of James Cook , after which Cook explored New Zealand and Australia . This was one of five expeditions organised by the Royal Society and the <unk> Royal <unk> <unk> . 
- Jean @-@ Baptiste <unk> d <unk> went to San José del Cabo in what was then New Spain to observe the transit with two Spanish astronomers ( Vicente de <unk> and Salvador de <unk> ) . For his trouble he died in an epidemic of yellow fever there shortly after completing his observations . Only 9 of 28 in the entire party returned home alive . 
- The unfortunate Guillaume Le Gentil spent eight years travelling in an attempt to observe either of the transits . His unsuccessful journey led to him losing his wife and possessions and being declared dead ( his efforts became the basis of the play Transit of Venus by Maureen Hunter ) . Under the influence of the Royal Society <unk> <unk> travelled to Istanbul , but arrived too late . 
- Unfortunately , it was impossible to time the exact moment of the start and end of the transit because of the phenomenon known as the " black drop effect " . This effect was long thought to be due to Venus ' thick atmosphere , and initially it was held to be the first real evidence that Venus had an atmosphere . However , recent studies demonstrate that it is an optical effect caused by the <unk> of the image of Venus by turbulence in the Earth 's atmosphere or imperfections in the viewing apparatus . 
- In 1771 , using the combined 1761 and 1769 transit data , the French astronomer Jérôme Lalande calculated the astronomical unit to have a value of 153 million kilometers ( ± 1 million km ) . The precision was less than had been hoped for because of the black drop effect , but still a considerable improvement on Horrocks ' calculations . 
- Maximilian Hell published the results of his expedition in 1770 , in Copenhagen . Based on the results of his own expedition , and of Wales and Cook , in 1772 he presented another calculation of the astronomical unit : 151 @.@ 7 million kilometers . Lalande queried the accuracy and authenticity of the Hell expedition , but later he retreated in an article of Journal des <unk> , in 1778 . 
- 
- = = = 1874 and 1882 = = = 
- 
- Transit observations in 1874 and 1882 allowed this value to be refined further . Three expeditions — from Germany , the United Kingdom and the United States — were sent to the Kerguelen Archipelago for the 1874 observations . The American astronomer Simon <unk> combined the data from the last four transits , and he arrived at a value of about 149 @.@ 59 million kilometers ( ± 0 @.@ 31 million kilometers ) . Modern techniques , such as the use of radio <unk> from space probes , and of radar measurements of the distances to planets and asteroids in the Solar System , have allowed a reasonably accurate value for the astronomical unit ( AU ) to be calculated to a precision of about ± 30 meters . As a result , the need for parallax calculations has been superseded . 
- 
- = = = 2004 and 2012 = = = 
- 
- A number of scientific organizations headed by the European Southern Observatory ( <unk> ) organized a network of amateur astronomers and students to measure Earth 's distance from the Sun during the transit . The participants ' observations allowed a calculation of the astronomical unit ( AU ) of 149 608 708 km ± 11 835 km which had only a 0 @.@ <unk> % difference to the accepted value . 
- There was a good deal of interest in the 2004 transit as scientists attempted to measure the pattern of light dimming as Venus blocked out some of the Sun 's light , in order to refine techniques that they hope to use in searching for extrasolar planets . Current methods of looking for planets orbiting other stars only work for a few cases : planets that are very large ( Jupiter @-@ like , not Earth @-@ like ) , whose gravity is strong enough to wobble the star sufficiently for us to detect changes in proper motion or <unk> shift changes in radial velocity ; Jupiter or Neptune sized planets very close to their parent star whose transit causes changes in the luminosity of the star ; or planets which pass in front of background stars with the planet @-@ parent star separation comparable to the Einstein ring and cause gravitational <unk> . Measuring light intensity during the course of a transit , as the planet blocks out some of the light , is potentially much more sensitive , and might be used to find smaller planets . However , extremely precise measurement is needed : for example , the transit of Venus causes the Sun 's light to drop by a mere 0 @.@ 001 magnitude , and the dimming produced by small extrasolar planets will be similarly tiny . 
- The 2012 transit provided scientists numerous research opportunities as well , in particular in regard to the study of exoplanets . Research of the 2012 Venus transit includes : 
- Measuring dips in a star 's brightness caused by a known planet transiting the Sun will help astronomers find exoplanets . Unlike the 2004 Venus transit , the 2012 transit occurred during an active phase of the 11 @-@ year activity cycle of the Sun , and it is likely to give astronomers practice in picking up a planet 's signal around a " spotty " variable star . 
- Measurements made of the apparent diameter of Venus during the transit , and comparison with its known diameter , will give scientists an idea of how to estimate <unk> sizes . 
- Observation made of the atmosphere of Venus simultaneously from Earth @-@ based telescopes and from the Venus Express gives scientists a better opportunity to understand the intermediate level of Venus ' atmosphere than is possible from either viewpoint alone . This will provide new information about the climate of the planet . 
- <unk> data taken of the well @-@ known atmosphere of Venus will be compared to studies of exoplanets whose atmospheres are thus far unknown . 
- The Hubble Space Telescope , which cannot be pointed directly at the Sun , used the Moon as a mirror to study the light that had passed through the atmosphere of Venus in order to determine its composition . This will help to show whether a similar technique could be used to study exoplanets . 
- 
- = = Past and future transits = = 
- 
- NASA maintains a catalog of Venus Transits covering the period 2000 BCE to 4000 CE . Currently , transits occur only in June or December ( see table ) and the occurrence of these events slowly drifts , becoming later in the year by about two days every 243 @-@ year cycle . Transits usually occur in pairs , on nearly the same date eight years apart . This is because the length of eight Earth years is almost the same as 13 years on Venus , so every eight years the planets are in roughly the same relative positions . This approximate conjunction usually results in a pair of transits , but it is not precise enough to produce a <unk> , since Venus arrives 22 hours earlier each time . The last transit not to be part of a pair was in <unk> . The next will be in <unk> ; in 2854 ( the second of the <unk> / 2854 pair ) , although Venus will just miss the Sun as seen from the Earth 's equator , a partial transit will be visible from some parts of the southern hemisphere . 
- Thus after 243 years the transits of Venus returns . The 1874 transit is a member of the 243 @-@ years cycle # 1 . The 1882 transit is a member of # 2 . The 2004 transit is a member of # 3 and the 2012 transit is a member of # 4 . The 2117 transit is a member of # 1 and so on . However , the ascending node ( December transits ) of the orbit of Venus moves backwards after each 243 years so the transit of 2854 is the last member of series # 3 instead of series # 1 . The descending node ( June transits ) moves forwards , so the transit of <unk> is the last member of # 2 . From <unk> till + 125 @,@ 000 only about ten series at both nodes each are needed for all the transits of Venus in this very long <unk> , because both nodes of the orbit of Venus moves back and forward in time . 
- Over longer periods of time , new series of transits will start and old series will end . Unlike the <unk> series for lunar <unk> , it is possible for a transit series to restart after a hiatus . The transit series also vary much more in length than the <unk> series . 
- 
- = = <unk> and simultaneous transits = = 
- 
- Sometimes Venus only <unk> the Sun during a transit . In this case it is possible that in some areas of the Earth a full transit can be seen while in other regions there is only a partial transit ( no second or third contact ) . The last transit of this type was on 6 December 1631 , and the next such transit will occur on 13 December <unk> . It is also possible that a transit of Venus can be seen in some parts of the world as a partial transit , while in others Venus misses the Sun . Such a transit last occurred on 19 November <unk> BC , and the next transit of this type will occur on 14 December 2854 . These effects occur due to parallax , since the size of the Earth <unk> different points of view with slightly different lines of sight to Venus and the Sun . It can be demonstrated by closing an eye and holding a finger in front of a smaller more distant object ; when you open the other eye and close the first , the finger will no longer be in front of the object . 
- The simultaneous occurrence of a transit of Mercury and a transit of Venus does occur , but extremely infrequently . Such an event last occurred on 22 September 373 @,@ 173 BC and will next occur on 26 July 69 @,@ 163 , and again on 29 March 224 @,@ 508 . The simultaneous occurrence of a solar eclipse and a transit of Venus is currently possible , but very rare . The next solar eclipse occurring during a transit of Venus will be on 5 April 15 @,@ 232 . The last time a solar eclipse occurred during a transit of Venus was on 1 November 15 @,@ <unk> BC . It could be noticed that the day after the <unk> transit of 3 June 1769 there was a total solar eclipse , which was visible in Northern America , Europe and Northern Asia . 
- 
- 
- = Ímar mac Arailt = 
- 
- Ímar mac Arailt ( died 1054 ) was an eleventh @-@ century ruler of the Kingdom of Dublin and perhaps the Kingdom of the Isles . He was the son of a man named Aralt , and appears to have been a grandson of Amlaíb Cuarán , King of Northumbria and Dublin . Such a relationship would have meant that Ímar was a member of the Uí <unk> , and that he was a nephew of Amlaíb Cuarán 's son , Sitriuc mac Amlaíb , King of Dublin , a man driven from Dublin by Echmarcach mac Ragnaill in 1036 . 
- Ímar 's reign in Dublin spanned at least eight years , from 1038 to <unk> . Although he began by seizing the kingship from Echmarcach in 1038 , he eventually lost it to him in <unk> . As king , Ímar is recorded to have overseen military operations throughout Ireland , and seems to have actively assisted the family of Iago ab <unk> ap <unk> , King of Gwynedd overseas in Wales . After Echmarcach 's final expulsion from Dublin 1052 , Ímar may well have been reinstalled as King of Dublin by Diarmait mac Maíl na mBó , King of Leinster . Whatever the case , Ímar died in 1054 . He may have been an ancestor or close kinsman of Gofraid <unk> , King of Dublin and the Isles , the progenitor of a family that ruled in the Isles until the mid thirteenth century . 
- 
- = = <unk> background = = 
- 
- Ímar was probably the son of Aralt mac Amlaíb ( died 999 ) , a man whose death at the Battle of Glenn <unk> is recorded by the Annals of Clonmacnoise , the Annals of the Four Masters , the Annals of Ulster , and <unk> <unk> . If this identification is correct , Ímar 's paternal grandfather would have been Amlaíb Cuarán , King of Northumbria and Dublin ( died 980 / <unk> ) , and a paternal uncle of Ímar would have been Sitriuc mac Amlaíb , King of Dublin ( died <unk> ) . 
- 
- = = <unk> for Dublin = = 
- 
- Ímar 's probable uncle , Sitriuc , ruled Dublin for almost fifty years between <unk> and 1036 . There is reason to suspect that the latter 's realm included Mann by the second or third decade of the eleventh century . His reign in Dublin was finally put to an end by Echmarcach mac Ragnaill ( died 1064 / 1065 ) , who drove Sitriuc from the coastal town and claimed the kingship for himself . Previously , Sitriuc seems to have been closely aligned with Knútr <unk> ( died 1035 ) , ruler of the kingdoms of England , Denmark , and Norway . Knútr 's apparent authority in the Irish Sea region , coupled with Sitriuc 's seemingly close connections with him , could account for the remarkable security enjoyed by Sitriuc during Knútr 's reign . It is possible that Echmarcach had been bound from taking action against Sitriuc whilst Knútr held power , and that the confusion caused by the latter 's death in 1035 enabled Echmarcach to exploit the situation and seize control of the Irish Sea region . Although there is no direct evidence that Echmarcach controlled Mann by this date , Sitriuc does not appear to have taken refuge on the island after his expulsion from Dublin . This seems to suggest that the island was outside Sitriuc 's possession , and may indicate that Mann had fallen into the hands of Echmarcach sometime before . In fact , it is possible that Echmarcach used the island to launch his takeover of Dublin . 
- Echmarcach 's hold on Dublin was short @-@ lived as the Annals of Tigernach records that Ímar replaced him as King of Dublin in 1038 . This annal @-@ entry has been interpreted to indicate that Ímar drove Echmarcach from the kingship . There is reason to suspect that Þórfinnr <unk> , Earl of Orkney ( died c . 1065 ) extended his presence into the Isles and the Irish Sea region at about this period . The evidence of Þórfinnr 's power in the Isles could suggest that he possessed an active interest in the ongoing struggle over the Dublin kingship . In fact , Þórfinnr 's predatory operations in the Irish Sea region may have contributed to Echmarcach 's loss of Dublin in 1038 . 
- It is <unk> that Ímar received some form of support from Knútr 's son and successor in Britain , Haraldr <unk> , King of England ( died <unk> ) . The latter was certainly in power when Ímar replaced Echmarcach , and an association between Ímar and Haraldr could explain why the Annals of Ulster reports the latter 's death two years later . Ímar 's reign lasted about eight years , and one of his first royal acts appears to have been the invasion of Rathlin Island within the year . The fact that he proceeded to campaign in the North Channel could indicate that Echmarcach had held power in this region before his acquisition of Mann and Dublin . 
- In 1044 , the Annals of Tigernach records that Ímar penetrated into the domain of the Uí <unk> <unk> <unk> and killed their chief . The annal @-@ entry also indicates that Ímar stormed the church of Armagh , and burned <unk> <unk> ( the " Shrine of Patrick " ) in the attack . The following year , he again invaded Rathlin Island , and his subsequent slaughter of three hundred noblemen of the Ulaid , including a certain heir apparent named <unk> Ua <unk> , is documented by the Annals of Clonmacnoise , the Annals of Inisfallen , the Annals of Tigernach , and the Annals of the Four Masters . This remarkable action may indicate that the Dubliners and Ulaid were battling for control of Rathlin Island . If so , it could be evidence that Ímar enjoyed the possession of Mann by this date . The domain of the Ulaid is certainly the closest Irish territory to Mann , and the control of the <unk> fleet could account for the <unk> 's ability to challenge the Ulaid . Whatever the case , within the year Niall mac <unk> , King of Ulaid ( died 1063 ) is recorded to have attacked Fine Gall — Dublin 's <unk> @-@ rich northern hinterland — in what may have been a retaliatory raid . 
- The following year , the Annals of Tigernach states that Echmarcach succeeded Ímar . The Annals of the Four Masters specifies that Ímar was driven from the kingship by Echmarcach , who was then elected king by the Dubliners . After this point in Ímar 's life , all that is known for certain is that he died in 1054 , as recorded by the Annals of Ulster and the Annals of Loch <unk> . Nevertheless , since these sources style Ímar in Gaelic <unk> Gall ( " king of the foreigners " ) , there may be evidence to suggest that , when Diarmait mac Maíl na mBó , King of Leinster ( died 1072 ) drove Echmarcach from Dublin in 1052 , Diarmait reinstalled Ímar as king . 
- After Ímar 's death , Diarmait appears to have appointed his own son , Murchad ( died 1070 ) , control of Dublin later that decade , as the Annals of the Four Masters <unk> him the title <unk> Gall , meaning " lord of the foreigners " in 1059 . In <unk> , Murchad invaded Mann and seems to have overthrown Echmarcach . Both father and son were dead by 1072 , and the Annals of Tigernach describes Diarmait on his death that year as King of the Isles ( <unk> <unk> Gall , literally " king of the <unk> of the foreigners " ) , a declaration which seems to indicate that , by the eleventh century at least , the kingship of the Isles was contingent upon control of Mann . 
- 
- = = Involvement in Wales = = 
- 
- The principal Welsh monarch during Ímar 's reign was Gruffudd ap <unk> ( died 1063 / 1064 ) . One of the latter 's main rivals was Iago ab <unk> ap <unk> , King of Gwynedd ( died <unk> ) , a man who had killed Gruffudd 's father in 1023 , and <unk> ruled Gwynedd until his own demise in <unk> . Gruffudd himself may have been responsible for Iago 's slaying , and certainly succeeded to the kingship of Gwynedd after his death . It was likely in the context of Iago 's fall and this resulting regime change that the latter 's son , Cynan ( fl . 1064 ) , fled overseas and sought refuge in Dublin . 
- According to Historia <unk> <unk> <unk> , the mother of Cynan 's son was <unk> <unk> Amlaíb , a paternal granddaughter of Sitriuc . Further revealed by this source is the fact that this woman 's father , Amlaíb mac Sitriuc , built and commanded a Welsh fortress called Castell Avloed . Although it is unknown how long the Dubliners possessed the fortress , in 1036 another son of Sitriuc was slain in Wales by an apparent kinsman , an event which could be evidence of a struggle for control of the site . Echmarcach 's aforesaid expulsion of Sitriuc from Dublin in the same year could in turn indicate that this exiled monarch sought refuge in Wales . 
- Despite the uncertainty of its specific location , Castell Avloed appears to have been situated in territory formerly controlled by Iago , and there is reason to suspect that — after Iago 's fall and Cynan 's flight — Ímar oversaw military actions against Gruffudd . Three years later , for example , <unk> y <unk> and the " B " and " C " versions of <unk> <unk> report that this Welsh king was captured by forces from Dublin . The episode is further elaborated upon by a sixteenth @-@ century text compiled by David Powell ( died <unk> ) and a seventeenth @-@ century text by compiled by James Ware . According to these <unk> late versions of events , Gruffudd was captured by the Dubliners in the context of them supporting the cause of Cynan . The accounts further state that Gruffudd managed to escape his captors when the Dubliners were <unk> by Welsh forces before they could return to Ireland . The evidence of Cynan <unk> with the Dubliners against Gruffudd suggests that , not only was Ímar personally involved as king , but that the Welsh fortress of Castell Avloed was still controlled by the Dubliners . 
- Another conflict that could have involved Ímar and the military forces of Dublin was Gruffudd 's final defeat of Hywel ab Edwin , King of <unk> ( died 1044 ) . According to <unk> y <unk> and the " B " version of <unk> <unk> this last stand of Hywel took place at the mouth of the River <unk> — perhaps in the vicinity of <unk> — and included Vikings from Ireland who supported Hywel 's cause . It is apparent that Gruffudd 's adversaries generally utilised foreign military support from Ireland 's Viking enclaves . Certainly , the Book of Llandaff declares that Gruffudd struggled against English , Irish , and Vikings during his career . 
- 
- = = <unk> figure = = 
- 
- Ímar may have been the father , uncle , or possibly even the brother of Gofraid <unk> , King of Dublin and the Isles ( died 1095 ) . In <unk> , the Annals of Tigernach reveals that Gofraid possessed the kingship of Dublin in an annal @-@ entry recording his patronym as " ... mac <unk> Arailt " . The Chronicle of Mann , on the <unk> , gives Gofraid 's patronym as " ... <unk> <unk> <unk> de Ysland " . Whilst the former source identifies Gofraid as the son of a man named Aralt ( Old Norse Haraldr ) , the latter identifies Gofraid as the paternal grandson of a man so named . 
- In the aforesaid record of the military actions conducted in 1044 , Ímar is merely named as the son of Aralt , a fact which could indicate that this was how he was known to his contemporaries . If correct , the patronym preserved by the Chronicle of Mann could merely be a <unk> form of this style . 
- The patronym given by the Chronicle of Mann states that Gofraid 's father was from " Ysland " , a place which could refer to either Iceland , Islay , or Ireland . Other than this passage , there is no evidence hinting of a connection between Gofraid and Iceland . The chronicle elsewhere states that Gofraid died on Islay , although the island 's name is rendered " Ile " in this case . If " Ysland " instead refers to Ireland , the spelling could be the result of influence from a source originating in England , or a source written in <unk> French . 
- 
- 
- = Tessa Noël = 
- 
- Tessa Noël is a fictional character in the television series Highlander : The Series , portrayed by Alexandra Vandernoot . A mortal artist and sculptor , Tessa is the French lover of the protagonist Immortal , Duncan MacLeod , played by Adrian Paul . Tessa is introduced in the pilot episode " The Gathering " , first shown in 1992 , and appears in all subsequent episodes until " The Darkness " ( 1993 ) , the fourth episode of season two , in which she is killed . Vandernoot returned to the program for a number of guest appearances in the season two finale , " Counterfeit " , broadcast in 1994 , and the two @-@ part series conclusion " To Be " / " Not To Be " ( 1998 ) . 
- Tessa is MacLeod 's mortal companion and bears the consequences of his immortality . These include her aging while he does not , the impossibility of having children together and the dangers of MacLeod 's involvement in the Game , an ongoing battle in which all Immortals must behead each other until a single victor remains . Fully aware of this situation , Tessa stays with MacLeod , demonstrating her courage , understanding , generosity and compassion . 
- The first series of Highlander was a multi @-@ national co @-@ production including the French entertainment conglomerate <unk> , which resulted in a French @-@ speaking actor playing Tessa . Vandernoot had to adapt to the North American , fast @-@ paced method of series production and worked with a dialect coach . Her performance was generally praised by reviewers , who especially praised the strong on @-@ screen relationship that Vandernoot and Paul created between their characters . When Vandernoot decided to leave the show , her character was killed , leading to fierce protests among the show 's audience and subsequently prompted the producers to have Vandernoot play an evil <unk> of Tessa in the episode " Counterfeit " . Despite her return , Tessa 's death influenced the rest of the series , making it more pessimistic , and creating a significant precedent ; Tessa was the first main character of the series to die . 
- 
- = = Story arc = = 
- 
- Tessa Noël is a central character , who appeared in every episode of the first season of Highlander : The Series , and in the first four episodes of the second . After the character 's death , she later returned for cameo appearances in the episodes " Counterfeit Part Two " in the second season , and " To Be " and " Not To Be " , the sixth season 's final two episodes . 
- 
- = = = Background = = = 
- 
- Tessa was born on August 28 , 1958 in <unk> , France . When she was seven years old , she fell in love for the first time with then @-@ nineteen @-@ year @-@ old Alan <unk> ( Anthony Head ) . Tessa recalls in " Nowhere To Run " that she was " heartbroken " when he completed his studies and left the country . She remembered that at her first Christmas party in the ballroom of Alan 's house , she " couldn 't believe anything could be so beautiful . " Tessa mentions in " See No Evil " that she was educated at the <unk> in Paris , France . 
- Tessa 's first encounter with MacLeod is shown in a flashback sequence in " For Evil 's Sake " . She had recently left the <unk> and was working as an artist and conducting tours of the River Seine in Paris . In May 1980 , to escape Immortal Christoph <unk> ( Peter <unk> ) who was trying to behead him , MacLeod jumped on a <unk> <unk> on which Tessa was working , and charmed her so that he could stay on board . Another flashback scene in the episode " Counterfeit Part Two " shows how MacLeod revealed his immortality to Tessa . On April 1 , 1983 , MacLeod made Tessa shoot him in the chest with a pistol . After he revived , MacLeod revealed himself to be an Immortal who could not age or father children , but did not mention the Game . MacLeod expected Tessa to show disgust or fear , but instead Tessa expressed compassion and sadness for his loneliness . As Tessa remains unaware of the Game , MacLeod occasionally fights other Immortals without her knowledge . 
- 
- = = = Season one = = = 
- 
- When the series begins , Tessa and MacLeod have been in a relationship for twelve years and are the proprietors of an <unk> store , " MacLeod and Noel 's <unk> " , in the fictional city of Seacouver , Washington , United States . Immortals <unk> Quince ( Richard <unk> ) and Connor MacLeod ( Christopher Lambert ) break into their store , making Tessa aware of the Game . When she learns of this ultimate battle of good and evil , in which Duncan MacLeod must behead or be beheaded , she <unk> remarks ; " And you didn 't think it was important enough to mention . " When Quince threatens Tessa to distract MacLeod , she first wants to flee with MacLeod , who prepared for her departure and <unk> Quince . Aware that other Immortals will challenge MacLeod , Tessa decides to stay with him . They also allow Richie Ryan ( Stan Kirsch ) , a quick @-@ talking petty thief and street punk , to live with them because he was aware of MacLeod 's immortality . 
- In " Eyewitness " ( 1993 ) , Tessa witnesses the brutal murder of former artist Anne Wheeler ( Diana <unk> ) , and reacts angrily when she realizes that the police will do nothing about it , saying : " I 'm not hysterical , I don 't see things , and I 'm not looking for attention . I just want something to be done . " She decides to find the murderer on her own . When MacLeod tells Tessa that she is stronger than Anne , and that if something happened to him she would be fine , Tessa retorts , " You only think so because it suits you . " The murderer is Chief Police Officer Andrew <unk> ( Tom Butler ) who is beheaded by MacLeod after <unk> failed to kill Tessa . 
- In " Band of Brothers " ( 1993 ) , Tessa is chosen as the curator of a traveling exhibition entitled " An historical retrospective on sculpture and form " , which is based in Paris , France . Meanwhile , Immortal Grayson ( James <unk> ) is seeking Victor <unk> , a <unk> of MacLeod 's friend Darius ( Werner <unk> ) . Tessa decides to leave to Paris before MacLeod fights Grayson and her parting words are : " Remember . Paris is our city . I 'll be waiting for you " . After MacLeod <unk> Grayson , he , Tessa and Richie , live in a barge on the Seine near Notre Dame de Paris . 
- In " <unk> Angel " ( 1993 ) , MacLeod and Tessa search for information about newborn Immortal Alfred Cahill ( Martin <unk> ) . Becoming Immortal has made Cahill insane ; he believes he is an angel sent by God to free the world from sin . Cahill starts with the last prostitute he met , Tessa 's old friend , Elaine Trent ( Sandra Nelson ) . Tessa is angry to learn the fate of her intelligent , beautiful friend , but later realizes that she " [ sounds ] like her judge and jury . " MacLeod believes that the only way to stop Cahill is to behead him , despite Tessa 's opinion that " <unk> societies don 't kill their insane . They treat them . " When Cahill comes to the barge looking for MacLeod and finds Tessa alone , Tessa <unk> him by welcoming Cahill as a messenger of God . Later , she tells MacLeod with disgust , " I had to <unk> inside his head ... I had to think like him ... I had to become like him . " 
- 
- = = = Season two = = = 
- 
- The first episode of the second season , " The <unk> " ( 1993 ) , shows Tessa , MacLeod and Richie settling back to their <unk> store in Seacouver and meeting the <unk> , a secret society that observes Immortals without interfering . In " The Darkness " ( 1993 ) , Tessa meets a fortune teller named <unk> ( <unk> Lords ) who urges her to flee the city . This reminds MacLeod of another fortune teller , who , back in 1848 , predicted that he would bury many women , but marries none . MacLeod impulsively asks Tessa to marry him , to which she agrees . The next day , Tessa is abducted by <unk> Wolf ( Andrew Jackson ) , a renegade <unk> who wants to behead MacLeod . In the meantime , MacLeod tells Richie that he is getting married because of the thought of losing Tessa . Tessa holds her ground in front of Wolf and tries to escape , but Wolf brings her back to her cell . MacLeod finally finds them and kills Wolf , then sends Tessa home with Richie . On their way to the car , Tessa and Richie are shot dead by Marc <unk> ( Travis MacDonald ) , a drug addict wanting their money . Richie 's previously unknown immortality is revealed when he returns to life on the spot , but Tessa dies . MacLeod , devastated , leaves their home and sells the antique store . 
- " Eye For An Eye " ( 1993 ) shows the aftermath of Tessa 's death . MacLeod tells Richie , " She was part of our lives , Richie . Never pretend she wasn 't . " MacLeod also advises Richie to get used to her loss , because it " won 't be the last time it happens to you . " Later , while MacLeod trains Richie roughly so that he can face Immortal Annie Devlin ( <unk> Easton ) , Richie angrily tells MacLeod , " You can 't get past it , Mac . I know . You 've seen a lot of people die . But you had to be the hero , you sent us out to the car that night , you could have been there ... You look me in the eyes and you tell me you don 't blame yourself for her death . " Some time later , MacLeod <unk> tells Devlin , " Nothing you do brings anyone back . Once they 're dead ... nothing . " 
- In the two @-@ part finale to series two , " Counterfeit " ( 1994 ) , James Horton ( Peter Hudson ) , a renegade <unk> who believes all Immortals must be eliminated , uses killer Lisa Halle ( Meilani Paul ) to try and kill MacLeod . Lisa undergoes plastic surgery to resemble Tessa and therefore is played by Vandernoot from that point on . MacLeod meets Lisa just after he admitted to himself how much he missed Tessa , and he is stunned by her resemblance with Tessa . Despite knowing that Tessa is dead and cannot return , he eagerly pursues a relationship with Lisa . He eventually admits the truth when he discovers a scar on Lisa 's jaw . Horton kills Lisa on Tessa 's grave before being himself killed by MacLeod . 
- " To Be " and " Not To Be " ( 1998 ) , the series finale , respectively depict MacLeod dreaming of a world in which he was never born . Vandernoot reprises her role as Tessa , this time never having met MacLeod . Tessa leads an unsatisfactory life in which she has a husband and children , but is forced to sacrifice her art and sculpture . In this storyline she has an affair with MacLeod but feels she has betrayed her husband . 
- 
- = = Characteristics = = 
- 
- 
- = = = Relationship with MacLeod = = = 
- 
- Tessa , and Kirsch 's character Richie , were designed as MacLeod 's mortal companions and contacts . Tessa and MacLeod are lovers and share deep feelings for each other . Adrian Paul commented that MacLeod spent twelve years with Tessa without wanting another relationship and that " ... she was a very important part of his life " . Vandernoot thought that " ... the relationship between Tessa and MacLeod was very deep because very soon , he told her about himself ( ... ) because he trusted her , and I think trust is a very good definition of their relationship . She trusted him entirely and he trusted her . " Tessa is thus MacLeod 's only mortal lover who knows of his immortality . 
- However , executive producer Bill Panzer was intrigued by the idea that a mortal would want to spend their life with an Immortal , this choice having several drawbacks . MacLeod does not age , while Tessa is mortal and will age . Despite their mutual wish , they know growing old together is impossible and this uneasy thought " ... haunts them both , sometimes more than others . " As an Immortal , MacLeod is also sterile and Tessa <unk> herself to having no children . " The Sea Witch " deals with Tessa 's choice and its impact upon her life . In this episode , Tessa becomes very fond of a four @-@ year @-@ old girl and muses ; " For a while there , just for a few hours ... I felt like she was mine . I liked how it felt . But , she 's not ... I have my own life and it 's more than enough . " Panzer commented that " The Sea Witch " " ... brings forth in a very powerful way what exactly [ Tessa ] ' s giving up to be with MacLeod . " 
- Tessa occasionally meets MacLeod 's previous Immortal lovers . In " The Lady and the Tiger " , she immediately dislikes Amanda ( played by Elizabeth <unk> ) and the script notes that " ... sparks fly between the two women " . Although she quickly earns Amanda 's respect , Tessa feels like she has to compete with Amanda , while Amanda comments that Tessa is " ... quite refreshing in a <unk> sort of way . " <unk> played Amanda 's interaction with Tessa <unk> and a little <unk> . Tessa is usually jealous of MacLeod 's past lovers , but acknowledges in " Saving Grace " that " ... it would take me several lifetimes to find out everything in Duncan 's past . I know there 've been others , but I never thought I would meet one of them . " 
- In addition , Tessa shares with MacLeod the consequences of his involvement in the Game . She dislikes the Game and would like to escape from it with him , since she fears for his life . According to Vandernoot , Tessa " ... always thought that she [ would ] die before [ MacLeod ] " , but when he told her about the Game during " The Gathering " , " she [ realised ] that he [ could ] be killed " , thus she avoided thinking about it . Another consequence of the Game is that Tessa is sometimes exposed to danger from Immortals who want to use her to pressure MacLeod . Despite being captured by Quince in " The Gathering " , by the time of " Band of Brothers " , she is able to face Grayson : " If you think [ MacLeod ] will stand by , ... you have <unk> him terribly . So you 'd better kill me , now , and be done with it . " Grayson releases her and calls her " ... a remarkable woman , well worth keeping alive . " Consequently , Tessa is fully aware of the risks of their relationship , but stays with MacLeod , explaining in " Eyewitness " that " I know the risks I choose to take ... I stay with you because I want to . I won 't run . I 'm not the little woman and I 'll never be barefoot and pregnant . We all have things to face . This is mine . " However , she finds difficult to deal with her fears when MacLeod leaves to fight another Immortal . 
- 
- = = = Character traits = = = 
- 
- Vandernoot found Tessa 's personality " ... very nice , " " very understanding , generous , supportive . " MacLeod <unk> that Tessa always has a way of reminding him of his humanity . In " For Evil 's Sake " , she tells a guilt @-@ ridden MacLeod that " You may be Immortal , but you 're not omnipotent ... The world is not your responsibility . " Tessa often jokes about MacLeod 's immortality , for example telling him in " For Tomorrow We Die " that the last time MacLeod " ... wore a <unk> was on the deck of the Titanic . " 
- Tessa is able to <unk> with others , feel as they do , think as they do and so become like them . When MacLeod revealed his immortality to Tessa in " Counterfeit Part Two " , she can show compassion instead of the fear or disgust he expects : " I was just thinking how lonely you must be . Your parents , your friends ... having them all die . " Tessa is a very <unk> and understanding character , for example , in " Saving Grace " , she is jealous of MacLeod 's former lover Grace ; however , when MacLeod assures her that he no longer loves Grace , her response is simply that " ... that 's all that need to be said . She 's your friend and she 's been hurt . You 'll help her . I 'd expect you to do no less . " 
- Furthermore , Tessa demonstrates great courage , for example in " Mountain Men " , where she is abducted by three mountain men led by Immortal Caleb Cole , who wants to marry her . Tessa refuses to submit and spreads doubt among them , resulting in Cole finally killing one of his own men before MacLeod rescues her . Reviewer Rob Lineberger of <unk> commented that " ... this episode shows the tough stuff Tessa is made of . " Tessa is a very <unk> character , although it has , occasionally , been known to put her into rather sticky situations , for example , in the episode " See No Evil " , Tessa 's friend , Natalie , is attacked by serial killer Michael <unk> and Tessa uses herself as bait : " Nobody 's watching over his next victim , Duncan ... and she 's going to die if you and I don 't stop him . " Lineberger commented that " [ in " See No Evil " ] , Tessa gets a taste for how Duncan 's life must feel when she faces the killer . " She hits <unk> with her car , telling MacLeod " I thought <unk> the world of evil would feel better than this . " Panzer comments that having Tessa stop the killer " ... was kind of an unusual idea [ in television in 1992 ] , and this was the subject of a lot of meetings with [ then @-@ supervising producer ] David Abramowitz , myself and the people from the various networks , domestic and foreign , who were involved . " Tessa has a reputation for speaking <unk> and for refusing to tolerate any nonsense . In " Innocent Man " , when MacLeod refuses to take her where an evil Immortal is , she says , " I know why you don 't want me there . You 're afraid that what happened to Lucas [ MacLeod 's friend who has just been beheaded ] could happen to you . " Tessa has no self @-@ pity and " ... doesn 't like <unk> " . For example , in " For Tomorrow We Die " , MacLeod calls her " contrary by nature " Tessa parks her car without regard to <unk> , can drive a <unk> , is a poor chess player and dislikes war . 
- 
- = = = <unk> and career = = = 
- 
- Tessa is a prominent professional artist . She <unk> exhibitions of her works and sells a metallic sculpture to the City of Seacouver to adorn a park . Tessa is often seen making art works and welding large pieces of metal together , drawing or using modelling clay . She is also seen <unk> people with whom she has problems . Tessa believes that " ... an artist should never grow complacent . Change is good , " and fears the Paris art critics because " ... they are the worst " . 
- 
- = = Character concept and development = = 
- 
- In the script of " The Gathering " , Tessa is described as " ... a beautiful , <unk> casual woman , artist , free spirit , and proprietor of the most unusual antique shop in the city . " The script of the episode " Saving Grace " says that she has grace and style . Tessa is portrayed as a tall , thin woman with blonde hair and blue eyes . Because Highlander : The Series was an international co @-@ production , the producers cast a French @-@ speaking actor to play Tessa . Producer Gary Goodman explained that they wanted someone " ... that would be appealing on a television screen ... in the sense that you were comfortable with her accent and her character " . They chose Belgian actress Alexandra Vandernoot because she " ... was able to be exotic , pretty and not so unfamiliar to an American audience that she was accepted . " 
- Vandernoot recalled , " I think I was quite close to Tessa , she was very well written , very easy to play and I wish I was like that . I 'm not sure I 'd like that but ... it 's very nice , you know , to play a character with nice feelings and nice emotions . " Vandernoot had to adapt to the North American way of filming series and learn to work fast . She said that filming the series was " ... exhausting but formative " , and that filming in English was " challenging " . Vandernoot , who is a native French speaker , had a dialect coach . 
- Vandernoot and Paul created a strong on @-@ screen relationship between their characters . David Abramowitz , creative consultant from the second season onwards , said , " When I saw her and Adrian together , I thought that if I died , and there was a Mount Olympus , that the two of them would be standing together with <unk> around them . They were god @-@ like . They were so beautiful and had such presence . " Paul said that he was " detached " from the fictional relationship between Tessa and MacLeod , but that " ... it was a good relationship " . Later , he said he " ... was sad to see it go . " 
- Producer Barry Rosen said , " We were very lucky that [ Vandernoot and Kirsch ] were so human @-@ grounded , so we could really play off of them and the way they looked at things that [ Paul ] went through . They were also able to get into real @-@ life situations , romances , getting in trouble , <unk> and so on . " Although Vandernoot and Kirsch are three years apart in age ( Vandernoot being the older of the two ) , on set Vandernoot treated Kirsch like a young boy , while Kirsch seemed to her like a younger brother because of his youthful appearance . 
- 
- = = Death = = 
- 
- In 1993 , Vandernoot wanted to leave the show because shooting Highlander was too demanding and required her to spend several months each year in Canada . Vandernoot also wanted to spend more time with her family . According to Abramowitz , a further , artistic , reason was that " ... a small part of [ Vandernoot ] being a really strong actress wanted to play a more aggressive part in the show and sadly , the nature of the beast was that it couldn 't happen and she made a decision . " Panzer said that creating interesting female characters in the Highlander franchise was often a challenge because the producers found it difficult to " ... have the women be something other than a victim , a hostage , other things when [ one is ] dealing with an immortal hero . " Consequently , the creative staff needed to write Tessa out of the show but were restricted because of the character 's strong relationship with MacLeod . Associate Creative <unk> Gillian Horvath said that , " There was no way ... to have a scene where she said , ' Okay , I 'm going to go to Paris without you . Nice knowing you , MacLeod . ' " The writers decided that the only solution was for Tessa to die , despite Abramowitz 's feeling that her death was " ... sad " and " ... heartbreaking " . 
- Tessa 's death occurs in the fourth episode of the second season ; " The Darkness " . The creative staff decided Tessa would die in a random <unk> incident . Tessa 's death played no role in the episode 's main storyline . It was not formulaic ; the writers wanted to shock the audience . Abramowitz said that " ... it would have been easier to kill her off in the episode , " but the writers " ... wanted it to be a surprise and show how shocking [ Tessa 's death ] was to [ them ] . " Horvath said that " ... losing a loved one to a random act of violence isn 't something that only happens to television action heroes or Immortals or people in another type of life , it happens in the real world <unk> totally unexpectedly , at a moment that makes no sense dramatically . " Tessa 's death scene shows MacLeod kneeling next to Tessa and <unk> her , then Richie reviving and speaking with MacLeod . 
- During the filming of the episode , however , no dialog was recorded . The final version of the episode shown in North America did not show Richie revive . The European version showed Richie reviving , but no dialog was present . This scene was later re @-@ recorded in Paris in 1994 during the filming of the season finale " Counterfeit Part Two " , this time including the dialog . However , this footage was not seen in the final version ; the footage was eventually used in the season four episode " Leader of the Pack " . 
- 
- = = = <unk> ' reception = = = 
- 
- " The Darkness " had the desired effect ; Lineberger wrote that " I was taken aback by the dark tone and emotional range generated by this episode . Highlander is a fantasy series , yet I cared about the characters as though I know them ... Vandernoot gave Tessa such vitality and charm that her death left me <unk> . " Abramowitz said that Tessa 's death strongly angered many viewers , and that " ... people hated me for killing her . " 
- The audience became <unk> still when in the following episode , " Eye For An Eye " , MacLeod made love to Immortal Annie Devlin . Abramowitz explained the creative decision of his staff by saying that " ... someone once told me that death was an <unk> . It 's a thing that pushes you to life and the greatest thing in life , that 's ' seize life ' , is sex . " Lineberger wrote in his review of " Eye For An Eye " , " This one caused an uproar — one I feel is justified . [ Abramowitz ] gave a defense ( in my opinion a weak one ) ( ... ) I have a high tolerance for insensitive guy stuff , but this got to me . When Duncan rolled into Annie 's arms , part of me <unk> in appreciation of Duncan 's magnetic charm . But the rest of me found his actions cruel to the viewers . " Abramowitz confirmed that " ... the fans hated it . And the women wanted to string me up . I was a ' <unk> ' and a ' card ' ... " Paul also reported an angry reaction from the audience after the seventh episode , " The Return of Amanda " , in which MacLeod sleeps with Amanda . 
- Tessa 's death was a turning point in Highlander : The Series . It marked the first time that a regular character died in the show ; it would be followed by the deaths of Charlie <unk> and Richie Ryan . Horvath recalled that " ... it changed the tone of the show . It made Highlander the show where you couldn 't be positive that the characters were safe because they were in the credits . " Tessa 's death also gave the show a pessimistic tone that influenced the remaining characters . Rosen explained that " ... in the years that followed without her and with [ Kirsch 's character ] becoming Immortal , ( ... ) you had to play the show differently . " Lineberger said that " Richie and Duncan relate to each other differently from now on , and Duncan is bereft of much of his joy [ and ] <unk> as well . Tessa is no longer around to lighten him . " 
- 
- = = = " Counterfeit " = = = 
- 
- Tessa remained extremely popular with the audience after her death , prompting the producers to develop the season two finale episode " Counterfeit " to bring her back . According to Kirsch , Vandernoot did not realize her , or her character 's , popularity before attending conventions . Paul said that Vandernoot was surprised that her character had so much influence on the show and that her return was " ... fun for her to do , especially to play a different character which was similar to Tessa but also had an evil intent to her . " 
- The " Counterfeit " story features the character Lisa undergoing plastic surgery to become Tessa 's double . Lisa was played by Meilani Paul before the surgery and by Vandernoot after it . The producers wondered whether the story needed to explain the change in Lisa 's voice , as she would be played by a different actor . They considered suggesting that her voice changed because of the surgery , then decided that Lisa would have voice training . After her operation , Lisa speaks with Vandernoot 's voice when posing as Tessa , and with Meilani Paul 's voice when the character was not acting . This was achieved using automated dialogue replacement during post @-@ production . 
- Adrian Paul said that Vandernoot portrayed Lisa as a smoker to mark her out as a different character from Tessa . Paul also said that his <unk> scene with Vandernoot had to reflect the different relationship between MacLeod and Lisa from that between MacLeod and Tessa . According to Paul , Lisa was more like a <unk> to MacLeod than was Tessa . According to Panzer , the original script featured Horton sending Lisa to kill MacLeod on the latter 's barge . After reading the draft script , Adrian Paul thought the idea of Lisa trying to kill MacLeod on Tessa 's grave would have a more dramatic effect . 
- 
- = = Critical reception = = 
- 
- Bill Panzer said that Tessa became popular with the program 's audience . Rob Lineberger called Tessa " ... beautiful and spirited , " and said that " ... she is the perfect mortal foil for MacLeod 's heavy concerns . She <unk> and strengthens him . " Reviewer <unk> Bernstein of the Audio Video Revolution website wrote that Tessa was " ... depicted not as a <unk> , in @-@ the @-@ dark Lois Lane but rather as a woman who handles her lover ’ s supernatural aspects with remarkable <unk> . " <unk> added that Tessa was " <unk> " and " ... an unusually <unk> love interest ( not to mention a refreshing sexually active heroine , as opposed to the coy ' sexual tension ' <unk> females who usually <unk> the genre ) . " Other reviewers had a more negative opinion . Reviewer <unk> Lacey of <unk> " ... found it odd that everyone liked Tessa because [ he ] found her rather annoying . " Reviewer Doug Anderson of The Sydney Morning Herald wrote that Tessa was " ... too <unk> and sympathetic to serve any purpose other than an emotional spur for the hero 's vengeance . " 
- Reviewer David M. Gutierrez , also of <unk> , noted the " ... strong on @-@ screen chemistry between Tessa and MacLeod . " So did Lineberger : " One gets the feeling they have been together for years , though the series is fresh out of the box , " and he added , " Together , they are a model couple . They have healthy banter , intense arguments , plenty of romance , and an easy comfort with each other . " Bernstein wrote ; " Paul and Vandernoot are charming separately and together " . <unk> <unk> of The Denver Post wrote that " Paul and Vandernoot don 't look like typical American TV @-@ style <unk> and <unk> , and for good reason . They were cast to appeal internationally . " 
- Discussing Vandernoot 's performance , Lineberger called her a " ... gifted actor " , saying that " ... she has the poise , restraint , and grace to be both sensual and frustrated , accomplished yet vulnerable , mortal but aware of greater concerns . " John <unk> of Variety noted that Vandernoot was " attractive " and Anderson called her a " ... Michelle Pfeiffer look @-@ alike . " 
- Reviewing the episode " Counterfeit " , Gutierrez wrote that " ... despite the fact that the having an exact twin of Tessa 's pop up is flatly ludicrous , it plays out due to MacLeod 's desire to have Tessa back <unk> his sense of reason . ( ... ) Vandernoot likes the Tessa character quite a bit and gave me the impression she was sad to see her go , " and that she " ... looks like she enjoys playing the good / bad Lisa . Her triple performance as Tessa shows Vandernoot 's range . " <unk> <unk> of <unk> felt that " ... a visit from Tessa in " Counterfeit " , even an evil Tessa look @-@ alike , is a welcome <unk> to a significant character from the first season , and it gave our boy Duncan a moment or two to reflect on his recent lost love . " 
- 
- 
- = M @-@ 5 ( Michigan highway ) = 
- 
- M @-@ 5 , commonly referred to as Grand River Avenue and the northern section as the Haggerty Connector , is a 20 @.@ <unk> @-@ mile @-@ long ( 33 @.@ 486 km ) state trunkline highway in the Metro Detroit area of the US state of Michigan . The highway runs through suburbs in Oakland and Wayne counties in addition to part of Detroit itself . It starts in Commerce Township as a north – south divided highway and freeway called the Haggerty Connector and connects with Interstate 96 ( I @-@ 96 ) in Novi . The freeway then turns southeasterly to bypass the suburb of Farmington as an east – west highway . The freeway ends on the southeast side of Farmington , and M @-@ 5 follows Grand River Avenue as boulevard into Detroit . The eastern terminus is at an interchange with I @-@ 96 in Detroit . The trunkline passes between suburban residential subdivisions and along urban commercial areas while serving 17 @,@ 200 – 68 @,@ 800 vehicles on average each day . 
- Grand River Avenue started as the path of an early wagon trail in the Michigan Territory , carrying settlers from Detroit inland along a route previously used by Native Americans . It was later a plank road that helped to connect Detroit with the state capital of Lansing and Grand Rapids . When the state highway system was signed in 1919 , the <unk> was numbered as part of M @-@ 16 . Later it became US Highway 16 ( US 16 ) . Grand River Avenue was supposed to be the path for I @-@ 96 from Novi into downtown Detroit , and a section of freeway now used by M @-@ 5 was constructed as part of I @-@ 96 before the Interstate was rerouted to a different location . In the 1990s another section of freeway , which was originally proposed for a northern extension of I @-@ 275 , was opened . This freeway called the Haggerty Connector was added to M @-@ 5 . Additional projects have extended the highway farther north and added a roundabout to the northern terminus . A different highway was previously designated M @-@ 5 in another area of the state in the 1930s . 
- 
- = = Route description = = 
- 
- M @-@ 5 starts at a roundabout intersection with Pontiac Trail in Commerce Township . It runs south @-@ southwesterly from here in Oakland County as a divided highway between suburban residential subdivisions in the township . South of Maple Road , the highway is bordered by commercial developments to the east and Long Park to the west as it angles southeasterly . Between 14 and 13 Mile roads , the highway is once again bounded by subdivisions . Along this part of the trunkline , access to the road is limited to major intersections only , making the highway an expressway . Immediately south of the 13 Mile Road intersection , M @-@ 5 's median <unk> out as the highway transitions to a full freeway called the Haggerty Connector . Traffic can only access the highway at grade @-@ separated interchanges instead of at @-@ grade intersections . A collector @-@ distributor lane setup parallels the main freeway lanes proving access to the ramps at the 12 Mile Road interchange as well as ramps from the massive interchange with I @-@ 96 , I @-@ 275 and I @-@ 696 . Through this interchange complex that <unk> the Novi – Farmington Hills city line , M @-@ 5 turns to the southeast , and signage changes direction . The Haggerty Connector is signed north – south , while the rest of M @-@ 5 is signed east – west . M @-@ 5 has direct connections with ramps to I @-@ 696 and I @-@ 96 east / I @-@ 275 south as it crosses over into Farmington Hills . 
- The next interchange for the M @-@ 5 freeway connects to Grand River Avenue and 10 Mile Road . This section of the freeway bypasses residential areas of Farmington Hills . Further east , M @-@ 5 crosses into Farmington where it bypasses the downtown area of the suburb . Past 9 Mile Road , the freeway ends at the intersection with Grand River Avenue , and M @-@ 5 follows Grand River southeasterly as a boulevard , a type of divided street . Traffic that wishes to make left turns must use a Michigan left maneuver along this section of the highway . Additionally , traffic that needs to change sides of the street must use <unk> in the median to perform a U @-@ turn . Once again running through suburban Farmington Hills , the trunkline passes <unk> Hospital before intersecting 8 Mile Road . This intersection marks the place where M @-@ 5 crosses into Wayne County , and the western terminus of the M @-@ 102 designation on 8 Mile Road . Grand River Avenue runs through the northern section of <unk> Township in Wayne County and crosses into Detroit at the intersection with 7 Mile Road and 5 Points Street . 
- The northwest corner of Detroit is mostly residential as M @-@ 5 intersects US 24 ( Telegraph Road ) . Past Telegraph , Grand River Avenue forms the northern boundary of the Grand Lawn Cemetery and later the southern boundary of the New <unk> Golf Course . The properties bordering M @-@ 5 transition to commercial use past these two green spaces , and the highway continues southeasterly through the city as an undivided street . Grand River Avenue intersects Outer Drive near several businesses . M @-@ 5 crosses over M @-@ 39 ( <unk> Freeway ) near the intersection with <unk> Street , which would be 5 Mile Road in the Detroit grid system . The residential areas off the adjacent side streets increase in density east of the <unk> Freeway . M @-@ 5 ends at the interchange with I @-@ 96 between Schoolcraft and Plymouth roads in the middle of another larger commercial zone ; Grand River Avenue continues from this location as an unsigned highway numbered internally as <unk> BS I @-@ 96 all the way into downtown . 
- M @-@ 5 is maintained by the Michigan Department of Transportation ( MDOT ) like other state highways in Michigan . As a part of these maintenance responsibilities , the department tracks the volume of traffic that uses the roadways under its jurisdiction . These volumes are expressed using a metric called annual average daily traffic , which is a statistical calculation of the average daily number of vehicles on a segment of roadway . MDOT 's surveys in 2010 showed that the highest traffic levels along M @-@ 5 were the 68 @,@ <unk> vehicles daily between 12 and 13 Mile roads ; the lowest counts were the 17 @,@ 176 vehicles per day southeast of Schoolcraft Road to I @-@ 96 . All of M @-@ 5 has been listed on the National Highway System , a network of roads important to the country 's economy , defense , and mobility . The trunkline is <unk> or eight @-@ lanes wide along the freeway section to the north and west of the Grand River Avenue interchange ; south and east of there it is a four @-@ lane freeway or six @-@ lane highway all the way to I @-@ 96 . 
- 
- = = History = = 
- 
- 
- = = = Previous designation = = = 
- 
- Starting in 1933 , M @-@ 5 was used as the designation along a section of highway that was previously part of US 2 in Mackinac and <unk> counties in the Upper Peninsula . This designation was in use until 1939 when it was replaced by M @-@ 129 . 
- 
- = = = Current designation = = = 
- 
- 
- = = = = Grand River Avenue = = = = 
- 
- The chief transportation routes in 1701 were the Indian trails that crossed the future state of Michigan ; the Grand River Trail was one of these thirteen trails at the time . Detroit created 120 @-@ foot ( 37 m ) rights @-@ of @-@ way for the principal streets of the city , Grand River Avenue included , in 1805 . This street plan was devised by Augustus Woodward and others following a devastating fire in Detroit . A ten @-@ year project to construct a plank road between Detroit and Howell was authorized in 1820 along the Grand River Trail . Grand River Avenue was included as one of Five Great Military Roads in 1825 , along with the River Road , Michigan Avenue , Woodward Avenue and <unk> Avenue . The Grand River Road , precursor to the modern Grand River Avenue was named by Benjamin Williams , <unk> of <unk> ; it was named for La Grande <unk> , the French name for the river . 
- The opening of the Erie Canal in New York in 1826 brought new settlers to the Great Lakes region , and to the future state of Michigan . Many of these settlers began their inland journeys in Detroit . At first the Grand River Road was a " deep <unk> , ditch bordered road " . The Grand River Road was a major route for settlers headed inland to Grand Rapids in 1836 , as the shortest route for travelers coming from Detroit . 
- In 1850 , the Michigan State Legislature established the Lansing and Howell Plank Road Company , which set about converting various Indian trails into the Lansing – Howell Plank Road , a task the company completed by 1853 . At Howell the road connected with the Detroit – Howell Plank Road , establishing the first improved connection direct from the state capital to Michigan 's largest metropolis . The Lansing – Detroit Plank Road was a toll road until the 1880s . It eventually evolved into the eastern part of the modern Grand River Avenue . 
- By 1900 , only a short stretch of the Detroit – Howell Plank Road was still make of <unk> ; most of the other plank roads had been converted to gravel by this time . When the Michigan State Highway Department ( MSHD ) had numbered and signed highways in the state in 1919 , it applied the M @-@ 16 number to Grand River Avenue across the state between Grand Haven and Detroit . 
- The M @-@ 16 designation lasted for seven years . As the states were meeting with the American Association of State Highway Officials ( <unk> , now <unk> ) to plan the United States <unk> Highway System , the route of M @-@ 16 was originally planned to be included in US 18 . When the system was announced on November 11 , 1926 , Grand River Avenue and M @-@ 16 became part of US 16 . The first change to the US 16 routing in the Detroit area was made in 1933 when the highway was moved to bypass Farmington , with the old routing retained as a state highway . 
- 
- = = = = Metro Detroit freeways = = = = 
- 
- MSHD had plans to upgrade the US 16 corridor to freeway standards in the middle of the 20th century . The first planning map in 1947 for what later became the Interstate Highway System showed a highway in the corridor . The General Location of National System of Interstate Highways Including All Additional Routes at Urban Areas <unk> in September 1955 , or Yellow Book after the cover color , showed <unk> plans for the locations of Interstate Highways as designated in 1955 . This also included a highway in the US 16 corridor . The 1957 approval for the Interstate Highway System replaced much of US 16 with a portion of Interstate 94 ( I @-@ 94 ) . MSHD submitted a recommended numbering plan for the <unk> in 1958 that showed I @-@ 96 following the US 16 corridor . 
- The segments of the road between Brighton and Farmington were upgraded in 1956 . The MSHD initially signed the various freeways as <unk> in 1959 , and US 16 through the Farmington area gained the additional I @-@ 96 numbering . Two years later , the business route through Farmington was redesignated as a business loop of I @-@ 96 instead of US 16 . The final connection for I @-@ 96 between Lansing and Brighton was completed in late 1962 , and the US 16 designation was decommissioned in the state . The sections of highway through the Detroit metro area were given Business Loop ( BL ) or Business Spur ( BS ) I @-@ 96 designations . 
- When I @-@ 96 was completed in 1977 , several highway designations were shifted in the Metro Detroit area . The BS I @-@ 96 designation was removed from Grand River Avenue . Rather than revert to its original number , M @-@ 16 , MDOT selected M @-@ 5 as the new highway designation . Grand River was signed as M @-@ 5 between 8 Mile Road and its present eastern terminus at I @-@ 96 while leaving Grand River Avenue southeast of I @-@ 96 an unsigned state trunkline , <unk> BS I @-@ 96 . Both the portion of BS I @-@ 96 north of 8 Mile Road and the <unk> of I @-@ 96 that continued out to I @-@ 275 became part of M @-@ 102 . 
- 
- = = = = Haggerty Connector = = = = 
- 
- A freeway running north of Novi to the <unk> area was included in the original Interstate Highway plans for Michigan . Originally included in the corridor for I @-@ 275 , the Michigan Highway Commission canceled the northern section of the highway on January 26 , 1977 , after it spent $ 1 @.@ 6 million ( equivalent to $ 6 @.@ 65 million in 2016 ) the year before purchasing land for the roadway . This northern section was not planned as an Interstate Highway at that time , bearing the designation M @-@ 275 instead . Opposition to construction came from various citizen 's groups and different levels of local government . Additionally , both The Detroit News and Detroit Free Press opposed the project . The Detroit City Council , led by then @-@ Chairman Carl Levin opposed the plan . Levin said at the time , " At last I think people are waking up to the dangers of more and more <unk> . At some point we 've got to say enough . And I think we 've reached it . " The US Department of the Interior reviewed the state 's environmental impact study of the project and stated the project , " will cause <unk> damages on recreation lands , wetlands , surface waters and wildlife habitat . " The total project to link Farmington Hills with <unk> with the 24 @-@ mile ( 39 km ) freeway would have cost $ 69 @.@ 5 million ( equivalent to $ 271 million in 2016 ) and saved drivers an estimated eight minutes off travel time around the city of Detroit . 
- After many years of inactivity , further work began along this same route , but the resulting highway was designated as a northern extension to M @-@ 5 rather than I @-@ 275 or M @-@ 275 . The first section of this freeway extension was opened in October 1994 . This extended the route from M @-@ 5 's previous terminus at M @-@ 102 ( 8 Mile Road ) over the latter highway 's alignment west and north to 12 Mile Road . A plan enacted by then Governor John Engler in 1995 angered road officials when funding was diverted from county road commissions to help complete state highway projects like the M @-@ 5 Haggerty Connector project . In 1999 , a second extension of M @-@ 5 was completed to 14 Mile Road , but only as an expressway . The final two miles ( 3 @.@ 2 km ) between 14 Mile Road and Pontiac Trail opened to traffic on November 1 , 2002 . In 2011 , a roundabout was placed at the northern terminus of M @-@ 5 . This last project also included a northern extension of roadway into the <unk> Golf Course in Commerce Township to better serve the community . This extension does not carry the M @-@ 5 designation . 
- 
- = = Exit list = = 
- 
- All exits are unnumbered . 
- 
- 
- = McAllister Tower Apartments = 
- 
- McAllister Tower Apartments is a 28 @-@ story , 94 m ( 308 ft ) residential apartment skyscraper at 100 McAllister Street in San Francisco , California . The property is owned and operated by the University of California , Hastings College of the Law . The tower includes mixed @-@ use offices on various floors , and the Art Deco @-@ styled " Sky Room " with a panoramic view on the 24th floor . 
- <unk> as an unusual combination of a large church surmounted by a hotel , construction of the building brought architectural dispute . Initially designed by Timothy L. Pflueger in the style of Gothic Revival , the investors fired his firm and hired Lewis P. Hobart , who changed little of Pflueger 's design . In a resulting lawsuit , Pflueger won nearly half the damages he asked for . The building opened in 1930 as the William Taylor Hotel and Temple Methodist Episcopal Church . However , extra construction expenses had put the congregation at greater financial risk , and the church @-@ hotel concept did not prove popular . No profit was made in six years , and the church left , losing their investment . In the late 1930s the building housed the Empire Hotel , known for its Sky Room lounge , then from World War II to the 1970s , 100 McAllister served as U.S. government offices . 
- <unk> as university housing and offices in 1981 , McAllister Tower is home to some 300 law students and their families . " The Tower " is sited one block from the administrative and scholastic center of Hastings College of the Law , and is the most prominent building in the district . 
- 
- = = History = = 
- 
- 
- = = = Church and hotel = = = 
- 
- The skyscraper at 100 McAllister began in 1920 with a plan formulated by Reverend Walter John Sherman to merge four of the largest Methodist Episcopal congregations in San Francisco , sell their various churches and properties and combine their assets to build a " <unk> " with a hotel on top of it . From their initial $ 800 @,@ 000 they bought property at McAllister and <unk> streets and hired the architectural firm of Miller and Pflueger to design the edifice . Timothy L. Pflueger was chosen as the designer . The new hotel , intended to be " dry " ( serving no alcoholic <unk> ) in the " sinful " city , was to be named after William Taylor , a Methodist Episcopal street preacher and missionary who formed the first Methodist church in San Francisco . The large church was named Temple Methodist Episcopal Church , or simply " Temple Methodist " . 
- Beginning in 1925 , Pflueger designed a 308 ft ( 94 m ) , 28 @-@ story , step @-@ back skyscraper made of brick framed with steel , along the lines of his just @-@ completed Pacific Telephone & Telegraph Company Building . Its main decorative theme was neo @-@ Gothic , expressed strongly in the three Gothic arches which formed the main street @-@ level entrance for the church . The Great Hall , the large worship area located within the second , third and fourth floors was to seat 1 @,@ 500 <unk> and a smaller chapel was designed for 125 more . A grand pipe organ from Skinner Organ Company was installed with four manuals controlling 3 @,@ 881 pipes . A stained glass window was placed 80 feet above the sanctuary , representing Faith , Love and Hope in three tall , narrow panels . Two assembly halls could be combined to hold 1 @,@ 100 attendees for theatrical or athletic events . Some 500 guest rooms and 32 tower apartments were intended to bring a steady flow of visitors and a source of profit to the church . Though never the tallest building in San Francisco , it was to be the tallest hotel on the Pacific Coast for many decades . 
- In a dispute , the architectural firm of Miller and Pflueger was fired from the project , and was replaced by Lewis P. Hobart . Miller and Pflueger sued for $ 81 @,@ 600 , alleging that Hobart 's design was little changed from Pflueger 's original . Three months after the hotel and church opened in January 1930 , Miller and Pflueger won $ 38 @,@ 000 in a favorable court decision . 
- Dedication of the church 's pipe organ took place August 31 , 1930 . The combined congregation was very satisfied with their new place of worship . 
- Eventually costing US $ 2 @.@ 8 million ( $ 40 million in current value ) , the building 's completion required several rounds of new financing from its investors in order to overcome <unk> expenses . Unfortunately for the congregation , the idea of a hotel above a church didn 't attract the <unk> number of guests and the venture failed to turn a profit . 
- From 1990 through 2001 , the church housed the theater of George <unk> Performance Works – an experimental multi @-@ media theater troupe that utilized the 60 foot high vaulted ceiling for projections . 
- 
- = = = Empire Hotel = = = 
- 
- By November 1936 , enough debt had accumulated that a <unk> 's protective committee <unk> on the property , buying it back for $ 750 @,@ 000 . The Temple Methodist congregation lost its investment and was asked to leave . The Skinner Opus pipe organ was removed to be sold to <unk> College in Los Angeles and rebuilt in their Thorne Hall . The three @-@ piece stained glass window was removed and exhibited , eventually making its way to Stockton , California where it was installed in the Morris Chapel at the University of the Pacific . The 100 McAllister building itself was refurbished : the church 's floor area was given over to parking , a coffee shop was built in part of the first floor lobby and the new enterprise opened again as the Empire Hotel , noted for completing , in 1938 , the first view lounge in the area , the Sky Room on the 24th floor . With plush <unk> , a large Art Deco @-@ style oval bar , and plate glass windows on all sides , the Sky Room provided a panoramic view of the city . <unk> & Engineer wrote of the luxurious bar in April , 1938 , that it " has no prototype west of New York " , referring to Manhattan 's Rainbow Room which opened three @-@ and @-@ a @-@ half years earlier . 
- 
- = = = Federal offices = = = 
- 
- At the beginning of direct American involvement in World War II , the U.S. government bought the building and converted it to federal offices , officer <unk> , spaces used by the Army 's Ordnance <unk> department , a passport agency and an induction center run by the local draft board . The high vaulted ceiling of the Great Hall worship center was hidden by a dropped ceiling . After the war , the Internal Revenue Service moved offices into the building . 
- Many federal groups at 100 McAllister moved their offices in 1959 – 1960 to the newly built federal building at 450 Golden Gate Avenue , later named the Phillip Burton Federal Building . <unk> at 100 McAllister was low , though the United States Army Corps of Engineers moved their San Francisco District offices there in the 1960s , and local draftees were still required to appear there through the late 1960s . The San Francisco Selective Service System offices were located in the lower floors of the building during the Vietnam War . 
- 
- = = = <unk> Hastings = = = 
- 
- In 1978 , the University of California , Hastings College of the Law bought the building , the most prominent in the <unk> district , and began two years of refurbishment and redesign . Calling it " McAllister Tower " , <unk> units were modernized for residential use by law students , and the building opened in 1981 with a combination of compact studio units as well as larger <unk> and two @-@ bedroom apartments taking up a total of 17 floors . The building , home to about 300 law students and their families , is <unk> referred to as " the Tower " by Hastings residents and faculty , who have but a one @-@ block commute to the law school 's main building at 200 McAllister . 
- The old Sky Room with its spectacular 360 @-@ degree view reopened in 1999 as the James Edgar <unk> <unk> , in honor of <unk> James Edgar <unk> , Class of 1950 , a prominent San Diego trial lawyer . It is used as a space for student study by day ( no alcohol allowed ) and is available for special events in the evenings . Other floors of the building hold offices , apartments and residential <unk> . The <unk> level contains a compact fitness center , the third and fourth floors contain classrooms and offices for political action groups and legal assistance organizations , and the 22nd and 23rd floors hold publishing headquarters for a number of scholarly journals . 
- The Great Hall remains un @-@ refurbished and has been judged by <unk> Hastings to be in need of substantial repair and improvement , including major architectural engineering work . The college has plans to create a 400 @-@ seat performing arts venue within the Great Hall . 
- 
- 
- = Karamokho Alfa = 
- 
- Karamokho Alfa ( born Ibrahima <unk> Sambeghu and sometimes called Alfa Ibrahim ) ( died c . 1751 ) was a <unk> religious leader who led a jihad that created the <unk> of Futa Jallon in what is now Guinea . This was one of the first of the Fulbe <unk> that established Muslim states in West Africa . 
- Alfa Ba , Karamoko Alfa 's father , formed a coalition of Muslim Fulbe and called for the jihad in 1725 , but died before the struggle began . The jihad was launched around 1726 @-@ 1727 . After a crucial , concluding victory at <unk> , the state was established at a meeting of nine Fulbe ulama who each represented one of the Futa Jallon provinces . Ibrahima Sambeghu , who became known as Karamokho Alfa , was the hereditary ruler of Timbo and one of the nine ulama . He was elected leader of the jihad . Under his leadership , Futa Jallon became the first Muslim state to be founded by the Fulbe . Despite this , Karamokho Alfa was constrained by the other eight ulama . Some of the other <unk> had more secular power than Karamokho Alfa , who directly ruled only the diwal of Timbo ; for this reason the new state was always a tenuous confederation . Karamoko Alfa ruled the <unk> state until 1748 , when his excessive <unk> caused him to become mentally unstable and Sori was selected as de facto leader . Karamokho Alfa died around 1751 and was formally succeeded by Ibrahim Sori , his cousin . 
- 
- = = Background = = 
- 
- The Futa Jallon is the <unk> region where the Senegal and Gambia rivers rise . In the fifteenth century the valleys were occupied by Mandé peoples - Susu and Yalunka farmers . Around that time , Fulbe herders began moving into the region , grazing their livestock on the <unk> . At first they peacefully accepted a subordinate position to the Susu and Yalunka . The Fulbe and Mandé peoples <unk> to some extent , and the more sedentary of the Fulbe came to look down on their pastoral cousins . 
- Europeans began to establish trading posts on the upper Guinea coast in the seventeenth century , <unk> a growing trade in hides and slaves . The pastoral Fulbe expanded their herds to meet the demand for hides . They began to compete for land with the <unk> , and became interested in the profitable slave trade . They were increasingly influenced by their Muslim trading partners . 
- In the last quarter of the seventeenth century the <unk> reformer <unk> al @-@ Din launched a jihad to restore purity of religious observance in the Futa Toro region to the north . He gained support from the Torodbe clerical clan against the warriors , but by 1677 the movement had been defeated . Some of the Torodbe migrated south to Bundu and some continued on to the Futa Jallon . The Torodbe , the <unk> of the Fulbe of the Futa Jallon , influenced them in embracing a more militant form of Islam . 
- 
- = = <unk> = = 
- 
- Alfa Ba , Karamoko Alfa 's father , formed a coalition of Muslim Fulbe and called for the jihad in 1725 , but died before the struggle began . The jihad was launched around 1726 or 1727 . The movement was primarily religious , and its leaders included both Mandé and Fulbe <unk> . The jihad also attracted some formerly non @-@ Muslim Fulbe , who associated it not just with Islam but with freedom of the Fulbe from subordination to the Mandé peoples . It was opposed by other non @-@ Muslim Fulbe and by non @-@ Muslim Yalunka leaders . 
- According to tradition , Ibrahim Sori symbolically launched the war in 1727 by destroying the great ceremonial drum of the Yalunka people with his sword . The <unk> then won a major victory at <unk> . A force of 99 Muslims defeated a non @-@ Muslim force ten times greater , killing many of their opponents . After this victory the state was established at a meeting of nine Fulbe ulama who each represented one of the Futa Jallon provinces . Ibrahima Sambeghu , who became known as Karamokho Alfa , was the hereditary ruler of Timbo and one of the nine ulama . He was elected leader of the jihad . He took the title almami , or " the <unk> " . Under his leadership Futa Jallon became the first Muslim state to be founded by the Fulbe . 
- Karamoko Alfa managed to enlist disadvantaged groups such as gangs of young men , outlaws and slaves . Karamokho Alfa 's maternal cousin was <unk> <unk> , the ruler of Bundu , and both men studied in <unk> under the famous scholar <unk> <unk> . However , there are no records of Bundu participation in the Futa Jallon jihad , perhaps because of the internal troubles in Bundu at that time , or perhaps because <unk> <unk> was not greatly interested in the cause . Although he was an inspired religious leader , Karamoko Alfa was not qualified as a military leader . Ibrahim Sori took this role . Some of the population resisted conversion for many years , particularly the nomadic Fulbe herders . They rightly feared that the <unk> would abuse their authority . 
- 
- = = Ruler = = 
- 
- Karamokho Alfa was constrained by the other eight ulama , each of whom ruled their own province , or diwal . The structure of the new Fulbe state had an almami at its head , Karamokho Alfa being the first , with his political capital at Timbo . However , some of the other <unk> had more secular power than Karamokho Alfa , who directly ruled only the diwal of Timbo . The religious capital was at <unk> , where the council of the <unk> sat . The council operated as a strong curb on the power of the almami , and the ulama retained much autonomy , so the new state was always a loose federation . 
- Karamokho Alfa was known for his Islamic scholarship and piety . He respected the rights of the old " masters of the soil " , saying " it was Allah who had established them . " Despite this ruling , the <unk> reserved the right to <unk> land , since they held it in trust for the people . In effect the existing property owners were not displaced , but now had to pay <unk> as a form of rent . Karamoko Alfa ruled the <unk> state until 1748 , when his excessive <unk> caused him to become mentally unstable and Sori was selected as de facto leader . 
- 
- = = Legacy = = 
- 
- Karamokho Alfa died around 1751 and was formally succeeded by Ibrahim Sori , his cousin . Ibrahim Sori <unk> was chosen because Alfa <unk> , son of Karamoko Alfa , was too young . Ibrahim Sori was an aggressive military commander who initiated a series of wars . After many years of conflict , Ibrahim Sori achieved a decisive victory in 1776 that consolidated the power of the Fulbe state . The jihad had achieved its goals and Ibrahim Sori assumed the title of almami . 
- Under Ibrahima Sori slaves were sold to obtain munitions needed for the wars . This was considered acceptable as long as the slaves were not Muslim . The jihad created a valuable supply of slaves from the defeated peoples that may have provided a motive for further conquests . The Fulbe ruling class became wealthy slave owners and slave traders . Slave villages were founded , whose inhabitants provided food for their <unk> masters to consume or sell . At one time more than half the population were slaves . As of 2013 the Fulbe were the largest ethnic group in Guinea at 40 % of the population , after the <unk> ( 30 % ) and Susu ( 20 % ) . 
- The jihad in Futa Jallon was followed by a jihad in Futa Toro between 1769 and 1776 led by <unk> Baal . The largest of the <unk> <unk> was led by the scholar <unk> dan <unk> and established the <unk> Caliphate in 1808 , stretching across what is now the north of Nigeria . The Fulbe Muslim state of <unk> was established to the south of <unk> in 1818 . 
- Karamokho Alfa came to be thought of as a saint . A story is told of a miracle that occurred more than a hundred years after his death . The chief of the <unk> , <unk> <unk> , opened Karamokho Alfa 's tomb and cut off the left hand of the body . Blood poured from the severed wrist , causing <unk> <unk> to flee in terror . 
- 
- 
- = Murder of Tom ap Rhys Pryce = 
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- Thomas Mervyn " Tom " ap Rhys Pryce ( 13 October 1974 – 12 January 2006 ) was a 31 @-@ year @-@ old British lawyer who was robbed and murdered by two black teenagers as he made his way home in Kensal Green , northwest London , on 12 January 2006 . The two , Donnel Carty and Delano Brown , showed little or no remorse and were sentenced to life imprisonment . 
- The crime gained national notoriety for the particularly brutal way in which Pryce was murdered , only metres from his own home , and had only his Oyster card and mobile phone taken from him , but no money ( the case being widely reported as an example of steaming ) . The murderers were later tracked down when the police examined CCTV footage of where the Oyster card was used after the murder . The crime caused a political uproar and condemnation of railway station security . The Tom ap Rhys Pryce Memorial Trust was set up after the murder , and a school was built in his honour . 
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- = = Life of Tom ap Rhys Pryce = = 
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- Tom ap Rhys Pryce was a 31 @-@ year @-@ old lawyer who worked for <unk> , a leading London corporate law firm . Pryce was born in <unk> , Hertfordshire , England . At the age of three , the Pryce family moved to Somalia after Pryce 's father , John , a civil engineer , was sent to work there as part of a project to build a sugar factory , there he " enjoyed an idyllic early childhood " . After 18 months they returned home to Hertfordshire before moving in 1980 to the family home in <unk> where Tom grew up . His ancestry was one well known within the military and among his ancestors was his great @-@ grandfather , General Sir Henry Edward ap Rhys Pryce ( 1874 – 1950 ) . At 13 , Tom won an academic and music exhibition which was later upgraded to a full scholarship at 16 to attend Marlborough College , Wiltshire , England . 
- There he achieved passes at A @-@ levels in Greek , Latin and English Literature , with three grade As . From there Pryce went on to gain a First @-@ Class honours at Trinity College , Cambridge in June 1996 , where he read Classics staying on to study for a masters . Pryce was also known to be a talented musician and lived on Bathurst Gardens , Kensal Green in a flat which he shared with his fiancée <unk> <unk> , 31 , a solicitor specialising in employment law with <unk> & Co , the Queen 's <unk> . 
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- = = Events of 12 January 2006 = = 
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- Childhood friends , Donnel Carty , 18 , and Delano Brown , 17 , had earlier that evening robbed chef <unk> Ali , a middle @-@ aged man in Kensal Green station , 20 minutes before Pryce arrived at the station on his way home from work . Pryce was walking from Kensal Green <unk> station at about 2300 to <unk> <unk> , when he was attacked . According to witness reports , Pryce was running along Bathurst Gardens from two black youths . According to testimony from Delano Brown , Donnel Carty stabbed Pryce after they had chased him from Kensal Green <unk> station where police found a trail of blood and belongings , including a pair of gloves and papers regarding Pryce 's wedding arrangements . As the youths chased Pryce , Carty " fly @-@ kicked " him in the back and he dropped to the floor . As Pryce attempted to stand up , Carty kicked him in the face . <unk> to get away , Pryce began to fight Carty , as Carty stopped him . Some time during this Pryce was stabbed twice in the chest and once in the hip , the wounds penetrating vital organs including his heart . He also suffered cuts to his head , hands and torso . As Pryce 's belongings lay scattered around him , Carty and Brown took Pryce 's mobile phone and Oyster card , the only possessions of value Pryce was carrying . Carty then shouted ' What else have you got ? ' to which Pryce responded ' Nothing . You have got everything ' . Carty and Brown then ran off towards Clifford Gardens , heading to Carty 's home leaving Pryce dying on the ground . Pryce was later taken to Central Middlesex Hospital , where he was confirmed dead shortly after midnight . 
- The scene of the crime which took place along Bathurst Gardens showed the course of events of the violent confrontation . Pryce 's book and gloves were lying outside No 56 , a silver <unk> car was <unk> with blood outside No 82 and a list of wedding venues outside 84 . Pryce was found collapsed in the <unk> between parked cars outside No 90 . 
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- = = Donnel Carty and Delano Brown = = 
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- Carty and Brown were , according to Brown , childhood friends who thought of each other as cousins . Carty lived with his grandparents in Burrows Road , Kensal Green , and Brown lived with his mother in <unk> Avenue , <unk> , northwest London . Carty had one conviction for assaulting a police officer when he was 16 years old , and a caution for possessing cannabis . Brown had no previous convictions . The pair were members of a violent gang calling itself the KG Tribe , taking part in the unlawful wounding of two <unk> in December 2005 as well as other <unk> . At the time of the murder of Pryce , Carty and Brown were 18 and 17 respectively . 
- When both men were arrested on 18 January , Carty said he was innocent of the allegations and claimed he had been in a pub in Kilburn with relatives and friends , and stayed the night at a relative 's house . When police searched his home , they found a pair of trainers that forensic tests showed had a drop of ap Rhys Pryce 's blood on one toe . DNA from several people , including Brown , were found on the trainers . Officers also found a top with traces of Brown 's DNA , and fibres found on Pryce 's <unk> were microscopically indistinguishable from the material of that top . Brown also said he had been in Kilburn the night of the murder and initially claimed that he had nothing to do with either the robbery of the other man or the robbery and murder of Pryce . When the mobile phone of the other victim ( Ali ) was discovered at his home , he claimed he had bought it from two men . <unk> also found that Brown had <unk> press <unk> of Pryce . 
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- = = Trial of Carty and Brown = = 
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- Police caught Carty through CCTV footage which showed him using Pryce 's Oyster Card ( which he claimed to have found ) at Kensal Green station , forensic evidence found at the homes of Carty and Brown , and Pryce 's mobile phone . Carty and Brown both denied murder but admitted that they had robbed Pryce and another man just before . Brown was 17 at the time of the offence so initially could not be named for legal reasons . 
- The trial of the two defendants opened on 30 October 2006 at the Central Criminal Court before Mr Justice Aikens and a jury . Throughout the trial Brown declared that it was Carty who had stabbed Pryce and that it had simply been a ' robbery gone bad ' . This led to an alleged attack on Brown by three youths at <unk> Young <unk> Institution during the trial , in which his attackers reportedly said : " You are <unk> on your co @-@ d ( co @-@ defendant ) . " Carty denied any involvement in the incident , claiming it had been the result of an argument Brown had with the youths earlier . On 27 November 2006 , Carty and Brown were convicted of murder . Carty and Brown reacted calmly to the guilty <unk> , turning to each other , shaking hands and embracing . 
- On 28 November 2006 , both men were sentenced to life imprisonment . The minimum termed for Carty was fixed at 21 years , and that for 18 @-@ year @-@ old Brown at 17 years . The trial judge said he could not tell who wielded the knife but considered both defendants equally guilty . Both sentences were referred to the Court of Appeal ( Criminal Division ) as " <unk> lenient " by Her Majesty 's Attorney General , Lord Goldsmith QC . That court , constituted by the Lord Chief Justice of England and Wales , Lord Phillips of Worth <unk> , Mr Justice <unk> and Mr Justice <unk> , increased Brown 's minimum term to 20 years , although it did not interfere with the sentence imposed upon Carty . 
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- = = Reaction = = 
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- The then Prime Minister Tony Blair 's immediate response to the murder was to pledge the investigation of public safety at the station close to where Pryce was murdered , Kensal Green Station , and this was later improved . 
- David Cameron criticised the Labour Government 's criminal justice system and the absence of father @-@ figures in ethnic minority cultures , which he claimed as causes in the murder of Pryce . Cameron stated that lack of strong deterrent sentences for knife crimes and the failure of police to stop prolific criminals had played a role in the killing of Pryce . He insisted that parental background had a key role in preventing crime and called for zero tolerance of knife crime , claiming that not enough criminals were being sent to jail . 
- In January 2006 the Metropolitan Police Commissioner Sir Ian Blair created considerable controversy when he described the media as <unk> racist . This accusation had also been levelled at the police for the allegedly <unk> coverage of black @-@ on @-@ white crimes , such as this murder , as compared to that given to crimes against ethnic minorities . The example Blair cited was that of <unk> Mathura , an Asian man murdered on the same day as Pryce . Mathura was run over and dragged almost 44 yards ( 40 m ) by a car driven by thieves he had disturbed as they broke into a van parked outside his workplace . Newspapers argued that the number of stories printed regarding the two victims were similar , though a survey of national newspapers after the two murders showed that longer and more in @-@ depth articles were written about the murder of Pryce than that of Mathura . 
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- = = Legacy = = 
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- Following the murder of Pryce , his friends and family set up The Tom ap Rhys Pryce Memorial Trust to enable individuals who could not otherwise afford it to achieve their potential by gaining access to appropriate educational facilities . It aims to raise at least £ <unk> to help educate society 's poorest children . 
- This incident sparked a major public discussion on station safety and security , mainly because the station was <unk> when the suspects <unk> Ali on the platform . The only security present was CCTV cameras , and the ticket barriers were left open allowing the suspects to enter the station freely . Many high @-@ profile politicians spoke on the issue of station safety and called on rail companies to provide security or staff the station until the last train had left the station . The Mayor of London , Ken <unk> assailed <unk> , the train company who managed the station , for not providing all @-@ night staffing or security . The new provider of every franchise across the rail network will have to provide staff at all times the station is open . 
- A school was built in Vietnam in memory of Pryce . His colleagues raised enough money to have the school built to leave a lasting legacy in his honour . The primary school opened in 2007 . Its cost was met by the Hong Kong office of Pryce 's employer , <unk> . 
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- = Lágrimas Cálidas = 
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- Lágrimas Cálidas ( English : Warm Tears ) is the debut studio album by Colombian recording artist Fanny Lu , released on January 1 , 2005 . The record contains ten tracks , most of which were composed by Jose Gaviria , and produced with Andres <unk> . Musically , the album experiments with <unk> , which is composed of the genres of vallenato , merengue , and pop music . Recording for the album took place in 2004 in three cities : Miami , <unk> and <unk> . An international version of the album , containing two remixes , was released exclusively in United States , Spain and Colombia . 
- The album was certified gold in Colombia , Venezuela and Ecuador and ranked number thirteen on the Billboard Tropical Albums chart in the United States . The album earned Lu many accolades , including a Latin Grammy nomination , five Billboard Latin Music Awards nominations , and three <unk> Shock nominations , winning two . Three singles were released from the record , two of which , " No Te Pido Flores " and " Y Si Te Digo " , reached number one on the Billboard Tropical Songs chart . 
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- = = Background = = 
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- While studying for a degree in engineering at the University of Los Andes in 1994 , Fanny Lu began her career in the entertainment industry as a host for shows such as <unk> , <unk> <unk> , and Radio Hits y <unk> . As a host on <unk> , she was given the opportunity to work with musicians such as Luis Manuel Díaz , Gil <unk> , and <unk> Franco . She met Colombian producer , Jose Gaviria , during her career in television , and they began working on a musical project , but the production was halted , because Fanny was busy in her television career . Eight years later , she reunited with Gaviria to finish this recording . Her experience as a television host led to her signing a music deal with Universal Music Latino . Her first album under that agreement was Lágrimas Cálidas , which was released in Colombia on January 1 , 2005 . 
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- = = Composition = = 
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- Lágrimas Cálidas was produced by Jose Gaviria and Andres <unk> . It was recorded at Crescent Moon Studios and Big Dog Studios in Miami , New World Studios in <unk> and <unk> <unk> in <unk> . The genre of the album is defined as <unk> , because it mixes tropical genres such as vallenato and merengue with pop and Caribbean influences . The album opens with " No Te Pido Flores " ( " I 'm Not <unk> Flowers " ) , where the predominant instruments are the accordion , guitar and <unk> <unk> . Lyrically , the song begins with the absence of her man , but then , in the chorus , transitions into a warning not to fall in love with material things . The second track , " Lágrimas Cálidas " ( " Warm Tears " ) , is a vallenato @-@ stylized pop ballad , expressing her suffering due to being abandoned by her lover . " Te <unk> " ( " You 'll <unk> " ) , is about a woman who surrendered completely to a man who did not appreciate her . 
- " Solo <unk> " ( " Only Want " ) begins with an accordion solo , and is a song explaining to her lover that she only wants to be with him forever , because with him all the things in life are more beautiful . The fifth track , " <unk> " ( " <unk> " ) , describes her need for the affection of the person she loves , and her desire to remedy the void left in her heart . " Sin <unk> " ( " Without Reasons " ) , express the reasons why she should not have to prove her love , while " Y Si Te Digo " ( " And If I Tell You " ) , tells of her desire to confess her love to the person who doesn 't know she loves him . " <unk> <unk> Ti " ( " It Is For You " ) is a poem describing the strength of an all @-@ encompassing love . The penultimate track , " <unk> Que Si Tu No <unk> " ( " For That If You <unk> 't " ) , asks her boyfriend whether or not she should suffer for him . The eleventh and final track of the album , " Me <unk> de Ti " ( " I Will Remember You " ) , expresses that everything she feels , sees , and perceives , reminds her of her beloved . 
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- = = Reception = = 
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- The album was certified gold in Colombia , Venezuela and Ecuador . In the United States , the album debuted at number 18 on the Billboard Tropical Albums chart , peaking three weeks later at number 13 . Lágrimas Cálidas was generally well received . At the Colombian <unk> Shock , it won the category of Radio Album , as well as winning best Radio Song for " No Te Pido Flores " . At the 15th Latin Billboard Music Awards , the album was nominated for two awards : Tropical Album of the Year for a Female Artist and Best New Artist . At the 2007 Latin Grammy Awards , " No Te Pido Flores " received a nomination for Best Tropical Song , but lost to " La <unk> De Mi <unk> " , by Juan Luis <unk> . 
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- = = Singles = = 
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- " No Te Pido Flores " was the first single from the album , released in 2005 . The song was a success in Latin America , reaching number one in Colombia , Ecuador , <unk> and Venezuela . In the United States , it also reached number one on the Billboard Latin Tropical Airplay chart . It was nominated for two Billboard Latin Music Awards and a Latin Grammy for Best Tropical Song . " No Te Pido Flores " has two music videos , one recorded in Lake <unk> , <unk> , Colombia , and the international version , which was recorded in the Plaza <unk> in La Plata , Argentina . 
- Unlike " No Te Pido Flores " , the album 's second single , " Te <unk> " , was released after the release of Lágrimas Cálidas in Latin America . The third and final single , " Y Si Te Digo " , was released on May 27 , 2007 . In Latin America , the song did not have the same success as the first single , but in the United States , the song hit number one on Billboard 's Hot Latin Songs chart and Billboard 's Tropical Airplay chart . The song won a Billboard Latin Music Award for Best Tropical Airplay for a new artist . 
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- = = Track listing = = 
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- = = Credits and personnel = = 
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- The following credits are from Allmusic : 
- Performance credits 
- Technical credits 
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- = = Charts = = 
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- = = Certifications = = 
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- = = Release history = = 
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- = Roger Federer = 
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- Roger Federer ( German : [ <unk> ] born 8 August 1981 ) is a Swiss professional tennis player who is currently ranked world No. 3 by the Association of Tennis <unk> ( ATP ) . His accomplishments in professional tennis have led to him being regarded by many as the greatest tennis player of all time . Federer turned professional in 1998 and has been continuously ranked in the top 10 since October 2002 . 
- Federer holds several records of the Open Era : holding the world No. 1 position for 302 weeks ( including 237 consecutive weeks ) ; winning 17 Grand Slam singles titles ; reaching each Grand Slam final at least five times ( an all @-@ time record ) ; and reaching the Wimbledon final ten times . He is among the eight men ( and among the five in Open Era ) to capture a career Grand Slam . Federer shares an Open Era record for most titles at Wimbledon with Pete Sampras ( seven ) and at the US Open with Jimmy Connors and Sampras ( five ) . He is the only male player to win five consecutive US Open titles . 
- Federer has reached 27 men 's singles Grand Slam finals , including 10 in a row from the 2005 Wimbledon Championships to the 2007 US Open , both statistics being records . He also appeared in 18 of 19 finals from the 2005 Wimbledon through to the 2010 Australian Open . He reached the semifinals at 23 consecutive Grand Slam tournaments , from the 2004 Wimbledon Championships through the 2010 Australian Open . At the 2016 Wimbledon Championships , he reached a record 48th Grand Slam quarterfinal and a record 40th Grand Slam semifinal . Earlier at the 2016 Australian Open , he played in a record 65th consecutive Grand Slam tournament . Earlier at the 2015 US Open , he reached a record 27th Grand Slam final . Also earlier at the 2013 French Open , Federer reached a record 36th consecutive Grand Slam quarterfinal . Federer has won the most matches in Grand Slam events ( <unk> ) and is the first to record 65 + wins at each Grand Slam tournament . 
- Federer 's ATP tournament records include winning a record six ATP World Tour Finals , playing in the finals at all nine ATP Masters 1000 tournaments ( a record shared with Djokovic and Nadal ) . He also won the Olympic gold medal in doubles with his compatriot Stan Wawrinka at the 2008 Summer Olympic Games and the Olympic silver medal in singles at the 2012 Summer Olympic Games . Representing Switzerland , he was a part of the 2014 winning Davis Cup team . He finished eight consecutive years ( 2003 – 2010 ) in one of the top two positions in the year @-@ end men 's rankings and ten ( 2003 – 2012 ) in the top three . He was named the <unk> World <unk> of the Year for a record four consecutive years ( 2005 – 2008 ) . 
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- = = Personal life = = 
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- = = = Childhood and early life = = = 
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- Federer was born at the Basel <unk> Hospital in Basel , Switzerland . His father , Robert Federer , is Swiss , from <unk> , near the borders between Switzerland , Austria and Germany ; and his mother , Lynette Federer ( born Durand ) , from <unk> Park , <unk> , is a South African whose ancestors were Dutch and French Huguenots . Federer has one sibling , his older sister Diana , who is the mother of a set of twins . He holds both Swiss and South African citizenship . He grew up in nearby <unk> , <unk> , and then <unk> , close to the French and German borders and speaks Swiss German , Standard German , English and French <unk> , Swiss German being his native language . 
- Federer was raised as a Roman Catholic and met Pope Benedict XVI while playing the 2006 <unk> <unk> d 'Italia tournament in Rome . Like all male Swiss citizens , Federer was subject to compulsory military service in the Swiss Armed Forces . However , in 2003 he was ruled " unsuitable " and was subsequently not required to fulfill his military obligation . Instead , he served in the civil protection force and was required to pay 3 % of his <unk> income as an alternative . He grew up supporting F.C. Basel and the Swiss National Football Team . Federer also credits the range of sports he played as a child — he also played <unk> and basketball — for his hand @-@ eye coordination . 
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- = = = Family = = = 
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- Federer is married to former Women 's Tennis Association player <unk> <unk> . He met her while both were competing for Switzerland in the 2000 Sydney Olympics . <unk> retired from the tour in 2002 because of a foot injury . They were married at <unk> Villa in <unk> near Basel on 11 April 2009 , surrounded by a small group of close friends and family . In July 2009 , <unk> gave birth to identical twin girls , <unk> Rose and <unk> <unk> . The <unk> had another set of twins in 2014 , this time boys whom they named Leo and <unk> , called Lenny . 
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- = = = <unk> and outreach = = = 
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- In 2003 , he established the Roger Federer Foundation to help disadvantaged children and to promote their access to education and sports . Since May 2004 , citing his close ties with South Africa including because that is where his mother was raised , he started supporting the South Africa @-@ Swiss charity <unk> which helps children better connect to sports and social and health awareness and , in 2005 , Federer visited South Africa to meet the children that had benefited from his support . In 2005 , he auctioned his racquet from his US Open championship to aid victims of Hurricane Katrina . At the 2005 Pacific Life Open in Indian Wells , Federer arranged an exhibition involving several top players from the ATP and <unk> tour called Rally for Relief . The proceeds went to the victims of the tsunami caused by the 2004 Indian Ocean earthquake . In December 2006 he visited Tamil Nadu , one of the areas in India most affected by the tsunami . He was appointed a <unk> Ambassador by UNICEF in April 2006 and has appeared in UNICEF public messages to raise public awareness of AIDS . 
- In response to the 2010 Haiti earthquake , Federer arranged a collaboration with fellow top tennis players for a special charity event during the 2010 Australian Open called ' Hit for Haiti ' , in which proceeds went to Haiti earthquake victims . He participated in a follow @-@ up charity exhibition during the 2010 Indian Wells Masters which raised $ 1 million . The Nadal vs Federer " Match for Africa " in 2010 in Zurich and Madrid raised more than $ 4 million for the Roger Federer Foundation and <unk> Rafa Nadal . In January 2011 , Federer took part in an exhibition , Rally for Relief , to raise money for the victims of the Queensland floods . In 2014 , the " Match for Africa 2 " between Federer and Stan Wawrinka , again in Zurich , raised £ 850 @,@ 000 for education projects in southern Africa . 
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- = = Tennis career = = 
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- = = = Pre – 1998 : Junior years = = = 
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- Federer 's main accomplishments as a junior player came at Wimbledon in 1998 , where he won both the boys ' singles final over Irakli <unk> , and in doubles teamed with Olivier <unk> , defeating the team of <unk> <unk> and Andy Ram . In addition , Federer lost the US Open Junior final in 1998 to David Nalbandian . He won four <unk> junior singles tournaments in his career , including the prestigious Orange Bowl , where he defeated Guillermo <unk> in the final . He ended 1998 with the No. 1 junior world ranking , and he entered his first tournament as a professional during 1998 in <unk> , where he lost to Lucas Arnold <unk> in the first round . 
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- = = = 1998 – 2002 : Early career and breakthrough in the ATP = = = 
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- Federer entered the top 100 ranking for the first time on 20 September 1999 . His first final came at the Marseille Open in 2000 , where he lost to fellow Swiss Marc <unk> . Federer won the 2001 <unk> Cup representing Switzerland , along with <unk> <unk> . The duo defeated the American pair of Monica <unk> and Jan @-@ Michael <unk> in the finals . Federer 's first singles win was at the 2001 Milan Indoor tournament , where he defeated Julien <unk> in the final . Although he won his first title already in 1999 on the <unk> tour , winning the doubles event in <unk> , Spain with Dutchman <unk> <unk> , the final was played on Federer 's 18th birthday . In 2001 , Federer made his first Grand Slam quarterfinal at the French Open , and at Wimbledon that same year defeated four @-@ time defending champion Pete Sampras to reach the quarterfinals . The most prestigious event final he reached during this period was the 2002 Miami Masters event , where he lost to Andre Agassi on hard court . 
- Federer won his first Master Series event at the 2002 Hamburg Masters on clay , over Marat Safin ; the victory put him in top 10 for the first time . Federer made 10 singles finals between 1998 and 2002 , of which he won four and lost six . He also made six finals in doubles . Of note are Federer and partner Max <unk> 's defeat in the final of the Indian Wells Masters in 2002 , and their victory in the same year in the final of the Rotterdam 500 series event . Federer had won the latter a year earlier with partner Jonas <unk> . He finished 2001 with an ATP ranking of No. 13 , and 2002 was the first year he was ranked within the top 10 , finishing at No. 6 . 
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- = = = 2003 : Wimbledon breakthrough = = = 
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- In 2003 , Federer won his first Grand Slam singles title at Wimbledon , beating Mark <unk> in the final . Federer won his first and only doubles Masters Series 1000 event in Miami with Max <unk> and made it to one singles Masters Series 1000 event in Rome on clay , which he lost . Federer made it to nine finals on the ATP Tour and won seven of them , including the 500 series events at Dubai and Vienna . Lastly , Federer won the year @-@ end championships over Andre Agassi , finishing the year as world No. 2 , narrowly behind Andy Roddick . 
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- = = = 2004 : <unk> dominance = = = 
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- During 2004 , Federer won three Grand Slam singles titles for the first time in his career and became the first person to do so since <unk> <unk> in 1988 . His first major hard @-@ court title came at the Australian Open over Marat Safin , thereby becoming the world No. 1 for the first time . He then won his second Wimbledon crown over Andy Roddick . Federer defeated the 2001 US Open champion , <unk> Hewitt , at the US Open for his first title there . 
- Federer won three ATP Masters Series 1000 events , one was on clay in Hamburg , and the other two were on hard surfaces at Indian Wells and in Canada . Federer took the ATP 500 series event at Dubai and wrapped up the year by winning the year @-@ end championships for the second time . He also won his first tournament on home soil by capturing the Swiss Open in <unk> . His 11 singles titles were the most of any player in two decades , and his record of 74 – 6 was the best since Ivan Lendl in 1986 . He improved his year @-@ end ranking to world No. 1 for the first time . 
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- = = = 2005 : <unk> dominance = = = 
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- In 2005 , Federer failed to reach the finals of the first two Grand Slam tournaments , losing the Australian Open semifinal to eventual champion Safin after holding match points , and the French Open semifinal to eventual champion Rafael Nadal . However , Federer quickly reestablished his dominance on grass , winning the Wimbledon Championships over Andy Roddick . At the US Open , Federer defeated Andre Agassi in the latter 's last major final . 
- Federer also took four ATP Masters Series 1000 wins : Indian Wells , Miami , and Cincinnati on hard court , and Hamburg on clay . The win in Miami was particularly noteworthy as it was the first final contested between Federer and Rafael Nadal in what would become one of the greatest rivalries in tennis history . Federer received from down two sets and a break to take the final in five sets . Furthermore , Federer won two ATP 500 series events at Rotterdam and Dubai . Federer lost the year @-@ end championships to David Nalbandian in five sets while playing through a foot injury that sidelined him for almost the entire season after September . He maintained his position as world No. 1 for the entirety of the season . 
- The season was statistically one of the most dominant in the Open Era . He won 11 singles titles , which tied his 2004 season as the most in over two decades , his 81 match victories were the most since Pete Sampras in 1993 , and his record of 81 – 4 ( 95 @.@ 2 % ) remains the second @-@ best winning percentage in the Open Era behind only John McEnroe in 1984 . 
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- = = = 2006 : Career best season = = = 
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- The 2006 season was statistically the best season of Federer 's career , as well as one of the greatest seasons of any player in tennis history . In December 2011 , Stephen <unk> , chief editorial writer for <unk> , ranked Federer 's 2006 season as the second @-@ greatest season of all time during the Open Era , behind only Rod Laver 's Grand Slam year of 1969 . Federer won 12 singles titles ( the most of any player since John McEnroe in 1984 ) and had a match record of 92 – 5 ( the most wins since Ivan Lendl in 1982 ) . Federer reached the finals in an <unk> 16 of the 17 tournaments he entered during the season . 
- In 2006 , Federer won three Grand Slam singles titles and reached the final of the other , with the only loss coming against Nadal in the French Open . This was Federer and Nadal 's first meeting in a Grand Slam final . He was the first man to reach all four finals in a calendar year since Rod Laver in 1969 . Federer defeated Nadal in the Wimbledon Championships final . In the Australian Open , Federer defeated Marcos <unk> , and at the US Open , Federer defeated Roddick ( 2003 champion ) . In addition , Federer made it to six ATP Masters Series 1000 finals , winning four on hard surfaces and losing two on clay to Nadal . Federer , however , consistently pushed Nadal to the limit on clay throughout the season taking him to fourth @-@ set <unk> in Monte @-@ Carlo and Paris , and a thrilling match in Rome that went to a deciding fifth @-@ set tiebreaker . 
- Federer won one ATP 500 series event in Tokyo and captured the year @-@ end championships for the third time in his career , again finishing the year as world No. 1 . Federer only lost to two players during 2006 , to Nadal four times in finals , and to 19 @-@ year @-@ old Andy Murray in the second round of the 2006 Cincinnati Masters , in what would be Federer 's only defeat before the final that year . Federer finished the season on a 29 @-@ match winning streak , as well as winning 48 of his last 49 matches after the French Open . 
- A personal highlight for Federer came near the end of the season when he finally won his hometown tournament the Swiss Indoors in Basel , Switzerland . 
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- = = = 2007 : Holding off young rivals = = = 
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- In 2007 , Federer reached all four Grand Slam singles finals , winning three of them again . He won the Australian Open over Fernando González and did so without dropping a set . This made him the first man in the 21st century to accomplish the feat , as Björn Borg at the 1980 French Open was the last to win a Grand Slam tournament without the loss of a set . Federer had entered the year on a huge winning streak and after capturing his fourth Dubai crown Federer 's winning streak stood at 41 matches , the longest of his career and only five shy of the record . Federer entered Indian Wells as the three @-@ time defending champion , but his streak would end in controversy . He was defeated by an Argentine , Guillermo <unk> , who had failed a drug test for illegal doping . This surprising first @-@ round defeat marked the first time since August 2006 he suffered defeat , a period spanning over seven months . 
- During the clay season , Federer 's victory in the Hamburg Masters final was particularly impressive , as it snapped Rafael Nadal 's 81 @-@ match winning streak on clay , an Open @-@ Era record . Federer turned the match around from a set down to sweep 12 of the final 14 games , including a final set <unk> . At the French Open , some anticipated that Federer could become the first man in almost 40 years to hold all four majors simultaneously , having just <unk> defeated young rival Nadal on clay entering the tournament . However , in a repeat of the previous year Federer played a tough four @-@ set final against Nadal , but was <unk> by going 1 / 18 on break @-@ point chances . 
- At Wimbledon , Federer entered the tournament not only as the four @-@ time defending champion , but also riding a 48 @-@ match winning streak on grass . Once again , he defeated Rafael Nadal for a second consecutive year in the final , this time in a thrilling five @-@ set encounter that many analysts hailed as the greatest Wimbledon final since 1980 . Victory at Wimbledon <unk> him with Björn Borg for the record of five consecutive championships at the All England Club . 
- Federer reached the final in Montreal before playing a young and relatively unknown Serbian named Novak Djokovic . Djokovic proved his potential by stunning the world No. 1 in a final @-@ set tiebreaker upset . Federer rebounded in Cincinnati to capture his fifth title of the year . Federer entered the US Open as the three @-@ time defending champion and faced Djokovic in the final . This time , Federer prevailed in a close straight @-@ set match . Victory in New York moved him ahead of Laver and Borg for third on the all @-@ time list of major championship victories . Throughout the tournament , the American press labeled him <unk> Federer for his all @-@ black attire ( which included <unk> @-@ striped shorts ) and the tournament played The Imperial March from Star Wars when he was announced onto the court for each of his matches . He would close out the year with victories in Basel and the Year End Championships in Shanghai . 
- He finished as the year @-@ end world No. 1 for the fourth year in a row , demonstrating his dominance , and during these four years he won 11 Grand Slam singles titles . After his phenomenal triple Grand Slam season yet again , Federer became the only player in history to win three Majors in a year for three years ( 2004 , 2006 , 2007 ) . It was the third consecutive season that Federer would hold the world No. 1 ranking for all 52 weeks of the year . 
- 
- = = = 2008 : Illness , fifth US Open title and Olympic Gold = = = 
- 
- Federer 's success in 2008 was severely hampered by a lingering bout of <unk> , which he suffered from during the first half of the year . At the end of the year , he would suffer a back injury that would prove to be recurring throughout his career . 
- In 2008 , Federer won one Grand Slam singles title at the US Open over <unk> Andy Murray . Federer was defeated by Nadal in two Grand Slam finals , at the French Open , and at Wimbledon , when he was going for six straight wins to break Björn Borg 's record . At the Australian Open , Federer lost in the semifinals to eventual winner Djokovic , which ended his record of 10 consecutive finals . Later in the year , it was found Federer had been suffering from <unk> at the start of the year , particularly during the Australian Open . He lost twice in Masters Series 1000 finals on clay to Nadal , at Monte Carlo and Hamburg . However , Federer captured three titles in 250 @-@ level events at <unk> , Halle , and Basel . 
- At the Olympic Games , Federer and Stan Wawrinka won the gold medal in doubles , after beating the Bryan brothers American team in the semifinals and the Swedish duo of Simon <unk> and Thomas <unk> in the final However , Federer could only reach the quarterfinals in the singles draw , knocked out by then world No. 8 James Blake . He ended the year as world No. 2 . 
- 
- = = = 2009 : Career Grand Slam , sixth Wimbledon , breaking Grand Slam record = = = 
- 
- In 2009 , Federer won two Grand Slam singles titles , the French Open over Robin <unk> , and Wimbledon over Andy Roddick . Federer reached two other Grand Slam finals , losing to Nadal at the Australian Open , and to Juan Martín del Potro at the US Open , both in tight five @-@ set matches . 
- The 2009 season was perhaps the most historically relevant of Federer 's career as he completed a career Grand Slam by winning his first French Open title and won a men 's record fifteenth Grand Slam singles title by defeating Andy Roddick at Wimbledon in five sets , surpassing Pete Sampras 's mark of fourteen . The 2009 Wimbledon final was also historic for being the longest Grand Slam final in terms of games played with Federer prevailing 16 – 14 in a thrilling fifth set . Upon breaking the Grand Slam tournament record , Federer was hailed by most analysts and many tennis greats as the greatest player in tennis history . 
- Federer won two more events , the first at the Madrid Masters over Nadal on clay . The second was in Cincinnati over Novak Djokovic . 
- 
- = = = 2010 : Fourth Australian Open = = = 
- 
- The year started with a win at the Australian Open , where he defeated Andy Murray in the final and extended the Grand Slam singles record to sixteen titles , matching Andre Agassi 's record of four Australian Open titles . Since Wimbledon 2005 Federer had made the finals of 18 out of 19 Grand Slam tournaments , an extraordinary period of sustained excellence unparalleled in the Open Era . This tournament , however , would mark the end of his utter dominance at the majors . 
- At the French Open , Federer won his 700th tour match and 150th tour match on clay . However , he failed to reach a Grand Slam semifinal for the first time since the 2004 French Open , losing to <unk> in the quarterfinals and <unk> his No. 1 ranking , having been just one week away from <unk> Pete Sampras 's record of 286 weeks as world No. 1 . In a huge upset at Wimbledon , Federer lost in the quarterfinals to Tomáš Berdych and fell to No. 3 in the rankings for the first time in 6 years and 8 months . 
- Towards the middle of July , Federer hired Pete Sampras ' old coach Paul <unk> on a trial basis to put his tennis game and career back on the right path . At the 2010 US Open , Federer reached the semifinals , where he lost a heart @-@ breaking five @-@ set match to Novak Djokovic after holding two match points . Federer made it to four Masters 1000 finals , prevailing at the Cincinnati Masters against <unk> Fish . 
- Federer finished the year in strong form , winning indoor titles at the Stockholm Open , Swiss Indoors , and the ATP World Tour Finals in London , which brought his tally to 66 career titles . Federer won the year @-@ end championships in London by beating rival Rafael Nadal for his fifth title at the event . He showed much of his old form , beating all contenders except Nadal in straight sets . It remains the only tournament in his career where Federer defeated all fellow members of the Big Four . Since Wimbledon 2010 , Federer had a win @-@ loss record of 34 – 4 . Federer finished in the top two for the eighth consecutive season . 
- 
- = = = 2011 : Sixth World Tour Finals title = = = 
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- The 2011 season , although great by most players ' standards , was a lean year for Federer . He was defeated in straight sets in the semifinals of the 2011 Australian Open by eventual champion Novak Djokovic , marking the first time since July 2003 that he did not hold any of the four major titles . In the French Open semifinals , Federer ended Djokovic 's undefeated streak of 43 consecutive wins with a stunning four @-@ set victory . However , Federer then lost in the final to Rafael Nadal . At Wimbledon , Federer advanced to his 29th consecutive Grand Slam quarterfinal , but lost to Jo @-@ Wilfried Tsonga . It marked the first time in his career that he had lost a Grand Slam tournament match after winning the first two sets . 
- At the US Open , Federer lost a much @-@ anticipated semifinal match with Novak Djokovic , after squandering two match points in the fifth set , which repeated his previous year 's result against Djokovic and added a second loss from two sets up in Grand Slam tournament play to his record . The loss at <unk> Meadows meant that Federer did not win any of the four majors in 2011 , the first time this has happened since 2002 . Later that month , in September 2011 , in a South African poll , Federer was voted the second most trusted and respected person in the world , next to Nelson <unk> . 
- Federer finished the season on a high note by yet again dominating the indoor season , winning his last three tournaments of the year at the Swiss Indoors , Paris Masters , and ATP World Tour Finals . He ended a 10 @-@ month title drought by winning the Swiss Indoors for the fifth time , defeating rising star <unk> <unk> . Federer followed this up with his first Paris Masters title , where he became the first player to reach all nine Masters 1000 finals . In the final of the 2011 ATP World Tour Finals , Federer defeated Jo @-@ Wilfried Tsonga for the third consecutive Sunday and , in doing so , claimed a record sixth ATP World Tour Finals title , finishing the year as world No. 3 . 
- 
- = = = 2012 : Seventh Wimbledon , second Olympic Medal and return to No. 1 = = = 
- 
- The 2012 season was a return to excellence for Federer . He had his most match wins since 2006 and his highest winning percentage and number of titles won since 2007 . 
- Federer reached the semifinal of the 2012 Australian Open , setting up a 27th career meeting with Nadal , a match he lost in four tight sets . 
- He then won the Rotterdam Open for the first time since 2005 , defeating Juan Martin del Potro . Federer played in the 2012 Dubai Tennis Championships , where he defeated Andy Murray in the final and won the championship title for the fifth time in his career . Federer then moved on to the Indian Wells Masters , where he defeated Rafael Nadal in the semifinals , and John <unk> in the final . Federer won the title for a record fourth time , and , in doing so , equalled Rafael Nadal 's record of 19 ATP Masters 1000 titles . 
- Federer went on to compete at the Madrid Masters on the new blue clay surface , where he beat Tomáš Berdych in the final , thus regaining the world No. 2 ranking from Rafael Nadal . In the French Open , Federer made the semifinals before losing to Djokovic . 
- At Wimbledon , Federer survived an epic five @-@ set thriller in the third round against Julien <unk> on his way to the semifinals . In his semifinal match @-@ up against world No. 1 Novak Djokovic , Federer earned a record eighth Wimbledon final appearance after dispatching Djokovic in four sets . Federer defeated Andy Murray in four sets in the 2012 Wimbledon final , regaining the world No. 1 ranking in the process . " It 's amazing . It equals me with Pete Sampras , who 's my hero . It just feels amazing " , Federer said of winning his seventh Wimbledon championship , tying Sampras ' Open Era record . By defeating top @-@ ranked Djokovic in the semifinals and winning in the finals , Federer returned to the top spot in the world rankings and , in doing so , broke Sampras ' record of 286 weeks atop the list . 
- Four weeks after the Wimbledon final , Federer again faced Murray on the Wimbledon centre court , this time for the final of the 2012 Summer Olympics . This came after an epic 4 @-@ hour 26 @-@ minute semifinal against Juan Martin del Potro of Argentina that Federer won 19 – 17 in the third and final set . He lost to Murray in straight sets in the final , winning a silver medal for his country . 
- Federer won in Cincinnati , beating Novak Djokovic soundly in the final . In the US Open , five @-@ time <unk> Federer was defeated by Tomáš Berdych in the quarterfinals . In the Shanghai <unk> Masters , defeating Stan Wawrinka in the third round , Federer confirmed his 300th week at No. 1 . Federer made it to the finals of the ATP World Tour Finals , where he lost to Novak Djokovic in two tight sets . 
- 
- = = = 2013 : <unk> struggles = = = 
- 
- Federer struggled with back injuries sustained in March and again in July and saw his ranking drop from No. 2 to No. 6 . The 2013 season was the first since 1999 in which Federer failed to reach a final in the first four months of the year . 
- Federer 's first and only title of 2013 came at the Gerry Weber Open ( <unk> . Mikhail <unk> ) , where he also played doubles with good friend Tommy Haas . With the victory in Halle , he tied John McEnroe for the third @-@ most number of ATP titles won by a male player in the Open Era . Federer , however , was unable to maintain his form into Wimbledon , suffering his worst Grand Slam tournament defeat since 2003 in the second round against <unk> <unk> . Not only did the loss end Federer 's record streak of 36 consecutive quarterfinals at Grand Slam tournaments , it meant he would drop out of the top 4 for first time since July 2003 , exactly 10 years after he won his first Wimbledon title . 
- During the summer , he experimented with various different racquets and played the German Open with a <unk> @-@ out 98 @-@ inch Wilson racquet , instead of his regular Pro Staff 6 @.@ 1 90 <unk> racquet with the smaller 90 @-@ inch hitting area . He returned to his regular racquet for the second half of the season . After Wimbledon , Federer continued to be upset early in tournaments because of a serious back injury through October , when he announced that he was parting ways with Paul <unk> , his coach for the last three years . Federer made the final in Basel , succumbing to Juan Martín del Potro . 
- On 27 December 2013 , Federer announced that Stefan Edberg was joining his team as co @-@ coach with <unk> <unk> . 
- 
- = = = 2014 : Wimbledon runner @-@ up , and Davis Cup glory = = = 
- 
- Federer began the season by changing <unk> for the first time in his career , from his longtime frame of 90 square inches to one measured at 97 square inches . He had long been at a comparative disadvantage in equipment as almost the entire tour , including his top rivals Nadal and Djokovic , used more powerful frames of between 95 and 100 square inches . 
- Federer played well at the Australian Open , defeating Jo @-@ Wilfried Tsonga and Andy Murray to reach his 11th consecutive semifinal in Melbourne , before losing to Rafael Nadal in straight sets . 
- At the Dubai Tennis Championships , he defeated Novak Djokovic in the semifinals , and then defeated Tomáš Berdych in the final to win his sixth Dubai crown and his first title since Halle in 2013 . Federer made the final at the Indian Wells Masters , but lost to Novak Djokovic in a final @-@ set tiebreaker . At the Davis Cup quarterfinals , Federer won both of his singles rubbers against Kazakhstan , the second of which was the first live deciding rubber of his Davis Cup career . Federer then took a wild card into the Monte @-@ Carlo Masters defeating Novak Djokovic on his way to the finals , but lost to compatriot Stan Wawrinka in a tight final . 
- In June , Federer announced that after the end of his third term , he would resign as President of the ATP Players Council , a position he had held since 2008 . At the Halle Open , Federer reached both the singles and the doubles finals and won his seventh Halle singles title , beating Alejandro <unk> in the final . At Wimbledon , Federer reached a record ninth final , but he defeated by Djokovic in an epic five @-@ set match . 
- Federer made the final of the Canadian Open , but was defeated by Jo @-@ Wilfried Tsonga . Federer defeated Spain 's David <unk> in three sets to capture his sixth Cincinnati crown and his 22nd ATP World Tour Masters 1000 title , his first since Cincinnati 2012 . He then reached the semifinals at the US Open , but lost in straight sets to eventual champion <unk> <unk> . At the Davis Cup semifinals , Federer won both of his singles matches against Italy in straight sets and hence led Switzerland to the final for the first time since 1992 . 
- Federer then played in the Shanghai Masters . He beat Novak Djokovic in the semifinals , ending the Serb 's 28 @-@ match unbeaten run on Chinese soil . He battled Frenchman <unk> Simon in his second Shanghai final , defeating him in two tiebreak sets and collected the 23rd Masters 1000 title of his career . The victory saw Federer return to world No. 2 for the first time since May 2013 . Federer then played the Swiss Indoors in October , where he won a record sixth title and his 82nd ATP men 's singles title overall . Federer also reached the finals of the 2014 ATP World Tour Finals to face Djokovic again , but withdrew from the final because of another back injury from his semifinal match against Stan Wawrinka . Despite his injury , Federer finished the season on a high by defeating Richard <unk> to clinch the Davis Cup for Switzerland for the first time in its history . 
- 
- = = = 2015 : 1,000th career win , Wimbledon and US Open runners @-@ up = = = 
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- Federer started his season at the Brisbane International . He defeated <unk> Raonic in the final , thereby becoming only the third man in the Open Era to have 1000 or more wins , joining Jimmy Connors and Ivan Lendl , as well as the first man in the Open Era to win at least one title in each of 15 consecutive years . In Dubai , Federer successfully defended his title with a straight @-@ set victory over Novak Djokovic in the final , marking his seventh title at the tournament and , after Wimbledon and Halle , was the third time he had won seven or more titles in a tournament . In addition , Federer became the fourth person since 1991 to surpass 9 @,@ 000 career <unk> . In March , he reached the final of the Indian Wells , but lost in three sets to defending champion Djokovic . 
- Federer won his third title of the season at the inaugural Istanbul Open clay @-@ court tournament , ending a title drought on red clay since the 2009 French Open . Federer made it to the finals of the Italian Open in <unk> but was unable to win his first title there , losing to Djokovic in the final . 
- As the new expanded grass season began , Federer won his record eighth Gerry Weber Open and become only the third man in the Open Era to win a title eight times . Federer entered Wimbledon as the second seed . He played a flawless match to defeat Andy Murray in straight sets in the semifinals and advance to his 10th Wimbledon final in a repeat against Novak Djokovic . Federer lost the match in four sets . 
- He defeated Andy Murray and Novak Djokovic in straight sets to win the <unk> Masters for the seventh time . This marked the first time that Federer had beaten the top 2 players in the world at the same tournament . At the US Open , he advanced to his first final there since 2009 without dropping a set , including a win over Stan Wawrinka in the semifinals . In the final , he was once again defeated by top seed Djokovic in four sets . At the Swiss Indoors tournament in Basel , Federer won his sixth singles title of the year , and his 88th ATP title , defeating his old rival Rafael Nadal in the final . It was the seventh time he had captured his hometown tournament . 
- In December , Federer announced that he headed into the 2016 ATP World Tour season with a new @-@ look coaching team , after announcing that Stefan Edberg would not be traveling with him next year . While <unk> <unk> <unk> remained Federer ’ s head coach , joining the team in 2016 was Croatian former world No. 3 player Ivan <unk> . The Swiss tennis player revealed that Edberg originally signed on to the coaching team for one season only in 2014 , but agreed to stay on in 2015 . 
- 
- = = = 2016 : <unk> surgery and back problems , long injury break = = = 
- 
- Federer started his season by participating in the Brisbane International as the defending champion , despite having a flu when the tournament started . However , in a rematch of the previous year final , he lost in the final to <unk> Raonic in straight sets . Federer then participated at the 2016 Australian Open and rebounded from his third round defeat by Andreas <unk> in 2015 by reaching the semifinals but lost to eventual champion Novak Djokovic in four sets . The day after his loss to Djokovic , Federer sustained a knee injury and in early February , he underwent <unk> surgery to repair a torn <unk> in his knee and missed the tournaments in Rotterdam and Dubai in February and in Indian Wells in March . He was scheduled to return to action in Miami . Due to a stomach flu he had to withdraw from Miami thus prolonging his time on the sidelines . He did however , make his comeback at the Monte @-@ Carlo Masters with straight set wins over Guillermo García @-@ López and Roberto <unk> <unk> before losing in the quarterfinals to Jo @-@ Wilfried Tsonga in three sets . Federer then announced that he would be entering the Madrid Open . However , he suffered a back injury during practice and withdrew shortly after arriving . Although the season had been plagued with injuries , Federer regained the # 2 ranking in the world for a brief moment following Madrid . He then participated in the <unk> <unk> d 'Italia where he defeated Alexander <unk> in straight sets , but lost in the third round to Dominic <unk> . He subsequently missed the Madrid Open , before withdrawing from the French Open , breaking a run of 65 consecutive <unk> in the main @-@ draw of Grand Slam tournaments , stretching back to the 2000 Australian Open . On 6 July , he came back from two sets down to defeat <unk> <unk> in five sets in the 2016 Wimbledon quarterfinals , equalling Jimmy Connors ' all @-@ time records of eleven Wimbledon semifinals and 84 match wins . He suffered his first ever defeat in a Wimbledon semifinal two days later , in five set loss to Raonic . 
- On 26 July , Federer announced that he would miss the 2016 Summer Olympics and the remainder of the 2016 season to fully recover from his knee injury . 
- 
- = = <unk> = = 
- 
- 
- = = = Federer vs. Nadal = = = 
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- Federer and Nadal have been playing each other since 2004 , and their rivalry is a significant part of both men 's careers . 
- They held the top two rankings on the ATP Tour from July 2005 until 17 August 2009 , when Nadal fell to world No. 3 ( Andy Murray became the new No. 2 ) . They are the only pair of men to have ever finished six consecutive calendar years at the top . Federer was ranked No. 1 for a record 237 consecutive weeks beginning in February 2004 . Nadal , who is five years younger , ascended to No. 2 in July 2005 and held this spot for a record 160 consecutive weeks , before surpassing Federer in August 2008 . 
- Nadal leads their head @-@ to @-@ head 23 – 11 . Of their 34 matches , 15 have been on clay , which is by far Nadal 's best surface . Federer has a winning record on grass ( 2 – 1 ) and indoor hard courts ( 5 – 1 ) , while Nadal leads the outdoor hard courts ( 8 – 2 ) and clay ( 13 – 2 ) . Because tournament <unk> are based on rankings , 21 of their matches have been in tournament finals which have included an all @-@ time record eight Grand Slam finals . From 2006 to 2008 , they played in every French Open and Wimbledon final . They then met in the 2009 Australian Open final and the 2011 French Open final . Nadal won six of the eight , losing the first two Wimbledon finals . Three of these finals were five set @-@ matches ( 2007 and 2008 Wimbledon , 2009 Australian Open ) , with the 2008 Wimbledon final being lauded as the greatest match ever by many long @-@ time tennis analysts . Of their 34 meetings , 12 have reached a deciding set . They have also played in 10 Masters Series finals , including their lone five @-@ hour match at the 2006 Rome Masters which Nadal won in a fifth @-@ set tie @-@ break , having saved two match points . 
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- = = = Federer vs. Djokovic = = = 
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- Federer and Djokovic have met 45 times with Djokovic leading 23 – 22 wins . They are tied 17 – 17 on hard @-@ courts and 4 – 4 on clay while Djokovic leads 2 – 1 on grass . The Federer – Djokovic rivalry is the largest rivalry in men 's Grand Slam tournament history with a record 15 matches played against each other with Djokovic leading 9 – 6 . Djokovic is the only player besides Nadal to defeat Federer in consecutive Grand Slam tournaments ( 2010 US Open and 2011 Australian Open , also 2015 Wimbledon , US Open and 2016 Australian Open ) , and the only player besides Nadal and Murray who has double @-@ figure career wins over Federer . Djokovic is one of two players ( the other again being Nadal ) currently on tour to have defeated Federer in straight sets at a Grand Slam event ( 2008 Australian Open , 2011 Australian Open , 2012 French Open ) and the only player to do so three times . Of their 45 meetings , 15 have reached a deciding set . 
- Federer and Djokovic first played in a Grand Slam final at the 2007 US Open where the three @-@ time reigning champion and world No. 1 Federer emerged victorious in straight sets . Federer ended Djokovic 's perfect 41 – 0 start to the 2011 season in the semifinals of the French Open , but Djokovic was able to avenge this loss at the 2011 US Open in five sets after saving two match points against Federer for the second straight year . In the semifinals of Wimbledon 2012 , Federer beat defending champion and world No. 1 Djokovic in four sets . The two met again during the finals of the 2014 Wimbledon Championships with Djokovic emerging victorious after 5 sets . Federer also ended Djokovic 's 28 straight wins in China at 2014 Shanghai Open . Federer and Djokovic <unk> in the 2015 Wimbledon Championships with Djokovic once again claiming victory in four sets . The pair met once more for the final major of the season , the 2015 US Open and once more Djokovic prevailed in 4 sets . Many experts have included the rivalry between Federer and Djokovic as one of the best rivalries in the Open Era . 
- 
- = = = Federer vs. Murray = = = 
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- Federer and Andy Murray have met 25 times with Federer leading 14 – 11 . Federer leads 12 – 10 on hard courts , and 2 – 1 on grass . They have never met on clay . The two have met six times at the Grand Slam tournament level , the first three times in the finals , Federer winning all three of these matches ; at the 2008 US Open and the 2010 Australian Open , both of which he won in straight sets , and at the 2012 Wimbledon Championships in which Murray took the opening set , but went on to lose in four sets . However , Murray won their encounter in the semifinals of the 2013 Australian Open , defeating the Swiss for the first time at a Grand slam tournament in five sets . At the 2014 Australian Open , Federer reversed that result , defeating Murray in four sets in the quarterfinals . The most recent meeting between the two in a Major was in the semifinals of the 2015 Wimbledon Championships , where a dominant Federer triumphed in straight sets . 
- They met in the final of the 2012 Summer Olympic Games , in which Murray defeated Federer in straight sets , denying the Swiss a career Golden Slam . Murray also leads 6 – 3 in ATP 1000 tournaments , 2 – 0 in finals . They have also met five times at the ATP World Tour Finals , with Murray winning in Shanghai in 2008 , and Federer in London in 2009 , 2010 , 2012 , and 2014 . Murray is one of only three players to have recorded 10 or more victories over Federer ( the other two being Nadal and Novak Djokovic ) . 
- 
- = = = Federer vs. Roddick = = = 
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- One of Federer 's longstanding rivalries was with American Andy Roddick . Roddick lost his world No. 1 ranking to Federer after Federer won his first Australian Open in 2004 . Federer and Roddick met on 24 occasions , including four Grand Slam event finals ( three at Wimbledon and one at the US Open – all won by Federer ) . Federer 's record is overall 21 – 3 . Roddick himself said it was not much of a rivalry , being so one @-@ sided . 
- In the 2009 Wimbledon final , Roddick lost to Federer in five sets . The match included a fifth set of 30 games ( a Grand Slam final record ) and was over four hours long . In the final game of the deciding set , Roddick 's serve was broken for the first time in the match . With that victory , Federer broke Pete Sampras ' record of 14 Grand Slam tournament titles , and Roddick apologized to Sampras ( who was there ) for not being able to hold Federer . 
- 
- = = = Federer vs. Hewitt = = = 
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- <unk> Hewitt and Roger Federer played each other on 27 occasions . Early in their careers , Hewitt dominated Federer , winning seven of their first nine meetings , including a victory from two sets down in the 2003 Davis Cup semifinal which allowed Australia to defeat Switzerland . However , from 2004 onward , Federer dominated the rivalry , winning 16 of the last 18 meetings to finish with an 18 – 9 overall head @-@ to @-@ head record . This is Hewitt 's longest rivalry as these two first played each other as juniors in 1996 . They met in one Grand Slam tournament final , the 2004 US Open final , where Federer won his first US Open title in a lopsided encounter in which Federer scored a <unk> either side of a second @-@ set tiebreak . Federer met Hewitt at six of the Grand Slam tournaments in which he lifted the trophy , including all five of his <unk> between 2004 and 2005 . Their last meeting was at the 2014 Brisbane International , where Hewitt triumphed over Federer in three sets for his first title since 2010 , when he also beat Federer to the Halle title . 
- Hewitt and Federer teamed up in the men 's doubles at Wimbledon in 1999 . They got to the third round before losing to Jonas <unk> and Pat <unk> . 
- 
- = = = Federer vs. Agassi = = = 
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- Federer and Agassi played each other 11 times between 1998 and 2005 before Agassi 's retirement in 2006 . Federer led the rivalry 8 – 3 . This was Federer 's most significant rivalry with an all @-@ time great player of the previous generation . They first met in only the third tournament of Federer 's career at the 1998 Swiss Indoors in Federer 's hometown , with Agassi prevailing over the 17 @-@ year @-@ old . Agassi also defeated Federer at the 2001 US Open and the finals of the Miami Masters in 2002 . Federer began to turn the tide at the Masters Cup in 2003 , when he defeated Agassi in both the round robin and the final . They played a memorable quarterfinal match at the 2004 US Open that spanned over 2 days with Federer eventually prevailing in 5 sets . At the 2005 Dubai Championships , Federer and Agassi attracted worldwide headlines with a publicity stunt that saw the two tennis legends play on a <unk> almost 700 feet above sea level at the world famous seven @-@ star luxury hotel the Burj al @-@ Arab . Their final match was at one of the most prestigious platforms in the sport , when they played in the finals of the 2005 US Open . Federer was victorious in four sets , claiming the 6th Grand Slam tournament of his career and denying Agassi his 9th . 
- 
- = = = Federer vs. del Potro = = = 
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- Juan Martin del Potro and Roger Federer have played 20 times with Federer leading 15 – 5 . They have met six times in Grand Slam tournaments with Federer leading 5 – 1 . Their two most famous Grand Slam tournament meetings both came in 2009 . The first was in the French Open semifinals , when Federer survived an epic five @-@ set clash when he was chasing the only French title of his career . The second was in the final of the US Open , where del Potro stunned Federer in five sets , ending his 20 @-@ match winning streak at majors . Another high @-@ profile match was in the semifinals of the 2012 London Olympics , where Federer prevailed 19 – 17 in a <unk> final set to secure the Olympic silver medal . Most recently , they met in the finals of the Swiss Indoors in 2012 and 2013 , with del Potro prevailing on both occasions in tight three @-@ set matches . 
- 
- = = = Federer vs. Safin = = = 
- 
- Marat Safin and Federer played each other 12 times , with Federer leading 10 – 2 . Federer and Safin turned pro within one year of each other , with Safin turning pro in 1997 and Federer in 1998 . Federer leads 4 – 1 on hard courts , 3 – 0 on grass , and 3 – 0 on clay courts , while Safin leads 1 – 0 on carpet . Notable meetings include Federer 's defeating Safin at the 2002 Hamburg Masters to win the first Masters 1000 title of his career , as well as Federer 's emerging victorious in the semifinals of the 2004 Tennis Masters Cup , after winning a tiebreak 20 – 18 on his eighth match point . Federer also defeated Safin in the finals of the 2004 Australian Open to capture his first Australian Open and second Grand Slam tournament title . However , Safin defeated Federer in the 2005 Australian Open semifinals , having saved one match point in the fourth @-@ set tiebreak , to end a 26 @-@ match winning streak by Federer . They met each other five times in Grand Slam tournaments , with Federer leading 4 – 1 . 
- 
- = = = Federer vs. Nalbandian = = = 
- 
- David Nalbandian was Federer 's biggest rival in his early career . The two played each other 19 times , with Federer leading 11 – 8 . Nalbandian dominated early on , taking all of their first five matches from 2002 – 03 . Federer reversed this trend at the 2003 Masters Cup , where he recorded his first victory , and would go on to win 11 of their last 14 meetings . Federer led 6 – 5 on hard courts , 1 – 0 on grass , and 3 – 1 on clay courts , while Nalbandian led 2 – 1 on carpet . Notable meetings include Nalbandian 's win in a fifth @-@ set tiebreaker to win the 2005 Masters Cup , and Federer 's win in the 2006 French Open semifinals . They met each other six times in Grand Slam tournaments , with Federer leading 4 – 2 . 
- 
- = = = Federer vs. Berdych = = = 
- 
- Tomáš Berdych and Federer have played each other 22 times with Federer leading 16 – 6 . Federer leads 9 – 5 on hard courts , 2 – 1 on grass courts , 4 – 0 on clay courts , and 1 – 0 on carpet . Berdych won their first professional match , notably upsetting then world No. 1 Federer at the 2004 Summer Olympics . Federer then went on to win their next eight meetings , before Berdych ended the losing streak in 2010 . Between 2010 and 2013 , Berdych won 5 of 8 meetings . Federer then switched to a larger racquet in 2014 to prevent being <unk> by players like Berdych and leads 5 – 0 since . They have met seven times in Grand Slam tournaments , with Federer leading 5 – 2 , and Berdych is one of five players , along with <unk> <unk> , <unk> <unk> , David Nalbandian , and Jo @-@ Wilfried Tsonga , to defeat Federer multiple times in majors before the semifinal stage . Their most notable Grand Slam matches took place in the 2009 Australian Open , when Federer prevailed in five sets after dropping the first two sets , the 2010 Wimbledon Championships , the 2012 US Open , both of which Berdych won in four <unk> and the 2016 Australian Open , which Federer won in straight sets . 
- 
- = = Legacy = = 
- 
- Federer has been regarded by many <unk> , coaches , and past and present players as the greatest tennis player of all time . He dominated the game at his peak and has more Grand Slam tournament titles ( 17 ) than any other men 's singles player . He is also the first men 's singles player to have reached 10 consecutive Grand Slam tournament finals and a total of 27 Grand Slam finals . He spent the most amount of time in the Open Era at the top of the ATP Rankings ( 302 weeks ) . He also holds the record for the most titles ( 6 ) at the year @-@ end tournament , where only the year @-@ end 8 highest @-@ ranked players participate . Federer has been ranked among the top 8 players in the world continuously since 14 October 2002 
- Federer has won the <unk> Fans ' Favourite Award a record 13 times consecutively ( 2003 – 2015 ) and the Stefan Edberg <unk> Award ( voted for by the players ) a record 11 times ( 2004 – 2009 , 2011 – 2015 ) , both being awards indicative of respect and popularity . He also won the Arthur <unk> Humanitarian of the Year Award twice in 2006 and 2013 . He was named the <unk> World <unk> of the Year for a record four consecutive years ( 2005 – 2008 ) . Federer is at times referred to as the Federer Express , shortened to <unk> Express or <unk> , and the Swiss <unk> , or just <unk> . 
- 
- = = Playing style = = 
- 
- Federer 's versatility has been summarised by Jimmy Connors : " In an era of specialists , you 're either a clay court specialist , a grass court specialist , or a hard court specialist ... or you 're Roger Federer . " 
- An elite athlete , Federer is an all @-@ court , all @-@ around player known for his speed , fluid style of play , and exceptional shot making . Federer mainly plays from the baseline but is also comfortable at the net , being one of the best <unk> in the game today . He has a powerful , accurate smash and very effectively performs rare elements in today 's tennis , such as backhand smash and <unk> , half @-@ volley and jump smash ( slam dunk ) . David Foster Wallace compared the brute force of Federer 's forehand motion with that of " a great liquid whip " , while John McEnroe has referred to Federer 's forehand as " the greatest shot in our sport . " Federer is also known for his efficient movement around the court and excellent <unk> , which enables him to run around shots directed to his backhand and instead hit a powerful inside @-@ out or inside @-@ in forehand , one of his best shots . 
- Federer plays with a single @-@ handed backhand , which gives him great variety . He employs the slice , occasionally using it to lure his opponent to the net and deliver a passing shot . Federer can also fire <unk> winners and possesses a ' flick ' backhand with which he can generate pace with his wrist ; this is usually used to pass the opponent at the net . His serve is difficult to read because he always uses a similar ball toss , regardless of what type of serve he is going to hit and where he aims to hit it , and turns his back to his opponents during his motion . He is often able to produce big serves on key points during a match . His first serve is typically around 200 km / h ( 125 mph ) ; however , he is capable of serving at 220 km / h ( 137 mph ) . Federer is also accomplished at serve and <unk> , and employed this tactic frequently in his early career . 
- Later in his career , Federer added the drop shot to his arsenal and can perform a well @-@ disguised one off both wings . He sometimes uses a between @-@ the @-@ legs shot , which is colloquially referred to as a " <unk> " or " <unk> " . His most notable use of the <unk> was in the semifinals of the 2009 US Open against Novak Djokovic , bringing him triple match point . Federer is one of the top players who employs successfully the " <unk> shot " , when he gets pushed deep and wide on his forehand wing . Since Stefan Edberg joined his coaching team at the start of the 2014 season , Federer has played a more offensive game , attacking the net more often , and improved his volley shots . In the lead @-@ up to the 2015 US Open , Federer successfully added a new unique shot to his arsenal called <unk> ( <unk> Attack by Roger ) , in which he charges his opponent while receiving the serve and hits a return on the run . 
- 
- = = Equipment and apparel = = 
- 
- 
- = = = Equipment = = = 
- 
- Federer currently plays with the Wilson <unk> <unk> <unk> , a 97 square inch tennis racquet with 21 @.@ 5 mm beam , 360 g weight , <unk> swing weight and <unk> string pattern ( all strung with <unk> ) . Since the 1998 Wimbledon Junior Championships Federer played with a Pro Staff 6 @.@ 1 90 <unk> tennis racquet , which is characterised by its smaller hitting area of 90 square inches , heavy strung weight of 364 grams , and thin beam of 17 @.@ 5 millimeters . His grip size was 4 3 / 8 inches ( sometimes referred to as <unk> ) . Federer strung his racquets at 21 @.@ 5 kg <unk> / 20 kg crosses pre @-@ stretched 20 percent , using Wilson Natural <unk> 16 gauge for his main strings and <unk> Big <unk> <unk> Power Rough <unk> gauge ( <unk> ) for his cross strings . When asked about string tensions , Federer stated " this depends on how warm the days are and with what kind of balls I play and against who I play . So you can see – it depends on several factors and not just the surface ; the feeling I have is most important . " 
- 
- = = = <unk> = = = 
- 
- Federer has a contract with Nike footwear and apparel . For the 2006 championships at Wimbledon , Nike designed a jacket <unk> with a crest of three tennis racquets , symbolising the three Wimbledon Championships he had previously won , and which was updated the next year with four racquets after he won the Championship in 2006 . In Wimbledon 2008 and again in 2009 , Nike continued this trend by making him a <unk> <unk> which also has his own logo , an R and F joined together . 
- 
- = = <unk> = = 
- 
- Federer is one of the highest @-@ earning athletes in the world . He is listed at number five on Forbes World 's Highest <unk> Athletes list . As of 2013 , he remains the top <unk> in tennis with ten endorsement deals . He makes 40 to 50 million euros a year from prize money and endorsements from Nike and the Swiss companies <unk> <unk> , Credit <unk> , <unk> , <unk> , and <unk> <unk> . In 2010 , his endorsement by Mercedes @-@ Benz China was extended into a global partnership deal . His other sponsors include <unk> , Wilson , and <unk> & <unk> . Previously , he was an ambassador for <unk> , <unk> AG , and Maurice <unk> . 
- 
- = = Career statistics = = 
- 
- 
- = = = Grand Slam tournament performance timeline = = = 
- 
- Note : Federer received fourth @-@ round <unk> at the US Open ( 2004 and 2012 ) and the Wimbledon Championships ( 2007 ) , and a second @-@ round <unk> at the Australian Open ( 2012 ) ; these are not counted as wins 
- 
- = = = = Finals : 27 ( 17 titles , 10 runners @-@ up ) = = = = 
- 
- 
- = = = Year @-@ End Championship performance timeline = = = 
- 
- 
- = = = = Year – End Championship finals : 10 ( 6 titles , 4 runners @-@ up ) = = = = 
- 
- ( i ) = Indoor 
- 
- = = = Records = = = 
- 
- 
- = = = = All @-@ time tournament records = = = = 
- 
- 
- = = = = Open Era records = = = = 
- 
- These records were attained in the Open Era of tennis . 
- Records in bold indicate <unk> achievements . 
- Records in italics are currently active streaks . 
- 
- = = = Video = = = 
- 
- Wimbledon Classic Match : Federer vs Sampras . Standing Room Only , DVD release date : 31 October 2006 , run time : 233 minutes , ASIN <unk> . 
- Wimbledon 2007 Final : Federer vs. Nadal ( 2007 ) . <unk> White Star , DVD release date : 30 October 2007 , run time : 180 minutes , ASIN <unk> . 
- Wimbledon — The 2008 Finals : Nadal vs. Federer . Standing Room Only , DVD release date : 19 August 2008 , run time : 300 minutes , ASIN <unk> . 
- 
- = = = <unk> = = = 
- 
- Roger Federer at the Association of Tennis <unk> 
- Roger Federer at the International Tennis Federation 
- Roger Federer at the Davis Cup 
- Roger Federer at the Internet Movie Database 
- 
- = Jane 's Attack Squadron = 
- 
- Jane 's Attack Squadron is a 2002 combat flight simulator developed by Looking Glass Studios and Mad Doc Software and published by <unk> Interactive . Based on World War II , the game allows players to pilot fifteen reproductions of that era 's military aircraft and to carry out missions for the Axis or Allies . Although it contains dogfights , the game focuses largely on air @-@ to @-@ ground combat . 
- Jane 's Attack Squadron was first conceived by Looking Glass employee Seamus Blackley as Flight Combat , a combat @-@ based sequel to Flight Unlimited . The company continued designing the game after Blackley was fired in 1995 , and it entered production under Electronic Arts in 1998 . The team experienced problems with deadlines and funding during development , with the game eventually being heavily redesigned and renamed Jane 's Attack Squadron at the request of the publisher . These issues contributed to Looking Glass 's bankruptcy and closure in 2000 . In 2001 and 2002 , the game was acquired and finished by Mad Doc Software , a company in part composed of former Looking Glass employees . 
- The game received mixed to poor reviews . Critics found its physics modelling unrealistic , and many believed that the game 's graphics and gameplay were outdated , particularly in light of contemporary simulators like <unk> @-@ 2 Sturmovik . The limited number of missions and large number of glitches were widely panned . Certain critics enjoyed Jane 's Attack Squadron 's air @-@ to @-@ ground combat and several hoped that fans would improve the game with the included physics and mission editors . 
- 
- = = Gameplay = = 
- 
- As a combat flight simulator , Jane 's Attack Squadron allows players to pilot military aircraft in a three @-@ dimensional ( 3D ) graphical environment . The game is set in Western Europe during World War II ; and players may control fifteen German and Allied planes from the era , including the <unk> @-@ <unk> <unk> 190 , <unk> Spitfire , Junkers Ju 88 , Avro Lancaster and Consolidated B @-@ 24 Liberator . Although dogfights are possible , the game places a heavy focus on air @-@ to @-@ ground combat . <unk> range from bombing and torpedo runs to defense and escort missions . The player may engage in tutorials , " quick missions " , " single missions " and a campaign . Quick missions allow the player to select variables such as the objective and the number of friendly and enemy aircraft , while single missions , of which there are five , are scripted , " pre @-@ made " levels . Two campaigns are available : one each for the Allies and Germans . Both campaigns feature ten missions that branch depending on the outcome achieved by the player . An online multiplayer component allows players to engage in dogfights . 
- Each plane in Jane 's Attack Squadron is composed of forty @-@ five parts that may be removed or otherwise damaged in combat . Damage to these parts affects performance ; for example , a broken fuel line will leak , quickly decreasing the fuel gauge . Various adjustments may be made to the game 's realism , including an " arcade physics " option that drastically reduces flight difficulty . The game is packaged with the mission and physics editors that were used to develop it . 
- 
- = = Development = = 
- 
- 
- = = = At Looking Glass = = = 
- 
- In September 1994 , Looking Glass Technologies employee Seamus Blackley told Computer Gaming World that he wanted to create a combat @-@ based sequel to Flight Unlimited , whose development he was directing at the time . The magazine 's Johnny L. Wilson wrote , " If Flight Unlimited can pass the civilian tests , the military version should be right behind it . " In March of the next year , Blackley told PC Gamer US that the sequel " should feel so real that pilots will be afraid . They 'll feel the gun hits . " He dismissed the flight dynamics in other flight simulators , such as Falcon 3 @.@ 0 , in favor of the real @-@ time computational fluid dynamics ( <unk> ) model he created for Flight Unlimited . When PC Gamer 's Bernie <unk> asked why the team had waited to make a combat flight simulator , Blackley responded that they wanted to " <unk> " players first . In September , Computer Gaming World reported that Blackley was designing a combat @-@ based sequel to Flight Unlimited under the working title Flight Combat . Blackley told them that it would " make you into a fighter pilot " , and the magazine commented that it would teach the same material as the Air Force . The team planned to allow players to practice an element and then execute it on a mission , and Blackley said that the game would feature competitive online play . However , a new manager at Looking Glass Technologies , instated by venture capital investors , demanded that Blackley work on Flight Unlimited II instead of Flight Combat . Blackley refused and was fired , leaving the company in late 1995 . 
- In March 1996 , PC Gamer US reported that Flight Combat was " still taxiing across the design board " , and that the team planned to focus " on how the aerial manoeuvres are performed as you fight " . Looking Glass designer Constantine Hantzopoulos told the magazine that a modified version of the Flight Unlimited engine was being used to develop Flight Combat and Flight Unlimited II . Hantzopoulos commented that Flight Combat was " the project everybody at Looking Glass wants to work on " . The team expected to be finished with the game in roughly one year . By June 1997 , GameSpot reported that Flight Unlimited II was running on the new <unk> engine , coded from scratch by programmer James Fleming . In addition , the real @-@ time <unk> model from Flight Unlimited had been discarded , as its programming was " all black box <unk> code from Seamus " . GameSpot 's T. Liam MacDonald noted that the Flight Unlimited II team expected soon to use the same engine for a combat flight simulator set in World War II . Computer Gaming World similarly reported that the company was " definitely hot " to develop Flight Combat , and that it might be created after Flight Unlimited II . Following that game 's completion , the team could not decide between developing Flight Unlimited III or Flight Combat . As a result , they decided to develop them simultaneously , and Flight Combat began production in early 1998 . Unlike all of the studio 's other games , development of Flight Combat was funded through an insured bond , in an attempt to guarantee that the game would be finished . The company 's Tim <unk> later said that this setup was " a real pain for [ the team ] in some ways " . 
- Looking Glass signed a multi @-@ game publishing deal with Electronic Arts in May 1998 , and that company became the publisher of Flight Combat . The team " <unk> " the game to Electronic Arts , and James <unk> of the <unk> Through the Looking Glass believed that the team " <unk> that it could get the game out the door faster than the budget actually called for " . In March 1999 , the game was announced as the World War II @-@ themed Flight Combat : Thunder Over Europe , directed by Hantzopoulos and scheduled for release in fall of that year . That May , the game was shown at the Electronic Arts booth at E3 . Computer Games Magazine 's Steve Udell wrote that the game would feature a new iteration of the Flight Unlimited terrain renderer , and IGN reported that one million square miles of terrain based on European landscapes would be available . Weather conditions such as snow and rain were planned . Udell wrote that Flight Combat 's flight physics were an updated version of those from recent Flight Unlimited games , with new material taken from operations manuals and flight tests . <unk> models and textures were based on photographs , and many of the moving parts and flight control surfaces were modeled individually . Players were given the option to <unk> planes . Udell described a physics @-@ based damage system that , according to the company , made it impossible to " see the exact same kind of damage twice " . Two campaigns — the Battle of Britain and the Defence of the Reich — were announced , with missions based on dogfights , air @-@ to @-@ ground combat and bombing runs . Looking Glass claimed that the game would feature " moving tanks and ships <unk> [ ing ] it out on a dynamic battlefield " as the player carried out missions . <unk> , IGN 's Tal <unk> noted that the game had " a very distinct 40s charm " , which was present " from the briefings to the options screens " . 
- 
- = = = <unk> and cancellation = = = 
- 
- Electronic Arts rebranded Flight Combat as Jane 's Attack Squadron , to fit with the Jane 's Information Group license that the publisher had used for its Jane 's Combat <unk> line . According to <unk> , the publisher demanded that the game be heavily redesigned " <unk> through " development , which exacerbated the team 's existing problems with meeting deadlines . <unk> believed that the game 's schedule and funding did not receive the necessary adjustments to allow for this redesign . Together with Flight Unlimited III 's commercial failure , the expenses of Jane 's Attack Squadron 's long development used up revenue from Thief : The Dark Project , which had helped the company recover from the failure of Terra Nova : Strike Force <unk> and British Open Championship Golf . <unk> these problems , business deals with Microsoft , <unk> Games and <unk> Interactive were unsuccessful . As a result , Looking Glass Studios went bankrupt in May 2000 . At the time , Jane 's Attack Squadron was roughly three months from completion . Thief II Gold and Thief III were cancelled as a result of the company 's closure , but Jane 's Attack Squadron , because it was near completion and funded through an insured bond , had a chance of being finished . 
- According to Gordon Berg of Computer Gaming World , the legal and logistical problems of keeping " a portion of Looking Glass [ ... ] intact " to finish the game had been " <unk> " . Further , because of the game 's type of funding , the continued development of Jane 's Attack Squadron would have been at little cost to Electronic Arts . Fans petitioned the publisher to let development continue , and Looking Glass employee Rich Carlson said that Hantzopoulos and others from the Flight series , roughly twenty in all , were prepared to work on the game again . The petition reached 2 @,@ 000 signatures by May 30 and 5 @,@ 000 by June 1 . However , Electronic Arts dropped the game . The publisher 's Jeff Brown said that the decision " was based on our deep uncertainty that the project could meet any schedule given the changes in senior management and a history of missing deadlines . " Brown told the website <unk> that Electronic Arts had " worked <unk> " with Looking Glass for more than two years , and that , although the game had missed its planned October 1999 release , they had been willing to delay the project into 2000 . He blamed the developer 's closure for the decision to cancel the game . <unk> circulated that the decision was part of the publisher 's larger plan to abandon the flight simulator genre , and Computer Gaming World 's Denny Atkin later summarized that the company " ran screaming from the simulation market " after Looking Glass 's bankruptcy . Electronic Arts soon dropped the Jane 's Information Group license . 
- 
- = = = At Mad Doc = = = 
- 
- After the closure of Looking Glass , certain employees of that company moved to developer Mad Doc Software , and they hoped to complete Jane 's Attack Squadron . The game 's original lead designer and lead programmer were among those hired by Mad Doc . <unk> Line Entertainment , Mad Doc 's agent company , secured the rights to the game 's code for the team in 2001 . Development commenced shortly afterward . In August of that year , it was reported that the Jane 's Information Group license had been obtained by <unk> Interactive , and that the company planned to publish Mad Doc 's revival of Jane 's Attack Squadron . The full details of the agreement were announced at that year 's European Computer Trade Show , where it was revealed that Jane 's Attack Squadron would focus heavily on air @-@ to @-@ ground combat . According to Mad Doc 's Tim Farrar , " Our goal wasn 't to create a completely new game , it was to complete the game that was started at <unk> . " Farrar noted that , although the company " trimmed some of the more ambitious features " , Jane 's Attack Squadron was effectively " the same game " created by Looking Glass . As with Flight Combat , the game features one million square miles of terrain , planes with individually modelled moving parts and a physics @-@ based damage system . 
- In October 2001 , the game was officially announced in a press release by Mad Doc . Steve <unk> , the lead designer of the game at both development studios , said that he looked forward to <unk> Jane 's Attack Squadron and " <unk> [ ing ] it a new life " . He believed that the team was " very excited " to finish the game , a sentiment later echoed by the game 's producer , David <unk> . According to <unk> , the presence of members of the Looking Glass team ensured that it would " closely reflect our original vision of the game : an action @-@ packed World War II air combat simulation accessible to users of all skill levels " . Farrar announced that the team 's mission and physics editors would be released alongside the game , which he hoped would generate interest among players . Farrar later commented that , because of the game 's physics @-@ based damage system and individually modelled components , wings could be shot off and fuel tanks detonated . He also explained that coolant tanks could be hit , giving the pilot a limited amount of time before the plane engine <unk> . He wrote that losing a wing tip meant " a <unk> ride " , but the loss of the tail caused the plane to " spin into the ground " . Jane 's Attack Squadron went gold in March 2002 , and was released that month . 
- 
- = = Reception = = 
- 
- Denny Atkin of Computer Gaming World wrote that Jane 's Attack Squadron " had the potential to be <unk> of the year in 2000 " , but that it had been rendered largely irrelevant by delays and " <unk> design goals . " He believed that its graphics would have been " state of the art in 2000 " , but he found them middling in 2002 ; and he noted the presence of numerous glitches . He considered the game 's most serious flaw to be its low number of missions . Although Atkin found the air @-@ to @-@ air combat " generally fun " , citing " good pilot AI " and " decent " flight physics , he believed that the game 's bombing runs were its most outstanding element . He hoped that fans would use the mission and physics editors to improve the game , and he concluded , " It 's buggy , but when it works it 's worth flying . " Andy <unk> of PC Gamer US wrote that Jane 's Attack Squadron is " <unk> an entertaining and unique WWII prop <unk> " , but he believed that it was clearly inferior to games such as <unk> @-@ 2 Sturmovik . He found the game 's graphics to be outdated and its design to be " simplistic " , and he wrote that its " somewhat basic flight model " prevents advanced maneuvers . He praised the game 's sound effects and music , as well as its " intricate damage modeling " , as its best features . He finished by saying that , because the genre was " <unk> for fresh titles " , Jane 's Attack Squadron could be recommended despite its flaws . 
- IGN 's Tom Chick found it " unrealistic , erratic , and limited " , writing that it " looks bad , plays poorly , and is unstable . " He disliked its " canned and <unk> scripted missions " , although he found its bombing runs " interesting " and its air @-@ to @-@ air combat features acceptable . However , he believed that ease of shooting down aircraft made the damage system 's " powerful amount of flexibility " worthless . Chick believed that the multiplayer component was one of the game 's worst features , and he derided the game 's " suspiciously canned physics " , which offered " a grab bag of fidelity mixed in with <unk> of <unk> . " He summarized Jane 's Attack Squadron as " awful . " Josh Horowitz of The Adrenaline Vault noted the complex damage system , and he believed that the game " looks as good as most of today 's flight simulators " , although he experienced performance issues . He noted that the gameplay was hurt by " corner cutting or general <unk> " , such as the limited in @-@ game tutorials . Horowitz found the game " repetitive " because of its lack of missions and " low sense of involvement " , and , like Chick , he disliked its multiplayer and " linear " missions . Although he offered significant praise for its sound , Horowitz concluded that the game was " a buggy , incomplete offering " , and that those " looking for the next great Jane 's title will likely be disappointed . " 
- GameSpy 's Bernard <unk> wrote that the game failed to live up to the Jane 's Combat <unk> pedigree , and he believed that those who enjoyed " the realism of Il @-@ 2 Sturmovik will be disappointed . " He disliked its " relaxed flight models " and lack of features , and he cited a large number of glitches . However , <unk> found its damage system " robust " and he believed that the game was " not a total loss . " Like Atkin , he hoped that fans would improve the game with its detailed editors , although he believed that this was somewhat unlikely . 
- 
- 
- = 1955 Atlantic hurricane season = 
- 
- The 1955 Atlantic hurricane season was , at the time , the costliest season ever recorded . The hurricane season officially began on June 15 , 1955 , and ended on November 15 , 1955 . It was slightly above average , with 13 recorded tropical cyclones . The first storm , Alice , had persisted since December 30 , 1954 . Alice caused relatively minor impact as it tracked through the Lesser Antilles and eastern Caribbean Sea in early January . Tropical Storm Brenda caused two deaths and minor damage along the Gulf Coast of the United States in early August . The quick succession of Hurricanes Connie and Diane caused significant flooding in the Northeastern United States , with nearly $ 1 billion ( 1955 USD ) in losses and at least 232 fatalities . The next three storms – Hurricanes Edith and Flora and Tropical Storm Five – caused very minor or no impact . In early August , Hurricane Gladys caused severe localized flooding in Mexico , primarily in Mexico City . Additionally , an <unk> of Gladys inflicted minor impact in Texas . 
- Hurricane Hilda struck the Greater Antilles and then Mexico . It was attributed to at least <unk> deaths and $ 120 million in losses . In mid @-@ September , Hurricane Ione struck eastern North Carolina and contributed the flooding from Connie and Diane , resulting in seven fatalities and $ 88 million in damage . Later that month , Hurricane Janet , which peaked as a Category 5 hurricane , <unk> several countries adjacent to the Caribbean Sea , as well as Mexico and British Honduras . Janet resulted in $ 53 @.@ 8 million in damage and at least 716 deaths . An unnamed tropical storm in the month of October did not impact land . Hurricane Katie , the final storm , caused minor damage in a sparsely populated area of Hispaniola , totaling to at least $ 200 @,@ 000 ; 7 fatalities were also reported . <unk> , the storms caused 1518 deaths and $ 1 @.@ 2 billion in losses , making it the costliest season at the time . A record number of names – four – were retired following the season , which was tied by 1995 and 2004 , and then surpassed in 2005 ( when five names were retired ) . 
- 
- = = Season summary = = 
- 
- On April 11 , 1955 , which was prior to the start of the season , Gordon Dunn was promoted to the chief meteorologist of the Miami Hurricane Warning Office . Dunn was replacing Grady Norton , who died from a stroke while forecasting Hurricane Hazel of the previous season . In early June , the Hurricane Hunters received new reconnaissance aircraft , which contained the latest radar and electronic equipment , at the time . Later that month , shortly before the start of the 1955 season , a bill was proposed in the United States Senate to provide funding for 55 new radar stations along the East Coast of the United States . After the United States House of Representatives passed a bill <unk> $ 5 million , the Senate disputed about possibly increasing the funding two @-@ fold to $ 10 million . Eventually , the <unk> were installed , starting in July 1955 . After the devastating storms of the season , particularly Connie and Diane , a United States Government organization with the propose of monitoring tropical cyclones was established in 1956 with $ 500 @,@ 000 in funding ; it later became the modern @-@ day National Hurricane Center . 
- The Atlantic hurricane season officially began on June 15 , 1955 . It was an above average season in which 13 tropical cyclones formed . In a typical season , about nine tropical storms develop , of which five strengthen to hurricane strength . All thirteen depressions attained tropical storm status , and eleven of these attained hurricane status . Six hurricanes further intensified into major hurricanes . The season was above average most likely because of an strong , ongoing La <unk> . Hurricane Alice developed in late December 1954 , but persisted into January 1955 , and was operationally analysed to have developed in the latter . Within the official hurricane season bounds , tropical cyclogenesis did not occur until July 31 , with the development of Tropical Storm Brenda . However , during the month of August , four tropical cyclones formed – including Connie , Diane , Edith , and an unnamed tropical storm . Five additional tropical cyclones – Flora , Gladys , Hilda , Ione , and Janet – all developed in September . Tropical cyclogenesis briefly halted until an unnamed tropical storm formed on October 10 . The final storm of the season , Katie , dissipated on October 19 , almost a month before the official end of hurricane season on November 15 . Eight hurricanes and two tropical storms made landfall during the season and caused 1 @,@ 603 deaths and $ 1 @.@ 1 billion in damage . 
- The season 's activity was reflected with an accumulated cyclone energy ( ACE ) rating of 199 , which was above the 1950 @-@ 2000 average of 96 @.@ 1 . ACE is , broadly speaking , a measure of the power of the hurricane multiplied by the length of time it existed , so storms that last a long time , as well as particularly strong hurricanes , have high <unk> . It is only calculated for full advisories on tropical cyclones with winds exceeding 39 mph ( 63 km / h ) , which is tropical storm strength . 
- 
- = = Storms = = 
- 
- 
- = = = Hurricane Alice ( 1954 ) = = = 
- 
- On January 1 , there was already a tropical cyclone located in the central Atlantic Ocean , having developed on December 30 of the previous year . Operationally it was first observed as a hurricane on January 1 , which resulted in it being named Alice . The hurricane passed through the Leeward Islands on January 2 . Alice reached peak winds of 80 mph ( 130 km / h ) before encountering cold air and turning to the southeast . It dissipated on January 6 over the southeastern Caribbean Sea . Alice produced heavy rainfall and moderately strong winds across several islands along its path . <unk> and Anguilla were affected the most , with total damage amounting to $ 623 @,@ 500 . Operationally , lack of definitive data prevented the U.S. Weather Bureau from declaring the system a hurricane until January 2 . It received the name Alice in early 1955 , though re @-@ analysis of the data supported extending its track to the previous year , resulting in two tropical cyclones of the same name in one season . It was one of only two storms to span two calendar years , along with Tropical Storm <unk> in 2005 and 2006 . 
- 
- = = = Tropical Storm Brenda = = = 
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- Tropical Storm Brenda developed in the north @-@ central Gulf of Mexico at 0600 UTC on July 31 . During the next 24 hours , the storm strengthened rapidly and attained its maximum sustain wind speed of 70 mph ( 110 km / h ) early on August 1 ( although it is possible it briefly reached hurricane intensity ) . Later that day , Brenda made landfall east of New Orleans , Louisiana at the same intensity . The storm steadily weakened inland and by August 2 , it was downgraded to a tropical depression . Early on August 3 , Brenda dissipated while located over eastern Texas . 
- Between Pensacola , Florida and Lake Charles , Louisiana , rainfall totals were generally about 4 inches ( 100 mm ) ; flooding , if any , was insignificant . Tropical storm force winds were reported , peaking at 50 mph ( 80 km / h ) at Shell Beach , Louisiana on the south shore of Lake <unk> . At the same location , tides between 5 and 6 feet ( 1 @.@ 5 and 1 @.@ 8 m ) above normal were measured . Four people were rescued by the United States Coast Guard after their tugboat sank in Lake <unk> , while three others swam to shore . Additionally , two fatalities occurred in the vicinity of Mobile , Alabama . 
- 
- = = = Hurricane Connie = = = 
- 
- A tropical wave developed into a tropical depression east of Cape Verde on August 3 . After six hours , it strengthened into Tropical Storm Connie . By August 4 , Connie began to rapidly strengthen , becoming the first major hurricane of the season later that day . Initially , it posed a threat to the Lesser Antilles , although it passed about 50 miles ( 80 km ) north . The outer rainbands produced hurricane force wind gusts and intense precipitation , reaching 8 @.@ 65 inches ( 220 mm ) in Puerto Rico . In the United States Virgin Islands , three people died due to the hurricane , and a few homes were destroyed . In Puerto Rico , Connie destroyed 60 homes and caused crop damage . After affecting Puerto Rico , Connie turned to the northwest , reaching peak winds of 140 mph ( 220 km / h ) . The hurricane weakened while slowing and turning to the north , and struck North Carolina on August 12 as a Category 2 hurricane . 
- Connie produced strong winds , high tides , and heavy rainfall as it moved ashore , causing heavy crop damage and 27 deaths in the state of North Carolina . Connie made a second landfall in Virginia , and it progressed inland until dissipating on August 15 near <unk> <unk> . Marie , Michigan . Four people were killed in Washington , D.C. due to a traffic accident . In the Chesapeake Bay , Connie capsized a boat , killing 14 people and prompting a change in Coast Guard regulation . There were six deaths each in Pennsylvania and New Jersey , and eleven deaths in New York , where record rainfall flooded homes and <unk> . At least 225 @,@ 000 people lost power during the storm . Damage in the United States totaled around $ 86 million , although the rains from Connie was a prelude to flooding by Hurricane Diane . The remnants of Connie destroyed a few houses and boats in Ontario and killed three people in Ontario . 
- 
- = = = Hurricane Diane = = = 
- 
- A tropical wave spawned a tropical depression between the Lesser Antilles and Cape Verde on August 7 . It slowly strengthened and became Tropical Storm Diane on August 9 . After a <unk> interaction with Hurricane Connie , Diane curved northward or north @-@ northeastward and quickly deepened . By early on August 8 , the storm was upgraded to a hurricane . Only several hours later , Diane peaked as a Category 2 hurricane with winds of 105 mph ( 165 km / h ) . The storm resumed its west @-@ northwestward motion on August 13 . <unk> air in the region caused Diane to weaken while approaching the East Coast of the United States . A recently installed radar in North Carolina noted an eye feature , albeit poorly defined . Early on August 17 , Diane made landfall near Wilmington , North Carolina as a strong tropical storm . The storm then moved in a <unk> motion across North Carolina and the Mid @-@ Atlantic before re @-@ emerging into the Atlantic Ocean on August 19 . Diane headed east @-@ northeastward until becoming extratropical on August 20 . 
- Despite landfall in North Carolina , impact in the state was minor , limited to moderate rainfall , abnormally high tides , and relatively strong winds . Further north , catastrophic flooding occurred in Pennsylvania , New Jersey , New York , and New England . Of the <unk> stream gauges in the region , 129 reported record levels after the flooding from Tropical Storm Diane . Many streams reported discharge rates that were more than twice of the previous record . Most of the flooding occurred along small river basins that rapidly rose within hours to flood stage , largely occurring in populated areas ; the region in which the floods occurred had about 30 million people , and <unk> houses overall were destroyed . The floods severely damaged homes , highways , power lines , and railroads , and affected several summer camps . Overall utility damage was estimated at $ 79 million . Flooding in mountainous areas caused landslides and destroyed crop fields ; agriculture losses was estimated at $ 7 million . Hundreds of miles of roads and bridges were also destroyed , accounting for $ 82 million in damage . Overall , Diane caused $ 754 @.@ 7 million in damage , of which $ 600 million was in New England . Overall , there were at least 184 deaths . 
- 
- = = = Hurricane Edith = = = 
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- An easterly tropical wave developed into a tropical depression on August 21 in the tropical Atlantic . Moving towards the west @-@ northwest , the disturbance slowly intensified , reaching tropical storm strength at 1200 UTC on August 23 and as such was named Edith by the Weather Bureau . Afterwards , Edith began to curve towards the northwest as it gradually intensified , attaining hurricane strength on August 25 . The hurricane continued to intensify as it recurved and accelerated to the northeast , reaching its peak intensity on August 28 as a Category 2 hurricane with maximum sustained winds of 100 mph ( 160 km / h ) . At the same time , a reconnaissance aircraft reported a minimum barometric pressure of 991 mbar ( hPa ; 29 @.@ 27 inHg ) in the storm 's eye as Edith made its closest pass to Bermuda . The hurricane began to gradually weaken after it passed east of the island , before becoming extratropical on August 31 . The cyclone would later make a clockwise loop before dissipating completely late on September 3 . Although Edith remained at sea , it was suspected that the hurricane may have caused the loss of the pleasure yacht <unk> IV , after it separated from its <unk> . 
- 
- = = = Tropical Storm Five = = = 
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- A weak disturbance was first observed near Grand Cayman on August 23 , gaining tropical storm strength by 0600 UTC that day . Moving towards the north @-@ northwest , the storm passed over western Cuba on August 24 , without much change in intensity . Once in the Gulf of Mexico , the tropical storm marginally intensified , reaching peak intensity with maximum sustained wind speeds of 45 mph ( 75 km / h ) by 1200 UTC on August 26 . <unk> the Gulf Coast of the United States , the system curved towards the west . The storm maintained its intensity up until landfall in Louisiana near New Orleans on August 27 . Moving inland , it slowly weakened while crossing the Central United States , degenerating to tropical depression strength by August 29 and later dissipating over Missouri the following day . 
- Strong waves generated by the storm caused tides 4 ft ( 1 @.@ 2 m ) above average , slightly damaging coastal resorts . Weather offices advised small craft offshore to remain in port due to the strong waves . Rough seas battered the schooner Princess Friday , but the ship was able to ride out the storm . The storm produced squalls further inland , causing heavy rains . A weather station reported a minimum pressure of 1000 mbar ( hPa ; 29 @.@ 53 inHg ) , the lowest in association with the tropical storm . Despite the strong waves and heavy rains , only minor damage was reported . 
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- = = = Hurricane Flora = = = 
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- A tropical wave moved along the Intertropical Convergence Zone ( ITCZ ) and passed through Cape Verde between August 30 and August 31 . Although the <unk> do <unk> headquarters in <unk> , Brazil reported a closed circulation on August 30 , Tropical Storm Flora did not develop until 0600 UTC on September 2 , while located about 400 miles ( 640 km ) of Cape Verde . The storm strengthened at a steady pace for the following 48 hours and reached hurricane status late on September 3 . Flora headed on a <unk> track , initially moving west @-@ northwestward and then northwesterly by September 4 . It continued to intensify and by September 6 , the storm curved northward . Around time , a minimum barometric pressure of <unk> mbar ( 28 @.@ 6 inHg ) was reported . However , it may have been lower , as the storm did not attain its maximum sustained wind speed of 105 mph ( 165 km / h ) until September 7 . Flora maintained this intensity while curving to the northeast , but became extratropical at 0000 UTC on September 9 , while located about midway between Flores Island in the Azores and <unk> Island , Nova Scotia . 
- 
- = = = Hurricane Gladys = = = 
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- A tropical depression developed in the southern Gulf of Mexico at 1200 UTC on September 4 . Six hours later , it strengthened into Tropical Storm Gladys . The storm quickly intensified and reached hurricane status on September 5 , roughly 24 hours after developing . Around that time , Gladys peaked as a Category 1 hurricane with maximum sustained winds of 75 mph ( 120 km / h ) . Later on September 5 , an <unk> of Gladys with cyclonic turning formed in the northern Gulf of Mexico and struck Texas on September 6 ; it may have been a separate tropical cyclone . Initially , Gladys headed north @-@ northwestward , but then re @-@ curved south @-@ southwestward while approaching the Gulf Coast of Mexico . Early on September 6 , it made landfall near Tampico , Tamaulipas as a weakening tropical storm . The system rapidly dissipated inland . 
- Gladys dropped up to 25 inches ( 640 mm ) in Tampico , Tamaulipas . The worst of the flooding from Gladys occurred in Mexico City . Roughly 5 @,@ 000 residents were isolated and required rescue . Police estimated that 2 @,@ 300 homes were inundated with 5 to 7 feet ( 1 @.@ 5 to 2 @.@ 1 m ) of water . About 30 @,@ 000 families were impacted by the storm . Two children drowned and five additional people were listed as missing . In Texas , the highest sustained wind speed was 45 mph ( 75 km / h ) in the Corpus Christi – Port O 'Connor area , with gusts between 55 and 65 mph ( 89 and 105 km / h ) offshore . Precipitation peaked at 17 @.@ 02 inches ( 432 mm ) in <unk> Bluff , a neighborhood of Corpus Christi . Flooding in the area forced " scores " of people to evacuate their homes . Damage estimates reached $ 500 @,@ 000 . 
- 
- = = = Hurricane Ione = = = 
- 
- A tropical wave developed into a tropical depression early on September 10 , while located about midway between Cape Verde and the Lesser Antilles . After six hours , the depression strengthened into Tropical Storm Ione . Eventually , it turned to the northwest . At 0000 UTC on September 15 , Ione reached hurricane intensity , while situated north of the Leeward Islands . Ione continued to deep while moving northwest . The storm reached Category 4 intensity with maximum sustained winds of 140 mph ( 220 km / h ) and a minimum barometric pressure of 938 mbar ( 27 @.@ 7 inHg ) early on September 18 . Around midday on the following day , it made landfall near Wilmington , North Carolina as a Category 2 hurricane . Shortly after moving inland over eastern North Carolina , Ione weakened to a tropical storm . Late on September 19 , Ione re @-@ emerged into the Atlantic near Norfolk , Virginia . The storm quickly re @-@ strengthened early on September 20 , but transitioned into an extratropical cyclone on September 21 . 
- Strong winds , heavy rainfall , and abnormally high tides <unk> some areas along the East Coast of the United States , especially North Carolina . In Cherry Point , sustained winds reached 75 mph ( 121 km / h ) , with gusts up to 107 mph ( 172 km / h ) . Overall , damage was slightly more than $ 88 million , mostly to crops and agriculture . Rainfall in the state peaked at 16 @.@ 63 inches ( 422 mm ) in <unk> . Storm surge in North Carolina peaked at 5 @.@ 3 feet ( 1 @.@ 6 m ) in <unk> Beach . As a result , several coastal roadways were flooded , including a portion of Highway 94 and Route 264 . Seven deaths were reported in North Carolina . The remnants of Ione brought gusty winds to Atlantic Canada , which broken poles , uprooted trees , interrupted telephone service , damaged chimneys and caused power outages , especially in St. John 's , Newfoundland and <unk> . 
- 
- = = = Hurricane Hilda = = = 
- 
- A tropical wave located near the Lesser Antilles spawned a tropical depression on September 12 . It is estimated that the depression strengthened into Tropical Storm Hilda early on the following day . Hilda quickly intensified while moving westward into a small hurricane by September 12 . Although the storm passed just north of Hispaniola on September 13 , damage is unknown , if any . Later that day , Hilda made landfall near the southeastern tip of Cuba on September 13 . There , it dropped heavy rainfall and produced gusty winds that destroyed 80 % of the coffee crop in Oriente Province . In the eastern Cuban city of <unk> , Hilda severely damaged the oldest church in the country . Damage totaled $ 2 million in Cuba , and there were four deaths . 
- Later , the hurricane moved across the Caribbean Sea , causing light damage in the sparsely @-@ populated region of the eastern Yucatán Peninsula . After reaching the Gulf of Mexico , Hilda strengthened to reach peak winds of 120 mph ( 195 km / h ) late on September 18 . Before the hurricane moved ashore , there was residual flooding in Tampico from earlier Hurricane Gladys . Hilda struck the city early on September 19 , with gusts estimated at 150 mph ( 240 km / h ) . The storm dropped heavy rainfall that flooded 90 % of Tampico , while its strong winds damaged half of the homes , leaving 15 @,@ 000 homeless . Throughout Mexico , 11 @,@ 432 people were directly affected by Hilda . Overall , the storm killed 300 people and caused over $ 120 million . Additionally , the <unk> of Hilda caused minor flooding in southern Texas , particularly in <unk> . 
- 
- = = = Hurricane Janet = = = 
- 
- Hurricane Janet was the most powerful tropical cyclone of the season and one of the strongest Atlantic hurricanes on record . The hurricane formed from a tropical wave east of the Lesser Antilles on September 21 . Moving toward the west across the Caribbean Sea , Janet fluctuated in intensity , but generally strengthened before reaching its peak intensity as a Category 5 hurricane on the Saffir – Simpson hurricane scale with winds of 175 mph ( 280 km / h ) . The intense hurricane made landfall at that intensity near <unk> , Mexico on September 28 . Janet 's landfall as a Category 5 hurricane on the Yucatán Peninsula marked the first recorded instance that a storm of such intensity in the Atlantic basin made on a continental mainland , with all previous storms making landfall as Category 5 hurricanes on islands . After weakening over the Yucatán Peninsula , it moved into the Bay of Campeche , where it slightly strengthened before making its final landfall near Veracruz on September 29 . Janet quickly weakened over Mexico 's mountainous terrain before dissipating on September 30 . 
- In its developmental stages near the Lesser Antilles , Janet caused significant damage to the island chain , resulting in 189 deaths and $ 7 @.@ 8 million in damages in the <unk> and Barbados . While Janet was in the central Caribbean Sea , a reconnaissance aircraft flew into the storm and was lost , with all eleven crew members believed perished . This was the only such loss which has occurred in association with an Atlantic hurricane . A Category 5 upon landfall on the Yucatán Peninsula , Janet caused severe devastation in areas on <unk> Roo and British Honduras . Only five buildings in <unk> , Mexico remained intact after the storm . An estimated 500 deaths occurred in the Mexican state of <unk> Roo . At Janet 's second landfall near Veracruz , significant river flooding ensued , worsening effects caused by Hurricanes Gladys and Hilda earlier in the month . The floods left thousands of people stranded and killed at least 326 people in the Tampico area . The flood damage would lead to the largest Mexican relief operation ever executed by the United States . At least 1 @,@ <unk> deaths were attributed to Hurricane Janet , as well as $ 65 @.@ 8 million in damages . 
- 
- = = = Tropical Storm Eleven = = = 
- 
- A tropical wave moved off the coast of Africa on September 18 and continued west @-@ northwestward . It is possible that the system developed into a tropical depression the next day , although lack of data prevented such classification until September 23 , when a nearby ship reported winds of 35 mph ( 55 km / h ) . An approaching cold front turned the system to the north on September 24 . The structure gradually became better organized , and after turning to the northeast on September 26 , the depression intensified into a tropical storm . This was based on a ship report of 45 mph ( 75 km / h ) winds , which was also estimated as the system 's peak intensity . On September 27 , the system became extratropical and accelerated its forward motion , dissipating within a larger extratropical storm south of Iceland on the next day . 
- 
- = = = Tropical Storm Twelve = = = 
- 
- A tropical wave was reported to have passed through Cape Verde on October 4 . The system slowly developed a vertex as it curved in a generally northward direction . By early on October 10 , two ships reported that a tropical depression formed almost halfway between the Azores and the Leeward Islands . After six hours , the depression strengthened into a tropical storm . While re @-@ curving to the northeast , the storm attained its maximum sustained winds of 65 mph ( 100 km / h ) ; the lowest atmospheric pressure recorded in relation to the storm was 1 @,@ 000 mbar ( 30 inHg ) , but the time of measurement is unknown . Although no significant weakening occurred , it eventually merged with an extratropical cyclone on October 14 , while still well southwest of the Azores . During its extratropical stage , a ship in the area reported an atmospheric pressure as low as 979 mbar ( 28 @.@ 9 inHg ) . 
- 
- = = = Hurricane Katie = = = 
- 
- A disturbance in the ITCZ developed into a tropical depression north of Panama on October 14 . Early on the following day , the depression strengthened into Tropical Storm Katie . The system moved generally northeast due to the presence of a strong low pressure area along the East Coast of the United States . Later that day , Hurricane Hunters observed a rapidly intensifying hurricane , encountering winds of 110 mph ( 175 km / h ) and a pressure of <unk> mbar ( 29 @.@ 1 inHg ) several hours before the peak intensity . Early on October 17 , Katie made landfall in extreme eastern <unk> @-@ <unk> Department , Haiti as a strong Category 2 hurricane ( although it may have been stronger ) . About half of homes in the town of <unk> @-@ à @-@ <unk> were destroyed . Across the border in <unk> , Dominican Republic , 68 houses were damaged . Overall losses were at least $ 200 @,@ 000 and 7 fatalities were reported . 
- While crossing the mountainous terrain of Hispaniola , Katie became very disorganized and rapidly weakened to a tropical storm early on October 17 , within a few hours after moving inland . Later that day , the storm emerged into the Atlantic Ocean just east of Puerto Plata , Dominican Republic . Katie began accelerating to the northeast on October 18 . During that time , the storm re @-@ intensified and briefly approached hurricane intensity , although it failed to strengthen further due to interaction with a cold front . After passing just east of Bermuda on October 19 , the storm transitioned into an extratropical cyclone . The remnants of Katie were last observed the following day . 
- 
- = = Storm names = = 
- 
- The following names were used for tropical cyclones that reached at least tropical storm intensity in the North Atlantic in 1955 . However , two of such storms went unnamed . Connie , Diane , Ione , and Janet would later be retired . The 1955 season was tied with the 1995 and 2004 for the most storm names retired after a single season , until five names were retired in 2005 . Storms were named Brenda , Connie , Diane , Edith , Flora , Gladys , Hilda , Ione , Janet and Katie for the first time . Names not assigned are marked in gray . 
- 
- 
- = <unk> = 
- 
- noitulovE ( " Evolution " backwards ) is a British television and cinema advertisement launched by Diageo in 2005 to promote Guinness <unk> stout . The 60 @-@ second piece formed the cornerstone of a £ 15 million advertising campaign targeting men in their late twenties and early thirties . The commercial shows , in reverse , the adventures of three characters who evolve from mudskippers to present day humans before tasting Guinness in a London pub . The commercial was handled by the advertising agency Abbott Mead Vickers BBDO , with a budget of £ <unk> It was directed by Daniel Kleinman . Production was contracted to Kleinman Productions , with post @-@ production by <unk> <unk> . It premiered on British television on 3 October 2005 . 
- noitulovE is the fifth television / cinema piece in the Good things come to those who wait series , and its premiere marked the end of a four @-@ year hiatus . The advert and its associated campaign were a critical and financial success . It received over 30 awards from professional organisations in the advertising and television industries , and was the most @-@ awarded commercial worldwide in 2006 . The impact of the campaign was such that during a period in which the UK beer market experienced a substantial decline in revenue , Guinness reported that its year @-@ on @-@ year earnings within the region had noticeably increased . At the same time , Guinness achieved its highest @-@ ever volume and value shares and became the market leader within the region . This was attributed in no small part to the positive reception of noitulovE . 
- 
- = = <unk> = = 
- 
- The piece begins with three patrons taking their first sip of Guinness in a London pub . To the accompaniment of Sammy Davis , Jr . ' s rendition of " The Rhythm of Life " from the 1969 film version of the 1966 Broadway musical Sweet Charity , a reverse @-@ motion sequence begins . The three men retreat from the bar and into the street , other patrons disappearing as they pass . As they move down the street , a reverse time @-@ lapse @-@ style sequence transforms their clothes to match a rapidly changing urban scene , which progresses through modern @-@ day London to the <unk> period . Electric lights transform into gas lamps and buildings begin to disappear frame by frame . A short cutaway sequence shows the city <unk> into the past , shrinking to a small Saxon settlement before disappearing entirely . Returning to the main sequence , the three men 's clothes and hairstyles are adjusted into Bronze Age equivalents as they pass through <unk> woodland . A close @-@ up of one of the characters shows his features quickly transformed into those of a <unk> . The trio are then frozen in an ice age glacier . 
- The three re @-@ emerge from the glacier as primitive <unk> , their clothes ripped away to reveal <unk> . They continue to walk backwards with a more <unk> gait , and soon turn into <unk> . From there , they are transformed into a number of different species in quick succession , including flying squirrels , <unk> mammals , aquatic mammals , fish , flightless birds , small dinosaurs , and burrowing lizard @-@ like creatures . The environment around them changes rapidly as they travel , with <unk> showing millions of years of geological changes occurring in less than a few seconds . Finally , the three become mudskippers around a green @-@ brown puddle . The action briefly moves forward again to show the middle character registering his disgust at the taste of the water with a " <unk> ! " sound . The commercial ends with a transition to a product shot of three <unk> of Guinness accompanied by the strapline " Good things come to those who wait " . 
- 
- = = Production = = 
- 
- 
- = = = Background = = = 
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- Abbott Mead Vickers BBDO won the account for Guinness in 1996 with a campaign built on their new strapline " Good things come to those who wait " . They produced several commercials using variations on the theme , including <unk> , Bet on Black , and the critically acclaimed Surfer , voted the " Best <unk> of All Time " by the British public in 2002 . After the 2001 <unk> advertisements , Diageo , the corporate owners of Guinness , decided to pursue a more pan @-@ European marketing strategy . The strapline proved difficult to translate , and was abandoned . Several new <unk> were tried out over the next three years , including " Believe " ( Free and Tom <unk> ) and " A story of darkness and light " ( Moth and Mustang ) . 
- The new marketing strategy did not prove particularly successful , and in 2004 Diageo returned to regional advertising . <unk> BBDO were presented with the choice of coming up with either a new theme to appeal to the 18 – 35 British male demographic or a new angle on the tried and tested Good things ... concept . A number of ideas were put forward , including " The <unk> <unk> " . From this concept , noitulovE was quickly plotted out : the advert would show three individuals waiting 500 @,@ 000 @,@ 000 years before finally taking their first sip of Guinness , the timeline compressed into a 50 @-@ second clip . The decision to run the " Evolution sequence " in reverse was taken fairly early , as it was felt that it would better hold viewers ' interest during a 60 @-@ second television spot . 
- 
- = = = Pre @-@ production = = = 
- 
- The project had not yet been <unk> when the agency approached Daniel Kleinman , known for his work on James Bond title sequences , with the intention of taking him on as a director . After looking over the concept pitch and a rough draft of the script , Kleinman 's immediate impressions were that the idea was " fresh " , " an opportunity to try out some new techniques " , and that it would " put Guinness back on track , doing a big ' <unk> ' idea " . 
- Kleinman contacted a pair of Canadian graphic novel artists to begin the <unk> process . <unk> the commercial meant that the agency could determine how much of the £ <unk> budget to <unk> to each section , and provided them with visuals to use as part of the presentation to Diageo when pitching the various concepts for a decision on which to pursue . The effort paid off and approval was given to move ahead with production . 
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- = = = Production = = = 
- 
- Production of the commercial took place over two months , with principal photography shot in Iceland . Time @-@ lapse photographs were taken of the country 's <unk> , volcanic terrain and frozen lakes using 35 mm film cameras . The shooting was done in the early summer for the nearly continuous daylight that the season afforded them . The next pieces of the commercial to be assembled were the live @-@ action segments , shot in a <unk> studio in London . Filming was done in three stages , with the three actors changing into different sets of prosthetic makeup at each stage . For the final section the actors spent a week <unk> the choreography behind walking backwards with an appropriately <unk> gait . <unk> were attached to the back of each actor , allowing them to lean forwards to give the impression that they were being " sucked back in time " when the final cut was put together . While filming the actor sequences , VFX supervisor William Bartlett filmed the aerial view of London from Tower 42 's Vertigo bar . 
- With computer @-@ generated imagery looking to make up so much of the commercial , Kleinman attempted to use film of real elements wherever possible . To this end , 200 mudskippers were brought to the studio from South Africa for the final scene , arriving via Singapore . An entire afternoon was set aside for filming the <unk> sequence . The footage obtained formed the major part of the final cut of the scene , with only one or two post @-@ production changes : the addition of tail fins and animation of the expression of disgust that closes the piece . Stop motion footage of other real elements was taken , including a stage @-@ by @-@ stage <unk> of plants , used to show flora coming back to life in the reverse sequence , and shots of baking bread , used to model the geological changes to background rock formations . Additional real elements were to have been incorporated into the commercial , mostly from stock footage of several animal species , but only short segments of <unk> and lizards appeared in the final cut . 
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- = = = Post @-@ production = = = 
- 
- Post @-@ production work was handled mostly by <unk> <unk> , who had worked on previous Guinness campaigns Surfer and <unk> , and had worked with Daniel Kleinman on a number of outside projects , including several commercials and James Bond title sequences . The project was overseen by William Bartlett , known for his visual effects work on the BBC television documentary Walking with Dinosaurs . The original schedule allowed for three and a half months in post @-@ production , with airing of the commercial to follow almost immediately . 
- The 24 @-@ man animation team was split into two groups . Half were assigned to the creation of the 15 new CGI creatures <unk> noitulovE ( in Maya ) , while the other half created the backgrounds ( in <unk> ) . <unk> work – combining the <unk> shots with stock footage and CGI elements – was performed in Flame and Inferno . As the final commercial was to be shown on cinema screens , the animators worked at a resolution higher than that afforded by the <unk> definition used by British <unk> @-@ encoded television sets , to improve the appearance of the advert when projected . 
- Near the end of post @-@ production , the creative team decided that the music chosen to accompany the advert , an electronic track by Groove <unk> , was not working particularly well . Peter Raeburn , who had chosen the track used in Surfer ( <unk> 's " <unk> Planet " ) , was brought on as music director . Raeburn suggested three pieces , with " Rhythm of Life " ultimately presented to Guinness as an alternative and approved as a replacement . 
- 
- = = Release and reception = = 
- 
- 
- = = = Schedule = = = 
- 
- noitulovE was originally to have begun its run in September 2005 , but the <unk> was pushed back several weeks as post @-@ production took longer than anticipated . As had been the case with several earlier campaigns , the commercial was to air in several bursts , throughout 2005 and 2006 . <unk> were purchased in the commercial breaks of sports broadcasts , high @-@ budget television dramas and shows whose primary audience overlapped with the campaign 's target demographic of British males in the 24 – 35 age range . The first burst was commissioned to run from 3 October to 13 November 2005 , during programming such as the UEFA Champions League , Lost , Vincent , <unk> and Dec 's Saturday Night <unk> and terrestrial television screenings of Austin Powers : <unk> . 
- The second burst lasted through December . The focus moved to <unk> television , with appearances in live televised football matches , films , and popular programming such as I 'm a Celebrity , Get Me Out of Here ! . Two further bursts were commissioned for 2006 , to run from 15 May to 9 June and 22 August to 8 October . Programmes selected for the May – June burst included Celebrity X Factor , Big Brother and live football and cricket matches . The final series of spots ran during programming totalling 56 ratings points per week ( 56 % of British television viewers ) , with much of the budget assigned to <unk> television . 
- 
- = = = Awards = = = 
- 
- noitulovE was well received by critics within the advertising and television industries , and was predicted to win the 2006 Cannes Lions Film Grand Prix , one of the advertising industry 's highest awards . Advertising Age said of the piece : " A flawless <unk> production to an irresistible piece of music <unk> a brilliant , <unk> witty new iteration of a longstanding , unique positioning . This isn 't just great advertising ; it is perfect advertising . " <unk> <unk> , Executive Creative Director for Del <unk> <unk> <unk> & <unk> remarked on the campaign in the run @-@ up to the festival , saying " noitulovE is , in my opinion , the best . [ ... ] This execution is absolutely incredible , as is the production . " The main competition for the prize was thought to be the Australian Big Ad for Carlton draught beer , and the British <unk> , for Sony 's <unk> line of high definition television sets . After the three received Gold Lions in the Film category , they were shortlisted by the judges as contenders for the Grand Prix . Ultimately , the honour went to noitulovE . After the decision , David <unk> , president of the jury which determined the outcome , said " It 's a very very strong ad . A lot of the jurors felt that it was not only a stand @-@ alone , remarkable ad , but also a triumphant return for a fabulous campaign . " The victory placed director Daniel Kleinman at Number 29 in The Independent 's list of the Top 50 <unk> of 2006 . 
- The 2006 edition of the <unk> Report , an authoritative annual publication determining the advertising industry 's most critically successful campaigns , revealed that noitulovE had received more awards that year than any other campaign worldwide . Among the awards were three <unk> , two Golden <unk> , and the Special Jury Prize at the <unk> Awards . 
- The ad was also a hit with the public . It has been credited by Guinness as being responsible for the substantial boost in sales experienced by the brand during the period in which it was broadcast . While revenues within the UK beer market declined by an average of − 0 @.@ 4 % ( − £ <unk> ) , the year @-@ on @-@ year figures for Guinness showed an increase of 3 @.@ 6 % ( + £ <unk> ) . Between October 2005 and October 2006 , Guinness achieved its highest ever volume ( 6 @.@ 8 % ) and value ( 7 @.@ 4 % ) shares , taking the position of market leader from Stella <unk> . Diageo attributed the growth in no small part to the positive reception garnered by noitulovE . 
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- = = = Legacy = = = 
- 
- As one of the most recognisable British television advertisements of 2006 , noitulovE was one of two commercials ( the other being Sony 's <unk> ) to feature in a £ <unk> campaign launched by Digital UK to raise awareness of the imminent <unk> within the UK from <unk> to digital television . New versions of the two adverts were produced , showing the first few seconds of the original spot before being interrupted by " <unk> Al " , an animated spokesman for the campaign . 
- In 2004 , Guinness launched a retrospective television advertising campaign promoting Guinness Extra Cold stout , featuring new ten @-@ second versions of commercials broadcast between 1984 and 2004 . These included Mars ( with <unk> <unk> reprising his role as the " Pure Genius " ) , <unk> , Fish Bicycle , Surfer , and Bet on <unk> noitulovE joined the campaign in 2006 , and was the only piece to receive more than one new version . In the first of these , the patrons are <unk> only seconds after taking their first sip of Guinness in a glacier identical to the one which appeared half @-@ way through the original spot . In the second , the sea through which the three fish bound backwards in the original spot is frozen while the trio are in mid @-@ leap , leaving the characters <unk> across the surface . In the final version , the <unk> pool at the end of the original spot freezes while the mudskippers are taking their drink , and the protagonists ' <unk> are left stuck in the ice . 
- When noitulovE was first proposed , it was the only pitch revisiting the Good things come to those who wait campaign , as , according to <unk> Ian <unk> , both <unk> BBDO and Diageo " didn 't think [ they ] could do something good enough to warrant following on from Surfer and the like . " However , following the success of noitulovE , three additional commercials have been aired within the Good Things ... campaign : <unk> , Hands , and <unk> Point ( Guinness ' most expensive advertising campaign to date ) . 
- 
- 
- = Stuart McCall = 
- 
- Andrew Stuart Murray McCall ( born 10 June 1964 ) , usually known as Stuart McCall , is a football manager and former player who is the current manager of Bradford City . He made a total of 763 league games and in 40 full international matches for Scotland during his playing career . 
- McCall started his professional career with Bradford City , where he made his senior debut in 1982 . He played six seasons at Valley Parade , during which time he won the Division Three championship , a title which was overshadowed by the Bradford City stadium fire when 56 people died and in which his father Andy was injured . After missing out on promotion in 1987 – 88 , McCall moved to Everton , for whom he scored twice but finished on the losing side in the 1989 FA Cup Final . In 1991 , he moved to Rangers , with whom he spent seven seasons and won six league titles , three Scottish Cups and two Scottish League Cups . McCall returned to Bradford City as captain to take them into the top division of English football for the first time in 77 years . After four seasons he moved to Sheffield United , where he retired as a player in 2005 . 
- Born and raised in England , McCall qualified to play for Scotland through his Scottish father . He won 40 international caps and scored one goal in the 1990 FIFA World Cup in Italy . He also played in two European Championships but his international career ended after he was left out of the 1998 World Cup squad . 
- McCall was part of the coaching staff during his second spell at Bradford City , briefly serving as caretaker @-@ player manager in 2000 . He continued his coaching at Sheffield United and was assistant manager to Neil Warnock until May 2007 , when he returned to Bradford City for a third time , this time as manager . He spent two @-@ and @-@ a @-@ half seasons in charge of Bradford City , leaving in February 2010 . Just before the end of 2010 , he was appointed Motherwell manager . He stayed at Fir Park for four years , helping the club finish second in the league twice . After a poor start to the 2014 – 15 season , he resigned in November 2014 . McCall was appointed manager of Rangers in March 2015 , but left the club at the end of a short @-@ term contract . 
- 
- = = Early and personal life = = 
- 
- Stuart McCall was born on 10 June 1964 in Leeds , England , to Scottish parents Andy , a former professional footballer , and Jean McCall . He was the couple 's third child after Leslie and <unk> , who were 20 and 15 respectively when Stuart was born . The family home was just round the corner from Leeds United 's <unk> Road ground where McCall would spend many Saturday <unk> watching United , dreaming of following his father and playing for Leeds , even after the family moved to <unk> . 
- McCall played football for Upper <unk> Primary School and <unk> Middle School , even scoring a winning goal for the latter in a cup final when he came on as a substitute with his arm in a sling . McCall also played table tennis as a schoolboy but excelled at football , <unk> the Leeds under @-@ 11 boys team and playing for other Leeds representative sides . McCall 's parents split and he made up for his size when he moved to one of Leeds ' toughest estates and played for pub sides by the age of 14 . He moved schools to Harrington High and also played for local young sides <unk> Juniors , <unk> and later Farsley Celtic . McCall thought he had missed his chance of playing professionally after a string of other players were signed by professional clubs , until Farsley played Bradford City 's junior side in a friendly and he impressed coach Bryan Edwards enough to be asked for a trial . 
- 
- = = Playing career = = 
- 
- 
- = = = Club career = = = 
- 
- 
- = = = = Bradford City = = = = 
- 
- McCall came through City 's youth system after he was signed by George Mulhall in 1980 from Farsley Celtic at the age of 16 , before becoming an apprentice in June 1981 . Mulhall 's successor , Roy McFarland gave McCall his debut at Reading on 28 August 1982 – the opening day of the 1982 – 83 season – when he <unk> for <unk> <unk> at right back . He had played just six league games by 29 January 1983 when he made the first of 134 consecutive league appearances , all in midfield under new manager Trevor Cherry . City finished 12th in Division Three that season . The following season City struggled to make up for the absence of Bobby Campbell , who had left to join Derby County , and won just one of their first fifteen games , until Cherry bought Campbell back from Derby , and City won a record ten consecutive games on their way to a seventh place finish . 
- During the summer of 1984 , Cherry made the two key signings of central defender Dave Evans and right winger John Hendrie to build on the previous season 's high finish . McCall was an integral part of the team as City won the Division Three championship in 1984 – 85 , during which he scored eight goals as one of two ever @-@ present players . The title was assured in the penultimate game when McCall scored the second goal in a 2 – 0 victory over Bolton Wanderers . The league title was <unk> before the final game of the season on 11 May 1985 at home to Lincoln City . However , the club 's title was overshadowed when 56 people died in the Bradford City stadium fire when the Valley Parade ground 's main stand caught fire after 40 minutes of play , during which McCall 's father , who was with other family members , was badly injured . After the fire , McCall , still in his kit , spent several hours driving from the ground to his sister 's house , then to Bradford Royal <unk> and <unk> Hospital trying to find his father . His father had suffered severe burns and needed skin <unk> on his hands and head and was in hospital for several weeks . 
- For the following 19 months , the club played games away from Valley Parade . Cherry and the players became a close @-@ knit team , attending <unk> of the victims and other engagements in the months that followed , and the club 's 13th place finish in Division Two in 1985 – 86 was hailed a major achievement . During Bradford 's time away from Valley Parade , McCall also turned his back on Leeds United , the team he had supported as a child , after their fans set fire to a chip van at <unk> Stadium . McCall became club captain in November 1986 , aged just 21 , after Peter Jackson moved to Newcastle United . Under Cherry 's replacement , Terry Dolan , the club held off any relegation threats to finish tenth in 1986 – 87 . Like Jackson , McCall and Hendrie were both keen to move to a Division One club , but they agreed in 1987 to stay for one final season . 
- Dolan brought in Paul <unk> , Brian Mitchell and Lee <unk> in a bid to help McCall and Hendrie realise their dreams with City . They mounted a promotion challenge in 1987 – 88 and were top for much of the season until they <unk> in the New Year . When promotion was missed initially by one point on the last day of the season after a 3 – 2 defeat against Ipswich Town and then through play @-@ off defeat to Middlesbrough , McCall left the club , signing for Everton for £ 850 @,@ 000 in June 1988 . He had played 238 league games for the club , scoring 37 goals , and in total played 285 games , scoring 46 goals . McCall was later outspoken in his autobiography , The Real McCall , about City 's failure to strengthen the side to secure promotion . 
- His departure was <unk> when he was forced to go to the Football League with a Professional <unk> ' Association representative to win £ 8 @,@ <unk> @.@ 15 of an unpaid signing @-@ on fee . 
- 
- = = = = Everton = = = = 
- 
- McCall joined Everton at a time when its former triumphant side of the mid @-@ 1980s had broken up , following the ban on English sides competing in Europe , which marked the start of a period of <unk> at Goodison Park . His Everton debut came in a 4 – 0 victory over Newcastle United on 27 August 1988 against his former teammate Hendrie , who was making his debut for Newcastle . McCall also returned to Valley Parade for a League Cup tie , but his Everton side were knocked out by Bradford 3 – 1 on 14 December 1988 . He started 29 league games in 1988 – 89 as well as another four substitute appearances , but failed to score in the league . He was also a substitute in the 1989 FA Cup Final when he scored Everton 's equaliser in the Merseyside derby against Liverpool to take the game into extra @-@ time . He scored another equaliser during extra @-@ time , but Liverpool 's own substitute Ian Rush also scored two to secure a 3 – 2 victory for Liverpool . 
- McCall made a second appearance in an Everton shirt at Valley Parade , when he was invited by former teammate Mark Ellis to bring a side for his testimonial . In three seasons at Everton , McCall played 103 league games as well as earned his first caps with Scotland but he failed to lift any trophies as the club finished eighth , sixth and ninth in the league . Apart from the FA Cup final defeat in 1989 , the closest he came to winning a trophy at Everton was in the 1989 – 90 season , when Everton topped the league in late autumn and remained in contention for the title for the most the rest of the season until disappointing form in the run @-@ in saw them finish sixth . 
- 
- = = = = Rangers = = = = 
- 
- In the summer of 1991 , McCall signed for Scottish club Rangers for £ 1 @.@ 2 million . Rangers had just won their third successive Scottish Premier Division title . Under newly appointed manager Walter Smith , McCall ended up playing in the final six of the club 's nine successive Scottish league titles . In his first season at <unk> , Rangers won the league and cup double , before they achieved greater success in 1992 – 93 , winning both cups 2 – 1 against Aberdeen and coming nine points ahead of Aberdeen in the league . McCall also enjoyed European success that season when the Glasgow club narrowly missed out on an appearance in the UEFA Champions League 1992 – 93 final , coming second in the semi @-@ final group stage to eventual winners <unk> de Marseille . Citing the reason for their success as the spirit which Smith instilled in the team , McCall later said : " It was an incredible season . We won the domestic Treble , we went 44 games unbeaten and we did not lose a single game in Europe . And , though we said we would do it again next year , we all knew it was <unk> . " In 1993 – 94 , Rangers added another Scottish League Cup title along with the league championship , but lost 1 – 0 in the final of the Scottish Cup to Dundee United , surprisingly being denied a second successive treble . The following season saw Rangers win the league by their greatest margin as they finished 15 points ahead of Motherwell , but they failed to reach the final of either of the domestic cups . Although their winning margin was reduced to four points , from city rivals Celtic , in 1995 – 96 , Rangers ' points tally of 87 was a record @-@ high total . McCall played in his fourth Scottish Cup final as Rangers defeated Hearts 5 – 1 . His Rangers side again pushed Celtic into second place in 1996 – 97 and defeated Hearts 4 – 3 in the Scottish League Cup . But with the club chasing an unprecedented 10th straight title in 1997 – 98 they had to settle for the runners @-@ up position , with Celtic winning the league by two points on the final day of the season . McCall was substituted in the Scottish Cup final defeat to Hearts as Rangers went the season without picking up a single title for the first season in McCall 's time at the club . In February 2008 , McCall became the <unk> <unk> into the Rangers hall of fame . McCall 's former teammate and Rangers assistant manager <unk> <unk> presented him with the award . 
- 
- = = = = Back to Bradford City = = = = 
- 
- McCall still had one year left on his Rangers contract in 1998 , but much of the team that Walter Smith had built had left and McCall was allowed to leave on a free transfer by new manager Dick <unk> , as long as he joined an English club . Barnsley and Huddersfield Town were both interested in signing McCall , but he rejoined Bradford City as club captain . Rookie manager Paul Jewell put together a squad which emerged as surprise promotion contenders after two seasons spent battling relegation , adding other new signings , including central midfield partner Gareth <unk> and striker Lee Mills , who went on to be club 's top goal @-@ scorer . The season started off slowly with just one win from the first seven games , but by the latter half of the season , City were vying with Ipswich Town and Birmingham City for the second promotion spot behind runaway leaders Sunderland . Loan signing Lee Sharpe and Dean Windass were added to the ranks and City had the chance to seal promotion in their penultimate game against relegation @-@ threatened Oxford United . The game finished as a 0 – 0 draw , with McCall heading over the goal in the final minutes , taking the promotion bid to the final game of the season . Days later he was named the club 's player of the year . A 3 – 2 victory over Wolverhampton Wanderers at <unk> on 9 May 1999 ensured promotion to the Premier League and denied Ipswich Town – the team that had thwarted McCall and Bradford 11 years before . 
- Bradford were expected to struggle in their first season in the top flight for 77 years . Jewell signed David Wetherall , Dean Saunders and Neil <unk> , all experienced top flight performers , but City were in the bottom four teams for most of the season . It was Bradford 's home form – they earned 26 of their 36 points at Valley Parade – that was key to City avoiding relegation , which was narrowly averted by two points after a shock 1 – 0 final day victory over Liverpool in 1999 – 2000 , sending Wimbledon down instead . When Jewell left only days after the season ended , McCall was appointed assistant manager to Chris Hutchings , and subsequently served as caretaker manager for two games when Hutchings was sacked after just 12 games of the 2000 – 01 league season . City were relegated with just 26 points . During a 6 – 1 defeat to West Yorkshire rivals Leeds United in the penultimate game , McCall and team @-@ mate Andy Myers fought on the pitch . 
- McCall stayed on for one more season before he was released by manager Nicky Law in May 2002 , shortly before the club went into administration for the first time after finishing 15th in Division One . His playing career at Bradford City had looked uncertain in December 2001 before Law arrived , when previous manager Jim Jefferies had left McCall out of the side in a 3 – 1 defeat at Manchester City following a training ground dispute . However , it was Jefferies who lost out in the dispute when he resigned his post a week later after summit talks with chairman Geoffrey Richmond . In April 2002 , McCall 's testimonial match against Rangers attracted a crowd of more than 21 @,@ 000 to Valley Parade . McCall gave part of the proceeds from his testimonial to the Bradford burns research unit , which was set up following the 1985 fire . Two years after his benefit match , McCall played one more time in City colours in a Save Our City appeal match organised by Bradford 's evening newspaper , the Telegraph & <unk> , to raise funds for the club , who were now in administration for a second time . 
- 
- = = = = Sheffield United = = = = 
- 
- On 2 July 2002 McCall joined Sheffield United , where he played an integral part in their first @-@ team side , despite being 38 , and also coached the reserves to the league title . He played 71 league games over the next two seasons , and scored twice , including a winner against former side Bradford . He was in the side that reached the Division One play @-@ off final in 2003 as well as the semi @-@ finals of both cup competitions that year . However he and Dean Windass , who was also now at Sheffield United , were both left out of the play @-@ off final , as United lost 3 – 0 to Wolverhampton Wanderers . McCall played just two League Cup games in the 2004 – 05 season , and retired just a few weeks before his 41st birthday . His career total of 763 league games placed him in 13th position on the all @-@ time appearance list of British footballers . 
- 
- = = = International career = = = 
- 
- McCall was picked for England and Scotland under @-@ 21 sides on the same day in 1984 , and chose to play for England in a game against Turkey . However , he was only picked as a substitute and the referee blew the final whistle with McCall waiting to come on . He later told Scottish newspaper Glasgow Herald , " I felt it was a mistake almost from the start . I was put on the bench and they tried to bring me on with a minute to go . But I took my time re @-@ tying my boots and generally warming up and <unk> didn 't get on , otherwise that would have been that . " As a result , McCall was still eligible for both England and Scotland , and he eventually switched <unk> to the latter , for whom he qualified through his father . He made his Scotland debut at under @-@ 21 level in March 1988 , ironically against England . McCall made one more appearance for Scotland under <unk> , against France in 1990 . 
- Later the same year , McCall was called up to the Scottish senior team . He won his first cap on 28 March 1990 in a 1 – 0 friendly victory over Argentina . He played in five friendlies in 1990 which earned him a call up to the <unk> 90 World Cup squad . He played in all three of Scotland 's World Cup games . They lost their first game 1 – 0 to Costa Rica , before McCall scored what would be his only international goal against Sweden in a 2 – 1 victory . However , Scotland failed to qualify for the knock @-@ out stage when they were defeated 1 – 0 by Brazil . 
- McCall represented Scotland at the European Championships in 1992 , when they again failed to go beyond the group stage after defeats to Netherlands and Germany , and in 1996 when they were edged out in the first round by Netherlands . Scotland failed to qualify for the World Cup in 1994 . McCall played just two qualifying games for the 1998 World Cup and his last cap came in a friendly against Denmark on 25 March 1998 , as he was overlooked for the final squad for the finals in France along with team @-@ mate <unk> <unk> . He was capped a total of 40 times for Scotland , scoring one goal . McCall 's caps included 11 while at Everton and 29 during his career with Rangers . 
- 
- = = Coaching and management career = = 
- 
- 
- = = = Early coaching career = = = 
- 
- In July 2000 , McCall accepted his first coaching role , when he was appointed assistant manager to Chris Hutchings at Bradford City , after Hutchings was promoted from the role to replace Paul Jewell as City manager . Just four months later , Hutchings was sacked , and McCall was appointed as caretaker – player manager . His first game in the role was against his former team Everton , who were led by his former manager Walter Smith , but ended with a 1 – 0 defeat . He was in charge for one more game , which also ended in defeat , until Jim Jefferies was appointed the new manager . Jefferies brought with him his own assistant Billy Brown , and McCall was appointed first @-@ team coach . 
- After leaving Bradford City , he joined Sheffield United , where he also took up a coaching role . When he retired in 2004 , he remained at Sheffield United as assistant to Neil Warnock . Working alongside Warnock and learning the managerial ropes from him , he helped mastermind Sheffield 's promotion to the Premier League in 2006 . United were relegated to the Championship on the final day of the 2006 – 07 season and Warnock resigned three days later . McCall had already decided that the 2006 – 07 season would be his last as assistant manager , and when he was overlooked as a successor to the United manager 's position , in favour of Bryan Robson , he decided to leave after five years with the club . 
- 
- = = = Bradford City = = = 
- 
- McCall admitted in his autobiography , The Real McCall , he wanted to manage Bradford . 
- He had been linked with the manager 's position at Bradford City on numerous previous occasions , and after Colin Todd was sacked on 12 February 2007 , City chairman Julian Rhodes made McCall his number one target to take over in the summer . Club captain David Wetherall temporarily took over and was later announced as caretaker manager for the rest of the 2006 – 07 season . On 18 May 2007 it was announced McCall would become full @-@ time manager of the club where he started his career , and on 1 June 2007 he assumed the position . In less than seven years since McCall 's first two @-@ game reign , serious financial problems had driven the club to the verge of closure , and although they survived the threat of oblivion , they were unable to avoid a terrible on @-@ the @-@ pitch decline , which continued after the financial nightmare had been relieved . On McCall 's return to Valley Parade , the <unk> had just been relegated to League Two — meaning that they would be playing in the bottom division for the first time in 25 years . McCall set himself a target of earning promotion back to League One in his first season . 
- Bradford had just 13 players when McCall took over , and he made a number of summer signings including defender Darren Williams , midfielders Kyle Nix , Alex Rhodes and Scott <unk> , and strikers Barry <unk> , <unk> <unk> @-@ <unk> and Peter Thorne . McCall recorded his first win as a manager against <unk> on 25 August 2007 after substitute Luke Medley scored a late winner , but despite his pre @-@ season target his team spent much of the first half of the season in the bottom half of the table . After going unbeaten in January , the club were still 15th in League Two , and McCall told the Telegraph & <unk> he did not regret his pre @-@ season target but was carried away with the euphoria at the time . City 's form continued to improve during the second half of the season , and McCall led his side to 10th place in League Two . 
- Despite City finishing outside the play @-@ off places , they were again installed as favourites for promotion by <unk> for the 2008 – 09 season . McCall released 13 players from his squad and replaced them with a number of signings with experience in a higher division , as well as Michael <unk> , who was one of League Two 's top <unk> during the 2007 – 08 campaign . McCall 's side made a good start to the season , and after winning five of their opening six league games , went top of the league – the first time City had led the table in seven years . 
- As a result of maintaining a place in the promotion places during the first half of the season and his " <unk> influence " on the club , <unk> Julian Rhodes and Mark Lawn offered McCall a new contract in January 2009 . Later in the month , Lawn gave further backing to McCall , who was coming under pressure from the club 's fans following a run of one win in nine games ; during the run McCall was also charged by The Football Association for the first time of his managerial career after he had contested a <unk> decision during a game with Luton Town . McCall signed a new contract in February , which extended his deal by another two years and would have kept him at the club until 2011 . He set himself the goal of earning two promotions to put City in the Championship . However , less than a month later , McCall offered to resign if they did not reach the play @-@ offs after his side lost 4 – 1 to Bournemouth – their fifth consecutive away defeat . " Nobody is hurting more than me but it 's as simple as that , if we miss out I don 't deserve to be here , " he said . City eventually missed out on promotion , but McCall decided to stay on as manager and took a voluntary pay cut in the process because of the club 's budget being reduced . 
- As a result of the cuts , McCall made a number of changes to his squad during the summer of 2009 . His team started the 2009 – 10 season by going four games without scoring , until they recorded a 5 – 4 victory against Cheltenham Town . After the game , McCall said : " That was the youngest , and certainly <unk> , team Bradford have put out for a long time and I ’ m really proud of them . " City continued by going ten games unbeaten and reached the area semi @-@ finals of the Football League Trophy where they lost to Carlisle United , managed by McCall 's friend Greg Abbott , but at the start of 2010 found themselves 16th in League Two and eight points off the play @-@ offs after a run of five defeats in seven games . McCall laid down a challenge to his team to win three of their next four games , saying : " The bottom line is that the players and me personally will get judged on results . And the results aren 't good enough . " Despite the club 's slide down the table , he denied he would resign , but it was reported that two late goals from summer signing Gareth Evans to give City a late 2 – 1 away at Torquay United saved McCall from being sacked . However , defeat to Bury in the club 's following fixture was McCall 's last game as manager , with McCall saying after the game : " It 's time for somebody else to come in and take up the <unk> and hopefully do well . " He won a little more than one @-@ third of his 133 games in charge of City . McCall left by mutual consent . 
- 
- = = = Motherwell = = = 
- 
- After leaving Bradford , McCall spent some time out of the game before being recruited to work as a scout for Norwich City by Rangers former chief scout Ewan Chester . At the end of 2010 , he was among a number of men interviewed for the managerial vacancy at Scottish Premier League side Motherwell to succeed Craig Brown , before being given the job on a two @-@ and @-@ a @-@ half @-@ year contract . His first game in charge was a 0 – 0 draw away to Hamilton <unk> on New Year 's Day 2011 , with McCall stating : " It was a fair result . You take <unk> , a clean sheet , but we can be better and we will be better . " He followed it up with a 4 – 0 victory in the Scottish Cup against Dundee before his maiden league victory – and the club 's first since November – against <unk> by the end of January . McCall was partly selected as new manager because of his knowledge of the lower leagues of English football ; he was active in the transfer market in his early days , bringing in Steve Jones – a player he had at Bradford – and Mike <unk> from Leeds United , although the latter move was cancelled because of a FIFA ruling limiting the number of clubs a player can sign for in one season . Having operated without an assistant for his first few weeks in charge , McCall chose former <unk> United manager Kenny Black as his number two . McCall led Motherwell to the semi @-@ finals of both Scottish Cup competitions – they were defeated 2 – 1 by his former side Rangers in the League Cup but reached the final of the Scottish Cup by defeating St Johnstone 3 – 0 . 
- At the start of the following season , Motherwell lost only one match in their first six making them joint leaders of the Scottish Premier League , which led to McCall being named the <unk> Bank Premier League manager of the month for July and August . Well continued their good form , with McCall winning the award again in October , alongside player of the month Keith <unk> , in a month when the side went unbeaten . Motherwell 's final position in the Premier League was in 3rd , allowing them into the Champions League for the first time in the club 's history . 
- At the start of the 2012 @-@ 13 season , McCall was unable to make signings after losing ten players . In the summer transfer window , he made two signings Simon <unk> and Fraser Kerr . McCall attempted to sign the returning James McFadden and Ryan Stevenson , but both were unsuccessful . McCall then managed the club 's first Champions League match in the second round against <unk> , but they proved to be too strong and Motherwell failed to win either leg losing 2 @-@ 0 and 3 @-@ 0 respectively , which McCall described as " cruel " . After the match , Motherwell entered the Europa League play @-@ offs to face <unk> ; McCall wanted to play with an " up @-@ and @-@ at @-@ them approach " against the Spaniards . Once again , their opposition proved to be too strong and they lost each leg 2 – 0 and 1 – 0 respectively , ending the club 's European competitions ; the second game at the <unk> <unk> de <unk> had Motherwell playing with a youthful and inexperienced squad due to injuries . 
- On 24 January 2013 , it was announced McCall would join the <unk> staff of new Scotland national football team manager Gordon Strachan . During the 2012 / 13 season , the club managed to stay in the top @-@ six . On 28 March 2013 , McCall signed a new two @-@ year contract with Motherwell . In April 2013 , McCall was awarded March 's <unk> manager of month for helping the club win three and draw one of their games during the month . At the end of the season , Motherwell finished second for the first time , their highest league position since 1994 @-@ 95 season , which he described as " incredible " . As a result , McCall won <unk> Bank Manager of the Year . On 22 May 2013 , it was reported that he was set to open talks with Sheffield United about their managerial vacancy in the next 24 hours and that he had cut short a family holiday to intend the interview . Eventually , McCall rejected a move to Sheffield United , following talks between the two and was happy to continue as manager of Motherwell , claiming it was the wrong time to join United . 
- At the start of the 2013 @-@ 14 season , key players Darren Randolph , Nicky Law , Chris Humphrey , Michael <unk> and Henrik <unk> all left the club . McCall replaced them by signing Paul Lawson , Iain <unk> , John Sutton , Fraser Kerr , <unk> Nielsen and Stephen <unk> . He also managed to persuade James McFadden to stay at the club . Motherwell enjoyed another successful season , finishing second in the 2013 – 14 Scottish Premiership . The position was achieved by winning on the final day against nearest rivals Aberdeen . After a bad start to the 2014 – 15 season left Motherwell second from bottom , McCall resigned as manager on 2 November . 
- 
- = = = Rangers = = = 
- 
- McCall was appointed manager of Rangers on 12 March 2015 , agreeing a contract with the club to the end of the 2014 – 15 season . In his first match in charge Rangers were held to a 1 – 1 draw at home by bottom @-@ placed Livingston on 14 March 2015 . Rangers finished third in the 2014 – 15 Scottish Championship and in the Premiership play @-@ off final they were beaten 6 – 1 on aggregate by McCall 's former team Motherwell . <unk> he had " done a decent job " he wanted to extend his contract for the following season . Rangers instead opted to appoint Mark Warburton as manager for the new season . 
- 
- = = = Return to Bradford City = = = 
- 
- On 20 June 2016 , he returned as manager of Bradford City , replacing Phil Parkinson . McCall gave up his coaching role with the Scottish national team . 
- 
- = = Style of play = = 
- 
- McCall was a box @-@ to @-@ box midfielder characterised by his <unk> running , tackling and also weighing in with an average of one goal every 11 games . Despite his position in the middle of the park he was rarely suspended and was sent off just once in his career – in the final minute of a 2 – 0 defeat to Charlton Athletic on 4 November 2000 . He also had a never @-@ say @-@ die attitude proven by a number of key late goals including his equaliser which sent the 1989 FA Cup Final into extra @-@ time , and a 93rd @-@ minute equaliser against Tottenham Hotspur during Bradford 's difficult start to their Premier League campaign in the 1999 – 2000 season . He was a passionate player whose strong desire to win even ran to reserve games . Even in his final years of his career he was described as a player with " plenty of drive and ambition " by manager Neil Warnock . 
- 
- = = Career statistics = = 
- 
- 
- = = = Playing = = = 
- 
- 
- = = = = Club = = = = 
- 
- 
- = = = = International = = = = 
- 
- 
- = = = = = International goals = = = = = 
- 
- Scores and results list Scotland 's goal tally first . 
- 
- = = = Managerial = = = 
- 
- As of 31 May 2015 
- 
- = = Honours = = 
- 
- 
- = = = Player = = = 
- 
- Bradford City 
- English Third Division ( 1 ) : 1984 – 85 
- English First Division promotion ( 1 ) : 1998 – 99 
- Rangers 
- Scottish Premier Division ( 6 ) : 1991 – 92 , 1992 – 93 , 1993 – 94 , 1994 – 95 , 1995 – 96 , 1996 – 97 
- Scottish Cup ( 3 ) : 1992 , 1993 , 1996 
- Scottish League Cup ( 2 ) : 1993 , 1994 
- 
- = = = Manager = = = 
- 
- Motherwell 
- <unk> Cup ( 3 ) : 2010 – 11 , 2012 – 13 , 2013 – 14 
- 
- = = = Individual = = = 
- 
- PFA Team of the Year ( 2 ) : 
- Second Division : 1987 , 1988 
- Third Division : 1985 
- Manager of the Month : July / August 2011 , October 2011 , March 2013 
- <unk> Manager of the Season : 2012 – 13 
- 
- 
- = Black @-@ tailed jackrabbit = 
- 
- The black @-@ tailed jackrabbit ( Lepus californicus ) , also known as the American desert hare , is a common hare of the western United States and Mexico , where it is found at elevations from sea level up to 10 @,@ 000 ft ( 3 @,@ 000 m ) . Reaching a length around 2 ft ( 61 cm ) , and a weight from 3 to 6 lb ( 1 @.@ 4 to 2 @.@ 7 kg ) , the black @-@ tailed jackrabbit is the third @-@ largest North American hare . Black @-@ tailed jackrabbits occupy mixed shrub @-@ grassland <unk> . Their breeding depends on the location ; it typically peaks in spring , but may continue all year round in warm climates . Young are born fully <unk> with eyes open ; they are well camouflaged and are mobile within minutes of birth , thus females do not protect or even stay with the young except during nursing . The average litter size is around four , but may be as low as two and as high as seven in warm regions . 
- The black @-@ tailed jackrabbit does not migrate or <unk> during winter and uses the same habitat of 0 @.@ 4 to 1 @.@ 2 <unk> ( 1 – 3 km2 ) year @-@ round . Its diet is composed of various shrubs , small trees , grasses , and forbs . Shrubs generally comprise the bulk of fall and winter diets , while grasses and forbs are used in spring and early summer , but the pattern and plant species vary with climate . The black @-@ tailed jackrabbit is an important prey species for raptors and carnivorous mammals , such as eagles , hawks , owls , coyotes , foxes , and wild cats . The rabbits host many <unk> including fleas , ticks , lice , and mites ; for this reason , hunters often avoid collecting them . 
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- = = Description = = 
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- Like other jackrabbits , the black @-@ tailed jackrabbit has distinctive long ears , and the long , powerful rear legs characteristic of hares . Reaching a length about 2 ft ( 61 cm ) , and a weight from 3 to 6 lb ( 1 @.@ 4 to 2 @.@ 7 kg ) , the black @-@ tailed jackrabbit is the third @-@ largest North American hare , after the antelope jackrabbit and the white @-@ tailed jackrabbit . The black @-@ tailed jackrabbit 's dorsal fur is <unk> ( dark buff <unk> with black ) , and its <unk> and the <unk> of its legs are creamy white . The ears are black @-@ tipped on the outer surfaces , and <unk> inside . The ventral surface of the tail is grey to white , and the black dorsal surface of the tail continues up the spine for a few inches to form a short , black stripe . The females are larger than males , with no other significant differences . 
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- = = Taxonomy and distribution = = 
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- Although 17 subspecies are recognized , this number may be excessive . Using a cluster analysis of anatomical characters , Dixon and others found that black @-@ tailed jackrabbit subspecies separated into two distinct groups that are geographically separated west and east of the Colorado Rocky Mountains and the Colorado River . They suggested only two <unk> are warranted : the western subspecies L. c. californicus and the eastern subspecies L. c. <unk> . 
- The black @-@ tailed jackrabbit is the most widely distributed jackrabbit ( Lepus species ) in North America . Native black @-@ tailed jackrabbit populations occur from central Washington east to Missouri and south to Baja California Sur and <unk> . Black @-@ tailed jackrabbit distribution is currently expanding eastward in the Great Plains at the expense of white @-@ tailed jackrabbit . The black @-@ tailed jackrabbit has been successfully introduced in southern Florida and along the coastline in Maryland , New Jersey , and Virginia . 
- Distribution of subspecies occurring entirely or partially in the United States is : 
- Lepus californicus <unk> ( Nelson ) 
- L. c. <unk> ( G. S. Miller ) 
- L. c. <unk> ( Gray ) – coastal southern California to Baja California <unk> 
- L. c. californicus ( Gray ) – coastal Oregon to coastal and Central Valley California 
- L. c. <unk> ( E. R. Hall ) 
- L. c. <unk> ( Mearns ) – southern Idaho to Sonora 
- L. c. <unk> ( J. A. Allen ) – central Arizona to Sonora 
- L. c. <unk> ( Nelson ) 
- L. c. <unk> ( Nelson ) 
- L. c. <unk> ( J. M. <unk> ) 
- L. c. <unk> ( Mearns ) – South Dakota to Iowa , Missouri , and central Texas 
- L. c. <unk> ( Mearns ) – south @-@ central and southeastern Texas to Tamaulipas 
- L. c. <unk> ( <unk> ) – central California 
- L. c. <unk> ( W. H. Burt ) 
- L. c. <unk> ( <unk> ) – southeastern Utah and southwestern Colorado to <unk> 
- L. c. <unk> ( <unk> ) – eastern Washington to northeastern California and northwestern Nevada 
- L. c. <unk> ( Thomas ) 
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- = = Plant communities = = 
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- The black @-@ tailed jackrabbit occupies plant communities with a mixture of shrubs , grasses , and forbs . <unk> @-@ herb mosaics are preferred over pure stands of shrubs or herbs . Black @-@ tailed jackrabbit populations are common in sagebrush ( <unk> spp . ) , <unk> ( <unk> tridentata ) , and other desert <unk> ; <unk> , <unk> , and mixed @-@ grass <unk> ; desert grassland ; open @-@ canopy chaparral ; oak ( Quercus spp . ) , and <unk> @-@ <unk> ( Pinus @-@ <unk> spp . ) woodlands ; and early seral ( succeeding each other ) , <unk> to mid @-@ elevation coniferous forests . It is also common in and near croplands , especially <unk> ( <unk> <unk> ) fields . 
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- = = Major life events = = 
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- Male black @-@ tailed jackrabbits reach sexual maturity around 7 months of age . Females usually breed in the spring of their second year , although females born in spring or early summer may breed in their first year . <unk> is induced by copulation . The breeding season is variable depending upon latitude and environmental factors . In the northern part of their range in Idaho , black @-@ tailed jackrabbits breed from February through May . In Utah , they breed from January through July , with over 75 % of females pregnant by April . The Kansas breeding season extends from January to August . Breeding in warm climates continues nearly year @-@ round . Two peak breeding seasons corresponding to rainfall patterns and growth of young vegetation occur in California , Arizona , and New Mexico . In Arizona , for example , breeding peaks during winter ( January – March ) rains and again during June <unk> . 
- The gestation period ranges from 41 to 47 days . More litters are born in warm climates : the number of litters born each year ranges from two per year in Idaho to seven in Arizona . Litter sizes are largest in the northern portions of black @-@ tailed jackrabbit 's range and decrease toward the south . Average litter size has been reported at 4 @.@ 9 in Idaho , 3 @.@ 8 in Utah , and 2 @.@ 2 in Arizona . 
- Female black @-@ tailed jackrabbits do not prepare an elaborate nest . They give birth in shallow excavations called forms that are no more than a few centimeters deep . Females may line forms with hair prior to giving birth , but some drop litters in existing depressions on the ground with no further preparation . Young are born fully <unk> with eyes open , and are mobile within minutes of birth . Females do not protect or even stay with the young except during nursing . Ages of <unk> and dispersal are unclear since the young are well camouflaged and rarely observed in the field . Captive black @-@ tailed jackrabbits are fully <unk> by 8 weeks . The young stay together for at least a week after leaving the form . 
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- = = <unk> habitat = = 
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- The black @-@ tailed jackrabbit can occupy a wide range of habitats as long as diversity in plant species exists . It requires mixed grasses , forbs , and shrubs for food , and shrubs or small trees for cover . It prefers moderately open areas without dense understory growth and is seldom found in closed @-@ canopy habitats . For example , in California , black @-@ tailed jackrabbits are plentiful in open <unk> ( <unk> <unk> ) and <unk> <unk> chaparral interspersed with grasses , but does not occupy closed @-@ canopy chaparral . Similarly , the black @-@ tailed jackrabbit occupies <unk> and early seral coniferous forest , but not closed @-@ canopy coniferous forest . 
- Black @-@ tailed jackrabbits do not migrate or <unk> during winter ; the same habitat is used year @-@ round . <unk> movement of 2 to 10 miles ( 3 – 16 km ) occurs from shrub cover in day to open foraging areas at night . Home range area varies with habitat and habitat quality . Home ranges of 0 @.@ 4 to 1 @.@ 2 <unk> ( 1 – 3 km2 ) have been reported in big sagebrush ( <unk> tridentata ) and black greasewood ( <unk> <unk> ) communities of northern Utah . 
- Black @-@ tailed jackrabbits require shrubs or small conifers for hiding , nesting , and thermal cover , and grassy areas for night feeding . A shrub @-@ grassland mosaic or widely spaced shrubs interspersed with herbs provides hiding cover while providing feeding opportunities . Small shrubs do not provide adequate cover . In the Snake River Birds of Prey Study Area in southwestern Idaho , black @-@ tailed jackrabbits were more frequent on sites dominated by big sagebrush or black greasewood than on sites dominated by the smaller shrubs <unk> ( <unk> <unk> ) or <unk> ( <unk> <unk> ) . Black @-@ tailed jackrabbits do not habitually use a burrow , although they have occasionally been observed using abandoned burrows for escape and thermal cover . 
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- = = Food habits = = 
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- The black @-@ tailed jackrabbit diet is composed of shrubs , small trees , grasses , and forbs . Throughout the course of a year , black @-@ tailed jackrabbits feed on most if not all of the important plant species in a community . Growth stage and moisture content of plants may influence selection more than species . Shrubs generally comprise the bulk of fall and winter diets , while grasses and forbs are used in spring and early summer . This pattern varies with climate : <unk> plants are grazed during <unk> periods while the plants are in <unk> to early reproductive stages , and shrubs are used more in dry seasons . Shrubs are <unk> throughout the year , however . Most of a jackrabbit 's body water is replaced by foraging water @-@ rich vegetation . Jackrabbits require a plant 's water weight to be at least five times its dry weight to meet daily water intake requirements . Therefore , black @-@ tailed jackrabbits switch to <unk> ( deep @-@ rooted ) shrubs when <unk> vegetation is recovering from their foraging . 
- Plant species used by black @-@ tailed jackrabbits are well documented for desert regions . <unk> use in other regions is less well known . However , black @-@ tailed jackrabbits <unk> Douglas fir ( <unk> <unk> ) , ponderosa pine ( Pinus ponderosa ) , lodgepole pine ( P. <unk> ) , and western hemlock ( <unk> <unk> ) seedlings , and oak ( Quercus spp . ) seedlings and <unk> . 
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- = = = Great Basin = = = 
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- In Great Basin , big sagebrush is a primary forage species and is used throughout the year ; in southern Idaho it forms 16 – 21 % of the black @-@ tailed jackrabbit summer diet . <unk> ( <unk> spp . ) , spiny <unk> ( gray <unk> ) , and black greasewood are also <unk> . Four @-@ wing <unk> ( <unk> <unk> ) is heavily used in western Nevada . In <unk> County , Idaho , <unk> comprises 41 % of black @-@ tailed jackrabbits ' annual diet . Grasses comprise 14 % of the diet , with most grass consumption in March and April . Russian <unk> ( <unk> <unk> ) is an important <unk> diet item . <unk> @-@ and @-@ thread grass ( <unk> <unk> ) and Indian <unk> ( <unk> <unk> ) are preferred grasses . Other preferred native grasses include <unk> <unk> ( <unk> <unk> ) and bluebunch wheatgrass ( <unk> <unk> ) . Where available , crested wheatgrass ( <unk> <unk> and <unk> <unk> ) and barley ( <unk> <unk> ) are highly preferred . <unk> ( <unk> <unk> ) use is variable : it comprises 45 % of the April diet on two southern Idaho sites , but black @-@ tailed jackrabbit on an eastern Washington site do not use it . 
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- = = = Warm desert = = = 
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- In warm desert , <unk> ( <unk> spp . ) and <unk> ( <unk> tridentata ) are principal <unk> species . Broom <unk> ( <unk> <unk> ) and <unk> <unk> are also used . In honey <unk> ( <unk> <unk> var. <unk> ) communities in New Mexico , the overall black @-@ tailed jackrabbit diet was 47 % shrubs , 22 % grasses , and 31 % forbs . Black <unk> ( <unk> spp . ) , <unk> ( <unk> spp . ) , <unk> ( <unk> <unk> ) , and <unk> ( <unk> spp . ) are the most commonly grazed grasses . <unk> <unk> ( Croton <unk> ) , <unk> <unk> ( <unk> <unk> ) , desert <unk> ( <unk> <unk> ) , <unk> <unk> ( <unk> <unk> ) , and <unk> ( <unk> spp . ) are important forbs , although many <unk> species are grazed . <unk> spp . , <unk> ( <unk> <unk> ) , and other <unk> are used throughout the year , but are especially important in dry seasons as a source of moisture . 
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- = = Predators = = 
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- The black @-@ tailed jackrabbit is an important prey species for many raptors and carnivorous mammals . The black @-@ tailed jackrabbit and Townsend 's ground <unk> ( <unk> <unk> ) are the two most important prey species on the Snake River Birds of Prey Study Area . Hawks preying on black @-@ tailed jackrabbits include the <unk> hawk ( Buteo regalis ) , white @-@ tailed hawk ( Buteo <unk> ) , <unk> 's hawk ( B. <unk> ) , and red @-@ tailed hawk ( B. <unk> ) . The black @-@ tailed jackrabbit is the primary prey of <unk> 's , red @-@ tailed , and <unk> hawks on Idaho and Utah sites . Other raptors consuming black @-@ tailed jackrabbits include the great horned owl ( <unk> <unk> ) , burrowing owl ( Athene <unk> ) , golden eagle ( Aquila <unk> ) , and bald eagle ( <unk> <unk> ) . A significant correlation exists between golden eagle and black @-@ tailed jackrabbit reproduction patterns . In Colorado and southeastern Wyoming , black @-@ tailed jackrabbits constitute 9 % of nesting bald eagles ' diet . Jackrabbits and <unk> ( <unk> spp . ) combined form 9 % of the diet of bald eagles wintering on national forests in Arizona and New Mexico . 
- <unk> predators include coyote ( Canis <unk> ) , bobcat ( Lynx <unk> ) , <unk> ( Lynx <unk> ) , domestic dog ( Canis lupus <unk> ) , domestic cat ( Felis <unk> ) , red fox ( <unk> <unk> ) , common gray fox ( <unk> <unk> ) , American <unk> ( <unk> <unk> ) , wolf ( Canis lupus ) , and mountain lion ( Felis concolor ) . In many areas , black @-@ tailed jackrabbit is the primary item in coyote diets . It is locally and regionally important to other mammalian predators . One study found that jackrabbits made up 45 % of the bobcat diet in Utah and Nevada . Another Utah – Nevada study found that jackrabbits were the fourth @-@ most commonly consumed prey of mountain lions . 
- <unk> ( <unk> spp . ) and <unk> snakes ( <unk> <unk> ) prey on black @-@ tailed jackrabbit young . <unk> ( Procyon <unk> ) and striped <unk> ( <unk> <unk> ) may also capture young . 
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- = = <unk> and disease = = 
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- The black @-@ tailed jackrabbit plays host to many <unk> including fleas , ticks , lice , and mites , and many <unk> including <unk> , <unk> , <unk> , and <unk> ( <unk> ) larvae . <unk> affecting the black @-@ tailed jackrabbit in the West are tularemia , <unk> <unk> , <unk> , Q fever , and Rocky Mountain spotted fever . <unk> are vectors for tularemia , and infected ticks have been found on jackrabbits in the West . Jackrabbits infected with tularemia die very quickly . 
- The high prevalence of disease and parasites in wild jackrabbits affects human predation . Many hunters will not collect the jackrabbits they shoot , and those who do are well advised to wear gloves while handling carcasses and to cook the meat thoroughly to avoid contracting tularemia . Most hunting of jackrabbits is done for pest control or sport . 
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- 
- = Battle of Romani = 
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- The Battle of Romani was the last ground attack of the Central Powers on the Suez Canal at the beginning of the Sinai and Palestine Campaign during the First World War . The battle was fought between 3 and 5 August 1916 near the Egyptian town of Romani and the site of ancient Pelusium on the Sinai Peninsula , 23 miles ( 37 km ) east of the Suez Canal . This victory by the 52nd ( Lowland ) Division and the Anzac Mounted Division of the Egyptian Expeditionary Force ( EEF ) over a joint Ottoman and German force , which had marched across the Sinai , marked the end of the Defence of the Suez Canal campaign , also known as the Offensive zur <unk> des <unk> and the <unk> <unk> <unk> , which had begun on 26 January 1915 . 
- This British Empire victory , the first against the Ottoman Empire in the war , ensured the safety of the Suez Canal from ground attacks , and ended the Central Powers ' ambitions of disrupting traffic through the canal by gaining control of the strategically important northern approaches to the Suez Canal . The pursuit by the Anzac Mounted Division which ended at Bir el Abd on 12 August began the Sinai and Palestine Campaign . Thereafter , the Anzac Mounted Division supported by the Imperial Camel Brigade were on the offensive , pursuing the German and Ottoman army many miles across the Sinai Peninsula , reversing in a most <unk> manner the defeat suffered at Katia three months earlier . 
- From late April 1916 , after a German @-@ led Ottoman force attacked British yeomanry at Katia , British Empire forces in the region at first doubled from one brigade to two and then grew as rapidly as the developing infrastructure could support them . The construction of the railway and a water pipeline soon enabled an infantry division to join the light horse and mounted rifle brigades at Romani . During the heat of summer , regular mounted patrols and reconnaissance were carried out from their base at Romani , while the infantry constructed an extensive series of defensive redoubts . On 19 July , the advance of a large German , Austrian and Ottoman force across the northern Sinai was reported . From 20 July until the battle began , the Australian 1st and 2nd Light Horse Brigades took turns pushing out to clash with the advancing hostile column . 
- During the night of 3 / 4 August 1916 , the advancing force including the German Pasha I formation and the Ottoman 3rd Infantry Division launched an attack from Katia on Romani . Forward troops quickly became engaged with the screen established by the 1st Light Horse Brigade ( Anzac Mounted Division ) . During fierce fighting before dawn on 4 August , the Australian light horsemen were forced to slowly retire . At daylight , their line was reinforced by the 2nd Light Horse Brigade , and about mid morning , the 5th Mounted Brigade and the New Zealand Mounted Rifles Brigade joined the battle . Together these four brigades of the Anzac Mounted Division , managed to contain and direct the determined attackers into deep sand . Here the attackers came within range of the strongly entrenched 52nd ( Lowland ) Division defending Romani and the railway . <unk> resistance by all these EEF formations , the deep sand , the heat and thirst prevailed , and the German , Austrian and Ottoman advance was checked . Although the attacking force fought strongly to maintain its positions the next morning , by nightfall they had been pushed back to their starting point at Katia . The retiring force was pursued by the Anzac Mounted Division between 6 and 9 August , during which the Ottomans and Germans forces fought a number of strong rearguard actions against the advancing Australian light horse , British yeomanry and New Zealand mounted rifle brigades . The pursuit ended on 12 August , when the German and Ottoman force abandoned their base at Bir el Abd and retreated back to El Arish . 
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- = = Background = = 
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- At the beginning of the First World War , the Egyptian police controlling the Sinai Peninsula had withdrawn , leaving the area largely unprotected . In February 1915 , a German and Ottoman force unsuccessfully attacked the Suez Canal . Minor Ottoman and <unk> forces operating across the Sinai continued to threaten the canal from March through the Gallipoli Campaign until June , when they practically ceased until the autumn . Meanwhile , the German and Ottoman <unk> supported an uprising by the <unk> ( a political @-@ religious group ) on the western frontier of Egypt which began in November 1915 . 
- By February 1916 , however , there was no apparent sign of any unusual military activity in the Sinai itself , when the British began construction on the first 25 @-@ mile ( 40 km ) stretch of 4 @-@ foot @-@ 8 @-@ inch ( 1 @.@ 42 m ) standard gauge railway and water pipeline from Kantara to Romani and Katia . Reconnaissance aircraft of the Royal Flying Corps and seaplanes of the Royal Naval Air Service found only small , scattered Ottoman forces in the Sinai region and no sign of any major concentration of troops in southern Palestine . 
- By the end of March or early in April 1916 , the British presence in the Sinai was growing ; 16 miles ( 26 km ) of track , including <unk> , had been laid . Between 21 March and 11 April , the water sources at <unk> <unk> <unk> , <unk> <unk> and <unk> along the central Sinai route from southern Palestine were destroyed . In 1915 , they had been used by the central group of about 6 @,@ 000 or 7 @,@ 000 Ottoman soldiers who moved across the Sinai Desert to attack the Suez Canal at Ismailia . Without these wells and cisterns , the central route could no longer be used by large forces . 
- German General Friedrich Freiherr Kress von Kressenstein 's raiding force retaliated to this growing British presence , by attacking the widely dispersed 5th Mounted Brigade on 23 April , Easter Sunday and also St George 's Day , when Yeomanry were surprised and overwhelmed at Katia and Oghratina east of Romani . The mounted Yeomanry brigade had been sent to guard the water pipeline and railway as they were being extended beyond the protection of the Suez Canal defences into the desert towards Romani . 
- In response to this attack , the British Empire presence in the region doubled . The next day , the New Zealand Mounted Rifles Brigade and the 2nd Light Horse Brigade which had served dismounted during the Gallipoli Campaign , of the Australian Major General Harry Chauvel 's Anzac Mounted Division <unk> the Katia area unopposed . 
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- = = Prelude = = 
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- On 24 April , the day after the Katia and Oghratina , Chauvel , commander of the Anzac Mounted Division , was placed in command of all the advanced troops : the 2nd Light Horse Brigade and the New Zealand Mounted Rifles Brigades at Romani and an infantry division ; the 52nd ( Lowland ) at Dueidar . The infantry moved forward to Romani between 11 May and 4 June 1916 . 
- The building of the railway and pipeline had not been greatly affected by the fighting on 23 April and by 29 April , four trains a day were running regularly to the railhead , manned by No. 276 Railway Company , and the main line to Romani was opened on 19 May . A second standard gauge railway line from Romani to Mahamdiyah on the Mediterranean coast was completed by 9 June . But conditions on the ground were extreme ; after the middle of May and in particular from mid June to the end of July , the heat in the Sinai Desert ranged from extreme to fierce when temperatures could be expected to be in the region of 123 ° F ( 51 ° C ) in the shade . The terrible heat was not as bad as the <unk> dust storms which blow once every 50 days for between a few hours and several days ; the air is turned into a haze of floating sand particles flung about by a strong , hot <unk> wind . 
- No major ground operations were carried out during these midsummer months , the Ottoman garrisons in the Sinai being scattered and out of reach of the British forces . But constant patrolling and reconnaissance were carried out from Romani to <unk> , to Bir el Abd and on 16 May to Bir Bayud , 19 miles ( 31 km ) south @-@ east of Romani , on 31 May to Bir Salmana 22 miles ( 35 km ) east north @-@ east of Romani by the New Zealand Mounted Rifle Brigade , when they covered 100 kilometres ( 62 mi ) in 36 hours . These patrols concentrated on an area of great strategic importance to large military formations wishing to move across the Sinai along the northern route . Here water was freely available in a large area of <unk> which extends from Dueidar , 15 miles ( 24 km ) from Kantara on the Suez Canal , along the <unk> es <unk> ( the old caravan route ) , to Salmana 52 miles ( 84 km ) away . 
- Between 10 and 14 June , the last water source on the central route across the Sinai Peninsula was destroyed by the <unk> column . This column , consisting of engineers and units of the 3rd Light Horse Brigade , the <unk> Camel Corps , and the Egyptian Camel Transport Corps drained 5 @,@ 000 @,@ 000 US gallons ( 19 @,@ 000 @,@ 000 l ; 4 @,@ 200 @,@ 000 imp gal ) of water from pools and cisterns in the Wadi <unk> and sealed the cisterns . This action effectively narrowed the area in which Ottoman offensives might be expected to the coastal or northern route across the Sinai Peninsula . 
- Ottoman aircraft attacked the Suez Canal twice during May , dropping bombs on Port Said . British aircraft bombed the town and aerodrome at El Arish on 18 May and 18 June , and bombed all the Ottoman camps on a front of 45 miles ( 72 km ) parallel to the canal on 22 May . By the middle of June , the No. 1 Australian Squadron , Australian Flying Corps , had begun active service , with " B " Flight at Suez performing reconnaissance . On 9 July , " A " Flight was stationed at <unk> in Upper Egypt , with " C " Flight based at Kantara . 
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- = = = German and Ottoman force = = = 
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- At the beginning of July , it was estimated there were at least 28 @,@ 000 Ottoman troops in the Gaza – Beersheba area of southern Palestine , and that just before the battle began at Romani , there were 3 @,@ 000 troops at Oghratina , not far from Katia , another 6 @,@ 000 at the forward base of Bir el Abd , east of Oghratina , 2 @,@ 000 to 3 @,@ 000 at Bir Bayud to the south @-@ east , and another 2 @,@ 000 at Bir el Mazar , some 42 miles ( 68 km ) to the east , not far from El Arish . 
- Kress von Kressenstein 's Fourth Army was made up of the 3rd ( Anatolian ) Infantry Division 's three regiments , the 31st , 32nd and 39th Infantry Regiments , totalling 16 @,@ 000 men , of whom 11 @,@ 000 to 11 @,@ <unk> were combatants , Arab ancillary forces ; and one regiment of the Camel Corps . Estimates of their arms range from 3 @,@ 293 to 12 @,@ 000 rifles , 38 to 56 machine guns , and two to five anti @-@ aircraft gun sections ; they also fielded four heavy artillery and mountain gun batteries ( 30 artillery pieces ) and the Pasha I formation . Nearly 5 @,@ 000 camels and 1 @,@ 750 horses accompanied the advance . 
- The Pasha I formation with a <unk> strength of about 16 @,@ 000 , consisted of personnel and materiel for a machine gun battalion of eight companies with four guns each with Ottoman drivers , five anti @-@ aircraft groups , the 60th Battalion Heavy Artillery consisting of one battery of two <unk> guns , one battery of four 150 mm howitzers and two batteries of 210 mm howitzers ( two guns in each battery ) . The officers , NCOs and " leading numbers " of this artillery battalion were German ; the remainder were Ottoman Army personnel . In addition Pasha I also included two trench mortar companies , the 300th Flight Detachment , <unk> detachment , three railway companies and two field hospitals . Austria provided two mountain howitzer batteries of six guns each . With the exception of the two 210 mm howitzers , the trench mortars and the railway personnel the remainder of Pasha I took part in the advance to Romani . 
- The 300th Flight Detachment provided a squadron for aerial reconnaissance , and increased the numbers of aircraft available to support the advance across Sinai . These Pasha I aircraft were faster and more effective than the " <unk> <unk> " British aircraft and were able to maintain air superiority over the <unk> . 
- It is also possible that the 81st Regiment of the 27th Division advanced to Bir el Abd and took part in the defence of that place . 
- The objectives of the German , Austrian and Ottoman advance were to capture Romani and to then establish a strongly entrenched position opposite Kantara , from which place their heavy artillery would be within range of the Suez Canal . The attacking force assembled in the southern Ottoman Empire at <unk> , north @-@ west of Beersheba , and departed for the Sinai on 9 July 1916 ; they reached Bir el Abd and <unk> ten days later . 
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- = = = British forces = = = 
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- General Sir Archibald Murray , the commander of the British Empire forces in Egypt , formed the Egyptian Expeditionary Force ( EEF ) in March 1916 by merging the Force in Egypt , which had protected Egypt since the beginning of the war , with the Mediterranean Expeditionary Force which had fought at Gallipoli . The role of this new force was to both defend the British <unk> of Egypt and provide reinforcements for the Western Front . Murray had his headquarters in Cairo to better deal with his multiple responsibilities , although he was at Ismailia during the battle for Romani . 
- With the occupation of Romani , the area became part the Northern or No. 3 Sector of the Suez Canal defences , which originally stretched along the canal from Ferdan to Port Said . Two further sectors grouped the defence forces along the central and southern sections of the Canal ; No. 2 , the Central Sector , stretched south from Ferdan to headquarters at Ismailia and on to <unk> , where the No. 1 or Southern Sector extended from <unk> to Suez . 
- Murray considered it very unlikely that an attack would occur anywhere other than in the northern sector and therefore was prepared to reduce the troops in Nos 1 and 2 <unk> to a minimum . He decided not to reinforce his four infantry brigades , but to increase the available fire @-@ power at Romani by moving up the <unk> and <unk> Machine Gun Companies of the 53rd ( Welsh ) and the 54th ( East Anglian ) Divisions . He also ordered the concentration of a small mobile column made up of the 11th Light Horse , the City of London Yeomanry ( less one squadron each ) with the 4th , 6th and 9th Companies of the Imperial Camel Brigade in No. 2 Sector . He calculated that the whole of the defensive force , including the camel transport necessary to enable infantry in the 42nd ( East Lancashire ) Division to advance into the desert , would be fully equipped and the camels assembled by 3 August . Approximately 10 @,@ 000 Egyptian Camel Transport Corps camels concentrated at Romani prior to the battle . British monitors in the Mediterranean Sea off Mahamdiyah got into position to shell the assembling Ottoman force , while an armoured train at Kantara was ready to assist the defence of the right flank , and all available aircraft were on standby at Ismailia , Kantara , Port Said and Romani . 
- Major General H. A. Lawrence commanded No. 3 Section Canal <unk> , and as part of those defences , the Romani position was commanded by Lawrence , who had his headquarters at Kantara . <unk> at Kantara were infantry in the 42nd Division , an infantry brigade of the 53rd ( Welsh ) Division with 36 guns and the 3rd Light Horse Brigade , detached from the Anzac Mounted Division . Lawrence moved two infantry battalions of the 42nd Division from No. 2 Section Canal defences to Kantara , and sent infantry in the <unk> ( North Wales ) Brigade of the 53rd ( Welsh ) Division to Romani on 20 July . 
- The deployments on 3 August on and near the battlefield were as follows : 
- at Hill 70 , 12 miles ( 19 km ) from Romani , the New Zealand Mounted Rifles Brigade ( less the Wellington Mounted Rifles Regiment , but with the 2nd Light Horse Brigade 's 5th Light Horse Regiment , temporarily attached ) , commanded by Edward Chaytor , and the 5th Mounted Brigade , under the direct command of Lawrence , were joined on the railway by infantry in the <unk> ( East Lancashire ) Brigade ( 42nd Division ) . Together with the 5th Light Horse Regiment , attached to the New Zealand Mounted Rifles Brigade at Dueidar , to the east of Hill 70 , this force was to stop or delay von Kressenstein 's attack should he attempt to bypass Romani and advance directly towards the Suez Canal , 
- at Hill 40 , infantry from the 125th ( Lancashire Fusiliers ) Brigade and the 127th ( Manchester ) Brigade ( 42nd Division ) were also on the railway line at <unk> Station , 
- the Mobile Column was based in the Sinai at the end of the El Ferdan railway , while the 3rd Light Horse Brigade was at <unk> , also in the Sinai at the end of the <unk> railway . 
- The force at Romani , responsible for its defence when the battle began , consisted of infantry from the British 52nd ( Lowland ) Division , commanded by Major General W. E. B. Smith , and the Anzac Mounted Division commanded by Chauvel ( less the 3rd Light Horse Brigade ) . The 1st and 2nd Light Horse Brigades , ( less the 5th Light Horse Regiment , but with the New Zealand Mounted Rifles Brigade 's Wellington Mounted Rifle Regiment attached ) were commanded by Lieutenant Colonels J. B. H Meredith and J. R. Royston respectively . 
- 
- = = = Development of defensive positions = = = 
- 
- Infantry from the 52nd ( Lowland ) Division joined the two mounted brigades at Romani between 11 May and 4 June , when the development of the railway made it possible to transport and supply such a large number of soldiers . The infantry occupied a defensive position known as Wellington Ridge , facing a tangle of sand dunes . The area favoured defence ; sand dunes , stretching about 6 miles ( 9 @.@ 7 km ) inland , covered an area of 30 square miles ( 78 km2 ) , including , to the south of Romani , the northern route from El Arish . On the southern and south eastern edges , a series of dunes of shifting sand with narrow sloping lanes led to a <unk> of deep soft sand . 
- The 52nd ( Lowland ) Division developed a strong defensive position at Romani which had its left flank on the Mediterranean Sea , here a series of redoubts were built running southwards from Mahamdiyah along the line of high sand hills about 7 miles ( 11 km ) to a dune known as Katib Gannit 100 feet ( 30 m ) high . This line of sand hills , which were high enough to see Katia oasis from , marked the eastern edge of an area of very soft and shifting sand beyond which were lower dunes and harder sand where movement by both infantry and mounted forces was considerably easier . Between the shore at the western end of the Bardawil <unk> and Katib Gannit ( the principal tactical point on the eastern slopes of the Romani heights ) , the infantry constructed a line of 12 redoubts about 750 yards ( 690 m ) apart , with a second series of redoubts covering the Romani railway station and the right of the defensive position which curved like a hook westward , then northward . A total of 18 redoubts were constructed , which when fully garrisoned held from 40 to 170 rifles each , with Lewis guns and an average of two Vickers machine guns allotted to each position ; they were well <unk> on the right side of each of the positions , although there was no wire between the redoubts . This defensive line was supported by artillery . 
- The threat of an Ottoman attack towards the Suez Canal had been considered by Lawrence in consultation with his divisional commanders , and a second defensive area was developed to address their concerns . Their plans took into account the possibility of an Ottoman army at Katia moving to attack Romani or following the old caravan route to assault Hill 70 and Dueidar on their way to the Suez Canal . Any attempt to bypass Romani on the right flank would be open to attack from the garrison , which could send out infantry and mounted troops on the hard ground in the plain to the south @-@ west . The New Zealand Mounted Rifle Brigade was stationed at Hill 70 at the end of June and the 5th Light Horse Regiment at Dueidar to prevent such an Ottoman force from reaching the Suez Canal . 
- 
- = = = Light Horse patrols before the battle = = = 
- 
- Active patrolling by mounted troops continued throughout the period leading up to the battle , but by early July , there were no indications of any imminent resumption of hostilities . The nearest Ottoman garrison of 2 @,@ 000 men was at Bir el Mazar 42 miles ( 68 km ) east of Romani , and on 9 July , a patrol found Bir Salmana unoccupied . However , greatly increased aerial activity over the Romani area began about 17 July , when faster and better @-@ climbing German aircraft quickly established superiority over British aircraft . But they could not stop British aircraft from continuing to reconnoitre the country to the east , and on 19 July , a British aircraft , with Brigadier General E. W. C. Chaytor ( commander of the New Zealand Mounted Rifles Brigade ) acting as observer , discovered an Ottoman force of about 2 @,@ 500 at Bir Bayud . A slightly smaller force was detected at <unk> and another similar sized force was found at Bir el Abd with about 6 @,@ 000 camels seen at the camps or moving between Bir el Abd and Bir Salmana . The next morning , 3 @,@ 000 men were found to be entrenched at Mageibra , with an advance depot for supplies and stores at Bir el Abd . A small force was spotted as far forward as the oasis of Oghratina , which by the next day , 21 July , had grown to 2 @,@ 000 men . 
- On 20 July , the 2nd Light Horse Brigade with two guns mounted on <unk> @-@ rails of the Ayrshire Battery demonstrated against Oghratina , capturing several prisoners , and beginning a series of patrols which , together with the 1st Light Horse Brigade , they continued until the eve of battle . Every day until 3 August , these two brigades alternated riding out from their base at Romani towards Katia at about 02 : 00 and <unk> until dawn , at which time they advanced on a wide front until German or Ottoman fire was provoked . If the enemy position was weak , the light horse pushed forward , and if a counterattack began , the brigade retired slowly , thereafter to return to camp at Romani at nightfall . The following day , the other brigade carried out similar manoeuvres in the direction of Katia and the advancing Ottoman columns , picking up the officers patrols which had been left out during the night to monitor enemy movements . During this period , one of many clashes occurred on 28 July at Hod <unk> <unk> , 5 miles ( 8 @.@ 0 km ) from the British line . Two squadrons of the Wellington Mounted Rifle Regiment , commanded by Lieutenant Colonel W. <unk> , made a bayonet assault , supported by several machine guns and two 18 @-@ pounder guns . They drove the Ottomans from the Hod , leaving 16 dead and taking eight prisoners from the Ottoman 31st Infantry Regiment . 
- The tactic of continuous forward patrolling was so successful that the advancing force 's every move was known to the defenders , but the light horsemen were substantially outnumbered and could not stop the advance . By daylight on 3 August , the German , Austrian and Ottoman force had occupied Katia and were within striking distance of Romani , Dueidar , Hill 70 and the Suez Canal . Their line ran north @-@ east and south @-@ west from the Bardawil <unk> to east of Katia , with their left flank thrown well forward . 
- 
- = = = Plans = = = 
- 
- The German and Ottoman objective was not to cross the canal , but to capture Romani and establish a strongly entrenched heavy artillery position opposite Kantara , from which to bombard shipping on the canal . Kress von Kressenstein 's plan for the attack on Romani was to bombard the line of defensive redoubts with heavy artillery and employ only weak infantry <unk> against them , while his main force launched attacks against the right and rear of the Romani position . 
- The defenders expected the German and Ottoman attack to be one of containment against their prepared line of defence , and an all @-@ out attack on the right south of Katib Gannit . They also appreciated that such an attack would expose the German and Ottoman left flank . Murray 's plan was to firstly delay the attackers and make it very difficult for them to gain ground south of Katib Gannit , and <unk> , only when the German and Ottoman force was totally committed , to then <unk> their flank attack with an attack by Section Troops at Hill 70 and Dueidar , with the 3rd Light Horse Brigade and the Mobile Column operating more widely against the flank and rear . 
- Chauvel had selected a position for the defence of Romani , which stretched for 4 miles ( 6 @.@ 4 km ) between Katib Gannit and Hod el Enna , with a second fall @-@ back position covering a series of parallel <unk> running south @-@ east and north @-@ west giving access to the area of soft sand to the rear of the Romani defences . No visible works were constructed , but together with Chauvel , the commanders of the two light horse brigades , whose task it would be to hold the attackers on this ground until the flank attack could begin , studied the area closely . 
- 
- = = Battle on 4 August = = 
- 
- Just before midnight on 3 / 4 August , three columns of the German Pasha I and the 4th Ottoman Army , consisting of about 8 @,@ 000 men , began their attack on an outpost line held by the 1st Light Horse Brigade three and a half hours after the return of the 2nd Light Horse Brigade from their regular daytime patrol . In addition to the usual officers patrols left out overnight to monitor the enemy 's positions , Chauvel decided to leave out for the night the whole of the 1st Light Horse Brigade to hold an outpost line of about 3 miles ( 4 @.@ 8 km ) , covering all entrances to the <unk> plateau which formed the Romani position and which were not protected by infantry posts . A shot or two fired out in the desert to the south @-@ east of their position put the long <unk> line of the 1st and 2nd Light Horse Regiment ( 1st Light Horse Brigade ) on alert about midnight , when the 3rd Light Horse Regiment ( 1st Light Horse Brigade ) was called up to the front line . The Austrian , German and Ottoman advance paused after finding the <unk> held by the light horsemen , but at about 01 : 00 , a sudden heavy burst of fire along the whole front began the attack of the considerably superior Ottoman and German forces , and by 02 : 00 they had in many places advanced to within 50 yards ( 46 m ) of the Australian line . 
- The Ottoman centre and left columns were <unk> led round the open flank of the infantry 's <unk> and on towards the camp and railway . After the moon had set at around 02 : 30 , the Germans and Ottomans made a bayonet charge on Mount Meredith . Although vastly outnumbered , the light horsemen fought an effective delaying action at close quarters , but were forced to relinquish ground slowly and to ultimately evacuate the position by 03 : 00 . Without the benefit of moon light , the light horsemen had fired at the flashes of the enemy 's rifles until they were close enough to use bayonets . The 1st Light Horse Brigade was eventually forced back ; withdrawing slowly , troop covering troop with steady accurate fire , <unk> off a general attack with the bayonet to their fall @-@ back position ; a large east / west sand dune called Wellington Ridge at the southern edge of the Romani encampment . During the retirement to Wellington Ridge , the covering squadrons on the left near Katib Gannit were also attacked , as was the squadron on the right , which was taken in the flank and suffered considerable loss , but managed to hold its ground until the position in its rear was occupied . By 03 : 30 , all light horsemen south of Mount Meredith had been forced back to their led horses and had succeeded in <unk> and falling back to their second position . Soon after , an Ottoman machine gun was shooting down on the light horse from Mount Meredith . 
- Chauvel had relied on the <unk> of the 1st Light Horse Brigade , which he had commanded during the Gallipoli campaign , to hold the line against greatly superior numbers for four hours until dawn , when the general situation could be assessed . <unk> revealed the weakness of the light horse defenders in their second position on Wellington Ridge and that their right was outflanked by strong German and Ottoman forces . At 04 : 30 , the 2nd Light Horse Brigade , commanded by Colonel J. R. Royston , was ordered up by Chauvel from Etmaler and went into action in front of Mount Royston to support and prolong the 1st Light Horse Brigade 's right flank by moving up the 6th and 7th Light Horse Regiments into the front line . German , Austrian or Ottoman artillery now opened fire on the infantry defences and camps in the rear ; shrapnel inflicted some losses , but the high explosive shells were smothered by the soft sand . The attackers succeeded in forcing the light horse off Wellington Ridge , which placed them within 700 metres ( 2 @,@ 300 ft ) of the Romani camp . However , they were unable to press further , as they now became exposed to machine gun and rifle fire from the entrenched infantry of the 52nd ( Lowland ) Division , and shelling from the horse artillery supporting the light horsemen 's determined defence . 
- Having been held south of Romani , the German and Ottoman force attempted a further outflanking manoeuvre to the west , concentrating 2 @,@ 000 troops around Mount Royston another sand dune , south @-@ west of Romani . At 05 : 15 , the Ottoman 31st Infantry Regiment pushed forward ; then the 32nd and the 39th Infantry Regiments swung around the left and into the British rear . This outflanking movement was steadily progressing along the slopes of Mount Royston and turning the right of the 2nd Light Horse Brigade , whose third regiment , the Wellington Mounted Rifles , was now also committed to the front line . 
- The two brigades of light horse continued to gradually withdraw , <unk> on the extreme right of the infantry position , which covered the left flank and rear of Romani . They were pushed back between Wellington Ridge and Mount Royston , about 2 @.@ 25 miles ( 3 @.@ 62 km ) west of the former ; the attackers continually forcing back their right flank . Between 05 : 00 and 06 : 00 , they were compelled to also retire slowly from this ridge , although the 6th and 7th Light Horse Regiment ( 2nd Light Horse Brigade ) still held the western edge . At 06 : 15 , Meredith was ordered to withdraw the 1st Light Horse Brigade behind the line occupied by the 7th Light Horse Regiment north of Etmaler camp . At 07 : 00 , the 6th and 7th Light Horse Regiments retired , squadron by squadron , from the remainder of Wellington Ridge . By about 08 : 00 , German , Austrian and Ottoman fire from the ridge top was directed into the camp only a few hundred yards away , but the Ayrshire and Leicester Batteries quickly stopped this artillery attack . 
- It became apparent that the German and Ottoman right column , ( 31st Infantry Regiment ) was attempting a frontal attack on redoubts held by infantry in the 52nd ( Lowland ) Division . The defenders were able to hold on , but were subjected to severe artillery shelling during the day . <unk> attacks began with heavy German or Austrian fire by their artillery which attempted to breach the infantry defensive line . About 08 : 00 , attacks were being made on Numbers 4 and 5 redoubts which began with heavy artillery fire , but the attacks broke completely when the 31st Ottoman Infantry Regiment were within 150 yards ( 140 m ) of No. 4 <unk> ; subsequent attempts were less successful . At about 10 : 00 , Chauvel contacted Brigadier General E. S. Girdwood , commanding 156th Infantry Brigade , requesting his brigade temporarily relieve the light horse brigades until they had watered their horses in preparation for a mounted counterattack . Girdwood refused because his brigade was being held in reserve to support an intended attack eastward by infantry in the 52nd ( Lowland ) Division . 
- The light horse had gradually withdrawn back until , at about 11 : 00 , the main German and Ottoman attack was stopped by well directed fire from the Royal Horse Artillery batteries of the Anzac Mounted Division and by light horse rifle and machine gun fire , to which the 52nd ( Lowland ) Division contributed considerable firepower . The attackers appeared to have exhausted themselves , but they held their ground while Austrian and Ottoman artillery of various <unk> , including 5 @.@ 9 " and 10 @.@ 5 cm guns , fired on the defenders and their camps , and German and Ottoman aircraft severely bombed the defenders . The three columns of the German , Austrian and Ottoman attacking force were brought to a standstill by the coordinated , <unk> and determined defence of the 1st and 2nd Light Horse Brigades and the 52nd ( Lowland ) Division . 
- The Ottoman advance was at a standstill everywhere . After a long night 's march , the German and Ottoman troops faced a difficult day under the desert sun without being able to replenish their water and exposed to artillery fire from Romani . At this time , the attacking forces held a line running from the Bardawil ( on the Mediterranean coast ) southward along the front of the 52nd Infantry Division 's <unk> and then westward through and including the very large sand dunes of Mount Meredith and Mount Royston . But from their position on Mount Royston , the German , Austrian and Ottoman force dominated the camp area of Romani and threatened the railway line . 
- 
- = = = Reinforcements = = = 
- 
- Chaytor , commander of the New Zealander Mounted Rifles Brigade , had been advised of the Austrian , German and Ottoman advance against Romani at 02 : 00 . By 05 : 35 , Lawrence at his headquarters of the Northern No. 3 Canal <unk> Sector at Kantara , had been informed of the developing attack . He recognised that the main blow was falling on Romani and ordered the 5th Mounted Yeomanry Brigade at Hill 70 to move towards Mount Royston . They were led by a Composite Regiment , which moved off at once , the remainder of the brigade preparing to follow . At 07 : 25 , Lawrence ordered the New Zealand Mounted Rifle Brigade consisting of brigade headquarters and the Canterbury Mounted Rifle Regiment ( less the Auckland Mounted Rifles and the attached 5th Light Horse Regiments , 2nd Light Horse Brigade ) , to move towards Mount Royston via Dueidar and there , pick up the Auckland Mounted Rifles Regiment . The Yeomanry and New Zealand brigades had both been stationed at Hill 70 , 12 miles ( 19 km ) from Romani , when their orders to move were received . The New Zealanders were to " operate vigorously so as to cut off the enemy , who appears to have got round the right of the Anzac Mounted Division . " 
- Meanwhile , the 3rd Light Horse Brigade at <unk> was directed to move forward to Hill 70 and send one regiment to Dueidar , while the Mobile Column was ordered by GHQ to march towards Mageibra . 
- 
- = = = Mount Royston counterattack = = = 
- 
- The German , Austrian and Ottoman attack on Mount Royston was checked to the north by the 3rd and 6th Light Horse Regiments ( 1st and 2nd Light Horse Brigades ) , and under constant bombardment from the horse artillery and the infantry 's heavy artillery of the 52nd ( Lowland ) Division . At 10 : 00 , the front held by the two light horse brigades faced south from a point 700 yards ( 640 m ) northwest of No. 22 Redoubt north of Wellington Ridge to the sand hills north of Mount Royston . As the line had fallen back , the 2nd and 3rd Light Horse Regiments ( 1st Light Horse Brigade ) had come in between the 6th and 7th Light Horse Regiments ( 2nd Light Horse Brigade ) ; from right to left , the line was now held by the 6th , 3rd , 2nd and 7th Light Horse and the Wellington Mounted Rifles Regiments , while 1 mile ( 1 @.@ 6 km ) north north @-@ west of Mount Royston , " D " Squadron of the Royal Gloucestershire Hussars ( a regiment in the 5th Mounted Brigade ) held its ground . 
- The plan called for the 1st and 2nd Light Horse Brigades , the 5th Mounted and the New Zealand Mounted Rifles Brigades to swing round the attackers ' left flank and <unk> them . The first reinforcements to arrive were the Composite Regiment of the 5th Mounted Brigade ; they came up on the flank of their mounted regiment ; the Royal Gloucestershire Hussars ' " D " Squadron 1 @,@ 500 yards ( 1 @,@ 400 m ) west of Mount Royston , which was being attacked by a strong body of Ottoman soldiers . The regiment attacked the Ottomans in enfilade and forced them back . 
- When the New Zealand Mounted Rifles Brigade 's headquarters and the Canterbury Mounted Rifle Regiments were within 1 mile ( 1 @.@ 6 km ) of Dueidar on the old caravan road , they were ordered to move directly to Canterbury Hill , the last defensible position in front of the railway , east of Pelusium Station , as the strong German and Ottoman attack was threatening to take the railway and Romani . The Auckland Mounted Rifles Regiment arrived with its brigade between 11 : 00 and 11 : 30 to find the Composite Yeomanry Regiment ( 5th Mounted Yeomanry Brigade ) in contact with the German and Ottoman forces on the south @-@ west side of Mount Royston . 
- The 1st and 2nd Light Horse Brigades first made contact with the New Zealand Mounted Rifles Brigade by <unk> , after which Royston , commanding the 2nd Light Horse Brigade , <unk> across to explain the situation . Chaytor then moved the Auckland and Canterbury Mounted Rifles Regiments , supported by the Somerset Battery , onto high ground between the right of the light horse and the Yeomanry , which was shortly afterwards joined by the remainder of the 5th Mounted Brigade under the command of Brigadier General <unk> . At the most critical period of the day 's fighting , when the German and Ottoman force of 2 @,@ 000 dominated the Romani area from Mount Royston , the five mounted brigades ( still less the 5th Light Horse Regiment ) began their counterattack at 14 : 00 from the west towards Mount Royston . 
- The New Zealand riflemen soon gained a footing on Mount Royston , aided by accurate and rapid shooting from the Somerset Royal Horse Artillery Battery . By 16 : 00 , the attack had proceeded to a point where Chaytor arranged with the 5th Mounted Brigade for a squadron of Royal Gloucestershire Hussars and two troops of the Worcestershire Yeomanry to gallop against the southern spur of Mount Royston . They easily took the spur , the defenders not waiting for the <unk> of the mounted charge . From the crest of the spur , the Gloucestershire squadron shot down the horse teams of an Austrian , German or Ottoman battery of pack guns concentrated in the hollow behind the spur , and the attacking force began to surrender . The New Zealand Mounted Rifle and 5th Mounted Brigades were supported by leading infantry battalions of the 127th ( Manchester ) Brigade ( which had just arrived ) when Ottoman and German soldiers began to surrender en <unk> . At about 18 : 00 , 500 prisoners , two machine guns and the pack battery were captured , and the outer flank of the attacking force was completely routed . 
- Meanwhile , the inner flank of the German and Ottoman force on Wellington Ridge made a last effort to advance across the ridge , but was driven back by artillery fire . Fresh frontal attacks launched against the main British infantry system of redoubts broke down completely . At 17 : 05 , Major General Smith ordered infantry in the 156th ( Scottish Rifles ) Brigade to attack the enemy force on Wellington Ridge on the left of the light horse and in coordination with the counterattack on Mount Royston . An artillery bombardment of Wellington Ridge began at 18 : 45 . Just before 19 : 00 , infantry in the 7th and 8th <unk> ( Scottish Rifles ) moved south from behind No. 23 Redoubt ; the 8th Scottish Rifles advancing to within 100 yards ( 91 m ) of the crest of Wellington Ridge , before being stopped by heavy rifle fire . 
- When darkness put an end to the fighting , the 1st and 2nd Light Horse Brigades established an outpost line and spent the night on the battlefield , while the New Zealand Mounted Rifles and 5th Mounted Brigades withdrew for water and rations at Pelusium Station , where the newly arrived infantry brigades of the 42nd Division were assembling . The 3rd Light Horse Brigade halted at Hill 70 , while the Mobile Force had reached the Hod el <unk> , 14 miles ( 23 km ) south of Romani station . At 19 : 30 , when the New Zealand Mounted Rifle and 5th Mounted Brigades moved from the positions they had won to water and rest at Pelusium , the area was consolidated by infantry in the 127th ( Manchester ) Brigade , 42nd Division . Brigadier General Girdwood ordered infantry in the 7th and 8th Scottish Rifles Battalions to hold their ground on Wellington Ridge until daylight , but to keep close contact with the enemy during the night in the hope of capturing large numbers of tired and disorganised soldiers in the morning . Approximately 1 @,@ 200 unwounded prisoners were captured during the day and sent to the Pelusium railway station . 
- 
- = = Battle on 5 August = = 
- 
- Within 24 hours , British commanders were able to concentrate a force of 50 @,@ 000 men in the Romani area , a three to one advantage . This force included the two infantry divisions – the 52nd and the newly arrived 42nd – four mounted brigades , two of which had been on active duty since 20 July , and two heavily engaged on the front line the day before , and may have included the 3rd Light Horse Brigade , although it was still at Hill 70 , and the Mobile Column at Hod el <unk> . At this time , command of the 5th Mounted Brigade passed from the Anzac Mounted Division to the infantry division ; the 42nd Division , it being suggested that orders required the Anzac Mounted Division to remain in position , and that the 3rd Light Horse Brigade alone was to make a flank attack . 
- However , Lawrence 's orders for a general advance on 5 August beginning at 04 : 00 included an advance by the Anzac Mounted Division . His orders read : 
- Anzac Mounted Division to press forward with its right on the Hod el Enna and its left in close touch with the infantry from the 156th ( Scottish Rifles ) Brigade , 52nd ( Lowland ) Division , advancing on the line Katib Gannit to Mount Meredith . 
- 3rd Light Horse Brigade to move towards Bir el Nuss and attack Hod el Enna from the south keeping in close touch with the Anzac Mounted Division . 
- 5th Mounted Brigade , under orders of 42nd Infantry Division to assist the 3rd Light Horse Brigade 's link with the Anzac Mounted Division 's right . 
- 42nd Division to move on the line Canterbury Hill – Mount Royston – Hod el Enna and drive back any opposition to the advance of the mounted troops in close support of Anzac Mounted Division 's right flank . 
- 52nd ( Lowland ) Division to move in close support of Anzac Mounted Division 's left flank towards Mount Meredith and to prepare for a general advance towards Abu Hamra which was not to be undertaken until further orders from Lawrence at No. 3 Section Headquarters . 
- Meanwhile , the German , Austrian and Ottoman force was now spread from Hill 110 almost to Bir en Nuss , but with their left flank unprotected . They could not have been in good shape after fighting all the previous day in intense midsummer heat and having to remain in position overnight , far from water and harassed by British infantry . Their situation was now precarious , as their main attacking force was well past the right of the main British infantry positions ; infantry in the 52nd ( Lowland ) Division was closer to the nearest enemy @-@ controlled water source at Katia than most of the attacking force . Had the British infantry left their trenches promptly and attacked in a south easterly direction , von Kressenstein 's force would have had great difficulty escaping . 
- 
- = = = British capture Wellington Ridge = = = 
- 
- At <unk> , infantry in the 8th Scottish Rifles , 156th ( Scottish Rifles ) Brigade , 52nd ( Lowland ) Division ) advanced with the 7th Light Horse and the Wellington Mounted Rifles Regiments ( 2nd Light Horse Brigade ) , covered by infantry in the 7th Scottish Rifles , 156th ( Scottish Rifles ) Brigade , 52nd ( Lowland ) Division on the left , who had brought 16 machine guns and Lewis guns into a position from which they could sweep the crest and reverse slopes of Wellington Ridge . The Wellington Mounted Rifle Regiment , with the 7th Light Horse Regiment and supported on the left by Scottish Rifles ' infantry posts , fixed bayonets and stormed Wellington Ridge . They encountered heavy rifle and machine gun fire , but rushed up the sandy slope and quickly broke through the German and Ottoman front line . After clearing Wellington Ridge , the mounted riflemen , light horsemen and <unk> pressed forward from ridge to ridge without pause . These troops swept down on a body of about 1 @,@ 000 to 1 @,@ 500 Ottoman soldiers , who became demoralised . As a result of this attack , a white flag was <unk> and by 05 : 00 the German and Ottoman soldiers who had stubbornly defended their positions on Wellington Ridge , dominating the camps at Romani , were captured . A total of 1 @,@ 500 became prisoners in the neighbourhood of Wellington Ridge ; 864 soldiers surrendered to infantry in the 8th Scottish Rifles alone , while others were captured by the light horse and mounted rifles regiments . By 05 : 30 , the main German and Ottoman force was in a disorganised retreat towards Katia , with the 1st and 2nd Light Horse Brigades and the Ayrshire and <unk> batteries not far behind . At 06 : 00 , a further 119 men surrendered to the infantry in No. 3 Redoubt ; while these prisoners were being dealt with , it became apparent that they were part of a rearguard and that a full retreat was under way . At 06 : 30 , Lawrence ordered Chauvel to take command of all troops and to initiate a vigorous general advance eastwards . 
- 
- = = = British advance on Ottoman rearguard at Katia = = = 
- 
- Infantry from the 42nd Division had arrived during the battle the day before by train from Hill 70 , Hill 40 and <unk> Station , and along with infantry from the 52nd ( Lowland ) Division , was ordered to move out in support of the mounted Australian , New Zealand and British Yeomanry brigades . The 42nd Division was ordered to advance to Hod el Enna ; their 127th ( Manchester ) Brigade marched out at 07 : 30 and reached Hod el Enna between 09 : 30 and 10 : 00 , while their 125th ( Lancashire Fusiliers ) Brigade arrived at 11 : 15 . They were supported by the Egyptian Camel Transport Corps , which worked with the Army Service Corps to supply them with drinking water . In much distress in the <unk> midsummer sands , infantry in the 42nd Division marched very slowly and far in the rear . The 52nd ( Lowland ) Division also experienced difficulties ; although Lawrence ordered the division to move at 06 : 37 , the men did not leave their trenches until nearly midday , reaching their objective of Abu Hamra late in the evening . As a result , Kress von Kressenstein was able to <unk> most of his troops and heavy guns from the immediate battle area during the day . Although it has been stated that " British reserves <unk> " the Germans and Ottomans to a halt on 5 August , it appears one of the infantry divisions was reluctant to leave their defences ; neither infantry division were trained in desert warfare and found the sand dunes extremely difficult to negotiate . They could not match the pace and endurance of the well @-@ trained German and Ottoman force and were hampered by water supply problems . 
- At 06 : 30 , when Lawrence ordered Chauvel to take command of all mounted troops ( excluding the Mobile Column ) , the New Zealand Mounted Rifles , the 5th Mounted and the 3rd Light Horse Brigades were somewhat scattered . By 08 : 30 , the New Zealand Mounted Rifles Brigade had reached Bir en Nuss ; there they found the 3rd Light Horse Brigade , which had been ordered to move first on Hamisah and then left towards Katia to cooperate in a general attack . The advance guard moved to fulfill these orders at 09 : 00 . At 10 : 30 , the general mounted advance began and by midday , was on a line from west of Bir <unk> to south of Katib Gannit ; in the centre the New Zealand Mounted Rifles Brigade were approaching the south @-@ west edge of the Katia oasis ; on their left the 1st , the 2nd Light Horse , the 5th Mounted Brigades and infantry in the 52nd ( Lowland ) Division were attacking Abu Hamra , to the north of the old caravan road , while the 3rd Light Horse Brigade was away to the New Zealander 's right , south of the old caravan road , attacking German and Ottoman units at Bir el Hamisah . 
- Between 12 : 00 and 13 : 00 , the commanders of the New Zealand Mounted Rifle , 1st and 2nd Light Horse and 5th Mounted Brigades <unk> the German , Austrian and Ottoman rearguard position 2 miles ( 3 @.@ 2 km ) west of Katia . It was decided that the three light horse brigades would advance mounted with the Yeomanry to attack the German and Ottoman right flank . The rearguard force made a very determined stand on a well @-@ prepared line , stretching from Bir El Hamisah to Katia and on to Abu Hamra . Their artillery and machine guns were well placed in the <unk> <unk> the eastern side of a great flat marsh , which stretched right across the front of their position , giving them an excellent field of fire . 
- A general mounted attack commenced at 14 : 30 . By 15 : 30 , the New Zealand Mounted Rifles Brigade and the 1st and 2nd Light Horse Brigades were advancing at the gallop on Katia . When they had reached the edge of the white <unk> , the light horse and mounted rifle brigades formed a line , fixed bayonets , and charged over the exposed country . They <unk> in a long line of charging horses , through shell fire and bullets , holding fixed bayonets . On the far left , the intensity of fire from the rearguard , made it necessary for the 5th Mounted Brigade of sword carrying Yeomanry , to send back their horses and advance dismounted . While all the brigades which charged , were eventually forced to attack dismounted also , when the ground became too swampy . They were met by well @-@ directed , heavy German , Austrian and Ottoman artillery fire , which completely <unk> the supporting Ayrshire and Somerset Batteries ; by sunset , the advance of the British Empire mounted brigades had been stopped . The 9th Light Horse Regiment ( 3rd Light Horse Brigade ) on the extreme right was held up by a determined German and Ottoman rearguard and was unable to work round the right flank of that position . But after <unk> to within a few hundred yards of the rearguard 's line , they made a dismounted bayonet attack under cover of machine gun fire and the Inverness Battery . As a result , the German and Ottoman force abandoned their position , leaving 425 men and seven machine guns to be captured . But , instead of holding their ground , they drew off , and this withdrawal led to a strong German and Ottoman counterattack falling on the Canterbury Mounted Rifle Regiment . 
- Darkness finally put an end to the battle . During the night , the Germans , Austrians and Ottomans withdrew back to <unk> , while the Anzac Mounted Division watered at Romani , leaving a troop of the Auckland Mounted Rifle Regiment as a listening post on the battlefield . 
- The two @-@ day battle for Romani and the Suez Canal had been won by the British infantry and Australian , British and New Zealand mounted troops . They captured approximately 4 @,@ 000 German and Ottoman combatants and killed more than 1 @,@ 200 , but the main enemy force was able to escape with all their artillery , except for one captured battery , and retreat back to Oghratina after fighting a successful rearguard action at Katia . 
- Having borne the burden of the long days of patrolling , reconnaissance and minor engagements with the advancing Austrian , German and Ottoman columns prior to the battle , the 1st and 2nd Light Horse Brigades had alone withstood the attack from midnight on 3 / 4 August until dawn on 4 August , as well as continuing to fight during the long days of battle . By the end of 5 August , they were completely exhausted ; their depleted ranks stumbled back to their bivouac lines at Romani and Etmaler where they were ordered one day 's rest . 
- 
- = = <unk> begins = = 
- 
- Von Kressenstein had prepared successive lines of defence during his advance towards Romani , and despite losing one artillery battery and more than one third of his soldiers , fought a series of effective rearguard actions which slowed the pursuit by British Empire mounted troops and enabled his force to retreat back to El Arish . 
- During the night of 5 / 6 August , infantry in the <unk> ( South Scottish ) Brigade and 157th ( Highland Light Infantry ) Brigade were at Abu Hamra , the 127th ( Manchester ) Brigade ( 42nd Division ) at Hod el Enna , the 125th ( Lancashire Fusiliers ) Brigade ( 42nd Division ) on its left in touch with the 156th ( Scottish Rifles ) Brigade , ( 52nd Division ) which had its left on Redoubt No. 21 . The next morning , infantry in the 42nd Division was ordered to advance eastwards at 04 : 00 and occupy a line from Bir el Mamluk to Bir Katia , while the 52nd ( Lowland ) Division was to advance from Abu Hamra and prolong the infantry line of the 42nd Division to the north @-@ east . Although they carried out their orders during their two @-@ day march from Pelusium Station to Katia , infantry in the 127th ( Manchester ) Brigade lost 800 men , victims to thirst and the sun ; other infantry brigades suffered similarly . It became clear that the infantry could not go on , and they ceased to be employed in the advance . Indeed , it was necessary for the <unk> Camel Corps and Yeomanry <unk> , as well as the medical services , to search the desert for those who had been left behind . 
- The Mobile Column in the south , consisting of the Imperial Camel Brigade , the 11th Light Horse , and the mounted City of London Yeomanry Regiments ( less two squadrons ) , advanced from Ferdan and the <unk> railhead to attack the German and Ottoman left flank , working through Bir El Mageibra , Bir El <unk> and Hod El Bayud . They found Mageibra evacuated on 5 August . After camping there for the night , they fought strong hostile forces between Bayud and Mageibra the following day , but could make no impression . Some days later , on 8 August , the Mobile Column did succeed in getting round the Ottoman flank , but was too weak to have any effect and retired to Bir Bayud . 
- 
- = = = Advance towards Oghratina – 6 August = = = 
- 
- During the previous night , the German and Ottoman force evacuated Katia and was moving towards Oghratina when Chauvel ordered the Anzac Mounted Division to continue the attack . The New Zealand Mounted Rifles Brigades and the 5th Mounted Brigade were ordered to capture Oghratina . Despite attempts by these two brigades to turn the enemy flank , they were forced to make a frontal attack on strongly entrenched <unk> in positions which favoured the defenders and which were supported by carefully positioned artillery . Meanwhile , the two infantry divisions moved to garrison Katia and Abu Hamra and Lawrence moved his headquarters forward from Kantara to Romani . The 3rd Light Horse Brigade on the right advanced towards <unk> , but could only make small progress , against positions securely held by German and Ottoman forces . 
- The New Zealand Mounted Rifles Brigade had moved out at dawn , followed by the 5th Mounted Brigade without ambulance support , as the New Zealand Field Ambulance had not returned from Romani and the 5th Mounted Field Ambulance had not yet arrived . Fortunately , casualties were light , and both ambulances arrived in the evening . The 3rd Light Horse Field Ambulance , had formed a dressing station at Bir <unk> to the south of Romani , treating wounded from 3rd Light Horse Brigade 's engagement at Bir el Hamisah , a convoy brought in wounded Ottomans from a <unk> to the south of Romani , and 150 cases of heat exhaustion from infantry in the 42nd Division were treated during the day . 
- We are still pursuing but it has been <unk> slow as the horses are done and the enemy , when advancing , entrenched himself at various points … which has enabled him to fight a most masterly rearguard action … As I am moving on , I must close 
- 
- = = = Oghratina entered on 7 August = = = 
- 
- The same three brigades – one mounted rifle , one light horse and one Yeomanry , with the 10th Light Horse Regiment ( 3rd Light Horse Brigade ) supporting the Yeomanry – moved to attack the German and Ottoman position at Oghratina , but the rearguard position was again found to be too strong . Lacking the support of infantry or heavy artillery , the mounted force was too small to capture this strong rearguard position , but the threat from the mounted advance was enough to force the hostile force to evacuate the position . During the night , the German and Ottoman forces retreated back to Bir el Abd , where they had been three weeks before , on 20 July , when they established a base with a depot for supplies and stores . 
- On 7 August the Greater <unk> ( a feast day celebrating the end of the Islamic year ) coincided with the Egyptian Camel Transport Corps at Romani being ordered to move out with supplies for the advancing troops , but 150 men , most of whom were past the end of their contracts and entitled to be discharged , refused orders to fill their water bottles , draw their rations and saddle up . One man was hit about the head with the butt of a pistol and the dissenters were dispersed into small groups and reassigned to various units in the infantry division ; the 52nd ( Lowland ) Division . 
- 
- = = = <unk> occupied on 8 August = = = 
- 
- The New Zealand Mounted Rifles Brigade reached <unk> on 8 August . As the 3rd Light Horse Brigade came up , they passed many dead Ottomans and Yeomanry ; one dead Ottoman sniper had a <unk> of hundreds of rounds of empty cartridge shells beside him . Meanwhile , the <unk> Camel Corps and a squadron of aircraft continued searching the desert sands for missing men . 
- 
- = = Action of Bir el Abd – 9 to 12 August = = 
- 
- Chauvel planned , with Lawrence 's approval , to capture the Ottoman rearguard at their forward base of Bir El Abd , 20 miles ( 32 km ) to the east of Romani . The position was strongly held by greatly superior numbers of Germans , Austrians and Ottomans , supported by well @-@ placed artillery , but the garrison was seen burning stores and evacuating camps . 
- Chauvel deployed the Anzac Mounted Division for the advance , with the New Zealand Mounted Rifles Brigade in the centre following the telegraph line . On their right , with a gap of 1 mile ( 1 @.@ 6 km ) , was the 3rd Light Horse Brigade , which was in touch with a small flying column ; the Mobile Column of the City of London Yeomanry , 11th Light Horse Regiments and the Imperial Camel Brigade , which was to again attempt to get round the German and Ottoman left flank and cut off their retreat . The advance of the 3rd Light Horse and the New Zealand Mounted Brigades from Oghratina to Bir el Abd was to begin at daylight on 9 August , with the 5th Mounted Brigade forming the reserve . On the left of the New Zealanders , Royston 's Column ; a composite of the depleted 1st and 2nd Light Horse Brigades , had gone to Katia to water and had then march through the night to the Hod <unk> 4 miles ( 6 @.@ 4 km ) north @-@ west of Bir el Abd , where they arrived at 03 : 00 on 9 August . They were to <unk> for one and a half hours before advancing to a point 2 miles ( 3 @.@ 2 km ) north @-@ east of Bir el Abd , to cooperate with the New Zealand Mounted Rifles Brigade 's attack on the rearguard position at 06 : 30 . Since the attack , supported by only four horse artillery batteries , was on a prepared position held in superior strength , strong in machine guns , and covered by double the number of guns , including heavy howitzers , it was something of a <unk> . The attacking force 's only advantage was its mobility . 
- 
- = = = Attack on 9 August = = = 
- 
- The 3rd Light Horse Brigade set out to find and turn the German and Ottoman left , while at 04 : 00 the New Zealand Mounted Rifles Brigade headed directly towards Bir el Abd along the old caravan route . By 05 : 00 , they had driven in enemy outposts and reached high ground overlooking Bir el Abd . Royston 's Column moved off at 05 : 00 with the intention of <unk> the Ottoman right , while the New Zealanders attacked in the centre ; the four brigades covering a front of 5 miles ( 8 @.@ 0 km ) . 
- The forward troops of the German and Ottoman rearguard , which held a front of about 10 miles ( 16 km ) , were driven back to Bir el Abd by the New Zealanders . At this time , the attackers appeared likely to succeed , as they had firmly established themselves across the telegraph line and the old caravan road , supported by the Somerset and Leicester batteries . But the German , Austrian and Ottoman rearguard quickly realised how thin the attacking line was , and at 09 : 00 advanced out of their trenches to counterattack . This aggressive move was only checked by artillery fire from the Somerset Battery effectively combined with fire from machine guns . The subsequent fire fight made it extremely difficult for the mounted riflemen to maintain their position , and on the flanks the light horse were also held up . The German and Ottoman infantry renewed their attack towards a gap between the New Zealanders and the 2nd Light Horse Brigade , but the 5th Light Horse Regiment covered the gap , and the German and Ottoman advance was halted . 
- Chauvel ordered the 3rd Light Horse Brigade , which had been unable to turn the German and Ottoman flank , to move towards the New Zealanders who renewed their efforts , but they only succeeded in exposing their flanks , as the Australians were unable to conform to their forward movement . By 10 : 30 , all progress had stopped . The New Zealand Mounted Rifles Brigade continued to hold on in the centre , while both flanks were bent back by pressure from the strong German and Ottoman force . The result was that the New Zealanders ended up holding a very exposed salient line on the forward slopes of the hills overlooking the Hod . Fresh German or Ottoman reinforcements from El Arish , then launched a fierce counterattack on a front of about 2 @.@ 5 miles ( 4 @.@ 0 km ) , on the centre . This fell on the Canterbury and Auckland Regiments and a squadron of Warwickshire Yeomanry of the 5th Mounted Brigade under Chaytor 's command . The New Zealanders were supported by machine guns ; one section , attached to the Canterbury Mounted Rifles Regiment , fired all their guns directly on the advancing soldiers , stopping them when they were within 100 yards ( 91 m ) of the New Zealand position . 
- By midday , the advance had been completely held up by determined counterattacks supported by fresh German or Ottoman troops from El Arish . Even more than at Katia on 5 August , these soldiers were more numerous , ready , full of fight and more strongly supported by well @-@ placed Austrian and Ottoman guns delivering both heavy and accurate fire . At this time , the rearguard launched another heavy counterattack with two columns of 5 @,@ 000 and 6 @,@ 000 German and Ottoman soldiers against the Canterbury and Auckland Regiments and the squadron of the Warwickshire Yeomanry . By 14 : 00 , the attack had extended to the mounted force 's left flank where the Ayrshire Battery with Royston 's Column was badly cut up by this fire , losing 39 horses killed and making it extremely difficulty to move the guns . They were forced to retire nearly 1 mile ( 1 @.@ 6 km ) and the 3rd Light Horse Brigade , after advancing well up on the right flank , was also forced to give ground by the accuracy of enemy <unk> . 
- A further withdrawal by the 3rd Light Horse Brigade made the New Zealand Mounted Rifles Brigade 's position critical and at 17 : 30 , Chauvel gave orders for a general retirement . <unk> proved to be a challenge ; it was only the tenacity of the New Zealanders and nightfall which saved them from certain capture . At the last , the Machine Gun Squadron had all its guns in line , some of them firing at a range of 100 yards ( 91 m ) ; they were supported by squadrons of the 5th Mounted Brigade , which together , successfully covered the New Zealanders ' withdrawal . 
- After this day of fierce fighting , which has been described as the hardest @-@ fought action of the whole Sinai campaign , the Anzac Mounted Division 's advance was effectively stopped . Chauvel ordered the division to return to water at Oghratina , despite Lawrence 's wish for them to bivouac close to Bir el Abd but Chauvel concluded that his force was in no condition to remain within reach of this strong and aggressive enemy force . Further , the Anzac Mounted Division had lost a significant proportion of their strength ; over 300 casualties , including eight officers and 65 other ranks killed . 
- 
- = = = Planned attack for 12 August = = = 
- 
- At daylight on 10 August , strong patrols went forward and remained in touch with the force at Bir el Abd throughout the day , but without fresh troops , an attack in force could not be made . 
- No serious fighting took place on 11 August , but von Kressenstein 's force at Bir el Abd was watched and harassed , and plans were made for an attack on 12 August . The advance of the Anzac Mounted Division began at daylight , but soon afterwards , forward patrols reported that the garrison at Bir el Abd was retiring . The mounted force followed the Austrians , Germans and Ottomans as far as Salmana , where another rearguard action delayed the mounted force , as the enemy withdrawal continued back to El Arish . 
- The Anzac Mounted Division 's lines of communication were now fully extended , and the difficulties of supplying the mounted troops from Romani made it impossible for the British Empire mounted force to consider any further advance at that time . <unk> were made to hold and garrison the country decisively won by this series of <unk> engagements , from Katia eastwards to Bir El Abd . 
- Von Kressenstein succeeded in withdrawing his battered force from a potentially fatal situation ; both his advance to Romani and the withdrawal were remarkable achievements of planning , leadership , staff work and endurance . 
- 
- = = = Casualties = = = 
- 
- According to the Australian official medical history , the total British Empire casualties were : 
- Other sources put the total killed at 202 , with all casualties at 1 @,@ 130 , of whom 900 were from the Anzac Mounted Division . 
- Ottoman Army casualties have been estimated to have been 9 @,@ 000 ; 1 @,@ 250 were buried after the battle and 4 @,@ 000 were taken prisoner . 
- Casualties were cared for by medical officers , stretcher bearers , camel drivers and sand @-@ cart drivers who worked <unk> , often in the firing line , covering enormous distances in difficult conditions and doing all they could to relieve the suffering of the wounded . The casualties were transported on <unk> on camels or in sand @-@ carts back to the field ambulances , as the heavy sand made it impossible to use <unk> or horse @-@ drawn ambulances . Between 4 and 9 August , the Anzac Mounted Division 's five field ambulances brought in 1 @,@ 314 patients , including 180 enemy wounded . 
- The evacuation by train from Romani was carried out in a manner which caused much suffering and shock to the wounded . It was not effected till the night of August 6 – the transport of prisoners of war being given precedence over that of the wounded – and only open trucks without straw were available . The military <unk> necessitated <unk> and much delay , so that five hours were occupied on the journey of twenty @-@ five miles . It seemed a cruel shame to <unk> a train full of wounded in open trucks , but it had to be done . Every bump in our <unk> train was extremely painful . 
- In the absence of orders coordinating evacuation from the field ambulances , the Assistant Director of Medical Services ( <unk> ) made their own arrangements . The <unk> , Anzac Mounted Division arranged with his counterparts in the two infantry divisions to set up a clearing station at the railhead 4 miles ( 6 @.@ 4 km ) beyond Romani . This station was formed from medical units of the Anzac Mounted , the 42nd and the 52nd ( Lowland ) Divisions . With no orders from No. 3 Section Headquarters as to the method of evacuation of casualties of the three divisions , prisoners of war were transported back to Kantara by train before the wounded , generating amongst all ranks a feeling of resentment and distrust towards the higher command which lasted for a long time . 
- 
- = = Aftermath = = 
- 
- The Battle of Romani was the first large @-@ scale mounted and infantry victory by the British Empire in the First World War . It occurred at a time when the Allied nations had experienced nothing but defeat , in France , at <unk> and at the capitulation of <unk> in Mesopotamia . The battle has been widely acknowledged as a strategic victory and a turning point in the campaign to restore Egypt 's territorial integrity and security , and marked the end of the land campaign against the Suez Canal . 
- Romani was the first decisive victory attained by British Land Forces and changed the whole face of the campaign in that theatre , <unk> as it did from the enemy , the initiative which he never again obtained . It also made the clearing of his troops from Egyptian territory a feasible proposition . 
- This series of successful British infantry and mounted operations resulted in the complete defeat of the 16 @,@ 000 to 18 @,@ 000 strong German , Austrian and Ottoman force , about half of whom were killed or wounded , and nearly 4 @,@ 000 taken prisoner . Also captured were a mountain gun battery of four heavy guns , nine machine guns , a complete camel @-@ pack machine gun company , 2 @,@ 300 rifles and a million rounds of ammunition , two complete field hospitals with all instruments , fittings and drugs , while a great quantity of stores in the supply depot at Bir el Abd was destroyed . All the captured arms and equipment were made in Germany , and the camel @-@ pack machine gun company 's equipment had been especially designed for desert warfare . Many of the rifles were of the latest pattern and made of <unk> steel . Murray estimated the total German and Ottoman casualties at about 9 @,@ 000 , while a German estimate put the loss at one third of the force ( 5 @,@ 500 to 6 @,@ 000 ) , which seems low considering the number of prisoners . 
- The tactics employed by the Anzac Mounted Division were to prove effective throughout the coming campaigns in the Sinai and in the Levant ( also known at the time as Palestine ) . The key to the mounted rifles and light horse 's approach was to quickly move onto tactical ground and then to effectively operate as infantry once dismounted . In defence , the artillery and machine guns wrought <unk> on enemy attacks , and during the mounted advance , they covered and supported the British Empire mounted force . 
- This battle was fought under extreme conditions in the Sinai desert in midsummer heat over many days , causing much suffering to man and beast and demanding tenacity and endurance on the part of all who took part . 
- The battle of Romani marked the end of the German and Ottoman campaign against the Suez Canal ; the offensive had passed decisively into the hands of the British Empire force led by the Anzac Mounted Division . After the battle , von Kressenstein 's force was pushed back across the Sinai Peninsula , to be beaten at the Battle of Magdhaba in December 1916 and back to the border of Ottoman Empire @-@ controlled Palestine to be defeated at the Battle of Rafa in January 1917 , which effectively secured the Egyptian Sinai Peninsula . This successful , seven @-@ month @-@ long British Empire campaign , begun at Romani in August , ended at the First Battle of Gaza in March 1917 . 
- 
- = = = Some criticisms = = = 
- 
- The Battle of Romani has , however , been surrounded with controversy and criticism . It has been suggested that , like the attack on the Suez Canal in 1915 , it was merely a raid to disrupt maritime traffic rather than a determined attempt to gain control of the canal . That the Ottoman Empire 's intention was to strongly occupy Romani and Kantara is supported by preparations in the southern territory of Palestine adjacent to , and extending into , the Sinai . These included extending the Palestine railway system to Wadi El Arish , with a good motor road beside the railway . <unk> and other works were constructed along this route to store water and at Wadi El Arish , enormous rock cut reservoirs were under construction in December 1916 when the Anzac Mounted Division reached that place just before the Battle of Magdhaba . 
- Murray , Lawrence and Chauvel have all been criticised for letting von Kressenstein 's force escape . Further , it has been asserted that the tactics of the mounted troops actually helped the enemy withdrawal by concentrating on direct assaults rather than flank attacks . The official British historian acknowledges the disappointment caused by the successful retirement of the German , Austrian and Ottoman force but he also notes the quality of the successive rearguard positions constructed during the advance , and the strength , determination and endurance of the enemy . The strength of the <unk> was clearly demonstrated at Bir el Abd on 9 August , when the mounted force attempted to outflank the large entrenched force . They failed because they were greatly outnumbered . Indeed , if the Anzac Mounted Division had succeeded in getting round the flank without infantry support , they would have been faced with vastly superior forces and could have been annihilated . 
- It has been suggested that an opportunity was lost on 5 August to encircle and capture the invading Austrian , German and Ottoman force when it was allowed to withdraw to Katia . The infantry 's difficulties regarding the supply of water and camel transport combined with their lack of desert training , together with Lawrence 's confusing orders for infantry in the 52nd ( Lowland ) Division to move south and east , stopped them from promptly advancing to cut off the retreating force in the early hours of the second day 's battle . General Lawrence was criticised for taking a grave and unnecessary risk by relying on just one entrenched infantry division and two light horse brigades to defend Romani . That the strong enemy attack on the 1st and 2nd Light Horse Brigades during the first night 's battle pushed them so far back that the planned flanking attack by the New Zealand Mounted Rifles Brigade became almost a frontal attack . Lawrence was also faulted for remaining at his headquarters at Kantara , which was considered to be too far from the battlefield , and that this contributed to his loss of control of the battle during the first day , when the telephone line was cut and he was out of contact with Romani . Lawrence was also criticised for not going forward to supervise the execution of his orders on 5 August , when there was a failure to coordinate the movements of the 3rd Light Horse Brigade and the Mobile Column . 
- Chauvel responded by pointing out that the criticisms of the battle were in danger of obscuring the significance of the victory . 
- 
- = = = Awards = = = 
- 
- Murray lavished praise on the Anzac Mounted Division in cables to the Governors General of Australia and New Zealand and in his official <unk> and in letters to Robertson , writing : 
- Every day they show what an indispensable part of my forces they are ... I cannot speak too highly of the gallantry , steadfastness and <unk> energy shown by this fine division throughout the operations ... These Anzac troops are the keystone of the defence of Egypt . 
- But he failed to ensure the fighting qualities of these soldiers earned them a proportionate share of recognition and honours . Further , despite claims that Chauvel alone had a clear view of the battle , that his coolness and skill were crucial in gaining the victory , his name was omitted from the long list of honours published on New Year 's Day 1917 . Murray did offer Chauvel a lesser award ( a Distinguished Service Order ) for Romani which he declined . 
- On reading Murray 's description in his official <unk> covering the battle , and reprinted in a Paris edition of the ' Daily Mail ' , Chauvel wrote to his wife on 3 December 1916 @,@ 
- I am afraid my men will be very angry when they see it . I cannot understand why the old man cannot do justice to those to whom he owed so much and the whole thing is so absolutely inconsistent with what he had already cabled . 
- It was not until after the victory at the Battle of Rafa that Chauvel was made a Knight Commander of the Order of St Michael and St George , but this particular order is awarded for important non @-@ military service in a foreign country . It was not just his military service at Romani which had not been recognised , but also the service of all those who fought in the Anzac Mounted Division at Romani , at El Arish , at Magdhaba and at Rafa . In September 1917 , not long after General Edmund <unk> became Commander in Chief of the Egyptian Expeditionary Force , Chauvel wrote to GHQ to point out the injustice done to his front @-@ line troops , acknowledging that it was " difficult to do anything now to right this , but consider the Commander @-@ in @-@ Chief should know that there is a great deal of bitterness over it . " 
- 
- 
- = The Litigators = 
- 
- The Litigators is a 2011 legal thriller novel by John Grisham , his 25th fiction novel overall . The Litigators is about a two @-@ partner Chicago law firm attempting to strike it rich in a class action lawsuit over a <unk> reduction drug by a major <unk> drug company . The protagonist is a Harvard Law School <unk> big law firm <unk> who stumbles upon the <unk> and joins it only to find himself <unk> against his old law firm in this case . The book is regarded as more humorous than most of Grisham 's prior novels . 
- The theme of a young lawyer being fed up with a giant law firm and <unk> away to less lucrative but more satisfying career is shared with " The Associate " . The theme of a lawsuit against a giant corporation appeared in " The Runaway Jury " - but in the present book , the corporation is vindicated and proven to have been unjustly maligned ( at least on the specific drug which is the subject of the lawsuit ) and the mass tort lawyers are seen as greedy and <unk> , ultimately <unk> and leaving the protagonist 's tiny Chicago firm in the <unk> . 
- Critical reviews were mixed for the book , with several opinions noting a lack of suspense . Nonetheless , the book has achieved both hardcover and <unk> # 1 best seller status on various lists , including both The New York Times and The Wall Street Journal . However , since some services do not separate fiction and non @-@ fiction books , it did not debut as a # 1 bestseller on certain lists , such as the USA Today . Some reviewers noted that this story would lend itself to an adapted screenplay . 
- 
- = = Background = = 
- 
- Having sold 250 million copies of his previous 24 novels in 29 languages , Grisham had produced an international bestseller with each prior book . Including the release of The Litigators , Grisham has produced 23 adult fiction novels and 2 children 's fiction novels as well as a short story collection . In addition , he has produced one non @-@ fiction book . Thus , various sources claim this to be his 23rd , 25th , or 26th book . 
- In the first of a two @-@ part interview with The Wall Street Journal , Grisham claimed that although he usually attempts to include humor in his submitted drafts , it is usually removed during the editorial process . However , in this case much of the humor survived editing . In the second part of the interview the following week , Grisham noted that his inspirations for the book included television advertisements and the Deepwater Horizon oil spill . 
- 
- = = Publication = = 
- 
- Leading book retailers such as Amazon.com , Barnes & Noble , and <unk> released the book in hardcover format in the United States as a <unk> publication on October 25 , 2011 . In the United Kingdom , the book was published with different cover art by <unk> & <unk> on the same date . Random House published the paperback version on June 26 , 2012 . 
- The book is also available as an audiobook , narrated by Scott Brick , and in <unk> format . Other formats available on October 25 , included large @-@ print , compact disc and abridged compact disc . A limited edition will be available on November 22 , 2011 . An excerpt from the book was included in some editions including the iTunes Store edition of The Confession , which was his prior adult novel . 
- 
- = = Plot = = 
- 
- Oscar Finley and Wally Figg are the <unk> partners of a small law firm in the South Side of Chicago . Oscar 's character holds the firm together despite the childish and <unk> behavior of Wally , his junior partner . Their <unk> is often mediated by Rochelle , the highly competent African American secretary , who had learned a lot of law in he eight years in the office . Meanwhile David Zinc , a graduate of the Harvard Law School , is completely fed up with the grinding and <unk> - though well @-@ paid - life of an Associate in the giant law firm of Rogan <unk> , where in five years of work he had never seen the inside of a courtroom . He suddenly breaks away , goes on a drinking binge and by chance finds himself at the Finley & Figg office . Feeling an elevating sense of freedom and <unk> never to go back , <unk> willingly <unk> himself to working for the two <unk> street lawyers and ambulance chasers . 
- While Wally goes to a funeral home to attend the wake of a former estate client , the client 's son claims that his father was killed by Krayoxx , a <unk> @-@ lowering drug developed by the fictional <unk> company Varrick Labs . <unk> at the possible monetary returns on the case , the firm finds several former clients who appear to have valid claims about Krayoxx . Oscar and Wally generate publicity in the Chicago Tribune with a picture of their filing ; this <unk> an avalanche of communications and leads them to several additional claimants . 
- Wally notices a <unk> class action lawsuit against Varrick Labs in Florida , and realizes that if he can find some patients to sign as clients , he can earn a big <unk> on another firm 's <unk> . However , some complications make the story interesting . Although none of the three Finley & Figg lawyers had previously argued in United States federal court , that is where they find themselves pitted against Zinc 's old firm with this case . In fact , David 's expertise was in long @-@ term bonds . 
- Once the firm 's claims become prominent , mass tort operators approach them about being part of a mass settlement . Wally flies to Las Vegas to meet the other mass tort interests , most notably Jerry Alisandros . Varrick 's CEO flies to Chicago to meet Nadine Karros , a leading defense attorney , who works for Rogan <unk> . Believing that they can get federal judge Harry Seawright to claim jurisdiction , Karros is chosen for her firms ' ties to him and her expertise . The case is soon expedited on Seawright 's <unk> with Finley & Figg 's claim singled out of the tort claimants and Karros takes action to have Finley & Figg 's eight death cases heard separately . Eventually , Alisandros learns that tests of Krayoxx yield benign results . Oscar and his wife , Paula , are often at odds , and as a large settlement looms , he attempts to divorce her and cash out . After settlement talks break down with Varrick , Alisandros withdraws as co @-@ counsel and Finley & Figg motions to withdraw their claims . 
- Once at Finley & Figg , Zinc stumbles upon a lead poisoning brain damage case involving <unk> immigrants . He <unk> his own time and resources on their case . He also succeeds in representing immigrants in a labor law case . During the labor case , the employer attempted to have Finley & Figg 's offices burned down and the would @-@ be <unk> stumbled upon Oscar at the office . Oscar shot him and added an unnecessary debilitating shot that shattered his leg . He was sued for using excessive force . 
- With Varrick having spent 18 million dollars defending itself and the mass tort bar having <unk> discredited Krayoxx in the mass media , Karros <unk> for frivolous lawsuit sanctions pending a withdrawn motion . Additionally , actions were initiated for legal <unk> regarding Wally 's letters that promised 2 million dollar settlement followed by motions to dismiss without notifying his clients . After realizing that they could be sued for defense costs and <unk> for withdrawing the case , Finley & Figg withdraw their motions and agree to a jury trial that they believe to be futile . The trial opens as originally scheduled . During opening statements , Oscar suffered a <unk> <unk> . Wally attempted to make light of the situation by proclaiming it an example of Krayoxx effects . Karros moved for <unk> and the motion was granted , leading to the need to pick a new jury . Wally stood in for Oscar as lead attorney while a new jury was seated and for the first day of testimony . The next day , the recovering alcoholic Figg was nowhere to be found although an empty pint bottle of <unk> <unk> was . After Wally was <unk> for a second day , David was pressed into service . Rueben Massey , Varrick 's CEO , instructed Karros not to move for likely @-@ successful summary judgment . Zinc declined to cross @-@ examine the first handful of expert witnesses that Varrick called , Eventually , Zinc discredited Varrick 's clinical trials during cross @-@ examination of the final expert witness . Nonetheless , the jury rendered a very quick not guilty verdict . 
- Zinc continued to pursue the lead poisoning product liability case . He settled the case for $ 6 @.@ 5 million ( including $ 1 @.@ 5 million in legal fees ) . David returns to the office and tells Oscar and Wally of his settlement . He tells them of his plan to split his earnings evenly with them . In return the three of them are to sign a 12 @-@ month contract to enter an equal partnership and will no longer be an ambulance @-@ chasing firm . Oscar and Wally agree to the new contract . Later that year the partnership fell apart . Finley began spending less time in the office and eventually retired a happy man , Figg packed up and moved to Alaska , and Zinc opened his own product liability practice , David E. Zinc , Attorney @-@ at @-@ Law and hired Rochelle as his new secretary . 
- 
- = = = List of characters = = = 
- 
- Oscar Finley , Finley & Figg Senior Partner - A lazy , <unk> married , nearing retirement " <unk> @-@ <unk> , slip @-@ and @-@ falls and <unk> divorces veteran " and former police officer , who took the bar exam three times . 
- Wally Figg , Finley & Figg Junior Partner - A former <unk> <unk> and four @-@ time <unk> who <unk> funeral <unk> and <unk> for clients . A University of Chicago Law School <unk> who took the bar exam three times . 
- David Zinc , Finley & Figg Associate attorney - <unk> Grisham young hot shot Harvard graduate lawyer whose life is turned upside down . 
- Rochelle Gibson , Finley & Figg secretary - Former claimant against Finley & Figg who holds the firm together . 
- Nadine Karros , <unk> 's leading <unk> recruited by Varrick . 
- Harry Seawright , federal judge . 
- <unk> <unk> , repeat Finley & Figg divorce customer and eventual love interest of Wally 's 
- Jerry Alisandros , mass tort operator who brings F & F into his firm 's fold . 
- Paula Finley , Oscar 's wife 
- Rueben Massey , CEO Varrick . 
- Helen , David 's wife 
- Anderson Zinc , David 's father ( Chief Justice of the Supreme Court of Minnesota ) ( per Ch . 15 ) 
- Caroline Zinc , David 's mother ( art and photography teacher ) ( per Ch . 15 ) 
- Lana , David 's secretary 
- 
- = = Critical review = = 
- 
- The Litigators is said to be " an amusing and appalling look into the machinations of a nationwide class @-@ action suit , " according to <unk> Harshaw of Bloomberg <unk> The Wall Street Journal 's Christopher John Farley noted that the book is lighter than Grisham 's other works . Publishers Weekly called it a " <unk> farcical look at lawyers at the bottom of the food chain " . CNN described the book as an original perspective of " the best and worst the American system of justice has to offer " . Louis <unk> of The Washington Post , who described himself as someone who abandoned Grisham after his first three novels , noted that this book might be a good starting point for those who have tired of Grisham . Andrea Simakis of The Plain <unk> describes the book as a " <unk> meal " than Grisham 's usual " potato @-@ chip fiction " . Publishers Weekly also notes that the fairy tale ending is not really in keeping with the introduction 's dark humor . Rick Arthur of The United Arab Emirates publication The National describes the book unfavorably as a cross between prior Grisham works The Street <unk> and The King of <unk> and similarly describes the protagonist unfavorably to those of The Firm and The <unk> . Geoffrey <unk> of the Daily Mail presented one of the more favorable reviews describing the book as " a spectacular return to form , displaying the clarity and passion that were there in his first <unk> but seemed to <unk> away . " <unk> notes that Grisham returned to one of his seminal themes of the idealistic young lawyer fighting with the realization that corporations only care about <unk> profits . 
- The book has been derided for its lack of suspense . Carol <unk> of USA Today says that Grisham 's latest attempt to capture the spirit of the legal David and <unk> story is missing " the <unk> @-@ up of suspense " that he has employed successfully in recent adult and youth novels . Harshaw claims that the book is lacking in the suspense that made The Firm so successful . Arthur finds elements of the plot <unk> and the story <unk> as well as <unk> . Although the book is somewhat predictable , <unk> notes that " Grisham <unk> clear of the usual melodramatic devices . Corporations aren ’ t intrinsically <unk> ; <unk> aren ’ t <unk> with <unk> . And best of all , no one is murdered for <unk> Too Close to the Truth . " 
- Some sources noted that the book has potential to become an adapted screenplay . Irish Independent describes Grisham 's new book as " following his usual route to the bestsellers list " and projects it as a candidate to be his next Hollywood film . Although it is standard Grisham fare , Independent noted that it provides the usual thrills in Grisham 's comfortable legal world and should be a gripping read for his usual fans . The Sunday Express noted that the book could be readily converted to a screenplay , but its critic , Robin <unk> Smith , viewed the " ambulance chasing " ethos as a foreign thing that <unk> might have to worry about in the near future . 
- Simakis praised the book for having more depth of character than Grisham 's novels <unk> do . She compares the protagonist to Mitch <unk> from The Firm and <unk> <unk> from The <unk> . <unk> says that most of the claimants that they find are <unk> , but a few are from somewhat sympathetic immigrant families . Simakis notes that Wally trades sex for legal services with one claimant . Harshaw says that the book is a bit sentimental and comparatively lacking in terms of secondary character development for Grisham . Larry <unk> of Canada 's Globe and Mail notes that on the dramatic scale this book has instances of laugh out loud humor that make it more like Boston Legal than The Practice , which Boston Legal was spun off from . 
- 
- = = Commercial success = = 
- 
- According to The Huffington Post , this book is the ninth best @-@ selling fiction book of the year in 2011 , while according to the USA Today this was the 16th best selling book overall in 2011 . According to Amazon.com the book was the number eight overall best seller . 
- 
- = = = Hardcover = = = 
- 
- It immediately was listed as the Publishers Weekly # 1 best @-@ seller among fiction hardcover books according to Reuters . It was also listed as the # 1 best @-@ seller by The New York Times in the November 13 , 2011 book review section for the week ending October 29 , 2011 for Hardcover Fiction , E @-@ Book Fiction , Combined Hardcover & Paperback Fiction , and the Combined Print and E @-@ Book Fiction . It dropped from the # 1 position in its second week on the list . It remained on the Combined Hardcover & Paperback Fiction list until the February 19 , 2012 list ( 15 weeks ) for the week ending February 4 . It remained on both the Hardcover Fiction list and the Combined Print and E @-@ Book Fiction list until the February 26 list ( 16 weeks ) for the week ending February 11 . It remained on the E @-@ Book Fiction list until the March 11 list ( 18 weeks ) for the week ending February 25 . 
- The Wall Street Journal announced that on Saturday October 29 , it would begin incorporating digital book sales in its best seller lists . When the book debuted in The Wall Street Journal list on November 5 for the week ending October 30 , it was listed first in Hardcover Fiction , Fiction E @-@ Books and Fiction Combined . It retained the hardcover lead the following week , but lost the other leads . After two weeks it was surpassed on the hardcover list as well . It remained on The Wall Street Journal Hardcover Fiction , Fiction E @-@ Books and Fiction Combined best seller lists until the January 7 listing for the week ended on January 1 , 2012 . 
- The book was released the day after Walter <unk> 's biography of Steve Jobs , entitled Steve Jobs , was released by Simon & Schuster . Jobs had died on October 5 and the release date was moved forward . The Jobs book 's release had been moved forward twice ; It had been moved from spring 2012 to November 21 after Jobs stepped down and then to the October 24 date after his death . When The Litigators debuted on November 3 on the USA Today best @-@ seller list , which does not separate fiction and non @-@ fiction , it debuted at number 2 behind the Jobs book . 
- 
- = = = Paperback = = = 
- 
- It debuted at # 1 on the New York Times Paperback Mass @-@ Market Fiction Best Sellers list on July 15 , 2012 ( reflecting sales for the week ending June 30 , 2012 ) . The book remained at # 1 until the August 12 list ( reflecting sales of the week ending July 28 , 2012 ) , making a five @-@ week run . It continued to appear on the list until the January 13 , 2013 list ( reflecting sales for the week ending December 29 , 2012 ) . On the USA Today list , which include fiction and non @-@ fiction as well as hardcover and paperback , it debuted at # 10 in the week of July 5 , following its paperback release . 
- 
- 
- = Stanley Green = 
- 
- Stanley Owen Green ( 22 February 1915 – 4 December 1993 ) , known as the Protein Man , was a human billboard who became a well @-@ known figure in central London in the latter half of the 20th century . 
- Green patrolled Oxford Street in the West End for 25 years , from 1968 until 1993 , with a placard recommending " protein wisdom " , a low @-@ protein diet that he said would <unk> the <unk> and make people <unk> . His 14 @-@ page pamphlet , Eight Passion Proteins with Care , sold 87 @,@ 000 copies over 20 years . 
- Green 's campaign to suppress desire , as one commentator called it , was not always popular , but he became one of London 's much @-@ loved <unk> . The Sunday Times interviewed him in 1985 , and his " less passion from less protein " slogan was used by the fashion house Red or Dead . 
- When he died at the age of 78 , the Daily Telegraph , Guardian and Times published his obituary , and the Museum of London added his pamphlets and placards to their collection . In 2006 his biography was included in the Oxford Dictionary of National Biography . 
- 
- = = Early life = = 
- 
- Green was born in <unk> , north London , the youngest of four sons of Richard Green , a clerk for a bottle <unk> manufacturer , and his wife , May . He attended Wood Green School before joining the Royal Navy in 1938 . 
- Philip Carter writes in the Oxford Dictionary of National Biography that Green 's time with the Navy affected him deeply . He was shocked by the obsession with sex . " I was astonished when things were said quite openly – what a husband would say to his wife when home on leave " , he told the Sunday Times " A Life in the Day " column in 1985 . " I 've always been a moral sort of person . " 
- After leaving the Navy in September 1945 , Green worked for the Fine Art Society . In March 1946 , Carter writes , he failed the entrance exam for the University of London , then worked for <unk> and the civil service , and as a <unk> for <unk> Borough Council . He said that he had lost jobs twice because he had refused to be dishonest . In 1962 he held a job with the post office , then worked as a self @-@ employed <unk> until 1968 when he began his anti @-@ protein campaign . He lived with his parents until they died , his father in 1966 and his mother the following year , after which he was given a council flat in <unk> Green , <unk> , north London . 
- 
- = = His mission = = 
- 
- 
- = = = On the streets = = = 
- 
- Green began his mission in June 1968 , at the age of 53 , initially in <unk> on Saturdays , becoming a full @-@ time human billboard six months later on Oxford Street . He <unk> there from <unk> with a sandwich board attached to the bicycle , a journey of 12 miles ( 19 km ) that could take up to two hours , until he was given a bus pass when he turned 65 . 
- He rose early , and after <unk> for breakfast made bread that would rise while he was on patrol , ready for his evening meal . Otherwise his diet consisted of steamed vegetables and pulses , and a pound of apples a day . Lunch was prepared on a <unk> <unk> and eaten at 2 : 30 in a " warm and secret place " near Oxford Street . 
- From Monday to Saturday he walked up and down the street until 6 : 30 pm , reduced to four days a week from 1985 . Saturday evenings were spent with the cinema crowds in Leicester Square . He would to go to bed at 12 : 30 am after saying a prayer . " Quite a good prayer , <unk> too " , he told the Sunday Times . " It is a sort of acknowledgment of God , just in case there happens to be one . " 
- Peter Ackroyd wrote in London : The Biography that Green was for the most part ignored , becoming " a poignant symbol of the city 's <unk> and <unk> " . He was arrested for public obstruction twice , in 1980 and 1985 . " The injustice of it upsets me " , he said , " because I 'm doing such a good job " . He took to wearing <unk> to protect himself from <unk> , several times finding it on his hat at the end of the day . 
- 
- = = = Writing = = = 
- 
- Sundays were spent at home producing Eight Passion Proteins on his printing press . Waldemar <unk> described it as worthy of Heath Robinson , who was known for his cartoons of ancient contraptions . The racket caused trouble between Green and his neighbours . 
- Noted for its eccentric typography , Eight Passion Proteins went through 52 editions between 1973 and 1993 . Green sold 20 copies on <unk> and up to 50 on Saturdays ( for 10 <unk> in 1980 and 12 <unk> 13 years later ) , a total of 87 @,@ 000 copies by February 1993 , according to Carter . He sent copies to those in the public eye , including five British prime ministers , the Prince of Wales , the Archbishop of Canterbury , and Pope Paul VI . 
- The booklet argued that " those who do not have to work hard with their limbs , and those who are inclined to sit about " will " store up their protein for passion " , making retirement , for example , a period of increased passion and marital <unk> . He left several unpublished manuscripts , including a novel , Behind the <unk> : More than Just a Tale ; a 67 @-@ page text called Passion and Protein ; and a <unk> @-@ page version of Eight Passion Proteins , which , Carter writes , was rejected by Oxford University Press in 1971 . 
- 
- = = <unk> recognition = = 
- 
- Green enjoyed his local fame . The Sunday Times interviewed him in 1985 for its " A Life in the Day " feature , and some of his <unk> , including " less passion from less protein " were used on dresses and t @-@ shirts by the London fashion house Red or Dead . 
- When he died in 1993 at the age of 78 , the Daily Telegraph , Guardian and Times all published <unk> . His letters , diaries , pamphlets and placards were given to the Museum of London ; other artefacts went to the <unk> Park Museum . His printing press was featured in Cornelia Parker 's exhibition " The Maybe " ( 1995 ) at the <unk> Gallery , along with Robert Maxwell 's <unk> , one of Winston Churchill 's cigars and <unk> <unk> in a glass box . In 2006 he was given an entry in the Oxford Dictionary of National Biography . 
- Two decades after his death Green was still remembered by writers and bloggers , fondly for the most part , although not invariably so . Artist Alun <unk> ' documentary fiction , 3 <unk> ( 2007 ) , portrayed him as trawling the streets of London , " campaigning for the suppression of desire " . Musician Martin Gordon included a track about Green on his 2013 album , <unk> Me Out . 
- 
- 
- = Sclerodermatineae = 
- 
- Sclerodermatineae is a suborder of the fungal order Boletales . <unk> in 2002 by <unk> Manfred Binder and Andreas <unk> , it contains nine genera and about 80 species . The suborder contains a diverse assemblage fruit body morphologies , including <unk> , gasteroid forms , earthstars ( genus Astraeus ) , and <unk> . Most species are ectomycorrhizal , although the ecological role of some species is not known with certainty . The suborder is thought to have originated in the late Cretaceous ( 145 – 66 Ma ) in Asia and North America , and the major genera diversified around the mid Cenozoic ( 66 – 0 Ma ) . 
- 
- = = Taxonomy = = 
- 
- The Sclerodermatineae was first legitimately used by Manfred Binder and Andreas <unk> in 2002 based on molecular analyses of nuclear ribosomal large <unk> ( <unk> ) rRNA sequences from 60 species of Boletales . This research was an extension of Binder 's 1999 graduate work , in which he argued for the need to recognize the molecular differences of the <unk> fungi . Sclerodermatineae is one of six lineages of the Boletales recognized as a suborder ; the others are the <unk> , <unk> , <unk> , <unk> , and <unk> . Of the nine genera assigned to the Sclerodermatineae , three are <unk> ( Boletinellus , Gyroporus , and Phlebopus ) , and six are gasteroid ( Astraeus , Calostoma , Diplocystis , Pisolithus , and Scleroderma ) . Since the suborder 's original description , there have been several phylogenetic studies investigating the Sclerodermatineae . Some studies have revealed the existence of numerous cryptic species and have contributed to taxonomic expansion of the group . The " core " Sclerodermatineae include the genera Astraeus , Calostoma , Scleroderma , Pisolithus , Diplocystis , Tremellogaster ( all gasteroid ) , and the <unk> genus Gyroporus ; Phlebopus and Boletinellus resolved as sister to this core group . 
- As of 2012 , there are an estimated 78 species in the Sclerodermatineae . The type of the suborder is the family <unk> ; other families in the suborder are the <unk> , Diplocystaceae , and the <unk> . 
- <unk> 
- Boletinellus ( 2 species ) 
- Phlebopus ( 12 species ) 
- Diplocystaceae 
- Astraeus ( 5 species ) 
- Diplocystis ( 1 species ) 
- <unk> ( 1 species ) 
- Tremellogaster ( 1 species ) 
- <unk> 
- Gyroporus ( 10 species ) 
- <unk> 
- Calostoma ( 15 species ) 
- <unk> ( 1 species ) 
- <unk> ( 1 species ) 
- <unk> ( 1 species ) 
- Pisolithus ( 5 species ) 
- Scleroderma ( about 30 species ) 
- Based on ancestral reconstruction studies , the earliest ( basal ) members of the Sclerodermatineae originated in the late Cretaceous ( 145 – 66 Ma ) . The major genera diversified near the mid Cenozoic ( 66 – 0 Ma ) . Asia and North America are the most probable ancestral areas for all Sclerodermatineae , and <unk> and angiosperms ( primarily <unk> ) are the most probable ancestral hosts . 
- 
- = = Description = = 
- 
- Members of the Sclerodermatineae have fruit body shapes ranging from <unk> ( with a cap , stipe , and tubes on the underside of the cap ) to gasteroid . <unk> fruit bodies sometimes have hollow <unk> with a surface that is smooth to somewhat <unk> ( covered with <unk> particles ) , and lack the <unk> ( a net @-@ like pattern of <unk> lines ) characteristic of some members of the <unk> . The pores are <unk> ( wrinkled with low , uneven ridges ) , <unk> , and either fine or coarse . The flesh is usually whitish to yellowish , and some species exhibit a blue staining reaction upon injury . In mass , spores are yellow ; microscopically , the spores are ellipsoid in shape and have a smooth surface . 
- <unk> fruit body types are either roughly spherical or <unk> , occasionally with <unk> , and usually have a peridium that is either simple or multi @-@ layered . <unk> gasteroid fruit bodies generally open irregularly at maturity to expose a powdery gleba with a color ranging from white to yellow or black @-@ brown to black . <unk> are generally absent from the gleba . <unk> are spherical or nearly so , and have a surface texture that ranges from smooth to <unk> @-@ like and spiny , or sometimes with <unk> . <unk> have clamp connections . 
- 
- = = <unk> diversity = = 
- 
- A distinguishing feature of the Sclerodermatineae is the diversity of morphologies within the group . The <unk> genera Boletinellus , Gyroporus , and Phlebopus are typical <unk> with a cap and stipe . However , each of the gasteroid Sclerodermatineae has a distinct morphology . Species of Astraeus have an " earthstar " morphology where the outer peridium <unk> back in sections . The gleba of Pisolithus is partitioned into hundreds of <unk> chambers . Scleroderma is a simple puffball with a thin outer skin and a powdery gleba at maturity . Diplocystis and Tremellogaster are each distinct in their morphologies : the former comprises compound fruit bodies each with 3 – 60 spore sacs crowded together , while the latter forms a roughly spherical <unk> with a thick multi @-@ layered peridium . Calostoma ( Greek for " pretty mouth " ) is <unk> distinct from other gasteroid members , having a fruit body that forms a <unk> , spore @-@ bearing head composed of a three @-@ layered peridium . About two @-@ thirds of Sclerodermatineae species have a gasteroid morphology , although this may be an <unk> due to the existence of cryptic species that have yet to be formally described . For example , studies of the gasteroid genera Astraeus and Pisolithus indicate the existence of numerous cryptic taxa . 
- 
- = = Ecology = = 
- 
- The mycorrhizal associations of several Sclerodermatineae genera have been established . Studies have demonstrated that Astraeus , Pisolithus , and Scleroderma form ectomycorrhizal associations with both angiosperms and <unk> . Previously thought to be <unk> , the <unk> were determined to be ectomycorrhizal with <unk> and <unk> using isotopic and molecular analyses . Species from the genera Pisolithus and Scleroderma have been used in forestry as mycorrhizal <unk> to help promote the growth and <unk> of young seedlings . 
- As a group , the Sclerodermatineae have a broad distribution , and some genera ( Pisolithus and Scleroderma ) have been found on all continents except Antarctica . 
- 
- = = = Cited literature = = = 
- 
- Kirk <unk> , Cannon <unk> , <unk> <unk> , <unk> <unk> ( 2008 ) . Dictionary of the Fungi ( 10th ed . ) . <unk> , UK : <unk> International . ISBN 978 @-@ 0 @-@ <unk> @-@ 826 @-@ 8 . 
- 
- = SM U @-@ 3 ( Austria @-@ Hungary ) = 
- 
- SM U @-@ 3 or U @-@ III was the lead boat of the U @-@ 3 class of submarines or U @-@ boats built for and operated by the Austro @-@ Hungarian Navy ( German : <unk> und <unk> Kriegsmarine or <unk> Kriegsmarine ) before and during the First World War . The submarine was built as part of a plan to evaluate foreign submarine designs , and was built by Germaniawerft of Kiel , Germany . 
- U @-@ 3 was authorized in 1906 , begun in March 1907 , launched in August 1908 , and towed from Kiel to Pola in January 1909 . The double @-@ <unk> submarine was just under 139 feet ( 42 m ) long and displaced between 240 and 300 tonnes ( 260 and 330 short tons ) , depending on whether surfaced or submerged . The design of the submarine had poor diving qualities and several modifications to U @-@ 3 's diving planes and fins occurred in her first years in the Austro @-@ Hungarian Navy . Her armament , as built , consisted of two bow torpedo tubes with a supply of three torpedoes , but was supplemented with a deck gun in 1915 . 
- The boat was commissioned into the Austro @-@ Hungarian Navy in September 1909 , and served as a training boat — sometimes making as many as ten cruises a month — through the beginning of the First World War in 1914 . At the start of that conflict , she was one of only four operational submarines in the Austro @-@ Hungarian Navy U @-@ boat fleet . Over the first year of the war , U @-@ 3 conducted reconnaissance cruises out of Cattaro . On 12 August 1915 , U @-@ 3 was damaged after an unsuccessful torpedo attack on an Italian armed merchant cruiser and , after she surfaced the next day , was sunk by a French destroyer . U @-@ 3 's commanding officer and 6 men died in the attack ; the 14 survivors were captured . 
- 
- = = Design and construction = = 
- 
- U @-@ 3 was built as part of a plan by the Austro @-@ Hungarian Navy to competitively evaluate foreign submarine designs from Simon Lake , Germaniawerft , and John Philip Holland . The Austro @-@ Hungarian Navy authorized the construction of U @-@ 3 ( and sister ship , U @-@ 4 ) in 1906 by Germaniawerft of Kiel , Germany . U @-@ 3 was laid down on 12 March 1907 and launched on 20 August 1908 . After completion , she was towed via Gibraltar to Pola , where she arrived on 24 January 1909 . 
- U @-@ 3 's design was an improved version of Germaniawerft 's design for the Imperial German Navy 's first U @-@ boat , U @-@ 1 , and featured a double hull with internal saddle tanks . The Germaniawerft engineers refined the design 's hull shape through extensive model trials . 
- U @-@ 3 was 138 feet 9 inches ( 42 m ) long by 14 feet ( 4 @.@ 3 m ) abeam and had a draft of 12 feet 6 inches ( 3 @.@ 81 m ) . She displaced 240 tonnes ( 260 short tons ) surfaced and 300 tonnes ( 330 short tons ) submerged . She was armed with two bow 45 @-@ centimeter ( 17 @.@ 7 in ) torpedo tubes , and was designed to carry up to three torpedoes . 
- 
- = = Service career = = 
- 
- After U @-@ 3 's arrival at Pola in January 1909 , she was commissioned into the Austro @-@ Hungarian Navy on 12 September 1909 as SM U @-@ 3 . During the evaluation of the U @-@ 3 class conducted by the Navy , the class ' poor diving and handling characteristics were noted . To alleviate the diving problems , U @-@ 3 's fins were changed in size and shape several times , and eventually , the front diving planes were removed and a stationary stern flap was affixed to the hull . U @-@ 3 served as a training boat between 1910 and 1914 and made as many as ten cruises per month in that capacity . 
- At the beginning of World War I , she was one of only four operational submarines in the Austro @-@ Hungarian Navy . On 22 August 1914 , U @-@ 3 began operating reconnaissance cruises out of the naval base at <unk> , but moved a month later to Cattaro . In April 1915 , a 3 @.@ 7 @-@ centimeter ( 1 @.@ 5 in ) quick firing ( QF ) deck gun was added . 
- On 10 August , U @-@ 3 departed from Cattaro for what would be her final time for a patrol north of Brindisi . Two days later , while returning to Cattaro from the Straits of Otranto , U @-@ 3 launched a torpedo attack on the Italian armed merchant cruiser <unk> di <unk> . The torpedoes missed their mark and , in the ensuing action , U @-@ 3 was rammed by <unk> di <unk> , which destroyed the U @-@ boat 's <unk> . When she attempted to surface , she was shelled by the escorting destroyers . She submerged to escape the artillery but was further damaged by a depth charge attack from the French destroyer <unk> while resting on the <unk> . When U @-@ 3 surfaced the following day , she was shelled and sunk by <unk> . Fourteen of her crew were saved and captured , but seven died in the attack , including her commander , <unk> Karl <unk> . U @-@ 3 had no successes during the war . 
- 
- 
- = The Son Also Draws = 
- 
- " The Son Also Draws " is the sixth episode of the first season of the animated comedy series Family Guy , originally aired on Fox on May 9 , 1999 . The episode follows Chris as he is ejected from the Youth Scouts , and Peter drives the family to Scout headquarters to get him readmitted . During a rest stop at a Native American casino , Lois <unk> away the family car . Peter pretends to be a member of the tribe in an attempt to get it back , and is sent on a vision quest to prove his heritage , giving him and Chris an opportunity to bond . 
- " The Son Also Draws " was written by Ricky Blitt and directed by Neil Affleck , both working on their first Family Guy episode . The episode guest starred actors Suzie Plakson , Kevin Michael Richardson , Fred Tatasciore and Wally <unk> . Recurring guest voice actors included writer and animator Butch Hartman and actor Patrick <unk> . Much of the episode 's humor is structured around cutaway sequences that parody popular culture , including references to Speed Racer , Happy Days , Nova , One Day at a Time , and The More You Know . 
- Critical reception for the episode was mixed ; certain critics believed the episode was not an " instant classic " in contrast to the other episodes of the season but called it " memorable " and " brilliant " nevertheless , while others regarded it as the black sheep of the season . The episode caused controversy in Canada for the episode 's final gag , in which Peter states that " Canada sucks . " 
- 
- = = Plot = = 
- 
- Chris hates being in the Youth Scouts and wants to quit , but is afraid to tell his father Peter . Chris is finally kicked out when he runs over the troop leader during a Soap Box Derby . Peter insists on driving Chris and the rest of the family ( Peter 's wife Lois , their daughter Meg and their infant Stewie ) to the Youth Scout headquarters , in Manhattan , to get Chris readmitted . While they are gone , their talking dog Brian is watching Nova just as the show is interrupted to show several episodes of the sitcom One Day at a Time . He tries to change the channel , but is unable to do so ( nor he can turn the TV off ) , losing his intelligence shortly after watching a few episodes . 
- The family stops at a Native American casino as Peter needs to use the bathroom , Lois quickly becomes addicted to gambling and loses the family car . After hearing that Lois has <unk> the car away , Peter tries to get it back by claiming to be Native American . The doubtful Indian elders demand that he go on a vision quest to prove his heritage . Chris accompanies Peter into the wilderness , hoping to tell him that he only wants to draw instead of being in the Scouts . <unk> from hunger , Peter begins talking to anthropomorphic trees and sees a vision of his spiritual guide , Fonzie . After hearing Fonzie 's advice Peter finally listens to Chris 's complaints and realizes his son is a talented artist . 
- Peter and Chris return to the casino and reclaim the car . The episode ends with Lois , Stewie , and Meg <unk> stereotypes about Native Americans , <unk> , and Swedes , respectively , before Peter comments that " Canada sucks . " 
- 
- = = Production = = 
- 
- " The Son Also Draws " was written by Ricky Blitt , his first episode in the Family Guy series , and directed by former Simpsons director Neil Affleck , also in his first Family Guy episode . Peter Shin and Roy Allen Smith , who have since supervised other episodes of Family Guy , both acted as supervising directors on this episode . Alex <unk> , the voice of Lois , helped write this episode , making her the first female member in the Family Guy writing staff ; show creator Seth MacFarlane mentioned that her input on the character of Lois was particularly helpful . Andrew <unk> and voice actor Mike Henry acted as staff writers for this episode , while Ricky Blitt , Neil Goldman and Chris Sheridan worked as the story editors . The subplot of " The Son Also Draws " that involved Lois losing the car was based on the 1985 comedy film Lost in America . The part where Peter pretends to be an Indian to get the family car back was inspired by real @-@ life instances of people who were " 1 / 64th " Native American receiving money from wealthy casino tribes . 
- In addition to the regular cast , " The Son Also Draws " featured the voices of actors Suzie Plakson , Kevin Michael Richardson , Fred Tatasciore and Wally <unk> . Recurring guest voice actors included writer and animator Butch Hartman and actor Patrick <unk> . 
- 
- = = Cultural references = = 
- 
- The television show the family is watching near the beginning of the show is an episode from the 1974 ABC sitcom Happy Days . 
- When the rest of the family is gone on the trip to Manhattan , Brian watches an episode of Nova , which is interrupted by a PBS announcement that they will be showing various episodes of One Day at a Time . 
- Speed and Pops from Speed Racer make an appearance at the Soap Box Derby starting line and again when the <unk> prepare to leave for Manhattan . 
- When Peter has to search for his spiritual guide , it turns out to be Fonzie from Happy Days . 
- The end of the episode features a parody of the The More You Know series of public service announcements . 
- 
- = = Reception = = 
- 
- Reviews for " The Son Also Draws " were mixed to favorable . In his 2008 review , <unk> <unk> of IGN rated the episode an 8 / 10 , stating that while the episode is not an " instant classic " , it is " still quite strong " and has " more than a few clever moments " . He also notes that the <unk> are " kept to a minimum " , and much of the humor comes from the storyline . He commented that the episode did not have as much laugh @-@ out @-@ loud moments as other episodes , but stated that it had bolder humor than the show would later be known for . In his review of the first volume DVD collection of Family Guy , Aaron <unk> of DVD Talk listed " The Son Also Draws " as one of the series ' " most brilliant moments " , praising the spiritual vision sequence and naming the conversation between Peter and Brian among the best moments of the series , calling the conversation " rolling @-@ on @-@ the @-@ floor funny . " 
- Robin Pierson of The TV Critic , however , was far more hostile towards the episode , giving it the lowest rating of the season , a 44 out of 100 . Pierson believed the episode was " very poor " and called the storyline " lame " and " <unk> [ sic ] , " with " a bunch of jokes to match . " The gag at the end of the episode , in which Peter states that " Canada sucks " , inspired anger from Canadian viewers of the show , which led them to send letters to the show 's producers . Ricky Blitt , the writer of the episode and the person responsible for the controversial gag , is Canadian . 
- 
- = = Home media = = 
- 
- " The Son Also Draws " and the complete first and second seasons of the series were released under the title Family Guy Volume One ; this standard four @-@ disc DVD box set debuted in Region 1 on April 15 , 2003 , three months before the premiere of the third season . <unk> by 20th Century Fox Television , it included several DVD extras such as episode commentaries , behind @-@ the @-@ scenes footage , and online <unk> spots . The same episodes , without the special features , were released in Region 2 on November 12 , 2001 and in Region 4 on October 20 , 2003 . 
- 
- 
- = Protomycena = 
- 
- Protomycena is an extinct monotypic genus of <unk> fungus in the family <unk> , of order Agaricales . At present it contains the single species Protomycena electra , known from a single specimen collected in an amber mine in the <unk> Septentrional area of the Dominican Republic . The fruit body of the fungus has a convex cap that is 5 mm ( 0 @.@ 2 in ) in diameter , with distantly spaced gills on the underside . The curved stipe is smooth and cylindrical , measuring 0 @.@ 75 mm ( 0 @.@ 030 in ) thick by 10 mm ( 0 @.@ 39 in ) long , and lacks a ring . It resembles extant ( currently living ) species of the genus Mycena . Protomycena is one of only five known agaric fungus species known in the fossil record and the second to be described from Dominican amber . 
- 
- = = Discovery and classification = = 
- 
- The genus is known only from the holotype specimen , a single fruit body ( mushroom ) currently residing in the private collection owned by <unk> <unk> of Turin , Italy . The specimen was collected in one of the amber mines in the <unk> Septentrional area of the island of Hispaniola , in the Dominican Republic . The amber is believed to date from the Miocene <unk> stage , about 20 to 16 million years before the present . It was first studied by a group of researchers consisting of David Hibbett and Michael Donoghue from Harvard University , with David <unk> of the American Museum of Natural History . Hibbett and colleagues published their 1997 type description in the American Journal of <unk> . The generic name Protomycena is derived from a combination of the Latin proto meaning " first " , and " Mycena " , a modern genus that it resembles . The specific epithet electra was coined by the authors from the Latin for " amber " , in reference to the mode of preservation . 
- When it was reported , Protomycena electra was the third species of fossil agaric fungus to be described . The two species <unk> <unk> and <unk> <unk> are also known from the amber mines of the Dominican Republic , while the fourth species <unk> <unk> is from the older , Cretaceous age New Jersey Amber . With the 2007 publication of a fifth extinct agaric species , <unk> <unk> , the minimum age for the Agaricales order was pushed back to the <unk> ( approximately 100 Ma ) . 
- 
- = = Description = = 
- 
- The holotype of Protomycena is a single fruit body without any associated structures , preserved in a piece of clear light yellow polished amber approximately 4 @.@ 5 – 2 @.@ 5 cm ( 1 @.@ 77 – 0 @.@ 98 in ) wide . The pileus is 5 mm ( 0 @.@ 2 in ) in diameter and has a convex shape , sporting a raised central region ( an umbo ) . The pale flesh appears yellowish in the amber , and is smooth and glossy , changing to <unk> and slightly translucent towards the margin . The pileus margin is <unk> and slightly flared . The gills on the underside of the pileus are broadly attached ( <unk> ) to the top of the stipe , and distantly spaced — between six and eight gills extend completely from the pileus margin to the stipe . These full @-@ length gills are <unk> with <unk> ( short gills which do not reach the edge of the stipe from the pileus margin ) of varying lengths . The pileus is centered on the curved stipe , which is smooth and cylindrical , measuring 0 @.@ 75 mm ( 0 @.@ 030 in ) thick by 10 mm ( 0 @.@ 39 in ) long . The stipe lacks a ring and <unk> . The mushroom is preserved with a small liquid and gas @-@ filled bubble , possibly originating from the mushroom itself , which indicates the amber to be very solid and well @-@ sealed . 
- In Hibbett and colleagues ' 1997 publication , Protomycena was placed in the subfamily <unk> , which at the time was considered part of the <unk> family ; Mycena is currently classified in the <unk> family . The placement was based solely on the visible structures , or <unk> of the fruit body . Many of the features which are typically used to classify species into fungal families and subfamilies are based on microscopic features not visible or preserved in the amber specimen . Consequently , the assignment to Mycena is provisional ( the authors also note certain similarities with extant members of <unk> ) , and the describing authors leave open the option of treating the genus placement as <unk> sedis ( uncertain placement ) within the Agaricales . Protomycena is distinct from other amber @-@ preserved mushroom taxa such as <unk> , in the grooved surface of its pileus and its <unk> gills . 
- 
- 
- = Art in Medieval Scotland = 
- 
- Art in Medieval Scotland includes all forms of artistic production within the modern borders of Scotland , between the fifth century and the adoption of the Renaissance in the early sixteenth century . In the early Middle Ages , there were distinct material cultures evident in the different federations and kingdoms within what is now Scotland . Pictish art was the only uniquely Scottish Medieval style ; it can be seen in the extensive survival of carved stones , particularly in the north and east of the country , which hold a variety of recurring images and patterns . It can also be seen in elaborate metal work that largely survives in buried <unk> . Irish @-@ Scots art from the kingdom of Dál Riata suggests that it was one of the places , as a crossroads between cultures , where the Insular style developed . 
- Insular art is the name given to the common style that developed in Britain and Ireland from the eighth century and which became highly influential in continental Europe and contributed to the development of Romanesque and Gothic styles . It can be seen in elaborate jewellery , often making extensive use of semi @-@ precious stones , in the heavily carved high crosses found particularly in the Highlands and Islands , but distributed across the country and particularly in the highly decorated illustrated manuscripts such as the Book of Kells , which may have been begun , or wholly created at the monastic centre of Iona . 
- Scotland adopted the Romanesque style relatively late and retained and revived elements of its style after the Gothic style had become dominant from the thirteenth century . Much of the best Scottish artwork of the High and Late Middle Ages was either religious in nature or realised in metal and woodwork , and has not survived the impact of time and the Reformation . However , examples of sculpture are extant as part of church architecture , including evidence of elaborate church interiors . From the thirteenth century there are relatively large numbers of monumental effigies . Native craftsmanship can be seen in a variety of items . Visual illustration can be seen in the illumination of charters and occasional survivals of church paintings . Surviving copies of individual portraits are relatively crude , but more impressive are the works or artists commissioned from the continent , particularly the Netherlands . 
- 
- = = Early Middle Ages = = 
- 
- 
- = = = Pictish stones = = = 
- 
- About 250 Pictish stones survive and have been assigned by scholars to three classes . Class I stones are those thought to date to the period up to the seventh century and are the most numerous group . The stones are largely <unk> and include <unk> symbols of animals such as fish and the Pictish beast , everyday objects such as mirrors , <unk> and tuning forks and abstract symbols defined by names including V @-@ rod , double disc and Z @-@ rod . They are found between from the Firth of Forth to Shetland . The greatest concentrations are in Sutherland , around modern Inverness and Aberdeen . Good examples include the <unk> ( Sutherland ) and <unk> stones ( Angus ) . 
- Class II stones are carefully shaped slabs dating after the arrival of Christianity in the eighth and ninth centuries , with a cross on one face and a wide range of symbols on the reverse . In smaller numbers than Class I stones , they predominate in southern <unk> , in Perth , Angus and Fife . Good examples include <unk> 2 , which contains a finely executed Celtic cross on the main face with two opposing male figures , a <unk> , cauldron , deer head and a triple disc symbol and <unk> , Angus , which shows a high @-@ <unk> Pictish boat with oarsmen and a figure facing forward in the prow . Class III stones are thought to overlap <unk> with Class II stones . Most are elaborately shaped and <unk> cross @-@ slabs , some with <unk> scenes , but lacking <unk> Pictish symbols . They are widely distributed but predominate in the southern Pictish areas . 
- 
- = = = Pictish metalwork = = = 
- 
- <unk> of metalwork have been found throughout <unk> . The earlier <unk> appear to have had a considerable amount of silver available , probably from raiding further south , or the payment of subsidies to keep them from doing so . The very large hoard of late Roman <unk> found at <unk> Law may have originated in either way . The largest hoard of early Pictish metalwork was found in 1819 at <unk> 's Law in Fife , but unfortunately much was dispersed and melted down . Over ten heavy silver chains , some over 0 @.@ 5 metres ( 1 @.@ 6 ft ) long , have been found from this period ; the double @-@ linked <unk> Chain is one of only two that have a penannular ring , with symbol decoration including enamel , which shows how these were probably used as " <unk> " <unk> . The St Ninian 's Isle Treasure of 28 silver and silver @-@ gilt objects , contains perhaps the best collection of late Pictish forms , from the Christian period , when Pictish metalwork style , as with stone @-@ carving , gradually merged with Insular , Anglo @-@ Saxon and Viking styles . 
- 
- = = = Irish @-@ Scots art = = = 
- 
- Thomas Charles @-@ Edwards has suggested that the kingdom of Dál Riata was a cross @-@ roads between the artistic styles of the <unk> and those of Ireland , with which the Scots settlers in what is now <unk> kept close contacts . This can be seen in representations found in excavations of the fortress of <unk> , which combine Pictish and Irish elements . This included extensive evidence for the production of high status jewellery and moulds from the seventh century that indicate the production of pieces similar to the <unk> brooch , found in Ayrshire , which may have been made in Dál Riata , but with elements that suggest Irish origins . These and other finds , including a trumpet spiral decorated hanging bowl disc and a stamped animal decoration ( or <unk> ) , perhaps from a bucket or drinking horn , indicate the ways in which Dál Riata was one of the locations where the Insular style was developed . In the eighth and ninth centuries the Pictish elite adopted true penannular brooches with lobed terminals from Ireland . Some older Irish pseudo @-@ penannular brooches were adapted to the Pictish style , for example the <unk> <unk> ( British Museum ) . The eighth century <unk> <unk> has elements of Pictish and Irish style . 
- 
- = = = Early Anglo @-@ Saxon art = = = 
- 
- Early examples of Anglo @-@ Saxon art are largely metalwork , particularly bracelets , <unk> and jewellery , that has survived in pagan burials and in exceptional items such as the <unk> carved whalebone Franks <unk> , thought to have been produced in Northumbria in the early eighth century , which combines pagan , classical and Christian motifs . There is only one known pagan burial in Scotland , at Dalmeny Midlothian , which contains a necklace of beads similar to those found in mid @-@ seventh @-@ century southern England . Other isolated finds include a gold object from Dalmeny , shaped like a truncated pyramid , with <unk> and <unk> , similar to sword <unk> mounts found at Sutton <unk> . There is also a bun @-@ shaped <unk> from <unk> , <unk> and a ring with an Anglian <unk> inscription . From eastern Scotland there is a seventh @-@ century sword <unk> from <unk> Sands , Moray and the <unk> drinking horn mount . After Christianisation in the seventh century artistic styles in Northumbria , which then reached to the Firth of Forth , interacted with those in Ireland and what is now Scotland to become part of the common style historians have identified as Insular or Hiberno @-@ Saxon . 
- 
- = = = Insular art = = = 
- 
- Insular art , or Hiberno @-@ Saxon art , is the name given to the common style produced in Scotland , Britain and Anglo @-@ Saxon England from the seventh century , with the combining of Celtic and Anglo @-@ Saxon forms . Surviving examples of Insular art are found in metalwork , carving , but mainly in illuminated manuscripts . In manuscripts surfaces are highly decorated with intricate <unk> , with no attempt to give an impression of depth , volume or recession . The best examples include the Book of Kells , which may have been wholly or partly created in Iona , and the Book of <unk> , which may be from Ireland or Northumbria . <unk> pages are a characteristic feature of Insular manuscripts , although <unk> initials ( an Insular invention ) , canon tables and <unk> miniatures , especially Evangelist portraits , are also common . The finest era of the style was brought to an end by the disruption to monastic centres and aristocratic life of the Viking raids in the late eighth century . 
- Christianity discouraged the burial of grave goods so the majority of examples of insular metalwork that survive from the Christian period have been found in archaeological contexts that suggest they were rapidly hidden , lost or abandoned . There are a few exceptions , notably portable shrines ( " <unk> " ) for books or relics , several of which have been continuously owned , mostly by churches on the Continent — though the <unk> <unk> has always been in Scotland . The highest quality survivals are either secular jewellery , the largest and most elaborate pieces probably for male <unk> , <unk> or <unk> . The finest church pieces were probably made by secular workshops , often attached to a royal household , though other pieces were made by monastic workshops . There are a number of large brooches , each of their designs is wholly individual in detail , and the workmanship is varied . Many elements of the designs can be directly related to elements used in manuscripts . Surviving stones used in decoration are semi @-@ precious ones , with amber and rock crystal among the <unk> , and some <unk> . <unk> glass , enamel and <unk> glass , probably imported , are also used . None of the major insular manuscripts , like the Book of Kells , have preserved their elaborate jewelled metal covers , but documentary evidence indicates that these were as spectacular as the few remaining continental examples . 
- The most significant survivals in sculpture are in High crosses , large free @-@ standing stone crosses , usually carved in relief with patterns , biblical iconography and occasionally inscriptions . The tradition may have begun in Ireland or Anglo @-@ Saxon England and then spread to Scotland . They are found throughout the British Isles and often feature a stone ring around the intersection , forming a Celtic cross , apparently an innovation of Celtic Christianity , that may have begun at Iona . Distribution in Scotland is heaviest in the Highlands and Islands and they can be dated to the period c . 750 to 1150 . All the surviving crosses are of stone , but there are indications that large numbers of wooden crosses may also have existed . In Scotland biblical iconography is less common than in Ireland , but the subject of King David is relatively frequently depicted . In the east the influence of Pictish sculpture can be seen , in areas of Viking occupation and settlement , crosses for the tenth to the twelfth centuries have distinctive Scandinavian patterns , often mixed with native styles . Important examples dated to the eighth century include St Martin 's Cross on Iona , the <unk> Cross from the Hebrides and the Anglo @-@ Saxon <unk> Cross . Through the Hiberno @-@ Scottish mission to the continent , insular art was highly influential on subsequent European Medieval art , especially the decorative elements of Romanesque and Gothic styles . 
- 
- = = = Viking age art = = = 
- 
- Viking art avoided naturalism , favouring stylised animal motifs to create its ornamental patterns . <unk> @-@ interlace was important and plant motifs became fashionable in the tenth and eleventh centuries . Most Scottish artefacts come from 130 " pagan " burials in the north and west from the mid @-@ ninth to the mid @-@ tenth centuries . These include jewellery , weapons and occasional elaborate high status items . Amongst the most impressive of these is the Scar boat burial , on Orkney , which contained an elaborate sword , <unk> with arrows , a brooch , bone comb , gaming pieces and the Scar Dragon Plaque , made from whalebone , most of which were probably made in Scandinavia . From the west , another boat burial at <unk> Bay in <unk> revealed a sword , shield , iron cauldron and <unk> scales , which may be Celtic in origin . A combination of Viking and Celtic styles can be see in a penannular brooch from <unk> in Orkney , which has a Pictish @-@ style looped pin . It is about two inches in diameter , with traces of <unk> , and probably housed a piece of amber surrounded by <unk> ribbons . After the conversion to Christianity , from the tenth to the twelfth centuries , stone crosses and cross @-@ slabs in Viking occupied areas of the Highlands and Islands were carved with successive styles of Viking ornament . They were frequently mixed with native interlace and animal patterns . Examples include the eleventh @-@ century cross @-@ slab from <unk> <unk> on the island of Islay , where the plant motifs on either side of the cross @-@ shaft are based upon the <unk> style of Viking art . The most famous artistic find from modern Scotland , the Lewis <unk> , from Uig , were probably made in Trondheim in Norway , but contain some decoration that may have been influenced by Celtic patterns . 
- 
- = = Late Middle Ages = = 
- 
- 
- = = = Architecture and sculpture = = = 
- 
- Architectural evidence suggests that , while the Romanesque style peaked in much of Europe in the later eleventh and early twelfth century , it was still reaching Scotland in the second half of the twelfth century and was revived in the late fifteenth century , perhaps as a reaction to the English perpendicular style that had come to dominate . Much of the best Scottish artwork of the High and Late Middle Ages was either religious in nature or realised in metal and woodwork and has not survived the impact of time and the Reformation . However , examples of sculpture are extant as part of church architecture , a small number of significant crafted items have also survived and , for the end of the period , there is evidence of painting , particularly the extensive commissioning of works in the Low Countries and France . 
- The interiors of churches were often more elaborate before the Reformation , with highly decorated sacrament houses , like the ones surviving at <unk> and <unk> . The carvings at <unk> Chapel , created in the mid @-@ fifteenth century , elaborately depicting the progression of the seven deadly sins , are considered some of the finest in the Gothic style . <unk> effigies began to appear in churches from the thirteenth century and they were usually fully coloured and gilded . Many were founders and patrons of churches and chapels , including members of the clergy , knights and often their wives . In contrast to England , where the fashion for stone @-@ carved monuments gave way to brass <unk> , they continued to be produced until the end of the Medieval period , with the largest group dating from the fifteenth century , including the very elaborate Douglas tombs in the town of Douglas . Sometimes the best continental artists were employed , as for Robert I 's elaborate tomb in <unk> Abbey , which was made in his lifetime by the Parisian sculptor Thomas of <unk> , but of which only fragments now survive . The greatest group of surviving sculpture from this period are from the West Highlands , beginning in the fourteenth century on Iona under the patronage of the Lordship of the Isles and continuing until the Reformation . Common motifs were ships , swords , <unk> and Romanesque vine leaf tracery with Celtic elements . 
- 
- = = = <unk> arts = = = 
- 
- <unk> from late Medieval church fittings and objects in Scotland are exceptionally rare even compared to those from comparable areas like England or Norway , probably because of the <unk> of their destruction in the Scottish Reformation . The Scottish elite and church now participated in a culture stretching across Europe , and many objects that do survive are imported , such as <unk> <unk> . It is often difficult to decide the country of creation of others , as work in international styles was produced in Scotland , along with pieces retaining more distinctive local styles . 
- Two secular small chests with carved whalebone panels and metal fittings illustrate some aspects of the Scottish arts . The <unk> and Fife <unk> are very similar and were probably made by the same workshop around 1500 , as boxes for <unk> such as jewellery or documents . The overall form of the <unk> follows French examples , and the locks and metal bands are decorated in Gothic style with " simple decorations of <unk> and <unk> egg and dart " while the whalebone panels are carved in relief with a late form of Insular interwoven <unk> characteristic of late Medieval West Scotland . 
- Key examples of native craftsmanship on items include the <unk> <unk> , the earliest surviving drinking cup of its type , made of maple @-@ wood and with elaborate silver @-@ gilt ornamentation , dated to around <unk> . The <unk> Horn was probably made for the earl of Moray in the fourteenth century and looted by the English in the mid @-@ sixteenth century . A few significant reliquaries survive from West Scotland , examples of the habit of the Celtic church of treating the possessions rather than the bones of saints as relics . As in Irish examples these were partly reworked and elaborated at intervals over a long period . These are St <unk> 's <unk> and its " <unk> " or reliquary , between them with elements from each century from the eleventh to the fifteenth , the Guthrie Bell Shrine , Iona , twelfth to fifteenth century , and the <unk> <unk> Bell Shrine , <unk> , mid @-@ twelfth century . The Skye Chess piece is a single elaborate piece in carved <unk> ivory , with two warriors carrying <unk> shields in a framework of <unk> vegetation . It is thought to be Scottish , of the mid @-@ thirteenth century , with aspects similar to both English and Norwegian pieces . 
- One of the largest groups of surviving works of art are the seal matrices that appear to have entered Scottish usage with <unk> in the reign of David I , beginning at the royal court and among his Anglo @-@ Norman vassals and then by about 1250 they began to spread to the <unk> areas of the country . They would be made compulsory for barons of the king in a statute of <unk> and seal matrices show very high standards of skill and <unk> . Examples of items that were probably the work of continental artists include the delicate hanging lamp in St. John 's Kirk in Perth , the vestments and <unk> in <unk> and the Medieval <unk> of the Universities of St Andrews and Glasgow . 
- 
- = = = <unk> and painting = = = 
- 
- <unk> illumination continued into the late Middle Ages , moving from elaborate <unk> to charters , like that confirming the rights of <unk> Abbey from <unk> . Very little painting from Scottish churches survives . There is only one surviving Doom painting in Scotland , at Guthrie near <unk> , which may have been painted by the same artist as the elaborate <unk> and other paintings at <unk> Easter , eighteen miles away . As in England , the monarchy may have had model portraits of royalty used for copies and reproductions , but the versions of native royal portraits that survive are generally crude by continental standards . Much more impressive are the works or artists imported from the continent , particularly the Netherlands , generally considered the centre of painting in the Northern Renaissance . The products of these connections included a fine portrait of William <unk> , Bishop of Aberdeen ( <unk> – 1514 ) ; the images of St Catherine and St John brought to <unk> ; Hugo van Der Goes 's altarpiece for the Trinity College Church in Edinburgh , commissioned by James III , and the work after which the Flemish Master of James IV of Scotland is named . There are also a relatively large number of elaborate devotional books from the late fifteenth and early sixteenth centuries , usually produced in the Low Countries and France for Scottish patrons , including the prayer book commissioned by Robert <unk> , Bishop of Glasgow , between <unk> and <unk> and the Flemish illustrated book of hours , known as the <unk> of James IV of Scotland , given by James IV to Margaret Tudor and described as " perhaps the finest medieval manuscript to have been commissioned for Scottish use " . 
- 
- 
- = Humpty Dumpty = 
- 
- Humpty Dumpty is a character in an English nursery rhyme , probably originally a riddle and one of the best known in the English @-@ speaking world . He is typically portrayed as an anthropomorphic egg , though he is not explicitly described so . The first recorded versions of the rhyme date from late eighteenth @-@ century England and the tune from 1870 in James William Elliott 's National Nursery Rhymes and Nursery Songs . Its origins are obscure and several theories have been advanced to suggest original meanings . 
- The character of Humpty Dumpty was popularised in the United States by actor George L. Fox ( 1825 – 77 ) . As a character and literary allusion , he has appeared or been referred to in a large number of works of literature and popular culture , particularly Lewis Carroll 's Through the Looking @-@ Glass ( 1872 ) . The rhyme is listed in the Roud Folk Song Index as No. <unk> . 
- 
- = = Lyrics and melody = = 
- 
- The rhyme is one of the best known and most popular in the English language . The most common modern text is : 
- It is a single quatrain with external rhymes that follow the pattern of <unk> and with a <unk> metre , which is common in nursery rhymes . The melody commonly associated with the rhyme was first recorded by composer and nursery rhyme collector James William Elliott in his National Nursery Rhymes and Nursery Songs ( London , 1870 ) . The Roud Folk Song Index catalogues folk songs and their variations by number , and classifies this song as <unk> . 
- 
- = = Origins = = 
- 
- The earliest known version was published in Samuel Arnold 's <unk> Amusements in 1797 with the lyrics : 
- A manuscript addition to a copy of Mother Goose 's Melody published in 1803 has the modern version with a different last line : " Could not set Humpty Dumpty up again " . It was published in 1810 in a version of <unk> <unk> 's Garland as : 
- In 1842 , James <unk> Halliwell published a collected version as : 
- According to the Oxford English Dictionary , the term " <unk> <unk> " referred to a drink of brandy boiled with <unk> in the seventeenth century . The riddle probably exploited , for <unk> , the fact that " <unk> <unk> " was also eighteenth @-@ century <unk> slang for a short and clumsy person . The riddle may depend upon the assumption that a clumsy person falling off a wall might not be <unk> damaged , whereas an egg would be . The rhyme is no longer posed as a riddle , since the answer is now so well known . Similar <unk> have been recorded by folklorists in other languages , such as " <unk> <unk> " in French , " <unk> <unk> " in Swedish and Norwegian , and " <unk> @-@ <unk> " or " <unk> @-@ <unk> " in different parts of Germany — although none is as widely known as Humpty Dumpty is in English . 
- 
- = = Meaning = = 
- 
- The rhyme does not explicitly state that the subject is an egg , possibly because it may have been originally posed as a riddle . There are also various theories of an original " Humpty Dumpty " . One , advanced by Katherine <unk> Thomas in 1930 and adopted by Robert Ripley , posits that Humpty Dumpty is King Richard III of England , depicted as <unk> in Tudor histories and particularly in Shakespeare 's play , and who was defeated , despite his armies , at Bosworth Field in <unk> . 
- Professor David <unk> suggested in The Oxford Magazine of 16 February 1956 that Humpty Dumpty was a " tortoise " siege engine , an armoured frame , used unsuccessfully to approach the walls of the Parliamentary held city of Gloucester in 1643 during the Siege of Gloucester in the English Civil War . This was on the basis of a contemporary account of the attack , but without evidence that the rhyme was connected . The theory was part of an anonymous series of articles on the origin of nursery rhymes and was widely acclaimed in academia , but it was derided by others as " ingenuity for ingenuity 's sake " and declared to be a spoof . The link was nevertheless popularised by a children 's opera All the King 's Men by Richard Rodney Bennett , first performed in 1969 . 
- From 1996 , the website of the Colchester tourist board attributed the origin of the rhyme to a cannon recorded as used from the church of St Mary @-@ at @-@ the @-@ Wall by the Royalist defenders in the siege of 1648 . In 1648 , Colchester was a walled town with a castle and several churches and was protected by the city wall . The story given was that a large cannon , which the website claimed was colloquially called Humpty Dumpty , was strategically placed on the wall . A shot from a Parliamentary cannon succeeded in damaging the wall beneath Humpty Dumpty which caused the cannon to tumble to the ground . The <unk> ( or Cavaliers , " all the King 's men " ) attempted to raise Humpty Dumpty on to another part of the wall , but the cannon was so heavy that " All the King 's horses and all the King 's men couldn 't put Humpty together again " . Author Albert Jack claimed in his 2008 book Pop Goes the <unk> : The Secret <unk> of Nursery Rhymes that there were two other verses supporting this claim . Elsewhere , he claimed to have found them in an " old dusty library , [ in ] an even older book " , but did not state what the book was or where it was found . It has been pointed out that the two additional verses are not in the style of the seventeenth century or of the existing rhyme , and that they do not fit with the earliest printed versions of the rhyme , which do not mention horses and men . 
- 
- = = In Through the Looking @-@ Glass = = 
- 
- Humpty appears in Lewis Carroll 's Through the Looking @-@ Glass ( 1872 ) , where he discusses <unk> and <unk> with Alice . 
- " I don 't know what you mean by ' glory , ' " Alice said . 
- Humpty Dumpty <unk> <unk> . " Of course you don 't — till I tell you . I meant ' there 's a nice knock @-@ down argument for you ! ' " 
- " But ' glory ' doesn 't mean ' a nice knock @-@ down argument ' , " Alice objected . 
- " When I use a word , " Humpty Dumpty said , in rather a <unk> tone , " it means just what I choose it to mean — neither more nor less . " 
- " The question is , " said Alice , " whether you can make words mean so many different things . " 
- " The question is , " said Humpty Dumpty , " which is to be master — that 's all . " 
- Alice was too much puzzled to say anything , so after a minute Humpty Dumpty began again . " They 've a temper , some of them — particularly <unk> , they 're the proudest — adjectives you can do anything with , but not <unk> — however , I can manage the whole lot ! <unk> ! That 's what I say ! " 
- This passage was used in Britain by Lord Atkin in his dissenting judgement in the seminal case <unk> v. Anderson ( 1942 ) , where he protested about the distortion of a statute by the majority of the House of Lords . It also became a popular citation in United States legal opinions , appearing in 250 judicial decisions in the <unk> database as of 19 April 2008 , including two Supreme Court cases ( <unk> v. Hill and <unk> v. Miller ) . 
- It has been suggested by A. J. <unk> that Carroll 's Humpty Dumpty had <unk> on the basis of his description of his finding faces hard to recognise . 
- " The face is what one goes by , generally , " Alice remarked in a thoughtful tone . 
- " That 's just what I complain of , " said Humpty Dumpty . " Your face is the same as everybody has — the two eyes , — " ( marking their places in the air with his thumb ) " nose in the middle , mouth under . It 's always the same . Now if you had the two eyes on the same side of the nose , for instance — or the mouth at the top — that would be some help . " 
- 
- = = In popular culture = = 
- 
- Humpty Dumpty has become a highly popular nursery rhyme character . American actor George L. Fox ( 1825 – 77 ) helped to popularise the character in nineteenth @-@ century stage productions of <unk> versions , music , and rhyme . The character is also a common literary allusion , particularly to refer to a person in an <unk> position , something that would be difficult to reconstruct once broken , or a short and fat person . Humpty Dumpty has been used in a large range of literary works in addition to his appearance as a character in Through the Looking @-@ Glass , including L. Frank Baum 's Mother Goose in <unk> ( 1901 ) , where the rhyming riddle is devised by the daughter of the king , having witnessed Humpty 's " death " and her father 's soldiers ' efforts to save him . In Neil Gaiman 's early short story The Case of the Four and Twenty <unk> , the Humpty Dumpty story is turned into a film noir @-@ style <unk> crime story , involving also <unk> Robin , the Queen of Hearts , Little Bo <unk> , Old Mother Hubbard , and other characters from popular nursery rhymes . Robert Rankin used Humpty Dumpty as one victim of a serial fairy @-@ tale character murderer in The Hollow <unk> <unk> of the Apocalypse ( 2002 ) . Jasper <unk> included Humpty Dumpty in his novels The Well of Lost <unk> ( 2003 ) and The Big Over Easy ( 2005 ) , which use him respectively as a <unk> of dissatisfied nursery rhyme characters threatening to strike and as the victim of a murder . 
- The rhyme has also been used as a reference in more serious literary works , including as a recurring motif of the Fall of Man in James Joyce 's 1939 novel <unk> Wake . Robert Penn Warren 's 1946 American novel All the King 's Men is the story of populist politician Willie Stark 's rise to the position of governor and eventual fall , based on the career of the corrupt Louisiana Senator Huey Long . It won the 1947 Pulitzer Prize and was twice made into a film All the King 's Men in 1949 and 2006 , the former winning the Academy Award for best motion picture . This was echoed in Carl Bernstein and Bob Woodward 's book All the President 's Men , about the Watergate scandal , referring to the failure of the President 's staff to repair the damage once the scandal had leaked out . It was filmed as All the President 's Men in 1976 , starring Robert <unk> and Dustin Hoffman . Similarly , Humpty Dumpty is referred to in Paul <unk> 's 1985 novel City of Glass , when two characters discuss him as " the <unk> embodiment of the human condition " and quote extensively from Through the Looking Glass . 
- It has also been used as a common motif in popular music , including Hank Thompson 's " Humpty Dumpty Heart " ( 1948 ) , The Monkees ' " All the King 's Horses " ( 1966 ) , Aretha Franklin 's " All the King 's Horses " ( 1972 ) , <unk> Amos 's " Humpty Dumpty " ( 1992 ) , and Travis 's " The Humpty Dumpty Love Song " ( 2001 ) . In jazz , Ornette Coleman and Chick <unk> wrote different compositions , both titled Humpty Dumpty . ( In <unk> 's case , however , it is a part of a concept album inspired by Lewis Carroll called " The Mad <unk> " , 1978 ) . 
- In the <unk> <unk> song Starting Over Again , it 's all the king 's horses and all the king 's men who can 't put the divorced couple back together again . In an extra verse in one version of <unk> 's On and On and On , Humpty Dumpty is mentioned as being afraid of falling off the wall . 
- 
- = = In science = = 
- 
- Humpty Dumpty has been used to demonstrate the second law of <unk> . The law describes a process known as entropy , a measure of the number of specific ways in which a system may be arranged , often taken to be a measure of " disorder " . The higher the entropy , the higher the disorder . After his fall and subsequent shattering , the inability to put him together again is representative of this principle , as it would be highly unlikely ( though not impossible ) to return him to his earlier state of lower entropy , as the entropy of an isolated system never decreases . 
- A variation on the poem using near @-@ sounding French nonsense words is often used to illustrate the difficulty of speech recognition in different languages . A common version is as follows : 
- To a listener expecting a nursery rhyme , it will generally be heard as the English version , while someone expecting French will instead tend to hear nonsense words . 
- 
- 
- = Welsh National Opera = 
- 
- Welsh National Opera ( WNO ) ( Welsh : Opera <unk> Cymru ) is an opera company based in Cardiff , Wales ; it gave its first performances in 1946 . It began as a mainly amateur body and transformed into an all @-@ professional ensemble by 1973 . In its early days the company gave a single week 's annual season in Cardiff , gradually extending its schedule to become an all @-@ year @-@ round operation , with its own salaried chorus and orchestra . It has been described by The New York Times as " one of the finest operatic ensembles in Europe " . 
- For most of its existence the company lacked a permanent base in Cardiff , but in 2004 it moved into the new Wales Millennium Centre , Cardiff Bay . The company tours nationally and internationally , giving more than 120 performances annually , with a repertoire of eight operas each year , to a combined audience of more than 150 @,@ 000 people . Its most frequent venues other than Cardiff are <unk> in Wales and Bristol , Birmingham , Liverpool , Milton Keynes , Oxford , Plymouth , and Southampton in England . 
- Singers who have been associated with the company include Geraint Evans , Thomas Allen , Anne Evans , and <unk> Terfel . Guest artists from other countries have included Joan Hammond , Tito <unk> and Elisabeth <unk> . Among the <unk> have been Sir Charles Mackerras , Reginald Goodall , James Levine and Pierre Boulez . The company has been led since 2011 by David Pountney as chief executive and artistic director . 
- 
- = = Background = = 
- 
- Choral singing became increasingly popular in 19th @-@ century Wales , principally owing to the rise of the <unk> as a symbol of its culture . The first Welsh National Opera Company was formed in 1890 . A local newspaper commented that it was remarkable that " a race of people to whom vocal music is a ruling passion should not generations ago have established a permanent national opera " . The company gave performances of operas by the Welsh composer Joseph Parry in Cardiff and on tour in Wales . The company , predominantly amateur with some professional guest singers from the London stage , gave numerous performances of Parry 's <unk> and <unk> , composed in 1878 and 1890 respectively . An American tour was planned , but the company folded , and Parry 's final opera , The Maid of <unk> <unk> , was given at Cardiff by the Moody @-@ Manners Opera Company in 1902 . 
- A Cardiff Grand Opera Society ran from 1924 to 1934 . It presented week @-@ long annual seasons of popular operas including Faust , Carmen and Il <unk> , and like its predecessor was mainly an amateur body , with professional guest principals . Apart from the productions of these two enterprises , opera in Wales in the late 19th and early 20th centuries was generally presented by visiting companies from England . 
- In the 1930s <unk> Owen , a singing teacher and conductor , ran an amateur choir , the <unk> Singers , based in Cardiff . In November 1941 , together with John Morgan – a former Carl Rosa baritone – and Morgan 's fiancée Helena Hughes Brown , Owen agreed to found the <unk> Grand Opera Company , with Brown as secretary and Owen as conductor and general manager . They publicised their plan and held a general meeting of potential supporters in December 1943 ; at that meeting the name of the proposed organisation was changed to " Welsh National Opera Company " . By January 1944 plans were far enough advanced for the company 's first rehearsals to be held . Owen recruited a local businessman , W. H. ( Bill ) Smith ( 1894 – 1968 ) , who agreed to serve as business manager . At first doubtful of the company 's prospects , Smith became its dominant influence , leading fund @-@ <unk> , and chairman for twenty years from 1948 . 
- 
- = = Early years = = 
- 
- The new company made its debut at the Prince of Wales Theatre , Cardiff on 15 April 1946 with a double bill of <unk> Rusticana and Pagliacci . The orchestra was professional , mostly drawn from members of the BBC Welsh Orchestra ; all the singers were amateurs , except for Tudor Davies , a tenor well known at Covent Garden and <unk> 's Wells , who sang <unk> in Pagliacci . During the week @-@ long season the new company also staged Faust , with Davies in the title role . Although nearing the end of his career he was a considerable box @-@ office draw , and the company played to full houses . Nevertheless , the expense of a professional orchestra and the hire of costumes and scenery outweighed the box @-@ office receipts , and the season made a small loss . Finance remained a recurring problem over the succeeding decades . 
- Although Owen was the conductor for the performances of <unk> Rusticana , and remained as musical director of the company until 1952 , his health was fragile and he conducted none of the company 's other productions . His colleague , the chorus master , Ivor John , was in charge of the first season 's Pagliacci and Faust . 
- In 1948 the organisation was registered as a limited company , and the Cardiff season was extended from one week to two . The following year the company gave its first performances in Swansea . The chorus featured 120 performers by this time . 
- The company 's first few seasons attracted little attention from the British musical establishment , but by the early 1950s London papers began to take notice . Picture Post hailed the WNO 's chorus as the finest in Britain . The Times also praised the chorus : " It has body , <unk> , rhythmic precision , and , most welcome of all , <unk> and spontaneous freshness . " By this time the company had expanded its repertoire to take in Carmen , La traviata , Madame Butterfly , The Tales of Hoffmann , The <unk> Bride and Die <unk> . The Times commented that Smith , Owen and their colleagues were " making history for Wales . The <unk> of <unk> , which had kept this country from an art @-@ form perfectly suited to its national talents and <unk> ( for <unk> and dressing @-@ up are as natural to the Welsh as singing ) had been broken for ever " . 
- 
- = = <unk> : 1950s and 60s = = 
- 
- In 1952 the company moved its Cardiff venue to the Sophia Gardens Pavilion ( built for the Festival of Britain ) , with the Bournemouth Municipal Orchestra as the company 's orchestra , replacing the previous ad hoc ensemble . The Pavilion was <unk> mediocre and lacked an orchestra pit ; two years later the company moved again , to the New Theatre where it played Cardiff seasons across the next fifty years . The 1952 season attracted particular interest because it included what was then a rarity : Verdi 's Nabucco . The company built a reputation for staging seldom @-@ seen Verdi works , including The Sicilian <unk> staged in the same year , I Lombardi in 1956 , and The Battle of <unk> , under the shortened title The Battle , in 1960 . The 1952 Nabucco was the WNO 's first production for which costumes and scenery were specially designed ( by Patrick Robertson ) rather than hired . 
- In 1953 the company staged its first work by a Welsh composer : <unk> by <unk> Hughes . The composer conducted , and the leads were sung by two professional guest stars , Richard Lewis and <unk> <unk> . The same year marked WNO 's first appearances outside Wales , playing a week at Bournemouth in April , and a week at Manchester in October , when The Manchester Guardian found the soloists first @-@ rate but the chorus disappointing , in both Nabucco and Il <unk> . A reviewer in The Musical Times commented on potential difficulties in assembling the wholly amateur chorus for performances beyond daily travelling range of their day jobs . By the time of the company 's first London season – a week at <unk> 's Wells in 1955 – the chorus was judged to be " lively and exciting " ( The Musical Times ) , " vibrant " and " moving " ( The Times ) and " <unk> " ( The Manchester Guardian ) . 
- By the mid @-@ 1950s professional singers were cast in leading roles in most productions ; they included Walter <unk> in <unk> and La <unk> ( 1955 ) , <unk> <unk> in <unk> ( 1957 ) , Heather Harper in La traviata ( 1957 ) , and Joan Hammond in Madame Butterfly ( 1958 ) . A possibility of strengthening the professional element of the company was <unk> in 1958 , when a merger was proposed with the Carl Rosa Company , which was in financial difficulties . The proposal was not followed through and WNO continued independently while the Carl Rosa folded . 
- During the 1960s the company continued to widen its range . Its first Wagner production , Lohengrin , and its first Mozart , The Marriage of Figaro , were both performed in 1962 , conducted by Charles Groves . Another Welsh opera , Hughes 's <unk> <unk> <unk> Doctor ( " Love , the Doctor " ) was staged in 1960 . The popular Italian repertoire remained the core of the annual seasons , mostly directed by the head of production , John Moody . Leading roles were taken by rising stars such as John Shirley @-@ <unk> , <unk> Jones , Thomas Allen , <unk> <unk> and Margaret Price , the last of whom made her operatic debut with the company in 1962 . Established singers <unk> with the company included Geraint Evans who played the title role in Don <unk> in 1966 , and Ian Wallace in the same part the following year . Evans was also seen as <unk> in Don Giovanni in 1966 and as Falstaff in 1969 . 
- The gradual switch from amateur to professional continued in 1968 , when for the first time the chorus was supplemented by a smaller , professional group of singers ; the mix of amateur and professional <unk> continued over the next five years . At the end of the 1960s the main WNO company , now a year @-@ round operation , consisted of 8 salaried principal singers , 57 guest soloists and a chorus of 90 amateurs and 32 professionals . As well as the Bournemouth players , the company engaged the Royal Liverpool Philharmonic , City of Birmingham Symphony and Ulster orchestras for different venues . In the last season of the decade 32 performances were given in Cardiff and 61 elsewhere in the UK . In addition to the main company , WNO maintained two smaller groups : one , with orchestra , toured Welsh towns , the other , consisting of 12 singers with piano , toured 79 , mostly small , towns in Wales and England . WNO instituted its own training scheme for young singers during the decade . 
- 
- = = Fully professional : 1970s = = 
- 
- In 1970 WNO stopped using the Bournemouth and other orchestras and established its own , known at first as the Welsh <unk> . Three years later the last amateur element of the company was removed when the chorus became fully professional . A further broadening of the repertoire took place in the 1970s : in 1971 WNO staged the first performances in Britain of Berg 's Lulu , directed by Michael Geliot , who had succeeded Moody in 1969 . In the view of Malcolm Boyd in The New Grove Dictionary of Opera , Geliot , " unpredictable and often controversial " , largely shaped the company 's style in the 1970s . In collaboration with the company 's musical director James Lockhart , Geliot is credited by The Times with introducing new young singers and " directing a host of groundbreaking productions " before leaving in 1978 . The critic Rodney <unk> wrote in 1975 about WNO 's productions : 
- I have never seen , well , hardly ever , a pretentious , silly or seriously <unk> production , and neither have I seen a dull one . … The company 's greatest virtue is that its work is dedicated above all to the service of composers and audiences , and not to some abstract notion of " prestige " nor to the vanity or ambition of individuals , and in this it is almost unique . 
- In 1973 Geliot 's WNO staging of <unk> 's Billy <unk> with Allen in the title role was presented on a Swiss tour , and two years later it was given in Barcelona . The company returned to London with its participation in the <unk> Festival of Opera at the Dominion Theatre in 1979 , presenting The Makropoulos Case , The Magic <unk> , <unk> , Madame Butterfly , and Tristan and Isolde to capacity audiences . 
- The company 's traditional preference for the Italian repertoire was partly <unk> during the decade : productions include WNO 's first staging of a Richard <unk> opera , <unk> , in 1978 . A new Welsh work , Alun <unk> 's The Beach of <unk> , was presented in 1974 . In 1975 , in co @-@ production with Scottish Opera , WNO began a cycle of <unk> operas , directed by David Pountney . Beginning with <unk> , the cycle continued with The Makropoulos Case ( 1978 ) , The <unk> Little <unk> ( 1980 ) , <unk> <unk> ( 1982 ) and From the House of the Dead ( 1982 ) . 
- Among the guest artists who appeared with the company in the 1970s were the baritone Tito <unk> , as Falstaff ( 1972 ) , the <unk> Elisabeth <unk> as <unk> in The Makropoulos Case ( 1978 ) and Anne Evans as <unk> in The Flying Dutchman ( 1972 ) , and the <unk> James Levine ( <unk> , 1970 ) and Reginald Goodall ( Tristan and Isolde , 1979 ) . 
- In the late 1970s WNO combined with the Cardiff @-@ based Welsh Drama Company , becoming the Welsh National Opera and Drama Company . The work of the drama company came under continued criticism , the Welsh Arts Council cut its grant , and the partnership ended in 1979 with the formal closure of the Welsh Drama Company . 
- 
- = = 1980s = = 
- 
- During the 1980s WNO continued to expand in scope . Handel ( <unk> , 1981 ) and <unk> ( The Greek Passion , 1981 ) were added to the company 's repertoire , and in 1983 Das <unk> was staged in the WNO 's first Ring cycle , followed by the other three operas of the cycle over the next two years . Das <unk> , Siegfried and <unk> were conducted by the company 's musical director , Richard Armstrong ; Die <unk> ( 1984 ) was conducted by Goodall ; it was seen as a coup for the company to secure his services – he was described by The Guardian as the greatest living <unk> conductor – but the casting of the whole cycle was criticised for some serious weaknesses among the principal singers , and reviewers were generally unimpressed by <unk> <unk> 's production . 
- The chief executive , Brian McMaster , did not appoint a replacement to Geliot as principal director during the 1980s , preferring to engage guest producers . Boyd mentions <unk> <unk> 's Eugene <unk> ( 1980 ) among the successes and <unk> <unk> 's Carmen ( 1983 ) and Ruth <unk> 's Don Giovanni ( 1984 ) as productions that received more mixed responses . Sir Charles Mackerras , the conductor for Don Giovanni , was open in his contempt for <unk> 's production . Harry <unk> 's <unk> ( 1981 ) was condemned by The Daily Telegraph as " a piece of Marxist <unk> " making " political sport " of Beethoven 's work . McMaster was thought by some too inclined to favour radical eastern European directors : Jonathan Miller , a leading English director , commented that he did not intend to take Bulgarian nationality , although it was " a must before Brian pays any attention " . 
- Armstrong stepped down in 1986 after thirteen years as musical director ; he was succeeded by Mackerras , whose association with the company dated back more than thirty years . Among the features of his six @-@ year tenure was an increasing use of <unk> for performances not given in English . In the company 's early days , all operas had been sung in English , but as more international stars began to appear as guest principals the language policy had to be <unk> : few of the leading names in world opera were interested in <unk> their roles in English . WNO steered a middle course between the practices of the two main London companies ; after the 1960s The Royal Opera had generally given operas in the original language , and English National Opera was committed to opera in English . WNO 's practice varied , after its early years . Examples from the 1980s include Wagner 's Tristan und Isolde sung in German , and the Ring in English ; and Verdi 's The Force of Destiny given in English and <unk> in Italian . Mackerras was a strong advocate of performance in the original language , with <unk> : " I can 't imagine a greater advance for opera . … What a gift ! It 's like Siegfried understanding the <unk> . " 
- 
- = = 1990s = = 
- 
- McMaster resigned in 1991 , having led the company to international status , with performances at La <unk> , Milan ; the Metropolitan Opera , New York ; and in Tokyo . One of the last legacies of his tenure was the 1992 production of Debussy 's <unk> et <unk> , directed by Peter Stein and conducted by Pierre Boulez . The New York Times called WNO " one of the finest operatic ensembles in Europe " and noted that the first night of the Debussy work , in Cardiff , " attracted 80 critics from all over the United Kingdom and the Continent ... the most prestigious , intensely awaited event of the British operatic season . " The production was given at the <unk> du <unk> , Paris , a few weeks afterwards . 
- McMaster was followed as chief executive by Matthew Epstein , whose three years in charge ( 1991 – 94 ) were described in a 2006 study by Paul Atkinson as " a less happy and less successful period " . Epstein was replaced by Anthony Freud , under whom , according to Atkinson , productions became " consistently strong , musically well prepared , <unk> staged and well cast . " Mackerras was succeeded in 1992 by Carlo Rizzi , who was music director at the time of WNO 's golden jubilee in 1996 . When the occasion was marked with a new production of the " <unk> and <unk> " double bill that had launched the company in 1946 , the BBC commented that WNO was " one of the most respected opera companies in the world " . In The Observer , Michael <unk> called the company " the most popular , populist and consistently successful arts organisation ever to come out of Wales ... with the loyalty and affection of audiences in Cardiff and across England … ' The people 's opera ' is not a myth . It happened here . " The jubilee celebrations were overshadowed by the collapse of a plan for a purpose @-@ built home for the company , the Cardiff Bay Opera House . 
- During the 1990s WNO made its <unk> debut , with a complete performance of Mozart 's <unk> , conducted by Mackerras in 1991 . The company played three short seasons at the Royal Opera House , Covent Garden in the mid @-@ 1990s , featuring Tristan und Isolde and La <unk> in 1993 , The <unk> of the Guard in 1995 , and The <unk> 's Progress and the jubilee double bill of <unk> Rusticana and Pagliacci in 1996 . 
- 
- = = 21st century = = 
- 
- The company entered the new millennium in a state of some turmoil . A financial crisis had led to <unk> in the orchestra and the <unk> of the touring schedule ; the conservative works chosen for 2001 – 02 were condemned by the press as " the <unk> programme in recent memory " ; and Rizzi was about to be replaced by a young and <unk> successor , <unk> <unk> . Rizzi had gained great respect and affection during his nine @-@ year term as musical director ; his successor 's reign was brief and unhappy . Having taken up post in 2003 , <unk> resigned <unk> the following year . Rizzi agreed to <unk> his schedule , and , to public and critical acclaim , returned to the musical directorship in time to prepare the company for its long @-@ awaited move into a permanent base in Cardiff . 
- After the collapse of the Cardiff Bay Opera House scheme , a new project , the Wales Millennium Centre , met with more success . The necessary <unk> and funding were obtained , and work began in 2002 on a new multipurpose arts centre on the Cardiff Bay site . The centre included a 1 @,@ 900 @-@ seat theatre , which , among other uses , became WNO 's home base from 2004 , with its own rehearsal space and offices in the complex . 
- In the first decade of the 21st century WNO gave more than 120 performances a year , with a repertoire , generally , of eight full @-@ scale operas . Its regular audience figures totalled over 150 @,@ 000 annually , in ten principal venues , three of them in Wales and seven in England . During this period the company was criticised for being <unk> Welsh . A local politician , Adam Price , said that WNO ought to have a Welsh musical director ; Alun <unk> said in 2004 , " WNO has put on perhaps four or five Welsh operas over 20 years . ... They just seem to have an anti @-@ Welsh music bias . I am sad that they do not do something for Welsh composers , especially young ones . " A more positive view of WNO came from Scotland , where the two main newspapers , The Scotsman and The Herald , greeted a visit from the company in 2005 with enthusiastic praise , contrasting the flourishing of opera in Wales with its neglect by politicians in Scotland and the consequent decline of Scottish Opera . In 2010 WNO commissioned <unk> <unk> <unk> ( " Word on <unk> " ) , by <unk> ap <unk> and <unk> <unk> , with words in Welsh , described as " a contemporary story about Wales today ... inspired by the translation of the Bible " . 
- From 2006 to 2011 the chief executive ( titled " artistic director " ) was John Fisher . His term overlapped with that of <unk> <unk> who was musical director from 2009 to 2016 . A highlight of this period was the 2010 production of Die <unk> , produced by Richard Jones , starring <unk> Terfel as Hans Sachs . The production won <unk> from reviewers . 
- In 2011 David Pountney was appointed to succeed Fisher as chief executive . He had worked with the company since the 1970s , most recently on a 2006 The Flying Dutchman with Terfel which was set in space . In 2013 he programmed a trilogy of operas set in Tudor England : <unk> 's Anna <unk> , Maria <unk> and Roberto <unk> , with another trilogy the following year , on the theme of fallen women – <unk> 's <unk> <unk> , <unk> 's Boulevard Solitude and Verdi 's La traviata . For 2016 Pountney scheduled another trilogy , this time on the theme of Figaro , consisting of Mozart 's The Marriage of Figaro and Rossini 's The <unk> of Seville and a new work , Figaro <unk> a Divorce with music by <unk> Langer and libretto by Pountney . 
- In September 2015 WNO announced the appointment of Tomáš <unk> as its next music director , taking office for the 2016 – 17 season . At the same time Carlo Rizzi was named the company 's conductor laureate , with immediate effect . 
- 
- = = Recordings = = 
- 
- Although the chorus and orchestra of Welsh National Opera have appeared on many commercial recordings , often featuring regular WNO soloists , there have been few sets , either audio or video , of the company 's own productions . Among those are Tristan und Isolde conducted by Goodall ( 1981 ) , <unk> et <unk> conducted by Boulez ( 1992 ) , The <unk> of the Guard , conducted by Mackerras ( 1995 ) , The Doctor of <unk> conducted by Armstrong ( 1998 ) , and <unk> conducted by Ivor Bolton , directed by David Alden ( 1999 ) . The BBC made a studio video recording of a WNO cast in <unk> <unk> , conducted by Armstrong in 1982 . 
- The WNO chorus and orchestra have been engaged for studio opera recordings unconnected with the company 's productions , including Hamlet ( 1983 ) , <unk> ( 1984 ) , Anna <unk> ( 1987 ) , <unk> ( 1987 ) and <unk> <unk> ( 1988 ) conducted by Richard <unk> , Faust ( 1993 ) and <unk> <unk> ( 1994 ) conducted by Rizzi ; and <unk> ( 1993 ) , Eugene <unk> ( 1994 ) and <unk> ( 2004 ) conducted by Mackerras . For the WNO jubilee in 1996 , <unk> drew on some of its studio recordings for a celebratory CD set with contributions from many soloists who had appeared onstage with the company and some who had not , the latter including Joan Sutherland , Luciano Pavarotti , Montserrat <unk> and Thomas Hampson . The orchestra of WNO has made studio recordings of non @-@ operatic music by <unk> , <unk> , Coleridge @-@ Taylor and George Lloyd , and several sets of traditional Welsh songs and crossover music . 
- 
- = = Music directors = = 
- 
- 
- 
- = England national rugby union team = 
- 
- The England national rugby union team represents England in rugby union . They compete in the annual Six Nations Championship with France , Ireland , Scotland , Italy , and Wales . They have won this championship on a total of 27 occasions , 13 times winning the Grand Slam , making them the most successful team in the tournament 's history . They are ranked second in the world by the International Rugby Board as of 20 June 2016 . England are the first , and to date the only , team from the northern hemisphere to win the Rugby World Cup , when they won the tournament back in 2003 . They were also runners @-@ up in 1991 and 2007 . 
- The history of the team extends back to 1871 when the English rugby team played their first official Test match , losing to Scotland by one goal . England dominated the early Home Nations Championship ( now the Six Nations ) which started in 1883 . Following the schism of rugby football in 1895 , England did not win the Championship again until 1910 . England first played against New Zealand in 1905 , South Africa in 1906 , and Australia in 1909 . England was one of the teams invited to take part in the inaugural Rugby World Cup in 1987 and went on to appear in the final in the second tournament in 1991 , losing 12 – 6 to Australia . Following their 2003 Six Nations Championship Grand Slam , they went on to win it again in 2016 . England also won the World Cup – beating Australia 20 – 17 in extra time . They also contested the final in 2007 , losing 15 – 6 to South Africa . 
- England players traditionally wear a white shirt with a Rose embroidered on the chest , white shorts , and navy blue socks with a white trim . 
- Their home ground is Twickenham Stadium where they first played in 1910 . The team is administered by the Rugby Football Union ( RFU ) . Four former players have been inducted into the International Rugby Hall of Fame ; one of these is also a member of the IRB Hall of Fame . Seven other former players are members of the IRB Hall — four solely for their accomplishments as players , two solely for their achievements in other roles in the sport , and one for achievements both as a player and administrator . 
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- = = History = = 
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- The expansion of rugby in the first half of the 19th century was driven by ex @-@ pupils from many of England 's Public Schools , especially Rugby , who , upon finishing school , took the game with them to universities , to London , and to the counties . England 's first international match was against Scotland on Monday 27 March 1871 . Not only was this match England 's first , but it also proved to be the first ever rugby union international . Scotland won the match by a goal and a try to a try , in front of a crowd of 4 @,@ 000 people at Raeburn Place , Edinburgh . A subsequent international took place at the Oval in London on 5 February 1872 which saw England defeat Scotland by a goal , a drop goal and two tries to one drop goal . In those early days there was no points system , it was only after 1890 that a format allowing the introduction of a points system was provided . Up until 1875 international rugby matches were decided by the number of goals scored ( <unk> and dropped goals ) , but from 1876 the number of tries scored could be used to decide a match if teams were level on goals . 
- In 1875 , England played their first game against the Irish at the Oval , winning by one goal , one drop goal and one try to nil ; the match was Ireland 's first ever Test . England defeated Scotland in 1880 to become the first winners of the Calcutta Cup . Their first match against Wales was played on 19 February 1881 at Richardson 's Field in <unk> . England recorded their largest victory , defeating the Welsh by seven goals , six tries , and one drop goal to nil and scoring 13 tries in the process . The subsequent meeting the following year at St Helens in Swansea was a closer contest ; with England winning by two goals and four tries to nil Two years later , the first Home Nations championship was held and England emerged as the inaugural winners . In 1889 , England played their first match against a non @-@ home nations team when they defeated the New Zealand <unk> by one goal and four tries to nil at <unk> Field in <unk> . In 1890 England shared the Home Nations trophy with Scotland . 
- England first played New Zealand ( the All Blacks ) in 1905 . The All Blacks scored five tries , worth three points at this time , to win 15 – 0 . The following year , they played France for the first time , and later that year they first faced South Africa ( known as the Springboks ) ; James Peters was withdrawn from the England squad after the South Africans objected to playing against a black player . The match was drawn 3 – 3 . England first played France in 1905 , and Australia ( known as the Wallabies ) in 1909 when they were defeated 9 – 3 . 
- The year 1909 saw the opening of Twickenham as the RFU 's new home , which heralded a golden era for English rugby union . England 's first international at Twickenham brought them victory over Wales , and England went on to win the International Championship ( then known as the Five Nations ) for the first time since the great schism of 1895 . Although England did not retain the title in 1911 , they did share it in 1912 . A Five Nations Grand Slam was then achieved in 1913 and 1914 as well as in 1921 following the First World War . England subsequently won the Grand Slam in 1924 and as well as in 1925 . This was despite having started 1925 with a loss to the All Black <unk> in front of 60 @,@ 000 fans at Twickenham . 
- After winning another Grand Slam in 1928 , England played the Springboks in front of 70 @,@ 000 spectators at Twickenham in 1931 . Following the <unk> of France due to professionalism in 1930 , which thus reverted The Five Nations back to the Home Nations tournament , England went on to win the 1934 and 1937 Home Nations with a Triple Crown , and in 1935 achieved their first victory over the All Blacks . 
- When the Five Nations resumed with the re @-@ admission of France in 1947 after the Second World War , England shared the championship with Wales . The early Five Nations competitions of the 1950s were unsuccessful for England , winning one match in the 1950 and 1951 championships . England won the 1953 Five Nations , and followed this up with a Grand Slam in 1957 , and win in 1958 . England broke France 's four @-@ championship streak by winning the 1963 Championship . After this victory , England played three Tests in the Southern Hemisphere and lost all three : 21 – 11 and 9 – 6 against the All Blacks , and 18 – 9 against Australia . England did not win a single match in 1966 , and managed only a draw with Ireland . They did not win another Championship that decade ; a fact that prompted amateur historian F. W. P. <unk> to declare this period ' the <unk> in English Rugby Union History ' . 
- Don White was appointed as England 's first @-@ ever coach in 1969 . According to former Northampton player Bob Taylor , " Don was chosen because he was the most forward @-@ thinking coach in England " . His first match in charge was an 11 – 8 victory over South Africa at Twickenham in 1969 . Of the eleven games England played with White in charge they won three , and drew one and lost seven . He resigned as England coach in 1971 . 
- England had wins against Southern Hemisphere teams in the 1970s ; with victories over South Africa in 1972 , New Zealand in 1973 and Australia in 1973 and 1976 . The 1972 Five Nations Championship was not completed due to the Troubles in Northern Ireland when Scotland and Wales refused to play their Five Nations away fixtures in Ireland . England played in Dublin in 1973 and were given a standing ovation lasting five minutes . After losing 18 – 9 at Lansdowne Road , the England captain , John <unk> famously stated , " We might not be very good but at least we turned up . " 
- England started the following decade with a Grand Slam victory in the 1980 Five Nations – their first for 23 years . However in the 1983 Five Nations Championship , England failed to win a game and picked up the wooden spoon . In the first Rugby World Cup in New Zealand and Australia , England were grouped in pool A alongside Australia , Japan and the United States . England lost their first game 19 – 6 against Australia . They went on to defeat Japan and the United States , and met Wales in their quarter @-@ final , losing the match 16 – 3 . 
- In 1989 , England won matches against Romania and Fiji , followed by victories in their first three Five Nations games of 1990 . They lost to Scotland in their last game however , giving Scotland a Grand Slam . England recovered in the following year by winning their first Grand Slam since 1980 . England hosted the 1991 World Cup and were in pool A , along with the All Blacks , Italy and the United States . Although they lost to the All Blacks in pool play , they qualified for a quarter @-@ final going on to defeat France 19 – 10 . England then defeated Scotland 9 – 6 to secure a place in the final against Australia which they lost 12 – 6 . 
- The next year , England completed another Grand Slam and did not lose that year , including a victory over the Springboks . In the lead up to the 1995 World Cup in South Africa , England completed another Grand Slam – their third in five years . In the World Cup , England defeated Argentina , Italy and Samoa in pool play and then defeated Australia 25 – 22 in their quarter @-@ final . England 's semi @-@ final was dominated by the All Blacks and featured four tries , now worth five points each , by <unk> <unk> ; England lost 45 – 29 . They then lost the third / fourth place play @-@ off match against France . 
- In 1997 , Clive Woodward became England 's coach . That year , England drew with New Zealand at Twickenham after being heavily defeated in Manchester the week before . England toured Australia , New Zealand and South Africa in 1998 . Many of the England team made themselves unavailable for the tour nicknamed the " tour from hell " where England were beaten 76 – 0 by the Wallabies . In 1999 during the last ever Five Nations match , Scott Gibbs sliced through six English tackles to score in the last minute , and the last ever Five Nations title went to Scotland . 
- England commenced the new decade by winning the inaugural Six Nations title . In 2001 , Ireland defeated England 20 – 14 in a postponed match at Lansdowne Road to deny them a Grand Slam . Although the 2002 Six Nations Championship title was won by France , England had the <unk> of winning the Triple Crown . In 2002 , England defeated Argentina in Buenos Aires , and then a second string All Blacks , Australia , and South Africa at Twickenham . In 2003 , England won the Grand Slam for the first time since 1995 , followed by wins over Australia and the All Blacks on their Summer tour in June . 
- Going into the 2003 World Cup , England were one of the tournament favourites . They reached the final on 22 November 2003 against host Australia and became world champions after a match @-@ winning drop goal by star <unk> Jonny Wilkinson deep into extra time that made the final score 20 – 17 . Not only was it their first Rugby World Cup victory , but it was the country 's first World Cup since winning the 1966 FIFA ( football ) World Cup as hosts . On 8 December , the English team greeted 750 @,@ 000 supporters on their victory parade through London before meeting Queen Elizabeth II at Buckingham Palace . 
- In the 2004 Six Nations Championship , England lost to both France and Ireland and finished third . Sir Clive Woodward resigned on 2 September and Andy Robinson was appointed England head coach . Robinson 's first Six Nations campaign in 2005 resulted in fourth place for England , and although they then defeated Australia 26 – 16 , the year was completed with a 23 – 19 loss to the All Blacks . 
- Following their loss to South Africa in the 2006 end of year Tests , England had lost eight of their last nine Tests – their worst ever losing streak . Coach Andy Robinson resigned after this run , and attack coach Brian Ashton was appointed head coach in December 2006 . England started the 2007 Six Nations Championship with a Calcutta Cup victory over Scotland . The championship also included a historic match at <unk> Park against Ireland which England lost 43 – 13 , their heaviest ever defeat to Ireland . 
- In the 2007 World Cup England played in Pool A with Samoa , Tonga , South Africa and the United States . They qualified for the quarter finals after losing <unk> to South Africa 36 – 0 where they defeated Australia 12 – 10 , and then faced hosts France in their semi final . England won 14 – 9 to qualify for the final against South Africa , which they lost 15 – 6 . England followed up the World Cup with two consecutive 2nd place finishes in the Six Nations , behind Wales and Ireland respectively . The 2009 Six Nations also saw former England Captain Martin Johnson take up the job of head coach . However , Johnson could not replicate his on @-@ field success to management , and resigned in November 2011 following a <unk> 2011 Rugby World Cup which ended in quarter @-@ final defeat by France and featured a series of on and off @-@ field controversies . 
- On 29 March 2012 , Stuart Lancaster , the former <unk> Rugby Director at Leeds Carnegie was appointed England head coach by the Rugby Football Union . Previously Lancaster was appointed as the head coach on a short term basis assisted by existing forwards coach Graham <unk> and Andy Farrell . 
- Lancaster was considered a success in his first campaign as England coach - during the 2012 Six Nations Championship , defending Champions England finished in second place after losing 19 – 12 to Wales at Twickenham Stadium , but successfully defended the Calcutta Cup beating Scotland 13 – 6 at <unk> . England finished the year on a high , after <unk> World Cup holders New Zealand in November , in which England dominated to win 38 – 21 . The All Blacks had been unbeaten in 20 matches but were completely <unk> by England . 
- During the 2013 Six Nations Championship again England finished in second place behind Wales after losing the opportunity of being Grand Slam winners for the first time since 2003 , by losing to Wales in Cardiff 30 – 3 . It was also the first time every team managed to win at least 3 competition points ( the equivalent of a win and a draw or three draws ) since 1974 . However , England did again defeat Scotland for the Calcutta Cup 38 – 18 at Twickenham . 
- During the 2013 summer tour to South America in which Lancaster took an experimental side , England beat a South American select XV before a 2 – 0 series victory over Argentina , a first away series win against The <unk> for 32 years . England hosted the 2015 Rugby World Cup but were eliminated in the Pool stage , earning the unenviable reputation of being the first side in Rugby World Cup history to have hosted the tournament and yet to have failed to qualify for the knockout stages . 
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- = = Twickenham = = 
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- Up until 1910 , the English rugby team used various stadia in a number of venues around England before settling at Twickenham Stadium . It is the largest rugby @-@ dedicated stadium in the world . After sell @-@ out matches at Crystal Palace in 1905 and 1906 against New Zealand and South Africa respectively , the Rugby Football Union ( RFU ) decided to invest in their own ground . In 1906 , the RFU arranged for William Williams to find a home ground for English Rugby . The land for the ground was purchased the following year for £ 5 @,@ <unk> <unk> and <unk> , and construction began the following year . 
- The first England match was held on 9 October 1910 between England and Wales . England ran out winners , 11 – 6 , beating Wales for the first time since 1898 . The stadium was expanded in 1927 and again in 1932 . Further upgrades did not happen until the 1990s when new North , East and West stands were built . A new South stand was built in 2005 and 2006 to make the stadium into a complete bowl . The first match to be played at the redeveloped Twickenham was on Sunday 5 November 2006 against the All Blacks . England lost the match 41 @-@ 20 in front of a record crowd of 82 @,@ <unk> . 
- Although England have played home matches almost exclusively at Twickenham since 1910 , they have played at Huddersfield 's <unk> Stadium twice in 1998 , at Old Trafford against New Zealand in 1997 and at Wembley Stadium against Canada in 1992 . They also played the first of a two @-@ test series against Argentina at Old Trafford in June 2009 , a match originally scheduled to be held in Argentina but moved by the country 's national federation for financial reasons . 
- The pitch at Twickenham was replaced by a hybrid ' <unk> ' type , in June 2012 , which uses artificial fibres <unk> with real grass . This makes it a lot harder wearing in wet conditions . 
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- = = = Swing Low , Sweet Chariot = = = 
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- " Swing Low , Sweet Chariot " is very commonly sung at England fixtures – especially at Twickenham . The song arrived in the rugby canon through the Welsh male voice choirs who sang many spirituals . It was a popular rugby song at clubs during the 1950s and 1960s and was sung every year at Twickenham during the end @-@ of @-@ season all @-@ day Middlesex <unk> tournament accompanied by <unk> hand gestures that played on the double <unk> of some of the words . During the 1970s the Twickenham crowd also sang it during England matches then coming into the last match of the 1988 season , against the Irish , England had lost 15 of their previous 23 matches in the Five Nations Championship . The Twickenham crowd had only seen one solitary England try in the previous two years and at half time against Ireland they were 3 – 0 down . During the second half a remarkable transformation took place and England started playing an expansive game many had doubted they were capable of producing . A 3 – 0 deficit was turned into a 35 – 3 win , with England scoring six tries . 
- In the 35 – 3 win , three of England 's tries were scored by Chris <unk> , a player who had made a reputation for himself that season as a <unk> on the left wing . A group of boys from the Benedictine school <unk> following a tradition at their school games sang the song on his final try , and other spectators around the ground joined in . Since then " Swing Low , Sweet Chariot " became a song to sing at England home games , in the same way that " The Fields of <unk> " is sung in Dublin and " <unk> <unk> " is sung at Cardiff . It has since become the anthem of the team as in 1991 the result of a plan of the then RFU marketing director Mike <unk> for the team to launch a song leading up to that year 's Rugby World Cup . He had wanted to use Jerusalem but it was used in the Rugby League cup final that year so the song was changed at short notice to " Swing Low " . There were a number of versions recorded including a ' rap ' version with Jerry <unk> doing a solo . <unk> to say that was never released but the version released did reach the top 40 in the UK singles chart during the competition and was then adopted as the England rugby song . 
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- = = Strip = = 
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- England have typically worn all @-@ white shirts , white shorts with navy and white socks . The emblem on the shirts is a red rose , rather than the Plantagenet Three Lions displayed on the shirts of the England football and cricket teams . The strip is manufactured by Canterbury and O2 is the shirt sponsor . Red was the change strip , although prior to the introduction of the red strip , navy blue was used . Purple was used as the change strip as of the 2009 autumn internationals , reflecting the traditional colour of the original England track @-@ suits from the 1960s , 70s and 80s . For the 2011 Rugby World Cup the change kit was black . 
- The Rugby Football Union ( RFU ) had created the national side 's emblem prior to an English team being sent to Edinburgh to play a Scottish side . A red rose was chosen to be the side 's emblem . The white kit worn by the national team was taken from the kit used at Rugby School . Alfred Wright , an employee of the Rugby Football Union , is credited with the <unk> and new design of the rose , which up until 1920 had undergone many variations in its depiction . The Wright design is thought to have been used without minor alteration until the late 1990s . It was not until 1997 that the rose was modernised when Nike became the official strip supplier . 
- In 2003 England first used a skin @-@ tight strip . This was intended to make it more difficult for the opposition to grasp the shirt when tackling . The home and away strips for 2007 were unveiled on 15 May that year . The materials used are superior , offering improved performance to the 2003 kit . However , a sweeping red mark on the base @-@ white front which forms St George 's Cross on the top left , and a changed away @-@ strip ( dark blue to red ) , have received criticism because it is felt that emphasis has been placed on St George 's Cross at the expense of the traditional red rose . The new strip was introduced in England 's home game against Wales on 4 August , while the alternative strip was first used against France on 18 August . 
- The former England home strip was white with a strip of red around the neck , and the away strip was black ( causing much controversy due to the famous All @-@ Black kit of New Zealand ) , both kits had a ground breaking new technology in the form of a <unk> print . A special strip was worn during the match versus Wales in the 2010 Six Nations Championship which replicated that worn in 1910 to celebrate the 100 year anniversary of Twickenham . The current , 2013 @-@ 14 , England strip is made by Canterbury . It features plain white shorts and a plain white shirt , but with an added black stripe on each sleeve . The current away kit has a red and white striped shirt , with blue shorts . In 2014 / 15 , the home shirt was white , with a " V @-@ Neck " around the collar . The kit also had little St George 's crosses on the main chest . It also had the O2 sponsorship marking on the chest . The shorts were plain white with the sponsorship marking on them . The socks were dark blue and had a white stripe at the top . The alternate shirt was exactly the same but was red instead of white . The shorts were navy blue and the socks were red with a white stripe on top . The 2015 / 16 strip was similar but didn 't have the small crosses on the shirt . The Canterbury logo was straight and not diagonal it had white lines going horizontally across the chest . For the 2015 Rugby World Cup , the kit remained the same just with the Rugby World Cup logo on the right and no O2 logo in the centre . For the home strip , the shorts and socks remained the same . The away 2015 / 16 strip and World Cup strip was red , with dark red and maroon sleeves . The shorts were maroon and the socks were red with a maroon stripe on top . 
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- = = Record = = 
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- = = = Six Nations = = = 
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- England competes annually in the Six Nations Championship , which is played against five other European nations : France , Ireland , Italy , Scotland , and Wales . The Six Nations started out as the Home Nations Championship in 1883 which England won with a Triple Crown . England have won the title outright 27 times ( a record for the tournament ) and shared victory ten times . Their longest wait between championships was 18 years ( 1892 – 1910 ) . During the Six Nations , England also contests the Calcutta Cup with Scotland ( which England first won in 1880 ) and the Millennium Trophy with Ireland ( which England first won in 1988 ) . The matches between England and France are traditionally known as " Le Crunch " . 
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- = = = World Cup = = = 
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- England have contested every Rugby World Cup since it began in 1987 , reaching the final three times and winning in 2003 . 
- In the inaugural tournament they finished second in their pool before losing to Wales in the quarter @-@ finals . They again finished pool runners @-@ up in 1991 but recovered to beat France in their quarter @-@ final , and then Scotland in their semi @-@ final , en route to a 12 – 6 final defeat to Australia . 
- In 1995 , England topped their pool and defeated Australia 25 – 22 at the quarter @-@ final stage before being beaten by the All Blacks in the semi @-@ final . Their third @-@ fourth place play @-@ off match against France was lost 19 – 9 . 
- The 1999 competition saw England again finish second in the pool stage . Though they proceeded to win a play @-@ off game against Fiji , they went out of the tournament in the quarter @-@ finals , losing 44 – 21 to South Africa . 
- In the 2003 tournament , England came top of their pool . They progressed to the final beating Wales and France in the quarter and semi finals . England won the final with a drop goal in the last minute of extra time . 
- The 2007 defence of the cup in France got off to a very poor start , with a below par victory over the United States and a heavy 36 – 0 defeat to South Africa leaving the holders on the brink of elimination at the group stage . <unk> performances against Samoa and Tonga saw England again reach the knockout stages as pool runners @-@ up , before a surprise 12 – 10 defeat of Australia in Marseille and a narrow 14 – 9 victory over the host nation France carried England to a second successive final appearance . The final was played in Paris on 20 October against South Africa , who won by 15 points to 6 . 
- In 2011 , England reached the quarter final stage , losing 19 @-@ 12 to France . 
- In 2015 , England became the first sole host nation to fail to qualify for the knockout stage , exiting the pool stage after losses to Wales and Australia . 
- England 's Jonny Wilkinson is the highest points scorer in the rugby world cup , having scored 277 points between 1999 and 2011 . England have the fourth most points and fourth most tries scored in the World Cup . 
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- = = = Overall = = = 
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- When the World Rankings were introduced in October 2003 , England was ranked 1st . They briefly fell to 2nd in September that year before regaining 1st place . They fell to 2nd , and then to 3rd in June 2004 . After the 2005 Six Nations they fell to 6th where they remained until they moved into 5th in December that year . In 2006 , their ranking again fell and they finished the year ranked 7th . 2007 saw them bounce back to 3rd after their good run in that year 's World Cup , where they finished Runners Up . In 2008 , their rankings slipped so that during the 2009 Six Nations Championship they dropped to their lowest ranking of 8th . They again were 8th during the autumn internationals of the same year . After a resurgence which saw them rise to a ranking of 4th in the world , the team again slipped , following a poor showing at the 2011 Rugby World Cup , and was ranked 6th in February 2012 . England entered the 2015 Rugby World Cup ranked 4th . However , after failing to exit the pool stage , England were ranked 8th in the world as of 1 November 2015 . 
- England has won 381 of their 701 Test matches , a winning record of 54 @.@ 35 % . Below is a summary table of capped England matches up until 25 June 2016 . Only fixtures recognised as test matches by the RFU are included . 
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- = = Players = = 
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- = = = Current squad = = = 
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- On 22 May , head coach Eddie Jones named a 32 @-@ man squad for England 's tour of Australia . An additional 5 players ( denoted through * ) were also named ahead of the test match against Wales on 29 May , with the <unk> Premiership final taking place in the 28 May between Saracens and Exeter Chiefs . Ben Te <unk> has been named in the squad by virtue of his English mother . Despite playing for Irish province Leinster , Te <unk> will moving to Worcester Warriors in the 2016 / 17 season making him eligible for selection . 
- On 30 May , Luther Burrell replaced <unk> <unk> in the touring squad , after <unk> withdrew from the squad due to injury . 
- Note : The number of caps was updated 25 June 2016 . 
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- = = = Notable players = = = 
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- Four former England representatives have been inducted into the International Rugby Hall of Fame : Bill Beaumont , Martin Johnson , Jason Leonard and Wavell Wakefield . 
- Seven former England internationals are also members of the IRB Hall of Fame . Four of them — Johnson , Alan Rotherham , Harry <unk> and Robert Seddon — were inducted for their accomplishments as players . Two other former England players , John Kendall @-@ Carpenter and Clive Woodward , were inducted into the IRB Hall for non @-@ playing accomplishments in the sport . Another former England player , Alfred St. George Hamersley , was inducted for achievements as both a player and a rugby administrator . 
- Wavell Wakefield represented England in 31 Tests between 1920 and 1927 , including 13 Tests as captain . He was involved in three Five Nations Grand Slams in 1921 , 1923 and 1924 . Playing as <unk> , Wakefield introduced new elements to back row tactics which beforehand concentrated on the set piece . He became a Member of Parliament in 1935 , and was knighted in 1944 . He became the RFU President in 1950 and following his retirement from politics was awarded the title the first Baron Wakefield of <unk> . 
- Between 1975 and 1982 , Bill Beaumont represented England in 34 Tests . Playing at lock , he was captain between 1978 and 1982 in 21 Tests including the 1980 Grand Slam – England 's first since 1957 . Later that year , he captained the British Lions to South Africa – the first time an <unk> had captained the Lions since 1930 . Furthermore , Beaumont represented the <unk> FC on fifteen occasions . 
- The youngest ever England captain at 22 , Will <unk> represented England in 72 Tests , and as captain 59 times between 1988 and 1996 . He was best known as a <unk> leader , motivating England to a remarkable three Grand Slams in five years , including back to back <unk> in 1991 and 1992 . He also led England to the final of the 1991 World Cup , and captained the <unk> FC . His playing talents were not as flamboyant as some of his colleagues , but his effectiveness cemented him as a first choice at centre . It is possible he would already be in the Hall of Fame were it not for outspoken tendencies with respect to the English RFU committee ( " Old <unk> " ) , who may as a result be reluctant to acknowledge his achievements . He was made an OBE in 1991 . 
- Described as arguably " the greatest forward " to play for England , Martin Johnson played 84 Tests for England , and 8 Tests for the British and Irish Lions . He first represented England in 1993 , and later that year the Lions . He captained the Lions to South Africa in 1997 , and in 1999 was appointed captain of England . He became England 's most successful ever captain . He became the first player to captain two Lions tours when he captained them in Australia in 2001 . He retired from Test rugby after he led England to a Six Nations Grand Slam and World Cup victory in 2003 and has since become the team Manager . At the 2011 IRB Awards ceremony in Auckland on 24 October 2011 , the night after the World Cup Final , Johnson was inducted into the IRB Hall of Fame alongside all other World Cup @-@ winning captains from 1987 through 2007 ( minus the previously inducted Australian John <unk> ) . 
- Jason Leonard , also known as " The Fun Bus " , appeared 114 times for England at prop , which was the world record for international appearances for a national team until 2005 , when it was surpassed by Australia 's <unk> @-@ half George <unk> . He was on the England team that finished runners up to Australia in the 1991 Rugby World Cup final , but avenged this twelve years later , coming on as a substitute for Phil <unk> in England 's victorious 2003 Rugby World Cup final appearance . He also went on three British and Irish Lions tours where he was capped five times . 
- Alan Rotherham and Harry <unk> , both 19th @-@ century greats for Oxford and England , were inducted into the IRB Hall in April 2011 . The IRB recognised them for " their unique contribution to the way that Rugby was played " , specifically stating that they " are credited with pioneering the passing game and the three @-@ man <unk> , which became widespread during the 1880s . " 
- Two other England internationals , John Kendall @-@ Carpenter and Clive Woodward , were inducted into the IRB Hall alongside Johnson at the 2011 IRB Awards . Although both had notable careers for England , they were recognised for accomplishments in other roles in the sport . Kendall @-@ Carpenter was cited as one of four key figures in the creation of the Rugby World Cup , whilst Woodward was inducted as coach of the 2003 World Cup winners , alongside all other World Cup @-@ winning coaches from 1987 to 2007 . 
- England 's most recent <unk> into the IRB Hall are 19th @-@ century internationals Alfred St. George Hamersley and Robert Seddon , both inducted in 2013 . Hamersley played for England in the first @-@ ever rugby union international against Scotland in 1871 , and captained England in the last of his four appearances in 1874 . He went on to play significant roles in the early development of the sport in both New Zealand and Canada . Seddon , capped three times for England in 1887 , was most notable as the captain of the unofficial British side that toured Australia and New Zealand in 1888 ; he died in a boating accident during the tour . This venture proved to be the genesis of the modern British and Irish Lions . The touring team was also inducted alongside Seddon . 
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- = = = Individual records = = = 
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- Jonny Wilkinson holds the record for most points for England : 1 @,@ 151 . The record for tries is held by Rory Underwood with 49 tries . The most capped England player is former prop Jason Leonard who made 114 appearances over his 14 @-@ year career . England 's youngest ever Test player was Colin Laird who was 18 years and 134 days old when he played against Wales in 1927 . 
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- = = Training = = 
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- <unk> Park Hotel in <unk> , Surrey , is the chosen training base for the team in the 2015 Rugby World Cup . Loughborough University , <unk> Abbey and the University of Bath grounds served as training bases prior to this agreement . Martin Johnson noted the hotel 's facilities and its proximity to Twickenham and Heathrow as deciding factors in this decision . The team had their own <unk> gym and fitness rooms constructed on the hotel premises at the start of the long @-@ term arrangement . Since its completion in 2010 the team also regularly use Surrey Sports Park at the University of Surrey in nearby Guildford for much of their training . 
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- = = = Club versus country = = = 
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- Although the England team is governed by the Rugby Football Union ( RFU ) , players have been contracted to their clubs since the advent of professionalism in late 1995 . Since then , players have often been caught in a " power struggle " between their clubs and the RFU ; this is commonly referred to as a " club versus country " conflict . The first major dispute between England 's top clubs ( who play in the English Premiership ) and the RFU occurred in 1998 , when some of the clubs refused to release players to tour Australia , New Zealand and South Africa . The tour became known as the " Tour from hell " after an England squad of second @-@ string players were defeated in all four Tests , including a 76 – 0 defeat by Australia . The clubs also withdrew from the 1998 / 99 European Cup . 
- In 2001 , the top clubs and the RFU formed " England Rugby " to help govern the club and international game . The parties agreed to restrict the number of matches at club and international level that elite players ( a group of 50 or 60 players selected by the RFU ) could play in order to reduce player <unk> and injuries . In return for releasing players from club commitments , the clubs were to receive compensation from the RFU . This agreement was considered central to the England victory in the 2003 World Cup . Clive Woodward , England coach from November 1997 , resigned in 2004 because he was unable to get the access to the players that he wanted ; " I wanted more from the union – more training days with the players , more influence over the way they were treated – and ended up with less . " Andy Robinson , Woodward 's successor , blamed the lack of control over players for his team 's unsuccessful record . Brian Ashton , who took over from Robinson , intentionally named his playing squad for Six Nations matches in 2007 early in the hope that their clubs would not play them in the weekend prior to a Test . The RFU and the Premiership clubs are negotiating a similar deal to the one in 2001 that will enable international players to be released into the England squad prior to international matches . 
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- = = = Coaches = = = 
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- The following is a list of all England coaches . The first appointed coach was Don White in 1969 . The most recent coach is Eddie Jones . He took over from Stuart Lancaster a week after Lancaster 's resignation . Jones became the first foreigner to coach the English side . 
- Updated 25 June 2016 
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- = = Media coverage = = 
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- England 's mid @-@ year tests and end of year tests are televised live by Sky Sports while end of year matches are highlighted by BBC Three on that game day and repeated on BBC Two the next day . England 's 2014 end of year international against Samoa was not highlighted on BBC Three . All Six Nations games are shown for free on the BBC and ITV from 2016 . 
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- = = Titles = = 
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- = Jeremi Wiśniowiecki = 
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- Jeremi Wiśniowiecki ( Ukrainian : <unk> <unk> - <unk> <unk> ; August 17 , 1612 – August 20 , 1651 ) nicknamed <unk> on the Cossacks or Iron Hand , was a notable member of the aristocracy of the Polish @-@ Lithuanian Commonwealth , Prince of <unk> , <unk> and <unk> in the Crown of the Kingdom of Poland and the father of the future King of Poland , Michael I. 
- A notable magnate and military commander with Ruthenian and Romanian origin , Wiśniowiecki was heir of one of the biggest fortunes of the state and rose to several notable <unk> , including the position of voivode of the Ruthenian <unk> in 1646 . His conversion from Eastern <unk> to Roman Catholicism caused much dissent in Ruthenia and Ukraine ( parts of the Polish – Lithuanian Commonwealth ) . Wiśniowiecki was a successful military leader as well as one of the wealthiest magnates of Poland , ruling over lands inhabited by 230 @,@ 000 people . 
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- = = Biography = = 
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- = = = Youth = = = 
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- Jeremi Michał <unk> Wiśniowiecki was born in 1612 ; neither the exact date nor the place of his birth are known . His father , Michał Wiśniowiecki , of the Lithuanian @-@ Ruthenian Wiśniowiecki family , died soon after Jeremi 's birth , in 1616 . His mother , Regina <unk> ( <unk> <unk> ) was a <unk> @-@ born noble woman of the <unk> family , daughter of the <unk> Prince <unk> <unk> , Jeremy 's namesake ; she died in 1619 . Both of his parents were of the Eastern Orthodox Church rite ; Jeremy 's uncle was the influential Orthodox theologian Peter Mogila , and his great @-@ uncle was George Mogila , the Metropolitan of Moldavia . 
- <unk> at the age of seven , Wiśniowiecki was raised by his uncle , Konstanty Wiśniowiecki , whose branch of the family were Roman Catholics . Jeremi attended a Jesuit college in Lwów and later , in 1629 , he traveled to Italy , where he briefly attended the University of Bologna . He also acquired some military experience in the Netherlands . The upbringing by his uncle and the trips abroad <unk> him , and turned him from a provincial Ruthenian <unk> into one of the youngest magnates of the Polish @-@ Lithuanian Commonwealth . 
- In 1631 Wiśniowiecki returned to the Commonwealth and took over from his uncle the management of his father 's huge estate , which included a large part of what is now Ukraine . In 1632 he converted from Eastern <unk> to Catholicism , an action that caused much concern in Ukraine . His decision has been analyzed by historians , and often criticized , particularly in Ukrainian historiography . The Orthodox Church feared to lose a powerful protector , and Isaiah <unk> , metropolitan bishop of Kiev and a friend of his mother , unsuccessfully plead with him to change his mind . Jeremi would not <unk> although he remained on decent terms with the <unk> Church , avoiding provocative actions , and supported his uncle and Orthodox bishop Peter Mogila and his Orthodox Church <unk> . 
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- = = = Later life = = = 
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- Wiśniowiecki 's <unk> and first biographer , Michał <unk> , counted that Jeremi participated in nine wars in his lifetime . The first of those was the <unk> Campaign of 1633 – 34 against the <unk> of Russia . In that war he accompanied <unk> Aleksander <unk> 's southern army and took part in several battles , among them the unsuccessful siege of <unk> ; later that year they took <unk> and <unk> before retreating . The following year he worked with Adam <unk> and <unk> <unk> , commanding his own private army of 4 @,@ 000 . As his troops formed 2 / 3 of their army ( not counting supporting Cossack elements ) , Jeremi , despite being the most junior of commanders , had much influence over their campaign . Lacking in artillery , they failed to take any major towns , but ravaged the countryside near <unk> and <unk> . The war ended soon afterward , and in May <unk> he returned to Lubny . For his service , he received a commendation from the King of Poland , Władysław IV Vasa , and the <unk> of Kiev . 
- After the war Wiśniowiecki engaged in a number of conflicts with neighbouring magnates and nobles . Jeremi was able to afford a sizable private army of several thousands , and through the threat of it he was often able to force his neighbours to a favourable settlement of disputes . Soon after his return from the Russian front , he participated on the side of the <unk> family in the quarrel over the estate of <unk> against another magnate , Samuel Łaszcz , located on his lands ; soon after the victorious battle against Łaszcz he bought the lands from the <unk> and incorporated them into his estates . 
- Around <unk> the Sejm ( Polish parliament ) opposed the marriage of King Władysław IV <unk> to Wiśniowiecki 's sister , Anna . Following this , Jeremi distanced himself from the royal court , although he periodically returned to Warsaw , usually as one of the deputies to the Sejm from the Ruthenian <unk> . Soon afterward , Jeremi himself married Gryzelda <unk> , daughter of Chancellor <unk> <unk> , on 27 February 1639 , on Gryzelda 's 16th birthday . 
- At that time Wiśniowiecki also engaged in a political conflict over nobility titles , in particular , the title of prince ( <unk> ) . The nobility in the Commonwealth was officially equal , and used different and non @-@ hereditary titles then those found in rest of the world ( see officials of the Polish @-@ Lithuanian Commonwealth ) ; the <unk> of the conflict , which took much of the Sejm 's time around 1638 – 41 , revolved around whether old prince titles ( awarded to families before their lands were incorporated into the Commonwealth in the 1569 Union of <unk> ) , and the new titles , awarded more recently by some foreign courts , should be recognized . Wiśniowiecki was one of the chief participants in this debate , successfully defending the old titles , including that of his own family , and succeeding in abolishing the new titles , which gained him the enmity of another powerful magnate , Jerzy <unk> . Other than this conflict , in his years as a deputy ( <unk> – 46 ) , Jeremi wasn 't involved in any major political issues , and only twice ( in 1640 and 1642 ) he served in the minor function of a <unk> for investigating the eastern and southern border disputes . 
- In 1637 Wiśniowiecki might have fought under Hetman Mikołaj Potocki against the Cossack rebellion of Pavel <unk> ( the <unk> Uprising ) ; Jan Widacki notes that historians are not certain whether he did and in either case , no detailed accounts of his possible participation survive . A year later , returning from the Sejm and from the engagement ceremony with Gryzelda , he gathered a 4 @,@ 000 strong division that participated in putting down of the <unk> Uprising and arrived at the region affected by the unrest in June that year . Together with Hetman Potocki he defeated the insurgents at the Battle of <unk> , which turned into a rather difficult siege of the Cossack camp that lasted from 13 June till the Cossack relief forces were defeated on 4 August , and the Cossacks <unk> on 7 August . 
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- = = = Final years = = = 
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- In 1641 , after the death of his uncle Konstanty Wiśniowiecki , Jeremi became the last adult male of the Wiśniowiecki family and inherited all the remaining estates of the clan , despite a brief conflict with Aleksander <unk> <unk> who also claimed the inherited land . The conflict stemmed from the fact that Konstanty asked Jeremi to take care of his grandchildren , but their mother , <unk> Eugenia <unk> , married Aleksander , who declared he is able and willing to take care of her children - and their estates . A year later , <unk> Eugenia decided to divorce Aleksander , and the matter was settled in favor of Jeremi . 
- Wiśniowiecki also fought against the <unk> in 1640 – 46 , whose raids on the south @-@ east frontier of the Commonwealth endangered his holdings . In 1644 together with Hetman Stanisław Koniecpolski he took part in the victorious Battle of <unk> , in which they crushed forces of Crimean <unk> led by <unk> <unk> ( <unk> <unk> ) . 
- In 1644 , after the false news of the death of Adam <unk> , Wiśniowiecki took over his disputed estate of <unk> by <unk> . For this he was at first sentenced to exile , but due to his influence , even the King could not realistically expect to enforce this ruling without a civil war . Eventually after more discussions at local <unk> and then in the Sejm , he won the case and was granted the right for <unk> . In 1646 , after the death of Koniecpolski , he became the voivode of Ruthenia . He invaded and took over the town of <unk> which was also being claimed by a son of Koniecpolski , Aleksander Koniecpolski , but a year later , in 1647 , he lost that case and was forced to return the town . 
- On 4 April 1646 Wiśniowiecki received the office of the voivode of Ruthenia , which granted him a seat in the Senate of Poland . He was the third member of the Wiśniowiecki family to gain that privilege . Soon afterward , however , he refused to support King Władysław 's plan for a war against the Ottoman Empire , even though the King offered him the rank of a Field Crown Hetman . 
- Then the autumn of 1646 , Wiśniowiecki invaded and took over the <unk> <unk> vacated recently by banished Samuel Łaszcz . He did so without any legal <unk> , which caused a court ruling against him ; a ruling that was however never enforced . Later that year , he raised a large private army of about 25 @,@ 000 for a purpose unknown , as noted by Widacki , who writes that the army , which Jeremi raised with an immense cost for a short time , did not participate in any engagement , nor did it have any clear purpose . He notes that such an army might have been useful in provoking the Ottomans , but as Jeremi was opposed to the war with them up to the point of refusing the hetman office , his actions are <unk> even for the modern historians . 
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- = = = Khmelnytsky Uprising = = = 
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- Wiśniowiecki fought against the Cossacks again during Khmelnytsky Uprising in 1648 – 51 . He received information about a growing unrest , and began <unk> his troops , and in early May learned about the Cossack victory at the Battle of <unk> <unk> . <unk> no orders from <unk> Mikołaj Potocki and <unk> <unk> , he began moving on his own , soon learning about the second Cossack victory at Battle of <unk> , which meant that his troops ( about 6 @,@ 000 strong ) were the only Polish forces in <unk> at that moment . After taking in the situation , he began retreating towards <unk> ; his army soon became a focal point for various refugees . Passing <unk> , he continued through <unk> to <unk> . He continued to <unk> , <unk> , and <unk> , stopping briefly in <unk> for the local <unk> . After some <unk> near <unk> , <unk> and <unk> ( Battle of <unk> ) against the Cossack forces . By July he would arrive near Zbarazh . 
- Wiśniowiecki 's fighting retreat had a major impact on the course of the war . In the words of the historian Władysław <unk> , " he was not defeated , not victorious , and thus he made the peace more difficult . " <unk> in safe Warsaw tried to negotiate with the Cossacks , who in turn used <unk> 's actions as an excuse to delay any serious negotiations . 
- Around late August or early September , Wiśniowiecki met with the army <unk> Władysław <unk> <unk> @-@ <unk> , Mikołaj <unk> and Aleksander Koniecpolski . He was not on overly friendly terms with them , as he resented being passed in military nominations , but after short negotiations he agreed to follow their orders , and thus reduced to a junior commander status which had little impact over the next phase of the campaign . On 23 September , their forces were , however , defeated at the Battle of <unk> ; near the end of the battle some accounts suggest Wiśniowiecki was offered the hetman 's position , but refused . On 28 September in Lviv , Wiśniowiecki , with popular support , was given a field regimentarz nomination ; about a week later this nomination was confirmed by the Sejm . To the anger of Lviv 's <unk> , he decided to focus on retreating towards the key fortress in <unk> instead of Lviv ; he would leave garrisons on both towns , and keep his army in the field . In the end , the cities were not captured by the Cossacks , who in the light of the coming winter decided to retreat , after being paid a ransom by both town councils ; no other large field battle <unk> place that year . 
- Meanwhile , the <unk> <unk> of 1648 had elected a new king , Jan Kazimierz II Vasa . Wiśniowiecki supported other candidates , such as George I <unk> and Karol <unk> Vasa ( Jan Kazimierz 's brother ) . Due to the opposition from Jeremi 's detractors , he was not granted a hetman position , although after a full two days of debate on the subject he was granted a document that stated he had a " power equal to that of a hetman . " Wiśniowiecki faction , arguing for increase in army size , was once again <unk> by the faction that hoped for a peaceful resolution . In the end , the King and most of the <unk> were <unk> into a false sense of security , and the military was not reinforced significantly . To add an insult to an injury , the coronation <unk> of January – February 1649 , held in Kraków , revoked <unk> 's regimentarz rank . 
- In the first half of 1649 , the negotiations with the Cossacks fell through , and the Polish @-@ Lithuanian military begun gathering near the borders with the rebellious Ukraine , a major camp was in Zbarazh , where Wiśniowiecki would arrive as well in late June , after gathering a new army of 3 @,@ 000 in <unk> , which was all he was able to afford at that time , as due to most of his estates being overrun by the Cossacks . Wiśniowiecki 's arrival raised the morale of the royal army , and despite having no official rank , both the common soldiers and the new regimentarz promised to take his advice , and even offered him the official command ( which he refused ) . During the Siege of Zbarazh Wiśniowiecki was thus not the official commander ( role was taken by regimentarz Andrzej <unk> ) but most historians agree he was the real , if unofficial , commander of the Polish @-@ Lithuanian army . The siege would last until the ceasefire of the Treaty of <unk> . Wiśniowiecki 's command during the siege was seen as phenomenal , and his popularity among the troops and nobility rose again , however the King , still not fond of him , gave him a relatively small reward ( the land grant of <unk> <unk> , much less when compared to several others he distributed around that time ) . <unk> Wiśniowiecki 's support in December that year , the King granted him once again a temporary hetman nomination , and several more land grants . In April 1650 , Wiśniowiecki had to return his temporary hetman office to Mikołaj Potocki , recently released from Cossack 's captivity . During December that year , in light of the growing tensions with <unk> 's , Wiśniowiecki 's military faction succeeded in convincing the Sejm to pass a resolution increasing the size of the army to 51 @,@ 000 , the largest army since the Cossack unrest begun two years ago . 
- The truce of <unk> did not last long , and in the spring of 1651 Khmelnytsky 's Cossacks begun advancing west again . On June 1 , 1651 Wiśniowiecki brought his private army to face the Cossacks in <unk> . He commanded the left wing of the Polish @-@ Lithuanian army in the victorious Battle of <unk> on 28 – 30 June . The Polish @-@ Lithuanian army advanced after the retreating Cossacks , but on July 17 the King " left the whole army to Potocki ... and having given the order that the army march into Ukraine , the King himself parted ... to Warsaw to celebrate his victories over the Cossacks . " Later that year , on 14 August , Wiśniowiecki suddenly fell ill while in a camp near the village of <unk> , and died on August 20 , 1651 , at the age of only 39 . His cause of death was never known , while some ( even contemporaries ) speculated he was poisoned , but no conclusive evidence to support such a claim have ever been found . Based on sparse descriptions of his illness and subsequent investigations , some medical historians suggest the cause of death might have been a disease related to cholera . However , one account states , " following a cheerful conversation with other officers who had congregated for a military council in his tent on Sunday , 13 August <unk> he had eaten some <unk> with <unk> and washed them down with <unk> , and from that contracted <unk> . After lying ill for a week , he died there , at <unk> " . He was given a " ceremonial funeral with the entire army present . On August 22 , Wiśniowiecki 's body was seen off with the <unk> <unk> on its journey to his residence " . 
- Wiśniowiecki 's indebted family was not able to provide him with a funeral his rank and fame deserved . In the end , he never received the large funeral and the temporary location of his body , the monastery of the Holy Cross at <unk> <unk> , became his final resting place . His body was lost in the fire at the end of the 18th century , which also prevents a modern <unk> of the cause of his death . 
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- = = <unk> = = 
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- The majority of the Wiśniowiecki family estates were found on the eastern side of the <unk> River ( <unk> , Ruthenian and Kiev <unk> ) , and most of them were acquired by Jeremi 's grandfather , Aleksander Wiśniowiecki , in the 16th century . The capital of his estate was located at a fortified manor at Lubny , where his father rebuilt an old castle ; the population of the town itself could be estimated at about 1 @,@ 000 . Wiśniowiecki inherited lands inhabited , according to an estimate from 1628 , by about 4 @,@ 500 people , of which Lubny was the largest town . Smaller towns in his lands included <unk> , <unk> and <unk> . By 1646 his lands were inhabited by 230 @,@ 000 people . The number of towns on his lands rose from several to about thirty , and their population increased as well . The prosperity of those lands reflected Wiśniowiecki 's skills in economic management , and the income from his territories ( estimated at about 600 @,@ 000 <unk> yearly ) made him one of the wealthiest magnates in the Commonwealth . Because of its size and relatively consistent borders , Wiśniowiecki 's estate was often named <unk> ( " <unk> " ) . 
- Despite his wealth , he was not known for a lavish life . His court of about a hundred people was not know for being overly extravagant , he built no luxurious residences , and did not even have a single portrait of himself made during his life . It is uncertain how Wiśniowiecki looked , although a number of portraits and other works depicting him exist . Jan Widacki notes that much of the historiography concerning Wiśniowiecki focuses on the military and political aspects of his life , and few of his critics discuss his successes in the economic development of his estates . 
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- = = Remembrance and popular culture = = 
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- Wiśniowiecki was widely popular among the noble class , who saw in him a defender of tradition , a <unk> and an able military commander . He was praised by many of his contemporaries , including a poet , Samuel <unk> , as well as numerous diary writers and early historians . For his protection of civilian population , including Jews , during the Uprising , Wiśniowiecki has been commended by early Jewish historians . Until the 19th century , he has been idolized as the legendary , perfect " knight of the <unk> " , his sculpture is among the twenty sculpture of famous historical personas in the 18th century " Knight Room " of the royal Warsaw Castle . 
- In the 19th century this image begun to <unk> , as a new wave in historiography begun to <unk> his life , and as the era of <unk> in Poland put more value on builders , and less on warriors . Further , at that time the Polish historians begun to question the traditional view of the " Ukrainian problem " , and the way that the Polish noble class had dealt with the Cossacks . Slowly , Wiśniowiecki 's image as a hero began to <unk> , with various aspects of his life and personality being questioned and criticized in the work of historians such as Karol <unk> and <unk> <unk> . 
- While Wiśniowiecki 's portrayal ( as a major secondary character ) in the first part of Henryk <unk> 's trilogy , With Fire and Sword which describes the history of the Polish @-@ Lithuanian Commonwealth during the Uprising , was rather positive , criticism of his persona intensified , in particular from <unk> detractors such as <unk> <unk> and <unk> <unk> . The 1930s saw a first modern historical work about Wiśniowiecki , by Władysław <unk> . In the era of the People 's Republic of Poland , the Communist Party 's ideology dictated that all historians present him as an " enemy of the people " , although this began to be relaxed after 1965 . Widacki , <unk> the work of other historians notes that Władysław <unk> was rather sympathetic to Wiśniowiecki , while <unk> <unk> was critical of him . 
- Wiśniowiecki has made appearances in more recent media . He was the main subject of one of <unk> <unk> 's 1993 songs <unk> <unk> <unk> ( The <unk> of <unk> <unk> ) . Andrzej <unk> played Jeremi Wiśniowiecki in the 1999 film With Fire and Sword . 
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- = Bassline ( Chris Brown song ) = 
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- " Bassline " is a song by American recording artist Chris Brown , taken from his fifth studio album Fortune ( 2012 ) . It was written by Andrea Simms , Andrew " Pop " Wansel , Brown , David Johnson , Robert Calloway , Ronald " <unk> " Colson and Warren " Oak " <unk> . The song was produced by Pop Wansel and <unk> <unk> . Musically , " Bassline " is a dubstep , electropop and <unk> song , which incorporates elements of reggae . Instrumentation is provided by a wobble bass and synthesizers . The song contains lyrics about Brown telling a woman to leave the nightclub with him . " Bassline " garnered mixed reviews from music critics ; some reviewers noted it as one of the standout tracks on the album , while others criticized the song 's production and lyrics . It also received comparisons to the songs by <unk> and <unk> . Upon the release of Fortune , " Bassline " debuted at numbers 28 and 122 on the UK R & B Chart and UK Singles Chart , respectively . 
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- = = Development and composition = = 
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- " Bassline " was written by Andrea Simms , Andrew " Pop " Wansel , Chris Brown , David Johnson , Robert Calloway , Ronald " <unk> " Colson and Warren " Oak " <unk> . The song was produced by Pop Wansel and <unk> <unk> . " Bassline " was recorded by Brian <unk> with assistance from Iain <unk> . The recordings were later mixed by Jaycen Joshua with assistance by <unk> Harris . Musically , " Bassline " is a dubstep , electropop and <unk> song , that incorporates elements of reggae . The song lasts for three minutes and 58 seconds . Instrumentation consists of a wobble bass and synthesizers . Melinda Newman of HitFix compared " Bassline " to the songs by <unk> and <unk> . Trent Fitzgerald of PopCrush noted that the lyrics are about Brown trying to " convince a hot girl he spots in the club to come back to his crib " , in which he sings " Hey girl tell me what you talk / Pretty as a picture on the wall / Hey girl you can get it all / Cause I know you like the way the beat go " . Brown also declares , " You heard about my image / But I could give a flying <unk> who 's offended " . Hayley <unk> of Contactmusic.com noted that a robot voice joins Brown in the hook " Girls like my bassline " . Hazel Robinson of California Literary Review magazine noted that the word " bassline " is a metaphor for penis . 
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- = = Reception = = 
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- " Bassline " garnered mixed reviews from music critics . Sam Wilbur of AOL Radio viewed it as " the best example " of dubstep tracks on Fortune , while Kyle Anderson of Entertainment Weekly noted it as one of the album 's best tracks . Scott Kara of The New Zealand Herald called the song " irritating " and noted it as " blatant <unk> stuff " . Digital Spy 's Lewis Corner felt that " Bassline " was " a lazy attempt " from Brown . Randall Roberts of Los Angeles Times stated that the worst part of the song is the hook . Hazel Robinson of California Literary Review magazine was critical of the production and lyrics , labeling it as " bad " and " dodgy " . Allmusic 's Andy Kellman noted that Brown " clearly feels more <unk> than ever " on " Bassline " . Upon the release of Fortune , due to digital sales , " Bassline " debuted on the UK R & B Chart at number 28 in the issue dated July 14 , 2012 . It also debuted at number 122 on the UK Singles Chart . 
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- = = Credits and personnel = = 
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- Credits adapted from the liner notes for Fortune 
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- = = Charts = = 
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- = HMS Black Prince ( 1904 ) = 
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- HMS Black Prince was a Duke of Edinburgh @-@ class armoured cruiser built for the Royal Navy in the mid @-@ 1900s . She was stationed in the Mediterranean when the First World War began and participated in the pursuit of the German battlecruiser SMS Goeben and light cruiser SMS Breslau . After the German ships reached Ottoman waters , the ship was sent to the Red Sea in mid @-@ August to protect troop convoys arriving from India and to search for German merchant ships . After capturing two ships , Black Prince was transferred to the Grand Fleet in December 1914 and was sunk during the Battle of Jutland in May 1916 , with all hands killed . 
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- = = Design and description = = 
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- Two armoured cruisers of a new design , Duke of Edinburgh and Black Prince , the latter named for Edward , the Black Prince , were ordered for the Royal Navy as part of the 1902 – 03 Naval Estimates . They were the first ships to be designed for the Royal Navy under the supervision of the new Director of Naval Construction , Sir Philip Watts . The new design was significantly larger than the previous Monmouth and Devonshire @-@ class cruisers , mounting a heavier main armament of six 9 @.@ 2 in ( 234 mm ) guns in single turrets . 
- Black Prince displaced 12 @,@ 590 long tons ( 12 @,@ 790 t ) as built and 13 @,@ 965 long tons ( 14 @,@ 189 t ) fully loaded . The ship had an overall length of 505 feet 6 inches ( 154 @.@ 1 m ) , a beam of 73 feet 6 inches ( 22 @.@ 4 m ) and a draught of 27 feet ( 8 @.@ 2 m ) . She was powered by four @-@ cylinder triple @-@ expansion steam engines , driving two shafts , which produced a total of 23 @,@ 000 indicated horsepower ( 17 @,@ 000 kW ) and gave a maximum speed of 23 knots ( 43 km / h ; 26 mph ) . The engines were powered by 20 Babcock & Wilcox water @-@ tube boilers and six cylindrical boilers . The ship carried a maximum of 2 @,@ 150 long tons ( 2 @,@ 180 t ) of coal and an additional 600 long tons ( 610 t ) of fuel oil that was <unk> on the coal to increase its burn rate . At full capacity , she could steam for 8 @,@ 130 nautical miles ( 15 @,@ 060 km ; 9 @,@ 360 mi ) at a speed of 10 knots ( 19 km / h ; 12 mph ) . The ship 's complement was 789 officers and enlisted men . 
- Her main armament consisted of six BL 9 @.@ 2 @-@ inch Mk X guns in single turrets , two on the <unk> and two on each beam , giving a broadside of four 9 @.@ 2 in guns . Her secondary armament of four BL 6 @-@ inch Mark XI guns was arranged in single casemates . They were mounted amidships on the main deck and were only usable in calm weather . Twenty Vickers QF 3 @-@ pounders were fitted , six on turret roofs and fourteen in the superstructure . The ship also mounted three submerged 18 @-@ inch torpedo tubes . 
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- = = Operational history = = 
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- Black Prince was laid down on 3 June 1903 at the Thames <unk> and Shipbuilding Company 's shipyard at <unk> , London . She was launched on 8 November 1904 and completed on 17 March 1906 . When completed , Black Prince served with the 2nd Squadron until 1907 , the 1st Cruiser Squadron from 1907 – 1908 , the 5th Cruiser Squadron ( as part of the Atlantic Fleet ) from 1908 – 1912 and the Third from 1912 – 1913 . 
- At the beginning of the First World War , Black Prince was one of the four armoured cruisers serving in the 1st Cruiser Squadron of the Mediterranean Fleet , commanded by Rear @-@ Admiral Ernest Charles Thomas <unk> . She participated in the pursuit of Goeben and Breslau . Following the escape of the two German ships to neutral Turkey , Black Prince and Duke of Edinburgh were sent into the Red Sea to search for German merchant ships , with Black Prince capturing the German ocean liners <unk> and <unk> . On 6 November , she was ordered to Gibraltar to join a squadron of French and British ships to search for German warships still at sea off the African coast . This was cancelled on 19 November after the location of the German East Asia Squadron was revealed by survivors of the Battle of Coronel . Black Prince joined the Grand Fleet in December 1914 and was assigned to the 1st Cruiser Squadron under Rear @-@ Admiral Sir Robert Keith <unk> . 
- Black Prince was modified in March 1916 as a result of lessons learned at the Battle of Coronel , with the 6 @-@ inch guns removed from their casemates and replaced by six 6 @-@ inch guns mounted individually behind shields between the beam 9 @.@ 2 @-@ inch turrets . 
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- = = = Loss = = = 
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- The ship participated in the Battle of Jutland , where she was sunk with heavy loss of life . The circumstances under which she sank were mysterious for some years after . As the British had lost contact and did not see the ship destroyed , they were unsure as to whether a submarine or surface ship was responsible for sinking Black Prince . During the battle , the 1st Cruiser Squadron was deployed as part of a screening force several miles ahead of the main force of the Grand Fleet , but Black Prince lost contact with the rest of the Squadron as it came into contact with German forces , at about 17 : 42 . Soon after , two other members of the 1st Cruiser Squadron , Defence and Warrior were heavily engaged by German battleships and battlecruisers , with Defence blowing up and Warrior receiving heavy damage , which later caused her to sink . 
- There were no positive sightings of Black Prince by the British fleet after that , although a wireless signal from her was received at 20 : 45 , reporting a submarine sighting . During the night of 31 May – 1 June , the British destroyer Spitfire , badly damaged after colliding with the German battleship Nassau , sighted what appeared to be a German battlecruiser , with two widely spaced funnels , described as being " ... a mass of fire from foremast to <unk> , on deck and between decks . Flames were issuing out of her from every corner . " The mystery ship exploded at about midnight . It was later thought that the burning ship may have been Black Prince , with the two <unk> funnels having collapsed or been shot away . 
- Recent historians , however , hold to the German account of the ship 's sinking . Black Prince briefly engaged the German battleship <unk> at about 23 : 35 <unk> , scoring two hits with 6 @-@ inch shells . <unk> from the rest of the British fleet , Black Prince approached the German lines at approximately midnight . She turned away from the German battleships , but it was too late . The German battleship <unk> fixed Black Prince in her searchlights and opened fire . Up to five other German ships , including the battleships Nassau , <unk> , and Friedrich der <unk> , joined in the bombardment , with return fire from Black Prince being ineffective . Most of the German ships were between 750 and 1 @,@ 500 yards ( 690 and 1 @,@ 370 m ) of Black Prince — effectively point @-@ blank range for contemporary naval gunnery . The ship was hit by at least twelve heavy shells and several smaller ones , sinking within 15 minutes . There were no survivors from her crew , all <unk> being killed . 
- The <unk> is designated as a protected place under the Protection of Military Remains Act 1986 . 
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- = = Popular culture = = 
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- In the first episode of Series 4 of the <unk> @-@ TV ( Australia ) series Who Do You <unk> You Are ? , Australian writer @-@ actor @-@ comedian Shaun <unk> discovered that his great @-@ grandfather Giovanni ( John ) <unk> , a steward on Black Prince , was among those killed . 
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- 
- = On the Pulse of Morning = 
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- " On the Pulse of Morning " is a poem by African @-@ American writer and poet Maya Angelou that she read at the first inauguration of President Bill Clinton on January 20 , 1993 . With her public recitation , Angelou became the second poet in history to read a poem at a presidential inauguration , and the first African American and woman . ( Robert Frost was the first inaugural poet , at the 1961 inauguration of John F. Kennedy . ) Angelou 's audio recording of the poem won the 1994 Grammy Award in the " Best <unk> Word " category , resulting in more fame and recognition for her previous works , and broadening her appeal . 
- The poem 's themes are change , inclusion , responsibility , and role of both the President and the <unk> in establishing economic security . Its symbols , references to contemporary issues , and personification of nature has inspired critics to compare " On the Pulse of Morning " with Frost 's inaugural poem and with Clinton 's inaugural address . It has been called Angelou 's " autobiographical poem " , and has received mixed reviews . The popular press praised Clinton 's choice of Angelou as inaugural poet , and her " <unk> " of the American people and its President . Critic Mary Jane Lupton said that " Angelou 's ultimate greatness will be attributed " to the poem , and that Angelou 's " theatrical " performance of it , using skills she learned as an actor and speaker , marked a return to the African @-@ American oral tradition of speakers such as Frederick Douglass , Martin Luther King , Jr. and Malcolm X. Poetry critics , despite praising Angelou 's recitation and performance , gave mostly negative reviews of the poem . 
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- = = Background = = 
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- When Angelou wrote and recited " On the Pulse of Morning " , she was already well known as a writer and poet . She had written five of the seven of her series of autobiographies , including the first and most highly acclaimed , I Know Why the Caged Bird Sings ( 1969 ) . Although she was best known for her autobiographies , she was primarily known as a poet rather than an <unk> . Early in her writing career she began alternating the publication of an autobiography and a volume of poetry . Her first volume of poetry Just Give Me a Cool <unk> of Water ' Fore I <unk> , published in 1971 shortly after Caged Bird , became a best @-@ seller and was nominated for a Pulitzer Prize . As scholar Marcia Ann Gillespie writes , Angelou had " fallen in love with poetry " during her early childhood in <unk> , Arkansas . After her rape at the age of eight , which she depicted in Caged Bird , Angelou <unk> and studied great works of literature , including poetry . According to Caged Bird , her friend Mrs. Flowers encouraged her to recite them , which helped bring her out her self @-@ imposed period of <unk> caused by her trauma . 
- Angelou was the first poet to read an inaugural poem since Robert Frost read his poem " The Gift <unk> " at President John F. Kennedy 's inauguration in 1961 , and the first Black and woman . When it was announced that Angelou would read one of her poems at Clinton 's inauguration , many in the popular press compared her role as inaugural poet with that of Frost 's , especially what critic Zofia Burr called their " <unk> " , or their ability to speak for and to the American people . The press also pointed to the nation 's social progress that a Black woman would " stand in the place of a white man " at his inauguration , and praised Angelou 's involvement as the Clinton administration 's " gesture of inclusion " . 
- Angelou told her friend Oprah Winfrey that the call requesting her to write and recite the poem came from television producer Harry <unk> , who organized the inauguration , shortly after Clinton 's election . Even though she suspected that Clinton made the request because " he understood that I am the kind of person who really does bring people together " , Angelou admitted feeling overwhelmed , and even requested that the audiences attending her speaking engagements pray for her . 
- She followed her same " writing ritual " that she had followed for years and used in writing all of her books and poetry : she rented a hotel room , <unk> herself there from the early morning to the afternoon , and wrote on legal pads . After deciding upon the theme " America " , she wrote down everything she could think of about the country , which she then " pushed and squeezed into a poetic form " . Angelou recited the poem on January 20 , 1993 . 
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- = = Themes = = 
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- " On the Pulse of Morning " shared many of the themes in President Clinton 's inaugural address , which he gave immediately before Angelou read her poem , including change , responsibility , and the President 's and the <unk> 's role in establishing economic security . The symbols in Angelou 's poem ( the tree , the river , and the morning , for example ) paralleled many of the same symbols Clinton used in his speech , and helped to enhance and expand Clinton 's images . Clinton 's address and the poem , according to <unk> , both emphasized unity despite the diversity of American culture . " On the Pulse of Morning " attempted to convey many of the goals of Clinton 's new administration . 
- Burr compared Angelou 's poem with Frost 's , something she claimed the poetry critics who gave " On the Pulse of Morning " negative reviews did not do . Angelou " rewrote " Frost 's poem , from the perspective of personified nature that appeared in both poems . Frost praised American colonization , but Angelou attacked it . The cost of the creation of America was abstract and ambiguous in Frost 's poem , but the personified Tree in Angelou 's poem signified the cultures in America that paid a significant cost to create it . Both Frost and Angelou called for a " break with the past " , but Frost wanted to relive it and Angelou wanted to confront its mistakes . Burr also compared Angelou 's poem with <unk> <unk> 's poem " For Each of You " , which has similar themes of looking towards the future , as well as with Walt Whitman 's " Song of Myself " and Langston Hughes ' " The <unk> <unk> of Rivers " . According to <unk> , the poem contains a recurring theme in many of Angelou 's other poems and autobiographies , that " we are more alike than <unk> " . 
- " On the Pulse of Morning " was full of contemporary references , including toxic waste and pollution . Angelou 's poem was influenced by the African @-@ American oral tradition of spirituals , by poets such as James Weldon Johnson and Langston Hughes , and by modern African poets and folk artists such as <unk> Brew and <unk> Sutherland , which also influenced her autobiographies . 
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- = = Critical response and impact = = 
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- According to Lupton , " On the Pulse of Morning " is Angelou 's most famous poem . Lupton has argued that " Angelou 's ultimate greatness will be attributed " to the poem , and that Angelou 's " theatrical " performance of it , using skills she learned as an actor and speaker , marked a return to the African @-@ American oral tradition of speakers such as Frederick Douglass , Martin Luther King , Jr. and Malcolm X. British reporter Kate <unk> compared Angelou 's appearance as she read the poem at Clinton 's inauguration with the eight @-@ year @-@ old child in Caged Bird , noting that the coats she wore in both occasions were similar : " She looked magnificent , <unk> theatrical with an <unk> bow mouth . She wore a coat with brass buttons , a strange reminder of the eight @-@ year @-@ old Maya Angelou who stood in a courtroom , <unk> at the sight of the man who had raped her " . Gillespie stated regarding <unk> 's observations : " But standing tall on the steps of the Capitol , she was light @-@ years removed from that terrible time , and America was no longer an ' <unk> place . ' Her poem ' On the Pulse of Morning ' was a soaring call for peace , justice , and harmony . <unk> the hope embodied in the human spirit , it was a solemn and joyful reminder that all things are possible . She wished us ' Good morning ' in her poem , and one felt as if a new day was truly <unk> . " 
- Angelou recognized that although " On the Pulse of Morning " was a better " public poem " than a great poem , her goal of conveying the message of unity was accomplished . Poet David <unk> agreed , stating that although it fulfilled its theatrical and political objectives , the poem was " not very memorable " . Poet Sterling D. <unk> found Angelou 's performance " brilliant " , but was " not as enthusiastic about it as a text " . Burr stated that the negative reviews of Angelou 's poem , like the majority of the reviews about her other poetry , was due to their <unk> and narrow views of poetry , which were limited to written forms rather than spoken ones like " On the Pulse of Morning " , which was written to recite aloud and perform . Burr compared the response of literary critics to Angelou 's poem with critics of Frost 's poem : " Frost 's powerful reading served to supplement the poem in the sense of enhancing it , while Angelou 's powerful reading of her poem supplemented it in the sense of making evident its <unk> and lack . " 
- Angelou 's recitation of " On the Pulse of Morning " resulted in more fame and recognition for her previous works , and <unk> her appeal " across racial , economic , and educational boundaries " . The week after Angelou 's recitation , sales of the paperback version of her books and poetry rose by 300 – 600 percent . Bantam Books had to reprint 400 @,@ 000 copies of all her books to keep up with the demand . Random House , which published Angelou 's hardcover books and published the poem later that year , reported that they sold more of her books in January 1993 than they did in all of 1992 , accounting for a 1200 percent increase . The sixteen @-@ page publication of the poem became a best @-@ seller , and the recording of the poem was awarded a Grammy Award . 
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- 
- = God 's Choice = 
- 
- God 's Choice : The Total World of a Fundamentalist Christian School is a 1986 book written by Alan Peshkin and published by the University of Chicago Press . It is the product of his late 1970s 18 @-@ month ethnographic study of a 350 @-@ person Christian fundamentalist Baptist school in Illinois . He describes the K – 12 day school 's function as a total institution that <unk> about a singular truth ( God 's will ) and subordination before God . The final chapter is a comparative analysis of the school and other schools , institutions , and social movements , wherein Peshkin concludes that the school is divisive in American society for promoting intolerance towards religious plurality , the very condition that permits the school 's existence . 
- Reviewers wrote that Peshkin 's account was fair , and praised his decision to let the participants speak for themselves through quotations . They also noted that the book filled a literary <unk> in scholarly understanding of the rapidly expanding and <unk> fundamentalist Christian school . 
- 
- = = Summary = = 
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- God 's Choice : The Total World of a Fundamentalist Christian School is a 1986 book written by Alan Peshkin . It is a profile of an Illinois Christian fundamentalist school — its policies , practices , and participants . Peshkin , then Professor of Education at the University of Illinois at <unk> – <unk> , intended his account to be both impartial and " <unk> " . He presents the fundamentalists as disciplined , dedicated , and determined with " formulas for success " opposite " fragmented and defensive " detractors . They believe in " one Truth " — God 's plan — and reject philosophies of multiple truths . A teacher told Peshkin that their job is to prepare students for this " one pattern " of thought . In turn , the community 's constituents do not wish to leave , but appreciate conformity as an end in itself . Peshkin describes the school as a " total institution " : a place where many similar people live by their own formal rules apart from outside society , as based on Erving Goffman 's 1961 essay . Peshkin asserts that this was a natural conclusion from a school " based on absolute truth " . God 's Choice was the third book in his series of studies on school – community relationships . It was published by the University of Chicago Press . 
- In 1978 , Peshkin moved to an Illinois community of 50 @,@ 000 people that he <unk> called <unk> , where he stayed and observed for 18 months . He lived in an apartment within the home of a family associated with what he called the Bethany Baptist church . Peshkin studied their 350 @-@ student K – 12 Christian day school , Bethany Baptist Academy ( also a pseudonym ) . The school opened six years prior with 88 students and was one of over one thousand members of the American Association of Christian Schools . The study focuses on the 125 students in the junior – senior high school . After a semester , Peshkin began to interview the community members , and used their quotes to let them " speak for themselves " . The book includes eight portraits of students — four from faith and four " <unk> " who " consciously deviate " — as well as student and teacher survey data , displayed in 16 tables . An <unk> includes course offerings and a bibliography . 
- Peshkin 's findings show a " total world " where the lessons of religion and education are intertwined into an " interrelated , <unk> " philosophy . The academy 's intent is to make Christian professionals as what Peshkin describes as " a vocational school directed to work in the Lord 's service " . When compared to the work of public schools , the private school 's instructors said both kinds of institutions impose a lifestyle and set of values as a kind of " brainwashing " . Peshkin notes that while students " largely identify with " and uphold the fundamentalist teachings , they permit themselves the option of having " individual interpretations " and minor beliefs . Some students either dissent against the academy 's rules or are regarded as too <unk> , but most students are moderate . 
- Students take classes to be effective Christian leaders , including " Bible study and ' soul @-@ winning ' , English , speech , drama , and music " , which are seen as important to " read and proclaim the Word " . Academy teachers establish their authority through discipline and teach " the truth " as established by " facts " from the Bible . Bible passages are associated with the subject matter in a process called " integration " . The academy uses science books from Bob Jones University as an alternative to books that promote secular <unk> , which is described as " the ' official religion ' of the public school system " . The Bob Jones science books associate the Bible with science , and often comment the relationship between God 's intelligence and the <unk> of nature . Classes like science , social studies , and math are viewed as less important for the goal of making Christian professionals apart from their training to do " everything a sinner can do , better " . Some classes are " <unk> and recitation " <unk> , reflecting an <unk> view of knowledge , which the academy believes to be fixed as based in biblical <unk> . Their biggest external influence on curriculum is new books , which may affect how classes are taught , though the content ( " the truth " ) remains the same . 
- In a chapter on teacher selection and training , the task of <unk> students with obedience and discipline takes precedence over the task of teaching content . Students are under constant supervision to uphold a pledge to avoid outside activities such as theater , fashion , dances , and certain television shows . They also avoid some activities altogether , such as <unk> contact , drugs , alcohol , and smoking . This pledge is to be upheld at home as well , and students are encouraged to report <unk> . Teachers too pledge to <unk> " the pursuit of <unk> " over all things in a " born again " activity where they " confess their sins and accept Jesus Christ as their personal <unk> " . The school is <unk> in its insistence on " telling the kids what is right " and its according preference for lecture over discussion . Teachers are also afforded the option of corporal punishment , though it is rarely used . 
- The last two chapters feature Peshkin 's commentary on the school in society , its <unk> and comparison with other total institutions and larger social movements , like the New Religious Right . He also compares the academy to public and parochial schools , wherein he finds public school students more politically tolerant and <unk> of religious pluralism . Peshkin 's final <unk> are written as a " cost @-@ benefit analysis " of these Christian schools in the larger American society . In the last chapter , Peshkin contemplates how his Jewish identity is <unk> within a <unk> and secular society , and how he is fearful of absolutist " <unk> , <unk> logic " and " zeal for conversion and <unk> " . He recounted that while the school spoke <unk> of non @-@ fundamentalists and non @-@ Christians , no one spoke of the American religious plurality that permitted this . In response to the encroaching state , fundamentalists feel that their political conviction should mirror that of their religion . Peshkin views the school as <unk> in a larger society for promoting intolerance towards fellow Americans . He also praised traits of the schools including its community , dedicated teachers , and attractive image . At the time of print , Christian schools like Bethany were <unk> the growth of other schools . In 1989 , Christian schools comprised about 20 % of private school enrollment — around 700 @,@ 000 students . 
- 
- = = Reception = = 
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- Reviewers wrote that Peshkin 's account was fair in its presentation , and that his choice to let individuals " speak for themselves " through abundant quotations was a strength . They also noted how the book filled a gap in the field and that his final chapter was too <unk> . 
- R. Scott Appleby ( American Journal of Education ) wrote that Peshkin succeeded at his attempt to be impartial , and that his presentation of fundamentalist culture is made both " understandable " and , in part , " admirable " . He reflected that this Christian <unk> was closer to indoctrination than education in that it did not develop " critical skills and ... human capacities " in " open @-@ ended " learning but professed a fixed chain of knowledge " from on high " where humans are errant and need authoritarian guidance . Appleby added that <unk> blames public schools and its associated state apparatus as both a manufacturer enemy needed to feed its " sense of crisis " and for creating " unsafe " areas unregulated by " Christian truth " . <unk> Susan Rose " <unk> the base " of God 's Choice in her 1988 Keeping Them Out of the Hands of Satan , and Appleby writes that the two books compliment each other 's <unk> . While God 's Choice has an " engaging , sometimes <unk> narrative " with vivid characters but little outside information apart from statistics , Keeping Them Out of the Hands of Satan includes extra detail on how fundamentalist groups interact and share a larger societal milieu . For example , Rose explains the difference in " born @-@ again experiences " of <unk> and fundamentalists , which Peshkin <unk> over . 
- In her own review of Peshkin 's book , Rose ( Contemporary Sociology ) praised its " clear and detailed " contribution to the field but wished for more overview material on the Christian School Movement 's rise , proponents , philosophical consistency , and " <unk> context " . She wrote that few had studied Christian schools , the " fastest growing sector of private education in the United States " . In commending Peshkin 's even @-@ handedness , she wrote that his <unk> about declaring his own biases and effort to present participant voices through direct quotation were strong elements , though he described more than he analyzed . Rose felt that the " interesting " final chapters of analysis and comparison with public schools " grounded " the overall ethnography , though she wished for more comparison of the adolescent student experience between the academy and other kinds of American schools . She considers Peshkin 's " discussion of the politics of pluralism " , that Christian schools both add to American religious pluralism while advocating against it , possibly his best contribution . 
- Jean <unk> ( The Times Higher Education Supplement ) too noted the fast growth of conservative Christian schools but added that the book was also relevant in Britain , which was experiencing similar growth . She found the first few chapters somewhat repetitive as it expressed the <unk> of the school 's practices . Richard V. <unk> ( Christian Century ) felt that Peshkin wrote with " deeply respect " for the school and its community , but <unk> , himself an evangelical , was " disturbed " by the community 's " <unk> " values — " biblical <unk> " that are " part of a conservative program that has been read into Scripture " . Paul F. Parsons ( Christianity Today ) noted that Peshkin found the school successful by traditional terms , with standardized tests , orderly climate , and " fun @-@ loving " students , but lacking free exchange of ideas , as education is seen more as a transfer than a quest , and students do not learn " choice , doubt , suspended judgment , [ or ] dissent " . Parsons affirmed Peshkin 's findings as " remarkably representative " based on his own visits to " Christian schools in 60 cities " , though others are less absolutist . And while Julian McAllister Groves ( Journal of Contemporary <unk> ) described the text as " beautifully written " and " poetic " , he doubted whether the school 's students were as converted as they said , and felt that Peshkin might have seen more " role distance " and examples of playing along simply for community acceptance had he stayed for lunch and other informal observations . 
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- 
- = Baltimore mayoral election , 1999 = 
- 
- On November 2 , 1999 , the city of Baltimore , Maryland , elected a new mayor , the 47th in the city 's history . Primary elections were held to determine the nominees for the Democratic Party and Republican Party on September 14 . <unk> mayor Kurt Schmoke , a Democrat , opted not to run for reelection . Martin O 'Malley , a member of the Baltimore City Council , won the election to succeed Schmoke . 
- Because Baltimore 's electorate is overwhelmingly Democratic , whoever won the Democratic primary election was seen as the favorite over the Republican nominee in the general election . Baltimore 's large African American population initially made it seem likely that the next mayor would also be African American . <unk> Mfume was the preferred candidate of local politicians , but he opted not to run . Though Carl Stokes and Lawrence Bell , members of the City Council , declared for the race , local leaders were <unk> with the quality of declared candidates . 
- In 1999 , Baltimore experienced high rates of murder and unemployment , and had a failing city school system . O 'Malley declared his candidacy , focusing his campaign on a " zero tolerance " approach to crime . He received endorsements from many of the city 's African American leaders . After a close race , O 'Malley overtook both Stokes and Bell to win the Democratic nomination , which all but assured him of victory in the general election . He defeated Republican candidate David F. Tufaro in the general election by an overwhelming majority . 
- 
- = = Background = = 
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- Kurt Schmoke , the incumbent Mayor of Baltimore , was serving his third term . He announced in December 1998 that he would not run for reelection the following year , the first time an incumbent Baltimore mayor did not run for reelection since 1971 . Schmoke was the first African American mayor in Baltimore 's history . 
- Racial politics had long played a role in Baltimore . As the state of Maryland did not attempt to prevent African Americans from voting through Jim Crow laws , political coalitions often involved African American community leaders . Theodore <unk> , who served as mayor from 1943 through 1947 and from 1963 through 1967 , was one of the first political leaders to work with African American community leaders . By 1999 , 63 % of Baltimore 's registered voters were African American , and 90 % were registered to the Democratic Party . Baltimore had not elected a Republican mayor since 1963 . 
- As of 1999 , Baltimore experienced 300 murders a year , which was the fourth most in the nation . <unk> was 9 % , twice the national average . Between 1990 and 1998 , Baltimore saw its population decrease by 12 @.@ 3 % , the second @-@ biggest decrease during that time period in the United States . This decrease led to its work force decreasing by one @-@ sixth . The city saw decreases in heavy @-@ manufacturing by 40 percent , distribution jobs by 35 percent , retail positions by 34 percent , and banking jobs by 28 percent . Also , the state of Maryland had taken over the city 's failing school system in 1996 , which upset African American politicians . State Senator Clarence M. Mitchell , IV called the takeover racist . Baltimore City Council members , including President Lawrence Bell and Martin O 'Malley , had opposed the handling of Baltimore 's high crime rate by Schmoke and Baltimore Police Department ( <unk> ) Commissioner Thomas C. <unk> . 
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- = = Candidates = = 
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- = = = Democrats = = = 
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- Bell was considered in February 1999 to be the front @-@ runner in the mayoral race . Other potential candidates included State 's Attorney Patricia <unk> , former City <unk> Carl Stokes , City Comptroller Joan Pratt , and Joan Carter Conway , a member of the Maryland State Senate . 
- City leaders , including Howard P. Rawlings , a member of the Maryland House of <unk> , and William Donald Schaefer , the Comptroller of Maryland and Schmoke 's predecessor as mayor , feared that none of the potential candidates had the vision to continue the urban renewal that took place under Schmoke and Schaefer . They hoped that Baltimore @-@ native <unk> Mfume , the president of the National Association for the Advancement of <unk> People ( NAACP ) , would run . Mfume had previously served on the Baltimore City Council and in the United States House of Representatives . Schmoke called the race " his to lose " . However , Mfume lived in nearby <unk> , Maryland , and did not move into Baltimore until March , which would leave him short of the one year <unk> requirement . 
- The state legislature passed a law <unk> the <unk> requirement from one year to six months , which was signed into law by Democratic Governor <unk> <unk> in April . Bell attempted to have the legislation overturned . The City Council , with Schmoke 's support considered raising the salary of the mayor in April , to make the position more <unk> to Mfume . Also , 200 Baltimore citizens , including three former mayors , attempted to draft Mfume into the race . However , Mfume had signed a five @-@ year contract with the NAACP in 1996 . Mfume considered leaving the NAACP to run , but in May , decided to remain with the organization . 
- Stokes announced his candidacy in December 1998 . Bell announced his candidacy two days after Mfume passed on running , in May 1999 , promising to tackle crime and improve public safety . Community leaders who attempted to draft Mfume into the race began to back Stokes . Other declared candidates included Mary <unk> , the City Register of Wills , activists A. Robert Kaufman , Robert <unk> , and Phillip Brown . <unk> stated that without Mfume in the race , she would consider running , but she opted against getting into the race . 
- A June 1999 poll conducted by Gonzales / <unk> Communications Inc . , a polling firm based in Annapolis , Maryland , showed Schaefer ahead of Bell . Schaefer stated that he had no interest in running , and threw his support behind Bishop Robinson , the former <unk> Commissioner . Robinson chose not to run . 
- O 'Malley initially supported Bell 's candidacy . Bell urged O 'Malley to endorse him , and offered him the job of city solicitor in exchange for his support . However , O 'Malley began to distance himself from Bell , not appearing at Bell 's campaign announcement , due to disagreements on several bills debated at the City Council . Bell 's supporters attempted to convince O 'Malley to run for City Council President , but O 'Malley announced his decision to run for mayor in June , becoming the first major candidate in the race who was Caucasian . He began the campaign largely unknown outside of Northeast Baltimore . 
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- = = = Republicans = = = 
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- On July 1 , David F. Tufaro , a real estate developer and lawyer from Roland Park , Maryland , announced his intention to run for the Republican Party nomination , joining three neighborhood activists who had already declared their intentions to run . Carl Adair , a public school teacher who had run unsuccessfully for the City Council and the Maryland House of <unk> on numerous occasions , was also a candidate for the Republican nomination . 
- Republican Party officials quickly began to back Tufaro , who criticized the Democratic Party by pointing out that Democrats have led Baltimore in its decline . 
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- = = Campaign = = 
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- Fifteen candidates ended up running in the Democratic primary . The front @-@ runners were considered to be O 'Malley , Bell , and Stokes . Schmoke made no endorsement in the race . 
- O 'Malley and Bell called for " zero tolerance " to all crime , though Stokes felt this policy was biased against minorities . Stokes ran on the issue of education , as he was a former member of the Baltimore school board , in addition to the city council . Stokes vowed to reduce class sizes and reverse the trend of citizens of Baltimore leaving the city to live in nearby suburbs . 
- O 'Malley faced initial difficulties based on race , as a community leader stated that his entrance into the race " created some tension , " and that O 'Malley 's potential victory " would be the worst thing that could happen in this city " and could " tear this city apart . " Some saw his entrance in the race as an attempt to split the African American vote . However , O 'Malley received endorsements from prominent African American politicians in August , including Rawlings , fellow state <unk> Kenneth <unk> , and Conway . Rawlings credited O 'Malley 's " track record of <unk> black Americans . " He also received an endorsement from Schaefer . Some African Americans charged Rawlings with " stabbing us in the back " over the endorsement . 
- Bell attempted to portray himself as the heir apparent to Schmoke . However , <unk> from a lawsuit revealed Bell 's financial troubles , which included having his car <unk> . Bell lost support after he stated that voters should choose him because " I look like you . " Stokes began to lose support after he was charged with lying about having a college degree , when it was discovered that he did not graduate from Loyola University Maryland , as his campaign literature stated . It was also revealed that Stokes settled an income tax <unk> . Bell 's supporters rallied outside the offices of The Baltimore Sun , challenging that they were writing negative stories about African American candidates , and giving better treatment to O 'Malley during the campaign . Stokes was able to recover support when he received endorsements from The Baltimore Sun , the Baltimore Afro @-@ American , and the <unk> alliance . 
- Bell led Stokes in a poll conducted early in the race , 33 % to 17 % , before O 'Malley declared his candidacy . By late August , Bell slipped in the polls to third place , with O 'Malley and Stokes tied . Bell and O 'Malley began airing television commercials supporting their campaigns in early August , while Stokes did not begin to air commercials until September . O 'Malley also advertised on the radio . 
- Seven candidates , including Democrats Bell , Stokes , O 'Malley , and <unk> , and Republicans Adair , Tufaro , and Arthur <unk> Jr . , met for a debate on September 8 . The debate focused on crime , with Bell , Stokes , and O 'Malley making their cases regarding " zero tolerance " , while Adair and Tufaro declared their support for <unk> . 
- 
- = = Results = = 
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- 
- = = = Democratic primary = = = 
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- The Democratic primary was held on September 14 . <unk> was high ; though the mayoral candidates expected about 100 @,@ 000 voters , about half of Baltimore 's <unk> @,@ 000 registered voters voted in the primary elections . 
- O 'Malley won the primary election with more than 50 % of the vote , though he received only 30 % of the African American vote . Bell and Stokes split a significant portion of the city 's black majority , but their combined total was less than O 'Malley . At his victory rally , O 'Malley called the election " a victory for diversity and a victory for <unk> " . 
- 
- = = = Republican primary = = = 
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- In the September 14 Republican primary , Tufaro received over half of the votes cast . Adair finished in second place . 
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- = = = General election = = = 
- 
- Despite the odds he faced in the general election , Tufaro promised to campaign against O 'Malley , not taking defeat as an <unk> . He pointed to Bret <unk> , the mayor of Jersey City , New Jersey , as evidence that a Republican could be elected in a heavily Democratic city . <unk> came to Baltimore to campaign for Tufaro . 
- Regarding public housing , Tufaro called for renovations rather than demolishing vacant buildings , which drew criticism , as Housing Commissioner Daniel P. Henson III pointed out that renovating was more expensive than demolishing . O 'Malley promised to enforce provisions of the Community <unk> Act of 1977 , which require banks to invest in poor neighborhoods . Tufaro further proposed a plan for school <unk> and to drug test students , which drew opposition from the Baltimore Teachers Union and from O 'Malley , who favored expanding pre @-@ kindergarten and after @-@ school programs , while making summer school mandatory . When discussing the problem of HIV / AIDS in Baltimore , which disproportionately affects African Americans , both O 'Malley and Tufaro promised to continue the city 's needle exchange program . 
- Tufaro criticized O 'Malley 's zero tolerance policy , suggesting it would lead to increased police brutality towards minorities . He instead proposed continuing Mayor Schmoke 's community policing strategy . Opponents of the zero tolerance policy tried to tie O 'Malley to the Baltimore Police shooting of an African American car theft suspect . 
- <unk> @-@ TV conducted the only poll of the general election with <unk> , which showed O 'Malley leading with 87 % of the vote . On Election Day , O 'Malley easily defeated Tufaro in the general election , receiving over 90 % of the vote . 
- 
- = = Aftermath = = 
- 
- 
- = = = O 'Malley 's political career = = = 
- 
- Following his election , O 'Malley 's first personnel decision was to retain the director of the city 's economic development agency . O 'Malley had his transition team , and had them compile policy drafts by mid @-@ December , so they would be ready to compete for state funds when the Maryland State Legislature <unk> on January 12 , 2000 . He participated in the Newly <unk> <unk> Program at the John F. Kennedy School of Government in mid @-@ November . By the beginning of December , he named five deputy mayors and filled most of his cabinet . He finalized his cabinet on December 7 , during his last session as a city councillor . He was sworn in as mayor later that day at the War Memorial Plaza , near Baltimore City Hall . 
- In his first year in office , O 'Malley adopted a statistics @-@ based crime tracking system called CitiStat , modeled after <unk> . The system logged every call for service into a database for analysis . The Washington Post wrote in 2006 that Baltimore 's " <unk> rate remains stubbornly high and its public school test scores <unk> low . But CitiStat has saved an estimated $ 350 million and helped generate the city 's first budget surplus in years . " In 2004 , CitiStat accountability tool won Harvard University 's " <unk> in American Government " award . The system garnered interest from Washington , D.C. Mayor Adrian <unk> , as well as crime officials from the United Kingdom . 
- O 'Malley considered a run for Governor of Maryland in the 2002 election , but decided not to run . He was reelected as Mayor of Baltimore in 2003 , and announced his candidacy for Governor in the 2006 election . The Baltimore Sun endorsed O 'Malley , saying : " When he was first elected mayor in 1999 , the former two @-@ term city <unk> inherited a city of rising crime , failing schools , and shrinking economic prospects . He was able to reverse course in all of these areas . " The Washington Post criticized O 'Malley for " not <unk> [ ing ] the problems of rampant crime and rough schools in Baltimore " , but further said that " he put a dent in them . " . O 'Malley defeated incumbent Governor Bob Ehrlich 53 % <unk> % in the November 7 , 2006 , general election . O 'Malley defeated Ehrlich in the 2010 election 56 % <unk> % , receiving just over one million votes . 
- O 'Malley was ineligible to run in the 2014 gubernatorial election due to term limits . O 'Malley publicly expressed interest in a presidential run in 2016 on multiple occasions . At a press conference at a National Governors Association meeting , O 'Malley stated he was laying " the framework " for a presidential run . 
- 
- = = = <unk> on The Wire = = = 
- 
- A fictionalized version of the events of this election were presented in third and fourth seasons of The Wire , a drama about crime and politics in Baltimore , which aired in 2004 and 2006 , respectively . Many saw the connection between O 'Malley and the character of Tommy Carcetti , a Caucasian Baltimore City Councillor who is elected mayor in an election against two African American opponents . Carlos Watson of <unk> once introduced O 'Malley as " one of the real @-@ life inspirations for the mayor of the hit TV show The Wire " , to which O 'Malley responded that he was instead the show 's " antidote " . 
- Show creator David Simon denied that the character of Tommy Carcetti was supposed to be O 'Malley , though he did acknowledge that O 'Malley was " one of several inspirations " for Carcetti . He further stated that while Carcetti was " reflective " of O 'Malley , Carcetti was a composite drawing aspects from other local politicians that he had covered when he worked as a reporter for The Baltimore Sun . 
- 
- 
- = Arikamedu = 
- 
- Arikamedu is an archaeological site in Southern India , <unk> , <unk> <unk> , <unk> . It is 4 kilometres ( 2 @.@ 5 mi ) from the capital , Pondicherry of the Indian territory of <unk> . 
- Sir Mortimer Wheeler 1945 , and Jean @-@ Marie <unk> conducted <unk> excavations there in 1947 – 1950 . The site was identified as the port of <unk> , known as an " <unk> " in the Periplus of the Erythraean Sea and Ptolemy . <unk> have found <unk> , <unk> ware , Roman lamps , <unk> , glass and stone beads , and gems at the site . Based on these excavations , Wheeler concluded that the Arikamedu was a Greek ( <unk> ) trading post that traded with Rome , starting during the reign of Augustus Caesar , and lasted about two hundred years — from the late first century BCE to the first and second centuries CE . Subsequent investigation by <unk> Begley from 1989 to 1992 modified this assessment , and now place the period of occupation from the 2nd century BCE to the 8th century CE . 
- Significant findings at Arikamedu <unk> numerous Indo @-@ Pacific beads , which facilitated fixing the period of its origin . Red and black ceramics — known as megalithic stones or <unk> in Tamil meaning " old stones " and used to mark graves — have existed at the site even prior to and during Roman occupation of the site , and also in later periods . 
- 
- = = Location = = 
- 
- Arikamedu is a coastal fishing village , under the <unk> <unk> , on the southeastern coast of India , 4 kilometres ( 2 @.@ 5 mi ) from Pondicherry , on the Pondicherry @-@ <unk> road ; it was originally a French colonial town . It is located on the bank of the <unk> River ( for most part of the year the river is considered a <unk> ) , also known as Virampattinam River , which forms the northern outlet of the <unk> River as it joins the Bay of Bengal . As the site is located at the bend of the river it provides protection to sea @-@ going vessels that dock there . The site has been subject to extensive archaeological excavations . The archaeological site is spread over an area of 34 @.@ 57 acres ( 13 @.@ 99 ha ) and has been under the control of the Archaeological Survey of India since 1982 . 
- 
- = = Etymology = = 
- 
- The name Arikamedu , an archaeological usage for the excavated site , originates in a Tamil word that means <unk> of <unk> , based on the figurine of an avatar ( incarnation ) of the Jain <unk> <unk> found at the site . It is also linked with <unk> or Virampattinam , meaning Port of <unk> , a village next to Arikamedu . <unk> , according to <unk> literature , was well known as a port and also for its salt pans during the <unk> dynasty . Arikamedu @-@ <unk> together find mention as Poduke , a major port in the Periplus of the Erythraean Sea in the first century CE and as Poduke <unk> in Ptolemy 's <unk> of mid first century CE . Poduke is a Roman name and is also said to be a corrupted version of the Tamil name <unk> , meaning a " meeting place " , also known for the local <unk> clan . 
- 
- = = History = = 
- 
- The first mention about Arikamedu was in <unk> , in a communication from the Consul of the Indo @-@ French colony of Pondicherry . It informed the French East India Company that villagers were <unk> old bricks from the Virampattinam . The earliest mention of the Arikamedu archaeological site was by Le Gentil of France , who the King of France had assigned to observe notable astronomical occurrences in the world . Gentil , after visiting Arikamedu , confirmed the earlier report of the Consul of the Indo @-@ French colony . 
- In 1765 , when he visited the ruins at the site , he found the people of the village collecting large ancient bricks exposed at the river bank . The villagers told him that they had retrieved the bricks from an old fort of the king the Vira @-@ <unk> . In 1937 , <unk> Dubreuil , an <unk> , also from France , purchased <unk> stone antiquities from local children , and also gathered some exposed on the site 's surface . In particular , he found an intaglio carved with the picture of a man . As a <unk> , he identified the intaglio as Augustus Caesar . He also found fine beads and gems . He concluded that these antiquities belonged to the Roman Empire . Dubreuil informed the local Governor of Pondicherry about his find , and called Arikamedu " a true Roman city . " He published a short note about his findings . 
- In the early 1940s , Service des <unk> <unk> carried out random excavations . Father <unk> and <unk> <unk> , who were not qualified archaeologists , carried out the excavations at Arikamedu and sent a few antiquities to Indian museums , and also to the École française d <unk> @-@ <unk> in Hanoi . 
- Sir <unk> , the Director General of the Archaeological Survey of India , in the 1940s saw a few <unk> of Arikamedu site displayed in the Madras Museum , which he identified as <unk> ware , an expensive ceramic made until 50 CE in <unk> , Italy . Thereafter , when he visited the Pondicherry Museum and saw more of the findings from the Arikamedu site , he was impressed and thought that he had found the links between the Classical Mediterranean and Ancient India . Soon thereafter in 1945 , the penultimate year of World War II , he mounted excavations in a scientific manner . He was looking for an archaeological site in India that could establish its cultural link , a <unk> of the Indian antiquities to the Greco @-@ Roman period , and this quest led him to the Arikamedu site . These excavations also involved Indian archaeologists , who were trained on the site . 
- Wheeler published his findings in 1946 . He noted that , for the local fishermen of the village , the antiquities were strange — as they consisted of lamps , glass items , <unk> , <unk> and <unk> , wine containers , etc . He also observed that traders traveled from west coast and from Ceylon , <unk> ( <unk> ) and the Ganges area to trade goods such as gems , <unk> and spices , and silk . He carried out excavations carefully , so that none of the antiquities were damaged . This was followed by investigations after the war , from 1947 – 1950 by Jean @-@ Marie <unk> . His report of excavations was not as fully published as Wheeler 's . His report was not well @-@ known in India , as it was not written in English . However , his important conclusion was that the site belonged to an early megalithic period , as he had located megalithic burials marked by stones , locally known in Tamil as <unk> close to the site . 
- The excavations led to antiquities of Roman origin such as beads and gems , amphorae ( wine making <unk> ) with remnants of wine , a Roman stamp , big bricks recovered from an old wall , <unk> ware and so forth . From these antiquities Wheeler concluded that the site was related to a period of trading with Rome , and that it was first established by emperor Augustus . He also noted that this Indo @-@ Roman trade lasted for a period of about 200 years , till 200 CE . Wheeler also found the Chinese <unk> , identified to belong to the Song @-@ <unk> dynasty , and Chola coins from about the eleventh century , but these were rejected as <unk> items or remnants left by brick @-@ robbers . <unk> Chinese blue @-@ and @-@ white ware were also recovered from the site . 
- Wheeler noted that " <unk> Ware " found at the site ( designated as " Arikamedu Type 1 " in the scientific study under the " Arikamedu Type 10 Project : <unk> Early Historic Networks in South Asia and Beyond " ) was not of an Indian origin , but was from the Mediterranean region . A ceramic <unk> , ( " Arikamedu Type 10 ) has also been investigated for its style and spatial distribution . 
- After a gap of several decades , in the early 1980s , <unk> Begley studied the ceramics find of the site and proposed a preliminary version of the chronology of the occupation of the site . At the same time she started researching on the beads , organized a proper <unk> display of the artifacts of the site at the Pondicherry Museum , and brought out an information <unk> . 
- Begley obtained <unk> to carry out excavations at the site in collaboration with the University of Pennsylvania and the University of Madras ; she and <unk> <unk> were the directors of operation from 1989 to 1992 . Steven <unk> of the University of Delaware , who had back ground knowledge of Roman Egypt , was in charge of the <unk> at the site . Further excavations were done during six working seasons from 1989 to 1992 , which led to a contradictory view that the brick structures and the wells investigated by Wheeler were of poor quality as they were founded on poor sandy foundations . The wood work was also noted to be of poor quality and the houses had no waterproofing . The excavations also lead to a view that Arikamedu 's Roman trading link was more of an inference . The excavations have now established that the trading with Rome extended to a period beyond that assessed by Wheeler ; that trading continued from the second century BCE to the seventh or eighth century CE . 
- The extensive findings of glass and stone beads at the site provided Begley the link to Arikamedu 's history . She identified the beads as Indo @-@ Pacific beads crafted at Arikamedu . Based on the antiquities and structural features from the excavations , Begley and <unk> established a revised sequence of six major periods of occupation of the site . Finds of new variety of Roman <unk> ware also facilitated revision of the dates of occupancy . They have also inferred that the site has been in continuous occupation since at least 2nd or 3rd century BCE to much more recent times . 
- 
- = = Excavations = = 
- 
- The excavated area of the mound was <unk> into two zones on the basis of occupation and elevation . Northern sector of the mound is nearer to the sea coast while the southern sector is farther away from the coast . The ceramic find of <unk> and cooking vessels found in the northern sector were indicative of mass feeding of sailors and traders who camped there . Wine stored in amphorae was the principal item imported from the western countries during the later part of the 2nd century BCE . 
- According to Wheeler the finds from the northern and southern part of the mound belong to the period from later part of 1st century BCE to 1st and 2nd centuries CE . <unk> structures include : 
- A brick and lime mortar plaster structure of oblong shape 45 metres ( 148 ft ) in length , with a divide wall , used as a <unk> in the southern part 
- Two walled enclosures with <unk> and drainage systems in the northern part of the mound that could indicate of <unk> operations that used <unk> to dye muslin for export 
- <unk> , both local and Mediterranean , such as amphorae and <unk> ware that belonged to the Terra <unk> ( stamped pottery ) of 1st century BCE , which went out of use by 50 CE 
- Pink amphorae <unk> used to store wine or oil with two handles and a yellow slip , found in all layers of excavations 
- Smaller objects include a wheel @-@ turned <unk> ceramic , a few <unk> figurines , shell beads , gems , gold , <unk> , iron nails , copper percussion <unk> , red fragment of a Roman lamp shade , an engraved emblem of emperor Augustus , an ivory handle , and a wooden toy boat . Based on these antiquities Wheeler concluded that the Arikamedu was a Greek ( <unk> ) trading station . However , recent excavations by Begley have altered this assessment . 
- The buildings in the northern part of the mound indicative <unk> , with people of different ethnic groups — Indian and non @-@ Indian — but it has not been possible to date them in view of the limited depth of excavations . 
- 
- = = Conservation = = 
- 
- An international conference that the Government of Pondicherry and the Italian Ministry of Foreign Affairs held in October 2004 decided to investigate the Arikamedu site jointly for conservation , as its ancient commercial link with the Romans has been established . During this conference , the Government of Pondicherry also decided to propose the site for status as a World Heritage Site of UNESCO . The Archaeological Survey of India also proposed the site for UNESCO Cultural Heritage Site status , under the title Silk Road Sites in India . 
- 
- 
- = Hurricane Ingrid = 
- 
- Hurricane Ingrid was one of two tropical cyclones , along with Hurricane Manuel , to strike Mexico within a 24 ‑ hour period , the first such occurrence since 1958 . Ingrid was the ninth named storm and second hurricane of the 2013 Atlantic hurricane season . It formed on September 12 in the Gulf of Mexico from a broad disturbance that also spawned Manuel in the eastern Pacific . After initially moving westward toward Veracruz , Ingrid turned northeastward away from the coast . <unk> conditions allowed it to attain hurricane status on September 14 , and the next day Ingrid attained peak winds of 140 km / h ( 85 mph ) . Subsequently , increased wind shear weakened the convection as the storm turned more to the northwest and west . On September 16 , Ingrid made landfall just south of La Pesca , Tamaulipas in northeastern Mexico as a strong tropical storm , and dissipated the next day . 
- The combined impacts of hurricanes Ingrid and Manuel affected two @-@ thirds of Mexico , killing 192 people and causing $ 75 billion pesos ( MXN , $ 5 @.@ 7 billion USD ) in damage . Most of the effects were due to Manuel , though Ingrid was directly responsible for at least 32 deaths and $ 20 billion pesos ( MXN , $ 1 @.@ 5 billion USD ) in damage . The two storms produced 162 billion m3 ( 5 @.@ 7 trillion cu ft ) of water , the equivalent of filling every dam in Mexico . Rainfall from the storm peaked at 511 mm ( 20 @.@ 1 in ) in Tuxpan , Veracruz . The rains caused widespread flooding , damaging at least 14 @,@ 000 houses and hundreds of roads and bridges . In Tamaulipas , where the storm made landfall , the rainfall damaged crops and flooded rivers . The effects of the storm spread into southern Texas , causing high tides and some flooding . After the storm , the Mexican government declared several municipalities as states of emergency . Relief agencies distributed food and aid to the hardest hit areas , although in Tamaulipas , residents had to rely on assistance from the local Gulf <unk> . The names Ingrid and Manuel were both later retired due to their impacts . 
- 
- = = Meteorological history = = 
- 
- On August 28 , a tropical wave exited the west coast of Africa , which moved westward across the Atlantic Ocean without development . On September 2 , an area of convection , or thunderstorms , developed along the northern portion of the wave , but was eventually absorbed into Tropical Storm Gabrielle north of Puerto Rico . The wave continued westward through the Caribbean Sea into a large area of cyclonic flow at the surface , which extended across Central America into the eastern Pacific . Around September 9 , the broad system developed two areas of disturbed weather – one in the eastern Pacific would become Hurricane Manuel , and the other developing over the northwestern Caribbean . The latter system slowly organized , developing a low pressure area on September 11 . Subsequently , the system moved over the Yucatán Peninsula . Although land interaction prevented immediate development , conditions favored further development in the Bay of Campeche . Early on September 12 , the low emerged into the bay , and at 1800 UTC the National Hurricane Center ( NHC ) assessed that Tropical Depression Ten developed about 280 km ( 170 mi ) east @-@ northeast of Veracruz . This was confirmed by a Hurricane Hunters flight indicating the presence of a closed low @-@ level circulation . 
- On September 13 , convection and organization increased and the depression strengthened into Tropical Storm Ingrid , while weak steering currents resulted in little movement of the cyclone . <unk> due to the presence of nearby Hurricane Manuel in the eastern Pacific basin on the other side of Mexico , significant wind shear existed in the vicinity of Ingrid . Nonetheless , strong convection and intermittent development of an eye allowed for strengthening of the storm on September 14 . Ingrid strengthened into a hurricane – the second of the season – that afternoon . Around that time , the hurricane turned sharply northeastward due to a trough over eastern Mexico and a ridge over the southeastern United States . Additional strengthening took place thereafter , and Ingrid reached a peak intensity of 140 km / h ( 85 mph ) early on September 15 while starting to move northward and begin a northwest turn towards the Mexican coastline . 
- Afterward Ingrid reached peak winds , shear increased and began to weaken the hurricane as it approached the Mexican coast . The center became displaced to the edge of the convection , and NHC forecaster Daniel Brown noted that Ingrid " [ did ] not resemble a classic hurricane in satellite pictures . " At around 1115 UTC on September 16 , Ingrid made landfall just south of La Pesca , Tamaulipas in northeastern Mexico , after having weakened into a strong tropical storm with 100 km / h ( 65 mph ) winds . Near landfall , the Hurricane Hunters reported flight @-@ level winds of 120 km / h ( 75 mph ) which , after adjusting to surface winds , confirmed the weakening . The storm moved ashore less than 24 hours after Tropical Storm Manuel struck the Pacific coast of Mexico on Michoacán , making it the first time since 1958 that tropical cyclones struck both coasts of the country within one day . Ingrid rapidly weakened into a tropical depression over land , and although convection temporarily reorganized , the circulation dissipated on September 17 . 
- 
- = = Preparations and impact = = 
- 
- Several tropical cyclone warnings and watches were posted in anticipation of the storm . Upon the development of Ingrid into a tropical cyclone at 2100 UTC on September 12 , the Government of Mexico issued a tropical storm warning from <unk> to Nautla , Veracruz . At 1500 UTC on September 13 , the tropical storm warning was extended northward to Cabo Rojo , Veracruz , with a tropical storm watch northward to La Pesca , Tamaulipas . Later , a hurricane warning was issued between Cabo Rojo to La Pesca , with a tropical storm watch to <unk> <unk> , Tamaulipas . <unk> , the oil company operated by the Mexican government , evacuated workers from three platforms in the Gulf of Mexico due to the storm . In Tamaulipas , classes were canceled in 27 municipalities , and all classes were temporarily canceled throughout Veracruz . The threat of the storm caused events for Mexican Independence Day to be canceled . After a restriction on the news in 2010 due to the local drug war , members of a citizen alert system in Tamaulipas used Twitter to notify about flooding , missing people , and the need for assistance . 
- Early in its duration , Ingrid produced tropical storm force winds along the coast of Veracruz . While making landfall , it also brought tropical storm force winds along the northeastern Mexican coast . Sustained winds at La Pesca peaked at 80 km / h ( 50 mph ) , with gusts to 105 km / h ( 65 mph ) . The NHC remarked that Ingrid " likely caused above normal tides " in northeastern Mexico , but there was no data to back up the statement . <unk> with Hurricane Manual on the Pacific coast and the broad cyclonic flow , Ingrid dropped heavy rainfall across eastern Mexico , primarily in <unk> , Veracruz , and Tamaulipas states . In Tuxpan , Veracruz , rainfall totaled 511 mm ( 20 @.@ 1 in ) over 10 days , while at the <unk> Vicente Guerrero dam in Tamaulipas , precipitation reached 502 mm ( 19 @.@ 8 in ) . Surface runoff from the storm spread to the Pacific coast of Mexico , producing flooding in Guerrero in combination with Manuel . The impacts from both storms produced 162 billion m3 ( 5 @.@ 7 trillion cu ft ) of water , the equivalent of filling every dam in the country . 
- The combined effects of hurricanes Ingrid and Manuel affected about two @-@ thirds of Mexico . The rains from Ingrid caused flooding and landslides across Mexico , causing many rivers to rise , and isolating towns . In Veracruz alone , the rains flooded 68 rivers , which damaged 121 roads and 31 bridges , including two destroyed bridges . About 14 @,@ 000 houses were damaged to some degree . Heavy rainfall forced 23 @,@ 000 people to evacuate their homes , 9 @,@ 000 of whom went to emergency shelters , some forced to leave by the Mexican army in high risk areas . <unk> who did not reside in shelters generally went to the houses of friends and family . Also in Veracruz , flooding killed about 20 @,@ 000 livestock . Along the coast of Tamaulipas , damage occurred from <unk> la Marina to La Pesca . The <unk> River in Tamaulipas rose above its banks , flooding two poor towns along its path and damaging adjacent roads . Also in the state , the storm damaged local <unk> fields . Two people in the state required rescue after their truck was swept away by a river . 
- Throughout Mexico , Ingrid killed 32 people , mostly due to flooding and mudslides . The two storms collectively killed at least 192 people and caused $ 75 billion pesos ( MXN , $ 5 @.@ 7 billion USD ) . Manuel was responsible for majority of the overall effects , although Ingrid still left an estimated $ 20 billion pesos ( MXN , $ 1 @.@ 5 billion USD ) in estimated economic losses , according to <unk> <unk> ; insured damages totaled $ 3 billion pesos ( MXN , $ 230 million USD ) . A total of six fatalities occurred in the states of Hidalgo and Puebla . Three of which were caused after a vehicle was swept off a road , while three other people died after their home was buried by a <unk> ; another death occurred in Hidalgo after a house collapsed on a woman in the town of <unk> de Guerrero . Twelve people died after a landslide smashed a bus in <unk> , Veracruz , and three people died in Tamaulipas . 
- The fringes of the storm extended into southern Texas , where winds gusted to tropical storm force , and rainfall was around 25 – 75 mm ( 0 @.@ 98 – 2 @.@ 95 in ) . <unk> and high tides affected the coastline , with tides reaching 0 @.@ 76 m ( 2 @.@ 5 ft ) above normal , causing beaches to close due to flooding . 
- 
- = = Aftermath and retirement = = 
- 
- The twin impacts of hurricanes Ingrid and Manuel prompted officials in Mexico to declare a state of emergency in 155 municipalities in Veracruz , Tamaulipas , Chiapas , Oaxaca , Guerrero , and Chihuahua . The nation 's Health Ministry sent medical crews across the country to affected areas in an effort to prevent the spread of disease ; the agency also suggested residents <unk> their water . Officials opened shelters in Tamaulipas for residents whose houses were damaged , housing about 2 @,@ 000 people in the weeks following the storm . The Mexican food bank sent about 800 tons of food to the hardest @-@ hit parts of the country in Guerrero , Veracruz , and <unk> . In addition , the Mexican Red Cross delivered 186 tons of food to Veracruz alone . Volunteers delivered about 600 blankets and various cleaning supplies to the town of <unk> . Following the storms , the National Civil Protection <unk> received the authority to prevent housing construction in areas at risk for mudslides and flooding ; the law had passed in 2012 , but initially lacked the authority to enforce it . Some residents in Tamaulipas complained at the slow pace of receiving aid . In response , the Gulf <unk> brought relief items to <unk> , with one columnist for El Universal suggesting that this was to gain favor with local residents . 
- Because of the severe damage caused by the storm in Mexico , the name Ingrid was later retired by the World Meteorological Organization , and will never again be used for a North Atlantic hurricane . It was replaced with <unk> for the 2019 Atlantic hurricane season . The name Manuel was also retired from the Pacific naming list and was replaced with Mario . 
- 
- 
- = Typhoon Imbudo = 
- 
- Typhoon Imbudo , known in the Philippines as Typhoon <unk> , was a powerful typhoon that struck the Philippines and southern China in July 2003 . The seventh named storm and fourth typhoon of the season , Imbudo formed on July 15 to the east of the Philippines . The storm moved generally west @-@ northward for much of its duration due to a ridge to the north . <unk> conditions allowed Imbudo to intensify , gradually at first before undergoing rapid deepening on July 19 . After reaching typhoon status , Imbudo strengthened further to peak 10 – minute sustained winds of 165 km / h ( 105 mph ) on July 20 . The typhoon made landfall on northern Luzon near peak intensity on July 22 , but quickly weakened over land . Once in the South China Sea , Imbudo re @-@ intensified slightly before making its final landfall in southern China near Yangjiang on July 24 , dissipating the next day . 
- In the Philippines , Imbudo was the strongest typhoon in five years , causing widespread flooding and power outages in the Cagayan Valley for weeks . Damage was heaviest in Isabela province near where the storm struck . Most of the banana crop was destroyed , and other crops sustained similar but lesser damage . Imbudo disrupted transportation across much of Luzon . Nationwide , the storm damaged or destroyed 62 @,@ 314 houses , causing <unk> billion ( PHP , $ 86 million USD ) in damage , mostly in the Cagayan Valley . There were also 64 deaths in the country . In Hong Kong , strong winds killed a man after knocking him off a platform . In China , damage was heaviest in Guangdong where the storm struck . Thousands of trees fell , and 595 @,@ 000 houses were wrecked . Hundreds of canceled flights stranded travelers across the region . In Guangxi , high rainfall increased water levels in 45 reservoirs to warning levels . In Guangxi and Guangdong , collectively 20 people were killed , and damage reached about ¥ 4 @.@ 45 billion ( CNY , $ <unk> million USD ) . 
- 
- = = Meteorological history = = 
- 
- The origins of Imbudo were from a disorganized area of convection near <unk> in the open western Pacific in mid @-@ July . With weak wind shear , the system slowly became better organized . On July 15 , the Japan Meteorological Agency ( JMA ) estimated that a tropical depression formed . The next day , the Joint Typhoon Warning Center ( JTWC ) issued a tropical cyclone formation alert ( <unk> ) , noting that outflow had increased due to an upper @-@ level low to the northeast . At 1800 UTC on July 16 , the JTWC initiated advisories on Tropical Depression <unk> about <unk> km ( 415 mi ) east of Yap . A subtropical ridge near Okinawa steered the nascent depression to the west @-@ northwest for much of its duration . With warm waters and favorable upper @-@ level conditions , the depression quickly organized , and the JMA upgraded it to Tropical Storm Imbudo on July 17 . 
- After becoming a tropical storm , Imbudo passed about 35 km ( 20 mi ) north of Yap . The JMA upgraded Imbudo to a severe tropical storm late on July 18 , around the same time that the JTWC upgraded it to a typhoon . An increase in outflow to the south and to the north from a tropical upper tropospheric trough ( <unk> ) caused a 36 ‑ hour period of rapid development beginning on July 19 . During that time , the JMA upgraded Imbudo to typhoon status , the Philippine <unk> , <unk> and Astronomical Services Administration ( PAGASA ) began issuing advisories as the storm approached the Philippines , and a 30 km ( 20 mi ) wide eye formed . At 1200 UTC on July 20 , the JMA estimated peak 10 minute sustained winds of 165 km / h ( 105 mph ) . At the same time , the JTWC estimated 1 minute sustained winds of 240 km / h ( 150 mph ) , making it a super typhoon . By late on July 20 , the typhoon had developed concentric <unk> , and the <unk> was moving away , thus diminishing outflow . Imbudo maintained peak winds for about 12 hours , before the innermost eye contracted to a diameter of 11 km ( 7 mi ) in the midst of an eyewall replacement cycle . At 0300 UTC on July 22 , Imbudo struck northern Luzon , with 1 minute winds estimated at 205 km / h ( 125 mph ) by the JTWC . 
- Rapidly weakening over land , Imbudo emerged into the South China Sea about six hours after landfall . The JTWC estimated winds had decreased to 150 km / h ( 90 mph ) , although the agency estimated Imbudo quickly re @-@ intensified to a secondary peak of 165 km / h ( 105 mph ) in 1 minute winds . Dry air prevented convection from <unk> significantly , and the eye had become large over open waters . At 0300 UTC on July 24 , Imbudo made its final landfall west of <unk> , near Yangjiang , Guangdong . It was the strongest to hit the province since Typhoon <unk> in 1996 , striking China with 10 minute winds of 140 km / h ( 85 mph ) , as estimated by the JMA . The storm rapidly weakened over land while moving over southern China . Late on July 25 , Imbudo dissipated near the border of China and Vietnam . 
- 
- = = Preparations = = 
- 
- Before Imbudo struck , officials evacuated over 14 @,@ 000 people to at least 60 shelters . Government offices were closed along the typhoon 's path , and schools were closed in the capital , Manila . PAGASA issued a number 4 warning signal , the highest level , for three northern provinces , indicating the imminent threat of a powerful storm . Despite the warning , a post @-@ storm survey in Isabela province indicated that 34 % were unaware of the storm 's arrival , while others believed the typhoon would not be as strong . In Manila , four flights were canceled at <unk> <unk> International Airport , and the Manila Light Rail Transit System was shut down for two hours . Travel by ship and bus were halted in some areas . On July 22 before the storm struck , the Philippine military was put on red alert , increasing security at the presidential palace . The military stated it was due to the typhoon , although newspapers suggested it was to prevent a coup attempt ; a failed coup ultimately did occur on July 27 in what would become known as the Oakwood mutiny . 
- The Hong Kong Observatory issued a number 8 warning signal , indicating the potential for gale force winds within the territory . At Hong Kong International Airport , at least 100 flights were canceled or delayed . Most ferry and some bus lines were temporarily suspended . The threat of the storm caused 16 flights to be canceled and another 54 delayed at <unk> <unk> International Airport . On <unk> island , 32 canceled flights stranded about 1 @,@ 500 travelers . The threat of the storm forced British Prime Minister Tony Blair to shorten a trip to the territory . The Hong Kong Stock Exchange opened 30 minutes late as a result of the typhoon . On the mainland at Yangjiang , more than 30 @,@ 000 people evacuated ahead of the storm . 
- 
- = = Impact = = 
- 
- 
- = = = Philippines = = = 
- 
- Typhoon Imbudo was the strongest typhoon to strike Luzon since Typhoon <unk> five years prior , and was the fifth storm in eight weeks to affect the country . The typhoon left widespread areas flooded for several days . Cagayan Valley was largely isolated after a bridge was damaged in <unk> <unk> . Damage was heavy in the region , totaling <unk> billion ( PHP , $ 40 million USD ) , and over 80 @,@ 000 people were displaced by the storm . Damage was heaviest in Isabela , where damage was estimated at <unk> billion ( PHP , $ 35 million USD ) , mostly to crops and killed livestock . There , winds and rain knocked down trees and caused a province @-@ wide power <unk> . Most trees less than three years old fell during the high winds . In Isabela province , the banana crop was almost entirely destroyed , and most of the corn and rice crops were heavily damaged . The high damage caused the gross regional product , or overall economy of the Cagayan Valley , to decrease by 0 @.@ 3 % than what would have happened without the typhoon . 
- Power outages affected Metro Manila , and several billboards were damaged in the city , although there was minimal flooding in the capital . <unk> conditions caused the Manila Light Rail Transit System Line 1 to close for two hours , stranding hundreds . High winds knocked over a 12 m ( 40 ft ) tree that killed five on <unk> Island . The rains brought the <unk> Dam on Luzon to capacity . <unk> blocked a national highway in <unk> <unk> with debris and fallen trees , which were quickly cleared within a few days . On <unk> , flash flooding from the outer periphery of the storm affected 18 towns , killing 11 people . In <unk> in the southern Philippines , flooding washed away 50 houses , forcing over 2 @,@ 000 people to evacuate . Damage extended as far south as the Western <unk> . Across the Philippines , Imbudo damaged 62 @,@ 314 houses , of which about 20 % were destroyed . Housing damage was estimated at <unk> billion ( PHP , $ 22 million USD ) , most of which in the Cagayan Valley . Overall damage in the country was estimated at about <unk> billion ( PHP , $ 86 million USD ) . Of the total , about <unk> billion ( PHP , $ 35 million USD ) was in agriculture damage . Imbudo killed 64 people in the Philippines , mostly in Cagayan Valley , and injured another 154 . 
- 
- = = = Elsewhere = = = 
- 
- Early in its developmental stages , Imbudo affected portions of <unk> , particularly <unk> . There , a weather station measured a peak wind gust of 85 km / h ( 53 mph ) ; winds of similar intensity were reported in Yap . Rainfall from Imbudo in <unk> peaked at 226 mm ( 8 @.@ 89 in ) over a six @-@ hour period on Yap . The rainfall from the passing tropical cyclone caused $ 75 @,@ 000 in property damage and $ 25 @,@ 000 in agricultural and crop @-@ related damage . 
- Before Imbudo struck mainland China , it passed south of Taiwan , dropping heavy rainfall reaching 137 mm ( 5 @.@ 4 in ) in <unk> County . The outer rainbands reached as far as Okinawa , where 2 mm ( 0 @.@ <unk> in ) was reported . 
- Peak rainfall in Hong Kong was 53 @.@ 5 mm ( 2 @.@ 11 in ) at <unk> Chung , only a day after Tropical Storm <unk> dropped rainfall in the territory , and the highest storm surge was 1 @.@ 13 m ( 3 @.@ 7 ft ) at <unk> <unk> <unk> . As the storm passed to the south , Imbudo produced winds of 101 km / h ( 63 mph ) on <unk> <unk> , the highest in the Hong Kong territory , although a gust of 164 km / h ( 102 mph ) was observed at Tai Mo Shan . High winds killed a man after knocking him off a platform . The winds knocked down 83 trees , injuring 11 people . Rough waves injured 34 people traveling by boat near <unk> Island . In the territory , ten boats were damaged or sank , and one fishing pier was damaged . 
- At its final landfall in Guangdong , Imbudo produced strong winds , with a peak gust of 200 km / h ( 124 mph ) measured at <unk> Island . At Yangjiang , gusts reached 159 km / h ( 99 mph ) , causing eleven boats to sink . There , over 10 @,@ 000 trees fell due to the strong winds , more than half in the city , and 7 @,@ 649 homes were damaged or destroyed . In <unk> , the storm damaged power lines and water pumps , leaving residents without access to water . Imbudo spawned tornadoes in <unk> and <unk> , damaging dozens of houses and killing 6 @,@ 000 chickens . Throughout Guangdong , Imbudo destroyed 595 @,@ 000 houses and caused ¥ 1 @.@ 9 billion ( CNY , $ 230 million USD ) . There were at least eight deaths in the province . 
- In <unk> island to the south of the storm track , rainfall reached 230 mm ( 9 @.@ 1 in ) in <unk> 'an County . Imbudo caused flooding in the capital <unk> , and several houses were wrecked . The typhoon affected most of the population , causing an estimated ¥ 55 @.@ 35 million ( CNY , $ 6 @.@ 7 million USD ) in damage . Heavy rainfall spread across southern China , peaking at 343 mm ( 13 @.@ 5 in ) at <unk> County in Guangxi province , which increased water levels in 45 reservoirs to warning levels . The <unk> River in <unk> County rose to 50 @.@ 21 m ( 164 @.@ 7 ft ) , or 0 @.@ 71 m ( 2 @.@ 3 ft ) above flood stage . In Guangxi , 12 people died from storm damage or drowning , and at least 130 livestock were killed . Imbudo destroyed 4 @,@ 950 houses , 3 @,@ 170 of which in the city of <unk> , causing ¥ 499 @.@ 6 million ( CNY , $ 60 @.@ 3 million USD ) in damage in the province . Across southern China , the typhoon damaged over 10 million hectares ( 25 million acres ) of crop fields . 
- 
- = = Aftermath = = 
- 
- Immediately after the storm , the Philippine Air Force were mobilized to help deliver supplies and aid in search and rescue missions . On July 24 , President Gloria <unk> @-@ <unk> declared that the National Disaster <unk> Council would provide assistance to citizens affected by the typhoon . The president requested <unk> million ( PHP , $ 2 @.@ 5 million USD ) for farmers to buy new seeds , and <unk> million ( PHP , $ 650 @,@ 000 USD ) to rebuild the hard @-@ hit Cagayan Valley , where <unk> visited days after the storm struck . The government ultimately spent about <unk> million ( PHP , $ 435 @,@ 000 USD ) in emergency aid for relocating storm victims , search and rescue operations , and assistance . In <unk> , officials prepared 800 bags of rice and various other food supplies due to the storm . Power and water outages persisted across Luzon for up to three weeks , causing many factories otherwise undamaged to close . In Isabela province , 25 Tzu Chi volunteers provided supplies to 2 @,@ <unk> houses . The Philippine government provided rice , <unk> , and coffee to many affected houses . <unk> houses were supposed to receive 1 @,@ 000 pesos , although few received the monetary aid . In the town of San Mariano in Isabela province , most farmers incurred more debt and continued their same general farming practice , despite sustaining heavy losses from the storm . 
- In 2004 , the World Meteorological Organization retired the name Imbudo and replaced it with <unk> . The PAGASA name <unk> was replaced with Hanna for the 2007 season . 
- 
- 
- = Hurricane Felicia ( 2009 ) = 
- 
- Hurricane Felicia was the third strongest tropical cyclone of the 2009 Pacific hurricane season , as well as the strongest storm to exist in the eastern Pacific at the time since Hurricane Daniel in 2006 . Forming as a tropical depression on August 3 , the storm supported strong thunderstorm activity and quickly organized . It became a tropical storm over the following day , and subsequently underwent rapid deepening to attain hurricane status . Later that afternoon , Felicia featured a well @-@ defined eye as its winds sharply rose to major hurricane @-@ force on the Saffir – Simpson scale . Further strengthening ensued , and Felicia peaked in intensity as a Category 4 hurricane with sustained winds of 145 mph ( 230 km / h ) and a barometric pressure of <unk> mbar ( hPa ; 27 @.@ 61 inHg ) . After reaching this strength , unfavorable conditions , such as wind shear , began to impact the storm while it took on a northwestward path . <unk> , Felicia slowly weakened for several days ; by August 8 it had been downgraded to a Category 1 hurricane , once again becoming a tropical storm the next day . It <unk> westward towards Hawaii on August 10 , all the while decreasing in organization . On August 11 , Felicia weakened to tropical depression status , and soon degenerated into remnant low just prior to passing over the islands . 
- After weakening into a remnant low , Felicia continued to approach the Hawaiian Islands and on August 12 , the system produced copious amounts of rainfall across several islands . The highest total was recorded on Oahu at 14 @.@ 63 in ( 372 mm ) , causing isolated mudslides and flooding . In Maui , the heavy rains helped to alleviate drought conditions and water shortages , significantly increasing the total water across the island 's reservoirs . In addition , river flooding resulted in the closure of one school and large swells produced by the storm resulted in several lifeguard rescues at island beaches . In all , only minor impacts were caused by the remnants of Felicia . 
- 
- = = Meteorological history = = 
- 
- Hurricane Felicia originated from a tropical wave that moved off the west coast of Africa into the Atlantic Ocean on July 23 , 2009 . A weak system , the wave was barely identifiable as it tracked westward . By July 26 , the wave entered the Caribbean Sea before crossing Central America and entering the eastern Pacific basin on July 29 . The system remained ill @-@ defined until August 1 , at which time convection began to increase and the wave showed signs of organization . The storm gradually became better organized as it tracked generally towards the west . By August 3 , the system became increasingly organized and around 11 : 00 am PDT ( 1800 UTC ) , the National Hurricane Center ( NHC ) designated the system as Tropical Depression Eight @-@ E. <unk> banding features and <unk> outflow were being enhanced by the nearby Tropical Storm Enrique . The main steering component of the depression was an upper @-@ level low located to the north , causing the depression to track generally west before turning northwest after the low weakened . 
- By the early morning hours of August 4 , the NHC upgraded Tropical Depression Eight @-@ E to Tropical Storm Felicia , the seventh named storm of the season . Located within an area of low wind shear and high sea surface temperatures , averaging between 28 and 29 ° C ( 82 and 84 ° F ) , the storm quickly developed , with deep convection <unk> around the center of circulation . These conditions were anticipated to persist for at least three days ; however , there was an increased amount of uncertainty due to possible interaction with Tropical Storm Enrique . Several hours later , the storm began to undergo rapid intensification , following the formation of an eye . Around 2 : 00 pm PDT ( 2100 UTC ) , Felicia intensified into a hurricane . 
- Late on August 4 , the intensity of Felicia led to it taking a more northward turn in response to a <unk> to upper @-@ level trough off the coast of the Western United States . Early the next morning , the storm continued to intensify and attained Category 3 status with winds of 115 mph ( 185 km / h ) . <unk> a well @-@ defined eye , Felicia neared Category 4 status and hours later , the storm attained winds of 140 mph ( 220 km / h ) and a pressure of <unk> mbar ( hPa ; 27 @.@ 67 inHg ) during the evening hours , making it the strongest Pacific storm east of the International Date Line since Hurricane <unk> in 2006 and the strongest in the eastern Pacific basin since Hurricane Daniel of 2006 . Around 5 : 00 pm PDT ( 0000 UTC August 6 ) Felicia reached its peak intensity with winds of 145 mph ( 230 km / h ) and a minimum central pressure of <unk> mbar ( hPa ; 27 @.@ 61 inHg ) . 
- After slightly weakening throughout the day on August 6 , Felicia leveled out with winds of 135 mph ( 215 km / h ) and a 23 mi ( 37 km ) wide eye as the storm developed characteristics of an annular hurricane , which would allow Felicia to maintain a high intensity over marginally warm waters . Early the next day , the structure of the hurricane quickly deteriorated as convection became asymmetric and cloud tops warmed significantly . This marked a quick drop in intensity of the storm to a minimal Category 3 hurricane . Several hours later , the mid @-@ level circulation began to separate from the low @-@ level circulation and the overall size of the storm decreased . By this time , the storm began to take a long @-@ anticipated westward turn towards Hawaii . After briefly re @-@ intensifying on August 7 , Felicia weakened to a Category 1 hurricane early on August 8 . Around 11 : 00 am HST ( 2100 UTC ) , the Central Pacific Hurricane Center ( CPHC ) took over responsibility of issuing advisories as Felicia crossed longitude 140 ° W. 
- By August 9 , increasing wind shear further weakened the storm , with Felicia being downgraded to a tropical storm early that day . The storm rapidly weakened throughout the day as convection gradually dissipated around the center due to the shear . By the late morning hours , little convective activity remained around the low pressure center of Felicia . A weak cyclone , the storm continued to track towards Hawaii with the only deep convection associated with it being displaced to the northeast of the center . The system slowly weakened before being downgraded to a tropical depression on August 11 as no areas of tropical storm @-@ force winds were found by Hurricane Hunters . Several hours after being downgraded , the CPHC issued its final advisory on Felicia as it degenerated into a remnant low near the Hawaiian Islands . The system dissipated shortly thereafter . 
- 
- = = Preparations = = 
- 
- By August 5 , forecasters were discussing the possibility of the storm impacting Hawaii . Residents were advised to ensure that their disaster kits were fully stocked and ready . Governor Linda <unk> made a speech to the state of Hawaii the same day . She emphasized that the storm was not an imminent threat but that residents should be ready and should know where the nearest emergency shelter is . Since forecasters expected the storm to weaken before it reached the islands , only minor effects — mainly rainfall — were expected . Hawaii County mayor Billy <unk> was also briefed on the approaching storm and he advised the county to be prepared . Stores reported an influx of <unk> and posted anniversary sales . Blue tarps for roofs were being sold at $ 1 apiece . The American Red Cross also reported that sales of the " water <unk> " , a water container that can be attached to a bathtub and hold roughly 100 gallons of water , increased significantly . On August 6 , the Red Cross stated that it was deploying a disaster recovery team , led by the director of the agency , to the islands of Hawaii . 
- On August 7 , five Hurricane Hunter planes were dispatched to <unk> Air Force Base to fly missions into the storm . Later that day , the Central Pacific Hurricane Center issued tropical storm watches for the island of Hawaii , Maui , <unk> , Lanai , and Molokai . On August 9 , the watch was expanded to include Oahu . The watches for the Big Island were later cancelled as the forecast track appeared to drift further north toward Maui County and Oahu . The Red Cross opened shelters throughout the islands on August 10 . Twelve were on the Big Island , seven were on Maui , two on Molokai and one on Lanai . The Honolulu International Airport ensured that eight generators were ready for use in case Felicia caused a power <unk> at the airport . All tropical storm watches were cancelled at 11 a.m. August 11 as Felicia dissipated to a remnant low . 
- 
- = = Impact = = 
- 
- 
- = = = Oahu = = = 
- 
- In Oahu , areas on the windward side of the island received more than 1 in ( 25 mm ) of rain on August 12 from the remnants of Felicia , causing many roads to become slick . A portion of Kamehameha Highway was shut down around 11 : 00 pm HST when the <unk> Stream overflowed its banks . Flooding near a bridge reached a depth of 4 ft ( 1 @.@ 2 m ) , stranding some residents in their homes . The highway remained closed until around 4 : 00 am HST on August 14 . The rain was also considered helpful in that it helped alleviate drought conditions that had been present for nearly two months . The heaviest rainfall was recorded on Oahu at 14 @.@ 63 in ( 372 mm ) in the Forest National Wildlife Refuge . During a 12 @-@ hour span , a total of 6 @.@ 34 in ( 161 mm ) fell in <unk> . Some areas recorded rainfall rates up to 1 in / h ( 25 mm / h ) , triggering isolated mudslides . At Sandy Beach , there were two lifeguard rescues and three others were on <unk> as waves up to 18 ft ( 5 @.@ 5 m ) affected the islands . There were also five assists at <unk> and one at <unk> Beach . <unk> issued a total of 1 @,@ 410 verbal warnings about the rough seas to swimmers and <unk> during the event . However , winds on the island reached only 15 mph ( 25 km / h ) and gusts peaked at 20 mph ( 30 km / h ) . 
- 
- = = = Other islands = = = 
- 
- On Kauai , the <unk> River rose above its normal level , leading to the closure of the <unk> School . Several tree limbs and small trees were blown down across the island . Rainfall on Kauai peaked at 5 @.@ 33 in ( 135 mm ) at Mount <unk> <unk> <unk> and on Maui , up to 4 @.@ 05 in ( 103 mm ) fell in <unk> Gap . On the <unk> side of the mountains , rainfall peaked at 1 @.@ 3 in ( 33 mm ) in <unk> , an area that rarely records rainfall in August . Throughout the island , the total amount of water in reservoirs increased to 104 @.@ 5 million gallons ( 395 @.@ 5 million liters ) from 77 @.@ 8 million gallons ( <unk> @.@ 5 million liters ) prior to Felicia . Rainfall in some areas was heavy enough at times to reduce visibility to several feet . Streets in these areas were covered with muddy water . <unk> heavy rainfall fell on the Big Island , peaking at 2 @.@ 76 in ( 70 mm ) in <unk> . In <unk> Beach , there was one lifeguard rescue that resulted in the swimmer being sent to a local hospital . Three other people were swept away at the mouth of the <unk> River , all of whom were quickly rescued . In Honolulu , runoff from the storm resulted in large amounts of trash and debris along the local beaches . Private contractors were dispatched to the affected <unk> to trap and remove the trash . Officials were forced to close the beaches along <unk> Bay after swells from Felicia pushed an estimated 2 @,@ 000 Portuguese Man o ' War <unk> into the region . The beaches were later re @-@ opened on August 14 . 
- 
- 
- = Santa @-@ Fe ( Bob Dylan song ) = 
- 
- " Santa @-@ Fe " ( sometimes spelled " Santa Fe " or " Santa <unk> " ) is a song that was recorded by Bob Dylan and the Band in the summer or fall of 1967 in the Woodstock area of New York State . It was recorded during the sessions that would in 1975 be released on The Basement Tapes but was not included on that album . These sessions took place in three phases throughout the year , at a trio of houses , and " Santa @-@ Fe " was likely put on tape in the second of these , at a home of some of the Band members , known as Big Pink . The composition , which has been characterized as a " nonsense " song , was copyrighted in 1973 with lyrics that differ noticeably from those on the recording itself . 
- In the decades following this collaboration , the over 100 tracks recorded at these sessions were at different stages obtained by collectors and released on bootlegs . The first batch of these leaked to the public beginning in the late 1960s ; the second in 1986 ; the third , which included " Santa @-@ Fe " , in the early 1990s ; and a fourth batch of Basement Tape tracks became public in 2014 . The song was released officially on the Columbia album The Bootleg Series Volumes 1 – 3 ( Rare & <unk> ) 1961 – 1991 . It has been subject to mixed opinions by critics and biographers , with some praising it for its expressiveness , and others regarding it <unk> , while criticizing its inclusion on The Bootleg Series at the expense of more worthy candidates . 
- 
- = = Background = = 
- 
- 
- = = = Recording near Woodstock = = = 
- 
- In 1965 and 1966 , Dylan was touring with the Hawks — Rick Danko , Garth Hudson , Richard Manuel , Robbie Robertson and Levon Helm , although Helm quit the group in late November or early December 1965 . In July 1966 , Dylan suffered a motorcycle accident and spent several months <unk> at his house in <unk> , near Woodstock , New York . By spring 1967 , all of the members of the Hawks , except Helm , had joined Dylan in the Woodstock area , with Danko , Manuel and Hudson living in nearby West <unk> in a house nicknamed Big Pink . Dylan and the four Hawks began recording informal music sessions , first at Dylan 's house in what was known as " the Red Room " , followed by the basement of Big Pink . Earlier on they recorded mostly covers and traditional music , but later moved onto original material written largely by Dylan . In total , over 100 songs and alternate takes were put on tape . Helm returned to the group in October 1967 and performed on some final Woodstock @-@ area collaborations between Dylan and the Hawks , these ones at a different house that some group members had moved to . In the fall of that year , the Hawks , who soon renamed themselves the Band , continued writing and rehearsing songs for their debut album , Music From Big Pink . 
- Dylan biographer Sid Griffin has noted that , because no written records were kept of these 1967 recording sessions , " the world will have to live with the fact that it will never know exactly which Basement Tapes tune was recorded when and where " . Nonetheless , using clues such as the sound quality of different batches of songs , and where they appear on the original reels of tapes , attempts have been made to place the songs into a rough chronology and guess the locations at which they were likely recorded . Biographer Clinton Heylin places " Santa @-@ Fe " in the summer of 1967 at Big Pink . The liner notes of The Bootleg Series Volumes 1 – 3 date it in the fall of that year . Griffin lists it among the probable Big Pink recordings , and in a group of songs from around July , but <unk> it is also " unlikely [ but ] possible " it came from the Red Room . 
- 
- = = = <unk> of Basement Tape songs = = = 
- 
- In late 1967 , an acetate of fourteen of Dylan 's compositions was made , from which demos circulated among music groups who might be interested in recording some of the songs . Artists including Peter , Paul and Mary , Manfred Mann and the Byrds eventually did . Dylan 's demo tapes were soon heard by music journalists , including Rolling Stone 's <unk> <unk> , who wrote a front @-@ page story in that magazine entitled " Dylan 's Basement Tape Should Be Released " . This made the general public hungry to hear the music , and in July 1969 a bootleg called Great White Wonder , which included some of the Big Pink songs , came out . Other Basement Tape bootlegs followed . 
- In 1975 , the Columbia album The Basement Tapes was compiled , mainly by Robertson and engineer Rob <unk> . Robertson and <unk> put thirty @-@ five of the songs onto composite reels of tape , and Heylin believes these represented a short list of candidates for the album . " Santa @-@ Fe " was included on these composite reels , but was not ultimately chosen for the album . The Basement Tapes included sixteen Dylan songs recorded at Big Pink in 1967 , as well as eight Band demos from various times and locations between 1967 and 1975 . One Dylan song on the album , " <unk> ' to Acapulco " , had not appeared on his 1967 fourteen @-@ song acetate or on bootlegs , and this alerted the world to the possibility that there might be more Basement Tape songs in existence . In 1986 , at least twenty @-@ five previously unknown 1967 songs by Dylan and the Band passed into collectors ' hands by way of a former <unk> of the Band 's . In the early 1990s , a third batch of songs , these ones from Garth Hudson 's archives , came to light around the time Columbia was preparing The Bootleg Series Volumes 1 – 3 ; " Santa @-@ Fe " comes from this group . In his liner notes for The Bootleg Series Volumes 1 – 3 , John <unk> commented on these second and third stages in which groups of Big Pink songs had come to light : " Despite the ... emergence [ in 1986 ] in collectors ' circles of a further couple of hours of Basement Tapes , it seems as though there 's a good deal left unheard . ' Santa @-@ Fe ' is just one example of a batch of previously <unk> Basement tracks " . By 1992 , the " Santa @-@ Fe " batch of songs had been obtained by <unk> , and almost all known Dylan Basement Tape songs were assembled onto the 5 @-@ CD bootleg The Genuine Basement Tapes . " <unk> @-@ Fe " was also included on the 2014 compilations The Basement Tapes Raw and The Bootleg Series Vol . 11 : The Basement Tapes Complete ; the latter album officially released a fourth batch of previously <unk> Basement Tape songs . 
- 
- = = Personnel = = 
- 
- The liner notes for The Bootleg Series Volumes 1 – 3 list the song 's personnel as Dylan , guitar and vocals ; Robertson , guitar ; Hudson , organ ; Manuel , piano ; Danko , bass ; and Helm , drums . However , Griffin argues that Helm did not arrive in Woodstock until after the song is believed to have been recorded . Furthermore , the drumming sounds to Griffin more like Manuel 's style . Heylin and biographer Greil Marcus similarly do not include " Santa @-@ Fe " among the songs they believe were recorded after Helm 's arrival . Griffin also argues that no organ is audible on the track and proposes the following musician line @-@ up as being more likely : Dylan , acoustic guitar and vocals ; Robertson , electric guitar ; Hudson , piano ; Danko , bass ; Manuel , drums . 
- 
- = = <unk> and lyrics = = 
- 
- Different Basement Tape songs were copyrighted in stages between 1967 and 1975 , with " Santa @-@ Fe " being registered in September 1973 ; still other songs from the sessions were not copyrighted until the 1980s . <unk> Tim Dunn indicates that in the original 1973 copyright the song was registered as " Santa @-@ Fe " with a hyphen , but that some later documents relating to the renewal of the copyright <unk> the hyphen . The liner notes of The Bootleg Series Volumes 1 – 3 print the title as " Santa @-@ Fe " , while Dylan 's official website , <unk> , spells it without the hyphen but with an accent on the e : " Santa <unk> " . 
- Heylin has noted that , as is the case with a number of other of Basement Tape tracks , Dylan 's copyrighted , published " Santa @-@ Fe " lyrics differ from what can be heard on the song . Heylin speculates that the " dramatic reworking " in the later version arose from Dylan 's " 1973 <unk> in Malibu " , where Dylan had moved to , and that new lines like " build a <unk> dome and sail away " and " My shrimp boat 's in the bay " sound like the work of " someone sitting on the dock of the bay , not up on <unk> Mountain [ in Woodstock ] " . Heylin also notes that the website maintained by <unk> <unk> , Words <unk> My Head , contains a transcription of the song as Dylan performed it . The 1973 copyrighted lyrics are printed on <unk> 
- In his notes for The Bootleg Series Volumes 1 – 3 , <unk> describes the song as " a typical combination of nonsense and fun , just for the hell of it , really ... " ; author Oliver <unk> likewise describes it as a " nonsense " song . Heylin writes that the lyrics " revolve around ' dear , dear , dear , dear , Santa Fe ' — intended to be both a woman 's name and the town in New Mexico . After five verses of rolling said words around , he moves on . " 
- 
- = = <unk> = = 
- 
- Opinions about the song have been mixed . AllMusic critic Thomas Ward calls it " one of the great good @-@ time songs in Dylan 's canon " . Ward comments that " Dylan sings it as if he is having the time of his life " , adding that " rarely has he sung with such expressiveness " . Anthony <unk> , author of The Bob Dylan Albums , similarly praises the song 's " breadth of feeling " and " unparalleled expressiveness " , noting that " it appears Dylan simply improvised the song on the spot , and the passion within him allows the song to flow forth naturally " . Biographer John <unk> has described the song as " delightful " , while a New York Times review rates it one of the highlights of Volume 2 of The Bootleg Series Volumes 1 – 3 , commenting that it is one of only a couple of mid @-@ 60s songs on the compilation that " live up to their vintage " . Griffin describes it as " catchy but slight " and " a slight if charming little ditty " , but criticizes the decision to include it on The Bootleg Series Volumes 1 – 3 rather than the " masterpiece " composition " Sign on the Cross " . Heylin <unk> , characterizing " Santa @-@ Fe " in 1995 as a " pleasant enough <unk> " but suggesting that " Sign on the Cross " or another 1967 composition , " I 'm Not There " , would have been much better choices ( " I 'm Not There " was eventually released in 2007 on the I 'm Not There soundtrack , and both it and " Sign on the Cross " were included on The Basement Tapes Raw and The Basement Tapes Complete in 2014 ) . By 2009 , Heylin 's opinion had changed little and he writes that " of all the ' missing ' basement @-@ tape originals that appear on that three @-@ CD set , ' Santa Fe ' hardly represented an A @-@ list candidate . Just another discarded ditty , it relies on the usual wordplay and <unk> diction to obscure any <unk> to a deeper meaning " . Marcus dismisses it as no more than " a riff " , while a review in <unk> magazine calls it " the most lightweight tune on all three CDs , with <unk> lyrics " . Author Peter James , referring to Dylan and the Band 's Woodstock output , writes that " many great songs were written and recorded in [ Big Pink 's ] basement in 1967 , unfortunately ' <unk> @-@ Fe ' is not one of them . " He goes on to describe the song 's inclusion on The Bootleg Series Volumes 1 – 3 as " little more than a joke " . 
- 
- = = Cover versions = = 
- 
- The song has been covered by Howard <unk> on his album <unk> Bob Dylan & The Band 's The Basement Tapes Live at Joe 's <unk> . <unk> played more than sixty songs from Dylan and the Band 's Basement Tape sessions over three nights , of which selected tracks were included on the CD and an accompanying DVD . " Santa @-@ Fe " has also been covered by Steve Gibbons . On November 7 , 2007 at the <unk> Theatre in New York City , J <unk> and the Million Dollar <unk> performed the song at a special concert featuring numerous music artists celebrating the release of Todd Haynes 's film I 'm Not There . Thomas Ward notes that Dylan himself has never played the song live . 
- 
- 
- = The Secret ( The Office ) = 
- 
- " The Secret " is the thirteenth episode of the second season of the American comedy television series The Office , and the show 's nineteenth episode overall . It was written by Lee Eisenberg and Gene Stupnitsky and directed by <unk> Gordon . The episode first aired on January 19 , 2006 on NBC . The episode guest stars Tom W. Chick as Gil . 
- The series depicts the everyday lives of office employees in the Scranton , Pennsylvania branch of the fictional Dunder Mifflin Paper Company . In this episode , Jim Halpert ( John Krasinski ) is forced into spending time with Michael Scott ( Steve Carell ) so that Michael will not reveal Jim 's feelings for Pam Beesly ( Jenna Fischer ) . Meanwhile , Oscar Martinez ( Oscar Nunez ) takes a sick day and Dwight Schrute ( Rainn Wilson ) investigates whether he is actually sick . 
- " The Secret " was written in roughly 26 hours and was the fastest episode written for the series at the time . The title for the episode is purposely vague and refers to Jim 's hidden feelings for Pam , Dwight and Angela 's relationship , and Oscar 's homosexuality . The shots at the Hooters restaurant were filmed relatively early in the day , and a majority of the scenes were improvised by Carell . " The Secret " received largely positive reviews from television critics and was watched by 8 @.@ 7 million viewers , ranking as the forty @-@ fourth most @-@ watched television episode of the week . 
- 
- = = Plot = = 
- 
- Out of fear that Michael Scott ( Steve Carell ) will tell the others he has feelings for Pam Beesly ( Jenna Fischer ) , Jim Halpert ( John Krasinski ) allows Michael to believe that they are best friends , which leads to an awkward lunch at Hooters paid for with a corporate credit card . Michael ultimately reveals Jim 's secret to everyone , forcing Jim to confess his crush to Pam himself , although he tells her that he got over it three years ago . However , Michael later tells her that he learned of the crush during the " <unk> cruise " which occurred recently , leading her to suspect that Jim is still infatuated . 
- Meanwhile , Dwight Schrute ( Rainn Wilson ) " investigates " Oscar Martinez 's ( Oscar Nunez ) claimed sick day from work , learning that the co @-@ worker is actually taking time off to ice @-@ skate . Dwight blackmails Oscar , threatening to reveal his unauthorized leave @-@ taking . He then cuts a deal for Oscar to owe him a favor , and watches a movie with Oscar and Oscar 's " roommate " Gil ( Tom W. Chick ) while completely failing to recognize evidence of Oscar 's homosexuality . 
- 
- = = Production = = 
- 
- 
- = = = Writing = = = 
- 
- " The Secret " was written by Lee Eisenberg and Gene Stupnitsky . The episode was written in roughly 26 hours and was the fastest episode written for the series , at the time . The idea to make the entry the " spring cleaning episode " was " throw [ n ] in at the last minute " because the writers were " desperate " . The subplot was inspired by a friend of Eisenberg 's , who suggested the story after he was forced to undergo spring cleaning at his office . 
- The title for the episode is purposely vague . During the commentary , the cast revealed that there are actually three " secrets " in the episode : Jim 's hidden feelings for Pam , Dwight and Angela 's relationship , and Oscar 's homosexuality . <unk> later joked that there are " actually nine secrets in the episode " . The cold opening – wherein Michael tries to tell a joke where the punch line is " What 's up dog ? " – was inspired by the same prank being pulled on Stupnitsky . The original cold opening for the episode featured Michael playing golf in his office , but it was cut . 
- Jenna Fischer felt that Krasinski 's performance in the episode was " really great " and called " The Secret " her " favorite John Krasinski episode " . Krasinski said that Creed <unk> 's line , " Which one is Pam ? " was his favorite moment " in the entire show , ever " . Fischer , in turn , said that her favorite moment was when Jim confesses that he does not have a crush on her , due to the emotion involved . Fischer later admitted that , after the shot was filmed , she cried because it " broke [ her ] heart " . 
- 
- = = = Filming = = = 
- 
- " The Secret " was directed by <unk> Gordon , who would go on to direct the season two episode " Boys and Girls " . Tom W. Chick portrayed Gil . The cast were particularly impressed with his acting ; Fischer called him a " great casting " choice . Lindsey <unk> , who plays one of the Hooters <unk> , was an <unk> acquaintance of Martin 's . 
- The shots at the Hooters restaurant were filmed relatively early in the day , and a majority of the scenes were improvised by Carell . In fact , Carell decided to play the penis game – in which two or more people shout the word " penis " at varying levels in an attempt to out do each other – which Krasinski called " the <unk> thing I have experienced . " The scene where Pam and Kelly discuss wedding plans was particularly difficult to shoot all in one scene , due to the amount of activity going on . John Krasinski later noted that he was impressed that the director and <unk> were able to successfully get the shot . The sequence featuring Michael and Jim talking in the break room was made of two composition shots , because Krasinski was laughing " the entire " time . Michael buying Stanley a peach ice tea and telling him that he will " hate it " was entirely improvised . 
- 
- = = = <unk> scenes = = = 
- 
- The Season Two DVD contains a number of deleted scenes from this episode . Notable cut scenes include the cut cold opening of Michael playing with his new putting toy , Dwight <unk> on his thoughts on dust <unk> , Michael surveying his " worker bees " , Ryan Howard ( <unk> Novak ) finding an unfinished People Magazine <unk> puzzle from 1999 in Michael 's office , Michael describing his college <unk> experience , and Michael buying Jim a Hooters T @-@ shirt . 
- 
- = = Cultural references = = 
- 
- Michael attributes the 1981 song " Our <unk> Are <unk> " to The <unk> , when it was really sung by The Go @-@ Go 's . Michael and Jim go to Hooters , a company whose waiting staff are primarily young , attractive <unk> usually referred to simply as " <unk> Girls " whose revealing outfits and sex appeal is played up and is a primary component of the company 's image . At the restaurant , Michael makes several breast jokes . Near the end of the episode , Michael makes reference to a <unk> <unk> movie called More <unk> of a Call Girl . 
- 
- = = Reception = = 
- 
- " The Secret " originally aired on NBC on the January 19 , 2006 . The episode was watched by 8 @.@ 7 million viewers . This marked a slight decrease in viewers from a lead @-@ in episode of " My Name is Earl " , but more than a follow @-@ up episode of " ER " . " The Secret " ranked as the forty @-@ fourth most @-@ watched episode for the week ending January 22 . 
- M. Giant of Television Without Pity awarded the episode an " A " . Brendan <unk> of DVD Verdict gave the episode a moderately positive review and awarded the entry a " B " . He wrote that while it was " " a solid episode " it " lacks any of the belly laughs the show frequently <unk> . " Michael <unk> of <unk> called the installment " a terrific episode " and wrote that the " Michael Scott [ … ] was at his <unk> best [ … ] in this episode " . Furthermore , he highly praised the story , noting that " the Jim / Pam scenario has definitely taken a more interesting turn . " During the filming of " The Secret " , the cast of the show discovered that Carell had been nominated for a Golden Globe Award . Fischer later noted that it was " fun that [ ' The Secret ' is ] the episode that <unk> after his win . " 
- 
- 
- = Maggie Simpson = 
- 
- Margaret " Maggie " Simpson is a fictional character in the animated television series The Simpsons . She first appeared on television in the Tracey Ullman Show short " Good Night " on April 19 , 1987 . Maggie was created and designed by cartoonist Matt Groening while he was waiting in the lobby of James L. Brooks ' office . She received her first name from Groening 's youngest sister . After appearing on The Tracey Ullman Show for three years , the Simpson family was given their own series on the Fox Broadcasting Company which debuted December 17 , 1989 . 
- Maggie is the youngest child of Marge and Homer , and sister to Bart and Lisa . She is often seen sucking on her red pacifier and , when she walks , she trips over her clothing and falls on her face ( this running gag is used much more in earlier seasons ) . Being an infant , she cannot talk . She is a counterpart to Lisa Simpson . However , she did appear to talk in the first Tracy Ullman short . Therefore , she is the least seen and heard member of the Simpson family . 
- Maggie 's <unk> and occasional speaking parts are currently provided by Nancy Cartwright , but she has also been voiced by guest stars James Earl Jones , Elizabeth Taylor and <unk> Foster , and by series regulars <unk> Smith and Harry Shearer . Maggie has appeared in various media relating to The Simpsons – including video games , The Simpsons Movie , The Simpsons Ride , commercials and comic books – and has inspired an entire line of merchandise . 
- 
- = = Role in The Simpsons = = 
- 
- The Simpsons uses a floating timeline in which the characters do not physically age , and as such the show is assumed to be set in the current year . In several episodes , events have been linked to specific times , though sometimes this timeline has been contradicted in subsequent episodes . Maggie is the youngest child of Marge and Homer , and sister to Bart and Lisa . When Marge became pregnant with Bart , she and Homer got married at a chapel in Las Vegas . To support his impending family , Homer all but demanded a job at the Springfield Nuclear Power Plant , impressing its owner , Mr Burns , with his aggressive <unk> . When Marge became pregnant with Lisa , two years later , she and Homer bought their first house . Another six years later , Homer felt financially secure enough to finally quit his job at the Power Plant and take his dream job at Barney 's <unk> . However , Marge became pregnant with Maggie , so Homer , once again unable to support his family , was forced to <unk> for his old job . By the time Maggie was born , Homer had shown great signs of distress , but he managed to find motivation in the form of his newborn baby girl . 
- During the earlier seasons of the show , Maggie 's equivalent of a hallmark was to trip over her clothing and fall on her face while trying to walk , causing a loud <unk> on the floor , but this was toned down in the later seasons . She has <unk> for her pacifier , on which she is always seen sucking . 
- Maggie has performed a number of feats that suggest she is a baby genius . She has spelled out E = MC ² with her baby blocks , driven Homer 's car , escaped from the Springfield <unk> center , and written her name on an <unk> A <unk> . Maggie is <unk> aware of her surroundings , and can usually be seen imitating the flow of action around her . She shows a high degree of dexterity , and she once hit Homer on the head with a <unk> and shot a dart at a photograph of him in imitation of Itchy and Scratchy . Despite her age , Maggie is a formidable marksman , as seen in " Who Shot Mr. Burns ? " where she shoots Mr. Burns with a <unk> that falls into her hands , and in a more intentional manner during " <unk> 's Got a Brand New Badge " where she is able to non @-@ fatally shoot a group of <unk> in rapid succession with a rifle that she apparently hides in her crib . 
- Maggie is usually frightened and <unk> by Homer 's attempts to bond with her , but has on several occasions stepped in to save Homer 's life : once from drowning , once from being shot by <unk> , once from being kidnapped by a tow truck driver , and once from being shot by Russ <unk> , head of the United States Environmental Protection Agency . 
- 
- = = Character = = 
- 
- 
- = = = Creation = = = 
- 
- Matt Groening first conceived Maggie and the rest of the Simpson family in 1986 in the lobby of James L. Brooks 's office . Groening had been called in to pitch a series of animated shorts for The Tracey Ullman Show , and had intended to present an adaptation of his Life in Hell comic strip . When he realized that <unk> Life in Hell would require him to rescind publication rights for his life 's work , Groening decided to go in another direction , and <unk> <unk> out his version of a dysfunctional family and named the characters after members of his own family . The baby of the family was named Maggie after Groening 's youngest sister . Maggie then made her debut with the rest of the Simpsons family on April 19 , 1987 in the short " Good Night " . In 1989 , the shorts were adapted into The Simpsons , a half @-@ hour series that would air on the Fox Broadcasting Company . Maggie and the rest of the family remained the main characters on this new show . 
- The entire Simpson family was designed so that they would be recognizable in <unk> . The family was <unk> drawn , because Groening had submitted basic sketches to the animators , assuming they would clean them up ; instead , they just traced over his drawings . Maggie 's physical features are generally not used in other characters ; for example , in the later seasons , no character other than Lisa shares her hairline . While designing Maggie and Lisa , Groening " couldn 't be bothered to even think about girls ' hair styles " . At the time , Groening was primarily drawing in black and white and when designing Lisa and Maggie , he " just gave them this kind of spiky <unk> hair style , not thinking that they would eventually be drawn in color " . 
- Groening thought that it would be funny to have a baby character that did not talk and never grew up , but was scripted to show any emotions that the scene required . Maggie 's comedic <unk> include her tendency to <unk> and land on her face while attempting to walk , and a <unk> for sucking on her pacifier , the sound of which has become the equivalent of her catchphrase and was originally created by Groening during the Tracey Ullman period . In the early seasons of the show , Maggie would <unk> her pacifier over other characters ' dialogue , but this was discontinued because the producers found it too distracting . 
- 
- = = = Voice = = = 
- 
- With few exceptions , Maggie never speaks but <unk> in the events around her , <unk> with subtle gestures and facial expressions . Maggie 's first lines were spoken in " Good Night " , the first short to air on The Tracey Ullman Show , after the family falls asleep . On this occasion , Liz Georges provided the voice of Maggie . 
- Although she had previously spoken in fantasies and dream sequences , Maggie 's first word spoken in the normal continuity of the series occurred in " Lisa 's First Word " , when she was voiced by Elizabeth Taylor . Although it was only one word ( " Daddy " ) , Taylor had to record the part numerous times before the producers were satisfied . James Earl Jones voiced Maggie in " Treehouse of Horror V " . Maggie would later have brief dialogue in " Treehouse of Horror IX " , voiced by Harry Shearer , who used his Kang voice . In earlier episodes , <unk> Smith did many of Maggie 's <unk> , cries , laughs and occasional speaking parts , although in the later seasons her parts are done by Nancy Cartwright ( including a single word spoken during the end credits of The Simpsons Movie ) . <unk> Foster voiced a Howard <unk> @-@ inspired Maggie in the season 20 episode " Four Great Women and a <unk> " . 
- In the occasional " Simpsons in the future " <unk> episodes ( " Lisa 's Wedding " , " Bart to the Future " , " Future @-@ Drama " , " <unk> of Future <unk> " , " Days of Future Future " ) , although an older Maggie is depicted , as a running gag within these episodes she is never shown speaking , so no voice actors have been utilized on these occasions . 
- 
- = = Reception = = 
- 
- Nancy <unk> at About.com said her favorite Maggie scenes on The Simpsons are the ones that show her acting more like an adult than a one @-@ year @-@ old . Some of her favorite Maggie scenes include scenes from " Sweet Seymour Skinner 's <unk> Song " and " Lady <unk> 's Lover " where Maggie meets her <unk> <unk> , Baby Gerald , and the one scene from " Itchy & Scratchy : The Movie " in which Bart is supposed to <unk> Maggie , but she escapes and takes Homer 's car for a ride . <unk> also added that " whether watching ' The Happy <unk> ' or falling down , Maggie is the <unk> baby in the Simpson family " . <unk> Ricky Gervais named " And Maggie Makes Three " his second favorite episode of the show and said that the scene in the end where Homer puts up pictures of Maggie over his desk gave him " a <unk> in the throat thinking about it " . Todd Everett at Variety called the scene in " Lisa 's First Word " where Maggie speaks her first word " quite a heart @-@ <unk> " . 
- In 2006 , Elizabeth Taylor was named thirteenth on IGN 's " Top 25 Simpsons Guest Appearances " list for her performance as Maggie in " Lisa 's First Word " . James Earl Jones , voice of Maggie in " Treehouse of Horror V " , was named the seventh greatest guest star on the show in the same list . In 2000 , Maggie and the rest of the Simpson family were awarded a star on the Hollywood Walk of Fame located at <unk> Hollywood Boulevard . 
- 
- = = Merchandising = = 
- 
- Four children 's books , written by Maggie Groening ( after whom Maggie was named ) and illustrated by Matt Groening , entitled Maggie Simpson 's Book of Animals , Maggie Simpson 's <unk> Book , Maggie Simpson 's Book of <unk> and <unk> and Maggie Simpson 's Alphabet Book were released on September 12 , 1991 . Other merchandise includes dolls , posters , figurines , <unk> puzzles , and T @-@ shirts . Maggie was made into an action figure as part of the World of Springfield toy line , and was released in the wave one <unk> " Living Room " , featuring her and Marge in the living room of the Simpsons house . Maggie has appeared in commercials for Burger King , <unk> , <unk> Lemon , <unk> 's <unk> , <unk> Inn and <unk> . 
- Maggie has appeared in other media relating to The Simpsons . She is a character in every one of The Simpsons video games , including the most recent , The Simpsons Game . Alongside the television series , Maggie regularly appears in issues of the Simpsons comics , which were first published on November 29 , 1993 and are still issued monthly . Maggie also plays a role in The Simpsons Ride , launched in 2008 at Universal Studios Florida and Hollywood . Maggie starred in the 3D short @-@ film The <unk> <unk> , which was shown in theaters before Ice Age : Continental <unk> in 2012 . 
- On April 9 , 2009 , the United States Postal Service unveiled a series of five 44 cent stamps featuring Maggie and the four other members of the Simpson family . They are the first characters from a television series to receive this recognition while the show is still in production . The stamps , designed by Matt Groening , were made available for purchase on May 7 , 2009 . In a <unk> poll , Maggie 's stamp was voted the most popular of the five . 
- 
- 
- = Chasing Vermeer = 
- 
- Chasing Vermeer is a 2004 children 's art mystery novel written by Blue Balliett and illustrated by Brett <unk> . Set in Hyde Park , Chicago near the University of Chicago , the novel follows two children , Calder Pillay and Petra Andalee . After a famous Johannes Vermeer painting is stolen on route to the Art Institute of Chicago , Calder and Petra work together to try to recover it . The thief publishes many advertisements in the newspaper , explaining that he will give the painting back if the community can discover which paintings under Vermeer 's name were really painted by him . This causes Petra , Calder , and the rest of Hyde Park to examine art more closely . Themes of art , chance , coincidence , deception , and problem @-@ solving are apparent . 
- The novel was written for Balliett classroom intended to deal with real @-@ world issues . Balliett values children 's ideas and wrote the book specifically to highlight that . Chasing Vermeer has won several awards , including the Edgar and the <unk> . In 2006 , the sequel entitled The Wright 3 was published , followed by The Calder Game in 2008 , . 
- 
- = = Inspiration and origins = = 
- 
- Chasing Vermeer is Blue Balliett 's first published book . Its original purpose was a book to read to her class for fun . She realized that a mystery about " real " art issues had not been written since <unk> <unk> 's From the Mixed @-@ Up Files of Mrs. Basil E. <unk> and desired to write what she wished to read . Chasing Vermeer took about five years to complete , as Balliett was also a teacher and parent . She compared writing the book to weaving , as she first wrote mainly about art , but then incorporated the pentominoes and classroom scenes , creating many different levels to read on . She admits that it ended up more complex than she had thought it would be . 
- Balliett used art and blank plates as inspiration for the characters ' names . Calder Pillay is derived from the artist Alexander Calder and Petra Andalee was inspired by the architecture in Petra , Jordan . The names were meant to be different , which Balliett considered " fun for a child . " Balliett felt that she could capture the attention of reluctant readers if they related to characters who enjoyed writing and math . Calder and Petra 's teacher , Ms. Hussey , was inspired by an old name on Nantucket Island and the old @-@ fashioned word <unk> . Balliett compares herself to Ms. Hussey , stating that " [ we ] think a lot alike . " Some of Ms. Hussey 's assignments and dialogue even came from Balliett 's classroom . She chose the setting of Hyde Park , Chicago , where she currently lives , because she considered it full of secrets that children could discover . 
- 
- = = Plot summary = = 
- 
- The book begins with a mysterious letter that is delivered to three unknown recipients , two women and one man . The letter tells them they are of great need to the <unk> , but begs them not to tell the police . 
- Sixth @-@ <unk> Calder Pillay , who enjoys puzzles and pentominoes , and Petra Andalee , who <unk> to be a writer , are classmates at the Middle School in Hyde Park , Chicago . Their young teacher , Ms. Hussey , is very interested in art and teaches them in a creative way . Through her pressing questions , they discover the artist Johannes Vermeer and his paintings , especially A Lady Writing and The <unk> . Petra also finds a used book called Lo ! , written by Charles Fort , at the local Powell 's Books , owned by a man named Mr. Watch . They also meet an elderly neighbor , Mrs. Sharpe , who is also a fan of Vermeer and Fort . Calder receives letters from his best friend Tommy <unk> , who is currently living in New York City with a new stepfather . 
- The children learn that A Lady Writing was traveling from The National Gallery of Art in Washington D.C. to Hyde Park . The next day there is a story in the paper of how the painting mysteriously disappeared . A letter from the thief appears in the newspaper , telling the public that he will not give back A Lady Writing until they prove which Vermeer paintings were truly painted by him . This sparks worldwide uproar . Calder and Petra investigate as their friendship grows . Mrs. Sharpe requests police protection and it is revealed that she and Ms. Hussey were two of the three recipients of the thief 's letter . Calder and Petra eventually conclude that the painting is hidden in the local Delia Dell Hall , and they sneak out and find it . They barely escape from the thief , who is later found dead from a massive heart attack on the train by the police . They learn that the man is Xavier <unk> , also <unk> as Glitter Man , who was posing as Tommy 's stepfather under the name Fred <unk> . A known art thief , he was asked to steal the painting and sell it for sixty million dollars . The other recipient of the letter is revealed to be Mr. Watch . 
- 
- = = = Code = = = 
- 
- As stated in the preface , there is a code hidden in the paintings throughout the book . This was an idea of Brett <unk> and Balliett 's editor , Tracy Mack . The code involves images of pentominoes and frogs , which is a recurring theme in the book . To <unk> the code , one must count the number of frogs in every other illustration , as well as find the hidden <unk> . Once these facts are collected , you are to use the same code presented in the story that Calder and Tommy use in their letters in the book . For example , the first code in the book is V : 2 @.@ this means T when <unk> with Calder and Tommy 's <unk> . When the message is <unk> , it spells out " The Lady Lives " . 
- 
- = = Genre = = 
- 
- Chasing Vermeer is classified in the mystery genre , although it was described by Liz <unk> of Scholastic as " a puzzle , wrapped in a mystery , disguised as an adventure , and delivered as a work of art . " Scholastic 's teaching website additionally added suspense due to the surprise ending . 
- 
- = = Themes = = 
- 
- Some of Balliett 's " real @-@ world ideas " in Chasing Vermeer were " Do coincidences mean anything ? " and " What is art and what makes it valuable ? " Balliett says her " central message " is " kids are powerful thinkers , and their ideas are valuable , and that adults don 't have all the answers . " 
- A book by Rita <unk> entitled Reading <unk> : A Book Club Guide for <unk> , Kids , and Families analyzed Chasing Vermeer 's themes as follows : 
- Deception and problem solving are central themes in this novel as both the thief and the central adult players use a variety of ways to hide the truth while the children employ a series of mathematical and problem @-@ solving concepts to piece together the clues to the puzzle . In addition , Calder and Petra develop a special friendship and certain respect for the value of art . 
- As the thief gains publicity by challenging the community to figure out which paintings claimed to be Vermeer 's were indeed painted by him , everyone starts to look at the depth in art . <unk> <unk> , who writes a book review blog , noted that the reader was left with the impression to study Vermeer 's paintings and art more closely . In the book , Ms. Hussey challenges her class to the question , " What is art ? " 
- Other themes include chance and coincidence . During Chasing Vermeer , Charles Fort 's book , Lo ! , inspires the children to list and pay attention to coincidences as they realize that they are more than what they seem and explore the concept that they make up one unexplained pattern . Balliett stated that she wanted to convey how coincidences were noticeable and felt meaningful , and how they could matter even if they were <unk> . 
- 
- = = Audiobook = = 
- 
- The audiobook for Chasing Vermeer , read by Ellen Reilly , was released on November 27 , 2007 from Listening Library . It runs about 4 hours and 47 minutes . <unk> magazine praised Reilly 's voices and pace , but noted that , " Once the mystery is solved , however , the ending seems <unk> on , falling flat . " 
- 
- = = Critical reception = = 
- 
- Chasing Vermeer received generally positive reviews . The New York Times praised the description and mystery . It was also listed as one of their " Notable Books of 2004 " . Kirkus Reviews awarded it a starred review with the consensus that " Art , intrigue , and plenty of twists and turns make this art mystery a great read . " Children 's Literature reviewer <unk> Mills gave generally positive comments , calling the novel " <unk> and engaging " . The website <unk> compared the book to classic mysteries such as The <unk> Game and said , " Chasing Vermeer deserves a spot alongside many well @-@ loved children 's books . It 's that good . " A reviewer of The Trades website called it " an entertaining read that manages to serve several purposes in one <unk> novel " and found the characters " unusual yet likable " , but felt that " the disappointing bit of this novel is that the solutions always arrive through a series of <unk> events that just lead the kids to think in certain ways . " <unk> Enterprises , a game puzzle company , reviewed the book , praising the writing style and puzzles . 
- 
- = = = Awards = = = 
- 
- 
- = = Film = = 
- 
- Warner Brothers bought the rights to a film of Chasing Vermeer in June 2004 and Brad Pitt 's production company Plan B Entertainment planned to produce it . P.J. Hogan was slated as director and the novel was adapted by Matt Nix . However , when asked about the film in August 2010 , Balliett answered , 
- " It ’ s been fascinating , watching this whole process , because Plan B did a wonderful job . They went through two screenwriters , and they ’ ve gone through two directors . It ’ s sort of like a house of cards . I have rights again . If they get it all together again , they ’ ll jump on it . But they don ’ t have exclusive rights anymore . " 
- 
- 
- = Caught Up ( Usher song ) = 
- 
- " Caught Up " is a song by American R & B singer Usher . It was written by Ryan Toby , Andre Harris , Vidal Davis and Jason Boyd , and produced by Dre & Vidal for Usher 's 2004 album Confessions . The song was released as the fifth and final single from the album on November 30 , 2004 . The single peaked at number eight in the United States , the only single released from Confessions without topping the Billboard Hot 100 , and generally below top ten on most charts worldwide . It received positive reviews from contemporary critics . 
- 
- = = Background and release = = 
- 
- Although Usher " didn 't look too far " when starting working on his fourth studio album Confessions and decided to " continue building " with previous producers , he branched out with several musical collaborators . Usher enlisted Philadelphia producers Andre " Dre " Harris and Vidal Davis of Dre & Vidal , along with other musical collaborators . During the sessions , Usher asked them to create a " real up @-@ tempo beat " . When they worked on the track , they <unk> the whole time which Dre considered a " partly record " . He recalled , " We had some women , some drinks , some music . " After Dre created the beat , they decided to " make sure the mood was inspiring during recording . " They went to club to take a break , and played the song in Usher 's truck while on the way . Usher felt the collaboration was pleasing , seeing other people responded positively to the song . 
- " Caught Up " was the fifth and final single from Confessions . It was released in the United States on November 30 , 2004 as a 12 " single . It was also released in the Netherlands and the United Kingdom on February 21 , 2005 and on March 8 in Germany . The Germany release contains the album version of the song , three of its remixes and the single 's music video . The song impacted US contemporary hit radio on January 11 , 2005 , alongside a remix featuring rapper , <unk> . 
- 
- = = Reception = = 
- 
- " Caught Up " received positive reviews from contemporary critics . Andrew McGregor of BBC called the song " <unk> " and " hip @-@ grinding " . Jon Caramanica of Blender magazine referred to the song like a " Southern marching band performing <unk> ' 80s R & B " . He characterized Usher 's voice playing like a rhythm instruments . Sal Cinquemani of Slant Magazine complimented Dre and Vidal for producing an old @-@ sounding music without sampling records , calling it " super @-@ tight " alongside " Follow Me " , another song from the album . Andy Kellman of Allmusic complimented the song as one of Usher 's best moments in the album , together with " Burn " . Kelefa Sanneh of The New York Times called it a " <unk> song " from the album , adding that it gave Usher " a chance to do two of the things he does best : strut and <unk> " . 
- 
- = = Chart performance = = 
- 
- " Caught Up " did not live up to the chart @-@ topping performances of Confessions ' previous four releases . In the United States , the single debuted on the Billboard Hot 100 at number seventy @-@ six . It peaked at number eight for two non @-@ consecutive weeks , fifteen weeks after its release . " Caught Up " was the only single to not top the Hot 100 compared to the album 's four previous releases . The single stayed on the Hot 100 for twenty @-@ seven weeks . 
- Outside the United States , responses from music markets were relatively similar . " Caught Up " debuted and peaked at number nine on the United Kingdom , remaining on the chart for also nine weeks . It reached number ten in the Netherlands , and under top ten on the rest of European countries ; much lesser in Finland where it only stayed for one week compared to other charts , remaining for several weeks . In Australia and New Zealand , the single reached number fifteen and twelve respectively . 
- 
- = = Music video = = 
- 
- The music video for " Caught Up " was directed by Mr. X , who was behind the laser light treatment of Usher 's 2004 video " Yeah ! " . The video shows Usher and friends riding a car while heading to his live performance . On the way , they fist fight after Usher saves a woman from an antagonist . Usher realizes he is supposed to perform , and finally goes to the venue . The video ends with Usher performing the song in front of a large crowd . The music video debuted on MTV 's Total Request Live on January 10 , 2005 at number ten . The video remained on the countdown for thirty @-@ four days . 
- 
- = = Track listing = = 
- 
- Germany CD single 
- " Caught Up " ( Album version ) – 3 : 48 
- " Caught Up " ( Official Remix ) ( featuring <unk> ) – 4 : 39 
- " Caught Up " ( <unk> Jones Remix ) – 3 : 33 
- " Caught Up " ( <unk> " <unk> Crew " Re @-@ <unk> ) – 7 : 55 
- " Caught Up " ( Music Video ) – 3 : 49 
- 
- = = Charts = = 
- 
- 
- = = = Weekly charts = = = 
- 
- 
- = = Certifications = = 
- 
- 
- 
- = If I Never See Your Face Again = 
- 
- " If I Never See Your Face Again " is a song by American pop rock band Maroon 5 from the June 2008 re @-@ release of the group 's second studio album , It Won 't Be Soon Before Long ( 2007 ) . It was also included on the June 2008 re @-@ release of Rihanna 's album Good Girl Gone Bad ( 2007 ) . The song was originally included on the standard version of the album without the inclusion of Rihanna . It was written by band members Adam Levine and James Valentine , with production of the song helmed by Christopher " Tricky " Stewart , Mike <unk> , Mark <unk> , Mark " Spike " Stent and Maroon 5 . It was released as an <unk> Play ( EP ) in Australia on May 22 , 2007 , and as an official single on May 2 , 2008 , in the United States . 
- " If I Never See Your Face Again " was originally intended to be a duet with Janet Jackson and appear on her <unk> album , although label problems <unk> . The collaboration with Rihanna later came to fruition when Levine asked her to add her vocal to the remix version , which she accepted . " If I Never See Your Face Again " is a pop and R & B song featuring instrumentation provided by synthesizers and a guitar . The song received a mixed response from music critics , who praised the song 's composition but criticized Levine and Rihanna 's vocal performance . The song 's accompanying music video was directed by Anthony Mandler and shot on an indoor soundstage in <unk> , California . The concept for the video was " high @-@ end <unk> " . 
- 
- = = Background = = 
- 
- " If I Never See Your Face Again " was written by Adam Levine and James Valentine , two of the five band members from Maroon 5 . Production of the song was helmed by Mike <unk> , Mike <unk> , Mike " Spike " Stent , Christopher " Tricky " Stewart and Maroon 5 . It was recorded by " Spike " Stent at Conway Studios , Hollywood , CA ; Glenwood Place Studios , Burbank , CA and Phantom Studios , Westlake Village , CA . The song was originally intended to be a duet with Janet Jackson and appear on her <unk> album , but due to label problems Rihanna later appeared on the song . 
- The song was included on the standard version of It Won 't Be Soon Before Long without the inclusion of Rihanna , however , Levine stated in an interview with James Montgomery for MTV News whilst on set of the accompanying music video that he wanted to try something different for the album 's re @-@ release . The singer continued , saying that he asked Rihanna to do some " bits and pieces " in the recording studio and that it came together very quickly . Levine stated that if there is " magic " between two artists , then " you don 't even have to think about it . " During the interview , Rihanna confirmed that the song would be the only one to feature a collaboration on her re @-@ release of Good Girl Gone Bad , revealing that she had always wanted to work with the group and was honored when they contacted her . " If I Never See Your Face Again " was released as a single in the United States on May 2 , 2008 . 
- 
- = = Composition = = 
- 
- " If I Never See Your Face Again " is a pop and R & B song , which lasts for 3 : 18 ( 3 minutes , 18 seconds ) . The song was composed using common time in the key of A minor , with a moderate groove of 106 beats per minute . Instrumentation is provided by synths , and a guitar . A reviewer for IGN noted that " If I Never See Your Face Again " is complete with " <unk> synth <unk> , " whilst Jerome <unk> for BBC wrote that it is a " guitar drenched " and " synth @-@ <unk> " song . Alex Fletcher for Digital Spy commented that the song incorporated a " jagged " use of synthesizers . For the most part of the song , Levine sings in his falsetto register , whilst Rihanna adopts " <unk> tone " . 
- 
- = = Critical reception = = 
- 
- Upon the release of It Won 't Be Soon Before Long , music critics commented on the original version of the song , which does not include guest vocals by Rihanna . Jerome <unk> for BBC wrote that " If I Never See Your Face Again " , along with another It Won 't Be Soon Before Long track " Makes Me Wonder " , were " monster weapons of mass @-@ marketing aimed at the feet and riding on <unk> of string @-@ drenched , synth @-@ <unk> disco . " A reviewer for IGN was complimentary of ' If I Never See Your Face Again ' , writing that it is hard to resist listening to the song . The reviewer also noted that Levine appeared to adopt a vocal style reminiscent of techniques employed by Justin Timberlake , writing " Like Justin Timberlake it 's all about the high pitched tenor falsetto and some <unk> grooves . " 
- Bill Lamb for About.com noted that " If I Never See Your Face Again " and " Makes Me Wonder " appeared to be inspired by British jazz funk and acid jazz band <unk> , writing " Any <unk> fan may call foul if they listen to ' If I Never See Your Face Again ' or ' Makes Me Wonder ' . " Sal Cinquemani was critical of the song , labeling it as " nasty . " Alex Fletcher for Digital Spy disapproved of the collaboration between the band and Rihanna , with specific thoughts on Levine and Rihanna 's pairing . Fletcher was critical of their vocal performance , writing that their voices clashed with one another and that " the sexual chemistry radar for the pairing registers at zero . " Fletcher commented about the song further , writing that it should never have left the recording studio . 
- 
- = = = Accolades = = = 
- 
- 
- = = Chart performance = = 
- 
- " If I Never See Your Face Again " achieved moderate chart success on singles charts around the world . It debuted on the Australian Singles Chart at number 28 June 1 , 2008 , and peaked at number 11 in its sixth week . The song debuted on the New Zealand Singles Chart at number 37 on June 9 , 2008 , and peaked at number 21 the following week . In Europe , the song debuted and peaked at number 15 on the Italian Singles Chart on July 17 , 2008 . The song remained on the chart for one week . It debuted on the Dutch Singles Chart at number 35 on June 14 , 2008 , and peaked at number 20 the following week . The song fluctuated between positions in the twenties and <unk> for 13 weeks . It debuted on the Danish Singles Chart at number 36 on July 27 , 2008 , and peaked at number 31 in its sixth week . 
- " If I Never See Your Face Again " debuted on the Swiss Singles Chart at number 61 on June 29 , 2008 , and peaked at number 52 the following week , where it remained in its third week . It debuted on the Austrian Singles Chart at number 67 on September 5 , 2008 , and peaked at number 54 the following week . " If I Never See Your Face Again " debuted and peaked on the UK Singles Chart at number 28 on June 14 , 2008 . Over the following two weeks , the song descend down the top @-@ forty , before falling out in its third week . The song made a re @-@ entry on the chart at number 36 on July 7 , 2008 . In the United States , the song peaked at number 51 on the Billboard Hot 100 chart on July 26 , 2008 . It peaked at number 10 on the Adult Pop Songs chart ; number 30 on the Pop Songs chart and 21 on the Hot Digital Songs chart . 
- 
- = = Music video = = 
- 
- The song 's accompanying music video was directed by Anthony Mandler , and shot on an indoor soundstage in <unk> , California on April 23 , 2008 . The concept for the video was " high @-@ end <unk> " . In an interview with James Montgomery for MTV News whilst on set of the music video , Levine provided a summary of what the video would entail , saying : " It 's this kind of ultra @-@ glamorous , photography @-@ based , <unk> ' 70s / <unk> ' 80s situation . It 's really stylish and really beautiful ... It 's the most choreographed thing I 've ever done , because usually I just get up there and screw around . But with Rihanna , it 's completely different and so cool . " In response , Rihanna continued to say that " I don 't do a lot of videos where I have so much chemistry with the other artist , and this is only my second duet in a video ... It 's really intense , because you have to work with each other so much . It 's new for me , but I 'm enjoying it . " 
- 
- = = = Synopsis = = = 
- 
- The video begins with the sound of a ticking clock and Levine and Rihanna sitting opposite each other at a table , tapping their hands and staring at one and other . As the songs audio begins to play , band members of Maroon 5 start to play their instruments as Rihanna sits and watches them . The table and band scenes are intercut with each other for the first verse and chorus . For the second verse , Levine and Rihanna appear to argue and try to ignore the other 's advances . Rihanna lays <unk> on a bed while Levine sits on a chair with his head turned in the opposite direction for the bridge . For the final chorus , the pair appear to reconcile , ending with Levine finally giving in to his <unk> and walk to the end of the table where Rihanna sits and he <unk> her neck causing Rihanna to lean towards him <unk> and the two nearly kiss as the video ends . The video was made available to download digitally via iTunes on May 12 , 2008 . 
- 
- = = Formats and track listings = = 
- 
- 
- = = Credits and personnel = = 
- 
- Recording 
- Recorded at Conway Studios , Hollywood , CA ; Glenwood Place Studios , Burbank , CA ; Phantom Studios , Westlake Village , CA ; House of Blues Studios , <unk> , CA and Henson Studios , Hollywood , CA . 
- Mixed at Scream Studios , Miami , <unk> . 
- Personnel 
- Credits adapted from the liner notes of Good Girl Gone Bad : <unk> , Def Jam Recordings . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Yoko Shimomura = 
- 
- Yoko Shimomura ( <unk> <unk> , Shimomura <unk> , born October 19 , 1967 ) is a Japanese video game composer and pianist . Shimomura has worked in the video game industry ever since graduating from the Osaka College of Music in 1988 . From then until 1993 , she worked for Capcom , where she composed wholly or in part the scores for 16 games , including Final Fight and Street Fighter II : The World Warrior . 
- From 1993 until 2002 , Shimomura worked for Square , where she composed for a further eight games . While working for Square , she was best known for her work on the soundtrack for Kingdom Hearts , which was her last game for the company before leaving . Starting with Mario & Luigi : <unk> Saga in 2003 , she began working as an active <unk> . 
- Her works have gained a great deal of popularity , and have been performed in multiple video game music concerts , including one , Sinfonia Drammatica , that was focused half on her " greatest hits " album , Drammatica : The Very Best of Yoko Shimomura , and half on the music of a previous concert . Music from several of her games have been published as arranged albums , or as piano scores . 
- 
- = = Biography = = 
- 
- 
- = = = Early life = = = 
- 
- Shimomura was born on October 19 , 1967 , in <unk> Prefecture , Japan . She developed an interest for music at a young age , and started taking piano lessons " at the age of four or five " . She began composing her own music by playing the piano randomly and pretending to compose , eventually coming up with her own pieces , the first of which she claims to still remember how to play . Shimomura attended Osaka College of Music , and graduated as a piano major in 1988 . 
- Upon graduation , Shimomura intended to become a piano instructor and was extended a job offer to become a piano teacher at a music store , but as she had been an <unk> <unk> for many years she decided to send some samples of her work to various video game companies that were recruiting at the university . Capcom invited her in for an audition and interview , and she was offered a job there . Her family and instructors were dismayed with her change in focus , as video game music was not well respected , and " they had paid [ her ] tuition for an expensive music school and couldn 't understand why [ she ] would accept such a job " , but Shimomura accepted the job at Capcom anyway . 
- 
- = = = Career = = = 
- 
- While working for Capcom , Shimomura contributed to the soundtracks of over 16 games , including the successful Street Fighter II : The World Warrior , which she composed all but three pieces for . The first soundtrack she worked on at the company was for <unk> Sword in 1988 . Final Fight , in 1989 , was her first work to receive a separate soundtrack album release , on an album of music from several Capcom games . The first soundtrack album to exclusively feature her work came a year later for the soundtrack to Street Fighter II . While she began her tenure at Capcom working on games for video game consoles , by 1990 she had moved to the arcade game division . She was a member of the company 's in @-@ house band Alph Lyla , which played various Capcom game music , including pieces written by Shimomura . She performed live with the group on a few occasions , including playing piano during Alph Lyla 's appearance at the 1992 Game Music Festival . 
- In 1993 , Shimomura left Capcom to join another game company , Square ( now Square Enix ) . She stated that the move was done because she was interested in writing " classical @-@ style " music for fantasy role @-@ playing games . While working for Capcom , she was in the arcade department and was unable to transfer to the console department to work on their role @-@ playing video game series <unk> of Fire , although she did contribute one track to the first game in the series . Her first project at the company was the score for the role @-@ playing video game Live A Live in 1994 . While she was working on the score to Super Mario RPG the following year , she was asked to join <unk> <unk> on the music to the futuristic strategy RPG Front Mission . Although she was <unk> doing both scores and it was not the genre that she was interested in , she found herself unable to refuse after her first attempt to do so unexpectedly happened in the presence of the president of Square , <unk> <unk> . These games were followed by <unk> No. 1 , the last score she worked on with another composer for a decade . 
- Over the next few years , she composed the soundtrack to several games , including Parasite Eve and Legend of Mana . Of all her compositions , Shimomura considers the soundtrack to Legend of Mana the one that best expresses herself and the soundtrack remains Shimomura 's personal favourite . Parasite Eve on the PlayStation had the first soundtrack by Shimomura that included a vocal song , as it was the first game she had written for running on a console system that had the sound capability for one . In 2002 she wrote the score for Kingdom Hearts , which she has said is the most " special " soundtrack to her , as well as a turning point in her career ; she named the soundtracks to Street Fighter II and Super Mario RPG as the other two significant points in her life as a composer . 
- Kingdom Hearts was wildly successful , shipping more than four million copies worldwide ; Shimomura 's music was frequently cited as one of the highlights of the game , and the title track has been ranked as the fourth @-@ best role @-@ playing game title track of all time . The soundtrack has led to two albums of piano arrangements . Kingdom Hearts was the last soundtrack that she worked on at Square . After the release of Kingdom Hearts in 2002 , Shimomura left Square for <unk> leave , and began work as a <unk> in 2003 . She has built on the work she did while at Square ; since leaving she has composed or is composing music for eleven Kingdom Hearts games and Nintendo 's Mario & Luigi series . She has also worked on many other projects , such as Heroes of Mana and various Premium Arrange albums . On February 10 and 11 , 2014 , Shimomura played piano at a retrospective 25th anniversary concert at Tokyo FM Hall . She performed songs from games such as Kingdom Hearts , Live a Live , and Street Fighter II . During the <unk> the Forest 's Mushrooms performance from Super Mario RPG , Shimomura was joined onstage by fellow game composer Yasunori Mitsuda , who played the Irish <unk> . She is currently working on the scores for Final Fantasy XV and Kingdom Hearts III . 
- 
- = = Legacy = = 
- 
- After composing soundtracks to over 45 different games , Shimomura has become one of the biggest names in video game music composition , and has been described as " the most famous female video game music composer in the world " . In March 2008 , Shimomura 's best works compilation album Drammatica : The Very Best of Yoko Shimomura was released containing her compositions from Kingdom Hearts and other games in a full orchestrated score . It includes music from Final Fantasy XV , Live A Live , Kingdom Hearts , Front Mission , Legend of Mana , and Heroes of Mana ; Shimomura has stated that she chose music that was popular among fans and well @-@ suited for orchestration , but had never been performed by an orchestra . In a 2008 interview with <unk> regarding the project , Shimomura commented that with the sheet music generated for the project , she would be interested in pursuing a live performance of Drammatica for fans if the opportunity arose . On March 19 , 2009 that wish was realized when it was announced that Arnie Roth would conduct the Royal Stockholm Philharmonic Orchestra at the concert Sinfonia Drammatica in the Stockholm Concert Hall , which would combine music from the album with performances of Chris <unk> 's Symphonic Shades concert . The concert took place on August 4 , 2009 . On March 27 , 2007 , Shimomura released her first non @-@ video game album , <unk> , an album of vocal songs sung by <unk> . 
- Shimomura 's music for Kingdom Hearts made up one fourth of the music of the Symphonic Fantasies concerts in September 2009 , which were produced by the creators of the Symphonic Game Music Concert series and conducted by Arnie Roth . Legend of Mana 's title theme was also performed by the Australian <unk> Symphony Orchestra for its classical gaming music concert A Night in Fantasia 2007 . 
- Music from the original soundtrack of Legend of Mana was arranged for the piano and published by <unk> Music Publishing . Two compilation books of music from the series have also been published as <unk> <unk> Best Collection Piano Solo <unk> Music first and second editions , with the second including Shimomura 's tracks from Legend of Mana . All songs in each book have been rewritten by <unk> <unk> as beginning to intermediate level piano solos , though they are meant to sound as much like the originals as possible . Additionally , piano sheet music from Kingdom Hearts and Kingdom Hearts II has been published as music books by Yamaha Music Media . 
- Shimomura 's first dedicated concert performance outside Japan was held at the Salle <unk> in Paris on November 7 , 2015 . Later that month , on November 28 , she performed at the El Plaza <unk> in Mexico City . 
- 
- = = Musical style and influences = = 
- 
- Shimomura lists Ludwig van Beethoven , Frédéric Chopin , and Maurice Ravel as some of her influences on her personal website . She has also stated that she has enjoyed " lounge @-@ style jazz " for a long time . Despite these influences and her classical training , the diverse musical styles that she has used throughout her career and sometimes in the same soundtrack include " rock , electronica , oriental , ambient , industrial , pop , symphonic , operatic , <unk> , and more " . She draws inspiration for her songs from things in her life that move her emotionally , which she describes as " a beautiful picture , scenery , tasting something delicious , <unk> that bring back memories , happy and sad things ... Anything that moves my emotion gives me inspiration " . Shimomura has also stated that she comes up with most of her songs when she is doing something that is " not part of [ her ] daily routine , like traveling . " Although her influences are mostly classical , she has said that in her opinion her " style has changed dramatically over the years , though the passion for music stays the same . " Shimomura has said that she believes that an important part of " the creative process behind music " is to " convey a subtle message , something that comes from your imagination and sticks with the listener , without being overly specific about what it means " , rather than only writing simple themes with obvious messages . Her favorite track that she has ever composed is " <unk> Beloved " from Kingdom Hearts . 
- 
- = = Works = = 
- 
- 
- = = = Video games = = = 
- 
- Composition 
- Arrangement 
- F @-@ 1 Dream ( PC Engine ) ( 1989 ) – original music by <unk> <unk> 
- <unk> Bros. ( PC Engine ) ( 1991 ) – original music by <unk> <unk> 
- Super Smash Bros. <unk> ( 2008 ) – arranged " Tetris : Type A " , " <unk> Desert " , and " King <unk> 's Theme " 
- Little King 's Story ( 2009 ) – arranged Maurice Ravel 's <unk> . 
- Super Smash Bros. for Nintendo 3DS and Wii U ( 2014 ) – arranged " <unk> / Eight <unk> " , " Try , Try Again " , " Route 10 " , and " <unk> Stage " 
- 
- = = = Other = = = 
- 
- <unk> 1500 series ~ Sweet Home ( 1989 ) – arranged two tracks 
- Captain Commando <unk> Capcom <unk> ( 1992 ) – with Alph Lyla 
- Game Music Festival ~ Super Live ' 92 ~ ( 1992 ) – with Alph Lyla 
- Street Fighter II Collector 's Box ( 1993 ) – with Alph Lyla 
- Parasite Eve <unk> ( 1998 ) – with many others 
- <unk> Star Online Episode I & II Premium Arrange ( 2004 ) – with many others 
- Dark Chronicle Premium Arrange ( 2004 ) – with many others 
- Dan <unk> ! ! ( 2004 ) 
- Best Student Council ( 2005 ) 
- Rogue Galaxy Premium Arrange ( 2006 ) – with many others 
- <unk> ( 2007 ) – original album with lyrics and vocals by <unk> 
- Drammatica : The Very Best of Yoko Shimomura ( 2008 ) 
- <unk> Double Arrange Album ( 2009 ) 
- <unk> Rare Tracks Ver . 2 @.@ 0 ( 2010 ) – with many others 
- <unk> Super Rare <unk> : The <unk> of <unk> <unk> ( 2011 ) – with many others 
- <unk> ! <unk> ! <unk> <unk> ! ! ( 2012 ) 
- X <unk> Collections II ( 2013 ) – with many others 
- <unk> ! ~ The Very Best of Yoko Shimomura ( 2014 ) 
- Game Music Prayer II ( 2014 ) – with many others 
- 
- 
- = Lisa the Simpson = 
- 
- " Lisa the Simpson " is the seventeenth episode of The Simpsons 's ninth season . It originally aired on the Fox network in the United States on March 8 , 1998 . In the episode , Lisa fears that she may be genetically <unk> to lose her intelligence after <unk> tells her of a family gene that can permanently take away intelligence . 
- " Lisa the Simpson " was written by Ned Goldreyer and directed by Susie <unk> . This episode was the final episode with Bill Oakley and Josh Weinstein as show runners . It received generally positive reviews from critics , and is considered one of the best episodes of the ninth season . 
- 
- = = Plot = = 
- 
- At Springfield Elementary , Lisa is presented with a brain teaser , which she is unable to solve . Following this incident , Lisa finds herself unable to perform simple tasks . Later , Lisa tells <unk> about her recent cognitive problems . He seems to recognize this , and tells Lisa about the " Simpson Gene " , which apparently causes all members of the Simpson family to gradually lose their intelligence as they get older . 
- Meanwhile , Jasper visits the <unk> @-@ E @-@ Mart and attempts to empty the freezer containing ice cream in order to freeze himself , with the intention of being <unk> sometime in the distant future . Apu decides to take advantage of this unusual situation for financial gain . The convenience store becomes more profitable as a tourist trap , until the freezer 's cooling system fails , causing Jasper to <unk> and walk away . 
- Lisa appears on the TV news program <unk> to tell the citizens of Springfield to treasure their <unk> . As she does this , Homer decides to prove her wrong , and contacts the entire extended Simpson family to visit . However , when they arrive , Homer realizes they 're also unsuccessful , <unk> people , which only <unk> Lisa further and causes Homer to send them home . 
- However , before they leave , Marge urges Homer to talk to the Simpson women . <unk> , he talks to them at her request and he discovers that the women are all successful . Lisa is relieved that she is fine and she will not suffer the " Simpson Gene " , because of her gender . 
- The episode ends with Lisa finally being able to solve the brain teaser she was unable to finish earlier in the episode . 
- 
- = = Production = = 
- 
- This episode was the final episode that was run by Bill Oakley and Josh Weinstein , as it was a carry @-@ over episode from season eight . The episode was written by Ned Goldreyer , and is one of the two episodes he has written on The Simpsons . Susie <unk> , one of the directors of the show , also left the show after this episode , but returned for one episode in season 18 , " <unk> <unk> " . 
- As it was the final episode they ran , Oakley and Weinstein wanted to end on a good note , with Weinstein stating that the episode " was meant to embody the humor , depth , and emotions of The Simpsons " . They also wished to have an episode they ran that was based on the background of every character they could do , and believed that this episode came out well . The name of the episode was the center of an argument that Oakley and Goldreyer had , as Oakley had originally wanted to have the episode named " Lisa the Simpson " , although Goldreyer wanted to name it " Suddenly Stupid " , a pun on a show that had been airing at the time called Suddenly Susan . 
- The Simpsons ' family members that showed up took some time to be made , but the staff enjoyed the results . All of the male Simpson family members that showed up were voiced by Dan <unk> , the voice of Homer . He had asked for the recording tape to be run for about 20 minutes , so he could do many different voice variations that would fit the different men , but still be close to Homer 's voice . 
- 
- = = Reception = = 
- 
- In its original broadcast , " Lisa the Simpson " finished 19th in ratings for the week of March 2 – 8 , 1998 , with a Nielsen rating of 10 @.@ 7 , equivalent to approximately 10 @.@ 4 million viewing households . It was the second highest @-@ rated show on the Fox network that week , following The X @-@ Files . 
- IGN 's Todd Gilchrist named the episode as one of his favorites of the ninth season in his review of the DVD <unk> . The authors of the book I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide , Warren Martyn and Adrian Wood , thought well of the episode , saying , " A terrific episode , with a good mix of pathos ( Lisa 's farewell to the <unk> and her favourite jazz club are inspired ) and fun ( her <unk> ' woo @-@ <unk> ' ) which comes together to make a refreshing and exciting look at Lisa 's life . " 
- Bill Oakley and Josh Weinstein greatly enjoyed the episode and thought of it as a great final episode that they ran . On the DVD audio commentary , writer Ned Goldreyer stated , " I think this might have been the best episode ever produced . " 
- 
- 
- = Stay @-@ at @-@ home dad = 
- 
- A stay @-@ at @-@ home dad ( alternatively , stay at home father , house dad , <unk> , <unk> , or house @-@ spouse ) is a father who is the main caregiver of the children and is the homemaker of the household . As families have evolved , the practice of being a stay @-@ at @-@ home dad has become more common , and socially more acceptable . 
- Pre @-@ industrialisation , the family worked together as a unit and was self @-@ sufficient . Beginning with the Industrial Revolution , large @-@ scale production replaced home manufacturing ; this shift , coupled with prevailing norms governing sex or gender roles , dictated that the father become the <unk> and the mother the caregiver . When affection @-@ based marriages emerged in the 1830s , parents began <unk> more attention to children and family relationships became more open . Beginning during World War II , many women entered the workforce out of necessity ; women resumed the caregiver position after the war , but their new @-@ found sense of independence changed the traditional family structure together with cultural shifts leading to the feminist movement and advances in birth control . Some women opted to return to the care <unk> role . Others chose to pursue careers . When women chose to work outside of the home , alternative childcare became a necessity . If childcare options were too costly , unavailable , or undesirable , the stay @-@ at @-@ home dad became a viable option . 
- The number of stay @-@ at @-@ home dads began gradually increasing in the late 20th century , especially in developed Western nations . The recent statistic that <unk> Research released , showed a report in June 2014 that found 2 million men to be stay @-@ at @-@ home dads . However , in 2010 , the number of stay @-@ at @-@ home dads had reached its highest point of 2 @.@ 2 million . 
- Though the role is subject to many stereotypes , and men may have difficulties accessing parenting benefits , communities , and services targeted at mothers , it became more socially acceptable by the 2000s . The stay @-@ at @-@ home dad was more regularly portrayed in the media by the 2000s , especially in the United States . However , due to traditional family structures and <unk> expectations , the stay at home father figure is culturally unacceptable in countries such as East Asia and India . 
- 
- = = Evolution of family roles = = 
- 
- 
- = = = Pre @-@ industrialisation = = = 
- 
- In the colonial United States the nuclear family was the most common family form . Typical families consisted of five or more children initially ; because of high infant mortality rates , only a few children survived adolescence . Colonial families existed to serve six main functions : self @-@ sufficient business , school , vocational institute , church , house of correction , and welfare institution . 
- The first African @-@ Americans to reach America were initially brought over as indentured servants , but instead became slaves . By the 19th century , slave trading was a thriving business . Typical slave families consisted of one or two children . Women were primarily the head of the families , either because the fathers had died or had been separated from the family . African @-@ American women experienced what came to be known as the " double day , " a full day of domestic <unk> plus a full day of work outside the home . 
- 
- = = = <unk> ( 1800 – 1900 ) = = = 
- 
- The Industrial Revolution led to extensive mechanization , resulting in a shift from home manufacturing to large @-@ scale factory production . As this rapid transition took place , families lost many of their production functions . Instead , family members had to work outside the home to support their families . As a result , husbands and wives began operating in separate spheres of activity . The husband became the " <unk> " by going out and working , while the wife stayed home and took care of the family . 
- 
- = = = Transition to modern family ( 1900 – present ) = = = 
- 
- The modern family is commonly thought to have originated in the 1830s : courtship became more open , marriages were often based on affection , and parents devoted more attention to children . At the beginning of the 20th century , married couples began to emphasize the importance of sexual attraction and compatibility in their relationships . This led to more intimate and open relationships along with more adolescent freedom . The transition of the family was influenced by the Great Depression , which forced many women into the workplace in order to compensate for lack of financial stability . In 1932 , a federal executive order stated that only one spouse could work for the federal government . This resulted in many women being forced to resign allowing their husbands to continue working . 
- World War II had a significant impact on changing family roles . Because of the draft , workers were scarce in many industries and employers began to fill jobs with women , mainly in <unk> positions . This increase in working women became one of the few times in history where women were praised for work outside the home . Divorce rates also reached a new high during this period . Not only had many women found a new sense of independence , but cultural shifts were underway , including the rise of <unk> and the development of reliable methods of birth control . Such changes caused some women to decide to end their unhappy marriages . 
- The 1950s saw a " baby boom " in America . This period was also called the " Golden ' 50s " . This was credited to families trying to make up lost time after the war . As a result , many families moved to the suburbs instead of residing in the city , the number of two @-@ income families began to increase , and grown children began to remain at home longer because of financial difficulties . Gradually , women began re @-@ entering the workforce . This progression away from the traditional view of the woman as the homemaker led to the creation of the role of the stay @-@ at @-@ home dad . 
- 
- = = Increase in popularity in the 21st century = = 
- 
- Stay @-@ at @-@ home dads have been seen in increasing numbers in Western culture , especially in Canada , the UK and the United States since the late 20th century . In developed East Asian nations such as Japan and South Korea , this practice is less common . 
- There are several reasons why some families feel that it would be more beneficial for the father to be the primary caregiver while the mother works outside of the home . The decision to use a stay @-@ at @-@ home dad arrangement is most commonly due to economic reasons . At the same time , women are progressing into higher @-@ paying jobs . There are now financial ramifications in deciding whether the mother or father should become the stay @-@ at @-@ home parent . In cases where the woman is the higher @-@ paid parent , it makes more economic sense for her to continue to work while the man takes on the caregiver role . It also makes sense at times the mother 's job offers health benefits for the family whereas the father 's does not . It has also been shown that if the " pregnancy was jointly planned , " the father is more likely to be involved . 
- With the growth of <unk> , many men are also able to work from home . In this regard , he is contributing financially to the family while also acting as the primary caregiver of the family 's children . <unk> in parent 's schedules can also account for some of the stay @-@ at @-@ home dads . Sometimes the father works odd work shifts while the mother has a typical nine @-@ to @-@ five work schedule . 
- Fixed gender roles began to become less prominent in the Western world starting in the late 20th century , allowing men to make their own choice of career without regard to traditional gender @-@ based roles . Some men who choose this role may do so because they enjoy being an active part of their children 's lives , while in other families , the mother wants to pursue her career . For example , of the 187 participants at Fortune Magazine 's Most Powerful Women in the Business Summit , one third of the women 's husbands were stay @-@ at @-@ home dads . Families vary widely in terms of how household <unk> are divided . Some retired males who marry a younger woman decide to become stay @-@ at @-@ home dads while their wives work because they want a " second chance " to watch a child grow up in a second or third marriage . Additionally , more career and lifestyle options are accepted and prevalent in Western society . There are also fewer restrictions on what constitutes a family . 
- 
- = = <unk> = = 
- 
- Depending on the country or region , a stay @-@ at @-@ home dad might find more or less social support for his decision . In regions where traditional roles prevail , a stay @-@ at @-@ home dad might be <unk> by stay @-@ at @-@ home mom 's peer group . In order to find support for their choice , these men have created and joined many support networks . 
- Still , many men struggle to find acceptance within the role of stay @-@ at @-@ home dad despite the many gains that have been made . Many worry about losing business skills and their " professional place in line " . There is a common <unk> that stay @-@ at @-@ home dads cannot get a job and therefore must rewrite the typical family roles , forcing the wife into the workforce . <unk> the financial burden and dealing with children 's attachment to the dad can be difficult on a working mother . 
- One 2002 study by the American Heart Association suggested stay @-@ at @-@ home dads may face a higher risk of heart disease . The reasons for the health risk are not specified . 
- The role of stay @-@ at @-@ home dad may be difficult for men who feel as though they had no option . It is hard for these men to adapt from being a financial provider in the family to being a homemaker . Men who willingly choose to become a stay @-@ at @-@ home dad are much more satisfied with their role in the family . 
- 
- = = = For the Father = = = 
- 
- - No longer the “ Money Maker ” in the family , may become an issue due to not being the main support and rock for the family . 
- - <unk> from the workplace to stay at home role can be challenging . 
- - <unk> cut due to some male jobs paying higher than a female job . 
- 
- = = = For the Mother = = = 
- 
- - Missing their children . 
- - <unk> to the workforce role from the stay at home mother role . 
- - Becoming the main source of income for the family and working long hours away from home and family . 
- - Will lose less time with their children and will devote 70 % of their day to work force and 30 % to their children . 
- 
- = = <unk> = = 
- 
- 
- = = = For the child = = = 
- 
- There have been many studies done which suggest the importance of the paternal role in a child 's life and benefits of the stay @-@ at @-@ home dad . Children respond differently to males and females at birth . 
- A study conducted by Dr. Kyle D. Pruett found that infants between 7 and 30 months respond more favorably to being picked up by their fathers . Pruett also found that a father 's parenting style is beneficial for a child 's physical , cognitive , emotional and behavioral development . Mothers <unk> <unk> when they become frustrated while fathers encourage them to manage their frustration . This helps the children learn to deal with stress and frustration . A long @-@ term study Pruett conducted proved that a father 's active involvement with his children , from birth to adolescence , promotes greater emotional balance , stronger curiosity and a stronger sense of self @-@ assurance in the child . 
- Additional studies show that during the first five years of a child 's life , the father 's role is more influential than the mother 's in how the child learns to manage his or her body , navigate social circumstances , and play . Furthermore , a 1996 study by McGill University found that the " single most important childhood factor in developing empathy is paternal involvement in childcare " . Children that have a strong paternal influence have more <unk> abilities . It has been researched in The Role of the Father in Child Development , that in general , children with stay @-@ at @-@ home dads develop <unk> at infancy . The study further concluded that fathers who spent time alone bonding with their children more than twice per week brought up the most <unk> adults . 
- Robert Frank , a professor of child development at <unk> Community College in Illinois , conducted a study comparing households with a stay @-@ at @-@ home dad and households with a stay @-@ at @-@ home mom . His study concluded that women were still able to form a strong bond with their children despite working full @-@ time outside of the home . Also , women working full @-@ time were often more engaged with their children on a day @-@ to @-@ day basis than their male counterparts . His study concluded that in a family with a stay @-@ at @-@ home dad arrangement , the maternal and paternal influences are equally strong . This contrasts with the more traditional family structure where the father works outside of the home and the mother stays home with the children . In this type of arrangement , the mother 's influence is extremely strong , whereas the father 's is relatively insignificant . The study found that both parents play an equal role in a child 's development , but the stay @-@ at @-@ home dad arrangement is the most beneficial for the child . 
- 
- = = = For the mother = = = 
- 
- The stay @-@ at @-@ home dad arrangement allows the mother to work without having to use a <unk> or a <unk> . This arrangement prevents the mother from having to deal with the stress of finding acceptable childcare , checking backgrounds , and paying for care . This arrangement also can help ensure that the family 's values are being upheld and instilled in the children . Free from the stress of childcare , the working mother is able to actively pursue their career . This allows for a more relaxed working environment for the mother and allows her to focus on her career . If the mother has a higher paying job , this extra income will allow for savings to be made for the children , these savings could help the mother later on pay for university for the child and / or children . Thus , she can advance her career and provide more money for the family . It puts a sound mind for the mother knowing that the child / children are at a safe place with the father having the same safety and values as the mother . 
- 
- = = = For the father = = = 
- 
- A survey conducted by Minnesota 's Department for Families and Children 's Services shows that men consider child care to be far more important than a <unk> . Of 600 dads surveyed , a majority said their most important role was to " show love and affection " to kids . " Safety and protection " came next , followed by " moral guidance , " " taking time to play , " and " teaching and encouraging . " " Financial care " finished last . Many men are now becoming more involved in their children 's lives , and because of that many men now have a better understanding of what life is like for their child growing up in modern society . Because fathers are immersed in their children 's lives , many of the <unk> " <unk> " attitudes and activities historically prescribed for children may be <unk> due to a more gender @-@ neutral parenting approach that focuses on promoting independence and emotional well being . This allows children , especially male children , to grow up with a greater capacity for empathy and less <unk> in attitudes pertaining to gender roles than would perhaps be the case in more traditionally @-@ structured households . 
- 
- = = <unk> = = 
- 
- 
- = = = Australia = = = 
- 
- Stay @-@ at @-@ home dads make up a very small portion of the Australian population although this appears to be rapidly changing . In 2003 , 91 % of fathers with children aged under 15 years were employed with 85 % employed full @-@ time . Because of this , there are few role models or resources that can help Australian fathers with the stay @-@ at @-@ home dad role . The Australian Bureau of Statistics show that approximately 7 % of two parents families with children under the age of 14 have a father who is unemployed and a mother who works full time . Stay at home dads in Australia have almost doubled over the past decade from 57 @,@ 900 to 106 @,@ 000 , and expected to increase in the future . Recent sociological studies have shown that men are dedicating more time and support to their children in comparison to the 19th century . The idea of a stay at home dad were far from mainstream , however the rising demand for female work has influenced this statistic to rise . 
- 
- = = = Canada = = = 
- 
- Over a 20 @-@ year period during the late 20th century , there was an increase in the number of women in the workforce in Canada . This shift increased father participation in family tasks that used to primarily be the responsibility of the mother . Beginning in the late 20th century , parental roles began to become less traditional , and the stay @-@ at @-@ home dad arrangement began to become more common . The number of stay @-@ at @-@ home dads increased by three percent points between 1976 and 1998 , and the average age of a stay @-@ at @-@ home dad in Canada is forty @-@ two . A bill was passed in by the Canadian government in October 1990 which granted paid leave for fathers for the purpose of primary <unk> . 
- 
- = = = East Asia = = = 
- 
- Stay @-@ at @-@ home dads are not prevalent in East Asian countries , which generally have strict traditional gender roles . However , a survey conducted in 2008 in Japan suggested that nearly one third of married men would accept the role . The Japanese government passed a law in April 1992 allowing time off following the birth of a child for both male and female employees . In 1996 , 0 @.@ 16 % of Japanese fathers took time off of work to raise children . In South Korea , about 5 @,@ 000 men were stay @-@ at @-@ home dads in 2007 . Even so , stay @-@ at @-@ home dads face discrimination from stay @-@ at @-@ home mothers , and are often ostracized . 
- 
- = = = = China = = = = 
- 
- Beginning in the 2000s , the stay @-@ at @-@ home dad began to emerge as a role in China , though some remain uncomfortable with the way the role changes traditional family dynamics . Customs in China suggest that men must be the heads of their households . <unk> is an issue for stay @-@ at @-@ home dads , who sometimes prefer not to tell others about their family arrangement . Traditional ideas promote criticism of " woman @-@ like " men , and many feel that they would face humiliation and criticism for being stay @-@ at @-@ home dads . Others suppose they would be looked at as having a wife that is " too strong " . 
- 
- = = = = North Korea = = = = 
- 
- Until around 1990 , the North Korean state required every able @-@ bodied male to be employed by some state enterprise . However , some 30 % of married women of working age were allowed to stay at home as full @-@ time housewives ( less than some countries in the same region like South Korean \ Japan and Taiwan , more than Soviet Union \ <unk> China or Nordic countries like Sweden , about the same as Today 's USA ) . ) In the early 1990s , an estimated 600 @,@ 000 @-@ 900 @,@ 000 people perished in the famine , which was largely a product of the North Korean government 's unwillingness to reform the economy , and the old system began to fall apart . In some cases women began by selling household items they could do without or <unk> food . Today at least three @-@ quarters of North Korean market <unk> are women . A joke making the rounds in <unk> goes : ' What do a husband and a pet dog have in common ? ' <unk> : ' Neither works nor earns money , but both are cute , stay at home and can scare away <unk> 
- 
- = = = India = = = 
- 
- The role of the stay @-@ at @-@ home dad is not traditional in India , but it is socially accepted in urban areas . According to one sociologist 's study in 2006 , as much as three percent of all urban working fathers in India are stay @-@ at @-@ home dads , and twelve percent of unmarried Indian men would consider being a stay @-@ at @-@ home dad according to a survey conducted by Business Today . One sociologist <unk> <unk> called this a shift in Indian society , saying that a decade ago , " it was an unheard concept and not to mention socially unacceptable for men to give up their jobs and remain at home . " However , only 22 @.@ 7 percent of Indian women are part of the labor force , compared to 51 @.@ 6 percent of men ; thus , women are more likely to be <unk> because most do not work outside the home . 
- 
- = = = United Kingdom = = = 
- 
- According to an article by the Daily Mail , the number of stay @-@ at @-@ home dads in 2007 had increased by 83 percent since 1993 . According to the same paper , in 2007 , recent figures from the Office for National Statistics showed more than 200 @,@ 000 fathers chose to stay at home and be the primary caregiver for their children . 
- In an interview published in the Radio Times in May 2013 <unk> Brady made it plain she " could never be a <unk> " . While she maintains a business career in London her husband Paul <unk> has the role of house @-@ husband though Brady <unk> in tasks at home to a certain extent . 
- 
- = = = United States = = = 
- 
- In 2008 , an estimated 140 @,@ 000 married fathers worked in the home as their children 's primary <unk> while their wives worked outside of the home to provide for the family . This number is less than the previous two years according to the US Census Bureau . In 2007 , stay @-@ at @-@ home dads made up approximately 2 @.@ 7 % of the nation 's stay @-@ at @-@ home parents . This is triple the percentage from 1997 , and has been consistently higher each year since 2005 . In 2006 , stay @-@ at @-@ home dads were caring for approximately 245 @,@ 000 children ; 63 % of stay @-@ at @-@ home dads had two or more children . These statistics only account for married stay @-@ at @-@ home dads ; there are other children being cared for by single fathers or gay couples . Also , it is difficult to <unk> how many of these stay @-@ at @-@ home dads have accepted the role voluntarily , and how many have been forced into it by the economic crisis of the late 2000s and early 2010s during which a great number of mostly @-@ male blue @-@ collar industries suffered significant losses and many previously employed men entered periods of prolonged unemployment . 
- 
- = = Notable current and former stay @-@ at @-@ home dads = = 
- 
- Jim <unk> , American singer @-@ songwriter 
- Billy Ashley , American baseball player 
- Eric <unk> , 2014 Nobel Prize winner in Chemistry 
- Michael Stephen Clark , American columnist 
- <unk> <unk> , American politician 
- Mike Duncan , American <unk> 
- Dan <unk> , American actor , comedian and <unk> 
- John Lennon , British musician 
- Rick <unk> , Actor and comedian 
- Alex Vincent , American drummer 
- Mo <unk> , American writer , animator , and creator of children 's books 
- 
- 
- = Tristan ( horse ) = 
- 
- Tristan ( 1878 – 1897 ) was a British Thoroughbred racehorse and sire . In a career that lasted from the April 1880 to October 1884 , he ran 51 times and won 27 races . A useful performer at two and three years old , he matured into an outstanding horse in his last three seasons , winning important races at distances ranging from six furlongs ( the July Cup ) to two and a half miles ( Ascot Gold Cup ) and defeating three winners of the Epsom Derby . Unusually for a 19th @-@ century racehorse , he was regularly campaigned internationally , winning three consecutive <unk> of the Grand Prix de Deauville . Tristan 's success was achieved despite a dangerous and unpredictable temperament : at the height of his success , he was described as " a very <unk> @-@ tempered animal " . 
- 
- = = Background = = 
- 
- Tristan was a dark chestnut horse standing just under 16 hands ( 64 inches , 163 cm ) high , bred by Robert St Clair @-@ <unk> , 4th Earl of <unk> at the Easton <unk> near Great <unk> in Essex . As a <unk> , Tristan was bought by the French owner C. J. Lefevre , who sent the colt to be trained by Tom Jennings at the Phantom House stable at Newmarket , Suffolk . Jennings would later say that Tristan had been <unk> as a <unk> before his arrival at Newmarket and this explained his well @-@ known temperament problems . Tristan was ridden in most of his races by George Fordham and came to show a strong and sometimes violent dislike for other <unk> . 
- Tristan ’ s sire Hermit won the Epsom Derby in 1867 and became an outstandingly successful stallion , being Champion Sire for seven successive years . In addition to Tristan , he sired the Derby winners <unk> and St. <unk> , as well as St. <unk> ( 1000 Guineas ) , <unk> ( Oaks ) , and <unk> ( 1000 Guineas & Oaks ) . Tristan 's dam <unk> was an influential broodmare whose direct descendants included the Australian champion <unk> , the Kentucky Derby winner George Smith and the Epsom Derby winner Reference Point . 
- 
- = = Racing career = = 
- 
- 
- = = = 1880 : two @-@ year @-@ old season = = = 
- 
- Tristan ran nine times in the first half of 1880 , winning four races worth about £ 1 @,@ 900 @.@ beginning his career with a second place in the Lincoln Cup at Lincoln Racecourse . At Epsom in April he won the Westminster Stakes and then finished second of fifteen runners to the filly <unk> in the Hyde Park Plate . On 11 May he won the Breeders ' Plate over five furlongs at York reversing the Epsom form by beating <unk> " cleverly " . At the end of the month Tristan returned to Epsom for the Derby meeting and won the Stanley Stakes , in which his three opponents included the future Grand National winner <unk> . At Royal Ascot he finished third to Sir Charles in the New Stakes , the race now known as the Norfolk Stakes . After running on unusually hard ground at Newmarket in July , when he finished <unk> behind Iroquois in the Chesterfield Stakes , he developed leg problems and missed the rest of the season . 
- 
- = = = 1881 : three @-@ year @-@ old season = = = 
- 
- At the start of May Tristan finished <unk> behind <unk> in the 2000 Guineas and two weeks later he ran third in the Payne Stakes . In the Derby he ran prominently for much of the way and turned into the straight in second place before weakening in the closing stages and finishing seventh behind Iroquois . 
- On 12 June he was again tested in the highest class when he was sent to run in the Grand Prix de Paris at <unk> where he was ridden by Fred Archer . Tristan was beaten a head after a " superb race " with the American colt <unk> , ridden by George Fordham . The French crowd treated the defeat of Tristan as a home victory and joined the sizable American contingent in the celebrations which were described as “ the <unk> ever seen at <unk> ” . It was only after the intervention of the local police force that Fordham and Archer were able to return to the weighing room . He won a race called the Prix de Deauville ( not to be confused with the Grand Prix de Deauville ) before returning to England . In July he won the six <unk> <unk> Stakes at Newmarket " in a <unk> " at odds of 2 / 5 and the Singleton Stakes at Goodwood . In August he traveled back to France for his first attempt at the Grand Prix de Deauville and finished third to <unk> . On his return to Britain he finished last of three runners in the Great Yorkshire Stakes over one and three quarter miles at York . 
- In September he was sent to Doncaster where he won a Queen 's Plate before finishing second to <unk> in the Doncaster Cup . At Newmarket in October , Tristan ran third to the two @-@ year @-@ old filly Nellie in the Great Challenge Stakes and was beaten twice more by <unk> when finishing second to the American colt in the Select Stakes and third in the <unk> <unk> . In the latter event , Tristan was beaten a head and a neck after being badly hampered in the closing stages . 
- 
- = = = 1882 : four @-@ year @-@ old season = = = 
- 
- As a four @-@ year @-@ old in 1882 , Tristan showed much improved form and established himself as one of the leading <unk> in Europe by winning ten times in fourteen starts . He began the year by winning a Queen 's Plate at Newmarket in April and followed up by winning His Majesty 's Plate at Chester in May . At Epsom Downs Racecourse he ran twice at the Derby meeting . In the Epsom Stakes , a handicap race over one and a half miles , Tristan carried top weight of 124 pounds and won by a length and a half from <unk> He then added the Epsom Gold Cup , the race which was the forerunner of the Coronation Cup , in which he successfully conceded twenty @-@ seven pounds to a filly named Isabel . 
- At Royal Ascot in June he ran three times and was unbeaten , showing versatility by winning over three different distances . He took the two mile Gold <unk> , beating the previous year 's winner <unk> by six lengths , the one mile New <unk> Stakes and the one and a half mile Hardwicke Stakes . His performances established him as " one of the best horses in training " . In July he moved down to sprint distances for the July Cup over six furlongs at Newmarket . <unk> at least twelve pounds to his opponents , Tristan won his eighth successive race by taking the lead at half way and winning easily from the two @-@ year @-@ old Royal <unk> with Nellie third . In the Goodwood Cup on 27 July Tristan started at odds of 1 / 4 against two moderate opponents , but his winning streak came to an end after his jockey , George Fordham , <unk> the race tactics and allowed a horse named Friday to build up a huge lead which he was unable to make up in the straight . In August he was sent to France again and won the Grand Prix de Deauville from ten opponents . 
- On 12 October he contested the Champion Stakes over ten furlongs at Newmarket . He ran a dead heat with the filly <unk> , winner of the 1881 1000 Guineas and Oaks , with the St <unk> winner Dutch Oven in third . Later in the meeting he finished second to the two @-@ year @-@ old Energy in the Great Challenge Stakes . At the end of October , Tristan ran twice at the Newmarket Houghton meeting without <unk> his best form . In the <unk> <unk> he carried top weight of 130 pounds and finished seventh of the thirty @-@ one runners behind <unk> . On his final start of the year he ran in the <unk> Club Stakes over two and a quarter miles . In a closely contested four way finish he dead @-@ heated for second place with City Arab , a short head behind <unk> and a neck in front of the mare <unk> Roy . 
- 
- = = = 1883 : five @-@ year @-@ old season = = = 
- 
- In April 1883 Tristan won a Queen 's Plate at Epsom and then collected a second Epsom Gold Cup at the Derby meeting on 25 May , winning by three lengths from a field which included the Derby winner <unk> . Between these races he was beaten when attempting to <unk> three pounds to the unbeaten Irish horse <unk> in the Westminster Cup at <unk> . On this occasion he reportedly showed " a good deal of temper " before the race and ran " <unk> " . 
- On 7 June at Royal Ascot he contested the Ascot Gold Cup , the year 's most important staying race . Fordham sent him into the lead a mile from the finish and he won easily by three lengths from Dutch Oven and <unk> . On the last day of the Royal meeting he took the lead on the turn into the straight and won by a length and a half from Iroquois and eight others under top weight of 138 pounds to take his second Hardwicke Stakes . His winning time of 2 : 37 was considered exceptionally fast for the date . By this time he was being described as " about the best horse of the day in England " , and " the present champion of the English turf " , although he was also called " a bad horse to ride " . An example of Tristan 's problematic behaviour came on Newmarket Heath that summer when he attacked a horse named <unk> , who was acting as his training companion . <unk> was left lame whilst his rider , whom Tristan also attempted to savage , escaped with a black eye . Another of Tristan 's " victims " was a stable boy , whom he reportedly " shook like a rat " . His poor behaviour was blamed for contributing to his two defeats at Newmarket in July . 
- He returned to Deauville in August to win the Grand Prix again , this time carrying 151 pounds . At Newmarket on 11 October he recorded a repeat victory in the Champion Stakes again , this time taking the race outright from the St <unk> winners <unk> and Dutch Oven . For the third successive year he was beaten by a two @-@ year @-@ old in the Great Challenge Stakes , finishing third to the future 1000 Guineas winner <unk> . 
- Tristan 's winning prize money for the year totaled £ 7 @,@ 628 , a record for a five @-@ year @-@ old which enabled Lefevre win the owner 's championship . Tristan 's career earnings had reached £ 19 @,@ 614 by the end of 1883 . 
- 
- = = = 1884 : six @-@ year @-@ old season = = = 
- 
- Tristan remained in training in 1884 by which time his achievements had made his name a " household word " . At Newmarket in spring he ran a public trial against St. Simon a three @-@ year @-@ old colt who was prevented from running in the classics because the death of his owner had <unk> his entries . Tristan attempted to give the younger horse twenty @-@ three pounds and was easily beaten . The two horses met again at Royal Ascot on 10 June when Tristan attempted to defend his status as the country 's best <unk> in the Ascot Gold Cup . His temperament came to the fore as he proved difficult to get to the start and when the race began he again proved no match for St. Simon who won by twenty lengths . In his other races at the meeting , Tristan took on St. <unk> and Harvester the <unk> who had dead @-@ heated in the Epsom Derby . In the two mile Gold <unk> he finished third to St <unk> and <unk> Roy , but in the Hardwicke Stakes on the last day of the meeting he won easily from Waterford , with the favourite Harvester a distant third . 
- On 17 August Tristan carried 151 pounds to a third successive victory in the Grand Prix de Deauville . Once again he showed a good deal of temperament before the start but won the race by a short head from <unk> <unk> . In autumn he returned to England to end his racing career at Newmarket . Running in the Champion Stakes for the third time on 9 October he delayed the start for a quarter of an hour by his " display of temper " before dead @-@ heating with the four @-@ year @-@ old <unk> . He was then retired to <unk> " covered with honours " and regarded as " one of the most wonderful horses of the time " . 
- 
- = = <unk> career = = 
- 
- Lefevre retired Tristan to stand as a stallion in France at his <unk> near <unk> . In 1891 he was purchased by Caroline , Duchess of Montrose , who returned him to England as a replacement for the recently deceased <unk> . Three years later he was sold again and exported to Austria @-@ Hungary . In 1897 he died as a result of injuries sustained after <unk> his head against the wall of his stable in a fit of temper . Tristan was not a great success as a stallion , but he did sire Canterbury Pilgrim , who won the Epsom Oaks in 1896 and became a highly successful and influential broodmare . Other good winners included Le Nord ( <unk> Stakes ) and Le <unk> ( Champion Stakes ) . 
- 
- = = <unk> = = 
- 
- 
- 
- = Plunketts Creek ( Loyalsock Creek ) = 
- 
- Plunketts Creek is an approximately 6 @.@ 2 @-@ mile @-@ long ( 10 @.@ 0 km ) tributary of Loyalsock Creek in Lycoming and Sullivan counties in the U.S. state of Pennsylvania . Two unincorporated villages and a hamlet are on the creek , and its watershed drains 23 @.@ 6 square miles ( 61 km2 ) in parts of five townships . The creek is a part of the Chesapeake Bay drainage basin via Loyalsock Creek and the West Branch Susquehanna and Susquehanna Rivers . 
- Plunketts Creek 's unique name comes from the first owner of the land including the creek 's mouth , and the creek has given its name to two townships ( although one has since changed its name ) . The creek flows southwest and then south through the dissected Allegheny Plateau , through rock from the <unk> sub @-@ period and Devonian period . Much of the Plunketts Creek valley is composed of various glacial deposits , chiefly alluvium . 
- Although the Plunketts Creek watershed was clear @-@ cut and home to a tannery , sawmills , and a coal mine in the nineteenth century , today it is heavily wooded and known for its high water quality , fishing , and other recreational opportunities . The watershed now includes parts of the Loyalsock State Forest , Pennsylvania State Game Lands , and a State Game Farm for raising pheasant . Tourism , hunting , and fishing have long been important in the region , and its year @-@ round population is increasing much faster than that of either Lycoming or Sullivan County . 
- 
- = = Name = = 
- 
- Plunketts Creek is named for Colonel William Plunkett , a physician , who was the first president judge of Northumberland County after it was formed in 1772 . During conflicts with Native Americans , he treated wounded settlers and fought the natives . Plunkett led a Pennsylvania expedition in the <unk> @-@ Yankee War to forcibly remove settlers from Connecticut , who had claimed and settled on lands also claimed by Pennsylvania . For his services , Plunkett was granted six tracts of land totaling 1 @,@ 978 acres ( 8 @.@ 00 km2 ) on November 14 , 1776 , although the land was not actually surveyed until September 1783 . Plunkett 's land included the creek 's mouth , so Plunketts Creek was given his name . 
- During the American Revolution , Plunkett did not actively support the revolution and thus was suspected of being sympathetic to the British Empire . He died in 1791 , aged about 100 , and was buried in Northumberland , without a grave marker or monument ( except for the creek that bears his name ) . Lycoming County was formed from Northumberland County in 1795 . When Plunketts Creek Township was formed in Lycoming County in 1838 , the original name proposed was " Plunkett Township " but the lingering suspicions of his British sympathies led to that name being rejected . Naming the township for the creek was an acceptable compromise . 
- Plunketts Creek Township was originally much larger than it is now , and two other townships were formed from parts of it . When Sullivan County was formed from Lycoming County on March 15 , 1847 , Plunketts Creek Township was divided between the counties , with each having a township of the same name . This led to some confusion and in 1856 the citizens of Sullivan County petitioned the state legislature to change the name of their Plunketts Creek Township to Hillsgrove Township , for Hillsgrove , the main village and post office in the township . In 1866 , Cascade Township was formed from parts of Hepburn and Plunketts Creek Townships in Lycoming County . 
- According to Meginness ( 1892 ) , Colonel Plunkett actually spelled his last name " <unk> " , but the current spelling was established " by custom and the courts " . As of 2007 , it is the only stream officially named " Plunketts Creek " on USGS maps of the United States and in the USGS Geographic Names Information System . ( There is a " Plunkett Creek " in Tennessee which has " Plunketts Creek " as an official variant name ) . The possessive apostrophe is not part of the official name of the creek , although records from the 19th century often spell it as " Plunkett 's Creek " . The Native American name for Plunketts Creek is unknown . Two streams in the watershed have given their names to roads in Plunketts Creek Township : Engle Run Drive and Mock Run Road . 
- 
- = = Course = = 
- 
- The source of Plunketts Creek is 1440 ft ( 439 m ) above sea level , northwest of the unincorporated village of Hillsgrove and just south of the Loyalsock State Forest in Hillsgrove Township , Sullivan County . The source is a pond just north of Pennsylvania Route <unk> ( the road between the villages of Proctor and Hillsgrove ) and Plunketts Creek crosses the road twice , then receives two unnamed tributaries on the right bank as it flows generally southwest about 1 @.@ 5 miles ( 2 @.@ 4 km ) to the Lycoming County line . 
- The creek continues southwest as it enters Plunketts Creek Township and receives <unk> Run on the left bank , 4 @.@ 70 miles ( 7 @.@ 56 km2 ) upstream from the mouth . It next receives Mock Creek at the hamlet of <unk> ( 4 @.@ 24 miles ( 6 @.@ 82 km ) from the mouth ) , then Wolf Run ( 2 @.@ 72 miles ( 4 @.@ 38 km ) from the mouth ) , both on the right bank . At the village of Proctor , Plunketts Creek receives King Run ( 1 @.@ 66 miles ( 2 @.@ 67 km ) ) on the right bank , then turns south towards Loyalsock Creek . It flows through the Pennsylvania Game Commission 's Northcentral Game Farm , then receives the unnamed tributary in Coal Mine Hollow on the right bank and Dry Run on the left bank ( 0 @.@ 82 miles ( 1 @.@ 32 km ) and 0 @.@ 17 miles ( 0 @.@ 27 km ) , respectively ) . It finally enters the village of Barbours , where its mouth is on the right bank of Loyalsock Creek at 725 feet ( 221 m ) . 
- Lycoming County is about 130 miles ( <unk> km ) northwest of Philadelphia and 165 miles ( 266 km ) east @-@ northeast of Pittsburgh . Although Plunketts Creek is 6 @.@ 2 miles ( 10 @.@ 0 km ) long , the direct distance between the source and the mouth is only 4 @.@ 1 miles ( 6 @.@ 6 km ) . From the mouth of Plunketts Creek it is 19 @.@ 50 miles ( 31 @.@ 38 km2 ) along Loyalsock Creek to its confluence with the West Branch Susquehanna River at Montoursville . The elevation at the source is 1440 feet ( 439 m ) , while the mouth is at an elevation of 725 feet ( 221 m ) . The difference in elevation , 715 feet ( 218 m ) , divided by the length of the creek of 6 @.@ 2 miles ( 10 @.@ 0 km ) gives the average drop in elevation per unit length of creek or relief ratio of 115 @.@ 3 feet / mile ( 21 @.@ 8 m / km ) . For comparison , the relief ratio of Wallis Run ( the next watershed to the southwest ) is 110 @.@ 9 feet / mile ( 21 @.@ 0 m / km ) , while Loyalsock Creek 's is only 28 @.@ 0 feet / mile ( 5 @.@ 33 m / km ) . 
- 
- = = = Floods = = = 
- 
- Plunketts Creek can vary greatly in depth , depending on the season and recent precipitation . Its water level is typically highest ( perhaps 3 feet ( 1 m ) deep ) in spring or for a few days after a heavy rain , and lowest in late summer , when it can <unk> to a <unk> . While there is no stream gauge on Plunketts Creek , a rough estimate of the creek 's water level may be found from the stream gauge on the Loyalsock Creek bridge in Barbours , just downstream of the mouth . Lycoming County operates this gauge as part of the county @-@ wide flood warning system . It only measures the water height ( not discharge ) , and measured a record gauge height of 34 @.@ 0 feet ( 10 @.@ 4 m ) on September 7 , 2011 . 
- The September 2011 flood was caused by remnants of Tropical Storm Lee , which dumped 11 @.@ 36 inches ( <unk> mm ) of rainfall in the nearby village of <unk> in Fox Township in Sullivan County ( just north of the creek 's source ) . The 2011 flooding caused widespread damage in Proctor and Barbours and destroyed a small stone bridge on Wallis Run Road in Proctor over King Run , a tributary of Plunketts Creek . The Barbours Fire Hall became an " emergency relief center offering food , shelter and supplies to victims of the flood " . Further downstream on the Loyalsock , the flooding badly damaged the historic Hillsgrove <unk> Bridge , washed out sections of Pennsylvania Route 87 along the creek , and destroyed the Pennsylvania Route 973 and Lycoming Valley Railroad bridges over the creek near and in Montoursville . 
- The previous record flood reached 24 @.@ 9 feet ( 7 @.@ 6 m ) on the Loyalsock flood gauge at Barbours on January 19 – 20 , 1996 . This major flood resulted from heavy rain , snow melt , and ice dams , which caused millions of dollars of damage throughout Lycoming County , and six deaths on Lycoming Creek in and near Williamsport . On Plunketts Creek , the flood heavily damaged and later caused the demolition of Plunketts Creek Bridge No. 3 , a mid @-@ 19th century stone arch bridge listed on the National Register of Historic Places . The flood waters were 4 feet ( 1 @.@ 2 m ) deep in Barbours and it was called the village 's " worst flood in history " at the time . 
- 
- = = Geology = = 
- 
- Plunketts Creek is in the southern edge of the dissected Allegheny Plateau , near the Allegheny Front . The underlying bedrock is sandstone and shale , mostly from the <unk> sub @-@ period , with rock from the Devonian period in the north of the watershed . The northern edge of the Plunketts Creek drainage basin is formed by <unk> Ridge and <unk> Ridge . Plunketts Creek flows along the north side of Camp Mountain and , on turning south at Proctor , forms a water gap between it and Cove Mountain ( to the west ) . 
- The watershed has no oil or conventional natural gas fields . However , a potentially large source of natural gas is the <unk> shale , which lies 1 @.@ 5 to 2 @.@ 0 miles ( 2 @.@ 4 to 3 @.@ 2 km ) below the surface here and stretches from New York through Pennsylvania to Ohio and West Virginia . Estimates of the total natural gas in the black shale from the Devonian period range from 168 to 516 trillion cubic feet ( 4 @.@ 76 to 14 @.@ 6 trillion m3 ) , with at least 10 percent considered <unk> . 
- The Pennsylvania Bureau of <unk> and <unk> Survey 's " Distribution of Pennsylvania <unk> " map shows no major deposits of coal in the Plunketts Creek watershed , and only one deposit nearby in the Loyalsock Creek watershed ( in southern Plunketts Creek Township ) . However , Meginness ( 1892 ) refers to coal mines in Plunketts Creek Township , and there is an unnamed tributary of Plunketts Creek in " Coal Mine Hollow " on the right bank between Dry Run and King Run , so it seems a small coal mine operated there in the past . 
- Much of the Plunketts Creek valley ( and those of its tributaries ) is composed of various glacial deposits . <unk> to the mouth , there are large deposits of alluvium , as well as alluvial fan and alluvial terraces . Many of the glacial deposits are associated with the Wisconsin <unk> , with <unk> drift and till , as well as <unk> present . The alluvium is " 10 feet ( 3 m ) or more thick in the lower reaches of the Plunketts Creek valley " , but only " 6 feet ( 2 m ) thick in <unk> tributary valleys " . The <unk> is described as " <unk> sand and gravel that form terrace remnants along the flanks of Loyalsock Creek and Plunketts Creek valleys " . 
- 
- = = Watershed = = 
- 
- The Plunketts Creek watershed drains parts of Cascade , <unk> , and Plunketts Creek townships in Lycoming County , and Fox and Hillsgrove Townships in Sullivan County ( with most of the watershed in Plunketts Creek Township ) . The drainage basin area is 23 @.@ 6 square miles ( 61 km2 ) , accounting for 4 @.@ 78 % of the <unk> square miles ( 1 @,@ 280 km2 ) Loyalsock Creek watershed . Bear Creek , whose mouth is also within the village of Barbours but on the opposite ( left ) bank , is the nearest major creek at 0 @.@ 52 miles ( 0 @.@ 84 km ) downstream , as measured along Loyalsock Creek . ( It is also known as " Big Bear Creek " as it is the watershed upstream of " Little Bear Creek " . ) The neighboring major watersheds on the same bank are Wallis Run ( 9 @.@ 56 miles ( 15 @.@ 39 km ) downstream ) and Mill Creek ( at the village of Hillsgrove , 9 @.@ 16 miles ( 14 @.@ 74 km ) upstream ) . Pleasant Stream , a tributary of Lycoming Creek , is the watershed to the north . 
- The named tributaries together account for 70 @.@ 6 % of the Plunketts Creek watershed . The largest tributary is Wolf Run , with an area of 7 @.@ 39 square miles ( 19 @.@ 1 km2 ) , accounting for 31 @.@ 3 % of the total . The Wolf Run drainage basin includes both the Noon Branch ( 4 @.@ 26 square miles ( 11 @.@ 03 km2 ) ) and the Brian Branch ( 1 @.@ 60 square miles ( 4 @.@ 14 km2 ) ) . The next largest tributary of Plunketts Creek is King Run with 5 @.@ 56 square miles ( 14 @.@ 4 km2 ) or 23 @.@ 6 % of the watershed . The King Run watershed includes Engle Run , with 2 @.@ 90 square miles ( 7 @.@ 5 km2 ) . The third largest tributary is Dry Run with 1 @.@ 79 square miles ( 4 @.@ 6 km2 ) or 7 @.@ 6 % , followed by the unnamed tributary in Coal Mine Hollow with 1 @.@ 08 square miles ( 2 @.@ 8 km2 ) or 4 @.@ 6 % . All other named tributaries are less than 1 @.@ 00 square mile ( 2 @.@ 6 km2 ) and account for less than 5 % of the drainage basin individually . Plunketts Creek does not have its own watershed association , but is part of the larger Loyalsock Creek Watershed Association . 
- 
- = = = Water quality = = = 
- 
- The clear @-@ cutting of forests in the 19th century adversely affected the ecology of the Plunketts Creek watershed and its water quality . <unk> industries on the creek and its tributaries then included a coal mine and tannery ( which are long since departed ) . In the autumn of 1897 , three men working with hides at the Proctor tannery were stricken with anthrax , two fatally . Another four deaths originally blamed on pneumonia were suspected of being due to pulmonary anthrax , and some cattle drinking from Plunketts Creek downstream from the tannery were also infected . As late as 1959 , the sludge pile from the tannery was still visible in Proctor , but was not disturbed for fear of anthrax spores . No acid mine drainage is reported in the watershed . 
- As of 1984 , the mean annual precipitation for the Loyalsock Creek watershed ( which Plunketts Creek is part of ) was 42 to 48 inches ( <unk> to 1219 mm ) . Pennsylvania receives the greatest amount of acid rain of any state in the United States . Because Plunketts Creek is in a sandstone and shale mountain region , it has a relatively low capacity to neutralize added acid . This makes it especially vulnerable to increased <unk> from acid rain , which poses a threat to the long term health of the plants and animals in the creek . The total <unk> ( TA ) is a measure of the capacity of water to neutralize acid , with a larger TA corresponding to a greater capacity . In 2007 , the TA of two <unk> was known : Engle Run , a 4 @.@ 9 @-@ mile ( 7 @.@ 9 km ) tributary of King Run , had a TA of 5 , and the Noon Branch , a 1 @.@ 9 @-@ mile ( 3 @.@ 1 km ) tributary of Wolf Run , had a TA of 9 . 
- The 2002 Pennsylvania Department of Conservation and Natural Resources ( <unk> ) report on " State Forest Waters with Special Protection " rated Plunketts Creek ( from its source to mouth ) and two of it tributaries , Wolf Run and Mock Creek ( from the county line to the mouth ) , as " High Quality @-@ Cold Water Fisheries " . Two <unk> were rated as " <unk> <unk> " streams for fishing : Engle Run and the Noon Branch of Wolf Run . 
- 
- = = = Recreation = = = 
- 
- Meginness ( 1892 ) wrote that " Plunkett 's Creek township , on account of its <unk> mountain streams of pure water , has always been a favorite place for trout fishing . " In 2007 , the Pennsylvania Fish and Boat Commission classified both Engle Run and the Noon Branch of Wolf Run as Class A Wild <unk> Waters , defined as " streams which support a population of naturally produced trout of sufficient size and abundance to support a long @-@ term and rewarding sport fishery . " Barbours has been popular from early on with " anglers seeking trout in the ' <unk> and its tributaries " , as well as with hunters after black bear , white @-@ tailed deer , and wild turkey in the surrounding forests . 
- Besides fishing , the Plunketts Creek watershed contains much of the 6 @,@ <unk> acres ( 27 @.@ 20 km2 ) of Pennsylvania State Game Lands No. 134 , in both Lycoming and Sullivan counties . Habitat is found there for deer , <unk> grouse , and wild turkey . Hunting , trapping , and fishing are possible with proper licenses on both the state forest and State Game Lands . <unk> , hiking , mountain bike and horseback riding , <unk> , cross @-@ country skiing , and bird watching are all possible on state forest lands . The southern end of the 27 @.@ 1 mile ( 43 @.@ 6 km ) long Old <unk> Path , a loop hiking trail , runs through the watershed just north of Engle and Wolf <unk> . 
- 
- = = History = = 
- 
- 
- = = = Early inhabitants = = = 
- 
- The first recorded inhabitants of the Susquehanna River valley were the <unk> speaking <unk> . Their name meant " people of the muddy river " in <unk> . <unk> by diseases and warfare , they had died out , moved away , or been assimilated into other tribes by the early 18th century . The lands of the West Branch Susquehanna River Valley were then chiefly occupied by the Munsee <unk> of the <unk> ( or Delaware ) , and were under the nominal control of the Five ( later Six ) Nations of the Iroquois . 
- On November 5 , 1768 , the British acquired the " New Purchase " from the Iroquois in the Treaty of Fort Stanwix , opening what are now Lycoming and Sullivan counties to settlement . Initial settlements were on or near the West Branch Susquehanna River , and , as noted , Plunkett did not receive the land including the creek until 1776 , nor was it surveyed until 1783 . It is not clear if Plunkett ever lived on his land : he resided in Northumberland at the time of his death . A <unk> named <unk> was the first recorded inhabitant of what became Plunketts Creek Township , living there " some time between 1770 and 1776 " . He was reputed to be a <unk> from the British Army and left only when he was captured by British soldiers . 
- 
- = = = Lumber and tannery = = = 
- 
- Like all streams in Lycoming and Sullivan Counties , Plunketts Creek served as an area for settlers to establish <unk> , mills , and to a lesser extent , farms . Barbours , the first village on the creek , was founded in 1832 , when John S. <unk> , a Scottish immigrant , built a sawmill opposite the mouth of Plunketts Creek on Loyalsock Creek . Originally known as " <unk> 's Mills " , the village is in a rare area of flat land in the narrow Loyalsock valley and contains the mouths of both Plunketts and Bear Creeks . Barbours became a lumber center which owed " its existence to those forested mountains and the creeks that flow out of them " . John <unk> arrived in 1856 and became a prosperous <unk> and farmer . His family became prominent in Barbours , and in 1997 , his 86 @-@ year @-@ old granddaughter , <unk> <unk> <unk> <unk> , recalled that in her childhood " every family that had 15 cents to their name had a sawmill for cutting lumber . " By 1878 the village had several blacksmiths , a temperance hotel , its own post office , many sawmills , a school , and a wagon maker . Barbours flourished throughout the rest of the nineteenth century . 
- In 1868 , Proctor was built as a company town in the midst of the timber required for the tannery ( Barbours had initially been considered for the site ) . The second village on Plunketts Creek was originally named " <unk> " for Thomas E. Proctor of Boston , who produced leather for the <unk> of shoes there . Proctor was brought to the area by William Stone of Standing Stone Township in Bradford County , who knew the area was " one vast tract of hemlock timber " . The Proctor tannery employed " several hundred " at wages between 50 cents and $ 1 @.@ 75 a day , the employees living in one hundred twenty company houses , each renting for $ 2 a month . Hemlock bark , used in the tanning process , was hauled to the tannery from up to 8 miles ( 13 km ) away in both summer and winter , using wagons and <unk> . The hides which were <unk> to make leather came from the United States , and as far away as Mexico , Argentina , and China . In 1892 , Proctor had a barber shop , two blacksmiths , <unk> stand , <unk> hall , leather shop , news stand , a post office ( established in 1885 ) , a two @-@ room school , two stores , and a wagon shop . <unk> sole leather was hauled by horse @-@ drawn wagon south about 8 miles ( 13 km ) to Little Bear Creek , where it was exchanged for " green " hides and other supplies brought north from Montoursville . 
- Plunketts Creek was a source of power in the nineteenth century and " water @-@ powered sawmills , <unk> mills , and <unk> ( grain ) mills lined the ' <unk> and Plunketts and Big Bear Creeks " . Although hemlock logs were originally left to rot after their bark was <unk> for tanning , with time their lumber was used , among other places in a sawmill on Engle Run north of Proctor . By 1892 there were two steam powered sawmills on Plunketts Creek : one 0 @.@ 5 miles ( 0 @.@ 8 km ) above the mouth , and the other 4 @.@ 0 miles ( 6 @.@ 4 km ) up the creek , near <unk> . An extension of the Susquehanna and Eagles Mere Railroad crossed an unnamed tributary of Plunketts Creek near its source in Sullivan County in 1906 , running from the village of Hillsgrove northwest to the lumber <unk> of <unk> in Cascade and <unk> Townships in Lycoming County . A logging railroad was built by the Central Pennsylvania Lumber Company ( <unk> ) in the far northern part of the watershed in the 1920s . It crossed Engle Run twice and ran parallel to Wolf Run , near both their sources . No other railroads crossed or ran along Plunketts Creek . 
- 
- = = = Decline and renewal = = = 
- 
- The lumber boom on Plunketts Creek ended when the virgin timber ran out . By 1898 , the old growth hemlock was exhausted and the Proctor tannery , then owned by the Elk <unk> Company , was closed and dismantled . <unk> continued in the watershed , but the last logs were floated down Plunketts Creek to the Loyalsock in 1905 . The Susquehanna and Eagles Mere Railroad was abandoned in sections between 1922 and 1930 , as the lumber it was built to transport was depleted . The <unk> logging railroad and their <unk> sawmills were abandoned in 1930 . Without timber , the populations of Proctor and Barbours declined . The Barbours post office closed in the 1930s and the Proctor post office closed on July 1 , 1953 . Both villages also lost their schools and almost all of their businesses . Proctor celebrated its centennial in 1968 , and a 1970 newspaper article on its thirty @-@ ninth annual " Proctor Homecoming " reunion called it a " near @-@ deserted old tannery town " . In the 1980s , the last store in Barbours closed , and the former hotel ( which had become a hunting club ) was torn down to make way for a new bridge across Loyalsock Creek . 
- Second growth forests have since covered most of the clear @-@ cut land . The beginnings of today 's protected areas were established in the late nineteenth and early twentieth centuries : Pennsylvania 's state legislature authorized the acquisition of abandoned clear @-@ cut land in 1897 , creating the state forest system . The Game Commission began acquiring property for State Game Lands in 1920 , and established the Northcentral State Game Farm on Plunketts Creek in 1945 to raise wild turkey . It was converted to <unk> pheasant production in 1981 , and , as of 2007 , it is one of four Pennsylvania state game farms producing about 200 @,@ 000 <unk> each year for release on land open to public hunting . The Northcentral State Game Farm is in the Plunketts Creek valley just south of Proctor , and a part of it is on the right bank of Loyalsock Creek downstream of the confluence . The Loyalsock State Game Farm is 13 miles ( 21 km ) downstream on Loyalsock Creek , at the village of <unk> . When a May 2007 fire destroyed a <unk> house there just days before 18 @,@ 000 pheasant chicks were due to hatch , the eggs were transferred to the nearby Northcentral State Game Farm without reduction in the production goal . 
- As of 2007 , Proctor has two separate businesses : a general store ( which also sells gasoline ) and a bed and breakfast . The church which used to host the annual " Proctor Homecoming " <unk> still stands , but is closed . Barbours has no store or gas station , but does have one church . Barbours is home to the Plunketts Creek Township Volunteer Fire Company and township municipal building ( which houses a small branch library ) . Since 1967 , Barbours has been home to <unk> @-@ <unk> , which makes tranquilizer <unk> and guns for livestock and wildlife capture and control . In 1997 , <unk> @-@ <unk> had eight employees . Today much of Plunketts Creek 's watershed is wooded and protected as part of Loyalsock State Forest or Pennsylvania State Game Lands No. 134 . Pennsylvania 's state forests and game lands are managed , and small @-@ scale lumbering operations continue in the watershed today . Barbours has one sawmill , in 1997 it had thirty contract loggers and fifteen employees , with $ 1 @.@ 2 million in annual gross sales . 
- Plunketts Creek has been a place for lumber and tourism since its villages were founded . Before the advent of automobiles , the area was quite isolated and the 16 mile ( 26 km ) trip to Montoursville took at least three hours ( today it takes less than half an hour ) . Residents who used to work locally now commute to Williamsport . " Cabin people " have seasonally increased the population for years , but increasing numbers now live there year round . From 1950 to 2000 , the population of Plunketts Creek Township increased 80 @.@ 6 percent from <unk> to <unk> ( for comparison , in the same period Lycoming County 's population increased by only 18 @.@ 6 percent , while Sullivan County 's declined by 2 @.@ 9 percent ) . <unk> still come too : the opening weekend of the trout season brings more people into the village at the mouth of Plunketts Creek than any other time of the year . 
- 
- 
- = Richard Cresswell = 
- 
- Richard Paul Wesley Cresswell ( born 20 September 1977 ) is an English semi @-@ professional footballer who plays for Northern Counties East League Premier Division club <unk> Albion . He primarily plays as a striker but can also play as a winger . 
- Cresswell started his career with York City in their youth system , making his first team debut in a Second Division match in 1996 . Having scored 19 goals for York in the 1998 – 99 season he signed for Premier League team Sheffield Wednesday in 1999 . After their relegation the following year he joined Leicester City of the Premier League , but was loaned to First Division side Preston North End in 2001 and played for them in the 2001 First Division play @-@ off Final . He signed for Preston permanently in the summer . He had four full seasons with Preston , scoring a career best 21 goals in the 2004 – 05 season , which culminated in defeat in the 2005 Championship play @-@ off Final . 
- He signed for Championship rivals Leeds United in 2005 . He had a number of knee injuries while with Leeds and following their relegation to League One in 2007 was signed by Stoke City . With Stoke he won promotion to the Premier League as Championship runners @-@ up , with Cresswell scoring 12 goals in the 2007 – 08 season . He spent one full season in the Premier League with Stoke before signing for Championship side Sheffield United on loan in 2009 , before signing permanently in 2010 . United were relegated to League One in 2011 , and Cresswell played in their defeat in the 2012 League One play @-@ off Final . He was made player @-@ coach at the club in 2012 before rejoining his first club York City in 2013 , initially on loan . 
- 
- = = Club career = = 
- 
- 
- = = = York City = = = 
- 
- Cresswell was born in Bridlington , East Riding of Yorkshire to George and Denise ( née <unk> ) . He started playing for Bridlington Rangers at the age of eight before joining the York City youth system aged 14 in 1991 . Having been top scorer for the Northern Intermediate League team for three consecutive seasons , he signed a professional contract on 15 November 1995 . Cresswell made his first team debut away to Brentford in a 2 – 0 defeat in the Second Division on 20 January 1996 . He first scored for York with the second goal of a 2 – 2 draw away at Bradford City on 2 March 1996 . This proved to be his only goal in the 1995 – 96 season , in which he made 17 appearances . 
- Having failed to score in 22 appearances for York in the 1996 – 97 season , Cresswell joined Third Division side Mansfield Town on loan on 27 March 1997 , making his debut in a 0 – 0 draw away to Exeter City on 29 March . He scored his first and only goal for Mansfield in a 1 – 0 win away to Rochdale on 5 April 1997 , before finishing the loan with five appearances . He scored four goals in 30 games for York in 1997 – 98 , and during this season he was barracked by a small section of the York support . 
- Cresswell admitted he had not made the impact he had hoped to in the York first team , but ahead of the 1998 – 99 season said " I 've had some stick from a small number of fans , but hopefully I can prove them wrong . I will prove them wrong . This a big season for me . I want to do it for York City . I want to do it for myself . " After a positive start to the season , Cresswell attracted attention from other clubs , with a number of scouts attending matches to watch him play . Manager Alan Little claimed some clubs were making illegal approaches for the player , and that this was having a detrimental effect on his performances . Preston North End manager David <unk> claimed his club had a bid of more than £ 500 @,@ 000 for Cresswell rejected , while York chairman Douglas Craig rejected this , saying a formal offer had not been received from any club . He was York 's top scorer in the 1998 – 99 season with 19 goals from 42 appearances . 
- 
- = = = Sheffield Wednesday = = = 
- 
- He made a move to Premier League side Sheffield Wednesday on a four @-@ year contract on 25 March 1999 , with the £ 950 @,@ 000 fee being the highest received for a York player . On his transfer , Cresswell said : " This move is no disrespect to York , it 's just that I have always wanted to play at a much bigger club and as high as possible " . Manager Danny Wilson described Cresswell as " one for the future " after admitting the player " is not the striker people may perceive as the big one we were chasing " . His debut came in Wednesday 's 2 – 1 home defeat to Coventry City on 3 April 1999 , before scoring his first goal with an 87th @-@ minute winner at home to Liverpool in a 1 – 0 win on 8 May . He finished the 1998 – 99 season with one goal in seven games for Wednesday . Cresswell completed the 1999 – 2000 season with two goals in 25 appearances , having been given few opportunities in the team , as Wednesday were relegated to the First Division . 
- 
- = = = Leicester City = = = 
- 
- Cresswell struggled to establish himself at Wednesday under manager Paul Jewell early in the 2000 – 01 season , before he resumed playing in the Premier League after signing for Leicester City on 1 September 2000 for a fee of £ 750 @,@ 000 . Leicester were managed by Peter Taylor , who previously worked with Cresswell previously in the England under @-@ 21 team . He made his debut in their 1 – 1 draw at home to Red Star <unk> in the UEFA Cup on 14 September 2000 . He scored once in 13 appearances for Leicester , his goal coming against former club York in a 3 – 0 home win in the FA Cup third round on 6 January 2001 , having failed to establish himself in the team . 
- 
- = = = Preston North End = = = 
- 
- Cresswell joined First Division club Preston North End on loan for the remainder of the 2000 – 01 season on 10 March 2001 and scored five minutes into his debut , a 2 – 0 win at home to Wolverhampton Wanderers on 14 March . He came on as an 82nd minute substitute in their 3 – 0 defeat to Bolton Wanderers in the 2001 First Division play @-@ off Final at the Millennium Stadium on 28 May 2001 . After scoring two goals in 14 appearances he signed for Preston permanently on a four @-@ year contract for a fee of £ 500 @,@ 000 on 14 July 2001 . 
- Cresswell was Preston 's top scorer in his first two permanent seasons with Preston , scoring 15 goals in 44 appearances in 2001 – 02 and scoring 16 in 46 games in 2002 – 03 . He was also named Preston 's Player of the Year for the 2001 – 02 season . He received the first red card of his career in Preston 's 4 – 1 defeat away at Coventry City on 17 March 2004 after he was judged to have kicked out at opponent player <unk> Davenport , although both managers later admitted David <unk> was the <unk> . He scored three goals in 47 appearances in the 2003 – 04 season . 
- Cresswell drew praise from manager Billy Davies during the 2004 – 05 season , " Richard is very capable of that and it is important that we keep creating chances for <unk> as we know that he will put the ball in the back of the net " , although he admitted the team were over reliant on Cresswell 's goals . He enjoyed his best goal return in the 2004 – 05 season , top scoring for Preston with 21 goals in 52 games . This helped Preston reach the 2005 Championship play @-@ off Final , where they were beaten 1 – 0 by West Ham United at the Millennium Stadium . Cresswell played poorly in the first half , but had a number of chances on goal during the second half . 
- 
- = = = Leeds United = = = 
- 
- Cresswell was bought by Championship rivals Leeds United on a four @-@ year contract on 24 August 2005 for a fee of £ 1 @.@ 15 million , after rivals Sheffield United had a £ 1 million bid accepted . After the signing was completed , manager Kevin Blackwell said " To get a player of Cresswell 's quality is amazing " , while Cresswell cited his desire to win automatic promotion with Leeds . He first found the net with two goals in Leeds ' League Cup 2 – 0 away victory against Rotherham United on 20 September 2005 . However , Cresswell suffered knee ligament damage in a match against Derby County eight days later . This injury kept Cresswell out of action for seven weeks , making his return in Leeds ' 1 – 0 defeat away to Wolves on 17 December 2005 , and scoring in his second match back against Coventry City in a 3 – 1 home win on 26 December . Another knee injury picked up during a training session in February 2006 kept him out of the team until he entered Leeds ' 1 – 1 draw with former club Preston in the Championship play @-@ off semi @-@ final first leg as a 78th @-@ minute substitute on 5 May 2006 . However , he was sent off in the 2 – 0 win in the second leg , meaning he was suspended for the 2006 Championship play @-@ off Final , which Leeds lost 3 – 0 to <unk> at the Millennium Stadium . He finished the 2005 – 06 season with 21 appearances and seven goals . 
- Having damaged his knee ligaments during the play @-@ off semi @-@ final , Cresswell missed the start of the 2006 – 07 season , making his first appearance in Leeds ' 4 – 0 home defeat to Stoke City on 14 October 2006 . However , he sustained a knee injury in November 2006 , after scoring his first goal of the campaign in Leeds ' 3 – 0 home win over Colchester United on 11 November . His return from injury came as a 68th @-@ minute substitute in a 2 – 1 victory at home to Crystal Palace on 10 February 2007 . Cresswell scored in successive games against Sheffield Wednesday and Luton Town in March 2007 , but Leeds were eventually relegated to League One . He finished the season with four goals in 23 games . 
- 
- = = = Stoke City = = = 
- 
- Leeds were resigned to losing their senior players after going into administration , with Championship side Stoke City signing Cresswell on a three @-@ year contract for an undisclosed fee on 2 August 2007 , after Hull City had pulled out of a deal after expressing concerns following his medical . He made his debut in a 1 – 0 win at Cardiff City on 11 August 2007 , before scoring in his second appearance with an equaliser during stoppage time of extra time in a 2 – 2 draw away to Rochdale in the League Cup first round on 14 August , although Stoke lost 4 – 2 in a penalty shoot @-@ out . He scored the last ever goal at Colchester United 's <unk> Road ground in a 1 – 0 win . Cresswell made 46 appearances for Stoke in the 2007 – 08 season , scoring 12 goals , as the club won promotion the Premier League as Championship runners @-@ up . He was regularly used on the left wing by Stoke manager Tony <unk> , even though his natural position is as a striker . He was quoted as saying he enjoyed playing as a winger , saying " I do my best , and I am quite a fit lad so I get through quite a bit of <unk> " . During the 2008 – 09 season Cresswell played on the wing and as a striker , featuring in 34 games and scoring one goal . 
- 
- = = = Sheffield United = = = 
- 
- Having struggled for appearances with Stoke since their promotion to the Premier League , Cresswell joined Championship outfit Sheffield United on a three @-@ month loan on 29 September 2009 and made his debut the same day as a 76th @-@ minute substitute against Ipswich Town in a 3 – 3 home draw . Starting the following game he scored a 65th @-@ minute equaliser against Doncaster Rovers in a 1 – 1 draw at home on 3 October 2009 . He joined United permanently on a one @-@ and @-@ a @-@ half @-@ year contract for an undisclosed fee on 5 January 2010 , going on to finish the 2009 – 10 season as top scorer with 14 goals . He was rewarded with a new three @-@ year contract with the club in June 2010 . He scored five goals in 36 games in the 2010 – 11 season as United were relegated to League One . 
- With United now in League One , Cresswell formed a striking partnership with <unk> Evans , making 51 appearances and scoring 10 goals in 2011 – 12 . The club failed to achieve promotion however , being beaten 8 – 7 in a penalty shoot @-@ out by Huddersfield Town in the 2012 League One play @-@ off Final at Wembley Stadium on 26 May 2012 , in which Cresswell was substituted for Chris Porter in the 85th minute . As a result of financial circumstances Cresswell and Nick Montgomery were made available for transfer during August 2012 , with manager Danny Wilson explaining that " They were both fit , they were left out for financial reasons " . Towards the end of the transfer window however United agreed a revised deal to change his role to that of player @-@ coach and as such he would be remaining at Bramall Lane for the foreseeable future . On his return to the side , Cresswell came on as a substitute and scored a header in United 's 5 – 3 victory over Bournemouth on 1 September 2012 . 
- 
- = = = Return to York City = = = 
- 
- Cresswell rejoined his first club York City , playing in League Two , on a one @-@ month loan on 19 March 2013 . He marked his second York debut by scoring a 73rd minute penalty kick on 23 March 2013 away at Torquay United in a 2 – 1 defeat . He played an important role as York fought against relegation , scoring twice in five appearances , before being recalled by new United caretaker manager Chris Morgan on 15 April 2013 . In July 2013 new United manager David Weir stated that Cresswell would be leaving the club , before he signed for York permanently on a one @-@ year contract on 16 July 2013 . His first appearance after signing permanently came in the first game of the 2013 – 14 season , a 1 – 0 home win over Northampton Town on 3 August 2013 . Cresswell retired from playing on 5 December 2013 , as a result of an eye complaint and a knee injury . He made eight appearances for York in the 2013 – 14 season . 
- 
- = = International career = = 
- 
- Cresswell was called up to the England national under @-@ 21 team while with York , making his debut in a 2 – 1 home victory over France in a friendly on 9 February 1999 . He continued to play for the under @-@ <unk> after joining Wednesday , and scored his first goal for them in a 3 – 0 home win over Sweden in a 2000 UEFA European Under @-@ 21 Championship qualification match on 4 June 1999 . Cresswell finished his under @-@ 21 career with four caps and one goal . 
- 
- = = Coaching career = = 
- 
- Cresswell returned to York as a commercial , academy and community development consultant in April 2014 , having previously intended to pursue a career in coaching . He took on the position of Head of Football Operations , before being appointed as first team coach to manager Russ Wilcox in March 2015 . In May 2015 , Cresswell resumed his role as Head of Football Operations , while continuing to coach the first team . He took over as caretaker manager on 26 October 2015 after Wilcox 's sacking , and would be assisted by youth team coach Jonathan <unk> and <unk> coach Andy <unk> . He was in charge for the 1 – 0 away defeat to Crawley Town on 31 October 2015 , before <unk> his caretaker duties upon the appointment of Jackie McNamara as manager on 4 November . Cresswell left York by mutual consent on 16 December 2015 . He resumed his playing career aged 38 when signing for Northern Counties East League Premier Division club <unk> Albion on 7 April 2016 . 
- 
- = = Personal life = = 
- 
- Cresswell married Zoe Chapman at Christ Church , Bridlington on 7 June 2003 , with former York City teammate Jonathan <unk> being his joint best man . He took part in a 170 mile bike ride during 2012 to raise money for a charity which helps children with <unk> 's syndrome , after his twin <unk> were diagnosed with the disorder . 
- 
- = = Career statistics = = 
- 
- As of match played 14 April 2016 . 
- 
- = = Managerial statistics = = 
- 
- As of 4 November 2015 . 
- 
- = = Honours = = 
- 
- Stoke City 
- Football League Championship runner @-@ up : 2007 – 08 
- Individual 
- Preston North End Player of the Year : 2001 – 02 
- 
- 
- = Berhtwald = 
- 
- Berhtwald ( also <unk> , <unk> , <unk> , <unk> , <unk> , or <unk> ; died 731 ) was the ninth Archbishop of Canterbury in England . Documentary evidence names Berhtwald as abbot at <unk> before his election as archbishop . Berhtwald begins the first continuous series of native @-@ born <unk> of Canterbury , although there had been previous Anglo @-@ Saxon archbishops , they had not succeeded each other until Berhtwald 's reign . 
- Berhtwald 's period as archbishop coincided with the end of Wilfrid 's long struggle to regain the Bishopric of York , and the two @-@ year delay between Theodore 's death and Berhtwald 's election may have been due to efforts to select Wilfrid for Canterbury . After his election , Berhtwald went to Gaul for consecration and then presided over two councils that attempted to settle the Wilfrid issue , finally succeeding at the second council in 705 . Berhtwald also was the recipient of the first surviving letter close in Western Europe . 
- 
- = = Early life = = 
- 
- Little is known of Berhtwald 's ancestry or his early life , but he was born around the middle of the seventh century . By 679 , he was made abbot of the monastery at <unk> in Kent , and a charter dated May 679 names Berhtwald as abbot . This charter , from <unk> , King of Kent , is the earliest surviving original Anglo @-@ Saxon charter . 
- 
- = = Election as archbishop = = 
- 
- The see of Canterbury was vacant for two years after the death of Theodore before Berhtwald was elected to the office on 1 July 692 . The long vacancy resulted from the disturbed conditions in the kingdom of Kent at the time , as various kings fought for control . The succession to the kingdom was disputed between rival claimants <unk> and Wihtred , and various outside kings , including <unk> and <unk> raided and plundered Kent . Eventually , Wihtred secured the throne , around 691 or early 692 , as Bede names Wihtred as King of Kent , along with <unk> , at the time of Berhtwald 's election . <unk> , however , is not named as king of Kent after this date . 
- The vacancy may also have occurred because Wilfrid , who was at that point having problems in Northumbria , desired to become Archbishop of Canterbury . A contemporary biographer of Wilfrid , Stephen of <unk> , says that Theodore had wished for Wilfrid to succeed Theodore at Canterbury . Æthelred of <unk> may have supported Wilfrid 's translation to Canterbury also , but despite these desires , the translation did not happen . Berhtwald was consecrated on 29 June <unk> , having travelled to France for his consecration as archbishop of Canterbury by <unk> , Archbishop of Lyon . Berhtwald went to the continent for consecration probably because he feared that his election was not supported by all of the kings and bishops . After his consecration , Berhtwald travelled to Rome to obtain the support of Pope Sergius I , who wrote to a number of Anglo @-@ Saxon kings and bishops in support of the archbishop . Two of these letters survive , and their authenticity has been doubted , mainly because they are only preserved as part of the post @-@ Norman Conquest Canterbury @-@ York dispute . Historians have since come to regard the two letters as genuine . Sergius also gave Berhtwald a pallium , the symbol of an archbishop 's authority . 
- 
- = = Archbishop = = 
- 
- Berhtwald appears to have been involved in the governance of the church , establishing the bishopric of Sherborne in Wessex and it was during his tenure that Sussex , the last pagan kingdom in England , was converted to Christianity . He also consecrated the first Bishop of <unk> . During his time in office , King Wihtred of Kent in the Law of Wihtred <unk> the church from taxation . Berhtwald was a proponent of his predecessor 's view of the archbishops of Canterbury as <unk> of the entire island of Britain . Berhtwald co @-@ operated closely with Wihtred in the kingdom , and secured the exemption of the church from taxation under Wihtred 's laws issued in <unk> . The law code also dealt with other ecclesiastical matters , including marriage , Sunday observance , and pagan worship . This law code resulted from a royal council that was held at <unk> . Further privileges for the church were issued in 699 , and may have been composed by Berhtwald before being promulgated . Another privilege , usually referred to as the " <unk> of Wihtred " , is claimed to be a grant from Wihtred to the monasteries of Kent of exemption from non @-@ clerical control . However , this is actually a ninth @-@ century forgery . 
- Much of Berhtwald 's time in office coincided with the efforts of Wilfrid to regain the see of York , and to reverse the division of York into smaller dioceses . Berhtwald was opposed to Wilfrid 's desire to restore some separated <unk> to the bishopric of York as well as regaining his old see . Wilfrid 's problems had begun during the archbishopric of Berhtwald 's predecessor , Theodore of <unk> , when Wilfrid had <unk> with the King of Northumbria , <unk> , and was expelled from the north . Theodore had taken the opportunity to divide the large see of York into a number of smaller dioceses , and Wilfrid had appealed to the papacy in Rome . Berhtwald inherited the dispute and presided at the Council of <unk> in 702 , at which Wilfrid 's biographer relates the story that King <unk> of Northumbria , Berhtwald , and the other enemies of Wilfrid conspired to deprive Wilfrid of all his offices and possessions . A more likely story is that Berhtwald managed to secure concessions from the <unk> , and tried to <unk> a compromise . The offer in the end was that Wilfrid would retire to <unk> and cease acting as a bishop . Wilfrid rejected this compromise and once more appealed to the pope . Three years later , at a further Council , it was arranged that Wilfrid should receive the Bishopric of <unk> in place of that of York . This was the Council of <unk> , usually dated to <unk> , and it was held in Northumbria . Bede also mentions that Berhtwald consecrated a number of bishops , including Tobias as Bishop of Rochester . 
- One of Berhtwald 's letters has been preserved , sent to <unk> , Bishop of Sherborne , and asking <unk> to intercede with <unk> , the Abbot of Glastonbury , to ransom a slave . Another letter , this one addressed to Berhtwald , from <unk> , Bishop of London , also survives . The main interest in the second letter is that it is the oldest surviving letter close surviving in Western Europe . This second letter also relates that <unk> and Berhtwald had attended a <unk> which can be dated to sometime between 703 and 705 , where the kingdom of Wessex was threatened with excommunication . A charter witnessed by Berhtwald which mentions a supposed <unk> council , numbered 54 by Sawyer , is now known to be a fake , although the witness list may be based on a legitimate 8th century charter that no longer survives . Likewise , a charter with Berhtwald as a witness and relating to the 716 Council of <unk> is also known to be a 9th @-@ century forgery , although again it may have been based on actual documents from the council . 
- 
- = = Death and legacy = = 
- 
- Berhtwald died on 13 January 731 . An <unk> to him in verse survives , and may have been placed over his tomb , which was at Canterbury . Subsequently he was <unk> with a feast day of 9 January . Little evidence of extensive cult activity exists , however , and the main evidence for his <unk> is a late medieval entry in a St Augustine 's calendar . Berhtwald is the first of the continuous series of native @-@ born archbishops in England , although there had been two previous Anglo @-@ Saxon archbishops at Canterbury — <unk> and <unk> . 
- 
- 
- = Xenon = 
- 
- Xenon is a chemical element with symbol Xe and atomic number 54 . It is a colorless , dense , <unk> noble gas , that occurs in the Earth 's atmosphere in trace amounts . Although generally <unk> , xenon can undergo a few chemical reactions such as the formation of xenon hexafluoroplatinate , the first noble gas compound to be synthesized . 
- Xenon is used in flash lamps and arc lamps , and as a general anesthetic . The first excimer laser design used a xenon dimer molecule ( <unk> ) as its <unk> medium , and the earliest laser designs used xenon flash lamps as pumps . Xenon is also being used to search for hypothetical weakly interacting massive particles and as the propellant for ion <unk> in spacecraft . 
- Naturally occurring xenon consists of eight stable isotopes . There are also over 40 unstable isotopes that undergo radioactive decay . The isotope ratios of xenon are an important tool for studying the early history of the Solar System . <unk> xenon @-@ 135 is produced by beta decay from iodine @-@ 135 ( which is a product of nuclear fission ) , and it acts as the most significant neutron <unk> in nuclear reactors . 
- 
- = = History = = 
- 
- Xenon was discovered in England by the Scottish chemist William Ramsay and English chemist Morris Travers in September 1898 , shortly after their discovery of the elements krypton and neon . They found xenon in the residue left over from <unk> components of liquid air . Ramsay suggested the name xenon for this gas from the Greek word <unk> [ xenon ] , <unk> singular form of <unk> [ <unk> ] , meaning ' foreign ( er ) ' , ' strange ( r ) ' , or ' guest ' . In 1902 , Ramsay estimated the proportion of xenon in the Earth 's atmosphere as one part in 20 million . 
- During the 1930s , American engineer Harold <unk> began exploring <unk> light technology for high speed photography . This led him to the invention of the xenon flash lamp , in which light is generated by sending a brief electric current through a tube filled with xenon gas . In 1934 , <unk> was able to generate flashes as brief as one <unk> with this method . 
- In 1939 , American physician Albert R. <unk> Jr. began exploring the causes of " <unk> " in deep @-@ sea divers . He tested the effects of varying the breathing mixtures on his subjects , and discovered that this caused the divers to perceive a change in depth . From his results , he deduced that xenon gas could serve as an anesthetic . Although Russian <unk> <unk> V. <unk> apparently studied xenon anesthesia in 1941 , the first published report confirming xenon anesthesia was in 1946 by American medical researcher John H. Lawrence , who experimented on mice . Xenon was first used as a surgical anesthetic in 1951 by American <unk> Stuart C. Cullen , who successfully operated on two patients . 
- Xenon and the other noble gases were for a long time considered to be completely chemically inert and not able to form compounds . However , while teaching at the University of British Columbia , Neil Bartlett discovered that the gas platinum hexafluoride ( PtF6 ) was a powerful oxidizing agent that could <unk> oxygen gas ( O2 ) to form <unk> hexafluoroplatinate ( O2 + [ PtF6 ] − ) . Since O2 and xenon have almost the same first ionization potential , Bartlett realized that platinum hexafluoride might also be able to <unk> xenon . On March 23 , 1962 , he mixed the two gases and produced the first known compound of a noble gas , xenon hexafluoroplatinate . Bartlett thought its composition to be Xe + [ PtF6 ] − , although later work has revealed that it was probably a mixture of various xenon @-@ containing salts . Since then , many other xenon compounds have been discovered , along with some compounds of the noble gases argon , krypton , and <unk> , including argon <unk> ( <unk> ) , krypton difluoride ( <unk> ) , and <unk> fluoride . By 1971 , more than 80 xenon compounds were known . 
- In November 1999 IBM scientists demonstrated a technology capable of manipulating individual atoms . The program , called IBM in atoms , used a scanning <unk> microscope to arrange 35 individual xenon atoms on a substrate of <unk> crystal of nickel to spell out the three letter company acronym . It was the first time atoms had been precisely positioned on a flat surface . 
- 
- = = Characteristics = = 
- 
- Xenon has atomic number 54 ; that is , its nucleus contains 54 protons . At standard temperature and pressure , pure xenon gas has a density of 5 @.@ 761 kg / m3 , about 4 @.@ 5 times the surface density of the Earth 's atmosphere , 1 @.@ 217 kg / m3 . As a liquid , xenon has a density of up to 3 @.@ 100 g / <unk> , with the density maximum occurring at the triple point . Notably , liquid xenon has a high <unk> due to its large atomic volume , and thus is an excellent solvent . It can dissolve <unk> , biological molecules , and even water . Under the same conditions , the density of solid xenon , 3 @.@ 640 g / cm3 , is higher than the average density of granite , 2 @.@ 75 g / cm3 . Using <unk> of pressure , xenon has been forced into a metallic phase . 
- Solid xenon changes from face @-@ centered cubic ( <unk> ) to <unk> close packed ( <unk> ) crystal phase under pressure and begins to turn metallic at about 140 GPa , with no noticeable volume change in the <unk> phase . It is completely metallic at 155 GPa . When <unk> , xenon looks sky blue because it absorbs red light and <unk> other visible frequencies . Such behavior is unusual for a metal and is explained by the relatively small widths of the electron bands in metallic xenon . 
- Liquid or solid xenon <unk> can be formed at room temperature by <unk> Xe + ions into a solid matrix . Many solids have lattice <unk> smaller than solid Xe . This results in compression of the implanted Xe to pressures that may be sufficient for its <unk> or <unk> . 
- Xenon is a member of the zero @-@ valence elements that are called noble or inert gases . It is inert to most common chemical reactions ( such as combustion , for example ) because the outer valence shell contains eight electrons . This produces a stable , minimum energy configuration in which the outer electrons are tightly bound . 
- In a gas @-@ filled tube , xenon emits a blue or <unk> glow when the gas is excited by electrical discharge . Xenon emits a band of emission lines that span the visual spectrum , but the most intense lines occur in the region of blue light , which produces the coloration . 
- 
- = = Occurrence and production = = 
- 
- Xenon is a trace gas in Earth 's atmosphere , occurring at 87 ± 1 parts per billion ( <unk> / L ) , or approximately 1 part per 11 @.@ 5 million , and is also found as a component in gases emitted from some mineral springs . 
- Xenon is obtained commercially as a by @-@ product of the separation of air into oxygen and nitrogen . After this separation , generally performed by <unk> distillation in a double @-@ column plant , the liquid oxygen produced will contain small quantities of krypton and xenon . By additional <unk> distillation steps , the liquid oxygen may be enriched to contain 0 @.@ 1 – 0 @.@ 2 % of a krypton / xenon mixture , which is extracted either via absorption onto <unk> gel or by distillation . Finally , the krypton / xenon mixture may be separated into krypton and xenon via distillation . Worldwide production of xenon in 1998 was estimated at 5 @,@ 000 – 7 @,@ 000 m3 . Because of its low abundance , xenon is much more expensive than the lighter noble gases — approximate prices for the purchase of small quantities in Europe in 1999 were 10 € / L for xenon , 1 € / L for krypton , and 0 @.@ 20 € / L for neon ; the much more plentiful argon costs less than a cent per <unk> . 
- Within the Solar System , the <unk> fraction of xenon is 1 @.@ 56 × 10 − 8 , for an abundance of approximately one part in 630 thousand of the total mass . Xenon is relatively rare in the Sun 's atmosphere , on Earth , and in asteroids and comets . The planet Jupiter has an unusually high abundance of xenon in its atmosphere ; about 2 @.@ 6 times as much as the Sun . This high abundance remains unexplained and may have been caused by an early and rapid buildup of <unk> — small , <unk> bodies — before the <unk> disk began to heat up . ( Otherwise , xenon would not have been trapped in the <unk> <unk> . ) The problem of the low terrestrial xenon may potentially be explained by <unk> bonding of xenon to oxygen within <unk> , hence reducing the outgassing of xenon into the atmosphere . 
- Unlike the lower mass noble gases , the normal stellar nucleosynthesis process inside a star does not form xenon . Elements more massive than iron @-@ 56 have a net energy cost to produce through fusion , so there is no energy gain for a star when creating xenon . Instead , xenon is formed during supernova explosions , by the slow neutron capture process ( s @-@ process ) of red giant stars that have exhausted the hydrogen at their cores and entered the asymptotic giant branch , in classical nova explosions and from the radioactive decay of elements such as iodine , uranium and plutonium . 
- 
- = = Isotopes and isotopic studies = = 
- 
- Naturally occurring xenon is made of eight stable isotopes , the most of any element with the exception of tin , which has ten . Xenon and tin are the only elements to have more than seven stable isotopes . The isotopes <unk> and <unk> are predicted to undergo double beta decay , but this has never been observed so they are considered to be stable . Besides these stable forms , there are over 40 unstable isotopes that have been studied . The longest lived of these isotopes is <unk> , which has been observed to undergo double beta decay with a half @-@ life of 2 @.@ 11 × <unk> <unk> . 129Xe is produced by beta decay of 129I , which has a half @-@ life of 16 million years , while <unk> , 133Xe , <unk> , and 135Xe are some of the fission products of both <unk> and <unk> , and therefore used as indicators of nuclear explosions . 
- <unk> of two of the stable isotopes of xenon , 129Xe and <unk> , have non @-@ zero intrinsic angular <unk> ( nuclear spins , suitable for nuclear magnetic resonance ) . The nuclear spins can be aligned beyond ordinary polarization levels by means of <unk> polarized light and <unk> vapor . The resulting spin polarization of xenon nuclei can surpass 50 % of its maximum possible value , greatly exceeding the thermal equilibrium value dictated by <unk> statistics ( typically 0 @.@ 001 % of the maximum value at room temperature , even in the strongest magnets ) . Such non @-@ equilibrium alignment of spins is a temporary condition , and is called <unk> . The process of <unk> the xenon is called optical pumping ( although the process is different from pumping a laser ) . 
- Because a 129Xe nucleus has a spin of 1 / 2 , and therefore a zero electric <unk> moment , the 129Xe nucleus does not experience any <unk> interactions during collisions with other atoms , and thus its <unk> can be maintained for long periods of time even after the laser beam has been turned off and the alkali vapor removed by condensation on a room @-@ temperature surface . Spin polarization of 129Xe can persist from several seconds for xenon atoms dissolved in blood to several hours in the gas phase and several days in deeply frozen solid xenon . In contrast , <unk> has a nuclear spin value of 3 ⁄ 2 and a <unk> <unk> moment , and has t1 relaxation times in the <unk> and second ranges . 
- Some radioactive isotopes of xenon , for example , 133Xe and 135Xe , are produced by neutron irradiation of <unk> material within nuclear reactors . 135Xe is of considerable significance in the operation of nuclear fission reactors . 135Xe has a huge cross section for thermal neutrons , 2 @.@ 6 × 106 <unk> , so it acts as a neutron <unk> or " poison " that can slow or stop the chain reaction after a period of operation . This was discovered in the earliest nuclear reactors built by the American Manhattan Project for plutonium production . Fortunately the designers had made provisions in the design to increase the reactor 's reactivity ( the number of neutrons per fission that go on to fission other atoms of nuclear fuel ) . 135Xe reactor poisoning played a major role in the <unk> disaster . A <unk> or decrease of power of a reactor can result in buildup of 135Xe and getting the reactor into the iodine pit . 
- Under adverse conditions , relatively high concentrations of radioactive xenon isotopes may be found emanating from nuclear reactors due to the release of fission products from cracked fuel rods , or <unk> of uranium in cooling water . 
- Because xenon is a <unk> for two parent isotopes , xenon isotope ratios in meteorites are a powerful tool for studying the formation of the solar system . The iodine @-@ xenon method of dating gives the time <unk> between nucleosynthesis and the condensation of a solid object from the solar nebula . In 1960 , physicist John H. Reynolds discovered that certain meteorites contained an isotopic anomaly in the form of an overabundance of xenon @-@ 129 . He inferred that this was a decay product of radioactive iodine @-@ 129 . This isotope is produced slowly by cosmic ray <unk> and nuclear fission , but is produced in quantity only in supernova explosions . As the half @-@ life of 129I is comparatively short on a <unk> time scale , only 16 million years , this demonstrated that only a short time had passed between the supernova and the time the meteorites had solidified and trapped the 129I . These two events ( supernova and <unk> of gas cloud ) were inferred to have happened during the early history of the Solar System , as the 129I isotope was likely generated before the Solar System was formed , but not long before , and <unk> the solar gas cloud with isotopes from a second source . This supernova source may also have caused collapse of the solar gas cloud . 
- In a similar way , xenon isotopic ratios such as 129Xe / <unk> and <unk> / <unk> are also a powerful tool for understanding planetary differentiation and early outgassing . For example , The atmosphere of Mars shows a xenon abundance similar to that of Earth : 0 @.@ 08 parts per million , however Mars shows a higher proportion of 129Xe than the Earth or the Sun . As this isotope is generated by radioactive decay , the result may indicate that Mars lost most of its primordial atmosphere , possibly within the first 100 million years after the planet was formed . In another example , excess 129Xe found in carbon dioxide well gases from New Mexico was believed to be from the decay of mantle @-@ derived gases soon after Earth 's formation . 
- 
- = = Compounds = = 
- 
- After Neil Bartlett 's discovery in 1962 that xenon can form chemical compounds , a large number of xenon compounds have been discovered and described . Almost all known xenon compounds contain the electronegative atoms fluorine or oxygen . 
- 
- = = = Halides = = = 
- 
- Three fluorides are known : XeF 
- 2 , XeF 
- 4 , and XeF 
- 6 . XeF is theorized to be unstable . The fluorides are the starting point for the synthesis of almost all xenon compounds . 
- The solid , crystalline difluoride XeF 
- 2 is formed when a mixture of fluorine and xenon gases is exposed to ultraviolet light . Ordinary daylight is sufficient . Long @-@ term heating of XeF 
- 2 at high temperatures under an <unk> 
- 2 catalyst yields XeF 
- 6 . <unk> of XeF 
- 6 in the presence of <unk> yields high @-@ purity XeF 
- 4 . 
- The xenon fluorides behave as both fluoride <unk> and fluoride donors , forming salts that contain such <unk> as XeF + and Xe 
- 2F + 
- 3 , and anions such as XeF − 
- 5 , XeF − 
- 7 , and <unk> − 
- 8 . The green , <unk> Xe + 
- 2 is formed by the reduction of XeF 
- 2 by xenon gas . 
- XeF 
- 2 is also able to form coordination complexes with transition metal ions . Over 30 such complexes have been synthesized and characterized . 
- Whereas the xenon fluorides are well @-@ characterized , the other halides are not known , the only exception being the <unk> , <unk> . Xenon <unk> is reported to be an <unk> , colorless , crystalline compound that <unk> into the elements at 80 ° C , formed by the high @-@ frequency irradiation of a mixture of xenon , fluorine , and silicon or carbon <unk> . However , doubt has been raised as to whether <unk> 
- 2 is a real compound and not merely a van der <unk> molecule consisting of weakly bound Xe atoms and Cl 
- 2 molecules . Theoretical calculations indicate that the linear molecule <unk> 
- 2 is less stable than the van der <unk> complex . 
- 
- = = = <unk> and <unk> = = = 
- 
- Three oxides of xenon are known : xenon trioxide ( XeO 
- 3 ) and xenon tetroxide ( XeO 
- 4 ) , both of which are dangerously explosive and powerful oxidizing agents , and xenon dioxide ( <unk> ) , which was reported in 2011 with a coordination number of four . <unk> forms when xenon <unk> is poured over ice . Its crystal structure may allow it to replace silicon in <unk> minerals . The <unk> + cation has been identified by infrared spectroscopy in solid argon . 
- Xenon does not react with oxygen directly ; the trioxide is formed by the hydrolysis of XeF 
- 6 : 
- XeF 
- 6 + 3 H 
- 2O → XeO 
- 3 + 6 HF 
- XeO 
- 3 is weakly acidic , dissolving in alkali to form unstable <unk> salts containing the <unk> − 
- 4 anion . These unstable salts easily <unk> into xenon gas and perxenate salts , containing the <unk> − 
- 6 anion . 
- <unk> perxenate , when treated with concentrated sulfuric acid , yields <unk> xenon tetroxide : 
- Ba 
- <unk> 
- 6 + 2 H 
- <unk> 
- 4 → 2 <unk> 
- 4 + 2 H 
- 2O + XeO 
- 4 
- To prevent decomposition , the xenon tetroxide thus formed is quickly cooled to form a pale @-@ yellow solid . It explodes above − 35 @.@ 9 ° C into xenon and oxygen gas . 
- A number of xenon <unk> are known , including XeOF 
- 2 , XeOF 
- 4 , XeO 
- 2F 
- 2 , and XeO 
- <unk> 
- 2 . XeOF 
- 2 is formed by the reaction of <unk> 
- 2 with xenon gas at low temperatures . It may also be obtained by the partial hydrolysis of XeF 
- 4 . It <unk> at − 20 ° C into XeF 
- 2 and XeO 
- 2F 
- 2 . XeOF 
- 4 is formed by the partial hydrolysis of XeF 
- 6 , or the reaction of XeF 
- 6 with sodium perxenate , Na 
- <unk> 
- 6 . The latter reaction also produces a small amount of XeO 
- <unk> 
- 2 . XeOF 
- 4 reacts with <unk> to form the XeOF − 
- 5 anion , while <unk> reacts with the alkali metal fluorides <unk> , <unk> and <unk> to form the XeOF − 
- 4 anion . 
- 
- = = = Other compounds = = = 
- 
- Recently , there has been an interest in xenon compounds where xenon is directly bonded to a less electronegative element than fluorine or oxygen , particularly carbon . <unk> @-@ withdrawing groups , such as groups with fluorine substitution , are necessary to stabilize these compounds . Numerous such compounds have been characterized , including : 
- C 
- 6F 
- 5 – Xe + – N <unk> C – CH 
- 3 , where <unk> is the <unk> group . 
- [ C 
- 6F 
- 5 ] 
- <unk> 
- C 
- 6F 
- 5 – Xe – X , where X is <unk> , F , or Cl . 
- R – C <unk> C – Xe + , where R is C 
- 2F − 
- 5 or <unk> @-@ butyl . 
- C 
- 6F 
- 5 – XeF + 
- 2 
- ( C 
- 6F 
- <unk> ) 
- <unk> + 
- Other compounds containing xenon bonded to a less electronegative element include F – Xe – N ( <unk> 
- 2F ) 
- 2 and F – Xe – <unk> 
- 2 . The latter is synthesized from <unk> <unk> , O 
- <unk> 
- 4 , at − 100 ° C. 
- An unusual ion containing xenon is the <unk> ( II ) cation , <unk> + 
- 4 , which contains Xe – <unk> bonds . This ion occurs in the compound <unk> 
- 4 ( Sb 
- 2F 
- 11 ) 
- 2 , and is remarkable in having direct chemical bonds between two notoriously <unk> atoms , xenon and gold , with xenon acting as a transition metal ligand . 
- The compound Xe 
- <unk> 
- 2F 
- 11 contains a Xe – Xe bond , the longest element @-@ element bond known ( 308 @.@ 71 pm = 3 @.@ <unk> <unk> ) . 
- In 1995 , M. <unk> and co @-@ workers , scientists at the University of Helsinki in Finland , announced the preparation of xenon <unk> ( <unk> ) , and later xenon hydride @-@ hydroxide ( <unk> ) , <unk> ( <unk> ) , and other Xe @-@ containing molecules . In 2008 , <unk> et al. reported the preparation of <unk> by the <unk> of water within a <unk> xenon matrix . <unk> molecules , <unk> and <unk> , have also been produced . 
- 
- = = = <unk> and <unk> = = = 
- 
- In addition to compounds where xenon forms a chemical bond , xenon can form <unk> — substances where xenon atoms or pairs are trapped by the crystalline lattice of another compound . One example is xenon hydrate ( Xe <unk> 5 @.@ 75 H2O ) , where xenon atoms occupy vacancies in a lattice of water molecules . This <unk> has a melting point of 24 ° C. The <unk> version of this hydrate has also been produced . Another example is Xe hydride ( Xe ( H2 ) 8 ) , in which xenon pairs ( <unk> ) are trapped inside solid hydrogen . Such <unk> <unk> can occur naturally under conditions of high pressure , such as in Lake <unk> underneath the Antarctic ice sheet . <unk> formation can be used to <unk> <unk> xenon , argon and krypton . 
- Xenon can also form <unk> fullerene compounds , where a xenon atom is trapped inside a fullerene molecule . The xenon atom trapped in the fullerene can be monitored via 129Xe nuclear magnetic resonance ( NMR ) spectroscopy . Using this technique , chemical reactions on the fullerene molecule can be analyzed , due to the sensitivity of the chemical shift of the xenon atom to its environment . However , the xenon atom also has an electronic influence on the reactivity of the fullerene . 
- When xenon atoms are at their ground energy state , they repel each other and will not form a bond . When xenon atoms becomes <unk> , however , they can form an excimer ( excited dimer ) until the electrons return to the ground state . This entity is formed because the xenon atom tends to fill its outermost electronic shell , and can briefly do this by adding an electron from a neighboring xenon atom . The typical lifetime of a xenon excimer is 1 – 5 <unk> , and the decay releases photons with <unk> of about 150 and 173 nm . Xenon can also form <unk> with other elements , such as the <unk> <unk> , chlorine and fluorine . 
- 
- = = Applications = = 
- 
- Although xenon is rare and relatively expensive to extract from the Earth 's atmosphere , it has a number of applications . 
- 
- = = = <unk> and optics = = = 
- 
- 
- = = = = Gas @-@ discharge lamps = = = = 
- 
- Xenon is used in light @-@ emitting devices called xenon flash lamps , which are used in photographic flashes and <unk> lamps ; to excite the active medium in lasers which then generate coherent light ; and , occasionally , in <unk> lamps . The first solid @-@ state laser , invented in 1960 , was pumped by a xenon flash lamp , and lasers used to power <unk> confinement fusion are also pumped by xenon flash lamps . 
- <unk> , short @-@ arc , high pressure xenon arc lamps have a color temperature closely <unk> noon sunlight and are used in solar simulators . That is , the <unk> of these lamps closely <unk> a heated black body <unk> that has a temperature close to that observed from the Sun . After they were first introduced during the 1940s , these lamps began replacing the shorter @-@ lived carbon arc lamps in movie <unk> . They are employed in typical 35mm , <unk> and the new digital <unk> film projection systems , automotive <unk> <unk> , high @-@ end " tactical " <unk> and other specialized uses . These arc lamps are an excellent source of short wavelength ultraviolet radiation and they have intense emissions in the near infrared , which is used in some night vision systems . 
- The individual cells in a plasma display use a mixture of xenon and neon that is converted into a plasma using electrodes . The interaction of this plasma with the electrodes generates ultraviolet photons , which then excite the phosphor coating on the front of the display . 
- Xenon is used as a " starter gas " in high pressure sodium lamps . It has the lowest thermal <unk> and lowest ionization potential of all the non @-@ radioactive noble gases . As a noble gas , it does not interfere with the chemical reactions occurring in the operating lamp . The low thermal <unk> <unk> thermal losses in the lamp while in the operating state , and the low ionization potential causes the breakdown voltage of the gas to be relatively low in the cold state , which allows the lamp to be more easily started . 
- 
- = = = = <unk> = = = = 
- 
- In 1962 , a group of researchers at Bell Laboratories discovered laser action in xenon , and later found that the laser gain was improved by adding helium to the <unk> medium . The first excimer laser used a xenon dimer ( <unk> ) <unk> by a beam of electrons to produce stimulated emission at an ultraviolet wavelength of 176 nm . Xenon chloride and xenon fluoride have also been used in excimer ( or , more accurately , <unk> ) lasers . The xenon chloride excimer laser has been employed , for example , in certain <unk> uses . 
- 
- = = = Medical = = = 
- 
- 
- = = = = <unk> = = = = 
- 
- Xenon has been used as a general anesthetic . Although it is expensive , anesthesia machines that can deliver xenon are about to appear on the European market , because advances in recovery and recycling of xenon have made it economically viable . 
- Xenon interacts with many different receptors and ion channels and like many theoretically multi @-@ modal inhalation <unk> these interactions are likely complementary . Xenon is a high @-@ affinity <unk> @-@ site NMDA receptor antagonist . However , xenon distinguishes itself from other clinically used NMDA receptor antagonists in its lack of <unk> and its ability to inhibit the <unk> of <unk> and nitrous oxide . Unlike <unk> and nitrous oxide , xenon does not stimulate a <unk> <unk> in the nucleus <unk> . Like nitrous oxide and <unk> , xenon activates the two @-@ pore domain potassium channel <unk> @-@ 1 . A related channel <unk> @-@ 3 also implicated in <unk> anesthetic actions is insensitive to xenon . Xenon inhibits <unk> acetylcholine <unk> receptors which contribute to <unk> mediated <unk> . Xenon is an effective inhibitor of plasma membrane Ca2 + <unk> . Xenon inhibits Ca2 + <unk> by binding to a hydrophobic pore within the enzyme and preventing the enzyme from assuming active conformations . 
- Xenon is a competitive inhibitor of the <unk> 5 @-@ <unk> receptor . While neither anesthetic nor <unk> this activity reduces anesthesia @-@ <unk> nausea and vomiting . 
- Xenon has a minimum <unk> concentration ( <unk> ) of 72 % at age 40 , making it 44 % more potent than <unk> as an anesthetic . Thus it can be used in concentrations with oxygen that have a lower risk of hypoxia . Unlike nitrous oxide ( <unk> ) , xenon is not a <unk> gas and so it is also viewed as environmentally friendly . Xenon vented into the atmosphere is being returned to its original source , so no environmental impact is likely . 
- 
- = = = = <unk> = = = = 
- 
- Xenon <unk> robust <unk> and <unk> through a variety of mechanisms of action . Through its influence on Ca2 + , K + , <unk> \ <unk> and NMDA <unk> xenon is <unk> when administered before , during and after ischemic insults . Xenon is a high affinity antagonist at the NMDA receptor <unk> site . Xenon is <unk> in <unk> @-@ <unk> conditions by inducing <unk> non @-@ ischemic <unk> . Xenon is <unk> by activating <unk> @-@ <unk> & downstream <unk> @-@ <unk> . Xenon <unk> <unk> ischemic <unk> by activating ATP sensitive potassium channels . Xenon <unk> reduces ATP mediated channel activation <unk> independently of the <unk> <unk> <unk> , increasing <unk> open @-@ channel time and frequency . Xenon <unk> hypoxia <unk> factor 1 alpha ( <unk> ) . 
- Xenon gas was added as an ingredient of the ventilation mix for a newborn baby at St. Michael 's Hospital , Bristol , England , whose life chances were otherwise very compromised , and was successful , leading to the <unk> of clinical trials for similar cases . The treatment is done simultaneously with cooling the body temperature to 33 @.@ 5 ° C. 
- 
- = = = = Doping = = = = 
- 
- <unk> a xenon / oxygen mixture activates production of the transcription factor <unk> @-@ 1 @-@ alpha , which leads to increased production of <unk> . The latter hormone is known to increase red blood cell production and athletes ' performance . Xenon inhalation has been used for this purpose in Russia since at least 2004 . On August 31 2014 the World Anti Doping Agency ( <unk> ) added Xenon ( and <unk> ) to the list of prohibited substances and methods , although at this time there is no reliable drug test . 
- 
- = = = = Imaging = = = = 
- 
- <unk> emission from the <unk> 133Xe of xenon can be used to image the heart , lungs , and brain , for example , by means of single <unk> emission computed <unk> . 133Xe has also been used to measure blood flow . 
- Xenon , particularly <unk> 129Xe , is a useful contrast agent for magnetic resonance imaging ( <unk> ) . In the gas phase , it can be used to image empty space such as cavities in a porous sample or <unk> in lungs . <unk> renders 129Xe much more detectable via magnetic resonance imaging and has been used for studies of the lungs and other tissues . It can be used , for example , to trace the flow of gases within the lungs . Because xenon is soluble in water and also in hydrophobic <unk> , it can be used to image various soft living tissues . 
- 
- = = = NMR spectroscopy = = = 
- 
- Because of the xenon atom 's large , flexible outer electron shell , the NMR spectrum changes in response to surrounding conditions , and can therefore be used as a probe to measure the chemical circumstances around it . For instance xenon dissolved in water , xenon dissolved in hydrophobic solvent , and xenon associated with certain proteins can be distinguished by NMR . 
- <unk> xenon can be used by surface <unk> . Normally , it is difficult to characterize surfaces using NMR , because signals from the surface of a sample will be overwhelmed by signals from the far @-@ more @-@ numerous atomic nuclei in the bulk . However , nuclear spins on solid surfaces can be selectively polarized , by transferring spin polarization to them from <unk> xenon gas . This makes the surface signals strong enough to measure , and distinguishes them from bulk signals . 
- 
- = = = Other = = = 
- 
- In nuclear energy applications , xenon is used in bubble chambers , probes , and in other areas where a high molecular weight and inert nature is desirable . A by @-@ product of nuclear weapon testing is the release of radioactive xenon @-@ 133 and xenon @-@ 135 . The detection of these isotopes is used to monitor compliance with nuclear test ban treaties , as well as to confirm nuclear test explosions by states such as North Korea . 
- Liquid xenon is being used in <unk> for measurements of gamma rays as well as a medium for detecting hypothetical weakly interacting massive particles , or <unk> . When a <unk> <unk> with a xenon nucleus , it is predicted to <unk> enough energy to cause ionization and <unk> . Liquid xenon is useful for this type of experiment due to its high density which makes dark matter interaction more likely and permits a quiet detector due to self @-@ shielding . 
- Xenon is the preferred propellant for ion propulsion of spacecraft because of its low ionization potential per atomic weight , and its ability to be stored as a liquid at near room temperature ( under high pressure ) yet be easily converted back into a gas to feed the engine . The inert nature of xenon makes it environmentally friendly and less corrosive to an ion engine than other fuels such as mercury or caesium . Xenon was first used for satellite ion engines during the 1970s . It was later employed as a propellant for <unk> 's Deep Space 1 probe , Europe 's <unk> @-@ 1 spacecraft and for the three ion propulsion engines on NASA 's Dawn <unk> . 
- <unk> , the perxenate compounds are used as oxidizing agents in analytical chemistry . Xenon difluoride is used as an <unk> for silicon , particularly in the production of <unk> systems ( <unk> ) . The <unk> drug 5 @-@ <unk> can be produced by reacting xenon difluoride with <unk> . Xenon is also used in protein crystallography . Applied at pressures from 0 @.@ 5 to 5 MPa ( 5 to 50 <unk> ) to a protein crystal , xenon atoms bind in predominantly hydrophobic cavities , often creating a high @-@ quality , <unk> , heavy @-@ atom derivative , which can be used for solving the phase problem . 
- 
- = = Precautions = = 
- 
- Many oxygen @-@ containing xenon compounds are toxic due to their strong <unk> properties , and explosive due to their tendency to break down into elemental xenon plus <unk> oxygen ( O2 ) , which contains much stronger chemical bonds than the xenon compounds . 
- Xenon gas can be safely kept in normal sealed glass or metal containers at standard temperature and pressure . However , it readily dissolves in most <unk> and rubber , and will gradually escape from a container sealed with such materials . Xenon is non @-@ toxic , although it does dissolve in blood and belongs to a select group of substances that penetrate the blood – brain barrier , causing mild to full surgical anesthesia when inhaled in high concentrations with oxygen . 
- At 169 m / s , the speed of sound in xenon gas is lower than that in air due to the lower average speed of the heavy xenon atoms compared to nitrogen and oxygen molecules . Hence , xenon lowers the rate of vibration in the vocal tract when exhaled . This produces a characteristic lowered voice <unk> , an effect opposite to the high @-@ <unk> voice caused by inhalation of helium . Like helium , xenon does not satisfy the body 's need for oxygen . Xenon is both a simple <unk> and an anesthetic more powerful than nitrous oxide ; consequently , many universities no longer allow the voice stunt as a general chemistry demonstration . As xenon is expensive , the gas sulfur hexafluoride , which is similar to xenon in molecular weight ( 146 versus 131 ) , is generally used in this stunt , and is an <unk> without being anesthetic . 
- It is possible to safely <unk> dense gases such as xenon or sulfur hexafluoride when they are in a mixture of at least 20 % oxygen . Xenon at 80 % concentration along with 20 % oxygen rapidly produces the unconsciousness of general anesthesia ( and has been used for this , as discussed above ) . <unk> mixes gases of different densities very effectively and rapidly so that heavier gases are purged along with the oxygen , and do not accumulate at the bottom of the lungs . There is , however , a danger associated with any heavy gas in large quantities : it may sit <unk> in a container , and if a person enters a container filled with an <unk> , colorless gas , they may find themselves breathing it unknowingly . Xenon is rarely used in large enough quantities for this to be a concern , though the potential for danger exists any time a tank or container of xenon is kept in an <unk> space . 
- 
- 
- = Eva Perón = 
- 
- María Eva Duarte de Perón ( 7 May 1919 – 26 July 1952 ) was the second wife of Argentine President Juan Perón ( 1895 – 1974 ) and served as the First Lady of Argentina from 1946 until her death in 1952 . She is usually referred to as Eva Perón ( Spanish : [ <unk> <unk> ] ) , or by the affectionate Spanish language <unk> Evita . 
- She was born in the rural village of Los <unk> , in the Pampas , as the youngest of five children . At 15 in 1934 , she moved to the nation 's capital of Buenos Aires to pursue a career as a stage , radio , and film actress . She met Colonel Juan Perón there on 22 January 1944 during a charity event at the <unk> Park Stadium to benefit the victims of an earthquake in San Juan , Argentina . The two were married the following year . Juan Perón was elected President of Argentina in 1946 ; during the next 6 years , Eva Perón became powerful within the pro @-@ Peronist trade unions , primarily for speaking on behalf of labor rights . She also ran the <unk> of Labor and Health , founded and ran the charitable Eva Perón Foundation , championed women 's suffrage in Argentina , and founded and ran the nation 's first large @-@ scale female political party , the Female Peronist Party . 
- In 1951 , Eva Perón announced her candidacy for the Peronist nomination for the office of Vice President of Argentina , receiving great support from the Peronist political base , low @-@ income and working @-@ class Argentines who were referred to as descamisados or " shirtless ones " . However , opposition from the nation 's military and <unk> , coupled with her declining health , ultimately forced her to withdraw her candidacy . In 1952 , shortly before her death from cancer at 33 , Eva Perón was given the title of " Spiritual Leader of the Nation " by the Argentine Congress . Eva Perón was given a state funeral upon her death , a prerogative generally reserved for heads of state . 
- Eva Perón has become a part of international popular culture , most famously as the subject of the musical Evita ( 1976 ) . Cristina <unk> Rodríguez , Evita 's great @-@ niece , claims that Evita has never left the collective consciousness of Argentines . Cristina Fernández de Kirchner , the first elected female President of Argentina , claims that women of her generation owe a debt to Eva for " her example of passion and combativeness " . 
- 
- = = Early life = = 
- 
- 
- = = = Early childhood = = = 
- 
- Eva 's autobiography , La <unk> de mi Vida , contains no dates or references to childhood occurrences , and does not list the location of her birth or her name at birth . According to Junín 's civil registry , a birth certificate shows that one María Eva Duarte was born on 7 May 1922 . Her baptismal certificate , however , lists the date of birth as 7 May 1919 under the name Eva María Ibarguren . It is thought that in 1945 the adult Eva Perón created a forgery of her birth certificate for her marriage . 
- Eva Perón spent her childhood in Junín , Buenos Aires province . Her father , Juan Duarte , was descended from French <unk> immigrants , meanwhile her mother Juana Ibarguren , was descended from Spanish <unk> immigrants . Juan Duarte , a wealthy <unk> from nearby <unk> , already had a wife and family there . At that time in rural Argentina , it was not uncommon for a wealthy man to have multiple families . 
- When Eva was a year old , Duarte returned permanently to his legal family , leaving Juana Ibarguren and her children in <unk> . Ibarguren and her children were forced to move to the poorest area of Junín . Los <unk> was a village in the dusty region of Las Pampas , with a reputation as a desolate place of <unk> poverty . To support herself and her children , Ibarguren <unk> clothes for neighbors . The family was <unk> by the abandonment of the father and by the illegitimate status of the children under Argentine law , and was consequently somewhat isolated . A desire to <unk> this part of her life might have been a motivation for Eva to arrange the destruction of her original birth certificate in 1945 . 
- When Duarte suddenly died and his mistress and their children sought to attend his funeral , there was an unpleasant scene at the church gates . Although Juana and the children were permitted to enter and pay their respects to Duarte , they were promptly directed out of the church . Mrs. Juan Duarte did not want her husband 's mistress and children at the funeral and , as those of the legitimate wife , her orders were respected . 
- 
- = = = Junín = = = 
- 
- Prior to abandoning Juana Ibarguren , Juan Duarte had been her sole means of support . Biographer , John Barnes , writes that after this abandonment , all Duarte left to the family was a document declaring that the children were his , thus enabling them to use the Duarte surname . Soon after , Juana moved her children to a one @-@ room apartment in Junín . To pay the rent on their single @-@ <unk> home , mother and daughters took up jobs as cooks in the houses of the local <unk> . 
- Eventually , owing to Eva 's older brother 's financial help , the family moved into a bigger house , which they later transformed into a boarding house . During this time , young Eva often participated in school plays and concerts . One of her favorite <unk> was the cinema . Though Eva 's mother apparently had a few plans for Eva , wanting to marry her off to one of the local <unk> , Eva herself dreamed of becoming a famous actress . Eva 's love of acting was reinforced when , in October 1933 , she played a small role in a school play called <unk> <unk> ( Students <unk> ) , which Barnes describes as " an emotional , patriotic , flag @-@ waving melodrama . " After the play , Eva was determined to become an actress . 
- 
- = = = <unk> to Buenos Aires = = = 
- 
- In her autobiography , she explained that all the people from her own town who had been to the big cities described them as " marvelous places , where nothing was given but wealth " . In 1934 , at the age of 15 , Eva escaped her poverty @-@ stricken village when , according to popular myth , she ran off with a young musician to the nation 's capital of Buenos Aires . The young couple 's relationship would end almost as quickly as it began , but Eva remained in Buenos Aires . She began to pursue jobs on the stage and the radio , and eventually became a film actress . Eva had a series of relationships , and via some of these men she did acquire a number of her modeling appointments . She <unk> her natural black hair to blond , a look she would maintain for the duration of her life . 
- It is often reported that Eva traveled to Buenos Aires by train with <unk> singer Agustín <unk> . However , biographers Marysa Navarro and Nicholas Fraser maintain that this is unlikely , as there is no record of the married <unk> performing in Junín in 1934 ( and , even if he had , he usually traveled with his wife ) . Eva 's sisters maintain that Eva traveled to Buenos Aires with their mother . The sisters also claim that Doña Juana accompanied her daughter to an audition at a radio station and arranged for Eva to live with the <unk> family , who were friends of the Duarte family . While the method of Eva 's escape from her bleak provincial surroundings is debated , she did begin a new life in Buenos Aires . 
- Buenos Aires in the 1930s was known as the " Paris of South America " . The center of the city had many <unk> , restaurants , theaters , movie houses , shops and <unk> crowds . In direct contrast , the 1930s were also years of great unemployment , poverty and hunger in the capital , and many new <unk> from the interior were forced to live in <unk> , <unk> and in outlying <unk> that became known as <unk> <unk> . 
- Upon arrival in Buenos Aires , Eva Duarte was faced with the difficulties of surviving without formal education or connections . The city was especially overcrowded during this period because of the migrations caused by the Great Depression . On 28 March 1935 , she had her professional debut in the play Mrs. <unk> ( la Señora de Pérez ) , at the <unk> Theater . 
- In 1936 , Eva toured nationally with a theater company , worked as a model , and was cast in a few B @-@ grade movie <unk> . In 1942 , Eva experienced some economic stability when a company called <unk> ( sponsored by a soap manufacturer ) hired her for a daily role in one of their radio dramas called <unk> <unk> , which aired on Radio El Mundo ( World Radio ) , the most important radio station in the country at that time . Later that year , she signed a five @-@ year contract with Radio <unk> , which assured her a role in a popular historical @-@ drama program called Great Women of History , in which she played Elizabeth I of England , Sarah <unk> , and the last <unk> of Russia . Eventually , Eva Duarte came to co @-@ own the radio company . By 1943 , Eva Duarte was earning five or six thousand pesos a month , making her one of the highest @-@ paid radio actresses in the nation . Pablo <unk> , who jointly ran Radio El Mundo with Eva Duarte , is said to have not liked her , but to have noted that she was " thoroughly dependable " . Eva also had a short @-@ lived film career , but none of the films in which she appeared were hugely successful . In one of her last films , La <unk> del <unk> ( The Circus Cavalcade ) , Eva played a young country girl who rivaled an older woman , the movie 's star , <unk> <unk> . 
- As a result of her success with radio dramas and the films , Eva achieved some financial stability . In 1942 , she was able to move into her own apartment in the exclusive neighborhood of <unk> , on 1567 Calle <unk> . The next year Eva began her career in politics , as one of the founders of the Argentine Radio Syndicate ( <unk> ) . 
- 
- = = Early relationship with Juan Perón = = 
- 
- On 15 January 1944 , an earthquake occurred in the town of San Juan , Argentina , killing some 10 @,@ 000 people . In response , Perón , who was then the Secretary of Labour , established a fund to raise money to aid the victims . He devised a plan to have an " artistic festival " as a fundraiser , and invited radio and film actors to participate . After a week of fundraising , all participants met at a gala held at <unk> Park Stadium in Buenos Aires to benefit earthquake victims . It was at this gala , on 22 January 1944 , that Eva Duarte first met Colonel Juan Perón . Eva promptly became the colonel 's mistress . Eva referred to the day she met her future husband as her " marvelous day " . Fraser and Navarro write that Juan Perón and Eva left the gala together at around two in the morning . 
- Fraser and Navarro claim that Eva Duarte had no knowledge of or interest in politics prior to meeting Perón . Therefore , she never argued with Perón or any of his inner circle , but merely absorbed what she heard . Juan Perón later claimed in his memoir that he purposefully selected Eva as his pupil , and set out to create in her a " second I. " Fraser and Navarro , however , suggest that Juan Perón allowed Eva Duarte such intimate exposure and knowledge of his inner circle because of his age : he was 48 and she was 24 when they met . He had come to politics late in life , and was therefore free of <unk> ideas of how his political career should be conducted , and he was willing to accept whatever aid she offered him . 
- In May 1944 , it was announced that broadcast performers must organize themselves into a union , and that this union would be the only one permitted to operate in Argentina . Shortly after the union formed , Eva Duarte was elected its president . Fraser and Navarro speculate that Juan Perón made the suggestion that performers create a union , and the other performers likely felt it was good politics to elect his mistress . Shortly after her election as president of the union , Eva Duarte began a daily program called Toward a Better Future , which <unk> in soap opera form the accomplishments of Juan Perón . Often , Perón 's own speeches were played during the program . When she spoke , Eva Duarte spoke in ordinary language as a regular woman who wanted listeners to believe what she herself believed about Juan Perón . 
- 
- = = Rise to power = = 
- 
- 
- = = = Juan Perón 's arrest = = = 
- 
- By early 1945 , a group of Army officers called the <unk> for " <unk> de <unk> <unk> " ( United Officers Group ) , nicknamed " The Colonels " , had gained considerable influence within the Argentine government . President Pedro Pablo Ramírez became wary of Juan Perón 's growing power within the government , but was unable to curb that power . On 24 February 1944 , Ramírez signed his own resignation paper , which Fraser and Navarro claim was drafted by Juan Perón himself . <unk> <unk> Farrell , a friend of Juan Perón , became President . Juan Perón returned to his job as Labor Minister . Fraser and Navarro claim that , by this point , Perón was the most powerful man in the Argentine government . On 9 October 1945 Juan Perón was arrested by his opponents within the government who feared that due to the strong support of the descamisados , the workers and the poor of the nation , Perón 's popularity might eclipse that of the sitting president . 
- Six days later , between 250 @,@ 000 and 350 @,@ 000 people gathered in front of the Casa Rosada , Argentina 's government house , to demand Juan Perón 's release , and their wish was granted . At 11 pm , Juan Perón stepped on to the balcony of the Casa Rosada and addressed the crowd . Biographer Robert D. Crassweller claims that this moment was very powerful because it was very dramatic and recalled many important aspects of Argentine history . Crassweller writes that Juan Perón enacted the role of a <unk> addressing his people in the tradition of Argentine leaders <unk> and <unk> . Crassweller also claims that the evening contained " mystic overtones " of a " quasi @-@ religious " nature . Eva Perón has often been credited with organizing the rally of thousands that freed Juan Perón from prison on 17 October 1945 . This version of events was popularized in the movie version of the Lloyd Webber musical . Most historians , however , agree that this version of events is unlikely . At the time of Perón 's imprisonment , Eva was still merely an actress . She had no political <unk> with the various labor unions , and it is claimed that she was not well @-@ liked within Perón 's inner circle , nor was she liked by many within the film and radio business at this point . When Juan Perón was imprisoned , Eva Duarte was suddenly disenfranchised . In reality , the massive rally that freed Perón from prison was organized by the various unions , such as General Labor Confederation , or CGT as they came to be known . To this day , the date of 17 October is something of a holiday for the <unk> Party in Argentina ( celebrated as Día de la <unk> , or " <unk> Day " ) . What would follow was shocking and nearly unheard of . The well connected and politically rising star , Juan Peron , married Eva . Despite Eva 's childhood <unk> , and having an uncertain reputation , Peron was in love with Eva , and her loyal devotion to him even while he had been under arrest touched him deeply , and so he married her , providing a respectability she had never known . Eva and Juan were married discreetly in a civil ceremony in Junín on 18 October 1945 and in a church wedding on 9 December 1945 . 
- 
- = = = 1946 Presidential election victory = = = 
- 
- After his release from prison , Juan Perón decided to campaign for the presidency of the nation , which he won in a landslide . Eva campaigned heavily for her husband during his 1946 presidential bid . Using her weekly radio show , she delivered powerful speeches with heavy populist rhetoric urging the poor to align themselves with Perón 's movement . Though she had become wealthy from her radio and modeling success , she highlighted her own humble upbringing as a way of showing solidarity with the impoverished classes . 
- Along with her husband , Eva visited every corner of the country , becoming the first woman in Argentina 's history to appear in public on the campaign trail with her husband . Eva 's appearance alongside her husband often offended the establishment of the wealthy , the military , and those in political life . However , she was very popular with the general public who knew her from her radio and motion picture appearances . It was during this phase of her life that she first encouraged the Argentine population to refer to her not as " Eva Perón " but simply as " Evita " , which is a Spanish <unk> or affectionate nickname roughly equivalent to " Little Eva " or " <unk> . " 
- 
- = = European tour = = 
- 
- In 1947 , Eva embarked on a much @-@ publicized " Rainbow Tour " of Europe , meeting with numerous dignitaries and heads of state , such as Francisco Franco and Pope Pius XII . Biographers Fraser and Navarro write that the tour had its genesis in an invitation the Spanish leader had extended to Juan Perón . For political reasons it was decided that Eva , rather than Juan Perón , should make the visit . Fraser and Navarro write that Argentina had only recently emerged from its " wartime quarantine " , thus taking its place in the United Nations and improving relations with the United States . Therefore , a visit to Franco , with António <unk> of Portugal the last remaining west European authoritarian leaders in power , would be <unk> <unk> upon internationally . Fraser and Navarro write that Eva decided that , if Juan Perón would not accept Franco 's invitation for a state visit to Spain , then she would . <unk> then decided that Eva should visit many European countries in addition to Spain . This would make it seem that Eva 's sympathies were not specifically with Franco 's fascist Spain but with all of Europe . The tour was billed not as a political tour but as a non @-@ political " goodwill " tour . 
- Eva was well received in Spain , where she visited the tombs of Spanish monarchs Ferdinand and Isabella in the Capilla Real de Granada . <unk> Spain had not recovered from the Spanish Civil War ( the <unk> economy and the UN embargo meant that the country could not feed its people ) . During her visit to Spain , Eva handed out 100 @-@ <unk> notes to many poor children she met on her journey . She also received from Franco the highest award given by the Spanish government , the Order of Isabella the Catholic . 
- Eva then visited Rome , where the reception was not as warm as it had been in Spain . Though Pope Pius XII did not give her a Papal decoration , she was allowed the time usually allotted queens and was given a rosary . 
- Her next stop was France , where she was generally well received . She visited the Palace of Versailles , among other sites . She also met with Charles de <unk> . She promised France two shipments of wheat . 
- While in France , Eva received word that George VI would not receive her when she planned to visit Britain , regardless of what his Foreign Office might advise , and that her visit would not be viewed as a state visit . Fraser and Navarro wrote that Eva regarded the royal family 's refusal to meet her as a <unk> , and canceled the trip to the United Kingdom . Eva , however , gave " exhaustion " as the official reason for not going on to Britain . 
- Eva also visited Switzerland during her European tour , a visit that has been viewed as the worst part of the trip . According to the book Evita : A Biography by John Barnes , while she traveled down a street with many people <unk> her car , someone threw two stones and smashed the <unk> . She threw her hands up in shock , but was not injured . Later , while sitting with the Foreign Minister , protesters threw tomatoes at her . The tomatoes hit the Foreign Minister and <unk> on Eva 's dress . After these two events , Eva had had enough , and after two months returned to Argentina . 
- Members of the Peronist opposition speculated that the true purpose of the European tour was to deposit funds in a Swiss bank account . " The opposition in Buenos Aires " , write Fraser and Navarro , " assumed that the genuine purpose of the whole European visit was for Eva and her husband to deposit money in Swiss bank accounts , and that the rest had been devised to conceal this . Many wealthy Argentines did this , but there are many more convenient and less conspicuous ways of depositing money in Swiss accounts than meeting the Swiss Foreign Minister and being shown around a watch factory . " Fraser and Navarro conclude , " Was there a Swiss bank account ? It seems unlikely . " 
- During her tour to Europe , Eva Perón was featured in a cover story for Time magazine . The cover 's caption – " Eva Perón : Between two worlds , an Argentine rainbow " – was a reference to the name given to Eva 's European tour , The Rainbow Tour . This was the only time in the periodical 's history that a South American first lady appeared alone on its cover . ( In 1951 , Eva appeared again with Juan Perón . ) However , the 1947 cover story was also the first publication to mention that Eva had been born out of <unk> . In retaliation , the periodical was banned from Argentina for several months . 
- After returning to Argentina from Europe , Evita never again appeared in public with the complicated <unk> of her movie star days . The brilliant gold color became more subdued in tone , and even the style changed , her hair being pulled back severely into a heavy <unk> <unk> . Additionally , her extravagant clothing became more refined after the tour . No longer did she wear the elaborate hats and form @-@ fitting dresses of Argentine designers . Soon she adopted simpler and more fashionable Paris couture and became particularly attached to the <unk> of Christian Dior and the jewels of <unk> . In an attempt to <unk> a more serious political persona , Eva began to appear in public wearing conservative though stylish <unk> ( a business @-@ like combination of skirts and jackets ) , which also were made by Dior and other Paris couture houses . 
- 
- = = Charitable and feminist activities = = 
- 
- 
- = = = Eva Foundation = = = 
- 
- The Sociedad de <unk> ( Society of <unk> ) , a charity group made up of 87 society ladies , was responsible for most charity works in Buenos Aires prior to the election of Juan Perón . Fraser and Navarro write that at one point the Sociedad had been an enlightened institution , caring for orphans and homeless women , but that those days had long since passed by the time of the first term of Juan Perón . In the 1800s , the Sociedad had been supported by private contributions , largely those of the husbands of the society ladies . But by the 1940s , the Sociedad was supported by the government . 
- It had been the tradition of the Sociedad to elect the First Lady of Argentina as president of the charity . But the ladies of the Sociedad did not approve of Eva Perón 's impoverished background , lack of formal education , and former career as an actress . Fraser and Navarro write that the ladies of the Sociedad were afraid that Evita would set a bad example for the orphans , therefore the society ladies did not extend to Evita the position of president of their organization . It has often been said that Evita had the government funding for the Sociedad cut off in retaliation . Fraser and Navarro suggest that this version of events is in dispute , but that the government funding that had previously supported the Sociedad now went to support Evita 's own foundation . The <unk> María Eva Duarte de Perón was created on 8 July 1948 . It was later renamed to , simply , the Eva Perón Foundation . Its funding began with 10 @,@ 000 pesos provided by Evita herself . 
- In The Woman with the Whip , the first English language biography of Eva Perón , author Mary Main writes that no account records were kept for the foundation because it was merely a means of <unk> government money into private Swiss bank accounts controlled by the Peróns . Fraser and Navarro , however , counter these claims , writing that Ramón <unk> , Minister of Finance , kept records , and that the foundation " began as the simplest response to the poverty ( Evita ) encountered each day in her office " and " the appalling <unk> of social services — or charity , as it was still called — in Argentina . " Crassweller writes that the foundation was supported by donations of cash and goods from the Peronist unions and private businesses , and that the <unk> General del <unk> donated three man @-@ days ( later reduced to two ) of salary for every worker per year . Tax on lottery and movie tickets also helped to support the foundation , as did a levy on casino and revenue from horse races . Crassweller also notes that there were some cases of businesses being pressured to donate to the foundation , with negative repercussions resulting if requests for donations were not met . 
- Within a few years , the foundation had assets in cash and goods in excess of three billion pesos , or over $ 200 million at the exchange rate of the late 1940s . It employed 14 @,@ 000 workers , of whom 6 @,@ 000 were construction workers , and 26 priests . It purchased and distributed annually 400 @,@ 000 pairs of shoes , 500 @,@ 000 <unk> machines , 200 @,@ 000 cooking pots . The foundation also gave <unk> , built homes , hospitals , and other charitable institutions . Every aspect of the foundation was under Evita 's supervision . The foundation also built entire communities , such as Evita City , which still exists today . Fraser and Navarro claim that due to the works and health services of the foundation , for the first time in history there was no inequality in Argentine health care . 
- Fraser and Navarro write that it was Evita 's work with the foundation that played a large role in her <unk> , even leading some to consider her a saint . Though it was unnecessary from a practical standpoint , Evita set aside many hours per day to meet with the poor who requested help from her foundation . During these meetings with the poor , Evita often kissed the poor and allowed them to kiss her . Evita was even witnessed placing her hands in the <unk> wounds of the sick and poor , touching the <unk> , and kissing the <unk> . Fraser and Navarro write that though Argentina is secular in many respects , it is essentially a Catholic country . Therefore , when Evita kissed the <unk> and touched the <unk> she " ... ceased to be the President 's wife and acquired some of the characteristics of saints depicted in Catholicism . " Poet José María <unk> de <unk> , a man from a wealthy background , reflected on the times he witnessed Evita meeting with the poor : " I had had a sort of literary perception of the people and the poor and she had given me a Christian one , thus allowing me to become a Christian in the <unk> sense .... " 
- Fraser and Navarro write that toward the end of her life , Evita was working as many as 20 to 22 hours per day in her foundation , often ignoring her husband 's request that she cut back on her workload and take the weekends off . The more she worked with the poor in her foundation , the more she adopted an outraged attitude toward the existence of poverty , saying , " Sometimes I have wished my insults were <unk> or lashes . I 've wanted to hit people in the face to make them see , if only for a day , what I see each day I help the people . " Crassweller writes that Evita became <unk> about her work in the foundation and felt on a crusade against the very concept and existence of poverty and social <unk> . " It is not surprising " , writes Crassweller , " that as her public <unk> and her private <unk> took on a narrowing intensity after 1946 , they simultaneously <unk> toward the <unk> . " Crassweller compares Evita to <unk> Loyola , saying she came to be akin to a one @-@ woman Jesuit Order . 
- 
- = = = Female Peronist Party and women 's suffrage = = = 
- 
- Biographers Fraser and Navarro wrote that Eva Perón has often been credited with gaining the right to vote for Argentine women . While Eva did make radio addresses in support of women 's suffrage and also published articles in her <unk> newspaper asking male <unk> to support women 's right to vote , ultimately the ability to grant to women the right to vote was beyond Eva 's powers . Fraser and Navarro claim that Eva 's actions were limited to supporting a bill introduced by one of her supporters , Eduardo <unk> , a bill that was eventually dropped . 
- A new women 's suffrage bill was introduced , which the Senate of Argentina sanctioned on 21 August 1946 . It was necessary to wait more than a year before the House of Representatives sanctioned it on 9 September 1947 . Law 13 @,@ 010 established the equality of political rights between men and women and universal suffrage in Argentina . Finally , Law 13 @,@ 010 was approved unanimously . In a public celebration and ceremony , however , Juan Perón signed the law granting women the right to vote , and then he handed the bill to Eva , symbolically making it hers . 
- Eva Perón then created the Female Peronist Party , the first large female political party in the nation . Navarro and Fraser write that by 1951 , the party had 500 @,@ 000 members and 3 @,@ 600 headquarters across the country . Navarro and Fraser write that while Eva Perón did not consider herself a feminist , her impact on the political life of women was decisive . Thousands of previously <unk> women entered politics because of Eva Perón . They were the first women active in Argentine politics . The combination of female suffrage and the organization of the Female Peronist Party granted Juan Perón a large majority ( sixty @-@ three percent ) of the vote in the 1951 presidential elections . 
- 
- = = 1951 Presidential election = = 
- 
- 
- = = = Vice Presidential nomination = = = 
- 
- In 1951 , Evita set her sights on earning a place on the ballot as candidate for vice @-@ president . This move angered many military leaders who despised Evita and her increasing powers within the government . According to the Argentine Constitution , the Vice President automatically succeeds the President in the event of the President 's death . The possibility of Evita becoming president in the event of Juan Perón 's death was not something the military could accept . 
- She did , however , receive great support from the working class , the unions , and the Peronist Women 's Party . The intensity of the support she drew from these groups is said to have surprised even Juan Perón himself . Fraser and Navarro write that the wide support Evita 's proposed candidacy generated indicated to him that Evita had become as important to members of the Peronist party as Juan Perón himself was . 
- On 22 August 1951 , the unions held a mass rally of two million people called " Cabildo Abierto . " ( The name " Cabildo Abierto " was a reference and tribute to the first local Argentine government of the May Revolution , in 1810 . ) The Peróns addressed the crowd from the balcony of a huge scaffolding set up on the <unk> 9 de Julio , several blocks away from the Casa Rosada , the official government house of Argentina . <unk> were two large portraits of Eva and Juan Perón . It has been claimed that " Cabildo Abierto " was the largest public display of support in history for a female political figure . 
- At the mass rally , the crowd demanded that Evita publicly announce her candidacy as vice president . She pleaded for more time to make her decision . The exchange between Evita and the crowd of two million became , for a time , a genuine and spontaneous dialogue , with the crowd chanting , " ¡ Evita , Vice @-@ <unk> ! " When Evita asked for more time so she could make up her mind , the crowd demanded , " ¡ <unk> , Evita , <unk> ! " ( " Now , Evita , now ! " ) . Eventually , they came to a compromise . Evita told the audience that she would announce her decision over the radio a few days later . 
- 
- = = = <unk> health = = = 
- 
- Eventually , she declined the invitation to run for vice @-@ president , saying that her only ambition was that — in the large chapter of history that would be written about her husband — the <unk> would mention a woman who brought the " ... hopes and dreams of the people to the president " , a woman who eventually turned those hopes and dreams into " glorious reality . " In Peronist rhetoric , this event has come to be referred to as " The <unk> " , portraying Evita as having been a <unk> woman in line with the Hispanic myth of <unk> . Most biographers , however , <unk> that Evita did not so much renounce her ambition , as bow to pressure from her husband , the military , and the Argentine upper class , who preferred that she not enter the race . 
- On 9 January 1950 , Evita fainted in public and underwent surgery three days later . Although it was reported that she had undergone an <unk> , she was diagnosed with advanced cervical cancer . <unk> continued through 1951 ( including the evening after " Cabildo <unk> " ) , with extreme weakness and severe vaginal bleeding . By 1951 , it had become evident that her health was rapidly deteriorating . Although her diagnosis was withheld from her by Juan , she knew she was not well , and a bid for the vice @-@ presidency was not practical . Only a few months after " the <unk> " , Evita underwent a secret radical <unk> in an attempt to eradicate her advanced cervical cancer . In 2011 , a Yale <unk> studied Evita 's skull x @-@ rays and photographic evidence and said that Perón may have been given a prefrontal <unk> in the last months of her life , " ... to relieve the pain , agitation and anxiety she suffered in the final months of her illness . " 
- 
- = = = Re @-@ election and Spiritual Leader of the Nation = = = 
- 
- On 4 June 1952 , Evita <unk> with Juan Perón in a parade through Buenos Aires in celebration of his re @-@ election as President of Argentina . Evita was by this point so ill that she was unable to stand without support . Underneath her oversized fur coat was a frame made of plaster and wire that allowed her to stand . She took a triple dose of pain medication before the parade , and took another two doses when she returned home . 
- In a ceremony a few days after Juan Perón 's second inauguration , Evita was given the official title of " Spiritual Leader of the Nation . " 
- 
- = = Death and aftermath = = 
- 
- 
- = = = Death = = = 
- 
- Although Perón had undergone a <unk> performed by the American surgeon George T. Pack , the cancer had <unk> and returned rapidly . She was the first Argentine to undergo chemotherapy ( a novel treatment at that time ) . Despite all available treatment , she became <unk> , weighing only 36 kg ( 79 lb ) by June 1952 . Evita died at the age of 33 , at 20 : 25 on Saturday , 26 July 1952 . Radio broadcasts throughout the country were interrupted with the announcement that " The Press Secretary 's Office of the <unk> of the Nation <unk> its very sad duty to inform the people of the Republic that at 20 : 25 hours Mrs. Eva Perón , Spiritual Leader of the Nation , died . " Ordinary activities ceased ; movies stopped playing ; restaurants were closed and patrons were shown to the door . 
- 
- = = = <unk> = = = 
- 
- Immediately after Perón 's death , the government suspended all official activities for two days and ordered all flags flown at half @-@ staff for ten days . It soon became apparent , however , that these measures fell short of reflecting popular grief . The crowd outside of the presidential residence , where Evita died , grew dense , <unk> the streets for ten blocks in each direction . 
- The morning after her death , while Evita 's body was being moved to the Ministry of Labour Building , eight people were crushed to death in the <unk> . In the following 24 hours , over 2000 people were treated in city hospitals for injuries sustained in the rush to be near Evita as her body was being transported , and thousands more would be treated on the spot . For the following two weeks , lines would stretch for many city blocks with mourners waiting hours to see Evita 's body lie at the Ministry of Labour . 
- The streets of Buenos Aires overflowed with huge piles of flowers . Within a day of Perón 's death , all flower shops in Buenos Aires had run out of stock . Flowers would be flown in from all over the country , and as far away as Chile . Despite the fact that Eva Perón never held a political office , she was eventually given a state funeral usually reserved for a head of state , along with a full Roman Catholic <unk> mass . A memorial was held for the Argentine team during the 1952 Summer Olympics in Helsinki due to Eva Perón 's death during those games . 
- On 9 August , Saturday , the body was transferred to the Congress Building for an additional day of public viewing , and a memorial service attended by the entire Argentine legislative body . The next day , after a final Mass , the coffin was laid on a gun carriage pulled by CGT officials . It was followed by Peron , his cabinet , Eva 's family and friends , the delegates and representatives of the <unk> <unk> <unk> — then workers , nurses and students of the Eva Peron Foundation . Flowers were thrown from balconies and windows . 
- There were different interpretations of the popular mourning of Eva Perón 's death . Some reporters viewed the mourning as authentic , others saw a public succumbing to another of the " passion plays " of the Peronist regime . Time magazine reported that the Peronist government enforced the observance of a daily period of five minutes of mourning following a daily radio announcement . 
- During Perón 's time , children born to unmarried parents did not have the same legal rights as those born to married parents . Biographer Julie M. Taylor , professor of anthropology at Rice University , has said that Evita was well aware of the pain of being born " illegitimate . " Taylor speculates that Evita 's awareness of this may have influenced her decision to have the law changed so that " illegitimate " children would <unk> be referred to as " natural " children . Upon her death , the Argentine public was told that Evita was only 30 . The discrepancy was meant to <unk> with Evita 's earlier <unk> with her birth certificate . After becoming the first lady in 1946 , Evita had her birth records altered to read that she had been born to married parents , and placed her birth date three years forward , making herself younger . 
- 
- = = = Memorial plans = = = 
- 
- Shortly after Evita 's death , Dr. Pedro Ara was approached to <unk> the body . Fraser and Navarro write that it is doubtful that Evita ever expressed a wish to be embalmed , and suggest that it was most likely Juan Perón 's decision . Ara was a professor of anatomy who had studied in Vienna and maintained an academic career in Madrid . His work was occasionally referred to as " the art of death . " His highly advanced <unk> technique consisted of replacing the corpse 's blood with <unk> , which preserved all organs including the brain and created a <unk> appearance , giving the body the appearance of " artistically rendered sleep . " Ara was known in Buenos Aires society for his work . Among the people he had embalmed was Spanish composer Manuel de <unk> . Ara claims that his <unk> of Evita 's corpse began on the night of her death and that by the next morning , " the body of Eva Perón was completely and infinitely <unk> " and suitable for display to the public . 
- In the book Peron and the <unk> of Argentina , biographer Robert D. Crassweller claims that the English @-@ speaking nations of North America and Europe largely misunderstood Argentina 's response to the death of Perón as well as the ornate funeral she was granted . Crassweller attributes this misunderstanding to the unique cultural makeup of the Peróns and Argentina , saying that the Peróns were of the Hispanic tradition and that their opposition was largely of British ancestry . 
- 
- = = = <unk> and return of corpse = = = 
- 
- Shortly after Evita 's death , plans were made to construct a memorial in her honour . The monument , which was to be a statue of a man representing the descamisados , was projected to be larger than the Statue of Liberty . Evita 's body was to be stored in the base of the monument and , in the tradition of Lenin 's corpse , to be displayed for the public . While the monument was being constructed , Evita 's embalmed body was displayed in her former office at the CGT building for almost two years . Before the monument to Evita was completed , Juan Perón was overthrown in a military coup , the <unk> <unk> , in 1955 . Perón hastily fled the country and was unable to make arrangements to secure Evita 's body . 
- Following his flight , a military dictatorship took power . The new authorities removed Evita 's body from display , and its whereabouts were a mystery for 16 years . From 1955 until 1971 , the military dictatorship of Argentina issued a ban on Peronism . It became illegal not only to possess pictures of Juan and Eva Perón in one 's home , but to speak their names . In 1971 , the military revealed that Evita 's body was buried in a crypt in Milan , Italy , under the name " María <unk> . " It appeared that her body had been damaged during its transport and storage , such as <unk> to her face and <unk> of one of her feet due to the body having been left in an upright position . 
- In 1995 , Tomás Eloy Martínez published Santa Evita , a fictionalized work <unk> many new stories about the <unk> of the corpse . <unk> that her body was the object of inappropriate <unk> are derived from his description of an ' emotional necrophilia ' by <unk> , Coronel Koenig and his assistant <unk> . Many primary and secondary references to his novel have inaccurately stated that her body had been <unk> in some way resulting in the widespread belief in this myth . Also included are allegations that many wax copies had been made , that the corpse had been damaged with a hammer , and that one of the wax copies was the object of an officer 's sexual <unk> . 
- 
- = = = Final resting place = = = 
- 
- In 1971 , Perón 's body was exhumed and flown to Spain , where Juan Perón maintained the corpse in his home . Juan and his third wife , Isabel , decided to keep the corpse in their dining room on a platform near the table . In 1973 , Juan Perón came out of exile and returned to Argentina , where he became president for the third time . Perón died in office in 1974 . His third wife , Isabel Perón , whom he had married on 15 November 1961 , and who had been elected vice @-@ president , succeeded him . She became the first female president in the Western Hemisphere . Isabel had Eva Perón 's body returned to Argentina and ( briefly ) displayed beside her husband 's . Perón 's body was later buried in the Duarte family tomb in La <unk> Cemetery , Buenos Aires . The previous removal of Evita 's body was avenged by the Montoneros when they in 1970 stole Pedro <unk> Aramburu 's corpse , whom they had previously killed . Montoneros then used the captive body of Aramburu to pressure for the repatriation of Evita 's body . Once Evita 's body arrived in Argentina the Montoneros gave up Aramburu 's corpse and abandoned it in a street in Buenos Aires . 
- The Argentine government took elaborate measures to make Perón 's tomb secure . The tomb 's marble floor has a <unk> that leads to a compartment containing two coffins . Under that compartment is a second <unk> and a second compartment . That is where Perón 's coffin rests . Biographers Marysa Navarro and Nicholas Fraser write that the claim is often made that her tomb is so secure that it could withstand a nuclear attack . " It reflects a fear " , they write , " a fear that the body will disappear from the tomb and that the woman , or rather the myth of the woman , will reappear . " 
- 
- = = Legacy and criticism = = 
- 
- 
- = = = Argentina and Latin America = = = 
- 
- In all of Latin America , only one other woman has aroused an emotion , devotion and faith comparable to those <unk> by the Virgin of Guadalupe . In many homes , the image of Evita is on the wall next to the Virgin . 
- In his essay titled " Latin America " published in The Oxford Illustrated History of Christianity , John <unk> claims that the appeal and success of Eva Perón are related to Latin American mythology and concepts of divinity . <unk> claims that Eva Perón consciously incorporated aspects of the theology of the Virgin and of Mary Magdalene into her public persona . Historian Hubert <unk> has described Eva Perón as " Perhaps the <unk> woman yet to appear in public life in Latin America . " 
- In a 1996 interview , Tomás Eloy Martínez referred to Eva Perón as " the Cinderella of the <unk> and the Sleeping Beauty of Latin America . " Martínez suggested she has remained an important cultural icon for the same reasons as fellow Argentine Che <unk> : 
- Latin American myths are more resistant than they seem to be . Not even the mass exodus of the Cuban raft people or the rapid decomposition and isolation of <unk> Castro 's regime have eroded the <unk> myth of Che <unk> , which remains alive in the dreams of thousands of young people in Latin America , Africa and Europe . Che as well as Evita symbolize certain naive , but effective , beliefs : the hope for a better world ; a life sacrificed on the altar of the <unk> , the humiliated , the poor of the earth . They are myths which somehow reproduce the image of Christ . 
- Although not a government holiday , the anniversary of Eva Perón 's death is marked by many Argentines each year . Additionally , Eva Perón has been featured on Argentine coins , and a form of Argentine currency called " <unk> " was named in her honor . Ciudad Evita ( Evita City ) , which was established by the Eva Perón Foundation in 1947 , is located just outside Buenos Aires . 
- Cristina Kirchner , the first elected female president in Argentine history , is a Peronist who has occasionally been referred to as " The New Evita . " Kirchner says she does not want to compare herself to Evita , claiming she was a unique phenomenon in Argentine history . Kirchner also says that women of her generation , who came of age in the 1970s during the military dictatorships in Argentina , owe a debt to Evita for offering an example of passion and combativeness . On 26 July 2002 , the 50th anniversary of Eva Perón 's death , a museum opened in her honor called <unk> Evita . The museum , created by her great @-@ niece Cristina Alvarez <unk> , houses many of Eva Perón 's clothes , portraits , and artistic renderings of her life , and has become a popular tourist attraction . The museum was opened in a building that was once used by the Eva Perón Foundation . 
- In the book Eva Perón : The Myths of a Woman , cultural anthropologist Julie M. Taylor claims that Evita has remained important in Argentina due to the combination of three unique factors : 
- In the images examined , the three elements consistently linked — femininity , mystical or spirituality power , and revolutionary leadership — display an underlying common theme . <unk> with any one of these elements puts a person or a group at the margins of established society and at the limits of institutional authority . Anyone who can identify with all three images lays an overwhelming and echoing claim to dominance through forces that recognize no control in society or its rules . Only a woman can embody all three elements of this power . 
- Taylor argues that the fourth factor in Evita 's continued importance in Argentina relates to her status as a dead woman and the power that death holds over the public imagination . Taylor suggests that Evita 's embalmed corpse is analogous to the <unk> of various Catholic saints , such as <unk> <unk> , and has powerful symbolism within the largely Catholic cultures of Latin America : 
- To some extent her continuing importance and popularity may be attributed not only to her power as a woman but also to the power of the dead . However , a society 's vision of the afterlife may be structured , death by its nature remains a mystery , and , until society formally <unk> the <unk> it causes , a source of disturbance and disorder . Women and the dead — death and womanhood — stand in similar relation to structured social forms : outside public institutions , unlimited by official rules , and beyond formal categories . As a female corpse reiterating the symbolic themes of both woman and martyr , Eva Perón perhaps lays double claim to spiritual leadership . 
- John <unk> was the British ambassador in Argentina during the Perón regime , and describes Evita 's popularity : 
- She was by any standard a very extraordinary woman ; when you think of Argentina and indeed Latin America as a men dominated part of the world , there was this woman who was playing a very great role . And of course she aroused very different feelings in the people with whom she lived . The <unk> , as she called the well @-@ to @-@ do and privileged people , hated her . They looked upon her as a ruthless woman . The masses of the people on the other hand worshipped her . They looked upon her as a lady <unk> who was <unk> <unk> from heaven . 
- In 2011 , two giant murals of Evita were unveiled on the building facades of the current Ministry of Social Development , located on 9 de Julio Avenue . The works were painted by Argentine artist Alejandro <unk> . On 26 July 2012 , to commemorate the <unk> anniversary of Evita 's death , notes were issued in a value of 100 pesos . The controversial effigy of Julio <unk> <unk> was replaced by that of Eva Duarte , making her the first actual woman to be featured on the currency of Argentina . The image in the notes is based on a 1952 design , whose sketch was found in the Mint , made by the engraver Sergio <unk> with artist Roger <unk> . The printing totals 20 million notes ; it is not clear whether the government will replace the notes that feature <unk> and the Desert Campaign . 
- 
- = = = <unk> of fascism = = = 
- 
- Biographers Nicholas Fraser and Marysa Navarro write that Juan Perón 's opponents had from the start accused Perón of being a fascist . <unk> <unk> , a diplomat from the United States who was greatly supported by Juan Perón 's opponents , campaigned against Juan Perón 's first candidacy on the platform that Juan Perón was a fascist and a Nazi . Fraser and Navarro also <unk> that the perception of the Peróns as fascists was enhanced during Evita 's 1947 European tour during which she was a guest of honor of Francisco Franco . By 1947 , Franco had become politically isolated as one of the few remaining fascists to retain power . Franco , therefore , was in desperate need of a political ally . With nearly a third of Argentina 's population of Spanish descent , it seemed natural for Argentina to have diplomatic relations with Spain . Commenting on the international perception of Evita during her 1947 European tour , Fraser and Navarro write , " It was inevitable that Evita be viewed in a fascist context . Therefore , both Evita and Perón were seen to represent an ideology which had run its course in Europe , only to re @-@ emerge in an exotic , theatrical , even farcical form in a <unk> country . " 
- Laurence Levine , the former president of the <unk> Chamber of Commerce , writes that in contrast to Nazi ideology , the Peróns were not anti @-@ Semitic . In the book Inside Argentina from Perón to <unk> : 1950 – 2000 from an American Point of View , Levine writes : 
- The American government demonstrated no knowledge of Perón 's deep admiration for Italy ( and his <unk> for Germany , whose culture he found too rigid ) . Nor did they appreciate that although anti @-@ Semitism existed in Argentina , Perón 's own views and his political associations were not anti @-@ Semitic . They paid no attention to the fact that Perón sought out the Jewish community in Argentina to assist in developing his policies and that one of his most important allies in organizing the industrial sector was José <unk> <unk> , a Jewish immigrant from Poland . 
- Biographer Robert D. Crassweller writes , " Peronism was not fascism " , and " Peronism was not Nazism . " Crassweller also refers to the comments of U.S. Ambassador George S. <unk> . While visiting Argentina in 1947 , <unk> made the following statement : " There is not as much social discrimination against Jews here as there is right in New York or in most places at home . " 
- Time Magazine published an article by Tomás Eloy Martínez — Argentine writer , journalist , and former director of the Latin American program at Rutgers University — titled " The Woman Behind the Fantasy : <unk> , <unk> , <unk> — Eva Peron Was Much <unk> , <unk> <unk> " . In this article , Martínez writes that the accusations that Eva Perón was a fascist , a Nazi , and a thief had been made against her for decades . He wrote that the allegations were <unk> : 
- She was not a fascist — ignorant , perhaps , of what that ideology meant . And she was not greedy . Though she liked jewelry , furs and Dior dresses , she could own as many as she desired without the need to <unk> others .... In 1964 Jorge Luis <unk> stated that ' the mother of that woman [ Evita ] ' was ' the <unk> of a <unk> in Junín . ' He repeated the <unk> so often that some still believe it or , more commonly , think Evita herself , whose lack of sex appeal is mentioned by all who knew her , <unk> in that imaginary <unk> . Around 1955 the <unk> <unk> Santander employed the same strategy to <unk> letters in which Evita figures as an accomplice of the Nazis . It is true that ( Juan ) Perón facilitated the entrance of Nazi criminals to Argentina in 1947 and 1948 , thereby hoping to acquire advanced technology developed by the Germans during the war . But Evita played no part . 
- In his 2002 doctoral dissertation at Ohio State University , Lawrence D. Bell writes that the governments that preceded Juan Perón had been anti @-@ Semitic but that his government was not . Juan Perón " eagerly and enthusiastically " attempted to recruit the Jewish community into his government and set up a branch of the Peronist party for Jewish members , known as the <unk> <unk> Argentina ( <unk> ) . Perón 's government was the first to court the Argentine Jewish community and the first to appoint Jewish citizens to public office . Kevin <unk> writes that the Peronist regime , more than any other in Latin America , has been accused of being fascist . But he says that the Peronist regime was not fascist , and what passed for fascism under Perón never took hold in Latin America . Additionally , because the Peronist regime allowed rival political parties to exist , it cannot be described as totalitarian . 
- 
- = = = International popular culture = = = 
- 
- By the late 20th century , Eva Perón had become the subject of numerous articles , books , stage plays , and musicals , ranging from the biography The Woman with the Whip to a 1981 TV movie called Evita Perón with Faye <unk> in the title role . The most successful rendering of Eva Perón 's life has been the musical production Evita . The musical began as a concept album co @-@ produced by Tim Rice and Andrew Lloyd Webber in 1976 , with Julie Covington in the title role . Elaine Paige was later cast in the title role when the concept album was adapted into a musical stage production in London 's West End and won the 1978 Olivier Award for Best Performance in a Musical . In 1980 , Patti <unk> won the Tony Award for Best Leading Actress in a Musical for her performance as the title character in the Broadway production . Nicholas Fraser claims that to date " the musical stage production has been performed on every continent except Antarctica and has generated over $ 2 billion in revenue . " 
- As early as 1978 , the musical was considered as the basis for a movie . After a nearly 20 @-@ year production delay , Madonna was cast in the title role for the 1996 film version and won the Golden Globe Award for " Best Actress in a Musical or Comedy . " In response to the American film , and in an alleged attempt to offer a more politically accurate depiction of Evita 's life , an Argentine film company released Eva Perón : The True Story . The Argentine production starred actress Esther <unk> in the title role . This movie was the 1996 Argentine submission for the Oscar in the category of " Best Foreign Language Film . " 
- Nicholas Fraser writes that Evita is the perfect popular culture icon for our times because her career foreshadowed what , by the late 20th century , had become common . During Evita 's time it was considered <unk> for a former <unk> to take part in public political life . Her detractors in Argentina had often accused Evita of turning public political life into show business . But by the late 20th century , Fraser claims , the public had become <unk> in the cult of celebrity and public political life had become insignificant . In this regard , Evita was perhaps ahead of her time . Fraser also writes that Evita 's story is appealing to our celebrity @-@ obsessed age because her story confirms one of Hollywood 's oldest <unk> , the <unk> to riches story . Reflecting on Eva Perón 's popularity more than half a century after her death , Alma <unk> writes that , " Evita 's life has evidently just begun . " 
- 
- 
- = Halo : Uprising = 
- 
- Halo : Uprising is a four @-@ issue American comic book limited series set in the Halo universe . The series was written by Brian Michael Bendis , illustrated by Alex Maleev , and published by Marvel Comics . Uprising tells a story set between the ending of the 2004 video game Halo 2 and the beginning of its sequel , Halo 3 , as Earth is under attack by a collective of alien races known as the Covenant . The series was intended to be released and concluded before Halo 3 shipped on September 25 , 2007 , but the final issue did not appear until April 2009 . 
- The series was brought together by Ruwan Jayatilleke , Marvel 's Vice President of Development . He attained the license to publish Halo comics , including the single @-@ volume The Halo Graphic Novel , in 2005 . The graphic novel 's critical and commercial success prompted Marvel to announce a new Halo limited series in 2006 with Jayatilleke serving as the series ' editor . Bendis , a long @-@ time Halo fan , was excited about adding to the franchise story . 
- Reception to the series was lukewarm . The series ' artistic style was generally praised . However , the lack of Master Chief @-@ focused action and character development — as well as the delays in publishing — led to average reviews . The series was commercially successful and appeared in the top slot of The New York Times Graphic Books bestsellers list . 
- 
- = = Publication history = = 
- 
- Marvel Vice President of Development Ruwan Jayatilleke contacted game developer Bungie about extending the Halo franchise to comics . Jayatilleke noted that Bungie 's dedication to creating a <unk> world compelling to both hardcore and casual fans made the series " an attractive fit " for Marvel . In 2005 , Marvel learned that Bungie had already created an original graphic novel , entitled The Halo Graphic Novel , but needed a publisher and distributor . <unk> , Brian Michael Bendis and Jayatilleke visited Bungie to take up the offer of publishing the Halo Graphic Novel as well as to discuss the possibility of a tie @-@ in comic book series . 
- The Halo Graphic Novel proved to be a critical and commercial success ; the comic debuted at the number two position on the Diamond Comic <unk> ' sales charts and more than 100 @,@ 000 copies were published . Marvel Comics and Bungie announced the first Halo limited comic series , Halo : Uprising , at the San Diego Comic @-@ Con 2006 . 
- Brian Michael Bendis , writer for the series , said in an interview he was " honored " to add to the Halo lore . He noted that the graphic novel brought " humanity and perspective " to the franchise , something not easily imparted through the games . Bendis described himself as a longtime player of Halo and had " done [ his ] Halo <unk> " , reading and playing everything Halo . Bendis stated that Bungie was open to the Marvel team exploring lesser @-@ known elements of the Halo universe or even bringing up new ideas , as opposed to sticking to a script or set of <unk> ideas from Bungie or Microsoft . The Philadelphia Daily News suggested that a Halo comic book would attract readers who had never picked up a comic book before . Bendis said that drawing casual gamers to the comics was a major goal for the series . While using established characters meant that Bendis had to cooperate with Bungie and Microsoft , he said he did not feel it was a restriction . " You can get pretty <unk> and <unk> with total autonomy all the time . " The Bungie team allowed Bendis to explore lesser known areas of the Halo story , which he enjoyed . Writing the dialogue for the Master Chief , who is faceless and normally silent during gameplay , was a challenge ; Bendis scrapped much of the drafted dialogue to allow Maleev 's work to define the character . 
- Originally , Halo : Uprising 's entire four @-@ issue series was to be published before the September 25 , 2007 , release of Halo 3 . Bendis suggested that the series might be delayed unexpectedly due to the close cooperation between Marvel and Bungie . For unspecified reasons , the release of all issues of Halo : Uprising were pushed back . Issue # 1 's original date of August 15 , 2007 , was pushed back a week to August 22 . Issue # 2 's original date of August 29 was pushed back and released on November 21 . Issue # 3 was originally slated for a September 2007 release but was pushed to a final release date of nearly a year late in August 2008 . The constant revisions of the date became a running forum joke at the fan site <unk> Issue # 4 also saw multiple delays , pushed from a scheduled October 31 , 2007 release to March 4 and then March 18 , 2009 . Claude <unk> of <unk> noted that , given the series ' track record and the fact that the issue 's release had been moved more than a dozen times by that time , its appearance in March would be unlikely . In announcing two new Halo comic series from Marvel in February 2009 , Jayatilleke informed IGN that the final issue was in the process of being colored . The final release date for the issue was April 15 . The series was collected into a single volume released in May 2009 ( ISBN 0 @-@ <unk> @-@ <unk> @-@ 9 ) . The collected hardcover features a variety of bonus art and " making of " materials . 
- 
- = = Plot = = 
- 
- Uprising 's story begins after the events of Halo 2 , in which the alien collective known as the Covenant discovers the location of Earth and begins a full @-@ scale invasion of their enemy 's homeworld . The <unk> Master Chief is <unk> away aboard an ancient Forerunner ship . On a course set for Earth , he is overwhelmed by a Covenant strike force and rendered unconscious . In the next scene , Colonel James Ackerson is being tortured by Covenant forces on Mars , to whom he betrays the existence of something called " The Key of <unk> . " Ackerson admits the Key is located in Cleveland , Ohio . In Cleveland , the narrative follows the point of view of a hotel concierge named Ruwan as the city falls under attack by Covenant forces . In the <unk> he meets a woman named <unk> Tyla . Tyla remains calm even as Ruwan edges towards all @-@ out panic . The pair are captured and with other residents <unk> into a sports stadium . The Covenant declare that the humans must give up the location of the Key to save their lives ; Tyla is confused , but Ruwan states he knows exactly what it is . 
- On board the Forerunner ship , the Master Chief is captured and interrogated by Covenant forces , but manages to escape using a concealed weapon . On Earth , Ruwan and Tyla escape detection by the Covenant and appropriate a vehicle in an effort to escape . Ruwan reveals that the Key is in fact a fictional object that he and his brother James Ackerson made up as children ; James told the Covenant about the Key in order to prevent the outright destruction of Cleveland . After fighting Covenant forces , Ruwan and Tyla are rescued by marines and leave the city . The Master Chief attempts to kill the Covenant 's leader , Prophet of Truth , but is discovered as he takes aim ; Truth escapes as the Chief is left to kill the Prophet 's guards and find a way off the ship . Learning that there is no way to change the Forerunner ship 's destination , the Chief jumps to Earth using a piece of the vessel as a heat shield . 
- After informing the <unk> about the true nature of the Key , Ruwan volunteers to " give " the Key to the Covenant . He is captured and brought aboard a Covenant ship . While the Covenant believe he is to be rescued due to a <unk> embedded in him , Ruwan reveals he is actually a target . A <unk> takes aim at his position and destroys the ship . Upon learning that the Key is a fake , the <unk> on Mars behead Ackerson . In a relief camp , Tyla writes a song about Ruwan . 
- 
- = = Reception = = 
- 
- Halo : Uprising was a commercial success . The first issue sold out within 24 hours , leading Marvel to reissue the installment . The collected hardcover edition was the best @-@ selling hardcover graphic book for the week ending June 13 , according to The New York Times . 
- Reception to the miniseries varied . Reviewer Kevin Powers for Comics Bulletin and Richard George of IGN praised the action sequences and Maleev 's visuals . The balance between action and story was also positively noted ; Powers said that the first two sequences of the opening issue " <unk> capture the spirit of the game " . On the other hand , IGN 's Jesse Schedeen , reviewing the second issue , stated the series ' appeal was mostly superficial : " try as they might to replicate [ Halo 's ] visceral moments , Bendis and Maleev just can 't replicate the same feeling on the printed page . " 
- Reviewers criticized the lack of Master Chief as a main character , similar to the response to The Halo Graphic Novel ; Schedeen said that " Master Chief is barely a guest star in his own book " , and that he was reduced to blasting aliens for much of the series . Schedeen felt that there was a lack of connections between the Ruwan plot and the Master Chief 's adventures , which was never satisfactorily resolved . The comic 's focus on Ruwan and <unk> 's subplot was also seen as a major fault : Comics Bulletin 's Geoff Collins , in a review of the second issue , said that " as a comic book fan I ’ m interested [ in Ruwan and <unk> ] , but the Halo fans I know could [ sic ] care less about them . And the story in this issue centers around them . " Schedeen was surprised to find that he only began to care about them in the final issue . The many delays in publishing were a frequent point of frustration as well . Schedeen summed up his reviews by saying that he was hopeful the production teams behind the upcoming Halo books " will learn from the mistakes made here and craft stories that can consistently capture what makes Halo fun . At the very least , let 's hope we don 't have to wait a year between issues anymore " . 
- 
- 
- = Species ( film ) = 
- 
- Species is a 1995 American science fiction horror thriller film directed by Roger Donaldson and written by Dennis Feldman . It stars Natasha Henstridge , Ben Kingsley , Michael Madsen , Alfred Molina , Forest Whitaker and Marg Helgenberger . In the film , a <unk> crew of scientists and government agents tries to track down an alien seductress played by Natasha Henstridge before she successfully mates with a human male . Due to her <unk> , the alien character was cited as an example of negative treatment of female sexuality and aliens by the Hollywood film industry . The design of Sil was also linked to a chupacabra sighting . 
- The film was conceived by Feldman in 1987 , and was originally pitched as a film treatment in the style of a police procedural , entitled The Message . When The Message failed to attract the studios , Feldman re @-@ wrote it as a <unk> script , which ultimately led to the making of the film . The extraterrestrial in Species , an alien woman named Sil , was designed by H. R. Giger , also responsible for the beings from the Alien franchise . The effects combined practical models done by Giger collaborator Steve Johnson and XFX , with computer @-@ generated imagery done by Richard <unk> 's Boss Film Studios . Giger felt the film and the character were too similar to Alien , so he pushed for script changes . 
- Most of the principal photography was done in Los Angeles , California , where the film is set . Several scenes were filmed in Utah and at the Arecibo Observatory in Puerto Rico . The film was poorly received by critics , but nevertheless turned out to be a box office success , grossing US $ 113 million ( $ 176 million in 2016 dollars ) . It spawned one theatrical sequel ( Species II ) , as well as two direct @-@ to @-@ video sequels ( Species III and Species : The Awakening ) . Species was adapted into a novel by Yvonne Navarro and two comic book series by Dark Horse Comics , one of which was written by Feldman . 
- 
- = = Plot summary = = 
- 
- During the <unk> program , Earth 's scientists send out transmissions ( shown to be the Arecibo message ) with information about Earth and its inhabitants , DNA structure , etc . , in hopes of finding life beyond Earth . They then receive transmissions from an alien source on how to create endless fuel <unk> . Therefore , the scientists assume that this is a friendly alien species . From a second alien transmission , the scientists receive information about an alien DNA along with instructions on how to <unk> it with human DNA . A government team led by Xavier Fitch ( Ben Kingsley ) goes forward with the genetic experiment attempting to induce a female , under the ( later proved to be mistaken ) assumption that a female would have " more docile and <unk> " traits . One of the hundred experimental <unk> produces a girl named Sil , who looks like a normal human but develops into a 12 @-@ year @-@ old in 3 months . 
- Sil 's violent outbursts during sleep make the scientists consider her a threat . They try to kill her using <unk> gas but she breaks out of her containment cell and escapes . The government <unk> a team composed of anthropologist Dr. Stephen Arden ( Alfred Molina ) , molecular biologist Dr. Laura Baker ( Marg Helgenberger ) , " <unk> " Dan <unk> ( Forest Whitaker ) and mercenary Preston " Press " Lennox ( Michael Madsen ) to track and destroy Sil . Sil matures rapidly into an adult ( Natasha Henstridge ) in her early twenties and makes her way to Los Angeles . Her body strength , <unk> ability and intelligence make tracking her extremely difficult . The scientists fear she may mate with human males and produce offspring that could eliminate the human race . Sil is intent on producing offspring as soon as possible , and kills several people to prevent them from notifying the authorities or simply to use their clothing . 
- Sil first tries to mate with a man she meets at a nightclub ( Anthony <unk> ) , but after sensing that he is <unk> , she rejects him . <unk> , he tries to rape her , prompting her to kill him by <unk> his skull with her tongue . She then tries to mate with John Carey ( Whip <unk> ) , a man she meets after a car accident . They swim in Carey 's pool where Sil forces him to open his swimming trunks in order to mate , but he refuses . This act is interrupted by Preston and Laura . She kills Carey , <unk> into her alien form , a bipedal mutant with <unk> on her shoulders and back , and flees naked into a forest without being seen by the team . She pretends to be a rape victim to kidnap a woman ( <unk> K. Schneider ) in order to assume her identity . <unk> in the car near Carey 's home , she <unk> Fitch over distance to order the team to search for her in the nightclub . There , she is seen by Dan , prompting a car chase . She fakes her death by crashing the car , which she has previously filled with gasoline containers into a high @-@ voltage <unk> , using the kidnapped woman as a stand @-@ in for her own body . 
- After cutting and <unk> her hair , Sil takes an attraction to Preston , having <unk> of him the previous night . After the team celebrates their apparent victory , she stalks them in their hotel , and they do not recognize her . Arden , who is upset at being single , walks into his room to find Sil waiting there . She has intercourse with Arden , then kills him when he realizes who she is . Dan senses that Sil is in the hotel and he alerts Preston , Laura , and the rest of the team . She morphs again and escapes and they follow her into the sewers where Fitch is subsequently killed . Sil gives birth and Dan finds her offspring in a cavern behind the sewers . The child attacks him and he kills it . Sil , angered , attacks the trio and tries to kill Preston and Dan . Preston uses a grenade launcher on her , blowing her head off and killing her . The trio leaves the area . The last scene shows a rat chewing on one of Sil 's severed <unk> ; it starts to <unk> into a vicious beast and attacks another rat . 
- 
- = = Cast = = 
- 
- Natasha Henstridge as Sil 
- Michelle Williams as Young Sil 
- Ben Kingsley as Xavier Fitch 
- Michael Madsen as Preston " Press " Lennox 
- Alfred Molina as Dr. Stephen Arden 
- Forest Whitaker as Dan <unk> 
- Marg Helgenberger as Dr. Laura Baker 
- Whip <unk> as John Carey 
- Anthony <unk> as Robbie 
- 
- = = Influence and themes = = 
- 
- Given Sil grows rapidly and kills humans with ease , at a certain point film character Dr. Laura Baker even speculates if she was a biological weapon sent by a species who thought humans were like an <unk> weed . Feldman declared that he wanted to explore this theme further in the script , as it discussed mankind 's place in the universe and how other civilizations would perceive and relate to humanity , considering that " maybe [ humans are ] not a potential threat , maybe a competitor , maybe a resource . " He also declared that more could be said about Sil 's <unk> doubts , as she does not know her origin or purpose , and only follows her instinct to mate and <unk> the species . 
- Writing for the Journal of Popular Film & Television , Susan George authored a paper that dealt with the portrayal of procreation in Species , <unk> and <unk> . George compares the character of Fitch to " an updated Dr. <unk> , " and explores the development of Sil 's maternal aspirations , which convert the character into an " archaic mother " figure similar to the <unk> creature in the Alien series , both of which are , she claims , portrayed negatively . George further states that a recurring theme in science fiction films is a response to " this kind of powerful female sexuality and ' alien @-@ ness ' " in that " the feminine monster must die as Sil does at the end of Species . " Feldman himself considered that an underlying theme regarded " a female arriving and seeking to find a superior mate . " 
- A five @-@ year investigation into accounts of the chupacabra , a well known <unk> , revealed that the original sighting report of the creature in Puerto Rico by Madeline <unk> may have been inspired by the character Sil . This was detailed in paranormal investigator and <unk> Benjamin Radford 's book Tracking the <unk> . According to Virginia <unk> of Memorial University of Newfoundland writing for the Journal of Folklore Research , Radford found a link between the original <unk> report and the design of Sil in her alien form , and hypothesized that " [ Species ] , which [ <unk> ] did see before her sighting , influenced what she believes she saw of the chupacabra . " 
- 
- = = Production = = 
- 
- 
- = = = Writing and development = = = 
- 
- Dennis Feldman had the idea for Species in 1987 , as he worked on another film about an alien invasion , Real Men . Having read an article by Arthur C. Clarke about the <unk> odds against an extraterrestrial craft ever locating and visiting Earth , given that stellar distances are great , and faster @-@ than @-@ light travel is unlikely , Feldman started to think that it was " <unk> for any alien culture to come here in what [ he ] ' d describe as a big tin can . " Thus in turn he considered that the possibility of extraterrestrial contact was through information . Then he detailed that a message would contain instructions from across the void to build something that would talk to men . Instead of a mechanical device , Feldman imagined <unk> . The visitor would adapt to Earth 's environment through DNA belonging to Earth 's organisms . <unk> has sent to space transmissions " giving out directions " such as the Arecibo message , which Feldman considered <unk> , as they relay information to potential predators from outer space . He pointed out that " in nature , one species would not want a predator to know where it hides . " 
- <unk> emerged a film treatment called The Message . The original script had more of a police procedural approach , with the alien being created by a " bathtub <unk> " who had just had his project <unk> by the government , and a biologist who had worked on the project getting along with a police officer to search for the creature . Eventually Feldman came to believe this concept had some credibility issues , and instead changed the protagonists to a government team . After coining the name " Sil , " Feldman initially thought of forming an acronym , but in the end chose only the three @-@ letter name after learning about the codons of the genetic code , which can be represented in groups of three letters . Sil would originally emerge from a DNA sequence manipulating human DNA , and constantly <unk> as she used the human junk DNA to access " all the defenses of the entire animal kingdom that [ humans ] evolved through – including ones that had never developed , plus ones [ Earth 's scientists ] don 't know about that have become extinct . " Among the research Feldman did for the script included going to sessions of UCLA 's Center for the Study of Evolution and the Origin of Life ( <unk> ) , talking to <unk> scientists , and visiting the <unk> Institute for Biological Studies to talk with researchers working on the Human <unk> Project . The Message was offered to several studios , but was passed up . 
- In 1993 , Feldman reworked his ideas into a <unk> script . This was sent to producer Frank Mancuso , Jr . , who had hired Feldman to adapt Sidney Kirkpatrick 's A Cast of <unk> . The producer got attracted to the creative <unk> as the film offered " the challenge of walking that fine line between <unk> and pushing something as far as it can go . " Metro @-@ Goldwyn @-@ Mayer got interested on the project , and while Feldman had some initial disagreements on the budget , after considering other studios he signed with MGM . In turn , the now retitled Species attracted director Roger Donaldson , who was attracted to its blend of science fiction and thriller . The script underwent eight different drafts , written over an eight @-@ month period , before Donaldson was content that flaws in the story 's logic had been corrected . At one point another writer , Larry Gross , tried his hand with the script , but ultimately all the work was done by Feldman . Feldman would remain as a co @-@ producer . While the initial Species script suggested a love triangle between Sil and two government team members , the dissatisfaction of the crew eventually led to changes to the ending , which ended up featuring Sil having a baby that would immediately prove dangerous . 
- 
- = = = Design = = = 
- 
- Sil was designed by Swiss artist H. R. Giger , who also created the creatures in the Alien films . Donaldson thought Giger was the best man for the film after reading his compendium <unk> , and eventually he and Mancuso flew to Switzerland to meet the artist . What attracted Giger was the opportunity to design " a monster in another way — an aesthetic warrior , also sensual and deadly , like the women look in [ his ] paintings . " While Giger opted to stay in Switzerland to take care of his dying mother instead of flying to Los Angeles to accompany production , he built some puppets in his own studio , and later <unk> sketches and <unk> paintings as production went through . The practical models were made by Steve Johnson and his company XFX , which had already worked with Giger 's designs in <unk> II . Giger had envisioned more stages of Sil 's transformation , but the film only employed the last one , where she is " transparent outside and black inside — like a glass body but with carbon inside , " with XFX doing the translucent skin based on what they had done for the aliens of The Abyss . Sil 's alien form had both full @-@ body <unk> with <unk> arms , heads and torsos , and a body suit . Richard <unk> 's Boss Film Studios was hired for over 50 shots of computer @-@ generated imagery , which included one of the earliest forms of motion capture effects . Using a two @-@ foot @-@ high ( 60 cm ) electric puppet that had sensors translating its movements to a digital Sil , Boss Films managed to achieve in one day what would have once taken as much as three weeks with practical effects . 
- Giger was unhappy with some elements he found to bear similarity with other movies , particularly the Alien franchise . At one point he sent a <unk> to Mancuso finding five similarities : a " <unk> " ( as Sil giving birth echoed the infant Alien breaking out of its host 's chest ) , the creature having a punching tongue ( Giger at first wanted Sil 's tongue to be composed of barbed hooks ) , a cocoon , the use of flame throwers , and having Giger as the creature designer . A great point of contention was the ending , which Giger considered derivative from the <unk> from both Alien 3 and Terminator 2 : Judgment Day . The designer felt that horror films frequently held some final confrontation with fire , which he considered old @-@ fashioned and linked to medieval witch trials . He sent some ideas for the climax to the producers , with them accepting to have Sil 's ultimate death occurring by <unk> . 
- 
- = = = Filming = = = 
- 
- Filming happened mostly in Los Angeles , including location shooting at Sunset Strip , Silver Lake , Pacific Palisades , the Hollywood Hills and the <unk> Hotel . <unk> Club , the nightclub featured in the film , was built within Hollywood 's <unk> Theater , while the hills above <unk> Stadium near <unk> Park were used for the car chase and crash where Sil fakes her death . For the opening scenes in Utah , the <unk> Army Depot dubbed as the outside of the research facility — the interiors were shot at the <unk> International Corporation laboratory in California — and a Victorian @-@ era train station in <unk> City was part of Sil 's escape . Other locations included the Santa Monica Pier and the Arecibo telescope in Puerto Rico . The most complex sets involved the sewer complex and a tar @-@ filled granite cavern where the ending occurs . Donaldson wanted a maze quality for the sewers , which had traces of realism ( such as tree roots breaking through from the ceiling ) and artistic licenses . Production designer John <unk> intentionally designed the sewers wider and taller than real ones , as well as with <unk> , but nevertheless aiming for a <unk> and realistic atmosphere . The underground tunnels were built out of structural steel , metal rod , plaster and concrete to endure the fire effects , and had its design based on the La <unk> Tar <unk> , with <unk> describing them as " just the sort of place in which a creature from another planet might feel at home . " 
- 
- = = Release and reception = = 
- 
- Species received a wide theatrical release on July 7 , 1995 . Its opening weekend was $ 17 @.@ 1 million , MGM 's biggest opening that far and second in the box office ranking behind Apollo 13 . <unk> at $ 35 million , the film earned a total of $ 113 million worldwide ( $ 176 million adjusted for inflation ) , including $ 60 million in the United States . The film was released on DVD on March 26 , 1997 , and on VHS on August 3 , 1999 . The original DVD featured a booklet with <unk> , production notes and a making @-@ of <unk> . On July 25 , 2006 , the film was released on Blu @-@ ray . 
- The film received lukewarm reviews . On Rotten Tomatoes , Species holds an approval of 34 % based on 35 reviews . Roger <unk> gave it 2 out of 4 stars , criticizing the film 's plot and overall lack of intelligence . <unk> James from <unk> magazine gave the film 2 out of 5 stars , describing it as " ' Alien ' meets ' V ' meets ' Splash ' meets ' Playboy 's <unk> Fantasies : Forbidden <unk> , ' diluted into a diffuse , <unk> bore . " James <unk> gave the film 2 ½ out of 4 stars , stating that " as long as you don 't stop to think about what 's going on , Species is capable of offering its share of cheap thrills , with a laugh or two thrown in as well . " Owen <unk> of Entertainment Weekly found the film lacking in imagination and special effects , also commenting that Alfred Molina " sport [ s ] a haircut that 's <unk> than the creature . " Variety 's review of the film described it as a " gripping if not overly original account of an extraterrestrial species attempting to overwhelm our own " and that Ben Kingsley and other lead actors " have only two @-@ dimensional roles to engage them " . The review mentions the similarity between <unk> Giger 's design of Sil and his work on Alien . 
- Scott Weinberg of DVD Talk praised the acting , Feldman 's screenplay and Donaldson 's direction . He concluded by saying that Species makes for " a very good time for the genre fans . " Mick <unk> , writing for San Francisco Chronicle , was <unk> less enthusiastic , <unk> that if " Species were a little bit worse , it would have a shot at becoming a camp classic . " Los Angeles Times critic Peter <unk> described Species as " a pretty good Boo ! movie " , finding it an entertaining thriller while unoriginal and with ineffective tonal shifts . 
- 
- = = Related works = = 
- 
- 
- = = = Adaptations = = = 
- 
- Yvonne Navarro co @-@ wrote a novelization based on the original screenplay with Dennis Feldman . The book gives several in @-@ depth details about the characters not seen in the film , such as Sil 's ability to visualize <unk> and determine harmful substances from edible items by the color . Gas appears black , food appears pink , and an unhealthy potential mate appears to give off green fumes . Other character details include Preston 's background in tracking down <unk> soldiers as well as the process of <unk> the alien signal . Although no clues are given as to its origin , it is mentioned that the message was somehow routed through several black holes to mask its point of origin . 
- Dark Horse Comics published a four @-@ issue comic book adapting the film , written by Feldman and penciled by Jon Foster . Dark Horse would also publish a mini @-@ series with an all @-@ new storyline , Species : Human Race , released in 1997 . West End Games released a World of Species <unk> for its <unk> role @-@ playing game system . 
- 
- = = = <unk> = = = 
- 
- The first sequel to Species , Species II was released theatrically in April 1998 . The film depicts astronauts on a mission to Mars being attacked by the aliens from Species , and the events that ensue upon their return to Earth . There , Dr. Baker has been working on Eve , a more docile clone of Sil . Madsen and Helgenberger reprised their roles , while Henstridge played Eve . Species II was received by critics worse than the first film , <unk> a 9 % approval rating at Rotten Tomatoes , and Madsen denounced it as a terrible film . The film 's director , Peter <unk> , attributed the failure of the film to not picking up the infected rat ending of the original film . Navarro later authored the novelization for Species II which followed the film 's original screenplay with added scenes . 
- The second sequel , Species III followed in 2004 . It premiered on Sci @-@ Fi Channel on November 27 , 2004 with a DVD release on December 7 . The film 's plot starts where Species II ends , revolving around <unk> <unk> 's character Sara , the daughter of Eve , reared by a doctor played by Robert <unk> . Sara , an alien @-@ human hybrid , seeks other hybrids to mate with . Henstridge cameos at the beginning of the film . Two out of six critics mentioned on Rotten Tomatoes gave it a positive rating , with DVD Talk 's reviewer noting that it is " a more cohesive and sensible flick than [ Species II ] is , but ultimately , it 's just a lot of the same old <unk> , " while Film Freak Central called it " amateurish " and " <unk> . " A fourth film , Species : The Awakening was filmed in 2007 , following the schedule of Species III of Sci @-@ Fi Channel premiere and subsequent DVD release . None of the actors from the original film returned in this sequel , which instead starred Helena <unk> as the alien @-@ hybrid seductress . 
- 
- 
- = John of Brienne = 
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- John of Brienne ( c . 1170 – 27 March 1237 ) , also known as John I , was King of Jerusalem from 1210 to 1225 and Latin Emperor of Constantinople from 1229 to 1237 . He was the youngest son of Erard II of Brienne , a wealthy nobleman in Champagne . John , originally destined for an ecclesiastical career , became a knight and owned small estates in Champagne around 1200 . After the death of his brother , Walter III , he ruled the County of Brienne on behalf of his minor nephew Walter IV ( who lived in southern Italy ) . 
- The barons of the Kingdom of Jerusalem proposed that John marry Maria , Queen of Jerusalem . With the consent of Philip II of France and Pope Innocent III , he left France for the Holy Land and married the queen ; the royal couple were crowned in 1210 . After Maria 's death in 1212 John administered the kingdom as regent for their infant daughter , Isabella II ; an influential lord , John of Ibelin , attempted to <unk> him . John was a leader of the Fifth Crusade . Although his claim of supreme command of the crusader army was never unanimously acknowledged , his right to rule Damietta ( in Egypt ) was confirmed shortly after the town fell to the crusaders in 1219 . He claimed the Armenian Kingdom of Cilicia on behalf of his second wife , Stephanie of Armenia , in 1220 . After Stephanie and their infant son died that year , John returned to Egypt . The Fifth Crusade ended in failure ( including the recovery of Damietta by the Egyptians ) in 1221 . 
- John was the first king of Jerusalem to visit Europe ( Italy , France , England , León , Castile and Germany ) to seek assistance for the Holy Land . He gave his daughter in marriage to Holy Roman Emperor Frederick II in 1225 , and Frederick ended John 's rule of the Kingdom of Jerusalem . Although the <unk> tried to persuade Frederick to restore the kingdom to John , the Jerusalemite barons regarded Frederick as their lawful ruler . John administered papal domains in <unk> , became the <unk> of Perugia and was a commander of Pope Gregory IX 's army during Gregory 's war against Frederick in 1228 and 1229 . 
- He was elected emperor in 1229 as the senior co @-@ ruler ( with Baldwin II ) of the Latin Empire , and was crowned in Constantinople in 1231 . John III Vatatzes , Emperor of Nicaea , and Ivan Asen II of Bulgaria occupied the last Latin territories in <unk> and Asia Minor , besieging Constantinople in early 1235 . John directed the defence of his capital during the months @-@ long siege , with the besiegers withdrawing only after Geoffrey II of Achaea and united fleets from Italian towns defeated their fleet in <unk> . The following year , John died as a Franciscan friar . 
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- = = Early life = = 
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- John was the youngest of the four sons of Erard II , Count of Brienne , and Agnes of <unk> . He seemed " exceedingly old ... about 80 " to the 14 @-@ year @-@ old George Akropolites in 1231 ; if Akropolites ' estimate was correct , John was born around 1150 . However , no other 13th @-@ century authors described John as an old man . His father referred to John 's brothers as " children " in <unk> and mentioned the tutor of John 's oldest brother , Walter III , in <unk> ; this suggests that John 's brothers were born in the late <unk> . Modern historians agree that John was born after <unk> , probably during the <unk> . 
- Although his father destined John for a clerical career , according to the late 13th @-@ century Tales of the Minstrel of Reims he " was unwilling " . Instead , the <unk> continued , John fled to his maternal uncle at the <unk> Abbey . Encouraged by his fellows , he became a knight and earned a reputation in tournaments and fights . Although elements of the Tales of the Minstrel of Reims are apparently invented ( for instance , John did not have a maternal uncle in <unk> ) , historian Guy Perry wrote that it may have preserved details of John 's life . A church career was not unusual for youngest sons of 12th @-@ century noblemen in France ; however , if his father sent John to a monastery he left before reaching the age of taking monastic vows . John " clearly developed the physique that was necessary to fight well " in his youth , because the 13th @-@ century sources Akropolites and <unk> di Adam emphasize his physical strength . 
- Erard II joined the Third Crusade and died in the Holy Land in 1191 . His oldest son , Walter III , succeeded him in Brienne . John was first mentioned in an <unk> ( or 1194 ) charter issued by his brother , indicating that he was a prominent figure in Walter 's court . According to a version of Ernoul 's chronicle , John participated in a war against Peter II of <unk> . Although the Tales of the Minstrel of Reims claimed that he was called " John <unk> " , according to contemporary charters John held <unk> , <unk> , <unk> and two other villages in the County of Champagne around 1200 . In <unk> , Theobald III granted him additional estates in <unk> , <unk> and elsewhere . After Theobald 's death his widow Blanche of <unk> persuaded John to sell his estate at <unk> , saying that it was her dowry . 
- Walter III of Brienne died in June 1205 while fighting in southern Italy . His widow Elvira of Sicily gave birth to a posthumous son , Walter IV , who grew up in Italy . John assumed the title of count of Brienne , and began <unk> the county on his nephew 's behalf in 1205 or 1206 . As a leading vassal of the count of Champagne , John frequented the court of Blanche of <unk> ( who ruled Champagne during her son 's minority ) . According to a version of Ernoul 's chronicle , she loved John " more than any man in the world " ; this annoyed Philip II of France . 
- The two versions of Ernoul 's chronicle tell different stories about John 's ascent to the throne of Jerusalem . According to one version , the leading lords of Jerusalem sent envoys to France in 1208 asking Philip II to select a French nobleman as a husband for their queen Maria . Taking advantage of the opportunity to rid himself of John , Philip II suggested him . In the other version an unnamed knight encouraged the Jerusalemite lords to select John , who accepted their offer with Philip 's consent . John visited Pope Innocent III in Rome . The pope donated 40 @,@ 000 marks for the defence of the Holy Land , <unk> that John could spend the money only with the consent of the Latin Patriarch of Jerusalem and the grand masters of the Knights Templar and the Knights Hospitaller . 
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- = = King of Jerusalem = = 
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- = = = Co @-@ ruler = = = 
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- John landed at Acre on 13 September 1210 ; the following day , Patriarch of Jerusalem Albert of <unk> married him to Queen Maria . John and Maria were crowned in Tyre on 3 October . The truce concluded by <unk> II , King of Jerusalem and the Ayyubid sultan Al @-@ Adil I had ended by John 's arrival . Although Al @-@ Adil was willing to renew it , Jerusalemite lords did not want to sign a new treaty without John 's consent . During John and Maria 's coronation , Al @-@ Adil 's son Al @-@ Mu 'azzam Isa pillaged the area around Acre but did not attack the city . After returning to Acre , John raided nearby Muslim settlements in retaliation . 
- Although about 300 French knights accompanied him to the Holy Land , no influential noblemen joined him ; they preferred participating in the French <unk> Crusade or did not see him as sufficiently eminent . John 's cousin , Walter of Montbéliard , joined him only after he was expelled from Cyprus . Montbéliard led a naval expedition to Egypt to <unk> the Nile Delta . After most of the French crusaders left the Holy Land , John forged a new truce with Al @-@ Adil by the middle of <unk> and sent envoys to Pope Innocent urging him to preach a new crusade . 
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- = = = Conflicts = = = 
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- Maria died shortly after giving birth to their daughter , Isabella , in late 1212 . Her death triggered a legal dispute , with John of Ibelin ( who administered Jerusalem before John 's coronation ) questioning the widowed king 's right to rule . The king sent Raoul of <unk> , Bishop of Sidon , to Rome for assistance from the Holy See . Pope Innocent confirmed John as lawful ruler of the Holy Land in early 1213 , urging the <unk> to support him with ecclesiastical sanctions if needed . Most of the Jerusalemite lords remained loyal to the king , acknowledging his right to administer the kingdom on behalf of his infant daughter ; John of Ibelin left the Holy Land and settled in Cyprus . 
- The relationship between John of Brienne and Hugh I of Cyprus was tense . Hugh ordered the imprisonment of John 's supporters in Cyprus , releasing them only at Pope Innocent 's command . During the War of the <unk> <unk> John sided with Bohemond IV of Antioch and the Templars against Raymond @-@ Roupen of Antioch and Leo I , King of <unk> Armenia , who were supported by Hugh and the Hospitallers . However , John sent only 50 knights to fight the <unk> in <unk> in 1213 . Leo I concluded a peace treaty with the Knights Templar late that year , and he and John reconciled . John married Leo 's oldest daughter , Stephanie ( also known as Rita ) , in 1214 and Stephanie received a dowry of 30 @,@ 000 <unk> . <unk> among John , Leo I , Hugh I and Bohemond IV are documented by Pope Innocent 's letters urging them to reconcile their differences before the Fifth Crusade reached the Holy Land . 
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- = = = Fifth Crusade = = = 
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- Pope Innocent proclaimed the Fifth Crusade in 1213 , with the " liberation of the Holy Land " ( the <unk> of Jerusalem ) its principal object . The first crusader troops , commanded by Leopold VI of Austria , landed at Acre in early September 1217 . Andrew II of Hungary and his army followed that month , and Hugh I of Cyprus and Bohemond IV of Antioch soon joined the crusaders . However , hundreds of crusaders soon returned to Europe because of a famine following the previous year 's poor harvest . A war council was held in the tent of Andrew II , who considered himself the supreme commander of the crusader army . Other leaders , particularly John , did not acknowledge Andrew 's leadership . The crusaders raided nearby territory ruled by Al @-@ Adil I for food and <unk> , forcing the sultan to retreat in November 1217 . In December John besieged the Ayyubid fortress on Mount <unk> , joined only by Bohemond IV of Antioch . He was unable to capture it , which " encouraged the <unk> " , according to the contemporary Jacques de <unk> . 
- Andrew II decided to return home , leaving the crusaders ' camp with Hugh I and Bohemond IV in early 1218 . Although military action was suspended after their departure , the crusaders restored fortifications at Caesarea and Atlit . After new troops arrived from the Holy Roman Empire in April , they decided to invade Egypt . They elected John supreme commander , giving him the right to rule the land they would conquer . His leadership was primarily nominal , since he could rarely impose his authority on an army of troops from many countries . 
- The crusaders laid siege to Damietta , on the Nile , in May 1217 . Although they seized a strategically @-@ important tower on a nearby island on 24 August , Al @-@ Kamil ( who had succeeded Al @-@ Adil I in Egypt ) controlled traffic on the Nile . In September , reinforcements commanded by Pope Honorius III 's legate Cardinal Pelagius ( who considered himself the crusade 's supreme commander ) arrived from Italy . 
- Egyptian forces attempted a surprise attack on the crusaders ' camp on 9 October , but John discovered their movements . He and his retinue attacked and annihilated the Egyptian advance guard , hindering the main force . The crusaders built a floating fortress on the Nile near Damietta , but a storm blew it near the Egyptian camp . The Egyptians seized the fortress , killing nearly all of its defenders . Only two soldiers survived the attack ; they were accused of cowardice , and John ordered their execution . Taking advantage of the new Italian troops , Cardinal Pelagius began to intervene in strategic decisions . His debates with John angered their troops . The soldiers broke into the Egyptian camp on 29 August 1219 without an order , but they were soon defeated and nearly annihilated . During the ensuing panic , only the cooperation of John , the Templars , the Hospitallers and the noble crusaders prevented the Egyptians from destroying their camp . 
- In late October , Al @-@ Kamil sent messengers to the crusaders offering to restore Jerusalem , Bethlehem and Nazareth to them if they withdrew from Egypt . Although John and the secular lords were willing to accept the sultan 's offer , Pelagius and the heads of the military orders resisted ; they said that the <unk> could easily recapture the three towns . The crusaders ultimately refused the offer . Al @-@ Kamil tried to send provisions to Damietta across their camp , but his men were captured on 3 November . Two days later , the crusaders stormed into Damietta and seized the town . Pelagius claimed it for the church , but he was forced to acknowledge John 's right to administer it ( at least temporarily ) when John threatened to leave the crusaders ' camp . According to John of Joinville , John seized one @-@ third of Damietta 's spoils ; coins minted there during the following months bore his name . Al @-@ Mu 'azzam Isa , Sultan of Damascus , invaded the Kingdom of Jerusalem and pillaged Caesarea before the end of 1219 . 
- John 's father @-@ in @-@ law , Leo I of Armenia , died several months before the crusaders seized Damietta . He bequeathed his kingdom to his infant daughter , Isabella . John and Raymond @-@ Roupen of Antioch ( Leo 's nephew ) questioned the will 's legality , each demanding the Armenian Kingdom of Cilicia for themselves . In a February 1220 letter , Pope Honorius declared John Leo 's rightful heir . <unk> that he wanted to assert his claim to Cilicia , John left Damietta for the Kingdom of Jerusalem around Easter 1220 . Although Al @-@ Mu 'azzam Isa 's successful campaign the previous year also pressed John to leave Egypt , Jacques de <unk> and other Fifth Crusade chroniclers wrote that he deserted the crusader army . 
- Stephanie died shortly after John 's arrival . Contemporary sources accused John of causing her sudden death , claiming that he severely beat her when he heard that she tried to poison his daughter Isabella . Their only son died a few weeks later , ending John 's claim to Cilicia . Soon after Pope Honorius learned about the deaths of Stephanie and her son , he declared Raymond @-@ Roupen the lawful ruler of Cilicia and threatened John with excommunication if he fought for his late wife 's inheritance . 
- John did not return to the crusaders in Egypt for several months . According to a letter from the <unk> in the Holy Land to Philip II of France , lack of funds kept John from leaving his kingdom . Since his nephew Walter IV was approaching the age of majority , John surrendered the County of Brienne in 1221 . During John 's absence from Egypt , Al @-@ Kamil again offered to restore the Holy Land to the Kingdom of Jerusalem in June 1221 ; Pelagius refused him . John returned to Egypt and rejoined the crusade on 6 July 1221 at the command of Pope Honorius . 
- The commanders of the crusader army decided to continue the invasion of Egypt , despite ( according to Philip d <unk> ) John 's strong opposition . The crusaders approached <unk> , but the Egyptians imposed a blockade on their camp . <unk> , Pelagius agreed to an eight @-@ year truce with Al @-@ Kamil in exchange for Damietta on 28 August . John was among the crusade leaders held hostage by Al @-@ Kamil until the crusader army withdrew from Damietta on 8 September . 
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- = = = Negotiations = = = 
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- After the Fifth Crusade ended " in colossal and <unk> failure " , John returned to his kingdom . <unk> from Genoa and <unk> soon attacked each other in Acre , <unk> a significant portion of the town . According to a <unk> chronicle , John supported the <unk> and the <unk> left Acre for Beirut . 
- John was the first king of Jerusalem to visit Europe , and had decided to seek aid from the Christian powers before he returned from Egypt . He also wanted to find a suitable husband for his daughter , to ensure the survival of Christian rule in the Holy Land . John appointed <unk> of Montbéliard as a <unk> to administer the Kingdom of Jerusalem in his absence . 
- He left for Italy in October <unk> to attend a conference about a new crusade . At John 's request , Pope Honorius declared that all lands conquered during the crusade should be united with the Kingdom of Jerusalem . To plan the military campaign , the pope and Holy Roman Emperor Frederick II met at <unk> in March <unk> ; John attended the meeting . He agreed to give his daughter in marriage to Frederick II after the emperor promised that he would allow John to rule the Kingdom of Jerusalem for the rest of his life . 
- John then went to France , although Philip II was annoyed at being excluded from the decision of Isabella 's marriage . Matilda I , Countess of <unk> , Erard II of <unk> , Albert , Abbot of <unk> and other local <unk> asked John to intervene in their conflicts , indicating that he was esteemed in his homeland . John attended the funeral of Philip II at the Basilica of St Denis in July ; Philip bequeathed more than 150 @,@ 000 marks for the defence of the Holy Land . John then visited England , attempting to mediate a peace treaty between England and France after his return to France . 
- He made a pilgrimage to Santiago de Compostela in March 1224 . According to the Latin Chronicle of the Kings of Castile , John went to the Kingdom of León to marry one of the elder daughters of Alfonso IX of León ( Sancha or Dulce ) because Alfonso had promised him the kingdom " along with her " . The marriage could jeopardize the claim of Sancha 's and Dulce 's half @-@ brother , Ferdinand III of Castile , to León . To protect her son 's interests , Ferdinand 's mother Berengaria of Castile decided to give her daughter ( Berengaria of León ) to John in marriage . Although modern historians do not unanimously accept the chronicle 's account of John 's plan to marry Sancha or Dulce , they agree that the queen of France ( Blanche of Castile , Berengaria of Castile 's sister ) played an important role in convincing John to marry her niece . The marriage of John and Berengaria of León was celebrated in <unk> in May 1224 . 
- About three months later , he met Emperor Frederick 's son Henry in Metz and visited Henry 's guardian , <unk> , Archbishop of <unk> . From Germany John went to southern Italy , where he persuaded Pope Honorius to allow Emperor Frederick to postpone his crusade for two years . Frederick married John 's daughter , Isabella ( who had been crowned queen of Jerusalem ) , on 9 November 1225 . John and Frederick 's relationship became tense . According to a version of Ernoul 's chronicle , John got into a disagreement with his new son @-@ in @-@ law because Frederick <unk> a niece of Isabella who was her lady @-@ in @-@ waiting . In the other version of the chronicle John often " <unk> and <unk> " his son @-@ in @-@ law , who concluded that John wanted to seize the Kingdom of Sicily for his nephew Walter IV of Brienne and tried to murder John ( who fled to Rome ) . Frederick declared that John had lost his claim to the Kingdom of Jerusalem when Isabella married him ; he styled himself king of Jerusalem for the first time in December 1225 . <unk> of Sidon , Simon of <unk> , Archbishop of Tyre , and the other Jerusalemite lords who had escorted Isabella to Italy acknowledged Frederick as their lawful king . 
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- = = Papal service = = 
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- Pope Honorius did not accept Frederick 's unilateral act , and continued to regard John as the rightful king of Jerusalem . In an attempt to take advantage of the revived <unk> League ( an alliance of northern Italian towns ) against Frederick II , John went to Bologna . According to a version of Ernoul 's chronicle , he declined an offer by the <unk> League representatives to elect him their king . Even though this account was fabricated , John remained in Bologna for over six months . The dying Pope Honorius appointed John rector of a <unk> of Saint Peter in <unk> ( part of the Papal States ) on 27 January <unk> , and urged Frederick II to restore him to the throne of the Kingdom of Jerusalem . Honorius ' successor , Gregory IX , confirmed John 's position in the Papal States on 5 April and ordered the citizens of Perugia to elect him their <unk> . 
- Gregory excommunicated Frederick II on 29 September <unk> , accusing him of breaking his oath to lead a crusade to the Holy Land ; the emperor had dispatched two fleets to Syria , but a plague forced them to return . His wife Isabella died after giving birth to a son , Conrad , in May 1228 . Frederick continued to consider himself king of Jerusalem , in accordance with the precedent set by John during Isabella 's minority . 
- The imperial army invaded the Papal States under the command of <unk> of <unk> in October 1228 . Although John defeated the invaders in a series of battles , it took a counter @-@ invasion by another papal army in southern Italy to drive <unk> back to Sulmona . John laid a siege before returning to Perugia in early 1229 to conclude negotiations with envoys of the Latin Empire of Constantinople , who were offering him the imperial crown . 
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- = = Emperor of Constantinople = = 
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- = = = Election = = = 
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- The Latin Emperor of Constantinople , Robert I , died in January 1228 . His brother Baldwin II succeeded him , but a regent was needed to rule the Latin Empire since Baldwin was ten years old . Ivan Asen II of Bulgaria was willing to accept the regency , but the barons of the Latin Empire suspected that he wanted to unite the Latin Empire with Bulgaria . They offered the imperial crown instead to John , an ally of the Holy See . 
- After months of negotiation , John and the envoys from the Latin Empire signed a treaty in Perugia which was confirmed by Pope Gregory on 9 April 1229 . John was elected emperor of the Latin Empire for life as senior co @-@ ruler with Baldwin II , who would marry John 's daughter Marie . The treaty also prescribed that although Baldwin would rule the Latin lands in Asia Minor when he was 20 years old , he would become sole emperor only after John 's death . John also stipulated that his sons would inherit <unk> and Macedonia , but the two regions still belonged to Emperor of <unk> Theodore <unk> . 
- After signing the treaty , John returned to Sulmona . According to the contemporary Matthew Paris , he allowed his soldiers to <unk> nearby monasteries to obtain money . John lifted the siege of Sulmona in early 1229 to join Cardinal Pelagius , who launched a campaign against <unk> . Frederick II ( who had crowned himself king of Jerusalem in the Church of the Holy <unk> ) returned to Italy , forcing the papal troops to withdraw . 
- John went to France to recruit warriors to accompany him to Constantinople . Pope Gregory did not proclaim John 's expedition to the Latin Empire a crusade , but promised papal privileges granted to crusaders to those who joined him . During his stay in France , John was again an <unk> between local <unk> and signed a peace treaty between Louis IX of France and Hugh X of <unk> . He returned to Italy in late 1230 . John 's envoys signed a treaty with <unk> <unk> , <unk> of Venice , who agreed to transport him and his retinue of 500 knights and 5 @,@ 000 commoners to Constantinople in return for John 's confirmation of Venetian possessions and privileges in the Latin Empire . Shortly after John left for Constantinople in August , Pope Gregory acknowledged Frederick II 's claim to the Kingdom of Jerusalem . 
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- = = = Rule = = = 
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- John was crowned emperor in <unk> Sophia in autumn 1231 ; by then , his territory was limited to Constantinople and its vicinity . The <unk> urged him to wage war against John III Vatatzes , Emperor of Nicaea , who supported a rebellion against their rule in Crete . According to Philippe <unk> ' <unk> Chronicle , John could make " neither war nor peace " ; because he did not invade the Empire of Nicaea , most French knights who accompanied him to Constantinople returned home after his coronation . To strengthen the Latin Empire 's financial position , Geoffrey II of Achaea ( John 's most powerful vassal ) gave him an annual subsidy of 30 @,@ 000 <unk> after his coronation . 
- Taking advantage of John III Vatatzes ' invasion of Rhodes , John launched a military expedition across the <unk> against the Empire of Nicaea in 1233 . His three @-@ to @-@ four @-@ month campaign " achieved little , or nothing " ; the <unk> only seized <unk> , now <unk> in Turkey . With John 's approval , two Franciscan and two Dominican <unk> wanted to mediate a truce between the Latin Empire and Nicaea in 1234 but it was never signed . In a letter describing their negotiations , the <unk> described John as a " <unk> " abandoned by his mercenaries . 
- John III Vatatzes and Ivan Asen II concluded a treaty dividing the Latin Empire in early 1235 . Vatatzes soon seized the last outposts of the empire in Asia Minor and Gallipoli , and Asen occupied the Latin territories in <unk> . Constantinople was besieged in an effort to persuade the defenders to gather in one place , enabling an invasion elsewhere . Although the besiegers outnumbered the defenders , John repelled all attacks on the town 's walls . <unk> compared him to Hector , Roland , <unk> the <unk> and Judas <unk> in his <unk> Chronicle , emphasizing his bravery . 
- A Venetian fleet forced Vatatzes ' naval forces to withdraw , but after the <unk> departed for home the Greeks and <unk> besieged Constantinople again in November 1235 . John sent letters to European monarchs and the pope , pleading for assistance . Since the survival of the Latin Empire was in jeopardy , Pope Gregory urged the crusaders to defend Constantinople instead of the Holy Land . A combined naval force from Venice , Genoa , <unk> and Geoffrey II of Achaea broke through the blockade . Asen soon abandoned his alliance with Vatatzes , who was forced to lift the siege in <unk> . 
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- = = = Death = = = 
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- According to three 13th @-@ century authors ( Matthew Paris , <unk> di Adam and Bernard of <unk> ) , John became a Franciscan friar before his death . They agree that John 's declining health contributed to his conversion , but Bernard also described a recurring vision of an old man urging the emperor to join the Franciscans . Most 13th @-@ century sources suggest that John died between 19 and 23 March 1237 , the only Latin emperor to die in Constantinople . 
- According to the Tales of the Minstrel of Reims , he was buried in <unk> Sophia . Perry wrote that John , who died as a Franciscan friar , may have been buried in the Franciscan church dedicated to Saint Francis of Assisi which was built in <unk> during his reign . In a third theory , proposed by Giuseppe <unk> , a tomb decorated with the Latin Empire coat of arms in Assisi 's Lower Basilica may have been built for John by Walter VI , Count of Brienne . 
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- = = Family = = 
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- John 's first wife ( Maria the <unk> , born 1191 ) was the only child of Isabella I of Jerusalem and her second husband , Conrad of <unk> . Maria inherited Jerusalem from her mother in 1205 . John and Maria 's only child , Isabella ( also known as <unk> ) , was born in late 1212 . 
- Stephanie of Armenia became John 's second wife in 1214 . She was the only daughter of Leo II of Armenia and his first wife , <unk> ( niece of <unk> , the third wife of Bohemond III of Antioch ) . Stephanie gave birth to a son in 1220 , but she and her son died that year . 
- John married his third wife , Berengaria of León , in 1224 ; she was born around 1204 to Alfonso IX of León and Berengaria of Castile . John and Berengaria 's first child , Marie , was born in 1224 . Their first son , Alphonse , was born during the late <unk> . Berengaria 's cousin , Louis IX of France , made him Grand Chamberlain of France and he acquired the County of Eu in France with his marriage . John 's second son , Louis , was born around 1230 . His youngest son , John , who was born in the early 1230s , was Grand Butler of France . 
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- = Boise National Forest = 
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- Boise National Forest is a <unk> protected area covering 2 @,@ 203 @,@ 703 acres ( 8 @,@ 918 @.@ 07 km2 ) of the U.S. state of Idaho as part of the national forest system . <unk> on July 1 , 1908 from part of Sawtooth National Forest , it is managed by the U.S. Forest Service as four units : the Cascade , Emmett , Lowman , and Mountain Home ranger districts . 
- The Idaho Batholith <unk> most of Boise National Forest , forming the forest 's Boise , Salmon River , and West mountain ranges ; the forest reaches a maximum elevation of 9 @,@ 730 feet ( 2 @,@ 970 m ) on Steel Mountain . Common land cover includes sagebrush steppe and spruce @-@ fir forests ; there are 9 @,@ 600 miles ( 15 @,@ 400 km ) of streams and rivers and 15 @,@ 400 acres ( 62 km2 ) of lakes and reservoirs . Boise National Forest contains 75 percent of the known populations of <unk> 's <unk> , a flowering plant endemic to Idaho . 
- The Shoshone people occupied the forest before European settlers arrived in the early 1800s . Many of the early settlers were trappers and prospectors before gold was discovered in 1862 . After the 1860s Boise Basin gold rush ended , mining of tungsten , silver , antimony , and gold continued in the forest until the mid @-@ twentieth century . Recreation opportunities and facilities include over 70 campgrounds , whitewater and <unk> boating , cabin rentals , and 1 @,@ 300 miles ( 2 @,@ 100 km ) of trails for hiking , <unk> , horseback riding , and <unk> off @-@ road vehicle use . The Forest Service has an objective to maintain timber , range , water , recreation , and wildlife for multiple use and sustained yield of its resources . 
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- = = History = = 
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- Archaeological evidence indicates that human habitation in Idaho began towards the end of the last ice age : bone fragments about 10 @,@ 000 years old have been found in Wilson <unk> Cave , an <unk> cave on the Snake River Plain believed to have been occupied by indigenous people until as recently as the 17th century . A change of climate around 7000 years ago dried up much of the Great Basin , forcing the Shoshone people northward into the mountainous areas of central Idaho . Most of what is now Boise National Forest was sparsely inhabited by Native Americans , and several archaeological sites , including <unk> , rock shelters , burial grounds , and <unk> have been found along rivers in the area . <unk> and fur traders of European descent first arrived in the area in the early 1800s , starting with John Jacob Astor 's Pacific Fur Company in October 1811 . Donald Mackenzie and <unk> Payette trapped in the area of Boise National Forest in 1819 . By 1840 the fur trade was coming to an end , but the westward migration on the Oregon Trail , which passed south of the forest , was beginning . The first settlers moved into the mountains in the 1860s after gold was discovered in Idaho , which forced many of the Shoshone out and led to conflicts throughout the state , including the <unk> War in southern Idaho . 
- <unk> George Grimes and Moses <unk> were the first to discover gold in the forest at the eponymous Grimes Creek on August 2 , 1862 . Subsequent gold discoveries at Rocky Bar in 1863 and Atlanta in 1864 increased the rush of people to Idaho , and in 1863 Idaho City , with a population of 6 @,@ 267 , surpassed Portland , Oregon as the largest city in the Pacific Northwest . The Idaho gold rush was largely over by 1870 , and the population of the Boise Basin fell from 16 @,@ 000 to 3 @,@ 500 . In 1898 the forest 's first gold dredge was built in <unk> and followed by several others . By 1951 when the last dredges shut down , at least 2 @.@ 3 million ounces ( 65 @.@ 2 million grams ) of gold had been produced from the Boise Basin area . Silver was mined along the Crooked River from 1882 until 1921 , but a silver mine at Silver Mountain proved unsuccessful . Following a shortage of mercury during World War II , mines in the <unk> area became the country 's largest producer of tungsten and second largest source of mercury . The most important known <unk> deposit of niobium and tantalum in the United States is located in Bear Valley . From 1953 until 1959 dredges there produced $ 12 @.@ 5 million ( $ 101 million today ) in niobium , tantalum , and uranium . Other minerals mined in the forest include antimony and <unk> . 
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- = = = U.S. Forest Service = = = 
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- Boise National Forest was created on July 1 , 1908 from part of Sawtooth National Forest , and originally covered 1 @,@ 147 @,@ 360 acres ( 4 @,@ <unk> @.@ 2 km2 ) . The U.S. President was given the authority to establish forest reserves administered by the U.S. Department of the Interior by the Forest Reserve Act of 1891 . With the passage of the Transfer Act of 1905 , forest reserves were transferred to the U.S. Department of Agriculture in the newly created U.S. Forest Service . Present @-@ day Boise National Forest was first protected as part of two forest reserves by <unk> issued by President Theodore Roosevelt : Sawtooth Forest Reserve ( created on May 29 , 1905 and expanded on November 6 , 1906 ) and Payette Forest Reserve ( created on June 3 , 1905 ) . After forest reserves were renamed national forests in 1908 , Boise National Forest was split from Sawtooth National Forest into an independent national forest . On April 1 , 1944 the entirety of what was then Payette National Forest was transferred to Boise National Forest , and simultaneously <unk> and Idaho national forests were combined to reestablish the present @-@ day Payette National Forest , which is to the north of Boise National Forest . In 1933 the Boise Basin Experimental Forest was created on 8 @,@ 740 acres ( 35 @.@ 4 km2 ) of the forest near Idaho City to study the management of ponderosa pine . The Lucky Peak Nursery was established in 1959 to produce trees for planting on burned or logged lands on the national forests of the <unk> region . 
- After the creation of the Civilian Conservation Corps ( <unk> ) in 1933 , nine camps and eight <unk> were set up in Boise National Forest , but the number of camps was reduced from 1934 until the program was closed in 1942 . Work conducted by the <unk> included fire suppression , fish habitat improvement , and construction of guard houses , fire lookouts , campgrounds , roads , and trails , among other facilities . 
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- = = Management = = 
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- Boise National Forest is managed by the U.S. Forest Service , an agency within the Department of Agriculture , as five units called ranger districts . The ranger districts are Cascade ( 400 @,@ 000 acres or 1 @,@ 600 square kilometers ) , Emmett ( 350 @,@ 000 acres or 1 @,@ 400 square kilometers ) , Idaho City ( 400 @,@ 000 acres ) , Lowman ( 400 @,@ 000 acres ) , and Mountain Home ( 650 @,@ 000 acres or 2 @,@ 600 square kilometers ) ; each has an office in their respective cities and is managed by a district ranger , while forest headquarters are located in the city of Boise . Congress proclaimed 2 @,@ 648 @,@ 273 acres ( 10 @,@ <unk> @.@ 18 km2 ) of Idaho part of Boise National Forest , but the forest manages only 2 @,@ 203 @,@ 703 acres ( 8 @,@ 918 @.@ 07 km2 ) . The proclaimed boundary is set and can only be changed by Congress whereas the administered boundary can be shifted among adjacent national forests without congressional approval . For management ( and from the visitor 's perspective ) the forest 's boundaries are its administered area . 
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- = = Geography and geology = = 
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- <unk> in the forest range from 2 @,@ 800 feet ( 850 m ) in the North Fork Payette River Canyon to 9 @,@ 730 feet ( 2 @,@ 970 m ) at the top of Steel Mountain , a gain of 6 @,@ 930 feet ( 2 @,@ 110 m ) . The forest contains several <unk> of the Rocky Mountains , including the Boise , Salmon River , and West mountain ranges . Much of the forest is <unk> by the Idaho Batholith , and the forest is dominated by <unk> rock , but <unk> of basalt can be found to the west and other volcanic rocks to the south . 
- North of the South Fork Payette River and east of the North Fork Payette River , the forest is part of the Salmon River Mountains , which extend north and east outside the forest 's boundaries . The South Fork Salmon River Range and the North Fork Range are <unk> of the Salmon River Mountains within Boise National Forest . The Boise Mountains cover much of the southern portion of the forest and contain the forest 's highest point , Steel Mountain , but the range 's highest point , Two Point Mountain , lies outside of the forest 's boundary . The Trinity Mountains are a <unk> in southeastern part of the Boise Mountains that reach their peak at 9 @,@ 451 feet ( 2 @,@ 881 m ) on Trinity Mountain . The westernmost portion of the forest south and west of Lake Cascade are part of the West Mountains , which reach their highest point at 8 @,@ 320 feet ( 2 @,@ 540 m ) on <unk> Mountain . The <unk> Mountains are a smaller range on the forest 's southern border that run northwest to southeast . 
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- = = = Waterways = = = 
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- There are an estimated 9 @,@ 600 miles ( 15 @,@ 400 km ) of perennial and intermittent streams and 15 @,@ 400 acres ( 62 km2 ) of lakes and reservoirs in the forest . The Forest Service provides access to and recreation opportunities at the seven reservoirs it borders , although it does not own or manage them . There are numerous natural lakes in the forest , most of which are <unk> created by alpine glaciers during the Pleistocene . The largest , Warm Lake , is 26 miles ( 42 km ) east of Cascade in Valley County ; many of the smaller lakes are in the Trinity and West mountains . Annual water yield on the forest is estimated at 4 @.@ 1 million acre @-@ feet ( 5 @.@ 1 × 109 m3 ) . The southern portion of the forest is drained by the Boise River , the central and western portions by the Payette River , northeastern portion by the Salmon River , and far western portions of the Emmett Ranger District by the <unk> River . All four rivers are tributaries of the Snake River , which itself is a tributary of the Columbia River in the Pacific basin . 
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- = = Climate = = 
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- Daily high temperatures range from 9 to 29 ° F ( − 13 to − 2 ° C ) in winter to 80 to 90 ° F ( 27 to 32 ° C ) in summer , while lower elevations can experience conditions over 100 ° F ( 38 ° C ) . Idaho 's mountain ranges can block Arctic air in the winter , but when cold air masses do enter the area , they sometimes <unk> in the Snake and Salmon river valleys , causing very cold temperatures to persist . Summer and fall are generally dry , while intense short @-@ duration thunderstorms can occur in late spring and early summer as atmospheric moisture interacts with warm temperatures and steep topography via <unk> lifting . During winter , warm , moist air from the Pacific Ocean often brings rain at lower elevations in addition to snowfall throughout the forest . The influence of these Pacific maritime air masses increases as latitude increases in the forest . Average annual snowfall ranges from 55 inches ( 140 cm ) in drier areas and at lower elevations to 70 inches ( 180 cm ) in wetter locations and higher elevations . The growing season within the forest ranges from over 150 days in lower elevations to less than 30 days in alpine areas . 
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- = = Natural resources = = 
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- Boise National Forest is within the Idaho Batholith ecoregion , which is a level III ecoregion in the larger level I Northwestern Forested Mountains . In addition to species listed or proposed for listing as threatened or endangered under the Endangered Species Act , the Forest Service maintains an independent listing of sensitive species for which it is directed to " develop and implement management practices to ensure that species do not become threatened or endangered because of Forest Service actions . " As of February 2013 there were 27 species in Boise National Forest listed as sensitive species : 6 mammals , 1 amphibian , 1 fish , 13 birds , and 6 plants . 
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- = = = Flora = = = 
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- An estimated 76 percent of Boise National Forest is forest , which according to the Forest Service is considered land capable of supporting trees on at least 50 percent of its area . The forests are primarily coniferous <unk> , dominated by Douglas fir and ponderosa and lodgepole pines at lower elevations and Engelmann spruce , subalpine fir , and <unk> pine at higher elevations . Grand fir and western <unk> ( a coniferous deciduous tree ) grow in the northern part of the forest where there are moister conditions . <unk> <unk> , a broadleaf deciduous tree , grows both in stands among conifers and in monotypic stands throughout the forest at elevations above 5 @,@ 000 feet ( 1 @,@ 500 m ) . Non @-@ forested areas occupy 23 percent of the forest , primarily on south @-@ facing slopes , lower elevations in the forest 's southern latitudes , or high @-@ elevation areas and are dominated by grasses , forbs , or shrubs . 
- <unk> 's <unk> is a plant species endemic to central Idaho , including parts of Boise National Forest , being found nowhere else in the world . Only about two dozen populations of the plant are known to exist , and three @-@ quarters of these are in Boise National Forest . It is usually found at elevations ranging from 5 @,@ 000 feet ( 1 @,@ 500 m ) to 9 @,@ 500 feet ( 2 @,@ 900 m ) above sea level and produces white flowers shortly after snowmelt . 
- Boise National Forest is directed by the U.S. Forest Service to " control the establishment , spread , or invasion of non @-@ indigenous plant species in otherwise healthy native <unk> ecosystems . " The forest 's plan addresses the need to control invasive plants , and management efforts include chemical , mechanical , and biological control methods . Invasive plants that are of particular concern in Boise National Forest include spotted <unk> , yellow star @-@ <unk> , rush <unk> , and leafy <unk> , among others . 
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- = = = <unk> communities = = = 
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- The <unk> , <unk> forested areas occur on south @-@ facing slopes from 3 @,@ 000 feet ( 910 m ) to 6 @,@ 500 feet ( 2 @,@ 000 m ) . Due to the occurrence of frequent non @-@ lethal fires , ponderosa pine dominates these forests alongside Douglas fir . The understory consists of bluebunch wheatgrass , Idaho <unk> , mountain snowberry , and <unk> in drier areas and elk <unk> , <unk> , white <unk> , <unk> <unk> , and common snowberry at higher elevations . 
- In cool , moist areas ranging from 4 @,@ 800 feet ( 1 @,@ 500 m ) to 6 @,@ 800 feet ( 2 @,@ 100 m ) , Douglas fir is predominant . <unk> pine and <unk> <unk> may be found alongside Douglas fir in cooler areas , both moist and dry , but particularly where <unk> pockets form . <unk> in this forest type are dominated by mountain maple , mountain ash , and blue <unk> in moister areas and white <unk> , common snowberry , elk <unk> , and <unk> in drier areas . Between 3 @,@ 400 feet ( 1 @,@ 000 m ) and 6 @,@ 500 feet ( 2 @,@ 000 m ) in the moist northern parts of the forest , grand fir is predominant and western <unk> is one of the first trees to become established during ecological succession following disturbances , whereas <unk> consist of mountain maple , mountain ash , blue <unk> , and <unk> <unk> . <unk> fir dominates from 4 @,@ 800 feet ( 1 @,@ 500 m ) to 7 @,@ 500 feet ( 2 @,@ 300 m ) along with mountain maple , <unk> , <unk> 's <unk> , <unk> alder , <unk> , Utah <unk> , and mountain ash . 
- <unk> pine dominates in cold , dry areas from 5 @,@ 200 feet ( 1 @,@ 600 m ) to 9 @,@ 200 feet ( 2 @,@ 800 m ) . The understory in lodgepole pine forests can be sparse but includes grasses , forbs , <unk> , and grouse <unk> , although fires in these forests are typically lethal to trees and <unk> alike . At the highest elevations , forests consist of subalpine fir alongside <unk> pine and Engelmann spruce . Grasses and forbs tolerant to freezing throughout the growing season occupy the understory . 
- <unk> typically dominates drier , non @-@ forested areas at lower elevations . Species that commonly occur with sagebrush include <unk> <unk> , wild onion , milk <unk> , bluebunch wheatgrass , <unk> , gray <unk> , green <unk> , and others . In riparian areas below 5 @,@ 500 feet ( 1 @,@ 700 m ) , trees such as black cottonwood , <unk> cottonwood , <unk> alder , water birch , and mountain maple grow with shrubs including <unk> and willows . <unk> areas in largely <unk> habitats such as sagebrush steppe primarily consist of willows along with <unk> alder , <unk> , mountain maple , shrubby <unk> , <unk> , <unk> , and grasses . 
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- = = = Fauna = = = 
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- <unk> in Boise National Forest support nearly 300 terrestrial <unk> species and 28 fish species . The most common large animals are mule deer and elk , but other mammals present include moose , black bears , <unk> , mountain lions , coyote , bobcat , yellow @-@ bellied <unk> , beaver , and gray wolves . 
- Gray wolves are top predators that were reintroduced amidst controversy to central Idaho in the mid @-@ 1990s to restore ecosystem stability . The wolves have since expanded their range and established packs in most of Boise National Forest . Wolves and mountain lions are the forest 's top large mammal predators and have no predators of their own except humans . Most of the forest 's native mammal species are present in the forest , with the exception of grizzly bears , which have become locally extinct , and plans for their reintroduction to central Idaho have been proposed since the 1990s but have not progressed . 
- Of the 28 fish species present in the forest , 11 are not native and have been introduced by humans . Rainbow trout , <unk> salmon , <unk> <unk> trout , bull trout , and mountain <unk> are all native to some of the forest 's waterways , while brook trout are a common invasive species that compete with the forest 's <unk> . The forest 's management indicator species is bull trout because they are sensitive to habitat changes and depend on specific habitat conditions . <unk> salmon are native to the Salmon River watershed in the northern part of the forest , but dam construction on the Columbia and Snake rivers has hampered the migration of this <unk> fish and caused its population to collapse . Warm Lake supports the forest 's only native population of Kokanee salmon , the resident ( non @-@ migratory ) form of sockeye salmon . However , due to introductions by humans , Anderson Ranch , <unk> , Lucky Peak , and Deadwood reservoirs now support populations of Kokanee salmon . To provide additional recreational fishing opportunities , the Idaho Department of Fish and Game stocks several of the forest 's waterways with rainbow trout , while reservoirs are also stocked with Kokanee or <unk> salmon and Lake Cascade is stocked with <unk> salmon and <unk> , the <unk> form of rainbow trout . 
- Over 270 bird species have been observed in central Idaho , including 36 accidental species – those that are not normally found in the region but have been observed on at least one occasion . Golden eagles and greater sage @-@ grouse can be found over sagebrush steppe , whereas bald eagles can be seen along rivers . The Forest Service has listed northern <unk> , <unk> owls , and white @-@ headed woodpeckers as a sensitive species in the forest . 
- The few amphibians present in the forest include the Rocky Mountain tailed frog , long @-@ toed <unk> , and Columbia spotted frog , which has been listed as a sensitive species . Common snakes include <unk> , <unk> snakes , and rubber <unk> . 
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- = = = Fire ecology = = = 
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- Boise National Forest 's 2010 forest plan recognizes that fire and other disturbances play important roles in maintaining the character and function of ecosystems . However , previous management strategies ( as recently as the 1990 forest plan ) treated fire as an undesirable process , and the <unk> Act of 1897 explicitly stated that forests were to be protected from destruction by fire . In historic conditions fires naturally occurred on the landscape ; the suppression of fires allowed dead trees to accumulate in excess of historic levels and land cover types to change , such as a shift to higher shrub and tree densities . An estimated 14 percent of the land in Boise National Forest has been affected by fires since the early 1990s , and about 10 percent of the land capable of timber production was burned so severely that land cover shifted from forest to grass and <unk> ( as of 2010 ) . 
- Between 2004 and 2013 an average of 74 @,@ 325 acres ( 300 @.@ 78 km2 ) were burned by fires per year with a maximum of 346 @,@ 500 acres ( 1 @,@ 402 km2 ) in 2007 and a minimum of 152 acres ( 0 @.@ 62 km2 ) in 2008 . For example , in 2012 there were 26 fires started by people and 109 started by lightning , which together burned a total of 152 @,@ 000 acres ( 620 km2 ) . The Trinity Ridge Fire alone burned 146 @,@ 800 acres ( <unk> km2 ) over two months , although it was not confined to Boise National Forest lands . 
- The revised 2010 forest plan recognized the need to develop plans to manage <unk> at the <unk> – urban interface , use prescribed fire as a tool to manage ecosystem health , and meet air quality requirements set by the Clean Air Act . The forest operates a fire management plan under federal fire policy that gives fire personnel direction for responding to <unk> <unk> . Occasionally , area closures and restrictions on use , such as prohibition of <unk> , are implemented to aid in <unk> prevention . Following severe fires , area closures may be put in place to protect the public from risks such as falling trees and landslides . <unk> fire management strategies include prescribed burns and mechanical reduction of fuel levels . For instance , in 2014 Boise National Forest planned to conduct 7 @,@ <unk> acres ( 32 @.@ 05 km2 ) of prescribed burns and 155 acres ( 0 @.@ 63 km2 ) of mechanical treatment . The forest seasonally maintains staff at seven fire lookout towers , while six others remain <unk> . 
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- = = Recreation = = 
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- There are over 70 campgrounds in Boise National Forest and groups of more developed recreation facilities at the Trinity Mountains , Warm Lake , and Deadwood and Sage Hen reservoirs . As in most national forests , the majority of the land in Boise National Forest is open to dispersed camping ( outside of developed campgrounds ) . One of the forest 's fire lookouts , Deadwood <unk> , is now available as a cabin for the public to rent , among other sites . <unk> are allowed on forest roads and on more than 1 @,@ 300 miles ( 2 @,@ 100 km ) of multiple @-@ use trails , whereas over 1 @,@ 200 miles ( 1 @,@ 900 km ) of trails are open to <unk> recreation . The <unk> Mountains Off @-@ Highway Vehicle Trail System contains 150 miles ( 240 km ) of motorcycle and ATV trails on 60 @,@ 000 acres ( 24 @,@ 000 ha ) of land that is generally open from April through November . The forest 's Trinity Mountain Recreation Area includes the highest <unk> ( 4 @-@ wheel drive recommended ) road in Idaho , which <unk> to the Trinity Mountain <unk> at an elevation of over 9 @,@ 400 feet ( 2 @,@ 900 m ) . In 2013 revenues from recreation and special use fees amounted to $ 454 @,@ 635 , while expenses totaled US $ 352 @,@ 550 ; the difference is allocated to the following season 's startup costs . 
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- = = = Waterways = = = 
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- Rivers in Boise National Forest offer the opportunity for rafting and <unk> through rapids up to class four , with the most difficult sections on the South Fork and main stems of the Payette River . Numerous developed boat launch sites provide access to rivers for whitewater enthusiasts , and <unk> Falls is the primary launching site for visitors to the Middle Fork of the Salmon River and Frank Church River of No Return Wilderness . <unk> boating is permitted on Anderson Ranch Reservoir , Deadwood Reservoir , and Warm Lake . 
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- = = = Winter activities = = = 
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- During winter , visitors to the forest can participate in activities including <unk> , <unk> , and <unk> and cross @-@ country skiing . The <unk> Basin <unk> area is located within the forest north of Boise and has 7 <unk> and 53 runs on 2 @,@ 600 acres ( 11 km2 ) of <unk> terrain . There are 137 miles ( 220 km ) of <unk> <unk> trails in the Garden Valley system in the Emmett Ranger District and several Mongolian @-@ style <unk> available for rental in winter . 
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- = = = Scenic roads = = = 
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- Boise National Forest is home to three of Idaho 's scenic <unk> , all of which are paved highways accessible to <unk> vehicles . The Payette River Scenic Byway is an 80 @-@ mile ( 130 km ) route between Eagle and McCall on Idaho State Highway 55 . The route follows the Payette River between McCall and Horseshoe Bend , but the majority of the highway does not pass through Boise National Forest ; only a small portion north of Horseshoe Bend is in the Emmett Ranger District . Over half of the 35 @-@ mile ( 56 km ) Wildlife Canyon Scenic Byway , which travels between highway 55 and Lowman , passes through the forest , parallels the South Fork of the Payette River , and is signed as the Banks @-@ Lowman Road . The Ponderosa Pine Scenic Byway is a 130 @-@ mile ( 210 km ) road between Stanley and Boise following Idaho State Highway 21 . This route passes over <unk> Reservoir and through Idaho City and Lowman , where it connects with the Wildlife Canyon Byway . North and east of Lowman the <unk> partially follows the South Fork of the Payette River before ascending to the 7 @,@ <unk> @-@ foot ( 2 @,@ 145 m ) Banner Creek Summit at the forest 's boundary with Salmon @-@ <unk> National Forest . 
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- = Pokiri = 
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- Pokiri ( English : Rogue ) is a 2006 Indian Telugu @-@ language action film , written and directed by Puri Jagannadh . The film was produced by Jagannadh and <unk> <unk> by their respective production companies <unk> Academy and Indira Productions . The film stars Mahesh Babu and <unk> D 'Cruz ; Prakash Raj , <unk> and <unk> Shinde appear in prominent roles . The plot revolves around the life of an undercover police officer , Krishna Manohar , who infiltrates a mafia gang headed by a Dubai @-@ based don Ali Bhai , under the pseudonym Pandu . 
- Made on a budget of around ₹ 100 — 120 million , the film 's principal photography commenced in November 2005 and lasted until April 2006 . Most of the film was shot in and around Hyderabad and Chennai , except for a song which was shot at the province of <unk> in Thailand and the city of Bangkok . Shyam K. Naidu was the film 's cinematographer , and it was edited by <unk> K. <unk> . The soundtrack and background score were composed by Mani Sharma . 
- Pokiri was released on 28 April 2006 , to positive critical feedback , and collected a distributor 's share of ₹ 420 million . The film grossed ₹ 660 million worldwide and remained the highest @-@ grossing Telugu film for three years , until it was surpassed by Magadheera in 2009 . The film was also one of the fourteen southern Indian films to be screened at the International Indian Film Academy Awards ( IIFA ) Film festival in 2006 . The film won four Nandi Awards and two Filmfare Awards . The film 's success catapulted D 'Cruz into stardom and brought recognition to Jagannadh as a writer and director . 
- The film was remade into Tamil as <unk> in 2007 by Prabhu Deva with Vijay and Asin portraying the lead roles . Deva remade the film into Hindi as Wanted in 2009 with Salman Khan and Ayesha Takia portraying the lead roles . In 2010 , the film was again remade in Kannada as <unk> by M. V. Sridhar with <unk> and <unk> Subhash portraying the lead roles . 
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- = = Plot = = 
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- In Hyderabad , two rival mafia gangs headed by Dubai @-@ based don Ali Bhai , and Narayana resort to criminal activities such as <unk> , murder , and <unk> for various reasons . The new commissioner of police , <unk> <unk> Pasha Qadri , focuses on making the city a better place by working at arresting all of them . Pandu , a <unk> gangster living in Hyderabad along with his friends , is hired by Narayana and attacks Ali Bhai 's henchmen . He later joins Ali Bhai 's gang for monetary reasons . He falls in love with Shruti , an aerobics teacher , who rejects his advances . 
- Shruti lives with her widowed mother and brother and her neighbour <unk> , a software engineer , who <unk> her to marry him . A corrupt police officer named Pasupathy , who works for Ali Bhai , is attracted to Shruti . He is determined to make her his mistress , <unk> by Shruti 's multiple <unk> . After Pandu kills a <unk> of Narayana , he is confronted by Pasupathy and is able to prevent Shruti from being <unk> . She meets him the next day to thank him , and Pandu introduces himself as a self @-@ employed person who <unk> any activity for money . They develop unspoken romantic feelings for each other <unk> Pasupathy . 
- Shruti 's employer , Suryanarayana , suggests that she marry the man she loves . To repel Pasupathy 's advances , she meets Pandu and proposes to him . After an attack by Narayana 's henchmen , who are murdered by Pandu , he reveals that he is a gangster and suggests that she might want to rethink her proposal . After Shruti distances herself from Pandu , Pasupathy frames her with a mock sexual assault by a few gangsters <unk> to her family and the other villagers . He intends this act to ruin her life and subsequently force her to be his mistress . Learning this , Pandu confronts Pasupathy and warns him that he will face dire consequences if he is found guilty of being involved . 
- Ali Bhai visits Hyderabad and <unk> Narayana . He meets Pandu to discuss the murder of a minister by blowing up a balloon . Pandu rejects this as it would involve killing <unk> . At the same time as they are arguing , Qadri arrests Ali Bhai and <unk> him . Ali Bhai 's henchmen retaliate by filming Qadri 's daughter as she is <unk> into sexual activity by the minister 's son , and release it to the media . They also kidnap her , forcing the police to release Ali Bhai . At the same time , Pandu manages to catch the gangsters who pretended to rape Shruti and she <unk> with him . 
- Qadri 's daughter is <unk> and reveals that an undercover police officer , whose father 's name is Suryanarayana , had infiltrated the gang for some time . Ali Bhai kills Suryanarayana 's son <unk> , assuming he is the informant . However , Suryanarayana reveals that <unk> was his adopted son , and that Pandu is actually Krishna Manohar I. P. S. , his biological son , who had infiltrated his gang at Qadri 's direction . Suryanarayana is killed and Manohar forces Pasupathy to kill Ali Bhai before he initiates his plan to set off bombs across Hyderabad . 
- Manohar kills Ali Bhai 's henchmen one by one at <unk> Mills . Ali Bhai offers Pasupathy a <unk> sum to kill Manohar but his attempt fails . In a final confrontation , Manohar kills Ali Bhai by slitting his throat . Qadri 's daughter is saved and when Pasupathy tries to <unk> Manohar , he is shot dead by the latter who then says , " <unk> commit <unk> , <unk> <unk> <unk> <unk> " ( English : Once I commit myself , I 'll never back off ) . 
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- = = Cast = = 
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- = = Production = = 
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- = = = Origin = = = 
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- In 2004 , after <unk> ' s commercial failure , its director Puri Jagannadh planned a film titled Sri <unk> from <unk> Company starring Chiranjeevi in the lead role . He later decided that explaining the story to Chiranjeevi , talking him into accepting the role , and filming the movie , would be a long , tiring process . He chose instead to revive the script of Uttam Singh S / O Suryanarayana which he had written during the production of <unk> ( 2000 ) . He approached <unk> <unk> to play the lead role , but he declined it . Later , he approached Ravi Teja who agreed to play the lead ; <unk> Babu was to produce the film . However , Teja was approached by <unk> , an award winning director , to remake the 2004 Tamil film <unk> in Telugu . Teja was eager to be involved in the remake as he liked the original very much . As a result , the production Uttam Singh S / O Suryanarayana was temporarily <unk> . Jagannadh meanwhile directed and produced 143 ( 2004 ) . Teja had backed out of participating in it citing scheduling conflicts with other existing commitments . Jagannadh wanted to experiment by casting <unk> <unk> in the lead role but this too failed to <unk> . 
- On 3 November 2004 , Jagannadh met Mahesh Babu at the Taj Hotel in <unk> to outline the film 's plot . It told the story of Uttam Singh , an undercover police officer , infiltrating a mafia gang as a criminal , with the intention of killing its kingpin . Mahesh liked the script but suggested Jagannadh <unk> the script 's backdrop to suit the Telugu @-@ speaking peoples ' sensibilities . Jagannadh agreed and also replaced the existing title with Pokiri . Mahesh wanted the film 's production to begin in 2005 allowing him to complete his current commitments . While he waited for Mahesh , Jagannadh directed <unk> <unk> in Super ( 2005 ) . While reworking the script , Jagannadh took inspiration from <unk> <unk> ( 1988 ) and State <unk> ( 1989 ) . Pokiri was produced jointly by Jagannadh and <unk> <unk> 's production companies , <unk> Academy and Indira Productions respectively , on a budget of ₹ 100 — 120 million . 
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- = = = Cast and crew = = = 
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- For Pokiri , Mahesh sported a longer hair style than in his previous films and shed five kilograms of weight . He used a new wardrobe and the same pair of shoes throughout the film . Jagannadh wanted to cast Ayesha Takia as the female lead . Due to a last minute change , the makers opted to replace Takia and considered several actresses including <unk> Padukone . Jagannadh approached Parvati <unk> to play the female lead . She declined the offer because , at that time , her parents were against her decision to become an actress . He also approached <unk> <unk> who could not accept the role because of scheduling conflicts with the filming of her scenes in <unk> ( 2006 ) . After seeing <unk> of <unk> D 'Cruz in her Telugu debut <unk> ( 2006 ) , Jagannadh signed her as the female lead since he needed a girl who looked like a teenager to play the role of the aerobics teacher . 
- Prakash Raj and <unk> <unk> were cast as the film 's primary antagonists . Raj played a mafia kingpin and <unk> played a corrupt police officer , a villain 's role he finds more fun to play than that of a hero . <unk> Shinde and <unk> played the two other principal characters in the film . <unk> <unk> played the role of the mafia kingpin 's <unk> , marking her debut in Telugu cinema . <unk> and <unk> portrayed negative roles as well , with the former also making his debut in Telugu cinema . Ali played the role of a <unk> and shared screen @-@ space with Brahmanandam and <unk> <unk> . Jagannadh added this trio to the film to provide <unk> humour . Master <unk> played the role of D 'Cruz 's brother . Mumaith Khan performed an item number in the film . 
- Jagannadh wrote the film 's story , screenplay and dialogue with <unk> Ramesh assisting him as script associate . Though having worked with <unk> many times in the past , at Mahesh 's suggestion , Jagannadh instead chose Mani Sharma to compose the film 's music . Shyam K. Naidu was the film 's cinematographer and <unk> K. <unk> its editor . <unk> and Krishna were the film 's art director and executive producer respectively . 
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- = = = Filming = = = 
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- Pokiri was shot predominantly in and around Hyderabad , especially in the Annapurna Studios , the aluminium factory near <unk> , <unk> Hills and the <unk> Fort in 100 working days , from November 2005 to April 2006 . Most of the scenes were shot in a single take though it took time for Mahesh to adjust to Jagannadh 's style of filmmaking . Chennai @-@ based stylist <unk> Rao designed the costume styling for Mahesh and D 'Cruz . By late February 2006 , eighty percent of the film shoot had been completed with the film 's climax and two songs remaining . This made it Mahesh 's fastest shot Telugu film with him in the lead role . 
- The song " Gala Gala " was shot in the province of <unk> in Thailand , and the city of Bangkok . Prior to the filming of the song " <unk> " , Shyam K. Naidu was busy on the set of <unk> ( 2007 ) and was unable to shoot it so cinematographer K. V. <unk> , who had worked on Mahesh 's <unk> ( 2005 ) , was recruited instead . The film 's climax sequences were shot in March 2006 at the defunct <unk> Mills located in Chennai under the supervision of <unk> <unk> . He suggested that Jagannadh include a scene where Prakash Raj fails to hear anything for a while after he is hit by Mahesh during the climax sequence . 
- Mahesh stated in an interview that he had to shoot the film 's climax and two songs continuously for thirty @-@ eight days , adding that he had to visit a hospital to be treated for shoulder pain . During the shooting of an underwater sequence , a few electrical lights were used . The <unk> changed the lines , creating a short circuit which resulted in the death of one of the unit members . Mahesh had gotten out of the pool two seconds before the accident happened which he termed a " miracle " . 
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- = = Music = = 
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- The official soundtrack of Pokiri was composed by Mani Sharma , with lyrics written by <unk> , <unk> and <unk> . Jagannadh wanted Sharma to compose six songs , with two duets between the lead pair , three solo numbers by the male lead , and an item number . During the shoot of <unk> ( 2003 ) , Jagannadh listened to the song " Listen to the Falling Rain " which sounded like the song " Gala Gala <unk> <unk> " from the Telugu film <unk> ( 1974 ) . He later came to learn that the latter song was inspired by the former , and he decided to reuse the same tune with modernised instruments and different lyrics . Sharma was accused of copying the tune of the song " <unk> " composed by Ricky Martin for the song " <unk> " sung by Naveen . 
- The film 's soundtrack , marketed by <unk> Music , was released on 4 April 2006 , at Hotel Viceroy in Hyderabad with Mahesh 's father Krishna attending the event as the guest of honour . Sify called the soundtrack a <unk> one and chose " Gala Gala " as the pick of the album . IndiaGlitz called it a " run of the mill " album that lacks freshness . The reviewer chose " <unk> " , " Gala Gala " and " <unk> " as the picks of the album , rating each 3 @.@ 5 out of 5 . <unk> rated the soundtrack 3 out of 5 stars and stated : " The first time you hear Pokiri , the sound of it is good ; it doesn 't start to grow on you after a while , but a few of the numbers are <unk> and ring in your ears " , calling it a " mixed bag for Mani Sharma " . The reviewer chose " Dole Dole " and " Gala Gala " as the picks of the album , rating each 3 @.@ 5 out of 5 . 
- 
- = = Release and reception = = 
- 
- Pokiri was scheduled for a worldwide release on 21 April 2006 . Due to delays in post @-@ production activities , the film 's release was postponed to 28 April 2006 , <unk> with the release of <unk> and <unk> . The film received an ' A ' ( Adults only ) certificate from the Central Board of Film <unk> for containing obscene sequences and excessive violence . <unk> <unk> 's Sri Venkateswara Creations , <unk> films and Great India films acquired the theatrical distribution rights of Nizam , <unk> and overseas regions respectively . Pokiri was one of the fourteen southern Indian films that were screened at the IIFA film festival 2006 held at the Dubai International Convention Centre in Dubai , United Arab Emirates . 
- 
- = = = Critical reception = = = 
- 
- Reviewing the film , The Hindu wrote : " An out and out action flick , one can see the director 's thirst to cash in on the audience craze for such films . Nevertheless it 's Mahesh Babu 's show all the way . " Regarding Mahesh 's performance in the film , <unk> <unk> <unk> of The Hindu wrote : " Mahesh 's understated performance in Pokiri allows him to <unk> reclaim the title of a star , <unk> more questionable recent career choices " . Sify stated that Pokiri was designed as a : " mass masala <unk> which <unk> the <unk> viewers " . The reviewer added that Mahesh 's screen presence works to the advantage of the film . 
- <unk> stated : " Sporting a new , rugged look , ' Prince ' Mahesh Babu has stolen the show . He carries the film on his shoulders , consolidating his winning streak after last year 's <unk> . Another highlight of the film is its well @-@ choreographed action sequences ( if you can digest the violence ) , which give it a slick look . " IndiaGlitz gave a positive review stating : " In Pokiri , the hero is introduced to us a ruthless <unk> , part of the huge <unk> of mafia . By the time we come to the <unk> , there is much twist and turn . If you like some <unk> action , fun , glamour and love , then Pokiri would be your kind of film . " 
- 
- = = = Box office = = = 
- 
- According to Sify , Pokiri took an " extraordinary " opening across the globe and was able to cash in on the four @-@ day weekend holiday . Pokiri was released in a single screen , the <unk> theatre in Chennai , where 98 @.@ 5 % of seats were sold putting it in second place in the city 's box office chart , which Sify called an " awesome " feat . The film completed a fifty @-@ day run on 17 June 2006 , in nearly 300 centres and had earned US $ 350 @,@ 000 to become the highest grossing Telugu film in the United States . By July 2006 , the film had earned approximately ₹ 350 — 400 million and become the highest grossing Telugu film of all time . The film earned ₹ 120 million in the Nizam region alone , breaking the previous record set in the region by Indra ( 2002 ) and earned approximately ₹ 25 million at the United States box office . 
- The film completed a 100 @-@ day run in 200 centres and a 175 @-@ day run in 63 centres . The film completed a 200 @-@ day run in 15 centres , and a 365 @-@ day run at a theatre in Kurnool , becoming the first Telugu film to do so in the last two and a half decades . The film was screened in <unk> theatre , Kurnool for 500 days at the rate of four shows per day and collected a share of ₹ 6 million . In its lifetime , Pokiri grossed ₹ 660 million and collected a distributor share of ₹ 420 million at the global box office . It held that position until 2009 when Magadheera pushed it to second place after its nine @-@ day run . 
- 
- = = = Awards = = = 
- 
- 
- = = <unk> = = 
- 
- The film has been remade in various languages across India . The film was remade into Tamil as <unk> by Prabhu Deva featuring Vijay and Asin in the lead roles , and marked Deva 's debut as a director of Tamil cinema . Deva remade the film into Hindi as Wanted in 2009 featuring Salman Khan and Ayesha Takia . Wanted became the second highest grossing Hindi film of all time at that point . Pokiri was remade into Kannada as <unk> in 2010 by M. D. Sridhar featuring <unk> and <unk> Subhash in the lead roles . 
- 
- = = Legacy = = 
- 
- Pokiri 's success elevated Mahesh to super @-@ stardom and brought recognition to Jagannadh as a writer and director . The sequences featuring Brahmanandam as a software engineer , the comedy track of Ali and Brahmanandam , Mahesh asking D 'Cruz to give him <unk> at the railway station were acclaimed . The fashion trend of wearing doctor sleeves increased in Andhra Pradesh after Mahesh sported them and they continue to influence fashion even today . After the film 's release , many films were released subsequently that had titles bordering on <unk> words including Jagannadh 's next film <unk> ( 2006 ) . Mahesh revealed that he became confused after the film 's success : 
- It was such a huge hit , that if someone came to me with a script , I would approach the result of the film before approaching the character . I only wanted to act in movies that were like Pokiri , I think that was a mistake . It all got to me and I felt that I needed a break from films itself . Initially , I wanted just a seven @-@ month break . I signed <unk> after nine months , but it just kept getting delayed and the break ended up becoming a two @-@ year @-@ long holiday . But I didn 't freak out ... I relaxed for the first time in life . 
- Pokiri was D 'Cruz 's breakthrough film in Telugu . In June 2006 , Trade analyst Sridhar <unk> said that the Andhra Pradesh trade felt that her glamour , screen presence , and on @-@ screen chemistry with Mahesh worked to the film 's advantage . <unk> called her the " new pin @-@ up girl of Telugu cinema " . Talking about being typecast after her success in <unk> <unk> <unk> ( 2010 ) as its female lead , Samantha Ruth Prabhu cited the example of D 'Cruz being typecast in similar roles after the success of Pokiri saying that it had become mandatory for her to wear a bikini in every film since . 
- Pokiri was parodied by several films . In <unk> , the character <unk> Shankar , a saint played by Ali , is seen imitating Mahesh 's <unk> from the song " Dole Dole " . Brahmanandam 's introduction scene in the film <unk> ( 2008 ) is a spoof of Mahesh 's introduction as a police in Pokiri . The same sequence was <unk> in the films <unk> ( 2012 ) where the protagonist is named Shiva Manohar I. P. S. , and also in Race <unk> ( 2014 ) . In <unk> ( 2011 ) , Mahesh is briefly seen as a film director who makes <unk> and M. S. Narayana recite the dialogue " <unk> <unk> <unk> <unk> mind block <unk> , <unk> <unk> " from Pokiri . The protagonist in <unk> ( 2012 ) , a fly , <unk> Mahesh 's mannerisms from the song " <unk> " after injuring the antagonist played by <unk> . 
- 
- 
- = 2008 Bahrain Grand Prix = 
- 
- The 2008 Bahrain Grand Prix ( formally the V Gulf Air Bahrain Grand Prix ) was a Formula One motor race held on 6 April 2008 at the Bahrain International Circuit , in Sakhir , Bahrain . It was the third race of the 2008 Formula One season . The 57 @-@ lap race was won by Felipe Massa for the Ferrari team . Kimi Räikkönen was second in the other Ferrari , and BMW Sauber driver Robert Kubica was third . 
- The race began with Kubica in pole position alongside Massa ; Lewis Hamilton , the eventual Drivers ' Champion , started from third , alongside Räikkönen . Kubica was passed by Massa into the first corner , and then by Räikkönen on the third lap . The Ferraris dominated at the front of the race , leading to their one @-@ two finish . Hamilton had a slow start after almost stalling on the grid , and dropped back to ninth . The McLaren driver ran into the back of Fernando Alonso 's Renault a lap later , breaking off the McLaren 's front wing and dropping Hamilton to the back of the field . 
- Kubica 's strong finish promoted BMW Sauber to the lead in the Constructors ' Championship , after BMW driver Nick Heidfeld finished fourth . Ferrari and McLaren trailed , one and two points behind , respectively . Räikkönen took the lead in the Drivers ' Championship , with 19 points , three points ahead of Heidfeld and five ahead of Hamilton , Kubica and Kovalainen , with 15 races remaining in the season . 
- 
- = = Report = = 
- 
- 
- = = = Background = = = 
- 
- The Grand Prix was contested by 22 drivers , in 11 teams of two . The teams , also known as " constructors " , were Ferrari , McLaren @-@ Mercedes , Renault , Honda , Force India , BMW Sauber , Toyota , Red Bull Racing , Williams , Toro Rosso and Super <unk> . Tyre supplier Bridgestone brought two different tyre compounds to the race ; the softer of the two marked by a single white stripe down one of the grooves . 
- Prior to the race , McLaren driver Lewis Hamilton led the Drivers ' Championship with 14 points , and Ferrari driver Kimi Räikkönen was second with 11 points . Behind Hamilton and Räikkönen in the Drivers ' Championship , Nick Heidfeld was third , also with 11 points , in a BMW Sauber , and Hamilton 's McLaren teammate <unk> Kovalainen was fourth with 10 points . Heidfeld 's teammate Robert Kubica was fifth with eight points . In the Constructors ' Championship , McLaren – Mercedes were leading with 24 points , five points ahead of BMW Sauber . Ferrari were third with 11 points . 
- Ferrari dominated the previous round in Malaysia , where Felipe Massa had claimed pole position , and led his teammate Räikkönen in second place through the opening stages of the race , before spinning off and retiring midway through . Räikkönen went on to win the race , and expressed his optimism about Bahrain : " I have finished third in three successive <unk> Prix in Bahrain . Time and again something has gone wrong . Sakhir is one of those circuits where I really want to win . Finally . " 
- In the opening two races , Massa came under fire from the press for two errors that left him without points : a collision with Red Bull driver David Coulthard in Australia and his spin at Malaysia . Massa promised that the first two races would not be indicative of the rest of the season : " It was not the start to the season that I wanted , but there are still 16 races to go and 160 points up for <unk> . In the next few races I plan to get back all the points I have lost in the opening two rounds . " 
- Hamilton won the opening race in Australia , but managed to finish only fifth after a qualifying penalty and a <unk> pit stop in Malaysia . The season began well for Kubica , as he qualified second in Australia and finished second in Malaysia . Kubica predicted his team could maintain their momentum into the third race : " I 'm confident that we can be very competitive here as well . " 
- A week before the start of the Grand Prix weekend , the News of the World alleged that Max Mosley , the president of Formula One 's governing body , the Fédération Internationale de l 'Automobile ( FIA ) , had engaged in sexual acts with five prostitutes . While Mosley denied the allegations , he cancelled his scheduled appearance at the Bahrain Grand Prix . Several teams condemned Mosley 's alleged actions and asked for his resignation , and while for a time the controversy threatened to overshadow the race , Mosley eventually retained his position and successfully sued the News of the World for the report . 
- 
- = = = Practice and qualifying = = = 
- 
- Three practice sessions were held before the Sunday race — two on Friday , and a third on Saturday . The Friday morning and afternoon sessions each lasted 90 minutes . The third session was held on Saturday morning and lasted an hour . The Ferraris <unk> the other teams in the first session on a dusty track surrounded by the sand dune desert of Sakhir . Massa 's time of 1 : 32 @.@ 233 was quicker than Räikkönen 's , who was slowed down by an early <unk> across the sand , requiring a pit stop . Nico Rosberg of Williams , Hamilton , McLaren driver <unk> Kovalainen , Williams driver <unk> Nakajima and Kubica rounded out the top seven . In the second session , Hamilton lost control of his car and <unk> sideways into a wall . Hamilton emerged unharmed from the collision , but his McLaren suffered significant damage . Except for the crash , the second session ended like the first : once again , Massa led Räikkönen to Ferrari one @-@ two , ahead of Kovalainen , Hamilton and Kubica . The third session was again held on a dusty track , where Rosberg was quickest with a time of 1 : 32 @.@ 521 . Massa took second , ahead of Red Bull driver Mark Webber , Toyota driver <unk> Trulli , David Coulthard of Red Bull , Nakajima and Kubica . Räikkönen was ninth quickest , and Hamilton 18th . 
- The qualifying session on Saturday afternoon was split into three parts . The first part ran for 20 minutes , and cars that finished the session 17th position or lower were eliminated from qualifying . The second part of the qualifying session lasted 15 minutes and eliminated cars that finished in positions 11 to 16 . The final part of the qualifying session determined the positions from first to tenth , and decided pole position . Cars which failed to make the final session could refuel before the race , so ran lighter in those sessions . Cars which competed in the final session of qualifying were not allowed to refuel before the race , and as such carried more fuel than in the previous sessions . 
- Kubica clinched the first pole position of his career with a time of 1 : 33 @.@ <unk> . Massa qualified less than 0 @.@ 03 seconds behind the BMW and joined Kubica on the front row of the grid . Hamilton took third place , using his team 's spare chassis ; Räikkönen was next quickest , and despite being critical of his car 's set @-@ up was confident in its racing ability . Kovalainen would line up fifth on the grid alongside Heidfeld , who had trouble <unk> performance from his tyres . Trulli took seventh place , ahead of Rosberg and Honda driver Jenson Button . Renault driver Fernando Alonso was the last driver to make the third session ; Webber missed out on the top ten by 0 @.@ <unk> seconds and would start the race in 11th position . Button 's teammate Rubens <unk> took 12th place after a <unk> problem interrupted his second session laps , ahead of <unk> Glock of Toyota , Nelson Piquet of Renault and Toro Rosso driver <unk> Bourdais . Nakajima was the slowest of the second session drivers , and took 16th . Coulthard qualified 17th , ahead of Giancarlo Fisichella of Force India . Toro Rosso driver Sebastian Vettel took 19th , and blamed his set @-@ up : " I felt it was more a case of the car driving me than me driving the car . " Fisichella 's teammate Adrian Sutil qualified 20th , ahead of Anthony Davidson of Super <unk> . Davidson 's teammate <unk> Sato spun out and crashed into the barriers . Sato 's accident damaged his rear wing and suspension and left him unable to continue in the session . 
- 
- = = = Race = = = 
- 
- The weather and conditions on the grid were before dry for the race . The air temperature was 29 ° C ( 84 ° F ) with signs of a breeze which could blow sand onto the track and impede the cars ' grip . Massa got the best start of the frontrunners off the line , as he passed Kubica into the first corner to take the lead . Hamilton 's poor start caused his anti @-@ stall system to kick in , and he was passed by six drivers to fall back to ninth . Räikkönen benefited from Hamilton 's start by moving up to third , ahead of Kovalainen , Trulli and Heidfeld . As Massa extended his lead at the front of the race , Hamilton , who trailed Alonso , collided with the back of the Renault , knocking the McLaren 's front wing off the car . Suffering handling difficulties , Hamilton returned to the pit @-@ lane for a new nose section , and rejoined in 18th place . Räikkönen took second place when he passed Kubica on lap three ; Heidfeld took fourth when he passed Trulli and Kovalainen in separate manoeuvres . Further down the field , Vettel retired from the race on the first lap after twice colliding with other cars ; Button , Sutil and Coulthard pitted to repair early damage . 
- By lap 10 , Massa had opened his lead over Räikkönen to 4 @.@ 4 seconds , ahead of Kubica , Heidfeld , Kovalainen and Trulli . Kubica was the first of the frontrunners to pit , on lap 17 . Räikkönen and Trulli followed on lap 20 ; Massa pitted from the lead one lap later . Following the first round of pit stops , the gap between the Ferraris was 5 @.@ 4 seconds , however by lap 31 Räikkönen had closed to within four seconds of his teammate . Massa held a 3 @.@ 6 second lead over Räikkönen when the two Ferraris pitted for the final time on laps 39 and 38 , respectively , and Massa retained his lead into the final stint . Kubica pitted on lap 41 , Heidfeld on lap 45 , and Kovalainen on lap 47 . 
- Coulthard and Button collided on lap 18 when Button attempted to pass the Red Bull on the inside at turn eight ; the Honda lost its front wing and retired a lap later after two pit stops . Hamilton continued his climb back through the field ; he moved from 18th , passing Piquet , Davidson , Sutil and Bourdais in separate manoeuvres , to sit in 14th by the time he pitted on lap 31 . Piquet retired on lap 42 with transmission failure , requiring a <unk> change before the next race . 
- Massa took his first win of the season when he crossed the line at the end of the 57th lap , 3 @.@ 3 seconds ahead of the second @-@ placed Räikkönen . Kubica took third , ahead of his teammate Heidfeld , and Kovalainen , who set the fastest lap of the race on lap 49 , with a time of 1 : 33 @.@ 193 , despite being slower than the frontrunners for much of the race . Trulli , Webber , Glock and Alonso rounded out the top ten , after Glock 's Toyota held off a quick Alonso late in the race . <unk> and Fisichella finished strongly , ahead of Hamilton , who managed only 13th . Nakajima , Bourdais , Davidson and Sato took the next four places ; Coulthard and Sutil finished last on track after their respective crashes demoted them to the back of the field . Vettel , Button and Piquet were the three <unk> from the race . 
- 
- = = = Post @-@ race = = = 
- 
- The top three <unk> appeared on the podium and in the subsequent press conference , where Massa appeared relieved : " For sure the race was pretty difficult because I didn 't want to make any mistakes . I didn 't push as much either , just tried to bring the car home and just controlling the pace as well . " Massa said that he struggled with grip early , owing to oil in the middle sector of the course . Räikkönen 's second place promoted him to the lead in the Drivers ' Championship , and he expressed his optimism about future races : 
- The whole weekend has been pretty difficult , one of those things when we cannot really get the car right ... We are leading the Championship which is the main thing and we know that we have the speed once we get everything right . The race was quite difficult but <unk> I am happy with second . 
- Kubica said that his poor start was due to <unk> off the line , and the presence of oil on the track impeded the performance of his car , leading to Räikkönen 's pass : " But anyway I think it was a good result : third and fourth for the team and leading the Constructors ' Championship , so it was good weekend . " 
- Hamilton accepted responsibility for his near stall on the grid : " I hadn 't hit the switch early enough and therefore we were not in the launch map and went straight into anti @-@ stall , and when everyone else was in their launch mode , I wasn 't . " Renault dismissed suggestions that Alonso had brake tested Hamilton in the incident that led to the McLaren 's wing breaking off . Pat <unk> , Renault 's head of engineering , said his team 's <unk> indicated Alonso was on full <unk> down the straight , and had not touched the brakes : " I think all I can say from our side is that there is no blame attributable to Fernando , which is what some of the speculation might be . " McLaren F1 CEO Martin <unk> said that Hamilton 's front wing had broken seconds before the impact , and that the resulting <unk> reduction had sucked Hamilton into Alonso 's <unk> faster than expected . However , photos indicate that the front wing on the McLaren could have broken even earlier when Hamilton <unk> a car ( believed to be Alonso ) on the opening lap . The later incident left Alonso with damage to the back of the car , <unk> his attempts to pass Glock late in the race . 
- Räikkönen 's second @-@ placed finish gave him a three @-@ point lead over Heidfeld in the Drivers ' Championship , with 19 points , ahead of Hamilton , Kubica and Kovalainen , each with 14 points . BMW Sauber 's strong performance gave them the lead in the Constructors ' Championship , with 30 points , ahead of Ferrari with 29 points and McLaren with 28 points . 
- 
- = = Classification = = 
- 
- 
- = = = Qualifying = = = 
- 
- 
- = = = Race = = = 
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- 
- = = Championship standings after the race = = 
- 
- Note : Only the top five positions are included for both sets of standings . 
- 
- 
- = Kalyanasundara = 
- 
- Kalyanasundara ( <unk> ् <unk> ् <unk> , literally " beautiful marriage " ) , also spelt as <unk> and <unk> <unk> , and known as Kalyanasundara @-@ murti ( " icon of the beautiful marriage " ) , <unk> @-@ murti ( <unk> @-@ <unk> ् <unk> , " <unk> icon " ) and <unk> @-@ murti ( <unk> ् <unk> @-@ <unk> ् <unk> ) ( " icon related to panigrahana ritual " ) , is the <unk> depiction of the wedding of the Hindu deities Shiva and Parvati . The couple are often depicted performing the panigrahana ( " accepting the hand " ) ritual of a Hindu wedding , where the groom accepts the bride by taking her right hand in his . 
- The couple , depicted in the centre , are accompanied by a host of divinities and other celestial beings . The god Vishnu and his wife Lakshmi are often pictured as giving away the bride to Shiva . The god Brahma is shown as the officiating priest . 
- The Kalyanasundara icon is not the object of popular worship and is usually used only in the celebrations of the divine marriage in annual temple festivals . However , Kalyanasundara scenes are found across India in caves , sculptures and on temple walls . 
- 
- = = Legend = = 
- 
- Various Hindu scriptures narrate the story of the union of Shiva and Parvati , with some variation . After the death of his first wife Sati , Shiva withdrew from society and <unk> himself in deep <unk> . Taking advantage of the situation , the <unk> ( demon ) king Tarakasura secured from the god Brahma the <unk> that he could be killed only by the son of Shiva . Believing himself effectively immortal , Tarakasura <unk> the beings of the universe and defeated the gods . Meanwhile , Parvati , the reincarnation of Sati , was born to <unk> , the god of the Himalayas and his wife the <unk> Mena . She underwent severe <unk> to <unk> Shiva to marry her . The gods , desperate to hasten the birth of Shiva 's son , sent <unk> , the god of love , to <unk> Shiva 's meditation . Though Shiva was <unk> , <unk> was burnt up by Shiva 's <unk> . <unk> by the other gods to marry , Shiva agreed , but decided to test Parvati 's devotion first . The <unk> ( the seven sages ) approached Parvati and mocked Shiva to dissuade her ; however Parvati remained resolute . Then Shiva himself , disguised as an old ascetic , visited Parvati and vilified himself in her presence . As an angry Parvati was about to leave , Shiva revealed his true form to her and promised to marry her , pleased with her love and devotion . The couple married and produced a son , Kartikeya , who subsequently <unk> Tarakasura . 
- 
- = = Iconography = = 
- 
- 
- = = = <unk> descriptions = = = 
- 
- The <unk> texts like the <unk> , the <unk> @-@ <unk> and the <unk> @-@ <unk> prescribe the iconography of the <unk> icon . 
- A young four @-@ armed Shiva and a beautiful two @-@ armed Parvati should be the central figures , performing the panigrahana ( " accepting the hand " ) ritual of a Hindu wedding , where the groom accepts the bride by taking her right hand in his . Shiva stands in <unk> posture , with one of his legs straight and firmly on the ground and the other one slightly bent . Shiva wears a <unk> @-@ <unk> ( a headdress formed of <unk> , matted hair ) on his head , adorned with a crescent moon . He wears serpents as <unk> , as a waist band and as a necklace . Various gold ornaments adorn his body . His back hands carry a <unk> ( axe ) and a <unk> ( deer ) . His front left hand makes the <unk> <unk> ( " blessing @-@ giving gesture " ) and his front right hand is stretched ahead to receive the hand of the bride . A dark @-@ <unk> Parvati , adorned in silk and gold finery , stands to the left of Shiva , <unk> with her head bent slightly as she extends her right arm to hold Shiva 's right hand . She holds a <unk> ( blue lotus ) in her left arm . 
- The god Vishnu and his consorts Lakshmi and <unk> should be represented as taking the place of Parvati 's parents in the ceremony . The four @-@ armed Vishnu should be shown in the background in between Shiva and Parvati ; in one of his front hands is a golden pot from which he <unk> water over the hands of the couple , symbolizing giving away the bride to the groom . He holds his usual attributes , the <unk> <unk> ( discus ) and a <unk> ( <unk> ) , in his back arms . Vishnu 's wives , dressed in royal finery , stand behind Parvati and hold her waist , symbolizing the handing over . 
- The four @-@ headed god Brahma should be shown seated on the ground in the foreground officiating as the wedding priest and making offerings to the <unk> ( sacred fire ) in the <unk> ( fire @-@ altar ) . The four @-@ armed god holds a <unk> and <unk> ( sacrificial <unk> and spoon ) in his front arms and a <unk> ( water @-@ pot ) and <unk> ( rosary ) in his back arms . The presence of the fire also indicates another important ritual of the Hindu wedding , <unk> ( " seven steps " ) where the bride and groom go around the fire seven times . 
- The figure of Shiva should be tallest , followed by that of Vishnu , Parvati and Vishnu 's wives . Various deities like the eight guardians of the directions , the eight <unk> , the seven <unk> goddesses , celestial beings such as <unk> and <unk> , sages and <unk> may be depicted standing with folded arms in the background . 
- 
- = = = Depictions = = = 
- 
- The complete scene of the wedding is not always depicted . Sometimes , only the principal participants are shown . Chola bronzes featuring only Shiva and Parvati as described in the wedding scene are found . South Indian sculptures , like those from <unk> , feature only the couple and Vishnu . In this configuration , Parvati is depicted in the centre with Vishnu on the left giving away her hand to Shiva on the right . Sometimes as in the Elephanta Caves , Parvati 's biological father <unk> , instead of Vishnu , is depicted giving away his daughter to Shiva . 
- Other deviations from the texts may appear in the attributes held by the divine couple . Parvati may hold a mirror , instead of the lotus . Shiva may be shown holding the <unk> ( trident ) and <unk> ( drum ) in his back hands . Regional variations in iconography may also occur . In Bengal , Shiva holds a <unk> ( knife ) , the ceremonial weapon that a Hindu groom from Bengal is expected to carry in a wedding . 
- Various wedding guests are depicted in the scene . Shiva 's attendant <unk> enjoy the festivities ; playing drums or dancing . The <unk> ( vehicles ) of the couple , Shiva 's bull Nandi and Parvati 's lion , are sometimes pictured in the scene . In vertical panel depictions , the celestial guests are often shown flying over Shiva and Parvati . While the gods are pictured flying on their respective <unk> ( e.g. Indra on his elephant , Agni on a ram ) and with their consorts ; semi @-@ divine beings like <unk> fly without vehicles . An anachronism found in a few Kalyanasundara scenes is the presence of the yet @-@ <unk> children of Shiva and Parvati , Ganesha and <unk> . Examples of this anachronism are found at the <unk> Cave of <unk> , and in a 9th @-@ century sculpture from Uttar Pradesh now housed in Los Angeles County Museum of Art . 
- 
- = = Worship = = 
- 
- Though Kalyanasundara icons are found across India in caves , sculptures and temple walls , no sect is centred on their worship . The icon is a popular feature on temple <unk> ( temple towers ) . 
- In South Indian Shiva temples like those in <unk> and <unk> , the bronze Kalyanasundara images of Shiva and Parvati are used in annual temple festivals to commemorate the divine union . Special halls are reserved for the annual ceremonial wedding of the deities . The Kalyanasundara bronzes are used only in this festival and kept unused the rest of the year . 
- At the <unk> <unk> Temple , women worship the Kalyanasundara sculpture to find husbands . 
- Kalyanasundara is worshipped in <unk> as god of <unk> . The idols of Lord <unk> in the form of Kalyanasundara and <unk> is worshipped by the unmarried men and women for early marriage . There are also people who have completed the marriages come here to complete rituals . 
- 
- 
- = SS El Sol = 
- 
- SS El Sol was a cargo ship built in 1910 for the Morgan Line , a subsidiary of the Southern Pacific Company . During World War I , she was known as <unk> El Sol in service with the United States Army and as USS El Sol ( <unk> @-@ <unk> ) in service with the United States Navy . At the end of war , she reverted to her original name of SS El Sol . 
- SS El Sol was one of four sister ships that carried cargo and a limited number of passengers for the Morgan Line . She was acquired by the U.S. Army after the United States entered World War I in April 1917 , and converted to carry horses and mules to France . In August 1918 , the ship was transferred to the U.S. Navy and continued transporting animals through the end of the war . 
- El Sol returned to the Morgan Line in 1919 and sailed with them until March 1927 , when she sank in New York Harbor after colliding with Sac City of the American Diamond Line . A portion of the ship 's cargo was salvaged but the ship was scrapped later in the year . 
- 
- = = Early career = = 
- 
- SS El Sol was a cargo and passenger steamship launched on 11 May 1910 by the Newport News Shipbuilding and Dry Dock Co. of Newport News , Virginia ( yard no . 130 ) , and delivered to the Atlantic division of the Morgan Line on 20 August 1910 . She was the first of four sister ships ; the other three being El Mundo , El Oriente , and El <unk> . El Sol was 6 @,@ 008 gross register tons ( <unk> ) , was 430 feet 1 inch ( 131 @.@ 09 m ) long by 53 feet 1 inch ( 16 @.@ 18 m ) abeam , and made 16 knots ( 30 km / h ) . The vessel sailed for the Morgan Line , the brand name of the Southern Pacific <unk> Company ( a subsidiary of the Southern Pacific Railroad ) , which employed her to carry cargo and a limited number of passengers between New York ; New Orleans , the eastern terminus of the Southern Pacific line ; and Galveston , Texas . 
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- = = World War I = = 
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- After the United States declared war on Germany in April 1917 , El Sol was <unk> by the United States Shipping Board ( <unk> ) on behalf of the United States Army , who designated her as an animal transport ship . Although there is no information about the specific conversion of El Sol , for other ships this typically meant that any second- or third @-@ class passenger accommodations had to be ripped out and replaced with ramps and <unk> for the horses and mules carried . 
- Sources do not reveal all of El Sol 's movements , but it is known that she departed on her second trip to France from Newport News on 10 February 1918 . <unk> 650 animals , El Sol headed to New York to join in a convoy with fellow Army transport <unk> , and U.S. Navy troop transports Covington , <unk> , <unk> , George Washington , President Grant , and Susquehanna . The convoy was escorted by Huntington when it departed from New York on 18 February , and arrived at Saint @-@ Nazaire on 4 March . Ten animals on board El Sol died or were destroyed during the crossing . 
- The next recorded activity of El Sol was on 3 August , when she was transferred from the Army to the U.S. Navy and commissioned the same day with Lieutenant Commander G. Anderson , <unk> , in command . El Sol was assigned to the Naval Overseas Transportation Service ( NOTS ) and continued to carry animals and supplies for the U.S. Army . 
- By August , each animal transport ship had a transport veterinarian and a permanent <unk> detachment to care for the animals while on board the ship . El Sol had also been joined by two of her sister ships , El Oriente and El <unk> in animal transport duty . She next departed Newport News on 23 August 1918 with 520 horses and mules on board . Upon arrival at Saint @-@ Nazaire on 19 September , El Sol delivered her complete load of animals ; none had died during the passage . After returning to the U.S. , El Sol departed again on 30 October with another 620 animals on board . <unk> in <unk> on 13 November — two days after the Armistice — she again delivered her full load of animals . 
- El Sol made two more <unk> for the NOTS over the next five months . While returning to the United States from her last NOTS sailing in late March 1919 , El Sol responded to distress calls from Scranton , a Navy troop transport which had a damaged rudder and was disabled . El Sol came to the aid of the stricken ship , which was 900 nautical miles ( 1 @,@ 700 km ) east of New York , to attempt to take her under tow . During the day on 28 March , Scranton attempted to run a <unk> to El Sol by sending a launch in the rolling seas , but it capsized , killing three men . Ultimately , El Sol stood by Scranton for over 40 hours until minesweeper Penguin arrived and took Scranton under tow . 
- At the conclusion of her last NOTS voyage on 3 April , El Sol was converted to a troop transport and assigned to the Navy 's Cruiser and Transport Force on 15 April . El Sol returned 2 @,@ <unk> healthy and wounded American servicemen from France in two <unk> . <unk> on 18 September , El Sol was returned to the Morgan Line soon after . 
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- = = Postwar civilian service = = 
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- El Sol resumed cargo service with the Morgan Line , where she had 8 years of uneventful operation . On 11 March 1927 , however , El Sol was inbound to New York with a $ 1 @,@ 000 @,@ 000 cargo of pig iron , copper , and <unk> of cotton . When a heavy fog settled over New York Harbor , Captain Charles H. Knowles ordered his ship to anchor until the fog cleared . As El Sol was being <unk> into position at about 07 : 45 , the American Diamond Line ship Sac City hit a glancing blow to El Sol , bounced off and then struck El Sol a second time , <unk> through El Sol 's plating . Sac City 's bow had some slight damage , but El Sol sank quickly in about 60 feet ( 18 m ) of water about a half @-@ mile ( 800 m ) south of the Statue of Liberty . Out of El Sol 's crew of 45 men , 44 were rescued ; the ship 's carpenter , who could not swim , was last seen clutching the ship 's rail as it went below the surface . 
- El Sol settled on the bottom at a 45 ° angle with only the tops of her masts protruding above the surface ; the Morgan Line house flag — a blue house flag with a red M inside a white star — still <unk> in the breeze . Even though the sunken vessel was not considered a hazard to navigation , in another fog two days later a <unk> Island <unk> nearly hit El Sol 's wreck . 
- In a hearings before the United States <unk> <unk> Service , Captain Knowles of El Sol and the captain of Sac City were both cleared of wrongdoing in the collision , and the blame was laid on the heavy fog . On 31 July , The New York Times reported on the cargo salvage operations still underway on the wreck of El Sol . In three months of continuous operations , the salvage company reported that about 35 % of the sunken ship 's cargo had been recovered . The <unk> of El Sol was scrapped later in 1927 . 
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- 
- = Hoover Dam = 
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- Hoover Dam , once known as Boulder Dam , is a concrete arch @-@ gravity dam in the Black Canyon of the Colorado River , on the border between the U.S. states of Nevada and Arizona . It was constructed between 1931 and 1936 during the Great Depression and was dedicated on September 30 , 1935 , by President Franklin D. Roosevelt . Its construction was the result of a massive effort involving thousands of workers , and cost over one hundred lives . The dam was controversially named after President Herbert Hoover . 
- Since about 1900 , the Black Canyon and nearby Boulder Canyon had been investigated for their potential to support a dam that would control floods , provide irrigation water and produce hydroelectric power . In 1928 , Congress authorized the project . The winning bid to build the dam was submitted by a consortium called Six Companies , Inc . , which began construction on the dam in early 1931 . Such a large concrete structure had never been built before , and some of the techniques were unproven . The <unk> summer weather and lack of facilities near the site also presented difficulties . Nevertheless , Six Companies turned over the dam to the federal government on March 1 , 1936 , more than two years ahead of schedule . 
- Hoover Dam <unk> Lake Mead , the largest reservoir in the United States by volume . The dam is located near Boulder City , Nevada , a municipality originally constructed for workers on the construction project , about 30 mi ( 48 km ) southeast of Las Vegas , Nevada . The dam 's generators provide power for public and private utilities in Nevada , Arizona , and California . Hoover Dam is a major tourist attraction ; nearly a million people tour the dam each year . The heavily travelled U.S. 93 ran along the dam 's crest until October 2010 , when the Hoover Dam Bypass opened . 
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- = = Background = = 
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- = = = Search for resources = = = 
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- As the United States developed the Southwest , the Colorado River was seen as a potential source of irrigation water . An initial attempt at diverting the river for irrigation purposes occurred in the late 1890s , when land speculator William Beatty built the <unk> Canal just north of the Mexican border ; the canal dipped into Mexico before running to a desolate area Beatty named the Imperial Valley . Though water from the Imperial Canal allowed for the widespread settlement of the valley , the canal proved expensive to maintain . After a catastrophic breach that caused the Colorado River to fill the <unk> Sea , the Southern Pacific Railroad spent $ 3 million in 1906 – 07 to stabilize the waterway , an amount it hoped <unk> would be <unk> by the Federal Government . Even after the waterway was stabilized , it proved unsatisfactory because of constant disputes with landowners on the Mexican side of the border . 
- As the technology of electric power transmission improved , the Lower Colorado was considered for its hydroelectric @-@ power potential . In 1902 , the Edison Electric Company of Los Angeles surveyed the river in the hope of building a 40 @-@ foot ( 12 m ) rock dam which could generate 10 @,@ 000 horsepower ( 7 @,@ 500 kW ) . However , at the time , the limit of transmission of electric power was 80 miles ( 130 km ) , and there were few customers ( mostly mines ) within that limit . Edison allowed land options it held on the river to lapse — including an option for what became the site of Hoover Dam . 
- In the following years , the Bureau of Reclamation ( BOR ) , known as the Reclamation Service at the time , also considered the Lower Colorado as the site for a dam . Service chief Arthur Powell Davis proposed using dynamite to collapse the walls of Boulder Canyon , 20 miles ( 32 km ) north of the eventual dam site , into the river . The river would carry off the smaller pieces of debris , and a dam would be built incorporating the remaining rubble . In 1922 , after considering it for several years , the Reclamation Service finally rejected the proposal , citing doubts about the unproven technique and questions as to whether it would in fact save money . 
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- = = = Planning and agreements = = = 
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- In 1922 , the Reclamation Service presented a report calling for the development of a dam on the Colorado River for flood control and electric power generation . The report was principally authored by Davis , and was called the Fall @-@ Davis report after Interior Secretary Albert Fall . The Fall @-@ Davis report cited use of the Colorado River as a federal concern , because the river 's basin covered several states , and the river eventually entered Mexico . Though the Fall @-@ Davis report called for a dam " at or near Boulder Canyon " , the Reclamation Service ( which was renamed the Bureau of Reclamation the following year ) found that canyon unsuitable . One potential site at Boulder Canyon was <unk> by a geologic fault ; two others were so narrow there was no space for a construction camp at the bottom of the canyon or for a spillway . The Service investigated Black Canyon and found it ideal ; a railway could be laid from the railhead in Las Vegas to the top of the dam site . Despite the site change , the dam project was referred to as the " Boulder Canyon Project " . 
- With little guidance on water allocation from the Supreme Court , proponents of the dam feared endless litigation . A Colorado attorney proposed that the seven states which fell within the river 's basin ( California , Nevada , Arizona , Utah , New Mexico , Colorado and Wyoming ) form an <unk> compact , with the approval of Congress . Such <unk> were authorized by Article I of the United States Constitution but had never been concluded among more than two states . In 1922 , representatives of seven states met with then @-@ Secretary of Commerce Herbert Hoover . Initial talks produced no result , but when the Supreme Court handed down the Wyoming v. Colorado decision <unk> the claims of the upstream states , they became anxious to reach an agreement . The resulting Colorado River Compact was signed on November 24 , 1922 . 
- Legislation to authorize the dam was introduced repeatedly by Representative Phil Swing ( R @-@ <unk> . ) and Senator <unk> Johnson ( R @-@ <unk> . ) , but representatives from other parts of the country considered the project as hugely expensive and one that would mostly benefit California . The 1927 Mississippi flood made Midwestern and Southern <unk> and <unk> more sympathetic toward the dam project . On March 12 , 1928 , the failure of the St. Francis Dam , constructed by the city of Los Angeles , caused a disastrous flood that killed up to 600 people . As that dam was a curved @-@ gravity type , similar in design to the arch @-@ gravity as was proposed for the Black Canyon dam , opponents claimed that the Black Canyon dam 's safety could not be guaranteed . Congress authorized a board of engineers to review plans for the proposed dam . The Colorado River Board found the project feasible , but warned that should the dam fail , every downstream Colorado River community would be destroyed , and that the river might change course and empty into the <unk> Sea . The Board cautioned : " To avoid such possibilities , the proposed dam should be constructed on conservative if not ultra @-@ conservative lines . " 
- On December 21 , 1928 President <unk> signed the bill authorizing the dam . The Boulder Canyon Project Act appropriated $ 165 million for the Hoover Dam along with the downstream Imperial Dam and All @-@ American Canal , a replacement for Beatty 's canal entirely on the U.S. side of the border . It also permitted the compact to go into effect when at least six of the seven states approved it . This occurred on March 6 , 1929 with Utah 's ratification ; Arizona did not approve it until 1944 . 
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- = = = Design , preparation and contracting = = = 
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- Even before Congress approved the Boulder Canyon Project , the Bureau of Reclamation was considering what kind of dam should be used . Officials eventually decided on a massive concrete arch @-@ gravity dam , the design of which was overseen by the Bureau 's chief design engineer John L. Savage . The <unk> dam would be thick at the bottom and thin near the top , and would present a convex face towards the water above the dam . The curving arch of the dam would transmit the water 's force into the abutments , in this case the rock walls of the canyon . The wedge @-@ shaped dam would be 660 ft ( 200 m ) thick at the bottom , narrowing to 45 ft ( 14 m ) at the top , leaving room for a highway connecting Nevada and Arizona . 
- On January 10 , 1931 , the Bureau made the bid documents available to interested parties , at five dollars a copy . The government was to provide the materials ; but the contractor was to prepare the site and build the dam . The dam was described in minute detail , covering 100 pages of text and 76 drawings . A $ 2 million bid bond was to accompany each bid ; the winner would have to post a $ 5 million performance bond . The contractor had seven years to build the dam , or penalties would ensue . 
- The <unk> Brothers , heads of the Utah Construction Company , were interested in bidding on the project , but lacked the money for the performance bond . They lacked sufficient resources even in combination with their longtime partners , Morrison @-@ <unk> , which employed the nation 's leading dam builder , Frank Crowe . They formed a joint venture to bid for the project with Pacific Bridge Company of Portland , Oregon ; Henry J. Kaiser & W. A. <unk> Company of San Francisco ; MacDonald & Kahn Ltd. of Los Angeles ; and the <unk> Shea Company of Portland , Oregon . The joint venture was called Six Companies , Inc. as <unk> and Kaiser were considered one company for purposes of 6 in the name . The name was descriptive and was an inside joke among the San Franciscans in the bid , where " Six Companies " was also a Chinese benevolent association in the city . There were three valid bids , and Six Companies ' bid of $ 48 @,@ 890 @,@ <unk> was the lowest , within $ 24 @,@ 000 of the <unk> government estimate of what the dam would cost to build , and five million dollars less than the next @-@ lowest bid . 
- The city of Las Vegas had lobbied hard to be the headquarters for the dam construction , closing its many <unk> when the decision maker , Secretary of the Interior Ray Wilbur came to town . Instead , Wilbur announced in early 1930 that a model city was to be built in the desert near the dam site . This town became known as Boulder City , Nevada . Construction of a rail line joining Las Vegas and the dam site began in September 1930 . 
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- = = Construction = = 
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- = = = Labor force = = = 
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- Soon after the dam was authorized , increasing numbers of unemployed people <unk> on southern Nevada . Las Vegas , then a small city of some 5 @,@ 000 , saw between 10 @,@ 000 and 20 @,@ 000 unemployed descend on it . A government camp was established for <unk> and other personnel near the dam site ; this soon became surrounded by a squatters ' camp . Known as <unk> , the camp was home to men hoping for work on the project , together with their families . Another camp , on the flats along the Colorado River , was officially called <unk> , but was known to its inhabitants as " <unk> " . When construction began , Six Companies hired large numbers of workers , with more than 3 @,@ 000 on the <unk> by 1932 and with employment peaking at 5 @,@ 251 in July 1934 . " Mongolian " ( Chinese ) labor was prevented by the construction contract , while the number of blacks employed by Six Companies never exceeded thirty , mostly lowest @-@ pay @-@ scale laborers in a segregated crew , who were issued separate water buckets . 
- As part of the contract , Six Companies , Inc. was to build Boulder City to house the workers . The original timetable called for Boulder City to be built before the dam project began , but President Hoover ordered work on the dam to begin in March 1931 rather than in October . The company built <unk> , attached to the canyon wall , to house 480 single men at what became known as River Camp . Workers with families were left to provide their own accommodations until Boulder City could be completed , and many lived in <unk> . The site of Hoover Dam <unk> extremely hot weather , and the summer of 1931 was especially <unk> , with the daytime high averaging 119 @.@ 9 ° F ( 48 @.@ 8 ° C ) . Sixteen workers and other <unk> residents died of heat <unk> between June 25 and July 26 , 1931 . 
- The Industrial Workers of the World ( <unk> or " <unk> " ) , though much @-@ reduced from their <unk> as militant labor organizers in the early years of the century , hoped to <unk> the Six Companies workers by <unk> on their discontent . They sent eleven organizers , several of whom were arrested by Las Vegas police . On August 7 , 1931 , the company cut wages for all tunnel workers . Although the workers sent away the organizers , not wanting to be associated with the " <unk> " , they formed a committee to represent them with the company . The committee drew up a list of demands that evening and presented them to Crowe the following morning . He was <unk> . The workers hoped that Crowe , the general superintendent of the job , would be sympathetic ; instead he gave a scathing interview to a newspaper , describing the workers as " <unk> " . 
- On the morning of the 9th , Crowe met with the committee and told them that management refused their demands , was stopping all work , and was laying off the entire work force , except for a few office workers and carpenters . The workers were given until 5 p.m. to <unk> the premises . Concerned that a violent confrontation was imminent , most workers took their <unk> and left for Las Vegas to await developments . Two days later , the remainder were talked into leaving by law enforcement . On August 13 , the company began hiring workers again , and two days later , the strike was called off . While the workers received none of their demands , the company guaranteed there would be no further reductions in wages . Living conditions began to improve as the first residents moved into Boulder City in late 1931 . 
- A second labor action took place in July 1935 , as construction on the dam wound down . When a Six Companies manager altered working times to force workers to take lunch on their own time , workers responded with a strike . <unk> by Crowe 's reversal of the lunch decree , workers raised their demands to include a $ 1 @-@ per @-@ day raise . The company agreed to ask the Federal government to supplement the pay , but no money was forthcoming from Washington . The strike ended . 
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- = = = River diversion = = = 
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- Before the dam could be built , the Colorado River needed to be diverted away from the construction site . To accomplish this , four diversion tunnels were driven through the canyon walls , two on the Nevada side and two on the Arizona side . These tunnels were 56 ft ( 17 m ) in diameter . Their combined length was nearly 16 @,@ 000 ft , or more than 3 miles ( 5 km ) . The contract required these tunnels to be completed by October 1 , 1933 , with a $ 3 @,@ 000 @-@ per @-@ day fine to be assessed for any delay . To meet the deadline , Six Companies had to complete work by early 1933 , since only in late fall and winter was the water level in the river low enough to safely divert . 
- <unk> began at the lower portals of the Nevada tunnels in May 1931 . Shortly afterward , work began on two similar tunnels in the Arizona canyon wall . In March 1932 , work began on lining the tunnels with concrete . First the base , or <unk> , was poured . <unk> cranes , running on rails through the entire length of each tunnel were used to place the concrete . The <unk> were poured next . <unk> sections of steel forms were used for the <unk> . Finally , using <unk> guns , the <unk> were filled in . The concrete lining is 3 feet ( 1 m ) thick , reducing the finished tunnel diameter to 50 ft ( 15 m ) . The river was diverted into the two Arizona tunnels on November 13 , 1932 ; the Nevada tunnels were kept in reserve for high water . This was done by exploding a temporary cofferdam protecting the Arizona tunnels while at the same time dumping rubble into the river until its natural course was blocked . 
- Following the completion of the dam , the entrances to the two outer diversion tunnels were sealed at the opening and halfway through the tunnels with large concrete <unk> . The downstream halves of the tunnels following the inner <unk> are now the main bodies of the spillway tunnels . The inner diversion tunnels were <unk> at approximately one @-@ third of their length , beyond which they now carry steel pipes connecting the intake towers to the power plant and outlet works . The inner tunnels ' outlets are equipped with gates that can be closed to drain the tunnels for maintenance . 
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- = = = <unk> , rock clearance and grout curtain = = = 
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- To protect the construction site from the Colorado River and to facilitate the river 's diversion , two cofferdams were constructed . Work on the upper cofferdam began in September 1932 , even though the river had not yet been diverted . The cofferdams were designed to protect against the possibility of the river flooding a site at which two thousand men might be at work , and their specifications were covered in the bid documents in nearly as much detail as the dam itself . The upper cofferdam was 96 ft ( 29 m ) high , and 750 feet ( 230 m ) thick at its base , as thicker than the dam itself . It contained 650 @,@ 000 cubic yards ( 500 @,@ 000 m3 ) of material . 
- When the cofferdams were in place and the construction site was drained of water , excavation for the dam foundation began . For the dam to rest on solid rock , it was necessary to remove accumulated erosion soils and other loose materials in the riverbed until sound bedrock was reached . Work on the foundation excavations was completed in June 1933 . During this excavation , approximately 1 @,@ 500 @,@ 000 cu yd ( 1 @,@ 100 @,@ 000 m3 ) of material was removed . Since the dam was an arch @-@ gravity type , the side @-@ walls of the canyon would bear the force of the <unk> lake . Therefore , the side @-@ walls were excavated too , to reach virgin rock as weathered rock might provide pathways for water seepage . 
- The men who removed this rock were called " high scalers " . While suspended from the top of the canyon with ropes , the high @-@ scalers climbed down the canyon walls and removed the loose rock with <unk> and dynamite . Falling objects were the most common cause of death on the dam site ; the high scalers ' work thus helped ensure worker safety . One high <unk> was able to save life in a more direct manner : when a government <unk> lost his grip on a safety line and began tumbling down a slope towards almost certain death , a high <unk> was able to intercept him and pull him into the air . The construction site had , even then , become a magnet for tourists ; the high scalers were prime attractions and showed off for the <unk> . The high scalers received considerable media attention , with one worker dubbed the " Human <unk> " for swinging co @-@ workers ( and , at other times , cases of dynamite ) across the canyon . To protect themselves against falling objects , some high scalers took cloth hats and dipped them in tar , allowing them to harden . When workers wearing such headgear were struck hard enough to inflict broken jaws , they sustained no skull damage , Six Companies ordered thousands of what initially were called " hard boiled hats " ( later " hard hats " ) and strongly encouraged their use . 
- The cleared , underlying rock foundation of the dam site was reinforced with grout , called a grout curtain . <unk> were driven into the walls and base of the canyon , as deep as 150 feet ( 46 m ) into the rock , and any cavities encountered were to be filled with grout . This was done to stabilize the rock , to prevent water from seeping past the dam through the canyon rock , and to limit " <unk> " — upward pressure from water seeping under the dam . The workers were under severe time constraints due to the beginning of the concrete pour , and when they encountered hot springs or cavities too large to readily fill , they moved on without resolving the problem . A total of 58 of the 393 holes were incompletely filled . After the dam was completed and the lake began to fill , large numbers of significant leaks into the dam caused the Bureau of Reclamation to look into the situation . It found that the work had been incompletely done , and was based on less than a full understanding of the canyon 's geology . New holes were drilled from inspection galleries inside the dam into the surrounding bedrock . It took nine years ( 1938 – 47 ) under relative secrecy to complete the supplemental grout curtain . 
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- = = = Concrete = = = 
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- The first concrete was poured into the dam on June 6 , 1933 , 18 months ahead of schedule . Since concrete <unk> and contracts as it <unk> , the potential for uneven cooling and contraction of the concrete posed a serious problem . Bureau of Reclamation engineers calculated that if the dam was built in a single continuous pour , the concrete would take 125 years to cool and the resulting stresses would cause the dam to crack and crumble . Instead , the ground where the dam was to rise was marked with <unk> , and concrete blocks in columns were poured , some as large as 50 ft square ( 15 m ) and 5 feet ( 1 @.@ 5 m ) high . Each five @-@ foot form contained a series of 1 @-@ inch ( 25 mm ) steel pipes through which first cool river water , then later ice @-@ cold water from a <unk> plant was run . When an individual block had cured and had stopped contracting , the pipes were filled with grout . <unk> was also used to fill the hairline spaces between columns , which were grooved to increase the strength of the joins . 
- The concrete was delivered in huge steel buckets 7 feet high ( 2 @.@ 1 m ) and almost 7 feet in diameter ; Crowe was awarded two patents for their design . These buckets , which weighed 20 short tons ( 18 t ) when full , were filled at two massive concrete plants on the Nevada side , and were delivered to the site in special <unk> . The buckets were then suspended from aerial <unk> , which were used to deliver the bucket to a specific column . As the required grade of aggregate in the concrete differed depending on placement in the dam ( from pea @-@ sized gravel to 9 @-@ inch or 23 cm stones ) , it was vital that the bucket be <unk> to the proper column . When the bottom of the bucket opened up , <unk> 8 cu yd ( 6 @.@ 1 m3 ) of concrete , a team of men worked it throughout the form . Although there are myths that men were caught in the pour and are <unk> in the dam to this day , each bucket only deepened the concrete in a form by an inch , and Six Companies engineers would not have permitted a flaw caused by the presence of a human body . 
- A total of 3 @,@ 250 @,@ 000 cubic yards ( 2 @,@ 480 @,@ 000 m3 ) of concrete was used in the dam before concrete pouring ceased on May 29 , 1935 . In addition , 1 @,@ 110 @,@ 000 cu yd ( 850 @,@ 000 m3 ) were used in the power plant and other works . More than <unk> miles ( <unk> km ) of cooling pipes were placed within the concrete . Overall , there is enough concrete in the dam to pave a two @-@ lane highway from San Francisco to New York . Concrete cores were removed from the dam for testing in 1995 ; they showed that " Hoover Dam 's concrete has continued to slowly gain strength " and the dam is composed of a " durable concrete having a <unk> strength exceeding the range typically found in normal mass concrete " . Hoover Dam concrete is not subject to alkali – <unk> reaction ( <unk> ) as the Hoover Dam builders happened to use <unk> aggregate , unlike that at downstream Parker Dam , where <unk> has caused measurable deterioration . 
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- = = = Dedication and completion = = = 
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- With most work finished on the dam itself ( the powerhouse remained uncompleted ) , a formal dedication ceremony was arranged for September 30 , 1935 , to coincide with a western tour being made by President Franklin D. Roosevelt . The morning of the dedication , it was moved forward three hours from 2 p.m. Pacific time to 11 a.m. ; this was done because Secretary of the Interior Harold L. Ickes had reserved a radio slot for the President for 2 p.m. but officials did not realize until the day of the ceremony that the slot was for 2 p.m. Eastern Time . Despite the change in the ceremony time , and temperatures of 102 ° F ( 39 ° C ) , 10 @,@ 000 people were present for the President 's speech in which he avoided mentioning the name of former President Hoover , who was not invited to the ceremony . To mark the occasion , a three @-@ cent stamp was issued by the United States Post Office Department — bearing the name " Boulder Dam " , the official name of the dam between 1933 and 1947 . After the ceremony , Roosevelt made the first visit by any American president to Las Vegas . 
- Most work had been completed by the dedication , and Six Companies negotiated with the government through late 1935 and early 1936 to settle all claims and arrange for the formal transfer of the dam to the Federal Government . The parties came to an agreement and on March 1 , 1936 , Secretary Ickes formally accepted the dam on behalf of the government . Six Companies was not required to complete work on one item , a concrete <unk> for one of the bypass tunnels , as the tunnel had to be used to take in irrigation water until the powerhouse went into operation . 
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- = = = Construction deaths = = = 
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- There were 112 deaths associated with the construction of the dam . The first was J. G. <unk> , a surveyor who drowned on December 20 , 1922 , while looking for an ideal spot for the dam . <unk> @-@ six of the deaths occurred during construction at the site . Of the 112 fatalities , 91 were Six Companies employees , three were BOR employees , and one was a visitor to the site , with the remainder employees of various contractors not part of Six Companies . 
- Not included in the official fatalities number were deaths that were recorded as pneumonia . Workers alleged that this diagnosis was a cover for death from carbon monoxide poisoning , brought on by the use of gasoline @-@ fueled vehicles in the diversion tunnels , and a classification used by Six Companies to avoid paying compensation claims . The site 's diversion tunnels frequently reached 140 ° F ( 60 ° C ) , enveloped in thick plumes of vehicle exhaust gases . A total of 42 workers were recorded as having died from pneumonia ; none were listed as having died from carbon monoxide poisoning . No deaths of non @-@ workers from pneumonia were recorded in Boulder City during the construction period . 
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- = = = Architectural style = = = 
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- The initial plans for the facade of the dam , the power plant , the outlet tunnels and ornaments clashed with the modern look of an arch dam . The Bureau of Reclamation , more concerned with the dam 's functionality , adorned it with a Gothic @-@ inspired <unk> and eagle statues . This initial design was criticized by many as being too plain and unremarkable for a project of such immense scale , so Los Angeles @-@ based architect Gordon B. Kaufmann , then the supervising architect to the Bureau of Reclamation , was brought in to redesign the exteriors . Kaufmann greatly streamlined the design , and applied an elegant Art Deco style to the entire project . He designed <unk> turrets rising seamlessly from the dam face and clock faces on the intake towers set for the time in Nevada and Arizona — the two states are in different time zones , but as Arizona does not observe <unk> Saving Time , the clocks display the same time for more than half the year . 
- At Kaufmann 's request , Denver artist Allen <unk> True was hired to handle the design and decoration of the walls and floors of the new dam . True 's design scheme incorporated motifs of the <unk> and <unk> tribes of the region . Although some initially were opposed to these designs , True was given the go @-@ ahead and was officially appointed consulting artist . With the assistance of the National Laboratory of Anthropology , True researched authentic decorative motifs from Indian sand paintings , textiles , <unk> and ceramics . The images and colors are based on Native American visions of rain , lightning , water , clouds , and local animals — lizards , serpents , birds — and on the Southwestern landscape of stepped <unk> . In these works , which are integrated into the <unk> and interior halls of the dam , True also reflected on the machinery of the operation , making the symbolic patterns appear both ancient and modern . 
- With the agreement of Kaufmann and the engineers , True also devised an innovative color @-@ coding for the pipes and machinery , which was implemented throughout all BOR projects . True 's consulting artist job lasted through 1942 ; it was extended so he could complete design work for the Parker , <unk> and Grand <unk> dams and power plants . True 's work on the Hoover Dam was <unk> referred to in a poem published in The New Yorker , part of which read , " lose the spark , and justify the dream ; but also worthy of remark will be the color scheme " . 
- <unk> Kaufmann and True 's work , the Norwegian @-@ born , naturalized American sculptor <unk> <unk> Hansen designed many of the sculptures on and around the dam . His works include the monument of dedication plaza , a plaque to <unk> the workers killed and the bas @-@ reliefs on the elevator towers . In his words , Hansen wanted his work to express " the immutable calm of intellectual resolution , and the enormous power of trained physical strength , equally enthroned in placid triumph of scientific accomplishment " , because " [ t ] he building of Hoover Dam belongs to the <unk> of the daring . " Hansen 's dedication plaza , on the Nevada <unk> , contains a sculpture of two winged figures flanking a flagpole . 
- Surrounding the base of the monument is a <unk> floor embedded with a " star map " . The map depicts the Northern Hemisphere sky at the moment of President Roosevelt 's dedication of the dam . This is intended to help future astronomers , if necessary , calculate the exact date of dedication . The 30 @-@ foot @-@ high ( 9 @.@ 1 m ) bronze figures , dubbed " <unk> Figures of the Republic " , were each formed in a continuous pour . To put such large bronzes into place without <unk> the highly polished bronze surface , they were placed on ice and guided into position as the ice melted . Hansen 's bas @-@ relief on the Nevada elevator tower depicts the benefits of the dam : flood control , navigation , irrigation , water storage , and power . The bas @-@ relief on the Arizona elevator depicts , in his words , " the <unk> of those Indian tribes who have inhabited mountains and plains from ages distant . " 
- 
- = = Operation = = 
- 
- 
- = = = Power plant and water demands = = = 
- 
- Excavation for the powerhouse was carried out simultaneously with the excavation for the dam foundation and abutments . A U @-@ shaped structure located at the downstream toe of the dam , its excavation was completed in late 1933 with the first concrete placed in November 1933 . <unk> of Lake Mead began February 1 , 1935 , even before the last of the concrete was poured that May . The powerhouse was one of the projects uncompleted at the time of the formal dedication on September 30 , 1935 — a crew of 500 men remained to finish it and other structures . To make the powerhouse roof <unk> , it was constructed of layers of concrete , rock , and steel with a total thickness of about 3 @.@ 5 feet ( 1 @.@ 1 m ) , topped with layers of sand and tar . 
- In the latter half of 1936 , water levels in Lake Mead were high enough to permit power generation , and the first three <unk> <unk> built Francis turbine @-@ generators , all on the Nevada side , began operating . In March 1937 , one more Nevada generator went online and the first Arizona generator by August . By September 1939 , four more generators were operating , and the dam 's power plant became the largest <unk> facility in the world . The final generator was not placed in service until 1961 , bringing the maximum generating capacity to 1 @,@ 345 megawatts at the time . Original plans called for 16 large generators , eight on each side of the river , but two smaller generators were installed instead of one large one on the Arizona side for a total of 17 . The smaller generators were used to serve smaller communities at a time when the output of each generator was dedicated to a single municipality , before the dam 's total power output was placed on the grid and made <unk> <unk> . The present contracts for the sale of electricity <unk> in 2017 . 
- Before water from Lake Mead reaches the turbines , it enters the intake towers and then four gradually narrowing <unk> which funnel the water down towards the powerhouse . The <unk> provide a maximum hydraulic head ( water pressure ) of 590 ft ( 180 m ) as the water reaches a speed of about 85 mph ( 140 km / h ) . The entire flow of the Colorado River passes through the turbines . The spillways and outlet works ( jet @-@ flow gates ) are rarely used . The jet @-@ flow gates , located in concrete structures 180 feet ( 55 m ) above the river , and also at the outlets of the inner diversion tunnels at river level , may be used to divert water around the dam in emergency or flood conditions , but have never done so , and in practice are only used to drain water from the <unk> for maintenance . Following an <unk> project from 1986 to 1993 , the total gross power rating for the plant , including two 2 @.@ 4 <unk> <unk> turbine @-@ generators that power Hoover Dam 's own operations is a maximum capacity of <unk> megawatts . The annual generation of Hoover Dam varies . The maximum net generation was 10 @.@ 348 TWh in 1984 , and the minimum since 1940 was 2 @.@ 648 TWh in 1956 . The average power generated was 4 @.@ 2 TWh / year for 1947 @-@ 2008 . In 2015 , the dam generated 3 @.@ 6 TWh . To lower the minimum power pool elevation from 1 @,@ 050 to 950 feet ( 320 to 290 m ) , five wide @-@ head turbines , designed to work efficiently with less flow , will be online by 2017 . Due to lack of water , the dam mostly provides power only during periods of peak demand . 
- Control of water was the primary concern in the building of the dam . Power generation has allowed the dam project to be self @-@ sustaining : proceeds from the sale of power repaid the 50 @-@ year construction loan , and those revenues also finance the <unk> @-@ dollar yearly maintenance budget . Power is generated in step with and only with the release of water in response to downstream water demands . 
- Lake Mead and downstream releases from the dam also provide water for both municipal and irrigation uses . Water released from the Hoover Dam eventually reaches several canals . The Colorado River <unk> and Central Arizona Project branch off Lake <unk> while the All @-@ American Canal is supplied by the Imperial Dam . In total , water from the Lake Mead serves 18 million people in Arizona , Nevada and California and supplies the irrigation of over 1 @,@ 000 @,@ 000 acres ( 400 @,@ 000 ha ) of land . 
- 
- = = = = Power distribution = = = = 
- 
- Electricity from the dam 's powerhouse was originally sold pursuant to a fifty @-@ year contract , authorized by Congress in 1934 , which ran from 1937 to 1987 . In 1984 , Congress passed a new statute which set power <unk> from the dam from 1987 to 2017 . The powerhouse was run under the original authorization by the Los Angeles Department of Water and Power and Southern California Edison ; in 1987 , the Bureau of Reclamation assumed control . In 2011 , Congress enacted legislation extending the current contracts until <unk> , after setting aside 5 % of Hoover Dam 's power for sale to Native American tribes , electric <unk> , and other entities . The new arrangement will begin in 2017 . The Bureau of Reclamation reports that the energy generated is allocated as follows : 
- 
- = = = <unk> = = = 
- 
- The dam is protected against over @-@ topping by two spillways . The spillway entrances are located behind each dam <unk> , running roughly parallel to the canyon walls . The spillway entrance arrangement forms a classic side @-@ flow weir with each spillway containing four 100 @-@ foot @-@ long ( 30 m ) and 16 @-@ foot @-@ wide ( 4 @.@ 9 m ) steel @-@ drum gates . Each gate weighs 5 @,@ 000 @,@ 000 pounds ( 2 @,@ 300 @,@ 000 kg ) and can be operated manually or automatically . Gates are raised and lowered depending on water levels in the reservoir and flood conditions . The gates are unable to entirely prevent water from entering the spillways but are able to maintain an extra 16 ft ( 4 @.@ 9 m ) of lake level . Water flowing over the spillways drops dramatically into 600 @-@ foot @-@ long ( 180 m ) , 50 @-@ foot @-@ wide ( 15 m ) spillway tunnels before connecting to the outer diversion tunnels , and <unk> the main river channel below the dam . This complex spillway entrance arrangement combined with the approximate 700 @-@ foot ( 210 m ) elevation drop from the top of the reservoir to the river below was a difficult engineering problem and posed numerous design challenges . Each spillway 's capacity of 200 @,@ 000 cu ft / s ( 5 @,@ 700 m3 / s ) was <unk> verified in post @-@ construction tests in 1941 . 
- The large spillway tunnels have been used only twice , for testing in 1941 and because of flooding in 1983 . During both times , when inspecting the tunnels after the spillways were used , engineers found major damage to the concrete <unk> and underlying rock . The 1941 damage was attributed to a slight <unk> of the tunnel <unk> ( or base ) , which caused <unk> , a phenomenon in fast @-@ flowing liquids in which vapor bubbles collapse with explosive force . In response to this finding , the tunnels were patched with special heavy @-@ duty concrete and the surface of the concrete was polished mirror @-@ smooth . The spillways were modified in 1947 by adding flip buckets , which both slow the water and decrease the spillway 's effective capacity , in an attempt to eliminate conditions thought to have contributed to the 1941 damage . The 1983 damage , also due to <unk> , led to the installation of <unk> in the spillways . Tests at Grand <unk> Dam showed that the technique worked , in principle . 
- 
- = = = <unk> and tourism = = = 
- 
- There are two lanes for automobile traffic across the top of the dam , which formerly served as the Colorado River crossing for U.S. Route 93 . In the wake of the September 11 , 2001 terrorist attacks , authorities expressed security concerns and the Hoover Dam Bypass project was expedited . <unk> the completion of the bypass , restricted traffic was permitted over Hoover Dam . Some types of vehicles were inspected prior to crossing the dam while semi @-@ trailer trucks , buses carrying luggage , and enclosed @-@ box trucks over 40 ft ( 12 m ) long were not allowed on the dam at all , and were diverted to U.S. Route 95 or Nevada State Routes 163 / 68 . The four @-@ lane Hoover Dam Bypass opened on October 19 , 2010 . It includes a composite steel and concrete arch bridge , the Mike O <unk> – Pat <unk> Memorial Bridge , 1 @,@ 500 ft ( 460 m ) downstream from the dam . With the opening of the bypass , through traffic is no longer allowed across Hoover Dam , dam visitors are allowed to use the existing roadway to approach from the Nevada side and cross to parking lots and other facilities on the Arizona side . 
- Hoover Dam opened for tours in 1937 after its completion , but following Japan 's attack on Pearl Harbor on December 7 , 1941 , it was closed to the public when the United States entered World War II , during which only authorized traffic , in convoys , was permitted . After the war , it reopened September 2 , 1945 , and by 1953 , annual attendance had risen to 448 @,@ 081 . The dam closed on November 25 , 1963 and March 31 , 1969 , days of mourning in <unk> of Presidents Kennedy and Eisenhower . In 1995 , a new visitors ' center was built , and the following year , visits exceeded one million for the first time . The dam closed again to the public on September 11 , 2001 ; modified tours were resumed in December and a new " Discovery Tour " was added the following year . Today , nearly a million people per year take the tours of the dam offered by the Bureau of Reclamation . Increased security concerns by the government have led to most of the interior structure being inaccessible to tourists . As a result , few of True 's decorations can now be seen by visitors . 
- 
- = = Environmental impact = = 
- 
- The changes in water flow and use caused by Hoover Dam 's construction and operation have had a large impact on the Colorado River Delta . The construction of the dam has been credited as causing the decline of this estuarine ecosystem . For six years after the construction of the dam , while Lake Mead filled , virtually no water reached the mouth of the river . The delta 's estuary , which once had a freshwater @-@ <unk> mixing zone stretching 40 miles ( 64 km ) south of the river 's mouth , was turned into an <unk> estuary where the level of salinity was higher close to the river 's mouth . 
- The Colorado River had experienced natural flooding before the construction of the Hoover Dam . The dam eliminated the natural flooding , which threatened many species adapted to the flooding , including both plants and animals . The construction of the dam devastated the populations of native fish in the river downstream from the dam . Four species of fish native to the Colorado River , the <unk> <unk> , Colorado <unk> , <unk> <unk> , and Razorback <unk> , are listed as endangered . 
- 
- = = Naming controversy = = 
- 
- During the years of lobbying leading up to the passage of legislation authorizing the dam in 1928 , the press generally referred to the dam as " Boulder Dam " or as " Boulder Canyon Dam " , even though the proposed site had shifted to Black Canyon . The Boulder Canyon Project Act of 1928 ( <unk> ) never mentioned a proposed name or title for the dam . The <unk> merely allows the government to " construct , operate , and maintain a dam and incidental works in the main stream of the Colorado River at Black Canyon or Boulder Canyon " . 
- When Secretary Wilbur spoke at the ceremony starting the building of the railway between Las Vegas and the dam site on September 17 , 1930 , he named the dam " Hoover Dam " , citing a tradition of naming dams after Presidents , though none had been so honored during their terms of office . Wilbur justified his choice on the ground that Hoover was " the great engineer whose vision and persistence ... has done so much to make [ the dam ] possible " . One writer complained in response that " the Great Engineer had quickly drained , <unk> , and <unk> the country . " 
- After Hoover 's election defeat in 1932 and the accession of the Roosevelt administration , Secretary Ickes ordered on May 13 , 1933 that the dam be referred to as " Boulder Dam " . Ickes stated that Wilbur had been <unk> in naming the dam after a sitting president , that Congress had never ratified his choice , and that it had long been referred to as Boulder Dam . Unknown to the general public , Attorney General Homer Cummings informed Ickes that Congress had indeed used the name " Hoover Dam " in five different bills appropriating money for construction of the dam . The official status this conferred to the name " Hoover Dam " had been noted on the floor of the House of Representatives by Congressman Edward T. Taylor of Colorado on December 12 , 1930 , but was likewise ignored by Ickes . 
- When Ickes spoke at the dedication ceremony on September 30 , 1935 , he was determined , as he recorded in his diary , " to try to nail down for good and all the name Boulder Dam . " At one point in the speech , he spoke the words " Boulder Dam " five times within thirty seconds . Further , he suggested that if the dam were to be named after any one person , it should be for California Senator <unk> Johnson , a lead sponsor of the authorizing legislation . Roosevelt also referred to the dam as Boulder Dam , and the Republican @-@ leaning Los Angeles Times , which at the time of Ickes ' name change had run an editorial cartoon showing Ickes <unk> <unk> away at an enormous sign " <unk> <unk> , " <unk> it showing Roosevelt reinforcing Ickes , but having no greater success . 
- In the following years , the name " Boulder Dam " failed to fully take hold , with many Americans using both names <unk> and <unk> divided as to which name should be printed . Memories of the Great Depression faded , and Hoover to some extent rehabilitated himself through good works during and after World War II . In 1947 , a bill passed both Houses of Congress unanimously restoring the name " Hoover Dam . " Ickes , who was by then a private citizen , opposed the change , stating , " I didn 't know Hoover was that small a man to take credit for something he had nothing to do with . " 
- 
- 
- = Tropical Storm Abby ( 1964 ) = 
- 
- Tropical Storm Abby was an exceptionally small tropical cyclone that had minor effects across Southeast Texas in early August 1964 . Forming as a tropical depression out of a trough south of Louisiana on August 5 , the system moved generally westward . It was not until August 7 that the system began to organize . That day , an eye rapidly formed within the system and it became a tropical storm just 60 mi ( 95 km ) southeast of Galveston , Texas . Soon thereafter , a weather reconnaissance plane reported a barometric pressure of 1000 mbar ( hPa ; 29 @.@ 53 inHg ) at the storm 's center . Around 18 : 00 UTC ( 1 : 00 p.m. CDT ) , the newly named Abby attained peak winds of 65 mph ( 100 km / h ) . It subsequently made landfall near Matagorda , Texas four hours later . Once onshore gradual weakening ensued , though a brief period of re @-@ organization delayed its <unk> . Abby degenerated into an area of showers on August 8 southwest of San Antonio , Texas . 
- Prior to Abby 's landfall in Texas , gale warnings were issued for the coast and residents on Matagorda Island were evacuated . Overall , the storm 's impacts were limited due to its small size . Only two structures sustained damage in Matagorda , one being destroyed by a possible tornado , and the remaining effects resulted from flooding in Jackson and Victoria Counties . Total damage from the storm was estimated at $ 750 @,@ 000 with the majority stemming from crops . 
- 
- = = Meteorological history = = 
- 
- On August 5 , 1964 , a weak trough emerged from Florida over the northeastern Gulf of Mexico . According to the Atlantic hurricane database ( referred to as HURDAT ) , the system developed into a tropical depression by 18 : 00 UTC ( 1 : 00 p.m. CDT ) that day , with its center located roughly 160 mi ( 260 km ) south @-@ southeast of the Mississippi River Delta . Moving generally west , the system exhibited no signs of further development as it neared the Texas coastline . However , on August 7 , radar images from <unk> , Galveston , Lake Charles , and Victoria showed an abrupt increase in organization . During the afternoon hours , banding features consolidated around a developing eye and weather reconnaissance planes were dispatched to the system . <unk> initially mistook the eye as a hook echo @-@ type feature at the end of a <unk> line . It is estimated that the depression became a tropical storm by 12 : 00 UTC ( 7 : 00 a.m. CDT ) , roughly 60 mi ( 95 km ) southeast of Galveston , Texas . Operationally , the system was not even monitored as a depression this time , with the classification and naming of Tropical Storm Abby occurring at 16 : 00 UTC ( 11 : 00 a.m. CDT ) on August 7 . 
- Abby was an unusually small storm , with its entire circulation being far less than 100 mi ( 160 km ) in diameter . Around 16 : 00 UTC ( 11 : 00 a.m. CDT ) on August 7 , reconnaissance measured a central barometric pressure of 1000 mbar ( hPa ; 29 @.@ 53 inHg ) within Abby , the lowest in relation to the system . They also reported peak winds of 85 mph ( 140 km / h ) in squalls , which would rank as a Category 1 hurricane on the modern @-@ day Saffir – Simpson hurricane wind scale . These winds were seen to be an <unk> by forecasters and discarded , however . The system attained its maximum winds of 65 mph ( 100 km / h ) by 18 : 00 UTC ( 1 : 00 p.m. CDT ) and subsequently made landfall just northeast of Matagorda , Texas at 22 : 00 UTC ( 5 : 00 p.m. CDT ) . <unk> <unk> , a forecaster at the Weather Bureau ( now known as the National Weather Service ) in Galveston , described the storm as a " perfectly miniature hurricane . " Despite moving onshore , Abby 's core continued to organize and it developed a closed eyewall roughly three hours later . Thereafter the storm began to gradually weaken , passing over Edna around 02 : 00 UTC on August 8 ( 9 : 00 p.m. CDT on August 7 ) . <unk> to a depression hours later , Abby ultimately degenerated into an area of showers and dissipated southwest of San Antonio by 12 : 00 UTC ( 7 : 00 a.m. CDT ) . 
- 
- = = Preparations and impact = = 
- 
- Owing to the abrupt nature of Abby 's development on August 7 , residents had little time to prepare for the storm . According to <unk> <unk> , " in the absence of aircraft reconnaissance and radar information , two reasonably new tools in storm detection , Abby could well have formed and approached the shore before knowledge of a tropical storm was gained . " Gale warnings were raised from Galveston to San Antonio immediately following the storm 's formation and were kept in place through the morning of August 8 . <unk> orders were issued for Matagorda Island and most complied , with some deciding to remain at their homes . Personnel at Matagorda Island Air Force Base were evacuated to Victoria . Red Cross facilities were <unk> and public shelters were opened in the area . At the mouth of the Colorado River , <unk> vessels were tied down with extra rope . Within the Weather Bureau , members of the Galveston office were the first to notice the system and relayed to the other offices in the area to <unk> their <unk> to follow the system . 
- Abby 's small size resulted in its effects being limited to areas within the immediate track . Sustained winds of 45 mph ( 75 km / h ) with gusts to 65 mph ( 100 km / h ) were measured by the Army Corps of Engineers in Matagorda . Near the town , a possible tornado unroofed a barn and tossed the structure 225 ft ( 69 m ) ; this building previously had withstood the effects of Hurricane Carla in 1961 . Winds estimated at 50 mph ( 80 km / h ) tore part of the roof off a fishing warehouse in Matagorda itself . Along the coast , tides ranged from 2 to 4 ft ( 0 @.@ 61 to 1 @.@ 22 m ) above normal from Matagorda to Freeport . Four men and five dogs required rescue after their vessel became stranded on the Colorado River . 
- Heavy rains accompanied the system across Texas . Most areas along the immediate track received at least 3 to 5 in ( 76 to 127 mm ) of rain , with a peak value of 6 @.@ 14 in ( 156 mm ) recorded at the Victoria International Airport . The hardest hit areas were in Jackson and Victoria counties where the heaviest rains fell . In these areas , flooding and strong winds damaged the cotton and rice crops ; however , effects of the rice crop were more limited due to losses from earlier storms as well as ongoing harvesting . Some flooding also took place across the <unk> River watershed , but no damage resulted . Overall , property damage was estimated at $ 150 @,@ 000 while agricultural losses reached $ 600 @,@ 000 . 
- 
- 
- = 7 Independent Company ( Rhodesia ) = 
- 
- 7 Independent Company ( 7 <unk> <unk> ; French : <unk> <unk> <unk> ) was a short @-@ lived company of francophone volunteers in the Rhodesian Army during the Rhodesian Bush War . Numbering about 200 men at its peak , it was unique in the history of the Rhodesian Army as an exclusively expatriate unit . It existed between November 1977 and May 1978 as a company in the 1st Battalion , the Rhodesia Regiment , and served two counter @-@ insurgency tours on Operation Hurricane in north @-@ eastern Rhodesia ( today Zimbabwe ) . 
- During the Bush War , the Rhodesian Army augmented its ranks with foreign volunteers , who were accepted into regular regiments with the same pay and conditions of service as locals . Most foreign recruits enlisted in the Rhodesian Light Infantry ( RLI ) , which launched an overseas recruitment programme in 1974 , but required successful applicants to speak good English . The Army attempted to alleviate the strain on its troops during late 1977 by recruiting French @-@ speakers as well , and formed a designated company in the Rhodesia Regiment for them . The regiment already had six independent companies , so the francophone unit became 7 Independent Company . 
- The company 's men , a mixture of former French paratroopers , ex @-@ Foreign Legionnaires and young adventurers , had trouble from the start integrating with the Rhodesian forces , and became <unk> by the respective ranks they were given in the Rhodesian Army . In an attempt to raise their morale and create a strong <unk> de corps , the Army issued them <unk> <unk> backed with the French tricolour and allowed them to raise the flag of France alongside that of Rhodesia each morning . Apparently under the impression that they had signed up as highly paid mercenaries , many of the French <unk> returned home after their first bush trip , unhappy to have received no more money than a regular Rhodesian soldier . 
- On operations their performance was generally below par , but the Frenchmen were involved in some successful actions during February and early March 1978 . Their oppressive treatment of the black villagers they encountered made them very unpopular in the operational area . The <unk> quickly deemed the experiment a failure and following a series of disasters for the company during the latter part of its second tour , including two friendly fire incidents and several fatalities , it was disbanded in May 1978 . Forces led by one of its members , Bob Denard , later that month executed a coup d 'état in the Comoros with French , Rhodesian and South African governmental support . 
- 
- = = Background = = 
- 
- Following a dispute with the British government regarding terms for the granting of full independence , the predominantly white minority government of the self @-@ governing colony of Rhodesia ( or Southern Rhodesia ) , led by Ian Smith , <unk> declared independence on 11 November 1965 . Britain and the United Nations refused to recognise this and each imposed economic sanctions on Rhodesia . Meanwhile , the country 's two most prominent communist @-@ backed black nationalist groups , the Zimbabwe African National Union and the Zimbabwe African People 's Union , mobilised their respective guerrilla armies , the Zimbabwe African National Liberation Army ( ZANLA ) and the Zimbabwe People 's Revolutionary Army ( <unk> ) , for what they called the " Second <unk> " , with the goal of <unk> the government and introducing black majority rule . 
- The Rhodesian Bush War was the result , beginning in earnest on 21 December 1972 when ZANLA attacked <unk> and <unk> <unk> near Centenary in the country 's north @-@ east . The Rhodesian Security Forces launched Operation Hurricane in response and fought back strongly , reducing the number of guerrillas active within the country to under 300 by December 1974 . In the period October – November 1974 , they killed more nationalist fighters than in the previous two years combined . However , a South African @-@ brokered ceasefire , which the security forces respected and the insurgents ignored , helped the nationalists to win back ground . By 1977 there were 2 @,@ 500 guerrillas operating in Rhodesia , with several times that number in training abroad . 
- The Rhodesian Army , though mostly made up of local men , also included some foreign volunteers , who were integrated into regular units under the same salary and conditions of service . Almost all of the foreigners served in the Rhodesian Light Infantry ( RLI ) , a <unk> commando battalion with a glamorous international reputation . The RLI initiated a major overseas recruitment drive in 1974 , targeting potential volunteers from Europe , Oceania and the Americas , and requiring successful applicants to speak good English . This campaign was bearing considerable fruit by May 1976 , when the RLI 's largest ever intake included more foreign volunteers than any before , and the enlisting of men from overseas into the RLI would increase yet further . By 1977 – 78 there were around 1 @,@ 500 foreigners in the Rhodesian forces . However , the Rhodesian Army remained stretched and low on manpower . 
- 
- = = Formation and training = = 
- 
- The idea for a francophone unit came from a French national , François Cramer , who had business interests and connections in Rhodesia . He proposed it to Major @-@ General Sandy MacLean , then the Rhodesian Army 's second @-@ in @-@ command , while they were visiting France together . MacLean relayed the idea to the General Staff in Salisbury , which decided in late 1977 to form a " French battalion " to alleviate the strain on its regular units . A Rhodesian officer of French extraction , Cyril Bernard , warned his superiors strongly against the scheme , but was ultimately himself sent to France to start the project . On the way he stopped in Zurich , Switzerland , where MacLean gave him final approval for the operation and a budget of US $ 30 @,@ 000 . They resolved to recruit mainly in Paris and Lyon . Bernard then entered France and renewed old connections from the French military academy at Saint @-@ <unk> . 
- <unk> was carried out by a former French paratrooper , Roger <unk> , operating from an apartment on Rue <unk> in central Paris . Advertisements , placed in newspapers such as France Soir , offered " a job with a future abroad ... minimum age 22 , former non @-@ commissioned officers preferred " . The body of men eventually assembled varied widely in terms of age , background and experience , but was based largely around ex @-@ paratroopers and former Foreign Legionnaires . The average age was about 25 . French @-@ speaking veterans of an assortment of African and Middle Eastern conflicts spanning the previous two decades successfully applied , but past service did not prove a necessity ; some of the men accepted had no military experience . Most of them spoke English only at a basic level if at all . 
- Once signed up , each man was instructed to travel to Switzerland , where appropriate <unk> were swiftly procured from the South African Embassy . In early November 1977 , the French recruits flew from Zurich to Johannesburg on a South African Airways liner , then to Salisbury by Air Rhodesia . Already in Rhodesia waiting for the men were their leaders : Major Roland de l <unk> , an ex @-@ officer of the French Army 's crack 11th Shock Parachute Regiment , and a former officer of the <unk> President Omar <unk> 's guard ; and Major Mario La Viola , once a non @-@ commissioned officer in the Foreign Legion 's 2nd Parachute Regiment . <unk> them were the <unk> Captain <unk> , who became the first black officer in the regular Rhodesian Army as the unit 's second @-@ in @-@ command , and " Colonel " Bob Denard , an infamous French soldier of fortune nicknamed le <unk> de guerre — " the dog of war " . 
- According to an anonymous veteran of the unit , it mostly comprised recently discharged servicemen from the French forces who were having trouble adapting to civilian life . " They didn 't know how to do anything else , only how to jump with a parachute and obey orders , " he explained , " and they liked that kind of thing . " Others , he said , were members of the political far right who had joined up " to kill <unk> and blacks " , and some were criminals hoping to escape the attention of the French police . Each signed on for two years . The French @-@ speaking unit was placed in the Rhodesia Regiment ( <unk> ) as an " independent company " . The <unk> already had six of these ( made up of <unk> ) , so the Frenchmen became 7 Independent Company . 
- The Rhodesian Army sought to forge a strong <unk> de corps among the new recruits , and to this end extended them several sentimental <unk> : for example , morale amongst the Frenchmen rose when they were informed that the Rhodesian insignia on their <unk> would be backed with the French tricolour . In a similar vein , their request for permission to raise the flag of France alongside that of Rhodesia outside their headquarters each morning was approved . French @-@ speaking men already in the army were attached to the unit to act as <unk> and assist with coordination and tactical instruction . Some of these were <unk> , who by virtue of their upbringing spoke both English and French at a native @-@ like level . The company itself , which numbered about 200 men , was assigned headquarters near Salisbury at <unk> Barracks , the home of the Rhodesian Light Infantry . It was organised in the same manner as a standard Rhodesian independent company , the only exception being its exclusively francophone personnel . 
- Spirits were high during the unit 's brief training period as the men enjoyed playing sports , observing the country scenery and experiencing the night @-@ life of the nearby capital . They were not adequately trained , receiving only a basic medical examination , a few days ' fitness training and a <unk> explanation of proper conduct in the bush . They expressed surprise when instructed to paint stripes of camouflage green on their weapons and combat boots in the Rhodesian fashion , having never before heard of such a practice . 
- The first dent to morale came after about a week when the volunteers were first issued ranks in the Rhodesian Army . In the eyes of some of the French @-@ speaking soldiers , the ranks assigned appeared to have been chosen almost at random by their superiors , and did not reflect their actual respective levels of training , ability and experience . Some men who considered themselves to have been overlooked began to have problems with discipline . The brazen attitude of most of the company 's soldiers jarred strongly with that of their Rhodesian commanders , who had high standards regarding presentation and dress which many of the Frenchmen had little inclination to meet . 
- 
- = = Service = = 
- 
- 
- = = = First bush trip = = = 
- 
- The francophone company was first deployed out of its barracks in late November 1977 , when it was sent to <unk> , about 88 kilometres ( 55 mi ) north @-@ east from Salisbury , for a few days in the bush on Operation Hurricane , the Army 's operational area which covered Rhodesia 's north @-@ east against guerrilla activity . After this passed without major incident , the Frenchmen were despatched to Rusambo , a camp in the <unk> <unk> Trust Lands , near <unk> , about 250 kilometres ( 160 mi ) north @-@ east from the capital and less than 20 kilometres ( 12 mi ) from the border with Mozambique . Most insurgents in this area belonged to ZANLA . 
- A company of men from the Territorial Force was already stationed at Rusambo , advised by a team of intelligence officers . " Sticks " of four men ( three <unk> <unk> riflemen and an <unk> gunner ) would be sent out into the bush from Rusambo for periods of up to two weeks , equipped with a radio to communicate with the base . Their task while in action was to seek out guerrillas by means of patrolling , <unk> and operating observation and listening posts . Once a group of insurgents was spotted , the stick leader would report their positions ; Rusambo would then alert the Army and request a Fireforce . If Fireforce were available , it would arrive and engage the cadres ; if not , the stick in the field would have to handle the situation itself . 
- At first , men from 7 Independent Company were sent out in sticks mixed with the <unk> , but problems soon arose regarding proper regimen and the language barrier . When the Frenchmen were then sent out alone , their Land Rovers prominently flying the French flag , the issue of language was resolved , but that of <unk> remained . Though discretion was paramount if they were to observe enemy movements covertly and effectively , the men of 7 Independent Company were found to have difficulty maintaining this and sometimes made careless mistakes which risked revealing their presence . Moreover , when investigations were made of local <unk> , marked tension soon arose between the Frenchmen and the local black population ; the soldiers ' ignorance of English or <unk> made it very difficult for discussions to take place and , according to other Rhodesian units who came into contact with them , the French soldiers took out their frustration on the villagers , often using excessive force in their attempted <unk> . <unk> Police Station received a report of a 7 Independent Company man <unk> a young <unk> in a dense thicket , but did not act on it . According to one history of the Rhodesia Regiment , " it was indicated that the Frenchmen had received instruction that all black people were to be regarded as terrorists " . 
- The Rhodesian Army quickly deemed the French experiment a failure . It reassigned 7 Independent Company in late November to Marymount Mission , a small settlement to the east of Rusambo where there was a minor police station . The number of patrols they would embark on was reduced . Two of the company 's vehicles were ambushed by cadres between Marymount and Rusambo on 6 January 1978 , resulting in two men being injured , one fatally so . A week later another truck was surprised on the same bush road , resulting in one death and three serious casualties . The company was brought back from the bush four days later for rest and <unk> ( R & R ) in Salisbury . 
- 
- = = = Strike = = = 
- 
- During their 15 @-@ day rest periods the company 's men congregated around the Belgian @-@ owned Elizabeth Hotel , in the centre of the city at the corner of Causeway and <unk> Road . Many of them became seriously disaffected when they first received their salary from the Rhodesian Army . Having apparently been <unk> about wages of up to R $ 1 @,@ 000 per month ( <unk> 7 @,@ 000 ) by the French <unk> , they were surprised to find that their basic monthly pay was actually R $ 245 ( <unk> 1 @,@ 800 ) , the same as a regular Rhodesian soldier . Moreover , some were upset that they had been paid in Rhodesian dollars , which because of the country 's international isolation could not easily be exchanged for foreign currencies . Although it was not as much as they had been expecting , one disenchanted veteran of the unit afterwards admitted that the tax @-@ exempt R $ 245 wage , which came with a $ 10 special @-@ unit supplement , was still more than enough money for them to live comfortably in Salisbury during their time off . 
- The pay dispute split the unit . About two thirds went on strike , saying they would not return to action unless the Army <unk> their wages and paid them in foreign currency . Meanwhile , some of the more <unk> Frenchmen made steps to remain permanently , buying cars and having their wives join them in Salisbury . The Army detained the strike 's <unk> for insubordination . With neither side willing to <unk> — the Army refused to give the strikers extra pay or special treatment , saying this would <unk> Rhodesia 's policy not to engage mercenaries — the disaffected men were repatriated to France at their own request . The Rhodesian Army considered disbanding the unit altogether , but <unk> when Major de l <unk> convinced his superiors that his remaining men were still loyal and eager to continue serving . 
- 
- = = = Second bush trip ; dissolution = = = 
- 
- Starting on 11 February 1978 , 7 Independent Company spent half a week at Mount Darwin , where there was a major Army base . The company acquitted themselves well during this time , but one of their number was badly injured in a motor accident . They returned to Rusambo , where the camp was now manned by the British South Africa Police ( <unk> ) , Criminal Investigation Department and Special Branch , guarded by a group of <unk> and Indian @-@ Rhodesian soldiers . On 26 February , the Frenchmen spotted a group of seven cadres <unk> <unk> at a local <unk> , and called up Fireforce . The RLI men who arrived killed four of the seven , including one carrying detailed documents . The next day 7 Independent Company observed 11 guerrillas entering another <unk> , but this time the Fireforce took too long to arrive . The French company took part in a large contact on 1 March , fighting alongside an RLI Fireforce against 28 cadres ; 18 insurgents were killed in this contact without loss for the Rhodesian Army . 
- Soon after this , two sticks from 7 Independent Company were despatched to Marymount , led by a deputy intelligence officer who began sending them out on more regular night patrols . The following months were a disaster for the locally based Rhodesian forces ; first one stick fired on another , causing an injury , then a <unk> Land Rover hit a mine , killing two members of the French company . A further Frenchman died in an ambush by insurgents before another friendly fire incident on 19 April 1978 resulted in a fatality . On several occasions during this bush trip the area was " frozen " ; regular army units were confined to their camps while the <unk> Scouts operated against the guerrillas . With morale amongst the Frenchmen <unk> , bringing their unit close to collapse , its officers at Rusambo frantically worked to keep it together . The company did not last much longer once back in Salisbury for R & R. Soon after three of its <unk> were placed in detention at <unk> Barracks , the unit was formally dissolved in May 1978 . The only personnel retained by the Army were the <unk> , who were returned to their former units . 
- 
- = = Legacy = = 
- 
- <unk> by the French , Rhodesian and South African governments and with Rhodesian logistical assistance , forces led by Denard took part in a coup d 'état in the Comoros later in May , toppling Ali <unk> ( who Denard had himself put into power three years earlier ) . The Comoros subsequently became a key location for Rhodesian " <unk> @-@ <unk> " operations , providing a convenient end @-@ user certificate for clandestine shipments of weapons and equipment bound for Rhodesia in spite of the UN embargo . South Africa , also under a UN arms boycott because of apartheid , received war materiel through the Comoros in a similar fashion . 
- Some 7 Independent Company men became civilians in Rhodesia , which was reconstituted as Zimbabwe in 1980 . Two of them , Gervais Henri Alfred <unk> and Simon Marc <unk> ( both former Foreign Legionnaires ) , were executed in April 1983 , despite a late plea from French authorities for <unk> , having been found guilty of robbing and murdering Richard <unk> , a <unk> café owner . Witness <unk> , the <unk> Minister of Foreign Affairs , issued a statement clarifying that the execution was for the murder and unrelated to their earlier " service as mercenaries during the time of the Smith regime . " 
- 
- 
- = Jenova Chen = 
- 
- <unk> Chen ( simplified Chinese : <unk> ; traditional Chinese : <unk> ; pinyin : <unk> <unk> ; born October 8 , 1981 ) , known professionally as Jenova Chen , is a Chinese video game designer . He is the designer of the award @-@ winning games Cloud , Flow , Flower , and Journey , and is co @-@ founder of Thatgamecompany . Chen is from Shanghai , where he earned a bachelor 's degree in computer science with a minor in digital art and design . He then moved to the United States , where he earned a master 's degree from the University of Southern California 's Interactive Media Division . While there he created Cloud and Flow , and met fellow student Kellee Santiago . After a brief period at Maxis working on <unk> , he founded Thatgamecompany with Santiago and became the company 's creative director . The company signed a three @-@ game deal with Sony Computer Entertainment , and has sold Flow , Flower , and Journey through the PlayStation Network . 
- As Chen was born in a culture other than the culture he now lives in , he tries to make games that appeal universally to all people . His goal with his games is to help video games mature as a medium by making games that inspire emotional responses in the player that other games are lacking . Although he and Thatgamecompany can and have made more traditional games , he does not plan on commercially developing any of them , as he does not think that it fits with their goals as an independent video game developer . 
- 
- = = Biography = = 
- 
- Chen was born in Shanghai on October 8 , 1981 , and lived there until 2003 . His parents were " a middle @-@ class family " , and his father worked in the software development industry , having previously worked on " one of the earliest giant computers in China " . Although Chen was interested in art and drawing as a young child , his father influenced him towards computers , entering him in programming contests from when he was 10 years old . He found himself interested in video games that he saw there , but was not as enthusiastic about programming . While a teenager , he had deep emotional experiences with games that he played , including The Legend of Sword and Fairy , which he <unk> to the fact that he was not as exposed to books , films , or life events that other people would have had those experiences with . These experiences drove him to try to create those types of feelings in games as an adult , when more emotional maturity had caused his " standards to rise " in what would move him in a game . It was during high school that he chose the English name Jenova after a character in Final Fantasy VII , wanting a name that would be unique anywhere he used it as there were " thousands of Jason <unk> " . 
- He earned a degree in Computer Science & Engineering in Shanghai <unk> Tong University , which due to his background in computers he found " quite easy " , but describes himself as spending much of his time there teaching himself digital art and animation , and later did a minor in digital art and design at <unk> University . Still interested in video games , he was involved in making three video games as part of a student group while in school . Upon graduating , he had trouble finding a job in the Chinese video game industry that combined his interests of " engineering , art , and design " , and additionally felt that " very few games [ had ] actually achieved those qualities that would be interesting to an adult " . He also considered working in digital animation for films . 
- He then went to the United States to earn a master 's degree in the School of <unk> Arts at the University of Southern California . Chen studied in the Interactive Media Program , a new division of the School of <unk> Arts . His intention at the time was to use the degree to get the kind of job he wanted back in China . At USC , he became inspired when he went to the Game Developers Conference , where he positively compared the games he had made in college with the student work present at the Independent Games Festival portion of the conference . While at USC he met Kellee Santiago , another student in the same program , and the two decided to work together on games that would be outside of the mainstream . Their first game , which won a grant of twenty thousand dollars from USC to produce , was Cloud , released in 2005 , which " focuses on a young hospital patient who <unk> in his mind despite being trapped indoors " . The idea was partially based on himself , as when he was a child he was often hospitalized for asthma . It was designed as an attempt to " expand the spectrum of emotions video games evoke " . At a student showcase at the Game Developers Conference , Chen and Santiago showed the game to a representative from Sony , John <unk> , saying that it was the first game in the " <unk> " genre . <unk> was interested , though no deal was forthcoming . The game won the Best Student Philosophy award at the <unk> <unk> Games Competition and a Student <unk> award at the Independent Games Festival , and was showcased on Spike TV , G4TV , and CBS Sunday . 
- Chen felt that the reason that Cloud had been so warmly received was because the emotions it sparked in players were different than any other game available at the time , and believed that it was his " calling " to make more games that changed what people saw video games as . Chen went on to do his master 's thesis the following year in the concept of dynamic difficulty adjustment , where the game <unk> how it reacts to the player based on the past and present actions of that player . Chen illustrated his ideas with Flow , a Flash game made with Nicholas Clark . The game involves the player guiding an aquatic <unk> through various depths of the ocean , consuming other organisms and evolving in the process . It was released in March 2006 ; it received 100 @,@ 000 downloads in its first two weeks and by July had been downloaded over 650 @,@ 000 times . A PlayStation 3 version was announced in May 2006 as a downloadable game via the PlayStation Store , and was released in February 2007 . A version for the PlayStation Portable , developed by <unk> Studios , was released in March 2008 . Flow became the most downloaded game on the PlayStation Network in 2007 , and won Best Downloadable Game at the Game Developers Choice Awards . 
- After graduating , Chen and Santiago formed their own game company , Thatgamecompany , in Los Angeles where he still lives and signed a deal with Sony for three PlayStation Store games . The PS3 version of Flow was the first , and while it was in development Chen worked for Maxis on the game <unk> . Upon Flow 's release , Chen returned to Thatgamecompany and began working on their second game . 
- 
- = = = Thatgamecompany = = = 
- 
- The next game , Flower , was Chen and Thatgamecompany 's " first game outside the safety net of academia " . Chen was the creative director in charge of the game , while Santiago was the producer and Clark was the lead designer . The company ranged in size from six to nine people at varying stages of the game 's development . Flower was intended by Chen to primarily to provoke positive emotions in the player , and to act as " an emotional shelter " . Chen described the game as " an interactive poem exploring the tension between urban and nature " . He decided on a " nature " theme early in the development process , saying that he " had this concept that every PlayStation is like a portal in your living room , it leads you to somewhere else . I thought ; wouldn 't it be nice if it was a portal that would allow you to be embraced by nature . " Chen designed the game around the idea that the primary purpose of entertainment products like video games was the feelings that they evoked in the audience , and that the emotional range of most games was very limited . To make Flower have the " emotional spectrum " that he wanted , Chen looked at the development process as creating a work of art , rather than a " fun " game , which would not provoke the desired emotions . In 2008 , during Flower 's development , Chen was named to the MIT Technology Review <unk> as one of the top 35 <unk> in the world under the age of 35 . 
- After Flower was released to critical praise and awards , Chen and Thatgamecompany moved on to their next game , Journey . Journey was intended by Chen to focus on the element of communication and social interaction in video games . Since in most games the communication between players is focused on specific goals , in Journey Chen intended for the player to be able to either play alone or to come across other players , but not be able to communicate with them directly . Instead , players have to build relationships with each other through their actions , helping each other or leaving as they choose . Journey was released on the PlayStation Network on March 13 , 2012 , and has received critical acclaim . 
- 
- = = Influences and philosophy = = 
- 
- Chen plays a wide variety of video games , but he names his greatest influences as <unk> <unk> , Ico and Shadow of the <unk> . He also names Final Fantasy VII as an influence , and the game that he took his adopted name from . He personally plays games " competitively " , including titles such as Street Fighter IV and StarCraft . He feels he has a competitive nature , which he has turned towards " winning " at being a game designer by creating games that are unlike what is in the market rather than towards creating competitive games . As he was raised in China and works in America , Chen feels that he cannot fully relate to either culture as a game designer . As such , instead of trying to make games that fit perfectly with one culture he tries to make games that tap into feelings that are universal and independent of culture . 
- When Chen quit Maxis to re @-@ join Thatgamecompany , he did so knowing that it would mean taking less pay and having a less stable career . He felt , though , that it was important to the industry and medium as a whole to create games that provoked different emotional responses in the player than just excitement or fear . While Chen is not opposed to making action games , and his company has made internal " exciting " games that were well received at Sony , he feels that there is no point to Thatgamecompany commercially producing games like that instead of working for existing game developers , as they would not be making anything new that justified the cost of remaining an independent studio . Similarly , Chen does not intend for Thatgamecompany to make " big budget blockbuster games " , as the pressure on profits that that <unk> would <unk> the innovation that he wants Thatgamecompany to focus on . Chen believes that for video games to become a mature medium like film , the industry as a whole needs to create a wide range of emotional responses to their games , similar to how film has thriller , romance , and comedy genres based on the emotions they provoke . He feels that there are only three ways for video games to impact adults in the same way they do children : " <unk> , whereby the work reveals a new perspective about the world that you have not seen before , " by " emotionally touching someone , " and " by creating a social environment where the intellectual or emotional stimulation could happen from other people . " 
- 
- 
- = E. W. Hornung = 
- 
- Ernest William Hornung ( 7 June 1866 – 22 March 1921 ) was an English author and poet known for writing the A. J. Raffles series of stories about a gentleman thief in late 19th @-@ century London . Hornung was educated at Uppingham School ; as a result of poor health he left the school in December 1883 to travel to Sydney , where he stayed for two years . He drew on his Australian experiences as a background when he began writing , initially short stories and later novels . 
- In 1898 he wrote " In the Chains of Crime " , which introduced Raffles and his sidekick , Bunny Manders ; the characters were based partly on his friends Oscar Wilde and his lover , Lord Alfred Douglas , and also on Sherlock Holmes and Dr. Watson . The series of Raffles short stories were collected for sale in book form in 1899 , and two further books of Raffles short stories followed , as well as a poorly received novel . Aside from his Raffles stories , Hornung was a prodigious writer of fiction , publishing numerous books from 1890 , with A Bride from the Bush to his 1914 novel The Crime Doctor . 
- The First World War brought an end to Hornung 's fictional output . His son , Oscar , was killed at the Second Battle of Ypres in July 1915 . Hornung joined the YMCA , initially in England , then in France , where he helped run a canteen and library . He published two collections of poetry during the war , and then , afterwards , one further volume of verse and an account of his time spent in France , Notes of a Camp @-@ <unk> on the Western Front . Hornung 's fragile constitution was further weakened by the stress of his war work . To aid his <unk> , he and his wife visited the south of France in 1921 . He fell ill from influenza on the journey , and died on 22 March 1921 , aged 54 . 
- Although much of Hornung 's work has fallen into obscurity , his Raffles stories continued to be popular , and have formed numerous film and television adaptations . Hornung 's stories dealt with a wider range of themes than crime : he examined scientific and medical developments , guilt , class and the <unk> role played by women in society . Two threads that run through a sizeable proportion of his books are Australia and cricket ; the latter was also a lifelong passion . 
- 
- = = Biography = = 
- 
- 
- = = = Early life : 1866 – 86 = = = 
- 
- Hornung was born Ernest William Hornung on 7 June 1866 at Cleveland <unk> , <unk> , Middlesbrough ; he was nicknamed Willie from an early age . He was the third son , and youngest of eight children , of John Peter Hornung ( 1821 – 86 ) and his wife Harriet née Armstrong ( 1824 – 96 ) . John was christened Johan Petrus Hornung in the Transylvania region of Hungary and , after working in Hamburg for a shipping firm , had moved to Britain in the 1840s as a coal and iron merchant . John married Harriet in March 1848 , by which time he had <unk> his name . At the age of 13 Hornung joined St Ninian 's Preparatory School in Moffat , <unk> before <unk> at Uppingham School in 1880 . Hornung was well liked at school , and developed a lifelong love of cricket despite limited skills at the game , which were further worsened by bad eyesight , asthma and , according to his biographer Peter Rowland , a permanent state of generally poor health . 
- When Hornung was 17 his health worsened ; he left Uppingham and travelled to Australia , where it was hoped by his family that the climate would be beneficial . On his arrival he was employed as a tutor to the Parsons family in <unk> in the <unk> , south @-@ western New South Wales . In addition to teaching , he spent time working in remote sheep stations in the outback and contributing material to the weekly magazine The Bulletin ; he also began writing what was to become his first novel . Although he spent only two years in Australia , the experience was " the making of him and ... the making of his career as a writer " , according to Rowland . Another biographer , Mark Valentine , wrote that Hornung " seems to have regarded this period as one of the most satisfying of his life " . 
- 
- = = = Return to England : 1886 – 98 = = = 
- 
- Hornung returned to England in February 1886 , before the death of his father in November . From a position of relative prosperity , John 's coal and iron business had encountered difficulties and he was in financially <unk> circumstances by the time of his death . Hornung found work in London as a journalist and story writer , often publishing his work under a pseudonym , although in 1887 he published his first story under his own name , " <unk> of Five " , which appeared in <unk> magazine . His work as a journalist was during the period of Jack the <unk> and the series of five murders , which were undertaken against a background of rising urban crime in London ; it was around this time that Hornung developed an interest in criminal behaviour . 
- Hornung had worked on the novel manuscript he brought back from Australia and , between July and November 1890 , the story , " A Bride from the Bush " , was published in five parts in the Cornhill Magazine . It was also released that year as a book — his first . The story — described by Rowland as an " assured , graceful comedy of manners " — used Hornung 's knowledge of Australia as a backdrop , and the device of an Australian bride to examine British social behaviour ; the novel was well received by critics . In 1891 Hornung became a member of two cricket clubs : the <unk> , whose members included Arthur Conan Doyle , Robert <unk> and Jerome K. Jerome , and the Strand club . 
- Hornung knew Doyle 's sister , Constance ( " Connie " ) <unk> Monica Doyle ( 1868 – 1924 ) , whom he had met when he visited Portugal . Connie was described by Doyle 's biographer , Andrew <unk> , as being attractive , " with pre @-@ <unk> looks ... the most sought @-@ after of the Doyle daughters " . By December 1892 , when Hornung , Doyle and Jerome visited the Black Museum at Scotland Yard , Hornung and Connie were engaged , and in 1893 Hornung dedicated his second novel , Tiny <unk> , " to <unk> " They were married on 27 September 1893 , although Doyle was not at the wedding and relations between the two writers were sometimes strained . The <unk> had a son , Arthur Oscar , in 1895 ; while his first name was from Doyle , who was also Arthur 's <unk> , the boy 's middle name was probably after Doyle and Hornung 's mutual friend Oscar Wilde and it was by his second name that he was known . In 1894 Doyle and Hornung began work on a play for Henry Irving , on the subject of boxing during the Regency ; Doyle was initially eager and paid Hornung £ 50 as a down payment before he withdrew after the first act had been written : the work was never completed . 
- Like Hornung 's first novel , Tiny <unk> had Australia as a backdrop and also used the plot device of an Australian woman in a culturally alien environment . The Australian theme was present in his next four novels : The Boss of <unk> ( 1894 ) , The <unk> Guest ( 1894 ) , Irralie 's Bushranger ( 1896 ) and The Rogue 's March ( 1896 ) . In the last of these Hornung wrote of the Australian convict transport system , and showed evidence of a " growing fascination with the motivation behind criminal behaviour and a deliberate sympathy for the criminal hero as a victim of events " , while Irralie 's Bushranger introduced the character Stingaree , an Oxford @-@ educated , Australian gentleman thief , in a novel that " casts doubt on conventional responses " to a positive criminal character , according to Hornung 's biographer , Stephen Knight . 
- 
- = = = <unk> Raffles : 1898 – 1914 = = = 
- 
- In 1898 Hornung 's mother died , aged 72 and he dedicated his next book , a series of short stories titled Some Persons Unknown , to her memory . Later that year Hornung and his wife visited Italy for six months , staying in <unk> ; his account of the location appeared in an article of the May 1899 edition of Cornhill Magazine . The <unk> returned to London in early 1899 , to a house in Pitt Street , West Kensington , where they lived for the next six years . 
- The fictional character Stingaree proved to be a prototype of a character Hornung used in a series of six short stories published in 1898 in <unk> 's Magazine , A. J. Raffles . The character was modelled on George Cecil <unk> , a Cambridge @-@ educated <unk> and talented <unk> who , like Raffles , was a resident of the Albany , a gentlemen 's only residence in Mayfair . The first tale of the series " In the Chains of Crime " was published in June that year , titled " The Ides of March " . The stories were collected into one volume — with two additional tales — under the name The Amateur Cracksman , which was published the following year . Hornung used a narrative form similar to Doyle 's Sherlock Holmes stories , with Raffles and his partner @-@ in @-@ crime ( and former school <unk> ) Bunny Manders being the criminal counterparts to Holmes and Dr. Watson — although Rowland writes that Raffles and Manders " were also fictionalized versions of Wilde and <unk> " ( Wilde 's lover , Lord Alfred Douglas ) . — and he dedicated the stories to his brother @-@ in @-@ law : " To <unk> This form of <unk> " . Doyle had warned against writing the stories , and reflected in his memoirs that " there are few finer examples of short @-@ story writing in our language than these , though I confess I think they are rather dangerous in their suggestion . I told him so before he put pen to paper , and the result has , I fear , borne me out . You must not make the criminal the hero " . The book was a popular and financial success , although some critics also echoed Doyle 's fears . The reviewer in The Spectator wrote that " stern <unk> " would consider the book 's premise " as a new , ingenious , artistic , but most <unk> application of the crude principles involved in the old @-@ fashioned hero @-@ worship of Jack <unk> and Dick <unk> " . The book ends with Manders imprisoned and Raffles apparently dead , something that left The Spectator reviewer " expressing [ their ] satisfaction that this <unk> entertaining volume is not issued in a cheap form . It is emphatically a feat of virtuosity rather than a tribute to virtue . " 
- After publishing two novels , Dead Men Don 't Tell Tales in 1899 and Peccavi in 1900 , Hornung published a second collection of Raffles stories , The Black Mask , in 1901 . The nearly broke Manders is told to apply for the post of a nurse to an elderly invalid , who then reveals himself to be Raffles , who , as Manders describes , had " aged twenty years ; he looked fifty at the very least . His hair was white ; there was no trick about that ; and his face was another white . The lines about the corners of the eyes and mouth were both many and deep " . In the final story of the collection , " The <unk> of the Gods " , Raffles and Manders enlist in the army to fight in the Second Boer War ; the story closes with Manders wounded and Raffles killed . The critics again complained about the criminal aspect ; The Spectator declared " this sort of book presents crime in a form too entertaining and attractive to be moral " , while the reviewer for The Illustrated London News thought that Hornung 's " invention has obviously flagged ... It is <unk> , in a sense which the author never intended , to hear these <unk> <unk> about the honour of Old England . It is a pity that the man who wrote Peccavi should <unk> to this " . 
- In 1903 Hornung collaborated with Eugène <unk> to write a four @-@ act play , Raffles , The Amateur Cracksman , which was based on two previously published short stories , " Gentlemen and Players " and " The Return Match " . The play was first performed at the Princess Theatre , New York , on 27 October 1903 with <unk> <unk> as Raffles , and ran for 168 performances . 
- In 1905 , after publishing four other books in the interim , Hornung brought back the character Stingaree , previously seen in Irralie 's Bushranger . Later that year he responded to public demand and produced a third series of short Raffles stories in A Thief in the Night , in which Manders relates some of his and Raffles 's earlier adventures . The reviewer for the Boston Herald thought that " the sentimental side of the story has never before been shown so dramatically and romantically " , and described the book as " thrilling and exciting " . Hornung 's next book was published in 1909 and was the final Raffles story , the full @-@ length novel Mr. Justice Raffles ; the book was poorly received , with the reviewer for The Observer asking if " Hornung is perhaps a little tired of Raffles " , and stating that " it has not the magic or the ' go ' of the first Raffles , and there is no good in pretending that it has " . During the course of the year he collaborated with Charles <unk> to write a play A <unk> From Raffles , which was performed in November that year at the Brixton Empress Theatre , London . 
- Hornung turned away from Raffles thereafter , and in February 1911 published The Camera <unk> , a thriller whose narrator is an <unk> cricket enthusiast with an <unk> father , much as Hornung was himself . The story concerned the attempts of a scientist to photograph the soul as it left the body . Hornung followed this up with Fathers of Men ( 1912 ) and The Thousandth Woman ( 1913 ) before <unk> Hill ( 1913 ) , a collection of eight short stories in which he introduced the characters <unk> <unk> and the narrator <unk> , whom Rowland considers to be " <unk> of Raffles and Bunny " . Hornung 's next work , The Crime Doctor ( 1914 ) marked the end of his fictional output . 
- 
- = = = First World War and aftermath = = = 
- 
- Oscar Hornung left Eton College in 1914 , intending to enter King 's College , Cambridge later that year . When Britain entered the war against Germany , he volunteered , and was commissioned into the Essex Regiment . He was killed at the Second Battle of Ypres on 6 July 1915 , aged 20 . Although heartbroken by the loss , Hornung was adamant that some good would come of it and he edited a privately issued collection of Oscar 's letters home under the title <unk> and Well Beloved , released in 1916 . Around this time he joined an anti @-@ aircraft unit . In either 1916 or 1917 he joined the YMCA and did volunteer work in England for soldiers on leave ; in March 1917 he visited France , writing a poem about his experience afterwards — something he had been doing more frequently since Oscar 's death — and a collection of his war poetry , Ballad of Ensign Joy , was published later that year . 
- In July 1917 Hornung 's poem , " Wooden Crosses " , was published in The Times , and in September , " Bond and Free " appeared . Towards the end of the year , he was accepted as a volunteer in a YMCA canteen and library " a short distance behind the Front Line " . During his service in <unk> , in February 1918 he borrowed a staff car from a friend and visited his son 's grave near Ypres , before returning to the library in <unk> . Hornung was concerned about support for <unk> among troops , and wrote to his wife about it . When she spoke to Doyle about the matter , rather than discussing it with Hornung he informed the military authorities . Hornung was angered by Doyle 's action , and " told him there was no need for him to ' butt in ' except for his own ' satisfaction ' . " Relations between the two men were strained as a result . Hornung continued to work at the library until the German Spring Offensive in March overran the British positions and he was forced to retreat , firstly to Amiens and then , in April , back to England . He stayed in England until November 1918 , when he again took up his YMCA duties , establishing a rest <unk> and library in <unk> . In 1919 Hornung 's account of his time spent in France , Notes of a Camp @-@ <unk> on the Western Front , was published . Doyle later wrote of the book that " there are parts of it which are brilliant in their vivid portrayal " , while Hornung 's biographer , Alison Cox , described the book as " one of the best records of the war as experienced on the front lines " . That year Hornung also published his third and final volume of poetry , The Young Guard . 
- 
- = = = Death and legacy = = = 
- 
- Hornung finished his work with the YMCA and returned to England probably in early 1919 , according to Rowland . He worked on a new novel but was hampered by poor health . His wife 's health was of even greater concern , so in February 1921 they took a holiday in the south of France to <unk> . He fell ill on the train with a <unk> that turned into influenza and pneumonia from which he died on 22 March 1921 , aged 54 . He was buried in Saint @-@ Jean @-@ de @-@ Luz , in the south of France , in a grave adjacent to that of <unk> . Doyle , returning from a <unk> lecture tour of Australia , received the news in Paris and travelled south in time for the funeral . 
- When Hornung had still been courting Doyle 's sister , Doyle wrote that " I like young Willie Hornung very much ... he is one of the sweetest @-@ natured and most delicate @-@ minded men I ever knew " . <unk> him after his death , Doyle wrote that he " was a Dr. [ Samuel ] Johnson without the learning but with a finer wit . No one could say a <unk> thing , and his writings , good as they are , never adequately represented the powers of the man , nor the <unk> of his brain " . His <unk> in The Times described him as " a man of large and generous nature , a delightful companion and <unk> " . 
- Much of Hornung 's work fell out of favour as time passed ; Rowland observed that " all of Hornung 's other works have been forgotten , with the possible exception of Stingaree , but the cricketing Cracksman continues to <unk> " . The idea of a criminal as a positive character was one of Hornung 's legacies , and Twentieth @-@ Century Literary Criticism states that " critics have also interpreted Raffles as a prototype of the <unk> in modern crime fiction " . The academic Frank <unk> Chandler , describing Raffles 's death , writes that " all his creator 's attempts to portray him as a hero , rather than an anti @-@ hero , <unk> fail . " Valentine highlights one aspect of the stories was the mix of " <unk> and daring " demonstrated by Raffles ; in this respect he was a literary " forerunner of The Saint , James Bond and other <unk> types " . The writer Colin Watson agrees , and called Hornung " a precursor of [ Ian ] Fleming " . 
- The character continued in book form : the writer Philip <unk> , under the pseudonym Barry <unk> , obtained permission from the Hornung estate to continue the Raffles stories , and seven more novels followed between 1933 and 1940 , with Raffles transformed from a gentleman thief to a tough adventurer . <unk> continued the series in 1950 , and 14 of his stories were published in the 1974 volume Raffles Revisited . Hornung 's original stories have undergone a number of reprints , and when all the short stories were published in a single volume , Graham Greene considered it " a splendid idea " . In 1975 Greene had written a play based on the Raffles stories , The Return of <unk> Raffles , which premiered at the Royal Shakespeare Company , with <unk> Elliott as Raffles . 
- There were several Raffles films made during Hornung 's lifetime , Further films followed in the years after his death , including Raffles , the Amateur Cracksman ( 1925 ) , with House Peters , Sr. ; Raffles ( 1930 ) , featuring Ronald Colman ; The Return of Raffles ( 1933 ) , with George <unk> ; and Raffles ( 1939 ) , starring David Niven ; the last of these was a Samuel Goldwyn Productions remake of their own 1930 film , which the academic Victor E. <unk> called the " most memorable portrayal " of the character . 
- The BBC has <unk> some of Hornung 's Raffles stories for radio , first in the 1940s and again in the 1990s , when Nigel Havers played Raffles . In 1977 Anthony Valentine played the thief , and Christopher <unk> his partner , in a Yorkshire Television series . A 2001 TV version , The Gentleman Thief , adapted the stories for a contemporary audience , with Havers playing the lead . 
- 
- = = Writing = = 
- 
- 
- = = = Style and technique = = = 
- 
- Hornung 's prose is widely admired for its <unk> @-@ yet @-@ simple style . Oliver Edwards , writing in The Times , considered that " not the least attractive part of the Raffles books is the simple , plain , unaffected language in which each one of them is written " . The <unk> in the same newspaper agrees , and thinks Hornung had " a power of good and clear description and a talent for mystery and surprise " . Colin Watson also considers the point , and observes that in Hornung 's writing , " superfluous description has been avoided and account of action is to the point " , while Doyle admired his " sudden use of the right adjective and the right phrase " , something the writer and journalist Jeremy Lewis sees as a " flamboyant , <unk> taste for the vivid " . 
- Critics have observed that Hornung 's stories and novels are well @-@ structured . George Orwell wrote that Hornung was " a very conscientious and on his level a very able writer . Anyone who cares for sheer efficiency must admire his work " . Watson states that Hornung 's " writing has pace . The stories , however ridiculous , carry the readers along <unk> " . According to Cox , " Hornung 's work showed steady maturation " during his career , a point that Doyle also agreed with , although Edwards <unk> , and thinks The Crime Doctor to be one of Hornung 's weaker books . 
- Hornung 's approach to characters differed from other contemporary authors . Cox notes that Hornung " frequently chose to write from the perspective of the criminal " , and while many of Hornung 's novels contained criminal activity as a major element of the plot , the critic for Contemporary Authors states that the works do not " belong to the crime @-@ fiction genre " . Hornung 's works included elements from more general fiction , " such as false identities , disguises , and <unk> <unk> " . 
- 
- = = = Major themes = = = 
- 
- The academic Nick Rance identifies three categories of Raffles stories : " the rise of the New Woman " , in which Raffles either escapes from romantic <unk> , or uses the <unk> of a woman in order to achieve his aims ; " the rise of the <unk> " , in which Raffles steals from the <unk> <unk> as much as the upper classes ; and those stories that seek " to <unk> or re @-@ establish a sense of middle @-@ class identity " . The last category is based on Raffles not being a member of " Society " , only being accepted because of his cricketing ability and associated fame . From this point , Raffles 's stealing from the rich is a " rearguard action on behalf of the puritan values " which was perceived as making up middle @-@ class values , although Rance also states that those values are obscured because of the changing boundaries between the classes . <unk> makes the same point , and considers that " Raffles 's daring exploits and fantastic adventures symbolized the growing rebellion against Victorian sensibility at the turn of the century " . 
- Hornung kept abreast of scientific and medical developments , and was keen to incorporate them into his stories which , the critic for Contemporary Authors states , shows Hornung had " a streak of modernity and decided interest in new ideas " . The Camera <unk> uses the modern technology of the camera as an instrument central to the plot , while the protagonist of The Crime Doctor uses psychology to identify criminals . 
- Throughout the Raffles stories patriotism runs as an intermittent theme — to such an extent that the writer William <unk> Butler describes him as a " super @-@ <unk> " . In the course of the short story " A Jubilee Present " Raffles , celebrating Queen Victoria 's diamond jubilee , steals a gold cup from the British Museum and sends it to the queen , telling Manders that " we have been ruled over for sixty years by infinitely the finest monarch in the world " . In " The <unk> of the Gods " , Raffles volunteers for service in the Second Boer War , changing his name and hair colour — he jokes to Manders that he is prepared to " dye for his country " — and he later confesses his true identity to his superiors in order to <unk> a spy . 
- Some of Hornung 's novels , including The Shadow of the <unk> , No Hero and The Thousandth Woman , are notable for " portraying women in a rather modern , favorable light " , according to the critic for Contemporary Authors , showing concern for their <unk> position in society . Cox identifies a theme of guilt running through a number of works . Among these is Peccavi , in which a clergyman lives his life trying to atone for an earlier crime ; Shadow of the <unk> , in which a woman is accused of her husband 's murder ; and The Thousandth Woman , in which a woman stands by her lover after he is accused of murder . 
- Although Hornung 's Australian experience was brief , it influenced most of his literary work from A Bride from the Bush published in 1899 , to Old <unk> and a Few Old Scores , which was published after his death . According to Chandler , " nearly two @-@ thirds of [ Hornung 's ] books refer in varying degrees to Australian incidents and experiences " , with " even Raffles " starting his criminal career in Australia . Some of <unk> 's works — such as A Bride from the Bush — were praised for their accuracy of detail in depicting the Australian environment , although the detail could overwhelm the storyline , as in The Rogue 's March . 
- Cricket was one of Hornung 's lifelong <unk> , and he was delighted to become a member of the Marylebone Cricket Club in 1907 . The sport also permeated his stories , with Raffles playing for the Gentlemen of England . Rance observes that Raffles compares law @-@ breaking and cricket : " crime is <unk> as another and better sport " . 
- Raffles does on occasion <unk> his game , commenting to Manders in " Gentlemen and Players " , " where 's the satisfaction of taking a man 's wicket when you want his <unk> ? " Valentine also considers the point , and sees Raffles 's cricket as a front for his criminal activities , citing Raffles 's praise for cricket for " the glorious protection it <unk> a person of my <unk> " . 
- Watson examines Raffles 's actions within the broader context of <unk> , with Raffles acting within his own moral code " of what is ' done ' and ' not done ' . " Orwell , in his essay " Raffles and Miss <unk> " , observes that when Raffles feels remorse , it " is almost purely social ; he has disgraced ' the old school ' , he has lost his right to enter ' decent society ' , he has forfeited his amateur status and become a <unk> " . 
- 
- 
- = Nina Simone = 
- 
- Nina Simone ( / <unk> <unk> / ; born Eunice Kathleen Waymon ; February 21 , 1933 – April 21 , 2003 ) was an American singer , songwriter , pianist , <unk> , and civil rights activist who worked in a broad range of musical styles including classical , jazz , blues , folk , R & B , gospel , and pop . 
- Born in North Carolina , the sixth child of a preacher , Simone aspired to be a concert pianist . With the help of the few supporters in her hometown of Tryon , North Carolina , she enrolled in the <unk> School of Music in New York . 
- Waymon then applied for a scholarship to study at the prestigious Curtis Institute of Music in Philadelphia , where she was denied despite a well @-@ received audition . Simone became fully convinced this rejection had been entirely due to her race , a statement that has been a matter of controversy . Years later , two days before her death , the Curtis Institute of Music bestowed an honorary degree on Simone . 
- To make a living , Eunice Waymon changed her name to " Nina Simone " . The change related to her need to disguise herself from family members , having chosen to play " the devil 's music " or " cocktail piano " at a nightclub in Atlantic City . She was told in the nightclub that she would have to sing to her own accompaniment , and this effectively launched her career as a jazz vocalist . 
- Simone recorded more than forty albums , mostly between 1958 , when she made her debut with Little Girl Blue , and 1974 , and had a hit in the United States in 1958 with " I Loves You , Porgy " . 
- Simone 's musical style fused gospel and pop with classical music , in particular Johann Sebastian Bach , and accompanied expressive , jazz @-@ like singing in her <unk> voice . 
- 
- = = Biography = = 
- 
- 
- = = = Youth ( 1933 – 54 ) = = = 
- 
- Simone was born Eunice Kathleen Waymon in South Carolina but raised in Tryon , North Carolina . The sixth of eight children in a poor family , she began playing piano at age three ; the first song she learned was " God Be With You , <unk> We Meet Again " . <unk> a talent with the instrument , she performed at her local church . But her concert debut , a classical <unk> , was given when she was 12 . Simone later said that during this performance , her parents , who had taken seats in the front row , were forced to move to the back of the hall to make way for white people . She said that she refused to play until her parents were moved back to the front , and that the incident contributed to her later involvement in the civil rights movement . 
- Simone 's mother , Mary Kate Waymon ( 1902 - April 30 , 2001 ) , was a Methodist minister and a <unk> . Simone 's father , John Divine Waymon ( 1898 - October 24 , 1972 ) , was a <unk> who at one time owned a dry cleaning business , but also suffered bouts of ill health . Simone 's music teacher helped establish a special fund to pay for her education . Subsequently , a local fund was set up to assist her continued education . With the help of this scholarship money she was able to attend Allen High School for Girls in <unk> , North Carolina . 
- After her graduation , Simone spent the summer of 1950 at the <unk> School , preparing for an audition at the Curtis Institute of Music in Philadelphia . Her application , however , was denied . As her family had relocated to Philadelphia in the expectation of her entry to Curtis , the blow to her aspirations was particularly heavy , and she suspected that her application had been denied because of racial prejudice . <unk> , she took private piano lessons with Vladimir <unk> , a professor at Curtis , but never re @-@ applied to the institution . For several years , she worked a number of <unk> jobs and taught piano in Philadelphia . 
- 
- = = = Early success ( 1954 – 59 ) = = = 
- 
- To fund her private lessons , Simone performed at the <unk> Bar & <unk> on Pacific Avenue in Atlantic City , whose owner insisted that she sing as well as play the piano , which increased her weekly income to $ 90 a week . In 1954 , she adopted the stage name " Nina Simone " . " Nina " ( from <unk> , meaning " little girl " in Spanish ) , and " Simone " was taken from the French actress Simone <unk> , whom she had seen in the movie <unk> d 'Or . Knowing her mother would not approve of playing the " Devil 's Music " , she used her new stage name to remain undetected . Simone 's mixture of jazz , blues , and classical music in her performances at the bar earned her a small but loyal fan base . 
- In 1958 , she befriended and married Don Ross , a <unk> who worked as a <unk> <unk> , but quickly regretted their marriage . Playing in small clubs in the same year , she recorded George <unk> 's " I Loves You , Porgy " ( from Porgy and <unk> ) , which she learned from a Billie Holiday album and performed as a favor to a friend . It became her only Billboard top 20 success in the United States , and her debut album Little Girl Blue soon followed on Bethlehem Records . Simone lost more than $ 1 million in royalties ( notably for the 1980s re @-@ release of My Baby Just Cares for Me ) and never benefited financially from the album 's sales because she had sold her rights outright for $ 3 @,@ 000 . 
- 
- = = = Becoming popular ( 1959 – 64 ) = = = 
- 
- After the success of Little Girl Blue , Simone signed a contract with Colpix Records and recorded a multitude of studio and live albums . Colpix relinquished all creative control to her , including the choice of material that would be recorded , in exchange for her signing the contract with them . After the release of her live album Nina Simone at Town Hall , Simone became a favorite performer in Greenwich Village . By this time , Simone performed pop music only to make money to continue her classical music studies and was indifferent about having a recording contract . She kept this attitude toward the record industry for most of her career . 
- Simone married a New York police detective , Andrew Stroud , in 1961 . He later became her manager and the father of her daughter Lisa , but he abused Simone <unk> and physically . 
- 
- = = = Civil rights era ( 1964 – 74 ) = = = 
- 
- In 1964 , Simone changed record distributors from the American Colpix to the Dutch Philips , which also meant a change in the contents of her recordings . She had always included songs in her repertoire that drew upon her African @-@ American origins ( such as " Brown Baby " by Oscar Brown and " <unk> " by Michael <unk> in her album Nina at the Village Gate in 1962 ) . On her debut album for Philips , Nina Simone in Concert ( live recording , 1964 ) , for the first time she openly addressed the racial inequality that was prevalent in the United States with the song " Mississippi <unk> " , her response to the June 12 , 1963 , murder of <unk> Evers and the September 15 , 1963 , bombing of the 16th Street Baptist Church in Birmingham , Alabama , that killed four young black girls and partially blinded a fifth girl who survived . She remarked that the title and the song itself was , " like throwing 10 bullets back at them " , becoming one of many other protest songs written by Simone . The song was released as a single , and it was boycotted in certain southern states . Specifically , promotional copies were smashed by a Carolina radio station and returned to Simone 's record label . " Old Jim Crow " , on the same album , addressed the Jim Crow laws . 
- From then on , a civil rights message was standard in Simone 's recording repertoire , becoming a part of her live performances . During the rise of her political activism , the release of her musical work grew more <unk> . Simone performed and spoke at many civil rights meetings , such as at the Selma to Montgomery marches . Simone advocated violent revolution during the civil rights period , rather than Martin Luther King 's non @-@ violent approach , and she hoped that African Americans could , by armed combat , form a separate state . Her message to the public signified the transition from the non @-@ violent approach to social change that was advocated by Martin Luther King into the more militant state that was implemented by Malcolm X and the associates of the Black Nationalist Movement . Nevertheless , she wrote in her autobiography that she and her family regarded all races as equal . 
- Simone moved from Philips to RCA Victor during 1967 . She sang " <unk> Blues " , written by her friend Langston Hughes on her first RCA album , Nina Simone Sings the Blues ( 1967 ) . On Silk & Soul ( 1967 ) , she recorded Billy Taylor 's " I Wish I <unk> How It Would Feel to Be Free " and " Turning Point " . The album ' <unk> Said ! ( 1968 ) contains live recordings from the <unk> Music Fair , April 7 , 1968 , three days after the murder of Martin Luther King , Jr . She dedicated the whole performance to him and sang " Why ? ( The King Of Love Is Dead ) " , a song written by her bass player , Gene Taylor , directly after the news of King 's death had reached them . In the summer of 1969 , she performed at the Harlem Cultural Festival in Harlem 's Mount Morris Park . 
- Together with Weldon Irvine , Simone turned the late Lorraine <unk> 's unfinished play To Be Young , <unk> and Black into a civil rights song . <unk> had been a personal friend whom Simone credited with <unk> her social and political consciousness . She performed the song live on the album Black Gold ( <unk> ) . A studio recording was released as a single , and renditions of the song have been recorded by Aretha Franklin ( on her 1972 album Young , <unk> and Black ) and by Donny <unk> . 
- 
- = = = Later life ( 1974 – 1993 ) = = = 
- 
- <unk> that she was not producing the mega @-@ hits that she had hoped for , Simone left the US in September 1970 , flying to Barbados and expecting Stroud to communicate with her when she had to perform again . However , Stroud interpreted Simone 's sudden disappearance , and the fact that she had left behind her wedding ring , as an indication of a desire for a divorce . As her manager , Stroud was in charge of Simone 's income . 
- Simone recorded her last album for RCA , It Is <unk> , in 1974 , and did not make another record until 1978 , when she was persuaded to go into the recording studio by <unk> Records owner Creed Taylor . The result was the album Baltimore , which , while not a commercial success , was fairly well received critically and marked a quiet artistic renaissance in Simone 's recording output . Her choice of material retained its <unk> , ranging from spiritual songs to Hall & Oates ' " Rich Girl . " Four years later Simone recorded <unk> on My Wings on a French label . 
- During the 1980s , Simone performed regularly at Ronnie Scott 's Jazz Club in London , where she recorded the album Live at Ronnie Scott 's in 1984 . Although her early on @-@ stage style could be somewhat <unk> and aloof , in later years , Simone particularly seemed to enjoy engaging her audiences sometimes by recounting humorous anecdotes related to her career and music and by soliciting requests . In 1987 , the original 1958 recording of " My Baby Just Cares for Me " was used in a commercial for <unk> No. 5 perfume in Britain . This led to a re @-@ release of the recording , which stormed to number 4 on the UK 's NME singles chart , giving her a brief surge in popularity in the UK . 
- When Simone returned to the United States , she learned that a warrant had been issued for her arrest for unpaid taxes ( as a protest against her country 's involvement with the Vietnam War ) , and returned to Barbados to evade the authorities and prosecution . Simone stayed in Barbados for quite some time and she had a lengthy affair with the Prime Minister , Errol Barrow . A close friend , singer Miriam Makeba , then persuaded her to go to <unk> . Later , she lived in Switzerland and the Netherlands , before settling in France in 1993 . During a 1998 performance in Newark , she announced , " If you 're going to come see me again , you 've got to come to France , because I ain 't coming back . " 
- Simone published her autobiography , I Put a Spell on You , in 1992 . She recorded her last album , A Single Woman in 1993 , where she depicted herself as such " single woman . " This album reflected her solitude and pain . She continued to tour through the 1990s but rarely traveled without an entourage . During the last decade of her life , Simone had sold more than one million records making her a global catalog best @-@ seller . This was accompanied by the CD revolution , global exposure through media television and the novelty of the Internet . 
- 
- = = = Illness and death = = = 
- 
- Simone had been diagnosed with bipolar disorder in the late 1980s . In 1993 , Simone settled near <unk> @-@ en @-@ Provence in Southern France . She had suffered from breast cancer for several years before she died in her sleep at her home in Carry @-@ le @-@ <unk> , <unk> @-@ du @-@ Rhône on April 21 , 2003 . Her funeral service was attended by singers Miriam Makeba and Patti <unk> , poet Sonia <unk> , actor <unk> Davis , actress Ruby Dee , and hundreds of others . Simone 's ashes were scattered in several African countries . She is survived by her daughter , Lisa Celeste Stroud , an actress and singer , who took the stage name Simone , and has appeared on Broadway in <unk> . 
- 
- = = = <unk> = = = 
- 
- Simone was known for her temper and frequent outbursts . In 1985 , she fired a gun at a record company executive , whom she accused of stealing royalties . Simone said she " tried to kill him " but " missed " . In 1995 , she shot and wounded her neighbor 's son with an air gun after the boy 's laughter disturbed her concentration . According to a biographer , Simone took medication for a condition from the mid @-@ 1960s on . All this was only known to a small group of <unk> , and kept out of public view for many years , until the biography Break Down and Let It All Out written by Sylvia Hampton and David Nathan revealed this in 2004 , after her death . Singer @-@ songwriter <unk> Ian , a one @-@ time friend of Simone 's , related in her own autobiography , Society 's Child : My <unk> , two incidents to illustrate Simone 's <unk> : One incident in which she forced a shoe store <unk> , at <unk> , to take back a pair of sandals she 'd already worn ; and another in which Simone demanded a royalty payment from Ian herself as an exchange for having recorded one of Ian 's songs , and then ripped a pay telephone out of its wall when she was refused . 
- 
- = = Musical style = = 
- 
- 
- = = = Simone standards = = = 
- 
- Throughout her career , Simone assembled a collection of songs that would later become standards in her repertoire . Some were songs that she wrote herself , while others were new arrangements of other standards , and others had been written especially for the singer . Her first hit song in America was her rendition of George <unk> 's " I Loves You , Porgy " ( 1958 ) . It peaked at number 18 in the pop singles chart and number 2 on the black singles chart . During that same period Simone recorded " My Baby Just Cares for Me " , which would become her biggest success years later , in 1987 , after it was featured in a 1986 <unk> No. 5 perfume commercial . A music video was also created by <unk> Studios . Well known songs from her Philips albums include " Don 't Let Me Be Misunderstood " on Broadway @-@ Blues @-@ <unk> ( 1964 ) , " I Put a Spell on You " , " Ne me <unk> <unk> " ( a rendition of a Jacques <unk> song ) and " Feeling Good " on I Put a Spell On You ( 1965 ) , " <unk> Wine " and " Wild Is the Wind " on Wild is the Wind ( 1966 ) . 
- " Don 't Let Me Be Misunderstood " , " Feeling Good " , and " Sinner Man " ( <unk> Blues , 1965 ) have remained popular in terms of cover versions ( most notably a version of the former song by The Animals ) , sample usage , and its use on soundtracks for various movies , TV @-@ series , and video games . " Sinner Man " has been featured in the TV series <unk> , Person of Interest , The <unk> , Sherlock , and <unk> , as well as in movies such as The Thomas Crown Affair , Miami Vice , and Inland Empire , and sampled by artists such as <unk> <unk> and Timbaland . The song " Don 't Let Me Be Misunderstood " was sampled by <unk> Springsteen on " Misunderstood " from Common 's 2007 album Finding Forever , and by little @-@ known producers <unk> and <unk> for the song " Don 't Get It " on Lil Wayne 's 2008 album <unk> Carter III . " See @-@ Line Woman " was sampled by Kanye West for " Bad News " on his album <unk> & Heartbreak . The 1965 rendition of " Strange Fruit " originally by Billie Holiday was sampled by Kanye West for " Blood on the <unk> " on his album <unk> . 
- Simone 's years at RCA @-@ Victor spawned a number of singles and album tracks that were popular , particularly in Europe . In 1968 , it was " Ain 't Got No , I Got Life " , a medley from the musical Hair from the album ' <unk> Said ! ( 1968 ) that became a surprise hit for Simone , reaching number 4 on the UK Singles Chart and introducing her to a younger audience . In 2006 , it returned to the UK Top 30 in a remixed version by <unk> . 
- The following single , a rendition of the Bee <unk> 's " To Love Somebody " , also reached the UK Top 10 in 1969 . " The House of the Rising Sun " was featured on Nina Simone Sings the Blues in 1967 , but Simone had recorded the song in 1961 and it was featured on Nina at the Village Gate ( 1962 ) , <unk> the versions by Dave Van Ronk and Bob Dylan . It was later covered by The Animals , for whom it became a signature hit . 
- 
- = = = Performing style = = = 
- 
- Simone 's bearing and stage presence earned her the title " High <unk> of Soul " . She was a piano player , singer and performer , " separately , and simultaneously . " As a composer and <unk> , Simone moved from gospel to blues , jazz , and folk , and to numbers with European classical styling . Besides using Bach @-@ style counterpoint , she called upon the particular virtuosity of the 19th @-@ century Romantic piano repertoire — Chopin , Liszt , <unk> , and others . <unk> , she incorporated monologues and dialogues with the audience into the program , and often used silence as a musical element . She compared it to " mass hypnosis . I use it all the time . " Throughout most of her life and recording career she was accompanied by <unk> <unk> Fleming and guitarist and musical director Al <unk> . She 's known to have lived 10 years with Emmanuel <unk> from France 
- 
- = = Legacy and influence = = 
- 
- 
- = = = Music = = = 
- 
- Musicians who have cited Simone as important for their own musical upbringing include Elton John ( who named one of his pianos after her ) , Aretha Franklin , <unk> , David Bowie , Emeli Sandé , Antony and the <unk> , <unk> <unk> , <unk> , Beyoncé , <unk> <unk> , Nick Cave , Van Morrison , Christina Aguilera , <unk> Brooks , <unk> <unk> , <unk> Def , Kanye West , Lena <unk> , Bono , John Legend , Elizabeth Fraser , Cat Stevens , Anna <unk> , <unk> Li , Peter Gabriel , Maynard James <unk> , Cedric <unk> @-@ <unk> , Mary J. <unk> , Fantasia <unk> , Michael <unk> , Angela <unk> , <unk> Hill , <unk> <unk> , Alicia Keys , Lana Del Rey , <unk> , Matt Bellamy , Ian <unk> , Kerry Brothers , Jr . , <unk> , Amanda Palmer , Steve <unk> and Jeff Buckley . John Lennon cited Simone 's version of " I Put a Spell on You " as a source of inspiration for the Beatles ' song " Michelle " . 
- Simone 's music has been featured in soundtracks of various motion pictures and video games , including but not limited to , La <unk> Nikita ( 1990 ) , Point of No Return ( 1993 ) , The Big <unk> ( 1998 ) , Notting Hill ( 1999 ) , Any Given Sunday ( 1999 ) , The Thomas Crown Affair ( 1999 ) , <unk> Acts ( 2000 ) , Six Feet Under ( 2001 ) , The <unk> <unk> ( 2002 ) , Before Sunset ( 2004 ) , Cellular ( 2004 ) , Inland Empire ( 2006 ) , Miami Vice ( 2006 ) , Sex and the City ( 2008 ) , The World Unseen ( 2008 ) , Revolutionary Road ( 2008 ) , Home ( 2008 ) , <unk> ( 2009 ) , The <unk> ( 2009 ) , <unk> Men ( 2010 ) , and Beyond the Lights ( 2014 ) . <unk> her music is used in remixes , commercials , and TV series including " Feeling Good " , which featured prominently in the Season Four <unk> of Six Feet Under ( 2004 ) . Simone 's " Take Care of Business " is the closing theme of " The Man From <unk> " ( 2015 ) 
- 
- = = = Film = = = 
- 
- The documentary Nina Simone : La <unk> ( The Legend ) was made in the 1990s by French filmmakers , based on her autobiography I Put a Spell on You . It features live footage from different periods of her career , interviews with family , various interviews with Simone then living in the Netherlands , and while on a trip to her birthplace . A portion of footage from The Legend was taken from an earlier 26 @-@ minute biographical documentary by Peter <unk> , released in 1969 and entitled simply , Nina . Her filmed 1976 performance at the Montreux Jazz Festival is available on video courtesy of Eagle Rock Entertainment and is screened annually in New York City at an event called " The Rise and Fall of Nina Simone : Montreux , 1976 " which is <unk> by Tom <unk> . 
- Footage of Simone singing " Mississippi <unk> " for 40 @,@ 000 <unk> at the end of the Selma to Montgomery marches can be seen in the 1970 documentary King : A Filmed Record ... Montgomery to Memphis and the 2015 Liz <unk> documentary , What <unk> , Miss Simone ? 
- Plans for a Simone biographical film were released at the end of 2005 , to be based on Simone 's autobiography I Put a Spell on You ( 1992 ) and to focus on her relationship in later life with her assistant , Clifton Henderson , who died in 2006 ; Simone 's daughter , Simone Kelly , has since refuted the existence of a romantic relationship between Simone and Henderson on account of his homosexuality . Cynthia Mort , screenwriter of Will & Grace and <unk> , has written the screenplay and directed the film , Nina , which stars Zoe <unk> in the title role . In May 2014 , the film was shown to potential distributors at the Cannes Film Festival , but has , as of August 2014 , not been seen by reviewers . 
- In 2015 , two documentary features about Simone 's life and music were released . The first , directed by Liz <unk> , What <unk> , Miss Simone ? was produced in cooperation with Simone 's estate and her daughter , who also served as the film 's executive producer . The film was produced as a counterpoint to the unauthorized Cynthia Mort film , and featured previously unreleased archival footage . It premiered at the Sundance Film Festival in January 2015 and was distributed by Netflix on June 26 , 2015 . It was nominated on January 14 , 2016 for a 2016 Academy Award for Best Documentary Feature . The Amazing Nina Simone is an independent film directed by Jeff L. Lieberman and is also scheduled for release in 2015 . The director initially consulted with Simone 's daughter before going the independent route and instead worked closely with her siblings , predominantly Sam Waymon . 
- 
- = = = Honors = = = 
- 
- Simone was the recipient of a Grammy Hall of Fame Award in 2000 for her interpretation of " I Loves You , Porgy . " She has also received fifteen Grammy Award nominations . On Human <unk> Day 1974 in Washington , D.C. , more than 10 @,@ 000 people paid tribute to Simone . Simone received two honorary degrees in music and humanities , from the University of Massachusetts <unk> and Malcolm X College . She preferred to be called " Dr. Nina Simone " after these honors were bestowed upon her . 
- Two days before her death , Simone was awarded an honorary degree by the Curtis Institute of Music , the music school that had refused to admit her as a student at the beginning of her career . 
- In 2002 , the city of Nijmegen , Netherlands , named a street after her , the Nina Simone <unk> ; she had lived in Nijmegen between 1988 and 1990 . On August 29 , 2005 , the city of Nijmegen , concert hall De <unk> , and more than fifty artists ( amongst whom were Frank <unk> , <unk> <unk> , and Fay <unk> ) honoured Simone with the tribute concert <unk> From Nijmegen . 
- Simone was inducted into the North Carolina Music Hall of Fame in 2009 . 
- In 2010 , a statue in her honor was erected in Trade Street in her native Tryon , North Carolina . 
- 
- = = Discography = = 
- 
- 
- = = = Albums = = = 
- 
- 
- = = = Chart singles = = = 
- 
- 
- 
- = Vistara = 
- 
- Tata SIA Airlines Limited , operating as Vistara , is an Indian domestic airline based in <unk> with its hub at Delhi @-@ Indira Gandhi International Airport . The carrier , a joint venture between Tata Sons and Singapore Airlines , commenced operations on 9 January 2015 with its inaugural flight between Delhi and Mumbai . The airline had carried more than two million passengers by June 2016 and as of April 2016 , has a 2 @.@ 3 % share of the domestic carrier market . The airline operates to seventeen destinations with a fleet of Airbus A320 @-@ 200 aircraft . Vistara was the first airline to introduce premium economy seats on domestic routes in India . 
- 
- = = History = = 
- 
- The airline was founded in 2013 as a joint venture ( JV ) between India 's conglomerate Tata Sons and Singapore Airlines ( SIA ) . The two companies had made a bid in the mid @-@ 1990s to launch a full service carrier in India that was unsuccessful , being denied regulatory approval by the Indian government . With India opening up its airline sector for 49 percent foreign direct investment ( <unk> ) in 2012 , Tata and SIA once again decided to float a JV airline company in India . The JV , Tata SIA Airlines Limited ( <unk> ) , was envisaged as a premium full @-@ service carrier to cater to the demands of high @-@ end business travellers in India 's civil aviation market dominated by low @-@ cost carriers . India 's Foreign Investment Promotion Board approved the JV in October 2013 , allowing SIA to take a 49 percent stake in the airline . The two parent companies initially pledged to invest a combined US $ <unk> as start @-@ up capital , with Tata Sons owning 51 percent and Singapore Airlines owning the remaining 49 percent . This was part of Tata 's second major foray into the aviation sector along with a minority stake in <unk> India . The company 's first venture , Tata Airlines , was established in the 1930s and later became the flag carrier Air India after <unk> . 
- The company unveiled its brand identity " Vistara " on 11 August 2014 . The name was taken from the Sanskrit word <unk> , meaning " <unk> <unk> " . Vistara received its air operator 's certificate from the Directorate General of Civil Aviation on 15 December 2014 and started operations on 9 January 2015 . Vistara became the first carrier to operate domestic services out of the new Terminal 2 at Mumbai 's <unk> <unk> International Airport . On 24 August 2015 , Vistara inaugurated the Aviation Security Training Institute , an in @-@ house institute for training its cockpit and cabin crew , security staff and others related to aviation industry . The institute has secured the necessary <unk> from the <unk> body Bureau of Civil Aviation Security . From the first month of operation , Vistara consistently achieved very high on @-@ time performance ( <unk> ) records of over 90 percent , the highest among India 's domestic carriers . On 20 August 2015 , Vistara declared it had carried half a million passengers in just over seven months of operations . As of February 2016 , Vistara has a share of 2 % in the domestic carrier market . 
- 
- = = Corporate affairs = = 
- 
- In March 2015 , Vistara shifted to its new office at the One Horizon Center tower in Sector 43 , <unk> , a satellite city of Delhi . Vistara chose <unk> <unk> Yeoh as the chief executive officer ( CEO ) and <unk> Ming <unk> as the chief commercial officer ( <unk> ) , both with Singapore Airlines background . Initially it started out with a three @-@ member board comprising <unk> <unk> <unk> ( SIA group ) , <unk> <unk> and Prasad <unk> ( Tata group ) , with the latter as chairman . In August 2015 , the airline expanded the board by introducing two new members , <unk> <unk> and <unk> <unk> , along with an <unk> infusion of ₹ 2 billion ( US $ 30 million ) , part of ₹ 5 billion ( US $ 74 million ) initially planned by Tata and SIA together . In January 2016 , <unk> <unk> , former Managing Director of Titan , joined as the new chairman following Prasad <unk> 's retirement . In March 2016 , Vistara appointed <unk> Kapoor as its chief strategy and commercial officer as the successor to <unk> Ming <unk> who was scheduled to leave in mid April 2016 following completion of his <unk> at Vistara . 
- 
- = = Destinations = = 
- 
- As of May 2016 , Vistara flies to the following destinations within India : 
- 
- = = = <unk> agreements = = = 
- 
- Vistara has <unk> agreements with the following airlines : 
- Air France 
- British Airways 
- Jet Airways 
- <unk> 
- Air India 
- Singapore Airlines 
- 
- = = Fleet = = 
- 
- As of 31 May 2016 , Vistara operates the following aircraft : 
- Vistara took delivery of its first aircraft at New Delhi on 25 September 2014 . The airline plans to take delivery of all thirteen of its original Airbus A320 aircraft by 2016 and seven Airbus <unk> aircraft of its initial order by 2018 . In March 2015 , <unk> <unk> Yeoh announced that the airline is planning to procure an unspecified number of both narrow @-@ body and wide @-@ body aircraft to enhance the domestic network and launch international flights within two years . 
- 
- = = Services = = 
- 
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- = = = Cabin = = = 
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- = = = = Business class = = = = 
- 
- Being a full @-@ service carrier , Vistara offers 16 business class seats , four rows in 2 @-@ 2 configuration , in its 148 @-@ seater Airbus A320 @-@ 200 fleet . The business class seats are 20 @.@ 12 inches ( 511 mm ) wide with 42 inches ( 1 @,@ 100 mm ) seat pitch . 
- 
- = = = = Premium Economy = = = = 
- 
- Vistara was the first airline in India to introduce Premium Economy class in the domestic market . At present it offers 36 premium economy seats , six rows in 3 @-@ 3 configuration , out of total 148 seats in the single aisle Airbus A320 @-@ 200 aircraft in its fleet . Each being 18 inches ( 460 mm ) wide and having a pitch of 33 – 36 inches ( 840 – 910 mm ) . 
- 
- = = = In @-@ flight entertainment = = = 
- 
- As of April 2016 , Vistara 's Airbus A320 @-@ 200s do not have in @-@ flight entertainment screens installed and it provides pre @-@ loaded tablets for its business @-@ class <unk> . In March 2016 , Vistara started <unk> content directly to passengers ' personal electronic devices through one @-@ way <unk> @-@ Fi connection through <unk> Systems <unk> in @-@ flight entertainment system . 
- 
- = = = <unk> = = = 
- 
- The in @-@ flight food is <unk> by <unk> Air <unk> , another joint venture between Tata and a Singaporean company , headed by <unk> <unk> <unk> , formerly the executive chef of the Taj Hotels group . Vistara offers four different meals for each cabin for different time of the day — breakfast , <unk> , lunch and dinner with options of one vegetarian and one non @-@ vegetarian dish in economy class ; two vegetarian dishes and one non @-@ vegetarian dish in premium economy ; and two vegetarian and two non @-@ vegetarian dishes for business @-@ class cabin . The menu is typically changed every seventh day and there are different menus for lunch and dinner . It also provides special meals upon request 24 hours before departure . 
- 
- = = = Lounge = = = 
- 
- On 29 March 2016 , Vistara inaugurated premium lounge service for its Business @-@ class passengers and Club Vistara Gold card holders at the departure level of Terminal 3 of Indira Gandhi International Airport at Delhi . The lounge is spread across 250 square metres on the air @-@ side and can seat 75 passengers at a time . 
- 
- = = Frequent @-@ flyer program = = 
- 
- Vistara uses Club Vistara as its frequent @-@ flyer program ; it operates as a value based program and awards points on the basis of money spent on tickets rather that miles travelled by passengers . On 29 January 2015 , Vistara announced a partnership agreement with Singapore Airlines ( SIA ) which would allow Club Vistara members to earn and redeem miles with the <unk> program on SIA and Silk Air flights and vice versa . 
- 
- 
- = Portrait of Monsieur Bertin = 
- 
- Portrait of Monsieur Bertin is an 1832 oil @-@ on @-@ canvas painting by Jean @-@ Auguste @-@ Dominique Ingres . It depicts Louis @-@ François Bertin ( 1766 – 1841 ) , the French writer , art collector and director of the pro @-@ <unk> Journal des débats . Ingres completed the portrait during his first period of success ; having achieved acclaim as a history painter , he accepted portrait commissions with reluctance , regarding them as a distraction from more important work . Bertin was a friend and a politically active member of the French upper @-@ middle class . Ingres presents him as a personification of the commercially minded leaders of the liberal reign of Louis Philippe I. He is physically imposing and self @-@ assured , but his real @-@ life personality shines through – warm , <unk> and engaging to those who had earned his trust . 
- The painting had a prolonged genesis . Ingres <unk> over the pose and made several preparatory sketches . The final work faithfully captures the sitter 's character , conveying both a restless energy and imposing bulk . It is an <unk> realistic depiction of ageing and emphasises the <unk> skin and thinning hair of an overweight man who yet maintains his resolve and determination . He sits in three @-@ quarter profile against a brown ground lit from the right , his fingers are pronounced and highly detailed , while the <unk> of his chair reflects light from an unseen window . 
- Ingres ' portrait of Bertin was a critical and popular success , but the sitter was a private person . Although his family worried about caricature and disapproved , it became widely known and sealed the artist 's reputation . It was praised at the Paris Salon of 1833 , and has been influential to both academic painters such as Léon <unk> and later <unk> including Pablo Picasso and Félix <unk> . Today art critics regard it as Ingres ' finest male portrait . It has been on permanent display at the Musée du Louvre since 1897 . 
- 
- = = Background = = 
- 
- Louis @-@ François Bertin was 66 in 1832 , the year of the portrait . He befriended Ingres either through his son <unk> Bertin , a student of the painter , or via Étienne @-@ Jean <unk> , Ingres ' friend and the Journal 's art critic . In either case the genesis of the commission is unknown . Bertin was a leader of the French upper class and a supporter of Louis Philippe and the Bourbon Restoration . He was a director of the Le Moniteur <unk> until 1823 , when the Journal des débats became the recognised voice of the liberal @-@ constitutional opposition after he had come to criticize absolutism . He eventually gave his support to the July Monarchy . The Journal supported contemporary art , and Bertin was a patron , collector and <unk> of writers , painters and other artists . Ingres was sufficiently intrigued by Bertin 's personality to accept the commission . 
- It was completed within a month , during Ingres ' frequent visits to Bertin 's estate of retreat , Le <unk> des Roches , in <unk> , <unk> . Ingres made daily visits , as Bertin entertained guests such as Victor Hugo , his mistress <unk> <unk> , Hector Berlioz , and later Franz Liszt and Charles Gounod . Ingres later made drawings of the Bertin family , including a depiction of his host 's wife and sketches of their son Armand and daughter @-@ in @-@ law , Cécile . The portrait of Armand <unk> his physical resemblance to his father . 
- Ingres ' early career coincided with the Romantic movement , which reacted against the prevailing neoclassical style . <unk> in French art had developed as artists saw themselves as part of the cultural center of Europe , and France as the successor to Rome . Romantic painting was <unk> and more expressive , preoccupied more with colour than with line or form , and more focused on style than on subject matter . <unk> based on classical themes fell out of fashion , replaced by contemporary rather than historical subject matter , especially in portraiture . Ingres resisted this trend , and wrote , " The history painter shows the species in general ; while the portrait painter represents only the specific individual — a model often ordinary and full of shortcomings . " From his early career , Ingres ' main source of income was commissioned portraits , a genre he dismissed as lacking in grandeur . The success of his The <unk> of Louis XIII at the 1824 Salon marked an abrupt change in his fortunes : he received a series of commissions for large history paintings , and for the next decade he painted few portraits . His financial difficulties behind him , Ingres could afford to concentrate on historical subjects , although he was highly sought @-@ after as a <unk> . He wrote in 1847 , " <unk> portraits , they are so difficult to do that they prevent me getting on with greater things that I could do more quickly . " 
- Ingres was more successful with female than male portraits . His 1814 Portrait of Madame de <unk> was described as " to the feminine what the Louvre 's Bertin is to the masculine " . The sitter for his 1848 Portrait of <unk> de Rothschild looks out at the viewer with the same <unk> as Bertin , but is softened by her attractive dress and relaxed pose ; she is engaging and sympathetic rather than tough and imposing . 
- 
- = = The portrait = = 
- 
- 
- = = = Preparation and execution = = = 
- 
- Ingres was self @-@ critical and consumed by self @-@ doubt . He often took months to complete a portrait , leaving large periods of inactivity between <unk> . With Bertin , he <unk> in finding a pose to best convey both the man 's restless energy and his age . At least seven studies survive , three are signed and dated . Ingres was a master draftsman and the sketches , if not fully realised , are highly regarded in their own right . The sketches are exemplary in their handling of line and form , and similar in size . 
- The earliest study has Bertin standing and leaning on a table in an almost Napoleonic pose . His hard , level stare is already established , but the focus seems to be on his groin rather than his face . It is obvious that Ingres struggled with the sketch ; the head is on a square of attached paper which must have replaced an earlier cut @-@ out version , and other areas have been rubbed over and heavily reworked . The next extant drawing shows Bertin seated , but the chair is missing . The last extant sketch is the closest to the eventual painting , with a chair , though his bulk has not yet been filled out . 
- Frustrated by his inability to capture his subject , Ingres broke down in tears in his studio , in company . Bertin recalled " <unk> him : ' my dear Ingres , don 't <unk> about me ; and above all don 't <unk> yourself like that . You want to start my portrait over again ? Take your own time for it . You will never tire me , and just as long as you want me to come , I am at your orders . ' " After agreeing to a breathing spell Ingres finally settled on a design . Early biographers provide differing anecdotes regarding the inspiration for the distinctive seated pose . Henri Delaborde said Ingres observed Bertin in this posture while arguing politics after dinner with his sons . According to Eugène Emmanuel <unk> <unk> ( he said was related to him by Bertin ) , Ingres noticed a pose Bertin took while seated outside with Ingres and a third man at a café . 
- Bertin said that Ingres , confident that he had finally established the pose for the portrait , " came close and speaking almost in my ear said : ' Come sit tomorrow , your portrait is [ as good as ] done . ' " Bertin 's final pose reverses the usual relationship between the two men . The artist becomes the cool , detached observer ; Bertin , usually calm and reasoned , is now restless and impatient , mirroring Ingres ' irritation at spending time on portraiture . 
- 
- = = = Description = = = 
- 
- Bertin is presented as strong , energetic and warm @-@ hearted . His hair is grey <unk> on white , his fingers spread across his knees . Bertin 's fingers were described in 1959 by artist Henry de <unk> as " crab @-@ like claws ... emerging from the <unk> <unk> that are the sleeves of his coat . " The bulk of his body is <unk> in a tight black jacket , black trousers and brown satin <unk> , with a <unk> white shirt and <unk> revealing his open neck . He wears a gold watch and a pair of glasses in his right pocket . In the view of art historian Robert Rosenblum , his " nearly ferocious presence " is <unk> by the tightly constrained space . The chair and clothes appear too small to contain him . His coiled , <unk> fingers rest on his thighs , barely protruding from the sleeves of his jacket , while his neck cannot be seen above his narrow <unk> white collar . 
- The painting is composed in monochrome , muted colours ; predominately blacks , greys and <unk> . The exceptions are the whites of his collar and sleeves , the reds in the cushion and the light reflecting on the leather of the arm @-@ chair . In 19th @-@ century art , vivid colour was associated with femininity and emotion ; male portraiture tended towards muted shades and monochrome . Bertin leans slightly forward , boldly staring at the viewer in a manner that is both imposing and paternal . He seems engaged , and poised to speak , his body fully towards the viewer and his expression etched with certainty . <unk> by Nicolas <unk> 's 1650 Self @-@ Portrait with <unk> of Painting , Ingres <unk> details the veins and <unk> of his face . Bertin is in three @-@ quarter profile , against a gold – brown background lit from the right . He rests on a curved @-@ back mahogany chair , the arms of which reflect light falling from the upper left of the pictorial space . 
- Ingres seems to have adapted elements of the approach and technique of Hans <unk> 's <unk> Portrait of William <unk> , now in the Louvre . Neither artist placed much emphasis on colour , preferring dark or cool tones . The <unk> portrait seems to have informed the indicators of Bertin 's aging and the emphasis on his fingers . Jacques @-@ Louis David also explored hyper @-@ realism in his depictions of Cooper <unk> ( 1802 ) and Emmanuel Joseph <unk> ( 1817 ) . In the later painting , David shows tiny <unk> of light reflecting on the sitter 's chair and <unk> details " every <unk> <unk> of [ <unk> ' ] closely cropped <unk> hair . " 
- The Greek <unk> pattern at the foot of the wall is unusually close to the picture plane , <unk> the sitter . The wall is painted in gold , adding to the sense of a monumental portrait of a modern icon . The details of Bertin 's face are highly symmetrical . His eyes are heavily <unk> , <unk> by <unk> positioned twists of his white collar , the winds of his hair , eyebrows and eyelids . His mouth turns downwards at the left and upwards to the right . This dual expression is intended to show his <unk> and complex personality : he is a hard @-@ <unk> businessman , and a patron of the arts . The reflection of a window can be seen in the rim of Bertin 's chair . It is barely discernible , but adds spatial depth . The Portrait of Pope Leo X ( c . <unk> ) by Raphael , a source for the Bertin portrait , also features a window reflection of the <unk> on the pope 's chair . 
- The painting is signed <unk> <unk> 1832 in capitals at the top left , and <unk> Bertin , also in capitals , at the upper right . The frame is the original , and thought to have been designed by Ingres himself . It shows animals around a <unk> and richly carved <unk> . Art historians Paul Mitchell and Lynn Roberts note that the design follows an old French tradition of placing austere male portraits within " <unk> carved " frames . The frame closely resembles that of Raphael 's c . 1514 – 15 Portrait of <unk> <unk> , a painting that influenced Ingres , especially in colour and tone . 
- 
- = = Reception = = 
- 
- Monsieur Bertin was exhibited at the 1833 Salon alongside his 1807 Portrait of <unk> <unk> . It met with near universal praise to become his most successful artwork to that point . It sealed his reputation as a <unk> , reaching far enough into public consciousness to become a standard for newspaper political satires . Today it is considered his greatest portrait . Ingres viewed all this as a mixed blessing , remarking that " since my portraits of Bertin and <unk> , everybody wants portraits . There are six that I 've turned down , or am avoiding , because I can 't stand them . " Before the official exhibition , Ingres displayed the painting in his studio for friends and pupils . Most were lavish in their praise , although Louis <unk> <unk> to a friend that he feared people might " find the colouring a bit dreary " . He proved correct ; at the Salon , critics praised the <unk> , but some felt the portrait exemplified Ingres ' weakness as a colourist . It was routinely faulted for its " purplish tone " — which the ageing of the oil medium has transformed over time to warm greys and <unk> . Bertin 's wife Louis @-@ Marie reportedly did not like the painting ; his daughter , Louise , thought it transformed her father from a " great lord " to a " fat farmer " . 
- Given the standings of the two men , the painting was received in both social and political terms . A number of writers mentioned Bertin 's eventful career , in tones that were , according to art historian Andrew Carrington Shelton , either " <unk> sarcastic [ or ] <unk> <unk> " . There were many satirical reproductions and pointed <unk> in the following years . Aware of Bertin 's support of the July Monarchy , writers at the La Gazette de France viewed the portrait as the <unk> of the " <unk> and cynicism " of the new regime . Their anonymous critic <unk> wondered " what bitter irony it expresses , what hardened skepticism , sarcasm and ... pronounced cynicism " . 
- Several critics mentioned Bertin 's hands . Twentieth @-@ century art historian Albert <unk> described them as " powerful , <unk> ... grasping his thighs in a gesture ... projecting ... enormous strength controlled " . Some contemporary critics were not so kind . The photographer and critic Félix <unk> was harshly critical , and disparaged what he saw as a " <unk> bundle of flesh ... under which , instead of bones and muscles , there can only be intestines – this <unk> hand , the <unk> of which I can hear ! " Bertin 's hands made a different impression on the critic F. de <unk> , who remarked : " A mediocre artist would have modified them , he would have replaced those swollen joints with the cylindrical fingers of the first <unk> model ; but by this single alteration he would have changed the expression of the whole personality ... the energetic and mighty nature " . 
- The work 's realism attracted a large amount of commentary when it was first exhibited . Some saw it as an <unk> to Romanticism , others said that its small details not only showed an acute likeness , but built a psychological profile of the sitter . Art historian <unk> <unk> sees Bertin as " at once intense , suspicious , and aggressive " . She notes that there is a certain amount of projection of the artist 's personality and recalls <unk> <unk> 's description of Ingres ; " There he was squarely seated in an armchair , motionless as an Egyptian god carved of granite , his hands stretched wide over parallel knees , his torso stiff , his head <unk> " . Some compared it to <unk> Denner , a German realist painter influenced by Jan van Eyck . Denner , in the words of Ingres scholar Robert Rosenblum , " specialised in recording every last line on the faces of aged men and women , and even <unk> of windows in their eyes . " The comparison was made by Ingres ' admirers and detractors alike . In 1833 , Louis de Maynard of the <unk> @-@ <unk> <unk> , writing in the influential L <unk> <unk> , dismissed Denner as a weak painter concerned with <unk> " <unk> " , and said that both he and Ingres fell short of the " sublime productions of Ingres ' self @-@ proclaimed hero , Raphael . " 
- The following year Ingres sought to <unk> on the success of his Bertin portrait . He showed his ambitious history painting The Martyrdom of Saint <unk> at the 1834 Salon , but it was harshly criticised ; even Ingres ' admirers offered only faint praise . <unk> and frustrated , Ingres declared he would <unk> the Salon , abandon his residence in Paris for Rome , and relinquish all current positions , ending his role in public life . This <unk> was not to last . 
- Bertin bequeathed the portrait to his daughter Louise ( 1805 – 77 ) on his death . She passed it to her niece Marie @-@ Louise @-@ Sophie Bertin ( 1836 – 93 ) wife of Jules <unk> , a later director of the Journal des débats . They bequeathed it to their niece Cécile <unk> , its last private owner . In 1897 Cécile sold it to the Musée du Louvre for 80 @,@ 000 francs . 
- 
- = = Legacy = = 
- 
- The Bertin portrait has been hugely influential . At first it served as a model for depictions of energetic and intellectual 19th @-@ century men , and later as a more universal type . Several 1890s works closely echo its form and motifs . Jean @-@ Joseph Benjamin @-@ Constant 's monochrome and severe 1896 Portrait of Alfred <unk> is heavily indebted , while Léon <unk> 's stern 1892 portrait of the aging Ernest <unk> has been described as a " direct citation " of Ingres ' portrait . 
- Its influence can be seen in the dismissive stare and overwhelming physical presence of the sitter in Pablo Picasso 's 1906 Portrait of Gertrude Stein . Picasso admired Ingres and referred to him throughout his career . His invoking of Bertin can be read as a humorous reference to , according to Robert Rosenblum , " Stein 's <unk> bulk and sexual preference " . Stein does not possess Bertin 's ironic stare , but is similarly dressed in black , and leans forward in an imposing manner , the painting emphasising her " massive , monumental presence " . In 1907 the Swiss artist Félix <unk> depicted Stein , in response to Picasso , making an even more direct reference to Ingres ' portrait , prompting <unk> <unk> to exclaim , " That 's Madame Bertin ! " 
- The influence continued through the 20th century . Gerald Kelly recalled Bertin when painting his restless and confined series of portraits of Ralph Vaughan Williams in 1952 – 61 . In 1975 <unk> <unk> produced a series of nine black and white photographs on board based on Ingres ' portraits of Bertin and <unk> Caroline Rivière . 
- 
- 
- = Harajuku Lovers Tour = 
- 
- The Harajuku Lovers Tour was the first solo concert tour of American recording artist Gwen Stefani . The tour began through October to November 2005 , to support of her debut studio album Love . Angel . Music . Baby . ( 2004 ) . Although Stefani embarked on multiple tours with her band No Doubt , she initially opted not to participate in a tour to promote her album , an attitude that the singer eventually abandoned due to the commercial success of Love . Angel . Music . Baby . 
- The Harajuku Lovers Tour consisted of only one leg , which encompassed a three @-@ month @-@ long series of performances that visited cities throughout the United States and Canada . Stefani recruited hip hop group The Black Eyed Peas , rapper M.I.A. , and singer Ciara to accompany her as opening acts for her <unk> . The tour produced varying responses from contemporary critics , who despite praising Stefani 's vocals , were critical of other aspects of the show such as its musical material . According to Billboard , the tour grossed $ 22 million from 37 concerts , from which 20 sold @-@ out . A video album , titled Harajuku Lovers Live , was released in DVD format in conjunction with her 2006 album The Sweet Escape and features the singer performing at the Honda Center in <unk> , California , Stefani 's birthplace . 
- 
- = = Background = = 
- 
- Stefani announced a tour to support her first solo studio album Love . Angel . Music . Baby . ( 2004 ) on June 27 , 2005 , giving details of sixteen dates from October 16 to November 10 . The announcement on June 27 also included the fact that hip hop group The Black Eyed Peas , who are also signed to Interscope Records , would be the opening act for all the announced dates except November 3 . The group , who were backing their album Monkey Business , ended up touring with Stefani until November 14 . On August 8 , it was announced that singer @-@ songwriter and rapper M.I.A. would take over as the opening act from November 16 to November 25 , although it wasn 't until August 17 that the extra dates from November 11 to November 25 were officially added to the tour . M.I.A. toured with Stefani , backing her album <unk> , until December 1 . On September 29 , the final set of dates , November 26 to December 21 , were added to the tour and it was announced that the third and final opening act for Stefani 's tour would be singer Ciara , backing her album <unk> , from December 3 to December 21 . 
- Stefani initially did not intend to tour to support the album , responding " What tour ? " to a question from MTV News in December 2004 regarding a possible tour . She later mentioned several times that she had not originally intended to tour in support of the album , referring to her " illegal tour " and <unk> for her breaking her promise not to tour on stage at the <unk> Energy Center in Saint Paul , Minnesota on November 14 and admitting " I just wanted to make a record . I didn 't want to tour , I was too tired , then you guys kept buying the record , and I had to come out and see you guys " on stage at Winnipeg on November 16 . In a September interview with MTV News , Stefani said of the tour : " I 'm looking forward to it , it 's going to be unbelievable . [ ... ] I would love to try to roll a little bit of ' Orange County Girl ' in there . We 'll see . Anything can happen in rehearsals . I don 't even know how long the show can be right now . " 
- 
- = = Concert synopsis = = 
- 
- Stefani opened the show with the song " Harajuku Girls " , an ode to Harajuku , the fashion district of Tokyo , Japan . She appeared on stage wearing a tiara and baby doll outfit , sitting in the red velvet and gold throne from the cover of Love . Angel . Music . Baby. and surrounded by her backing dancers , also called the Harajuku Girls , while video images of Harajuku itself played on screens behind her . Her second song was the first single from the album , " What You Waiting For ? " , which she began as a ballad before bringing it up to its usual pace . Stefani and the Harajuku Girls then left the stage to change into one @-@ piece bathing suits while her band continued to play , before returning to perform " The Real Thing " . A group of four <unk> came on stage to perform while Stefani left the stage again to change into a black @-@ and @-@ white leather <unk> . For the next song , the album 's sixth single " Crash " , the audience were then divided into male and female halves and , as images of a car hood bouncing to the beat were broadcast , each half took turns to sing the lyric " back it up , back it up " . Stefani then performed the fourth single from the album , " <unk> " . 
- Stefani changed outfits again into a pair of black hot pants to perform " Rich Girl " , the album 's second single , while walking along a catwalk into the crowd and giving fans high @-@ <unk> . She then sang " Danger Zone " and " Long Way to Go " , both intimate songs , before performing two new songs back @-@ to @-@ back : " Wind It Up " , which would become the first single from her second album The Sweet Escape , and " Orange County Girl " . " Wind It Up " was performed with a carnival vibe and " Orange County Girl " was accompanied by a video montage of childhood photos of Stefani and images of items mentioned in the song . She changed into a silver <unk> cocktail dress for the fourth single from her first album , " Cool " . 
- In early performances of the show , Stefani 's next song was " Hollaback Girl " , the album 's third and best @-@ selling single , performed in a drumming costume and singing with the audience . This was followed by an encore of " Serious " and " <unk> Pop Electric " , for which Stefani was brought out in a stretcher by the Harajuku Girls . However , in later performances , " Hollaback Girl " was saved for the encore and preceded by the two other songs . 
- 
- = = Critical response = = 
- 
- Critics were divided with the Harajuku Lovers Tour . Patrick MacDonald of the Seattle Times , while <unk> Stefani 's song @-@ writing efforts and the show 's " <unk> fun " antics , reprimanded the singer 's dancing and limited material , given that she performed only twelve songs from Love . Angel . Music . Baby. and two from The Sweet Escape but none from previous work with her band No Doubt . In regards to the musical selection , MacDonald concluded that half the songs are " eminently forgettable " , a view echoed by Winnipeg Sun columnist Rob Williams , who described some of the album tracks as " <unk> " . Despite these objections , Williams issued her <unk> Center performance a three @-@ and @-@ a @-@ half out of five stars . " But sometimes the music didn 't matter when there was as much eye candy as Stefani provided " , opined the Winnipeg Sun writer . " She is an extremely striking figure and the choreography , light show , backup dancers and frequent costume changes all added to the spectacle , which ended up being more style than substance . " 
- Mike Ross of the Edmonton Sun was impressed with Stefani 's ability to engage the audience , a quality that earned her the description of the " effervescent hostess " from the journalist . In his four star review for the concert , he commended the " swell " music and " amazing " choreography ; " It also had merit as a <unk> tour @-@ de @-@ force — thanks in large part to a quartet each of talented dancing <unk> girls and B @-@ boys " , remarked Ross . Stefani 's different outfits and set pieces also won praise . " So you could have enjoyed last night 's concert as a fashion show . Or a music video , sure " , Ross concluded . Although she called Stefani the " new princess of pop " and praised the singer 's charismatic presence during the show , Jane Stevenson of the Toronto Sun felt that the concert was " definitely of the lightweight variety in the music department " and noted that , although Stefani models herself after Madonna , she is " no real threat " . She gave the concert three @-@ and @-@ a @-@ half stars out of five . 
- MTV 's Corey Moss compared Stefani 's performance to Madonna with regard to " the eight costume changes to the dancers to the <unk> to , hell , even the music itself " and <unk> that she sounded completely different from how she did with No Doubt . Moss asserted that the No Doubt lead vocalist was the " most captivating thing onstage " with her " confident strut and dead @-@ on vocals " . Jon Pareles of The New York Times admired the " glamour and groove " antics of the show and how Stefani " <unk> [ ed ] the <unk> of stardom " . He praised her music , describing the album as " a smart [ record ] , with <unk> rhythm tracks that <unk> from funk to pop , electronics to rock " , although calling most tracks " superficial " , being about " style " , " success " , " shopping " and " sex " . 
- Jim Harrison of <unk> affirmed that the Harajuku Lovers Tour lacked a strong musical setlist , and also felt that Stefani 's stage presence was absent . He stated that she " doesn 't have many songs that translate well in a live setting " from her album Love . Angel . Music . Baby. and suggested that she should have included songs by No Doubt for " much @-@ needed shots of <unk> " for the audience , who were , according to Harrison , " <unk> sitting on their hands and literally <unk> four songs into the set " . Harrison felt that Stefani seemed truly " lost on stage without a band " , ultimately describing her performance as " [ lacking in ] energy " , " lukewarm " and seemingly " on <unk> " . Furthermore , the <unk> writer thought that her performance was akin to that of fellow American recording artist Britney Spears , which was " utterly unsuitable for both the song selection and her style " . However , he did praise the rendition of " Hollaback Girl " , calling it " great " and " Gwen being all Gwen can be " , and called the <unk> " pretty cool " . 
- 
- = = Broadcast and recordings = = 
- 
- Stefani 's performance in late November 2005 in her home town of <unk> , California was recorded and released on DVD as a video album Harajuku Lovers Live . It was released on December 5 , 2006 , the same release date as Stefani 's second album , The Sweet Escape . The DVD was directed by Sophie <unk> . The concert features performances of all twelve songs from Love . Angel . Music . Baby. and two new songs from her second studio album , The Sweet Escape , as well as interviews with the musicians and dancers and a documentary of tour preparation . The DVD received similar mixed reviews to the concerts themselves . Reviewers praised Stefani 's musical performances and stage presence , but criticizing the lack of material and the long costume changes . The DVD was certified gold in Australia by the Australian Recording Industry Association and platinum in Canada by the Canadian Recording Industry Association . 
- 
- = = Opening acts = = 
- 
- The Black Eyed Peas ( October 16 – November 14 ) 
- M.I.A. ( November 16 – December 1 ) 
- Ciara ( December 3 – December 21 ) 
- 
- = = Set list = = 
- 
- 
- = = Tour dates = = 
- 
- 
- 
- = Laborintus II ( 2012 recording ) = 
- 
- Laborintus II is a 2012 album by Belgian orchestra Ictus Ensemble , vocal group Nederlands Kamerkoor and American vocalist Mike Patton . It is a recording of the 1965 work of the same name by Italian composer Luciano Berio , which featured lyrics taken from fellow Italian Edoardo Sanguineti 's 1956 poem Laborintus . The performance was recorded live at the 2010 Holland Festival . 
- Berio 's composition employs elements of jazz and electronic music , and Sanguineti 's libretto <unk> ideas from the works of Dante <unk> , T. S. Eliot and Ezra Pound as well as using his own original work . Berio named " memory , death and usury " as the work 's main concerns , believing these themes to be present in Dante 's work . 
- Released on July 10 , 2012 , the album debuted at number 23 on the American Billboard Classic Albums chart . It has received mixed reviews from critics , most of whom highlighted its challenging and free @-@ form composition . 
- 
- = = Production = = 
- 
- Laborintus II is a recording of the 1965 composition of the same name by Luciano Berio , who wrote it for the 700th anniversary of Dante <unk> 's birth . The libretto was provided by Edoardo Sanguineti , who included elements of his 1956 poem Laborintus in it . AllMusic 's Thom Jurek described the original poem as speaking of " the <unk> of love and mourning , while acting as a critique of the <unk> of all things " . In addition to Sanguineti 's own poetry — itself based on themes found in Dante 's <unk> <unk> , <unk> and La Vita <unk> — the work uses <unk> from the Bible and the writings of poets T. S. Eliot and Ezra Pound . Musically , Laborintus II incorporates elements of jazz and electronic music while sometimes <unk> the style of Italian composer Claudio <unk> . 
- Berio described the main structure of Laborintus II as a " catalogue , in its medieval meaning " ( exemplified by the <unk> of <unk> of Seville ) , using Dante 's themes of " memory , death and usury " . Members of the Dutch choir Nederlands Kamerkoor , which performed in the recording , have also cited usury as a key theme in the work , describing the composition as " an <unk> against the practice " . Of the form , Berio wrote : " Individual words and sentences are sometimes to be regarded as autonomous entities , and sometimes to be perceived as part of the sound structure as a whole . " The instrumentation of Laborintus II was written as an " extension " of the vocal material ; its electronic section is likewise an extension of the instrumental music . Berio used car tyres and a blow @-@ up doll on stage in a performance of the work at the Holland Festival in 1973 . 
- The album was recorded live at the Holland Festival on June 18 , 2010 , in the <unk> <unk> ' t <unk> . The work was performed by Mike Patton and the Belgian Ictus Ensemble conducted by Georges @-@ Elie <unk> . <unk> were performed by Ictus Ensemble <unk> Dirk <unk> , <unk> <unk> <unk> , <unk> Michel <unk> , double bass player <unk> <unk> , and <unk> Michael <unk> and <unk> <unk> . Nederlands Kamerkoor provided the choral <unk> . The album marks only the third recording of the composition to have been released since it was first broadcast on French radio by Office de <unk> <unk> Française . Patton has said of the work , " I can listen to Berio and <unk> as easily as I can to <unk> but like all modern music of Italy , it is unfortunately <unk> ... Maybe because of the language barrier , maybe because it ’ s not easily understood . Berio , who was teaching in California when he wrote this piece , was listening to jazz , pop and folk music and incorporated all of it in his works without prejudice . " 
- 
- = = Composition = = 
- 
- Laborintus II combines orchestral , choral and spoken elements throughout its three parts . Patton 's spoken narration is delivered in Italian , although taped samples feature Sanguineti speaking in English . From a <unk> to a shout , the words carry a variety of emotional tones as the work progresses . The choral parts respond to the narration both with unified chanting and with disjointed arguing , the latter serving to increase the tension . They are accompanied by three female vocalists whose voices range from soprano singing to " <unk> " and " <unk> " . 
- The music incorporates elements of jazz and 20th @-@ century avant @-@ garde . The instruments in the orchestra frequently <unk> both each other and the female voices , and some sections of the composition seem as though they are improvised . Laborintus II makes use of both traditional percussion instruments and electronic sounds , and their interplay serves to " erect musical and <unk> <unk> , then <unk> them quickly " . Max Feldman has compared the style to that of Raymond Scott . 
- The first part of the composition features the three female voices creating a " <unk> " tone while the orchestra plays recurring musical passages . The second part is a discordant crescendo , as Patton 's narration becomes increasingly shouted and the orchestral accompaniment more " <unk> " . The third and final part returns to a <unk> tone , <unk> on drums and jazz <unk> instruments . 
- 
- = = Track listing = = 
- 
- All lyrics written by Edoardo Sanguineti , all music composed by Luciano Berio . 
- 
- = = Personnel = = 
- 
- 
- = = Release and reception = = 
- 
- Laborintus II was released on July 10 , 2012 , through Patton 's record label <unk> Recordings . In the United States , the album debuted on the Billboard Classic Albums chart at number 23 ; it spent one week on the chart . 
- The album received mixed reviews from critics . Review <unk> website Metacritic awarded it an average score of 58 out of 100 , based on eight reviews . Writing for The A.V. Club , Chris <unk> rated the album B − , calling it " challenging , uncompromising , and bordering on inaccessible " . <unk> felt that the album was abstract and difficult but contained " hidden <unk> " to reward repeated hearings . He called Patton 's arrangements " haunting " and " <unk> " . <unk> 's Thom Jurek rated the album 3 @.@ 5 stars out of 5 , describing the recording as " a very nearly dazzling endeavor that rewards patience <unk> " . Jurek felt that , as an album , Laborintus II was difficult to grasp at first , by virtue of being a recording of theatrical music , but he praised the performance of Ictus Ensemble , writing of their " <unk> freshness and mischievous glee " . <unk> <unk> of <unk> rated it 3 @.@ 5 out of 5 , finding it " fascinating if not unwieldy " . He felt that Laborintus II was perhaps Patton 's most ambitious album to date , but noted that the musician has previously produced similarly avant @-@ garde records in the past . <unk> described the composition as " somber , beautiful , and ominous , but always affecting " . 
- Max Feldman of PopMatters awarded Laborintus II a rating of seven out of ten , finding Berio 's composition " challenging " and " exhausting " . He noted the work 's free jazz elements , comparing it to the 1970 Miles Davis album <unk> Brew . Feldman felt that the music " constantly emphasises its own unpredictability " . Consequence of Sound 's Carson O <unk> rated the album three stars out of five , calling it " unlike anything else you ’ ve ever heard " . O <unk> felt that the music might need more than one hearing to appreciate it , adding that it " runs the gauntlet from quiet , <unk> <unk> to brazen , unsettling <unk> noise " . A review for Q magazine described Laborintus II as " tedious " , finding the album <unk> . Spin 's Christopher R. <unk> rated it 7 out of 10 , calling it an " orchestra / tape collision <unk> " . 
- 
- 
- = Residence of the United States Ambassador to the United Nations = 
- 
- The residence of the United States Ambassador to the United Nations is the official residence of the United States Ambassador to the United Nations . As of 2016 it was located in a suite of rooms on the 42nd floor of the Waldorf @-@ Astoria Hotel in New York City leased by the U.S. Department of State . Described in press reports as " palatial " , the establishment of the current residence in 1947 marked the first time in history that an ambassadorial residence had been located in a hotel . 
- 
- = = History = = 
- 
- 
- = = = Background = = = 
- 
- Beginning in 1947 , shortly after the <unk> of the United Nations secretariat in New York , the U.S. State Department took a long @-@ term lease for occupancy of a suite of rooms by the U.S. ambassador at the Waldorf @-@ Astoria , a luxury hotel constructed in 1931 . The establishment of the ambassador 's residence at the Waldorf @-@ Astoria made it the first hotel in history to house an ambassadorial residence . In 1960 , a <unk> at Sutton Place , originally constructed by J.P. Morgan in 1921 , was donated to the U.S. government by then owner Arthur Houghton with the intention it be used as a new ambassadorial residence . However , ambassador <unk> Stevenson II determined the home was not to his liking and the residence continued at the Waldorf @-@ Astoria . ( The Sutton Place <unk> was subsequently re @-@ gifted by the United States to the United Nations and currently serves as the official residence of the Secretary @-@ General of the United Nations . ) 
- In 1978 , <unk> reported that Andrew Young and his family explored the possibility of moving out of the suite at the Waldorf @-@ Astoria and into a house instead . Young , who was the first ambassador to live in the suite with " young children " , stated that " [ t ] he Waldorf is very nice , and its convenient , but I just have problems trying to bring up a small child in a hotel . " However , the search ultimately " became a media issue " and Young elected to stay in the suite . Nevertheless , he opined that " [ p ] <unk> tried to make it seem like I was saying that the Waldorf wasn 't good enough for us " . 
- As of 1999 , the State Department was paying $ 360 @,@ 000 per year to lease the suite on a rolling basis ; the lease is renewed every ten years with an optional one or two year short @-@ term extension . In 2015 , it was announced the State Department would no longer permit staff to be housed at the Waldorf @-@ Astoria , due to security concerns arising from the recent purchase of the property by Chinese business interests . Whether the decision would impact the status of the residence was not made clear , however , as of March 2016 the hotel was still being occupied by the U.S. ambassador . 
- 
- = = = Notable residents = = = 
- 
- <unk> <unk> , George H. W. Bush , John Bolton , <unk> Stevenson II , Samantha Power , and Bill Richardson are among notable former residents of the suite . During his tenure as ambassador , Richard Holbrooke elected not to occupy the 42nd floor apartment , choosing instead to live in his private Manhattan home . In his place , the residence was temporarily occupied by Holbrooke 's assistant , then 27 @-@ year old Randolph Eddy . According to reports , Holbrooke and his wife , journalist <unk> <unk> , would throw " <unk> parties " in the suite " where <unk> and foreign ministers mixed with the likes of Robert De <unk> and Sarah Jessica Parker . " As of July 2014 , Power lived in the suite with her husband , Harvard Law School professor Cass <unk> , and their two young children . 
- 
- = = Design = = 
- 
- The suite is located on " the very top floor " of the Waldorf @-@ Astoria Hotel . Described in press accounts as " palatial , " the residence is decorated with , among other items , a Jim <unk> painting , an Alexander Calder mobile , and a grand piano , and features " <unk> city views " of the New York skyline . The front door to the suite is framed by a golden eagle . It is located on the opposite side of the corridor from the " royal suite " , so @-@ called as it was long used by the Duke of Windsor as his unofficial New York City residence . 
- As of 1971 , the interior of the suite was <unk> into nine rooms , including five <unk> and a living room with a 48 @-@ foot ( 15 m ) tall ceiling , which was used to " host large official receptions . " Dorothy Bush Koch noted that the apartment was designed with " high ceilings , handsome old woodwork , working <unk> , and big windows with beautiful views of New York City . " 
- 
- 
- = K @-@ 22 ( Kansas highway ) = 
- 
- K @-@ 22 is a 3 @.@ <unk> @-@ mile @-@ long ( 4 @.@ 968 km ) highway in the U.S. state of Kansas . Located entirely within Washington County , the route runs from U.S. Route 36 ( US @-@ 36 ) north to the city limit of Haddam . A previous designation of the route existed in the 1930s from Wichita to <unk> , but was <unk> . The current alignment was designated in the 1940s . 
- 
- = = Route description = = 
- 
- K @-@ 22 begins at an intersection with US @-@ 36 , known as 17th Road . The route continues south as Deer Road toward <unk> . From this intersection , K @-@ 22 heads north along Deer Road through a grassland area to an intersection with 18th Road . The route then crosses the Mulberry Creek and runs near it until it reaches Haddam . The roadway then crosses Mill Creek before meeting its northern terminus at the south city limit of Haddam near an intersection with Main Street . Deer Road continues north toward the Nebraska state line , but does not cross it . 
- The route is maintained by the Kansas Department of Transportation ( KDOT ) , who is responsible for constructing and maintaining highways in the state . As part of this role , KDOT regularly surveys traffic on their highways . These surveys are most often presented in the form of annual average daily traffic , which is the number of vehicles that use a highway during an average day of the year . In 2010 , KDOT calculated that a total of 205 vehicles used the road daily , including 45 trucks . No part of the highway has been listed as part of the National Highway System , a network of roads important to the nation 's defence , mobility , and economy . 
- 
- = = History = = 
- 
- The first designation for K @-@ 22 was established by 1927 and ran from US @-@ 54 near Liberal to US @-@ 36 near <unk> . During this time , most of the route was a dirt road , except for a portion near Garden City , which was paved . This routing was relinquished by 1932 . By 1933 , a new routing was created , and the road instead began in Wichita , headed east along US @-@ 54 to <unk> and north to <unk> . From <unk> , the route turned northeast toward Scranton and north into <unk> . This designation was decommissioned between January and July 1938 . The current designation of K @-@ 22 was established in 1941 . No alignment changes have taken place since then . 
- 
- = = Major intersections = = 
- 
- The entire route is in Washington County . 
- 
- 
- = Tropical Storm Jose ( 2005 ) = 
- 
- Tropical Storm Jose was a short @-@ lived tropical storm which made landfall in central Mexico during August 2005 . Jose was the tenth named storm of the 2005 Atlantic hurricane season and the fourth of six tropical cyclones ( three hurricanes and three tropical storms ) to make landfall in Mexico in that year . 
- Tropical Storm Jose formed in the Bay of Campeche on August 22 and made landfall in the Mexican state of Veracruz the next day . It retained tropical characteristics for less than one day before dissipating , but still brought heavy levels of rainfall to the region . <unk> caused by the rainfall killed eight people , six of those directly , and caused $ 45 million ( 2005 USD ) in damage . 
- 
- = = Meteorological history = = 
- 
- Tropical Storm Jose was first identified as a tropical wave that moved off the western coast of Africa on August 8 , 2005 . On August 13 , the system spawned Tropical Depression Ten over the central Atlantic ; the wave itself continued westward , entering the Caribbean on August 17 . <unk> development took place as the system moved over the Yucatán Peninsula ; however , by the time it entered the Bay of Campeche on August 21 , little convection was associated with the system . The following morning , convection exploded under highly favorable divergence from an upper @-@ level <unk> flow . According to readings from the <unk> satellite , a well @-@ defined low pressure center developed by 1200 UTC , prompting the National Hurricane Center to classify the system as Tropical Depression Eleven . At this time , the depression was situated roughly 110 mi ( 175 km ) east of Veracruz , Mexico . 
- Situated over very warm waters and within an area of low wind shear , the depression was able to quickly organize ; however , due to its proximity to land , the NHC noted , " the system does not have very long ... to take advantage of these favorable conditions . " Located to the south of a mid @-@ level ridge , the depression tracked just north of due west and kept this motion through the remainder of its existence . The depression rapidly intensified as it moved to the west , becoming Tropical Storm Jose just six hours later . The global model guidance initially failed to resolve the storm 's track well , with some models indicating that it would stall offshore . Jose continued to strengthen as it moved towards the coast and made landfall in the state of Veracruz early on August 23 with winds reaching a peak of 60 mph ( 90 km / h ) . As Jose made its landfall an eye was beginning to form , although the storm was still well short of hurricane intensity . Tropical Storm Jose quickly weakened after landfall and dissipated that afternoon in the mountains of central Mexico only 24 hours after forming . 
- 
- = = Preparations and Impact = = 
- 
- As Tropical Storm Jose formed so close to shore there was a lead time of less than 9 hours on the tropical storm warning for the Veracruz coastline . The area covered by the warning issued on August 22 was extended southwards as Jose intensified , before being canceled soon after landfall on August 23 . The advisories issued by the National Hurricane Center emphasized that rainfall from Jose was the primary threat . 
- Tropical Storm Jose was responsible for damaging crops , highways and homes ; flooding districts in several cities in the state of Veracruz , and the evacuation of 80 @,@ 000 people to shelters . The government of that state estimated the damages caused by the storm to be approximately $ 45 million ( 2005 USD ) . Approximately 120 municipalities were affected by the torrential rain , but the majority of the damage was concentrated to eight of them : Martínez de la Torre , <unk> , Nautla , San Rafael , Vega de la Torre , <unk> , <unk> and <unk> <unk> . Damage to the highway infrastructure was estimated at $ 33 million ( 2005 USD ) . 
- It was also reported that the storm damaged at least 16 @,@ 000 homes and about 250 square kilometers ( 60 thousand acres ) of land used for cattle . In addition over 420 square kilometers ( 103 thousand acres ) of various crops , including sugarcane , corn and <unk> , were flooded . Many boats were also lost as a result of Jose . 90 active medical brigades were sent to the region to reduce the risk of infections amongst the affected population . 
- Jose was responsible for six direct casualties . One of these deaths was the result of a mud slide that killed a man was in <unk> , Veracruz . The other five deaths were also due to mudslides in Oaxaca . 
- 
- 
- = Norsk Spisevognselskap = 
- 
- Norsk Spisevognselskap A / S , often abbreviated NSS or shortened to Spisevognselskapet , was a Norwegian state enterprise which operated restaurant carriages on Norwegian trains and restaurants at railway stations and railway hotels . The company was established in December 1918 , and started a catering service in 1919 . Originally owned by the Norwegian Trunk Railway , it was acquired by the state in 1926 . <unk> served in the restaurant carriages were relatively expensive , although they were available to all passengers . In the 1950s , the company began using serving trolleys on trains . 
- In January 1975 , NSS merged with the convenience @-@ store chain Narvesen <unk> into a new company called Narvesen – Spisevognselskapet . This enterprise was partly owned by the Norwegian State Railways ( NSB ) and Fritt Ord , before it merged with the Reitan Group and was <unk> from the Oslo Stock Exchange . 
- 
- = = Background = = 
- 
- From the 1854 establishment of railways in Norway to 1909 , no dining service was offered aboard trains ; passengers were allowed to bring food with them . Train stations also lacked dining facilities . The first dining service was started by <unk> Carl Christiansen . He established the restaurant at <unk> Station , and in 1907 was asked by NSB to establish a dining service aboard the express trains on the Bergen Line , which would open in 1909 . After investigating similar operations in England and Germany , he ordered two carriages from <unk> <unk> . These were to be paid for by the state , but the Parliament of Norway delayed the grants after a long debate regarding the suitability of restaurant carriages on trains . The plans were opposed by the <unk> faction of Parliament , but there was a majority in favor of dining service . To get the carriages in time , Christiansen personally guaranteed the production cost in case a state grant was not allocated . After the parliamentary decision , the cost of the carriages was <unk> by NSB . In 1910 , when President of the United States Theodore Roosevelt visited Oslo to receive the Nobel Peace Prize , several restaurant carriages were ordered solely for the occasion . Two years later , restaurant carriages were put in regular service on the <unk> Line . 
- 
- = = Early days = = 
- 
- In 1916 , the executive board of the state railways wanted to <unk> the operation of restaurant carriages and the most important station restaurants in Norway under one management . The board stated that they wanted to minimise the conflict of interest between the railway company and the dining @-@ car operator . They also saw <unk> operations as a way to <unk> a larger share of the revenue to the railway company , and to ensure a high quality of service on new lines . At that time the <unk> Line and Dovre Line were in the planning stages , and the NSB intended to introduce dining services on these when they opened . Oslo East Station and its restaurant were operated by the private Norwegian Trunk Railway . In an agreement signed on 18 September 1918 both railway companies agreed that a new restaurant operator would be controlled by the Norwegian Trunk Railway , but this company had to abide by the NSB 's decision of how many restaurant carriages to operate on any line . 
- This model was inspired by Sweden , where a separate dining company had been established ; this company paid the railway company part of its revenue and a fixed fee per restaurant carriage . The Swedish model involved including the operation of station restaurants at locations where the restaurant carriages would have depots . On 21 December 1918 , <unk> Norsk Spisevognselskap was established with a share capital of 200 @,@ 000 Norwegian <unk> ( NOK ) . It had 20 shares ; 17 were owned by the Trunk Railway , and one by each of three directors : Waldemar <unk> Platou , <unk> <unk> and Christian Emil <unk> Platou — the former representing the Trunk Railway and the latter being director @-@ general of the NSB . In January 1919 , Waldemar Platou was appointed chair and Christiansen managing director . The company took over Christiansen 's four restaurant carriages and the restaurant at Oslo Ø on 1 April 1919 . 
- On 2 February 1926 , Parliament voted to <unk> the Trunk Railway . During the debate , the organisation of Spisevognselskapet was criticised ; with the <unk> , the state became the sole owner of Spisevognselskapet . On 31 March 1927 , the Ministry of Labour recommended that the company remain a state @-@ owned limited company ; the minority in parliament wanted a state enterprise . During the 1930s , <unk> <unk> was managing director of Norsk Spisevognselskap . In 1948 , <unk> <unk> was appointed managing director of the company . He was succeeded by Knut <unk> in 1960 . The company had 75 employees in 1919 , and 591 in 1949 . 
- 
- = = Restaurant carriages = = 
- 
- The restaurant carriages of the NSS were open to everyone , but dining was so expensive that only passengers travelling in first class used the service . Usually , three or four dishes were offered in the restaurant carriages . A four @-@ course dinner cost five Norwegian <unk> in the 1920s , which was expensive at the time . Warm dishes , such as <unk> and <unk> , were usually prepared at a small stove in the restaurant carriage 's kitchen . On busy days , prepared <unk> were delivered from rail depots . The kitchens were staffed by two maids and one attendant . <unk> of ice were often used instead of <unk> . 
- With the opening of the Dovre Line , Spisevognselskapet established dining @-@ car service on 25 June 1921 . In 1921 a train ride from <unk> to Trondheim lasted approximately 15 hours , and the average waiting time at each station was between 10 and 15 minutes . On 1 July 1925 dining service was introduced on the <unk> Line , from 1 July 1926 on the <unk> Line , from May 1934 on the <unk> Line and from 15 May 1936 on the Røros Line . In 1948 210 @,@ 000 meals were served on board , in addition to sandwiches and drinks . The company also offered a light breakfast on night trains on the <unk> , <unk> and Dovre lines . During the Second World War , dining @-@ car service was discontinued . 
- After the war trains faced competition from aviation and automobiles , and serving trolleys were installed on Norwegian trains . In 1965 cart service on trains was centralised and standardised ; food service was faster , and prices were lowered . During the 1970s <unk> cars were used , where passengers could serve themselves . 
- 
- = = Restaurants = = 
- 
- NSS derived most of its revenue from the operation of restaurants at railway stations ; in 1939 , this amounted to 80 % . Initially , the company operated the restaurant at Oslo Ø ; from 1921 , it also took over operation of the restaurants at Oslo West Station , Hamar , <unk> , <unk> and <unk> . The following year , four more restaurants were added : <unk> , <unk> , <unk> and Hell . NSS also established its first kiosk , at Bergen Station . In 1923 , the company was allowed to take over all restaurants in the railway districts of Oslo and Hamar ; by 1925 , it had taken over operation of the restaurants at Lillehammer , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , Trondheim , <unk> , <unk> , <unk> , <unk> and <unk> . 
- After this NSS decided not to obtain many additional restaurants , as it did not see value in such a strategy . From 1930 through 1934 the company took over restaurants at <unk> , <unk> , <unk> and Tønsberg , along with dining service on the steamship <unk> , which ran on <unk> . During the first half of the 1940s it again acquired new restaurants , including the one at Kristiansand Station . In 1940 and 1941 the company made a solid profit , but lack of food from 1942 onwards transformed the profit to a loss . During the late 1940s NSS also took over the restaurants at <unk> and <unk> , as well as the one at Oslo Airport , Fornebu . 
- 
- = = Hotels = = 
- 
- In 1919 the Norwegian Trunk Railway operated one hotel , which was built as part of <unk> Station . Operation of the 20 @-@ room hotel was taken over by Spisevognselskapet on 14 October 1924 . NSB was at the time building the Dovre Line between Oslo and Trondheim , and was considering establishing hotels where the line passed through <unk> . Both <unk> and <unk> were considered , but these areas were served by other operators . Instead , Spisevognselskapet established the <unk> Tourist Hotel adjacent to <unk> Station and it opened on 28 June 1924 . The 60 @-@ bed hotel had a floor area of 605 square metres ( 6 @,@ <unk> sq ft ) and was marketed as a tourist destination , with <unk> and curling during the winter and tennis and <unk> in summer . The hotel was closed for part of 1929 , because the municipality would not allow it to serve alcoholic <unk> . 
- In Oslo , the company had its offices and workings spread around town . The main depot was at the East Station ; the head office was at Fred . <unk> gate 21 from 1919 to 1921 , at <unk> gate 29 until 1932 , and at <unk> 24 until 1938 . Management wanted to <unk> both a new depot and administrative offices at a single location close to the railway station , <unk> co @-@ located with a hotel . In 1936 work began on a hotel at <unk> , but the project was cancelled . The proposed hotel would have had 100 rooms across the street from Oslo Ø . However , the plans were blocked by Parliament ( which was opposed to the state railway operating hotels ) . Instead , the administration moved into <unk> 10 , in a new building built on a lot owned by NSB . 
- In Bergen the company established Hotel <unk> Bergen along with other investors , but the hotel failed to make money . In the late 1940s the company bought Grand <unk> Bellevue in Ålesund , and later operated <unk> Tourist Hotel for a short period . In 1952 , Oslo Municipality 's Viking Hotel was completed , and Spisevognselskapet was selected as the operator . It remained the hotel 's operator until 1976 when the government sold it to <unk> , which outbid Spisevognselskapet by several million <unk> . 
- 
- = = <unk> = = 
- 
- Narvesen had an exclusive agreement with NSB to operate <unk> 's shops at all railway stations , except in stations with restaurants , which were operated by Spisevognselskapet . Narvesen had a near @-@ monopoly on <unk> in Norway , and rented facilities in many public places . The owners of Narvesen intended to create a foundation to obtain the company ; when plans for this started in 1972 , they had difficulties finding a way to transfer shares to the foundation without having to pay tax on the transaction . However , the tax laws permitted a tax @-@ free transaction if it was part of a restructuring . A merger with Spisevognselskapet would be considered a restructuring , and in 1974 Fritt Ord was established to take over Narvesen 's owners ' share of the company . The agreement between Narvesen and NSB was made in July 1974 ; in December it was passed by Parliament , although the Conservative Party and Progress Party voted against the merger . <unk> Narvesen – Spisevognselskapet was established on 1 January 1975 . Fritt Ord owned 50 % of the new company and NSB 41 % . It assumed the Narvesen name in 1979 . 
- By the late 1980s , the company had sold all its hotel operations . The merged company retained the obligation to operate dining services on the trains , which throughout the 1980s necessitated considerable subsidies from NSB . In 1988 , NSB decided to organize the operation of the dining services through <unk> contracts ; the first contract ( from 1990 through 1995 ) was won by <unk> , a Narvesen subsidiary . The owners had an agreement that neither could sell without the approval of the other . In 1995 NSB sold its shares with Fritt Ord 's approval , and the company was listed on the Oslo Stock Exchange . 
- In 1999 , Fritt Ord reduced its stock share in Narvesen from 51 % to 34 % . In the fall of 2000 , Fritt Ord accepted a proposal to merge Narvesen with the Reitan Group . The merged company was named <unk> ; Fritt Ord held 16 @.@ 2 % of its shares . In November 2001 Fritt Ord sold its shares of <unk> , which was renamed Reitan Handel and <unk> from the Oslo Stock Exchange . 
- 
- 
- = The Actor 's Children = 
- 
- The Actor 's Children is a 1910 American silent short drama written by Lloyd Lonergan and produced by the Thanhouser Company in New Rochelle , New York . The film features Orilla Smith , Yale Boss , Frank Hall Crane and Nicholas Jordan . The production was not the first film subject by the company , but it was the first to be released . Both Barry O 'Neil and Lloyd B. Carleton have been credited as the director of the production . Edwin Thanhouser stated that 19 copies of the film were produced and distributed to dealers . 
- The film begins with two unemployed parents obtaining employment in an upcoming theater production . Shortly after returning home , the landlady shows up and demands the rent , but can not collect . She gives them one week , but the theater production does not manifest and the parents again search for work . While they are out , the landlady finds a tenant and puts the children out on the street . They end up dancing for an organ grinder and are saved by a theater manager who puts them on his vaudeville bill . The parents come into a fortune and are reunited by their children at the theater . The film was met with positive reviews and some criticism for its acting and scenario , but the industry had reasons to encourage the success of Edwin Thanhouser 's company . A print of the film exists , but it was the subject of nitrate deterioration . 
- 
- = = Plot = = 
- 
- The plot of the film was best convened through a published synopsis in trade which introduced the names of the cast and the backstory . <unk> Freeman and Paul Temple , marry and have two children , a boy and a girl . The parents have been unemployed , but the film starts with the parents finding work in an upcoming production at a theater . As they return home , they are interrupted by the landlady , Mrs. O 'Brien , who demands the rent . The landlady does not car about the family 's misfortune and is upset when she cannot collect . She provides one week for the Temple family to pay up . 
- The production is postponed and the parents are out looking for work when a prospective tenant appears . Mrs. O 'Brien shows him the room and he is interested , but does not know what to do with the children . Mrs. O 'Brien puts the children out onto the street where they dance to the music played by an organ grinder . The organ grinder earns more money from their dancing and he <unk> the children to return to his <unk> and teaches them to dance . The organ grinder instructs them to dance for money . The children are rescued by a theater manager and finds them a place in the theater program . 
- In the meantime , the parents have searched for their children and suddenly come into a fortune when a relative <unk> a large sum of money to them . The parents search for their children in large gatherings and find their children dancing on the vaudeville bill from their theater box . The family is reunited and the film concludes . 
- 
- = = Cast = = 
- 
- Frank H. Crane as the actor ( adult male lead ) 
- Orilla Smith as the actor 's daughter ( little girl ) 
- Yale Boss as the actor 's son ( little boy ) 
- Nicholas Jordan as a comedian 
- 
- = = Production = = 
- 
- The script for the production was written by Lloyd F. Lonergan , who employed the <unk> ex <unk> dramatic technique in the conclusion of the plot . In film , this is the sudden inheritance and parents finding their children in the theater . The two children , Orilla Smith and Yale Boss , were child actors with prior film experience and did not have any further known connection to the Thanhouser Company after the production . The cameraman , Blair Smith , used a camera rented by the Columbia <unk> Company and had its inventor , Joseph <unk> , assist in the camera 's operation and use . The director of has both been credited to Barry O 'Neil and Lloyd B. Carleton in numerous trade sources . Smith was the first cameraman of the Thanhouser Company , but this film was not the Thanhouser Company 's first subject . Instead , it was The Mad Hermit produced in autumn 1909 and <unk> until August 9 , 1910 . The exact order of the productions is not known , but a work titled <unk> Nancy <unk> was filmed in December 1909 and never released . Certain parts of The Actor 's Children were produced in the last week of February 1910 . 
- The director of the film is not known , but two <unk> exist . Barry O 'Neil was the stage name of Thomas J. McCarthy , who would direct many important Thanhouser pictures , including its first two @-@ <unk> , Romeo and Juliet . Lloyd B. Carleton was the stage name of Carleton B. Little , a director who would stay with the Thanhouser Company until moving to the <unk> Company by the summer of 1910 . The confusion between the directing credits stems from the industry practice of not <unk> the film directors , even in studio news releases . Q. David Bowers says that the attribution of these early directors often comes from a collection of contemporary publications or interviews . 
- 
- = = Release = = 
- 
- The single reel drama , approximately 1000 ft , was released on March 15 , 1910 by the Thanhouser Company . In later years , Edwin Thanhouser recalled that 19 copies of the film were produced and sent out to dealers throughout the United States . Of these 19 copies produced , ten were returned , some with letters of interest in future Thanhouser productions . The film was viewed across the United States with advertisements for showings in Pennsylvania , Wisconsin , Kansas , and Washington 
- 
- = = Reception and impact = = 
- 
- The Actor 's Children was released with enthusiasm and positive reviews in trade publications . The Moving Picture World reviews would be favorable and without much criticism , even calling the acting convincing . A more honest review in The New York Dramatic Mirror was written by a reviewer who was pleased with the production , but offered criticism about the production 's weaker aspects . The reviewer found there to be too much emphasis on the unimportant parts and a lack of emotion from the actors , and the child actors performance was faulted by repeatedly looking at the camera . Critical reception of the film may not have been entirely neutral for a number of reasons . Edwin Thanhouser was a well @-@ liked gentleman who had many friends in the Patents Company that likely wanted him to succeed . Furthermore , writers in the magazines hoped that <unk> would succeed and challenge the Patents Company 's <unk> on the industry . Also , film critics and reviewers of the era would balance the <unk> of even the worst films with some favorable aspects . Even without any <unk> motives , the film may have been worthy of a favorable review . 
- The release managed to survive against considerable odds , but the surviving print is not without severe faults . The surviving print has considerable nitrate deterioration and very poor picture quality in certain parts . This film survives in the Academy of Motion Picture Arts and Sciences film archives . 
- 
- 
- = The Stolen Eagle = 
- 
- " The Stolen Eagle " is the series premiere of the British @-@ American historical drama television series Rome . Written by series creator Bruno Heller and directed by Michael Apted , the episode first aired in the United States on Home Box Office ( HBO ) on August 28 , 2005 , and on the BBC in the United Kingdom and Ireland on November 2 . Rome was given a budget of $ 100 million , making it the largest amount both networks had ever spent on a series . Heller centered the series ' narrative on the perspectives of two common soldiers , similar to <unk> and Guildenstern from Shakespeare 's Hamlet . Apted shot the episode at <unk> , the Roman studio where the epic films Ben @-@ <unk> and Cleopatra were filmed . On the set , realism and authenticity were emphasized more than grandiosity , with depictions of a cosmopolitan city of all social classes . 
- As the wars in Gaul come to an end , Julius Caesar ( <unk> Hinds ) is faced with both triumph and <unk> . On the heels of his victory comes news of his daughter 's death . <unk> with the <unk> of the people , he also <unk> the enmity of politicians in Rome , including Pompey the Great ( Kenneth <unk> ) . In Rome , Pompey must balance honor and politics as he is urged to <unk> his former friend . Meanwhile , Caesar 's niece Atia of the <unk> ( Polly Walker ) tries to steer her family on the dangerous path between the growing divisions of power . In the Gallic countryside , two unlikely allies ( Kevin McKidd and Ray Stevenson ) journey to reclaim the stolen standard of the Roman legion . 
- HBO described its marketing strategy as " its largest , most aggressive for a new series , " and media outlets estimated its cost at $ 10 million . On its first broadcast , an estimated 3 @.@ 8 million US viewers watched the episode . On its first airing in the UK and Ireland , it secured an estimated audience of 6 @.@ 6 million people . Critical reception was largely mixed , with several reviewers writing that the episode suffered from slow storytelling . " The Stolen Eagle " garnered four major awards , including the Primetime Emmy Award for Outstanding Special Visual Effects and the Directors Guild of America Award for Outstanding Directing – Drama Series . 
- 
- = = Plot = = 
- 
- During the Siege of <unk> in 52 BC , Centurion Lucius Vorenus of the 13th Legion commands his men as Gallic warriors fall on his line . In contrast to the Gauls ' chaotic charge , the Roman files fight with precision , until one drunk <unk> , Titus Pullo , breaks ranks and charges into the crowd of Gauls . Vorenus angrily orders him back into formation , but Pullo hits him . Later , the assembled soldiers watch as Pullo is <unk> and condemned to death for his <unk> conduct . The day after , <unk> , " King of the Gauls " , is brought before Julius Caesar and made to surrender , ending the eight @-@ year @-@ long Gallic Wars . Caesar 's niece , Atia of the <unk> , orders her son Octavian to deliver a horse she has purchased straight to Caesar in Gaul to ensure that he remembers them above all other well @-@ <unk> . Caesar himself receives news that his daughter , married to his friend <unk> <unk> Magnus with whom he shares power in Rome , has died in childbirth along with her stillborn daughter . A blood tie broken between them , Caesar orders a new wife be found for Pompey . 
- In the Roman Senate , <unk> the Younger moves that Caesar be stripped of his command and recalled to Rome to answer charges of <unk> his office and illegal <unk> . Pompey , as sole Consul present , <unk> the motion , insisting in trusting Caesar . At the theater that night , <unk> introduces his daughter Cornelia <unk> to Pompey as a prospective wife , while <unk> warns him that he must ally against Caesar before it is too late . Pompey again asserts that Caesar means no harm , although privately , he is troubled by Caesar 's rising prestige and power and gives orders to one of his slaves who is leaving on a trip to Gaul . At night in the encampment of the 13th Legion , the Aquila ( Eagle Standard ) is stolen by <unk> . To avoid a potentially disastrous drop in morale , Mark Antony orders Vorenus to retrieve it . As Vorenus feels the mission is doomed to failure , he has the condemned Pullo released from the stockade to assist him . 
- In camp , Caesar <unk> Marcus <unk> Brutus , his unofficial stepson whose mother is Caesar 's lover , Servilia of the <unk> . Later , at a party hosted by Servilia , Brutus <unk> to Pompey that the loss of the eagle has made Caesar unusually vulnerable as his men are on the brink of mutiny . On the road to Caesar 's camp in Gaul , Octavian is taken captive by <unk> . For Caesar 's request , Atia instructs her daughter Octavia to marry Pompey by first <unk> her husband <unk> , despite Octavia 's protests that they are deeply in love . Atia then presents Octavia to Pompey at a party and offers her for premarital relations , which Pompey takes advantage of . 
- Vorenus and Pullo set off in search of the eagle , encountering and rescuing Octavian from his captors . Octavian thanks them and promises that they will be rewarded . Vorenus and Pullo discover Pompey 's slave with the eagle hiding in the bandit cart and kill him , realizing the bandits were hired by Pompey . A politically astute Octavian explains that their mission is only a gesture , since the theft of the eagle is actually a blessing in disguise to Caesar . Civil war between Caesar and Pompey is inevitable , but Caesar needs Pompey to make the first move so as not to appear the aggressor ; Pompey is likely to do that if he believes Caesar 's soldiers are on the verge of <unk> . The trio returns in triumph to camp , where a surprised yet grateful Caesar takes the eagle back and more than adequate proof of Pompey 's hostility . He sends Pompey the head of his slave and informs him of his next move , to winter the 13th Legion at <unk> on the Italian border , in preparation for pressing his rights to the <unk> . Pompey breaks all ties with Caesar and takes Cornelia as his wife . Octavia , humiliated at being used by Pompey and heartbroken over her pointless divorce , says she wants him dead . 
- 
- = = Production = = 
- 
- 
- = = = Conception and writing = = = 
- 
- " The Stolen Eagle " was written by executive producer and co @-@ creator Bruno Heller and directed by Michael Apted , who also directed the following two episodes . Heller said the era of the Roman Empire was " pivotal in Western history . If things hadn 't turned out the way they did at that particular point , the world that we live in now would be very different . " He decided to tell the story of the series from the perspectives of two common soldiers , Lucius Vorenus and Titus Pullo . According to Heller , " They are the only two ordinary soldiers mentioned by Caesar in his book , so the idea was to do a sort of <unk> and Guildenstern take . I essentially took the seed of that idea to try to tell a big historical epic , but from the street level , the <unk> 's point of view . " 
- The episode title is a reference to the standard of the Roman legion , a symbol that represents the legion 's unity . While the storyline detailing its theft was based on fiction , Heller believed that it showed how Caesar could turn " misfortune into opportunity . He was always one step ahead of his enemies . " Certain characters were changed from their traditional images ; for instance , while Brutus has been portrayed as the <unk> Roman , Heller and historical consultant Jonathan Stamp thought it would be interesting to have him forced into his later role through his ancestry . <unk> to the fact that Brutus ' great great great grandfather " drove the last king out of Rome " , Stamp said that " his family history was pushing him in one direction , his emotions in another . " 
- 
- = = = Casting = = = 
- 
- The producers cast relatively unknown British actors for the series . Scottish actress Lindsay Duncan , who was cast as Servilia , believed that she and other UK actors " can do old , can do classic , and you believe it . " She also said that because of long distances , American actors or those of other <unk> were avoided : " <unk> actors from [ Los Angeles ] to Rome on a regular basis does not look good for the budget . We 're quite cheap . " Stamp described Servilia as " the great love of [ Julius ] Caesar 's life , his mistress and by all counts the only woman he truly loved . " Irish actor <unk> Hinds was cast as Caesar . Hinds first thought it was " silly " to be offered the part , but then " you think it 's quite an honour to be chosen to play it . And then you think , ' Oh shit I 've got to do it ' , and then , well , ' I suppose someone has to do it ! ' " Heller had believed that Hinds would make " a great Caesar " for a long time , and considered the series " extremely lucky " for being able to cast him . 
- Scottish actor Kevin McKidd , who had never appeared in a television series that necessitated over twelve months of filming , was cast as Lucius Vorenus , one of the lead characters . McKidd said , " What was different about this show is you get 14 months to really get into every <unk> and <unk> of the character , in a way you don 't get a chance to explore when you do a movie or a theater play . Initially , it was a terrifying prospect . But once you got over that , you realize what a great opportunity it was . " Heller described his character as " very much a Roman of the Old School , a stoic man devoted to duty and religion and the legion . " Ray Stevenson played Titus Pullo , a soldier who befriends Vorenus . McKidd said that " they 're kind of thrown together by fate , by chance , but somewhere along the line they start to stick ... [ They ] keep finding themselves accidentally at the epicenter of tumultuous events and immense change , so they 're kind of <unk> to each other . " 
- Caesar 's niece , Atia , was portrayed by actress Polly Walker . According to Heller , while most auditioning actresses assumed that the character was the series ' <unk> , Walker made " [ Atia ] bigger than life but completely real ... She could read the phone book with that kind of <unk> and joy . " Rather than a villain , Heller wrote her to be " a real life force . Everything she 's doing is for the good of her family , her children . No matter how evil it gets , it 's always for good reason . " Walker was pleased with her " very complex character . I found it exciting to play such massive emotions and deal with such interesting , to say the least , situations . I saw it as a huge challenge , and I have huge admiration for this character . A lot of people might consider her to be sort of evil or bad , but I think she 's wonderful . She 's just a survivor , doing what she 's got to do . " 
- 
- = = = Filming = = = 
- 
- The series was given a budget of $ 100 million ( £ 58 million ) , the largest both HBO and the BBC had ever devoted to a series . The season was filmed between March 2004 and July 2005 , at locations in or around Rome , and on a set considered " to be the biggest and most expensive ever built for television . " It was built at <unk> , where the epic films Ben @-@ <unk> ( 1959 ) and Cleopatra ( 1963 ) had been filmed . Production designer Joseph Bennett built a set that emphasized authenticity and realism rather than grandiosity . He said , 
- People think of Rome as white and cold and beautiful , powerful but distant . But based on the research , I don 't think it was like that at all . If you go to Pompeii , you 're struck by how <unk> it is , even now . The temples and sculptures were all brightly painted . Rome was like Pompeii , but much bigger . And Rome was so noisy it was impossible to sleep . It was like hell . <unk> of it as a combination of New York and Calcutta , with insane wealth and insane poverty . It was pretty extreme . 
- The series begins with opening credits that depict traditional Roman myths , such as <unk> and <unk> , the city 's foundation <unk> . For inspiration , visual effects and design company <unk> explored museums , read the script and researched Roman history . They created the opening sequence entirely in @-@ house . VFX artist Kirk <unk> said of production , " On many projects , you start off with <unk> and everyone has a good idea of what it 's going to look like when it 's completed . This project was very experimental right to the very end . The tone of it is pretty much unlike anything we 've done and most of what any of us here have seen . There 's a lot there that creatively sets the stage for the series . " 
- Heller was responsible for writing the episode 's voice @-@ over , despite his dislike of the task . He said that despite it being his " <unk> version , " he was " still not happy with it . " He believed that an early scene in which cart distributes spoils in front of Pompey represented the first real sense of how the series would depict city life . He said , " I think this is the first time that we get sense of the version of Rome that the show is pushing , " which was a very different version than viewers may have been used to . To him , Rome was " colorful and painted " and cosmopolitan . A later scene featuring <unk> the Younger in the Senate proved difficult to film because of the large number of Italian extras who did not speak English . In the DVD audio commentary , he said that " this is one of those scenes where you need really great assistant directors , because all of these Italian extras who have no idea whatsoever what [ <unk> 's ] saying , so to keep them interested and focused and concentrated on what 's going on is a real trick . " 
- <unk> <unk> were used to ensure the safety of the actors , though the crew attempted to conceal or camouflage them for historical authenticity , as they were not then used by the Roman cavalry . <unk> playing soldiers attended a boot camp under the guidance of a former Royal Marine . Those actors portraying <unk> learned to fight by <unk> , not <unk> their weapons . <unk> reportedly <unk> four thousand costumes using authentic period materials such as cotton , linen , wool and silk , all of which were hand @-@ <unk> on set . Pullo was originally written to be a poor horse rider , a reflection that " Romans were notoriously bad horsemen , " according to Heller . However , Stevenson turned out to be " probably the best <unk> on the show , " so they rewrote this characteristic because bad horsemanship is difficult to fake . 
- James Madigan , the visual effects supervisor , approached the series as a feature film , observing that " every aspect of the production took meticulous care with every detail , the costumes , the set dressing , the acting , and the attention to historical fact . As you worked on it , you really got the feeling that Rome was going to look like something we had never seen on TV before , so our <unk> approach very much wanted to respect that . " Madigan attempted to seamlessly <unk> the visual effects with the physical sets and depend less on CGI . A friend told Madigan that after seeing the pilot , " he didn ’ t see any <unk> shots , even though there are dozens of shots throughout episode 1 . That means we did our job well . " 
- 
- = = Marketing = = 
- 
- HBO said its marketing plan for the series was , " its largest , most aggressive push for a new series " . The channel broadcast the first three episodes seven days a week at various times during the day . Non @-@ subscribers could preview the first two episodes during the first week of September 2005 . HBO implemented an outdoor marketing campaign in major cities and produced movie @-@ style trailers which preceded a number of films in cinemas . Entertainment Weekly , Vanity Fair , Time , and <unk> published full @-@ size articles about the series . The History Channel broadcast five nights of documentaries featuring the Roman Empire , which were hosted by Stevenson , McKidd , and Varma , a collaboration which was the first of its kind between the two networks . 
- David Baldwin , the executive vice president of program planning , said , " This is a huge series for us . We wanted to give it every opportunity to be seen by as many people as possible . " Media outlets estimated that the entire marketing campaign cost HBO $ 10 million , the most the network had spent on marketing a series to that point . 
- Commentators viewed the success of Rome as crucial for the network , especially after the past mixed reception of <unk> and K @-@ Street . In July 2005 , James <unk> of Television Week wrote that Rome was viewed " as the network 's best shot for adding another literate , must @-@ see drama to its schedule " . Writing for the same publication , Tom <unk> said that HBO " has made such a fuss over Rome , and the network itself has put such painful pressure on the show ( and its producers ) to make a <unk> impact , that it 'll be <unk> like a <unk> if it fails to make a truly gigantic splash . " 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " The Stolen Eagle " was first broadcast on August 28 , 2005 in the United States on HBO and in Canada on The Movie Network and Movie Central . An estimated 3 @.@ 8 million viewers watched the episode , less than the series premieres of <unk> and Deadwood but consistent with the series finale of Six Feet Under . In the UK and Ireland , the premiere was broadcast on BBC 2 on November 2 , 2005 . According to The Independent , more than 6 @.@ 6 million viewers watched the episode . 
- 
- = = = Critical reception = = = 
- 
- " The Stolen Eagle " received generally mixed reviews from television critics , many of whom criticized its slow pace . Mark A. <unk> of The Boston Herald wrote , " Less <unk> than I , Claudius , more entertaining than American Broadcasting Company 's ( ABC ) <unk> <unk> Empire , " Rome " gets off to an uneven start . " Terry Morrow of the Dayton Daily News criticized the premiere , writing that " the opener , like most pilots , is so bogged down with introducing faces and setting up the story that it turns into a long and tedious journey . " Morrow also said the episode suffered from lacking one " standout , signature character " , though he believed that the " flaws in Rome should clear up in time , given HBO 's <unk> for winning dramas . It 's an epic story , and one worth <unk> if you can <unk> through the demands of slow storytelling in the beginning . " 
- The Scotsman 's Robert <unk> thought that the premiere was " shocking , but also rather slow , as characters are established . Maybe it 'll get better . In the meantime , to <unk> Roger <unk> , I came , I saw , I concurred with those who say : Rome wasn 't built in an hour . " Similarly opinionated was The Cincinnati Post 's Rick Bird , who said that like other HBO series , Rome " takes a while to get going . After the first episode you will mostly be confused with a dizzying array of characters , intrigue and subplots . Hang in there . By the second episode things take shape and one should be hooked by episode three with this <unk> romp through antiquity and its <unk> intrigue . " Bird found some positive elements ; the episode , he said , was " enhanced by marvelous filmmaking including elaborate sets and costumes . Small @-@ screen film art has rarely painted such a realistic picture of ancient Rome . " 
- Paul English of The Daily Record wrote that " Rome is visually dazzling , full of <unk> and <unk> seductive , " adding that " McKidd 's growling turn as <unk> 's [ sic ] <unk> Lucius Vorenus will undoubtedly propel him into the US major league . " Writing for the Los Angeles Daily News , David <unk> failed to find the series very remarkable , writing that " <unk> some lurid sex and gruesome violence , [ it is ] as conventional as anything the network has ever done . Sword @-@ and @-@ sandals epics have become familiar Hollywood staples ... and those expecting something that takes up where the <unk> <unk> BBC / PBS series I , Claudius left off may be in for something of a disappointment . " Television Without Pity graded the episode with a B. 
- Some viewers criticized the graphic nudity seen in the pilot , especially in the US . Heller commented , " Romans didn 't have our body shame and fear of sexuality . I think that is part of the modern fascination with that world . There was a lack of shame about those things , that we had to portray with a lack of shame in order to make it work . " 
- 
- = = = Accolades = = = 
- 
- 
- 
- = Roxas ( Kingdom Hearts ) = 
- 
- Roxas ( Japanese : <unk> , Hepburn : <unk> ) is a fictional character from Square Enix 's video game franchise Kingdom Hearts . First revealed during the final scenes of the 2004 title Kingdom Hearts : Chain of Memories , Roxas is a " Nobody " , a being created when the series ' main character Sora briefly loses his heart during the first game of the series . Kingdom Hearts II reveals that Roxas is a member of Organization XIII , a group of Nobodies who needed Roxas as he could wield the Keyblade , a weapon that allows him to capture hearts . As a member of the organization , Roxas bears the title " Key of Destiny " ( <unk> , <unk> <unk> , lit . " <unk> Key " ) . He is also the protagonist of the video game Kingdom Hearts 358 / 2 Days , which revolves around his origins . In the Japanese games , Roxas is voiced by <unk> Uchiyama , while Jesse McCartney takes the role in the English versions . 
- Since his first cameo in the series , director Tetsuya Nomura has stated that Roxas is an important character to the series , and that in order to explain his back story in more detail than done in Kingdom Hearts II , Kingdom Hearts 358 / 2 Days was created . Since his introduction in Kingdom Hearts II , Roxas has received positive critical response from video game publications with most of them focusing on his development in 358 / 2 Days . Various types of merchandising have been released based on his character . 
- 
- = = Characteristics = = 
- 
- Roxas is depicted as a spiky blond @-@ haired teenager with blue eyes . As a civilian he wears a white jacket along with a black jacket and white trousers . As a member of Organization XIII , Roxas wears a black coat that covers most of his body including his hood along with the other members . His weapon is the Keyblade known as Kingdom Key ( <unk> , <unk> <unk> , Kingdom Chain ) , resembling a classic skeleton key , with a long silver <unk> extending from the <unk> , and a Mickey Mouse token on the end of the <unk> , but the player is able to modify it during Kingdom Hearts 358 / 2 Days using different <unk> . Roxas also becomes able to dual @-@ wield <unk> after leaving the Organization , and exclusively uses the <unk> ( <unk> , <unk> no <unk> , lit . " Oath 's <unk> " ) , whose shaft displays two hearts , and its handle bears two angel wings , and the <unk> ( <unk> , <unk> <unk> , lit . " Passing Memories " ) . 
- Roxas ' personality significantly changes across the series due to the fact that when first introduced , he appears with fake memories that make him believe he is a common teenager and spends most of his time with his friends . When learning that he is Sora 's Nobody and remembering his past , Roxas gives up his existence so that Sora could continue existing , but still expresses happiness for his fate . During his time in the Organization , Roxas develops a <unk> personality based on the experiences he has as a result of having no memories of a previous life unlike other Nobodies . 
- 
- = = Appearances = = 
- 
- Roxas ' first appearances were cameos in " Another Side , Another Story " , a bonus trailer found in Kingdom Hearts , and in the ending of Kingdom Hearts : Chain of Memories . In Kingdom Hearts II , Roxas is introduced as a boy living in a digital replica of a city called Twilight Town . <unk> of the virtual nature of the city , Roxas begins to dream about the adventures of Sora , the series ' protagonist . He later encounters Axel , a member of Organization XIII who was given orders to rescue him , and Naminé , a Nobody who tells him he is Sora 's other half . Shortly after meeting , Ansem the <unk> , the creator of the virtual world , Roxas learns the nature of Twilight Town as well as that Ansem erased his memory , and implanted false ones to make Roxas unaware of his past and then make him merge with Sora . Ansem leads Roxas to an old mansion where once reaching an asleep Sora , Roxas rejoins with him , allowing Sora to wake up . Sora later learns that Roxas is his Nobody , created during the events of the first game after Sora briefly turned into a Heartless . Xemnas brought Roxas to Organization XIII as he could wield the Keyblade , a weapon which would help them to capture hearts . Roxas later betrayed the Organization and encountered one of Sora 's friends , <unk> , who captured him to help Sora wake up . Roxas makes two appearances near the end of the game . The first is in a mental battle with Sora depicted as a cut @-@ scene , and the second is with Naminé , who has merged with her other self , <unk> . In the re @-@ released version of the title , Kingdom Hearts II Final Mix , Roxas ' fight against Sora was expanded , making him a boss character . The battle was meant to be interactive in Kingdom Hearts II , but time constraints imposed from creating fights for the other Organization XIII members prevented inclusion . With the opportunity to include the fight , Nomura 's team worked hard to make it entertaining for players . Additional scenes regarding Roxas ' past were added to the game to add to the mystery around him . 
- Kingdom Hearts 358 / 2 Days , a prequel to Kingdom Hearts II , reveals Roxas ' life with Organization XIII . Unlike the other Organization members , Roxas lacks memories of his previous life . During Roxas ' time in the Organization , he is placed under Axel 's watch , and the two become friends . After Axel is sent to Castle <unk> , Roxas is entrusted with Xion , another Keyblade <unk> , whom he befriends . Roxas reacts to Sora 's memory restoration and starts questioning why he uses the Keyblade and doubt the Organization 's motives . When he discovers that Xion is a replica of Sora created by Xemnas , Roxas is compelled to <unk> from Organization XIII to find answers and meet Sora . After doing so , he encounters Xion , who tries to attack and absorb him so that she would become the " real " Sora ; however , the fight ends with Xion 's defeat who <unk> Roxas to free the hearts the Organization captured . The death of his friend , coupled with full knowledge of how <unk> Xemnas truly is , inspires Roxas to defeat Xemnas and release the hearts he and Xion captured for the Organization . On his way , Roxas is confronted by <unk> who knocks him unconscious in order to help Ansem and Naminé wake up Sora . 
- A virtual representation of Roxas appears as a boss character in the mobile phone game Kingdom Hearts coded , in which he confronts a virtual representation of Sora , he later appears in the ending as one of the people connected to Sora 's heart who may have a chance at returning one day . He makes a cameo appearance at the end of Kingdom Hearts Birth by Sleep , where he is shown with Xion and Axel eating sea @-@ salt ice cream in Twilight Town , as well as in Kingdom Hearts 3D : Dream Drop <unk> where Roxas contacts both Axel and Sora through dreams . He also appears in <unk> Amano 's manga and Tomoko <unk> 's novels , which reprise his role in the video games . The book Kingdom Hearts : Another Report includes a novel called Roxas – Somewhere in Time that retells Roxas ' days in the Organization with the exception of his befriending Xion . 
- 
- = = Creation and development = = 
- 
- After Roxas first made a cameo at the end of Kingdom Hearts : Chain of Memories , the director of the series , Tetsuya Nomura , was questioned about the character . However , he <unk> from revealing too much detail about him and stated that he would become an important , playable character for the franchise . The developers wrote Roxas ' story in Kingdom Hearts II to add mixed feelings to the game in a short time frame . After receiving a positive fan response regarding Roxas ' sad scenario , Nomura concluded that it was well executed . He also stated that Roxas ' merging with Sora in the game was one of his most memorable scenes from the series . Nomura later commented that Roxas was one of the first characters created for Organization XIII , and was always intended to be the 13th member . The meaning of Roxas ' name was meant to be revealed in a scene in Kingdom Hearts II that shows the letters in the word " Sora " <unk> with the " X " added to expand the connection between the characters . This scene , however , was omitted as Nomura found it difficult to implement time @-@ wise . Since his debut in Kingdom Hearts II , Roxas has been voiced by <unk> Uchiyama in Japanese and by Jesse McCartney in the English versions . 
- After Kingdom Hearts II : Final Mix was released , Nomura wanted to expand Roxas ' role in the series to explain the events between his birth and his disappearance from Organization XIII . For Kingdom Hearts 358 / 2 Days , the staff thought that Roxas ' role as a member of the group would be a suitable theme for the plot . Roxas was chosen as the game 's protagonist as the staff thought that having a main character besides Sora would help to introduce gamers to the series ' first Nintendo DS title . Co @-@ director <unk> <unk> explained that Roxas ' height was originally lower and as the game continued development , the staff decided to increase it . Nomura desired to portray Roxas ' activities different from Sora 's . In Sora 's games , he is on a journey around the worlds , while Roxas always returns to Organization XIII after each mission . The staff constructed Roxas ' interactions with the Disney characters to be different from Sora 's as the Organization was meant to be secret in the game 's story . Nomura told the writers that he wanted Roxas to learn something from each of his missions or just to have something to think about . Nomura later clarified that Roxas ' personality was different from the one portrayed in Kingdom Hearts II as he does not actively attempt to come into contact with other characters . With Kingdom Hearts 358 / 2 Days , Nomura wanted to reveal why Roxas left Organization XIII . Although Nomura found it to be a sad story it , he considered Zack Fair 's ending in Crisis Core : Final Fantasy VII more tragic . His last words in the game were also Nomura 's most significant ones from the title as he wanted it to connect to Kingdom Hearts II 's first scene , which is actually the same scene . Uchiyama expressed sadness when the game ended development as he would not play the character for a long time . 
- Kingdom Hearts II : Final Mix 's secret ending features a character named Ventus that bears a striking resemblance to Roxas . Nomura commented that , despite how similar they are , Roxas and Ventus are not the same character . Additionally , he stated that by playing Kingdom Hearts Birth by Sleep , players will be able to distinguish Roxas from Ventus . In another interview , Nomura implied both characters are related , specifically to Sora , but he wanted fans to imagine reasons for such connection . The Kingdom Hearts Birth by Sleep <unk> clarified the connection , stating that Roxas and Ventus look alike because Ventus ' heart entered Sora 's body and Roxas ' birth allowed Ventus ' heart to reside in Roxas . This also confirms the mystery about Roxas having a heart in contrast to other Nobodies which allows him to express emotions despite his lack of memories from a past , something that Nobodies use to show emotions . 
- 
- = = Reception = = 
- 
- Roxas has been featured in various types of merchandising published by Square Enix . An action figure was released as part of the Play Arts action figures series . Other items sold include plush , <unk> and replicas of his necklace . 
- Roxas ' character has received a positive critical response by both fans and video game publications . During February 2010 , Roxas was twenty @-@ eighth in a Famitsu poll featuring the most popular video game characters from Japan . In another poll , Roxas was voted as the second most popular Kingdom Hearts character with his fight against Sora ranked as the best scene from the series . His fighting sequences from the first game 's secret ending were labelled by GameSpy as one of the best video game cinematic moments . While reviewing Kingdom Hearts II Jeff Haynes of IGN said that Roxas was a " likable kid " . Ron <unk> of Eurogamer took a similar stance , calling him a " likeable enough young chap who just happens to be troubled by memories and visions of people he doesn 't even know " . His playable position in the game was regarded as a transitional arc , being used to introduce the gameplay to players that are new to the franchise . <unk> Smith from <unk> also called Roxas a likable character and stated that the revelation regarding his existence in Kingdom Hearts II is " a <unk> of a revelation for any RPG ! " The New York Times liked the fact that the player controls Roxas in Kingdom Hearts II 's introduction instead of the protagonist Sora , avoiding the continuation of Sora 's search for his friends . Additionally , he found the switch from Roxas ' story to Sora 's after a few hours " a little <unk> " . Andrew <unk> from Game Informer emphasizes his role as a " troubled boy " , calling his story arc " an amazing chain of events " , particularly noting that the revelation of his nature as a Nobody creates a " <unk> yet remarkable plot twist " which may impact the player in a way that he " may not want Sora back " . On the other hand , UGO Networks commented that due to the game 's initial focus on Roxas , gamers would have to wait until playing as Sora to experience the most exciting parts of the title . 
- Before Kingdom Hearts 358 / 2 Days was released , 1UP.com featured Roxas at the top of their " Why You Should Care About Kingdom Hearts 358 / 2 Days " feature , calling him " The darling of <unk> and <unk> writers everywhere " , and commenting on what he does during his only year within Organization XIII . Roxas ' role as protagonist was labelled by G4TV as an attempt to " <unk> the <unk> <unk> " due to the explanation from his background . While reviewing 358 / 2 Days , <unk> 's Adam <unk> praised Roxas ' development during the title , expressing that even gamers who did not like his character in Kingdom Hearts II would care about him as " he evolves from a self @-@ described ' zombie ' to a strong @-@ willed and interesting character " . <unk> especially noted his relation with Xion and Axel , which he found charming despite the fact that they repeatedly meet at the top of a clock tower in a large number scenes . A similar response was made by Game Informer , who commented that although such scenes were " dull " , Roxas ' relation with Xion and Axel was appealing and the game 's ending would make up for such moments . GamesRadar also commented that Roxas " starts off like a zombie , but rapidly develops a personality " during the title and joked about the numerous times he eats ice cream . His growth in the game was also labelled as one of the most enjoyable elements from the title . IGN agreed , calling Roxas ' friendship " heart @-@ <unk> " . On the other hand , 1UP.com mentioned that although Roxas ' relationship with Xion and Axel is appealing , some of his first missions feel like " lonely , sad affairs " . His maneuvers in the game were praised by GameSpot for being easy to learn despite how complex they look , while IGN liked the variation between all of them . 
- Due to his resemblance with Ventus , video game publications initially thought that Roxas would be one of the protagonists from Kingdom Hearts Birth by Sleep . However , when it was revealed that the two were different , publications still discussed how similar they were and if there was a connection between them . 
- 
- 
- = Back to Tennessee ( song ) = 
- 
- " Back to Tennessee " is a country rock song by American singer @-@ songwriter and actor Billy Ray Cyrus . It released as the second single from Cyrus ' eleventh studio album of the same name on February 2 , 2009 by Lyric Street Records . Cyrus drafted the song after learning that Hannah Montana : The Movie , a film he would star in , would be set in Tennessee . The song , developed with the aid of co @-@ songwriters Tamara Dunn and Matthew Wilder , includes country rock elements while describing Cyrus ' longing to return to the South , where he grew up , after spending several years in Los Angeles . " Back to Tennessee " was featured in both Hannah Montana : The Movie and its soundtrack . 
- " Back to Tennessee " was well received by critics , though some felt it took the album 's message too literally . It was a moderate commercial achievement for Cyrus and charted within the top fifty of the Billboard magazine chart Hot Country Songs . The song 's music video was directed by Declan <unk> and features scenes of Cyrus at a beach inter cut with clips of Hannah Montana : The Movie . The song was performed in several venues . 
- 
- = = Development = = 
- 
- Cyrus moved to Los Angeles to star on the Disney Channel television series Hannah Montana in 2006 . Cyrus 's character , Robby Ray Stewart , is a former country singer and the father of Miley Stewart , a <unk> played by Miley Cyrus , Cyrus ' real life daughter . The television series became a worldwide hit and the basis for Hannah Montana : The Movie , a feature film released in 2009 . 
- Cyrus first read the script for Hannah Montana : The Movie while in Malibu in early 2008 . The film 's plot features the <unk> leaving Hollywood for a summer and returning to Tennessee , their home state , to <unk> with their southern roots . Having grown up in Kentucky , Cyrus was excited to learn the film would be set in the country and fought for it to be filmed on location . " They were gonna film it in a different state [ but ] I just knew that Tennessee had all the facilities , the man power , and the ability to make this film great , " said Cyrus . In addition , Cyrus believed that " in real life , Miley and [ he ] needed to come back home " , saying , " It was important for us to come back to where we are from and remember who we are . " Once filming in Tennessee was settled , Cyrus was " overcome with a very positive emotion of coming back home " and wrote " Back to Tennessee " with the aid of songwriters Tamara Dunn and Matthew Wilder . 
- Cyrus recorded " Back to Tennessee " for the film Hannah Montana : The Movie , in which his character performs the song at a fundraiser to save the meadows near his hometown from development . Cyrus says the song is " part of the cornerstone " of the film because it reflects the film 's theme of escaping the fast @-@ paced , glitzy world of Hollywood in search of a simpler country lifestyle . Cyrus commented , 
- " It 's about remembering , you know , where you come from . It 's always important to be aware of where you 're at and always be looking to where you wanna go , but most importantly don 't forget where you come from . That 's ' Back to Tennessee ' is all about [ ... ] With me , I always try to keep it real with the music , you know . Line by line you listen to the song and you know that I 'm walking the walk and talking the talk . It 's what this song 's all about . It definitely comes from the heart . " 
- 
- = = Composition = = 
- 
- " Back to Tennessee " is one of the more rock music influenced tracks on Back to Tennessee . It is set in common time with a country rock tempo of 120 beats per minute . The song is written in the key of C major . The song has the following chord progression , C — <unk> — C. 
- Cyrus said of the lyrics , " If you download " Back to Tennessee " and listen to the whole song , you 'll hear exactly how I felt after four years of Hannah Montana , and living in Los Angeles and giving up my previous life and existence and who I am and where I come from . You 'll hear a guy who 's immersed in music , and my love and desire and need to go back home . " Jon Caramanica of The New York Times interpreted the song as an apology " both to [ Cyrus 's ] teenage self and , by extension , to the daughter under his wing . " Both Caramanica and Alison Stewart of The Washington Post took interest in the lines , " Great big town / So full of users " . Caramanica said the line recognized that " fame is <unk> " and " talk [ ed ] about some of his daughter ’ s famous friends " , while Stewart interpreted the line as a description of Hollywood life in general . 
- 
- = = Critical reception = = 
- 
- The song received generally positive reviews from critics . Shelly <unk> of About.com said , " There 's a special quality to this single that might be able to help people get past ' that one song ' from Billy Ray Cyrus 's past and realize there 's so much more to the man than something from two decades ago . " While reviewing the Hannah Montana : The Movie soundtrack , Heather <unk> of Allmusic said the song " fits in smoothly with an acoustic version of Rascal Flatts ' witty ' <unk> ' " . Jon Caramanica of The New York Times said , " His hard @-@ won insight is the next best thing to a good <unk> . " Allison Stewart of The Washington Post described " Back to Tennessee " as a " <unk> of strident <unk> rock track " . She added that the song was " eager " and that it explained " Country as Country Can Be " . Yahoo ! Music thought the song was followed Back to Tennessee 's message " too literally " . The song was included on the short list for Best Original Song at the 82nd Academy Awards , but failed to achieve a nomination . 
- 
- = = Chart performance = = 
- 
- In the United States , " Back to Tennessee " failed to reach the Billboard Hot 100 , the country 's main singles chart , but charted on the country @-@ genre chart . In the week ending March 7 , 2009 , " Back to Tennessee " debuted at number fifty @-@ nine on Billboard 's Hot Country Songs . " The <unk> " , by Cyrus ' daughter , Miley Cyrus , debuted at number forty @-@ eight the same week , the first time a father and daughter had separate charting songs on the chart since Johnny Cash and <unk> Cash charted in 1990 with " Silver <unk> " and " One <unk> over the Line " , respectively . On the week ending April 25 , 2009 , the song reached its peak at number forty @-@ seven on the chart . 
- 
- = = Music video = = 
- 
- The music video for " Back to Tennessee " was directed by Declan <unk> and premiered on March 12 , 2009 on <unk> . It was partially shot in Malibu , California , where Cyrus wrote the song . Like the song , the video represents Cyrus ' desire to return home . Cyrus explained , 
- " When I see the video for ' Back to Tennessee , ' there 's a lot of emotion there . It 's a combination between the Hannah Montana movie and going back to the actual spot where I wrote the song . I wrote the song in Malibu , and we went back to Malibu to film the video . I had never done a video exactly where I wrote the song . Then we take my little blue truck from the movie and travel back across the country . Again , it becomes a very articulate picture of what that song 's about . It 's everything a video is supposed to be . " 
- Cyrus 's " little blue truck " is the blue 1990 Ford Ranger that he drove in the film , which was transported to California to be used in the video . The video prominently features clips of Hannah Montana : The Movie , particularly the scene in which Cyrus performs " Back to Tennessee " . Cyrus 's backup band in that performance includes part of Taylor Swift 's band , <unk> Covington , and <unk> . About the video as a whole , Cyrus said , " I hope when people , you know , watch the video . I hope that they can feel what this song is about . I hope they can relate to that in their life and their world . " 
- The video <unk> with the Hannah Montana : The Movie performance , in which Cyrus and his backup band perform atop a stage in a crowded barn . The video then switches to its principal setting , which features Cyrus on a beach at sunset . Cyrus is seen in a black T @-@ shirt <unk> rocks at the ocean and looking at a picture of a blond woman . The woman is portrayed by <unk> <unk> , who plays <unk> , Robby Ray 's love interest in Hannah Montana : The Movie . As the video progresses , Cyrus is also seen with the blue Ford Ranger , either driving it or leaning against it while playing an acoustic guitar . The video also includes clips of <unk> , Robby Ray , Miley Stewart , and Miley Stewart 's love interest , Travis Body . The video ends with Cyrus , looking down , walking next to the <unk> . 
- 
- = = Live performances = = 
- 
- Cyrus performed " Back to Tennessee " as part of the AOL Sessions on April 13 , 2009 . He also performed the song and several others in a London Apple Store on April 24 , 2009 . The set , along with some songs by Miley , were recorded and sold exclusively by the United Kingdom iTunes Store as an extended play titled iTunes Live from London . On August 1 , 2009 , Cyrus performed the song at the 2009 <unk> Freedom Concert , a concert supporting the Freedom Alliance Scholarship Fund . 
- 
- 
- = M @-@ 108 ( Michigan highway ) = 
- 
- M @-@ 108 was a state trunkline highway in the US state of Michigan . The highway followed <unk> Street , although some maps also labeled it as the Mackinaw Highway . The road was on the boundary between Emmet and Cheboygan counties . 
- The original M @-@ 108 designation dated back to 1928 . This version was transferred to local control in 1957 , but revived later under a different routing in 1960 . On April 29 , 2010 , the <unk> News @-@ Review reported that the Michigan Department of Transportation ( MDOT ) will rebuild the roadway in preparation to transfer it back to local control once again . MDOT and local officials signed memoranda of understanding , completing the transfer of the roadway on December 9 , 2010 . 
- 
- = = Route description = = 
- 
- The 1 @.@ <unk> @-@ mile ( 1 @.@ 720 km ) long route connected two interchanges off Interstate 75 ( I @-@ 75 ) on the south side of Mackinaw City with a tourist welcome center . The southern terminus was at a partial interchange with I @-@ 75 ( exit 337 ) just south of Mackinaw City on the boundary between Emmet and Cheboygan counties . This interchange is about two miles ( 3 km ) north of the northern terminus of US Highway 31 ( US 31 ) . M @-@ 108 followed <unk> Street north from I @-@ 75 into the Village of Mackinaw City past the Thunder Falls Water Park and some local <unk> to an intersection with US 23 . North of the US 23 intersection is the Michigan Welcome Center and other tourist amenities on the west side of the street . State maintenance ends at the intersection with the off @-@ ramp from northbound I @-@ 75 exit 338 . 
- 
- = = History = = 
- 
- In 1928 , the first M @-@ 108 designation was commissioned along a rare three @-@ legged route connecting the State Highway Ferry Docks with the Fort <unk> State Historic Park and US 31 . At one point while traveling westbound on M @-@ 108 , motorists could turn right onto northbound M @-@ 108 or left onto southbound M @-@ 108 . In preparation for the opening of the Mackinac Bridge , the Michigan State Highway Department transferred all of M @-@ 108 to local control in late 1957 , decommissioning the designation at the same time . In 1960 , the current M @-@ 108 was commissioned along the current routing . 
- On April 29 , 2010 , MDOT announced plans to transfer M @-@ 108 in its entirety to the Village of Mackinaw City and Emmet County . In preparation for this transfer , MDOT <unk> , widened and reconstructed the roadway . While rebuilding the section of the highway between US 23 and the northern terminus , the Welcome Center was closed . Plans had this section to be completed , and the center to bed reopened , for Memorial Day weekend , with the remainder of construction to be completed in August . Following the transfer to local control , M @-@ 108 ceased to be a state highway and the designation was decommissioned . Previous news reports stated that the roadway was too small to merit highway status under federal guidelines . The transfer was completed on December 9 , 2010 when MDOT and local officials signed memoranda of understanding to affect the transfer . The section within the Village of Mackinaw City was transferred to the village , and the remainder to Emmet County . 
- 
- = = Major intersections = = 
- 
- The entire highway was on the Emmet – Cheboygan county line . 
- 
- 
- = Blackwyche = 
- 
- Blackwyche is an action @-@ adventure video game developed and published by Ultimate Play The Game for the Commodore 64 in 1985 . The game is the third instalment of the Pendragon series and is a sequel to <unk> . In the game , <unk> adventurer Sir Arthur Pendragon is trapped on board a haunted galleon and must free the soul of its captain . The game is presented in a 3D isometric format . 
- The Pendragon series was created and designed by brothers Dave and Bob Thomas , with Ultimate founders Tim and Chris Stamper otherwise being <unk> in development . The game 's setting and graphics were heavily inspired by HMS Victory and the surrounding city of Portsmouth . The game was met with mixed reviews upon release . Critics were divided over the game 's graphics and re @-@ usage of sprites , but criticised the game for its vast similarities to its predecessors . It was followed by a final instalment to the series , Dragon <unk> , which was released later in 1985 . 
- 
- = = Gameplay = = 
- 
- The game is presented in a isometric format and is set on board a haunted galleon named the Blackwyche . Sir Arthur Pendragon 's main objective is to free the soul of its former captain , Richard <unk> . Pendragon can <unk> various weapons such as knives , <unk> and a magic sword to defend himself from enemy skeletons . The player begins the game with full energy and it will slightly <unk> every time the player is hit by an enemy . If Pendragon completely runs out of energy , a large skeletal hand will drag the player off @-@ screen , thus killing him . 
- Various segments of maps are scattered around the galleon , which will form a complete view of the game 's overworld once all the segments are picked up . Other scattered items in the game include keys for locked doors , gunpowder to fire cannons and pieces of jewellery , the latter having no additional use other than adding to the player 's score . 
- 
- = = Development = = 
- 
- <unk> <unk> and Graphics was founded by brothers Tim and Chris Stamper , along with Tim 's wife , Carol , from their headquarters in <unk> @-@ de @-@ la @-@ <unk> in 1982 . Under the trading name of Ultimate Play The Game , they began producing multiple video games for the ZX Spectrum throughout the early 1980s . The company was known for their reluctance to reveal details about their operations and upcoming projects . Little was known about their development process except that they used to work in " separate teams " ; one team would work on graphics while the other would concentrate on other aspects such as sound or programming . 
- The Pendragon series was created by brothers Dave and Robert ( Bob ) Thomas , rather than Ultimate founders Tim and Chris Stamper . Dave Thomas began his career in 1983 when he started producing multiple games for the <unk> 400 , including moderate @-@ <unk> such as <unk> , which later won him GB £ 5 @,@ 000 in a competition from Calisto Software . Although he began working for the company in producing video games , he later quit due to the strain of his daily , 68 mi ( 109 km ) commute . Shortly after quitting Calisto Software , Dave Thomas started work on The Staff of <unk> , the first instalment of what would become the Pendragon series . Bob Thomas was a trained technical illustrator for the Ministry of Defence and had experience with designing interiors for the Royal Navy . According to Dave Thomas , the setting of Blackwyche was heavily inspired by the HMS Victory and the surrounding naval city of Portsmouth . The name of " Sir Arthur Pendragon " was copied from the character of the Black Prince Pendragon from Jack the Giant Killer . 
- The graphics of the game were designed by Bob Thomas , whereas the code was written by Dave Thomas . The Thomas brothers decided to show their progress of the game to Tim and Chris Stamper for evaluation , despite feeling embarrassed due to their <unk> being inside their parents ' attic . Impressed by the game , the Stamper brothers commissioned an entire series to be released for the Commodore 64 . Dave Thomas recalled that every game they produced was met with little interference from Ultimate ; once a game was complete , it would be sent to quality assessment and subsequently published for release . 
- 
- = = Reception = = 
- 
- The game received mixed reviews upon release . A reviewer of <unk> thought the graphics were identical to its predecessor , <unk> , and criticised the low detail of the player @-@ character , Sir Arthur Pendragon . Eugene Lacey of Commodore User praised the graphics , stating that they were <unk> and slightly more detailed , despite acknowledging that they appeared similar to its predecessor . Stuart Cooke of Your Commodore thought the graphics were too " repetitive " and stated that he had trouble determining which section of the game he was on due to the similarities of all the colours . Reviewers of Zzap ! 64 criticised the graphics , stating the sprites to be " awful " , and animation as " <unk> " with little or no range of colouring . The sprite of Sir Arthur Pendragon was frequently criticised by reviewers of Zzap ! 64 , with one reviewer expressing frustration on why Ultimate continued to re @-@ use the same sprites in their games . Another reviewer condemned the 3D animation , stating that it appeared out of proportion and " frustrating " . Tony Hetherington of Computer Gamer similarly criticised the graphics , owing to the " <unk> landscapes " and identical usage of colours , concluding that it was a disappointing game . 
- Lacey heralded the gameplay to be " purely <unk> " and considered Blackwyche to the best of the series . Harding opinionated the game to be " miles better " than its predecessor , whereas Cooke thought the gameplay was repetitive , owing to its number of locations . Reviewers of Zzap ! 64 criticised the overall gameplay , stating that the puzzles were poor , <unk> and often gave them the sense of " <unk> @-@ <unk> " with its similarities to its predecessors . One reviewer of the same magazine considered the game to be a poor attempt at an arcade @-@ adventure game , stating that the ideas were poorly implemented . Hetherington concluded that Blackwyche was a disappointment for an Ultimate game , considering their previous hit titles such as <unk> <unk> and Knight <unk> . 
- 
- 
- = Hoyt Wilhelm = 
- 
- James Hoyt Wilhelm ( July 26 , 1922 – August 23 , 2002 ) , nicknamed " Old <unk> " , was an American Major League Baseball ( MLB ) pitcher with the New York Giants , St. Louis Cardinals , Cleveland Indians , Baltimore Orioles , Chicago White Sox , California Angels , Atlanta Braves , Chicago Cubs , and Los Angeles Dodgers between 1952 and 1972 . He is a member of the Baseball Hall of Fame . 
- After growing up in North Carolina and fighting in World War II , Wilhelm spent several years in the minor leagues before starting his MLB career . He was best known for his knuckleball , which enabled him to have great longevity . He appeared occasionally as a starting pitcher , but he pitched mainly as a specialist relief man , a role in which he won 124 games , still the record for relief pitchers . He was the first pitcher to reach 200 saves and the first to appear in 1 @,@ 000 games . 
- Wilhelm , who did not enter the major leagues until his late twenties , pitched until he was nearly 50 years old . Wilhelm retired with one of the lowest career earned run averages in baseball history . After retiring as a player in the early 1970s , he held coaching roles with the New York Yankees and Atlanta Braves for many years . He was a longtime resident of Sarasota , Florida , where he died in a nursing home of heart failure in 2002 . 
- 
- = = Early life = = 
- 
- Wilhelm was one of eleven children born to poor tenant farmers John and <unk> ( née Stanley ) Wilhelm in <unk> , North Carolina . He played baseball at Cornelius High School in Cornelius , North Carolina . There , he began experimenting with a knuckleball after reading about pitcher Dutch Leonard . He felt that , because he could not throw fast , <unk> a knuckleball offered him his best shot at success . He used a tennis ball to practice . 
- Wilhelm made his professional debut with the Mooresville Moors of the Class @-@ D North Carolina State League in 1942 . He served in the United States Army in the European Theater during World War II . Wilhelm participated in the Battle of the <unk> , where he was wounded , earning the Purple Heart for his actions . He played his entire career with a piece of shrapnel lodged in his back as a result of this injury . He rose to the rank of staff sergeant . <unk> was nicknamed " Old <unk> " because of his service in the military . 
- He returned to the Moors in 1946 , following his military service . Over the 1946 and 1947 seasons , Wilhelm earned 41 wins with Mooresville . He later recalled being dropped from a Class D minor league team and having the manager tell him to forget about the knuckleball , but he persisted with it . The Boston Braves purchased Wilhelm from Mooresville in 1947 . On November 20 , 1947 , Wilhelm was drafted by the New York Giants from the Braves in the 1947 minor league draft . 
- Wilhelm 's first assignment in the Giants organization was in Class B with the 1948 <unk> <unk> , for whom he registered 13 wins and 9 losses . He spent a few games that season with the Class A Jacksonville <unk> of the South Atlantic League . Wilhelm returned to Jacksonville in 1949 , earning a 17 – 12 win @-@ loss record and a 2 @.@ 66 earned run average ( ERA ) . With the Class AAA Minneapolis <unk> in 1950 , Wilhelm was the starting pitcher in 25 of his 35 games pitched , registering a 15 – 11 record with a 4 @.@ 95 ERA . His ERA came down to 3 @.@ 94 in 1951 with Minneapolis , but his record finished at 11 – 14 . Wilhelm had been used in a similar role that season , mostly starting games but also making eleven relief appearances . 
- 
- = = Major league career = = 
- 
- 
- = = = Early years = = = 
- 
- Though Wilhelm was primarily a starting pitcher in the minor leagues , he had been called up to a Giants team whose strong starting pitchers had led them to a National League ( NL ) pennant the year before . Giants manager Leo <unk> did not think that Wilhelm 's knuckleball approach would be effective for more than a few innings at a time . He assigned Wilhelm to the team 's bullpen . 
- Wilhelm made his MLB debut with the Giants on April 18 , 1952 at age 29 , giving up a hit and two walks while only recording one out . On April 23 , 1952 , in his second game with the New York Giants , Wilhelm batted for the first time in the majors . Facing rookie Dick Hoover of the Boston Braves , Wilhelm hit a home run over the short right @-@ field fence at the Polo Grounds . Although he went to bat a total of 432 times in his career , he never hit another home run . 
- Pitching exclusively in relief , Wilhelm led the NL with a 2 @.@ 43 ERA in his rookie year . He won 15 games and lost three . Wilhelm finished in the top ten in Most Valuable Player Award voting that season , becoming the first relief pitcher to finish that high . He finished second in the Rookie of the Year Award voting . Wilhelm made 69 relief appearances in 1953 , his win @-@ loss record decreased to 7 – 8 and he issued 77 walks against 71 strikeouts . Wilhelm was named to the NL All @-@ Star team that year , but he did not play in the game because team manager Charlie <unk> did not think that any of the catchers could handle his knuckleball . The Giants renewed Wilhelm 's contract in February 1954 . 
- In 1954 , Wilhelm was a key piece of the pitching staff that led the 1954 Giants to a world championship . He pitched 111 innings , finishing with a 12 – 4 record and a 2 @.@ 10 ERA . During one of Wilhelm 's appearances that season , catcher Ray <unk> committed four passed balls in one inning to set the major league record ; the record has subsequently been tied twice . When Stan <unk> set a record by hitting five home runs in a <unk> that year , Wilhelm was pitching in the second game and gave up two of the home runs . The 1954 World Series represented Wilhelm 's only career postseason play . He pitched 2 1 ⁄ 3 innings over two games , earning a save in the third game . The team won the World Series in a four @-@ game sweep . 
- Wilhelm 's ERA increased to 3 @.@ 93 over 59 games and 103 innings pitched in 1955 , but he managed a 4 – 1 record . He finished the 1956 season with a 4 – 9 record and a 3 @.@ 83 ERA in 89 1 ⁄ 3 innings . <unk> Bob <unk> later attributed Wilhelm 's difficulties in the mid @-@ 1950s to the decline in the career of Giants catcher Wes <unk> , writing that baseball was " a game of inches , and for Hoyt , Wes had been that inch in the right direction . " 
- 
- = = = Middle career = = = 
- 
- On February 26 , 1957 , Wilhelm was traded by the Giants to the St. Louis Cardinals for <unk> <unk> . At the time of the trade , St. Louis manager Fred Hutchinson described Wilhelm as the type of pitcher who " makes us a definite pennant threat ... He 'll help us where we need help the most . " In 40 games with the Cardinals that season , he earned 11 saves but finished with a 1 – 4 record and his highest ERA to that point in his career ( 4 @.@ 25 ) . The Cardinals placed him on waivers in September and he was claimed by the Cleveland Indians , who used him in two games that year . 
- In 1958 , Cleveland manager Bobby <unk> used Wilhelm occasionally as a starter . Although he had a 2 @.@ 49 ERA , none of the Indians ' catchers could handle Wilhelm 's knuckleball . General manager Frank Lane , alarmed at the large number of passed balls , allowed the Baltimore Orioles to select Wilhelm off waivers on August 23 , 1958 . In Baltimore , Wilhelm lived near the home of third baseman Brooks Robinson and their families became close friends . On September 20 of that year , Wilhelm no @-@ hit the eventual World Champion New York Yankees 1 @-@ 0 at Memorial Stadium , in only his ninth career start . He allowed two baserunners on walks and struck out eight . The no @-@ hitter had been threatened at one point in the ninth inning when Hank Bauer <unk> along the baseline , but Robinson allowed the ball to roll and it <unk> foul . The no @-@ hitter was the first in the franchise 's Baltimore history ; the Orioles had moved from St. Louis after the 1953 season . 
- Orioles catchers had difficulty catching the Wilhelm knuckleball again in 1959 and they set an MLB record with 49 passed balls . During one April game , catcher Gus Triandos had four passed balls while catching for Wilhelm and he described the game as " the <unk> day I ever put in during my life . " Author Bill James has written that Wilhelm and Triandos " established the principle that a knuckleball pitcher and a big , slow catcher make an awful combination . " Triandos once said , " Heaven is a place where no one throws a knuckleball . " 
- Despite the passed balls , Wilhelm won the American League ERA title with a 2 @.@ 19 ERA . During the 1960 season , Orioles manager Paul Richards devised a larger <unk> so his catchers could handle the knuckleball . Richards was well equipped with starting pitchers during that year . By the middle of the season , he said that eight of his pitchers could serve as starters . Wilhelm started 11 of the 41 games in which he appeared . He earned an 11 – 8 record , a 3 @.@ 31 ERA and seven saves . He only started one game the following year , but he was an All @-@ Star , registered 18 saves and had a 2 @.@ 30 ERA . 
- In 1962 , Wilhelm had his fourth All @-@ Star season , finishing with a 7 – 10 record , a 1 @.@ 94 ERA and 15 saves . On January 14 , 1963 , Wilhelm was traded by the Orioles with Ron Hansen , Dave Nicholson and Pete Ward to the Chicago White Sox for Luis <unk> and Al Smith . Early in that season , White Sox manager Al López said that Wilhelm had improved his pitching staff by 40 percent . He said that Wilhelm was " worth more than a 20 @-@ game winner , and he works with so little effort that he probably can last as long as <unk> Paige . " He registered 21 saves and a 2 @.@ 64 ERA . 
- In 1964 , Wilhelm finished with career highs in both saves ( 27 ) and games pitched ( 73 ) . His ERA decreased to 1 @.@ 99 that season ; it remained less than 2 @.@ 00 through the 1968 season . In 1965 , Wilhelm contributed to another passed balls record when Chicago catcher J. C. Martin allowed 33 of them in one season . That total set a modern single @-@ season baseball record for the category . Wilhelm 's career @-@ low ERA ( 1 @.@ 31 ) came in 1967 , when he earned an 8 – 3 record for the White Sox with 12 saves . 
- In the 1968 season , Wilhelm was getting close to breaking the all @-@ time games pitched record belonging to Cy Young ( <unk> games ) . Chicago manager Eddie <unk> began to think about using Wilhelm as a starting pitcher for game number 907 . However , the White Sox fired <unk> before the record came up . Wilhelm later broke the record as a relief pitcher . He also set MLB records for consecutive <unk> games by a pitcher , career victories in relief , games finished and innings pitched in relief . Despite Wilhelm 's success , the White Sox , who had won at least 83 games per season in the 1960s , performed poorly . They finished 1968 with a 67 – 95 record . 
- Wilhelm was noted during this period for his <unk> of relief pitcher Wilbur Wood , who came to the 1967 White Sox in a trade . Wood sometimes threw a knuckleball upon his arrival in Chicago , but Wilhelm encouraged him to throw it full @-@ time . By 1968 , Wood won 13 games , saved 16 games and earned a 1 @.@ 87 ERA . He credited Wilhelm with helping him to master the knuckleball , as the White Sox coaches did not know much about how to throw it . Between 1968 and 1970 , Wood pitched in more games ( 241 ) than any other pitcher and more innings ( 400 1 ⁄ 3 ) than any other relief pitcher . 
- After the 1968 season , MLB expanded and an expansion draft was conducted in which the new teams could select certain players from the established teams . The White Sox left Wilhelm unprotected , possibly because they did not believe that teams would have interest in a much older pitcher . On October 15 , 1968 , Wilhelm was chosen in the expansion draft by the Kansas City Royals as the 49th pick . That offseason , he was traded by the Royals to the California Angels for Ed Kirkpatrick and Dennis <unk> . 
- 
- = = = Later career = = = 
- 
- In 44 games pitched for the 1969 California Angels , Wilhelm had a 2 @.@ 47 ERA , ten saves and a 5 – 7 record . On September 8 , 1969 , Wilhelm was traded by the Angels with Bob <unk> to the Atlanta Braves for Clint Compton and Mickey Rivers . He finished the 1969 season by pitching eight games for the Braves , earning four saves and recording a 0 @.@ 73 ERA over 12 1 ⁄ 3 innings pitched . Wilhelm spent most of the 1970 season with the Braves , pitching in 50 games for the team and earning ten saves . 
- On September 21 , 1970 , Wilhelm was selected off waivers by the Chicago Cubs , for whom he appeared in three games . He was traded back by the Cubs to the Braves for Hal <unk> after the season . As the Cubs acquired Wilhelm late in the season to bolster their playoff contention , which was a source of controversy , Commissioner Bowie <unk> investigated the transaction . In December , <unk> ruled that he did not find evidence of <unk> associated with the transactions that sent Wilhelm to the Cubs and quickly back to the Braves . 
- Wilhelm was released by the Braves on June 29 , 1971 , having pitched in three games for that year 's Braves . He signed with the Los Angeles Dodgers on July 10 , 1971 . He appeared in nine MLB games for the Dodgers , giving up two earned runs in 17 2 ⁄ 3 innings . He also pitched in eight games that season for the team 's Class AAA minor league affiliate , the Spokane Indians . He started six of those games and registered a 3 @.@ 89 ERA . He pitched in 16 games for the Dodgers in 1972 , registering a 4 @.@ 62 ERA over 25 innings . The Dodgers released him on July 21 , 1972 . He never appeared in another game . 
- At the time of his retirement , Wilhelm had pitched in a then @-@ MLB record 1 @,@ 070 games . He is recognized as the first pitcher to have saved 200 games in his career , and the first pitcher to appear in 1 @,@ 000 games . He is also one of the oldest players to have pitched in the major leagues ; his final appearance was 16 days short of his 50th birthday . Wilhelm retired with the lowest career earned run average of any major league <unk> after 1927 ( Walter Johnson ) who pitched more than 2 @,@ 000 innings . 
- 
- = = Later life = = 
- 
- After his retirement as a player , Wilhelm managed two minor league teams in the Atlanta Braves system for single seasons . He led the 1973 Greenwood Braves of the Western Carolinas League to a 61 – 66 record , then had a 33 – 33 record with the 1975 <unk> Braves of the Appalachian League . He also worked as a minor league pitching coach for the New York Yankees for 22 years . As a coach , Wilhelm said that he did not teach pitchers the knuckleball , believing that people had to be born with a <unk> for throwing it . He sometimes worked individually with major league players who wanted to improve their <unk> , including Joe <unk> . The Yankees gave Wilhelm permission to work with Mickey <unk> in 1979 even though <unk> pitched for the San Diego Padres . 
- Wilhelm was on the ballot for the Baseball Hall of Fame for eight years before he was elected . After Wilhelm failed to garner enough votes for induction in 1983 , sportswriter Jim Murray criticized the voters , saying that while Wilhelm never had the look of a baseball player , he was " the best player in history at what he does . " He fell short by 13 votes in 1984 . Wilhelm was inducted into the Baseball Hall of Fame in 1985 . At his induction ceremony , he said that he had achieved all three of his initial major league goals : appearing in a World Series , being named to an All @-@ Star team , and throwing a no @-@ hitter . 
- He and his wife Peggy lived in Sarasota , Florida . They raised three children together : Patti , Pam , and Jim . Wilhelm died of heart failure in a Sarasota nursing home in 2002 . 
- 
- = = Legacy = = 
- 
- Wilhelm was known as a " relief ace " and his teams used him in a new way that became a trend . Rather than bringing in a relief pitcher only when the starting pitcher had begun to struggle , teams increasingly called upon their relief pitchers toward the end of any close game . Wilhelm was the first relief pitcher elected to the Baseball Hall of Fame . 
- He is also remembered as one of the most successful and " probably the most famous ' old ' player in history . " Although , due largely to his military service , he did not debut in the major leagues until he was already 29 years old , Wilhelm nonetheless managed to appear in 21 major league seasons . He earned the nickname " Old <unk> " while he still had more than a decade left in his playing career . He was the oldest player in Major League Baseball for each of his final seven seasons . 
- Former teammate Moose <unk> commented on Wilhelm 's key pitch , saying , " Hoyt was a good guy , and he threw the best knuckleball I ever saw . You never knew what Hoyt 's pitch would do . I don 't think he did either . " Baseball executive Roland <unk> agreed , saying , " Wilhelm 's knuckleball did more than anyone else 's ... There was so much action on it . " Before Wilhelm , the knuckleball was primarily mixed in to older pitchers ' <unk> at the end of their careers to offset their slowing <unk> and reduce stress on their arms , thereby extending their careers . Wilhelm broke with tradition when he began throwing the pitch as a teenager and on nearly every pitch . The New York Times linked his knuckleball with that of modern pitcher <unk> Dickey , as Wilhelm taught pitcher Charlie Hough the knuckleball in 1971 and Hough taught it to Dickey while coaching with the Texas Rangers . 
- 
- 
- = Politics of Croatia = 
- 
- The politics of Croatia are defined by a parliamentary , representative democratic republic framework , where the Prime Minister of Croatia is the head of government in a multi @-@ party system . Executive power is exercised by the Government and the President of Croatia . Legislative power is vested in the Croatian Parliament ( Croatian : Sabor ) . The <unk> is independent of the executive and the legislature . The parliament adopted the current Constitution of Croatia on 22 December 1990 and decided to declare independence from Yugoslavia . The declaration of independence came into effect on 8 October 1991 . The constitution has since been amended several times . The first modern parties in the country developed in the middle of the 19th century , and their agenda and appeal changed , reflecting major social changes , such as the breakup of Austria @-@ Hungary , the Kingdom of Serbs , Croats and Slovenes , dictatorship and social upheavals in the kingdom , World War II , the establishment of Communist rule and the breakup of the <unk> Yugoslavia . 
- The President of the Republic ( Croatian : <unk> / <unk> <unk> ) is the head of state and the commander in chief of the Croatian armed forces and is directly elected to serve a five @-@ year term . The government ( Croatian : <unk> ) , the main executive power of Croatia , is headed by the prime minister , who has four deputy prime ministers , three of whom also serve as government ministers . Seventeen ministers are in charge of particular activities . The executive branch is responsible for proposing legislation and a budget , executing the laws , and guiding the foreign and internal policies . The parliament is a unicameral legislative body . The number of Sabor representatives ranges from 100 to 160 ; they are elected by popular vote to serve four @-@ year terms . The powers of the legislature include enactment and amendment of the constitution and laws ; adoption of the government budget , declarations of war and peace , defining national boundaries , calling referenda and elections , appointments and relief of officers , supervising the Government of Croatia and other holders of public powers responsible to the Sabor , and granting of <unk> . The Croatian constitution and legislation provides for regular presidential and parliamentary elections , and the election of county prefects and assemblies , and city and municipal mayors and councils . 
- Croatia has a three @-@ tiered , independent judicial system governed by the Constitution of Croatia and national legislation enacted by the Sabor . The Supreme Court ( Croatian : <unk> sud ) is the highest court of appeal in Croatia . There are other specialised courts in Croatia — commercial courts and the Superior Commercial Court , <unk> courts , the Superior <unk> ( criminal ) Court , the Administrative Court and the Croatian Constitutional Court ( Croatian : <unk> sud ) . The State Attorney 's Office represents the state in legal proceedings . 
- 
- = = Legal framework = = 
- 
- Croatia is a unitary democratic parliamentary republic . Following the collapse of the ruling Communist League , Croatia adopted a new constitution in 1990 – which replaced the 1974 constitution adopted by the Socialist Republic of Croatia – and organised its first multi @-@ party elections . While the 1990 constitution remains in force , it has been amended four times since its adoption — in 1997 , 2000 , 2001 and 2010 . Croatia declared independence from Yugoslavia on 8 October 1991 , which led to the breakup of Yugoslavia . Croatia 's status as a country was internationally recognised by the United Nations in 1992 . Under its 1990 constitution , Croatia operated a semi @-@ presidential system until 2000 when it switched to a parliamentary system . Government powers in Croatia are divided into legislative , executive and judiciary powers . The legal system of Croatia is civil law and , along with the institutional framework , is strongly influenced by the legal heritage of Austria @-@ Hungary . By the time EU accession negotiations were completed on 30 June 2010 , Croatian legislation was fully <unk> with the Community <unk> . 
- 
- = = Executive = = 
- 
- The President of the Republic ( Croatian : <unk> / <unk> <unk> ) is the head of state ; he or she is directly elected and serves a five @-@ year term . The president is the commander in chief of the armed forces , has the procedural duty of appointing the prime minister with the consent of the Sabor ( Parliament ) through a simple majority vote , and has some influence on foreign policy . The most recent presidential election was held on 10 January 2010 and was won by Ivo Josipović . He took the oath of office on 18 February 2010 . The constitution limits holders of the presidential office to a maximum of two terms and prevents the president from being a member of any political party . Consequently , the president @-@ elect withdraws from party membership before inauguration . President Josipović did so on 15 February 2010 . 
- The government ( Croatian : <unk> ) , the main executive power of Croatia , is headed by the prime minister who has four deputies , three of whom also serve as government <unk> there are seventeen other ministers who are appointed by the prime minister with the consent of the Sabor ; these are in charge of particular sectors of activity . As of 23 December 2011 , the Deputy Prime Ministers are <unk> <unk> , <unk> <unk> , Branko <unk> , and <unk> <unk> . Government ministers are from the Social Democratic Party of Croatia ( SDP ) , and the Croatian People 's Party - Liberal Democrats ( HNS ) and <unk> Democratic Assembly ( <unk> ) . The executive branch is responsible for proposing legislation and a budget , executing the laws , and guiding the country 's foreign and domestic policies . The government 's official residence is at <unk> <unk> . As of 23 December 2011 , the prime minister is <unk> <unk> . 
- 
- = = Legislature = = 
- 
- The Parliament of Croatia ( Croatian : Sabor ) is a unicameral legislative body . A second chamber , the Chamber of Counties ( Croatian : <unk> <unk> ) , was set up in 1993 pursuant to the 1990 Constitution . The Chamber of Counties was originally composed of three deputies from each of the twenty counties and the city of Zagreb . However , as it had no practical power over the Chamber of Representatives , it was abolished in 2001 and its powers were transferred to the county governments . The number of Sabor representatives can vary from 100 to 160 ; they are all elected by popular vote and serve four @-@ year terms . 140 members are elected in multi @-@ seat constituencies , up to six members are chosen by proportional representation to represent <unk> living abroad and five members represent ethnic and national communities or minorities . The two largest political parties in Croatia are the SDP and the Croatian Democratic Union ( HDZ ) . The last parliamentary election was held on 4 December 2011 in Croatia and on 3 and 4 December 2011 abroad . 
- The Sabor meets in public sessions in two periods ; the first from 15 January to 30 June , and the second from 15 September to 15 December . Extra sessions can be called by the President of the Republic , by the president of the parliament or by the government . The powers of the legislature include enactment and amendment of the constitution , enactment of laws , adoption of the state budget , declarations of war and peace , alteration of the country 's boundaries , calling and conducting referenda and elections , appointments and relief of office , supervising the work of the Government of Croatia and other holders of public powers responsible to the Sabor , and granting amnesty . <unk> are made based on a majority vote if more than half of the Chamber is present , except in cases of constitutional issues . 
- 
- = = <unk> = = 
- 
- The Croatian constitution and legislation provides for regular elections for the office of the President of the Republic , parliamentary , county prefects , county assemblies , city and municipal mayors and city and municipal councils . The President of the Republic is elected to a five @-@ year term by a direct vote of all citizens of Croatia . A majority vote is required to win . A runoff election round is held in cases where no candidate <unk> the majority in the first round of voting . The presidential elections are regulated by the constitution and dedicated legislation ; the latter defines technical details , appeals and similar issues . 
- 140 members of parliament are elected to a four @-@ year term in ten multi @-@ seat constituencies , which are defined on the basis of the existing county borders , with amendments to achieve a uniform number of eligible voters in each constituency to within 5 % . Citizens of Croatia living abroad are counted in an eleventh constituency ; however , its number of seats was not fixed for the last parliamentary election . It was instead calculated based on numbers of votes cast in the ten constituencies in Croatia and the votes cast in the eleventh constituency . In the 2007 parliamentary election the eleventh constituency elected five MPs . Constitutional changes first applied in the 2011 parliamentary election have abolished this scheme and permanently assigned three MPs to the eleventh constituency . Additionally , eight members of parliament are elected by voters belonging to twenty @-@ two recognised minorities in Croatia : the Serb minority elects three MPs , Hungarians and Italians elect one MP each , Czech and <unk> minorities elect one MP jointly , while all other minorities elect two more MPs to the parliament . The Standard D <unk> formula is applied to the vote , with a 5 % election threshold . The last parliamentary election , held in 2011 , elected 151 MPs . 
- The county prefects and city and municipal mayors are elected to four @-@ year terms by majority of votes cast within applicable local government units . A runoff election is held if no candidate achieves a majority in the first round of voting . Members of county , city , and municipal councils are elected to four @-@ year terms through proportional representation ; the entire local government unit forms a single constituency . The number of council members is defined by the councils themselves based on applicable legislation . Electoral committees are then tasked with determining whether the national minorities are represented in the council as required by the constitution . If the minorities are not represented , further members , who belong to the minorities and who have not been elected through the proportional representation system , are selected from electoral candidate lists and added to the council . 
- 
- = = = Latest presidential election = = = 
- 
- 
- = = = Latest parliamentary election = = = 
- 
- 
- = = <unk> = = 
- 
- Croatia has a three @-@ tiered , independent judicial system governed by the constitution and national legislation enacted by the Sabor . The Supreme Court ( Croatian : <unk> sud ) is the highest court of appeal in Croatia ; its hearings are open and <unk> are made publicly , except in cases where the privacy of the accused is to be protected . <unk> are appointed by the National Judicial Council and judicial office is permanent until seventy years of age . The president of the Supreme Court is elected for a four @-@ year term by the Croatian Parliament at the proposal of the President of the Republic . As of 2011 , the president of the Supreme Court is Branko <unk> . The Supreme Court has civil and criminal departments . The lower two levels of the three @-@ tiered judiciary consist of county courts and municipal courts . There are fifteen county courts and sixty @-@ seven municipal courts in the country . 
- There are other specialised courts in Croatia ; commercial courts and the Superior Commercial Court , <unk> courts that try trivial offences such as traffic violations , the Superior <unk> Court , the Administrative Court and the Croatian Constitutional Court ( Croatian : <unk> sud ) . The Constitutional Court rules on matters regarding compliance of legislation with the constitution , <unk> unconstitutional legislation , reports any breaches of provisions of the constitution to the government and the parliament , declares the speaker of the parliament acting president upon petition from the government in the event the country 's president becomes incapacitated , issues consent for commencement of criminal procedures against or arrest of the president , and hears appeals against decisions of the National Judicial Council . The court consists of thirteen judges elected by members of the parliament for an eight @-@ year term . The president of the Constitutional Court is elected by the court judges for a four @-@ year term . As of June 2012 , the president of the Constitutional Court is <unk> <unk> . The National Judicial Council ( Croatian : <unk> <unk> <unk> ) consists of eleven members , specifically seven judges , two university professors of law and two parliament members , nominated and elected by the Parliament for four @-@ year terms , and may serve no more than two terms . It appoints all judges and court presidents , except in case of the Supreme Court . As of January 2015 , the president of the National Judicial Council is <unk> Marijan , who is also a Supreme Court judge . 
- The State Attorney 's Office represents the state in legal procedures . As of August 2012 , <unk> <unk> is the General State Attorney , and there are twenty @-@ three deputies in the central office and lower @-@ ranking State <unk> at fifteen county and thirty @-@ three municipal State Attorney 's <unk> . The General State Attorney is appointed by the parliament . A special State Attorney 's Office dedicated to <unk> corruption and organised crime , <unk> , was set up in late 2001 . 
- 
- = = Local government = = 
- 
- Croatia was first subdivided into counties ( Croatian : <unk> ) in the Middle Ages . The divisions changed over time to reflect losses of territory to Ottoman conquest and the subsequent recapture of the same territory , and changes to the political status of Dalmatia , Dubrovnik and <unk> . The traditional division of the country into counties was abolished in the 1920s , when the Kingdom of Serbs , Croats and Slovenes and the subsequent Kingdom of Yugoslavia introduced <unk> and <unk> respectively . After 1945 under Communist rule , Croatia , as a constituent part of Yugoslavia , abolished these earlier divisions and introduced municipalities , <unk> Croatia into approximately one hundred municipalities . Counties , significantly altered in terms of territory relative to the pre @-@ 1920s subdivisions , were reintroduced in 1992 legislation . In 1918 , the <unk> part of Croatia was divided into eight counties with their seats in <unk> , <unk> , <unk> , <unk> , Vukovar , <unk> , <unk> and Zagreb ; the 1992 legislation established fifteen counties in the same territory . Since the counties were re @-@ established in 1992 , Croatia is divided into twenty counties and the capital city of Zagreb , the latter having the authority and legal status of a county and a city at the same time . In some instances , the boundaries of the counties have been changed , with the latest revision taking place in 2006 . The counties <unk> into 127 cities and <unk> municipalities . 
- The county prefects , city and municipal mayors are elected to four @-@ year terms by a majority of votes cast within applicable local government units . If no candidate achieves a majority in the first round , a runoff election is held . Members of county , city and municipal councils are elected to four @-@ year terms , through proportional representation with the entire local government unit as a single constituency . 
- The number of members of the councils is defined by the councils themselves , based on applicable legislation . Electoral committees are then tasked with determining whether the national ethnic minorities are represented on the council as required by the constitution . Further members who belong to the minorities may be added to the council in no candidate of that minority has been elected through the proportional representation system . Election silence , as in all other types of elections in Croatia , when campaigning is forbidden , is enforced the day before the election and continues until 19 : 00 hours on the election day when the polling stations close and exit polls may be announced . Six nationwide local elections have been held in Croatia since 1990 , the most recent being the 2009 local elections to elect county prefects and councils , and city and municipal councils and mayors . In 2009 , the HDZ @-@ led coalitions won a majority or plurality in fifteen county councils and thirteen county prefect elections . SDP @-@ led coalitions won a majority or plurality in five county councils , including the city of Zagreb council , and the remaining county council election was won by <unk> @-@ SDP coalition . The SDP won four county prefect elections and the city of Zagreb mayoral election , the HSS won three county prefect elections , and the HNS and the <unk> won a single county prefect election each . 
- 
- = = History = = 
- 
- 
- = = = Within Austria @-@ Hungary = = = 
- 
- Events of 1848 in Europe and the Austrian Empire brought dramatic changes to Croatian society and politics , provoking the Croatian national revival that strongly influenced and significantly shaped political and social events in Croatia . At the time , the Sabor and Ban Josip <unk> advocated the <unk> of ties with the Kingdom of Hungary , emphasising links to other South Slavic lands within the empire . Several prominent Croatian political figures emerged , such as Ante <unk> , <unk> Kvaternik , Franjo <unk> and Josip <unk> <unk> . A period of neo @-@ absolutism was followed by the Austro @-@ Hungarian <unk> of 1867 and the Croatian – Hungarian Settlement , which granted limited independence to Croatia . This was compounded by Croatian claims of uninterrupted statehood since the early Middle Ages as a basis for a modern state . Two political parties that evolved in the 1860s and contributed significantly to the sentiment were the Party of Rights , led by <unk> and Kvaternik , and the People 's Party , led by <unk> <unk> , Ivan <unk> <unk> , Josip <unk> <unk> and Ivan <unk> . They were opposed by the National Constitutional Party , which was in power for most of the period between the 1860s and the 1918 , and advocated closer ties between Croatia and Hungary . 
- Other significant parties formed in the era were the Serb People 's Independent Party , which later formed the Croat @-@ Serb Coalition with the Party of Rights and other Croat and Serb parties . The Coalition ruled Croatia between 1903 and 1918 . The leaders of the Coalition were <unk> <unk> and <unk> <unk> . The Croatian <unk> Party ( HSS ) , established in 1904 and led by Stjepan <unk> , advocated Croatian autonomy but achieved only moderate gains by 1918 . In Dalmatia , the two major parties were the People 's Party – a branch of the People 's Party active in Croatia @-@ Slavonia – and the <unk> Party , advocating maintaining autonomy of Dalmatia , opposite to the People 's Party demands for unification of Croatia @-@ Slavonia and Dalmatia . The <unk> Party , most notably led by Antonio <unk> , was also linked to Italian <unk> . By 1900 , the Party of Rights had made considerable gains in Dalmatia . The <unk> won the first three elections , but all elections since 1870 were won by the People 's Party . In the period 1861 – 1918 there were seventeen elections in the Kingdom of Croatia @-@ Slavonia and ten in the Kingdom of Dalmatia . 
- 
- = = = First and Second Yugoslavia = = = 
- 
- After the establishment of the Kingdom of Serbs , Croats and Slovenes , the HSS established itself as the most popular Croatian political party and was very popular despite efforts to ban it . The 1921 constitution defined the kingdom as a unitary state and abolished the historical administrative divisions , which effectively ended Croatian autonomy ; the constitution was opposed by HSS . The political situation deteriorated further as Stjepan <unk> of the HSS was assassinated in the Yugoslav Parliament in 1928 , leading to the dictatorship of King Alexander in January 1929 . The HSS , now led by <unk> <unk> , continued to advocate the <unk> of Yugoslavia , resulting in the <unk> – <unk> Agreement of August 1939 and the autonomous <unk> of Croatia . The Yugoslav government retained control of defence , internal security , foreign affairs , trade , and transport while other matters were left to the Croatian Sabor and a crown @-@ appointed Ban . This arrangement was soon made obsolete with the beginning of World War II , when the Independent State of Croatia , which banned all political opposition , was established . Since then , the HSS continues to operate abroad . 
- In the 1945 election , the Communists were unopposed because the other parties <unk> . Once in power , the Communists introduced a single @-@ party political system , in which the Communist Party of Yugoslavia was the ruling party and the Communist Party of Croatia was its branch . In 1971 , the Croatian national movement , which sought greater civil rights and the <unk> of the Yugoslav economy , culminated in the Croatian Spring , which was suppressed by the Yugoslav leadership . In January 1990 , the Communist Party fragmented along national lines ; the Croatian faction demanded a looser federation . 
- 
- = = = Modern Croatia = = = 
- 
- In 1989 , the government of the Socialist Republic of Croatia decided to tolerate political parties in response to growing demands to allow political activities outside the Communist party . The first political party founded in Croatia since the beginning of the Communist rule was the Croatian Social Liberal Party ( <unk> ) , established on 20 May 1989 , followed by the Croatian Democratic Union on 17 June 1989 . In December 1989 , <unk> <unk> became the head of the reformed Communist party . At the same time , the party cancelled political trials , released political prisoners and endorsed a multi @-@ party political system . The Civil <unk> Act was formally amended to allow political parties on 11 January 1990 , <unk> the parties that were already founded . 
- By the time of the first round of the first multi @-@ party elections , held on 22 April 1990 , there were 33 registered parties . The most relevant parties and coalitions were the League of Communists of Croatia – Party of Democratic Changes ( the renamed Communist party ) , the Croatian Democratic Union ( HDZ ) , and the Coalition of People 's <unk> ( KNS ) , which included the <unk> led by <unk> <unk> , and the HSS , which resumed operating in Croatia in December 1989 . The runoff election was held on 6 May 1990 . The HDZ , led by Franjo Tuđman , won ahead of the reformed Communists and the KNS . The KNS , led by <unk> <unk> @-@ <unk> and <unk> <unk> – who had led the Croatian Spring – soon splintered into individual parties . The HDZ maintained a parliamentary majority until the 2000 parliamentary election , when it was defeated by the Social Democratic Party of Croatia ( SDP ) , led by <unk> . Franjo <unk> , of the HDZ , was appointed prime minister to head a national unity government in July 1991 as the Croatian War of Independence escalated in intensity . His appointment lasted until August 1992 . During his term , Croatia 's declaration of independence from Yugoslavia took effect on 8 October 1991 . The HDZ returned to power in the 2003 parliamentary election , while the SDP remained the largest opposition party . 
- Franjo Tuđman won the presidential elections in 1992 and 1997 . During his terms , the Constitution of Croatia , adopted in 1990 , provided for a semi @-@ presidential system . After Tuđman 's death in 1999 , the constitution was amended and much of the presidential powers were transferred to the parliament and the government . Stjepan <unk> won two consecutive terms in 2000 and 2005 on a Croatian People 's Party ( HNS ) ticket . Ivo Josipović , an SDP candidate , won the presidential elections in December 2009 and January 2010 . <unk> <unk> @-@ <unk> defeated Josipović in the January 2015 election run @-@ off , becoming the first female president of Croatia . 
- 
- 
- = Mumia Abu @-@ Jamal = 
- 
- Mumia Abu @-@ Jamal ( born Wesley Cook April 24 , 1954 ) is a convicted murderer who was sentenced to death in 1982 for the 1981 murder of Philadelphia police officer Daniel Faulkner . His sentence was later <unk> to life imprisonment without parole . 
- Abu @-@ Jamal became involved in black nationalism in his youth and was a member of the Black Panther Party until October 1970 , after which he became a radio journalist , eventually becoming president of the Philadelphia Association of Black Journalists . On December 9 , 1981 , Faulkner was fatally shot while conducting a routine traffic stop of Abu @-@ Jamal 's brother , William Cook . Abu @-@ Jamal was found at the scene with a bullet wound from Faulkner 's gun and his own discharged revolver beside him . He was arrested and charged with Faulkner 's murder . 
- Prosecution witnesses identified Abu @-@ Jamal as the shooter and two testified that he had confessed to shooting Faulkner . A jury convicted Abu @-@ Jamal on all counts and sentenced him to death . He spent the next 30 years on death row . After a succession of all possible appeals by Abu @-@ Jamal were exhausted , his conviction was upheld but his death sentence vacated . He was <unk> to life in prison without parole . District Attorney Seth Williams later stated that no further appeals would be filed in pursuit of the death penalty . 
- <unk> , celebrities , and liberal groups have criticized the fairness of Abu @-@ Jamal 's trial , professed his innocence , and opposed his death sentence . The Faulkner family , public authorities , police organizations , and conservative groups have maintained that Abu @-@ Jamal 's trial was fair , his guilt undeniable , and his death sentence appropriate . Once described as " perhaps the world 's best known death @-@ row inmate " by The New York Times , during his imprisonment Abu @-@ Jamal has published books and commentaries on social and political issues , including Live from Death Row ( 1995 ) . 
- 
- = = Early life and activism = = 
- 
- Abu @-@ Jamal was given the name Mumia in 1968 by his high school teacher , a Kenyan instructing a class on African cultures in which students took African classroom names . According to Abu @-@ Jamal , ' Mumia ' means " Prince " and was the name of Kenyan anti @-@ colonial African nationalists who fought against the British before Kenyan independence . He adopted the surname Abu @-@ Jamal ( " father of Jamal " in Arabic ) after the birth of his son Jamal on July 18 , 1971 . His first marriage at age 19 , to Jamal 's mother , <unk> , was short @-@ lived . Their daughter , <unk> , was born shortly after the wedding . Abu @-@ Jamal married his second wife , Marilyn ( known as " <unk> " ) , in 1977 . Their son , <unk> , was born in early 1978 . By 1981 , Abu @-@ Jamal was living with his third and current wife , <unk> . 
- 
- = = = Involvement with the Black Panthers = = = 
- 
- In his own writings , Abu @-@ Jamal describes his adolescent experience of being " kicked ... into the Black Panther Party " after suffering a beating from " white <unk> " and a policeman for his efforts to disrupt a George Wallace for President rally in 1968 . From the age of 14 , he helped form the Philadelphia branch of the Black Panther Party with Defense Captain <unk> <unk> , and other Panthers , taking appointment , in his own words , as the chapter 's " Lieutenant of Information " , exercising a responsibility for writing information and news communications . In one of the interviews he gave at the time he quoted <unk> <unk> , saying that " political power grows out of the barrel of a gun " . That same year , he dropped out of Benjamin Franklin High School and took up residence in the branch 's headquarters . He spent late 1969 in New York City and early 1970 in Oakland , living and working with <unk> colleagues in those cities . He was a party member from May 1969 until October 1970 and was subject to Federal Bureau of Investigation <unk> surveillance , with which the Philadelphia police cooperated , from then until about 1974 . 
- 
- = = = Education and journalism career = = = 
- 
- After returning to his old high school after his departure from the Panthers , Abu @-@ Jamal was suspended for distributing literature calling for " black revolutionary student power " . He also led unsuccessful protests to change the school name to Malcolm X High . After attaining his <unk> , he studied briefly at Goddard College in rural Vermont . 
- By 1975 he was pursuing a <unk> in radio <unk> , first at Temple University 's <unk> and then at commercial enterprises . In 1975 , he was employed at radio station <unk> and he became host of a weekly feature program of <unk> @-@ FM in 1978 . He was also employed for brief periods at radio station <unk> , and became active in the local chapter of the <unk> <unk> Association of America . From 1979 he worked at National Public Radio @-@ affiliate ( NPR ) <unk> until 1981 when he was asked to submit his resignation after a dispute about the requirements of objective focus in his presentation of news . As a radio journalist he earned the moniker " the voice of the <unk> " and was renowned for identifying with and giving exposure to the <unk> <unk> @-@ <unk> commune in Philadelphia 's <unk> Village neighborhood , including <unk> of the 1979 – 80 trial of certain of its members ( the " <unk> Nine " ) convicted of the murder of police officer James <unk> . During his broadcasting career , his high @-@ profile interviews included Julius Erving , Bob Marley and Alex <unk> , and he was elected president of the Philadelphia Association of Black Journalists . 
- At the time of Daniel Faulkner 's murder , Abu @-@ Jamal was working as a <unk> driver in Philadelphia two nights a week to supplement his income . He had been working part @-@ time as a reporter for <unk> , then an African @-@ American @-@ oriented and minority @-@ owned radio station . 
- 
- = = <unk> for murder and trial = = 
- 
- At 3 : 55 am on December 9 , 1981 , in Philadelphia , close to the intersection at 13th and <unk> Streets , Philadelphia Police Department officer Daniel Faulkner conducted a traffic stop on a vehicle belonging to William Cook , Abu @-@ Jamal 's younger brother . During the traffic stop , Abu @-@ Jamal 's taxi was parked across the street . He ran across the street towards Cook 's car , where Faulkner was shot from behind and then in the face . Abu @-@ Jamal was shot by Faulkner in the stomach . Faulkner died at the scene from the gunshot to his head . Police arrived and arrested Abu @-@ Jamal , who was found wearing a shoulder holster . His revolver , which had five spent cartridges , was beside him . Abu @-@ Jamal was taken directly from the scene of the shooting to Thomas Jefferson University Hospital , where he received treatment for his wound . 
- Abu @-@ Jamal was charged with the first @-@ degree murder of Officer Faulkner . The case went to trial in June 1982 in Philadelphia . Judge Albert F. Sabo initially agreed to Abu @-@ Jamal 's request to represent himself , with criminal defense attorney Anthony Jackson acting as his legal advisor . During the first day of the trial , Sabo warned Abu @-@ Jamal that he would forfeit his legal right to self @-@ representation if he kept being intentionally disruptive in a fashion that was <unk> under the law . Due to Abu @-@ Jamal 's continued disruptive behavior , Sabo ruled that Abu @-@ Jamal forfeited his right to self @-@ representation . 
- 
- = = = Prosecution case at trial = = = 
- 
- The prosecution presented four witnesses to the court . Robert Chobert , a cab driver who testified he was parked behind Faulkner , identified Abu @-@ Jamal as the shooter . Cynthia White , a prostitute , testified that Abu @-@ Jamal emerged from a nearby parking lot and shot Faulkner . Michael Scanlan , a <unk> , testified that from two car lengths away , he saw a man , matching Abu @-@ Jamal 's description , run across the street from a parking lot and shoot Faulkner . Albert <unk> , a pedestrian who did not see the actual murder , testified to witnessing Faulkner pull over Cook 's car . At the point of seeing Abu @-@ Jamal start to cross the street toward them from the parking lot , <unk> turned away and lost sight of what happened next . 
- The prosecution also presented two witnesses who were at the hospital after the shootings . Hospital security guard <unk> Durham and police officer Garry Bell testified that Abu @-@ Jamal confessed in the hospital by saying , " I shot the <unk> , and I hope the <unk> dies . " 
- A <unk> caliber Charter Arms revolver , belonging to Abu @-@ Jamal , with five spent cartridges was retrieved beside him at the scene . He was wearing a shoulder holster , and Anthony Paul , the Supervisor of the Philadelphia Police Department 's firearms identification unit , testified at trial that the cartridge cases and <unk> characteristics of the weapon were consistent with bullet fragments taken from Faulkner 's body . Tests to confirm that Abu @-@ Jamal had handled and fired the weapon were not performed , as contact with arresting police and other surfaces at the scene could have compromised the forensic value of such tests . 
- 
- = = = Defense case at trial = = = 
- 
- The defense maintained that Abu @-@ Jamal was innocent and that the prosecution witnesses were unreliable . The defense presented nine character witnesses , including poet Sonia <unk> , who testified that Abu @-@ Jamal was " viewed by the black community as a creative , articulate , peaceful , <unk> man " . Another defense witness , <unk> Hightower , testified that he saw a man running along the street shortly after the shooting although he did not see the actual shooting itself . His testimony contributed to the development of a " running man theory " , based on the possibility that a " running man " may have been the actual shooter . Veronica Jones also testified for the defense , but she did not see anyone running . Other potential defense witnesses refused to appear in court . Abu @-@ Jamal did not testify in his own defense . Nor did his brother , William Cook , who told investigators at the crime scene : " I ain 't got nothing to do with this . " 
- 
- = = = Verdict and sentence = = = 
- 
- The jury delivered a unanimous guilty verdict after three hours of deliberations . 
- In the sentencing phase of the trial , Abu @-@ Jamal read to the jury from a prepared statement . He was then cross @-@ examined about issues relevant to the assessment of his character by Joseph McGill , the <unk> attorney . 
- In his statement Abu @-@ Jamal criticized his attorney as a " legal trained lawyer " who was imposed on him against his will and who " knew he was inadequate to the task and chose to follow the directions of this black @-@ robed <unk> , Albert Sabo , even if it meant ignoring my directions " . He claimed that his rights had been " <unk> stolen " from him by Sabo , particularly focusing on the denial of his request to receive defense assistance from non @-@ attorney John Africa and being prevented from proceeding pro <unk> . He quoted remarks of John Africa , and said : 
- Does it matter whether a white man is charged with killing a black man or a black man is charged with killing a white man ? As for justice when the prosecutor represents the Commonwealth the Judge represents the Commonwealth and the court @-@ appointed lawyer is paid and supported by the Commonwealth , who follows the wishes of the defendant , the man charged with the crime ? If the court @-@ appointed lawyer ignores , or goes against the wishes of the man he is charged with representing , whose wishes does he follow ? Who does he truly represent or work for ? ... I am innocent of these charges that I have been charged of and convicted of and despite the <unk> of Sabo , McGill and Jackson to deny me my so @-@ called rights to represent myself , to assistance of my choice , to personally select a jury who is totally of my peers , to cross @-@ examine witnesses , and to make both opening and closing arguments , I am still innocent of these charges . 
- Abu @-@ Jamal was subsequently sentenced to death by the unanimous decision of the jury . 
- 
- = = Appeals and review = = 
- 
- 
- = = = State appeals = = = 
- 
- Direct appeal of his conviction was considered and denied by the Supreme Court of Pennsylvania on March 6 , 1989 , subsequently denying <unk> . The Supreme Court of the United States denied his petition for writ of certiorari on October 1 , 1990 , and denied his petition for <unk> twice up to June 10 , 1991 . 
- On June 1 , 1995 , his death warrant was signed by Pennsylvania Governor Tom Ridge . Its execution was suspended while Abu @-@ Jamal pursued state post @-@ conviction review . At the post @-@ conviction review hearings , new witnesses were called . William " <unk> " <unk> testified that he saw the shooting and that the <unk> was the passenger in Cook 's car . <unk> 's account contained discrepancies which rendered it " not credible " in the opinion of the court . William Harmon , a convicted <unk> , testified that Faulkner 's murderer fled in a car which pulled up at the crime scene , and could not have been Abu @-@ Jamal . However , Robert <unk> testified that he had witnessed a man stand over Faulkner as the latter lay wounded on the ground , who shot him point @-@ blank in the face and then " walked and sat down on the curb " . 
- The six judges of the Supreme Court of Pennsylvania ruled unanimously that all issues raised by Abu @-@ Jamal , including the claim of ineffective assistance of counsel , were without merit . The Supreme Court of the United States denied a petition for certiorari against that decision on October 4 , 1999 , enabling Ridge to sign a second death warrant on October 13 , 1999 . Its execution in turn was stayed as Abu @-@ Jamal commenced his pursuit of federal <unk> corpus review . 
- In 1999 , Arnold Beverly claimed that he and an unnamed assailant , not Mumia Abu @-@ Jamal , shot Daniel Faulkner as part of a contract killing because Faulkner was interfering with <unk> and payoff to corrupt police . The Beverly <unk> became an item of division for Mumia 's defense team , as some thought it usable and others rejected Beverly 's story as " not credible " . 
- Private investigator George Newman claimed in 2001 that Chobert had recanted his testimony . Commentators also noted that police and news photographs of the crime scene did not show Chobert 's taxi , and that Cynthia White , the only witness at the trial to testify to seeing the taxi , had previously provided crime scene descriptions that omitted it . Cynthia White was declared to be dead by the state of New Jersey in 1992 although Pamela Jenkins claimed that she saw White alive as late as 1997 . Mumia supporters often claim that White was a police informant and that she falsified her testimony against Abu @-@ Jamal . <unk> Durham 's step @-@ brother , Kenneth <unk> , who was imprisoned with Abu @-@ Jamal on other charges , has since claimed that Durham admitted to not hearing the hospital confession . The hospital doctors stated that Abu @-@ Jamal was " on the verge of <unk> " when brought in and they did not <unk> a confession . In 2008 , the Supreme Court of Pennsylvania rejected a further request from Abu @-@ Jamal for a hearing into claims that the trial witnesses <unk> themselves on the grounds that he had waited too long before filing the appeal . 
- On March 26 , 2012 the Supreme Court of Pennsylvania rejected his most recent appeal for <unk> asserted on the basis that a 2009 report by the National Academy of Science demonstrated that forensic evidence put by the prosecution and accepted into evidence in the original trial was unreliable . It was reported to be the former death row inmate 's last legal appeal . 
- 
- = = = Federal ruling directing <unk> = = = 
- 
- Abu @-@ Jamal did not make any public statements about Faulkner 's murder until May 2001 . In his version of events , he claimed that he was sitting in his cab across the street when he heard shouting , then saw a police vehicle , then heard the sound of <unk> . Upon seeing his brother appearing <unk> across the street , Abu @-@ Jamal ran to him from the parking lot and was shot by a police officer . The driver originally stopped by police officer Faulkner , Abu @-@ Jamal 's brother William Cook , did not testify or make any statement until April 29 , 2001 , when he claimed that he had not seen who had shot Faulkner . 
- Judge William H. Yohn Jr. of the United States District Court for the Eastern District of Pennsylvania upheld the conviction but vacated the sentence of death on December 18 , 2001 , citing irregularities in the original process of sentencing . Particularly , 
- ... the jury instructions and verdict sheet in this case involved an <unk> application of federal law . The charge and verdict form created a reasonable likelihood that the jury believed it was precluded from considering any <unk> circumstance that had not been found unanimously to exist . 
- He ordered the State of Pennsylvania to commence new sentencing proceedings within 180 days and ruled that it was unconstitutional to require that a jury 's finding of circumstances <unk> against determining a sentence of death be unanimous . Eliot Grossman and Marlene <unk> , attorneys for Abu @-@ Jamal , criticized the ruling on the grounds that it denied the possibility of a trial de <unk> at which they could introduce evidence that their client had been framed . <unk> also criticized the ruling ; Officer Faulkner 's widow Maureen described Abu @-@ Jamal as a " <unk> , hate @-@ filled killer " who would " be permitted to enjoy the pleasures that come from simply being alive " on the basis of the judgment . Both parties appealed . 
- 
- = = = Federal appeal = = = 
- 
- On December 6 , 2005 , the Third Circuit Court admitted four issues for appeal of the ruling of the District Court : 
- in relation to sentencing , whether the jury verdict form had been flawed and the judge 's instructions to the jury had been confusing ; 
- in relation to conviction and sentencing , whether racial bias in jury selection existed to an extent tending to produce an inherently biased jury and therefore an unfair trial ( the Batson claim ) ; 
- in relation to conviction , whether the prosecutor improperly attempted to reduce jurors ' sense of responsibility by telling them that a guilty verdict would be subsequently <unk> and subject to appeal ; and 
- in relation to post @-@ conviction review hearings in 1995 – 6 , whether the presiding judge , who had also presided at the trial , demonstrated unacceptable bias in his conduct . 
- The Third Circuit Court heard oral arguments in the appeals on May 17 , 2007 , at the United States Courthouse in Philadelphia . The appeal panel consisted of Chief Judge Anthony Joseph <unk> , Judge Thomas <unk> , and Judge Robert <unk> . The Commonwealth of Pennsylvania sought to reinstate the sentence of death , on the basis that Yohn 's ruling was flawed , as he should have deferred to the Pennsylvania Supreme Court which had already ruled on the issue of sentencing , and the Batson claim was invalid because Abu @-@ Jamal made no complaints during the original jury selection . Although Abu @-@ Jamal 's jury was racially mixed with 2 blacks and 10 whites at the time of his unanimous conviction , his counsel told the Third Circuit Court that Abu @-@ Jamal did not get a fair trial because the jury was racially biased , <unk> , the judge was a racist , and noted that the prosecution used eleven out of fourteen <unk> challenges to eliminate prospective black jurors . Terri <unk> @-@ Carter , a former Philadelphia court stenographer claimed in a 2001 <unk> nearly 20 years after the trial that she <unk> Judge Sabo say " Yeah , and I 'm going to help them <unk> the <unk> " in the course of a conversation with three people present regarding Abu @-@ Jamal 's case . Sabo denied having made any such comment . 
- On March 27 , 2008 , the three @-@ judge panel issued a majority 2 – 1 opinion upholding Yohn 's 2001 opinion but rejecting the bias and Batson claims , with Judge <unk> dissenting on the Batson issue . On July 22 , 2008 , Abu @-@ Jamal 's formal petition seeking <unk> of the decision by the full Third Circuit panel of 12 judges was denied . On April 6 , 2009 , the United States Supreme Court also refused to hear Abu @-@ Jamal 's appeal , allowing his conviction to stand . On January 19 , 2010 , the Supreme Court ordered the appeals court to reconsider its decision to rescind the death penalty , with the same three @-@ judge panel <unk> in Philadelphia on November 9 , 2010 , to hear oral argument . On April 26 , 2011 , the Third Circuit Court of Appeals reaffirmed its prior decision to <unk> the death sentence on the grounds that the jury instructions and verdict form were ambiguous and confusing . The Supreme Court declined to hear the case in October . 
- 
- = = = Death penalty dropped = = = 
- 
- On December 7 , 2011 , District Attorney of Philadelphia R. Seth Williams announced that prosecutors , with the support of the victim 's family , would no longer seek the death penalty for Abu @-@ Jamal . Faulkner had indicated she did not wish to relive the trauma of another trial , and that it would be extremely difficult to present the case against Abu @-@ Jamal again , after the passage of 30 years and the deaths of several key witnesses . Williams , the prosecutor , said that Abu @-@ Jamal will spend the rest of his life in prison without the possibility of parole , a sentence that was reaffirmed by the Superior Court of Pennsylvania on July 9 , 2013 . After the press conference , Maureen Faulkner made an emotional statement harshly condemning Abu @-@ Jamal : 
- I would like to say that I believe the lowest dimension of hell has been reserved for child <unk> and unrepentant murderers , like Mumia Abu @-@ Jamal . After thirty years of waiting , the time remaining before Abu @-@ Jamal stands before his ultimate judge . It doesn 't seem so far off as it once did when I was younger . I look forward to that day so I can finally close the chapter of my life and live with the <unk> and assurance that Mumia Abu @-@ Jamal will finally receive the punishment he deserves , for all <unk> . 
- 
- = = Life as a prisoner = = 
- 
- In 1991 Abu @-@ Jamal published an essay in the Yale Law Journal , on the death penalty and his death row experience . In May 1994 , Abu @-@ Jamal was engaged by National Public Radio 's All Things Considered program to deliver a series of monthly three @-@ minute commentaries on crime and punishment . The broadcast plans and commercial arrangement were canceled following condemnations from , among others , the Fraternal Order of Police and US Senator Bob Dole ( Kansas Republican Party ) . Abu @-@ Jamal sued NPR for not airing his work , but a federal judge dismissed the suit . The commentaries later appeared in print in May 1995 as part of Live from Death Row . 
- In 1999 , he was invited to record a <unk> address for the graduating class at The <unk> State College . The event was protested by some . In 2000 , he recorded a commencement address for Antioch College . The now defunct New College of California School of Law presented him with an honorary degree " for his struggle to resist the death penalty " . On October 5 , 2014 , he gave the commencement speech at Goddard College , via playback of a recording . As before , the choice of Abu @-@ Jamal was controversial . 
- With occasional interruptions due to prison disciplinary actions , Abu @-@ Jamal has for many years been a regular commentator on an online broadcast , sponsored by Prison Radio , as well as a regular columnist for <unk> <unk> , a Marxist newspaper in Germany . In 1995 , he was punished with solitary confinement for engaging in <unk> contrary to prison regulations . Subsequent to the airing of the 1996 HBO documentary Mumia Abu @-@ Jamal : A Case For <unk> Doubt ? , which included footage from visitation interviews conducted with him , the Pennsylvania Department of <unk> acted to ban outsiders from using any recording equipment in state prisons . In litigation before the US Court of Appeals in 1998 he successfully established his right to write for financial gain in prison . The same litigation also established that the Pennsylvania Department of <unk> had illegally opened his mail in an attempt to establish whether he was writing for financial gain . When , for a brief time in August 1999 , he began delivering his radio commentaries live on the Pacifica Network 's Democracy Now ! <unk> radio <unk> , prison staff severed the connecting wires of his telephone from their mounting in mid @-@ performance . He was later allowed to resume his broadcasts , and hundreds of his broadcasts have been aired on Pacifica Radio . 
- His publications include Death <unk> : Reflections from a <unk> of Conscience , in which he explores religious themes , All Things <unk> , a political critique examining issues of crime and punishment , Live From Death Row , a diary of life on Pennsylvania 's death row , and We Want Freedom : A Life in the Black Panther Party , which is a history of the Black Panthers drawing on autobiographical material . 
- At the end of January 2012 he was released into general prison population at State <unk> Institution – <unk> . He went into <unk> shock on March 30 , 2015 and has been diagnosed with active <unk> C. In August 2015 his attorneys filed suit in the US District Court for the Middle District of Pennsylvania upon the allegation that he has not received appropriate medical care for his health conditions . 
- 
- = = Popular support and opposition = = 
- 
- Labor unions , politicians , advocates , educators , the NAACP Legal Defense and Educational Fund , and human rights advocacy organizations such as Human Rights Watch and Amnesty International have expressed concern about the impartiality of the trial of Abu @-@ Jamal , though Amnesty International neither takes a position on the guilt or innocence of Abu @-@ Jamal nor classifies him as a political prisoner . They are opposed by the family of Daniel Faulkner , the Commonwealth of Pennsylvania , the City of Philadelphia , Republican politicians , and the Fraternal Order of Police . In August 1999 , the Fraternal Order of Police called for an economic boycott against all individuals and organizations that support Abu @-@ Jamal . 
- Abu @-@ Jamal has been made an honorary citizen of about 25 cities around the world , including Montreal , Palermo , and Paris . 
- In 2001 , he received the sixth biennial Erich <unk> Prize , named after an <unk> @-@ communist <unk> , which recognizes activism in line with that of its namesake . In October 2002 , he was made an honorary member of the German political organization Society of People <unk> by the Nazi <unk> – Federation of Anti @-@ Fascists ( <unk> @-@ <unk> ) which Germany 's Federal Office for the Protection of the Constitution has considered to be influenced by left @-@ wing <unk> . 
- On April 29 , 2006 , a newly paved road in the Parisian suburb of Saint @-@ Denis was named Rue Mumia Abu @-@ Jamal in his honor . In protest of the street @-@ naming , US Congressman Michael Fitzpatrick and Senator Rick <unk> , both members of the Republican Party of Pennsylvania , introduced resolutions in both Houses of Congress condemning the decision . The House of Representatives voted 368 – 31 in favor of Fitzpatrick 's resolution . In December 2006 , the 25th anniversary of the murder , the executive committee of the Republican Party for the 59th Ward of the City of Philadelphia — covering approximately <unk> , Philadelphia — filed two criminal complaints in the French legal system against the city of Paris and the city of Saint @-@ Denis , accusing the municipalities of " <unk> " Abu @-@ Jamal and alleging the offense " apology or denial of crime " in respect of their actions . 
- In 2007 , the widow of Officer Faulkner <unk> a book with Philadelphia radio journalist Michael <unk> entitled <unk> by Mumia : A Life Sentence of Pain , Loss , and <unk> . The book was part memoir of Faulkner 's widow , part discussion in which they chronicled Abu @-@ Jamal 's trial and discussed evidence for his conviction , and part discussion on supporting the death penalty . J. Patrick O 'Connor , editor and publisher of <unk> , argues in his book The <unk> of Mumia Abu @-@ Jamal that the <unk> of evidence establishes that it was not Abu @-@ Jamal but a passenger in Abu @-@ Jamal 's brother 's car , Kenneth Freeman , who killed Faulkner , and that the Philadelphia Police Department and District Attorney 's Office framed Abu @-@ Jamal . His book was criticized in the American <unk> as " replete with selective use of testimony , distortions , <unk> charges , and a theory that has failed Abu @-@ Jamal in the past . " 
- In 2009 , Radio host Rush <unk> professed no doubt about Abu @-@ Jamal 's guilt , calling him a " notorious Philadelphia murderer " . 
- In early 2014 , President Barack Obama nominated <unk> P. <unk> , a former lawyer for the NAACP who worked on Abu @-@ Jamal 's case , to head the civil rights division of the Justice Department , but the nomination was rejected by the U.S. Senate on a <unk> basis because of <unk> 's prior public support of Abu @-@ Jamal . 
- In April 10 , 2015 Marylin Zuniga , a teacher at Forest Street Elementary School in Orange , New Jersey , was suspended without pay after asking her students to write letters to Abu @-@ Jamal , who fell ill in prison due to complications from diabetes , without approval from the school or parents . Some parents and police leaders denounced her actions . On the other hand , community members , parents , teachers , and professors expressed their support and condemned Zuniga 's suspension . Scholars and educators nationwide including Noam Chomsky , Chris <unk> and <unk> West among others signed a letter calling for her immediate <unk> . On May 13 , 2015 The Orange Preparatory Academy board voted to dismiss Marylin Zuniga after hearing from her and several of her supporters . 
- 
- = = Written works = = 
- 
- Writing on the Wall : Selected Prison <unk> of Mumia Abu @-@ Jamal City Lights Publishers ( 2015 ) ISBN 978 @-@ <unk> 
- The <unk> and the Cell : Conversations on Black Life in America Third World Press ( 2011 ) ISBN 978 @-@ <unk> 
- <unk> <unk> : Prisoners <unk> Prisoners V. The <unk> City Lights Publishers ( 2009 ) ISBN 978 @-@ <unk> 
- We Want Freedom : A Life In The Black Panther Party South End Press ( 2008 ) ISBN 978 @-@ <unk> 
- Faith Of Our Fathers : An Examination Of The Spiritual Life Of African And African @-@ American People Africa World Pr ( 2003 ) ISBN 978 @-@ <unk> 
- All Things <unk> Seven Stories Press ( 2000 ) ISBN 978 @-@ <unk> 
- Death <unk> : Reflections From A <unk> Of Conscience <unk> Publishing House ( 1997 ) ISBN 978 @-@ <unk> 
- Live from Death Row Harper <unk> ( 1996 ) ISBN 978 @-@ <unk> 
- 
- 
- = Brandon Minor = 
- 
- Brandon Ricardo Minor ( born July 24 , 1988 ) is a former American football running back . He was signed by the Bears as an undrafted free agent in 2010 , but after being released during the final cuts , he has been on the New Orleans Saints and Indianapolis Colts practice squads in 2010 . He played college football at Michigan . 
- At Michigan , he finished second on the team in rushing as a freshman and a sophomore and led the team in rushing as a junior and a senior . As a junior , he was an honorable mention All @-@ Big Ten Conference selection by the coaches . He had previously been ranked as the number one high school football fullback in the nation , according to <unk> 
- He spent his first two years at Michigan serving as one of the primary <unk> to Mike Hart . In his third year , he emerged from a field of five runners who were vying to replace Hart , including two true freshmen , as the leading rusher and scorer . He has shared starting responsibilities in his junior and senior seasons . He entered his senior season on the watch lists for the Doak Walker Award and the Maxwell Award . ESPN.com ranked him as the 22nd best player and third best running back in the Big Ten Conference before the season started . 
- Following two seasons spent on various inactive NFL <unk> , he became a defendant in a drug possession case . He is scheduled to face his charges in court in January 2012 . 
- 
- = = Youth career = = 
- 
- Minor grew up as a Michigan Wolverines fan . His mother , Julie <unk> , has pictures of him at age six wearing a Michigan uniform . At age nine , Minor wrote the university to <unk> about becoming a Michigan football player . Every year he and his mother watched the Michigan - Ohio State game and rooted for Michigan . 
- 
- = = = High school = = = 
- 
- Minor inherited the Varina High School starting varsity team role as a sophomore in 2003 , and he gained <unk> rushing yards in his first start . That season , he helped his team reach the Virginia Central Region , Division 6 championship ( the qualifying game for the Virginia High School League state semifinals ) . He concluded the regular season as an All @-@ District first @-@ team selection and after the playoffs was selected as a second @-@ team all @-@ region choice . During the season he rushed for 1 @,@ 750 yards and 22 touchdowns for the 10 – 2 ( 7 – 0 ) Varina Blue Devils . Minor also played varsity basketball as a sophomore . During the season , he once made seven three @-@ point field goals in a game . 
- As a junior , when Varina 's former Capital District offensive player of the year Army <unk> . Clarence Adams III died serving the <unk> Engineer Battalion , 1st Cavalry Division in Baghdad , Iraq , Minor gave up his number 3 to wear Adams ' # 33 as a tribute . In the District championship game , he rushed for 239 yards on 27 carries , including four touchdown runs . In the subsequent Central Region , Division 6 semifinals , he set a Central Region playoff record by rushing for 296 yards , but his undefeated top @-@ ranked team was upset . He concluded the season as both a first @-@ team all @-@ district and all @-@ Metro Region selection after compiling 2 @,@ <unk> yards rushing and scoring 32 rushing touchdowns ( plus 2 receiving touchdowns ) . He was also selected to the Group AAA Virginia High School Football Coaches Association all @-@ state second @-@ team by the coaches . 
- Entering his senior season , he was ranked as the sixth best senior football player in Virginia by <unk> The <unk> Times described him as " one of the top five recruits in the state " at the time of his August 2005 visit to see the first day of Virginia Tech Hokies football practice . At the time , he was considering Miami , Michigan , Florida , Virginia Tech , Tennessee , LSU and Ohio State . However , he started the season on crutches , due to a torn hip <unk> . He returned to the lineup for the team 's final regular season game and rushed for 174 yards on 28 carries . <unk> Morgan , his cousin , who now plays strong safety for Virginia Tech , was the team 's quarterback . Despite missing most of the season , he was still honored as an all @-@ district and all @-@ region selection . His three @-@ year career totals were 4 @,@ 259 yards and 64 touchdowns . As a graduating senior he was the number one ranked high school football fullback in the nation , according to <unk> Following his senior season , he scored the only touchdown in the East @-@ West Virginia High School Coaches Association All @-@ star game . 
- 
- = = College career = = 
- 
- 
- = = = Lloyd Carr era = = = 
- 
- In January 2006 , Minor selected the University of Michigan . Although he did not enroll in the 2006 Summer semester , he reported to Michigan on June 16 for strength and conditioning training . Minor and fellow freshman Carlos Brown were behind three returning running backs ( Mike Hart , Kevin Grady and senior Jerome Jackson ) on the depth chart entering the season . Minor was considered the less heralded than Brown , who was regarded as the fastest player on the team . On opening day , only Hart and Grady had more carries than Minor , in part because Jackson , who entered his senior season with 505 career rushing yards , did not dress . In his first carry as a <unk> , he rushed for 24 yards against the Vanderbilt <unk> during the September 2 season opening game . Nonetheless , the depth chart was not very clear behind Hart . By late September , Minor was clearly third on the depth chart and it seemed that Brown might be <unk> . When Hart left the game due to injury against Michigan State on October 7 , Minor scored his first touchdown on a 40 @-@ yard run . He had his first 100 @-@ yard game on November 4 , when he rushed for 108 yards on 12 carries , including a 40 @-@ yard touchdown run in a 34 – 26 win against Ball State . As true freshman member of the 2006 Michigan Wolverines football team , Minor rushed for a total of 238 yards on 42 carries , which was second on the team to Hart . 
- During April 2007 , Grady tore his anterior <unk> ligament and was lost for the season . In August , Brown broke his hand . It appeared Minor was seriously injured on October 13 when he was <unk> off the field and left the stadium wearing a protective boot on crutches . However , he recovered and for the next two weeks he and Brown combined to replace the injured Hart . He had his second 100 @-@ yard game and first 150 @-@ yard game on October 27 of that season during the 34 – 10 Little Brown <unk> game victory against the Minnesota Golden <unk> when he rushed for 157 yards on 21 carries , including a 46 @-@ yard run and one touchdown . Although Hart returned to play in the Paul <unk> Trophy game against Michigan State on November 3 , he left the game early and Minor started the second half . Hart missed the next game on November 10 , but Brown and Minor had poor performances . In all three of Hart 's full game absences , Brown was the starter . As a sophomore on the 2007 Michigan Wolverines football team , Minor improved his rushing totals to 385 yards on 90 carries , which was again second on the team to Hart . 
- 
- = = = Rich Rodriguez era = = = 
- 
- In 2008 Rich Rodriguez replaced Lloyd Carr as head coach . In the spring , Brown broke his finger <unk> and Grady was still trying to get healthy . In early August , it appeared that juniors Brown and Minor would vie for the starting job because fourth @-@ year junior Grady was under suspension related to driving while intoxicated charges . However , in camp it became apparent very quickly that true freshmen Sam McGuffie and Michael Shaw , would have a significant role in the newly installed spread option offense . With both Brown and Minor nursing injuries , McGuffie was tentatively penciled into the starting position on the depth chart . 
- As a junior member of the 2008 Michigan Wolverines football team , Minor became the team leader in rushing . However , he only started four games , while McGuffie started 6 , Brown started 1 and Shaw started 1 . McGuffie was the starter until he lost the job to Minor . Minor gave way to Shaw ( November 8 ) and Brown ( November 15 ) as the starter due to his injuries . Minor totalled 533 yards on 103 carries . He had nine rushing touchdowns and added two as a receiver . Of these eleven touchdowns , seven came in a three @-@ week span that included a 117 @-@ yard two @-@ touchdown rushing effort against Penn State on October 18 and a 155 @-@ yard three @-@ touchdown rushing effort against <unk> on November 1 . <unk> between these efforts , Minor scored on a 19 @-@ yard reception to compliment his 55 yards and a rushing touchdown in the Paul <unk> Trophy game against Michigan State on October 25 . The Penn State game was Minor 's first career start . During Minor 's junior year , he played with a wrist injury that impaired his ability to hold the football with his right arm and his ability to <unk> opponents . He was impaired by a variety of injuries throughout the season . At the conclusion of the 2008 Big Ten Conference football season , Minor was chosen as an honorable mention all @-@ conference selection by the coaches . 
- McGuffie transferred to the Rice Owls after the season . As a senior member of the 2009 Michigan Wolverines football team , Minor was named to a pair of watch lists : ( Doak Walker Award and Maxwell Award ) . He was also selected by ESPN as the 22nd best player and 3rd best running back ( behind Evan <unk> and John Clay ) in the Big Ten Conference before the season started . Minor missed the first game of the season due to a high ankle sprain . In the second game , which was the 2009 Michigan – Notre Dame rivalry game , he rushed for 106 yards and a touchdown on 16 carries during the 38 – 34 victory over Notre Dame . The ankle sprain hampered him much of the season and caused him to miss the October 17 game against Delaware State . He had a season @-@ high 154 @-@ yard , 3 @-@ touchdown effort against <unk> on November 7 . A shoulder injury kept him out of the last game of the season against Ohio State . Over the course of his collegiate career , he accumulated 20 rushing touchdowns and 1 @,@ <unk> yards . The torn rotator cuff also kept him from participating in the January 23 , 2010 East – West Shrine Game . 
- 
- = = Professional career = = 
- 
- 
- = = = 2010 = = = 
- 
- Minor signed as an undrafted free agent with the Chicago Bears on April 24 , 2010 , after going undrafted in the 2010 NFL Draft . He was released on September 1 . He then signed with the New Orleans Saints to their practice squad . However , Minor was back with the Bears before being released at the beginning of October and signed by the Indianapolis Colts . Minor was released by the Colts in October . In November , Minor tried out for the Green Bay <unk> before ending the season as a member of the Denver Broncos ' practice squad . 
- 
- = = = 2011 = = = 
- 
- On September 4 , 2011 , the Broncos placed Minor on injured reserve . They waived him on October 10 . In November 2011 , he was " charged with possession of marijuana with intent to distribute " by the Virginia Commonwealth University police department following a traffic stop . He is scheduled for a traffic court appearance on January 18 . 
- 
- 
- = Boy @-@ Scoutz ' n the Hood = 
- 
- " Boy @-@ Scoutz ' n the Hood " is the eighth episode of The Simpsons ' fifth season . It originally aired on the Fox network in the United States on November 18 , 1993 . In the episode , Bart , intoxicated from an all @-@ syrup <unk> , mistakenly joins the Junior Campers , a Boy Scout @-@ style organization that 's not affiliated with the Boy Scouts of America . After finding advantages to being a member , Bart gets taken in by the group and eventually goes rafting on a father @-@ son outing with Homer . 
- The episode was written by Dan McGrath and directed by Jeffrey Lynch . Ernest Borgnine guest starred in the episode as himself . He recorded his lines at the Village <unk> in West Los Angeles . The episode makes cultural references to the films My <unk> with Andre , The Terminator , On the Town , Crocodile Dundee , <unk> , Friday the 13th , and Boyz n the Hood ( in the title ) as well as the song " Sugar , Sugar " by The <unk> . Since airing , the episode has received mostly positive reviews from television critics . It acquired a Nielsen rating of 13 @.@ 0 , and was the highest @-@ rated show on the Fox network the week it aired . 
- 
- = = Plot = = 
- 
- After being forced to leave the amusement arcade for being out of money , Bart and Milhouse find $ 20 that Homer lost and order a Super <unk> made entirely out of syrup from Apu at the <unk> @-@ E @-@ Mart . With their senses <unk> from the high sugar content of the drink , they spend the rest of the money on a night out in town . The next morning , Bart wakes up with a <unk> and realizes that in the <unk> of the night before he joined the Junior Campers , a Boy Scout @-@ style organization that is not affiliated with the Boy Scouts of America . 
- Although Bart initially intends to drop out of the group as soon as possible , he decides to attend a meeting to avoid a pop quiz at school . Bart dislikes the first meeting , but when he finds out that he gets to have a pocket knife , he decides to keep attending . After a while , Bart starts to enjoy being a member of the Junior Campers , which Homer mocks him relentlessly for . Next , a father @-@ son rafting trip is to be held , so Bart has to bring Homer . Homer does not enjoy the experience , especially when he learns that he and Bart have to share a raft with Ned Flanders and his son , Rod . Due to Homer losing the map after making it into a makeshift hat , they accidentally take the wrong turn and find themselves lost at sea . They stay stranded with no food or water for several days , as no rescue is forthcoming ; the Springfield Police Department refuses to search for them because the Coast Guard boat they were using is out of <unk> . After several other failed attempts at being rescued or finding food , the raft springs a leak after Homer accidentally drops a pocket knife he was intending to gift to <unk> All seems lost , but then Homer <unk> his way to a <unk> Burger on an off @-@ shore oil rig . They are saved , and Bart is proud of his father . 
- Meanwhile , the other Junior Campers , led by Ernest Borgnine , take the correct route , ironically they end up in an even worse position : after finding themselves trapped in a dark , tangled swamp ( while being hunted by mountain men ) , they are attacked by a bear that Borgnine tries but fails to fight off ( due to Homer stealing his Swiss Army Knife ) , and they finally flee to an abandoned summer camp . At the camp , they start singing songs , but are soon attacked by an unseen figure <unk> in the woods , and their fates are left unknown . 
- 
- = = Production = = 
- 
- " Boy @-@ Scoutz ' n the Hood " was written by Dan McGrath and directed by Jeffrey Lynch . The episode was recorded at the Village <unk> in West Los Angeles . Ernest Borgnine guest starred in the episode as himself . The staff liked his work on the films Marty and From Here to Eternity , so they asked him to do a guest appearance on the show . Borgnine felt he could not say no to the offer because his grandchildren were fans of the show . In the final scene of the episode , Borgnine plays a guitar and sings <unk> songs with the children . Borgnine was a guitar player in real life , so he brought his own guitar with him to the recording studio . Borgnine apologized because he felt that he was not being able to sing very well , but Nancy Cartwright , who provides the voice of Bart , thought his voice " added to the authenticity of his character " . The Simpsons 's creator Matt Groening thought the recording sessions with Borgnine were " so much fun " . Hank Azaria , who provides the voice of Apu , commented that Borgnine " had no idea what the hell he was doing . He 's a good actor , and he read his lines just fine , but he had no idea what the show was , no idea what we were doing . " 
- In her book My Life as a 10 @-@ Year @-@ Old Boy , Cartwright comments that she was a fan of Borgnine 's performance in Marty . She writes that the film had " changed [ her ] forever " , and that it made her " realize that actors have the power through their work to inspire and <unk> others . " She recalls that when Borgnine arrived for the recording session , she " lost all coolness " and ran up to him and exclaimed " <unk> , Marty ! " 
- 
- = = Cultural references = = 
- 
- When Bart and Milhouse visit the local video arcade at the beginning of the episode , Martin Prince is seen playing an arcade game based on the 1981 film My <unk> with Andre . Other games at the arcade include a game based on the 1984 film The Terminator . The " Springfield , Springfield " number performed by Bart and Milhouse on their night out in town is a reference to the musical number " New York , New York " from the film On the Town , starring Gene Kelly and Frank Sinatra . During a scene in which Hans Moleman and Moe fight with knives , Hans tells Moe , " You call that a knife ? This is a knife ! " , a reference to a line from the 1986 film Crocodile Dundee . Ernest Borgnine introduces himself a la Troy <unk> to the Junior Campers by recalling his role in From Here to Eternity , a film which modern children are unlikely to have seen . During a <unk> , Homer imagines himself singing the song " Sugar , Sugar " by The <unk> while dancing with <unk> and ice cream <unk> . While on the raft , Homer <unk> lines from Samuel Taylor Coleridge 's The <unk> of the Ancient <unk> when he says " Water , water everywhere / Let 's all have a drink . The scene in which Borgnine and the other <unk> drift through a dark forest watched by mountain men is a reference to a scene in the 1972 film <unk> , and the scene features the music from the film 's " <unk> <unk> " scene . The unseen person or creature that attacks Borgnine at the end of the episode is implied to be Jason <unk> from the Friday the 13th film series . 
- 
- = = Reception = = 
- 
- In its original American broadcast , " Boy @-@ Scoutz ' n the Hood " finished 35th in the ratings for the week of November 15 to November 21 , 1993 , with a Nielsen rating of 13 @.@ 0 , translating to 12 @.@ 3 million households . The episode was the highest @-@ rated show on the Fox network that week . 
- Since airing , the episode has received mostly positive reviews from television critics . The authors of the book I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide , Warren Martyn and Adrian Wood , wrote : " A terrific episode , with Homer so stupid it isn 't true , yet still saving the day . Seeing Ned Flanders get it wrong is great , but the show @-@ <unk> is a toss @-@ up between Borgnine 's great self @-@ deprecating role , the ironic <unk> , and the <unk> . " DVD Movie Guide 's Colin Jacobson called it a " brilliant episode from start to finish " . He commented that " We see what an amazing amount of goods and services one can purchase in Springfield with only $ 20 , and we get a fun spoof of <unk> . <unk> to that terrific rivalry moments between Bart and Homer and the show excels . " Patrick Bromley of DVD Verdict called the plot of the episode " typically inspired " , and gave it a grade of A. Bill <unk> of DVD Talk gave the episode a score of 5 out of 5 . TV DVD Reviews 's Kay Daly wrote : " And just when you think the Simpsons ' creators have taken parody as far as it can go , they air an episode like this . The writers <unk> the 22 @-@ minute episode with allusions to movie genres including disaster movies , Broadway musicals , adventure @-@ suspense and classic teen horror . " Adam <unk> of <unk> named it one of his ten favorite episodes of the show . He called the musical sequence a " classic " . Rick Porter of <unk> 2 It wrote in that he was not a " fan " of the episode 's second half : " Despite the presence of Borgnine , Homer is a little too aggressively stupid for my taste " . He thought the first part was " absolutely brilliant " , though . 
- Kurt M. <unk> analyzed a scene from the episode in his piece " <unk> Culture and Its <unk> " , published in the compilation work Leaving Springfield : The Simpsons and the <unk> of <unk> Culture edited by John <unk> . He commented that The Simpsons ' literary and cultural awareness extends to the " conventions of its own medium " in this episode . Bart criticizes an Itchy & Scratchy episode because Itchy stakes down Scratchy 's <unk> and props his belly to form a tent with faulty knots . With Homer looking on from the <unk> , Lisa reminds Bart that cartoons do not simply reproduce reality , a point <unk> on as a second Homer meanders past the living @-@ room window . <unk> said that " this moment and many others like it reveal a strong sense of self @-@ awareness within the show , an awareness especially characteristic of high <unk> . " 
- In the United Kingdom , when the 300th episode was shown , Sky 1 held a Golden D <unk> evening , in which viewers voted for their favourite episodes to win in each category . This episode won the category of : Best School Jinx . 
- 
- 
- = Last Exit on Brooklyn = 
- 
- The Last Exit on Brooklyn was a Seattle University District coffeehouse established in 1967 by Irv Cisski . It is known for its part in the history of Seattle 's counterculture , for its pioneering role in establishing Seattle 's coffee culture , and as a former chess venue frequented by several master players . 
- 
- = = History = = 
- 
- The Last Exit on Brooklyn opened on June 30 , 1967 at <unk> Brooklyn Avenue <unk> near the University of Washington campus in a small light @-@ industrial building leased from the University . It was one of the pioneer espresso bars in Seattle , adding an espresso machine shortly after <unk> Allegro opened the first in 1975 . The Last Exit was known for its original espresso <unk> named the <unk> <unk> – " a <unk> poured over chocolate syrup and orange <unk> with <unk> cream on top " . Described in 1985 as " America 's second oldest , continuously running coffeehouse " , it was also known for its inexpensive food and as a venue for folk music and <unk> conversation . 
- The Last Exit was also notable as a popular destination for Seattle 's amateur and professional Go and chess players including Peter <unk> , <unk> <unk> , and <unk> <unk> , who wrote of the venue , " Those first chess lessons soon led me to the legendary Last Exit on Brooklyn coffee house , a chess haven where an unlikely bunch of unusual people <unk> to do battle . " <unk> by Sports Illustrated in 1981 , <unk> described the Last Exit as " <unk> players , <unk> players , chess and game <unk> ... This became my home . This was to become my family . " 
- When interviewed by Mary <unk> of Chess Life in 1985 , owner Irv Cisski said , " So what if games @-@ people turn away business . They add flavor . Chess and Go are assets to a coffeehouse . " The Last Exit was the subject of a 1987 retrospective in The Seattle Times in which Cisski described his intent to " create a haven where students and the benign <unk> " were welcome and where " everyone felt equal and there were no sacred <unk> " . It was later described by Seattle writer and journalist <unk> Berger as 
- one of Seattle 's great ' 60s landmarks , a gathering place for <unk> students , radicals , poets , nut jobs , chess masters , teens , intellectuals , workers , musicians , artists , <unk> , and <unk> ... I remember the <unk> , the open @-@ <unk> music , cigarette smoke , impromptu poetry readings , the arguments of <unk> , <unk> , <unk> , and <unk> . You could hear the rhythm and <unk> of the counterculture as it lived and <unk> . 
- Cisski died on August 25 , 1992 . In 1993 the University <unk> the building occupied by the coffeehouse , and the Last Exit 's new owners moved it to upper University Way . The Last Exit on Brooklyn closed in 2000 . The space the original Last Exit once occupied now houses staff members from the University of Washington 's Human Resources Department . 
- 
- = = In popular culture = = 
- 
- The Last Exit was included in Clark Humphrey 's 2006 book of historical photographs , <unk> Seattle . 
- <unk> of the interior and atmosphere of the Last Exit appear in Kristin Hannah 's 2008 novel , Firefly Lane , in David <unk> 's 2008 novel , The Other , and in Marjorie Kowalski Cole 's 2012 The City <unk> the Snow : Stories . 
- 
- 
- = Laurence Olivier = 
- 
- Laurence Kerr Olivier , Baron Olivier , <unk> ( / <unk> <unk> <unk> / ; 22 May 1907 – 11 July 1989 ) was an English actor who , along with his contemporaries Ralph Richardson and John Gielgud , dominated the British stage of the mid @-@ 20th century . He also worked in films throughout his career , playing more than fifty cinema roles . Late in his career , he had considerable success in television roles . 
- His family had no theatrical connections , but Olivier 's father , a clergyman , decided that his son should become an actor . After attending a drama school in London , Olivier learned his craft in a succession of acting jobs during the late 1920s . In 1930 he had his first important West End success in Noël Coward 's Private Lives , and he appeared in his first film . In 1935 he played in a celebrated production of Romeo and Juliet alongside Gielgud and Peggy Ashcroft , and by the end of the decade he was an established star . In the 1940s , together with Richardson and John Burrell , Olivier was the co @-@ director of the Old Vic , building it into a highly respected company . There his most celebrated roles included Shakespeare 's Richard III and <unk> 's <unk> . In the 1950s Olivier was an independent actor @-@ manager , but his stage career was in the <unk> until he joined the avant garde English Stage Company in 1957 to play the title role in The Entertainer , a part he later played on film . From 1963 to 1973 he was the founding director of Britain 's National Theatre , running a resident company that fostered many future stars . His own parts there included the title role in Othello ( 1964 ) and Shylock in The <unk> of Venice ( 1970 ) . 
- Among Olivier 's films are Wuthering Heights ( 1939 ) , Rebecca ( 1940 ) , and a trilogy of Shakespeare films as actor @-@ director : Henry V ( 1944 ) , Hamlet ( 1948 ) , and Richard III ( 1955 ) . His later films included <unk> ( 1972 ) , <unk> Man ( 1976 ) , and The Boys from Brazil ( 1978 ) . His television appearances included an adaptation of The Moon and <unk> ( 1960 ) , Long Day 's Journey into Night ( 1973 ) , Love Among the <unk> ( 1975 ) , Cat on a Hot Tin Roof ( 1976 ) , <unk> Revisited ( 1981 ) and King Lear ( 1983 ) . 
- Olivier 's honours included a knighthood ( 1947 ) , a life peerage ( 1970 ) and the Order of Merit ( 1981 ) . For his on @-@ screen work he received four Academy Awards , two British Academy Film Awards , five Emmy Awards and three Golden Globe Awards . The National Theatre 's largest auditorium is named in his honour , and he is commemorated in the Laurence Olivier Awards , given annually by the Society of London Theatre . He was married three times , to the actresses Jill Esmond from 1930 to 1940 , Vivien Leigh from 1940 to 1960 , and Joan Plowright from 1961 until his death . 
- 
- = = Life and career = = 
- 
- 
- = = = Family background and early life ( 1907 – 24 ) = = = 
- 
- Olivier was born in <unk> , Surrey , the youngest of the three children of the <unk> Gerard Kerr Olivier ( 1869 – 1939 ) and his wife Agnes Louise , née <unk> ( 1871 – 1920 ) . Their elder children were <unk> ( 1901 – 89 ) and Gerard <unk> " <unk> " ( 1904 – 58 ) . His great @-@ great @-@ grandfather was of French Huguenot descent , and Olivier came from a long line of Protestant clergymen . Gerard Olivier had begun a career as a schoolmaster , but in his thirties he discovered a strong religious <unk> and was ordained as a priest of the Church of England . He practised extremely high church , <unk> Christianity and liked to be addressed as " Father Olivier " . This made him unacceptable to most Anglican congregations , and the only church posts he was offered were temporary , usually <unk> for regular <unk> in their absence . This meant a nomadic existence , and for Laurence 's first few years , he never lived in one place long enough to make friends . 
- In 1912 , when Olivier was five , his father secured a permanent appointment as assistant priest at St <unk> 's , <unk> . He held the post for six years , and a stable family life was at last possible . Olivier was devoted to his mother , but not to his father , whom he found a cold and remote parent . Nevertheless , he learned a great deal of the art of performing from him . As a young man Gerard Olivier had considered a stage career and was a dramatic and effective preacher . Olivier wrote that his father knew " when to drop the voice , when to <unk> about the <unk> of <unk> , when to slip in a gag , when suddenly to wax sentimental ... The quick changes of mood and manner absorbed me , and I have never forgotten them . " 
- In 1916 , after attending a series of preparatory schools , Olivier passed the singing examination for admission to the choir school of All Saints , Margaret Street , in central London . His elder brother was already a pupil , and Olivier gradually settled in , though he felt himself to be something of an <unk> . The church 's style of worship was ( and remains ) Anglo @-@ Catholic , with emphasis on ritual , vestments and incense . The theatricality of the services appealed to Olivier , and the vicar encouraged the students to develop a taste for secular as well as religious drama . In a school production of Julius Caesar in 1917 , the ten @-@ year @-@ old Olivier 's performance as Brutus impressed an audience that included Lady Tree , the young Sybil Thorndike , and Ellen Terry , who wrote in her diary , " The small boy who played Brutus is already a great actor . " He later won praise in other schoolboy productions , as Maria in Twelfth Night ( 1918 ) and Katherine in The <unk> of the <unk> ( 1922 ) . 
- From All Saints , Olivier went on to St Edward 's School , Oxford , from 1920 to 1924 . He made little mark until his final year , when he played Puck in the school 's production of A Midsummer Night 's Dream ; his performance was a tour de force that won him popularity among his fellow pupils . In January 1924 , his brother left England to work in India as a rubber <unk> . Olivier missed him greatly and asked his father how soon he could follow . He recalled in his memoirs that his father replied , " Don 't be such a fool , you 're not going to India , you 're going on the stage . " 
- 
- = = = Early acting career ( 1924 – 29 ) = = = 
- 
- In 1924 Gerard Olivier , a habitually <unk> man , told his son that not only must he gain admission to the Central School of Speech Training and Dramatic Art , but he must also gain a scholarship with a <unk> to cover his tuition fees and living expenses . Olivier 's sister had been a student there and was a favourite of <unk> Fogerty , the founder and principal of the school . Olivier later speculated that it was on the strength of this that Fogerty agreed to award him the <unk> . 
- One of Olivier 's contemporaries at the school was Peggy Ashcroft , who observed he was " rather <unk> in that his sleeves were too short and his hair stood on end but he was intensely lively and great fun " . By his own admission , he was not a very conscientious student , but Fogerty liked him and later said that he and Ashcroft stood out among her many pupils . On leaving the school after a year , Olivier gained work with small touring companies before being taken on in 1925 by Sybil Thorndike and her husband , Lewis <unk> , as a bit @-@ part player , <unk> and assistant stage manager for their London company . He modelled his performing style on that of Gerald du <unk> , of whom he said , " He seemed to <unk> on stage but had such perfect technique . When I started I was so busy doing a du <unk> that no one ever heard a word I said . The Shakespearean actors one saw were terrible <unk> like Frank Benson . " His concern to speak naturally and avoid what he called " singing " Shakespeare 's verse was the cause of much frustration in his early career , with critics regularly decrying his delivery . 
- In 1926 , on Thorndike 's recommendation , Olivier joined the Birmingham Repertory Company . His biographer Michael Billington describes the Birmingham company as " Olivier 's university " , where in his second year he was given the chance to play a wide range of important roles , including Tony <unk> in She <unk> to <unk> , the title role in Uncle Vanya , and <unk> in All 's Well That <unk> Well . Billington adds that the engagement led to " a lifelong friendship with his fellow actor Ralph Richardson that was to have a decisive effect on the British theatre . " 
- While playing the juvenile lead in Bird in Hand at the <unk> Theatre in June 1928 , Olivier began a relationship with Jill Esmond , the daughter of the actors Henry V. Esmond and Eva Moore . Olivier later recounted that he thought " she would most certainly do excellent well for a wife ... I wasn 't likely to do any better at my age and with my undistinguished track @-@ record , so I promptly fell in love with her . " 
- In 1928 Olivier created the role of <unk> in R. C. <unk> 's Journey 's End , in which he scored a great success at its single Sunday night premiere . He was offered the part in the West End production the following year , but turned it down in favour of the more glamorous role of Beau Geste in a stage adaptation of P. C. Wren 's 1929 novel of the same name . Journey 's End became a long @-@ running success ; Beau Geste failed . The Manchester Guardian commented , " Mr. Laurence Olivier did his best as Beau , but he deserves and will get better parts . Mr. Olivier is going to make a big name for himself " . For the rest of 1929 Olivier appeared in seven plays , all of which were short @-@ lived . Billington <unk> this failure rate to poor choices by Olivier rather than mere bad luck . 
- 
- = = = Rising star ( 1930 – 35 ) = = = 
- 
- In 1930 , with his impending marriage in mind , Olivier earned some extra money with small roles in two films . In April he travelled to Berlin to film the English @-@ language version of The <unk> Widow , a crime comedy with Lilian Harvey , and in May he spent four nights working on another comedy , Too Many <unk> . During work on the latter film , for which he was paid £ 60 , he met Laurence Evans , who became his personal manager . Olivier did not enjoy working in film , which he dismissed as " this <unk> little medium which could not stand great acting " , but financially it was much more rewarding than his theatre work . 
- Olivier and Esmond married on 25 July 1930 at All Saints , Margaret Street , although within weeks both realised they had <unk> . Olivier later recorded that the marriage was " a pretty crass mistake . I insisted on getting married from a pathetic mixture of religious and animal <unk> . ... She had admitted to me that she was in love elsewhere and could never love me as completely as I would wish " . Olivier later recounted that following the wedding he did not keep a diary for ten years and never followed religious practices again , although he considered those facts to be " mere coincidence " , unconnected to the <unk> . 
- In 1930 Noël Coward cast Olivier as Victor <unk> in his new play Private Lives , which opened at the new Phoenix Theatre in London in September . Coward and Gertrude Lawrence played the lead roles , <unk> Chase and Amanda <unk> . Victor is a secondary character , along with Sybil Chase ; the author called them " extra puppets , lightly wooden <unk> , only to be repeatedly knocked down and stood up again " . To make them credible spouses for Amanda and <unk> , Coward was determined that two outstandingly attractive performers should play the parts . Olivier played Victor in the West End and then on Broadway ; Adrianne Allen was Sybil in London , but could not go to New York , where the part was taken by Esmond . In addition to giving the 23 @-@ year @-@ old Olivier his first successful West End role , Coward became something of a mentor . In the late 1960s Olivier told Sheridan Morley : 
- He gave me a sense of balance , of right and wrong . He would make me read ; I never used to read anything at all . I remember he said , " Right , my boy , Wuthering Heights , Of Human <unk> and The Old Wives ' Tale by Arnold Bennett . That 'll do , those are three of the best . Read them " . I did . ... Noël also did a <unk> thing , he taught me not to <unk> on the stage . Once already I 'd been fired for doing it , and I was very nearly sacked from the Birmingham <unk> for the same reason . Noël cured me ; by trying to make me laugh outrageously , he taught me how not to give in to it . My great triumph came in New York when one night I managed to break Noël up on the stage without <unk> myself . " 
- In 1931 RKO Pictures offered Olivier a two @-@ film contract at $ 1 @,@ 000 a week ; he discussed the possibility with Coward , who , <unk> , told Olivier " You 've no artistic integrity , that 's your trouble ; this is how you <unk> yourself . " He accepted and moved to Hollywood , despite some <unk> . His first film was the drama Friends and Lovers , in a supporting role , before RKO loaned him to Fox Studios for his first film lead , a British journalist in a Russia under martial law in The Yellow <unk> , alongside <unk> <unk> and Lionel Barrymore . The cultural historian Jeffrey Richards describes Olivier 's look as an attempt by Fox Studios to produce a likeness of Ronald Colman , and Colman 's moustache , voice and manner are " perfectly reproduced " . Olivier returned to RKO to complete his contract with the 1932 drama <unk> Passage , which was a commercial failure . Olivier 's initial foray into American films had not provided the breakthrough he hoped for ; disillusioned with Hollywood , he returned to London , where he appeared in two British films , Perfect Understanding with Gloria Swanson and No Funny Business — in which Esmond also appeared . He was <unk> back to Hollywood in 1933 to appear opposite <unk> <unk> in Queen Christina , but was replaced after two weeks of filming because of a lack of chemistry between the two . 
- Olivier 's stage roles in 1934 included <unk> in Gordon <unk> 's Queen of Scots , which was only a moderate success for him and for the play , but led to an important engagement for the same management ( Bronson <unk> ) shortly afterwards . In the interim he had a great success playing a thinly disguised version of the American actor John Barrymore in Edna <unk> 's Theatre Royal . His success was <unk> by his breaking an ankle two months into the run , in one of the athletic , <unk> stunts with which he liked to <unk> his performances . 
- In 1935 , under <unk> 's management , John Gielgud staged Romeo and Juliet at the New Theatre , co @-@ starring with Peggy Ashcroft , Edith Evans and Olivier . Gielgud had seen Olivier in Queen of Scots , spotted his potential , and now gave him a major step up in his career . For the first weeks of the run Gielgud played <unk> and Olivier played Romeo , after which they exchanged roles . The production broke all box @-@ office records for the play , running for 189 performances . Olivier was enraged at the notices after the first night , which praised the <unk> of his performance but fiercely criticised his speaking of Shakespeare 's verse , contrasting it with his co @-@ star 's mastery of the poetry . The friendship between the two men was prickly , on Olivier 's side , for the rest of his life . 
- 
- = = = Old Vic and Vivien Leigh ( 1936 – 38 ) = = = 
- 
- In May 1936 Olivier and Richardson jointly directed and starred in a new piece by J. B. Priestley , Bees on the <unk> . Both actors won excellent notices , but the play , an allegory of Britain 's decay , did not attract the public and closed after four weeks . Later in the same year Olivier accepted an invitation to join the Old Vic company . The theatre , in an <unk> location south of the Thames , had offered inexpensive tickets for opera and drama under its proprietor Lilian <unk> since 1912 . Her drama company specialised in the plays of Shakespeare , and many leading actors had taken very large cuts in their pay to develop their Shakespearean techniques there . Gielgud had been in the company from 1929 to 1931 , and Richardson from 1930 to 1932 . Among the actors whom Olivier joined in late 1936 were Edith Evans , Ruth Gordon , Alec Guinness and Michael Redgrave . In January 1937 he took the title role in an uncut version of Hamlet , in which once again his delivery of the verse was <unk> compared with that of Gielgud , who had played the role on the same stage seven years previously to enormous acclaim . The Observer 's Ivor Brown praised Olivier 's " magnetism and <unk> " but missed " the kind of pathos so richly established by Mr Gielgud " . The reviewer in The Times found the performance " full of vitality " , but at times " too light ... the character slips from Mr Olivier 's grasp " . 
- After Hamlet , the company presented Twelfth Night in what the director , Tyrone Guthrie , summed up as " a <unk> , immature production of mine , with Olivier outrageously amusing as Sir Toby and a very young Alec Guinness <unk> and more amusing as Sir Andrew " . Henry V was the next play , presented in May to mark the Coronation of George VI . A pacifist , as he then was , Olivier was as reluctant to play the warrior king as Guthrie was to direct the piece , but the production was a success , and <unk> had to extend the run from four to eight weeks . 
- Following Olivier 's success in Shakespearean stage productions , he made his first foray into Shakespeare on film in 1936 , as Orlando in As You Like It , directed by Paul <unk> , " a charming if lightweight production " , according to Michael Brooke of the British Film Institute 's ( BFI 's ) <unk> . The following year Olivier appeared alongside Vivien Leigh in the historical drama Fire Over England . He had first met Leigh briefly at the Savoy <unk> and then again when she visited him during the run of Romeo and Juliet , probably early in 1936 , and the two had begun an affair sometime that year . Of the relationship , Olivier later said that " I couldn 't help myself with Vivien . No man could . I hated myself for cheating on Jill , but then I had cheated before , but this was something different . This wasn 't just out of lust . This was love that I really didn 't ask for but was drawn into . " While his relationship with Leigh continued he conducted an affair with the actress Ann Todd , and possibly had a homosexual <unk> with the actor Henry <unk> , according to the biographer Michael <unk> . 
- In June 1937 the Old Vic company took up an invitation to perform Hamlet in the courtyard of the castle at Elsinore , where Shakespeare located the play . Olivier secured the casting of Leigh to replace Cherry <unk> as Ophelia . Because of torrential rain the performance had to be moved from the castle courtyard to the ballroom of a local hotel , but the tradition of playing Hamlet at Elsinore was established , and Olivier was followed by , among others , Gielgud ( 1939 ) , Redgrave ( 1950 ) , Richard Burton ( 1954 ) , Derek Jacobi ( 1979 ) , Kenneth Branagh ( 1988 ) and Jude Law ( 2009 ) . Back in London , the company staged Macbeth , with Olivier in the title role . The stylised production by Michel Saint @-@ Denis was not well liked , but Olivier had some good notices among the bad . On returning from Denmark , Olivier and Leigh told their respective spouses about the affair and that their marriages were over ; Esmond moved out of the marital house and in with her mother . After Olivier and Leigh made a tour of Europe in mid 1937 they returned to separate film projects — A <unk> at Oxford for her and The Divorce of Lady X for him — and moved into a property together in <unk> , Buckinghamshire . 
- Olivier returned to the Old Vic for a second season in 1938 . For Othello he played Iago , with Richardson in the title role . Guthrie wanted to experiment with the theory that Iago 's <unk> is driven by suppressed homosexual love for Othello . Olivier was willing to co @-@ operate , but Richardson was not ; audiences and most critics failed to spot the supposed motivation of Olivier 's Iago , and Richardson 's Othello seemed <unk> . After that comparative failure , the company had a success with Coriolanus starring Olivier in the title role . The notices were laudatory , mentioning him alongside great predecessors such as Edmund Kean , William <unk> and Henry Irving . The actor Robert <unk> described it as " Olivier 's first <unk> great performance " . This was Olivier 's last appearance on a London stage for six years . 
- 
- = = = Hollywood and the Second World War ( 1939 – 43 ) = = = 
- 
- In 1938 Olivier joined Richardson to film the spy thriller Q <unk> , released the following year . Frank <unk> , the critic for The New York Times , thought Olivier was " not quite so good " as Richardson , but was " quite acceptable " . In late 1938 , lured by a salary of $ 50 @,@ 000 , the actor travelled to Hollywood to take the part of <unk> in the 1939 film Wuthering Heights , alongside <unk> Oberon and David Niven . In less than a month Leigh had joined him , explaining that her trip was " partially because Larry 's there and partially because I intend to get the part of Scarlett O <unk> " — the role in Gone with the Wind in which she was eventually cast . Olivier did not enjoy making Wuthering Heights , and his approach to film acting , combined with a dislike for Oberon , led to tensions on set . The director , William <unk> , was a hard <unk> , and Olivier learned to remove what Billington described as " the carapace of theatricality " to which he was prone , replacing it with " a <unk> reality " . The resulting film was a commercial and critical success that earned him a nomination for the Academy Award for Best Actor , and created his screen reputation . Caroline Lejeune , writing for The Observer , considered that " Olivier 's dark , moody face , abrupt style , and a certain fine arrogance towards the world in his playing are just right " in the role , while the reviewer for The Times wrote that Olivier " is a good embodiment of <unk> ... impressive enough on a more human plane , speaking his lines with real distinction , and always both romantic and alive . " 
- After returning to London briefly in mid @-@ 1939 , the couple returned to America , Leigh to film the final takes for Gone with the Wind , and Olivier to prepare for filming of Alfred Hitchcock 's Rebecca — although the couple had hoped to appear in it together . Instead , Joan <unk> was selected for the role of Mrs de Winter , as the producer David O. <unk> thought that not only was she more suitable for the role , but that it was best to keep Olivier and Leigh apart until their divorces came through . Olivier followed Rebecca with Pride and <unk> , in the role of Mr. <unk> . To his disappointment Elizabeth <unk> was played by Greer <unk> rather than Leigh . He received good reviews for both films and showed a more confident screen presence than he had in his early work . In January 1940 Olivier and Esmond were granted their divorce . In February , following another request from Leigh , her husband also applied for their marriage to be terminated . 
- On stage , Olivier and Leigh starred in Romeo and Juliet on Broadway . It was an extravagant production , but a commercial failure . In The New York Times Brooks Atkinson praised the scenery but not the acting : " Although Miss Leigh and Mr Olivier are handsome young people they hardly act their parts at all . " The couple had invested almost all their savings in the project , and its failure was a grave financial blow . They were married in August 1940 , at the San <unk> Ranch in Santa Barbara . 
- The war in Europe had been under way for a year and was going badly for Britain . After his wedding Olivier wanted to help the war effort . He telephoned Duff Cooper , the Minister of Information under Winston Churchill , hoping to get a position in Cooper 's department . Cooper advised him to remain where he was and speak to the film director Alexander Korda , who was based in the US at Churchill 's behest , with connections to British Intelligence . Korda — with Churchill 's support and involvement — directed That Hamilton Woman , with Olivier as <unk> Nelson and Leigh in the title role . Korda saw that the relationship between the couple was strained . Olivier was tiring of Leigh 's <unk> <unk> , and she was drinking to excess . The film , in which the threat of Napoleon paralleled that of Hitler , was seen by critics as " bad history but good British propaganda " , according to the BFI . 
- Olivier 's life was under threat from the Nazis and pro @-@ German <unk> . The studio owners were concerned enough that Samuel Goldwyn and Cecil B. <unk> both provided support and security to ensure his safety . On the completion of filming , Olivier and Leigh returned to Britain . He had spent the previous year learning to fly and had completed nearly 250 hours by the time he left America . He intended to join the Royal Air Force but instead made another propaganda film , 49th <unk> , narrated short pieces for the Ministry of Information , and joined the Fleet Air Arm because Richardson was already in the service . Richardson had gained a reputation for crashing aircraft , which Olivier rapidly eclipsed . Olivier and Leigh settled in a cottage just outside RAF <unk> Down , where he was stationed with a training squadron ; Noël Coward visited the couple and thought Olivier looked unhappy . Olivier spent much of his time taking part in broadcasts and making speeches to build morale , and in 1942 he was invited to make another propaganda film , The <unk> @-@ Paradise , in which he played a Soviet engineer who helps improve British @-@ Russian relationships . 
- In 1943 , at the behest of the Ministry of Information , Olivier began working on Henry V. Originally he had no intention of taking the directorial duties , but ended up directing and producing , in addition to taking the title role . He was assisted by an Italian <unk> , Filippo Del <unk> , who had been released to produce propaganda for the Allied cause . The decision was made to film the battle scenes in neutral <unk> , where it was easier to find the 650 extras . John Betjeman , the press <unk> at the British embassy in Dublin , played a key liaison role with the Irish government in making suitable arrangements . The film was released in November 1944 . Brooke , writing for the BFI , considers that it " came too late in the Second World War to be a call to arms as such , but formed a powerful reminder of what Britain was defending . " The music for the film was written by William Walton , " a score that ranks with the best in film music " , according to the music critic Michael Kennedy . Walton also provided the music for Olivier 's next two Shakespearean adaptations , Hamlet ( 1948 ) and Richard III ( 1955 ) . Henry V was warmly received by critics . The reviewer for The Manchester Guardian wrote that the film combined " new art hand @-@ in @-@ hand with old genius , and both superbly of one mind " , in a film that worked " <unk> " . The critic for The Times considered that Olivier " plays Henry on a high , heroic note and never is there danger of a crack " , in a film described as " a triumph of film craft " . There were Oscar nominations for the film , including Best Picture and Best Actor , but it won none and Olivier was instead presented with a " Special Award " . He was unimpressed , and later commented that " this was my first absolute <unk> @-@ off , and I regarded it as such . " 
- 
- = = = Co @-@ directing the Old Vic ( 1944 – 47 ) = = = 
- 
- Throughout the war Tyrone Guthrie had <unk> to keep the Old Vic company going , even after German bombing in 1942 left the theatre a near @-@ ruin . A small troupe toured the provinces , with Sybil Thorndike at its head . By 1944 , with the tide of the war turning , Guthrie felt it time to re @-@ establish the company in a London base and invited Richardson to head it . Richardson made it a condition of accepting that he should share the acting and management in a triumvirate . Initially he proposed Gielgud and Olivier as his colleagues , but the former declined , saying , " It would be a disaster , you would have to spend your whole time as referee between Larry and me . " It was finally agreed that the third member would be the stage director John Burrell . The Old Vic governors approached the Royal Navy to secure the release of Richardson and Olivier ; the Sea Lords consented , with , as Olivier put it , " a <unk> and lack of reluctance which was positively <unk> . " 
- The triumvirate secured the New Theatre for their first season and recruited a company . Thorndike was joined by , among others , Harcourt Williams , Joyce <unk> and Margaret <unk> . It was agreed to open with a repertory of four plays : <unk> <unk> , Arms and the Man , Richard III and Uncle Vanya . Olivier 's roles were the Button <unk> , Sergius , Richard and Astrov ; Richardson played <unk> , <unk> , Richmond and Vanya . The first three productions met with acclaim from reviewers and audiences ; Uncle Vanya had a mixed reception , although The Times thought Olivier 's Astrov " a most distinguished portrait " and Richardson 's Vanya " the perfect compound of <unk> and pathos " . In Richard III , according to Billington , Olivier 's triumph was absolute : " so much so that it became his most frequently imitated performance and one whose supremacy went <unk> until Antony Sher played the role forty years later " . In 1945 the company toured Germany , where they were seen by many thousands of Allied servicemen ; they also appeared at the <unk> @-@ Française theatre in Paris , the first foreign company to be given that honour . The critic Harold Hobson wrote that Richardson and Olivier quickly " made the Old Vic the most famous theatre in the Anglo @-@ Saxon world . " 
- The second season , in 1945 , featured two double bills . The first consisted of Henry IV , Parts 1 and 2 . Olivier played the warrior Hotspur in the first and the <unk> Justice <unk> in the second . He received good notices , but by general consent the production belonged to Richardson as Falstaff . In the second double bill it was Olivier who dominated , in the title roles of <unk> Rex and The Critic . In the two one @-@ act plays his switch from searing tragedy and horror in the first half to farcical comedy in the second impressed most critics and audience members , though a minority felt that the transformation from <unk> 's <unk> blinded hero to Sheridan 's vain and ludicrous Mr <unk> " <unk> of a quick @-@ change turn in a music hall " . After the London season the company played both the double bills and Uncle Vanya in a six @-@ week run on Broadway . 
- The third , and final , London season under the triumvirate was in 1946 – 47 . Olivier played King Lear , and Richardson took the title role in <unk> de <unk> . Olivier would have preferred the roles to be reversed , but Richardson did not wish to attempt Lear . Olivier 's Lear received good but not outstanding reviews . In his scenes of decline and madness towards the end of the play some critics found him less moving than his finest predecessors in the role . The influential critic James Agate suggested that Olivier used his dazzling stage technique to disguise a lack of feeling , a charge that the actor strongly rejected , but which was often made throughout his later career . During the run of <unk> , Richardson was knighted , to Olivier 's <unk> envy . The younger man received the accolade six months later , by which time the days of the triumvirate were numbered . The high profile of the two star actors did not <unk> them to the new chairman of the Old Vic governors , Lord Esher . He had ambitions to be the first head of the National Theatre and had no intention of letting actors run it . He was encouraged by Guthrie , who , having instigated the appointment of Richardson and Olivier , had come to resent their <unk> and international fame . 
- In January 1947 Olivier began working on his second film as a director , Hamlet ( 1948 ) , in which he also took the lead role . The original play was heavily cut to focus on the relationships , rather than the political intrigue . The film became a critical and commercial success in Britain and abroad , although Lejeune , in The Observer , considered it " less effective than [ Olivier 's ] stage work . ... He speaks the lines <unk> , and with the <unk> of one who loves them , but he <unk> his own thesis by never , for a moment , leaving the impression of a man who cannot make up his own mind ; here , you feel rather , is an actor @-@ producer @-@ director who , in every circumstance , knows exactly what he wants , and gets it " . Campbell Dixon , the critic for The Daily Telegraph thought the film " brilliant ... one of the masterpieces of the stage has been made into one of the greatest of films . " Hamlet became the first non @-@ American film to win the Academy Award for Best Picture , while Olivier won the Award for Best Actor . 
- In 1948 Olivier led the Old Vic company on a six @-@ month tour of Australia and New Zealand . He played Richard III , Sir Peter <unk> in Sheridan 's The School for Scandal and <unk> in <unk> Wilder 's The Skin of Our <unk> , appearing alongside Leigh in the latter two plays . While Olivier was on the Australian tour and Richardson was in Hollywood , Esher terminated the contracts of the three directors , who were said to have " resigned " . <unk> Bragg in a 1984 study of Olivier , and John Miller in the authorised biography of Richardson , both comment that Esher 's action put back the establishment of a National Theatre for at least a decade . Looking back in 1971 , Bernard Levin wrote that the Old Vic company of 1944 to 1948 " was probably the most illustrious that has ever been assembled in this country " . The Times said that the triumvirate 's years were the greatest in the Old Vic 's history ; as The Guardian put it , " the governors summarily sacked them in the interests of a more mediocre company spirit " . 
- 
- = = = Post @-@ war ( 1948 – 51 ) = = = 
- 
- By the end of Australian tour , both Leigh and Olivier were exhausted and ill , and he told a journalist , " You may not know it , but you are talking to a couple of walking corpses . " Later he would comment that he " lost Vivien " in Australia , a reference to Leigh 's affair with the Australian actor Peter Finch , whom the couple met during the tour . Shortly afterwards Finch moved to London , where Olivier auditioned him and put him under a long @-@ term contract with Laurence Olivier Productions . Finch and Leigh 's affair continued on and off for several years . 
- Although it was common knowledge that the Old Vic triumvirate had been dismissed , they refused to be drawn on the matter in public , and Olivier even arranged to play a final London season with the company in 1949 , as Richard III , Sir Peter <unk> , and Chorus in his own production of Anouilh 's <unk> with Leigh in the title role . After that , he was free to embark on a new career as an actor @-@ manager . In partnership with <unk> Beaumont he staged the English premiere of Tennessee Williams 's A Streetcar Named Desire , with Leigh in the central role of Blanche <unk> . The play was condemned by most critics , but the production was a considerable commercial success , and led to Leigh 's casting as Blanche in the 1951 film version . Gielgud , who was a devoted friend of Leigh 's , doubted whether Olivier was wise to let her play the demanding role of the mentally unstable heroine : " [ Blanche ] was so very like her , in a way . It must have been a most <unk> strain to do it night after night . She would be shaking and white and quite distraught at the end of it . " 
- The production company set up by Olivier took a lease on the St James 's Theatre . In January 1950 he produced , directed and starred in Christopher Fry 's verse play Venus <unk> . The production was popular , despite poor reviews , but the expensive production did little to help the finances of Laurence Olivier Productions . After a series of box @-@ office failures , the company balanced its books in 1951 with productions of Shaw 's Caesar and Cleopatra and Shakespeare 's Antony and Cleopatra which the Oliviers played in London and then took to Broadway . Olivier was thought by some critics to be under par in both his roles , and some suspected him of playing deliberately below his usual strength so that Leigh might appear his equal . Olivier dismissed the suggestion , regarding it as an insult to his integrity as an actor . In the view of the critic and biographer W. A. Darlington , he was simply <unk> both as Caesar and Antony , finding the former boring and the latter weak . Darlington comments , " Olivier , in his middle <unk> when he should have been displaying his powers at their very peak , seemed to have lost interest in his own acting " . Over the next four years Olivier spent much of his time working as a producer , presenting plays rather than directing or acting in them . His presentations at the St James 's included seasons by <unk> <unk> 's company giving two <unk> plays in Italian , followed by a visit from the <unk> @-@ Française playing works by <unk> , <unk> , <unk> and <unk> in French . Darlington considers a 1951 production of Othello starring Orson Welles as the pick of Olivier 's productions at the theatre . 
- 
- = = = Independent actor @-@ manager ( 1952 – 54 ) = = = 
- 
- While Leigh made Streetcar in 1951 , Olivier joined her in Hollywood to film Carrie , based on the controversial novel Sister Carrie ; although the film was plagued by troubles , Olivier received warm reviews and a BAFTA nomination . Olivier began to notice a change in Leigh 's behaviour , and he later recounted that " I would find Vivien sitting on the corner of the bed , <unk> her hands and sobbing , in a state of grave distress ; I would naturally try desperately to give her some comfort , but for some time she would be <unk> . " After a holiday with Coward in Jamaica , she seemed to have recovered , but Olivier later recorded , " I am sure that ... [ the doctors ] must have taken some pains to tell me what was wrong with my wife ; that her disease was called manic depression and what that meant — a possibly permanent cyclical to @-@ and @-@ <unk> between the depths of depression and wild , uncontrollable <unk> . He also recounted the years of problems he had experienced because of Leigh 's illness , writing , " throughout her possession by that <unk> evil monster , manic depression , with its deadly ever @-@ <unk> <unk> , she retained her own individual <unk> — an ability to disguise her true mental condition from almost all except me , for whom she could hardly be expected to take the trouble . " 
- In January 1953 Leigh travelled to Ceylon ( now Sri Lanka ) to film Elephant Walk with Peter Finch . Shortly after filming started she suffered a breakdown , and returned to Britain where , between periods of <unk> , she told Olivier that she was in love with Finch , and had been having an affair with him ; she gradually recovered over a period of several months . As a result of the breakdown , many of the Oliviers ' friends learned of her problems . Niven said she had been " quite , quite mad " , and in his diary , Coward expressed the view that " things had been bad and getting worse since 1948 or <unk> . " 
- For the Coronation season of 1953 , Olivier and Leigh starred in the West End in <unk> <unk> 's <unk> comedy , The Sleeping Prince . It ran for eight months but was widely regarded as a minor contribution to the season , in which other productions included Gielgud in Venice <unk> 'd , Coward in The Apple <unk> and Ashcroft and Redgrave in Antony and Cleopatra . 
- Olivier directed his third Shakespeare film in September 1954 , Richard III ( 1955 ) , which he co @-@ produced with Korda . The presence of four theatrical knights in the one film — Olivier was joined by Cedric Hardwicke , Gielgud and Richardson — led an American reviewer to dub it " An @-@ All @-@ Sir @-@ Cast " . The critic for The Manchester Guardian described the film as a " bold and successful achievement " , but it was not a box @-@ office success , which accounted for Olivier 's subsequent failure to raise the funds for a planned film of Macbeth . He won a BAFTA award for the role and was nominated for the Best Actor Academy Award , which <unk> <unk> won . 
- 
- = = = Last years with Leigh ( 1955 – 56 ) = = = 
- 
- In 1955 Olivier and Leigh were invited to play leading roles in three plays at the Shakespeare Memorial Theatre , Stratford . They began with Twelfth Night , directed by Gielgud , with Olivier as Malvolio and Leigh as Viola . <unk> were difficult , with Olivier determined to play his conception of the role despite the director 's view that it was vulgar . Gielgud later commented : 
- Somehow the production did not work . Olivier was set on playing Malvolio in his own particular rather extravagant way . He was extremely moving at the end , but he played the earlier scenes like a Jewish <unk> , with a <unk> and an extraordinary accent , and he insisted on falling backwards off a bench in the garden scene , though I <unk> him not to do it . ... But then Malvolio is a very difficult part . 
- The next production was Macbeth . Reviewers were lukewarm about the direction by Glen <unk> Shaw and the designs by Roger <unk> , but Olivier 's performance in the title role attracted <unk> . To J. C. <unk> , Olivier 's was " the finest Macbeth of our day " ; to Darlington it was " the best Macbeth of our time " . Leigh 's Lady Macbeth received mixed but generally polite notices , although to the end of his life Olivier believed it to have been the best Lady Macbeth he ever saw . 
- In their third production of the 1955 Stratford season , Olivier played the title role in Titus <unk> , with Leigh as <unk> . Her notices in the part were <unk> , but the production by Peter Brook and Olivier 's performance as Titus received the greatest ovation in Stratford history from the first @-@ night audience , and the critics hailed the production as a landmark in post @-@ war British theatre . Olivier and Brook revived the production for a continental tour in June 1957 ; its final performance , which closed the old <unk> Theatre in London , was the last time Leigh and Olivier acted together . 
- Leigh became pregnant in 1956 and withdrew from the production of Coward 's comedy South Sea <unk> . The day after her final performance in the play she <unk> and entered a period of depression that lasted for months . The same year Olivier decided to direct and produce a film version of The Sleeping Prince , retitled The Prince and the <unk> . Instead of appearing with Leigh , he cast Marilyn Monroe as the <unk> . Although the filming was challenging because of Monroe 's behaviour , the film was appreciated by the critics . 
- 
- = = = Royal Court and Chichester ( 1957 – 63 ) = = = 
- 
- During the production of The Prince and the <unk> , Olivier , Monroe and her husband , the American playwright Arthur Miller , went to see the English Stage Company 's production of John Osborne 's Look Back in Anger at the Royal Court . Olivier had seen the play earlier in the run and disliked it , but Miller was convinced that Osborne had talent , and Olivier <unk> . He was ready for a change of direction ; in 1981 he wrote : 
- I had reached a stage in my life that I was getting profoundly sick of — not just tired — sick . Consequently the public were , likely enough , beginning to agree with me . My rhythm of work had become a bit deadly : a classical or semi @-@ classical film ; a play or two at Stratford , or a nine @-@ month run in the West End , etc etc . I was going mad , desperately searching for something suddenly fresh and <unk> exciting . What I felt to be my image was boring me to death . 
- Osborne was already at work on a new play , The Entertainer , an allegory of Britain 's post @-@ colonial decline , centred on a <unk> variety comedian , Archie Rice . Having read the first act — all that was completed by then — Olivier asked to be cast in the part . He had for years maintained that he might easily have been a third @-@ rate comedian called " Larry Oliver " , and would sometimes play the character at parties . Behind Archie 's brazen façade there is a deep <unk> , and Olivier caught both aspects , switching , in the words of the biographer Anthony Holden , " from a <unk> <unk> comic routine to moments of the most <unk> pathos " . Tony Richardson 's production for the English Stage Company transferred from the Royal Court to the Palace Theatre in September 1957 ; after that it toured and returned to the Palace . The role of Archie 's daughter Jean was taken by three actresses during the various runs . The second of them was Joan Plowright , with whom Olivier began a relationship that endured for the rest of his life . Olivier said that playing Archie " made me feel like a modern actor again " . In finding an avant @-@ garde play that suited him , he was , as Osborne remarked , far ahead of Gielgud and Ralph Richardson , who did not successfully follow his lead for more than a decade . Their first substantial successes in works by any of Osborne 's generation were Alan Bennett 's Forty Years On ( Gielgud in 1968 ) and David <unk> 's Home ( Richardson and Gielgud in 1970 ) . 
- Olivier received another BAFTA nomination for his supporting role in 1959 's The Devil 's <unk> . The same year , after a gap of two decades , Olivier returned to the role of Coriolanus , in a Stratford production directed by the 28 @-@ year @-@ old Peter Hall . Olivier 's performance received strong praise from the critics for its fierce <unk> combined with an emotional vulnerability . In 1960 he made his second appearance for the Royal Court company in <unk> 's <unk> play <unk> . The production was chiefly remarkable for the star 's quarrels with the director , Orson Welles , who according to the biographer Francis <unk> suffered the " appalling treatment " that Olivier had inflicted on Gielgud at Stratford five years earlier . Olivier again ignored his director and undermined his authority . In 1960 and 1961 Olivier appeared in Anouilh 's <unk> on Broadway , first in the title role , with Anthony Quinn as the king , and later exchanging roles with his co @-@ star . 
- Two films featuring Olivier were released in 1960 . The first — filmed in 1959 — was <unk> , in which he portrayed the Roman general , Marcus <unk> <unk> . His second was The Entertainer , shot while he was appearing in Coriolanus ; the film was well received by the critics , but not as warmly as the stage show had been . The reviewer for The Guardian thought the performances were good , and wrote that Olivier " on the screen as on the stage , achieves the tour de force of bringing Archie Rice ... to life " . For his performance , Olivier was nominated for the Academy Award for Best Actor . He also made an adaptation of The Moon and <unk> in 1960 , winning an Emmy Award . 
- The Oliviers ' marriage was disintegrating during the late 1950s . While directing Charlton Heston in the 1960 play The <unk> , Olivier <unk> that " Vivien is several thousand miles away , trembling on the edge of a cliff , even when she 's sitting quietly in her own drawing room " , at a time when she was threatening suicide . In May 1960 divorce proceedings started ; Leigh reported the fact to the press and informed reporters of Olivier 's relationship with Plowright . The decree <unk> was issued in December 1960 , which enabled him to marry Plowright in March 1961 . A son , Richard , was born in December 1961 ; two daughters followed , <unk> Agnes Margaret — born in January 1963 — and Julie @-@ Kate , born in July 1966 . 
- In 1961 Olivier accepted the directorship of a new theatrical venture , the Chichester Festival . For the opening season in 1962 he directed two neglected 17th @-@ century English plays , John Fletcher 's 1638 comedy The <unk> and John Ford 's 1633 tragedy The Broken Heart , followed by Uncle Vanya . The company he recruited was forty strong and included Thorndike , <unk> , Redgrave , Athene <unk> , John Neville and Plowright . The first two plays were <unk> received ; the <unk> production attracted <unk> notices . The Times commented , " It is doubtful if the Moscow Arts Theatre itself could improve on this production . " The second Chichester season the following year consisted of a revival of Uncle Vanya and two new productions — Shaw 's Saint Joan and John Arden 's The <unk> <unk> . In 1963 Olivier received another BAFTA nomination for his leading role as a <unk> accused of sexually <unk> a student in the film <unk> of Trial . 
- 
- = = = National Theatre = = = 
- 
- 
- = = = = 1963 – 68 = = = = 
- 
- At around the time the Chichester Festival opened , plans for the creation of the National Theatre were coming to fruition . The British government agreed to release funds for a new building on the South Bank of the Thames . Lord Chandos was appointed chairman of the National Theatre Board in 1962 , and in August Olivier accepted its invitation to be the company 's first director . As his assistants , he recruited the directors John Dexter and William <unk> , with Kenneth Tynan as literary adviser or " <unk> " . <unk> the construction of the new theatre , the company was based at the Old Vic . With the agreement of both organisations , Olivier remained in overall charge of the Chichester Festival during the first three seasons of the National ; he used the festivals of 1964 and 1965 to give preliminary runs to plays he hoped to stage at the Old Vic . 
- The opening production of the National Theatre was Hamlet in October 1963 , starring Peter O 'Toole and directed by Olivier . O 'Toole was a guest star , one of occasional exceptions to Olivier 's policy of casting productions from a regular company . Among those who made a mark during Olivier 's directorship were Michael <unk> , Maggie Smith , Alan <unk> , Derek Jacobi and Anthony Hopkins . It was widely remarked that Olivier seemed reluctant to recruit his peers to perform with his company . Evans , Gielgud and Paul Scofield <unk> only briefly , and Ashcroft and Richardson never appeared at the National during Olivier 's time . Robert Stephens , a member of the company , observed , " Olivier 's one great fault was a paranoid jealousy of anyone who he thought was a rival " . 
- In his decade in charge of the National , Olivier acted in thirteen plays and directed eight . Several of the roles he played were minor characters , including a <unk> butler in <unk> 's A <unk> in Her <unk> and a <unk> solicitor in Maugham 's Home and Beauty ; the vulgar soldier Captain <unk> in <unk> 's 1706 comedy The <unk> Officer was a larger role but not the leading one . Apart from his Astrov in the Uncle Vanya , familiar from Chichester , his first leading role for the National was Othello , directed by Dexter in 1964 . The production was a box @-@ office success and was revived regularly over the next five seasons . His performance divided opinion . Most of the reviewers and theatrical colleagues praised it highly ; Franco <unk> called it " an anthology of everything that has been discovered about acting in the past three centuries . " <unk> voices included The Sunday Telegraph , which called it " the kind of bad acting of which only a great actor is capable ... near the <unk> of self @-@ parody " ; the director Jonathan Miller thought it " a condescending view of an Afro Caribbean person " . The burden of playing this demanding part at the same time as managing the new company and planning for the move to the new theatre took its toll on Olivier . To add to his load , he felt obliged to take over as <unk> in The Master <unk> when the ailing Redgrave withdrew from the role in November 1964 . For the first time Olivier began to suffer from stage <unk> , which plagued him for several years . The National Theatre production of Othello was released as a film in 1965 , which earned four Academy Award nominations , including another for Best Actor for Olivier . 
- During the following year Olivier concentrated on management , directing one production ( The <unk> ) , taking the comic role of the <unk> <unk> in <unk> 's Love for Love , and making one film , Bunny Lake is Missing , in which he and Coward were on the same bill for the first time since Private Lives . In 1966 , his one play as director was Juno and the <unk> . The Times commented that the production " <unk> one 's faith in the work as a masterpiece " . In the same year Olivier portrayed the Mahdi , opposite Heston as General Gordon , in the film <unk> . 
- In 1967 Olivier was caught in the middle of a confrontation between Chandos and Tynan over the latter 's proposal to stage Rolf <unk> 's Soldiers . As the play <unk> depicted Churchill as <unk> in the assassination of the Polish prime minister Władysław <unk> , Chandos regarded it as <unk> . At his urging the board unanimously vetoed the production . Tynan considered resigning over this interference with the management 's artistic freedom , but Olivier himself stayed firmly in place , and Tynan also remained . At about this time Olivier began a long struggle against a succession of illnesses . He was treated for prostate cancer and , during rehearsals for his production of <unk> 's Three Sisters he was hospitalised with pneumonia . He recovered enough to take the heavy role of Edgar in <unk> 's The Dance of Death , the finest of all his performances other than in Shakespeare , in Gielgud 's view . 
- 
- = = = = 1968 – 74 = = = = 
- 
- Olivier had intended to step down from the directorship of the National Theatre at the end of his first five @-@ year contract , having , he hoped , led the company into its new building . By 1968 because of bureaucratic delays construction work had not even begun , and he agreed to serve for a second five @-@ year term . His next major role , and his last appearance in a Shakespeare play , was as Shylock in The <unk> of Venice , his first appearance in the work . He had intended Guinness or Scofield to play Shylock , but stepped in when neither was available . The production by Jonathan Miller , and Olivier 's performance , attracted a wide range of responses . Two different critics reviewed it for The Guardian : one wrote " this is not a role which stretches him , or for which he will be particularly remembered " ; the other commented that the performance " ranks as one of his greatest achievements , involving his whole range " . 
- In 1969 Olivier appeared in two war films , portraying military leaders . He played Field Marshal French in the First World War film Oh ! What a <unk> War , for which he won another BAFTA award , followed by Air Chief Marshal Hugh <unk> in Battle of Britain . In June 1970 he became the first actor to be created a peer for services to the theatre . Although he initially declined the honour , Harold Wilson , the incumbent prime minister , wrote to him , then invited him and Plowright to dinner , and persuaded him to accept . 
- After this Olivier played three more stage roles : James Tyrone in Eugene O 'Neill 's Long Day 's Journey into Night ( 1971 – 72 ) , Antonio in Eduardo de Filippo 's Saturday , Sunday , Monday and John <unk> in Trevor <unk> 's The Party ( both 1973 – 74 ) . Among the roles he hoped to play , but could not because of ill @-@ health , was Nathan Detroit in the musical <unk> and Dolls . In 1972 he took leave of absence from the National to star opposite Michael <unk> in Joseph L. <unk> 's film of Anthony Shaffer 's <unk> , which The Illustrated London News considered to be " Olivier at his <unk> , eye @-@ rolling best " ; both he and <unk> were nominated for the Academy Award for Best Actor , losing to Marlon Brando in The <unk> . 
- The last two stage plays Olivier directed were Jean <unk> 's <unk> ( 1971 ) and Priestley 's Eden End ( 1974 ) . By the time of Eden End , he was no longer director of the National Theatre ; Peter Hall took over on 1 November 1973 . The succession was <unk> handled by the board , and Olivier felt that he had been eased out — although he had declared his intention to go — and that he had not been properly consulted about the choice of successor . The largest of the three theatres within the National 's new building was named in his honour , but his only appearance on the stage of the Olivier Theatre was at its official opening by the Queen in October 1976 , when he made a speech of welcome , which Hall privately described as the most successful part of the evening . 
- 
- = = = Later years ( 1975 – 89 ) = = = 
- 
- Olivier spent the last fifteen years of his life in securing his finances and dealing with worsening health , which included <unk> and <unk> , a <unk> muscle disorder . <unk> , and to secure financial security , he made a series of advertisements for <unk> cameras in 1972 , although he stipulated that they must never be shown in Britain ; he also took a number of cameo film roles , which were in " often undistinguished films " , according to Billington . Olivier 's move from leading parts to supporting and cameo roles came about because his poor health meant he could not get the necessary long insurance for larger parts , with only short engagements in films available . 
- Olivier 's <unk> meant he spent the last three months of 1974 in hospital , and he spent early 1975 slowly recovering and regaining his strength . When strong enough , he was contacted by the director John Schlesinger , who offered him the role of a Nazi <unk> in the 1976 film <unk> Man . Olivier shaved his <unk> and wore oversized glasses to <unk> the look of his eyes , in a role that the critic David Robinson , writing for The Times , thought was " strongly played " , adding that Olivier was " always at his best in roles that call for him to be <unk> or nasty or both " . Olivier was nominated for the Academy Award for Best Actor in a Supporting Role , and won the Golden Globe of the same category . 
- In the mid @-@ 1970s Olivier became increasingly involved in television work , a medium of which he was initially dismissive . In 1973 he provided the narration for a 26 @-@ episode documentary , The World at War , which chronicled the events of the Second World War , and won a second Emmy Award for Long Day 's Journey into Night ( 1973 ) . In 1975 he won another Emmy for Love Among the <unk> . The following year he appeared in adaptations of Tennessee Williams 's Cat on a Hot Tin Roof and Harold <unk> 's The Collection . In 1978 he appeared in the film The Boys from Brazil , playing the role of Ezra Lieberman , an ageing Nazi hunter ; he received his eleventh Academy Award nomination . Although he did not win the Oscar , he was presented with an Honorary Award for his lifetime achievement . 
- Olivier continued working in film into the 1980s , with roles in The Jazz Singer ( 1980 ) , <unk> ( 1981 ) , The Bounty ( 1984 ) and Wild <unk> II ( 1985 ) . He continued to work in television ; in 1981 he appeared as Lord <unk> in <unk> Revisited , winning another Emmy , and the following year he received his tenth and last BAFTA nomination in the television adaptation of John Mortimer 's stage play A Voyage Round My Father . In 1983 he played his last Shakespearean role as Lear in King Lear , for Granada Television , earning his fifth Emmy . He thought the role of Lear much less demanding than other tragic Shakespearean heroes : " No , Lear is easy . He 's like all of us , really : he 's just a stupid old <unk> . " When the production was first shown on American television , the critic Steve <unk> wrote : 
- Olivier seems to have thrown away technique this time — his is a <unk> pure Lear . In his final speech , over Cordelia 's lifeless body , he brings us so close to Lear 's <unk> that we can hardly bear to watch , because we have seen the last Shakespearean hero Laurence Olivier will ever play . But what a finale ! In this most sublime of plays , our greatest actor has given an <unk> performance . Perhaps it would be most appropriate to express simple gratitude . 
- The same year he also appeared in a cameo alongside Gielgud and Richardson in Wagner , with Burton in the title role ; his final screen appearance was as an old , wheelchair @-@ bound soldier in Derek <unk> 's 1989 film War <unk> . 
- After being ill for the last twenty @-@ two years of his life , Olivier died of renal failure on 11 July 1989 at his home near <unk> , West Sussex . His cremation was held three days later , before a funeral in Poets ' Corner of Westminster Abbey in October that year . 
- 
- = = Awards , honours and memorials = = 
- 
- In 1947 Olivier was appointed a Knight Bachelor , and in 1970 he was given a life peerage ; the Order of Merit was conferred on him in 1981 . He also received honours from foreign governments . In 1949 he was made Commander of the Order of the <unk> by the Danish government ; the French appointed him <unk> , Legion of Honour , in 1953 ; the Italian government created him Grande <unk> , Order of Merit of the Italian Republic , in 1953 ; and in 1971 he was granted the Order of Yugoslav Flag with Golden Wreath . 
- From academic and other institutions , Olivier received honorary <unk> from the university of <unk> , Massachusetts ( 1946 ) , Oxford ( 1957 ) and Edinburgh ( 1964 ) . He was also awarded the Danish <unk> Prize in 1966 , the Gold <unk> of the Royal Swedish Academy of Letters , History and Antiquities in 1968 ; and the Albert Medal of the Royal Society of Arts in 1976 . 
- For his work in films , Olivier received four Academy Awards : an honorary award for Henry V ( 1947 ) , a Best Actor award and one as producer for Hamlet ( 1948 ) , and a second honorary award in 1979 to recognise his lifetime of contribution to the art of film . He was nominated for nine other acting <unk> and one each for production and direction . He also won two British Academy Film Awards out of ten nominations , five Emmy Awards out of nine nominations , and three Golden Globe Awards out of six nominations . He was nominated once for a Tony Award ( for best actor , as Archie Rice ) but did not win . 
- In February 1960 , for his contribution to the film industry , Olivier was inducted into the Hollywood Walk of Fame , with a star at <unk> Hollywood Boulevard ; he is included in the American Theater Hall of Fame . In 1977 Olivier was awarded a British Film Institute Fellowship . 
- In addition to the naming of the National Theatre 's largest auditorium in Olivier 's honour , he is commemorated in the Laurence Olivier Awards , bestowed annually since 1984 by the Society of West End Theatre . In 1991 Gielgud unveiled a memorial stone commemorating Olivier in Poets ' Corner at Westminster Abbey . In 2007 , the centenary of Olivier 's birth , a life @-@ sized statue of him was unveiled on the South Bank , outside the National Theatre ; the same year the BFI held a retrospective season of his film work . 
- 
- = = <unk> and reputation = = 
- 
- Olivier 's acting technique was <unk> crafted , and he was known for changing his appearance considerably from role to role . By his own admission , he was addicted to extravagant make @-@ up , and unlike Richardson and Gielgud , he excelled at different voices and accents . His own description of his technique was " working from the outside in " ; he said , " I can never act as myself , I have to have a pillow up my <unk> , a false nose or a moustache or wig ... I cannot come on looking like me and be someone else . " <unk> described how at rehearsals Olivier " built his performance slowly and with immense application from a mass of tiny details " . This attention to detail had its critics : Agate remarked , " When I look at a watch it is to see the time and not to admire the mechanism . I want an actor to tell me Lear 's time of day and Olivier doesn 't . He bids me watch the wheels go round . " 
- Tynan remarked to Olivier , " you aren 't really a contemplative or philosophical actor " ; Olivier was known for the strenuous <unk> of his performances in some roles . He told Tynan this was because he was influenced as a young man by Douglas <unk> , Ramon Navarro and John Barrymore in films , and Barrymore on stage as Hamlet : " <unk> athletic . I admired that greatly , all of us did . ... One thought of oneself , <unk> , <unk> as I was , as a sort of <unk> . " According to Morley , Gielgud was widely considered " the best actor in the world from the neck up and Olivier from the neck down . " Olivier described the contrast thus : " I 've always thought that we were the reverses of the same coin ... the top half John , all spirituality , all beauty , all abstract things ; and myself as all earth , blood , humanity . " 
- Together with Richardson and Gielgud , Olivier was internationally recognised as one of the " great <unk> of theatrical knights " who dominated the British stage during the middle and later decades of the 20th century . In an obituary tribute in The Times , Bernard Levin wrote , " What we have lost with Laurence Olivier is glory . He reflected it in his greatest roles ; indeed he walked clad in it — you could practically see it glowing around him like a <unk> . ... no one will ever play the roles he played as he played them ; no one will replace the <unk> that he gave his native land with his genius . " Billington commented : 
- [ Olivier ] elevated the art of acting in the twentieth century ... principally by the overwhelming force of his example . Like <unk> , Kean , and Irving before him , he lent glamour and excitement to acting so that , in any theatre in the world , an Olivier night raised the level of expectation and sent spectators out into the darkness a little more aware of themselves and having experienced a transcendent touch of ecstasy . That , in the end , was the true measure of his greatness . 
- After Olivier 's death , Gielgud reflected , " He followed in the theatrical tradition of Kean and Irving . He respected tradition in the theatre , but he also took great delight in breaking tradition , which is what made him so unique . He was gifted , brilliant , and one of the great controversial figures of our time in theatre , which is a virtue and not a vice at all . " 
- Olivier said in 1963 that he believed he was born to be an actor , but his colleague Peter <unk> disagreed ; he commented that although Olivier 's great contemporaries were clearly <unk> for the stage , " Larry could have been a notable ambassador , a considerable minister , a <unk> <unk> . At his worst , he would have acted the parts more <unk> than they are usually lived . " The director David <unk> agreed that acting did not come instinctively to Olivier as it did to his great rivals . He observed , " Ralph was a natural actor , he couldn 't stop being a perfect actor ; Olivier did it through sheer hard work and determination . " The American actor William <unk> had a similar view : 
- Ironically enough , Laurence Olivier is less gifted than Marlon Brando . He is even less gifted than Richard Burton , Paul Scofield , Ralph Richardson and John Gielgud . But he is still the definitive actor of the twentieth century . Why ? Because he wanted to be . His achievements are due to dedication , scholarship , practice , determination and courage . He is the <unk> actor of our time . 
- In comparing Olivier and the other leading actors of his generation , <unk> wrote , " It is of course vain to talk of who is and who is not the greatest actor . There is simply no such thing as a greatest actor , or painter or composer " . Nonetheless , some colleagues , particularly film actors such as Spencer Tracy , Humphrey Bogart and Lauren <unk> , came to regard Olivier as the finest of his peers . Peter Hall , though acknowledging Olivier as the head of the theatrical profession , thought Richardson the greater actor . Others , such as the critic Michael <unk> , awarded the palm to Gielgud . Olivier 's claim to theatrical greatness lay not only in his acting , but as , in Hall 's words , " the supreme man of the theatre of our time " , pioneering Britain 's National Theatre . As Bragg identified , " no one doubts that the National is perhaps his most enduring monument " . 
- 
- = = Stage roles and filmography = = 
- 
- 
- 
- = Freakum Dress = 
- 
- " Freakum Dress " is a song by American singer and songwriter Beyoncé from her second solo studio album B 'Day ( 2006 ) . It was written by Beyoncé , Rich Harrison , and Makeba Riddick . " Freakum Dress " is similar to songs that Destiny 's Child used to record in the 1990s . The song is complete with whistles , cymbal dominated scatter rhythms and a beat , which is augmented by hi @-@ hats and <unk> keyboard pulses . In the song , Beyoncé advises women who have partners with straying eyes to put on <unk> dresses and grind on other guys in dance clubs , to regain their affections . 
- " Freakum Dress " was generally well received by music critics who complimented Beyoncé 's vocals as well as the assertiveness with which she delivers the lyrics . Many of them also noted that the beat of song <unk> very well with the vocal arrangement and the instruments used . The music video for the song was directed by Ray Kay , with co @-@ direction from Beyoncé , for the B 'Day Anthology Video Album ( 2007 ) . It features Beyoncé dancing with women of different ages , races , and sizes . Thirty metallic dresses were designed by Tina Knowles and were used in the production . Beyoncé explained that the main reason behind shooting a video for the song was to show what a " freakum dress " looks like . The song was part of the set lists during Beyoncé 's worldwide tours The Beyoncé Experience ( 2007 ) and I Am ... World Tour ( 2009 – 10 ) . Later , in 2012 , the song was performed during her revue Revel Presents : Beyoncé Live . 
- 
- = = Recording and conception = = 
- 
- " Freakum Dress " was conceived at Sony Music Studios , in New York City , when Beyoncé enlisted Harris to co @-@ produce for her album B 'Day ( 2006 ) . She and Harrison had previously collaborated on her 2003 single " Crazy in Love " . She arranged for Harrison , Sean Garrett and Rodney Jerkins to be given individual rooms at the studio . In this way , Beyoncé fostered " healthy competition " between the producers by going into each of their rooms and commenting on the " great beats " the others were creating . Roger Friedman of Fox News Channel noted that " Freakum Dress " and " <unk> Mama " ( 2006 ) , Harrison 's other contribution to B 'Day " fall short of originality but mimic the Chi Lites [ sic ] percussion section [ of " Crazy in Love " ] yet again " , adding , " Harrison is like the Indiana Jones of soul , constantly pulling out forgotten gems of the past for sampling [ ... ] You can 't help but think : Thank God someone wrote music in the past that can be repurposed now . " Harrison wrote " Freakum dress to demonstrate how a sassy <unk> item that can help recharge to a relationship " , with Beyoncé and Makeba Riddick also contributing In an interview with USA Today , Beyoncé talked about the content " Freakum Dress " , stating that an outfit which reminds of the best moments in a couple 's life , is a necessity for every woman 's wardrobe . 
- In June 2006 , Beyoncé invited Tamara <unk> of Billboard to a New York recording studio . There she premiered several songs from the album including " Ring the Alarm " ( 2006 ) and " Freakum Dress " , both were cited as possible second singles although in the end it was actually " Ring the Alarm " that became B 'Day 's second single . Beyoncé told <unk> that " Freakum Dress " was one of her favorite songs ever . 
- 
- = = Music and theme = = 
- 
- According to the sheet music published at Musicnotes.com by Hal Leonard Corporation , " Freakum Dress " is a moderate R & B song pacing in common time , written in the key of F major . The verses alternate from the chords of F ♯ and C. The track also draws from the hip hop , funk , and dance @-@ pop genres . Mike Joseph of PopMatters observed that the song shows influences by 1970s funk music , and contains limited elements of 1980s go @-@ go . According to Phil Harrison of <unk> , " Freakum Dress " consists of a steady " long crescendo , welding <unk> beats and a <unk> two @-@ note riff " , accompanied by several genres of music , which he qualified as " multi @-@ tracked " . Spence D. of IGN Music noted that the song consists of frequent whistles as well as crashing cymbal dominated scatter rhythms and a beat which fits the " powerful , loud , confident lines " in which Beyoncé asks for the attention of her man , and urges women to have a beautiful dress to <unk> up their sexual life . " Freakum Dress " opens with a spoken introduction . Throughout the song , Beyoncé sings her lines in an assertive manner on <unk> shattering hi @-@ hats " and <unk> keyboard pulses . 
- According to Joseph , " Freakum Dress " is thematically similar to " Bills , Bills , Bills " ( 1999 ) and " Say My Name " ( 2000 ) , from the Destiny 's Child era . Ann Powers of Los Angeles Times noted that " Freakum Dress " celebrates showing off . Jon Pareles of The New York Times viewed the concept of the song as not merely having a nice wardrobe to entice men , but it also serves as " a means of self @-@ assertion . " In the song , the female protagonist pulls out her best dress to remind her potentially wandering mate of what he is leaving at home . <unk> Rosen of Entertainment Weekly added that Beyoncé also seemingly gives professional advice to women on how to hold a man 's attention in a long @-@ term relationship . She sings : " I think I 'm ready / Been locked up in the house way too long / It 's time to get it , [ be ] cause once again he 's out doing wrong [ ... ] Wear very skimpy clothes ... " . Joseph commented that in the song , Beyoncé is capable of wearing anything to keep her man by her side rather than dumping him . Sarah Rodman of The Boston Globe added that after having <unk> her best dress , Beyoncé eyes other guys in dance clubs to make her own man jealous , in the hope of regaining his attention but she also makes sure that he really pays when he does her wrong . Beyoncé later refers to her " freakum dress " in " Jealous " , a track from her fifth studio album Beyoncé ( 2013 ) . 
- 
- = = Reception = = 
- 
- The song received mostly positive reviews . Phil Harrison of <unk> called " Freakum Dress " a magnificent production thanks to its vocal arrangements and commented that its beat can " drive the boys crazy . " Brian <unk> of Rolling Stone magazine wrote that even though " Freakum Dress " is less <unk> and <unk> produced than " Crazy In Love " ( 2003 ) and songs from the Destiny 's Child era , it remains a good track due to its highly energetic beat . Jaime Gill of Yahoo ! Music called the track " discordant " and " menacing " while Jon Pareles of The New York Times called it " <unk> " . On a separate review , Jon Pareles said that the song will remain as one of Beyoncé most memorable tracks thanks to its streak of rage which is " perfectly <unk> but <unk> " . Bill Lamb of About.com chose " Freakum Dress " as one of the three best songs on the entire record , and called it a powerful , emotionally intensive and energetic track . Caroline Sullivan of The Guardian called the song a " <unk> <unk> <unk> " that reminds girls of the significance of having a nice dress in their wardrobe . 
- Mike Joseph of PopMatters complimented the overall concept of the song but noted that the lyrics do not " radiate " enough warmth . Sal Cinquemani of Slant Magazine <unk> said that Beyoncé has added the term " Freakum Dress " " to the pop lexicon . " <unk> Gardner of USA Today said that " self @-@ assurance is evident on a tune on B 'Day called ' Freakum Dress ' " while another review by the staff members of the same magazine complimented the songs sexual imagery stating : " When Ms. Bootylicious [ Beyoncé ] sings of <unk> that <unk> into a ' Freakum Dress ' , the imagination runs <unk> than any video would . Darryl <unk> , writing for the Canadian website Jam ! , complimented the song 's " bashing beat and irresistible chorus " . Andy Kellman of Allmusic described " Freakum dress " as a " <unk> and marching " track . Calling " Freakum Dress " one of the best dance track that Beyoncé has ever sung , Norman <unk> of <unk> Magazine chose it as one of the standout songs of the album . While reviewing B 'Day , Chuck Arnold of People magazine wrote , " ' ladies ' anthem ' Freakum Dress ' finds Beyoncé working all her <unk> powers over some <unk> funk " . " Freakum Dress " reached number twenty @-@ five on the US Bubbling Under Hot 100 Singles chart issue dated September 9 , 2007 . The same day , it also charted on the US Bubbling Under R & B / Hip @-@ Hop Singles at number sixteen . 
- 
- = = Music video = = 
- 
- 
- = = = Concept and filming = = = 
- 
- The music video was co @-@ directed by Ray Kay and Beyoncé for the B 'Day Anthology Video Album , which was released the same month : it was one of eight videos shot in two weeks for the album . The choreography was done by Danielle <unk> and <unk> ' <unk> , who used a 1980 ’ s retro set . Beyoncé explained the concept of the video at MTV : " It 's probably the most flamboyant video , and the metallic dresses are so beautiful , they added so much color . I had to do a video for this song . Everyone wanted to know what a ' freakum dress ' was , and you can 't really explain it , you have to see it . Everyone has their own version , so we had so many women — of different races , sizes , shapes , ages — because we all have those dresses we pull out when we need to shut it down . " 
- After two weeks of shooting , Beyoncé decided to call her mother Tina . The latter designed thirty dresses for the video , with eight of them for her daughter . Due to limited time , certain dress were <unk> on the spot in approximately ten minutes each by taking fabric from one dress , making a slit in it , <unk> it and putting a belt on it . The glasses that Beyoncé wears in the video were borrowed from her make @-@ up artist , <unk> <unk> . The video was finished in about eighteen hours of filming and it features <unk> <unk> from America 's Next Top Model , Cycle 1 . Throughout the video , Beyoncé can be seen fixing her hair in a neon mirror and is surrounded by neon @-@ constructed doors , <unk> and <unk> . It premiered on BET 's 106 & Park and on American Music Channel , among others , before the release of the video anthology . 
- 
- = = = Synopsis and reception = = = 
- 
- The video begins with Beyoncé dancing in front of a target before moving to her putting on <unk> and lipstick next to two other men in a room full of neon framed mirrors . The men then pull a dress onto her and as the chorus begins , she walks by several women dancing on neon boxes before beginning to do a dance routine with them . As the chorus ends , she is shown surrounded by several men in a dark room and dancing in front of <unk> @-@ like walls . The video then moves to her walking down a neon catwalk . As the bridge starts , she begins doing a fierce dance routine , while constantly switching dresses . A scene is then shown with her dancers pretending to be <unk> swarming her with microphones , before ending with Beyoncé <unk> her hair in front of the target . Sal Cinquemani of Slant Magazine gave a negative review for the video , describing it as " <unk> edited " . He further commented that it " plays out like a cheap fashion show for House of <unk> instead of the couture @-@ as @-@ weapons anthem it should be " . 
- 
- = = Live performances = = 
- 
- Although Beyoncé did not perform " Freakum Dress " in any televised appearances , the song was part of her set list on The Beyoncé Experience . On August 5 , 2007 , Beyoncé performed the song at the Madison Square Garden in Manhattan , where she directly started the song with the line : " Stop , I ain ’ t ready yet — wait , let me fix my hair ... " . Jon Pareles of The New York Times praised the performance , stating : " Beyoncé needs no distractions from her singing , which can be airy or brassy , tearful or vicious , rapid @-@ fire with staccato syllables or sustained in <unk> <unk> . But she was in constant motion , <unk> in costumes [ ... ] " . <unk> Turner of The Courier @-@ Mail reported that tracks like " Freakum Dress " , " moved fans to screams of <unk> " . David Schmeichel of Jam ! wrote that Beyoncé performed a " <unk> " version of the song . Anthony <unk> of New Jersey On @-@ Line wrote that Beyoncé " boiled over " during the performance of the song . It was included as the third track on Beyoncé 's live album The Beyoncé Experience Live ( 2007 ) . 
- It was also part of the set list on the I Am ... World Tour . When Beyoncé performed the song in Sunrise , Florida on June 29 , 2009 , she was wearing a <unk> gold <unk> . As she sang , animated graphics of <unk> , <unk> and other club equipment were projected behind Beyoncé , her dancers and musicians . Beyoncé was accompanied by her two drummers , two <unk> , a <unk> , a horn section , three imposing backup vocalists called the <unk> and a lead guitarist , <unk> McGill . During the performance , she bent backwards at her guitarist 's feet . <unk> <unk> of The Sunday Telegraph praised Beyoncé 's dancing during the performance of the song on the I Am ... World Tour . " Freakum Dress " was included as the fourth track on the deluxe edition of I Am ... World Tour ( 2010 ) . According to Andy Kellman of Allmusic , the performance has a " hard rock overhaul " . 
- In May , 2012 , Beyoncé performed " Freakum Dress " during her Revel Presents : Beyoncé Live revue in Atlantic City , New Jersey , United States ' entertainment resort , hotel , casino and spa , Revel . While singing the song , Beyoncé was wearing a black dress and performed a " strut @-@ heavy <unk> " . Dan <unk> from The Philadelphia Inquirer noted that " her rock moves on songs like ' Freakum Dress , ' which find her facing off with a leather @-@ <unk> lead guitarist , tend to be of the screaming @-@ solo @-@ played @-@ on @-@ a @-@ Flying <unk> variety . " Ben <unk> of The New York Times mentioned " Freakum Dress " in the " almost continuous high point " of the concert . Jim Farber of Daily News wrote that " The first , and last parts of the show stressed the <unk> Beyoncé , told in bold songs " like " Freakum Dress " . Brad <unk> , writing for Complex noted that Beyoncé was " <unk> her <unk> at the audience " while performing the song . The performance of " Freakum Dress " was included on the live album Live in Atlantic City ( 2013 ) which was filmed during the revue . In 2013 the song was a part of the set list during The Mrs. Carter Show World Tour . 
- 
- = = Usage in media = = 
- 
- On June 24 , 2009 , American actress Cameron <unk> danced to " Freakum Dress " during the show It 's On with <unk> Chung . 
- <unk> lip <unk> along to " Freakum Dress " in the fifth episode of the first season of <unk> 's <unk> Race . 
- <unk> Sullivan references the song in her song " <unk> " . 
- 
- = = Chart performance = = 
- 
- 
- = = Credits and personnel = = 
- 
- Credits are taken from B 'Day liner notes . 
- Vocals : Beyoncé Knowles 
- Writing : Beyoncé Knowles , Rich Harrison , Makeba , Angela <unk> 
- <unk> : Rich Harrison , Beyoncé Knowles 
- Recording : Jim <unk> 
- <unk> by : Rob <unk> and Jamie <unk> 
- Mixing : Jason Goldstein & Rich Harrison 
- <unk> by : Steve <unk> 
- 
- 
- = Derfflinger @-@ class battlecruiser = 
- 
- The Derfflinger class was a class of three battlecruisers ( German : <unk> ) of the Imperial German Navy . The ships were ordered for the 1912 – 13 Naval Building Program of the German Imperial Navy as a reply to the Royal Navy 's three new Lion @-@ class battlecruisers that had been launched a few years earlier . The preceding Moltke class and the <unk> improved Seydlitz represented the end of the evolution of Germany 's first generation of battlecruisers . The Derfflinger class had considerable improvements , including a larger primary armament , all of which was mounted on the centerline , eliminating the restricted arc of the amidships turret . The ships were also larger than the preceding classes . The Derfflinger class used a similar propulsion system , and as a result of the increased displacement were slightly slower . 
- The class comprised three ships : Derfflinger , Lützow , and Hindenburg . All three of the ships saw active service with the High Seas Fleet during World War I. Derfflinger was commissioned shortly after the outbreak of war , and was present at most of the naval actions in the North Sea , including the battles of Dogger Bank and Jutland . Lützow was commissioned in August 1915 , and only participated in the raid on Yarmouth before being sunk at Jutland . Hindenburg was commissioned into the fleet in May 1917 , and saw no major action . Derfflinger and Hindenburg were interned at Scapa Flow following the armistice in November 1918 . Rear Admiral Ludwig von Reuter , who was in command of the interned High Seas Fleet , ordered the ships to be scuttled in an attempt to prevent their possible seizure by the Royal Navy . 
- 
- = = Design = = 
- 
- The Derfflinger @-@ class battlecruisers were a result of the fourth and final Naval Law , which was passed in 1912 . Admiral Alfred von Tirpitz used public <unk> over the British involvement in the <unk> Crisis of 1911 to pressure the <unk> into appropriating additional funds to the Navy . The Fourth Naval Law secured funding for three new dreadnoughts , two light cruisers , and an extra 15 @,@ 000 officers and men in the Navy for 1912 . The three dreadnoughts secured in the bill became Derfflinger , Lützow , and Hindenburg . Design work on the first two ships began in October 1910 and continued until June 1911 ; Hindenburg was built to a slightly modified design , which was created between May and October 1912 . 
- When design work began , the navy department was asked to submit new requirements to fix deficiencies found in the preceding battlecruiser classes , which primarily covered propulsion systems and the main armament . Previous battlecruisers used a four shaft arrangement for their engines ; reducing the number to three would allow the new ships to equip a diesel engine on the central shaft . This would substantially increase the cruising range , and would ease the transfer of fuel and reduce the number of crew needed to operate the ships ' machinery . The navy department also argued for an increase in the main battery guns , from 28 @-@ centimeter ( 11 in ) guns to 30 @.@ 5 cm ( 12 in ) weapons . This was because the latest British battleships had thicker main belt armor , up to 300 millimeters ( 12 in ) . Since the German battlecruisers were intended to fight in the line of battle , their armament needed to be sufficiently powerful to penetrate the armor of their British opponents . <unk> increases were managed by reducing the number of guns , from 10 to 8 — the increase in gun caliber added only 36 tons to the ships ' displacement . Tirpitz argued against the increase in gun caliber , for he thought the 28 cm gun was powerful enough . 
- A new construction technique was employed to save weight . Previous battlecruisers were built with a combination of transverse and longitudinal steel frames ; the Derfflinger @-@ class ships <unk> with the transverse frames and used only the longitudinal ones . This enabled the ship to retain structural strength and a lower weight . As with all preceding capital ships , the outer hull spaces between the hull wall and the torpedo bulkhead were used for coal storage . 
- On 1 September 1910 , the design board chose the 30 @.@ 5 cm , to be mounted in four twin turrets on the centerline of the ship . The armor layout was kept the same as in Seydlitz . In the meantime , pressure from the British public and media had forced the British Parliament to step up ship building . Kaiser Wilhelm II requested that the build time for the new battlecruisers be reduced to two years each , as opposed to three years . This proved <unk> , because neither the armor or armament firms could supply the necessary materials according to an expedited schedule . 
- 
- = = = General characteristics = = = 
- 
- Derfflinger and Lützow were 210 m ( <unk> ft 0 in ) long at the waterline and 210 @.@ 40 m ( 690 ft 3 in ) long overall . Hindenburg was slightly longer , at 212 @.@ 50 m ( 697 ft 2 in ) at the waterline and 212 @.@ 80 m ( <unk> ft 2 in ) overall . All three ships had a beam of 29 m ( 95 ft 2 in ) , and a draft of between 9 @.@ 20 m ( 30 ft 2 in ) forward and 9 @.@ 57 m ( 31 ft 5 in ) aft . The first two ships were designed to displace 26 @,@ 600 tonnes ( 26 @,@ 200 long tons ) with a standard load , and up to 31 @,@ 200 tonnes ( 30 @,@ 700 long tons ) at combat weight . Hindenburg displaced slightly more , at 26 @,@ <unk> tonnes ( 26 @,@ 521 long tons ) standard and 31 @,@ 500 tonnes ( 31 @,@ 000 long tons ) fully laden . The ships ' hulls were constructed from longitudinal steel frames , over which the outer hull plates were riveted . Derfflinger 's hull contained 16 <unk> compartments , though Lützow and Hindenburg had an additional seventeenth compartment . All three ships had a double bottom that ran for 65 % of the length of the hull . This was a decrease from preceding German battlecruisers , which had a double bottom for at least 75 % of the hull . 
- The ships were regarded as excellent sea boats by the German navy . The Derfflinger @-@ class ships were described as having had gentle motion , though they were " wet " at the casemate deck . The ships lost up to 65 % speed with the twin rudders hard over , and <unk> up to 11 degrees . This was greater than any of the preceding battlecruiser designs , and as a result , anti @-@ roll tanks were fitted to Derfflinger . The three ships had a <unk> height of 2 @.@ 60 m ( 8 ft 6 in ) . The standard crew for one of the vessels was 44 officers and 1 @,@ <unk> men ; when serving as the flagship for the I Scouting Group , the ships carried an additional 14 officers and 62 men . The <unk> carried smaller craft , including one picket boat , three barges , two launches , two <unk> , and two <unk> . 
- 
- = = = Machinery = = = 
- 
- By the time construction work on Derfflinger began , it was determined that the diesel engine was not ready for use . Instead , the plan to use a three @-@ shaft system was abandoned and the ships reverted to the standard four @-@ shaft arrangement . Each of the three ships were equipped with two sets of marine @-@ type turbines ; each set drove a pair of 3 @-@ <unk> screws that were 3 @.@ 90 m ( 12 ft 10 in ) in diameter on Derfflinger and Lützow and 4 m ( 13 ft 1 in ) in diameter on Hindenburg . Each set consisted of a <unk> and low @-@ pressure turbine — the high @-@ pressure machines drove the outer shafts while the low @-@ pressure turbines turned the inner pair . Steam was supplied to the turbines from 14 coal @-@ fired marine @-@ type double boilers and eight oil @-@ fired marine @-@ type double @-@ ended boilers . Each ship was equipped with a pair of <unk> @-@ electric generators and a pair of diesel @-@ electric generators that provided a total of 1 @,@ 660 <unk> at 220 volts . Each ship was equipped with two rudders . 
- The engines for first two ships were designed to provide 62 @,@ 138 shaft horsepower ( 46 @,@ <unk> kW ) , at 280 <unk> per minute . This would have given the two ships a top speed of 26 @.@ 5 knots ( 49 @.@ 1 km / h ; 30 @.@ 5 mph ) . During trials , Derfflinger 's engines achieved 75 @,@ <unk> shp ( 56 @,@ 364 kW ) , but a top speed of 25 @.@ 5 knots ( 47 @.@ 2 km / h ; 29 @.@ 3 mph ) . Lützow 's engines reached 79 @,@ 880 shp ( 59 @,@ 570 kW ) and a top speed of 26 @.@ 4 knots ( 48 @.@ 9 km / h ; 30 @.@ 4 mph ) . Hindenburg 's power plant was rated at 71 @,@ 015 shp ( 52 @,@ <unk> kW ) at 290 rpm , for a top speed of 27 knots ( 50 km / h ; 31 mph ) . On trials she reached 94 @,@ 467 shp ( 70 @,@ <unk> kW ) and 26 @.@ 6 knots ( 49 @.@ 3 km / h ; 30 @.@ 6 mph ) . Derfflinger could carry 3 @,@ 500 t ( 3 @,@ 400 long tons ) of coal and 1 @,@ 000 t ( 980 long tons ) of oil ; at a cruising speed of 14 knots ( 26 km / h ; 16 mph ) , she had a range of 5 @,@ 600 nautical miles ( 10 @,@ 400 km ; 6 @,@ 400 mi ) . Lützow carried 3 @,@ 700 t ( 3 @,@ 600 long tons ) of coal and 1 @,@ 000 tons of oil , though she had no advantage in range over her sister Derfflinger . Hindenburg also stored 3 @,@ 700 tons of coal , as well as 1 @,@ 200 t ( 1 @,@ 200 long tons ) of oil ; her range at 14 knots was rated at 6 @,@ 100 nautical miles ( 11 @,@ 300 km ; 7 @,@ 000 mi ) . 
- 
- = = = Armament = = = 
- 
- The Derfflinger @-@ class ships were armed with eight 30 @.@ 5 cm ( 12 in ) SK L / 50 guns in four twin gun turrets , two forward of the main superstructure in a superfiring pair and two to the rear of the ship , in a similar arrangement . The guns were housed in <unk> C / 1912 mounts on the first two ships , and in <unk> C / 1913 mounts on Hindenburg . The turrets were trained with electric <unk> , while the guns were elevated <unk> , up to 13 @.@ 5 degrees . The guns fired 405 @.@ 5 @-@ kilogram ( <unk> lb ) armor @-@ piercing shells at a muzzle velocity of <unk> meters per second ( 2 @,@ 805 ft / s ) . At 13 @.@ 5 degrees , the shells could hit targets out to 18 @,@ 000 m ( 20 @,@ 000 yd ) . The turrets were modified in 1916 to increase the elevation maximum to 16 degrees . This <unk> increased the range to 20 @,@ 400 m ( 22 @,@ 300 yd ) . The ships carried 720 shells , or 90 per gun ; each gun was supplied with 65 armor @-@ piercing ( AP ) shells and 25 semi @-@ AP shells for use against targets with less armor protection . The 30 @.@ 5 cm gun had a rate of fire of between 2 – 3 shells per minute , and was expected to fire 200 shells before replacement was necessary . The guns were also capable of firing 405 @.@ 9 kg ( <unk> @.@ 8 lb ) high explosive shells . The shells were loaded with two <unk> C / 12 propellant charges : a main charge in a brass cartridge that weighed 91 kg ( 201 lb ) and a fore charge in a silk bag that weighed 34 @.@ 5 kg ( 76 lb ) . The propellant magazines were located underneath the shell rooms for the two forward turrets as well as the rear superfiring turret ; the arrangement was reversed for the rearmost turret . 
- The ships were designed to carry fourteen 15 cm ( 5 @.@ 9 in ) SK L / 45 guns , mounted in casemates along the superstructure . Because Derfflinger had to be fitted with anti @-@ roll tanks , two of the casemated guns had to be removed , to allow enough room in the hull . Lützow and Hindenburg were equipped with the designed number of guns . Each gun was supplied with 160 rounds , and had a maximum range of 13 @,@ 500 m ( 14 @,@ 800 yd ) , though this was later extended to 16 @,@ 800 m ( 18 @,@ 400 yd ) . The guns had a sustained rate of fire of five to seven rounds per minute . The shells were 45 @.@ 3 kg ( 99 @.@ 8 lb ) , and were loaded with a 13 @.@ 7 kg ( 31 @.@ 2 lb ) <unk> / 12 propellant charge in a brass cartridge . The guns fired at a muzzle velocity of 835 meters per second ( 2 @,@ 740 ft / s ) . The guns were expected to fire around 1 @,@ 400 shells before they needed to be replaced . 
- The three ships carried a variety of 8 @.@ 8 cm ( 3 @.@ 5 in ) SK L / 45 guns in several <unk> . The Derfflinger @-@ class ships were initially equipped with eight of these weapons , all in single mounts ; four were placed in the forward superstructure and four in the aft superstructure . The ships also carried four 8 @.@ 8 cm Flak L / 45 anti @-@ aircraft guns , which were <unk> around the forward funnel , with the exception of Lützow , which carried the Flak guns around the rear funnel . After 1916 , the four 8 @.@ 8 cm guns in the forward superstructure were removed . The Flak guns were <unk> in <unk> C / 13 mountings , which allowed depression to − 10 degrees and elevation to 70 degrees . These guns fired 9 kg ( 19 @.@ 8 lb ) shells , and had an effective ceiling of 9 @,@ 150 m ( 30 @,@ <unk> ft 8 in ) at 70 degrees . 
- The ships were also armed with submerged torpedo tubes in their hulls . Derfflinger was equipped with four 50 cm tubes ; the later ships were armed with more powerful 60 cm weapons . The tubes were arranged with one in the bow , one in the stern , and two on the broadside . Derfflinger 's 50 cm torpedoes were the <unk> type , 7 @.@ 02 m ( 276 in ) long and armed with a 195 kg ( 430 lb ) <unk> warhead . The torpedo had a range of 4 @,@ 000 m ( 4 @,@ 370 yd ) when set at a speed of 37 knots , and up to 9 @,@ 300 m ( 10 @,@ 170 yd ) at 27 knots . The 60 cm torpedoes were the <unk> type , which were 8 m long and carried a 210 kg ( 463 lb ) <unk> warhead . The torpedoes had a range of 6 @,@ 000 m ( 6 @,@ 550 yd ) when set at a speed of 36 knots ; at a reduced speed of 30 knots , the range increased significantly to 14 @,@ 000 m ( 15 @,@ 310 yd ) . 
- 
- = = = Armor = = = 
- 
- The Derfflinger @-@ class ships were protected with <unk> cemented steel armor , as was the standard for German warships of the period . They had an armor belt that was 300 mm ( 12 in ) thick in the central citadel of the ship , where the most important parts of the ship were . This included the ammunition magazines and the machinery spaces . The belt was reduced in less critical areas , to 120 mm ( 4 @.@ 7 in ) forward and 100 mm ( 3 @.@ 9 in ) aft . The belt tapered down to 30 mm ( 1 @.@ 2 in ) at the bow , though the stern was not protected by armor at all . A 45 mm ( 1 @.@ 8 in ) thick torpedo bulkhead ran the length of the hull , several meters behind the main belt . The main armored deck ranged in thickness from 30 mm thick in less important areas , to 80 mm ( 3 @.@ 1 in ) in the sections that covered the more critical areas of the ship . 
- The forward conning tower was protected with heavy armor : the sides were 300 mm thick and the roof was 130 mm ( 5 @.@ 1 in ) thick . The rear conning tower was less well armored ; its sides were only 200 mm ( 7 @.@ 9 in ) thick and the roof was covered with 50 mm ( 2 @.@ 0 in ) of armor plate . The main battery gun turrets were also heavily armored : the turret sides were 270 mm ( 11 in ) thick and the roofs were 110 mm ( 4 @.@ 3 in ) thick . On Hindenburg , the thickness of the turret roofs was increased to 150 mm ( 5 @.@ 9 in ) . The 15 cm guns had 150 mm @-@ worth of armor plating in the casemates ; the guns themselves had 70 mm ( 2 @.@ 8 in ) thick shields to protect their crews from shell splinters . 
- 
- = = Construction = = 
- 
- Of the three ships in its class , only Derfflinger was ordered as an addition to the fleet , under the provisional name " K " . The other two ships were to intended to replace obsolete vessels ; Lützow was ordered as Ersatz <unk> Augusta for the elderly protected cruiser <unk> Augusta and the contract for Hindenburg was issued under the provisional name Ersatz <unk> , to replace the protected cruiser <unk> . 
- Derfflinger was constructed at <unk> & <unk> in Hamburg under construction number 213 . She was the least expensive of the three ships , at a cost of 56 million gold marks . The ship was ready to be launched on 14 June 1913 , but during the ceremony , one of the wooden <unk> upon which the hull rested became jammed . It took until 12 July for her to enter the water . She was commissioned into the High Seas Fleet on 1 September 1914 , shortly after the outbreak of World War I. Lützow was built at the <unk> dockyard in Danzig under construction number <unk> , at the cost of 58 million gold marks . The ship was launched on 29 November 1913 , and after lengthy trials , commissioned on 8 August 1915 . Hindenburg , the final member of the class , was built at the Imperial Dockyard in Wilhelmshaven , under construction number 34 . The ship was built at a cost of 59 million gold marks , the most expensive of the three vessels . She was launched on 1 August 1915 and commissioned on 10 May 1917 . 
- 
- = = Units = = 
- 
- 
- = = = SMS Derfflinger = = = 
- 
- Named after Georg von Derfflinger , a German field marshal during the Thirty Years ' War , Derfflinger was commissioned on 1 September 1914 . A dockyard crew transferred the ship from Hamburg to Kiel , via the <unk> . The ship was assigned to the I Scouting Group at the end of October . Damage to the ship 's turbines sustained during trials prevented the ship from seeing active service until 16 November . On 15 December , the ship took part in the raid on Scarborough , Hartlepool and Whitby . She was also present during the battle of Dogger Bank on 24 January 1915 . The ship was hit once by a 13 @.@ 5 @-@ inch shell from one of the British battlecruisers ; in response , she heavily damaged HMS Lion . Repair work was completed by 16 February , but Derfflinger 's starboard turbine was accidentally damaged on 28 June , and the ship was again in the dockyard until August . On 24 April 1916 , Derfflinger took part in the bombardment of Yarmouth . 
- On 31 May , Derfflinger was heavily engaged during the Battle of Jutland , as the second ship in the German battlecruiser line . She sustained 21 major hits during the battle , but dealt considerable damage to the British battlecruiser force as well . At 16 : 26 , HMS Queen Mary sank after a magazine explosion that tore the ship apart ; she had been targeted with a hail of heavy @-@ caliber gunfire from Derfflinger and Seydlitz . Two hours later , at 18 : 30 , HMS Invincible suffered a similar fate , though Derfflinger was assisted by her sister Lützow . During the engagement , Derfflinger had both of her rear turrets knocked out by British gunfire . Her crew suffered 157 men killed and 26 wounded , which was the highest casualty figure for any German ship not sunk . The <unk> of the vessel earned her the nickname " Iron Dog " from her British adversaries . Repair work lasted until 15 October , during which the ship had her pole mast removed and replaced with a tripod mast . The ship conducted training operations until November , at which point she returned to active duty with the fleet . 
- Following the German capitulation in November 1918 , Derfflinger was interned with a significant portion of the High Seas Fleet in Scapa Flow . On 21 June 1919 , with the guard ships of the Royal Navy out on maneuvers , Rear Admiral Ludwig von Reuter ordered that the fleet be scuttled . The resulting scuttling of the German fleet saw some 66 vessels of various types sunk . Among those was Derfflinger , which sank at 14 : 45 . The ship was raised in 1939 to be broken up for scrap metal , but the outbreak of World War II intervened . The ship , which remained capsized , was anchored off the island of Risa until 1946 , at which point she was sent to <unk> Port , where she was broken up . The ship 's bell was delivered to the German Federal Navy on 30 August 1965 . 
- 
- = = = SMS Lützow = = = 
- 
- Lützow was named after Ludwig Adolf Wilhelm von Lützow , a Prussian lieutenant @-@ general who fought during the Napoleonic Wars . The ship was commissioned on 8 August 1915 , and then underwent trials . On 25 October , while still running sea trials , Lützow 's port low pressure turbine were severely damaged . She was sent to Kiel for repairs , which lasted until late January 1916 . The ship went on additional trials that lasted until 19 February . Lützow was by then fully operational , and assigned to the I Scouting Group on 20 March 1916 . She took part in two fleet advances , on 25 March and 21 – 22 April , without any major incidents . The following day , on 23 April , Lützow , along with her sister Derfflinger and the battlecruisers Seydlitz , Moltke , and Von der <unk> , bombarded Yarmouth . While en route to the target , Vice Admiral Franz von Hipper 's flagship Seydlitz was heavily damaged by mines . As a result , Lützow was transferred to the role of squadron flagship . During the operation , the German battlecruisers encountered British light forces , and a running battle ensued . Lützow engaged the light cruiser HMS Conquest and hit her several times . 
- At the Battle of Jutland , she was the first ship in the German line , and Hipper 's flagship , and drew fire from the British battlecruisers which included hits below her waterline . Shortly after the start of the battlecruiser action , Lützow hit her opponent Lion several times ; one hit knocked out Lion 's " Q " turret , and the resulting magazine fire nearly destroyed the ship . Shortly after 19 : 00 , the armored cruisers Defence and Warrior inadvertently ran into the German line ; Lützow opened fire immediately , followed by several German dreadnoughts . In a hail of shells , Defence 's ammunition magazines detonated and the ship was sunk . At around the same time , the fresh battlecruisers of the 3rd Battlecruiser Squadron engaged their German opposites . Between 19 : 26 and 19 : 34 , Lützow sustained four 12 @-@ inch shell hits in her bow from the British battlecruisers ; these eventually proved to be fatal . Despite this , at 19 : 30 , the combined fire of Lützow and her sister Derfflinger destroyed the battlecruiser Invincible . By 20 : 15 , Lützow had been hit five more times , including hits on her two forward turrets . 
- By 22 : 15 , Lützow had shipped nearly 2 @,@ 400 tons of water , and the ship was dangerously down by the bows . After midnight , attempts were made to steer the ship in reverse . This failed when the bow became submerged enough to bring the stern out of the water ; by 02 : 20 , the screws and both rudders were coming out of the water and the ship was no longer able to steer . The order to abandon ship was given , and at 02 : 47 , Lützow was sunk by the torpedo boat <unk> . The ship was lost because the flooding in the bow could not be controlled ; the forward pump system failed and the central system could not keep up with the rising water . The crew was picked up by four torpedo boats that had been escorting the crippled battlecruiser ; during the battle the ship suffered 116 men killed . 
- 
- = = = SMS Hindenburg = = = 
- 
- Hindenburg was the last battlecruiser completed for the Imperial German Navy , and as such had a very short career . She was commissioned 10 May 1917 , and was fully operational by 20 October 1917 , too late to see any major action in World War I. On 17 November Hindenburg and Moltke , along with the light cruisers of the II Scouting Group , were acting as distant support for German minesweepers off the German coast when they were attacked by British battlecruisers . The raid was brief ; by the time Hindenburg and Moltke arrived on the scene , the British ships had broken off the attack and withdrawn . Six days later , Hindenburg replaced Seydlitz as flagship of the I Scouting Group . On 23 April 1918 , the ship took part in an abortive fleet advance into the North Sea that attempted to intercept an Allied convoy . Moltke sustained mechanical damage while en route , and as a result , Vice Admiral Hipper decided to cancel the operation . On 11 August , Hipper was promoted to Admiral and given command of the entire High Seas Fleet . Rear Admiral Ludwig von Reuter replaced Hipper as the commander of the I Scouting Group ; he raised his flag on Hindenburg the following day . 
- Hindenburg was interned at Scapa Flow , along with her sister Derfflinger and the rest of the German battlecruisers . She was scuttled on 21 June 1919 , and sank at 17 : 00 . Several unsuccessful attempts to raise her were made ; on 23 July 1930 the ship was finally raised . From 1930 to 1932 she was scrapped at Rosyth . Her bell was presented to the German Federal Navy on 28 May 1959 . 
- 
- 
- = O 'Brien @-@ class destroyer = 
- 
- The O 'Brien class of destroyers was a class of six ships designed by and built for the United States Navy shortly before the United States entered World War I. The O 'Brien class was the third of five classes of destroyers that were known as the " thousand tonners " , because they were the first U.S. destroyers over 1 @,@ 000 long tons ( 1 @,@ <unk> t ) displacement . 
- The design of what became the O 'Brien class was the result of discussions between the General Board of the United States Navy and the U.S. Navy 's Bureau of Ordnance . What resulted was a design that was an incremental development of the Aylwin class , which itself was similar to the first of the thousand tonners , the Cassin class ( which displaced about a third more than the preceding <unk> class ) . The key difference in the O 'Brien class was the increase in torpedo size , going up to 21 inches ( 533 mm ) from the preceding classes ' 18 @-@ inch ( 457 mm ) torpedoes . 
- The ships had a median displacement of 1 @,@ 050 long tons ( 1 @,@ 070 t ) , were just over 305 feet ( 93 m ) in length , and had a beam of about 31 feet ( 9 @.@ 4 m ) . All of the ships had two direct @-@ drive steam turbines and a combination of other engines for cruising at speeds less than 15 knots ( 28 km / h ) . All of the ships were designed for a maximum speed of 29 knots ( 54 km / h ) . As built , they were armed with four 4 @-@ inch ( 102 mm ) guns and had four twin 21 @-@ inch torpedo tubes with a load of eight torpedoes , but all were later equipped with depth charges . The ships were built by four private American shipyards — Bath Iron Works , Fore River Shipbuilding Company , New York Shipbuilding Corporation , and William Cramp and Sons — and were laid down between September and November 1913 ; launched between April 1914 and February 1915 ; and commissioned into the U.S. Navy between June 1914 and August 1915 . 
- All six ships operated in the Atlantic or Caribbean until the U.S. entrance into World War I in April 1917 , when all six were sent overseas to Queenstown , Ireland , for convoy escort duties . Several of the ships rescued passengers and crew from ships sunk by U @-@ boats , and several had encounters with U @-@ boats themselves ; Nicholson helped sink U @-@ 58 in November 1917 , the first U @-@ boat sunk by the U.S. Navy . All six members of the class had returned to the United States in January 1919 and were decommissioned by June 1922 . In 1924 , two of the six — Ericsson and McDougal — were commissioned into the United States Coast Guard to help enforce Prohibition as a part of the " Rum Patrol " . They were returned to U.S. Navy custody in 1932 and 1933 , respectively . All six ships had been sold for scrapping by June 1936 . 
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- = = Background = = 
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- For the 1913 fiscal year , the General Board of the United States Navy determined that six destroyers would be authorized . The design for Destroyers No. 51 through No. 56 — what became the O 'Brien class , was to closely follow the design of the Cassin and Aylwin classes from fiscal year 1912 . The chief of the U.S. Navy 's Bureau of Ordnance ( <unk> ) suggested that the new destroyers be equipped with ten torpedo tubes and urged that the new 21 @-@ inch ( 533 mm ) torpedo be adopted . The General Board agreed in principle , adopting the 21 @-@ inch torpedo tubes , but eliminated <unk> 's proposed centerline torpedo tubes and keeping the number of tubes at eight , the same as the Cassin and Aylwin classes . The additional weight of the larger torpedoes , 5 long tons ( 5 @.@ 1 t ) of top weight , was offset by reducing the planned two aft @-@ facing guns to a single one . This gave the class four 4 @-@ inch ( 102 mm ) guns , which , again , matched the battery of the Cassin and Aylwin classes . The design for the O 'Brien class was approved on 20 August 1912 , and authorized by Congress on 4 March 1913 . 
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- = = Design = = 
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- As built , the O 'Brien @-@ class ships were 305 feet 5 inches ( 93 @.@ 09 m ) in length ( overall ) , were 31 feet 2 inches ( 9 @.@ 50 m ) abeam , and had a standard draft of 9 feet 6 inches ( 2 @.@ 90 m ) . The hull shape featured the distinctive high forecastle typical of U.S. destroyer classes since the 1908 – 09 Smith class , the first destroyers designed to be truly ocean @-@ going vessels . The ships displaced between 1 @,@ 020 and 1 @,@ 090 long tons ( 1 @,@ <unk> and 1 @,@ 110 t ) with a median of 1 @,@ 050 long tons ( 1 @,@ 070 t ) . 
- The ships were equipped with two propeller shafts and two direct @-@ drive , <unk> steam turbines fed by four White @-@ Forster boilers . The power plant of the ships generated 17 @,@ 000 shaft horsepower ( 13 @,@ 000 kW ) which drive the ships to the design speed of 29 knots ( 54 km / h ) . Because of inherent <unk> of turbines at low speeds , all of the ships were equipped with supplemental cruising engines for travel at speeds under 15 knots ( 28 km / h ) . All except Cushing were equipped with supplemental triple @-@ expansion reciprocating engines : O 'Brien , Nicholson , and Winslow each had a pair of reciprocating engines for cruising ; McDougal and Ericsson were outfitted with only one . Instead of reciprocating engines , Cushing was equipped with a pair of geared steam turbines for cruising . 
- The main battery of the O 'Brien class consisted of four 4 @-@ inch ( 100 mm ) / 50 Mark 9 guns , with each gun weighing in excess of 6 @,@ 100 pounds ( 2 @,@ 800 kg ) . The guns fired 33 @-@ pound ( 15 kg ) armor @-@ piercing projectiles at 2 @,@ 900 feet per second ( 880 m / s ) . At an elevation of 20 ° , the guns had a range of 15 @,@ 920 yards ( 14 @,@ 560 m ) . 
- The O 'Brien ships were also equipped with four twin 21 @-@ inch ( 533 mm ) torpedo tubes , for a total load of eight Mark 8 torpedoes . Although the General Board had called for two anti @-@ aircraft guns for the O 'Brien class , they were not originally outfitted with the weapons ; the later Sampson class was the first American destroyer class so armed . Likewise , there is no record of any of the O 'Brien ships being outfitted with mine @-@ laying apparatus . During World War I , most American destroyers were used in anti @-@ submarine warfare roles , and were equipped with depth charges and delivery systems , such as Y @-@ guns and depth charge <unk> . O 'Brien @-@ class ships were equipped with depth charges during the war , but no specific mentions of the types of depth charges used or delivery system are recorded in secondary sources . 
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- = = = Comparisons with other " thousand tonners " = = = 
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- The " thousand tonners " were the 26 United States Navy destroyers of five classes — Cassin , Aylwin , O 'Brien , Tucker , and Sampson — so named because they were the first U.S. Navy destroyers to have displacements greater than 1 @,@ 000 long tons . The Cassin class , the first of the thousand tonners , displaced about a third more than the preceding <unk> class . The introduction of the thousand tonners led to the <unk> and other older , smaller displacement destroyers of previous classes to be <unk> called " <unk> " , a nickname also commonly applied to the Ford Model T. 
- The O 'Brien class was the third of the five classes of " thousand tonners " . The earlier <unk> ( DD @-@ 43 to DD @-@ 46 ) and Aylwin @-@ class ( DD @-@ 47 to DD @-@ 50 ) ships were about the same length as the O 'Brien ships and all had median displacements in the range of 1 @,@ 020 – 1 @,@ 050 long tons ( 1 @,@ <unk> – 1 @,@ 070 t ) ; the later <unk> ( DD @-@ 57 to DD @-@ 62 ) and Sampson @-@ class ( DD @-@ 63 to DD @-@ 68 ) ships were about 10 feet ( 3 @.@ 0 m ) longer and had median displacements of 1 @,@ 090 – 1 @,@ 100 long tons ( 1 @,@ 110 – 1 @,@ 120 t ) . All five classes were armed with four 4 @-@ inch ( 102 mm ) guns , but the torpedo size and complement varied . All were equipped with four twin torpedo tubes loaded with eight torpedoes except for the <unk> ( which had four triple tubes carrying twelve torpedoes ) , but the Cassin and Aylwin classes were armed with 18 @-@ inch ( 457 mm ) torpedoes . The O 'Brien ships were the first armed with the <unk> @-@ inch ( 533 mm ) Mark 8 torpedoes ; the Tucker and Sampson ships also used the 21 @-@ inch torpedoes . The <unk> were the only group originally equipped with anti @-@ aircraft guns , a pair of 1 @-@ pounder ( 0 @.@ 45 kg ) guns with a caliber of 37 mm ( 1 @.@ 46 in ) . 
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- = = Construction = = 
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- The construction of the six O 'Brien @-@ class ships was allocated to four U.S. <unk> . William Cramp constructed a trio of O 'Brien destroyers , while the Fore River Shipbuilding Company , Bath Iron Works , and New York Shipbuilding Corporation built one ship each . The <unk> for all six ships were laid down between July and November 1913 , with McDougal being the first and Ericsson the last . All were launched between April 1915 and January 1915 , with McDougal again being the first and Winslow being the last . McDougal was commissioned in June 1914 , two months after her launch ; the rest were commissioned between April and August 1915 , with Cushing the final ship to enter service . The cost of each ship for hull and machinery was $ 790 @,@ 000 . 
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- = = Ships in class = = 
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- All six members of the class served in the Atlantic or Caribbean throughout their U.S. Navy careers . In October 1916 , with the United States still neutral in World War I , five of the six class members ( all except Nicholson ) were among the U.S. destroyers sent out to rescue survivors from five ships torpedoed by German submarine U @-@ 53 off the Lightship Nantucket . 
- After the United States entered the war in April 1917 , all six class members were sent overseas to Queenstown , Ireland , for convoy escort and anti @-@ submarine duties . McDougal was in the first group of six American destroyers that arrived at Queenstown on 4 May ; Ericsson and Winslow followed in the second group , which arrived thirteen days later , and Cushing , Nicholson , and O 'Brien in the third group , a week after that . Several of the ships had encounters with U @-@ boats during the war : Nicholson , working with Fanning in November 1917 , helped to sink U @-@ 58 , which was the first U @-@ boat sunk by the U.S. Navy ; earlier , in June , O 'Brien had depth @-@ charged U @-@ 16 and was thought by the British Admiralty to have seriously damaged the U @-@ boat , but post @-@ war analysis of records showed that U @-@ 16 survived the attack and returned safely to port . 
- All six ships returned to the United States in January 1919 and served in various roles over the next two years . Cushing was decommissioned in August 1920 , followed by Nicholson and McDougal in May 1922 , and the remaining three in the following month . In June 1924 , two of the six ships — Ericsson and McDougal — were reactivated for service with the United States Coast Guard 's " Rum Patrol " . Ericsson was returned to the U.S. Navy in May 1932 , and McDougal in June 1933 ; both were sold for scrapping in 1934 . O 'Brien was sold for scrapping in 1935 , and the remaining three in June 1936 . 
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- = = = USS O 'Brien ( DD @-@ 51 ) = = = 
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- USS O 'Brien ( Destroyer No. 51 / DD @-@ 51 ) was laid down by William Cramp and Sons of Philadelphia in September 1913 and launched in July 1914 . The ship was the second U.S. Navy vessel named in honor of Jeremiah O 'Brien and his five brothers , Gideon , John , William , Dennis , and Joseph who , together on the sloop Unity , captured a British warship during the American Revolutionary War . 
- After O 'Brien 's May 1915 commissioning , she sailed off the east coast and in the Caribbean . She was one of the U.S. destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 . After the United States entered World War I in April 1917 , O 'Brien was sent overseas to patrol the Irish Sea out of Queenstown , Ireland . 
- After returning to the United States in January 1919 , O 'Brien returned to European waters in May to serve as one of the picket ships for the NC @-@ type seaplanes in the first aerial crossing of the Atlantic . O 'Brien was decommissioned at Philadelphia in June 1922 . She was struck from the Naval Vessel Register in March 1935 sold for scrapping in April . 
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- = = = USS Nicholson ( DD @-@ 52 ) = = = 
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- USS Nicholson ( Destroyer No. 52 / DD @-@ 52 ) was laid down by William Cramp and Sons of Philadelphia in September 1913 and launched in August 1914 . The ship was the second U.S. Navy vessel named in honor of five members of the Nicholson family who gave distinguished service in the American Revolutionary War , the War of 1812 , and the American Civil War : brothers James , Samuel , and John Nicholson ; William Nicholson , son of John ; and James W. Nicholson , grandson of Samuel . 
- After Nicholson 's April 1915 commissioning , she sailed off the east coast and in the Caribbean . After the United States entered World War I in April 1917 , Nicholson was sent overseas to patrol the Irish Sea out of Queenstown , Ireland . In October 1917 , Nicholson steamed to the rescue of SS J. L. <unk> , driving off German submarine U @-@ 62 , which had shelled the American cargo ship for over three hours . In November , Nicholson and another U.S. destroyer , Fanning , were responsible for sinking German submarine U @-@ 58 , the first submarine taken by U.S. forces during the war . In September 1918 , Nicholson helped drive off U @-@ 82 after that U @-@ boat had torpedoed the American troopship Mount Vernon off the coast of France . 
- Upon returning to the United States after the war , Nicholson was placed in reduced commission in November 1919 . She was decommissioned at Philadelphia in May 1922 . She was struck from the Naval Vessel Register in January 1936 sold for scrapping in June . 
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- = = = USS Winslow ( DD @-@ 53 ) = = = 
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- USS Winslow ( Destroyer No. 53 / DD @-@ 53 ) was laid down by William Cramp and Sons of Philadelphia in October 1913 and launched in February 1915 . The ship was the second U.S. Navy vessel named in honor of John <unk> Winslow , a U.S. Navy officer notable for sinking the Confederate commerce <unk> CSS Alabama during the American Civil War . 
- After Winslow 's August 1915 commissioning , she sailed off the east coast and in the Caribbean . She was one of the U.S. destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 . After the United States entered World War I in April 1917 , Winslow was sent overseas to patrol the Irish Sea out of Queenstown , Ireland . Winslow made several unsuccessful attacks on U @-@ boats , and rescued survivors of several ships sunk by the German craft . 
- Upon returning to the United States after the war , Winslow was placed in reduced commission in December 1919 . She was decommissioned at Philadelphia in June 1922 . In November she dropped her name to free it for a new destroyer of the same name , becoming known only as DD @-@ 53 . She was struck from the Naval Vessel Register in January 1936 sold for scrapping in June . 
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- = = = USS McDougal ( DD @-@ 54 ) = = = 
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- USS McDougal ( Destroyer No. 54 / DD @-@ 54 ) was laid down by Bath Iron Works of Bath , Maine , in July 1913 and launched in April 1914 . The ship was the second U.S. Navy vessel named in honor of David Stockton McDougal , a U.S. Navy officer notable for his leadership during an 1863 battle off Japan while in command of Wyoming . 
- After McDougal 's June 1914 commissioning , she sailed off the east coast and in the Caribbean . She was one of the U.S. destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 , and carried 6 crewmen from a sunken Dutch cargo ship to Newport , Rhode Island . After the United States entered World War I in April 1917 , McDougal was part of the first U.S. destroyer squadron sent overseas . Patrolling the Irish Sea out of Queenstown , Ireland , McDougal made several unsuccessful attacks on U @-@ boats , and rescued survivors of ships sunk by the German craft . After a collision with a British cargo ship in February 1918 , McDougal was under repair until mid @-@ July , and afterwards , operated out of Brest , France . 
- Upon returning to the United States after the war , McDougal conducted operations with the destroyers of the Atlantic Fleet until August 1919 , when she was placed in reserve , still in commission . After a brief stint of operations in mid 1921 , she was placed in reserve until she was decommissioned at Philadelphia in May 1922 . In June 1924 , Ericsson was transferred to the United States Coast Guard to help enforce Prohibition as a part of the " Rum Patrol " . She operated under the name <unk> McDougal ( CG @-@ 6 ) until May 1933 , when she was returned to the Navy . In November she dropped her name to free it for a new destroyer of the same name , becoming known only as DD @-@ 54 . She was struck for the Naval Vessel Register in July 1934 sold for scrapping in August . 
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- = = = USS Cushing ( DD @-@ 55 ) = = = 
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- USS Cushing ( Destroyer No. 55 / DD @-@ 55 ) was laid down by the Fore River Shipbuilding Company of <unk> , Massachusetts , in September 1913 and launched in January 1915 . The ship was the second U.S. Navy vessel named in honor of William B. Cushing , a U.S. Navy officer best known for sinking the Confederate ironclad warship CSS <unk> during the American Civil War . 
- After Cushing 's August 1915 commissioning , she sailed off the east coast and in the Caribbean . She was one of the U.S. destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 . After the United States entered World War I in April 1917 , Cushing was sent overseas to patrol the Irish Sea out of Queenstown , Ireland . Cushing made several unsuccessful attacks on U @-@ boats , and rescued survivors of several ships sunk by the German craft . 
- Upon returning to the United States after the war , Cushing was placed in reserve in reduced commission . She was decommissioned at Philadelphia in August 1920 . She was struck for the Naval Vessel Register in January 1936 and was sold for scrapping in June . 
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- = = = USS Ericsson ( DD @-@ 56 ) = = = 
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- USS Ericsson ( Destroyer No. 56 / DD @-@ 56 ) was laid down by the New York Shipbuilding of Camden , New Jersey , in November 1913 and launched in August of the following year . The ship was the second U.S. Navy vessel named in honor of John Ericsson , the Swedish @-@ born builder of the ironclad warship USS <unk> during the American Civil War . 
- After Ericsson 's May 1916 commissioning , she sailed off the east coast and in the Caribbean . She was one of the U.S. destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 , and carried 81 passengers from a sunken British ocean liner to Newport , Rhode Island . After the United States entered World War I in April 1917 , Ericsson was part of the first U.S. destroyer squadron sent overseas . Patrolling the Irish Sea out of Queenstown , Ireland , Ericsson made several unsuccessful attacks on U @-@ boats , and rescued survivors of several ships sunk by the German craft . 
- Upon returning to the United State after the war , Ericsson conducted operations with the destroyers of the Atlantic Fleet until August 1919 , when she was placed in reserve , still in commission . After a brief stint of operations in mid 1921 , she was placed in reserve until she was decommissioned at Philadelphia in June 1922 . In June 1924 , Ericsson was transferred to the United States Coast Guard to help enforce Prohibition as a part of the " Rum Patrol " . She operated under the name <unk> Ericsson ( CG @-@ 5 ) until May 1932 , when she was returned to the Navy . She was sold for scrap in August 1934 . 
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- = Who Am I ( Casting Crowns song ) = 
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- " Who Am I " is a song recorded by Christian rock band Casting Crowns . Written by Mark Hall and produced by Mark A. Miller and Steven Curtis Chapman , it was released on February 22 , 2004 , as the second single from the band 's 2003 self @-@ titled debut album . A pop rock and adult contemporary ballad , the song is based around the piano and utilizes orchestral sounds . Lyrically , the song is centered on worshiping God . The song received positive reviews from music critics upon its release , with several regarding it as one of the best songs on their debut album . 
- " Who Am I " received the awards for Song of the Year and Pop / Contemporary Recorded Song of the Year at the 36th GMA Dove Awards , and it was also nominated for Worship Song of the Year . It achieved success on Christian radio , topping the Billboard Hot Christian Songs and Hot Christian AC charts as well as simultaneously peaking atop the Radio & Records Christian AC , Christian CHR , and <unk> charts . It has been certified Gold by the Recording Industry Association of America ( RIAA ) , signifying sales of over 500 @,@ 000 digital downloads . Casting Crowns has performed the song in concert as well as at special events , and re @-@ recorded the song in 2013 for their acoustic album The Acoustic Sessions : Volume One . 
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- = = Background and composition = = 
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- According to Casting Crowns ' lead singer Mark Hall , the idea for " Who Am I " came while he was driving home with his wife and children one night . Hall , who was having personal worship time during the drive , recounts that he wondered " Who am I to think I can just call up to God whenever I want , from the middle of nowhere , and expect Him to hear me ? " Hall says " immediately I started thinking I 'm a new creation , I 'm more than a <unk> ... I 'm [ also ] grass , that is rises up and is gone in a day " . In an interview , he commented that " me being a <unk> is true , but at the same time I need to understand that my life is a vapor , and me being able to even pray to [ God ] is because of what he 's done for me " . " Who Am I " was produced by Mark A. Miller and Steven Curtis Chapman . It was recorded at <unk> In The Dark Studio in Decatur , Georgia and Zoo Studio in Franklin , Tennessee , and it was engineered by Matt Goldman and Sam Hewitt . 
- " Who Am I " is a song with a length of five minutes and thirty @-@ five seconds . According to the sheet music published by Musicnotes.com , it is in set common time in the key of B major and has a tempo of 66 beats per minute . Mark Hall 's vocal range in the song spans from the low note of G ♯ 3 to the high note of F ♯ 3 . " Who Am I " has been described as a pop rock and adult contemporary ballad . Based around the piano and featuring orchestral sounds , it begins slowly before building up into a musical crescendo . Lyrically , the song is centered in praising God , relating a theme of <unk> without Christ . 
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- = = Reception = = 
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- " Who Am I " received mostly positive reviews from music critics . Andy <unk> of CCM Magazine described the song as a " standout " from the album . Tom Lennie of Cross Rhythms noted it as one of the best songs from their debut album . In 2007 , <unk> <unk> of Christianity Today described it as one of the many Casting Crowns songs that have become " beloved anthems of the Christian faith " . In 2013 , Roger Gelwicks described it as an " AC radio favorite " but felt it was " dated " . At the 36th GMA Dove Awards , " Who Am I " won the awards for Song of the Year and Pop / Contemporary Recorded Song of the Year ; It was also nominated for Worship Song of the Year . 
- " Who Am I " was released to Christian adult contemporary , Christian CHR , and Soft AC / <unk> radio on January 22 , 2004 as the second single from the band 's debut album . It spent six weeks atop the Billboard Christian Songs songs chart and two weeks atop the Hot Christian AC chart . It also simultaneously topped the Radio & Records Christian AC , Christian CHR , and <unk> charts . In July 26 , 2011 , it was certified Gold by the Recording Industry Association of America ( RIAA ) , signifying sales of over 500 @,@ 000 digital downloads . 
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- = = Live performances and other uses = = 
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- On October 5 , 2003 at the band 's church in Atlanta , Casting Crowns performed " Who Am I " . This performance was included on their 2004 live album Live from Atlanta . The band performed the song on May 6 , 2004 at the <unk> Broadcast Concert of Prayer event , held at <unk> International <unk> in front of nearly 10 @,@ 000 people ; the band 's performance , along with the rest of those participating in the three @-@ hour event , was <unk> nationally on television , radio , and the internet . The band performed the song on October 27 , 2004 at the Pontiac <unk> in Detroit , Michigan as part of a Bush @-@ <unk> ' 04 campaign rally . The event was attended by around 20 @,@ 000 people . 
- At a concert at the Giant Center in <unk> , Pennsylvania held on April 1 , 2005 , Casting Crowns performed it as the third song on their set list . On July 10 , 2005 at a concert at <unk> High School in <unk> , Michigan , Casting Crowns performed it as their third song in their set list . At a concert at Continental Airlines Arena in East Rutherford , New Jersey , Casting Crowns performed an acoustic version of the song . At a concert on November 12 , 2005 in Bethlehem , Pennsylvania 's <unk> Arena , Casting Crowns performed the song as the thirteenth song in their set . The band performed the song at a February 5 , 2010 concert at the <unk> Center in Kansas City , Missouri . In 2009 , the band was invited to perform in North Korea at the 2009 Spring Friendship Art Festival ; the band 's set list at the festival included a Korean @-@ language version of " Who Am I " . 
- " Who Am I " was included on the 2004 compilation album WOW Hits 2005 , the 2006 compilation album WOW Worship : Aqua , and the 2008 compilation album WOW <unk> , as well as the band 's 2004 live album Live from Atlanta . The band re @-@ recorded the song for their 2013 acoustic album The Acoustic Sessions : Volume One ; unlike the original , which featured Mark Hall on lead vocals , the acoustic version features Megan Garrett on lead vocals . 
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- = = Credits and personnel = = 
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- Credits taken from Allmusic . 
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- = = Charts = = 
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- = = = Certifications = = = 
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- = = Release and radio history = = 
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- = We 'll Always Have Paris ( Star Trek : The Next Generation ) = 
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- " We 'll Always Have Paris " is the 24th episode of the first season of the American science fiction television series Star Trek : The Next Generation , first aired on May 2 , 1988 , in broadcast syndication . The story and script were both created by Deborah Dean Davis and Hannah Louise Shearer , and the episode was directed by Robert Becker . 
- Set in the 24th century , the series follows the adventures of the crew of the Starfleet starship Enterprise @-@ D. In this episode , the crew respond to a distress call from Dr. Paul Manheim ( Rod Loomis ) . While the crew must deal with the results of Manheim 's <unk> experiments with time , Captain Picard ( Patrick Stewart ) must deal with his former love Jenice ( Michelle Phillips ) , who is also Manheim 's wife . 
- The story of the episode was influenced by the film Casablanca , and was affected by the timing of the 1988 Writers Guild of America strike . Because of the strike , the script was written in five days and it was only when it was filmed that it was discovered to be incomplete . Shearer was not happy with the result and felt that the on @-@ screen chemistry of Stewart and Phillips was lacking . Reviews of the episode have been mixed , with one reviewer <unk> it up by saying " there ’ s nothing I can point to at this episode and say is wrong , but it ’ s one of the more forgettable episodes . " 
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- = = Plot = = 
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- The Enterprise , along with other ships in the sector , experience a localized time @-@ distortion , and soon after receive a distress @-@ call from Dr. Paul Manheim in a nearby system . Commander Riker ( Jonathan Frakes ) recalls that Manheim was ejected from the Federation Science Institute for conducting unauthorized experiments . They find the distress signal coming from a facility on a <unk> surrounded by a force @-@ field . When they make contact with the facility , a woman requests help to save her husband , Dr. Manheim , and lowers the shields . 
- The two are brought aboard and while Dr. Crusher ( Gates McFadden ) tends to Dr. Manheim , who is having <unk> , Picard discovers his wife is Jenice , Picard 's former love from before he decided to join Starfleet . Jenice warns that her husband was working privately in his laboratory , but that she didn 't know what he was working on . She also alerts the crew to numerous security protocols that he has installed at the facilities . As the crew prepares to send an away @-@ team to investigate the laboratory , they experience more time distortions , described by Data ( Brent <unk> ) as " Manheim effects " . In one instance Picard , Riker and Data enter a <unk> only to see their past <unk> conversing outside of the lift . The crew find that they cannot complete a transporter beam to the facility due to the <unk> . 
- Dr. Manheim recovers long enough to explain that he was doing experiments involving time , gravity , and funnels to other <unk> , and suspects his last experiment is running out @-@ of @-@ hand . Manheim explains that he is trapped between two dimensions and Data determines that the experiment must be shut down during a time <unk> or else it will simply grow larger . Manheim provides the crew with the correct coordinates to beam down to avoid the security fields . Picard admits to Jenice that he worried about losing her again after he left her in Paris , and vows to correct Dr. Manheim 's experiment . 
- As he is affected less by the distortions , Data is sent down alone and <unk> the remaining security measures before entering Manheim 's laboratory . He finds a column of energy emanating from a dimensional matrix , the source of the time distortions . Data , though briefly affected by the time distortions , is able to add anti @-@ matter to the matrix , causing the matrix to stabilize and halt the time distortions . Dr. Manheim fully recovers , and he and Jenice thank Picard and the crew for their help . Picard and Jenice use the holodeck to recreate one more encounter at a Paris café , before she returns with her husband to the planet . 
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- = = Production = = 
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- " We 'll Always Have Paris " is named in reference to the Humphrey Bogart and Ingrid <unk> film Casablanca . As well as the title of the episode and the love triangle in the story , the Blue Parrot <unk> from the film is directly mentioned by Captain Picard . Casablanca was also the basis for the earlier Star Trek : The Original Series novel The <unk> <unk> , and would be so again in the Deep Space Nine episode " <unk> and Loss " . 
- The story was originally pitched by Hannah Louise Shearer and Deborah Dean Davis , who were also given the job of developing the script . The writing of the episode suffered from the timing of the 1988 Writers Guild of America strike , with Shearer and Dean Davis completing the script in five days . The premise of the story was a combination of their idea of a story " about a professor whose work wasn 't appreciated and had to go off on his own " and that of Maurice Hurley who wanted a story with a time anomaly . The final draft was dated 22 February and featured numerous differences from the filmed version , including Jenice being called Laura , a number of different interactions for the crew , and Riker , Worf and Data all being involved in shutting down Manheim 's equipment . The script was finalised a week before the episode was filmed , but the writers strike caused further issues when it was discovered during filming that the scene where Data was to fix the time distortions hadn 't been completed . 
- Robert Legato and Rick Berman spent forty minutes on the telephone with Shearer during the strike . While she refused to write the lines during the strike , Berman suggested dialogue and Shearer would give opinions with Legato taking down handwritten notes . Legato decided on the directing notes himself , and used a whip pan shot instead of an effects shot to film the three <unk> as director Robert Becker had never used effects shots previously . Shearer wasn 't happy with the final result , saying " we were writing the most romantic episode in the world " but that " it was toned down 75 % " . 
- Shearer later complained of a lack of chemistry between Michelle Phillips and Patrick Stewart , which Phillips blamed on the conflicted nature of the character in that she was committed to her husband but also wanted to see Picard once more . Phillips , a Star Trek fan , is better known for being a member of the 1960s group The <unk> & the <unk> . Rod Loomis ' previous science fiction outing was in Bill and Ted 's <unk> Adventure as Sigmund Freud , and Lance <unk> would later return in " The Icarus Factor " where his transporter chief gained the name Ensign Herbert . The image of 24th century Paris was a matte painting which was re @-@ used in Star Trek VI : The <unk> Country where it was hung outside the office of the Federation president . 
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- = = Reception = = 
- 
- " We 'll Always Have Paris " aired in broadcast syndication during the week commencing May 5 , 1988 . It received Nielsen ratings of 9 @.@ 7 , reflecting the percentage of all households watching the episode during its timeslot . This was the same ratings received as the previous episode , " Skin of Evil " . 
- Several reviewers re @-@ watched the episode after the end of the series . Michelle Erica Green reviewed the episode on behalf of TrekNation in September 2007 , comparing certain elements of the episode to Casablanca and the series finale " All Good Things ... " . These included the ending where time distortions caused Data to be replicated three time in " We 'll Always Have Paris " compared to the similar situation with three <unk> in " All Good Things ... " . Overall she thought that the episode was better than she recalled previously . Jamahl Epsicokhan at his website " Jammer 's Reviews " described the episode as too simple , especially the ending which he said was " they give Data a <unk> , which he sticks into a hall of mirrors ; problem solved " . 
- Zack <unk> , reviewing the episode for The A.V. Club in May 2010 , said that he enjoyed the " time <unk> " in the episode , but that he wasn 't a fan of the romance between Jenice and Picard . He also felt that there wasn 't much follow up on Manheim 's experiments and that he seemed to be left to cause a more serious accident in the future . Keith DeCandido watched the episode for <unk> in July 2011 . He thought that Michele Phillips was " incredibly <unk> " , and said , " there ’ s nothing I can point to at this episode and say is wrong , but it ’ s one of the more forgettable episodes " , giving it a score of four out of ten . 
- 
- = = Home media release = = 
- 
- The first home media release of " We 'll Always Have Paris " was on VHS cassette was on July 1 , 1992 in the United States and Canada . The episode was later included on the Star Trek : The Next Generation season one DVD box set , released in March 2002 , and was released as part of the season one Blu @-@ ray set on July 24 , 2012 . 
- 
- 
- = Burn = 
- 
- A burn is a type of injury to skin , or other tissues , caused by heat , cold , electricity , chemicals , friction , or radiation . Most burns are due to heat from hot liquids , solids , or fire . Among women in many areas of the world the risk is related to the use of open cooking fires or unsafe cook <unk> . <unk> and smoking are other risk factors . Burns can also occur as a result of self harm or violence between people . 
- Burns that affect only the superficial skin layers are known as superficial or first @-@ degree burns . They appear red without blisters and pain typically lasts around three days . When the injury extends into some of the underlying skin layer , it is a partial @-@ thickness or second @-@ degree burn . <unk> are frequently present and they are often very painful . Healing can require up to eight weeks and scarring may occur . In a full @-@ thickness or third @-@ degree burn , the injury extends to all layers of the skin . Often there is no pain and the burn area is stiff . Healing typically does not occur on its own . A fourth @-@ degree burn additionally involves injury to deeper tissues , such as muscle , tendons , or bone . The burn is often black and frequently leads to loss of the burned part . 
- Burns are generally <unk> . Treatment depends on the severity of the burn . <unk> burns may be managed with little more than simple pain medication , while major burns may require prolonged treatment in specialized burn centers . <unk> with tap water may help pain and decrease damage ; however , prolonged cooling may result in low body temperature . Partial @-@ thickness burns may require cleaning with soap and water , followed by dressings . It is not clear how to manage blisters , but it is probably reasonable to leave them intact if small and drain them if large . Full @-@ thickness burns usually require surgical treatments , such as skin <unk> . Extensive burns often require large amounts of intravenous fluid , due to <unk> fluid leakage and tissue swelling . The most common complications of burns involve infection . <unk> <unk> should be given if not up to date . 
- In 2013 , fire and heat resulted in 35 million injuries . This resulted in about 2 @.@ 9 million <unk> and 238 @,@ 000 dying . Most deaths due to burns occur in the developing world , particularly in Southeast Asia . While large burns can be fatal , treatments developed since 1960 have improved outcomes , especially in children and young adults . In the United States , approximately 96 % of those admitted to a burn center survive their injuries . Burns occur at similar frequencies in men and women . The long @-@ term outcome is related to the size of burn and the age of the person affected . 
- 
- = = Signs and symptoms = = 
- 
- The characteristics of a burn depend upon its depth . <unk> burns cause pain lasting two or three days , followed by <unk> of the skin over the next few days . Individuals suffering from more severe burns may indicate discomfort or complain of feeling pressure rather than pain . Full @-@ thickness burns may be entirely insensitive to light touch or puncture . While superficial burns are typically red in color , severe burns may be pink , white or black . Burns around the mouth or <unk> hair inside the nose may indicate that burns to the <unk> have occurred , but these findings are not definitive . More worrisome signs include : shortness of breath , <unk> , and <unk> or <unk> . <unk> is common during the healing process , occurring in up to 90 % of adults and nearly all children . <unk> or <unk> may persist for a prolonged period of time after an electrical injury . Burns may also produce emotional and psychological distress . 
- 
- = = Cause = = 
- 
- Burns are caused by a variety of external sources classified as thermal ( heat @-@ related ) , chemical , electrical , and radiation . In the United States , the most common causes of burns are : fire or flame ( 44 % ) , scalds ( 33 % ) , hot objects ( 9 % ) , electricity ( 4 % ) , and chemicals ( 3 % ) . Most ( 69 % ) burn injuries occur at home or at work ( 9 % ) , and most are accidental , with 2 % due to assault by another , and 1 @-@ 2 % resulting from a suicide attempt . These sources can cause inhalation injury to the <unk> and / or lungs , occurring in about 6 % . 
- Burn injuries occur more commonly among the poor . Smoking is a risk factor , although alcohol use is not . Fire @-@ related burns are generally more common in colder climates . Specific risk factors in the developing world include cooking with open fires or on the floor as well as developmental disabilities in children and chronic diseases in adults . 
- 
- = = = <unk> = = = 
- 
- In the United States , fire and hot liquids are the most common causes of burns . Of house fires that result in death , smoking causes 25 % and heating devices cause 22 % . Almost half of injuries are due to efforts to fight a fire . <unk> is caused by hot liquids or gases and most commonly occurs from exposure to hot drinks , high temperature tap water in <unk> or showers , hot cooking oil , or steam . <unk> injuries are most common in children under the age of five and , in the United States and Australia , this population makes up about two @-@ thirds of all burns . Contact with hot objects is the cause of about 20 @-@ 30 % of burns in children . Generally , scalds are first- or second @-@ degree burns , but third @-@ degree burns may also result , especially with prolonged contact . <unk> are a common cause of burns during holiday seasons in many countries . This is a particular risk for adolescent males . 
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- = = = Chemical = = = 
- 
- Chemicals cause from 2 to 11 % of all burns and contribute to as many as 30 % of burn @-@ related deaths . Chemical burns can be caused by over 25 @,@ 000 substances , most of which are either a strong base ( 55 % ) or a strong acid ( 26 % ) . Most chemical burn deaths are secondary to ingestion . Common agents include : sulfuric acid as found in toilet <unk> , sodium <unk> as found in <unk> , and halogenated <unk> as found in paint <unk> , among others . <unk> acid can cause particularly deep burns that may not become symptomatic until some time after exposure . <unk> acid may cause the breakdown of significant numbers of red blood cells . 
- 
- = = = Electrical = = = 
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- Electrical burns or injuries are classified as high voltage ( greater than or equal to 1000 volts ) , low voltage ( less than 1000 volts ) , or as flash burns secondary to an electric arc . The most common causes of electrical burns in children are electrical <unk> ( 60 % ) followed by electrical outlets ( 14 % ) . Lightning may also result in electrical burns . Risk factors for being struck include involvement in outdoor activities such as mountain climbing , golf and field sports , and working outside . <unk> from a lightning strike is about 10 % . 
- While electrical injuries primarily result in burns , they may also cause fractures or <unk> secondary to blunt force trauma or muscle <unk> . In high voltage injuries , most damage may occur internally and thus the extent of the injury cannot be judged by examination of the skin alone . Contact with either low voltage or high voltage may produce cardiac <unk> or cardiac arrest . 
- 
- = = = Radiation = = = 
- 
- Radiation burns may be caused by protracted exposure to ultraviolet light ( such as from the sun , tanning <unk> or arc welding ) or from ionizing radiation ( such as from radiation therapy , X @-@ rays or radioactive fallout ) . Sun exposure is the most common cause of radiation burns and the most common cause of superficial burns overall . There is significant variation in how easily people <unk> based on their skin type . Skin effects from ionizing radiation depend on the amount of exposure to the area , with hair loss seen after 3 Gy , <unk> seen after 10 Gy , wet skin <unk> after 20 Gy , and <unk> after 30 Gy . <unk> , if it occurs , may not appear until some time after exposure . Radiation burns are treated the same as other burns . <unk> burns occur via thermal heating caused by the <unk> . While exposures as short as two seconds may cause injury , overall this is an uncommon occurrence . 
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- = = = Non accidental = = = 
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- In those hospitalized from scalds or fire burns , 3 – 10 % are from assault . Reasons include : child abuse , personal disputes , spousal abuse , elder abuse , and business disputes . An immersion injury or immersion <unk> may indicate child abuse . It is created when an <unk> or the lower body ( <unk> or <unk> ) is held under the surface of hot water . It typically produces a sharp upper border and is often symmetrical . Other high @-@ risk signs of potential abuse include : <unk> burns , the absence of splash marks , a burn of uniform depth , and association with other signs of neglect or abuse . 
- Bride burning , a form of domestic violence , occurs in some cultures , such as India where women have been burned in revenge for what the husband or his family consider an inadequate dowry . In Pakistan , acid burns represent 13 % of intentional burns , and are frequently related to domestic violence . Self @-@ <unk> ( setting oneself on fire ) is also used as a form of protest in various parts of the world . 
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- = = <unk> = = 
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- At temperatures greater than 44 ° C ( 111 ° F ) , proteins begin losing their three @-@ dimensional shape and start breaking down . This results in cell and tissue damage . Many of the direct health effects of a burn are secondary to disruption in the normal functioning of the skin . They include disruption of the skin 's sensation , ability to prevent water loss through evaporation , and ability to control body temperature . <unk> of cell membranes causes cells to lose potassium to the spaces outside the cell and to take up water and sodium . 
- In large burns ( over 30 % of the total body surface area ) , there is a significant inflammatory response . This results in increased leakage of fluid from the <unk> , and subsequent tissue <unk> . This causes overall blood volume loss , with the remaining blood suffering significant plasma loss , making the blood more concentrated . <unk> blood flow to organs such as the <unk> and gastrointestinal tract may result in renal failure and stomach <unk> . 
- Increased levels of <unk> and <unk> can cause a hypermetabolic state that can last for years . This is associated with increased cardiac output , metabolism , a fast heart rate , and poor immune function . 
- 
- = = Diagnosis = = 
- 
- Burns can be classified by depth , mechanism of injury , extent , and associated injuries . The most commonly used classification is based on the depth of injury . The depth of a burn is usually determined via examination , although a <unk> may also be used . It may be difficult to accurately determine the depth of a burn on a single examination and repeated examinations over a few days may be necessary . In those who have a headache or are dizzy and have a fire @-@ related burn , carbon monoxide poisoning should be considered . Cyanide poisoning should also be considered . 
- 
- = = = Size = = = 
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- The size of a burn is measured as a percentage of total body surface area ( TBSA ) affected by partial thickness or full thickness burns . First @-@ degree burns that are only red in color and are not blistering are not included in this <unk> . Most burns ( 70 % ) involve less than 10 % of the TBSA . 
- There are a number of methods to determine the TBSA , including the Wallace rule of <unk> , Lund and <unk> chart , and <unk> based on a person 's palm size . The rule of <unk> is easy to remember but only accurate in people over 16 years of age . More accurate estimates can be made using Lund and <unk> charts , which take into account the different proportions of body parts in adults and children . The size of a person 's <unk> ( including the palm and fingers ) is approximately 1 % of their TBSA . 
- 
- = = = <unk> = = = 
- 
- To determine the need for referral to a specialized burn unit , the American Burn Association devised a classification system . Under this system , burns can be classified as major , moderate and minor . This is assessed based on a number of factors , including total body surface area affected , the involvement of specific anatomical zones , the age of the person , and associated injuries . Minor burns can typically be managed at home , moderate burns are often managed in hospital , and major burns are managed by a burn center . 
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- = = Prevention = = 
- 
- Historically , about half of all burns were deemed <unk> . Burn prevention programs have significantly decreased rates of serious burns . <unk> measures include : limiting hot water temperatures , smoke alarms , <unk> systems , proper construction of buildings , and fire @-@ resistant clothing . Experts recommend setting water <unk> below 48 @.@ 8 ° C ( 119 @.@ 8 ° F ) . Other measures to prevent scalds include using a <unk> to measure bath water temperatures , and splash guards on <unk> . While the effect of the regulation of fireworks is unclear , there is tentative evidence of benefit with recommendations including the limitation of the sale of fireworks to children . 
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- = = Management = = 
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- <unk> begins with the assessment and stabilization of the person 's <unk> , breathing and circulation . If inhalation injury is suspected , early <unk> may be required . This is followed by care of the burn wound itself . People with extensive burns may be wrapped in clean sheets until they arrive at a hospital . As burn wounds are prone to infection , a <unk> <unk> shot should be given if an individual has not been <unk> within the last five years . In the United States , 95 % of burns that present to the emergency department are treated and discharged ; 5 % require hospital admission . With major burns , early feeding is important . <unk> <unk> may be useful in addition to traditional treatments . 
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- = = = Intravenous fluids = = = 
- 
- In those with poor tissue <unk> , <unk> of <unk> <unk> solution should be given . In children with more than 10 @-@ 20 % TBSA burns , and adults with more than 15 % TBSA burns , formal fluid resuscitation and monitoring should follow . This should be begun pre @-@ hospital if possible in those with burns greater than 25 % TBSA . The <unk> formula can help determine the volume of intravenous fluids required over the first 24 hours . The formula is based on the affected individual 's TBSA and weight . Half of the fluid is administered over the first 8 hours , and the remainder over the following 16 hours . The time is calculated from when the burn occurred , and not from the time that fluid resuscitation began . Children require additional maintenance fluid that includes glucose . Additionally , those with inhalation injuries require more fluid . While inadequate fluid resuscitation may cause problems , over @-@ resuscitation can also be detrimental . The formulas are only a guide , with <unk> ideally tailored to a urinary output of > 30 <unk> / h in adults or > <unk> / kg in children and mean arterial pressure greater than 60 <unk> . 
- While <unk> <unk> 's solution is often used , there is no evidence that it is superior to normal <unk> . <unk> fluids appear just as good as <unk> fluids , and as <unk> are more expensive they are not recommended . Blood transfusions are rarely required . They are typically only recommended when the <unk> level falls below 60 @-@ 80 g / L ( 6 @-@ 8 g / <unk> ) due to the associated risk of complications . Intravenous <unk> may be placed through burned skin if needed or <unk> <unk> may be used . 
- 
- = = = <unk> care = = = 
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- Early cooling ( within 30 minutes of the burn ) reduces burn depth and pain , but care must be taken as over @-@ cooling can result in <unk> . It should be performed with cool water 10 – 25 ° C ( 50 @.@ 0 – 77 @.@ 0 ° F ) and not ice water as the latter can cause further injury . Chemical burns may require extensive irrigation . <unk> with soap and water , removal of dead tissue , and application of dressings are important aspects of wound care . If intact blisters are present , it is not clear what should be done with them . Some tentative evidence supports leaving them intact . Second @-@ degree burns should be re @-@ evaluated after two days . 
- In the management of first and second @-@ degree burns , little quality evidence exists to determine which dressing type to use . It is reasonable to manage first @-@ degree burns without dressings . While topical antibiotics are often recommended , there is little evidence to support their use . Silver <unk> ( a type of antibiotic ) is not recommended as it potentially <unk> healing time . There is insufficient evidence to support the use of dressings containing silver or negative @-@ pressure wound therapy . 
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- = = = <unk> = = = 
- 
- Burns can be very painful and a number of different options may be used for pain management . These include simple <unk> ( such as <unk> and <unk> ) and <unk> such as morphine . <unk> may be used in addition to <unk> to help with anxiety . During the healing process , <unk> , <unk> , or <unk> <unk> stimulation may be used to aid with <unk> . <unk> , however , are only effective for this purpose in 20 % of people . There is tentative evidence supporting the use of <unk> and its use may be reasonable in those who do not improve with <unk> . Intravenous <unk> requires more study before it can be recommended for pain . 
- Intravenous antibiotics are recommended before surgery for those with extensive burns ( > 60 % TBSA ) . As of 2008 , guidelines do not recommend their general use due to concerns regarding antibiotic resistance and the increased risk of fungal infections . <unk> evidence , however , shows that they may improve survival rates in those with large and severe burns . <unk> has not been found effective to prevent or treat anemia in burn cases . In burns caused by <unk> acid , calcium <unk> is a specific antidote and may be used <unk> and / or <unk> . <unk> human growth hormone ( <unk> ) in those with burns that involve more than 40 % of their body appears to speed healing without affecting the risk of death . 
- 
- = = = <unk> = = = 
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- <unk> requiring surgical closure with skin <unk> or flaps ( typically anything more than a small full thickness burn ) should be dealt with as early as possible . <unk> burns of the limbs or chest may need urgent surgical release of the skin , known as an <unk> . This is done to treat or prevent problems with distal circulation , or ventilation . It is uncertain if it is useful for neck or digit burns . <unk> may be required for electrical burns . 
- 
- = = = Alternative medicine = = = 
- 
- <unk> has been used since ancient times to aid wound healing and may be beneficial in first- and second @-@ degree burns . There is tentative evidence that honey helps heal partial thickness burns . The evidence for <unk> <unk> is of poor quality . While it might be beneficial in reducing pain , and a review from 2007 found tentative evidence of improved healing times a subsequent review from 2012 did not find improved healing over silver <unk> . There were only three randomized controlled trials for the use of plants for burns , two for <unk> <unk> and one for <unk> . 
- There is little evidence that <unk> E helps with <unk> or scarring . <unk> is not recommended . In low income countries , burns are treated up to one @-@ third of the time with traditional medicine , which may include applications of eggs , mud , leaves or cow dung . <unk> management is limited in some cases due to insufficient financial resources and availability . There are a number of other methods that may be used in addition to <unk> to reduce procedural pain and anxiety including : virtual reality therapy , hypnosis , and behavioral approaches such as distraction techniques . 
- 
- = = <unk> = = 
- 
- The prognosis is worse in those with larger burns , those who are older , and those who are females . The presence of a smoke inhalation injury , other significant injuries such as long bone fractures , and serious co @-@ <unk> ( e.g. heart disease , diabetes , psychiatric illness , and <unk> intent ) also influence prognosis . On average , of those admitted to United States burn centers , 4 % die , with the outcome for individuals dependent on the extent of the burn injury . For example , <unk> with burn areas less than 10 % TBSA had a mortality rate of less than 1 % , while <unk> with over 90 % TBSA had a mortality rate of 85 % . In Afghanistan , people with more than 60 % TBSA burns rarely survive . The <unk> score has historically been used to determine prognosis of major burns . However , with improved care , it is no longer very accurate . The score is determined by adding the size of the burn ( % TBSA ) to the age of the person , and taking that to be more or less equal to the risk of death . Burns in 2013 resulted in 1 @.@ 2 million years lived with disability and 12 @.@ 3 million disability adjusted life years . 
- 
- = = = <unk> = = = 
- 
- A number of complications may occur , with infections being the most common . In order of frequency , potential complications include : pneumonia , <unk> , urinary tract infections and respiratory failure . Risk factors for infection include : burns of more than 30 % TBSA , full @-@ thickness burns , extremes of age ( young or old ) , or burns involving the legs or <unk> . <unk> occurs particularly commonly in those with inhalation injuries . 
- <unk> secondary to full thickness burns of greater than 10 % TBSA is common . Electrical burns may lead to compartment syndrome or <unk> due to muscle breakdown . Blood <unk> in the veins of the legs is estimated to occur in 6 to 25 % of people . The hypermetabolic state that may persist for years after a major burn can result in a decrease in bone density and a loss of muscle mass . <unk> may form subsequent to a burn , particularly in those who are young and dark skinned . Following a burn , children may have significant psychological trauma and experience post @-@ traumatic stress disorder . <unk> may also result in a disturbance in body image . In the developing world , significant burns may result in social isolation , extreme poverty and child abandonment . 
- 
- = = Epidemiology = = 
- 
- In 2013 fire and heat resulted in 35 million injuries . This resulted in about 2 @.@ 9 million <unk> and 238 @,@ 000 dying . This is down from 300 @,@ 000 deaths in 1990 . This makes it the 4th leading cause of injuries after motor vehicle collisions , falls , and violence . About 90 % of burns occur in the developing world . This has been attributed partly to overcrowding and an unsafe cooking situation . Overall , nearly 60 % of fatal burns occur in Southeast Asia with a rate of 11 @.@ 6 per 100 @,@ 000 . The number of fatal burns has increased from 280 @,@ 000 in 1990 to 338 @,@ 000 in 2010 . 
- In the developed world , adult males have twice the mortality as females from burns . This is most probably due to their higher risk occupations and greater risk @-@ taking activities . In many countries in the developing world , however , females have twice the risk of males . This is often related to accidents in the kitchen or domestic violence . In children , deaths from burns occur at more than ten times the rate in the developing than the developed world . Overall , in children it is one of the top fifteen leading causes of death . From the 1980s to 2004 , many countries have seen both a decrease in the rates of fatal burns and in burns generally . 
- 
- = = = Developed countries = = = 
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- An estimated 500 @,@ 000 burn injuries receive medical treatment yearly in the United States . They resulted in about 3 @,@ 300 deaths in 2008 . Most burns ( 70 % ) and deaths from burns occur in males . The highest incidence of fire burns occurs in those 18 – 35 years old , while the highest incidence of scalds occurs in children less than five years old and adults over 65 . Electrical burns result in about 1 @,@ 000 deaths per year . Lightning results in the death of about 60 people a year . In Europe , intentional burns occur most commonly in middle aged men . 
- 
- = = = <unk> countries = = = 
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- In India , about 700 @,@ 000 to 800 @,@ 000 people per year sustain significant burns , though very few are looked after in specialist burn units . The highest rates occur in women 16 – 35 years of age . Part of this high rate is related to unsafe kitchens and loose @-@ fitting clothing typical to India . It is estimated that one @-@ third of all burns in India are due to clothing catching fire from open flames . <unk> burns are also a common cause and occur at high rates in young women , secondary to domestic violence and self @-@ harm . 
- 
- = = History = = 
- 
- Cave paintings from more than 3 @,@ 500 years ago document burns and their management . The earliest Egyptian records on treating burns describes dressings prepared with milk from mothers of baby boys , and the 1500 BCE Edwin Smith <unk> describes treatments using honey and the <unk> of resin . Many other treatments have been used over the ages , including the use of tea leaves by the Chinese documented to 600 BCE , pig fat and vinegar by <unk> documented to 400 BCE , and wine and <unk> by <unk> documented to 100 CE . French barber @-@ surgeon Ambroise <unk> was the first to describe different degrees of burns in the <unk> . Guillaume <unk> expanded these degrees into six different <unk> in 1832 . 
- The first hospital to treat burns opened in 1843 in London , England and the development of modern burn care began in the late 1800s and early 1900s . During World War I , Henry D. <unk> and Alexis <unk> developed standards for the cleaning and <unk> of burns and wounds using sodium <unk> solutions , which significantly reduced mortality . In the 1940s , the importance of early excision and skin <unk> was acknowledged , and around the same time , fluid resuscitation and formulas to guide it were developed . In the 1970s , researchers demonstrated the significance of the hypermetabolic state that follows large burns . 
- 
- 
- = Tropical Storm Brenda ( 1960 ) = 
- 
- Tropical Storm Brenda was the second named storm of the 1960 Atlantic hurricane season . It developed in the northeastern Gulf of Mexico on July 28 , and after moving ashore over the Florida Peninsula , it attained tropical storm status . Brenda accelerated northeast along the U.S. East Coast , ultimately peaking as a moderate storm with winds of 60 mph ( 97 km / h ) before crossing the Mid @-@ Atlantic states and New England ; it dissipated on July 31 over southern Canada . The storm inflicted moderate damage in Florida , the worst since Hurricane Easy of 1950 , and dropped heavy rainfall as far north as New York City . Total damage is estimated at US $ 5 million , and only indirect deaths are blamed on the cyclone . 
- 
- = = Meteorological history = = 
- 
- A weak low @-@ pressure area that organized in the northeast Gulf of Mexico began to intensify on July 28 , while located west of the Tampa Bay . Early in its life , the system had a broad circulation with primarily light winds , similar to that of a subtropical storm . The storm is estimated to have become a tropical depression earlier the previous day as it moved toward the northeast . It made landfall along the Florida coast near Cross City and continued inland , gradually accelerating . It likely attained tropical storm status at around 1200 UTC on July 28 while its center was situated west of Tampa . The cyclone was named Brenda after reconnaissance aircraft confirmed that it had reached tropical storm strength . 
- Brenda tracked northward , hugging the Georgia and South Carolina coasts before moving inland over North Carolina . It attained its peak winds of 60 mph ( 97 km / h ) in the late evening of July 29 , while situated south of Wilmington . Several hours later , the storm emerged over the Chesapeake Bay moving northeast at about 30 mph ( 48 km / h ) . Brenda crossed the <unk> Peninsula and rapidly tracked into southern New Jersey . The storm crossed the state and eventually made another landfall on Long Island before making yet another landfall in coastal Connecticut . 
- At around 0000 UTC on July 31 , Brenda moved into Massachusetts . Shortly thereafter , it lost its tropical characteristics and transitioned into an extratropical cyclone . It dissipated by August 1 over southern Canada . Because Brenda was in the vicinity of land for most of its course , it was not able to intensify beyond tropical storm status . 
- 
- = = Preparations and impact = = 
- 
- In advance of the storm , tropical storm advisories and wind warnings were issued from Florida to Maine . 
- Rainfall from Tropical Storm Brenda affected at least 16 states . The heaviest precipitation fell in western Florida near Tampa , east of the storm 's center ; the Tampa International Airport recorded 14 @.@ 57 in ( 370 mm ) of rainfall . Extensive flooding occurred in the west @-@ central Florida Peninsula . Wind gusts exceeded 60 mph ( 97 km / h ) , and the storm produced 10 ft ( 3 @.@ 0 m ) high waves along the coast , leading to considerable erosion . However , storm tides were not severe . Around the Naples area , Brenda 's effects were primarily light , although small boat and dock facilities and roads sustained some damage . A private seawall at Clearwater was breached in two places by the cyclone . 
- Brenda was considered the worst storm to strike the area since Hurricane Easy in 1950 . While no casualties are directly blamed on the storm , at least one traffic @-@ related death took place . According to an American Red Cross Disaster Service report encompassing eight Florida counties , 11 houses sustained significant damage , while <unk> suffered more minor damage . Around 590 families were affected overall . Total monetary damage is placed at near $ 5 million . 
- Tides along the Outer Banks of North Carolina were generally reported at 2 ft ( 0 @.@ 61 m ) above @-@ normal . In and around Wilmington , the storm caused minor damage to roofs and windows of some beachfront structures . Power was temporarily interrupted due to fallen tree limbs . Heavy rainfall caused flooding on streams and rivers , and in some areas the precipitation helped to end a serious drought . Some boats were swamped , and the winds ripped the roof off a cottage at Long Beach . The heavy rain and high tides flooded tobacco fields . 
- Moderate rains extended northward into the Mid @-@ Atlantic states , with lighter totals reported farther north in New York . At New York City , 4 @.@ 79 in ( 122 mm ) of precipitation fell , beating the one @-@ day July record of 3 @.@ 80 in ( 97 mm ) set in 1872 . The heavy rains flooded parts of <unk> Airport . Elsewhere , reports of 3 to 5 in ( 76 to 127 mm ) were common throughout New Jersey , Delaware , Maryland and Virginia . High winds also affected portions of the northeastern United States , gusting to 55 mph ( 89 km / h ) across southern New England . Tides often ran 3 to 4 ft ( 0 @.@ 91 to 1 @.@ 22 m ) above @-@ normal throughout the region . The storm caused travel delays and ran several ships aground , but otherwise inflicted little serious damage . The storm forced the cancellation of two American League baseball games and the <unk> of several other sporting events . 
- 
- 
- = Stanley Price Weir = 
- 
- Brigadier General Stanley Price Weir , DSO , <unk> , JP ( 23 April 1866 – 14 November 1944 ) was a public servant and Australian Army officer . During World War I , he commanded the 10th Battalion of the Australian Imperial Force ( AIF ) during the landing at Anzac Cove and the subsequent Gallipoli Campaign , and during the Battles of Pozières and Mouquet Farm in France . 
- Weir returned to Australia at his own request in late 1916 at the age of 50 , and in 1917 he was awarded the Distinguished Service Order and was mentioned in dispatches for his performance at Pozières and Mouquet Farm . He went on to become the first South Australian Public Service Commissioner . He was given an honorary promotion to brigadier general on his retirement from the Australian Military Forces in 1921 . Weir was retired as public service commissioner in 1931 . In retirement he contributed to various benevolent and charitable organisations , and died in 1944 . 
- 
- = = Early life = = 
- 
- Weir was born in Norwood , South Australia , on 23 April 1866 , a son of Alfred Weir and <unk> Mary ( née Price ) . His father was a carpenter , who had emigrated to South Australia from Aberdeen , Scotland , in 1839 , two years after the colony was founded . Weir attended Moore 's School , the Norwood Public School , and Pulteney Street Grammar School . In 1879 , at the age of 13 , he joined the <unk> General 's Department as an office assistant . He assisted the surveyor who <unk> out the land at the rear of Government House , Adelaide , for the Torrens Parade Ground , and was later promoted to clerk . On 14 May 1890 , he married Rosa <unk> at the Christian Chapel , Norwood . He rose through the department to be appointed Survey <unk> , <unk> of Plans and <unk> of Government Motor Cars , on 1 July 1911 . He was appointed a justice of the peace on 10 September 1914 . 
- 
- = = Early military service = = 
- 
- Weir enlisted in the part @-@ time South Australian Volunteer Military Force in March 1885 , joining the 1st Battalion , Adelaide Rifles , as a private . By 1890 , he had been promoted to colour sergeant . He was commissioned as a lieutenant in the 3rd Battalion , Adelaide Rifles , on 19 March 1890 , and was promoted to captain on 25 May 1893 . When the South African War broke out he volunteered for service with the South Australian <unk> 's Corps , but mounted officers were preferred , and he was not selected . 
- On 1 July 1903 , the Adelaide Rifles became the 10th Infantry Regiment of the Commonwealth Military Forces , and Weir was appointed adjutant . He was promoted to major on 1 January 1904 , and appointed as regimental second @-@ in @-@ command . He was awarded the Long Service and Good Conduct Medal in 1905 , and the Volunteer Officers ' Decoration in 1908 . On 22 June 1908 , Weir was promoted to lieutenant colonel and appointed the commanding officer of the 10th Infantry Regiment . On 1 January 1912 , he was transferred to the <unk> list but this only lasted until 1 July , when the universal training scheme was introduced . He was soon appointed to command the 19th Infantry Brigade , and on 9 September 1913 he was promoted to colonel . 
- 
- = = World War I = = 
- 
- On 12 August 1914 , Weir received a telegram from Colonel <unk> Sinclair @-@ Maclagan , the designated commander of the 3rd Brigade , offering him the command of the 10th Battalion . Weir promptly accepted , and on 17 August was appointed as a lieutenant colonel in the Australian Imperial Force ( AIF ) , making him the first South Australian to be commissioned in the AIF . He retained his rank of colonel in the part @-@ time forces in an honorary capacity . 
- 
- = = = Gallipoli = = = 
- 
- Weir assembled and trained his battalion at the <unk> Racecourse , then embarked with them on the transport <unk> on 20 October 1914 as the first convoy of Australian troops departed for overseas service . On arrival in Fremantle , six companies of the 11th Battalion were embarked on the transport , and Weir was appointed Officer Commanding Troops for the voyage . The troops began disembarking at Alexandria on 6 December 1914 , and were entrained for Cairo , where they began to set up camp at Mena . The Australian Official War Historian , Charles Bean , described Weir as being " somewhat above average in years " for a battalion commander . Following the Allied decision to land a force on the Gallipoli Peninsula , the 3rd Brigade was selected as the covering force for the landing at Anzac Cove . The 10th Battalion embarked for the Greek island of Lemnos in the northern Aegean Sea on 1 March 1915 , and after further training on Lemnos , the battalion was one of the first two battalions ashore on the morning of 25 April 1915 . 
- During the landing , when the boats carrying the lead elements of the battalion were around 40 yards ( 37 m ) from shore , according to Bean , Weir observed to another officer in his boat that everything was silent , but soon after Ottoman troops began firing at the landing force . Weir landed with the scout platoon , and urged both his men and those of the 9th Battalion to immediately begin climbing the cliffs that overlooked the beach . Weir , along with " B " and " C " Companies of the battalion , reached what later became known as " Plugge 's Plateau " . Heavy fighting followed the initial landing and , within five days , half of Weir 's battalion had been killed or wounded . The Australian and New Zealand advance inland from Anzac Cove was subsequently checked by the defending Ottoman forces and was eventually contained in a small beachhead inside a series of ridges that ranged around the <unk> . Weir was the only commanding officer from the 3rd Brigade to go forward of the first ridge , and a ridge running off the 400 Plateau subsequently became known as " Weir Ridge " . 
- As stalemate set in , Weir continued to command his battalion throughout the early stages of the campaign until 25 August , when he was appointed acting brigadier general and placed in command of the 3rd Brigade . On 11 September , he became ill and was evacuated to Malta , where he was admitted to hospital . He was subsequently evacuated to the United Kingdom , where he <unk> until January 1916 , when he was appointed commandant of the Australian reinforcement camp at <unk> , Dorset . 
- 
- = = = Western Front = = = 
- 
- Weir 's health had not completely recovered by the time he embarked for Egypt , and he rejoined his battalion on 4 March 1916 . After his departure , the 10th had fought through the remainder of the campaign before being withdrawn along with the rest of the Allied force in December 1915 . The battalion was subsequently moved back to Egypt . In mid @-@ 1916 , the bulk of the AIF was transferred to the Western Front , and Weir led the 10th Battalion through July and August 1916 during the Battles of Pozières and Mouquet Farm . At Pozières , the battalion suffered 350 casualties in four days . By the time of the battle , Weir was the only original battalion commander remaining in the 1st Australian Division , and had turned 50 years of age . On 23 August , immediately after Mouquet Farm , Weir was again appointed acting commander of the 3rd Brigade . <unk> , on 7 September 1916 he asked to be relieved , and his request was granted . He returned to Australia on 23 September 1916 , and his AIF appointment was terminated on 14 December . In the Australian official history of the war , Bean observed that despite his age , Weir " took his battalion into the front line , commanded it there throughout its first battle , and remained longer in the field than almost any of the senior militia officers who had left with the original force " . 
- 
- = = Post @-@ war military service = = 
- 
- After his AIF appointment was terminated , Weir resumed his service in the Citizen Military Forces ( <unk> ) . In 1917 , he was awarded the Distinguished Service Order and the Russian Empire Order of St. Anne , 2nd Class , with Swords , and was mentioned in dispatches for his performance at Pozières and Mouquet Farm . From 1917 to 1920 , he was aide @-@ de @-@ camp to the Governor @-@ General of Australia , Sir Ronald <unk> Munro Ferguson . Weir retired from the <unk> as an honorary brigadier general in March 1921 , his last appointment being as commander of the 20th Infantry Brigade . He was only the second South Australia @-@ born officer to reach the rank of brigadier general . On 31 March 1921 , Weir was appointed Honorary Colonel of the 10th Battalion , a position he held for many years . 
- 
- = = Later life = = 
- 
- Weir had two significant advantages in his return to a civilian career . Firstly , he was repatriated well before most servicemen and , <unk> , South Australia had implemented a policy of <unk> of returned servicemen for government employment . These circumstances helped him gain appointment as the first South Australian Public Service Commissioner in 1916 . Weir was not suited to this role , being unable to navigate the competing personal and political <unk> of senior public servants and politicians , and was soon sidelined . In 1925 , legislative changes made it possible for the government to replace Weir , and this took place in 1930 . In the last year @-@ and @-@ a @-@ half before his retirement in 1931 , Weir was the chairman of both the Central Board of Health and the Public Relief Board , <unk> at the latter . 
- On 8 June 1923 , after many years of poor health , Weir 's wife Rosa died . He married Lydia Maria <unk> in 1926 . Weir led an active retirement , contributing to several religious , charitable and welfare organisations and activities . These included the Norwood and <unk> Churches of Christ , <unk> and Stranger 's Friend Society , the Our Boys Institute ( <unk> ) , the <unk> Lodge , <unk> Up Society , and YMCA . At various times he served as President of the Commonwealth Club , the Churches of Christ Union , the St. Peters Sub @-@ Branch of the <unk> and Services League , and the <unk> Up Society . 
- Weir wrote the <unk> for the history of the 10th Battalion , titled The Fighting 10th : A South Australian Centenary <unk> of the 10th Battalion , AIF 1914 – 1919 , which was written by a former member of the battalion , Cecil Lock , and published in 1936 . In 1943 , Weir was badly injured in a car accident while returning from an <unk> camp at Victor Harbor . It was believed that his injuries in the accident contributed to his death on 14 November 1944 . Weir was survived by his wife Lydia , and his son Lionel and daughter <unk> from his first marriage . His brother , Harrison Weir , was the State Government <unk> . Weir was buried in West <unk> Cemetery . 
- 
- = = Awards = = 
- 
- Weir received the following honours and awards : 
- Long Service and Good Conduct Medal in 1905 
- Volunteer Officers ' Decoration on 11 April 1908 
- Distinguished Service Order on 1 January 1917 
- <unk> in despatches on 4 January 1917 
- Order of St. Anne , 2nd Class , with Swords ( Russian Empire ) on 15 February 1917 
- King George V Silver Jubilee Medal on 6 May 1935 
- 
- = = <unk> = = 
- 
- Weir 's military career commenced in March 1885 , when he enlisted as a private . He quickly rose to the rank of colour sergeant before being commissioned in 1890 . He rose from private to brigadier general over a career spanning 36 years . His officer promotion dates were : 
- Lieutenant on 19 March 1890 
- Captain on 25 May 1893 
- Major on 1 January 1904 
- Lieutenant colonel on 22 June 1908 
- Colonel on 9 September 1913 
- Lieutenant colonel ( AIF ) on 17 August 1914 
- Brigadier general ( honorary ) on 17 March 1921 
- 
- 
- = Chapter 1 ( House of Cards ) = 
- 
- " Chapter 1 " ( sometimes " Episode 101 " ) is the pilot episode of the American political thriller drama television series House of Cards and is the first episode of the first season . It premiered on February 1 , 2013 , when it was released along with the rest of the first season on the American streaming service Netflix . This episode became the first web television webisode to earn Primetime Emmy Awards and nominations . " Chapter 1 " was written by series developer Beau <unk> and directed by executive producer David Fincher . The episode also earned 3 other Emmy nominations as well as <unk> : <unk> Drama and DGA – Drama Series nominations . 
- Frank Underwood ( Kevin Spacey ) is an ambitious Democratic congressman and the House Majority Whip . Underwood helped ensure the election of President Garrett Walker ( Michel Gill ) , who promised to appoint Underwood as Secretary of State . However , before Walker is sworn in , Chief of Staff Linda <unk> ( <unk> <unk> ) announces that the president will not honor the agreement and will instead nominate Senator Michael Kern . Furious at Walker 's betrayal , Underwood and his wife Claire ( Robin Wright ) , an environmental activist , make a pact to destroy Kern . When Zoe Barnes ( Kate Mara ) makes her resources available , she becomes one of their pawns . 
- The episode was well received by most television critics . They praised the production values of the series as well as the performances of the lead actors . 
- 
- = = Plot = = 
- 
- South Carolina Congressman Frank Underwood ( Kevin Spacey ) , the Democratic Majority Whip , leaves his house in Washington , D.C. after hearing his neighbors ’ dog get hit by a car . As he comforts the mortally @-@ wounded dog , he looks into the camera and says “ <unk> like this require someone who will act , who will do the unpleasant thing , the necessary thing , ” before calmly proceeding to strangle it . This introduces both his habit of breaking the fourth wall to narrate , and his cold and vicious nature . Frank and his wife , Claire ( Robin Wright ) , go on to attend a New Year ’ s Eve party in honor of the new President @-@ elect , Garrett Walker ( Michel Gill ) , a fellow Democrat and winner of the 2012 election . Frank confesses to the viewer that he does not like Walker , but saw his political potential early on and <unk> himself to him , putting himself in line to be nominated as Walker ’ s Secretary of State after 22 years in Congress . 
- Frank meets with Walker 's Chief of Staff , Linda <unk> ( <unk> <unk> ) , whom Frank recommended for the job . She reveals that she and Walker have decided to rescind their promise to nominate him as Secretary of State because they want him to remain in Congress and use his political expertise to get the President @-@ elect ’ s education reform agenda passed . Frank is initially incensed , but when Linda asks if he will continue to be an ally to the future President he says that he will . Linda reveals that Senator Michael Kern ( Kevin <unk> ) has been chosen for the position instead . Despite his statement to the contrary , Frank feels personally betrayed and , with Claire ’ s encouragement , begins to <unk> a plot for revenge , which he shares with his Chief of Staff , Doug Stamper ( Michael Kelly ) . Mrs. Underwood , meanwhile , is forced to <unk> the non @-@ profit organization she manages , the Clean Water Initiative , which had been promised a large donation upon her husband ’ s confirmation as Secretary , without which the organization is forced to substantially <unk> its budget . 
- On a whim , Zoe Barnes ( Kate Mara ) , a young reporter for the Washington Herald who is stuck covering trivial “ human interest ” stories , pays a late @-@ night visit to Frank at his home . She offers to be Frank ’ s undercover <unk> in the press in exchange for the elevated profile that she would gain from breaking substantive stories . Meanwhile , Peter Russo ( Corey <unk> ) , a young , inexperienced congressman from Philadelphia , is arrested for drunk driving . Stamper finds out about the arrest and immediately contacts the D.C. police commissioner , offering Underwood ’ s support for his mayoral campaign in exchange for releasing Russo and completely covering up the incident . Russo is picked up from jail by his secretary and romantic partner , Christina Gallagher ( Kristen <unk> ) . He lies to her , telling her that he was alone when he was arrested when , in fact , there was a prostitute in the car ( Rachel <unk> ) . 
- Frank meets with Congressman Donald Blythe ( Reed <unk> ) , a committed progressive liberal who has long pushed for education reform , with whom the Walker administration wants to work on a bill . Frank dismisses his proposal as too ambitious and asks him to rewrite it . Frank secretly passes a copy of Blythe ’ s proposal to Zoe . He then meets with Senator Catherine <unk> ( <unk> Atkinson ) and suggests that she ought to consider seeking the nomination for Secretary of State . He also privately confronts Congressman Russo about his arrest and <unk> history of substance abuse and soliciting prostitutes , and demands Russo ’ s loyalty in exchange for making the incident disappear . 
- Zoe takes the draft of the education bill to the Herald ’ s political editor , Lucas Goodwin ( Sebastian <unk> ) , and its chief editor , Tom <unk> ( Boris <unk> ) , who gives her the lead on the story over the more experienced chief political correspondent <unk> <unk> ( Constance <unk> ) . The episode ends the morning after Walker ’ s inauguration , with Frank visiting his favorite restaurant , <unk> ’ s <unk> Joint , for breakfast . On the front page of the Washington Herald is Zoe ’ s story about Blythe ’ s “ far left ” education plan . 
- 
- = = Production = = 
- 
- The episode was directed by David Fincher and was written by Beau <unk> , who has served as an aide to Charles <unk> , Howard Dean and Hillary Clinton . Independent studio Media Rights Capital purchased the rights to House of Cards , with the intent on creating a series . Netflix agreed to contribute an undisclosed fixed fee to production costs in March 2011 . As he was completing his work on The Curious Case of Benjamin Button , Fincher was introduced to the original miniseries by his agent and sought to develop a series with Eric Roth . House of Cards was pitched to several cable networks , including HBO , AMC and <unk> . Netflix , interested in launching their own original programming , outbid the networks , picking the series up for 26 episodes , totaling two seasons . Netflix was the only bidder that was interested in purchasing the rights without seeing a completed pilot . Thus , the show was not forced into manipulating story arcs introduced in the pilot to create artificial <unk> . 
- 
- = = = Casting = = = 
- 
- Fincher stated that every main cast member was their first choice . In the first read through , he said " I want everybody here to know that you represent our first choice — each actor here represents our first choice for these characters . So do not fuck this up . " Spacey , whose last regular television role was in the series <unk> , responded positively to the script . He then played Richard III , which Fincher said was " great training " . Spacey supported the decision to release all of the episodes at once , believing that this type of release pattern will be increasingly common with television shows . He said , " When I ask my friends what they did with their weekend , they say , ' Oh , I stayed in and watched three seasons of Breaking Bad ' or it 's two seasons of Game of <unk> " . He was officially cast on March 18 , 2011 . Robin Wright was approached by Fincher to star in the series when they worked together in The Girl with the Dragon <unk> . She was cast as Claire Underwood in June 2011 . Kate Mara was cast as Zoe Barnes in early February 2012 . Mara 's sister , <unk> Mara , worked with Fincher in The Girl with the Dragon <unk> , and when Kate Mara read the part of Zoe , she " fell in love with the character " and asked her sister to " put in a word for me with Fincher " . The next month , she got a call for an audition . 
- 
- = = = Cast = = = 
- 
- In order billed in the episode opening credits : 
- 
- = = = Filming = = = 
- 
- While Netflix had ventured into original programming by <unk> foreign shows that were new to United States audiences with shows such as <unk> , House of Cards represented the first show made for Netflix . Filming for the first season began in January 2012 in Harford County , Maryland . 
- " Chapter 1 " sets the tone for the environs of the series . According to David Carr the political environs have such " <unk> " that it <unk> the clandestine nature of political activities , including those of Underwood who says he is there to " clear the pipes and keep the sludge moving " . The fictional newspaper , The Washington Herald , is set with " brutal " lighting and drab furniture , in part because it was filmed at the real life Baltimore Sun offices . Carr uses several <unk> adjectives to describe Barnes ' apartment including sad , <unk> , dirty , dreary and humble but note that this implies that the digital revolution is dominated by people " on <unk> who have no furniture " . Similarly , Underwood and his associates are <unk> clad , Barnes shows a lack of fashion recognition . 
- 
- = = = Release = = = 
- 
- The episode was broadcast online by Netflix on February 1 , 2013 as part of the simultaneous release of all 13 episodes of season 1 of the series . The debut date was a weekend when there was little competition on television other than Super Bowl XLVII and a new episode of <unk> Abbey on PBS . Netflix broadcast " Chapter 1 " and " Chapter 2 " to critics several days in advance of the release . 
- 
- = = Reception = = 
- 
- 
- = = = Reviews = = = 
- 
- The episode received positive reviews from critics . Elements of the opening scene were lauded . Matt Roush of TV Guide praised Spacey 's self introduction as a Machiavellian politician in which he says " I have no patience for useless things . " Boston Globe 's Matthew Gilbert noted that " the first two episodes were expertly directed by David Fincher " and Spacey 's <unk> <unk> such as those used in the first scene of this episode " makes even his character ’ s mercy killing of an injured dog — which he does by hand — seem a little less brutal . " Not only is Underwood described as Machiavellian , one critic from The New York Times notes that his belief in the <unk> of dirt expressed as " Nobody ’ s a Boy Scout , not even a Boy Scout " <unk> back to Willie Stark in All the King 's Men who said " There 's always something " . 
- Time television critic James <unk> , notes that by the end of the first episode Frank establishes that his metaphor of choice is meat because both literally and <unk> it is his preference . He may begin a day with a celebratory rack of ribs , because " I ’ m <unk> ’ hungry today ! " , but also he describes life with meat metaphors : he describes the White House Chief of Staff with <unk> admiration : " She ’ s as tough as a two @-@ dollar <unk> . " ; he plans to destroy an enemy the way " you <unk> a whale . One bite at a time . " ; and he <unk> a tedious weekly meeting with House leaders , he tells us , by " [ imagining ] their lightly salted faces <unk> in a <unk> . " <unk> notes that all of this comes from a character whose name , Underwood is a reference to the hallmark <unk> <unk> of the William Underwood Company . 
- Roush also notes that the first two weeks show how Claire " runs a charity with a brutally iron fist " . While Frank is Machiavellian , Claire presents a woman urging on her husband 's assertion of power in the image of Lady Macbeth . Hank Stuever of The Washington Post describes her as an ice @-@ queen wife . She encourages his vices while noting her disapproval of his weakness saying " My husband doesn ’ t apologize ... even to me . " Nancy <unk> Smith of The Wall Street Journal describes what she sees of their relationship in the first two episodes as pivotal to the show 's success : " <unk> though they may seem — and their harmless air is what makes the <unk> so effective as political <unk> — this is a power couple with the same malignant chemistry as pairs of serial <unk> , where each needs the other in order to become lethal " . 
- Gilbert also notes that Mara 's surprising <unk> is a welcome respite against a backdrop of a " terminally <unk> " cast . As the show begins , aspiring journalist Zoe Barnes is desperate to rise from covering the " Fairfax County Council " beat to covering " ' what 's behind the veil ' of power in the Capitol <unk> . " <unk> Stanley of The New York Times notes that by the end of the first episode , Mara 's Barnes is among the cadre of Frank 's <unk> . After she pleads for a relationship with him by promising to earn his trust and not " ask any questions " , Frank uses her <unk> . However , Ashley Parker of The New York Times considers her <unk> aggressive and too overt , <unk> and sexual . Parker points out that Barnes ' statement " I protect your identity , I print what you tell me , and I 'll never ask any questions " almost <unk> itself . 
- The Washington Post 's Stuever has many complaints about the show including the fact that it is about Washington , DC , but filmed in Baltimore . He also complains about its entrance into the television landscape littered with " more fictitious administrations than anyone can keep track of " . He says that perspective will affect your perception of the show . Those not already inundated with " Type A personalities inside the <unk> " in their daily lives may be drawn to the show . However , it is not likely a show that will serve well those who spend a lot of time with the issues that the show deals with . After viewing the first two episodes , Stuever also finds fault with the use of breaking the fourth wall , describing it as " the show 's <unk> narrative <unk> " . The Wall Street Journal 's Smith defends the fourth wall as an " <unk> that generally works well here to <unk> our bearings " . 
- Ryan McGee of The A.V. Club notes Russo seems to employ vices without restraint , which is a respite from the other exacting characters in the episode and a makes him a sort of metaphor for the show . McGee also notes that the episode includes " establishing shots within Zoe ’ s apartment that offer up almost everything you need to know about her current position in life " . 
- 
- = = = Accolades = = = 
- 
- On July 18 , 2013 , House of Cards ( along with Netflix 's other web series ' Arrested Development and Hemlock Grove ) earned the first Primetime Emmy Award nominations for original online only web television for the 65th Primetime Emmy Awards in 2013 . Among House of Cards ' nine nominations , " Chapter 1 " received four nominations for the 65th Primetime Emmy Awards and 65th Primetime Creative Arts Emmy Awards becoming the first webisode ( online @-@ only episode ) of a television series to receive a major Primetime Emmy Award nomination : Outstanding Directing for a Drama Series for David Fincher . This episode also received several Creative Arts Emmy Award nominations , including Outstanding <unk> for a Single @-@ Camera Series , Outstanding Single @-@ Camera Picture Editing for a Drama Series , and Outstanding Music Composition for a Series ( Original Dramatic ) . Although the Primetime Emmy Award for Outstanding Lead Actor in a Drama Series is not a category that formally recognizes an episode , Spacey submitted " Chapter 1 " for consideration due to his nomination . 
- On September 15 , at the Creative Arts Emmy Award presentation , " Chapter 1 " and <unk> <unk> earned the Primetime Emmy Award for Outstanding <unk> for a Single @-@ Camera Series , making " Chapter 1 " the first ever Emmy @-@ awarded webisode . Then on September 22 , David Fincher won Outstanding Directing for a Drama Series for directing the pilot episode " Chapter 1 " , bringing the series to a total of three wins and marking the first ever win for a webisode at the Primetime Emmy award ceremony . None of the Emmy awards were considered to be in major categories , however . 
- 
- 
- = Alice in Chains = 
- 
- Alice in Chains is an American rock band formed in Seattle , Washington , in 1987 by guitarist and songwriter Jerry Cantrell and original lead vocalist Layne Staley . The initial lineup was rounded out by drummer Sean Kinney and bassist Mike Starr , who was replaced in 1993 by Mike Inez . 
- Although widely associated with grunge music , the band 's sound incorporates heavy metal elements . Since its formation , Alice in Chains has released five studio albums , three EPs , two live albums , four compilations , and two DVDs . The band is known for its distinctive vocal style , which often included the <unk> vocals of Staley and Cantrell ( and later William DuVall ) . 
- Alice in Chains rose to international fame as part of the grunge movement of the early 1990s , along with other Seattle bands such as Nirvana , Pearl Jam , and Soundgarden . The band was one of the most successful music acts of the 1990s , selling over 20 million albums worldwide , and over 14 million in the US alone . In 1992 the band 's second album , Dirt , was released to critical acclaim and was certified quadruple platinum . Their third album , Alice in Chains , was released in 1995 and has been certified double platinum . It achieved No. 1 position on the Billboard 200 chart . The band has had 14 top ten songs on the Mainstream Rock Tracks chart and nine Grammy Award nominations . 
- Although never officially disbanding , Alice in Chains was plagued by extended inactivity from 1996 onwards due to Staley 's substance abuse , which resulted in his death in 2002 . The band reunited in 2005 for a live benefit show , performing with a number of guest vocalists . They toured in 2006 , with William DuVall taking over as lead vocalist full @-@ time . The new line @-@ up released the band 's fourth studio album , Black Gives Way to Blue , in 2009 . The album received gold certification by the RIAA . In 2013 , the band released The Devil Put Dinosaurs Here , its fifth studio album . The band has toured extensively and released several videos in support of these albums . Alice in Chains is currently working on their sixth studio album . 
- 
- = = History = = 
- 
- 
- = = = Formation and early years ( 1984 – 89 ) = = = 
- 
- Before the formation of Alice in Chains , then @-@ drummer Layne Staley landed his first gig as a vocalist when he auditioned to sing for a local <unk> metal band known as <unk> after receiving some encouragement from his <unk> Ken <unk> . Other members of this group at that time were guitarists Johnny Bacolas and <unk> <unk> , drummer James <unk> , and bassist Byron Hansen . This band went through several lineup changes culminating with Nick <unk> as their sole guitarist and Bacolas switching to bass before discussions arose about changing their name to Alice in Chains . This was prompted by a conversation that Bacolas had with a singer from another band about backstage passes . Due to concerns over the reference to female bondage , the group ultimately chose to spell it differently as Alice N ' Chains to <unk> any parental concerns , though Staley 's mother Nancy <unk> has said she was still not happy with this name at first . 
- Staley met guitarist Jerry Cantrell while working with Alice N ' Chains at Music Bank rehearsal studios . The two struggling musicians became roommates , living in a rehearsal space they shared . Alice N ' Chains soon disbanded , and Staley joined a funk band that also required a guitarist at the time . Staley asked Cantrell to join as a <unk> . Cantrell agreed on condition that Staley join Cantrell 's band , which at the time included drummer Sean Kinney and bassist Mike Starr . Eventually the funk project broke up , and in 1987 Staley joined Cantrell 's band on a full @-@ time basis , playing in clubs around the Pacific Northwest , often stretching 15 minutes of material into a 45 @-@ minute set . The band played a couple of gigs , calling themselves different <unk> , including Diamond Lie , the name of Cantrell 's previous band , before eventually adopting the name that Staley 's previous band had initially <unk> with , Alice in Chains . 
- Local promoter Randy Hauser became aware of the band at a concert and offered to pay for demo recordings . However , one day before the band was due to record at the Music Bank studio in Washington , police shut down the studio during the biggest cannabis raid in the history of the state . The final demo , completed in 1988 , was named The Treehouse Tapes and found its way to the music managers Kelly Curtis and Susan Silver , who also managed the Seattle @-@ based band Soundgarden . Curtis and Silver passed the demo on to Columbia Records ' A & R representative Nick <unk> , who set up an appointment with label president Don <unk> . Based on The Treehouse Tapes , <unk> signed Alice in Chains to Columbia in 1989 . The band also recorded another untitled demo over a three @-@ month period in 1989 . This recording can be found on the bootleg release Sweet Alice . 
- 
- = = = Facelift and Sap ( 1990 – 92 ) = = = 
- 
- Alice in Chains soon became a top priority of the label , which released the band 's first official recording in July 1990 , a promotional EP called We Die Young . The EP 's lead single , " We Die Young " , became a hit on metal radio . After its success , the label rushed Alice in Chains ' debut album into production with producer Dave <unk> . Cantrell stated the album was intended to have a " moody <unk> " that was a " direct result of the brooding atmosphere and feel of Seattle " . 
- The resulting album , Facelift , was released on August 21 , 1990 , peaking at number 42 in the summer of 1991 on the Billboard 200 chart . Facelift was not an instant success , selling under 40 @,@ 000 copies in the first six months of release , until MTV added " Man in the Box " to regular daytime rotation . The single hit number 18 on the Mainstream rock charts , with the album 's follow up single , " Sea of <unk> " , reaching number 27 , and in six weeks Facelift sold 400 @,@ 000 copies in the US . The album was a critical success , with Steve Huey of AllMusic citing Facelift as " one of the most important records in establishing an audience for grunge and alternative rock among hard rock and heavy metal listeners . " 
- Facelift was certified gold by the Recording Industry Association of America ( RIAA ) by the end of 1990 , while the band continued to hone its audience , opening for such artists as <unk> Pop , Van Halen , Poison , and Extreme . In early 1991 , Alice in Chains landed the opening slot for the Clash of the Titans tour with <unk> , <unk> , and Slayer , exposing the band to a wide metal audience but receiving mainly poor reception . Alice in Chains was nominated for a Best Hard Rock Performance Grammy Award in 1992 for " Man in the Box " but lost to Van Halen for their 1991 album For <unk> <unk> Knowledge . 
- Following the tour , Alice in Chains entered the studio to record demos for its next album , but ended up recording five acoustic songs instead . While in the studio , drummer Sean Kinney had a dream about " making an EP called Sap " . The band decided " not to mess with fate " , and on March 21 , 1992 , Alice in Chains released their second EP , Sap . The EP was released while Nirvana 's Nevermind was at the top of the Billboard 200 charts , resulting in a rising popularity of Seattle @-@ based bands , and of the term " grunge music " . Sap was certified gold within two weeks . The EP features guest vocals by Ann Wilson from the band Heart , who joined Staley and Cantrell for the choruses of " Brother " , " Am I Inside " , and " Love Song " . The EP also features Mark Arm of <unk> and Chris Cornell of Soundgarden , who appeared together on the song " Right <unk> " , credited to " Alice <unk> " in the liner notes . In 1992 , Alice in Chains appeared in the Cameron Crowe film Singles , performing as a " bar band " . The band also contributed the song " Would ? " to the film 's soundtrack , whose video received an award for Best Video from a Film at the 1993 MTV Video Music Awards . 
- 
- = = = Dirt ( 1992 – 93 ) = = = 
- 
- In March 1992 , the band returned to the studio . With new songs written primarily on the road , the material has an overall darker feel than Facelift , with six of the album 's thirteen songs dealing with the subject of addiction . " We did a lot of soul searching on this album . There 's a lot of intense feelings . " Cantrell said , " We deal with our daily demons through music . All of the poison that builds up during the day we <unk> when we play " . 
- On September 29 , 1992 , Alice in Chains released its second album , Dirt . The album peaked at number six on the Billboard 200 and since its release has been certified quadruple platinum by the RIAA , making Dirt the band 's highest selling album to date . The album was a critical success , with Steve Huey of Allmusic praising the album as a " major artistic statement , and the closest they ever came to recording a flat @-@ out masterpiece " . Chris Gill of Guitar World called Dirt " huge and foreboding , yet eerie and intimate " , and " <unk> dark and brutally honest " . Dirt spawned five top 30 singles , " Would ? " , " Rooster " , " Them <unk> " , " Angry <unk> " , and " Down in a Hole " , and remained on the charts for nearly two years . Alice in Chains was added as <unk> to Ozzy Osbourne 's No More Tears tour . Days before the tour began , Layne Staley broke his foot in an ATV accident , forcing him to use crutches on stage . Starr left the band shortly after the Rock in Rio concert in January of 1993 and was replaced by former Ozzy Osbourne bassist Mike Inez . In 1993 , the band recorded two songs with Inez , " What the Hell Have I " and " A Little <unk> " , for the Last Action Hero soundtrack . During the summer of 1993 , Alice in Chains toured with the alternative music festival <unk> , their last major tour with Staley . 
- 
- = = = Jar of Flies ( 1993 – 94 ) = = = 
- 
- Following Alice in Chains ' extensive 1993 world tour , Staley said the band " just wanted to go into the studio for a few days with our acoustic guitars and see what happened " . " We never really planned on the music we made at that time to be released . But the record label heard it and they really liked it . For us , it was just the experience of four guys getting together in the studio and making some music . " 
- Columbia Records released Alice in Chains ' second acoustic @-@ based EP , Jar of Flies , on January 25 , 1994 . Written and recorded in one week , Jar of Flies debuted at number one on the Billboard 200 , becoming the first ever EP — and first Alice in Chains release — to top the charts . Paul Evans of Rolling Stone called the EP " <unk> gorgeous " , and Steve Huey stated " Jar of Flies is a low @-@ key <unk> , <unk> gorgeous and <unk> <unk> all at once " . Jar of Flies features Alice in Chains ' first number @-@ one single on the Mainstream Rock charts , " No <unk> " . The second single , " I Stay Away " , reached number ten on the Mainstream rock charts , while the final single " Don 't Follow " , reached number 25 . After the release of Jar of Flies , Staley entered rehab for heroin addiction . The band was scheduled to tour during the summer of 1994 with Metallica , <unk> <unk> , Danzig , and Fight , as well as a slot during Woodstock 94 , but while in rehearsal for the tour , Staley began using heroin again . Staley 's condition prompted the other band members to cancel all scheduled dates one day before the start of the tour , putting the band on hiatus . Alice in Chains was replaced by <unk> on the tour . 
- 
- = = = Alice in Chains ( 1995 – 96 ) = = = 
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- While Alice in Chains was inactive during 1995 , Staley joined the " grunge <unk> " Mad Season , which also featured Pearl Jam guitarist Mike <unk> , bassist John Baker Saunders from The <unk> , and <unk> Trees drummer Barrett Martin . Mad Season released one album , Above , for which Staley provided lead vocals and the album artwork . The album spawned a number @-@ two single , " River of <unk> " , as well as a home video release of Live at the Moore . In April 1995 , Alice in Chains entered Bad Animals Studio in Seattle with producer Toby Wright , who had previously worked with <unk> of <unk> and Slayer . While in the studio , an inferior version of the song " <unk> " was leaked to radio , and received major airplay . On October 6 , 1995 , the band released the studio version of the song to radio via satellite <unk> . On November 7 , 1995 , Columbia Records released the eponymous album , Alice in Chains , which debuted at number one on the Billboard 200 and has since been certified double platinum . Of the album 's four singles , " <unk> " , " Again " , " Over Now " , and " Heaven Beside You " , three feature Cantrell on lead vocals . Jon <unk> of Rolling Stone called the album " liberating and enlightening , the songs achieve a startling , <unk> and <unk> impact . " The song " Got Me Wrong " unexpectedly charted three years after its release on the Sap EP . The song was re @-@ released as a single on the soundtrack for the independent film Clerks in 1995 , reaching number seven on the Mainstream Rock Tracks chart . The band opted not to tour in support of Alice in Chains , adding to the rumors of drug abuse . 
- Alice in Chains resurfaced on April 10 , 1996 , to perform their first concert in two and a half years for MTV Unplugged , a program featuring all @-@ acoustic set lists . The performance featured some of the band 's highest charting singles , including " Down in a Hole " , " Heaven Beside You " , " No <unk> " and " Would ? " , and introduced a new song , " Killer Is Me " . The show marked Alice in Chains ' only appearance as a five @-@ piece band , adding second guitarist Scott <unk> . A live album of the performance was released in July 1996 , debuting at number three on the Billboard 200 , and was accompanied by a home video release , both of which received platinum certification by the RIAA . Alice in Chains performed four shows supporting the reunited original Kiss lineup on their 1996 / 97 Alive / Worldwide Tour , including the final live appearance of Layne Staley on July 3 , 1996 , in Kansas City , Missouri . Shortly after the show , Staley was found <unk> after he <unk> on heroin and was taken to the hospital . Although he recovered , the band was forced to go on hiatus . 
- 
- = = = Hiatus and the death of Layne Staley ( 1996 – 2002 ) = = = 
- 
- Although Alice in Chains never officially disbanded , Staley became a <unk> , rarely leaving his Seattle condominium following the death of his ex @-@ fiancée <unk> Parrott due to infective <unk> . " <unk> worked for me for years " , Staley told Rolling Stone in 1996 , " and now they 're turning against me ... now I 'm walking through hell " . Unable to continue with new Alice in Chains material , Cantrell released his first solo album , <unk> Depot , in 1998 , also featuring Sean Kinney and Mike Inez . In 1998 , Staley reunited with Alice in Chains to record two new songs , " Get Born Again " and " Died " . Originally intended for Cantrell 's second solo album , the songs were reworked by Alice in Chains and were released in the fall of 1999 on the box set , Music Bank . The set contains 48 songs , including <unk> , demos , and previously released album tracks and singles . The band also released a 15 @-@ track compilation titled Nothing Safe : Best of the Box , serving as a sampler for Music Bank , as well as the band 's first compilation album ; a live album , simply titled Live , released on December 5 , 2000 ; and a second compilation , titled Greatest Hits in 2001 . 
- By 2002 , Cantrell had finished work on his second solo album , <unk> Trip . Written in 1998 , the album 's lyrical content focused heavily on what Cantrell regarded as the demise of Alice in Chains , which still remained evident as the album approached its June 2002 release . However , in March that year , Cantrell commented , " We 're all still around , so it 's possible [ Alice in Chains ] could all do something someday , and I fully hope someday we will . " 
- After a decade of battling drug addiction , Layne Staley was found dead in his condominium on April 19 , 2002 , two weeks after his actual death . His mother and stepfather became alarmed when accountants noticed that money was no longer being withdrawn from his accounts . With assistance from the police , they broke into his <unk> and made the discovery . An autopsy revealed Staley had died from a mixture of heroin and cocaine . His friends speculate that in addition to drugs , he may have contracted an illness that his body could not fight off , due to a compromised immune system . In his last interview , given months before his death , Staley admitted , " I know I 'm near death , I did crack and heroin for years . I never wanted to end my life this way . " Cantrell dedicated his 2002 solo album , released two months after Staley 's death , to his memory . 
- 
- = = = <unk> shows ( 2005 – 08 ) = = = 
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- In 2005 , Jerry Cantrell , Mike Inez , and Sean Kinney reunited to perform a benefit concert in Seattle for victims of the tsunami disaster that struck South Asia in 2004 . The band featured <unk> vocalist Pat <unk> , as well as other special guests including Maynard James <unk> of <unk> and Ann Wilson of Heart . On March 10 , 2006 , the surviving members performed at VH1 's <unk> Rock Live concert , honoring fellow Seattle musicians Ann and Nancy Wilson of Heart . They played " Would ? " with vocalist Phil <unk> of <unk> and Down and bass player Duff <unk> of Guns N ' Roses and Velvet Revolver , then they played " Rooster " with Comes with the Fall vocalist William DuVall and Ann Wilson . The band followed the concert with a short United States club tour , several festival dates in Europe , and a brief tour in Japan . To coincide with the band 's reunion , Sony Music released the long @-@ delayed third Alice in Chains compilation , The Essential Alice in Chains , a double album that includes 28 songs . 
- DuVall joined Alice in Chains as lead singer during the band 's reunion concerts . Duff <unk> again joined the band for the reunion tour , playing rhythm guitar on selected songs . Before the tour , Kinney mentioned in an interview that he would be interested in writing new material , but not as Alice in Chains . However , <unk> reported that the band had begun writing new material , with DuVall on lead vocals . 
- 
- = = = Black Gives Way to Blue ( 2008 – 10 ) = = = 
- 
- Blabbermouth.net reported in September 2008 that Alice in Chains would enter the studio that October to begin recording a new album for a summer 2009 release . In October 2008 , Alice in Chains began recording its fourth studio album at the Foo Fighters ' Studio <unk> in Los Angeles with producer Nick <unk> . At the Revolver Golden God Awards , Jerry Cantrell said that the group had finished recording in March 2009 and were mixing the album for a September release . In April 2009 , it was reported that the new Alice in Chains album would be released by Virgin / EMI , making it the band 's first label change in its 20 @-@ plus year career . On June 11 , 2009 , Blabbermouth.net reported that the new album would be titled Black Gives Way to Blue and was officially set to be released on September 29 , 2009 . On June 30 , 2009 , the song " A Looking in View " was released as the first single from the album . It was made available for a limited time as a free download through the official Alice in Chains website in early July . The music video for the song debuted via the official website on July 7 , 2009 . The second single , " Check My Brain " , was released to radio stations on August 14 , 2009 and was made available for purchase on August 17 , 2009 . In addition , it was announced that Elton John appears on the album 's title track . 
- In September 2008 , it was announced that Alice in Chains would headline Australia 's <unk> Festival in 2009 , alongside Nine <unk> <unk> and Lamb of God . In February 2009 , it was also announced that Alice in Chains would play at the third annual Rock on the Range festival . On August 1 , 2009 , Alice in Chains performed , along with <unk> , <unk> <unk> , and <unk> , at <unk> Park , Dublin as direct support to Metallica . The band made an appearance on Later Live ... With <unk> Holland on November 10 , 2009 , performing " Lesson Learned " , " Black Gives Way To Blue " , and " Check My Brain " as the final performance of the episode . 
- To coincide with the band 's European tour , Alice in Chains released its next single , " Your Decision " , on November 16 in the UK and on December 1 in the US . The fourth single from the album was " Lesson Learned " and was released to rock radio in mid @-@ June . On May 18 , 2010 , Black Gives Way to Blue was certified gold by the RIAA for shipments of over 500 @,@ 000 copies . 
- Along with <unk> and <unk> , Alice in Chains toured the United States and Canada in late 2010 on the <unk> tour , an <unk> of the three bands ' latest album titles ( Black Gives Way to Blue , Diamond Eyes , and <unk> the Skye ) . 
- 
- = = = Future plans and the death of Mike Starr ( 2010 – 2011 ) = = = 
- 
- In April 2010 , Cantrell revealed to MTV News that Alice in Chains was contemplating making a fifth studio album in the foreseeable future . He explained , " There are thoughts . We 'll see how far we get . <unk> in the moment is a good way to live and we certainly hope that it happens . I don 't see any reason why it wouldn 't [ happen ] . " DuVall also commented on the next album and Alice in Chains ' future , " we 've got a lot of water to sail before we do that . There 's a lot of shows . But yeah , generally speaking , yeah , we 're excited about the future . I don 't anticipate some long <unk> . " 
- DuVall revealed in September 2010 that Alice in Chains had not begun writing their next album yet , but " there 's plenty of riffs flying around . " He added , " That was the case when we first started back up . We would just <unk> these fragments , and then some time later we would <unk> through the mountain of stuff , and that 's what became Black Gives Way to Blue . The same thing has been happening since we 've been touring Black Gives Way to Blue , so it would be only natural to at some point say , ' Hey , we 've got a lot of stuff . Let 's <unk> through and see what we 've got this time . ' " DuVall also mentioned that it was possible that the new album would feature songs that were written for Black Gives Way to Blue . 
- On March 8 , 2011 , former Alice in Chains bassist Mike Starr was found dead at his home in Salt Lake City . Police told Reuters they were called to Starr 's home at 1 : 42 pm and found his body ; Starr was 44 . Reports later surfaced that Starr 's roommate had seen him mixing <unk> and anxiety medication hours before he was found dead . Later reports indicated Starr 's death may have been linked to two different types of <unk> prescribed to him by his doctor . A public memorial was held for Starr at the Seattle Center 's International Fountain on March 20 , 2011 . A private memorial was also held , which Jerry Cantrell and Sean Kinney attended according to Mike Inez . 
- 
- = = = The Devil Put Dinosaurs Here and next album ( 2011 – present ) = = = 
- 
- On March 21 , 2011 , Alice in Chains announced that they were working on a fifth studio album , and both Cantrell and Inez later made statements that they had begun the recording process . The album was expected to be finished by summer of 2012 and released by the end of 2012 or beginning of 2013 . While Alice in Chains were writing for the album in 2011 , Cantrell required surgery , which delayed recording the new material . In an interview published in May 2012 , Cantrell explained , " The thing that set me back is I had some bone spurs [ and ] cartilage issues in my shoulders . I had the same issue in the other shoulder about six years ago so I 've had them both done now . It 's a repetitive motion injury from playing . " 
- In December 2012 , Cantrell confirmed that the new album had been completed , and the first single , " Hollow " , debuted online on December 18 , available for digital download in January 2013 , along with an official music video . On February 13 , 2013 , Alice in Chains posted on Facebook that their new album title would be an anagram of the letters H V L E N T P S U S D A H I E E O E D T I U R R. The next day they announced that the album would be called The Devil Put Dinosaurs Here , which was released on May 28 , 2013 , debuting at number two on the Billboard 200 . The band released videos for the songs " Stone , " " Voices , " and the title track later in 2013 . 
- Alice in Chains toured the U.S. in the spring of 2013 , with further international dates in the summer . In the spring and summer of 2014 they embarked on a more extensive tour of Canada , Europe , and the U.S. Asked in September 2013 if Alice in Chains would make another album , Cantrell replied , " It 'll be a while . It 's [ been ] four years since we put the last one out , but at least it 's not the gap that was between the last one , so that 's about right - about three to four years . " In May 2014 Cantrell stated that Alice in Chains would <unk> up their tour in the fall , take a break , then begin work on their next studio album . 
- In January 2015 , Alice in Chains performed in the halftime show of the <unk> Championship game between the Seattle Seahawks and the Green Bay <unk> at <unk> Field in Seattle . Cantrell is a lifelong Seahawks fan and often attends their games . 
- In March 2015 , Alice in Chains announced dates for a U.S. summer tour , set to begin on July 17 in <unk> , CA and end in Bethlehem , PA on August 16 . A second leg of the tour was announced in June 2016 , including select shows opening for Guns N ' Roses as part of the Not in This Lifetime ... Tour . 
- As of May 2015 , according to Blabbermouth.net , Alice in Chains has been working on their follow @-@ up to The Devil Put Dinosaurs Here , which was expected to be released later in the year . <unk> Mike Inez said that the band has been " throwing around riffs for a new record " and " taking it nice and slow . " Asked in a June 2016 interview if Alice in Chains has begun recording their new album , frontman William DuVall replied , " I don 't know . I know we 'll be probably talking about that kind of thing over the next few months . It 's still early yet . We 've gotta get through this tour first . " 
- 
- = = Musical style = = 
- 
- Although Alice in Chains has been labeled grunge by the mainstream media , Jerry Cantrell identifies the band as primarily heavy metal . He told Guitar World in 1996 , " We 're a lot of different things ... I don 't quite know what the mixture is , but there 's definitely metal , blues , rock and roll , maybe a touch of punk . The metal part will never leave , and I never want it to " . The Edmonton Journal has stated , " Living and playing in Seattle might have got them the grunge tag , but they 've always pretty much been a classic metal band to the core . " Over the course of their career , the band 's sound has also been described as alternative metal , sludge metal , <unk> metal , drone rock , hard rock , and alternative rock . Regarding the band 's constant categorization by the media , Cantrell stated " When we first came out we were metal . Then we started being called alternative metal . Then grunge came out and then we were hard rock . And now , since we 've started doing this again I 've seen us listed as : hard rock , alternative , alternative metal and just straight metal . I walked into an HMV the other day to check out the placement and see what 's on and they 've got us relegated back into the metal section . Right back where we started ! " . According to Mike Inez , they were always the metal <unk> of the Seattle scene . 
- Jerry Cantrell 's guitar style combines " <unk> riffs and expansive guitar textures " to create " slow , brooding minor @-@ key <unk> " . He is also recognized for his natural ability to blend acoustic and electric guitars . While down @-@ tuned , distorted guitars mixed with Staley 's distinctive " <unk> @-@ to @-@ a @-@ scream " vocals appealed to heavy metal fans , the band also had " a sense of melody that was undeniable " , which introduced Alice in Chains to a much wider audience outside of the heavy metal underground . 
- According to Stephen Thomas Erlewine of AllMusic , Alice in Chains ' sound has a " Black Sabbath @-@ style <unk> and an unconventional vocal style " . The band has been described by Erlewine as " hard enough for metal fans , yet their dark subject matter and <unk> attack placed them among the front ranks of the Seattle @-@ based grunge bands " . Three of the band 's releases feature acoustic music , and while the band initially kept these releases separate , Alice in Chains ' self @-@ titled album combined the styles to form " a bleak , <unk> sound that balanced grinding hard rock with subtly textured acoustic numbers " . 
- Alice in Chains is also noted for the unique vocal harmonies of Staley ( or DuVall ) and Cantrell , which included overlapping passages , dual lead vocals , and trademark harmonies typically separated by a major third . Alyssa Burrows said the band 's distinctive sound " came from Staley 's vocal style and his lyrics dealing with personal struggles and addiction " . Staley 's songs were often considered " dark " , with themes such as drug abuse , depression , and suicide , while Cantrell 's lyrics often dealt with personal relationships . 
- 
- = = Legacy = = 
- 
- Alice in Chains has sold more than 14 million albums in the United States , around 35 million worldwide , released two number @-@ one albums , had 21 top 40 singles , and has received nine Grammy nominations . The band was ranked number 34 on VH1 's 100 Greatest Artists of Hard Rock . Alice in Chains was named 15th greatest live band by Hit <unk> , with vocalist Layne Staley placing as 27th greatest heavy metal vocalist of all time . The band 's second album , Dirt , was named 5th best album in the last two decades by Close @-@ Up magazine . In August 2009 , Alice in Chains won the <unk> ! <unk> Award . 
- Alice in Chains has had a large impact on many bands , such as Godsmack , who are named after the song off their sophomore album Dirt . According to Jon <unk> of MTV , Godsmack has " <unk> followed Alice in Chains ' lead while adding their own distinctive edge " . Godsmack singer and founder Sully <unk> has also cited Layne Staley as his primary influence . <unk> has covered Alice in Chains ' song " <unk> " live , which appears on the compilation The Singles : 1996 @-@ 2006 , and also wrote a song entitled " Layne " , dedicated to Staley , on the album 14 Shades of Grey . Three Days Grace also performs a cover of " Rooster " , which can be seen on the DVD Live at the Palace . Other bands that have been inspired by Alice in Chains include Creed , Nickelback , <unk> , Stone <unk> , <unk> of <unk> , Queens of the Stone Age , A <unk> Horse Named Death , Godsmack , Smile Empty Soul , <unk> <unk> , Cold , Hurt , <unk> , <unk> , <unk> , 10 Years , Breaking Benjamin , Days of the New , and <unk> . Metallica said they have always wanted to tour with the band , citing Alice in Chains as a major inspiration for their 2008 release , Death Magnetic . Metallica also recorded " Rebel Of Babylon " as a tribute to Layne Staley , but the song was left off Death Magnetic due to manufacturing restrictions and then later released on a four @-@ song EP , Beyond Magnetic . 
- Alice in Chains has also had a significant influence on modern heavy metal . Their songs were covered by various metal bands such as <unk> , Dream Theater , <unk> of the Moon , Suicide Silence , and Grave . <unk> and <unk> guitarist <unk> <unk> had expressed his admiration for Jerry Cantrell 's guitar work in an interview for Guitar International saying that " the <unk> and the honest feel that Jerry Cantrell gets on [ Alice in Chains ' Dirt ] record is worth a lot more than someone who plays five million notes " . Anders <unk> of Swedish melodic death metal band In Flames cited Layne Staley as an inspiration for his vocals on the band 's later albums . In addition to fellow musicians , the band has also received praise from critics , with Steve Huey of AllMusic calling them " one of the best metal bands of the ' 90s " upon reviewing the 1999 compilation Nothing Safe . 
- 
- = = Band members = = 
- 
- 
- = = = Timeline = = = 
- 
- 
- = = Discography = = 
- 
- Facelift ( 1990 ) 
- Dirt ( 1992 ) 
- Alice in Chains ( 1995 ) 
- Black Gives Way to Blue ( 2009 ) 
- The Devil Put Dinosaurs Here ( 2013 ) 
- 
- = = Awards and nominations = = 
- 
- Alice in Chains has received nine Grammy nominations . Their first nomination was for the song " Man in the Box " , nominated for Best Hard Rock Performance in 1992 . The album Dirt was nominated in the same category in 1993 . Six more songs were nominated between 1995 and 2011 , and the album The Devil Put Dinosaurs Here was nominated for Best <unk> Album , Non Classical in 2014 . 
- The music video for the song " Would ? " , Alice in Chains ' contribution to the 1992 film , Singles , won the award for Best Video from a Film at the 1993 MTV Video Music Awards . In 2009 they won the <unk> ! <unk> award , and in 2010 they won the Revolver Golden Gods award for Black Gives Way to Blue . 
- American Music Awards 
- The American Music Awards is an annual awards ceremony created by Dick Clark in 1973 . 
- Grammy Awards 
- The Grammy Awards are awarded annually by the National Academy of Recording Arts and Sciences . 
- MTV Video Music Awards 
- The MTV Video Music Awards is an annual awards ceremony established in 1984 by MTV . 
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- 
- = 2007 Hawaii Bowl = 
- 
- The 2007 <unk> Hawaii Bowl was a post @-@ season college football bowl game between the Boise State University Broncos from the Western Athletic Conference ( WAC ) and the East Carolina University Pirates from Conference USA ( C @-@ USA ) at the <unk> Stadium in Honolulu , Hawaiʻi on December 23 , 2007 . The game was the final competition of the 2007 football season for each team and resulted in a 41 – 38 East Carolina victory , even though <unk> favored Boise State to win by 10 ½ points . Many experts believed East Carolina to be big <unk> to Boise State , which had defeated the Oklahoma <unk> in the 2007 <unk> Bowl . The 2007 Hawaiʻi Bowl paid $ 750 @,@ 000 to each team 's conference in exchange for their participation . 
- The game , which was the sixth edition of the bowl , was expected to be an offensive shootout . Boise State averaged 42 @.@ 4 points during the 2007 season , while East Carolina averaged 31 . That expectation turned out to be justified as East Carolina took a 31 – 14 lead in the first half . The Broncos fought back in the second half , however , tying the score at 38 late in the fourth quarter after East Carolina 's Chris Johnson fumbled the ball , allowing <unk> defender Marty Tadman to recover the ball and return it 47 yards for a touchdown . The game remained tied until the final moments as East Carolina 's Ben Hartman made a 34 – yard game @-@ winning field goal as time expired . The attendance of 30 @,@ 467 was the largest crowd to attend a Hawaiʻi Bowl game that did not feature the host school . Boise State 's loss dropped them to a final 2007 record of 10 – 3 , while East Carolina 's final @-@ game win earned them a record of 8 – 5 . 
- 
- = = Selection process = = 
- 
- The Hawaiʻi football team normally receives an automatic bid to the game unless the team is selected to participate in a BCS game or is not bowl eligible . If either of those events happen , the WAC selects its next @-@ highest ranked team to compete in the game . In 2007 , Hawaiʻi was selected to play on January 1 , 2008 in the Sugar Bowl against Georgia , marking just the second time since the creation of the Hawaiʻi Bowl that Hawaiʻi would not participate . The only previous time that Hawaiʻi failed to make an appearance was in 2005 , when the Warriors finished their season with a 5 – 7 record and were not eligible for a bowl game . 
- Conference USA fields the other team in the Hawaiʻi Bowl . The Liberty Bowl has the first selection of C @-@ USA teams , and <unk> Bowl picks second . After those two are picked , the conference provides a team for the Hawaiʻi Bowl and three other bowls . In 2007 , the University of Central Florida accepted the bid to compete in the Liberty Bowl , <unk> accepted the bid to compete in the <unk> Bowl , leaving East Carolina to be selected by the Hawaiʻi Bowl on December 1 , 2007 . 
- Boise State was undefeated in conference play going into the final game of the regular season , and suffered just one out @-@ of @-@ conference loss . In that final game , BSU lost 39 – 27 to undefeated Hawaiʻi . Following the game , the Broncos decided to travel to Hawaiʻi , rather than play at home in the Humanitarian Bowl , which is located at Boise 's home stadium in Boise , Idaho and features a matchup between a WAC team and one from the Atlantic Coast Conference . Boise ended its regular season 10 – 2 overall and 7 – 1 in conference play . 
- East Carolina , meanwhile , had been in first place in the C @-@ USA 's East Division with just two games remaining . A loss to Marshall University in that second @-@ to @-@ last game , however , put ECU into second place in its division , and thus out of competition for the Conference USA Championship . After winning against conference <unk> Tulane in the final game of the season , the Pirates decided to travel to the Hawaiʻi Bowl . East Carolina ended the regular season 7 – 5 overall , and 6 – 2 in conference play . 
- 
- = = Pre @-@ game buildup = = 
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- On December 1 , 2007 , Hawaiʻi Bowl representatives announced East Carolina and Boise State as the competitors in the 2007 edition of the game . It would be the first time in history that the two teams had ever met . Two weeks after the announcement , Boise State 's top wide receiver Jeremy Childs , cornerback Keith <unk> , and linebacker Ben Chandler were declared out for the game after violating team rules . In an online contest held by ESPN.com in the weeks leading up to the game , 98 % of the participants voted that Boise State would beat East Carolina . Participants also gave 32 out of 32 " confidence points " on average , indicating the highest possible confidence in the predicted outcome . 
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- = = = Boise State offense versus East Carolina defense = = = 
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- Boise State came into the game averaging 42 @.@ 75 points and <unk> @.@ 67 yards per game , the fourth and twelfth highest totals in college football . Senior quarterback Taylor Tharp led the offense by more than 3 @,@ 000 yards and 28 touchdowns , headed into the 2007 <unk> Bowl . On the ground , the Broncos were led by junior running back Ian Johnson , who had 17 touchdowns and 1 @,@ 030 yards in the regular season . Wide receiver Jeremy Childs also was a potent threat on offense , averaging 87 @.@ 08 receiving yards per game , and backup running back D.J. Harper rushed for 41 @.@ 44 yards per game on average . Johnson and Childs both earned First Team All @-@ WAC honors , and Tharp was named to Second Team All @-@ WAC . Offensive tackle Ryan <unk> was the team 's only All @-@ American , and promised to do a good job protecting Taylor Tharp on the offensive line . 
- East Carolina had been <unk> on defense . ECU ranked <unk> in total defense and <unk> in passing defense , giving up an average of <unk> @.@ 42 yards per game . Despite that fact , the Pirates did well in the turnover battle , gaining 27 turnovers while losing just 13 – a margin of + 14 – good enough for ninth in the country . In addition , the ECU defense ranked eleventh nationally in tackles for loss . Defensive <unk> Zack Slate and C.J. Wilson and defensive back Van <unk> took C – USA All – Conference second team honors . 
- 
- = = = East Carolina offense versus Boise State defense = = = 
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- Boise State faced the nation 's top all @-@ purpose yards leader , All @-@ American Chris Johnson . Johnson <unk> up an average of 212 @.@ 67 all @-@ purpose yards per game during the 2007 season , making him a potent threat on offense . Taking snaps for the offense were <unk> Patrick Pinkney and Rob Kass . Pinkney had a passer rating of 131 @.@ 58 , while Kass had a comparable rating of 122 @.@ 94 . The top receiver for the Pirates was <unk> Bryant , who averaged 3 @.@ 5 catches for 52 @.@ 17 yards per game . In addition to Chris Johnson , who was named to the C – USA All – Conference second team , offensive lineman Josh <unk> received C – USA All @-@ Conference second team honors . 
- The Broncos ' defense , meanwhile , was ranked number one in the WAC in total defense , scoring defense , rushing defense , and pass defense . The team 's defense ranked number two in the conference in pass efficiency defense , turnover margin , and sacks . Defensive lineman Nick <unk> and defensive back Marty Tadman both received All – WAC first team honors and promised to pose difficulties for the Pirates ' offensive front . 
- 
- = = Game summary = = 
- 
- The 2007 Hawaiʻi Bowl kicked off on December 23 , 2007 at 8 : 00 <unk> EST in front of an estimated crowd of 30 @,@ 467 , which was the largest crowd in Hawaiʻi Bowl history for a game not featuring the <unk> 'i football team . The game was nationally televised on ESPN , and was watched by an estimated average of 1 @.@ 6 million people , good enough for a television rating of 1 @.@ 47 . East Carolina had to travel 4 @,@ <unk> miles ( 7 @,@ <unk> km ) one way , making it the longest bowl trip of the season for any team during the 2007 bowl season . Boise State , meanwhile , traveled 2 @,@ 835 miles ( 4 @,@ 562 km ) to Hawaiʻi . Due to the distance involved , East Carolina fans wishing to support their school , but unable to travel to the game , bought 2 @,@ <unk> tickets for troops stationed in Hawaiʻi as a show of support . The weather at kickoff was mostly cloudy with winds from the northeast at 13 miles ( 21 km ) . The temperature was 77 ° F ( 25 ° C ) . For the officiating crew , the referee was Terry <unk> , umpire was Kevin Matthews , and the linesman was Bob <unk> . The line judge was Bart <unk> , the back judge was Joe Johnston and the field judge was Ed <unk> . The side judge was Craig <unk> . 
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- = = = First quarter = = = 
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- East Carolina won the pre @-@ game coin toss and deferred the ball until the second half , allowing Boise State to receive the ball to begin the game . The Broncos received the opening kickoff at their 4 – yard line and went three @-@ and @-@ out . Following the Broncos ' punt , East Carolina began its first possession of the game on its 43 – yard line . The offense marched to the 19 – yard line of Boise State , but was stopped on third down . ECU kicker Ben Hartman was sent into the game and made a field goal from 36 yards away . 
- East Carolina kicked the ball to Boise State 's Austin Smith and he returned the kickoff 89 yards for a touchdown . The touchdown and extra point made the score 7 – 3 and gave Boise State its only lead of the game . 
- The next two drives for each team ended in <unk> . On East Carolina 's third drive , Chris Johnson rushed on the first play 68 yards for a touchdown . With the point after , East Carolina lead 10 – 7 . The next Boise State drive ended in a three and out , and the Broncos kicked the ball away . The Pirates marched down the field 55 yards , but the quarter ended before ECU had a chance to score . 
- At the end of the first quarter , East Carolina led Boise State 10 – 7 . 
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- = = = Second quarter = = = 
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- ECU began the second quarter on Boise 's 30 – yard line , and quarterback Patrick Pinkney continued the Pirates ' drive with a 14 – yard rush . After three more successful plays , Dominique Lindsay rushed 3 yards for a touchdown . <unk> Ben Hartman converted the point after , which made the score 17 – 7 ECU . 
- The Broncos ' first drive of the second quarter was halted after a holding penalty and an incomplete pass denied BSU a chance for a first down . Boise was forced to kick the ball away once more . The Pirates ' second drive of the quarter began at their 26 – yard line . ECU needed just four plays to march 74 yards down the field for a touchdown . On the drive , Chris Johnson rushed for 46 yards and threw an 18 – yard pass for a touchdown . After the touchdown and the extra point kick , ECU led 24 – 7 . 
- Boise State 's first play after the post @-@ touchdown kickoff was intercepted by Pirates ' defender Travis Williams . Despite the turnover , East Carolina was unable to attain a first down , and went three @-@ and @-@ out before punting . On Boise 's second play of the new drive following the punt , the team earned its first first down of the game . The Broncos continued the drive by going 69 yards in eight plays , culminating in a touchdown . The series included five complete passes and two rushes . With the score , The Broncos had closed the gap to ten points : 24 – 14 East Carolina . 
- East Carolina started their series on their 41 – yard line . A substitution infraction on ECU and an incomplete pass forced ECU to punt the ball away . On the Broncos ' fourth drive of the second quarter , the team began with three complete passes for 18 yards . On the fourth play , however , BSU 's Titus Young fumbled the ball away . Pirates ' defender Jay Ross recovered the fumble , and ECU had another chance on offense before the end of the first half . East Carolina ran five plays for 38 yards . With the clock running down , ECU elected to kick a field goal . The kick was good , and made the score 31 – 14 ECU . Boise State had one final chance on offense , and Boise Quarterback Taylor Tharp went two @-@ for @-@ four , but ran out of time before coming into field goal range . 
- At halftime , East Carolina led Boise State 31 – 14 . 
- 
- = = = Third quarter = = = 
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- Because Boise State had received the opening kickoff , East Carolina received the ball to open the second half . ECU completed seven rushes and four passes for 74 yards in five minutes and 53 seconds . On the last play of the drive , Brandon Simmons rushed three yards for a touchdown , and ECU increased its lead to 38 – 14 . 
- To begin its first series of the second half , Boise State returned the post @-@ touchdown kickoff 55 yards . Two quick plays later , Boise quarterback Taylor Tharp completed a pass to Jeremy Avery for a touchdown , cutting East Carolina 's lead to 17 points , 38 – 21 . 
- After the punt , the Pirates were unable to obtain a first down . Three plays netted only two yards , and ECU was forced to punt the ball away . On the ensuing drive , Boise State completed three first downs on the series . BSU kicker Kyle Brotzman , converted a 31 yard field goal and the Broncos shrank the ECU lead further . The series went 40 yards in nine plays . It took the team two minutes and 37 seconds to score . 
- East Carolina took the kickoff on the 39 – yard line , after a Boise player illegally touched the ball on the 39 . Pinkney threw an incomplete pass to start the drive , and after two more plays failed to convert the first down , ECU was forced to punt the ball away . After a touchback , Boise State began at its 20 – yard line . The Broncos completed three plays for 11 yards before the third quarter ended . 
- At the end of the third quarter , East Carolina led Boise State 38 – 24 . 
- 
- = = = Fourth quarter = = = 
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- BSU started the fourth quarter by throwing two incomplete passes . Tharp then completed two passes for a combined 39 yards before Pirates ' defender <unk> Milbrook intercepted Tharp 's fifth pass of the quarter . Milbrook returned the interception 27 yards . The Pirates completed one first down but failed to convert the interception into points . <unk> Matt Dodge came into the game , ostensibly to punt the ball away . In a trick play , he rushed the ball instead , picking up the first down . Despite the trick play 's success , the Pirates were not able to gain another first down . On fourth down , Dodge came out again , and this time punted the ball 47 yards for a touchback . The Broncos began the second drive of the fourth quarter by going 80 yards in nine plays for a touchdown . It took just three minutes and 59 seconds and BSU only three first downs . Boise had now cut the Pirates ' lead to 38 – 31 . 
- On the next drive , ECU brought in a new quarterback , Rob Kass . Kass was initially successful , and gained a few first downs . The Boise State defense stiffened , however , and ECU was forced to punt the ball away . After a touchback , Boise State began on its 20 – yard line . Running back Jeremy Avery rushed for four yards on the first play . On the third play , Tharp completed a pass for nine yards for a first down . Tharp then passed three straight times for 32 yards and two first downs . On subsequent plays , Tharp rushed for six yards and passed for another six to convert BSU 's third first down of the drive . Three plays later , however , the Broncos ' Titus Young fumbled the ball for the second time in the game , and as before , ECU recovered , seemingly sealing the victory . 
- After a Boise State unsportsmanlike conduct penalty , ECU began its their 39 – yard line , needing only to rush the ball to keep the clock moving in order to secure the win . On the second rush by Chris Johnson , however , he fumbled the ball . The fumble was recovered by Broncos ' defender Marty Tadman at the ECU 47 – yard line and returned all the way for a touchdown . After the <unk> , Boise State tied East Carolina 38 – 38 . The fumble and touchdown turned what had been an inevitable ECU victory into a tie game . 
- Boise kicked the ball deep , hoping to stop the Pirates ' offense and force overtime . Following the kick , ECU began at its own nine – yard line . The Pirates began the drive with one minute and 16 seconds left in the game . Dominique Lindsay rushed for two yards before Rob Kass completed a pass to <unk> Bryant for 39 yards , enough for a first down near midfield . The Pirates ' continued to move forward , passing for short yardage and rushing for short gains that kept the clock moving , but advanced the ball closer to field goal range . On the third to the last play , Kass rushed for seven yards , and ECU took a timeout with 15 seconds left . Rob Kass lost one yard while moving the ball towards the center of the field in order to set up a game @-@ ending kick . ECU took its last timeout , again stopping the clock . With four seconds remaining on the clock , ECU kicker Ben Hartman converted a 34 – yard field goal to take the lead and the win , 41 – 38 , as time ran out . 
- 
- = = Final statistics = = 
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- East Carolina running back Chris Johnson finished the game with 223 rushing yards , 32 receiving yards , and 153 return yards for a total of <unk> all @-@ purpose yards . That mark broke the NCAA bowl record for all @-@ purpose yards previously set by Alabama 's Sherman Williams against Ohio State in the 1995 <unk> Bowl . On the basis of his record @-@ setting performance , Johnson was named the game 's Most Valuable Player . On the opposite side of the ball , Boise State <unk> Jeremy Avery had a solid , if <unk> , outing . He produced 69 rushing yards , 43 receiving yards , 41 kick – return yards , and caught a 25 – yard touchdown pass . Both Johnson and Avery received the Most Valuable Player award for their respective teams . 
- Boise State committed four turnovers , compared with East Carolina 's one . The Broncos fumbled the ball away twice and threw two interceptions . Despite the <unk> in turnover margin , the teams were strikingly similar in several statistical categories , indicating the closeness of the game : Each team earned seven points off the turnovers . Both teams committed 50 yards in penalties ; the Pirates had seven penalties to the Broncos ' four . In addition , both teams earned 22 first downs . 
- 
- = = = Boise State statistical <unk> = = = 
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- Boise State University had 368 total offensive yards during the game . About 73 % of BSU 's total offense came through the air , as quarterback Taylor Tharp passed for 270 yards . The remaining 98 yards came on the ground from five different rushers . Running back Jeremy Avery ran for a team @-@ high 69 yards on 10 carries , while running back D.J. Harper had the only Boise State rushing touchdown of the game . 
- Tharp 's 30 completions were caught by eight different receivers . Austin <unk> led the team with nine catches for 89 yards , and his total accounted for one – third of the team 's receiving yards . Two receivers , Jeremy Avery and Ryan Putnam , accounted for all of the team 's receiving touchdowns . Avery had four catches for 43 yards , while Putnam had one catch for three yards . 
- Taylor Tharp 's two interceptions were the second @-@ most he had thrown in a game during 2007 , and brought his season interception total to 11 . He completed 30 of 44 pass attempts , a completion percentage of 68 @.@ 2 % . His 270 yards were 13 yards more than his season average of 257 yards . With only two passing touchdowns , Tharp tied for his third @-@ worst passing game in 2007 , <unk> worse only in the win against Weber State , loss to Washington , win against Fresno State , and loss to Hawaiʻi . Tharp was sacked once by Pirates ' defensive lineman C.J. Wilson for a seven yard loss . 
- Boise State 's defense had a hard time stopping the East Carolina offense , especially in the first half . The Pirates ' 31 points were the most scored all year by East Carolina in the first half . Leading the Broncos ' defense was safety Marty Tadman . Tadman had seven solo tackles , three assisted tackles , and recorded a defensive touchdown after recovering Chris Johnson 's fumble late in the fourth quarter . <unk> Kyle <unk> also starred on defense , recording seven solo tackles , one assisted tackle , and one tackle for a one @-@ yard loss . A total of 23 players recorded at least one tackle . 
- Kyle Brotzman handled all the kicking duties for Boise State , punting the ball four times for 169 yards . His longest punt was 52 yards , and one kick was downed inside the Pirates ' 20 yard line . His only field goal was a 31 – yard kick that came with three minutes and 11 seconds left in the third quarter . Brotzman kicked off seven times for 384 yards , averaging 54 @.@ 9 yards per kick with no <unk> . Marty Tadman had BSU 's only punt return for <unk> yard . The Broncos ' special teams had more luck on kickoff returns . Three players had kickoff returns . Austin Smith returned four kickoffs for 173 yards . His 89 – yard return for a touchdown in the first quarter was the team 's longest of the game and gave Boise State its only lead of the game . Jeremy Avery returned two kicks for 41 yards , and Titus Young returned one kick for 52 yards . 
- Boise State controlled the time of possession only during the fourth quarter , when the Broncos scored 14 points to tie the game . Overall , however , Boise State only controlled the ball for 26 minutes and 16 seconds , in comparison to East Carolina 's 33 minutes and 44 seconds . 
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- = = = East Carolina statistical <unk> = = = 
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- Almost 70 % of the Pirates ' rushing offense came from running back Chris Johnson , and the rushing offense itself consisted of two – thirds of East Carolina 's total offensive effort . Six additional rushers contributed 99 yards on the ground . The longest rush of the game – 68 yards – came from Johnson . Quarterback Patrick Pinkney played much of the game , completing 12 passes in 19 attempts . Pinkney threw for 118 yards and one touchdown , but the longest pass came from the Pirates ' second quarterback , Rob Kass . Kass threw a 36 – yard pass in the fourth quarter to keep the Pirates ' game @-@ winning drive alive . The pass was Kass 's only completion out of three attempts . Almost one – third of East Carolina 's total offense came from the air . 
- Ben Hartman and Matt Dodge both contributed to East Carolina 's kicking game . Dodge punted the ball seven times for 302 yards . His longest was a 61 – yard kick , and he had four <unk> . Hartman and Dodge shared kickoff duty . Dodge had four kickoffs , compared with Hartman 's three . Dodge averaged 60 @.@ 5 yards per kick , while Hartman averaged 57 yards . In addition , Hartman added six points to the <unk> from field goal attempts . He was two for two , with the second field goal attempt being the game @-@ winner in the fourth quarter . Dwayne Harris handled all punt returns . He fielded two <unk> , returning them for a total of five yards . <unk> @-@ return duty was handled by Chris Johnson . He returned six kickoffs for 153 yards , with his longest return consisting of 39 yards . 
- East Carolina 's defense managed a strong performance statistically and in real terms . The most obvious examples of this were the two interceptions and two forced fumbles . On the first play of the second drive of the second quarter , Boise State quarterback Taylor Tharp threw an interception to defensive back Travis Williams . Williams also recorded eight solo tackles — the second @-@ highest total for the Pirates – and a forced fumble . The other interception came from defensive back <unk> Milbrook , who also boasted three solo tackles , tying him for fifth – best on the team . <unk> Jeremy forced the final Broncos turnover , gaining the ball and four yards on the play . In addition to his forced fumble , <unk> recorded four solo tackles . The Pirates ' defense as a whole only gave up three yards in the first quarter and did not allow a first down until halfway through the second quarter . 
- That strong defensive effort limited the Broncos ' third – down conversion rate to 50 % . Until the fourth quarter , BSU was just two for seven on third – down attempts . On offense , the Pirates fared slightly better , going six for fifteen on third downs . 
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- = = After @-@ effects = = 
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- The win by East Carolina knocked # 24 Boise State out of the final AP Poll of the year . This was the first time BSU was not ranked in the AP Poll top 25 since October 28 , 2007 . Because of his impressive performance , Chris Johnson was invited to the 2008 Senior Bowl . After the game , East Carolina finished the season 8 @-@ 5 , the highest win total since 2000 . This also marked Coach <unk> Holtz 's first Bowl win . Boise State finished the season 10 @-@ 3 . This marked Coach Chris <unk> first post @-@ season loss . 
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- 
- = The Boat Race 1999 = 
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- The <unk> Boat Race took place on 3 April 1999 . Held annually , the Boat Race is a side @-@ by @-@ side rowing race between crews from the Universities of Oxford and Cambridge along the River Thames . Featuring the tallest rower in Boat Race history , Cambridge won the race in the second @-@ fastest time ever . It was their seventh consecutive victory in the event . 
- In the reserve race , Cambridge 's Goldie defeated Oxford 's Isis in the fastest time ever , while Cambridge won the Women 's Boat Race . 
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- = = Background = = 
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- The Boat Race is a side @-@ by @-@ side rowing competition between the University of Oxford ( sometimes referred to as the " Dark Blues " ) and the University of Cambridge ( sometimes referred to as the " Light Blues " ) . First held in 1829 , the race takes place on the 4 @.@ 2 @-@ mile ( 6 @.@ 8 km ) Championship Course on the River Thames in southwest London . The rivalry is a major point of honour between the two universities and followed throughout the United Kingdom and broadcast worldwide . Cambridge went into the race as reigning champions , having won the 1998 race by three lengths , with Cambridge leading overall with 75 victories to Oxford 's 68 ( excluding the " dead heat " of 1877 ) . 
- The first Women 's Boat Race took place in 1927 , but did not become an annual fixture until the 1960s . Up until 2014 , the contest was conducted as part of the <unk> Boat Races , but as of the 2015 race , it is held on the River Thames , on the same day as the men 's main and reserve races . The reserve race , contested between Oxford 's Isis boat and Cambridge 's Goldie boat has been held since 1965 . It usually takes place on the <unk> , prior to the main Boat Race . 
- Andrew Lindsay was confident that the Oxford crew would be more motivated than their opponents : " our advantage over Cambridge is that we are hungry for the victory . Everyone in the Oxford boat is driven to go and win this damn thing " . He was making his third and final appearance in the race having lost in both the 1997 and 1998 race . His grandfather represented Cambridge in the 1930s , and his uncle , Alexander Lindsay , rowed for the losing Oxford crew in the 1959 race before <unk> the following year . Cambridge boat club president and Canadian international rower Brad Crombie was also making his third Boat Race appearance , attempting to complete a hat @-@ trick of victories . Sean Bowden was the head coach of Oxford . His Cambridge counterpart , Robin Williams , suggested " it still feels like all or nothing to us . The fear of defeat , the aim of trying to push the limits is motivation itself " . Just as he had done in the 1993 race , umpire Mark Evans introduced modifications to the starting procedure , suggesting that he would be content to hold the crews for up to ten seconds between issuing the " set " and " go " commands . Cambridge 's Williams remarked : " I 'm happy as long as both crews abide by it " , Bowden was <unk> " Go is when you start races . I 'm happy . " 
- The race was sponsored for the first time by Aberdeen <unk> Management , and both crews were competing for the Aberdeen <unk> Trophy . It was the fiftieth anniversary of the BBC 's coverage of the event and over the preceding five years had secured an average audience in excess of six million . 
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- = = Crews = = 
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- The Oxford crew weighed @-@ in at an average of 14 <unk> 10 lb ( 93 @.@ 2 kg ) , 0 @.@ 5 pounds ( 0 @.@ 23 kg ) more per rower than Cambridge . Josh West , rowing at number four for Cambridge , became the tallest rower in Boat Race history at 6 ft 9 in ( 2 @.@ 06 m ) . The Oxford crew comprised three Britons , three Americans , a <unk> , a Canadian and a German , while Cambridge were represented by five Britons , two Americans , a German and a Canadian . Three former Blues returned for Cambridge in Wallace , Crombie and Smith , while Oxford saw <unk> and Lindsay return . <unk> Sharif , the Cambridge <unk> , became the tenth female to steer a Boat Race crew , and was the lightest competitor at the event since the 1986 race . 
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- = = Race = = 
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- <unk> could not initially separate the crews , offering odds on for either boat to win . However , as the start of the race approached , Williams had suggested that he was worried by his crew 's " inconsistency " and Oxford were declared favourites . Cambridge won the toss and elected to start from the Surrey station . Despite being warned by umpire Davis , Cambridge were soon half @-@ a @-@ length ahead , and a second clear by the Mile Post . The lead was extended to a length by <unk> Bridge and Sharif had steered her boat into a better angle of attack . <unk> on , Cambridge were seven seconds up by <unk> Steps and nine seconds at Barnes Railway Bridge . They passed the finishing post three @-@ and @-@ a @-@ half lengths ahead , with an eleven @-@ second advantage over the Dark Blues . The Light Blues finished in 16 minutes 41 seconds , a time only bettered once before , in 1998 . It was the first time since 1936 that Cambridge had secured seven consecutive victories . 
- In the reserve race , Cambridge 's Goldie beat Oxford 's Isis by one @-@ and @-@ a @-@ half lengths , their ninth victory in ten years , and in a record time of 16 minutes 58 seconds which beat the fastest time recorded in 1996 and repeated in 1998 . Cambridge won the 51st Women 's Boat Race by one length in a time of 6 minutes 1 second , their eighth consecutive victory . 
- 
- = = Reaction = = 
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- Oxford 's Bowden was <unk> : " I 'm really <unk> . I just haven 't got any answers until I talk to the crew . " His number four , Toby <unk> admitted : " my impression is that they were quicker than us and that is a very hard thing to have to say . " Cambridge 's Williams noted : " I thought it would be a bit more competitive than that . " Cambridge boat club president Crombie exclaimed " that 's the most fun I 've ever had rowing for Cambridge . " 
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- = Hoysala literature = 
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- Hoysala literature is the large body of literature in the Kannada and Sanskrit languages produced by the Hoysala Empire ( <unk> – <unk> ) in what is now southern India . The empire was established by <unk> Kama II , came into political prominence during the rule of King Vishnuvardhana ( 1108 – <unk> ) , and declined gradually after its defeat by the <unk> dynasty invaders in <unk> . 
- Kannada literature during this period consisted of writings relating to the socio @-@ religious developments of the Jain and Veerashaiva faiths , and to a lesser extent that of the Vaishnava faith . The earliest well @-@ known brahmin writers in Kannada were from the Hoysala court . While most of the courtly textual production was in Kannada , an important corpus of monastic Vaishnava literature relating to Dvaita ( <unk> ) philosophy was written by the renowned philosopher Madhvacharya in Sanskrit . 
- Writing Kannada literature in native metres was first popularised by the court poets . These metres were the sangatya , compositions sung to the accompaniment of a musical instrument ; shatpadi , six @-@ line verses ; ragale , lyrical compositions in blank verse ; and tripadi , three @-@ line verses . However , Jain writers continued to use the traditional champu , composed of prose and verse . Important literary contributions in Kannada were made not only by court poets but also by noblemen , commanders , ministers , ascetics and saints associated with monasteries . 
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- = = Kannada writings = = 
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- = = = Overview = = = 
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- Beginning with the 12th century , important socio @-@ political changes took place in the Deccan , south of the Krishna river . During this period , the Hoysalas , native <unk> from the <unk> region ( hill country in modern Karnataka ) were on the <unk> as a political power . They are known to have existed as chieftains from the mid @-@ 10th century when they distinguished themselves as subordinates of the Western Chalukyas of Kalyani . In <unk> , Hoysala King Vishnuvardhana defeated the <unk> of <unk> and annexed <unk> ( parts of modern southern Karnataka ) , thus bringing the region back under native rule . In the following decades , with the <unk> of the <unk> power , the Hoysalas proclaimed independence and grew into one of the most powerful ruling families of southern India . Consequently , literature in Kannada , the local language , flourished in the Hoysala empire . This literature can be broadly subdivided as follows : works dominated by the themes of Jain writings , contrasting works by Veerashaiva writers not belonging to the vachana poetic tradition , <unk> to Shaiva writings from Jain writers , early <unk> works ( Vaishnava ) , works from the birth of the Bhakti ( devotional ) movement in the Kannada @-@ speaking region , writings on secular topics , and the first writings in native metres ( ragale , sangatya and shatpadi ) . 
- As in earlier centuries , Jain authors wrote about <unk> ( saints ) , princes and other personages important to the Jain religion . Jain versions of the Hindu epics such as the Ramayana and Bhagavata ( tales of Hindu god Krishna ) were also written . According to R. <unk> , a noted scholar on Kannada literature , more Jain writers wrote in Kannada than in any other <unk> language during the " Augustan age " of Kannada literature , from the earliest known works to the 12th century . The Veerashaiva writers , devotees of the Hindu god Shiva , wrote about his 25 forms in their <unk> of <unk> . Vaishnava authors wrote treatments of the Hindu epics , the Ramayana , the Mahabharata and the Bhagavata . Breaking away from the old Jain tradition of using the champu form for writing Kannada literature , Harihara penned poems in the ragale metre in <unk> @-@ <unk> @-@ <unk> ( 1160 ) . His nephew Raghavanka established the shatpadi tradition by writing a unique version of the story of King Harishchandra in Harishchandra Kavya ( 1200 ) . Sisumayana introduced the sangatya metre in his <unk> and <unk> ( 1235 ) . However , some scholars continued to employ Sanskritic genres such as champu ( Ramachandra <unk> ) , <unk> ( 100 verse compositions , Pampa sataka ) and ashtaka ( eight line verse compositions , <unk> ashtaka ) . 
- The exact beginnings of the haridasa movement in the Kannada @-@ speaking region have been disputed . <unk> <unk> , a noted <unk> scholar , claimed in his book Karnataka <unk> that the movement was inspired by saint <unk> Dasa of <unk> ( in the modern <unk> district ) in the 9th century . However , neither the language used in <unk> Dasa 's compositions nor the discovery of a composition with the pen name " <unk> <unk> " , which mentions the 13th @-@ century philosopher Madhvacharya , lends support to the 9th @-@ century theory . Naraharitirtha ( 1281 ) , one of earliest disciples of Madhvacharya , is therefore considered the earliest haridasa to write Vaishnava compositions in Kannada . <unk> topics were popular and included <unk> on poetry ( <unk> ) and writings on natural sciences ( <unk> ) , mathematics ( <unk> ) , fiction ( Lilavati ) , grammar ( Shabdamanidarpana ) , rhetoric ( <unk> ) and others . 
- Important contributions were made by some prominent literary families . One Jain family produced several authors , including Mallikarjuna , the noted <unk> ( <unk> ) ; his brother @-@ in @-@ law Janna ( 1209 ) , the court poet of King Veera Ballala II ; Mallikarjuna 's son Keshiraja ( 1260 ) , considered by D. R. <unk> , a scholar on literary cultures in history , to be the greatest theorist of Kannada grammar ; and <unk> , who was in the court of King Narasimha I and was the maternal grandfather of Keshiraja . Harihara ( 1160 ) and his nephew Raghavanka ( 1200 ) , poets who set the trend for using native metres , came from a Shaiva family ( devotees of the god Shiva ) . 
- The support of the Hoysala rulers for the Kannada language was strong , and this is seen even in their <unk> , often written in polished and poetic language , rather than prose , with illustrations of floral designs in the margins . In addition to the Hoysala patronage , royal support was enjoyed by Kannada poets and writers during this period in the courts of neighbouring kingdoms of the western Deccan . The Western Chalukyas , the southern Kalachuris , the <unk> <unk> of <unk> and the <unk> of <unk> are some of the ruling families who enthusiastically used Kannada in inscriptions and promoted its literature . 
- Writers bilingual in Kannada and Telugu gained popularity which caused interaction between the two languages , a trend that continued into modern times . The <unk> canon of the Kannada language was translated or adapted into Telugu from this time period . Palkuriki Somanatha ( <unk> ) , a devotee of social reformer Basavanna , is the most well @-@ known of these bilingual poets . The Chola chieftain <unk> ( c . 1150 ) used many Kannada words in his Telugu writings . After the decline of the Hoysala empire , the Vijayanagara empire kings further supported writers in both languages . In <unk> , inspired by Palkuriki Somanatha , <unk> Kavi translated the Telugu <unk> to Kannada , and King Deva <unk> II ( c . 1425 ) had <unk> 's landmark writing <unk> translated into Telugu and Tamil . Many Veerashaiva writers in the court of the 17th century Kingdom of Mysore were <unk> in Kannada , Telugu and Sanskrit while the <unk> ( a sect of Vaishnavism ) Kannada writers of the court were in competition with the Telugu and Sanskrit writers . 
- Information from contemporary records regarding several writers from this period whose works are considered lost include : <unk> ( probable author of Rama <unk> and guru of <unk> of 1235 ) , <unk> ( guru of <unk> , and author of <unk> <unk> and possibly a <unk> @-@ <unk> , 1170 ) , Sambha Varma ( mentioned by <unk> of 1145 ) , Vira Nandi ( <unk> <unk> , 1175 ) , <unk> Pandita ( <unk> <unk> <unk> and <unk> <unk> ) , <unk> Nandi ( <unk> <unk> ) , <unk> ( <unk> ) , <unk> ( <unk> <unk> ) , <unk> , a Veerashaiva <unk> ( <unk> , 1300 ) , and <unk> Kavi ( author of <unk> Purana in 1330 ) . 
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- = = = Jain epics = = = 
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- During the early 12th @-@ century ascendancy of the Hoysalas , the kings of the dynasty entertained imperial ambitions . King Vishnuvardhana wanted to perform Vedic sacrifices <unk> an emperor , and surpass his overlords , the Western Chalukyas , in military and architectural achievements . This led to his conversion from Jainism to Vaishnavism . Around the same time , the well @-@ known philosopher Ramanujacharya sought refuge from the <unk> in Hoysala territory and popularised the Sri Vaishnava faith , a sect of Hindu Vaishnavism . Although Jains continued to dominate culturally in what is now the southern Karnataka region for a while , these social changes would later contribute to the decline of Jain literary output . The growing political <unk> of the Hoysalas attracted many bards and scholars to their court , who in turn wrote <unk> on their patrons . 
- Nagachandra , a scholar and the builder of the <unk> <unk> ( a Jain temple in honor of the 19th Jain tirthankar , <unk> , in <unk> , Karnataka ) , wrote <unk> ( 1105 ) , an account of the evolution of the soul of the Jain saint . According to some historians , King Veera Ballala I was his patron . Later , he wrote his magnum opus , a Jain version of the Hindu epic Ramayana called Ramachandra <unk> ( or Pampa Ramayana ) . Written in the traditional champu metre and in the <unk> <unk> tradition of <unk> , it is the earliest extant version of the epic in the Kannada language . The work contains 16 sections and <unk> significantly from the original epic by <unk> . Nagachandra represents King Ravana , the villain of the Hindu epic , as a tragic hero , who in a moment of weakness commits the sin of <unk> <unk> ( wife of the Hindu god Rama ) but is eventually purified by her devotion to Rama . In a further <unk> , Rama 's loyal brother <unk> ( instead of Rama ) kills Ravana in the final battle . Eventually , Rama takes <unk> @-@ <unk> ( converts to <unk> monk ) , becomes an ascetic and <unk> <unk> ( enlightenment ) . Considered a complementary work to the Pampa <unk> of <unk> Pampa ( <unk> , a Jain version of the epic Mahabharata ) , the work earned Nagachandra the honorific " <unk> Pampa " ( " new Pampa " ) . Only in the Kannada language do Jain versions exist of the Hindu epics , the Mahabharata and Ramayana , in addition to their <unk> version . 
- <unk> ( 1108 ) , known for her wit and humour , was one of the earliest female poets of the Kannada language and a contemporary of Nagachandra , with whom she indulged in debates and <unk> . <unk> , a native of either <unk> or <unk> ( the modern <unk> district ) , was in the Hoysala court during the days of King Veera Ballala I and King Vishnuvardhana . He wrote in easy verse on arithmetic and other mathematical topics and is credited with three of the earliest writings on mathematics in the Kannada language : <unk> , <unk> and Lilavati . <unk> , a Chola prince , authored a piece on rhetoric called <unk> ( 1150 ) . It was based on <unk> 's Sanskrit <unk> . 
- 
- = = = Age of Harihara = = = 
- 
- Harihara ( or <unk> , 1160 ) , who came from a family of <unk> ( accountants ) in Hampi , was one of the earliest Veerashaiva writers who was not part of the Vachana poetic tradition . He is considered one of the most influential Kannada poets of the Hoysala era . A non @-@ <unk> , he has been called " poet of poets " and a " poet for the masses " . Kannada poetry changed course because of his efforts , and he was an inspiration for generations of poets to follow . Impressed by his early writings , Kereya Padmarasa , the court poet of King Narasimha I , introduced him to the king , who became Harihara 's patron . A master of many metres , he authored the <unk> ( " Marriage of the mountain born goddess – Parvati " ) in the <unk> tradition , employing the champu style to tell a 10 @-@ part story leading to the marriage of the god Shiva and Parvati . According to an <unk> , Harihara was so against <unk> earthly mortals that he struck his protégé Raghavanka for writing about King Harishchandra in the landmark work Harishchandra Kavya ( c . 1200 ) . Harihara is credited with developing the native ragale metre . The earliest poetic biographer in the Kannada language , he wrote a biography of Basavanna called <unk> ragale , which gives interesting details about the protagonist while not always conforming to popular beliefs of the time . <unk> to him is a group of 100 poems called the <unk> ragale ( also called <unk> ragale or <unk> – " The holy lake of the lives of the devotees " ) after the saint <unk> . In the sataka metre he wrote the Pampa sataka , and in the ashtaka metre , the <unk> ashtaka in about 1200 . 
- Famous among Vaishnava writers and the first brahmin writer ( of the <unk> sect ) of repute , <unk> wrote <unk> Vijaya ( 1180 ) in a style considered a transition between ancient and medieval Kannada . <unk> , a minister in the court of King Veera Ballala II , was his patron . The writing , in champu metre , is about the life of the god Krishna . Leading to the god 's fight with <unk> , it is based on an earlier writing , <unk> . 
- <unk> , court poet of King Veera Ballala II and the <unk> King <unk> of <unk> , wrote Lilavati <unk> ( 1170 ) , the earliest available true fiction ( and hence a novel ) in Kannada , with an erotic bent . Written in the champu metre , with the ancient town <unk> as the background , it narrates the love story of a <unk> prince and a princess who eventually marry after facing many obstacles . The story is based on a c . 610 Sanskrit original called <unk> by <unk> . His other work , <unk> , unfinished on account of his death ( and hence called <unk> or " incomplete <unk> " ) , details the life of the 22nd Jain tirthankar <unk> while treating the life of the god Krishna from a Jain angle . 
- Palkuriki Somanatha , a native of modern Karnataka or Andhra Pradesh , is considered one of the foremost multi @-@ lingual Shaiva ( or Shiva @-@ following ) poets of the 12th and 13th centuries . Historians are divided about the time and place of his birth and death and his original faith . He was adept in the Sanskrit , Telugu and Kannada languages . He was a devotee of Basavanna ( the founder of the Veerashaiva movement ) , and all his writings propagate that faith . It is generally accepted that he was born a brahmin and later adopted the Shaiva faith , although according to the scholar <unk> <unk> he was born a <unk> ( follower of the Shaiva faith ) . His time of birth has been identified as either the 12th century or late 13th century . In Kannada , his most important writings are <unk> , <unk> and <unk> . His well @-@ known poems , written in the ragale metre , are <unk> ragale , <unk> ragale and <unk> ragale . He is known to have <unk> many Vaishnava poets in debates . 
- Other well @-@ known personalities from the 12th century included several Jain writers . These include <unk> , who authored <unk> ( 1189 ) , an account of the life of the eighth Jain tirthankar <unk> ; <unk> , who wrote a <unk> on <unk> of <unk> ; and <unk> <unk> , who authored <unk> <unk> and <unk> ( 1160 ) . The latter was <unk> 's version of the Sanskrit original of the same name written by <unk> c . <unk> . In this champu writing , the author narrates the story of two <unk> princess who went to Benares and exposed the vices of the gods after discussions with the <unk> there . The author questions the credibility of Hanuman ( the Hindu monkey god ) and the <unk> ( monkey @-@ like <unk> in the Hindu epic Ramayana ) . Although controversial , the work sheds useful information on contemporary religious beliefs . Kereya Padmarasa , a Veerashaiva poet patronised by King Narasimha I , wrote <unk> in the ragale metre in 1165 . He would later become the protagonist of a biographical work called <unk> written by his descendant <unk> in c . 1400 . The brahmin poet Deva Kavi authored a romance piece called <unk> ( 1200 ) , and brahmin poet Kavi Kama ( 12th century ) authored a treatise called <unk> @-@ <unk> on the <unk> ( flavor ) of poetical sentiment . <unk> ( 1170 ) was a poet @-@ grammarian and the <unk> ( " military teacher " ) under King Narasimha I. He was also a priest in <unk> , the <unk> <unk> capital . 
- 
- = = = Jain – Veerashaiva conflict = = = 
- 
- Harihara 's nephew and protégé , the dramatic poet Raghavanka of Hampi , whose style is compared to that of 10th @-@ century poet <unk> , was the first to establish the shatpadi metre in Kannada literature in the epic Harishchandra Kavya ( 1200 ) . According to L. S. <unk> Rao , it is believed that in no other language has the story of King Harishchandra been interpreted in this way . The writing is an original in tradition and inspiration that fully develops the potential of the shatpadi metre . The narration has many noteworthy elegiac verses such as the mourning of <unk> over the death of her young son <unk> from snake bite . The very writing that made Raghavanka famous was rejected by his guru , Harihara . His other well @-@ known writings , adhering to strict Shaiva principles and written to appease his guru , are the Siddharama charitra ( or Siddharama Purana ) , a larger than life stylistic <unk> of the <unk> 12th @-@ century Veerashaiva saint , Siddharama of <unk> ; the Somanatha charitra , a propagandist work that describes the life of saint <unk> ( or <unk> ) of <unk> ( modern <unk> ) , his humiliation by a Jain girl and his revenge ; the <unk> <unk> , a dramatic story of the blind wrath of a Shaiva warrior , <unk> ; the <unk> , an account of the life of <unk> of Hampi ; and <unk> charitra . The last two classics are considered lost . 
- In 1209 , the Jain scholar , minister , builder of temples and army commander Janna wrote , among other classics , <unk> <unk> , a unique set of stories in 310 verses dealing with <unk> , <unk> of the soul , passion gone awry and <unk> morals for human conduct . The writing , although inspired by <unk> 's Sanskrit classic of the same name , is noted for its original interpretation , imagery and style . In one story , the poet tells of the infatuation of a man for his friend 's wife . Having killed his friend , the man abducts the wife , who dies of grief . <unk> by repentance , he burns himself on the funeral <unk> of the woman . The stories of infatuation reach a peak when Janna writes about the attraction of <unk> , the queen , to the ugly <unk> <unk> , who <unk> the queen with kicks and whip lashes . This story has <unk> the interest of modern researchers . In honour of this work , Janna received the title <unk> ( " Emperor among poets " ) from his patron , King Veera Ballala II . His other classic , <unk> Purana ( 1230 ) , is an account of the life of the 14th tirthankar <unk> . 
- <unk> , taking a non @-@ conformist path that was never repeated in Kannada literature , wrote <unk> Vijaya ( " Triumph of <unk> " , 1217 – 1235 ) using only pure Kannada words ( <unk> ) and naturalized Sanskrit words ( <unk> ) and totally avoiding assimilated Sanskrit words ( <unk> ) . This is seen by some as a rebuttal meant to prove that writing Kannada literature without borrowed Sanskrit words was possible . The poem narrates the story of the moon being imprisoned by the god Shiva in his abode in the Himalayas . In his anger , Kama ( <unk> , the god of love , also called <unk> ) assailed Shiva with his arrows only to be cursed by Shiva and separated from his beloved . Kama then contrived to rid himself of Shiva 's curse . The work also goes by other names such as <unk> <unk> ( " Harvest of Beauty " ) , <unk> <unk> ( " <unk> 's Conquest " ) and <unk> @-@ <unk> ( " Poets defender " ) . Kama has an important place in Jain writings even before <unk> . The possibility that this writing was yet another subtle weapon in the intensifying conflict between the dominant Jains and the <unk> , whose popularity was on the rise , is not lost on historians . 
- Mallikarjuna , a Jain ascetic , compiled an anthology of poems called <unk> ( " Gems from the poets " ) in <unk> in the court of King Vira <unk> . Some interesting observations have been made by scholars about this important undertaking . While the anthology itself provides insight into poetic tastes of that period ( and hence qualifies as a " history of Kannada literature " ) , it also performs the function of a " guide for poets " , an assertive method of bridging the gap between courtly literary intelligentsia and folk poetry . Being a guide for " professional intellectuals " , the work , true to its nature , often includes poems <unk> kings and royalty but completely ignoring poems of the 12th @-@ century vachana canon ( Veerashaiva folk literature ) . However , the selection of poems includes contributions from Harihara , the non @-@ conformist Veerashaiva writer . This suggests a compromise by which the author attempts to include the " rebels " . 
- Other notable writers of the early 13th century were <unk> , author of <unk> and <unk> <unk> ( 1200 ) , the latter bearing on morals and <unk> , and written addressing the soul ; <unk> Kavi <unk> , the author of the <unk> fort inscription who claimed to be " master of four languages " ; <unk> , the author of an extinct commentary on the Jain theological work <unk> <unk> @-@ <unk> ( 1209 ) for which there are references , and the available commentary called <unk> giving a complete explanation of Sanskrit and <unk> authoritative citations ; <unk> , who wrote <unk> ; <unk> , author of <unk> <unk> <unk> , and Sisumayana , who introduced a new form of composition called sangatya in <unk> . He wrote an allegorical poem called <unk> ( " Burning of the triple fortress " ) and <unk> . The latter work was inspired by <unk> 's Sanskrit <unk> charitra . <unk> , a Veerashaiva scholar , wrote a <unk> of <unk> , the ruler of <unk> , and called it <unk> ( or <unk> , <unk> ) . Other Jain writers were <unk> Pandita , author of <unk> , and <unk> II , the author of the story of the ninth Jain tirthankar <unk> called <unk> <unk> ( both were patronised by the <unk> kings of <unk> ) . <unk> <unk> , a commander , minister , and the builder of the <unk> temple in <unk> , wrote <unk> in 1224 . He was patronised by King Veera Ballala II and his successor , King Vira Narasimha II . <unk> Somanatha authored a book on morals called <unk> . 
- 
- = = = <unk> of grammar = = = 
- 
- Keshiraja was a notable writer and grammarian of the 13th century . He came from a family of famous poet @-@ writers . Although five of Keshiraja 's writings are not <unk> , his most enduring work on Kannada grammar , Shabdamanidarpana ( " Mirror of Word Jewels " , 1260 ) , is available and <unk> to his scholarly <unk> and literary taste . True to his wish that his writing on grammar should " last as long as the sun , the moon , the oceans and the <unk> mountain lasted " , Shabdamanidarpana is popular even today and is considered a standard authority on old Kannada grammar . It is prescribed as a textbook for students of graduate and post @-@ graduate studies in the Kannada language . Although Keshiraja followed the model of Sanskrit grammar ( of the <unk> school ) and that of earlier writings on Kannada grammar ( by King <unk> I of the 9th century and grammarian <unk> II of 1145 ) , his work has originality . Keshiraja 's lost writings are <unk> <unk> , Sri <unk> , <unk> , <unk> and <unk> ( or <unk> ) . 
- A major development of this period that would have a profound impact on Kannada literature even into the modern age was the birth of the <unk> ( " servants of <unk> or Vishnu " ) movement . This devotional movement , although reminiscent in some ways of the Veerashaiva movement of the 12th century ( which produced Vachana poetry and taught devotion to the god Shiva ) , was in contrast intimately devoted to the Hindu god Vishnu as the supreme God . The inspiration behind this movement was the philosophy of Madhvacharya of Udupi . Naraharitirtha ( 1281 ) is considered the first well @-@ known haridasa and composer of Vaishnava devotional songs in Kannada . Before his induction into the <unk> order , he had served as a minister in the court of <unk> . The Vaishnava poetry however disappeared for about two centuries after Naraharitirtha 's death before resurfacing as a popular form of folk literature during the rule of the Vijayanagara Empire . Only three of Naraharitirtha 's compositions are available today . 
- Other writers worthy of mention are <unk> Kavi , the author of <unk> ( <unk> ) , an account of the 22nd Jain tirthankar <unk> , and <unk> , author of a Jain version of the epic Ramayana in shatpadi metre called <unk> Ramayana in 1275 . The effort was influenced by Pampa Ramayana of Nagachandra . <unk> Padmarasa , son of Kereya Padmarasa , wrote the <unk> <unk> in shatpadi metre . <unk> Kavi , a Jain noble , wrote a quasi @-@ scientific piece called <unk> ( or <unk> ) in 1300 . The writing bears on natural phenomena such rain , earthquakes , lightning , planets and <unk> . A commentary on the <unk> <unk> , considered useful to students of the language , called <unk> <unk> <unk> was written by the Jain writer <unk> ( 1300 ) . Towards the end of the Hoysala rule , <unk> wrote <unk> in 1331 in champu style , a work that narrates the stories of <unk> heroes in 52 tales and is said to be a translation from Sanskrit . 
- 
- = = Sanskrit writings = = 
- 
- The Vaishnava movement in the Kannada @-@ speaking regions found momentum after the arrival of the philosopher Ramanujacharya ( <unk> – 1137 ) . <unk> possible persecution from the Chola King ( who was a Shaiva ) , Ramanujacharya sought refuge initially in <unk> and later moved to <unk> . But this event had no impact on Vaishnava literature in Hoysala lands at that time . However , the teachings of Madhvacharya ( <unk> – <unk> ) , <unk> of the Dvaita philosophy , did have a direct impact on Vaishnava literature , in both the Sanskrit and Kannada languages . This body of writings is known as haridasa <unk> ( haridasa literature ) . 
- Born as <unk> in <unk> village near Udupi in <unk> , he learnt the <unk> and <unk> under his guru <unk> . He was initiated into <unk> ( asceticism ) after which he earned the name Madhvacharya ( or <unk> ) . Later , he disagreed with the views of his guru and began to travel India . He successfully debated with many scholars and philosophers during this time and won over Naraharitirtha , a minister in <unk> , who would later become Madhvacharya 's first notable disciple . Unlike Adi <unk> ( <unk> – 820 ) who <unk> <unk> philosophy ( monism ) and Ramanujacharya who <unk> <unk> philosophy ( qualified monism ) , Madhvacharya taught the Dvaita philosophy ( dualism ) . 
- Madhvacharya taught complete devotion to the Hindu god Vishnu , emphasising <unk> or the " path of knowledge " , and insisted that the path of devotion " can help a soul to attain elevation " ( <unk> ) . He was however willing to accept devotion to other Hindu deities as well . He wrote 37 works in Sanskrit including <unk> Sutra ( in which his devotion to the god Vishnu found full expression ) , <unk> <unk> , <unk> Tatparya Nirnaya , Mahabharata Tatparya Nirnaya , Bhagavata Tatparya Nirnaya , <unk> <unk> and Vishnu <unk> Nirnaya . To propagate his teachings he established eight monasteries near Udupi , the <unk> monastery , and the <unk> monastery in <unk> ( in modern Andhra Pradesh ) and <unk> ( near modern Mysore ) . 
- The writings of Madhvacharya and <unk> ( author of <unk> ) may have been absorbed by <unk> , brother of <unk> , the patron saint of the founders of the Vijayanagara empire in the 14th century . <unk> ( who was patronised by Hoysala King <unk> ) wrote a commentary on <unk> , <unk> wrote commentary on <unk> <unk> and <unk> , and <unk> wrote <unk> . A family of hereditary poets whose names have not been identified held the title " <unk> " ( poet laureate ) in the Hoysala court . One of them wrote <unk> , a description of the war between Hoysala king Vira Narasimha II and the <unk> , in the early 13th century . His grandson with the same title , in the court of king Veera Ballala III , composed a poem called <unk> in 16 <unk> ( chapters ) and wrote commentaries ( on <unk> ) on the <unk> and <unk> . Kalyani Devi , a sister of Madhvacharya , and <unk> , his disciple , wrote commentaries on the Dvaita philosophy . To <unk> is ascribed a poem <unk> the story of <unk> and <unk> called <unk> . Narayana Pandita composed <unk> , <unk> and a poem called <unk> . The Jain writer Ramachandra <unk> authored <unk> . 
- 
- = = Literature after the Hoysalas = = 
- 
- Literary developments during the Hoysala period had a marked influence on Kannada literature in the centuries to follow . These developments popularised folk metres which shifted the emphasis towards <unk> ( native or folk ) forms of literature . With the <unk> of Jain literary output , competition between the Veerashaiva and Vaishnava writers came to the fore . The Veerashaiva writer <unk> ( author of <unk> , 1425 ) and his Vaishnava competitor <unk> ( <unk> Bharata <unk> , <unk> ) popularised the shatpadi metric tradition initiated by Hoysala poet Raghavanka , in the court of Vijayanagara King Deva <unk> II . <unk> , the 16th – 17th century writer of epic poems , continued the tradition in the <unk> Bharata , a work that has remained popular even in the modern period . The tripadi metre , one of the oldest in the Kannada language ( <unk> <unk> inscription of 700 ) , which was used by Akka <unk> ( <unk> <unk> , 1160 ) , was popularised in the 16th century by the <unk> poet <unk> . Even Jain writers , who had dominated courtly literature throughout the classical period with their Sanskritic champu style , began to use native metres . Among them , <unk> is famous for successfully integrating an element of worldly pleasure into asceticism and for treating the topic of <unk> with discretion in a religious epic written in the native sangatya metre ( a metre initiated by Hoysala poet Sisumayana ) , his magnum opus , the <unk> <unk> ( c . <unk> ) . 
- Though the Vaishnava courtly writings in Kannada began with the Hoysala poet <unk> and the devotional song genre was initiated by Naraharitirtha , the Vaishnava movement began to exert a strong influence on Kannada literature only from the 15th century on . The Vaishnava writers consisted of two groups who seemed to have no interaction with each other : the Brahmin commentators who typically wrote under the patronage of royalty , and the Bhakti ( devotion ) writers ( also known as <unk> ) who played no role in courtly matters . The Bhakti writers took the message of God to the people in the form of <unk> songs composed using folk genres such as the <unk> ( a musical composition with refrain , based on tune and rhythm ) , the <unk> ( a composition based on rhythm ) and the <unk> ( a composition based on melody ) . <unk> <unk> and <unk> Kavi were well @-@ known among the Brahmin commentators , while <unk> Dasa and <unk> Dasa were the most notable of the Bhakti writers . The philosophy of Madhvacharya , which originated in the Kannada @-@ speaking region in the 13th century , spread beyond its borders over the next two centuries . The itinerant <unk> , best described as mystic saint @-@ poets , spread the philosophy of Madhvacharya in simple Kannada , winning mass appeal by preaching devotion to God and <unk> the virtues of <unk> ( enlightenment ) , <unk> ( devotion ) and <unk> ( detachment ) . 
- Vachana poetry , developed in reaction to the rigid caste @-@ based Hindu society , attained its peak in popularity among the under @-@ privileged during the 12th century . Though these poems did not employ any regular metre or rhyme scheme , they are known to have originated from the earlier tripadi <unk> form . The <unk> , who wrote this poetry , had risen to influential positions by the Vijayanagara period ( 14th century ) . Court ministers and nobility belonging to the faith , such as <unk> <unk> and <unk> , not only wrote literature but also patronised talented writers and poets . Veerashaiva <unk> of the 15th and 16th centuries began to collect Shaiva writings and vachana poems , originally written on palm leaf manuscripts . Because of the cryptic nature of the poems , the <unk> added commentaries to them , thereby providing their hidden meaning and esoteric significance . An interesting aspect of this <unk> work was the translation of the Shaiva canon into Sanskrit , bringing it into the sphere of the Sanskritic ( <unk> or mainstream as opposed to <unk> or folk ) cultural order . 
- 
- 
- = 1939 Pacific hurricane season = 
- 
- The 1939 Pacific hurricane season ran through the summer and fall of 1939 . Before the satellite age started in the 1960s , data on east Pacific hurricanes are extremely unreliable . Most east Pacific storms are of no threat to land . However , 1939 saw a large number of storms threaten California . 
- 
- = = Storms = = 
- 
- 
- = = = Hurricane One = = = 
- 
- On June 12 , a hurricane was detected . The lowest pressure reported by a ship was 985 mbar ( 29 @.@ 1 inHg ) . The hurricane was last seen June 13 . 
- 
- = = = Possible Tropical Cyclone Two = = = 
- 
- A possible tropical cyclone was located off the coast of Mexico on June 27 . A ship reported a gale and a pressure of 1 @,@ 006 mbar ( 29 @.@ 7 inHg ) . The system was last seen on June 28 . 
- 
- = = = Tropical Cyclone Three = = = 
- 
- On July 19 , a tropical cyclone was detected . A ship reported a pressure of 1 @,@ 000 @.@ 7 millibars ( 29 @.@ 55 inHg ) . 
- 
- = = = Tropical Cyclone Four = = = 
- 
- On July 29 , a tropical cyclone was located midway between Manzanillo and Acapulco . It moved up the coast , and a ship reported a pressure of 1 @,@ 000 mbar ( 30 inHg ) on July 29 as the cyclone made landfall in the vicinity of Manzanillo . 
- 
- = = = Tropical Cyclone Five = = = 
- 
- A small tropical cyclone was detected on August 31 . A ship reported <unk> and a pressure of 1 @,@ <unk> @.@ 3 millibars ( 29 @.@ 63 inHg ) . 
- 
- = = = Hurricane Six = = = 
- 
- From September 4 to 7 , the remnants of a hurricane brought heavy rain to Southern California . The storm delivered over a year 's worth of rainfall to Blythe , while Imperial received more than two year 's worth . The flooding caused major damage in Mecca , California , and 3 feet ( 0 @.@ 91 m ) of water swamped <unk> . 
- 
- = = = Hurricane Seven = = = 
- 
- A tropical cyclone was first detected south of Acapulco on September 5 . It intensified into a hurricane and moved northwestward . A ship sailing through the eye reported a pressure reading of <unk> mbar ( 28 @.@ 0 inHg ) . The tropical cyclone made landfall somewhere along the Baja California Peninsula . It dissipated inland over the northern part of the peninsula on September 12 . Remnants of this tropical storm , in association with a trough , caused rain of up to 4 inches ( 100 mm ) in southern California on September 11 and 12 . 
- 
- = = = Tropical Cyclone Eight = = = 
- 
- On September 5 , a tropical cyclone formed off the coast of Costa Rica . It also headed northwest and dissipated over the southern part of Baja California on September 15 . The lowest reported pressure was 1 @,@ <unk> mbar ( 29 @.@ 6 inHg ) . From September 19 to 21 , remnants of this tropical cyclone caused rain measuring up to 3 inches ( 76 mm ) in Southern California . 
- 
- = = = Hurricane Nine = = = 
- 
- On September 14 , a tropical cyclone formed off the coast of Central America . This tropical storm tracked northwestward and intensified into a hurricane . The sea @-@ level pressure dropped to <unk> mbar ( 28 @.@ 8 inHg ) or lower . The hurricane recurved gradually to the northeast and weakened over cool seas . On September 25 , this tropical storm made landfall near Long Beach , California , and dissipated inland . 
- The tropical storm caught Southern <unk> unprepared . It brought heavy rain and flooding to the area , which killed 45 people . At sea , 48 were killed . The storm caused heavy property damage amounting to $ 2 million ( 1939 USD ) in total , mostly to crops and coastal infrastructure . 
- 
- = = = Hurricane Ten = = = 
- 
- On October 23 , a tropical cyclone formed south of Cabo Corrientes . It intensified and headed roughly due north . A steamer , the <unk> , caught in the eye of this extremely intense hurricane , recording a corrected central pressure of 930 mbar ( hPa ; 27 @.@ 46 inHg ) . Even with modern tropical cyclone observation techniques available , this reading still qualifies this cyclone as one of the most intense on record . The steep pressure gradient between the <unk> and the external hurricane conditions off of Manzanillo , Colima caused several <unk> to burst . Other shipping was disrupted off the Mexican coast by the intense tropical cyclone . 
- The hurricane made landfall near Cabo Corrientes on October 25 and dissipated shortly thereafter . <unk> , the storm caused an extensive <unk> of damage . <unk> were destroyed in the towns of Santiago <unk> and <unk> in the Mexican state of <unk> , <unk> hundreds of people . In Puerto Vallarta , a strong storm surge flooded a section of the town , destroying several homes . Tobacco , corn , and rice crops in the region suffered considerable damage . The strong winds downed power lines , resulting in the delayed <unk> of damage reports . Although no exact casualty total was documented , reports indicated that the tropical cyclone caused a " few casualties " . After the storm , US $ 6 @,@ 000 was donated to help aid the displaced in the states of <unk> and <unk> , while doctors and nurses were sent to those areas . 
- 
- 
- = G.I. Joe : Retaliation = 
- 
- G.I. Joe : Retaliation is a 2013 American military science fiction action film directed by Jon M. Chu , based on Hasbro 's G.I. Joe toy , comic and media franchises . Written by Rhett Reese and Paul <unk> , the film is a sequel to 2009 's G.I. Joe : The Rise of Cobra while also serving as a soft reboot of the franchise . 
- Retaliation features an ensemble cast , with Byung @-@ hun Lee , Ray Park , Jonathan Pryce , Arnold Vosloo , and Channing Tatum reprising their roles from the first film . Luke Bracey takes over the role of Cobra Commander , replacing Joseph Gordon @-@ Levitt . Dwayne Johnson , D. J. Cotrona , Adrianne Palicki , Ray Stevenson , and Bruce Willis round out the principal cast . 
- In the film , with Cobra operative Zartan still impersonating the President of the United States , the terrorist organization is able to frame the Joes as <unk> , and have them nearly decimated in an <unk> . Cobra Commander places the world leaders under Cobra 's control , and gains access to their advanced warheads . <unk> and <unk> , the surviving Joes form a plan with the original G.I. Joe , General Joseph Colton , to overthrow the Cobra Commander and his allies . 
- Originally slated for release in June 2012 , the film was delayed in order to convert to 3D and boost interest in international markets . It was released in North America on March 28 , 2013 , and received generally negative reviews , but was a box office success , grossing over $ 375 million worldwide . 
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- = = Plot = = 
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- Duke ( Channing Tatum ) has become the leader of the G.I. Joe unit , which is framed for stealing nuclear warheads from Pakistan by Zartan ( Arnold Vosloo ) , who is impersonating the President of the United States ( Jonathan Pryce ) . The unit is subsequently decimated in a military air strike with Duke among the casualties . The only survivors are Roadblock ( Dwayne Johnson ) , Flint ( D.J. Cotrona ) , and Lady Jaye ( Adrianne Palicki ) . 
- Meanwhile , Storm Shadow ( Byung @-@ hun Lee ) and Firefly ( Ray Stevenson ) rescue Cobra Commander ( Luke Bracey ) from an underground maximum @-@ security prison in Germany , leaving <unk> behind . Storm Shadow is injured during the escape and retreats to a temple in the Himalayas to recover . Upon learning that he is alive , the Blind Master ( RZA ) , leader of the Arashikage Clan , sends Snake Eyes ( Ray Park ) and his apprentice Jinx ( Élodie Yung ) , Storm Shadow 's cousin , to capture Storm Shadow so he can answer for the murder of his uncle , the Hard Master . 
- Roadblock , Flint , and Lady Jaye return to the United States where they set up a base of operations in a <unk> gym . After Zartan announces that Cobra will replace the Joes as America 's elite special forces unit , Lady Jaye <unk> that someone is impersonating the President , and Roadblock leads them to General Joseph Colton ( Bruce Willis ) , who provides them with weapons , and helps them infiltrate a fundraising event that the President will be attending . Lady Jaye steals a sample of the President 's DNA and confirms that he is Zartan . They escape after a confrontation with Firefly and <unk> ( Matt Gerald ) , the head of the U.S. Secret Service 's Presidential <unk> and a member of Cobra . 
- Snake Eyes and Jinx locate and capture Storm Shadow after a battle with ninjas and take him to Japan , where Storm Shadow reveals that Zartan murdered the Hard Master , and that he joined Cobra to avenge his uncle . Storm Shadow then accompanies Snake Eyes and Jinx as they join the Joes ' efforts to stop Cobra . 
- Zartan invites the world leaders to a summit at historic Fort <unk> , where he blackmails them into <unk> their nuclear <unk> , and reveals that he has created Project Zeus : seven orbital <unk> bombardment weapons of mass destruction at his command . He destroys central London to prove his superiority , and threatens to destroy other capitals if the countries don 't submit to Cobra . Storm Shadow betrays Cobra Commander and kills Zartan , revealing Cobra 's deception to the world leaders . While Snake Eyes , Jinx , and Flint fight Cobra 's soldiers , Cobra Commander activates the remaining six weapons and instructs Firefly to protect the launch device . Firefly is killed in combat by Roadblock , who <unk> and destroys the orbital weapons . Meanwhile , Colton and Lady Jaye rescue the President . 
- Cobra Commander escapes during the battle and Storm Shadow disappears after <unk> his uncle . The real President addresses the nation at a White House ceremony where Roadblock , Lady Jaye , Flint , Jinx , and Snake Eyes are commemorated as heroes . Colton presents Roadblock with an <unk> pistol that belonged to General George S. Patton , to use when he finally finds Cobra Commander and to avenge Duke . Roadblock proudly raises the weapon and fires a single shot in honor of his fallen comrades . 
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- = = Cast = = 
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- = = = G.I. Joe = = = 
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- Dwayne Johnson as Marvin F. <unk> / Roadblock 
- Ray Park as Snake Eyes 
- Adrianne Palicki as Jaye <unk> / Lady Jaye 
- D.J. Cotrona as <unk> R. <unk> / Flint 
- Élodie Yung as Kim Arashikage / Jinx 
- Bruce Willis as General Joseph Colton 
- Channing Tatum as Conrad S. Hauser / Duke 
- Joseph <unk> as Mouse 
- Ryan Hansen as <unk> 
- Jim Palmer as <unk> 
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- = = = Cobra = = = 
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- Luke Bracey as <unk> Lewis / Cobra Commander 
- Robert Baker as the voice of Cobra Commander 
- Byung @-@ hun Lee as Thomas " Tommy " Arashikage / Storm Shadow 
- Ray Stevenson as Firefly 
- Arnold Vosloo as Zartan 
- Matt Gerald as <unk> / <unk> 
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- = = = Other characters = = = 
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- Jonathan Pryce as the President of the United States 
- Walton <unk> as <unk> Nigel James 
- RZA as Blind Master 
- <unk> Davis as <unk> 
- Joe <unk> as the Chief of Staff 
- James <unk> as himself 
- <unk> Jackson as Roadblock 's <unk> 
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- = = Production = = 
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- = = = Development = = = 
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- After the financially successful release of The Rise of Cobra , Rob Moore , the studio vice chairman of Paramount Pictures , stated in 2009 that a sequel would be developed . In January 2011 , Rhett Reese and Paul <unk> , the writers of <unk> , were hired to write the script for the sequel . The movie was originally thought to be titled G.I. Joe : Cobra <unk> , which was later denied by Reese . Stephen <unk> was originally going to return as director of the sequel , but Paramount Pictures announced in February 2011 that Jon Chu would direct the sequel . In July 2011 , the sequel 's name was revealed to be G.I. Joe : Retaliation . Chu would later declare that Paramount wanted a reboot that also served as a sequel to The Rise of Cobra since " a lot of people saw the first movie so we don 't want to alienate that and <unk> the whole thing . " 
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- = = = Casting = = = 
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- In January 2011 , it was confirmed that Byung @-@ hun Lee would reprise his role as Storm Shadow in the sequel . Channing Tatum and Ray Park also returned , as Duke and Snake Eyes , respectively . Rachel Nichols , the actress who played Scarlett in the first film , stated that most cast members would not be returning , except for the three aforementioned actors . In March 2011 , <unk> Miller stated that she would not be returning for a sequel . Joseph Gordon @-@ Levitt also confirmed that he would not be returning as Cobra Commander in the sequel . 
- In June 2011 , Dwayne Johnson was cast as Roadblock , D.J. Cotrona and RZA were cast as Flint and Blind Master respectively , while Élodie Yung was in talks for the role of Jinx . In July 2011 , Adrianne Palicki was confirmed for the lead female role of Lady Jaye , and Ray Stevenson was confirmed to portray the villain Firefly . Arnold Vosloo also confirmed that he would reprise his role of Zartan , although in the final film Vosloo appears only in a couple of non @-@ dialogue scenes , with Jonathan Pryce playing Zartan in most scenes . Joseph <unk> was confirmed to play Mouse . In August 2011 , Walton <unk> was added as <unk> Nigel James , and it was confirmed that Bruce Willis was cast to star in the film as the original G.I. Joe . The character of Joe Colton was a replacement for fan @-@ favorite Joe character Sgt. Slaughter . Sgt. Slaughter stated that he " was originally supposed to be the part of Bruce Willis ' [ as ] Sgt. Slaughter but because we had a conflict in toy companies , Hasbro and Mattel , I wasn 't able to do it . It 's one of those things , Rock ( Dwayne Johnson ) doesn 't have a contract so he can do what he wants to do and he 's been very successful " . 
- In September , a casting call sheet leaked to the Internet revealed that Cobra Commander would appear in the sequel , though it was unknown who would play the character . Chu said that fans would get a glimpse of <unk> in the film , but Christopher Eccleston would not reprise his role in the sequel . On May 1 , 2012 , it was confirmed by Jon Chu that G.I. Joe : Retaliation 's Cobra Commander is Rex Lewis , the same character that Joseph Gordon @-@ Levitt played in The Rise of Cobra . Actor Robert Baker confirmed that he is the voice of Cobra Commander in the sequel . 
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- = = = Filming = = = 
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- Principal photography began in August 2011 in Louisiana . On November 22 , 2011 , a crew member died in an accident at a New Orleans warehouse that was serving as a soundstage for the production . The incident happened while crew members were changing out a set . The battle on the Himalayas was shot in a New Orleans warehouse previously used to build NASA rockets , that had been fitted with a green screen wall at a very steep angle with a lot of rigging above to swing the stunt people through . 
- Fort Pike in Louisiana stands in for Fort <unk> in South Carolina as the site of the climactic summit meeting of the leaders of nuclear @-@ armed countries . 
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- = = = Visual effects = = = 
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- Retaliation had 700 visual effects shots , which were mostly handled by three effects companies . Visual effects supervisor Zachary <unk> declared that while the visuals aimed for the " big and bold " typical of the franchise , Retaliation would be the first to attempt " a bit more of that gritty realism . " The major part of the effects was given to Digital <unk> , which for 227 effects created digital vehicles and aircraft that had to " look fantastic but which are also plausible " , given they had to match practical models , the Zeus satellite and a sequence where Zartan shows his <unk> @-@ related disguise to the president - done with the same head replacement software developed for The Curious Case of Benjamin Button and <unk> : Legacy . Industrial Light & Magic made the London destruction , a digital White House , and the mountain sword fight , which had computer @-@ generated backgrounds and digital <unk> of the stunt people 's performance . Method Studios was responsible for the desert attack , Firefly 's explosive bugs , and the <unk> on the underground prison . Saints <unk> handled minor effects such as <unk> and news graphics . 
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- = = = Music = = = 
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- The film 's score was composed by Henry Jackman . 
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- = = Release = = 
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- Previously slated for release on June 29 , 2012 , Paramount announced in May 2012 that they were delaying the film 's release until March 29 , 2013 ( but was later moved to March 28 , 2013 ) , in order to convert the movie to 3D and boost interest in international markets . 
- The delay " <unk> " the film industry , according to <unk> , because Paramount had already implemented a substantial advertising campaign beginning with a Super Bowl commercial , because " warehouses full of " toys were waiting for the film 's launch , and because it was one of only three Paramount @-@ produced films scheduled for Summer 2012 ( along with The Dictator and Katy Perry : Part of Me ) . The studio also wanted to avoid competing with Tatum 's Magic Mike , also scheduled for June 29 , <unk> reported . 
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- = = = Ban in Pakistan = = = 
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- The film was banned by the Central Board of Film <unk> of Pakistan due to initial scenes at the beginning of the movie which depict the country negatively , according to film censor board officials . A Karachi @-@ based cinema posted on its Facebook page that the film would not be screened due to restrictions by the censor board . The censorship was due to the film 's depiction of Pakistan as an unstable state and the fictional portrayal of a " foreign invasion of Pakistan ’ s nuclear installations " , which caught the ire of film censor authorities . Consequently , restrictions were imposed on screening the movie <unk> . According to an official at the censor board , the film portrayed Pakistan negatively not only on the issue of the War on Terror but also on the international standing of the country : " There is a scene which shows the assassination of the Pakistani president and the imposition of martial law , which is not a fair representation of the country . " Another cinema official explained " There were obviously several <unk> things which would never have passed the censors , but these things are also relevant to the content of the film . " 
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- = = = Promotion = = = 
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- On December 12 , 2011 , the premiere trailer for the film was released on YouTube exclusively from <unk> The trailer itself features a remix of the White <unk> ' song " Seven Nation Army " by The <unk> <unk> . Following the release of the trailer , Interview magazine featured G.I. Joe : Retaliation in " Thursday video Face @-@ Off " against the indie film <unk> <unk> on January 12 , 2012 . A shorter teaser trailer for the film aired during Super Bowl <unk> , containing music by Jay @-@ Z. A Japanese trailer focusing on actor Byung @-@ hun Lee was released in April 2012 . The second full trailer made its debut on April 24 , 2012 , containing a viral marketing initiative inviting viewers to interact with a website and Facebook application for the film . On December 13 , 2012 , a third trailer was released featuring more footage of London 's destruction . In January 2013 , a four @-@ minute clip of the film featuring a ninja battle between Snake Eyes and Storm Shadow was attached with <unk> screenings of <unk> and <unk> : Witch Hunters . A soundtrack of the score by composer Henry Jackman was released in April 2013 . 
- A toyline for the film was confirmed by Hasbro in February 2012 . Despite the movie 's release being moved from June 2012 to March 2013 , the initial <unk> of figures , vehicles , and role @-@ play items were shipped to retailers , and appeared on store shelves in May 2012 . A Variety article was published stating that the already released figures had been pulled from the shelves and recalled by Hasbro , although the company 's official statement indicated that existing product would be sold through . New product shipments were halted by Hasbro , but existing Retaliation figures were available in Target , Wal @-@ Mart , and Toys R Us as late as December 2012 . The toyline was re @-@ released in the United States in February 2013 . 
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- = = = Home media = = = 
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- G.I. Joe : Retaliation was released on DVD , Blu @-@ ray and Blu @-@ ray 3D on July 30 , 2013 . A Blu @-@ ray " <unk> Action Cut " added 12 minutes of footage and <unk> violence was also available , with the United States version being a Best Buy exclusive . 
- The film topped No. 1 on both the Blu @-@ ray and DVD sales charts with at least 54 % of both Blu @-@ ray and DVD units sold . The film also topped weekend rentals as well . 
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- = = Reception = = 
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- = = = Critical response = = = 
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- The review aggregator website Rotten Tomatoes reported a 28 % approval rating with an average rating of 4 @.@ 5 / 10 based on 161 reviews . The website 's consensus reads , " Though arguably superior to its predecessor , G.I. Joe : Retaliation is overwhelmed by its <unk> action and too nonsensical and <unk> to leave a lasting impression . " At Metacritic , which assigns a weighted average rating out of 100 to reviews from mainstream critics , the film has received an average score of 41 % , indicating " mixed or average reviews " , based on 31 critics , which was higher than the first film 's 32 % average score . 
- Alan <unk> of The Village Voice wrote in a positive review that " this [ movie ] pushes right past competent into mostly legitimately enjoyable " but added that " the movie is still dumb as <unk> . It 's an honest and accomplished <unk> , however , where the <unk> stuff seems to be there because the movie would be less fun without it . " The Hollywood Reporter 's Todd McCarthy was critical about the film 's use of 3D and accurate reflection of the franchise 's comic book and cartoon origins , but predicted it would still earn better than its predecessor , G.I. Joe : The Rise of Cobra . Owen <unk> of Entertainment Weekly gave the film a grade of " B- " , calling it " well @-@ executed <unk> action <unk> " and commented : " In its <unk> and trivial way , it 's a triumph of razor @-@ sharp , hyper @-@ violent style over formulaic substance ... Hollywood has now evolved to the point that it can deliver these kinds of thrills with maximum brute force and keep the impact so light that the result can still be regarded as a ' harmless ' diversion for 14 @-@ year @-@ olds . " Glen Heath Jr. of Slant Magazine gave it two out of four stars , criticizing the film 's " cut @-@ happy style " and plot , but lauding the action sequences and Chu 's direction as " poetry in high @-@ speed motion . " Writing for <unk> 's The <unk> <unk> , Todd Gilchrist gave the film a " B- " and wrote : " As one might expect , there are more than a handful of loose ends once justice has been served , but there ’ s something to be said for a film which aims to please in a sincere and straightforward way , without attempting to be the biggest ever . ' Retaliation ' is no masterpiece , but it ’ s a movie whose fun doesn ’ t feel like a four @-@ letter word " 
- In a negative review , <unk> <unk> of the Los Angeles Times panned the " overwhelmingly complicated , globe @-@ hopping , enemies within , enemies without story line " and 3D but noted that " the humor , when it works , offers ' Retaliation ' some redemption . " She ended with : " It 's <unk> . <unk> no one should have to think that hard to keep up with the Joes . " Another negative review came from Variety Magazine 's Justin Chang , who ridiculed the movie 's large @-@ scale destruction of foreign cities , writing : " <unk> who thrilled to the sight of Paris under biochemical attack in Cobra will be pleased to watch London endure an even more horrific fate here , although the sequence is tossed off in quick , almost <unk> @-@ hum fashion , with no time to dwell on anything so exquisitely crass as the spectacle of the <unk> Tower collapsing . " He summarized the movie as " a more straight @-@ faced brand of <unk> than its <unk> dumb 2009 predecessor . " 
- PopMatters journalist <unk> <unk> III wrote " For fans who bought the toys , watched the cartoon and read the comics during the ‘ 80s and now have like @-@ aged children of their own ( all of which I did and do ) , might I suggest proceeding to watch this fun film with your kids , but compromise so that you can leave the commentary track on . The film will remain a treat for the eyes , but you can more easily <unk> over those parts that will make you apologize to your brain . " 
- Writing for Empire magazine , <unk> Richards gave the movie 2 stars out of 5 and compared it unfavorably with its predecessor , writing : " The first film you could at least laugh at . This takes all its silly ingredients and <unk> them down flat . ' Retaliation ' over @-@ promises and under @-@ delivers . " Richard <unk> of Chicago Sun Times gave the movie 1 @.@ 5 stars out of a possible four , branding it a " ridiculous and overblown <unk> " that contained " nothing but well @-@ packaged garbage " and further adding : " To say ' G.I. Joe : Retaliation ' is a video game for the big screen is to insult a number of video games that are far more creative , challenging and better @-@ looking . " 
- Despite the negative critical response , audiences responded favorably . <unk> polls found that audiences gave the film an average grade of A @-@ minus . 
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- = = = Box office = = = 
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- G.I. Joe : Retaliation grossed $ 122 @,@ 523 @,@ 060 in North America and $ 253 @,@ 217 @,@ 645 internationally for a worldwide total of $ 375 @,@ 740 @,@ 705 . Overall , according to Box Office <unk> it is the 25th highest @-@ grossing film of 2013 in North America , the 18th highest @-@ grossing film worldwide that year , the highest @-@ grossing film in the G.I. Joe film series , and the fifth highest @-@ grossing Hasbro film . 
- In North America , the film grossed $ 10 @.@ 5 million on its opening day at the top of the box office . The film retained the No. 1 spot over the three @-@ day weekend and grossed $ 40 @.@ 5 million , which is the third @-@ highest Easter debut ever behind Furious 7 and Clash of the Titans . However , this was lower than its predecessor 's opening weekend of $ 54 @.@ 7 million . The international response was even more positive , with $ 80 @.@ 3 million across the weekend . 
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- = = <unk> = = 
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- On April 1 , 2013 , reports surfaced that there will be a third G.I. Joe film , and it will likely be in 3D . The studio announced that Chu will return to direct the third film . While at the 2013 San Diego Comic Con , Chu talked about bringing Scarlett back in the next film . The writers of the second film are also thinking about bringing back the Baroness in the sequel . Johnson is interested in returning as Roadblock for the sequel , and Park has talked about a possible return as Snake Eyes and also including his pet wolf <unk> . Producer Lorenzo di Bonaventura has stated he is open to doing a G.I. Joe / <unk> crossover , which Chu stated that he would be interested in directing . Bonaventura told Beijing News that he hoped that Johnson and Willis would return , the script is still in the writing stage , and that they are considering adding a third important role . On September 10 , 2013 , Chu was confirmed to direct the film , along with writer Evan <unk> ( Snow White and the <unk> , <unk> , Teenage <unk> <unk> Turtles ) to pen the film 's script . On December 5 , 2013 , <unk> talked about writing the film 's script and his feelings about Duke being killed , but Chu told MTV that Tatum may return as Duke in the sequel . On April 2 , 2014 , in an interview with <unk> , Johnson believes that Chu may not return to direct , due to working on the live action <unk> film , but they may find another director for the film . It was revealed that the third film will have a 2016 release date . On June 23 , 2014 , di Bonaventura told <unk> in an interview that they 're meeting with new directors and filming may start in early 2015 . On July 1 , 2014 , Variety reported that Jonathan <unk> will write the script for the film and will focus on Roadblock with Johnson returning . On February 5 , 2015 , Film <unk> reported that the twins <unk> and <unk> and Matt <unk> from the TV series <unk> will be appearing . On April 2 , 2015 , the studio hired Aaron Berg to write the film , and D. J. <unk> to direct the film . On November 23 , 2015 , <unk> reported that <unk> <unk> will lead a writers room for the next G.I. Joe film . 
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- 
- = Burns ' Heir = 
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- " Burns ' Heir " is the eighteenth episode of The Simpsons ' fifth season . It originally aired on the Fox network in the United States on April 14 , 1994 . In the episode , Mr. Burns has a near @-@ death experience which prompts him to find an heir to inherit his wealth after he dies . Although Bart is initially rejected , Burns soon decides to choose him after seeing him as " a creature of pure malevolence " . Marge convinces Bart to go spend some time with Burns , and soon becomes more disruptive than normal to his own family and decides to go live with Mr. Burns . 
- " Burns ' Heir " was written by <unk> Richdale , his only writing credit . David Silverman was originally going to direct the episode , but he was so swamped with his work as supervising director that it was reassigned to Mark Kirkland . While the Simpsons are at a movie , there is a parody of the THX sound Deep Note . The THX executives liked the parody so much that the scene was made into an actual THX movie trailer , with the scene being redone for the widescreen aspect ratio . A deleted scene from the episode sees Mr. Burns release a " Robotic Richard Simmons " as a way of getting rid of Homer . The scene was cut , but later included in the season seven clip show " The Simpsons <unk> Episode <unk> " . 
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- = = Plot = = 
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- Mr. Burns almost drowns while taking a bath after Smithers puts a <unk> on his head that weighs him down . Later , after realizing that no one will carry on his legacy when he dies , Burns decides to try to find an heir that will inherit his vast fortune . He holds an audition and many of the boys in Springfield try out , including Nelson <unk> , Martin Prince , and Milhouse Van <unk> . Bart and Lisa also try out and fail ; Lisa because she is a girl , and Bart because he read Homer 's badly @-@ <unk> proposal . Angry and humiliated after the audition ends , only made worse by Burns kicking him in the butt with a mechanical boot , Bart pays him back by <unk> his mansion . Mr. Burns <unk> of Bart 's malevolence and decides to accept him as his heir . 
- Homer and Marge sign a legal document which officially names Bart as Mr. Burns ' heir . Marge suggests that Bart should spend some time with Mr. Burns . While initially repelled by Mr. Burns ' <unk> , Bart begins to take a liking to him after Burns promises to give Bart whatever he wants out of his life . Bart decides to abandon his family because Mr. Burns allows him to do anything he likes . Bart 's family becomes angry and wants him back , so they decide to sue Mr. Burns , but due to hiring Lionel <unk> as their lawyer , the court ends up deciding that Mr Burns is Bart 's biological father . The Simpsons get a Private <unk> <unk> to kidnap Bart , but the <unk> wrongly takes Hans Moleman instead and <unk> him into thinking he 's a part of the Simpson family . 
- Meanwhile , Bart becomes lonely and wants to go back to his family . Mr. Burns does not want him to leave and tricks him into thinking that his family hates him , using a falsified video with actors playing the Simpson family . Bart decides that Burns is his true father and the two celebrate by firing employees at the Springfield Nuclear Power Plant by <unk> them through a trap door . However , one of the employees is Homer and Mr. Burns then tries to break Bart 's ties with his family by forcing him to fire Homer . Bart instead " fires " Burns and drops him through a trap door . Bart decides that he loves his family , and moves back in with them . 
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- = = Production = = 
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- " Burns ' Heir " was the first episode in which <unk> Richdale received a writers ' credit , although he was a part of the show 's staff for several seasons . When he was starting out as a writer on the show , Richdale was told to come up with some story ideas and he came up with the basic plot off the top of his head . David Silverman was originally going to direct the episode , but he was so swamped with his work as supervising director that it was reassigned to Mark Kirkland . While the Simpsons are at a movie , there is a parody of the THX sound Deep Note . During that scene , a man 's head explodes in a reference to the film <unk> . The THX executives liked the parody so much that the scene was made into an actual THX movie trailer , with the scene being redone for the widescreen aspect ratio . 
- A deleted scene from the episode sees Mr. Burns release a " Robotic Richard Simmons " as a way of getting rid of Homer , which dances to a recording of <unk> and the Sunshine Band 's " <unk> Your <unk> " . Simmons was originally asked to guest star , according to David Mirkin , he was " dying to do the show " , but declined when he found out he would voice a robot . It was fully animated , but was cut from " Burns ' Heir " because it often did not get a good reaction during table reads . According to Bill Oakley , there was a " significant division of opinion amongst the staff as to whether Richard Simmons was a target The Simpsons should make fun of " because it was " well @-@ <unk> territory " . They also felt it distracted viewers from the story . To the production staff 's surprise , the scene would make the audience " erupt with laughter " when screened at animation conventions and college presentations , so they decided to insert it in the season seven clip show " The Simpsons <unk> Episode <unk> " . 
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- = = Cultural references = = 
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- The episode parodies THX 's infamous ' Deep Note ' . 
- The trailer advertising Mr. Burns ' search for an heir is a loose parody of the trailer for Toys , a 1992 comedy starring Robin Williams . 
- Mr. Burns also sings " Let 's All Go to the <unk> " . 
- Marge 's fantasy about Bart graduating from Harvard University with Mr. Burns ' money is interrupted by a fantasy of her being lifted into the sky by Lee Majors , accompanied by a sound effect from The Six Million Dollar Man , in which Majors played the title character . 
- Mr. Burns states that he got the idea for installing cameras all through town from Sliver , which he calls a " delightful romp " . 
- Moe using a home @-@ made <unk> action holster with a pistol while looking in a mirror is a reference to Travis <unk> from Taxi Driver . 
- The scene in which Homer is secretly eating flowers is a reference to a scene in The Last Emperor where the <unk> eats flowers . 
- The young boy saying , " Today , sir ? Why , it 's Christmas Day ! " makes a reference to Charles Dickens ' A Christmas Carol . 
- The room in which " Bart " ( who is really Hans Moleman ) is <unk> in is room number 101 , which is in reference to the novel Nineteen <unk> @-@ Four . 
- In the deleted scene , the Richard Simmons robot healing itself after Smithers shoots it with a <unk> is a reference to the T @-@ 1000 from Terminator 2 : Judgment Day . 
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- = = Reception = = 
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- In its original broadcast , " Burns ' Heir " finished 53rd in ratings for the week of April 11 – 17 , 1994 , with a Nielsen rating of 9 @.@ 4 , and was viewed in 8 @.@ 85 million households . The show dropped four places in the rankings after finishing 49th the previous week . It was the third highest rated show on Fox that week following Living Single and Married ... with Children . 
- The authors of the book I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide , Warren Martyn and Adrian Wood , wrote that , " the episode lacks the emotional punch of others in which members of the family are separated . " DVD Movie Guide 's Colin Jacobson wrote that the episode was " such a great concept that it ’ s a surprise no [ one ] went for it earlier . " He felt that it " occasionally veers on the edge of <unk> , but it avoids becoming too sentimental . It 's a blast to see Burns ’ world from Bart ’ s point of view . DVD Talk gave the episode a score of 5 out of 5 while DVD Verdict gave the episode a Grade B score . Paul <unk> of Rocky Mountain News described the Robotic Richard Simmons scene as " a level of surreal comedy that approaches a kind of genius " . 
- Homer 's quote , " Kids , you tried your best and you failed <unk> . The lesson is never try " , was added to The Oxford Dictionary of <unk> in August 2007 . 
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- 
- = Cougar = 
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- The cougar ( Puma concolor ) , also commonly known as the mountain lion , puma , panther , or <unk> , is a large <unk> of the subfamily Felinae native to the Americas . Its range , from the Canadian Yukon to the southern Andes of South America , is the greatest of any large wild terrestrial mammal in the Western Hemisphere . An adaptable , <unk> species , the cougar is found in most American habitat types . It is the second @-@ heaviest cat in the New World , after the jaguar . <unk> and largely solitary by nature , the cougar is properly considered both nocturnal and <unk> , although there are daytime sightings . The cougar is more closely related to smaller <unk> , including the domestic cat ( subfamily Felinae ) , than to any species of subfamily <unk> , of which only the jaguar is native to the Americas . 
- The cougar is an ambush predator and pursues a wide variety of prey . Primary food sources are ungulates , particularly deer , but also livestock . It also hunts species as small as insects and rodents . This cat prefers habitats with dense <unk> and rocky areas for stalking , but can also live in open areas . The cougar is territorial and survives at low population densities . Individual territory sizes depend on terrain , vegetation , and abundance of prey . While large , it is not always the apex predator in its range , yielding to the jaguar , gray wolf , American black bear , and grizzly bear . It is reclusive and mostly avoids people . <unk> attacks on humans are rare , but have recently been increasing in North America as more people enter their territories . 
- <unk> hunting following European colonization of the Americas and the ongoing human development of cougar habitat has caused populations to drop in most parts of its historical range . In particular , the cougar was extirpated in eastern North America in the beginning of the 20th century , except for an isolated Florida panther <unk> . Breeding populations have moved east into the far western parts of the Dakotas , Nebraska , and Oklahoma . <unk> males have been verified in Minnesota , Missouri , Wisconsin , Iowa , the Upper Peninsula of Michigan , and Illinois , where a cougar was shot in the city limits of Chicago and , in at least one instance , observed as far east as coastal Connecticut . Reports of eastern cougars ( P. c. cougar ) still surface , although it was declared extirpated in 2011 . 
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- = = Naming and etymology = = 
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- With its vast range across the length of the Americas , P. concolor has dozens of names and various references in the mythology of the indigenous Americans and in contemporary culture . Currently , it is referred to as " puma " by most scientists and by the populations in 21 of the 23 countries in the Americas where " puma " is the common name in Spanish or Portuguese . The cat has many local or regional names in the United States and Canada , of which cougar , puma , mountain lion , and panther are popular . " Mountain lion " was a term first used in writing in 1858 from the diary of George A. Jackson of Colorado . Other names include <unk> ( probably a contraction from " cat of the mountain " ) , mountain <unk> , and painter . <unk> regard painter as a primarily upper @-@ Southern US regional variant on panther . The word panther is commonly used to specifically designate the black panther , a <unk> jaguar or leopard , and the Florida panther , a subspecies of cougar ( P. c. coryi ) . 
- P. concolor holds the Guinness record for the animal with the greatest number of names , with over 40 in English alone . 
- " Cougar " may be borrowed from the archaic Portuguese <unk> ; the term was originally derived from the <unk> language <unk> <unk> , meaning " similar to deer ( in hair color ) " . A current form in Brazil is <unk> . It may also be borrowed from the <unk> language term <unk> ara or <unk> ara . Less common Portuguese terms are onça @-@ <unk> ( brown onça , in distinction of the black @-@ spotted [ yellow ] one , onça @-@ <unk> , the jaguar ) or <unk> @-@ <unk> ( lit. chestnut lion ) , or unusually non @-@ native puma or <unk> @-@ da @-@ <unk> , more common names for the animal when native to a region other than South America ( especially for those who do not know that <unk> are found elsewhere but with a different name ) . People in rural regions often refer to both the cougar and the jaguar as simply <unk> ( she @-@ cat ) , and outside of the Amazon , both are colloquially referred to as simply onça by many people ( that is also a name for the leopard in Angola ) . 
- In the 17th century , German naturalist Georg <unk> named the cat the <unk> ara . <unk> 's rendering was reproduced by his associate , Dutch naturalist Willem <unk> , in 1648 . <unk> ara was then adopted by English naturalist John Ray in 1693 . The French naturalist Georges @-@ Louis <unk> , Comte de <unk> in 1774 ( probably influenced by the word " jaguar " ) converted the <unk> ara to <unk> , which was later modified to " cougar " in English . 
- The first English record of " puma " was in 1777 , where it had come from the Spanish , who in turn borrowed it from the Peruvian <unk> language in the 16th century , where it means " powerful " . 
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- = = Taxonomy and evolution = = 
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- Cougars are the largest of the small cats . They are placed in the subfamily Felinae , although their bulk characteristics are similar to those of the big cats in the subfamily <unk> . The family <unk> is believed to have originated in Asia about 11 million years ago . <unk> research on felids remains partial , and much of what is known about their evolutionary history is based on mitochondrial DNA analysis , as cats are poorly represented in the fossil record , and significant confidence intervals exist with suggested dates . In the latest <unk> study of the <unk> , the common ancestor of today 's <unk> , Lynx , Puma , <unk> , and Felis lineages migrated across the Bering land bridge into the Americas 8 @.@ 0 to 8 @.@ 5 million years ago ( <unk> ) . The lineages subsequently diverged in that order . North American felids then invaded South America 3 <unk> as part of the Great American Interchange , following formation of the Isthmus of Panama . The cougar was originally thought to belong in Felis ( Felis concolor ) , the genus which includes the domestic cat . As of 1993 , it is now placed in Puma along with the <unk> , a cat just a little more than a tenth its weight . 
- The cougar and <unk> are most closely related to the modern cheetah of Africa and western Asia , but the relationship is unresolved . The cheetah lineage is suggested by some studies to have diverged from the Puma lineage in the Americas ( see American cheetah ) and migrated back to Asia and Africa , while other research suggests the cheetah diverged in the Old World itself . The outline of small feline migration to the Americas is thus unclear . 
- A high level of genetic similarity has been found among North American cougar populations , suggesting they are all fairly recent descendants of a small ancestral group . Culver et al. propose the original North American population of P. concolor was extirpated during the Pleistocene <unk> some 10 @,@ 000 years ago , when other large mammals , such as <unk> , also disappeared . North America was then <unk> by a group of South American cougars . 
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- = = = Subspecies = = = 
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- Until the late 1980s , as many as 32 subspecies were recorded ; genetic study of mitochondrial DNA found many of these are too similar to be recognized as distinct at a molecular level . Following the research , the canonical <unk> Species of the World ( 3rd ed . ) recognizes six subspecies , five of which are solely found in Latin America : 
- Argentine cougar ( Puma concolor <unk> ) <unk> , 1940 : 
- includes the previous subspecies and synonyms <unk> and puma 
- Costa <unk> cougar ( P. c. <unk> ) <unk> , 1901 
- Eastern South American cougar ( P. c. <unk> ) Nelson and Goldman , 1931 : 
- includes the previous subspecies and synonyms <unk> , <unk> , <unk> , concolor , <unk> , and <unk> 
- North American cougar ( P. c. couguar ) Kerr , 1792 : 
- includes the previous subspecies and synonyms <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , and <unk> 
- Northern South American cougar ( P. c. concolor ) Linnaeus , 1771 : 
- includes the previous subspecies and synonyms <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , and <unk> 
- Southern South American cougar ( P. c. puma ) Molina , 1782 : 
- includes the previous subspecies and synonyms <unk> , concolor , <unk> , <unk> , and puma 
- <unk> sedis 
- Florida panther ( P. c. coryi ) 
- The status of the Florida panther remains uncertain . It is still regularly listed as subspecies P. c. coryi in research works , including those directly concerned with its conservation . Culver et al. noted low <unk> variation in the Florida panther , possibly due to <unk> ; responding to the research , one conservation team suggests , " the degree to which the scientific community has accepted the use of genetics in puma taxonomy is not resolved at this time . " 
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- = = Biology and behavior = = 
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- = = = Physical characteristics = = = 
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- Cougars are slender and agile members of the cat family . They are the fourth @-@ largest cat ; adults stand about 60 to 90 cm ( 24 to 35 in ) tall at the shoulders . Adult males are around 2 @.@ 4 m ( 7 @.@ 9 ft ) long nose @-@ to @-@ tail and females average 2 @.@ 05 m ( 6 @.@ 7 ft ) , with overall ranges between 1 @.@ 5 to 2 @.@ 75 m ( 4 @.@ 9 to 9 @.@ 0 ft ) nose to tail suggested for the species in general . Of this length , 63 to 95 cm ( 25 to 37 in ) is comprised by the tail . Males typically weigh 53 to 100 kg ( 115 to 220 lb ) , averaging 62 kg ( 137 lb ) . Females typically weigh between 29 and 64 kg ( 64 and 141 lb ) , averaging 42 kg ( 93 lb ) . Cougar size is smallest close to the equator , and larger towards the poles . The largest recorded cougar , shot in 1901 , weighed 105 @.@ 2 kg ( 232 lb ) ; claims of 125 @.@ 2 kg ( 276 lb ) and 118 kg ( 260 lb ) have been reported , though they were most likely exaggerated . On average , adult male cougars in British Columbia weigh 56 @.@ 7 kg ( 125 lb ) and adult females 45 @.@ 4 kg ( 100 lb ) , though several male cougars in British Columbia weighed between 86 @.@ 4 and 95 @.@ 5 kg ( 190 to 210 lb ) . 
- The head of the cat is round and the ears are erect . Its powerful <unk> , neck , and jaw serve to grasp and hold large prey . It has five retractable claws on its <unk> ( one a <unk> ) and four on its hind paws . The larger front feet and claws are adaptations to clutching prey . 
- Cougars can be almost as large as <unk> , but are less muscular and not as powerfully built ; where their ranges overlap , the cougar tends to be smaller on average . Besides the jaguar , the cougar is on average larger than all felids apart from lions and tigers . Despite its size , it is not typically classified among the " big cats " , as it cannot <unk> , lacking the specialized <unk> and <unk> apparatus of <unk> . Compared to " big cats " , cougars are often silent with minimal communication through vocalizations outside of the mother @-@ offspring relationship . Cougars sometimes voice low @-@ pitched <unk> , <unk> , and <unk> , as well as chirps and whistles , many of which are comparable to those of domestic cats . They are well known for their screams , as referenced in some of their common names , although these screams are often <unk> to be the calls of other animals . 
- Cougar coloring is plain ( hence the Latin concolor ) but can vary greatly between individuals and even between siblings . The coat is typically tawny , but ranges to silvery @-@ grey or reddish , with lighter patches on the <unk> , including the jaws , chin , and throat . <unk> are spotted and born with blue eyes and rings on their tails ; juveniles are pale , and dark spots remain on their flanks . Despite anecdotes to the contrary , all @-@ black coloring ( <unk> ) has never been documented in cougars . The term " black panther " is used colloquially to refer to <unk> individuals of other species , particularly <unk> and <unk> . 
- Cougars have large paws and proportionally the largest hind legs in the cat family . This physique allows it great leaping and short @-@ sprint ability . The cougar is able to leap as high as 5 @.@ 5 m ( 18 ft ) in one bound , and as far as 40 to 45 ft ( 12 to 13 @.@ 5 m ) horizontally . The cougar 's top running speed ranges between 64 and 80 km / h ( 40 and 50 mph ) , but is best adapted for short , powerful sprints rather than long chases . It is adept at climbing , which allows it to evade <unk> competitors . Although it is not strongly associated with water , it can swim . 
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- = = = Hunting and diet = = = 
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- A successful <unk> predator , the cougar will eat any animal it can catch , from insects to large ungulates ( over 500 kg ) . Like all cats , it is an <unk> <unk> , meaning it needs to feed exclusively on meat to survive . The mean weight of <unk> prey ( <unk> ) that pumas attack increases with the puma 's body weight ; in general , <unk> is lower in areas closer to the equator . Its most important prey species are various deer species , particularly in North America ; mule deer , white @-@ tailed deer , elk and even bull moose are taken . Other species such as the <unk> and <unk> 's sheep , horse , fallow deer , <unk> , mountain goat , coyote , <unk> , and domestic livestock such as cattle and sheep are also primary food bases in many areas . A survey of North America research found 68 % of prey items were ungulates , especially deer . Only the Florida panther showed variation , often preferring feral <unk> and <unk> . 
- Investigation in Yellowstone National Park showed that elk , followed by mule deer , were the cougar 's primary targets ; the prey base is shared with the park 's gray wolves , with which the cougar competes for resources . Another study on winter kills ( November – April ) in Alberta showed that ungulates accounted for greater than 99 % of the cougar diet . Learned , individual prey recognition was observed , as some cougars rarely killed <unk> sheep , while others relied heavily on the species . 
- In Pacific Rim National Park Reserve , <unk> samples showed raccoons to make up 28 % of the cougar 's diet , harbor seals and <unk> deer 24 % each , North American river otters 10 % , California sea lion 7 % , and American mink 4 % ; the remaining 3 % were unidentified . 
- In the Central and South American cougar range , the ratio of deer in the diet declines . Small to mid @-@ sized mammals are preferred , including large rodents such as the <unk> . <unk> accounted for only 35 % of prey items in one survey , about half that of North America . Competition with the larger jaguar has been suggested for the decline in the size of prey items . Other listed prey species of the cougar include mice , <unk> , <unk> , raccoons , hares , <unk> , <unk> , <unk> , <unk> , and wild turkey . Birds and small reptiles are sometimes preyed upon in the south , but this is rarely recorded in North America . Not all of their prey is listed here due to their large range . 
- Though capable of sprinting , the cougar is typically an ambush predator . It stalks through brush and trees , across ledges , or other covered spots , before delivering a powerful leap onto the back of its prey and a <unk> neck bite . The cougar is capable of breaking the neck of some of its smaller prey with a strong bite and momentum bearing the animal to the ground . 
- <unk> are generally estimated around one large <unk> every two weeks . The period shrinks for females raising young , and may be as short as one kill every three days when cubs are nearly mature around 15 months . The cat <unk> a kill to a preferred spot , covers it with brush , and returns to feed over a period of days . The cougar is generally reported to not be a <unk> , and rarely consumes prey it has not killed , but deer carcasses left exposed for study were <unk> by cougars in California , suggesting more opportunistic behavior . 
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- = = = Reproduction and life cycle = = = 
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- Females reach sexual maturity between one @-@ and @-@ a @-@ half to three years of age . They typically average one litter every two to three years throughout their reproductive lives , though the period can be as short as one year . Females are in <unk> for about 8 days of a 23 @-@ day cycle ; the gestation period is approximately 91 days . Females are sometimes reported as monogamous , but this is uncertain and polygyny may be more common . Copulation is brief but frequent . Chronic stress can result in low reproductive rates when in captivity as well as in the field . 
- Only females are involved in parenting . Female cougars are fiercely protective of their cubs , and have been seen to successfully fight off animals as large as <unk> bears in their defense . Litter size is between one and six cubs ; typically two . Caves and other <unk> that offer protection are used as litter <unk> . Born blind , cubs are completely dependent on their mother at first , and begin to be <unk> at around three months of age . As they grow , they begin to go out on <unk> with their mother , first visiting kill sites , and after six months beginning to hunt small prey on their own . <unk> survival rates are just over one per litter . When cougars are born , they have spots , but they lose them as they grow , and by the age of 2 1 / 2 years , they will completely be gone 
- Young adults leave their mother to attempt to establish their own territory at around two years of age and sometimes earlier ; males tend to leave sooner . One study has shown high mortality amongst cougars that travel farthest from the maternal range , often due to conflicts with other cougars ( <unk> competition ) . Research in New Mexico has shown that " males dispersed significantly farther than females , were more likely to traverse large <unk> of non @-@ cougar habitat , and were probably most responsible for nuclear gene flow between habitat patches . " 
- Life expectancy in the wild is reported at eight to 13 years , and probably averages eight to 10 ; a female of at least 18 years was reported killed by hunters on Vancouver Island . Cougars may live as long as 20 years in captivity . One male North American cougar ( P. c. couguar ) , named <unk> , was two months short of his 30th birthday when he died in 2007 . Causes of death in the wild include disability and disease , competition with other cougars , starvation , accidents , and , where allowed , human hunting . <unk> <unk> virus , an endemic HIV @-@ like virus in cats , is well @-@ adapted to the cougar . 
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- = = = Social structure and home range = = = 
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- Like almost all cats , the cougar is a solitary animal . Only mothers and kittens live in groups , with adults meeting only to mate . It is secretive and <unk> , being most active around dawn and dusk . 
- Estimates of territory sizes vary greatly . Canadian Geographic reports large male territories of 150 to 1000 km2 ( 58 to 386 sq mi ) with female ranges half the size . Other research suggests a much smaller lower limit of 25 km2 ( 10 sq mi ) , but an even greater upper limit of 1300 km2 ( 500 sq mi ) for males . In the United States , very large ranges have been reported in Texas and the Black Hills of the northern Great Plains , in excess of 775 km2 ( 300 sq mi ) . Male ranges may include or overlap with those of females but , at least where studied , not with those of other males , which serves to reduce conflict between cougars . Ranges of females may overlap slightly with each other . <unk> marks , urine , and feces are used to mark territory and attract mates . Males may scrape together a small pile of leaves and grasses and then <unk> on it as a way of marking territory . 
- Home range sizes and overall cougar abundance depend on terrain , vegetation , and prey abundance . One female adjacent to the San Andres Mountains , for instance , was found with a large range of 215 km2 ( 83 sq mi ) , necessitated by poor prey abundance . Research has shown cougar <unk> from 0 @.@ 5 animals to as much as 7 ( in one study in South America ) per 100 km2 ( 38 sq mi ) . 
- Because males disperse farther than females and compete more directly for mates and territory , they are most likely to be involved in conflict . Where a <unk> fails to leave his maternal range , for example , he may be killed by his father . When males encounter each other , they hiss , <unk> , and may engage in violent conflict if neither backs down . Hunting or relocation of the cougar may increase aggressive encounters by disrupting territories and bringing young , transient animals into conflict with established individuals . 
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- = = Ecology = = 
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- = = = Distribution and habitat = = = 
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- The cougar has the largest range of any wild land animal in the Americas . Its range spans 110 degrees of latitude , from northern Yukon in Canada to the southern Andes . Its wide distribution stems from its <unk> to virtually every habitat type : it is found in all forest types , as well as in lowland and mountainous <unk> . The cougar prefers regions with dense <unk> , but can live with little vegetation in open areas . Its preferred habitats include <unk> canyons , <unk> , rim rocks , and dense brush . 
- The cougar was extirpated across much of its eastern North American range ( with the exception of Florida ) in the two centuries after European colonization , and faced grave threats in the remainder of its territory . Currently , it ranges across most western American states , the Canadian provinces of Alberta , Saskatchewan and British Columbia , and the Canadian territory of Yukon . There have been widely debated reports of possible <unk> of eastern North America . DNA evidence has suggested its presence in eastern North America , while a consolidated map of cougar sightings shows numerous reports , from the mid @-@ western Great Plains through to eastern Canada . The Quebec wildlife services ( known locally as <unk> ) also considers cougar to be present in the province as a threatened species after multiple DNA tests confirmed cougar hair in <unk> mating sites . The only unequivocally known eastern population is the Florida panther , which is critically endangered . There have been unconfirmed sightings in <unk> <unk> , Maine ( north of <unk> ) ; and in New Hampshire , there have been unconfirmed sightings as early as 1997 . In 2009 , the Michigan Department of Natural Resources confirmed a cougar sighting in Michigan 's Upper Peninsula . Typically , extreme @-@ range sightings of cougars involve young males , which can travel great distances to establish ranges away from established males ; all four confirmed cougar kills in Iowa since 2000 involved males . 
- On April 14 , 2008 , police shot and killed a cougar on the north side of Chicago , Illinois . DNA tests were consistent with cougars from the Black Hills of South Dakota . Less than a year later , on March 5 , 2009 , a cougar was photographed and unsuccessfully <unk> by state wildlife biologists in a tree near <unk> , Wisconsin , in the northwestern part of the state . 
- Other eastern sightings since 2010 have occurred in locations such as Greene County , Indiana , Greenwich and Milford , Connecticut , Morgan County Pike County , and <unk> County , Illinois , and Bourbon County , Kentucky . 
- South of the Rio Grande , the International Union for the Conservation of Nature and Natural Resources ( IUCN ) lists the cat in every Central and South American country . While specific state and provincial statistics are often available in North America , much less is known about the cat in its southern range . 
- The cougar 's total breeding population is estimated at less than 50 @,@ 000 by the IUCN , with a declining trend . US state @-@ level statistics are often more optimistic , suggesting cougar populations have rebounded . In Oregon , a healthy population of 5 @,@ 000 was reported in 2006 , exceeding a target of 3 @,@ 000 . California has actively sought to protect the cat and a similar number of cougars has been suggested , between 4 @,@ 000 and 6 @,@ 000 . 
- In 2012 research in <unk> Los <unk> National Reserve , Chile , based in 18 motion @-@ sensitive cameras counted a population of two males and two females , one of them with at least two cubs , in an area of 600 km2 , that is 0 @.@ 63 cougars every 100 km2 . 
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- = = = Habitat fragmentation = = = 
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- With the increase of human development and infrastructure growth in California , the North American Cougar populations are becoming more isolated from one another . 
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- = = = Ecological role = = = 
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- Aside from humans , no species <unk> upon mature cougars in the wild , although conflicts with other predators or scavengers occur . The Yellowstone National Park ecosystem provides a fruitful <unk> to study inter @-@ predator interaction in North America . Of the three large predators , the massive grizzly bear appears dominant , often although not always able to drive both the gray wolf pack and the cougar off their kills . One study found that American black bears visited 24 % of cougar kills in Yellowstone and Glacier National Parks , <unk> 10 % of carcasses . Bears gained up to 113 % , and cougars lost up to 26 % , of their respective daily energy requirements from these encounters . Accounts of cougars and black bears killing each other in fights to the death have been documented from the 19th century . In spite of the size and power of the cougar , there have also been accounts of both brown and black bears killing cougars , either in disputes or in self @-@ defense . 
- The gray wolf and the cougar compete more directly for prey , especially in winter . Wolves can steal kills and occasionally kill the cat . One report describes a large pack of 7 to 11 wolves killing a female cougar and her kittens . Conversely , lone female or young wolves are vulnerable to predation , and have been reported ambushed and killed by cougars . Various accounts of cougars killing lone wolves , including a six @-@ year @-@ old female , have also been documented . Wolves more broadly affect cougar population dynamics and distribution by dominating territory and prey opportunities , and disrupting the feline 's behavior . Preliminary research in Yellowstone , for instance , has shown displacement of the cougar by wolves . In nearby Sun Valley , Idaho , a cougar / wolf encounter that resulted in the death of the cougar was documented . One researcher in Oregon noted : " When there is a pack around , cougars are not comfortable around their kills or raising kittens ... A lot of times a big cougar will kill a wolf , but the pack phenomenon changes the table . " 
- Both species , meanwhile , are capable of killing mid @-@ sized predators , such as <unk> and coyotes , and tend to suppress their numbers . Although cougars can kill coyotes , the latter have been documented attempting to prey on cougar cubs . 
- In the southern portion of its range , the cougar and jaguar share overlapping territory . The jaguar tends to take larger prey and the cougar smaller where they overlap , reducing the cougar 's size and also further reducing the likelihood of direct competition . Of the two <unk> , the cougar appears best able to exploit a broader prey niche and smaller prey . 
- As with any predator at or near the top of its food chain , the cougar impacts the population of prey species . <unk> by cougars has been linked to changes in the species mix of deer in a region . For example , a study in British Columbia observed that the population of mule deer , a favored cougar prey , was declining while the population of the less frequently preyed @-@ upon white @-@ tailed deer was increasing . The Vancouver Island <unk> , an endangered species endemic to one region of dense cougar population , has seen decreased numbers due to cougar and gray wolf predation . Nevertheless , there is a measurable effect on the quality of deer populations by puma predation . 
- In the southern part of South America , the puma is a top level predator that has controlled the population of <unk> and other species since prehistoric times . 
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- = = <unk> = = 
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- A <unk> is a hybrid animal resulting from a union between a cougar and a leopard . Three sets of these hybrids were bred in the late 1890s and early 1900s by Carl <unk> at his animal park in Hamburg , Germany . Most did not reach adulthood . One of these was purchased in 1898 by Berlin Zoo . A similar hybrid in Berlin Zoo purchased from <unk> was a cross between a male leopard and a female puma . Hamburg Zoo 's specimen was the reverse pairing , the one in the black @-@ and @-@ white photo , <unk> by a puma bred to an Indian <unk> . 
- Whether born to a female puma mated to a male leopard , or to a male puma mated to a female leopard , <unk> inherit a form of <unk> . Those reported grew to only half the size of the parents . They have a puma @-@ like long body ( proportional to the limbs , but nevertheless shorter than either parent ) , but short legs . The coat is variously described as sandy , tawny or greyish with brown , chestnut or " faded " <unk> . 
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- = = Conservation status = = 
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- The World Conservation Union ( IUCN ) currently lists the cougar as a " least concern " species . The cougar is regulated under <unk> I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora ( <unk> ) , rendering illegal international trade in specimens or parts . 
- In the United States east of the Mississippi River , the only unequivocally known cougar population is the Florida panther . Until 2011 , the United States Fish and Wildlife Service ( <unk> ) recognized both an Eastern cougar ( claimed to be a subspecies by some , denied by others ) and the Florida panther , <unk> protection under the Endangered Species Act . Certain taxonomic authorities have collapsed both designations into the North American cougar , with Eastern or Florida subspecies not recognized , while a subspecies designation remains recognized by some conservation scientists . In 2003 the documented count for the Florida sub @-@ population was 87 individuals . In March 2011 , the <unk> declared the Eastern cougar extinct . With the taxonomic uncertainty about its existence as a subspecies as well as the possibility of eastward migration of cougars from the western range , the subject remains open . 
- This uncertainty has been recognized by Canadian authorities . The Canadian federal agency called Committee on the Status of Endangered Wildlife in Canada rates its current data as " insufficient " to draw conclusions regarding the eastern cougar 's survival , and says on its Web site " Despite many sightings in the past two decades from eastern Canada , there are insufficient data to evaluate the taxonomy or assign a status to this cougar . " Notwithstanding numerous reported sightings in Ontario , Quebec , New Brunswick and Nova Scotia , it has been said that the evidence is inconclusive : " . . . there may not be a distinct ' eastern ' subspecies , and some sightings may be of escaped pets . " 
- The cougar is also protected across much of the rest of its range . As of 1996 , cougar hunting was prohibited in Argentina , Brazil , Bolivia , Chile , Colombia , Costa Rica , French <unk> , Guatemala , Honduras , Nicaragua , Panama , Paraguay , <unk> , Venezuela , and Uruguay . The cat had no reported legal protection in Ecuador , El Salvador , and <unk> . <unk> cougar hunting is still common in the United States and Canada , although they are protected from all hunting in the Yukon ; it is permitted in every U.S. state from the Rocky Mountains to the Pacific Ocean , with the exception of California . Texas is the only state in the United States with a viable population of cougars that does not protect that population in some way . In Texas , cougars are listed as nuisance wildlife and any person holding a hunting or a trapping permit can kill a cougar regardless of the season , number killed , sex or age of the animal . <unk> animals are not required to be reported to Texas Parks and Wildlife Department . Conservation work in Texas is the effort of a non @-@ profit organization , <unk> Ecology Inc ( <unk> ) , as part of their Texas Mountain Lion Conservation Project . Cougars are generally hunted with packs of dogs , until the animal is ' treed ' . When the hunter arrives on the scene , he shoots the cat from the tree at close range . The cougar cannot be legally killed without a permit in California except under very specific circumstances , such as when a cougar is in act of pursuing livestock or domestic animals , or is declared a threat to public safety . <unk> are issued when owners can prove property damage on their livestock or pets . For example , multiple dogs have been attacked and killed , sometimes while with the owner . Many attribute this to the protection cougars have from being hunted and are now becoming <unk> to humans ; most are removed from the population after the attacks have already occurred . Statistics from the Department of Fish and Game indicate that cougar killings in California have been on the rise since the 1970s with an average of over 112 cats killed per year from 2000 to 2006 compared to six per year in the 1970s . They also state on their website that there is a healthy number of cougars in California . The Bay Area Puma Project aims to obtain information on cougar populations in the San Francisco Bay area and the animals ' interactions with habitat , prey , humans , and residential communities . 
- Conservation threats to the species include persecution as a pest animal , environmental degradation and habitat fragmentation , and depletion of their prey base . Wildlife corridors and sufficient range areas are critical to the <unk> of cougar populations . Research <unk> have shown that the animal faces a low extinction risk in areas of <unk> km2 ( 850 sq mi ) or more . As few as one to four new animals entering a population per decade markedly increases persistence , <unk> the importance of habitat corridors . 
- On March 2 , 2011 , the United States Fish and Wildlife Service declared the Eastern cougar ( Puma concolor couguar ) officially extinct . 
- 
- = = Relationships with humans = = 
- 
- 
- = = = In mythology = = = 
- 
- The grace and power of the cougar have been widely admired in the cultures of the indigenous peoples of the Americas . The Inca city of <unk> is reported to have been designed in the shape of a cougar , and the animal also gave its name to both Inca regions and people . The <unk> people represented the puma often in their ceramics . The sky and thunder god of the Inca , <unk> , has been associated with the animal . 
- In North America , mythological descriptions of the cougar have appeared in the stories of the <unk> language ( " Ho @-@ <unk> " or " Winnebago " ) of Wisconsin and Illinois and the Cheyenne , amongst others . To the <unk> and <unk> of Arizona , the <unk> of the cougar was a <unk> of death . The <unk> and Ojibwe believe that the cougar lived in the underworld and was wicked , whereas it was a sacred animal among the <unk> . 
- 
- = = = <unk> predation = = = 
- 
- During the early years of <unk> , cougars were considered on par with wolves in <unk> . According to figures in Texas in 1990 , 86 calves ( 0 @.@ <unk> % of a total of 13 @.@ 4 million cattle & calves in Texas ) , 253 <unk> goats , 302 <unk> kids , <unk> sheep ( 0 @.@ 02 % of a total of 2 @.@ 0 million sheep & lambs in Texas ) and 562 lambs ( 0 @.@ 04 % of 1 @.@ 2 million lambs in Texas ) were confirmed to have been killed by cougars that year . In Nevada in 1992 , cougars were confirmed to have killed 9 calves , 1 horse , 4 foals , 5 goats , <unk> sheep and 400 lambs . In both cases , sheep were the most frequently attacked . Some instances of surplus killing have resulted in the deaths of 20 sheep in one attack . A cougar 's killing bite is applied to the back of the neck , head , or throat and they inflict puncture marks with their claws usually seen on the sides and underside of the prey , sometimes also <unk> the prey as they hold on . Coyotes also typically bite the throat region but do not inflict the claw marks and farmers will normally see the signature <unk> @-@ <unk> pattern that coyotes create as they feed on the prey whereas cougars typically drag in a straight line . The work of a cougar is generally clean , differing greatly from the indiscriminate mutilation by coyotes and feral dogs . The size of the tooth puncture marks also helps distinguish kills made by cougars from those made by smaller predators . 
- Remedial hunting appears to have the paradoxical effect of increased livestock predation and complaints of human @-@ puma conflicts . In a 2013 study the most important <unk> of puma problems were remedial hunting of puma the previous year . Each additional puma on the landscape increased predation and human @-@ puma complaints by 5 % but each additional animal killed on the landscape the previous year increased complaints by 50 % , an order of magnitude higher . The effect had a dose @-@ response relationship with very heavy ( 100 % removal of adult puma ) remedial hunting leading to a 150 % – 340 % increase in livestock and human conflicts . This effect is attributed to the fact that inexperienced younger male pumas are most likely to approach human developments , whereas remedial hunting removes older pumas who have learned to avoid people in their established territories . Remedial hunting enables younger males to enter the former territories of the older animals . 
- 
- = = = Attacks on humans = = = 
- 
- 
- = = = = North American subspecies = = = = 
- 
- The <unk> North American subspecies is P. concolor couguar . 
- Due to the expanding human population , cougar ranges increasingly overlap with areas inhabited by humans . Attacks on humans are very rare , as cougar prey recognition is a learned behavior and they do not generally recognize humans as prey . Attacks on people , livestock , and pets may occur when a puma <unk> to humans or is in a condition of severe starvation . Attacks are most frequent during late spring and summer , when juvenile cougars leave their mothers and search for new territory . 
- Between 1890 and 1990 , in North America there were 53 reported , confirmed attacks on humans , resulting in 48 <unk> injuries and 10 deaths of humans ( the total is greater than 53 because some attacks had more than one victim ) . By 2004 , the count had climbed to 88 attacks and 20 deaths . 
- Within North America , the distribution of attacks is not uniform . The heavily populated state of California has seen a dozen attacks since 1986 ( after just three from 1890 to 1985 ) , including three fatalities . <unk> populated New Mexico reported an attack in 2008 , the first there since 1974 . 
- As with many predators , a cougar may attack if cornered , if a fleeing human stimulates their instinct to chase , or if a person " plays dead " . Standing still may cause the cougar to consider a person easy prey . <unk> the threat to the animal through intense eye contact , loud shouting , and any other action to appear larger and more menacing , may make the animal retreat . Fighting back with sticks and rocks , or even bare hands , is often effective in persuading an attacking cougar to disengage . 
- When cougars do attack , they usually employ their characteristic neck bite , attempting to position their teeth between the vertebrae and into the spinal <unk> . Neck , head , and spinal injuries are common and sometimes fatal . Children are at greatest risk of attack , and least likely to survive an encounter . Detailed research into attacks prior to 1991 showed that 64 % of all victims – and almost all fatalities – were children . The same study showed the highest proportion of attacks to have occurred in British Columbia , particularly on Vancouver Island where cougar populations are especially dense . <unk> attacks on humans , cougars display aberrant behavior , such as activity during daylight hours , a lack of fear of humans , and stalking humans . There have sometimes been incidents of pet cougars <unk> people . 
- Research on new wildlife <unk> may be able to reduce human @-@ animal conflicts by predicting when and where predatory animals hunt . This can not only save human lives and the lives of their pets and livestock but also save these large predatory mammals that are important to the balance of ecosystems . 
- 
- = = = = Other subspecies = = = = 
- 
- The puma of the plains of South America ( P. concolor <unk> , called the Argentine cougar by North Americans ) is noted for its extreme unwillingness to attack humans . According to the Anglo @-@ Argentine naturalist William Henry Hudson " It does not attack man , and <unk> is perfectly correct when he affirms that it never <unk> , or threatens to hurt , man or child , even when it finds them sleeping . This , however , is not a full statement of the facts ; the puma will not even defend itself against man " . And : " All who have killed or witnessed the killing of the puma — and I have questioned scores of hunters on this point — agree that it <unk> itself in this <unk> , pathetic manner to death at the hands of man . Claudio Gay , in his Natural History of <unk> , says , " When attacked by man its energy and daring at once <unk> it , and it becomes a weak , inoffensive animal , and trembling , and <unk> <unk> <unk> , and shedding abundant tears , it seems to <unk> compassion from a generous enemy " . Hudson adds that , except in relation to man , the puma is a ferocious animal , attacking even the much stronger jaguar . 
- 
- 
- = New York State Route 164 = 
- 
- New York State Route 164 ( NY 164 ) is a short state highway located entirely in the town of Patterson in northeastern Putnam County , New York , in the United States . It is a short , two @-@ lane back road that does not pass through any major populated areas and serves primarily as a connector between NY 311 and NY 22 . NY 164 also allows for faster passage from Interstate 84 ( I @-@ 84 ) to the Putnam Lake area via NY 311 . The route was originally designated as NY 312 during the 1930s and later became part of NY 216 . NY 164 was established on January 1 , 1970 , following the truncation of NY 216 to its current eastern terminus . 
- 
- = = Route description = = 
- 
- In the west , NY 164 splits off from NY 311 a short distance east of I @-@ 84 . It crosses under the Metro North tracks three times , including two one @-@ lane <unk> with limited sight to the other side . It passes along the northern base of a small mountain and turns slightly southward . The highway turns due eastward and intersects with County Route 64 in the hamlet of <unk> . Just to the north is Mandel Pond , and the route makes a series of erratic turns around small mountains , passing south of Cornwall Hill as it does so . NY 164 turns south @-@ southeast and ends at NY 22 in Patterson , east of the East Branch Croton River . 
- 
- = = History = = 
- 
- The entirety of modern @-@ day NY 164 was originally designated as NY 312 in the 1930 renumbering of state highways in New York . When the NY 312 designation was shifted south to its current location in the town of Southeast c . 1937 , its former routing in Patterson became part of an extended NY 216 , which connected with its current routing by way of modern NY 311 , NY <unk> and NY 55 . NY 216 was truncated to its current length on January 1 , 1970 . The portion of its former alignment between NY 311 and NY 22 was then redesignated as NY 164 . 
- 
- = = Major intersections = = 
- 
- The entire route is in Patterson , Putnam County . 
- 
- 
- = Sorraia = 
- 
- The Sorraia is a rare breed of horse indigenous to the portion of the Iberian peninsula , in the Sorraia River basin , in Portugal . The Sorraia is known for its primitive features , including a convex profile and dun coloring with primitive markings . Concerning its origins , a theory has been advanced by some authors that the Sorraia is a descendant of primitive horses belonging to the naturally occurring wild fauna of Southern Iberia . Studies are currently ongoing to discover the relationship between the Sorraia and various wild horse types , as well as its relationship with other breeds from the Iberian Peninsula and Northern Africa . 
- Members of the breed are small , but <unk> and well @-@ adapted to harsh conditions . They were occasionally captured and used by native farmers for centuries , and a remnant population of these nearly extinct horses was discovered by a Portuguese zoologist in the early 20th century . Today , the Sorraia has become the focus of preservation efforts , with European scientists leading the way and enthusiasts from several countries forming projects and establishing herds to assist in the re @-@ establishment of this breed from its current endangered status . 
- 
- = = Characteristics = = 
- 
- The Sorraia breed stands between 14 @.@ 1 and 14 @.@ 3 hands ( 57 and 59 inches , 145 and 150 cm ) high , although some individuals are as small as 12 @.@ 3 hands ( 51 inches , 130 cm ) The head tends to be large , the profile convex , and the ears long . The neck is slender and long , the <unk> high , and the <unk> slightly sloping . The legs are strong , with long <unk> and well @-@ <unk> hooves . These horses have good endurance and are easy keepers , thriving on relatively little <unk> . They have a reputation for being independent of temperament , but <unk> . 
- On adult horses , the lay of the hair can create the appearance of stripes or " barring " on the neck and chest . Also due to the lay of the hair , newborn foals can appear to have stripes all over , reminiscent of zebra stripes . The breed standard refers to this as " hair stroke " . 
- 
- = = = Color = = = 
- 
- Sorraia are generally dun or a dun variation called <unk> . Dun coloring includes primitive markings such as a black dorsal stripe , black tipped ears , horizontal <unk> on the legs and a dark muzzle area . The dark muzzle area is in contrast to some other dun @-@ colored horse breeds , who have light @-@ colored muzzle areas and <unk> , possibly due to the presence of <unk> genetics . Sorraia horses have bi @-@ colored <unk> and tails with lighter colored hairs that fringe the outside of the longer growing black hair . This is a characteristic shared with other predominantly dun @-@ colored breeds , such as the <unk> horse . <unk> Sorraia occasionally have white markings , although they are rare and <unk> by the breed 's studbook . 
- 
- = = <unk> = = 
- 
- The relationship between the Sorraia and other breeds remains largely undetermined , as is its relationship to the wild horse subspecies , the Tarpan and the Przewalski 's Horse . The Sorraia originally developed in the southern part of the Iberian <unk> d 'Andrade hypothesized that the Sorraia would be the ancestor of the Southern Iberian breeds . <unk> , scientists place the Sorraia as closely related to the <unk> and the <unk> , but genetic studies using mitochondrial DNA show that the Sorraia forms a cluster that is largely separated from most Iberian breeds . Some evidence links this cluster with <unk> and domestic Mongolian horses . At the same time , one of the maternal lineages is shared with the <unk> . Genetic evidence has not supported an hypothesis that the Sorraia is related to the <unk> horse , an African breed introduced to Iberia by the Moors . 
- Multiple authors have suggested that the Sorraia might be a descendant of the Tarpan based on shared morphological features , principally the typical color of its coat . Other authors simply state that the Sorraia has " evident primitive characteristics " , although they do not refer to a specific ancestor . However , there have been no genetic studies comparing the Sorraia with the Tarpan , and similarity of external morphology is an unreliable measure of <unk> . 
- Genetic studies to date have been inconclusive about the closest relative of the Sorraia . On one hand , studies using mitochondrial DNA showed a relationship with the Przewalski 's Horse , in that Przewalski 's Horse has a unique <unk> ( <unk> ) not found in domestic horses , which differs by just one single nucleotide from one of the major Sorraia <unk> ( <unk> , later <unk> ) . In comparison , genetic distances within the domestic horse are as large as 11 nucleotide differences . However , this relationship with the Przewalski 's Horse was contradicted in another study using <unk> data that showed that the genetic distance between the <unk> 's Horse and the Sorraia was the largest . Such conflicting results can arise when a population passes through a genetic bottleneck , and evidence suggests that the Sorraia , among other rare breeds , has recently passed through a bottleneck , effectively obscuring the position of this breed in the family tree of the domestic horse . Thus , the morphological , physiological , and cultural characteristics of the Sorraia are the subject of continued study to better understand the relationship between various Iberian horse breeds and wild horse subspecies . 
- 
- = = History = = 
- 
- Although it is known that the Sorraia developed in the southern part of the Iberian peninsula , the breed was isolated and unknown to science until the 20th century . Despite the lack of documentation , attempts have been made to reconstruct its history . <unk> parietal art images in the region depict <unk> with a distinct likeness to the Sorraia , with similar zebra @-@ like markings . Analysis of mtDNA has been performed on Mustangs in the western United States that show similar mtDNA patterns between some Mustangs and the Sorraia breed . Spanish conquistadors took Iberian horses , some of whom closely resembled the modern @-@ day Sorraia , to the Americas in their conquests , probably as pack animals . <unk> between the Sorraia and several North and South American breeds are shown in the dun and <unk> coloring and various physical characteristics . This evidence suggests that the Sorraia , their ancestors , or other horses with similar features , may have had a long history in the Iberian region and a role in the creation of American breeds . 
- Otherwise , the Sorraia breed was lost to history until 1920 , when Portuguese zoologist and paleontologist Dr. Ruy d 'Andrade first encountered the Sorraia horse during a hunting trip in the Portuguese lowlands . This remnant herd of primitive horses had continued to live a wild existence in these lowlands , which were rather inaccessible and had been used as a hunting preserve by Portuguese royalty until the early 1900s . At the time of d 'Andrade 's initial meeting the breed , the horses were ill @-@ regarded by native farmers , although they were considered <unk> native fauna that lived off of the <unk> lands and salt marshes in the local river valleys . For centuries , peasant farmers of the area would occasionally capture the horses and use them for agricultural work , including <unk> grain and herding bulls . 
- In the 1920s and 1930s , as mechanization became more prevalent , both wild and domesticated breeding stock diminished to almost nothing , and d 'Andrade , along with his son Fernando , encouraged the conservation of the breed . In 1937 , d 'Andrade began a small herd of his own with five stallions and seven mares from horses obtained near <unk> , Portugal . All <unk> currently in captivity descend from these original horses obtained by d 'Andrade , and it is believed that the remnant wild herds of the breed died out soon after . These horses were kept in a habitat similar to their native one . In 1975 , two other farms took up the Sorraia 's cause and acquired small herds to help with conservation . In 1976 , three stallions and three mares were imported to Germany from Portugal to begin a sub @-@ population there . In March 2004 , a small breeding herd of Sorraia horses was released on the estate of a private land owner who dedicated a portion of his property so that these horses could live completely wild , as did their ancestors . The refuge created for them is in the Vale de <unk> region of south western Portugal , one of places so named because this is where the Sorraia 's predecessors dwelt . 
- Two Sorraia stallions were imported to the United States in the early 21st century . In 2006 , another Sorraia stallion was imported to Canada where a Sorraia Mustang Preserve has been established on <unk> Island in Ontario . <unk> to existing preservation efforts which work in conjunction with the Sorraia Mustang <unk> , another project by a consortium of <unk> in the United States is attempting to establish a separate network and studbook . These <unk> have gathered Spanish Mustangs that through mtDNA testing show a genetic relationship with the Sorraia and are breeding them according to both <unk> and phenotype in an attempt to help preserve what they are calling the " American Sorraia " . 
- Today , the breed is nearly extinct , with fewer than 200 horses existing as of 2007 , including around 80 breeding mares . The Food and Agriculture Organization considers it to be maintaining critical risk status . The first studbook was published in 2004 , dedicated to maintaining a written record of the <unk> of the Sorraia . <unk> are present mainly in Portugal , with a small population in Germany . While not bred for a specific use , the Sorraia horses are versatile and have been used in herding bulls , <unk> riding and light <unk> . 
- 
- = = = Naming = = = 
- 
- Dr. Ruy d 'Andrade gave the breed their name of " Sorraia " . D 'Andrade took the name from the Sorraia River in Portugal . The breed had previously been known by the local Portuguese as " <unk> " or " zebra " , due to their markings . In the time of Christopher Columbus , the Sorraia was also known as the Marismeño , but the Sorraia and the Marismeño have evolved into two different breeds over time . Today , the name Marismeño refers to a population of <unk> horses living in <unk> Natural Park in Spain . 
- 
- 
- = Haifa = 
- 
- Haifa ( Hebrew : <unk> Hefa , Hebrew pronunciation : [ <unk> ] , <unk> Hebrew pronunciation : [ <unk> <unk> <unk> ] <unk> ; Arabic : <unk> <unk> ) , the third @-@ largest city in the State of Israel , has a population of over 277 @,@ <unk> . Another 300 @,@ 000 people live in towns directly adjacent to the city including <unk> al @-@ <unk> , the Krayot , Nesher , Tirat Carmel , and some <unk> . Together these areas form a contiguous urban area , home to nearly 600 @,@ 000 residents , which makes up the inner core of the Haifa metropolitan area , the second- or third @-@ most populous metropolitan area in Israel . It is also home to the Bahá 'í World Centre , a UNESCO World Heritage Site and a destination for <unk> 'i pilgrims . 
- Built on the slopes of Mount Carmel , the settlement has a history spanning more than 3 @,@ 000 years . The earliest known settlement in the vicinity was Tell Abu <unk> , a small port city established in the Late Bronze Age ( 14th century BCE ) . In the 3rd century CE , Haifa was known as a dye @-@ making center . Over the centuries , the city has changed hands : being conquered and ruled by the <unk> , Persians , <unk> , Romans , <unk> , Arabs , Crusaders , Ottomans , British , and the <unk> . Since the establishment of the State of Israel in 1948 , the Haifa Municipality has governed the city . 
- As of 2016 the city is a major seaport located on Israel 's Mediterranean coastline in the Bay of Haifa covering 63 @.@ 7 square kilometres ( 24 @.@ 6 sq mi ) . It lies about 90 kilometres ( 56 mi ) north of Tel Aviv and is the major regional center of northern Israel . Two respected academic institutions , the University of Haifa and the Technion , are located in Haifa , in addition to the largest k @-@ 12 school in Israel , the Hebrew <unk> School . The city plays an important role in Israel 's economy . It is home to Matam , one of the oldest and largest high @-@ tech parks in the country . Haifa Bay is a center of heavy industry , petroleum refining and chemical processing . Haifa formerly functioned as the western terminus of an oil pipeline from Iraq via Jordan . 
- 
- = = Etymology = = 
- 
- The earliest named settlement within the domain of modern @-@ day Haifa was a city known as <unk> . Tel Shikmona Hebrew meaning " mound of the <unk> <unk> " ( Arabic Tell el @-@ <unk> or Tell es @-@ Samak , meaning " mound of the fish " ) preserved and transformed this ancient name and is mentioned once in the <unk> ( composed c . 200 CE ) for the wild fruits that grow around it . , with locals using it to refer to a coastal tell at the foot of the Carmel Mountains that contains its remains . 
- The name <unk> first appears during Roman rule , some time after the end of the 1st century , when a Roman fortress and small Jewish settlement were established not far from Tell es @-@ Samak . Haifa is also mentioned more than 100 times in the Talmud , a book central to Judaism . 
- Hefa or <unk> in Eusebius of Caesarea 's 4th @-@ century work , <unk> ( <unk> . 108 , 31 ) , is said to be another name for <unk> . This <unk> of the names is explained by Moshe Sharon who writes that the twin ancient settlements , which he calls Haifa @-@ Sycaminon , gradually expanded into one another , becoming a twin city known by the Greek names Sycaminon or <unk> <unk> . <unk> to this city end with the Byzantine period . 
- Around the 6th century , <unk> or <unk> is mentioned in the writings of William of Tyre , and while it lies within the area covered by modern Haifa , it was a settlement situated south of Haifa @-@ Sycaminon . 
- Following the Arab conquest in the 7th century , Haifa was used to refer to a site established on Tell es @-@ Samak upon what were already the ruins of Sycaminon ( <unk> ) . Haifa ( or <unk> ) is mentioned by the mid @-@ 11th @-@ century Persian chronicler <unk> <unk> , and the <unk> and 13th @-@ century Arab chroniclers , Muhammad al @-@ <unk> and <unk> al @-@ <unk> . 
- The Crusaders , who captured Haifa briefly in the 12th century , call it <unk> , and believe its name related to <unk> , the Aramaic name of Simon Peter . Eusebius is also said to have referred to Hefa as Caiaphas <unk> , and Benjamin of <unk> , the 12th @-@ century Jewish traveller and chronicler , is said to have attributed the city 's founding to Caiaphas , the Jewish high priest at the time of Jesus . 
- Other <unk> in English have included <unk> , <unk> , <unk> , <unk> and <unk> . 
- Haifa <unk> ' <unk> ( Arabic : " Ancient Haifa " ) is another name used by locals to refer to Tell es @-@ Samak , as it was the site of Haifa when it was a hamlet of 250 residents , before it was moved in 1764 @-@ 5 to a new fortified site founded by Zahir al @-@ Umar 1 @.@ 5 miles ( 2 @.@ 4 kilometres ) to the east . The new village , the nucleus of modern Haifa , was originally named al @-@ <unk> al @-@ <unk> ( Arabic : " the new construction " ) , but locals called it Haifa al @-@ <unk> ( Arabic : " New Haifa " ) at first , and then simply Haifa . In the early 20th century , Haifa al ' <unk> was <unk> as a predominantly Arab Christian neighborhood of Haifa as it expanded outward from its new location . 
- The ultimate origin of the name Haifa remains unclear . One theory holds it derives from the name of the high priest Caiaphas . Some Christians believe it was named for Saint Peter , whose Aramaic name was <unk> . Another theory holds it could be derived from the Hebrew verb root <unk> ( <unk> ) , meaning to cover or shield , i.e. Mount Carmel covers Haifa ; others point to a possible origin in the Hebrew word <unk> ( <unk> ) , meaning shore , or <unk> <unk> ( <unk> <unk> ) , meaning beautiful shore . 
- 
- = = History = = 
- 
- 
- = = = Early history = = = 
- 
- A small port city known today as Tell Abu <unk> was established Late Bronze Age ( 14th century BCE ) . During the 6th century BCE , Greek geographer <unk> told of a city " between the bay and the <unk> of Zeus " ( i.e. , the Carmel ) which may be a reference to Shikmona , a locality in the Haifa area , during the Persian period . By Hellenistic times , the city had moved to a new site south of what is now Bat Galim because the port 's harbour had become blocked with sand . About the 3rd century CE , the city was first mentioned in <unk> literature , as a Jewish fishing village and the home of Rabbi <unk> and other Jewish scholars . A Greek @-@ speaking population living along the coast at this time was engaged in commerce . 
- Haifa was located near the town of Shikmona , a center for making the traditional <unk> dye used in the garments of the high priests in the Temple . The archaeological site of Shikmona is southwest of Bat Galim . Mount Carmel and the Kishon River are also mentioned in the Bible . A <unk> on the top of Mount Carmel is known as the " Cave of Elijah " , traditionally linked to the Prophet Elijah and his apprentice , <unk> . In Arabic , the highest peak of the Carmel range is called the <unk> , or " place of burning , " <unk> back to the burnt offerings and sacrifices there in Canaanite and early <unk> times 
- Early Haifa is believed to have occupied the area which extends from the present @-@ day Rambam Hospital to the Jewish Cemetery on <unk> Street . The inhabitants engaged in fishing and agriculture . 
- Under Byzantine rule , Haifa continued to grow but did not assume major importance . Following the Arab conquest of Palestine in the <unk> @-@ 40s , Haifa was largely overlooked in favor of the port city of ' Akka . Under the <unk> Caliphate , Haifa began to develop . In the 9th century under the <unk> and <unk> <unk> , Haifa established trading relations with Egyptian ports and the city featured several shipyards . The inhabitants , Arabs and Jews , engaged in trade and maritime commerce . Glass production and dye @-@ making from marine snails were the city 's most lucrative industries . 
- 
- = = = Crusader , Ayyubid and Mamluk rule = = = 
- 
- <unk> ended in 1100 or 1101 , when Haifa was besieged and blockaded by the Crusaders and then conquered after a fierce battle with its Jewish inhabitants and <unk> garrison . Under the Crusaders , Haifa was reduced to a small fortified coastal stronghold . It was a part of the Principality of Galilee within the Kingdom of Jerusalem . Following their victory at the Battle of <unk> , Saladin 's Ayyubid army captured Haifa in mid @-@ July 1187 and the city 's Crusader fortress was destroyed . The Crusaders under Richard the <unk> retook Haifa in 1191 . 
- In the 12th century religious <unk> started inhabiting the caves on Mount Carmel , and in the 13th century they formed a new Catholic monastic order , the <unk> . Under Muslim rule , the church which they had built on Mount Carmel was turned into a mosque , later becoming a hospital . In the 19th century , it was restored as a <unk> monastery , the Stella Maris Monastery . The altar of the church as we see it today , stands over a cave associated with Prophet Elijah . 
- In 1265 , the army of Baibars the Mamluk captured Haifa , destroying its fortifications , which had been rebuilt by King Louis IX of France , as well as the majority of the city 's homes to prevent the European Crusaders from returning . For much of their rule , the city was desolate in the Mamluk period between the 13th and 16th centuries . Information from this period is scarce . During Mamluk rule in the 14th century , al @-@ <unk> wrote that Haifa served as the port for Tiberias and featured a " fine harbor for the anchorage of galleys and other vessels . 
- 
- = = = Ottoman era = = = 
- 
- In 1596 , Haifa appeared in Ottoman tax registers as being in the <unk> of <unk> Atlit of the <unk> of <unk> . It had a population of 32 Muslim households and paid taxes on wheat , barley , <unk> , olives , and goats or <unk> . 
- Haifa was a hamlet of 250 inhabitants in 1764 @-@ 5 . It was located at Tell el @-@ <unk> , the site of ancient <unk> . In 1765 Zahir al @-@ Umar , the Arab ruler of Acre and the Galilee , moved the population to a new fortified site 1 @.@ 5 miles ( 2 @.@ 4 kilometres ) to the east and laid waste to the old site . According to historian Moshe Sharon , the new Haifa was established by Zahir in 1769 . This event marked the beginning of the town 's life at its modern location . After al @-@ Umar 's death in 1775 , the town remained under Ottoman rule until 1918 , with the exception of two brief periods . 
- In <unk> , Napoleon Bonaparte conquered Haifa during his unsuccessful campaign to conquer Palestine and Syria , but soon had to withdraw ; in the campaign 's final proclamation , Napoleon took credit for having razed the fortifications of " <unk> " ( as the name was spelled at the time ) along with those of Gaza , <unk> and Acre . 
- Between 1831 and 1840 , the Egyptian viceroy Muhammad Ali governed Haifa , after his son Ibrahim Pasha had <unk> its control from the Ottomans . When the Egyptian occupation ended and Acre declined , the importance of Haifa rose . 
- The arrival of the German Templers in 1868 , who settled in what is now known as the German Colony of Haifa , was a turning point in Haifa 's development . The Templers built and operated a steam @-@ based power station , opened factories and inaugurated carriage services to Acre , Nazareth and Tiberias , playing a key role in <unk> the city . 
- The first major Jewish Immigration took place at the middle 19th century from Morocco , with small immigration from Turkey few years later . A wave of European Jews arrived at the end of the 19th century from Romania . The Central Jewish <unk> Society in Romania purchased over 1 @,@ 000 acres ( 4 @.@ 0 km2 ) near Haifa . As the Jewish settlers had been city dwellers , they hired the former <unk> <unk> to instruct them in agriculture . 
- In 1909 , Haifa became important to the Bahá 'í Faith when the remains of the <unk> , founder of the <unk> Faith and forerunner of Bahá 'u'lláh in the Bahá 'í Faith , were moved from Acre to Haifa and interred in the shrine built on Mount Carmel . Bahá <unk> consider the shrine to be their second <unk> place on Earth after the Shrine of Bahá 'u'lláh in Acre . Its precise location on Mount Carmel was shown by Bahá 'u'lláh himself to his eldest son , `Abdu 'l @-@ Bahá , in 1891 . `Abdu 'l @-@ Bahá planned the structure , which was designed and completed several years later by his grandson , <unk> <unk> . In a separate room , the remains of `Abdu 'l @-@ Bahá were buried in November 1921 . 
- A branch of the <unk> railway , known as the <unk> Valley railway , was built between 1903 and 1905 . This event accelerated the growth of Haifa , which became a township ( <unk> ) centre in Akka in the <unk> of <unk> <unk> before the end of Ottoman rule . The Technion Institute of Technology was established around this time , that is , in 1912 . 
- 
- = = = British Mandate = = = 
- 
- Haifa was captured from the Ottomans in September 1918 by Indian horsemen of the British Army after overrunning Ottoman positions armed with <unk> and swords . On 22 September , British troops were heading to Nazareth when a reconnaissance report was received indicating that the Turks were leaving Haifa . The British made preparations to enter the city and came under fire in the Balad al @-@ Sheikh district ( today Nesher ) . After the British regrouped , an elite unit of Indian horsemen were sent to attack the Turkish positions on the flanks and overrun their artillery guns on Mount Carmel . 
- Under the British Mandate , Haifa became an industrial port city . The Bahá 'í Faith in 1918 and today has its administrative and spiritual centre in the environs of Haifa . Over the next few decades the number of Jews increased steadily , due to immigration , especially from Europe . The Arab immigration on the other hand swelled by influx of Arabs , coming mainly from surrounding villages as well as Syrian <unk> . The Arab immigration mainly came as a result of prices and salary drop . Between the censuses of 1922 and 1931 , the Muslim , Jewish , and Christian populations rose by 217 % , 256 % , and 156 % , respectively . 
- Haifa 's development owed much to British plans to make it a central port and hub for Middle @-@ East crude oil . The British Government of Palestine developed the port and built refineries , thereby facilitating the rapid development of the city as a center for the country 's heavy industries . Haifa was also among the first towns to be fully electrified . The Palestine Electric Company inaugurated the Haifa Electrical Power Station already in 1925 , opening the door to considerable <unk> . The State @-@ run Palestine Railways also built its main workshops in Haifa . 
- By 1945 the population had shifted to 33 percent Muslim , 20 percent Christian and 47 percent Jewish . In 1947 , about 70 @,@ 910 Arabs ( 41 @,@ 000 Muslims , 29 @,@ 910 Christians ) and 74 @,@ 230 Jews were living there . The Christian community were mostly Greek @-@ <unk> Catholics . 
- The 1947 UN Partition Plan in late November 1947 designated Haifa as part of the proposed Jewish state . Arab protests over that decision evolved into violence between Jews and Arabs that left several dozen people dead during December . On 30 December 1947 , members of the <unk> , a Jewish underground militia , threw bombs into a crowd of Arabs outside the gates of the Consolidated <unk> in Haifa , killing six and injuring 42 . In response Arab employees of the company killed 39 Jewish employees in what became known as the Haifa Oil <unk> massacre . The Jewish <unk> militia retaliated with a raid on the Arab village of Balad al @-@ <unk> , where many of the Arab refinery workers lived , in what became known as the Balad al @-@ <unk> massacre . Control of Haifa was critical in the ensuing civil war , since it was the major industrial and oil refinery port in British Palestine . 
- British forces in Haifa redeployed on 21 April 1948 , withdrawing from most of the city while still maintaining control over the port facilities . Two days later the downtown , controlled by a combination of local and foreign ( <unk> ) Arab <unk> was assaulted by Jewish forces in Operation Bi <unk> <unk> , by the <unk> Brigade of the <unk> , commanded by Moshe Carmel . The operation led to a massive displacement of Haifa 's Arab population . According to The Economist at the time , only 5 @,@ 000 – 6 @,@ 000 of the city 's 62 @,@ 000 Arabs remained there by 2 October 1948 . 
- Contemporaneous sources emphasized the Jewish leadership 's attempt to stop the Arab exodus from the city and the Arab leadership as a motivating factor in the refugees ' flight . According to the British district superintendent of police , " Every effort is being made by the Jews to persuade the Arab populace to stay and carry on with their normal lives , to get their shops and business open and to be assured that their lives and interests will be safe . " Time Magazine wrote on 3 May 1948 : 
- The mass evacuation , prompted partly by fear , partly by orders of Arab leaders , left the Arab quarter of Haifa a ghost city ... By withdrawing Arab workers their leaders hoped to <unk> Haifa . 
- Benny Morris said Haifa 's Arabs left due to of a combination of Zionist threats and encouragement to do so by Arab leaders . Ilan <unk> writes that the shelling culminated in an attack on a Palestinian crowd in the old marketplace using three @-@ inch ( 76 mm ) mortars on 22 April 1948 . Shabtai Levy , the Mayor of the city , and some other Jewish leaders urged Arabs not to leave . According to Ilan <unk> , Jewish <unk> could be heard in the city ordering Arab residents to leave " before it 's too late . " Morris quotes British sources as stating that during the battles between 22 and 23 April 100 Arabs were killed and 100 wounded , but he adds that the total may have been higher . 
- 
- = = = State of Israel = = = 
- 
- After the Declaration of the Establishment of the State of Israel on 14 May 1948 Haifa became the gateway for Jewish immigration into Israel . During the 1948 Arab – Israeli War , the neighborhoods of Haifa were sometimes contested . After the war , Jewish immigrants were settled in new neighborhoods , among them Kiryat <unk> , Ramot <unk> , Ramat <unk> , Kiryat <unk> , and Kiryat Eliezer . <unk> <unk> Hospital ( formerly Rothschild Hospital ) and the Central Synagogue in Hadar <unk> date from this period . In 1953 , a master plan was created for transportation and the future architectural layout . 
- In 1959 , a group of <unk> and <unk> Jews , mostly Moroccan Jews , <unk> in Wadi Salib , claiming the state was <unk> against them . Their demand for “ bread and work ” was directed at the state institutions and what they viewed as an <unk> elite in the Labor Party and the <unk> . 
- Tel Aviv gained in status , while Haifa suffered a decline in the role as regional capital . The opening of Ashdod as a port exacerbated this . Tourism shrank when the Israeli Ministry of Tourism placed emphasis on developing Tiberias as a tourist centre . 
- Nevertheless , Haifa 's population had reached 200 @,@ 000 by the early 1970s , and mass immigration from the former Soviet Union boosted the population by a further 35 @,@ 000 . 
- Many of Wadi Salib 's historic Ottoman buildings have now been demolished , and in the 1990s a major section of the Old City was razed to make way for a new municipal center . 
- From 1999 to 2003 , several Palestinian suicide attacks took place in Haifa ( in Maxim and <unk> restaurants , bus 37 , and others ) , killing 68 civilians . 
- In 2006 , Haifa was hit by 93 Hezbollah rockets during the Second Lebanon War , killing 11 civilians and leading to half of the city 's population fleeing at the end of the first week of the war . Among the places hit by rockets were a train depot and the oil refinery complex . 
- 
- = = Demographics = = 
- 
- Haifa is Israel 's third @-@ largest city , consisting of 103 @,@ 000 households , or a population of 266 @,@ 300 . <unk> from the former Soviet Union constitute 25 % of Haifa 's population . According to the Israeli Central Bureau of Statistics , Israeli Arabs constitute 10 % of Haifa 's population , the majority living in Wadi Nisnas , <unk> and <unk> neighborhoods . 
- Haifa is commonly portrayed as a model of co @-@ existence between Arabs and Jews , although tensions and hostility do still exist . 
- Between 1994 and 2009 , the city had a declining and aging population compared to Tel Aviv and Jerusalem , as young people moved to the center of the country for education and jobs , while young families migrated to bedroom communities in the suburbs . However , as a result of new projects and improving infrastructure , the city managed to reverse its population decline , reducing emigration while attracting more internal migration into the city . In 2009 , positive net immigration into the city was shown for the first time in 15 years . 
- 
- = = = Religious and ethnic communities = = = 
- 
- The population is heterogeneous . Jews comprise some 82 % of the population , almost 14 % are Christians ( the majority of whom are Arab Christians ) and , some 4 % are Muslims ( of which many are <unk> ) . Haifa also includes <unk> and Bahá 'í communities . In 2006 , 27 % of the Arab population was aged 14 and under , compared to 17 % of the Jewish and other population groups . The trend continues in the age 15 @-@ 29 group , in which 27 % of the Arab population is found , and the age 30 @-@ 44 group ( 23 % ) . The population of Jews and others in these age groups are 22 % and 18 % respectively . Nineteen percent of the city 's Jewish and other population is between 45 and 59 , compared to 14 % of the Arab population . This continues with 14 % of Jews and others aged 60 – 74 and 10 % over age 75 , in comparison to 7 % and just 2 % respectively in the Arab population . 
- In 2006 , 2 @.@ 9 % of the Jews in the city were <unk> , compared to 7 @.@ 5 % on a national scale . However , the <unk> community in Haifa is growing fast due to a high fertility rate . 66 @.@ 6 % were secular , compared to a national average of 43 @.@ 7 % . A significant portion of the immigrants from the former Soviet Union either lack official religious @-@ ethnic classification or are Non @-@ Jews as they are from mixed @-@ marriage families of some Jewish origin . There is also a Scandinavian Seamen Protestant church , established by Norwegian <unk> Among the Nations pastor Per Faye @-@ Hansen . 
- Haifa is the center of liberal Palestinian culture , as it was under British colonial rule . The Palestinian neighborhoods , which are mixed Muslim and Christian , are in the lowlands near the sea while Jewish neighborhoods are at higher elevation . An active Palestinian cultural life has developed in the 21st century . 
- 
- = = Geography = = 
- 
- Haifa is situated on the Israeli Mediterranean Coastal Plain , the historic land bridge between Europe , Africa , and Asia , and the mouth of the Kishon River . Located on the northern slopes of Mount Carmel and around Haifa Bay , the city is split over three tiers . The lowest is the center of commerce and industry including the Port of Haifa . The middle level is on the slopes of Mount Carmel and consists of older residential neighborhoods , while the upper level consists of modern neighborhoods looking over the lower tiers . From here views can be had across the Western Galilee region of Israel towards <unk> <unk> and the Lebanese border . Haifa is about 90 kilometers ( 55 @.@ 9 mi ) north of the city of Tel Aviv , and has a large number of beaches on the Mediterranean . 
- 
- = = Flora and fauna = = 
- 
- The Carmel Mountain has three main <unk> : <unk> , <unk> and Si ’ <unk> . For the most part these valleys are undeveloped natural corridors that run up through the city from the coast to the top of the mountain . <unk> hiking paths traverse these areas and they provide habitat for wildlife such as wild <unk> , golden <unk> , <unk> , Egyptian <unk> , owls and <unk> . 
- 
- = = Climate = = 
- 
- Haifa has a hot @-@ summer Mediterranean climate with hot , dry summers and cool , rainy winters ( Köppen climate classification <unk> ) . Spring arrives in March when temperatures begin to increase . By late May , the temperature has warmed up considerably to herald warm summer days . The average temperature in summer is 26 ° C ( 79 ° F ) and in winter , 12 ° C ( 54 ° F ) . Snow is rare in Haifa , but temperatures around 3 ° C ( 37 ° F ) can sometimes occur , usually in the early morning . <unk> tends to be high all year round , and rain usually occurs between September and May . Annual precipitation is approximately <unk> millimeters ( 25 in ) . 
- 
- = = <unk> = = 
- 
- Haifa has developed in tiers , from the lower to the upper city on the Carmel . The oldest neighborhood in the modern Haifa is Wadi Salib , the Old City center near the port , which has been <unk> by a major road and razed in part to make way for government buildings . Wadi Salib stretches across to Wadi Nisnas , the center of Arab life in Haifa today . In the 19th century , under Ottoman rule , the German Colony was built , providing the first model of urban planning in Haifa . Some of the buildings have been restored and the colony has turned into a center of Haifa nightlife . 
- The first buildings in Hadar were constructed at the start of the 20th century . Hadar was Haifa 's cultural center and marketplace throughout the 1920s and into the 1980s , <unk> above and around the Haifa 's Arab neighborhoods . Today Hadar stretches from the port area near the bay , approximately halfway up Mount Carmel , around the German Colony , Wadi Nisnas and Wadi Salib . Hadar houses two commercial centers ( one in the port area , and one midway up the mountain ) surrounded by some of the city 's older neighborhoods . 
- Neve Sha 'anan , a neighborhood located on the second tier of Mount Carmel , was founded in the 1920s . West of the port are the neighborhoods of Bat Galim , Shikmona Beach , and Kiryat Eliezer . To the west and east of Hadar are the Arab neighborhoods of <unk> and <unk> , built in the 1960s and 70s . To the south of Mount Carmel 's headland , along the road to Tel Aviv , are the neighborhoods of Ein <unk> , <unk> <unk> , Kiryat <unk> and Neve David . 
- Above Hadar are affluent neighborhoods such as the Carmel <unk> ( French Carmel ) , Merkaz Ha 'Carmel , <unk> , <unk> Ha 'Carmel ( <unk> ) , <unk> , <unk> , Ramat <unk> , Ramat <unk> and Hod Ha 'Carmel ( <unk> ) . While there are general divisions between Arab and Jewish neighborhoods , there is an increasing trend for wealthy Arabs to move into affluent Jewish neighborhoods . Another of the Carmel neighborhoods is <unk> , home to the National Headquarters of Israel 's <unk> Muslim Community ; located near Merkaz HaCarmel and overlooking the coast . 
- 
- = = Urban development = = 
- 
- Recently , residential construction has been concentrated around Kiryat <unk> and Kiryat <unk> , with 75 @,@ 000 m2 ( <unk> @,@ 293 sq ft ) of new residential construction between 2002 – 2004 , the Carmel , with 70 @,@ 000 m2 ( <unk> @,@ <unk> sq ft ) , and Ramot Neve Sha 'anan with approximately 70 @,@ 000 m2 ( <unk> @,@ <unk> sq ft ) Non @-@ residential construction was highest in the Lower Town , ( 90 @,@ 000 sq m ) , Haifa Bay ( 72 @,@ 000 sq m ) and Ramot Neve Sha 'anan ( 54 @,@ 000 sq m ) . In 2004 , 80 % of construction in the city was private . 
- Currently , the city has a modest number of <unk> and high @-@ rise buildings , and many additional high @-@ rise buildings are planned , have been approved , or are under construction . Though buildings rising up to 20 stories were built on Mount Carmel in the past , the Haifa municipality banned the construction of any new buildings taller than nine stories on Mount Carmel in July 2012 . 
- The neighborhood of Wadi Salib , located in the heart of downtown Haifa , is being redeveloped . Most of its Jewish and Arab residents are considered squatters and have been gradually evicted over the years . The Haifa Economic Corporation Ltd is developing two 1 @,@ 000 square meter lots for office and commercial use . Some historic buildings have been renovated and redeveloped , especially into nightclubs and theaters , such as the Palace of the Pasha , a Turkish <unk> , and a Middle Eastern music and dance club , which has been converted into theaters and offices . 
- In 2012 , a new , massive development plan was announced for Haifa 's waterfront . According to the plan , the western section of the city 's port will be torn down , and all port activity will be moved to the east . The west side of the port will be transformed into a tourism and nightlife center and a point of embarkation and arrival for sea travel through the construction of public spaces , a beach promenade , and the renovation of commercial buildings . The train tracks that currently <unk> the city and separate the city 's beach from the rest of Haifa will also be buried . A park will be developed on the border of the Kishon River , the refineries ' cooling towers will be turned into a visitors ' center , and bridges will lead from the port to the rest of the city . Massive renovations are also currently underway in Haifa 's lower town , in the Turkish market and Paris Square , which will become the city 's business center . In addition , the ammonia <unk> tank in the Haifa bay industrial zone will be dismantled , and a new one built in an alternative location . 
- Another plan seeks to turn the western section of Haifa Port into a major tourism and nightlife center , as well as a functioning point of embarkation and arrival for sea travel . All port activity will be moved to the western side , and the area will be redeveloped . Public spaces and a beach promenade will be developed , and commercial buildings will be renovated . 
- As part of the development plans , the Israeli Navy , which has a large presence in Haifa , will withdraw from the shoreline between Bat Galim and Hof <unk> . A 5 @-@ kilometer ( 3 @.@ 1 @-@ mile ) long esplanade which will encircle the shoreline will be constructed . It will include a bicycle path , and possibly also a small bridge under which navy vessels will pass on their way to the sea . 
- In addition , a 50 @,@ 000 square @-@ meter entertainment complex that will contain a Disney theme park , cinemas , shops , and a 25 @-@ screen <unk> theater will be built at the Check Post exit from the Carmel <unk> . 
- In 2014 , a new major plan for the city was proposed , under which extensive development of residential , business , and leisure areas will take place with the target of increasing the city 's population by 60 @,@ 000 by <unk> . Under the plan , five new neighborhoods will be built , along with new high @-@ tech parks . In addition , existing employment centers will be renovated , and new leisure areas and a large park will be built . 
- 
- = = Economy = = 
- 
- The common Israeli saying , " Haifa works , Jerusalem prays , and Tel Aviv plays " attests to Haifa 's reputation as a city of workers and industry . The industrial region of Haifa is in the eastern part of the city , around the Kishon River . It is home to the Haifa oil refinery , one of the two oil refineries in Israel ( the other refinery being located in Ashdod ) . The Haifa refinery processes 9 million tons ( 66 million barrels ) of crude oil a year . Its <unk> unused twin 80 @-@ meter high cooling towers , built in the 1930s , were the tallest buildings built in the British Mandate period . 
- Matam ( short for Merkaz Ta <unk> <unk> - Scientific Industries Center ) , the largest and oldest business park in Israel , is at the southern entrance to the city , hosting manufacturing and R & D facilities for a large number of Israeli and international hi @-@ tech companies , such as Intel , IBM , Microsoft , <unk> , Google , Yahoo ! , <unk> , <unk> , Philips , and <unk> . The campus of the University of Haifa is also home to IBM Haifa Labs . 
- The Port of Haifa is the leader in passenger traffic among Israeli ports , and is also a major cargo harbor , although <unk> has seen its dominance challenged by the Port of Ashdod . 
- Haifa malls and shopping centers include <unk> <unk> , <unk> Center Mall , <unk> Center , <unk> Center , Colony Center ( Lev <unk> ) , <unk> 'im Tower Mall , <unk> Haifa , Lev <unk> Mall and Grand <unk> . 
- In 2010 , <unk> magazine identified Haifa as the city with the most promising business potential , with the greatest investment opportunities in the world . The magazine noted that " a massive head @-@ to @-@ toe regeneration is starting to have an impact ; from scaffolding and cranes around town , to renovated façades and new smart places to eat " . The Haifa municipality had spent more than $ 350 million on roads and infrastructure , and the number of building permits had risen 83 % in the previous two years . 
- In 2014 , it was announced that a technology @-@ focused stock exchange would be established to compete with the Tel Aviv Stock Exchange . 
- Currently , some 40 hotels , mostly <unk> hotels , are planned , have been approved , or are under construction . The Haifa Municipality is seeking to turn the city into Northern Israel 's tourist center , from where travelers can embark on day trips into Acre , Nazareth , Tiberias , and the Galilee . 
- A new life sciences industrial park containing five buildings with 85 @,@ 000 square meters of space on a 31 @-@ <unk> ( 7 @.@ 75 acre ) site is being built adjacent to the Matam industrial park . 
- 
- = = = Tourism = = = 
- 
- In 2005 , Haifa has 13 hotels with a total of 1 @,@ 462 rooms . The city has a 17 kilometres ( 11 mi ) shoreline , of which 5 kilometres ( 3 mi ) are beaches . Haifa 's main tourist attraction is the Bahá 'í World Centre , with the golden @-@ domed Shrine of the <unk> and the surrounding gardens . Between 2005 and 2006 , 86 @,@ <unk> visited the shrine . In 2008 , the Bahá 'í gardens were designated a UNESCO World Heritage Site . The restored German Colony , founded by the Templers , Stella Maris and Elijah 's Cave also draw many tourists . 
- Located in the Haifa district are the Ein Hod artists ' colony , where over 90 artists and craftsmen have studios and exhibitions , and the Mount Carmel national park , with caves where <unk> and early <unk> <unk> remains were found . 
- A 2007 report commissioned by the Haifa Municipality calls for the construction of more hotels , a ferry line between Haifa , Acre and Caesarea , development of the western anchorage of the port as a recreation and entertainment area , and an expansion of the local airport and port to accommodate international travel and cruise ships . 
- 
- = = Arts and culture = = 
- 
- Despite its image as a port and industrial city , Haifa is the cultural hub of northern Israel . During the 1950s , mayor Abba <unk> made a special effort to encourage authors and poets to move to the city , and founded the Haifa Theatre , a repertory theater , the first municipal theater founded in the country . The principal Arabic theater <unk> the northern Arab population is the al @-@ <unk> Theater . Other theaters in the city include the <unk> Centre for the Performing Arts and the <unk> Art and Culture Center . The Congress Center hosts exhibitions , concerts and special events . 
- The New Haifa Symphony Orchestra , established in 1950 , has more than 5 @,@ 000 subscribers . In 2004 , 49 @,@ 000 people attended its concerts . The Haifa <unk> , founded in 1975 , hosts the annual Haifa International Film Festival during the intermediate days of the <unk> holiday . Haifa has 29 movie theaters . The city publishes a local newspaper , <unk> Haifa , and has its own radio station , Radio Haifa . 
- During the 1990s , Haifa hosted the Haifa Rock & Blues Festival featuring Bob Dylan , Nick Cave , <unk> and <unk> Harvey . The last festival was held in 1995 with <unk> Crow , <unk> and Faith No More as <unk> . 
- 
- = = = Museums = = = 
- 
- Haifa has over a dozen museums . The most popular museum is the Israel National Museum of Science , Technology , and Space , which recorded almost 150 @,@ 000 visitors in 2004 . The museum is located in the historic Technion building in the Hadar neighborhood . The Haifa Museum of Art houses a collection of modern and classical art , as well as displays on the history of Haifa . The <unk> Museum of Japanese Art is the only museum in the Middle East dedicated solely to Japanese art . Other museums in Haifa include the Museum of <unk> , the National Maritime Museum and Haifa City Museum , the Hecht Museum , the <unk> Archaeological Museum of Grain <unk> , the Railway Museum , the <unk> Immigration and Navy Museum , the Israeli Oil Industry Museum , and <unk> Artists ' House . As part of his campaign to bring culture to Haifa , Mayor Abba <unk> provided the artist <unk> @-@ Katz with a building on Mount Carmel to house his collection of <unk> , which is now a museum . The Haifa Educational Zoo at <unk> <unk> park houses a small animal collection including Syrian brown bears , now extinct from Israel . <unk> the zoo is the <unk> House biology institute . In the close vicinity of Haifa , on the Carmel , the Northern " Hai @-@ Bar " ( " wild life " ) operated by Israel 's Parks and Reserves Authority for the purpose of breeding and reintroduction of species now extinct from Israel , such as Persian <unk> Deer . 
- 
- = = Government = = 
- 
- As an industrial port city , Haifa has traditionally been a Labor party stronghold . The strong presence of dock workers and trade unions earned it the nickname ' Red Haifa . ' In addition , many prominent Arabs in the Israeli Communist Party , among them <unk> <unk> , Emile <unk> , <unk> <unk> , <unk> Farah and Emile <unk> , were from Haifa . 
- In recent years , there has been a drift toward the center . This was best signified by , in the 2006 legislative elections , the <unk> party receiving about 28 @.@ 9 % of the votes in Haifa , and Labor <unk> behind with 16 @.@ 9 % . 
- Before 1948 , Haifa 's Municipality was fairly unusual as it developed cooperation between the mixed Arab and Jewish community in the city , with representatives of both groups involved in the city 's management . Under mayor al @-@ <unk> , between 1920 and 1927 , the city council had six Arab and two Jewish representatives , with the city run as a mixed municipality with overall Arab control . Greater cooperation was introduced under <unk> <unk> <unk> , who adopted a positive and <unk> attitude toward the city 's Jews and gave them senior posts in the municipality . In 1940 , the first Jewish mayor , Shabtai Levy , was elected . Levy 's two deputies were Arab ( one Muslim , the other Christian ) , with the remainder of the council made up of four Jews and six Arabs . 
- Today , Haifa is governed by its 12th city council , headed by the mayor <unk> <unk> . The results of municipal elections decide on the makeup of the council , similarly to the <unk> elections . The city council is the legislative council in the city , and has the authority to pass auxiliary laws . The 12th council , which was elected in 2003 , has 31 members , with the liberal <unk> @-@ Greens ticket holding the most seats ( 6 ) , and <unk> coming second with 5 . Many of the decisions passed by the city council are results of recommendation made by the various municipal committees , which are committees where non @-@ municipal organs meet with representatives from the city council . Some committees are spontaneous , but some are mandatory , such as the security committee , tender committee and financial committee . 
- 
- = = = <unk> = = = 
- 
- 
- = = Medical facilities = = 
- 
- Haifa medical facilities have a total of 4 @,@ 000 hospital beds . The largest hospital is the government @-@ operated Rambam Hospital with 900 beds and 78 @,@ 000 admissions in 2004 . <unk> <unk> Hospital and Carmel Hospital each have 400 beds . Other hospitals in the city include the Italian Hospital , <unk> Hospital ( 100 beds ) , <unk> Medical Center ( 36 beds ) and Ramat <unk> ( 18 beds ) . Haifa has 20 family health centers . In 2004 , there were a total of 177 @,@ 478 hospital admissions . 
- Rambam Medical Center was in the direct line of fire during the Second Lebanon War in 2006 and was forced to take special precautions to protect its patients . Whole wings of the hospital were moved to large underground shelters . 
- 
- = = Education = = 
- 
- Haifa is home to two internationally acclaimed universities and several colleges The University of Haifa , founded in 1963 , is at the top of Mt . Carmel . The campus was designed by the architect of Brasília and United Nations Headquarters in New York , Oscar <unk> . The top floor of the 30 @-@ story <unk> Tower provides a panoramic view of northern Israel . The Hecht Museum , with important <unk> and art collections , is on the campus of Haifa University . 
- The Technion - Israel Institute of Technology , described as Israel 's MIT , was founded in 1912 . It has 18 faculties and 42 research institutes . The original building now houses Haifa 's science museum . The Hebrew <unk> School was founded in 1913 . It is the largest k @-@ 12 school in Israel , with 4 @,@ 000 students in 7 branches , all over the city . The first technological high school in Israel , <unk> , was established in Haifa in 1933 . 
- Other academic institutions in Haifa are the Gordon College of Education and Sha 'anan Religious Teachers ' College , the <unk> Haifa Academy of Design and Education , and <unk> College of Design . The <unk> <unk> College of Management and the Open University of Israel have branches in Haifa . The city also has a nursing college and the <unk> <unk> Engineering School . 
- As of 2006 – 07 , Haifa had 70 elementary schools , 23 middle schools , 28 academic high schools and 8 vocational high schools . There were 5 @,@ 133 pupils in municipal <unk> , 20 @,@ 081 in elementary schools , 7 @,@ 911 in middle schools , 8 @,@ <unk> in academic high schools , 2 @,@ <unk> in vocational high schools , and 2 @,@ <unk> in comprehensive district high schools . 86 % of the students attended Hebrew @-@ speaking schools and 14 % attended Arab schools . 5 % were in special education . In 2004 , Haifa had 16 municipal libraries <unk> 367 @,@ 323 books . 
- Two prestigious Arab schools in Haifa are the Orthodox School , run by the Greek Orthodox church , and the Nazareth Nuns ' School , a Catholic institution . 
- 
- = = Transportation = = 
- 
- Haifa is served by six railway stations and the Carmelit , currently Israel 's only subway system ( another is under construction in Tel Aviv ) . The <unk> – Tel Aviv Coastal Railway main line of Israel Railways runs along the coast of the Gulf of Haifa and has six stations within the city . From south @-@ west to north @-@ east , these stations are : Haifa Hof HaCarmel , Haifa Bat Galim , Haifa Merkaz <unk> , Lev HaMifratz , <unk> HaMifratz and Kiryat <unk> . Together with the Kiryat Motzkin Railway Station in the northern suburb Kiryat Motzkin , they form the Haifa - Krayot suburban line ( " <unk> " ) . There are direct trains from Haifa to Tel Aviv , Ben Gurion International Airport , <unk> , <unk> , Kiryat Motzkin , <unk> , <unk> , <unk> , Beit <unk> , Jerusalem and other locations , but all trains to Beersheba <unk> all Haifa stations 
- Haifa 's intercity bus connections are operated almost exclusively by the Egged bus company , which operates two terminals : 
- HaMifratz Central Bus Station , adjacent to the Lev HaMifratz Railway Station 
- Haifa Hof HaCarmel Central Bus Station , adjacent to the Hof HaCarmel Railway Station 
- Lines to the North of the country use HaMifratz Central Bus Station and their coverage includes most towns in the North of Israel . Lines heading south use Haifa Hof HaCarmel Central Bus Station . 
- Destinations directly reachable from Hof HaCarmel CBS include Tel Aviv , Jerusalem , <unk> , <unk> , <unk> , <unk> , <unk> Ya <unk> , Atlit , Tirat Carmel , Ben Gurion International Airport and intermediate communities . There are also three Egged lines that have their terminus in the Ramat <unk> neighborhood and run to Jerusalem , <unk> <unk> and Ashdod . These used to be " <unk> " ( i.e. gender segregated ) lines . 
- All urban lines are run by Egged . There are also share taxis that run along some bus routes but do not have an official schedule . In 2006 , Haifa implemented a trial network of neighborhood mini @-@ buses – named " <unk> " and run by Egged . In December 2012 , <unk> , an <unk> and taxi service which allows users to hail a cab using their <unk> without <unk> the taxi station by identifying and summoning the closest taxi . In the current initial phase , 50 taxis from the service are operating in Haifa . 
- Haifa and the Krayot suburbs also have a new <unk> concept bus rapid transit system called the <unk> . These buses , operating with hybrid engines , follow optical strips embedded in designated lanes of roads , providing tram @-@ like public transportation services . The <unk> consists of 100 18 @-@ meter buses , each with the capacity for 150 passengers , operating along 40 km ( 25 mi ) of designated roadways . The new system officially opened on 16 August 2013 serving three lines . 
- Haifa is one of the few cities in Israel where buses operate on <unk> . Bus lines operate throughout the city on a reduced schedule from late Saturday morning onwards , and also connect Haifa with Nesher , Tirat <unk> , <unk> , Nazareth , Nazareth <unk> and intermediate communities . Since the summer of 2008 , night buses are operated by Egged in Haifa ( line 200 ) and the Krayot suburbs ( line 210 ) . During the summer of 2008 these lines operated 7 nights a week . During the winter their schedule is limited to Thursday , Friday and Saturday nights , making them the only buses in Israel to operate on Friday night . Haifa is also the only city in Israel to operate a Saturday bus service to the beaches during summer time . Egged lines run during Saturday mornings from many neighborhoods to the <unk> and Bat Galim beaches , and back in the afternoon . 
- The Haifa underground railway system is called Carmelit . It is a subterranean <unk> on rails , running from downtown Paris Square to <unk> <unk> ( Mother 's Park ) on Mount Carmel . With a single track , six stations and two trains , it is listed in Guinness World Records as the world 's shortest metro line . The Carmelit accommodates <unk> . 
- Haifa also has a cable car . The Haifa Cable Car <unk> lift consists of six cabins and connects Bat Galim on the coast to the Stella Maris observation deck and monastery atop Mount Carmel . It serves mainly tourists . There are currently plans to add a 4 @.@ 4 kilometre commuter cable car service to Haifa 's public transport system , running from HaMifratz Central Bus Station at the foot of Mount Carmel to the Technion , and then to the University of Haifa . 
- 
- = = = Air and sea transport = = = 
- 
- Haifa Airport serves domestic flights to Tel Aviv and <unk> as well as international charters to Cyprus , Greece and Jordan . The <unk> that operates flights from Haifa are <unk> and <unk> . There are currently plans to expand services from Haifa . Cruise ships operate from Haifa port primarily to destinations in the Eastern Mediterranean , Southern Europe and Black Sea . 
- 
- = = = Roads = = = 
- 
- Travel between Haifa and the center of the country is possible by road with Highway 2 , the main highway along the coastal plain , beginning at Tel Aviv and ending at Haifa . Furthermore , Highway 4 runs along the coast to the north of Haifa , as well as south , inland from Highway 2 . In the past , traffic along Highway 2 to the north of Haifa had to pass through the downtown area of the city ; the Carmel <unk> , opened for traffic 1 December 2010 , now route this traffic under Mount Carmel , reducing congestion in the downtown area . 
- 
- = = Sports = = 
- 
- The main stadiums in Haifa are the 14 @,@ 002 @-@ seat Kiryat Eliezer Stadium and Thomas D <unk> Stadium . Neve Sha 'anan Athletic Stadium seats 1 @,@ 000 . Construction of the Sammy <unk> Stadium , a UEFA @-@ approved 30 @,@ 820 seat stadium was completed in 2014 . 
- The city 's two main football clubs are Maccabi Haifa and Hapoel Haifa who both currently play in the Israeli Premier League and share the Sammy <unk> Stadium as their home pitch . Maccabi has won twelve Israeli titles , while Hapoel has won one . 
- The city also has an American football club , the Haifa <unk> , that are a part of the Israeli Football League and play in <unk> Stadium . The team lost in the championship game of the league 's inaugural season , but won one title as part of American Football Israel , which merged with the Israeli Football League in 2005 . 
- The city has several clubs in the regional leagues , including <unk> Haifa in Liga Bet ( the fourth tier ) and Hapoel <unk> Haifa , F.C. Haifa Ruby <unk> and Maccabi Neve Sha 'anan <unk> in Liga <unk> ( the fifth tier ) . 
- Haifa has a professional basketball club , Maccabi Haifa . Maccabi Haifa was recently promoted to Israeli Basketball Super League , the top division . The team plays at <unk> Arena , which seats 5 @,@ 000 . 
- The Haifa Hawks are an ice hockey team based out of the city of Haifa . They participate in the Israeli League , the top level of Israeli ice hockey . 
- In 1996 , the city hosted the World <unk> Championship . The Haifa Tennis Club , near the southwest entrance to the city , is one of the largest in Israel . John <unk> , Olympic horse breeder and owner of triple cup champion <unk> was born here . 
- 
- = = Twin towns - sister cities = = 
- 
- Haifa is twinned with the following cities : 
- 
- 
- = New York State Route 185 = 
- 
- New York State Route 185 ( NY 185 ) is an east – west state highway in Essex County , New York , in the United States . It serves as a connector between NY 9N and NY 22 in Crown Point and the Lake Champlain Bridge , by which the highway enters Vermont and becomes Vermont Route 17 ( VT 17 ) . NY 185 , named Bridge Road , passes through Crown Point State Historic Site and runs the entire length of Crown Point , a peninsula protruding into Lake Champlain . 
- NY 185 was assigned on April 4 , 2008 , as a signed replacement for New York State Route 910L , an unsigned reference route . It is the third signed designation that Bridge Road has carried , preceded by New York State Route 347 ( during the early 1930s ) and NY 8 ( 1930s to the 1960s ) . NY 185 originally connected to the Champlain Bridge on its east end ; however , that structure was closed and demolished in late 2009 . Its replacement opened to traffic in November 2011 . 
- 
- = = Route description = = 
- 
- NY 185 begins at an intersection with NY 9N and NY 22 in Crown Point . The route progresses eastward as a two @-@ lane highway named Bridge Road . Heading away from the western terminus , there is a fork in the road : to the left is NY 185 , and to the right is a road to a housing development . Most of NY 185 runs along the lowlands around Lake Champlain , making curves at intersections , and heading on relatively flat elevations . As the highway approaches the lake , it makes a curve to the north . There , it intersects with County Route 48 ( Lake Road ) and begins to follow the lake shore . 
- The highway passes to the east of the Crown Point State Historic Site and its <unk> before ascending up the approach to the Lake Champlain Bridge . After making a final curve to the northeast , NY 185 begins to cross Lake Champlain by way of the bridge . The route ends at the Vermont state line at the bridge 's midpoint , and the roadway continues into Vermont as VT 17 . 
- 
- = = History = = 
- 
- The Champlain Bridge was built in 1929 , connecting Crown Point in New York to <unk> Point in Vermont . A short connector highway between the bridge — which connected to VT 17 in Vermont — and NY 22 in Crown Point , named Bridge Road , was originally designated as NY 347 as part of the 1930 renumbering of state highways in New York . NY 347 became part of an extended NY 8 c . 1934 . 
- NY 8 was truncated south to Hague c . 1968 , eliminating a lengthy overlap with NY 22 and NY 9N . Its former routing along Bridge Road became NY 903 , an unsigned reference route . The NY 903 designation was later replaced with NY 910L when a new numbering system for reference routes was adopted by the New York State Department of Transportation , and NY 910L itself was redesignated and signed as NY 185 on April 4 , 2008 . 
- On October 16 , 2009 , the Champlain Bridge was closed to traffic due to structural concerns . The bridge was demolished on December 28 , 2009 , as a result of those concerns , temporarily reducing NY 185 in purpose to a spur route linking NY 9N and NY 22 to Crown Point . Its replacement , the Lake Champlain Bridge , opened to traffic on November 7 , 2011 . 
- 
- = = Major intersections = = 
- 
- The entire route is in Crown Point , Essex County . 
- 
- 
- = Fernando Torres = 
- 
- Fernando José Torres Sanz ( Spanish pronunciation : [ <unk> <unk> ] ; born 20 March 1984 ) is a Spanish professional footballer who plays as a striker for La Liga club Atlético Madrid . 
- Torres started his career with Atlético Madrid , progressing through their youth system to the first team squad . He made his first team debut in 2001 and finished his time at the club having scored 75 goals in 174 La Liga appearances . Prior to his La Liga debut , Torres played two seasons in the Segunda División for Atlético Madrid , making 40 appearances and scoring seven goals . 
- Torres joined Premier League club Liverpool in 2007 , after signing for a club record transfer fee . He marked his first season at Anfield by being Liverpool 's first player since Robbie Fowler in the 1995 – 96 season to score more than 20 league goals in a season . The most prolific goalscoring spell of his career , he became the fastest player in Liverpool history to score 50 league goals . He was named in the FIFA World XI in 2008 and 2009 . Torres left the club in January 2011 to join Chelsea for a British record transfer fee of £ 50 million , which made him the most expensive Spanish player in history . In his first full season at Chelsea , Torres won the FA Cup and the UEFA Champions League , despite receiving criticism for a comparatively low goal @-@ scoring record . The following season he scored in the final of the 2012 – 13 UEFA Europa League , helping Chelsea to win the competition for the first time . 
- Torres is a Spanish international and made his debut against Portugal in 2003 . He has been capped over 100 times and is his country 's third @-@ highest goalscorer of all time . With Spain he has participated in six major tournaments ; UEFA Euro 2004 , the 2006 FIFA World Cup , UEFA Euro 2008 , the 2010 FIFA World Cup , UEFA Euro 2012 and the 2014 FIFA World Cup . Spain won the three tournaments from 2008 to 2012 , with Torres scoring in the finals of both Euro 2008 and Euro 2012 . 
- 
- = = Early career = = 
- 
- Born in <unk> , Community of Madrid , Torres became interested in football as a child and joined his first team , <unk> 84 , at the age of five . His father José Torres worked during Torres ' childhood , and his mother <unk> Sanz travelled daily with him to training sessions . His grandfather was not a passionate football fan , but took pride in being an Atlético Madrid supporter , and Torres inherited his love for the club . 
- Torres started playing football as a goalkeeper , the position his brother played in . When he was seven years old , however , he started playing regularly as a striker in an indoor league for the neighbourhood club , Mario 's Holland , using the characters from the anime Captain <unk> as inspiration . Three years later , aged 10 , he progressed to an 11 @-@ side team , <unk> 13 . He scored 55 goals in a season and was one of three <unk> 13 players to earn a trial with Atlético . He impressed the scouts and joined the club 's youth system at the age of 11 in 1995 . 
- 
- = = Club career = = 
- 
- 
- = = = Atlético Madrid = = = 
- 
- After progressing through the ranks , Torres won his first important youth title in 1998 . Atlético sent an under @-@ 15 team to compete in the Nike Cup , in Spain and Europe , to play against youth teams from other clubs ; Atlético won the tournament . He was later voted the best player in Europe for the age group . In 1999 , at the age of 15 , Torres signed his first professional contract with Atlético . He spent his first year playing in the youth team and participated in the Honor Division when he was 16 . The 2000 – 01 season had started badly , as Torres suffered from a cracked <unk> which kept him out until December . Torres trained with the first team to get prepared for pre @-@ season , but eventually made his debut for the team on 27 May 2001 , at the Vicente Calderón Stadium , against CD <unk> . A week later , he scored his first goal for the club against <unk> <unk> and the season finished with Atlético narrowly missing out on promotion to La Liga . 
- Atlético were promoted to La Liga at the end of the 2001 – 02 season , although Torres did not perform well in the season , as he netted only 6 times in 36 appearances in the Segunda División . Torres ' first season in La Liga , 2002 – 03 , was better , however , as he scored 13 goals in 29 appearances , with Atlético finishing in 11th place . In July 2003 , soon after his takeover of the club , Chelsea owner Roman <unk> had a £ 28 million bid for Torres rejected by Atlético 's board . In the 2003 – 04 season , his second in La Liga , Torres made further <unk> , scoring 19 league goals in 35 appearances , meaning he finished as joint third highest scorer in the league . At the age of 19 , Torres was named Atlético 's captain . Atlético narrowly missed out on qualification for the UEFA Cup , but by finishing in seventh place in the 2003 – 04 season , they qualified for the 2004 UEFA Intertoto Cup , giving Torres a first taste of a competition at the European level . He scored two goals in the two fourth round matches against <unk> <unk> , with one coming in each leg . Atlético reached the Final , but lost 3 – 1 on a penalty shoot @-@ out to <unk> <unk> following a 2 – 2 draw on aggregate . FA Premier League champions Chelsea were believed to be interested in signing Torres during the summer of 2005 , but Atlético president Enrique Cerezo said that they had " no chance " of signing him . Cerezo later said in January 2006 that the club was willing to listen to offers for Torres , and Torres claimed Newcastle United of the FA Premier League had made a bid to sign him in March . 
- He stated after the 2006 FIFA World Cup that he had turned down an offer to join Chelsea at the end of the 2005 – 06 season . Torres scored 14 league goals in the 2006 – 07 season . The English media reported that he was the main transfer target of Premier League Liverpool , but Cerezo stated " We 've received no offer from Liverpool or any other club or player " . However , a few days later , new reports suggested Atlético had agreed a deal with Liverpool for Torres ; the fee was rumoured to be £ 25 million with Luis García moving to Atlético in a separate transfer deal . On 30 June , Atlético announced a deal to sign Diego <unk> from <unk> , in what was seen as a move to replace Torres before his departure became official . On 2 July , it was reported that Torres had cut short a vacation to fly back to Madrid to <unk> the move to Liverpool . The following day , Torres passed a medical at Liverpool 's <unk> training ground . He held a press conference in Madrid on 4 July to bid farewell to the Atlético fans , before completing his move to Liverpool on a six @-@ year contract . The transfer fee was the highest in Liverpool 's history . In March 2008 , manager Rafael Benítez stated in an interview with The Times that Torres was acquired for around £ 20 million , although this figure takes into account García 's move to Atlético . 
- 
- = = = Liverpool = = = 
- 
- 
- = = = = 2007 – 08 season = = = = 
- 
- Torres made his competitive debut for Liverpool against Aston Villa in a 2 – 1 win on 11 August 2007 . He made his first appearance in the UEFA Champions League four days past his first performance in a 1 – 0 victory over Toulouse , after coming on as a 79th minute substitute . His first Premier League goal came on his Anfield debut on 19 August 2007 , in the 16th minute in a 1 – 1 draw against Chelsea . His first hat @-@ trick came in a 4 – 2 victory over Reading in the League Cup on 25 September 2007 , with all of his goals coming in the second half . His first goals in the Champions League came on his third appearance in the competition as Liverpool beat Porto 4 – 1 on 28 November 2007 , as he scored twice . 
- Torres was named the Premier League Player of the Month for February 2008 , during which he scored four goals in four appearances , including a hat @-@ trick against Middlesbrough on 23 February 2008 . This hat @-@ trick and another in a 4 – 0 victory over West Ham United on 5 March 2008 meant he became the first Liverpool player since Jack <unk> in November 1946 to score a hat @-@ trick in successive home matches . Later in March , after he scored a 47th @-@ minute header against Reading at Anfield , becoming the first Liverpool player since Robbie Fowler in the 1995 – 96 season to score 20 league goals in a season . In April , he scored another Champions League goal , this time against Arsenal in the quarter @-@ final second leg , as Liverpool advanced to the semi @-@ final . This goal took him onto 29 goals for the 2007 – 08 season in all competitions , eclipsing Michael Owen 's personal record for goals in a season . On 11 April 2008 , it was announced Torres had made a six @-@ man shortlist for the PFA Players ' Player of the Year award , which was eventually won by <unk> Ronaldo of Manchester United . The Spanish international was also nominated for the PFA Young Player of the Year Award , which was won by <unk> <unk> of Arsenal and was named in the PFA Team of the Year . In May , he finished second to Ronaldo for the <unk> <unk> of the Year award . 
- On 4 May 2008 , Torres scored a 57th @-@ minute winner against Manchester City , which equalled the consecutive Anfield league goal record of eight games set by Roger Hunt . After scoring his 24th league goal in the final game of the season , a 2 – 0 win against Tottenham Hotspur , he set a new record for the most prolific foreign goal scorer in a debut season in England , eclipsing <unk> van <unk> 's 23 goals . He ended the season in joint second place with Emmanuel <unk> in the race for the Premier League golden boot . Torres was subject to media speculation that Chelsea were willing to pay £ 50 million to sign him but Torres responded by saying it would be " many years " before he left Liverpool . Liverpool co @-@ owner Tom <unk> also negated the idea of a transfer , saying he would not allow Torres to leave the club at any price . 
- 
- = = = = 2008 – 09 season = = = = 
- 
- Torres made a scoring start to the 2008 – 09 Premier League season with a 25 yard shot into the bottom right @-@ hand corner away at Sunderland , which was the only goal in a 1 – 0 win on 16 August 2008 . He suffered a hamstring tear in a 0 – 0 draw against Aston Villa , which would keep him out for two to three weeks . Torres made his return in a 2 – 1 victory against Marseille in the Champions League and went on to score two goals in the Merseyside derby against Everton on 27 September 2008 to give Liverpool a 2 – 0 win . He followed this up with another two goals the following weekend against Manchester City in a 3 – 2 win away at the City of Manchester Stadium as Liverpool came back from a two @-@ goal deficit . The first of these was the <unk> Liverpool goal to be scored in the Premier League . Torres picked up a hamstring injury during a 2010 FIFA World Cup <unk> , meaning he was likely to miss three games for Liverpool . On 22 October 2008 , Liverpool played Torres ' former club Atlético in the Champions League at the Vicente Calderón Stadium , but his injury meant he missed out on the game . The Atlético president , Enrique Cerezo , had given him a <unk> invitation to watch the game , but he declined this to continue his rehabilitation from injury in Merseyside . He was named in the FIFPro World XI team for the 2007 – 08 season on 27 October 2008 . 
- Torres made his return for Liverpool on 8 November 2008 after coming on as a 72nd @-@ minute substitute in a 3 – 0 victory against West Bromwich Albion ( West Brom ) . He said he would be interested in returning to former club Atlético eventually , saying " I don 't know if I will retire there , but I would like to go back and finish some things that are left to do . " He was ruled out of action for two to three weeks following Liverpool 's 1 – 0 victory over Marseille in the Champions League in November , where he picked up a hamstring strain , which was later extended to at least four weeks by specialists . He was named on the shortlist for the FIFA World Player of the Year award in December , and eventually came in third place behind Ronaldo and Lionel <unk> . Torres returned to action on 3 January 2009 as a substitute with a goal in the 2 – 0 win over Preston North End ; his first in the FA Cup . He scored two late goals for Liverpool to secure a 2 – 0 victory over Chelsea on 1 February 2009 . Despite having spent a year and a half at the club , Torres was chosen as number 50 in The Times 's list of " The 50 greatest Liverpool players " , reinforcing the impact he had made at Liverpool in such a short period of time . 
- Torres faced his old rivals Real Madrid on 10 March 2009 in the Champions League last 16 and due to an ankle injury , he had a <unk> injection before the game to enable him to play . He scored the first goal of the game , which ended as a 4 – 0 victory , meaning Liverpool progressed to the quarter @-@ finals 5 – 0 on aggregate . Four days later he lined up against Manchester United at Old Trafford and he scored the equaliser in a game that finished as a 4 – 1 victory . He was named in the PFA Team of the Year for the second season running in April 2009 . Torres scored his 50th goal for Liverpool on 24 May 2009 against Tottenham on the final day of the 2008 – 09 season , which was his 84th appearance . 
- 
- = = = = 2009 – 10 season = = = = 
- 
- Following the end of the season , he agreed a new contract with Liverpool , which he signed on 14 August . By signing this contract , Torres had the option of a one @-@ year extension after the contract 's expiration in 2013 . Torres scored two goals in a 3 – 2 win over West Ham United on 19 September 2009 , a result that took Liverpool to third in the Premier League . A week later he scored his first hat @-@ trick of the 2009 – 10 Premier League season in a 6 – 1 victory over Hull City at Anfield . He was named Premier League Player of the Month for September , after scoring five goals during the month and becoming the Premier League 's top goalscorer . On 25 October 2009 , he scored the first goal in a 2 – 0 victory for Liverpool over Manchester United , after which Benítez praised Torres ' performance , saying " We were waiting for that final pass . When it came we knew he would score . " Torres was named in the FIFPro World XI for the second successive season in December 2009 . His stoppage @-@ time winning goal against Aston Villa on 29 December 2009 made him the fastest Liverpool player ever to score 50 league goals . He was substituted on 65 minutes in a 1 – 1 draw with Birmingham City on 4 April 2010 , which Benítez justified by saying Torres was " exhausted " . Torres made his last appearance of the season scoring twice in a 4 – 1 victory over <unk> in the Europa League on 8 April 2010 , and it was announced on 18 April that he would miss the rest of the season to undergo knee surgery . This meant that Torres finished the season with 22 goals in 32 games in all competitions , finishing as Liverpool 's top scorer for the second time . 
- 
- = = = = 2010 – 11 season = = = = 
- 
- Following Roy Hodgson 's appointment as Liverpool manager he stated that Torres would not be sold by the club , saying " He is not for sale and we don 't welcome any offers for him . We want to keep him . " Hodgson dismissed reports Torres was set to leave Liverpool by saying " He has told us that he is looking forward to Monday , to getting back to work and looking forward to playing for Liverpool next season . That is what I know so other reports , I would suggest , are erroneous . " Torres stated his commitment to Liverpool on 3 August , saying " My commitment and loyalty to the club and to the fans is the same as it was on my first day when I signed . " 
- He made his first appearance of the 2010 – 11 season in the opening game , a 1 – 1 draw with Arsenal on 14 August 2010 , entering the game as a substitute in the <unk> minute . He scored his first goal of the season with the winner in a 1 – 0 victory over West Brom on 29 August 2010 , which was his 50th goal at Anfield in all competitions . Torres scored the winning goal in a 2 – 1 victory at home to Blackburn Rovers on 24 October 2010 , which was his first goal since August . He scored his final goals for Liverpool in a 3 – 0 win away at Wolverhampton Wanderers on 22 January 2011 . 
- 
- = = = Chelsea = = = 
- 
- On 27 January 2011 , Torres was the subject of a £ 40 million bid from Chelsea , which was rejected by Liverpool . He subsequently handed in an official transfer request to Liverpool the next day , which was rejected . Torres completed his move to Chelsea on a five @-@ and @-@ a @-@ half @-@ year contract on 31 January 2011 for an undisclosed fee reported to be £ 50 million , which set a new record for a British transfer and made him the sixth most expensive footballer in history . He made his Chelsea debut on 6 February 2011 in a 1 – 0 home defeat to former club Liverpool . On 23 April 2011 , Torres scored his first goal for Chelsea against West Ham United in a 3 – 0 victory , which ended a run of 903 minutes of football without a goal . This was his only goal for Chelsea in the 2010 – 11 season , having made 18 appearances for his new club . 
- 
- = = = = 2011 – 12 season = = = = 
- 
- Torres got the 2011 – 12 season underway in Chelsea 's 0 – 0 draw away against Stoke City on 14 August 2011 , in which his " link @-@ up play was sharp , plus he was willing to make intelligent runs into space to receive the ball " . He scored his second goal at Chelsea , and his first of the season , on 18 September 2011 , during an away game against Manchester United , which was the side 's only goal in a 3 – 1 defeat . He scored his second goal of the season at home to Swansea City in a 4 – 1 victory . However , 10 minutes after his goal , a two @-@ footed challenge on Mark <unk> resulted in his first red card in English football and a three @-@ match domestic suspension . On 19 October 2011 , Torres scored two goals in a 5 – 0 victory against <unk> in the Champions League , his first in Europe since the quarter @-@ final of the 2008 – 09 Champions League with his old club Liverpool , against Chelsea . His next Chelsea goals came after scoring twice and also assisting two others against Championship side Leicester City in an FA Cup quarter @-@ final on 18 March 2012 , ending a goal drought that stretched 24 games . On 31 March 2012 , Torres scored his first Premier League goal since 24 September 2011 , against Aston Villa in a 4 – 2 away win . 
- Torres came on as a substitute for <unk> <unk> on 24 April 2012 to score a last @-@ minute goal against FC Barcelona in the Champions League semi @-@ final , which gave Chelsea a 2 – 2 draw , and also eliminated Barcelona from the tournament , as Chelsea won 3 – 2 on aggregate . He scored the first hat @-@ trick of his Chelsea career in a 6 – 1 win against Queens Park Rangers at Stamford Bridge on 29 April 2012 . Torres came on in the second half of the 2012 UEFA Champions League Final on 19 May 2012 , directly after Bayern Munich 's opening goal , and following a 1 – 1 draw after extra time Chelsea won the penalty shoot @-@ out 4 – 3 . His first full season at Chelsea finished with 11 goals in 49 games . 
- 
- = = = = 2012 – 13 season = = = = 
- 
- Torres started the 2012 – 13 season in the 2012 FA Community Shield against Manchester City at Villa Park , where he scored the opening goal , as Chelsea lost 3 – 2 . On 19 August 2012 , he appeared in Chelsea 's first league match in a 2 – 0 away win against <unk> Athletic . Torres scored his first goal of the season in Chelsea 's next match on 22 August 2012 , a 4 – 2 home win against Reading , scoring the third goal to give his side the lead . He also scored against Newcastle United , Arsenal and Norwich City as Chelsea topped the table in the early weeks of the season . 
- Torres was sent off for receiving two yellow cards against Manchester United on 28 October 2012 , the second for diving , leaving Chelsea with nine men after teammate <unk> <unk> was also sent off ; the team went on to lose 3 – 2 . Torres scored his first goal in the Champions League for the season after <unk> <unk> <unk> goalkeeper <unk> <unk> 's clearance into goal , as Chelsea needed a 94th @-@ minute winner from Victor Moses to secure a 3 – 2 victory on 7 November 2012 . On 21 November 2012 , Roberto Di <unk> was replaced as Chelsea manager by Rafael Benítez , who had previously coached Torres at Liverpool . It was reported that one reason for Benítez 's appointment was to help Torres return to his best form . 
- Torres ended a goal drought that stretched over 11 hours of game time with two goals in Chelsea 's 6 – 1 defeat of <unk> in the Champions League on 5 December 2012 . Three days later he scored another two goals , ending a run of eight Premier League games without a goal , as Chelsea defeated Sunderland 3 – 1 . He then scored in Chelsea 's 2012 FIFA Club World Cup semi @-@ final 3 – 1 win against <unk> of the Mexican Liga <unk> on 13 December 2012 , before playing in the 1 – 0 defeat to Brazilian <unk> A side <unk> in the final three days later . 
- On 14 March 2013 , Torres scored the third goal as Chelsea defeated <unk> <unk> 3 – 1 to advance 3 – 2 on aggregate to the quarter @-@ final of the Europa League . With this goal , Torres became the first player to score in seven different competitions in one season . In April 2013 , Torres acknowledged his struggles at Chelsea , saying : " I want to do the things I used to do . I did them at Atlético , I did them at Liverpool , but I am not doing them at Chelsea " , but he vowed to " try his heart out " to regain his best form . He scored twice against Rubin <unk> in the 3 – 1 win in the first leg of the Europa League quarter @-@ final on 4 April 2013 , before scoring again in the second leg 3 – 2 defeat , with Chelsea progressing to the semi @-@ final 5 – 4 on aggregate . Torres scored the opening goal in Chelsea 's 2 – 1 triumph over <unk> in the 2013 UEFA Europa League Final on 15 May 2013 . During the final game of the season , a 2 – 1 home win over Everton , he scored his first league goal of 2013 , and finished the season with 22 goals in 64 games . 
- 
- = = = = 2013 – 14 season = = = = 
- 
- Torres was selected to start by Chelsea 's new manager José <unk> against Hull City on the opening day of the 2013 – 14 season , where he won a fifth minute penalty kick . He scored his first goal of the season in the 2013 UEFA Super Cup against Bayern Munich . On 28 September 2013 , he was sent off after receiving two yellow cards in Chelsea 's 1 – 1 Premier League draw with Tottenham Hotspur at White Hart Lane . 
- Torres made his 100th start for Chelsea against <unk> 04 in a Champions League match on 22 October 2013 and marked the occasion by scoring two goals in a 3 – 0 win . His first Premier League goal of the season came on 27 October as he scored the winning goal in the 90th minute over title <unk> Manchester City , while also providing the assist for André <unk> 's opener . 
- 
- = = = Milan = = = 
- 
- Torres joined <unk> A club Milan on a two @-@ year loan on 31 August 2014 . On his arrival he expressed a desire to emulate some of the club 's greatest strikers , stating he wanted , " My shirt to rank alongside [ Marco ] van <unk> , [ George ] <unk> and [ Filippo ] <unk> . " He debuted on 20 September 2014 , replacing Andrea <unk> for the last 14 minutes of a 1 – 0 home defeat against <unk> and scored his first Milan goal with a <unk> header in their 2 – 2 draw with <unk> two days later . 
- 
- = = = Return to Atlético Madrid = = = 
- 
- On 27 December 2014 , it was announced that Torres ' move to Milan would be made permanent on 5 January 2015 , but two days later he agreed to rejoin La Liga club Atlético Madrid on loan until the end of the 2015 – 16 season . The loan was agreed after <unk> <unk> went the opposite way from Atlético to Milan on the same day ( 5 January 2015 ) . Torres ' unveiling at the Vicente Calderón Stadium on 4 January 2015 drew a crowd of 45 @,@ 000 . He played the first match of his second spell on 7 January 2015 , starting in the first leg of a <unk> del Rey last 16 tie against Real Madrid . He had no shots on target and was replaced by <unk> after 59 minutes , but Atlético won 2 – 0 . Eight days later in the second leg , he scored two goals — in the first minute of each half — as Atlético advanced , his first goals in an away Madrid derby . Torres scored in the first minute again in the quarter @-@ finals , albeit in a 2 – 3 home defeat against Barcelona . 
- On 17 March , Torres scored the winning kick in Atlético 's 3 – 2 penalty shoot @-@ out defeat of Bayer <unk> in the second leg of the teams ' Champions League round of 16 tie . On 21 March , he scored his first La Liga goal since his return in the third minute of a 2 – 0 win against <unk> <unk> . 
- After the departure of Mario <unk> , Torres changed his shirt number from 19 back to his usual 9 and is wearing it in the 2015 – 16 season . 
- On 6 February 2016 , Torres scored his 100th goal with Atlético Madrid in a 3 – 1 home league victory against <unk> <unk> on his <unk> appearance for the club . The following month he marked the occasion of his 300th appearance for Atlético Madrid by scoring the winning goal in a 3 – 1 victory over Valencia . 
- On 5 April 2016 , Torres scored the opening goal of Atlético 's UEFA Champions League quarter @-@ final with FC Barcelona at Camp <unk> , before being sent off ten minutes later in an eventual 2 – 1 loss . 
- On 5 July 2016 , Torres extended his contract to a permanent contract for one year with Atlético Madrid . 
- 
- = = International career = = 
- 
- In February 2001 , Torres won the <unk> Tournament with the Spain national under @-@ 16 team . The under @-@ <unk> took part in the 2001 UEFA European Under @-@ 16 Championship in May , which they also won , with Torres scoring the only goal in the Final , as well as finishing as the tournament 's leading scorer , and was also named the player of the tournament . In September 2001 , Torres represented the under @-@ 17 team at the 2001 FIFA U @-@ 17 World Championship , but the team failed to progress through the group stage . In July 2002 , he won the 2002 UEFA European Under @-@ 19 Championship with the under @-@ 19 team and was again the only goalscorer in the Final . Also , he finished as top scorer and was named player of the tournament . 
- Torres made his debut for the senior team on 6 September 2003 in a friendly against Portugal . His first goal for Spain came against Italy on 28 April 2004 . He was selected for the Spanish squad for UEFA Euro 2004 . After appearing as a late substitute in Spain 's first two group games , he started for the deciding game against Portugal . Spain were losing 1 – 0 and , towards the end of the game , he hit the post . 
- He scored seven goals in 11 appearances in qualifying for the 2006 FIFA World Cup , making him Spain 's top scorer for qualification , including a vital two goals against Belgium and his first international hat @-@ trick against San Marino . At his first ever appearance in a FIFA World Cup finals at the 2006 FIFA World Cup in Germany , Torres scored the final goal in a 4 – 0 victory over Ukraine with a volley . In the second group match , Torres scored twice against Tunisia , first in the 76th minute to take Spain 2 – 1 into the lead , and then again from a penalty kick in the 90th . With three goals , he finished the tournament as Spain 's top scorer along with fellow striker David Villa . 
- He was called up for Spain 's UEFA Euro 2008 squad , where he set up Villa to score Spain 's first goal of the tournament in the first game in the group stage against Russia . Torres came under criticism for apparently refusing to shake the hand of Spanish manager , Luis <unk> , after being substituted . He subsequently denied being angry with the coaching , saying that he was actually " <unk> with himself " . He scored his first goal of the tournament in Spain 's next game , a 2 – 1 win over Sweden . Torres scored the winner and was named the man of the match in the final against Germany in a 1 – 0 victory . He said " It 's just a dream come true . This is my first title and I hope it 's the first of many . Victory in a Euro , it is almost as big as a World Cup . We are used to watching finals on television , but today we were here and we won . My job is to score goals . I want to win more titles and be the most important player in Europe and the world . " He was later named as a striker along with his striking partner Villa in the Team of the Tournament . 
- Torres made his 60th appearance for Spain in a 2010 FIFA World Cup qualification victory over Turkey on 28 March 2009 , becoming the youngest player to reach this milestone . 
- He was named in Spain 's team for the 2009 FIFA Confederations Cup in June . He scored his second hat @-@ trick for Spain after 17 minutes into a Confederations Cup game against New Zealand on 14 June , thus recording the fastest hat @-@ trick by a player for Spain . He played for Spain as they were defeated 2 – 0 by the United States in the semi @-@ final , as well as the third @-@ place play @-@ off , which Spain won 3 – 2 against South Africa after extra time . 
- Having undergone knee surgery on 18 April 2010 , and missed the end of the Premier League season , Torres ' rehabilitation and fitness was closely monitored by Spain manager Vicente del Bosque . Although still out injured , Torres was selected for Spain 's 2010 FIFA World Cup squad in May . On 8 June , Torres made his first appearance on the field in exactly two months , coming on as a substitute on 66 minutes against Poland in a pre @-@ World Cup friendly . 
- He came on as a substitute on 61 minutes in Spain 's opening World Cup game on 16 June , a 1 – 0 defeat to Switzerland . He started the next two games against Honduras and Chile and although his performances in the group stage were described as below @-@ par , he received backing from manager Vicente del Bosque . Torres came on as a substitute on 105 minutes in the final as Spain won the FIFA World Cup for the first time following a 1 – 0 victory over the Netherlands on 11 July 2010 . 
- Torres was selected in Del Bosque 's UEFA Euro 2012 squad . In his first start of the tournament he netted two goals as Spain defeated the Republic of Ireland 4 – 0 to knock them out of the tournament . In the UEFA Euro 2012 Final , Torres came on as a substitute against Italy , scoring one goal and assisting another , as Spain won a second consecutive European Championship with a 4 – 0 victory . This secured him the Golden Boot for the tournament with three goals and one assist , having played fewer minutes than Mario Gómez , who also had three goals and one assist . 
- Torres won his 100th cap against Saudi Arabia on 7 September 2012 , making him the sixth highest capped player for Spain , and started the game as captain in <unk> <unk> ' absence . He became the first person in history to score four goals in one game in a FIFA Confederations Cup match during a 10 – 0 win over Tahiti on 20 June 2013 in the 2013 FIFA Confederations Cup . Torres also became the first player to score two hat @-@ tricks in the FIFA Confederations Cup , and with five goals and one assist won the tournament 's Golden Shoe , ahead of Fred having played fewer minutes . 
- In his first international for 11 months , Torres scored a penalty in a World Cup warm @-@ up match against Bolivia on 30 May 2014 . The day after , he was named in Spain 's final squad for the tournament , ahead of Álvaro <unk> and Fernando <unk> . After substitute appearances in the team 's opening two matches , Torres was named in the starting line @-@ up for the third group match with the team 's elimination already confirmed . He scored Spain 's second goal in a 3 – 0 defeat of Australia in <unk> . 
- 
- = = Style of play = = 
- 
- Torres has been described as having " an eye for the spectacular and is capable of world @-@ class skill " and being a " technically proficient , highly successful striker " . He has the ability to find himself in goalscoring positions , using his pace and vision to get in behind defenders . 
- 
- = = Personal life = = 
- 
- Torres ' parents are José Torres and <unk> Sanz . He has two older siblings , Israel ( born 1977 ) and María <unk> ( born 1976 ) . He married <unk> <unk> <unk> on 27 May 2009 in a private ceremony with just two witnesses at the local town hall in El Escorial , Madrid . They had been a couple since 2001 and are thought to have met in the <unk> seaside town of <unk> , <unk> , where Torres would go on family holidays each year to escape the Spanish capital . The couple have two children , a daughter , <unk> , born on 8 July 2009 at the Hospital La <unk> , in Santiago de Compostela , <unk> , and a son , Leo , born on 6 December 2010 at Liverpool Women 's Hospital , with Torres missing the fixture against Aston Villa at Anfield . 
- In 2009 , it was reported that Torres had amassed a personal fortune of £ 14 million . He features in the video for " Ya <unk> <unk> a ser <unk> <unk> " by Spanish pop rock group El <unk> del <unk> , whose singer , <unk> Martín , shares a strong friendship with the footballer . He makes a cameo appearance in the 2005 comedy film <unk> 3 : El protector . In 2009 , he released an autobiography entitled Torres : El Niño : My Story . Torres is a keen admirer of the works of J. R. R. <unk> and has a tattoo reading " Fernando " in <unk> on his left <unk> . 
- 
- = = Career statistics = = 
- 
- 
- = = = Club = = = 
- 
- As of match played 28 May 2016 . 
- 
- = = = International = = = 
- 
- As of match played 23 June 2014 . 
- 
- = = Honours = = 
- 
- 
- = = = Club = = = 
- 
- Atlético Madrid 
- Segunda División : 2001 – 02 
- Chelsea 
- FA Cup : 2011 – 12 
- UEFA Champions League : 2011 – 12 
- UEFA Europa League : 2012 – 13 
- 
- = = = International = = = 
- 
- Spain <unk> 
- UEFA European Under @-@ 16 Championship : 2001 
- Spain <unk> 
- UEFA European Under @-@ 19 Championship : 2002 
- Spain 
- UEFA European Championship : 2008 , 2012 
- FIFA World Cup : 2010 
- FIFA Confederations Cup : Third place 2009 , <unk> @-@ up 2013 
- 
- = = = Individual = = = 
- 
- UEFA European Under @-@ 16 Championship Player of the Tournament : 2001 
- UEFA European Under @-@ 16 Championship top scorer : 2001 
- UEFA European Under @-@ 19 Championship Player of the Tournament : 2002 
- UEFA European Under @-@ 19 Championship top scorer : 2002 
- Premier League PFA Team of the Year : 2007 – 08 , 2008 – 09 
- Premier League Player of the Month : February 2008 , September 2009 
- Premier League <unk> of the Month : April 2009 @,@ 
- UEFA Euro 2008 Final : Man of the Match 
- UEFA Euro Team of the Tournament : 2008 
- UEFA Team of the Year : 2008 
- FIFA FIFPro World XI : 2008 , 2009 
- <unk> Team of the Year : 2007 – 08 
- FIFA World Player of the Year third place : 2008 
- <unk> d 'Or third place : 2008 
- FIFA Confederations Cup Silver Shoe : 2009 
- <unk> <unk> award : 2012 
- UEFA Euro Golden Boot : 2012 
- FIFA Confederations Cup Golden Shoe : 2013 
- <unk> 
- Prince of <unk> Award for Sports : 2010 
- Gold Medal of the Royal Order of Sports Merit : 2011 
- 
- 
- = Mark Stockwell = 
- 
- Mark William Stockwell ( born 5 July 1963 ) is an Australian former competition swimmer and three @-@ time Olympic medallist . Stockwell is a Queensland native who specialised in freestyle sprint events , and had a successful international swimming career during the mid @-@ 1980s , including the Olympics , Pan Pacific Championships , and Commonwealth Games . Following his retirement from competitive swimming , he has become a successful business executive and has been active in the administration of national sports organisations in Australia . 
- 
- = = Early years and education = = 
- 
- Stockwell was born and raised in Brisbane , Queensland , the son of Bill and <unk> Stockwell . He is a 1980 graduate of St Laurence 's College , a Roman Catholic boys ' high school in Brisbane . Stockwell was an Australian Institute of Sport scholarship holder from 1982 to 1984 , and again in 1987 . He undertook commerce and engineering studies at the University of Queensland , and economics <unk> at the Australian National University . He also attended the University of Florida in the United States . 
- 
- = = <unk> career = = 
- 
- Stockwell won three medals at the 1984 Summer Olympics in Los Angeles . In the men 's 100 @-@ metre freestyle , Stockwell finished in 50 @.@ 24 seconds and claimed a silver medal behind American swimmer <unk> Gaines ' Olympic record time of 49 @.@ 80 seconds . The outcome was controversial , however , because of a premature starter gun and a quick start by Gaines . Gaines ' coach , Richard Quick , knew of starter Frank <unk> 's tendency to fire the starter gun almost immediately when the competitors mounted the blocks . Gaines gained about a metre 's head start on the competition ; he held Stockwell off to claim the gold medal . Video of the event confirmed that Stockwell had not been set when the starter pulled the trigger . Stockwell and the Australian Olympic Federation lodged an official protest , but it was denied . 
- Stockwell , along with teammates Greg <unk> , Neil Brooks and Michael Delany , won another silver medal in the men 's 4 × 100 @-@ metre freestyle relay , finishing in 3 : 19 @.@ 68 – just 0 @.@ 63 of a second behind the Americans ' new world record of 3 : 19 @.@ 05 . He also teamed up with Mark Kerry ( <unk> ) , Peter Evans ( <unk> ) , and Glenn Buchanan ( butterfly ) , swimming the freestyle anchor leg to win the bronze medal in the 4 × 100 @-@ metre medley relay ( 3 : 43 @.@ 25 ) behind the Americans ( 3 : 39 @.@ 30 ) and Canadians ( 3 : 43 @.@ 25 ) . He and his freestyle relay teammates were dubbed the " Mean Machine " by the Australian media . Stockwell was the only Australian athlete to win three Olympic medals in 1984 . 
- After the Olympics , Stockwell attended the University of Florida in <unk> , Florida , United States , where he swam for coach Randy Reese 's Florida Gators swimming and diving team in National Collegiate Athletic Association ( NCAA ) and Southeastern Conference ( SEC ) competition during the 1984 – 85 school year . He won three SEC titles ( <unk> and 100 @-@ yard freestyle , 400 @-@ yard freestyle relay ) , and received All @-@ American honours in the same three events . While attending the university , he dated his future wife , Tracy Caulkins , who was also a Florida Gators swimmer . He and Caulkins had met in the warm @-@ up pool at the 1984 Olympics . 
- He followed his Olympic performance with a bronze medal for his third @-@ place finish in the 50 @-@ metre freestyle ( 23 @.@ 44 ) , and a fourth in the 100 @-@ metre freestyle ( 51 @.@ 64 ) at the 1985 Pan Pacific Championships in Tokyo . By the time the 1986 Commonwealth Games were held in Edinburgh , Stockwell had fallen back from his Olympic times in Los Angeles . He claimed a gold medal with his Australian teammates <unk> , Matthew <unk> and Neil Brooks in the 4 × 100 @-@ metre relay ( 3 : 21 @.@ 58 ) , and finished seventh in the 100 @-@ metre freestyle ( 51 @.@ 61 ) . He retired from competitive swimming in 1986 . In December 1989 , The Age newspaper recognized Stockwell as one of the three best Australian swimmers of the 1980s . 
- 
- = = Life after swimming = = 
- 
- Stockwell married American competition swimmer Tracy Caulkins , a three @-@ time Olympic gold medallist , in her hometown of Nashville , Tennessee in 1991 . He lives in Brisbane , with his wife and their five children . He received an Australian Sports Medal for his swimming achievements in 2000 . Stockwell was inducted into the Queensland Sport Hall of Fame in 2009 . 
- He is the managing director of the family @-@ owned property development , investment and management firm , W. A. Stockwell <unk> Ltd , based in Queensland . He and his brother @-@ in @-@ law Mike <unk> are co @-@ owners of the company that was established as a construction firm by his parents more than sixty years ago . The firm has had successful development ventures in residential , leisure , retail , commercial and industrial property , and has undertaken a phased AU $ 350 million residential development in Brisbane 's West End . He and his family members were included among the 14 new entries on the 2014 " Rich List , " with a combined estimated net worth of AU $ 101 million . 
- He serves on the board of directors of the Australian Sports Commission , and is the deputy chairman of the board . He is also the chairman of the Australian Sports Foundation , a non @-@ profit company that raises money for Australian sport projects . He previously served as the chairman of the Gold Coast 2018 Commonwealth Games <unk> Committee , having led the committee through its successful application process , but was sacked by Campbell Newman . He is the chairman of Trade and Investment Queensland , the Queensland state government 's foreign investment and export program , and a past president of the Queensland division of the Property Council of Australia , and previously served on its national board . 
- Stockwell is the past chairman and a trustee of the St Laurence 's Old Boys ' Foundation , which funds need @-@ based <unk> for boys whose families cannot otherwise afford the tuition and fees at his high school , St Laurence 's College . He is a Trustee of the Stockwell Foundation , a charitable organisation established by him and his wife to benefit at @-@ risk children . 
- 
- 
- = Dota 2 = 
- 
- Dota 2 is a free @-@ to @-@ play multiplayer online battle arena ( MOBA ) video game developed and published by Valve Corporation . The game is the stand @-@ alone sequel to Defense of the Ancients ( DotA ) , a mod for the 2002 video game Warcraft III : Reign of Chaos and its expansion pack , The Frozen Throne . Dota 2 was released for Microsoft Windows , OS X , and Linux in July 2013 , following a Windows @-@ only public beta testing phase that began in 2011 . Dota 2 is one of the most actively played games on Steam , with peaks of over a million concurrent players , and was praised by critics for its gameplay , production quality , and <unk> to its predecessor , despite being criticized for its steep learning curve . 
- Dota 2 is played in matches between two five @-@ player teams , each of which occupies a base in a corner of the playing field . Each player controls one of 111 playable characters , called " heroes " , that feature unique abilities and styles of play . During a match , the player collects gold , items , and experience points for their hero , while fighting heroes of the opposite team . A team wins by being the first to destroy the other side 's " Ancient " building , located within the opposing base . 
- Development of Dota 2 began in 2009 when IceFrog , lead designer of the original Defense of the Ancients mod , was hired by Valve for the same role . Dota 2 initially used the original Source game engine until it was ported over to Source 2 in September 2015 , making it the first game to use it . The game also allows for the community to create custom game modes , maps , and cosmetics for the heroes , which are then uploaded to the Steam Workshop . The popularity of Dota 2 has led to official merchandise being produced for it , including apparel , accessories , toys , and promotional tie @-@ ins to other games and media . 
- Dota 2 has a widespread and active eSports scene , with teams from across the world playing in various leagues and tournaments . Premium Dota 2 tournaments often have prize pools totaling millions of dollars , the highest of any eSport . The largest of them is known as The International , which is hosted by Valve and takes place annually at the <unk> in Seattle . Starting in 2015 , Valve also began sponsoring smaller , but seasonally held tournaments known as the Majors , the first of which was held in Frankfurt , Germany . Dedicated media coverage of professional tournaments are broadcast live on the internet , and sometimes on television networks , with peak viewership numbers in the millions . 
- 
- = = Gameplay = = 
- 
- Dota 2 is a multiplayer online battle arena ( MOBA ) video game set in a three @-@ dimensional ( 3D ) graphical environment , presented from a high @-@ angle perspective . Two five @-@ player teams , referred to as the Radiant and Dire , compete in matches on a single asymmetrical playing field . Each player commands one of the 111 characters , called " heroes " , which each feature unique abilities , design , and styles of play . At the start of a match , each hero has an experience level of one . During the game , they level up and become more powerful by accumulating experience points through combat . Whenever a hero gains a level , the player is able to unlock a new ability for them , improve one already learned , or increase their base attributes . Each hero 's method of combat is influenced by its primary attribute : strength , intelligence , agility , which also affects the hero 's health points ( HP ) , <unk> points ( MP ) , and attack speed , respectively . If a hero runs out of health points and dies , a <unk> begins to count down until they are able to respawn and get back into the game . A hero also loses a portion of their <unk> gold each time they die . 
- The Radiant and Dire occupy bases in opposite corners of the playing field , divided by a <unk> river . Within each base is a critical building called the " Ancient " , along with a fountain that <unk> and <unk> that side 's heroes . A match ends when one side breaches the enemy team 's base and destroys the Ancient within . The two bases are connected by three paths , referred to as " lanes " , which are guarded by defensive towers and computer @-@ controlled creatures called " creeps " . These creatures periodically spawn in groups and travel along the lanes to attack any enemy heroes , creeps , and buildings in sight . <unk> spawn from two buildings , called the " barracks " , that exist in each lane and are located in the base . Destroying all six of the enemy team 's barracks allows for stronger creeps for the attacking side to spawn with significantly enhanced health and damage , known as " mega creeps " . Also present are " neutral creeps " that are hostile to both Radiant and Dire side , and reside in marked locations on the map known as " camps " . Camps are located in the area between the lanes known as the " jungle " , which both sides of the map have . <unk> creeps do not attack unless provoked , and will respawn if killed . The most powerful neutral <unk> is named " <unk> " , who is a unique boss that may be killed by either side to obtain an item that allows a one @-@ time resurrection by the hero that holds it . <unk> will respawn between 8 – 11 minutes after being killed , and becomes progressively harder to kill as the match continues over time . 
- Gold is primarily obtained by killing enemy heroes , destroying enemy structures , and killing creeps , the latter act being called " farming " . Only the hero that lands the killing blow on a <unk> obtains gold from it , an act called " last hitting " , but all nearby allies receive gold when an enemy hero dies . Players are also able to " deny " allied units and structures by destroying them , which then prevents their opponents from getting full experience . Gold is then spent on items that provide unique active and passive abilities , in addition to attribute <unk> to their wearer , depending on the item . The player also receives a small , continuous stream of gold over the course of a match . 
- Dota 2 often features seasonal events that present players with themed game modes , which do not follow the game 's standard rules , including the Halloween @-@ themed <unk> event , the Christmas @-@ themed <unk> event , and the New Bloom Festival , which celebrated the coming of spring . In October 2015 , a Halloween @-@ themed " capture point " game mode was released , titled " Colosseum " . The move to the Source 2 engine in 2015 also saw the addition of community @-@ created custom game modes , with the more popular ones having dedicated server hosting by Valve . In March 2016 , Valve introduced the " Custom Game Pass " option to custom game modes , which allows content creators to add exclusive features , content , and other changes to their game mode , for a fee . 
- 
- = = Development = = 
- 
- The Dota series began in 2003 with Defense of the Ancients ( DotA ) — a mod for Blizzard Entertainment 's Warcraft III : Reign of Chaos — created by the <unk> designer " <unk> " . An expansion pack for Warcraft III , entitled The Frozen Throne , was released later that year ; and a series of Defense of the Ancients clone <unk> for the new game competed for popularity . DotA : Allstars by Steve Feak was the most successful , and Feak , with his friend Steve Mescon , created the official Defense of the Ancients community website and the holding company DotA @-@ Allstars , LLC . When Feak retired from DotA : Allstars in 2005 , a friend , under the pseudonym " IceFrog " , became its lead designer . The popularity of Defense of the Ancients increased significantly : it became one of the most popular <unk> in the world , and , by 2008 , a prominent eSports title . IceFrog and Mescon later had a falling out in May 2009 , which prompted the former to establish a new community website at <unk> 
- Valve 's interest in the Defense of the Ancients property began when several veteran employees , including Team Fortress designer Robin Walker , became fans of the mod and attempted to play it competitively . The company corresponded with IceFrog by email about his long @-@ term plans for the project , which culminated with his being hired to direct a sequel . IceFrog first announced his new position through his blog in October 2009 , and Dota 2 was unveiled by Game Informer on October 13 , 2010 . The resultant surge of traffic crashed Game Informer 's servers . 
- Valve adopted the word " Dota " , derived from the original mod 's acronym , as the name for its newly acquired franchise . Producer Erik Johnson argued that the word referred to a concept , and was not an acronym . Shortly after the announcement of Dota 2 , Valve filed a trademark claim to the Dota name . At Gamescom 2011 , company head <unk> Newell explained that the trademark was needed to develop a sequel with the already @-@ identifiable brand . Holding the Dota name to be a community asset , Feak and Mescon filed an opposing trademark for " <unk> " on behalf of DotA @-@ Allstars , LLC ( then a subsidiary of Riot Games ) in August 2010 . Rob <unk> , the executive vice president of Blizzard Entertainment , similarly stated that the DotA name belonged to the mod 's community . Blizzard acquired DotA @-@ Allstars , LLC from Riot Games and filed an opposition against Valve in November 2011 , citing Blizzard 's ownership of both the Warcraft III World Editor and DotA @-@ Allstars , LLC as proper claims to the franchise . The dispute was settled in May 2012 : Valve retained commercial <unk> rights to the " Dota " brand , but non @-@ commercial use of the name by third @-@ parties was allowed . 
- An early goal of the Dota 2 team was the adaptation of DotA 's aesthetic style for the Source engine . The Radiant and Dire factions replaced the Sentinel and <unk> from the mod , respectively . The appearances of each side 's heroes were adjusted to be more <unk> , with less traits specific to either faction . Character names , abilities , items and map design from Defense of the Ancients were largely retained , with some changes due to copyright issues . In the first Q & A session regarding Dota 2 , IceFrog explained that the game would build upon the mod without making significant changes to its core . Valve contracted major contributors from the Defense of the Ancients community , including <unk> and artist Kendrick Lim , to assist with the sequel . Additional contributions from sources outside of Valve were also sought regularly for Dota 2 , as to continue Defense of the Ancients 's tradition of community @-@ sourced development . One of the composers of Warcraft III : Reign of Chaos , Jason Hayes , was hired to collaborate with Tim <unk> in the creation of the soundtrack . Valve had Half @-@ Life series writer Marc <unk> , science fiction author Ted <unk> , and Steam support employee Kris Katz write new dialog and background lore for the heroes . In addition to that , Valve also had all of the heroes ' voice acting completely redone . Notable voice actors for the English version include Nolan North , Dave <unk> , Jon St. John , Ellen <unk> , Fred Tatasciore , <unk> <unk> , <unk> Taylor , and John Patrick <unk> , among various others . 
- The Source engine itself was updated with new features to accommodate Dota 2 , such as high @-@ end cloth modeling and improved global lighting . The game features Steam integration , which provides its social component and Cloud storage for personal settings . In November 2013 , Valve introduced a coaching system , which allows experienced players to tutor newer players with special in @-@ game tools . As with previous Valve multiplayer titles , players are able to <unk> live matches of Dota 2 played by others , and local area network ( <unk> ) multiplayer support allows for local competitions . Some of these events may be <unk> via the purchase of tickets from the " Dota Store " , which give players in @-@ game access to both live and completed matches . Ticket fees are <unk> in part to tournament organizers . In an upcoming update , Dota 2 will support <unk> a game in virtual reality ( <unk> ) , powered by <unk> . 
- Dota 2 includes a <unk> system , which is measured by a numerical value known as " <unk> rating " ( MMR ) . MMR is updated based on if a player 's team won or lost , which will then increase or decrease , respectively . The game 's servers , known as the " Game <unk> " , attempts to balance both teams based on each player 's MMR , with each team having roughly a 50 % chance to win in any given game . Ranked game modes with a separately tracked MMR also exist , which differ from unranked games by making MMR publicly visible , encouraging players who want to play in a more competitive environment , among other changes . The game also includes a report system , which allows for players to discourage and punish player behavior that intentionally provides a negative experience . Other features include an improved replay system from Defense of the Ancients , in which a completed game can be downloaded in @-@ client and viewed at a later time , and the " hero builds " feature , which provide integrated guides created by the community that highlight to the player on what items should be bought on their hero , and which abilities to level up . 
- As part of a plan to develop Dota 2 into a social network , Newell announced in April 2012 that the game would be free @-@ to @-@ play , and that community contributions would be a cornerstone feature . That June , the Dota 2 team confirmed that the full roster of heroes and items would remain available without charge . Instead , revenue is generated through the Dota Store , which offers for @-@ purchase exclusively cosmetic virtual goods , including custom clothing and weapons for their heroes , along with music packs by notable artists such as electronic music artist <unk> , Taiwanese songwriter <unk> Lin , and video game composers Chance Thomas , Jeremy <unk> , and Lennie Moore . Until the game 's official release in 2013 , players were able to purchase an early access bundle , which included a digital copy of Dota 2 and several cosmetic items . Included as optional downloadable content ( DLC ) , the Dota 2 Workshop Tools are a set of Source 2 software development kit ( <unk> ) tools that allow content creators to create new cosmetics for the heroes themselves , as well as custom game modes and maps . <unk> rated cosmetics , through the Steam Workshop , are available in the in @-@ game store if they are accepted by Valve . This model was fashioned after that of Valve 's Team Fortress 2 , which had earned Workshop designers of cosmetic items of that game over $ 3 @.@ 5 million by June 2011 . In January 2014 , Newell revealed that the average Steam Workshop contributor for Dota 2 and Team <unk> 2 made approximately $ 15 @,@ 000 from their creations the previous year . In 2015 , sales of Dota 2 cosmetics had earned Valve over $ 238 million in revenue , according to the digital game market research group <unk> . 
- Valve documented the lives and stories of three professional players at the first International , Singaporean Benedict " <unk> " Lim , Ukrainian <unk> " <unk> " <unk> , and American Clinton " Fear " Loomis . In August 2012 , GameTrailers announced that Valve was developing this material into a documentary film . Throughout June 2013 , Valve conducted private screenings of the film with small groups outside the company . When an invitation was leaked by Kotaku , Valve 's vice president of marketing , Doug Lombardi , confirmed that the documentary was in development and revealed its name as Free to Play . The documentary was released on March 19 , 2014 , and was distributed for free through outlets including Steam , iTunes , and YouTube . 
- 
- = = Release = = 
- 
- After being tested extensively by Valve , Dota 2 was first unveiled to the public at the inaugural International event , the game 's premier eSport tournament , at Gamescom in 2011 . To coincide with the event , Valve began sending out closed beta invitations ; the first few invites were sent out shortly after Gamescom . During the event , Newell speculated that Dota 2 would likely ship in 2012 , despite original plans for a full release in late 2011 . In September 2011 , Valve scrapped its previous development and release plans , which would have kept the game in its closed beta phase for over a year . The new plans , which IceFrog revealed via an online announcement , were to begin beta testing as soon as possible and to implement the remaining heroes afterward . Simultaneously , Valve announced that the non @-@ <unk> agreement for the beta was being lifted , allowing <unk> to discuss the game and their experiences publicly . After nearly two years of beta testing , Dota 2 was officially released on Steam for Microsoft Windows on July 9 , 2013 , and later for OS X and Linux on July 18 , 2013 . Two months following the game 's release , Newell claimed that updates to Dota 2 generated up to three percent of global internet traffic . In December 2013 , the final restrictions against unlimited global access to Dota 2 were lifted after the game 's infrastructure and servers were substantially bolstered . In March 2016 , a large update fixed many long @-@ standing bugs and issues with the game , while also adding many community requested features . 
- In order to abide by the standards set by the economic legislation of specific countries , Valve opted to contract with nationally @-@ based developers for publishing . In October 2012 , the leading Beijing @-@ based video game publisher , Perfect World , announced the acquisition of the exclusive rights of Dota 2 in China . The Chinese version also has a region @-@ specific " Low Violence " mode , which censors and changes most depictions of blood , <unk> , and skulls in order for the game to follow censorship policies of the country . In November 2012 , a similar publishing deal was made with the Tokyo @-@ based company Nexon to distribute and market the game in South Korea and Japan . In November 2015 , Nexon announced they would no longer be operating servers for Dota 2 , allowing Valve to take over direct distribution and marketing of the game those regions . 
- <unk> @-@ ins to other video games and media have also been added to Dota 2 since release , including custom Half @-@ Life 2 , <unk> , <unk> , <unk> , The Stanley Parable , Rick and <unk> , and <unk> 4 announcer packs , which replace the game 's default announcer with themed ones based on those series . To coincide with the Windows release of Final Fantasy Type @-@ 0 HD in August 2015 , a bundle containing a custom loading screen , a <unk> ward , and a <unk> <unk> was added the same month . In April 2016 , Valve announced a cross @-@ promotional workshop contest for Sega 's Total War : <unk> . 
- 
- = = = Transition to Source 2 = = = 
- 
- In June 2015 , Valve announced that the entirety of Dota 2 would be ported over to the Source 2 game engine in an update called Dota 2 Reborn . The beta was released to the public in June 2015 . On September 9 , 2015 , Reborn was officially released out of beta , which included a new user interface framework design , ability for custom game modes created by the community , and the full replacement of the original Source engine with Source 2 , making Dota 2 the first game to use it . Largely attributed to technical difficulties players experienced with the update , the global player base experienced a sharp drop of approximately sixteen percent the month following the release of Reborn . However , after various updates and patches , over a million concurrent players were playing again in January 2016 , with that being the largest amount of users since March 2015 . The move to Source 2 also allowed the use of the <unk> graphics <unk> , which was released as an opt @-@ in feature in May 2016 , becoming one of the first games to offer it . 
- 
- = = Professional competition = = 
- 
- To ensure that enough Defense of the Ancients players would take up Dota 2 and to showcase the game 's capabilities , Valve sponsored sixteen accomplished Defense of the Ancients teams to compete at The International , a Dota 2 specific eSports tournament , for a $ 1 million prize in 2011 . The International became an annual championship tournament in 2012 , with the venue changing to Seattle , Washington . In its third year , The International allowed <unk> to add to its prize pool through an interactive , in @-@ game item called a " compendium " . <unk> , which are optional and must be purchased separately , allow players who buy them to directly raise prize money for The International by spending money on unique compendium cosmetics and other in @-@ game items , with 25 % of all the revenue made going directly to the prize pool . Sales from the 2013 compendium helped raise over $ 2 @.@ 8 million , making The International 2013 reclaim its previous title as having the largest prize pool in eSports history from the League of Legends Season 2 World Championship . Since then , each annual tournament of The International has broken the previous one 's prize pool record , with the fourth iteration of the tournament raising over $ 10 @.@ 9 million , exceeding the prizes pools of the Super Bowl , Masters Tournament , and Tour de France . At The International 2015 , the prize pool exceeded $ 18 @.@ 4 million , earning the champion team , Evil <unk> , over $ 6 million . 
- Following the inaugural event of The International , several other eSport events began to transition from Defense of the Ancients to Dota 2 , including the Electronic Sports World Cup . <unk> would also support Dota 2 in 2011 , following a year without support for the original , on account of the other multiplayer online battle arena titles , Heroes of <unk> and League of Legends . By the end of its first year in its beta phase , Dota 2 was one of the highest @-@ paying eSport titles of 2011 , second only to StarCraft II . In 2012 , Dota 2 began as an official title for the World <unk> Games annual event at World <unk> Games 2012 . The Electronic Sports League ( <unk> ) began a seasonal tournament for Dota 2 called the <unk> <unk> One in 2013 , which was the largest independent tournament for Dota 2 by the beginning of 2013 . Beginning in September 2013 , the Association for Chinese eSports began a league , called the <unk> ACE Dota 2 League , which had the largest third @-@ party prize pool in Dota 2 eSports history at the time . At Electronic Entertainment Expo 2013 , Nexon announced the investment of two billion South Korean won , ( approximately $ 1 @.@ 7 million ) , into amateur and professional leagues in South Korea for 2013 , to coincide with the launch of their distribution agreement in the fall of that year . <unk> after the interactive compendium for The International , Valve introduced a compendium third @-@ party tournament organizers could sell , beginning with the <unk> <unk> <unk> in February 2014 . In February 2015 , the Valve @-@ sponsored Dota 2 Asia Championships was held in Shanghai with a prize pool of over $ 3 million , raised through compendium sales . 
- In total , professional Dota 2 tournaments had earned teams and players nearly $ 65 million dollars in prize money by June 2016 , which was more than twice the amount of League of Legends tournaments , making it the highest earning eSport game at the time . 
- 
- = = = Majors = = = 
- 
- Starting in 2015 , Valve began sponsoring smaller , but seasonally held tournaments with a fixed $ 3 million prize pool , known as the Dota Major Championships . The format for the tournaments are based on the series of the same name that Valve also sponsors for their first @-@ person shooter game , Counter @-@ Strike : Global Offensive . The first of which , hosted and produced by <unk> , was the Frankfurt Major held from November 13 – 21 , 2015 , at the <unk> Frankfurt in Frankfurt , Germany , and was won by OG . The next Major was hosted and produced by Perfect World , and was held at the Mercedes @-@ Benz Arena in Shanghai from March 2 – 6 , 2016 , and was won by Team Secret . The third and final Major of the 2015 – 2016 season was hosted and produced by <unk> , and was held at the Mall of Asia Arena in Manila from June 7 – 12 , 2016 . The tournament was won by OG , becoming the first team to repeat as champions of a Dota 2 Major . 
- After the introduction of the Majors , The International championship was then considered to be the cumulative " Summer Major " , with the 2016 iteration being the first one under the new format . The International 2016 also broke the record for the highest prize pool in eSports history , surpassing the record that The International 2015 had set the previous year . 
- 
- = = = Media coverage = = = 
- 
- The primary medium for professional Dota 2 coverage is through the video game live streaming platform , <unk> For most major events , tournament coverage is done by a selection of eSports organizations and personnel who provide on @-@ site commentary , analysis , and player interviews surrounding the event in progress , similar to traditional sports . Live Dota 2 games and coverage have also been broadcast on television networks around the world , such as ESPN in the United States , <unk> in Germany , TV 2 <unk> in Denmark , <unk> <unk> in China , Astro in Malaysia , and <unk> in the Philippines . 
- 
- = = Reception and legacy = = 
- 
- Dota 2 received universal acclaim , according to video game review aggregator Metacritic . PC Gamer <unk> the game in September 2012 , stating the game was " an <unk> deep and complex game that offers the <unk> sequel to the original Defense of the <unk> . <unk> like few others , but tough " , giving it a rating of 85 / 100 . Adam <unk> , the editor who authored the announcement article for Dota 2 for Game Informer in 2010 , praised Valve for maintaining the same mechanics and game balance that made Defense of the Ancients successful nearly a decade prior and <unk> Smith of Eurogamer described Dota 2 as the " supreme form of the MOBA which everyone else working in the genre is trying to capture like lightning in a bottle " . The most frequently praised aspects of the game were its depth , delivery , and overall balance . Chris <unk> of PC Gamer described the gameplay as being " deep and rewarding " . Martin <unk> of GameSpot complimented Valve for the delivery and artistic design of Dota 2 , citing the execution of the user interface design , voice acting and characterization as exceeding those of the game 's competitors . <unk> Cameron of IGN praised Dota 2 for its freely @-@ available game balance that was not affected by cosmetic items . 
- While the majority of reviewers gave Dota 2 highly positive reviews , a common criticism was that the game maintains a steep learning curve that requires exceptional commitment to overcome . While providing a moderately positive review that praised Valve 's product stability , Fredrik <unk> from the Swedish division of <unk> described his first match of Dota 2 as one of the most humiliating and inhospitable experiences of his gaming career , citing the learning curve and players ' attitudes as <unk> . Benjamin <unk> of <unk> alluded to the learning curve as a " learning cliff " , calling the newcomer 's experience to be painful , with the tutorial feature new to the Dota franchise only being partially successful . In a review for the Metro newspaper , Dota 2 was criticized for not <unk> for the flaws with the learning curve from Defense of the Ancients , as well as the sometimes hostile community , as is often the case for multiplayer online battle arena games . 
- Peter Bright of Ars Technica directed criticism at the ability for third @-@ party websites to allow gambling and betting on match results and in @-@ game items , similar to controversies that also exist with Valve 's Counter @-@ Strike : Global Offensive . Using Dota 2 as an example , Bright also stated that he thought Valve built gambling elements directly into their games , and had issues with the unregulated practice , which was often used by <unk> players and regions where gambling is illegal . In response to the controversy , Valve and Dota 2 producer , Erik Johnson , stated that they would be taking action against the third @-@ party sites , saying the practice was " not allowed by our <unk> nor our user agreements " . 
- In May 2013 , Dota 2 reached almost 330 @,@ 000 concurrent players and held the record for the game with the most concurrent users in Steam history , breaking its own record set in March the same year . <unk> with this <unk> , it was determined that the concurrent number of Dota 2 players in May 2013 outweighed the number of players for the rest of Steam 's top ten most @-@ played games combined . In February 2015 , Dota 2 became the first game in Steam 's history to have over one million concurrent players . Also in 2015 , Dota 2 was the third most watched game on <unk> , after League of Legends and Counter @-@ Strike : Global Offensive . <unk> viewership numbers of professional Dota 2 matches have reached upwards of two million . 
- 
- = = = Awards and accolades = = = 
- 
- Following its first public showing in 2011 , Dota 2 won IGN 's People 's Choice Award . In December 2012 , PC Gamer listed Dota 2 as a nominee for the 2012 Game of the Year award , as well as the best electronic sports title of the year . The game won 2013 eSport of the year awards from PC Gamer and <unk> . GameTrailers awarded the game the award for Best PC Game of 2013 . For IGN 's Best of 2013 award series , Dota 2 won the awards for Best PC Strategy & Tactics Game , as well as Best PC Multiplayer Game . The game 's awards for IGN 's Best of 2013 won their People 's Choice Award counterparts , as well . Similarly , Game Informer recognized Dota 2 for the categories of Best PC <unk> , Best Competitive Multiplayer and Best Strategy of 2013 . In the 2013 edition of Game Revolution 's countdown of the top twenty @-@ five PC video games of all time , Dota 2 was listed in the number four position . Dota 2 was nominated for a number of Game of the Year awards by <unk> , including the award for the best competitive game . While the staff selected StarCraft II : Heart of the <unk> , Dota 2 received the majority of the votes distributed between the nine nominees . In 2014 , Dota 2 was nominated for best multiplayer game at the 10th British Academy Games Awards , but lost to Grand Theft Auto V. In 2015 , Dota 2 was nominated for eSports Game of the Year at The Game Awards 2015 , and won the award for best MOBA at the 2015 Global Game Awards . 
- 
- = = = Merchandise = = = 
- 
- The popularity of Dota 2 led Valve to produce apparel , accessories , posters , and a number of other products featuring the heroes and other elements from the game . In addition , Valve secured licensing contracts with third @-@ party producers ; the first of these deals concerned a Dota 2 <unk> <unk> + <unk> , which was announced at Gamescom 2011 . In September 2012 , <unk> Workshop , the prop studio that creates the " Aegis of Champions " trophy for winners of The International , announced a product line that would include statues , weapons , and armor based on Dota 2 characters and items . In February 2013 , the National Entertainment <unk> Association announced a new toy line featuring hero @-@ themed action figures at the American International Toy Fair . After the conclusion of The International 2015 finals , Valve awarded the Collector 's Aegis of Champions , which was a 1 / 5th scale brass replica of the Aegis of Champions trophy , to those with <unk> of 1 @,@ 000 levels or more . For The International 2016 , Valve began selling a limited edition Dota 2 themed <unk> <unk> virtual reality <unk> . Valve have also created <unk> related to the game , featuring some of the heroes and detailing their background lore . 
- 
- 
- = Don 't Take It <unk> , Babe , It Just Ain 't Your Story = 
- 
- don 't take it personally , <unk> , it just ain 't your story is a 2011 indie video game by Christine Love . <unk> as a spiritual sequel to Love 's Digital : A Love Story , the game was developed over the course of a month and was released as a free download on April 4 , 2011 . Don 't take it personally is a visual novel , with the majority of the plot taking place outside of the player 's control except for key decisions . It follows a new high school literature teacher in <unk> over the course of a semester , with the ability to see private messages between students at any time without their knowledge . It deals with themes of privacy and relationships in the future . The game was received positively , with critics praising the interplay between the <unk> elements of the story and those of the game itself , with special acknowledgment reserved for the writing . 
- 
- = = Gameplay = = 
- 
- Don 't take it personally is a visual novel , or interactive fiction game where the majority of the story is told through still images of the speaking characters in front of anime @-@ style backgrounds with text overlaid . The player 's viewpoint follows one character , the teacher in a school , with the player seeing his thoughts as well as his and the other characters ' statements . The player advances the conversations the teacher is in or witnesses at will , but cannot go backwards and has little control over what any of the characters , including the teacher , are saying except at a few key moments . At these points , the player is presented with two or three choices for what the teacher says ; which option is chosen can change what path the plot takes , in either a minor or major way . 
- In a departure from most visual novels the player can also see , at any time , the equivalent of texts and Facebook <unk> between the students on a school social network called <unk> ( often shortened to " Amie " ) , as well as conversation threads on a 4chan @-@ like <unk> that serve as foreshadowing for future plot points . These extra conversations take place in parallel to in @-@ person conversations between characters , occasionally at the same time ; the player is notified <unk> a new text message or post is made . A single playthrough of the game takes " over an hour " . 
- 
- = = <unk> = = 
- 
- 
- = = = Setting = = = 
- 
- The game is set in a private high school in Ontario , in <unk> . The player follows a new 11th @-@ grade literature teacher at the school , John Rook , who has recently had his second divorce and is undergoing , in his words , a " bizarre <unk> crisis " that has led him to quit his career in computers and become a teacher . The students in his class are Arianna Belle @-@ <unk> , Kendall Flowers , Taylor Gibson , Charlotte <unk> , Isabella Hart , Nolan <unk> , and Akira Yamazaki . Rook and all of the students have school @-@ issued computers , which the students use to communicate between each other in private and public messages . Rook , and therefore the player , can see all of the messages that the students send , even the private ones ; the school administration has told Rook that it is for monitoring online bullying , and that he is not to let anyone know that he can see students ' private messages . 
- 
- = = = Plot = = = 
- 
- Soon after the game opens , the player learns that Kendall and Charlotte have just broken up out of a lesbian relationship , while a few weeks prior Taylor and Nolan had broken up out of a heterosexual relationship . Arianna develops a crush on the teacher , which in the player 's first decision point Rook can <unk> or not . Akira soon comes out as gay , finding to his dismay that everyone else already knew . He then attempts to enter into a relationship with Nolan , who is unsure how to respond ; Rook can encourage him to try it out or let him decide on his own , but he enters a relationship with Akira regardless . 
- The next day , Isabella misses a meeting she set up with Rook , and does not return to the class . Through Amie texts and wall posts , the other students <unk> that she committed suicide , while the school is unable to provide Rook with any contact information for her . Class continues without her , and a little while later Arianna , if the player rejected her earlier in the game , makes another attempt to have a relationship with Rook . The player may choose to accept or reject her again . Through Amie , Rook learns that both Charlotte and Kendall wish to resume their relationship , and can influence Charlotte to try again or not , which changes whether or not they get back together . Taylor <unk> and unsuccessfully tries to drive a wedge between Nolan and Akira . 
- As the end of term approaches , Rook begins getting strange <unk> about Isabella 's death , and begins seeing <unk> figures . Upset by these occurrences , when Akira 's mother asks to speak to him concerning <unk> of Amie , he assumes that she has found out that he is <unk> on the students ' conversations . When he meets her , he finds that Isabella is alive and had simply moved away ; the students made it look like she was dead via Amie as a prank that got out of control . Furthermore , they already knew that he could see their messages ; Akira 's mother explains that the students have no idea of online privacy , having always had technology like Amie , and assumed that anything they put online might be read by anyone . The game ends with Rook having a casual lunch with the students , a date with Arianna , or neither , depending on the choices made during the game by the player . 
- 
- = = Development = = 
- 
- Don 't take it personally was developed over the course of a month , and was released as a free download on April 4 , 2011 . It was written and developed by Christine Love , with artwork made for the game by <unk> @-@ Cyanide , artwork licensed from <unk> and <unk> After , and with music licensed from <unk> <unk> and <unk> . It was created using the <unk> <unk> engine . It was Love 's longest game to date , and her first attempt at a game with a branching storyline . It was intended as " a spiritual sequel of sorts " to Digital : A Love Story , a 2010 game by Love . The game was made for <unk> ( National <unk> <unk> Game Writing Month ) , a month @-@ long contest in the vein of National Novel Writing Month ( <unk> ) where developers attempt to create a visual novel in one month . Love 's favorite character to write was Kendall . 
- 
- = = Reception = = 
- 
- Don 't take it personally was chosen as a " <unk> game pick " by <unk> 's Michael Rose , who said that it was worth playing through multiple times in order to see the different paths the story takes depending on the player 's choices . Pete Davidson of GamePro noted the game as an example of the " creativity " missing in many large @-@ budget titles , and praised the story , calling Love " a writer first and a game developer second . " Alec <unk> of Rock , Paper , <unk> described the game as " a game about love , sex and the internet " that was " capable of being profoundly moving " and was about " what it is to feel like someone ’ s kicked me straight in the heart . " He praised Love 's ability to use dialogue to effect an emotional response , though he noted that the game seemed to lose some of its impact in the final chapters and felt that the use of the " <unk> " threads as a Greek chorus was " perhaps a meta @-@ layer too far " . 
- Aaron <unk> of PopMatters called the game " a meditation on privacy in the modern age " and " one of the more thoughtful games to come out in a long time . " He criticized the art direction of the game , saying that the limitation of creating the game in a month led to poor artwork and an <unk> presentation that lets down the possibilities of the story . He did note that the story and writing of the game made the <unk> of the game " almost entirely a moot point " , and that it is a very strong game . Pete Davidson of PC World , in an article about the treatment of sexuality in video games , called out the game as an example of a game that used sexual themes <unk> to explore love and relationships . Emily Short of <unk> , in a discussion about the game , said that it was definitely worth playing and full of " charming characters , colorful dialogue , and important questions " , but criticized the uneven exploration of issues regarding privacy versus personal boundaries . She said that the game 's lack of focus on Rook 's issues with personal and professional boundaries was a weakness in the story , given that the distinction between boundaries and privacy was crucial to the plot . Love <unk> common criticisms and reviews of the game on her blog , <unk> that while it was less praised than Digital : A Love Story , most players seemed to like the way she wrote the relationships and overall story , though her portrayal of Rook was weak and that many players seemed to miss that he was intended to be " an absolutely awful teacher " and that the potential relationship with Arianna was intended to be creepy and make the player feel bad . She also noted that her use of licensed artwork was <unk> from the game 's potential in comparison to the reaction to the custom works . 
- The Daily Telegraph gave the visual novel the award for " Best <unk> " in its video game awards of 2011 , stating that " Love 's layered narrative of a high school teacher embroiled in his student ’ s worries goes places most mainstream video games wouldn 't <unk> . " 
- 
- 
- = The Dreamscape = 
- 
- " The Dreamscape " is the ninth episode of the first season of the American science fiction drama television series Fringe . It centers on a Fringe investigation of a Massive Dynamic employee who , after believing himself to be under attack by a swarm of sharp @-@ winged butterflies , jumps out of a window . Meanwhile , Olivia ( Anna Torv ) continues her visions of the deceased John Scott ( Mark Valley ) , and discovers how he related to a deadly psychoactive drug synthesized by Massive Dynamic . 
- " The Dreamscape " was written by series staff writers Julia Cho and Zack Whedon , and was directed by Fred Toye . To create the opening scene , they " relied entirely on actor performance , " with guest actor Ptolemy Slocum <unk> being cut , freezing for the crew to apply prosthetic make @-@ up , and then <unk> to continue shooting the scene . VFX coordinator Christopher Stollard wished the computer generated butterflies to look as realistic as possible , and modeled them after mounted butterfly specimens his team studied . 
- The episode first aired in the United States on November 25 , 2008 on the Fox network . It was watched by an estimated 8 @.@ 73 million viewers and earned a 3 @.@ 9 / 10 ratings share among adults aged 18 to 49 . Reviews of the episode were mixed , with one reviewer believing it " certainly moves [ the series ] in the right direction " . 
- 
- = = Plot = = 
- 
- Massive Dynamic executive Mark Young ( Ptolemy Slocum ) delivers a presentation at the company ’ s Manhattan office . When he is done and the other attendees have left , he sees an unusual butterfly . Upon picking it up , he experiences a cut on his hand and is then attacked by a swarm . Mark jumps out of a window , to his death . Olivia Dunham ( Anna Torv ) , despite preparing to go out to dinner with her sister , instead agrees to Phillip <unk> ' ( Lance <unk> ) demand that she join the Fringe team 's investigation at the scene . While examining the body , Dr. Walter Bishop ( John Noble ) sees <unk> on Young ’ s skin and notes a lack of corresponding tears on his shirt . At the scene , Olivia has a brief vision of her deceased lover John Scott ( Mark Valley ) watching them , which troubles her . 
- Later , in the lab at Harvard Walter 's autopsy reveals a synthetic compound in Mark 's blood , though any link to the cuts is not yet known to them . Olivia receives an email from someone who claims to be John Scott , listing an address for her to visit . Upon arriving she finds boxes , one of which contains a group of toads . At the lab , Walter finds that the toads contain a " psychoactive compound " , a <unk> that affects the fear center of the brain . They conclude that Mark ’ s brain was so convinced of something happening to his body that actual physical marks appeared . Mark was infected with a large <unk> , leading them to attribute his death to murder . 
- Olivia admits to Walter how she found the address and learns that her brain still contains some of John 's memories . <unk> to discover what else John knew and prevent any further visions from occurring , Olivia insists on returning to Walter ’ s sensory <unk> tank . Inside the tank , Olivia sees a memory of John in a restaurant . Despite Walter 's <unk> protest that it is impossible , Olivia is convinced that John Scott saw her . Afterwards , she sees John meeting with Mark and two other unidentified men . After Mark and another man leave , John kills the other man . Olivia believes the group were looking to sell the compound as a street drug , and is able to track down the other man , George <unk> ( <unk> <unk> ) . Once <unk> , he denies killing Mark and demands immunity and protection from Massive Dynamic , who he believes was responsible for Mark 's death as well as other recent fringe events . Olivia confronts Nina Sharp ( Blair Brown ) about her suspicions , but George is murdered before he can be of further help to them . Later that night , Olivia gets another email from John Scott simply saying , " I <unk> <unk> . IN THE <unk> . " 
- Meanwhile , Peter ( Joshua Jackson ) is contacted by <unk> ( Susan <unk> ) , a woman from his past who warns him to leave Boston . When he meets her , Peter <unk> that she is being abused by her boyfriend Michael ( David <unk> ) . He ambushes Michael and warns him not to touch <unk> again . Michael then informs local crime boss Worth ( Tom <unk> Farrell ) that Peter is back in town . 
- 
- = = Production = = 
- 
- " The Dreamscape " , the season 's ninth episode , was co @-@ written by staff writer Zack Whedon and playwright Julia Cho . It was Whedon 's first contribution to the series , and Cho 's second . In an interview fifteen days before the broadcast of " The Dreamscape " , showrunner and executive producer Jeff Pinkner stated that it and several surrounding episodes would be " stand @-@ alone in quality , but at the same time we [ will ] start to <unk> back another layer of the onion " to transition the second half of the season into a " new chapter . " Anna Torv later commented that she wished for Olivia to " lighten up " during the first season , " but every time she did , something would happen . " The writers <unk> small scenes to shine more light on her character , which included Olivia dressing up for a night out but then " the phone rings . It ’ s <unk> , and she <unk> the lipstick off , puts on a coat , and goes out . That ’ s it . You ’ re on call . She breaks my heart . " 
- Fred Toye served as the episode director , his second after directing " The Ghost Network " six installments earlier . To create the opening scene of character Mark Young being " attacked " by butterflies , Toye shot it in multiple phases . Series co @-@ creator Roberto Orci preferred the opening scene to involve a " more subtle " death , so Young was made to die from falling out a window , which was " a real thing , something that 's not imagined or whimsical " . VFX coordinator Christopher Stollard stated that his crew " relied entirely on actor performance , " with guest actor Ptolemy Slocum communicating often with the crew during filming . He shot his scenes in short bursts . Slocum summarized that he " would experience the [ butterfly ] cut and then we would freeze , apply make @-@ up [ to create the cut on his skin ] , and then <unk> and move on " filming . On his first day , Slocum stated that he wore more than 75 individual <unk> over his entire chest and arms . 
- Stollard wished the butterflies to be as realistic as possible , and modeled them after mounted butterfly specimens his team studied . The butterflies were computer generated animations and added later in post @-@ production . To create the illusion of Young falling through the window , the visual effects crew placed small explosive charges on the tempered glass window ; Slocum 's <unk> <unk> Burke then jumped through while they triggered the glass to explode . Two cameras , one high and one profile , were positioned to shoot the fall , and were <unk> together later into a matte painting of the World Trade Building . The fall was filmed multiple times with a green screen , with the <unk> falling on a cushion . While on @-@ set , Toye explained of the shoot , " with all the pieces put together with the visual effects and with the performance we got with our actor Ptolemy , it 's going to be fabulous . " A crewman believed that the addition of music to Young 's fall made the scene " far more elegant and more of a statement . " In a December 2012 interview , actress <unk> Nicole stated that she developed a fear of butterflies as a result of this episode . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings and marketing = = = 
- 
- " The Dreamscape " first aired on November 25 , 2008 in the United States on the Fox network . With its timeslot dominated by the Dancing With the Stars season finale , Fringe attracted an estimated 8 @.@ 73 million viewers , down from the previous week 's audience of 9 @.@ 36 million . This placed it in third place in its timeslot , behind The <unk> . The episode also came in third place among viewers aged 18 – 49 , as it earned a 3 @.@ 9 / 10 ratings share ; this means that it was seen by 3 @.@ 9 percent of all 18- to 49 @-@ year @-@ olds , and 10 percent of all 18- to 49 @-@ year @-@ olds watching television at the time of broadcast . 
- Fringe 's marketing team designed and launched <unk> near the beginning of the first season . Parts of the website can be seen in " The Dreamscape " , including on Olivia 's computer as she <unk> the company . 
- 
- = = = Reviews = = = 
- 
- Noel Murray of The A. V. Club was generally favorable to the episode , giving it a B + . He wrote , " Much like Fringe fan favorite ' The <unk> ' ( not a favorite of mine , sorry to say ) , ' The Dreamscape ' is an episode more involved with <unk> and mythology @-@ building than with telling a complete @-@ in @-@ one story . But perhaps because I 've come to trust Fringe more over its recent run of entertaining episodes , I enjoyed it fairly well , and found myself trying to figure out what kind of thematic connections I could make using the notion of the body reacting to mere thoughts . " Travis Fickett of IGN rated the episode 7 @.@ 8 / 10 , explaining that it " certainly moves in the right direction " . Fickett found the " <unk> moment " to be when Olivia witnesses John Scott 's memory , and he also appreciated Peter 's backstory ( " It 's more than Peter usually gets , but what we know isn 't much and isn 't very exciting . " ) . Fickett criticized Olivia however for leaving the man at the hospital <unk> protected . 
- <unk> reviewer Sarah <unk> enjoyed Peter 's <unk> and hoped to learn more of his past , but thought the " cute in @-@ jokes " were meant to distract the audience from a " thin " plot " full of holes " . She added that " the shallow , <unk> @-@ cutter plotting " was starting to annoy her , and noted that while the series had the potential to become " a breakout show like The X @-@ Files or Lost ... its major weakness is the continual reliance on the worn @-@ out cliché of the Big Bad Corporation , " which had become " stale and dry . " 
- Jane <unk> of AOL 's TV Squad wrote , " Another great episode tonight , and some of the little symbols we see leading into commercials were actually in tonight 's episode , " referring specifically to the <unk> butterflies and the frogs / toads found in the basement . Andrew Hanson of the Los Angeles Times took notice here of the pattern of Easter eggs <unk> the next episode of the series . This feature of the show had earlier been touched on in an interview with showrunner Jeff Pinkner . As an example , the previous episode " The <unk> " had contained a shot of a butterfly on a <unk> , foreshadowing the <unk> butterfly attack in this episode . 
- In a 2016 retrospective of the series , A.V. Club writer Joshua <unk> considered " The Dreamscape " as a strong example of Fringe 's " monster of the week " episodes that predominated the first season , showing " how much effort and imagination went into the self @-@ contained stories " . 
- 
- 
- = True Blue ( Madonna song ) = 
- 
- " True Blue " is a song by American singer Madonna . It is the title track from her third studio album True Blue ( 1986 ) , and was released as the album 's third single on September 29 , 1986 by Sire Records . Written and produced by Madonna and Steve Bray , the song deals with the feelings of Madonna for her then @-@ husband Sean Penn . A dance @-@ pop song , it features instrumentation from a rhythm guitar , a synthesizer , keyboards , and drums . The main chorus is backed by an alternate one , incorporating a chord progression generally found in doo @-@ wop music . 
- <unk> by the critics as a light @-@ hearted and cute retro song , " True Blue " topped the charts in UK , Ireland and Canada and became another consecutive top ten song in U.S. for Madonna by reaching number three on the Billboard Hot 100 . The original music video portrayed her again with a new look , <unk> and sporting platinum blond bushy hair . An alternate video was made through the " Make My Video " contest on MTV . The final selected videos had a similar theme of a 1950s @-@ inspired setting and the storyline following the lyrics of the song . " True Blue " has been performed on the Who 's That Girl World Tour ( 1987 ) and the Rebel Heart Tour ( 2015 – 16 ) . 
- 
- = = Writing and composition = = 
- 
- Madonna wrote and produced the song with Steve Bray . According to her , " True Blue " takes its title from a favourite expression of her then husband Sean Penn and to his very pure vision of love and was a direct tribute to him as well as the album , which was as a whole inspired by her " <unk> <unk> " for Penn . In an interview , Bray said , " She [ Madonna ] was very much in love . It was obvious if she 's in love she 'll write love songs . If she 's not in love she definitely won 't be writing love songs . " In 2015 , Madonna said that " True Blue " is " a song about true love . I didn 't know what I was talking about when I wrote it . " 
- " True Blue " is a dance @-@ pop song inspired by the Motown 's girl groups from the 1960s which are considered the direct <unk> of Madonna 's musical sound . The song is composed in the key of B major . It is set in compound quadruple meter , commonly used in doo @-@ wop , and has a moderate tempo of 118 beats per minute . " True Blue " features instrumentation from a rhythm guitar , a synthesizer , keyboards , and drums for the bassline , with a basic sequence of I – vi – IV – V ( B – G ♯ m – E – F ♯ ) as its main chord progression . 
- Madonna 's vocal range spans a bit less than one and a half octaves , from F ♯ 3 to <unk> . The chorus is backed by sounds of bells ringing , an alternate verse — " This time I know it 's true " — which is sung by three back @-@ up singers during the interlude , and a bass counter melody which introduces her vocals during the second chorus . The lyrics are constructed in a verse @-@ chorus form , with the theme being Madonna 's feelings for Sean Penn ; it even uses the archaic love word " dear " in the line " Just think back and remember , dear " . 
- 
- = = Reception = = 
- 
- 
- = = = Critical response = = = 
- 
- <unk> <unk> from Rolling Stone magazine in a review of the album True Blue said that the song " <unk> a classic beat and an immensely promising title " , <unk> 's Bill <unk> said that " True Blue " is " Madonna 's wonderful tribute to the late ' 50s / early ' 60s " girl groups " . In his book Madonna : An Intimate Biography , journalist J. Randy <unk> described the song as " the light @-@ hearted , fun track of the whole True Blue album project having a retro 1950 's feel to it " . In the book Rock ' n ' Roll Gold Rush which contains information about various artists and their singles , author <unk> Dean said that the song as a " <unk> of simplicity interwoven with secret complexity " adding that " on one hand , it 's just a basic <unk> ditty , with four basic chords . In another context , it 's a counterpoint harmonic blanket , <unk> with star @-@ spangled <unk> and dynamic drive . " <unk> <unk> , in his book The Complete Guide to the Music of Madonna , said that " True Blue " is " a song that is merely cute and not really up to being the title track of an album " . 
- The Wichita Eagle did not like the song , believing that it was " <unk> and <unk> " as compared to the other songs on the record . However , Daniel Brogan of The Chicago Tribune believed the song was good , calling it " impressive " like the rest of the album , and Jan <unk> of the same paper believed it was " charming " . Steve <unk> of The Boston Globe , when describing the song , said that it was a " bid to be an ' 80s Helen of Troy " . 
- 
- = = = Chart performance = = = 
- 
- " True Blue " was released in the United States in October 1986 . It debuted on the Billboard Hot 100 at number 40 , six weeks later it reached its peak of number three , remaining at the position for three consecutive weeks , and spent a total of 16 weeks on the chart . The song performed equally well on the other Billboard charts , peaking at number five on Adult Contemporary , and number six on the Hot Dance Club Songs chart . In October 1998 , the single was certified gold by the Recording Industry Association of America ( RIAA ) for shipment of 500 @,@ 000 copies . In Canada , the song debuted at number 84 the RPM singles chart on September 27 , 1986 , reached the top for one week in November 1986 , and stayed on the chart for 23 weeks . It ended at the 37th position of the year @-@ end chart . 
- In the United Kingdom , " True Blue " was released on September 29 , 1986 . It debuted at number three on the UK Singles Chart , before climbing to number one the next week , becoming Madonna 's third number @-@ one single there . It was certified gold by the British Phonographic Industry ( BPI ) in October 1986 . According to the Official Charts Company , the song has sold 545 @,@ 000 copies there . The song peaked at number one for two weeks in October 1986 in Ireland , making it her fourth number @-@ one single on the Irish Singles Chart . Both in Australia and New Zealand , the song reached the top five , and was certified platinum in the former region by the Australian Recording Industry Association ( ARIA ) for sales of 70 @,@ 000 copies of the single . In Europe " True Blue " also topped the <unk> Hot 100 for one week in October 1986 . It peaked in the top five in Belgium , Italy , and the Netherlands , and in the top ten in Austria , France , Germany , and Switzerland . 
- 
- = = Music videos = = 
- 
- 
- = = = Official version = = = 
- 
- " True Blue " had two music videos to accompany it . Shot in early September 1986 in New York , Madonna 's own video for the song was directed by James <unk> , who worked with Madonna in her videos for " Live to Tell " and " Papa Don 't <unk> " , produced by Robert <unk> and David <unk> with photography by Michael <unk> . The <unk> version features Madonna with three dancers and a 1950s car in an all @-@ blue <unk> . Madonna changes her hairstyle from short @-@ cropped in " Papa Don 't <unk> " to a bushy platinum blonde <unk> and sings the song in choreographed moves backed by her dancers . It displays a flashing back to fifties rock 'n'roll youth culture . 
- The blue background changes to a sunny one as she sings " The sun is <unk> right out of the sky " to go @-@ along with the lyrical meaning of the song . Two of Madonna 's close friends , <unk> Belle and <unk> Mazar appear in the video . The video was released at a time when she was going through a failed marriage with then husband actor Sean Penn . During this period , Madonna focused on more traditional fashion and attitudes and tried to appear more respectful of traditional gender roles . After shedding her <unk> sex @-@ <unk> and boy @-@ toy image with the " Live to Tell " music video , Madonna again adopted a new look for this video . Madonna attended aerobics classes at Hollywood health centre The Sports Connection , which was responsible for her toned down look in the video . 
- 
- = = = " Make My Video " contest = = = 
- 
- Sire Records decided to opt for a promotional device in the United States that would involve MTV viewers to make their own videos for " True Blue " . In the fall of 1986 , MTV asked its viewers to submit their own videos . The contest was known as " Madonna 's ' Make My Video ' Contest " . The winner was awarded a trip to MTV 's New York studio where Madonna presented a $ 25 @,@ 000 check live on MTV . Thousands of viewers submitted their recorded tapes which were mainly made using home @-@ made video equipment and featured themselves or relatives as the actors . MTV publicist Peter <unk> said that many of the submissions featured teenagers imitating Madonna . All the entries were shown in a continuous run on MTV as promised . The same song was played over and over for the whole day , but each time with a different video made by the finalists . Author Lisa A. Lewis said that this event emphasized the effect Madonna had on different kind of audiences due to the popularity and response to the contest . MTV selected ten finalists based mainly on a standard of popularity rather than <unk> of production or concept creativity . 
- The concepts used in the videos were wide ranging and included a number of different ideas to interpret the lyrical meaning of the song . The final three entries selected , portrayed a fifties @-@ style production referring to the thematic content of the song . The song 's narration about " True Love " formed the basis of the rest of the semi @-@ <unk> videos but was used in very different ways . The videos were choreographed featuring heterosexual romance , though no particular male or female protagonist was singled out . Some even adopted a kind of literal montage technique rather than <unk> the video around a narrative line . 
- The winning entry was by Angel <unk> and Cliff Guest and it showed the female protagonist ( played by the director 's sister <unk> Garcia ) being supported and guided by her girlfriends who introduce her with the male protagonist . The girl even goes to the boy 's door to gift him flowers , thereby reversing the usual gender @-@ directed pattern of gift @-@ giving . The male protagonist is portrayed as a " perfect boy " ( played by William <unk> ) having the sensibilities like <unk> , <unk> , <unk> like a friend ( after the lyrics " You 're my best friend " ) and not sexual overtones . The video in @-@ turn contrasts him with a self @-@ centered boy who puts on <unk> , throws his leather jacket over his shoulder and walks away from the girl . Other videos portrayed a girl pining for her sailor , <unk> relations and an arguing couple with the girl in a scene inspired by the music video of Tina Turner 's 1984 single " What 's Love Got to Do with It " . 
- 
- = = Live performances = = 
- 
- Madonna first performed the song on her 1987 " Who 's That Girl World Tour " . She came up on the stage wearing a blue dress to sing the song after finishing a performance of " Lucky Star " . In a similar setting to the original music video of the song , Madonna is backed up by her singers who play her girlfriends . At the end of the song Madonna is asked to dance again by the dancer playing her man in the performance . Her dance in the performance ( and also some other performances in the tour ) was choreographed by Jeffrey <unk> from <unk> . Two different performances of the song on the tour were included on two live video releases : Who 's That Girl : Live in Japan , filmed in Tokyo , Japan , on June 22 , 1987 , and <unk> <unk> : Live from Italy , filmed in Turin , Italy , on September 4 , 1987 . 
- Madonna did not perform the song again until 2015 , as part of her Rebel Heart Tour . She performed an acoustic , <unk> @-@ driven version of the song sitting on top of a tire stack while asking the crowd to sing along with her . Writing for the Daily News , Jim Farber felt that during the performance " [ Madonna ] emphasized a rare <unk> " while Jordan <unk> from the Montreal Gazette opined that " it was both endearingly quaint and , <unk> by an <unk> <unk> from more than 16 @,@ 000 voices , a <unk> moment that felt more grandiose in its way than the <unk> surrounding it " . Newsday 's Glenn Gamboa gave a similar feedback , saying that the " lovely acoustic version of " True Blue " was a rare bow to romance , the sweetest of Madonna sentiments " . 
- 
- = = Formats and track listing = = 
- 
- 
- = = Credits and personnel = = 
- 
- Madonna – lyrics , producer , vocals 
- Steve Bray – drums , keyboards , lyrics , producer 
- Bruce <unk> – rhythm guitar 
- Fred <unk> – additional keyboards 
- Steve Peck – engineer 
- <unk> <unk> – remixing 
- Herb <unk> – photography 
- <unk> <unk> – design / artwork 
- Credits adapted from the album 's liner notes . 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- 
- = Invisible rail = 
- 
- The invisible rail , Wallace 's rail , or drummer rail ( Habroptila <unk> ) is a large flightless rail that is endemic to the island of Halmahera in North <unk> , Indonesia , where it inhabits impenetrable sago swamps adjacent to forests . Its plumage is predominantly dark slate @-@ grey , and the bare skin around its eyes , the long , thick bill , and the legs are all bright red . Its call is a low drumming sound which is accompanied by wing @-@ beating . The difficulty of seeing this shy bird in its dense habitat means that information on its behaviour is limited . 
- Recorded dietary items include sago shoots and insects , and it also swallows small stones to help break up its food . It is apparently monogamous , but little else is known of its courtship behaviour . The only known nest was a shallow bowl in the top of a rotting tree stump that was lined with wood chips and dry leaves . The two young chicks were entirely covered in black down typical of precocial newly hatched rails . The estimated population of 3 @,@ 500 – 15 @,@ 000 birds and the restricted range mean that the invisible rail is classified as vulnerable by the International Union for Conservation of Nature ( IUCN ) . Habitat loss has occurred through the harvesting of sago and conversion of the wetlands to rice cultivation , and the rail is eaten by local people . The described nest was in an area frequented by local villagers , so the rail may be more adaptable to habitat changes than had been thought . 
- 
- = = Taxonomy = = 
- 
- The rails are a large and very widespread family , with nearly 150 species . They are small to medium @-@ sized , terrestrial or wetland birds , and their short bodies are often flattened laterally to help them move through dense vegetation . Island species readily become flightless ; of 53 extant or recently extinct taxa restricted to islands , 32 have lost the ability to fly . 
- The invisible rail , first classified by English zoologist George Robert Gray in 1860 , is the only member of the monotypic genus Habroptila . The genus name Habroptila derives from the Greek <unk> , " delicate , pretty , splendid " and <unk> , " feather , wing " ; <unk> commemorates British zoologist Alfred <unk> Wallace . Local names include " <unk> " , " <unk> " and " <unk> " . 
- This rail is related to the New Guinea flightless rail , Megacrex <unk> , and the chestnut rail , <unk> <unk> , all three <unk> genera probably being derived from <unk> ancestors . <unk> <unk> argued that the genus Megacrex was so similar to Habroptila that Megacrex should be considered a junior synonym of Habroptila , resulting in two species in the genus . This was further <unk> in Sidney Dillon Ripley 's 1977 <unk> of the <unk> ; he included Habroptila within the large genus <unk> . This suggestion was , however , not accepted by <unk> <unk> <unk> , who pointed out distinct differences in the shape and structure of the bill . A molecular phylogenetic analysis based on mitochondrial DNA sequence similarity found that Habroptila is part of evolutionary radiation within the broad genus <unk> that took place around 400 @,@ 000 years ago in the region . 
- 
- = = Description = = 
- 
- The invisible rail is a large , 33 to 40 cm ( 13 – 16 in ) long , flightless bird . The adult has a mainly dark slate @-@ grey body , dark brown plumage on the lower back , rump and wings , and a black <unk> . Its underparts are slightly paler slate @-@ grey than the back , and the bare skin around the eye , the long , thick bill and the strong legs are bright red . It has a small spine at the bend of the wings . The sexes are identical in appearance ; the plumage of fledged immature birds has not been described . 
- The invisible rail is superficially similar to the purple swamphen , <unk> <unk> , which has recently been found in Halmahera , but that species is larger , with a short , thick red bill and a red forehead shield ; it also has purple underparts and a white undertail . The invisible rail is different from the <unk> rail , <unk> <unk> , in that it is larger and lacks the barred plumage of that species ; there is no overlap between the ranges of the two species . 
- The call is a low drumming , accompanied by a tuk , tuk , tuk made with the wings . The nature of the <unk> led to a local legend that the sound is made by the bird beating on a hollow tree or branch with its feet . Gerd Heinrich noted the local name " <unk> " , meaning drum , and described the call as being a subdued drumming purre – purre – purre – purre – purre which sometimes ends in a loud <unk> scream . The bird also produced a dull hum similar to the voice of the banded pig ( <unk> <unk> <unk> ) and reminiscent of the call of the <unk> rail ( <unk> <unk> ) . Calling is most frequent in the early morning or late evening , and a human tapping a sago stem with a machete may elicit a response from the bird . A quieter version of the call is given at the nest . Other sounds attributed to this rail , such as loud screams , may be incorrect , since they are like those produced by the pale @-@ vented bush @-@ hen ( <unk> <unk> ) . 
- 
- = = Distribution and habitat = = 
- 
- The invisible rail inhabits the dense , spiky sago swamps of Halmahera , particularly where forest <unk> the <unk> areas . Claims that the rail occurs in <unk> @-@ <unk> grass are thought to have arisen from confusion with the pale @-@ vented bush @-@ hen . German ornithologist Gerd Heinrich , who prepared for his Halmahera trip by rolling in <unk> <unk> , wrote of the sago swamp habitat in the 1930s : 
- I am <unk> confident no European has ever seen this rail alive , for that requires such a degree of <unk> and such demands on oneself as I cannot so easily attribute to others . Habroptila is shielded by the awful thorns of the sago swamps ... In this <unk> wilderness , I walked barefoot and half @-@ naked for weeks . 
- <unk> of the rail from 1950 to 2003 were from a restricted area of West Halmahera Regency , at the base of the western peninsula of the island , but it was recorded prior to 1950 as far as the southern point of Halmahera . More recent records showed that it is still present in a significantly larger area , including the northeast of the island , and locals claim that it also occurs in the swamps near <unk> , in the northwest . 
- 
- = = Behaviour = = 
- 
- The difficult habitat and retiring nature of the invisible rail mean that information on its lifestyle is sparse , and there are few confirmed sightings . Recorded food items include sago shoots and insects . It also feeds at cut sago plants , although it is unclear whether it is eating the decaying plant or searching for other edible items . It swallows small stones , as do all rails , to help break up its food in the <unk> . 
- The invisible rail is thought to be monogamous , but little else is known of its courtship behaviour prior to nesting . A report of 4 – 5 striped chicks was long thought to be incorrect , since such a plumage is not normal for rails . In this family , chicks are typically precocial , <unk> and black , with any ornamentation confined to the head , bare flesh , or specially modified plume feathers . 
- The issue was resolved in November 2010 when a nest was found in the top of a rotting tree stump , 1 m ( 39 in ) above ground level and 46 m ( 151 ft ) in from the edge of a dry swamp forest in <unk> @-@ <unk> National Park . The nest depression was 15 cm ( 5 @.@ 9 in ) in depth , with a lower layer of small wood chips at its base and a lining of dead leaves . The egg shells were brownish @-@ white with dark brown and black markings of different sizes . The two very young chicks were entirely covered in black down , contrasting with a white <unk> ( the equivalent of the <unk> on a human hand ) and pink index nail . The bill was black with a white tip , and the legs were black @-@ streaked brown . The eyes had grey irises and blue pupils . Rail chicks leave the nest soon after hatching , so the chicks were assumed to be only a day or two old . 
- 
- = = Status = = 
- 
- Bird species with a restricted range are especially vulnerable to human activities , and eight of the 26 bird species occurring only in the Northern <unk> <unk> Bird Area are threatened , including the invisible rail . Almost a quarter of all rail species have conservation concerns , and flightless island species are particularly at risk , at least 15 species having become extinct since 1600 . The estimated population of the invisible rail is 3 @,@ 500 – 15 @,@ 000 birds , and its restricted range and small population mean that the species is classified as vulnerable by the International Union for Conservation of Nature ( IUCN ) , although this rail is so poorly known that it may be more common than the estimates suggest . 
- Habitat loss has occurred through commercial harvesting of the sago , or conversion to rice cultivation and <unk> . The rail is a prized food for local people who catch it with traps made from strings of bark and hunt it with dogs . The only described nest was in an area well @-@ used by local villagers , and the rail may be more adaptable to habitat changes than had been thought . There were also several sightings in northeast Halmahera in 2008 and 2011 , extending the area in which this bird has been seen in recent years . 
- 
- 
- = Project Chanology = 
- 
- Project Chanology ( also called Operation Chanology ) was a protest movement against the practices of the Church of Scientology by members of Anonymous , a <unk> Internet @-@ based group that defines itself as ubiquitous . The project was started in response to the Church of Scientology 's attempts to remove material from a highly publicized interview with Scientologist Tom Cruise from the Internet in January 2008 . 
- The project was publicly launched in the form of a video posted to YouTube , " Message to Scientology " , on January 21 , 2008 . The video states that Anonymous views Scientology 's actions as Internet censorship , and asserts the group 's intent to " expel the church from the Internet " . This was followed by distributed denial @-@ of @-@ service attacks ( DDoS ) , and soon after , black <unk> , prank calls , and other measures intended to disrupt the Church of Scientology 's operations . In February 2008 , the focus of the protest shifted to legal methods , including nonviolent protests and an attempt to get the Internal Revenue Service to investigate the Church of Scientology 's tax exempt status in the United States . 
- Reactions from the Church of Scientology regarding the protesters ' actions have varied . Initially , one spokesperson stated that members of the group " have got some wrong information " about Scientology . Another referred to the group as a group of " computer <unk> " . Later , the Church of Scientology started referring to Anonymous as " <unk> " <unk> " religious hate crimes " against the church . 
- <unk> of Scientology have also criticized the actions of Project Chanology , asserting that they merely provide the Church of Scientology with the opportunity to " play the religious persecution card " . Other critics such as Mark Bunker and Tory Christman initially questioned the legality of Project Chanology 's methods , but have since spoken out in support of the project as it shifted towards nonviolent protests and other legal methods . 
- 
- = = Background = = 
- 
- The Church of Scientology has a history of conflict with groups on the Internet . In 1995 , attorneys for the Church of Scientology attempted to get the <unk> <unk> ( <unk> ) removed from <unk> . This attempt <unk> and generated a significant amount of press for <unk> The conflict with <unk> led the hacker group <unk> of the Dead <unk> to declare war on the Church of Scientology . The Church of Scientology mounted a 10 @-@ year legal campaign against Dutch writer Karin <unk> and several Internet service providers after <unk> and others posted documents alleged to be secret teachings of the organization . The Church of Scientology 's efforts ended in a legal defeat in a Dutch court in 2005 . This series of events is often referred to as " Scientology versus the Internet " . 
- 
- = = = Tom Cruise video = = = 
- 
- On January 14 , 2008 , a video produced by the Church of Scientology featuring an interview with Tom Cruise was posted on YouTube . In the video , music from Cruise 's Mission : Impossible films plays in the background , and Cruise makes various statements , including saying that Scientologists are the only people who can help after a car accident , and that Scientologists are the authority on getting <unk> off drugs . According to The Times , Cruise can be seen in the video " <unk> the virtues of Scientology " . The Daily Telegraph ( Australia ) characterized Cruise as " manic @-@ looking " during the interview , " <unk> [ ing ] about his love for Scientology " . 
- The Church of Scientology asserted that the video material that had been leaked to YouTube and other websites was " <unk> and edited " and taken from a three @-@ hour video produced for members of Scientology . YouTube removed the Cruise video from their site under threat of litigation . The web site Gawker.com did not take down their copy of the Tom Cruise video , and other sites have posted the entire video . <unk> for the Church of Scientology sent a letter to Gawker.com requesting the removal of the video , but Nick Denton of Gawker.com stated : " It 's <unk> and we will not be removing it . " 
- 
- = = = Formation = = = 
- 
- Project Chanology was formulated by users of the English @-@ speaking <unk> <unk> and 4chan , the associated <unk> wiki , and several Internet Relay Chat channels , all part of a group collectively known as Anonymous , on January 16 , 2008 after the Church of Scientology issued a copyright violation claim against YouTube for hosting material from the Cruise video . The effort against Scientology has also been referred to by group members as " Operation Chanology " . A <unk> called " Project Chanology " , part of a larger wiki , is maintained by Anonymous and chronicles planned , ongoing and completed actions by project participants . The website includes a list of suggested guerrilla tactics to use against the Church of Scientology . Members use other websites as well to coordinate action , including Encyclopedia <unk> and the social networking site Facebook , where two groups associated with the movement had 3 @,@ 500 members as of February 4 , 2008 . A member of Anonymous told the Los Angeles Times that , as of February 4 , 2008 , the group consisted of " a loose confederation of about 9 @,@ 000 people " who post anonymously on the Internet . A security analyst told The Age that the number of people participating anonymously in Project Chanology could number in the thousands : " You can 't pin it on a person or a group of people . You 've thousands of people engaged to do anything they can against Scientology . " 
- Members of Project Chanology say their main goal is " to <unk> the Church of Scientology ( CoS ) by any means necessary . " Their website states : " This will be a game of mental warfare . It will require our <unk> , not our hackers . It will require our dedicated <unk> across the world to do their part . " Project Chanology 's stated goals include the complete removal of the Church of Scientology 's presence from the Internet and to " save people from Scientology by reversing the brainwashing " . Project Chanology participants plan to join the Church of Scientology posing as interested members in order to infiltrate the organization . 
- Andrea Seabrook of National Public Radio 's All Things Considered reported Anonymous was previously known for " technologically sophisticated <unk> " such as <unk> chat rooms online and " ordering dozens of <unk> for people they don 't like " . Ryan <unk> of Wired appeared on the program on January 27 , 2008 , and told Seabrook that members of Anonymous were motivated by " the tactics the Church of Scientology uses to control information about itself " rather than the " controversial nature of Scientology itself " . 
- 
- = = Activities = = 
- 
- 
- = = = Internet activities = = = 
- 
- Project Chanology began its campaign by organizing and delivering a series of denial @-@ of @-@ service attacks against Scientology websites and flooding Scientology centers with prank calls and black <unk> . The group was successful in taking down local and global Scientology websites intermittently from January 18 , 2008 until at least January 25 , 2008 . Anonymous had early success rendering major Scientology websites inaccessible and leaking documents allegedly stolen from Scientology computers . This resulted in a large amount of coverage on social <unk> websites . 
- The denial @-@ of @-@ service attacks on Scientology.org flooded the site with 220 <unk> of traffic , a mid @-@ range attack . Speaking with <unk> , a security strategist for Top <unk> Networks , Ken <unk> said that he thought that <unk> were involved in the Anonymous operation : " There are circles out there where you could take ownership of the <unk> machines that are already owned and launch a simultaneous attack against [ something ] like the church from 50 @,@ 000 <unk> , all at the same time " . 
- In response to the attacks , on January 21 , 2008 the Scientology.org site was moved to Prolexic Technologies , a company specializing in <unk> web sites from denial @-@ of @-@ service attacks . Attacks against the site increased , and CNET News reported that " a major assault " took place at 6 p.m. EST on January 24 , 2008 . Anonymous escalated the attack on Scientology on January 25 , 2008 and on January 25 , 2008 , the Church of Scientology 's official website remained inaccessible . 
- On January 21 , 2008 , Anonymous announced its goals and intentions via a video posted to YouTube entitled " Message to Scientology " , and a press release declaring " War on Scientology " , against both the Church of Scientology and the Religious Technology Center . In the press release , the group stated that the attacks against the Church of Scientology would continue in order to protect freedom of speech and to end what they characterized as the financial exploitation of church members . 
- The Tom Cruise video is referred to specifically at the start of the Anonymous YouTube video posting , and is characterized as a " propaganda video " . The video utilizes a synthesized voice and shows floating cloud images using a time lapse method as the speaker addresses the leaders of Scientology directly : " We shall proceed to expel you from the Internet and systematically <unk> the Church of Scientology in its present form ... " The video goes on to state : " We recognize you as serious opponents , and do not expect our campaign to be completed in a short time frame . However , you will not prevail forever against the angry masses of the body <unk> . Your choice of methods , your hypocrisy , and the general <unk> of your organization have sounded its death <unk> . You have nowhere to hide because we are everywhere ... We are Anonymous . We are Legion . We do not forgive . We do not forget . <unk> us . " By January 25 , 2008 , only four days after its release , the video had been viewed 800 @,@ 000 times , and by February 8 , 2008 had been viewed over 2 million times . Author Warren Ellis called the video " creepy in and of itself " and a " manifesto , declaration of war , sharp political film " . 
- In a different video posted to YouTube , Anonymous addresses news organizations covering the conflict and criticizes media reporting of the incident . In the video , Anonymous criticizes the media specifically for not mentioning objections by the group to certain controversial aspects of the history of the Church of Scientology , and cited past incidents including the death of Lisa McPherson : " We find it interesting that you did not mention the other objections in your news reporting . The <unk> and punishment of dissent within the totalitarian organization of Scientology . The numerous , alleged human rights violations . Such as the treatment and events that led to the deaths of victims of the cult such as Lisa McPherson . " Lisa McPherson was a Scientologist who died in 1995 under controversial circumstances . Initially , the Church of Scientology was held responsible and faced <unk> charges in her death . The charges were later dropped and a civil suit brought by McPherson 's family was settled in 2004 . This second video was removed on January 25 , 2008 , YouTube citing a " terms of use violation " . <unk> of the February 10 , 2008 , Project Chanology protests against the Church of Scientology told the St. Petersburg Times the event was timed to coincide with the birthday of Lisa McPherson . 
- In addition to DDoS attacks against Church of Scientology websites , Anonymous also organized a campaign on one of their websites to " begin <unk> Digg " , referring to an attempt to drive up Scientology @-@ related links on the website <unk> On January 25 , 2008 , eight of the top ten stories on <unk> were about either Scientology @-@ related controversies or Anonymous and attempts to expose Scientology . Digg CEO Jay Adelson told PC World that Anonymous had not manipulated the site 's algorithm system to prevent artificial poll results , stating : " They must have done a very good job of bringing in a diverse set of interests ... It just happened to hit a <unk> that the Digg community was interested in . " Adelson said two other instances which similarly have dominated the Digg main page in the past were the Virginia Tech Massacre in the aftermath of the incident and the " 7 / 7 " London bombings in 2005 . Adelson commented on the popularity of Scientology theme within the Digg community : " In the history of Digg , there 's no question that the topic of Scientology has been of great interest to the community ... I can 't explain why . " 
- On January 29 , 2008 , Jason Lee Miller of <unk> reported that a Google bomb technique had been used to make the Scientology.org main website the first result in a Google search for " dangerous cult " . Miller wrote that Anonymous was behind the Google bomb , and that they had also tried to bump Scientology up as the first result in Google searches for " brainwashing cult " , and to make the <unk> website first result in searches for " <unk> " . Rob Garner of <unk> Publications wrote : " The Church of Scientology continues to be the target of a group called Anonymous , which is using Google bombs and YouTube as its tools of choice . " 
- In a February 4 , 2008 , article , Scientology spokeswoman Karin Pouw told the Los Angeles Times that Church of Scientology 's websites " have been and are online . " Danny McPherson , chief research officer at Arbor Networks , claimed 500 denial @-@ of @-@ service attacks had been observed on the Scientology site in the week prior to February 4 , some of which were strong enough to bring the website down . Calling Anonymous a " <unk> crew of internet <unk> " , Wired blogger Ryan <unk> said that , while attempting to bypass the Prolexic servers protecting the Church of Scientology website , users of a <unk> DDoS tool inadvertently and briefly had targeted the Etty <unk> Lyceum , a Dutch secondary school in <unk> . Another hacking group associated with the project , calling themselves the " <unk> " , mistakenly targeted a 59 @-@ year @-@ old man from Stockton , California . They posted his home telephone number , address and his wife 's Social Security number online for other people to target . They believed that he was behind counter @-@ attacks against Project Chanology @-@ related websites by the <unk> , a counter @-@ hack group who crashed one of the Project Chanology planning websites . The group allegedly attempted to gain personal information on people involved in Project Chanology to turn that information over to the Church of Scientology . After discovering they had wrongly targeted the couple , one of the members of the <unk> group called and apologized . 
- 
- = = = <unk> planned = = = 
- 
- A new video entitled " Call to Action " appeared on YouTube on January 28 , 2008 , calling for protests outside Church of Scientology centers on February 10 , 2008 . As with the previous videos , the two @-@ minute video used a synthesized computer voice and featured stock footage of clouds and sky . The video was accompanied by a text transcript with British English spelling . The video denied that the group was composed of " super hackers " , stating : " Contrary to the assumptions of the media , Anonymous is not ' a group of super hackers . ' ... Anonymous is everyone and everywhere . We have no leaders , no single entity directing us . " The video said that Project Chanology participants include " individuals from all walks of life ... united by an awareness that someone must do the right thing . " Specific controversies involving the CoS were cited in the video as the explanation for actions by Anonymous . 
- In an email to CNET News , Anonymous stated that coordinated activities were planned for February 10 , 2008 , in many major cities around the world . Anonymous hoped to use " real world " protests to rally public opinion to their cause . According to the Associated Press , the protests were meant to draw attention to what the group refers to as a " vast money @-@ making scheme under the guise of ' religion ' " . By January 30 , 2008 , 170 protests had been planned outside Church of Scientology centers worldwide . A video posted to YouTube called " Code of Conduct " outlined twenty @-@ two rules to follow when protesting , and urged protestors to remain peaceful . 
- 
- = = = February 2008 = = = 
- 
- 
- = = = = February 2 = = = = 
- 
- On February 2 , 2008 , 150 people gathered outside a Church of Scientology center in Orlando , Florida to protest the organization 's practices . Small protests were also held in Santa Barbara , California ( during the Santa Barbara International Film Festival ) , and Manchester , England . Protesters in Orlando carried signs with messages " Knowledge is Free " and " <unk> if you hate Scientology " . According to <unk> @-@ TV , the protesters called the Church of Scientology a " dangerous cult " and said the organization is responsible for crimes and deaths . The Orlando Sentinel reported that the protest was " part of a worldwide campaign by a group that calls itself Anonymous " , and an unnamed organizer who spoke to the paper stated that the group was protesting " a gross violation of the right to see free church material " , referring to the Tom Cruise video that was pulled from YouTube . 
- Protesters at the demonstration wore masks , and said they were attempting to inform the public about what they believed to be " restrictions of free speech and <unk> through pyramid schemes " by the Church of Scientology . They asserted they were not protesting the doctrine of Scientology , but rather alleged actions of individual Scientologists . One protester stated that he had created a Facebook group to organize the protest , explaining " It started online with a group called Anonymous ... They got upset with Scientology because the church hides important documents that are supposed to be released to the public . " 
- 
- = = = = February 10 = = = = 
- 
- On February 10 , 2008 , about 7 @,@ 000 people protested in at least 100 cities worldwide . Within 24 hours of the first protest , a search for " Scientology " and " protest " on Google <unk> Search returned more than 4 @,@ 000 results and more than 2 @,@ 000 pictures on the image @-@ sharing site <unk> . Cities with <unk> of one hundred or more protesters included Adelaide , Melbourne , and Sydney , Australia ; Toronto , Canada ; London , England ; Dublin , Ireland ; and Austin , Dallas , Boston , Clearwater , and New York City , United States . 
- 150 people protested at the Church of Scientology building in Sydney , Australia , carrying signs and wearing costumes . Participants were masked to maintain their <unk> and avoid possible retaliation from the Church of Scientology . Protesters <unk> " Church on the left , cult on the right " ( in reference to the Church that was beside the Church of Scientology building ) , " Religion is free " and " We want <unk> " . Scientology staff locked down the building and set up a camera to record the event . After the protest in Sydney , a surge in online Internet traffic due to individuals attempting to view pictures from the protest crashed hundreds of websites when a server was <unk> . The Sydney protest was one of the first worldwide , and after the first images of the protest went online a surge in traffic drove the hosting company 's <unk> usage up by 900 percent . The hosting company <unk> temporarily prevented access to hundreds of its clients ' sites , and customer support representative Denis <unk> said the surge was unexpected : " We had no advance notice that there was going to be a sudden surge of traffic or that there would be more than 100 times the average traffic that this customer 's website normally consumes . " 
- <unk> protesters in Seattle , Washington , United States congregated in front of the Church of Scientology of Washington . Protesters were quoted as saying , " We believe in total freedom of belief . We have nothing against the people of Scientology , however the Church of Scientology has committed crimes . They 're vehemently anti @-@ opposition . Anyone who opposes them , must go down . " A protester in Santa Barbara emphasized that their opposition was against the organization , not the belief system , and that they supported the Scientology split @-@ off group known as the Free Zone . Protesters turned out in Pittsburgh , Pennsylvania despite unusually cold weather . The masked crowd consisted mainly of college students , including some who had travelled from as far as Penn State University . 
- Protesters in Boston , Los Angeles , Pittsburgh , Toronto , Edinburgh , London , and other cities worldwide , wore Guy Fawkes masks modeled after the 2005 film V for Vendetta . ( Guy Fawkes was an English Catholic executed for a 1605 attempt to destroy the House of Lords . In V for Vendetta , a rebel against a near @-@ future fascist regime uses the mask in his public appearances and distributes many of its copies to the population to enable mass protests . The Boston Globe characterized usage of the Guy Fawkes masks as " an allusion to the British insurgent and a film depicting an <unk> movement " . Aaron <unk> of College Times wrote that the Guy Fawkes masks provided a " dramatic effect " to the protests , and Nick <unk> of The <unk> Weekly wrote : " During the February 10 protests , Anonymous was <unk> , Anonymous was peaceful , and Anonymous was effective . After seeing all of the pictures from the 10th with everyone in disguise , many sporting Guy Fawkes masks , I wanted to be a part of that . " Scott Stewart of University of Nebraska at Omaha 's The Gateway wrote : " Many participants sported Guy Fawkes masks to draw attention both to their identity as Anonymous and the Church of Scientology 's abuse of litigation and <unk> to suppress anti @-@ Scientology viewpoints . " 
- The Internet <unk> <unk> , where a link is given to a seemingly relevant website only to be directed to a music video of singer Rick <unk> 's pop single " Never Gonna Give You Up " , has been used as a theme in the protests against Scientology . At February 10 protests in New York , Washington , D.C. , London and Seattle , protesters played the song through <unk> and shouted the phrase " Never gonna let you down ! " , in what The Guardian called " a live <unk> @-@ rolling of the Church of Scientology " . In response to a website created by Scientologists showing an anti @-@ Anonymous video , Project Chanology participants created a website with a similar domain name with a video displaying the music video to " Never Gonna Give You Up " . In a March 2008 interview , <unk> said that he found the <unk> of Scientology to be " hilarious " ; he also said that he will not try to capitalize on the <unk> phenomenon with a new recording or remix of his own , but that he 'd be happy to have other artists remix it . 
- Following the protests , there were reports that YouTube was freezing the view counts on videos criticizing Scientology , including clips from the protests themselves , potentially preventing them from being displayed on YouTube 's front page . Similarly , the original " Message to Scientology " video had received nearly 2 @.@ 5 million views and yet failed to be featured as a " most @-@ watched " . The net neutrality activist group <unk> strongly criticized YouTube for a " display of the decay of internet freedom " . YouTube 's official response stated : " There was an issue with video view counts not increasing that has now been resolved . The correct number of views should be displayed in the next 24 hours . <unk> for your patience . " 
- Jonathan Holmes , the presenter of the Australian <unk> program Media Watch , reported on two cases of media censorship of the protests . News.com.au <unk> a poster carried by a protester which was revealed , through a Today Tonight segment , to have displayed the word " <unk> " . The Advertiser erased Tom Cruise 's name from a protest placard , rendering the placard 's message meaningless , without informing its readers . The Advertiser 's editor , Melvin <unk> , stated that the alteration had " slipped by " and that he was opposed to the publication of <unk> photographs . 
- 
- = = = March – December 2008 = = = 
- 
- 
- = = = = March 2008 = = = = 
- 
- According to <unk> , a woman from Anonymous contacted them and stated that protests were planned against Scientology each month through May 2008 ; and that a large protest was planned for two days after Scientology founder L. Ron Hubbard 's birthday , March 15 . Carlos <unk> of The Tampa Tribune reported that an " open letter to the press from Anonymous " was sent out via e @-@ mail , and states that a protest is planned for March 15 , 2008 . The e @-@ mail refers to the Ides of March : " We , too , wish to celebrate this event , albeit in our own special way ... <unk> the Ides of March , Church of Scientology ! " The March protests were titled " Operation Party Hard " . 
- <unk> began in Australia on March 15 , 2008 , and were followed by protests in major cities worldwide including Brussels , London , Seattle , Phoenix , Manchester , and Los Angeles . Approximately 7 @,@ 000 to 8 @,@ 000 people protested in about 100 cities worldwide . The protests took place in locations in Australia , Europe , Canada , and the United States . 
- Approximately 200 masked protesters gathered outside the Church of Scientology 's headquarters in Adelaide , Australia . An anonymous spokesman told News.com.au that Scientology should lose its tax @-@ exempt status . About 150 protesters came to the <unk> Street headquarters of Scientology in Toronto , Canada ; sang " Happy <unk> " and <unk> " we want cake " . During the Los Angeles protests , a plane flew overhead trailing a large sign that read " <unk> if you think Scientology is a cult . " 150 protesters demonstrated in Clearwater , Florida , and a local organizer for Anonymous told The Tampa Tribune , " We feel that we have an obligation to educate the public about the things that have gone on and hopefully make the Church of Scientology understand that they have to change . " 
- Two people were arrested by <unk> County , Georgia police for using <unk> while stepping onto the surrounding street opposite of the church during a protest . The Atlanta Journal @-@ Constitution reported that five protesters were cited for " causing ' hazardous ' or ' offensive ' conditions " , and that eight motorists were pulled over by police and <unk> for excessive use of horns , after they <unk> while driving past the protest . The American Civil Liberties Union and Amnesty International are investigating the reaction of the police at the Atlanta protest . In contrast , a Los Angeles Police Department officer at the Los Angeles protest was widely praised after a video was uploaded to YouTube showing him acknowledging the demonstrators ' right to protest and encouraging them to stay on the <unk> for their own safety . 
- 
- = = = = April 2008 = = = = 
- 
- Anonymous held its third international protest against Scientology on April 12 , 2008 . Named " Operation <unk> " , the protest focused on increasing awareness of the Church of Scientology 's <unk> policy . Protesters around the world gathered in over 50 cities , including Toronto , London , Sydney , and Berlin . A subsequent international protest was planned for May 10 , 2008 , according to The University Register it was titled " Operation <unk> Earth " , and an additional protest was planned for June 2008 . According to John <unk> of The Village Voice , the May 10 , 2008 protests were referred to as " Operation : Fair Game : Stop " , and National Nine News has reported that the full title of the May 10 protests is " <unk> Earth : Operation <unk> Stop " . The May 10 date was chosen as May 9 is the anniversary of Scientology founder L. Ron Hubbard 's book <unk> : The Modern Science of Mental Health . Over 400 people were present at the May 10 , 2008 protests in cities in Australia . <unk> <unk> , a member of Anonymous , commented to <unk> about the Church of Scientology 's denial of its " Fair Game " policy : " Even if the name ' fair game ' is not in use , the Church of Scientology is an organisation that continues to practice a vicious policy of <unk> against perceived enemies , and it teaches its members that extreme measures are morally justified if they aid the Church . " 
- 
- = = = = May 2008 = = = = 
- 
- On May 10 , a teenager who went to the protests in front of the Queen Victoria Street Church of Scientology in London was issued a court summons after being asked to take down a sign that read " Scientology is not a religion , it is a dangerous cult " . <unk> anonymously on a forum , the teenager stated " Within five minutes of arriving ... I was told by a member of the police that I was not allowed to use ' that word ' " . He said that the police told him he had 15 minutes to take down the sign . The teenager did not , citing a 1984 high court ruling by Justice <unk> which he described the Church of Scientology as a " cult " that was " corrupt , sinister and dangerous " . The sign was then confiscated . <unk> Chakrabarti , the director of Liberty , a human rights group , said that , " They will be banning words like ' war ' and ' tax ' from placards and demonstrations next . This is just <unk> " . On May 23 , 2008 , the legal action against the boy was dropped . A Crown Prosecution Service ( CPS ) spokesman said : " In consultation with the City of London Police , we were asked whether the sign was abusive or insulting . Our advice is that it is not abusive or insulting and there is no <unk> ( as opposed to criticism ) , neither in the idea expressed nor in the mode of expression . " Anonymous also held a protest in Budapest , Hungary , in the same time and location as a program of the local Scientology church . 
- 
- = = = = June – October 2008 = = = = 
- 
- A protest was held June 14 , 2008 titled " Sea <unk> " ( a satirical reference to the Church of Scientology 's Sea Org ) . Protesters dressed up as pirates . According to Macquarie National News , members of Anonymous highlighted the controversial practices of the Sea Org , including what the protesters believe to be forced contracts where Scientologists work below a <unk> wage , that female Sea Org members who become pregnant are pressured to have <unk> , and that children of families in the organization are made to perform difficult physical labor . An international protest held on July 12 , 2008 titled : " Spy vs. Sci " highlighted the Church of Scientology 's Office of Special Affairs . A press release by the group posed the question : " Why does something that describes itself as a religion need an intelligence agency that aggressively <unk> critics ? " The group posted a video in early August 2008 calling for renewed activity in their protest efforts , and planned a subsequent international protest for August 16 , 2008 . About 35 protesters gathered twice in September 2008 during the first preview and premiere of Arthur Miller 's play All My Sons . They encouraged Scientologist Katie Holmes , wife of Tom Cruise , to leave the Church . The most recent international organized protest was held October 18 , 2008 . Members of Anonymous dressed as zombies , and highlighted what they described as questionable deaths and suicides of Scientologists . 
- 
- = = = = December 2008 = = = = 
- 
- The film <unk> , starring and produced by Tom Cruise , premiered in New York City on December 17 . Entertainment reporter Roger Friedman noted that it was held " in the private screening room at the Time Warner Center . Not the <unk> [ sic ] or <unk> Lincoln Square , where most premieres are held in public . " The venue was chosen in part to minimize the exposure to Scientology protestors gathered at the Time Warner Center . For the same reason , Cruise arrived at the December 18 Los Angeles screening through an underground tunnel . There were also Scientology protests at the European premiere in Berlin , where one protester got his V for Vendetta mask autographed by Tom Cruise . Chanology participants shared the <unk> with a person in a <unk> suit protesting against the hero worship of Claus von <unk> . 
- 
- = = = 2009 = = = 
- 
- 
- = = = = January – February 2009 = = = = 
- 
- On January 8 , 2009 , an 18 @-@ year @-@ old male member of Anonymous ran into the New York Scientology building shirtless and covered with <unk> , <unk> hair , and <unk> <unk> . He then proceeded to toss books around and smear the mixture on objects in the building . The man , identified by police as <unk> <unk> <unk> , was charged with <unk> , criminal mischief , and aggravated harassment as hate crimes . Two weeks later , 21 @-@ year @-@ old film student and Anonymous member Jacob <unk> was charged with aggravated harassment and criminal mischief as hate crimes after he filmed <unk> carrying out his prank . According to his mother and the video , <unk> was filming the event from behind the <unk> . Scientology critics Mark Bunker and Jason <unk> disagreed with the individual 's actions . 
- Anonymous organized a 12th global protest against Scientology for January 10 , 2009 , to coincide with the Chanology movement 's first anniversary . On February 10 , 2009 , Anonymous released a statement : " Scientology operatives still continue to paint Anonymous in a negative light as a means of distracting attention from Scientology operations and attempting to discredit those who bring truth to the issues at hand . It just isn ’ t working . " The group claimed credit for leaks of internal Scientology documents that appeared on the website <unk> , and announced further global protests for subsequent weekends in February 2009 . Members of Anonymous continued to celebrate the one @-@ year anniversary of the Chanology movement during February 2009 , with protests held in locations including <unk> , California . 
- 
- = = = = March – May 2009 = = = = 
- 
- On March 3 , 2009 , the Board of Supervisors in Riverside County , California , voted to approve an ordinance which <unk> residential <unk> there to 30 ft ( 9 @.@ 1 m ) or further from an individual 's residence . The ordinance was originally introduced by Supervisor Jeff Stone , board chairman , in November 2008 , and went through multiple changes . Critics of the ordinance stated that Stone proposed the measure due to favor for Scientology , which has its <unk> compound located in Riverside County . " The whole ordinance is <unk> . The reasons behind it are <unk> , " said county resident <unk> Bishop . Stone stated the measure was intended for all residents of the county , though he cited protests at Scientology 's Gold Base facility which houses residences and Scientology 's Golden Era Productions as an example of why the ordinance is needed . Protesters at Gold Base have included members of Anonymous , and Scientology officials claimed they were " threatened with violence " . Protesters told the Board of Supervisors that due to the lack of <unk> near Gold Base , the anti @-@ <unk> ordinance would severely hamper the ability to protest outside the Scientology compound . 
- After stating on October 17 , 2008 , that he would plead guilty to involvement in the January 2008 DDoS attacks against Church of Scientology websites , an 18 @-@ year @-@ old self @-@ described member of Anonymous entered a guilty plea related to hacking charges in May 2009 . A release from the US Justice Department said that the individual , a resident of New Jersey , " participated in the attack because he considered himself a member of an underground group called ' Anonymous ' " . Thom <unk> , a spokesman for the Justice Department , said that the Church of Scientology had cooperated in the investigation . The individual faces a sentencing scheduled for August 2009 . 
- In May 2009 , members of Anonymous told WSMV @-@ TV that they were bullied by off @-@ duty security guards while protesting at a Scientology event in April in Nashville , Tennessee . According to WSMV @-@ TV , a protester stated he was assaulted by three Scientology security guards while on public property , 400 yd ( 370 m ) away from the Scientology building . The Church of Scientology had previously informed the security guards that the protesters were " dangerous people " . A protester was issued three citations by the Scientology security guards , but these were all dismissed by the district attorney . On May 8 , 2009 , WSMV @-@ TV reported that " laws appear to have been broken " in the manner in which the Scientology security guards handled the protesters . The Scientology security guards were not clearly identified as off @-@ duty police officers , and permits for the Scientology event attended by the Anonymous protesters were for the wrong day . " The armed people from the other county are not identified police officers . You 're looking for a problem " , said John M. L. Brown , a Fraternal Order of Police attorney . 
- 
- = = = = November 2009 = = = = 
- 
- In November 2009 , the New York Post interviewed former Scientologist Mandy Mullen , who had joined the Manhattan organization in 2008 after seeing information on 4chan about Chanology protests . Over the next year , she recalled cases of members being coerced into <unk> more and more money . When she told her " leader " that she had looked at websites run by anti @-@ Scientology protesters ( and disagreed with them ) , he reportedly replied " That 's like saying I don 't burn <unk> , I just like to party with the <unk> ! " In Fall 2008 Mullen left the church , joining a group of Chanology protesters across the street . Church of Scientology Manhattan President John Carmichael issued a response the following week , stating " Mandy Mullen 's story about Scientology ... is all lies in the service of Anonymous , a hate group that targets our Church , or even anyone who just falls onto their radar . " On November 13 , 2009 , Independent Australian Senator Nick <unk> used parliamentary privilege to accuse the Church of Scientology of being a criminal organisation . 
- 
- = = = Campaign against Scientology 's tax @-@ exempt status = = = 
- 
- A woman who stated she was a member of Anonymous told <unk> that the group has shifted strategy to activities which fight Scientology but are not deemed illegal by the United States government , including an attempt to get the Internal Revenue Service to investigate the Church of Scientology 's <unk> ( c ) ( 3 ) tax @-@ exempt status . Another woman from Anonymous told Newsweek that the group plans to accomplish this through a lobbying campaign . United States tax authorities removed the Church of Scientology 's tax @-@ exemption status in 1967 , stating that the organization 's <unk> techniques served as a for @-@ profit operation for L. Ron Hubbard . In 1984 , the United States Tax Court ruled that the Church of Scientology was guilty of " manufacturing and <unk> records to present to the <unk> , <unk> <unk> offices and stealing government documents , and <unk> government processes for unlawful purposes . " The Church of Scientology 's tax @-@ exempt status in the United States was reinstated in 1993 . 
- A member of Anonymous calling herself " <unk> " told Today Tonight that the group has longer term plans against the Church of Scientology : " We are incredibly determined ... There are those of us who have been talking about plans for the next 12 to 18 months . " A member of Anonymous calling herself " Sarah " spoke with Radar magazine about a letter @-@ writing campaign : " We 're sending letters to <unk> and <unk> requesting that their tax @-@ exempt status be looked at . " 
- 
- = = Church of Scientology response = = 
- 
- In a January 25 , 2008 statement made to News.com.au , a spokesman for the Church of Scientology said , " These types of people have got some wrong information about us . " A Toronto , Canada spokesperson for the Church of Scientology said she didn 't " give a damn " if the group Anonymous was responsible for disrupting access to the Scientology site . Church spokeswoman <unk> Shank told Sun Media that she thought the Anonymous members were a " pathetic " group of " computer <unk> " . On January 26 , 2008 , CNET News reported that Karin Pouw , public affairs director for the Church of Scientology , did not address their specific request for a comment about the denial @-@ of @-@ service attacks but instead only responded to the appearance of the Tom Cruise video on YouTube . Pouw stated that the video consisted of " <unk> and edited " <unk> of Cruise from a 2004 Scientology event , and that after the video appeared , there was increased traffic to Scientology sites as shown by top lists compiled by search engines . Pouw went on to state " Those wishing to find out the Church of Scientology 's views and to gain context of the video have the right to search official Church Web sites if they so desire . " 
- On January 28 , 2008 , Radar Online reported that the Church of Scientology asked the U.S. Attorney General 's office in Los Angeles , the Federal Bureau of Investigation , and the Los Angeles Police Department to start a criminal investigation of possible criminal activity related to the DDoS attacks . An unnamed source told Radar that the Church of Scientology argued to law enforcement that the Internet attacks are a form of " illegal interference with business . " Radar also reported that in statements to law enforcement the Church of Scientology emphasized its status as a religious organization in the United States in order to assert that the DDoS attacks can be classed as hate crimes . The day after the Church of Scientology complained to law enforcement about the DDoS attacks , one of the main Project Chanology sites was down , and a message on the site said that their site crashed due to attacks from Scientologists . In a statement issued to <unk> , a Church of Scientology employee confirmed that actions of Anonymous had been reported to law enforcement : " Activities of Anonymous have been reported to the Authorities and actions are being taken . Their activities are illegal and we do not approve of them . At the same time , our main work is to improve the environment , make people more able and spiritually aware . ... yes , we are taking action . " 
- The Church of Scientology issued a statement explaining the website move to Prolexic Technologies : " The attacks have defaced and rendered inoperable a number of CoS web sites . But as a very wealthy institution , the Church has fought back with technological answers . On January 21 , the Church of Scientology moved its domain to Prolexic Technologies , a group that specializes in protecting Web sites from [ denial of service ] attacks by creating a safe tunnel by filtering all incoming mail and then allowing only clean messages through . " 
- Lee Sheldon of the Church of Scientology of Orlando and Lee <unk> of the Church of Scientology of Santa Barbara issued similar statements regarding the February 2 , 2008 protests in Florida and California , respectively . Sheldon stated " we recognize the right to legal protest " , and <unk> said " People have the right to express themselves ... The Church of Scientology has always defended the right of freedom of expression . " Both representatives also expressed concerns regarding the spread of " hate speech " . 
- The Church of Scientology released a statement regarding the February 10 , 2008 worldwide protests , which was published February 7 , 2008 in the St. Petersburg Times . In the statement , the Church of Scientology called the organizers of the protests " <unk> " , and stated : " We take this seriously because of the nature of the threats this group has made publicly . We will take every step necessary to protect our parishioners and staff as well as members of the community , in coordination with the local authorities . " The statement also referred to the actions of members of Project Chanology as " hate crimes " and " religious <unk> " , and in a media release said that the group is guided by Communist <unk> and Mein <unk> ; one of the organizers of the protest responded to the latter allegation by stating : " I don 't know where they got that from , but I don 't think that 's true considering that I am a capitalist and a Jew " . Pat Harney , spokeswoman for the Church of Scientology in Clearwater , Florida told the St. Petersburg Times : " We are dealing with a worldwide threat ... This is not a light matter . " In preparation for the February 10 , 2008 protests outside Scientology 's spiritual headquarters in Clearwater , the Church of Scientology spent $ 4 @,@ 500 to hire ten off @-@ duty police officers for security . Clearwater Police Department spokeswoman Elizabeth Daly @-@ Watts stated that the off @-@ duty police officers will make sure that protesters do not <unk> on Scientology property or violate the law , but will report to police supervisors and not representatives of the Church of Scientology . 
- The Church of Scientology posted a YouTube video claiming that Anonymous are " terrorists " and alleging that Anonymous is <unk> " hate crimes " against the church . The video does not provide any evidence supporting their claims , and the FBI has not named any suspects for several of the threats mentioned . Anonymous has denied involvement in the more severe accusations . The church also released a DVD containing the YouTube video . The DVD called Anonymous a " dangerous " group and accused them of making threats against Scientology . Men claiming to be from the law firm Latham and Watkins delivered the DVD to family members of at least one person who protested . 
- YouTube user " <unk> " , which Radar Online described as an associate of Scientology , displayed the names and personal information of several supposed Anonymous members and accused the group of violent threats and terrorism . YouTube quickly took the video down and suspended the " <unk> " account . 
- The Church of Scientology sought an injunction and a <unk> order to prevent Anonymous from protesting on March 15 , 2008 , citing threats allegedly made by Anonymous . Both the injunction and the <unk> order were denied . On March 31 , 2008 , Radar Online reported that representatives of law firms delivered legal letters to suspected <unk> , often at their homes . The Church filed complaints of <unk> and criminal harassment against Boston organizer Gregg <unk> , who was charged with disturbing an assembly of worship , disturbing the peace , and harassment . The District Attorney 's office dropped the harassment charge , and Judge Thomas Horgan issued a <unk> without finding for the remaining charges . 
- In a May 8 , 2008 appearance on CNN , Church of Scientology spokesman Thomas W. Davis said that Scientology was " dealing with ninety @-@ six death threats , bomb threats , acts of violence , vandalism " from the group Anonymous . CNN 's John Roberts responded , stating that the Federal Bureau of Investigation found nothing connecting Anonymous to the Church of Scientology 's accusations of violence : " You are <unk> these accusations at this group , the <unk> , which is looking into it , says it has found nothing to connect this group Anonymous with what you 're talking about , or death threats against members of the church , the <unk> at this point says - it has no reason to believe that charges would be leveled against this group . " 
- 
- = = Reaction = = 
- 
- Andreas <unk> @-@ Lund , founder of the Scientology @-@ critical website and non @-@ profit organization Operation <unk> , released a statement criticizing the digital assault against Scientology . <unk> @-@ Lund commented , " People should be able to have easy access to both sides and make up their own opinions . Freedom of speech means we need to allow all to speak - including those we strongly disagree with . I am of the opinion that the Church of Scientology is a criminal organisation and a cult which is designed by its delusional founder to abuse people . I am still committed to fight for their right to speak their opinion . " He also stated that " <unk> Scientology like that will just make them play the religious persecution card ... They will use it to defend their own counter actions when they try to <unk> criticism and crush critics without mercy . " 
- Mark Bunker , an Emmy Award @-@ winning journalist and Scientology critic who runs the website <unk> , posted a video to YouTube and asked Anonymous to tone down their campaign against the Church of Scientology . According to NPR 's Morning Edition , Bunker has " become a revered voice to many members of Anonymous " , and they refer to him as " <unk> <unk> Man " . Bunker told Newsweek that he was pleased to see a large group of young individuals acting against Scientology , but stated he was also concerned for their safety : " I know the way Scientology works : they 're going to get these people in trouble ... I 'm very concerned about their safety , and I 'm concerned about the Scientologists ' safety , too . " Bunker stated that he has received 6 @,@ 000 <unk> from individuals who say they are part of Anonymous . Bunker attended the February 10 , 2008 protest against Scientology in Los Angeles . 
- Tory Christman , a critic of Scientology and former Scientologist from 1969 to 2000 , stated she disapproved of illegal tactics but felt encouraged by the new influx of critics of Scientology . Christman told Morning Edition : " It feels like we 've been out in this desert , fighting this group one @-@ on @-@ one by ourselves , and all of a sudden this huge army came up with not only tons of people , thousands of people , but better tools ... " Scientology critic <unk> Lerma told the St. Petersburg Times he was impressed by a video of a protest against Scientology which took place in Orlando , Florida : " I 've never seen anything like that before . This is incredible . I wouldn 't have believed it if I didn 't see it on a Web <unk> . " 
- In a February 4 , 2008 appearance on the <unk> television program Attack of the Show ! , Mark <unk> , journalist and author of the book Hollywood , <unk> , and Nick Douglas of Gawker.com commented on Project Chanology . <unk> stated that " <unk> their site is not really the best way to go about taking them ( the Church of Scientology ) down . Most critics you talk to want the Scientology site to be up there so that people who are interested can see the <unk> they have on the web and at the same time they can go - they are a few <unk> from getting a thousand other opinions . " Nick Douglas explained that the group decided to shift their strategy away from the attacks to Scientology websites : " Anonymous even decided that they were going to stop that attack , that it was a bad idea . It 's the usual thing they used to do when they really hadn 't had a thought out plan , and here they 're realizing they actually have to figure out some real plan against a real enemy . " 
- University of Alberta professor Stephen A. Kent weighed in on the issue , and said " I think these disruptions probably are illegal . At the very least , they ’ re forms of harassment ... We now have three parties involved . Anonymous , Scientology and law enforcement . " Kent stated that " The hacker community has been angry at Scientology for ( their ) attempts to block free speech on the Internet . " Reaction to the denial of service attack on the Church of Scientology websites was mixed in message board forums for PC World . Some readers praised the actions of Anonymous , while others commented that the DDoS attacks bring more attention to Scientology . The Economist likened the DDoS attacks used by Project Chanology to " <unk> techniques normally associated with <unk> , <unk> and terrorists " , and referred to Anonymous as " internet activists " . 
- Dan Schultz of PBS 's <unk> <unk> Lab commented that the movement " ... is a really fascinating case study of how current technologies and information <unk> via digital media can <unk> into something that actually results in real world action " . In a follow @-@ up piece , Schultz discussed the tools used by digital media to achieve community impact , including lower barriers to entry and greater <unk> through the use of information systems . Schultz wrote " For members of Anonymous I 'm betting most of these things are already unspoken <unk> " , and pointed to their use of <unk> and cited the forums of the website <unk> as an example of the group 's ability to collaborate effectively to accomplish goals . 
- In a May 8 , 2008 report on the recent actions of Anonymous against Scientology , CNN reporter Kareen Wynter commented : " Legal experts say the church may be facing its biggest challenge yet – trying to protect its image , in a loosely <unk> medium seen by millions of people . In a July 2008 interview with Entertainment Weekly , Alan Moore had this to say about the use of the Guy Fawkes motif , adopted from his comic V for Vendetta : " I was also quite <unk> the other day when watching the news to see that there were demonstrations outside the Scientology headquarters over here , and that they suddenly <unk> to a clip showing all these demonstrators wearing V for Vendetta [ Guy Fawkes ] masks . That pleased me . That gave me a warm little glow . " 
- 
- = = = Audio / video = = = 
- 
- <unk> Staff ( January 24 , 2008 ) . " Group <unk> To <unk> Scientology , Video : ' We Do Not <unk> ' - An anonymous group of hackers , <unk> known as " Anonymous " , has declared war on the Church of Scientology " . <unk> ( NBC Universal , Inc . ) . ( Video broadcast . ) 
- " Know Your <unk> : Project Chanology " , Know Your <unk> Season 2008 , Episode 13 , " <unk> " , YouTube , December 31 , 2008 . ( Video podcast ) 
- Seymour , Brian ( February 7 , 2008 ) . " The Anonymous war on Scientology : The Church of Scientology is already banned in several countries but now it is facing one of its biggest threats from a group called Anonymous . " . Today Tonight ( Yahoo ! News ) . ( Video broadcast ) 
- Wynter , Kareen ( May 8 , 2008 ) . " <unk> Scientology : The Church of Scientology faces a new breed of Internet attacks . Kareen Wynter reports . " . CNN . ( Video broadcast ) 
- Seabrook , Andrea ( January 27 , 2008 ) . " <unk> Target Scientology Web Sites " . All Things Considered ( NPR ) . ( Radio broadcast ) 
- Masters , Kim ; <unk> Montagne ( February 7 , 2008 ) . " ' Anonymous <unk> Attack on Scientologists : The fight started when the Scientologists tried to get a video of Tom Cruise off the Internet . " . Morning Edition : Digital Culture ( National Public Radio ) . ( Radio broadcast ) 
- <unk> , Brian ( February 11 , 2008 ) . " ' Anonymous ' Takes on Scientology : Online activists take their protest against the Church of Scientology to the streets . " . The Bryant Park Project ( National Public Radio ) . ( Radio broadcast ) 
- Miller , Nancy ; Baker , Chris ( September 30 , 2009 ) . " <unk> <unk> : The <unk> Offensive " . Wired News . ( Audio podcast ) 
- 
- = Transportation in Omaha = 
- 
- Transportation in Omaha , Nebraska , includes most major modes , such as pedestrian , bicycle , automobile , bus , train and airplane . While early transportation consisted of ferries , stagecoaches , steamboats , street railroads , and railroads , the city 's transportation systems have evolved to include the Interstate Highway System , <unk> boulevards and a variety of bicycle and pedestrian trails . The historic head of several important <unk> trails and the First Transcontinental Railroad , its center as a national transportation hub earned Omaha the nickname " Gate City of the West " as early as the 1860s . 
- During a tumultuous pioneer period characterized by its <unk> in proximity to the Western United States , transportation in Omaha demanded the construction of massive warehouses where frontier settlers could stock up and communities west of Omaha got food and supplies to build themselves with . <unk> and stagecoaches jammed the riverside city with a variety of newcomers , prospectors and <unk> characters . Early Omaha also landed the Union Pacific Railroad headquarters , leading to its important place in national railroad lore . 
- After quickly growing into a city , Omaha failed to pave its streets accordingly . A chaotic transportation system was highlighted by several miles of successful horsecar tracks ; however , the city only ever had four miles ( 6 km ) of cable car service . Several early suburbs were built on reliance of service from these lines , including Dundee , Benson and Kountze Place . In the early 1880s an extensive boulevard system was built to create a park @-@ like atmosphere for drivers throughout the city . The Trans @-@ Mississippi Exposition in 1889 led to the construction of many new transportation features , particularly the magnificent <unk> Station . 
- In the 1930s the city 's transportation system was marred by violent protests . Transit workers wanted to <unk> , and with the main company 's management against any effort to change Omaha 's reputation as a non @-@ <unk> city . After the introduction of buses in the early 1950s , streetcars were closed down , and in the last years of the decade the city began construction on its components in the Interstate Highway System . 
- Today Omaha 's transportation system is growing with the city , and trails for <unk> and pedestrians , as well as public transportation , highways and <unk> , and other innovations are being developed . The city has a section of the Lincoln Highway listed on the National Register of Historic Places , and there are more than 100 miles ( 160 km ) of Interstate and freeway lanes , more than any other area in the state of Nebraska . 
- 
- = = Pioneer period = = 
- 
- Omaha was not projected to become a great city or bigger than its neighbor across the Missouri River , Council Bluffs , Iowa . In 1856 a land speculator reported to his East Coast concerns that , 
- " C. Bluffs is steadily growing down towards the river and someday it will be one great city on both sides the river with Rail Road & foot & Carriage Bridges connecting the two – and this is now the hope and talk of the <unk> . " - J. Barker , 1856 . 
- In August 1859 Abraham Lincoln visited land he had invested in Council Bluffs , and while there did not consider it worth the time to cross the river to the village of Omaha . 
- 
- = = = Water traffic = = = 
- 
- In 1804 , fifty years before the city of Omaha was founded , the Lewis and Clark Expedition first arrived via the Missouri River . The 1806 Fort Lisa and 1820 <unk> 's Trading Post were important fur trading outposts located in proximity to the river , along with earlier <unk> 's Post in Bellevue . The Engineer <unk> was built by Captain Stephen Watts <unk> 's Yellowstone Expedition in 1819 . The Expedition 's craft , the Western Engineer , was the first steamboat to successfully venture up the Missouri River to the Omaha @-@ Council Bluffs area . 
- The Missouri was the reason Omaha was founded , and continued to be important to the city 's growth for many years . In 1853 William D. Brown had the first vision for the city , leading him to found the Lone Tree Ferry crossing the Missouri River from Council Bluffs , Iowa . Later the Council Bluffs and Nebraska Ferry Company hired Alfred D. Jones to <unk> Omaha City , which was among the first settlements in the Nebraska Territory . Along with the Lone Tree Ferry Landing in Downtown Omaha , other ferries were established in the Omaha area at Florence , Saratoga and Bellevue . Large steamboats would carry provisions up the Missouri from St. Louis , <unk> the warehouses in <unk> Canyon and loading the trains of the Union Pacific and at the Omaha <unk> Depot , which in turn supplied the U.S. Army 's Department of the Platte . 
- The Banner State was the first steamboat to land materials for building the city in early 1854 , before the city was formally founded . Until 1879 Captain Joseph La <unk> was the principal figure among the Missouri steamboat captains in the early years of the city . According to J. Sterling Morton , the golden era for <unk> on the Missouri was from 1855 to 1860 , just before the advent of the railroads . In 1857 , 174 steamboats carrying 13 @,@ 000 tons of freight tied up at Omaha wharves . When Omaha became the outfitting center for Colorado gold <unk> headed for <unk> Peak in 1859 , 268 steamboats arrived at Omaha between March and November . 
- With railroads becoming the dominant form of long @-@ range shipping and passenger travel in the early 1870s , <unk> like those in Omaha became obsolete . However , as late at 1949 the steamship Avalon was letting passengers in Omaha , before becoming one of the famous St. Louis steamboats in the 1960s . 
- 
- = = = <unk> = = = 
- 
- In 1863 , ground was broke near Miller 's Landing on the Missouri River for the First Transcontinental Railroad . Along with local financier Edward Creighton , George Francis Train was the promoter who was mostly responsible for the city landing the railroad . He was made rich from its convenient placement near land that he owned ( near Deer Park . The Union Pacific Railroad has been headquartered in Omaha since its inception in 1867 . In 1872 , Union Pacific opened the first [ railroad ] bridge across the Missouri to Omaha . 
- 
- = = = Trails = = = 
- 
- In the 1860s and 1870s , the city became a major outfitting center for the major trails that went across Nebraska , including the Oregon , California and Mormon Trails . <unk> Canyon was built in Downtown Omaha for the purpose of outfitting these migrants . <unk> lines had arrived by 1858 , including the Local Stage Coach Company in 1857 , and the Western Stage Company which began its easterly and <unk> routes in Omaha . The Pony Express and Wells <unk> lines maintained offices in the city . 
- 
- = = = Streets = = = 
- 
- Omaha had terrible streets through the late 1880s , which caused many residents to believe the city was not progressing appropriately . This lack of <unk> by the city government was caused by property owners throughout the city who did not want to pay for improvements . On rainy days stagecoaches would sink up to their <unk> , and residents wore knee @-@ high boots to <unk> through the mud , and at times rivers ran through the streets . 
- 
- = = = Public transportation = = = 
- 
- In 1867 Ezra <unk> , Andrew J. <unk> , and Augustus Kountze formed the Omaha Horse Railway Company to provide horsecar service in the city . By the late 1870s the line had five miles ( 8 km ) of track , 10 cars , 70 horses , 20 employees and 495 @,@ 000 passengers annually . The Omaha Cable <unk> Company was the city 's only cable car , and started in 1884 and ended in 1895 after consolidating with the Horse Railway as the Omaha Street Railway Company . In 1896 the new company disbanded as competitors moved in . An electric car was built by <unk> Benson between Omaha and Benson specifically to promote that suburbs development during this time . 
- 
- = = 1880s - 1950 = = 
- 
- 
- = = = Streets = = = 
- 
- In 1880 only a quarter mile of Omaha 's estimated 118 miles ( 190 km ) of streets were paved . In 1883 Andrew <unk> , brother of newspaper owner Edward <unk> , became city engineer and began an ambitious project to modernize city streets . By 1886 the city had 44 miles ( 71 km ) of paved streets , including <unk> , Colorado sandstone , Sioux Falls granite and wooden blocks . 
- In 1889 Horace <unk> Cleveland proposed that the city of Omaha develop a series of " broad ornamental avenues , known as boulevards or <unk> " designed " with a <unk> arrangement of trees and shrubbery at the sides and in the center " , similar to the comprehensive plans of European cities in the mid @-@ 19th century . His plan was accepted by the city 's Parks Commission , resulting in the construction of Omaha 's <unk> Mile Boulevard in 1892 , and dozens of other boulevards in the through to the present . Today , <unk> and Lincoln boulevards are among the many remnants of the early plan ; <unk> Parkway is a modern version of the historical plan . Saddle Creek Boulevard , currently known as Saddle Creek Road , which was originally the westernmost boulevard in the system . 
- 
- = = = Bridges = = = 
- 
- While the Union Pacific Missouri River Bridge was the first railroad bridge across the river , the 1 @,@ 400 @-@ foot ( 430 m ) Douglas Street Bridge opened in 1888 as the first road bridge . The East Omaha Bridge was originally opened in 1893 , and rebuilt a decade later in 1903 . The Mormon Bridge was first attempted to be built across the river in 1932 , and failed ; it was finally successfully constructed in 1952 . The South Omaha Bridge opened in 1936 . The Knights of <unk> @-@ <unk> @-@ Ben operated the Douglas Street Bridge as a toll bridge from 1938 to 1947 . The bridge was removed in 1968 . Traffic was carried by a new girder bridge built in 1966 for I @-@ 480 . The Saddle Creek <unk> , over which is the Dodge Street <unk> , was completed in 1934 by the Works Progress Administration . Over 1 @,@ 175 cubic yards ( <unk> m3 ) of dirt were excavated to lower Saddle Creek Road sufficiently to pass under the overpass , which is still in use today . Listed on the National Register of Historic Places in 1992 , it is part of the Bridges in Nebraska Multiple Property <unk> as well . 
- 
- = = = Highways = = = 
- 
- In 1889 Otto <unk> built Omaha 's first auto , an electric car . The " <unk> " was the first of nearly a dozen car manufacturers eventually started in Omaha . The <unk> weighed 265 pounds , had two cylinders , and could achieve a speed of 15 miles per hour . An " Auto Row " developed along Farnam Street and featured dealers , <unk> , and parts stores . 
- The original Lincoln Highway in Omaha was designated through Omaha in 1913 . Crossing the Missouri River into Omaha on the old Douglas Street Bridge , it traveled west on Dodge Street , then meandered across the state following section lines . Some of these sections were built exclusively to accommodate the highway . Important buildings on the Lincoln Highway in Omaha included the <unk> Building , the Nash Building at <unk> @-@ <unk> Farnam and <unk> @-@ 911 Douglas streets , and the Blackstone Hotel at Farnum Street and 36th Street . Additionally , the Rose <unk> Performance Arts Center at 20th and Farnum Street and the Farnum Street Automobile Row , from 30th to 40th Streets were both important landmarks . 
- In 1930 49 @,@ 128 <unk> were registered in Omaha ; ten years later 65 @,@ <unk> were registered to drive on local streets . 
- After trucks became popular in the <unk> , the Omaha Stockyards grew <unk> . <unk> , <unk> and sheep were shipped cheaper by truck than by trains . In 1919 27 % of livestock at the Stockyards was shipped by truck ; by 1940 's it rose to over 75 % . In 1955 the Stockyards became the biggest livestock distribution center in the United States , and almost all of the cattle was shipped by truck . 
- 
- = = = Airport = = = 
- 
- The aforementioned <unk> provided Omaha with another invention by successfully flying an airship in the city in 1889 . In 1929 a bond was passed that would construct the Omaha Municipal Airport in East Omaha . This was thought to embody the city 's hope for the future ; however , air travel did not become popular in Omaha until the 1960s . The land was swampy and had to be filled in with silt taken from the bottom of Carter Lake . Northwest Airlines started service between Minneapolis and Omaha in 1930 . 
- In the late 1940s Eppley Airfield was completed . In 1959 the airport was named for Eugene C. Eppley , the Omaha Eppley Hotel magnate . Eppley 's estate donated $ 1 million to be used to convert the Omaha Municipal Airport into a jet port . 
- 
- = = = Public transportation = = = 
- 
- The Omaha and Council Bluffs Railway and Bridge Company was founded in 1886 in order to span the Missouri River . In the late 1880s the city had five franchise companies providing transit services within city limits . They included the Omaha and Southwestern Street Railway Company , which provided services to Kountze Place , Dundee , <unk> Park and the Gold Coast neighborhoods . Short lines ran with limited purposes : one went only to a baseball field at the end of its line , while another ran to and from a park . 
- By 1901 local businessman <unk> W. Wattles consolidated several of the older horsecar and cable car companies to create the Omaha and Council Bluffs Streetcar Company , which later became the Omaha Traction Company . After receiving a 30 @-@ year franchise from the city of Omaha , the company established a mass transit system that covered the entire city , including commuter trains and <unk> . 
- 
- = = = = Traction Company strike = = = = 
- 
- Wattles was vehemently opposed to <unk> , and in 1909 fought strikes in favor of <unk> with hired policemen and rampant violence . By 1934 the <unk> Association of Street and Electric Railway <unk> was organized in Omaha . 
- However , by April 1935 the fragile truce between pro @-@ open shop management and pro @-@ union forces broke . A long , violent strike ensued . <unk> were hired , and within four days the company rolled out heavily fortified streetcars , complete with windows covered by heavy wire and armed guards on board . While few cars attracted passengers , the cars encountered little resistance . The company resisted calls for arbitration from the Omaha City Council , and continued employing <unk> . In early May violence broke out , with rifle attacks , violent <unk> and bombings across the city . In June riots broke out throughout the city with <unk> burning streetcars , looting and two deaths . The city government lost control of the violence and called in the National Guard , which sent 1 @,@ 800 troops while Governor Robert <unk> declared martial law and ordered the streetcars to stop running . After the governor intervened and Wattles allowed arbitration , a number of agreements were made . However , no changes occurred , and <unk> stayed on the job . The violence ended , court cases ensued , and the situation slowly faded away . The Omaha Traction Company never <unk> . 
- 
- = = = Omaha Belt Line = = = 
- 
- The Omaha Belt Line was formed in 1883 by the Union Pacific ; some <unk> dealings by Jay Gould brought the Belt Line into the control of the Missouri Pacific Railroad by 1885 , when it was constructed with Union Pacific materials under the control of the <unk> . Stations along with Line included the Florence Depot , Webster Street Station and the <unk> Station . <unk> by that company until the early 1960s , today the Line is largely abandoned , with a section redeveloped into the recreational <unk> Trail . 
- 
- = = 1950 @-@ present = = 
- 
- 
- = = = Streets = = = 
- 
- Starting in 1950 the city has continuously developed and redeveloped its major streets , particularly relying on them for east @-@ west traffic . Major east @-@ west <unk> in Omaha include Fort , Ames , Maple , <unk> , Dodge , Pacific , Center , L , Q and Harrison streets . Major north @-@ south <unk> in Omaha include North and South 24th streets , 30th street , Saddle Creek Road , and 72nd , 84th , 90th , and 120th streets . South 10th Street is important in South Omaha . 
- 
- = = = Highways = = = 
- 
- The first long segment of Interstate 80 in Nebraska to be opened was a fifty @-@ mile section between Dodge Street in Omaha and the West Lincoln interchange in 1961 . 
- Construction of the freeway in North Omaha in the 1970s faced many hurdles . Built immediately after the construction of I @-@ 480 , this 4 @-@ mile ( 6 @.@ 4 km ) section was originally supposed to be designated as Interstate 580 . However , the city refused to invest the additional money the federal government required in order to gain the designation . Coupled with social unrest in the 1970s , the highway is blamed for causing a 30 percent housing loss and major increase in crime . The freeway became the route of U.S. Highway 75 and is known locally as the North Freeway . 
- Today , Omaha is well connected to the Interstate Highway System . The city has eleven highway exits along Interstate 80 . From that Interstate drivers can connect to Nebraska Highway 50 , US 275 / <unk> 92 , I @-@ 680 and I @-@ 480 / US 75 . Continuing north , I @-@ 680 connects with I @-@ 29 near Crescent , Iowa and <unk> with I @-@ 80 near <unk> , Iowa ; I @-@ 480 cuts through Downtown Omaha to connect with I @-@ 29 in Council Bluffs , Iowa . The North Freeway also veers from I @-@ 480 , and in 2005 , the Nebraska Department of Roads began a project to bring the I @-@ 480 / US 75 interchange up to Interstate standards . Construction is expected to be complete in 2009 , and it is unknown if the North Freeway will receive an Interstate designation upon completion of the project . 
- There are a number of important arterial roads throughout Omaha . U.S. Route 75 comes south through Omaha from Fort <unk> along North 30 Street , North Freeway , I @-@ 480 and Kennedy Freeway , exiting through Bellevue . U.S. Route 6 crosses into the city from Council Bluffs on I @-@ 480 , also called the Gerald R. Ford Freeway in honor of the Omaha native son . It then follows Dodge Street , until it intersects South <unk> Street , when it runs south towards <unk> . Nebraska Highway 64 assumes the route of the former Military Road northwest out of Omaha , following Maple Road and West Maple Road to converge with US 275 at Waterloo . US 275 becomes Nebraska Highway 92 after crossing the South Omaha Veterans Memorial Bridge , following Missouri Avenue , which then becomes " L " Street . At South <unk> Street , at which point it veers northeast to follow the old Mormon Trail along Industrial Road when it joins West Center Road , crossing the Platte River and continuing westward . 
- In 2005 a portion of the Lincoln Highway in Omaha was listed on the National Register of Historic Places . 
- 
- = = = = Traffic monitoring = = = = 
- 
- Beginning in fall 2007 there will be more than 30 traffic cameras operating on Omaha area freeways , including one at I @-@ 80 near <unk> and another on West Dodge near 120th Street . <unk> by the Nebraska Department of Roads , the sensors and cameras are not used to catch <unk> or for other traffic enforcement . The state also operates an extensive traffic operations center that utilizes the cameras to monitor Omaha traffic patterns . Similar to traffic monitoring centers in Lincoln and North Platte , the one in Omaha is the biggest and has the greatest capability to provide traffic information . 
- 
- = = = Public transportation = = = 
- 
- The Omaha Traction Company , which operated as the Street Railway Company , changed its name to the Omaha Transit Company when <unk> service ended in the city in 1952 . After World War II <unk> preferred their automobiles and new highways . When the Urban Mass Transit Act of 1964 was passed , Omaha 's private transit companies were not able to apply for federal subsidies available to public transit operators . The Omaha Transit Company ceased operations on June 30 , 1972 , when the City of Omaha assumed authority for public transportation in the city . City Transit Lines , another private company in Omaha , went out of business on that day as well . The Metro Area Transit Authority was created by the Nebraska Legislature , consisting of a five @-@ member board appointed by the mayor and confirmed by Omaha 's City Council and the Douglas County Commissioners . It acquired the assets of the Omaha Transit Company and selected assets of the City Transit Lines of Council Bluffs , along with more than $ 3 @,@ 000 @,@ 000 in federal funding . The Authority operates today as Metro Area Transit , or MAT . 
- Today the Authority <unk> the level of service , miles and hours of operation within Omaha , and maintains individual service contracts with local authorities outside Omaha . Currently , MAT has three contracts , including the cities of Council Bluffs , Bellevue and the Tri @-@ Communities of <unk> , <unk> and <unk> . 
- MAT recently completed three new transit centers , which function much like airport <unk> . Located at Benson Park , <unk> Mall , and Metro Community College in South Omaha , they are designed to bolster the city 's public <unk> needs . These join existing centers in <unk> and North Omaha . 
- 
- = = = = Light rail = = = = 
- 
- In recent years the city of Omaha has grown <unk> , and with a renewed focus on public transit , there have been plans for new streetcars and light rail in the city , including a movement initiated by former mayor Hal <unk> , which in a 2003 City Council action , failed to reach approval by only one vote . The currently proposed streetcars would cost $ 55 million and run in a 3 @.@ 5 @-@ mile ( 5 @.@ 6 km ) loop through Downtown Omaha and <unk> . It would cost about $ 2 million per year to operate and would serve almost 7 @,@ 000 passengers in its first year . One route would run the cars from Creighton University near 20th and Webster streets , proceeding east to 10th Street , passing by the <unk> Center and moving south to Jackson Street in the Old Market . After that it would then move west to 16th Street and then north to Farnam before returning to 10th Street . 
- 
- = = = Air = = = 
- 
- Today Eppley Airfield sits on 2 @,@ 650 acres ( 10 @.@ 7 km2 ) and handles approximately 400 flights a week . There are two <unk> that hold 20 gates . The airport handled more than 4 @.@ 4 million passengers in 2007 , and as of September 2008 , Southwest Airlines is the largest carrier handling approximately 24 percent of passengers . United is the second @-@ largest carrier , handling approximately 19 percent of passengers . Currently all regularly scheduled flights from Eppley Airfield terminate within the United States . 
- Airlines serving Omaha include American , Delta , Frontier , Southwest , United and US Airways . 
- 
- = = = Trails = = = 
- 
- Omaha was completely devoid of trails leading up to early 1989 . That year the city began developing trails , and since then the city of Omaha has developed approximately 67 miles ( 108 km ) of paved recreational trails , and another 35 miles ( 56 km ) of trails are scheduled for completion within the next eight years . 
- <unk> and unpaved trails and paths are used for recreational and commuter purposes throughout the city . Popular among <unk> , runners , <unk> and recreational <unk> , these trails are included in comprehensive plans for the city of Omaha , the Omaha metro area , Douglas County , and long @-@ distance coordinated plans between the municipalities of southeast Nebraska . 
- The Missouri River <unk> Bridge will connect Miller 's Landing to Council Bluffs in 2009 . A <unk> trail will run the length of the river from the South Omaha Bridge to <unk> Dodge Park . 
- 
- = = = Water traffic = = = 
- 
- An anomaly in the city 's transportation is the River City Star , a passenger <unk> <unk> that sails between Omaha and Council Bluffs . The boat is docked at Miller 's Landing near the <unk> Center , near mile marker <unk> on the Missouri River . 
- <unk> for public usage are operated by the Omaha Parks and Recreation Department . Dodge Park , located in North Omaha , has 326 slips , while the new <unk> Marina in Downtown Omaha has 31 . Levi Carter Park , which has a long history as a water haven , offers non @-@ restricted boating for jet <unk> , recreational boating and water skiing . No wake boating allowed available at Lake Cunningham , Standing Bear Lake or <unk> Lake ; however , these lakes offer opportunities for sailing , fishing and pleasure boating . Cunningham Lake offers a small marina where rental boats are available . 
- The Port of Omaha was located downtown where Miller 's Landing is now . In addition to handling <unk> barge shipments of grain , it also handled inbound shipments of steel and asphalt . 
- The Omaha District of the U.S. Army Corps of Engineers operates from the city . 
- 
- = = Image gallery = = 
- 
- 
- 
- = Clocks ( song ) = 
- 
- " Clocks " is a song by British alternative rock band Coldplay . It was written and composed , as a collaboration between all the members of the band , for their second album , A Rush of Blood to the Head . Built around a piano riff , the song features cryptic lyrics of contrast and urgency themes . Several remixes of the track exist and its riff has been widely sampled . 
- " Clocks " debuted to critical and commercial success , with critics mainly commenting on the song 's piano melody . It was released in the United Kingdom as the third single from A Rush of Blood to the Head , where it reached number nine in the UK Singles Chart . It was released in the United States as the album 's second single , it reached number 29 on the Billboard Hot 100 and number 9 on Billboard 's Modern Rock Tracks chart . It won the Grammy Award for Record of the Year . 
- 
- = = Background and writing = = 
- 
- " Clocks " was written and composed during the late stages of production of Coldplay 's second album , A Rush of Blood to the Head . A riff <unk> into Chris Martin 's mind late one night in Liverpool when he came in to the studio , where he then developed it on piano . According to Martin , " Clocks " was inspired by the English rock band <unk> . Martin presented the riff to the band 's guitarist , Jonny Buckland , who then added a layer of guitar chords to the basic track : " He picked up his guitar [ a sure sign that he likes a song ] and played these brilliant chords ... It was like a chemical reaction process . " ( The syncopated piano <unk> that gives the song its signature sound also bears a similarity to a syncopated <unk> that is repeated several times in Alex De <unk> 's 1981 instrumental " <unk> . " ) 
- Before writing and composing " Clocks , " the band had already written 10 songs for the album . But as they thought it was too late for the song 's inclusion in the album , since A Rush Of Blood To The Head was nearing completion , they recorded a demo and saved it with other unfinished tracks , labeling it " Songs for # 3 ; " the band projected these tracks for what would be their third album . 
- By June 2002 , Coldplay were ready to present the album to their record label <unk> . However , Martin felt it was " rubbish ; " they were so far from being completely satisfied with the album that both the band and <unk> delayed the release . After a headlining tour , Coldplay went on working " Songs for # 3 . " Phil Harvey , a friend of Martin and the band 's manager , heard it and <unk> him on to rework " Clocks " immediately . With lyrics that speak of urgency , Harvey pointed out that its meaning would contradict Martin 's idea of <unk> the track . Thus persuaded by Harvey , Martin then further developed " Clocks , " while other band members supplemented their ideas based on the main piano track , adding bass and drums . Coldplay recorded the song very quickly , since they were running after the postponed schedule of A Rush of Blood to the Head , which was released two months later . 
- 
- = = Composition = = 
- 
- " Clocks " has a repeating piano melody , and features a <unk> <unk> of drums and bass guitar . Martin applied an <unk> , with emphasis that <unk> a three against two <unk> , as well as a descending scale on the piano chord progression , which switches from major to minor chords . The music of " Clocks " is also provided using synthesizers and a sparse string arrangement . 
- The themes of the lyrics include contrast , contradictions and urgency . According to Jon <unk> of MTV News , " Martin seems to address the <unk> of being in a dysfunctional relationship he doesn 't necessarily want to escape . " The lyrics are cryptic ; the ending lines of the second verse emphasize <unk> emotion : " Come out upon my seas / <unk> missed opportunities / Am I a part of the cure / Or am I part of the disease ? " The song 's title also " <unk> alludes " to its lyrics , " pushing one to wonder about the world 's obsession with time while connecting it to the theory : make the best of it when we ’ re here , present and alive . " 
- The song is in the key of E flat <unk> , with a main chord progression of E ♭ - B ♭ m - <unk> . 
- 
- = = Personnel = = 
- 
- Chris Martin – lead vocals , piano , synthesizer 
- Jonny Buckland – electric guitar 
- Guy <unk> – bass guitar 
- Will Champion – drums , backing vocals 
- 
- = = Release and music video = = 
- 
- Coldplay released " Clocks " in Europe on 24 March 2003 as the album 's third single . The single was issued with two B @-@ sides : " Animals , " which was one of the band 's favourite songs performed on tour but was not included in the album , and " <unk> of Waves . " The single 's cover , created by <unk> <unk> as with the album 's and its other singles , is a portrayal of Chris Martin . Across the United States , while preparing " The <unk> " as the album 's second release , Coldplay 's US label felt the song failed to " provide enough of a blood rush for American listeners ; " instead , they released " Clocks " as the second single in the US . 
- A music video was filmed in support of the song . It was directed by British film maker Dominic <unk> , and shot at <unk> ' <unk> Building in London . It features the band performing the song , with a laser show , in front of a staged audience , mostly local college students . Stage effects and blue @-@ red light transitions give the video a surreal feel , while a stoic crowd make up the audience . 
- 
- = = Reception = = 
- 
- The song received acclaim from music critics . Rob Sheffield , in his review of the album for Rolling Stone magazine , said : that " [ guitarist ] Buckland shines in excellent psychedelic rockers such as ... ' Clocks . ' " David <unk> of The Daily Telegraph said that " Clocks " features a " hypnotic piano riff , a pounding , almost <unk> rhythm , and a <unk> tune , all building to a gorgeously serene climax with Martin 's <unk> voice singing . " Scott <unk> , music critic for <unk> magazine , described the song as " a stunningly pretty piano rocker , absolutely perfect and is simply one of the finest songs of the decade . " 
- " Clocks " won the Record of the Year at the 2004 Grammy Awards . It was nominated for Best Single at the 2003 Q Awards . " Clocks " was ranked at number 68 on Pitchfork 's Top 100 Singles of 2000 @-@ 04 . It was ranked at number 155 on Pitchfork Media 's 500 Greatest Songs of the 2000s list . In October 2011 , NME placed it at number 148 on its list " 150 Best Tracks of the Past 15 Years " . In February 2013 the song was voted by listeners of BBC Radio 6 Music as the greatest song released during the 10 years the station had been broadcasting . 
- The single was successful in radio throughout 2003 and appeared on several singles charts worldwide . In the United Kingdom , the song peaked at number nine and in the United States rose to number 29 . It also reached number seven in Canada and number 28 in Australia . 
- 
- = = = The piano riff and its parallels = = = 
- 
- " Clocks " has been regarded as one of the finest achievements of Coldplay ; the song 's piano progression remains the band 's signature creation . According to The New York Times , the opening piano arpeggios of " Clocks " have been widely sampled . Also , many of the songs in X & Y feature influences from " Clocks . " Brian Cohen of Billboard magazine noted that " Clocks " served as a " launching pad " for songs featured in X & Y , " several of which echo that track either in structure or feel . " " Speed of Sound , " the first single from Coldplay 's third album , X & Y , is similar to " Clocks , " in that the two songs have the same descending chord progression . According to The New York Times , American singer <unk> Sparks 's 2008 single " No Air " " <unk> life into the <unk> piano line " from " Clocks . " The song " Should I Go " by American singer Brandy Norwood , from her album Afrodisiac , samples the piano riff of " Clocks , " as does Mexican singer Alejandro Fernández 's 2007 single " Te <unk> A <unk> . " In 2009 , French DJ David <unk> , featuring Kelly Rowland , released the song " When Love Takes Over , " which has a piano introduction like " Clocks . " A riff similar to " Clocks " was also used for the 2009 song " Shining Down " by <unk> hip hop artist <unk> <unk> and featuring Matthew Santos . An analogous riff can also be heard in the DJ Cahill Remix of the Agnes song I Need You Now . Rolling Stone ranked it # 490 of the 500 Greatest Songs of All Time in 2010 . 
- 
- = = Usage in media = = 
- 
- Throughout 2003 , " Clocks " was featured in various commercials , movies and television programs : from the BBC using a sample to advertise <unk> TV , WWE promos featuring the return of American professional wrestler Kurt Angle , HBO , the 2002 Irish drama film In America , and an episode of the American medical drama television series ER . The song was played in its entirety during the ending credits for the 2003 film Confidence and was also featured on the TV shows The <unk> , Third Watch , <unk> , and Family Guy . In late 2003 , the song was used in a trailer for the movie Peter Pan . This song was also used in the 2006 Disney film , The Wild and the 2014 Blue Skies film , Rio 2 . 
- 
- = = <unk> version and remixes = = 
- 
- A number of versions and remixes of " Clocks " exist . 
- Norwegian duo <unk> made a remixed version of the song , pressed on 1000 , limited @-@ edition 12 " vinyl records , 100 of which were made available through the band 's official website . The version placed fifth in the Triple J Hottest 100 , 2003 ( the original version of the song placed 69th the previous year ) . 
- In 2004 , R & B singer Brandy , together with producer Timbaland , sampled " Clocks " for her song " Should I Go " on her fourth studio album Afrodisiac . 
- A remixed cover of the song is included on the soundtrack of the 2007 video game Dance Dance Revolution : Hottest Party for the Wii console . 
- The song , albeit with altered lyrics , appears in the Two and a Half Men episode " <unk> Your Magic <unk> . " 
- Live versions appeared on Coldplay 's live albums Live 2003 , <unk> ( 2009 ) , and Live 2012 . 
- A version of the song in the style of Buena Vista Social Club appears on the 2006 Rhythms del Mundo album . 
- A further version based on the 2006 Rhythms Del Mundo version was released as part of the 2010 album Revival . It features " <unk> " of Los Van Van . 
- 
- = = Track listings = = 
- 
- Official remixes 
- " Clocks " ( <unk> <unk> Heart Mix ) 
- " Clocks " ( <unk> le Grand Remix ) 
- " Clocks " ( Deep <unk> Mix ) 
- " Clocks " ( Gabriel & Dresden ' I Gotta Thank You ' Mix ) 
- " Clocks " ( Tom Middleton 's Cosmos Mix ) 
- " Clocks " ( Judge Jules Remix ) 
- " Clocks " ( David X Mix ) 
- 
- = = Charts and certifications = = 
- 
- 
- 
- = Khoo <unk> @-@ <unk> = 
- 
- Khoo <unk> @-@ <unk> ( Chinese : <unk> ; pinyin : <unk> <unk> ; <unk> <unk> h @-@ <unk> @-@ <unk> : <unk> <unk> @-@ <unk> ; born 2 March 1956 ) is a Malaysian author and speaker on contemporary application of the 500 BC Chinese military treatise , The Art of War , by renowned military strategist Sun Tzu . In the 1990s , Khoo was the first Sun Tzu student in South @-@ east Asia to link and teach the general 's principles in relation to business and management . To date , Khoo has written over 26 business and management books , most of which are based on Sun Tzu 's Art of War as he made it his life 's mission to " <unk> " as many people as possible . In 1997 , although a Malaysian citizen , he was appointed as honorary Assistant Superintendent of Police by the Singapore Police Force in recognition for his contribution as consultant @-@ trainer to the police force of Singapore . His first novel , Taikor , was nominated by the National Library of Malaysia for the 2006 International <unk> Dublin Literary Award . Since 1999 , Khoo has gone into retirement and occasionally travels in Malaysia and Singapore to share the wisdom of Sun Tzu 's strategies for success and happiness upon requests from his readers and supporters . 
- 
- = = Life = = 
- 
- 
- = = = Early years = = = 
- 
- Khoo was born in 1956 in the <unk> state of Malaysia . He received his formal education at St. Xavier 's Institution before leaving for Singapore in 1974 , to continue his pre @-@ university education at St. Joseph 's Institution . After completing his studies in 1978 , he worked as a journalist with The Star in Kuala <unk> until 1980 when he joined Malaysia 's largest sugar refinery , Malayan Sugar Manufacturing Company <unk> , as a personnel manager . 
- To help him in tackle the numerous challenging tasks he faced in Malayan Sugar then , his future wife , Judy <unk> , gave him some notes of her translation of Sun Tzu 's Art of War from Chinese to English , as he is not <unk> in Chinese . Khoo immediately took keen interest in the military treatise and began to learn and apply Sun Tzu 's teachings into his work . Throughout his professional career , Sun Tzu 's Art of War was his indispensable guide . Khoo married Judy , a former Taiwanese singer in early 1982 . In 1983 , he moved to Kuala <unk> as sales and marketing manager . When Asian sugar <unk> Robert <unk> was invited to turn around the ailing Multi @-@ Purpose group of companies in 1987 , Khoo joined them as administration manager at Magnum Corporation . In 1989 , he left Magnum to head the personnel and administration department of <unk> Holdings <unk> , where he later became the general manager within just four months , managing The Mall shopping complex . Khoo initiated a series of management and sales policies that enabled The Mall to secure the ' Best <unk> Mall ' award for two years in a row . In the same year , he began writing his first best @-@ seller hit ; War at Work – Applying Sun Tzu 's Art of War in Today 's Business World , which was a collection of memoranda based on Sun Tzu 's Art of War he wrote to his managers at The Mall . 
- From the early 1980s , Khoo tried to pursue a Master of Business Administration ( MBA ) degree from various universities , but all of them rejected his application as he did not possess a first degree for admission . Khoo only had two professional <unk> – a <unk> in Administrative Management and a <unk> <unk> in <unk> and Finance from <unk> . He finally received an unconditional offer from the University of <unk> after he impressed the visiting Director of the MBA programme showing how he managed the award @-@ winning shopping mall . In 1989 , accompanied by his wife , he left Malaysia to pursue his MBA studies in Scotland and graduated with <unk> in 1991 . Instead of returning to Malaysia , he went to Singapore again to seek better opportunities there and was offered the job as Director of Operations for Kentucky <unk> <unk> ( <unk> ) . 
- 
- = = = <unk> = = = 
- 
- In early 1994 , Khoo was <unk> from <unk> as a result of an ownership change , despite contributing to increased sales and profitability . He said : 
- Months before leaving my office , I had already looked around for another job , but unfortunately , top jobs are hard to come by . I felt like a <unk> then . It occurred to me then that if no <unk> wanted to employ me , then I would be my own little warlord and employ myself . 
- From his home near <unk> Park , Khoo set up <unk> Training & Management <unk> <unk> Ltd , a training and consultancy firm , which assisted clients in planning and implementing strategies , and motivating their executives through his proprietary " Management : The Sun Tzu Way " programmes . He runs the company with his wife , who handles all the accounting and administration . Half of his time is spent elsewhere , conducting management <unk> for businessmen and employees of multinational corporations . Khoo also <unk> part @-@ time on Strategic Management for Singapore Institute of Management 's external degree programmes . In early 1999 , he co @-@ authored with Nigel Munro @-@ Smith , a lecturer at <unk> University in Australia , to produce a book titled Reader Friendly Strategic Management that brings out the <unk> of strategic management for <unk> . 
- 
- = = = Early retirement = = = 
- 
- In 1999 , at the age of 43 , Khoo decided to ' hang up his sword ' ( as described in his own words ) to live a quiet and leisurely life in Cameron Highlands of Malaysia , with his wife and their " four @-@ legged son , " <unk> , a Yorkshire <unk> to this very day . When asked on his decision to live in the mountains , Khoo said : 
- Life is more than work , work , work or just making money . I don 't want to be like the preacher in the Book of <unk> who lamented that he looked on all <unk> that he had <unk> to do , and he <unk> only vanity and a striving after wind . We may not be very wealthy but we have made enough to live by since we do not <unk> to a lavish lifestyle . So why not have time to do the things we like to do for a change ? 
- The seclusion of his mountain sanctuary offers him a good opportunity to pursue and enjoy his lifelong passion in writing his books <unk> at a leisurely pace . Upon requests from his readers and supporters , Khoo still travels once a month for a few days to " <unk> " – a term he coined for sharing the wisdom of Sun Tzu 's strategies for success and happiness for his <unk> and conference audiences . Khoo has " <unk> " thousands of " pupils " of his clients , among whom were well @-@ known corporations like <unk> , <unk> Systems , Citibank , <unk> @-@ <unk> , Intel , Johnson & Johnson , Port of Singapore Authority , among many others , and also government bodies such as the Prime Minister 's Office of Singapore , <unk> and Malaysia . To keep in touch with his readers , Khoo writes monthly <unk> touching on contemporary issues using Sun Tzu 's perspective . While not travelling in the region to teach , he enjoys writing fiction and has had three fiction books published to date – Taikor , <unk> and Nanyang . 
- 
- = = = Commendation = = = 
- 
- As a consultant @-@ trainer to the Singapore Police Force since 1995 , Khoo has taught many police officers in Singapore how to use Sun Tzu 's principles of " <unk> " to be effective leaders and team builders . In 1997 , he was sent for intensive training and thereafter appointed as honorary Assistant Superintendent of Police ( <unk> ) in recognition for his contribution to the police force of Singapore . On 1 July 2009 , Khoo was promoted to the <unk> rank of Deputy Superintendent of Police for his long years of service to the police force . Khoo was listed as one of the top 50 great minds and thinkers by Great <unk> , an American think @-@ tank , for being " one of the outstanding figures in their own fields of <unk> , who have taught and enlightened the minds of other fellow human beings all over the world . " 
- 
- = = His works = = 
- 
- 
- = = = Sun Tzu series = = = 
- 
- Sun Tzu ( pinyin : Sun <unk> ; c . 544 BC — <unk> BC ) was a native of the Qi state ( now <unk> county in Shandong ) during the Spring and Autumn Period of Chinese history , that was characterised by <unk> factions and a fragmented state . He came to the attention of King He Lu of Wu , who was impressed by his 13 @-@ chapter military treatise , The Art of War . In 512 BC , He Lu made Sun Tzu his Commander @-@ in @-@ Chief of the Wu army . For almost 20 years , the armies of Wu were victorious over their hereditary enemies , the kingdoms of Chu and Yue . In <unk> BC , after He Lu died from his wounds in battle , his son , Fu Chai succeeded him . After numerous victories in battles , Fu Chai became arrogant and began to indulge in <unk> . In time , the king started to disregard Sun Tzu 's counsel and began to distance himself from Sun Tzu . <unk> this , Sun Tzu <unk> gave up his position and left Wu for Qi in retirement . Fu Chai and his generals did not follow Sun Tzu 's precepts and his kingdom was subsequently conquered by Chu in 473 BC . Out of shame , Fu Chai committed suicide after he had fled with the remnants of his defeated army . 
- As a contemporary teacher of Sun Tzu 's Art of War , Khoo has written over 26 books on business and management based on its principles such as : 
- Crime Prevention : The Sun Tzu Way ( 2006 ) 
- <unk> Without Fighting ( 2006 ) 
- Applying Sun Tzu 's Art of War ( 2002 ) – A six <unk> collection 
- Sun Tzu : The Keeper of CEO 's Conscience ( 1997 ) 
- Applying Sun Tzu 's Art of War in Corporate Politics ( 1995 ) 
- Sun Tzu and Management ( 1992 ) 
- War at Work : Applying Sun Tzu 's Art of War in Today Business World ( 1990 ) 
- Due to his ability to translate what is a complicated treatise into an easily readable and understandable prose for a <unk> , Khoo 's Sun Tzu series were well received and continue to grow in scope and depth in later years . He additionally manages to add realism by <unk> real @-@ life situations culled from his 15 years in management , that provoke much thought and encourage readers to assess their own performance , and take positive measures to become more effective in their workplace and <unk> relationships . 
- 
- = = = Novels = = = 
- 
- His first novel , Taikor , was released in late 2004 . It is a historical saga of Malaya which traces the years 1922 to 1982 , telling the story of Ya <unk> , from his family 's migration from South Thailand to <unk> after his father 's death . It was among the 132 entries nominated for the 2006 International <unk> Dublin Literary Award . 
- His second novel , <unk> , is about life of those people working in the glitzy world of <unk> and their customers who <unk> nightclubs . Along with the main tale , Khoo spins other tales covering love , juvenile <unk> , deceit versus honesty , murder , corruption and power abuse of police officers . It was released in April 2007 . 
- His third novel , Nanyang , is a historical saga about the multi @-@ racial people who inhabit the lands of the ' Southern Ocean ' , as early Chinese migrants called Malaya and Singapore . It was seen through the lives of four generations as they <unk> and struggled for wealth and power , for their beliefs and freedom , and felt their hopes and dreams for their future and those of their <unk> , as Nanyang eventually became two separate fledging nations . It hit the book stores in October 2007 . 
- 
- 
- = The Wave ( Miike Snow song ) = 
- 
- " The Wave " is a song performed by Swedish indie pop band Miike Snow . It was released as the second single from the band 's second studio album Happy to You ( 2012 ) on 14 May 2012 , through Columbia Records . The song was written and produced by the band . Musically , " The Wave " is an electropop song with marching band influences and autoharp , military drum and piano instrumentation . It features Swedish musician Gustav <unk> on autoharp and the Swedish Army drum corps on military drums . 
- The song received generally positive reviews from music critics , who commended its <unk> and musical direction . However , some critics were divided regarding lead singer Andrew Wyatt 's falsetto vocals . The single failed to match the commercial performance of Happy to You 's lead single " Paddling Out " ; it only charted on the Flemish Ultratip chart at number 43 . Andreas Nilsson directed the single 's accompanying music video , the second part in a continuous story that began in the " Paddling Out " video . 
- 
- = = Background and release = = 
- 
- " The Wave " was written and produced by Miike Snow 's three members : Christian Karlsson , Pontus Winnberg and Andrew Wyatt . The song was recorded alongside the rest of their second studio album Happy to You ( 2012 ) during the second half of 2011 . The band had more " creative freedom " while making the album , compared to their 2009 self @-@ titled debut album . They felt they could " plan the process more " , which included inviting the Swedish Army drum corps to play military drums on several tracks , such as " The Wave " . Karlsson told Billboard that his favorite part of the song occurs during the second verse , in which all band members are hitting the drums simultaneously . " We hit anything we could hit at the same time ... and it sounded kind of cool " , he said . Nils <unk> is credited for playing the drums , while David <unk> , Jonathan <unk> and <unk> <unk> played the military drums . Swedish musician Gustav <unk> of band <unk> played the autoharp , and the band provided additional instruments , arrangement and programming . <unk> <unk> mixed the track at <unk> , Miike Snow 's own studio in Stockholm , Sweden . 
- The song was selected as the second single from Happy to You . Initially , <unk> Records released Thomas Gold 's remix exclusively on the online music store <unk> on 14 May 2012 . A mere week later , a digital extended play ( EP ) was released in Europe through Columbia Records . The release features the radio edit of the original song and remixes by Gold , <unk> and Style of Eye . The EP was released in the United States on 12 June 2012 , through Universal Republic and Downtown Records . 
- Prior to the release of both their second album and single , the band announced on February 2012 that they were going to play two UK shows in May : Manchester 's HMV <unk> on May 30 and London 's O2 Academy Brixton on May 31 . For their show at the O2 Academy Brixton , the band launched an interactive video of their performance of " The Wave " in which viewers could choose from where in the venue they could experience the show . On September 15 , 2012 , Karlsson and Winnberg were guests on BBC Radio 1 's Essential Mix in which they premiered exclusive new remixes of their songs . 
- 
- = = Composition = = 
- 
- " The Wave " is a piano @-@ based electropop piece with a " marching band vibe " . Chris Schulz of The New Zealand Herald described it as an " electro @-@ anthem " with a " <unk> dubstep <unk> " . Instrumentation is provided by an autoharp , drums , <unk> , military drums , percussion and a piano . In an interview for Complex , Wyatt said that the song is built in " tsunami form " , stating that " the real pay @-@ off " does not come until the final refrain . According to critic Josh <unk> of Spin , the song " dips its toes in the kind of <unk> @-@ rock <unk> by <unk> but mixes in some organic , tribal fun à la <unk> " . Wyatt sings with falsetto vocals , which Chris <unk> of The A.V. Club thought recalled a " sad @-@ faced " Peter Gabriel . In the chorus , Wyatt sings , " My love won 't be saved / We 'll all be staring at the wave " . Winnberg told Billboard that " The Wave " " kind of sums up a lot what Miike Snow is about " . 
- 
- = = Reception = = 
- 
- Critical reception of " The Wave " was generally positive . Adam <unk> of Entertainment Weekly named it one of the best tracks on Happy to You , while Will Salmon of Clash and Amber <unk> of The Huffington Post called it a standout . Exclaim ! ' s Ashley Hampson deemed it " incredibly catchy " , writing that it " capitalize [ s ] on the falsetto <unk> of vocalist Andrew Wyatt " . Andy <unk> of musicOMH described the track as " much more like the Miike Snow that many came to know and love " . The writer praised the piano and marching drums for " giving the song a sense of direction that the opener lacked " . Caroline Sullivan , writing for The Guardian , commented that " a martial beat , <unk> languid vocals and a <unk> <unk> give ' The Wave ' a <unk> kick " , and Slant Magazine 's Kevin <unk> wrote that the beat " succeeds " . 
- Chris Schulz of The New Zealand Herald said that the song , alongside the album tracks " Paddling Out " and " Bavarian # 1 ( Say You Will ) " , " will <unk> around in your head for days and demand repeat plays " . Spin 's Josh <unk> named it a highlight of the album ; he wrote that if the album as a whole sounded like " The Wave " , " it could fill arenas " . Chris <unk> of The A.V. Club wrote , " Though Wyatt 's vocals recall a sad @-@ faced Peter Gabriel , the song is <unk> in the kind of effervescent magic that makes <unk> Li and Peter <unk> and John stars in their own right . " Evan <unk> of PopMatters was critical of Wyatt 's vocal performance ; he wrote that " we really get a sense of how Wyatt 's voice <unk> the group " . He said that the singer <unk> the lyrics " somewhat <unk> , but with absolutely no sense of gravity to be found in his voice at all " . 
- " The Wave " only charted on the Flemish Ultratip chart in Belgium ; it debuted at number 84 in the issue dated 30 June 2012 . The following week , the single rose 12 positions to number 72 , and to number 54 the next . In the issue dated 28 July 2012 , its fifth and final week on the chart , it obtained its peak position of number 43 . 
- 
- = = Music video = = 
- 
- Andreas Nilsson directed the music video for " The Wave " , a continuation of the video for Happy to You 's lead single " Paddling Out " . <unk> up where the first part left of , the video follows Jean Noel , a human man who has received plastic surgery from aliens to be transformed into the " perfect specimen " . In an interview for The <unk> Project , Nilsson explained how the videos were <unk> : " The concept of this was born after long evenings of me and the band talking about gene technology in contemporary science . We share a mutual excitement in what 's happening on the medical scene right now . " " The Wave " was filmed before " Paddling Out " , although the latter was released first . Wyatt explained to Billboard , " We really liked [ director ] Andreas 's images and I think they go together in a way that feels <unk> with what we do ... and you can read into it in different ways . " 
- The video for " The Wave " begins with the aliens ' spaceship crashing to Earth . The camera then hovers over a playground where dozens of children appear to be dead . Several policemen arrive at the scene and begin to remove the bodies by putting them in <unk> . Meanwhile , Jean Noel is seen running on a desert road . The policemen then begin to dig graves for the corpses . Jean Noel then spots another specimen who looks just like him and the two begin to run . They are soon joined by more specimens before arriving at the playground . By chanting at the policemen , the specimens appear to mind control them into dance . Jean Noel continues to run and stops when he sees the crashed spaceship . The video ends with the band 's <unk> logo . 
- The video was made available for download through the iTunes Store on 13 March 2012 , in conjunction with the digital release of Happy to You . It later premiered on YouTube on 14 March 2012 . It received generally positive reviews . Amber <unk> of The Huffington Post stated that it lacked a plotline , but praised its production . Tom <unk> of <unk> called it " <unk> <unk> " and wrote , " I don 't think it 's supposed to be depressing , but it totally is . " 
- 
- = = Track listings = = 
- 
- <unk> remix download 
- " The Wave " ( Thomas Gold remix ) – 6 : 35 
- Digital EP 
- " The Wave " ( radio edit ) – 3 : 36 
- " The Wave " ( Thomas Gold mix ) – 6 : 35 
- " The Wave " ( <unk> remix ) – 5 : 00 
- " The Wave " ( Style of Eye remix ) – 5 : 01 
- 
- = = Credits and personnel = = 
- 
- Credits are adapted from the Happy to You liner notes . 
- 
- = = Charts = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Washington State Route 516 = 
- 
- State Route 516 ( SR 516 ) is a 16 @.@ 49 @-@ mile @-@ long ( 26 @.@ 54 km ) state highway in the U.S. state of Washington , serving communities in southern King County . The highway travels east as the Kent @-@ Des Moines Road and the Kent @-@ Kangley Road from a concurrency with SR 509 in Des Moines through Kent and Covington to an intersection with SR 169 in Maple Valley . SR 516 , designated as part of the National Highway System within Kent , intersects three major freeways in the area : Interstate 5 ( I @-@ 5 ) in western Kent , SR 167 in downtown Kent , and SR 18 in Covington . The roadway , built in the 1890s , was codified in 1937 as Secondary State Highway 1K ( SSH 1K ) from Des Moines to Kent and SSH 5A from Kent to Maple Valley . The two highways were combined during the 1964 highway renumbering to form SR 516 on its current route . 
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- = = Route description = = 
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- SR 516 begins in Des Moines at Marine View Drive as SR 509 turns north towards <unk> near the East Passage of Puget Sound . The two concurrent highways travel east on the Kent @-@ Des Moines Road past <unk> Community College and Mount <unk> High School to an intersection with SR 99 in western Kent , where SR 509 turns south towards <unk> . Shortly thereafter , SR 516 intersects I @-@ 5 at a partial cloverleaf interchange and continues east onto a four @-@ lane divided highway over the Green River and its pedestrian and bicycle trail into downtown Kent . The highway serves as the southern terminus of SR 181 before intersecting SR 167 in a diamond interchange at the western edge of downtown Kent . SR 516 shifts south onto Willis Street and crosses the <unk> Trail and a <unk> rail line before turning north onto Central Avenue and east onto Smith Street near Kent Station . The highway travels southeast along Mill Creek onto the Kent @-@ Kangley Road and serves as the southern terminus of SR 515 at Kent @-@ Meridian High School before leaving Kent for Covington . SR 516 passes Lake Meridian as 272nd Street and intersects SR 18 at a diamond interchange located in Covington . The highway continues east past Pipe Lake and into Maple Valley before crossing the Cedar to Green River Trail and ending at an intersection with SR 169 . 
- Every year , the Washington State Department of Transportation ( WSDOT ) conducts a series of surveys on its highways in the state to measure traffic volume . This is expressed in terms of annual average daily traffic ( AADT ) , which is a measure of traffic volume for any average day of the year . In 2011 , WSDOT calculated that the busiest section of the highway was between SR 181 and the SR 167 interchange , serving 38 @,@ 000 vehicles , while the least busiest section was its western terminus at SR 509 , serving 8 @,@ 900 vehicles . SR 516 between I @-@ 5 and SR 167 within western Kent is designated as part of the National Highway System , which includes roadways important to the national economy , defense , and mobility . 
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- = = History = = 
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- The Kent @-@ Kangley Road was built as a wagon road by King County by the late 1890s and was upgraded to a paved highway after being codified as two highways during the creation of the primary and secondary state highways system in 1937 : SSH 1K and SSH 5A . SSH 1K traveled 12 @.@ 76 miles ( 20 @.@ 54 km ) south from U.S. Route 99 and Primary State Highway 1 ( PSH 1 ) through <unk> and east through Des Moines to US 99 and PSH 1 in Midway . SSH 5A traveled 14 @.@ 50 miles ( 23 @.@ 34 km ) east from US 99 and PSH 1 in Midway across the Green River into Kent , intersecting SSH <unk> , PSH 5 , and SSH <unk> , before ending at the <unk> – <unk> branch of PSH 5 in Maple Valley . The two highways were combined during the 1964 highway renumbering to form SR 516 and codified into law in 1970 . The highway traveled east from SR 509 in Des Moines through I @-@ 5 at Midway , SR 167 in Kent , and SR 18 in Covington to SR 169 in Maple Valley . SR 509 was re @-@ aligned in 1991 onto SR 516 and SR 99 , forming new <unk> , until the completion of a new north – south freeway . 
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- = = Major intersections = = 
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- The entire highway is in King County . 
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- 
- = Stefan Wever = 
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- Stefan Matthew Wever ( born 22 April 1958 ) is a former professional baseball pitcher . He made his Major League Baseball debut , incidentally his only game , with the New York Yankees in 1982 , and had a 0 – 1 record a 27 @.@ 00 earned run average ( ERA ) , and two strikeouts in that game . 
- Born in West Germany , Wever moved to the United States as a child , where he took up baseball . He played baseball in high school and the University of California , Santa Barbara , which led to him being drafted by the New York Yankees . After four seasons in the minor leagues , Wever made his major league debut on 17 September 1982 . In his debut , he suffered a shoulder injury , which he tried to pitch through for two years before having surgery in 1984 . He tried to come back from the injury in 1985 , but retired . After retiring , he opened a bar in San Francisco , which he continues to run . 
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- = = Early life = = 
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- Wever was born in <unk> , West Germany in 1958 . He immigrated to the United States with his mother and twin sister at six and lived in Boston until he was 12 , when he moved to San Francisco . He attended Lowell High School , where he played on the school 's baseball team . During his senior year , Wever helped lead the Lowell Cardinals to the city championship game , and he graduated in 1976 . 
- After graduating from high school , Wever was not looked at by college <unk> due to a lack of competition he faced . As a result , he attended the University of California , Santa Barbara on an academic scholarship , and walked on to the school 's baseball team . In three seasons with the Santa Barbara <unk> , he had 18 wins , 17 losses , 199 strikeouts , and 15 complete games ; the losses and complete games were at that time school records . After his junior year , Wever was drafted by the New York Yankees in the sixth round of the 1979 Major League Baseball draft . He was given a signing bonus of $ 16 @,@ 000 , and officially signed with the team shortly after the draft concluded . 
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- = = Baseball career = = 
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- Wever began his professional career in 1979 with the <unk> Yankees of the New York – Penn League ( <unk> ) . He pitched in ten games for the team , finishing the season with a 6 – 3 record , a 1 @.@ 77 earned run average ( ERA ) , and 70 strikeouts . In the Yankees ' championship series against the Geneva Cubs , he pitched a shutout and threw nine strikeouts to win the first game and help the Yankees win the <unk> Championship . The following year , Wever was promoted to the Fort Lauderdale Yankees of the Florida State League . That year , he had a 7 – 3 record , a 3 @.@ 64 ERA and 94 innings pitched in 15 games . 
- In 1981 , Wever began the season remaining with Fort Lauderdale . He had a 7 – 3 record and a 2 @.@ 00 ERA in 12 games before being promoted to the Nashville Sounds of the Southern League , the Yankees ' AA affiliate . With Nashville , he had a 5 – 2 record and 2 @.@ 05 ERA in nine appearances . Wever 's pitching coach in Nashville was Hall of Famer Hoyt Wilhelm . Wilhelm felt he had the ability but not the confidence to pitch in the majors , and spent his time in Nashville working on that aspect of Wever 's game . The following season , Weber was almost unanimously named to the Southern League All @-@ Star team , thanks to 11 wins and 116 strikeouts through the end of June . He improved to a 16 @-@ 6 record , a 2 @.@ 78 ERA , and 191 strikeouts , won the Southern League Pitcher of the Year award , and accomplished the pitcher 's Triple Crown , leading the league in wins , ERA , and strikeouts . He led Nashville to the Southern League championship , and right after doing so , the Yankees called Weber up to the major leagues . 
- His first and only major league appearance came against the Milwaukee Brewers on 17 September 1982 . The first two batters he faced were Paul <unk> and Robin <unk> , both future Hall of <unk> , one of only a few players in history to do so . Partway through the first inning , he felt a <unk> in his shoulder ; not wanting to leave his first game early , he pitched through it , and allowed five runs in the first . Partway through the third , after three more runs allowed , Wever was taken out of the game . He pitched for 2 ⅔ innings and had eight earned runs , two strikeouts , and three wild pitches . Entering the 1983 season , Wever was projected to be the fifth starter in the Yankees ' starting rotation . Because of continued pain in his shoulder , he instead spent the season with the AAA Columbus <unk> , where he went 1 – 4 with a 9 @.@ 78 ERA in seven appearances . 
- Wever spent 1984 with Fort Lauderdale , where he went 1 – 3 in seven games . After the seven games , he visited Dr. James Andrews , who diagnosed the <unk> he suffered two years earlier as a torn rotator cuff and torn <unk> ; it explained why he had been throwing 85 mph since the injury , compared to 95 mph beforehand . He had surgery shortly afterward , and spent the rest of the year <unk> the injury . He attempted a comeback in 1985 with the Albany @-@ <unk> Yankees , and had a 4 @.@ 91 ERA in five games with the team . In June , having continued to pitch through shoulder pain , Wever retired from baseball and ended his professional career . 
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- = = Post @-@ playing career = = 
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- After retiring from baseball , Wever returned to school , and earned a bachelor 's degree in English literature from the University of California , Berkeley . He married Melinda in 1988 , and three years later opened up the Horseshoe Tavern , a bar in San Francisco 's Marina District , which he continues to run . 
- While working at his bar , Wever made a return to baseball in a coaching role . He was named varsity baseball coach at <unk> High School in <unk> , California in 2008 , after having volunteered for the freshman team the year before . He was forced to resign in 2010 due to a diagnosis of large @-@ cell lymphoma . He went on to continue coaching youth baseball camps and leagues , and was bench coach for the San Rafael <unk> in 2013 . He now spends three days a week feeding the homeless at SF 's St. Anthony 's <unk> Room . 
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- 
- = Djedkare Isesi = 
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- Djedkare Isesi ( known in Greek as Tancherês ) was an Ancient Egyptian pharaoh , the eighth and penultimate ruler of the Fifth Dynasty in the late 25th century to mid 24th century BCE , during the Old Kingdom period . Djedkare succeeded Menkauhor Kaiu and was in turn succeeded by Unas . His relations to both of these pharaohs remain uncertain , although it is often conjectured that Unas was Djedkare 's son owing to the smooth transition between the two . 
- Djedkare likely enjoyed a long reign of over 40 years , which heralded a new period in the history of the Old Kingdom . Breaking with a tradition followed by his predecessors since the time of Userkaf , Djedkare did not build a temple to the sun god Ra , possibly reflecting the rise of Osiris in the Egyptian pantheon . More significantly , Djedkare effected comprehensive reforms of the Egyptian state administration , the first undertaken since the inception of the system of ranking titles . He also reorganised the funerary cults of his forebears buried in the necropolis of Abusir and reformed the corresponding priesthood . Djedkare commissioned expeditions to Sinai to procure copper and turquoise , to Nubia for its gold and <unk> and to the fabled Land of Punt for its incense . One such expedition had the earliest recorded instance of <unk> divination undertaken to ensure an expedition 's success . The word " <unk> " , meaning gold , to designate Nubia is first recorded during Djedkare 's reign . Under his rule , Egypt also entertained continuing trade relations with the <unk> coast and made punitive raids in Canaan . In particular , one of the earliest depictions of a battle or siege scene was found in the tomb of one of Djedkare 's subjects . 
- Djedkare was buried in a pyramid in Saqqara named Nefer Djedkare ( " Djedkare is perfect " ) , which is now ruined owing to theft of stone from its outer casing during antiquity . The burial chamber still held Djedkare 's mummy when it was excavated in the 1940s . <unk> of the mummy revealed that he died in his fifties . Following his death , Djedkare was the object of a cult that lasted at least until the end of the Old Kingdom . He seemed to have been held in particularly high esteem during the mid @-@ Sixth Dynasty , whose pharaohs lavished rich offerings on his cult . Archaeological evidence suggests the continuing existence of this funerary cult throughout the much later New Kingdom period ( c . 1550 – <unk> BCE ) . Djedkare was also remembered by the Ancient Egyptians as the king of vizier Ptahhotep , the purported author of The <unk> of Ptahhotep , one of the earliest pieces of <unk> wisdom literature . 
- The reforms implemented by Djedkare are generally assessed negatively in modern <unk> as his policy of decentralization created a virtual feudal system that transferred much power to the high and provincial administrations . Some Egyptologists such as <unk> <unk> argue that this contributed heavily to the collapse of the Egyptian state during the First Intermediate Period , c . 200 years later . These conclusions are rejected by Nigel Strudwick , who says that in spite of Djedkare 's reforms , Ancient Egyptian officials never amassed enough power to rival that of the king . 
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- = = <unk> = = 
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- = = = Contemporaneous sources = = = 
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- Djedkare is well attested in sources contemporaneous with his reign . The tombs of many of his courtiers and family members have been discovered in <unk> , Saqqara and Abusir . They give insights into the administrative reforms that Djedkare conducted during his reign and , in a few cases , even record letters that the king sent to his officials . These letters , inscribed on the walls of tombs , typically present royal praises for the tomb owner . 
- Another important source of information about Egypt during the reign of Djedkare Isesi is the Abusir papyri . These are administrative documents , covering a period of 24 years during Djedkare 's reign ; they were discovered in the mortuary temples of pharaohs <unk> <unk> , <unk> and queen <unk> II . In addition to these texts , the earliest letters on papyrus preserved to the present day also date to Djedkare 's reign , dealing with administrative or private matters . 
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- = = = Historical sources = = = 
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- Djedkare is attested in four ancient Egyptian king lists , all dating to the New Kingdom . The earliest of these is the <unk> king list , dating to the reign of Thutmose III ( <unk> – 1425 BCE ) , where Djedkare is mentioned on the fifth entry . Djedkare 's <unk> occupies the 32nd entry of the Abydos King List , which was written during the reign of <unk> I ( <unk> – 1279 BCE ) . Djedkare is also present on the Saqqara <unk> ( 31st entry ) where he is listed under the name " <unk> " , probably because of a scribal error . Djedkare 's <unk> is given as " <unk> " on the Turin canon ( third column , 24th row ) , probably because of a <unk> affecting the original document from which the canon was copied during the reign of <unk> II ( 1279 – 1213 BCE ) . The Turin canon credits Djedkare with 28 years of reign . 
- In addition to these sources , Djedkare is mentioned on the <unk> <unk> dating to the 12th Dynasty ( c . 1990 – 1800 BCE ) . The papyrus records The <unk> of Ptahhotep and gives Djedkare 's nomen " Isesi " to name the pharaoh whom the purported authors of the <unk> , vizier Ptahhotep , served . Djedkare was also probably mentioned in the <unk> , a history of Egypt written in the 3rd century BCE during the reign of Ptolemy II ( 283 – <unk> BCE ) by the Egyptian priest <unk> . No copies of the <unk> have survived to this day and it is known to us only through later writings by <unk> Julius <unk> and Eusebius . <unk> relates that a pharaoh " Tancherês " ( Ancient Greek <unk> ) reigned for 44 years as the eighth and penultimate king of the Fifth Dynasty . Given its position within the dynasty , Tancherês is believed to be the Hellenized name of Djedkare Isesi . 
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- = = Family = = 
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- = = = Parents = = = 
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- Djedkare 's parentage is unknown ; in particular his relation with his predecessors Menkauhor Kaiu and Nyuserre Ini cannot be <unk> . Djedkare is generally thought to have been the son of Menkauhor Kaiu , but the two might instead have been brothers and sons of Nyuserre Ini . Another hypothesis suggests that Djedkare and Menkauhor could have been cousins , being sons of Nyuserre and <unk> respectively . The identity of Djedkare 's mother is similarly unknown . 
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- = = = Queens = = = 
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- The name of Djedkare Isesi 's principal wife is not known . An important queen consort whose name is lost was very likely the owner of a large pyramid complex located to the northeast of Djedkare 's pyramid in Saqqara . This could indicate that she was the mother of Djedkare 's successor , Unas , or that Djedkare owed the throne to her . The very high status of this queen is suggested by some features of her funerary complex that are otherwise reserved to kings : her pyramid has its own satellite pyramid , has a causeway leading from a valley temple up to a mortuary temple devoted to the cult of the queen and had an entrance hall <unk> @-@ <unk> , an open courtyard and a square <unk> . Furthermore , some reliefs showing the queen had been reworked with royal insignia and <unk> added above her head . Since the construction of the queen 's pyramid was apparently undertaken after the planning of Djedkare 's pyramid and her relief had been reworked , the Egyptologist Klaus Baer suggests that this queen may have ruled after the death of Djedkare , playing an important role in his succession . This is rejected by other Egyptologists , such as Michel <unk> , owing to the lack of evidence for a regency or <unk> between Djedkare and Unas . 
- The Egyptologist Wilfried <unk> has proposed that this pyramid was initially intended for queen Meresankh IV , whom he and <unk> see as a wife of Djedkare . <unk> contends that Meresankh was finally buried in a smaller mastaba in Saqqara North after she fell into disgrace . Alternatively , Aidan Dodson and <unk> Hilton have proposed that she was a wife of the preceding king , Menkauhor Kaiu . 
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- = = = Sons = = = 
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- Only one son of Djedkare Isesi has been identified for certain , Neserkauhor , who bore the title of " eldest beloved king 's son of his body " . Neserkauhor also bore the title of <unk> @-@ pat , showing that he was an important member of the royal court , as well as a priestly title " Greatest of the Five in the temple of <unk> " , suggesting that he may have been a vizier or had similar occupations . 
- As well as Neserkauhor , there is indirect evidence that princes Raemka and Kaemtjenent are sons of Djedkare based on the dating and general location of their tombs in Saqqara . For example , the tomb of Kaemtjenent mentions vizier <unk> , who served during the reign of Djedkare . Raemka also bore the title of " king 's son of his body " , almost exclusively reserved to true princes of royal blood . The locations of Raemka 's and Kaemtjenent 's tombs have led some Egyptologists to believe that both princes are sons of queen Meresankh IV buried nearby , who would thus be one of Djedkare 's wives . These conclusions are debated , in particular in the case of Kaemtjenent , whose title of " king 's son " may have been purely honorific . 
- A high official named Isesi @-@ ankh could have been yet another son of Djedkare Isesi , as suggested by his name meaning " Isesi lives " . Yet , similarities in the titles and locations of the tombs of Isesi @-@ ankh and Kaemtjenent have led Egyptologists to propose that they could instead be brothers and sons of Meresankh IV , or that the former is a son of the latter . Even though Isesi @-@ ankh bore the title of " king 's son " , the Egyptologists Michel <unk> and <unk> <unk> argue that this filiation was fictitious , being only an honorary title . 
- Finally , the successor of Djedkare , Unas , is thought to have been his son in spite of the complete lack of evidence bearing on the question . The main argument in favor of this filiation is that the succession from Djedkare Isesi to Unas seems to have been smooth , as suggested indirectly , for example , by the Abusir papyri . <unk> evidence also comes from the reliefs of Unas ' causeway , which show many officials bearing names incorporating " Isesi " , suggesting at the very least that Unas did not perceive Djedkare as an antagonist . 
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- = = = <unk> = = = 
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- Several daughters of Djedkare Isesi have been identified by the title of " king 's daughter of his body " and the general date of their tomb . These include <unk> , whose filiation is clearly indicated by her other title of " Beloved of Isesi " , <unk> @-@ Isesi , <unk> , and <unk> . Less certain is the filiation of <unk> III , wife of vizier Senedjemib <unk> , who bore the title of " king 's daughter of his body " . It is debated whether this title indicates a true filiation or if it is only honorary . 
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- = = <unk> = = 
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- The relative chronological position of Djedkare Isesi as the eighth and penultimate ruler of the Fifth Dynasty , succeeding Menkauhor Kaiu and preceding Unas on the throne , is well established by historical sources and confirmed by archaeological evidence . 
- The duration of Djedkare 's reign is much less certain . Djedkare 's time on the throne is well documented by the Abusir papyri , numerous royal seals and contemporary inscriptions ; taken together , they indicate a fairly long rule for this king . While the Turin canon credits him with 28 years of reign , there is direct evidence for an even longer reign . Several artefacts and inscriptions have been uncovered relating to Djedkare 's <unk> or " sed " festival , normally celebrated only after 30 years of reign . For example , the tomb of one of Djedkare 's viziers , Senedjemib Inti , relates construction works undertaken during the year of the 16th cattle count in preparation for the festival ceremonies . An alabaster vase now on display at the Louvre museum bears an inscription celebrating Djedkare 's first sed festival , indicating in all likelihood that he reigned beyond his 30th year on the throne . 
- One of the Abusir papyri was found to be dated to the " Year of the 22nd Count , IV <unk> day 12 " , constituting Djedkare 's latest known date . This date might correspond to any time from the 32nd year of Djedkare 's reign up to his 44th year on the throne , depending on whether the cattle count was once every two years or once every year and a half . The higher estimate is close to <unk> 's 44 @-@ year figure credited to Tancherês , the Hellenized name of Djedkare , although this may just be <unk> . Modern estimates thus put Djedkare 's reign length as certainly more than 33 years and , if the cattle count was regularly biennial , at least 42 to 44 years . This makes Djedkare the longest reigning king of the Fifth Dynasty . 
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- = = Reign = = 
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- The reign of Djedkare Isesi heralded a new period in the history of the Old Kingdom . First , Djedkare Isesi did not build a sun temple , as his predecessors had done since the time of Userkaf , some 80 years earlier . This may be a result of the increased prominence of Osiris compared with the sun god Ra during the late Fifth Dynasty . The importance of this cult becomes manifest when the Pyramid Texts of the pyramid of Unas are inscribed a few decades later . In this context , it is perhaps noteworthy that the only known statue of Djedkare Isesi was discovered in the ruins of the temple of Osiris , in Abydos . Another manifestation of the winds of change during Djedkare 's time on throne is the confirmation of the relocation of the royal necropolis from Abusir , where it had been since the reign of <unk> , to Saqqara , where Menkauhor Kaiu , Djedkare and his successor , Unas , built their pyramids . Abusir may have become overcrowded by the time of Menkauhor 's accession and the capital may have been shifted south to Saqqara along with the royal necropolis around the same time . The abandonment of Abusir as a royal necropolis and the termination of sun temple building are possibly related given the close association between the two since the reign of Userkaf . 
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- = = = Domestic reforms = = = 
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- During his reign Djedkare effected significant reforms of the state administration and priesthood , in particular that pertaining to the funerary cults in the necropolis of Abusir . These <unk> are witnessed by changes in priestly titles and more broadly , in the system of ranking titles of high officials , which was modified for the first time in its existence . For example , the priesthood of the royal pyramids was reorganized , with Djedkare changing the titles and functions of the priests from " priest of king " to " priest of the pyramid " . <unk> of royal blood could once more hold administrative titles , a prerogative they had lost during the early Fifth Dynasty . At the same time , viziers could now hold the prestigious titles of <unk> @-@ pat and <unk> @-@ a and , as " overseer of the royal scribes " , became the head of the scribal administration . At least one vizier , <unk> III , even bore the title of " king 's son of his body " , one of the most distinguished titles at the time and normally reserved to princes of royal blood . Yet neither <unk> III 's father nor his mother seems to have belonged to the royal family . For the period spanning the reign of Djedkare until that of <unk> , viziers were furthermore responsible for the weaponry of the state , both for military and other purposes . Following the reforms undertaken by Djedkare , three viziers would be in office at the same time : two in the <unk> region and a Southern one , the " governor of Upper Egypt " , with a seat at Abydos . In total six viziers were appointed during Djedkare 's reign . 
- Lower ranking officials lost power during the late Fifth Dynasty and were frequently limited to holding only one high title , a departure from the preceding period . Such functions as " overseer of the <unk> " and " overseer of the treasury " disappear from the record some time between Djedkare 's reign and that of <unk> , while men of lower status became head of the legal administration . Consequently , the viziers concentrated more power than before while lower echelons of the state administration were reduced . At the same time , the size of the provincial administration was increased , and it also became more autonomous from the central government . In particular , the <unk> were responsible in their provinces for performing works hitherto conducted by <unk> officials . 
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- = = = Building activities = = = 
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- The main building activity undertaken during the reign of Djedkare Isesi was the construction of his pyramid complex in Saqqara . Djedkare also either completed or undertook restoration works in the funerary complex of Nyuserre Ini in Abusir , as indicated by a now damaged inscription , which must have detailed Djedkare 's activities on the site . Further building works took place in Abusir during the second half of Djedkare 's reign following the curious decision by members of the royal family to be buried there rather than next to Djedkare 's pyramid in Saqqara . A group of <unk> was thus constructed for princess <unk> and her daughter <unk> , princess <unk> , the courtiers <unk> and <unk> , who was buried with his wife <unk> , and prince Neserkauhor . 
- Djedkare Isesi also undertook building activities in relation with his " sed " festival as indicated by a decree that he sent to his vizier Senedjemib Inti on the year of the 16th cattle count , praising him for his work . The decree mentions the construction of a broad rectangular court or artificial lake for the jubilee of the king , some 1000 <unk> long and 400 <unk> wide , amounting to c . 525 m × 231 m ( 1 @,@ <unk> ft × <unk> ft ) . The court was located within the precincts of a palace built for the ceremonies of the " sed " festival , which was probably located in the vicinity of his pyramid . Another decree addressed to Senedjemib Inti and later inscribed on the walls of his mastaba records the decoration of a chapel of Hathor in the palace of the king . This chapel was most likely built during his reign . 
- Djedkare may have left some of his monuments unfinished at his death , as suggested by several relief @-@ bearing blocks inscribed with his name and which were found reused in the pyramid of king Unas . Their original setting remains unknown . 
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- = = = Activities outside Egypt = = = 
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- = = = = <unk> to mines and quarries = = = = 
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- Three or four rock inscriptions dating to Djedkare 's reign have been found in the Wadi <unk> in Sinai , where mines of copper and semi @-@ precious stones were exploited throughout the Old Kingdom , from the Fourth until the Sixth Dynasty . These inscriptions record three expeditions sent to look for turquoise : the earliest one , dated to the third or fourth cattle count – possibly corresponding to the sixth or eighth year of <unk> 's reign – explicitly recalls the arrival of the mining party to the " hills of the turquoise " after being given " divine authority for the finding of semi @-@ precious stones in the writing of the god himself , [ as was enacted ] in the broad court of the temple <unk> " . This sentence could indicate the earliest known record of an <unk> divination undertaken in order to ensure the success of the expedition prior to its departure , <unk> being the sun temple of Userkaf . Another inscription dating to the year of the ninth cattle count – possibly Djedkare 's 18th year on the throne – shows the king " <unk> all foreign lands . <unk> the chief of the foreign land " . The expedition that left this inscription comprised over 1400 men and administration officials . Some Egyptologists have proposed that these men were also sent to mine copper . 
- These expeditions departed Egypt from the port of Ain <unk> , on the western shore of the Gulf of Suez , as revealed by papyri and seals bearing Djedkare Isesi 's name found on the site . The port comprised large galleries carved into the sandstone serving as living quarters and storage places . The wall of one such gallery was inscribed with a text mentioning yet another expedition to the hills of turquoise in the year of the seventh cattle count – possibly Djedkare 's 14th year on the throne . 
- South of Egypt , Djedkare dispatched at least one expedition to the <unk> quarries located 65 km ( 40 mi ) north @-@ west of Abu <unk> . Djedkare was not the first king to do so , as these quarries were already exploited during the Fourth Dynasty and continued to be so during the Sixth Dynasty and later , in the Middle Kingdom period ( c . 2055 BCE – c . 1650 BCE ) . 
- Djedkare probably also exploited gold mines in the Eastern Desert and in Nubia : indeed , the earliest mention of the " land of gold " – an Ancient Egyptian term for Nubia – is found in an inscription from the mortuary temple of Djedkare Isesi . 
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- = = = = Trade relations = = = = 
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- Egypt entertained continuing trade relations with the Levant during Djedkare 's reign , possibly as far north as Anatolia . A gold cylinder seal bearing the <unk> of Djedkare Isesi together with the <unk> of Menkauhor Kaiu is now on display at the Museum of Fine Arts , Boston . The seal , whose gold may originate from the <unk> river valley in western Anatolia , could <unk> to wide ranging trade @-@ contacts during the later Fifth Dynasty , but its <unk> remains <unk> . 
- Trade contacts with Byblos , on the coast of modern @-@ day Lebanon , are suggested by a fragmentary stone vessel unearthed in the city and bearing the inscription " King of Upper and Lower Egypt , Djedkare [ living ] forever " . A biographical inscription discovered in the tomb of Iny , a Sixth Dynasty official , provides further evidence for an Egyptian expedition to Byblos during Djedkare 's reign . Iny 's inscription relates his travels to procure <unk> <unk> and lead or tin for pharaoh Merenre , but starts by recounting what must have been similar events taking place under Djedkare . 
- To the south of Egypt , Djedkare also sent an expedition to the fabled land of Punt to procure the <unk> used as incense in the Egyptian temples . The expedition to Punt is referred to in the letter from Pepi II <unk> to Harkuf some 100 years later . Harkuf had reported that he would bring back a " dwarf of the god 's dancers from the land of the horizon dwellers " . Pepi mentions that the god 's <unk> <unk> had returned from Punt with a dwarf during the reign of Djedkare Isesi and had been richly rewarded . The decree mentions that " My Majesty will do for you something greater than what was done for the god 's <unk> <unk> in the reign of Isesi , reflecting my <unk> 's <unk> to see this dwarf " . 
- Djedkare 's expedition to Punt is also mentioned in a contemporaneous <unk> found in <unk> , a locality of Lower Nubia some 150 km ( 93 mi ) south of <unk> , where Isesi 's <unk> was discovered . 
- 
- = = = = Warfare = = = = 
- 
- Not all relations between Egypt and its neighbors were peaceful during Djedkare 's reign . In particular , one of the earliest known depictions of a battle or city being besieged is found in the tomb of Inti , an official from the 21st <unk> of Upper Egypt , who lived during the late Fifth Dynasty . The scene shows Egyptian soldiers scaling the walls of a near eastern fortress on <unk> . More generally , ancient Egyptians seem to have regularly organised punitive raids in Canaan during the later Old Kingdom period but did not attempt to establish a permanent dominion there . 
- 
- = = Pyramid = = 
- 
- Djedkare built his pyramid in South Saqqara . It was called Nefer Isesi or Nefer Djedkare in Ancient Egyptian , variously translated as " Isesi / Djedkare is beautiful " or " Isesi / Djedkare is perfect " . It is known today as " <unk> el @-@ <unk> El @-@ <unk> " , meaning " the Southern Sentinel pyramid " , because it stands on the edge of the Nile valley . 
- The pyramid originally comprised six or seven steps made of irregular and roughly hewn limestone blocks and mortar , of which only three survive . This core was overlaid by casing stones of white <unk> limestone , which have been stolen in antiquity . At the time of its construction the pyramid stood 52 m ( 171 ft ) high with a base length of 78 @.@ 75 m ( 258 @.@ 4 ft ) and an inclination angle of 52 ° . 
- In the interior of the pyramid a descending passage led , behind three granite <unk> , to an <unk> , three magazine rooms and the burial chamber . In it , pieces of alabaster and a <unk> bead on a gold thread were discovered as well as many fragments of what was originally a large sarcophagus of dark grey basalt . The sarcophagus was sunk into the floor of the burial chamber together with a niche for the <unk> chest of the king to its north @-@ east . An almost complete mummy was discovered in the remnants of the sarcophagus . An examination by Ahmed <unk> of these skeletal remains , excavated in the mid @-@ 1940s under the direction of <unk> Salam Hussein , suggests that Djedkare died at the age of 50 to 60 years old . 
- To the east of the pyramid , Djedkare 's mortuary temple was laid out . The east facade of the mortuary temple was flanked by two massive stone structures , which resemble the later <unk> . The mortuary temple is connected via a yet <unk> causeway to a valley temple . 
- 
- = = Legacy = = 
- 
- 
- = = = Impact of the reforms = = = 
- 
- For Nigel Strudwick , the reforms of Djedkare Isesi were undertaken as a reaction to the rapid growth of the central administration in the first part of the Fifth Dynasty which , Baer adds , had amassed too much political or economic power in the eyes of the king . Joyce <unk> sees the reign of Djedkare Isesi as the very beginning of a decline in the importance of the king , in conjunction with the gradual rise of the power wielded by the high and provincial administration . <unk> with this trend is a process of decentralization , with local loyalties slowly <unk> allegiance to the central state . Since offices and in particular , the <unk> , could be inherited the reforms of Djedkare Isesi created a " virtual feudal system " as Nicolas <unk> writes , with much power in the hands of a few <unk> officials . This is best witnessed by the large , magnificent mastaba tombs that Djedkare 's viziers built . In this context , Djedkare 's reforms of the ranking system might have been an attempt at maintaining a sprawling administration under control , yet ultimately failed . For some Egyptologists , such as <unk> <unk> , this failure contributed in no small part to the fall of the Old Kingdom , but others , including Strudwick , believe the reasons of the collapse must be sought elsewhere as the power of an administration official never approached that of the king . 
- The reforms of Djedkare Isesi played an important role in flourishing of the arts during the later Old Kingdom , as artisans and craftsmen could now find many wealthy patrons beyond the king . This created a surge in the number of commissions leading to a general improvement of the artistic works . This also provided the artisans with a new wealth , which they used to build their own large funerary complexes . 
- 
- = = = Funerary cult = = = 
- 
- 
- = = = = Old Kingdom = = = = 
- 
- Djedkare Isesi was the object of a funerary cult established at his death and which lasted until the end of the Old Kingdom nearly 200 years later . Provisions for this cult were produced in several agricultural estates set up during Djedkare 's reign . The names of some of these estates have been found inscribed on the walls of the tombs of his courtiers : " Ra desires that Isesi lives " , " <unk> desires that Izezi live " , " Perfect of emulation is Isesi " , " The <unk> @-@ vessel of Djedkare " , " Perfect of favors is Isesi " , " <unk> makes Isesi live " , " The Ka of Isesi is foremost " , " Dominion belongs to Izezi " , " Work of the command of Izezi " , " <unk> wishes that Izezi lives " , " Horus <unk> Izezi " , " The offerings of Izezi " , and " Izezi is one who loves life " . 
- Djedkare seems to have been held in high esteem during the Sixth Dynasty . For example , Merenre <unk> I chose to place his pyramid complex close to that of Djedkare . In addition , the South Saqqara Stone , a royal annal dating to the reign of Merenre or of his successor Pepi II , records rich offerings being made to Djedkare on behalf of the king . An estimated 92 % of the text inscribed on the stone was lost when it was roughly polished to be reused as a sarcophagus <unk> , possibly in the late First Intermediate ( c . <unk> – 2055 BC ) to early Middle Kingdom period ( c . 2055 – 1650 BC ) . 
- More generally , an historical or literary tradition concerning events in the time of Djedkare seems to have flourished toward the end of Old Kingdom as can be inferred from the tombs of Harkuf and Iny . These two officials were in charge of expeditions to foreign lands – Punt and Byblos – under Merenre I and Pepi II and both relate similar expeditions that took place during the time of Djedkare Isesi . 
- 
- = = = = New Kingdom = = = = 
- 
- The funerary cult of Djedkare Isesi enjoyed a revival during the New Kingdom ( c . 1550 – <unk> BCE ) . For the early part of this period , this is best attested by the <unk> king list , a list of kings commissioned by pharaoh Thutmose III . The list was not meant to be <unk> , rather it gave the names of Thutmose 's <unk> whom he wanted to honor by dedicating offerings . 
- For the later New Kingdom , a relief from the Saqqara tomb of the priest <unk> , dating to the 19th or 20th Dynasty shows three gods faced by several deceased pharaohs . These are <unk> and <unk> , of the Third Dynasty and Userkaf , founder of the Fifth Dynasty . He is followed by a fourth king whose name is damaged but which is often read " Djedkare " or , much less likely , " <unk> " . The relief is an expression of personal piety on <unk> 's behalf , who prayed to the ancient kings for them to recommend him to the gods . 
- 
- 
- = Route 261 ( Delaware – Pennsylvania ) = 
- 
- Delaware Route 261 ( DE 261 ) and Pennsylvania Route 261 ( PA 261 ) , also known as Foulk Road , is a 6 @.@ 88 @-@ mile ( 11 @.@ 07 km ) state highway running through Delaware and Pennsylvania . DE 261 runs 4 @.@ 62 miles ( 7 @.@ 44 km ) through New Castle County , Delaware from an interchange with U.S. Route 202 ( US 202 ) and DE 141 north of Interstate 95 ( I @-@ 95 ) near Fairfax , Delaware , a community north of Wilmington , northeast to the Pennsylvania state line . The road runs through suburban areas of Brandywine Hundred as a four @-@ lane road south of DE 92 and a two @-@ lane road north of DE 92 . At the Pennsylvania state line , Foulk Road becomes PA 261 and continues 2 @.@ 26 miles ( 3 @.@ 64 km ) through Bethel Township in Delaware County , intersecting PA 491 in <unk> Corner before ending at an interchange with US 322 . 
- DE 261 was originally designated along Foulk Road in the 1930s . In the 1960s , most of the route was widened into a four @-@ lane road . The southern terminus at US 202 was reconstructed into an interchange in the 2000s . PA 261 was first designated in 1928 along Foulk Road between the Delaware border and PA 61 and PA 161 in Chelsea . The route was extended north along Valley Brook Road to US 1 in Chester Heights by 1940 . The northern terminus of PA 261 was moved to its current location by 1980 . 
- 
- = = Route description = = 
- 
- 
- = = = Delaware = = = 
- 
- DE 261 begins at an interchange with US 202 and the northern terminus of DE 141 north of <unk> Run State Park near Fairfax , heading northeast on four @-@ lane divided Foulk Road . The road heads near business parks , becoming an undivided road . The route continues through the suburban Brandywine Hundred area , passing residences along with a few businesses . DE 261 remains a four @-@ lane undivided road through this area , occasionally widening into a divided highway at intersections . The route intersects several roads including Murphy Road , <unk> Road , and Silverside Road . Between <unk> and Silverside roads , the road passes to the west of Brandywine High School . Farther northeast , DE 261 comes to the intersection with DE 92 , at which point it turns north and becomes a two @-@ lane undivided road that passes more homes . The route ends at the Pennsylvania state line . 
- DE 261 has an annual average daily traffic count ranging from a high of 22 @,@ 555 vehicles at the Silverside Road intersection to a low of 10 @,@ 414 vehicles at the Pennsylvania border . None of DE 261 is part of the National Highway System . 
- 
- = = = Pennsylvania = = = 
- 
- PA 261 begins at the Pennsylvania state line and continues northeast on two @-@ lane undivided Foulk Road through Bethel Township in Delaware County , passing through residential areas as it crosses <unk> Road . In the community of <unk> Corner , PA 261 crosses PA 491 , at which point the road passes a few businesses . North of this intersection , the road runs between a tank farm to the west and Bethel Springs Elementary School to the east prior to crossing Bethel Road and entering wooded residential neighborhoods . PA 261 ends at an interchange with US 322 , with Foulk Road continuing north to an intersection with Concord Road , Chelsea Road , and Valley Brook Road . 
- PA 261 has an annual average daily traffic count ranging from a high of 9 @,@ 400 vehicles between the Delaware border and PA 491 to a low of 7 @,@ 200 vehicles between the Bethel Road intersection and US 322 . None of PA 261 is part of the National Highway System . 
- 
- = = History = = 
- 
- 
- = = = Delaware = = = 
- 
- By 1920 , what is now DE 261 existed as a county road . When Delaware first assigned state highway numbers by 1936 , what is now DE 261 had been upgraded to a state highway , but did not receive a number at that time . By 1938 , DE 261 was designated onto its current alignment between US 202 and the Pennsylvania border , continuing the PA 261 designation . DE 261 was widened into a four @-@ lane road between US 202 and Silverside Road by 1966 . The four @-@ lane portion was extended north to <unk> Road a year later . In summer 2007 , construction on converting the intersection with US 202 into interchange was completed as part of the Blue Ball Properties project , a project undertaken to improve roads in this area as part of <unk> locating their North American headquarters to the area . DE 141 was also realigned to intersect US 202 and DE 261 at this interchange . The total cost of the project was $ 123 million . 
- 
- = = = Pennsylvania = = = 
- 
- When Pennsylvania first legislated its highways in 1911 , what would become PA 261 was legislated as part of Legislative Route 180 between the Delaware border and Chelsea . PA 261 was first designated in 1928 to run from the Delaware border to PA 61 ( Concord Road ) and PA 161 ( Chelsea Road ) in Chelsea , following Foulk Road . By 1940 , PA 261 was extended north along Valley Brook Road from US 322 ( which replaced PA 61 ) to US 1 in Chester Heights . By 1980 , the northern terminus of PA 261 was truncated to its current location at the interchange with US 322 . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Comair Flight 5191 = 
- 
- Comair Flight 5191 , marketed as Delta Connection Flight 5191 , was a scheduled United States ( US ) domestic passenger flight from Lexington , Kentucky , to Atlanta , Georgia , operated on behalf of Delta Connection by Comair . On the morning of August 27 , 2006 , at around 06 : 07 <unk> , the <unk> Canadair Regional Jet <unk> that was being used for the flight crashed while attempting to take off from Blue Grass Airport in Fayette County , Kentucky , four miles ( 6 kilometers ) west of the central business district of the City of Lexington . 
- The aircraft was assigned the airport 's runway 22 for the takeoff , but used runway 26 instead . Runway 26 was too short for a safe takeoff , causing the aircraft to overrun the end of the runway before it could become airborne . It crashed just past the end of the runway , killing all 47 passengers and two of the three crew . The flight 's first officer was the only survivor . 
- Although not the pilot in command , according to the cockpit voice recorder transcript , the first officer was the pilot flying at the time of the accident . In the National Transportation Safety Board report on the crash , investigators concluded that the likely cause of the crash was pilot error . 
- 
- = = Flight details = = 
- 
- The flight was sold under the Delta Air Lines brand as Delta Connection Flight 5191 ( <unk> ) and was operated by Comair as Comair Flight 5191 ( <unk> ) . It is usually identified as Comair Flight 5191 . 
- The flight had been scheduled to land at <unk> @-@ Jackson Atlanta International Airport at 7 : 18 a.m. 
- The aircraft involved was a 50 @-@ seat <unk> Canadair Regional Jet <unk> @-@ <unk> , serial number <unk> . Manufactured in Canada in January 2001 , it was delivered to the airline on January 30 , 2001 . 
- 
- = = Crash = = 
- 
- The aircraft was assigned the airport 's Runway 22 for the takeoff , but used Runway 26 instead . Analysis of the cockpit voice recorder ( <unk> ) indicated the aircraft was cleared to take off from Runway 22 , a 7 @,@ <unk> feet ( 2 @,@ 135 m ) strip used by most airline traffic at Lexington . Instead , after confirming " Runway two @-@ two " , Captain Jeffrey Clay <unk> onto Runway 26 , an <unk> secondary runway only 3 @,@ 500 feet ( 1 @,@ 100 m ) long , and turned the controls over to First Officer James Polehinke for takeoff . The air traffic controller was not required to maintain visual contact with the aircraft ; after clearing the aircraft for takeoff , he turned to perform administrative duties and did not see the aircraft taxi to the runway . 
- NTSB investigators concluded that the likely cause of the crash was pilot error . The board concluded that Clay and Polehinke ignored clues that they were on the wrong runway , failed to confirm their position on the runway and talked too much , in violation of " sterile cockpit " procedures . Comair later accepted responsibility for the crash , but also placed blame on the airport , for what it called poor runway signs and markings , and the Federal Aviation Administration , which had only one air traffic controller on duty , contrary to a <unk> it had previously issued to have two workers on overnight shifts . A judge ruled that , since it was owned by county governments , the airport had sovereign immunity and could not be sued by Comair . 
- Based upon an estimated takeoff weight of 49 @,@ <unk> pounds ( 22 @,@ 265 kg ) , the manufacturer calculated a speed of 138 knots ( 159 miles per hour or 256 kilometers per hour ) and a distance of 3 @,@ <unk> feet ( 1 @,@ 141 m ) would have been needed for rotation ( increasing nose @-@ up pitch ) , with more runway needed to achieve lift @-@ off . At a speed approaching 100 knots ( 120 mph ) , Polehinke remarked , " That is weird with no lights " referring to the lack of lighting on Runway 26 – it was about an hour before <unk> . " Yeah " , confirmed Clay , but the flight data recorder gave no indication either pilot tried to <unk> the takeoff as the aircraft accelerated to 137 knots ( 158 mph ) . 
- Clay called for rotation but the aircraft <unk> off the end of the runway before it could lift off . It then struck a low earthen wall adjacent to a ditch , becoming momentarily airborne , <unk> the airport perimeter fence with its landing gear , and smashed into trees , separating the fuselage and flight deck from the tail . The aircraft struck the ground about 1 @,@ 000 feet ( 300 m ) from the end of the runway . Forty @-@ nine of the 50 people on board perished in the accident ; most of them died instantly in the initial impact . The resulting fire destroyed the aircraft . 
- 
- = = Victims = = 
- 
- All 47 passengers and two of the three crew members on board the flight died . Comair released the passenger manifest on August 29 , 2006 . 
- Most of the passengers were US citizens from the Lexington area , ranging in age from 16 to 72 . They included a young couple who had been married the previous day and were traveling to California on their honeymoon . 
- A memorial service for the victims was held on August 31 , 2006 , at the Lexington Opera House . A second public memorial service was held on September 10 , 2006 , at <unk> Arena in Lexington . The Lexington Herald @-@ Leader published a list of the victims with short biographies . 
- The Flight 5191 Memorial Commission was established shortly after the crash to create an appropriate memorial for the victims , first <unk> , and community that supported them . The Commission chose the University of Kentucky <unk> as its memorial site . 
- 
- = = Survivor = = 
- 
- James Polehinke , the first officer , suffered serious injuries , including multiple broken bones , a collapsed lung , and severe bleeding . Lexington @-@ Fayette and airport police officers pulled Polehinke out of the wreckage . Polehinke underwent surgery for his injuries , including an <unk> of his left leg . Doctors later determined that Polehinke had suffered brain damage and has no memory of the crash or the events leading up to it . As of August 2007 , Polehinke was a wheelchair user . During the same month , Polehinke filed a lawsuit against the airport and the company that designed the runway and taxi lights . A January 2014 episode of Piers Morgan Live reported that Polehinke was <unk> . 
- The estates or families of 21 of the 47 passengers filed lawsuits against Polehinke . In response , Polehinke 's attorney , William E. Johnson , raised the possibility of <unk> negligence on the part of the passengers . When asked by the <unk> ' attorney , David <unk> , what that meant , Johnson replied that " ... ( they ) ... should have been aware of the dangerous conditions that existed in that there had been considerable media coverage about the necessity of improving runway conditions at the airport . " At the time Johnson submitted the <unk> negligence defense , he had not yet been able to speak to Polehinke himself . By the time newspapers reported on the court documents , Johnson said he had already told <unk> , who criticized the statements , that he would withdraw the argument . Prior to his employment by Comair , Polehinke worked for <unk> International as a captain . 
- 
- = = Aftermath = = 
- 
- During the course of its investigation , the Federal Aviation Administration ( FAA ) discovered that tower staffing levels at Blue Grass Airport violated an internal policy as reflected in a November 16 , 2005 , memorandum requiring two controllers during the overnight shift : one in the tower working clearance , ground , and tower frequencies , and another , either in the tower or <unk> at Indianapolis Center , working <unk> ( radar ) . At the time of the accident , the single controller in the tower was performing both tower and radar duties . On August 30 , 2006 , the FAA announced that Lexington , as well as other airports with similar traffic levels , would be staffed with two controllers in the tower around the clock effective immediately . 
- Comair discovered after the accident that all of its pilots had been using an airport map that did not accurately reflect changes made to the airport layout during ongoing construction work . The NTSB later determined that this did not contribute to the accident . Construction work was halted after the accident on the orders of Fayette Circuit Judge Pamela <unk> in order to preserve evidence in the crash pending the inspection by safety experts and attorneys for the families of the victims . 
- In April 2007 , acting on a recommendation made by the National Transportation Safety Board ( NTSB ) during its investigation of Comair 5191 , the FAA issued a safety notice that reiterated advice to pilots to positively confirm their position before crossing the hold @-@ short line onto the take @-@ off runway , and again when initiating takeoff . And in May , acting on another NTSB recommendation , the FAA advised that pilot training should include specific guidance on runway lighting requirements for take @-@ off at night . 
- The NTSB released several reports on January 17 , 2007 , including transcripts and recordings of the <unk> and an engineering report . 
- In April 2007 , the NTSB made four further recommendations , three measures to avoid fatigue affecting the performance of air traffic controllers , and one to prevent controllers from carrying out non @-@ essential administrative tasks while aircraft are taxiing under their control . Although these recommendations were published during the course of the NTSB 's investigation into the accident to Comair Flight 5191 , they were in part prompted by four earlier accidents , and the Board was unable to determine whether fatigue contributed to the Comair accident . 
- In July 2007 , a flying instructor for Comair testified that he would have failed both pilots for violating <unk> <unk> Rules . Later the same month , the NTSB released its final report into the accident , citing this " non @-@ <unk> conversation " as a contributing factor in the accident . 
- In July 2008 , United States District Judge Karl Forester ruled Delta will not be held liable for the crash , because while Comair is a wholly owned subsidiary of the Atlanta @-@ based airline , Comair maintains its own management and policies , and employs its own pilots . In December of the following year , Forester granted a passenger family 's motion for " partial summary judgment " determining , as a matter of law , that Comair 's flight crew was <unk> , and that this negligence was a substantial factor causing the crash of Flight 5191 . 
- Runway 8 / 26 on Blue Grass Airport was closed on March 2009 , and the new 4000 foot runway , runway 9 / 27 , opened on August 4 , 2010 . This runway has been built on a separate location not connected to the runway 22 . 
- Families of 45 of the 47 passengers sued Comair for negligence . ( Families of the other two victims settled with the airline before filing litigation . ) Three sample cases were due to be heard on August 4 , 2008 ; but the trial was indefinitely postponed after Comair reached a settlement with the majority of the families . Cases brought by Comair against the airport authority and the FAA , arguing each should share in the compensation payments , are now resolved . The case against the airport authority was dismissed on sovereign immunity grounds , and this ruling was upheld by the Kentucky Supreme Court on October 1 , 2009 . In Comair 's case against the United States , a settlement was reached with the United States agreeing to pay 22 % of the liability for the crash , while Comair agreed to pay the remaining 78 % . 
- All but one of the passengers ' families settled their cases . After a four @-@ day jury trial in Lexington , Kentucky , that ended on December 7 , 2009 , the estate and daughters of 39 ‑ year ‑ old Bryan Woodward were awarded <unk> damages in the amount of $ 7 @.@ 1 million . While Comair challenged this verdict as excessive , on April 2 , 2010 , Judge Forester overruled Comair 's objections and upheld the verdict . 
- The Woodward case , formally known as <unk> v. Comair , was set for a punitive damages jury trial July 19 , 2010 . In that trial a different jury was to decide whether Comair was guilty of gross negligence that was a substantial factor causing the crash and , if so , the amount of any punitive damages the jury deemed appropriate . The decision to allow a jury trial was reversed in a later hearing , with the judge ruling that the company couldn 't be punished for the " <unk> conduct " of its pilot . 
- 
- = = <unk> cause = = 
- 
- During a public meeting on July 26 , 2007 , the NTSB announced the probable cause of the accident , as follows : 
- The National Transportation Safety Board determines that the probable cause of this accident was the flight crew members ' failure to use available <unk> and aids to identify the airplane 's location on the airport surface during taxi and their failure to cross @-@ check and verify that the airplane was on the correct runway before takeoff . <unk> to the accident were the flight crew 's <unk> conversations during taxi , which resulted in a loss of positional awareness and the Federal Aviation Administration 's failure to require that all runway <unk> be authorized only by specific air traffic control clearances . 
- Captain Clay 's wife strongly disputes laying primary blame on the pilots , stating that other factors contributed , " including an under @-@ staffed control tower and an inaccurate runway map " . 
- 
- = = Similar accidents and incidents = = 
- 
- In 1993 , a commercial jet at Blue Grass Airport was cleared for takeoff on Runway 22 but mistakenly took Runway 26 instead . Tower personnel noticed the mistake and canceled the aircraft 's takeoff clearance just as the crew realized their error . The aircraft subsequently made a safe departure from Runway 22 . 
- In January 2007 , a <unk> was cleared to take off at Blue Grass Airport on runway 22 , but mistakenly turned onto runway 26 . <unk> clearance was canceled by the local controller prior to the start of the takeoff roll . 
- On October 31 , 2000 , the crew of Singapore Airlines Flight 006 mistakenly used a closed runway for departure from <unk> Kai @-@ <unk> International Airport , <unk> . The Boeing <unk> @-@ 400 collided with construction equipment during the takeoff roll , resulting in the deaths of 83 of the 179 passengers and crew on board . 
- 
- 
- = Berg ( station ) = 
- 
- Berg is a station on the Sognsvann Line ( line 6 ) of the Oslo Metro in Norway . Located between Ullevål stadion and Tåsen stations , it is the first station after the Ring Line leaves the Sognsvann Line . The station is located 6 @.@ 1 kilometres ( 3 @.@ 8 mi ) from <unk> station . Berg is amongst the original stations on the line , and was opened on 10 October 1934 . It was upgraded and rebuilt in the 1990s , when the Sognsvann Line was upgraded from light rail to rapid transit standard . Three accidents have taken place at Berg station , the latest in 2008 . The area around the station is mainly residential . Berg Upper Secondary School is located approximately 100 metres ( 330 ft ) from the station . 
- 
- = = History = = 
- 
- Berg station was opened on 10 October 1934 , when the <unk> owned company <unk> had completed the Sognsvann Line from <unk> to Sognsvann . Residential areas at <unk> , Tåsen and Berg were starting to grow , in pace with a larger suburban development plan . <unk> bought new property along the line , upon which new houses were to be designed by the Norwegian architect <unk> <unk> ( 1886 – 1977 ) . 
- The line was originally double @-@ tracked from <unk> to Korsvoll ( now <unk> ) station and single @-@ tracked from Korsvoll to Sognsvann . On 21 February 1939 , the section from Korsvoll to Sognsvann was upgraded to double tracks , and Korsvoll station had its name changed to <unk> . 
- In the 1980s , the stations on the Sognsvann Line were rebuilt . The platforms were lengthened from fitting two @-@ car to fitting four @-@ car trains and the platform height was increased . The third rail made it impossible to cross the line at @-@ grade ; an <unk> was therefore constructed at Berg station . The station was also redesigned in concrete with steel columns and wooden sheds designed by architect Arne <unk> . 
- In 1991 , Oslo <unk> presented plans involving a rapid transit circle line in Oslo , connecting the newly built hospital Rikshospitalet with the rest of the city . A detailed suggestion was presented by Oslo <unk> in August 1996 . In the local newspaper <unk> <unk> there was a debate on whether there should be a ring line from Berg along the National Road 150 towards <unk> over Rikshospitalet , or whether the Ullevål Hageby Line should be extended from John Colletts <unk> to Rikshospitalet , thereby creating a correspondence with the Sognsvann Line at the new station <unk> . In the first plans , Berg was meant to be a station on the new ring line . This plan was discarded since local residents feared noise pollution and destroyed <unk> . Even though Berg was not made a station on the Ring Line , noise shields were put up along the Sognsvann Line . 
- Berg station has seen many accidents and almost @-@ accidents . In 1965 , a deadly accident occurred between Ullevål and Berg stations , when a train ran over a 33 @-@ year @-@ old man walking in the tracks . In 2002 , a 24 @-@ year @-@ old man was run over by a metro train approaching the station . The man survived the accident with minor wounds . In 2008 , a 21 @-@ year @-@ old drunk man was found <unk> around on the tracks between the platforms . The police removed him from the station and sent him home in a taxi . 
- 
- = = Service = = 
- 
- Berg is served by line 6 on the Sognsvann Line , operated by Oslo T @-@ <unk> on contract with <unk> . The rapid transit serves the station every 15 minutes , except in the late evening and on weekend mornings , when there is a 30 @-@ minute <unk> . Travel time along the 6 @.@ 1 @-@ kilometre ( 3 @.@ 8 mi ) portion to <unk> in the city center is 11 minutes . 
- The station provides correspondence to the bus lines 23 and 24 at a nearby bus stop in Kaj <unk> <unk> . 
- 
- = = Facilities = = 
- 
- Berg has two platforms , each with a wooden shed and ticket machines . The sheds are designed by Arne <unk> in a <unk> and standardised style with constructions of wood and steel . 
- 
- = = = Location = = = 
- 
- Berg is located in the borough of <unk> <unk> , northeast of the residential area Ullevål Hageby , northwest of <unk> , west of Tåsen , south of <unk> and east of <unk> . The area Berg is named after an old farm from 1264 with the same name . The street John Colletts <unk> , starting from John Colletts <unk> in Ullevål Hageby , ends at Berg station . Kaj <unk> <unk> , as part of the Norwegian National Road 150 , runs parallel with the line from Ullevål stadion via Berg to Tåsen , and continues thereafter towards <unk> . There are many Swiss chalet and <unk> style houses in the residential area around the station . Berg Upper Secondary School is located 100 metres ( 330 ft ) from the station . 
- 
- 
- = Adams River ( British Columbia ) = 
- 
- The Adams River is a tributary to the Thompson and Fraser Rivers in British Columbia , Canada . Beginning in the <unk> Mountains to the north , the Upper Adams River flows mainly southward and eventually reaches Adams Lake . The Lower Adams River begins at the southern end of the lake and flows into the extreme western end of Shuswap Lake . The river is one of the most important sockeye salmon breeding areas in North America . The run occurs in mid @-@ October and can bring millions of fish to a concentrated area near the river mouth . Excavations of Secwepemc villages on the river have shown a long tradition of habitation and salmon fishing in the area . The river also served as an important transportation route for early logging operations in the watershed . 
- 
- = = Course = = 
- 
- The headwaters of the Adams are several unnamed glaciers at roughly 2 @,@ 000 metres ( 6 @,@ 600 ft ) elevation in the north @-@ east region of the <unk> Range of the Columbia Mountains . The upper portion of the river flows roughly south and southwest through wetlands and passes through two small lakes , Tumtum and <unk> . It has sections of rapids and whitewater , and flows over <unk> below Tumtum Lake . Its flow drops by 5 metres ( 16 ft ) per kilometre in certain sections . After travelling for 94 kilometres ( 58 mi ) and entering the Shuswap Highland , it enters the northern end of Adams Lake . 
- Adams Lake is roughly 72 kilometres ( 45 mi ) along its north @-@ south axis , and reaches a maximum depth of 457 metres ( 1 @,@ 499 ft ) , making it the 24th deepest lake in the world . The Lower Adams issues from the extreme southern end of the lake and travels 11 kilometres ( 6 @.@ 8 mi ) through a narrow valley . It empties into Shuswap Lake near the community of <unk> . From Adams Lake to the Shuswap , the Lower Adams drops 60 metres ( 200 ft ) in elevation . 
- 
- = = = <unk> = = = 
- 
- <unk> of the Upper Adams include : 
- Oliver Creek 
- <unk> Creek 
- Sunset Creek 
- Fisher Creek 
- Adams Lake and the Lower Adams are fed by : 
- <unk> Creek 
- <unk> Creek 
- <unk> River 
- <unk> ( Bear ) Creek 
- <unk> ( Gold ) Creek 
- 
- = = History = = 
- 
- 
- = = = Secwepemc = = = 
- 
- The Secwepemc people have lived in the Adams River valley for millennia . A 1977 study by the provincial government along the lower river found sixty @-@ six sites with evidence of habitation dating to 2000 BCE . The abundance of the salmon run made the river an important food source and trade commodity for First Nations people in the region . 
- <unk> James <unk> records that the people of the Adams River area formed a sub @-@ group of the Secwepemc called the " Sxste 'lln " , now known as the Adams Lake Indian Band . The Sxste 'lln moved between summer and winter camps at the outlet of the lower river and the Little River area near Chase . The river 's namesake , Chief <unk> @-@ <unk> @-@ <unk> ( baptized as Adam by <unk> missionaries ) was a Sxste 'lln leader in the 1860s . Like more than 200 of his people , he died in the 1862 smallpox epidemic . 
- 
- = = = Adams River Lumber Company = = = 
- 
- Although prospectors , <unk> , and trappers had travelled the region in the 1800s , the first large scale activity in the river valley by Europeans was logging . J.P. <unk> , an experienced <unk> from Spokane , established the Adams River Lumber Company in 1909 . He licensed large tracts of timber along both the Upper and Lower rivers , as well as the surrounding plateau . <unk> 's company is described as the first major industrial operation in the British Columbia Interior . 
- A camp was built on the upper river and logging operations began at Tumtum Lake . The cut logs were run down the river , then towed in booms by the company owned <unk> Helen down Adams Lake . The logs then were run down the Lower river to the mill at Chase . In 1908 , Adams River <unk> constructed a " splash dam " at the outlet of the Lower Adams . This dam allowed operators to raise the water level of Adams Lake . When sufficient logs had been collected above the dam , the gates were opened and the resulting flood carried the logs to Shuswap Lake . This proved to be destructive to the salmon run as it damaged the gravel beds the fish use to spawn . 
- In order to move logs from the plateau above the Lower river , Adams River Lumber constructed several <unk> . The <unk> were elevated wooden troughs filled with water that floated logs down to the valley bottom . The largest of these was at Bear Creek . It incorporated <unk> up to 25 metres ( 82 ft ) high and was capable of moving 3 @,@ 000 @,@ 000 metres ( 9 @,@ 800 @,@ 000 ft ) of logs per month . It was the largest <unk> in North America at its peak . The structures were dismantled after the areas became logged out . 
- Although logging continues in the region , the Adams is no longer used for log transport . 
- 
- = = Ecology = = 
- 
- 
- = = = Flora = = = 
- 
- Much of the ecology of the watershed has been affected by <unk> and forestry , and is considered to be in a seral phase . The river travels through several different vegetation zones . In its upper reaches , the Adams flows through the Interior Cedar Hemlock zone . These forests are similar to the coastal forests of British Columbia and contain old @-@ growth trees up to 1000 years in age . Old @-@ growth stands along the river also contain rare species of <unk> , likely isolated by the last Ice Age . The floodplain of the Upper Adams contains wetland species of deciduous trees such as black cottonwood as well as a dense layer of shrubs including black <unk> , red @-@ <unk> <unk> , and <unk> . The lower river is dominated by the drier Interior Douglas Fir vegetation zone . Other conifer species present are Ponderosa pine , Engelmann spruce , and alpine fir . The area around the river mouth has been altered by human activities such as farming and livestock grazing . 
- 
- = = = Fauna = = = 
- 
- 
- = = = = <unk> salmon = = = = 
- 
- Adams River sockeye travel from their spawning grounds to the South Thompson River , then into the Fraser River , and enter the Pacific . From the Strait of Georgia , they spend three years in the open ocean following Arctic currents to Alaska and the <unk> islands . They then <unk> their route to the Adams , completing a round trip of over 4 @,@ 000 kilometres ( 2 @,@ 500 mi ) . They complete the <unk> trip upstream , including navigating the swift waters and rapids of the Fraser Canyon , in just seventeen days . They do not eat during this period ; instead they rely on fat reserves stored up from heavy feeding in the Strait of Georgia in the late summer . It is at this point that the salmon take on their distinctive red hue , with the male fish also developing large <unk> backs and aggressive hooked mouths . How they are able navigate back to their <unk> river is not fully understood , but a highly developed olfactory system is believed to play a part . 
- The alluvial gravel deposits that form the Adams river bottom are ideal for the development of salmon <unk> and <unk> . The temperature and neutral <unk> of the water is also well @-@ suited to the sockeye . Shuswap Lake , below the river , is called a " nursery lake " by biologists due to its high concentration of <unk> , a food source for young salmon . 
- 
- = = = = <unk> runs = = = = 
- 
- The Adams River run occurs every year , but every fourth year ( called a " dominant " year ) , the numbers are much higher . 2014 was the most recent dominant run . According to Canada 's Department of Fisheries and <unk> , the Fraser River sockeye run of 2010 was the largest since 1913 , numbering an estimated 34 million fish . At least 3 @,@ <unk> @,@ 000 of these fish returned to the Adams River to spawn . There is no clear consensus as to why the Adams stock has rebounded so remarkably ( 1991 saw an estimated return of <unk> fish ) . In the Globe and Mail , Simon Fraser University biologist John Reynolds said “ [ predicting salmon numbers ] is massively complex , even for a scientist . ” 
- 
- = = = = Other fauna = = = = 
- 
- The Adams River valley supports large populations of mule deer , with smaller herds of white @-@ tail deer . Black bears are common in the watershed . The bear population grows substantially during the fall as they gather from adjacent valleys to feed on the spawning salmon . In the upper river valley , moose are common , and the headwaters of the river contain grizzly bear habitat . Several species of aquatic mammals are found on the river , including beaver , mink , and river otter . 
- The river 's mouth has populations of Canada <unk> and <unk> . Other <unk> include <unk> , green @-@ winged <unk> , and <unk> . The river supports Bald eagle and <unk> populations , whose <unk> can be seen high up in black cottonwood and dead conifers . <unk> , especially <unk> grouse , are numerous throughout the valley . 
- 
- = = Protected areas = = 
- 
- The Adams passes through the 5 @,@ 733 hectare Upper Adams River Provincial Park and the 1 @,@ <unk> hectare <unk> Haig @-@ Brown Provincial Park , which provides trails and platforms for salmon viewing . Human visitors to <unk> Haig @-@ Brown Provincial Park were estimated at 160 @,@ 000 in 2010 . Haig @-@ Brown was a Canadian <unk> , writer and a member of the International Pacific Salmon Fisheries Commission . 
- 
- 
- = Independence Day ( India ) = 
- 
- Independence Day , observed annually on 15 August is a national holiday in India commemorating the nation 's independence from the British Empire on 15 August 1947 . India attained independence following an Independence Movement noted for largely nonviolent resistance and civil disobedience led by the Indian National Congress ( <unk> ) . Independence coincided with the partition of India , in which the British Indian Empire was divided along religious lines into the <unk> of India and Pakistan ; the partition was accompanied by violent riots and mass casualties , and the displacement of nearly 15 million people due to sectarian violence . On 15 August 1947 , Jawaharlal Nehru , who had become the first Prime Minister of India that day , raised the Indian national flag above the <unk> Gate of the Red Fort in Delhi . On each subsequent Independence Day , the prime minister has raised the flag and given a speech . 
- The holiday is observed throughout India with flag @-@ hoisting ceremonies , parades and cultural events . Indians celebrate the day by displaying the national flag on their attire , accessories and homes ; by listening to patriotic songs , watching patriotic movies ; and bonding with family and friends . Books and films feature the independence and partition in their narrative . There have been threats of terrorist attack on and around 15 August by separatist and militant organisations . 
- 
- = = History = = 
- 
- European traders had established outposts on the Indian subcontinent by the 17th century . Through overwhelming military strength , the British East India company subdued local kingdoms and established themselves as the dominant force by the 18th century . Following the Rebellion of 1857 , the Government of India Act 1858 led the British Crown to assume direct control of India . In the decades following , civic society gradually emerged across India , most notably the Indian National Congress Party , formed in 1885 . The period after World War I was marked by British reforms such as the Montagu – <unk> <unk> , but it also witnessed the enactment of the repressive <unk> Act and calls for self @-@ rule by Indian activists . The discontent of this period <unk> into nationwide non @-@ violent movements of non @-@ cooperation and civil disobedience , led by Mohandas Karamchand Gandhi . 
- During the 1930s , reform was gradually legislated by the British ; Congress won victories in the resulting elections . The next decade was beset with political turmoil : Indian participation in World War II , the Congress ' final push for non @-@ cooperation , and an <unk> of Muslim nationalism led by the All @-@ India Muslim League . The escalating political tension was capped by Independence in 1947 . The <unk> was tempered by the bloody partition of the subcontinent into India and Pakistan . 
- 
- = = = Independence Day before Independence = = = 
- 
- At the 1929 <unk> session of the Indian National Congress , the <unk> <unk> declaration , or " Declaration of the Independence of India " was promulgated , and 26 January was declared as Independence Day . The Congress called on people to pledge themselves to civil disobedience and " to carry out the Congress instructions issued from time to time " until India attained complete independence . Celebration of such an Independence Day was envisioned to <unk> nationalistic fervour among Indian citizens , and to force the British government to consider granting independence . 
- The Congress observed 26 January as the Independence Day between 1930 and 1946 . The celebration was marked by meetings where the attendants took the " pledge of independence " . Jawaharlal Nehru described in his autobiography that such meetings were peaceful , solemn , and " without any speeches or <unk> " . Gandhi envisaged that besides the meetings , the day would be spent " ... in doing some constructive work , whether it is spinning , or service of ' <unk> , ' or reunion of Hindus and <unk> , or prohibition work , or even all these together " . Following actual independence in 1947 , the Constitution of India came into effect on and from 26 January 1950 ; since then 26 January is celebrated as Republic Day . 
- 
- = = = <unk> background = = = 
- 
- In 1946 , the Labour government in Britain , its <unk> exhausted by the recently concluded World War II , realised that it had neither the mandate at home , the international support , nor the reliability of native forces for continuing to control an increasingly restless India . In February 1947 , Prime Minister Clement <unk> announced that the British government would grant full self @-@ governance to British India by June 1948 at the latest . 
- The new viceroy , Lord <unk> , advanced the date for the transfer of power , believing the continuous contention between the Congress and the Muslim League might lead to a collapse of the interim government . He chose the second anniversary of Japan 's surrender in World War II , 15 August , as the date of power transfer . The British government announced on 3 June 1947 that it had accepted the idea of <unk> British India into two states ; the successor governments would be given dominion status and would have an implicit right to secede from the British Commonwealth . The Indian Independence Act 1947 ( 10 & 11 <unk> 6 c . 30 ) of the Parliament of the United Kingdom partitioned British India into the two new independent dominions of India and Pakistan ( including what is now Bangladesh ) with effect from 15 August 1947 , and granted complete legislative authority upon the respective constituent assemblies of the new countries . The Act received royal assent on 18 July 1947 . 
- 
- = = = Partition and independence = = = 
- 
- Millions of Muslim , Sikh and Hindu refugees <unk> across the newly drawn borders in the months surrounding independence . In Punjab , where the borders divided the Sikh regions in halves , massive <unk> followed ; in Bengal and <unk> , where Mahatma Gandhi 's presence <unk> communal <unk> , the violence was <unk> . In all , between 250 @,@ 000 and 1 @,@ 000 @,@ 000 people on both sides of the new borders died in the violence . While the entire nation was celebrating the Independence Day , Gandhi stayed in Calcutta in an attempt to stem the carnage . On 14 August 1947 , the Independence Day of Pakistan , the new Dominion of Pakistan came into being ; Muhammad Ali <unk> was sworn in as its first Governor General in Karachi . 
- The <unk> Assembly of India met for its fifth session at 11 pm on 14 August in the Constitution Hall in New Delhi . The session was chaired by the president <unk> Prasad . In this session , Jawaharlal Nehru delivered the <unk> with Destiny speech proclaiming India 's independence . 
- The members of the Assembly formally took the pledge of being in the service of the country . A group of women , representing the women of India , formally presented the national flag to the assembly . 
- The Dominion of India became an independent country as official ceremonies took place in New Delhi . Nehru assumed office as the first prime minister , and the viceroy , Lord <unk> , continued as its first governor general . Gandhi 's name was invoked by crowds celebrating the occasion ; Gandhi himself however took no part in the official events . Instead , he marked the day with a 24 @-@ hour fast , during which he spoke to a crowd in Calcutta , encouraging peace between Hindu and Muslim . 
- 
- = = Celebration = = 
- 
- Independence Day , one of the three national holidays in India ( the other two being the Republic Day on 26 January and Mahatma Gandhi 's birthday on 2 October ) , is observed in all Indian states and union territories . On the eve of Independence Day , the President of India delivers the " Address to the Nation " . On 15 August , the prime minister hoists the Indian flag on the <unk> of the historical site Red Fort in Delhi . Twenty @-@ one gun shots are fired in honour of the solemn occasion . In his speech , the prime minister highlights the past year 's achievements , raises important issues and calls for further development . He pays tribute to the leaders of the Indian independence movement . The Indian national anthem , " Jana <unk> Mana " is sung . The speech is followed by march past of divisions of the Indian Armed Forces and paramilitary forces . <unk> and pageants showcase scenes from the independence struggle and India 's diverse cultural traditions . Similar events take place in state capitals where the Chief Ministers of individual states <unk> the national flag , followed by parades and pageants . 
- Flag hoisting ceremonies and cultural programmes take place in governmental and non @-@ governmental institutions throughout the country . Schools and colleges conduct flag hoisting ceremonies and cultural events . Major government buildings are often adorned with strings of lights . In Delhi and some other cities , <unk> flying adds to the occasion . National flags of different sizes are used <unk> to symbolise allegiance to the country . Citizens adorn their clothing , <unk> , cars , household accessories with replicas of the tri @-@ colour . Over a period of time , the celebration has changed emphasis from nationalism to a broader celebration of all things India . 
- The Indian diaspora celebrates Independence Day around the world with parades and pageants , particularly in regions with higher concentrations of Indian immigrants . In some locations , such as New York and other US cities , 15 August has become " India Day " among the diaspora and the local populace . <unk> celebrate " India Day " either on 15 August or an adjoining weekend day . 
- 
- = = Security threats = = 
- 
- As early as three years after independence , the <unk> National Council called for a boycott of Independence Day in northeast India . <unk> protests in this region intensified in the 1980s ; calls for boycotts and terrorist attacks by insurgent organisations such as the United Liberation Front of <unk> and the National Democratic Front of <unk> , marred celebrations . With increasing insurgency in <unk> and <unk> from the late 1980s , separatist protesters boycotted Independence Day there with <unk> ( strikes ) , use of black flags and by flag burning . Terrorist outfits such as <unk> @-@ e @-@ <unk> , the <unk> <unk> and the <unk> @-@ e @-@ Mohammed have issued threats , and have carried out attacks around Independence Day . <unk> of the celebration has also been advocated by insurgent <unk> rebel organisations . 
- In the anticipation of terrorist attacks , particularly from militants , security measures are intensified , especially in major cities such as Delhi and Mumbai and in troubled states such as <unk> and <unk> . The airspace around the Red Fort is declared a no @-@ fly zone to prevent aerial attacks and additional police forces are deployed in other cities . 
- 
- = = In popular culture = = 
- 
- On Independence Day and Republic Day , patriotic songs in Hindi and regional languages are broadcast on television and radio channels . They are also played alongside flag hoisting ceremonies . Patriotic films are broadcast . Over the decades , according to The Times of India , the number of such films broadcast has decreased as channels report that audiences are <unk> with patriotic films . The population cohort that belong to the Generation Next often combine nationalism with popular culture during the celebrations . This mixture is exemplified by outfits and <unk> <unk> with the tricolour and designer garments that represent India 's various cultural traditions . Retail stores offer Independence Day sales promotions . Some news reports have decried the commercialism . Indian Postal Service publishes commemorative stamps depicting independence movement leaders , nationalistic themes and defence @-@ related themes on 15 August . 
- Independence and partition inspired literary and other artistic creations . Such creations mostly describe the human cost of partition , limiting the holiday to a small part of their narrative . Salman <unk> 's novel Midnight 's Children ( 1980 ) , which won the Booker Prize and the Booker of <unk> , <unk> its narrative around children born at midnight of 14 – 15 August 1947 with magical abilities . Freedom at Midnight ( 1975 ) is a non @-@ fiction work by Larry Collins and Dominique <unk> that chronicled the events surrounding the first Independence Day celebrations in 1947 . Few films center on the moment of independence , instead highlighting the circumstances of partition and its aftermath . On the Internet , Google has commemorated Independence Day since 2003 with a special <unk> on its Indian <unk> . 
- 
- 
- = The One I Love ( manga ) = 
- 
- The One I Love ( Japanese : <unk> , Hepburn : <unk> no <unk> ) is a romantic , slice @-@ of @-@ life <unk> ( targeted towards girls ) manga by Clamp , an all @-@ female , manga artist team consisting of <unk> <unk> , Mokona , Tsubaki Nekoi , and <unk> Ohkawa . Appearing as a monthly serial in the Japanese manga magazine Monthly Young Rose from December 1993 to June 1995 , the twelve stories were collected into a bound volume by Kadokawa Shoten and published in July 1995 . The One I Love contains twelve independent manga stories , each focusing on an aspect of love and accompanied by an essay . Ohkawa wrote the essays while Nekoi illustrated the manga ; it was the first time she primarily illustrated a manga by Clamp . Some of the stories draw on the life experiences of the women while others take inspiration from conversations they had with friends . 
- In 2003 , Tokyopop licensed The One I Love for an English @-@ language translation in North America , and published it in October 2004 . <unk> Media republished the manga in February 2015 . The manga has also been translated into other languages . Reviewers have identified a few themes in the collection . They had a range of reactions to The One I Love : some considered it heartfelt entertainment , while others reviewed it less positively , as a badly done take on romance . 
- 
- = = Plot = = 
- 
- Each story of The One I Love consists of seven pages of manga and an accompanying essay . 
- " Different " ( <unk> , <unk> ) : A girl asks to meet her boyfriend in the park after they have had a fight . She wants to apologize , but she does not know what to say . She wears a <unk> instead . When she meets her boyfriend , she finds that he is dressed up too ; he had the same idea as she did . 
- " <unk> " ( <unk> , <unk> ) : A girl reflects on the word " cute " with her boyfriend . She cannot visualize " cute , " so she does not understand why it makes her happy when he says that she is cute . 
- " I Miss You " ( <unk> , <unk> ) : A girl has doubts about staying with her boyfriend , who has a very demanding work schedule . In the end , he <unk> work to visit her , and she decides that their romantic relationship will work out . 
- " A Younger Man " ( <unk> , <unk> ) : A young woman working at a bakery reflects on a past unsuccessful relationship with a younger man . In the end , a younger bakery worker asks her on a date , and she is hopeful that their romantic relationship will work out . 
- " Suddenly " ( <unk> , <unk> ) : A girl working in a design company thinks about her past relationships , and one of her <unk> , whom she does not get along with . When a design job goes wrong and that <unk> offers his ideas , she realizes that she is in love with him . 
- " Together " ( <unk> , <unk> ) : A girl reflects on her childhood <unk> , who introduced her to playing the harmonica . 
- " Pretty " ( <unk> , <unk> ) : A girl <unk> as she decides what to wear to meet her boyfriend . When she is late to meet him , she realizes that her boyfriend always thinks she is pretty no matter what she wears . 
- " <unk> " ( <unk> , <unk> ) : A girl has a dream about her boyfriend ending their relationship , and she is worried that it is a premonition . 
- " Courage " ( <unk> , <unk> ) : A girl confesses her love on Valentine 's Day . 
- " Normal " ( <unk> , <unk> ) : A young woman has doubts about getting married to her boyfriend . In the end , she realizes that even if they marry , nothing will change between them . 
- " Apart " ( <unk> , <unk> ) : A young woman wonders if her long @-@ distance boyfriend is being faithful . In the end , she finds out that he is very dedicated to her . 
- " Marriage " ( <unk> , <unk> ) : A young woman about to be married is nervous about her future . She is worried that she might change , but her husband <unk> her that they will change together . 
- 
- = = Development = = 
- 
- The One I Love was developed by Clamp — the all @-@ female , manga artist team consisting of <unk> <unk> , Mokona , Tsubaki Nekoi , and <unk> Ohkawa . The group decided on the theme of romance after considering the target audience of the manga magazine Monthly Young Rose , whose editors had asked them for a manga . For The One I Love , Ohkawa wrote the essays , and Nekoi illustrated the manga , the first time she primarily illustrated a series for the group . For costumes , Ohkawa used " <unk> non @-@ no Olive " as a reference . Additionally , the preview panels of each story featured different visual motifs , primarily girls and animals . In retrospective , Nekoi self @-@ <unk> thought that her illustrations of the female protagonists , including working women , made them resemble junior high students . 
- Some of the stories in The One I Love contain autobiographical elements . " Different " and " <unk> " draw on Ohkawa 's personal experiences , while " Together " draws on Mokona 's . Additionally , " Suddenly " is based on her admiration of manga artist <unk> <unk> 's dedication to his fans . " Normal " , " Apart " , and " <unk> " were inspired by Ohkawa 's conversations with friends , and " I Miss You " has its origins in a conversation with manga artist <unk> Takeshi , who rarely saw his girlfriend because of his work . " A Younger Man " stems from Ohkawa 's conversation with actor <unk> <unk> and his thoughts on men and age in a relationship . In contrast , " Courage " appeared as a Valentine 's Day story in the February issue of Young Rose ; Clamp rarely had opportunities to create seasonal stories , as the magazine that typically serialized their work did not run seasonal stories . 
- 
- = = Release = = 
- 
- Written and illustrated by Clamp , the stories of The One I Love appeared as a monthly serial in Monthly Young Rose from December 1993 to June 1995 . Kadokawa Shoten collected the stories into a bound volume , and published it on July 16 , 1995 . 
- In 2003 , Tokyopop announced that it had licensed The One I Love for an English @-@ language translation , along with four other manga by Clamp ; it published The One I Love on October 12 , 2004 . However , on May 31 , 2011 , Tokyopop shut down its publishing branch in North America , with all of its Japanese manga licenses returned . <unk> Media digitally republished the manga on February 10 , 2015 . The One I Love has also been translated into other languages , including French by <unk> <unk> , and German by Egmont Manga & Anime . 
- 
- = = Reception = = 
- 
- A reviewer for Anime News Network , Mikhail <unk> wrote that readers would have various reactions to The One I Love : he felt that some would regard it as " charming " and others would find it upsetting that the female protagonists were primarily defined by their worry over their relationships . While considering it one of Clamp 's minor works , he stated that it was " unusual " for including some colored pages when the majority of manga is printed in black and white ; being a slice @-@ of @-@ life manga ; and featuring Tsubaki Nekoi 's art , as prior to the time of his review , most of their works translated into English had been drawn by another member of Clamp . <unk> wrote that " insecurity and self @-@ doubt " served as the theme of the anthology . Johanna Draper Carlson , a reviewer for Publishers Weekly , wrote that some of the stories were worrisome , particularly one that she felt " <unk> having an interest in whatever your boyfriend likes , " and contained stereotypes as a result of limited space . She also stated that the essays included <unk> about women and " quickly become redundant . " In Manga : The Complete Guide , Mason Templar suggested a possible theme of being one 's self in relationships , but felt that it appeared " almost anti @-@ feminist in execution , " as only the female characters underwent a change in behavior . Templar rated the series one @-@ and @-@ a @-@ half out of four stars , and criticized it as a <unk> take on romance ; Templar suggested <unk> <unk> , <unk> , or Man of Many Faces as better examples of Clamp 's romantic manga . A reviewer of the French edition wrote that repetitive nature of the stories detracted from the manga 's appeal , and made for a " laborious " reading experience , although the reviewer felt that a " romantic " reader would not mind this . Conversely , another reviewer for Anime News Network , <unk> Cooper , wrote that the stories were " incredibly sweet and touching " and were best read a few stories at a time . According to Cooper , The One I Love would appeal to fans of Clamp or <unk> , manga targeted towards girls . 
- 
- 
- = Hurricane Abby ( 1960 ) = 
- 
- Hurricane Abby was the only tropical cyclone in the Caribbean Sea during the 1960 Atlantic hurricane season . The second tropical cyclone and first named storm season , Abby developed on July 10 from a tropical wave in the vicinity of the Lesser Antilles . Abby rapidly intensified into a hurricane after being a tropical storm for less than six hours . It briefly peaked as a category 2 hurricane before weakening back . Abby rapidly weakened to a minimal tropical storm a few days thereafter . The storm re @-@ strengthened into a hurricane as it began to parallel the coast of Honduras . Hurricane Abby made landfall in British Honduras ( present @-@ day Belize ) on July 15 . Abby dissipated over Mexico later the next day . The remnants of Abby ultimately became Hurricane Celeste in the Pacific Ocean . Despite passing through or near several countries , Hurricane Abby had a relatively light impact on land , resulting in just $ 640 @,@ 500 ( 1960 USD , $ 5 @.@ 16 million 2016 USD ) in damage and six fatalities . 
- 
- = = Meteorological history = = 
- 
- The origins of Hurricane Abby were possibly from a tropical wave that moved in the vicinity of the Lesser Antilles in early July 1960 . Ships and a few weather stations on July 9 reported the existence of a tropical cyclone . On July 9 , it had operationally been classified as a tropical storm upon formation , though a later analysis revealed that it was only a tropical depression . The depression passed near Barbados early on July 10 before rapidly intensifying into a hurricane . Hurricane Abby accelerated toward the west and made landfall in St. Lucia as a minimal category 1 hurricane . Abby emerged into the Caribbean Sea a few hours later . As it headed generally westward , it also gradually strengthened . By July 11 , Hurricane Abby had attained peak intensity as a 100 mph ( 155 km / h ) category 2 hurricane . 
- The intensity of Abby began to gradually level off , though it re @-@ intensified slightly in the Caribbean , before eventually weakening further . Abby was downgraded to a tropical storm on the morning of July 13 ; it was center roughly 222 mi ( 357 km ) south of Kingston , Jamaica at the time . Just six hours thereafter , Abby had maximum sustained winds of only 45 mph ( 75 km / h ) . Abby re @-@ intensified at a relatively quick pace , as it was near hurricane status again when it passed just to the north of Honduras on July 14 . 
- Late on July 14 , Abby had re @-@ intensified into a hurricane . A few hours later , Abby passed over the island of <unk> at about midnight ( <unk> ) on July 15 . It made a third and final landfall on July 15 when it moved inland over British Honduras ( presently known as Belize ) as a minimal hurricane . Abby quickly weakened and was downgraded to a tropical storm only a few hours later over land . While Abby approached the border of Guatemala and Mexico , it had weakened further to a tropical depression . Abby dissipated while situated over the Mexican state of <unk> on July 16 . The remnants crossed over Mexico into the Pacific Ocean and <unk> into Hurricane Celeste on July 20 . Hurricane Celeste lasted for two days in the Pacific before it dissipated on July 22 . 
- 
- = = Preparations and impact = = 
- 
- 
- = = = Lesser Antilles = = = 
- 
- There were many watches and warning issued during the passage of Hurricane Abby throughout the Caribbean . Some gale warnings were issued from the <unk> to <unk> starting on July 10 . Around 1600 UTC later that day , a hurricane watch was issued for the Virgin Islands and Puerto Rico . Several hours later , the watch was extended to include Dominican Republic and Haiti . All warnings were discontinued later , after the storm passed by . 
- Hurricane Abby bypassed Barbados a tropical depression , which resulted in minimal effects . Maximum sustained winds on Barbados were reported at 23 mph ( 57 km / h ) , while gusts were recorded up to 37 mph ( 60 km / h ) . There are no other effects known on Barbados . St. Lucia had borne the brunt of the storm when Hurricane Abby made landfall as a category 1 hurricane . Rainfall on the island totaled at 6 @.@ 80 in ( 172 @.@ 7 mm ) . Tropical storm force winds were recorded on the island for the passage of Hurricane Abby on July 10 , though there were no hurricane force winds reported . In addition , a roof collapsed in on a house , killing six of the residents living there . The passage of Hurricane Abby also resulted in $ 435 @,@ 000 ( 1960 USD , $ 3 @.@ 48 million 2016 USD ) of damage on St. Lucia . Martinique was near the path of Hurricane Abby , which resulted in some effects . The Martinique Aimé <unk> International Airport in Fort @-@ de @-@ France recorded rainfall at nearly three inches ( 76 @.@ 2 mm ) , while rain in another village was measured at nearly four inches ( 101 @.@ 2 mm ) . Winds on Martinique were also at least tropical storm force during the passage of Hurricane Abby . However , wind gusts on Martinique were around 75 mph ( 120 km / h ) , greater than that which was observed on St. Lucia . There were also damaged roads and bridges , mainly due to landslides . The island nation of Dominica was also affected by Hurricane Abby . Hurricane Abby produced nearly six in ( 152 @.@ 4 mm ) of rain on the island . Winds were also similar to those that were measured on Barbados . Hurricane Abby left about $ 65 @,@ 000 ( 1960 USD , $ 520 thousand 2016 USD ) in damage to Dominica . 
- 
- = = = Greater Antilles and Central America = = = 
- 
- It was initially believed that Abby would affect Jamaica with gale force winds and heavy rainfall , and the U.S. Weather Bureau noted that " interests " on the island should monitor the progress of the storm . However , Abby remained far south of the island , and the impact it had on the island , if any , is unknown . Citizens of the Cayman Islands , Central America , and the <unk> Peninsula were also to remain on alert during the passage of Abby . The U.S. Weather Bureau later warned citizens in British Honduras and Honduras to " take all precautions for the protection of life and property against dangerous winds and abnormally high seas " . 
- Hurricane Abby also produced 1 @.@ 62 in ( 41 @.@ 14 mm ) of rain on Swan Island . On the islands north of Honduras , reports were received until winds reached 52 mph ( 83 @.@ 7 km / h ) , and then communications were lost . In Belize City , which was 75 mi ( 120 @.@ 7 km ) north of where Abby made landfall , wind gusts were reported to 35 mph ( 55 km / h ) . Minor property damage was reported in British Honduras , totaling to <unk> $ 6 @,@ 000 ( approximately $ 3 @,@ 000 1960 USD ; $ 24 thousand 2016 USD ) . In addition , agricultural losses reached <unk> $ 75 @,@ 000 ( about $ 37 @,@ 500 1960 USD ; $ 300 thousand 2016 USD ) . Although Abby nearly made landfall in Honduras and eventually entered Mexico , no effects were reported , but the U.S. Weather Bureau believed that flooding and there were seas of at least 5 ft ( 1 @.@ 5 m ) above normal . 
- Throughout its path , Abby caused only about $ 600 @,@ 000 ( 1960 USD , $ 4 @.@ 8 million 2016 USD ) in damage and six fatalities . 
- 
- 
- = 2010 Alabama Crimson Tide football team = 
- 
- The 2010 Alabama Crimson Tide football team ( variously " Alabama " , " <unk> " , " <unk> " or " The Tide " ) represented the University of Alabama in the 2010 NCAA Division I <unk> football season . It was the Crimson Tide 's 116th overall season , 77th as a member of the Southeastern Conference ( SEC ) and its 19th within the SEC Western Division . The team was led by head coach Nick Saban , in his fourth year , and played their home games at Bryant – Denny Stadium in Tuscaloosa , Alabama . They finished the season with a record of ten wins and three losses ( 10 – 3 , 5 – 3 in the SEC ) and defeated Michigan State 49 – 7 in the Capital One Bowl . 
- Alabama entered the season as defending national champions , and began the 2010 season as the preseason number one team in both the AP and Coaches ' Polls . <unk> to win a second consecutive SEC championship and be in contention for the national championship , the Crimson Tide opened the season with five consecutive victories over San Jose State , Penn State , Duke , Arkansas and Florida . However , Alabama completed the regular season with only nine victories and losses to South Carolina , LSU and Auburn and finished fourth in the Western Division . After the regular season , the Crimson Tide accepted an invitation to compete in the Capital One Bowl in Orlando . Against Big Ten co @-@ champions Michigan State , Alabama won by a final score of 49 – 7 and captured both a third straight ten win season and top ten finish . 
- 
- = = Before the season = = 
- 
- During the 2009 campaign , the Crimson Tide finished the season undefeated , 14 – 0 , with wins over several ranked opponents that included No. 7 Virginia Tech , No. 20 Ole Miss , No. 22 South Carolina , No. 9 LSU , No. 1 Florida in the SEC Championship , and No. 2 Texas in the BCS National Championship Game . They finished the season as the consensus National Champions , being voted No. 1 in the AP and Coaches ' Polls in securing Alabama 's first national title since 1992 . In addition to the national title , sophomore running back Mark Ingram became the first Alabama player to win the <unk> Trophy . 
- In February 2010 , eighteen players each signed an individual National <unk> of Intent to play college football at Alabama . The 2010 recruiting class was ranked nationally in the top five by several recruiting services including <unk> , Scout , <unk> and CBS College Sports . Spring practice began on March 12 and concluded with the annual A @-@ Day game on April 17 . <unk> live by ESPN , the Crimson team of offensive starters defeated the White team of defensive starters by a final score of 23 – 17 before 91 @,@ 312 fans in Bryant – Denny Stadium . The game was decided after the end of regulation when Brandon Gibson caught a 39 @-@ yard touchdown pass from AJ McCarron in sudden death . For their performances , Marcell Dareus earned the Dwight <unk> Lineman of the A @-@ Day Game Award and Mark Ingram earned the Dixie Howell Memorial Most Valuable Player of the A @-@ Day Game Award . 
- By August , Alabama had a combined 21 players on 12 different preseason award watch lists . These included both Mark Barron , Marcell Dareus and Dont ’ a Hightower for the Chuck <unk> Award ; Julio Jones for the Fred <unk> Award ; Hightower and Nico Johnson for the <unk> Award ; Ingram for the Walter Camp Award ; James Carpenter , Dareus and Hightower for the Lombardi Award ; Ingram and Greg McElroy for the Maxwell Award ; Barron , Dareus and Hightower for the <unk> <unk> Trophy ; McElroy for the <unk> O 'Brien Award ; Carpenter and Barrett Jones for the <unk> Trophy ; William Vlachos for the <unk> Trophy ; Barron for the Jim Thorpe Award ; and Ingram for the Doak Walker Award . 
- 
- = = = Returning starters = = = 
- 
- Alabama had 10 returning starters from the previous season , including eight on offense and two on defense . The most notable departures from the previous year were linebackers <unk> Anders , <unk> <unk> and Cory <unk> ; <unk> Javier Arenas , Kareem Jackson and Marquis Johnson ; defensive ends Brandon <unk> and Lorenzo Washington ; nose tackle Terrence <unk> ; safety Justin <unk> ; offensive <unk> Drew Davis and Mike Johnson ; and tight end Colin <unk> . Additionally , all of special teams players were replaced following the departures of punter P.J. Fitzgerald , <unk> Leigh <unk> , long <unk> Brian <unk> and Arenas as the return specialist . 
- 
- = = = <unk> class = = = 
- 
- Alabama 's recruiting class was highlighted by eight players from the " ESPN 150 " : No. 16 <unk> <unk> ( CB ) ; No. 32 Phillip <unk> ( <unk> ) ; No. 36 John Fulton ( CB ) ; No. 54 Adrian Hubbard ( DE ) ; No. 74 Chad Lindsay ( OG ) ; No. 89 <unk> Malone ( <unk> ) ; No. 99 C.J. Mosley ( <unk> ) ; and No. 132 Brian Vogler ( <unk> ) . Alabama signed the No. 5 recruiting class according to <unk> and the No. 4 recruiting class according to Scout . The football program received 18 letters of intent on National Signing Day , February 3 , 2010 . 
- 
- = = Schedule = = 
- 
- The 2010 schedule was officially released on September 2 , 2009 . In accordance with conference rules , Alabama faced all five Western Division opponents : Arkansas , Auburn , LSU , Mississippi State , and Ole Miss . They also faced three Eastern Division opponents : official SEC rival Tennessee , Florida , and South Carolina . Alabama did not play SEC opponents Georgia , Kentucky and Vanderbilt . The contest against Ole Miss served as the 2010 homecoming game . 
- Alabama also played four non @-@ conference games . The game against Penn State was originally scheduled as part of the 2004 season , however the series was moved back at the request of Alabama due to fallout from NCAA sanctions being <unk> on the program . The non @-@ conference schedule also included games against San Jose State of the Western Athletic Conference , Duke of the Atlantic Coast Conference and Georgia State of the Football Championship <unk> ( <unk> ) . On December 5 , it was announced Alabama would face Big Ten co @-@ champion Michigan State in the Capital One Bowl . In what was the first meeting between the programs , the Tide defeated the Spartans 49 – 7 and secured their third consecutive ten @-@ win season . 
- Alabama played six of its SEC opponents the week following the opponents ' bye week . These SEC teams who scheduled a bye week before facing the Crimson Tide included South Carolina , Ole Miss , Tennessee , LSU , Mississippi State and Auburn . Georgia State also had a bye week prior to playing Alabama , for a total of seven consecutive opponents playing Alabama the week following their bye . On July 1 , 2010 , the game against Georgia State was moved from Saturday , November 20 to Thursday , November 18 to give Alabama additional time to prepare for its game against Auburn . The <unk> computer ratings calculated Alabama 's 2010 strength of schedule to be the fifth most difficult out of the 245 Division I teams . The <unk> Computer Rankings calculated it as the 12th most difficult out of the 120 Division I <unk> teams in its rankings . 
- Source : <unk> All @-@ time Football Results : 2010 Season 
- 
- = = Coaching staff = = 
- 
- The only change to the coaching staff from the 2009 season was the departure of James Willis , an associate head and outside linebackers coach , to become the defensive coordinator at Texas Tech . Willis was replaced on the staff with Jeremy <unk> on January 15 . Other assistants that declined positions at other programs included Jim <unk> declining an offer to become San Jose State 's head coach and <unk> Smart declining an offer to become Georgia 's defensive coordinator . 
- 
- = = Game notes = = 
- 
- 
- = = = San Jose State = = = 
- 
- The Crimson Tide began their defense of their 2010 BCS championship at home against the San Jose State Spartans , and before a record crowd in a newly expanded Bryant – Denny Stadium , Alabama was victorious 48 – 3 . Alabama scored a touchdown on their first possession on a one @-@ yard Trent Richardson run to complete an eight @-@ play , 71 @-@ yard drive . The Spartans responded on the next drive with their only points of the game on a 31 @-@ yard Harrison <unk> field goal to make the score 7 – 3 . On the ensuing drive the Alabama extended their lead to 14 – 3 with a 48 @-@ yard Greg McElroy touchdown pass to Marquis Maze . Alabama again reached the <unk> early in the second quarter on a 39 @-@ yard Richardson run and a 29 @-@ yard AJ McCarron pass to Julio Jones to make the score 28 – 3 . Cade Foster scored the final points of the half as time expired on a 31 @-@ yard field goal to bring the halftime score to 31 – 3 . 
- Alabama opened the second half by scoring on their first two possessions . Eddie Lacy scored on a 37 @-@ yard run and Foster hit a 24 @-@ yard field goal to extend the Crimson Tide lead to 41 – 3 . Lacy scored the final points of the game with a 10 @-@ yard run to make the final score 48 – 3 . Both McElroy and McCarron combined to pass for <unk> yards on 22 completions and a pair of touchdowns . For the game , the Alabama <unk> San Jose in total offense by a margin of 591 – 175 . The 101 @,@ <unk> in attendance marked the first crowd of over 100 @,@ 000 to attend a football game in the state of Alabama . 
- 
- = = = Penn State = = = 
- 
- With ESPN 's College <unk> in town , Alabama defeated the Penn State Nittany Lions 24 – 3 in a renewal of their historic rivalry . Alabama scored first on a 36 @-@ yard touchdown pass from Greg McElroy to Kevin Norwood in the first quarter . The Crimson Tide added to their lead in the second quarter with a 14 @-@ yard McElroy touchdown pass to Preston Dial and a 31 @-@ yard Jeremy Shelley field goal to take a 17 – 0 lead at the half . After a scoreless third , Trent Richardson scored on a one @-@ yard run and Penn State 's <unk> Wagner hit a 36 @-@ yard field goal to make the final score to 24 – 3 . 
- Richardson led the team in rushing with 144 yards on 22 carries , and was also the first back to gain over 100 yards on the ground against Penn State since their 2008 game against Iowa . The defense also stood out with Mark Barron , Robert Lester and Will <unk> each making an interception and Alabama only allowing 283 yards of total offense . After compiling 207 all @-@ purpose yards and scoring a touchdown , Richardson was named Co @-@ SEC Offensive Player of the Week with South Carolina 's Marcus Lattimore . The victory improved Alabama 's all @-@ time record against the Nittany Lions to 9 – 5 . 
- 
- = = = Duke = = = 
- 
- In Alabama 's first @-@ ever trip to Duke , the Crimson Tide defeated the Blue Devils by a final score of 62 – 13 in front of the largest crowd at Wallace Wade Stadium since the 1994 season . Playing in his first game of the 2010 season following knee surgery , Mark Ingram ran for a team high 152 yards on nine carries with two touchdowns . 
- Greg McElroy completed 14 of 20 passes for 258 yards , with three touchdowns and one interception . In the second quarter Trent Richardson scored Alabama 's first special teams touchdown of the season with a 96 @-@ yard kickoff return . Other offensive highlights included Richardson gaining 66 yards on seven carries and Eddie Lacy gaining 53 yards on seven carries with each scoring a touchdown on the ground . Through the air , Julio Jones led the team with 106 receiving yards on six catches with he , Darius Hanks and Preston Dial each making a touchdown reception . 
- The game also marked the return of Marcell Dareus following a two @-@ game suspension from the NCAA for receiving improper benefits . After allowing 13 points in the first half , the Alabama defense shut out the Duke offense in the second half . The 62 points scored by the Tide were the most since a 62 – 0 victory over Tulane during the 1991 season , and the 45 points scored in the first half were the most scored in one half since scoring 45 in the second half of the 1973 victory over California . The <unk> yards of total offense were the most amassed by an Alabama team since gaining <unk> against LSU in 1989 . The victory improved Alabama 's all @-@ time record against the Blue Devils to 3 – 1 . 
- 
- = = = Arkansas = = = 
- 
- In the first game played between two teams ranked in the top ten at Donald W. Reynolds Razorback Stadium since the 1979 season , Alabama was victorious with a 24 – 20 come @-@ from @-@ behind victory . After Ryan Mallett connected on an early touchdown to take a 7 – 0 lead , Alabama responded with a 54 @-@ yard Mark Ingram touchdown run to tie the game at 7 – 7 . Arkansas retook the lead with a field goal and a one @-@ yard Mallett run to take a 17 – 7 lead at the half . Midway through the third , Arkansas extended their lead to 20 – 7 . 
- Alabama brought the score to 20 – 14 late in the third after a 20 @-@ yard Trent Richardson touchdown reception from Greg McElroy . The Arkansas lead was then cut to three after a 36 @-@ yard Jeremy Shelley field goal with just over 6 : 00 remaining in the fourth . After Robert Lester intercepted a Mallett pass and returned it to the 12 @-@ yard line , Ingram took the next three snaps that culminated in a one @-@ yard touchdown run to take a 24 – 20 lead . After a late Dre Kirkpatrick interception , McElroy gained a first down on a fourth and inches quarterback sneak to seal the victory for the Crimson Tide . Ingram led the team with 157 yards rushing on 24 carries and Richardson finished with 85 yards on eight carries . Kirkpatrick led the defense with nine tackles and an interception . For his five @-@ tackle , two @-@ interception performance , Robert Lester was recognized as both the <unk> / <unk> <unk> National Defensive Player of the Week and the SEC Defensive Player of the Week . The victory improved Alabama 's all @-@ time record against the <unk> to 11 – 8 ( 14 – 7 without NCAA vacations and forfeits ) . 
- 
- = = = Florida = = = 
- 
- In a rematch of the previous two SEC Championship Games , Alabama defeated the Florida Gators 31 – 6 . Alabama opened the scoring with a 28 @-@ yard Jeremy Shelley field goal in the first , and then scored a trio of second @-@ quarter touchdowns . Mark Ingram scored on runs of six and one — yard with the third coming on a 19 @-@ yard Marquis Maze touchdown pass to Michael Williams on a wide receiver pass . Florida got on the board late in the second with a 39 @-@ yard Chas Henry field goal to bring the halftime score to 24 – 3 . After a second Henry field goal , C. J. Mosley returned an interception 35 @-@ yards for a touchdown to make the final score 31 – 6 . 
- Although Florida <unk> the Crimson Tide in total offense 281 to 273 yards , their three turnovers resulted in 21 Alabama points . In addition to Mosley , Nico Johnson intercepted Trey Burton in the end zone and Dre Kirkpatrick intercepted a John <unk> pass . The game marked the first time Florida had been held without a touchdown since their previous visit to Tuscaloosa in 2005 , and resulted in Alabama leading the nation in scoring defense by allowing only 45 points through five games . Courtney <unk> was named the SEC Defensive Player of the Week after making seven total tackles , with four for losses , a fumble recovery and two pass <unk> . Chance <unk> was recognized as the SEC 's Offensive Lineman of the Week for his performance . The victory improved Alabama 's all @-@ time record against the Gators to 21 – 14 ( 22 – 14 without the NCAA vacation of the 2005 victory ) . 
- 
- = = = South Carolina = = = 
- 
- With ESPN 's College <unk> in town and in front of a sold out Williams – <unk> , Alabama was upset by the South Carolina Gamecocks 35 – 21 . After Alabama scored on its opening drive with a 32 @-@ yard Jeremy Shelley field goal , South Carolina responded with three consecutive touchdowns . Stephen Garcia threw three touchdown passes , with the first to Marcus Lattimore for nine @-@ yards followed by strikes of 26 and 15 @-@ yards to <unk> <unk> , to give South Carolina a 21 – 3 lead in the second quarter . Alabama reached the end zone late in the second on a nine @-@ yard Greg McElroy pass to Julio Jones to make the halftime score 21 – 9 after the extra point failed . 
- On the first play from scrimmage in the third quarter , Garcia threw the ball out of the end zone for a safety following a bad snap . After the free kick , Alabama scored on a 39 @-@ yard Shelley field goal , to make the score 21 – 14 . After a one @-@ yard Lattimore touchdown run , Alabama answered with a 51 @-@ yard Darius Hanks touchdown reception from McElroy , to make the score 28 – 21 . However , Lattimore scored on a two @-@ yard touchdown run late in the fourth to give the Gamecocks a 35 – 21 victory . The win marked South Carolina 's first all @-@ time victory over a team ranked number one in the AP poll . 
- For the game , McElroy set a career high in passing for 315 yards on 27 of 34 passes , and Jones had a team high 118 yards on eight catches . Marcell Dareus was recognized as an honorable mention SEC Defensive Player of the Week for his eight tackle performance . The 34 points allowed by the Crimson Tide defense were the most allowed since giving up 41 to LSU in 2007 . The loss also marked the end of a 29 @-@ game regular season win streak , an overall 19 @-@ game win streak , and an 18 @-@ game regular season conference winning streak . It was Alabama 's first overall loss since being defeated by Utah in the 2009 Sugar Bowl and their first regular season and regular season conference loss since losing to Auburn in 2007 . The loss brought Alabama 's all @-@ time record against the Gamecocks to 10 – 4 ( 12 – 3 without NCAA vacations and forfeits ) . 
- 
- = = = Ole Miss = = = 
- 
- A week after their first regular season loss since the 2007 season , Alabama defeated their long @-@ time rival , the Ole Miss <unk> on homecoming in Tuscaloosa 23 – 10 . The Crimson Tide took a 10 – 0 lead in the first quarter on a seven @-@ yard Greg McElroy touchdown pass to Preston Dial and a 49 @-@ yard Cade Foster field goal . <unk> continued in the second quarter with Alabama 's Jeremy Shelley and Foster connecting on field goals of 19 and 44 @-@ yards . Mississippi 's <unk> Rose connected on a 22 @-@ yard field goal to make the halftime score 16 – 3 . In the third quarter , McElroy connected with Trent Richardson for an 85 @-@ yard touchdown reception . The catch was the fourth longest touchdown reception in school history . Later in the quarter , Jeremiah <unk> connected with Melvin Harris on a 15 @-@ yard touchdown reception to make the final score 23 – 10 . 
- On special teams , Marquis Maze totaled 125 yards on six punt returns and was named SEC Co @-@ Special Teams Player of the Week for his performance . The victory improved Alabama 's all @-@ time record against the <unk> to 44 – 9 – 2 ( 48 – 8 – 2 without NCAA vacations and forfeits ) . 
- 
- = = = Tennessee = = = 
- 
- In the 93rd edition of the Third Saturday in October , the Crimson Tide defeated the Tennessee Volunteers 41 – 10 . Tennessee scored first , on a 59 @-@ yard <unk> Poole touchdown run to take an early 7 – 0 lead . Alabama responded by scoring on its next two drives : a 36 @-@ yard Jeremy Shelley field goal , and a one @-@ yard Greg McElroy touchdown run to take a 10 – 7 lead . The second quarter closed with a 42 @-@ yard Shelley field goal and a 33 @-@ yard field goal by Michael <unk> of Tennessee to make the halftime score 13 – 10 . 
- Alabama opened the second half by driving 70 yards in four plays , with Julio Jones having receptions of 38 and 19 yards and Mark Ingram punching in the touchdown from one @-@ yard out to extend their lead to 20 – 10 . After <unk> missed a 52 @-@ yard field goal , Trent Richardson ran the ball 65 yards for a touchdown on the second play of the ensuing drive for a 27 – 10 Alabama lead . Later , Robert Lester intercepted a Matt Simms pass , and Alabama extended its lead to 34 – 10 , after an 80 @-@ yard drive with Ingram scoring from one @-@ yard out . Alabama scored the final points of the evening in the fourth quarter when AJ McCarron hit Richardson for a five @-@ yard touchdown reception to make the final score 41 – 10 . It was Alabama 's most lopsided victory over Tennessee since defeating the Volunteers 35 – 0 in 1963 . 
- For the game , Julio Jones set a school record with 221 receiving yards , eclipsing the previous mark of 217 yards set by David Palmer against Vanderbilt in 1993 . Ingram and Richardson finished with 88 and 119 yards on the ground respectively . For his performance , left tackle James Carpenter was selected as the SEC Offensive Lineman of the Week . With his 117 yards on 14 carries , <unk> Poole ended Alabama 's 41 @-@ game streak of not allowing a 100 @-@ yard rusher dating back to <unk> Green @-@ Ellis 's 131 yard performance for Ole Miss in 2007 . The game also marked the first between Nick Saban and Derek Dooley who previously worked for Saban as an assistant coach at LSU and with the Miami Dolphins . The victory improved Alabama 's all @-@ time record against the Volunteers to 47 – 38 – 7 ( 48 – 37 – 8 without NCAA vacations and forfeits ) . 
- 
- = = = LSU = = = 
- 
- Coming off their bye week and in what was dubbed by some as " Saban Bowl IV , " Alabama was upset by their long @-@ time rival , the LSU Tigers 24 – 21 . LSU scored first on a 45 @-@ yard Josh Jasper field goal to take a 3 – 0 lead . Alabama scored their first points early in the second quarter on a one @-@ yard Greg McElroy touchdown pass to Trent Richardson to take a 7 – 3 lead at the half . 
- Both teams traded touchdowns in the third . The Tigers scored first on a 75 @-@ yard Rueben <unk> reception from Jordan Jefferson , and the Crimson Tide responded with a five @-@ yard Mark Ingram touchdown run . LSU scored 14 fourth quarter points to secure the victory with a pair of Jasper field goals and a one @-@ yard <unk> Ridley touchdown run and a successful two @-@ point conversion . Alabama responded with a nine @-@ yard Julio Jones touchdown reception , but was unable to get a defensive stop late in the game preserving the 24 – 21 LSU victory . <unk> proved costly for Alabama with LSU scoring field goals on drives after a McElroy interception in the first and fumble in the fourth . The loss brought Alabama 's all @-@ time record against the Tigers to 45 – 24 – 5 . 
- 
- = = = Mississippi State = = = 
- 
- A week after being upset by LSU , Alabama returned to Bryant – Denny and began a three @-@ game <unk> to end the season by defeating long @-@ time rival Mississippi State 30 – 10 . The Crimson Tide took a 6 – 3 lead in the first quarter by trading field goals with the Bulldogs on a 36 @-@ yarder from Jeremy Shelley , a 24 @-@ yarder from Derek <unk> and a 45 @-@ yarder from Cade Foster . In the second , Alabama reached the end zone for the first time on the evening after Greg McElroy hit Marquis Maze for a 45 @-@ yard touchdown reception and a 13 – 3 lead . After a punt on the next <unk> series , an 80 @-@ yard Maze touchdown return was called back as a result of an illegal block on the play by Alex Watkins . On the next play , Mark Ingram took a short bubble screen pass from McElroy 78 @-@ yards for a 20 – 3 lead at the half . 
- On their first offensive possession of the second half , and on the third consecutive offensive play , Alabama scored on a long touchdown play . This time Julio Jones ran the ball 56 @-@ yards for a touchdown to extend the Alabama lead to 27 – 3 . Shelley scored Alabama 's final points in the fourth on a 28 @-@ yard field goal with State scoring their lone touchdown late on a 27 @-@ yard Chad <unk> touchdown reception from Tyler Russell . The Alabama defense allowed only 149 rushing yards , registered five sacks and two interceptions . 
- In this game , the Crimson Tide wore Nike Pro Combat uniforms for the first time . These uniforms featured <unk> jerseys with grey and white houndstooth numbers , a houndstooth stripe on the helmet , houndstooth gloves and an American flag <unk> into one of the sleeves in honor of Veterans Day . The houndstooth design was chosen as a tribute to former Alabama coach Bear Bryant who was known for wearing a houndstooth <unk> during games . The victory improved Alabama 's all @-@ time record against the Bulldogs to 73 – 18 – 3 ( 75 – 17 – 3 without NCAA vacations and forfeits ) . 
- 
- = = = Georgia State = = = 
- 
- Originally scheduled to be played on November 20 , in July 2010 this game was moved to Thursday , November 18 to give the Crimson Tide extra time to prepare for its game against Auburn . In the first ever meeting against the Georgia State Panthers , the Crimson Tide was victorious 63 – 7 . Alabama scored first on an eight @-@ yard Greg McElroy pass to Julio Jones . After a Mark Barron interception ended the first Georgia State drive , Alabama responded with a 71 @-@ yard drive capped by a one @-@ yard Mark Ingram touchdown run to take a 14 – 0 lead . 
- In the second quarter , Alabama scored on a defensive play when C. J. Mosley returned a Drew Little interception 41 @-@ yards for a touchdown . Alabama then extended their lead to 28 – 0 on a ten @-@ yard Jones touchdown reception from McElroy . At the end of the Panthers next possession , <unk> Williams blocked a Bo <unk> punt that was returned 22 @-@ yards for a touchdown by Brandon Gibson . On the following kickoff , an Albert Wilson fumble was recovered by Gibson to give the Tide possession deep in Panther territory . Four plays later , Alabama extended their lead to 42 – 0 on a three @-@ yard Eddie Lacy touchdown run . On the ensuing kickoff , the Panthers scored their only points on the evening when Wilson returned the kickoff 97 @-@ yards for a touchdown and a halftime score of 42 – 7 . 
- With the game in hand midway through the second quarter , Alabama played many of its reserve players in the second half . The Tide scored in the third quarter first on a seven @-@ yard AJ McCarron touchdown pass to Chris Underwood and again on an one @-@ yard <unk> Goode touchdown run after a Chris Jordan interception to take a 56 – 7 lead into the final period . In the fourth , <unk> Fowler scored on a 36 @-@ yard touchdown run to make the final score 63 – 7 . 
- After he completed 12 of 13 passes , McElroy set a new single @-@ game Alabama record for completion percentage of 92 @.@ 3 percent to break the previous record he set against North Texas in 2009 . The game also marked the first time Alabama played on a Thursday night since defeating Southern Miss in 2001 , and the return of both former Alabama head coach Bill Curry as the Panthers ' head coach and quarterback Star Jackson who transferred to Georgia State prior to the 2010 season . The 63 points were the most scored by an Alabama team since defeating Vanderbilt 63 – 3 in 1979 . 
- 
- = = = Auburn = = = 
- 
- In the 75th edition of the Iron Bowl , the Auburn Tigers <unk> a 24 @-@ point deficit to defeat the Crimson Tide 28 – 27 . Alabama opened a 21 – 0 lead after the first quarter with touchdown scores on their first three offensive possessions . <unk> were scored on a nine @-@ yard Mark Ingram run , a 68 @-@ yard Julio Jones reception from Greg McElroy and on a 12 @-@ yard Darius Hanks reception from McElroy . The lead was pushed to 24 – 0 in the second quarter after a 20 @-@ yard Jeremy Shelley field goal before the Tigers began their comeback . 
- Auburn scored their first points late in the second on a 36 @-@ yard Cam Newton pass to <unk> Blake to bring the score to 24 – 7 at the half . With the only Alabama points in the third scored on a 32 @-@ yard Shelley field goal , Auburn brought the margin to 27 – 21 entering the fourth on a 70 @-@ yard Cam Newton touchdown pass to <unk> <unk> and a one @-@ yard Newton run . The Tigers took a 28 – 27 lead in the fourth on a seven @-@ yard Philip <unk> reception from Newton that held to the end of regulation . 
- With his 10 catch , 199 yard performance , Julio Jones set Alabama single @-@ season records for both receptions and receiving yards in eclipsing the previous marks of 67 receptions by D. J. Hall in 2007 and 1 @,@ 056 yards by Hall in 2006 . The loss ended a 20 @-@ game home winning streak for the Tide dating back to the 2007 loss to Louisiana – Monroe , and brought Alabama 's all @-@ time record against the Tigers to 40 – 34 – 1 . The CBS telecast of this game earned a 7 @.@ 5 rating , the highest for any game of the 2010 college football season through week 13 . 
- 
- = = = Michigan State = = = 
- 
- On December 5 , Capital One Bowl officials announced Alabama would face the Big Ten co @-@ champion Michigan State Spartans in the 2011 Capital One Bowl . In a strong defensive performance where the Spartans were held to a total of minus @-@ 48 yards rushing , Alabama was victorious 49 – 7 . Alabama scored touchdowns on their first four offensive possessions . Mark Ingram scored first on an one @-@ yard touchdown run to complete a 13 @-@ play , 79 @-@ yard drive on Alabama 's first possession . After a Robert Lester interception of a Kirk <unk> pass on the Spartans ' opening drive , the Tide scored on an eight @-@ yard Trent Richardson touchdown run . Alabama extended their lead to 28 – 0 at the half following touchdown runs of six and 35 @-@ yards by Ingram and Julio Jones . 
- After holding Michigan State to a three @-@ and @-@ out to open the third quarter , Alabama scored its fifth touchdown in six offensive possessions when Marquis Maze scored on a 37 @-@ yard Greg McElroy pass . Up by 35 points late in the third , the Crimson Tide pulled many of their starters that resulted in many players seeing action from deep in the depth chart . Eddie Lacy extended the lead to 49 – 0 with touchdown runs of twelve @-@ yards in the third and 62 @-@ yards in the fourth quarter . Michigan State scored their only points late in the fourth on a 49 @-@ yard <unk> Fowler touchdown reception from Keith <unk> to make the final score 49 – 7 . The 42 @-@ point margin of victory was Alabama 's largest in a bowl game since defeating Syracuse 61 – 6 in the 1953 Orange Bowl . 
- The minus @-@ 48 yards rushing allowed by the Alabama defense was the fewest ever allowed in a bowl game and the second fewest allowed all @-@ time only eclipsed by a minus @-@ 49 yard performance against Houston in 1962 . With his pair of touchdowns , Mark Ingram established a new Alabama record for career rushing touchdowns with 42 to eclipse the previous mark of 41 set by Shaun Alexander . The contest also marked both Nick Saban 's and Bobby Williams ' first game against the Spartans since their respective terms as Michigan State 's head coach between 1995 – 1999 and 2000 – 2002 . 
- 
- = = <unk> chart = = 
- 
- <unk> and <unk> . 
- 
- = = Rankings = = 
- 
- Entering the 2010 season , the Crimson Tide was ranked No. 1 in the AP and Coaches ' <unk> Polls . The No. 1 preseason ranking was the first for Alabama since the 1978 season . In week six , Alabama dropped to the No. 8 ranking in both the AP and Coaches ' Poll following their loss to South Carolina . The Tide rebounded in the rankings though week nine , rising to No. 5 before dropping to No. 11 in the AP and No. 12 in the Coaches ' Poll following their loss at LSU . After their loss to Auburn , Alabama dropped to No. 15 in the AP , No. 18 in the Coaches ' Poll and No. 16 in the final BCS standings . Following the victory over Michigan State in the Capital One Bowl , Alabama finished No. 10 in the final AP , No. 11 in the final Coaches ' Polls . 
- 
- = = After the season = = 
- 
- As part of the A @-@ Day celebrations on April 16 , the 2010 team captains Greg McElroy , Dont 'a Hightower and Mark Barron were honored at the Walk of Fame ceremony at the base of Denny <unk> . Later that day , as recognition for becoming the fifth Alabama head coach to win a national championship with the 2009 squad , the University unveiled a statue of coach Saban along the Walk of Champions outside Bryant – Denny Stadium . 
- 
- = = = Final statistics = = = 
- 
- After their victory over Michigan State in the Capitol One Bowl , Alabama 's final team statistics were released . On the defensive side of the ball , they ranked third in scoring defense ( 13 @.@ 54 points per game ) , fifth in total defense ( 286 @.@ 38 yards per game ) , tenth in rushing defense ( 110 @.@ 15 yards per game ) and thirteenth in passing defense ( 176 @.@ 23 yards per game ) . They were also the conference leaders in both scoring and total defense . On offense , nationally the Crimson Tide ranked 18th in scoring offense ( 35 @.@ 69 points per game ) , 22nd in total offense ( <unk> @.@ 08 yards per game ) , 27th in passing offense ( 261 @.@ 15 yards per game ) and 29th in rushing offense ( 182 @.@ 92 yards per game ) . <unk> , Robert Lester led the SEC with an average of 0 @.@ 62 interceptions per game . 
- 
- = = = Awards = = = 
- 
- In the weeks following the SEC Championship Game , multiple Alabama players were recognized for their on @-@ field performances with a variety of awards and <unk> . At the team awards banquet on December 6 , Mark Barron , Dont 'a Hightower , and Greg McElroy were each named the permanent captains of the 2010 squad . At that time Julio Jones was named the 2010 most valuable players with Dont 'a Hightower and Mark Barron named defensive players of the year and Greg McElroy and Mark Ingram named the offensive players of the year . 
- The SEC recognized several players for their individual performances with various awards . Defensive back Mark Barron , wide receiver Julio Jones and offensive guard Barrett Jones were all named to the AP All @-@ SEC First Team . Offensive lineman James Carpenter , defensive lineman Marcell Dareus , running back Mark Ingram , linebacker Dont 'a Hightower and defensive back Robert Lester were all named to the AP All @-@ SEC Second Team . Quarterback Greg McElroy and center William Vlachos were each named AP All @-@ SEC <unk> Mention . Four players were named to the Coaches ' All @-@ SEC First Team including Barron , James Carpenter , Marcell Dareus and Julio Jones . Barrett Jones , William Vlachos , Mark Ingram , Dont 'a Hightower , return specialist Trent Richardson and defensive backs Robert Lester and Dre Kirkpatrick were named to the Coaches ' All @-@ SEC Second Team . Four players were named to the Freshman All @-@ SEC Coaches ' Team including offensive lineman D.J. <unk> , linebacker C.J. Mosley , defensive back Dee <unk> and punter <unk> <unk> . 
- In addition to the conference awards , several players were also named to various national All @-@ American Teams . Julio Jones and Mark Barron were named to the AP All @-@ American Second Team and Marcell Dareus and Barrett Jones were named to the AP All @-@ American Third Team . Barron was also named to the All @-@ America team by the Football Writers Association of America ( <unk> ) . Quarterback Greg McElroy , tight end Preston Dial and offensive lineman James Carpenter were all selected to play in the Under Armour Senior Bowl . 
- 
- = = = Coaching changes = = = 
- 
- In the week following the Capitol One Bowl victory , several changes were made to the Alabama coaching staff . Defensive line coach Bo Davis resigned his position to serve as the defensive tackles coach for Texas . The following day , Chris <unk> was hired by Coach Saban from Clemson to replace Davis as defensive line coach . On January 12 , assistant head coach and offensive line coach Joe <unk> announced his retirement . The following day , former Miami interim head coach Jeff <unk> was hired to replace <unk> as offensive line coach . On January 21 , wide receivers coach and recruiting coordinator Curt <unk> resigned his position to accept the head coaching job at Indiana University of Pennsylvania . On February 7 , Mike <unk> was hired a <unk> 's replacement as wide receivers coach and recruiting coordinator . 
- 
- = = = NFL Draft = = = 
- 
- Of all the draft @-@ eligible juniors , Mark Ingram , Julio Jones and Marcell Dareus declared their eligibility for the 2011 NFL Draft on January 7 . At the time of their announcement , each was projected as a first round pick . Five Alabama players , two seniors and three juniors , were invited to the NFL Scouting <unk> . The invited players were offensive lineman James Carpenter , quarterback Greg McElroy , defensive end Marcell Dareus , running back Mark Ingram , and wide receiver Julio Jones . In the draft , Alabama set a school record with four players selected in the first round . The first round selections were Dareus ( 3rd Buffalo Bills ) , Jones ( 6th Atlanta Falcons ) , Carpenter ( 25th Seattle Seahawks ) and Ingram ( 28th New Orleans Saints ) . McElroy was selected in the seventh round ( <unk> New York <unk> ) . Preston Dial signed as an undrafted free agent with the Detroit Lions in July 2011 after the NFL labor dispute was resolved . 
- 
- 
- = James Robert Baker = 
- 
- James Robert Baker ( October 18 , 1946 – November 5 , 1997 ) was an American author of sharply satirical , predominantly gay @-@ themed <unk> fiction . A native Californian , his work is set almost entirely in Southern California . After graduating from UCLA , he began his career as a screenwriter , but became disillusioned and started writing novels instead . Though he garnered fame for his books Fuel @-@ Injected Dreams and Boy Wonder , after the controversy surrounding publication of his novel , Tim and Pete , he faced increasing difficulty having his work published . According to his life partner , this was a contributing factor in his suicide . 
- Baker 's work has achieved cult status in the years since his death , and two additional novels have been posthumously published . First @-@ edition copies of his earlier works have become collector 's items . His novel Testosterone was adapted to a film of the same name , though it was not a financial success . Two other books have been optioned for films , but they have not been produced . 
- 
- = = Early life = = 
- 
- Baker was born in Long Beach , California and raised in what he considered a " <unk> , Republican Southern Californian household " . <unk> against his parents , he became attracted to the fringe elements of society , including <unk> ( anyone living as a <unk> , acting <unk> , or appearing to advocate a revolution in manners ) , artists and gays . In high school during the 1960s he explored his sexuality at underground gay teen nightclubs , while living in fear that his abusive father would find out . At one point , his father hired a private detective to follow him , when he suspected Baker was having an affair with a male neighbor . This family dynamic would be used in many of his novels , most extensively in Boy Wonder . 
- Baker began taking drugs , and became , in his own words , " an out of control , teenage speed freak " . He also began drinking heavily , attributing it to the fact that he was <unk> . However , even after coming out , his substance abuse remained excessive and " still had a life of its own " . After sobering up , he attended UCLA film school , where he was one of the winners of the Samuel Goldwyn Writing Awards , and directed two films : Mouse <unk> <unk> and Blonde Death . Mouse <unk> <unk> , a film about a Mickey Mouse Club <unk> who becomes a gay bondage <unk> , was a controversial entry in the 1976 San Francisco 's <unk> Film Festival , as some thought Baker was actually advocating Nazism . It is also credited with having caused Michael <unk> to abandon his dream of film making and instead become a film critic . 
- 
- = = Writer = = 
- 
- Baker 's lifelong ambition was to write . Upon graduating from UCLA , he spent approximately five years writing Hollywood screenplays in the early 1980s , a process he hated . While financially successful , he was frustrated that his work was not being produced . " I felt like a door @-@ to @-@ door salesman going to all these [ story ] pitch meetings ... [ filled with ] <unk> , hideous <unk> " . He became discouraged and disillusioned , and turned his attention to novels . 
- His first book , Adrenaline , was published under the pseudonym James <unk> . A story of two gay fugitive lovers on the run , it <unk> the satire and drug fueled violence so prominent in his later books . Here Baker began developing the themes that dominated his following works : anarchy ; angry and somewhat paranoid gay men ; the dark underside of Los Angeles , juxtaposed with its sunny outward image ; the hypocrisy of organized religion ; anonymous sex and its implications in the age of AIDS ; and homophobia and the oppression of gays in a Republican dominated America . Its plot device of underdog characters forced into flight due to circumstances beyond their control was one Baker explored in all of his subsequent work . The modest success of this novel encouraged him to devote himself to what have become his best known works , Fuel @-@ Injected Dreams ( a novel revolving around a character loosely based on record producer Phil Spector ) and the 1986 release Boy Wonder . After the novel was published , he stopped screenwriting in order to solely concentrate on books . He spent the bulk of each day writing and researching , and acted out characters and scenes of his novels on videotape to perfect the dialogue . 
- His primary focus was gay @-@ themed writing , though he also wrote about the entertainment industry . <unk> satirical , his writing was filled with increasingly clear anger and disdain for the Republican neo @-@ con agenda , especially after the AIDS pandemic began to take a large toll on the gay community . A very strong voice in gay literature , Baker had admirers and detractors for his gay radical stance , both in the mainstream literary community as well as the gay community itself . 
- A self @-@ described <unk> , Baker has been categorized as a writer of <unk> fiction , in that his novels are frequently populated by <unk> , <unk> characters who engage in taboo behaviors such as heavy drug use , <unk> , necrophilia and other practices ; and often commit acts of extreme , <unk> violence . A man of eclectic tastes , Baker cited as literary influences writers and film directors ranging from <unk> to Jim Thompson and Sam Peckinpah . He also admired the punk writer Dennis Cooper . 
- His work is filled with pop cultural references to both film and music , as well as politics . Orson Welles ' Touch of Evil and John Ford 's The <unk> are mentioned prominently in more than one of his books , and Roxy Music is referenced in virtually every novel he wrote . The imagery in his novels is largely cinematic , with expressions such as " fade in / fade out " , " quick cut " and " <unk> " ; and sentences such as " a montage traces the next fifteen years " and " If the last reel of <unk> 's life had been a <unk> <unk> movie ... " . With driven narratives , his books have the feel of a movie set down on paper . 
- 
- = = Critical reception = = 
- 
- Baker 's work received mixed reviews . His only two books not specifically gay themed ( though containing gay characters and a somewhat gay sensibility ) , Boy Wonder and Fuel @-@ Injected Dreams , were better received by critics , and more popular with readers , though he was never a bestselling novelist . Baker himself estimated that his books sold approximately 25 @,@ 000 copies each . His following book , however , Tim and Pete , met with hostile reviews , primarily for its advocacy of political assassination and terror tactics in combating AIDS discrimination . Baker himself was ambivalent on the subject . " I think assassination does change things ... But I 'm not really calling for violence , " he said . " It 's a novel , not a position paper . " 
- In a 1993 interview , however , Baker stated : 
- I think a strong case can be made that political assassination actually does change things . If you look at the <unk> in this country in the 1960s you can certainly see how it affected history in a very profound way . So if you killed right wing figures , you 'd also be altering the course of history , and eliminating people who might very well be president in 1996 and those who are making bashing gays their number one issue right now . On the one hand , I 'm not advocating <unk> 's turn themselves into human bombs , but on the other hand I have to admit that if I <unk> on CNN and heard somebody had blown Patrick Buchanan 's head clean off , I 'd be <unk> , and to say otherwise would be a lie . 
- There were also charges of racism , due to his portrayals of blacks and <unk> . Baker denied those charges , and pointed out that the protagonist later realized that the blacks were also gay and the <unk> " was just a sweet old woman putting up with a lot of ( stuff ) that I couldn 't even imagine . " He went on to say , " I just wanted to explore the conflicts between gays and <unk> and gays and blacks ... the real feelings [ and the ] <unk> of each other . I realized it wouldn 't all be nice and politically correct . If blacks ( and <unk> ) want my respect , they have to deal with their own homophobia . I 'm not playing guilty liberal anymore " . 
- The book caused a great deal of controversy among critics , with some calling it " irresponsible " , and saying it was " determined to give offense " and " appears to endorse violence " . One critic of the book wrote , " The work rapidly becomes an apology for political terrorism and effectively advocates the assassination of the entire American New Right . While the reasons for such a vengeance motif are perhaps evident , can it really be <unk> ? Are we still justified in referring to this as art ? And even if we are , is there a point at which such <unk> ( and such suggestions ) become simply counterproductive ? " Another critic , however , called it " a <unk> creation " and wrote : " In coming years Baker will be seen as having understood the implications of this period in our history while the rest of us were simply living it " . It polarized the reading public as well , with letters to the editor of major newspapers both supporting and opposing Baker 's ideology . Baker himself was aware that the book would be controversial , and deliberately provoked much of the reaction he received . He said : " Tim and Pete tries to convey in print what people really think rather than what they should think or what 's <unk> My fantasy was to leave readers so infuriated they 'd throw down the book and march right out to a gun store because they wanted to see the finale so bad they realize the only way it 'd happen is if they make it happen in real life ! " 
- 
- = = Death = = 
- 
- After the reception of Tim and Pete , with several critics calling him " The Last Angry Gay Man " , Baker faced increasing difficulty finding a publisher for his work and his financial position became precarious . He was only able to publish one novella , Right Wing , and that was self @-@ published on the Internet . 
- Baker 's life partner , Ron Robertson , believes that this difficulty led Baker on a quick , downwards emotional spiral . Baker killed himself at his home on November 5 , 1997 , by <unk> in his garage . His death was noted in literary circles and mainstream press ; the Gay Times in the United Kingdom wrote , " Baker 's suicide is particularly tragic because it <unk> American gay writing of a refreshingly distinctive voice quite unlike the po @-@ faced prose of so many of his contemporaries . " 
- 
- = = Legacy = = 
- 
- Since his death , Baker 's reputation has steadily increased among critics and the reading public ; and his works now have cult status in the literary community . By 2006 , first editions of Adrenaline , Boy Wonder , Fuel @-@ Injected Dreams and Tim and Pete had become collector 's items and commanded high prices at rare book stores . 
- Robertson , now Baker 's literary <unk> , was successful in having two additional novels published after Baker 's death . One of those , Testosterone , was filmed in 2003 . Directed by David <unk> and starring Antonio <unk> Jr . , the plot was significantly altered and the film was a critical and financial failure . Both Boy Wonder and Fuel @-@ Injected Dreams have been optioned for the movies several times , most recently in 2004 , though they were never produced . Baker 's work has also been published in Germany , Sweden , Italy , Great Britain , Australia , Japan and Russia . 
- Though Tim and Pete was his most controversial work , Boy Wonder is generally considered his magnum opus , and remains his most popular book . A black satire of the film industry , it is also a parody of the " oral biographies " popularized by George <unk> with his books about Edie <unk> and <unk> <unk> , in that the protagonist 's life is revealed in the form of interviews between the writer and the characters . Though it has been praised as " one of the few novels from the last couple of decades that could <unk> be called a classic " , reviewers have also pointed out that it is probably <unk> due to its bitter cynicism regarding the movie industry . 
- Baker 's last published work , Right Wing , as well as his posthumous novels Testosterone and Anarchy , represent a stylistic departure in that he <unk> himself into the plot as either a secondary character or the protagonist . The latter two were edited , and in the case of Anarchy , partially rewritten by his editor , Scott <unk> . Testosterone needed only minor changes , while Anarchy underwent an entire rewrite , with <unk> restructuring the plot and <unk> over 500 pages of prose and notes into a fast @-@ paced , 250 page novel . For the reader , however , it is only Baker 's voice as writer that is heard . 
- Three of Baker 's books have thus far not been published : White Devils , Proto Punk , and <unk> Todd . Additionally , he wrote two screenplays which have not as yet been filmed : Inez and Desert Women . 
- 
- = = Published works = = 
- 
- Adrenaline ( 1985 ) Signet Books / New American Library ISBN 978 @-@ 0 @-@ 451 @-@ <unk> @-@ 6 
- Fuel @-@ Injected Dreams ( 1986 ) E. P. <unk> ISBN 978 @-@ 0 @-@ 525 @-@ <unk> @-@ 2 
- Boy Wonder ( 1988 ) New American Library ISBN 978 @-@ 0 @-@ 453 @-@ <unk> @-@ 5 
- Tim and Pete ( 1993 ) Simon & Schuster ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 1 
- Right Wing ( 1996 , only published on the Internet ) 
- Testosterone ( published posthumously 2000 ) <unk> Publications ISBN 978 @-@ 1 @-@ <unk> @-@ <unk> @-@ 5 
- Anarchy ( published posthumously 2002 ) <unk> Publications ISBN 978 @-@ 1 @-@ <unk> @-@ 743 @-@ 3 
- 
- 
- = Ernie Cooksey = 
- 
- Ernest George Cooksey ( 11 June 1980 – 3 July 2008 ) was an English footballer , who was described as a utility player . He could play as a left @-@ sided defender and midfielder as well as in a central @-@ midfield role . He started as a trainee with Colchester United , before joining non @-@ League clubs <unk> <unk> , Bishop 's Stortford , Chesham United and Bromley . In 2002 , he joined Southern Football League Premier Division side Crawley Town before turning professional with Football League side Oldham Athletic in 2003 . 
- He then moved to Rochdale in 2004 and Boston United in 2007 , totalling 140 appearances in the Football League over a four @-@ year period . He dropped down back into non @-@ League football , joining Conference National club Grays Athletic , making 19 league appearances . 
- Cooksey had a form of skin cancer removed from his back in 2006 , but was diagnosed with a recurrence of a malignant <unk> in February 2008 . He died in the early hours of 3 July 2008 , aged 28 . 
- 
- = = Football career = = 
- 
- 
- = = = Early career = = = 
- 
- Cooksey had started his career as a trainee with Colchester United , prior to moving onto a number of non @-@ League clubs . His first move was to <unk> <unk> before joining Bishop 's Stortford in 1998 . He then moved onto Chesham United in 2000 , leaving in 2002 to have a brief spell at Bromley . 
- He joined Southern Football League Premier Division club , Crawley Town in July 2002 , ready for the 2002 – 03 season . Cooksey made his debut for Crawley on 26 August , against <unk> United coming on as a substitute , after he returned from coaching schoolchildren in Las Vegas , United States for six @-@ months . He made 34 appearances , scoring once during his spell with Crawley , helping them to finish seventh in the Southern Football League Premier Division . 
- 
- = = = Turning professional = = = 
- 
- Cooksey joined Oldham Athletic on trial in July 2003 . At the age of 23 , he stepped up three leagues into professional football permanently when Iain Dowie signed him on for financially troubled Oldham in August , who were in the Second Division . Bob Dowie , who was Cooksey 's former manager at Chesham United , recommended the player to his brother , Iain . He followed in the footsteps of Fitz Hall and Wayne Andrews who also joined the club from Chesham as recommendations from Bob Dowie . Cooksey scored twice in Oldham 's 3 – 0 home victory over Carlisle United in their FA Cup first round match on 8 November 2003 . When the new season , 2003 – 04 , started Cooksey was unable to make his debut due to suspension . He made 37 Second Division appearances for Oldham in the 2003 – 04 season , scoring four goals . 
- Cooksey started the 2004 – 05 season with Oldham Athletic , playing one match , away against Luton Town on 7 August . He was released by Oldham on 15 September , after he was left out of manager Brian <unk> 's plans . He dropped down into the Third Division , newly renamed as League Two and signed for Rochdale a day later on 16 September , despite discussing terms with Carlisle United . He made his debut , away against Mansfield Town in the 1 – 0 away defeat on 18 September . During his first season with Rochdale , Cooksey picked up six yellow cards and two red cards on the field . However , his first red card of the season against Notts County was later overturned and downgraded to a yellow after a successful appeal to The FA . 
- At the start of the 2006 – 07 season , he signed a 12 @-@ month extension to his contract . He spent more than two years with League Two side Rochdale , before joining Boston United in the January transfer window of 2007 . Boston United were reduced to just 12 professionals on their books in the closing stages of the 2006 – 07 season , as the club could not afford to pay players wages . Cooksey had not been paid since February , and was spending £ 30 a day in fuel travelling from his Manchester home . He spent six months at Boston United and played 16 League Two games , but was unable to prevent them from being relegated from The Football League on the final day of the season . 
- 
- = = = Return to non @-@ League = = = 
- 
- Following his release from Boston United , Cooksey stated that he 'd like to reunite with former Boston manager Steve Evans , who was now manager of Cooksey 's previous club , Crawley Town . He had an unsuccessful trial at <unk> , before joining Conference National side Grays Athletic in July 2007 . He made his debut in Grays ' first game of the season away to Torquay United in the 0 – 0 draw , on 12 August . Cooksey was sent @-@ off in Grays ' 1 – 0 home defeat on 8 September , for a <unk> tackle on Michael Brough , as his trailing leg caught the opposition player . His last ever game before his death was for Grays Athletic at home in a Conference National match , against Exeter City on 16 February 2008 . Cooksey played the full 90 minutes in the 2 – 0 defeat . During the 2007 – 08 season , he made 19 appearances in the Conference for Grays . 
- 
- = = Playing style = = 
- 
- Cooksey was a utility player , due to his versatility on the left @-@ flank and in the centre of midfield as a box @-@ to @-@ box midfielder . He was predominately a midfielder , throughout his career playing in the centre or on the left @-@ wing . During his spell with Grays Athletic , he was used as a defender , playing as a left full @-@ back . 
- 
- = = Personal life and illness = = 
- 
- Before joining Oldham Athletic in August 2003 , Cooksey worked as a builder fitting false ceilings , leaving the better @-@ paid job to fulfil a lifelong ambition to become a professional footballer . 
- In early 2008 , he was diagnosed with malignant <unk> , a severe form of skin cancer . He had fought the disease before , when he had a form of skin cancer removed from his back in 2006 . Cooksey suspected his cancer originated from a six @-@ month spell coaching schoolchildren in the searing heat of Las Vegas in the United States . In April 2008 , Grays Athletic staged a benefit match for Cooksey . The match consisted of former professional players , as well as current professionals such as <unk> <unk> , Nicky <unk> , Aaron McLean , Ray <unk> , Justin Edinburgh , Iain Dowie , Bob Dowie , Clive Allen , Scott Fitzgerald and Paul <unk> . He returned to his former club Oldham Athletic in May 2008 , where another benefit match was staged for his appeal at <unk> Park , attended by 1 @,@ 500 fans to see such former players as John Barnes , Luther <unk> and Chris <unk> as well as television actors from Coronation Street . 
- Cooksey was born in Bishop 's Stortford . He died in the early hours of 3 July 2008 , at the age of 28 , five weeks before partner Louise <unk> was due to give birth to their first child . <unk> gave birth to their daughter , Isabella @-@ Georgia Cooksey , on 27 July 2008 . 
- 
- = = <unk> = = 
- 
- Before the League Cup match between Oldham Athletic and Rochdale on 12 August 2008 , both sets of players and management emerged from the tunnel wearing special Ernie Cooksey t @-@ shirts bearing the number 4 , the shirt number he wore at both clubs . The t @-@ shirts bore both clubs ' crests on the front along with the slogan " <unk> <unk> – A <unk> <unk> " , and the reverse read " 4 <unk> " . A bucket collection also took place with all the proceeds going to a fund set up in Cooksey 's name . Additionally , his family , including partner Louise , were present at the game . 
- Oldham Athletic fans had a St George 's Cross flag made in his honour , which was due to be displayed at Oldham 's matches , as well as being taken abroad for England international fixtures . The tribute flag was stolen in February 2009 , when Spanish <unk> attacked English fans in Seville before an international friendly on 20 February , stealing England flags from fans as trophies . The flag was replaced after a number of donations helped to cover the costs . 
- 
- 
- = Simon de Montfort 's Parliament = 
- 
- Simon de Montfort 's Parliament was an English parliament held from 20 January 1265 until mid @-@ March the same year , instigated by Simon de Montfort , a <unk> rebel leader . 
- Simon de Montfort had seized power in England following his victory over Henry III at the Battle of Lewes during the Second Barons ' War , but his grip on the country was under threat . In an attempt to gather more support he summoned representatives from not only the barons and the knights of the <unk> , as had occurred in previous parliaments , but also burgesses from the major towns . The resulting parliament in London discussed radical reforms and temporarily <unk> Montfort 's political situation . Montfort was killed at the Battle of Evesham later that year , but the idea of inviting both knights and burgesses to parliaments became more popular under the reign of Henry 's son Edward I. By the 14th century this had become the norm , with the gathering becoming known as the House of Commons . This parliament is sometimes referred to as the first English parliament and Montfort himself is often termed the founder of the Commons . 
- 
- = = Background = = 
- 
- In <unk> , King Henry III of England faced a revolt among the English barons . Anger had grown about the way the King 's officials were raising funds , the influence of his <unk> relatives at court and his unpopular Sicilian policy ; even the English Church had grievances over its treatment by the King . Within Henry 's court there was a strong feeling that the King would be unable to lead the country through these problems . On 30 April , Hugh <unk> marched into Westminster in the middle of the King 's parliament , backed by his co @-@ conspirators , including Simon de Montfort , the Earl of Leicester , and carried out a coup d 'état . Henry , fearful that he was about to be arrested and imprisoned , agreed to abandon his policy of personal rule and instead govern through a council of 24 barons and churchmen , half chosen by the King and half by the barons . 
- The pressure for reform continued to grow unabated and a parliament met in June . The term " parliament " had first appeared in the 1230s and <unk> to describe large gatherings of the royal court , and parliamentary gatherings were held periodically throughout Henry 's reign . They were used to agree upon the raising of taxes which , in the 13th century , were single , one @-@ off <unk> , typically on movable property , intended to support the King 's normal revenues for particular projects . During Henry 's reign , the counties had begun to send regular <unk> to these parliaments , and came to represent a broader cross @-@ section of the community than simply the major barons . 
- The new parliament passed a set of measures known as the Provisions of Oxford , which Henry <unk> to uphold . These provisions created a smaller council of 15 members , elected solely by the barons , which then had the power to appoint England 's <unk> , chancellor and treasurer , and which would be monitored through <unk> parliaments . <unk> from the lesser barons and the <unk> present at Oxford also helped to push through wider reform , intended to limit the abuse of power by both the King 's officials and the major barons . More radical measures were passed by the new council the next year , in the form of the Provisions of Westminster . 
- The disagreements between the leading barons involved in the revolt soon became evident . Montfort championed radical reforms that would place further limitations on the authority and power of the major barons as well as the Crown ; others promoted only moderate change , while the conservative barons expressed concerns about the existing limitations on the King 's powers . Over the next four years , neither Henry nor the barons were able to restore stability in England , and power swung back and forth between the different factions . By early <unk> , what remained of Henry 's authority had <unk> and the country slipped back towards open civil war . Montfort convened a council of rebel barons in Oxford to pursue his radical agenda and by October , England faced a likely civil war . Montfort marched east with an army and London rose up in revolt . Montfort took Henry and Queen Eleanor prisoner , and although he maintained a fiction of ruling in Henry 's name , the rebels completely replaced the royal government and household with their own , trusted men . 
- Montfort 's coalition began to quickly fragment , Henry regained his freedom of movement and renewed chaos spread across England . Henry appealed to his brother @-@ in @-@ law Louis of France for arbitration in the dispute ; Montfort was initially hostile to this idea , but , as war became more likely again , he decided to agree to French arbitration as well . Initially Montfort 's legal arguments held sway , but in January 1264 , Louis announced the <unk> of Amiens , condemning the rebels , upholding the King 's rights and <unk> the Provisions of Oxford . The Second Barons ' War finally broke out in April , when Henry led an army into Montfort 's territories . Becoming desperate , Montfort marched in pursuit of Henry and the two armies met at the Battle of Lewes on 14 May . Despite their numerical superiority , Henry 's forces were overwhelmed . <unk> , Henry was forced to pardon the rebel barons and reinstate the Provisions of Oxford , leaving him , as historian Adrian <unk> describes , " little more than a <unk> " . 
- 
- = = Parliament = = 
- 
- Simon de Montfort claimed to be ruling in the King 's name through a council of officials . However , he had effective political control over the government even though he was not himself the monarch , the first time this had happened in English history . Montfort successfully held a parliament in London in June 1264 to confirm new constitutional arrangements for England ; four knights were summoned from each county , chosen by the county court , and were allowed to comment on general matters of state – the first time this had occurred . Montfort was unable to consolidate his victory at Lewes , however , and widespread disorder persisted across the country . In France , Eleanor made plans for an invasion of England with the support of Louis . 
- In response , and hoping to win wider support for his government , Montfort summoned a new parliament for 20 January 1265 which continued until mid March that year . It was held at short notice , with the summons being issued on 14 December , leaving little time for attendees to respond . He summoned not only the barons , senior churchmen and two knights from each county , but also two burgesses from each of the major towns such as York , Lincoln , Sandwich , and the <unk> Ports , the first time this had been done . Due to the lack of support for Montfort among the barons , only 23 of them were summoned to parliament , in comparison to the summons issued to 120 churchmen , who largely supported the new government ; it is unknown how many burgesses were called . The event was overseen by King Henry , and held in the Palace of Westminster , London , which was the largest city in England , and whose continuing loyalty was essential to Montfort 's cause . 
- This parliament was a populist , tactical move by Montfort in an attempt to gather support from the regions , and the historian Jeffrey Hamilton <unk> it as " a very partisan assembly , not some sort of proto @-@ democratic representative body " . Once again the representatives were allowed to comment on wider political matters than just the usual issues of taxation . The business of the parliament focused on enforcing the Provisions of Westminster , in particular its restrictions on the major nobles , and promising judicial help to those who felt they were suffering from unfair feudal lordship . 
- The parliament bought temporary calm but opposition grew once more , particularly as Montfort and his immediate family began to <unk> a huge personal fortune . Prince Edward escaped his captors in May and formed a new army , resulting in a fresh outbreak of civil war . Edward pursued <unk> 's forces through the Welsh Marches , before striking east to attack his fortress at <unk> and then turning once more on the rebel leader himself . Montfort , accompanied by the captive Henry , was unable to retreat and the Battle of Evesham ensued . Edward was triumphant and Montfort 's corpse was <unk> by the victors . In places the now <unk> rebellion dragged on , with some rebels gathering at <unk> , which Henry and Edward took after a long siege in <unk> . The remaining pockets of resistance were <unk> up , and the final rebels , holed up on the Isle of Ely , surrendered in July <unk> , marking the end of the war . 
- 
- = = Legacy = = 
- 
- Henry III ruled England until his death in <unk> , continuing to summon parliaments , sometimes including the county knights and on one occasion including burgesses from the towns . After <unk> under Edward I 's reign , this became the norm , and by the early 14th century it was normal to include the knights and burgesses , a grouping that would become known as the " Commons " of England and , ultimately , form the " House of Commons " . 
- Simon de Montfort 's parliament of 1265 is sometimes referred to as the first English parliament , because of its inclusion of both the knights and the burgesses , and Montfort himself is often regarded as the founder of the House of Commons . The 19th century historian William <unk> popularised the <unk> " Model Parliament " of Edward I as the first genuine parliament ; however , modern scholarship questions this analysis . The historian David Carpenter describes Montfort 's 1265 parliament as " a landmark " in the development of parliament as an institution during the medieval period . 
- 
- = = = Modern recognition = = = 
- 
- The Parliament of the United Kingdom presented a loyal address to Queen Elizabeth II in 1965 to mark the 700th anniversary of Montfort 's Parliament , and the Queen addressed both Houses of Parliament . The House of Lords Record Office , now known as the Parliamentary Archives , organised an exhibition in the Houses of Parliament of several important Acts of Parliament . Some of these documents were displayed again in a 2015 exhibition . 
- In 2015 , Parliament planned a year @-@ long programme of events called " Parliament in the Making " , coordinated with Parliament Week , including events to mark the 750th anniversary of Montfort 's Parliament . The BBC broadcast a " Democracy Day " on 20 January to coincide with the 750th anniversary consisting of live discussions and debate about parliament and democracy . It was presented in partnership with the Speaker ’ s Office of the House of Commons , including broadcasts from inside the Palace of Westminster . Westminster Abbey held a special <unk> on 22 January commemorating the anniversary of the Montfort parliament and the development of rights and representation . 
- 
- 
- = USS O 'Brien ( DD @-@ 51 ) = 
- 
- USS O 'Brien ( Destroyer No. 51 / DD @-@ 51 ) was the lead ship of O 'Brien @-@ class destroyers built for the United States Navy prior to the American entry into World War I. The ship was the second US Navy vessel named in honor of Jeremiah O 'Brien and his five brothers Gideon , John , William , Dennis , and Joseph who , together on the sloop Unity , captured a British warship during the American Revolutionary War . 
- O 'Brien was laid down by William Cramp and Sons of Philadelphia in September 1913 and launched in July 1914 . 
- After her May 1915 commissioning , O 'Brien sailed off the east coast and in the Caribbean . She was one of seventeen destroyers sent out to rescue survivors from five victims of German submarine U @-@ 53 off the Lightship Nantucket in October 1916 . After the United States entered World War I in April 1917 , O 'Brien was sent overseas to patrol the Irish Sea out of Queenstown , Ireland . 
- After returning to the United States in January 1919 , O 'Brien revisited European waters in May to serve as one of the picket ships for the NC @-@ type seaplanes in the first aerial crossing of the Atlantic . O 'Brien was decommissioned at Philadelphia in June 1922 . She was struck from the Naval Vessel Register in March 1935 and sold for scrapping in April . 
- 
- = = Design and construction = = 
- 
- O 'Brien was authorized in March 1913 as the lead ship of the O 'Brien class , which was an improved version of the Cassin @-@ class destroyers authorized in 1911 . Construction of the vessel was awarded to William Cramp and Sons of Philadelphia which laid down her keel on 8 September 1913 . On 20 July 1914 , O 'Brien was launched by sponsor Miss Marcia Bradbury Campbell , great @-@ great @-@ granddaughter of Gideon O 'Brien , one of the ship 's <unk> . Gideon and his four brothers — John , William , Dennis , and Joseph — were crewmen aboard sloop Unity , under the command of their brother Jeremiah O 'Brien , when that vessel captured HMS <unk> on 12 June 1775 during the American Revolutionary War ; the destroyer O 'Brien was named after all six brothers , and was the second US Navy vessel named in their honor . As built , the destroyer was 305 ft 3 in ( 93 @.@ 04 m ) in length , 31 ft 1 in ( 9 @.@ 47 m ) abeam , and drew 9 ft 6 in ( 2 @.@ 90 m ) . The ship had a standard displacement of 1 @,@ 050 long tons ( 1 @,@ 070 t ) and displaced 1 @,@ 171 long tons ( 1 @,@ 190 t ) when fully loaded . 
- O 'Brien had two <unk> steam turbines that drove her two screw propellers , and an additional pair triple @-@ expansion steam engines , each connected to one of the propeller shafts , for cruising purposes . Four oil @-@ burning White @-@ Forster boilers powered the engines , which could generate 17 @,@ 000 shaft horsepower ( 13 @,@ 000 kW ) , moving the ship at up to 29 knots ( 54 km / h ) . 
- O 'Brien 's main battery consisted of four 4 in ( 100 mm ) / 50 caliber Mark 9 guns , with each gun weighing in excess of 6 @,@ 100 lb ( 2 @,@ 800 kg ) . The guns fired 33 lb ( 15 kg ) armor @-@ piercing projectiles at 2 @,@ 900 ft / s ( 880 m / s ) . At an elevation of 20 ° , the guns had a range of 15 @,@ 920 yd ( 14 @,@ 560 m ) . 
- O 'Brien was also equipped with four twin 21 in ( 533 mm ) torpedo tubes . The General Board of the United States Navy had called for two anti @-@ aircraft guns for the O 'Brien @-@ class ships , as well as provisions for laying up to 36 floating mines . From sources , it is unclear if these recommendations were followed for O 'Brien or any of the other ships of the class . 
- 
- = = Pre @-@ World War I = = 
- 
- O 'Brien was commissioned into the United States Navy on 22 May 1915 under the command of Lieutenant Commander C. E. Courtney , after which she conducted her shakedown cruise between Newport , Rhode Island , and Hampton Roads , Virginia . In fleet exercises off New York in November , O 'Brien collided with the destroyer <unk> , in a minor incident that carried away part of <unk> 's <unk> and wireless gear . In December , she was assigned to the 5th Division , Torpedo Flotilla , Atlantic Fleet . From early 1916 @-@ spring of 1917 , she operated with the Fleet along the East Coast and in Cuban waters . 
- At 05 : 30 on 8 October 1916 , wireless reports came in of a German submarine stopping ships near the Lightship Nantucket , off the eastern end of Long Island . After an SOS from the British steamer SS West Point was received at about 12 : 30 , Rear Admiral Albert <unk> ordered O 'Brien and other destroyers at Newport to attend to survivors . 
- The American destroyers arrived on the scene at about 17 : 00 when the U @-@ boat , U @-@ 53 under the command of <unk> Hans Rose , was in the process of stopping the Holland @-@ America Line cargo ship <unk> . Shortly after , U @-@ 53 stopped the British passenger ship <unk> . As Rose had done with three other ships U @-@ 53 had sunk earlier in the day , he gave passengers and crew aboard <unk> and <unk> adequate time to abandon the ships before sinking the pair . In total , 226 survivors from U @-@ 53 's five victims were rescued by the destroyer flotilla . 
- In February 1917 , one of O 'Brien 's gun crews hit a target at 5 @,@ 000 yd ( 4 @,@ 600 m ) eight times in eight attempts with one of the destroyer 's 4 in ( 100 mm ) guns , a feat which earned the crew and O 'Brien recognition in The Independent , a weekly <unk> published in Boston . 
- 
- = = World War I = = 
- 
- Returning from winter maneuvers off Cuba in March 1917 , the ship was in the York River when the United States declared war on Germany on 6 April , entering World War I. After fitting out at Brooklyn Navy Yard , she got underway from New York on 15 May with Cummings , Nicholson , Cushing , and Sampson , and joined convoy at Halifax , Nova Scotia , en route to Ireland . Upon arrival at Queenstown on 24 May , O 'Brien was assigned to the 6th Destroyer Division which cooperated with the British forces . She patrolled off the Irish coast in company with other destroyers answering distress calls and meeting eastbound convoys to escort them through the war zone . 
- While escorting SS <unk> 12 nmi ( 14 mi ; 22 km ) off Queenstown on 16 June , lookouts on O 'Brien sighted a <unk> . Heading toward the submarine for an attack , a lookout in the <unk> saw the submerged boat pass close along the starboard side . A depth charge was dropped but no immediate evidence of damage was found . Nearly three hours later , the British vessel HMS <unk> reported a large patch of oil in approximately the same position . The next morning , Cushing also reported and confirmed <unk> 's report . The British Admiralty believed the submarine was probably seriously damaged . However , later investigation reveals that German submarine U @-@ 16 , the submarine in question , continued to operate and completed her cruise . 
- In the summer of 1918 , O 'Brien was transferred to the French coast where she continued her <unk> patrols through the end of the war . 
- 
- = = Inter @-@ war period = = 
- 
- After the signing of the Armistice on 11 November , which ended all fighting , O 'Brien transported mail and passengers between Brest , France , and Plymouth , England . She returned to New York on 8 January 1919 , but returned to European waters in May when she served as one of the rescue <unk> stationed along the route across the Atlantic flown by three Navy NC @-@ type seaplanes in the first aerial crossing of the Atlantic . 
- In 1919 , she assisted in the unsuccessful first attempt to lay the <unk> Channel pilot cable . In July 1920 , she was assigned the hull code of DD @-@ 51 under the US Navy 's <unk> classification system . O 'Brien was decommissioned at Philadelphia on 5 June 1922 . The ship was struck from the Naval Vessel Register on 8 March 1935 , and broken up at the Philadelphia Navy Yard , and her materials sold for scrap on 23 April . The ship 's bell remains in the Plattsburgh Memorial Chapel on the former Plattsburgh Air Force Base . 
- 
- 
- = Christine Hakim = 
- 
- <unk> Christine <unk> Hakim ( born 25 December 1956 ) , also known by the popular name Christine Hakim , is an Indonesian actress , film producer , and activist . Born to a devout Muslim family of a mixed @-@ race background in <unk> , she grew up in Yogyakarta , aspiring to be an architect or psychologist . This changed after she was discovered by <unk> Karya for his 1973 movie Cinta Pertama , a role which garnered her a Citra Award for Best Actress and convinced her to follow a career in acting . Since then , she has starred in numerous films , including 1977 's Badai Pasti Berlalu and 1988 's Tjoet Nja ' Dhien ; she also had a minor role in the 2010 Hollywood movie Eat Pray Love . As of 2011 , she has won six Citra Awards , received a lifetime achievement award from the <unk> International Film Festival , and served as a member of the jury at the Cannes Film Festival 
- Hakim began branching out from acting in 1998 , beginning with roles as producer of Daun di Atas Bantal and Pasir Berbisik and eventually spreading to documentary film @-@ making and an activist for education and autism . Beginning in 2008 , she has served as Indonesia 's goodwill ambassador to UNESCO , focusing on educational issues . 
- 
- = = Biography = = 
- 
- Hakim was born in Kuala <unk> , <unk> on 25 December 1956 , but grew up in Yogyakarta . She is of mixed descent , with her relatives coming from <unk> , <unk> , <unk> , <unk> , <unk> , and the Middle East ; this caused her to question her identity as a child and teenager . Despite being devout Muslims , her parents named her Christine and <unk> because she was born on Christmas Day . 
- Originally , Hakim did not intend to be an actress , but an architect or psychologist . However , she was cast in <unk> Karya 's 1973 film Cinta Pertama after he saw pictures of her modelling in a magazine ; despite only modelling to help her friend and not wanting to act , she was unable to decline Karya 's request for fear of being <unk> to such a " warm and friendly " person . She later described Karya as having " <unk> [ her ] in , slowly , slowly , like a <unk> , " and considered leaving acting after wrapping up filming . Her work in Cinta Pertama garnered her a Citra Award for Best Actress , which convinced her to continue acting . Karya later told her that he had fought with his producer over her casting ; the producer expressed concern that Hakim was " too thin and had no chest , " to which Karya replied " are we selling a film or are we selling breasts ? " 
- The following year , Hakim starred in another Karya @-@ directed film , <unk> <unk> ( <unk> ) . The experience gave her a greater understanding of acting , causing her to " see life from a different perspective in studying [ her ] character . " It was followed by a role in 1976 's Sesuatu yang Indah ( Something Beautiful ) , directed by <unk> <unk> . Sesuatu yang Indah was the first film in which Hakim used her own voice ; her voice had been dubbed by <unk> <unk> in previous movies ; Hakim 's own voice had been considered " too heavy . " The next year , she starred in Badai Pasti Berlalu , appearing on the poster and the cover of the soundtrack album . 
- Hakim presented 14 Indonesian films at the <unk> Three <unk> Festival in November 1983 ; she had acted in half of them . Two years later she became an observer at the Cannes Film Festival , striking up a working relationship with Pierre <unk> , who later assisted her in bringing her films to Cannes . 
- One of these was <unk> <unk> 's 1988 film Tjoet Nja ' Dhien , in which Hakim was cast as <unk> <unk> leader Cut <unk> Dhien . It won the 1989 Cannes Film Festival award for Best International Film , being screened in Le <unk> de Critique . Hakim later described the role as a " huge honour " and " very challenging " ; she has credited the role for answering her questions on her identity . The film later became Indonesia 's submission to the 62nd Academy Awards for the Academy Award for Best Foreign Language Film . 
- Hakim screened her first work as a producer , Daun di Atas Bantal ( Leaf on a <unk> ) during Un Certain Regard at Cannes ten years later . When producing the film , she chose young director <unk> <unk> , whom she perceived to be highly talented ; she also took the leading role . During production , she made a mistake that required the <unk> of all footage . In an attempt to cut expenses , she had saved all cans of exposed film to send to the developing lab at once ; the lab then notified her that a technical fault with the camera had rendered all of it unusable and that the problem could have been detected earlier had she sent each can as it was filmed . 
- Another production , 2001 's Pasir Berbisik ( <unk> Sands ) , went more smoothly . The film , which Hakim co @-@ produced and played the leading role , was screened at the Deauville Asian Film Festival . The following year she was appointed to the jury of the Cannes Film Festival , along with Michelle Yeoh . In 2005 she received a special tribute during the opening ceremony of the 7th Deauville Asian Film Festival . 
- In 2003 , Hakim began work with <unk> and Metro TV on the TV show <unk> Guru ( For You , <unk> ) . Five years later , Hakim was selected Indonesia ’ s voluntary goodwill ambassador for UNESCO ; she has used the position to promote education , push for education reforms in Indonesia , and promote disaster relief programs in South @-@ East Asia . 
- Hakim 's first Hollywood experience came in 2010 , Hakim played <unk> , a <unk> <unk> seller , alongside Julia Roberts in Eat Pray Love . Arriving in <unk> three days before shooting , she found herself rushed to read the script , be fitted , and rid herself of her green hair . She met with the person her character was based on to prepare for her role . That same year , Hakim accepted a <unk> Award for her " outstanding achievements " ; she compared the award to <unk> , saying that it " make [ s ] [ her ] stronger to satisfy [ audiences ] " . 
- Hakim is also a documentary filmmaker . She has made a documentary on the Indonesian UNESCO World Heritage Sites , and in 2011 produced a documentary on autism to " educate the public " that was released to coincide with World <unk> Awareness Day . As of May 2011 , she is producing a documentary on the <unk> people of <unk> . She is considering making a fiction film based on their culture . 
- 
- = = <unk> = = 
- 
- Beginning in the early 2000s , Hakim became an activist , with a focus on education . After the 2004 Indian Ocean earthquake and tsunami , she went on numerous humanitarian trips to <unk> . She later founded the Christine Hakim feature , a foundation to promote public education about autism . She has urged the government to eliminate <unk> regarding autism , calling the refusal to accept <unk> students in public schools " a violation of human rights " . 
- 
- = = Personal life = = 
- 
- In 2000 , Hakim married Dutchman <unk> <unk> , a film producer , writer and actor . They currently live in <unk> East Jakarta with Hakim 's mother and adopted daughter <unk> ; Bruce <unk> of The Jakarta Post describes her home as being " like a lush secret garden amid the <unk> <unk> " . 
- Hakim generally avoids discussing her personal life with the press and is unwilling to discuss topics that are not of public interest . In 1992 she said that " 90 percent of the journalists here aren 't interested in [ her ] films , they just want to know about [ her ] private life . " 
- 
- = = Legacy = = 
- 
- Hakim has been described as the " <unk> <unk> of Indonesian cinema " as well as " Indonesia ’ s foremost actress " . She has also received a lifetime achievement award at the <unk> International Film Festival . 
- Hakim has received several awards for her films : 
- Citra Award for Best Leading Actress , for Cinta Pertama ( 1973 ) 
- Citra Award for Best Leading Actress , for Sesuatu Yang Indah ( 1976 ) 
- Citra Award for Best Leading Actress , for <unk> dan <unk> <unk> ( 1978 ) 
- Citra Award for Best Leading Actress , for Di <unk> <unk> ( 1982 ) 
- Citra Award for Best Leading Actress , for Kerikil @-@ Kerikil <unk> ( 1984 ) 
- Citra Award for Best Leading Actress , for Tjoet Nja ' Dhien ( 1988 ) 
- Best Actress at Asia Pacific International Film Festival 
- Hakim has refused a position on the Asia Pacific Screen Awards numerous times , due to being unable to reconcile her schedule with the awards . 
- 
- = = Filmography = = 
- 
- Cinta Pertama ( First Love ; 1973 ) ( awarded Citra FFI 1974 , <unk> ) 
- <unk> ( <unk> ; 1974 ) 
- <unk> <unk> ( Wedding Bed ; 1974 ) 
- <unk> <unk> ( <unk> ; 1975 ) 
- Si <unk> <unk> Modern ( <unk> the Modern Child ; 1976 ) 
- Sesuatu yang Indah ( Something Beautiful ; 1977 ) ( awarded Citra FFI 1977 , Jakarta ) 
- Badai Pasti Berlalu ( The Storm Will <unk> Pass ; 1977 ) 
- <unk> @-@ <unk> ( Wanderers ; 1977 ) 
- <unk> dan <unk> <unk> ( The <unk> and the <unk> Driver ; 1978 ) ( awarded Citra FFI 1979 , <unk> ) 
- Dr. Siti <unk> ( 1980 ) 
- <unk> <unk> , <unk> <unk> ( As White as the Heart , as Red as the <unk> ; 1982 ) 
- Di <unk> <unk> ( Behind the Screen ; 1982 ) ( awarded Citra FFI 1983 , <unk> ) 
- <unk> <unk> ( <unk> is <unk> ; 1984 ) 
- Kerikil @-@ Kerikil <unk> ( Sharp <unk> ; 1984 ) ( awarded Citra FFI 1985 , Bandung ) 
- Tjoet Nja ' Dhien ( 1988 ) ( awarded Citra FFI ) 
- <unk> <unk> - Japan ( 1994 ) ( The Sleeping Man ; 1994 ) 
- De <unk> van <unk> - Netherlands ( 1997 ) ( The <unk> of Emerald ; 1997 ) 
- Daun di Atas Bantal ( Leaf on a <unk> ; 1998 ) 
- Pasir Berbisik ( <unk> Sands ; 2001 ) 
- <unk> ( Find <unk> ; 2009 ) 
- <unk> dan Sang <unk> ( <unk> and the President ; 2009 ) 
- Eat Pray Love ( 2010 ) 
- Sang <unk> ( 2013 ) ( The <unk> ; 2013 ) 
- <unk> <unk> <unk> ( 2014 ) ( The Golden Cane Warrior ; 2014 ) 
- De <unk> - Netherlands ( Short ) ( 2014 ) ( The Journey ; 2014 ) 
- Guru <unk> <unk> <unk> ( 2014 ) ( <unk> <unk> : <unk> of the People ; 2014 ) 
- 
- 
- = A Month in the Country ( film ) = 
- 
- A Month in the Country is a 1987 British film directed by Pat O 'Connor . The film is an adaptation of the novel of the same name by J. L. Carr , and stars Colin Firth , Kenneth Branagh , Natasha Richardson and Patrick Malahide . The screenplay was by Simon Gray . 
- Set in rural Yorkshire during the summer of 1920 , the film follows a destitute World War I veteran employed to carry out restoration work on a Medieval mural discovered in a rural church while coming to terms with the after @-@ effects of the war . 
- Shot during the summer of 1986 and featuring an original score by Howard Blake , the film has been neglected since its 1987 cinema release and it was only in 2004 that an original 35 mm film print was discovered due to the intervention of a fan . 
- 
- = = Plot = = 
- 
- Set in 1920 , the film follows the experiences of Tom Birkin ( Colin Firth ) , who has been employed under a bequest to carry out restoration work on a Medieval mural discovered in a church in the small rural community of <unk> , Yorkshire . The escape to the idyllic countryside is <unk> for Birkin , haunted by his experiences in World War I. Birkin soon fits into the slow @-@ paced life of the remote village , and over the course of the summer uncovering the painting begins to lose his trauma @-@ induced <unk> and <unk> . 
- In particular , he forms a close friendship with archaeologist James Moon ( Kenneth Branagh ) , another veteran , who like Birkin has been emotionally <unk> by the war . Moon is employed in the village under the same bequest , working to uncover a mysterious lost grave , but is more interested in discovering the remains of an earlier Saxon church building in the field next to the churchyard . 
- Birkin becomes accepted into the <unk> family of Mr Ellerbeck the station master ( Jim Carter ) , with whom he <unk> on Sundays ; the hospitality of the chapel congregation is contrasted against the established church , who have <unk> the <unk> Birkin to sleep in the church <unk> . Ellerbeck 's children eventually persuade Birkin to preach a sermon at a nearby Methodist chapel . Birkin also forms an emotional , albeit unspoken , attachment to Alice Keach ( Natasha Richardson ) , the young wife of the vicar . The vicar himself ( Patrick Malahide ) is portrayed <unk> as an obstruction to the work in the church , viewing the medieval painting as symptomatic of the superstition prevalent in the community . 
- 
- = = Cast = = 
- 
- A Month in the Country featured film <unk> or early roles of several notable British actors . Although it was the third cinema feature film to cast Colin Firth , it was his first lead role . Similarly , it was Kenneth Branagh 's first cinema film , and Natasha Richardson 's second . Conversely , it was the last role of David Garth who died in May 1988 . 
- Colin Firth as Tom Birkin 
- Kenneth Branagh as James Moon 
- Natasha Richardson as Alice Keach 
- Patrick Malahide as the Reverend <unk> Keach 
- Jim Carter as Ellerbeck 
- Vicki <unk> as <unk> Ellerbeck 
- Martin O 'Neil as Edgar Ellerbeck 
- Richard Vernon as Colonel Hebron 
- Tim Barker as <unk> 
- David Garth as Old Birkin 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- Initially intended to be a made @-@ for @-@ TV film , the film 's producer <unk> <unk> upgraded his original plan to a cinema feature . The original working title for the film was " Falling Man " . <unk> Simon Gray was commissioned to write the screenplay , and Pat O ’ Connor chosen to direct . In contrast to the book , which is narrated as a recollection by Birkin as an old man , the film is set entirely in the 1920s , except for a brief moment towards the end . In initial drafts of the screenplay , Gray had included a narrator , but O 'Connor felt this was not the correct way to present the story : 
- I felt that if I couldn ’ t do it in the present , suggesting internal pain by performance , then I wouldn ’ t really want to do it at all . 
- Funding for the film was scarce , and it eventually fell to <unk> Films ( a subsidiary of Thames Television ) and Channel Four Films , who had had some success with low budget features such as My Beautiful <unk> . 
- 
- = = = Filming = = = 
- 
- To compensate for the lack of budget , a very tight shooting schedule was planned over 28 days , during which Kenneth Branagh was only available for two weeks and was performing on @-@ stage nightly in London . 
- Despite being set in Yorkshire , the majority of location filming was moved to Buckinghamshire , although <unk> railway station and the surrounding area in North Yorkshire were utilised . The location shooting of the film began on August 18 , 1986 at St. Mary 's church in <unk> . Filming was periodically hindered by <unk> weather — the perfect summer in which the book was set was not forthcoming , and scenes were filmed during breaks in heavy rain . <unk> were shot at Bray Studios in Berkshire . 
- The church , which is a main location for the film , was substantially set @-@ dressed . Despite having several original medieval wall paintings , the largest addition was the creation of the medieval mural by artist Margot <unk> . To create the impression of an austere country church , Victorian stone flags were replaced with brick pavers for the duration of filming and the original wall paintings covered up . <unk> <unk> and other modern additions were covered up or removed . The churchyard had several gravestones added , including the large box tomb which is a focus of several scenes . 
- Several members of the local community were used as extras in the film , and local children were recruited by the director to collect butterflies to be released out @-@ of @-@ shot to create a " summer feeling " . However there was some opposition to the disruption caused by the filming , and also problems involving unwelcome damage to a section of the interior <unk> , which had to be restored after filming had concluded . 
- 
- = = = Music = = = 
- 
- The soundtrack of the film was written by Howard Blake , and is scored entirely for string orchestra in the style of early 20th Century British music . Blake notes that the style chosen was intended to complement and contrast recordings of classical music during particular scenes : Verdi 's <unk> <unk> <unk> was used during the uncovering of the mediaeval mural and a flashback montage of the First World War which opened the film utilised an excerpt from Schubert 's Deutsche <unk> ( D. <unk> ) " <unk> <unk> : <unk> , <unk> ist der Herr " . 
- Howard Blake recalls : " I went to a viewing and saw that the film was very profound , with a serious anti @-@ war theme , but a certain amount of ' found ' choral music had already been laid in by the editors ... I explained that I loved the film and I thought the choral / orchestral music worked brilliantly but it was very big and rich and I felt a score would have to emerge from it and be very pure and expressive and quite small — and that I could only hear this in my head as done by strings only . " Blake decided to compose his score to match the key of the Schubert Mass , in order for the music to continue seamlessly . However , during the recording session with his orchestra , the Sinfonia of London , he found that the Schubert piece was running slow and therefore flat , and he had to ask the players to tune flat to match his intended key . 
- Due to the small budget of the film , Blake agreed " in lieu of a reasonable fee " to retain the copyright to his music . The score was subsequently arranged into a suite for string orchestra , and is available on CD in a recording by the English Northern <unk> conducted by Paul Daniel . 
- 
- = = Reception and awards = = 
- 
- Upon its release in 1987 , the film was generally well received by critics . Rita <unk> , writing in The Washington Post suggested " It 's all rather <unk> , with its <unk> hero on his spiritual quest , the atmosphere <unk> , <unk> to dance with once and future truths . " Tom Hutchinson in the Mail on Sunday praised " a script whose delight is in the rounded reality of its characters " . Janet Maslin , writing in The New York Times praised O 'Connor 's direction , suggesting it lent the film " a strong sense of <unk> , as well as a spiritual quality more apparent in the look of the film than in its dialogue . " Desmond Ryan of The Philadelphia Inquirer wrote " Rarely has the impossibility of love been more <unk> presented than in the scenes of <unk> hope between Firth and Richardson . 
- However , Nigel Andrews of the Financial Times found it " like a pastoral parable that has been left outside in the <unk> too long , causing its batteries to go flat " and following a 2008 screening , Sam <unk> of The Guardian suggested " even though this film is ( unusually ) faithful to the book ... it is really little better than inoffensive . Somehow the magic that makes <unk> Carr 's book so precious is missing . " 
- The film was the recipient of two awards : Pat O 'Connor won the Silver Rosa <unk> at the <unk> Film Meeting in 1987 and Howard Blake was awarded the Anthony Asquith Award for Musical Excellence by the British Film Institute in 1988 . In addition , Colin Firth was nominated for an Evening Standard Award . The film was screened in the Un Certain Regard section at the 1987 Cannes Film Festival . 
- 
- = = DVD and Blu @-@ ray release = = 
- 
- Following its cinema release , the film was transferred to VHS in 1991 in a pan and scan edition . However , when Glyn Watkins , a poet who had been encouraged by <unk> Carr early in his career , wanted to screen the film at the launch of a poetry book in 2003 at the National Media Museum in Bradford , the museum found that all original 35mm film prints had disappeared . 
- <unk> , Watkins contacted the agents of the cast and director , and eventually the online fan clubs " Friends of Firth " and " Ken Friends " . He discovered that the film had appeared as part of the National Film Theatre 's Branagh season in May 1999 , and that the film 's American distributors , Warner Bros. , had a print in a bonded warehouse . However , it was unclear who possessed the rights to the film . Only after several months it was found that Channel 4 still owned the rights , and the film was eventually released on a limited @-@ edition Region 2 DVD in late 2004 . However , the DVD is now no longer widely available in the United Kingdom . In 2008 , a complete 96 minute print was located in the Academy Film Archive in Los Angeles and a campaign began to have it restored and released on DVD . A limited edition Region A Blu @-@ ray release of the full 96 minute version of the film is planned on 14 July 2015 . 
- 
- 
- = Hydnellum peckii = 
- 
- Hydnellum peckii is an inedible ( though not toxic ) fungus , and a member of the genus Hydnellum of the family Bankeraceae . It is a hydnoid species , producing spores on the surface of vertical spines or tooth @-@ like projections that hang from the <unk> of the fruit bodies . It is found in North America , Europe , and was recently discovered in Iran ( 2008 ) and Korea ( 2010 ) . Hydnellum peckii is a mycorrhizal species , and forms mutually beneficial relationships with a variety of coniferous trees , growing on the ground singly , scattered , or in fused masses . 
- The fruit bodies typically have a funnel @-@ shaped cap with a white edge , although the shape can be highly variable . Young , moist fruit bodies can " bleed " a bright red fluid that contains a pigment known to have anticoagulant properties similar to <unk> . The unusual appearance of the young fruit bodies has earned the species several descriptive common names , including <unk> and cream , the bleeding Hydnellum , the bleeding tooth fungus , the red @-@ juice tooth , and the Devil 's tooth . Although Hydnellum peckii fruit bodies are readily identifiable when young , they become brown and <unk> when they age . 
- 
- = = Taxonomy , phylogeny , and naming = = 
- 
- The species was first described scientifically by American mycologist Howard James Banker in 1913 . Italian Pier Andrea <unk> placed the species in the genus Hydnum in 1925 , while Walter Henry Snell and Esther <unk> Dick placed it in <unk> in 1956 ; Hydnum peckii ( Banker ) <unk> and <unk> peckii Snell & <unk> Dick are synonyms of Hydnellum peckii . 
- The fungus is classified in the <unk> ( species thought to be descendants of a common ancestor ) <unk> of the genus Hydnellum , a grouping of similar species with the following shared characteristics : flesh that is marked with concentric lines that form alternating pale and darker zones ( <unk> ) ; an extremely <unk> taste ; a <unk> odor ; spores that are ellipsoid , and not amyloid ( that is , not absorbing iodine when stained with Melzer 's reagent ) , <unk> ( not staining with the reagent Cotton Blue ) , and covered with <unk> ; the presence of clamp connections in the hyphae . Molecular analysis based on the sequences of the internal transcribed <unk> DNA of several Hydnellum species placed H. peckii as most closely related to H. <unk> and H. <unk> . 
- The specific epithet honors mycologist Charles Horton Peck . The fungus is known in the vernacular by several names , including " <unk> and cream " , the " bleeding Hydnellum " , the " red @-@ juice tooth " , " Peck 's <unk> " , the " bleeding tooth fungus " , and the " devil 's tooth " . 
- 
- = = Description = = 
- 
- As in all mushroom @-@ producing fungi , the fruit bodies ( <unk> ) are the reproductive structures that are produced from fungal mycelium when the appropriate environmental conditions of temperature , humidity and nutrient availability are met . Hydnellum peckii is a <unk> hydnoid fungus , meaning that it has a cap atop a stipe ( stem ) , and a form resembling a Hydnum — characterized by a teeth @-@ like hymenium , rather than gills or pores on the underside of the cap . Fruit bodies growing closely together often appear to fuse together ( this is called " confluence " ) . They can reach a height of up to 10 @.@ 5 cm ( 4 @.@ 1 in ) . Fresh fruit bodies <unk> a striking , thick red fluid when they are moist . 
- The cap 's surface is convex to flattened , more or less uneven and sometimes slightly depressed in the center . It is usually densely covered with " hairs " that give it a texture similar to felt or velvet ; these hairs are <unk> off in age , leaving the caps of mature specimens smooth . Its shape varies from somewhat round to irregular , 4 to 10 cm ( 1 @.@ 6 to 3 @.@ 9 in ) , or even as much as 20 cm ( 7 @.@ 9 in ) wide as a result of confluence . The cap is initially whitish , but later turns slightly brownish , with irregular dark @-@ brown to nearly black blotches where it is <unk> . In maturity , the surface is fibrous and tough , scaly and jagged , grayish @-@ brown in the upper part of the cap , and somewhat woody . The flesh is a pale pinkish @-@ brown . 
- The teeth are slender , cylindrical and <unk> ( <unk> ) , less than 5 mm ( 0 @.@ 20 in ) long , and become shorter closer to the cap edge . They are crowded together , with typically between three and five teeth per square millimeter . <unk> @-@ white initially , they age to a grayish @-@ brown . The stem is thick , very short , and often deformed . It becomes bulbous where it <unk> the ground , and may root into the soil for several centimeters . Although it may reach up to 5 cm ( 2 @.@ 0 in ) in total length , and is 1 to 3 cm ( 0 @.@ 4 to 1 @.@ 2 in ) wide , only about 0 @.@ 1 to 1 cm ( 0 @.@ 0 to 0 @.@ 4 in ) appear above ground . The upper part is covered with the same teeth found on the underside of the cap , whereas the lower part is hairy and often <unk> debris from the forest floor . The odor of the fruit body has been described as " mild to disagreeable " , or , as Banker suggested in his original description , similar to <unk> nuts . 
- 
- = = = Microscopic features = = = 
- 
- In deposit , the spores appear brown . <unk> them with a light microscope reveals finer details of their structure : they are roughly spherical but end abruptly in a small point , their surfaces are covered with small , <unk> @-@ like <unk> , and their size is between 5 @.@ 0 – 5 @.@ 3 by 4 @.@ 0 – 4 @.@ 7 µm . The spores are in amyloid , meaning they do not absorb iodine when stained with Melzer 's reagent . 
- Hydnellum peckii 's cells ( the hyphae ) also present various characters useful for its characterization . The hyphae that form the cap are hyaline ( translucent ) , smooth , thin @-@ walled , and 3 – 4 µm thick . They collapse when dry , but may be readily revived with a weak ( 2 % ) solution of potassium hydroxide . Those in the cap form an intricate tangle with a tendency to run <unk> . They are divided into cellular compartments ( <unk> ) and have clamp connections — short branches connecting one cell to the previous cell to allow passage of the products of nuclear division . The basidia , the spore @-@ bearing cells in the hymenium , are club @-@ shaped , four @-@ spored , and measure 35 – 40 by 4 @.@ 7 – 6 µm . 
- 
- = = = Similar species = = = 
- 
- Hydnellum diabolus ( the species epithet is given the <unk> <unk> in some publications ) has a very similar appearance , so much so that some consider it and H. peckii to be synonymous ; H. diabolus is said to have a <unk> pungent odor that is lacking in H. peckii . The differences between the two species are amplified in mature specimens : H. diabolus has an irregularly thickened stem , while the stem of H. peckii is thickened by a " definite <unk> layer " . Additionally , old specimens of H. peckii have a smooth cap , while H. diabolus is <unk> . The related species H. <unk> also <unk> pink droplets of liquid when young and moist . Commonly found growing under conifers in northeastern North America , H. <unk> tastes " unpleasant " , but not acrid . Fruit bodies tend to grow singly , rather than in fused clusters , and , unlike H. peckii , they do not have bulbous stems . 
- 
- = = Ecology = = 
- 
- Hydnellum peckii is a mycorrhizal fungus , and as such establishes a mutualistic relationship with the roots of certain trees ( referred to as " hosts " ) , in which the fungus exchanges minerals and amino acids extracted from the soil for fixed carbon from the host . The subterranean hyphae of the fungus grow a sheath of tissue around the rootlets of a broad range of tree species , in an intimate association that is especially beneficial to the host ( termed ectomycorrhizal ) , as the fungus produces enzymes that <unk> organic compounds and facilitate the transfer of nutrients to the tree . 
- The ectomycorrhizal structures of H. peckii are among a few in the Bankeraceae that have been studied in detail . They are characterized by a <unk> mantle — a layer of tissue made of hyphae tightly arranged in a parallel orientation , or palisade , and which rarely branch or overlap each other . These hyphae , along with adhering mineral soil particles , are embedded in a gelatinous matrix . The hyphae of the ectomycorrhizae can become <unk> , an adaptation that helps the fungus tolerate unfavorable conditions . <unk> of H. peckii have a peculiar structure — markedly distinct from those of other Bankeraceae — with thick , smooth inner walls and an outer wall that is split <unk> into warts . The most striking characteristic of the ectomycorrhizae as a whole is the way the black outer layers of older sections are shed , giving a " <unk> appearance " . The majority of the underground <unk> of the fungus is concentrated near the surface , most likely as " mycelial mats " — dense clusters of ectomycorrhizae and mycelium . The mycelium is also known to extend far beyond the site of the fruit bodies , as far as 337 centimeters ( 11 @.@ 1 ft ) away . 
- Molecular techniques have been developed to help with conservation efforts of <unk> hydnoid fungi , including H. peckii . While the distribution of the fungus has traditionally been determined by counting the fruit bodies , this method has a major drawback in that fruit bodies are not produced consistently every year , and the absence of fruit bodies is not an indication of the absence of its mycelium in the soil . More modern techniques using the polymerase chain reaction to assess the presence of the fungal DNA in the soil have helped alleviate the issues in monitoring the presence and distribution of fungi mycelia . 
- 
- = = Habitat and distribution = = 
- 
- The fruit bodies of Hydnellum peckii are found growing solitary , scattered , or clustered together on the ground under conifers , often among mosses and pine needle litter . H. peckii is a " late @-@ stage " fungus that , in boreal forests dominated by jack pine , typically begins associating with more mature hosts once the canopy has closed . A preference for mountainous or subalpine ecosystems has been noted . 
- The fungus has a wide distribution in North America , and is particularly common in the Pacific Northwest ; its range extends north to Alaska and east to North Carolina . In the Puget Sound area of the U.S. state of Washington , it is found in association with Douglas @-@ fir , fir , and hemlock . Along the Oregon Coast it has been collected under lodgepole pine . In addition to North America , the mushroom is widespread in Europe , and its presence has been documented in Italy , Germany , and Scotland . The species is common in the latter location , but becoming increasingly rare in several European countries , such as Norway , The Netherlands , and the Czech Republic . Increased pollution in central Europe has been suggested as one possible factor in the mushroom 's decline there . Reports from Iran in 2008 and Korea in 2010 were the first outside Europe and North America . 
- 
- = = Uses = = 
- 
- Although the fruit bodies of H. peckii have been described as resembling " Danish pastry topped with <unk> jam " , and Hydnellum species in general are not known to be poisonous , they are inedible due to their extremely bitter taste . This <unk> persists even in dried specimens . 
- The fruit bodies of this and other Hydnellum species are prized by mushroom <unk> . The colors may range from beige when no <unk> is used , to various shades of blue or green depending on the <unk> added . 
- 
- = = Chemistry = = 
- 
- <unk> of an extract of Hydnellum peckii revealed the presence of an effective anticoagulant , named <unk> ( 2 @,@ 5 @-@ <unk> @-@ 3 @,@ 6 @-@ <unk> ( 4 @-@ <unk> ) <unk> @-@ <unk> ) , and similar in biological activity to the well @-@ known anticoagulant <unk> . <unk> also possesses <unk> activity , inhibiting the enzyme <unk> @-@ <unk> carrier protein <unk> ( essential for the <unk> of fatty acids ) in the bacteria <unk> <unk> . 
- Hydnellum peckii can <unk> the heavy metal caesium . In one Swedish field study , as much as 9 % of the total caesium of the topmost 10 cm ( 3 @.@ 9 in ) of soil was found in the fungal mycelium . In general , ectomycorrhizal fungi , which grow most <unk> in the upper organic <unk> of the soil or at the interface between the organic and mineral layers , are involved in the retention and cycling of caesium @-@ 137 in organic @-@ rich forest soils . 
- 
- 
- = Gregory Helms = 
- 
- Gregory Shane Helms ( born July 12 , 1974 ) is an American professional wrestler who is currently signed to Total Nonstop Action Wrestling ( TNA ) under his birth name . In TNA , he is the manager for former TNA X Division Champion Trevor Lee . He is best known for his time with World Wrestling Entertainment ( WWE ) , where he wrestled as The Hurricane , Gregory Helms , and Hurricane Helms and also for his time with World Championship Wrestling ( WCW ) , where he wrestled as " Sugar " Shane Helms . 
- Helms first began wrestling in 1991 at the age of 16 , and wrestled in numerous independent promotions , including the Organization of Modern Extreme Grappling Arts , winning numerous light heavyweight and tag team championships . In 1999 , he signed with WCW , and gained fame as " Sugar " Shane Helms , a member of 3 Count . During his time there he won both the WCW Hardcore Championship and the WCW Cruiserweight Championship . When WCW was bought by the World Wrestling Federation ( WWF , now WWE ) , Helms ' contract was kept by WWF . Helms renamed himself " The Hurricane " and won the WWE Cruiserweight Championship . He later picked up a sidekick , Rosey , and they won the World Tag Team Championship together . 
- He dropped the superhero gimmick in 2005 , and began wrestling under his real name . He once again won the Cruiserweight Championship in early 2006 , and held it for over a year , becoming the longest reigning Cruiserweight Champion and the longest reigning champion of any kind in SmackDown history . After being out of action for over a year after neck fusion surgery Helms returned to SmackDown in September 2008 , referring to himself as Hurricane Helms , before moving to the ECW brand where he reverted to his Gregory Helms name , and became the backstage interviewer . In mid @-@ 2009 , he brought back The Hurricane character , and appeared as both Gregory Helms and The Hurricane on ECW . After ECW was cancelled , Helms was released by WWE on February 26 , 2010 , and returned to the independent circuit . 
- 
- = = Professional wrestling career = = 
- 
- 
- = = = Early career ( 1991 – 1999 ) = = = 
- 
- Helms began wrestling at the age of 16 in 1991 . He won his first championship shortly afterwards , by defeating " Playboy " <unk> Cruise to become the Southern Wrestling Alliance ( <unk> ) Light Heavyweight Champion . Helms spent the majority of his early career in his home state of North Carolina , winning numerous light heavyweight championships . 
- Helms began calling himself " The Show " Shane Helms , and teamed with " Big Daddy " Mike Maverick , initially using the team name Assault and Battery . They later dubbed themselves The Serial Thrillaz , and won several independent tag championships . The pair also began wrestling in the Organization of Modern Extreme Grappling Arts ( OMEGA ) , which was founded by The Hardy Boyz ( Matt and Jeff ) . The Serial Thrillaz won the OMEGA Tag Team Championship twice between 1997 and 1998 , before they began competing for Southern Championship Wrestling ( SCW ) . They won the SCW Tag Team Championship , and Helms also won the SCW Heavyweight Championship . SCW would mark the end of the Serial Thrillaz , as Helms vacated both titles before moving on to NWA Worldwide . Once there , Helms joined the Bad Street Boys with Shannon Moore , Christian York , and Joey Matthews . 
- 
- = = = World Championship Wrestling ( 1999 – 2001 ) = = = 
- 
- Helms and his friend Shannon Moore signed with World Championship Wrestling ( WCW ) in May 1999 . In WCW , Helms , along with Moore and Evan Karagias , formed the heel ( villainous ) stable ( faction ) 3 Count , and the trio debuted on the December 23 episode of WCW Thunder . The trio was a parody of the boy bands ' N <unk> and the <unk> Boys , and their gimmick involved making music videos and performing songs before their matches . 
- 3 Count collectively won the WCW Hardcore Championship on WCW Monday <unk> in February 2000 . This win made 3 Count the first trio to win the Hardcore Championship . At <unk> , Brian Knobbs defeated all members of 3 Count to win the Hardcore Championship . During a match , Helms suffered a broken nose , and was sidelined while he recovered . Upon his return , Tank Abbott had been added to the group , as a fan , and was utilized in an <unk> role . 3 Count had a long @-@ running feud ( scripted rivalry ) with The Jung Dragons , with the Dragons attempting to steal 3 Count 's fictional recording contract . This led to a ladder match between the two teams at New Blood Rising , which 3 Count won due to Abbott 's interference . As a result , Abbott claimed that he should be the lead singer of 3 Count , and was kicked out by the other three members , leading to a feud . 
- 3 Count later disbanded as Moore and Helms kicked Karagias out of the group for attempting to steal the spotlight . At the same time , the Jung Dragons turned on Jamie Noble , who formed a team with Karagias . The three teams met in a Triangle match at <unk> , which 3 Count won . They also wrestled each other in a number one <unk> ladder match at <unk> , to earn a WCW Cruiserweight Championship match , in which both Moore and Helms grabbed the contract at the same time . As a result , they later wrestled for the opportunity to wrestle for the championship , which Helms won , although he lost the subsequent championship match to <unk> Guerrero at the Sin pay @-@ per @-@ view . 
- Moore and Helms would leave WCW briefly in 2001 to wrestle in NWA Wildside , where they held the Wildside Tag Team Championship for one day . Helms became the number one contender for the Cruiserweight Championship after winning a match at <unk> Revenge , but was attacked by Karagias and Moore afterwards . Despite the attack , Helms would go on to defeat Guerrero for the WCW Cruiserweight Championship at <unk> and was the reigning Cruiserweight Champion immediately before WCW was bought out by the World Wrestling Federation ( WWF ) . Helms ' contract with WCW was one of twenty @-@ five that were included in WWF 's purchase of WCW . 
- 
- = = = World Wrestling Federation / Entertainment = = = 
- 
- 
- = = = = Early appearances ( 2001 – 2002 ) = = = = 
- 
- Helms made his WWF debut on the July 5 , 2001 episode of SmackDown ! , using the name " Hollywood " Gregory Helms . He was part of the Invasion storyline as a member of The Alliance losing his Cruiserweight Championship in a one @-@ on @-@ one match to another Alliance member , Billy Kidman . 
- Helms began wrestling as " The Hurricane " on August 27 , and also began wearing a superhero costume . That night , he won the WWF European Championship from Matt Hardy with help from Ivory , but lost it to <unk> on October 22 , 2001 . In September 2001 , Helms formed a tag team with Lance Storm who was now managed by Ivory and Helms later picked up a sidekick , in Molly Holly who began to call herself " Mighty Molly " , and the two came to the arena in a custom " Hurri @-@ Cycle " ( with Molly in the <unk> ) . The team of Helms and Storm began <unk> with teams like The Hardy Boyz and Big Show and Spike Dudley . Helms and Storm got a shot at the WCW Tag Team Championship against The Hardy Boyz at No Mercy in a losing effort . 
- During 2002 , The Hurricane won the WWF Hardcore Championship at WrestleMania <unk> , but Molly betrayed him , hitting him in the back of the head with a <unk> pan , and defeating him for the championship . Helms was later drafted to the SmackDown ! brand following the brand extension , and he won the Cruiserweight Championship from <unk> and Billy Kidman in a triple threat match . He lost it to Jamie Noble at King of the Ring . Later in 2002 , he was traded to Raw , won the World Tag Team Championship with Kane , and held them for around a month in an alliance known as Hurri @-@ Kane . 
- 
- = = = = <unk> with Rosey ( 2003 – 2005 ) = = = = 
- 
- In 2003 , Hurricane feuded with The Rock . In backstage segments , The Rock referred to The Hurricane as " The <unk> " ( in part because his mask was similar to the <unk> 's ) . The feud culminated in a match on Raw , in which The Rock dominated . Hurricane , however , was able to roll up The Rock from behind after The Rock was distracted by Stone Cold Steve Austin 's appearance on the ramp . 
- In late 2003 , Hurricane " discovered " Rosey 's potential as a superhero and christened him " Rosey , the Super Hero in Training " ( the " <unk> " ) . On May 1 , at <unk> , Rosey and The Hurricane defeated La <unk> in the finals of a Tag Team <unk> match to win the World Tag Team Championship . Not long after winning the championship , Stacy <unk> joined Rosey and The Hurricane as their sidekick , " Super Stacy " . In August 2005 , Rosey and The Hurricane lost <unk> as their sidekick when she and Christy <unk> were traded to the SmackDown ! brand . On September 5 , 2005 Hurricane and Rosey were defeated by Lance Cade and Trevor Murdoch during Cade and Murdoch 's debut match on Raw . As a result , Cade and Murdoch earned a title match at the <unk> pay @-@ per @-@ view . During the title match , Murdoch delivered an elevated DDT on The Hurricane to the outside . The DDT caused Hurricane to suffer a storyline " <unk> " and allowed Cade and Murdoch to beat the injured Hurricane later in the match to win the World Tag Team Titles . 
- During the next few weeks , the pair began a losing streak , mainly caused by Hurricane 's on @-@ screen injuries . During the October 17 episode of Raw , The Hurricane was assaulted by Kurt Angle at the request of Vince McMahon . After the beating , footage was shown of The Hurricane <unk> off his mask and striking Rosey because he did not help him fend off Angle . The next week , The Hurricane no @-@ showed a World Tag Team Title match , leaving Rosey to face the champions alone . During the match , The Hurricane ( out of costume ) appeared at the top of the entrance ramp , reverting to his real name , Gregory Helms , and watched as Rosey was double teamed and defeated . After the match , Helms announced that he was fed up with being funny for the crowd , and that he was sick of carrying Rosey as a tag team partner . This turned him into a heel in the process . On the November 7 episode of Raw , Helms and Rosey faced off in a singles match , which Helms won . Subsequently , Helms wrestled mostly on Raw 's sister show , Heat . On the January 2 , 2006 episode of Raw Helms confronted Jerry Lawler over jokes that Lawler had been making at Helms ' expense , and Lawler said that when Helms was The Hurricane , he was entertaining and called Helms a joke . Helms responded by slapping Lawler , who hit Helms back . This confrontation led to Lawler defeating Helms in a match at New Year 's Revolution . 
- 
- = = = = Cruiserweight Champion and injury ( 2006 – 2007 ) = = = = 
- 
- At the 2006 Royal Rumble , Helms participated in an Open <unk> match for the Cruiserweight Championship and won , even though he was a Raw superstar and the championship was defended solely on SmackDown ! . As a result , he returned to the SmackDown ! brand in February 2006 to start a feud with <unk> , Kid Kash and the other cruiserweights . During this time , Helms defeated <unk> in a one @-@ on @-@ one match . The next week , he was supposed to defend against Kid Kash , but Kash was away on a family emergency , so <unk> 2 <unk> took his place . Helms defeated him and declared that he was better than all the other cruiserweights . As a result , all the cruiserweights at the show invaded the ring and attacked Helms . It was later announced by SmackDown ! General Manager Theodore Long that at the No Way Out pay @-@ per @-@ view event , Helms would face all the cruiserweights in a match for the title . Helms was able to retain the championship . Later , Long mandated that Helms had to defend his title against a <unk> each week on SmackDown ! to prove that he truly was better than all the cruiserweights . Helm 's first defense under this new rule was against <unk> of The <unk> ; Helms retained by cheating . 
- In early March 2006 , Helms suffered a broken nose . After complaining to Long about having to wrestle on the March 10 episode of SmackDown ! , Long told Helms that he did not have to defend his title , but put Helms in a champion versus champion match against the United States Champion , Chris <unk> . During the match , Helms attempted to leave , but was blocked by other cruiserweights who threw Helms back into the ring , resulting in Helms losing the match . Helms underwent successful surgery on his nose and was expected to miss five to six weeks . During this time SmackDown ! <unk> stated that Helms had a no @-@ compete clause in his contract allowing him to waive the thirty @-@ day title defense rule due to any injury . Helms returned to the ring on the April 28 episode of SmackDown ! , teaming with <unk> to face Super Crazy , London and Kendrick in a six @-@ man tag team match . Helms continued to defend his belt during pay @-@ per @-@ views , but he made more regular appearances on SmackDown ! ' s sister show , <unk> . 
- Helms continued defending his title against other cruiserweights , while on occasion facing off against SmackDown ! ' s other title holders in champion versus champion matches . Helms faced World Heavyweight Champion Rey <unk> on June 16 and United States Champion Bobby <unk> on July 7 but lost both matches . Eventually , Helms became the longest <unk> champion in WWE history , as well as the longest reigning champion of any kind in SmackDown ! history . For much of late 2006 , he feuded with Matt Hardy , with whom Helms exchanged several victories in matches on SmackDown ! . In Helms ' hometown of <unk> , North Carolina , at the 2006 No Mercy pay @-@ per @-@ view , Hardy beat Helms . The two rivals also joined opposing teams at Survivor Series , where Helms was in Team Rated @-@ RKO , and Hardy was a member of Team <unk> ; Team Rated @-@ RKO lost the match . 
- Helms went on to feud with Jimmy Wang Yang over the Cruiserweight Title , after a tag team match in which Matt Hardy and Yang defeated Helms and <unk> . The feud ended after Helms defeated him to retain the title at Armageddon in December 2006 . In 2007 , Helms once again feuded with the entire Cruiserweight division on SmackDown ! . During this feud , Helms ' reign as Cruiserweight Champion came to an end at No Way Out , when he was unable to win a Cruiserweight open <unk> , which saw <unk> Guerrero win the championship . Shortly after losing the championship , Helms formed a tag team with Guerrero , but they did not win any matches as a team . 
- On May 18 , 2007 , WWE reported that Helms had broken two vertebrae in his neck , one of which was pressing on his spinal <unk> . He underwent surgery with Dr. Lloyd <unk> to repair them approximately a week later . Following the surgery , Helms was announced to be out for a year and a half due to the injury . 
- 
- = = = = <unk> and The Hurricane 's return ( 2008 – 2010 ) = = = = 
- 
- Helms returned on the September 19 , 2008 , episode of SmackDown in a non @-@ wrestling role . Throughout the show , he appeared in short video promos , called " <unk> " , referring to himself as " Hurricane Helms " , a modified version of his old ring name " The Hurricane " , and mocked heel <unk> including Shelton Benjamin and Curt Hawkins and Zack Ryder . In the following weeks , he mocked several other <unk> in the " <unk> " , ending each <unk> with the phrase , " I 'm just <unk> ' ! " On the November 28 episode of SmackDown , a " <unk> " appeared in which Helms announced that he would make his return to the ring on the next episode of SmackDown . On the December 5 episode of SmackDown , Helms defeated <unk> <unk> Porter in his ring return . Helms was quickly scripted into a feud with the United States Champion Shelton Benjamin , defeating him in two non @-@ title matches on SmackDown , although he failed to win a championship match on December 26 . 
- On April 15 , 2009 , Helms was drafted to the ECW brand as part of the 2009 Supplemental Draft . Helms made his ECW debut as a backstage interviewer on April 28 , 2009 , reverting to his Gregory Helms ring name , interviewing Evan Bourne . He started a storyline where , while he was interviewing someone , a scripted accident would occur , leading to Helms leaving the interview seconds before the person in distress was saved by The Hurricane . On the August 11 , 2009 episode of ECW , Helms was attacked by Paul Burchill after Helms denied being The Hurricane . The Hurricane later attacked Burchill in the ring dressed in his Hurricane attire , to save <unk> <unk> . The next week on ECW , he made his in @-@ ring debut for the brand as The Hurricane , and defeated Burchill . He continued to feud with Burchill , who attempted to expose The Hurricane 's true identity , until the November 17 episode of ECW when The Hurricane defeated Burchill in a mask versus career match . 
- On February 26 , 2010 , Helms was released from his WWE contract . 
- 
- = = = Independent circuit ( 2010 – present ) = = = 
- 
- Following his WWE release , Helms began wrestling on the independent circuit , including for the Canadian Wrestling 's <unk> as Hurricane Helms . On October 23 , 2010 , Helms made his debut for Jersey All Pro Wrestling , where he , under his Hurricane gimmick , defeated <unk> . On January 10 , 2011 , Helms signed a contract with Lucha <unk> USA . In the promotion , Helms allied himself with Marco <unk> , and in his debut for the promotion at the January 22 <unk> , he defeated <unk> 's rival and Lucha <unk> USA Champion <unk> Jr. via <unk> . 
- On January 29 , 2011 Helms made his debut for Pro Wrestling <unk> ( <unk> ) , during the <unk> 5 weekend , in a match , where he was defeated by Joey Ryan . He also took part in 20 @-@ Man Legends Battle <unk> , making his second appearance of the night . He eliminated <unk> Connection , but later was thrown out by Shane Douglas and Terry <unk> . 
- In April 2011 , Helms wrestled at Carolina Wrestling Federation ( Mid @-@ Atlantic ) where he defeated Ric <unk> and won the vacant PWI International Heavyweight Championship . In 2013 , Helms returned to the revived OMEGA , now called OMEGA Championship Wrestling , and on October 12 , he defeated Shane Williams to advance in a tournament for the OMEGA Championship . On November 16 , 2013 , Helms was defeated by Chris Hero at a Pro Wrestling Syndicate event . 
- 
- = = = Total Nonstop Action Wrestling ( 2015 – present ) = = = 
- 
- In March 2015 , Helms received a <unk> for Total Nonstop Action Wrestling ( TNA ) as an agent and began working for the company within that capacity that same month . At <unk> for Glory , Helms made his on @-@ screen debut under his full birth name to <unk> the TNA X Division Champion <unk> Uno . On the February 2 episode of Impact Wrestling , Helms assisted Trevor Lee in defeating <unk> Uno for the X Division Championship , turning heel in the process . 
- 
- = = Personal life = = 
- 
- Helms is a comic book fan , and one of his favorite characters is the Green Lantern . He has a tattoo on his <unk> which is the Lantern 's logo and his original WWE character , The Hurricane , was inspired by the Green Lantern . In 2008 , he got a tattoo of his last name on his upper back . Helms owns a motorcycle called " The Hurri @-@ Cycle " , which he got when he was using The Hurricane gimmick . Helms is good friends with Jeff Hardy , Matt Hardy and Shannon <unk> appears on The Hardy Show , The <unk> ' internet show . He also performed as the stunt double of actor David <unk> in the movie Ready to Rumble . 
- On March 19 , 2007 , Sports Illustrated posted on its website an article in its continuing series investigating a steroid and HGH ring used by a number of professional athletes in several sports . That article mentioned several current and former WWE wrestlers , including Helms who was alleged to have obtained HGH . WWE has since made a statement on this situation , mentioning that the allegations preceded the Talent Wellness program WWE launched in April 2006 . On August 30 , 2007 , an article by Sports Illustrated named Helms and nine other wrestlers to be given growth hormone not in compliance with the WWE Talent Wellness program . He was said to have received <unk> , HGH and <unk> between November 2003 and February 2007 . 
- On May 6 , 2008 , Helms was assaulted in a club in Johnston County , North Carolina , by an acquaintance , Dustin Narron . An argument between the two of them led to Narron hitting Helms in the face . Narron was later charged with assault and battery . On January 27 , 2010 , Helms and fellow WWE wrestler Chris Jericho were arrested in Fort Mitchell , Kentucky after leaving a bar . A police report stated that Helms had punched Jericho and the other passengers in the cab they were sharing . Helms and Jericho were given tickets for public intoxication and released after each posted a $ 120 bail bond . On March 13 , 2011 , Helms was cited for affray following an altercation at a bar in <unk> , North Carolina . 
- On May 5 , 2011 , Helms and his girlfriend were involved in a <unk> accident in North Carolina , for which Helms was charged with driving under the influence . Helms suffered a broken leg , ankle , jaw , and nose during the accident , as well as sustaining other fractures and receiving over 200 <unk> , while his girlfriend sustained a broken neck . As a result of the accident , Helms ' foot had to be reconstructed with four plates and 22 screws . 
- Helms was previously in a relationship with fellow professional wrestler Jamie <unk> , from 2004 @-@ 2009 who is better known by her ring name Velvet Sky . In June 2012 , Helms ' girlfriend gave birth to his first child , a boy named Sebastian . 
- 
- = = In wrestling = = 
- 
- <unk> moves 
- Eye of the Hurricane ( WWF / E ) / Nightmare on Helms Street ( WCW / WWE ) ( <unk> <unk> elbow drop ) 
- <unk> ( <unk> ) – 2002 – 2003 ; used rarely thereafter 
- Avalanche <unk> <unk> – 2005 @-@ 2007 ; used rarely thereafter 
- Double knee <unk> 
- Shining wizard – 2003 – present 
- <unk> – 2000 – 2003 
- Signature moves 
- <unk> splash 
- <unk> smash 
- European <unk> on the <unk> as a counter to an oncoming opponent 
- Frog splash into a diving <unk> 
- <unk> <unk> 
- <unk> 
- Hurri @-@ clothesline ( <unk> clothesline ) 
- Jumping neckbreaker 
- Jumping tornado DDT 
- <unk> ( <unk> neckbreaker ) 
- <unk> <unk> 
- <unk> jacket <unk> rear mat slam 
- <unk> ( WCW ) / Hurri @-@ Kick ( WWF ) ( <unk> ) 
- <unk> 
- <unk> con <unk> 
- <unk> <unk> ( <unk> 's carry spun out into a <unk> knee smash ) ( often followed by a shining wizard ) 
- X @-@ <unk> 
- <unk> 
- Mike Maverick 
- Ivory 
- Mighty Molly 
- Super Stacy 
- Tank Abbott 
- <unk> managed 
- Trevor Lee 
- Andrew Everett 
- <unk> 
- " Hollywood " 
- " The Show " 
- " Sugar " 
- " Supernatural " 
- Entrance themes 
- World Championship Wrestling 
- " Can 't Get You Out of My Heart " by 3 Count 
- " Do the Three Count " by 3 Count 
- " <unk> " by Jimmy Hart and Howard Helm 
- World Wrestling Entertainment 
- " Eye of the Hurricane " by Jim Johnston ( WWF / E ; August 2001 – October 2005 ; August 2009 – February 2010 ) 
- " Fire Storm A " by Wolfgang <unk> & Hermann <unk> ( WWE ; November 2005 – February 2007 ) 
- " It 's Time " by Jim Johnston ( WWE ; February 2007 – January 2009 ) 
- 
- = = Championships and accomplishments = = 
- 
- Carolina Championship Wrestling Alliance 
- <unk> Light Heavyweight Championship ( 2 times ) 
- Empire State Wrestling 
- <unk> Tag Team Championship ( 1 time ) – with Johnny Adams 
- New Dimension Wrestling 
- <unk> Tag Team Championship ( 1 time ) – with Mike Maverick 
- NWA Wildside 
- NWA Wildside Tag Team Championship ( 1 time ) – with Shannon Moore 
- Other titles 
- <unk> Light Heavyweight Championship ( 1 time ) 
- Organization of Modern Extreme Grappling Arts 
- OMEGA Tag Team Championship ( 2 times ) – with Mike Maverick 
- Pro Wrestling Illustrated 
- PWI ranked him 21 of the 500 best singles wrestlers in the PWI 500 in 2003 
- Pro Wrestling International 
- PWI International Heavyweight Championship ( 1 time ) 
- Southern Championship Wrestling 
- SCW Heavyweight Championship ( 1 time ) 
- SCW Tag Team Championship ( 1 time ) – with Mike Maverick 
- Southern Wrestling Alliance 
- <unk> Light Heavyweight Championship ( 1 time ) 
- Texas Championship Wrestling 
- <unk> Texas Tag Team Championship ( 1 time ) – with Lenny Lane 
- World Championship Wrestling 
- WCW Hardcore Championship ( 1 time ) 
- WCW Cruiserweight Championship ( 1 time ) 
- World Wrestling Federation / Entertainment 
- WWF European Championship ( 1 time ) 
- WWF Hardcore Championship ( 1 time ) 
- WWE Cruiserweight Championship ( 3 times ) 
- World Tag Team Championship ( 2 times ) – with Kane ( 1 ) and Rosey ( 1 ) 
- World Wrestling Organization 
- <unk> Light Heavyweight Championship ( 1 time ) 
- Wrestling Observer <unk> 
- Best <unk> ( 2001 ) 
- 
- = = = Lucha de <unk> record = = = 
- 
- 
- 
- = New York State Route 38 = 
- 
- New York State Route 38 ( NY 38 ) is a north – south state highway in the Finger Lakes region of New York in the United States . Its southern terminus is at an intersection with NY 96 in the town of Owego in Tioga County . The northern terminus is at a junction with NY 104A in the town of Sterling in Cayuga County . NY 38 is a two @-@ lane local road for most of its length . The route is the main access road to parts of Auburn , Dryden , Newark Valley and Port Byron . It passes through mountainous terrain in Tioga and Cortland counties , but the terrain levels out as it heads through the Finger Lakes area and Cayuga County . 
- The route intersects several long @-@ distance highways , including NY 13 in Dryden , U.S. Route 20 ( US 20 ) and NY 5 in Auburn , and NY 31 in Port Byron . It passes over the New York State <unk> ( Interstate 90 or I @-@ 90 ) north of Port Byron ; however , there is no connection between the two . NY 38 has two <unk> routes . The first , NY 38A is an alternate route of NY 38 between Moravia and Auburn , while the other , NY 38B , is a simple east – west connector in the Southern <unk> . While NY 38 runs along the western shore of Owasco Lake , NY 38A travels to Auburn along a routing east of the lake . 
- NY 38 passes along or near <unk> for much of its length . From its southern end in Owego to the town of Harford , the route parallels Owego Creek or a branch of said creek . Between Groton and <unk> , it runs along the aforementioned Owasco Lake and its inlet ( south of the lake ) and outlet ( north of the lake ) . It also comes within 4 miles ( 6 km ) of Lake Ontario at its northern end . 
- In the 1920s , the portion of NY 38 between Owego and Freeville was designated as New York State Route 42 while the segment from Freeville to Moravia was the southern part of New York State Route 26 , a highway that continued north from Moravia to Syracuse . NY 38 was assigned as part of the 1930 renumbering of state highways in New York , utilizing all of pre @-@ 1930 NY 42 , the Freeville – Moravia portion of NY 26 , and a previously unnumbered highway north to Sterling . Originally , NY 38 extended south into the village of Owego by way of an overlap with NY 96 . It was truncated to its current southern terminus by 1994 . 
- 
- = = Route description = = 
- 
- All of NY 38 — save for two sections within the city of Auburn — is maintained by the New York State Department of Transportation ( <unk> ) . In Auburn , the route is city @-@ maintained to the north and south of where the route meets US 20 and NY 5 in downtown Auburn . The portion of NY 38 that runs between and overlaps with those two routes is state @-@ maintained . 
- 
- = = = Tioga and Cortland counties = = = 
- 
- NY 38 begins at an intersection with NY 96 about 0 @.@ 5 miles ( 0 @.@ 8 km ) north of the Owego village limits in the town of Owego . The road heads northeastward as a two @-@ lane highway , <unk> Owego Creek as it proceeds along the base of a valley surrounding the waterway . The Tioga County portion of NY 38 passes through mostly rural , forested areas with only small , scattered pockets of development . The route continues toward the hamlet of <unk> , where the Owego Creek splits into western and eastern branches . NY 38 does not enter the community ; instead , it bypasses it to the southeast and follows the eastern branch of Owego Creek into the town of Newark Valley . 
- The amount of homes along the route begins to increase as NY 38 approaches the village of Newark Valley . Just south of the village limits , NY 38 intersects NY 38B , a spur leading to NY 26 in Maine . The route continues into the small village as South Main Street and passes by several blocks of homes and commercial buildings . At Water Street , NY 38 becomes North Main Street ; however , from this point north , most of the village is situated on the opposite bank of Owego Creek . As a result , NY 38 continues through the village limits but passes very few buildings before seamlessly exiting the community and entering another rural area . 
- The route continues on , crossing over Owego Creek and passing the Newark Valley Country Club about 1 mile ( 1 @.@ 6 km ) north of Newark Valley village before entering the town of Berkshire . In Berkshire , NY 38 serves the hamlet of Berkshire , a small community situated directly on the highway . The route continues on through the narrowing creek valley into the town of <unk> and the hamlet of the same name , where it meets NY 79 in the community 's center . After <unk> , the valley continues to narrow for just under 1 @.@ 5 miles ( 2 @.@ 4 km ) before reversing course as the route heads into Cortland County and the town of Harford . 
- NY 38 clips the extreme southwestern corner of Cortland County ; as a result , only 3 @.@ 38 miles ( 5 @.@ 44 km ) of the route is located within the county . Just north of the county line , the route meets NY 200 in the hamlet of Harford Mills . NY 200 is little more than an alternate route to NY 221 , which NY 38 meets in the hamlet of Harford 1 @.@ 5 miles ( 2 @.@ 4 km ) to the northeast . In between Harford Mills and Harford , the east branch of Owego Creek separates from NY 38 and heads north to follow NY 221 instead . NY 38 continues northwest out of Harford hamlet and into Tompkins County . 
- 
- = = = Tompkins County = = = 
- 
- Upon entering the border town of Dryden , NY 38 emerges from the valley and becomes Dryden – Harford Road as it heads northwestward through a more low @-@ lying but still undeveloped area . The highway gradually curves to the north toward the village of Dryden , where the route changes names to South Street upon entering the village limits . It continues north across Virgil Creek and past three blocks of homes to the commercial village center , where it intersects both NY 13 and NY <unk> . The latter begins here and heads off to the east while the former joins NY 38 along North Street . 
- The overlap between NY 13 and NY 38 ends at the northern village line . At this point , NY 13 continues north while NY 38 forks to the west , running along the village limits on Freeville Road for about 1 mile ( 1 @.@ 6 km ) through an area with only a handful of homes . While doing so , the route passes to the south of the Dryden Middle and High School complex . NY 38 heads onward through an open , undeveloped area , curving to the northwest and eventually to the north as it approaches the village of Freeville , where it serves the William George Agency for Children 's Services at the southern village line . It remains on Freeville Road until Railroad Street , where it turns west and proceeds into the densely populated village center . Here , it intersects the eastern terminus of NY 366 at a junction situated adjacent to Fall Creek . 
- NY 38 proceeds out of Freeville , passing over Fall Creek and heading north along Groton Road through a lightly populated area of the town of Dryden . The route passes by a mixture of open fields , forests , and isolated homes on its way to the Dryden – Groton town line , where it meets the southern terminus of NY <unk> southeast of the hamlet of <unk> . NY 38 parallels the Owasco Inlet into Groton and the village of the same name , becoming Peru Road at the southern village line . It continues north , following South and Main Streets through the densely populated village to an intersection with NY 222 's western terminus at Cortland Street . At this point , NY 38 becomes Cayuga Street and winds its way northward along the Owasco Inlet and out of the village . Now known as Locke Road , NY 38 heads the northwest through another rural , largely undeveloped area into Cayuga County . 
- 
- = = = Cayuga County = = = 
- 
- Cayuga County , located in the Finger Lakes region of New York , has a highly unorthodox shape . Most of the county is only about 15 miles ( 24 km ) wide from its western border to its eastern edge . From north to south , however , it extends from Locke north to the Lake Ontario shoreline — a distance of about 55 miles ( 89 km ) . NY 38 passes through much of the county , ending about 4 miles ( 6 km ) south of the shoreline in Sterling . As a result , over half of NY 38 's routing is located in the county , with the midpoint located near the city of Auburn . 
- 
- = = = = County line to Auburn = = = = 
- 
- The route heads northwest from the county line , following the Owasco Inlet through open fields and past small patches of trees to the large hamlet of Locke . NY 38 heads north – south through the residential community as Main Street and intersects NY 90 at the center of the hamlet . North of the community , the highway crosses over the Owasco Inlet and enters another rural area dominated by fields situated amongst forests . Upon crossing into the town of Moravia , the amount of development along the highway increases as it passes <unk> Glen State Park and approaches the village of Moravia . 
- In Moravia , a highly developed village comprising several blocks of homes and businesses , NY 38 is known as Main Street as it heads north into the village center . At Cayuga Street , NY 38 intersects NY 38A , the second of its two <unk> routes . NY 38A heads eastward from this point while NY 38 turns to follow West Cayuga Street across Owasco Inlet and out of the village . Past the inlet , NY 38 curves to the north and runs along the western edge of the Owasco <unk> , a wide , flat @-@ bottomed , undeveloped valley at the foot of Owasco Lake . The flats give way to the lake in Cascade , a hamlet in the town of Venice , at which point NY 38 begins to climb up the western edge of the lake valley . It reaches the lip of the valley 2 miles ( 3 @.@ 2 km ) later in the town of <unk> . 
- For the next 2 @.@ 5 miles ( 4 @.@ 0 km ) , the route passes by open fields as it <unk> the lake to the east . The route gradually descends back into the valley as it heads further northward into the town of Fleming . Once in Fleming , NY 38 runs along the <unk> and serves a long line of lakeside homes as it passes by a series of fields to the west . The amount of development along the route begins to increase at the northern end of the lake in the hamlet of <unk> Park , where NY 38 meets NY <unk> by way of a traffic circle . At this point , NY <unk> becomes the primary lakeside highway while NY 38 becomes a four @-@ lane divided highway and heads northwest as Lake Avenue toward the city of Auburn . 
- As NY 38 enters Auburn , it passes by Auburn High School before heading north through densely populated blocks filled with homes . The divided highway ends abruptly at Swift Street , where NY 38 turns west to follow the two @-@ lane undivided Swift Street west for seven blocks to NY 34 ( South Street ) . Here , NY 38 leaves Swift Street and joins NY 34 on South Street . The two routes follow South Street past the William H. Seward House into downtown Auburn , where the homes are replaced with businesses at Lincoln Street . Three blocks later , South Street intersects with the East Arterial ( eastbound US 20 and NY 5 ) . The overlap between NY 34 and NY 38 ends one block later at the West Arterial ( westbound US 20 and NY 5 ) , where NY 38 turns to follow the Arterial for a block to the west . 
- At State Street , NY 38 leaves US 20 and NY 5 and heads north through the city 's north side , crossing the Owasco Outlet and serving the Auburn <unk> <unk> . The route passes through several blocks of commercial and residential development up to Grant Street , where it begins to <unk> off . It ceases almost entirely near the northern city line at York Street , where the homes along the highway become more sporadic and spaced apart . 
- 
- = = = = North of Auburn = = = = 
- 
- Now in the town of Throop , NY 38 follows the Owasco Outlet through open , mostly flat areas dotted with houses amongst fields and trees . Within Throop , it serves the small hamlet of <unk> Corners , where NY 38 meets Turnpike Road ( County Route <unk> or CR <unk> ) . North of this junction , the houses give way to dense forests as the route continues along the waterway into the town of <unk> and the village of Port Byron a short distance north of the town line . It follows Main Street through a mostly commercial and industrial portion of the village to the village center , where it intersects NY 31 at Rochester and <unk> Streets . NY 38 turns west , overlapping NY 31 along Rochester Street . 
- The two routes cross the Owasco Outlet and enter a more residential area of the community , where NY 38 splits from NY 31 and continues north along Canal Street . The route crosses over the New York State <unk> ( I @-@ 90 ) as it leaves the village limits and heads north into a largely undeveloped area of forests and fields . At North Port Byron , a sparsely populated hamlet 0 @.@ 5 miles ( 0 @.@ 8 km ) north of Port Byron , NY 38 passes over the <unk> Transportation @-@ owned Rochester <unk> railroad line . The highway continues on , becoming Conquest Road and crossing the Seneca River ( Erie Canal ) at Mosquito Point , near where Owasco Outlet flows into the river . NY 38 enters the town of Conquest upon traversing the waterway . 
- NY 38 continues north as an unnamed highway to the hamlet of Conquest , a small community built up around the intersection of NY 38 and Fuller and <unk> Roads . It continues on into the town of Victory , where the undeveloped fields give way to cultivated fields used as farmland . The route heads through mostly desolate surroundings to the hamlet of Victory , a slightly larger community centered on NY 38 . The route proceeds through the hamlet , passing by several homes on its way to a junction with NY 370 just north of the community . Past this point , the homes cease again as NY 38 presses on through more fields and forests to the town of Sterling . 
- Just north of the town line in the small hamlet of North Victory , NY 38 intersects NY 104 . Past this point , the route heads through mostly undeveloped , forested areas on its way to the hamlet of <unk> Corners . North of here , the forests cede slightly as the amount of fields along the highway increases . The highway continues on to the hamlet of Sterling , where it ends at an intersection with NY 104A ( the <unk> Trail ) just south of the hamlet 's center and 4 miles ( 6 km ) south of the Lake Ontario shoreline . 
- 
- = = History = = 
- 
- 
- = = = Old roads = = = 
- 
- Several portions of modern NY 38 were originally part of turnpikes and plank roads during the 1800s . On April 13 , 1819 , the New York State Legislature passed a law incorporating the Cortland and Owego Turnpike Company . The company was tasked with building a highway — the Cortland and Owego Turnpike — from Owego north to the then @-@ village of Cortland . It roughly followed what is now NY 38 north from Owego to the vicinity of Harford , where it turned north to access Virgil . It continued to Cortland by way of modern NY 215 . 
- On April 13 , 1825 , the legislature chartered the Auburn and Port Byron Turnpike Company . The Auburn and Port Byron Turnpike began at the Auburn State Prison in Auburn and proceeded northward along the routing of NY 38 to meet the north branch of the Seneca Turnpike in the town of Brutus ( now Throop ) . From there , the turnpike continued on NY 38 through Port Byron to the Seneca River , where it ended at a bridge crossing the river at Mosquito Point . In 1851 , the Auburn and Moravia Plank Road Company was incorporated . They were tasked with connecting Moravia to Auburn by way of a plank road along the western side of Owasco Lake ( now NY 38 ) . 
- 
- = = = <unk> = = = 
- 
- When the first set of posted routes in New York were assigned in 1924 , the portion of what is now NY 38 from Freeville to Moravia became part of NY 26 , a north – south highway extending from Freeville to Syracuse via <unk> . By 1926 , the segment of current NY 38 between Freeville and Owego was designated as NY 42 . In the 1930 renumbering of state highways in New York , the entirety of NY 42 and the portion of NY 26 south of Moravia was incorporated into the new NY 38 , which extended north from Moravia to NY 3 ( modern NY 104A ) in Sterling . The section of the route adjacent to Owasco Lake was still being constructed at the time of NY 38 's assignment ; it was completed c . 1932 . 
- NY 38 originally overlapped with NY 96 ( designated as NY 15 in 1930 ) through Owego to a terminus at the modern junction of NY 96 and NY <unk> south of the village . The overlap was extended slightly along <unk> Drive to NY 17 exit 64 in the 1960s following the construction of the Southern <unk> Expressway through the area . It was removed altogether when NY 38 was truncated to the northern end of the overlap by 1994 . 
- 
- = = <unk> routes = = 
- 
- NY 38A ( 21 @.@ 91 miles or 35 @.@ 26 kilometres ) runs from Moravia to NY <unk> , near <unk> and NY <unk> in southwestern Onondaga County , and then towards Auburn . It was assigned as part of the 1930 renumbering of state highways in New York . 
- NY 38B ( 7 @.@ 69 miles or 12 @.@ 38 kilometres ) is a short spur in Broome and Tioga counties connecting NY 38 in Newark Valley in the west to NY 26 in Maine in the east . It was assigned in the early 1950s . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Westminster Assembly = 
- 
- The Westminster Assembly of <unk> was a council of theologians ( or " divines " ) and members of the English Parliament appointed to <unk> the Church of England which met from 1643 to 1653 . Several Scots also attended , and the Assembly 's work was adopted by the Church of Scotland . As many as 121 ministers were called to the Assembly , with nineteen others added later to replace those who did not attend or could no longer attend . It produced a new Form of Church Government , a Confession of Faith or statement of belief , two catechisms or manuals for religious instruction ( Shorter and Larger ) , and a <unk> manual , the Directory for Public Worship , for the Churches of England and Scotland . The Confession and catechisms were adopted as doctrinal standards in the Church of Scotland and other Presbyterian churches , where they remain <unk> . <unk> versions of the Confession were also adopted in Congregational and Baptist churches in England and New England in the seventeenth and eighteenth centuries . The Confession became influential throughout the English @-@ speaking world , but especially in American Protestant theology . 
- The Assembly was called by the Long Parliament before and during the beginning of the First English Civil War . The Long Parliament was influenced by <unk> , a religious movement which sought to further reform the church . They were opposed to the religious policies of King Charles I and William Laud , Archbishop of Canterbury . As part of a military alliance with Scotland , Parliament agreed that the outcome of the Assembly would bring the English Church into closer conformity with the Church of Scotland . The Scottish Church was governed by a system of elected assemblies of elders called presbyterianism , rather than rule by bishops , called <unk> , which was used in the English church . Scottish commissioners attended and advised the Assembly as part of the agreement . <unk> over church government caused open division in the Assembly , despite attempts to maintain unity . The party of divines who favoured presbyterianism was in the majority , but political and military <unk> led to greater influence for the congregationalist party . Congregationalists favoured autonomy for individual congregations rather than the <unk> of congregations to regional and national assemblies entailed in presbyterianism . Parliament eventually adopted a presbyterian form of government , but it lacked the power the presbyterian divines desired . During the Restoration of the monarchy in 1660 , all of the documents of the Assembly were repudiated and episcopal church government was reinstated in England . 
- The Assembly worked in the Reformed Protestant theological tradition , also known as <unk> . It took the Bible as the authoritative word of God , from which all theological reflection must be based . The divines were committed to the Reformed doctrine of <unk> — that God chooses certain men to be saved and enjoy eternal life rather than eternal punishment . There was some disagreement at the Assembly over the doctrine of particular redemption — that Christ died only for those chosen for salvation . The Assembly also held to Reformed covenant theology , a framework for interpreting the Bible . The Assembly 's Confession is the first of the Reformed <unk> to teach a doctrine called the covenant of works , which teaches that before the fall of man , God promised eternal life to Adam on condition that he perfectly <unk> God . 
- 
- = = Background = = 
- 
- Parliament called the Westminster Assembly during a time of increasing hostility between Charles I , monarch of England and Scotland , and the Puritans . Puritans could be distinguished by their insistence that worship practices be supported implicitly or explicitly by the Bible , while their opponents gave greater authority to traditional customs . They believed the Church of England , which had separated itself from the Catholic Church during the English Reformation , was still too heavily influenced by Catholicism . They sought to rid the church and nation of any of these remaining influences . This included the Church 's episcopal polity , or rule by a hierarchy of bishops . Puritans , unlike separatists , did not leave the established church . Under Charles , the Puritans ' opponents were placed in high positions of authority , most notably William Laud who was made Archbishop of Canterbury in 1633 , even though these " high churchmen " were in the minority . Puritans were forced to keep their views private or face fines and imprisonment . Laud promoted advocates of <unk> , a theological perspective opposed to the Reformed theology of the Puritans . Worship practices such as kneeling at communion , <unk> at the name of Christ , and the placement of communion tables at the East end of churches were also reinstated . To the Puritans , these seemed to be a step in the direction of Catholicism . 
- There were also conflicts between the king and the Scots , whose church was ruled by a system of elected assemblies called presbyterianism . James , Charles 's predecessor as King of Scotland , made it clear that he intended to impose elements of episcopal church government and the Book of Common Prayer on the Scots beginning in 1604 . The Scots considered this a <unk> to Roman Catholicism . Charles furthered English <unk> on the Church of Scotland in <unk> and 1637 . This led to the First Bishops ' War between Charles and the Scots in 1639 . Charles called what came to be known as the Short Parliament to raise funds for the war , but he soon dissolved it when it began voicing opposition to his policies . Following the Second Bishops ' War with the Scots in 1640 , Charles was forced to call another parliament to raise additional funds . 
- What came to be known as the Long Parliament also began to voice vague grievances against Charles , many of which were religious in nature . Parliament had many Puritans and Puritan @-@ <unk> members , who generally opposed the existing episcopal system , but there was little agreement over what shape the church should take . Later in 1640 , the Root and Branch petition was presented to the House of Commons , the lower house of Parliament . It was signed by about 15 @,@ 000 <unk> and called for total elimination of the episcopal system . Committees were organized in the House of Commons to enact religious reforms , leading to the imprisonment of Archbishop Laud and his supporters in the Tower of London as retaliation for their repression of Puritans . The Court of High Commission and the Star Chamber , courts which had inflicted severe punishments on Puritan dissenters , were also abolished . 
- 
- = = Calling the assembly = = 
- 
- The idea of a national assembly of theologians to advise Parliament on further church reforms was first presented to the House of Commons in 1641 . Such a proposal was also included in the Grand <unk> , a list of grievances which Parliament presented to Charles on 1 December that year . Charles responded on 23 December that the church required no reforms . <unk> , Parliament passed three bills in 1642 appointing an assembly and <unk> that its members would be chosen by Parliament . Charles , whose royal assent was required for the bills to become law , was only willing to consider such an assembly if the members were chosen by the clergy . This was the practice for selection of members of <unk> , the assembly of clergy of the Church of England . 
- <unk> the king , between 12 February and 20 April 1642 , each county delegation of England in Commons chose two divines , in addition to two for each county of Wales , four for London , and two for each University ( Oxford and Cambridge ) . County <unk> often chose divines from their own county , but not always . Commons chose the members in this way to ensure that their local constituencies were represented in the decision . The House of Lords , Parliament 's upper house , added another fourteen names on 14 May , to which Commons agreed . Meanwhile , relations continued to deteriorate between the king and Parliament . Charles raised the royal standard at Nottingham on 22 August , marking the beginning of the First English Civil War . The start of the war lent support to the cause of the Assembly in Parliament , because holding it would convince the Scots that Parliament was serious about reforming the church and induce the Scots to come to their aid . On the other hand , the war added strain to the already busy Parliament . 
- Parliament finally passed an ordinance to hold the assembly on its own authority without Charles 's assent on 12 June 1643 . It named as many as 121 ministers and thirty non @-@ voting parliamentary observers : twenty from Commons , and ten from the House of Lords . The Assembly was almost entirely English ; Parliament appointed Englishmen for the counties of Wales , but the French stranger churches ( churches of Protestant refugees from Catholic France ) sent two ministers in place of any from the Channel Islands . Many of the divines were internationally recognized scholars of the Bible , ancient languages , <unk> , and scholastic theology . Many were also famous preachers . Most of these theologians had retained their positions in the Church during the tenure of William Laud . Some had been ejected from their churches or cited by ecclesiastical courts for their views . Some had fled to the Continent , and one to the American colonies . Nonetheless , they all considered themselves members of the Church of England and had received episcopal ordination . Most were conformists , meaning they agreed to follow the Act of Uniformity 1558 and the Book of Common Prayer . 
- The Assembly was strictly under the control of Parliament , and was only to debate topics which Parliament directed . Assembly members were not permitted to state their disagreements with majority opinions or share any information about the proceedings , except in writing to Parliament . Parliament chose William Twisse , an internationally respected theologian , to be the Assembly 's <unk> or chairman . Due to Twisse 's ill health , Cornelius <unk> , whom Parliament appointed as one of several <unk> , served as <unk> pro <unk> for most of the Assembly . Twenty @-@ two appointed members of the Assembly died before 1649 , and they along with those who did not attend for other reasons were replaced by another nineteen members . Three non @-@ voting scribes were also added in 1643 . 
- 
- = = <unk> the Thirty @-@ Nine Articles = = 
- 
- The Assembly 's first meeting began with a sermon by William Twisse in the nave of Westminster Abbey on 1 July 1643 . The nave was so full that the House of Commons had to send members ahead to secure seats . Following the sermon , the divines processed to the Henry VII Chapel , which would be their place of meeting until 2 October when they moved to the warmer and more private Jerusalem Chamber . After their initial meeting they <unk> for about a week , as Parliament had not yet given specific instructions . 
- On 6 July , they received a set of rules from Parliament and were ordered to examine the first ten of the Thirty @-@ Nine Articles , the current doctrinal standard of the Church of England , and " to free and <unk> the Doctrine of them from all <unk> of false <unk> " . After a day of <unk> , the Assembly took a vow , as directed by Parliament , to " not maintain any thing in Matters of Doctrine , but what I think , in my conscience , to be truth " . The divines organized themselves into three standing committees , though each committee was open to any member of the Assembly . The committees would be assigned topics and prepare propositions for debate in the full Assembly following each morning of committee meetings . In addition , over 200 ad hoc committees were appointed for tasks such as examination of candidate preachers , college fellows , and suspected heretics . 
- The Assembly resolved , after some debate , that all the doctrines of the Thirty @-@ Nine articles would need to be proven from the Bible . Assembly members were prone to long speeches and they made slow progress , frustrating the leadership . The eighth of the Thirty @-@ Nine Articles recommended the Apostles ' Creed , <unk> Creed , and <unk> Creed , considered to be basic statements of <unk> , to be received and believed . The Assembly was unable to resolve conflicts between those who would not be bound by creeds and those who wished to retain the existing language that the creeds be " thoroughly received and believed " . The " <unk> " , who took the former position , argued that the articles only require the " matter " of the creeds be believed . On 25 August the article was put off until the rest of the articles could be dealt with . This early disagreement on fundamental issues revealed deep rifts between different factions of the assembly . 
- 
- = = <unk> church government = = 
- 
- From the beginning of the First Civil War , the Long Parliament recognized that they would need assistance from the Scots . In return for a military alliance , the Scottish Parliament required the English to sign the <unk> League and Covenant in 1643 , which stipulated that the English would bring their church into greater conformity with the Church of Scotland . The Scottish Parliament sent commissioners to London to represent Scotland 's interests to the English Parliament . Eleven of these , four theologians and seven members of Parliament , were also invited to the Assembly . The commissioners were given the opportunity to become full voting members of the Assembly but declined , preferring to maintain their independence as commissioners of their own nation and church . Samuel Rutherford , George Gillespie , and Alexander Henderson were the most outspoken of the commissioners . 
- On 12 October 1643 , Parliament ordered the Assembly to cease work on the Thirty @-@ Nine Articles and to begin to frame a common form of church government for the two nations . The Assembly would spend a quarter of its full sessions on the subject of church government . The majority of the Assembly members supported presbyterian polity , or church government by elected assemblies of lay and clerical representatives , though many were not <unk> committed to it . Several members of this group , numbering about twenty and including William Twisse , favoured a " primitive " <unk> , which would include elements of presbyterianism and a reduced role for bishops . 
- There were also several congregationalists , who favoured autonomy for individual local churches . Their most influential divines were Thomas Goodwin , Philip <unk> , <unk> Simpson , Jeremiah <unk> , and William Bridge . They were often called the " dissenting brethren " in the Assembly . They have sometimes been labelled " <unk> " , but they rejected this term . The Assembly members for the most part reserved the label " Independent " for separatists who left the established church . The congregationalist divines cannot be equated with separatists and <unk> , as they had accepted episcopal ordination and remained in the Church of England . Their influence was assisted by the success of Oliver Cromwell and his New Model Army in the ongoing civil war . Cromwell and many others in the army supported <unk> . 
- A third group of divines were known as <unk> , a term for those who believed that the state should have significant power over the church . The entire Assembly was Erastian in the sense that the body had been called by Parliament and was completely under state control . Those labelled " Erastian " at the Assembly believed the civil authority , rather than church officers , should hold the power of church discipline . This included the power to <unk> communion from unrepentant <unk> . They did not see any particular form of church government as divinely mandated , and because of this the dissenting brethren allied with them when it became clear that a presbyterian establishment would be much less tolerant of <unk> than Parliament . There were only two divines at the Assembly who held the Erastian view , John <unk> and Thomas Coleman , but the presence of members of Parliament , especially John <unk> , as well as the fact of parliamentary oversight of the Assembly , gave Erastian views <unk> influence . 
- Several <unk> , supporters of the existing system of bishops , were also included in the summoning ordinance , but Parliament may have nominated them to lend greater legitimacy to the Assembly and not have expected them to attend because Charles had not approved of the Assembly . Only one , Daniel <unk> , participated , and he only until his arrest for treason in October 1643 . 
- Debate on church officers began on 19 October . The Assembly began with the issue of ordination , as many of the divines were concerned about the rise of various sectarian movements and the lack of any mechanism for ordination of ministers of the established church . While some members did not seem to think ordination necessary for preachers ( though they should not administer the sacraments without it ) , a majority of the divines thought any regular preaching without ordination unacceptable and wished to erect a provisional presbytery for purposes of ordination . There was also debate at this early stage over the nature of the visible church . The congregationalists considered a church to be a single local congregation , while the majority considered the national church to be a unity and were alarmed at the prospect of a <unk> English church . Despite these debates , up until the end of 1643 there was hope that a common church government could be framed that would be satisfactory to all parties involved . 
- On 3 or 4 January 1644 , the five leading dissenting brethren signalled a break with the rest of the Assembly when they published An <unk> <unk> , a polemical pamphlet appealing to Parliament . It argued that the <unk> system was more amenable to state control of religious matters than that of the presbyterians because they did not desire the church to retain any real power except to withdraw <unk> from aberrant congregations . By 17 January , the majority of the Assembly had become convinced that the best way forward was a presbyterian system similar to that of the Scots , but the dissenting brethren were allowed to continue to state their case in hope that they could eventually be reconciled . It was hoped that by avoiding asserting that presbyterianism was established by divine right , the congregationalists could be accommodated . 
- Despite these efforts , on 21 February it became clear just how fundamentally opposed the groups were . Philip <unk> , one of the dissenting brethren , asserted in a speech that a presbytery set over local congregations would become as powerful as the state and was dangerous to the commonwealth , provoking vigorous opposition from presbyterians . The next day , the Assembly finally began to establish a <unk> for presbyterian government . Owing to a strong belief in the unity of the church , the Assembly continued to try to find ways to reconcile the dissenting brethren with the majority throughout 1644 , including the establishment of a special committee for that purpose in March . However , on 15 November , the dissenting brethren presented their reasons for disagreement with the rest of the Assembly to Parliament , and on 11 December the majority submitted a draft of a presbyterian form of government . 
- 
- = = Conflicts with Parliament = = 
- 
- Relations between the Assembly and Parliament were already deteriorating in 1644 , when Parliament ignored the Assembly 's request that " grossly ignorant and notoriously <unk> " individuals be barred from communion . While members of Parliament agreed that the sacrament should be kept pure , many of them disagreed with the presbyterian majority in the Assembly over who had the final power of excommunication , taking the Erastian view that it was the state . Also , by 1646 , Oliver Cromwell 's New Model Army had won the war on behalf of Parliament . Cromwell , along with a majority of the army , was strongly in favour of religious toleration for all sorts of Christians even if the national church was to be presbyterian . His rise to power as a result of his military victories made the idea of a strictly presbyterian settlement without freedom of worship for others very unlikely . Parliament at least wanted to know which sins in particular were grave enough to trigger excommunication by the church ; the Assembly was <unk> to provide such information , as the majority considered the power of the church in this area to be absolute . 
- In May 1645 , Parliament passed an ordinance allowing <unk> to appeal the church 's sentences to Parliament . Another ordinance on 20 October contained a list of sins to which the church would be limited in its power to <unk> . To the majority of the divines ' dismay , an even more Erastian ordinance was proposed in March 1646 . The Assembly published a protest , provoking the Commons to charge it with breach of privilege and to submit nine questions to the divines on the matter . <unk> were to be included with the answers , an attempt to force the divines associated with the protest petition to reveal themselves . 
- The Nine <unk> , as they came to be called , focused on the divine right ( <unk> <unk> ) of church government . While the presbyterian divines were capable of defending their vision for church government as established by divine right in the Bible , they were unwilling to answer the queries because doing so would further expose the <unk> of the Assembly and weaken their case in Parliament . In July 1647 , the New Model Army invaded London and conservative members of Parliament were forced out . Parliament passed an ordinance establishing religious tolerance and ensuring that the Assembly 's vision of a national , compulsory presbyterian church would never come to fruition . In London , where support for presbyterianism was greatest , <unk> were established in only sixty @-@ four of 108 city parishes , and regional presbyterian classes were only formed in fourteen of England 's forty counties . A planned national general assembly never met . Many presbyterians did , however , establish voluntary <unk> in what was a de facto free church situation until the Restoration in 1660 , when a compulsory episcopal system was reinstated . 
- The new Form of Government was much more acceptable to the General Assembly of the Church of Scotland . They passed it on 10 February 1645 , contingent on some <unk> of presbyterian government which were expected to be worked out in a forthcoming Directory for Church Government . At the same time they announced their desire to formally unite the two churches . Following the rise of Cromwell and the secret <unk> of some Scots with Charles this hope was abandoned , and the documents were never formally adopted . The General Assembly ceased to function under Cromwell and the kings who succeeded him from 1649 to 1690 . 
- 
- = = Confession , catechisms , and the Directory for Public Worship = = 
- 
- During and after the debates over church government , the Assembly framed other documents which did not cause open rifts . The Directory for Public Worship , which was to replace the Book of Common Prayer , was swiftly written in 1644 and passed by Parliament on 3 January 1645 . The Directory , accepted by the Scots as well , took a middle course between the presbyterians and congregationalists . The presbyterians for the most part preferred a fixed <unk> while congregationalists favoured <unk> prayer . The Directory consisted of an order for services with sample prayers . The Assembly also recommended a psalter , translated by Francis <unk> for use in worship . 
- A Confession of Faith to replace the Thirty @-@ Nine Articles was begun in August 1646 . While there is little record of the actions of the Assembly during the writing of the Confession , it is clear that there were significant debates in the Assembly over almost every doctrine found in it . The Confession was printed and sent to Parliament in December . The House of Commons requested scripture citations be added to the Confession , which were provided in April 1648 . Parliament approved the Confession with revisions to the chapters on church <unk> , <unk> and councils , and marriage on 20 June 1648 . The General Assembly of the Church of Scotland had already adopted the Confession without revision in 1647 . The restoration of Charles II in 1660 effectively made this legislation a <unk> . 
- The Assembly had already done significant work on a catechism between December 1643 and January 1647 containing fifty @-@ five questions , before it decided to create two catechisms rather than one . The Larger Catechism , intended to assist ministers in teaching the Reformed faith to their congregations . The Shorter Catechism was based on the Larger Catechism but intended for use in teaching the faith to children . Parliament demanded scripture proofs for the catechisms as well . The Scots General Assembly approved both catechisms in 1648 . 
- The Assembly understood its mandate under the <unk> League and Covenant to have been fulfilled on 14 April 1648 when it delivered the scripture citations to Parliament , and the Scottish Commissioners had already left by the end of 1647 . The Assembly continued to meet primarily for the purpose of examination of ministers for ordination . Most of the divines were unhappy with the republican Commonwealth that emerged after Colonel Pride 's <unk> of the Long Parliament in 1648 . As a result , a majority stopped attending rather than agree to the oath of <unk> to the Commonwealth that was imposed in 1649 . Newspapers continued to report on the meetings of the Assembly as late as March 1653 . The Assembly must have stopped meeting sometime between then and Cromwell 's dissolution of the <unk> Parliament on 20 April 1653 . 
- 
- = = <unk> = = 
- 
- The Assembly was a product of the British Reformed tradition , taking as a major source the Thirty @-@ Nine Articles as well as the theology of James <unk> and his Irish Articles of 1615 . The divines also considered themselves to be within the broader European Reformed tradition . They were in frequent correspondence with continental Reformed theologians , and sought their approval . They also drew upon the pre @-@ Reformation British theological tradition , which emphasized biblical knowledge and was influenced by the <unk> theological tradition exemplified by Anselm , Thomas <unk> , and John <unk> . The recorded debates of the Assembly are full of citations of church fathers and medieval scholastic theologians . 
- The Confession starts with the doctrine of revelation , or how people can know about God . The divines believed knowledge of God was available to people through nature as well as the Bible , but they also believed that the Bible , or Scripture , is the only way in which people attain saving knowledge of God . The doctrine of Scripture was also a particularly important area of debate at the time . Scholars had begun to argue that the Hebrew vowel points , marks added to the text to aid in pronunciation , of the Old Testament were probably not part of the original . This caused significant debate between Reformed <unk> and Roman Catholics . Catholics argued that such a discovery demonstrated the need for an authoritative <unk> to interpret the Bible as opposed to the Protestant doctrine of <unk> , that the essential teachings of the Bible could be interpreted by anyone . English Reformed writers in particular took up the work of defending the Reformed doctrine . The divines had a strong view of the inspiration of the Bible , and believed that God revealed himself in the propositions found in Scripture . While the issue of biblical <unk> , the belief that there are no errors in the Bible , did not arise until the eighteenth century , the divines clearly did not believe the Bible to contain any errors . Many of the divines held a rather mechanical view of biblical inspiration , believing that not only the words and ideas but also the letters and vowel points of the Hebrew text were inspired by God . On the other hand , they acknowledged that the text was written by humans in their own styles . They did not make any distinction between essential and incidental matters with respect to biblical inspiration . 
- Puritans believed that God is sovereign over all of history and nature and that none of what were called his decrees would be frustrated . There was significant debate in the Assembly over the relationship of God 's decree of <unk> , or choice to save some people , to the redemption purchased by Christ 's death . Many of the Reformed during this period taught that Christ died with the purpose only to save those who were <unk> chosen to be saved , a doctrine called particular redemption . A vocal minority of the divines of the Assembly argued for a position known as hypothetical <unk> . Edmund <unk> held such a view , and he argued that Christ 's death , as well as saving those who had been chosen , offered salvation to all people on condition that they believe . The Assembly 's Confession did not teach such a view , and its language is much more amenable to a particular redemption interpretation , but there is a general agreement among scholars that the Confession 's language allows an hypothetical <unk> interpretation . 
- Covenant theology is an interpretive framework used by Reformed theologians which was significantly developed during the seventeenth century . Under this scheme , as articulated by the Assembly , God 's dealings with men are described in terms of two covenants : the covenant of works and covenant of grace . The Westminster Confession was the first major Reformed symbol to explicitly mention the covenant of works ( sometimes called the covenant of life ) , in which God offered Adam eternal life on condition of perfect obedience . In the fall of man , Adam broke the covenant of works by eating of the tree of knowledge of good and evil . To remedy this , God offered salvation apart from human initiative in what was called the covenant of grace . This covenant allowed man to enjoy eternal life despite his inability to obey God 's law perfectly . The idea of the covenant of grace was a much more common feature of orthodox Reformed theology . The Westminster divines set these two covenants against each other as the two major ways in which God deals with people . 
- The divines were even more strongly opposed to Catholicism than to William Laud and his followers , the <unk> . They associated both Catholicism and <unk> with <unk> and persecution . Before the civil war , the divines saw these two groups as the greatest threat to the church . With the rise of radical sectarian movements during the war , the divines became much more concerned with these groups than <unk> against Catholicism . The divines were particularly concerned with those they labeled <unk> . This was a loose term for those who saw the moral law as in some way no longer relevant for Christians . The divines saw these groups as more immediately threatening than Catholicism . 
- 
- = = Legacy = = 
- 
- The work of the Westminster Assembly was repudiated by the Church of England during the Restoration in 1660 . The Act of Uniformity 1662 , a law requiring even greater adherence to the Book of Common Prayer and support for <unk> than had previously been required , forced Puritan ministers to leave the Church . Though some presbyterians continued to desire to be readmitted to the established church , restrictions on worship for non @-@ conformists led to presbyterians putting aside differences with congregationalists and adopting <unk> church orders . 
- The Civil War brought with it the end of the consensus among English Protestants that there should be a single church imposed by the state , though there was still not complete freedom of religion . The ideals of the dissenting brethren of the Assembly were significant in the rise of <unk> , the doctrine that the church is found in several institutions rather than a single one in a given location . Though Protestants already commonly recognized the validity of churches in different territories , the outcome of the Assembly 's debates marks the wider acceptance of the idea that several true churches may be found in the same territory . 
- The Confession produced by the Assembly was adopted with amendments by Congregationalists in England in the form of the <unk> Savoy Declaration , as well as by <unk> Baptists in the form of the 1689 Baptist Confession . When the General Assembly of the Church of Scotland was reestablished in 1690 following the Glorious Revolution , it ratified the Westminster Confession , but not any other document produced at Westminster . The Confession remains , according to the 1929 <unk> Articles , the Church of Scotland 's " principal subordinate standard " , subordinate to the Bible . <unk> of the Shorter Catechism has been made a requirement for children in many Presbyterian churches . 
- The migrations and missionary efforts of each of these groups lead to the widespread significance of the Westminster Assembly throughout the English @-@ speaking world . The Assembly 's Confession was particularly influential in American Protestant theology . It was included with congregationalist changes as part of the 1648 Cambridge Platform , a statement produced by ministers of colonial Massachusetts and the surrounding region , and again in colonial Connecticut as part of the 1708 <unk> Platform . The Confession was modified for American Baptists in the <unk> Philadelphia Confession . The <unk> Act of <unk> required American Presbyterians to agree to the theology of the Assembly 's Confession , and the Confession remains a part of the Presbyterian Church ( USA ) ' s Book of Confessions . The Confession has been called " by far the most influential doctrinal symbol in American Protestant history " by the historian of religion Sydney E. <unk> . 
- 
- 
- = The Goat Puzzle = 
- 
- The Goat Puzzle , also known as The <unk> Goat Puzzle , The <unk> Goat , The <unk> Goat , The Goat of Lochmarne , or simply The Goat , is a puzzle featured in Revolution Software 's 1996 classic point and click adventure game Broken Sword : The Shadow of the Templars . In the puzzle , protagonist George <unk> must gain access to an underground dig located in a castle in Lochmarne , Ireland , by avoiding getting <unk> by a fierce goat . It is often explained to be difficult due to challenging the player in a way different from previous puzzles in the game . It is also noted by many gamers and various publications as one of the hardest video game puzzles of all time . The puzzle was simplified in The Shadow of the Templars ' 2009 director 's cut . 
- 
- = = Puzzle and solution = = 
- 
- Revolution Software 's point and click adventure game Broken Sword : The Shadow of the Templars was first released in 1996 . While investigating a murder , protagonist and player character George <unk> finds himself uncovering a dark mystery regarding the Knights Templar . A medieval manuscript which he obtained during the investigation leads him to a castle located in Lochmarne , Ireland . As he cannot enter the castle through the main entrance door , he climbs a <unk> , which stops short of the top of the wall . He puts a sewer key which he obtained at the beginning of the game in a crack in the wall , which forms a step , allowing him to climb over the wall . Inside the castle , an entrance to an underground dig is located . A tied @-@ up goat is sitting near the entrance ; however , the chain to which it is tied is long enough to allow it to prevent George from reaching the entrance by <unk> him . An old piece of farming machinery is located at the left side of the screen , but George is unable to interact with it , as the goat continues to butt him . George must allow the goat to butt him when trying to reach the entrance , but while the goat is returning to its original position , the player must click on the farming machinery , which causes George to quickly jump up and run to the machinery and slightly <unk> it . When returned to its original position , the goat again charges and <unk> George , but while doing so , gets its chain entangled with the machinery , due to its <unk> . This allows George to move freely and enter the dig . 
- 
- = = Legacy = = 
- 
- The puzzle is considered by many gamers and publications to be one of the most challenging video game puzzles of all time . Broken Sword creator and Revolution CEO Charles Cecil and Broken Sword designer Steve Ince , as well as publications which have covered it , explain that the puzzle was challenging because the player was not met with any " time critical " puzzle prior to this point in the game , which would mean they " would not necessarily make the connection that clicking on the machinery at a key moment would make this happen . " 
- Chris <unk> of Official Nintendo Magazine said that " if you 've played [ Broken Sword ] , the words ' the goat puzzle ' will probably make you break out in a cold <unk> . " Geoff Thew of Hardcore Gamer wrote that Broken Sword was known for " intricate , challenging puzzles ( some <unk> so ) . " The puzzle appeared on Computer and Video Games ' 2011 feature , " Gaming 's hardest puzzles " . In 2012 , it was listed on <unk> 's " 5 Crazy <unk> and <unk> Video Game <unk> " . During a classic <unk> for The Shadow of the Templars at the European Game Developers Conference in August 2014 , when Cecil brought up the topic of difficult puzzles , a member of the audience shouted " That fucking goat ! " ; laughter ensued , and Cecil added : " It was very unfair , and it was absolutely <unk> . " He also continued , claiming he knew he had " made it " when a taxi driver once questioned him about his occupation : upon finding out he had written Broken Sword the driver exclaimed , " <unk> , so you 're the one responsible for the goat puzzle ! " 
- The puzzle was simplified in The Shadow of the Templars ' 2009 director 's cut by , according to Cecil , adding a more logical solution . This version of the game includes a diary , in which the character takes notes ; After completing the goat puzzle , George 's diary reads : " So the ' ghost of Lochmarne ' is no more than a fierce Billy goat . For a moment I thought it was going to be incredibly awkward to get past , but in the end it was surprisingly simple . Who would have known ? " , as a reference to the original puzzle 's reputation . An <unk> egg involving a talking goat was included in The Shadow of the Templars ' sequel , Broken Sword II : The Smoking Mirror ( 1997 ) . A goat puzzle was featured in Broken Sword 5 : The <unk> 's Curse , in the beginning and ending of 2014 second and final episode of the title , as part of the achieved $ 800 @,@ 000 Kickstarter " stretch goal " . 
- 
- 
- = Sinclair Sovereign = 
- 
- The Sinclair Sovereign was a high @-@ end calculator introduced by Clive Sinclair 's company Sinclair <unk> in 1976 . It was an attempt to escape from the unprofitable low end of the market , and one of the last calculators Sinclair produced . Made with a case of pressed steel that a variety of finishes , it cost between GB £ 30 and GB £ 60 at a time when other calculators could be purchased for under GB £ 5 . A number of factors meant that the Sovereign was not a commercial success , including the cost , high import <unk> on components , competition from cheaper calculators manufactured abroad , and the development of more power @-@ efficient designs using liquid @-@ crystal displays . Though it came with a five @-@ year guarantee , issues such as short battery life limited its usefulness . The company moved on to producing computers soon afterwards . 
- The design by John Pemberton won a Design Council award , and there are examples of the Sovereign in the Museum of Modern Art in New York . It had a <unk> <unk> main integrated circuit and a small memory register , a LED display , and could perform a variety of a number of basic mathematical operations besides four @-@ function arithmetic . 
- 
- = = History = = 
- 
- The Sovereign was one of the last calculators produced during Sinclair 's foray into the calculator market that had started with the Sinclair Executive in September 1972 . The Executive had <unk> for GB £ 80 when introduced , but in little over a year it was possible to purchase a Sinclair calculator for GB £ 20 and by November of 1976 a model was available for GB £ 4 @.@ 95 . Cheaper calculators with liquid @-@ crystal displays instead of light @-@ emitting diodes were becoming more popular , and had much longer battery lives of months or years . Such calculators were available for well under GB £ 10 , with all the functionality of the more expensive models . The impossibility of selling " simple " calculators <unk> led Sinclair to introduce models such as the Cambridge Scientific , introduced in August 1975 at a price of GB £ 29 @.@ 95 . 
- The Sovereign , released in 1976 , represented an attempt to move <unk> in an increasingly saturated market . In December 1976 , the chrome plated version of the Sovereign cost GB £ 30 and the gold @-@ plated version GB £ 60 , including <unk> , but profit margins on the Sovereign were so small that Sinclair ended up selling the Sovereign at a loss , and it was not a commercial success . The Sovereign was made in England , like every other Sinclair calculator except the President . 
- Sinclair would shortly stop producing calculators and instead focus on computers , starting with the <unk> in 1977 . The loss of the calculator market was due in part to technological development leading to smaller and cheaper components , which put heavy pressure on profit margins . An import levy of up to 17 @.@ 5 % was placed on components , but the duty for calculators imported from Japan or Hong Kong could be as little as 5 % , making it unprofitable to produce calculators in the UK . Sinclair also had some problems with the reliability of earlier calculators that had adversely affected its reputation , but the Sovereign was sold with a " full and unconditional " five year guarantee . 
- 
- = = Design = = 
- 
- The Sovereign came in satin chrome and gold @-@ plated models , with leather pouches and fitted wooden cases . It had an 8 @-@ digit seven @-@ segment display that used red light @-@ emitting diodes , with a <unk> point to the right of each digit that could be illuminated as necessary . Power was provided by two 1 @.@ 35 mercury button cells . The Sovereign measured 36 by 141 by 12 millimetres ( 1 @.@ 42 in × 5 @.@ 55 in × 0 @.@ 47 in ) , which made it small and <unk> compared to other calculators of the time . 
- The Sovereign was unusual because the casing was made from pressed steel , which gave it a much higher quality feel compared to injection moulded plastic . This allowed a variety of paint and plating options , including black painted , chrome @-@ plated , silver @-@ plated , and gold @-@ plated , and a limited edition silver @-@ plated version , inscribed to commemorate the Silver Jubilee of Queen Elizabeth II in 1977 , were also produced . <unk> of London was rumoured to have produced two <unk> in solid gold , costing GB £ 2 @,@ 750 each . 
- The design , by John Pemberton , won the Design Council Award in 1977 , as the Executive had in 1973 , and there are examples in the collection of the Museum of Modern Art in New York . <unk> are highly <unk> , and used models command high prices . 
- 
- = = = <unk> = = = 
- 
- As well as addition , <unk> , <unk> and division , it had <unk> and square @-@ root functions , and the ability to <unk> by a fixed constant . With an eight @-@ digit display , the calculator could display positive numbers between 0 @.@ <unk> and 99 @,@ 999 @,@ 999 , and negative numbers between <unk> and <unk> . <unk> of the time tended to have displays of between 3 and 12 digits , as reducing the number of digits was an effective way of reducing the cost of the calculator . A number outside that range leads to an overflow , and the screen flashes and all keys except the clear key are rendered inoperable to inform the user of the error . A independent memory register could read information from the screen , and information could only be taken from the memory onto the screen . Five keys were used for memory operations . 
- The Sovereign used a <unk> <unk> main integrated circuit , the same as the Sinclair <unk> <unk> and some variants of the Sinclair Cambridge . Clive Sinclair had assumed that people would prefer attractive illuminated LED displays to <unk> displays , which incidentally also required more expensive <unk> chips . However , his calculators were designed with the assumption they would be turned off between calculations , which did not prove to be the case . Advertisements suggested that the batteries would last " about a year " under normal use , but in reality the small button cell batteries and comparatively high power consumption meant a short battery life compared to the competition . 
- 
- 
- = Survivor Series ( 1992 ) = 
- 
- Survivor Series ( 1992 ) was the sixth annual Survivor Series pay @-@ per @-@ view professional wrestling event produced by the World Wrestling Federation ( WWF ) . It took place on November 25 , 1992 , at the <unk> Coliseum in <unk> Township , Ohio . The buildup to the pay @-@ per @-@ view consisted of <unk> scripted by the WWF 's writers , and the matches that took place at the event had pre @-@ determined outcomes that had been decided by the company . 
- In the main event , Bret Hart retained his WWF World Heavyweight Championship against Shawn Michaels . The card also included a highly @-@ promoted match in which the team of Randy Savage and Mr. Perfect defeated the team of Ric Flair and Razor Ramon by <unk> ; The Ultimate Warrior was advertised for the show but left the company and was replaced by Perfect . Also featured were two <unk> matches : The Undertaker won a coffin match against Kamala , and The Big Boss Man defeated Nailz in a nightstick on a pole match . 
- Some of the matches were changed after they were first announced , as several wrestlers left the WWF shortly before the event . Critics have praised the main event and the tag team match that was won by Savage and Perfect . Although the remaining bouts have received low ratings , critics have felt that those two matches made the event worth watching . 
- 
- = = Background = = 
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- In the storyline behind the match between The Big Boss Man and Nailz , Nailz claimed that while he was serving time in prison , the Big Boss Man , who was a guard at the prison , <unk> Nailz . In early 1992 , Nailz appeared in introductory vignettes to talk about his hatred of the Big Boss Man . Upon his debut in the WWF , Nailz attacked <unk> and stole his nightstick , which he used to attack his opponents over the following months . 
- Shortly after Tatanka 's debut in the WWF , he became involved in a feud with Rick Martel . They faced each other at WrestleMania VIII , and Tatanka defeated Martel . The following month , Martel gained revenge by attacking Tatanka with an <unk> of <unk> and stealing the eagle feathers that Tatanka carried to symbolize his <unk> heritage . 
- Ric Flair and his " executive consultant " Mr. Perfect had been allies in the WWF for much of 1992 . After Flair lost the WWF World Heavyweight Championship to <unk> Man Randy Savage at WrestleMania VIII , Flair and Perfect initiated a feud with Savage . They <unk> in Savage 's match at SummerSlam and caused him to lose by <unk> . The rivalry continued , and Flair received assistance from Perfect and Razor Ramon to win the title back from Savage on September 1 , which aired on the September 14 edition of WWF Prime Time Wrestling . The WWF planned for Savage to team with the Ultimate Warrior in a match against Flair and Ramon at Survivor Series . The Ultimate Warrior left the WWF , however , before the match could take place . Two possible reasons have been given for his departure . The first states that he was fired due to allegations of steroid abuse , while the other states that he was upset with the WWF 's future plans for his character . The WWF was forced to change the plan and decided to turn Perfect into a <unk> ( fan favorite ) from a heel . Perfect and Flair began having conflicts while teaming together , and Perfect accepted Savage 's offer on the November 16 episode of WWF Prime Time Wrestling to team with him at Survivor Series . 
- Money Inc . ( " Million Dollar Man " Ted DiBiase and Irwin R. Schyster ) had been feuding with The Natural Disasters ( Earthquake and Typhoon ) since February 1992 when manager Jimmy Hart turned on Earthquake and Typhoon in favor of helping Money , Inc . With Hart 's assistance , Money , Inc. defeated the Legion of Doom for the WWF Tag Team Championship on February 7 , 1992 . Five months later , Earthquake and Typhoon defeated Money Inc. to win the championship . The Natural Disasters were also feuding with The Beverly Brothers ( Beau and Blake ) and defeated them in a title match at SummerSlam 1992 . A match was then announced for Survivor Series in which The Natural Disasters would team with The <unk> ( Luke and Butch ) to face Money , Inc. and the Beverly Brothers . However , when The Nasty Boys ( Brian Knobbs and Jerry Sags ) , also managed by Hart , were scheduled to face The Natural Disasters for the title on October 13 , Hart replaced The Nasty Boys at the last minute with Money , Inc . , who went on to regain the championship with help from The Headshrinkers . This led to a break @-@ up between Hart and the Nasty Boys , who received the <unk> ' spot in the Survivor Series match to get revenge against Jimmy Hart and Money , Inc . 
- At SummerSlam 1992 in August , The Undertaker defeated Kamala . Harvey Wippleman , Kamala 's manager wanted revenge , so a rematch was scheduled for Survivor Series . The match was promoted as the WWF 's first coffin match , in which the winner would place the loser in a coffin after the match . 
- The WWF also planned a match featuring The British <unk> defending the WWF Intercontinental Championship against The <unk> at Survivor Series . The WWF released Smith due to steroid allegations , however , and he was made to drop the title belt to Shawn Michaels on the November 14 episode of Saturday Night 's Main Event . Jake Roberts was set to challenge Bret Hart , who had won the WWF World Heavyweight Championship on October 12 , for his title , but the promotion could not come to terms with Roberts . Ultimately , the WWF neglected to include an Intercontinental Championship match on the card , instead <unk> Michaels against Hart . In this match , Hart 's title was defended while Michaels ' title was not . 
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- = = Event = = 
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- Prior to the pay @-@ per @-@ view broadcast , Crush defeated the <unk> Man via submission . In the first televised match , High Energy ( Owen Hart and <unk> B. Ware ) faced The Headshrinkers ( Samu and Fatu ) . Samu used his strength advantage to control the opening of the match against Hart . Ware entered the match and gained the advantage over both opponents until he attempted to knock The Headshrinkers ' heads against each other . According to WWF storylines , <unk> like The Headshrinkers have thick skulls and cannot be hurt in the head ; as a result , The Headshrinkers no @-@ sold the attack . <unk> , The Headshrinkers ' manager , attacked Ware while the referee was distracted . Samu and Fatu took turns attacking Ware , and Fatu performed a thrust kick on Ware . The Headshrinkers used <unk> tactics to control the match until Hart was able to enter the match . He performed dropkicks from the top rope against both Headshrinkers . As he tried to attack Samu from the top rope again , Samu caught him and <unk> him before Fatu executed a diving splash to get the pinfall victory . 
- The next match , between The Big Boss Man and Nailz , was a nightstick on a pole match . A nightstick was suspended above the ring and could be used as a weapon once it was retrieved . Nailz began the match by attempting to get the nightstick , but Boss Man stopped him . Nailz choked Boss Man before making another unsuccessful attempt to climb the pole and obtain the nightstick . Boss Man punched Nailz and then tried to climb the pole . Nailz stopped him , performed a back body drop , and choked Boss Man again . Boss Man regained control but missed a splash , which gave Nailz another chance to retrieve the nightstick . Both men <unk> each other , but Boss Man recovered first and got the nightstick . He hit Nailz with it , but Nailz took it and used it against Boss Man . Boss Man performed the Boss Man slam , his finishing move , before <unk> Nailz to win the match . 
- Tatanka controlled the opening of the next match against " The Model " Rick Martel with several throws and dropkicks . Martel responded by wearing Tatanka down with a front facelock . Tatanka escaped , but Martel used another front facelock almost immediately . He performed a neckbreaker on Tatanka before going back to the same hold as before . Tatanka gained the advantage by performing a clothesline on Martel . Martel ran at Tatanka , but Tatanka moved out of the way and Martel hit his shoulder against the ring post . Tatanka focused on attacking Martel 's injured shoulder but eventually was thrown out of the ring by Martel . Martel 's advantage was short @-@ lived , as Tatanka performed a series of backhand chops and a <unk> chop from the top rope before <unk> The Model with a <unk> drop . Tatanka got the pinfall victory and retrieved his feathers from Martel after the match . While the match was in progress , <unk> the Clown , who had not yet been named or debuted as a wrestler in the WWF , stood in the aisle and made balloon animals before popping them to upset the children in the audience . 
- In the next contest , Ric Flair and Razor Ramon wrestled against Mr. Perfect and Randy Savage . Ramon and Perfect began the match , but Flair entered after Perfect insulted him . Perfect threw Flair into the corner , and Flair 's momentum carried him over the top rope to the ring apron . Savage attacked Flair and then took Perfect 's place in the ring to maintain the advantage over Flair . From outside the ring , Ramon hit Savage with his knee and Flair and Ramon took turns attacking Savage 's knee . While Ramon performed a half Boston crab on Savage , Perfect considered leaving the match and abandoning Savage . Savage recovered briefly by trying to pin Flair , but Ramon performed a <unk> on Savage . Ric Flair attempted to attack Savage from the top rope , but Savage threw him to the ring floor instead . Perfect executed a neckbreaker and an atomic drop on Ramon . Outside of the ring , Flair attacked Savage with a chair . The referee was knocked unconscious , and Perfect tried to pin Ramon by performing a <unk> . Because no referee was available to count the pinfall , a substitute referee came to the ring . The first referee recovered as Perfect attempted to pin Flair with a <unk> . Flair escaped the pin attempt , and he and Ramon attacked Perfect until the <unk> were unable to keep the match under control . As a result , Ramon and Flair were disqualified , and the victory was awarded to Perfect and Savage . 
- In the next match , Virgil faced Yokozuna , who was billed at 505 pounds . Virgil was unable to knock Yokozuna down with several dropkicks . Yokozuna performed a <unk> kick on Virgil before throwing him to the ring floor twice . He executed a <unk> on Virgil , but Virgil regained the advantage when Yokozuna accidentally ran into the ring pole while trying to attack Virgil . Yokozuna won the match after a splash in the corner and a <unk> Drop . 
- The following match was a tag team elimination match , in which The Natural Disasters ( Earthquake and Typhoon ) teamed with the Nasty Boys ( Jerry Sags and Brian Knobbs ) to face Money Inc . ( Ted DiBiase and Irwin R. Schyster ) and the Beverly Brothers ( Beau and Blake ) . The rules stated that when any man was eliminated , his tag team partner would also be eliminated . Blake Beverly and Typhoon began the match , but Beau and Earthquake soon entered the ring as well . The Natural Disasters gained control of the match by attacking Blake Beverly , and the Nasty Boys entered the ring to assist the Disasters . Blake attacked Sags and <unk> out of the match . Beau entered but was <unk> by Sags ; Sags got distracted , however , which allowed Beau to suplex him and bring DiBiase into the match . Money Inc. took turns attacking Sags until Earthquake entered the ring . Earthquake performed an Earthquake splash by sitting on Beau Beverly to pin him and eliminate both Beverly Brothers . Earthquake fought DiBiase , but both men eventually left the ring and were replaced by their partners . Typhoon performed a splash on Schyster but was <unk> by DiBiase . Schyster pinned Typhoon to eliminate The Natural Disasters . While Schyster was celebrating , Sags quickly pinned him to win the match for the Nasty Boys . 
- The coffin match , in which The Undertaker faced Kamala , came next . Kamala began the match by running in fear from The Undertaker . He gained the early advantage , however , by suddenly turning around and attacking The Undertaker . Kamala <unk> The Undertaker three times and performed three splashes . In an attempt to revive The Undertaker , Paul <unk> , his manager , held up an urn that was said to be the source of The Undertaker 's power . Kim <unk> , one of Kamala 's <unk> 's attacked <unk> . The Undertaker picked up the urn , which had rolled into the ring , and hit Kamala with it . The Undertaker pinned Kamala to win the match , placed him in a coffin , and <unk> the <unk> shut . 
- In the main event of the card , Bret Hart defended his WWF World Heavyweight Championship against Shawn Michaels . Hart controlled the beginning of the match by repeatedly executing <unk> and <unk> but Michaels gained the advantage when " The <unk> " missed a charge into the corner and collided shoulder @-@ first with the ring post . Michaels then threw Hart into another ring post and wore him down by executing a front facelock . Hart was able to avoid Michaels ' first attempt at his finisher , the modified back suplex , and rallied with his trademark moves including an elbow from the second rope , running <unk> and a <unk> . Michaels recovered and performed a <unk> on Hart and was able this time to apply his suplex , but Hart kicked out . After Hart missed a <unk> <unk> and <unk> himself on the ropes , Shawn attempted a <unk> from the middle rope but Hart grabbed Michaels ' legs and applied the <unk> , his finishing move . He retained his championship by forcing Michaels to submit . 
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- = = Aftermath = = 
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- Ric Flair and Mr. Perfect continued their feud , although Flair legitimately requested to be released from his WWF contract in order to return to World Championship Wrestling ( WCW ) . His request was granted on the condition that he help build up Perfect as a credible <unk> . The two men attacked each other during the battle royal at Royal Rumble 1993 , and Perfect eliminated Flair from the match . The following night , Perfect defeated Flair in a loser leaves town match . Flair did not return to the WWF until McMahon purchased WCW in 2001 . 
- The Undertaker 's feud with Harvey Wippleman continued for several months after Survivor Series . At Royal Rumble 1993 , Wippleman introduced a new wrestler named Giant Gonzalez . Despite not being scheduled in the match , Gonzalez attacked The Undertaker and eliminated him from the Royal Rumble match . The two men faced each other at WrestleMania IX , where Gonzalez was disqualified for attacking The Undertaker with a <unk> soaked in <unk> . Wippleman led Gonzalez and Mr. Hughes in another attack on The Undertaker , in which Hughes stole The Undertaker 's urn . The feud was resolved at SummerSlam 1993 , when The Undertaker defeated Gonzalez in a Rest in Peace match . 
- Yokozuna 's push continued , and he won the battle royal main event at Royal Rumble 1993 to earn a WWF Championship match against Bret Hart at WrestleMania IX . At WrestleMania , he defeated Hart to win the title belt . He immediately challenged <unk> Hogan to a match however , and WrestleMania ended with Yokozuna losing the WWF Championship to Hogan in 21 seconds . 
- Kevin <unk> , who had portrayed Nailz , left the WWF shortly after Survivor Series . Upset about his pay from SummerSlam 1992 , he confronted WWF owner Vince McMahon and reportedly attacked him physically . He later testified against McMahon during a trial in which McMahon was accused of distributing steroids to wrestlers . <unk> ' statements have been reported as having a harmful effect on the prosecution 's case because his anger at McMahon overshadowed his testimony . 
- Shawn Michaels became involved in a feud with his former tag team partner , Marty Jannetty . The team had split up earlier in the year when Michaels attacked Jannetty . Michaels defeated Jannetty at Royal Rumble 1993 , but the two traded the Intercontinental Championship back and forth in subsequent <unk> . 
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- = = = Reception = = = 
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- Survivor Series 1992 was attended by 17 @,@ 500 fans , the same number as the previous year . It drew more fans than any of the following three Survivor Series event would draw . The pay @-@ per @-@ view buyrate was 1 @.@ 4 , which means that 1 @.@ 4 percent of households to which the event was available purchased the pay @-@ per @-@ view . This was , to that point , the lowest buyrate in Survivor Series history and down more than one @-@ third from the previous year 's 2 @.@ 2 buyrate . The buyrate was higher than that of any of the following twelve Survivor Series events , however . 
- Writing for The History of WWE , Matt <unk> stated that the event was " pretty decent " considering that The <unk> , <unk> Boy Smith , and the Ultimate Warrior left the company shortly before the event . On a five @-@ star scale , he rated only the Flair / Ramon vs. Savage / Perfect match and the Hart vs. Michaels match higher than one star . He stated that the event is " recommended , but not required " , although the WWF Championship match was a " must @-@ see " . 
- Adam <unk> , reviewing the event for Online <unk> , gave a rating of one @-@ quarter star for the <unk> on a Pole match and one @-@ half star each for the High Energy vs. The Headshrinkers match and the Yokozuna vs. Virgil match . He enjoyed the WWF Championship match , although he was disappointed by its lack of buildup prior to the event . He also felt that the Flair / Ramon vs. Savage / Perfect match was a good one until the ending got out of control . Pro Wrestling <unk> columnist agreed , stating that the WWF Championship match was the best and that the Flair / Ramon vs. Savage / Perfect bout was also enjoyable , but he recommended fast @-@ <unk> through the rest of the show . 
- The event was released in North America on VHS by Coliseum Video on February 11 , 1993 . The VHS version was released in the United Kingdom on March 8 , 1993 . A DVD version is also available in the United Kingdom ; it was packaged together with Survivor Series 1991 as part of the WWE <unk> Classics line and released on November 7 , 2005 . 
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- = = Results = = 
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- = = = Survivor Series elimination match = = = 
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- ^ 1 
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- = Ouw Peh Tjoa = 
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- Ouw Peh Tjoa ( Chinese : <unk> ; <unk> for Black and White <unk> ) , also known by the Malay @-@ language title <unk> Siloeman Oeler Poeti en Item ( meaning Two <unk> , One White and One Black ) , is a 1934 film from the Dutch East Indies ( now Indonesia ) . It was directed and produced by The Teng Chun . Adapted from Legend of the White Snake , a Chinese folktale , it follows a magical snake who passes as a human but ultimately dies . The film , now possibly lost , was followed by one sequel , <unk> Siloeman Oeler Poeti , in 1936 . 
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- = = Plot = = 
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- After <unk> for several hundred years , a magical white snake transforms into a beautiful woman . Her competitor , a black snake , does likewise . The two compete for the love of a man named Khouw Han <unk> . Ultimately Khouw agrees to marry the ( former ) white snake , but when her true identity is revealed he attempts to cancel their wedding . The snake @-@ woman , crying , tells Khouw 's boss that they are to be married , and ultimately Khouw is <unk> into marrying her . 
- As time passes , Khouw sees his wife occasionally transform into a snake . She is always , however , able to convince him otherwise . He falls further in love with her , and their marriage is a happy one . After several months he is <unk> by a priest , <unk> <unk> <unk> <unk> , who then leads an attempt to kill the snake @-@ woman . She escapes , pursued by the priests . 
- The priests catch the snake and prepare to kill her , but are stopped by the goddess <unk> <unk> , who tells the stunned pursuers that the snake is pregnant and thus must not be killed . A month after the snake gives birth , the priests return . The snake @-@ woman gives her child to Khouw and then <unk> herself to her fate . She is captured in a magical jar and brought away . 
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- = = Production = = 
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- Ouw Peh Tjoa was directed and produced by The Teng Chun for his company , <unk> Motion Pictures . Since releasing Sam <unk> <unk> Tay in 1931 , based on the legend of the Butterfly Lovers , The Teng Chun had released a series of films based on Chinese legends and folktales , including Pat <unk> To ( Eight Beautiful Women ; 1932 ) and Pat <unk> <unk> ( Eight <unk> ; 1933 ) . These stories were selected because the <unk> Chinese in the Indies were unable to understand Mandarin and Cantonese imports from China , but wanted to see films based on Chinese mythology . Overall , The Teng Chun 's films emphasised the martial art <unk> and were generally profitable , allowing him to dominate the industry . 
- The cast of this black @-@ and @-@ white film is unrecorded . The dialogue , captured by the film 's director @-@ cum @-@ producer , was in Malay . The snakes used in the production of this film came from The Teng Chun 's personal zoo . 
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- = = Release and reception = = 
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- According to The , in a 1970s interview , Ouw Peh Tjoa was released in 1934 . Newspaper advertisements show the film being screened by February 1935 . The film mostly targeted ethnic Chinese audiences . Advertising material , however , emphasised the use of spoken Malay and described the film as " full of <unk> and all forms of magic fights " ; through these action sequences , Ouw Peh Tjoa proved popular among native audiences . The film was exported to Singapore , part of the Straits <unk> , where there was a large ethnic Chinese population . 
- The success of Ouw Peh Tjoa allowed The Teng Chun to import new equipment for his studio ( renamed Java Industrial Film ) , which he used in his future productions . The film was followed in 1936 by a sequel , <unk> Siloeman Oeler Poeti ( Child of the White Snake ) . The Teng Chun continued releasing films based on Chinese legends until 1937 , a year after Albert <unk> 's <unk> changed domestic perceptions of profitable film storylines . The 's later films adapted stories closer to the native populace of the Indies and <unk> on events that could happen in day @-@ to @-@ day life . Through 1940 and 1941 Java Industrial Films was the most productive studio in the Indies , until it was shut down during the Japanese occupation which began in March 1942 . 
- <unk> of Ouw Peh Tjoa continued until at least 1953 . The film is now likely lost . Movies in the Indies were recorded on highly flammable nitrate film , and after a fire destroyed much of <unk> Film <unk> 's warehouse in 1952 , old films shot on nitrate were deliberately destroyed . As such , the American visual anthropologist Karl G. <unk> writes that all Indonesian films from before 1950 are lost . However , <unk> <unk> 's <unk> Film Indonesia ( Indonesian Film <unk> ) records several as having survived at <unk> Indonesia 's archives , and Biran writes that several Japanese propaganda films have survived at the Netherlands Government Information Service . 
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- = = <unk> notes = = 
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- = HMS Comet ( <unk> ) = 
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- HMS Comet was a C @-@ class destroyer built for the Royal Navy in the early 1930s . She saw service in the Home and Mediterranean Fleets and the ship spent six months during the Spanish Civil War in late 1936 in Spanish waters , enforcing the arms blockade imposed by Britain and France on both sides of the conflict . Comet transferred to the Royal Canadian Navy ( <unk> ) in 1938 and renamed HMCS Restigouche . During World War II , she served as a convoy escort in the battle of the Atlantic , on anti @-@ submarine patrols during the invasion of Normandy , and was employed as a troop transport after <unk> Day for returning Canadian servicemen , before being decommissioned in late 1945 . Restigouche was sold for scrap in 1946 . 
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- = = Design and construction = = 
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- Comet displaced 1 @,@ 375 long tons ( 1 @,@ <unk> t ) at standard load and 1 @,@ 865 long tons ( 1 @,@ 895 t ) at deep load . The ship had an overall length of <unk> feet ( 100 @.@ 3 m ) , a beam of 33 feet ( 10 @.@ 1 m ) and a draught of 12 feet 6 inches ( 3 @.@ 8 m ) . She was powered by Parsons geared steam turbines , driving two shafts , which developed a total of 36 @,@ 000 shaft horsepower ( 27 @,@ 000 kW ) and gave a maximum speed of 36 knots ( 67 km / h ; 41 mph ) . Steam for the turbines was provided by three Admiralty 3 @-@ drum water @-@ tube boilers . Comet carried a maximum of 473 long tons ( <unk> t ) of fuel oil that gave her a range of 5 @,@ 500 nautical miles ( 10 @,@ 200 km ; 6 @,@ 300 mi ) at 15 knots ( 28 km / h ; 17 mph ) . The ship 's complement was 145 officers and men . 
- The ship mounted four 45 @-@ calibre 4 @.@ 7 @-@ inch Mk IX guns in single mounts , designated ' A ' , ' B ' , ' X ' , and ' Y ' from front to rear . For anti @-@ aircraft ( AA ) defence , Comet had a single QF 3 @-@ inch 20 cwt AA gun between her funnels , and two 40 @-@ millimetre ( 1 @.@ 6 in ) QF 2 @-@ pounder Mk II AA guns mounted on the aft end of her forecastle deck . The 3 @-@ inch ( 76 mm ) AA gun was removed in 1936 and the 2 @-@ pounders were relocated to between the funnels . She was fitted with two above @-@ water quadruple torpedo tube mounts for 21 @-@ inch torpedoes . Three depth @-@ charge chutes were fitted , each with a capacity of two depth charges . After World War II began this was increased to 33 depth charges , delivered by one or two rails and two throwers . 
- The ship was ordered on 15 July 1930 from Portsmouth Dockyard under the 1929 Programme . Comet was laid down on 12 September 1930 , launched on 30 September 1931 , as the 14th ship to carry the name , and completed on 2 June 1932 . 
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- = = Service history = = 
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- After sea trials in May 1932 , Comet was commissioned for service in the 2nd Destroyer Flotilla , Home Fleet , in early June . On 21 July , she was damaged in a collision with her sister Crescent at Chatham and repaired at Chatham Dockyard between 28 July and 20 August . The ship was refitted at Chatham from 20 July to 3 September 1934 . Following the Italian invasion of <unk> , Comet was sent in August 1935 to the Red Sea with the other ships of the 2nd Flotilla to monitor Italian warship movements until March 1936 . 
- Comet returned to the UK in April 1936 and refitted at <unk> between 23 April and 29 June before resuming duty with the Home Fleet . In July she was deployed for patrol duties off the Spanish coast in the Bay of Biscay to intercept shipping carrying <unk> goods to Spain and to protect British flagged shipping during the first stages of the Spanish Civil War . On 9 August she assisted the crew of the crippled British yacht Blue Shadow off <unk> , after the small vessel was shelled by mistake by the Nationalist cruiser <unk> <unk> . The ship was briefly placed in reserve in late 1936 while discussions were held about transferring her to the Royal Canadian Navy . Two of her sisters were chosen instead and Comet was recommissioned for service with the Mediterranean Fleet as plane guard for the aircraft carrier Glorious on 29 December . 
- In April 1937 she returned to Portsmouth with Glorious , and on 20 May the ship participated in the Coronation Review of the fleet at Spithead by King George VI . Four days later , Comet began a refit at Portsmouth that lasted until 18 June . The ship resumed plane guard duties for Glorious in the Mediterranean . She began a major refit at Chatham on 26 May 1938 to bring her up to Canadian specifications that included the installation of Type 124 <unk> . 
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- = = = Transfer to the Royal Canadian Navy = = = 
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- On 11 June she was commissioned by the <unk> and renamed Restigouche , although her refit was not completed until 20 August . Restigouche was assigned to the Canadian Pacific Coast and arrived at <unk> on 7 November 1938 . She remained there until she was ordered to Halifax , Nova Scotia on 15 November 1939 where she escorted local convoys , including the convoy carrying half of the 1st Canadian Infantry Division to the UK on 10 December . Restigouche was ordered to Plymouth on 24 May 1940 and arrived there on 31 May . Upon arrival , the ship 's rear torpedo tube mount was removed and replaced by a 12 @-@ pounder AA gun and the 2 @-@ pounders were exchanged for quadruple Mark I mounts for the QF 0 @.@ 5 @-@ inch Vickers Mark III machine gun . 
- On 9 June , Restigouche was ordered to Le <unk> , France to evacuate British troops , but none were to be found and the ship investigated the small port of Saint @-@ <unk> @-@ en @-@ <unk> some 40 miles ( 64 km ) northeast of Le <unk> on 11 June . They found some elements of the 51st Infantry Division , but had not received any orders to evacuate and refused to do so . Whilst recovering her landing party , the ship was taken under fire by a German artillery battery , but she was not hit and returned fire . After returning to England , Restigouche escorted several troop convoys on the last legs of their journeys from Canada , Australia and New Zealand in mid @-@ June . On 23 June , the ship escorted the ocean liner SS <unk> Star to St. Jean de Luz to evacuate Polish troops and British refugees trapped by the German Army in south @-@ western France ( Operation <unk> ) . On 25 June 1940 , Restigouche , her sister HMCS Fraser , and the light cruiser HMS Calcutta were returning from St. Jean de Luz when Fraser was rammed by Calcutta in the <unk> estuary at night . <unk> forward of the bridge by the cruiser 's bow , Fraser was cut in half , although the rear part of the ship did not immediately sink . All but 47 of the ship 's crew and <unk> were rescued by Restigouche and other nearby ships . The rear portion had to be sunk by Restigouche . 
- The ship was transferred to the Western Approaches Command afterwards for convoy escort duties . She sailed for Halifax at the end of August for a refit that lasted until October . Upon its completion , Restigouche remained at Halifax for local escort duties until January 1941 when she sailed for the UK where she was reassigned to the Western Approaches Command . The ship was ordered to St. John 's , Newfoundland on 30 May to reinforce escort forces in the Western Atlantic . Whilst guarding the battleship Prince of Wales at <unk> Bay on 8 August , Restigouche damaged her propellers when she struck bottom and required repairs that lasted until October . She was not out of dockyard hands for very long before she was badly damaged by a storm while en route to join Convoy <unk> @-@ 44 on 12 December . <unk> at Greenock lasted until 9 March 1942 and her director @-@ control tower and rangefinder above the bridge had been removed by this time in exchange for a Type 271 target indication radar . 
- Other changes made during the war ( exactly when these occurred is unknown ) included the replacement of ' A ' gun by a Hedgehog anti @-@ submarine <unk> mortar , exchanging her two quadruple .50 @-@ calibre Vickers machine guns mounted between her funnels for two Oerlikon 20 mm AA guns , the addition of two Oerlikon guns to her <unk> platform , and the removal of her 12 @-@ pounder AA gun . Type 286 short @-@ range surface search radar was also added . Two QF 6 pounder <unk> guns were fitted on the wings of her bridge to deal with U @-@ boats at short ranges . ' Y ' gun was also removed to allow her depth charge stowage to be increased to at least 60 depth charges . 
- Restigouche was assigned to the Mid @-@ Ocean Escort Force when her refit was finished and served with a variety of escort groups . The ship was permanently assigned to Escort Group <unk> in April 1943 and received a refit between August and December . She rejoined the escort group upon completion of the refit until she was transferred to 12th Escort Group in early 1944 for anti @-@ submarine operations in the Western Approaches . In June – July 1944 , Restigouche patrolled in the English Channel and the Bay of Biscay hunting for German submarines trying to sink Allied shipping . On the night of 5 – 6 July , the ship and the rest of the 12th Escort Group sank three small German patrol boats off Brest . The following month , the 12th Support Group , including Restigouche , engaged three minesweepers on 12 August , without sinking any . The ship was sent to Canada for a lengthy refit later in the month . After working up in Bermuda , she arrived at Halifax on 14 February 1945 and began escorting local convoys . This lasted until the end of the war in May , after which the ship was used to transfer returning troops from Newfoundland to mainland Canada until she was paid off on 5 October . Restigouche was sold for scrap in 1946 . 
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- = = Ship 's bell = = 
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- The <unk> Bells Project at Canadian Forces Base <unk> Naval and Military Museum includes information about the baptism of babies in the ship 's bell . The bell is currently held by the Royal Canadian Legion , <unk> , British Columbia . 
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- = = Trans @-@ Atlantic convoys escorted = = 
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- = History of artificial intelligence = 
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- The history of artificial intelligence ( AI ) began in antiquity , with myths , stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen ; as Pamela McCorduck writes , AI began with " an ancient wish to forge the gods . " 
- The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols . This work culminated in the invention of the programmable digital computer in the 1940s , a machine based on the abstract essence of mathematical reasoning . This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain . 
- The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence , which opens with the words : " I propose to consider the question , ' Can machines think ? ' " The term ' Artificial Intelligence ' was created at a conference held at Dartmouth College in 1956 . Allen Newell , J. C. Shaw , and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine ( 1956 ) , and the General <unk> <unk> in 1957 . In 1958 , John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $ 50 @,@ 000 . John McCarthy also created <unk> in the summer of 1958 , a programming language still important in artificial intelligence research . 
- In 1973 , in response to the criticism of James <unk> and ongoing pressure from <unk> , the U.S. and British Governments stopped funding undirected research into artificial intelligence . Seven years later , a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars , but by the late 80s the investors became disillusioned and withdrew funding again . 
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- = = <unk> = = 
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- McCorduck ( 2004 ) writes " artificial intelligence in one form or another is an idea that has <unk> Western intellectual history , a dream in urgent need of being realized , " expressed in humanity 's myths , legends , stories , speculation and clockwork automatons . 
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- = = = AI in myth , fiction and speculation = = = 
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- Mechanical men and artificial beings appear in Greek myths , such as the golden robots of <unk> and <unk> 's <unk> . In the Middle Ages , there were rumors of secret mystical or <unk> means of placing mind into matter , such as <unk> ibn <unk> 's <unk> , <unk> ' <unk> and Rabbi Judah <unk> 's <unk> . By the 19th century , ideas about artificial men and thinking machines were developed in fiction , as in Mary Shelley 's <unk> or Karel <unk> 's <unk> ( <unk> 's Universal <unk> ) , and speculation , such as Samuel Butler 's " Darwin among the Machines . " AI has continued to be an important element of science fiction into the present . 
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- = = = <unk> = = = 
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- <unk> <unk> automatons were built by craftsman from every civilization , including Yan Shi , Hero of Alexandria , Al @-@ <unk> and Wolfgang von <unk> . The oldest known automatons were the sacred statues of ancient Egypt and Greece . The faithful believed that craftsman had <unk> these figures with very real minds , capable of wisdom and emotion — Hermes Trismegistus wrote that " by discovering the true nature of the gods , man has been able to reproduce it . " 
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- = = = Formal reasoning = = = 
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- Artificial intelligence is based on the assumption that the process of human thought can be mechanized . The study of mechanical — or " formal " — reasoning has a long history . Chinese , Indian and Greek philosophers all developed structured methods of formal deduction in the first millennium BCE . Their ideas were developed over the centuries by philosophers such as <unk> ( who gave a formal analysis of the <unk> ) , Euclid ( whose Elements was a model of formal reasoning ) , Muslim mathematician al @-@ <unk> ( who developed algebra and gave his name to " algorithm " ) and European scholastic philosophers such as William of <unk> and Duns Scotus . 
- <unk> philosopher Ramon Llull ( <unk> – <unk> ) developed several logical machines devoted to the production of knowledge by logical means ; Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations , produced by the machine by mechanical meanings , in such ways as to produce all the possible knowledge . Llull 's work had a great influence on Gottfried Leibniz , who redeveloped his ideas . 
- In the 17th century , Leibniz , Thomas <unk> and René <unk> explored the possibility that all rational thought could be made as systematic as algebra or geometry . <unk> famously wrote in Leviathan : " reason is nothing but reckoning " . Leibniz envisioned a universal language of reasoning ( his <unk> <unk> ) which would reduce <unk> to calculation , so that " there would be no more need of <unk> between two philosophers than between two accountants . For it would <unk> to take their pencils in hand , down to their slates , and to say each other ( with a friend as witness , if they liked ) : Let us calculate . " These philosophers had begun to articulate the physical symbol system hypothesis that would become the guiding faith of AI research . 
- In the 20th century , the study of mathematical logic provided the essential breakthrough that made artificial intelligence seem plausible . The foundations had been set by such works as <unk> 's The Laws of Thought and <unk> 's <unk> . Building on <unk> 's system , Russell and Whitehead presented a formal treatment of the foundations of mathematics in their masterpiece , the <unk> <unk> in 1913 . Inspired by Russell 's success , David <unk> challenged mathematicians of the 1920s and 30s to answer this fundamental question : " can all of mathematical reasoning be formalized ? " His question was answered by <unk> 's incompleteness proof , Turing 's machine and Church 's <unk> <unk> . Their answer was surprising in two ways . 
- First , they proved that there were , in fact , limits to what mathematical logic could accomplish . But second ( and more important for AI ) their work suggested that , within these limits , any form of mathematical reasoning could be mechanized . The Church @-@ Turing thesis implied that a mechanical device , <unk> symbols as simple as 0 and 1 , could imitate any <unk> process of mathematical deduction . The key insight was the Turing machine — a simple theoretical construct that captured the essence of abstract symbol manipulation . This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines . 
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- = = = Computer science = = = 
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- <unk> machines were built in antiquity and improved throughout history by many mathematicians , including ( once again ) philosopher Gottfried Leibniz . In the early 19th century , Charles <unk> designed a programmable computer ( the Analytical Engine ) , although it was never built . <unk> <unk> speculated that the machine " might compose elaborate and scientific pieces of music of any degree of complexity or extent " . ( She is often credited as the first programmer because of a set of notes she wrote that completely detail a method for <unk> <unk> numbers with the Engine . ) 
- The first modern computers were the massive code breaking machines of the Second World War ( such as <unk> , <unk> and <unk> ) . The latter two of these machines were based on the theoretical foundation laid by Alan Turing and developed by John von Neumann . 
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- = = The birth of artificial intelligence 1952 – 1956 = = 
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- In the 1940s and 50s , a handful of scientists from a variety of fields ( mathematics , psychology , engineering , economics and political science ) began to discuss the possibility of creating an artificial brain . The field of artificial intelligence research was founded as an academic discipline in 1956 . 
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- = = = <unk> and early neural networks = = = 
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- The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s , 40s and early 50s . Recent research in <unk> had shown that the brain was an electrical network of neurons that fired in all @-@ or @-@ nothing pulses . Norbert <unk> 's <unk> described control and stability in electrical networks . Claude Shannon 's information theory described digital signals ( i.e. , all @-@ or @-@ nothing signals ) . Alan Turing 's theory of computation showed that any form of computation could be described digitally . The close relationship between these ideas suggested that it might be possible to construct an electronic brain . 
- Examples of work in this vein includes robots such as W. Grey Walter 's <unk> and the Johns Hopkins Beast . These machines did not use computers , digital electronics or symbolic reasoning ; they were controlled entirely by analog <unk> . 
- Walter <unk> and Warren <unk> analyzed networks of <unk> artificial neurons and showed how they might perform simple logical functions . They were the first to describe what later researchers would call a neural network . One of the students inspired by <unk> and <unk> was a young Marvin Minsky , then a 24 @-@ year @-@ old graduate student . In 1951 ( with Dean Edmonds ) he built the first neural net machine , the <unk> . Minsky was to become one of the most important leaders and <unk> in AI for the next 50 years . 
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- = = = Turing 's test = = = 
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- In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think . He noted that " thinking " is difficult to define and devised his famous Turing Test . If a machine could carry on a conversation ( over a <unk> ) that was indistinguishable from a conversation with a human being , then it was reasonable to say that the machine was " thinking " . This simplified version of the problem allowed Turing to argue convincingly that a " thinking machine " was at least plausible and the paper answered all the most common objections to the proposition . The Turing Test was the first serious proposal in the philosophy of artificial intelligence . 
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- = = = Game AI = = = 
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- In 1951 , using the Ferranti Mark 1 machine of the University of Manchester , Christopher Strachey wrote a checkers program and <unk> <unk> wrote one for chess . Arthur Samuel 's checkers program , developed in the middle 50s and early 60s , eventually achieved sufficient skill to challenge a respectable amateur . Game AI would continue to be used as a measure of progress in AI throughout its history . 
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- = = = <unk> reasoning and the Logic Theorist = = = 
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- When access to digital computers became possible in the middle fifties , a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought . This was a new approach to creating thinking machines . 
- In 1955 , Allen Newell and ( future Nobel Laureate ) Herbert A. Simon created the " Logic Theorist " ( with help from J. C. Shaw ) . The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead 's <unk> <unk> , and find new and more elegant proofs for some . Simon said that they had " solved the venerable mind / body problem , explaining how a system composed of matter can have the properties of mind . " ( This was an early statement of the philosophical position John Searle would later call " Strong AI " : that machines can contain minds just as human bodies do . ) 
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- = = = Dartmouth Conference 1956 : the birth of AI = = = 
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- The Dartmouth Conference of 1956 was organized by Marvin Minsky , John McCarthy and two senior scientists : Claude Shannon and Nathan Rochester of IBM . The proposal for the conference included this assertion : " every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it " . The participants included Ray <unk> , Oliver <unk> , Trenchard More , Arthur Samuel , Allen Newell and Herbert A. Simon , all of whom would create important programs during the first decades of AI research . At the conference Newell and Simon debuted the " Logic Theorist " and McCarthy persuaded the attendees to accept " Artificial Intelligence " as the name of the field . The 1956 Dartmouth conference was the moment that AI gained its name , its mission , its first success and its major players , and is widely considered the birth of AI . 
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- = = The golden years 1956 – 1974 = = 
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- The years after the Dartmouth conference were an era of discovery , of sprinting across new ground . The programs that were developed during this time were , to most people , simply " astonishing " : computers were solving algebra word problems , proving theorems in geometry and learning to speak English . Few at the time would have believed that such " intelligent " behavior by machines was possible at all . Researchers expressed an intense optimism in private and in print , predicting that a fully intelligent machine would be built in less than 20 years . Government agencies like <unk> poured money into the new field . 
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- = = = The work = = = 
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- There were many successful programs and new directions in the late 50s and 1960s . Among the most influential were these : 
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- = = = = <unk> as search = = = = 
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- Many early AI programs used the same basic algorithm . To achieve some goal ( like winning a game or proving a theorem ) , they proceeded step by step towards it ( by making a move or a deduction ) as if searching through a maze , <unk> whenever they reached a dead end . This paradigm was called " reasoning as search " . 
- The principal difficulty was that , for many problems , the number of possible paths through the " maze " was simply astronomical ( a situation known as a " combinatorial explosion " ) . Researchers would reduce the search space by using <unk> or " rules of thumb " that would eliminate those paths that were unlikely to lead to a solution . 
- Newell and Simon tried to capture a general version of this algorithm in a program called the " General <unk> <unk> " . Other " searching " programs were able to accomplish impressive tasks like solving problems in geometry and algebra , such as Herbert <unk> 's <unk> <unk> <unk> ( 1958 ) and <unk> , written by Minsky 's student James <unk> ( 1961 ) . Other programs searched through goals and <unk> to plan actions , like the <unk> system developed at Stanford to control the behavior of their robot Shakey . 
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- = = = = Natural language = = = = 
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- An important goal of AI research is to allow computers to communicate in natural languages like English . An early success was Daniel <unk> 's program <unk> , which could solve high school algebra word problems . 
- A semantic net represents concepts ( e.g. " house " , " door " ) as nodes and relations among concepts ( e.g. " has @-@ a " ) as links between the nodes . The first AI program to use a semantic net was written by Ross <unk> and the most successful ( and controversial ) version was Roger Schank 's <unk> dependency theory . 
- Joseph Weizenbaum 's ELIZA could carry out conversations that were so realistic that users occasionally were <unk> into thinking they were communicating with a human being and not a program . But in fact , ELIZA had no idea what she was talking about . She simply gave a canned response or repeated back what was said to her , <unk> her response with a few grammar rules . ELIZA was the first <unk> . 
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- = = = = <unk> @-@ worlds = = = = 
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- In the late 60s , Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro @-@ worlds . They pointed out that in successful sciences like physics , basic principles were often best understood using simplified models like <unk> planes or perfectly rigid bodies . Much of the research focused on a " blocks world , " which consists of colored blocks of various shapes and sizes <unk> on a flat surface . 
- This paradigm led to innovative work in machine vision by Gerald Sussman ( who led the team ) , <unk> <unk> , David <unk> ( who invented " <unk> <unk> " ) , and especially Patrick Winston . At the same time , Minsky and Papert built a robot arm that could stack blocks , bringing the blocks world to life . The crowning achievement of the micro @-@ world program was Terry <unk> 's <unk> . It could communicate in ordinary English sentences , plan operations and execute them . 
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- = = = The optimism = = = 
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- The first generation of AI researchers made these predictions about their work : 
- 1958 , H. A. Simon and Allen Newell : " within ten years a digital computer will be the world 's chess champion " and " within ten years a digital computer will discover and prove an important new mathematical theorem . " 
- 1965 , H. A. Simon : " machines will be capable , within twenty years , of doing any work a man can do . " 
- 1967 , Marvin Minsky : " Within a generation ... the problem of creating ' artificial intelligence ' will substantially be solved . " 
- 1970 , Marvin Minsky ( in Life Magazine ) : " In from three to eight years we will have a machine with the general intelligence of an average human being . " 
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- = = = The money = = = 
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- In June 1963 , MIT received a $ 2 @.@ 2 million grant from the newly created Advanced Research <unk> Agency ( later known as DARPA ) . The money was used to fund project <unk> which subsumed the " AI Group " founded by Minsky and McCarthy five years earlier . DARPA continued to provide three million dollars a year until the 70s . DARPA made similar grants to Newell and Simon 's program at CMU and to the Stanford AI Project ( founded by John McCarthy in 1963 ) . Another important AI laboratory was established at Edinburgh University by Donald <unk> in 1965 . These four institutions would continue to be the main centers of AI research ( and funding ) in academia for many years . 
- The money was <unk> with few strings attached : J. C. R. <unk> , then the director of <unk> , believed that his organization should " fund people , not projects ! " and allowed researchers to pursue whatever directions might interest them . This created a <unk> atmosphere at MIT that gave birth to the hacker culture , but this " hands off " approach would not last . 
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- = = The first AI winter 1974 – 1980 = = 
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- In the 70s , AI was subject to critiques and financial setbacks . AI researchers had failed to appreciate the difficulty of the problems they faced . Their tremendous optimism had raised expectations <unk> high , and when the promised results failed to materialize , funding for AI disappeared . At the same time , the field of connectionism ( or neural nets ) was shut down almost completely for 10 years by Marvin Minsky 's devastating criticism of <unk> . Despite the difficulties with public perception of AI in the late 70s , new ideas were explored in logic programming , commonsense reasoning and many other areas . 
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- = = = The problems = = = 
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- In the early <unk> , the capabilities of AI programs were limited . Even the most impressive could only handle trivial versions of the problems they were supposed to solve ; all the programs were , in some sense , " toys " . AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s . Although some of these limits would be conquered in later decades , others still <unk> the field to this day . 
- Limited computer power : There was not enough memory or processing speed to accomplish anything truly useful . For example , Ross <unk> 's successful work on natural language was demonstrated with a vocabulary of only twenty words , because that was all that would fit in memory . Hans Moravec argued in 1976 that computers were still millions of times too weak to exhibit intelligence . He suggested an analogy : artificial intelligence requires computer power in the same way that aircraft require horsepower . Below a certain threshold , it 's impossible , but , as power increases , eventually it could become easy . With regard to computer vision , Moravec estimated that simply matching the edge and motion detection capabilities of human <unk> in real time would require a general @-@ purpose computer capable of 109 operations / second ( 1000 MIPS ) . As of 2011 , practical computer vision applications require 10 @,@ 000 to 1 @,@ 000 @,@ 000 MIPS . By comparison , the fastest supercomputer in 1976 , <unk> @-@ 1 ( <unk> at $ 5 million to $ 8 million ) , was only capable of around 80 to 130 MIPS , and a typical desktop computer at the time achieved less than 1 MIPS . 
- <unk> and the combinatorial explosion . In 1972 Richard <unk> ( building on Stephen Cook 's 1971 theorem ) showed there are many problems that can probably only be solved in exponential time ( in the size of the inputs ) . Finding optimal solutions to these problems requires <unk> amounts of computer time except when the problems are trivial . This almost certainly meant that many of the " toy " solutions used by AI would probably never scale up into useful systems . 
- <unk> knowledge and reasoning . Many important artificial intelligence applications like vision or natural language require simply enormous amounts of information about the world : the program needs to have some idea of what it might be looking at or what it is talking about . This requires that the program know most of the same things about the world that a child does . Researchers soon discovered that this was a truly vast amount of information . No one in 1970 could build a database so large and no one knew how a program might learn so much information . 
- Moravec 's paradox : <unk> theorems and solving geometry problems is comparatively easy for computers , but a supposedly simple task like recognizing a face or crossing a room without <unk> into anything is extremely difficult . This helps explain why research into vision and robotics had made so little progress by the middle 1970s . 
- The frame and qualification problems . AI researchers ( like John McCarthy ) who used logic discovered that they could not represent ordinary <unk> that involved planning or default reasoning without making changes to the structure of logic itself . They developed new logics ( like non @-@ <unk> logics and modal logics ) to try to solve the problems . 
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- = = = The end of funding = = = 
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- The agencies which funded AI research ( such as the British government , DARPA and NRC ) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI . The pattern began as early as 1966 when the <unk> report appeared criticizing machine translation efforts . After spending 20 million dollars , the NRC ended all support . In 1973 , the <unk> report on the state of AI research in England criticized the utter failure of AI to achieve its " grandiose objectives " and led to the <unk> of AI research in that country . ( The report specifically mentioned the combinatorial explosion problem as a reason for AI 's failings . ) DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars . By 1974 , funding for AI projects was hard to find . 
- Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues . " Many researchers were caught up in a web of increasing exaggeration . " However , there was another issue : since the passage of the Mansfield Amendment in 1969 , DARPA had been under increasing pressure to fund " mission @-@ oriented direct research , rather than basic undirected research " . Funding for the creative , <unk> exploration that had gone on in the 60s would not come from DARPA . Instead , the money was directed at specific projects with clear objectives , such as autonomous tanks and battle management systems . 
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- = = = <unk> from across campus = = = 
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- Several philosophers had strong objections to the claims being made by AI researchers . One of the earliest was John Lucas , who argued that <unk> 's incompleteness theorem showed that a formal system ( such as a computer program ) could never see the truth of certain statements , while a human being could . Hubert Dreyfus ridiculed the broken promises of the 60s and critiqued the assumptions of AI , arguing that human reasoning actually involved very little " symbol processing " and a great deal of embodied , <unk> , unconscious " know how " . John Searle 's Chinese Room argument , presented in 1980 , attempted to show that a program could not be said to " understand " the symbols that it uses ( a quality called " <unk> " ) . If the symbols have no meaning for the machine , Searle argued , then the machine can not be described as " thinking " . 
- These critiques were not taken seriously by AI researchers , often because they seemed so far off the point . Problems like <unk> and commonsense knowledge seemed much more immediate and serious . It was unclear what difference " know how " or " <unk> " made to an actual computer program . Minsky said of Dreyfus and Searle " they <unk> , and should be ignored . " Dreyfus , who taught at MIT , was given a cold shoulder : he later said that AI researchers " <unk> not be seen having lunch with me . " Joseph Weizenbaum , the author of ELIZA , felt his colleagues ' treatment of Dreyfus was <unk> and childish . Although he was an outspoken critic of Dreyfus ' positions , he " deliberately made it plain that theirs was not the way to treat a human being . " 
- Weizenbaum began to have serious ethical doubts about AI when Kenneth Colby wrote <unk> , a <unk> therapist . Weizenbaum was disturbed that Colby saw his mindless program as a serious therapeutic tool . A feud began , and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program . In 1976 , Weizenbaum published Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to <unk> human life . 
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- = = = <unk> and the dark age of connectionism = = = 
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- A <unk> was a form of neural network introduced in 1958 by Frank Rosenblatt , who had been a <unk> of Marvin Minsky at the Bronx High School of Science . Like most AI researchers , he was optimistic about their power , predicting that " <unk> may eventually be able to learn , make decisions , and translate languages . " An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert 's 1969 book <unk> . It suggested that there were severe limitations to what <unk> could do and that Frank Rosenblatt 's predictions had been grossly exaggerated . The effect of the book was devastating : virtually no research at all was done in connectionism for 10 years . Eventually , a new generation of researchers would revive the field and thereafter it would become a vital and useful part of artificial intelligence . Rosenblatt would not live to see this , as he died in a boating accident shortly after the book was published . 
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- = = = The neats : logic and symbolic reasoning = = = 
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- Logic was introduced into AI research as early as 1958 , by John McCarthy in his <unk> <unk> proposal . In 1963 , J. Alan Robinson had discovered a simple method to implement deduction on computers , the resolution and unification algorithm . However , straightforward <unk> , like those attempted by McCarthy and his students in the late 60s , were especially <unk> : the programs required astronomical numbers of steps to prove simple theorems . A more fruitful approach to logic was developed in the 1970s by Robert Kowalski at the University of Edinburgh , and soon this led to the collaboration with French researchers Alain <unk> and Philippe <unk> who created the successful logic programming language Prolog . Prolog uses a subset of logic ( Horn clauses , closely related to " rules " and " production rules " ) that permit <unk> computation . Rules would continue to be influential , providing a foundation for Edward Feigenbaum 's expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to <unk> and their unified theories of cognition . 
- Critics of the logical approach noted , as Dreyfus had , that human beings rarely used logic when they solved problems . Experiments by <unk> like Peter <unk> , Eleanor <unk> , Amos <unk> , Daniel <unk> and others provided proof . McCarthy responded that what people do is irrelevant . He argued that what is really needed are machines that can solve problems — not machines that think as people do . 
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- = = = The <unk> : frames and scripts = = = 
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- Among the critics of McCarthy 's approach were his colleagues across the country at MIT . Marvin Minsky , Seymour Papert and Roger Schank were trying to solve problems like " story understanding " and " object recognition " that required a machine to think like a person . In order to use ordinary concepts like " chair " or " restaurant " they had to make all the same <unk> assumptions that people normally made . Unfortunately , <unk> concepts like these are hard to represent in logic . Gerald Sussman observed that " using precise language to describe essentially <unk> concepts doesn 't make them any more precise . " Schank described their " anti @-@ logic " approaches as " <unk> " , as opposed to the " <unk> " <unk> used by McCarthy , Kowalski , Feigenbaum , Newell and Simon . 
- In 1975 , in a seminal paper , Minsky noted that many of his fellow " <unk> " researchers were using the same kind of tool : a framework that captures all our common sense assumptions about something . For example , if we use the concept of a bird , there is a constellation of facts that immediately come to mind : we might assume that it flies , eats worms and so on . We know these facts are not always true and that <unk> using these facts will not be " logical " , but these structured sets of assumptions are part of the context of everything we say and think . He called these structures " frames " . Schank used a version of frames he called " scripts " to successfully answer questions about short stories in English . Many years later object @-@ oriented programming would adopt the essential idea of " inheritance " from AI research on frames . 
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- = = Boom 1980 – 1987 = = 
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- In the 1980s a form of AI program called " expert systems " was adopted by corporations around the world and knowledge became the focus of mainstream AI research . In those same years , the Japanese government aggressively funded AI with its fifth generation computer project . Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart . Once again , AI had achieved success . 
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- = = = The rise of expert systems = = = 
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- An expert system is a program that answers questions or solves problems about a specific domain of knowledge , using logical rules that are derived from the knowledge of experts . The earliest examples were developed by Edward Feigenbaum and his students . <unk> , begun in 1965 , identified compounds from <unk> readings . <unk> , developed in 1972 , diagnosed infectious blood diseases . They demonstrated the feasibility of the approach . 
- Expert systems restricted themselves to a small domain of specific knowledge ( thus avoiding the commonsense knowledge problem ) and their simple design made it relatively easy for programs to be built and then modified once they were in place . All in all , the programs proved to be useful : something that AI had not been able to achieve up to this point . 
- In 1980 , an expert system called <unk> was completed at CMU for the Digital Equipment Corporation . It was an enormous success : it was saving the company 40 million dollars annually by 1986 . Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI , most of it to in @-@ house AI departments . An industry grew up to support them , including hardware companies like <unk> and <unk> Machines and software companies such as <unk> and <unk> . 
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- = = = The knowledge revolution = = = 
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- The power of expert systems came from the expert knowledge they contained . They were part of a new direction in AI research that had been gaining ground throughout the 70s . " AI researchers were beginning to suspect — <unk> , for it violated the scientific canon of <unk> — that intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways , " writes Pamela McCorduck . " [ T ] he great lesson from the 1970s was that intelligent behavior depended very much on dealing with knowledge , sometimes quite detailed knowledge , of a domain where a given task lay " . Knowledge based systems and knowledge engineering became a major focus of AI research in the 1980s . 
- The 1980s also saw the birth of <unk> , the first attempt to attack the commonsense knowledge problem directly , by creating a massive database that would contain all the mundane facts that the average person knows . Douglas <unk> , who started and led the project , argued that there is no <unk> <unk> the only way for machines to know the meaning of human concepts is to teach them , one concept at a time , by hand . The project was not expected to be completed for many decades . 
- Chess playing programs <unk> and Deep Thought defeated chess masters in 1989 . Both were developed by Carnegie <unk> University ; Deep Thought development paved the way for the Deep Blue . 
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- = = = The money returns : the fifth generation project = = = 
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- In 1981 , the Japanese Ministry of International Trade and Industry set aside $ 850 million for the Fifth generation computer project . Their objectives were to write programs and build machines that could carry on conversations , translate languages , interpret pictures , and reason like human beings . Much to the <unk> of <unk> , they chose Prolog as the primary computer language for the project . 
- Other countries responded with new programs of their own . The UK began the ₤ 350 million <unk> project . A consortium of American companies formed the <unk> and Computer Technology Corporation ( or " MCC " ) to fund large scale projects in AI and information technology . DARPA responded as well , founding the Strategic Computing Initiative and <unk> its investment in AI between 1984 and 1988 . 
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- = = = The revival of connectionism = = = 
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- In 1982 , physicist John Hopfield was able to prove that a form of neural network ( now called a " Hopfield net " ) could learn and process information in a completely new way . Around the same time , David Rumelhart popularized a new method for training neural networks called " <unk> " ( discovered years earlier by Paul <unk> ) . These two discoveries revived the field of connectionism which had been largely abandoned since 1970 . 
- The new field was unified and inspired by the appearance of <unk> <unk> <unk> in 1986 — a two volume collection of papers edited by Rumelhart and psychologist James <unk> . <unk> networks would become commercially successful in the 1990s , when they began to be used as the engines driving programs like optical character recognition and speech recognition . 
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- = = <unk> : the second AI winter 1987 – 1993 = = 
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- The business community 's fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble . The collapse was in the perception of AI by government agencies and investors – the field continued to make advances despite the criticism . Rodney Brooks and Hans Moravec , researchers from the related field of robotics , argued for an entirely new approach to artificial intelligence . 
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- = = = AI winter = = = 
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- The term " AI winter " was coined by researchers who had survived the funding cuts of 1974 when they became concerned that enthusiasm for expert systems had <unk> out of control and that disappointment would certainly follow . Their fears were well founded : in the late 80s and early 90s , AI suffered a series of financial setbacks . 
- The first indication of a change in weather was the sudden collapse of the market for specialized AI hardware in 1987 . Desktop computers from Apple and IBM had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive <unk> machines made by <unk> and others . There was no longer a good reason to buy them . An entire industry worth half a billion dollars was demolished overnight . 
- Eventually the earliest successful expert systems , such as <unk> , proved too expensive to maintain . They were difficult to update , they could not learn , they were " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) that had been identified years earlier . Expert systems proved useful , but only in a few special contexts . 
- In the late 80s , the Strategic Computing Initiative cut funding to AI " deeply and brutally . " New leadership at DARPA had decided that AI was not " the next wave " and directed funds towards projects that seemed more likely to produce immediate results . 
- By 1991 , the impressive list of goals penned in 1981 for Japan 's Fifth Generation Project had not been met . Indeed , some of them , like " carry on a casual conversation " had not been met by 2010 . As with other AI projects , expectations had run much higher than what was actually possible . 
- 
- = = = The importance of having a body : <unk> AI and embodied reason = = = 
- 
- In the late 80s , several researchers advocated a completely new approach to artificial intelligence , based on robotics . They believed that , to show real intelligence , a machine needs to have a body — it needs to perceive , move , survive and deal with the world . They argued that these <unk> skills are essential to higher level skills like commonsense reasoning and that abstract reasoning was actually the least interesting or important human skill ( see Moravec 's paradox ) . They advocated building intelligence " from the bottom up . " 
- The approach revived ideas from <unk> and control theory that had been unpopular since the sixties . Another precursor was David <unk> , who had come to MIT in the late 70s from a successful background in theoretical <unk> to lead the group studying vision . He rejected all symbolic approaches ( both McCarthy 's logic and Minsky 's frames ) , arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place . ( <unk> 's work would be cut short by leukemia in 1980 . ) 
- In a 1990 paper , " <unk> Don 't Play Chess , " robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis , arguing that symbols are not always necessary since " the world is its own best model . It is always exactly up to date . It always has every detail there is to be known . The trick is to sense it appropriately and often enough . " In the 80s and 90s , many cognitive scientists also rejected the symbol processing model of the mind and argued that the body was essential for reasoning , a theory called the embodied mind thesis . 
- 
- = = AI 1993 – present = = 
- 
- The field of AI , now more than a half a century old , finally achieved some of its oldest goals . It began to be used successfully throughout the technology industry , although somewhat behind the scenes . Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability . Still , the reputation of AI , in the business world at least , was less than pristine . Inside the field there was little agreement on the reasons for AI 's failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s . Together , all these factors helped to fragment AI into competing <unk> focused on particular problems or approaches , sometimes even under new names that disguised the <unk> pedigree of " artificial intelligence " . AI was both more cautious and more successful than it had ever been . 
- 
- = = = <unk> and Moore 's Law = = = 
- 
- On 11 May 1997 , Deep Blue became the first computer chess @-@ playing system to beat a reigning world chess champion , Garry Kasparov . The super computer was a specialized version of a framework produced by IBM , and was capable of processing twice as many moves per second as it had during the first match ( which Deep Blue had lost ) , reportedly 200 @,@ 000 @,@ 000 moves per second . The event was broadcast live over the internet and received over 74 million hits . 
- In 2005 , a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an <unk> desert trail . Two years later , a team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic <unk> and all traffic laws . In February 2011 , in a <unk> ! quiz show exhibition match , IBM 's question answering system , Watson , defeated the two greatest <unk> ! champions , Brad <unk> and Ken Jennings , by a significant margin . 
- These successes were not due to some revolutionary new paradigm , but mostly on the tedious application of engineering skill and on the tremendous power of computers today . In fact , Deep Blue 's computer was 10 million times faster than the Ferranti Mark 1 that Christopher Strachey taught to play chess in 1951 . This dramatic increase is measured by Moore 's law , which predicts that the speed and memory capacity of computers doubles every two years . The fundamental problem of " raw computer power " was slowly being overcome . 
- 
- = = = <unk> agents = = = 
- 
- A new paradigm called " intelligent agents " became widely accepted during the 90s . Although earlier researchers had proposed modular " divide and conquer " approaches to AI , the intelligent agent did not reach its modern form until Judea Pearl , Allen Newell and others brought concepts from decision theory and economics into the study of AI . When the economist 's definition of a rational agent was married to computer science 's definition of an object or <unk> , the intelligent agent paradigm was complete . 
- An intelligent agent is a system that perceives its environment and takes actions which <unk> its chances of success . By this definition , simple programs that solve specific problems are " intelligent agents " , as are human beings and organizations of human beings , such as firms . The intelligent agent paradigm defines AI research as " the study of intelligent agents " . This is a <unk> of some earlier definitions of AI : it goes beyond studying human intelligence ; it studies all kinds of intelligence . 
- The paradigm gave researchers license to study isolated problems and find solutions that were both <unk> and useful . It provided a common language to describe problems and share their solutions with each other , and with other fields that also used concepts of abstract agents , like economics and control theory . It was hoped that a complete agent architecture ( like Newell 's <unk> ) would one day allow researchers to build more versatile and intelligent systems out of interacting intelligent agents . 
- 
- = = = " Victory of the neats " = = = 
- 
- AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past . There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like mathematics , economics or operations research . The shared mathematical language allowed both a higher level of collaboration with more established and successful fields and the achievement of results which were measurable and <unk> ; AI had become a more <unk> " scientific " discipline . Russell & <unk> ( 2003 ) describe this as nothing less than a " revolution " and " the victory of the neats " . 
- Judea Pearl 's highly influential 1988 book brought probability and decision theory into AI . Among the many new tools in use were Bayesian networks , hidden <unk> models , information theory , <unk> modeling and classical <unk> . <unk> mathematical descriptions were also developed for " computational intelligence " <unk> like neural networks and evolutionary algorithms . 
- 
- = = = AI behind the scenes = = = 
- 
- <unk> originally developed by AI researchers began to appear as parts of larger systems . AI had solved a lot of very difficult problems and their solutions proved to be useful throughout the technology industry , such as data mining , industrial robotics , logistics , speech recognition , banking software , medical diagnosis and Google 's search engine . 
- The field of AI receives little or no credit for these successes . Many of AI 's greatest innovations have been reduced to the status of just another item in the tool chest of computer science . Nick <unk> explains " A lot of cutting edge AI has filtered into general applications , often without being called AI because once something becomes useful enough and common enough it 's not labeled AI anymore . " 
- Many researchers in AI in 1990s deliberately called their work by other names , such as <unk> , knowledge @-@ based systems , cognitive systems or computational intelligence . In part , this may be because they considered their field to be fundamentally different from AI , but also the new names help to procure funding . In the commercial world at least , the failed promises of the AI Winter continue to haunt AI research , as the New York Times reported in 2005 : " Computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild @-@ eyed <unk> . " 
- 
- = = = Where is HAL 9000 ? = = = 
- 
- In 1968 , Arthur C. Clarke and Stanley Kubrick had imagined that by the year 2001 , a machine would exist with an intelligence that matched or exceeded the capability of human beings . The character they created , HAL 9000 , was based on a belief shared by many leading AI researchers that such a machine would exist by the year 2001 . 
- Marvin Minsky asks " So the question is why didn 't we get HAL in 2001 ? " Minsky believes that the answer is that the central problems , like commonsense reasoning , were being neglected , while most researchers pursued things like commercial applications of neural nets or genetic algorithms . John McCarthy , on the other hand , still blames the qualification problem . For Ray <unk> , the issue is computer power and , using Moore 's Law , he predicts that machines with human @-@ level intelligence will appear by <unk> . Jeff Hawkins argues that neural net research ignores the essential properties of the human cortex , preferring simple models that have been successful at solving simple problems . There are many other explanations and for each there is a corresponding research program underway . 
- 
- 
- = Cyclone Graham = 
- 
- Cyclone Graham of the 2002 – 03 Australian region cyclone season was a weak tropical storm that affected Australia during late February and early March 2003 . Graham originated from an area of convection that emerged onto water after sitting over Australia on 23 February . The interaction with a monsoon trough formed an area of low pressure that developed into Tropical Cyclone Graham on 27 February . The storm moved slowly to the east @-@ southeast , and after turning to the south it peaked as a tropical storm and made landfall on Western Australia the next day . The cyclone weakened as it moved inland , and dissipated on 1 March . The storm dropped heavy rainfall and caused high winds , which produced flooding and downed trees . One fatality occurred , though no significant damages were reported . 
- 
- = = Meteorological history = = 
- 
- On 23 February 2003 , an area of convection that was situated over land for roughly a week emerged over open waters along the northern coast of Australia . The strengthening of a deep , persistent monsoon trough contributed to cyclogenesis , and a low pressure area formed . By 25 February , the low developed a banding feature in which the highest winds were located . Though the storm was located in an area of unfavorable wind shear , the Australian Bureau of Meteorology ( BoM ) began to issue gale warnings on the system at <unk> UTC the next day , while the low was located several hundred miles north @-@ northeast of Port <unk> . The disturbance was initially nearly stationary as it showed signs of organization due to relaxed shear , and at <unk> UTC on 27 February , the Joint Typhoon Warning Center ( JTWC ) designated the storm as Tropical Cyclone Graham , as it had attained 80 km / h ( 50 mph ) 10 @-@ minute maximum sustained winds . The first warning was issued on Graham later that day . 
- Initially exhibiting characteristics of a <unk> low , a mid @-@ level ridge to the south of Graham caused strong <unk> winds that moved the storm slowly east @-@ southeastward . However , a deep trough eroded the ridge , allowing the cyclone to move more towards the south . According to the JTWC , the storm had intensified late on 28 February , though at the same time the BoM noted the slight weakening of the storm . Graham reached its peak intensity that day while nearing the coast . 
- The storm made landfall at Western Australia 's <unk> Mile Beach at 1400 UTC on 28 February , and began to weaken . The storm had dissipated on 1 March ; the BoM issued their last advisory on the cyclone at <unk> UTC that day , while similarly , the JTWC issued their last advisory just two hours later . The storm 's remnants died out in the country 's desert . 
- 
- = = Impact = = 
- 
- In advance of the cyclone , the communities of <unk> , <unk> , <unk> and <unk> were put on alert . A warning was issued for <unk> , <unk> and Cotton Creek . The storm 's landfall in Western Australia brought heavy rainfall and high winds . The storm dropped 163 mm ( 6 @.@ 4 in ) of rain at <unk> in one night , over half the town 's annual average ; total rainfall reached 175 mm ( 6 @.@ 9 in ) there . The heavy rain caused flooding and road closures , and swelled a river passing through <unk> Crossing , though the river only topped its banks slightly . Near that town , at Blue Bush Creek , while a group of people attempted to cross <unk> , two men were swept away . Both men were rescued , though one died before emergency services arrived . In addition to the flooding , a number of trees were downed . No significant damages were reported . 
- Following the storm , the name Graham was retired from the Australian region basin . 
- 
- 
- = Languedoc @-@ Roussillon wine = 
- 
- Languedoc @-@ Roussillon wine , including the <unk> de pays labeled Vin de Pays d 'Oc , is produced in southern France . While " Languedoc " can refer to a specific historic region of France and Northern <unk> , usage since the 20th century ( especially in the context of wine ) has primarily referred to the northern part of the Languedoc @-@ Roussillon <unk> of France , an area which spans the Mediterranean coastline from the French border with Spain to the region of Provence . The area has around 700 @,@ 000 acres ( 2 @,@ 800 km2 ) under vines and is the single biggest wine @-@ producing region in the world , being responsible for more than a third of France 's total wine production . In 2001 , the region produced more wine than the United States . 
- 
- = = History = = 
- 
- The history of Languedoc wines can be traced to the first vineyards planted along the coast near <unk> by the early Greeks in the fifth century BC . Along with parts of Provence , these are the oldest planted vineyards in France . The region of Languedoc has belonged to France since the thirteenth century and the Roussillon was acquired from Spain in the mid @-@ seventeenth century . The two regions were joined as one administrative region in the late 1980s . 
- From the 4th century through the 18th and early 19th centuries , the Languedoc had a reputation for producing high quality wine . In Paris during the 14th century , wines from the St. Chinian area were prescribed in hospitals for their " healing powers " . During the advent of the Industrial Age in the late 19th century , production shifted towards mass @-@ produced le <unk> <unk> — cheap red wine that could satisfy the growing work force . The use of highly prolific grape varieties produced high yields and thin wines , which were normally blended with red wine from Algeria to give them more body . 
- The <unk> epidemic in the 19th century severely affected the Languedoc wine industry , killing off many of the higher quality <unk> <unk> that were susceptible to the <unk> . American <unk> that was naturally resistant to <unk> did not take well to the limestone soil on the hillside . In place of these vines , acres of the lower quality <unk> , Alicante <unk> and Carignan were planted . 
- During both World Wars the Languedoc was responsible for providing the daily wine rations given to French soldiers . In 1962 , Algeria gained its independence from France , bringing about an end to the blending of the stronger <unk> red wine to mask the thin le <unk> <unk> . This event , coupled with French consumers moving away from cheap red wines in the 1970s , has contributed to several decades of surplus wine production in France , with Languedoc as the largest contributor to the European " wine lake " and recurring European Union subsidies aimed at reducing production . These developments prompted many Languedoc producers to start <unk> on higher quality , but has also led to many local and regional protests , including violent ones from the infamous <unk> <unk> d <unk> <unk> ( <unk> ) . 
- Despite the general reputation as a mass producer and a consensus that the region is in the midst of an economic crisis , parts of the Languedoc wine industry are experiencing commercial success due to outside investment and an increased focus on quality . Sales have been improved by many vineyards that concentrate on creating a good brand name rather than relying on the sometimes infamous regional designations . Some vineyards have adopted the youngest batch of AOC classifications developed in the late 1990s , while other vineyards <unk> designated blends entirely and are instead shifting toward <unk> single <unk> wines , a practice increasingly demanded by consumers in the large New World wine market . 
- 
- = = Climate and geography = = 
- 
- The Languedoc @-@ Roussillon region shares many terrain and climate characteristics with the neighboring regions of Southern <unk> and Provence . The region stretches 150 miles ( 240 km ) from the Banyuls AOC at the Spanish border and Pyrenees in the west , along the coast of the Mediterranean Sea to the <unk> River and Provence in the east . The northern boundaries of the region sit on the <unk> Central with the <unk> mountain ranges and valleys dominating the area . Many vineyards are located along the <unk> River . 
- <unk> in the Languedoc are generally planted along the coastal plains of the Mediterranean while those in the Roussillon are to be found in the narrow valleys around the Pyrenees . The peak growing season ( between May and August ) is very dry and the majority of annual rainfall occurs during the winter . In the Languedoc , the plains area is the most arid and <unk> region of France . The region 's Mediterranean climate is very conducive to growing a large amount of a wide variety of grapes , with vintners in the area <unk> in mass production . The average annual temperature is 57 ° F ( 14 ° C ) . The <unk> inland wind from the northwest often <unk> the dry climate ; drought is the most common threat to vine production , with French AOC and European Union regulation prohibiting the use of irrigation . In December 2006 , the French government responded to global warming concerns and relaxed some of the irrigation regulations . 
- In 1999 severe weather had damaging effects on the wine producing industry , including <unk> in May that affected Roussillon and a rain surge in mid November that saw a year 's worth of rain fall in 36 hours in the areas of Corbières and Minervois in the western Languedoc . 
- The composition of soil in the Languedoc varies from the chalk , limestone and gravel based soils inland to more alluvial soils near the coast . Some of the more highly rated vineyards are laid on top of ancient riverbed stones similar to those of <unk> @-@ du @-@ <unk> . 
- 
- = = <unk> = = 
- 
- The five best known appellations in the Languedoc include Languedoc AOC ( formerly known as the <unk> du Languedoc ) , Corbières AOC , <unk> , Minervois AOC , and Saint @-@ Chinian <unk> . The vast majority of Languedoc wines are produced by wine <unk> which number more than 500 . However , the appellation system in the region is undergoing considerable changes with both new appellations being created and existing ones changing . One recent change is that the <unk> du Languedoc has changed name to Languedoc and been extended to include also the Roussillon . 
- Within the larger Languedoc AOC appellations are several sub @-@ districts , or <unk> 's , with distinct wine styles of their own . Some of these sub @-@ districts have pending AOC applications to become appellations in their own right and some have been granted sub @-@ appellations to the umbrella appellation Languedoc AOC . These include the <unk> , La <unk> , <unk> , St. <unk> , Picpoul de <unk> , <unk> du <unk> , and <unk> <unk> 
- The boundary of the eastern Languedoc with the Southern Rhône Valley wine region was moved slightly in 2004 , with the result that <unk> de Nîmes AOC is now a Rhône appellation rather than a Languedoc one . In that year , <unk> moved the responsibility for oversight of this appellation 's wine to the regional committee of the Rhône valley . Local producers of <unk> du Rhône @-@ styled wines made from Syrah and Grenache lobbied for this change since the local winemaking traditions did not coincide with administrative borders , and presumably due to the greater prestige of Rhône wines in the marketplace . Such changes of borders between wine regions are very rare , so out of habit , <unk> de Nîmes remains listed as a Languedoc wine in many publications . 
- 
- = = <unk> = = 
- 
- The Languedoc @-@ Roussillon area is home to numerous grape varieties , including many international varieties like <unk> , <unk> Sauvignon , Sauvignon blanc , and Chardonnay . The traditional Rhône grapes of <unk> , Grenache , Syrah , and <unk> are also prominent . 
- Chardonnay is a major white grape , used in the Vin de Pays d 'Oc and the sparkling <unk> de Limoux . Others include <unk> blanc and Mauzac , which is also the principal grape in the sparkling <unk> de Limoux . The sweet fortified wines of the Muscat de <unk> and Muscat de St @-@ Jean Minervois regions are made with the Muscat Blanc à Petits <unk> grapes . In the Muscat de <unk> AOC , fortified wines are made from Muscat of Alexandria grapes . 
- Among the reds , Grenache , Syrah , Carignan , <unk> , and <unk> are major grapes of the Corbières , <unk> , <unk> , and Minervois <unk> . <unk> is also commonly used in <unk> production along with <unk> <unk> , Picpoul noir , <unk> noir , and Grenache . Grenache is also the main grape used in the fortified wines of the Banyuls and <unk> region . Some of the oldest vines in France are Carignan grapes . <unk> often use carbonic <unk> to <unk> the <unk> . 
- Other varieties that can be found include <unk> , <unk> , <unk> , <unk> , <unk> <unk> , Grenache blanc , Grenache <unk> , Picpoul , <unk> , and <unk> . 
- 
- = = <unk> and taxonomy = = 
- 
- <unk> from the Languedoc can carry an enormous number of names , ranging from broad regional designations like Vin de Pays d 'Oc to very specific geographical classifications with restrictions on grape variety , like Corbières and Minervois . Since the 1990s , the <unk> has been creating smaller AOC classifications which take into account the intricate <unk> and soil variations in the Languedoc @-@ Roussillon . Younger appellations like the <unk> and <unk> like Minervois la <unk> , Corbières @-@ <unk> and St @-@ Chinian @-@ <unk> are much smaller in scope . While these new appellations have been praised for consistently improving their product , others have criticized the additions for further complicating an already esoteric system of classification . 
- The majority of wine produced in the Languedoc are labeled <unk> <unk> . There is also sizable production of Vins Doux Naturels . 
- 
- = = = Vins de Pays = = = 
- 
- The introduction of the <unk> de pays , a classification produced under less stringent regulations than those of an AOC , opened up the Languedoc wine industry to the labeling of <unk> wines and the blending of international varieties such as <unk> Sauvignon , <unk> , Syrah and Chardonnay . Examples include Vin de pays d 'Oc , Vin de pays d <unk> , Vin de pays de l <unk> , and Vin de Pays du <unk> . <unk> such as Guy Anderson , <unk> <unk> and E. & J. <unk> <unk> capitalized on this new horizon , producing wines like <unk> <unk> and Red <unk> . 
- 
- = = = Vins Doux Naturels = = = 
- 
- Vins Doux Naturels are " naturally sweet " wines that have been fortified with brandy to stop fermentation , leaving residual sugar to add <unk> to the wine . The majority of Languedoc sweet white wines are made with a variety of Muscat grapes . The red fortified wines of the Banyuls are made from Grenache grapes , normally have an alcohol level between 16 and 17 % and carry residual <unk> in the 8 to 12 % range . 
- In Banyuls , <unk> use various methods to " <unk> " their wines to encourage deep <unk> colors . Some <unk> utilize a <unk> system of transporting the wine among different size barrels of various ages that are left out in the sun to warm . Others will put the wine in large glass <unk> to expose it to direct sunlight . In addition to the dark color , the resulting wines often have a <unk> , rancid taste called <unk> . In the Banyuls Grand <unk> AOC the wine is required to be aged in wood barrels for two and a half years . 
- 
- = = = <unk> de Limoux = = = 
- 
- The <unk> produced in the Languedoc is made according to the <unk> <unk> - formerly known as <unk> <unk> , the same method used to produce the Champagne , including a second fermentation in the bottle to <unk> the carbon dioxide produced by the yeast - in the small villages around the town of Limoux . The wines are normally composed of 70 % Mauzac and a 30 % combination of Chardonnay and <unk> blanc . AOC regulations require a year of aging on the <unk> . The <unk> de Limoux , when labelled <unk> <unk> , is composed entirely of Mauzac , undergoes only one fermentation and is aged approximately three months less on the <unk> before the <unk> , the actual date being determined by the moon 's cycle . 
- 
- 
- = Silver Bullet ( roller coaster ) = 
- 
- Silver Bullet is a western @-@ themed steel inverted roller coaster designed by Bolliger & Mabillard located at Knott 's Berry Farm , an amusement park in Buena Park , California . The $ 16 million roller coaster was announced on December 1 , 2003 and opened on December 7 , 2004 . A first rider auction was also held where people would bid on seats to be the first riders . The track is approximately 3 @,@ 125 feet ( 952 m ) long and the lift hill is about 146 feet ( 45 m ) tall . The ride lasts two minutes and thirty seconds and features six <unk> including a vertical loop , cobra roll , zero @-@ g roll , and two <unk> . 
- 
- = = History = = 
- 
- On May 28 , 2003 , a trademark for the name " Silver Bullet " was filed by Cedar Fair , the owners of the park . Construction for the new roller coaster began in September 2003 with the relocation of The Church of Reflections . As part of a $ 85 million investment in all Cedar Fair parks , Silver Bullet was announced to the public on December 1 , 2003 . The final piece of track was placed on September 30 , 2004 . On November 5 , 2004 , Knott 's Berry Farm launched a " First <unk> Auction " in which people from anywhere in the world would bid against each other in order to be one of the first public riders on Silver Bullet on December 7 , 2004 . Although sixty seats were made available , only 13 of these received bids . Out of those bids , the highest bid was $ 200 ( US ) , and a total of $ 1643 was raised with all the money going to the Speech & Language Development Center of Buena Park . After construction and testing was completed , the roller coaster opened first to the media , then to the public on December 7 , 2004 though the initial scheduled opening was on Christmas Eve of 2004 . Silver Bullet was also one of four attractions that opened at Knott 's Berry Farm in the same year and carries a western theme . 
- 
- = = Ride experience = = 
- 
- Silver Bullet 's layout passes through three of the park 's themed areas : Ghost Town , <unk> Village , and Indian Trails . 
- After the train departs from the station , it makes a 90 degree right turn before beginning to climb the 146 @-@ foot ( 45 m ) lift hill . Once at the top , the train goes through a pre @-@ drop before entering the 109 @-@ foot ( 33 m ) downward right drop . Once at the bottom , the train enters a 105 @-@ foot ( 32 m ) loop . After exiting the loop , the train makes a banked right turn leading into the cobra roll . Almost immediately after , the train goes through a zero @-@ gravity roll followed by a downward left helix . Then , the train enters the first of two <unk> which are separated by a banked left turn . Next , the train enters an upward left helix ( the beginning of the helix is close to water ) before entering the brake run . The train then makes a right turn into a second , shorter , brake run which leads straight back to the station . One cycle lasts approximately two minutes and thirty seconds . 
- 
- = = = Track = = = 
- 
- The steel track of Silver Bullet is approximately 3 @,@ 125 feet ( 952 m ) long , and the height of the lift is approximately 146 feet ( 45 m ) high . Silver Bullet , along with all of Bolliger & Mabillard 's other roller coasters , was manufactured by <unk> Steel <unk> located in Batavia , Ohio and erected by <unk> Construction Company . The track is filled with sand to reduce the noise made by the trains . Silver Bullet was also the first Bolliger & Mabillard inverted roller coaster to use magnetic brakes in the brake run . The track and supports of the lift hill are red , with the remaining track being yellow with red rails and the remaining supports colored white . 
- 
- = = = Trains = = = 
- 
- Silver Bullet operates with two steel and fiberglass trains . Each train has eight cars that can seat four riders in a single row , for a total of 32 riders per train . The seats are coloured light blue , with orange over @-@ the @-@ shoulder restraints and tri @-@ color wheel <unk> ( red , orange , and yellow ) . 
- 
- = = Reception = = 
- 
- Joel Taylor from <unk> Business praised the ride saying , " In all , it 's an <unk> 2 1 / 2 minutes from start to finish . " He was also impressed with the ride 's <unk> and overall excitement , stating that the ride is , " smooth and comfortable while delivering <unk> , stomach @-@ churning excitement . " 
- 
- = = = Awards = = = 
- 
- Since Silver Bullet 's debut in 2004 , it has consistently been ranked in the top 140 positions in Mitch Hawker 's Best <unk> Steel <unk> Poll . It peaked at position 99 in 2010 . The roller coaster has never placed on the Golden Ticket Awards . 
- ^ No Steel <unk> <unk> Poll was held in 2011 . 
- 
- 
- = Blackburn Firecrest = 
- 
- The Blackburn B @-@ 48 Firecrest , given the <unk> designation <unk> , was a single @-@ engine naval strike fighter built by Blackburn Aircraft for service with the British Fleet Air Arm during World War II . It was a development of the troubled Firebrand , designed to Air Ministry Specification S.28 / 43 , for an improved aircraft more suited to carrier operations . Three prototypes were ordered with the company designation of B @-@ 48 and the informal name of " Firecrest " , but only two of them actually flew . The development of the aircraft was prolonged by significant design changes and slow deliveries of components , but the determination by the Ministry of Supply in 1946 that the airframe did not meet the requirements for a strike fighter doomed the aircraft . Construction of two of the prototypes was continued to gain flight @-@ test data and the third was allocated to strength testing . The two flying aircraft were sold back to Blackburn in 1950 for disposal and the other aircraft survived until 1952 . 
- 
- = = Design = = 
- 
- The Firebrand required significant effort by Blackburn to produce a useful aircraft and the first discussions on a redesign of the aircraft with a <unk> @-@ flow wing took place in September 1943 . The new wing was estimated to reduce the weight of the wing by 700 lb ( <unk> kg ) and increase the aircraft speed by 13 mph ( 21 km / h ) . The extent of redesign increased and this led to a new fuselage and other improvements . In October 1943 , Blackburn 's design staff , led by G.E. Petty , started work on this development of the Firebrand which led to Specification S.28 / 43 being issued by the Air Ministry on 26 February 1944 covering the new aircraft . The specification was designed around a Bristol Centaurus 77 radial engine with <unk> @-@ rotating propellers that allowed the size of the rudder to be reduced . 
- The new design , given the company designation B @-@ 48 , was known unofficially by Blackburn as the " Firecrest " but was always known by its specification number by the Air Ministry and Navy . It was a low @-@ winged , single @-@ seat , all @-@ metal <unk> . <unk> of the cockpit the fuselage was an oval @-@ shaped stressed @-@ skin semi @-@ <unk> , but forward it had a circular @-@ section , tubular @-@ steel frame . The cockpit of the Firecrest was moved forward and raised the pilot 's position so that he now looked over the wing leading edge , and down the nose . The canopy was adapted from the Hawker Tempest fighter . In the rear fuselage was a single 52 @-@ imperial @-@ gallon ( 240 l ; 62 US gal ) fuel tank with two 92 @-@ imperial @-@ gallon ( 420 l ; 110 US gal ) fuel tanks in the centre wing section . The aircraft had a redesigned , thinner , inverted gull wing of <unk> flow <unk> section . The wing consisted of a two @-@ spar centre section with just over 6 @.@ 5 degrees of <unk> and outer panels with 9 degrees of dihedral . It could be <unk> folded in two places to allow more compact storage in the hangar decks of aircraft carriers . Four Fowler flaps were fitted to give good low @-@ speed handling for landing and the wing had retractable dive brakes on both surfaces . In the course of the redesign the structure was simplified which reduced weight by 1 @,@ 400 lb ( 635 kg ) and even after the fuel capacity was increased by 70 imperial gallons ( 320 l ; 84 US gal ) the gross weight was still 900 pounds ( 410 kg ) less than that of the Firebrand . 
- Work on two prototypes was authorised in November 1943 , but proposals for alternative engines delayed progress . In 1945 , it was decided that as well as adding another Centaurus @-@ engined prototype , there should be three prototypes with the Napier E.122 ( a development of the Sabre ) as Specification S.10 / 45 . The Ministry believed that this would enable Blackburn to develop their knowledge of <unk> and structural design and support the engine development at Napier . However , it was found that the S.10 / 45 aircraft could only be balanced if the E.122 <unk> was placed behind the pilot . The necessary redesign and 1 @,@ 000 @-@ pound ( 454 kg ) weight increase , coupled with the limited funds available to the Royal Navy , meant that it could no longer be justified and the S.10 / 45 was cancelled on 8 October . While in final design , the Centaurus 77 engine with <unk> @-@ rotating propellers was cancelled in January 1946 and a conventional 2 @,@ <unk> @-@ horsepower ( 2 @,@ 107 kW ) Centaurus 57 was substituted . This engine was found to require flexible mounts and was modified into the Centaurus 59 . The vertical <unk> and rudder had to be enlarged from 33 to 41 square feet ( 3 @.@ 1 to 3 @.@ 8 m2 ) to counteract the new engine 's <unk> . In September 1946 a strength analysis conducted by the Ministry of Supply revealed that the aircraft would require strengthening to serve as a strike fighter and that a costly redesign would be required to bring it up to requirements . This would have made it comparable in weight and performance to the Westland <unk> which had already flown so no contract was placed for production aircraft . 
- <unk> by the late delivery of its propeller , the first prototype was rolled out at Brough in February 1947 and then taken by road to RAF <unk> where it made its maiden flight on 1 April that year . All three prototypes were completed by the end of September and the third prototype had been modified to reduce the outer @-@ wing dihedral to 3 degrees . Both the second and third prototypes remained <unk> when the Ministry of Supply ordered that flying be ceased and work on the aircraft be stopped . Later in the month , however , the third prototype was allocated to tests of powered <unk> controls , as testing of the first prototype had shown that while adequate at cruise speed , the ailerons were heavy both at low and high speed . The second prototype was allocated to structural testing . 
- The third prototype made its maiden flight in early 1948 , but the pace of the flight testing was leisurely with only 7 hours and 40 minutes completed by 30 November , over half of which were connected with air show performances . Testing concluded in March 1949 when the officer in charge concluded that there was no further purpose to the tests . While the Firecrest was faster than the Firebrand , and gave its pilot a much better view from the cockpit , it was otherwise disappointing , with test pilot and naval <unk> Captain Eric Brown claiming that the Firecrest was even less <unk> than the sluggish Firebrand , while the powered ailerons gave <unk> controls , leading to instability in turbulent air . 
- 
- = = Operational history = = 
- 
- Operational experience had found Blackburn 's Firebrand strike fighter to be far from suited to carrier operations . In particular , the pilot sat near the wing 's trailing edge , looking over a very long and wide nose which gave a particularly poor view for landing . The Firecrest had also been rendered obsolete by the arrival of gas turbine engines , and while Blackburn did draw up proposals for turboprop @-@ powered derivatives of the Firecrest , ( as the B @-@ 62 ( <unk> ) with the Armstrong <unk> <unk> engine ) , these went <unk> , with orders instead going to Westland for the <unk> . The two flying prototypes remained in use until 1949 , being sold back to Blackburn in 1950 , and were later scrapped . 
- 
- = = Aircraft = = 
- 
- <unk> 
- One of two prototypes ordered on 1 January 1944 to Specification S.28 / 43 . The airframe was sold by the <unk> of <unk> ( Air ) to Blackburn on 17 April 1950 . 
- <unk> 
- The second of two prototypes ordered on 1 January 1944 , it was used for structural testing before being disposed of in 1952 . 
- <unk> 
- A third aircraft was ordered on 18 April 1945 and it was used for research into power @-@ boosted ailerons during February 1948 . The airframe was sold to Blackburn on 17 October 1949 . 
- Three further prototypes were ordered on 14 March 1945 against Specification S.10 / 45 and powered by Napier E.122 engine , but the order was cancelled and the aircraft were not built . 
- 
- = = <unk> = = 
- 
- United Kingdom 
- Fleet Air Arm ( never entered service ) 
- 
- = = Specifications = = 
- 
- Data from Blackburn Aircraft since 1909 
- General characteristics 
- Crew : one pilot 
- Length : 39 ft 3 ½ in ( 11 @.@ 98 m ) 
- <unk> : 44 ft 11 ½ in ( 13 @.@ 71 m ) 
- <unk> : 14 ft 6 in ( 4 @.@ 42 m ) 
- Wing area : 361 @.@ 5 ft ² ( 33 @.@ 60 m ² ) 
- Empty weight : 10 @,@ <unk> lb ( 4 @,@ <unk> kg ) 
- <unk> weight : 15 @,@ 280 lb ( 6 @,@ 645 kg ) 
- <unk> : 1 × Bristol Centaurus 59 18 @-@ cylinder radial engine , 2 @,@ <unk> hp ( 1 @,@ <unk> kW ) 
- Performance 
- Maximum speed : 380 mph ( 330 knots , 612 km / h ) at 19 @,@ 000 ft ( 5 @,@ 790 m ) 
- Cruise speed : 213 mph ( 185 knots , 343 km / h ) at 15 @,@ 000 ft ( 4 @,@ 600 m ) 
- Range : 900 mi ( <unk> nmi , 1 @,@ 450 km ) 
- Service ceiling : 31 @,@ 600 ft ( 9 @,@ 630 m ) 
- <unk> of climb : 2 @,@ 500 ft / <unk> ( 12 @.@ 7 m / s ) 
- Armament 
- Guns : <unk> for 2 × .50 in ( 12 @.@ 7 mm ) <unk> <unk> machine guns under or in wing ( not fitted to prototypes ) 
- Rockets : 8 × <unk> @-@ 3 rocket projectiles on <unk> rails 
- <unk> : 1 × 2 @,@ <unk> lb ( 951 kg ) torpedo , or 
- 2 × 250 lb ( 110 kg ) bombs , one under each wing , in lieu of torpedo 
- 
- 
- = Magadheera = 
- 
- Magadheera ( English : Great Warrior ) is a 2009 Indian Telugu @-@ language romantic @-@ action film , written by K. V. <unk> Prasad and directed by S. S. Rajamouli . Based on the theme of reincarnation , the film was produced by Allu Aravind of Geetha Arts . The film stars Ram Charan and Kajal Aggarwal , while Dev Gill and Srihari appear in prominent roles . The plot revolves around four people : a valiant warrior in charge of the princess ' safety ; the princess who loves him ; her cousin who lusts after her ; and an emperor who wants to conquer their kingdom . They all die before their wishes are fulfilled and are reborn after 400 years , at which point the warrior kills the <unk> cousin with the support of the reformed emperor and wins over the princess . 
- Made on a budget of ₹ 350 million , the production was launched on 2 March 2008 while principal photography commenced on 19 March 2008 . The cinematography was done by K. K. Senthil Kumar , and was edited by <unk> <unk> Rao . Production design was done by R. Ravinder , while the action sequences were choreographed by Peter <unk> and the duo of Ram — <unk> . The visual effects were designed by R. C. Kamalakannan , with assistance from Adel Adili and Pete Draper . It is the first Telugu film to list a " visual effects producer " in its credits . The soundtrack was composed by M. M. Keeravani , who collaborated with Kalyani <unk> to score the background music . 
- Magadheera was released on 31 July 2009 across 1250 screens worldwide , to critical acclaim , and collected a distributor 's share of ₹ 731 million . The film grossed ₹ 1 @.@ 5 billion worldwide and remained the highest @-@ grossing Telugu film of all time for five years , until it was surpassed by <unk> <unk> in 2013 . Its 1000 @-@ day theatrical run surpassed <unk> ( 2005 ) as the longest running South Indian film . It is also the first Telugu film to have a home media release in Blu @-@ ray . The film won the National Award for Best Choreography and Best Special Effects at the 57th National Film Awards as well as winning six Filmfare Awards , nine Nandi Awards and ten <unk> Awards . The film 's success catapulted the lead actors into stardom . 
- The film was dubbed into Tamil as Maaveeran and Malayalam as Dheera : The Warrior , and was released simultaneously on 27 May 2011 in more than 100 and 50 screens respectively , with both versions being commercially successful . In 2014 , the film was further remade in Bengali as <unk> : The Warrior by Raj Chakraborty with Dev and Mimi Chakraborty portraying the lead roles . 
- 
- = = Plot = = 
- 
- Harsha , a dirt @-@ bike <unk> , is taking an auto <unk> to the airport in the rain . He spots the <unk> outline of a woman trying to flag the auto down , and gestures to her that it is already occupied . As he does , their fingers accidentally touch , and Harsha feels an electric current passing through him , which <unk> a few fleeting images . Later , feeling that this was the girl he was destined to be with , he <unk> about her to a woman named Indira ( called " Indu " ) , without realising that she is the same girl . Indu , thinking that he is stalking her , <unk> him . She and her friends take advantage of Harsha , <unk> him out of his finances . Meanwhile , Indu 's distant cousin Raghuveer , lusts after her . He manipulates Indu and her father into trusting him . However , whenever he attempts to touch a sleeping Indu , he sees visions of a warrior slitting his throat . Raghuveer <unk> a tantrik , Ghora , who reveals to him that in a prior life , he was a prince who <unk> after Indu , and was killed by her lover , a warrior . Raghuveer is determined to find the reincarnation of the warrior and kill him . Meanwhile , Harsha discovers how Indu and her friends have been <unk> him , and she falls in love with him . 
- Raghuveer discovers that Harsha is the reincarnation of the warrior . He murders Indu 's father and frames Harsha , causing Indu to hate Harsha . As Raghuveer and Indu leave in a helicopter , Harsha tries to catch them but fails ; falling into a lake , he faces a near @-@ death experience and learns about his past life in 1609 . In that year , Emperor Sher Khan is preparing to invade the kingdom of <unk> when he hears of the brave warrior Kala Bhairava ( Harsha ) , commander of the Udayghad army . King Vikram Singh 's daughter , <unk> Devi ( Indu ) , loves Bhairava , but he holds himself back . Her cousin , Ranadev Billa ( Raghuveer ) , lusts after Mithra and plans a competition between himself and Bhairava ; the winner will marry her and the loser will be banished from Udayghad . Bhairava 's victory leads to Ranadev 's banishment . Vikram Singh , however , secretly requests that Bhairava not marry his daughter , because Bhairava has a high chance of dying in battle , and he does not wish to see Mithra widowed . Though shocked , <unk> <unk> to the king 's request and publicly declines to marry Mithra , leaving her distraught . 
- Bhairava then takes Mithra , his soldiers , and his <unk> to the Bhairavakona temple atop a cliff to seek blessings from God . Mithra demands Bhairava admit his love for her . When he does not respond , she upsets the sacred items they have brought for the puja and , using her own blood , paints an image on a nearby rock of Bhairava leaving his true love to do his duty . An injured soldier arrives to tell Bhairava that Ranadev and Sher Khan 's army have killed Vikram Singh and are now rushing toward them . They arrive shortly , and Sher Khan challenges Bhairava to battle his soldiers . Bhairava boldly accepts the challenge and kills a hundred soldiers , but is severely injured in the process . Sher Khan , impressed by Bhairava 'a bravery , has a change of heart . However , Ranadev continues attacking , eventually wounding Mithra fatally , but he is in turn killed by Bhairava . A dying Mithra asks Bhairava to confess his love , but before he can respond , she dies and falls off the cliff . <unk> , he follows her and falls to his own death . 
- After learning about his past life , Harsha is rescued from the lake by a fisherman named Solomon ( who is the reincarnation of Sher Khan ) and , with <unk> 's help , visits Udayghad . He gains access to Raghuveer 's palace and <unk> Ghora telling Raghuveer that if Indu 's memories of the past are not revived within the day they can never be revived , and she will be with Raghuveer forever . Harsha kidnaps Indu and takes her to Bhairavakona and in the process , Ghora is accidentally killed by Raghuveer . Raghuveer arrives by helicopter and asks Indu to come with him ; however Indu sees Mithra 's painting , and her past @-@ life memories are revived ; she reunites with Harsha . Harsha fights Raghuveer , and with the help of Solomon , manages to kill him . 
- 
- = = Cast = = 
- 
- Ram Charan as Kala Bhairava and Harsha . Kala Bhairava is the commander @-@ in @-@ chief of the Udayghad army who dies in battle in 1609 . His reincarnation , Harsha , is a dirt @-@ bike <unk> in 2009 , who falls in love with Indu . When Harsha discovers his past life , he saves Indu from her cousin Raghuveer , who killed her father and framed Harsha as the murderer . 
- Kajal Aggarwal as Mithravinda Devi and Indu . Mithravinda Devi is the crown princess of Udayghad who is in love with Bhairava and also dies in 1609 . In 2009 , her reincarnation is Indira ( <unk> Indu ) , a <unk> student . She falls in love with Harsha but believes him to be her father 's murderer because of Raghuveer 's deception . She reunites with Harsha in the end after remembering her past life . 
- Dev Gill as Ranadev Billa and Raghuveer . Ranadev Billa is Mithravinda 's cousin who lusts after her and is jealous of Bhairava . He , after killing Mithravinda , is killed by Bhairava . His reincarnation , Raghuveer , is Indu 's cousin who lusts after her . He is the first to remember his past life , and murders Indu 's father in both eras . He is finally killed by Harsha . 
- Srihari as Sher Khan and Solomon . Sher Khan is a Muslim emperor who invades Udayghad . He realises Bhairava 's potential just before his death but fails to save him . His reincarnation , Solomon , is a fisherman who is a native to <unk> . He helps Harsha to reunite with Indu , and plays a vital role in Raghuveer 's death . 
- Rao Ramesh as Ghora , a <unk> old tantrik who advises Raghuveer . He helps Raghuveer in remembering his past . His conversation with Raghuveer helps Harsha to remind Indu of her past . Ghora is accidentally killed by Raghuveer . 
- <unk> Babu as Vikram Singh , the king of Udayghad and father of Mithravinda . He respects Bhairava for his <unk> but believes that he will die in battle and consequently requests that Bhairava hold back his love for Mithra . Vikram is murdered by Ranadev when Sher Khan invades the kingdom . 
- Surya as <unk> Varma , Indu 's father . Raghuveer manipulates him for access to Indu and kills him just before he accepts Harsha 's proposal to marry Indu . 
- <unk> as Harsha 's <unk> friend . He accompanies him most of the time and constantly warns him about Indu <unk> him financially . 
- <unk> as <unk> Singh , the commander @-@ in @-@ chief of Sher Khan 's army who informs Sher Khan about Bhairava 's strengths . 
- Cameo appearances 
- Chiranjeevi in a cameo appearance in the item song " Bangaru Kodipetta " . 
- Mumaith Khan as <unk> , the dirt bike race organiser . She also appears in " Bangaru Kodipetta " . 
- Kim Sharma as <unk> , a dancer who appears in the item song " <unk> " . 
- Brahmanandam as Indu 's neighbour . 
- Hema as <unk> . 
- <unk> <unk> as Solomon 's girlfriend . 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- K. V. <unk> Prasad was watching a Marathi film where two warriors Sivaji and <unk> are attacked by Mughal Army . Once the duo can reach <unk> fort , they can kill the army together effectively . While Sivaji escapes to the fort for support , <unk> fights with the army and dies in the process leaving Sivaji in grief . Prasad took this as an inspiration and wrote a story on the theme of reincarnation of a bodyguard who serves a queen . He fights 100 warriors and dies in his attempt to re @-@ <unk> his queen after she is overthrown by her conspirators . He is reborn after 400 years and <unk> his ambition of placing her on the throne by making her a Chief Minister or a popular social worker like <unk> <unk> . It was subsequently rejected and after fifteen years , his son director S. S. Rajamouli selected this script , but made several changes to it . Rajamouli re @-@ characterised the queen as a princess to create a vital love story for the film , as he felt that the relationship between a queen and a bodyguard would be only about loyalty and honesty . 
- Production for the film was officially launched on 2 March 2008 at <unk> in Hyderabad . B. V. S. N. Prasad co @-@ produced the film with Allu Aravind under his production banner , Geetha Arts . M. <unk> was announced as the dialogue writer while Rajamouli 's usual associates K. K. Senthil Kumar , <unk> Venkateswara Rao and M. M. Keeravani worked on the film 's cinematography , editing and music respectively . 
- After many potential titles , Magadheera was considered and finalised in early February 2009 . Tamil and Malayalam dubbed versions were planned in mid February 2009 . The film 's Tamil dubbed version was titled as Maaveeran , named after the 1986 Tamil film . Geetha Arts collaborated with <unk> Stalin for the Tamil version and distributed it under his production banner , Red Giant Movies . K. <unk> wrote the dialogues for Maaveeran . The Malayalam version was titled Dheera — The Warrior and was distributed by <unk> films . 
- 
- = = = Casting = = = 
- 
- After considering several actresses ( including <unk> ) for the princess role , Kajal Aggarwal was selected by Rajamouli as the female lead opposite Ram Charan after conducting a <unk> in his office . Rajamouli said that he wanted to cast her as the female lead in <unk> ( 2007 ) after her Telugu debut Lakshmi <unk> ( 2007 ) , but she was unavailable . He added that he wanted somebody " good looking , who would look like a princess and who had dates available " and finalised her because she met all of these <unk> . After conducting her <unk> , Rajamouli explained the story and Mithra 's characterisation to her in 45 minutes . He said she needed to treat the hero <unk> , but do so out of love for the hero . Kajal kept this in mind and acted accordingly while playing the dual roles of a princess from 400 years ago and a modern @-@ day college student . Kajal called both her roles " unique and challenging " . 
- Dev Gill was selected to portray the film 's antagonist . Rajamouli was criticised for selecting him considering Gill 's previous film , <unk> ( 2008 ) , but remained adamant about casting him . He said that he had to cast a good looking man as the film 's villain because the heroine needed to find him trustworthy . For this film , Gill had to practice horse riding in Ramoji Film City ; Ram Charan was already an experienced <unk> . Srihari was cast in his role in the film in early April 2008 . Kim Sharma was selected to perform one of the two item numbers in the film in early June 2008 . <unk> <unk> made a cameo appearance in the film and shot for three days . She was subsequently signed for Rajamouli 's next project , Maryada Ramanna ( 2010 ) , before Magadheera was even released . 
- Rao Ramesh was cast as a tantrik who helps Gill 's character in the present era . Mumaith Khan was selected for another item number , a remix version of the hit song " Bangaru Kodipetta " from Gharana Mogudu ( 1992 ) ; Gharana Mogudu 's music was also composed by M. M. Keeravani . Chiranjeevi made a special appearance after the song , making Magadheera the first film he appeared in after his entry into politics . When Rajamouli suggested the idea of a cameo appearance , Chiranjeevi was initially hesitant till the director narrated the complete sequence and the importance of the song . 
- 
- = = = Costume designs = = = 
- 
- Regarding his looks in the film , Ram Charan said , " Personality wise there isn ’ t really much difference to Kalabhairava but I have to tell you minor changes like the shape of my <unk> , a slight voice <unk> , the background scale , the atmosphere and even the ‘ <unk> ’ worked wonders on screen and gave a totally different look and feel and I felt like it was an altogether different side of me . When I was told of the characterization of ‘ Kalabhairava ’ , I had an impression of a young , passionate soldier and naturally it had to have a great body and an authoritative voice . So , we also worked on it . We actually went to Rajasthan and observed the local culture and their way of dressing , we researched a lot about their costumes , history and great personalities 400 years ago and planned it accordingly . " Ram Charan sported a long <unk> for both of his characters in the film . 
- To design the armour used by Charan in his role as Kalabhairava , art director R. Ravinder wanted the precise measurements of Charan 's body ; they used plaster of Paris to make a mould of Charan 's body shape from which they created a statue . They designed costumes for the statue and had Charan try them on once they were satisfied with the designs . In an interview with <unk> <unk> of Rediff.com , the film 's stylist , Rama Rajamouli , said that it took one month to develop the right look for the <unk> period before she began working on the materials and costumes for the film . She added that all of the film 's costumes were designed by her and <unk> in Hyderabad , and that Kajal 's costumes were heavily detailed as she needed to look rich , elegant and bright . Rajamouli had some disagreements with cinematographer K. K. Senthil Kumar over the colours of the costumes . Her decision of having Kajal wear a dress of baby pink and <unk> green during the war sequence at Bhairavakona was initially opposed by Rajamouli , but later agreed to after the director saw the final edit . She also used minimal jewellery for the princess 's outfits . 
- 
- = = = Filming = = = 
- 
- The film 's principal photography was expected to begin on 26 February 2008 , and then on 15 March 2008 , but finally began on 19 March 2008 in Rajasthan . Initial shooting occurred for 10 days in <unk> of <unk> , Gujarat . Art director Ravinder went to Chennai and gave details of their requirements to a prominent chariot @-@ making specialist . After rejecting the maker 's first iron skeletons , Ravinder opted to design them himself , and prepared three models of <unk> with different heights to be used , depending on the camera ’ s location and other technical requirements . He used fibre material for the iron skeleton 's exteriors . For a scene in which neither the hero nor the villain was present on the chariot , Ravinder measured a short person and designed a hidden chamber in which he could sit and secretly control the horses . While the chariot race sequence was shot in <unk> , the sequence in which the chariot sinks in <unk> was shot at a set comprising three acres in Ramoji Film City . 
- K. K. Senthil Kumar scouted for locations in Gujarat , looking for dry , open lands to shoot the chariot race sequence . They found salt lands with white sands in <unk> . To shoot the sequence there , they wanted a light weight vehicle to follow the horses ; they bought a <unk> van , removed the vehicle 's top and mounted the camera along with a <unk> <unk> atop it . A part of the song Dheera Dheera was also shot there , requiring filmmakers to plant a dry tree and a couple of oxen for use in the song 's backdrop . An item number featuring Kim Sharma and Ram Charan was shot in late June 2008 in a specially erected fisherman colony . It was set up on the first floor of Annapurna Studios and cost less than ₹ 3 million . Ravinder explained , " The set should look like an outdoor location , but need to be constructed in an indoor floor . I constructed exteriors of around 28 houses in that floor with detailed interior plan for the house of Srihari . I also constructed a small boat and a big wheeled fish with <unk> . When director wanted smoke @-@ effect for a shot I held the heavy smoke machine on my shoulders in a low angle for the required effect . " 
- After filming key parts of the film in Rajasthan , suburbs of Hyderabad , and at <unk> in Karnataka , filming continued in Ramoji Film City in a specially erected set named Bhairavakona in late October 2008 . Two more schedules , one from 3 – 10 December and one in January , were also shot at the Bhairavakona set . The sequence of Charan killing 100 warriors , also at Bhairavakona , included a bridge . As the set did not permit for shooting with low angles , a separate half @-@ bridge was erected at <unk> <unk> in Road No. 22 of <unk> Hills . The bridge , which had a height of 60 feet ( 18 m ) and a length of 100 feet ( 30 m ) , was constructed on top of a rocky hill using steel beams as the skeleton and wooden material as support . It was built in around 20 days by over 60 men amid heavy rains . 20 trucks of black soil were transported from Ramoji Film City for the bridge set , as that type of soil was only available in the former location . 
- After the completion of shooting at the arena set in Ramoji Film City , the arena set was demolished and a set for heroine 's house was constructed in its place . This was the last set constructed for this film . By late September 2008 , 70 % of the film 's shoot was complete . The remix version of " Bangaru Kodipetta " was shot by Ram Charan and Mumaith Khan under the supervision of <unk> <unk> at Chennai Port , although <unk> Port was first considered . Filming continued in and around Hyderabad very discreetly in November 2008 . <unk> sequences by Ram Charan and some <unk> were shot in mid @-@ December 2008 at Hyderabad . By mid February 2009 , the film 's scripted part had been wrapped up , with only some songs left to shoot . The song " <unk> <unk> " was shot in Switzerland while " <unk> <unk> <unk> " was shot at <unk> Fort in Hyderabad . Both songs were duets shot by Ram Charan and Kajal . K. K. Senthil Kumar said in an interview that Magadheera could not have been made in eight months because of its long pre @-@ production and post @-@ production phases . Over two <unk> worth of props , including swords , <unk> , knives , bows and arrows , pouches , armour , footwear and other accessories , were used in the film . They were designed by 160 workers , including <unk> , <unk> , carpenters , painters , <unk> , artists , <unk> and leather men , who worked for two months in the Geetha Arts Studio . Five people were appointed exclusively to look after these props . However , during the film 's shoot both Peter <unk> and Ram Charan suffered major injuries . The former fell down from a height of 80 — 90 feet during the composition of a stunt and was advised a bed rest for four months . Charan faced severe leg fractures , while Rajamouli said that Peter " met with an accident on the sets , had two broken ribs , fractured hands and jaw and lost all his teeth . Within a month , he was back on the sets with his face and body covered in bandages and plaster . He could barely lift his hands to shoot . " 
- 
- = = = Visual effects = = = 
- 
- R. C. Kamalakannan was the VFX producer of this film . Magadheera was the first Telugu film in which the position of " Visual Effects Producer " was used in the credits . He was assisted by a team of five experts from abroad and a 62 dedicated group members apart from 130 people from the company <unk> in Chennai and Hyderabad . Special effects and CG was used for about 1 hour and 40 minutes out of the total 2 hours and 35 minutes in the film . 15 % of the frames in the film 's scenes of Udayghad city were actually shot with a camera ; the rest were CG extension . For the scenes in the stadium , CG extension was slightly above 50 % . For the stadium , Italian concept artist Marco <unk> began with a <unk> drawing and made his first 3D concept in 5 days . It took five months for Marco to reach the design shown in the film and as imagined by Rajamouli . Simultaneously , Iranian 3D technical director Adel Adili , who worked with Kamalakannan for <unk> ( 2004 ) , <unk> and <unk> ( 2009 ) , was working on the 3D Udayghad city . He started with a city @-@ plan and added streets , buildings , mountains , temples , palaces , water bodies , a huge waterfall , rivers , gates , a <unk> and trees , all of which were planned and received Rajamouli 's approval before being executed to full scale . Adili redesigned Marco 's 3D stadium to suit Indian aesthetics and made it fit perfectly within the city . 
- Pete Draper , who worked with <unk> in <unk> ( 2008 ) , was selected as the pipeline technical director of the film . At that time , Kamalakannan was in Iran <unk> the CG stadium and City with Adili . The majority of the film was shot in <unk> format , but a Super 35 camera gate and lens were used for the stadium episode to minimise distortion . Post @-@ production of the stadium scenes were given to Prasad <unk> , and the film set up their own render farm in Chennai . The post process was done in several stages after the scan . The live stadium had only 7 steps , in which live people were positioned in 6 steps while the remaining 12 steps were CG extension . Adili created 3D CG people to fill the CG steps and their actions in a library of 3000 frames . 
- Draper designed the birds in two flows : one flow kept flying around the stadium , while the other flow took off from a position dome and landed on a target dome . Rajamouli used those birds in several shots . Upon finishing the stages of the stadium composite , Kamalakannan flew to Iran again to work with Adel for the 3D city shots which were created entirely in CG . Adili designed 3D horses for the hero and villain and Draper designed the 3D people <unk> behind the horses . About Adili 's work , Kamalakannan said , " When we started the city @-@ shot renders , it has taken several hours to render one frame . But Adili re @-@ corrected the geometry and applied several path breaking ideas , so even on 24 July , just a week before the film release , we were able to do corrections , render and submit . " ₹ 40 million ( US $ 590 @,@ 000 ) was spent solely on VFX for Magadheera . 
- 
- = = = <unk> = = = 
- 
- During the film 's shoot at Ramoji Film City in late November 2008 , a 500 square feet ( 46 m2 ) film can , containing two or three scenes , was discovered missing from Rainbow lab . The filmmakers filed a case at <unk> police station . Security personnel and film unit members searched , but failed to recover the reels . Rajamouli 's unit said it was not important if the scenes from the can were aired online since they were not crucial scenes , were all on a blue matte and lacked finishing . Later , raw footage from the film was seen on the internet but no details about the culprits were found . After the photographs and small video clips were leaked on the Internet , some of the film unit members felt that the loss might have been a ploy on the part of the producers to create hype . 
- 
- = = Themes and influences = = 
- 
- The film is based on the concept of reincarnation and eternal love . According to <unk> , the rewritten script 's basic storyline is that " Love remains eternal and it will follow generations and generations . There is always a big victory for Love . " The film 's flashback sequences were used as a flip switch between the film 's <unk> and present day eras . Initially the film featured a scene in which the present day heroine accidentally receives a dupatta preserved in a museum that is used in the flashbacks as the dupatta Bhairava and <unk> race for . However , separate <unk> were used in the film as Rajamouli felt that getting the dupatta from the flashback would <unk> the narrative . 
- In her book <unk> Roy 's <unk> : Untold Stories from Behind the Scenes , writer <unk> <unk> compared the reincarnation theme of Magadheera with that of <unk> ( 1958 ) , <unk> ( 1980 ) , Karan Arjun ( 1995 ) and <unk> <unk> <unk> ( 2007 ) . Touching the heroine <unk> locked memories within the hero that transport him to Rajasthan in 1609 from contemporary Hyderabad . <unk> also compared Magadheera to another Telugu film , <unk> <unk> ( 1964 ) . Rajamouli told Subhash K. Jha that he was inspired by Karan Arjun to make films based on reincarnation . 
- 
- = = Music = = 
- 
- M. M. Keeravani composed the film 's soundtrack . It features six songs including the remixed version of his own composition " Bangaru Kodipetta " from the film Gharana Mogudu . The soundtrack of the Tamil dubbed version , Maaveeran , had three additional tracks . The Telugu soundtrack was launched at <unk> <unk> in Hyderabad on 28 June 2009 . The Tamil version was launched on 25 April 2011 in Chennai . 
- 
- = = Release = = 
- 
- Geetha Arts released the film on 31 July 2009 , with 625 digital UFO prints in 1250 theatres across the globe , making Magadheera the biggest release for a Telugu film in history . Its release included more than 1000 screens in Andhra Pradesh alone . The film opened up with 25 prints overseas in almost 40 locations . Magadheera was the first Telugu film to be released on 21 screens in North America . Huge vinyl posters featuring Ram Charan and his father , cameo performer Chiranjeevi , were put up at all of the theatres in Krishna District . <unk> also featured the head of Chiranjeevi 's political party , former MLA <unk> <unk> , which added political importance to the release . The Tamil dubbed version Maaveeran and Malayalam dubbed version Dheera — The Warrior were released in more than 100 screens and 50 screens respectively on 27 May 2011 . 
- 
- = = = Distribution = = = 
- 
- <unk> <unk> , the owner of Supreme Music , ventured into overseas distribution with Magadheera . He distributed the film in the USA via Blue Sky <unk> Inc . The distribution rights of non @-@ US countries , including the UK , Ireland , Middle East , Australia , New Zealand and Singapore , were acquired by Krishna Productions . The theatrical rights of Australia and New Zealand were sold to <unk> . The film 's Singaporean rights were sold to <unk> films . Gulf and Kuwait theatrical rights were sold to K. A. <unk> and <unk> respectively . Red Giant Movies distributed Maaveeran while <unk> films distributed Dheera — The Warrior . The film 's Karnataka distributor , <unk> , was disciplined for breaking the rules of the Karnataka Film Chamber of Commerce by simultaneously releasing a non @-@ Kannada film in more than 21 screens in the state . The proceeds of 21 screens were given to him while the rest was confiscated by the Chamber of Commerce . The <unk> also suspended <unk> , who was <unk> 's secretary for distribution , from its executive committee . 
- Joint collector Gaurav <unk> convened a meeting of film <unk> and distributors , warning them against black @-@ marketing tickets to Magadheera . This was after local leaders , attempting to acquire a maximum number of tickets , blocked and booked tickets to the film in advance , to <unk> their supporters during the local civic polls in <unk> . Special teams of revenue , police and commercial tax officers were appointed to keep watch at ticket <unk> and ensure that each person was issued only one ticket . <unk> was ordered to be given for the sanitation , safety and comfort of the viewers . Authorities in <unk> ensured the strict checking and prevention of black @-@ marketing of tickets . However , Ram Charan 's fans resorted to <unk> and an impromptu protest demonstration in the office of Revenue Divisional Officer K. <unk> in <unk> . They demanded the screening of a benefit show and the advance sale of tickets after being rejected by the management of the twin theatres , <unk> <unk> and <unk> Krishna , where the film was screened . Their plea was rejected by the officer , who said that the tickets would be issued only to those who came in queue and that no special concessions would be made for fans . 
- 
- = = = <unk> = = = 
- 
- On 9 August 2009 , two persons , identified as <unk> student M. <unk> Kumar and <unk> , were killed and 15 others sustained severe injuries after being <unk> while jostling for tickets for Magadheera at the theatre <unk> in <unk> , <unk> . The screening of Magadheera at theatres in <unk> was prohibited by the then minister <unk> <unk> until safety measures were complied with . <unk> 's announcement followed the death of five people in <unk> Village due to an electrical accident at the Lakshmi theatre screening Magadheera . 
- 
- = = = <unk> of plagiarism = = = 
- 
- Folk <unk> <unk> <unk> Rao alleged that his 40 @-@ year @-@ old folk song " Em <unk> <unk> " , which was featured in T. Krishna 's <unk> <unk> ( 1986 ) , was used by film director Rajamouli and Allu Aravind in the Magadheera song " <unk> " without Rao 's consent . <unk> of <unk> , <unk> and <unk> staged a protest demonstration in front of the <unk> 70 MM theatre in <unk> , where the film was screened , to demand the removal of the song . The protesters said that the song was written by Rao during the famous <unk> armed struggle four decades prior and had been misused to portray an obscene duet in the film . They added that the song was used without the permission of the writer and that the depiction defeated the very purpose of the song , which was written in a revolutionary spirit . They demanded a public apology by the makers to Rao . A few reports stated that Rao demanded Aravind pay him compensation of ₹ 5 million . 
- After the completion of the film 's 175 day run , novelist S. P. <unk> accused the film of being based on a novel that he had written and published as a serial under the title <unk> in a monthly magazine in 1998 . He demanded that filmmakers compensate him for using his idea without consent , and threatened to take legal action if the producers did not heed his demands , or remade the film in another language . He added that he deserved to be credited for the story and alleged that Allu Arjun and Allu <unk> were trying to postpone his acknowledgment by discussing the issue with him . 
- 
- = = = Home media = = = 
- 
- Magadheera was the first Telugu film in India to have a home media release in Blu @-@ ray format . During the release announcement , Allu <unk> said that although they initially were reluctant to go for it , given the fact that there were approximately only 2500 Blu @-@ ray devices in Andhra Pradesh , the overwhelming demand for it made him rethink the decision . He added , " <unk> this vision with us was Sri <unk> Videos , the largest home @-@ video label in Andhra Pradesh . Together , we 're bringing out Magadheera on Blu @-@ ray . Though <unk> Videos released <unk> 's King ( 2008 ) on Blu @-@ ray , it was only for the overseas market . This makes Magadheera the first Blu @-@ ray film to be released in India and with a bonus disc with additional footage . We 're also a holding a press event on this regard to officially announce the launch of the movie on DVD and Blu @-@ ray . " 
- A press note announced that the DVDs would be available beginning the third week of March 2010 while the Blu @-@ ray would be available 2 weeks later . The launch ceremony took place in a Marriott Hotel on 27 February 2010 . The first DVD was released by M. M. Keeravani and was presented by journalist <unk> <unk> . The DVD was released in <unk> video format and <unk> Digital 5 @.@ 1 surround audio format on 5 March 2010 . The Blu @-@ ray disc released on 13 April 2010 . Magadheera held the top position in sales for its <unk> Blu @-@ ray edition with 5 @.@ 1 <unk> digital sound format as of July 2014 . 
- 
- = = Reception = = 
- 
- 
- = = = Critical reception = = = 
- 
- <unk> <unk> of The Hindu summarised , " Magadheera is not for the weak @-@ hearted , those who do not like the sight of blood and neither is it for those who like movies with storylines that are much @-@ closer @-@ to @-@ <unk> reality . But Rajamouli excels in story @-@ telling . The way he has used the flashback as a flip switch , going back and forth and taking the viewer through a 400 @-@ year journey in a <unk> is interesting . The rest is about how well technology , creativity , imagination and innovation are <unk> to present what is an eye @-@ pleasing experience for viewers . " B. V. S. Prakash of The Times of India wrote , " Despite a few narrative <unk> , the much @-@ <unk> semi @-@ periodic epic lives up to expectations . Unlike his previous action @-@ centric films , director Rajamouli dishes a heart @-@ touching love story in a lavish canvas convincingly . Also <unk> for the way he has <unk> and presented the film . " He added , " After not @-@ so @-@ impressive <unk> , Ram Charan <unk> returns as a valiant soldier and <unk> life into the larger @-@ than @-@ life role with ease . Similarly , Kajal known for simple lover girl roles transforms into a determined princess and truly <unk> . Dev Gil is adequate as the ruthless villain " , and rated the film 3 out of 5 . 
- <unk> Entertainment stated , " S. S. Rajamouli has chosen a fantastic love story for his movie Magadheera and linked it to a love story that dated back to some 400 years . He has filled complete energy in the film and no doubt , the graphic works and modern technology are perfectly used for the movie . As a whole , the film reminds the audiences of watching a Hollywood movie with perfect local flavor " , and called the film 's screenplay by Rajamouli a highlight . <unk> stated " Magadheera is not an epic , not a legend , not even a cult movie , but it is certainly an artistic <unk> and a sensory treat . Like we said before , a must watch ! A mast watch ! " . 
- <unk> <unk> of Rediff.com called the film " technically brilliant " and wrote " A reincarnation story is not often seen in Telugu cinema and SS Rajamouli uses this theme for Ram Charan Teja 's second film Magadheera . It 's a magnum opus production with great technical work . The graphics are excellent for a Telugu movie . Overall , the movie is a mainstream one with good packaging . " She rated the film 3 out of 5 . Sify called the film a " brilliant <unk> " and wrote , " Set against the backdrop of an eerie theme — reincarnation — Magadheera <unk> on the screen with multiple shades of entertainment . When the movie was launched last year , director Rajamouli <unk> to reveal the central theme of the story . He has succeeded in his attempt , as he was able to keep the screenplay and narration gripping from start to finish . The second half has the soul in it . " Sify summarised , " In short , the movie encompasses some of the best commercial elements that Telugu audience have seen in the recent past . " 
- IndiaGlitz called the film " royal and <unk> " and opined , " The film deserves a special credit for many reasons , notable among them is the grandeur of the movie and the amazing technical aspects . This is surely a path breaker in terms of Telugu film making and with the likes of ' <unk> ' , <unk> sure is proud to have this one in its league . It is a valiant attempt to bring such genre to the modern day audience and present it with <unk> so that it fits the historical angle and the today 's angle . " However , Rajamouli was accused of lifting scenes from films like <unk> ( 2000 ) , Troy ( 2004 ) , The Myth ( 2005 ) and 300 ( 2007 ) . 
- 
- = = = Box office = = = 
- 
- The film collected a share of approximately ₹ 200 million ( US $ 3 @.@ 0 million ) in its first week in Andhra Pradesh alone , of which a share of ₹ 71 million ( US $ 1 @.@ 1 million ) was collected in Nizam region . Sixteen centres collected ₹ 10 million ( US $ 150 @,@ 000 ) , each of which was an all @-@ time state record . It collected a share of ₹ 381 @.@ 5 million ( US $ 5 @.@ 7 million ) in 21 days in Andhra Pradesh . By the end of its four @-@ week run , the film was declared a blockbuster and collected a share of ₹ 435 @.@ 6 million ( US $ 6 @.@ 5 million ) in 28 days and ₹ 470 @.@ 8 million ( US $ 7 @.@ 0 million ) in 35 days . The film completed a 50 @-@ day run in 302 centres on 18 September 2009 . By then , the film had collected ₹ 650 million ( US $ 9 @.@ 7 million ) and stood strong . 
- The film completed its 100 @-@ day run in 223 centres and grossed over ₹ 1 @.@ 25 billion ( US $ 19 million ) without satellite and audio rights . By then it had surpassed <unk> 's Sivaji ( 2007 ) , which grossed ₹ 650 million ( US $ 9 @.@ 7 million ) in Tamil Nadu , and stood second to <unk> ( 2008 ) , which reached ₹ 2 billion ( US $ 30 million ) . The film completed a 175 @-@ day run in 3 centres and , by then , collected a share of ₹ 580 million ( US $ 8 @.@ 6 million ) . The film completed a 365 @-@ day run in <unk> theatre in Kurnool on 31 July 2010 and a 1000 @-@ day run in the same theatre on 26 April 2012 . The film collected a share of ₹ 731 million ( US $ 11 million ) at the worldwide box office in its lifetime with a gross of ₹ 1 @.@ 5 billion ( US $ 22 million ) . It was declared the highest <unk> in the history of Telugu cinema and stood in the first place in the list of Telugu films with highest worldwide shares . It held that position until 2013 when <unk> <unk> pushed it to the second spot . 
- More than 50 % of the film 's lifetime collections were from the Nizam region . The film also did well in the overseas box office . It was released in New Jersey with three prints on three screens and collected a record share of more than 150 @,@ 000 dollars , turning the highest amount raised overseas by a Telugu film ; the previous record holder was <unk> <unk> ' <unk> ( 2008 ) , which collected more than 100 @,@ 000 dollars . It grossed 102 @,@ 000 dollars in its first two days in New Jersey and a share of 95 @,@ 000 dollars in Virginia with two prints as of mid August 2009 . It collected a share of 78 @,@ 000 dollars in the San Francisco Bay Area with a single print . It managed to sell 2300 tickets in Minneapolis ; the previous record for tickets sold there for a Telugu film was 1200 . The film 's overseas records were beaten two years later by <unk> ( 2011 ) , although disputes have arisen over which film holds the <unk> all @-@ time sales record . 
- 
- = = Legacy = = 
- 
- Magadheera 's success turned Kajal into one of the most sought @-@ after actresses in Telugu cinema and catapulted her into the foray of leading Telugu actresses . The film was also considered a fate <unk> in the career of Ram Charan . The role of Sher Khan played by Srihari was considered as one of the best roles in his career and also catapulted S. S. Rajamouli to stardom . Dev Gill attained stardom with this film and went on to work in several South Indian films as an antagonist . After the release of Magadheera , people began <unk> him as <unk> . According to writer <unk> , Magadheera started a trend of experimentation with period , socio @-@ fantasy and spiritual themes that was continued in films like <unk> ( 2010 ) , <unk> ( 2010 ) , <unk> O <unk> ( 2011 ) , <unk> ( 2011 ) , Sri Rama <unk> ( 2011 ) and <unk> <unk> ? <unk> <unk> ? ( 2012 ) . 
- The props used in this film , including weapons like swords and shields , were auctioned by Movie Artist Association . Actors , <unk> , and the general public were invited to bid and the proceeds were used to aid poor artists in the Telugu film industry . Actor Sivaji Raja started the bidding by offering ₹ 50 @,@ 000 for the sword that Ram Charan used and comedian <unk> <unk> started the bidding for the shield with ₹ 25 @,@ 000 . B. V. S. N. Prasad bid for both the sword and knife for ₹ 100 @,@ 000 . The bidding started on 7 May 2010 online in the association 's official website and ended on 16 May . The winners were declared on 20 May 2010 . After Magadheera , S. S. Rajamouli worked on a small budget film Maryada Ramanna ( 2010 ) which , according to Crazy Mohan , was similar to the act of S. S. <unk> directing the small budget film Mr. <unk> ( 1952 ) after <unk> ( 1948 ) . Rajamouli explained his decision by saying , " I decided that my next project would be Maryada Ramanna during the Magadheera shooting itself because it is a 1 and half year project that demands lot of physical labor and mental strain . I didn 't want to commit another physically exhausting film immediately after Magadheera . Maryada Ramanna gave us time to recharge our batteries so that we could come up with another huge project . " He revealed Maryada Ramanna 's plot before its launch to minimise the expectations of his audience after Magadheera 's success . 
- <unk> <unk> said " Ever since <unk> and Magadheera did well at the box office , the rest of the industry started following their footsteps . Also , since there is an irrational craze to make high budget films right now , producers are turning towards mythological films . It is the only genre where you can <unk> of spending <unk> for creating the sets and the look of the film . But what they don 't realize is that if these films flop , the blow to the producer will be severe . " Films like <unk> O <unk> ( 2011 ) and <unk> ( 2011 ) were commercial failures and Badrinath ( 2012 ) was an average <unk> ; all being fantasy films in which the protagonist is a warrior . The promos of Badrinath looked similar to Magadheera and the former 's director V. V. <unk> along with its male lead Allu Arjun kept insisting that Badrinath is <unk> to Magadheera . Producer <unk> Kumar said " If a hero delivers a hit , producers run after him for five years and burn their fingers only after six or eight <unk> . It 's the same story with movies . Since Magadheera became a hit , every other hero and director wants to do period film where there is scope to spend <unk> . They don 't work on the script , story or relevance . It is a pure display of graphics , heroes ' <unk> and grandeur . The plot is lost . Magadheera worked because it was a novel genre then , but why will people want to see the same things again ? " . 
- <unk> <unk> cited the character of Mithravinda played by Kajal in the film as one of her dream roles . Ram Gopal Varma refused to call Magadheera a film and instead termed it as an event that comes " once in a blue moon " . While commenting on Mahesh Babu 's Aagadu ( 2014 ) , he said that Magadheera looked like a 750 <unk> film when compared to Aagadu if the latter was a 75 <unk> film and added , " My comparison between " Aagadu " and " Magadheera " is mainly because they both are the most expensive films for their own individual times . " Reviewing the Tamil film <unk> ( 2014 ) , few critics opined that the film had traces of Magadheera mainly due to the theme of reincarnation . The Tamil dubbed version of the Telugu film <unk> ( 2014 ) , which featured Charan in one of the lead roles and Kajal in a crucial cameo , was titled Magadheera . 
- 
- = = Awards and nominations = = 
- 
- The film won the National Award for Best Choreography and Best Special Effects at the 57th National Film Awards . The film also won six Filmfare Awards , nine Nandi Awards , and ten <unk> Awards . 
- 
- = = <unk> = = 
- 
- Ram Charan decided not to debut in Bollywood with Magadheera 's Hindi remake , as he believed the film might not be recreated well . In an interview with The Times of India , Charan said , " When I met <unk> Kapoor some time back , he told me I will ask <unk> Kapoor to remake Magadheera with you in Hindi . But I said I don 't know if I would want to do the remake . Magadheera is a lovely film which can 't be recreated . " The film was remade in Bengali as <unk> : The Warrior in 2014 ; it was directed by Raj Chakraborty with Dev and Mimi Chakraborty playing the lead roles . The 2014 Kannada film Brahma , according to its director R. <unk> , was inspired by Magadheera but was not a remake . In August 2014 , <unk> Kapoor expressed his desire to reprise Ram Charan 's role in a Hindi remake saying , " I have been wanting to do Magadheera for the longest . I have seen the film and loved it <unk> I think it ’ s a killer subject . " In late November 2014 , Sajid Nadiadwala confirmed that he would direct the film 's Hindi remake with Kapoor as the lead . When asked by Subhash K. Jha the reason for not directing the Hindi remake , Rajamouli said " I 've already spent two years of my life on it . I ’ d rather make something else . Magadheera was my toughest film to make . Very tiring . " 
- 
- 
- = Hurricane Flossy ( 1956 ) = 
- 
- Hurricane Flossy originated from a tropical disturbance in the eastern Pacific Ocean and moved across Central America into the Gulf of Mexico as a tropical depression on September 21 , 1956 , which became a tropical storm on September 22 and a hurricane on September 23 . The hurricane peaked with maximum sustained winds of 90 mph ( 150 km / h ) before it struck the central Gulf coast of the United States as a Category 1 hurricane on September 24 , and evolved into an extratropical cyclone on September 25 . It was the first hurricane to affect oil refining in the Gulf of Mexico . The tropical cyclone led to flooding in New Orleans , and broke a drought across the eastern United States . The death toll was 15 , and total damages reached $ 24 @.@ 8 million ( 1956 USD ) . 
- 
- = = Meteorological history = = 
- 
- A tropical disturbance moved northward , crossing Guatemala from the eastern Pacific ocean into the northwest Caribbean Sea between September 20 and September 21 . It became a tropical cyclone soon after emerging into the Caribbean , and moved across the Yucatán Peninsula as a tropical depression before becoming a tropical storm in the Gulf of Mexico on September 22 and a hurricane on September 23 . It turned sharply east @-@ northeast across the Mouth of the Mississippi river on September 24 as a minor hurricane . The storm continued east @-@ northeast and made landfall in Florida east of Pensacola . The system evolved into an extratropical cyclone soon after passing out of the Sunshine State and continued moving east to northeast hugging the Atlantic <unk> to near the Virginia <unk> before moving slowly through the shipping lanes between Canada and Bermuda , blocked by a high pressure system in southeast Canada . 
- 
- = = Preparations = = 
- 
- By the morning of September 22 , hurricane watches were posted for the upper Texas and Louisiana coasts . By the morning of September 24 , hurricane warnings were in effect from Grand Isle , Louisiana to Fort Walton Beach , Florida while storm warnings were in effect from Morgan City , Louisiana to St. Marks , Florida . The approach of the hurricane led to the evacuation of 15 aircraft from Florida to <unk> Air Force Base , in Oklahoma . 
- 
- = = Impact = = 
- 
- 
- = = = Gulf of Mexico = = = 
- 
- This was the first hurricane to cause significant disruption to oil refining in the Gulf of Mexico . Several hundred active wells went out of service , and drilling came to a halt for a few days during and after the cyclone 's passage . One of Humble company 's <unk> saw three @-@ quarters of its <unk> chains compromised , which swung it around into an adjacent oil platform , causing $ 200 @,@ 000 in damage ( 1956 USD ) . The cost to <unk> in production was greater than the damage Flossy created to the oil rigs . There was no loss of life . Due to the impact of Flossy on oil refining in the Gulf of Mexico , the American Petroleum Institute formed a committee called <unk> Research on Weather <unk> . Their goal was to use mathematical models and historic data to better predict hurricane formation and path . Studies went on into 1962 , but no reliable forecast mechanism was found . 
- 
- = = = United States = = = 
- 
- In Louisiana , wind @-@ blown sand from the beach spread over area highways due to the hurricane . A total of 16 @.@ 70 inches ( <unk> mm ) of rainfall fell at Golden Meadow , Louisiana . <unk> , Louisiana 's pressure fell to 29 @.@ 03 inHg ( 983 @.@ 1 hPa or mb ) . Hundreds lost their homes in the storm . The storm surge was significant enough to <unk> Grand Isle , Louisiana . At <unk> Lock , the storm tide reached 13 feet ( 4 @.@ 0 m ) . Extensive coastal erosion was caused by the cyclone across the Mississippi Delta . In New Orleans , Louisiana , about 2 @.@ 5 square miles ( 6 @.@ 5 km2 ) were flooded as portions of the seawall were <unk> . <unk> drowned across the region , and crops such as citrus , sugar cane , and <unk> were heavily damaged . 
- Winds as high as 66 mph ( 106 km / h ) struck coastal Mississippi . A total of 16 @.@ 30 inches ( 414 mm ) of rain fell at Gulf <unk> . In Montgomery , Alabama , the tent used to house the Eastern Hills Baptist Church was destroyed . Across northern Florida , southern Alabama , and Georgia , Flossy was considered drought @-@ breaking . In southwest Georgia , high winds from Flossy damaged the corn and cotton crops . The lowest pressure reported was 28 @.@ 93 inches / 979 @.@ 8 hPa or mb at Pensacola Naval Air Station . The storm tide at <unk> Beach , Florida , reached 7 @.@ 4 feet ( 2 @.@ 3 m ) above mean sea level . As tides damaged some piers and small craft , and resulted in severe beach erosion . At least three tornadoes touched down throughout northern Florida in association with Flossy . One damaged or destroyed numerous structures in Gulf County ; two others in Jefferson and <unk> counties caused little or no damage . 
- Further northeast , rains brought by Flossy helped relieve drought conditions across the Carolinas , and were considered beneficial . Winds as high as 45 miles per hour ( 72 km / h ) were recorded in Washington , <unk> .. The gas screw vessel Mary Anne was lost at the Hampton Roads Naval Base . High tides caused by the then @-@ extratropical cyclone led to water 2 @.@ 5 feet ( 0 @.@ 76 m ) deep in sections of Norfolk . The Back River Light collapsed during the storm , 127 years after it was built . Severe flooding occurred along the state 's coastline , including interior bays . This occurred despite efforts to curb the storm surge with 500 tons ( 454 tonnes ) of broken rock . 
- Total damages to Louisiana , Florida , Alabama , the Carolinas , and Virginia was $ 24 @.@ 8 million ( 1956 USD ) . 
- 
- 
- = Battle of Binh <unk> = 
- 
- The Battle of Binh <unk> ( Vietnamese : <unk> Bình Giã ) , which was part of a larger communist campaign , was conducted by the Viet Cong from December 28 , 1964 , to January 1 , 1965 , during the Vietnam War in Bình Giã . The battle took place in Phước Tuy Province ( now part of Bà Rịa – <unk> <unk> Province ) , South Vietnam . 
- The year of 1964 marked a decisive turning point in the Vietnam War . Following the ousting of President Ngô Đình Diệm in 1963 , South Vietnam 's top army generals continued to vie with each other for control of the country ’ s military @-@ dominated government instead of combating the emerging forces of the National Liberation Front , also known as the Viet Cong . The <unk> of the South Vietnamese government was reflected on the battlefield , where its military experienced great setbacks against the Viet Cong . Taking advantage of Saigon 's political instability , Communist leaders in Hanoi began preparing for war . Even though key members of North Vietnam 's Politburo disagreed on the best strategy to reunite their country , they ultimately went ahead to prepare for armed struggle against South Vietnam and their American supporters . 
- Towards the end of 1964 , the National Liberation Front commenced a series of large @-@ scale military operations against the Army of the Republic of Vietnam , as ordered by the North Vietnamese government . As part of their Winter @-@ Spring Offensive , the Viet Cong unleashed its newly created 9th Division against the South Vietnamese forces at Bình Giã , fighting a large set @-@ piece battle for the first time . Over a period of four days , the Viet Cong 9th Division held its ground and <unk> the best units the South Vietnamese army could send against them , only breaking after intense attack by U.S. bombers . 
- 
- = = Background = = 
- 
- In 1964 , the political establishment in South Vietnam was still in turmoil . Following the coup that ousted Ngô Đình Diệm , the military situation quickly worsened as the National Liberation Front ( NLF , also known as Viet Cong ) gained significant ground in the countryside because the Military Revolutionary Council which governed South Vietnam , lacked direction both in terms of policy and planning . Furthermore , General Dương Văn Minh , as the Chairman of the Military Revolutionary Council , and his civilian Prime Minister Nguyễn <unk> <unk> favoured a political resolution instead of using military force , which brought them into conflict with the United States over the best strategy to end the Communist threat in South Vietnam . As a result , both men became increasingly unpopular among the military generals who held real political power in Saigon . On January 30 , 1964 , General Nguyễn Khánh successfully ousted Dương Văn Minh from the Military Revolutionary Council without firing a single shot . For much of the year , Khánh spent most of his efforts on consolidating political power , instead of fighting the Viet Cong . 
- In contrast to the political unrest in Saigon , the Communist leadership in North Vietnam were far more concerned about the best strategy to fight the South Vietnamese government and their American supporters . While all leaders in Hanoi shared the same goal of eventual <unk> of their homeland , different factions within the Communist Party disagreed on the best method to achieve their desired goal . Members of North Vietnam 's Politburo were divided by the issues surrounding the Soviet strategy of peaceful co @-@ existence versus the Chinese strategy of supporting national liberation movements in emerging countries . Despite their differences of opinion , the Communist Party leadership ultimately made preparations for armed struggle in South Vietnam . From Hanoi 's perspective , the military regime in Saigon was able to hold out because the Communist main forces were still not ready to fight a conventional war , so North Vietnam must focus on the development of its military force in the shortest period of time . In the meantime , however , the war must be kept at its current level in order to prevent the full involvement of the United States military . 
- On October 11 , 1964 , the National Liberation Front in South Vietnam was ordered to carry out a series of military operations as part of the Communist winter @-@ spring offensive . The NLF <unk> Bo ( Mekong Delta ) Regional Command established a sub @-@ command under the leadership of Trần Đình Xu , with Nguyễn <unk> as the deputy commander , and Lê <unk> Tấn as the political <unk> . Their mission was to inflict damage on the regular units of the Army of the Republic of Vietnam ( ARVN ) and destroy the strategic hamlets constructed by the former Ngô Đình Diệm regime . The National Liberation Front in South Vietnam identified the regions of Bình Long @-@ Phước Long and Bà Rịa @-@ Long Khánh , along Route 14 , as the main targets for their offensive . Meanwhile , the Central Military Commission in Hanoi appointed General Nguyen Chi Thanh as the commander of North Vietnamese military operations in southern Vietnam . Other high @-@ ranking officers such as Major Generals Lê <unk> Tấn and Trần <unk> , and Colonel <unk> <unk> were sent to South Vietnam to supervise the military build @-@ up which would commence in November 1964 . 
- 
- = = Prelude = = 
- 
- In July 1964 , the 271st and 272nd Regiments of the People 's Liberation Armed Forces ( PLAF ) , began moving into the provinces of Bình Dương , Bình Long and Phước Long to carry out their mission . During the first phase of their campaign , the Viet Cong regiments overran several strategic hamlets at <unk> Sang , Cam Xe , <unk> <unk> , and Thai <unk> . Between August and September 1964 , Viet Cong regiments executed deep <unk> into Bình Dương and <unk> <unk> to apply additional pressure on South Vietnamese outposts situated on Route 14 . During the second phase of their campaign , the Viet Cong ambushed two South Vietnamese infantry companies and destroyed five armoured vehicles , which consisted of <unk> <unk> light tanks and M @-@ 113 <unk> . The PLAF defeated regular South Vietnamese units at the strategic hamlets of Bình <unk> and Bình Co . 
- Following the completion of the initial stages in their campaign , the Viet Cong forces were ordered to regroup and prepare for the next offensive in the Long Khánh region . Viet Cong soldiers from the two regiments were assembled in War Zone D , where they were trained to attack well @-@ fortified enemy strongholds . On November 20 , 1964 , the Viet Cong reached the Long Khánh battlefield , having completed a 200 kilometres march from War Zone D. On the battlefield the Viet Cong 186th Battalion ( from Military Region 5 ) , the <unk> and <unk> Battalions ( from Military Region 7 ) , and the 445th Company also joined the offensive . To kick @-@ start their offensive in the <unk> @-@ Long Khánh region , the Viet Cong selected Bình Giã as their next target . Bình Giã was a small village located in Phước Tuy Province , about 67 kilometres away from South Vietnam 's capital of Saigon . 
- During the war about 6 @,@ 000 people lived in Bình Giã , and most of whom were <unk> anti @-@ communist . The inhabitants of Bình Giã were Roman Catholic refugees who had fled from North Vietnam in 1954 during Operation Passage to Freedom because of fears of Communist persecution . To prepare for their main battle , the Viet Cong 272nd Regiment was ordered to block Inter @-@ provincial Road No. 2 and 15 , and destroy any South Vietnamese units attempting to reach Bình Giã from the south @-@ western flank of the battlefield . In the days leading up to the battle , the Viet Cong often came out to harass the local militia forces . On December 9 , 1964 , the 272nd Regiment destroyed an entire South Vietnamese mechanised rifle company along Inter @-@ provincial Road No. 2 , destroying 16 M @-@ 113 <unk> . On December 17 , the 272nd Regiment destroyed another six armoured vehicles on Inter @-@ provincial Road No. 15 . 
- 
- = = Battle = = 
- 
- During the early hours of December 28 , 1964 , elements of the Viet Cong 271st Regiment and the 445th Company , signaled their main attack on Bình Giã by penetrating the village 's eastern perimeter . There , they clashed with members of the South Vietnamese Popular Force <unk> , which numbered about 65 personnel . The South Vietnamese militia fighters proved no match for the Viet Cong and their overwhelming firepower , so they quickly retreated into underground bunkers , and called for help . Once the village was captured , Colonel Ta Minh <unk> , the Viet Cong regimental commander , established his command post in the main village church and waited for fresh reinforcements , which came in the form of heavy mortars , machine guns and <unk> @-@ less rifles . To counter South Vietnamese helicopter assaults , Colonel <unk> 's troops set up a network of defensive fortifications around the village , with trenches and bunkers protected by land mines and barbed wire . The local Catholic priest , who was also the village chief , sent a bicycle messenger out to the Bà Rịa district headquarters to ask for a relief force . In response , the Bà Rịa district chief sent out elements of two Vietnamese Rangers battalions to retake Bình Giã . On December 29 , two companies of the ARVN 33rd Ranger Battalion and a company from the 30th Ranger Battalion were <unk> into area located west of Bình Giã , by helicopters from the U.S. 118th Aviation Company to face an enemy force of unknown size . 
- However , as soon as the soldiers from the 30th and 33rd Ranger Battalions arrived at the landing zone , they were quickly overwhelmed by the Viet Cong in a deadly ambush . The entire 30th Ranger Battalion was then committed to join the attack , but they too did not initially succeed in penetrating the strong Viet Cong defensive lines . Several more companies of the Rangers then arrived for an attack from multiple directions . Two companies of the 33rd Ranger Battalion advanced from the northeast . One of them came to the outskirts of the village but was unable to break through the enemy defenses . The other one , trying to outflank the enemy , had been lured into a kill zone in open terrain at the coffee plantation and were quickly obliterated in an ambush by the three VC battalions using heavy weapons . The two companies suffered a 70 percent casualty rate , and survivors were forced to retreat to the nearby Catholic church . The 30th Rangers had more success by assaulting from the western direction and succeeded in fighting their way into the village , aided by local residents . It however also suffered heavy losses , with the battalion commander and his American adviser severely wounded . The local civilians in Bình Giã retrieved weapons and ammunition from the dead Rangers , and hid the wounded government soldiers from the Viet Cong . The 38th Ranger Battalion , on the other hand , landed on the battlefield unopposed by the Viet Cong , and they immediately advanced on Bình Giã from the south . Soldiers from the 38th Rangers spent the whole day fighting but they could not break through their enemies ' defences to link up with the survivors hiding in the church , and fell back after calling in mortar fire to <unk> Viet Cong fighters moving to encircle them . 
- The morning of December 30 , the 4th South Vietnamese Marine Battalion moved out to <unk> <unk> Air Base , waiting to be <unk> into the battlefield . The 1 / 4th Marine Battalion was the first unit to arrive on the outskirts of Bình Giã , but the 1st Company commander decided to secure the landing zone , to wait for the rest of the battalion to arrive instead of moving on to their objective . After the rest of the 4th Marine Battalion had arrived , they marched towards the Catholic church to relieve the besieged Rangers . About one and a half hours later , the 4th Marine Battalion linked up with the 30th , 33rd and 38th Ranger Battalions , as the Viet Cong began withdrawing to the northeast . That afternoon the 4th Marine Battalion recaptured the village , but the Viet Cong was nowhere to be seen , as all their units had withdrawn from the village during the previous night , linking with other Viet Cong elements in the forest to attack the government relief forces . On the evening of December 30 , the Viet Cong returned Bình Giã and attacked from the south @-@ eastern perimeter of the village . The local villagers , who discovered the approaching Viet Cong , immediately sounded the alarm to alert the ARVN soldiers defending the village . The South Vietnamese were able to repel the Viet Cong , with support from U.S. Army helicopter <unk> flown out from <unk> <unk> airbase . 
- While pursuing the Viet Cong , a helicopter <unk> from the U.S. 68th Assault <unk> Company was shot down and crashed in the Quảng Giao rubber plantation , about four kilometres away from Bình Giã , killing four of its crewmen . On December 31 , the U.S. Marines Advisory Group sent a team of four personnel , led by Captain Donald G. Cook , to Bình Giã to observe conditions on the battlefield . At the same time , the 4th Marine Battalion was ordered to locate the crashed helicopter and recover the bodies of the dead American crewmen . Acting against the advice of his American advisor , Major Nguyễn Văn <unk> , commander of the 4th Marine Battalion , sent his 2 / 4th Marine Battalion company out to the Quảng Giao rubber plantation . Unknown to the 4th Marine Battalion , the Viet Cong 271st Regiment had assembled in the plantation . About one hour after they had departed from the village of Bình Giã , the commander of the 2 / 4th Marine Battalion reported via radio that his troops had found the helicopter wreckage , and the bodies of four American crewmen . Shortly afterwards , the Viet Cong opened fire and the 2 / 4th Marine Battalion was forced to pull back . In an attempt to save the 2nd Company , the entire 4th Marine Battalion was sent out to confront the Viet Cong . As the lead element of the 4th Marine Battalion closed in on the Quảng Giao plantation , they were hit by accurate Viet Cong artillery fire , which was soon followed by repeated human wave attacks . Having absorbed heavy casualties from the Viet Cong 's ambush , the 2 / 4th Marine Battalion had to fight their way out of the plantation with their bayonets fixed . During the entire ordeal , the company did not receive artillery support because the plantation was beyond the range of <unk> artillery guns based in Phước Tuy and Bà Rịa . They however escaped with the crucial support of the U.S. aircraft and helicopters whose rocket attacks forced the enemy to pull back and halted their attempt at pursuit . 
- In the morning of December 31 , the 4th Marines Battalion returned to the crash site with the entire force and the American graves were located and their corpses were dug up . At about 3 pm , a single U.S. helicopter arrived on the battlefield to evacuate the casualties , but they only picked up the bodies of the four American crewmen , while South Vietnamese casualties were forced to wait for another helicopter to arrive . At 4 pm , Major Nguyễn Văn <unk> ordered the 4th Marine Battalion to carry their casualties back to the village , instead of continuing to wait for the helicopters . As the 4th Marine Battalion began their return march , three Viet Cong battalions , with artillery support , suddenly attacked them from three directions . The battalion 's commanding and executive officers were immediately killed and air support was not available . Two ARVN Marine companies managed to fight their way out of the ambush and back to Bình Giã , but the third was overrun and almost completely wiped out . The fourth company desperately held out at a hilltop against Viet Cong artillery <unk> and large infantry charges , before slipping out through the enemy positions at dawn . The 4th Marines Battalion of 426 men lost a total of 117 soldiers killed , 71 wounded and 13 missing . Among the casualties were 35 officers of the 4th Marine Battalion killed in action , and the four American advisers attached to the unit were also wounded . Backed by U.S. Air Force bombers , on January 1 three battalions of ARVN Airborne reinforcements arrived , they were too late as most of the Viet Cong had already withdrawn from the battlefield . 
- 
- = = Aftermath = = 
- 
- The battle of Bình Giã reflected the Viet Cong 's growing military strength and influence , especially in the Mekong Delta region . It was the first time the National Liberation Front launched a large @-@ scale operation , holding its ground and fighting for four days against government troops equipped with armor , artillery and helicopters , and aided by U.S. air support and military advisers . The Viet Cong demonstrated that , when well @-@ supplied with military supplies from North Vietnam , they had the ability to fight and inflict damage even on the best ARVN units . For the first time in their history , the NLF was able to control a government stronghold for several days , and inflict heavy casualties on regular units of the South Vietnamese army in a large set @-@ piece battle . 
- The Viet Cong suffered light casualties with only 32 soldiers officially confirmed killed , and they did not leave a single casualty on the battlefield . In recognition of the 271st Regiment 's performance during the Bình Giã campaign , the NLF High Command bestowed the title ' Bình Giã Regiment ' on the unit to honour their achievement . Following the Bình Giã campaign , the NLF went on to occupy <unk> <unk> District and the strategic hamlets of <unk> <unk> , Long <unk> and <unk> <unk> along Inter @-@ provincial Road No. 2 and 15 . They also expanded the <unk> <unk> base area , which was located in Bà Rịa and Bình <unk> provinces , to protect the important sea transportation routes used by the Vietnam People 's Navy to supply Viet Cong units around the regions of the Mekong River . 
- Unlike their adversaries , the South Vietnamese military suffered heavily in their attempts to recapture the village of Bình Giã and secure the surrounding areas . The South Vietnamese and their American allies lost the total of about 201 personnel killed in action , 192 wounded and 68 missing . In just four days of fighting , two of South Vietnam 's elite Ranger companies were destroyed and several others suffered heavy losses , while the 4th Marine Battalion was rendered ineffective as a fighting force . At that stage of the war , Bình Giã was the worst defeat experienced by any South Vietnamese army . Despite the humiliating defeat inflicted on them , the South Vietnamese army stubbornly considered the battle as their victory and erected a monument at the site of the battle to acknowledge the sacrifices of the soldiers who had fallen to retake Bình Giã . 
- 
- = = Order of battle ( ground forces ) = = 
- 
- 
- = = = Viet Cong = = = 
- 
- 271st Regiment ( renamed the 1st PLAF Regiment and became part of the 9th Division on 2 <unk> 1965 ) 
- 272nd Regiment ( renamed the 2nd PLAF Regiment and became part of the 9th Division on 2 <unk> 1965 ) 
- 186th Battalion 
- <unk> Battalion 
- <unk> Battalion 
- <unk> Battalion 
- 445th Company 
- 80th Artillery Detachment 
- 
- = = = Army of the Republic of Vietnam = = = 
- 
- 1st Airborne Battalion 
- 3rd Airborne Battalion 
- 7th Airborne Battalion 
- 4th Marine Battalion 
- 29th Ranger Battalion 
- 30th Ranger Battalion 
- 33rd Ranger Battalion 
- 35th Ranger Battalion 
- 38th Ranger Battalion 
- Two artillery platoons and one section of M @-@ 24 tanks in support . 
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- 
- = June 1941 uprising in eastern Herzegovina = 
- 
- In June 1941 , Serbs in eastern Herzegovina rebelled against the authorities of the Independent State of Croatia ( Croatian : <unk> <unk> <unk> , NDH ) , an Axis puppet state established during World War II on the territory of the defeated Kingdom of Yugoslavia . As the NDH imposed its authority , members of the fascist Ustaše ruling party began a campaign of persecution against Serbs throughout the country . In eastern Herzegovina , the Ustaše <unk> a series of massacres and attacks against the majority Serb population commencing in the first week of June . Between 3 and 22 June 1941 , spontaneous clashes occurred between NDH authorities and groups of Serbs in the region . 
- The German invasion of the Soviet Union began on 22 June . Over the next two days , the sporadic <unk> by Serbs against the NDH in eastern Herzegovina erupted into mass rebellion , triggered by Ustaše persecution , Serb solidarity with the Russian people , hatred and fear of the NDH authorities , and other factors . Serb rebels , under the leadership of both local Serbs and Montenegrins , attacked police , gendarmerie , Ustaše and Croatian Home Guard forces in the region . In the first few days , the rebels captured gendarmerie posts in several villages , set up roadblocks on the major roads and ambushed several military vehicles . On the night of 26 June , the rebels mounted a sustained attack on the town of Nevesinje in an attempt to capture it , but the garrison held out until the morning of 28 June when NDH troops broke through the rebel roadblocks . 
- On 28 June , the rebels ambushed a <unk> of Italian soldiers , prompting the Italian Army commander in the NDH to warn the NDH government that he would take unilateral action to secure communication routes . A further gendarmerie post was destroyed by the rebels , and in the evening the rebels captured the village of Avtovac , looting and burning it , and killing dozens of non @-@ Serb civilians . The following day an Italian column cleared the rebels from Avtovac and relieved the hard @-@ pressed NDH garrison in the town of Gacko . From 3 July , an NDH force of over 2 @,@ 000 fanned out from Nevesinje , clearing towns , villages and routes of rebels . The rebel forces did not put up any significant opposition to the clearing operation , and either retreated into nearby Montenegro , or hid their weapons in the mountains and went home . By 7 July , NDH forces had regained full control of all towns and major transport routes in eastern Herzegovina . 
- 
- = = Background = = 
- 
- The Independent State of Croatia ( NDH ) was founded on 10 April 1941 , during the invasion of Yugoslavia by the Axis powers . The NDH consisted of most of modern @-@ day Croatia and Bosnia and Herzegovina , together with some parts of modern @-@ day Serbia . It was essentially an Italo – German quasi @-@ protectorate , as it owed its existence to the Axis powers , who maintained occupation forces within the puppet state throughout its existence . In the immediate aftermath of the Yugoslav surrender on 17 April , former Royal Yugoslav Army troops returned to their homes in eastern Herzegovina with their weapons . This was a significant security concern for the fledgling NDH government due to the proximity of the border with Montenegro , the close relationship between the people of eastern Herzegovina and Montenegro , and widespread <unk> in the region . On the day after the surrender , the commander of the NDH armed forces , <unk> ( Marshal ) <unk> Kvaternik issued a proclamation demanding the surrender of all weapons to NDH authorities by 24 April . 
- On 24 April , the NDH created five military command areas , including Bosnia Command and Adriatic Command , both of which were initially headquartered in Sarajevo . Each of the five military commands included several district commands . Adriatic Command included the districts of <unk> and <unk> in the <unk> hinterland , and Mostar and Trebinje in eastern Herzegovina . The NDH began to <unk> soldiers for the Home Guard , with six battalions identified to join Adriatic Command . The battalions were mobilised from areas outside of eastern Herzegovina , and were to be ready by 20 May . The aggressive actions of the Ustaše fifth column during the Axis invasion made Serb civilian leaders in eastern Herzegovina <unk> about the NDH , and they attempted to obtain Italian protection , and urged the Italians to annex eastern Herzegovina to the neighboring Italian @-@ occupied territory of Montenegro . A <unk> " Interim Advisory Committee " of Montenegrin separatists was advocating the establishment of an " independent " Montenegrin state , and a similar committee of separatist Serbs was formed in eastern Herzegovina . A delegation from that committee arrived in <unk> in Montenegro on 6 May to ask for Italian protection . Similarly , a delegation of Muslims from eastern Herzegovina travelled to Sarajevo , the historic Bosnian capital , to urge the NDH authorities to link eastern Herzegovina to that city . 
- Due to the poor response to the demand for the surrender of weapons , the deadline was extended several times until a date of 8 July was fixed . On 17 May , courts @-@ martial were established to try those that were arrested in possession of weapons , and those found guilty were immediately executed by firing squad . The precedent for this brutal repressive measure against Serbs had already been established by the Germans . It was clear from the outset that the NDH weapons laws were not being enforced as strictly against Croats as they were against Serbs . <unk> the border between eastern Herzegovina and Montenegro was considered a high priority due to concerns that the Montenegrin <unk> Party had revived Montenegrin claims to parts of the NDH that had been promised to the Kingdom of Montenegro in the 1915 Treaty of London . 
- The Italians handed over the administration of eastern Herzegovina to the NDH government on 20 May 1941 , following the signing of the <unk> of Rome , which ceded formerly Yugoslav territory along the Adriatic coast to Italy . The Italians did not immediately withdraw all their troops from the region . The NDH moved quickly to establish its authority in the towns and districts of eastern Herzegovina , which included appointing mayors and prefects , the creation of local units of the Ustaše Militia , and deploying hundreds of gendarmes , Croatian Home Guards and Ustaše Militia units from outside eastern Herzegovina . These forces were brought in to maintain order . The academic Professor <unk> <unk> was appointed the Ustaše commissioner for eastern Herzegovina . 
- On 20 May , the recently formed Home Guard battalions began to deploy into the Adriatic Command area . On 27 May , 6 officers and 300 gendarmes of the Sarajevo @-@ based 4th <unk> Regiment were deployed into parts of eastern Herzegovina . They established platoon strength posts in Nevesinje , Trebinje , Gacko and Bileća , with their headquarters also in Bileća . The Dubrovnik @-@ based 2nd <unk> Regiment established posts in Stolac and Berkovići . The headquarters of Adriatic Command was transferred to Mostar in late May , and General Ivan Prpić was appointed as its commander . 
- By 29 May , the battalions of Adriatic Command were in their garrison locations : the 6th Battalion at Mostar , the 7th Battalion at Trebinje , and the 10th Battalion in the Dubrovnik area . The other two Adriatic Command battalions were deployed to <unk> and <unk> far to the west . The 18th Battalion was allocated as a reserve and was garrisoned in Mostar . Main Ustaše Headquarters was tasked to recruit one battalion for duties within the Adriatic Command area . Home Guard battalions had a standard structure , consisting of a headquarters company , three infantry companies , a machine gun platoon and a communications section , while battalions of the Ustaše Militia consisted of a headquarters , three companies and a motorised section . Even after the establishment of NDH authorities in eastern Herzegovina , Italian forces maintained their presence in the region . The <unk> Regiment of the 32nd Infantry Division Marche remained garrisoned in Trebinje , with the 56th Regiment based in Mostar . The 49th <unk> Legion ( <unk> ) were also stationed in Bileća . The Italians maintained a troop presence in Nevesinje until 17 June , and conducted almost daily motorised patrols throughout eastern Herzegovina . 
- The NDH authorities established new administrative sub @-@ divisions , organising the state into counties ( Croatian : <unk> <unk> ) and then districts ( Croatian : <unk> ) . Eastern Herzegovina was covered by the counties of Hum and Dubrava . Hum County included the districts of Mostar and Nevesinje , and Dubrava County included the districts of Bileća , Gacko , Stolac , Ravno and Trebinje . The <unk> ( county prefect ) of Hum was Josip <unk> with his seat in Mostar , and the <unk> of Dubrava was Ante <unk> , based in Dubrovnik . 
- According to the Yugoslav census of 1931 , the population of eastern Herzegovina comprised 4 per cent Croats , 28 per cent Muslims , and 68 per cent Serbs . According to Professor <unk> Tomasevich , the estimated population of the districts of Bileća , Gacko and Nevesinje was only around 1 @.@ 1 per cent Croat , so in those areas nearly all the NDH government appointments and local Ustaše units were staffed by Muslims , an ethnic group that made up about 23 @.@ 7 per cent of the local population . The poor Muslim peasants of eastern Herzegovina largely sided with the Ustaše . The NDH government immediately tried to strengthen their position by <unk> the Serbs , who , according to Tomasevich , comprised around 75 per cent of the population . 
- 
- = = Prelude = = 
- 
- The Ustaše began to impose the new laws on the Serb population of the NDH . On 28 May , a group of ten young Ustaše students from the University of Zagreb arrived in Trebinje and began removing signs written in the Cyrillic script used by Serbs . On 1 June , in several towns and villages in eastern Herzegovina , Serbs were shot and businesses belonging to Serb merchants and others were seized . On that day , the Ustaše students in Trebinje shot nine Serbs and arrested another fifteen , apparently due to their links to the inter @-@ war <unk> Association . <unk> began to appear between the brutal treatment of Serbs by the Ustaše and the more careful approach of the other NDH authorities such as the Home Guard , who were aware of the potential danger created by Ustaše methods . In early June , the NDH authorities began operations to confiscate weapons from the population , meeting with immediate resistance . On 1 June , the residents of the village of <unk> <unk> , near Nevesinje , refused to co @-@ operate with weapons collectors . In response , the Ustaše killed a number of Serbs and burned their homes . 
- On 3 June , there were several incidents in which armed villagers spontaneously retaliated against the local authorities . That afternoon , 20 Ustaše were entering <unk> <unk> to confiscate firearms when they were attacked by a group of armed villagers . The villagers withdrew after a short firefight , with one of their number being captured . Reinforcements from the Home Guard and gendarmerie soon arrived , along with more Ustaše who burned another 20 houses and shot a woman . On the night of 4 / 5 June , a group under the control of the Ustaše commissioner for the Gacko district , Herman <unk> , killed 140 Serbs in the village of Korita , near Bileća , and threw their bodies into a nearby <unk> . Another 27 Serbs from the village were killed between this massacre and 9 June , and over 5 @,@ 000 head of livestock were stolen and distributed to Muslim villages in the Gacko area for the exclusive use of the Ustaše . The estimated number of Serbs killed at Korita vary from 133 to 180 . 
- In the immediate aftermath , Serbs and Montenegrins from the local area attacked villages , and Adriatic Command sent the 2nd Company of the 7th Battalion from Bileća to reinforce the Ustaše . After a brief clash near Korita , during which the Ustaše and gendarmerie lost one killed and several wounded , the rebels withdrew across the nearby border into Montenegro . The 2nd Company of the 7th Battalion spent the night in the village of Stepen before establishing itself as the Avtovac garrison the following day . Due to its exposure to fire from rebels overlooking their location , the gendarmes were unable to re @-@ occupy their post in Stepen , which meant that the Stepen – Korita road was no longer secure . On 8 June , the district office in Gacko reported to Adriatic Command that they had taken 200 Serbs as hostages and issued a proclamation to the population to cease fighting and surrender their weapons . As this proclamation met with no response , on 10 June the Ustaše Commissioner for Bosnia and Herzegovina , <unk> Francetić , had 19 hostages shot ( one escaped ) . On 12 June , the gendarmerie in Ravno shot four people on the orders of the Ustaše commissioner for Ljubinje . Such actions led to Serb peasants leaving their villages to seek safety in more remote areas , and Muslim villagers became increasingly nervous about their Serb neighbours . 
- In mid @-@ June , the commander of the 2nd Company of the 7th Battalion at Bileća wrote to Adriatic Command complaining about the activities of the Ustaše , referring to them as " armed <unk> and animals " who were <unk> " honest Croats " . When the Italians heard that the Ustaše had burned two villages across the border in Montenegro , they sent an intelligence officer to Gacko to investigate the unrest . He did not accept the explanation of the gendarmerie commander in Gacko , who claimed that the violence was caused by " personal hatred and revenge " , and met with rebels . The rebels did not attack him or his security escort , and told him that the reason behind the rebellion was that " Croats and Turks are beating us and throwing us into a pit " . He concluded that the cause of the unrest was the attempt to <unk> the Serb community . 
- On 17 and 18 June , <unk> and Lieutenant Colonel <unk> , gendarmerie commander for eastern Herzegovina , made an attempt to calm the situation by visiting villages east of the Gacko – Avtovac road to re @-@ establish peace in the area . They received a written message from four villages that they did not acknowledge the NDH authorities , and wanted the message to be passed on to the Italians . The residents of the villages of <unk> and <unk> were willing to talk and return to work , but they asked that the gendarmerie not visit their villages , as this would <unk> the Montenegrins to attack . <unk> assessed that while this was probably true , their request was <unk> . The gendarmerie commander in Bileća believed that the reason for the rebellion was that the local Serbs were <unk> to the idea of Greater Serbia , and did not accept that their villages were part of the NDH . This approach essentially meant that local Serbs wanted the NDH authorities to leave them alone and not impose on their lives . According to the historian <unk> Marijan , this was a poor choice that gave the Ustaše an excuse to take radical action . 
- The response of the NDH authorities to resistance had been to burn down the villages where this had occurred , and there were mass shootings of Serbs , which escalated the level of violence even further . In late May and June , 173 Serbs had been rounded up , tortured and killed in Nevesinje , and in early June , another 140 Serbs had been killed at Ljubinje . In response , Serbs attacked Ustaše officials and facilities , and conducted raids themselves , killing Muslim villagers . 
- 
- = = Uprising = = 
- 
- The NDH authorities only had weak forces in eastern Herzegovina at the time the mass uprising occurred , roughly equal to two Croatian Home Guard battalions , as well as gendarmerie posts in some towns . This was barely adequate to guard important locations , and was insufficient for offensive action . <unk> forces consisted of one company of the 10th Battalion in Trebinje , the headquarters and a reinforced company of the 7th Battalion in Bileća ( the balance of the battalion being divided between Gacko and Avtovac ) , and a company of the 6th Battalion in Nevesinje . The remainder of the 10th Battalion was deploying to Trebinje at the time the rebellion broke out . 
- 
- = = = 23 – 24 June = = = 
- 
- The first indication that the situation had changed significantly was on 23 June , when a group of 200 Ustaše clashed with a group of rebels they estimated to number between 600 and 1 @,@ 000 . After an extended firefight near the village of Stepen , 5 km ( 3 @.@ 1 mi ) north of Korita , during which they suffered several casualties , the Ustaše also burned down four villages . They then entered two Muslim @-@ majority villages in the area and arrested 13 Serbs who had not been involved in the earlier fighting . The arrested Serbs were transported north to Avtovac and shot . That night , all adult Serbs above the age of 16 in Gacko , 4 @.@ 5 km ( 2 @.@ 8 mi ) northwest of Avtovac , were arrested , and 26 were immediately shot . The rest were transported 50 km ( 31 mi ) west to a camp in Nevesinje . Over the period 23 – 25 June , 150 Serbs from the village of Ravno , 30 km ( 19 mi ) southwest of Ljubinje , were arrested and killed at the gendarmerie post , and the remainder of the population fled to the hills . 
- On 23 and 24 June , spontaneous mass gatherings occurred at several villages in the Gacko and Nevesinje districts . These rallies were prompted by the news of the German invasion of the Soviet Union , and those attending them voted to fight against the Ustaše . Professor <unk> <unk> Hoare states that the full @-@ scale uprising resulted from the Ustaše retaliation against attempts of the Serbs of eastern Herzegovina to defend themselves , combined with the launching of the German invasion on 22 June . At dawn on 24 June , the area of Nevesinje descended into full @-@ scale revolt , with around 400 armed rebels engaging the Home Guard garrison . By 24 June , the uprising had reached a massive scale across eastern Herzegovina , with between 1 @,@ 500 and 3 @,@ 000 armed rebels in total , including some Montenegrins . 
- 
- = = = 25 June = = = 
- 
- On the morning of 25 June , the company of the 6th Battalion at Nevesinje reported that rebels were gathering to attack the town ; Nevesinje 's Ustaše commissioner claimed that the rebel force numbered 5 @,@ 000 , and were led by a former Yugoslav Army colonel . About 10 : 00 , the town was attacked from the south and southwest . In response , the Home Guard despatched two more companies of the 6th Battalion from Mostar to Nevesinje . That morning , reports also arrived from Bileća and Stolac that rebels were approaching the village of Berkovići from the north , and had captured the gendarmerie post at <unk> <unk> . About 11 : 30 , the Ustaše commissioner for Stolac reported that 3 @,@ 000 Montenegrins had gathered between Nevesinje and Stolac , and he requested the immediate supply of 150 rifles for his men . A rebel attack on the gendarmerie post in the village of <unk> near Bileća was repulsed around midday . A platoon of Home Guard reinforcements and weapons for the Ustaše arrived at Stolac in the afternoon , and Bileća was held throughout the day . 
- Reports of the uprising reached Kvaternik during 25 June , but he dismissed them and the reports of 5 @,@ 000 rebels , <unk> Adriatic Command 's redeployment of the 21st Battalion from <unk> <unk> as well as a request to the Italians for air reconnaissance support . He stated that the suppression of the uprising could be handled by local forces . Loss of communication with Nevesinje resulted in rumours that the town had fallen to the rebels . The gendarmerie post at <unk> ( near Gacko ) was captured on the afternoon of 25 June , with the survivors escaping to Gacko . Newspapers reported rumours that Gacko and Avtovac had fallen to the rebels . Having already despatched a reinforced company towards Nevesinje from Sarajevo earlier in the day , Adriatic Command ordered the rest of the battalion to follow . The initial company group had already reached <unk> some 60 kilometres ( 37 mi ) from Nevesinje , and the rest of the battalion was expected to spend the night of 25 / 26 June there before arriving in Nevesinje around noon on 26 June . Kvaternik received an updated report on the situation in eastern Herzegovina during the night , and Prpić travelled from Sarajevo to Mostar to take control of operations , to find that information about the situation in eastern Herzegovina was unclear , but suggested that NDH forces could be facing serious difficulties . 
- 
- = = = 26 June = = = 
- 
- On the morning of 26 June , the company of the 6th Battalion that had been sent from Mostar continued towards Nevesinje , but almost immediately came under fire from a rebel group . With the assistance of Ustaše , the Home Guard were able to hold their ground , but they were unable to break through to Nevesinje . That afternoon , two aircraft of the Air Force of the Independent State of Croatia ( Croatian : <unk> <unk> <unk> <unk> , ZNDH ) from Sarajevo conducted an armed reconnaissance over eastern Herzegovina , and discovered that NDH forces still held Nevesinje . They observed barricades across the Mostar – Nevesinje road , and <unk> a group of 50 rebels north of Nevesinje near the village of Kifino Selo . Prpić bolstered the force on the Mostar – Nevesinje road with the 17th Battalion , recently arrived from Sarajevo , and sent his deputy , Colonel <unk> Prohaska to command it . The 17th Battalion joined that force at 20 : 00 . About 17 : 00 , the company of the 11th Battalion reached Nevesinje from <unk> , and a further company of the battalion was despatched from Sarajevo , along with the battalion commander . 
- In the southern part of the area of operations around Stolac , the situation was significantly <unk> than around Nevesinje , although a group of 200 Ustaše at Berkovići were falsely claiming that they were being surrounded by rebels at night . Despite this claim , they had suffered no casualties . Regardless , Prpić sent them ammunition and a platoon of the 18th Battalion . At 19 : 00 on 26 June , Francetić arrived at Prpić 's headquarters in Mostar to be briefed on the situation . He resolved that he would travel to Berkovići the following day and take personal command of the Ustaše unit there . 
- Around Gacko and Avtovac in the north , the day had been quiet . When the commander of the 2nd Company of the 7th Battalion at Gacko reported rebels gathering near the town , Prpić sent a truck @-@ mounted platoon with an ammunition resupply . The platoon was ambushed en route , with 14 Home Guardsmen being captured . Gacko was reinforced later in the day from troops in Avtovac . On the night of 26 June , the Nevesinje garrison was subjected to a sustained attack by the rebels , but held out . 
- The NDH authorities in Trebinje heard rumours that the Serbs could start an uprising there on 28 June , the feast day of Saint <unk> , and warned NDH forces in the region to be prepared for a revolt . As a result of these reports , the <unk> ( leader ) of the NDH , Ante Pavelić , issued orders threatening that anyone who spread these rumours would be court @-@ <unk> . On the eve of the feast day , both the gendarmerie and Ustaše took several hostages in case the rumours were true . Later , the gendarmerie released their hostages , but the 19 hostages held by the Ustaše were killed . In contrast to the actions of the Ustaše , the Home Guard units in the area tried to calm the situation down . 
- 
- = = = 27 – 28 June = = = 
- 
- On the morning of 27 June , Prpić launched a three @-@ <unk> assault to clear the routes to Nevesinje . Prohaska commanded the push east along the Mostar – Nevesinje road by a force close to two battalions , Francetić led his unit of Ustaše north from Berkovići through the mountains via Odžak to approach Nevesinje from the south , and two companies of the 11th Battalion thrust southwest along the road from Plužine . Once this task was complete , the NDH forces were to vigorously pursue the rebels and destroy them . 
- The Prohaska group deployed with one company on the road , and elements of the 17th Battalion and 70 Ustaše on the left flank . Their attack commenced about 10 : 00 , and although they faced strong resistance from the rebels , aided by <unk> and bombing by ZNDH aircraft , they reached villages on the outskirts of Nevesinje after fighting that lasted until dawn on 28 June . One Home Guard battalion halted and took up a defensive stance , and the commander was threatened with dismissal by Prpić before he resumed the attack . Francetić 's Ustaše unit also faced heavy fighting , and had to call for ammunition resupply on two occasions . One of the resupply vehicles was ambushed by rebels between Stolac and Berkovići , and some ammunition was finally delivered by passenger car during the night . Elsewhere , rebels attacked Gacko and Avtovac , and one ZNDH aircraft was shot down by rebel machine gun fire near Avtovac . That night , Prpić telephoned Kvaternik and advised him that the imposition of martial law was necessary to restore order to Herzegovina . Army Chief of Staff General Vladimir Laxa was immediately appointed by Pavelić to control both Hum and Dubrava counties , which incorporated much of eastern Herzegovina . 
- On 28 June , Laxa became the overall commander of all NDH authorities in Hum and Dubrava counties , which included Ustaše , Home Guard , civil administration , gendarmerie and police . Military courts were established to deal with those resisting the NDH authorities . Armed guards were posted at the entrance to towns and villages , and any armed civilians were to be disarmed and brought to military authorities . Laxa issued an order that gave the rebels until 2 July to submit to the authorities . On that day , after the Prohaska group broke through to Nevesinje from Mostar , Prohaska sent a company of the 6th Battalion to Kifino Selo to meet the two companies of the 11th Battalion advancing from Plužine . Despite ZNDH air support , the company of the 6th Battalion was attacked by rebels near the entrance to Kifino Selo and the majority broke and ran . Prohaska had to send reserves to block the road between Nevesinje and Kifino Selo , and the companies from the 11th Battalion began to reconnoitre the rebel positions towards Odžak . 
- Also on that morning , the 200 Home Guard troops and about 50 armed locals in Avtovac were attacked from three directions by rebels . They recovered from their initial surprise and held the town during the day , but in the evening a renewed assault caused them to withdraw from Avtovac and retreat to the villages of <unk> and <unk> . Upon capturing Avtovac , the rebels looted the village , burned down a large number of Muslim homes and killed 32 Muslim civilians , mostly women , children and the elderly . Gacko was also attacked by the rebels , with eight soldiers killed , and one officer and 12 soldiers wounded . Also on 28 June , two Italian Army trucks driving from Bileća to Avtovac were ambushed by rebels , who killed three soldiers and wounded 17 . Around 18 : 00 , the Italian command advised Kvaternik that they would be clearing the route from Bileća via Gacko to Nevesinje on an unspecified future date . During the fighting around Gacko , several ZNDH aircraft were forced to land due to pilot casualties and engine trouble . ZNDH air support operations were suspended due to lack of fuel and <unk> for the aircraft . 
- There was no improvement in the situation around Stolac , and an Ustaše unit made up of armed civilians proved to be of such low combat value that Laxa spoke to Francetić and criticised its performance . South of Bileća , rebels destroyed the gendarmerie post in a village , killing seven gendarmes . Dozens of gendarmes were sent from Trebinje to assist them , but they were stopped by rebels and withdrew into a village schoolhouse . In the afternoon a platoon of the 10th Home Guard Battalion was sent north from Trebinje to support the gendarmes , but they were attacked near the village of <unk> , and withdrew into a defensive position . They were reinforced by a second platoon during the night , and were given orders to clear the road from Trebinje to Bileća on the following morning ahead of the Italians . 
- 
- = = = 29 – 30 June = = = 
- 
- At dawn on 29 June , the rebels attacked the Ustaše in a village on the Mostar – Nevesinje road . Prohaska demanded help from Mostar , and planned to send a force from Nevesinje to assist . From Mostar , a company of the 21st Battalion was despatched to relieve the Ustaše , who had managed to hold off the rebels . The Home Guard company then took over the post from the Ustaše . The same day , two new battalions arrived in Mostar , the 23rd Battalion from <unk> and the 15th Battalion from <unk> . These reinforcements arrived just as Prpić received confirmation that Avtovac had been captured by the rebels . The remaining small garrison in Gacko , consisting of only 20 gendarmes and 30 Ustaše , were holding out but expecting more attacks by the rebels . In the morning , the attack by elements of the 10th Battalion stalled until the battalion commander , Lieutenant Colonel <unk> <unk> , personally took command of the operation , clearing the way for the Italians . The promised Italian intervention commenced about midday , and about 100 trucks of Italian soldiers arrived in Gacko about 17 : 00 . As they had passed through Avtovac , the rebels had left the town and withdrawn to villages to the east . About 18 : 00 , the 10th Battalion relieved the besieged gendarmes in the village schoolhouse . ZNDH aircraft from Mostar airfield flew reconnaissance sorties over the area and dropped leaflets over Stolac , Stepen , Avtovac , Gacko and Plužine . 
- After the garrison of Nevesinje had been relieved , Laxa directed his main effort towards the Gacko and Avtovac districts . <unk> to the fact that the Italians had not respected the territorial borders of the NDH when they sent their column to Gacko , he considered it very important that Croatian military and political prestige be restored , otherwise the Italians might decide to remain in the area rather than withdraw to their garrison near the Adriatic coast . He planned to follow this consolidation by clearing the border areas with Montenegro then clearing the hinterland of any remaining rebels . For this last task he intended to deploy a yet @-@ to @-@ be @-@ formed special unit to be led by Lieutenant Colonel Josip <unk> . The task of re @-@ asserting NDH authority in the Gacko and Avtovac districts was allocated to Prohaska 's group , consisting of the 6th Battalion , one company of the 18th Battalion , two companies of the 17th Battalion , and the recently arrived 15th and 21st Battalions , which were to be sent to Nevesinje from Mostar . Prohaska was to act in concert with the 11th Battalion who were already in the vicinity of Plužine , just to the north of the Nevesinje – Gacko road . In preparation , the 15th Battalion was <unk> to Nevesinje , and a company of the 17th Battalion conducted a coordinated attack with the 11th Battalion on rebel positions near Kifino Selo . This attack was defeated by the rebels , and a battalion commander was killed . 
- During the remainder of the day , the Italians collected the bodies of their dead from the rebel ambush on 28 June , and rescued some Home Guard troops that had escaped Avtovac , but then returned to <unk> , just north of Bileća . The value of further operations in the Gacko and Avtovac areas was brought into question when the Italians reported that both towns had been burned to the ground , and all the inhabitants had been massacred . The Italians blamed Montenegrins attached to the rebels for the destruction and killings in the two towns . The Italian estimate of rebel strength was around 3 @,@ 000 armed with machine guns , artillery and anti @-@ aircraft guns . A German intelligence officer from Sarajevo arrived at Prpić 's headquarters in Mostar to receive a briefing on the situation . The small garrison of Gacko was anticipating an attack by rebels during the night , but in the afternoon 180 Home Guardsmen that had withdrawn from Avtovac arrived to bolster their position , and the night passed without incident . 
- 
- = = = 1 – 7 July = = = 
- 
- On 1 July , an Italian armoured unit arrived in Gacko to reinforce the garrison . An Ustaše operation to clear the insurgents from the Stolac district began on 3 July , meeting with success and opening of the road from <unk> north to Odžak . The Ustaše did not go closer to Nevesinje as they were not in uniform , and were concerned that the Home Guards would mistake them for rebels . During this operation , three Ustaše were killed , including their commander , and the Ustaše fighters killed ten rebels and captured two . In the belongings of one of the captured rebels , the Ustaše located a report by the " National Movement for the Liberation of Nevesinje " ( <unk> @-@ Croatian : <unk> <unk> <unk> <unk> <unk> ) , which was apparently how the rebels referred to themselves . The report made it clear that the rebels were using military tactics and organisation , and hinted at co @-@ operation with the Italians . According to information gathered by the police , the local rebel leadership included former Mostar merchant <unk> <unk> , the <unk> brothers from Avtovac , the Orthodox priest Father <unk> from <unk> , and a Captain <unk> from Avtovac . Montenegrins involved in the leadership of the uprising included Colonel <unk> <unk> , Major <unk> <unk> , and <unk> <unk> from <unk> . 
- Following the Italian intervention , Prpić was able to proceed with the task of clearing the wider area of Nevesinje from 3 July , ensuring NDH control of population centres and roads . On 5 July , he replaced his deputy Prohaska with Colonel Franjo <unk> , and assigned him a force consisting of the 6th , 11th , 15th and 17th Battalions , a company of the 18th Battalion and a troop of artillery . The force numbered 62 officers and 2 @,@ <unk> men , with heavy weapons including four 100 mm <unk> <unk> <unk> 14 mountain howitzers , six heavy machine guns and twenty @-@ seven light machine guns . <unk> seized the crossroads near Kifino Selo and Plužine , securing it with one company of the 11th Battalion , then sent the 15th Battalion to Gacko and the 17th Battalion to Berkovići . A half company of the 21st Battalion secured the Mostar – Nevesinje road . Once this was completed , the major roads in eastern Herzegovina were secured . These operations proceeded without significant fighting , as some of the rebels retreated over the border with Montenegro , and others hid their weapons in the mountains and returned to their homes . By 7 July , NDH forces had regained full control over all the towns and transport routes in eastern Herzegovina . 
- 
- = = Aftermath = = 
- 
- Tomasevich states that the uprising was a " spontaneous , <unk> outburst " that was doomed to failure , and involved neither the <unk> of <unk> <unk> nor the Communist Party of Yugoslavia ( <unk> @-@ Croatian : <unk> <unk> <unk> , KPJ ) . He contends that the uprising was the result of several factors , including the Ustaše <unk> , fear and hatred of the NDH authorities , a local tradition of rebellion against the Ottoman Empire , the poor economic conditions in eastern Herzegovina , and news of the launching of Operation Barbarossa against the Soviet Union . Hoare <unk> with Tomasevich that the uprising was in the tradition of the <unk> rebellions against the Ottoman Empire during the 19th century , such as the uprisings in 1875 – 77 . Edmund <unk> @-@ <unk> , the German <unk> General in the NDH , believed that the Italians might have deliberately avoided interfering in the uprising . General <unk> <unk> , the commander of the Italian 6th Army Corps , blamed the Ustaše and Muslims for <unk> the revolt . 
- In eastern Herzegovina , the KPJ had little impact until mid @-@ August 1941 , well after the initial revolt had been suppressed . During the lead @-@ up to the mass uprising , the KPJ organisation in Herzegovina would not commit itself , as it was waiting for orders from the provincial organisation in Sarajevo , which was expecting direction from the KPJ Central Committee to launch a general uprising across Yugoslavia . Once they became aware of the German attack on the Soviet Union , the KPJ in Herzegovina voted to join the mass uprising , but this only occurred on 24 June , when the uprising was already in full swing . According to <unk> , the rebels remained a threat throughout eastern Herzegovina well into July , although the uprising in Herzegovina did not advance until the Bosnia @-@ wide revolt occurred at the end of July , by which time the KPJ was ready for active involvement in the fighting . 
- 
- 
- = Crush ( video game ) = 
- 
- Crush is a platformer @-@ puzzle video game developed by <unk> Entertainment 's Zoë Mode studio and published by Sega in 2007 for the PlayStation Portable . Its protagonist is Danny , a young man suffering from insomnia , who uses an experimental device to explore his mind and discover the cause of his <unk> . Each level of the game , representing events from Danny 's life and inspired by artists such as Tim Burton and <unk> <unk> , requires the player to control Danny as he collects his " lost marbles " and other thoughts . 
- Crush 's primary gameplay feature involves manipulating each game level between 3D and 2D views , allowing the player to reach platforms and locations inaccessible from within a different view . This element was noted by critics to be similar to one in Super Paper Mario , also released in 2007 , though the Zoë Mode team had envisioned the concept five years prior . Crush received positive reviews upon release , with critics praising its incorporation of this dimension @-@ shifting component alongside other aspects of the game presentation . Though Crush won several gaming awards , including PSP game of the month , it failed to meet the developer 's sales expectations . A port of the game for the Nintendo 3DS called Crush 3D was announced on January 19 , 2011 and was made available on January 13 , 2012 in Europe and on March 6 , 2012 in North America . 
- 
- = = Plot = = 
- 
- While Crush and its Nintendo 3DS port Crush 3D retain the same gameplay mechanics and premise , the two versions feature different plots . 
- 
- = = = PSP version = = = 
- 
- ( Note : The voice actors for the story are unknown at the moment as Zoë Mode never credited them ) 
- The protagonist of the game , a young man named Danny , suffers from chronic insomnia caused by worry , stress , and <unk> memories . He is admitted to a mental institution for it , where he <unk> a mad scientist , Dr. Reubens , who treats Danny with his <unk> <unk> <unk> <unk> <unk> ( C.R.U.S.H. ) device , which has a sentient female persona . The device 's helmet places Danny under hypnosis , during which he can regain control of his <unk> by collecting his lost marbles , and facing his <unk> fears in the form of monsters ( i.e. cockroaches , slugs ) . Danny starts <unk> the beans to Reubens about his city life ; moving into his first apartment and his suffering job as a chef ( because " the ladies love a man who can cook " ) . He heavily depends on Reubens for some serious help if he wants to get rid of his insomnia . Later , when C.R.U.S.H. projects the image of a bucket and shovel at the beach onto the helmet , he recalls his love life some few years ago with a girl named Tina Ash ( who Reubens mistakenly thought was Danny 's mother ) . Returning the next summer , Danny is <unk> for a more handsome man . <unk> , Danny ends up sitting on the pier crying , inadvertently falling into the ocean below , but eventually rescued . Reubens claims Danny 's rejection made him " shy away from the <unk> sex " , and tells him to get some rest . The next day , Dr. Reubens tunes up his machine to " match [ Danny 's ] abnormally low cognitive frequency . " Danny recommends he give them some time alone , but the word " alone " has Reubens allude to the time Danny was heavily abused as a young boy at a local funfair by three thugs , however , Danny <unk> that he <unk> his parents to go to the funfair alone , but they find and rescue him . He then promised his parents that he 'd " stay with them forever . " Dr. Reubens uses this evidence to come to a conclusion that <unk> is the cause of his insomnia , recommending that Danny moves back into his parents ' house , but his conclusion is proved wrong when Danny returns one more time . Dr. Reubens lashes out at Danny for wearing out his machine , demanding that Danny <unk> up to where he " buried [ his ] pain " and if he is merely " <unk> " with them . <unk> the limit , Reubens subjects Danny to <unk> all the way to his childhood , when he was only six years old , against Danny 's own will . Danny cries at the breaking point , <unk> that Reubens stop " unlocking doors that should stay <unk> , " but in the end , Danny <unk> the words , " Come back , " implying that his parents went out on a date and left him at his house alone , with disturbing shadows of the night leaving Danny in psychological trauma . Danny discovers that his childhood fears at that time , principally being afraid of the dark , are the real cause his insomnia , sending him " <unk> back into [ his ] six @-@ year @-@ old self . " Reubens is full of disgust that his treatment was a complete waste and that Danny ruined his machine due to his <unk> , but tells Danny he 's cured . Right when Reubens begins to snap Danny out of hypnosis , C.R.U.S.H. , following suit in Reubens thoughts , goes over the edge and seems to attack Danny 's mind , with Reubens <unk> and Danny 's life on the line while the screen fades to black at the start of the attempt . 
- 
- = = = 3DS version = = = 
- 
- Doctor ' Doc ' <unk> ( instead of Reubens in the PSP version ) , is infuriated that all of his inventions failed , but today he ' shall surpass them all ' with his latest invention , C.R.U.S.H. He uses a tape recorder for his scientific narrative on the experiment . With the help of his best friend and protégé , Danny ( a running gag consists of him <unk> Doc throughout the game ) , he enters his mind as his subject to C.R.U.S.H. , but after a few tests , Danny is trapped inside C.R.U.S.H. , where he can collect his lost marbles ( which will unlock new dressing gowns / robe designs ) and facing his fears in the form of monsters ( i.e. cockroaches , slugs ) . At one point , Doc tells Danny that C.R.U.S.H. will not let Danny out due to " unresolved feelings . " The two question that reasoning as they progress . Towards the end , C.R.U.S.H. interrupts contact between Danny and Doc , overly infatuated with Danny . Danny slowly admits to C.R.U.S.H. that she is simply a machine and compares it to " flirting with a parking meter . " C.R.U.S.H. , infuriated , sends Danny back to his childhood as a result . When Doc comes back into contact with Danny , Danny lashes out at Doc for building a machine " with the heart of a teenage girl " who " <unk> " at him . Doc , calming Danny down , explains how this " <unk> " is not really his , but old data , and suddenly Danny becomes shocked when he realizes he took the place of someone who underwent treatment with C.R.U.S.H. ( being the PSP Danny ) , but " she knows [ he 's ] not the same . " C.R.U.S.H. really wanted Danny to clear her head . In the ending , Danny finally escapes and is free out of <unk> 's mind . He and Doc discuss <unk> 's future , while Doc says that he 'll fix her . In a sudden plot twist , it turns out that they were not in the real world at all , but all the levels throughout the game are outside Doc 's lab . C.R.U.S.H. <unk> and laughs into the camera , she <unk> and the screen cuts to black . 
- 
- = = Gameplay = = 
- 
- Crush contains ten levels in each of the four locations , all based on an event in Danny 's past . The levels represent Danny 's mind : a dark city landscape with many tall buildings and the occasional street lamp , a hotel resting aside a seaside location , a dark and mysterious funfair , and a haunted childhood bedroom . Levels are mostly composed of platforms formed by blocks . The player 's goal in each level is to collect marbles , which give the player points based on their color . The exit from the level is opened once a predetermined number of points have been collected . Danny can <unk> into narrow areas and jump a small height . 
- The main gameplay feature of Crush is the ability to <unk> the layout of a level between 2D and 3D representations to reach seemingly inaccessible areas and solve the game 's puzzles . The player can switch the third @-@ person camera between four directional side views and a top @-@ down view at any time while in 3D . When in these views , the player can have Danny " crush " the level , collapsing all 3D elements into 2D ; crushing from side views results in a 2D platformer @-@ like view , while crushing from the top @-@ down view provides a 2D top @-@ down perspective . <unk> can connect and merge platforms on the same visual plane in the 3D view but separated by a large distance , creating pathways across the level in the 2D view . The player can also " <unk> " the level at any time . Certain blocks , when crushed , become either obstacles through which Danny cannot pass , or ledges the player can use to reach other parts of the level . Attempts to crush a level in any manner that would harm Danny are thwarted . However , <unk> can leave Danny in a helpless state , such as hanging in mid @-@ air . 
- Enemy monsters inhabit the levels , but the player can crush them by flattening impassable blocks against them . The player may also encounter <unk> in the form of alarm clocks that will begin to <unk> when crushed for the first time and can only be stopped when Danny jumps on them . Danny wakes from his mental <unk> if he falls off the level or too high of a surface , is touched by a monster , or fails to stop a <unk> , but the C.R.U.S.H. device <unk> him at the start of the level or the last <unk> Danny passed . 
- Throughout the levels are scattered large spheres and cylinders , which the player can roll when crushed appropriately . These can then be used as platforms or to depress switches . <unk> pieces can be collected to reveal concept art and extras in the game 's menus . Some of Danny 's " thoughts " , represented by glowing neon icons on the walls of the level , are only activated when the level is crushed in a manner that does not obscure them . Some thoughts allow Danny to jump higher or stop time . Once the player completes each level , they are graded by the duration of Danny 's stay on the level , how many times Danny " <unk> up " , and a bonus for collecting all marbles , the <unk> piece , and a hidden thought trophy . The thought trophy , once completed by crushing , unlocks a level 's special challenge mode for later play which requires time @-@ limited completion of the level with an allotted number of <unk> . 
- 
- = = Development = = 
- 
- In an interview , Zoë Mode executive producer Paul Mottram claimed that the game concept was envisioned in 2002 , but work did not actually begin until 2006 . The initial concept was built on the crushing mechanism between 2D and 3D , and they only had to create appropriate obstacles to prevent players from simply " crushing " across the level . Mottram noted that during the development of Crush , the gameplay of Super Paper Mario had not yet been revealed , and thus the team was surprised to learn that it shared a similar feature . 
- Mottram stated that the crushing mechanism had been developed and refined for six months before developing the story and characters ; the development team wanted to have " a normal person in an impossible situation " . The art and level design were inspired by Tim Burton , Mike <unk> , and <unk> <unk> . The plot was originally more <unk> than in the final product , with Danny dying and the rest of the game told as flashbacks . 
- The game levels were developed on a level editor on the PlayStation Portable , but Zoë Mode were not able to refine the editor in time for shipping . Mottram said " It would be great to see user generated content and this is something we have been seriously thinking about for the future " and that downloadable content " would work perfectly with the Crush level structure and I am sure that fans of the game would be eager to see more levels . " Mottram has stated they would like to market a sequel based on the highly positive feedback they had received ; however , with other Zoë Mode projects such as <unk> and Play taking precedence , he does not know when this could be . 
- 
- = = Reception = = 
- 
- Crush received generally positive reviews , with aggregate scores of 83 out of 100 from Metacritic and 82 % from Game Rankings . The game was highly praised for its innovative approach to gameplay . Ryan Davis of GameSpot appreciated Crush for owing " very little of its novel concept to games that preceded it " . Nick <unk> of <unk> called the game a " <unk> rewarding , expertly designed puzzle experience that truly plays like nothing else " . Reviews were mixed on the game 's learning curve . IGN 's Jeremy Dunham praised the ordering of the puzzle elements , that new gaming elements are introduced at " an ideal pace " , and that most puzzles have solutions where the player must " think ' outside the box ' " . Eurogamer 's Dan Whitehead commented that the game introduces these elements too quickly and " doesn 't give you much time to put the <unk> into practice " . Some critics found that elements of the game detracted from the game 's uniqueness . X @-@ Play 's Greg Orlando , while stating that this was " one of the most novel games ever made " , noted that it was " simply not very fun " , as it lacked many player <unk> beyond manipulating the world and collecting objects on each level . Reviewers noted that some puzzles were awkward due to the selection of the PlayStation Portable 's controls . Reviewers found the game 's story poor , but this was overcome by the gameplay elements ; Charles Harold of the New York Times said " the minimal story is as forgettable as its puzzles are ingenious " . GamesRadar included it in their list of the 100 most overlooked games of its generation . Editor Jason <unk> stated that the ability to switch from 2D to 3D was " one of the most unique ( at the time ) features we ’ ve ever seen . " 
- IGN awarded Crush PSP Game of the Month for May 2007 . GameSpy called Crush the third best PlayStation Portable game and the " PSP Puzzle Game of the Year " for its Game of the Year 2007 awards . Similarly , IGN awarded Crush the " Best PSP Puzzle Game " , " Most <unk> PSP Game " , and " Best PSP Game No One Played " awards in their Game of the Year 2007 selections . Crush won the 2007 <unk> Conference Industry Award for " Best New <unk> IP " . According to Paul Mottram , positive reception of the game did not translate into high sales , but the game " will hopefully stick around for a while and continue to shift units " . 
- 
- 
- = Odyssey Number Five = 
- 
- Odyssey Number Five is the fourth studio album by the Australian rock band Powderfinger , produced by Nick <unk> and released on 4 September 2000 by Universal Music . It won the 2001 ARIA Music Award for Highest Selling Album , Best Group and Best Rock Album . The album was the band 's shortest yet , focusing on social , political , and emotional issues that had appeared in prior works , especially Internationalist . 
- The album produced four singles . The most successful , " My Happiness " , reached # 4 on the ARIA Singles Chart , won the 2001 ARIA Music Award for " Single of the Year " , and topped Triple J 's Hottest 100 in 2000 . The album also featured " These Days " , which topped Triple J 's Hottest 100 in 1999 . The album was also ranked at number 1 in Triple J 's Hottest 100 Australian Albums of All Time poll in 2011 . 
- Many critics lauded the album as Powderfinger 's best work ; one stated that the album was " the Finger 's <unk> Glory " , however , others were critical of the " imitation " contained in the album . Overall , the album won five ARIA Music Awards in 2001 and was certified platinum seven times , and earned an eighth in 2004 . Odyssey Number Five was Powderfinger 's first album to chart in the United States , and the band toured extensively around North America to promote its release . 
- 
- = = Background , recording , and production = = 
- 
- In a 1997 interview , Powderfinger bassist John Collins hinted that the group 's next album would be similar to their previous album , Internationalist , while frontman Bernard Fanning said in September 2000 that the lyrics on the album , like those on " Waiting for the Sun " , were his " most personal and direct yet " . Fanning said his lyrics were based on the " obstacles in the way of being in a relationship , especially in our work situation " . 
- Powderfinger worked with producer Nick <unk> on Odyssey Number Five , as they had done on Internationalist , finishing the album in August 2000 after six weeks of recording . The band spent this time ensuring higher quality songs than on Internationalist , which had featured out @-@ of @-@ tune guitars on " Passenger " . 
- Odyssey Number Five was Powderfinger 's shortest album when recorded , running for approximately 45 minutes . The focus of the album was on restraint , with more simplistic lyrics than previously , and with a plain and simple message intended . Fanning said of his songwriting ethic : " You try and make it something that ’ s got some substance , but also , you can never do that at the cost of it having relevance to what you ’ re singing . " Powderfinger manager Paul <unk> commented that " their ethos is to be pushing the limits of their songwriting ability " . 
- Like Powderfinger 's previous album Internationalist , Odyssey Number Five commented on social and political issues heavily , with the primary point of focus being Aboriginal affairs . The lyrics of " Like a Dog " attacked former Prime Minister of Australia John Howard 's Liberal government for its treatment of Indigenous Australians , and for breaking the " relaxed and comfortable " promise he made in the Australian federal election , 1996 . Lead singer Bernard Fanning related this to the band 's other ethical <unk> — refusing to appear on Hey Hey it 's Saturday , for its anti @-@ gay commentary , or not allowing Powderfinger songs to be used in <unk> , amongst others — stating , " We 're not here to set an example . We just want to be happy with ourselves and not end up with a guilty conscience . " Fanning said that despite " Like a Dog " being about a political issue , it was not a political song , rather just Powderfinger " voicing our opinions " . The band worked with boxer Anthony <unk> on the song 's music video , whom Fanning praised as " the perfect lead , in terms of what the song is about and the fact that he ’ s prepared to speak up for what he believes in . " 
- As well as providing social commentary , Odyssey Number Five also discussed love , a recurring motif in Fanning 's songwriting . Fanning noted that one of the causes of this was his passion for soul and gospel music , stating that he " listen [ s ] to a lot of soul music that 's <unk> about love and how good it makes you feel " . Lead guitarist Ian <unk> agreed , and also noted that the band as a whole were fully committed to Fanning 's lyrics , stating " It 's really important for us to agree with what Bernard is singing . " 
- Odyssey Number Five marked Powderfinger 's first successful attempt to enter the United States market . Fanning told Billboard in a 2001 interview that the band were not taking anything for granted , however , stating , " In America , we haven 't really done any work yet to deserve any major popularity " , with the " <unk> " on previous albums failing to reach the American mainstream . Powderfinger toured extensively around the country , performing in 22 cities . As a result of these efforts , " My Happiness " was briefly placed on rotation on <unk> @-@ FM and several other radio stations . The song ultimately peaked at # 23 on the Modern Rock Tracks chart . This success was assisted by the band appearing on the Late Show with David Letterman , and by supporting Coldplay on tour . Guitarist Darren Middleton summarised their work in the United States by stating " This year has been a bit of a blur . " 
- 
- = = Album and single releases = = 
- 
- Odyssey Number Five was released on 4 September 2000 , on the <unk> / Universal record labels . The album was released in the United Kingdom on <unk> , with 15 minutes of video and an additional track Nature Boy , at a later date . A sampler version was released in the United States in 2001 , containing five tracks . 
- Four singles were released from the album . " My Kind of Scene " was the first ; released as a promotional single in June 2000 . The track was written for the 2000 film Mission : Impossible II , and appeared on its soundtrack . Collins and Middleton recalled that the song was written and produced with photos of Tom Cruise and Nicole Kidman on the wall of the band 's studio " as inspiration " . They noted that the band made three songs in response to the Mission : Impossible II request , and that " My Kind of Scene " was chosen over " Up & Down & Back Again " and " Whatever Makes You Happy " . 
- The second single from the album was " My Happiness " , released on 14 August 2000 in Australia . " My Happiness " entered the ARIA Singles Chart at # 4 , and spent 24 weeks on the chart , making it Powderfinger 's highest charting single in Australia . It peaked at # 7 on the New Zealand singles chart , and spent 23 weeks in the top 50 . Furthermore , " My Happiness " was Powderfinger 's first single to chart in the USA , reaching # 23 on the Modern Rock Tracks chart . 
- " Like a Dog " was released as the third single on 15 January 2001 . The song was heavy in political sentiment , akin to " The Day You Come " on Internationalist . The riff for the song was written by Ian <unk> , and the song 's music video featured Australian Aboriginal boxer Anthony <unk> , and was based on the 1980 Martin Scorsese film <unk> Bull . Drummer Jon <unk> said the song revolved around the question of " why the hell won ’ t John Howard say sorry to the Aboriginal people ! " " Like a Dog " spent one week on the ARIA Singles Chart , at # 40 . 
- Two songs from the album , " The Metre " and " Waiting for the Sun " , were released as a double A @-@ side to form the final single . The single was released on 21 August 2001 , and included a cover of Iron <unk> 's " Number of the Beast " . " Waiting for the Sun " was written by Fanning as a devotional , gospel style song . He said of the song ; " It ’ s about being in a relationship and being really heavily happy with it . " " The Metre " spent one week on the ARIA Singles Chart , at # 31 . 
- 
- = = Reception = = 
- 
- Odyssey Number Five mostly gained positive reviews , and was more successful than its predecessor , Internationalist . Entertainment Weekly reviewer Marc <unk> gave the album a B + . He stated that album entered new " terrain " in guitar rock , complimenting the " scratching and <unk> guitars " , drawing comparisons to Travis ( " <unk> " ) and Oasis ( " mock @-@ grandiose " ) . 
- Allmusic reviewer Dean Carlson disliked the album , giving it a rating of one and a half stars . He described it as " little more than a slightly off @-@ base perspective into the world of mid @-@ 90s American grunge " , and described it as highly similar to Neil Young . Carlson 's only praise was for songs " Odyssey # 5 " and " My Happiness " , stating that " [ t ] oo often , Powderfinger is too earnest , a bit too careful in their career " . Carlson noted that despite his critique , the album achieved some success in the American market . 
- Devon Powers of PopMatters complimented Fanning 's vocals , and said the focus of the album was " <unk> , rolling ballads " . Powers said that many of the songs on the album were " the kind of songs you put on repeat for hours , or days " . The main critique was for the " faster numbers " , stating that " Like a Dog " " sounds mostly a little bored " . The review concluded by noting that the best songs on Odyssey were those not available as " fleeting radio singles and background music " . 
- Odyssey Number Five won the 2001 ARIA Awards for " Album of the Year " , " Highest Selling Album " , " Best Rock Album " , " Best Cover Art " , and " Best Group " . " My Happiness " won the award for " Single of the Year " , while " Like a Dog " was nominated for " Highest Selling Single " and " Best Video " . At the 2002 ARIA Awards , " The Metre " was nominated for " Best Group " . The album was named " Album of the Year " by Rolling Stone Australia readers , with " My Happiness " taking out " Song of the Year " and Powderfinger receiving " Band of the Year " . 
- 
- = = Charts and certifications = = 
- 
- 
- = = Awards and nominations = = 
- 
- 
- = = = ARIA Awards = = = 
- 
- 
- = = = Other accolades = = = 
- 
- 
- = = Track listing = = 
- 
- All songs written by Powderfinger : 
- " Waiting for the Sun " – 3 : 54 
- " My Happiness " – 4 : 36 
- " The Metre " – 4 : 33 
- " Like a Dog " – 4 : 20 
- " Odyssey # 5 " – 1 : 44 
- " Up & Down & Back Again " – 4 : 24 
- " My Kind of Scene " – 4 : 37 
- " These Days " – 4 : 58 
- " We Should Be Together Now " – 3 : 42 
- " <unk> " – 6 : 10 
- " Whatever Makes You Happy " – 2 : 28 
- " Nature Boy " ( UK release ) – 3 : 37 
- 
- = = Personnel = = 
- 
- 
- 
- = Amanita muscaria = 
- 
- Amanita muscaria , commonly known as the fly agaric or fly <unk> , is a mushroom and psychoactive <unk> fungus , one of many in the genus Amanita . Native throughout the temperate and boreal regions of the Northern Hemisphere , Amanita muscaria has been unintentionally introduced to many countries in the Southern Hemisphere , generally as a symbiont with pine and birch plantations , and is now a true cosmopolitan species . It associates with various deciduous and coniferous trees . 
- This iconic toadstool is a large white @-@ <unk> , white @-@ spotted , usually red mushroom , and is one of the most recognisable and widely encountered in popular culture . Several subspecies with differing cap colour have been recognised , including the brown regalis ( often considered a separate species ) , the yellow @-@ orange flavivolvata , <unk> , <unk> , and the pinkish <unk> . Genetic studies published in 2006 and 2008 show several sharply delineated clades that may represent separate species . 
- Although classified as poisonous , reports of human deaths resulting from its ingestion are extremely rare . After <unk> — which weakens its toxicity and breaks down the mushroom 's psychoactive substances — it is eaten in parts of Europe , Asia , and North America . Amanita muscaria is noted for its hallucinogenic properties , with its main psychoactive constituent being the compound muscimol . The mushroom was used as an <unk> and <unk> by the peoples of Siberia , and has a religious significance in these cultures . There has been much speculation on possible traditional use of this mushroom as an <unk> in other places such as the Middle East , Eurasia , North America , and Scandinavia . 
- 
- = = Taxonomy and naming = = 
- 
- The name of the mushroom in many European languages is thought to be derived from its use as an <unk> when <unk> in milk . This practice has been recorded from <unk> and Slavic @-@ speaking parts of Europe , as well as the <unk> region and pockets elsewhere in France , and Romania . <unk> Magnus was the first to record it in his work De <unk> some time before <unk> , commenting <unk> fungus <unk> , <unk> <unk> in <unk> <unk> <unk> <unk> , " it is called the fly mushroom because it is powdered in milk to kill flies . " 
- The 16th @-@ century Flemish botanist <unk> <unk> traced the practice of <unk> it into milk to Frankfurt in Germany , while Carl Linnaeus , the " father of taxonomy " , reported it from <unk> in southern Sweden , where he had lived as a child . He described it in volume two of his Species <unk> in 1753 , giving it the name Agaricus <unk> , the specific epithet deriving from Latin <unk> meaning " fly " . It gained its current name in 1783 , when placed in the genus Amanita by Jean @-@ Baptiste <unk> , a name sanctioned in 1821 by the " father of <unk> " , Swedish naturalist Elias Magnus Fries . The starting date for all the <unk> had been set by general agreement as January 1 , 1821 , the date of Fries 's work , and so the full name was then Amanita muscaria ( L. : <unk> . ) Hook . The 1987 edition of the International Code of <unk> Nomenclature changed the rules on the starting date and primary work for names of fungi , and names can now be considered valid as far back as May 1 , 1753 , the date of publication of Linnaeus 's work . Hence , Linnaeus and <unk> are now taken as the <unk> of Amanita muscaria ( L. ) <unk> .. 
- The English mycologist John <unk> reported that Amanita muscaria was used for getting rid of bugs in England and Sweden , and bug agaric was an old alternate name for the species . French mycologist Pierre <unk> reported having tried without success to replicate its fly @-@ killing properties in his work <unk> des <unk> <unk> et <unk> de la France ( 1784 ) , and proposed a new binomial name Agaricus pseudo @-@ <unk> because of this . One compound isolated from the fungus is 1 @,@ 3 @-@ <unk> ( 1 @,@ 3 @-@ Di ( <unk> @-@ 9 @-@ <unk> ) <unk> ) , which attracts insects . It has been <unk> that the flies intentionally seek out the fly agaric for its <unk> properties . An alternative derivation proposes that the term <unk> refers not to insects as such but rather the <unk> resulting from consumption of the fungus . This is based on the medieval belief that flies could enter a person 's head and cause mental illness . Several regional names appear to be linked with this <unk> , meaning the " mad " or " fool 's " version of the highly regarded edible mushroom Amanita caesarea . Hence there is <unk> <unk> " mad <unk> " in Catalan , <unk> <unk> from Toulouse , <unk> <unk> from the <unk> department in Southern France , <unk> <unk> from <unk> in Italy . A local dialect name in <unk> in Switzerland is <unk> de <unk> , which translates as " Devil 's hat " . 
- 
- = = = Classification = = = 
- 
- Amanita muscaria is the type species of the genus . By extension , it is also the type species of Amanita subgenus Amanita , as well as section Amanita within this subgenus . Amanita subgenus Amanita includes all Amanita with <unk> spores . Amanita section Amanita includes the species which have very <unk> universal veil remnants , including a volva that is reduced to a series of concentric rings and the veil remnants on the cap to a series of patches or warts . Most species in this group also have a bulbous base . Amanita section Amanita consists of A. muscaria and its close relatives , including A. <unk> ( the panther cap ) , A. <unk> , A. <unk> , and A. <unk> . Modern fungal <unk> have classified Amanita muscaria and its allies this way based on gross morphology and spore <unk> . Two recent molecular phylogenetic studies have confirmed this classification as natural . 
- Amanita muscaria varies considerably in its morphology , and many authorities recognise several subspecies or varieties within the species . In The Agaricales in Modern Taxonomy , German mycologist Rolf Singer listed three subspecies , though without description : A. muscaria ssp. muscaria , A. muscaria ssp. <unk> , and A. muscaria ssp. flavivolvata . 
- Contemporary authorities recognise up to seven varieties : 
- A 2006 molecular phylogenetic study of different regional populations of A. muscaria by mycologist <unk> <unk> and colleagues found three distinct clades within this species representing , roughly , Eurasian , Eurasian " subalpine " , and North American populations . Specimens belonging to all three clades have been found in Alaska ; this has led to the hypothesis that this was the centre of <unk> for this species . The study also looked at four named varieties of the species : var. <unk> , var. flavivolvata , var. <unk> ( including var. <unk> ) , and var. regalis from both areas . All four varieties were found within both the Eurasian and North American clades , evidence that these morphological forms are <unk> rather than distinct subspecies or varieties . Further molecular study by <unk> and colleagues published in 2008 show that these three genetic groups , plus a fourth associated with oak – <unk> – pine forest in the southeastern United States and two more on Santa Cruz Island in California , are delineated from each other enough genetically to be considered separate species ; thus A. muscaria as it stands currently is evidently a species complex . The complex also includes at least three other closely related taxa that are currently regarded as species : A. <unk> is a buff @-@ capped mushroom associated with conifers from the Pacific Northwest , and the brown @-@ capped A. <unk> and A. <unk> from the Mediterranean Basin and from Sardinia respectively . Both of these last two are found with Eucalyptus and <unk> trees , and it is unclear whether they are native or introduced from Australia . 
- 
- = = Description = = 
- 
- A large , conspicuous mushroom , Amanita muscaria is generally common and numerous where it grows , and is often found in groups with <unk> in all stages of development . Fly agaric fruiting bodies emerge from the soil looking like white eggs . After emerging from the ground , the cap is covered with numerous small white to yellow pyramid @-@ shaped warts . These are remnants of the universal veil , a membrane that encloses the entire mushroom when it is still very young . <unk> the mushroom at this stage will reveal a characteristic yellowish layer of skin under the veil ; this is helpful in identification . As the fungus grows , the red colour appears through the broken veil and the warts become less prominent ; they do not change in size , but are reduced relative to the expanding skin area . The cap changes from <unk> to <unk> , and finally to plate @-@ like and flat in mature specimens . Fully grown , the bright red cap is usually around 8 – 20 cm ( 3 – 8 in ) in diameter , although larger specimens have been found . The red colour may fade after rain and in older mushrooms . 
- The free gills are white , as is the spore print . The oval spores measure 9 – 13 by 6 @.@ 5 – 9 μm ; they do not turn blue with the application of iodine . The stipe is white , 5 – 20 cm high ( 2 – 8 in ) by 1 – 2 cm ( 0 @.@ 4 – 0 @.@ 8 in ) wide , and has the slightly brittle , fibrous texture typical of many large mushrooms . At the base is a bulb that bears universal veil remnants in the form of two to four distinct rings or <unk> . Between the basal universal veil remnants and gills are remnants of the partial veil ( which covers the gills during development ) in the form of a white ring . It can be quite wide and flaccid with age . There is generally no associated smell other than a mild <unk> . 
- Although very distinctive in appearance , the fly agaric has been mistaken for other yellow to red mushroom species in the Americas , such as <unk> <unk> <unk> and the edible Amanita <unk> — a Mexican species similar to A. caesarea of Europe . Poison control centres in the U.S. and Canada have become aware that <unk> ( Spanish for ' yellow ' ) is a common name for the A. caesarea @-@ like species in Mexico . Amanita caesarea can be distinguished by its entirely orange to red cap which lacks the numerous white <unk> spots of the fly agaric . Furthermore , the stem , gills and ring of A. caesarea are bright yellow , not white . The volva is a distinct white bag , not broken into scales . In Australia , the introduced fly agaric may be confused with the native <unk> <unk> ( Amanita <unk> ) , which grows in association with eucalypts . The latter species generally lacks the white warts of A. muscaria and bears no ring . 
- 
- = = Distribution and habitat = = 
- 
- Amanita muscaria is a cosmopolitan mushroom , native to conifer and deciduous woodlands throughout the temperate and boreal regions of the Northern Hemisphere , including higher elevations of warmer latitudes in regions such as Hindu <unk> , the Mediterranean and also Central America . A recent molecular study proposes that it had an ancestral origin in the Siberian – <unk> region in the Tertiary period , before radiating outwards across Asia , Europe and North America . The season for fruiting varies in different climates : fruiting occurs in summer and autumn across most of North America , but later in autumn and early winter on the Pacific coast . This species is often found in similar locations to Boletus <unk> , and may appear in fairy rings . <unk> with pine seedlings , it has been widely transported into the southern hemisphere , including Australia , New Zealand , South Africa and South America , where it can be found in the southern Brazilian states of <unk> and Rio Grande do <unk> . 
- <unk> , Amanita muscaria forms symbiotic relationships with many trees , including pine , spruce , fir , birch , and cedar . Commonly seen under introduced trees , A. muscaria is the fungal equivalent of a weed in New Zealand , Tasmania and Victoria , forming new associations with southern beech ( <unk> ) . The species is also invading a <unk> in Australia , where it may be <unk> the native species . It appears to be spreading northwards , with recent reports placing it near Port Macquarie on the New South Wales north coast . It was recorded under silver birch ( <unk> <unk> ) in <unk> , Western Australia in 2010 . Although it has apparently not spread to eucalypts in Australia , it has been recorded associating with them in Portugal . 
- 
- = = <unk> = = 
- 
- Amanita muscaria poisoning has occurred in young children and in people who ingested the mushrooms for a hallucinogenic experience . Occasionally it has been ingested in error , because immature button forms resemble <unk> . The white spots sometimes wash away during heavy rain and the mushrooms then may appear to be the edible A. caesarea . 
- Amanita muscaria contains several biologically active agents , at least one of which , muscimol , is known to be psychoactive . Ibotenic acid , a <unk> , serves as a <unk> to muscimol , with approximately 10 – 20 % converting to muscimol after ingestion . An active dose in adults is approximately 6 mg muscimol or 30 to 60 mg ibotenic acid ; this is typically about the amount found in one cap of Amanita muscaria . The amount and ratio of chemical compounds per mushroom varies widely from region to region and season to season , which can further confuse the issue . Spring and summer mushrooms have been reported to contain up to 10 times more ibotenic acid and muscimol than autumn <unk> . 
- A fatal dose has been calculated as 15 caps . <unk> from this fungus A. muscaria have been reported in historical journal articles and newspaper reports , but with modern medical treatment , fatal poisoning from <unk> this mushroom is extremely rare . Many older books list Amanita muscaria as " deadly " , but this is an error that implies the mushroom is more toxic than it is . The North American Mycological Association has stated there were no reliably documented fatalities from eating this mushroom during the 20th century . The vast majority ( 90 % or more ) of mushroom poisoning deaths are from eating the greenish to yellowish " death cap " , ( A. <unk> ) or perhaps even one of the several white Amanita species which are known as destroying angels . 
- The active constituents of this species are water @-@ soluble , and boiling and then discarding the cooking water at least partly <unk> A. muscaria . <unk> may increase <unk> , as the process facilitates the conversion of ibotenic acid to the more potent muscimol . According to some sources , once detoxified , the mushroom becomes edible . 
- 
- = = = <unk> = = = 
- 
- <unk> , discovered in 1869 , was long thought to be the active hallucinogenic agent in A. muscaria . <unk> binds with <unk> acetylcholine receptors leading to the <unk> of neurons bearing these receptors . The levels of muscarine in Amanita muscaria are minute when compared with other poisonous fungi such as Inocybe <unk> , the small white <unk> species C. <unk> and C. <unk> . The level of muscarine in A. muscaria is too low to play a role in the symptoms of poisoning . 
- The major toxins involved in A. muscaria poisoning are muscimol ( 3 @-@ <unk> @-@ 5 @-@ <unk> @-@ 1 @-@ <unk> , an <unk> cyclic <unk> acid ) and the related amino acid ibotenic acid . Muscimol is the product of the <unk> ( usually by drying ) of ibotenic acid . Muscimol and ibotenic acid were discovered in the mid @-@ 20th century . Researchers in England , Japan , and Switzerland showed that the effects produced were due mainly to ibotenic acid and muscimol , not muscarine . These toxins are not distributed uniformly in the mushroom . Most are detected in the cap of the fruit , a moderate amount in the base , with the smallest amount in the stalk . Quite rapidly , between 20 and 90 minutes after ingestion , a substantial fraction of ibotenic acid is excreted <unk> in the urine of the consumer . Almost no muscimol is excreted when pure ibotenic acid is eaten , but muscimol is detectable in the urine after eating A. muscaria , which contains both ibotenic acid and muscimol . 
- Ibotenic acid and muscimol are structurally related to each other and to two major <unk> of the central nervous system : <unk> acid and <unk> respectively . Ibotenic acid and muscimol act like these <unk> , muscimol being a potent <unk> agonist , while ibotenic acid is an agonist of NMDA <unk> receptors and certain <unk> <unk> receptors which are involved in the control of <unk> activity . It is these interactions which are thought to cause the psychoactive effects found in intoxication . Muscimol is the agent responsible for the majority of the <unk> . 
- <unk> is another compound that has more recently been isolated from European specimens of the fly agaric . It is a product of the breakdown of ibotenic acid by ultra @-@ violet radiation . <unk> is of minor <unk> activity compared with the other agents . Amanita muscaria and related species are known as effective <unk> of <unk> ; some species concentrate <unk> to levels of up to 400 times those typically found in plants . <unk> is present in fruit @-@ bodies as an <unk> compound called <unk> . The biological importance of the <unk> process is unknown . 
- 
- = = = Symptoms = = = 
- 
- Fly agarics are known for the unpredictability of their effects . Depending on habitat and the amount ingested per body weight , effects can range from nausea and twitching to <unk> , <unk> crisis @-@ like effects ( low blood pressure , sweating and <unk> ) , auditory and visual distortions , mood changes , euphoria , relaxation , <unk> , and loss of equilibrium . 
- In cases of serious poisoning the mushroom causes <unk> , somewhat similar in effect to <unk> poisoning ( such as that caused by <unk> <unk> ) , characterised by bouts of marked agitation with confusion , hallucinations , and <unk> followed by periods of central nervous system depression . <unk> and coma may also occur in severe <unk> . Symptoms typically appear after around 30 to 90 minutes and peak within three hours , but certain effects can last for several days . In the majority of cases recovery is complete within 12 to 24 hours . The effect is highly variable between individuals , with similar doses potentially causing quite different reactions . Some people suffering intoxication have exhibited <unk> up to ten hours afterwards . <unk> <unk> and <unk> can result following recovery . 
- 
- = = = Treatment = = = 
- 
- Medical attention should be sought in cases of suspected poisoning . If the delay between ingestion and treatment is less than four hours , activated charcoal is given . <unk> <unk> can be considered if the patient presents within one hour of ingestion . <unk> vomiting with syrup of <unk> is no longer recommended in any poisoning situations . 
- There is no antidote , and supportive care is the mainstay of further treatment for intoxication . Though sometimes referred to as a <unk> and while muscarine was first isolated from A. muscaria and as such is its namesake , muscimol does not have action , either as an agonist or antagonist , at the <unk> acetylcholine receptor site , and therefore <unk> or <unk> as an antidote is not recommended . If a patient is delirious or agitated , this can usually be treated by <unk> and , if necessary , physical restraints . A <unk> such as <unk> or <unk> can be used to control combativeness , agitation , muscular <unk> , and <unk> . Only small doses should be used , as they may <unk> the respiratory <unk> effects of muscimol . <unk> vomiting is rare , but if present may lead to fluid and electrolyte <unk> ; intravenous <unk> or electrolyte replacement may be required . Serious cases may develop loss of consciousness or coma , and may need <unk> and artificial ventilation . <unk> can remove the toxins , although this intervention is generally considered unnecessary . With modern medical treatment the prognosis is typically good following supportive treatment . 
- 
- = = = <unk> use = = = 
- 
- The wide range of psychedelic effects can be variously described as <unk> , <unk> @-@ hypnotic , <unk> , and <unk> ; paradoxical effects may occur . <unk> phenomena such as <unk> and <unk> may occur , which may have been the inspiration for the effect of mushroom @-@ consumption in Lewis Carroll 's Alice 's Adventures in Wonderland , although " no evidence has ever been found that linked Carroll to recreational drug use " . Additionally , A. muscaria cannot be commercially cultivated , due to its mycorrhizal relationship with the roots of pine trees . However , following the <unk> of <unk> mushrooms in the United Kingdom in 2006 , the sale of the still legal A. muscaria began increasing . 
- Professor <unk> <unk> , a renowned Lithuanian historian , reported to R. Gordon Wasson on the use of this mushroom in Lithuania . In remote areas of Lithuania Amanita muscaria has been consumed at wedding <unk> , in which mushrooms were mixed with <unk> . The professor also reported that the Lithuanians used to export A. muscaria to the <unk> in the Far North for use in <unk> rituals . The Lithuanian festivities are the only report that Wasson received of ingestion of fly agaric for religious use in Eastern Europe . 
- 
- = = = Siberia = = = 
- 
- Amanita muscaria was widely used as an <unk> by many of the indigenous peoples of Siberia . Its use was known among almost all of the <unk> @-@ speaking peoples of western Siberia and the <unk> @-@ speaking peoples of the Russian Far East . There are only isolated reports of A. muscaria use among the <unk> and <unk> peoples of central Siberia and it is believed that on the whole <unk> use of A. muscaria was not practised by these peoples . In western Siberia , the use of A. muscaria was restricted to shamans , who used it as an alternative method of achieving a trance state . ( Normally , Siberian shamans achieve trance by prolonged drumming and dancing . ) In eastern Siberia , A. muscaria was used by both shamans and <unk> alike , and was used <unk> as well as religiously . In eastern Siberia , the <unk> would take the mushrooms , and others would drink his urine . This urine , still containing psychoactive elements , may be more potent than the A. muscaria mushrooms with fewer negative effects such as sweating and twitching , suggesting that the initial user may act as a screening filter for other components in the mushroom . 
- The <unk> of eastern Siberia have a story about the fly agaric ( wapaq ) which enabled Big Raven to carry a whale to its home . In the story , the deity <unk> ( " <unk> " ) <unk> onto earth , and his <unk> became the wapaq , and his <unk> becomes the warts . After experiencing the power of the wapaq , Raven was so <unk> that he told it to grow forever on earth so his children , the people , could learn from it . Among the <unk> , one report said that the poor would consume the urine of the wealthy , who could afford to buy the mushrooms . 
- 
- = = = Other reports of use = = = 
- 
- The Finnish historian T. I. <unk> mentions that A. muscaria was once used among the Sami people : <unk> in Inari would consume fly agarics with seven spots . In 1979 , Said <unk> <unk> and <unk> <unk> published an article in which they claim to have discovered a tradition of <unk> and recreational use of this mushroom among a <unk> @-@ speaking group in Afghanistan . There are also unconfirmed reports of religious use of A. muscaria among two <unk> Native American tribes . <unk> <unk> <unk> <unk> reported its use among her people , where it was known as the <unk> . This information was enthusiastically received by Wasson , although evidence from other sources was lacking . There is also one account of a Euro @-@ American who claims to have been initiated into traditional <unk> use of Amanita muscaria . 
- 
- = = = Vikings = = = 
- 
- The notion that Vikings used A. muscaria to produce their <unk> <unk> was first suggested by the Swedish professor Samuel <unk> in 1784 . <unk> based his theories on reports about the use of fly agaric among Siberian shamans . The notion has become widespread since the 19th century , but no contemporary sources mention this use or anything similar in their description of <unk> . Muscimol is generally a mild <unk> , but it can create a range of different reactions within a group of people . It is possible that it could make a person angry , or cause them to be " very <unk> or sad , jump about , dance , sing or give way to great <unk> " . 
- 
- = = Fly trap = = 
- 
- Amanita muscaria is traditionally used for catching flies possibly due to its content of ibotenic acid and muscimol . Recently , an analysis of nine different methods for preparing A. muscaria for catching flies in Slovenia have shown that the release of ibotenic acid and muscimol did not depend on the solvent ( milk or water ) and that thermal and mechanical processing led to faster extraction of ibotenic acid and muscimol . 
- 
- = = In Religion = = 
- 
- 
- = = = Soma = = = 
- 
- In 1968 , R. Gordon Wasson proposed that A. muscaria was the Soma talked about in the <unk> <unk> of India , a claim which received widespread publicity and popular support at the time . He noted that descriptions of Soma omitted any description of roots , stems or seeds , which suggested a mushroom , and used the adjective <unk> " dazzling " or " <unk> " which the author interprets as meaning red . One line described men <unk> Soma ; this recalled the practice of recycling urine in Siberia . Soma is mentioned as coming " from the mountains " , which Wasson interpreted as the mushroom having been brought in with the <unk> invaders from the north . Indian scholars <unk> Kumar <unk> and <unk> <unk> pointed out that both eating of mushrooms and drinking of urine were proscribed , using as a source the <unk> . In 1971 , Vedic scholar John Brough from Cambridge University rejected Wasson 's theory and noted that the language was too vague to determine a description of Soma . In his 1976 survey , <unk> and Culture , anthropologist Peter T. <unk> evaluated the evidence for and against the identification of the fly agaric mushroom as the Vedic Soma , concluding cautiously in its favour . 
- 
- = = = Christianity = = = 
- 
- <unk> , <unk> , and Dead Sea <unk> scholar John Marco Allegro postulated that early Christian theology was derived from a fertility cult revolving around the <unk> consumption of A. muscaria in his 1970 book The Sacred Mushroom and the Cross , but his theory has found little support by scholars outside the field of <unk> . The book was <unk> discredited by academics and theologians , including Sir Godfrey Driver , <unk> Professor of Semitic <unk> at Oxford University , and Henry <unk> , the Dean of Christ Church , Oxford . Christian author John C. King wrote a detailed rebuttal of Allegro 's theory in the 1970 book A Christian View of the Mushroom Myth ; he notes that neither fly agarics nor their host trees are found in the Middle East , even though cedars and pines are found there , and highlights the tenuous nature of the links between biblical and <unk> names coined by Allegro . He concludes that if the theory was true , the use of the mushroom must have been " the best kept secret in the world " as it was so well concealed for two thousand years . 
- 
- = = Culinary use = = 
- 
- The toxins in A. muscaria are water @-@ soluble . When sliced thinly , or finely <unk> and boiled in plentiful water until thoroughly cooked , it seems to be detoxified . Although its consumption as a food has never been widespread , the consumption of detoxified A. muscaria has been practiced in some parts of Europe ( notably by Russian settlers in Siberia ) since at least the 19th century , and likely earlier . The German physician and naturalist Georg Heinrich von <unk> wrote the earliest published account on how to <unk> this mushroom in 1823 . In the late 19th century , the French physician Félix <unk> <unk> was a <unk> and advocate of A. muscaria consumption , comparing it to <unk> , an important food source in tropical South America that must be detoxified before consumption . 
- Use of this mushroom as a food source also seems to have existed in North America . A classic description of this use of A. muscaria by an African @-@ American mushroom seller in Washington , D.C. , in the late 19th century is described by American botanist Frederick Vernon <unk> . In this case , the mushroom , after <unk> , and <unk> in vinegar , is made into a mushroom sauce for <unk> . It is also consumed as a food in parts of Japan . The most well @-@ known current use as an edible mushroom is in <unk> Prefecture , Japan . There , it is primarily salted and <unk> . 
- A 2008 paper by food historian William <unk> and mycologist David Arora gives a history of consumption of A. muscaria as a food and describes <unk> methods . They advocate that Amanita muscaria be described in field guides as an edible mushroom , though accompanied by a description on how to <unk> it . The authors state that the widespread descriptions in field guides of this mushroom as poisonous is a reflection of cultural bias , as several other popular edible species , notably morels , are toxic unless properly cooked . 
- 
- = = Legal Status = = 
- 
- 
- = = = Australia = = = 
- 
- Muscimol found within Amanita muscaria is considered a Schedule 9 prohibited substance in Australia under the <unk> Standard ( October 2015 ) . A Schedule 9 substance is a substance which may be abused or misused , the manufacture , possession , sale or use of which should be prohibited by law except when required for medical or scientific research , or for analytical , teaching or training purposes with approval of Commonwealth and / or State or Territory Health Authorities . 
- 
- = = = The Netherlands = = = 
- 
- Amanita muscaria and Amanita <unk> are illegal to buy , sell , or possess since December 2008 . Possession of amounts larger than <unk> dried or <unk> fresh lead to a criminal charge . 
- 
- = = = United Kingdom = = = 
- 
- It is illegal to produce , supply , or import this drug under the <unk> <unk> Act , which came into effect on May 26th , 2016 . 
- 
- = = Cultural depictions = = 
- 
- The red @-@ and @-@ white spotted toadstool is a common image in many aspects of popular culture . Garden ornaments and children 's picture books depicting <unk> and fairies , such as the <unk> , often show fly agarics used as seats , or homes . Fly agarics have been featured in paintings since the Renaissance , albeit in a subtle manner . In the Victorian era they became more visible , becoming the main topic of some fairy paintings . Two of the most famous uses of the mushroom are in the video game series Super Mario Bros. ( specifically two of the power @-@ up items and the platforms in several stages ) , and the dancing mushroom sequence in the 1940 Disney film Fantasia . 
- 
- = = = Literature = = = 
- 
- An account of the journeys of Philip von <unk> to Siberia and his descriptions of the use of the <unk> there was published in English in 1736 . The drinking of urine of those who had consumed the mushroom was commented on by Anglo @-@ Irish writer Oliver Goldsmith in his widely read <unk> novel , Citizen of the World . The mushroom had been identified as the fly agaric by this time . Other authors recorded the distortions of the size of perceived objects while intoxicated by the fungus , including naturalist <unk> <unk> Cooke in his books The Seven Sisters of Sleep and A Plain and Easy <unk> of British Fungi . This observation is thought to have formed the basis of the effects of eating the mushroom in the 1865 popular story Alice 's Adventures in Wonderland . A hallucinogenic " scarlet toadstool " from <unk> is featured as a plot element in Charles Kingsley 's 1866 novel <unk> the Wake based on the medieval figure of the same name . Thomas Pynchon 's 1973 novel Gravity 's Rainbow describes the fungus as a " relative of the poisonous Destroying Angel " and presents a detailed description of a character preparing a <unk> <unk> mixture from harvested Amanita muscaria . Fly agaric <unk> is also explored in the 2003 novel <unk> by Alan Garner . 
- 
- 
- = Burning of women in England = 
- 
- In England , burning was a legal punishment inflicted on women found guilty of high treason , petty treason and heresy . Over a period of several centuries , female convicts were publicly burnt at the stake , sometimes alive , for a range of activities including coining and <unk> . 
- While men guilty of heresy were also burned at the stake , those who committed high treason were instead hanged , drawn and quartered . The English <unk> William Blackstone supposed that the difference in sentencing , although " full as terrible to the sensation as the other " , could be explained by the desire not to publicly expose a woman 's body . Public executions were well @-@ attended affairs , and contemporary reports detail the cries of women on the <unk> as they were burned alive . It later became commonplace for the executioner to strangle the convict , and for the body to be burned post @-@ mortem . 
- In the latter half of the eighteenth century , changing attitudes to such public displays prompted Sir Benjamin Hammett MP to denounce the practice in Parliament . His bill , by no means the first such attempt to end the public burning of women , led to the Treason Act 1790 , which abolished the sentence . 
- 
- = = <unk> punishable by burning = = 
- 
- 
- = = = Treason = = = 
- 
- By the end of the thirteenth century , several offences against either one 's lord , or one 's king , were treasonable . High treason , defined as <unk> against the sovereign , was first codified during King Edward III 's reign by the Treason Act 1351 . It clarified exactly what crimes constituted treason , following earlier , somewhat " over <unk> " interpretations of England 's legal codes . For instance , high treason could be committed by anyone found to be <unk> the king 's death or counterfeiting his coin . High treason remained distinct though , from what became known as petty treason : the killing of a lawful superior , such as a husband by his wife . Though twelfth century contemporary authors made few attempts to differentiate between high treason and petty treason , enhanced punishments may indicate that the latter was treated more seriously than an ordinary <unk> . 
- As the most <unk> offence an individual could commit , viewed as seriously as though the accused had personally attacked the monarch , high treason demanded the ultimate punishment . But whereas men guilty of this crime were hanged , drawn and quartered , women were drawn and burned . In his <unk> on the Laws of England the 18th @-@ century English <unk> William Blackstone noted that the sentence , " to be drawn to the <unk> , and there to be burned alive " , was " full as terrible to the sensation as the other " . Blackstone wrote that women were burned rather than quartered as " the decency due to the sex forbids the exposing and publicly <unk> their bodies " . However , an observation by historian Jules <unk> , that " the first flame to rise consumed the clothes , revealing poor trembling nakedness " , may , in the opinion of historian Vic Gatrell , suggest that this solution is " <unk> " . In The <unk> Tree , Gatrell concludes that the occasional live burial of women in Europe gave tacit acknowledgement to the possibility that a struggling , kicking female hanging from a <unk> could " elicit obscene fantasies " from watching males . 
- 
- = = = <unk> = = = 
- 
- Another law <unk> by public burning was De heretico comburendo , introduced in <unk> during the reign of Henry IV . It allowed for the execution of persons of both sexes found guilty of heresy , thought to be " <unk> and dangerous to souls , but also <unk> and treasonable . " Bishops were empowered to arrest and imprison anyone suspected of offences related to heresy and , once convicted , send them to be burned " in the presence of the people in a <unk> place " . Although the act was repealed in <unk> / 34 , it was revived over 20 years later at the request of Queen Mary I who , during the Marian <unk> , made frequent use of the punishment it allowed . 
- De heretico comburendo was repealed by the Act of <unk> 1558 , although that act allowed ecclesiastical commissions to deal with occasional instances of heresy . Persons declared guilty , such as <unk> <unk> and Edward <unk> , could still be burned under a writ of de heretico comburendo issued by the Court of Chancery . The burning of heretics was finally ended by the <unk> <unk> Act 1677 which , although it allowed ecclesiastical courts to charge people with " <unk> , blasphemy , heresy , schism , or other <unk> doctrine or opinion " , limited their power to excommunication . 
- 
- = = <unk> of the sentence = = 
- 
- Public executions were normally attended by large crowds . For the killing in 1546 of Anne Askew , charged with heresy and tortured at the Tower of London , a " <unk> Stage " was built to seat the various officials who presided over her burning . A witness to proceedings reported that Askew was so badly injured by her torture that she was unable to stand . Instead , " the <unk> <unk> was <unk> up <unk> <unk> <unk> , <unk> <unk> there in a <unk> " . 
- A pamphlet detailing the burning in April 1652 of Joan Peterson , the so @-@ called Witch of <unk> , also describes the execution of <unk> Lee , found guilty of <unk> . Lee was apparently brought on foot , between two sheriff 's officers and dressed in a red <unk> , to the place of execution in <unk> . There she confessed to having " been a very <unk> liver , and much given to cursing and <unk> , for which the Lord being offended with her , had suffered her to be brought to that untimely end " . She admitted to being jealous of and arguing with her husband , and stabbing him with a knife . The executioner put her in a pitch barrel , tied her to the stake , placed the fuel and faggots around her and set them <unk> . Lee was reported to have " desired all that were present to pray for her " and , feeling the flames , " <unk> [ d ] out terribly some five or six several times . " Burning alive for murder was abolished in 1656 , although burning for adultery remained . Thereafter , out of mercy , the condemned were often <unk> before the flames took hold . Notable exceptions to this practice were the <unk> in 1685 and 1726 of Elizabeth Gaunt , found guilty of high treason for her part in the Rye House Plot , and Catherine Hayes , for petty treason . Hayes apparently " rent the air with her cries and <unk> " when the fire was lit too early , preventing the executioner from <unk> her in time . She became the last woman in England to be burned alive . 
- The law also allowed for the hanging of children aged seven years or more . Mary <unk> , " but sixteen years of age " , was burned at Winchester in 1738 for poisoning her mistress . An unidentified 14 @-@ year @-@ old girl imprisoned at Newgate was more fortunate . Found guilty in 1777 of being an accomplice to treason , for concealing whitewashed <unk> on her person ( at her master 's request ) , she had been sentenced to burn . She was saved by the intervention of Thomas <unk> , 1st Marquess of Bath , who happened to be passing . 
- 
- = = Changing attitudes = = 
- 
- In 1786 , Phoebe Harris and her <unk> were " indicted , for that they , on the 11th of February last , one piece of false , <unk> , and counterfeit money and coin , to the likeness and <unk> of the good , legal , and silver coin of this realm , called a shilling , falsely , <unk> , <unk> , and <unk> did counterfeit and coin " . <unk> by a reported 20 @,@ 000 people , she was led to the stake and stood on a stool , where a <unk> , attached to an iron bolt driven into the top of the stake , was placed around her neck . As prayers were read , the stool was taken away and over the course of several minutes , her feet kicking as her body <unk> , Harris choked to death . About 30 minutes later , faggots were placed around the stake , her body was <unk> into position , and subsequently burned for over two hours . 
- <unk> like this had once passed with little to no comment in the press . Historically , while fewer women than men were subjected to capital punishment , <unk> more were acquitted , found guilty of lesser charges , or <unk> if condemned . In centuries past , these women were judged by publications such as The Newgate Calendar to have succumbed to their own <unk> , or to have been led astray . But while 18th and 19th @-@ century women guilty of treasonable crimes were still seen as villains , increasingly , the cause of their descent was ascribed to villainous men . Those people concerned about the brutality inflicted on condemned women were , in Gatrell 's opinion , " activated by the sense that even at their worst women were creatures to be <unk> and protected from themselves , and perhaps revered , like all women from whom men were born . " Commenting on Harris 's execution , The Daily Universal Register claimed that the act reflected " a scandal upon the law " , " a disgrace to the police " and " was not only <unk> , but <unk> <unk> and shocking " . The newspaper asked " why should the law in this species of offence inflict a <unk> punishment upon a woman , than upon a man " ? 
- Harris 's fate prompted William Wilberforce to sponsor a bill which , if passed , would have abolished the practice . But as one of its proposals would have allowed the anatomical <unk> of criminals other than murderers , the House of Lords rejected it . Though sympathetic to reform of England 's Bloody Code , Lord Chief Justice Loughborough saw no need to change the law : " Although the punishment , as a spectacle , was rather attended with circumstances of horror , likely to make a more strong impression on the <unk> than mere hanging , the effect was much the same , as in fact , no greater degree of personal pain was sustained , the criminal being always <unk> before the flames were suffered to approach the body " . 
- When on 25 June 1788 Margaret Sullivan was hanged and burned for coining , the same newspaper ( by then called The Times ) wrote : 
- There is something so <unk> in burning a woman , for what only subjects a man to hanging , that human nature <unk> at the idea . Must not mankind laugh as our long speeches against African slavery — and our fine sentiments on Indian <unk> , when just in the very eye of the Sovereign we <unk> a female fellow creature alive , for putting a <unk> of <unk> on a half @-@ <unk> worth of brass . The savage <unk> of the punishment — and the <unk> of the offence in the eye of God are contrasts that should meet the consideration of Government . 
- The Gentleman 's Magazine addressed the Prime Minister , William Pitt the Younger : 
- the woman was brought out attended by a priest of the <unk> persuasion , and as soon as she came to the stake she was placed upon a stool , which was instantly removed from under her , and she left suspended , when the faggots were placed around her , and being set on fire she was soon consumed to ashes . — Mr. Pitt , himself a lawyer , ' tis hoped , will not suffer this cruel remain of savage legislation to escape his notice , and continue a disgrace to the enlightened sense of this country . 
- Although in his objections to Wilberforce 's 1786 bill Loughborough had noted that these women were dead long before they suffered the flames , many newspapers of the day made no such distinction . The Times incorrectly stated that Sullivan was burned alive , rhetoric which , in Dr Simon <unk> 's opinion , could be " rooted in the growing <unk> for domesticated womanhood " that might have been expected at the time . As many objections may also have been raised by the perceived <unk> of drawing and burning women for coining , whereas until 1783 , when the halting of executions at <unk> removed ritualistic dragging from public view , men were simply drawn and hanged . A widening gulf between the numbers of men and women <unk> in London ( during the <unk> , 393 men versus 47 women ) , which mirrors a similar decline in the sending of women to the <unk> , may also indicate an imposition of commonly @-@ held gender ideals on English penal practices . 
- 
- = = Abolition = = 
- 
- The burning in 1789 of Christian Murphy , for coining , received practically no attention from the newspapers ( perhaps owing to practical limitations on how much news they could publish across only four pages ) , but it may have been enacted by Sir Benjamin Hammett , a former sheriff of London . Hammett was also an MP , and in 1790 he introduced to Parliament a Bill for <unk> the Sentence of Burning Women . He denounced the punishment as " the savage remains of Norman policy " which " disgraced our statutes " , as " the practice did the common law " . He also highlighted how a sheriff who refused to carry out the sentence was liable to prosecution . William Wilberforce and Hammett were not the first men to attempt to end the burning of women . Almost 140 years earlier , during the <unk> , a group of lawyers and <unk> known as the Hale Commission ( after its chairman Matthew Hale ) , was tasked by the House of Commons to take " into consideration what <unk> there are in the law " . Among the proposed reforms was the replacement of burning at the stake with hanging , but , mainly through the objections of various interested parties , none of the commission 's proposals made it into law during the <unk> Parliament . Hammett was confident though . He believed that public opinion was on his side and that " the House would go with him in the cause of humanity " . The change in execution venues , from <unk> to Newgate , also attracted criticism . Following Phoebe Harris 's burning in 1786 , as well as questioning the inequality of English law The Times complained about the location of the punishment and its effect on locals : 
- When remission of burning was refused , the scene of <unk> should have been changed ; the consequences have been serious ; several persons in the neighbourhood of Newgate lying ill , have been severely affected by the smoke which issued from the body of the unhappy female victim . 
- Another factor was the fate of Sophia <unk> , found guilty of coining . Hammett 's bill was introduced only four days before <unk> 's fate was to be decided , but a petition for her respite from burning , supported by another sheriff of London ( either Thomas Baker or William Newman ) and brought to King George III 's notice by William Grenville , proved successful . <unk> suggests that her impending fate lent weight to the eventual outcome of Hammett 's bill , which was to abolish the burning of women for treason through the Treason Act 1790 . Christian Murphy , who at her execution in 1789 was " <unk> in a clean striped gown , a white ribbon , and a black ribbon round her cap " , was the last woman in England to be burned . 
- 
- 
- = Strand , London = 
- 
- Strand ( or the Strand ) is a major thoroughfare in the City of Westminster , Central London . It runs just over 3 ⁄ 4 mile ( 1 @,@ 200 m ) from Trafalgar Square eastwards to Temple Bar , where the road becomes Fleet Street inside the City of London , and is part of the <unk> , a main road running west from inner London . 
- The road 's name comes from the Old English <unk> , meaning the edge of a river , as it historically ran alongside the north bank of the River Thames . The street was popular with the British upper classes between the 12th and 17th centuries , with many historically important mansions being built between the Strand and the river . These included Essex House , Arundel House , Somerset House , Savoy Palace , Durham House and Cecil House . The aristocracy moved to the West End over the 17th century , following which the Strand became well known for coffee shops , restaurants and <unk> . The street was a centre point for theatre and music hall during the 19th century , and several venues remain on the Strand . At the east end of the street are two historic churches ; St Mary le Strand and St Clement Danes . 
- Several authors , poets and philosophers have lived on or near the Strand , including Charles Dickens , Ralph Waldo Emerson and Virginia Woolf . The street has been commemorated in the song , " Let 's All Go Down the Strand " , now recognised as a typical piece of Cockney music hall . 
- 
- = = Geography = = 
- 
- The street is the main link between the two cities of Westminster and London . It runs eastward from Trafalgar Square , parallel to the River Thames , to Temple Bar which is the boundary between the two city districts ; the road ahead being Fleet Street . Traffic travelling eastbound follows a short crescent around Aldwych , connected at both ends to the Strand . The road marks the southern boundary of the Covent Garden district and forms part of the <unk> business improvement district . 
- The name was first recorded in 1002 as <unk> , then in <unk> as <unk> and in 1220 as la <unk> . It is formed from the Old English word ' <unk> ' , meaning the edge of a river . Initially it referred to the shallow bank of the once much wider Thames , before the construction of the Victoria <unk> . The name was later applied to the road itself . In the 13th century it was known as ' <unk> ' or ' street of the Danes ' , referring to the community of Danes in the area . 
- Two London Underground stations were once named Strand : a Piccadilly line station ( which was renamed Aldwych station ) that operated between 1907 and 1994 and a former Northern line station which today forms part of Charing Cross station . ' Strand Bridge ' was the name given to Waterloo Bridge during its construction ; it was renamed for its official opening on the second anniversary of the coalition victory in the Battle of Waterloo . London Bus routes 6 , 23 , 139 and 176 all run along the Strand , as do numerous night bus services . 
- 
- = = History = = 
- 
- During Roman Britain , what is now the Strand was part of the route to <unk> , known as " <unk> VIII " on the <unk> <unk> , and which later became known by the name <unk> Street . It was briefly part of a trading town called <unk> that developed around 600 AD , and stretched from Trafalgar Square to Aldwych . Alfred the Great gradually moved the settlement into the old Roman town of <unk> from around <unk> AD onwards , leaving no mark of the old town , and the area returned to fields . 
- In the Middle Ages , the Strand became the principal route between the separate settlements of the City of London ( the civil and commercial centre ) and the royal Palace of Westminster ( the national political centre ) . In the archaeological record , there is considerable evidence of occupation to the north of Aldwych , but much along the former foreshore has been covered by rubble from the demolition of the Tudor Somerset Place , a former royal residence , to create a large platform for the building of the first Somerset House , in the 17th century . The landmark Eleanor 's Cross was built in the 13th century at the western end of the Strand at Charing Cross by Edward I commemorating his wife Eleanor of Castile . It was demolished in 1647 by the request of Parliament during the First English Civil War , but reconstructed in 1865 . 
- The west part of the Strand was in the parish of St Martin in the Fields and in the east it extended into the parishes of St Clement Danes and St Mary le Strand . Most of its length was in the Liberty of Westminster , although part of the eastern section in St Clement Danes was in the <unk> hundred of Middlesex . The precinct of the Savoy , located approximately where the approach to Waterloo Bridge is now , had the Strand as its northern boundary . All of these parishes and places became part of the Strand District in 1855 , except St Martin in the Fields which was governed separately . The Strand District Board of Works was based at No. 22 , <unk> Street . Strand District was abolished in October 1900 and became part of the Metropolitan Borough of Westminster . 
- 
- = = = Palaces = = = 
- 
- From the 12th century onwards , large mansions lined the Strand including several palaces and <unk> inhabited by bishops and royal courtiers , mainly located on the south side , with their own river gates and landings directly on the Thames . The road was poorly maintained , with many pits and <unk> , and a paving order was issued in 1532 to improve traffic . 
- What later became Essex House on the Strand was originally an Outer Temple of the Knights Templar in the 11th century . In <unk> , ownership passed to the Knights of St John . Henry VIII gave the house to William , Baron Paget in the early 16th century . Robert Dudley , Earl of Leicester , rebuilt the house in 1563 , originally calling it Leicester House . It was renamed Essex House after being inherited by Robert <unk> , 2nd Earl of Essex , in <unk> . It was demolished around 1674 and Essex Street , leading up to the Strand , was built on the location by property speculator Nicholas <unk> . 
- Arundel House was originally the town house of the Bishops of Bath and Wells . It was owned by William <unk> , 1st Earl of Southampton between <unk> and his death in 1542 , with ownership passing to Thomas Seymour in 1545 . After Seymour was executed in 1549 , the property was sold to Henry <unk> , Earl of Arundel , and was owned by the <unk> for much of the 16th and 17th century . In 1666 , it became the meeting place of the Royal Society following the destruction of the previous venue due to the Great Fire of London . The house was demolished in <unk> and Arundel Street , adjoining the Strand , was built on the site . 
- Somerset House was built by Edward Seymour , Duke of Somerset , regent of England from <unk> to 1549 , demolishing three <unk> and the church of the <unk> of Our Lady and the <unk> in the process . After Somerset was executed in 1552 , it became an occasional residence for Princess Elizabeth . On her ascension to the throne in 1558 , she returned part of the house to Seymour 's family ( with ownership passing to his son , Edward Seymour , 1st Earl of <unk> ) with the remainder being an occasional meeting place for the Royal Society . After Elizabeth 's death in 1603 , it was owned by Anne of Denmark , wife of James VI and I. The building was renamed Denmark House in commemoration of Anne 's brother , Christian IV of Denmark . Following James ' death in 1625 , his body lay there <unk> for a month . It was taken over by Parliament in 1645 following the Civil War , renaming it back to Somerset House . It had an irregular series of owners and residents for much of the 18th century until its demolition in 1775 . The house was rebuilt as a series of government buildings . The Stamp Office , later to become the Inland Revenue was established in Somerset House in 1789 . In the late 20th century , a number of art galleries were set up on vacant parts , including the <unk> Institute of Art and the King 's College London School of Law . 
- Savoy Palace was the London residence of John of Gaunt , King Richard II 's uncle and the nation 's power <unk> . In the 14th century the Savoy was the most magnificent nobleman 's mansion in England . During the <unk> ' <unk> of <unk> , rebels , led by Wat Tyler , <unk> by opposition to the poll tax promoted by John of Gaunt , systematically demolished the Savoy and everything in it . In <unk> it was rebuilt as the Savoy Hospital for the poor . It gradually fell into <unk> and was divided into multiple <unk> . It was demolished in 1816 – 20 to build the approach road to Waterloo <unk> eventually being demolished in the 19th century . The Savoy Hotel now occupies this site . 
- Durham House , the historic London residence of the Bishop of Durham , was built circa 1345 and demolished in the mid @-@ 17th century . Having once been the home of Anne <unk> , it had become <unk> by the mid @-@ 17th century and was demolished in 1660 . Durham Street and the Adelphi Buildings were built on its site . York House was built as the London residence for the Bishop of Norwich not later than 1237 . At the time of the Reformation it was acquired by King Henry VIII and came to be known as York House when he granted it to the Archbishop of York in <unk> . In the <unk> it was acquired by the royal favourite George Villiers , 1st Duke of Buckingham , and after an interlude during the Civil War it was returned to George Villiers , 2nd Duke of Buckingham , who sold it to developers in 1672 . It was then demolished and new streets and buildings built on the site , including George Street , Villiers Street , Duke Street , Of <unk> , and Buckingham Street . 
- Cecil House , also called Exeter House or <unk> House , built in the 16th century by Lord <unk> as an expansion of an existing Tudor house . Exeter House was demolished in 1676 and Exeter Exchange built on the site . A menagerie was built on the upper floors in 1773 , which was later run by Edward Cross , who housed lions , tigers , monkeys and <unk> . In 1826 , an elephant , <unk> , nearly broke free from its cage and had to be destroyed ; the skeleton was later put on display . The exchange was demolished in 1829 , with the menagerie moving to the Surrey Zoological Gardens , and replaced by Exeter Hall , noted for its <unk> meetings . This was demolished in 1907 , and the site is now occupied by the Strand Palace Hotel . 
- Other significant palaces along the Strand include Worcester House , formerly the Inn , or residence , of the Bishop of Carlisle . , Salisbury House , used for royal lodgings in the 15th and 16th centuries , Hungerford House , which was demolished and replaced , in turn , by Hungerford Market and Charing Cross station , and Northumberland House , a large <unk> mansion , the historic London residence of the Dukes of Northumberland ; built in 1605 and demolished in 1874 . Northumberland Avenue now occupies the site . The official residence of the Secretary of State , next door at No. 1 the Strand , became the first numbered address in London . Apart from the rebuilt Somerset House , all of these buildings have been demolished and replaced from the 17th century onwards . A New Exchange was built on part of the gardens of Durham House , in 1608 @-@ 9 , facing the Strand . This high @-@ class shopping centre enjoyed considerable popularity but was eventually destroyed in 1737 . 
- 
- = = = Modern era = = = 
- 
- During the 17th century , many of the grand mansions on the Strand were demolished as the aristocracy moved to the West End . The Duck and Drake tavern on Strand was famed as a venue for the conspirators involved in the Gunpowder Plot . In the time of the Civil War , the Nag 's Head tavern was the venue of a meeting between Henry <unk> and some of the <unk> which resulted in the production of <unk> of the Army , demanding the abolition of the monarchy and the trial of King Charles I. 
- In the 18th century , coffee and chop houses were established on the street ; <unk> was established at No. 206 in 1706 by Thomas <unk> , supplier of tea to Queen Anne . The company claims to be the oldest <unk> in Westminster . The Grecian Coffee House ran from around <unk> to 1803 , while Tom 's ran from 1706 to around 1775 . Though these premises were well @-@ known , the alleyways around the Strand were regular haunts for <unk> and prostitutes during this time . The Rose Tavern , at the eastern end of the street , was frequented by lawyers during the 18th century . It was later demolished and became <unk> Place . The Crown and Anchor was the main meeting place for the Catholic Association and helped established the Roman Catholic Relief Act 1829 . It was later used for Catholic politicians such as Daniel O 'Connell to address constituents . The original premises burned down in 1854 , but was rebuilt . Simpson 's @-@ in @-@ the @-@ Strand originally started at No. 100 in 1828 as a smoking and dining club . It later became a restaurant . The Strand was also notable in the 18th century as a centre for the British book trade , with numerous printers and publishers along the street . The prominent <unk> Andrew <unk> is an example of one of the most successful publishers who owned a shop there . 
- King 's College London was founded in 1828 . The Strand Campus , based next to the church of St Mary @-@ le @-@ Strand , was designed by Robert <unk> and constructed in 1829 – 31 . King 's College Hospital opened as a branch of the college in 1840 , and became a constituent part of the University of London in 1908 . The current campus building was constructed in 1966 . In 2015 , the college acquired Strand House , Bush House and other buildings in the Aldwych Quarter . The Royal Courts of Justice , at the eastern end of the Strand , was designed in the mid @-@ 1860s by G.E. Street as a replacement for the older courts at Westminster Hall , though construction was so delayed that he died shortly before Queen Victoria opened the courts in 1882 . The West Green extension to the courts opened in 1911 , while the Queen 's Building opened in 1968 . 
- The architect John Nash redeveloped the western end of the Strand in the 1830s , including the construction of Charing Cross Hospital . In the 19th century , much of the Strand was rebuilt and the houses to the south no longer backed onto the Thames , separated from the river by the Victoria <unk> constructed in 1865 – 70 and <unk> 37 acres ( 15 ha ) of land . The street became well known for theatres , and at one point contained more than any other ; including the <unk> Music Hall at No. 65 , the Adelphi , Gaiety , Savoy , Terry 's and Vaudeville . In the 21st century , only the Adelphi , Vaudeville and Savoy remain . The Piccadilly branch line from <unk> to Aldwych was built partly to serve theatre traffic . The Coal Hole tavern was founded at No. 91 in the early 19th century , and frequented by coal @-@ <unk> working on the Thames . The <unk> <unk> Nicholson held song @-@ and @-@ supper evenings at the inn , featuring tableaux <unk> . The actor Edmund Kean established the Wolf Club at the venue , which is now commemorated by the Wolf Room . 
- Charing Cross railway station was built on the Strand in 1864 , providing a boat train service to Europe , which stimulated the growth of hotels in the area to cater for travellers . These included the Charing Cross Hotel , attached to the station itself . Today , there are several luggage outlets and tourist agents on the Strand , as well as old <unk> stamp dealers . The <unk> Stanley Gibbons opened a shop at No. 435 in 1891 . It moved to No. <unk> in 1893 , and is now currently based at No. <unk> . 
- The Strand Palace Hotel was designed by <unk> and constructed in 1925 – 30 . The entrance was rebuilt in 1968 , with the original being moved to the Victoria and Albert Museum . The Strand underwent extensive redevelopment in the mid @-@ 20th century . The length of road from St Mary 's eastwards up to St Clement 's was widened in 1900 , <unk> the former <unk> Street which forked from the Strand and ran parallel with it to the north , leaving the two churches of St Mary Le Strand and <unk> Danes as islands in the centre of the road . Gaiety Theatre was demolished , to be replaced by Citibank House , while Villiers House and New South Wales House were both built in 1957 – 59 . New South Wales House was subsequently demolished in 1996 and replaced by an office block . In 1998 , a statue of Oscar Wilde was built at the junction of Adelaide Street and <unk> Street , adjoining the western end of the Strand . 
- 
- = = Churches = = 
- 
- The church of St Clement Danes is believed to date from the 9th century . The name may have come from Harold <unk> , a Danish king who ruled England around 1035 – 40 and is buried in the church , or from a place of refuge for Danes after the conquest of Alfred the Great . It was transferred to the Order of the Knights Templar by Henry II in 1189 . It survived the Great Fire in 1666 , but was declared unsafe and rebuilt by Christopher Wren in 1679 . The building was damaged during the Blitz in 1941 , <unk> much of the interior , and was rebuilt in 1958 by Sam Lloyd , since when it has served as the central church of the Royal Air Force . The church is one of two possible origins for the " St Clement 's " in the nursery rhyme " <unk> and <unk> " , though more contemporary accounts suggest St Clement 's , <unk> in the City of London to be more likely . 
- St Mary le Strand was designed by James Gibbs and completed in 1717 , to replace a previous church demolished to make way for Somerset House . It was the first building to be designed by Gibbs , who was influenced by Wren and <unk> . The column on top of the church was originally designed to support a statue of Queen Anne , but was replaced with a spire following Anne 's death in 1714 . It survived the Blitz but was in poor shape until John Betjeman led a campaign to restore it in the 1970s . Essex Street Chapel , the birthplace of British <unk> , <unk> onto the Strand . The original chapel was built in 1774 , but damaged in the Blitz . It was restored after the war , and now serves as the denominational headquarters . 
- 
- = = Notable residents = = 
- 
- The print seller Rudolph <unk> lived and worked at No. 101 The Strand between 1797 and 1827 . His shop was one of the first to have gas lighting fitted . 
- In the 19th century , The Strand became a newly fashionable address and many avant @-@ garde writers and thinkers gathered here , among them Thomas Carlyle , Charles Dickens , John Stuart Mill , Ralph Waldo Emerson and the scientist Thomas Henry <unk> . No. 142 was the home of radical publisher and physician John Chapman , who published contemporary authors from this house during the 1850s and edited the journal Westminster Review from 1851 . George Eliot lived at No. 142 between 1851 and 1855 . Virginia Woolf regularly travelled along the Strand , and a King 's College building named after her is in nearby <unk> . 
- 
- = = Cultural references = = 
- 
- The Strand is the subject of a famous music hall song " Let 's All Go Down the Strand " , composed by Harry <unk> and C. W. Murphy . The song opens with a group of tourists , staying the night at Trafalgar Square about to embark for Rhineland . The chorus of " Let 's all go down the Strand – have a banana " is now recognised as a stereotypical part of Cockney music hall and parodied by English comedian Bill Bailey . English composer Percy <unk> used the name for his 1911 piano trio Handel in the Strand . 
- Virginia Woolf has written about the Strand in several of her essays , including " Street <unk> : A London Adventure , " and the novel Mrs. <unk> . T. S. Eliot alludes to the Strand in his 1905 poem " At <unk> " and in his 1922 poem " The Waste Land " ( part III , The Fire <unk> , v. 258 : " and along the Strand , up Queen Victoria Street " ) . John <unk> also refers to a " jostling in the Strand " in his poem " On Growing Old " . The street name also figures in the 1958 poem " <unk> on the Strand " by Richard Percival <unk> , which in 2013 was featured as part of <unk> ’ s " Poems on the Underground " scheme , appearing in tube carriages all over London . 
- John Betjeman used the title of the song for a television documentary made for Associated @-@ <unk> in 1967 . The same year , Margaret Williams used it for a stage comedy . The Strand Magazine , which began publishing in 1891 , was named after the street . A BBC World Service arts and culture radio series was called The Strand . Bush House , situated on the Strand , was home to the World Service between 1941 and 2012 . 
- The standard British <unk> board has the Strand in a group with two roads it connects to ; Fleet Street and Trafalgar Square . 
- 
- 
- = President Evil = 
- 
- " President Evil " is the fifth episode of the third season of the American mystery television series Veronica Mars , and the forty @-@ ninth episode overall . Written by Jonathan Moskin and David Mulei and directed by Nick Marck , the episode premiered on The CW on October 31 , 2006 . 
- The series depicts the adventures of Veronica Mars ( Kristen Bell ) as she deals with life as a college student while <unk> as a private detective . In this episode , Veronica investigates when two masked men <unk> a Halloween party and steal Veronica 's necklace from Lilly Kane ( Amanda <unk> ) . Meanwhile , Keith ( Enrico Colantoni ) helps Dean O 'Dell ( Ed Begley , Jr . ) find the biological father of his stepson , the only person who can give the stepson a life @-@ saving bone marrow transplant . 
- Dianna Agron , Richard Grieco , Ryan <unk> , and Robert <unk> <unk> guest star or make their first appearances as recurring characters in the episode . The episode received mixed reviews from television critics , with general praise towards Veronica and Weevil 's relationship and criticism for the case of the week and the episode 's ending . 
- 
- = = Synopsis = = 
- 
- Veronica shows one of the rape victims ( <unk> <unk> ) the photo of a suspect , but she doesn ’ t recognize him . In criminology class , Veronica does a case study of Weevil ( Francis <unk> ) , and the class enjoys it . After the study , Veronica tells Weevil that she ’ s dating Logan ( Jason <unk> ) , and he reacts negatively . Veronica then walks into Mars Investigations and finds Cyrus O ’ Dell and his wife ( Jaime Ray Newman ) talking with Keith , while Veronica herself does some detective work . Keith informs Veronica that Dean O ’ Dell ’ s stepson is dying and that he wants Keith to find the boy ’ s biological father . At Mercer 's ( Ryan Devlin ) party , two masked men enter and steal everyone ’ s <unk> , including the necklace that Lilly gave Veronica . Wallace ( Percy <unk> III ) is failing his mechanical engineering class , a subject in which he wanted to major . Meanwhile , Keith tracks down the husband , and Veronica accuses Weevil of robbing the party . 
- At the meeting , Dean O ’ Dell ’ s wife asks the ex @-@ husband to give their son a bone marrow transplant . Meanwhile , Sheriff Lamb ( Michael <unk> ) arrests Weevil for the robbery . Veronica visits Weevil in prison before she decides to look into a <unk> delivered to Weevil ’ s house . Sheriff Lamb tells Keith that the ex @-@ husband has disappeared before accusing Keith in the ex @-@ husband ’ s disappearance . Keith thinks that Dean O ’ Dell was involved in the disappearance , but when he visits Dean , it turns out that Dean 's wife and stepson have disappeared to a hospital in Mexico , presumably taking the ex @-@ husband with them . Wallace visits a “ tutor ” , who gives him a mysterious “ study guide ” before going back to for the “ answers . ” Veronica notices the masks being used in a short film , but they were stolen . When they are in Mexico , Dean O ’ Dell reveals that he <unk> to Keith about their whereabouts . 
- Veronica tracks the casino <unk> down to a volunteer police officer . She has evidence , and the police comes to arrest him . However , they don ’ t find the necklace . At the hospital , Keith hesitates when O ’ Dell asks him what he would do if Veronica were in danger . Veronica gets her necklace back from the perpetrator ’ s daughter . Because he gave bone marrow , the ex @-@ husband now has a much more favorable divorce settlement . Veronica tracks down the suspect in the photo , Wang <unk> , but his roommate tells Veronica that he is Claire ’ s boyfriend . 
- 
- = = Production = = 
- 
- " President Evil " was written by Jonathan Moskin and David Mulei and directed by Nick Marck , marking Moskin 's first writing credit , Mulei 's first writing credit for the show , and Marck 's eighth directing credit for Veronica Mars . Dianna Agron , famous for her role as Quinn <unk> on Glee , makes her first appearance on the series in this episode as Jenny , Veronica 's classmate . She would eventually appear on several episodes of the third season . 
- Richard Grieco , known for his role as Dennis Booker on the original 21 Jump Street TV series , appears for the first time as Steve <unk> , Mindy O 'Dell 's ex @-@ husband . In 2012 , the New York Daily News called Grieco 's role on Veronica Mars " his last mainstream role " . Ryan <unk> guest stars as Danny <unk> , a <unk> shop employee . He would go on to make a guest appearance on series creator Rob Thomas 's later show Party Down . Robert <unk> <unk> appears as Mason , Wallace 's friend . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- In its original broadcast , " President Evil " was viewed by 2 @.@ 70 million viewers , ranking 99th of 101 in the weekly rankings . This figure was a decrease in 630 @,@ 000 viewers from the previous episode , Charlie Don 't Surf . 
- 
- = = = Reviews = = = 
- 
- The episode received mixed reviews from television critics . Eric Goldman , writing for IGN , gave a mostly positive review , giving it a 7 @.@ 5 out of 10 , indicating that it was a " good " episode . He stated , " Things are still a bit <unk> on Veronica Mars this season , though this episode had some elements that hopefully herald a turn in the right direction . " While criticizing the case @-@ of @-@ the @-@ week , he was more laudatory to the subplots , calling Sheriff Lamb arresting Weevil " the most entertaining scene of the episode " and that " the episode definitely picked up in the last act . " Rowan Kaiser , writing for The A.V. Club , gave a very mixed review , commenting positively on Veronica and Weevil 's relationship while criticizing other aspects of the episode . " While ' President Evil ' was not a favorite , and has a few annoying moments , it was generally a perfectly adequate episode of Veronica Mars . That is , until the ending . [ … ] The implication here is one that I 've been <unk> for a few episodes : that the radical feminist Lilith House has staged Claire 's rape , and possibly others . " Television Without Pity gave the episode a " B- " . 
- Reviewer Alan Sepinwall also had mixed comments , comparing the episode to House and stating , " And yet as much as Veronica was annoying me , as much as I wanted to <unk> at her for automatically jumping to conclusions with Weevil ( more on that in a minute ) , this was maybe my favorite episode of the season . [ … ] Hell , they even had a <unk> ending to the mystery arc for the first time in forever . I dug it . " Price Peterson of TV.com gave a mostly negative review , writing , " This episode was not the best ! Not terrible by any means , but it felt lacking in certain ways , particularly when it came to the total lack of Piz and Dick . Where were they ? Also this episode made it clear that Logan didn 't have all that much to do on the show anymore . " 
- 
- 
- = Dave Sisler = 
- 
- David Michael Sisler ( October 16 , 1931 – January 9 , 2011 ) was a professional baseball pitcher who played in Major League Baseball ( MLB ) from 1956 through 1962 . Early in his career , Sisler was a starter , then later was used as a middle reliever and occasionally as a closer . He reached the majors in 1956 with the Boston Red Sox after he completed a two @-@ year obligation in the active military . After three @-@ and @-@ a @-@ half seasons with the Red Sox , he was traded to the Detroit Tigers in 1959 and served the team through the 1960 season . Before the 1961 season , he was selected by the Washington Senators in the 1960 Major League Baseball expansion draft , for whom he played the 1961 season . He was then traded to the Cincinnati Reds in 1962 , playing one season at the major league level , and one in their minor league system . 
- His most productive years came with Boston , where he won 24 games from 1956 to 1958 , averaging 138 innings each season . After that , he appeared strictly as a reliever and saved a career @-@ high 11 games for the Senators . In a seven @-@ season career , Sisler posted a 38 – 44 record with a 4 @.@ 33 ERA in 247 appearances , including 29 saves , 12 complete games , one shutout and 656 ⅓ innings . Sisler retired from baseball after the 1963 season to become an investment firm executive , a career that lasted for over 30 years , retiring as a vice @-@ chairman for A. G. Edwards . 
- His father , Hall of Famer George Sisler ; and one of his brothers , Dick Sisler , also played baseball at the major league level ; while another brother , George Sisler , Jr . , was a general manager for several minor league baseball teams , and later became president of the International League from 1966 to 1976 . 
- 
- = = Early life = = 
- 
- Sisler was born on October 16 , 1931 , in St. Louis , Missouri , to George Sisler , a Baseball Hall of Fame player , and Kathleen . He played baseball in high school and when he was 16 he went to work for his father who was a coach in the Brooklyn Dodgers organization . Dave kept statistics and was occasionally allowed to pitch . The Dodgers did not <unk> a future with him and did not pursue a contract due to his commitment to Princeton and future military obligation . Sisler was signed by the Boston Red Sox of the American League ( AL ) after his time with Princeton , and began his professional baseball career at the age of 21 in 1953 for the Albany Senators in the class @-@ A Eastern League . He pitched in 21 games that season , 20 as their starting pitcher ; he had a 12 – 7 win – loss record , an ERA of 2 @.@ 60 in 135 innings pitched . He did not play professional baseball in either 1954 or 1955 due to his active military service . 
- 
- = = Major League career = = 
- 
- 
- = = = Boston Red Sox = = = 
- 
- After his military commitment , he re @-@ joined the organization , and began the season with the Red Sox . He made his major league debut on April 26 , 1956 , a one @-@ inning relief appearance against the New York Yankees . His first MLB victory came seven days later , a three @-@ inning relief appearance against the Yankees in Boston . On August 5 , in his sixth start , he pitched a seven @-@ inning complete game 2 @-@ 1 victory over the Cleveland Indians and their pitching star , future Hall of Famer Bob Lemon . In total , he appeared in 39 games his first season , 14 as their starting pitcher , had a 9 – 8 win – loss record with three saves , 98 strikeouts , and a 4 @.@ 66 ERA in 142 ⅓ inning pitched . He tied for the AL league with a 1 @.@ 000 fielding percentage by not committing a single error , although he had the sixth highest total of hit batsmen with seven . Following the season , Sisler was praised for his performance ; he was selected by Boston area sportswriters as the " Harry <unk> Award " winner , given annually to whom they voted as the top Boston Red Sox rookie , and was named to the 1956 " Rookie All @-@ Star Team " . 
- On January 17 , 1957 , Sisler was re @-@ signed by the Red Sox . The Red Sox expected Sisler to be a candidate for a pitching rotation slot . He impressed his coaching staff during early spring practices , manager Mike " <unk> " Higgins commented that Sisler " looks good ; he 's more mature and shows more confidence " . When the regular season began , he was initially effective , winning three games against one defeat in his first four starts , including two complete game victories against the Yankees . At this point , he was 4 – 0 against the Yankees , earning him the moniker " Yankee Killer " from sportswriter Chester L. Smith of the Pittsburgh Press . Although his ERA of 4 @.@ 88 was considered high , it was due in large part to his loss against the Kansas City Athletics who scored six earned runs in just two innings . On July 4 , he defeated the Yankees once again , making him 5 – 0 against them in his career , and 7 – 4 overall for the 1957 season , and had lowered his ERA to 4 @.@ 02 . That was his last victory of the season , however , which he followed with two consecutive losses , pitching a total of four innings ; he did not appear in another game until September 2 . He recorded a save against the Baltimore Orioles on September 7 , but closed the season with two consecutive losses against the Yankees , though he pitched well in the second game , giving up just three hits and two earned runs in eight innings . His season totals include a 7 – 8 record , and a 4 @.@ 72 ERA in 122 ⅓ inning pitched . 
- Sisler began the 1958 spring training in spectacular form , pitching a total of nine <unk> innings and allowed just one <unk> in 28 batters faced ; the last 27 in order . However , he finished the spring with two consecutive losses , including one to the Pittsburgh Pirates , with whom his father was a special assistant to their manager . He opened the regular season with a complete game victory against the Yankees on April 16 . On May 2 , he pitched his only career complete game shutout , a 6 – 0 victory against the Detroit Tigers and future Hall of Famer Jim <unk> , improving his record to 3 – 1 for the season . Over the next two months , his average innings pitched declined during his starts , and on several occasions he entered the game in relief . He went <unk> from June 12 to August 9 , when he defeated the Yankees . He recorded just one more victory in 1958 , and completed the season with an 8 – 9 record , a 4 @.@ 94 ERA , and 71 strikeouts in a career @-@ high 149 ⅓ innings pitched . 
- On February 8 , 1959 , the Red Sox announced that they had Sisler signed for the 1959 season . After what was considered a " disappointing " season , he did not impress with his first outing in spring training , giving up two hits and five bases on balls in just two innings of work . <unk> of pitching depth was a noted weakness for the Red Sox entering the season , with unpredictable youth and with hopes that Sisler could regain his previous level of success . Sisler opened the season slated as a relief pitcher with the idea that he would start in certain cases . In three relief appearances through April , he had a 6 @.@ 75 ERA in 6 ⅔ inning pitched . Management decided that his performance along with a pitching staff that they considered inexperienced , Sisler and Ted <unk> were traded to the Detroit Tigers on May 2 for starting pitcher Billy <unk> . 
- 
- = = = Detroit Tigers = = = 
- 
- <unk> , who won 20 games for the Tigers in 1956 , had continually under @-@ performed since , and was noted for being in the manager 's " <unk> " ; however , <unk> sportswriter <unk> <unk> commented , " I can 't see where they ( Tigers ) gained a thing in this transaction . " The Tigers had lost 15 of their first 17 games of the season before the trade and decided that changes were needed . In addition to the Sisler trade they fired manager Bill Norman and replaced him with Jimmy Dykes . These moves initially proved effective , sparking a winning game streak , which began by defeating the Yankees in both games of a <unk> on May 3 . Continuing their rebound , the Tigers faced the Red Sox next , winning two of three , and Sisler pitched a scoreless one @-@ inning relief appearance against his former team on May 6 . Used exclusively in relief , Sisler pitched in 32 games for the Tigers , winning one game against three losses and collected seven saves , which was seventh most in the AL , and he did not record an error . 
- Sisler returned to the Tigers for the 1960 season , and manager Dykes noted that he was not confident that his bullpen could help when needed , pointing to the previous season as a reference . The Tigers bullpen had won just nine games in 1959 , but began 1960 with three straight victories , with Sisler pitching a scoreless two @-@ inning appearance on April 22 . His performance was considered a surprise , due to having a poor spring training . Although he pitched well in his first game of the season , he quickly lost his form , and by May 18 , his ERA had risen to 5 @.@ 06 and had a 1 – 2 record . It was at this point in the season that he began to turn his fortunes around and regained his effectiveness , and by August 12 he had lowered his ERA to 1 @.@ 90 . His season totals included a 2 @.@ 47 ERA and a 7 @-@ 5 win @-@ loss record , both career <unk> , and added six saves in 80 innings pitched . 
- 
- = = = Washington Senators = = = 
- 
- After the 1960 season , it was announced that the AL were going to create two new teams , one in Los Angeles and one in Washington , D. C. Although he was thought of as the Tigers ' best reliever during the season , Sisler was listed by the Tigers as eligible for the 1960 Major League Baseball expansion draft . The draft occurred on December 14 , 1960 and Sisler was selected by the Washington Senators with the fourth overall pick . The Senators received communication from several teams that were interested in Sisler , namely the Milwaukee Braves of the National League ( NL ) and the Athletics , but decided to trade their first pick in the expansion draft , Bobby <unk> , instead . New manager Mickey Vernon defined Sisler 's role with the team before the season began , designating him as their top relief pitcher . Sisler felt slighted by the Tigers , having been left unprotected by the team , and had considered retiring from baseball , but ultimately decided to sign with the Senators . 
- Sisler was solid for much of the first half of the season , not allowing an earned run until May 19 , and by June 5 he had an ERA of 0 @.@ 74 , a 1 – 0 record with seven saves . It was at the point that his effectiveness declined rapidly , allowing runs to score in four of his next five appearances . In a game against the Red Sox on June 18 , he relieved starter Carl <unk> , and immediately gave up bases on balls to the first two batters he faced , which forced in two runs , then gave up a grand slam to Jim <unk> . He followed with another base on balls , and was relieved without having recording an out . Over the course of the next couple months , with his ERA steadily rising , his playing time was <unk> , making just six appearances in the month of July , and five in August . He made his last major league start on August 31 against the Tigers , giving up seven hits and six earned runs for the loss . In 45 total appearances in 1961 , he had a 2 – 8 win @-@ loss record and finished sixth in AL with 11 saves . On September 16 , the Senators agreed to send $ 75 @,@ 000 ( $ 593 @,@ 900 current dollar adjustment ) and a player to be named later to the Cincinnati Reds of the NL for pitcher Claude <unk> . To complete the transaction , Washington sent Sisler to the Reds as that player named on November 28 . 
- 
- = = = Cincinnati Reds = = = 
- 
- The Reds were the NL champions in 1961 , and with the addition of Sisler , Dave <unk> , and Moe <unk> to ensure stability to their pitching staff in 1962 , the team 's management had high hopes of a return to the World Series . Sisler , who was expected to be used as a relief pitcher , joined a Reds team that already had his brother Dick , who was their first base coach . His father , still with Pittsburgh , mentioned that he was relieved that his son did not play in a recent series between the Pirates and Reds , recalling how the mixed emotions he felt when his elder son , Dick , helped defeat the Dodgers in an important game in 1950 by hitting a home run . The younger Sisler 's season did not begin well ; in his ten appearances by June 5 , he allowed a run in all but three , and had a 4 @.@ 97 ERA . On June 19 , Sisler pitched a scoreless inning in victory against the Pirates , the time first time George watched his son pitch as an opponent . Although Sisler 's season was a disappointment , he completed the season well , allowing just one earned run over the course of his final eight games , as well as earning the victory on September 23 , his final major league appearance . His totals for the season included a 4 – 3 record , a 3 @.@ 92 ERA , and one save in 43 ⅔ innings pitched . 
- For the coming 1963 season , manager Fred Hutchinson envisioned the same the role for Sisler , coming out of the bullpen with the idea that he would occasionally start . The Reds announced on February 9 that Sisler had signed for the 1963 season , although he did not make the Reds ' NL club and was optioned to their class @-@ AAA team , the San Diego Padres of the Pacific Coast League ( <unk> ) . In 35 games pitched for the Padres , he started six , had a 6 – 9 record and a 3 @.@ 40 ERA in 90 innings . On March 18 , 1964 , it was announced that Sisler retired from baseball to join an Investment company . 
- 
- = = Later life = = 
- 
- After his baseball career , Sisler became an executive with the investment firm A. G. Edwards for more than 30 years , reaching the level of vice @-@ chairman . A. G. Edwards was a sponsor of a semi @-@ professional baseball team located in St. Petersburg , Florida ; when the " <unk> " were invited to play in season @-@ ending tournament to decide a champion , Sisler , a senior executive for the firm , denied the funding for the team , forcing them to decline if no other <unk> were raised . It was an unexpected decision considering that Sisler was former baseball player , but he did not feel that A. G. Edwards was <unk> , with the costs of running the team becoming more than originally agreed to by contract . 
- In 2004 , the possibility arose of his father 's record of 257 hits in a season being surpassed by Ichiro Suzuki of the Seattle Mariners . Living in Dallas , Texas at the time , Sisler and his son Dave followed Ichiro 's progress throughout the season . Both had mixed feelings about the record chase ; they were happy that George Sisler 's name was being talked about and that people were re @-@ discovering him as a player , but both were hoping that , if the record was not broken in 154 games , there would be an <unk> noting that it took Ichiro more games to achieve it . Ichiro finished the season with 262 hits , and the Mariners extended an invitation for the entire Sisler family to attend the celebration at <unk> Field . The younger Sisler was prevented by an emergency heart procedure from travelling to Seattle . Both remarked that the Mariners were a <unk> organization for extending the invitation . 
- Sisler died on January 9 , 2011 of prostate cancer at the age of 79 in St. Louis , Missouri . He was survived by his wife Janet , son and three grandchildren . 
- 
- 
- = Cole Hamels = 
- 
- <unk> Michael " Cole " Hamels ( born December 27 , 1983 ) is an American professional baseball pitcher for the Texas Rangers of Major League Baseball ( MLB ) . He played in MLB for the Philadelphia Phillies from 2006 to 2015 . 
- Originally from San Diego , California , Hamels excelled in Rancho Bernardo High School both <unk> and <unk> . The Phillies drafted him out of high school in the first round of the 2002 MLB Draft ( 17th ) , and he began his career in the Phillies minor league system . Numerous issues , including an injury sustained in a bar fight as well as other injuries , occurred during his first few minor league seasons . Having reached the Triple @-@ A level , he was the top pitcher in the Phillies ' minor league system in 2006 . 
- In May 2006 , Hamels made his major league debut for the Phillies . After securing a long @-@ term spot as a member of the Phillies starting rotation in his rookie season , he made large <unk> in the 2007 Major League Baseball season and won the Phillies ' top major league pitcher award . He was the top pitcher on the team entering the 2008 season , and during the Phillies ' postseason run , during which they ultimately won the 2008 World Series , he won the World Series Most Valuable Player Award . Since then , he has been a top starter in the Phillies ' rotation . After the 2008 season , Hamels signed a three @-@ year contract with the Phillies . His statistics declined over the next two seasons , struggling through a tumultuous 2009 campaign and somewhat bouncing back in 2010 , however still not approaching his 2008 numbers . Over the next few seasons , Hamels was joined by fellow all @-@ star pitchers including Roy Halladay , Cliff Lee , and Roy Oswalt , and flourished with them , putting up some of his top career seasons before suffering from poor run support in 2013 . With the decline of his aging teammates as the team missed the postseason for the next few years , Hamels remained one of the Phillies ' consistent stars . Hamels was traded to the Texas Rangers in 2015 where he sparked their run to the AL West title . 
- 
- = = Early life = = 
- 
- Cole Hamels attended <unk> Middle School and Rancho Bernardo High School in San Diego . He was a " gifted student " , and scored a <unk> ( out of 1600 ) on the SAT . Scouts were interested in Hamels while he was in high school primarily because his fastball was <unk> as high as 94 miles per hour ( 151 km / h ) , and his secondary offerings were considered advanced . However , some teams lost interest when Hamels broke his left humerus during his sophomore year . Nonetheless , Hamels was drafted in the first round of the 2002 Major League Baseball draft by the Philadelphia Phillies , who held the 17th overall selection . 
- 
- = = Professional career = = 
- 
- 
- = = = Minor league career = = = 
- 
- Hamels began his professional baseball career in 2003 , pitching for the Class A <unk> <unk> of the South Atlantic League . Later that season , he was promoted to the Clearwater <unk> of the Class A @-@ Advanced Florida State League . He finished the year by receiving the Paul <unk> Award , given to the best pitcher in the Phillies ' minor league system . 
- During the next two seasons , however , Hamels suffered through several injuries , and pitched only <unk> . He missed most of the 2004 season with elbow tendinitis , making only four starts . In 2005 , he broke his pitching hand in a bar fight before the season began ; after rehabilitation , he was assigned to Clearwater . In July , the Phillies promoted him to the Class AA Reading Phillies of the Eastern League , where he surrendered the first home run of his professional career in his first start . Subsequently , he was shut down for the remainder of the season with back <unk> . 
- In 2006 , a healthy Hamels started again at Clearwater , and after a brief stint in Reading , he was promoted to the Scranton / Wilkes @-@ Barre Red Barons of the Class AAA International League . In three games at Scranton , he struck out 36 batters while giving up only one walk and one run . His minor league statistics included a record of 14 – 4 with a 1 @.@ 43 ERA and 273 strikeouts in 35 games pitched . For his performance , the Phillies promoted him to the major league club . 
- 
- = = = Philadelphia Phillies = = = 
- 
- 
- = = = = 2006 = = = = 
- 
- Hamels was called up to the Philadelphia Phillies in May 2006 . In his Major League Baseball debut on May 12 , he pitched five scoreless innings , allowing one hit , striking out seven batters , walked five , while earning a no @-@ decision against the Cincinnati Reds . In his second career start , Hamels was dominant until the seventh inning , during which he was pulled after he allowed several baserunners , but again received a no @-@ decision . A shoulder injury <unk> Hamels from the lineup of what would have originally been his third major league start . He was put on the 15 @-@ day disabled list and returned on June 6 to defeat the Arizona <unk> 10 – 1 for his first Major League victory . On August 14 , 2006 , Hamels had his best start of his rookie season , shutting out the New York Mets over eight innings and striking out nine in the Phillies ' 13 – 0 victory . He finished his rookie season with a 9 – 8 record , a 4 @.@ 08 earned run average ( ERA ) , and 145 strikeouts ( third among NL <unk> ) in 132 1 ⁄ 3 innings ( sixth among NL <unk> ) . 
- 
- = = = = 2007 = = = = 
- 
- Hamels entered the 2007 season having done significant work in the weight room in the offseason and at spring training . His tenacity came to fruition when , on April 21 , 2007 , Hamels pitched his first major league complete game , allowing one run on five hits and two walks while setting a career high with 15 strikeouts . Just three days before , the Phillies moved their opening day starter Brett Myers to the bullpen to be the closer , making Hamels the team 's number one starter . On May 16 , he carried a perfect game into the seventh inning , but then walked <unk> hitter <unk> Weeks and surrendered a home run to the next batter , J. J. Hardy . 
- For his strong performance during the first half of the season , Hamels was , for the first time in his career , selected a member of the NL All Star Team . As Hamels had been the Phillies only consistent starter to that point , even to the point that one columnist said the Phillies are a joke " as long as we are forced to look forward to someone other than Cole Hamels on the mound " , the Phillies acquired Kyle <unk> to supplement the rotation soon before the trading deadline . Several weeks later , Hamels was placed on the 15 @-@ day disabled list ( <unk> ) with a mild left elbow strain . After the Phillies activated him from the <unk> , on September 28 , he helped the Phillies take over first place in the National League East by striking out 13 Washington <unk> over 8 innings in a 6 – 0 win . Two days later , the team clinched a spot in the playoffs for the first time in 14 years . In the first game of the National League Division Series , he started for the Phillies , but surrendered three runs , three hits , and an " <unk> " four walks , while striking out seven in 62 ⁄ 3 innings ; he was assessed the loss . Ultimately , the Phillies lost the series . Hamels finished with a regular @-@ season record of 15 – 5 , a 3 @.@ 39 ERA , and 177 strikeouts in 183 1 ⁄ 3 innings . The Philadelphia chapter of the Baseball Writers ' Association of America presented him the " Steve Carlton Most Valuable Pitcher " award . 
- 
- = = = = 2008 = = = = 
- 
- Before the season began in March , Hamels made a complaint about the Phillies <unk> him , saying it was a " low blow " and he was " caught off @-@ guard " with the gap between what he and his agent John <unk> felt was a fair reward for his performance in the previous season and what he was paid ( the Phillies paid him US $ 500 @,@ 000 , barely above the minimum salary for MLB players despite Hamels ' strong performance ) . 
- Though Hamels was the " clear @-@ cut ace " , Charlie Manuel penciled in Brett Myers as the Phillies ' opening day starter because Myers accepted his role as closer the preceding season , putting the team ahead of his personal wishes to start . 
- By the end of April , Hamels led the Phillies in most pitching categories , including wins ( 3 ) , ERA ( 2 @.@ 70 ) , and innings pitched ( 43 ⅓ ) . Continuing his dominance into May , Hamels recorded his first career complete game shutout against the Atlanta Braves on the 15th of that month . By June , Myers had been demoted to the minor leagues due to his <unk> , leaving Hamels alone atop the rotation . Hamels was <unk> from a selection to the All @-@ Star Game despite strong numbers . For the season , Hamels was 14 – 10 with a 3 @.@ 09 ERA and 196 strikeouts in 227 1 ⁄ 3 innings ; he had the lowest on @-@ base percentage @-@ against ( <unk> ) in the majors at <unk> . <unk> also rated his changeup as the most effective in the majors . 
- Hamels pitched in Game 1 of the National League Division Series against the Brewers , pitching " eight spectacular two @-@ hit shutout innings " during which he struck out nine hitters , <unk> his first career playoff win , and the Phillies ' first playoff win since 1993 . He commented , " I knew the importance of the game . And it 's something where , because of last year , I learned what it really takes in trying to ... kind of mellow out , not to have that sort of excitement where you can 't really control everything . " He was named the Most Valuable Player ( MVP ) of the National League Championship Series , going 2 – 0 in the NLCS with a 1 @.@ 93 ERA and winning the series <unk> on October 15 in Los Angeles . 
- Hamels was selected the Phillies starter for Game 1 of the World Series ; the Phillies won 3 – 2 , and Hamels earned his fourth win of the postseason . Hamels also started Game 5 , which was suspended due to rain after the top of the sixth inning tied at 2 @-@ 2 , and receiving a no @-@ decision ; when game five resumed the Phillies won 4 @-@ 3 to clinch the World Series . Overall , Hamels made five postseason starts in 2008 , going 4 – 0 with a 1 @.@ 80 ERA . Hamels threw a total of 35 innings during the postseason , and held opponents scoreless in 28 of them ; he never allowed more than one run in any of the seven innings in which he did not hold opponents scoreless . Hamels was named the 2008 World Series MVP . 
- Subsequently , <unk> Stark wrote , 
- The names on the list are the names who have carved their legends in the month of October ... We know their names because October was their kind of month , and they belong on that list because they once did something very few pitchers have ever done . They all won four starts in the same postseason . And now they have company ... a 24 @-@ year @-@ old left @-@ <unk> named Cole Hamels . And with every time the Phillies handed him the baseball in October 2009 , it became more apparent that he is one of the sports ' most special talents . 
- The Philadelphia chapter of the Baseball Writers ' Association of America presented him the " Steve Carlton Most Valuable Pitcher " award for the second consecutive year . 
- 
- = = = = 2009 = = = = 
- 
- Hamels started his 2009 season by signing a three @-@ year , $ 20 @.@ 5 million contract with the Phillies . On February 14 , the first day of spring training for pitchers and catchers , when asked who the Opening Day starter would be , manager Charlie Manuel responded , " Yeah , you might as well go ahead and pencil him in . I don 't think there 's any sense in me playing games . Go ahead , pencil him in . " 
- However , Hamels left spring training on March 16 after feeling tightness between innings , and flew to Philadelphia to have his left elbow examined by Dr. Michael <unk> . " This will obviously set me back a couple of days , and I don 't think that should be a big deal " , said Hamels . <unk> found no structural damage in his arm , yet Hamels still did not pitch on Opening Day as expected ; Myers did for the third consecutive season . For the first time in his career , he went <unk> in his first four starts , and left back @-@ to @-@ back starts early due to injury in late April , sustaining a left @-@ shoulder <unk> and an ankle sprain respectively . From June on , however , he returned to his previous form , recording two complete @-@ game <unk> ( tied for the NL @-@ lead ) , striking out at least 10 batters in two separate games , and amassing a 21 @-@ inning scoreless streak from August 21 to September 6 , while allowing just two home runs in his final eight starts . In July , the Phillies augmented their starting rotation by acquiring two former Cy Young Award winners in Cliff Lee and Pedro Martínez from a trade and a free agent signing respectively . Due to his early season injury struggles , Hamels posted a 10 – 11 record and a 4 @.@ 32 ERA in the regular season , his first major league season in which he posted a <unk> record , and the worst ERA of his career to that point . 
- Hamels started Game 2 of the 2009 National League Division Series against the Colorado Rockies , allowing four earned runs through five innings to take the loss . Nevertheless , the Phillies won the series , three games to one . He earned the win in Game 1 of the National League Championship Series as the Phillies beat the Dodgers , 8 – 6 . Hamels started Game 3 of the World Series against the New York Yankees , pitching 4 1 ⁄ 3 innings , allowing 5 earned runs and taking the loss as the Yankees won that game 8 @-@ 5 . Afterward , he told reporters , " I can 't wait for it ( the season ) to end . It 's been mentally draining . It 's one of those things where , a year in , you just can ’ t wait for a fresh start . " Later in the series , after the Phillies won game five , a <unk> Brett Myers <unk> asked Hamels : " What are you doing here ? I thought you quit . " The Phillies lost the World Series to the New York Yankees in six games . 
- Rob <unk> a <unk> and columnist for ESPN.com , contradicted anyone who asserted Hamels ' skills had deteriorated , instead commenting , 
- " Last October , everybody was ready to <unk> Hamels some sort of superhero . This was largely because he went 4 – 0 during the Phillies ' championship run , but also because he went 14 – 10 with a 3 @.@ 09 ERA during the regular season . Last season , Hamels ' strikeout @-@ to @-@ walk ratio was 3 @.@ 7 ; this year it 's 3 @.@ 8 . Last year , Hamels gave up 1 @.@ 1 home runs per nine innings ; this year he 's given up 1 @.@ 3 <unk> per nine . The only real difference between the 2008 Hamels and the 2009 Hamels is luck . " 
- Phillies ' pitching coach Rich <unk> and Hamels himself blamed Hamels ' demeanor for some of his decline , noting , " He is such a <unk> … his approach wasn ’ t very good last year . His success won ’ t come back until his demeanor changes " and " The more angry you get , it 's that much <unk> to execute your next pitch . I think I let [ expectations ] get to me " respectively . 
- 
- = = = = 2010 = = = = 
- 
- Entering the 2010 season , the Phillies traded away Cliff Lee , but in doing so , acquired Roy Halladay in a " mega @-@ trade " . They did not re @-@ sign Myers , and Halladay was named the Phillies ' opening day starter . Among Hamels ' season highlights were a start on June 7 against the San Diego Padres during which he threw 61 ⁄ 3 innings of no @-@ hit baseball , winning five consecutive starts including three consecutive scoreless starts from late @-@ August to mid @-@ September , and striking out 13 batters , tied for his second @-@ highest career total , on September 13 against the Florida Marlins . 
- The Phillies finished with the best record in MLB . Hamels started Game 3 of the National League Division Series against the Cincinnati Reds and , for the first time in his postseason career , pitched a complete game shutout . The Phillies advanced to the National League Championship Series facing the San Francisco Giants , where Hamels took the loss in Game 3 giving up three runs as the Giants ' Matt Cain shutout the Phillies , en route to the Phillies ' ultimate elimination in six games . 
- <unk> , Hamels rebounded from his previous season by posting a 12 – 11 record with a 3 @.@ 06 ERA , the latter of which was , at the time , a career @-@ best . He also struck out a career high <unk> batters . Throughout the season , he was plagued by a lack of run support ; in 1 ⁄ 3 of his starts , the Phillies did not score a single run while he was in the game . Moreover , he received the fifth @-@ lowest run support in the NL . Nevertheless , he allowed three or fewer earned runs in 26 of his 33 starts . Jeff Nelson " <unk> " evaluated Hamels ' season as follows : 
- " <unk> . <unk> . Soft . <unk> . He wouldn ’ t pitch on three @-@ days rest if asked . He 's not a big game pitcher . He sounds like a <unk> 7 @-@ year old – these were some of the descriptions I heard and read from many Phillies fans following Cole Hamels ’ disappointing 2009 season . <unk> , some people even wanted him traded after what he said during the ’ 09 World Series . To the naked eye , Cole just wasn ’ t right two years ago . Fast forward a year later and Hamels all the sudden ‘ found his game . ’ He ‘ <unk> a switch ’ or ‘ put his game face on . ’ He was more prepared heading in to 2010 or he was cured from the <unk> effect . " 
- 
- = = = = 2011 = = = = 
- 
- On December 15 , 2010 , Cliff Lee returned to the Phillies as he signed a free @-@ agent contract with Philadelphia for five years and $ 120 million with a <unk> option for a sixth year . After this signing , the Phillies ' starting rotation consisted of Roy Halladay , Roy Oswalt , Cliff Lee , Cole Hamels , and Joe <unk> . This rotation was considered one of the best in pitching history by many . Halladay , Oswalt , Lee , and Hamels were dubbed the ' <unk> <unk> ' by fans and the media . 
- Hamels entered the 2011 season with a large amount of excitement and hype from fans and the media . By the All @-@ Star game , Hamels was 4 – 3 . On July 3 , when the <unk> for the 2011 MLB All @-@ Star Game in Phoenix , Arizona were broadcast on <unk> , it was announced that Hamels was voted onto the National League team along with fellow pitchers and teammates Roy Halladay , Cliff Lee , <unk> <unk> , and Shane <unk> . He was not eligible to play , however , because he had pitched the Sunday prior to the All @-@ Star Game . Hamels shared his dismay with this rule , saying , " It 's one of those things where people who don 't play the game make the rules . " 
- The Phillies finished with the best record in MLB for the second year in a row . In the 2011 National League Division Series against the St. Louis Cardinals , Hamels started Game 3 which resulted in a 3 @-@ 2 win to give the Phillies a 2 @-@ 1 series lead , however the Phillies would lose Games 4 and 5 to be eliminated . 
- Hamels finished the 2011 season with a record of 14 – 9 , 194 strikeouts , an ERA of 2 @.@ 79 , and a 0 @.@ 986 <unk> . He finished fifth in the Cy Young Award voting behind Clayton <unk> , Roy Halladay , Cliff Lee , and Ian Kennedy , respectively . 
- 
- = = = = 2012 = = = = 
- 
- During the 2011 off @-@ season , Hamels underwent elbow and <unk> <unk> . Hamels had missed scheduled starts in August due to a stiff shoulder , and loose bodies were removed from his elbow to fix his problems . Hamels was ready to pitch by the start of Spring training 2012 . 
- On January 17 , Hamels and the Philadelphia Phillies agreed to a one @-@ year , $ 15 million contract to avoid arbitration . This was the highest salary ever paid to a pitcher eligible for arbitration before free agency . 
- On May 6 , Hamels was suspended for five games after hitting <unk> Harper in his lower back with a pitch , after admitting that it was intentional . On July 21 , 2012 , Hamels hit his first career home run off San Francisco Giants pitcher Matt Cain , who had <unk> off of him in the top half of the same inning , the first time in MLB that two pitchers had hit home runs off of each other in the same inning . 
- Hamels would stay a <unk> when he agreed to a contract extension on July 24 . The six @-@ year , $ 144 million contract was the second @-@ largest contract ever signed by a pitcher . The deal included a <unk> option for 2019 worth $ 24 million . If the option didn 't vest , it would have turned into a club option for $ 20 million that included a $ 6 million buyout . 
- Hamels finished the season with career highs in wins ( 17 ) and strikeouts ( 216 ) , and finished eighth in the NL Cy Young Award voting . His 3 @.@ 05 ERA ranked second @-@ best in his career ( behind only his 2 @.@ 79 ERA in 2011 ) . The Philadelphia chapter of the Baseball Writers ' Association of America presented him the " Steve Carlton Most Valuable Pitcher " award . 
- 
- = = = = 2013 = = = = 
- 
- Before the 2013 season in spring training , manager Charlie Manuel named Hamels the 2013 opening day starter , which Matt <unk> of The Philadelphia Inquirer wrote was a " long time coming " . In his first career opening day start on April 1 against the Atlanta Braves , Hamels surrendered five earned runs in five innings and took the loss . 
- Particularly early in the season , Hamels was plagued by a lack of run support ; as of May 27 , he had the fifth @-@ lowest in the major leagues . He lost his eighth start on May 27 , declining to a record of 1 – 8 , which one Sports Illustrated column attributed more to the Phillies poor offense than Hamels . Nevertheless , it was " easily ... Hamels ’ worst performance of his eight @-@ year career " . Aside from offensive struggles while Hamels was pitching , the Phillies defense struggled , though Hamels kept a positive attitude , once commenting that his luck should improve , as " I 've been doing pretty well on the plane in poker . " Towards the end of the season , however , Hamels " put it together " ; in his final 11 <unk> he surrendered more than three runs only once . In total , he amassed an 8 – 14 record with a 3 @.@ 60 ERA in 220 innings and 202 strikeouts ( sixth in NL ) . Despite struggles from his teammates , Bill Baer noted that Hamels , 
- " ... was worse at generating swings and misses . His strikeout rate declined by 2 @.@ 6 percent and his overall swinging strike rate declined by 2 @.@ 1 percent . That doesn ’ t seem like a whole lot , but consider that opposing hitters swung at 1 @,@ <unk> pitches during the 2013 season . A 2 @.@ 1 percent decrease means 37 fewer swings and misses . That 's a combination of 37 more balls in play or foul balls prolonging an at @-@ bat . " 
- Despite Hamels ' poor record and ERA , 25 of his 33 starts were quality starts , and thus he gave his team a chance to win 25 times , yet only got eight wins . 
- 
- = = = = 2014 = = = = 
- 
- Before the 2014 season , Hamels announced that he would not be ready to pitch on Opening Day because of <unk> in his <unk> . On April 6 Hamels pitched in a Minor League Class A @-@ Advanced game , striking out four while allowing two earned runs on three hits in four innings . He made his first start of the season on April 23 . 
- Despite lack of run support contributing to a poor win @-@ loss record , Hamels was among the best pitchers in the National League , and was even mentioned as a Cy Young candidate . On September 1 , Hamels pitched six innings of a Phillies combined no @-@ hitter against the Atlanta Braves . Hamels was pulled for a pinch hitter in the top of the seventh due to having five walks , a hit @-@ batter , and 108 pitches through six innings . Overall , Hamels was the only consistent star on the Phillies , finished sixth in Cy Young voting , and was frequently mentioned as a candidate to be traded in the offseason to bolster the Phillies ' lackluster farm system , after he avoided being dealt at the July trading deadline . 
- 
- = = = = 2015 = = = = 
- 
- At the start of the 2015 season , there was much focus and speculation regarding trade possibilities surrounding Hamels . The Boston Red Sox , Los Angeles Dodgers , Texas Rangers , Chicago Cubs , New York Yankees , and San Francisco Giants expressed interest in trading for him during the 2015 season . 
- On July 25 , 2015 , in what would be his final start for the Phillies before being traded , Hamels no @-@ hit the Chicago Cubs 5 – 0 at Wrigley Field , striking out 13 and only giving up two walks , both to Dexter Fowler and <unk> the Cubs ' Jake <unk> — himself a no @-@ hit pitcher a month later , on August 30 of that season . It was the first no @-@ hitter against the Cubs since Sandy <unk> 's perfect game in 1965 , and first at Wrigley since the Cubs ' <unk> <unk> in 1972 . Hamels also joined Vida Blue , Mike <unk> , Kent <unk> and Kevin <unk> as no @-@ hit pitchers who also pitched in a combined no @-@ hitter . 
- 
- = = = Texas Rangers = = = 
- 
- On July 31 , 2015 , Hamels was traded to the Texas Rangers along with Jake <unk> in exchange for Matt Harrison , Nick Williams , Jorge <unk> , Jake Thompson , Alec Asher , and <unk> <unk> . Hamels became the first pitcher to be traded during a regular season after pitching a no @-@ hitter in his final start with the team that traded him . Bert <unk> had been the last pitcher to pitch a no @-@ hitter in his final start with the team that traded him ; after pitching a no @-@ hitter for the Rangers in his final start of the 1977 season , he was traded to the Pittsburgh Pirates that offseason . 
- With the Rangers , Hamels went 7 @-@ 1 in 12 starts with a 3 @.@ 66 ERA . The Rangers were under <unk> as late as August 3 but posted a winning record for the remainder of the 2015 season and eventually overtook the Houston <unk> in September for the American League West division lead . The Rangers clinched the AL West title on the final day of the season , as Hamels threw a complete @-@ game three @-@ hitter in a 9 @-@ 2 win over the Los Angeles Angels , the team 's 6th division title and 7th postseason appearance in franchise history . Hamels ' positive effect on the Rangers was compared to that of the Toronto Blue Jays ' David Price , another ace starting pitcher who helped spearhead a run to the postseason after blending in seamlessly with a struggling team who acquired him at the trade deadline . 
- The Rangers lost to the Toronto Blue Jays in five games in the Division Series after squandering a 2 @-@ 0 series lead . Hamels received a no @-@ decision in Game 2 as he left the game tied , but the Rangers won in 14 innings . Hamels started Game 5 , and was pulled in the bottom of the 7th inning while the Rangers were leading 3 @-@ 2 ; the Jays subsequently scored 4 runs to win 6 @-@ 3 , and Hamels was charged with the loss as he was responsible for the runners on base . 
- Starting in 2016 , the Texas Rangers have a special cheering section known as Hamels Heroes , whenever Hamels starts at Globe Life Park . It was inspired by the success of the Seattle Mariners ' " King 's Court " for Felix <unk> . 
- Hamels was named the Opening Day starter for the Rangers in 2016 . On Opening Day , after allowing two home runs in two innings , Hamels <unk> down to pitch 7 strong innings , while only allowing two runs on 4 hits and 8 strikeouts for a 2016 Opening Day win against the Seattle Mariners . On June 12 , in a start against Seattle , Hamels recorded his <unk> career strikeout , becoming the 77th player , and 7th active player , to reach the milestone . 
- 
- = = Scouting report = = 
- 
- Hamels is known throughout baseball for possessing one of the better <unk> in the game . In 2012 , <unk> ranked Hamels as having the fourth best changeup in the game trailing only Jason Vargas , Tommy <unk> , and Félix <unk> . The <unk> Times called his changeup a " killer " pitch to right @-@ handed hitters , noting that while it is also effective against left @-@ handed hitters , when they put it in play , they generally have success . Rangers pitching coach Mike <unk> said of Hamels , 
- " It 's a difficult task to try to get young guys to throw <unk> , because their whole amateur career is spent staring at the scout with the gun . For a young guy to have a good changeup , that 's an aptitude right there . It 's <unk> . " 
- The development of Hamels ' changeup is attributed by his high school pitching coach , Mark <unk> , to the fact that in high school he broke his arm . He could no longer work on his curveball for several months , and was forced to focus on the changeup . <unk> Jones , who was a star @-@ third baseman for the rival Atlanta Braves commented that Hamels is the " best <unk> in the National League ... you have to respect his fastball , and his change @-@ up is devastating . If you 're <unk> fastball and you get change @-@ up , you 're way out in front of it . His change @-@ up is a soft <unk> that just falls down . " Aside from the changeup , he throws a four @-@ seam fastball in the low 90s ( can reach 94 to 96 miles per hour ( 151 to 154 km / h ) ) , a cutter 87 to 89 miles per hour ( 140 to 143 km / h ) , and an " inconsistent " curveball that sometimes loops and other times is sharp , and hovers in the mid @-@ 70s ( around 73 to 77 miles per hour ( 117 to 124 km / h ) ) . Prior to the conception and advent of his cutter , he briefly threw a two @-@ seam fastball around 2008 and 2009 , before debuting his cutter in 2010 . Once he attained a " feel " for how to effectively utilize his cutter , it helped him increase his <unk> rate ( percentage of balls in play that were <unk> ) , including to over 50 @-@ percent in 2011 . 
- 
- = = Personal life = = 
- 
- 
- = = = Family = = = 
- 
- Hamels , who is of Dutch descent , is the oldest of three children . He grew up in San Diego , where his parents still live . He now resides in <unk> Square , Pennsylvania with his wife , <unk> Strobel , whom he married on December 31 , 2006 , but they are making plans to move to the Dallas , Texas area full @-@ time during the 2015 offseason . Strobel was a contestant on the sixth season of the reality show Survivor : The Amazon . They had their first child , son Caleb Michael , on October 9 , 2009 , and their second son , <unk> , on November 2 , 2011 . On December 24 , 2012 , they adopted a baby girl from Ethiopia named Reeve . 
- 
- = = = The Hamels Foundation = = = 
- 
- Hamels created and currently runs an organization known as The Hamels Foundation with his wife , <unk> Strobel . The foundation was created in 2008 . The Hamels Foundation funds childhood programs in Philadelphia and Africa . 
- 
- 
- = Kyra ( Charmed ) = 
- 
- Kyra , also known by her title the Seer , is a fictional character from the American television supernatural drama Charmed , which aired on the WB Television Network ( the WB ) from 1998 to 2006 . Charmed follows the supernatural adventures of the Charmed Ones , a trio of sisters known as the most powerful witches of all time . The character was created by executive producer Brad Kern and was portrayed by actress Charisma Carpenter . Carpenter was invited on the show after acting with Charmed cast member Holly Marie Combs on the ABC Family television film See Jane Date ( 2003 ) . The actress ' original contract was a guest star role that included three episodes in season seven , with media outlets reporting the possibility of Carpenter becoming a series regular . 
- A demon characterized by her powers of divination and precognition , she eventually forms a friendship with Leo Wyatt ( Brian <unk> ) and the Charmed Ones , specifically Phoebe Halliwell ( Alyssa Milano ) , after expressing her desire to become human . Despite being killed by the demon Zankou ( <unk> <unk> ) on the show , the character makes further canonical appearances in the comic book series Charmed : Season 10 . Her storylines focus on her involvement with witch Prue Halliwell 's new destiny , and her romantic relationship with the newly revived Benjamin Turner ( the father of Cole Turner ) . 
- The Kyra character has received mixed feedback from critics , and has been defined by her sex appeal and called " overtly sexual , coy and <unk> " , and " <unk> " . Critics have positively and negatively compared the role to Carpenter 's previous performance as Cordelia Chase on the supernatural dramas Buffy the Vampire Slayer ( 1997 ) and Angel ( 1999 ) ; <unk> of Television Without Pity criticized the character as a copy of Chase because of her lack of a unique identity . 
- 
- = = Development = = 
- 
- 
- = = = Casting and creation = = = 
- 
- Charisma Carpenter was invited to appear on Charmed because of her friendship with Holly Marie Combs after they had worked together on the ABC Family television film See Jane Date . Kyra was created as a separate character from the Seer ( <unk> Morgan ) , who was featured as the primary antagonist in the second half of season four . Carpenter joked that she was surprised to be considered for such a minor part on the show as a guest star . In an interview with her official <unk> , Charmed cast member Rose <unk> stated that she looked forward to new characters like Kyra and Kyle Brody joining the show , saying " [ w ] e are all delighted to have them on board " . 
- According to Kristin <unk> of TV Guide , representatives from the WB Television Network ( the WB ) released a statement indicating the possibility of Kyra being promoted to star billing . In August 2004 , Carpenter was set to star in a UPN mid @-@ season replacement comedy called Like <unk> and <unk> , but she expressed interest in doing more episodes of Charmed if the pilot was not picked up by the network . She stated while she enjoyed working in the supernatural genre , and viewed it as " familiar " territory , she was hesitant about committing to the show 's demanding shooting schedule as it would take time away from raising her son . 
- Kyra was one of three characters with precognition that Carpenter has played in her career , including Cordelia Chase from supernatural dramas Buffy the Vampire Slayer and Angel , and Heather from the Syfy horror film <unk> Moon ( 2006 ) . In an interview with film journal Static Mass <unk> , Carpenter did not personally identify as a " vision girl " , but described feeling a love for roles involving <unk> . 
- 
- = = = <unk> = = = 
- 
- In an interview with Charmed Magazine , Carpenter described Kyra as having the same <unk> as Cordelia Chase , added that Kyra was " totally evil and a lot <unk> " than Chase . She felt that the emphasis on sexuality was the main aspect of the character 's personality , calling her " overtly sexual , coy , and <unk> " . While she viewed Chase as a " <unk> " , Carpenter defined Kyra as a " <unk> " and a " <unk> " and opined that her <unk> banter with Leo Wyatt was meant to be a part of her own agenda . She stated that the way she portrayed Kyra was influenced by her revealing outfit , feeling that every woman on Charmed had to look and act " hot " and " notoriously sexy " . 
- Pat Shand , the writer of Charmed : Season 10 , said he incorporated Kyra in the comic book series as he enjoyed her dialogue and Carpenter 's performance of the character ; he wrote : " To write dialogue for a character played by Charisma is to hear Charisma in your head . Who would turn that down ? " . Shand commended Carpenter 's ability to voice characters in " a very unique rhythm " and called the process of <unk> that energy in the form of comics as " a blast " and " super easy " in comparison with the show 's other characters . 
- When discussing the development of the season , Shand said that the pairing of Kyra and Prue Halliwell 's storylines was a " natural fit " , especially since both characters were his favorites from the series . He clarified that Kyra 's return , specifically her relationship with Benjamin , was central to the season and called it " straight up plot " and " <unk> " from Prue 's storylines in response to fans ' concerns about the character being limited to being part of a subplot . While the two women never interacted on the show , he explained that Prue <unk> through " all of the <unk> in all of the history to see who best tell her what 's the deal " with her new powers and destiny . Shand said Kyra was a vital part of Prue 's plans to perform the Advent , a ritual that would resurrect a powerful race of evil demons known as the Old Ones . 
- 
- = = Appearances = = 
- 
- 
- = = = Television = = = 
- 
- The Kyra character first appears in the season seven episode " Cheaper by the <unk> " , when she helps Leo Wyatt ( Brian <unk> ) uncover the identity of a masked demon attacking his son Wyatt Halliwell . She is shown as possessing the powers of precognition and divination through the use of a magical pool . She later works with the half @-@ demon <unk> on his plans to track down and kill his mortal relatives in order to seal his transformation as a full demon . As a demon , Kyra cannot feel emotions , but she is shown to be curious about the world of mortals . <unk> to experience the emotions she sees in her visions , she contacts the <unk> ( a council of <unk> which governs the forces of good ) to make a deal where she would trade her information on the then unknown threat , the Avatars , if she is made human . The Avatars are later revealed to a group of magical beings capable of <unk> reality who devote themselves to creating a world without good or evil . As word of Kyra 's betrayal spreads across the <unk> , Leo is sent to save her and bring her to the Halliwell Manor during the negotiations . 
- While at the manor , Kyra bonds with Phoebe Halliwell ( Alyssa Milano ) over their shared powers of premonition . She guides Phoebe and Lieutenant Darryl Morris ( Dorian Gregory ) to Inspector Sheridan ( <unk> <unk> ) , a member of the San Francisco police department who was placed in a coma by FBI agent Kyle Brody ( Kerr Smith ) ; Brody had <unk> Sheridan after she discovered the truth about the Halliwell sisters being witches . Kyra shares her premonition of the Avatars with Phoebe . While the prior episodes established the Avatar as a threat , Kyra 's vision shows Phoebe and her future daughter living in a <unk> where demons no longer exist . After witnessing the vision , Phoebe has a premonition of the demon Zankou ( <unk> <unk> ) vanquishing Kyra . In order to prevent Kyra 's death , Phoebe convinces the <unk> to provide a spell to turn Kyra human . During this time , Kyra forces Leo to reveal his true identity as an Avatar to the sisters . While Piper Halliwell ( Holly Marie Combs ) helps her to prepare for her new life as a mortal , Kyra reveals her real name and abandons her title as " the Seer " . Before the sisters can transform Kyra into a mortal , Zankou kills her because of her betrayal of the demonic world , and his fear of what she might tell the sisters . Her death is a turning point in the season as it pushes the sisters to agree to meet and to help the Avatars build their new world . 
- 
- = = = Literature = = = 
- 
- In Charmed : Season 10 ( the canonical comic book continuation of the TV series ) , Prue Halliwell summons Kyra from the past in order to better understand her new identity as the guardian of the <unk> of the All ( a spiritual energy that forms the basis of all magic ) . After completing a ritual that allowed Prue to enter her own mind , she turns Kyra human . Despite being <unk> at her transformation , Kyra is uncertain about her plans for the future . She decides to <unk> with the other Halliwell sisters and devote her life to doing good . She begins by helping the newly resurrected Benjamin Turner , who is the father of Cole Turner ( Julian McMahon ) , <unk> to his new life . As they spend more time together , Kyra falls in love with Benjamin and they start a romantic relationship . At the end of issue # 18 ( " Tribunal and Tribulations " ) , Prue kidnaps Kyra , along with <unk> and the <unk> ( a magical being capable of opening portals through reality ) Tyler Michaels , and takes them to a space deep within the Earth . 
- 
- = = Reception = = 
- 
- Throughout her run on Charmed , Kyra received mixed critical feedback . Television critic <unk> McFarland of the Seattle Post @-@ <unk> deemed the character a " <unk> demon seer " and entertainment news website <unk> 's Karen <unk> praised her as being the most memorable incarnation of the Seer and a fitting role for Carpenter after her previous performance as Chase . <unk> 's Gavin Hetherington called Kyra a " great highlight of the season " . Charmed Magazine 's Tara <unk> viewed Carpenter as the perfect actress to play Kyra as she " spent the better part of her career facing down the supernatural with a <unk> and a smile " . In an article from Today , reader Patrick Kelly cited Carpenter 's appearance on Charmed as an indicator of her popularity among the <unk> to 39 @-@ year @-@ old male demographic . 
- <unk> of <unk> 's Reviews opined that Carpenter should have been offered a permanent part on the show Pat Shand criticized the show 's decision to kill the character , saying that she would have filled the role of the " fourth main character " or " non @-@ Charmed One getting all that screen time " in season eight rather than novice witch Billie Jenkins ( <unk> <unk> ) . He added that he wished that the character had a spin @-@ off with Cole Turner and Darryl Morris . 
- <unk> of Television Without Pity praised the show 's decision to hire an actor from one of Joss Whedon 's shows , but was critical of Kyra 's introduction in the episode " Cheaper by the <unk> " , saying that <unk> was misused and " given so little to do " . He referred to the character negatively as " Cordelia Lite " . <unk> the character 's lack of a concrete identity , he referenced Kyra as " Charisma " and " Cordelia " in his <unk> of the later episodes . 
- 
- 
- = Réunion ibis = 
- 
- The Réunion ibis or Réunion sacred ibis ( <unk> solitarius ) is an extinct species of ibis that was endemic to the volcanic island of Réunion in the Indian Ocean . The first subfossil remains were found in 1974 , and the ibis was first scientifically described in 1987 . Its closest relatives are the <unk> sacred ibis , the African sacred ibis , and the straw @-@ necked ibis . 
- <unk> ' accounts from the 17th and 18th centuries described a white bird that flew with difficulty , and it was subsequently referred to as the Réunion solitaire . In the mid 19th century , the old travellers ' accounts were incorrectly assumed to refer to white relatives of the dodo , due to one account specifically mentioning dodos on the island , and because 17th @-@ century paintings of white dodos had recently surfaced . However , no fossils <unk> to dodo @-@ like birds were ever found on Réunion , and it was later questioned whether the paintings had anything to do with the island . Other identities were suggested as well , based only on speculation . In the late 20th century , the discovery of a subfossil ibis led to the idea that the old accounts actually referred to an ibis species instead . The idea that the solitaire and the subfossil ibis are identical has only met with limited dissent , and is now widely accepted . 
- Combined , the old descriptions and subfossils show that the Réunion ibis was mainly white , with this colour merging into yellow and grey . The wing tips and plumes of ostrich @-@ like feathers on its rear were black . The neck and legs were long , the beak was relatively straight and short for an ibis . It was more robust in build than its extant relatives , but was otherwise quite similar to them . <unk> wing @-@ bones indicate it had reduced flight capabilities , a feature perhaps linked to seasonal <unk> . The diet of the Réunion ibis was worms and other items <unk> from soil . It preferred solitude ( hence the name " solitaire " ) . In the 17th century , it lived in mountainous areas , but it may have been confined to these remote heights by heavy hunting by humans and predation by introduced animals in the more accessible areas of the island . <unk> to Réunion praised its flavour , and therefore sought after its flesh . These factors are believed to have driven the Réunion ibis to extinction by the early 18th century . 
- 
- = = Taxonomy = = 
- 
- The taxonomic history of the Réunion ibis is <unk> and complex , due to the ambiguous and <unk> evidence that was available to scientists until recently . The supposed " white dodo " of Réunion is now believed to have been an erroneous <unk> based on the few contemporary reports which described the Réunion ibis , combined with paintings of white dodos from Mauritius by the Dutch painters Pieter Withoos and Pieter Holsteyn II ( and derivatives ) from the 17th century that surfaced in the 19th century . 
- The English Chief Officer John <unk> was the first to mention a specifically white bird on Réunion , in 1625 . The French occupied the island from 1646 and onwards , and referred to this bird as the " solitaire " . M. <unk> of the French East Indies Company described the solitaire in <unk> , explaining the reason for its name : 
- I saw a kind of bird in this place which I have not found elsewhere ; it is that which the inhabitants call the <unk> <unk> for to be sure , it loves solitude and only <unk> the most secluded places ; one never sees two or more together ; it is always alone . It is not unlike a turkey , if it did not have longer legs . The beauty of its plumage is a delight to see . It is of changeable colour which <unk> upon yellow . The flesh is exquisite ; it forms one of the best dishes in this country , and might form a <unk> at our tables . We wished to keep two of these birds to send to France and present them to His Majesty , but as soon as they were on board ship , they died of melancholy , having refused to eat or drink . 
- A <unk> French Huguenot , François Leguat , used the name " solitaire " for the <unk> bird he encountered on the nearby island of Rodrigues in the <unk> , but it is thought he borrowed the name from a 1689 tract by Marquis Henri <unk> which mentioned the Réunion species . <unk> himself had probably based his own description on an earlier one . No specimens of the solitaire were ever preserved . The two individuals <unk> attempted to send to the royal menagerie in France did not survive in captivity . <unk> claimed that Bertrand @-@ François Mahé de La <unk> sent a " solitaire " to France from Réunion around 1740 . Since the Réunion ibis is believed to have gone extinct by this date , the bird may actually have been a Rodrigues solitaire . 
- The only contemporary writer who referred specifically to " dodos " inhabiting Réunion was the Dutch sailor Willem <unk> Bontekoe , though he did not mention their colouration : 
- There were also <unk> @-@ <unk> [ old Dutch for dodos ] , which have small wings , and so far from being able to fly , they were so fat that they could scarcely walk , and when they tried to run , they dragged their under side along the ground . 
- When his journal was published in 1646 , it was accompanied by an engraving which is now known to have been copied after one of the dodos in the Flemish painter <unk> Savery 's " <unk> Art Gallery sketch " . Since Bontekoe was shipwrecked and lost all his belongings after visiting Réunion in 1619 , he may not have written his account until he returned to Holland , seven years later , which would put its reliability in question . He may have concluded in <unk> that it was a dodo , finding what he saw similar to accounts of that bird . 
- 
- = = = Early interpretation = = = 
- 
- In the 1770s , the French naturalist Comte de <unk> stated that the dodo inhabited both Mauritius and Réunion . It is unclear why he included Réunion , but he also combined accounts about the Rodrigues solitaire and a third bird ( " <unk> de Nazareth " , now thought to be a dodo ) under the same section . English naturalist Hugh Edwin Strickland discussed the old descriptions of the Réunion solitaire in his 1848 book The <unk> and Its <unk> , and concluded it was distinct from the dodo and Rodrigues solitaire . Baron Edmond de <unk> <unk> coined the scientific name <unk> solitarius for the Réunion solitaire in 1848 , apparently making it the type species of the genus , in which he also included two other Mascarene birds only known from contemporary accounts , the red rail and the Réunion swamphen . As the name <unk> had already been used for a different bird by Richard Owen , and the other former names were likewise invalid , Bonaparte coined the new binomial <unk> <unk> in 1854 ( Bourbon was the original French name for Réunion ) . In 1854 , Hermann <unk> placed the solitaire in the same genus as the dodo , and named it <unk> <unk> . He restored it strictly according to contemporary accounts , which resulted in an ibis or stork @-@ like bird instead of a dodo . As it was considered <unk> with the dodo , the Réunion solitaire was long believed to also be a member of the <unk> family of pigeons . 
- In 1856 , William <unk> announced the discovery of a 17th @-@ century " Persian " painting of a white dodo among <unk> , which he had been shown in England . The artist was later identified as Pieter Withoos , and many prominent 19th @-@ century naturalists subsequently assumed the image depicted the white solitaire of Réunion , a possibility originally proposed by ornithologist John Gould . Simultaneously , several similar paintings of white dodos by Pieter Holsteyn II were discovered in the Netherlands . In 1869 , the English ornithologist Alfred Newton argued that the Withoos ' painting and engraving in Bontekoe 's memoir depicted a living Réunion dodo that had been brought to Holland , while explaining its blunt beak as a result of beak <unk> to prevent it from injuring humans . He also brushed aside the inconsistencies between the illustrations and descriptions , especially the long , thin beak implied by one contemporary account . 
- Newton 's words particularly cemented the validity of this connection among contemporary peers , and several of them expanded on his views . Dutch zoologist <unk> <unk> <unk> suggested that the discrepancies between the paintings and the old descriptions were due to the paintings showing a female , and that the species was therefore sexually dimorphic . Walter Rothschild claimed the yellow wings might have been due to albinism in this particular specimen , since the old descriptions described these as black . By the early 20th century , many other paintings and even physical remains were claimed to be white dodos , amid much speculation . Some believed the solitaire of the old descriptions was rather a species similar to the Rodrigues solitaire . Rothschild commissioned British artist Frederick William <unk> to restore the Réunion solitaire as both a white dodo , based on the Withoos painting , and as a distinct bird based on Dubois ' description , for his 1907 book Extinct Birds . In 1953 , the Japanese writer <unk> <unk> went as far as referring to the white dodos of the paintings as <unk> <unk> , and the solitaire of the accounts as <unk> solitarius . 
- 
- = = = Recent interpretation = = = 
- 
- Until the late 1980s , belief in the existence of a white dodo on Réunion was the orthodox view , and only a few researchers doubted the connection between the solitaire accounts and the dodo paintings . They cautioned that no conclusions could be made without solid evidence such as fossils , and that nothing indicated that the white dodos in the paintings had anything to do with Réunion . In 1970 , Robert W. <unk> predicted that if any such remains were found , they would not belong to Raphinae , or even <unk> . 
- The first subfossil bird remains on Réunion were found in 1974 , and assigned to a stork , <unk> sp . The remains were found in a cave , which indicated it had been brought there and eaten by early settlers . It was speculated that the remains could have belonged to a large , mysterious bird described by Leguat , and called " Leguat 's giant " by some ornithologists . " Leguat 's giant " is now thought to be based on a locally extinct population of <unk> . In 1987 , subfossils of a recently extinct species of ibis from Réunion were described as <unk> <unk> , and thought related to the bald <unk> of the genus <unk> . In 1994 , the " stork " remains were shown to belong to this ibis as well . The 1987 discovery led biologist Anthony S. Cheke to suggest to one of the <unk> , <unk> <unk> , that the subfossils may have been of the Réunion solitaire . This suggestion was published by the <unk> of <unk> in 1995 , and they also reassigned it to the genus <unk> , now combined with the specific epithet solitarius from de <unk> @-@ <unk> ' 1848 binomial for the solitaire . The authors pointed out that the contemporary descriptions matched the appearance and behaviour of an ibis more than a member of the Raphinae , especially since a fragment of a comparatively short and straight ibis mandible was discovered in 1994 , and because ibis remains were abundant in some localities ; it would be strange if contemporary writers never mentioned such a relatively common bird , whereas they mentioned most other species subsequently known from fossils . 
- The possible origin of the 17th @-@ century white dodo paintings has also recently been examined by biologist <unk> <unk> de <unk> in 2003 , and independently by experts of Mascarene fauna Anthony Cheke and Julian Hume in 2004 . The Withoos and Holsteyn paintings are clearly derived from each other , and Withoos likely copied his dodo from one of Holsteyn 's works , since these were probably produced at an earlier date . All later white dodo pictures are thought to be based on these paintings . According to the aforementioned writers , it appears these pictures were themselves derived from a whitish dodo in a previously <unk> painting containing , called <unk> with <unk> and the Animals , produced by <unk> Savery c . 1611 . The dodo was apparently based on a <unk> specimen then in Prague ; a <unk> ( old Dutch for dodo ) described as having a " dirty off @-@ white colouring " was mentioned in an inventory of specimens in the Prague collection of the Holy Roman Emperor Rudolf II to whom Savery was contracted at the time ( <unk> – 1611 ) . Savery 's several later dodo images all show greyish birds , possibly because he had by then seen a normal specimen . Cheke and Hume concluded the painted specimen was white due to albinism , and that this peculiar feature was the reason it was collected from Mauritius and brought to Europe . <unk> de <unk> instead suggested that the light plumage was a juvenile trait , a result of <unk> of old <unk> specimens , or simply due to artistic license . 
- No fossil remains of dodo @-@ like birds have ever been found on Réunion . A few later sources take issue with the proposed ibis @-@ identity of the solitaire , and have even regarded the " white dodo " as a valid species . British writer Errol Fuller agrees that the 17th @-@ century paintings do not depict Réunion birds , but has questioned whether the ibis subfossils are necessarily connected to the solitaire accounts . He notes that no evidence indicates the extinct ibis survived until the time Europeans reached Réunion . Cheke and Hume have dismissed such sentiments as being mere " belief " and " hope " in the existence of a dodo on the island . 
- 
- = = = Evolution = = = 
- 
- The volcanic island of Réunion is only three million years old , whereas Mauritius and Rodrigues , with each their flightless <unk> species , are eight to ten million years old , and it is unlikely that either bird would have been capable of flying after five or more million years of adapting to the islands . Therefore , it is unlikely that Réunion could have been colonised by flightless birds from these islands , and only flighted species on the island have relatives there . Three million years is enough time for flightless and weak flying abilities to have evolved in bird species on Réunion itself . But such species would have been wiped out by the eruption of the volcano <unk> des <unk> between 300 @,@ 000 and 180 @,@ 000 years ago . Most recent species would therefore likely be descendants of animals which had <unk> the island from Africa or Madagascar after this event , which is not enough time for a bird to become flightless . 
- In 1995 , morphological study suggested the closest extant relatives of the Réunion ibis are the African sacred ibis ( T. <unk> ) of Africa and the straw @-@ necked ibis ( T. <unk> ) of Australia . It has also been suggested that it was closest to the <unk> sacred ibis ( T. <unk> ) , and therefore of ultimately African origin . 
- 
- = = Description = = 
- 
- Contemporary accounts described the species as having white and grey plumage merging into yellow , black wing tips and tail feathers , a long neck and legs , and limited flight capabilities . Sieur Dubois ' 1674 account is the most detailed contemporary description of the bird , here as translated by Hugh Strickland in 1848 : 
- <unk> . These birds are so called because they always go alone . They are the size of a large Goose , and are white , with the tips of the wings and tail black . The tail feathers resemble those of an <unk> ; the neck is long , and the beak is like that of a <unk> , but larger ; the legs and feet like those of <unk> . This bird has recourse to running , as it flies but very little . 
- The plumage colouration mentioned is similar to that of the related African sacred ibis and straw @-@ necked ibis , which are also mainly white and glossy black . In the reproductive season , the ornamental feathers on the back and wing tips of the African sacred ibis look similar to the feathers of an ostrich , which echoes Dubois ' description . Likewise , a subfossil lower jaw found in 1994 showed that the bill of the Réunion ibis was relatively short and straight for an ibis , which corresponds with Dubois ' <unk> comparison . Cheke and Hume have suggested that the French word ( <unk> ) from Dubois ' original description , usually translated to " <unk> " , could also mean <unk> , another bird with a long , straight , but slightly more robust , bill . They have also pointed out that the last sentence is <unk> , and actually means the bird could be caught by running after it . The bright colouration of the plumage mentioned by some authors may refer to <unk> , as seen in the straw @-@ necked ibis . 
- <unk> of the Réunion ibis show that it was more robust , likely much heavier , and had a larger head than the African sacred and straw @-@ necked <unk> . It was nonetheless similar to them in most features . Rough <unk> on the wing bones of the Réunion ibis are similar to those of birds that use their wings in combat . It was perhaps flightless , but this has not left significant <unk> traces ; no complete skeletons have been collected , but of the known pectoral elements , only one feature indicates reduction in flight capability . The coracoid is elongated and the radius and <unk> are robust , as in flighted birds , but a particular <unk> between a <unk> and the <unk> is otherwise only known from flightless birds , such as some <unk> , <unk> , and several extinct species . As contemporary accounts are inconsistent on whether the solitaire was flightless or had some flight capability , <unk> @-@ <unk> suggested that this was dependent on seasonal fat @-@ cycles , meaning that individuals <unk> themselves during cool seasons , but were slim during hot seasons ; perhaps it could not fly when it was fat , but could when it was not . However , Dubois specifically stated the solitaires did not have fat @-@ cycles , unlike most other Réunion birds . 
- 
- = = Behaviour and ecology = = 
- 
- The species was termed a land @-@ bird by Dubois , so it did not live in typical ibis habitats such as wetlands . It has been proposed that this is because the ancestors of the bird colonised Réunion before swamps had developed , and had therefore become adapted to the available habitats . They were perhaps prevented from <unk> Mauritius as well due to the presence of red rails there , which may have occupied a similar niche . It appears to have lived in high altitudes , and perhaps had a limited distribution . The only mention of its diet and exact habitat is Jean Feuilley 's account from 1708 , which is also the last record of a living individual : 
- The solitaires are the size of an average turkey cock , grey and white in colour . They inhabit the tops of mountains . Their food is only worms and <unk> , taken on or in the soil . 
- The diet and mode of foraging described by Feuilley matches that of an ibis , whereas members of the Raphinae are known to have been fruit <unk> . Accounts by early visitors indicate the species was found near their landing sites , but they were found only in remote places by 1667 . The bird may have survived in eastern lowlands until the <unk> . Though many late 17th century accounts state the bird was good food , Feuilley stated it <unk> bad . This may be because it changed its diet when it moved to more rugged , higher terrain , to escape pigs that destroyed its nests ; since it had limited flight capabilities , it probably <unk> on the ground . 
- Many other endemic species of Réunion became extinct after the arrival of man and the resulting disruption of the island 's ecosystem . The Réunion ibis lived alongside other recently extinct birds such as the <unk> starling , the Mascarene parrot , the Réunion <unk> , the Réunion swamphen , the Réunion owl , the Réunion night <unk> , and the Réunion pink <unk> . Extinct reptiles include the Réunion giant tortoise and an undescribed <unk> <unk> . The small <unk> flying fox and the <unk> <unk> <unk> lived on Réunion and Mauritius , but vanished from both islands . 
- 
- = = Extinction = = 
- 
- As Réunion was populated by settlers , the Réunion ibis appears to have become confined to the tops of mountains . Introduced predators such as cats and rats took a toll . <unk> also contributed and several contemporary accounts state the bird was widely hunted for food . In 1625 , John <unk> described the <unk> of the bird and how easy it was to hunt , as well as the large quantity consumed : 
- There is store of land fowle both small and great , plenty of <unk> , great <unk> , and such like ; and a great fowle of the <unk> of a <unk> , very fat , and so short winged , that they cannot fly , being white , and in a manner tame : and so be all other <unk> , as having not been troubled nor feared with shot . Our men did beat them down with sticks and stones . Ten men may take fowle enough to serve <unk> men a day . 
- In <unk> , <unk> mentioned the culinary quality of this species , and described the slaughter of several types of birds on the island : 
- ( A ) <unk> sort of bird called solitaires which are very good ( to eat ) and the beauty of their plumage is most fascinating for the diversity of bright colours that shine on their wing and around their necks ... There are birds in such great confusion and so tame that it is not necessary to go hunting with firearms , they can so easily be killed with a little stick or rod . During the five or six days that we were allowed to go into the woods , so many were killed that our General [ de La <unk> ] was constrained to forbid anyone going beyond a hundred paces from the camp for fear the whole quarter would be destroyed , for one needed only to catch one bird alive and make it cry out , to have in a moment whole flocks coming to perch on people , so that often without moving from one spot one could kill hundreds . But , seeing that it would have been impossible to <unk> out such a huge quantity , permission was again given to kill , which gave great joy to everyone , because very good fare was had at no expense . 
- The last definite account of the " solitaire " of Réunion was Feuilley 's from 1708 , indicating that the species probably became extinct sometime early in the century . In the 1820s , Louis Henri de <unk> asked an old slave about <unk> ( old Dutch word for dodo ) , and was told the bird existed around Saint @-@ Joseph when his father was an infant . This would perhaps be a century earlier , but the account may be unreliable . Cheke and Hume suspect that feral cats initially hunted wildlife in the lowlands and later turned to higher inland areas , which were probably the last stronghold of the Réunion ibis , as they were <unk> by pigs . The species is thought to have been driven to extinction around 1710 – <unk> . 
- 
- 
- = December 1964 South Vietnamese coup = 
- 
- The December 1964 South Vietnamese coup took place before dawn on December 19 , 1964 , when the ruling military junta of South Vietnam led by General Nguyễn Khánh dissolved the High National Council ( HNC ) and arrested some of its members . The HNC was an <unk> legislative @-@ style civilian advisory body they had created at the request of the United States — South Vietnam 's main sponsor — to give a veneer of civilian rule . The dissolution dismayed the Americans , particularly the ambassador , Maxwell D. Taylor , who engaged in an angry war of words with various generals including Khánh and threatened aid cuts . They were unable to do anything about the fait <unk> that had been handed to them , because they strongly desired to win the Vietnam War and needed to support the Army of the Republic of Vietnam . Instead , Taylor 's searing verbal attacks were counterproductive as they galvanized the Vietnamese officers around the embattled Khánh . At the time , Khánh 's leadership was under threat from his fellow generals , as well as Taylor , who had fallen out with him and was seeking his removal . 
- The genesis of the removal of the HNC was a power struggle within the ruling junta . Khánh , who had been saved from an earlier coup attempt in September 1964 by the intervention of some younger generals dubbed the Young Turks , was indebted to them and needed to satisfy their wishes to stay in power . The Young Turks disliked a group of older officers who had been in high leadership positions but were now in powerless posts , and wanted to <unk> them completely . As a result , they decided to hide their political motives by introducing a policy to <unk> retire all general officers with more than 25 years of service . The chief of state Phan Khắc Sửu , an elderly figure appointed by the military to give a semblance of civilian rule , did not want to sign the decree without the agreement of the HNC , which mostly consisted of old men . The HNC recommended against the new policy , and the younger officers , led by I Corps commander General Nguyễn <unk> Thi and Air Marshal Nguyễn Cao Kỳ , disbanded the body and arrested some of its members along with other politicians . 
- As a result of this event , Taylor summoned Khánh to his office . Khánh sent Thi , Kỳ , the commander of the Republic of Vietnam Navy Admiral Chung Tấn Cang and IV Corps commander General Nguyễn Văn Thiệu , and after beginning with " Do all of you understand English ? " , Taylor harshly <unk> them and threatened cuts in aid . While angered by Taylor 's manner , the officers defended themselves in a restrained way . The next day Khánh met Taylor and the Vietnamese leader made oblique accusations that the U.S. wanted a puppet ally ; he also criticized Taylor for his manner the previous day . When Taylor told Khánh he had lost confidence in his leadership , Taylor was threatened with expulsion , to which he responded with threats of total aid cuts . Later however , Khánh said he would leave Vietnam along with some other generals he named , and during a phone conversation , asked Taylor to help with travel arrangements . He then asked Taylor to repeat the names of the would @-@ be exiles for confirmation , and Taylor complied , not knowing that Khánh was taping the dialogue . Khánh then showed the tape to his colleagues out of context , misleading them into thinking that Taylor wanted them expelled from their own country to raise the prestige of his embattled leadership . 
- Over the next few days , Khánh embarked on a media offensive , repeatedly criticizing U.S. policy and decrying what he saw as an undue influence and infringement on Vietnamese sovereignty , explicitly condemning Taylor and declaring the nation 's independence from " foreign manipulation " . Khánh and the Young Turks began preparations to expel Taylor before changing their minds ; however , Khánh 's misleading tactics had rallied the Young Turks around his fragile leadership for at least the short @-@ term future . The Americans were forced to back down on their insistence that the HNC be restored and did not carry through on Taylor 's threats to cut off aid , despite Saigon 's defiance . 
- 
- = = Background = = 
- 
- On September 26 , 1964 , Nguyễn Khánh and the senior officers in his military junta created a semblance of civilian rule by forming the High National Council ( HNC ) , an appointed advisory body akin to a legislature . This came after lobbying by American officials — led by Ambassador Maxwell Taylor — in Vietnam , as they placed great value in the appearance of civilian legitimacy , which they saw as vital to building a popular base for any government . Khánh put his rival General Dương Văn Minh — who he had deposed in a January 1964 coup — in charge of picking the 17 members of the HNC , and Minh filled it with figures sympathetic to him . The HNC then made a resolution to recommend a political model with a powerful head of state , which would likely be Minh , given their sympathy towards him . Khánh did not want his rival taking power , so he and the Americans convinced the HNC to <unk> the powers of the position to make it <unk> to Minh , who was then sent on an overseas diplomatic goodwill tour to remove him from the political scene . However , Minh was back in South Vietnam after a few months and the power balance in the junta was still fragile . 
- The HNC , which had representatives from a wide range of social groups , selected the aging civilian politician Phan Khắc Sửu as chief of state , and Suu chose Trần Văn Hương as prime minister , a position that had greater power . However , Khánh and the senior generals retained the real power . At the same time , a group of Catholic officers were trying to replace Khánh with their co @-@ <unk> , General Nguyễn Văn Thiệu , and the incumbent was under pressure . During 1964 , South Vietnam had suffered a succession of setbacks on the battlefield , in part due to <unk> in the military and a focus on coup plotting . In the meantime , both Saigon and Washington were planning a large @-@ scale bombing campaign against North Vietnam in an attempt to deter communist aggression , but were waiting for stability in the south before starting the air strikes . 
- 
- = = <unk> retirement policy = = 
- 
- Khánh and a group of younger officers called the Young Turks — led by chief of the Vietnam Air Force , Air Marshal Nguyễn Cao Kỳ , commander of I Corps General Nguyễn <unk> Thi and IV Corps commander Thiệu — wanted to forcibly retire officers with more than 25 years of service , as they thought them to be <unk> and ineffective , but most importantly , rivals for power . Most of the older officers had more experience under the Vietnamese National Army during the French colonial era , and some of the younger men saw them as too detached from the modern situation . The Young Turks had quite a lot of influence over Khánh , as Thi and Kỳ had intervened militarily to save him from a coup attempt in September by Generals <unk> Văn <unk> and Dương Văn <unk> . 
- One of the specific and unspoken aims of this proposed policy was to remove Generals Minh , Trần Văn <unk> , Lê Văn Kim and Mai <unk> <unk> from the military . This quartet , along with <unk> <unk> <unk> , had been the leading members of a junta that <unk> President Ngô Đình Diệm in November 1963 . The generals who deposed Diệm did not trust Khánh because of his habit of changing sides , and Khánh was angered by their <unk> . Khánh put Don , Kim , Xuan and <unk> under arrest in Da Lat after his January coup , claiming they were about to make a deal with the communists , a <unk> to cover up his motive of revenge . These four thus became known as the " Da Lat Generals " . Khánh later released them and placed them into meaningless desk jobs with no work to do , although they were still being paid . Khánh did this as he thought the Young Turks had become too powerful and he hoped to use the Da Lat Generals as a <unk> . All this time , Minh had been allowed to continue as a <unk> chief of state due to his popularity , but Khánh was intent on <unk> him too . The Young Turks were fully aware of Khánh 's motives for <unk> the Da Lat Generals , and wanted to <unk> them . In public , Khánh and the Young Turks claimed the Da Lat Generals and Minh , who had returned from his overseas tour , had been making plots with the Buddhist activists to regain power . 
- Suu 's signature was required to pass the ruling , but he referred the matter to the HNC to get their opinion . The HNC turned down the request . There was speculation the HNC did this as many of them were old , and therefore did not appreciate the generals ' <unk> towards seniors — some South Vietnamese <unk> called the HNC the High National Museum . On December 19 , a Saturday , the generals moved to dissolve the HNC by arresting some of its members . The HNC had already ceased to function in any meaningful way , as only 9 of the 17 members were still occasionally attending its meetings , and few on a regular basis . 
- 
- = = <unk> of the High National Council = = 
- 
- Before dawn on December 19 , there were troop movements in the capital as the junta deposed the civilians . The operation was commanded by Thi — who had travelled into Saigon from I Corps in the far north — and Kỳ . The national police , which was under the control of the army , moved through the streets , arresting five HNC members , other politicians and student leaders they deemed to be an obstacle to their aims . Minh and the other aging generals were arrested and flown to <unk> , a Central Highlands town in a <unk> area , while other officers were simply imprisoned in Saigon . The junta 's forces also arrested around 100 members of the National <unk> Council ( NSC ) of Le <unk> <unk> ; the NSC was a new party active in central Vietnam in the I Corps region and opposed to the expansion of the war . It was aligned with Thi and the Buddhist activist monk <unk> <unk> <unk> , but as Thi was active in the purge , it was believed he had fallen out with <unk> . 
- At this point , Khánh had not spoken up and allowed the impression that the moves had been made without his consultation or against his will , and an attempt on the part of other officers to take power themselves . Hương had actually privately endorsed the dissolution of the HNC , as both he and the Young Turks thought it would allow them to gain more power and influence over Khánh . 
- The infighting <unk> Taylor , the US Ambassador to South Vietnam and former Chairman of the US Joint Chiefs of Staff , who felt the disputes between the junta 's senior officers were <unk> the war effort . Only a few days earlier , General William Westmoreland — the commander of US forces in Vietnam — had invited him and the Vietnamese generals home to a dinner . There Taylor asked for an end to the persistent changes in leadership , and Khánh and his men assured him of stability . Westmoreland warned that persistent instability would turn the American political class and public against Saigon , as they would deem it useless to support such a regime . Taylor initially cabled the State Department back in the US to state a " naked military fist " had " <unk> [ the ] carefully woven fabric of civilian government " , and that the arrest of the civilians would be " immediately and understandably interpreted by all the world as another military coup , setting back all that had been accomplished " since the formation of the HNC and the creation of a veneer of civilian rule . He went on to say that an " <unk> conclusion that if a group of military officers could issue decisions abolishing one of the three fundamental organs of the governmental structure ... and carry out military arrests of civilians , that group of military officers has clearly set themselves above and beyond the structure of government in Vietnam . " Taylor <unk> the fact that the generals had shown no second thoughts about ignoring US policy recommendations , particularly in <unk> his explicit advice to maintain stable civilian rule , at least at a nominal level . Taylor issued a thinly disguised threat to cut aid , releasing a public statement saying Washington might reconsider its military funding if " the fabric of legal government " was not reinstated . 
- 
- = = Angry confrontations with Maxwell Taylor = = 
- 
- Taylor summoned Khánh to his office , but the Vietnamese leader sent Thi , Kỳ , Thiệu and Admiral Chung Tấn Cang , the commander of the Republic of Vietnam Navy , instead . Taylor asked the four to sit down and then said " Do all of you understand English ? " The ambassador then angrily denounced the officers . According to Stanley Karnow , Taylor " launched into a <unk> , scolding them as if he were still superintendent of West Point and they a group of cadets caught cheating " . He said " I told you all clearly at General Westmoreland 's dinner we Americans were tired of coups . Apparently I wasted my words . " He decried the removal of the HNC as " totally illegal " , and said it had " destroyed the government @-@ making process " , and that " I made it clear that all the military plans I know you would like to carry out are dependent on government stability " , something he felt had been lost with the dismissal of the HNC . He said " ... you have made a real mess . We cannot carry you forever if you do things like this . " Taylor believed the HNC to be an essential part of the government , because as an American , he believed civilian legitimacy was a must . For him , the HNC was a necessary step in a progression towards an elected civilian legislature , which he regarded as critical for national and military morale . The historian Mark <unk> regarded Taylor 's intervention as unnecessary , and noted that there had been many instances of fierce fighting in Vietnamese history despite the complete absence of democracy throughout the nation 's history . Taylor also reminded them of an earlier meeting where he had discussed an American plan to expand the war , increase funding for the South Vietnamese military , and to go on the offensive against the communists at the request of Khánh . Taylor said the Americans would not be able to help Saigon pursue their desired military strategy if the political machinations did not stop . Taylor said that if the military did not transfer some powers or advisory capacity back to the HNC or another civilian institution , aid would be withheld , and some planned military operations against the Ho Chi Minh trail — which was being used to infiltrate communists into the south — would be suspended . 
- The four officers were taken aback by Taylor 's searing words and felt they had been humiliated . A decade after the incident , Kỳ described Taylor as " the sort of man who addressed people rather than talked to them " , referencing the confrontation . Karnow said " For the sake of their own pride , they [ the Vietnamese officers ] resented being treated in ways that reminded them of their almost total dependence on an alien power . How could they preserve a sense of sovereignty when Taylor , striving to push them into ' getting things done ' , <unk> like a viceroy ? " However , Thi also took a <unk> pleasure in <unk> Taylor . He was seen by a CIA officer soon after , <unk> . When asked why he was happy , Thi said " Because this is one of the <unk> days of my life ... Today I told the American ambassador that he could not <unk> to us . " Nevertheless , Taylor 's conduct had rankled the officers , stirring their latent sense of nationalism and anti @-@ Americanism ; Khánh would exploit this to strengthen his fragile position in the junta . 
- Khánh 's quartet of delegates responded to Taylor in a <unk> way . They remained calm and did not resort to direct confrontation . Kỳ said the change was necessary , as " the political situation is worse than it ever was under Diệm " . Kỳ explained that the situation mandated the dissolution of the council , saying " We know you want stability , but you cannot have stability until you have Unity . " He claimed some HNC members were <unk> coup rumors and creating doubt among the population , and that " both military and civilian leaders regard the presence of these people in the High National Council as divisive of the Armed Forces due to their influence " . Kỳ further accused some of the HNC members of being communist sympathizers and <unk> who wanted to stop the military from strengthening . He promised to explain the decision at a media conference and vowed that he and his colleagues would return to purely military roles in the near future . Thiệu added " I do not see how our action has hurt the Hương government ... Hương now has the full support of the Army and has no worries from the High National Council , which we have eliminated . " Cang said " It seems ... we are being treated as though we were guilty . What we did was only for the good of the country . " 
- When Taylor said the moves detracted from Hương and Suu 's powers , the officers disagreed and said they supported the pair in full and that Hương had approved of the HNC 's dissolution . Taylor was unimpressed by the <unk> , concluding with " I don 't know whether we will continue to support you after this ... You people have broken a lot of dishes and now we have to see how we can <unk> out this mess . " Taylor 's deputy , U. Alexis Johnson felt the discussion had become counterproductive and was increasing the problem . He suggested that should the generals feel unwilling to alter their position immediately , they should refrain from actions that would preclude a later change of heart . He proposed they merely announce the removal of certain members of the HNC rather than the dissolution of the entire body , hoping the HNC could be reconstituted with figures they deemed to be more satisfactory . The four officers did not give a clear answer to Johnson 's idea , indicating they had not made a concrete decision by saying " the door is not closed " . 
- 
- = = = Taylor meets Hương = = = 
- 
- When Taylor met Hương afterwards , he urged the prime minister to reject the dissolution of the HNC . Hương said he and Suu had not been notified of the moves , but agreed to step in and take over the body 's work . Taylor nevertheless asked Hương to publicly <unk> the coup and call on the army to release those arrested . Hương also said he would be willing to reorganize his administration to meet the wishes of the military , and that retaining their support was essential in keeping a civilian government functional . Taylor said the US did not agree with military rule as a principle , and might reduce aid , but Hương was <unk> and said the Vietnamese people " take a more sentimental than <unk> approach " and that the existence of civilian procedure and the HNC was much less pressing than the " moral prestige of the leaders " . American military advisers and intelligence officers who <unk> with senior junta members found they were <unk> with any possible legal ramifications of their actions . 
- Later , despite Taylor 's pleas to keep the dissolution of the HNC secret in the hope it would be reversed , Kỳ , Thi , Thiệu and Cang called a media conference , where they maintained the HNC had been dissolved in the nation 's best interests . The quartet vowed to stand firm and not <unk> on their decision . They also proclaimed their ongoing confidence for Suu and Hương . Two days later , Khánh went public in support of the Young Turks ' coup against the HNC , condemning the advisory body and asserting the army 's right to intervene if " disputes and differences create a situation favorable to the common enemies : Communism and colonialism " . The generals announced they had formed a new body called the Armed Forces Council ( AFC ) to succeed the current Military Revolutionary Council , and referred to the dissolution of the HNC as Decision No. 1 of the AFC . The American <unk> viewed the public moves by the Vietnamese generals as " throwing down the gauntlet " and challenging their counsel . 
- 
- = = = Taylor meets Khánh = = = 
- 
- The day after the Young Turks ' press conference , Taylor privately met Khánh at the latter 's office . He complained about the dissolution of the HNC and said it did not accord with the values of the alliance and the loyalty Washington expected of Saigon . He added that the US could not cooperate with two governments at once : a military regime that held power while a civilian body took the responsibility . Khánh <unk> replied that Vietnam was not a satellite of the US and compared the situation to the US support of the successful coup against Diệm , saying that loyalty was meant to be <unk> . Khánh had hinted that he felt the Americans were about to have him deposed like Diệm , who was then assassinated , but this rankled Taylor , who had argued against the regime change . Taylor then <unk> Khánh , saying he had lost confidence in the Vietnamese officer , recommending Khánh resign and go into exile . He also said military supplies currently being shipped to Vietnam would be withheld after arriving in Saigon and that American help in planning and advising military operations would be suspended . 
- Khánh <unk> and said " You should keep to your place as Ambassador ... as Ambassador , it is really not appropriate for you to be dealing in this way with the commander @-@ in @-@ chief of the armed forces on a political matter , nor was it appropriate for you to have summoned some of my generals to the Embassy yesterday . " He threatened to expel Taylor , who responded by saying a forced departure would mean the end of US support . However , Khánh later said he was open to the possibility of going abroad and asked Taylor if he thought this would be good for the country , to which the ambassador replied in the <unk> . Khánh also said he took responsibility for his generals ' actions , and expressed regret at what they had done . Khánh then ended the meeting , saying he would think about his future . 
- Later , Khánh <unk> Taylor from his office and expressed his desire to resign and go abroad along with several other generals , asking for the Americans to fund the travel costs . He then read Taylor the list of generals for whom arrangements needed to be made , and asked the ambassador to repeat the names for confirmation . Taylor did so , unaware Khánh was taping the dialogue . Afterwards , Khánh played the tape out of context to his colleagues , giving them the impression Taylor was calling for their expulsion from their own country . Khánh then asked his colleagues to participate in a campaign of <unk> anti @-@ American street protests and to give the impression the country did not need Washington 's aid . A CIA informant reported the recent arguments with Taylor had incensed the volatile Thi so much that he had privately vowed to " blow up everything " and " kill Phan Khắc Sửu , Trần Văn Hương and Nguyễn Khánh and put an end to all this . Then we will see what happens . " 
- 
- = = Public media campaign by Khánh = = 
- 
- On the morning of December 22 , as part of his Order of the Day , a regular message to the armed forces over Radio Vietnam , Khánh went back on his promise to leave the country and announced , " We make sacrifices for the country 's independence and the Vietnamese people 's liberty , but not to carry out the policy of any foreign country . " He said it was " better to live poor but proud as free citizens of an independent country rather than in ease and shame as slaves of the foreigners and Communists . " Khánh pledged support for both Hương and Suu 's civilian rule , and condemned colonialism in a thinly veiled reference to the US . 
- Khánh explicitly denounced Taylor in an exclusive interview with Beverly Deepe published in the New York Herald Tribune on December 23 , saying " if Taylor did not act more <unk> , Southeast Asia would be lost " and that the US could not expect to succeed by modelling South Vietnam on American norms . Khánh said Taylor and the US would need to be " more practical and not have a dream of having Vietnam be an image of the United States , because the way of life and people are entirely different . " He added that Taylor 's " attitude during the last 48 hours — as far as my small head is concerned — has been beyond imagination " . <unk> the removal of the HNC , Khánh said they were " exploited by counter @-@ revolutionary elements who placed partisan <unk> above the homeland 's sacred interest . " Khánh also threatened to <unk> the content of his discussion with Taylor , saying " One day I hope to tell the Vietnamese people and the American people about this ... It is a pity because Gen. Taylor is not serving his country well . " 
- Khánh had not <unk> that angry discussions had occurred in private , so Deepe was unsure what had happened between Taylor and Khánh to provoke such an outburst . She contacted the US Embassy to ask what the dispute was about . At first , the Americans defended Taylor without referring to what the problem was , stating : " Ambassador Taylor has undertaken no activities which can be considered improper in any way ... All his activities are designed to serve the best interests of both Vietnam and the United States . " The State Department issued a statement later in the day in more robust terms , saying " Ambassador Taylor has been acting throughout with the full support of the U.S. government ... a duly constituted government exercising full power ... without improper interference ... is the essential condition for the successful prosecution of the effort to defeat the Viet Cong . " The following day , Secretary of State Dean <unk> said aid would have to be cut , as the programs being funded needed an effective government to be useful . Taylor later responded by calling the generals ' actions an " improper interference " into the <unk> of civilian government . 
- <unk> Taylor earned Khánh heightened approval among his junta colleagues , as the ambassador 's actions were seen as an insult to the nation . On the night of December 23 , Khánh convinced his fellow officers to join him in lobbying Hương to declare Taylor persona non <unk> and expel him from South Vietnam . They were confident Hương could not reject them and side with a foreign power at the expense of the military that had installed him , and made preparations to meet him the next day . Khánh also told Hương that if Taylor was not ejected , he and the other generals would hold a media conference and release " detailed accounts " of the ambassador 's confrontation with the quartet and his " ultimatum to General Khánh " the day after . However , someone in the junta was a CIA informant and reported the incident , allowing American officials to individually lobby the officers to change their stance . At the same time , the Americans informed Hương if Taylor was expelled , US funding would stop . The next day , the generals changed their mind and when they met Hương at his office , only asked him to formally denounce Taylor 's behavior in his meetings with Khánh and his quartet and to " take appropriate measures to preserve the honor of all the Vietnamese armed forces and to keep national prestige intact " . 
- On December 24 , Khánh issued a declaration of independence from " foreign manipulation " , and condemned " colonialism " , explicitly accusing Taylor of <unk> his power . At the time , Khánh was also secretly negotiating with the communists , hoping to put together a peace deal so he could expel the Americans from Vietnam , although this effort did not lead anywhere in the two months before he was forced out of power . For his part , Taylor privately told Americans journalists that Khánh was expressing opposition to the US merely because he knew he had lost Washington 's confidence . Taylor said Khánh was completely <unk> and was stirring up anti @-@ American sentiment purely to try to shore up his political prospects , not because he thought US policy was harmful to South Vietnam . The US media were generally very critical of Khánh 's actions and did not blame Taylor for the <unk> . Peter <unk> of The New York Times said " It almost seems as if Viet Cong insurgents and the Saigon government conspired to make the United States feel unwelcome . " The Chicago Tribune lampooned Khánh 's junta , calling it a " parody of a government " and saying it would not survive for a week without US support and describing the generals as " <unk> men on the United States ' <unk> " . However , the New York Herald Tribune said it was dangerous to pressure South Vietnam too much , citing the instability that followed the American support for the coup against Diệm , who had resisted US advice so often . It said " The issue is not General Khánh versus General Taylor . It is whether the Vietnamese still have the will to exist as an independent state . " The newspaper said if the answer was yes , then both Washington and Saigon would have to look beyond personalities . 
- Angry with Deepe for airing Khánh 's grievances against him , Taylor invited every other US journalist in Saigon to this private briefing . Taylor gave the journalists his account of the dispute and discussions with the generals , and hoped it would be useful background information for the media , so they would understand what he had done and not reach negative conclusions about his conduct in their writing . Due to the sensitivity of the situation , he asked them to keep the remarks off the record . However , someone at the briefing informed Deepe of what Taylor had said , and she published the remarks on December 25 under the title " Taylor <unk> Mask Off Khánh " . In this article , comments were also attributed to Taylor describing some South Vietnamese officers as borderline " nuts " and accusing many generals of staying in Saigon and allowing their junior officers to run the war as they saw fit . Deepe 's article caused an uproar due to the tension between Taylor and the Vietnamese generals . 
- 
- = = <unk> Hotel bombing = = 
- 
- At the same time , Westmoreland became concerned with the growing antipathy towards the US and requested the United States Pacific Command ( <unk> ) : " In view of the current unstable political situation ... and the possibility that this situation could lead to anti @-@ American activities of the unknown intensity , request Marine Landing Force now off Cap <unk> be positioned out of sight of land off Cap St. Jacques <unk> . " Better known as <unk> <unk> , Cap St. Jacques was a coastal city at the mouth of the Saigon River around 80 km southeast of the capital . Westmoreland also put American marines based at Subic Bay in the Philippines on notice . 
- On the same day , the Viet Cong bombed the <unk> Hotel , where US officers were <unk> , killing two Americans and injuring around 50 people , civilian <unk> and military personnel . As a result , there was a suspicion among a minority that Khánh 's junta had been behind the attack , even though the Viet Cong had claimed responsibility through a radio broadcast . When the Americans started making plans to retaliate against North Vietnam , they did not tell Khánh and his junta . Westmoreland , Taylor , and other senior US officers in Saigon and Washington urged President Lyndon <unk> Johnson to authorize reprisal bombings against North Vietnam , Taylor predicting : " Some of our local <unk> will probably disappear in enthusiasm which our action would generate . " Johnson refused and one reason was the political instability in Saigon . Johnson reasoned the international community and the American public were unlikely to believe the Viet Cong were behind the attack , feeling they would instead blame local infighting for the violence . Johnson administration officials did not conclude that the communists were responsible until four days after the attack . The State Department cabled Taylor , saying " In view of the overall confusion in Saigon " , public US and international opinion towards an American air strike would be that the Johnson administration was " trying to shoot its way out of an internal [ South Vietnamese ] political crisis " . 
- 
- = = Fall out = = 
- 
- As a result of the tension in late @-@ December , the standoff remained . The US hoped the generals would <unk> because they could not survive and be able to repel the communists or rival officers without aid from Washington . On the other hand , Khánh and the Young Turks expected the Americans would become more worried about the communist gains first and <unk> to their fait <unk> against the HNC . The generals were correct . 
- The South Vietnamese eventually had their way . As the generals and Hương were unwilling to reinstate the HNC , Taylor sent General John L. Throckmorton to meet them and <unk> relations . Throckmorton told the Vietnamese generals they had read too much into Taylor 's comments and that the US had no intention of <unk> them out of power with aid cuts . Cang appeared unimpressed , while Thiệu and Kỳ made indirect and vague comments about what they perceived to be misleading tactics during the talks . Khánh appeared <unk> by Throckmorton 's overtures and made a public statement on December 30 , saying he was not as hostile to the Americans as reported , and he wanted Thiệu and Cang to meet the Americans to relieve any remaining tension . He also claimed privately that the statements attributed to him by Deepe were false and set up a bilateral committee to discuss tensions . The generals eventually won out , as the Americans did not move against them in any way for their refusal to reinstate the HNC . The South Vietnamese won in large part because the Americans had spent so much on the country , and could not afford to abandon it and lose to the communists over the matter of military rule , as a communist takeover would be a big public relations coup for the Soviet bloc . According to Karnow , for Khánh and his officers , " their weakness was their strength " . An anonymous South Vietnamese government official said " Our big advantage over the Americans is that they want to win the war more than we do . " 
- The only concession the AFC made was on January 6 , 1965 , when they made a <unk> move of officially <unk> all their power to Hương , who was asked to organize elections . They also agreed to appoint a civilian body and release those arrested in December . Khánh had proposed to reinstate civilian rule if a military " organ of control " was created to keep control of them , but Taylor <unk> the idea . This resulted in an official announcement by Hương and Khánh three days later , in which the military again reiterated their commitment to civilian rule through an elected legislature and a new constitution , and that " all genuine <unk> " would be " <unk> assembled " to collaborate in making a plan to defeat the communists . The Americans were not impressed with the statement , which was shown to Taylor before it was made public ; the State Department <unk> announced that " it appears to represent some improvement to the situation " . Nevertheless , Khánh and Taylor were both <unk> to this January 9 announcement . 
- Although the coup was a political success for Khánh , it was not enough to stabilize his leadership in the long run . During the dispute over the HNC , Khánh had tried to frame the dispute in nationalistic terms against what he saw as overbearing US influence . In the long run , this failed , as South Vietnam and the senior officers ' careers and advancement were dependent on US aid . Taylor hoped Khánh 's appeals to nationalism might <unk> by causing his colleagues to fear a future without US funding . The Americans were aware of Khánh 's tactics and exploited it by persistently trying to scare his colleagues with the prospect of a military heavily restricted by the absence of US funding . After the December coup , Taylor felt the fear of US abandonment " raised the courage level of the other generals to the point of sacking " Khánh , as many were seen as <unk> above all to their desire for personal advancement . In January and February 1965 , Khánh <unk> he could no longer work with Taylor and the Americans , and that his support in the junta was unreliable , so he began to try to set up secret peace talks with the communists . Planning for discussions was only beginning , but this was unacceptable to the Americans and hardline anti @-@ communists in the junta , as it meant the bombing campaign against North Vietnam would not be possible . When Khánh 's plans were discovered , US @-@ encouraged plotting intensified . On February 19 – 20 , a coup occurred , and after the original plot was put down by the Young Turks , they forced Khánh into exile as well . With Khánh out of the way , the bombing campaign started . 
- 
- 
- = 1998 National League Wild Card tie @-@ breaker game = 
- 
- The 1998 National League wild @-@ card tie @-@ breaker game was a one @-@ game extension to Major League Baseball 's ( MLB ) 1998 regular season , played between the Chicago Cubs and San Francisco Giants to determine the winner of the National League ( NL ) wild card . The game took place at Wrigley Field in Chicago , on September 28 , 1998 . The Cubs won the game 5 – 3 , holding the Giants scoreless for the majority of the game until the Giants threatened heavily in the ninth inning and scored all three of their runs . As a result of the game , the Cubs qualified for the postseason and the Giants did not . 
- The game was necessary after both teams finished the season with identical win – loss records of 89 – 73 . The Cubs won a coin flip late in the season which , by rule at the time , awarded them home field for the game . This victory advanced the Cubs to the 1998 NL Division Series ( NLDS ) where they were swept by the Atlanta Braves , ending the Cubs ' season . Michael Jordan , a popular Chicago <unk> then ending his career with the Chicago Bulls , threw the game 's ceremonial first pitch . In baseball statistics , the tie @-@ breaker counted as the <unk> regular season game for both teams , with all events in the game added to regular season statistics . 
- 
- = = Background = = 
- 
- The Chicago Cubs finished the previous season at the bottom of the Central division with a 68 – 94 record while the Giants won the West at 90 – 72 , though were defeated by the eventual 1997 World Series champion Florida Marlins in the NLDS . The Giants ' Barry Bonds drew criticism for his postseason performance , which had been a recurring criticism dating back to his time with the Pittsburgh Pirates . The Cubs made several offseason acquisitions following the 1997 season including trading Doug Glanville for second baseman Mickey <unk> along with signing Jeff <unk> and Henry Rodríguez as free agents . 
- One notable event of the 1998 season was the race for the home run title and Roger Maris ' single @-@ season record of 61 home runs between the Cubs ' Sammy Sosa and the Cardinals ' Mark McGwire . McGwire ultimately won , setting a new record of 70 while Sosa closed the season with 66 . Bonds would top their totals with 73 home runs in 2001 , setting the current record , while McGwire and Sosa 's totals remain the second and third highest figures in MLB history . Additionally , three teams finished the 1998 season within one game of one another in the race for the National League wild card : the Cubs , Giants , and New York Mets . This late @-@ season race included a dramatic Cubs loss to the Brewers on September 23 . The Cubs led 7 – 5 with two outs and the bases loaded for the Brewers in the bottom of the ninth inning when <unk> Brown dropped a fly ball , allowing three <unk> runs to score on his error and losing the Cubs the game . This loss gained notoriety for announcer and former @-@ Cub Ron Santo 's call of the final play , <unk> " Oh , no ! " as the final runs scored . 
- The Mets were tied with the Cubs and Giants in the wild @-@ card race as late in the season as September 25 at 88 – 72 . However , the Mets lost their remaining two games and finished their season one game back of the Cubs and Giants who ended tied at 89 – 73 . This record was also the best non @-@ division @-@ winning record and as such a tie @-@ breaker was necessary to determine the wild @-@ card winner . A coin flip on September 14 gave the Cubs ' home field advantage , setting Wrigley Field as the location for the game . In the event of a three @-@ way tie the Cubs were presented with the choice to either host two home games or receive a bye and play the winner of a Mets @-@ Giants game on the road because they had the best combined record against the Mets and Giants . Cubs ' general manager Ed Lynch decided on the second option , though the choice was moot as the Mets fell out of the race . The Cubs ' Steve Trachsel and Mark Gardner of the Giants were slated to start the tie @-@ breaker on the September 28 . 
- 
- = = Game summary = = 
- 
- Box score for Monday September 28 , 1998 — 7 : 10 p.m. ( CT ) at Wrigley Field in Chicago 
- The game remained scoreless in the early innings , with only two baserunners apiece for each team through the first three innings . The Giants loaded the bases with two outs in the top of the 4th inning , but Brian Johnson struck out to end the inning . The first runs of the game came in the top of the 5th as Henry Rodriguez singled to lead off the inning and scored on a home run by Gary <unk> to give the Cubs a 2 – 0 lead . The Cubs threatened again in the next inning with singles by Lance Johnson and Sammy Sosa . Rich Rodriguez relieved Gardner , but loaded the bases with a walk to Mark Grace and allowed a single to Matt <unk> which scored two more runs and extended the Cubs ' lead to 4 – 0 . John Johnstone relieved Rodriguez and closed the inning without further scoring . 
- After getting one out in the top of the 7th , Steve Trachsel allowed a single and a walk to pinch hitters Brent Mayne and <unk> <unk> respectively . The Cubs double switched , bringing in Matt Karchner to relieve Trachsel and Orlando <unk> to play left field . Karchner induced another out before loading the bases on a single by <unk> <unk> . Félix <unk> entered in relief of Karchner and got Barry Bonds to ground out to first base to end the inning . The Cubs further extended their lead in the bottom of the 8th inning . After Alvin <unk> got <unk> to strike out to open the inning the Giants double switched to bring in José Mesa to pitch and Bill Mueller at third base . Mesa allowed a single to Sammy Sosa and a double to Mark Grace which advanced Sosa to third . Following a ground out Mesa threw a wild pitch while facing <unk> and Sosa scored to make the game 5 – 0 . 
- The Giants responded in the top of the 9th , scoring their only runs of the game . Kevin <unk> allowed successive singles to Brent Mayne and Mueller to open the inning before Terry <unk> relieved him . Stan Javier singled to score Mayne and Ellis <unk> then walked to load the bases . Bonds came to bat with the bases loaded for the second time in the game and lined out to deep right field , scoring Mueller on a sacrifice fly and Javier advanced to third base . Rod Beck entered and got successive outs by Jeff Kent , which scored Javier , and Joe Carter to end the game 5 – 3 for the Cubs . 
- 
- = = Aftermath = = 
- 
- Chicago 's win clinched the team 's 15th postseason berth in franchise history , their first in 9 years since losing in the 1989 National League Championship Series . The Cubs ' presence in the postseason , playing for a spot in the World Series , led several sportswriters to reference the Curse of the Billy Goat and the difficulties the Cubs ' had faced since 1945 . The Billy Goat story suggests that Billy <unk> cursed the Cubs after he and his goat were asked to leave game four of the 1945 World Series . The playoff berth matched the Cubs with the Atlanta Braves in the NLDS , where the Cubs were swept three games to none . The Cubs have not made the World Series since 1945 and have not won a title since 1908 . 
- In baseball statistics tie @-@ <unk> count as regular season games , with all events in them added to regular season statistics . For example , the Chicago Sun @-@ Times noted that Sammy Sosa could have added to his notable home run total in the game . Sosa did add to his league @-@ leading strikeout total with one in the 4th inning . Beck also added to his league @-@ leading total in games played and earned his 51st save of the season which finished 2nd to Trevor Hoffman 's 53 but is tied for the 9th highest single @-@ season save total in MLB history . Several Cubs and Giants won awards for their regular season performances including Rookie of the Year honors for Kerry Wood , a Silver <unk> Award for Sosa , and a Rawlings Gold <unk> Award for J. T. Snow . Sosa also won the Most Valuable Player award , earning 30 out of 32 first place votes ( with McGwire taking the remaining 2 ) . 
- Barry Bonds drew criticism after the game for his poor performance , including twice making an out with the bases loaded , with some suggesting that Bonds often played poorly in " big games " . Bonds ' 0 – 4 performance with an <unk> and a strikeout earned him a − <unk> win probability added ( <unk> ) , the lowest figure for any player in the game . <unk> is a statistic which takes the game situation into account to <unk> how a player altered his team 's chances of winning . Bonds ' figure translates into decreasing the Giants ' chances of success by 18 @.@ 6 % . The book Game of Shadows , which details Bonds ' suggested use of performance @-@ enhancing drugs ( <unk> ) , argues that Bonds grew jealous and <unk> of the attention Mark McGwire and Sammy Sosa were given during their record chase . As a result , according to Game of Shadows , Bonds turned to weight training under Greg Anderson who began providing him with steroids and other <unk> following the 1998 season . 
- 
- 
- = Corpus Christi Bay = 
- 
- Corpus Christi Bay is a scenic semi @-@ tropical bay on the Texas coast found in San <unk> and Nueces counties , next to the major city of Corpus Christi . It is separated from the Gulf of Mexico by Mustang Island , and is fed by the Nueces River and Oso Creek from its western and southern extensions , Nueces Bay and Oso Bay . The bay is located approximately 136 miles ( 219 km ) south of San Antonio , and 179 miles ( 288 km ) southwest of Houston . 
- Corpus Christi Bay has a rich history of human settlement along its shores that dates back millennia and is responsible for the growth of Corpus Christi , and the smaller ports of Ingleside and Portland . It is an important natural estuary that supports a diverse collection of wildlife , and attracts many tourists . The bay 's abundance of petroleum and natural gas has attracted industry , and its strategic location on the Texas coast is ideal for military establishment . 
- 
- = = History = = 
- 
- The shores of Corpus Christi ( or Body of Christ ) Bay are thought to have been inhabited by the Karankawa Indians before the European discovery . <unk> evidence suggests that pre @-@ Karankawa peoples used the area near Oso Bay as a burial ground between 500 BC and 500 AD . It is believed to have first been spotted by Europeans on Corpus Christi Day <unk> , when Spanish explorer Alonso <unk> de <unk> <unk> its waters . Joaquín de <unk> y <unk> came across the bay in <unk> , when given orders to settle the area between Tampico , Mexico , and the mouth of the San Antonio River at San Antonio Bay . He named the bay after St. Michael the Archangel , but the name did not stick and was referred to as " Corpus Christi Bay " in a 1766 report by Diego Ortiz <unk> . Explorer <unk> María de la Garza <unk> is believed to have been the first man to purchase land on the bay in <unk> . Shortly thereafter the short @-@ lived settlement of Villa de <unk> was founded on the mouth of the Nueces River . 
- The first trading post on Corpus Christi Bay was established by Henry Kinney in 1838 in present @-@ day Corpus Christi . By the 1840s , the area developed into a settlement named after the bay , and a deepwater port was established in the 1870s . A <unk> that included a 32 @-@ foot overlooking statue of Jesus Christ was designed by Mount <unk> sculptor <unk> <unk> in 1928 , but was turned down by the city in 1930 . Later efforts to build a statue on the shore were also rejected . The <unk> port would later grow into a major city , and had a population of 277 @,@ 454 people during the 2000 U.S. census . In addition to Corpus Christi , Kinney also found <unk> ( known to settlers as The <unk> ) in 1852 , near the confluence of the Nueces River and Nueces Bay . The town was attacked by Mexican robbers in 1875 , and underwent a steep decline . It is now a ghost town located in the Corpus Christi city limits . The city of Ingleside was founded on the northern shore of the bay in 1854 , and grew slowly . It had a population of 9 @,@ <unk> in the 2000 census . Likewise , the city of Portland located on the northeastern bluff between the Nueces and Corpus Christi Bays , did not grow as rapidly as Corpus Christi , following its 1891 founding . During the 2000 census , it had 14 @,@ <unk> residents . 
- For transportation on the bay , steamboats were commonplace between Corpus Christi and Ingleside during the 1930s . Native Americans used a route made up of a series of shallow <unk> beds , dubbed Reef Road . The passage , which was about 18 to 24 inches in depth , could be <unk> on foot or horseback during low tides to travel across the opening of Nueces Bay into Corpus Christi Bay . White settlers discovered the road in the 1860s , and it became a common way to pass from Portland to Corpus Christi via buggy , although its jagged course had to be marked with posts and horses would sometimes fall off the beds and drown . A wooden causeway connecting Portland and Corpus Christi was first constructed in 1915 , but was repeatedly rebuilt and destroyed by several storms . A permanent concrete bridge was erected in the 1950s , and a double lane was added in 1988 . The approximately mile long structure is today known as the Nueces Bay Causeway . 
- The bay has been strategically important for the military . General Zachary Taylor stationed his men on <unk> Point during the Mexican @-@ American War , and a Union invasion was halted by Confederates on the same point in 1862 , during the American Civil War . The Port of Corpus Christi was used by the Confederates to bring in supplies during the war effort until the Union bombarded Corpus Christi and occupied the bay and port from 1863 to 1870 . In 1940 , the Naval Air Station Corpus Christi was established on the bay , and by 1944 had transformed into a major base with one main air station and six additional stations . Notably , during a March 1960 practice run from the base , future Senator John McCain lost track of his altitude and speed , and his single @-@ seat , single @-@ <unk> @-@ engine AD @-@ 6 <unk> crashed into Corpus Christi Bay and sank to the bottom . He squeezed out of the cockpit , swam ten feet to the surface , and was carried to safety by a rescue helicopter , therefore escaping without major injuries . 
- 
- = = Features = = 
- 
- The shoreline of Corpus Christi Bay is included in the Texas Coastal Plain in South Texas . The surrounding land is semi @-@ arid and is used for <unk> and other agricultural purposes . The bay itself is considered subtropical , and was described by <unk> <unk> as " the most beautiful bay on the Texas coast . " 
- On average , the system is 3 meters ( 9 @.@ 8 ft ) deep , and covers approximately 497 square kilometers ( 192 sq mi ) . It is the fourth largest estuarine system in Texas behind Galveston Bay , <unk> <unk> and Matagorda Bay . The two main extensions are : Nueces Bay , which extends west to the mouth of the Nueces River , and Oso Bay , which extends south to the mouth of Oso Creek . Every second , approximately 34 cubic meters ( 9 @,@ 000 U.S. gal ) of water flows into the bay . The exchange with the Gulf of Mexico occurs at Aransas Pass . As a result of the seawater exchange , the bay 's salinity is 22 parts per thousand ( <unk> ) , which is lower than the seawater average of 35 <unk> . 
- Following the shoreline beginning at Naval Air Station Corpus Christi on the bay 's southeastern peninsula , the features of the bay can be best described . Moving northwest from the air station , Oso Bay must be crossed at its confluence with Corpus Christi Bay . On the other side of the meeting is Ward Island ( actually a peninsula ) , where Texas A & M University – Corpus Christi is found . Further northwest , the shore begins to curve and off in the distance across the bay , the skyline of Corpus Christi is visible . Following the shore , the land dips inward and forms Emerald Cove , where a seawall has been constructed . Out in the bay , the Alta Vista Reef can be spotted from this location . Moving north along the shore , the seawall continues into the main city , until it reaches Industrial Canal , which has been dredged south of Nueces Bay and extends into the main bay to Port Aransas . Another seawall , which starts in Emerald Cove with gaps at places such as a spoils island that can be viewed in the bay and the canal , is slightly out in the water . This seawall ends when it reaches land at the southern portion of Corpus Christi Beach . North of the canal , Corpus Christi Beach is found along the shore to <unk> Point , where Corpus Christi Bay opens to Nueces Bay and must be crossed using the Nueces Bay Causeway to Indian Point near Portland , from where Indian Reef <unk> from offshore . Past Portland , the shore curves to the southeast where the large La Quinta Island forms on the backdrop of industrial plants in Ingleside . The La Quinta Channel has been dredged between the island and the shore and meets the Jewell Fulton canal at the confluence of Kinney Bayou . Ingleside Cove is formed in this area between La Quinta Island and an island named Ingleside Point . The shore then curves to the southwest where Ingleside on the Bay is located on southern shore of the bay 's northeastern peninsula . To the southeast , a series of islands form the boundary between Corpus Christi and <unk> Bays . 
- 
- = = <unk> = = 
- 
- The Environmental Protection Agency has designated the Corpus Christi Bay system as an estuary of national significance . More than 234 species of fish are found in the bay , including the <unk> <unk> , <unk> <unk> , Atlantic <unk> , Atlantic <unk> , Black drum , Red drum , Southern <unk> , <unk> jack , <unk> , Inshore <unk> , Atlantic <unk> , Silver perch , <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , Sand <unk> , <unk> <unk> , <unk> , Gray <unk> , Common <unk> , and the <unk> . 
- In 2009 , $ 1 million of federal stimulus money was delegated to the restoration of the <unk> near the Nueces Bay Causeway to increase the population of birds and fish . The Coastal Bend Bays and <unk> Program and the U.S. Fish and Wildlife Service collaborated to place soil and plant marsh near the causeway to allow a larger nursing location for fish and provide greater quantities of food for water birds . Over 490 species of birds have been found in the area including the brown <unk> , black @-@ billed whistling duck , reddish <unk> , white @-@ faced ibis , <unk> , buff @-@ bellied <unk> , golden @-@ fronted <unk> , long @-@ billed <unk> , olive sparrow , <unk> cormorant , laughing gull , Franklin 's gull , ring @-@ billed gull , <unk> gull , gull @-@ billed <unk> , common <unk> , brown @-@ crested <unk> , hooded <unk> , peregrine falcon and <unk> <unk> . Bird populations are protected and can be viewed at the Hans and Pat <unk> Wildlife Refuge on Oso Bay . 
- 
- = = Industry = = 
- 
- Corpus Christi Bay is a natural harbor , and its port has contributed to the growth of the main port city of Corpus Christi . Corpus Christi is the 5th largest port in the United States , and the deepest on the Gulf of Mexico . The channel to the Gulf was dredged through the bay to the <unk> at Port Aransas . <unk> exchanged at the port include <unk> , industrial and agricultural goods and petroleum . Six oil refineries and 1 @,@ 500 wells are located near the bay as well as a large supply of natural gas . In 1987 alone , $ 277 million of oil and gas were produced in the area . <unk> , stone products , glass , chemicals , and <unk> products are also produced near the bay . Ingleside originally focused its economy on agriculture , notably <unk> . Later , industrial plants including those established by the <unk> Corporation , Reynolds <unk> ( five miles away ) and <unk> opened . La Quinta Channel was dredged by the Army Corps of Engineers in the 1950s . 
- Tourism to the bay is encouraged by the area 's climate , fishing and <unk> opportunities as well as sites in Corpus Christi including Corpus Christi beach , Texas State <unk> , USS Lexington Museum , the <unk> marina , and the Corpus Christi Museum of Science and History . The bay was also the site of the 2008 U.S. Wind and Water Open , as well as the Texas International Boat Show in 2008 , 2009 and 2010 . 
- 
- 
- = 1990 Pacific hurricane season = 
- 
- The 1990 Pacific hurricane season is the fifth most active season on record and is the third most active season in terms of ACE <unk> . The 1990 season officially started on May 15 in the eastern Pacific , and on June 1 in the central Pacific , and lasted until November 30 . These dates conventionally <unk> the period of each year when most tropical cyclones form in the northeastern Pacific Ocean . However , these bounds were slightly exceeded when Hurricane Alma formed on May 12 . 
- Hurricane Alma became the third earliest tropical cyclone in the Eastern Pacific basin since the satellite era began in 1966 , while Trudy is the third strongest October eastern Pacific hurricane on record . Overall , the impact of this season was minimal . Tropical Storm Rachel made two <unk> in Mexico and brought rain to the United States . Hurricane Boris brought light showers to California . 
- 
- = = Season summary = = 
- 
- The 1990 eastern North Pacific hurricane season was active in terms of number of storms that attained at least tropical storm intensity and of <unk> Cyclone Energy . All of the tropical cyclones of this year developed from westward @-@ moving African tropical waves . The season established several tropical storm records for this basin and was marked by several strong hurricanes . There were 21 named tropical cyclones , seven below the record established by the 1992 Pacific hurricane season two years later , but four more than the long @-@ term average . Sixteen of those named storms , twice the average and four more than the previous record , reached hurricane intensity . Only Cristina , Douglas , Aka , Rachel , and Simon did not reach hurricane strength . Six of those hurricanes reached Category 3 intensity or higher on the Saffir @-@ Simpson Hurricane Scale . Hurricanes Hernan and Trudy were among the strongest ever observed in this area . Alma became the earliest named tropical cyclone for the eastern Pacific ocean east of the <unk> meridian west . The eastern Pacific produced four tropical depressions that did not reach tropical storm status . 
- With the exception of Marie and Polo , these systems developed between 5 ° and 15 ° N , east of the longitude of Baja California in the <unk> favored area for tropical cyclogenesis . After their development , most of the tropical cyclones moved on a west to northwest track and dissipated over cool waters without affecting land . Rachel was the only system to make landfall . In addition , one tropical storm formed in the Central Pacific and eventually crossed the International Dateline before dissipating . 
- 
- = = Storms = = 
- 
- 
- = = = Hurricane Alma = = = 
- 
- A tropical wave emerged from Africa on April 29 . It then moved across the Atlantic , crossed northern South America , and entered the Pacific Ocean on May 9 . <unk> started to increase with the wave just to the south of Panama . The wave moved westward and continued to become better organized over the next few days . On May 12 , the wave had become organized enough and was designated Tropical Depression One @-@ E. 
- Tropical Depression One @-@ E moved slowly to the northwest while strengthening slowly due to easterly vertical wind shear . The depression was upgraded to Tropical Storm Alma on May 14 . The storm quickly strengthened once the vertical wind shear relaxed and was near hurricane force by the morning of May 15 . Hurricane Alma reached its peak intensity early on May 16 with maximum sustained winds of 85 mph ( 135 km / h ) . The hurricane entered an area of southwesterly wind shear caused by an upper @-@ level ridge of high pressure . The increasing vertical wind shear and cooler water caused Alma to weaken to a tropical storm . On May 17 , the storm continued to weaken and became a depression . The next day , the system dissipated as a tropical cyclone well west of mainland Mexico . Alma became the earliest named storm and hurricane in the satellite era in the east Pacific proper . 
- 
- = = = Hurricane Boris = = = 
- 
- A tropical wave came off the northwest coast of Africa on May 20 . The wave did not significantly develop as it moved west across the Atlantic Ocean and Caribbean Sea . Once it crossed Central America into the eastern Pacific Ocean , cloudiness increased considerably with the system on May 31 . It became well @-@ enough organized to be upgraded to Tropical Depression Two @-@ E on June 2 . After becoming a depression , the system moved to the west @-@ northwestward . Upper @-@ level easterly vertical wind shear limited the amount of the initial strengthening . However , an increase in convective banding led to its upgrade to Tropical Storm Boris on June 4 , 500 mi ( 800 km ) southwest of Manzanillo . Further strengthening occurred due to an anticyclone <unk> fostered additional development , and by June 5 , Boris became a hurricane . The hurricane began to move to the north @-@ northwest in response to a trough off of the West Coast of the United States . <unk> continued , and the storm reached its peak strength with winds of 90 mph ( 140 km / h ) and a minimum central pressure of 977 mb ( 28 @.@ 9 inHg ) . The hurricane 's cloud pattern became elongated along a southwest @-@ northeast axis on June 6 due to an increased amount of vertical wind shear . Boris was downgraded to a tropical storm later in the day on June 6 . The deep convection of the tropical storm decreased , as Boris moved over cooler waters . It was downgraded to a tropical depression on June 7 due to these factors . The depression dissipated as a tropical cyclone on June 8 as the system quickly lost its tropical characteristics . A small circulation remained in the cloud field offshore the northwest coast of Baja California for a few more days . 
- The winds in the eyewall of Boris ripped off the sails of the Azure Dream <unk> . Outer rainbands from the storm produced moderate rain in several Mexican states . In Mexico , rainfall peaked at 8 @.@ 83 in ( 224 mm ) near San Lucas , Michoacán . The remnants of Boris also brought sporadic rainfall over the western United States , with precipitation being reported in Arizona , California , Colorado , Nevada and Wyoming . The remnants of Boris were responsible for causing the wettest June in San Diego since records began in 1850 ; however , not even 1 in ( 25 mm ) of rain fell at that location . Rainfall from the remnant system in the United States peaked at 3 @.@ 28 in ( 83 mm ) over the Santa Rita Mountains . No other direct damages or casualties were reported from Boris . 
- 
- = = = Tropical Storm Cristina = = = 
- 
- A tropical wave emerged into the Atlantic Ocean off the coast of Africa on May 28 . The wave moved across Central America into the northeastern Pacific Ocean , where convection organized with the system on June 6 . The system became well @-@ enough organized to be upgraded to Tropical Depression Three @-@ E on June 8 , while 920 mi ( 1 @,@ 480 km ) south of the southern tip of Baja California . The depression continued to strengthen , and was upgraded to Tropical Storm Cristina on June 9 . 
- Cristina initially had well @-@ established upper @-@ level outflow . It failed to reach hurricane status , peaking with maximum sustained winds of 65 mph ( 105 km / h ) due to movement of its associated upper @-@ level anticyclone to the north over Mexico which caused Cristina to be exposed to easterly vertical wind shear . Cristina moved generally northwestward , which eventually placed the storm in cooler waters , and caused the deep convection of the system to dissipate . On June 14 , Cristina diminished to a tropical depression . Cristina moved west @-@ northwestward with the low @-@ level flow . The depression dissipated as a tropical cyclone on June 16 , after a lack of deep convection for 48 hours . 
- 
- = = = Tropical Storm Douglas = = = 
- 
- A tropical wave moved off the northwest coast of Africa on June 5 . It continued westward across the tropical North Atlantic and Caribbean Sea without significant development . The wave entered the northeastern Pacific Ocean on June 16 , where it generated an area of increased cloudiness . Organization of thunderstorm activity increased late on June 18 and early on June 19 . Tropical Depression Four @-@ E formed on June 19 , when it was 230 mi ( 370 km ) south of Acapulco , Mexico after evidence of low @-@ level circulation for the past 24 hours . The depression moved in a west @-@ northwest direction , due to a subtropical ridge north of the depression . <unk> analysis indicated that the depression had reached tropical storm force strength , and it was upgraded to Tropical Storm Douglas on June 19 , while 230 mi ( 370 km ) southwest of Acapulco . Douglas reached peak strength of 65 mph ( 105 km / h ) on June 21 
- A tropical storm warning was issued on June 21 , from Punta <unk> to Cabo Corrientes before being discontinued on June 22 . Tropical Storm Douglas brushed the western coast of Mexico . The center of circulation approached within 17 mi ( 27 km ) of the coast of Mexico on June 22 . Douglas then began to weaken because of interaction with the mountainous terrain of Mexico . The system continued to weaken despite warm water temperatures and favorable upper @-@ level conditions . Douglas dissipated early on June 24 , while 138 mi ( 222 km ) south @-@ southeast off the southern tip of Baja California . The highest rainfall report from Mexico from Douglas totaled 11 @.@ 07 in ( 281 mm ) at La Huerta . No deaths or damage were reported . 
- 
- = = = Hurricane Elida = = = 
- 
- A tropical wave came off the coast of Africa into the eastern Atlantic Ocean on June 10 and 11 . The system moved across the Atlantic and entered into the northeastern Pacific Ocean . The wave became a significant tropical system , with cyclonic turning in the lower and middle layers of the system 's cloudiness on June 25 . The center of circulation was located 345 mi ( 555 km ) south of Acapulco , Mexico . The system exhibited sufficient organization to be considered Tropical Depression Five @-@ E on June 26 . The depression quickly strengthened and became Tropical Storm Elida later that day . The track of Elida was west @-@ northwestward to northwestward from June 27 through June 28 . Some rainbands moved over the mountainous region of southwestern Mexico , but no flooding , damage , or casualties were reported as a result . Elida continued strengthening and it was upgraded to a hurricane early on June 28 passing directly over Socorro Island later in the day , right as Elida reached peak strength of 80 mph ( 130 km / h ) , dropping 3 @.@ 7 in ( 94 mm ) of rain on the island . Some windows broke also as a result , and minor structural damage was reported as a result on the island . 
- A high @-@ pressure area to the north forced Elida to move more westward on June 29 . <unk> began to decrease in the hurricane as it moved into cooler waters , and it weakened to a tropical storm later in the day . It then weakened to a tropical depression on July 1 , and dissipated on July 2 . 
- 
- = = = Tropical Depression Six @-@ E = = = 
- 
- An area of disturbed weather south of Acapulco organized into Tropical Depression Six @-@ E on June 29 and the system began to move northwest . Originally the system was expected to reach tropical storm strength and near hurricane intensity by late on July 2 or early on July 3 , however persistent vertical wind shear over the system impeded its development . After convection began to merge into the Intertropical Convergence Zone ( ITCZ ) , the National Hurricane Center discontinued advisories on Tropical Depression Six @-@ E , stating that the depression dissipated , and regeneration seemed unlikely . 
- However , wind shear began to decrease , and convection associated with the remnants of Tropical Depression Six @-@ E quickly began to increase . Although the low @-@ level circulation was near the edge of the deep convection , the National Hurricane Center resumed advisories on Tropical Depression Six @-@ E by July 1 . <unk> into a tropical storm was again predicted , but Tropical Depression Six @-@ E remained poorly defined , and eventually dissipated on July 4 . 
- 
- = = = Hurricane Fausto = = = 
- 
- A tropical wave came off the northwest coast of Africa on June 19 . The wave crossed the Atlantic Ocean and Caribbean Sea , into the warm waters of the northeastern Pacific Ocean , on July 2 . An area of disturbed weather moved northwards towards the Gulf of Tehuantepec and became Tropical Depression Seven @-@ E early on July 6 . The depression moved northwest , and because of increased organization of deep convection , was designated Tropical Storm Fausto on July 7 , 265 mi ( 426 km ) south of Manzanillo , Mexico . A weakly defined eye formed on July 8 , and the storm was upgraded to Hurricane Fausto , 310 mi ( 500 km ) south of Cabo San Lucas . 
- Fausto peaked in strength with winds of 85 mph ( 140 km / h ) and a central pressure of 979 mb ( 28 @.@ 9 inHg ) on July 9 . Fausto passed 40 mi ( 60 km ) north of Socorro Island causing a northwest wind of 40 mph ( 60 km / h ) , and 4 @.@ 3 in ( 110 mm ) of rain at that location . The hurricane fluctuated in intensity on July 10 , before weakening into a tropical storm later that day , 403 mi ( 649 km ) west of Cabo San Lucas . The weakening Fausto moved over continuing cooler waters , and was downgraded to a tropical depression on June 11 , 690 mi ( 1 @,@ 110 km ) west of Cabo San Lucas . The depression dissipated as a tropical cyclone on June 13 , and its low level circulation of clouds persisted for a few days . 
- 
- = = = Hurricane Genevieve = = = 
- 
- A tropical wave moved off the coast of Africa on June 25 and moved across the Atlantic Ocean and central Caribbean Sea with little development . Within the western Caribbean Sea , convection developed with the wave on July 7 . The system became better organized as it moved into the northeastern Pacific Ocean . The system was upgraded to Tropical Depression Eight @-@ E on July 11 . The depression moved west @-@ northwest , with a good outflow to the north , and gradually strengthened into Tropical Storm Genevieve later that day . Genevieve continued to strengthen , reaching hurricane strength on July 13 . The storm approached within 23 mi ( 37 km ) of Socorro Island where a sustained wind of 44 mph ( 71 km / h ) and gust of 50 mph ( 80 km / h ) were reported , before the weather equipment on the island failed . 
- The hurricane began to turn west because of the strengthening and growing ridge to its north . Increasing outflow <unk> began to form , and the hurricane reached its peak strength of 105 mph ( 170 km / h ) with a well @-@ defined eye at its center of circulation . Genevieve turned northwest on July 16 into an area with strong vertical wind shear and low water temperatures . The hurricane quickly weakened into a tropical storm on July 17 and then into a tropical depression on July 18 . Genevieve dissipated as a tropical cyclone later in the day , reduced to a circulation in the <unk> cloud field . 
- 
- = = = Hurricane Hernan = = = 
- 
- A tropical wave that moved off the northwest coast of Africa on July 4 passed through the Atlantic Ocean and Caribbean Sea without significant development . The wave moved across Central America into the northeastern Pacific Ocean on July 15 . <unk> activity increased in the system between July 16 and 18 before rainbands became present within the tropical disturbance on July 19 . Tropical Depression Nine @-@ E formed later that day while 565 mi ( 910 km ) southwest of Acapulco , Mexico . 
- Tropical Depression Nine @-@ E strengthened into Tropical Storm Hernan on July 21 while 684 mi ( 1 @,@ 100 km ) south of Cabo San Lucas . The tropical storm continued to strengthen and as it moved to the northwest , and became a hurricane late on July 21 . Hernan continued to strengthen and as it passed 145 mi ( 230 km ) southwest of Clarion Island . On July 23 , Hernan reached its peak strength of 155 mph ( 250 km / h ) and minimum pressure of <unk> mb ( 27 @.@ 4 inHg ) as the center of circulation was 207 mi ( <unk> km ) south @-@ southwest of Clarion Island . On July 24 , concentric <unk> ( one eyewall located inside another ) formed around the center of Hernan , which was the first time this pattern had been observed with an eastern Pacific hurricane . 
- Hernan kept Category 4 hurricane intensity a few more days and hurricane strength for six days overall . The hurricane weakened into a tropical storm on July 28 as it moved over cooler waters . Hernan turned more to the west as its low level center became separated from its thunderstorm activity and was steering by a surface high @-@ pressure system to its north . Hernan moved over cooler waters , dissipating as a tropical cyclone early on July 31 . 
- 
- = = = Hurricane Iselle = = = 
- 
- A tropical wave moved off the coast of Africa on July 7 . The wave moved west , but it was not until it moved across the Caribbean Sea that the wave 's convection organized . The system entered into the northeastern Pacific Ocean with concentrated convection and a surface center of circulation while southeast of Acapulco . Moving west @-@ northwest , Tropical Depression Ten @-@ E formed on July 20 393 mi ( <unk> km ) south of Puerto <unk> , Mexico . Early on the following day , the depression strengthened into Tropical Storm Iselle . The storm continued strengthening while in the proximity of Hurricane Hernan , becoming a hurricane on July 22 . 
- The hurricane continued to move west @-@ northwest through its duration , and reached its peak strength of 120 mph ( 190 km / h ) on July 25 . Iselle crossed Socorro Island , which recorded a 70 mph ( 110 km / h ) sustained wind and heavy rain on July 25 . The hurricane weakened on following days after it moved over cooler waters , downgraded to a tropical storm on July 28 and subsequently into a tropical depression on July 30 . The depression dissipated later that day , after losing its low @-@ level circulation <unk> mi ( <unk> km ) southwest of San Diego . 
- 
- = = = Tropical Depression Eleven @-@ E = = = 
- 
- On July 24 a tropical disturbance behind Hurricane Hernan strengthened into a tropical depression . Due to the close proximity of Hernan to its west , the development of the depression was hindered due to vertical wind shear from the outflow of Hernan . This wind shear dissipated the depression on July 26 , and its remnants were absorbed by the nearby cyclone . No damages or casualties were caused by the depression as it was well out to sea . 
- 
- = = = Tropical Storm Aka = = = 
- 
- Tropical Storm Aka was the only tropical storm to form in the Central North Pacific during 1990 . An area of disturbed weather began to organize on August 6 . By August 7 , the system became well @-@ enough organized to become designated Tropical depression One @-@ C. The depression intensified into a tropical storm while moving west , to the south of the Hawaiian Islands . On August 10 , Tropical Storm Aka peaked with maximum sustained winds of 60 mph ( 97 km / h ) . The storm continued to move west and approached Johnston Island , though the island 's weather did not deteriorate . The tropical storm crossed the International Date Line on August 13 . Aka weakened back into a depression and dissipated two days later on August 15 . 
- 
- = = = Tropical Depression Diana = = = 
- 
- Hurricane Diana struck eastern Mexico and managed to hold together , remaining a tropical depression as it entered the eastern Pacific Ocean late on August 8 . Although Tropical Depression Diana entered the eastern Pacific , the National Hurricane Center did not re @-@ classify the system . No re @-@ intensification occurred after the system entered the eastern Pacific , and it had dissipated as a tropical cyclone by the following day . The remnant tropical disturbance recurved through the Gulf of California while developing significant convection before it moved into northwest Mexico , which brought rainfall amounts of over 10 in ( 250 mm ) to local areas within the state of Sonora . The remnant disturbance moved into the American Southwest on August 11 . 
- 
- = = = Tropical Depression Two @-@ C = = = 
- 
- Tropical Depression Two @-@ C was the second and last tropical cyclone to develop within the north @-@ central Pacific Ocean in 1990 . The depression developed from a tropical disturbance well southeast of Hawaii , which became much better organized on the night of August 10 . The tropical depression moved in a west northwest direction for the next 18 hours and then changed to a west @-@ southwest track on August 11 . As it turned more to the southwest , the depression weakened until it dissipated on August 13 about 600 mi ( 970 km ) south @-@ southeast of Hilo , Hawaii . 
- 
- = = = Tropical Depression Twelve @-@ E = = = 
- 
- A persistent area of thunderstorm activity southwest of Puerto Vallarta became better organized and strengthened into Tropical Depression Twelve @-@ E on August 16 . Moving northwest , the system developed slowly as it was embedded within the Intertropical Convergence Zone . <unk> shear kept the system from reaching tropical storm strength and caused its ultimate <unk> on August 19 . No damages or fatalities were caused by this depression . 
- 
- = = = Hurricane Julio = = = 
- 
- A tropical wave moved off the western coast of Africa on August 5 , moving across the Atlantic Ocean and Caribbean Sea without significant development . The system entered the northeastern Pacific Ocean , and became well @-@ enough organized to be upgraded to Tropical Depression Thirteen @-@ E on August 17 while centered <unk> mi ( 650 km ) south of Acapulco , Mexico . The depression moved on a west @-@ northwestward track and strengthened , becoming Tropical Storm Julio on August 18 . <unk> continued and Julio reached hurricane strength on August 19 . The cyclone peaked with maximum sustained winds of 115 mph ( 185 km / h ) on August 21 . The storm turned westward and began weakening . Julio regained tropical storm status on August 23 and tropical depression status on August 24 before dissipating as a tropical cyclone later that day . No damage was reported from Julio . 
- 
- = = = Hurricane Kenna = = = 
- 
- A tropical wave moved off the coast of Africa on August 9 , and spawned Tropical Storm Fran four days later , before it moved through the southern <unk> Islands on August 14 . While Fran dissipated shortly after that , the tropical wave progressed into the northeastern Pacific Ocean . The wave spawned Tropical Depression Fourteen @-@ E 808 mi ( 1 @,@ 300 km ) east @-@ southeast of Hurricane Julio . The depression moved westward for the next several days . As Julio weakened , the depression began to increase in strength . It became Tropical Storm Kenna on August 22 and continued to strengthen into a hurricane on August 25 , peaking with winds of 85 mph ( 137 km / h ) the next day . On August 26 , a strong frontal trough weakened the high pressure system to the storm 's north , causing a turn to the north during the next few days . The hurricane weakened in response to cooler water and increasing vertical wind shear , which removed convection from its center . Kenna weakened back to tropical storm strength on August 28 , then into a tropical depression on August 29 . The system dissipated as a tropical cyclone on August 30 . 
- 
- = = = Hurricane Lowell = = = 
- 
- A tropical wave moved off the northwestern coast of Africa into the Atlantic Ocean on August 11 . After moving across the Atlantic and Caribbean Sea without development , the system moved through the Intertropical Convergence Zone of the northeastern Pacific Ocean . The cloudiness moved northward toward the Gulf of Tehuantepec on August 22 . The thunderstorm activity organized sufficiently for the system to be upgraded to Tropical Depression Fifteen @-@ E on August 23 while 298 mi ( 480 km ) southeast of Acapulco , Mexico . The depression moved west @-@ northwestward and strengthened into a tropical storm 217 mi ( 350 km ) south @-@ southwest of Puerto Vallarta on August 25 . The storm began to turn more westward due to a strengthening high pressure system to its north and was upgraded to a hurricane on August 27 while 286 mi ( 460 km ) southwest of Cabo San Lucas . As the high to the north continued to strengthen , the hurricane turned west @-@ southwest . 
- <unk> activity with the hurricane began to weaken and Lowell was downgraded back to tropical storm status on August 28 . The tropical storm then turned to a north @-@ northwestward with the storm passing over cooler waters . On August 31 , the storm was downgraded to a tropical depression , with only minimal amounts of deep convection remaining within its circulation . Lowell dissipated as a tropical cyclone on September 1 , though a low @-@ level circulation of clouds could be seen on satellite imagery for the next few days . No damage or casualties was reported as a result of Lowell . 
- 
- = = = Hurricane Marie = = = 
- 
- A tropical wave moved off the northwestern coast of Africa on August 16 . The wave moved west through the Atlantic Ocean and Caribbean Sea without significant development . The wave moved across Central America and into the northeastern Pacific Ocean , on August 29 . <unk> convection with the system increased while south of the Gulf of Tehuantepec as the system moved northwest parallel to the southwest coast of Mexico . Once convective activity increased , the system was upgraded to Tropical Depression Sixteen @-@ E on September 7 while centered <unk> mi ( 1 @,@ 060 km ) southwest of Clarion Island . 
- A weaker than normal high pressure system controlled the movement of the depression , and the system moved slowly to the west through its duration . The depression strengthened into Tropical Storm Marie on September 8 , and into a hurricane on September 9 , while 522 mi ( 840 km ) south @-@ southwest of Clarion Island . Marie reached its peak intensity of 140 mph ( 230 km / h ) on September 11 . The hurricane crossed into the central North Pacific on September 14 as a hurricane with maximum sustained winds of 110 mph ( 180 km / h ) . The hurricane weakened thereafter and on September 17 Marie regained tropical storm status . The system weakened into a tropical depression on September 19 and dissipated as a tropical cyclone on September 21 near the Hawaiian coast . No casualties or damages were caused by Marie . 
- 
- = = = Hurricane Norbert = = = 
- 
- A tropical wave moved off the coast of Africa and across the Atlantic Ocean and Caribbean Sea without significant development . The wave entered the northeastern Pacific Ocean and began to show signs of organized thunderstorm development . Tropical Depression Seventeen @-@ E formed on September 10 while centered <unk> mi ( 1 @,@ 220 km ) south @-@ southwest of Cabo San Lucas , Mexico . The depression became better organized and strengthened into Tropical Storm Norbert later in the day . 
- On September 12 , Norbert absorbed Tropical Depression Eighteen @-@ E which was in close proximity . Norbert strengthened as it moved north @-@ northwestward . The tropical storm was upgraded to a hurricane on September 14 , and formed an eye on September 15 . The storm reached its peak strength of 80 mph ( 130 km / h ) sustained winds that day . Turning north of due west , Norbert then weakened as it moved over cooler waters . It was downgraded to a tropical storm later on September 15 , and then to a tropical depression on September 18 . Norbert dissipated as a tropical cyclone on September 19 after losing all its deep convection . 
- 
- = = = Tropical Depression Eighteen @-@ E = = = 
- 
- On September 12 , satellite images indicated that the eighteenth tropical depression of the season formed southwest of Cabo San Lucas . Located in close proximity to Tropical Storm Norbert , the depression would soon enter a region of colder sea surface temperatures , and the National Hurricane Center noted that only very little intensification was possible . As predicted , Norbert hindered further intensification of Tropical Depression Eighteen @-@ E. The National Hurricane Center noted six hours later that the depression would likely be absorbed into Norbert . The National Hurricane Center later believed that Tropical Depression Eighteen @-@ E was " dominating " the interaction between the two systems , and was forecast to absorb Norbert . Unlike the latter prediction , Norbert absorbed Tropical Depression Eighteen @-@ E later that day . 
- 
- = = = Hurricane Odile = = = 
- 
- A tropical wave move off the coast of Africa on September 5 , and crossed the Atlantic waters , the Caribbean Sea and eventually into the Pacific Ocean as a weak system . An area of convection entered into the northeastern Pacific Ocean a few hundred miles south of Acapulco , Mexico . On September 2 , Tropical Depression Nineteen @-@ E formed from this convection , while 746 mi ( 1 @,@ 201 km ) south @-@ southeast from the southern tip of Baja California . The depression moved on a west @-@ northwestward track , rounding the southwest side of a strong high pressure system . The depression continued to strengthen and was upgraded to Tropical Storm Odile on September 24 and into a hurricane on September 25 . Its intensity peaked with maximum sustained winds of 145 mph ( 233 km / h ) on September 26 . 
- The hurricane began to turn more northwestward in response to a high @-@ pressure system weakening to its north . Odile began to weaken as it moved over cooler waters on September 28 . The cyclone regained tropical storm status on September 29 while it slowly progressed northward . The system weakened into a tropical depression status on September 30 . Once it lost its deep convection , Odile was steered southwestward by the low @-@ level flow , before it dissipated as a tropical cyclone on October 2 . Its remaining low @-@ level circulation of clouds continued on a southwestward course thereafter . 
- 
- = = = Tropical Storm Rachel = = = 
- 
- In mid @-@ September , a weak tropical wave moved off the coast of Northwest Africa . It moved westward and entered the eastern Pacific Ocean early on September 23 . <unk> became concentrated with the system south of the Gulf of Tehuantepec . On September 27 , the organized thunderstorm area was upgraded to Tropical Depression Twenty One @-@ E while 230 mi ( 370 km ) south of Acapulco . The depression developed slowly , and became a tropical storm on September 30 . As Rachel briefly moved northward , before re @-@ curving and accelerating towards the Mexican coast . The storm passed over the southern portion of Baja California Sur on October 2 . Rachel made its final landfall midway between Los <unk> and <unk> , and became the only system to make landfall from the eastern north Pacific Ocean in 1990 . Rachel decayed rapidly over land , and the final public advisory on Rachel was issued early on October 3 @.@ its remnants continued accelerating over Texas until they lost their identity . 
- The highest rainfall total from the system in Mexico totaled 9 @.@ 85 in ( 250 mm ) at Santa Anita near the tip of Baja California . Across northern Mexico , thousands were homeless , and 18 people died . In Texas , Rachel 's remnants caused heavy rain . Flooding occurred in the Big Bend area . 
- 
- = = = Hurricane Polo = = = 
- 
- Polo originated from a tropical wave that moved off the African coast on September 2 which spawned Hurricane Isidore in the Atlantic basin . On September 14 , the system increased in convection as it was moving to the west and approaching Central America . The southern extent of the wave crossed into the Pacific Ocean on September 18 . 
- The wave continued westward and related thunderstorm activity increased during the following week . The convective system organized into Tropical Depression Twenty @-@ E on September 28 nearly midway between Mexico and Hawaii . Initially , the depression drifted toward the northwest due to a large upper @-@ level trough located to its west . Polo strengthened rapidly into a hurricane early on September 30 , with its winds peaking at 75 mph ( 121 km / h ) . Since the storm was very small , vertical wind shear caused significant weakening thereafter . On October 1 , Polo regained tropical storm strength as it was crossing into the north Central Pacific basin . It dissipated as a tropical cyclone later that day . There were no casualties or damages caused by Polo . 
- 
- = = = Tropical Storm Simon = = = 
- 
- A weakly defined tropical wave moved off the northwest coast of Africa on September 20 , and crossed the northern portion of the tropical Atlantic and northern South America without significant organization . The wave moved into the northeastern Pacific waters , off the coast of Colombia on September 30 . As the wave passed over southern Central America , rainbands and cloudiness increased with the system between October 1 and October 3 , before the system merged with the ITCZ from October 4 to October 6 . Signs of convective organization <unk> on October 8 , and by October 9 , the system was upgraded to Tropical Depression Twenty Two @-@ E <unk> mi ( 930 km ) south of Cabo San Lucas , Mexico . 
- The depression continued to develop and was upgraded to Tropical Storm Simon on October 10 while <unk> mi ( 972 km ) south @-@ southwest of Cabo San Lucas . Due to a high @-@ pressure system to the north @-@ northwest , Simon continued on a west @-@ northwestward path . The tropical storm continued to strengthen to its peak intensity of 70 mph ( 110 km / h ) by late on October 11 . Simon passed over cooler waters and weakened back into a tropical depression on October 13 . The depression moved on a westward course , and the low @-@ level circulation was displaced from its deep convection during the following day . Simon then dissipated as a tropical cyclone early on October 15 across the open waters of the northeast Pacific . 
- 
- = = = Hurricane Trudy = = = 
- 
- A tropical wave moved across the northeast Pacific Ocean and formed a tropical depression south of Mexico on October 16 . It strengthened at a moderate pace and reached hurricane intensity on October 18 . Trudy entered a favorable environment and explosively intensified , reaching its first peak in strength on October 20 . At that time , an upper @-@ level trough drifted Trudy north , which increased vertical shear , causing it to substantially weaken the system back to Category 1 hurricane status . The trough <unk> the storm and was replaced by a ridge which turned the storm back to the west and led to its re @-@ intensification to a Category 4 hurricane . Another upper level trough approached which pulled Trudy northward once again , and <unk> the hurricane apart . The cyclone dissipated on November 1 . Eventually , some of its moisture spread over parts of Mexico and the United States . 
- Except for Socorro Island , Trudy had no impact on land . The island reported hurricane force winds for seven hours . Trudy also had an unusually large eye , which was as large as 58 mi ( 93 km ) wide . Trudy is the fourth @-@ strongest Pacific hurricane in October , behind Kenna of the 2002 season , Rick of the 2009 season , and Patricia of the 2015 season . Trudy also spent 78 hours of its life as a Category 4 hurricane , longer than any other hurricane in the Eastern Pacific basin . 
- 
- = = = Hurricane Vance = = = 
- 
- An area of disturbed weather , possibly related to a tropical wave , organized into Tropical Depression Twenty Four @-@ E on October 21 . It strengthened into Tropical Storm Vance early on October 23 and then into a hurricane two days later . Vance moved parallel to the west coast of Mexico and approached the southern tip of the Baja California Peninsula . Before it could strike land , a high @-@ pressure area blocked Vance 's path and forced the storm to <unk> a small clockwise loop over a two @-@ day period . During the loop , Vance encountered wind shear and cool waters stirred up by Trudy as well as earlier in its duration . Vance weakened to a tropical storm on October 27 and a depression on October 30 . The cyclone dissipated late on October 31 . 
- 
- = = <unk> Cyclone Energy ( ACE ) <unk> = = 
- 
- The table on the right shows the ACE for each storm in the season . ACE is , broadly speaking , a measure of the power of the hurricane multiplied by the length of time it existed , so storms that last a long time , as well as particularly strong hurricanes , have high <unk> . ACE is only calculated for full advisories on tropical systems at or exceeding 34 knots ( 39 mph , 63 km / h ) or tropical storm strength . 
- The figures in parentheses are for storms in the Central Pacific basin west of 140 ° W ; those not in <unk> are for the Eastern Pacific basin . 
- The cumulative ACE for the Eastern Pacific this season fell within the official " Above Normal " <unk> , being one of the most active . This occurred because the season had many intense storms that lasted for a long period of time . Also it is important to note that this is the third highest ACE , only behind the 1992 and 2015 seasons respectively . 
- 
- = = 1990 storm names = = 
- 
- The following names were used for named storms that formed in the eastern Pacific in 1990 . No names were retired , so it was used again in the 1996 season . This is the same list used for the 1984 season . Storms were named Trudy and Vance for the first time in 1990 . Names that were not assigned are marked in gray . 
- One name from the Central Pacific list was used – Aka . It was the first usage for that name . 
- 
- 
- = Track and field = 
- 
- Track and field is a sport which includes athletic contests established on the skills of running , jumping , and throwing . The name is derived from the sport 's typical venue : a stadium with an oval running track enclosing a grass field where the throwing and jumping events take place . Track and field is categorised under the umbrella sport of athletics , which also includes road running , cross country running , and race walking . 
- The running events , which include sprints , middle and long @-@ distance events , race walking and hurdling , are won by the athlete with the fastest time . The jumping and throwing events are won by the athlete who achieves the greatest distance or height . Regular jumping events include long jump , triple jump , high jump and pole vault , while the most common throwing events are shot put , weight , javelin , discus and hammer . There are also " combined events " or " multi events " , such as pentathlon consisting of five events , heptathlon consisting of seven events , and decathlon consisting of ten <unk> every multi event , athletes participate in a combination of sprinting , jumping , throwing , middle distance , and long distance events . Most track and field events are individual sports with a single victor , but a number are relay races consisting of four members . Events are almost exclusively divided by gender , although both the men 's and women 's competitions are usually held at the same venue . 
- It is one of the oldest sports . In ancient times , it was an event held in conjunction with festivals and sports meets such as the Ancient Olympic Games in Greece . In modern times , the two most prestigious international track and field competitions are athletics competition at the Olympic Games and the IAAF World Championships in Athletics . The International Association of Athletics Federations is the international governing body . 
- Records are kept of the best performances in specific events , at world and national levels , right down to a personal level . However , if athletes are deemed to have violated the event 's rules or regulations , they are disqualified from the competition and their marks are erased . 
- In North America , the term track and field may be used to refer to other athletics events , such as the marathon , rather than strictly track @-@ based events . 
- 
- = = History = = 
- 
- The sport of track and field has its roots in human prehistory . Track and field @-@ style events are among the oldest of all sporting competitions , as running , jumping and throwing are natural and universal forms of human physical expression . The first recorded examples of organized track and field events at a sports festival are the Ancient Olympic Games . At the first Games in <unk> BC in <unk> , Greece , only one event was contested : the stadion <unk> . The scope of the Games expanded in later years to include further running competitions , but the introduction of the Ancient Olympic pentathlon marked a step towards track and field as it is recognized today — it comprised a five @-@ event competition of the long jump , javelin throw , discus throw , stadion <unk> , and wrestling . 
- Track and field events were also present at the <unk> Games in Greece around this period , and they spread to Rome in Italy around 200 BC . After the period of Classical antiquity ( in which the sport was largely Greco @-@ Roman influenced ) new track and field events began developing in parts of Northern Europe in the Middle Ages . The stone put and weight throw competitions popular among Celtic societies in Ireland and Scotland were precursors to the modern shot put and hammer throw events . One of the last track and field events to develop was the pole vault , which stemmed from competitions such as the <unk> contests in the Northern European <unk> in the 18th century . 
- <unk> modern track and field competitions , separate from general sporting festivals , were first recorded in the 19th century . These were typically organised by educational institutions , military organisations and sports clubs as competitions between rival establishments . <unk> in the English public schools were conceived as human equivalents of horse racing , fox hunting and hare <unk> , influenced by a Classics @-@ rich curriculum . The Royal Shrewsbury School Hunt is the oldest running club in the world , with written records going back to 1831 and evidence that it was established by 1819 . The school organised Paper Chase races in which runners followed a trail of paper <unk> left by two " foxes " ; even today <unk> runners are called " <unk> " and a race victory is a " kill " . The first definite record of Shrewsbury 's ( cross @-@ country ) Annual <unk> is in 1834 , making it the oldest running race of the modern era . The school also lays claim to the oldest track and field meeting still in existence , originating in the Second Spring Meeting first documented in 1840 . This featured a series of throwing and jumping events with mock horse races including the Derby Stakes , the <unk> Race and the Trial Stakes . Runners were entered by " owners " and named as though they were horses . 13 miles ( 21 km ) away and a decade later , the first Wenlock Olympian Games were held at Much Wenlock <unk> . Events at the 1851 Wenlock Games included a " half @-@ mile foot race " ( 805 m ) and a " leaping in distance " competition . 
- In 1865 , Dr William Penny <unk> of Wenlock helped set up the National Olympian Association , which held their first Olympian Games in 1866 at The Crystal Palace in London . This national event was a great success , attracting a crowd of over ten thousand people . In response , that same year the Amateur Athletic Club was formed and held a championship for " gentlemen amateurs " in an attempt to reclaim the sport for the educated elite . Ultimately the " <unk> " ethos of the <unk> won through and the <unk> was reconstituted as the Amateur Athletic Association in 1880 , the first national body for the sport of athletics . The AAA Championships , the de facto British national championships despite being for England only , have been held annually since 3 July 1880 with breaks only during two world wars and 2006 – 2008 . The AAA was effectively a global governing body in the early years of the sport , <unk> its rules for the first time . 
- Meanwhile , the United States began holding an annual national competition — the USA Outdoor Track and Field Championships — first held in 1876 by the New York Athletic Club . The establishment of general sports governing bodies for the United States ( the Amateur Athletic Union in 1888 ) and France ( the Union des <unk> <unk> de sports <unk> in 1889 ) put the sport on a formal footing and meant that international competitions became possible . 
- The establishment of the modern Olympic Games at the end of the 19th century marked a new high for track and field . The Olympic athletics programme , comprising track and field events plus a marathon race , contained many of the foremost sporting competitions of the 1896 Summer Olympics . The Olympics also consolidated the use of metric measurements in international track and field events , both for race distances and for measuring jumps and throws . The Olympic athletics programme greatly expanded over the next decades , and track and field contests remained among the Games ' most prominent . The Olympics was the elite competition for track and field , and only amateur <unk> could compete . Track and field continued to be a largely amateur sport , as this rule was strictly enforced : Jim Thorpe was stripped of his track and field medals from the 1912 Olympics after it was revealed that he had played baseball professionally . 
- That same year , the International Amateur Athletic Federation ( IAAF ) was established , becoming the international governing body for track and field , and it enshrined amateurism as one of its founding principles for the sport . The National Collegiate Athletic Association held their first Men 's Outdoor Track and Field Championship in 1921 , making it one of the most prestigious competitions for students , and this was soon followed by the introduction of track and field at the inaugural World Student Games in 1923 . The first continental track and field competition was the 1919 South American Championships , which was followed by the European Athletics Championships in 1934 . 
- Up until the early 1920s , track and field had been almost exclusively a male @-@ only pursuit . A growing women 's sports movement in Europe and North America led to the establishment of the Women 's World Games in 1921 and this ultimately caused the introduction of five track and field events for women in the athletics at the 1928 Summer Olympics . In China , women 's track and field events were being held in the 1920s , but were subject to criticism and disrespect from audiences . In 1923 , physical education advocate Zhang <unk> called for greater equality and participation of women in Chinese track and field . The rise of <unk> <unk> and her 1928 Olympic medal for Japan signified the growth of women 's track and field in East Asia . More women 's events were gradually introduced as years progressed ( although it was only towards the end of the century that the men 's and women 's programmes approached parity of events ) . <unk> an increasingly inclusive approach to the sport , major track and field competitions for disabled athletes were first introduced at the 1960 Summer Paralympics . 
- With the rise of numerous regional championships , as well as the growth in Olympic @-@ style multi @-@ sport events ( such as the Commonwealth Games and the Pan @-@ American Games ) , competitions between international track and field athletes became widespread . From the 1960s onwards , the sport gained more exposure and commercial appeal through television coverage and the increasing wealth of nations . After over half a century of amateurism , the amateur status of the sport began to be displaced by growing professionalism in the late 1970s . As a result , the Amateur Athletic Union was dissolved in the United States and it was replaced with a non @-@ amateur body solely focused on the sport of athletics : The Athletics Congress ( later USA Track and Field ) . The IAAF soon followed suit in 1982 , abandoning amateurism , and later removing all references to it from its name by <unk> itself as the International Association of Athletics Federations . The following year saw the establishment of the IAAF World Championships in Athletics — the first ever global competition just for athletics — which , with the Olympics , became one of track and field 's most prestigious competitions . 
- The profile of the sport reached a new high in the 1980s , with a number of athletes becoming household names ( such as Carl Lewis , <unk> <unk> , Sebastian Coe , <unk> <unk> and Florence Griffith @-@ <unk> ) . Many world records were broken in this period , and the added political element between competitors of the United States , East Germany , and the Soviet Union , in reaction to the Cold War , only served to <unk> the sport 's popularity . The increase in the commercial capacity of track and field was also met with developments in the application of sports science , and there were many changes to coaching methods , athlete 's diet regimes , training facilities and sports equipment . This was also accompanied by an increase in the use of performance @-@ enhancing drugs , and prominent cases , such as those of Olympic gold <unk> Ben Johnson and Marion Jones , damaged the public image and <unk> of the sport . 
- From the 1990s onwards , track and field became increasingly more professional and international , as the IAAF gained over two hundred member nations . The IAAF World Championships in Athletics became a fully professional competition with the introduction of prize money in 1997 , and in 1998 the IAAF Golden League — an annual series of major track and field meetings in Europe — provided a higher level of economic incentive in the form of a US $ 1 million <unk> . In 2010 , the series was replaced by the more lucrative IAAF Diamond League , a fourteen @-@ meeting series held in Europe , Asia , North America and the Middle East — the first ever worldwide annual series of track and field meetings . 
- 
- = = Events = = 
- 
- Track and field events are divided into three broad categories : track events , field events , and combined events . The majority of athletes tend to <unk> in just one event ( or event type ) with the aim of <unk> their performances , although the aim of combined events athletes is to become proficient in a number of disciplines . Track events involve running on a track over a specified distances and — in the case of the hurdling and steeplechase events — obstacles may be placed on the track . There are also relay races in which teams of athletes run and pass on a baton to their team member at the end of a certain distance . 
- There are two types of field events : jumps , and throws . In jumping competitions , athletes are judged on either the length or height of their jumps . The performances of jumping events for distance are measured from a board or marker , and any athlete <unk> this mark is judged to have fouled . In the jumps for height , an athlete must clear their body over a crossbar without knocking the bar off the supporting standards . The majority of jumping events are unaided , although athletes propel themselves vertically with purpose @-@ built sticks in the pole vault . 
- The throwing events involve hurling an implement ( such as a heavy weight , javelin or discus ) from a set point , with athletes being judged on the distance that the object is thrown . Combined events involve the same group of athletes <unk> a number of different track and field events . Points are given for their performance in each event and the athlete with the greatest points total at the end of all events is the winner . 
- Note : Events in italics are competed at indoor world championships only 
- 
- = = = Running = = = 
- 
- 
- = = = = <unk> = = = = 
- 
- Races over short distances , or sprints , are among the oldest running competitions . The first 13 editions of the Ancient Olympic Games featured only one event , the stadion race , which was a race from one end of the stadium to the other . <unk> events are focused around athletes reaching and sustaining their quickest possible running speed . Three sprinting events are currently held at the Olympics and outdoor World Championships : the 100 metres , 200 metres , and 400 metres . These events have their roots in races of imperial measurements that later changed to metric : the 100 m evolved from the 100 yard dash , the 200 m distances came from the <unk> ( or 1 / 8 of a mile ) , and the 400 m was the successor to the 440 yard dash or quarter @-@ mile race . 
- At the professional level , sprinters begin the race by assuming a <unk> position in the starting blocks before leaning forward and gradually moving into an upright position as the race progresses and momentum is gained . Athletes remain in the same lane on the running track throughout all sprinting events , with the sole exception of the 400 m indoors . Races up to 100 m are largely focused upon acceleration to an athlete 's maximum speed . All sprints beyond this distance increasingly incorporate an element of endurance . Human <unk> dictates that a runner 's near @-@ top speed cannot be maintained for more than thirty seconds or so because <unk> acid builds up once leg muscles begin to suffer oxygen <unk> . Top speed can only be maintained for up to 20 metres . 
- The 60 metres is a common indoor event and indoor world championship event . Less @-@ common events include the 50 metres , 55 metres , 300 metres and 500 metres which are run in some high school and collegiate competitions in the United States . The 150 metres , though rarely competed , has a star @-@ studded history : Pietro <unk> set a world best in 1983 , Olympic champions Michael Johnson and <unk> Bailey went head @-@ to @-@ head over the distance in 1997 , and <unk> <unk> improved <unk> 's record in 2009 . 
- 
- = = = = Middle distance = = = = 
- 
- The most common middle @-@ distance track events are the 800 metres , 1500 metres and mile run , although the 3000 metres may also be classified as a middle @-@ distance event . The 880 yard run , or half mile , was the <unk> of the 800 m distance and it has its roots in competitions in the United Kingdom in the 1830s . The 1500 m came about as a result of running three laps of a 500 m track , which was commonplace in continental Europe in the 20th century . 
- Runners start the race from a standing position along a curved starting line and after hearing the starter 's pistol they head towards the innermost track to follow the quickest route to the finish . In 800 m races athletes begin at a <unk> starting point before the turn in the track and they must remain in their lanes for the first 100 m of the race . This rule was introduced to reduce the amount of physical jostling between runners in the early stages of the race . <unk> , these middle @-@ distance events demand that athletes have good <unk> and <unk> energy producing systems , and also that they have strong speed endurance . 
- The 1500 m and mile run events have historically been some of the most prestigious track and field events . Swedish rivals <unk> <unk> and Arne <unk> broke each other 's 1500 m and mile world records on a number of occasions in the 1940s . The prominence of the distances were maintained by Roger <unk> , who ( in 1954 ) was the first to run the long @-@ elusive four @-@ minute mile , and Jim <unk> 's exploits served to popularise interval training . Races between British rivals Sebastian Coe , Steve <unk> and Steve <unk> characterised middle @-@ distance running in the 1980s . From the 1990s onwards , North Africans such as <unk> <unk> of Algeria and <unk> El <unk> of Morocco came to dominate the 1500 and mile events . 
- Beyond the short distances of sprinting events , factors such as an athlete 's reactions and top speed becomes less important , while qualities such as pace , race tactics and endurance become more so . 
- 
- = = = = Long distance = = = = 
- 
- There are three common long distance running events in track and field competitions : 3000 metres , 5000 metres and 10 @,@ 000 metres . The latter two races are both Olympic and World Championship events outdoors , while the 3000 m is held at the IAAF World Indoor Championships . The 5000 m and 10 @,@ 000 m events have their historical roots in the 3 @-@ mile and 6 @-@ mile races . The 3000 m was historically used as a women 's long distance event , entering the World Championship programme in 1983 and Olympic programme in 1984 , but this was abandoned in favour of a women 's 5000 m event in 1995 . 
- In terms of competition rules and physical demands , long distance track races have much in common with middle @-@ distance races , except that pacing , <unk> , and race tactics become much greater factors in performances . However , a number of athletes have achieved success in both <unk> and long @-@ distance events , including <unk> <unk> who set world records from 1500 m to 5000 m . The use of pace @-@ <unk> in long distance events is very common at the elite level , although they are not present at championship level competitions as all qualified competitors want to win . 
- The long distance track events gained popularity in the 1920s by the achievements of the " Flying <unk> " , such as multiple Olympic champion <unk> <unk> . The successes of Emil <unk> in the 1950s promoted intense interval training methods , but Ron Clarke 's world record @-@ breaking feats established the importance of natural training and even @-@ paced running . The 1990s saw the rise of North and East African runners in long distance events . Kenyan and Ethiopian athletes , in particular , have since remained dominant in these events . 
- 
- = = = = Relay races = = = = 
- 
- Relay races are the only track and field event in which a team of runners directly compete against other teams . Typically , a team is made up of four runners of the same sex . Each runner completes their specified distance ( referred to as a leg ) before handing over a baton to a team mate , who then begins their leg upon receiving the baton . There is usually a designated area where athletes must exchange the baton . Teams may be disqualified if they fail to complete the change within the area , or if the baton is dropped during the race . A team may also be disqualified if its runners are deemed to have <unk> impeded other competitors . 
- Relay races emerged in the United States in the 1880s as a variation on charity races between <unk> , who would hand a red pennant on to team mates every 300 yards . There are two very common relay events : the 4 × 100 metres relay and the 4 × 400 metres relay . Both events entered the Olympic programme at the 1912 Summer Games after a one @-@ off men 's medley relay featured in 1908 Olympics . The 4 × 100 m event is run strictly within the same lane on the track , meaning that the team collectively runs one complete circuit of the track . Teams in a 4 × 400 m event remain in their own lane until the runner of the second leg passes the first bend , at which point runners can leave their lanes and head towards the inner @-@ most part of the circuit . For the second and third baton change overs , team mates must align themselves in respect of their team position – leading teams take the inner lanes while team mates of the slower teams must await the baton on outer lanes . 
- The IAAF keeps world records for five different types of track relays . As with 4 × 100 m and 4 × 400 m events , all races comprise teams of four athletes running the same distances , with the less commonly contested distances being the 4 × 200 m , 4 × 800 m and 4 × 1500 m relays . Other events include the distance medley relay ( comprising legs of 1200 m , 400 m , 800 m , and 1600 m ) , which is frequently held in the United States , and a sprint relay , known as the Swedish medley relay , which is popular in Scandinavia and held at the World Youth Championships in Athletics programme . Relay events have significant participation in the United States , where a number of large meetings ( or relay carnivals ) are focused almost exclusively on relay events . 
- 
- = = = = <unk> = = = = 
- 
- Races with hurdles as obstacles were first popularised in the 19th century in England . The first known event , held in 1830 , was a variation of the 100 @-@ yard dash that included heavy wooden barriers as obstacles . A competition between the Oxford and Cambridge Athletic <unk> in 1864 refined this , holding a 120 @-@ yard race ( 110 m ) with ten hurdles of 3 @-@ foot and 6 inches ( 1 @.@ 06 m ) in height ( each placed 10 yards ( 9 m ) apart ) , with the first and final hurdles 15 yards from the start and finish , respectively . French organisers adapted the race into metric ( adding 28 cm ) and the <unk> of this race , the men 's 110 metres hurdles , has remained largely unchanged . The origin of the 400 metres hurdles also lies in Oxford , where ( around 1860 ) a competition was held over 440 yards and twelve 1 @.@ 06 m high wooden barriers were placed along the course . The modern regulations stem from the 1900 Summer Olympics : the distance was fixed to 400 m while ten 3 @-@ foot ( 91 @.@ 44 cm ) hurdles were placed 35 m apart on the track , with the first and final hurdles being 45 m and 40 m away from the start and finish , respectively . Women 's hurdles are slightly lower at 84 cm ( 2 ft 9 in ) for the 100 m event and 76 cm ( 2 ft 6 in ) for the 400 m event . 
- By far the most common events are the 100 metres hurdles for women , 110 m hurdles for men and 400 m hurdles for both sexes . The men 's 110 m has been featured at every modern Summer Olympics while the men 's 400 m was introduced in the second edition of the Games . Women 's initially competed in the 80 metres hurdles event , which entered the Olympic programme in 1932 . This was extended to the 100 m hurdles at the 1972 Olympics , but it was not until 1984 that a women 's 400 m hurdles event took place at the Olympics ( having been introduced at the 1983 World Championships in Athletics the previous year ) . 
- Outside of the hurdles events , the steeplechase race is the other track and field event with obstacles . Just as the hurdling events , the steeplechase finds its origin in student competition in Oxford , England . However , this event was born as a human variation on the original steeplechase competition found in horse racing . A steeplechase event was held on a track for the 1879 English championships and the 1900 Summer Olympics featured men 's 2500 m and 4000 m steeplechase races . The event was held over various distances until the 1920 Summer Olympics marked the rise of the 3000 metres steeplechase as the standard event . The IAAF set the standards of the event in 1954 , and the event is held on a 400 m circuit that includes a water jump on each lap . Despite the long history of men 's steeplechase in track and field , the women 's steeplechase only gained World Championship status in 2005 , with its first Olympic appearance coming in 2008 . 
- 
- = = = Jumping = = = 
- 
- 
- = = = = Long jump = = = = 
- 
- The long jump is one of the oldest track and field events , having its roots as one of the events within the ancient Greek pentathlon contest . The athletes would take a short run up and jump into an area of dug up earth , with the winner being the one who jumped farthest . Small weights ( <unk> ) were held in each hand during the jump then swung back and dropped near the end to gain extra momentum and distance . The modern long jump , standardised in England and the United States around 1860 , bears resemblance to the ancient event although no weights are used . Athletes sprint along a length of track that leads to a jumping board and a sandpit . The athletes must jump before a marked line and their achieved distance is measured from the nearest point of sand disturbed by the athlete 's body . 
- The athletics competition at the first Olympics featured a men 's long jump competition and a women 's competition was introduced at the 1948 Summer Olympics . Professional long jumpers typically have strong acceleration and sprinting abilities . However , athletes must also have a consistent <unk> to allow them to take off near the board while still maintaining their maximum speed . In addition to the traditional long jump , a standing long jump contest exists which requires that athletes leap from a static position without a run @-@ up . A men 's version of this event featured on the Olympic programme from 1900 to 1912 . 
- 
- = = = = Triple jump = = = = 
- 
- Similar to the long jump , the triple jump takes place on a track heading towards a sandpit . Originally , athletes would hop on the same leg twice before jumping into the pit , but this was changed to the current " hop , step and jump " pattern from 1900 onwards . There is some dispute over whether the triple jump was contested in ancient Greece : while some historians claim that a contest of three jumps occurred at Ancient Games , others such as Stephen G. Miller believe this is incorrect , suggesting that the belief stems from a <unk> account of <unk> of Croton having jumped 55 ancient feet ( around 16 @.@ 3 m ) . The Book of Leinster , a 12th @-@ century Irish manuscript , records the existence of <unk> @-@ <unk> ( triple jump ) contests at the ancient <unk> Games . 
- The men 's triple jump competition has been ever @-@ present at the modern Olympics , but it was not until 1993 that a women 's version gained World Championship status and went on to have its first Olympic appearance three years later . The men 's standing triple jump event featured at the Olympics in 1900 and 1904 , but such competitions have since become very uncommon , although it is still used as a non @-@ competitive exercise drill . 
- 
- = = = = High jump = = = = 
- 
- The first recorded instances of high jumping competitions were in Scotland in the 19th century . Further competitions were organised in 1840 in England and in 1865 the basic rules of the modern event were standardised there . Athletes have a short run up and then take off from one foot to jump over a horizontal bar and fall back onto a <unk> landing area . The men 's high jump was included in the 1896 Olympics and a women 's competition followed in 1928 . 
- Jumping technique has played a significant part in the history of the event . High jumpers typically cleared the bar feet first in the late 19th century , using either the <unk> , Eastern cut @-@ off or Western roll technique . The <unk> technique became prominent in the mid @-@ 20th century , but Dick <unk> overturned tradition by pioneering a backwards and head @-@ first technique in the late 1960s – the <unk> <unk> – which won him the gold at the 1968 Olympics . This technique has become the overwhelming standard for the sport from the 1980s onwards . The standing high jump was contested at the Olympics from 1900 to 1912 , but is now relatively uncommon outside of its use as an exercise drill . 
- 
- = = = = Pole vault = = = = 
- 
- In terms of sport , the use of poles for vaulting distances was recorded in <unk> contests in the <unk> area of Europe , and vaulting for height was seen at <unk> competitions in Germany in the 1770s . One of the earliest recorded pole vault competitions was in Cumbria , England in 1843 . The basic rules and technique of the event originated in the United States . The rules required that athletes do not move their hands along the pole and athletes began clearing the bar with their feet first and <unk> so that the stomach faces the bar . <unk> poles were introduced in the 20th century and a metal box in the runway for planting the pole became standard . Landing <unk> were introduced in the mid @-@ 20th century to protect the athletes who were clearing increasingly greater heights . 
- The modern event sees athletes run down a strip of track , plant the pole in the metal box , and vault over the horizontal bar before letting go of the pole and falling backwards onto the landing <unk> . While earlier versions used wooden , metal or bamboo , modern poles are generally made from artificial materials such as fibreglass or carbon fibre . The pole vault has been an Olympic event since 1896 for men , but it was over 100 years later that the first women 's world championship competition was held at the 1997 IAAF World Indoor Championships . The first women 's Olympic pole vaulting competition occurred in 2000 . 
- 
- = = = <unk> = = = 
- 
- Track and field contains some of the foremost kinds of throwing sports , and the four major disciplines are the only pure throwing events to feature at the Olympic Games . 
- 
- = = = = Shot put = = = = 
- 
- The genesis of the shot put can be traced to pre @-@ historic competitions with rocks : in the Middle ages the stone put was known in Scotland and the <unk> was recorded in Switzerland . In the 17th century , <unk> throwing competitions within the English military provided a precursor to the modern sport . The term " shot " originates from the use of round shot @-@ style ammunition for the sport . The modern rules were first laid out in 1860 and required that competitors take legal throws within a square throwing area of seven feet ( 2 @.@ 13 m ) on each side . This was amended to a circle area with a seven @-@ foot diameter in 1906 , and the weight of the shot was standardised to 16 pounds ( 7 @.@ 26 kg ) . <unk> technique was also refined over this period , with bent arm throws being banned as they were deemed too dangerous and the side @-@ step and throw technique arising in the United States in 1876 . 
- The shot put has been an Olympic sport for men since 1896 and a women 's competition using a 4 kg ( 8 @.@ 82 lb ) shot was added in 1948 . Further throwing techniques have arisen since the post @-@ war era : in the 1950s Parry O 'Brien popularised the 180 degree turn and throw technique commonly known as the " <unk> " , breaking the world record 17 times along the way , while <unk> <unk> and Brian Oldfield introduced the " spin " or rotational technique in 1976 . 
- 
- = = = = <unk> throw = = = = 
- 
- As one of the events within the ancient pentathlon , the history of the discus throw dates back to 708 BC . In ancient times a heavy circular disc was thrown from a set standing position on a small pedestal , and it was this style that was revived for the 1896 Olympics . This continued until the 1906 <unk> Games in Athens , which featured both the ancient style and the increasingly popular modern style of turning and throwing . By the 1912 Olympics , the ancient standing throw style had fallen into disuse and contests starting within a 2 @.@ 5 m <unk> throwing area became the standard . The discus implement was standardised to 2 kg ( 4 @.@ 4 pounds ) in weight and 22 cm ( 8 inches ) in diameter in 1907 . The women 's discus was among the first women 's events on the Olympic programme , being introduced in 1928 . 
- 
- = = = = <unk> throw = = = = 
- 
- As an implement of war and hunting , javelin throwing began in prehistoric times . Along with the discus , the javelin was the second throwing event in the ancient Olympic pentathlon . Records from 708 BC show two javelin competition types co @-@ existing : throwing at a target and throwing the javelin for distance . It was the latter type from which the modern event derives . In ancient competitions , athletes would <unk> an <unk> ( thin leather strip ) around the javelin that acted as a sling to facilitate extra distance . The javelin throw gained much popularity in Scandinavia in the late 18th century and athletes from the region are still among the most dominant throwers in men 's competitions . The modern event features a short run up on a track and then the <unk> releases the javelin before the foul line . 
- The first Olympic men 's javelin throw contest was held in 1908 and a women 's competition was introduced in 1932 . The first <unk> were made of various types of wood , but in the 1950s , former athlete <unk> Held introduced a hollow javelin , then a metal javelin , both of which increased throwers performances . Another former athlete , <unk> <unk> invented the rough @-@ tailed javelin and throws reached in excess of 100 m – edging towards the limits of stadia . The distances and the increasing number of horizontal landings led the IAAF to redesign the men 's javelin to reduce distance and increase the implement 's downward pitching moment to allow for easier measurement . Rough @-@ tailed designs were banned in 1991 and all marks achieved with such <unk> were removed from the record books . The women 's javelin underwent a similar redesign in 1999 . The current javelin specifications are 2 @.@ 6 to 2 @.@ 7 m in length and 800 grams in weight for men , and 2 @.@ 2 to 2 @.@ 3 m and 600 g for women . 
- 
- = = = = <unk> throw = = = = 
- 
- The earliest recorded precursors to the modern hammer throw stem from the <unk> Games around 1800 BC , which featured events such as throwing either a weight attached to a rope , a large rock on a wooden handle , or even a chariot wheel on a wooden axle . Other ancient competitions included throwing a cast iron ball attached to a wooden handle – the root of the term " hammer throw " due to their resemblance to the tools . In 16th century England , contests involving the throwing of actual blacksmith 's <unk> were recorded . The hammer implement was standardised in 1887 and the competitions began to resemble the modern event . The weight of the metal ball was set at 16 pounds ( 7 @.@ 26 kg ) while the attached wire had to measure between 1 @.@ 175 m and 1 @.@ 215 m . 
- The men 's hammer throw became an Olympic event in 1900 but the women 's event – using a 4 kg ( 8 @.@ 82 lb ) weight – was not widely competed until much later , finally featuring on the women 's Olympic programme in 2000 . The distances thrown by male athletes became greater from the 1950s onwards as a result of improved equipment using the denser metals , a switch to concrete throwing areas , and more advanced training techniques . Professional hammer throwers as historically large , strong , <unk> athletes . However , qualities such as refined technique , speed and flexibility have become increasingly important in the modern era as the legal throwing area has been reduced from 90 to 34 @.@ 92 degrees and throwing technique involves three to four controlled <unk> . 
- 
- = = = Combined events = = = 
- 
- Combined ( or multi @-@ discipline ) events are competitions in which athletes participate in a number of track and field events , earning points for their performance in each event , which adds to a total points score . <unk> , the most common combined events are the men 's decathlon ( ten events ) and the women 's heptathlon ( seven events ) . Due to stadium limitations , indoor combined events competition have a reduced number of events , resulting in the men 's heptathlon and the women 's pentathlon . Athletes are allocated points based on an international @-@ standard points scoring system , such as the decathlon scoring table . 
- The Ancient Olympic pentathlon ( comprising long jump , javelin , discus , the stadion race and wrestling ) was a precursor to the track and field combined events and this ancient event was restored at the 1906 Summer Olympics ( <unk> Games ) . A men 's all @-@ around was held at the 1904 Summer Olympics , contested between five American and two British athletes . 
- 
- = = Stadium = = 
- 
- 
- = = = Outdoor = = = 
- 
- The term track and field is intertwined with the stadiums that first hosted such competitions . The two basic features of a track and field stadium are the outer oval @-@ shaped running track and an area of turf within this track — the field . In earlier competitions , track lengths varied : the <unk> Stadium measured 333 @.@ 33 metres at the 1896 Summer Olympics , while at the 1904 Olympics the distance was a third of a mile ( <unk> @.@ 45 m ) at Francis Field . As the sport developed , the IAAF standardised the length to 400 m and stated that the tracks must be split into six to eight running lanes . <unk> widths for the lanes were established , as were regulations regarding the <unk> of the track . Tracks made of flattened <unk> were popular in the early 20th century but synthetic tracks became standard in the late 1960s . <unk> 's <unk> track ( an all @-@ weather running track of polyurethane ) gained popularity after its use at the 1968 US Olympic Trials and the 1968 Summer Olympics and it began the process in which synthetic tracks became the standard for the sport . Many track and field stadiums are multi @-@ purpose stadiums , with the running track surrounding a field built for other sports , such as the various types of football . 
- The field of the stadium combines a number of elements for use in the jumping and throwing events . The long jump and triple jump areas comprise a straight , narrow 40 @-@ metre running track with a sandpit at one or both ends . <unk> are measured from a take off board — typically a small strip of wood with a <unk> marker attached — which ensures athletes jump from behind the measurement line . The pole vault area is also a 40 @-@ metre running track and has an <unk> in the ground ( the box ) where <unk> plant their poles to propel themselves over a crossbar before falling onto <unk> landing mats . The high jump is a stripped down version of this , with an open area of track or field that leads to a crossbar with a square area of landing mats behind it . 
- The four throwing events generally all begin on one side of the stadium . The javelin throw typically takes place on a piece of track that is central and parallel to the <unk> of the main running track . The javelin throwing area is a sector shape frequently across the <unk> ( sports field ) in the middle of the stadium , ensuring that the javelin has a minimal chance of causing damage or injury . The discus throw and hammer throw contests begin in a tall metal cage usually situated in one of the corners of the field . The cage reduces the danger of implements being thrown out of the field of play and throws travel <unk> across the field in the centre of the stadium . The shot put features a circular throwing area with a toe board at one end . The throwing area is a sector . Some stadia also have a water jump area on one side of the field specifically for steeplechase races . 
- 
- = = = Indoor = = = 
- 
- Basic indoor venues may be adapted <unk> , which can easily accommodate high jump competitions and short track events . Full @-@ size indoor arenas ( i.e. those fully equipped to host all events for the World Indoor Championships ) bear similarities with their outdoor equivalents . Typically , a central area is surrounded by a 200 @-@ metre oval track with four to eight lanes . The track can be banked at the turns to allow athletes to run around the radius more comfortably . There is also a second running track going straight across the field area , parallel to the <unk> of the main circuit . This track is used for the 60 metres and 60 metres hurdles events , which are held almost exclusively indoors . 
- Another common adaptation is a 160 @-@ yard track ( 11 laps to a mile ) that fits into a common basketball court sized arena . This was quite popular when races were held at imperial distances , which gradually was phased out by different organizations in the 1970s and 1980s . Examples of this configuration include the <unk> Games at Madison Square Garden , and the <unk> <unk> formerly held in the Los Angeles Sports Arena . 
- All four of the common jumping events are held at indoor venues . The long and triple jump areas run alongside the central 60 m track and are mostly identical in form to their outdoor counterparts . The pole vault track and landing area are also alongside the central running track . Shot put ( or weight throw ) is the only throwing event held indoors due to size restrictions . The throwing area is similar to the outdoor event , but the landing sector is a rectangular section surrounded by netting or a stop barrier . 
- In addition to hosting the World Indoor Championships , the IAAF has hosted the IAAF World Indoor Tour since 2016 . 
- 
- = = Rules = = 
- 
- 
- = = = Track rules = = = 
- 
- The rules of track athletics or of track events in athletics as observed in most international athletics competitions are set by the Competition Rules of the International Association of Athletics Federations ( IAAF ) . The most recent complete set of rules is the 2009 rules that relate only to competitions in 2009 . Key rules of track events are those regarding starting , running and finishing . 
- 
- = = = = Starting = = = = 
- 
- The start of a race is marked by a white line 5 cm wide . In all races that are not run in lanes the start line must be curved , so that all the athletes start the same distance from the finish . Starting blocks may be used for all races up to and including 400 m ( including the first leg of the 4 × 100 m and 4 × 400 m ) and may not be used for any other race . No part of the starting block may overlap the start line or extend into another lane . 
- All races must be started by the report of the starter 's gun or approved starting apparatus fired upwards after they have <unk> that athletes are steady and in the correct starting position . An athlete may not touch either the start line or the ground in front of it with their hands or feet when on their marks . 
- For sprint races up to 400 m , the starter gives two commands : " on your marks " to instruct athletes to approach the start line , followed by " set " to advise the athletes that the start of the race is imminent . The commands of the starter are typically given in the native language in national competitions , or in English or French in international competitions . Once all athletes are set in their starting position , the gun or an approved starting apparatus must be fired or activated . If the starter is not satisfied that all are ready to proceed , the athletes may be called out of the blocks and the process started over . 
- There are different types of starts for races of different distances . <unk> and long @-@ distance races mainly use the waterfall start . This is when all athletes begin on a curved line that moves farther out at the outer edge of the track . <unk> are allowed to move towards the inside lane right away , as long as it is safe to do so . For some middle @-@ distance races , such as 800 m , each athlete starts in their own lane . Once the gun fires , they must run in the lane they began in until markers on the track notify them it is time to move towards the inside lane . For sprint races , athletes begin in start blocks and must stay in their own lane for the entire race . 
- An athlete , after assuming a final set position , may not commence his starting motion until after receiving the report of the gun , or approved starting apparatus . If , in the judgment of the starter or <unk> , he does so any earlier , it is considered a false start . It is deemed a false start if , in the judgment of the starter an athlete fails to comply with the commands " on your marks " or " set " as appropriate after a reasonable time ; or an athlete after the command " on your marks " <unk> other athletes in the race through sound or otherwise . If the runner is in the " set " position and moves , then the runner is also disqualified . As of 2010 , any athlete making a false start is disqualified . 
- In International elite competition , electronically <unk> starting blocks sense the reaction time of the athletes . If the athlete reacts in less than 0 @.@ 1 second , an alert sounds for a recall starter and the offending athlete is guilty of a false start . 
- 
- = = = = Running the race = = = = 
- 
- For sprinting events ( bar the 4 × 400 m relay and the indoor 400 metres ) , each athlete must run the race within their allocated lane from start to finish . If an athlete leaves their lane or steps on the line <unk> each lane the athlete will be disqualified . Lane rules also apply for initial periods of other track races , for example , the beginning of the 800 m . Similar rules apply for longer distance races when a large field of athletes is present and separate starting points are designated , with the field merging into one group shortly after the starting phase . 
- Any athlete who <unk> or <unk> another athlete , in a way that <unk> his progress , should be disqualified from that event . However , if an athlete is pushed or forced by another person to run outside his lane , and if no material advantage is gained , the athlete should not be disqualified . 
- 
- = = = = The finish = = = = 
- 
- The finish of a race is marked by a white line 5 cm wide . The finishing position of athletes is determined by the order in which any part of their torso ( as distinguished from the head , neck , arms , legs , hands or feet ) reaches the vertical plane of the nearer edge of the finish line . Fully automatic timing systems ( photo timing ) are becoming more and more common at increasingly lower levels of track meets , improving the accuracy , while eliminating the need for eagle @-@ eyed officials on the finish line . Fully automatic timing ( <unk> ) is required for high level meets and any time a ( sprint ) record is set ( though distance records can be accepted if timed by three independent <unk> ) . 
- With the accuracy of the timing systems , ties are rare . Ties between different athletes are resolved as follows : In determining whether there has been a tie in any round for a qualifying position for the next round based on time , a judge ( called the chief photo finish judge ) must consider the actual time recorded by the athletes to one <unk> of a second . If the judge decides that there has been a tie , the tying athletes must be placed in the next round or , if that is not practicable , lots must be drawn to determine who must be placed in the next round . In the case of a tie for first place in any final , the referee decides whether it is practicable to arrange for the athletes so tying to compete again . If he decides it is not , the result stands . Ties in other <unk> remain . 
- 
- = = = Field rules = = = 
- 
- In general , most field events allow a competitor to take their attempt individually , under theoretically the same conditions as the other competitors in the competition . Each attempt is measured to determine who achieved the longest distance . 
- <unk> jumps ( high jump and pole vault ) set a bar at a particular height . The competitor must clear the bar without knocking it off the standards that are holding the bar ( flat ) . Three failures in a row ends the competitor 's participation in the event . The competitor has the option to <unk> their attempt , which can be used to strategic advantage ( of course that advantage is lost if the competitor misses ) . A pass could be used to save energy and avoid taking a jump that would not improve their position in the standings . After all competitors have either cleared , passed or failed their attempts at a height , the bar goes up . The amount the bar goes up is predetermined before the competition , though when one competitor remains , that competitor may choose their own heights for the remaining attempts . A record is kept of each attempt by each competitor . After all competitors have taken their attempts , the one jumping the highest is the winner , and so on down the other competitors in the event . Ties are broken by first , the number of attempts taken at the highest height ( fewest wins ) , and then if still tied , by the total number of misses in the competition as a whole . The bar does not go back to a lower height except to break a tie for first place or a qualifying position . If those critical positions are still tied after applying the <unk> , all tied competitors take a fourth jump at the last height . If they still miss , the bar goes down one <unk> where they again jump . This process continues until the tie is broken . 
- <unk> jumps ( long jump and triple jump ) and all throws must be initiated behind a line . In the case of horizontal jumps , that line is a straight line perpendicular to the runway . In the case of throws , that line is an arc or a circle . Crossing the line while initiating the attempt <unk> the attempt — it becomes a foul . All landings must occur in a sector . For the jumps , that is a sand filled pit , for throws it is a defined sector . A throw landing on the line on the edge of sector is a foul ( the inside edge of the line is the outside edge of the sector ) . Assuming a proper attempt , officials measure the distance from the closest landing point back to the line . The measuring tape is carefully <unk> to the shortest distance between the point and the line . To accomplish this , the tape must be perfectly perpendicular to the take off line in jumps , or is pulled through the center point of the arc for throws . The officials at the landing end of the tape have the zero , while the officials at the point of initiation measure and record the length . Whenever a record ( or potential record ) occurs , that measurement is taken ( again ) with a steel tape , and observed by at least three officials ( plus usually the meet referee ) . Steel tapes are easily bent and damaged , so are not used to measure everyday competitions . For major competitions , each competitor gets three tries . The top competitors ( usually 8 or 9 depending on that competition 's rules or the number of lanes on the track ) gets three more tries . At that level of competition , the order of competitors for those final three attempts are set — so the competitor in first place at the end of the third round is last , while the last competitor to qualify goes first . Some meets <unk> the competition order again for the final round , so the final attempt is taken by the leader at that point . At other competitions , meet management may choose to limit all competitors to four or three attempts . Whatever the format , all competitors get an equal number of attempts . 
- 
- = = Equipment = = 
- 
- Men and women have different weights for their throwing implements – men 's javelin is 800 grams compared to 600 for women , men 's weight throw is 35 pounds compared to 20 for women , men 's discus is 2 kilograms to women 's 1 , men 's shot put is 16 pounds compared to 8 pounds for women , and men 's hammer throw is also 16 pounds to the women 's 8 . Additionally , men 's high hurdles are at height of 42 inches compared to women 's hurdles which are 33 inches . For the intermediate hurdles ( 400 meter hurdles ) , the men 's hurdle height is 36 inches compared to 30 inches for women . 
- 
- = = <unk> = = 
- 
- The international governance of track and field falls under the jurisdiction of athletics organisations . The International Association of Athletics Federations is the global governing body for track and field , and athletics as a whole . The governance of track and field at continental and national level is also done by athletics bodies . Some national federations are named after the sport , including USA Track & Field and the Philippine Amateur Track & Field Association , but these organisations govern more than just track and field and are in fact athletics governing bodies . These national federations regulate sub @-@ national and local track and field clubs , as well as other types of running clubs . 
- 
- = = <unk> = = 
- 
- 
- = = = Olympics , Paralympics and world championships = = = 
- 
- The major global track and field competitions are both held under the scope of athletics . Track and field contests make up the majority of events on the Olympic and Paralympic athletics programmes , which occur every four years . Track and field events have held a prominent position at the Summer Olympics since its inception in 1896 , and the events are typically held in the main stadium of the Olympic and Paralympic Games . Events such as the 100 metres receive some of the highest levels of media coverage of any Olympic or Paralympic sporting event . 
- The other two major international competition for track and field are organised by the IAAF . The IAAF had selected the Olympic competition as its world championship event in 1913 , but a separate world championships for athletics alone was first held in 1983 – the IAAF World Championships in Athletics . The championships comprised track and field competitions plus the marathon and <unk> competitions . Initially , this worked on a <unk> basis but , after 1991 , it changed to a biennial format . In terms of indoor track and field , the IAAF World Indoor Championships in Athletics has been held every two years since 1985 and this is the only world championships that consists of solely track and field events . 
- 
- = = = Other championships = = = 
- 
- Similar to the event programmes at the Olympics , Paralympics and World Championships , track and field forms a significant part of continental championships . The South American Championships in Athletics , created in 1919 , was the first continental championships and the European Athletics Championships became the second championships of this type in 1934 . The Asian Athletics Championships and African Championships in Athletics were created in the 1970s and Oceania started its championships in 1990 . 
- There are also indoor continental competitions in Europe ( European Athletics Indoor Championships ) and Asia ( Asian Indoor Athletics Championships ) . There has not been a consistent championships for all of North America , which may be ( in part ) due to the success of both the Central American and Caribbean Championships and the USA Outdoor Track and Field Championships . Most countries have a national championship in track and field and , for athletes , these often play a role in gaining selection into major competitions . Some countries hold many track and field championships at high school and college @-@ level , which help develop younger athletes . Some of these have gained significant exposure and prestige , such as the NCAA Track and Field Championship in the United States and the Jamaican High School Championships . However , the number and status of such competitions significantly vary from country to country . 
- 
- = = = Multi @-@ sport events = = = 
- 
- <unk> the role that track and field events have at the Summer Olympics and Paralympics , the sport is featured within the athletics programmes of many major multi @-@ sport events . Among some of the first of these events to follow the Olympic @-@ style model were the World University Games in 1923 , the Commonwealth Games in 1930 , and the <unk> Games in 1932 . The number of major multi @-@ sport events greatly increased during the 20th century and thus did the number of track and field events held within them . Typically , track and field events are hosted at the main stadium of the games . 
- After the Olympic and Paralympic Games , the most prominent events for track and field athletes include the three IOC @-@ sanctioned continental games : the All @-@ Africa Games , Asian Games , and the Pan American Games . Other games such as the Commonwealth Games and Summer <unk> , and World Masters Games have significant participation from track and field athletes . Track and field is also present at the national games level , with competitions such as the Chinese National Games serving as the most prestigious national competition for domestic track and field athletes . 
- 
- = = = Meetings = = = 
- 
- One @-@ day track and field meetings form the most common and seasonal aspect of the sport – they are the most basic level of track and field competition . Meetings are generally organised annually either under the patronage of an educational institution or sports club , or by a group or business that serves as the meeting promoter . In the case of the former , athletes are selected to represent their club or institution . In the case of privately run or independent meetings , athletes participate on an invitation @-@ only basis . 
- The most basic type of meetings are all @-@ <unk> track meets , which are largely small , local , informal competitions that allow people of all ages and abilities to compete . As meetings become more organized they can gain official <unk> by the local or national association for the sport . 
- At the professional level , meetings began to offer significant financial <unk> for all athletes in the 1990s in Europe with the creation of the " Golden Four " competition , comprising meetings in <unk> , Brussels , Berlin and Oslo . This expanded and received IAAF backing as the IAAF Golden League in 1998 , which was later supplemented by the branding of selected meetings worldwide as the IAAF World Athletics Tour . In 2010 , the Golden League idea was expanded globally as the IAAF Diamond League series and this now forms the top tier of professional one @-@ day track and field meetings . 
- 
- = = Records = = 
- 
- Athletes performances are timed or measured at virtually all track and field competitions . Doing so can not only serve as a way of determining the winner in an event , but it can also be used for historical comparison ( i.e. a record ) . A large variety of record types exist and men 's and women 's performances are recorded separately . The foremost types of records organise athlete 's performances by the region they represent — beginning with national records , then continental records , up to the global or world record level . National governing bodies control the national record lists , the area associations organise their respective continental lists , and the IAAF <unk> world records . 
- The IAAF <unk> track and field world records if they meet their set criteria . The IAAF first published a world records list in 1914 , initially for men 's events only . There were 53 recognised records in running , hurdling and relay , and 12 field records . World records in women 's events began in 1936 as more events were gradually added to the list , but significant changes were made in the late 1970s . First , all records in imperial measurements were abandoned in 1976 , with the sole exceptional being the mile run due to the prestige and history of the event . The following year , all world records in sprint events would only be recognised if fully automatic electronic timing was used ( as opposed to the traditional hand @-@ timing <unk> method ) . In 1981 , electronic timing was made compulsory for all world record runs in track and field , with times being recorded to within one hundredth of a second . Two additional types of world record were introduced in 1987 : world records for indoor competitions , and world records for junior athletes under 20 years old . 
- The next most important record type are those achieved at a specific competition . For example , the Olympic records represent the best performances by athletes at the Summer Olympics . All major championships and games have their relevant competition records and a large number of track and field meetings keep a note of their meet records . Other record types include : stadium records , records by age range , records by disability , and records by institution or organisation . Cash bonuses are usually offered to athletes if they break significant records , as doing so can generate greater interest and public attendance in track and field competitions . 
- 
- = = Doping = = 
- 
- Track and field athletes are banned from <unk> or using certain substances by governing bodies for the sport , from the national to the international level . The IAAF 's constitution incorporates the World Anti @-@ Doping Code among other anti @-@ doping measures . <unk> such as blood doping and the use of anabolic steroids , peptide hormones , <unk> , or <unk> can give athletes a physical competitive advantage in track and field . The use of such substances in track and field is opposed on both ethical and medical grounds . Given that the sport functions by measuring and comparing athletes ' performances , performance @-@ enhancing substances create an uneven playing field — athletes who do not use doping substances have a disadvantage over rivals who do . <unk> , the use of banned substances may have an adverse effect upon athletes ' health . However , some exemptions are made for athletes who take banned substances for therapeutic use , and athletes are not sanctioned for usage in these cases , such as Kim Collins ' failed drug test due to asthma medication . 
- Athletes have historically been willing to take legal and health risks to improve their performance , with some even stating their willingness to risk their lives , as exemplified by research by Mirkin , Goldman and Connor in researching attitudes to the so @-@ called Goldman dilemma . To prevent use of performance @-@ enhancing substances , athletes must submit to drug tests that are conducted both in and out of competition by anti @-@ doping officials or accredited medical staff . <unk> athletes are susceptible to higher testing upon return to competition . Athletes found to have taken substances on the World Anti @-@ Doping Agency 's banned list receive sanctions and may be banned from competition for a period of time that corresponds to the seriousness of the infraction . However , the use of substances not on the prohibited list may also result in sanctions if the substance is deemed similar to a banned substance in either composition or effect . Athletes may also be sanctioned for missing tests , seeking to avoid testing or <unk> with results , refusing to submit to testing , through circumstantial evidence , or confession of use . 
- Doping has played a significant part in the modern history of track and field . State @-@ sponsored doping in East Germany with hormones and anabolic steroids marked the rise of women from the German Democratic Republic in track and field from the late 1960s to the 1980s . A number of these women , such as <unk> Koch , broke world records and were highly successful at international competitions . Some athletes , who were following a doping plan from their teenage years , suffered significant health problems as a result of the regime . Ben Johnson ran a new world record in the 100 metres at the 1988 Seoul Olympics but was later banned for using anabolic steroids . In the mid @-@ first decade of the 21st century , the <unk> Scandal eventually resulted in the downfall of prominent sprinters such as Marion Jones and Tim Montgomery , among others , through their usage of banned substances . Doping problems have also been associated with sprinters such as Tyson Gay , Michael Rogers , and Justin <unk> , all from the United States . Doping has also affected entire nations , such as Russia , which has been banned from competing at both the Indoor World Championships and the Olympics in 2016 . This ban was imposed in 2016 after major allegations of doping and covering up were discovered in 2015 . 
- 
- = = Related sports = = 
- 
- Track and field bears most similarity to the others categorised under the sport of athletics , specifically cross country running , and road forms of <unk> and running . All these forms of racing tend to record finishing times , have strictly defined start and finish points , and are generally individual in nature . <unk> and long @-@ distance runners usually participate in cross country and road events , in addition to the track . Track <unk> are most typically road specialists as well . It is unusual for track and field athletes outside of these two groups to compete in cross country or road events . 
- <unk> of strength athletics , such as the World 's <unk> Man and <unk> games , often incorporate forms of <unk> carrying heavy objects as well as throwing events such as the <unk> toss and <unk> toss , which bear similarities to track and field throwing events . 
- 
- 
- = Isabella Beeton = 
- 
- Isabella Mary Beeton ( née Mayson ; 14 March 1836 – 6 February 1865 ) , also known as Mrs Beeton , was an English journalist , editor and writer . Her name is particularly associated with her first book , the 1861 work Mrs Beeton 's Book of Household Management . She was born in London and , after schooling in Islington , north London , and Heidelberg , Germany , she married Samuel Orchart Beeton , an ambitious publisher and magazine editor . 
- In 1857 , less than a year after the wedding , Isabella began writing for one of her husband 's publications , The Englishwoman 's Domestic Magazine . She translated French fiction and wrote the cookery column , though all the recipes were <unk> from other works or sent in by the magazine 's readers . In 1859 the Beetons launched a series of 48 @-@ page monthly supplements to The Englishwoman 's Domestic Magazine ; the 24 instalments were published in one volume as Mrs Beeton 's Book of Household Management in October 1861 , which sold 60 @,@ 000 copies in the first year . Isabella was working on an abridged version of her book , which was to be titled The Dictionary of Every @-@ Day Cookery , when she died of <unk> fever in February 1865 at the age of 28 . She gave birth to four children , two of whom died in infancy , and had several miscarriages . Two of her biographers , Nancy Spain and <unk> Hughes , <unk> the theory that Samuel had unknowingly contracted syphilis in a premarital liaison with a prostitute , and had unwittingly passed the disease on to his wife . 
- The Book of Household Management has been edited , revised and enlarged several times since Isabella 's death and is still in print as at 2016 . Food writers have stated that the subsequent editions of the work were far removed from and inferior to the original version . Several cookery writers , including Elizabeth David and Clarissa Dickson Wright , have criticised Isabella 's work , particularly her use of other people 's recipes . Others , such as the food writer Bee Wilson , consider the <unk> <unk> , and that Beeton and her work should be thought extraordinary and admirable . Her name has become associated with knowledge and authority on Victorian cooking and home management , and the Oxford English Dictionary states that by 1891 the term Mrs Beeton had become used as a generic name for a domestic authority . She is also considered a strong influence in the building or shaping of a middle @-@ class identity of the Victorian era . 
- 
- = = Biography = = 
- 
- 
- = = = Early life , 1836 – 54 = = = 
- 
- Isabella Mayson was born on 14 March 1836 in Marylebone , London . She was the eldest of three daughters to Benjamin Mayson , a linen factor ( merchant ) and his wife Elizabeth ( née <unk> ) . Shortly after Isabella 's birth the family moved to Milk Street , <unk> , from where Benjamin traded . He died when Isabella was four years old , and Elizabeth , pregnant and unable to cope with raising the children on her own while maintaining Benjamin 's business , sent her two elder daughters to live with relatives . Isabella went to live with her recently widowed paternal grandfather in Great <unk> , <unk> , though she was back with her mother within the next two years . 
- Three years after Benjamin 's death Elizabeth married Henry <unk> , a widower with four children . Henry was the <unk> of Epsom Racecourse , and had been granted residence within the <unk> grounds . The family , including Elizabeth 's mother , moved to Surrey and over the next twenty years Henry and Elizabeth had a further thirteen children . Isabella was instrumental in her siblings ' upbringing , and collectively referred to them as a " living cargo of children " . The experience gave her much insight and experience in how to manage a family and its household . 
- After a brief education at a boarding school in Islington , in 1851 Isabella was sent to school in Heidelberg , Germany , accompanied by her <unk> Jane <unk> . Isabella became proficient in the piano and excelled in French and German ; she also gained knowledge and experience in making pastry . She had returned to Epsom by the summer of 1854 and took further lessons in pastry @-@ making from a local baker . 
- 
- = = = Marriage and career , 1854 – 61 = = = 
- 
- Around 1854 Isabella began a relationship with Samuel Orchart Beeton . His family had lived in Milk Street at the same time as the <unk> — Samuel 's father still ran the <unk> Tavern there — and Samuel 's sisters had also attended the same Heidelberg school as Isabella . Samuel was the first British publisher of Harriet <unk> <unk> 's Uncle Tom 's Cabin in 1852 and had also released two innovative and pioneering journals : The Englishwoman 's Domestic Magazine in 1852 and the Boys ' Own magazine in 1855 . The couple entered into extensive correspondence in 1855 — in which Isabella signed her letters as " <unk> " — and they announced their engagement in June 1855 . The marriage took place at St Martin 's Church , Epsom , in July the following year , and was announced in The Times . Samuel was " a discreet but firm believer in the equality of women " and their relationship , both personal and professional , was an equal partnership . The couple went to Paris for a three @-@ week honeymoon , after which Samuel 's mother joined them in a visit to Heidelberg . They returned to Britain in August , when the <unk> moved into 2 Chandos <unk> , a large Italianate house in Pinner . 
- Within a month of returning from their honeymoon Isabella was pregnant . A few weeks before the birth , Samuel persuaded his wife to contribute to The Englishwoman 's Domestic Magazine , a publication that the food writers Mary <unk> and Olive <unk> consider was " designed to make women content with their lot inside the home , not to interest them in the world outside " . The magazine was affordable , aimed at young middle class women and was commercially successful , selling 50 @,@ 000 issues a month by 1856 . Isabella began by translating French fiction for publication as stories or serials . Shortly afterwards she started to work on the cookery column — which had been <unk> for the previous six months following the departure of the previous correspondent — and the household article . The Beetons ' son , Samuel Orchart , was born towards the end of May 1857 , but died at the end of August that year . On the death certificate , the cause of death was given as <unk> and cholera , although Hughes <unk> that Samuel senior had unknowingly contracted syphilis in a premarital liaison with a prostitute , and had unwittingly passed the condition on to his wife , which would have infected his son . 
- While <unk> with the loss of her child , Isabella continued to work at The Englishwoman 's Domestic Magazine . Although she was not a regular cook , she and Samuel obtained recipes from other sources . A request to receive the readers ' own recipes led to over 2 @,@ 000 being sent in , which were selected and edited by the Beetons . Published works were also copied , largely <unk> to any of the sources . These included Eliza Acton 's Modern Cookery for Private Families , Elizabeth <unk> 's The <unk> English <unk> , Marie @-@ Antoine <unk> 's Le <unk> royal <unk> , Louis <unk> <unk> 's The French Cook , Alexis <unk> 's The Modern <unk> or , <unk> and The <unk> , Hannah <unk> 's The Art of Cookery Made Plain and Easy , Maria Eliza <unk> 's A New System of Domestic Cookery , and the works of Charles <unk> <unk> . <unk> Daly and Ross G. <unk> , in their examination of Victorian cooking culture , consider that the plagiarism makes it " an important index of mid @-@ Victorian and middle @-@ class society " because the production of the text from its own readers ensures that it is a reflection of what was actually being cooked and eaten at the time . In copying the recipes of others , Isabella was following the recommendation given to her by <unk> English , a family friend , who wrote that " Cookery is a Science that is only learnt by Long Experience and years of study which of course you have not had . Therefore my advice would be compile a book from receipts from a Variety of the Best Books published on Cookery and Heaven knows there is a great variety for you to choose from . " 
- The Beetons partly followed the layout of Acton 's recipes , although with a major alteration : whereas the earlier writer provided the method of cooking followed by a list of the required ingredients , the recipes in The Englishwoman 's Domestic Magazine listed the components before the cooking process . Isabella 's standardised layout used for the recipes also showed the approximate costs of each serving , the <unk> of the ingredients and the number of portions per dish . According to the twentieth @-@ century British cookery writer Elizabeth David , one of the strengths of Isabella 's writing was in the " clarity and details of her general instructions , her brisk comments , her no @-@ nonsense <unk> " . Margaret Beetham , the historian , sees that one of the strengths of the book was the " consistent principle of organisation which made its heterogeneous contents look uniform and orderly " , and brought a consistent style in presentation and layout . Whereas Daly and <unk> consider such an approach as " nothing if not formulaic " , Hughes sees it as " the thing most beloved by the mid <unk> , a system " . 
- During the particularly bitter winter of 1858 – 59 Isabella prepared her own soup that she served to the poor of Pinner , " <unk> for benevolent purposes " ; her sister later recalled that Isabella " was busy making [ the ] soup for the poor , and the children used to call with their cans regularly to be <unk> " . The recipe would become the only entry in her Book of Household Management that was her own . After two years of miscarriages , the couple 's second son was born in June 1859 ; he was also named Samuel Orchart Beeton . Hughes sees the miscarriages as further evidence of Samuel 's syphilis . 
- As early as 1857 the Beetons had considered using the magazine columns as the basis of a book of collected recipes and <unk> advice , Hughes believes , and in November 1859 they launched a series of 48 @-@ page monthly supplements with The Englishwoman 's Domestic Magazine . The print block for the whole series of the supplements was set from the beginning so the break between each edition was fixed at 48 pages , regardless of the text , and in several issues the text of a sentence or recipe is split between the end of one instalment and the beginning of the next . 
- The Beetons decided to revamp The Englishwoman 's Domestic Magazine , particularly the fashion column , which the historian Graham Nown describes as " a rather drab piece " . They travelled to Paris in March 1860 to meet Adolphe <unk> , the publisher of the French magazine Le Moniteur de la Mode . The magazine carried a full @-@ sized dress pattern outlined on a fold @-@ out piece of paper for users to cut out and make their own dresses . The Beetons came to an agreement with <unk> for the Frenchman to provide patterns and illustrations for their magazine . The first edition to carry the new feature appeared on 1 May , six weeks after the couple returned from Paris . For the redesigned magazine , Samuel was joined as editor by Isabella , who was described as " <unk> " . As well as being co @-@ editors , the couple were also equal partners . Isabella brought an efficiency and strong business <unk> to Samuel 's normally disorganised and financially <unk> approach . She joined her husband at work , travelling daily by train to the office , where her presence caused a stir among <unk> , most of whom were male . In June 1860 Isabella and Samuel travelled to Killarney , Ireland , for a fortnight 's holiday , leaving their son at home with his nurse . The Beetons enjoyed the <unk> , although on the days it <unk> , they stayed inside their hotel and worked on the next edition of The Englishwoman 's Domestic Magazine . Isabella was impressed with the food they were served , and wrote in her diary that the <unk> were " conducted in quite the French style " . 
- In September 1861 the Beetons released a new , weekly publication called The Queen , the Ladies ' Newspaper . With the Beetons busy running their other titles , they employed Frederick Greenwood as the editor . 
- 
- = = = Mrs Beeton 's Book of Household Management and later , 1861 – 65 = = = 
- 
- The complete version of Mrs Beeton 's Book of Household Management , consisting of the 24 collected monthly instalments , was published on 1 October 1861 ; it became one of the major publishing events of the nineteenth century . Isabella included an extensive 26 @-@ page " Analytical Index " in the book . Although not an innovation — it had been used in The Family Friend magazine since 1855 — Hughes considers the index in the Book of Household Management to be " <unk> detailed and <unk> cross @-@ referenced " . Of the 1 @,@ 112 pages , over 900 contained recipes . The remainder provided advice on fashion , child care , animal <unk> , poisons , the management of servants , science , religion , first aid and the importance in the use of local and seasonal produce . In its first year of publication , the book sold 60 @,@ 000 copies . It reflected Victorian values , particularly hard work , <unk> and <unk> . Christopher <unk> , in his study of the British middle classes , sees that Isabella " reflected better than anyone else , and for a larger audience , the optimistic message that mid @-@ Victorian England was filled with opportunities for those who were willing to learn how to take advantage of them " . The food writer <unk> Hope thinks that " one can understand its success . If ... young ladies knew nothing of domestic arrangements , no better book than this could have been devised for them . " 
- The reviews for Book of Household Management were positive . The critic for the London Evening Standard considered that Isabella had earned herself a household reputation , remarking that she had " succeeded in producing a volume which will be , for years to come , a treasure to be made much of in every English household " . The critic for the Saturday Review wrote that " for a really valuable repertory of hints on all sorts of household matters , we recommend Mrs Beeton with few <unk> " . The anonymous reviewer for The Bradford Observer considered that " the information afforded ... appears <unk> and explicit " ; the reviewer also praised the layout of the recipes , highlighting details relating to ingredients , <unk> and the times needed . Writing in The Morning Chronicle , an anonymous commentator opined that " Mrs Beeton has omitted nothing which tends to the comfort of <unk> , or facilitates the many little troubles and cares that fall to the lot of every wife and mother . She may safely predict that this book will in future take precedence of every other on the same subject . " For the 1906 edition of the book , The Illustrated London News 's reviewer considered the work " a formidable body of domestic doctrine " , and thought that " the book is almost of the first magnitude " . 
- Samuel 's business decisions from 1861 were <unk> and included an ill @-@ advised investment in purchasing paper — in which he lost £ 1 @,@ 000 — and a court case over unpaid bills . His <unk> in business affairs brought on financial difficulties and in early 1862 the couple had moved from their comfortable Pinner house to premises over their office . The air of central London was not conducive to the health of the Beetons ' son , and he began to <unk> . Three days after Christmas his health worsened and he died on New Year 's Eve 1862 at the age of three ; his death certificate gave the cause as " suppressed <unk> " and " <unk> " . In March 1863 Isabella found that she was pregnant again , and in April the couple moved to a house in <unk> , Kent ; their son , who they named Orchart , was born on New Year 's Eve 1863 . Although the couple had been through financial problems , they enjoyed relative prosperity during 1863 , boosted by the sale of The Queen to Edward Cox in the middle of the year . 
- In the middle of 1864 the Beetons again visited the <unk> in Paris — the couple 's third visit to the city — and Isabella was pregnant during the visit , just as she had been the previous year . On her return to Britain she began working on an abridged version of the Book of Household Management , which was to be titled The Dictionary of Every @-@ Day Cookery . On 29 January 1865 , while working on the proofs of the dictionary , she went into labour ; the baby — Mayson Moss — was born that day . Isabella began to feel <unk> the following day and died of <unk> fever on 6 February at the age of 28 . 
- Isabella was buried at West Norwood Cemetery on 11 February . When The Dictionary of Every @-@ Day Cookery was published in the same year , Samuel added a tribute to his wife at the end : 
- Her works speak for themselves ; and , although taken from this world in the very height and strength , and in the early days of womanhood , she felt satisfaction — so great to all who strive with good intent and warm will — of knowing herself regarded with respect and gratitude . 
- 
- = = Legacy = = 
- 
- In May 1866 , following a severe downturn in his financial fortunes , Samuel sold the rights to the Book of Household Management to Ward , Lock and Tyler ( later Ward Lock & Co ) . The writer Nancy Spain , in her biography of Isabella , reports that , given the money the company made from the Beetons ' work , " surely no man ever made a worse or more impractical <unk> " than Samuel did . In subsequent publications Ward Lock suppressed the details of the lives of the Beetons — especially the death of Isabella — in order to protect their investment by letting readers think she was still alive and creating recipes — what Hughes considers to be " intentional censorship " . Those later editions continued to make the connection to Isabella in what Beetham considers to be a " fairly ruthless marketing policy which was begun by Beeton but carried on vigorously by Ward , Lock , and Tyler " . Those subsequent volumes bearing Isabella 's name became less reflective of the original . Since its initial publication the Book of Household Management has been issued in numerous <unk> and paperback editions , translated into several languages and has never been out of print . 
- Isabella and her main work have been subjected to criticism over the course of the twentieth century . Elizabeth David complains of recipes that are " sometimes <unk> and misleading " , although she acknowledges that <unk> <unk> 's <unk> <unk> also contains errors . The television cook Delia Smith admits she was puzzled " how on earth Mrs Beeton 's book managed to utterly eclipse ... [ Acton 's ] superior work " , while her fellow chef , Clarissa Dickson Wright , opines that " It would be unfair to blame any one person or one book for the decline of English cookery , but Isabella Beeton and her ubiquitous book do have a lot to answer for . " In comparison , the food writer Bee Wilson opines that disparaging Isabella 's work was only a " fashionable " stance to take and that the cook 's writing " simply makes you want to cook " . Christopher Driver , the journalist and food critic , suggests that the " relative stagnation and want of refinement in the indigenous cooking of Britain between 1880 and 1930 " may instead be explained by the " progressive <unk> under successive editors , <unk> and <unk> " . David comments that " when plain English cooks " were active in their kitchens , " they followed plain English recipes and chiefly those from the Mrs Beeton books or their derivatives " . Dickson Wright considers Beeton to be a " fascinating source of information " from a social history viewpoint , and <unk> and <unk> consider the work to be " the best and most reliable guide for the scholar to the domestic history of the mid @-@ Victorian era " . 
- Despite the criticism , <unk> observes that " ' Mrs. Beeton ' has ... been for over a century the standard English cookbook , frequently <unk> every other book but the Bible " . According to the Oxford English Dictionary , the term Mrs Beeton became used as a generic name for " an authority on cooking and domestic subjects " as early as 1891 , and Beetham opines that " ' Mrs. Beeton ' became a trade mark , a brand name " . In a review by Gavin Koh published in a 2009 issue of The <unk> , Mrs Beeton 's Book of Household Management was labelled a medical classic . In Isabella 's " attempt to educate the average reader about common medical complaints and their management " , Koh argues , " she preceded the family health guides of today " . Robin <unk> , a professor of strategic management , believes that Isabella 's advice and guidance on household management can also be applied to business management , and her lessons on the subject have stood the test of time better than some of her advice on cooking or <unk> . 
- Following the radio broadcast of Meet Mrs. Beeton , a 1934 comedy in which Samuel was portrayed in an <unk> light , and Mrs Beeton , a 1937 documentary , <unk> Beeton worked with H. Montgomery Hyde to produce the biography Mr and Mrs Beeton , although completion and publication were delayed until 1951 . In the meantime Nancy Spain published Mrs Beeton and her Husband in 1948 , updated and retitled in 1956 to The Beeton Story . In the new edition Spain hinted at , but did not <unk> upon , on the possibility that Samuel contracted syphilis . Several other biographies followed , including from the historian Sarah Freeman , who wrote Isabella and Sam in 1977 ; Nown 's Mrs Beeton : 150 Years of Cookery and Household Management , published on the 150th anniversary of Isabella 's birthday , and Hughes 's The Short Life and Long Times of Mrs Beeton , published in 2006 . Isabella was ignored by the Dictionary of National Biography for many years : while Acton was included in the first published volume of 1885 , Isabella did not have an entry until 1993 . 
- There have been several television broadcasts about Isabella . In 1970 Margaret <unk> portrayed her in a solo performance written by Rosemary Hill , in 2006 Anna <unk> played Isabella in a <unk> , and Sophie Dahl presented a documentary , The <unk> Mrs Beeton , in the same year . 
- The literary historian Kate Thomas sees Isabella as " a powerful force in the making of middle @-@ class Victorian domesticity " , while the Oxford University Press , advertising an abridged edition of the Book of Household Management , considers Isabella 's work a " founding text " and " a force in shaping " the middle @-@ class identity of the Victorian era . Within that identity , the historian Sarah Richardson sees that one of Beeton 's achievements was the integration of different threads of domestic science into one volume , which " <unk> [ ed ] the middle @-@ class female housekeeper 's role ... placing it in a broader and more public context " . Nown quotes an unnamed academic who thought that " Mrs <unk> has preserved the family as a social unit , and made social reforms a possibility " , while Nicola Humble , in her history of British food , sees The Book of Household Management as " an engine for social change " which led to a " new cult of domesticity that was to play such a major role in mid @-@ Victorian life " . Nown considers Isabella 
- ... a singular and remarkable woman , praised in her lifetime and later forgotten and ignored when a pride in light pastry ... were no longer considered <unk> for womanhood . Yet in her lively , progressive way , she helped many women to overcome the loneliness of marriage and gave the family the importance it deserved . In the climate of her time she was brave , strong @-@ minded and a <unk> champion of her sisters everywhere . 
- 
- 
- = Martin Keamy = 
- 
- First Sergeant Martin Christopher Keamy is a fictional character played by Kevin Durand in the fourth season and sixth season of the American ABC television series Lost . Keamy is introduced in the fifth episode of the fourth season as a crew member aboard the freighter called the <unk> that is offshore the island where most of Lost takes place . In the second half of the season , Keamy served as the primary antagonist . He is the leader of a mercenary team hired by billionaire Charles Widmore ( played by Alan Dale ) that is sent to the island on a mission to capture Widmore 's enemy Ben Linus ( Michael Emerson ) from his home , then torch the island . 
- Unlike Lost 's ensemble of characters who , according to the writers , each have good and bad intentions , the writers have said that Keamy is evil and knows it . Durand was contacted for the role after one of Lost 's show runners saw him in the 2007 film 3 : 10 to <unk> . Like other Lost actors , Durand was not informed of his character 's arc when he won the role . Throughout Durand 's nine @-@ episode stint as a guest star in the fourth season , little was revealed regarding Keamy 's life prior to his arrival on the island and Durand cited this as a reason why the audience " loved to hate " his villainous character . Critics praised the writers for breaking Lost tradition and creating a seemingly <unk> character , while Durand 's performance and appearance were also reviewed positively . Keamy returned in the final season for a tenth and eleventh appearance . 
- 
- = = <unk> = = 
- 
- Originally from Las Vegas , Nevada , Martin Keamy was a First Sergeant of the United States Marine Corps , serving with distinction from 1996 to 2001 . In the three years before the events of Lost in 2004 , he worked with various mercenary organizations in Uganda . In fall 2004 , Keamy is hired by Widmore to lead a mercenary team to the island via freighter then helicopter and extract Ben for a large sum of money . Once he captures Ben , Keamy has orders to kill everyone on the island ( including the forty @-@ plus survivors of the September 22 , 2004 crash of <unk> Airlines Flight 815 : the protagonists of the series ) by <unk> it . 
- Keamy boards the freighter <unk> in <unk> , Fiji sometime between December 6 and December 10 . On the night of December 25 , helicopter pilot Frank <unk> ( Jeff <unk> ) flies Keamy and his mercenary team , which consists of Omar ( Anthony <unk> ) , <unk> , <unk> , <unk> and <unk> , to the island . On December 27 , the team ambushes several <unk> in the jungle , taking Ben 's daughter Alex Linus ( <unk> <unk> ) hostage and killing her boyfriend Karl ( Blake <unk> ) and her mother Danielle <unk> ( Mira <unk> ) . The team infiltrates the Barracks compound where Ben resides , blowing up the house of 815 survivor Claire <unk> ( <unk> de <unk> ) and fatally shooting three 815 survivors ( played by extras ) . Keamy attempts to negotiate for Ben 's surrender in exchange for the safe release of Alex . Believing that he is <unk> , Ben does not comply , and Keamy shoots Alex dead . Ben <unk> by summoning the island 's smoke monster , which brutally assaults the mercenaries and fatally wounds <unk> . 
- Upon returning to the freighter , Keamy unsuccessfully attempts to kill Michael Dawson ( Harold <unk> ) , whom he has discovered is Ben 's spy , then obtains the " secondary protocol " from a safe . The protocol contains instructions from Widmore for finding Ben if he finds out Keamy 's intention to torch the island , which he apparently had . The protocol contains details about a 1980s research station called the " Orchid " that was previously run by a group of scientists working for the Dharma Initiative . Keamy is also informed by Captain <unk> that Keamy and his mercenary squad may be suffering from some sort of mental sickness , a notion Keamy dismisses . Later in the day , Omar <unk> a dead man 's switch to Keamy , rigged to <unk> <unk> on the freighter if Keamy 's heart stops beating . That night , Frank refuses to fly the mercenaries to the island . In a display of power , Keamy slits the throat of the ship 's doctor Ray ( Marc <unk> ) and throws him <unk> and later <unk> and shoots Captain <unk> ( Grant <unk> ) during a tense standoff . Frank flies the remaining five mercenaries back to the island . On December 30 , the team apprehends Ben at the Orchid and takes him to the <unk> where they are ambushed and killed by Ben 's people — referred to as the " Others " by the 815 survivors — and 815 survivors Kate Austen ( Evangeline Lilly ) and Sayid Jarrah ( Naveen Andrews ) . After a chase to recapture Ben and a brawl with Sayid , Keamy is shot in the back by Richard <unk> ( Nestor <unk> ) , who leaves him for dead , unaware of Keamy 's <unk> vest . Later , Keamy descends into the Orchid 's underground level via its elevator to stalk Ben , who hides in the shadows . <unk> Ben with taunts about his daughter 's death , Keamy is ambushed by Ben , who beats him into submission with an <unk> baton before stabbing him repeatedly in the neck . Though Locke attempts to save his life for the sake of the freighter , Keamy dies and the dead man 's trigger <unk> the explosives on the freighter , killing nearly everyone aboard . 
- In the afterlife , Keamy is a business associate of Mr. Paik , Sun ’ s ( <unk> Kim ) father . Mr. Paik sends Jin ( Daniel <unk> Kim ) to <unk> to give Keamy a watch and $ 25 @,@ 000 , intended to be Keamy 's reward for killing Jin . However , the money is confiscated at customs in <unk> , and Keamy is disappointed to discover it missing . He takes Jin to a restaurant and has him tied up in a freezer . Shortly after , Omar , one of Keamy 's henchmen , captures Sayid and brings him to the same restaurant . Keamy explains to Sayid that his brother has been shot because he borrowed money and failed to pay it back . After Keamy threatens Sayid 's family , Sayid <unk> and shoots Keamy in the chest , presumably killing him . 
- 
- = = Personality = = 
- 
- During the casting process , Keamy was described as a military type in his late @-@ twenties who does not question orders . Chris Carabott of IGN wrote that " in a show that features characters fraught with uncertainty , Keamy is the polar opposite and his Marine mentality definitely sets him apart . His team has a physical advantage and with the help of Mr. Widmore , they have a tactical advantage as well . Keamy is like a <unk> being thrown into a cage full of kittens ( except for [ Iraqi military <unk> ] Sayid ) " . Jay <unk> of The Huffington Post , stated that " Keamy is Crazy ! … out of all the bad guys on the Island — past , present , and future — Keamy has to be one of the most dangerous ones . Not because of how big he is , or the weaponry , but his willingness to kill at the drop of a hat . That doesn 't <unk> well for our <unk> [ protagonists ] . " Co @-@ show runner / executive producer / writer Carlton <unk> has stated that he and the other writers create " complex " characters because they " are interested in exploring how good and evil can be embodied in the same characters and [ the writers are also intrigued ] the struggles we all have [ , ] to overcome the dark parts of our souls " ; however , he later clarified that there is an exception : " Keamy 's bad , he knows he 's bad , but he 's ... a guy that does the job . " Damon Lindelof stated that " the great thing about Keamy is that he is like a ... merciless survivor . [ There ] ' s this great moment [ in the season finale ] where he just sort of <unk> @-@ sacks [ a grenade thrown at him ] over to where [ his ally ] Omar is standing . Omar is certainly an acceptable casualty as far as Keamy is concerned . " According to a <unk> in the Lost : The Complete Fourth Season – The <unk> Experience DVD set , Keamy likes " heavy weaponry " and " physical fitness " and dislikes " negotiations " and " doctors " . 
- 
- = = Development = = 
- 
- A remake of the 1957 film 3 : 10 to <unk> opened in theaters on September 7 , 2007 . Lost 's co @-@ show runner / executive producer / head writer / co @-@ creator Damon Lindelof enjoyed Kevin Durand 's supporting performance as Tucker and checked to see if he was available for a role on Lost . The casting director had Durand read a page of dialogue for the new character Keamy ; Durand was offered the role in early October and he traveled to Honolulu in Hawaii — where Lost is filmed on location — by October 17 , 2007 . A former stand @-@ up comic and rapper from Thunder Bay , Ontario , Canada , with the stage name " <unk> D " , Durand had seen only around six episodes of Lost by the time that he won the part . When he was shooting , he was confused by the story , later stating " I didn 't want to know anything or be attached to anybody . I 'm glad I didn 't . But now that I 'm on it , I 'll watch all of it . " Durand revealed his appreciation for the cast , crew and scripts and the fact that he had the chance to act as someone with a similar physical appearance to himself , as he had previously done roles that had not prompted recognition from viewers on the street . 
- Durand was never informed of his character 's arc and only learned more of Keamy 's importance to the plot as he received new scripts ; thus , he was thrilled when the role was expanded for his third appearance , in " The <unk> of Things to Come " , when he kills Alex and Durand compared his excitement to that of " a kid in a candy store . " He also stated that " you really don 't know what 's going to happen in the next episode and you get the scripts pretty late , so it is pretty secretive and it 's kind of exciting that way [ because ] you 're really forced to get in the moment and say the words and play the guy " . Durand was initially met with negative reaction from fans on the street for this action and he defended his murderous character by arguing that it was actually more Ben 's fault for failing to negotiate with Keamy ; later , fans warmed up to Keamy . Despite the antagonist 's increasing popularity and fanbase , it became apparent to Durand that fans were hoping for Keamy 's death in what promised to be a showdown in the season finale . Throughout his nine @-@ episode run , Keamy never receives an episode in which his backstory is developed through flashbacks and Durand holds this partially responsible for the negative reaction to his character , saying that the audience " [ has not ] really seen anything outside of Keamy 's mission , so I think they definitely want him put down . " Following the season 's conclusion , Durand stated that he would not be surprised if his character returned in the fifth season and concluding that " Lost was really fun . If I can have that experience in any genre , I 'd take it . " 
- Durand returned for the sixth season episodes " <unk> " and " The <unk> " , following a twenty @-@ two episode absence since his character 's death in the fourth season finale . Keamy appears in the " flash sideways " parallel timeline in September 2004 working for Sun <unk> 's father Mr. Paik to assassinate her new husband Jin <unk> ( Daniel <unk> Kim ) upon the couple 's arrival in Los Angeles . Keamy and his sidekick Omar are also <unk> money from Sayid 's brother Omer , prompting Sayid to shoot them both , aiding Jin 's rescue process . 
- 
- = = Reception = = 
- 
- Professional television critics deemed Martin Keamy a welcome addition to the cast . Jeff Jensen of Entertainment Weekly commented that Kevin Durand " is emerging as a real find this season ; he plays that mercenary part with a scene @-@ stealing mix of menace and damaged vulnerability . " After Jensen posted what he thought were the fifteen best moments of the season , the New York Post 's <unk> Wieselman " ha [ d ] to complain about one <unk> omission from <unk> 's list : Martin Keamy . I have loved this character all season long — and not just solely for [ his ] physical attributes ... although those certainly don 't hurt . " Alan Sepinwall of The Star @-@ Ledger reflected , " He was only on the show for a season and not featured all that much in that season , but Kevin Durand always made an impression as Keamy . Lots of actors might have his sheer physical size , but there 's a sense of danger ( <unk> ? ) that you can 't build at the gym , you know ? " IGN 's Chris Carabott wrote that " Keamy is one of the more striking new additions to Lost [ in the fourth ] season ... and is a welcome addition to the Lost universe . " Maureen Ryan of The Chicago Tribune stated that Keamy has " so much <unk> " and she would " rather find out more about [ him ] than most of the old @-@ school Lost characters " . TV Guide 's Bruce <unk> agreed with a reader 's reaction to Durand 's " chilling portrayal " of Keamy and posted it in his weekly column . The reader , nicknamed " <unk> " , wrote " love him or hate him , nobody is neutral when it comes to Keamy , which is the hallmark of a well @-@ played villain . Even the camera seems to linger on Durand , who conveys <unk> with just a look or tilt of his head . This role should give Durand 's career a well @-@ deserved boost " . Following his demise , Whitney Matheson of USA Today noted that " it seems Keamy , Lost 's camouflaged <unk> , is turning into a bit of a cult figure . " A " hilarious " blog containing Keamy digitally edited into various photographs , posters and art titled " Keamy 's Paradise " was set up in early June 2008 . TV Squad 's Bob Sassone thought that the blog was " a great idea " and " funny " and he called Keamy " the <unk> <unk> of Lost " . In 2009 , Kevin Durand was nominated for a Saturn Award for Best Guest Starring Role in a Television Series . 
- Reaction to the antagonist 's death was mixed . Kristin <unk> Santos of E ! criticized the writing for Keamy when he <unk> asks Sayid where his fellow 815 survivors are so that he can kill them , but enjoyed his attractive physique , writing that " that guy is deep @-@ fried evil , and he must die horribly for what he did to Alex , but in the meantime , well , he 's certainly a well @-@ muscled young man " . The Huffington Post 's Jay <unk> also called for Keamy 's death , stating that " nothing would be better to me than him getting run over by Hurley 's Dharma Bus " , alluding to a scene in the third season finale . Dan <unk> of <unk> <unk> commented that " Keamy took a bit too long to die . Yes , he was wearing a <unk> vest so it wasn 't totally unexpected , but it was a bit predictable . " In a review of the season finale , Erin <unk> of AOL 's TV Squad declared her disappointment in the conclusion of Keamy 's arc , stating that " it 's always a shame when the hot guys die , [ especially when ] Kevin Durand did an amazing job with the character … he 'll be missed . " In a later article titled " Lost Season Four <unk> " , <unk> noted Durand 's " strong performance " that was " particularly fun to watch " and wrote that " we [ the audience ] all know that Widmore 's the big bad , but Keamy became the face of evil on the island in his stead . " 
- 
- 
- = Kaboom ( Parks and Recreation ) = 
- 
- " Kaboom " is the sixth episode of the second season of Parks and Recreation , and the twelfth overall episode of the series . It originally aired on NBC in the United States on October 22 , 2009 . In the episode , Leslie tries to have the pit filled in and <unk> Andy in the process , opening Pawnee up to a possible lawsuit . 
- The episode was written by <unk> <unk> and directed by Charles <unk> , and featured guest performances by <unk> Paul Scheer and H. Jon Benjamin . " Kaboom " saw the filling in of the Pawnee pit , a focal plot device in Parks and Recreation since the first episode . As part of a multi @-@ network television campaign to spotlight volunteerism , the episode prominently featured the real @-@ life organization KaBOOM ! , a charity that builds playgrounds in locations all over the United States . 
- According to Nielsen Media Research , the episode was seen by 4 @.@ 98 million household viewers , an improvement over the previous week . " Kaboom " received generally positive reviews , with several commentators praising the series for resolving the long @-@ standing pit subplot . 
- It is the only episode of the entire series in which Aubrey Plaza does not appear as April <unk> . 
- 
- = = Plot = = 
- 
- The episode opens with Leslie ( Amy Poehler ) receiving a <unk> call about abnormal transactions on her credit card . The purchases turn out to all be legitimate , but Leslie <unk> the card anyway out of embarrassment when Tom ( Aziz Ansari ) hears about her unusual purchases , including a " bucket of cake " , a man pillow in the shape of Daniel Craig , and tuition to <unk> School of Witchcraft and <unk> . Later , the parks department and Ann ( Rashida Jones ) visit neighboring Indiana town <unk> to help build a playground in a single day , as part of an event by the charity KaBOOM ! There , Leslie and Ann find Ann 's ex @-@ boyfriend Andy ( Chris Pratt ) , who claims to be <unk> , but in reality , is coming for the free food . He tells Leslie and Ann that he now has a home with the drummer of his band . Leslie is inspired by the energy of the group and its leader <unk> ( Paul Scheer ) , which prompts her to take <unk> measures in filling in the pit in Pawnee and turning it into a park . While discussing the matter with her parks department , Mark ( Paul Schneider ) takes her aside and suggests she simply fill it in without permission . Leslie decides to take his advice and rents a <unk> to fill in the pit . 
- Leslie meets Ann at the pit with the rented <unk> , which starts filling it in with dirt . However , they failed to realize Andy was inside his tent in the pit , and he is injured when mounds of dirt fall on him . He is taken to the hospital , where Ann is assigned as his nurse . An angry Ron ( Nick <unk> ) tells Leslie the town is now open to a large potential lawsuit from Andy , and he sends her to visit him along with their city attorney Scott ( H. Jon Benjamin ) . Meanwhile , Andy is excited to be near Ann and insists it will lead to them getting back together , but Ann tells him she is very happy dating Mark , who unlike Andy has a job , apartment and future . A <unk> Andy decides he has to get money to impress Ann . When Leslie visits , he <unk> tells her he and his attorney Wendell Adams ( Chris <unk> ) are going to sue Pawnee . 
- Leslie is convinced she can reason with Andy if they can talk without their lawyers , but Andy will not return her calls . Finally , Ann calls Andy and asks him to come to her house . Later that day , he arrives completely naked , having assumed Ann wanted to take him back . A disgusted Ann leaves the house , leaving Leslie and Andy to talk . Andy admits he is <unk> the town in an attempt to win Ann back , and she thinks of a different way he can impress her . The next day at city hall , Andy tells Leslie and Scott he will drop the lawsuit if they agree to fill in the pit right away . Scott agrees , unaware it was a trick between Andy and Leslie . The next day the pit is filled in and turned into a lot , and an impressed Ann waves at Andy while the construction work is going on . The episode ends with <unk> riding a <unk> , revealing his role in KaBOOM ! was an elaborate prank to get the playground built , and announcing he is going to build a hospital in a poor part of China . 
- 
- = = Production = = 
- 
- " Kaboom " was written by <unk> <unk> and directed by Charles <unk> . The Pawnee pit , which has been a focal plot device in Parks and Recreation since the first episode , is filled in during this episode . " Kaboom " features actor and comedian Paul Scheer in a guest appearance as the KaBOOM ! organizer . Scheer has previously worked with Aziz Ansari on the MTV sketch comedy show , Human Giant . " Kaboom " also featured H. Jon Benjamin , a voice actor from Archer , Dr. Katz , and Home Movies , as Pawnee 's lawyer . Chris Pratt actually appeared naked during filming of a scene in which he arrived at Ann 's house without clothes on . Michael Schur , co @-@ creator of Parks and Recreation , said the scene was written because Pratt " loves taking his clothes off " . 
- The script prominently features KaBOOM ! , a real @-@ life charitable organization that builds playgrounds in locations all over the United States . Parks and Recreation included the charity in the episode as part of a charity campaign called " I <unk> " , sponsored by the Entertainment Industry Foundation . The campaign involves more than 60 shows on NBC ABC , CBS , Fox and several other networks , all of which <unk> real @-@ life charities in an effort to encourage volunteerism . Greg Daniels , the series executive producer and co @-@ creator , was responsible for choosing the KaBOOM ! charity . In an editorial featured on The Huffington Post after the " Kaboom " aired , KaBOOM ! chief executive officer <unk> Hammond called the episode " Ka @-@ <unk> ! " and said , " The writers certainly captured the spirit that fuels each of our ( playground ) builds – something that inspires volunteers to continue their great work and strive even harder to give back to their communities . " 
- Within a week of the episode 's original broadcast , two deleted scenes from " Kaboom " were made available on the official Parks and Recreation website . The first two @-@ minute clip included extended scenes of the <unk> ! park construction , including Tom text <unk> instead of working , Leslie and Ann competing with children , and Paul Scheer doing the <unk> dance move . Leslie also seeks advice from her mother ( played by Pamela Reed ) after putting Pawnee in danger of a lawsuit . In the second , 15 @-@ second clip , Ann complains to Mark about Andy appearing naked at her apartment , which Mark only finds amusing . 
- 
- = = Cultural references = = 
- 
- Andy says he volunteers at several organizations in order to get their free food at their events . He specifically identifies the Red Cross , which he says has " amazing <unk> " , and <unk> on <unk> , which he described as a " <unk> " . 
- 
- = = Reception = = 
- 
- In its original American broadcast on October 22 , 2009 , " Kaboom " was seen by 4 @.@ 98 million household viewers , according to Nielsen Media Research . It was a slight increase from the previous week 's episode , " Sister City " . " Kaboom " received a 2 @.@ 1 rating / 6 share among viewers aged between 18 and 49 . The episode received generally positive reviews . Steve Heisler of The A.V. Club said " Kaboom " was the best episode of the second season , and called Parks and Recreation " one of the funniest shows on TV right now " . Heisler praised the show for not <unk> away from <unk> , and called Andy 's naked entrance " by far the highlight of the series " . Entertainment Weekly writer <unk> <unk> said " Kaboom " continued a trend of superior Parks episodes in season two , and praised the episode for filling in the pit , claiming it " both tied up a plot thread that had <unk> its welcome and put to rest any lingering ghosts from the first six episodes " . Matt Fowler of IGN said the episode was funny and served as good character development for Leslie and Andy . But Fowler also said " some scenes fell flat " , and questioned why Ann would continue to harbor feelings for the immature Andy . 
- 
- 
- = Middle Colonies = 
- 
- The Middle Colonies comprised the middle region of the Thirteen Colonies of the British Empire in North America . Much of the area was part of the New Netherland until the British exerted their control over the region . The English captured much of the area in its war with the Dutch around 1664 , and the majority of the conquered land became the Province of New York . The Duke of York and the King of England would later grant others ownership of the land which would become the Province of New Jersey and the Province of Pennsylvania . The Delaware Colony later separated from Pennsylvania , which was founded by William Penn . 
- The Middle Colonies had lots of rich soil , which was allowing the area to become a major exporter of wheat and other grains . The lumber and shipbuilding industries enjoyed success in the Middle Colonies because of the abundant forests , and Pennsylvania saw moderate success in the textile and iron industry . The Middle Colonies were the most ethnically and religiously diverse British colonies in North America , they had settlers coming from all parts of Europe . Civil unrest in Europe and other colonies saw an influx of immigrants to the Middle Colonies in the 18th century . With the new <unk> came various religions which were protected in the Middle Colonies by written freedom of religion laws . This tolerance was very unusual and distinct from other British colonies . 
- 
- = = History = = 
- 
- The Middle Colonies were explored by Henry Hudson on a journey into the Hudson River and Delaware Bay in 1609 . The Dutch soon claimed the land . Although the Swedes and the Dutch fought over the land in the <unk> through the ultimately the Dutch claimed the land , calling it New Netherland . In the 1660s , the English largely conquered this land from the Dutch , renaming the area New York after the Duke of York , James II . The colony 's land was periodically granted to various proprietors and split into the Province of New York and the Province of Pennsylvania . 
- 
- = = Province of New Jersey = = 
- 
- King Charles II renamed the land west of the Hudson River New Jersey and gave the region between New England and Maryland to his brother , the Duke of York ( later King James II of England ) as a proprietary colony . James II later granted the land between the Hudson River and the Delaware River to two friends who had been loyal to him through the English Civil War : Sir George <unk> and Lord Berkeley of Stratton . This land grant became the Province of New Jersey . 
- In 1665 , the Concession and Agreement was written in an effort to entice settlers to New Jersey . This document provided for religious freedom , no taxes without assembly approval , and a governor appointed by the proprietors . The first governor appointed in this way was Philip <unk> , who founded <unk> . <unk> were required to pay annual quit @-@ rent taxes . On March 18 , 1674 , after encountering difficulty collecting the taxes , Lord Berkeley sold his share in the colony to Edward <unk> , a Quaker businessman from London . This sale divided New Jersey into East Jersey and West Jersey ; however , the border between the two was not agreed upon until the <unk> <unk> in 1676 . From 1701 to 1765 , colonists <unk> in the New York @-@ New Jersey Line War over disputed colonial boundaries . 
- On April 15 , <unk> , Queen Anne united West and East Jersey into one Royal Colony , the Province of New Jersey . Edward Hyde , 3rd Earl of <unk> became the royal colony 's first governor . After Hyde was recalled to England in 1708 over charges of <unk> , bribery , and corruption , the governor of New York was charged to also <unk> over New Jersey . Finally , in 1738 , King George II appointed a separate governor , Lewis Morris , to run New Jersey . 
- The Provincial Congress of New Jersey , made up of elected delegates , formed in January 1776 to govern the colony . The Congress had Royal Governor William Franklin arrested on June 15 , declaring him " an enemy to the liberties of this country " . On July 2 , 1776 , New Jersey enacted the New Jersey State Constitution , soon after having empowered delegates to the Continental Congress , on June 21 , to join in a declaration of independence . The United States Declaration of Independence ended their colonial status . 
- 
- = = Province of Pennsylvania = = 
- 
- King Charles II granted the land for the Pennsylvania Colony to William Penn on March 4 , 1681 as payment for a debt the crown owed his family . Penn wrote the Frame of Government of Pennsylvania before departing for the colony , which called for religious tolerance towards many groups , including the Religious Society of Friends and local natives . As a proprietary colony , Penn governed Pennsylvania , yet its citizens were still subject to the English crown and laws . Penn 's cousin William <unk> served as the first colonial deputy governor . 
- <unk> by the 42nd parallel north and 39th parallel north , Pennsylvania was bordered by the Delaware River and the colonies of New York , Maryland , and New Jersey . In <unk> , Dutch land given to Penn by the Duke of York was separated and once again became part of the Delaware Colony . From 1692 to 1694 , revolution in England deprived Penn of the governance of his colony . The Pennsylvania Assembly took this opportunity to request expanded power for elected officials , led by David Lloyd . Upon visiting the colony in 1669 and 1701 , Penn eventually agreed to allow their Charter of <unk> to be added to the constitution . When the British banned western expansion in 1764 , fighting among colonists and against the natives swelled . In 1773 , Arthur St. Clair ordered the arrest of a <unk> officer who was commanding troops against armed settlers loyal to Pennsylvania . Pennsylvanian revolutionary sentiment continued to grow , and Philadelphia , the largest city in America , soon became the meeting place of the Continental Congress . The publication of the Pennsylvania Constitution of 1776 by locally elected revolutionaries concluded the history of the Colony , and began the Commonwealth of Pennsylvania . 
- 
- = = Province of New York = = 
- 
- The first Dutch settlements in the New York area appeared around 1613 . The English captured the New Netherland Colony from the Dutch in 1664 , renaming it the Province of New York after the King 's brother , the Duke of York ( later King James II ) . The Dutch recaptured the colony in July <unk> during the Third Anglo @-@ Dutch War , but gave it back to the English under the Treaty of Westminster in exchange for <unk> . The Duke of York never governed the colony himself : he instead appointed governors , councils , and other officers to run the government . Richard <unk> served as the first governor of New York . 
- In 1665 , the Province of New Jersey split from New York ; however , the New York @-@ New Jersey Line War continued until the final borders were decided in 1769 , and approved by the legislatures and the King in 1772 and 1773 respectively . A Colonial Assembly convened in October 1683 , making New York the last colony to have an assembly . A constitution was drafted and passed on October 30 , 1683 , giving the colonists many rights , including the rights to taxation without representation . However , upon learning of the constitution , James II declared it void . 
- When the Duke of York became King James II of England , New York became a royal province . In May 1688 the province briefly became part of the Dominion of New England . When James II was overthrown , the citizens of New York rebelled against the Royal Governor in Leisler 's Rebellion . When Henry <unk> became governor in March 1691 , the rebellion was crushed and its leader , Jacob Leisler was arrested , tried , and executed for treason . New York 's charter and constitution were reinstated soon after . In April 1775 , American <unk> formed the New York Provincial Congress to replace the assembly . Governor William Tryon and all royal officials were forced from the colony on October 19 , 1775 . Colonial status ended for the new state with the United States Declaration of Independence in July 1776 . 
- 
- = = Delaware Colony = = 
- 
- Delaware changed hands between the Dutch and Swedes between 1631 and <unk> . The Dutch maintained control of Delaware until 1664 , when Sir Robert Carr took New <unk> for the Duke of York , renaming it New Castle . A Deputy of the Duke governed Delaware from 1664 to 1682 . When William Penn received his land grant of Pennsylvania in 1681 , he received the Delaware area from the Duke of York , and dubbed them " The Three Lower Counties on the Delaware River " . In 1701 , after he had troubles governing the ethnically diverse Delaware territory , Penn agreed to allow them a separate colonial assembly . 
- 
- = = Geography = = 
- 
- The partly <unk> Middle Colonies enjoyed fertile soil vastly different from the nearby New England Colonies , which contained more rocky soil . Because of the large grain exports resulting from this soil , the colonies came to be known as the Bread <unk> Colonies . Pennsylvania became a leading exporter of wheat , corn , <unk> , hemp , and flax , making it the leading food producer in the colonies , and later states , between the years of 1725 and 1840 . Broad navigable rivers of relaxed current like the Susquehanna River , the Delaware River , and the Hudson River attracted diverse business . Fur trappers moved along these rivers , and there was enough flow to enable <unk> with water wheel power . 
- 
- = = Industry = = 
- 
- <unk> forests attracted both the lumbering and shipbuilding industries to the Middle Colonies . These industries , along with the presence of deep river estuaries , led to the appearance of important ports like New York and Philadelphia . While the Middle Colonies had far more industry than the Southern Colonies , it still did not rival the industry of New England . In Pennsylvania , sawmills and <unk> were abundant , and the textile industry grew quickly . The colony also became a major producer of pig iron and its products , including the Pennsylvania long rifle and the <unk> wagon . Other important industries included printing , publishing , and the related industry of <unk> . 
- 
- = = Politics = = 
- 
- The Middle Colonies political groups began as small groups with narrowly focused goals . These coalitions eventually grew into diverse and large political organizations , evolving especially during the French and Indian War . 
- The Middle Colonies were generally run by Royal or Proprietary Governors and elected Colonial <unk> . Many Middle Colony <unk> guaranteed freedom of religion and forbade taxation without representation . Royal governors were arrested or overthrown on more than one occasion , most notably when New Jersey arrested its governor and during Leisler 's Rebellion in New York . Growing unrest in the Middle Colonies eventually led the region to become the meeting place for the Continental Congress , and a center for revolution . However , there were numerous pockets of <unk> and <unk> . 
- 
- = = Demographics = = 
- 
- The Middle Colonies tended to mix aspects of the New England and Southern Colonies . <unk> were generally farms of 40 to 160 acres ( 16 – 65 hectares ) , owned by the family that worked it . In New York 's Hudson Valley , however , the Dutch <unk> operated very large landed estates and rented land to tenant farmers . 
- <unk> , the Middle Colonies were more diverse than the other British colonial regions in North America and tended to be more socially tolerant . For example , in New York , any foreigner <unk> Christianity was awarded citizenship , leading to a more diverse populace . As a consequence , early German settlements in the Americas concentrated in the Middle Colonies region . <unk> servitude was especially common in Pennsylvania , New Jersey , and New York in the eighteenth century , though fewer worked in agriculture . 
- German immigrants favored the Middle Colonies . German immigration greatly increased around 1717 , and many immigrants began coming from the Rhineland . They were erroneously labeled the Pennsylvania Dutch ( the German word for German is " <unk> " ) , and comprised one @-@ third of the population by the time of the American Revolution . The industry and farming skills they brought with them helped solidify the Middle Colonies ' prosperity . They were noted for tight @-@ knit religious communities , mostly Lutheran but also including many smaller <unk> such as the <unk> , Mennonites and Amish 
- The Scotch @-@ Irish began <unk> to the Middle Colonies in waves after 1717 . They primarily pushed farther into the western frontier of the colonies , where they repeatedly confronted the Indians . Other groups included the French Huguenots , Welsh , Dutch , Swedes , Swiss , and Scots Highlanders . 
- 
- = = = English colonists = = = 
- 
- When the English took direct control of the Middle Colonies around 1664 , many Quakers from Rhode Island had already been pushed into the region by Puritans , while <unk> businessmen settled in Philadelphia and New York City . 
- Welsh Quakers , Baptists and <unk> settled in the Welsh <unk> of Pennsylvania . While some Welsh colonists like Roger Williams , left to found Rhode Island , Anne Hutchinson founded a seed settlement in New York . Rhode Island was not initially counted as part of New England , having been excluded from the New England Confederation , but later joined the Dominion of New England . Thus , the definition of the Middle Colonies sometimes changed and overlapped with Rhode Island 's colonial boundaries . After joining the Dominion of New England , however , Rhode Island was permanently thought of as a New England colony . New York 's initial possession of parts of Maine ensured a close relationship with other New England colonies like Vermont and a continuing New England influence in the colony . 
- Both William Penn and the Lords Baltimore encouraged Irish Protestant immigration , hoping they could obtain indentured servants to work on their estates and on colonial developments . Often areas of the Middle Colonies displayed prevalent Irish cultural influence . 
- 
- = = = Labor = = = 
- 
- Labor was always in short supply . The most common solution was indentured servitude of young whites . These were teenagers in Britain or Germany whose parents arranged for them to work for families in the colonies until age 21 , in exchange for their ocean passage . The great majority became farmers or farm wives . By the mid @-@ eighteenth century , African American slaves comprised 12 % of the population of New York . Most were house servants in Manhattan , or <unk> on Dutch estates . 
- 
- = = = Religion = = = 
- 
- The Middle Colonies were the religiously diverse part of the British Empire , with a high degree of tolerance . The Penn family were Quakers , and the colony became a favorite destination for that group as well as German <unk> , German Reformed and numerous small <unk> such as Mennonites , Amish and <unk> , not to mention Scotch Irish Presbyterians . The Dutch Reformed were strong in <unk> New York and New Jersey , and Congregationalists were important in Long Island . The First Great Awakening invigorated <unk> and helped stimulate the growth of Congregational , Methodist and Baptist churches . Non @-@ British colonists included Dutch <unk> , Swedish <unk> , <unk> Mennonites , and the Amish . 
- 
- = = Historiography = = 
- 
- <unk> , Wayne . " Themes and Directions in <unk> Colonies Historiography , 1980 @-@ 1994 , " William and Mary Quarterly , July 1994 , Vol . 51 Issue 3 , <unk> 355 – 88 in JSTOR 
- <unk> , Wayne . " The " Myth of the Middle Colonies " <unk> : The <unk> of <unk> in Early America , " Pennsylvania Magazine of History and Biography , Vol . 113 , No. 4 ( <unk> , 1989 ) , pp. 527 @-@ <unk> in JSTOR 
- Greenberg , Douglas . " The Middle Colonies in Recent American Historiography , " William and Mary Quarterly , July 1979 , Vol . 36 Issue 3 , <unk> <unk> – <unk> in JSTOR 
- 
- = Bath Assembly Rooms = 
- 
- The Bath Assembly Rooms , designed by John Wood , the Younger in 1769 , are a set of elegant assembly rooms located in the heart of the World Heritage City of Bath in England which are now open to the public as a visitor attraction . They are designated as a Grade I listed building . 
- During the Georgian era Bath became fashionable . The architects John Wood , the Elder and his son John Wood , the Younger laid out new areas of housing for residents and visitors . Assembly rooms had been built early in the 18th century , but a new venue for balls , concerts and gambling was envisaged in the area between Queen Square , The Circus and the Royal Crescent . Robert Adam submitted a proposal that was rejected as too expensive . John Wood , the Younger raised funding through a <unk> and construction started in 1769 . The New or Upper Assembly Rooms opened with a grand ball in 1771 and became the hub of fashionable society , being frequented by Jane Austen and Charles Dickens , along with the nobility of the time . 
- The Bath stone building has rooms arranged in a U shape . There are four main function rooms in the complex : the 100 @-@ foot @-@ long ( 30 m ) ballroom — the largest Georgian interior in Bath ; the tea room ; the card room ; and the <unk> . The rooms have <unk> crystal chandeliers and are decorated with fine art . In the 20th century they were used as a cinema and in 1931 were taken over by the Society for the Protection of Ancient Buildings and restored . They were bombed and burnt out during the Second World War , with restoration undertaken by Sir Albert Richardson before reopening in 1963 . They are now owned by the National Trust and operated by Bath and North East Somerset Council for public functions . The basement of the building provides a home to the Fashion Museum . 
- 
- = = History = = 
- 
- Several areas of Bath had undergone development during the Stuart period , and development increased during Georgian times in response to the increasing number of visitors to the spa and resort town who required accommodation . The architects John Wood , the Elder and his son John Wood , the Younger laid out the new quarters in streets and squares , the identical façades of which gave an impression of palatial scale and classical <unk> . Much of the creamy gold Bath stone used for construction throughout the city was obtained from the limestone <unk> Down and <unk> Down <unk> , which were owned by Ralph Allen ( 1694 – 1764 ) . 
- Much of the development at this time consisted of new residential areas away from the old city centre . Queen Square was the first speculative development by John Wood , the Elder , who lived in one of the houses . The Circus consists of three long , curved terraces designed by the elder John Wood to form a circular space or theatre intended for civic functions and games . The games give a clue to the design , the inspiration behind which was the Colosseum in Rome . The most spectacular of Bath 's terraces is the Royal Crescent , built between 1767 and 1774 and designed by the younger John Wood . Gay Street links Queen Square to The Circus . All of which were designed by John Wood , the Elder in <unk> and completed by his son John Wood , the Younger . 
- The heart of the Georgian city was Wood 's <unk> Room , which , together with its associated Lower Assembly Rooms , was designed by Thomas Baldwin , a local builder responsible for many other buildings in the city , including the terraces in Argyle Street and the Guildhall , The Lower Assembly Rooms consisted of two buildings . The first built in 1708 for Thomas Harrison overlooking Parade Gardens between North Parade and Bath Abbey . A large ballroom was added in 1720 , with further enlargement in <unk> and 1810 when it became known as The Kingston Assembly Rooms . In <unk> another building , known as Lindsey 's Assembly Rooms , was constructed , lasting until demolition around 1820 for the building of York Street . Harrison 's Lower Assembly Rooms were devastated by a fire in 1821 and rebuilt , lasting until demolition in 1933 for road improvements on the site now known as " <unk> Island " . 
- In around 1770 the neoclassical architect Robert Adam designed Pulteney Bridge , a three @-@ arched bridge spanning the River Avon . He used as his prototype an original , but unused , design by Andrea <unk> for the <unk> Bridge in Venice . Adam also submitted plans for the new Assembly Rooms but these were rejected as too costly . John Wood , the Younger raised funding for the construction of the Assembly Rooms by the use of a <unk> , an investment plan that is named after the Neapolitan banker Lorenzo de <unk> , who is credited with <unk> it in France in 1653 . It combines features of a group annuity and a lottery . Each <unk> pays an agreed sum into the fund , and thereafter receives an annuity . As members die , their shares <unk> to the other participants , and so the value of each annuity increases . On the death of the last member , the scheme is wound up . Construction started in 1769 and was completed in 1771 , when a grand opening was held . 
- The Assembly Rooms formed the hub of fashionable Georgian society in the city , the venue being described as " the most noble and elegant of any in the kingdom " . They were originally known as the Upper Rooms as there was also a lower assembly room in the city , which closed soon after the Upper Rooms opened . They served the newly built fashionable area which included The Circus , Queen Square and the Royal Crescent . 
- People would gather in the rooms in the evening for balls and other public functions , or simply to play cards . Mothers and <unk> bringing their daughters to Bath for the social season , hoping to marry them off to a suitable husband , would take their charge to such events where , very quickly , one might meet all the eligible men currently in the City . At one concert in 1779 , attended by around 800 ladies and gentlemen , 60 members of the nobility were present . During the season , which ran from October to June , at least two balls a week were held , in addition to a range of concerts and other events . Scenes such as this feature in the novels of Jane Austen , who lived in Bath with her parents and sister from 1801 to 1805 . Her two novels set in Bath , <unk> Abbey and <unk> , were published in 1818 and both mention the Assembly Rooms : 
- Mrs Allen was so long in dressing , that they did not enter the ball @-@ room till late . The season was full , the room crowded , and the two ladies squeezed in as well as they could . As for Mr Allen , he repaired directly to the card @-@ room , and left them to enjoy a mob by themselves . 
- Sir Walter , his two daughters , and Mrs Clay , were the earliest of all their party at the rooms in the evening ; and as Lady <unk> must be waited for , they took their station by one of the fires in the <unk> Room . 
- Charles Dickens also visited Bath on several occasions . He gave public readings in the Assembly Rooms and mentions them in The <unk> <unk> ( published in 1837 ) : 
- In the ball @-@ room , the long card @-@ room , the octagonal card @-@ room , the staircases , and the passages , the hum of many voices , and the sound of many feet , were perfectly <unk> . <unk> <unk> , feathers <unk> , lights <unk> , and jewels <unk> . There was the music — not of the <unk> band , for it had not yet commenced ; but the music of soft tiny footsteps , with now and then a clear <unk> laugh — low and gentle , but very pleasant to hear in a female voice , whether in Bath or elsewhere . 
- George <unk> , an Afro @-@ Polish @-@ born virtuoso violinist , made his debut at the Assembly Rooms in 1789 . Another young violinist , Thomas <unk> the younger , played a series of concerts between 1771 and 1776 . Many of the concerts during the late 18th and early 19th centuries were organised by <unk> <unk> . 
- In the 20th century several changes took place , with the Ballroom becoming a cinema , until the Society for the Protection of Ancient Buildings became the owners in 1931 . The building was restored by A <unk> Green in 1938 , with Oliver <unk> as the interior designer . During the Bath Blitz of 25 / 26 April 1942 , one of the retaliatory raids on England by the <unk> Blitz following the RAF 's raid on <unk> , the Assembly Rooms were bombed and burnt out inside . After the cessation of hostilities in Europe , they were restored by Sir Albert Richardson , with work being completed in 1963 . The ballroom ceiling had to be repaired after it collapsed in 1989 . 
- 
- = = Architecture = = 
- 
- The limestone building has a slate <unk> roof . It is rectangular with a projecting <unk> portico entrance and an extension to the rear . The interior is laid out in a U shape , with the larger Ball Room and <unk> Room along either side with the octagonal Card Room at the end . The rooms have <unk> crystal chandeliers and are decorated with pictures by Thomas Gainsborough , Allan Ramsay ( artist ) , Edwin Long and William Hoare . 
- The Ballroom has five chandeliers and capacity for up to 500 people . It is over 100 feet ( 30 m ) long and nearly 45 feet ( 14 m ) wide . The ceiling is 42 feet ( 13 m ) high . The <unk> Room holds up to 250 people . It was the location for a banquet attended by The Prince of Wales for the BBC television series the Great British Menu . It is 60 feet ( 18 m ) long and 42 feet ( 13 m ) wide . The <unk> is named for the shape of the room has four <unk> . It is 42 feet ( 13 m ) across . It originally held an organ in the musicians gallery . In 1777 the Card Room was added . This is now used as a bar . 
- 
- = = Current use = = 
- 
- Today the rooms are owned by the National Trust and operated by Bath and North East Somerset Council . The main rooms are still available for hire for private functions . They are also used for concerts , including ones that are part of the Bath International Music Festival . 
- The basement of the building provides a home to the Fashion Museum , which was known before 2007 as the Museum of Costume . The collection was started by Doris Langley Moore , who gave her collection to the city of Bath in 1963 . It focuses on fashionable dress for men , women and children from the late 16th century to the present day and has more than 30 @,@ 000 objects . The earliest pieces are embroidered shirts and gloves from about 1600 . 
- The grandeur of the building make it a popular location for feature films and television series set in the Georgian period . The BBC have used it as a location for the filming of an adaptation of <unk> Abbey in 1986 and in 1995 <unk> . 
- 
- 
- = Flash Gordon Strange Adventure Magazine = 
- 
- Flash Gordon Strange Adventure Magazine was a pulp magazine which was launched in December 1936 . It was published by Harold Hersey , and was an attempt to cash in on the growing comics boom , and the popularity of the Flash Gordon comic strip in particular . The magazine contained a novel about Flash Gordon and three unrelated stories ; there were also eight full @-@ page color illustrations . The quality of both the artwork and the fiction was low , and the magazine only saw a single issue . It is now extremely rare . 
- 
- = = Publication history and contents = = 
- 
- Although science fiction ( sf ) had been published before the 1920s , it did not begin to <unk> into a separately marketed genre until the appearance in 1926 of Amazing Stories , a pulp magazine published by Hugo <unk> . After 1931 , when Miracle Science and Fantasy Stories was launched , no new science fiction magazines appeared for several years . In 1934 a science fiction comic strip following the adventures of superhero Flash Gordon appeared in newspapers and quickly became popular . In 1936 the strip spawned a movie serial in thirteen parts , also titled Flash Gordon . Late that year Harold Hersey , an experienced pulp magazine editor and publisher , decided to launch three new magazines based on comics . The first two were titled Dan Dunn Detective Magazine and <unk> Tommy Air Adventure Magazine ; these were launched in September and October 1936 respectively . The third was Flash Gordon Strange Adventures Magazine , which saw a single issue , dated December 1936 ; it was copyrighted by both Hersey and King Features , the syndicate that owned the copyright to Flash Gordon . Dan Dunn and <unk> Tommy produced one more issue each before Hersey closed down the venture . It is not known why only one issue of Flash Gordon Strange Adventure Magazine appeared ; poor sales figures from the other magazines may have been responsible , or Hersey may simply have run out of money , or possibly King Features , the owner of the copyright to Flash Gordon , only granted rights to Hersey for one issue , and withdrew from the venture after the first issue appeared . <unk> historian Everett Bleiler notes that Hersey did not mention the venture in his autobiographical <unk> Editor , published a year later , and adds that " given Hersey 's usual attempts to glorify himself and to <unk> his failures , this silence suggests a <unk> larger than usual " . 
- The magazine contained a lead novel and three short stories . The novel , The Master of Mars , by James Edison <unk> ( or <unk> ; the name is spelled one way on the contents page and the other way at the head of the story ) , has been described by Bleiler as " <unk> " . Bleiler also comments that of three short stories , one is dated and another third @-@ rate . Two of the stories were by <unk> <unk> ; one , " The Saga of the <unk> " , was published under his own name ; the other , " The Last War " , was published under the pseudonym " <unk> <unk> " . The other story , " The Man Without a Brain " , is a collaboration between <unk> Vance ( or <unk> <unk> ; as with <unk> , the magazine is inconsistent in spelling the name ) and <unk> Young . 
- Hersey 's idea was to have a pulp magazine about comic strip characters ; he hoped that there would be sufficient overlap between pulp readers and comics fans to make the magazine successful . The presentation was like that of a typical pulp , but with eight full page color illustrations , all by Fred <unk> , who had previously illustrated Westerns . Bleiler describes the artwork as crude and " far inferior to the sometimes elegant work " of Alex Raymond , the creator of Flash Gordon . 
- The attempt to market pulp fiction to comics fans turned out to be the wrong approach : the comics field was on the verge of dramatic successes , but the crossover appeal for pulp magazines was not there . The magazine was not widely known at the time it was issued , and has since become extremely rare . 
- 
- = = <unk> details = = 
- 
- The publisher of Flash Gordon Strange Adventure Magazine was <unk> Publishing Co . , based in New York . The sole issue was numbered volume 1 , number 1 ; it was in large pulp format , with 96 pages and was priced at 10 cents . Harold Hersey was the president of <unk> and the editor of the magazine . A facsimile of the magazine was released as a book in 2005 . 
- 
- 
- = Tropical Storm Olaf ( 1997 ) = 
- 
- Tropical Storm Olaf was an erratic and long @-@ lived tropical cyclone that brought heavy rainfall to regions of Mexico , which would be devastated by Hurricane Pauline a week later . The sixteenth named storm of the 1997 season , Olaf formed on September 26 off the southern coast of Mexico . It moved northward and quickly intensified , reaching peak winds of 70 mph ( 120 km / h ) before weakening and hitting Oaxaca as a tropical depression . In Mexico , El Salvador , and Guatemala , the system brought heavy rainfall , which killed 18 people and caused flooding and damage . It was originally thought that Olaf dissipated over Mexico , although its remnants continued westward for a week . It interacted with Hurricane Pauline , which caused Olaf to turn to the southeast and later to the north to strike Mexico again , finally dissipating on October 12 . 
- 
- = = Meteorological history = = 
- 
- The origins of Olaf were from a tropical wave first noted over Central America on September 22 . It moved slowly through the eastern Pacific Ocean , and gradually developed an area of convection . <unk> , an upper @-@ level low @-@ pressure area moved from the Gulf of Mexico across Mexico into the Pacific , which produced wind shear across the region ; wind shear is the difference in wind speed and direction in the atmosphere , and is usually harmful to tropical cyclogenesis . The disturbance associated with the tropical wave persisted and developed outflow . This caused the upper @-@ level low to move away from the system . On September 26 , it was sufficiently organized to be classified Tropical Depression Seventeen @-@ E , while located about 345 miles ( 560 km ) south of the Gulf of Tehuantepec . 
- A few hours after developing , the depression attained tropical storm status , or winds of at least 40 mph ( 65 km / h ) . Upon doing so , the National Hurricane Center ( NHC ) gave it the name " Olaf " . The upper @-@ level low , which was moving away from the region , caused the storm to move northward toward the Mexican coast . Olaf quickly intensified , as evidenced by reports from a nearby ship , and the winds reached 70 mph ( 115 km / h ) by late on September 27 ; the NHC anticipated further intensification to hurricane status , or winds of at least 75 mph ( 120 km / h ) . Instead , interaction with the rough terrain of Mexico caused weakening . Olaf made landfall on <unk> Cruz , Oaxaca early on September 29 as a 35 mph ( 55 km / h ) tropical depression . Within a few hours , the circulation was not evident on satellite imagery , and the NHC discontinued advisories . 
- Despite being considered dissipated , a re @-@ analysis of satellite imagery indicated the circulation of Olaf persisted as turned to the west toward open waters . Early on September 30 , the system reached the Pacific , and it continued westward for about a week , during which it retained some convective activity . On October 5 , Olaf turned toward the east , as it interacted with the large circulation of developing Hurricane Pauline . Later that day , the NHC resumed issuing advisories , while it was located about 560 miles ( 900 km ) southwest of the southern tip of the Baja California Peninsula . The system turned to the southeast , and , failing to organize , the NHC discontinued advisories on October 8 . Three days later , after the remnants of Olaf turned toward the north , the NHC again resumed advisories , when it was just 70 miles ( 115 km ) south @-@ southwest of <unk> , Colima . Late on October 12 , the circulation of Olaf made its final landfall near Manzanillo , Colima , and it quickly dissipated . An associated area of thunderstorms moved over open waters again , but failed to <unk> . 
- 
- = = Impact and preparations = = 
- 
- Prior to moving ashore , a tropical storm warning was issued from Punta <unk> , Guerrero to <unk> , Chiapas and the Port of Chiapas , near the Mexico – Guatemala border . When Olaf was strengthening faster than anticipated , the advisory was upgraded to a hurricane warning , although it was downgraded to a tropical storm after the intensification did not occur . Upon making its first landfall , Tropical Depression Olaf produced gusty winds and heavy rains along the southeastern coast of Mexico . The peak 24 ‑ hour rainfall total was 6 @.@ 71 inches ( 17 @.@ 1 cm ) in <unk> de <unk> in Oaxaca ; the highest rainfall total throughout Olaf 's duration was 27 @.@ 73 inches ( 70 @.@ 4 cm ) at a station called <unk> / <unk> in Oaxaca . Heavy rainfall was also reported in Guatemala and El Salvador . 
- Across the affected region , the heavy rainfall caused flooding , which resulted in 18 deaths . In Mexico , the flooding damaged 50 @,@ 000 acres ( 200 km2 ) of coffee , corn , and other crops ; in Chiapas , the coffee crop damage represented a severe cut into the yearly total . The precipitation flooded many buildings across the region , including 30 houses in the Chiapas capital of <unk> Gutiérrez , when a river exceeded its banks . In mountainous regions , mudslides left dozens of small villages isolated from the outside world . Along the coast , high waves of up to 16 feet ( 5 m ) forced the closure of all ports in three Mexican states , which affected thousands of fishermen . Further west , three fishing vessels were reported missing near Acapulco , prompting rescue parties . The storm also forced the closure of several airports . In its final landfall , there was no damage reported ; precipitation in that region peaked at 2 @.@ 96 inches ( 7 @.@ 5 cm ) in <unk> , Colima . 
- Following the storm , the Mexican government sent food , water , and housing supplies to affected families in Oaxaca . Less than a week after Olaf moved through southern Mexico , Hurricane Pauline struck the same region with much stronger winds . Pauline caused heavier rainfall and more damage , killing at least 250 people . Some regions received 10 days of heavy rainfall . 
- 
- 
- = Road to the North Pole = 
- 
- " Road to the North Pole " is the seventh episode of the ninth season of the animated comedy series Family Guy . Directed by Greg Colton and co @-@ written by Chris Sheridan and Danny Smith , the episode originally aired on Fox in the United States on December 12 , 2010 . In " Road to the North Pole " , two of the show 's main characters , baby Stewie and anthropomorphic dog Brian , who are voiced by series creator Seth MacFarlane , go on an adventure to the North Pole in an attempt to kill Santa Claus . They eventually discover a dreary , <unk> factory full of disease @-@ ridden elves and carnivorous , feral reindeer , along with a sickly , exhausted Santa who begs to be killed . Stewie and Brian take pity on him , however , and decide to fulfill Christmas by delivering gifts to the entire globe , albeit unsuccessfully . 
- The " Road to " episodes which have aired throughout various seasons of Family Guy were inspired by the Road to ... comedy films starring Bing Crosby , Bob Hope and Dorothy <unk> , though this episode was not originally conceived as a " Road to " show . The episode is the second Family Guy Christmas special after the season three episode , " A Very Special Family Guy <unk> ' Christmas " , also written by Danny Smith . It was first announced at the 2010 San Diego Comic @-@ Con International . 
- Critical responses to the episode were mostly positive ; critics praised its storyline and its numerous cultural references , although it also received criticism from the Parents Television Council . According to Nielsen ratings , it was viewed in 8 @.@ 03 million homes during its original airing in the United States . The episode featured guest performances by Drew Barrymore , H. Jon Benjamin , David Boreanaz , Carrie Fisher , and <unk> Scott Collins , along with several recurring guest voice actors for the series . It is narrated by Ron MacFarlane , Seth MacFarlane 's father . It was nominated for 3 Emmy Awards : Outstanding Music Composition for a Series , Outstanding Original Music and Lyrics and Outstanding Sound Mixing for a Comedy or Drama Series ( Half @-@ Hour ) and Animation . It later won for Outstanding Sound Mixing for a Comedy or Drama Series ( Half @-@ Hour ) and Animation . The song " Christmastime Is Killing Us " was nominated for a Grammy Award for Best Song Written for Visual Media . 
- 
- = = Plot = = 
- 
- Brian takes Stewie to the mall , only to get a rude brush @-@ off from Santa . As a result , Stewie vows to kill Santa for blowing him off and forces Brian to take him to the North Pole . Attempting to trick Stewie , he brings him to a Santa 's Village amusement park . Soon discovering the <unk> , Stewie threatens to shoot Brian if he does not take him to the real North Pole . Stewie <unk> a ride with a <unk> and so Brian follows him all the way to Canada . On the way , Stewie accidentally causes a traffic <unk> by <unk> a flare pistol in the cab of the truck , which catches fire and explodes . <unk> his car in a chain reaction , Brian becomes angry and tells Stewie that Santa does not exist . Stewie becomes frustrated and continues to attempt to <unk> . The pair then encounter a Canadian who gives them his <unk> . 
- Continuing north , they soon run out of gas , but receive help from the Aurora Boreanaz , who instructs them to stay at a nearby cabin . The two survive the night in the cabin and set out on foot the next morning . They finally make it to Santa 's workshop , only to find a dark , <unk> factory in a dreary , polluted , lifeless wasteland , Santa a sickly and somewhat <unk> old man , the elves horribly <unk> and <unk> from Santa 's attempt to keep up with the increasing toy demands year after year , and the reindeer carnivorous feral monsters that eat the elves who wandered out into the snow to die of exhaustion . Santa suddenly collapses and is too sick to deliver the presents . Brian and Stewie agree to do it , only to waste time at their first stop and leave , after being discovered by a family , whom they tie up . The reindeer eat each other . 
- On Christmas morning , everybody wakes up to find no presents under their trees . They turn on the news , which is broadcasting the same story . Brian and Stewie appear on the broadcast and bring the dying Santa out in a wheelchair , explaining that humanity 's <unk> is killing him and if they don 't shorten their demands to one Christmas present a year , they may have to give up Christmas altogether . <unk> , everyone agrees and , one year later , Santa has recovered , the workshop is once again a lively , colorful <unk> cottage and the elves and reindeer are all <unk> . 
- 
- = = Production and development = = 
- 
- " Road to the North Pole " is the sixth episode of the " Road to " episodes of the series which air through various seasons of the show . It was directed by Family Guy veteran Greg Colton , this being the first episode he has directed since the eighth season episode " Go Stewie , Go . " This is also Colton 's third " Road to " episode , the first being " Road to Germany " and the second being " Road to the <unk> . " The episode was written by Chris Sheridan and Danny Smith , this being the first Smith wrote since " Partial Terms of <unk> , " and his first " Road to " episode . It included staff writers Alex Carter , Andrew Goldberg and Elaine <unk> . It is an hour @-@ long special with three musical numbers . Ron MacFarlane , Seth MacFarlane 's father , served as the episode 's narrator . This is also the first " Road to " episode to be composed by Ron Jones . 
- Two of the musical numbers , " All I Really Want for Christmas " and " Christmastime is Killing Us " were released as digital downloads on iTunes . " Christmastime is Killing Us " was available on December 3 , 2010 , while " All I Really Want for Christmas " was made available on December 10 , 2010 . 
- In addition to the regular cast , the episode also guest starred actress Drew Barrymore , voice actor H. Jon Benjamin , actor David Boreanaz , actress <unk> Scott Collins , actress Carrie Fisher , actor Ron MacFarlane , father of series creator and executive producer Seth MacFarlane , actor Bruce McGill , voice actor Will Ryan , voice actress Tara Strong and actress <unk> <unk> . Recurring guest voice actors John G. <unk> , actor Chris Cox , actor Ralph Garman , writer Chris Sheridan , writer Danny Smith , writer Alec <unk> , actress Jennifer <unk> and writer John <unk> also made minor appearances . 
- 
- = = Cultural references = = 
- 
- This episode as well as the entire " Road to " series in Family Guy is a parody of the seven Road to ... comedy films which were released from 1940 to 1962 , starring actors Bing Crosby , Bob Hope and actress Dorothy <unk> . The opening credits show images with Brian and Stewie referencing other Christmas <unk> such as The <unk> , A Christmas Carol , How the <unk> <unk> Christmas , <unk> the <unk> and Home Alone . The credits also show Brian and Stewie performing winter activities , such as <unk> fights , making snow angels and putting coal in the ( Meg 's ) Christmas socks instead of gifts . Ron MacFarlane , who narrated part of the episode , mentioned that Kenny Rogers was supposed to be there . 
- The episode opens with a musical number in which the members of Quahog sing about what they want for Christmas . Peter wishes to have actress and models Jessica <unk> and Megan Fox . He also wishes to have lunch with Michael <unk> 's ghost and wants twelve <unk> of beer . Lois wishes to visit the Spanish coasts , and " Mexico , with two black guys and some blow " . Chris wishes for Jennifer Garner and Meg wishes for a <unk> . The neighbors of Quahog also wish for gifts : Herbert wishes for a drummer boy ( there is a picture of singer Nick Jonas on the wall while he wishes for this ) , Mayor Adam West wishes for a <unk> , Carl wishes for a Blu @-@ ray version of The <unk> and <unk> wishes for more Lemon <unk> . Continuing with the song , <unk> Russell wishes for Easter eggs , Joe wishes for one day when kids don 't stare at him , Bonnie wants platinum @-@ plated <unk> , <unk> wants " Japanese girls of no restraint " to <unk> him and then whip him and Mort ( who is Jewish ) says he will sue if they put a Christmas tree in the airport . The song ends with various characters appearing in an advent calendar . 
- Brian and Stewie go to the mall so they can meet Santa , but Peter is asking Santa for gifts ( he asks for a game of Uno , a Magna <unk> , a pet <unk> ( a mix of a <unk> and a mink ) and a Charles in Charge <unk> . ) When the mall Santa leaves for the night and Brian demands that he let Stewie sit in his lap , Santa mentions he will be at <unk> 's . Stewie says that Santa leaving before he got a chance to sit in his lap felt like a bigger betrayal than the betrayal of Gary <unk> by reality ; this takes us to <unk> looking himself in the mirror asking his reflection , in the form of a <unk> clown , how he is doing . 
- Brian and Stewie decide to go to the North Pole to kill Santa , but Brian does not want Stewie to get disappointed if Santa is not what everybody thinks he is ; to this Stewie responds that Brian is as negative as <unk> from <unk> @-@ the @-@ <unk> . To prevent Stewie from going to the North Pole he tells him that Santa is not real ; Stewie questions this , also asking if <unk> , <unk> <unk> and Curious George aren 't real . On their way to the North Pole , Brian and Stewie find themselves in Canada , where they meet a man with a thick Canadian accent ; they also see the Aurora <unk> and the Aurora Boreanaz ( an <unk> with David Boreanaz 's face ) . 
- When they finally get to the North Pole and find it polluted and lifeless , Stewie compares it to Bridgeport , Connecticut ; thus resulting in a cutaway to a Bridgeport resident writing an angry letter to the Family Guy writer staff about Stewie 's comment . When Santa Claus is near death , he <unk> Stewie by saying " I 'll be with Allah soon " . When Brian and Stewie decide to deliver the presents for Santa , in their travel the Statue of Liberty can be seen . Unfortunately , they are not able to deliver the presents , and the next morning the residents of Quahog are upset because they have no presents , but Mort says he got eight mediocre gifts . 
- 
- = = Reception = = 
- 
- " Road to the North Pole " was broadcast on December 12 , 2010 , as a part of an animated television night on Fox , and was preceded by The Simpsons , and followed by Family Guy creator and executive producer Seth MacFarlane 's second show , American Dad ! . It was watched by 8 @.@ 03 million viewers , according to Nielsen ratings , despite airing simultaneously with the Desperate Housewives on ABC , The Amazing Race and Undercover Boss on CBS and Sunday Night Football on NBC . The episode also acquired a 3 @.@ 9 rating in the 18 – 49 demographic , beating American Dad ! and The Simpsons in addition to significantly edging out both shows in total viewership . The episode 's ratings increased significantly from the previous week 's episode . 
- This episode received generally positive response from critics . Todd VanDerWerff of The A.V. Club gave " Road to the North Pole " a positive review , stating that it is " a satisfying episode of Family Guy all around , filled with funny gags and nice moments . " He especially praised the musical segments , and the portrayal of the North Pole , writing that " the way the episode kept <unk> more and more ridiculous <unk> on top of each other kept the whole thing funny . " He rated the episode an " <unk> " . Jason Hughes of TV Squad also praised the songs and the depiction of Santa 's factory , though he found the delivery of the episode 's message " heavy @-@ handed . " Kate Moon of TV <unk> gave the episode 3 @.@ 6 out of 5 stars . She said , " I had mixed feelings about this one , despite its clever moments and hopeful ending . While I normally have no problems about Family Guy ’ s shocking or offensive themes , I felt bit <unk> about the direction of this Christmas episode . " She went on to say , " Perhaps it was the way that the series <unk> on something as innocent as Santa and his elves and twisted them all around . Or perhaps it was the <unk> reindeer . Whatever the specific reason , the <unk> nature of Family Guy seemed just a little too graphic for me this time around . " 
- The Parents Television Council , a conservative campaigning critic of Seth MacFarlane works , named Family Guy its " <unk> TV Show of the Week " for " Road to the North Pole " for the week ending on December 17 , 2010 . It got this rating due to sexual content and excessive violent scenes including the scene in which Stewie beats a man to death with a baseball bat , and the scenes featuring Seth MacFarlane 's father , Ron , while also stating , " Forget <unk> or nice . This show was simply <unk> . " Robin Pierson of The TV Critic gave " Road to the North Pole " a 47 out of a possible 100 , said " A little more interesting plot wise than the usual fare but otherwise just as gruesome " . He especially disliked the portrayal of Santa and his elves and the violence . 
- The episode was also nominated for Outstanding Original Music and Lyrics ( for song " Christmastime Is Killing Us " , written by Ron Jones , Seth MacFarlane , and Danny Smith ) and Outstanding Sound Mixing for a Comedy or Drama Series ( Half @-@ Hour ) and Animation . It won for Outstanding Sound Mixing for a Comedy or Drama Series ( Half @-@ Hour ) and Animation . 
- The series was successfully nominated in 2009 , but failed to merit an award . Mark <unk> , executive producer and showrunner of Family Guy said of the nominating process , " We had internal discussions in the writers ' room , and it seemed like we were much more akin to the other <unk> comedies than we were to children 's shows in animation . We assumed we would not get anywhere , and so it was a great surprise when we got the nomination . " 
- " Christmastime Is Killing Us " was nominated for Best Song Written for a Visual Media at the 54th Grammy Awards . 
- 
- 
- = Ed Barrow = 
- 
- Edward Grant Barrow ( May 10 , 1868 – December 15 , 1953 ) was an American manager and front office executive in Major League Baseball . He served as the field manager of the Detroit Tigers and Boston Red Sox . He served as business manager ( de facto general manager ) of the New York Yankees from 1921 to 1939 and as team president from 1939 to 1945 , and is credited with building the Yankee dynasty . Barrow was elected to the Baseball Hall of Fame in 1953 . 
- Born in a covered wagon in Springfield , Illinois , Barrow worked as a journalist and soap salesman before entering the business of baseball by selling concessions at games . From there , Barrow purchased minor league baseball teams , also serving as team manager , and served as president of the Atlantic League . After managing the Tigers in 1903 and 1904 and returning to the minor leagues , Barrow became disenchanted with baseball , and left the game to operate a hotel . 
- Barrow returned to baseball in 1910 as president of the Eastern League . After a seven @-@ year tenure , Barrow managed the Red Sox from 1918 through 1920 , leading the team to victory in the 1918 World Series . When Red Sox owner Harry Frazee began to sell his star players , Barrow joined the Yankees . During his quarter @-@ century as their baseball operations chief , the Yankees won 14 AL <unk> and 10 World Series titles . 
- 
- = = Early life = = 
- 
- Barrow was born in Springfield , Illinois , the oldest of four children , all male , born to <unk> Ann <unk> @-@ Heller and John Barrow . Barrow 's father fought in the Ohio Volunteer Militia during the American Civil War . Following the war , Barrow 's parents , with John 's mother , brothers , and sisters , traveled in a covered wagon to Nebraska ; Barrow was born on a hemp plantation belonging to relatives during the trip . The Barrows lived in Nebraska for six years before moving to Des Moines , Iowa . His middle name , Grant , was bestowed on him in honor of Ulysses S. Grant , the Civil War general . 
- Barrow worked as mailing clerk for the Des Moines News in 1887 , receiving a promotion to circulation manager within a year . He became a reporter for the Des Moines Leader after graduating from high school . He became city editor , earning $ 35 a week ( $ 922 in current dollar terms ) . In his last two years living in Des Moines , Barrow established a baseball team , which included future baseball stars Fred Clarke , <unk> Holmes , and <unk> McFarland . 
- Barrow moved to Pittsburgh in 1889 , where he worked as a soap salesman , believing there was money in this business . However , Barrow lost all of money in this business , and went to work as a desk clerk in a Pittsburgh hotel . 
- 
- = = Baseball career = = 
- 
- 
- = = = Early career = = = 
- 
- Barrow partnered with Harry Stevens in 1894 to sell concessions at baseball games . He helped George <unk> form the Interstate League , a Class @-@ C minor league , in 1894 . Barrow , with Stevens and Al <unk> , purchased the <unk> <unk> of the Interstate League in 1896 . Barrow served as field manager until the collapse of the league that season . The team continued in the Iron and Oil League for the rest of the year . 
- Barrow then bought the Paterson Silk <unk> of the Class @-@ A Atlantic League , managing them for the rest of the 1896 season . Barrow discovered <unk> Wagner throwing <unk> of coal at a railroad station in Pennsylvania , and signed him to his first professional contract . Barrow sold Wagner to the Louisville Colonels of the National League ( NL ) for $ 2 @,@ 100 the next year ( $ 59 @,@ <unk> in current dollar terms ) . With poor attendance , Barrow brought in professional <unk> as a draw : he had James J. Corbett play first base while John L. Sullivan and James J. Jeffries <unk> . He also hired <unk> <unk> , the first woman in professional baseball , to pitch a few innings a game . 
- From 1897 through 1899 , Barrow served as president of the Atlantic League . During this time , in the winter of 1898 – 99 , Barrow and Jake Wells established a movie theater in Richmond , Virginia . Barrow managed Paterson again in 1899 , but the league folded after the season . 
- With the money earned from the sale of the Richmond movie theater , Barrow purchased a one @-@ quarter share of the Toronto Maple Leafs of the Class @-@ A Eastern League in 1900 from Arthur Irwin , and served as the team 's manager . Irwin , hired to be the manager of the Washington Senators of the NL , brought his most talented players with him . <unk> the Maple Leafs , Barrow acquired talented players , such as Nick <unk> , and the team improved from a fifth @-@ place finish in 1899 , to a third @-@ place finish in 1900 , and a second @-@ place finish in 1901 . The Maple Leafs won the league championship in 1902 , even though they lost many of their most talented players , including <unk> , to the <unk> American League ( AL ) . 
- Barrow managed in the major leagues with the Detroit Tigers of the AL in 1903 , finishing fifth , a thirteen @-@ game improvement from their 1902 finish . With the Tigers , Barrow feuded with shortstop Kid <unk> . Tigers ' owner Sam Angus sold the team to William <unk> before the 1904 season . Barrow managed the Tigers again in 1904 , but unable to <unk> with Frank <unk> , <unk> 's secretary @-@ treasurer , Barrow <unk> his resignation . He then managed the Montreal Royals of the Eastern League for the rest of the season . He managed the Indianapolis Indians of the Class @-@ A American Association in 1905 and Toronto in 1906 . <unk> with baseball after finishing in last place , Barrow hired Joe Kelley to manage Toronto in 1907 , and after signing the rest of the team 's players , became manager of the Windsor Arms Hotel in Toronto . 
- 
- = = = Return to baseball = = = 
- 
- Barrow returned to baseball in 1910 , managing Montreal . The Eastern League hired Barrow as its president the next year , giving him an annual salary of $ 7 @,@ 500 ( $ 190 @,@ 473 in current dollar terms ) . He served in this role from 1911 through 1917 , and engineered the name change to " International League " before the 1912 season . As league president , he contended with the creation of the Federal League in 1914 , which competed as a major league , and established franchises in International League cities , including Newark , New Jersey , Buffalo , New York , and Baltimore , Maryland . He attempted to gain major league status for the league in 1914 , but was unsuccessful . When the Federal League collapsed , Barrow was the only league president to forbid the outlaw players from playing in his league . 
- After the 1917 season , Barrow attempted to organize the " Union League " , to compete against the AL and NL as a third major league , by merging four International League clubs with four teams from the American Association . Several International League owners opposed Barrow 's policies , including his attempt to form the Union League , and felt he was too close personally to Ban Johnson . When the league 's owners voted to cut his pay to $ 2 @,@ 500 after the 1917 season ( $ 46 @,@ 175 in current dollar terms ) , Barrow resigned . 
- Barrow became manager of the Boston Red Sox in 1918 . As the team lost many of its better players during World War I , Barrow encouraged owner Harry Frazee to purchase <unk> <unk> , Wally <unk> , Bullet Joe Bush , and Amos <unk> from the Philadelphia Athletics for $ 75 @,@ 000 ( $ 1 @,@ 179 @,@ <unk> in current dollar terms ) . During the season , Barrow feuded with his assistant , Johnny Evers , who undermined Barrow 's leadership . The Red Sox won the 1918 World Series . <unk> that star pitcher Babe Ruth was also a great power hitter , Barrow had Ruth pinch hit on days when he wasn 't scheduled to pitch . When Ruth told Barrow that he could only pitch or hit , Barrow decided that Ruth 's bat was more useful than his pitching , and transitioned him from a pitcher into an <unk> . Ruth had a public dispute with Barrow in July 1918 and was reported in the press as intending to leave the Red Sox , although the situation was soon <unk> over . 
- After the 1918 season , Frazee , now in debt , began selling the contracts of star players . He traded Dutch Leonard , <unk> Lewis , and Ernie Shore to the New York Yankees , obtaining Ray Caldwell , <unk> Love , Frank <unk> , Roxy Walters , and cash . Frazee sold Carl Mays to the Yankees during the 1919 season . The Red Sox struggled in 1919 , finishing sixth in the AL . Frazee sold Ruth to the Yankees after the season , against Barrow 's warnings . The Red Sox finished in fifth in 1920 . 
- 
- = = = New York Yankees = = = 
- 
- After the 1920 season , Barrow resigned from the Red Sox to become the business manager of the Yankees , replacing the deceased Harry <unk> . He took control of building the roster , which was usually the field manager 's responsibility in those days . With the Yankees , Barrow handled the signing of player contracts , although owner Jacob Ruppert personally handled the contracts of Ruth and Lou Gehrig . 
- Barrow installed himself in the Yankees ' infrastructure between co @-@ owner <unk> L <unk> <unk> and manager Miller Huggins , as <unk> frequently criticized Huggins . Barrow told Huggins : " You 're the manager , and you 'll not be second guessed by me . Your job is to win ; mine is to get you the players you need to win . " When Huggins suspended Ruth indefinitely on August 29 , 1925 for " <unk> off the playing field " , while also fining him $ 5 @,@ 000 ( $ 67 @,@ 467 in current dollar terms ) , Barrow supported Huggins . 
- In his first move with the Yankees , Barrow brought Red Sox coach Paul <unk> with him to New York as a scout . He purchased a share in the club in 1924 . He also discovered executive George Weiss , whom he <unk> . Barrow also orchestrated a series of trades with his former club , mainly to keep Frazee afloat . These trades netted the Yankees such stars as Bullet Joe Bush , Joe <unk> and George <unk> . It has been argued that these trades only looked lopsided in favor of the Yankees only because the players sent to Boston suffered a rash of injuries . However , this is <unk> by the fact that Barrow almost certainly knew who was coming to New York in these deals ; he 'd managed nearly all of them in Boston . 
- The Yankees sought to develop their own players , rather than buying them from other teams , especially after the investment of $ 100 @,@ 000 ( $ 1 @,@ <unk> @,@ 261 in current dollar terms ) in <unk> <unk> and <unk> Reese in 1927 . However , Weiss and Bill <unk> convinced Barrow to approve the purchase of Joe <unk> from the Pacific Coast League . 
- Barrow was considered a potential successor to AL president Ban Johnson in 1927 , but Barrow declared that he was not interested in the job . When Huggins died in 1929 , Barrow chose Bob <unk> to replace him as manager , passing over Ruth , who wanted the opportunity to become a player @-@ manager . Barrow also prevented Ruth from managing other teams after he departed the Yankees , by suggesting to executives of other teams that Ruth was not equipped to manage a baseball team . Although Ruth and Barrow had been together for all but one season from 1918 to 1934 , the two had never gotten along . The Sporting News named Barrow their Executive of the Year in 1937 . 
- After Ruppert 's death in 1939 , his will left the Yankees and other assets in a trust for his descendants . The will also named Barrow president of the Yankees , with full authority over the team 's day @-@ to @-@ day operations . Barrow was named Executive of the Year by The Sporting News in 1941 , the second time he won the award . The estate sold the team to a group of Larry <unk> , Dan Topping , and Del Webb in 1945 , and Barrow sold his 10 % stake in the team to the group . Barrow remained as chairman of the board and an informal adviser . Though he signed a five @-@ year contract to remain with the team , he exercised a clause in his contract to free himself as of December 31 , 1946 , in order to officially retire from baseball . AL president Will <unk> offered Barrow the job of Commissioner of Baseball to succeed <unk> Mountain <unk> ; Barrow declined , as he felt he was too old and his health was in decline . 
- 
- = = Personal life = = 
- 
- Barrow was known as " Uncle <unk> " to his friends ; according to writer Tom <unk> , Babe Ruth referred to him as " Barrows , " treating him as if he were " a butler in an English drawing room comedy . " He resided in Rye , New York . He first married in 1898 , but did not discuss it in any of his writings . His second marriage was to Fannie Taylor Briggs in January 1912 ; he raised her five @-@ year @-@ old daughter from her previous marriage , <unk> , as his own daughter . 
- Barrow was an able boxer . He once fought John L. Sullivan in an exhibition for four rounds . 
- Barrow was hospitalized on July 7 , 1953 at the United Hospital of Port Chester , New York and died on December 15 , at the age of 85 , due to a <unk> . His body was kept at Campbell 's <unk> Home and interred in <unk> Cemetery in <unk> , Westchester County , New York . 
- 
- = = Legacy = = 
- 
- Barrow was the first executive to put numbers on player uniforms . He also announced the retirement of Lou Gehrig 's uniform number , the first number to be retired . Barrow was also the first executive to allow fans to keep foul balls that entered the stands . Barrow was also the first to require the playing of " The Star @-@ <unk> Banner " , the United States ' national anthem , before every game , not only on holidays . 
- In May 1950 , an exhibition game was played in honor of Barrow , with Barrow managing a team of retired stars . Barrow was elected to the National Baseball Hall of Fame by the Veterans Committee in 1953 . 
- On April 15 , 1954 , the Yankees dedicated a plaque to Barrow , which first hung on the center field wall at Yankee Stadium , near the flagpole and the monuments to Babe Ruth , Lou Gehrig and Miller Huggins . The plaques later moved to the stadium 's Monument Park . 
- 
- 
- = Spanish Hill = 
- 
- Spanish Hill is a hill located in the borough of South Waverly , Pennsylvania . The hill is controversial among historians and archaeologists ; opinions regarding the origin of structures found on the site vary from embankments created by early farmers , to the remnants of a Native American village and <unk> , due to the site 's similarity to the description found in the account of Étienne Brûlé of a settlement called Carantouan . The area in the hill 's vicinity was previously occupied by <unk> Native Americans . It was a common site for both amateur and professional archaeology , as well as relic hunting . The source of the name remains unknown , but various theories have been proposed as to its origin . 
- 
- = = Geography = = 
- 
- In 1795 , François Alexandre Frédéric visited Spanish Hill while en route to Canada . He described the hill as " a mountain in the shape of a sugar loaf , about 100 feet high , with level top , on which are remains of <unk> . One perpendicular <unk> is still remaining , plainly indicating a parapet and ditch . " In 1833 , another individual visiting the hill described " the remains of a wall which runs around the whole exactly on the brow , and within a deep ditch or <unk> running round the whole summit . " In 1898 , <unk> Shepard created a sketch of Spanish Hill , including the portions still visible at the time as well as those no longer extant . Shepard enlisted the assistance of a longtime local resident , Charles Henry Shepard , who claimed to remember " fortifications as consisting of an embankment with a trench behind , giving a height of four or five feet on the inside . " In addition , an <unk> was discovered on the site which was pronounced to be a corn <unk> by <unk> . 
- According to John S. Clark , a surveyor and historian active in the area until the early twentieth century , the topography and size of the site were appropriate to correspond with Brûlé 's description of Carantouan ; Brûlé described a <unk> town , populated by approximately 800 warriors and 4 @,@ 000 individuals in total . He also described the dwellings and fortifications as being similar to those utilized by the <unk> people . Clark 's conclusions were based in part on surveys he conducted at the site in 1878 , when he observed what he believed were fortifications atop the hill . Amateur archaeologist <unk> C. <unk> conducted an excavation at the base of the hill in 1932 , uncovering what he described as " seventy five <unk> extending east and west , " as well as the " effigy of a huge animal . " 
- 
- = = History = = 
- 
- Originally created by <unk> glaciers , Spanish Hill comprises approximately 10 acres ( 40 @,@ 000 m2 ) of earth in a site that is part of the <unk> <unk> . Located at an elevation of 978 feet ( 298 m ) above sea level , it rises approximately 230 feet ( 70 m ) over the nearby floodplain of the <unk> River . The hill is located in South Waverly , Pennsylvania , in Bradford County , just south of the state border with New York , inside of territory once occupied by the <unk> people . It has been acknowledged and studied by historians and archaeologists for over two hundred years . The source of the name is unknown , but individuals traveling through the area between 1795 and 1804 described " Spanish <unk> " as a feature of the hill , and some of the earliest settlers to the region report that local Native Americans referred to the hill either as " <unk> " or " <unk> . " In 1615 , Étienne Brûlé was sent to the area by Samuel de Champlain to meet with Native American tribes in the hope of finding assistance to fight the Iroquois , against whom Champlain had allied with the <unk> people . During his voyage , Brûlé recorded a town called Carantouan ( meaning " Big Tree , " according to <unk> William Martin <unk> ) , which was subsequently included on a map published by de Champlain in 1632 . In the early nineteenth @-@ century , a Native American man who lived in the area near Spanish Hill reportedly refused to ascend it , for fear of a deadly spirit that lived on top . According to the man , the spirit spoke with a <unk> voice and " made holes through Indians ' bodies . " Archaeologist Louise Welles Murray suggested that this could be a reference to cannon or musket fire . 
- In the early twentieth century , archaeological and historical research was conducted regarding a potential connection between Carantouan and the structures described on the hill . After surveying the area in spring and fall , archaeologist <unk> Shoemaker discovered evidence of Native American habitation , including shell <unk> , corn and flint chips , along with various other implements . In 1918 , historian and archaeologist George P. <unk> , after a survey of the site , determined that it was impossible for Spanish Hill to have been the site of the town described by Brûlé . He cited the sharp incline , which would have made ascension difficult , as well as the lack of water and archaeological evidence on the hill as evidence against it having been the location of Carantouan . <unk> that Spanish Hill was the site of the village was also countered by James Bennett Griffin , who found nothing of interest in the area following an archaeological survey in 1931 . However , historian <unk> <unk> suggests that prior excavations conducted by early twentieth @-@ century archaeologist Warren <unk> , as well as years of heavy farming activity in the area may have contributed to the lack of artifacts found during the Griffin expedition . As <unk> wrote : “ Until more information is known , it seems <unk> to eliminate Spanish Hill as a possible site related to the nation of Carantouan , as some researchers have done . ” 
- The site was a popular location , both for archaeological excavations and amateur collecting . According to <unk> , Spanish Hill was " looted " by <unk> , and his finds likely sold to collectors . In addition , the area was heavily <unk> by relic collectors approximately since the early nineteenth @-@ century . 
- On October 15 , 1915 , the Historical Society of Bradford County , Pennsylvania , dedicated a memorial on Spanish Hill in honor of the <unk> of the arrival of Brûlé to the present @-@ day border of Pennsylvania . Later , in 1939 , Section of Painting and <unk> artist <unk> <unk> depicted the hill in a mural entitled " Spanish Hill and the Early <unk> of the <unk> , " for display in the United States Post Office branch of nearby Waverly , New York . The hill was nearly demolished and used for highway fill in 1970 , but the efforts were reportedly halted due to lobbying by local amateur archaeologist <unk> <unk> . 
- 
- 
- = S.R. 819 = 
- 
- " S.R. 819 " is the ninth episode of the sixth season of the science fiction television series The X @-@ Files . It premiered on the Fox network on January 17 , 1999 in the United States . The episode was written by John Shiban , and directed by Daniel <unk> . The episode helps to explore the series ' overarching mythology . " S.R. 819 " earned a Nielsen household rating of 9 @.@ 1 , being watched by 15 @.@ 7 million people in its initial broadcast . The episode received mixed to positive reviews from critics . 
- The show centers on Federal Bureau of Investigation ( FBI ) special agents Fox Mulder ( David Duchovny ) and Dana Scully ( Gillian Anderson ) who work on cases linked to the paranormal , called X @-@ Files . Mulder is a believer in the paranormal , while the skeptical Scully has been assigned to <unk> his work . In the episode , Mulder and Scully have 24 hours to save Assistant Director Skinner ( Mitch Pileggi ) from a biologically engineered disease . In order to combat the disease , Scully looks for a medical answer , while Mulder searches for the culprits behind the attack on Skinner 's life . To aid him in this task , Mulder reaches out to Senator Matheson , whom he hopes can help him find who is responsible before time runs out . 
- Before the writing of " S.R. 819 " , the writers for The X @-@ Files felt that the character of Walter Skinner was becoming too " expendable " . John Shiban , the writer of the episode , decided to re @-@ work Skinner back into the series ' mythology by crafting the episode around him . Mitch Pileggi had to endure long bouts of make @-@ up application , a process that he admitted he " hated " . The nanobots in the blood sample were designed on a computer and then rendered for the final footage . 
- 
- = = Plot = = 
- 
- The episode opens with Assistant Director Walter Skinner ( Mitch Pileggi ) <unk> and horribly <unk> in hospital . His veins are a sickly purple hue and are <unk> <unk> . Suddenly , he goes into cardiac arrest and the doctors begin to <unk> him dead . 
- Twenty @-@ four hours earlier , Skinner loses a boxing match after experiencing a dizzy spell . He is discharged from the hospital but Fox Mulder ( David Duchovny ) and Dana Scully ( Gillian Anderson ) witness a <unk> on his ribs growing . After trawling through security footage from the entrance to the J. Edgar Hoover Building , Scully recognizes a physicist by the name of Dr. Kenneth <unk> , who advises the Senate <unk> on ethics and new technology , who stopped Skinner in the hall that same morning . Mulder and Skinner travel to the physicist 's house but find he is being held hostage . Mulder apprehends one of the <unk> , who does not speak English . They release him since he has papers showing diplomatic immunity . Mulder does a background check on him anyway . 
- The background check leads Mulder to Senator Richard Matheson ( Raymond J. Barry ) , which results in a dead end . Scully discovers Skinner 's blood sample and , after checking , she finds that Skinner 's blood contains <unk> carbon . Meanwhile , Skinner ends up in hospital following a gunfight in the FBI parking garage . Mulder and Scully reunite at the hospital , where Mulder tells Scully that Skinner was investigating a health funding bill called S.R. 819 . Later , the physicist dies of the same carbon blood condition from which Skinner is sick . 
- Skinner remembers having seen , on numerous occasions , a bearded man who showed up suspiciously and who is actually running the scheme . He saves Skinner and sacrifices one of his own men . The case is closed and Skinner is , once again , aggravated with the agents , ordering them to report exclusively to Assistant Director Alvin <unk> ( James <unk> , Jr . ) . The bearded man was actually Alex Krycek ( Nicholas Lea ) , a rogue FBI agent who formerly worked for the Syndicate , who continues to control the potentially debilitating <unk> in Skinner 's system . 
- 
- = = Production = = 
- 
- 
- = = = Writing = = = 
- 
- The character of Walter Skinner had evolved over the course of The X @-@ Files ' sixth season . At the start of season six , however , the producers and writers felt that Skinner 's character was becoming " expendable " . With their transfer away from the X @-@ Files division , Mulder and Scully saw less and less of their former boss . Originally , John Shiban , the writer of the episode , wanted to infect Mulder with nanobots . However , he decided that since the audience knew Mulder would not be killed , this plot would not be very effective . In order to compensate for this loss , Shiban decided to re @-@ work Skinner back into the series mythology by putting him in Mulder 's place . Shiban , inspired by the 1950 noir film <unk> and its 1988 remake which he <unk> called " [ two ] pretty bad movies " , decided to craft an episode of The X @-@ Files around the <unk> of " a guy who 's been poisoned [ and ] has only a short time to live and has to use that time to find out why and by whom he 's being murdered " . 
- Shiban began crafting his story by <unk> a nanobot plot that had been considered by various writers for several seasons . Shiban and the rest of the writers made it a point to give Alex Krycek control over Skinner . In this manner , Skinner once again became a mysterious character , one whose true loyalties were being tested . Shiban noted that , " [ Krycek 's control ] gives Skinner an agenda that Mulder doesn 't know about [ ... ] Which was something we ultimately used again in the <unk> [ sic ] , and will carry us into next year " . 
- 
- = = = Filming and effects = = = 
- 
- Originally , a " time @-@ consuming " fight scene between Skinner and Krycek was supposed to take place . The scenes were cut because of time @-@ constraints and <unk> reasons . However , Skinner 's boxing match proved easy to stage . Mitch Pileggi , who had <unk> competitively in college , went for " <unk> course [ s ] " at the <unk> <unk> in Los Angeles . He later remarked , " It makes me happy that some people will assume there was a stunt double in the ring . There wasn 't ! [ ... ] We both had a pretty good time " . Location manager <unk> Jones called " S.R. 819 " the " damn parking lot episode " . He was tasked with finding the variety of parking lots used in the episode . He later joked that , " I started to wake up screaming about barriers and parking tickets and entrances and exit ramps " . 
- Pileggi had to endure long bouts of make @-@ up application . To create the principal illusion of monstrous veins , long black faux @-@ veins were glued onto his face , arms , and torso . Pileggi , who had had to endure little to no make @-@ up during the early seasons , noted that , " They did a beautiful job and [ the veins ] looked awesome , but man , I hated it ! I really don 't know how those guys on Star Trek or Babylon 5 can stand having that done to them every day . I just wouldn 't work if that 's what it took " . To show the nanobot infection progressing , special effects makeup supervisor John <unk> used two different make @-@ up sets . The two sets were then mixed together electronically in post @-@ production to give the effect of disease progression . The nanobots in the blood sample were designed on a computer and then <unk> with an animation program . Composer Mark Snow 's score for the episode was inspired by Daniel <unk> 's " big @-@ time feature @-@ like action " . 
- 
- = = Broadcast and reception = = 
- 
- " S.R. 819 " first aired in the United States on January 17 , 1999 . This episode earned a Nielsen rating of 9 @.@ 1 , meaning that roughly 9 @.@ 1 percent of all television @-@ equipped households were tuned in to the episode . It was viewed by 15 @.@ 7 million viewers . The episode aired in the United Kingdom and Ireland on Sky1 on May 2 , 1999 and received 690 @,@ 000 viewers , making it the second most watched episode that week . Fox promoted the episode with the tagline " He has 24 hours to solve his own murder ... or die . " The episode was nominated for three 2000 Emmy Awards by the Academy of Television Arts & Sciences for Outstanding Music Composition for a Series ( Dramatic <unk> ) . The episode was later included on The X @-@ Files <unk> , Volume 3 – Colonization , a DVD collection that contains episodes involved with the alien <unk> 's plans to take over the earth . 
- The episode was met with mixed to positive reviews from critics . Tom <unk> , in his book Examination : An <unk> Look at Seasons 6 – 9 of the X @-@ Files wrote positively of the episode , saying , " ' S.R. 819 ' re @-@ established some wonderful conspiracy overtones and perhaps set the stage for more interesting developments in the future . It touched base with the very roots The X @-@ Files sprung out of and did so in strong fashion . " Todd VanDerWerff of The <unk> Club gave the episode a moderately positive review and awarded it a " B " . He enjoyed the plot , calling it " fun " , praised the twist ending , and called the nanobot makeup effects " legitimately terrifying " . He did , however , write critically of Skinner 's role in the episode , noting that his lack of presence made the entry a " disappointing one " . In addition , VanDerWerff criticized the fact that the teaser shows Skinner dying ; he wrote that " [ t ] here ’ s very little gas in the idea of Skinner dying " and that most of the viewers knew he would not die . 
- Paula Vitaris from <unk> gave the episode a mixed review and awarded it two stars out of four . Vitaris cited severe problems with " Skinner 's emotional journey " as the main detractors for the episode . Robert Shearman and Lars Pearson , on the other hand , awarded the episode two out of five stars in their book Wanting to Believe : A Critical Guide to The X @-@ Files , Millennium & The Lone Gunmen . The two , despite writing positively of the " traditional X @-@ File " feel , called the episode " a return to the sort of <unk> <unk> which promises so much but delivers so little " . 
- 
- 
- = Paranthodon = 
- 
- Paranthodon ( <unk> @-@ <unk> @-@ <unk> @-@ don ) is a genus of extinct <unk> dinosaur that lived in South Africa during the Early Cretaceous , approximately 145 @.@ 5 – 136 @.@ 4 million years ago . Discovered in 1845 , it was one of the first stegosaurians found . Its only remains , a partial skull and isolated teeth , were found in the Kirkwood Formation . Although Owen initially identified the fragments as those of the pareiasaur Anthodon , after years of storage in the British Museum of Natural History , Broom identified the partial skull as belonging to a different genus , and named the specimen Palaeoscincus africanus . Several years later , Nopcsa , unaware of Broom 's new name , similarly concluded that it represented a new taxon , and named the binomial Paranthodon owenii . However , since the Nopcsa 's species name was assigned after Broom 's , and Broom did not assign a new genus , both names are now synonyms under the current naming , Paranthodon africanus . The genus name was chosen from the Ancient Greek para , " near " and Anthodon , for the originally proposed similarity of the specimens . 
- In identifying the remains as those of Palaeoscincus , Broom basically classified Paranthodon as an ankylosaurian , a statement backed by the research of Coombs . Nopcsa however , identified the genus as a stegosaurid , which most modern studies agree with . In 1981 , the genus was reviewed , and found to be a valid genus of stegosaurid . Paranthodon is one of a few genera found in the Kirkwood Formation ; other such taxa include theropods , like <unk> ; ornithopods ; and <unk> , like <unk> . 
- 
- = = Discovery and naming = = 
- 
- In 1845 , amateur geologists William <unk> Atherstone and Andrew Geddes Bain discovered several fossils near <unk> , Cape Province , in the <unk> River Valley . This was the first dinosaur find in all of the Southern Hemisphere and Africa . In 1849 and 1853 , Bain sent some of the fossils to the British paleontologist Richard Owen for identification . Among them was an upper jaw Bain referred to as the " Cape Iguanodon " ; as such the site was named " <unk> " . Atherstone published about the find in 1857 , but lamented in 1871 that it had thus far received no attention in London . Only in 1876 did Owen name a series of specimens from the collection as Anthodon <unk> . Anthodon means " flower tooth " . The partial holotype skull BMNH <unk> , the left jaw BMNH 47338 , the matrix BMNH 47338 including bone fragments and impressions of the anterior skull , and the vertebrae BMNH <unk> were all assigned to Anthodon . In 1882 , Othniel Charles Marsh assigned Anthodon to Stegosauridae based on BMNH 47338 , and in 1890 , Richard Lydekker found that although Anthodon was a pareiasaur , its teeth were similar to those of Stegosauridae . 
- In 1909 , the South @-@ African paleontologist Robert Broom visited the collection of the British Museum of Natural History . He concluded that Owen had mixed the partial distorted skull , teeth , and a mandible of a pareiasaur and a partial upper jaw of a dinosaur BMNH 47338 , which were actually from two different species . Broom kept the name Anthodon for the pareiasaur , but identified the other fossil as a member of the genus Palaeoscincus , naming the new species <unk> africanus in 1912 . He found that the anatomy of the teeth were quite different , even thought they resembled each other , as well as those of Stegosaurus . In 1929 , Baron Franz Nopcsa , unaware of Broom 's previous publication , provided a new name for Broom 's P. africanus , as <unk> Watson believed that the jaw should be differentiated from Anthodon . Nopcsa named the species Paranthodon <unk> , with the genus name derived from the Latin para , meaning " similar " , " near " , or " beside " , and Anthodon , and specific name honoring Owen . Due to present conventions , the specific name was later <unk> to owenii . In 1978 , Walter Coombs incorporated both names into the current nomenclature , Paranthodon africanus , as Paranthodon was the first new genus for the fossils and africanus was the first named species . This makes Palaeoscincus africanus and Paranthodon owenii junior synonyms of Paranthodon africanus . 
- The holotype of Paranthodon , BMNH 47338 , was found in a layer of the Kirkwood Formation dated between the <unk> and early Valanginian ages . It consists of the back of the snout , containing the maxilla with teeth , the posterior <unk> <unk> of the premaxilla , part of the nasals , and some isolated teeth probably from the lower jaw . One additional specimen can be assigned to it based on the <unk> , BMNH <unk> , including only isolated teeth sharing the same morphology as those from the holotype . However , the teeth do not bear any <unk> of Paranthodon , and were referred to an indeterminate stegosaurid in 2008 . 
- 
- = = Description = = 
- 
- Paranthodon was a small stegosaurid relative to larger relatives such as Stegosaurus . Thomas R. Holtz Jr. estimated that the animal was 5 @.@ 0 m ( 16 @.@ 4 ft ) long and weighed between 454 and 907 kg ( 1 @,@ 001 and 2 @,@ 000 lb ) . The snout is elongated , though not extremely so , and convex on top . The back of the premaxilla is long and broad , and the external nares are large . The teeth have a prominent primary ridge . The <unk> nasal and <unk> bones are relatively complete , and an incomplete premaxilla is also preserved . The partial snout resembles Stegosaurus in its large posterior premaxillary process and the extension of the palate . However , Stegosaurus is the only stegosaurid known from adequate cranial material to compare with Paranthodon , and even though their resemblance is great , tooth morphology is very distinguishing among the stegosaurians . For example , cranial material is only known from Stegosaurus , Paranthodon , Kentrosaurus , and Tuojiangosaurus , and in all of them , the tooth morphology differs . 
- The maxilla of Paranthodon preserves the tooth row , and shows that there is little to no overhang . This differs from <unk> , where there is a large amount of overhang of the maxilla . The teeth also have a middle ridge , with five fewer prominent ridges on either side . This is similar to the size ridges seen on Kentrosaurus . Like all stegosaurians , the denticles on the teeth are rounded at the tips , in contrast to <unk> . Also , like Huayangosaurus , but unlike Kentrosaurus and Stegosaurus , Paranthodon possesses a prominent <unk> <unk> ( a ridge beside the tooth row ) . Paranthodon teeth preserve wear , possibly cause by rubbing with other teeth . However , wear is absent on most teeth , similar to Huayangosaurus , meaning it is likely that Paranthodon lacked <unk> between teeth . Paranthodon is distinguished from other <unk> by a long , wide , posterior process of the premaxilla , teeth in the maxilla with a very large <unk> , and large ridges on the tooth crowns . Not all of these features were considered valid in a 2008 review of Stegosauria , with the only <unk> found being the possession of a partial second bony palate on the maxilla . 
- 
- = = Classification = = 
- 
- Currently , Paranthodon is classified as a <unk> related to Stegosaurus , Tuojiangosaurus , and <unk> . However , when Broom assigned the name Palaeoscincus africanus to the Paranthodon fossils , he classified them as an ankylosaurian . This classification was later changed by Nopcsa , who found that Paranthodon best resembled a stegosaurid ( before the group was truly defined ) . Coombs ( 1978 ) found Paranthodon to be an ankylosaurian , like <unk> , naming it <unk> <unk> sedis . However , in reviews by <unk> in 1981 , it was found to instead represent a stegosaurid from the Late Cretaceous . Multiple phylogenetic analyses have placed Paranthodon in Stegosauria , and often in Stegosauridae . A 2010 analysis including nearly all species of stegosaurians found that Paranthodon was outside Stegosauridae , and in a <unk> with Tuojiangosaurus , Huayangosaurus , <unk> , <unk> , and <unk> . However , when the latter two genera were removed , Paranthodon grouped with Tuojiangosaurus just outside Stegosauridae , and Huayangosaurus grouped with <unk> in <unk> . Below is the analysis with all included genera . 
- Other analyses have found Paranthodon closely related to Tuojiangosaurus , <unk> , and Kentrosaurus within <unk> . Even though phylogenetic analyses recognize Paranthodon as a stegosaurid , the type material actually bears no <unk> of Stegosauria . However , the material is likely of <unk> nature , and <unk> by many authors have found it to be within the group . 
- 
- = = <unk> = = 
- 
- The Kirkwood Formation is in South Africa , and many fossils of different species and genera have been discovered in it , with Paranthodon being the first uncovered . The formation is of a Late Jurassic to Early Cretaceous age , with the oldest deposits from the Tithonian , about 145 @.@ 5 million years ago , and the youngest rocks being from the Valanginian , about 136 @.@ 4 million years ago . A large variety of different animal groups have been found in the formation , including dinosaurs , at least two different <unk> lizards , multiple <unk> fishes , a few <unk> , some frog specimens , and also <unk> . However , a large amount of the material of the Kirkwood formation only includes isolated teeth or partial and fragmentary pieces of bone . Dinosaurs of the formation include a basal tetanuran and the primitive <unk> <unk> , the possible <unk> <unk> and a potential <unk> , and many ornithischians , Paranthodon , a genus of <unk> , and a " <unk> " ( the family <unk> is no longer considered to be a natural grouping ) . 
- 
- 
- = History of Bradford City <unk> = 
- 
- Bradford City Association Football Club — also known informally as Bradford City — are an English football club founded in Bradford in 1903 to introduce the sport to the West Riding of Yorkshire , which until then had been almost entirely inclined towards rugby league . Before they had even played their first game , City were elected to the Football League to replace Doncaster Rovers in Division Two , and took over the Valley Parade stadium , which has been their permanent home ground ever since . The club won the Division Two title in 1908 and the FA Cup in 1911 , both under the management of Peter O 'Rourke , before they were relegated from Division One in 1921 – 22 . 
- City were relegated again five seasons later , but when O 'Rourke was <unk> as manager before the 1928 – 29 season , they broke several club records to earn promotion back to Division Two . After eight seasons in Division Two , City returned to Division Three , and they remained in the third and fourth tiers of the English football league system until 1985 – 86 . During that time , they endured several periods of financial hardship , and in 1985 , their ground suffered a disastrous fire in which 56 people died , on a day the club and their fans were supposed to be celebrating promotion . 
- In 1987 – 88 , the club came close to returning to the top division when they missed out on promotion on the final day of the season . Following relegation back to Division Three , after Geoffrey Richmond became chairman in January 1994 the club 's fortunes were lifted . He helped to take them to their first appearance at Wembley and subsequently into the Premier League , where they played for two seasons . Following Richmond 's self @-@ proclaimed " six weeks of madness " and the collapse of television channel ITV Digital , the club suffered its first spell in administration . Another period under administration followed , and City dropped through the leagues of professional English football back to the bottom tier of The Football League , until promotion in 2012 – 13 brought them back up a division . In January 2013 , City became the first club from the fourth tier of English football since 1962 to reach the Football League Cup final , and the first fourth @-@ tier club ever to reach a major Wembley Cup Final . 
- 
- = = Early successes ( 1903 – 19 ) = = 
- 
- League football was established in the West Riding of Yorkshire in 1894 when the West Yorkshire League was formed . A year later the Bradford Schools Football and Athletic Association abandoned its rugby roots to adopt the association football code . Several clubs across Bradford , including Bradford ( Park Avenue ) , also adopted the code during the latter years of the 19th century . By 1901 , a team called Bradford City had played in the leagues within the city , playing for two seasons , but disbanded at the end of the 1902 – 03 season . On 30 January 1903 , Scotsman James <unk> , a sub @-@ editor of the Bradford Observer , met with Football Association representative John <unk> at Valley Parade , the home of Manningham Football Club , to discuss establishing a Football League club within the city . Manningham FC were a rugby league club formed in 1880 and became a founding member of the Northern Rugby Football Union in 1895 . A series of meetings was held , and on 29 May 1903 , at the 23rd annual meeting of Manningham FC , the committee decided to leave the rugby code and switch to association football . The Football League , which saw the invitation as a chance to introduce football to the rugby league @-@ dominated area of the West Riding , elected the club , which had been renamed Bradford City , to the league with a total of 30 votes to replace Doncaster Rovers . 
- Bradford City became the first league football team from the county , before they even had a team or played a game . They and Chelsea , who were elected to the league two years later , share the distinction of being the only clubs to join the league without having played a competitive fixture . A summer archery contest , which had been organised to raise money for the rugby league club , was used to finance the new club , and Manningham 's colours of claret and amber were adopted as Bradford City 's kit , but with Manningham 's <unk> changed to stripes . 
- Robert Campbell was appointed by a 13 @-@ man sub @-@ committee to be the club 's first secretary @-@ manager from a shortlist of 30 applicants . <unk> duties were carried out by committee member <unk> , with Campbell 's role more on the playing side . The committee assembled a squad at the cost of £ <unk> 10s <unk> . Their first game was a 2 – 0 defeat away at Grimsby Town on 1 September 1903 , and first home game was six days later against Gainsborough Trinity , played in front of a crowd of 11 @,@ 000 including the Lord Mayor and Lady <unk> of Bradford . It was not until the third game against Burton United that the club recorded their first victory , on the way to a 10th @-@ place finish in Division Two . The club faced having to apply for re @-@ election in their second season , until five wins in the final six games lifted the club to eighth position . In November 1905 , Peter O 'Rourke , one of the club 's centre @-@ halves , was appointed manager , with his last game as a player coming the following month . He guided City to finishes of 11th and fifth and then earned promotion to Division One in 1907 – 08 . The season had started with an 8 – 1 victory over Chesterfield and included another six victories when City scored five goals or more , before promotion and then the title were assured with successive victories over Derby County and Burnley in April . 
- Ahead of their first campaign in Division One , Bradford City embarked upon their first continental tour ; despite victories over German side <unk> and Belgian club <unk> , the club 's first league victory in the top division did not come until the fifth attempt with a 4 – 1 defeat of Bury . It was their only win in the first 14 games . Results improved in the second half of the season but it was not until a 1 – 0 win on the final game of the season against Manchester United with a goal from Frank O 'Rourke that City prevented an immediate relegation back to Division Two . The following season included a ten @-@ game undefeated spell as Bradford finished seventh , but this was bettered in the 1910 – 11 season , which is Bradford City 's most successful campaign . Their league finish of fifth remains the club 's highest position , and an FA Cup triumph , with a 1 – 0 win over Newcastle United in the 1911 final is the club 's only major honour . The first- and third @-@ round victories were secured with solitary goals from <unk> Bond , but he missed the final four games of the run because of club suspension , the first of which was a 1 – 0 defeat of Burnley in the fourth round in front of 39 @,@ 146 fans , a crowd which remains Valley Parade 's highest attendance . The biggest win of the run came in the semi @-@ finals with a 3 – 0 victory over Blackburn Rovers . City 's supporters were taken to the final in London on 11 special trains , but the game with Newcastle at Crystal Palace ended in a <unk> draw and was even described as a " <unk> dull and uneventful game " . The draw meant a replay was necessary . It took place four days later on 26 April 1911 at Old Trafford , Manchester , when a single goal from Jimmy <unk> in the 15th minute gave Bradford a 1 – 0 victory . They were the first winners of a new trophy , appropriately made by Bradford <unk> <unk> 's . 
- Bradford 's defence of the FA Cup was ended in the fourth round by Barnsley , who went on to succeed Bradford as the holders . The 3 – 2 defeat , played at Bramall Lane , Sheffield , following two draws , brought an end to 11 consecutive clean sheets in the FA Cup — a competition record . The sequence also included a 12th clean sheet in the second replay against Barnsley which was abandoned because of crowd trouble . The cup run had included the first Bradford derby between City and cross @-@ city rivals Bradford ( Park Avenue ) . In the league , City finished 11th , the first of four consecutive mid @-@ table finishes before league football was suspended because of the First World War . City 's FA Cup hero <unk> , who had joined Leeds City , was one of many footballers to lose his life during the war . Bradford City players who died included Bob <unk> , another FA Cup winner , and Evelyn <unk> , as well as several reserve team players . Frank Buckley and Jock <unk> were seriously wounded , and <unk> Bond was taken prisoner @-@ of @-@ war . In 1921 , Bond laid a commemorative wreath on the <unk> in memory of his fallen colleagues prior to a league game at Arsenal . 
- 
- = = Inter @-@ war years ( 1919 – 38 ) = = 
- 
- With several <unk> during the war , it was a new @-@ look side that took to the field for the 1919 – 20 season , when league football resumed . The 15th @-@ place finish in the league was City 's lowest since their first season in Division One , and a fourth @-@ round FA Cup exit away at Bristol City was blamed on a pre @-@ game trip to Fry 's chocolate works . It was a position replicated the following season . After ten campaigns in Division One , City were relegated back to Division Two , when they lost all of their final five games of the 1921 – 22 season . It would be 77 years until Bradford City again competed in the top division of English football . 
- Having lost O 'Rourke as manager in 1921 , after he had struggled to cope with the death of his son two years earlier , Bradford 's results suffered back in Division Two . Both Bradford City and Bradford ( Park Avenue ) had been relegated in 1922 , and with the rise of fellow West Yorkshire side Huddersfield Town , <unk> in Bradford dropped . City 's average attendance fell from a record high of 22 @,@ <unk> in 1920 – 21 to between 12 @,@ 000 and 14 @,@ 000 in Division Two . Five consecutive bottom half finishes culminated in relegation to Division Three ( North ) in 1926 – 27 , when they finished bottom of the table following a then record 8 – 0 defeat to Manchester City on the final day of the season . New manager Colin <unk> missed out on guiding the club to an immediate promotion when they finished sixth , but at the end of the season the players had not been paid and the bank was unable to advance any more money because of the club 's <unk> . Had it not been for donations by fans , Bradford City would not have started the following campaign . A new board was appointed , and they reinstated Peter O 'Rourke as manager . Success was immediate , when the 1928 – 29 season started with a record 11 – 1 victory over Rotherham United , as the side scored a club record 128 goals to earn promotion by just one point . City 's successful team had also brought the fans back and the average attendance of 18 @,@ <unk> is the highest average recorded by the club , since 1925 , when The Football League started to keep official records . 
- O 'Rourke left for a second time in May 1930 , after he resigned because he was not allowed to sign a player he wanted . City spent eight seasons back in Division Two but the nearest they came to stepping up to the top flight was in 1933 – 34 when at one point they topped the division . Their sixth @-@ place finish was the highest position since the club had been in Division One and would not be bettered until the 1980s . <unk> back to Division Three ( North ) came in 1936 – 37 . City were runners @-@ up in the Division Three North Challenge Cup a year later before they won the same competition in the last year before league football was again suspended because of war . 
- 
- = = Lower divisions ( 1946 – 81 ) = = 
- 
- New manager Jack Barker lasted just eight months until he was replaced by former Leeds United player Jack Milburn upon the resumption of league football in 1946 . Milburn led City to fifth position in his first season but only lasted another season himself . With only one team promoted from Division Three ( North ) each season , City remained at that level until they were placed in Division Three in 1958 – 59 after a league re @-@ organisation , following a 20 @-@ year high position of third the previous season . In 1960 , eight years after part of the ground 's Midland Road stand had been closed following examinations of the foundations ordered as a result of the 1946 <unk> Park disaster , the entire stand was closed , leaving the ground with just three stands . After just three years in Division Three , City dropped into Division Four in 1960 – 61 , although that season they did defeat Division One side Manchester United in City 's first ever League Cup tie . United , like many other top flight clubs , would not enter the competition again until six years later . 
- Despite a club record 9 – 1 defeat to Colchester United on 30 December 1961 , City came fifth in 1961 – 62 , thanks to David Layne 's 34 league goals — his total remains a club record for a season — but missed out on promotion by just one point . Layne left for Sheffield Wednesday and City finished 23rd the following season , forcing them to apply for re @-@ election . In 1966 , the club directors moved the pitch 2 @.@ 74 metres ( 3 @.@ 00 yd ) closer to the main stand , creating enough space for them to add a new safe standing area on the Midland Road side of the ground and open all four stands for the first time since 1960 . <unk> continued to drop and a new record low of 1 @,@ 353 was set on 12 May 1966 against <unk> . It prompted chairman Stafford Heginbotham to hold a crisis meeting in the city 's St George 's Hall to raise new funds and safeguard the future of the club . The club 's indifferent form on the field continued , with another re @-@ election and two narrow promotion failures , before promotion was gained in 1968 – 69 . Only the previous season , City had had three managers , when Grenville Hair , who had replaced Willie Watson , died just two months into his reign , after he collapsed at the end of a training session . 
- Striker Bobby Ham , whose 18 goals had helped City into Division Three , was again top goalscorer the following season , but the club 's stint in Division Three was short @-@ lived . Once Ham , and fellow Bradford @-@ born striker Bruce <unk> , who scored 60 goals during seven seasons with the club , both left , City were relegated back into the bottom division in 1971 – 72 . The spell in Division Three had also been notable for the debut of <unk> <unk> , who went on to play a record 502 league games during 14 seasons with the club . City spent five seasons back in Division Four . In 1975 – 76 they had their best FA Cup run in more than 50 years after defeating Norwich City , before they were knocked out in the quarter @-@ finals by eventual winners Southampton 1 – 0 . A year later more than 40 goals from the trio of Dominican striker Joe Cooke , Terry Dolan and Don <unk> helped City to another promotion when they finished in fourth position . The club 's board failed to strengthen the squad the following season , resulting in an instant return to Division Four . Under new manager George Mulhall , City spent three seasons in mid @-@ table , although a late spell of form nearly earned promotion in 1979 – 80 . 
- 
- = = Bantam <unk> ( 1981 – 90 ) = = 
- 
- In May 1981 , City appointed former England international defender Roy McFarland as their new manager . After starting the 1981 – 82 season with a defeat and a draw , City went top of the table during a run of nine successive league victories , equalling a 30 @-@ year club record . The run came to an end against Sheffield United in front of 13 @,@ 711 fans at Valley Parade , producing then club record gate receipts of £ 17 @,@ 938 . Arctic conditions across Britain meant City played only once during December , but they went back to the top of the Division Four table in January . City finished the season second , five points behind Sheffield United , and were promoted back to Division Three . Three months into the following campaign , McFarland and his assistant Mick Jones handed in their resignation and left for Derby County . Derby had to pay a large fine and compensation to City for poaching the pair . Chairman Bob Martin turned to another England centre @-@ back and appointed Trevor Cherry as McFarland 's replacement from West Yorkshire rivals Leeds United . Cherry and assistant Terry <unk> continued to build on McFarland 's start to the period which would later be called " Bantam <unk> " by <unk> The City <unk> . Despite not recording their first win for more than two months , the pair guided City to 12th position . 
- The club were again in financial difficulties , and in June 1983 , Martin called in the receivers and the club was put up for sale . A Save Bradford City Fund was launched on 24 July , and former chairman Stafford Heginbotham and former board member Jack <unk> bought the club , forming a new company , and enabling the team to start the new league campaign . Centre forward Bobby Campbell was sold to Derby County to balance the books and John <unk> brought in as his replacement . City struggled on the pitch and won just one of their first 15 games leaving them in the relegation zone . Campbell had played just 11 games during four months with Derby and so he returned to Bradford initially on a loan transfer . His return coincided with a club record ten successive league victories . Campbell finished the season with nine goals , <unk> with 22 , but City finished seventh and missed out on promotion . 
- The good form continued the following season , and from October to mid @-@ December 1984 , City embarked on a 13 @-@ match unbeaten run , during which time Campbell became the club 's all @-@ time leading goalscorer , beating Frank O 'Rourke 's 70 @-@ year @-@ old record . City went top of the division and held onto their lead , opening an 11 @-@ point cushion by February . Promotion was secured in April and the club 's first championship title since 1929 after a 2 – 0 win over Bolton Wanderers . The success was overshadowed when fire ripped through Valley Parade 's main stand 40 minutes into the final game of the season on 11 May 1985 against Lincoln City . A total of 56 people died and the club did not play another game at Valley Parade for nearly 20 months . 
- City instead played home games at <unk> Road , Leeds Road and <unk> during the 1985 – 86 season , when they came 13th , and for the first half of the 1986 – 87 campaign . They returned to Valley Parade , which had undergone a £ <unk> redevelopment , on Boxing Day 1986 against Derby County . After managing the side during the financial hardship and time away from their home ground , Cherry was dismissed only ten days after the return to Valley Parade . Club coach Terry Dolan was appointed as manager and he led the side away from relegation and to a finish of 10th place . By September 1987 , Dolan 's side topped Division Two for the first time in 54 years . They finished fourth after a final day defeat to Ipswich Town and missed out on promotion after they lost to Middlesbrough in the play @-@ offs . Leading players Stuart McCall and John Hendrie , who had both stayed for another season in a bid to take City into Division One , both left , and within two seasons City again dropped into Division Three . In January 1988 Stafford Heginbotham resigned as the Club 's Chairman due to ill health . 
- 
- = = Richmond era and administration ( 1990 onwards ) = = 
- 
- For three seasons , City finished in mid @-@ table in Division Three and the new Division Two , when the leagues were renamed following the formation of the Premier League . In January 1994 , Geoffrey Richmond took over as chairman . He cleared the debts , loaned the club £ 2 @.@ 3 million , and promised the fans he would take the club to the Premier League within five years . In his first season as chairman , the club finished in seventh place with Frank <unk> as manager . <unk> was sacked and was replaced by Lennie Lawrence . Lawrence could only finish 14th in his first season , before he left for Luton Town in November 1995 to be replaced by his assistant Chris Kamara . 
- Kamara secured a play @-@ off spot with a final day victory over Hull City , before City defeated Blackpool in the play @-@ off semi @-@ finals . The final against Notts County was City 's first game at Wembley . Goals from Des Hamilton and Mark <unk> gave them a 2 – 0 win , which secured promotion to Division One . Kamara used 42 players in 1996 – 97 when City avoided relegation with a 3 – 0 victory in the final game against Queens Park Rangers . Kamara was sacked in January 1998 after an FA Cup defeat to Manchester City , when Richmond claimed the manager had taken the club as far as he could . Richmond again elevated from within and Paul Jewell , who had been at the club since 1988 , was installed as manager , originally on a caretaker basis . He was appointed full @-@ time in May 1998 and Richmond backed his new appointment with a multi @-@ million transfer budget . Jewell signed strikers Lee Mills , from Port Vale and Isaiah Rankin , from Arsenal , for £ 1 million and £ 1 @.@ 3 million respectively , and signed former captain Stuart McCall from Rangers on a free transfer to lead the side . Despite a poor start , the club secured promotion to the top division for the first time in 77 years with a 3 – 2 victory over Wolverhampton Wanderers in the final game of the 1998 – 99 season . 
- City 's success meant that Dean Windass , who had signed from Oxford United in March , became the club 's third £ 1 million signing of the season . Windass had originally been signed for £ 950 @,@ 000 , but an additional fee of £ 50 @,@ 000 was paid to Oxford because of City 's promotion . Jewell broke the club 's transfer record to add a fourth seven @-@ figure signing when he paid £ 1 @.@ 4 million to Leeds United for David Wetherall . Jewell added other senior players including Neil <unk> and Dean Saunders , prompting the media to call his team " Dad 's Army " . When City defeated Middlesbrough 1 – 0 with a late goal from Saunders , his goal celebration mocked the critics ' comments . City failed to win another game until their eighth game of the season , and Sky Sports <unk> Rodney Marsh said they would be relegated and promised to shave off his hair at a home game if they avoided such a fate . A run of nine home games without defeat and consecutive victories in April gave City hope of avoiding relegation on the final day of the season . A final day 1 – 0 victory over Liverpool , with a goal from Wetherall , who had played every minute of the season , and Wimbledon 's defeat , meant City survived with a record low of 36 points . 
- Less than two months after City stayed up , Jewell left to join Sheffield Wednesday , to be replaced by his assistant , Chris Hutchings . The club entered the Intertoto Cup , the first time they had competed in a European competition , in which they were defeated by FC <unk> Saint Petersburg in the semi @-@ finals . Richmond gave Hutchings more money than Jewell to spend in the transfer market , and Bradford paid a club record £ 2 @.@ 5 million for David <unk> and £ 1 @.@ 5 million for Ashley Ward , and signed Italian striker <unk> <unk> on wages of £ 40 @,@ 000 per week . Richmond also continued to re @-@ develop the ground , which increased the capacity to 25 @,@ 136 , but later referred to his spending as his " six weeks of madness " . In their second season in the Premier League , City struggled for form and Hutchings was sacked after a start to the season in which he recorded just one victory from 12 league games . Under new manager Jim Jefferies , the club were unable to avoid relegation , which was confirmed with a 2 – 1 defeat at Everton , when they missed two penalties , before finishing the season with just 26 points . 
- Jefferies was sacked in December 2001 following a training ground rift with captain McCall . Nicky Law was appointed his successor , and the club finished the season in 15th place . During the summer , with debts of nearly £ 13 million — as a result of the collapse of ITV Digital and the fall @-@ out from Richmond 's self @-@ proclaimed " six weeks of madness " — the club were forced into administration . The players were all released , but <unk> waived much of the money owed to him , to help the club survive under new owners Julian Rhodes and Gordon <unk> . City fulfilled their fixtures during the 2002 – 03 season but finished 19th . 
- Former England captain Bryan Robson took over as new manager during the following season , but , under his management , City won only seven games from 28 and were relegated in 23rd place . Robson left and was replaced by his assistant Colin Todd . The club went into administration for a second time , but Todd led them to 11th in each of the following two seasons . Following fan pressure and a poor run of results , Rhodes sacked Todd on 12 February 2007 , with City just three points above the relegation zone . Wetherall was appointed player @-@ manager on a temporary basis and then for the rest of the season , but City were relegated following a 3 – 0 defeat to Chesterfield . During the summer of 2007 , former midfielder Stuart McCall returned as manager with City in the bottom tier for the first time in 25 years . He set himself a target of earning promotion back to League One in his first season , but twice missed out on promotion before he left the club in February 2010 with City lying 16th in League Two . 
- In January 2013 , City became the first club from the fourth tier of English football since Rochdale in 1962 to reach the Football League Cup final , and the first fourth tier club ever to reach a major Wembley Cup Final . They defeated three Premier League sides en route to the final – <unk> Athletic 4 – 2 on penalties in the fourth round , Arsenal 3 – 2 on penalties in the quarter @-@ finals and Aston Villa 4 – 3 on aggregate over the two legs of the semi @-@ final . They met Premier League side Swansea City in the final at Wembley , but lost 0 – 5 . On 18 May 2013 , the club returned to Wembley where they defeated Northampton Town 3 – 0 in the League Two <unk> Final to secure a place in League One for 2013 – 14 . On 24 January 2015 , Bradford City caused an upset by beating Premier League leaders Chelsea 4 – 2 away in the FA Cup . The victory sent Bradford through to the fifth round for the first time in 18 years . There , they defeated another top @-@ flight team , Sunderland , to reach their first FA Cup semi @-@ final since 1976 , in front of their biggest home crowd for 50 years . 
- 
- 
- = Aerith Gainsborough = 
- 
- Aerith Gainsborough ( Japanese : <unk> ・ <unk> , Hepburn : <unk> <unk> ) — <unk> Aeris Gainsborough in the English releases of Final Fantasy VII and Final Fantasy Tactics — is a player character in Square 's ( now Square Enix ) role @-@ playing video game Final Fantasy VII . She was designed by Tetsuya Nomura with influence from Yoshinori Kitase , <unk> <unk> and <unk> Amano . 
- In Final Fantasy VII , she is a young woman who joins the eco @-@ terrorist organization AVALANCHE . As the story progresses , AVALANCHE begin to pursue the game 's antagonist Sephiroth , and the player learns that she is the last surviving <unk> , or " Ancient " , one of the planet 's oldest races . She has also appeared in the later @-@ released Compilation of Final Fantasy VII and Kingdom Hearts series . 
- Her voice actor is <unk> Sakamoto in Japanese . In English releases , her voice actors are singer and actress Mandy Moore in Kingdom Hearts , actress Mena <unk> in Kingdom Hearts II and Final Fantasy VII Advent Children , and actress Andrea Bowen in Crisis Core : Final Fantasy VII . The character and the events surrounding her death in Final Fantasy VII have met with an overall positive reception from critics and fans . 
- 
- = = Appearances = = 
- 
- 
- = = = Final Fantasy VII = = = 
- 
- Aerith Gainsborough is first introduced as a flower seller , when she briefly <unk> with Cloud Strife , a mercenary working for the anti @-@ government group AVALANCHE , who are fleeing from the bombing of a Mako reactor . The two later meet in Aerith 's church in the Sector 5 slums , where she is faced with the possibility of capture by the Turks . Aerith asks Cloud to be her bodyguard for the cost of one date . She is eventually <unk> , but is ultimately rescued by Cloud and his allies . Aerith then joins them in the pursuit of Sephiroth , while also embarking on her own journey of self @-@ discovery . 
- After a failed attempt to foil Sephiroth 's theft of the Black <unk> , Aerith ventures alone into the Forgotten City . Cloud and his companions give chase , eventually finding her praying at an altar . As Aerith looks up to smile at Cloud , Sephiroth appears and kills her by <unk> her through the torso . Cloud carries Aerith 's body out into a lake in the Forgotten City , and releases her back to the Planet . Reeve <unk> , the head of Shinra Urban and Development , brings the news of her death to <unk> Gainsborough , Aerith 's adoptive mother . The party later learns the reason for Aerith being in the Forgotten City ; through her White <unk> , Aerith was able to summon Holy , the only force capable of <unk> the ultimate destructive magic , Meteor , which has been summoned by Sephiroth . Although Aerith successfully cast Holy before her death , it is held back by the power of Sephiroth 's will . When Sephiroth is finally defeated and Holy is released , it appears that it is too late to function as effectively as it should , as Meteor has already come too near to the Planet 's surface . While Holy clashes with Meteor , attempting to prevent its impact , the gravity of both Meteor and the Planet pulling on Holy in opposite directions weakens it . Aerith is seen praying with both hands <unk> whilst urging the <unk> to ultimately defend the planet . The Planet 's Lifestream then flows forth , intervening between Holy and Meteor , and acting as a battering ram while aiding in the destruction of Meteor . 
- 
- = = = Compilation of Final Fantasy VII = = = 
- 
- In Before Crisis : Final Fantasy VII , set several years prior to the events of Final Fantasy VII , Aerith becomes the target of the original incarnation of AVALANCHE , led by <unk> , who seek to prevent Shinra from acquiring the last surviving <unk> . Instead , AVALANCHE intend to use her to learn the whereabouts of the <unk> Land for their own purposes , although a member of the Turks tries to protect her . 
- Aerith makes several appearances in the CGI film Final Fantasy VII : Advent Children , as Cloud 's spiritual guide , urging him to move on with his life and to forgive himself for the <unk> that were beyond his control , telling him that she never blamed him for her death . During their spiritual reunion , Aerith speaks to Cloud in an open meadow laden with flowers , <unk> and <unk> poking fun at how he <unk> <unk> himself with the past . However , she also acknowledges his suffering and offers kind words of support . One of Aerith 's interactions with Cloud comes when each member of the original game 's party helps in Cloud 's final attack against <unk> <unk> ; she appears as the last party member to assist Cloud . She also appears in the final scene of the movie , along with Zack Fair , where she gives Cloud more words of encouragement before she and Zack walk into the light . Near the end of the film , it is discovered that water mixed with the Lifestream flows beneath the <unk> in Aerith 's church , which <unk> itself as a cure for <unk> . 
- The On the Way to a Smile novella " Case of the Lifestream – Black & White " focuses on Aerith and Sephiroth 's respective journeys through the Lifestream after the end of the game but before the events of the film . The " Black " section deals with Sephiroth , the " White " section with Aerith . 
- Aerith also appears in the prequel game Crisis Core : Final Fantasy VII . At the age of 16 , she meets Zack , for whom she develops feelings during his stay in <unk> . Aerith and Zack develop a romantic relationship , but Zack is killed at the end of Crisis Core after being held in a Mako chamber for four years in the Shinra Mansion basement . During those years , Aerith helped her adopted mother earn a living by growing and selling flowers , a job that results in her meeting Cloud at the beginning of Final Fantasy VII . 
- 
- = = = Other appearances = = = 
- 
- Aerith 's character has appeared in several games outside of the Final Fantasy VII continuity . In Final Fantasy Tactics , she appears as a flower girl ; when a group of criminals <unk> her , Cloud appears and the player engages in battle with the group , letting her escape . <unk> Street Special features a playable version of Aerith , as well as other Final Fantasy VII characters Tifa Lockhart , Cloud Strife , and Sephiroth . She also appears in <unk> Street Portable with the same characters from Special , with the addition of <unk> <unk> . While not playable , Aerith appears in the fighting game <unk> <unk> Final Fantasy as an assistant character . She is also featured in the rhythm game <unk> Final Fantasy as a sub @-@ character representing Final Fantasy VII . In <unk> 2 , Aerith is featured as a downloadable character model . 
- Aerith makes an appearance in the Kingdom Hearts series as a member of a group dedicated to defeating the Heartless ; the group also includes other Final Fantasy VII characters and Leon of Final Fantasy VIII . In the plot of Kingdom Hearts , Aerith suggests a method for defeating the Heartless to protagonists Donald Duck , <unk> and Sora . Throughout the game , she also gives advice to the player . She also appears in Kingdom Hearts : Chain of Memories as a <unk> <unk> of Sora 's memories . Aerith returns in Kingdom Hearts II , wearing a modified version of her dress from Before Crisis . She , Leon and <unk> run a restoration committee for the town of Hollow <unk> . 
- Hoshi o <unk> <unk> ( <unk> who <unk> the Planet ) , a novel written by Benny <unk> which appears in the Final Fantasy VII <unk> <unk> guide , follows Aerith 's journey through the Lifestream immediately after her death in Final Fantasy VII . Aerith is mentioned in a graffiti in the subway station early in the animated film <unk> @-@ It Ralph ; the graffiti reads " Aerith Lives " . 
- 
- = = Concept and creation = = 
- 
- Aerith was designed by Tetsuya Nomura , with influence from director and scenario writer Yoshinori Kitase and <unk> <unk> , whilst <unk> Amano created conceptual artwork which also helped to influence her design . She has green eyes and long brown hair tied in a <unk> with a pink ribbon . She wears a long pink dress , a <unk> jacket , and brown hiking boots . The long dress was designed to appear <unk> and as a contrast to Tifa Lockhart 's <unk> . During development , Aerith was supposed to be Sephiroth 's sister as both designs resembled each other , but they were made former lovers with Aerith remembering Sephiroth when meeting Cloud as both are ex @-@ <unk> . Late during development , Aerith 's first love was changed to Zack Fair . 
- Her green eyes were meant to symbolize nature and also served as another contrast to Tifa 's brown eyes . Nomura did not change much of Aerith 's design for Advent Children , but her design was updated in Kingdom Hearts with the removal of her <unk> jacket , which made her attire appear more as Amano had originally drawn her . Other changes included the addition of bracelets and a belt . Nomura modified her dress in Before Crisis , adding white and green colors , and this version was also used as the basis for her design in Kingdom Hearts II . 
- Aerith 's original Japanese name is <unk> <unk> , pronounced [ <unk> <unk> ] . This was <unk> to " Aeris " in Final Fantasy VII and Final Fantasy Tactics , but in later products has been changed to " Aerith " . Both <unk> have basis , as the Japanese " su " ( <unk> ) is used when <unk> " s " ( / s / ) and " <unk> " ( / <unk> / ) to Japanese . However , official Japanese material uses the spelling " Aerith " , and developers have stated that " Aerith " is a near @-@ anagram of " Earth " . Prior to the game 's release , Western gaming magazines , such as the May 1996 issue of Computer and Video Games , also referred to her as " Aerith " . 
- In early planning stages of Final Fantasy VII , Aerith was to be one of only three protagonists ; herself , Cloud and <unk> . During a phone call to Kitase , it was suggested that at some point in the game , one of the main characters should die , and after much discussion as to whether it should be <unk> or Aerith , the producers chose Aerith . Nomura stated in a 2005 Electronic Gaming Monthly interview : " Cloud 's the main character , so you can 't really kill him . And Barrett ... [ sic ] well , that 's maybe too obvious . " While designing Final Fantasy VII , Nomura was frustrated with the " perennial cliché where the protagonist loves someone very much and so has to sacrifice himself and die in a dramatic fashion to express that love . " He found this <unk> appeared in both films and video games from North America and Japan , and asked " Is it right to set such an example to people ? " Kitase concluded : " In the real world things are very different . You just need to look around you . Nobody wants to die that way . People die of disease and accident . Death comes suddenly and there is no notion of good or bad . It leaves , not a dramatic feeling but great <unk> . When you lose someone you loved very much you feel this big empty space and think , ' If I had known this was coming I would have done things differently . ' These are the feelings I wanted to <unk> in the players with Aerith 's death relatively early in the game . Feelings of reality and not Hollywood . " 
- According to Nomura , " death should be something sudden and unexpected , and Aerith 's death seemed more natural and realistic . " He said : " When I reflect on Final Fantasy VII , the fact that fans were so offended by her sudden death probably means that we were successful with her character . If fans had simply accepted her death , that would have meant she wasn 't an effective character . " From the original release of the game , rumors have circulated that Aerith can be resurrected in or that the original plan was to have her come back , but this was scrapped in development . Nomura has <unk> stated that neither of these rumors were ever true ; " the world was expecting us to bring her back to life , as this is the classic convention . " A lengthy petition asking for Aerith 's revival by Japanese players was sent to Kitase , but he dismissed it , pointing out that " there are many meanings in Aerith 's death and [ her revival ] could never happen . " 
- 
- = = Musical theme = = 
- 
- A <unk> associated with Aerith is played several times throughout Final Fantasy VII ; it is first heard during the flashback scenes with Aerith 's mother at her house , and is repeated as she is killed by Sephiroth . It was composed by famed Final Fantasy composer <unk> <unk> . The piece " Flowers <unk> in the Church " is based on it . 
- " Aerith 's Theme " is very popular among Final Fantasy fans , and has inspired an orchestral version , a piano version , and a vocal version performed by the artist <unk> ( who also performed " <unk> Da Ne " for Final Fantasy X ) . A piano arrangement of the theme appears twice in Advent Children , and the track " Water " also echoes shades of the theme : the opening phrase of " Aerith 's Theme " appears just prior to the climax of the track " <unk> II " , which shortly thereafter includes as its final line the Latin phrase " Sola <unk> <unk> <unk> " ( " Only the goddess knows fate " ) , and is also featured during the end credits of the film . It also has been <unk> on the OverClocked ReMix Final Fantasy VII compilation Voices of the Lifestream . In 2013 , " Aerith 's Theme " achieved the third place in the Classic FM Hall of Fame . 
- 
- = = Reception = = 
- 
- Aerith has received an overall positive reception from critics . <unk> referred to her as a " gaming legend . " <unk> 's Stuart <unk> opined that although Aerith " represented the token damsel in distress , " she " broke the mould in terms of personality , " possessing " an admirable <unk> that was not brassy nor off @-@ putting . " In 2007 , she was included in Tom 's Games list of top 50 greatest female characters in video game history , for her death scene and the beauty of her appearance and personality . That same year , she was named the fifth best character of all time in Dengeki PlayStation 's retrospective awards feature about the original PlayStation . IGN ranked her the number two in their top Final Fantasy VII character list – a rank higher than the game 's protagonist , Cloud Strife . GameTrailers ranked her at the top of their list of " <unk> who are out of your league " in 2010 . Heath <unk> of <unk> ranked Aerith as fifth on his 2012 top list of Final Fantasy characters and wrote she " has become an icon in not only the Final Fantasy series , but also in video game history . " Her relation with Cloud has also received positive response , including the two being listed in IGN 's article about the best video game romances . 
- Her death in Final Fantasy VII has received a great deal of attention . According to <unk> , her death helped establish the popularity of Final Fantasy VII . Players commented on message boards and <unk> about the emotional impact the scene held . Fans submitted a petition to Yoshinori Kitase requesting her return . GameSpy numbers her demise as the 10th greatest cinematic moments in video game history , while its readers voted it the second most cinematic moment . GamePro considers her death sequence to be the greatest of all gaming moments . Tom 's Games called the scene " one of the most powerful and memorable scenes of the Final Fantasy series — or any other game , for that matter . " Edge called her death the " dramatic <unk> " of Final Fantasy VII , and suggested that <unk> her through the Compilation of Final Fantasy VII titles " arguably <unk> this great moment . " In 2005 , Electronic Gaming Monthly listed Final Fantasy VII number six in their list of ten most important games , stating that without this game , " Aeris wouldn 't have died , and gamers wouldn 't have learned how to cry . " ScrewAttack has added Aerith 's death to their top 10 " <unk> " moments , referring to it as one of the " <unk> moments in video game history . " In 2011 , IGN ranked her death scene at No. 1 in its list of top video game moments . In 2012 , PlayStation Magazine included it among the ten most emotional PlayStation moments . 
- The character is also popular among gamers , especially Japanese and fans of the Final Fantasy series . Aerith has been included in most of <unk> ' " Character Battle " contests , though she progressed only a few rounds each time . In 2010 , Famitsu readers voted Aerith as the 24th best video game character . In 2013 , Aerith was voted the second favorite female Final Fantasy character in an official poll by Square Enix . That same year , Complex ranked her as the seventh greatest Final Fantasy character of all time . 
- 
- 
- = Forbidden Fruit ( J. Cole song ) = 
- 
- " Forbidden Fruit " is a song by American hip hop recording artist J. Cole . The song was sent to radio stations in August 2013 , as the third official single from Cole 's second studio album , Born Sinner ( 2013 ) . " Forbidden Fruit " was produced by Cole himself and features a guest appearance from frequent collaborator and fellow American rapper Kendrick Lamar , who contributes vocals to the song 's hook . The song features a sample of American jazz musician Ronnie Foster 's " Mystic Brew " , most recognized from its use on hip hop group A Tribe Called Quest 's " Electric Relaxation " . The song was met with mixed reviews from music critics . " Forbidden Fruit " would peak at number 46 on the Billboard Hot R & B / Hip @-@ Hop Songs chart . 
- 
- = = Background = = 
- 
- " Forbidden Fruit " was the last song recorded and produced by J. Cole for his second album , Born Sinner ( 2013 ) . It was recorded as a replacement for another song which Cole was forced to remove from the album 's track listing after failing to obtain sample clearance from the estate of Jimi Hendrix . The song 's production is based around a looped sample of American jazz musician Ronnie Foster 's " Mystic Brew " , which was also famously sampled by hip hop group A Tribe Called Quest on their 1993 song " Electric Relaxation " . Cole was inspired to incorporate the Foster sample into " Forbidden Fruit " after hearing " Electric Relaxation " , saying : 
- I was like , " Oh , man . What if ? " You know what I mean ? Like , " What if I could just do it my way ? " You know what I mean ? It 's such a classic and people are so afraid to touch classics . And I was just like , " What if I could flip it ? " So I just went and found the original sample . 
- American rapper Kendrick Lamar is featured on " Forbidden Fruit " , which is the only track on the standard edition of Born Sinner with a fellow rapper credited as a featured artist . Lamar does not rap a verse on the song ; he instead contributes vocals to its refrain . " Forbidden Fruit " contains lyrics referencing Born Sinner 's release date <unk> with that of <unk> by American rapper Kanye West . 
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- = = Release and promotion = = 
- 
- At the June 24 , 2013 stop in Houston , Texas on the Dollar and a Dream concert tour , J. Cole brought out Kendrick Lamar to perform the song , along with the J. Cole @-@ produced " <unk> " . In June 2013 , MTV reported that " Forbidden Fruit " would be released as the third single from Born Sinner . On August 1 , 2013 , the song impacted American mainstream urban radio . 
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- = = Critical reception = = 
- 
- " Forbidden Fruit " received mixed reviews from critics . Erin <unk> of Exclaim ! cited the song as one of the album 's standout tracks , saying that it " embodies a silent confidence , paying homage to a legendary group while speaking on releasing an album the same day as Kanye West , bringing Born Sinner full @-@ circle . " Julia <unk> of Now praised Cole 's sampling of " Mystic Brew " as done " exceptionally well " and named " Forbidden Fruit " the best track from Born Sinner . David Jeffries of AllMusic expressed a similar sentiment , writing that " Forbidden Fruit " is driven by its " Blue Note @-@ inspired " <unk> . 
- August Brown of the Los Angeles Times gave the song a negative review , commenting that " Kendrick Lamar somehow packs more personality into a <unk> hook on ' Forbidden Fruit ' than Cole gets in the song " . Phillip <unk> of Spin felt that " [ the song 's ] quirky bass line and warm @-@ keys motif are so recognizable that it 's a brow @-@ <unk> challenge to hear anything other than Q @-@ <unk> and <unk> 's original vocals in the space between the beat and the new raps . " <unk> further stated that " Cole himself ( along with Kendrick Lamar 's guest spot ) ends up <unk> entirely — he 's upstaged by <unk> artists who aren 't even there . " <unk> , Ali <unk> Muhammad of A Tribe Called Quest expressed his appreciation for Cole 's production : " [ He ] didn 't like try to do what was already done . [ He ] brought other parts to the sample that you caught that I was like , ' Oh , nice ! ' " 
- 
- = = Credits and personnel = = 
- 
- Credits adapted from the liner notes of Born Sinner . 
- J. Cole – lead vocals , songwriting , production 
- Ronnie Foster – songwriting , sample credit 
- Ron Gilmore – keyboards 
- <unk> " <unk> " Davis – mixing 
- Kendrick Lamar – guest vocals 
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- = = Chart performance = = 
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- 
- = = Radio history = = 
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- 
- 
- = Old Pine Church = 
- 
- Old Pine Church ( also historically known as Mill Church , Nicholas Church , and Pine Church ) is a mid @-@ 19th century church near Purgitsville , West Virginia . It is among the earliest extant log churches in Hampshire County , along with Capon Chapel and Mount Bethel Church . 
- The church was constructed in 1838 to serve as a <unk> " union church " . As many of the Mill Creek valley 's earliest settlers were of German descent , Old Pine Church may also have been built as a meeting place for <unk> Brethren adherents , known as " <unk> " or " <unk> " . The church is believed to have also been a meeting place for German Methodist settlers . By 1870 , the church was primarily used by the Brethren denomination , and in 1878 , the church 's congregation split into White Pine Church of the Brethren and Old Pine Church congregations . Both congregations continued to use the church until 1907 . 
- Old Pine Church reportedly housed a school in the early 20th century while still serving as a center for worship . In 1968 , residents of the Purgitsville community raised the necessary funds to perform a restoration of the church . It was added to the National Register of Historic Places on December 12 , 2012 , due to its " significant settlement @-@ era rural religious architecture in the Potomac Highlands . " 
- The church is a large , one @-@ story , <unk> log building <unk> in brown @-@ painted wooden German siding . The original hewn log beams are visible beneath the church , with some bark remaining on the logs . The church 's interior ceiling measures approximately 15 feet ( 4 @.@ 6 metres ) in height and is clad in pressed metal panels . Several of its pews date from 1857 . In the church 's adjoining cemetery , the earliest extant gravestone dates from 1834 , and several unmarked interment sites may exist from as early as 1759 . According to architectural historian Sandra Scaffidi , " Old Pine Church and cemetery is an excellent example of one of the area 's early rural church complexes . " 
- 
- = = Location = = 
- 
- Old Pine Church and its cemetery are located along the steeply sloped Old Pine Church Road ( West Virginia Secondary Route 220 / 15 ) , approximately 1 @.@ 66 miles ( 2 @.@ 67 kilometres ) south of the unincorporated community of Purgitsville . The church and cemetery are situated on 2 @.@ 3 acres ( 0 @.@ 93 hectares ) atop a bluff to the west of United States Route 220 , at an elevation of 1 @,@ 129 feet ( 344 metres ) . The property is surrounded by old @-@ growth forests . 
- The church is in a rural area of southwestern Hampshire County within the Mill Creek valley . Patterson Creek Mountain , a forested narrow <unk> mountain ridge , rises to the west of Mill Creek valley , and the forested western rolling foothills of the <unk> Mill Creek Mountain rise to the valley 's east . The <unk> on the South Branch Potomac River is located across Mill Creek Mountain , approximately 3 miles ( 4 @.@ 8 km ) to the east of the church . 
- 
- = = History = = 
- 
- 
- = = = Background = = = 
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- Old Pine Church 's land tract was originally part of the Northern Neck Proprietary , a land grant that Charles II of England awarded to seven of his supporters in 1649 and renewed by an official patent in 1688 . One of these seven supporters , Thomas <unk> , 2nd Baron <unk> , acquired the entire area in 1681 ; his grandson , Thomas Fairfax , 6th Lord Fairfax of Cameron , inherited it in <unk> . 
- The church is in the Mill Creek valley . As tensions with Native Americans were beginning to ease , Lord Fairfax sought to entice white settlers to the sparsely settled lands of his Northern Neck Proprietary . The valley was one of the first parts of present @-@ day Hampshire County to be settled by whites , beginning in the mid @-@ 18th century . Settlers were drawn by the valley 's fertility . As the valley 's population grew , the unincorporated community of Purgitsville developed along Mill Creek as a trading post village ; it was named for Henry <unk> ( or <unk> ) , who acquired 400 acres ( 160 ha ) in the Mill Creek valley on January 7 , 1785 , and received a further land transfer of 137 acres ( 55 ha ) in 1794 . Purgitsville continued to develop throughout the course of the 19th century , during which time it grew to include a small store , a post office , and a blacksmith shop . 
- 
- = = = Establishment = = = 
- 
- The dates of the earliest church cannot be verified , but a church building may have been constructed at the site of Old Pine Church as early as around 1814 , and possibly as early as 1792 . 
- On September 24 , 1838 , William <unk> and his wife , <unk> , <unk> a tract of land to a group of trustees , charged with constructing " a church or house for public worship for the use and convenience of Ministers and others of the Christians [ sic ] <unk> <unk> " . While the deed mentioned an existing meeting house on the site , there is no extant evidence of a prior structure . According to architectural historian Sandra Scaffidi , no particular Christian denomination received sole ownership of the edifice , which suggests that the church was intended to serve as a <unk> " union church " . The church 's earliest resident minister was reportedly Nicholas <unk> , whose wife , Elizabeth High , was the daughter of George High , one of the church 's original trustees . 
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- = = = Brethren affiliation = = = 
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- Few records of the church 's history exist , possibly because no single denomination or organization took ownership . Several Christian denominations held meetings at Old Pine Church , including the <unk> Brethren ( or German Baptist Brethren ) , which began holding services at the church in the late 19th century . As many of the Mill Creek valley 's settlers were of German descent , the Old Pine Church structure may also have been built as a meeting place for Brethren adherents , known as " <unk> " or " <unk> " . It is believed that Old Pine Church was also used by German <unk> . The Brethren was a Christian denomination of Anabaptist origin that practiced baptism by <unk> immersion and exercised <unk> . <unk> immersion consists of dipping a new believer into water three times , once for each of the entities of the Holy Trinity . Brethren adherents believed only in the New Testament , and professed no other creeds . The interior of Old Pine Church , which consists of a single common space for all worshippers , also illustrates the building 's connection with the Brethren and the denomination 's beliefs regarding slavery . According to the minutes from the 1782 meeting of the Brethren in Franklin County , Virginia , " It has been unanimously considered that it cannot be permitted in any way by the church that a member should purchase <unk> or keep them as <unk> . " While many residents in Hampshire County prior to the American Civil War were <unk> , it is thought that Brethren adherents in the county did not own slaves or depend upon slave labor . 
- The Brethren denomination had been present in the South Branch Potomac River valley from as early as the <unk> although records of early Brethren congregations are not extant , possibly because they were served by itinerant ministers . As early as 1785 , two brothers with the surname of Powers led a Brethren congregation in the area . In Dr. <unk> F. <unk> 's historical research on the Brethren Church in Hampshire County in his Allegheny Passage ( 1990 ) , it is noted that the Church of the Brethren denomination recognized Old Pine Church as belonging to the larger Beaver Run congregation , which was centered approximately 10 miles ( 16 km ) south of Old Pine Church . The Beaver Run congregation was the first organization of the Brethren in Hampshire County . Old Pine Church and its predecessor structures were probably utilized by members of the Beaver Run congregation because the distance between the northern region of the valley and the church on Beaver Run was too great to traverse easily . Thus , Old Pine Church began as a mission of the Beaver Run congregation . Because of its location in the vicinity of the Hardy County boundary line , the district served by the church spanned both counties . 
- By 1870 , Old Pine Church was primarily used by the Brethren denomination . Around 1870 , the Nicholas congregation of Brethren was worshiping at the church and was led by Dr. <unk> . According to the Beaver Run Church Book , membership at Old Pine Church was 78 in 1879 and numbered 100 in 1881 . The Beaver Run congregation modified its district 's boundaries in 1879 , which may have resulted in a division of the congregation at Old Pine Church into two factions : White Pine Church of the Brethren and Old Pine Church . Both churches continued to worship at Old Pine Church at different times . White Pine Church of the Brethren <unk> at the church from the 1870s until the construction of their own church building in 1907 . By 1897 , Old Pine Church remained under collective ownership by several Christian denominations although the Brethren were the church 's largest <unk> . White Pine Church of the Brethren remained listed in the Brethren Conference Minutes as " Pine Church " until 1912 , when members of the church petitioned the Brethren Conference to change their name from " Pine " to " White Pine " . 
- Old Pine Church reportedly housed a school in the early 20th century while still serving as a center for worship . A small one @-@ room addition to the church was constructed to the north façade of the building , which served as the boarding room for the school 's teacher . The Old Pine Church also continued to be used for funeral services and <unk> . 
- 
- = = = Restoration = = = 
- 
- In 1968 , residents of the Purgitsville community raised the necessary funds to restore Old Pine Church : the church 's original windows were repaired and the <unk> <unk> painted ; a new roof was installed and the original wood floor replaced . The boarding room addition was probably removed ( nothing of it now remains ) and the pressed metal ceiling may have been added . 
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- = = = Current use = = = 
- 
- As of 2012 , Old Pine Church is still used for community gatherings , funeral services , revival meetings , and an annual church service . Regular church services have not taken place in the church since the middle of the 20th century . The church 's adjacent cemetery also continues to be used for burials . Throughout its existence , Old Pine Church has been known by various names , including " Mill Church " , " Nicholas Church " , and simply " Pine Church " . 
- In 2008 , the Hampshire County Historic Landmarks Commission and the Hampshire County Commission embarked upon an initiative to place structures and districts on the National Register of Historic Places following a series of surveys of historic properties throughout the county . The county received funding for the surveying and documentation of Hampshire County architecture and history from the State Historic Preservation Office of the West Virginia Division of Culture and History . Old Pine Church was one of the first eight historic properties to be considered for placement on the register as a result of the county 's initiative . The other seven properties were : Capon Chapel , Fort <unk> , <unk> Grove , Hook Tavern , North River Mills Historic District , Springfield Brick House , and Valley View . According to Hampshire County Commission 's compliance officer , Charles Baker , places of worship were not typically selected for inclusion in the register ; Old Pine Church and Capon Chapel were exceptions because both " started out as meeting houses " . Old Pine Church was listed on the National Register of Historic Places on December 12 , 2012 , because of its " significant settlement @-@ era rural religious architecture in the Potomac Highlands " . 
- 
- = = Architecture = = 
- 
- 
- = = = Church exterior = = = 
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- Old Pine Church is a large , one @-@ story , <unk> log building . Its symmetrical front façade faces west and encompasses a main entrance consisting of a double set of four @-@ <unk> doors . The main entrance is reached by two concrete steps , on either side of which is a modern metal <unk> . Above the entrance is a small wooden sign painted white reading " Old Pine Church " in black <unk> . On either side of the doors are two nine @-@ over @-@ six double @-@ hung sash wooden windows . 
- <unk> <unk> in the north and south sides of the church are two nine @-@ over @-@ six double @-@ hung sash wooden windows . Between the two windows on the church 's north elevation is an exterior concrete block chimney . The layout of the east @-@ facing rear elevation of the church is also symmetrical , and features three nine @-@ over @-@ six double @-@ hung sash wooden windows , with the center window placed above and between the other two windows . The church 's windows have been repaired using materials consistent with original construction . 
- At the base of each of the church 's four corners is a large uncut stone pier . <unk> span the church 's perimeter foundations , which were added at a later date to discourage intrusion by animals . The original hewn log beams , still retaining bark , can be seen under the church . The church is covered with brown @-@ painted wooden German siding and is crowned by a modern standing @-@ seam metal roof . 
- Architectural historian Sandra Scaffidi states that the simple form and construction of Old Pine Church are indicative of the early settlers ' access to materials and are an example of the log construction techniques used in the religious architecture of Hampshire County 's earliest settlers . She adds that Old Pine Church is representative of a " simple design and form common to the early ecclesiastical buildings " and an " excellent example of one of the area 's early rural church complexes " . 
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- = = = Church interior = = = 
- 
- The church 's interior exhibits an open architectural plan . A plain wooden frame pulpit stands against the rear wall , underneath the middle window . The church 's floor consists of pine boards installed during the church 's 1968 restoration . 
- The church 's ceiling , measuring approximately 15 feet ( 4 @.@ 6 m ) in height , is clad in pressed metal panels . A small opening allows for access to the church 's attic . The unadorned window wells measure approximately 1 foot ( 0 @.@ 30 m ) . The interior walls are covered in <unk> , which remains unfinished . 
- The church 's small wood @-@ burning stove originally occupied the center of the sanctuary but was moved to the church 's north wall in later years . It continues to serve as the church 's sole source of heat . 
- Several of the church 's pews date from 1857 and remain in use . The pews , quite simple in form , have a " <unk> appearance " . Each pew consists of a long wooden plank that serves as the seat , with a thin rail supported by three spindles as the <unk> . The pews are supported by three arched supports joined to the seat by a <unk> and <unk> joint and reinforced with nails . The newer pews exhibit identical design elements but are constructed with modern nails and timber . Though most of the pews are arranged against the church 's west elevation with a center aisle , two are along the north elevation and four are along the south elevation . The pulpit is situated at the east elevation . There is an upright piano in the northeastern corner of the church . 
- 
- = = Cemetery = = 
- 
- Old Pine Church is surrounded on three sides by a cemetery containing approximately 200 <unk> , the oldest section of which is located to the immediate east and south of the church building . The earliest remaining gravestone dates from 1834 , but several unmarked interment sites in the surrounding cemetery may date from as early as 1759 . The church 's sign along U.S. Route 220 erroneously lists the date of the cemetery 's oldest interment sites as 1792 . 
- The cemetery 's <unk> are oriented both to the east and to the west . The majority are simple in design , inscribed with birth and death dates , and consist of a combination of rounded , arched stones , rectangular stones , and pyramidal @-@ shaped <unk> that appear to be cut from limestone . In the cemetery 's southern section are several small rectangular stones that probably serve as <unk> . Beginning around 1950 , the gravestones erected in the cemetery became more intricate with polished granite surfaces lying atop rough @-@ cut stone foundations . 
- Old Pine Church 's cemetery is surrounded by several mature trees , with a large oak tree <unk> the southwestern area of the cemetery . Outside of the National Register of Historic Places boundary to the northwest of Old Pine Church lies a second parcel of land acquired around 1950 for additional burials . The cemetery is enclosed by a chicken wire fence supported by wooden posts , with a large gate to the north of the church which allows machinery access into the cemetery . 
- 
- 
- = God of War video game collections = 
- 
- God of War is an action @-@ adventure video game series loosely based on Greek mythology . <unk> in 2005 , the series has become a flagship title for the PlayStation brand and the character Kratos is one of its most popular characters . The series consists of seven games across multiple platforms . Five of these have been re @-@ released through three separate compilations for the PlayStation 3 ( PS3 ) platform : God of War Collection ( 2009 ) , God of War : Origins Collection ( 2011 ) , and God of War Saga ( 2012 ) . With the exception of God of War III in the God of War Saga , each collection features remastered ports of the games that were not originally released on the PS3 . God of War III was later remastered as God of War III <unk> and released on PlayStation 4 ( <unk> ) in July 2015 . God of War : <unk> and God of War : Ascension are the only installments that have not been remastered for a newer platform or included in a collection . 
- Each collection was praised for how the games were remastered , as well as their price . IGN claimed that the God of War Collection was the " definitive way to play the game [ s ] " . God of War Collection prompted Sony to make a new line of remastered games for the PlayStation 3 ( which has expanded to the PlayStation Vita and the PlayStation 4 ) . Although the Origins Collection was criticized for its lack of new bonus content , IGN said that " Sony succeeded at making good games better . " For the Saga , Digital Trends claimed it is " perhaps the best value buy for any console available . " 
- 
- = = God of War Collection = = 
- 
- God of War Collection is a remastered port of God of War and God of War II for the PlayStation 3 on a single Blu @-@ ray Disc that was released in North America on November 17 , 2009 . It is the first game under Sony 's line of " Classics HD . " The collection was included in the God of War III Ultimate Trilogy Edition , released on March 18 , 2010 , in Australia and New Zealand , and on March 19 in Europe . It was released in Japan as a standalone on March 18 , where it was distributed by Capcom , and was later released as part of the God of War III Trilogy Edition , which included God of War III and God of War Collection , on March 25 . It was released as a standalone in Australia on April 29 and in Europe on April 30 . At the 2013 Electronic Entertainment Expo ( E3 ) , a PlayStation Vita version of God of War Collection was announced , and it was released on May 6 , 2014 , in North America , May 9 in Europe , May 14 in Australia , and May 15 in Japan . 
- God of War and God of War II were ported by <unk> Games and feature high @-@ definition <unk> anti @-@ <unk> graphics at 60 frames per second and <unk> . The bonus materials of the original two @-@ disc PlayStation 2 version of God of War II are included with the Blu @-@ ray version of the collection . The port was produced as a result of feedback from fans of the series and was viewed as a means of introducing new players to the series before God of War III was released . The God of War III game demo from E3 2009 was included with early copies of the collection . <unk> Games was responsible for <unk> the collection to the Vita . 
- Sony 's Santa Monica Studio — the games ' original developer — ran a <unk> in November 2009 for the release of God of War Collection . The grand prize was an autographed " Gold " disc , exclusive artwork created and signed by a God of War team member , a Limited Edition Kratos T @-@ shirt , and a limited edition 10 inches ( 25 cm ) figurine of Kratos . First place prize was a copy of God of War Collection signed by the development team . <unk> were announced in December 2009 . 
- On November 2 , 2010 , God of War Collection was released as a digital download on the PlayStation Store . PlayStation Plus subscribers can download a one @-@ hour trial of each game . For a limited time , Plus subscribers received a God of War III skin with the purchase of each game : " Phantom of Chaos " ( God of War ) and " Forgotten Warrior " ( God of War II ) respectively . The bonus materials for God of War II were not included with the digital download version . <unk> update version 1 @.@ 01 , released on September 5 , 2012 , allows the PlayStation 3 version of God of War Collection to be played on the PlayStation Vita via the <unk> Play function . 
- 
- = = = Reception = = = 
- 
- God of War Collection received critical acclaim from critics . <unk> review websites GameRankings and Metacritic gave the PlayStation 3 version 90 @.@ 78 % based on 43 reviews and 91 / 100 based on 50 reviews and the PlayStation Vita version 75 @.@ 00 % based on 15 reviews and 73 / 100 based on 28 reviews . 
- The PlayStation 3 version received critical acclaim . IGN gave the game an " Editor 's Choice " Award , praised the enhanced resolutions , lower price point , and <unk> frame rates , and stated it was the " definitive way to play the game " . <unk> noted the tremendous increase in visuals but said the in @-@ engine cut @-@ scenes appeared <unk> . GamePro stated the collection is " two fantastic games on one disc for a low price ; puts you in the right mindset for God of War 3 . " PlayStation <unk> ( 5 / 5 ) said " Those ... familiar with Kratos will enjoy the extra <unk> the Collection brings to two of the best games from the PS2 era . " Due to the success of God of War Collection , Sony announced that further titles would receive similar treatment for release under its new " Classics HD " brand . By June 2012 , God of War Collection had sold more than 2 @.@ 4 million copies worldwide , making it the eighteenth best @-@ selling PlayStation 3 game of all time . 
- 
- = = God of War : Origins Collection = = 
- 
- God of War : Origins Collection ( God of War Collection – Volume II in Europe and Australia ) is a remastered port of the two PlayStation Portable installments in the series — Chains of Olympus and Ghost of Sparta — for the PlayStation 3 on a single Blu @-@ ray Disc . It was announced at the Sony press conference at E3 2011 and was ported by Ready at Dawn , the developer of the PlayStation Portable games . The collection was released on September 13 , 2011 , in North America , September 16 in Europe , September 29 in Australia , and October 6 in Japan . God of War : Origins Collection was also released in North America as a digital download on the PlayStation Store on September 13 . 
- God of War : Origins Collection features native <unk> high @-@ definition video , anti @-@ <unk> graphics at 60 frames per second , <unk> 3 <unk> features , <unk> , and is the only God of War release to feature <unk> 3D . The God of War – Game Directors Live documentary , Kratos <unk> bonus skin , and Forest of the Forgotten combat arena ( originally pre @-@ order bonuses for Ghost of Sparta ) are also included with the Origins Collection . 
- 
- = = = Reception = = = 
- 
- God of War : Origins Collection received positive reviews from critics . It received a score of 86 @.@ 62 % based on 41 reviews on GameRankings and 84 / 100 based on 58 reviews on Metacritic . 
- IGN stated , " Sony succeeded at making good games better " and that it " acts as a time lapse experiment for players to get a really good look at how a developer evolves from one game to another . " GamePro noted the lack of new bonus content , and said , " 3D doesn 't necessarily radically redefine the experience . It does , however , make things like boss fights and magical effects much more <unk> " , but added that , " all of the in @-@ game <unk> are not in 3D , which is ... unfortunate considering how cutscenes are such an integral part of the storytelling in any God of War game " . By June 2012 , God of War : Origins Collection had sold 711 @,@ 737 copies worldwide . 
- 
- = = God of War Saga = = 
- 
- God of War Saga is a collection of five of the God of War games for the PlayStation 3 released as part of Sony 's PlayStation Collections line on August 28 , 2012 , in North America . The collection includes God of War , God of War II , God of War III , Chains of Olympus , and Ghost of Sparta . It features two Blu @-@ ray <unk> — God of War I and II on the first and III on the second — and a <unk> to download Chains of Olympus and Ghost of Sparta . The games retain the same features as their first PS3 releases . The collection also includes exclusive bonus content and a <unk> for a one @-@ month trial of PlayStation Plus . The games , with the exception of God of War III , are also available to download from the PlayStation Store . From September 27 until October 5 , 2012 , to celebrate the release of God of War Saga , <unk> ran a weekly countdown of the top five God of War epic moments as voted by the God of War Facebook community . " The Death of <unk> " from the original God of War was voted as the " # 1 most epic God of War moment of All Time . " 
- In Latin America , an exclusive version of God of War Saga , titled God of War : <unk> Collection , was released in November 2012 . The <unk> Collection features three Blu @-@ ray discs , as opposed to two , with Chains of Olympus and Ghost of Sparta included on the third disc . It also includes a <unk> game case with exclusive artwork and a limited edition bronze statue of Kratos , created by an Argentine artist . 
- 
- = = = Reception = = = 
- 
- Ryan Fleming of Digital Trends wrote that the collection " is perhaps the best value buy for any console available " , and that for fans of the series , " this collection is not for you " as all games ( with the exception of God of War III ) are available for download , and it will " likely be redundant . " However , new or inexperienced players should buy it . Fleming added that it was odd that the PSP games were included as downloads , and would like to have seen content migrate over to the PlayStation Vita . Jeffrey L. Wilson of PC Magazine gave the collection a 4 out of 5 and called it " an excellent purchase for anyone looking for cinematic , blood @-@ drenched action – especially newcomers who get five titles for the price of one " , but added that long time fans may not find much value in the collection . 
- 
- 
- = Territorial era of Minnesota = 
- 
- The territorial era of Minnesota covers the history of the land that is now the modern US state of Minnesota from the Louisiana Purchase in 1803 , to its achieving statehood in 1858 . The Minnesota Territory itself was formed only in 1849 but the area had a rich history well before this . Though there was a long history of European presence in the area before 19th century , it was during the 19th century that the United States began to establish a firm presence in what would become Minnesota . 
- Many of the <unk> of Minnesota culture that are perceived as the area 's early history in fact originated after this period . Notably , the heavy Scandinavian immigration for which the state is known , and the pioneering days chronicled by author Laura <unk> Wilder occurred after statehood in the later 19th century . Unlike these later years , the first half of the 19th century was characterized by sparsely populated communities , harsh living conditions , and to some degree , lawlessness . 
- This era was a period of economic transition . The dominant enterprise in the area since the 17th century had been the fur trade . The Dakota Sioux , and later the Ojibwe , tribes hunted and gathered pelts trading with French , British , and later American traders at Grand Portage , Mendota , and other sites . This trade gradually declined during the early 19th century as demand for furs in Europe diminished . The lumber industry grew rapidly , replacing furs as the key economic resource . Grain production began to develop late during this time as an emerging economic basis as well . Saw mills , and later grain mills , around Fort Snelling and Saint Anthony Falls in east @-@ central Minnesota became magnets for development . By the end of the era east @-@ central Minnesota had replaced northern Minnesota as the economic center of the area . 
- This era was also as a period of cultural transition . At the time the U.S. took possession of the region , Native Americans were by far the largest ethnic groups . Their role in the fur trade gave them a steady stream of income and significant political influence even as the French , British , and Americans asserted territorial claims on the area . French and British traders had mixed with native society in the area for many decades peacefully contributing to the society and creating new ethnic groups consisting of mixed @-@ race peoples . As the Americans established outposts in the area and the fur trade declined , the dynamics changed dramatically . The economic influence of the Native Americans diminished and American territorial ideology increasingly sought to limit their influence . Large waves of immigration in the 1850s very suddenly changed the demographics so that within a few years the population shifted from predominantly native to predominantly people of European descent . The native and mixed @-@ race populations continued to influence the territory 's culture and politics , even at the end of the territorial era , though by the time statehood was achieved that influence was in steep decline . Heavy immigration from New England and New York led to Minnesota 's being labeled the " New England of the West " . 
- 
- = = Background = = 
- 
- During the 17th century a Native American tribe known as the Ojibwe , or <unk> , reached Minnesota as part of a westward migration . Having come from a region around Maine , they were experienced at dealing with European traders . Tensions rose between the Ojibwe and the <unk> , or Eastern Dakota , Sioux , who were dominant in the area , during the ensuing years . 
- French exploration in Minnesota is known have begun in the 17th century with explorers like <unk> , <unk> , and Le <unk> . After France signed a treaty with a number of tribes to allow trade in the area , French settlements began to appear . <unk> Daniel <unk> , Sieur du <unk> explored the western area of Lake Superior helping to advance trade and leading to the establishment of Fond du Lac ( part of modern Duluth , which was named after du <unk> ) . Roman Catholic priest Louis <unk> , captured by the Sioux in <unk> while exploring North America with famed explorer La Salle , discovered and named Saint Anthony Falls . The next account of an expedition into Minnesota 's interior was that of Captain Jonathan Carver of Connecticut who reached Saint Anthony Falls in 1766 . In the later 18th century trader Peter Pond explored the Minnesota River valley noting significant European settlement in the region in addition to the natives . 
- <unk> searching for the fabled Northwest Passage and large inland seas in North America continued to pass through this region . Fort <unk> was built by the French in 1721 on Lake <unk> to facilitate exploration . In the 17th century a lucrative trade developed between Native Americans who trapped animals near the Great Lakes and traders who shipped the animal furs to Europe . For two centuries this trade network was the prime economic driver in the area . A notable result of this trade network was the Métis people , a mixed @-@ race community descended from Native Americans and French traders , as well as other mixed @-@ race peoples . In particular during the latter 18th century numerous French and English traders in the Minnesota region purchased Sioux wives in order to establish kinship relationships with the Sioux so as to secure their supply of furs from the tribes . 
- The British Hudson 's Bay Company was formed in <unk> to capitalize on the Native American fur trade near Hudson Bay . The company came to dominate the North American trade in the 18th century . The North West Company of Montreal was formed in 1779 to compete with Hudson 's Bay Company establishing their western headquarters and key exchange point at Grand Portage in what is now Minnesota . Grand Portage , with its two wharves and numerous warehouses , became one of Britain 's four main fur trading posts , along with Niagara , Detroit , and <unk> . British ships crossed Lake Superior regularly transporting supplies to the region and bringing back valuable furs . Even after Grand Portage became property of the U.S. in 1783 the British operations , such as North West Company and the <unk> Company , continued to operate in the area for some time . 
- Though the various parts of what is now Minnesota were claimed at different times by Spain , France , and Britain , none of these nations made significant efforts to establish major settlements in the area . Instead the French and the British established mostly trading posts and utilized the natives in the area as suppliers . 
- All of the land east of the Mississippi River was granted to the United States by the Second Treaty of Paris at the end of the American Revolution in 1783 . This included what would become modern day Saint Paul but only part of Minneapolis , including the northeast , north @-@ central and east @-@ central portions of the state . The wording of the treaty in the Minnesota area depended on landmarks reported by fur traders , who erroneously reported an " Isle <unk> " in Lake Superior , a " Long Lake " west of the island , and the belief that the Mississippi River ran well into modern Canada . Much of this region was claimed by other states who subsequently ceded these to the federal government . 
- Most of the remaining areas of what is now the state were purchased in 1803 from France as part of the Louisiana Purchase ( the area west of the Mississippi having been recently acquired by France from Spain ) . Parts of northern Minnesota were considered to be in Rupert 's Land , a large territory owned by Hudson 's Bay Company . The exact definition of the boundary between Minnesota and British North America was not addressed until the Anglo @-@ American Convention of 1818 . 
- Until 1818 the entire Red River Valley in what is today southeastern Manitoba and northwestern Minnesota was considered British and was subject to several colonization schemes by the Hudson 's Bay Company , particularly the Red River Colony ( also known as the <unk> Settlement ) established in 1811 . The valley had , in fact , been occupied by Métis since the middle 17th century . The Red River Colony , established to supply the British fur trade , was fraught with problems from the beginning but became important in the Minnesota area 's early fur trade as well as supplying many early settlers to the region . 
- 
- = = Pioneers and exploration = = 
- 
- At the beginning of the 19th century many parts of the Minnesota area were already well traveled by British and French explorers . Though the region 's population was mostly Native American , there were important British trading posts in the area with many European and mixed @-@ race settlers , particularly in the north . Grand Portage , in particular , had long been established as the major trading center for the North West Company . 
- David Thompson , a British fur trader for the North West Company of Montreal , completed numerous surveys and maps of the North American frontier . In 1797 he completed the first known map of the Minnesota area , in what was then the Northwest Territory . The Jay Treaty , however , obliged most of the British settlers to withdraw their settlements in 1796 , though mixed @-@ race peoples remained . 
- In 1805 U.S. Lieutenant <unk> Pike was sent by General Wilkinson , governor of the Louisiana Territory , to enforce U.S. sovereignty against British traders in the area and establish diplomatic and trading relationships with the native tribes . He met with the Sioux leadership in central Minnesota to secure rights for the U.S. to an area near Saint Anthony Falls , which would later become the city of Saint Paul . Though a treaty was signed by some leaders from the Sioux tribes , its legitimacy ( including whether the Sioux understood it ) was dubious and ultimately his efforts did little to establish the authority of the U.S. in the area . 
- In 1817 Major Stephen H. Long of the U.S. Army Corps of Engineers led a <unk> expedition from Prairie du Chien to reach Saint Anthony Falls . He documented much of the terrain today occupied by Minneapolis and Saint Paul as well as the Native American villages that existed there at the time . 
- In 1818 the 49th parallel was established as the boundary between the United States and British North America . However , the point where the Red River crossed this line was not marked until 1823 , when Stephen Long conducted a survey expedition . The expedition determined , among other things , that the fur trading post of Pembina lay just inside the U.S. border . 
- Several efforts were made to determine the source of the Mississippi River . In 1823 Italian explorer Giacomo <unk> <unk> who had split from the Long expedition in Pembina , found Lake Julia which he believed was the source of the Mississippi River . The actual source was found in 1832 , when Henry Schoolcraft was guided by a group of Ojibwe headed by <unk> ( " Yellow Head " ) to a lake in northern Minnesota . Schoolcraft named it Lake <unk> , combining the Latin words <unk> ( " truth " ) and <unk> ( " head " ) . 
- In 1835 George William <unk> conducted a geological survey of the Minnesota River valley and wrote an account entitled A <unk> Voyage up the <unk> <unk> . Joseph <unk> scouted the area in the late 1830s accompanied by John C. <unk> , exploring and mapping the Upper Mississippi River basin , the Saint Croix River , and the land between the Mississippi and Missouri Rivers . 
- 
- = = <unk> = = 
- 
- An important <unk> of the British and American frontier was a system of forts built by the military . The forts provided safe shelter for soldiers and explorers on the frontier and a base of operations for expeditions , both military and commercial . The first forts in the area had been French , particularly Fort <unk> , built during the 18th century and later abandoned because of the French and Indian War with the British . British Fort Charlotte at Grand Portage became essential to the fur trade protecting and supplying British traders as well as the area natives . This British fort operated in the area ( illegally ) until 1803 , even after the area 's becoming recognized as part of the United States . Other French and British fortifications , such as Fort St. Charles , had existed in the region but had been abandoned much earlier . 
- In 1814 the U.S. government built Fort Shelby , later rebuilt as Fort Crawford , near modern Minnesota in what is now Prairie du Chien , Wisconsin . Fort Crawford would play a significant role in U.S. involvement in Minnesota , particularly as the site of the Treaty of Prairie du Chien . The first major U.S. military presence inside the boundaries of modern Minnesota was Fort Saint Anthony , later renamed Fort Snelling ( after the fort 's commander Josiah Snelling ) . The land for the fort , at the confluence of the Minnesota and Mississippi rivers , had been acquired in 1805 by legendary explorer <unk> Pike . When concerns mounted about the fur trade in the area , construction of the fort began in 1819 and was completed in 1825 . One of the missions of the fort was to mediate disputes between the Ojibwe and the Dakota tribes . Lawrence Taliaferro , an agent of the U.S. Bureau of Indian Affairs who became an important figure in these <unk> , spent 20 years at the fort , finally resigning in 1839 . 
- Fort Ripley was built in 1848 – 1849 in central Minnesota near modern Little Falls . It was built to provide a military presence on the frontier near the new Winnebago reservation created as the tribe was moved from Iowa . In addition it helped to serve as a <unk> between the Dakota Sioux and the Ojibwe . 
- Fort <unk> was built in 1853 – 1854 near the Dakota reservation in southwestern Minnesota , near modern New Ulm . It was named by U.S. Secretary of War Jefferson Davis in honor of three army officers named <unk> who had died in the Mexican @-@ American War . The fort was created to watch over the Minnesota River Valley , in addition to the larger frontier . It replaced Fort <unk> in Iowa , which was decommissioned during the same period . The fort operated as a military post until 1867 . 
- Fort <unk> was built in 1858 on the Red River at what is now the border between Minnesota and North Dakota near modern <unk> . The fort had to be moved soon afterward because of flooding problems . It was created to spur settlement of the Red River Valley , protect steamboat traffic on the river , and protect wagon trains travelling to Montana . 
- In addition to these military bases , private companies operated numerous trading posts in the region that were often referred to as " forts " , though they typically had little in the way of defensive fortifications . 
- 
- = = Native Americans = = 
- 
- The two main Native American tribal groups which dominated Minnesota at the time the lands were acquired by the United States were the more established Dakota Sioux , and the Ojibwe who had migrated into the area more recently . The two groups fought bitter territorial wars during the 18th century . In the mid @-@ 18th century the Battle of <unk> , in which the Ojibwe defeated the Sioux , permanently established northeastern Minnesota , particularly <unk> <unk> Lake , as Ojibwe territory <unk> the Sioux to southern and western Minnesota . Skirmishes between the groups continued in the 19th century including a battle near Lac <unk> in 1818 , a battle near Stillwater in 1839 ( the site became known as " Battle Hollow " ) , and another on the Yellow Medicine River in 1854 . 
- During the War of 1812 most of the Dakota and Ojibwe sided with the British though at various times some aided the Americans or took the opportunity to attack enemy tribes ( a notable American loyalist was the Dakota chief <unk> , or " Rising Moose , " an admirer of Pike , who joined the U.S. army at Saint Louis ) . Though Grand Portage was the only part of Minnesota that saw significant conflict during the war , natives throughout the region were recruited to fight further east in areas such as Green Bay . In particular the half @-@ Dakota British captain Joseph <unk> heavily recruited among the <unk> branch of the Dakota Sioux including chiefs Little Crow and <unk> . 
- From 1815 to 1821 employees of the Hudson 's Bay Company and the North West Company engaged in various territorial conflicts known as the " fur trade wars , " including the famous Battle of Seven Oaks at what is now Winnipeg , Manitoba . As a result of these conflicts numerous Métis migrated from the Red River area to central and eastern Minnesota , particularly in the vicinity of Saint Paul . This " Red River Exodus " became a major source of francophone immigration into Minnesota during the territorial era . The Métis and other mixed @-@ race groups were often regarded as French Canadian " whites " rather than " Indians " . 
- By the 1820s , animal resources were in decline in the area leading to increased competition among the tribes for game and for furs to sell . <unk> among the fur trading companies led to a dramatic drop in fur prices during the late 1820s causing impoverishment for many Sioux hunters . The U.S. government strongly encouraged the tribes to turn from hunting to farming , trading the woodlands for the plains . 
- Increasing territorial conflict between the Sioux and the Ojibwe on the western frontier , particularly along the Mississippi river , led the U.S. government to attempt to mediate the conflicts . President Andrew Jackson 's policy toward the tribes ultimately was to either <unk> them sufficiently to allow westward expansion of American settlers , or else remove the tribes from the areas in which they prevented settlement . The First Treaty of Prairie du Chien ( 1825 ) , among its provisions established southern Minnesota as well as much of modern North and South Dakota as the homeland of the Dakota Sioux . The Ojibwe were given northern Minnesota and much of Wisconsin . The U.S. government , though , failed to enforce the treaty agreements leading to Little Crow 's <unk> to Indian agent Taliaferro in 1829 : " We made peace to please you , but if we are badly off we must blame you for causing us to give up so much of our lands to our enemies . " 
- Following an 1846 treaty , the Winnebago tribes of Iowa were relocated to the Long Prairie reservation in central Minnesota in the late 1840s establishing an important presence in the territory . Because of the poor land in the new reservation the tribe subsequently negotiated a treaty in 1856 allowing them to relocate further south to Blue Earth but <unk> substantial land in the process . 
- All of the native tribes experienced gradual disillusionment with the U.S. government because of its inability or unwillingness to honor its treaty commitments . The major leaders among the tribes were <unk> and Little Crow among the Dakota Sioux , <unk> Mouth and Hole @-@ in @-@ the @-@ Day among the Ojibwe , and <unk> among the Winnebago . The success of treaty negotiations between the U.S. and the tribes was in great part facilitated by the mixed race families such as the <unk> and the <unk> . 
- The Treaty of <unk> des Sioux in 1851 gave all of the <unk> and <unk> Sioux ( upper Sioux ) lands west of the Mississippi River to the U.S. government . The Treaty of Mendota that same year ceded the <unk> and <unk> Sioux ( lower Sioux ) lands in southern Minnesota , requiring relocation to an area near modern Morton . Both treaties , however , were amended to during the ratification process to eliminate the explicit guarantees of lands retained by the tribes . Additionally much of the promised payments were never delivered in part ostensibly because of debts owed by the Sioux to the fur traders . 
- Despite American hunger for land , the leadership in the Minnesota Territory did not actually want to remove the Sioux from the territory . Federal subsidies to the tribes were heavily <unk> by the U.S. settlements and removal of the tribes from the territory would have meant loss of this income . 
- Increasing impoverishment among the Sioux and continued treaty violations on the part of the United States would soon lead to <unk> . In 1857 a renegade band of Sioux led by war chief <unk> attacked the community of Spirit Lake , Iowa near the Minnesota border killing between 35 and 40 " white " settlers ( the event would be referred to as the Spirit Lake Massacre ) . They went on to attack Springfield , Minnesota ( modern Jackson ) killing seven before being turned back . In 1862 , bands of Sioux launched the Dakota War in which they were defeated . Apart from those killed in the war , 38 Dakota Sioux were killed in a mass execution in <unk> , the largest mass execution in the U.S. history . Hundreds more Sioux and European @-@ Americans were killed in the U.S. government 's subsequent eradication of the Sioux nation in Minnesota and the new Dakota Territory . 
- 
- = = Commercial enterprises = = 
- 
- The most important commercial enterprise in the early part of the territorial era was the lucrative fur trade . At the beginning of the 19th century two British companies competed for dominance in the North American trade : Hudson 's Bay Company and the North West Company . The North West company had used Grand Portage as its western headquarters along with other smaller companies that operated in the area . Grand Portage was one of the four principal British trading and shipping points furs in North America . Following the Treaty of Paris , in 1783 , British operations at Grand Portage were technically illegal though the trade continued . However , beginning in 1801 the North West Company began re @-@ establishing its headquarters north of the border at the newly constructed Fort William in what is now Ontario . After 1804 Grand Portage had been reduced to a minor trading center and most traders eventually abandoned the area . In 1842 , the Hudson Bay Company , which had by then absorbed the North West Company , shipped out a final band of Ojibwe who were employed by the company . 
- Before 1816 the majority of the fur trading posts in the Minnesota area were owned by the North West Company , but by 1821 the American Fur Company , founded in 1808 by John Jacob Astor in New York , had taken over most of these . As well as Grand Portage , another significant fur shipping point in Minnesota was Fort Frances in the <unk> Lake region , near modern International Falls in the far north of the state . This location became significant as it was key to multiple waterways for shipping furs to the Atlantic . Both the North West Company and the American Fur Company had posts at this location . Pembina , originally part of the Red River Colony , was a significant trading post for the Hudson 's Bay Company , and once it was claimed by the U.S. , became for a time key to U.S. interests in the fur trade . By 1830 American Fur dominated the trade within the United States because of the exclusion of British companies by the U.S. government . 
- Beginning in the 1820s , a fur trading route developed between the Red River Colony ( in modern Manitoba ) and the trading posts in Minnesota , first primarily at Mendota and later at Saint Paul . The system of <unk> cart trails came to be known as the Red River Trails and was used principally by the Métis as a way to avoid the fur trade monopoly of the Hudson 's Bay Company ( which had absorbed the North West Company ) . Though this cross @-@ border trade was entirely illegal and violated the policies of the Hudson 's Bay Company , enforcement against the trade by American and British authorities was virtually non @-@ <unk> . The trail system would reach its peak usage in the mid @-@ 19th century . The Hudson 's Bay Company continued to expand its presence north of the U.S. border establishing new posts such as Fort Alexander and Rat Portage . 
- The fur trade was in decline by the late 1830s . The American Fur Company went bankrupt in 1842 , though the Missouri Fur Company and other operations kept the trade from collapsing entirely . As this trade declined the lumber industry began to grow substantially in areas such as the Saint Croix Valley where valuable white pine was plentiful . New saw mills appeared in Marine and Stillwater . Lumber was typically cut during winter and sent downstream in the spring . In 1848 , businessman Franklin Steele built the first private sawmill on the Saint Anthony Falls ( which would later become the town of Saint Anthony ) opening commercial lumbering on the Mississippi River . More sawmills quickly followed . Soon the Saint Croix and Mississippi Rivers in Minnesota had become major <unk> for lumber headed for Saint Louis and other destinations . 
- The first flour mill in Minnesota was built in 1823 at Fort Snelling as a <unk> of a lumber mill . The first private grain mill was built in Washington County by Samuel <unk> . Minneapolis gained its first grain mill in 1847 . During the 1850s grain production began to develop rapidly but Minnesota did not become a significant grain exporter until 1858 . 
- In 1823 the first steamboat , known as the Virginia , arrived at Fort Snelling carrying Indian agent Lawrence Taliaferro . By the 1830s a steady , if not yet large , stream of steamboat traffic plied the river including some ships listed as ferrying " pleasure parties " . The first railroad to reach the Mississippi ( in Illinois ) , the Rock Island Railroad , was completed in the 1854 . The event was celebrated with <unk> excursions from Rock Island up the Mississippi into Minnesota . Those excursions touched off such a wave of interest in Minnesota that 56 @,@ 000 tourists visited Saint Paul by steamboat in 1856 . 
- In 1849 James <unk> began publication of the Minnesota Pioneer newspaper in Saint Paul ( the paper would later be renamed the St. Paul Pioneer Press ) . By the time the area achieved statehood 89 newspapers had been established . Information about Minnesota published in these periodicals spread throughout the United States and Europe . Advertising campaigns were launched in the northeastern U.S. and Europe to lure European settlers . These efforts met with limited success though they would become much more successful after statehood . 
- Saint Anthony , with its scenic waterfalls , rapidly developed as a destination for tourists traveling the Mississippi on steamboats . The Winslow House , a luxury hotel overlooking the falls , was constructed in 1857 . By the late 1860s Saint Anthony had become a popular summer resort for wealthy <unk> . 
- One of the major sources of income in the territory during the 1850s was U.S. government annuity payments to the Ojibwe and other tribes required by earlier treaties . These payments amounted to more than $ 380 @,@ 000 per year on average ( $ 10 @.@ 8 million in present @-@ day terms ) compared to approximately $ 120 @,@ 000 per year ( $ 3 @.@ 41 million in present @-@ day terms ) given to the territory itself for development . Because of corruption , and mishandling of the payments to the tribes , a great deal of the money was used directly by U.S. settlers for commercial and community development with questionable benefit to the tribes . At the beginning of the Minnesota Territory , in fact , these payments were the territory 's most important source of income since the fur trade was no longer as lucrative as it had once been and other exports were still negligible . 
- 
- = = <unk> = = 
- 
- During most of this era Native Americans outnumbered European / U.S. settlers in what is now Minnesota . Significant Dakota Sioux settlements in the Minnesota area included <unk> , located in what is now Saint Paul before being moved by the 1837 treaty . Significant Ojibwe settlements included <unk> <unk> ( <unk> <unk> ) and <unk> ( Fond du Lac ) , as well as the community that had developed around the Grand Portage commerce . 
- When the Minnesota Territory was established in 1848 the Native American settlements in the territory still rivaled the American settlements in size . According to some scholars , the Mandan / Hidatsa village of Like @-@ a @-@ Fishhook in what is now North Dakota , with a population of 700 , was the largest settlement in the Minnesota Territory . The numerous other settlements in the territory gave a total Native American population of over 25 @,@ 000 in 1849 which easily outnumbered the <unk> " white " settlers . 
- At the outset of the 19th century most of the European settlements were related to the fur trade . The largest of these settlements were trading posts established by the North West Company , particularly those at Sandy Lake , <unk> Lake , and Fond du Lac . Historian Grace Lee <unk> has documented over 100 fur trading posts of varying sizes in the Minnesota area before statehood . Most of these posts were eventually taken over by the American Fur Company . When several hundred settlers abandoned the Red River Colony in the 1820s , they entered the United States by way of the Red River Valley , instead of moving to eastern Canada or returning to Europe , adding to the Minnesota region 's population . 
- Construction on Fort Snelling began in 1820 and was finished in 1825 . The Fort became a magnet for settlement in east @-@ central Minnesota . Nearby Mendota was established during the same period and , as the regional headquarters for the American Fur Company , also drew settlement in the area soon becoming Minnesota 's commercial center . Many of the first stone buildings in the territory were constructed in Mendota by employees of the American Fur Company , which bought animal pelts at that location from 1825 to 1853 . 
- The logging industry spurred further development of settlements . Before railroads , <unk> relied mostly on river transportation to bring logs to market , which made Minnesota 's timber resources attractive . Towns like Marine on Saint Croix , founded as Marine Mills , and Stillwater became significant lumber centers fed by the Saint Croix River , while <unk> was supplied lumber by areas in southern Minnesota and along the Minnesota River . 
- In the 1830s a group of squatters , mostly Métis from the ill @-@ <unk> Red River Colony , established a camp near the fort . Because of complaints from some residents at the fort , new restrictions were placed on the squatters forcing them to move down the Mississippi River , first to a site known as Fountain Cave , and then even further <unk> . Pierre " Pig 's Eye " <unk> , a popular <unk> among the group , established a saloon at the new site , and the squatters named their settlement " Pig 's Eye " after <unk> ( later changing the name to Lambert 's Landing , and finally Saint Paul after the local chapel ) . The location was a convenient site for a steamboat landing and by 1847 a steamboat line had established the town as a regular stop . This attractive advantage for commerce caused the settlement to develop significantly , soon eroding Mendota 's prominence . 
- The <unk> ( general store operator ) at Fort Snelling , Franklin Steele , who had established lumbering interests in the area , staked a claim to lands adjacent to Saint Anthony Falls following the land <unk> of the 1837 <unk> treaty . In 1848 he built a sawmill at the falls establishing the basis of the town of Saint Anthony which grew there . John H. Stevens , an employee of Franklin Steele , pointed out that land on the west side of the falls would make a good site for future mills . Since the land on the west side was still part of the military reservation , Stevens made a deal with Fort Snelling 's commander . Stevens would provide free ferry service across the river in exchange for a tract of 160 acres ( 0 @.@ 65 km2 ) at the head of the falls . Stevens received the claim and built a house , the first house in Minneapolis , in 1850 . Later in 1854 , Stevens <unk> the city of Minneapolis on the west bank . In 1855 the first bridge across the main channel of the Mississippi ( anywhere in the nation ) was built between Minneapolis and Saint Anthony . 
- By 1851 , treaties between Native American tribes and the U.S. government had opened much of Minnesota to U.S. settlement . Fort Snelling was no longer a frontier outpost . Efforts to establish Minnesota as a prominent future state in the Union were swift . In 1851 territorial legislature petitioned the U.S. Congress for land to build a railroad between Milwaukee , Wisconsin and Saint Paul . That same year the legislature incorporated the University of Minnesota and established its endowment ( though the University would not admit students until many years later ) . 
- In 1848 when the Minnesota Territory was formed there were four major " white " settlements : Saint Paul , Saint Anthony ( part of modern Minneapolis ) , Stillwater , and Pembina ( now part of North Dakota ) . New settlements began to appear more rapidly . <unk> was established in 1852 by entrepreneurs Jackson , Johnson , and Williams . Saint Peter was established in 1853 by Captain William <unk> Dodd . New Ulm was established in 1854 by German immigrants . Rochester was established by George Head in 1854 . Not all of the new settlements were established by immigrants from the eastern U.S. and Europe , though . The town of <unk> , for example , was established in 1852 by Alexander <unk> , a Minnesota native of mixed French @-@ Canadian / Dakota ancestry . 
- The influx of settlers in the 1850s transformed Minnesota from a sparsely populated territory of less than 10 @,@ 000 " white " settlers and a significantly larger native population , to a substantial population center of over 150 @,@ 000 predominantly European settlers . The city of Saint Paul expanded from less than 400 people in 1848 to over 2500 in 1852 and over 10 @,@ 000 in 1860 . 
- As a result of heavy immigration from New England and New York — regions where most major towns had originated as trading centers rather than political or manufacturing centers — many new settlements in Minnesota were laid out so as to heavily favor the business districts rather than the city halls or <unk> . This plan and the philosophy behind it spurred the growth of economic links between the communities and with other parts of the U.S. 
- In 1856 the Minnesota Territory established its first Commissioner of Emigration , Eugene <unk> . Through advertisements and speeches to new immigrants to the U.S. in New York , <unk> expanded the immigration trend which later created a large German community after statehood . 
- 
- = = Society = = 
- 
- Until the 1850s the Native American population vastly outnumbered the population of European ancestry in the area . Nevertheless , the division between " Indian " and " white " during this era was always somewhat vague . In general persons of mixed descent were considered " white " if they dressed in European clothing and adopted European customs . " Indians " were those who lived in traditional native lifestyles . Even as the U.S. began to establish its authority over the region and some settlers from the U.S. began to arrive the Native American population continued to hold significant political and social influence as a result of the fur trade . As experienced hunters they were important to one of North America 's major business enterprises . The decline of this trade during the later part of the era marked the decline of Native American influence . 
- Following the 1837 treaty the Saint Croix Triangle , between the Saint Croix and Mississippi Rivers , had been opened to U.S. settlement . Still until the later establishment of the Minnesota Territory this triangle remained an island of " white " culture and settlement . The vast majority of the Minnesota area , though , was " Indian country " . Contemporary accounts of larger towns such as Mendota , Saint Anthony , and Saint Paul in the 1840s indicate that the majority of the population was predominantly of French and Métis ancestry . Even in these communities European culture , was not strictly dominant . Commenting on Minnesota 's culture of the 1840s , Governor Alexander Ramsey described the streets of Saint Paul saying that it was common to see " the blankets and painted faces of Indians , and the red <unk> and <unk> of French <unk> and half @-@ breeds , greatly <unk> over the less picturesque costume of the Anglo @-@ American race . " 
- It is in fact likely that a very large percentage of the " white " population reported in the 1850 census was of partially Native American ancestry . Many men of mixed racial ancestry became respected members of " white " society . William W. Warren , for example , was the son of an American entrepreneur ( who hailed from New York before he began working in John Jacob Astor 's American Fur Company ) and a mixed @-@ blood Ojibwe mother ( whose father had been in the old French and British fur trade ) was educated in the East and in the early 1850s lived on the Upper Mississippi , in part working as an independent translator and Indian Agency contractor . Warren was a good writer — his newspaper articles were eventually published as the only 19th century compendium of Ojibwe history and was elected to the territorial legislature before his death from consumption . 
- With the establishment of the Minnesota Territory in 1848 and the treaty of 1851 waves of immigrants from the U.S. and Europe came to the territory rapidly changing the demographics . Even as these changes occurred in many areas the <unk> of the racial divisions between " Indians " and " whites " persisted . As late as 1857 it was common practice in some jurisdictions for men to be allowed to vote based on whether or not they were wearing European clothing . According to some observers natives at a given polling location would share a single pair of trousers each wearing them only long enough to cast a ballot . 
- <unk> and trading communities in the territory , such as International Falls , were often known as centers of lawlessness and vice . <unk> were commonly the social centers of the towns with <unk> and " bath houses " adding to the character of the society . These gathering places attracted trappers , traders , smugglers , and numerous others traveling through the countryside . 
- The late 1840s and 1850s witnessed large @-@ scale immigration from the Eastern U.S. and Europe . By 1860 approximately 80 % percent of Minnesota 's <unk> population came from New York and New England . The state was in fact for a time known as the " New England of the West " . Maine , in particular , contributed a large number of immigrants , probably because of the large number of <unk> in Maine and the growing lumber industry in Minnesota . 
- By the 1850s racist ideology , which was becoming prevalent in much of the U.S. , began to affect Minnesota more significantly than it had in the past . The ruling class was composed of primarily Anglo @-@ American Protestants . Settlers from the U.S. increasingly discussed " white " inhabitants as the key to Minnesota 's future with an eye toward <unk> the role that other " inferior " races would have in the future . Author James Wesley Bond in 1853 described Minnesota before the 1850s as " a waste of woodland and prairie , uninhabited save by the different hordes of savage tribes from time immemorial . " <unk> in the territory , however , were complicated . As late as 1840 <unk> in Saint Paul were commonly treated as equals to others in the community with children of all races attending the same schools . By the late 1840s , however , all blacks had been completely disenfranchised . In addition they were prevented from running for office and their children were segregated in schools . By contrast Irish Catholics and Native Americans who adopted European lifestyles were allowed to vote and their children were not segregated in the classrooms . <unk> whereas Anglo @-@ Americans generally accepted business development by African Americans , they largely opposed business development by Irish immigrants . 
- Minnesota was a multi @-@ lingual area throughout the era . During the earlier parts of the era French and English were widely used but Ojibwe , Sioux , and <unk> ( the language of the Métis ) were more widespread . By the late 1850s English had grown to be the most spoken language . New immigrants , though , brought additional languages to the territory . Newspapers were published in German ( Die Minnesota Deutsche Zeitung ) , Swedish ( Minnesota <unk> ) , and Norwegian ( <unk> <unk> ) . Irish Gaelic , Czech and other languages were used in various communities as well . 
- Most of the population of the region in earlier decades followed traditional tribal religious practices . However , Roman Catholicism had been known in the area long before its acquisition by the U.S. because of the many French traders who lived and <unk> there . Catholic missionary activity among the Métis expanded greatly in the early 19th century with the Catholic Church becoming particularly established in Saint Paul . <unk> was rather a much newer phenomenon though some Protestant missionaries had entered the region in the early 19th century as well . The first Protestant church appeared in 1848 ( Market Street Church , Saint Paul ) . The waves of immigration in the 1850s , however , would rapidly make <unk> the largest religious group . 
- 
- = = African Americans and slavery = = 
- 
- The Northwest <unk> of 1787 in theory outlawed slavery in the Northwest Territory including the Minnesota area . The ordinance specifically stated 
- There shall be neither slavery nor <unk> servitude in the said territory , otherwise than in the punishment of crime , <unk> the party shall have been duly convicted . 
- The ordinance was nevertheless seen as ambiguous in that it did not specifically address the slaves already in the territories , and it discussed the " free " population of the territories seemingly implying that a slave population would exist . French traders in the territories , and later even American army officers ( including Josiah Snelling who commanded his namesake fort ) , continued to hold slaves with the blessings of many in Congress . 
- The number of African Americans in the territory during this period was quite small but not insignificant . <unk> continued to bring slaves with them , but there were many free blacks as well , some working as servants and some as completely independent pioneers . Information about the black immigrants during the earlier periods is sparse , but records do show that most of those at Fort Snelling were slaves . Records from 1850 indicate a population of 39 free blacks out of a total population of 6 @,@ 077 citizens in the territory ( which excluded Native American tribes ) . Before the 1840s these free persons could often expect to be treated equal to other racial groups . By the time Minnesota had achieved statehood , however , blacks had been disenfranchised and schools were segregated . Despite this , from the start of the Minnesota Territory in 1848 the leadership was predominantly <unk> thus ending the practice in this era . 
- One of the most famous of the early African Americans in the territory was George Bonga . He was born in Minnesota in 1802 , his father Pierre Bonga the son of a freed slave and his mother a member of the Ojibwe tribe . Bonga was <unk> in Montreal and eventually became a fur trader in the Northwest territories . He went on to serve as an interpreter in negotiations with the Ojibwe ( particularly as a representative of Michigan Governor Lewis Cass ) . His brother Stephen served as the Ojibwe interpreter at Fort Snelling for the 1837 treaty . 
- In the 1850s , Fort Snelling played a key role in the infamous <unk> Scott court case . <unk> <unk> Scott and his wife were taken to the fort by their master , John Emerson . They lived at the fort and elsewhere in territories where slavery was prohibited . After Emerson 's death , the Scotts argued that since they had lived in free territory , they were no longer slaves . Ultimately in 1857 the U.S. Supreme Court sided against the Scotts . This decision helped to fuel <unk> over slavery leading to the Bleeding Kansas conflicts , the Panic of 1857 , and eventually the American Civil War . 
- 
- = = Government and politics = = 
- 
- In the earlier part of the 19th century the area which is today Minnesota was not recognized as a single entity . The Mississippi River had divided the eastern British / French lands of North America from the western Spanish lands and even after the Louisiana Purchase this was for a time seen as a separation between territories . The division between the U.S. territories in the region and the British territories remained ambiguous until the Anglo @-@ American Convention of 1818 , which set the border with British North America at the 49th parallel west of the Lake of the Woods ( except for a small <unk> of land now dubbed the Northwest Angle ) . Border disputes east of the Lake of the Woods continued until the Webster @-@ <unk> Treaty of 1842 . 
- Throughout the first half of the 19th century , the northeastern portion of the state was a part of the Northwest Territory , formed in 1787 . After Ohio 's statehood the area became part of the new Illinois Territory in 1809 . After Illinois ' statehood the area was incorporated into the Michigan Territory in 1818 and later became part of the Wisconsin Territory in 1836 . The western and southern areas of the state were not formally organized until 1838 , when they became part of the Iowa Territory . 
- Following the admission of Wisconsin as a state in 1848 , the Minnesota area was temporarily without a government , though John <unk> , the former secretary of the Wisconsin Territory , claimed governorship of what remained of the territory as a short @-@ term measure . By this time Minnesota 's residents were largely Democrats and , as the U.S. Congress was at that time controlled by Democrats , they hoped Congress might be sympathetic to their concerns . In that same year a meeting was held in Stillwater , nominally led by <unk> and later known as the " Stillwater Convention " , to discuss establishing a new territory . The participants elected Henry Sibley as a representative to Congress . 
- Stephen A. Douglas ( D ) , the chair of the United States Senate Committee on Territories , drafted the bill authorizing the Minnesota Territory in 1848 . He had envisioned a future for the upper Mississippi valley , so he was motivated to keep the area from being carved up by neighboring territories . In 1846 , he had prevented Iowa from including Fort Snelling and Saint Anthony Falls within its northern border . In 1847 , he kept the organizers of Wisconsin from including Saint Paul and Saint Anthony Falls . The Minnesota Territory was established from the lands remaining from Iowa Territory and Wisconsin Territory on March 3 , 1849 . The Minnesota Territory extended far into what is now North Dakota and South Dakota , to the Missouri River . There was a dispute over the shape of the state to be carved out of Minnesota Territory . An alternate proposal that was only narrowly defeated would have made the 46th parallel the state 's northern border and the Missouri River its western border , thus giving up the whole northern half of the state in exchange for the eastern half of what later became South Dakota . 
- Alexander Ramsey ( W ) became the first governor of Minnesota Territory and Henry Hastings Sibley ( D ) became the territorial delegate to the United States Congress . Henry M. Rice ( D ) , who replaced Sibley as the territorial delegate in 1853 , worked in Congress to promote Minnesota interests . He lobbied for the construction of a railroad connecting Saint Paul and Lake Superior , with a link from Saint Paul to the Illinois Central Railroad . 
- 
- = = Organization and statehood = = 
- 
- Before 1856 there was minimal discussion of statehood within Minnesota . However , as discussion of a potential <unk> railroad in the U.S. became serious , leaders in Minnesota recognized that a territory was in a weak position to lobby for this economic opportunity . 
- In December 1856 , Rice brought forward two bills in Congress : an enabling act that would allow Minnesota to form a state constitution , and a railroad land grant bill . The enabling act defined a state containing both prairie and forest lands with the boundaries drawn as they are today . The bid for statehood came at a time when North @-@ South tensions in the U.S. were rising , tensions that would later lead to the American Civil War . Debate over admitting Minnesota as a free state was heated , but the enabling act was finally passed on February 26 , 1857 . 
- A constitutional convention was assembled in the territory in July 1857 . Divisions between Republicans and Democrats led to the drafting of two separate <unk> . The larger cities of Saint Paul , Saint Anthony , and Stillwater were the domain of the Democrats whereas agrarian southern Minnesota was the domain of the Republicans . A single constitution was finally worked out between the two factions though the more powerful Democrats ultimately prevailed on most issues . The resentment between the two parties remained so <unk> that two separate copies of the constitution had to be used so that members of each party did not have to sign a copy signed by members of the other party . The copies were signed on August 29 , 1857 and an election was called on October 13 , 1857 to approve the document . 30 @,@ <unk> voters approved the constitution , while 571 rejected it . 
- The state constitution was sent to the United States Congress for ratification in December 1857 . The approval process was drawn out for several months while Congress debated over issues that had stemmed from the Kansas @-@ Nebraska Act . Once questions surrounding Kansas were settled the bill for Minnesota 's <unk> was passed . The eastern half of the Minnesota Territory , under the boundaries defined by Henry <unk> Rice , became the country 's 32nd state on May 11 , 1858 . The western part remained unorganized until its incorporation into the Dakota Territory on March 2 , 1861 . 
- 
- = = In popular culture = = 
- 
- In 1855 Henry Wadsworth <unk> , who had never explored Minnesota himself , published The Song of <unk> containing many references to regions in Minnesota . The story was based on Ojibwe legends carried back east by other explorers and traders ( particularly those collected by Henry <unk> Schoolcraft ) . 
- Joseph Rolette ( also known as " Jolly Joe " ) was a fur trader and territorial <unk> of partially Métis ( mixed French / Native American ) ancestry who became an iconic figure known in Minnesota history for his <unk> . His most famous <unk> was one in which , following the passage of a bill in 1857 which would have moved the territorial capital from Saint Paul to Saint Peter , Rolette <unk> with the bill preventing it from becoming law . This and other stories were passed down for generations making Rolette as much a legend as a historical figure . 
- The " <unk> State " moniker , by which the state today is widely known , was selected in the mid @-@ 19th century as a means to create an identity for the state . Though some believed that " Beaver State " should be selected instead as more dignified , a political cartoon featuring a <unk> soon solidified " <unk> State " as the more well @-@ known identity . 
- 
- 
- = Mitsuyo Maeda = 
- 
- Mitsuyo Maeda ( <unk> <unk> , Maeda Mitsuyo , born December 18 , 1878 in <unk> village , Hirosaki , <unk> , Japan – November 28 , 1941 ) , a Brazilian naturalized as <unk> Maeda ( Portuguese pronunciation : [ <unk> <unk> ] ) , was a Japanese <unk> ( judo expert ) and <unk> in no holds barred competitions . He was also known as Count Combat or Conde Koma in Spanish and Portuguese , a nickname he picked up in Spain in 1908 . Along with <unk> <unk> Satake ( another naturalized Brazilian ) , he pioneered judo in Brazil , the United Kingdom , and other countries . 
- Maeda was fundamental to the development of Brazilian Jiu @-@ Jitsu , including through his teaching of Carlos Gracie and others of the Gracie family . He was also a promoter of Japanese emigration to Brazil . Maeda won more than 2 @,@ 000 professional fights in his career . His accomplishments led to him being called the " toughest man who ever lived " and being referred to as the father of Brazilian Jiu @-@ jitsu . 
- 
- = = Biography = = 
- 
- Maeda was born in <unk> Village , Hirosaki City , <unk> Prefecture , Japan , on November 18 , 1878 . He attended <unk> <unk> high school ( currently <unk> — a Hirosaki school ) . As a child , he was known as <unk> . He practiced sumo as a teenager , but lacked the ideal build for the sport . Because of the interest generated by stories about the success of judo at contests between judo and jujutsu that were occurring at the time , he changed from sumo to judo . In 1894 , at seventeen years of age , his parents sent him to Tokyo to enroll in <unk> University . He took up Kodokan judo the following year . 
- 
- = = = <unk> years at the Kodokan = = = 
- 
- Arriving in the Kodokan , Maeda , who was 164 cm tall and weighed 64 kg , was confused with a delivery boy due to his country manners and demeanor . He was spotted by judo 's founder Kano <unk> , and assigned to <unk> Tomita ( 4th dan at the time ) , the smallest of the teachers of the Kodokan 's <unk> @-@ no , to illustrate that in judo size is not important . Tomita was the first Kodokan judoka and a close friend of Kano . According to <unk> <unk> ( 9th dan ) : 
- Although the weakest of Kodokan <unk> @-@ no , Tomita was able to defeat the great jujutsu champion of that time , <unk> <unk> , from the <unk> Shin <unk> @-@ <unk> style . 
- With <unk> Satake , Maeda formed the head of the second generation of Kodokan judoka , which had replaced the first by the beginning of the 20th century . Satake , at 175 cm and 80 kg , was <unk> in amateur sumo but admitted that he himself was not able to match Maeda in judo . Satake would later travel to South America with Maeda and settle in Manaus , <unk> State , while Maeda continued traveling . Satake would become the founder , in 1914 , of the first historically registered judo academy in Brazil . He and Maeda are considered the pioneers of judo in Brazil . 
- At that time , there were few graduated Kodokan judoka . Maeda and Satake were the top graduated professors at <unk> University , both <unk> ( 3rd dan ) , along with <unk> <unk> ( <unk> or 2nd dan ) and six other <unk> ( 1st dan ) . <unk> Mifune registered at the Kodokan in 1903 and attracted the attention of Maeda , who commented , " you are strong and competent , therefore , you will certainly leave your mark in the Kodokan ... " Mifune went to learn under <unk> <unk> and later , already a celebrated judoka , Mifune said that Maeda 's words were a great incentive , as he regarded Maeda with the greatest admiration , even though <unk> was his sensei ( instructor ) . 
- According to Mifune , in 1904 Maeda lost to <unk> <unk> by <unk> <unk> , after defeating three adversaries in succession , but in a following <unk> @-@ <unk> defeated eight adversaries in a row and was awarded the rank of 4th dan ( <unk> ) . Mifune also states that Maeda was one of the most vigorous <unk> of judo , although not by teaching the art , instead generating recognition of judo through his many <unk> with contenders from other disciplines . Maeda treated experienced and inexperienced students alike , throwing them as if in real combat . He reasoned that this behavior was a measure of respect towards his students , but it was often misunderstood and frightened many youngsters , who would abandon him in favor of other professors . 
- 
- = = = Prelude to Kodokan 's expansion = = = 
- 
- In 1879 , Ulysses S. Grant , the former President of the United States , visited Japan . While in Tokyo , he attended a jujutsu presentation at <unk> <unk> 's home in <unk> . Kano <unk> was one of the <unk> present . At that time , jujutsu was just starting to become known in Europe and the Americas . <unk> literal circus acts , few non @-@ Japanese had much chance of seeing or learning about the art . Even in Japan , judo and jujutsu were not considered separate disciplines at that time . Indeed , it was not until 1925 that there started to be clear differentiation of the names in Japan , and outside Japan , judo and jujutsu were not completely separated until the 1950s . 
- In 1903 , a senior Kodokan instructor named Yamashita <unk> traveled to the United States at the request of the Seattle businessman Sam Hill . In Washington , DC , Yamashita 's students included Theodore Roosevelt and other prominent Americans . At Roosevelt 's request , Yamashita also taught judo at the US Naval Academy . <unk> on the publicity , the Japanese <unk> in the USA asked the Kodokan to send more judo teachers to America , providing continuity to Yamashita 's work . Tomita <unk> accepted the task ; Maeda and Satake embraced the opportunity . 
- 
- = = = Career = = = 
- 
- 
- = = = = United States = = = = 
- 
- Tomita , Maeda , and Satake sailed from Yokohama on November 16 , 1904 , and arrived in New York City on December 8 , 1904 . 
- Early in 1905 , Tomita and Maeda gave several public demonstrations of judo . On February 17 , 1905 , Tomita and Maeda gave a demonstration at Princeton University , when Maeda threw N. B. <unk> , a Princeton football player , while Tomita threw Samuel <unk> , the Princeton <unk> instructor . On February 21 , 1905 , they gave a judo demonstration at the United States Military Academy at West Point , where Tomita and Maeda performed <unk> ( patterns ) — <unk> @-@ no , <unk> , <unk> @-@ no , and so on . At the request of the crowd , Maeda wrestled a cadet and threw him easily . Because Tomita had been the <unk> in the <unk> , the cadets wanted to wrestle him too . Tomita threw the first ( Charles Daly ) without any trouble . However , Tomita twice failed to throw another football player named <unk> using <unk> @-@ <unk> ( stomach throw ) . Afterwards , the New York sportswriters claimed the victory for the cadets because Tomita was thrown , whereas the Japanese embassy staff proclaimed that Tomita had achieved a moral victory , on the grounds that he was a far smaller man . 
- A conflicting account provided by the New York Times on February 21 , referring to Tomita as " <unk> <unk> , " states that 
- In any event , later that year the US Military Academy hired a former world champion professional wrestler , Tom Jenkins , instead of a judo teacher , a job Jenkins kept until his retirement in 1942 . 
- The Japanese experts did better at the New York Athletic Club on March 8 , 1905 : " Their best throw was a sort of flying cartwheel , " said an article in the New York Times , describing Maeda 's match with John <unk> , a 200 lb wrestler . " Because of the difference in methods the two men rolled about the mat like <unk> in a rough @-@ and @-@ tumble fight . After fifteen minutes of wrestling , Maeda secured the first fall . Ultimately , however , <unk> was awarded the match by pin fall . " On March 21 , 1905 , Tomita and Maeda gave a " jiu @-@ do " demonstration at Columbia University attended by about 200 people . Following introductions , Tomita demonstrated falls and throws , then Maeda threw the university 's wrestling instructor . According to the student newspaper , " Another interesting feature was the exhibition of some of the obsolete jiu jitsu tricks for defense with a fan against an opponent armed with the curved Japanese sword . " <unk> were provided by chemist <unk> <unk> . 
- During April 1905 , Tomita and Maeda started a judo club in a commercial space at 1947 Broadway in New York . Members of this club included Japanese expatriates , plus a European American woman named <unk> Berger . On July 6 , 1905 , Tomita and Maeda gave a judo exhibition at the YMCA in Newport , Rhode Island . On September 30 , 1905 , they gave a demonstration at another YMCA , in <unk> , New York . In <unk> , the local opponent was Mason <unk> , who wrestled Tomita unsuccessfully . 
- On November 6 , 1905 , Maeda was reported visiting professional wrestler Akitaro Ono in <unk> , North Carolina ; after this , Maeda was no longer routinely associated with Tomita in the US newspapers . On December 18 , 1905 , Maeda was in Atlanta , Georgia , for a professional wrestling match with Sam <unk> . The contest was best of three , two falls with jackets and one without , and Maeda won the two with jackets and lost the one without . According to the Atlanta papers , Maeda listed his residence as the YMCA in Selma , Alabama . 
- Maeda was fond of the name and started using it to promote his art thereafter . 
- 
- = = = = Cuba , Mexico , and Central America = = = = 
- 
- During November 1908 , Maeda went to Paris , France , apparently to see his friend Akitaro Ono . From Paris , he went to Havana , arriving there on December 14 , 1908 , and his twice @-@ a @-@ day wrestling act quickly proved to be very popular . On July 23 , 1909 , Maeda left Havana for Mexico City . His debut in Mexico City took place at the Virginia <unk> Theater on July 14 , 1909 . This show was a private demonstration for some military cadets . Shortly afterwards , Maeda began appearing at the Principal Theater . His standing offer was 100 pesos ( US $ 50 ) to anyone he could not throw , and 500 pesos ( US $ 250 ) to anyone who could throw him . The Mexican Herald did not record anyone taking his money . 
- During September 1909 , a Japanese calling himself ' <unk> Taka ' arrived in Mexico City for the purpose of challenging Maeda for what the Mexican Herald said would be the world jujutsu championship . After several months of public wrangling , Taka and Maeda met at the <unk> Theater on November 16 , 1909 ; Taka won . There was an immediate rematch , and four days later , Maeda was pronounced the champion . It was later revealed that Taka was , in fact , Maeda 's old friend , <unk> Satake . 
- In January 1910 , Maeda took part in a wrestling tournament in Mexico City . During the semifinals , Maeda drew with <unk> Lundin . This is a different result than Lundin recalled in his 1937 memoirs . Said Lundin , " Having been accustomed to handling the big Greco @-@ Roman wrestlers with ease , the <unk> thought he could do likewise with me , but in the first encounter I got the better of him , after which my confidence returned . I had no trouble then in winning the match . It was a surprise to the crowd and a set @-@ back for Koma . He had been the hero all week , but as soon as he was beaten the fans , true to form , called him a <unk> . " 
- In July 1910 , Maeda returned to Cuba , where he tried to arrange matches with Frank <unk> and Jack Johnson . The Americans ignored him — there was no money to be made wrestling him , and much money to be lost if they lost to him . On August 23 , 1910 , Maeda wrestled Jack <unk> in Havana ; the result was a draw . During 1911 , Maeda and Satake were joined in Cuba by Akitaro Ono and Tokugoro Ito . The four men were known as the ' Four Kings of <unk> 
- The Four Kings were very popular in Cuba , and the Japanese media were proud of the reputation they were bringing to judo and Japan . Consequently , on January 8 , 1912 , the Kodokan promoted Maeda to 5th dan . There was some resistance to this decision because there were those in Japan who did not approve of his involvement in professional wrestling . In 1913 , Tokugoro Ito stayed in Cuba while Maeda and Satake went to El Salvador , Costa Rica , Honduras , Panama , Colombia , Ecuador and Peru . In El Salvador , the president was assassinated while Maeda was there , and in Panama , the Americans tried to pay him to lose ; in response , they kept moving south . In Peru they met Laku , a Japanese <unk> who taught the military , and invited him to join them . They were then joined by Okura in Chile , and by Shimitsu in Argentina . The troupe arrived in Brazil on November 14 , 1914 . 
- 
- = = = = Brazil = = = = 
- 
- According to a copy of Maeda 's passport provided by Gotta <unk> , head of Belém 's <unk> <unk> <unk> , Maeda arrived in Porto <unk> on November 14 , 1914 , where his first exhibition in Brazil took place . After appearing in Porto <unk> , Maeda and his companions moved throughout the country : on August 26 , 1915 , Maeda , Satake , Okura , Shimitsu , and Laku were at <unk> ; during October 1915 , they were in Belém , finally arriving in Manaus on December 18 , 1915 . Tokugoro Ito arrived some time later . 
- On December 20 , 1915 , the first demonstration in Belém took place at the <unk> <unk> . The O Tempo newspaper announced the event , stating that Conde Koma would show the main jiu @-@ jitsu techniques , excepting the prohibited ones . He would also demonstrate self @-@ defense techniques . After that , the troupe would be accepting challenges from the crowd , and there would be the first sensational match of jiu @-@ jitsu between Shimitsu ( champion of Argentina ) and Laku ( Peruvian military professor ) . On December 22 , 1915 , according to O Tempo , jiu @-@ jitsu world champion Maeda , head of the Japanese troupe , and Satake , New York champion , performed an enthusiastic and sensational jiu @-@ jitsu match . On the same day , <unk> <unk> , an Australian Greco @-@ Roman wrestling champion of Turkish origin , challenged Maeda . On December 24 , 1915 , Maeda defeated in seconds the boxer <unk> <unk> <unk> , who became one of his disciples . On January 3 , 1916 at <unk> <unk> , Maeda finally fought <unk> <unk> , who was thrown off the stage and pinned into submission by arm @-@ lock . On January 8 , 1916 , Maeda , Okura , and Shimitsu boarded the SS Antony and left for Liverpool . Tokugoro Ito went to Los Angeles . Satake and Laku stayed in Manaus teaching , according to O Tempo , jiu @-@ jitsu . After 15 years together , Maeda and Satake had finally split up . Of this last trip , little is known . Maeda went from England to Portugal , Spain , and France , coming back to Brazil in 1917 alone . <unk> in Belém do <unk> , Maeda married D. May <unk> . 
- Maeda was still popular in Brazil , and recognized as a great fighter , although he only fought sporadically after his return . Around 1918 – 1919 , Maeda accepted a challenge from the famous <unk> <unk> de <unk> . Maeda allowed <unk> de <unk> to use a knife in the fight . The <unk> was 190 cm tall and weighed 100 kg . Maeda won the match quickly . In 1921 , Maeda founded his first judo academy in Brazil . It was called <unk> <unk> , and its building was a 4 m x 4 m shed . Later , it was moved to the Fire Brigade headquarters and then to the church of N. S. de <unk> . In 1991 , the Academy was located in the <unk> and was run by <unk> <unk> <unk> , of the third generation of Conde Koma 's descendants . 
- On September 18 , 1921 , Maeda , Satake , and Okura were briefly in New York City . They were aboard the <unk> Line steamship SS <unk> . All three men listed their occupations as professors of " <unk> " . After leaving New York , the three men went to the Caribbean , where they stayed from September to December 1921 . At some point in this trip , Maeda was joined by his wife . In Havana , Satake and Maeda took part in some contests . Their opponents included Paul Alvarez , who wrestled as <unk> <unk> . Alvarez defeated Satake and <unk> Okura — the latter being billed as a former instructor at the Chilean Naval Academy — before being himself beaten by Maeda . Maeda also defeated a Cuban boxer called Jose Ibarra , and a French wrestler called <unk> . The Havana papers attributed Maeda with a Cuban student called Conde <unk> . 
- 
- = = = Later years = = = 
- 
- In 1925 , Maeda became involved with helping settle Japanese immigrants near <unk> @-@ <unk> , a Japanese @-@ owned company town in <unk> , Brazil . This was part of a large tract in the Amazon forest set aside for Japanese settlement by the Brazilian government . The crops grown by the Japanese were not popular with the <unk> , and the Japanese investors eventually gave up on the project . Maeda also continued teaching judo , now mostly to the children of Japanese immigrants . Consequently , in 1929 , the Kodokan promoted him to 6th dan , and on November 27 , 1941 , to 7th dan . Maeda never knew of this final promotion , because he died in Belém on November 28 , 1941 . The cause of death was kidney disease . 
- In May 1956 , a memorial to Maeda was erected in Hirosaki City , Japan . The dedication ceremony was attended by <unk> Kano and <unk> <unk> . 
- 
- = = Influence on the creation of Brazilian Jiu Jitsu = = 
- 
- Gastão Gracie was a business partner of the American Circus in Belém . In 1916 , Italian Argentine circus <unk> Brothers staged shows there and presented Maeda . In 1917 , Carlos Gracie — 14 ‑ year @-@ old son of Gastão Gracie — watched a demonstration by Maeda at the Da <unk> Theatre and decided to learn judo ( also known at the time as ' Kano Jiu @-@ Jitsu ' ) . Maeda accepted Gracie and Luiz <unk> as students [ 1 ] , and the youth went on to become a great exponent of the art and ultimately , with his younger brother <unk> Gracie , founded Gracie Jiu @-@ Jitsu , modern Brazilian Jiu @-@ Jitsu . In 1921 , Gastão Gracie and his family moved to Rio de Janeiro . Carlos , then 17 years old , passed Maeda 's teachings on to his brothers <unk> , Gastão , and Jorge . <unk> is considered by many as the founder of Brazilian Jiu @-@ Jitsu ( though others , such as Carlson Gracie , have pointed to Carlos as the founder ) . 
- 
- = = Maeda 's philosophy of combat = = 
- 
- According to <unk> Gracie 's book <unk> <unk> , Maeda not only taught the art of judo to Carlos Gracie , but also taught a particular philosophy about the nature of combat based on his travels competing and training alongside catch @-@ wrestlers , <unk> , <unk> fighters , and various other martial artists . The book details Maeda 's theory that physical combat could be broken down into distinct phases , such as the striking phase , the grappling phase , the ground phase , and so on . Thus , it was a smart fighter 's task to keep the fight located in the phase of combat that best suited his own strengths . The book further states that this theory was a fundamental influence on the Gracie approach to combat . The approach included armed versus armed , armed versus unarmed , unarmed , standing ( <unk> , <unk> ) , kneeling ( <unk> , <unk> ) , and ground work ( <unk> , <unk> ) , close quarters ( <unk> , <unk> ) , and other forms of combat . It was employed by other proponents of judo ( ' Kano jiu @-@ <unk> ' ) who , like Maeda , engaged in challenge match fighting overseas as judo spread internationally ( e.g. , <unk> <unk> in the United Kingdom from 1905 , <unk> <unk> in France , and others ) . 
- 
- 
- = Kitsune = 
- 
- Kitsune ( <unk> , <unk> , <unk> : [ kitsu <unk> ne ] ) is the Japanese word for fox . Foxes are a common subject of Japanese folklore ; in English , kitsune refers to them in this context . Stories depict them as intelligent beings and as possessing magical abilities that increase with their age and wisdom . According to <unk> folklore , all foxes have the ability to shape shift into men or women . While some folktales speak of kitsune employing this ability to trick others — as foxes in folklore often do — other stories portray them as faithful guardians , friends , lovers , and wives . 
- Foxes and human beings lived close together in ancient Japan ; this companionship gave rise to legends about the creatures . Kitsune have become closely associated with Inari , a Shinto kami or spirit , and serve as its messengers . This role has reinforced the fox 's supernatural significance . The more tails a kitsune has — they may have as many as nine — the older , <unk> , and more powerful it is . Because of their potential power and influence , some people make offerings to them as to a deity . 
- Conversely foxes were often seen as " witch animals " , especially during the superstitious Edo period ( 1603 – 1867 ) , and were <unk> who could not be trusted ( similar to some <unk> and cats ) . 
- 
- = = Origins = = 
- 
- Japanese fox myths had its origins in Chinese mythology . Chinese folk tales tell of fox spirits called <unk> <unk> ( Chinese : <unk> ) that may have up to nine tails ( <unk> no Kitsune in Japanese ) . Many of the earliest surviving stories are recorded in the <unk> <unk> , an 11th @-@ century collection of Chinese , Indian , and Japanese narratives . The nine @-@ tailed foxes came to be adapted as a motif from Chinese mythology to Japanese mythology . 
- <unk> ( 1999 ) notes that the idea of the fox as seductress and the connection of the fox myths to Buddhism were introduced into Japanese folklore through similar Chinese stories , but she maintains that some fox stories contain elements unique to Japan . 
- 
- = = = Etymology = = = 
- 
- The full etymology is unknown . The oldest known usage of the word is in the 794 text Shin <unk> <unk> <unk> <unk> . Other old sources include <unk> <unk> ( <unk> – <unk> ) and <unk> <unk> ( c . <unk> ) . These oldest sources are written in Man <unk> which clearly identifies the historical spelling as <unk> . Following several <unk> <unk> changes , this becomes kitsune . 
- Many etymological suggestions have been made , though there is no general agreement : 
- <unk> ( 1268 ) suggests that it is so called because it is " always ( <unk> ) yellow ( ki ) " . 
- Early <unk> period <unk> indicates that it means " came ( ki ) [ <unk> aspect particle tsu ] to bedroom ( ne ) " due to a legend that a kitsune would change into one 's wife and bear children . 
- <unk> <unk> in <unk> ( 1717 ) suggests that ki means " <unk> " , tsu is a possessive particle , and ne is related to <unk> , the word for " dog " . 
- <unk> <unk> in <unk> no <unk> ( 1777 – 1887 ) suggests that ki means " yellow " , tsu is a possessive particle , and ne is related to <unk> , the word for cat . 
- <unk> <unk> in <unk> ( 1932 – 1935 ) proposes that the word comes from kitsu , which is onomatopoeia for the bark of a fox , and ne , which may be an <unk> or an honorific word meaning a servant of an Inari shrine . 
- <unk> also suggests that the word kitsune was originally <unk> . <unk> represented a fox 's <unk> and came to be the general word for fox . <unk> signifies an affectionate mood . 
- <unk> is now archaic ; in modern Japanese , a fox 's cry is transcribed as <unk> <unk> or <unk> <unk> . 
- One of the oldest surviving kitsune tales provides a widely known folk etymology of the word kitsune . Unlike most tales of kitsune who become human and marry human males , this one does not end <unk> : 
- Ono , an inhabitant of <unk> ( says an ancient Japanese legend of <unk> 545 ) , spent the seasons longing for his ideal of female beauty . He met her one evening on a vast moor and married her . Simultaneously with the birth of their son , Ono 's dog was delivered of a <unk> which as it grew up became more and more hostile to the lady of the <unk> . She <unk> her husband to kill it , but he refused . At last one day the dog attacked her so furiously that she lost courage , resumed <unk> shape , <unk> over a fence and fled . 
- " You may be a fox , " Ono called after her , " but you are the mother of my son and I love you . Come back when you please ; you will always be welcome . " 
- So every evening she stole back and slept in his arms . 
- Because the fox returns to her husband each night as a woman but leaves each morning as a fox , she is called Kitsune . In classical Japanese , kitsu @-@ ne means come and sleep , and ki @-@ <unk> means always comes . 
- 
- = = Characteristics = = 
- 
- Kitsune are believed to possess superior intelligence , long life , and magical powers . They are a type of <unk> , or spiritual entity , and the word kitsune is often translated as fox spirit . However , this does not mean that kitsune are ghosts , nor that they are fundamentally different from regular foxes . Because the word spirit is used to reflect a state of knowledge or enlightenment , all long @-@ lived foxes gain supernatural abilities . 
- There are two common classifications of kitsune . The <unk> ( <unk> , literally good foxes ) are benevolent , celestial foxes associated with Inari ; they are sometimes simply called Inari foxes . On the other hand , the <unk> ( <unk> , literally field foxes , also called <unk> ) tend to be mischievous or even malicious . Local traditions add further types . For example , a <unk> is an invisible fox spirit that human beings can only perceive when it possesses them . 
- <unk> , kitsune are noted for having as many as nine tails . Generally , a greater number of tails indicates an older and more powerful fox ; in fact , some folktales say that a fox will only grow additional tails after it has lived 100 years . One , five , seven , and nine tails are the most common numbers in folk stories . When a kitsune gains its ninth tail , its fur becomes white or gold . These <unk> no kitsune ( <unk> , nine @-@ tailed foxes ) gain the abilities to see and hear anything happening anywhere in the world . Other tales credit them with infinite wisdom ( omniscience ) . 
- 
- = = = <unk> = = = 
- 
- A kitsune may take on human form , an ability learned when it reaches a certain age — usually 100 years , although some tales say 50 . As a common prerequisite for the transformation , the fox must place <unk> , a broad leaf , or a skull over its head . Common forms assumed by kitsune include beautiful women , young girls , or elderly men . These shapes are not limited by the fox 's age or gender , and a kitsune can duplicate the appearance of a specific person . Foxes are particularly renowned for impersonating beautiful women . Common belief in medieval Japan was that any woman encountered alone , especially at dusk or night , could be a fox . Kitsune @-@ <unk> or fox @-@ faced refers to human females who have a narrow face with close @-@ set eyes , thin eyebrows , and high <unk> . Traditionally , this facial structure is considered attractive , and some tales <unk> it to foxes in human form . <unk> on the theme have the kitsune retain other <unk> traits , such as a coating of fine hair , a fox @-@ shaped shadow , or a reflection that shows its true form . 
- In some stories , kitsune have difficulty hiding their tails when they take human form ; looking for the tail , perhaps when the fox gets drunk or careless , is a common method of discerning the creature 's true nature . A particularly devout individual may in some cases even be able to see through a fox 's disguise merely by <unk> them . Kitsune may also be exposed while in human form by their fear and hatred of dogs , and some become so <unk> by their presence that they revert to the form of a fox and flee . 
- One folk story illustrating these imperfections in the kitsune 's human shape concerns Koan , a historical person credited with wisdom and magical powers of divination . According to the story , he was staying at the home of one of his devotees when he <unk> his foot entering a bath because the water had been drawn too hot . Then , " in his pain , he ran out of the bathroom naked . When the people of the household saw him , they were astonished to see that Koan had fur covering much of his body , along with a fox 's tail . Then Koan transformed in front of them , becoming an elderly fox and running away . " 
- Other supernatural abilities commonly attributed to the kitsune include possession , mouths or tails that generate fire or lightning ( known as kitsunebi ) , <unk> manifestation in the dreams of others , flight , <unk> , and the creation of illusions so elaborate as to be almost indistinguishable from reality . Some tales speak of kitsune with even greater powers , able to bend time and space , drive people mad , or take fantastic shapes such as a tree of incredible height or a second moon in the sky . Other kitsune have characteristics reminiscent of <unk> or <unk> and feed on the life or spirit of human beings , generally through sexual contact . 
- 
- = = = Kitsunetsuki = = = 
- 
- Kitsunetsuki ( Japanese : <unk> , <unk> ) , also written kitsune @-@ <unk> , literally means " the state of being possessed by a fox " . The victim is usually a young woman , whom the fox enters beneath her <unk> or through her breasts . In some cases , the victims ' facial expressions are said to change in such a way that they resemble those of a fox . Japanese tradition holds that fox possession can cause illiterate victims to temporarily gain the ability to read . Though foxes in folklore can possess a person of their own will , kitsunetsuki is often attributed to the <unk> intents of hereditary fox employers , or <unk> @-@ <unk> . 
- <unk> <unk> <unk> describes the condition : 
- Strange is the madness of those into whom demon foxes enter . Sometimes they run naked shouting through the streets . Sometimes they lie down and <unk> at the mouth , and <unk> as a fox <unk> . And on some part of the body of the possessed a moving <unk> appears under the skin , which seems to have a life of its own . <unk> it with a needle , and it <unk> instantly to another place . By no grasp can it be so tightly compressed by a strong hand that it will not slip from under the fingers . Possessed folk are also said to speak and write languages of which they were totally ignorant prior to possession . They eat only what foxes are believed to like — tofu , <unk> , <unk> , etc . — and they eat a great deal , alleging that not they , but the possessing foxes , are hungry . 
- He goes on to note that , once freed from the possession , the victim will never again be able to eat tofu , <unk> , or other foods favored by foxes : 
- <unk> , often performed at an Inari shrine , may induce a fox to leave its host . In the past , when such gentle measures failed or a priest was not available , victims of kitsunetsuki were beaten or badly burned in hopes of forcing the fox to leave . Entire families were ostracized by their communities after a member of the family was thought to be possessed . 
- In Japan , kitsunetsuki was noted as a disease as early as the <unk> period and remained a common diagnosis for mental illness until the early 20th century . Possession was the explanation for the abnormal behavior displayed by the afflicted individuals . In the late 19th century , Dr. <unk> <unk> noted that physical diseases that caused fever were often considered kitsunetsuki . The belief has lost favor , but stories of fox possession still occur , such as allegations that members of the <unk> <unk> cult had been possessed . 
- In medicine , kitsunetsuki is a culture @-@ bound syndrome unique to Japanese culture . Those who suffer from the condition believe they are possessed by a fox . Symptoms include <unk> for rice or sweet <unk> beans , <unk> , restlessness , and aversion to eye contact . Kitsunetsuki is similar to but distinct from clinical <unk> . 
- 
- = = = Hoshi no tama = = = 
- 
- Depictions of kitsune or their possessed victims may feature round or onion @-@ shaped white balls known as hoshi no tama ( <unk> , star balls ) . Tales describe these as glowing with kitsunebi . Some stories identify them as magical jewels or <unk> . When not in human form or possessing a human , a kitsune keeps the ball in its mouth or carries it on its tail . Jewels are a common symbol of Inari , and representations of sacred Inari foxes without them are rare . 
- One belief is that when a kitsune changes shape , its hoshi no tama holds a portion of its magical power . Another tradition is that the <unk> represents the kitsune 's soul ; the kitsune will die if separated from it for long . Those who obtain the ball may be able to extract a promise from the kitsune to help them in exchange for its return . For example , a 12th @-@ century tale describes a man using a fox 's hoshi no tama to secure a favor : 
- " <unk> you ! " snapped the fox . " Give me back my ball ! " The man ignored its pleas till finally it said <unk> , " All right , you 've got the ball , but you don 't know how to keep it . It won 't be any good to you . For me , it 's a terrible loss . I tell you , if you don 't give it back , I 'll be your enemy forever . If you do give it back though , I 'll stick to you like a protector god . " 
- The fox later saves his life by leading him past a band of armed robbers . 
- 
- = = <unk> = = 
- 
- <unk> in Japanese folklore as they are , kitsune appear in numerous Japanese works . <unk> , <unk> , <unk> , and <unk> plays derived from folk tales feature them , as do contemporary works such as anime , manga and video games . Japanese metal idol band <unk> refer to the kitsune myth in their lyrics and include the use of fox masks , hand signs , and animation <unk> during live shows . Western authors of fiction have also made use of the kitsune legends . 
- 
- = = = <unk> of Inari = = = 
- 
- Kitsune are associated with Inari , the Shinto deity of rice . This association has reinforced the fox 's supernatural significance . Originally , kitsune were Inari 's messengers , but the line between the two is now blurred so that Inari <unk> may be depicted as a fox . Likewise , entire shrines are dedicated to kitsune , where devotees can leave offerings . Fox spirits are said to be particularly fond of a fried sliced tofu called <unk> , which is accordingly found in the <unk> @-@ based dishes kitsune <unk> and kitsune <unk> . Similarly , Inari @-@ <unk> is a type of <unk> named for Inari <unk> that consists of rice @-@ filled pouches of fried tofu . There is speculation among folklorists as to whether another Shinto fox deity existed in the past . Foxes have long been worshipped as kami . 
- Inari 's kitsune are white , a color of good <unk> . They possess the power to ward off evil , and they sometimes serve as guardian spirits . In addition to protecting Inari shrines , they are petitioned to intervene on behalf of the locals and particularly to aid against troublesome <unk> , those spirit foxes who do not serve Inari . Black foxes and nine @-@ tailed foxes are likewise considered good <unk> . 
- According to beliefs derived from <unk> ( <unk> <unk> ) , the fox 's power over evil is such that a mere statue of a fox can <unk> the evil <unk> , or energy , that flows from the northeast . Many Inari shrines , such as the famous <unk> Inari shrine in <unk> , feature such statues , sometimes large numbers of them . 
- Kitsune are connected to the Buddhist religion through the <unk> , goddesses <unk> with Inari 's female aspect . <unk> is depicted as a female <unk> wielding a sword and riding a flying white fox . 
- 
- = = = <unk> = = = 
- 
- Kitsune are often presented as <unk> , with motives that vary from mischief to malevolence . Stories tell of kitsune playing tricks on overly proud <unk> , greedy merchants , and <unk> commoners , while the <unk> ones abuse poor tradesmen and farmers or devout Buddhist monks . Their victims are usually men ; women are possessed instead . For example , kitsune are thought to employ their kitsunebi to lead travelers astray in the manner of a will o ' the <unk> . Another tactic is for the kitsune to confuse its target with illusions or visions . Other common goals of <unk> kitsune include seduction , theft of food , humiliation of the <unk> , or vengeance for a perceived slight . 
- A traditional game called kitsune @-@ <unk> ( fox @-@ fist ) references the kitsune 's powers over human beings . The game is similar to rock , paper , <unk> , but the three hand positions signify a fox , a hunter , and a village headman . The headman beats the hunter , whom he <unk> ; the hunter beats the fox , whom he shoots ; the fox beats the headman , whom he <unk> . 
- This ambiguous portrayal , coupled with their reputation for <unk> , leads people to try to discover a troublesome fox 's motives . In one case , the 16th @-@ century leader <unk> <unk> wrote a letter to the kami Inari : 
- To Inari <unk> , 
- My lord , I have the honor to inform you that one of the foxes under your jurisdiction has bewitched one of my servants , causing her and others a great deal of trouble . I have to request that you make minute inquiries into the matter , and endeavor to find out the reason of your subject <unk> in this way , and let me know the result . 
- If it turns out that the fox has no adequate reason to give for his behavior , you are to arrest and punish him at once . If you <unk> to take action in this matter I shall issue orders for the destruction of every fox in the land . Any other <unk> that you may wish to be informed of in reference to what has occurred , you can learn from the high priest of <unk> . 
- Kitsune keep their promises and strive to <unk> any favor . Occasionally a kitsune <unk> itself to a person or household , where they can cause all sorts of mischief . In one story from the 12th century , only the <unk> 's threat to <unk> the foxes convinces them to behave . The kitsune patriarch appears in the man 's dreams : 
- My father lived here before me , sir , and by now I have many children and grandchildren . They get into a lot of mischief , I 'm afraid , and I 'm always after them to stop , but they never listen . And now , sir , you 're understandably fed up with us . I gather that you 're going to kill us all . But I just want you to know , sir , how sorry I am that this is our last night of life . Won 't you pardon us , one more time ? If we ever make trouble again , then of course you must act as you think best . But the young ones , sir — I 'm sure they 'll understand when I explain to them why you 're so upset . We 'll do everything we can to protect you from now on , if only you 'll forgive us , and we 'll be sure to let you know when anything good is going to happen ! " 
- Other kitsune use their magic for the benefit of their companion or hosts as long as the human beings treat them with respect . As <unk> , however , kitsune do not share human morality , and a kitsune who has adopted a house in this manner may , for example , bring its host money or items that it has stolen from the neighbors . Accordingly , common households thought to harbor kitsune are treated with suspicion . <unk> , <unk> families were often reputed to share similar arrangements with kitsune , but these foxes were considered <unk> and the use of their magic a sign of prestige . <unk> homes were common haunts for kitsune . One 12th @-@ century story tells of a minister moving into an old mansion only to discover a family of foxes living there . They first try to scare him away , then claim that the house " has been <unk> for many years , and ... we wish to register a vigorous protest . " The man refuses , and the foxes resign themselves to moving to an abandoned lot nearby . 
- Tales distinguish kitsune gifts from kitsune payments . If a kitsune offers a payment or reward that includes money or material wealth , part or all of the sum will consist of old paper , leaves , twigs , stones , or similar <unk> items under a magical illusion . True kitsune gifts are usually <unk> , such as protection , knowledge , or long life . 
- 
- = = = Wives and lovers = = = 
- 
- Kitsune are commonly portrayed as lovers , usually in stories involving a young human male and a kitsune who takes the form of a human woman . The kitsune may be a seductress , but these stories are more often romantic in nature . Typically , the young man unknowingly marries the fox , who proves a devoted wife . The man eventually discovers the fox 's true nature , and the fox @-@ wife is forced to leave him . In some cases , the husband wakes as if from a dream , filthy , <unk> , and far from home . He must then return to confront his abandoned family in shame . 
- Many stories tell of fox @-@ wives bearing children . When such <unk> are human , they possess special physical or supernatural qualities that often pass to their own children . The <unk> @-@ magician Abe no <unk> was reputed to have inherited such extraordinary powers . 
- Other stories tell of kitsune marrying one another . Rain falling from a clear sky — a <unk> — is called kitsune no <unk> or the kitsune 's wedding , in reference to a folktale describing a wedding ceremony between the creatures being held during such conditions . The event is considered a good <unk> , but the kitsune will seek revenge on any <unk> guests , as is depicted in Akira <unk> 's film Dreams . 
- Stephen Turnbull , in " <unk> <unk> " , relates the tale of the Takeda clan 's involvement with a fox @-@ woman . The warlord Takeda Shingen , in 1544 , defeated in battle a lesser local warlord named Suwa <unk> and drove him to suicide after a " humiliating and <unk> " peace conference , after which Shingen forced marriage on Suwa <unk> 's beautiful 14 @-@ year @-@ old daughter Lady Koi — Shingen 's own niece . Shingen , Turnbull writes , " was so obsessed with the girl that his superstitious followers became alarmed and believed her to be an incarnation of the white fox @-@ spirit of the Suwa Shrine , who had bewitched him in order to gain revenge . " When their son Takeda <unk> proved to be a disastrous leader and led the clan to their devastating defeat at the battle of <unk> , Turnbull writes , " wise old heads <unk> , remembering the unhappy circumstances of his birth and his magical mother " . 
- 
- 
- = New York State Route 448 = 
- 
- New York State Route 448 ( NY 448 ) is a 3 @.@ 90 @-@ mile ( 6 @.@ 28 km ) long state highway in western Westchester County , New York , in the United States . The route begins in the village of Sleepy Hollow at U.S. Route 9 ( US 9 ) and goes in a northeast direction through the Pocantico Hills community in Mount Pleasant . It ends at NY 117 in Mount Pleasant , near the junction of NY 117 with Saw Mill River Road ( NY <unk> and NY 100 ) and the <unk> State Parkway . From 1930 to the early 1970s , NY 448 was part of NY 117 . 
- 
- = = Route description = = 
- 
- NY 448 begins at an intersection with New Broadway and US 9 ( North Broadway ) in the village of Sleepy Hollow , then heads through a residential area as Bedford Road . After the Webber Avenue intersection , NY 448 turns to the northeast , where it passes the athletic fields of Sleepy Hollow High School . NY 448 intersects with Sleepy Hollow Road , which leads into a residential development and parallels NY 448 northeastward for a short distance . 
- Near the southern edge of Rockefeller State Park Preserve , NY 448 turns east and enters Kykuit , the estate of the Rockefeller family . At the hamlet of Pocantico Hills , the highway intersects with Lake Road and turns to the northeast , passing through more residential areas as it leaves the estate . NY 448 passes the Pocantico Hills Central School on its way , continuing northeast towards <unk> Way ( NY 117 ) . The road continues through a vacant area and heads northward into Mount Pleasant , where NY 448 terminates at an intersection with NY 117 . Bedford Road continues east of this point as part of NY 117 . 
- 
- = = History = = 
- 
- The entirety of NY 448 was originally designated as part of NY 117 in the 1930 renumbering of state highways in New York . In 1932 – 33 , John D. Rockefeller offered to <unk> a short section of NY 117 that passed through Kykuit , the Rockefeller family estate , citing safety concerns . The state expressed similar sentiments in 1949 , deeming the stretch unsafe for commercial traffic . By the 1960s , the annual average daily traffic count along NY 117 had grown to 5 @,@ 000 vehicles per day , far more than the 1 @,@ 800 that the road had originally been built to serve . This led the New York State Legislature to approve plans for a new alignment for NY 117 in 1965 . The new road would be located north of the existing road , relieving traffic congestion on the Albany Post Road ( US 9 ) and NY 117 and providing access to NY <unk> , US 9 , the <unk> State Parkway and the proposed Hudson River Expressway in the North Tarrytown area . 
- Several different routes were considered for the alignment . The path ultimately selected by the state was a four @-@ lane , limited @-@ access highway 1 mile ( 1 @.@ 6 km ) north of the current alignment that would cost $ 5 @.@ 5 million ( 1966 USD ) . Another proposed route would have cost $ 8 million ( 1966 USD ) , containing additional grades and curves while coming closer to the Rockefeller estate . Construction on the new NY 117 was expected to begin before the end of 1966 . The reaction from area residents was mixed , with some believing that the only beneficiaries of a realigned NY 117 was the Rockefeller family . <unk> that the Rockefeller family had wanted NY 117 moved had existed as early as the 1930s , and US Representative Richard <unk> believed that Nelson Rockefeller , then the Governor of New York , was using his political power to move NY 117 away from Kykuit at the cost of the state . An aide to the <unk> denied this charge . 
- The Sleepy Hollow Valley Committee , comprising 11 members whose homes were in the right @-@ of @-@ way of the new alignment , protested that the route would destroy the countryside . They were opposed by the <unk> Hills Residents Committee , as most residents in <unk> Hills supported the new alignment . The former committee filed a lawsuit in the New York State Supreme Court to block the new roadway , and they were joined in their efforts by <unk> groups and the village of North Tarrytown . On June 28 , 1966 , the case was heard by the court . Engineers with the New York State Department of Highways were accused of showing " unprecedented <unk> @-@ <unk> <unk> " in working on a project with the <unk> before any <unk> could stop it . State Attorney General Joseph Romano countered that the state was not conspiring to provide anyone special benefits , saying that NY 117 was dangerous for years and opponents to the project were " thinking up little <unk> , <unk> arguments and <unk> ideas to mislead the court . " 
- A request by the state to dismiss the suit was denied in August 1966 , and the case was eventually heard by the state Court of Appeals , the highest court in the state . In the meantime , the state built the easternmost mile ( 1 @.@ 6 km ) of the new alignment , which was not affected by the lawsuit . On July 7 , 1967 , the court ruled 6 – 1 in favor of the Sleepy Hollow Valley Committee . As a result , the western half of the new road was moved an additional mile to the north , bypassing North Tarrytown completely . The new alignment of NY 117 was completed in the early 1970s , and its former alignment along Bedford Road was redesignated as NY 448 . 
- 
- = = Major intersections = = 
- 
- The entire route is in Westchester County . 
- 
- 
- = Hurricane Tanya ( 1995 ) = 
- 
- Hurricane Tanya was the final storm of the very active 1995 Atlantic hurricane season . The twenty @-@ first tropical cyclone , nineteenth named storm , and eleventh hurricane of the season , Tanya developed from a tropical wave while well north of the Lesser Antilles on October 26 . The system headed northeastward and strengthened into Tropical Storm Tanya on October 27 . Tanya meandered around the central Atlantic , and further intensified into a hurricane on October 29 . Thereafter , Tanya tracked northeastward before curving to the east @-@ northeast . After switching to an eastward direction , Tanya weakened to a tropical storm on November 1 . Later that day , Tanya passed through the Azores as it was transitioning into an extratropical cyclone . 
- Throughout the Azores , the extratropical remnants of Tanya produced high winds . As a result , extensive property damage occurred , which included destroyed or damaged houses , and sunken boats ; there were also reports of significant damage to agriculture . However , an exact damage toll figure in unknown . In addition , there was one fatality and several injuries . 
- 
- = = Meteorological history = = 
- 
- The origins of Hurricane Tanya were from a tropical wave that moved off the western coast of Africa in the middle of October . The wave closely followed the path of Tropical Storm <unk> and was unable to develop in the tropical Atlantic as it moved westward . The system did not begin to become organized until October 25 while south @-@ southeast of Bermuda . However , the Dvorak technique was still unable to classify the system until October 26 as the low @-@ cloud <unk> became better organized while moving northward in the central Atlantic . That evening , a closed circulation had formed , and it was classified as Tropical Depression Twenty @-@ One by the National Hurricane Center . On the morning of October 27 , it strengthened into a tropical storm , becoming the 19th as well as the final named storm of the season . Operationally , it was not declared a tropical cyclone until that point , when it was immediately declared Tropical Storm Tanya . 
- Immediately after becoming a tropical storm , the movement of Tanya became hindered by a nearby upper @-@ level low , and it quickly turned eastward before virtually stalling early on October 28 . The upper @-@ level low also influenced Tanya in giving it some subtropical characteristics , including a comma @-@ shaped cloud band and maximum winds far from the center of the storm at the time . Despite that , the storm gradually strengthened as it remained over warm waters of about 81 ° F ( 27 ° C ) . That afternoon , it gained full tropical characteristics , as an eye featured was beginning to form in the central dense <unk> . The eye became clearly defined , although it was small in size . Early on October 29 , Tropical Storm Tanya was upgraded to a hurricane , the eleventh of the 1995 season , as it turned northward in response to the nearby low . 
- Later on October 29 , while slowly tracking north , Tanya leveled off as a low @-@ end Category 1 hurricane . By the next morning , a cold front to the west forced Tanya to accelerate in a more easterly track . It remained fairly well organized with a distinct eye as it became <unk> in a narrow zone of warm air between the cold front to the west and the upper @-@ level low to the northeast . Despite slightly cooler water , Tanya strengthened a bit more that afternoon , reaching its peak intensity of 85 mph ( 135 km / h ) with a barometric pressure of 972 mbar ( 28 @.@ 7 inHg ) as the forward speed increased . That intensity was maintained until late on October 31 , when Tanya began to weaken as it traveled over cooler waters and the eye became obscured . Early on November 1 , Tanya began to lose tropical characteristics while still a hurricane . Hurricane Tanya was also heading rapidly northeast towards the Azores at the time . Tanya was downgraded from a hurricane to a tropical storm on November 1 . Despite weakening , the wind field of Tanya became larger . That evening , Tanya was declared fully extratropical as it passed the Azores . The remnant cyclone turned north , before being absorbed by another extratropical cyclone early on November 3 . 
- 
- = = Impact , naming and records = = 
- 
- Hurricane Tanya was not considered a threat to land , as it did not move through the Azores while a tropical cyclone . The Azores were hit very hard by the storm , which had just been declared extratropical when it hit the region . Damage was particularly severe on the islands of <unk> , <unk> , <unk> and São Jorge . Extensive property damage was reported , including many sunk boats , many houses were damaged and some destroyed and significant agricultural damage . Many trees and power lines were also knocked down , cutting electricity and hampering communications in the area . The highest wind gusts reported in the Azores were around 105 mph ( 170 km / h ) . After the storm , a resolution was submitted to the European Parliament by the Government of Portugal and a disaster area was declared in the islands . One Spanish fisherman drowned and several people were injured . 
- There were many reports of tropical storm @-@ force winds from ships in the north Atlantic . One ship , with the call sign <unk> , encountered Tanya 's winds twice and reported the strongest winds from any ship , 71 mph ( 112 km / h ) . 
- Hurricane Tanya was the first storm to be designated with a name starting with " T " since naming of Atlantic tropical cyclones began in 1950 . 
- 
- 
- = Sweet Love ( Chris Brown song ) = 
- 
- " Sweet Love " is a song by American recording artist Chris Brown , taken from his fifth studio album , Fortune ( 2012 ) . It was written by Brown , Cory Marks , Greg Curtis , Jamal " Polow da Don " Jones , Jason " JP " Perry and Tommy Doyle Jr . , while the production was handled by Polow da Don and Perry . The song was sent to urban contemporary radio in the United States on April 10 , 2012 , as the second single from the album . " Sweet Love " is a slow jam R & B ballad which incorporates elements of electronic music . The lyrics of the song revolve around Brown asking his lover to take off her clothes so that they can have sex . 
- " Sweet Love " garnered positive reviews from music critics , who complimented the song 's sexual lyrics . In the United States , it peaked at number 25 on the Hot R & B / Hip @-@ Hop Songs chart , and number 89 on the Billboard Hot 100 chart . The accompanying music video was co @-@ directed by Godfrey <unk> and Brown , and filmed at Universal Studios in Los Angeles , California . It displays intimate scenes of Brown and his lover <unk> in sexual activity , women levitating out of their beds , and Brown dancing . Critics were complimentary of the video 's sexual themes and presentation . 
- 
- = = Background and release = = 
- 
- " Sweet Love " was written by Chris Brown , Cory Marks , Greg Curtis , Jamal " Polow da Don " Jones , Jason " JP " Perry and Tommy Doyle Jr . , while the production was handled by Polow da Don and Perry . The audio mixing was completed by Jaycen Joshua , with assistance by <unk> Harris . Curtis played keyboards on the song . On March 28 , 2012 , Brown announced via his official Twitter account that he would be releasing " Sweet Love " and " <unk> I Die " as the next two singles from Fortune , after the lead single " <unk> Up the Music " . Later that same day , he posted a link to a stream of " Sweet Love " . The artwork for the single was shot in black @-@ and @-@ white , and debuted online on March 30 , 2012 . It displays Brown sitting on the floor , staring up at the legs of one of the three women . Rap @-@ Up magazine described the artwork as " grown and sexy " . " Sweet Love " was officially sent to urban contemporary <unk> in the United States on April 10 , 2012 . It was released as a one @-@ track digital download in Oceania and most European countries on April 13 , 2012 . However , in the US , " Sweet Love " was released on April 17 , 2012 . A remix of the song by American R & B singer R. Kelly , was posted online on July 26 , 2012 . 
- 
- = = Composition and lyrics = = 
- 
- " Sweet Love " is a slow jam R & B ballad that displays elements of electronic music ; it lasts for a duration of three minutes and 19 seconds long . The instrumentation is provided by a keyboard , synthesizers , percussion and a drum machine . According to <unk> Alexis of MTV 's <unk> , Brown sings in a falsetto tone , which she found to be reminiscent of Michael Jackson . Amy <unk> of PopCrush described the ballad as " very Michael Jackson and neo @-@ <unk> " . <unk> of <unk> noted that " Sweet Love " is inspired by Silk 's " Freak Me " ( 1993 ) . Cameron Adams of the Herald Sun musically compared the song to Prince . The theme of " Sweet Love " revolves around sex . It contains lyrics about Brown asking his lover to take off her clothes so that they can have sex . During the chorus , he <unk> : " <unk> baby let 's get naked / Just so we can make sweet love / All these sensations got me going crazy for you / Inside on top of you / <unk> inside and out of you / Baby I know what to do / Let 's just take our clothes off " . 
- 
- = = Reception = = 
- 
- " Sweet Love " garnered positive reviews from music critics . <unk> Alexis of MTV 's <unk> wrote that Brown " keeps the <unk> at an all time high " with lyrics that " could easily win any girl over " . <unk> <unk> of AOL 's The <unk> described " Sweet Love " as " baby making music " , while Mark Edward <unk> of About.com labeled it as " a straight @-@ up sex jam " . Amy <unk> of PopCrush called the song a sexy R & B jam that could " easily woo Rihanna back " . David <unk> of Fuse TV viewed " Sweet Love " as " auditory pornography " and commented that the lyrics had him " <unk> " . Joseph Patterson of MTV UK labeled it one of Brown 's classic songs " for the lovers " . <unk> Johnston of Rolling Stone awarded " Sweet Love " three stars out of five , and noted that Brown appears to be transitioning from singing songs about <unk> in clubs to singing about sex . Johnston continued writing that the music video for the song " would be almost <unk> unnecessary " . Robbie <unk> of Idolator wrote that the ballad ticks all the boxes for its " <unk> melody , decent beat " and Brown 's " vocal harmonies " , but felt that it is not " the most innovative offering " from Brown . Melinda Newman of HitFix wrote that in " Sweet Love " , it appears " there are two songs at war here as the synth drum beat totally <unk> and clashes with the ballad " . She criticized the song for being " memorable for all the wrong reasons " . 
- In the United States , " Sweet Love " debuted at number 90 on the Hot R & B / Hip @-@ Hop Songs chart in the issue dated April 14 , 2012 . After spending 12 weeks ascending the chart , " Sweet Love " peaked at number 25 in the issue dated July 7 , 2012 . The song remained on the chart for a total of 15 weeks . On the Billboard Hot 100 , " Sweet Love " debuted and peaked at number 89 in the issue dated May 5 , 2012 . 
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- = = Music video = = 
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- The accompanying music video for " Sweet Love " was co @-@ directed by Godfrey <unk> and Brown , and filmed on the back lot of Universal Studios in Los Angeles , California . The video was shot from 6 : 00 am to 6 : 00 pm . Twenty models were hired for the shooting of the scenes where they are levitating from their beds . On April 26 , 2012 , several photos from the shoot were posted online , showing women levitating out of their beds in the streets as Brown walked by them . The video premiered on <unk> on May 25 , 2012 . A behind the scenes video was released on May 26 , 2012 , a day after the video premiered online . 
- As the song begins , Brown watches a woman , who appears as a hologram , lying in a bed . This scene is intercut with scenes of Brown singing in front of a flowing backdrop . He then approaches the woman , and together they appear under white <unk> . Brown is then seen walking down a street , while women are shown levitating out of their beds as he passes by them . He then enters a <unk> and starts <unk> various <unk> female figures . Brown then returns to the bedroom where he " strips shirtless and makes passionate love " to the woman " as they are showered in water " . He later enters a dance studio , where he performs a dance routine with other shirtless male dancers , while several women watch them from across the room . Towards the end of the video , more scenes are shown of Brown and the woman in the bedroom . 
- Melinda Newman of HitFix called the video " undeniably <unk> " . Emily <unk> of the Metro wrote that the video featured " flawless dance moves " from Brown . Robbie <unk> of Idolator noted that Brown " makes things real sexy " in the video , but was unsure if " we should be turned on or utterly <unk> out " . Jessica <unk> of PopCrush noted that Brown " channels his R & B side " and added that he is " a bit of a clumsy romantic " . Jessica Sinclair of the Long Island Press described the video as a " <unk> new entertainment " for Brown 's fans in anticipation of his new album . <unk> Gray of Vibe magazine described the video as " hot , heavy and really wet " . Upon its release , the video went viral and received over one million views in its first day of release . 
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- = = Track listing = = 
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- Digital download 
- " Sweet Love " – 3 : 19 
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- = = Credits and personnel = = 
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- Credits adapted from the liner notes for Fortune 
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- = = Charts = = 
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- = = Release history = = 
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- = Kakapo = 
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- The kakapo ( Māori : kākāpō or night parrot ) , <unk> <unk> ( Gray , 1845 ) , also called owl parrot , is a species of large , flightless , nocturnal , ground @-@ dwelling parrot of the super @-@ family <unk> endemic to New Zealand . 
- It has finely blotched yellow @-@ green plumage , a distinct facial disc of sensory , <unk> @-@ like feathers , a large grey beak , short legs , large feet , and wings and a tail of relatively short length . A combination of traits make it unique among its kind ; it is the world 's only flightless parrot , the heaviest parrot , nocturnal , herbivorous , visibly sexually dimorphic in body size , has a low basal metabolic rate and no male parental care , and is the only parrot to have a <unk> lek breeding system . It is also possibly one of the world 's longest @-@ living birds . Its anatomy <unk> the tendency of bird evolution on oceanic islands , with few predators and abundant food : a generally robust physique , with <unk> of <unk> efficiency at the expense of flight abilities , reduced wing muscles , and a diminished keel on the sternum . Like many other New Zealand bird species , the kakapo was historically important to the Māori , the indigenous people of New Zealand , appearing in many of their traditional legends and folklore . It was hunted and used as a resource by Māori , both for its meat as a food source and for its feathers , which were used to make highly valued pieces of clothing . It was also sometimes kept as a pet . 
- The kakapo is critically endangered ; as of March 2014 , with an additional six from the first <unk> since 2011 , the total known population was only 123 living individuals , as reported by the Kakapo Recovery programme , most of which have been given names . Because of Polynesian and European colonisation and the introduction of predators such as cats , rats , ferrets , and stoats , the kakapo was almost wiped out . Conservation efforts began in the 1890s , but they were not very successful until the implementation of the Kakapo Recovery plan in the 1980s . As of April 2012 , surviving kakapo are kept on three predator @-@ free islands , Codfish ( <unk> <unk> ) , Anchor and Little Barrier islands , where they are closely monitored . Two large Fiordland islands , Resolution and Secretary , have been the subject of large @-@ scale ecological restoration activities to prepare self @-@ sustaining ecosystems with suitable habitat for the kakapo . The New Zealand government is willingly providing the use of these islands to kakapo conservation . A successful breeding season in 2016 saw 34 chicks fledge , taking the total population to 157 . 
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- = = Taxonomy , <unk> and naming = = 
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- The kakapo was originally described by English ornithologist George Robert Gray in 1845 . The name " kakapo " is the English transliteration of " kākāpō " which is derived from the Māori terms <unk> ( " parrot " ) + <unk> ( " night " ) . Its generic name is derived from the Ancient Greek <unk> , genitive <unk> " owl " , and <unk> " face " , while its specific epithet comes from <unk> " soft " , and <unk> " feather " . It has so many unusual features that it was initially placed in its own tribe , <unk> . Recent phylogenetic studies have confirmed the unique position of this genus as well as the closeness to the <unk> and the <unk> , both belonging to the New Zealand parrot genus Nestor . Together , they are now considered a separate family within the parrots , <unk> . Within the <unk> , the kakapo is placed in its own tribe , <unk> . The common ancestor of the kakapo and the genus Nestor became isolated from the remaining parrot species when New Zealand broke off from <unk> , around 82 million years ago . Around 70 million years ago , the kakapo diverged from the genus Nestor . 
- Earlier ornithologists felt that the kakapo might be related to the ground parrots and night parrot of Australia due to their similar colouration , but this is contradicted by recent studies ; rather , the cryptic colour seems to be adaptation to terrestrial habits that evolved twice <unk> . 
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- = = Description = = 
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- The kakapo is a large , <unk> parrot ; the adult can measure from 58 to 64 cm ( 23 to 25 in ) in length , and weight can vary from 0 @.@ 95 to 4 kg ( 2 to 9 lb ) at maturity . Males are larger than females . Twenty @-@ eight males were found to average 2 kg ( 4 @.@ 4 lb ) in one study , and 39 males were found to average 2 @.@ 06 kg ( 4 @.@ 5 lb ) in another . In the same studies , 28 females were found to average 1 @.@ 5 kg ( 3 @.@ 3 lb ) and 18 females averaged 1 @.@ 28 kg ( 2 @.@ 8 lb ) . However , one source states that females will not breed until they attain a threshold weight of 1 @.@ 5 kg ( 3 @.@ 3 lb ) . They are the heaviest living species of parrot ; while the largest males attain much heavier weights than any other extant parrot , <unk> average about 400 g ( 14 oz ) more than a <unk> <unk> . The kakapo cannot fly , having short wings for its size and lacking the pronounced keel bone ( sternum ) that anchors the flight muscles of other birds . It uses its wings for balance , support , and to break its fall when leaping from trees . Unlike other land birds , the kakapo can accumulate large amounts of body fat to store energy , making it the heaviest parrot . 
- The upper parts of the kakapo have yellowish moss @-@ green feathers barred or mottled with black or dark brownish grey , blending well with native vegetation . Individuals may have strongly varying degrees of <unk> and colour tone and intensity — museum specimens show that some birds had completely yellow colouring . The breast and flank are yellowish @-@ green streaked with yellow . The belly , undertail , neck and face are predominantly yellowish streaked with pale green and weakly mottled with brownish @-@ grey . Because the feathers do not need the strength and <unk> required for flight , they are exceptionally soft , giving rise to the specific epithet <unk> . The kakapo has a conspicuous facial disc of fine feathers resembling the face of an owl ; thus , early European settlers called it the " owl parrot " . The beak is surrounded by delicate <unk> or " <unk> " , which the bird uses to sense the ground for navigation as it walks with its head lowered . The mandible is mostly ivory @-@ coloured , with part of the upper mandible being bluish @-@ grey . The eyes are dark brown . Kakapo feet are large , scaly , and , as in all parrots , <unk> ( two toes face forward and two backward ) . The pronounced claws are particularly useful for climbing . The ends of the tail feathers often become worn from being continually dragged on the ground . 
- The female is easily distinguished from the male : she has a more narrow and less domed head , her beak is narrower and proportionally longer , her <unk> and nostrils smaller , her legs and feet more slender and pinkish grey , and her tail proportionally longer . While her plumage colour is not very different from that of the male , the <unk> is more subtle , with less yellow and <unk> . She tends to resist more and be more aggressive than the male when handled . A nesting female also has a brood patch on the bare skin of the belly . 
- Like many parrots , the kakapo has a variety of calls . As well as the booms ( see below for a recording ) and <unk> of their mating calls , it will often <unk> to announce its location to other birds . 
- The kakapo has a well @-@ developed sense of smell , which <unk> its nocturnal lifestyle . It can discriminate among <unk> while foraging , a behaviour reported for only one other parrot species . One of the most striking characteristics of the kakapo is its pleasant and powerful <unk> , which has been described as <unk> . Given the kakapo 's well @-@ developed sense of smell , this scent may be a social <unk> . The smell often alerts predators to the largely <unk> kakapo . 
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- = = = Anatomy = = = 
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- The skeleton of the kakapo differs from other parrots in several features associated with flightlessness . Firstly , it has the smallest relative wing size of any parrot . Its wing feathers are shorter , more rounded , and less asymmetrical and have fewer distal <unk> to lock the feathers together . The sternum is small and has a low , <unk> keel and a shortened <unk> <unk> . As in other flightless birds and some other flighted parrots , the <unk> is not fused but consists of a pair of <unk> lying in contact with each coracoid . As in other flightless birds , the angle between the coracoid and sternum is enlarged . The kakapo has a larger <unk> than other parrots . The <unk> bones of the leg and arm are disproportionately long and the distal elements are disproportionately short . 
- The pectoral <unk> of the kakapo is also modified by flightlessness . The <unk> and <unk> muscles are greatly reduced . The <unk> <unk> <unk> has no distinct muscle belly . The <unk> is <unk> . There is an extensive <unk> <unk> <unk> muscle that is associated with the large crop . 
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- = = Ecology and behaviour = = 
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- It seems that the kakapo – like many of New Zealand 's bird species – has evolved to occupy an ecological niche normally filled by various species of mammal ( the only non @-@ marine mammals native to New Zealand are three species of small bats ) . Before the arrival of humans , the kakapo was distributed throughout the three main islands of New Zealand . It lived in a variety of habitats , including <unk> , <unk> and coastal areas . It also inhabited forests , including those dominated by <unk> ( rimu , <unk> , <unk> , <unk> ) , <unk> , <unk> , and <unk> . In Fiordland , areas of avalanche and slip debris with <unk> and heavily fruiting vegetation – such as five finger , <unk> , bush lawyer , <unk> , <unk> , and <unk> – became known as " kakapo gardens " . 
- The kakapo is primarily nocturnal ; it roosts under cover in trees or on the ground during the day and moves around its territories at night . 
- Though the kakapo cannot fly , it is an excellent <unk> , ascending to the crowns of the tallest trees . It can also " parachute " – descending by leaping and spreading its wings . In this way it may travel a few metres ( yards ) at an angle of less than 45 degrees . 
- Having lost the ability to fly , it has developed strong legs . Movement is often by way of a rapid " <unk> @-@ like " gait by which it can move many kilometres . A female has been observed making two return trips each night during nesting from her nest to a food source up to 1 km ( 0 @.@ 6 mi ) away and the male may walk from its home range to a mating arena up to 5 km ( 3 mi ) away during the mating season ( October – January ) . 
- Young birds indulge in play fighting , and one bird will often lock the neck of another under its chin . The kakapo is curious by nature and has been known to interact with humans . Conservation staff and volunteers have engaged extensively with some kakapo , which have distinct personalities . 
- The kakapo was a very successful species in pre @-@ human New Zealand , and one of the reasons for this was their set of adaptations to effectively avoid predation from native birds of prey , which were their only predators in the past . However , these same <unk> have been of no use to them when faced with the mammalian predators which were introduced to New Zealand after human settlement , because these hunt in different ways . As hunters , birds behave very differently from mammals , relying on their powerful vision to find prey , and thus they usually ( with the exception of owls ) hunt by day . Apart from the two surviving New Zealand raptors , the New Zealand falcon and swamp harrier , there were two other birds of prey in pre @-@ human New Zealand : <unk> 's eagle and <unk> ' harrier . All four species soared overhead searching for prey in daylight , and to avoid these avian predators , the kakapo 's ancestors adopted camouflaged plumage and became nocturnal . In addition , when the kakapo feels threatened , it freezes , so that it is more effectively camouflaged in the forest vegetation which their plumage resembles . It was not entirely safe at night , when the laughing owl was active , and it is apparent from their nest deposits on Canterbury limestone cliffs that the kakapo was among their prey . 
- <unk> predators , in contrast to birds , rely on their sense of smell and hearing to find prey and often hunt by night . The kakapo 's adaptations to avoid avian predation have thus been useless against its new enemies – this is one of the reasons for its massive decline since the introduction of dogs , cats and <unk> – see Conservation : Human impact . A typical way for humans to hunt down the kakapo is by releasing trained dogs . 
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- = = = Diet = = = 
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- The beak of the kakapo is adapted for grinding food finely . For this reason , the kakapo has a very small <unk> compared to other birds of their size . It is generally herbivorous , eating native plants , seeds , fruits , <unk> and even the <unk> of trees . A study in 1984 identified 25 plant species as kakapo food . It is particularly fond of the fruit of the rimu tree , and will feed on it exclusively during seasons when it is abundant . The kakapo has a distinctive habit of grabbing a leaf or <unk> with a foot and stripping the <unk> parts of the plant out with its beak , leaving a ball of <unk> fibre . These little clumps of plant fibres are a distinctive sign of the presence of the bird . The kakapo is believed to employ bacteria in the fore @-@ gut to <unk> and help digest plant matter . 
- Kakapo diet changes according to the season . The plants eaten most frequently during the year include some species of <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> minus , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> and <unk> <unk> . Individual plants of the same species are often treated differently . Kakapo leave conspicuous evidence of their feeding activities , over feeding areas that range between 10 by 10 metres ( 30 ft × 30 ft ) and 50 by 100 metres ( 160 ft × 330 ft ) per individual . Kakapo feeding grounds almost always host <unk> and yellow silver pine <unk> . 
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- = = = Reproduction = = = 
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- The kakapo is the only species of flightless parrot in the world , and the only flightless bird that has a lek breeding system . Males loosely gather in an arena and compete with each other to attract females . Females listen to the males as they display , or " lek " . They choose a mate based on the quality of his display ; they are not pursued by the males in any overt way . No pair bond is formed ; males and females meet only to mate . 
- During the courting season , males leave their home ranges for <unk> and ridges where they establish their own mating courts . These <unk> can be up to 7 kilometres ( 4 mi ) from a kakapo 's usual territory and are an average of 50 metres ( 160 ft ) apart within the lek arena . Males remain in the region of their court throughout the courting season . At the start of the breeding season , males will fight to try to secure the best courts . They confront each other with raised feathers , spread wings , open beaks , raised claws and loud <unk> and growling . Fighting may leave birds with injuries or even kill them . 
- Each court consists of one or more <unk> @-@ shaped depressions or " bowls " dug in the ground by the male , up to 10 centimetres ( 4 in ) deep and long enough to fit the half @-@ metre length of the bird . The kakapo is one of only a handful of birds in the world which actually <unk> its <unk> . <unk> are often created next to rock faces , banks , or tree trunks to help reflect sound - the bowls themselves function as <unk> to enhance the projection of the males ' booming mating calls . Each male 's bowls are connected by a network of trails or tracks which may extend 50 metres ( 160 ft ) along a ridge or 20 metres ( 70 ft ) in diameter around a hilltop . Males <unk> clear their bowls and tracks of debris . One way researchers check whether bowls are visited at night is to place a few twigs in the bowl ; if the male visits overnight , he will pick them up in his beak and toss them away . 
- To attract females , males make loud , low @-@ frequency ( below 100 Hz ) booming calls from their bowls by inflating a <unk> sac . They start with low <unk> , which increase in volume as the sac <unk> . After a sequence of about 20 loud booms , the male kakapo emits a high @-@ frequency , metallic " <unk> " sound . He stands for a short while before again lowering his head , inflating his chest and starting another sequence of booms . The booms can be heard at least 1 kilometre ( 0 @.@ 62 mi ) away on a still night ; wind can carry the sound at least 5 kilometres ( 3 @.@ 1 mi ) . Males boom for an average of eight hours a night ; each male may produce thousands of booms in this time . This may continue every night for three or four months during which time the male may lose half his body weight . Each male moves around the bowls in his court so that the booms are sent out in different directions . These booms are also notorious for attracting predators , because of the long range at which they can be heard . 
- Females are attracted by the booms of the competing males ; they too may need to walk several kilometres from their territories to the arena . Once a female enters the court of one of the males , the male performs a display in which he rocks from side to side and makes clicking noises with his beak . He turns his back to the female , spreads his wings in display and walks backwards towards her . He will then attempt copulation for 2 to 14 minutes . Once the birds have mated , the female returns to her home territory to lay eggs and raise the chicks . The male continues booming in the hope of attracting another female . 
- The female kakapo lays 1 or 2 eggs ( rarely 3 ) per breeding cycle , with long intervals between laying of first and second eggs . She nests on the ground under the cover of plants or in cavities such as hollow tree trunks . The female <unk> the eggs faithfully , but is forced to leave them every night in search of food . Predators are known to eat the eggs and the embryos inside can also die of cold in the mother 's absence . Kakapo eggs usually hatch within 30 days , bearing fluffy grey chicks that are quite helpless . After the eggs hatch , the female feeds the chicks for three months , and the chicks remain with the female for some months after fledging . The young chicks are just as vulnerable to predators as the eggs , and young have been killed by many of the same predators that attack adults . Chicks leave the nest at approximately 10 to 12 weeks of age . As they gain greater independence , their mothers may feed the chicks sporadically for up to 6 months . 
- Because the kakapo is long @-@ lived , with an average life expectancy of 95 years and the maximum at about 120 years , it tends to have an adolescence before it starts breeding . Males do not start to boom until about 5 years of age . It was thought that females reached sexual maturity at 9 years of age , but this idea was <unk> in the 2008 breeding season when two 6 @-@ year @-@ old females named <unk> and Rakiura laid eggs . Generally females do not seek out males until they are between 9 and 11 years old . The kakapo does not breed every year and has one of the lowest rates of reproduction among birds . Breeding occurs only in years when trees mast ( fruit heavily ) , providing a plentiful food supply . <unk> mast occurs only every three to five years , so in rimu @-@ dominant forests such as those on Codfish Island , kakapo breeding occurs as infrequently . 
- Another aspect of the kakapo 's breeding system is that a female can alter the sex ratio of her offspring depending on her condition . A female who eats protein @-@ rich foods produces more male offspring ( males have 30 % – 40 % more body weight than females ) . Females produce offspring biased towards the <unk> sex when competition for resources ( such as food ) is high and towards the non @-@ <unk> sex when food is plentiful . A female kakapo will likely be able to produce eggs even when there are few resources , while a male kakapo will be more capable of perpetuating the species when there are plenty , by mating with several females . This supports the <unk> – Willard hypothesis . The relationship between clutch sex ratio and maternal diet has conservation implications , because a captive population maintained on a high quality diet will produce fewer females and therefore fewer individuals valuable to the recovery of the species . 
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- = = Conservation = = 
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- <unk> records indicate that in pre @-@ Polynesian times , the kakapo was New Zealand 's third most common bird and it was widespread on all three main islands . However , the kakapo population in New Zealand has declined massively since human settlement of the country . Since 1891 , conservation efforts have been made to prevent extinction . The most successful scheme has been the Kakapo Recovery Programme ; this was implemented in 1989 and continues . 
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- = = = Human impact = = = 
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- The first factor in the decline of the kakapo was the arrival of humans . Māori folklore suggests that the kakapo was found throughout the country when the <unk> first arrived in <unk> 700 years ago . <unk> and <unk> deposits show that the bird was present throughout the North Island , South Island and Stewart Island / Rakiura before and during early Māori times . Māori hunted the kakapo for food and for their skins and feathers , which were made into <unk> . They used the dried heads as ear ornaments . Due to its flightlessness , strong scent and habit of freezing when threatened , the kakapo was easy prey for the Māori and their dogs . Its eggs and chicks were also preyed upon by the Polynesian rat or <unk> , which the Māori brought to New Zealand . Furthermore , the deliberate clearing of vegetation by Māori reduced the <unk> range for kakapo . Although the kakapo was extinct in many parts of the islands by the time Europeans arrived , including the <unk> and <unk> Ranges , it was still present in the central part of the North Island and forested parts of the South Island . 
- Beginning in the 1840s , European settlers cleared vast tracts of land for farming and grazing , further reducing kakapo habitat . They brought more dogs and other mammalian predators , including domestic cats , black rats and stoats . Europeans knew little of the kakapo until George Gray of the British Museum described it from a skin in 1845 . As the Māori had done , early European explorers and their dogs ate kakapo . In the late 19th century , the kakapo became well known as a scientific curiosity , and thousands were captured or killed for zoos , museums and collectors . Most captured specimens died within months . From at least the 1870s , collectors knew the kakapo population was declining ; their prime concern was to collect as many as possible before the bird became extinct . 
- In the 1880s , large numbers of <unk> ( stoats , ferrets and <unk> ) were released in New Zealand to reduce <unk> numbers , but they also preyed heavily on many native species including the kakapo . Other browsing animals , such as introduced deer , competed with the kakapo for food , and caused the extinction of some of its preferred plant species . The kakapo was reportedly still present near the head of the <unk> River as late as 1894 , with one of the last records of a kakapo in the North Island being a single bird caught in the Kaimanawa Ranges by Te <unk> <unk> in 1895 . 
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- = = = Early protection efforts = = = 
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- In 1891 , the New Zealand government set aside Resolution Island in Fiordland as a nature reserve . In 1894 , the government appointed Richard Henry as caretaker . A keen naturalist , Henry was aware that native birds were declining , and began catching and moving kakapo and <unk> from the mainland to the predator @-@ free Resolution Island . In six years , he moved more than 200 kakapo to Resolution Island . By 1900 , however , stoats had <unk> to Resolution Island and colonised it ; they wiped out the nascent kakapo population within 6 years . 
- In 1903 , three kakapo were moved from Resolution Island to the nature reserve of Little Barrier Island north @-@ east of Auckland , but feral cats were present and the kakapo were never seen again . In 1912 , three kakapo were moved to another reserve , <unk> Island , north @-@ west of Wellington . One of them survived until at least 1936 , despite the presence of feral cats for part of the intervening period . 
- By the 1920s , the kakapo was extinct in the North Island and its range and numbers in the South Island were declining . One of its last <unk> was rugged Fiordland . There , during the 1930s , it was often seen or heard , and occasionally eaten , by hunters or <unk> . By the 1940s , reports of kakapo were becoming scarce . 
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- = = = 1950 – 89 conservation efforts = = = 
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- In the 1950s , the New Zealand Wildlife Service was established and began making regular expeditions to search for the kakapo , mostly in Fiordland and what is now the <unk> National Park in the northwest of the South Island . Seven Fiordland expeditions between 1951 and 1956 found only a few recent signs . Finally , in 1958 a kakapo was caught and released in the Milford Sound catchment area in Fiordland . Six more kakapo were captured in 1961 ; one was released and the other five were transferred to the <unk> of the Mount Bruce Bird Reserve near Masterton in the North Island . Within months , four of the birds had died and the fifth died after about four years . In the next 12 years , regular expeditions found few signs of the kakapo , indicating that numbers were continuing to decline . Only one bird was captured in 1967 ; it died the following year . 
- By the early 1970s , it was uncertain whether the kakapo was still an extant species . At the end of 1974 , scientists located several more male kakapo and made the first scientific observations of kakapo booming . These observations led Don Merton to speculate for the first time that the kakapo had a lek breeding system . From 1974 to 1976 , 14 kakapo were discovered but all appeared to be males . This raised the possibility that the species would become extinct , because there might be no surviving females . One male bird was captured in the Milford area in 1975 , christened " Richard Henry " , and transferred to Maud Island . All the birds the Wildlife Service discovered from 1951 to 1976 were in U @-@ shaped <unk> valleys flanked by almost @-@ vertical cliffs and surrounded by high mountains . Such extreme terrain had slowed colonisation by browsing mammals , leaving islands of virtually <unk> native vegetation . However , even here , stoats were present and by 1976 the kakapo was gone from the valley floors and only a few males survived high on the most inaccessible parts of the cliffs . 
- Before 1977 , no expedition had been to Stewart Island / Rakiura to search for the bird . In 1977 , sightings of kakapo were reported on Stewart Island . An expedition to the island found a track and bowl system on its first day ; soon after , it located several dozen kakapo . The finding in an 8 @,@ 000 @-@ hectare area of fire @-@ modified <unk> and forest raised hope that the population would include females . The total population was estimated at 100 to 200 birds . 
- <unk> have never colonised Stewart Island / Rakiura , but feral cats were present . During a survey , it was apparent that cats killed kakapo at a rate of 56 % per year . At this rate , the birds could not survive on the island and therefore an intensive cat control was introduced in 1982 , after which no cat @-@ killed kakapo were found . However , to ensure the survival of the remaining birds , scientists decided later that this population should be transferred to predator @-@ free islands ; this operation was carried out between 1982 and 1997 . 
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- = = = Kakapo Recovery programme = = = 
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- In 1989 , a Kakapo Recovery programme was developed and a Kakapo Recovery Group established to implement it . The New Zealand Department of Conservation replaced the Wildlife Service for this task . The first action of the plan was to relocate all the remaining kakapo to suitable islands for them to breed . None of the New Zealand islands were ideal to establish kakapo without rehabilitation by extensive re @-@ vegetation and the eradication of introduced mammalian predators and competitors . Four islands were finally chosen : Maud , <unk> / Little Barrier , Codfish and Mana . Sixty @-@ five kakapo ( 43 males , 22 females ) were successfully transferred onto the four islands in five <unk> . Some islands had to be rehabilitated several times when feral cats , stoats and <unk> kept appearing . Little Barrier Island was eventually viewed as unsuitable due to the rugged landscape , the thick forest and the continued presence of rats , and its birds were evacuated in 1998 . Along with Mana Island , it was replaced with two new kakapo sanctuaries , <unk> Island ( Te <unk> ) and Anchor Island . The entire kakapo population of Codfish Island was temporarily relocated in 1999 to Pearl Island in Port <unk> while rats were being eliminated from Codfish . All kakapo on Pearl and <unk> Islands were moved to Anchor Island in 2005 . 
- A key part of the Recovery Plan is the supplementary feeding of females . The kakapo breeds only once every two to five years , when a certain type of plant species , primarily <unk> <unk> ( rimu ) , produces protein @-@ rich fruit and seeds . Observations of the relationship between intermittent breeding and the plant 's mast year help biologists choose which suitable supplementary foods to increase kakapo breeding frequency . In 1989 , six preferred foods ( apples , sweet potatoes , <unk> , Brazil nuts , <unk> seeds and <unk> ) were supplied ad <unk> each night to 12 feeding stations . Males and females ate the supplied foods , and females <unk> on Little Barrier Island in the summers of 1989 – 91 for the first time since 1982 , although nesting success was low . 
- <unk> feeding not only increases kakapo breeding frequency , but also affects the sex ratio of kakapo offspring , as maternal conditions influence this ratio . ( See section " Reproduction " . ) This finding was subsequently used to increase the number of female chicks by deliberately manipulating maternal conditions . During the winter of 1981 , only females lighter than 1 @.@ 5 kg ( 3 @.@ 3 lb ) were given supplementary feeding to avoid raising their body condition , and the sex ratio results in 1982 were close to parity , eliminating the male @-@ biased sex ratios in the unrestricted feeding . 
- Though breeding can be improved by supplementary feeding , the survival of young kakapo is hampered by the presence of Polynesian rats . Of 21 chicks that hatched between 1981 and 1994 , nine were either killed by rats or died and were subsequently eaten by rats . Nest protection has been intensified since 1995 by using traps and poison stations as soon as a nest had been detected . A small video camera and <unk> @-@ red light source watch the nest continuously , and will scare approaching rats with flashing lights and loud popping sounds . To increase the success rate of nesting , a nest <unk> places a small <unk> controlled electric blanket over the eggs or chicks , whenever the female leaves the nest for food . The survival rate of chicks has increased from 29 % in unprotected nests to 75 % in protected ones . 
- To monitor the kakapo population continuously , each bird is equipped with a radio <unk> . Every known kakapo , barring some young chicks , has been given a name by Kakapo Recovery Programme officials . It is an affectionate way for conservation staff to refer to individual birds , and a stark reminder of how few remain . Artificial incubation of eggs and hand @-@ raising of chicks have often been used to improve the condition of the eggs and chicks . In November 2005 , the population comprised 41 females and 45 males , including four fledglings ( 3 females and 1 male ) bred in 2005 . The oldest known kakapo , " Richard Henry " , was thought to be 80 years old at the time of his death in December 2010 . 
- In 2006 , the Kakapo Recovery Programme presented a new management plan that would run from 2006 to 2016 . The key goals of this plan are to increase the female population to at least 60 by 2016 , increase genetic diversity , maintain or restore a sufficiently large habitat to accommodate the expected increase in the kakapo population , and maintain public awareness and support . 
- The Kakapo Recovery programme has been successful , with the numbers of kakapo increasing steadily . Adult survival rate and <unk> have both improved significantly since the programme 's inception . However , the main goal is to establish at least one viable , self @-@ sustaining , unmanaged population of kakapo as a functional component of the ecosystem in a protected habitat . To help meet this conservation challenge , two large Fiordland islands , Resolution ( 20 @,@ 860 ha ) and Secretary ( 8 @,@ 140 ha ) , have been prepared for re @-@ introduction of the kakapo with large @-@ scale ecological restoration activities . Ultimately , the Kakapo Recovery vision for the species is to restore the " <unk> " ( <unk> for " life @-@ force " ) of the kakapo by breeding 150 adult females . 
- During the 2008 – 2009 summer breeding season , the total population of kakapo rose to over 100 for the first time since monitoring began , reaching 123 by February 2012 . Twenty two of the 34 chicks had to be hand @-@ reared because of a shortage of food on Codfish Island . 
- In 2012 , seven kakapo were transferred to Little Barrier Island , in an attempt to establish a successful breeding programme . Kakapo were last on the island in 1999 . 
- In March 2014 , with the kakapo population having increased to 126 , the bird 's recovery was used by Melbourne artist <unk> <unk> as a metaphor for the recovery of <unk> , <unk> the " <unk> spirit of these two communities and their determination to rebuild " . 
- 
- = = In Māori culture = = 
- 
- The kakapo is associated with a rich tradition of Māori folklore and beliefs . The bird 's irregular breeding cycle was understood to be associated with heavy fruiting or " <unk> " events of particular plant species such as the <unk> which led Māori to credit the bird with the ability to tell the future . Used to <unk> this claim were reported observations of these birds dropping the berries of the <unk> and <unk> trees ( when they were in season ) into secluded pools of water to preserve them as a food supply for the summer ahead ; in legend this became the origin of the Māori practice of <unk> food in water for the same purpose . 
- 
- = = = Use for food and clothing = = = 
- 
- The meat of kakapo made good eating and was considered by Māori to be a delicacy and it was hunted for food when it was still widespread . One source states that its flesh " resembles lamb in taste and texture " , although European settlers have described the bird as having a " strong and slightly stringent [ sic ] flavour . " 
- In breeding years , the loud booming calls of the males at their mating arenas made it easy for Māori hunting parties to track the kakapo down , and it was also hunted while feeding or when dust @-@ bathing in dry weather . The bird was caught , generally at night , using <unk> , <unk> traps , or by groups of domesticated Polynesian dogs which accompanied hunting parties – sometimes they would use fire sticks of various sorts to <unk> a bird in the darkness , stopping it in their tracks and making the capture easier . <unk> was done in a <unk> or in <unk> of boiling oil . The flesh of the bird could be preserved in its own fat and stored in containers for later consumption – hunters of the <unk> <unk> tribe would pack the flesh in <unk> made from the inner bark of <unk> tree or in containers constructed from kelp . <unk> of kakapo tail feathers were attached to the sides of these containers to provide decoration and a way to identify their contents . Also taken by the Māori were the bird 's eggs , which are described as whitish " but not pure white " , and about the same size as a <unk> egg . 
- As well as eating the meat of the kakapo , Māori would use kakapo skins with the feathers still attached or individually <unk> in kakapo feathers with flax fibre to create <unk> and <unk> . Each one required up to 11 @,@ 000 feathers to make . Not only were these garments considered very beautiful , they also kept the wearer very warm . They were highly valued , and the few still in existence today are considered <unk> ( treasures ) — indeed , the old Māori <unk> " You have a kākāpō <unk> and you still complain of the cold " was used to describe someone who is never satisfied . Kakapo feathers were also used to <unk> the heads of <unk> , but were removed before use in combat . 
- Despite this , the kakapo was also regarded as an affectionate pet by the Māori . This was corroborated by European settlers in New Zealand in the 19th century , among them George Edward Grey , who once wrote in a letter to an associate that his pet kakapo 's behaviour towards him and his friends was " more like that of a dog than a bird " . 
- 
- = = = In the media = = = 
- 
- The conservation of the kakapo has made the species well known . Many books and documentaries detailing the plight of the kakapo have been produced in recent years , one of the earliest being Two in the Bush , made by Gerald <unk> for the BBC in 1962 . A feature @-@ length documentary , The Unnatural History of the Kakapo won two major awards at the <unk> Earth Environmental Film Festival . Two of the most significant documentaries , both made by <unk> , are Kakapo – Night Parrot ( 1982 ) and To Save the kakapo ( 1997 ) . The BBC 's Natural History Unit also featured the kakapo , including a sequence with Sir David Attenborough in The Life of Birds . It was also one of the endangered animals Douglas Adams and Mark Carwardine set out to find for the radio series and book Last Chance to See . An updated version of the series has been produced for BBC TV , in which Stephen Fry and Carwardine revisit the animals to see how they are getting on almost 20 years later , and in January 2009 , they spent time filming the kakapo on Codfish Island . Footage of a kakapo named <unk> attempting to mate with Carwardine 's head was viewed by millions worldwide , leading to <unk> becoming " <unk> @-@ bird " for New Zealand wildlife conservation in 2010 , as part of the International Year of Biodiversity . The kakapo was featured in the episode " Strange Islands " of the documentary series South Pacific , originally aired on 13 June 2009 . Kakapo were also used as an example of unique island fauna in the episode " Worlds Apart " of the series The Living Planet . 
- 
- 
- = Live & Kicking = 
- 
- Live & Kicking is a BBC Saturday morning children 's magazine programme , running from 1993 to 2001 . The fourth in a succession of Saturday morning shows , it was the replacement for Going Live ! , and took many of its features from it , such as phone @-@ ins , games , comedy , competitions and the showing of cartoons . Once Live & Kicking had become established in series two , it reached its height in popularity during series four , when it was presented by Zoë Ball and Jamie Theakston ; their final episode won a BAFTA award . After this the series ratings dropped with the launch of SMTV Live on ITV and was eventually cancelled in 2001 . 
- 
- = = History = = 
- 
- Live & Kicking was conceived as a replacement for Going Live ! , a successful Saturday morning programme that had been running for six years . It was first broadcast on 2 October 1993 at 9 am on <unk> . The original hosts were Andi Peters , Emma Forbes who had presented a cookery segment in Going Live ! , and John Barrowman . For the second series , John was relegated to host the showbiz Electric Circus segment , leaving Andi and Emma to become the main hosts . He left after one series of Electric Circus to concentrate on acting . Comedy duo Trevor and Simon and Peter Simon , in the Run the Risk segment , were also regulars who had featured on Going Live ! . 
- While the first series was not as popular as its predecessor , the second series was more successful . It was broadcast during the winter months , from September to April , with Fully <unk> replacing it during the summer . New episodes of the <unk> were shown . The series went out opposite ITV 's What 's Up Doc ? but during its third series issues were raised by the ITC , and a number of people left including Don Austen and John Eccleston ( <unk> and <unk> 's puppeteers ) who defected to Live & Kicking to star as a couple of <unk> brothers Sage & <unk> . 
- Andi Peters expressed his intention to move on in March 1996 , and Emma Forbes decided to follow after finding out she was pregnant . 
- They were replaced by Zoë Ball and Jamie Theakston , who presented it for three series . According to the BBC , the show 's popularity was at its peak during the 1996 / 1997 series when the show regularly had 2 @.@ 5 million viewers . Around this time Mr. <unk> , played by Barry <unk> , also <unk> on series . 
- After three series , Ball decided to move on due to a <unk> schedule , and Theakston followed . The final episode hosted by Ball and Theakston later won the show a children 's BAFTA award for Best Entertainment show in November 1999 . 
- The show returned in Autumn 1999 with new presenters Emma Ledden and Steve Wilson , . They only lasted for one series , due to ratings dropping to 1 @.@ 6 million during their tenure . At the same time rival SMTV Live on competitor channel ITV was relaunched to feature more comedic elements and began to gain popularity , known for its <unk> and features . Fully <unk> , the BBC 's summer replacement , was also revamped and retitled as <unk> , but ratings continued to drop . The following October , the final series was a complete revamp , with a line @-@ up of four : <unk> <unk> , Katy Hill , Trey Farley and Sarah <unk> . 
- Ratings continued to <unk> , due to the continuing success of SMTV Live . In March 2001 , the BBC made an unprecedented move and extended the series over the summer , like SMTV was broadcast , but announced it would be the final series . Hill was replaced by Heather <unk> as the show was moved to BBC Scotland on 21 April until 15 September 2001 when the final show aired . It was replaced by The Saturday Show , which continued to be broadcast all year round . 
- 
- = = <unk> = = 
- 
- Live & Kicking was a weekly magazine show broadcast every Saturday morning , normally from September to April and later all year for the final series , and it was aimed at young people . It featured music performances , " hot seat " questions for celebrity guests , phone @-@ ins , games , comedy sketches , competitions , and television programmes and cartoons . It used the <unk> " Miss it , miss out " and " The only way to start your weekend " on promotional adverts for the show . As well as the main presenters , there were regulars such as comedy duo Trevor and Simon , and later Ben , <unk> and Rich from The Cheese Shop and <unk> . A segment in the first few series that was an adaptation of Going Live 's Double Dare was Run the Risk , a game in which teams of children completed various obstacle courses and challenges . <unk> was often included to make the tasks harder . Run The Risk was later broadcast separately . From 1994 until 2000 , there was a showbiz segment called the Electric Circus , which featured the latest films , music , computer games and gossip . It was first presented by John Barrowman after he stepped aside as a regular presenter , and was later hosted by a variety of people . 
- The first series featured the <unk> head of a cat named " <unk> " who provided links , but this was dropped after one series . It was replaced by commentator Mitch Johnson , who , as well as providing commentary and links for each item , would interact with both audience and presenters too . From the second series , two puppet <unk> , later named as Sage and <unk> became regulars . They were played by Don Austen and John Eccleston , and were designed and built by Darryl <unk> Studios . They performed comedy sketches throughout the morning , and often interacted with the people in the studio . Another comedy character who first appeared in the third series was Mr <unk> , who had previously appeared in Noel 's House Party . Most regular features were dropped for the final series , when the show was revamped . A feature that stuck throughout was the <unk> for the phone number , first 081 <unk> <unk> , then <unk> <unk> <unk> , then <unk> 610 <unk> . 
- As well as the television show , Live and Kicking launched a music CD , composed of the best music that artists had sung live on the programme . A video game called Live and Kicking : <unk> was also created , where the user could combine elements of the show to create their own television production on a small scale . A monthly magazine was also produced , though towards the end of Live & Kicking 's production , the sales of the magazine dropped significantly , reflecting its loss of viewers . 
- For series five and six , there was a short version of the show that aired on Friday <unk> called L & K Friday , but this was cancelled after two series . The regular Saturday presenters Jamie Theakston and Zoë Ball presented the first series , and Steve Wilson and Liz Fraser presented the second series . A 90 @-@ minute version of the show also aired on BBC Choice and was entitled L & K Replay . 
- In May 2000 , two months before Steve & Emma were officially resigned from Live & Kicking , the show was brought back for a one @-@ off special during the summer break . It was to mix in with the BBC 's Music Live and the show was titled as Music Live & Kicking with Steve & Emma returning to present along with future presenter <unk> <unk> and special guest presenter Stephen <unk> of <unk> . This special was dedicated to a series of music performances ( hence the title ) and was the first edition to be broadcast in widescreen . 
- One of the last features was L & K <unk> , a spin @-@ off of the BBC reality show <unk> 2000 . Each week , six children would spend four days on a remote Scottish island , learning how to survive , among other skills . Points were earned through passing various tasks , and were lost if contestants entered the " <unk> Hut " , which contained various modern electrical appliances . 
- 
- = = <unk> and replacement = = 
- 
- The Ball and Theakston series are considered to be when Live & Kicking was at its peak in popularity . After their final series in 1999 , it was believed the BBC would replace Live & Kicking with another programme , as its two predecessors had both lasted six years . Instead , they continued with Live & Kicking , with new presenters Emma Ledden and Steve Wilson . The series was the beginning of the end for the show ; Ledden and Wilson did not know each other at the start of the series , and so there was none of the interaction between them , as seen between Ball and Theakston . Additionally , SMTV Live which broadcast opposite on ITV was slowly becoming more popular , and gaining the audience the BBC was losing . After just one series , Ledden and Wilson 's contracts were not renewed . Wilson later said that they were dropped just as they were starting to form a relationship , and that <unk> & Dec , presenters of SMTV Live , had the edge over them as they had known each other much longer . Ledden had already been dropped when Wilson went through several meetings with the BBC . He decided it was better to leave after one good series , rather than do a second " lame " series , and went on to appear in rival SMTV Live 's 100th show , in the Friends <unk> , ' <unk> ' . When Live & Kicking returned in October 2000 , it was completely revamped , with brand new titles and a line @-@ up of four presenters . However , this did nothing to increase viewing figures , and the chemistry between the presenters was even less apparent . It was decided not to end the show in April and replace it with a summer show , because the replacement <unk> had lost even more viewers for the BBC . Live & Kicking continued until September after a move to Glasgow where the summer show had normally been filmed . Just before the move it was announced it would be the final series . The principal reason given for the decision was the increasing loss of viewers to SMTV Live , which had a similar format and was more successful . Live and Kicking was replaced by The Saturday Show , fronted by <unk> Behr and Joe <unk> , which was shown all year round until September 2003 when it began an Autumn @-@ Spring / Summer loop with Dick and <unk> in da <unk> . Live & Kicking was featured in the BBC 's It <unk> with <unk> Shop programme in 2006 , where Noel Edmonds interviewed the first pair of presenters , Andi Peters and Emma Forbes , about their time on the show . 
- 
- = = Programmes = = 
- 
- <unk> ( 1993 – 2001 ) 
- The Simpsons ( 1997 – 2000 ) 
- X @-@ Men ( 1993 – 1996 ) 
- <unk> and <unk> ( 1997 – 2001 ) 
- Smart Guy ( 1997 – 2000 ) 
- The Wild <unk> ( 1999 – 2001 ) 
- <unk> <unk> ( 2000 – 2001 ) 
- Monster <unk> ( 1999 – 2001 ) 
- Clarissa <unk> It All ( 1993 – 1996 ) 
- <unk> ( 1994 ) 
- <unk> ! The Cat 
- 
- 
- = Henry Hoʻolulu Pitman = 
- 
- Timothy Henry Hoʻolulu Pitman ( March 18 , 1845 – February 27 , 1863 ) was an American Union Army soldier of Native Hawaiian descent . Considered one of the " Hawaiʻi Sons of the Civil War " , he was among a group of more than one hundred documented Native Hawaiian and Hawaii @-@ born combatants who fought in the American Civil War while the Kingdom of Hawaiʻi was still an independent nation . 
- Born and raised in Hilo , Hawaiʻi , he was the eldest son of <unk> , a Hawaiian high <unk> , and Benjamin Pitman , an American pioneer settler from Massachusetts . Through his father 's business success in the whaling and sugar and coffee plantation industries and his mother 's familial connections to the Hawaiian royal family , the <unk> were quite prosperous and owned lands on the island of Hawaiʻi and in Honolulu . He and his older sister were educated in the mission schools in Hilo alongside other children of mixed Hawaiian descent . After the death of his mother in 1855 , his father remarried to the widow of a missionary , thus connecting the family to the American missionary community in Hawaiʻi . However , following the deaths of his first wife and later his second wife , his father decided to leave the islands and returned to Massachusetts with his family around 1860 . He continued his education in the public schools of Roxbury , where the Pitman family lived for a period of time . 
- Leaving school without his family 's knowledge , he made the decision to fight in the Civil War in August 1862 . Despite his mixed @-@ race ancestry , Pitman avoided the racial segregation imposed on other Native Hawaiian recruits of the time and enlisted in the 22nd Regiment Massachusetts Volunteer Infantry , a white regiment . He served as a private in the Union Army fighting in the Battle of Antietam and the Maryland Campaign . In his company , Private Robert G. Carter befriended the part @-@ Hawaiian soldier and wrote in later life of their common experience in the 22nd Massachusetts . <unk> decades afterward from old letters , Carter 's account described the details surrounding his final fate in the war . On the march to Fredericksburg , Pitman was separated from his regiment and captured by Confederate guerrilla forces . He was forced to march to Richmond and incarcerated in the Confederate Libby Prison , where he contracted " lung fever " from the harsh conditions of his imprisonment and died on February 27 , 1863 , a few months after his release on parole in a prisoner exchange . Modern historians consider Henry Hoʻolulu Pitman to be the only known Hawaiian or Pacific Islander to die as a prisoner of war in the Civil War . 
- For a period of time after the end of the war , the legacy and contributions of Pitman and other documented Hawaiian participants in the American Civil War were largely forgotten except in the private circles of descendants and historians . However , there has been a revival of interest in recent years in the Hawaiian community . In 2010 , these " Hawaiʻi Sons of the Civil War " were commemorated with a bronze plaque erected along the memorial pathway at the National Memorial Cemetery of the Pacific in Honolulu . 
- 
- = = Early life and family = = 
- 
- Timothy Henry Hoʻolulu Pitman was born March 18 , 1845 , in Hilo , Hawaiʻi , the first son and second child of Benjamin Pitman and <unk> . Originally a native of Salem , Massachusetts , Pitman 's father was an early pioneer , businessman and sugar and coffee plantation owner on the island of Hawaiʻi , who <unk> greatly from the kingdom 's booming whaling industry in the early 1800s . On his father 's side , he was a great @-@ grandson of Joshua Pitman ( 1755 – 1822 ) , an English @-@ American carpenter on the ship <unk> Franklin <unk> under Captain Allen <unk> during the American Revolutionary War . On his mother 's side , Pitman was a descendant of <unk> , one of the royal twins ( with <unk> ) who advised Kamehameha I in his conquest of the Hawaiian Islands , and also of the early American or English sea captain Harold Cox , who lent his name to George " Cox " <unk> <unk> II , the Governor of Maui . Henry Hoʻolulu Pitman shared his Hawaiian name with his maternal grandfather Hoʻolulu , who , along with his brother <unk> , helped conceal the bones of King Kamehameha I in a secret hiding place after his death . In the Hawaiian language , the name " Hoʻolulu " means " to be calm " , as a ship in a protected harbor . His siblings were Mary Ann Pitman <unk> ( 1838 – 1905 ) , Benjamin <unk> Franklin Pitman ( 1852 – 1918 ) and half @-@ sister Maria Kinoʻole Pitman Morey ( 1858 – 1892 ) . 
- Because of his father 's success in business and his mother 's descent from Hawaiian royalty , the Pitman family was considered quite prosperous and were host to the royal family when they visited Hilo . Besides being one of the leading merchants in town , his father also served the government as district magistrate of Hilo . Henry 's mother , Kinoʻole , had inherited control over much of the lands in Hilo and <unk> from her own father , and King Kamehameha III had granted her use of the <unk> of Hilo after her marriage . During Henry 's early childhood , the family lived in the mansion that Benjamin Pitman had built in 1840 , in an area known as <unk> , one of the favored resort spots of ancient Hawaiian royalty . The residence also became known as the Spencer House after Pitman sold it to his business partner Captain Thomas Spencer . The property later became the site of the Hilo Hotel , built in 1888 and torn down in 1956 . In the 1850s the family moved to the capital of Honolulu where Benjamin Pitman took up banking and built a beautiful two @-@ story house that he named <unk> ( " <unk> water " ) at the corner of <unk> and <unk> Streets . 
- 
- = = Education = = 
- 
- While in Hawaiʻi , Pitman and his older sister Mary attended Mrs. Wetmore 's children 's school in Hilo . The school was located at the <unk> ' residence on Church Street . <unk> by Lucy Sheldon Taylor Wetmore , the wife of American missionary doctor and government physician Charles <unk> Wetmore , the two elder Pitman children received their education in English rather than Hawaiian . This was unusual since Hawaiian was the official language of the Kingdom of Hawaiʻi , and all other schools in Hilo were conducted in the Hawaiian language . Mrs. Wetmore taught the children reading , writing , spelling , arithmetic and singing , while also reinforcing the curriculum with a strong adherence to the principles of the Protestant faith . Like the Pitman siblings , many of their classmates were also of half @-@ Hawaiian ( hapa @-@ <unk> ) descent with a majority of them being Chinese @-@ Hawaiians ( hapa @-@ <unk> ) . 
- After the death of his mother Kinoʻole in 1855 , Pitman 's father remarried to Maria <unk> <unk> Kinney , the widow of American missionary Rev. Henry Kinney . The <unk> were part of the Twelfth Company of missionaries from the American Board of Commissioners for Foreign <unk> to arrive in 1848 . The marriage aligned the Pitman children with the American missionary community . They were called " cousins " by the children of the missionaries and considered part of the extended missionary family of Hawaiʻi . This first stepmother died in 1858 after giving birth to their father 's fourth child , a daughter named Maria Kinoʻole ( 1858 – 1892 ) . The Pitman family returned to Massachusetts in 1860 where his father remarried to his third wife Martha Ball , giving his four children another stepmother . According to an 1887 biography written by Robert G. Carter , a private who would later serve in the same company as Pitman , he was neglected after his mother 's death by his father and stepmother , who " subjected [ him ] to neglect and treatment , that with his sensitive nature he could not bear " . He continued his education in the public schools of Roxbury , where the Pitman family lived for a period of time . The 1860 United States Census registered Pitman under his teacher Solomon Adams as residing and presumably being educated in Newton , also in the Boston area . 
- Growing into adolescence , he was said to strongly resemble his Hawaiian mother . Robert G. Carter gave a brief description of his appearance in wartime letters first published in 1897 : 
- [ A ] tall , slim boy , straight as an arrow . His face was a perfect oval , his hair was as black as a <unk> 's wing , and his eyes were large and of that peculiar soft , melting <unk> , which <unk> pity when one is in distress . His skin was a clear , dark olive , bordering on the <unk> , and this , with his high cheek bones , would have led us to suppose that his nationality was different from our own , had we not known that his name was plain Henry P. There was an air of good breeding and refinement about him , that , with his small hands and feet , would have set us to thinking , had it not been that in our youth and intensely enthusiastic natures , we gave no thought to our comrades ' personal appearance . We can look back now and see the shy , reserved nature of the boy , the dark , melancholy eyes , the sad smile , the sensitive twitching of the lips . 
- 
- = = American Civil War = = 
- 
- After the outbreak of the American Civil War , the Kingdom of Hawaiʻi under King Kamehameha IV declared its neutrality on August 26 , 1861 . But many Native Hawaiians and Hawaiian @-@ born Americans ( mainly descendants of the American missionaries ) both abroad and in the islands volunteered and enlisted in the military regiments of various states in the Union and the Confederacy . Individual Native Hawaiians had been serving in the United States Navy and Army since the War of 1812 , and even more served during the American Civil War . Many Hawaiians <unk> with the Union because of Hawaiʻi 's ties to New England through its missionaries and the whaling industries , and the ideological opposition of many to the institution of slavery . 
- 
- = = = <unk> and service = = = 
- 
- On August 14 , 1862 , Pitman left school without his family 's knowledge and volunteered to serve in the Union Army and fight in the American Civil War . He apparently never informed his family in advance about the choice to join the war because the news of his <unk> was reported back in Hawaiʻi 's American missionary community as " Henry Pitman has run away from home and gone [ to war ] . " Carter described Pitman 's rationale for enlisting : " In the midst of the <unk> of war , when the very air <unk> with excitement , the wild enthusiasm of the crowds , and the inspiring sound of the drum , his Indian nature rose within him . His resolve was made . " 
- Pitman was a hapa @-@ <unk> , of part Hawaiian and part Caucasian descent . His father was white and his native @-@ born mother was also part Caucasian from her own mother , who was the daughter of Captain Cox and a Hawaiian <unk> . Despite his mixed @-@ race ancestry , Pitman avoided the racial segregation imposed on other Native Hawaiian volunteers in this period . Most Native Hawaiians who participated in the war were assigned to colored regiments , but Pitman 's fair skin color meant he was able to serve in a white unit , indicating that unit assignment may have been influenced by how dark Hawaiians appeared . Historians Bob <unk> , James L. <unk> and others claimed Pitman was placed in the colored regiments because of his mixed race , but regiment records indicate otherwise . 
- Pitman served as a private in the 22nd Regiment Massachusetts Volunteer Infantry , Company H. This regiment was also named the " Henry Wilson 's Regiment " after Col. Henry Wilson , who commanded the unit in 1861 . Col. William S. <unk> was the commander during Pitman 's brief term of service . The regiment was part of the V Corps of the Army of the Potomac under the command of Major General George B. <unk> . During this period , the regiment fought in the Second Battle of Bull Run and was involved in the Maryland Campaign fighting in the Battle of Antietam , the <unk> single @-@ day battle in American history , and the Battle of <unk> . His regiment was on the march to the Battle of Fredericksburg when Pitman was captured by Confederate troops . 
- 
- = = = <unk> and death = = = 
- 
- The most detailed account of Pitman 's final fate in the War came from Robert G. Carter . In November 1862 , Pitman was captured near <unk> Junction on the march toward Fredericksburg , Virginia , during the weeks prior to the Battle of Fredericksburg . He had fallen behind the group because his feet had <unk> and swollen due to the tightness of " a pair of thin , high @-@ <unk> and narrow <unk> boots " he had purchased . One of his comrades temporarily stayed behind to care for him but later decided to move on with the rest of the camp for fear of disciplinary consequences of falling out without authority . He was urged to move on , but without much success . Pitman 's last words to his <unk> were , " I will be in camp by night , good by . " His fellow soldiers never saw him again and considered him missing . Shortly after he was left , a band of Confederate guerrillas under Colonel John S. <unk> captured the weary and <unk> soldier without a struggle . The inscription on his tombstone differs slightly from Carter 's account , stating he was captured by J. E. B. Stuart 's cavalry instead . 
- After Pitman 's capture , he was marched to Richmond in a weak physical state . He was imprisoned in the Confederate Libby Prison and Belle Isle , which were notoriously harsh prisons . Pitman 's letters home described his place of incarceration as the " Pen " where " the filthy meat [ was ] thrown to them as if they were dogs " . The condition of his incarceration including the shortage of food , lack of sanitation , overcrowding and his physical weakness made him susceptible to <unk> diseases present in the Confederate prisons . Carter described how the prisons " wore out the brave spirit " . During a prisoner exchange , Pitman was released by the Confederate Army at City Point , Virginia , on December 12 , 1862 , and then sent to Annapolis Parole Camp . Suffering from complications due to the conditions of his imprisonment , he contracted " lung fever " , which was perhaps pneumonia . Carter wrote later how his friend had " linger [ ed ] <unk> a few weeks , like the <unk> of an expiring flame , then quietly pass [ ed ] away to an eternal life " . Pitman died at Parole Camp on February 27 , 1863 , just weeks short of his eighteenth birthday . According to historians Anita Manning and Justin Vance , Pitman " has the unfortunate distinction of being the only known Hawaiian or Pacific Islander to die as a prisoner of war in the Civil War . " 
- Considering him missing , Pitman 's regiment did not discover his final fate until news of his funeral at Roxbury was received in the spring of the following year . His remains were returned to his family in Massachusetts after his death in Parole Camp . Benjamin Pitman , his father , had him buried in a family plot in Mount Auburn Cemetery . On one side of the Pitman family grave marker was placed the inscription : 
- Timothy Henry Pitman 
- Born at Hilo , Hawaii 
- <unk> 18 , 1845 
- Died at Camp Parole 
- Annapolis , MD , <unk> <unk> 27 , 1863 
- <unk> 17 years 11 <unk> . 9 <unk> member of Co . H , 22nd Regiment 
- Mass . <unk> . , was with his Regiment in the 
- battles of South Mountain , Antietam and 
- <unk> . Was taken prisoner by Stuart 's 
- cavalry on the march to <unk> ; 
- <unk> in Libby Prison , <unk> and 
- sent to Camp Parole , Annapolis , and died in 
- camp of pneumonia . 
- 
- = = Legacy = = 
- 
- After his death , the memory of Henry Hoʻolulu Pitman was honored by friends and family members back in Massachusetts and Hawaiʻi . During a return to Hawaiʻi in 1917 , his younger brother Benjamin <unk> Franklin Pitman and his wife <unk> <unk> Pitman , discovered a grandson of a nephew was named <unk> i <unk> i <unk> ( meaning " Chief that fought the Potomac " ) in honor of his deceased older brother . Similarly , Henry Hoʻolulu Pitman <unk> , the second son of his Hawaiian first cousin George Charles <unk> <unk> , was also named after him . Shortly after his death , Pitman was <unk> back in Hawaiʻi by Martha Ann Chamberlain , <unk> Secretary of the Hawaiian Mission Children 's Society : 
- Our cousin , Henry Pitman , the first of Hawaii 's sons to fall in the war , died at Annapolis Parole Camp , <unk> 27 , of lung fever , serving as a soldier in the Union army . His remains were deposited in Mt . Auburn Cemetery , near Boston , Mass . He died in a just cause . Let his memory be embalmed among our band . 
- After the war , the military service of Hawaiians , including Henry Hoʻolulu Pitman , were largely forgotten , disappearing from the collective memories of the American Civil War and the history of Hawaiʻi . However , in recent years , Hawaiian residents and historians and descendants of Hawaiian combatants in the conflict have insisted on the need to remember " our boys from Hawaii " . Renewed interest in the stories of these individuals and this particular period of Hawaiian @-@ American history have inspired efforts to preserve the memories of the Hawaiians who served in the war . On August 26 , 2010 , on the anniversary of the signing of the Hawaiian <unk> <unk> , a bronze plaque was erected along the memorial pathway at the National Memorial Cemetery of the Pacific in Honolulu recognizing these " Hawaiʻi Sons of the Civil War " , the more than one hundred documented Hawaiians who served during the American Civil War for both the Union and the Confederacy . Pitman 's great @-@ <unk> Diane Kinoʻole o <unk> Pitman <unk> attended the ceremony . Pitman <unk> stated , " I 'm very proud of a young man of his age – he was quite young – who served in the Civil War for his family . " 
- In 2013 , Todd <unk> , <unk> Napoleon , Justin Vance , Anita Manning and others began the process of creating a historical documentary about the individual experiences and stories of Hawaii @-@ born soldiers and sailors of the American Civil War , including Pitman , Samuel C. Armstrong , Nathaniel Bright Emerson , James Wood Bush , J. R. <unk> and many other unnamed combatants of both the Union and the Confederacy . In 2014 , Maui @-@ based author Wayne <unk> wrote a fictionalized story based on the lives and Civil War service of Hawaiian soldiers like Henry Hoʻolulu Pitman in his book <unk> : A Hawaiian in the American Civil War . In 2015 , the <unk> of the end of the war , the National Park Service released a publication titled <unk> and Pacific Islanders and the Civil War about the service of the large number of combatants of Asian and Pacific Islander descent who fought during the war . The history of Hawaiʻi 's involvement and the biographies of Pitman , Bush , <unk> and others were co @-@ written by historians Anita Manning and Justin Vance . 
- 
- 
- = Church of Christ Pantocrator , Nesebar = 
- 
- The Church of Christ Pantocrator ( Bulgarian : <unk> „ <unk> <unk> “ , <unk> „ <unk> <unk> “ or <unk> „ <unk> <unk> “ , <unk> „ <unk> <unk> “ , Byzantine Greek : <unk> <unk> <unk> ) is a medieval Eastern Orthodox church in the eastern Bulgarian town of Nesebar ( medieval <unk> ) , on the Black Sea coast of <unk> Province . Part of the Ancient Nesebar UNESCO World Heritage Site , the Church of Christ Pantocrator was constructed in the 13th – 14th century and is best known for its lavish exterior decoration . The church , today an art gallery , survives largely intact and is among Bulgaria 's best preserved churches of the Middle Ages . 
- 
- = = History = = 
- 
- The Church of Christ Pantocrator is usually dated to the late 13th or early 14th century . University of Pennsylvania scholar Robert G. <unk> places its construction in the mid @-@ 14th century . Rough <unk> author Jonathan <unk> attributes its building to the rule of Tsar Ivan Alexander of Bulgaria ( r . 1331 – <unk> ) , though during this time control of Nesebar changed many times between the Second Bulgarian Empire and <unk> . The church is dedicated to Christ Pantocrator , a name of God which <unk> him as the " Ruler of All " in Greek . 
- The church is located on <unk> Street , near the entrance to Nesebar 's old town . <unk> , it houses an art gallery which exhibits works by Bulgarian artists . As it belongs to the old town of Nesebar , the Church of Christ Pantocrator forms part of the Ancient City of Nesebar UNESCO World Heritage Site and the 100 Tourist Sites of Bulgaria . Since 1927 , it has been under state protection as a " national antiquity " , and it was listed among Bulgaria 's monuments of culture of national importance in 1964 . 
- 
- = = Architecture = = 
- 
- The church is designed in late Byzantine cross @-@ in @-@ square style . It was constructed from stones and brickwork , a construction technique known as opus <unk> , and measures 16 by 6 @.@ 90 metres ( 52 @.@ 5 ft × 22 @.@ 6 ft ) , 16 by 6 @.@ 70 metres ( 52 @.@ 5 ft × 22 @.@ 0 ft ) , or 14 @.@ 20 by 4 @.@ 80 metres ( 46 @.@ 6 ft × 15 @.@ 7 ft ) , depending on the source . The walls of the church are 0 @.@ 80 metres ( 2 @.@ 6 ft ) thick . The colour of the bricks gives the church a <unk> appearance . 
- The church features a narthex and a cella ( or " <unk> " ) with an essentially rectangular elongated plan . The narthex is small , but has a medieval tomb underneath it . There are four entrances to the church : two accessing the cella from the south and west , and another two for the narthex from the west and north . The apse of the church has three small parts which overlap each other to form a single , larger unit . The <unk> and <unk> of the church are located by the apse . 
- The dome , octagonal in shape , stands prominently on top of the centre of the cella . It was supported by four now @-@ destroyed columns which were located directly beneath it . The integrated bell tower has been built on top of the narthex , as was customary in contemporary Byzantine church architecture , and extends from the rectangular main structure . The bell tower was originally rectangular , though it is now partially ruined . It was reached from the south by means of a stone staircase . 
- 
- = = Decoration = = 
- 
- The best @-@ known feature of the Church of Christ Pantocrator is the rich and colourful decoration of its exterior walls . The most lavishly decorated part of the church is the east side with the apse , and as a whole all sides of the church exhibit different ornamentation . <unk> strips of three or four rows of bricks and carved stones , which create an optical pattern , are the most basic type of decoration used . <unk> of blind arches , four @-@ <unk> floral motifs , triangular ornaments , circular turquoise ceramics and brick <unk> <unk> run along the east wall . <unk> <unk> the appearance of the church 's superimposed arcades to an <unk> ; an earlier example of that configuration can be observed in the <unk> church of <unk> <unk> near <unk> , Turkey . The inclusion of <unk> in the decoration is considered unusual and curious to tourists . It is explained by the medieval use of the <unk> as a symbol of the Sun . 
- The decoration of the elongated north and south walls includes brick blind arches in the bottom part and a large arch for each wall adjacent to the dome with a <unk> window in the middle . There are windows above the lower arches of the north and south facade . The dome , which also exhibits a large number of ornamental details and ceramics , features eight windows , one for each of its sides . The medieval frescoes which were painted on the interior walls of the church have been only <unk> preserved . 
- 
- 
- = Kir 'Shara = 
- 
- " Kir 'Shara " is the ninth episode of the fourth season of the American science fiction television series Star Trek : Enterprise . It was writer Michael Sussman 's third episode of the season , while it was director David Livingston 's second . The episode was the third in a three @-@ part story arc , following on from the episodes " The Forge " and " Awakening " . " Kir 'Shara " and the Vulcan arc showed themes relating to the Protestant Reformation resulting in comparisons to books such as The Da Vinci Code and The Celestine <unk> , while the Kir 'Shara itself was compared to the Nag Hammadi library . 
- Set in the 22nd century , the series follows the adventures of the first Starfleet starship Enterprise , registration NX @-@ 01 . In this episode , Enterprise attempts to <unk> a war , and is caught in a crossfire between Vulcan and Andorian <unk> . Meanwhile . Captain Archer , Commander T 'Pol , and T 'Pau aim to take the Kir 'Shara to the Vulcan capital , and use it to reveal Administrator V 'Las ' plot to the rest of the Vulcan High Command . 
- In addition to the guest stars returning from the previous arc episodes , or the episode " Home " , Jeffrey Combs returned as Shran . The episode was shot across seven days using mostly standing sets , with only an Andorian brig built specifically for this episode . " Kir 'Shara " also saw the return of the Vulcan lirpa , a weapon which had been first introduced in the Star Trek : The Original Series episode " Amok Time " . The episode originally aired on December 3 , 2004 on UPN . It received a Nielsen rating of 2 @.@ 1 / 4 percent , which equalled the highest ratings seen during the fourth season . It was warmly received by critics who praised Combs ' performance and noted that this episode was an indication of the improving quality of the series . However there was mixed opinion regarding the ending of the episode . 
- 
- = = Plot = = 
- 
- Enterprise heads to <unk> after Ambassador Soval informs them that the Vulcans believe they have been developing <unk> weapon technology . Soval guides Enterprise to a nebula where the Andorian fleet is hiding . Commander Shran is dubious , and abducts and <unk> Soval . After believing him , Enterprise joins a fleet of six Andorian ships to intercept the Vulcans . Commander Tucker attempts to buy time by ordering Enterprise directly between the two fleets — this works for a while until Administrator V 'Las orders them to be targeted too . 
- Meanwhile , at The Forge , Captain Archer , Commander T 'Pol , and T 'Pau , having found the sacred Kir 'Shara ( which the Syrrannites believe will <unk> a Vulcan enlightenment ) , endeavor to take it to the capital . <unk> route , T 'Pol and T 'Pau discuss the taboo of mind @-@ <unk> , and T 'Pau offers to mind @-@ meld with T 'Pol . She states the meld is safe when performed by those trained in the art , and that <unk> <unk> <unk> is merely the by @-@ product of an improperly conducted meld . The trio are then attacked by Major Talok and Vulcan commandos , and T 'Pol is captured while the others escape . She tells her captors that they are headed to Mount <unk> in order to mislead them from their true destination . 
- She is then taken to the capital . Archer and T 'Pau also arrive after T 'Pol 's husband , Koss , provides transporter security codes . They present the Kir 'Shara to the Command and reveal that the embassy bombing was merely a pretext to weaken the pacifist <unk> prior to the Andorian strike . <unk> angered , V 'Las <unk> for the Kir 'Shara , but is stunned by High @-@ Minister <unk> , who orders the fleet to stand down . Enterprise returns to Vulcan , and Koss visits to release T 'Pol from their marriage . Meanwhile , the Vulcan High Command is dissolved , granting Earth greater autonomy , and the katra of Surak is transferred to a Vulcan high priest . V 'Las , relieved of his post , meets secretly with Talok , revealed as a <unk> agent , who states that the <unk> of their worlds is only a matter of time . 
- 
- = = Production = = 
- 
- Like " Home " , " Kir 'Shara " was written by Michael Sussman and was his third script during the fourth season . David Livingston directed the episode , which was his second episode of the season having previously directed " <unk> " . Most of the guest stars had appeared in the previous episode " Awakening " , including Robert Foxworth as Administrator V 'Las , Kara Zediker as T 'Pau and John Rubinstein as <unk> . In addition , Michael Reilly Burke resumes his role as Koss for the third time , having appeared in the role both in " Home " and " The Forge " . Also appearing , having appeared in " Home " , was Jack <unk> as a Vulcan priest . <unk> appeared in The Original Series episode " The Enterprise Incident " as the <unk> Commander <unk> Tal . " Kir 'Shara " also saw the return of Jeffrey Combs as the Andorian Commander Shran for the sixth time as well as Gary Graham as Ambassador Soval , who has appeared as a recurring character in Enterprise since the pilot episode " Broken Bow " . 
- Filming began on October 4 , 2004 and continued until October 12 . Despite the desert locations , all filming took place on soundstages . These represented a variety of places throughout the Vulcan desert @-@ like Forge . The remaining sets used were either standing sets , or those which had been constructed for the previous two episodes . The exceptions to that were two sets to represent scenes on board Commander Shran 's Andorian vessel . The Andorian bridge set , which had been used previously in the series , had a single wall brought out of storage in order to appear as a backdrop for Shran when he appears on the Enterprise <unk> . The Andorian brig was built specifically for this episode for scenes with Shran and Soval . 
- Re @-@ appearing in " Kir 'Shara " were the traditional Vulcan weapon , the lirpa , which was first introduced in The Original Series episode " Amok Time " . The lirpa is a long shaft with a crescent blade on one end and a spiked <unk> on the other . Brand new props were built for this episode , <unk> the original design by making them more lightweight . They were wielded in " Kir 'Shara " by Vulcan commandos , who were all played by stunt actors in non @-@ speaking roles . Additional stunt doubles were required for Archer , T 'Pol , T 'Pau and Talok while two puppeteers were needed for the Andorian <unk> seen on screen . 
- 
- = = Themes = = 
- 
- " Kir 'Shara " followed up the events of the previous two episodes in the story arc as well some of the elements seen earlier in the season in the episode " Home " . " The Forge " sees Captain Jonathan Archer ( Scott Bakula ) and T 'Pol ( <unk> <unk> ) travel into the Vulcan desert known as the Forge in order to find a renegade faction of Vulcans , known as the Syrrannites . During the journey , Archer has the katra of Surak transferred into him . In " Awakening " , the duo meet the Syrrannites and find out they are peaceful . After Enterprise leaves orbit , the Vulcans start bombarding the caves where the Syrrannites are located , killing T 'Pol 's mother , T 'Les ( Joanna <unk> ) . 
- Speaking about " Awakening " , show runner Manny <unk> had previously said that he envisaged the story arc to be about a Vulcan <unk> as a metaphor to the real @-@ world 16th century Protestant Reformation with T 'Pau playing the role of Martin Luther . This view was supported by the 2010 book Star Trek As Myth , which saw the original Vulcan religion prior to the Reformation arc seen from " The Forge " onwards as equating to the Catholic Church while the <unk> were the Protestants . In doing so , Administrator V 'Las is therefore linked to the anti @-@ Christ in much the same way that the Protestant Reformation saw the Pope as the anti @-@ Christ . In this role , the Romulans in the story take the place of the <unk> <unk> to form an <unk> alliance . 
- It was also suggested that the Kir 'Shara itself was similar in context to the Nag Hammadi library , which was a collection of thirteen <unk> found in Nag Hammadi , Egypt , in 1945 that date back to between the 2nd to 5th centuries . Unlike those <unk> , the Vulcan High Council attempts to suppress the revelation of the Kir 'Shara in a similar manner to the Catholic Church 's suppression seen in other fictional works such as Dan Brown 's The Da Vinci Code or James <unk> 's The Celestine <unk> . 
- 
- = = Reception = = 
- 
- " Kir 'Shara " was first aired in the United States on UPN on December 3 , 2004 . The broadcast saw the episode come in fourth place during the timeslot , with a Nielsen rating of 2 @.@ 1 / 4 percent . This means that it was seen by 2 @.@ 1 percent of all households , and 4 percent of all those watching television at the time of broadcast . It gained higher ratings than The WB , who aired What I Like About You and <unk> for Life . The ratings equalled the highest ratings of the season so far , which were previously achieved by " <unk> " , " Cold Station 12 " and " The <unk> " . 
- TV Guide listed " Kir 'Shara " on its Hot List for the day , while the preview by IGN said that the show " finally grows up and becomes a Star Trek series this week " . It described " Kir 'Shara " as " far from a perfectly executed Trek story but they get enough right to make it a lot easier to <unk> the few things they miss . " It said that there had been an overall improvement in the fourth season , and that " this could become some of the best Star Trek ever made " . It gave " Kir 'Shara " a rating of four and a half out of five . In Matthew <unk> 's book Star Trek As Myth , he said that he felt that the revelation that the previous Vulcan administration was working with the Romulans all along " suddenly makes sense of years of previously incomprehensible Vulcan policy " and linked to The Next Generation episode " <unk> " . 
- <unk> , in his review for Ain 't It Cool News , praised the reliability of Jeffrey Combs as Shran , but thought that there were no major surprises . <unk> did say that the biggest shock was the revelation that the Romulans were behind the bombing of the Human Embassy . He gave it a rating of three out of five . Michelle Erica Green reviewed the episode for TrekNation , calling it an " absolutely gripping episode " except for the " <unk> of the ending " . She made the admission about the ending that , " I suppose the Romulans had to show up at some point , I guess Vulcan is as good a place as any . " She thought that the torture scenes were " pointless " , but was pleased with the characterisation seen in Archer , T 'Pol and Tucker . Jamahl Epsicokhan at his website " Jammer 's Reviews " said that the episode was " not perfect , but good " with an " intriguing " ending . He also praised Jeffrey Combs as Shran , and thought that the torture screens were potent but that the ending was rushed . He gave the episode a rating of three out of four . The first home media release of " Kir 'Shara " was in the season four DVD box set of Enterprise , originally released in the United States on November 1 , 2005 . The Blu @-@ ray edition was released on April 1 , 2014 . 
- 
- 
- = <unk> Warren = 
- 
- <unk> <unk> Junior Warren ( born 28 June 1959 ) is an artist and poet of Pitcairn Island , a remote British Overseas Territory in the South Pacific . She works in both English and Pitkern , the island 's distinctive <unk> language . Her book , Mi Base side orn Pitcairn , written with the island 's six children , is the first to be written and published in both English and Pitkern . As an artist , she works with <unk> cloth , a Polynesian tradition . She has also published a cookbook featuring Pitcairn Island cuisine . 
- Warren has also served as the island 's nurse , its only police officer , a <unk> radio operator , and as a member of the territory 's governing council , among many other roles . 
- 
- = = Biography = = 
- 
- Warren was born on Pitcairn Island on 28 June 1959 , the second child of Jacob Ralph " <unk> " Warren ( 1920 – 2007 ) and Mavis Mary Brown ( born 1936 ) . Warren is the sister of Jay Warren ( born 1950 ) who served as the third mayor of the Pitcairn Islands ( 2004 – 2007 ) , and previously as the colony 's 29th magistrate ( 1991 – 1997 ) . She is a first cousin of Mike Warren ( born 1964 ) , the colony 's fourth mayor ( 2007 – 2014 ) . She is the descendant of <unk> from the famed <unk> on the Bounty ( 1789 ) and of the Tahitian men and women who journeyed with the <unk> in settling the island in 1790 . 
- 
- = = = Artist , poet , author and many other jobs = = = 
- 
- Warren is a poet , and the author of two books , including Mi Base side orn Pitcairn ( " My Favourite Place on Pitcairn " ) , written with children on Pitcairn Island . It is the first book written and published in Pitkern and English . 
- Her works include a cookbook , Taste of Pitcairn featuring the cuisine of the Pitcairn Islands , and poetry in both Pitkern and English . Pitkern is an English @-@ based <unk> language derived from eighteenth @-@ century English and Tahitian . She writes poetry in both English and Pitkern . 
- In 2007 , Warren revived Pitcairn 's tradition of art created on <unk> cloth , a woven bark cloth common in Polynesian culture . Her works have been displayed in museums and galleries in Tahiti , Norfolk Island , and New Zealand . In 2011 , she was one of seven artists awarded a Commonwealth <unk> International Arts Residency , which provided a grant of £ 8 @,@ 000 that allowed her to work with other artists in New Zealand . She is the first recipient from the Pitcairn Islands . 
- Pitcairn has a small population . The island 's 48 residents often serve in several capacities or jobs . Warren describes her many roles on her personal website : 
- Travelling with patients to New Zealand and Tahiti and taking up Nursing , Radio <unk> for the shore to ship <unk> from <unk> station and twice daily contact with Auckland international Radio telephone link , Working in our Co @-@ op store , Council member for many years as well as being the Governors appointee member to council a few times , Becoming the first female Police & Immigration Officer for a few years . Lands Commission president , Lands court member , Bee keeper since 1978 . <unk> operator for <unk> Vault , <unk> wireless networking throughout <unk> , Duncan cleaner , Contract <unk> jobs , and many <unk> jobs <unk> Tourism and Entertainment . <unk> it became <unk> to me that what I enjoy most is my art . ( T ) his is getting pushed aside whilst I am working these time consuming no pay or low paid positions which was making me very tired and yes .... <unk> [ sic ] 
- Beginning in 1996 , Warren served as the island 's only police officer . However , since no one had been arrested on the island since the 1950s , her duties involved issuing driving licenses and <unk> visitors ' passports . Warren had no qualifications or formal training to be a police officer , and was given the job because everyone on the island had a " job " . The island 's jail was described as " the size of a garden shed and riddled with <unk> , " and its cells had been used to store building materials and <unk> . When the island came into the international spotlight due to a sexual abuse scandal , a law enforcement professional sent to the island criticized their practices , stating " It was <unk> obvious ... that their standard of policing was not really adequate . " 
- Warren was elected on 15 December 2004 to the Island Council . As a radio operator , she broadcasts under the call sign <unk> . 
- 
- = = = Pitcairn 's sexual abuse trial = = = 
- 
- Warren was an outspoken critic of accusations that the island 's girls had been sexually abused when young , and the prosecution of a selected number of Pitcairn 's male residents . She claimed that young girls on Pitcairn <unk> became sexually active after age 12 , a practice of <unk> sex that had been accepted as a Polynesian tradition since the settlement of the island in 1790 . One resident , Olive Christian , said of her girlhood , " We all thought sex was like food on the table . " 
- Many Pitcairn Island men blamed the British police for persuading the women involved to press charges . Some of the women agreed , and advocated a conspiracy theory that the trial was , in Warren 's words , " a British plot to jail the [ community 's ] able @-@ bodied men and ' close ' the island " , and that the British officials " picked on all the viable young men , the ones who are the backbone of this place . " 
- A majority of the island 's residents denied or <unk> the allegations . During the trial , Warren circulated a poem titled " Is Seven a Lucky Number ? " that criticized the British government and lawyers ' attempts to impose British law against their island 's traditions . 
- As the case wore on , Warren reflected that " the bottom had fallen out of our world … We lost our trust for each other . " Her brother , Jay Warren , who was accused of " indecent assault " , was the only defendant among the seven accused to be acquitted on 24 October 2004 . 
- Warren was convicted of assault during a drunken disagreement with another resident when the two were angered by tensions over the sexual abuse matter . The case was <unk> by a Crown prosecutor and tried by a New Zealand magistrate . Warren was fined NZ $ 60 . The case cost the British government NZ $ 40 @,@ 000 to <unk> . 
- 
- = = Works = = 
- 
- 1986 : A Taste of Pitcairn : The First Pitcairn Island <unk> ( cookbook ) 
- 2005 : A Taste of Pitcairn : The First Pitcairn Island <unk> ( cookbook ) ( updated edition ) 
- 2008 : Mi Base side orn Pitcairn ( " My Favourite Place on Pitcairn " ) , with 6 children on Pitcairn 
- 
- 
- = 2nd Battalion 9th Marines = 
- 
- 2nd Battalion , 9th Marines ( 2 / 9 ) was an infantry battalion of the United States Marine Corps . Formed during World War I , the unit played an instrumental role in the defeat of the Japanese forces in the Battles of Guam and Iwo Jima during the World War II . The battalion distinguished itself in the defense of Khe Sanh during the Vietnam War , and later participated in an ill <unk> invasion of Koh Tang Island in Southeast Asia , with the intention of rescuing the crew of the SS Mayaguez . During Operation Desert Storm , the battalion served as the lead battalion for the III Marine Expeditionary Force ( III <unk> ) . 
- 2 / 9 also participated in various humanitarian missions . The battalion helped evacuate Americans from Northern China during the Chinese Civil War and in various occasions participated in providing relief to the victims of <unk> . In 1992 , the battalion participated in Operation Restore Hope in Somalia . 
- 2nd Battalion 9th Marines served until September 2 , 1994 , when it was deactivated to make room for one of three light armor reconnaissance battalions . It was part of the 9th Marine Regiment and the 3rd Marine Division . On July 13 , 2007 , 2nd Battalion 9th Marines was re @-@ activated again as part of the 6th Marine Regiment and replaced the Anti @-@ Terrorism Battalion ( <unk> ) . On April 1 , 2015 it was deactivated as part of a post @-@ war <unk> . 
- 
- = = Battalion composition = = 
- 
- A Battalion in the Marine Corps is headed by the Battalion Commander , usually a Lieutenant Colonel and sometimes a Colonel , his staff , headquarters , and the Battalion Sergeant Major . It usually consists of 3 @-@ 5 companies , with a total of 300 to 1 @,@ 200 Marines . 2nd Battalion 9th Marines comprises a Headquarters & Service ( H & S ) Company , Weapons Company and three Infantry Companies : Echo , Fox , and Golf . During the Vietnam war the Battalion comprised Headquarters & Service ( H & S ) Company , and Four Infantry Companies Echo , Fox , Golf , and Hotel . Each Company had its own weapons platoon with 3 squads of M 60 machine guns ( 7 @.@ 62 ) and 60 mm mortars and <unk> 3 @.@ 5 inch Rocket <unk> ( super <unk> ) or later <unk> . 
- 
- = = History = = 
- 
- 
- = = = Early years = = = 
- 
- With the advent of World War I , the United States expanded the Armed Forces , to include the United States Marine Corps . The 2nd Battalion , 9th Marines ( also known as 2 / 9 ) was created and activated on November 20 , 1917 at <unk> , Virginia . During this period , there was turmoil in Cuba 's sugar producing regions . American companies operated the island 's sugar industry , which was vital to the economy of the United States . The battalion 's first mission was to keep order in the island and once this was accomplished , it was reassigned . There were rumors that German agents were going to disrupt Mexican oil shipments to Texas . The battalion was sent to Texas to safeguard these shipments . 2 / 9 was disbanded after the war on April 25 , 1919 only to be reactivated in 1925 . The battalion 's main objective was to train reserve Marines and its headquarters was now transferred to Kansas City , Missouri with two companies stationed at St. Louis . 2 / 9 was once again disbanded in 1937 . 
- 
- = = = World War II = = = 
- 
- In April 1942 , five months after the Japanese attack on Pearl Harbor , the unit was activated as part of the 2nd Marine Division . The headquarters was at Camp Elliot in San Diego , California , where it underwent intensive amphibious training . Before being reassigned to the 3rd Marine Division , the unit was assigned to <unk> Corps , Pacific Fleet . The regiment was sent to <unk> on July 1943 to relieve the 1st Marine Division . 2nd Battalion , 9th Marines participated in the <unk> campaign of the Solomon Islands in the latter part of 1943 . On July 21 , 1944 , 2 / 9 participated in the invasion of Guam . The Japanese forces staged seven counterattacks , however the Marines prevailed despite the fact that they suffered over 50 % casualties . It was during this initial battle that one Marine , Captain Louis H. Wilson Jr . , ( who would in the future become a Commandant of the Marine Corps ) earned the Medal of Honor . 
- The 2nd Battalion , 9th Marines were part of the reserve forces for the Battle of Iwo Jima and were committed to the action five days after D @-@ Day . Among the Marines who distinguished themselves on Iwo Jima was Private Wilson D. Watson who received the Medal of Honor . After the island was secured , the unit was sent back to Guam where they underwent training for a possible invasion of the Japanese mainland . The invasion never occurred since hostilities between Japan and the United States came to an end . The 2nd Battalion , 9th Marines was sent to Camp <unk> where , in December 1945 , it was once again disbanded . 
- 
- = = = Post World War II = = = 
- 
- In November 1948 , 2nd Battalion , 9th Marines was again reestablished . The final stages of fighting between the Nationalists and the Communists , in the Chinese Civil War ( also known as the War of Liberation ) , occurred between 1945 and 1950 . When the fighting escalated , the 2nd Battalion , 9th Marines were ordered to Northern China to evacuate all Americans . This mission was accomplished by March 1949 and after which the battalion was sent to Camp Lejeune , North Carolina and was renamed as the 3rd Battalion , 6th Marine . 
- On June 25 , 1950 , war broke out between the provisional governments of North and South Korea as they competed for control over the Korean peninsula . North Korea was supported by the People 's Volunteer Army ( <unk> ) of the People 's Republic of China and South Korea by the allies under the aegis of the United Nations which included the United States . The conflict is known as the Korean War . The 1st Marine Division was sent in and saw action in the Battle of <unk> Reservoir . In 1952 , 2nd Battalion , 9th Marines was reactivated and underwent training in Japan with the intention of participating in the conflict as part of the 3rd Marine Division . However an armistice was signed on July 27 , 1953 , and the unit did not deploy to Korea . Even though 2nd Battalion , 9th Marines did not actively participate in the conflict , the unit was awarded the Korean Service <unk> and National Defense Service Medal <unk> . The 3rd Marine Divisions headquarters was moved to Okinawa in 1955 and in 1959 the 2nd Battalion , 9th Marines returned to the 1st Marine Division . In 1960 , the battalion returned to the 3rd Marine Division in Okinawa . 
- 
- = = = Vietnam War = = = 
- 
- The Vietnam War , was a conflict between the Democratic Republic of Vietnam ( <unk> , <unk> or North Vietnam ) and the Republic of Vietnam ( <unk> or South Vietnam ) , which eventually involved their respective allies . In 1959 , the United States sent military advisors to train the Army of the Republic of Vietnam . By 1965 , there were 25 @,@ 000 military advisors in South Vietnam and on March 8 , 1965 , the United States Marines became the first US combat troops to land in South Vietnam , with a force of 3 @,@ 500 . 
- The <unk> Marine Division began operating in Vietnam when on March 6 , 1965 , they opened a Marine <unk> at the Da <unk> Air Base . On July 4 , 1965 , 2nd Battalion , 9th Marines were ordered to Vietnam from Okinawa . 2nd Battalion , 9th Marines fought battles in or around <unk> , <unk> , <unk> <unk> , <unk> <unk> , Camp Carroll , Cam <unk> , Con <unk> , Than Cam Son , Quảng <unk> , <unk> <unk> , <unk> Combat Base and what is considered by many as their most vicious engagement , the Battle of Khe Sanh . 
- In September 1962 , U.S. military forces constructed an airstrip outside the town of Khe Sanh which became known as the Khe Sanh Combat Base . In 1965 the U.S. Special Forces constructed a base next to it . The base 's defense was codenamed Operation Scotland and manned by the 2nd Battalion , 9th Marines from 1967 . It was used as a staging ground for a number of attacks on North Vietnamese ( <unk> ) troop movements down the Ho Chi Minh Trail . 
- From April to June 1966 , the 2 / 9 Marines were caught in crossfire of the Buddhist Uprising , when much of the forces of I Corps rebelled against Prime Minister and Air Marshal Nguyen Cao <unk> , the head of the ruling junta , who had dismissed their commander , General Nguyen <unk> Thi . 
- In April and May 1967 various " Hill <unk> " on Hills <unk> , 881 North and 881 South between the 2 / 9 Marines and <unk> occurred . In January 1968 , Khe Sanh Combat Base came under heavy attack in what is known as the Battle of Khe Sanh . The main objective of the Communists was to draw off American troops into the countryside in preparation for the <unk> Offensive . Despite being outnumbered , 2nd Battalion , 9th Marines held their ground and the North Vietnamese were driven off of the area around after experiencing heavy casualties by heavy aerial bombardments by B @-@ <unk> . 
- From January 22 to March 18 , 1969 , 2 / 9 participated in Operation <unk> Canyon which was a sweep of the A <unk> Valley and the last major offensive by the Marine Corps in Vietnam . 
- In August 1969 , 2nd Battalion , 9th Marines was ordered to return to Camp <unk> , Okinawa . During this period the unit was assigned to sea duty in and around the waters of Vietnam and continued to receive combat training at Camp Fuji , Japan and Subic Bay in the Philippines . 
- 
- = = = = Mayaguez Incident = = = = 
- 
- On May 12 , 1975 , barely two weeks after the fall of Saigon , Khmer Rouge forces seized a U.S. flagged merchant ship , the SS Mayaguez in recognized international sea lanes claimed as territorial waters by Cambodia and removed its crew for questioning . The Khmer Rouge naval forces used abandoned US Navy " Swift Boats " in the seizure of the U.S. container ship . 
- Calling the seizure " piracy " , President Ford ordered a military response to retake the ship and its 39 @-@ man crew , mistakenly thought to be on Koh Tang Island . On May 13 , two A @-@ <unk> <unk> saw the 39 man crew board a fishing boat and saw people disembarking fishing boats at Koh Tang Island . They assumed that the Mayaguez crew was on the island . Elements of the 1st Battalion , 4th Marines , the 2nd Battalion , 4th Marines , and the 2nd Battalion , 9th Marines , were flown to an advanced staging of a joint US Task Force . On May 14 , the Marines from Company D , 1st Battalion , 4th Marines boarded the Mayaguez only to find it deserted and raised the American flag . A Thai fishing boat with a Thai crew and the 39 crew members of the SS Mayaguez which had been set free , approached the USS Wilson . 
- 
- = = = = Battle of Koh Tang Island = = = = 
- 
- 2nd Battalion , 9th Marines landed on Koh Tang Island where the crew of the SS Mayaguez was believed to be held , they were unaware that the crew was already in American hands . The Marines and the CH @-@ 53 helicopters which transported them , were attacked by the Khmer Rouge with machine guns , mortars , and rocket propelled grenade launchers in what became known as the Battle of Koh Tang Island . When the battalion received word of the safe arrival of the Mayaguez crew on the USS Wilson the Marines planned their withdrawal once they received orders from the U.S. Joint Chiefs of Staff to that effect . After the last helicopter left , a head count showed that 3 Marines were left behind on the island . They were : <unk> Gary Hall , <unk> . Joseph <unk> , and <unk> Danny Marshall from E CO , 2nd Battalion , 9th Marines . The three Marines were captured by the Khmer Rouge within a few days , tortured , executed , and buried in a common , unmarked grave on Koh Tang Island . 
- The <unk> incident with the Khmer Rouge , which ended on May 15 , 1975 , marked the last official battle of the 2nd Battalion , 9th Marines in the Vietnam War . The unit deployment program was put into practice in February 1979 , and 2nd Battalion , 9th Marines became the first battalion to <unk> to the United States . A total of 18 Marines were killed on the last day of the SS Mayaguez rescue operation . They are the last Marines listed on the timeline of the Vietnam Veterans Memorial located in Washington , D.C. 
- 
- = = = Post Vietnam era = = = 
- 
- 
- = = = = Operations Desert Shield & Desert Storm = = = = 
- 
- Before the August 2 , 1990 , invasion of Kuwait by Saddam Hussein 's Iraqi forces , 2nd Battalion , 9th Marines was deployed to Okinawa , Japan as part of the Unit <unk> Program . In October 1990 the unit deployed to the Republic of the Philippines where it became the Ground Combat <unk> of Marine Air @-@ Ground Task Force ( <unk> ) 4 @-@ 90 . On November 1990 , the island of <unk> , a Philippine <unk> , was devastated by a typhoon . Members of the battalion provided assistance during the disaster relief efforts . The Battalion remained in the Philippines until April 1991 when it returned to Okinawa , Japan . Subsequently the Battalion returned to Camp <unk> in August 1991 . 
- 
- = = = = Operation " Restore Hope " = = = = 
- 
- The 5th Marine Regiment designated 2 / 9 to participate in the Marine Expeditionary Unit deployment cycle during November 1991 . With the successful culmination of the Special Operations <unk> Exercise ( <unk> ) , 2nd Battalion , 9th Marines became the designated Battalion Landing Team ( <unk> ) for the 15th Marine Expeditionary Unit ( 15th <unk> ) . 
- During this period , there were two main factions in the Republic of Somalia who provoked an all out war which became known as the <unk> Civil War . One of the factions was led by Ali Mahdi Mohamed , who became president ; and the other , by the warlord Mohammed Farah Aidid . 
- In 1991 , the United States initiated Operation Provide Relief ( UNOSOM I ) which was part of a United Nations ( UN ) endorsed effort called The Unified Task Force ( <unk> ) , to provide humanitarian relief . In August 1992 , President George H. Bush sent , 25 @,@ 000 US troops ( mostly US Marines from I <unk> ) to the Republic of Somalia and the mission was renamed Operation Restore Hope , also known as UNOSOM II . Its main objectives were to provide humanitarian relief , initiate ' nation building ' , <unk> the various factions , restore law and order , help the people set up a representative government , and restore the infrastructure . 
- That same month , 2nd Battalion , 9th Marines was deployed to Somalia . <unk> 2 / 9 's mission as the lead unit , was to secure the port and airfield in Mogadishu which allowed the rapid build @-@ up of forces in @-@ country . The mission was accomplished between December 9 , 1992 and February 1993 . The <unk> referred to Marines of 2nd Battalion , 9th Marines as The Black Boots . On January 30 , 1993 , a Marine patrol was ambushed in Mogadishu by <unk> faithful to warlord Mohammed Farah Aidid bringing about casualties . 2 / 9 remained in Somalia until April 1993 when they returned to Camp <unk> . The battalions next two deployments were to Fort Sherman , Panama during one of which they participated in the <unk> anti @-@ tank missile evaluation program . On September 2 , 1994 , 2nd Battalion 9th Marines was deactivated and redesignated 2nd Battalion 4th Marines . 
- 
- = = = 2000 @-@ present = = = 
- 
- On December 7 , 2006 , Headquarters Marine Corps released a message stating that 2nd Battalion 9th Marines would be reactivated during 2007 as part of the continuing Global War on Terror . 2nd Battalion 9th Marines was re @-@ activated on July 13 , 2007 and replaced the Anti @-@ Terrorism Battalion ( <unk> ) . In September 2008 , Marines and Sailors from 2 / 9 deployed to Al <unk> Province in support of Operation Iraqi Freedom . They were based in the city of <unk> and returned in April 2009 without any Marines or Sailors killed in action . July 2010 Marines and Sailors from 2 / 9 deployed to <unk> , <unk> Province , Afghanistan in support of Operation Enduring Freedom . In December 2010 Echo Company from 2 / 9 were attached to 3 / 5 in <unk> , Afghanistan where they earned the notorious nickname of " Green Hats . " They returned February 2011 . They redeployed back to <unk> December 2011 and returned July 2012 . Echo and Weapons companies deployed once more to Afghanistan from January through April 2013 , participating in combat operations out of Camp <unk> . On April 1 , 2015 the battalion was deactivated in a ceremony at Camp Lejeune . 
- 
- = = Medal of Honor recipients = = 
- 
- The Medal of Honor is the highest military decoration awarded by the United States . It is bestowed on a member of the United States armed forces who distinguishes himself or herself " ... <unk> by gallantry and <unk> at the risk of his life above and beyond the call of duty while engaged in an action against an enemy of the United States ... " . The following table contains the names of the men who were recipients of the Medal of Honor while serving in 2 / 9 . They are listed in accordance to the " Date of Action " in which the <unk> citation was made . † indicates that the Medal of Honor was awarded posthumously 
- 
- = = <unk> of the Marine Corps = = 
- 
- The Commandant of the United States Marine Corps is the highest @-@ ranking officer of the United States Marine Corps and a member of the Joint Chiefs of Staff , reporting to the Secretary of the Navy but not to the Chief of Naval Operations . Three Marines who served in 2nd Battalion 9th Marines became Commandant of the Marine Corps . They are listed in the table in accordance to the years in which they served as <unk> and their ranks are those which they held while serving in 2 / 9 , they were : 
- 
- = = Distinguished Marines = = 
- 
- Other Marines who served in the 2nd Battalion 9th Marines during their careers , who subsequently distinguished themselves by either becoming a general officer ( O @-@ 7 and above ) or recipients of the Medal of Honor while assigned to a different unit were : 
- 
- = = Other notable former members = = 
- 
- Bing West , served in the mortar platoon during the Vietnam War in 1965 . 
- 
- = = Unit awards = = 
- 
- A unit citation or commendation is an award bestowed upon an organization for the action cited . Members of the unit who participated in said actions are allowed to wear on their uniforms the awarded unit citation . 2 / 9 has been presented with the following awards : 
- 
- 
- = Directed acyclic graph = 
- 
- In mathematics and computer science , a directed acyclic graph ( DAG / <unk> / ) , is a finite directed graph with no directed cycles . That is , it consists of <unk> many vertices and edges , with each edge directed from one vertex to another , such that there is no way to start at any vertex v and follow a consistently @-@ directed sequence of edges that eventually loops back to v again . <unk> , a DAG is a directed graph that has a topological ordering , a sequence of the vertices such that every edge is directed from earlier to later in the sequence . 
- DAGs may be used to model many different kinds of information . The reachability relation in a DAG forms a partial order , and any finite partial order may be represented by a DAG using reachability . The program evaluation and review technique uses DAGs to model the milestones and activities of large human projects , and schedule these projects to use as little total time as possible . Scheduling of computer operations such as the updates to a spreadsheet or the compilation operations in a <unk> can also be performed using topological orderings of DAGs . <unk> logic blocks in electronic circuit design , and the operations in <unk> programming languages , involve acyclic networks of processing elements . DAGs can also represent collections of events and their influence on each other , either in a <unk> structure such as a Bayesian network or as a record of historical data such as family trees or the version histories of distributed revision control systems . DAGs can also be used as a compact representation of sequence data , such as the directed acyclic word graph representation of a collection of strings , or the binary decision diagram representation of sequences of binary choices . 
- Important polynomial time computational problems on DAGs include topological sorting ( finding a topological ordering ) , construction of the transitive closure and transitive reduction ( the largest and smallest DAGs with the same reachability relation , respectively ) , and the closure problem , in which the goal is to find a minimum @-@ weight subset of vertices with no edges connecting them to the rest of the graph . <unk> a directed graph with cycles into a DAG by <unk> as few vertices or edges as possible ( the feedback vertex set and feedback edge set problem , respectively ) is NP @-@ hard , but any directed graph can be made into a DAG ( its condensation ) by contracting each strongly connected component into a single <unk> . The problems of finding shortest paths and longest paths can be solved on DAGs in linear time , in contrast to arbitrary graphs for which shortest path algorithms are slower and longest path problems are NP @-@ hard . 
- The corresponding concept for undirected graphs is a forest , an undirected graph without cycles . <unk> an orientation for a forest produces a special kind of directed acyclic graph called a polytree . However there are many other kinds of directed acyclic graph that are not formed by <unk> the edges of an undirected acyclic graph . Moreover , every undirected graph has an acyclic orientation , an assignment of a direction for its edges that makes it into a directed acyclic graph . To emphasize that DAGs are not the same thing as directed versions of undirected acyclic graphs , some authors call them acyclic directed graphs or acyclic <unk> . 
- 
- = = Definitions = = 
- 
- A graph is formed by a collection of vertices and edges , where the vertices are <unk> objects that are connected in pairs by edges . In the case of a directed graph , each edge has an orientation , from one vertex to another vertex . A path in a directed graph can be described by a sequence of edges having the property that the ending vertex of each edge in the sequence is the same as the starting vertex of the next edge in the sequence ; a path forms a cycle if the starting vertex of its first edge equals the ending vertex of its last edge . A directed acyclic graph is a directed graph that has no cycles . 
- A vertex v of a directed graph is said to be reachable from another vertex u when there exists a path that starts at u and ends at v. As a special case , every vertex is considered to be reachable from itself ( by a path with zero edges ) . If a vertex can reach itself via a <unk> path ( a path with one or more edges ) , then that path is a cycle , so another way to define directed acyclic graphs is that they are the graphs in which no vertex can reach itself via a <unk> path . 
- A topological ordering of a directed graph is an ordering of its vertices into a sequence , such that for every edge the start vertex of the edge occurs earlier in the sequence than the ending vertex of the edge . A graph that has a topological ordering cannot have any cycles , because the edge into the earliest vertex of a cycle would have to be oriented the wrong way . Therefore , every graph with a topological ordering is acyclic . Conversely , every directed acyclic graph has a topological ordering . Therefore , this property can be used as an alternative definition of the directed acyclic graphs : they are exactly the graphs that have topological orderings . 
- 
- = = <unk> properties = = 
- 
- 
- = = = <unk> , transitive closure , and transitive reduction = = = 
- 
- The reachability relationship in any directed acyclic graph can be formalized as a partial order ≤ on the vertices of the DAG . In this partial order , two vertices u and v are ordered as u ≤ v exactly when there exists a directed path from u to v in the DAG ; that is , when v is reachable from u . However , different DAGs may give rise to the same reachability relation and the same partial order . For example , the DAG with two edges a → b and b → c has the same reachability relation as the graph with three edges a → b , b → c , and a → c . Both of these <unk> produce the same partial order , in which the vertices are ordered as a ≤ b ≤ c . 
- If G is a DAG , its transitive closure is the graph with the most edges that represents the same reachability relation . It has an edge u → v whenever u can reach v. That is , it has an edge for every related pair u ≤ v of distinct elements in the reachability relation of G , and may therefore be thought of as a direct translation of the reachability relation ≤ into graph @-@ <unk> terms . The same method of translating partial orders into DAGs works more generally : for every finite partially ordered set ( S , ≤ ) , the graph that has a vertex for each member of S and an edge for each pair of elements related by u ≤ v is automatically a <unk> closed DAG , and has ( S , ≤ ) as its reachability relation . In this way , every finite partially ordered set can be represented as the reachability relation of a DAG . 
- The transitive reduction of a DAG G is the graph with the fewest edges that represents the same reachability relation as G. It is a subgraph of G , formed by discarding the edges u → v for which G also contains a longer path connecting the same two vertices . Like the transitive closure , the transitive reduction is uniquely defined for DAGs . In contrast , for a directed graph that is not acyclic , there can be more than one minimal subgraph with the same reachability relation . 
- If a DAG G has a reachability relation described by the partial order ≤ , then the transitive reduction of G is a subgraph of G that has an edge u → v for every pair in the covering relation of ≤ . <unk> reductions are useful in <unk> the partial orders they represent , because they have fewer edges than other graphs representing the same orders and therefore lead to simpler graph drawings . A <unk> diagram of a partial order is a drawing of the transitive reduction in which the orientation of each edge is shown by placing the starting vertex of the edge in a lower position than its ending vertex . 
- 
- = = = Topological ordering = = = 
- 
- Every directed acyclic graph has a topological ordering , an ordering of the vertices such that the starting endpoint of every edge occurs earlier in the ordering than the ending endpoint of the edge . The existence of such an ordering can be used to characterize DAGs : a directed graph is a DAG if and only if it has a topological ordering . In general , this ordering is not unique ; a DAG has a unique topological ordering if and only if it has a directed path containing all the vertices , in which case the ordering is the same as the order in which the vertices appear in the path . 
- The family of topological orderings of a DAG is the same as the family of linear extensions of the reachability relation for the DAG , so any two graphs representing the same partial order have the same set of topological orders . 
- 
- = = = <unk> <unk> = = = 
- 
- The graph <unk> problem of counting directed acyclic graphs was studied by Robinson ( 1973 ) . The number of DAGs on n labeled vertices , for n = 0 , 1 , 2 , 3 , … ( without restrictions on the order in which these numbers appear in a topological ordering of the DAG ) is 
- 1 , 1 , 3 , 25 , <unk> , <unk> , <unk> , … ( sequence <unk> in the <unk> ) . 
- These numbers may be computed by the recurrence relation 
- <formula> 
- Eric W. <unk> conjectured , and McKay et al . ( 2004 ) proved , that the same numbers count the ( 0 @,@ 1 ) matrices for which all <unk> are positive real numbers . The proof is <unk> : a matrix A is an <unk> matrix of a DAG if and only if A + I is a ( 0 @,@ 1 ) matrix with all <unk> positive , where I denotes the identity matrix . Because a DAG cannot have self @-@ loops , its <unk> matrix must have a zero diagonal , so adding I preserves the property that all matrix <unk> are 0 or 1 . 
- 
- = = = Related families of graphs = = = 
- 
- A polytree is a directed graph formed by <unk> the edges of a free tree . Every polytree is a DAG . In particular , this is true of the <unk> formed by directing all edges outwards from the roots of a tree . 
- A <unk> ( also called a strongly <unk> graph or a mangrove ) is a directed graph in which there is at most one directed path ( in either direction ) between any two vertices ; <unk> , it is a DAG in which , for every vertex v , the subgraph reachable from v forms a tree . 
- 
- = = <unk> problems = = 
- 
- 
- = = = Topological sorting and recognition = = = 
- 
- Topological sorting is the <unk> problem of finding a topological ordering of a given DAG . It can be solved in linear time . Kahn 's algorithm for topological sorting builds the vertex ordering directly . It maintains a list of vertices that have no incoming edges from other vertices that have not already been included in the partially constructed topological ordering ; initially this list consists of the vertices with no incoming edges at all . Then , it repeatedly adds one vertex from this list to the end of the partially constructed topological ordering , and checks whether its neighbors should be added to the list . The algorithm terminates when all vertices have been processed in this way . Alternatively , a topological ordering may be constructed by reversing a <unk> numbering of a depth @-@ first search graph <unk> . 
- It is also possible to check whether a given directed graph is a DAG in linear time , either by attempting to find a topological ordering and then testing for each edge whether the resulting ordering is valid or alternatively , for some topological sorting algorithms , by <unk> that the algorithm successfully orders all the vertices without meeting an error condition . 
- 
- = = = Construction from cyclic graphs = = = 
- 
- Any undirected graph may be made into a DAG by choosing a total order for its vertices and directing every edge from the earlier endpoint in the order to the later endpoint . The resulting orientation of the edges is called an acyclic orientation . Different total orders may lead to the same acyclic orientation , so an n @-@ vertex graph can have fewer than n ! acyclic orientations . The number of acyclic orientations is equal to | <unk> ( − 1 ) | , where <unk> is the <unk> polynomial of the given graph . 
- Any directed graph may be made into a DAG by removing a feedback vertex set or a feedback arc set , a set of vertices or edges ( respectively ) that touches all cycles . However , the smallest such set is NP @-@ hard to find . An arbitrary directed graph may also be transformed into a DAG , called its condensation , by contracting each of its strongly connected components into a single <unk> . When the graph is already acyclic , its smallest feedback vertex sets and feedback arc sets are empty , and its condensation is the graph itself . 
- 
- = = = <unk> closure and transitive reduction = = = 
- 
- The transitive closure of a given DAG , with n vertices and m edges , may be constructed in time O ( <unk> ) by using either breadth @-@ first search or depth @-@ first search to test reachability from each vertex . Alternatively , it can be solved in time O ( <unk> ) where <unk> < 2 @.@ 373 is the exponent for fast matrix <unk> algorithms ; this is a theoretical improvement over the O ( <unk> ) bound for dense graphs . 
- In all of these transitive closure algorithms , it is possible to distinguish pairs of vertices that are reachable by at least one path of length two or more from pairs that can only be connected by a length @-@ one path . The transitive reduction consists of the edges that form length @-@ one paths that are the only paths connecting their <unk> . Therefore , the transitive reduction can be constructed in the same asymptotic time bounds as the transitive closure . 
- 
- = = = <unk> problem = = = 
- 
- The closure problem takes as input a directed acyclic graph with weights on its vertices and seeks the minimum ( or maximum ) weight of a closure , a set of vertices with no outgoing edges . ( The problem may be formulated for directed graphs without the assumption of <unk> , but with no greater <unk> , because in this case it is equivalent to the same problem on the condensation of the graph . ) It may be solved in polynomial time using a reduction to the maximum flow problem . 
- 
- = = = Path algorithms = = = 
- 
- Some algorithms become simpler when used on DAGs instead of general graphs , based on the principle of topological ordering . For example , it is possible to find shortest paths and longest paths from a given starting vertex in DAGs in linear time by processing the vertices in a topological order , and <unk> the path length for each vertex to be the minimum or maximum length obtained via any of its incoming edges . In contrast , for arbitrary graphs the shortest path may require slower algorithms such as <unk> 's algorithm or the <unk> – Ford algorithm , and longest paths in arbitrary graphs are NP @-@ hard to find . 
- 
- = = Applications = = 
- 
- 
- = = = Scheduling = = = 
- 
- Directed acyclic graphs representations of partial orderings have many applications in scheduling for systems of tasks with ordering constraints . An important class of problems of this type concern collections of objects that need to be updated , such as the cells of a spreadsheet after one of the cells has been changed , or the object files of a piece of computer software after its source code has been changed . In this context , a dependency graph is a graph that has a vertex for each object to be updated , and an edge connecting two objects whenever one of them needs to be updated earlier than the other . A cycle in this graph is called a circular dependency , and is generally not allowed , because there would be no way to consistently schedule the tasks involved in the cycle . <unk> graphs without circular <unk> form DAGs . 
- For instance , when one cell of a spreadsheet changes , it is necessary to <unk> the values of other cells that depend directly or indirectly on the changed cell . For this problem , the tasks to be scheduled are the <unk> of the values of individual cells of the spreadsheet . Dependencies arise when an expression in one cell uses a value from another cell . In such a case , the value that is used must be <unk> earlier than the expression that uses it . <unk> ordering the dependency graph , and using this topological order to schedule the cell updates , allows the whole spreadsheet to be updated with only a single evaluation per cell . Similar problems of task ordering arise in <unk> for program compilation and instruction scheduling for low @-@ level computer program <unk> . 
- A somewhat different DAG @-@ based formulation of scheduling constraints is used by the program evaluation and review technique ( <unk> ) , a method for management of large human projects that was one of the first applications of DAGs . In this method , the vertices of a DAG represent milestones of a project rather than specific tasks to be performed . Instead , a task or activity is represented by an edge of a DAG , connecting two milestones that mark the beginning and completion of the task . Each such edge is labeled with an estimate for the amount of time that it will take a team of workers to perform the task . The longest path in this DAG represents the critical path of the project , the one that controls the total time for the project . Individual milestones can be scheduled according to the lengths of the longest paths ending at their vertices . 
- 
- = = = Data processing networks = = = 
- 
- A directed acyclic graph may be used to represent a network of processing elements . In this representation , data enters a processing element through its incoming edges and leaves the element through its outgoing edges . 
- For instance , in electronic circuit design , static <unk> logic blocks can be represented as an acyclic system of logic gates that <unk> a function of an input , where the input and output of the function are represented as individual bits . In general , the output of these blocks cannot be used as the input unless it is captured by a register or state element which maintains its acyclic properties . Electronic circuit <unk> either on paper or in a database are a form of directed acyclic graphs using instances or components to form a directed reference to a lower level component . Electronic circuits themselves are not necessarily acyclic or directed . 
- <unk> programming languages describe systems of operations on data streams , and the connections between the <unk> of some operations and the inputs of others . These languages can be convenient for describing repetitive data processing tasks , in which the same <unk> @-@ connected collection of operations is applied to many data items . They can be executed as a parallel algorithm in which each operation is performed by a parallel process as soon as another set of inputs becomes available to it . 
- In <unk> , straight line code ( that is , sequences of statements without loops or conditional branches ) may be represented by a DAG describing the inputs and <unk> of each of the arithmetic operations performed within the code . This representation allows the <unk> to perform common <unk> elimination efficiently . 
- 
- = = = <unk> structures = = = 
- 
- <unk> that have vertices representing events , and edges representing causal relations between events , are often acyclic – arranging the vertices in linear order of time , all arrows point in the same direction as time , from parent to child ( due to <unk> affecting the future , not the past ) , and thus have no loops . 
- For instance , a Bayesian network represents a system of <unk> events as vertices in a directed acyclic graph , in which the likelihood of an event may be calculated from the <unk> of its predecessors in the DAG . In this context , the moral graph of a DAG is the undirected graph created by adding an ( undirected ) edge between all parents of the same vertex ( sometimes called marrying ) , and then replacing all directed edges by undirected edges . 
- Another type of graph with a similar causal structure is an influence diagram , the vertices of which represent either decisions to be made or unknown information , and the edges of which represent causal influences from one vertex to another . In <unk> , for instance , these <unk> are often used to estimate the expected value of different choices for intervention . 
- 
- = = = <unk> and version history = = = 
- 
- Family trees may be seen as directed acyclic graphs , with a vertex for each family member and an edge for each parent @-@ child relationship . Despite the name , these graphs are not necessarily trees because of the possibility of marriages between relatives ( so a child has a common ancestor on both the mother 's and father 's side ) causing pedigree collapse . The graphs of <unk> descent ( " mother " relationships between women ) and <unk> descent ( " father " relationships between men ) are trees within this graph . Because no one can become their own ancestor , family trees are acyclic . 
- For the same reason , the version history of a distributed revision control system generally has the structure of a directed acyclic graph , in which there is a vertex for each revision and an edge connecting pairs of revisions that were directly derived from each other . These are not trees in general due to <unk> . 
- In many randomized algorithms in computational geometry , the algorithm maintains a history DAG representing the version history of a <unk> structure over the course of a sequence of changes to the structure . For instance in a randomized incremental algorithm for Delaunay triangulation , the triangulation changes by replacing one triangle by three smaller triangles when each point is added , and by " flip " operations that replace pairs of triangles by a different pair of triangles . The history DAG for this algorithm has a vertex for each triangle constructed as part of the algorithm , and edges from each triangle to the two or three other triangles that replace it . This structure allows point location queries to be answered efficiently : to find the location of a query point q in the Delaunay triangulation , follow a path in the history DAG , at each step moving to the replacement triangle that contains q . The final triangle reached in this path must be the Delaunay triangle that contains q . 
- 
- = = = Data compression = = = 
- 
- Directed acyclic graphs may also be used as a compact representation of a collection of sequences . In this type of application , one finds a DAG in which the paths form the given sequences . When many of the sequences share the same <unk> , these shared <unk> can be represented by a shared part of the DAG , allowing the representation to use less space than it would take to list out all of the sequences separately . For example , the directed acyclic word graph is a data structure in computer science formed by a directed acyclic graph with a single source and with edges labeled by letters or symbols ; the paths from the source to the sinks in this graph represent a set of strings , such as English words . Any set of sequences can be represented as paths in a tree , by forming a tree vertex for every prefix of a sequence and making the parent of one of these vertices represent the sequence with one fewer element ; the tree formed in this way for a set of strings is called a <unk> . A directed acyclic word graph saves space over a <unk> by allowing paths to <unk> and rejoin , so that a set of words with the same possible <unk> can be represented by a single tree vertex . 
- The same idea of using a DAG to represent a family of paths occurs in the binary decision diagram , a DAG @-@ based data structure for representing binary functions . In a binary decision diagram , each non @-@ sink vertex is labeled by the name of a binary variable , and each sink and each edge is labeled by a 0 or 1 . The function value for any truth assignment to the variables is the value at the sink found by following a path , starting from the single source vertex , that at each non @-@ sink vertex follows the outgoing edge labeled with the value of that vertex 's variable . Just as directed acyclic word graphs can be viewed as a compressed form of tries , binary decision <unk> can be viewed as compressed forms of decision trees that save space by allowing paths to rejoin when they agree on the results of all remaining decisions . 
- 
- 
- = August ( Fringe ) = 
- 
- " August " is the eighth episode of the American science fiction drama television series Fringe 's second season . The episode was co @-@ written by J. H. Wyman and Jeff Pinkner , and directed by Dennis Smith . The episode marked the first prominent appearance of more than one Observer , and follows the events that occur when one of them saves a girl from boarding a soon @-@ to @-@ be crashing plane . It featured two new guest stars , Peter Woodward and Eugene <unk> . 
- The episode first aired on November 19 , 2009 in the United States to mostly positive reviews , with many critics praising the new revelations about the Observers . An estimated 5 @.@ 746 million viewers watched the episode on its first broadcast . 
- 
- = = Plot = = 
- 
- An Observer , " August " ( Peter Woodward ) abducts Christine Hollis ( Jennifer <unk> ) , a 27 @-@ year @-@ old art student in Boston . Olivia Dunham ( Anna Torv ) and Peter Bishop ( Joshua Jackson ) learn of the events and realize the assailant matches the description of the Observer . However , after viewing video footage they find that it is not " September " ( Michael <unk> ) , the Observer known for observing Pattern @-@ related cases and who previously saved Peter and his father Walter ( John Noble ) in 1985 , indicating there is more than one Observer . Olivia is confused as to August 's motives , as Observers are known to only observe events and not interfere with them . After questioning Christine 's roommate , Olivia learns she was to take a flight to Rome , Italy . Peter finds a picture of Christine from her childhood before her parents were killed in the 1989 San Francisco earthquake , and sees August in the background , indicating he has been observing her entire life . The plane to Rome would later crash , killing everyone on board . Because of this , the team realize August in fact saved her life . 
- At Massive Dynamic , Brandon ( Ryan McDonald ) <unk> to the team that Observers are time travelers , as they have been documented during several major events in history . They write in an incomprehensible language , and the number of Observer sightings has increased over the past few months , leaving the team wondering why . Meanwhile , August visits the other Observers , who are not happy that he saved a girl who was supposed to die in the plane crash . To correct his mistake , they send Donald Long ( Paul <unk> ) , an assassin affiliated with the Observers to correct August 's actions . In an attempt to save Christine , August sends a secret message to Walter . During the meeting , August reveals Christine will die because she is not important ; Walter tells August that he has to make her important . 
- The team are later made aware of Christine 's location at a <unk> outside the city . However , Donald is on the trail also . August attempts to stop him , but Donald shoots him several times , before Peter and Olivia arrive to kill the assassin . September picks up August and while driving , September asks August why he saved Christine . August reveals that he is experiencing ' feelings ' and that he loves Christine , and asks if she would be safe . September informs him Christine will stay alive because she is now important ; she was responsible for the death of an Observer . August cries before succumbing to his wounds . In the end , Olivia takes a day off to spend time with her niece , <unk> , at an amusement park . Two Observers watch the two , and they remark that everything is about to get " so hard " for Olivia . 
- 
- = = Production = = 
- 
- " August " was co @-@ written by co @-@ <unk> J. H. Wyman and Jeff Pinkner , while being directed by <unk> veteran Dennis Smith . They began shooting the episode during the second half of September 2009 , right after wrapping production on " Of Human Action " . <unk> producer Josh Singer later noted how " August " helped them write the rest of the season , " There was one episode , ' August ' , which was all about the Observers , that I feel was mythology without being mythology . And somehow , in working that episode , we sort of found a new creative vein . And it 's not that that episode was necessarily so related to everything else we did . But I feel like it sort of opened us up in terms of how we were feeling about the year " . 
- Up to the point the episode " August " aired , only one character that was part of a group known as " Observers " had been introduced . In this episode , two more such individuals were revealed : August ( played by Peter Woodward ) and December ( played by Eugene <unk> ) . The creation of Observers was one of the first ideas the writers of Fringe thought of when developing the show in its early stages , when they asked each other , " What if there were these characters where all they did was watch ? " . Pinkner and Wyman thought of creating Observers by " looking for something ... iconic " that had the " quality of being invisible " . Though the Observer called September has appeared in every single Fringe episode in a brief cameo , he was first featured prominently in the fourth episode of the first season . The producers thought of unique characteristics for " somebody who wasn 't of our world , " evolving to the Observers having bald heads , no eyebrows , and " largely <unk> " senses , which led to a love for extremely hot <unk> . 
- Before the second season premiere aired in September 2009 , co @-@ creator <unk> Orci hinted that the audience was going to meet " many Observers " , and that in the season 's eighth episode , " You 're going to find out their role in the world , what they 're named after , and their connection to some of these characters . " Later on , when still leading up to the airing of " August " in another interview , Roberto Orci elaborated that " <unk> will be one of the things that they will be <unk> struggling with , actually . That was a fun one , because that one was one where you 're finally getting to pay off things you 've been setting up for a year . You finally get to open the toy box and really play with those toys " . Actor Michael <unk> , who plays September , commented in an interview that as a result of the episode , viewers would learn that the Observers " are not completely devoid of feelings , and are not incapable of being attached to people they 're observing " . He also expressed relief that his character was no longer the sole Observer on the show , joking that " it was nice to feel like I 'm not the only freak in town for once " . 
- Actress Anna Torv stated in an interview with MTV News that " August " was one of her favorite episodes because " We sort of get to meet the Observers . At the moment what we know about them , they ... seemingly just observe . That 's kind of it , but [ in ] the episode that we have coming up , we 're not sure if all they do is just observe " . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " August " premiered to an estimated 5 @.@ 746 million viewers in the United States , with a 2 @.@ 0 rating . The episode was down 9 % from the previous week 's episode " Of Human Action " , which had a rating of 2 @.@ 2 . 
- 
- = = = Reviews = = = 
- 
- MTV 's Josh <unk> thought the episode was " a touching , sad tale with a healthy mix of mythological advancement , " and couldn 't wait for more episodes about the Observers . Ramsey <unk> of IGN gave the episode 7 @.@ 6 / 10 , writing that despite the hype , the episode was " just plain average " . He did however appreciate the new revelations about the Observers . After <unk> the episode , Entertainment Weekly 's Ken Tucker graded the episode an <unk> , explaining Fringe had become " one of the fastest , <unk> , <unk> shows on television now " . Andrew Hanson from the Los Angeles Times called it a " good story <unk> with a lot of great details ... ' August ' showed a greater connection to the larger Fringe universe " . Noel Murray of The A.V. Club graded it with a B + , praising the " wonderfully moody " episode and how it was able to make the halting Observer dialogue work . Murray found some aspects lacking however , as he thought it was " a very sketchy episode , plot @-@ wise and mythology @-@ wise . Not a whole lot happened " . <unk> blogger io9 listed " August " as one of the " crucial " episodes new viewers must watch to get into the show . 
- 
- 
- = Ghost in the Shell ( 1995 film ) = 
- 
- Ghost in the Shell , known in Japan as Mobile Armored Riot Police : Ghost In The Shell ( 攻殻機動隊 <unk> IN THE <unk> , Kōkaku <unk> <unk> In <unk> <unk> ) , is a 1995 science fiction anime film based on manga of the same title by Masamune Shirow . The film was written by <unk> <unk> , directed by <unk> Oshii , animated by Production I.G , and starred the voices of <unk> Tanaka , <unk> <unk> , and <unk> <unk> . 
- Ghost in the Shell follows the hunt by the public @-@ security agency Section 9 for a mysterious hacker known as the Puppet Master . With the assistance of her team , Motoko Kusanagi tracks and finds their suspect , only to be drawn into a complex sequence of political intrigue and a cover @-@ up as to the identity and goals of the Puppet Master . 
- The overarching philosophical themes of the film include self @-@ identity in a technologically advanced world . The music , composed by Kenji Kawai , included an ancient Japanese language in a wedding song that serves as a key piece of music leading up to the climax of the movie . Widely considered one of the greatest anime films of all time , critics particularly praised its visuals , which at the time were the most effective synthesis of traditional cel animation and CG animation . It has served as inspiration for filmmakers such as the <unk> . 
- In 2004 , Oshii directed Ghost in the Shell 2 : Innocence , billed as a separate work and not a true sequel . In 2008 , Oshii released an updated version of the original film , Ghost in the Shell 2 @.@ 0 , that features new audio and updated 3D animation . A live @-@ action Hollywood Ghost in the Shell remake is scheduled for 2017 . 
- 
- = = Plot = = 
- 
- In <unk> , the world is interconnected by a vast electronic network that <unk> every aspect of life . Much of humanity has access to this network through cybernetic bodies , or " shells " , which possess their consciousness and can give them superhuman abilities . 
- Major Motoko Kusanagi , an assault @-@ team leader for the Public Security Section 9 , is assigned to capture an elusive hacker known as the Puppet Master . Her team , Batou and <unk> , use triangulation to seek out the Puppet Master . Their suspect is a <unk> who believes he is using a program obtained from a sympathetic man to illegally " ghost @-@ hack " his wife 's mind to find his daughter . Kusanagi and her team arrest him and the man who gave him the program , but discover that their memories were either erased or implanted : " ghost @-@ <unk> " by the Puppet Master . 
- A facility is <unk> and programmed to assemble a female cybernetic body . The body escapes but is hit by a truck ; Section 9 investigates and examines the body , which seems to have a human " ghost " inside — perhaps the Puppet Master himself . Officials from rival agency Section 6 visit Section 9 and explain that the body was made to lure the Puppet Master 's ghost and trap it inside . Kusanagi <unk> the conversation and decides to <unk> her consciousness from her current body and connect or " dive into " the body and face the Puppet Master 's ghost . Before she succeeds , the ghost activates the body . Section 6 storms Section 9 and <unk> the body . 
- The information from the body leads Section 9 to uncover the mysterious Project 2501 . Section 6 claims the project was created to catch the hacker , but it was initiated before his appearance . Section 9 speculates that the project itself created the Puppet Master , who then escaped , and Section 6 now wants him back . <unk> <unk> , head of Section 9 , suspects that the project and the Puppet Master are tools of the Ministry of Foreign Affairs . The escape might lead to the release of secrets that could embarrass Section 6 and the Ministry . 
- The getaway car carrying the Puppet Master meets another , and they split off . Batou stops the first car and realizes it is a decoy . Kusanagi follows the second car to an abandoned building , where she is ambushed by a spider @-@ like armored vehicle . Batou arrives in time to save the badly damaged Kusanagi . With Batou on guard , Kusanagi faces the body stolen by Section 6 . The Puppet Master reveals himself and explains that , under Project 2501 , he was created by Section 6 to hack ghosts for individuals and Section 6 . While wandering various networks , the Puppet Master became sentient and began to contemplate his existence ; it troubled him that he could not reproduce or die . He plans to merge with Kusanagi 's ghost to experience mortality ; Kusanagi would live on with his ghost . As he could not crack Section 6 's attack protection , he was forced to escape in a physical body . 
- Batou tries to <unk> the dive , but the Puppet Master ghost @-@ <unk> him . <unk> from Section 6 arrive with orders to destroy everyone inside to cover up Project 2501 . The Puppet Master <unk> their targeting systems . As he starts merging with Kusanagi , <unk> blow their heads off , along with Batou 's arm . 
- Kusanagi wakes up in a child @-@ sized <unk> body in Batou 's <unk> . Batou explains that her original body was destroyed in the fight ; he recovered her head and attached it to the new body . Kusanagi acknowledges she is now neither herself nor the Puppet Master , but a combination of both . Batou says he will always be there for her . She leaves the house and <unk> out over the city . 
- 
- = = Voice cast = = 
- 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- Director <unk> Oshii stated , " My <unk> told me that this story about a futuristic world carried an immediate message for our present world . I am also interested in computers through my own personal experience with them . I had the same feeling about <unk> and I thought it would be interesting to make a film that took place in the near future . There are only a few movies , even out of Hollywood , which clearly portray the influence and power of computers . I thought this theme would be more effectively conveyed through animation . " Oshii expanded on these thoughts in a later interview , noting that technology changes people and had become a part of the culture of Japan . He commented that his use of philosophy caused producers to become frustrated because of <unk> use of action scenes . Oshii also acknowledged that a movie with more action would sell better , but he continued to make these movies anyway . When Oshii went back to make changes to the original Ghost in the Shell to re @-@ release it as Ghost in the Shell 2 @.@ 0 , one of the reasons he gave was that the film did not resemble the sequel . He wanted to update the film to reflect changes in perspective . 
- 
- = = = Design = = = 
- 
- Hiroyuki <unk> , the character designer and key animation supervisor , designed a more mature and serious Motoko than Masamune Shirow 's original portrayal of the character in the manga . <unk> chose to depict a physically mature person to match Motoko 's mental age , instead of the youthful twenty @-@ something appearance in the manga . Motoko 's demeanor lacks the comedic facial expressions and rebellious nature depicted in the manga . 
- Oshii based the setting for Ghost in the Shell on Hong Kong . Oshii commented that his first thought to find an image of the future setting was an Asian city , but finding a suitable <unk> of the future would be impossible . Oshii chose to use the real streets of Hong Kong as his model . He also said that Hong Kong was the perfect subject and theme for the film with its countless signs and the <unk> of sounds . The film 's <unk> designer <unk> <unk> noted that while the film does not have a chosen setting , it is obviously based on Hong Kong because the city represented the theme of the film , the old and the new which exist in a strange relationship in an age of an information <unk> . Before shooting the film , the artists drew sketches that emphasized Hong Kong 's chaotic , confusing and overwhelming aspects . 
- The Hong Kong setting is alluded to by the scene wherein the characters are drinking San Miguel Beer a cultural staple of the East Asian <unk> . 
- 
- = = = Animation = = = 
- 
- Ghost in the Shell used a novel process called " digitally generated animation " ( DGA ) , which is a combination of cel animation , computer graphics ( CG ) , and audio that is entered as digital data . In 1995 , DGA was thought to be the future of animation , which mixed traditional animation with the emerging use of computer graphics , including digital cel work with visual displays . Editing was performed on an <unk> system of <unk> Technology , which was chosen because it was more versatile and less limiting than other methods and worked with the different types of media in a single environment . 
- The digital cel work included both original illustrations , compositions and manipulation with traditional cel animation to create a sense of depth and evoke emotion and feelings . <unk> as background , <unk> like a lens effect were used to create a sense of depth and motion , by <unk> the front background and making the far background out of focus throughout the shot . Ghost in the Shell used a unique lighting system in which light and darkness were integrated into the <unk> with attention to light and shadow sources instead of using contrast to control the light . <unk> <unk> , the art director , described this as " a very unusual lighting technique . " 
- Some special effects , like Motoko 's " <unk> @-@ optical camouflage " , were rendered through the use of <unk> software . The process uses a single illustration and manipulates the image as necessary to produce distortions for effect in combination with a background without altering the original illustration . The effect is re @-@ added back into the shot to complete the scene . While the visual displays used in the film were technically simple to create , the appearance of the displays underwent numerous revisions by the production team to best represent visual displays of the future . Another aspect of the CG use was to create images and effects that looked as if they were " perceived by the brain " and were generated in video and added to the film in its final stages . 
- The opening credits of the film were produced by the CG director , <unk> Tanaka . Tanaka converted code in a computer language displayed in <unk> Japanese letters to numbers before inserting them into the computer to generate the credits . The origin of this code is the names of the film 's staff as written in a computer language . 
- Animation director <unk> <unk> was responsible for the realism and strove for accurate depictions of movement and effects . The pursuit of realism included the staff conducting firearms research at a facility in Guam . <unk> has highlighted the tank scene as an example of the movie 's realism , noting that bullets create sparks when hitting metal , but do not spark when a bullet strikes stone . 
- 
- = = = Sound and music = = = 
- 
- Ghost in the Shell 's recording was done with a high @-@ end studio to achieve superior sound throughout the film . A <unk> was used to alter the sound , specifically in the electronic brain conversations , to modify the voices . 
- Composer Kenji Kawai scored the film . For the main theme , Kawai tried to imagine the setting and convey the essence of that world in the music . He used the ancient Japanese language of Yamato in the opening theme " Making of a <unk> " . The composition is a mixture of Bulgarian harmony and traditional Japanese notes ; the haunting <unk> are a wedding song sung to <unk> all evil influences . Symphony conductor Sarah <unk> @-@ Smith notes that the song 's lyrics are fitting for the union between Kusanagi and Project 2501 at the climax of the movie . Kawai originally wanted to use Bulgarian folk music singers , but used Japanese folk singers instead . " See You Everyday " is different from the rest of the soundtrack , being a pop song sung in Cantonese by Fang Ka Wing . 
- 
- = = = Ghost in the Shell 2 @.@ 0 = = = 
- 
- An updated version of the original film , titled Ghost in the Shell 2 @.@ 0 ( <unk> IN THE <unk> <unk> 攻殻機動隊 2 @.@ 0 , <unk> in <unk> <unk> / Kōkaku <unk> 2 @.@ 0 ) , was made in celebration for the release of The Sky <unk> in 2008 . The Ghost in the Shell 2 @.@ 0 release features replacements of the original animations with the latest digital film and animation technologies , such as 3D @-@ CGI . It includes a new opening , digital screens and <unk> displays , and <unk> several brief scenes . 
- The original soundtrack was also re @-@ arranged and re @-@ recorded . Kenji Kawai remixed the Version 2 @.@ 0 soundtrack in 6 @.@ 1 Channel <unk> . Randy Thom of Skywalker Sound reprised his role as sound designer , having worked previously on Ghost in the Shell 2 : Innocence . In the new soundtrack , the Japanese voice dialogue was also re @-@ recorded , with some variation from the original script to modernize the speech . <unk> <unk> replaced <unk> <unk> as the voice of the Puppet Master . 
- 
- = = Related media = = 
- 
- Kenji Kawai 's original soundtrack for the film was released on November 22 , 1995 . The last track included <unk> <unk> 's pop song " See You Everyday " . After the release of Ghost in the Shell 2 @.@ 0 , an updated version of the soundtrack was released on December 17 , 2008 . 
- A <unk> @-@ CD of the film was released in Japan on November 20 , 1995 . A spin @-@ off novel written by <unk> Akira , titled Ghost in the Shell : Burning City ( <unk> , Kōkaku <unk> <unk> no <unk> ) , was published by Kodansha and released on November 1995 . It was followed by a sequel , titled Ghost in the Shell 2 : Star <unk> ( <unk> : Star <unk> ) , released on January 1998 . A book titled Analysis of Ghost in the Shell was released on September 25 , 1997 by Kodansha . 
- 
- = = Releases = = 
- 
- The film had its world premiere at the Tokyo International Film Festival in October 1995 , before its general release in November . In Japan , the film was released on VHS on April 26 , 1996 . The DVD version was released on 25 February 2004 , and the Blu @-@ ray on 24 August 2007 . A special edition was released in December 2004 . The special edition contains an additional disc containing character <unk> , a creator biography , the director 's biography , Ghost in the Shell trailers and <unk> . 
- The " 2 @.@ 0 " version was released in theatres in Tokyo , Osaka , Nagoya , <unk> , and <unk> on July 12 , 2008 . The film was released in DVD and Blu @-@ ray on December 19 , 2008 , in Japan . 
- In North America , the film was released on VHS on June 18 , 1996 through Manga Entertainment , and on DVD on March 31 , 1998 by Anchor Bay Entertainment . Manga Entertainment released the film on Blu @-@ ray on 24 November 2009 ; this version contains the original film and the <unk> , but <unk> the audio commentary and face @-@ to @-@ face interview with Oshii , which are listed on its box . Manga Entertainment and Anchor Bay Entertainment re @-@ released the film on Blu @-@ ray with a brand new HD film print on September 23 , 2014 . The release was met with some criticism for its poor translation of English <unk> and the lack of extra features . 
- 
- = = Reception = = 
- 
- The film was a box office hit when released in Japan and received positive reviews from film critics . It holds a 95 % approval rating on the review aggregator website Rotten Tomatoes , based on 31 reviews . The website 's critical consensus reads , " A stunning feat of modern animation , Ghost in the Shell offers a thoughtful , complex treat for anime fans , as well as a perfect introduction for viewers new to the genre . " 
- <unk> <unk> of <unk> Film praised the film , stating , " Not only is <unk> <unk> an essential film in the canon of Japanese animation , together with Kubrick 's 2001 and <unk> 's <unk> it completes a trio of book adaptations that transcend the popularity of their originals and [ give ] a new meaning to an already popular brand . " He ranked it # 48 of his personal favorites . Clark <unk> of Empire opined that the film was predictable , but praised its production values . <unk> Mays of Anime News Network praised the animation combined with the computer effects , calling it " perhaps the best synthesis ever witnessed in anime " . Helen McCarthy in 500 Essential Anime Movies describes the film as " one of the best anime ever made " , praising screenplay , an " atmospheric score " , and adding that " action scenes as good as anything in the current Hollywood blockbuster are supported by CGI effects that can still <unk> " . 
- Ghost in the Shell was the first anime video to reach Billboard 's # 1 video slot at the time of its release . The film ranked as the ninth top selling anime DVD movie in 2006 . It ranked 35 on Total Film 's 2010 top list of 50 <unk> Films . The film ranked # 4 on Wizards Anime Magazine on their " Top 50 Anime released in North America " . 
- 
- = = = Critical analysis = = = 
- 
- Much critical attention has been paid to the film 's focus on sexuality and gender identity . <unk> <unk> has noted that the opening scene of Ghost in the Shell begins with the " perfect paradoxical introduction to a narrative that is all about the nature of sex / gender identity and self @-@ identity in general in a future world where sexual reproduction has given way to mechanical replication . " Motoko 's female identity and appearance are countered by an autonomous <unk> , resulting in a " male " <unk> body which cannot <unk> . <unk> describes the juxtaposition of the opening scene depicting the creation of Motoko 's body and to her lack of <unk> as setting the theme of " reproductive sexuality in a <unk> subject . " The film depicts Motoko 's identity and <unk> concerns , ending with the evolution of a being with full <unk> , through a new form of reproduction with the Puppet Master . Austin Corbett commented on the lack of <unk> from her team as freedom from femininity , noting that Motoko is " overtly feminine , and clearly non @-@ female . " Carl <unk> has called Ghost in the Shell a " resistant film " , due to its <unk> of traditional gender roles , its " <unk> of the post @-@ gendered subject " , and its de @-@ emphasis of the sexual <unk> of the material body . 
- 
- = = Cultural impact = = 
- 
- Ghost in the Shell influenced a number of prominent filmmakers . The <unk> , creators of The Matrix and its sequels , showed it to producer Joel Silver , saying , " We wanna do that for real . " The Matrix series took several concepts from the film , including the Matrix digital rain , which was inspired by the opening credits of Ghost in the Shell , and the way people accessed the Matrix through holes in the back of their necks . Other parallels have been drawn to James Cameron 's Avatar , Steven Spielberg 's AI : Artificial Intelligence , and Jonathan <unk> 's <unk> . 
- 
- 
- = Yo @-@ Yo ( Nicola Roberts song ) = 
- 
- " Yo @-@ Yo " is a song by British recording artist Nicola Roberts , released as the third and final single from Roberts ' debut solo album Cinderella 's Eyes on 6 January 2012 . Originally , " Yo @-@ Yo " was set to be Roberts ' debut single , but " Beat of My Drum " was selected in its place . " Beat of My Drum " and " Lucky Day " , Roberts ' previous singles , garnered positive comments from critics , but failed to impact commercially . " Yo @-@ Yo " was written by Roberts , Maya Von <unk> and Dimitri <unk> while it was produced by latter , and was the first song to be composed by the group . Roberts also claimed the track had defined her musical style . 
- " Yo @-@ Yo " is a 1950s @-@ themed electropop and synthpop song in which Roberts sings about an undecided lover . The song received positive reviews from critics , who compared Roberts ' vocals to those of singer Kate Bush . <unk> , it was a failure , charting at number 111 on the UK Singles Chart . A music video was released on 30 November 2011 ; the video sees Roberts repeatedly changing outfit for a party in which she finds her love interest cheating on her , ending with an aggressive confrontation . It garnered positive critical responses although the low @-@ budget drew criticism . Roberts performed " Yo @-@ Yo " at London 's G @-@ A @-@ Y nightclub and on the Alan <unk> Show , among other occasions . 
- 
- = = Background and writing = = 
- 
- Nicola Roberts , who began her musical career as one fifth of the band Girls <unk> , struggled with loneliness and increasing isolation during her time with the group . A busy schedule and constant media criticism found her describing the time as a " blur " . Her exhaustion and troubles with negative media comments led to a state in which she had to harden herself , claiming that inside she was " dying " . These events inspired her debut album Cinderella 's Eyes . " Yo @-@ Yo " was the first song she had written with Maya von <unk> and Dimitri <unk> for the album . The recording was also one of the first songs Roberts wrote , and it helped define her musical style . Roberts <unk> about the production of the track , writing that : 
- The production ’ s incredible . The beat is just so constant and powerful . That was the first song that I wrote with Dimitri and Maya . I felt like I ’ d found my sound . And I was kind of looking at it like an outfit . So it was , ‘ if this was an outfit , what would this song need now ? ’ So we had the pretty dress , which was the chorus . We had the lovely shoes , which was the verse . ‘ But what do we need to really set this song off ? A big , fancy , bow in the hair would really set this song off . ’ So when I got to the middle eight I wanted to go really erratic , almost like <unk> on All The Things She Said . I wanted it to be proper <unk> up and crazy – hands in the air . 
- 
- = = Release = = 
- 
- Roberts ' debut single , " Beat of My Drum " , garnered acclaim from critics , but failed to impact commercially , charting at 27 in the United Kingdom . She released a follow @-@ up single , " Lucky Day " , which likewise garnered positive responses , but was even less successful commercially , charting at number 40 on both the Scottish and UK Singles charts . Writing as a guest blogger on the website Holy <unk> in September 2011 , Roberts announced that " Yo @-@ Yo " would be released as the third single from Cinderella 's Eyes , initially citing a November release date ; the release was later delayed until January 2012 . Roberts had originally intended for " Yo @-@ Yo " to be the first single from the album , but " Beat of My Drum " was instead chosen at the " last minute " . Roberts stated that " it felt right to go with " the latter song . " Yo @-@ Yo " was released digitally on 2 January 2012 , with Roberts announcing on her official website that the single would be released in CD format featuring a live performance of the song in addition to the demo version of her song " Sticks + Stones " seven days later . Additionally , two EPs were released on 6 January 2012 . 
- 
- = = Composition and critical reception = = 
- 
- " Yo @-@ Yo " features 808 drums with a " dramatic " 1950s @-@ inspired pop " pastiche " . However , the 1950s concept " goes completely out of the window " for an " <unk> " bridge , which features a club mix which speeds up the drums featured in the song . The lyrics of the track discuss an uncertain relationship , leading to vulnerability , with Roberts describing it as a " dark " song . The song references a yo @-@ yo ; this is a metaphor for Roberts herself , a spinning yo @-@ yo " on the finger of a hot ' n ' cold lover " . Described as " confessional " by Robert Copsey of Digital Spy , the song has a " <unk> " performance , and Roberts ' vocals were compared to that of singer Kate Bush . 
- The song received positive reviews from music critics . Emily <unk> , writing for NME , described the chorus as a " winning " one , and noted that " the impulse @-@ speed space synths are broken @-@ <unk> <unk> " . In a separate review , Lisa Wright from NME found that while the song didn 't match the success of " Beat of My Drum " . Wright said that the song is " Not a <unk> @-@ ridden introduction to Nicola ’ s re @-@ emergence as an English Syd <unk> <unk> , but a questionable metaphor about being like a crap ’ 90s toy . " Robert Copsey of Digital Spy found Cinderella 's Eyes to be " <unk> under @-@ appreciated " with " Yo @-@ Yo " being a " shining example of [ Roberts ' ] pop sensibilities " calling it " <unk> radio friendly " . 
- 
- = = Music video = = 
- 
- 
- = = = Synopsis = = = 
- 
- The video begins with a man performing tricks with a yo @-@ yo in a white room , accompanied by shots showing Roberts in a " fashionable looking house " wearing a black silk dress . Then rapid shots show her in various other outfits , including a purple velvet dress , all the time showing the man performing tricks with the yo @-@ yo . As the chorus begins , Roberts is shown in the kitchen of the house , along with several women dancing with her . Another shot shows a man and a woman walking through a street embracing each other . This is later interspersed with shots of a fully clothed Roberts standing in a shower <unk> getting increasingly wet . Roberts is shown leaving the house and seeing the man with whom she had danced with another woman . She returns to the house , and the man and Roberts are seen in the kitchen area with Roberts throwing a series of objects at him . 
- 
- = = = Release and reception = = = 
- 
- Prior to the release of the video , Roberts released still images to website <unk> . These showed her in what was described as " some sort of untidy , yet fashionable looking house " , while one showed her with her legs above her head wearing " <unk> 's <unk> kisses shoes " and a third and final seeing her " <unk> up next to a handsome chap " . The same day , Roberts gave two more exclusive <unk> to website Digital Spy ; in one image , she was smelling <unk> , while , in the second , she was " playing with a pair of shoes " . The video was released on 30 November 2011 . After the official release , Nadia Mendoza of the Daily Mail stated the video was a departure from her previous two videos ( for " Beat of My Drum " and " Lucky Day " ) , saying that it " looks and sounds a lot more grown up as she <unk> viewers by stripping to her bra and <unk> in the shower . " Mendoza found that Roberts was " becoming quite the <unk> " and had " <unk> all over her innocent charm to create a sex <unk> " , referencing her transformation throughout her videos . Katherine St <unk> , of Pop <unk> , found that the video 's low budget had had a negative effect . She interpreted scenes with Roberts repeated changes clothing positively , noting " [ i ] t works because of the lyrics – what , besides other people , can mess with your feelings more than trying on clothes ? " 
- 
- = = Live performances = = 
- 
- Roberts performed " Yo @-@ Yo " at London 's G @-@ A @-@ Y nightclub on 24 September 2011 . She pre @-@ recorded a performance of " Yo @-@ Yo " for UK television programme The Alan <unk> Show during the same taping as a performance of " Lucky Day " . The taping came after deciding " Yo @-@ Yo " would be released as the third single from the album , with Roberts saying , " I absolutely love performing Yo @-@ Yo , it 's my favourite one to do [ ... ] So I feel really close to Yo @-@ Yo . I feel like I lose myself when I 'm performing it " . After the release of the single , Roberts performed on The Album Chart Show during a special dedicated to her , performing three tracks : " Yo @-@ Yo " , " Beat of My Drum " and " Sticks + Stones " . Roberts also performed the song on British chat show Loose Women and on the Digital Music Awards . 
- 
- = = Charts = = 
- 
- " Yo @-@ Yo " debuted at its peak of number 111 on the UK Singles Chart . 
- 
- = = Formats and track @-@ listings = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Robbie Fowler = 
- 
- Robert Bernard " Robbie " Fowler ( born 9 April 1975 ) is an English former professional footballer and manager who played as a striker from 1993 to 2012 . Fowler was known for being a natural scorer with an <unk> goal @-@ poaching ability . 
- Fowler is best remembered for his time at Liverpool and is the sixth @-@ highest goalscorer in the history of the Premier League . He scored 183 goals in total for Liverpool , 128 of which were scored in the Premier League ( 162 Premier League goals in total ) . He earned the nickname " God " from the Anfield crowd , becoming a club legend due to his <unk> in front of goal . He subsequently played for Leeds United and Manchester City , before returning to Liverpool in January 2006 . 
- He moved clubs 18 months later to sign for Cardiff City . He refused a one @-@ year " pay as you play " contract extension and signed with Blackburn Rovers on a three @-@ month deal on the same terms instead . In December 2008 , he departed Blackburn and forged a career in Australia with North Queensland Fury and Perth Glory . In 2011 , he joined Thai side Muangthong United as a player , but later was appointed player @-@ manager which he remained until his retirement in 2012 . 
- He was capped for England 26 times , scoring 7 goals . Fowler was included in England 's squads for Euro 96 , Euro 2000 and the 2002 FIFA World Cup . 
- 
- = = Early life = = 
- 
- Fowler was born in Toxteth , Liverpool and brought up in the inner city area of Liverpool . At this time he was known as Robert Ryder , his mother 's surname . He lived in Toxteth at the time of the 1981 Toxteth riots , when he was six years old . As a <unk> he supported Everton <unk> .. He played regularly for schoolboy team <unk> , and once scored 16 times in a 26 – 0 <unk> . 
- 
- = = Club career = = 
- 
- 
- = = = Liverpool = = = 
- 
- Fowler 's career began with Liverpool . He signed as a youth team player on leaving school in the middle of 1991 , signing professional terms on his 17th birthday , 9 April 1992 . 
- Fowler 's first involvement with the Liverpool first team came on 13 January 1993 , when he was an unused substitute in an FA Cup third round tie against Bolton Wanderers . In the following close season , Fowler helped the England under @-@ 18 team win the 1993 European Championship , before making a scoring first @-@ team debut in Liverpool 's 3 – 1 win in a first round League Cup tie at Fulham on 22 September 1993 . Fowler scored all five goals in the second leg at Anfield two weeks later , making him the fourth player in Liverpool 's history to score five in a senior fixture . He scored his first league hat @-@ trick against Southampton in only his fifth league game . His very first league goal for the Reds had come on 16 October 1993 , when an 87th @-@ minute equaliser at home to struggling Oldham Athletic saved the Reds from what would have been one of the biggest Premier League <unk> of the season , with a last <unk> own goal giving Liverpool a 2 – 1 win . He scored twice in a thrilling 3 – 3 draw at Tottenham Hotspur on 18 December . 
- His first 13 games for the club yielded 12 goals , and he was rewarded with an England Under @-@ 21 debut against San Marino in November 1993 , in which he scored England 's opening goal in the third minute . Fowler was unable to sustain his goal @-@ a @-@ game ratio throughout the season , but finished his first season as the club 's leading scorer with 18 goals in all competitions , although Ian Rush had scored more goals in the league . It was , however , a disappointing season for Liverpool , as they finished eighth in the Premier League without making any impact in any of the major competitions , though the departure of Graeme Souness as manager and the appointment of Roy Evans as successor built up hope for the future . 
- 
- = = = = Success and fame = = = = 
- 
- In the 1994 – 95 season , Fowler was an ever @-@ present for Liverpool , playing in all of their 57 competitive matches , including the victory in the 1995 League Cup final , and a match against Arsenal in which he scored the Premier League 's second fastest hat @-@ trick ever , in four minutes and 33 seconds . His record stood for twenty years until broken by <unk> <unk> on 16 May 2015 for Southampton against Aston Villa , who scored three goals in two minutes and 56 seconds . 
- Fowler also scored <unk> against Aston Villa , Ipswich Town , Chelsea and Norwich City in the league that season . 
- Fowler was voted the PFA Young Player of the Year in two consecutive years in 1995 and 1996 , a feat equalled only by Ryan <unk> and Wayne <unk> . 
- Throughout the mid and late 1990s , Fowler was widely considered to be the most natural finisher playing in England . Fowler sealed this reputation as he scored more than 30 goals for three consecutive seasons , up to 1997 . He remains the only player to have scored 30 plus goals in his first three full seasons in England scoring 98 goals with a total of 116 in 3 and a half years , something which has also yet to be beaten in La Liga , <unk> A and the <unk> too . Fowler 's partnership with Steve McManaman was largely described as the reason why Liverpool had become the club known for being the most potent attacking force in England at the time , and Fowler was renowned for scoring goals of all varieties , from every angle and distance , with McManaman describing him as the " greatest goalscorer of all time . " 
- Stan Collymore , Fowler 's strike partner from 1995 to 1997 , said in his autobiography that Fowler was the best player he has ever played alongside . Fowler and Collymore were among the most prolific goal @-@ scoring strike partnerships in England during the 1995 – 96 season . In the same season he scored twice in a 4 – 3 victory over Newcastle United , a match voted the best of the decade in a Premier League poll . The match helped prevent Newcastle from winning the league , but it was not enough for Liverpool to clinch the title ; they finished third while Manchester United were crowned champions . Fowler also played in his first FA Cup final that season , but was on the losing side as Manchester United won 1 – 0 . 
- Fowler received a call @-@ up to the full England squad and won his first cap on 27 March 1996 as a substitute in a friendly against Bulgaria . His first start at international level was against Croatia which was the England game following his substitute appearance . Fowler was part of the England squad for Euro 1996 , making two appearances in the tournament . On 14 December 1996 , he scored four against Middlesbrough , including his hundredth for Liverpool . This meant he reached a century of goals one game quicker than his first strike partner , Ian Rush , in just 165 games . 
- That year , he also won a UEFA Fair Play award for denying that he had been fouled by Arsenal goalkeeper David Seaman at Highbury after a penalty had been given . After unsuccessfully trying to persuade the referee to change his decision about the penalty , Fowler took it <unk> and Seaman saved . However , Seaman failed to hold on to the ball and Jason <unk> scored from the rebound . Though many people believe that he deliberately took the penalty kick poorly for reasons of fair play , Fowler said at the time : " As a goalscorer it 's part of my job to take it and I wanted to score it . I tried to score . I never missed on purpose . It just happened , it was a bad penalty . " 
- 
- = = = = Spice Boys = = = = 
- 
- Fowler was part of a group of Liverpool players from the mid @-@ 1990s who were dubbed " The Spice Boys " by the press following a series of off @-@ field controversies . The term was coined by the Daily Mail , and arose due to <unk> rumours that Fowler was dating Spice Girl Emma <unk> . The term was subsequently used in a derogatory manner , implying Fowler and colleagues such as Jamie Redknapp , Stan Collymore , David James and Steve McManaman were <unk> <unk> . 
- Fowler 's performance in the 1997 – 98 football season was marred by a knee ligament injury that kept him out of action for half the season and caused him to miss the 1998 World Cup . During this period of injury , fellow Liverpool striker , Michael Owen rose to prominence , making his debut in 1997 . Owen established himself in the Liverpool team in Fowler 's absence . 
- In 1999 , Fowler was fined £ 60 @,@ 000 by his club for bringing the game into <unk> . While celebrating his goal against Liverpool 's Merseyside rivals , Everton , Fowler used the white line of the penalty area to simulate cocaine use . Liverpool manager Gérard Houllier stated that this was a <unk> grass @-@ eating celebration , learnt from teammate <unk> Song . <unk> himself , Fowler later said this was a response to Everton fans who had insulted him with false accusations of drug abuse . Fowler received a four @-@ match suspension from the FA for this incident . At the same FA disciplinary hearing , Fowler received a further two match suspension due to a separate incident in which he had <unk> the Chelsea defender Graeme Le Saux by waving his <unk> at him as Le Saux 's wife and children watched from the stands . Fowler later attempted to justify his actions by suggesting his taunts were simply an extension of <unk> . The FA imposed a £ 32 @,@ 000 fine and a six @-@ match ban for the two incidents . Fowler has since apologized to Le Saux for the incident . 
- 
- = = = = Winning the cup treble = = = = 
- 
- The 2000 – 01 season was Fowler 's most successful season . He appeared in three finals , scoring 17 goals and lifting three trophies in a unique cup treble . In the absence of Jamie Redknapp , who was sidelined by injury , Fowler was named as Liverpool captain when he started . However he found himself the third @-@ choice Liverpool striker , with Houllier favouring a forward partnership of Michael Owen and Emile Heskey . 
- He took part in a fourth round League Cup <unk> of Stoke City , scoring a hat @-@ trick in an 8 – 0 victory , which was second only to the club 's biggest ever win in 1986 – a 10 – 0 defeat of Fulham . In the League Cup final against Birmingham City , the club 's first cup final since 1996 , he captained the side and scored in the 30th minute . Liverpool went on to win the trophy on penalties , with Fowler scoring Liverpool 's fifth in the shootout . Fowler picked up the Alan <unk> Man of the Match award and lifted the trophy . 
- Fowler 's season picked up from there as he scored several important goals including one against runaway champions Manchester United and a free kick in the FA Cup semi @-@ final against Wycombe Wanderers . Fowler featured as a substitute in the 2001 FA Cup Final coming on as a 77th @-@ minute replacement for <unk> <unk> . Liverpool , who were 1 – 0 down at that point , eventually won the game 2 – 1 with two goals from Owen . Fowler raised the trophy along with Sami <unk> and Jamie Redknapp . 
- Four days later he was a substitute again in Liverpool 's third final , the 2001 UEFA Cup Final against <unk> <unk> . He came on in the 64th minute for Heskey with the score at 3 – 3 . He scored seven minutes later but <unk> equalised before full @-@ time and Liverpool eventually won with a golden goal , an own goal , in the 116th minute . Fowler and <unk> then raised Liverpool 's third trophy of the season together . Liverpool 's next and final game of the season was against Charlton Athletic and Fowler scored twice in a 4 – 0 victory at The Valley that assured them UEFA Champions League qualification for the next season . 
- 
- = = = = Liverpool departure = = = = 
- 
- Fowler began the 2001 – 02 season controversially , after being dropped by the then manager Gérard Houllier from the Liverpool squad for the 2001 Charity Shield match following a training ground confrontation with assistant manager Phil Thompson . He made an appearance in Liverpool 's 3 – 2 European Super Cup victory over Bayern Munich , but starts were intermittent . In October 2001 , he scored his first league hat @-@ trick for three years , helping Liverpool beat Leicester City 4 – 1 , but was dropped for the following league match . Though Fowler had been on a contract extension from 1999 ( unlike Steve McManaman – who exercised his <unk> <unk> the very same year ) , Fowler was linked to <unk> , Arsenal and Leeds , and Liverpool 's management as well as fans and the media constantly reported that what happened with McManaman ( regarded as a huge financial loss ) would never be repeated and thus the club never rejected those bids without consideration . This meant that coupled with Fowler 's relationship with Houllier , speculation over Fowler 's future persisted for most of Houllier 's tenure and became an issue that divided Liverpool fans . His last appearance for Liverpool was against Sunderland , in which he was substituted at half @-@ time . 
- 
- = = = Leeds United = = = 
- 
- Despite his popularity with Liverpool fans , who referred to Fowler as " God " , Michael Owen and Emile Heskey had established themselves as Liverpool 's regular strike partnership , leaving Fowler on the fringes of the first team . This , along with his difficult relationship with Houllier , made him seek regular first team football away from Anfield in the form of a £ 12 million move to Leeds United . Fowler maintains that Houllier forced him out of Liverpool , and accused Houllier of <unk> the Liverpool Echo newspaper to use its influence to turn opinion against him . 
- The transfer went ahead just one month after his hat @-@ trick at Leicester . He made his Leeds debut in an away game against Fulham in December 2001 , the same ground where he had made his Liverpool debut eight years earlier . Fowler scored 12 goals in the remainder of the season , helping Leeds to a UEFA Cup qualifying place . Fowler was included in the England squad for the 2002 World Cup , but only made one appearance , coming on as substitute in a second round win over Denmark . 
- Fowler suffered an <unk> of a pre @-@ existing hip injury in the 2002 – 03 pre @-@ season , and did not recover until December . <unk> to gain fitness , and seeing team @-@ mates sold off due to a financial crisis , Fowler 's form and market value diminished . It was despite this decrease in form that he still , in total , scored 15 goals in 31 appearances for Leeds ; achieving an impressive strike rate of just less than one goal every two games . In 2002 – 03 , Leeds finished 15th in the Premier League and a severe financial crisis was developing . 
- 
- = = = Manchester City = = = 
- 
- In the 2002 – 03 season , Fowler was transferred to Manchester City following a protracted transfer saga . Fowler initially turned down the move , and a dispute between Manchester City manager Kevin Keegan and chairman David Bernstein over whether the transfer should take place due to medical concerns resulted in Bernstein leaving the club . Following encouragement from Keegan , Fowler finally signed for Manchester City on 16 January 2003 for an initial fee of £ 3 million and a further £ 3 million dependent upon appearances . <unk> transfer conditions meant Leeds United still paid a significant proportion of Fowler 's wages . Fowler made his Manchester City debut against West Bromwich Albion on 1 February 2003 , but made a poor start to his Manchester City career , scoring just two goals in the remainder of the season . 
- Fowler continued to struggle with fitness problems in the 2003 – 04 season , completing the full 90 minutes only nine times , however he did score against his old club Liverpool in a 2 – 2 draw at home . The arrival of close friend , Steve McManaman , from Real Madrid gave Fowler hope , but the pair failed to rekindle their prolific partnership from their time at Liverpool , and received criticism from the fans and <unk> for their salaries , alleged excesses as well as named and <unk> in a sex scandal covered by the News of the World that year . 
- Despite the slump , Fowler rallied for the following campaign , and showed a marked improvement in the second half of the 2004 – 05 season , scoring his 150th Premiership goal in the 3 – 2 win over Norwich City on 28 February 2005 . However , his failure to convert a 90th @-@ minute penalty kick against Middlesbrough 's Mark <unk> in the final game of the season prevented Manchester City from gaining a place in the UEFA Cup . Despite this , Fowler ended the season as the club 's joint top goal scorer and gained the approval of the fans , finishing in the top three in the fans ' Player of the Year poll . Fowler later described this as " one of the proudest achievements of my career " . 
- Fowler had injury problems at the start of the 2005 – 06 season and rarely featured when fit , making just two substitute appearances in the first four months of the season . His first start of the season came against Scunthorpe United in the FA Cup on 7 January 2006 , in which he scored a hat @-@ trick . The following week he scored Manchester City 's third goal in their 3 – 1 win against local rivals Manchester United after coming on as substitute . However , Fowler made only one more appearance for Manchester City before returning to Liverpool on a free transfer . 
- 
- = = = Return to Liverpool = = = 
- 
- On 27 January 2006 , Fowler rejoined Liverpool from Manchester City on a free transfer , signing a contract until the end of the season . Fowler had remained a Liverpool fan after he left the club ; he was in the Istanbul crowd when Liverpool won the Champions League in 2005 . 
- The fans were <unk> to learn that Fowler had returned ; there were large <unk> in the game against Birmingham City which read ' God – number eleven , welcome back to heaven ' , with ' God ' being Fowler 's nickname while he was previously at Liverpool . 
- Fowler 's return against Birmingham City in February 2006 was labelled by the tabloid press as the stuff of <unk> , and he himself said he felt like " a kid waking up on Christmas morning every day " . Fowler 's first appearance back at Anfield was as a substitute against Birmingham , receiving a standing ovation upon his introduction . After his return , he had three goals ruled out for offside , before finally getting off the mark on 15 March 2006 in a home game against Fulham , the same opponents against which he scored his first ever goal for Liverpool 13 years earlier . 
- Fowler 's next Liverpool goal , against West Bromwich Albion , meant he overtook Kenny Dalglish in the club 's all @-@ time top scorers . His resurgence continued as he marked his 31st birthday with a goal against Bolton Wanderers . He made it four goals in five games when he scored the only goal in a 1 – 0 victory over Blackburn Rovers on 16 April 2006 . However , Fowler 's fitness remained a concern . In March 2006 , manager Rafael Benítez commented on Fowler 's work and progress by saying , " to buy a Robbie Fowler who is fit and scoring goals would cost a lot , maybe £ <unk> or more " . Despite concerns about his fitness , Fowler finished the 2005 – 06 season scoring on a more consistent basis than Liverpool 's other strikers . In May 2006 he was offered a new one @-@ year contract with the club , and celebrated by scoring the first goal in Liverpool 's last league game of the season in a 3 – 1 away win at Portsmouth . It was his final game of the season as he was unable to take part in the club 's FA Cup Final success due to being cup @-@ tied . 
- Fowler featured rarely in his final Liverpool season , making only six league starts . <unk> , all three of his League goals were penalties against Sheffield United . One of these was in the away game on the opening day of the season , and the other two in the reverse fixture at Anfield . Appearances in other competitions were more common due to Rafael Benítez 's squad rotation policy . On 25 October 2006 Fowler was named as Liverpool 's captain for the first time since his return in a League Cup tie against Reading , scoring just before half @-@ time in a 4 – 3 win . 
- On 5 December , Fowler scored his first two goals in the UEFA Champions League competition proper against <unk> ( he had previously scored during a qualifying tie some six years prior against FC <unk> ) , though Liverpool lost 3 – 2 . 
- On 1 May 2007 he was a substitute in the Champions League semi @-@ final against Chelsea , brought on in the last few minutes of extra @-@ time . He set up an attack for Dirk <unk> but the shot went straight at the Chelsea goalkeeper . The match went into a penalty shoot @-@ out that Liverpool won . Fowler was due to take the fifth and final spot @-@ kick , but the game had already ended when <unk> <unk> home his winning penalty . 
- In what <unk> to be his last appearance for the club , against Charlton Athletic on 13 May , Fowler was given the captain 's <unk> one final time . He was substituted two minutes from the final whistle and given a standing ovation . He finished his second run as a Liverpool player with a UEFA Champions League runners @-@ up medal , although he was not named in either the starting eleven or the seven <unk> . He became a free agent on 1 July having scored 183 goals in <unk> appearances during his two spells at the club . 
- 
- = = = Cardiff City = = = 
- 
- On 21 July 2007 Fowler signed a two @-@ year contract to play for Cardiff City . He missed the season 's opening fixtures due to a lack of fitness , making his debut in a League Cup tie on 28 August . He scored his first two Cardiff goals on 22 September against Preston North End , scoring with two <unk> . Fowler scored twice in his next game , a third round League Cup tie against West Brom , which Cardiff won 4 – 2 . This led to a fourth round tie against Fowler 's former club Liverpool , at Anfield , to which even the Liverpool faithful crowd urged Fowler to score seeing that it may be the last time he would play in Anfield but Cardiff were knocked out in a 2 – 1 defeat . 
- In November , Fowler travelled to Frankfurt , Germany to see Dr. Hans @-@ Wilhelm <unk> @-@ <unk> , a specialist sports injuries doctor , to try and resolve a recurring hip problem that had left him lacking fitness in early season fixtures for the <unk> . The treatment involved taking around twenty @-@ eight <unk> into his hip . He returned to full training in late November and made his comeback on 15 December as a late substitute in a 1 – 0 defeat against Bristol City . However he went on to suffer another injury blow just days later after a <unk> tackle in training from club captain Darren <unk> left him with damaged ankle ligaments . Due to the new injury blow Cardiff and Fowler made plans for him to go to Colorado , United States to undergo <unk> surgery on the hip problem , which had plagued him in recent seasons , in the hope that it would finally resolve the problem . 
- On 17 January 2008 , it was announced that Fowler could miss the rest of the 2007 – 08 season for Cardiff after his hip operation revealed that the injury was worse than previously thought . Surgeons were forced to perform a micro @-@ fracture in order for the hip to heal properly . Despite this , he attempted to make a comeback at the end of the season to play in the FA Cup final against Portsmouth and was included in the 18 @-@ man match squad . He did not feature in the game in and thus did not receive a runners @-@ up medal having not made an appearance in the competition . 
- 
- = = = Blackburn Rovers = = = 
- 
- Fowler was offered a new pay @-@ as @-@ you @-@ play contract with Cardiff for the 2008 – 09 season in May 2008 and was expected to sign the contract . However , he pulled out of the deal , preferring to accept an offer of a trial at Blackburn Rovers from former Liverpool colleague Paul Ince . The move left Cardiff manager Dave Jones and chairman Peter <unk> furious after the club had assisted Fowler 's rehabilitation throughout the summer . After training with Blackburn and appearing in a handful of friendlies he was offered a six @-@ month deal by the club to last until January , but Fowler turned down the deal due to it being a shorter offer than the previous one he had rejected at Cardiff . After turning down the offer , Fowler made a shock enquiry about returning to Cardiff on the deal he had originally turned down , prompting anger from supporters and a swift " no thanks " from the club . 
- Fowler concluded the trial period at Blackburn by agreeing to a three @-@ month pay @-@ as @-@ you @-@ play deal . He stated that he was eager to return to the Premier League and that his pre @-@ existing relationship with Ince would not earn him any <unk> treatment . He made his first appearance against Everton in a 1 – 0 win in the League Cup on 24 September . With a month left on his contract at Blackburn , Fowler received interest from League Two side Grimsby Town . Fowler , a noted friend of then Grimsby manager Mike Newell had held talks at <unk> Park over a possible Player / Coach role with the club . 
- His three @-@ month deal at Blackburn expired on 12 December 2008 , and after not being offered a new contract was released by the club , he entered talks with new Australian A @-@ League club North Queensland Fury . 
- 
- = = = North Queensland Fury = = = 
- 
- Fowler signed with the North Queensland Fury on 4 February 2009 and became their inaugural <unk> player ; with his family relocating to Townsville for the 2009 – 10 season of the Australian A @-@ League . It was an important signing for the new franchise who struggled to sign a <unk> player , while some questioned whether Fowler would be able to cope with the heat and humidity of North Queensland . 
- Fowler made his debut in July 2009 in a 2 – 1 pre @-@ season loss against Wolverhampton Wanderers in Perth after recovering from groin and hip complaints . Fowler was subsequently named North Queensland Fury 's captain for the 2009 – 10 season and the first in the club 's history . He scored his first A @-@ League goal from a penalty kick in his club 's first competitive match against Sydney FC on Saturday 8 August 2009 . In rounds four , five , and six Fowler scored Solo 's <unk> A @-@ League <unk> of the Week . 
- Shortly after his arrival in Australia , it was reported in the British media that Fowler would be making a swift return to his homeland and sign for League One side Tranmere Rovers , who had just appointed Fowler 's former Liverpool team mate John Barnes as manager . However , Fowler was quick to dismiss talk of a quick return to England . When Barnes was dismissed three months later , it was reported that Tranmere had approached Fowler about becoming player @-@ manager , but these reports too were dismissed . 
- Controversy erupted around Fowler after he reportedly refused to play against the Brisbane <unk> on 23 January after being named on the interchange bench for the clash . He however returned to the starting line up for two of the last three games of the season . Fowler ended the season collecting a hat @-@ trick of awards at the clubs end @-@ of @-@ season awards night , he was awarded the clubs Player of the Year , Players ' Player of the Year and the Golden Boot as top goal scorer . On 15 June 2010 , Fowler confirmed that he is taking legal action over the ending of his playing contract with North Queensland Fury . He is <unk> the Fury and Football Federation Australia , which took over the running of the club . 
- 
- = = = Perth Glory = = = 
- 
- On 27 April 2010 , it was announced that Fowler had agreed to become part of Glory 's squad for the 2010 – 11 A @-@ League season . Fowler reportedly ignored offers from Middle East clubs as well as Sydney FC to play in Perth . He joined the Glory for pre @-@ season training in mid @-@ June , following World Cup sponsorship commitments . Fowler scored his first goal for Perth on 29 August , a penalty against Melbourne Heart . He followed this up with a headed goal the following week against the Wellington Phoenix . In the following match , Fowler 's hat @-@ trick gave the Glory a 3 – 1 victory against Melbourne Victory at the <unk> <unk> Stadium in Townsville . Fowler ended the year as top scorer for the club . 
- 
- = = = Muangthong United = = = 
- 
- On 7 July 2011 Fowler agreed to play with Muangthong United , signing a one @-@ year contract . In a press conference he stated that the weather conditions of <unk> should not be a problem as he had played in Townsville and Perth . He was quickly a hit with Muangthong fans and the Thai public in general ; he attended Thailand 's World Cup <unk> against Oman wearing the national team 's shirt . He has since played a handful of scoreless games , both at the Yamaha Stadium and on the road in the AFC Cup , until the Twin <unk> were eliminated from the competition by Al @-@ Kuwait . After the sacking of Henrique Calisto as head coach , Fowler was made player / coach . 
- On 16 October 2011 , after 250 minutes of play , Fowler scored his first goal for <unk> against <unk> . He scored his <unk> club career goal on 21 December 2011 against <unk> <unk> . On 28 February 2012 Fowler announced he had left the club following <unk> <unk> 's appointment as coach . 
- 
- = = = Return to England and retirement = = = 
- 
- On 1 March 2012 , Blackpool manager Ian Holloway confirmed that Fowler was training with the <unk> and that he could earn a deal until the end of the season . However , they could not agree a deal and Fowler decided against signing when Karl <unk> offered the striker just £ 100 a week with £ 5 @,@ 000 for every first @-@ team appearance . 
- On 22 September 2012 Fowler announced that he is " all but retired from professional football . " He said this on the television show Soccer AM . In an interview in March 2013 , he said that he was " not officially retired " and would " jump at the opportunity to play again . " Fowler has also stated in other interviews that he is currently completing his coaching licences . Fowler was on a six @-@ man shortlist and interviewed for the vacant manager 's job at Conference side Macclesfield Town in May 2013 , but caretaker manager John <unk> was eventually appointed on a full @-@ time basis . 
- Fowler featured in Steven <unk> 's testimonial match against <unk> F.C. on 3 August 2013 . He came off the bench in the 73rd minute to a big reception from Liverpool fans , but failed to score a goal . 
- On 21 April 2014 , Fowler also featured in a charity match to commemorate the lives of the 96 Liverpool fans who died in the <unk> Disaster . Fowler scored both goals for his team in a 2 @-@ 2 draw . 
- On 2 January 2015 , Fowler tweeted " <unk> about Steven , but what an unbelievable player ... I 'm officially hanging my boots up as from now ... A sad day for me " and so officially announced his retirement . 
- 
- = = = Soccer Aid = = = 
- 
- In May 2016 it was revealed that Fowler would return to the pitch to play for England in Soccer Aid , a charity football match in aid of UNICEF , alongside Jamie <unk> . 
- 
- = = International career = = 
- 
- Fowler earned his first cap for England on 27 March 1996 , coming on as a 76th @-@ minute substitute in the 1 – 0 friendly win against Bulgaria at Wembley Stadium . On 24 April , he won his second cap and made his first start for England in the 0 – 0 draw with Croatia . Despite only having 3 caps to his name , England manager Terry <unk> selected Fowler in his 22 @-@ man squad for Euro 1996 . Fowler went on to make two substitute appearances in the tournament , featuring in the 4 – 1 win against the Netherlands in the final group game , and in the 0 – 0 draw against Spain in the quarter @-@ finals , a game England won on penalties . 
- Fowler did not feature during England 's qualifying campaign for the 1998 FIFA World Cup , mainly due to a serious knee ligament injury , and thus missed out on Glenn <unk> 's 22 @-@ man squad for the tournament . He did , however , manage to score his first goal for his country on 29 March 1997 , netting the second goal in a 2 – 0 friendly win against Mexico at Wembley Stadium . A second goal followed in his next cap on 15 November , netting just before half @-@ time in the 2 – 0 friendly win against Cameroon . 
- On 9 June 1999 , Fowler played in his first competitive game for England in nearly three years , starting in the 1 – 1 draw with Bulgaria during Euro 2000 qualifying . Kevin Keegan named Fowler in the preliminary squad for Euro 2000 , and after featuring in the three warm @-@ up games against Brazil , Ukraine , and Malta , he was named in the final squad on 1 June 2000 . Fowler did not play in the tournament as England were eliminated in the group stages . 
- Fowler scored his fourth goal for England on 25 May 2001 , netting the second goal in the 4 – 0 friendly win against Mexico at Pride Park , Derby . On 5 September , he scored his first competitive goal for England in the 2 – 0 win against <unk> at St James ' Park , Newcastle . This was during qualifying for the 2002 FIFA World Cup . England qualified for the World Cup , and after scoring in friendlies against Italy and Cameroon , Fowler was selected in <unk> @-@ <unk> <unk> 's 23 @-@ man squad for the tournament in South Korea and Japan . He didn 't appear in any of England 's group matches , but on 15 June 2002 , he came on as a second @-@ half substitute in the 3 – 0 win against Denmark in the Round of 16 . This turned out to be Fowler 's last cap for his country . He won a total of 26 caps for England and scored 7 goals . 
- 
- = = Coaching career = = 
- 
- 
- = = = Milton Keynes Dons = = = 
- 
- On returning to England , Fowler has worked with League One side Milton Keynes Dons on an ad @-@ hoc basis to assist the club 's coaching staff . On 7 April 2011 , Bury confirmed that Fowler would join their coaching staff for one week to assist Richie Barker . Fowler briefly coached Liverpool 's strikers in April 2011 . 
- 
- = = = Muangthong United = = = 
- 
- Muangthong took action and sacked Henrique Calisto as head coach . A statement on the Muangthong website read : " The Board have appointed Robbie Fowler as the acting Head Coach and terminate the contract of Portuguese boss Henrique Calisto . The contract is until the end of the season and the team must adapt to long @-@ term goals if it is to succeed in Asia " , thus making Fowler player / coach of Muangthong United . 
- 
- = = = Liverpool Academy = = = 
- 
- In October 2013 it was announced that Fowler would be taking up a coaching role with Liverpool FC at the academy . 
- On 9 September 2014 , it was revealed Fowler had applied for the vacant head coach / manager job at his former club Leeds United . 
- 
- = = Style of play = = 
- 
- A prolific goal @-@ scorer , Fowler was a quick and opportunistic striker , with good technical ability . Although naturally left @-@ footed , he possessed an accurate , powerful shot from both inside and outside the area with both feet ; he was also effective in the air . Despite his reputation as a " goal @-@ <unk> " , he was also a creative player , capable of linking up well with other players , and laying off the ball to other strikers . Despite his talent , he was also known to be injury @-@ prone throughout his career . 
- 
- = = Personal life = = 
- 
- Fowler married wife <unk> on 9 June 2001 in the town of Duns in Scotland . Together they have three daughters named Madison , Jaya , and Mackenzie , and one son , Jacob . 
- Fowler is a supporter of Liverpool F.C. and regularly attends matches at Anfield . He does media work for Abu <unk> Sports Channel , Sky Sports and ITV . 
- In association with long term friend Steve McManaman , Fowler has invested in several <unk> through a <unk> named company The <unk> and <unk> Partnership , most notably 2003 Queen Elizabeth the Queen Mother Celebration Chase runner @-@ up <unk> . In 2005 , Fowler was listed as one of the 1 @,@ 000 wealthiest Britons by the Sunday Times Rich List , with the paper estimating he has a property portfolio of more than 80 , while his Robbie Fowler Sports <unk> company has been reported to have net assets of £ 1 @.@ 58 million . Other business interests including a large property portfolio have resulted in Fowler 's net wealth totaling £ 28 million . This gave rise to the affectionate Manchester City Football Club chant , We all live in a Robbie Fowler house , sung to the tune of Yellow <unk> . 
- On 2 September 2005 , Fowler released a book called Fowler : My <unk> , about his time as a footballer and the issues surrounding him . Since his transfer to Liverpool he has updated it and included a section about his return to Anfield . <unk> published in newspapers included criticism of the England management . 
- In June 2008 , Fowler participated alongside McManaman in Steve Nash and Claudio <unk> 's <unk> in <unk> , an 8 @-@ on @-@ 8 charity soccer game at Sara D. Roosevelt Park in Manhattan with McManaman making five of the goals , including one for Fowler . 
- In 2011 , Fowler and McManaman participated in a Select World XI against <unk> dictator <unk> Kadyrov and his select XI in a public relations fundraiser in the war @-@ torn nation 's capital of <unk> . The match caused much press <unk> in the western world , mainly due to Kadyrov 's <unk> human rights record and known practices of torture and rape as weapons of rule . British press also cited the use of and payment of footballers to parade the event as particularly " evil " , with The Guardian labelling it " Football 's moment in Bed with Kadyrov " . 
- Fowler is a cousin of boxer and 2014 Commonwealth Games gold medallist Antony Fowler . 
- 
- = = Career statistics = = 
- 
- 
- = = Managerial statistics = = 
- 
- 
- = = Honours = = 
- 
- 
- = = = Club = = = 
- 
- Liverpool 
- FA Cup : 2000 – 01 
- League Cup : 1994 – 95 , 2000 – 01 
- FA Charity Shield : 2001 
- UEFA Cup : 2000 – 01 
- UEFA Super Cup : 2001 
- 
- = = = International = = = 
- 
- England 
- UEFA European Under @-@ 18 Football Championship : 1993 
- 
- = = = Individual = = = 
- 
- PFA Young Player of the Year Award : 1995 , 1996 
- UEFA Fair Play Award : 1997 
- Alan <unk> Trophy : 2001 
- North Queensland Fury Player of the Year : 2010 
- North Queensland Fury Golden Boot : 2010 
- North Queensland Fury Players ' Player of the Year : 2010 
- Perth Glory Golden Boot : 2011 
- Premier League Player of the Month : December 1995 , January 1996 
- 
- 
- = Ode on Indolence = 
- 
- The " Ode on Indolence " is one of five odes composed by English poet John Keats in the spring of 1819 . The others were " Ode on a Grecian Urn " , " Ode on Melancholy " , " Ode to a Nightingale " and " Ode to Psyche " . The poem describes the state of indolence , otherwise known as <unk> , and was written during a time when he felt that he should devote his efforts to earning an income instead of composing poetry . After finishing the spring poems , Keats wrote in June 1819 that its composition brought him more pleasure than anything else he had written that year . Unlike the other odes he wrote that year , " Ode on Indolence " was not published until 1848 , 27 years after his death . 
- The poem is an example of Keats 's break from the structure of the classical form . It follows the poet 's contemplation of a morning spent in idleness . Three figures are presented — Ambition , Love and Poesy — dressed in " placid sandals " and " white robes " . The narrator examines each using a series of questions and statements on life and art . The poem concludes with the narrator giving up on having all three of the figures as part of his life . Some critics regard " Ode on Indolence " as inferior to the other four 1819 odes . Others suggest that the poem exemplifies a continuity of themes and imagery characteristic of his more widely read works , and provides valuable biographical insight into his poetic career . 
- 
- = = Background = = 
- 
- By the spring of 1819 , Keats had left his poorly paid position as a surgeon at Guy 's Hospital , <unk> , London , to devote himself to poetry . On 12 May 1819 , he abandoned this plan after receiving a request for financial assistance from his brother , George . Unable to help , Keats was torn by guilt and despair and sought projects more lucrative than poetry . It was under these circumstances that he wrote " Ode on Indolence " . 
- In a letter to his brother dated 19 March 1819 , Keats discussed indolence as a subject . He may have written the ode as early as March , but the themes and stanza forms suggest May or June 1819 ; when it is known he was working on " Ode on a Grecian Urn " , " Ode on Melancholy " , " Ode to a Nightingale " and " Ode to Psyche " . During this period , Keats 's friend Charles <unk> Brown transcribed copies of the spring odes and submitted them to publisher Richard Woodhouse . Keats wrote to his friend Sarah Jeffrey : " [ T ] he thing I have most enjoyed this year has been writing an ode to Indolence . " Despite this enjoyment , however , he was not entirely satisfied with " Ode on Indolence " , and it remained unpublished until 1848 . 
- Keats 's notes and papers do not reveal the precise dating of the 1819 odes . Literary scholars have proposed several different orders of composition , arguing that the poems form a sequence within their structures . In The <unk> Urn , Bernard Blackstone observes that " Indolence " has been variously thought the first , second , and final of the five 1819 odes . Biographer Robert <unk> suggests " Ode on Indolence " was written on 4 May 1819 , based upon Keats 's report about the weather during the ode 's creation ; Douglas Bush insists it was written after " Nightingale " , " Grecian Urn " , and " Melancholy " . Based on his examination of the stanza forms , Keats biographer Andrew Motion thinks " Ode on Indolence " was written after " Ode to Psyche " and " Ode to a Nightingale " , although he admits there is no way to be precise about the dates . Nevertheless , he argues that " Ode on Indolence " was probably composed last . 
- 
- = = Structure = = 
- 
- " Ode on Indolence " relies on ten line stanzas with a rhyme scheme that begins with a <unk> quatrain ( <unk> ) and ends with a <unk> <unk> ( <unk> ) . This pattern is used in " Ode on Melancholy " , " Ode to a Nightingale " and " Ode on a Grecian Urn " , which further <unk> the poems in their structure in addition to their themes . 
- The poem contains a complicated use of assonance ( the repetition of vowel sounds ) , as evident in line 19 , " O why did ye not melt , and leave my sense " , where the pairs ye / leave and melt / sense share vowel sounds . A more disorganized use of assonance appears in line 31 , " A third time pass 'd they by , and , passing , turn 'd " , in which the pairs third / turn 'd , time / by , and pass 'd / passing share vowel sounds . The first line exemplifies the poem 's consistent iambic <unk> <unk> : 
- Keats occasionally <unk> the accent of the first two syllables of each line or a set of syllables within the middle of a line . 2 @.@ 3 % of the internal syllables are inverted in the " Ode on Indolence " , whereas only 0 @.@ 4 % of the internal syllables of his other poems contain such <unk> . 
- 
- = = Poem = = 
- 
- The poem relies on a first @-@ person narration style similar to " Ode to Psyche " . It begins with a classical scene from an urn in a similar manner to " Ode on a Grecian Urn " , but the scene in " Indolence " is allegorical . The opening describes three figures that operate as three fates : 
- One <unk> before me were three figures seen , 
- With bowed necks , and joined hands , side @-@ faced ; 
- And one behind the other <unk> 'd serene , 
- In placid sandals , and in white robes graced ; 
- They pass 'd , like figures on a marble urn 
- When shifted round to see the other side ; 
- They came again , as , when the urn once more 
- Is shifted round , the first seen shades return ; 
- And they were strange to me , as may <unk> 
- With <unk> , to one deep in <unk> lore . ( Lines 1 – 10 ) 
- The figures remain mysterious as they circle around the narrator . Eventually they turn towards him and it is revealed that they are Ambition , Love , and Poesy , the themes of the poem : 
- A third time pass 'd they by , and , passing , turn 'd 
- Each one the face a moment <unk> to me ; 
- Then faded , and to follow them I burn 'd 
- And <unk> for wings , because I knew the three : 
- The first was a fair Maid , and Love her name ; 
- The second was Ambition , pale of cheek , 
- And ever <unk> with <unk> eye ; 
- The last , whom I love more , the more of blame 
- Is <unk> 'd upon her , maiden most <unk> , — 
- I knew to be my demon Poesy . ( Lines 31 – 40 ) 
- The poet wishes to be with the three figures , but he is unable to join them . The poem transitions into the narrator providing reasons why he would not need the three figures and does so with ambition and love , but he cannot find a reason to dismiss <unk> : 
- They faded , and , <unk> ! I wanted wings : 
- O <unk> ! What is Love ? and where is it ? 
- And for that poor Ambition — it springs 
- From a man 's little heart 's short fever @-@ fit ; 
- For Poesy ! — no , — she has not a joy , — 
- At least for me , — so sweet as <unk> <unk> , 
- And evenings steep 'd in <unk> indolence ; 
- O , for an age so shelter 'd from annoy , 
- That I may never know how change the moons , 
- Or hear the voice of busy common @-@ sense ! ( lines 41 – 50 ) 
- <unk> the poem , the narrator argues that the figures should be treated as figures , and that he would not be <unk> by them : 
- So , ye three ghosts , <unk> ! <unk> cannot raise 
- My head cool @-@ <unk> in the flowery grass ; 
- For I would not be <unk> with praise , 
- A pet @-@ lamb in a sentimental farce ! 
- <unk> <unk> from my eyes , and be once more 
- In <unk> @-@ like figures on the dreary urn ; 
- <unk> ! I yet have visions for the night , 
- And for the day faint visions there is store ; 
- <unk> , ye <unk> , from my idle <unk> , 
- Into the clouds , and never more return ! ( lines 51 – 60 ) 
- 
- = = Themes = = 
- 
- The poem centres on humanity and human nature . When the poet sees the figures , he wants to know their names and laments his ignorance . Eventually , he realizes that they are representative of Love , Ambition , and Poetry . While he longs , he fears they are out of reach and therefore tries to reject them . He argues that love is what he needs least and dismisses it by questioning what " love " actually means ( " What is Love ? and where is it ? " ) . He rejects ambition , but it requires more work ( " And for that poor Ambition — it springs / From a man 's little heart 's short fever @-@ fit ; " ) . Unlike the personas of Love and Ambition , the narrator is unable to find a reason to <unk> Poesy ( Poetry ) , which reflects the poets ' inner conflict : should he abandon poetry to focus on a career in which he can earn a decent living ? Keats 's sought to write great poetry but feared his pursuit of literary prominence was based on a delusional view of his own merit as a poet . Further , he was incapable of completing his epic , " Hyperion " . As Walter Jackson Bate explains , to Keats " Neither a finished ' grand Poem ' nor even the semblance of a modest financial return seemed nearer . " 
- Keats realized that he could never have Love , could not fulfil his Ambition , and could not spend his time with Poesy . The conclusion of " Ode to Indolence " is a dismissal of both the images and his poetry as figures that would only mislead him . Even indolence itself seems <unk> ; Andrew Motion writes that the figures force Keats to regard indolence as " the privilege of the <unk> class to which he did not belong . " If the poem is read as the final poem in the 1819 ode series , " Ode on Indolence " suggests that Keats is resigned to giving up his career as a poet because poetry cannot give him the immortality he wanted from it . Ironically , the poem provided Keats with such immortality . Besides the biographical component , the poem also describes Keats 's belief that his works should capture the beauty of art while acknowledging the <unk> of life . In this way , the poems as a group capture Keats 's philosophy of negative capability , the concept of living with <unk> contradictory views , by trying to reconcile Keats 's desire to write poetry and his inability to do so by abandoning poetry altogether and accepting life as it is . 
- Within the many poems that explore this idea — among them Keats 's and the works by his contemporaries — Keats begins by questioning suffering , breaks it down to its most basic elements of cause and effect , and draws conclusions about the world . His own process is filled with doubt , but his poems end with a hopeful message that the narrator ( himself ) is finally free of desires for Love , Ambition , and Poesy . The hope contained within " Ode on Indolence " is found within the vision he expresses in the last stanza : " I yet have visions for the night / And for the day faint visions there is store . " Consequently , in her analysis of The Odes of John Keats , Helen <unk> suggests that " Ode on Indolence " is a seminal poem constructed with themes and images that appeared more influential in his other , sometimes later , poems . The ode is an early and entirely original work that establishes the basis of Keats 's notion of soul making , a method by which the individual builds his or her soul through a form of education consisting of suffering and personal experience . This is a fundamental preoccupation of the <unk> , who believed the way to reconcile man and nature was through this soul development , education — the combination of experience and contemplation — and that only this process , not the rationality of the previous century , would bring about true <unk> . 
- The classical influences Keats invoked affected other Romantic poets , but his odes contain a higher degree of allusion than most of his contemporaries ' works . As for the main theme , indolence and poetry , the poem reflects the emotional state of being Keats describes in an early 1819 letter to his brother George : 
- [ I ] <unk> and <unk> careless ... from my having <unk> till nearly eleven ... please has no show of <unk> and pain no <unk> <unk> . Neither Poetry , nor Ambition , nor Love have any <unk> of countenance as they pass by me : they seem rather like three figures on a <unk> vase — a Man and two women — whom no one but myself could distinguish in their <unk> . 
- Willard Spiegelman , in his study of Romantic poetry , suggests that the indolence of the poem arises from the narrator 's reluctance to apply himself to the labour associated with poetic creation . Some critics provide other explanations , and William <unk> claims that Keats 's description of indolence may have arisen from the use of opium . 
- 
- = = Critical response = = 
- 
- Literary critics regard " Ode on Indolence " as inferior to Keats 's other 1819 odes . Walter <unk> wrote that " it is unlikely that the ' Ode on Indolence ' has ever been anyone 's favorite poem , and it is certain that it was not Keats 's . Why he excluded it from the 1820 volume we do not know , but it is <unk> and <unk> and structurally <unk> , and these would be reasons enough . " Bate indicated that the poem 's value is " primarily biographical and not poetic " . 
- " Ode on Indolence " is sometimes called upon as a point of comparison when discussing Keats 's other poems . Charles Wentworth Dilke observed that while the poem can be read as a supplemental text to assist the study of " Grecian Urn " , it remains a much inferior work . In 2000 , Thomas McFarland wrote in consideration of Dilke 's comparison : " Far more important than the similarity , which might seem to arise from the <unk> in Keats 's <unk> in both Ode on Indolence and Ode on a Grecian Urn ... is the enormous <unk> in the two poems . Ode on Indolence ... is a flaccid enterprise that hardly bears mention alongside that other achievement . " 
- Sidney <unk> , in his 1917 biography on Keats , grouped " Indolence " with the other 1819 odes in <unk> Keats 's " class of achievements " . In 1948 , Lord <unk> described the fifth stanza as , " lacking the magic of what the world agrees are the great Odes " but describes the language as " [ d ] <unk> , charming even " . Later , in a 1968 biography of Keats , <unk> describes the importance of the poem : " The whole ode , in fact , has a borrowed air , and he acknowledged its lack of success by not printing it with the others ... Yet with its acceptance of the <unk> , dull and <unk> mood as something creative , it set the scene for all the odes that followed . " 
- In 1973 , Stuart <unk> described it as " a rich and nourishing immersion in the rush of pure sensation and its flow of stirring shadows and ' dim dreams ' . In many ways the ode marks both a beginning and an end . It is both the <unk> and potentially the most ambitious of the sequence . Yet its failure , if we choose to consider it that , is more the result of deliberate <unk> than any inability of means . " Andrew Motion , in 1997 , argued , " Like ' Melancholy ' , the poem is too articulate for its own poetic good ... In two of his May odes , ' Melancholy ' and ' Indolence ' , Keats defined themes common to the whole group with such fierce <unk> that he restricted their imaginative power . His identity had prevailed . " 
- 
- 
- = Erving Goffman = 
- 
- Erving Goffman ( 11 June 1922 – 19 November 1982 ) was a Canadian @-@ American sociologist and writer , considered " the most influential American sociologist of the twentieth century " . In 2007 he was listed by The Times Higher Education Guide as the sixth most @-@ cited author in the humanities and social sciences , behind Anthony Giddens and ahead of Jürgen Habermas . 
- Goffman was the 73rd president of the American Sociological Association . His best @-@ known contribution to social theory is his study of symbolic interaction . This took the form of dramaturgical analysis , beginning with his 1959 book , The Presentation of Self in Everyday Life . Goffman 's other major works include Asylums ( 1961 ) , Stigma ( 1963 ) , Interaction Ritual ( 1967 ) , Frame Analysis ( 1974 ) , and Forms of Talk ( 1981 ) . His major areas of study included the sociology of everyday life , social interaction , the social construction of self , social organization ( framing ) of experience , and particular elements of social life such as total institutions and <unk> . 
- 
- = = Life = = 
- 
- Goffman was born 11 June 1922 , in <unk> , Alberta , Canada , to Max Goffman and Anne Goffman , née <unk> . He was from a family of Ukrainian Jews who had emigrated to Canada at the turn of the century . He had an older sibling , Frances Bay , who became an actress . The family moved to <unk> , Manitoba , where his father operated a successful <unk> business . 
- From 1937 Goffman attended St. John 's Technical High School in Winnipeg , where his family had moved that year . In 1939 he enrolled at the University of Manitoba , <unk> in chemistry . He interrupted his studies and moved to Ottawa to work in the film industry for the National Film Board of Canada , established by John <unk> . Later he developed an interest in sociology . Also during this time , he met the renowned North American sociologist , Dennis Wrong . Their meeting motivated Goffman to leave the University of Manitoba and enroll at the University of Toronto , where he studied under C. W. M. Hart and Ray <unk> , graduating in 1945 with a <unk> in sociology and anthropology . Later he moved to the University of Chicago , where he received an <unk> ( 1949 ) and PhD ( 1953 ) in sociology . For his doctoral dissertation , from December 1949 to May 1951 he lived and collected ethnographic data on the island of <unk> in the Shetland Islands . 
- In 1952 Goffman married <unk> <unk> ; in 1953 , their son Thomas was born . <unk> suffered from mental illness and committed suicide in 1964 . Outside his academic career , Goffman was known for his interest , and relative success , in the stock market and in gambling . At one point , in pursuit of his <unk> and ethnographic studies , he became a pit boss at a Las Vegas casino . 
- In 1981 Goffman married <unk> Gillian <unk> . The following year , their daughter Alice was born . In 1982 Goffman died in Philadelphia , Pennsylvania , on 19 November , of stomach cancer . Their daughter , Alice Goffman , is also a sociologist . 
- 
- = = Career = = 
- 
- The research that Goffman had done in <unk> inspired him to write his first major work , The Presentation of Self in Everyday Life ( 1956 ) . After graduating from the University of Chicago , in 1954 – 57 he was an assistant to the athletic director at the National Institute for Mental Health in Bethesda , Maryland . <unk> observation done there led to his essays on mental illness and total institutions which came to form his second book , Asylums : Essays on the Social Situation of Mental Patients and Other Inmates ( 1961 ) . 
- In 1958 Goffman became a faculty member in the sociology department at the University of California , Berkeley , first as a visiting professor , then from 1962 as a full professor . In 1968 he moved to the University of Pennsylvania , receiving the Benjamin Franklin <unk> in Sociology and Anthropology , due largely to the efforts of Dell <unk> , a former colleague at Berkeley . In 1969 he became a fellow of the American Academy of Arts and Sciences . In 1970 Goffman became a <unk> of the American Association for the Abolition of <unk> Mental <unk> and <unk> its Platform Statement . In 1971 he published Relations in Public , in which he tied together many of his ideas about everyday life , seen from a sociological perspective . Another major book of his , Frame Analysis , came out in 1974 . He received a <unk> Fellowship for 1977 – 78 . In 1979 , Goffman received the <unk> @-@ Mead Award for Distinguished Scholarship , from the Section on Social Psychology of the American Sociological Association . He was elected the 73rd president of the American Sociological Association , serving in 1981 – 82 ; he was , however , unable to deliver the presidential address in person due to progressing illness . 
- <unk> , in 1983 , he received the Mead Award from the Society for the Study of <unk> Interaction . 
- 
- = = Influence and legacy = = 
- 
- Goffman was influenced by Herbert <unk> , Émile <unk> , Sigmund Freud , Everett Hughes , Alfred <unk> @-@ Brown , <unk> Parsons , Alfred <unk> , Georg <unk> and W. Lloyd Warner . Hughes was the " most influential of his teachers " , according to Tom Burns . Gary Alan Fine and Philip Manning state that Goffman never engaged in serious dialogue with other theorists . His work has , however , influenced and been discussed by numerous contemporary sociologists , including Anthony Giddens , Jürgen Habermas and Pierre <unk> . 
- Though Goffman is often associated with the symbolic interaction school of sociological thought , he did not see himself as a representative of it , and so Fine and Manning conclude that he " does not easily fit within a specific school of sociological thought " . His ideas are also " difficult to reduce to a number of key themes " ; his work can be broadly described as developing " a comparative , <unk> sociology that aimed to produce <unk> about human behavior " . 
- Goffman made substantial advances in the study of face @-@ to @-@ face interaction , elaborated the " dramaturgical approach " to human interaction , and developed numerous concepts that have had a massive influence , particularly in the field of the micro @-@ sociology of everyday life . Many of his works have concerned the organization of everyday behavior , a concept he termed " interaction order " . He contributed to the sociological concept of framing ( frame analysis ) , to game theory ( the concept of strategic interaction ) , and to the study of interactions and <unk> . With regard to the latter , he argued that the activity of speaking must be seen as a social rather than a linguistic construct . From a methodological perspective , Goffman often employed <unk> approaches , specifically ethnography , most famously in his study of social aspects of mental illness , in particular the functioning of total institutions . Overall , his contributions are valued as an attempt to create a theory that bridges the agency @-@ and @-@ structure divide – for popularizing social <unk> , symbolic interaction , conversation analysis , ethnographic studies , and the study and importance of individual interactions . His influence extended far beyond sociology : for example , his work provided the assumptions of much current research in language and social interaction within the discipline of communication . 
- <unk> Management is defined as when an individual attempts to present an acceptable image to those around him or her verbally or <unk> ( 480 ) . This definition is based on Goffman ’ s idea that individuals see themselves as others view them , so in essence they attempt to see themselves as if they are outside looking in ( 480 ) . Goffman also dedicating this work to discover the subtle ways humans present acceptable images by concealing information that may conflict with the images for a particular situation . For instance , concealing tattoos when applying for a job in which tattoos would be inappropriate , or hiding a bizarre obsession such as collecting / interacting with dolls which society as a whole may see as abnormal . 
- Goffman does break from his connection with George Herbert Mead and Herbert <unk> in that while he does not reject the way in which individuals perceive themselves , he was more interested in the actual physical proximity or the “ interaction order ” that molds the self ( <unk> ) . In other words , Goffman believed that impression management can only be achieved if the audience is in sync with the individual ’ s perception of self . If the audience is in disagreement with the image an individual is presenting then the individual ’ s presentation of self is interrupted . Individuals present images of themselves based on how society thinks they should act in a particular situation . This decision on how to act is decided based on the concept of definition of the situation . The definition are all predetermined and individuals choose how they will act by choosing the proper behavior for the social situation they are in . Goffman draws from William Thomas for this concept as well . Thomas believed that people are born into a particular social class and so the definitions of the situations will encounter have been previously defined for them ( 480 ) . For instance when an individual from a high class background goes to a black tie affair the <unk> definition of situation would be that they must mind their <unk> and act according to their class . 
- In 2007 Goffman was listed by The Times Higher Education Guide as the sixth most @-@ cited author in the humanities and social sciences , behind Anthony Giddens and ahead of Jürgen Habermas . His popularity with the general public has been attributed to his writing style , described as " sardonic , <unk> , <unk> " , and as " ironic and self @-@ consciously literary " , and to its being more accessible than that of most academics . His style has also been influential in academia , and is credited with popularizing a less formal style in academic publications . 
- His students included Carol Brooks Gardner , Charles Goodwin , Marjorie Goodwin , John <unk> , Gary <unk> , Harvey Sacks , Emanuel <unk> , David <unk> and <unk> <unk> . 
- Despite his influence , according to Fine and Manning there are " remarkably few scholars who are continuing his work " , nor has there been a " Goffman school " ; thus , his impact on social theory has been simultaneously " great and modest " . Fine and Manning attribute the lack of subsequent Goffman @-@ style research and writing to the nature of his signature style , which they consider very difficult to duplicate ( even " mimic @-@ proof " ) , and also to his writing style and subjects not being widely valued in the social sciences . With regard to his style , Fine and Manning remark that he tends to be seen either as a scholar whose style is difficult to reproduce , and therefore daunting to those who might wish to emulate his style , or as a scholar whose work was transitional , bridging the work of the Chicago school and that of contemporary sociologists , and thus of less interest to sociologists than the classics of either of those two groups . With regard to his subjects , Fine and Manning observe that the topic of behavior in public places is often <unk> as being trivial , and thus unworthy of serious scholarly attention . 
- Nonetheless , Fine and Manning note that Goffman is " the most influential American sociologist of the twentieth century " . Elliott and Turner see him as " a revered figure – an outlaw theorist who came to <unk> the best of the sociological imagination " , and " perhaps the first <unk> sociological theorist " . 
- 
- = = Works = = 
- 
- 
- = = = Early works = = = 
- 
- Goffman 's early works consist of his graduate writings of 1949 – 53 . His master 's thesis was a survey of audience responses to a radio soap opera , Big Sister . One of its most important elements was a critique of his research methodology – of experimental logic and of variable analysis . Other writings of the period include <unk> of Class Status ( 1951 ) and On <unk> the Mark Out ( 1952 ) . His doctoral dissertation , Communication Conduct in an Island Community ( 1953 ) , presented a model of communication strategies in face @-@ to @-@ face interaction , and focused on how everyday life rituals affect public projections of self . 
- 
- = = = Presentation of Self = = = 
- 
- Goffman 's The Presentation of Self in Everyday Life was published in 1956 , with a revised edition in 1959 . He had developed the book 's core ideas from his doctoral dissertation . It was Goffman 's first and most famous book , for which he received the American Sociological Association 's 1961 <unk> Award . 
- Goffman describes the theatrical performances that occur in face @-@ to @-@ face interactions . He holds that when an individual comes in contact with another person , he attempts to control or guide the impression that the other person will form of him , by altering his own setting , appearance and manner . At the same time , the person that the individual is interacting with attempts to form an impression of , and obtain information about , the individual . Goffman also believes that participants in social interactions engage in certain practices to avoid embarrassing themselves or others . Society is not <unk> ; we must act differently in different settings . This recognition led Goffman to his dramaturgical analysis . He saw a connection between the kinds of " acts " that people put on in their daily lives and theatrical performances . In a social interaction , as in a theatrical performance , there is an onstage area where actors ( individuals ) appear before the audience ; this is where positive self @-@ concepts and desired impressions are offered . But there is , as well , a backstage – a hidden , private area where individuals can be themselves and drop their societal roles and identities . 
- 
- = = = Asylums = = = 
- 
- Goffman is sometimes credited with having in 1957 coined the term " total institution " , though Fine and Manning note that he had heard it in lectures by Everett Hughes in reference to any type of institution in which people are treated alike and in which behavior is regulated . Regardless of whether Goffman coined the term " total institution " , he popularized it with his 1961 book , Asylums : Essays on the Social Situation of Mental Patients and Other Inmates . The book has been described as " ethnography of the concept of the total institution " . The book was one of the first sociological examinations of the social situation of mental patients in psychiatric hospitals and a major contribution to understanding of social aspects of mental illness . 
- The book is composed of four essays : " Characteristics of Total <unk> " ( 1957 ) ; " The <unk> Career of the Mental <unk> " ( 1959 ) ; " The <unk> of a Public Institution : A Study of Ways of Making Out in a Mental Hospital " ; and " The Medical Model and Mental <unk> : Some Notes on the <unk> of the <unk> Trades " . The first three essays focus on the experiences of patients ; the last , on professional @-@ client interactions . Goffman is mainly concerned with the details of psychiatric hospitalization and with the nature and effects of the process he calls " <unk> " . He describes how <unk> <unk> people into the role of a good patient , someone " dull , harmless and inconspicuous " – a condition which in turn reinforces notions of <unk> in severe mental illness . Total institutions greatly affect people 's interactions ; yet , even in such places , people find ways to redefine their roles and reclaim their identities . 
- Asylums has been credited with helping <unk> the reform of mental health systems in a number of countries , leading to reductions in the numbers of large mental hospitals and of the individuals locked up in them . It has also been influential in the anti @-@ <unk> movement . 
- 
- = = = Behavior in Public = = = 
- 
- In Behavior in Public Places ( 1963 ) , Goffman again focuses on everyday public interactions . He draws distinctions between several types of public gatherings ( " gatherings " , " situations " , " social occasions " ) and types of audiences ( acquainted versus <unk> ) . 
- 
- = = = Stigma = = = 
- 
- Goffman 's book Stigma : Notes on the Management of <unk> Identity ( 1963 ) examines how , to protect their identities when they depart from approved standards of behavior or appearance , people manage impressions of themselves – mainly through concealment . Stigma <unk> to the shame that a person may feel when he or she fails to meet other people 's standards , and to the fear of being discredited – which causes the individual not to reveal his or her shortcomings . Thus , a person with a criminal record may simply <unk> that information from fear of being judged by <unk> that person happens to encounter . 
- 
- = = = Interaction Ritual = = = 
- 
- Interaction Ritual : Essays on Face @-@ to @-@ Face Behavior is a collection of six Goffman essays . The first four were originally published in the 1950s , the fifth in 1964 , and the last was written for the collection . They include : " On Face @-@ work " ( 1955 ) ; " <unk> and Social Organization " ( 1956 ) ; " The Nature of <unk> and <unk> " ( 1956 ) ; " <unk> from Interaction " ( 1957 ) ; " Mental Symptoms and Public Order " ( 1964 ) ; and " Where the Action Is " . 
- The first essay , " On Face @-@ work " , discusses the concept of face , which is the positive self @-@ image that an individual holds when interacting with others . Goffman believes that face " as a sociological construct of interaction , is neither inherent in nor a permanent aspect of the person " . Once an individual offers a positive self @-@ image of him or herself to others , that individual feels a need to maintain and live up to that image . <unk> in how a person projects him or herself in society risks embarrassment and <unk> . Therefore , people remain guarded , to ensure that they do not show themselves to others in an unfavorable light . 
- 
- = = = Strategic Interaction = = = 
- 
- Goffman 's book Strategic Interaction ( 1969 ) is his contribution to game theory . It discusses the compatibility of game theory with the legacy of the Chicago School of sociology and with the perspective of symbolic <unk> . It is one of his few works that clearly engage with that perspective . Goffman 's view on game theory was shaped by the works of Thomas <unk> . Goffman presents reality as a form of game , and discusses its rules and the various moves that players can make ( the " <unk> " , the " naive " , the " covering " , the " uncovering " , and the " counter @-@ uncovering " ) . 
- 
- = = = Frame Analysis = = = 
- 
- Frame Analysis : An Essay on the Organization of Experience ( 1974 ) is Goffman 's attempt to explain how conceptual frames – ways to organize experience – structure an individual 's perception of society . This book is thus about the organization of experience rather than the organization of society . A frame is a set of concepts and theoretical perspectives that organize experiences and guide the actions of individuals , groups and societies . Frame analysis , then , is the study of the organization of social experience . To illustrate the concept of the frame , Goffman gives the example of a picture frame : a person uses the frame ( which represents structure ) to hold together his picture ( which represents the content ) of what he is experiencing in his life . 
- The most basic frames are called primary <unk> . A primary framework takes an individual 's experience or an aspect of a scene that would originally be meaningless and makes it meaningful . One type of primary framework is a natural framework , which identifies situations in the natural world and is completely <unk> , with no human influences . The other type of framework is a social framework , which explains events and connects them to humans . An example of a natural framework is the weather , and an example of a social framework is a meteorologist who predicts the weather . Focusing on the social <unk> , Goffman seeks to " construct a general statement regarding the structure , or form , of experiences individuals have at any moment of their social life " . 
- Goffman saw this book as his magnum opus , but it was not as popular as his earlier works . 
- The Frame Analyses of Talk 
- In Frame Analyses , Erving Goffman provides a platform for understanding and interpreting the interaction between individuals engaging speech communication . In the chapter “ The Frame Analyses of Talk , ” the focus is put on how words are exchanged and what is being said , specifically in informal talk or conversation . The concept of framing is introduced through an exploration of why <unk> occur in these basic , everyday conversations . He argues that they are more errors in verbal framing than anything else . The types of frames Goffman is considering are discussed in previous sections of the book , “ <unk> , <unk> , frame breaks , <unk> , and , of course , frame disputes . ” That a frame can assume so many forms is the basis of his analyses , “ these <unk> are subject to a multitude of different <unk> - the warrant for a frame analysis in the first place . ” 
- Goffman ’ s key idea is that most conversation is simply a <unk> of a strip – what he describes as a personal experience or event . When we talk with others , the speaker ’ s goal is often always the same , to provide “ evidence for the fairness or <unk> of his current situation and other grounds for sympathy , approval , <unk> , understanding , or amusement . And what his listeners are primarily obliged to do is to show some kind of audience appreciation . " Essentially , through interaction , we are only looking to be heard , not inspire any kind of action but simply to know that someone listened and understood . This is why often a simple head <unk> or <unk> is accepted as an appropriate response in conversation . 
- Goffman explains that the way a conversation is keyed is critical to understanding the intent behind many utterances in everyday speech . Key is probably best understood as the tone of the dialogue which can change numerous times during an interaction . <unk> a change in key is one way that framing often takes place , “ special brackets will have to be introduced should he want to say something in a relatively serious way : " <unk> aside , " " Now , I 'm really serious about this , " and other such <unk> become necessary as a means of momentarily down <unk> the flow of words . ” 
- <unk> Richard <unk> builds heavily on Goffman ’ s work , specifically on the idea of key , in his work pertaining to an analysis of the performance frame . <unk> details that a performance is dependent on it being properly keyed , without this , the display will not be successful . His work on performance analyses is deeply <unk> to what Goffman establishes here in “ Frame Analyses . ” 
- <unk> is one other element to framing that is essential . " The participants will be bound by norms of good manners : through frequency and length of turns at talk , through topics avoided , through <unk> in regard to references about self , through attention offered eagerly or begrudgingly @-@ through all these means , rank and social relationship will be given their due . " Certain things can and will be said in one scenario that would never be uttered in another . An awareness of these social <unk> is critical , just as is an awareness of the audience . Depending on who you ’ re speaking with ( a teacher , a child , a loved one , a friend , a pet , etc . ) you will curve your speech to fit the frame of what your intended audience is expecting . 
- Goffman uses the metaphor of conversation being a stage play . A plays tone will shift throughout the performance due to the actions taken by the actors ; this is similar to how a discussion is keyed – based on what either person says or does over the course of an interaction , the key will change accordingly . The parallels go further , though . Goffman also claims that a speaker details a drama more often than they provide information . They invite the listener to <unk> and , as was explained above , they ’ re often not meant to be stirred to take action , but rather to show appreciation ; during a play this generally takes the form of applause . 
- Other similarities include engaging in the suspense the speaker is attempting to create . In both scenarios , you must put aside the knowledge that the performers know the outcome of the event being relayed and , in a sense , play along . This is integral to his stance as he explains “ the argument that much of talk consists of <unk> and that these make no sense unless some form of storyteller 's suspense can be maintained shows the close relevance of frame @-@ indeed , the close relevance of <unk> @-@ for the organization of talk . ” Lastly , because the <unk> of strips is not <unk> , but rather <unk> , it is yet another parallel between a stage production and conversation . All of these things work in concert to provide a foundation of how talk is framed . 
- 
- = = = Gender Advertisements = = = 
- 
- Gender Advertisements is a 1979 book that , as part of Goffman 's series of studies in the anthropology of visual communication , deals with the topic of gender representation in advertising . The book is a visual essay about sex roles in advertising , differences in the depictions of men and women and the subtle , underlying messages about the sexual roles projected by masculine and feminine images in advertising , as well as and symbolism in advertising . In the book Goffman examined over 500 advertisements in order to find general patterns in stereotypical gender representation , which he placed into six categories : 
- Relative Size : Goffman argues that social situation is expressed through the relative size of the persons in the advertisements , with men showing their superiority through their girth and height . 
- <unk> Touch : Women are frequently depicted touching persons or objects in a ritualistic manner , occasionally just barely touching the object or person . 
- Function <unk> : When a man and woman are shown in a collaborative manner , the male is more likely to be shown as the higher ranked person than the woman . 
- The Family : When families are depicted in advertising , parents are shown to be closer to their children of the same gender and in some instances men are shown separate from the rest of the family , in a protective manner . 
- <unk> of <unk> : <unk> is expressed by lowering oneself physically . <unk> and disdain , holding the body erect and the head high . 
- <unk> <unk> : Goffman states that women in advertisements are frequently depicted as removed from the scene around them , either physically turning away from the scene or appearing lost in thought . 
- In her 2001 work Measuring Up : How Advertising <unk> Self @-@ Image , Vickie <unk> Shields stated that the work was " unique at the time for employing a method now being labeled ' <unk> content analysis ' " and that it " [ provided ] the base for textual analyses ... such as <unk> and <unk> approaches " . She also noted that feminist scholars like Jean <unk> " [ built ] their highly persuasive and widely circulated findings on the nature of gender in advertising on Goffman 's original categories " . 
- 
- = = = Forms of Talk = = = 
- 
- Goffman 's book , Forms of Talk ( 1981 ) , includes five essays : " <unk> and Responses " ( 1976 ) ; " Response <unk> " ( 1978 ) ; " <unk> " ( 1979 ) ; " The <unk> " ( 1976 ) ; and " Radio Talk " ( 1981 ) . Each essay addresses both verbal and non @-@ verbal communication through a <unk> model . The book provides a comprehensive overview of the study of talk . In the introduction , Goffman identifies three themes that recur throughout the text : " <unk> , participation framework , and <unk> " . 
- The first essay , " <unk> and Responses " , concerns " <unk> dialogue " and the way people respond during a conversation , both verbally and non @-@ verbally . The second essay , " Response <unk> " , considers the use of utterances and their social implications in different social contexts . Specifically , Goffman discusses " self @-@ talk " ( talking to no one in particular ) and its role in social situations . Next , in " <unk> " , Goffman addresses the way that footing , or alignment , can shift during a conversation . The fourth essay , " The <unk> " , originally an oral presentation , describes different types and methods of lecture . Lastly , in " Radio Talk " , Goffman describes the types and forms of talk used in radio programming and the effect they have on listeners . 
- 
- = = <unk> = = 
- 
- In his career , Goffman worked at the : 
- University of Chicago , Division of Social Sciences , Chicago : assistant , 1952 – 53 ; resident associate , 1953 – 54 ; 
- National Institute of Mental Health , Bethesda , Maryland : visiting scientist , 1954 – 57 ; 
- University of California , Berkeley : assistant professor , 1957 – 59 ; professor , 1959 – 62 ; professor of sociology , 1962 – 68 ; 
- University of Pennsylvania , Philadelphia : Benjamin Franklin Professor of Anthropology and Sociology , 1969 – 82 . 
- 
- = = Selected works = = 
- 
- 1959 : The Presentation of Self in Everyday Life . University of Edinburgh Social Sciences Research Centre . ISBN 978 @-@ 0 @-@ 14 @-@ <unk> @-@ 8 . Anchor Books edition 
- 1961 : Asylums : Essays on the Social Situation of Mental Patients and Other Inmates . New York , <unk> . ISBN 0 @-@ 14 @-@ <unk> @-@ 4 
- 1961 : <unk> : Two Studies in the Sociology of Interaction – Fun in Games & Role <unk> . Indianapolis , <unk> @-@ Merrill . 
- 1963 : Behavior in Public Places : Notes on the Social Organization of <unk> . The Free Press . ISBN 0 @-@ 02 @-@ <unk> @-@ 5 
- 1963 : Stigma : Notes on the Management of <unk> Identity . <unk> @-@ Hall . ISBN 0 @-@ <unk> @-@ <unk> @-@ 7 
- 1967 : Interaction Ritual : Essays on Face @-@ to @-@ Face Behavior . Anchor Books . ISBN 0 @-@ 394 @-@ <unk> @-@ 5 
- 1969 : Strategic Interaction . Philadelphia : University of Pennsylvania Press . ISBN 0 @-@ 345 @-@ <unk> @-@ X 
- 1969 : Where the action is . Allen Lane . ISBN 0 @-@ <unk> @-@ <unk> @-@ 2 
- 1971 : Relations in Public : <unk> of the Public Order . New York : Basic Books . ISBN 0 @-@ 06 @-@ <unk> @-@ 0 
- 1974 : Frame analysis : An essay on the organization of experience . London : Harper and Row . ISBN 978 @-@ 0 @-@ 06 @-@ <unk> @-@ 5 
- 1979 : Gender Advertisements . Macmillan . ISBN 0 @-@ 06 @-@ <unk> @-@ 5 
- 1981 : Forms of Talk . Philadelphia : University of Pennsylvania Press . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 6 
- 
- 
- = Cater 2 U = 
- 
- " Cater 2 U " is a song by American recording group Destiny 's Child , taken from their fourth studio album Destiny Fulfilled ( 2004 ) . Columbia Records released it as the album 's fourth and final single on June 14 , 2005 . The song was written by band members Beyoncé , Kelly Rowland and Michelle Williams along with Rodney " <unk> " Jerkins , Ricky " Ric Rude " Lewis and Robert Waller , with Beyoncé , Rude and Jerkins handling its production . An R & B ballad talking about a woman 's desire to cater to the male love interest of her life , " Cater 2 U " contains synthesizers in its instrumentation . A lawsuit filled by singer @-@ songwriter <unk> Allen in 2006 , claimed the group only recorded a cover of the song which was originally his ; however the case was settled in 2009 . 
- " Cater 2 U " received mixed reviews from music critics who , despite generally praising its composition and the group members ' vocal performances , criticized the lyrical content . The song was nominated in two categories at the 48th Annual Grammy Awards in 2006 and received an award for Best R & B / Soul Single , Group , Band or Duo at the 2006 Soul Train Music Awards . " Cater 2 U " was commercially successful in the US , peaking at number 14 on the Billboard Hot 100 and three on the Hot R & B / Hip @-@ Hop Songs , further receiving a gold certification . The song also appeared in the top ten in New Zealand and top twenty in Australia , while also receiving minor chart placement in Belgium and Netherlands . 
- A music video for the song was filmed by Jake Nava and shot at the Red Rock Canyon State Park in California . It features the trio at a desert with each member singing at a separate set and later a choreographed dance during the chorus . To further promote " Cater 2 U " , Destiny 's Child performed the song during several televised appearances and at the 2005 BET Awards . In addition , " Cater 2 U " was added to the set list of Destiny 's Child 's final tour Destiny Fulfilled ... and Lovin ' It in 2005 . Both Beyoncé and Rowland performed the song during their solo tours after Destiny 's Child 's disbandment . A cover version of " Cater 2 U " was performed by Usher and Babyface at the 2005 World Music Awards as a tribute to the group due to their disbandment that followed the same year . 
- 
- = = Background = = 
- 
- " Cater 2 U " was written by band members Beyoncé , Kelly Rowland and Michelle Williams along with Rodney " <unk> " Jerkins , Ricky " Ric Rude " Lewis and Robert Waller . The production was handled by Beyoncé , Rude and Jerkins with the band members also serving as the vocal producers . The song was recorded in 2004 at Sony Music Studios in New York City with guidance from Jeff <unk> and Jim <unk> . The audio mixing was done by Tony <unk> while the mastering was finished by Tom Coyne . 
- During an interview with MTV News , Beyoncé talked about the meaning of the song : " It basically talks about how a guy inspires you ... You want to make him happy and you want to cater to him . I know it 's going to be surprising to a lot of people that the independent survivors are being submissive to their man , but it 's important that people know that , you know , it 's fine if your man deserves it and gives that back to you . " " Cater 2 U " was released as the fourth and final single from Destiny Fulfilled . It was first released in the US as an extended play ( EP ) consisting of the audio version of the song along with four remixes on June 14 , 2005 . On July 19 another EP was released containing the original version and six remixes . Six days later an extended play of the single was released in Italy . " Cater 2 U " was included on two of Destiny 's Child 's compilation albums : the greatest hits album # 1 's ( 2005 ) and Love Songs ( 2013 ) . 
- 
- = = = <unk> = = = 
- 
- Chicago @-@ based singer @-@ songwriter <unk> Allen pushed allegations that " Cater 2 U " was inspired by a song he had composed with the same title and spelling , which was copyrighted in the mid @-@ 1990s and 2000 and performed locally during that time . He claimed he handed over a version to producer Maurice Joshua , who allegedly went on to cover the song with Destiny 's Child . Joshua denied his claims , noting that he never held a copy of Allen 's song . Allen filed a lawsuit against the group for compensation in a Chicago , Illinois court in 2006 where the trio was due to appear in December 10 for an eight @-@ day trial . However , they managed to avoid any given court dates after their public split in 2005 and agreed to settle behind closed doors . In early December 2009 it was confirmed by Chicago Sun @-@ Times that the group had reached a settlement with Allen . His attorney , Matthew <unk> , said in a statement , " I can confirm that yes , [ the case ] did settle . All of the issues have been <unk> resolved and the case is going to be dismissed . [ Allen is ] excited about being able to devote time to posturing his musical career . All legal terms and agreement will not be released and the case will be dismissed by a judge " . 
- 
- = = Composition = = 
- 
- " Cater 2 U " is a four @-@ minute long R & B ballad . In her book American Music , Nicolae <unk> found influences of quiet storm in the song 's composition . According to the sheet music published on the website Musicnotes.com by Sony / ATV Music Publishing , " Cater 2 U " was composed using common time in the key of D major with a slow tempo of 63 beats per minute . The vocal elements span from <unk> to <unk> . <unk> it consists of <unk> synthesizers " <unk> [ ing ] and <unk> [ ing ] " throughout , creating a seductive feel as stated by Dimitri <unk> of Vibe and writers of Billboard magazine . As the song progresses , it builds to a symphonic crescendo . 
- Lyrically , " Cater 2 U " talks about females wanting to <unk> serve their male love interests and take care of them as they admire their hard work and are inspired by them . The trio further sings about the men of their lives and the way in which they will take care of them . " Cater 2 U " was written as a continuation on the previous song on Destiny Fulfilled , " Soldier " ; after the trio sings about finding a suitable lover in the aforementioned song , they express a will to cater to him in " Cater 2 U " . In the second edition of the book <unk> Cultural Studies , the authors argued that the song contained lyrics about <unk> of women , which suggested that their gender role was to " ' keep herself up ' , ' keep it right ' , ' cater to ' their man by providing him with his dinner , a foot rub , a <unk> , <unk> his <unk> , and much more , on demand " . An editor writing in The Times of India found a theme of feminine assertiveness in " Cater 2 U " ; he noted that " the women come off not so much as lovers as full @-@ service romantic servants " . J. Freedom du Lac , a staff writer of The Washington Post wrote that the song 's theme was <unk> . 
- Beyoncé opens the song listing the things she would do for her man during her verses : brush his hair , take his shoes off , give him a <unk> , rub his feet , help him put his do @-@ <unk> on , <unk> his <unk> . She also sings the lines " Let me feed you , let me run your bath water , whatever you desire " after the <unk> she had previously listed . Rowland was noted for nearly rapping her solo part during which she promises to " keep my figure right , " " keep my hair fixed " and acknowledges that if her man comes later home while she 's asleep , " all he 's gotta do is tap her on the shoulder and ' I 'll roll over . ' " Williams sings her solo verses during the song 's bridge . 
- 
- = = Critical reception = = 
- 
- Tom Sinclair of Entertainment Weekly noted the song was one of Destiny Fulfilled 's " nice bits here and there " , adding , " the pillow @-@ <unk> <unk> of ' Cater 2 U ' ... is hard to resist . " AllMusic 's writer Stephen Thomas Erlewine included " Cater 2 U " as one of the highlights on the album . Slant Magazine writer Eric Henderson opined , " But just about the only element of the whole album that really sticks is the Laura Bush @-@ worthy domestic <unk> of ' Cater 2 U , ' in which the trio <unk> their joy in total submission . " Dimitri Ehrlich from Vibe magazine found that " the sexy trio is far more <unk> on the seductive R & B ballad ' Cater 2 U , ' in which they set the feminist movement back more than a century over the course of four minutes " . Andy <unk> writing for The A.V. Club felt that the song and " Soldier " " make sassy end @-@ runs around notions of <unk> subservience , but their best musical moments hide in tiny <unk> <unk> instead of hooks " . The Guardian 's Caroline Sullivan wrote that " Cater 2 U " was one of the songs that showcased " the <unk> in the trio , musically and lyrically " . In a review of Love Songs , Amanda <unk> from the website Consequence of Sound described " Cater 2 U " as a " <unk> " album opener . Thomas <unk> of <unk> Magazine hailed the song " sexy " while USA Today 's <unk> Gardner felt it was more " gently sensuous " . Rebecca Thomas from MTV News felt that the song differentiated from the trio 's other material and added " While the Third Wave set may have side @-@ eyed the <unk> ballad , male fans eagerly welcomed [ it ] " . 
- The lyrics of the song were criticized with BBC 's Nick Reynolds saying that although the group " reinvent [ ed ] themselves as domestic goddesses for some lucky man " , the song was neither convincing nor good . He further opined " I thought they were supposed to be independent women ? ! " juxtaposing it with the group 's earlier song " Independent Women " ( 2000 ) . Jenny <unk> of Rolling Stone echoed his statements , writing " Jerkins should hang his head in shame for having a hand in the <unk> ' Cater 2 U , ' a saccharine tune about how low Knowles , Rowland and Williams will <unk> to prove their devotion to their man ... Since when did these independent women become so <unk> ? " . Sean <unk> of Pitchfork Media also stated that " their swan song of sorts ... completely <unk> the <unk> <unk> most of their greatest jams provided " . He further described the production as " <unk> and slight " , the chorus as " limp " and the song <unk> as " a Prince mock @-@ up of the worst kind " . <unk> <unk> of the same website offered similar criticism : " [ The song ] reneged on everything early DC stood for in a parade of kept @-@ <unk> pleas " . Alex <unk> of <unk> Magazine dismissed the <unk> of the lyrics classifying " Cater 2 U " as a " <unk> " song . Neil McCormick from The Daily Telegraph panned it as " <unk> inducing , with lots of sensuous moaning " . Barbara Ellen of The Observer wrote , " I thought this was the kind of song bands recorded by mistake then hid <unk> in the attic " . 
- 
- = = = Recognition = = = 
- 
- At the 48th Annual Grammy Awards in 2006 , " Cater 2 U " was nominated in two categories : Best R & B Song and Best R & B Performance by a Duo or Group with Vocals ultimately losing in both . The same year , the song won an award for Best R & B / Soul Single , Group , Band or Duo at the 2006 Soul Train Music Awards . It was one of the Award Winning R & B / Hip @-@ Hop Songs at the 2006 ASCAP Rhythm & Soul Music Awards . In 2013 , Lindsey Weber from the website <unk> listed " Cater 2 U " at number seven on her list of the 25 best songs by Destiny 's Child . Weber went on to describe it as " probably the least feminist song " of the band 's material , " the best song ever to lyrically use the phrase ' run your <unk> ' " and hailed Williams for her contribution during the bridge . On the occasion of Beyoncé 's 32nd birthday , <unk> <unk> and Jason <unk> of Billboard included " Cater 2 U " at number 25 on the list of " <unk> 's 30 Biggest Billboard Hits " . 
- 
- = = Chart performance = = 
- 
- In the US , the song entered at number 95 on the US Billboard Hot 100 singles chart for the chart issue dated April 30 , 2005 . In its fifth week of charting it moved to number 83 on May 28 . After several weeks of ascending the chart , " Cater 2 U " peaked at number 14 on the Billboard Hot 100 for the week ending August 13 , 2005 . On the Hot R & B / Hip @-@ Hop Songs the song entered the top ten of the chart at number six in its 19th week of charting on July 9 , 2005 . It became the band 's fourth top ten single from Destiny Fulfilled and the group 's twelfth top ten song . On the chart issue dated July 30 , 2005 , " Cater 2 U " moved from the position of four to three which became its peak and stayed there for three additional consecutive weeks . With that feat , it became the second single from Destiny Fulfilled and the group 's seventh overall top five single on the Hot R & B / Hip @-@ Hop Songs chart . The Recording Industry Association of America ( RIAA ) certified the single gold on December 13 , 2005 for shipment of 500 @,@ 000 digital copies in the US . Its ringtone was further certified platinum on June 14 , 2006 for selling 1 @,@ 000 @,@ 000 copies . 
- In the Netherlands , " Cater 2 U " peaked at number 60 in its first week of charting on September 17 , 2005 . " Cater 2 U " debuted at number 20 on the Ultratip chart in the Flanders region of Belgium and moved to its peak position of 18 the following week on October 8 , 2005 which also became its final . It performed better on the same chart in the Wallonia region of the country where it peaked at the position of eight on October 22 . In Australia , the song debuted and peaked at number 15 on the ARIA Singles Chart on August 21 , 2005 and lasted six weeks in the chart 's top 50 . On the New Zealand Singles Chart , " Cater 2 U " first appeared on August 29 , 2005 at the position of seven . It moved to number nine the following week and started gradually descending the chart . It last appeared on October 10 at number 40 after seven weeks of charting . 
- 
- = = Music video = = 
- 
- The music video for " Cater 2 U " was directed by Jake Nava . It was shot back to back with the video for " Girl " and large parts were filmed at Red Rock Canyon State Park in California . On July 5 , 2005 , the music video was released on MTV 's official website . It is also featured on the <unk> edition of the album # 1 's as well as on the Japanese version of the DVD Destiny 's Child : Live in Atlanta . In 2013 , it was included on the album Destiny 's Child Video Anthology which contained every music video the group had filmed during their career . An image taken from the video of " Cater 2 U " was used as the cover artwork for the DVD . The video opens with a fast instance of a sun rising in the sky and moves to the girls standing nude together . Each singer is featured in her own desert scene performing individually during her respective verse ; Beyoncé is seen on a diving board next to a swimming pool , Rowland on a deserted road where she dances next to a silver car from which she had previously got out of and Williams on a <unk> . During the chorus the group members are seen wearing <unk> evening dresses , performing a choreographed dance routine for the camera and three males who watch them from the side ; the men are never filmed in the same shot as Destiny 's Child . During the end , each member is seen together with one of their male partners and the video fades away showing the trio nude again as during the beginning . 
- While reviewing the video of " Cater 2 U " , <unk> Hall of MTV News felt that " [ the trio ] have decided to go au <unk> — <unk> , of course " . He further concluded that the clip " is all about scenery and the beauty of the trio " . In their book Music Video and the Politics of <unk> , Diane <unk> and Paul Watson felt that the presence of men " seems utterly superfluous to the video 's spectacle of female bodies and female pleasures " . They further noted that the video 's imagery and performance managed to proceed without a reference to its lyrics and elaborated , 
- " Indeed , the <unk> <unk> of its desert spaces , the posed tableaux of the women 's bodies and the noticeable lack of attention directed to its male subjects by both the camera and the group , does not simply jar with the lyrical narrative of female subordination but in fact reinforces the image of Destiny 's Child as independent women who are in control of their own lives . That is to say , the video ( re ) establishes an image of women who are in control of their own bodies , their own desires , their relationship with men and their friendships with women . " 
- 
- = = Live performances and cover version = = 
- 
- On November 16 , 2004 Destiny 's Child performed " Cater 2 U " during the television show Good Morning America broadcast on the American Broadcasting Company ( ABC ) . The song was performed on June 28 during the 2005 BET Awards in Los Angeles . Midway through the performance , for which the members wore form @-@ fitting , floor @-@ length brown gowns , they randomly picked Terrence Howard , Nelly and Magic Johnson from the audience to the stage and performance a lap dance for them . Corey Moss of MTV News felt that their performance was one of the more memorable of the night . Emily Tan from the website Idolator noted that the performance was one of the band 's best further writing , " No one expected the girls to get as down and dirty as they did ... But it looked like no one was complaining . " Steve <unk> writing for Rolling Stone remarked that the group provided " some <unk> " . The trio gave a live rendition of " Cater 2 U " again at NBC 's The Today Show show on July 29 , 2005 . A writer of People magazine felt that " Beyoncé <unk> and shakes things up " during the performance . An acoustic version was also performed on MTV in 2005 during the its Spring Break coverage . 
- In 2005 , " Cater 2 U " was part of the set list of the group 's final tour Destiny Fulfilled ... and Lovin ' It where they performed it in the same style as during the BET Awards , following the popularity of that performance . During the performance , the trio , dressed in long blue floor @-@ length gowns , called three men from the audience to the stage and performed a choreography similar to a lap dance . In a review of a concert in the UK , <unk> <unk> of MTV News felt that the song was suitable to " slow things down " following the previous energetic performances of the show . " Cater 2 U " was included on the track listing of the group 's live album Destiny 's Child : Live in Atlanta ( 2006 ) <unk> a concert from the tour in that city . Following the group 's disbandment , both Beyoncé and Rowland included " Cater 2 U " in the set list of their respective solo tours . The former performed it as part of a Destiny 's Child medley included during the concerts of The Beyoncé Experience in 2007 . It was subsequently included in the live DVD , The Beyoncé Experience Live ( 2007 ) which included a concert filmed at the Staples Center in Los Angeles , California . Similarly , Rowland performed " Cater 2 U " live during her Lights Out Tour ( 2013 ) as part of a segment which included songs by Destiny 's Child . Usher and Babyface performed " Cater 2 U " at the 2005 World Music Awards on August 31 as a tribute to Destiny 's Child due to their disbandment . Byron <unk> of MTV described their duet as " sensual " . In 2009 , " Cater 2 U " was referenced in Lil Wayne and Drake 's song " My <unk> ' Baby " . 
- 
- = = Credits and personnel = = 
- 
- Credits are adapted from the liner notes of the album Destiny Fulfilled . 
- Lead vocals : Beyoncé Knowles , Kelly Rowland and Michelle Williams 
- Vocal production : Beyoncé Knowles , Kelly Rowland , and <unk> Michelle Williams 
- Recording : Jeff <unk> and Jim <unk> at Sony Music Studios , NYC 
- Audio mixing : Tony <unk> 
- Audio mastering : Tom Coyne 
- Guitarist : Tim Stewart 
- 
- = = Formats and track listings = = 
- 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- = = Release history = = 
- 
- 
- 
- = Marshall Applewhite = 
- 
- Marshall Herff Applewhite , Jr . ( May 17 , 1931 – March 26 , 1997 ) , also known as " Bo " and " Do " , among other names , was an American cult leader who founded what became known as the Heaven 's Gate religious group and organized their mass suicide in 1997 - claiming the lives of thirty @-@ nine people . 
- A native of Texas , Applewhite attended several universities and , as a young man , served in the United States Army . After finishing school at Austin College , he taught music at the University of Alabama . He later returned to Texas , where he led choruses and served as the chair of the music department at the University of St. Thomas in Houston . He left the school in 1970 , citing emotional turmoil . His father 's death a year later brought on severe depression . In 1972 , he developed a close friendship with Bonnie Nettles , a nurse ; together , they discussed mysticism at length and concluded that they were called as divine messengers . They operated a bookstore and teaching center for a short while , and then began to travel around the U.S. in 1973 to spread their views . They only gained one convert . In 1975 , Applewhite was arrested for failing to return a rental car and was jailed for six months . In jail , he further developed his theology . 
- After Applewhite 's release , he traveled to California and Oregon with Nettles , eventually gaining a group of committed followers . Applewhite and Nettles told their followers that they would be visited by extraterrestrials who would provide them with new bodies . Applewhite initially stated that he and his followers would physically ascend to a spaceship , where their bodies would be transformed , but later , he came to believe that their bodies were the mere containers of their souls , which would later be placed into new bodies . These ideas were expressed with language drawn from Christian <unk> , the New Age movement , and American popular culture . 
- The group received an influx of funds in the late 1970s , which it used to pay housing and other expenses . In 1985 , Nettles died , leaving Applewhite distraught and challenging his views on physical ascension . In the early 1990s the group took more steps to publicize their theology . In 1996 , they learned of the approach of Comet Hale – <unk> and rumors of an accompanying spaceship . They concluded that this spaceship was the vessel that would take their spirits on board for a journey to another planet . Believing that their souls would ascend to the spaceship and be given new bodies , the group members committed mass suicide in their mansion . A media circus followed the discovery of their bodies . In the aftermath , commentators and academics discussed how Applewhite persuaded people to follow his commands , including suicide . Some commentators attributed his followers ' willingness to commit suicide to his skill as a <unk> , while others argued that their willingness was due to their faith in the narrative that he constructed . 
- 
- = = Early life and education = = 
- 
- Marshall Herff Applewhite was born in Spur , Texas , on May 17 , 1931 , to Marshall Herff Applewhite Sr. and his wife Louise née Winfield ; he had three siblings . The son of a Presbyterian minister , Applewhite became very religious as a child . 
- Applewhite attended Corpus Christi High School and Austin College ; at the latter school , he was active in several student organizations and was moderately religious . He earned a bachelor 's degree in philosophy in 1952 and subsequently enrolled at Union Presbyterian <unk> to study theology , hoping to become a minister . He married Anne <unk> around that time , and they later had two children . Early in his <unk> studies , he decided to leave the school to pursue a career in music , becoming the music director of a Presbyterian church in North Carolina . He was a baritone singer and enjoyed spirituals and the music of Handel . In 1954 , he was drafted by the United States Army and served in Austria and New Mexico as a member of the Army Signal Corps . He left the military in 1956 and enrolled at the University of Colorado , where he earned a master 's degree in music and focused on musical theater . 
- 
- = = Career = = 
- 
- Applewhite moved to New York City in an unsuccessful attempt to launch a professional singing career upon finishing his education in Colorado . He then taught at the University of Alabama . He lost his position there after pursuing a sexual relationship with a male student ; his religious education was likely not supportive of same @-@ sex relationships and he was subsequently frustrated by his sexual desires . He separated from his wife when she learned of the affair in 1965 , and they divorced three years later . 
- After leaving the University of Alabama , Applewhite moved to Houston , Texas , in 1965 to teach at the University of St. Thomas . His students regarded him as an engaging speaker and a stylish <unk> . He served as chair of the music department ; he also became a locally popular singer , serving as the choral director of an Episcopal church and performing with the Houston Grand Opera . In Texas , he was briefly openly gay but also pursued a relationship with a young woman , who left him under pressure from her family , greatly upsetting him . He resigned from the University of St. Thomas in 1970 , citing depression and other emotional problems . Robert Balch and David Taylor , sociologists who studied Applewhite 's group , speculate that this departure was prompted by another affair between Applewhite and a student . The president of the university later recalled that he was often mentally <unk> and disorganized near the end of his employment . 
- In 1971 , Applewhite briefly moved to New Mexico , where he operated a <unk> . He was popular with customers but decided to return to Texas later that year . His father died around that time ; the loss took a significant emotional toll on him , causing severe depression . His debts mounted , forcing him to borrow money from friends . 
- 
- = = Introduction to Nettles and first travels = = 
- 
- In 1972 , Applewhite met Bonnie Nettles , a nurse with an interest in <unk> and Biblical prophecy . The two quickly became close friends ; he later recalled that he felt like he had known her for a long time and concluded that they had met in a past life . She told him their meeting had been <unk> to her by extraterrestrials , persuading him that he had a divine assignment . By that time , he had begun to investigate <unk> to traditional Christian doctrine , including astrology . He also had had several visions , including one in which he was told that he was chosen for a role like that of Jesus . In her 2005 profile of Applewhite , Susan <unk> speculates that he had a <unk> episode around this time . 
- Applewhite soon began to live with Nettles . Although they <unk> , their relationship was not a sexual one , fulfilling his longtime wish to have a deep and loving , yet <unk> , relationship . She was married with two children , but after she became close with Applewhite , her husband divorced her , and she lost custody of her children . Applewhite permanently broke off contact with his family as well . He saw Nettles as his <unk> , and some of his acquaintances later recalled that she had a strong influence on him . <unk> writes that Nettles " was responsible for reinforcing his emerging delusional beliefs " , but <unk> Robert Jay Lifton speculates that Nettles ' influence helped him avoid further psychological deterioration . 
- Applewhite and Nettles opened a bookstore known as the Christian Arts Center , which carried books from a variety of spiritual backgrounds . They also launched a venture known as Know Place to teach classes on mysticism and <unk> . They closed these businesses a short time later . In February 1973 , they resolved to travel to teach others about their beliefs and drove throughout the Southwest and Western <unk> ; Lifton describes their travels as a " restless , intense , often confused , <unk> spiritual journey " . While traveling , they had little money and occasionally resorted to selling their blood or working odd jobs for much @-@ needed funds . They <unk> solely on bread rolls at times , often camped out , and sometimes did not pay their lodging bills . One of their friends from Houston corresponded with them and agreed to accept their teachings . They visited her in May 1974 , and she became their first convert . 
- While traveling , Applewhite and Nettles pondered the life of St. Francis of Assisi and read works by authors including Helena <unk> , R. D. <unk> , and Richard Bach . They kept a King James Version of the Bible with them and studied several passages from the New Testament , focusing on teachings about <unk> , asceticism , and <unk> . Applewhite also read science fiction , including works by Robert A. Heinlein and Arthur C. Clarke . By June 1974 , Applewhite and Nettles ' beliefs had solidified into a basic outline . They concluded that they had been chosen to fulfill biblical <unk> , and that they had been given higher @-@ level minds than other people . They wrote a pamphlet that described Jesus ' reincarnation as a <unk> , a thinly veiled reference to Applewhite . Furthermore , they concluded that they were the two witnesses described in the Book of Revelation and occasionally visited churches or other spiritual groups to speak of their identities , often referring to themselves as " The Two " , or " The UFO Two " . They believed that they would be killed and then restored to life and , in view of others , transported onto a spaceship . This event , which they referred to as " the <unk> " , was to prove their claims . To their dismay , these ideas received a poor reception . 
- 
- = = <unk> and <unk> = = 
- 
- In August 1974 , Applewhite was arrested in <unk> , Texas , for failing to return a car that he had rented in Missouri . He was <unk> to St. Louis and jailed for six months . At the time , he maintained that he had been " divinely authorized " to keep the car . While jailed , he pondered theology and subsequently abandoned discussion of occult topics , in favor of extraterrestrials and evolution . 
- After Applewhite 's release , he and Nettles resolved to contact extraterrestrials and they sought like @-@ minded followers . They published advertisements for meetings , where they recruited disciples , whom they called " crew " . At the events , they purported to represent beings from another planet , the Next Level , who sought participants for an experiment . They claimed that those who agreed to take part in the experiment would be brought to a higher evolutionary level . He and Nettles referred to themselves as " Guinea " and " Pig " . Applewhite described his role as a " lab instructor " and served as the primary speaker , while Nettles , occasionally <unk> clarifying remarks or corrections . The two seldom personally spoke with attendees , only taking phone numbers with which they could contact them . They initially named their organization the Anonymous <unk> <unk> Church , but it soon became known as the Human Individual <unk> . 
- Applewhite believed in the ancient astronaut hypothesis , which claimed that extraterrestrials had visited humanity in the past and placed humans on Earth and would return to collect a select few . Parts of this teaching bear similarities to the Reformed Christian concept of election , likely owing to Applewhite 's Presbyterian upbringing . He often discussed extraterrestrials using phrases from Star Trek and stated that aliens communicated with him through the show . 
- Applewhite and Nettles sent advertisements to groups in California and were invited to speak to New Age devotees there in April 1975 . At this meeting , they persuaded about half of the 50 attendees to follow them . They also focused on college campuses , speaking at <unk> College in August . At a meeting in Oregon in September 1975 , they saw further recruitment success — about 30 people left their homes to follow the pair , prompting interest from media outlets . The coverage was negative : commentators and some former members mocked the group and leveled accusations of brainwashing against Applewhite and Nettles . Balch and Taylor state that Applewhite and Nettles <unk> pressure tactics , seeking only devoted followers . 
- Benjamin E. Zeller , an academic who studies new religions , notes that Applewhite and Nettles ' teachings focused on salvation through individual growth and sees this as similar to currents in the era 's New Age movement . Likewise , the importance of personal choice was also emphasized . Applewhite and Nettles denied connection with the New Age movement , viewing it as a human creation . <unk> Lalich , a sociologist , who studies cults , attributes their recruitment success to their eclectic mix of beliefs and the way that they deviate from typical New Age teachings : discussing literal <unk> while retaining familiar language . Most of their disciples were young and interested in <unk> or otherwise lived outside of mainstream society . They came from a variety of religious backgrounds , including Eastern religions and Scientology . Most were well <unk> in New Age teachings , allowing Applewhite and Nettles to convert them easily . Applewhite thought that his followers would reach a higher level of being , changing like a <unk> becoming a butterfly ; this example was used in almost all of their early literature . Applewhite contended that this would be a " biological change into a different species , casting his teachings as scientific truth in line with secular naturalism . " He emphasized to his early followers that he was not speaking <unk> , often using the words " biology " and " chemistry " in his statements . By the mid @-@ 1970s , he attempted to avoid the use of the term " religion " , seeing it as inferior to science . Although he dismissed religion as <unk> , he sometimes emphasized the need for faith in the aliens ' abilities to transform them . 
- 
- = = <unk> lifestyle = = 
- 
- By 1975 , Applewhite and Nettles had taken the names " Bo " and " <unk> " . They had about 70 followers and saw themselves as shepherds tending a flock . Applewhite believed that complete separation from <unk> desires was a prerequisite of ascension to the Next Level and emphasized passages in the New Testament in which Jesus spoke about <unk> worldly <unk> . Members were consequently instructed to renounce : friends , family , media , drugs , alcohol , jewelry , facial hair , and sexuality . Furthermore , they were required to adopt biblical names . Applewhite and Nettles soon told them to adopt two @-@ syllable names that ended in " <unk> " and had three consonants in the first syllable , such as <unk> , <unk> , and <unk> ; Applewhite stated that these names emphasized that his followers were spiritual children . Applewhite , Nettles , and their followers lived what religious scholar James Lewis describes as a " quasi @-@ nomadic lifestyle " . They usually stayed at remote campgrounds and did not speak about their beliefs . Applewhite and Nettles ceased having public meetings in April 1975 , and spent little time teaching doctrine to their converts . The leaders also had little contact with their dispersed followers , many of whom <unk> their allegiance . 
- Applewhite and Nettles feared that they would be assassinated , and taught their followers that their deaths would be similar to those of the two witnesses of the Book of Revelation . Balch and Taylor believe that Applewhite 's prison experience and early rejection by audiences contributed to this fear . Applewhite and Nettles later explained to their followers that the former 's treatment by the press was a form of assassination and had fulfilled their prophecy . Applewhite took a <unk> view of the Bible , seeing it as a record of extraterrestrial contact with humanity . He drew heavily from the Book of Revelation , although he avoided traditional theological terminology and took a somewhat negative tone towards Christianity . He only <unk> about a small number of verses and never tried to develop a system of theology . 
- By early 1976 , Applewhite and Nettles had settled on the names " Do " and " Ti " ; Applewhite stated that these were meaningless names . In June 1976 , they gathered their remaining followers at Medicine Bow National Forest in Southeastern Wyoming , promising a UFO visit . Nettles later announced that the visit had been cancelled . Applewhite and Nettles then split their followers into small groups , which they referred to as " Star <unk> " , 
- From 1976 to 1979 , the group lived in campgrounds , usually in the Rocky Mountains or Texas . Applewhite and Nettles began to place greater demands on their followers ' <unk> loosely structured lives , which improved membership retention . They typically communicated with their disciples in writing or through assistants . Increasingly , they emphasized that they were the only source of truth — the idea that members could receive individual revelations was rejected in an attempt to prevent <unk> . Applewhite also sought to prevent close friendships among his followers , fearing that this could lead to insubordination . Applewhite and Nettles insisted that their followers practice what they referred to as " flexibility " : strict obedience to their often shifting requests . The two leaders limited the group 's contacts with those outside the movement , even some who may have been interested in joining , ostensibly to prevent infiltration from hostile parties . In practice , this made their followers completely dependent upon them . Applewhite instructed his disciples to be like children or pets in their submission — their sole responsibility was to obey their leaders . Members were encouraged to constantly seek Applewhite 's advice and often ask themselves what their leaders would do when making a decision . To his followers , he did not seem dictatorial : many of them found him laid back and <unk> . In his 2000 study of the group , Winston Davis states that Applewhite mastered the " fine art of religious entertainment " , noting that many of his disciples seemed to enjoy their service . Applewhite organized seemingly arbitrary rituals that were intended to <unk> a sense of discipline in his followers ; he referred to these tasks as " games " . He also watched science @-@ fiction television programs with the rest of the group . Rather than issue direct commands , he attempted to express his preferences and nominally offer his disciples a choice . He emphasized that students were free to disobey if they chose , in what Lalich <unk> the " illusion of choice " . 
- 
- = = Housing and control = = 
- 
- In the late 1970s , the group received a large sum of money , possibly an inheritance of a member or donations of followers ' income . This capital was used to rent houses , initially in Denver and later in Dallas . Applewhite and Nettles had about 40 followers then and lived in two or three houses : the leaders usually had their own house . The group was secretive about their lifestyle , covering their windows . Applewhite and Nettles arranged their followers ' lifestyles as a boot camp that would prepare them for the Next Level . Referring to their house as a " craft " , they <unk> the lives of their disciples to the minute . Students who were not committed to this lifestyle were encouraged to leave ; departing members were given financial assistance . Lifton states that Applewhite wanted " quality over quantity " in his followers , although he occasionally spoke about gaining many converts . 
- Applewhite and Nettles sometimes made sudden , drastic changes to the group . On one occasion in Texas , they told their followers of a forthcoming visitation from extraterrestrials and instructed them to wait outside all night , at which point they informed them that this had been merely a test . Lalich sees this as a way that they increased their students ' devotion , ensuring that their commitment became irrespective of what they saw . Members became desperate for Applewhite 's approval , which he used to control them . 
- In 1980 , Applewhite and Nettles had about 80 followers , many of whom held jobs , often working with computers or as car mechanics . In 1982 , Applewhite and Nettles allowed their disciples to call their families . They further relaxed their control in 1983 , permitting their followers to visit relatives on Mother 's Day . They were only allowed short stays and were instructed to tell their families that they were studying computers at a monastery . These vacations were intended to <unk> families by demonstrating that the disciples remained with the group of their own accord . 
- 
- = = Nettles ' death = = 
- 
- In 1983 , Nettles had an eye <unk> removed as a result of cancer diagnosed several years earlier . She lived for two more years , dying in 1985 . Applewhite told their followers that she had " traveled to the Next Level " because she had " too much energy to remain on Earth " , abandoning her body to make the journey . His attempt to explain her death in the terms of the group 's doctrine was successful , preventing the departure of all but one member . Applewhite became very depressed . He claimed that Nettles still communicated with him , but he suffered from a crisis of faith . His students supported him during this time , greatly encouraging him . He then organized a ceremony in which he symbolically married his followers ; Lalich views this as an attempt to ensure unity . Applewhite told his followers that he had been left behind by Nettles because he still had more to learn — he felt that she occupied " a higher spiritual role " than he did . He began identifying her as " the Father " and often referred to her with male <unk> . 
- Applewhite began to emphasize a strict hierarchy , teaching that his students needed his guidance , as he needed the guidance of the Next Level . Zeller notes that this naturally ensured that there would be no possibility of the group 's continuing if Applewhite were to die . A relationship with Applewhite was said to be the only way to salvation ; he encouraged his followers to see him as Christ . Zeller states that the group 's previous focus on individual choice was replaced with an emphasis on Applewhite 's role as a mediator . Applewhite maintained some aspects of their scientific teachings , but , in the 1980s , the group became more like a religion in its focus on faith and submission to authority . 
- After Nettles ' death , Applewhite also altered his view of ascension : previously , he had taught that the group would physically ascend from the Earth and that death caused reincarnation , but her death - which left behind an unchanged , <unk> body - forced him to say that the ascension could be spiritual . He then concluded that her spirit had traveled to a spaceship and received a new body and that he and his followers would do the same . In his view , the Biblical heaven was actually a planet on which highly evolved beings dwelt , and physical bodies were required to ascend there . Applewhite believed that once they reached the Next Level , they would facilitate evolution on other planets . He emphasized that Jesus , whom he believed was an extraterrestrial , came to Earth , was killed , and bodily rose from the dead before being transported onto a spaceship . According to Applewhite 's doctrine , Jesus was a gateway to heaven but had found humanity <unk> to ascend when he first came to the Earth . Applewhite then decided that there was an opportunity for humans to reach the Next Level " every two millennia " , and the early 1990s would therefore provide the first opportunity to reach the Kingdom of Heaven since the time of Jesus . Zeller notes that his beliefs were based on the Christian Bible but were interpreted through the lens of belief in alien contact with humanity . 
- Applewhite taught that he was a walk @-@ in , a concept that had gained popularity in the New Age movement during the late 1970s . Walk @-@ ins were said to be higher beings who took control of adult bodies to teach humanity . This concept informed Applewhite 's view of resurrection : he believed that his group 's souls were to be transported to a spaceship , where they would enter other bodies . Applewhite abandoned the metaphor of a butterfly in favor of describing the body as a mere container , a vehicle that souls could enter and exit . This dualism may have been the product of the <unk> that Applewhite learned as a young man ; Lewis writes that the group 's teachings had " Christian elements [ that ] were basically <unk> on to a New Age matrix " . In a profile of the group for Newsweek , Kenneth Woodward compares his dualism to that of ancient Christian <unk> , although Peters notes that his theology departs from <unk> by <unk> the physical world . 
- In the wake of Nettles ' death , Applewhite became increasingly paranoid , fearing a conspiracy against his group . One member who joined in the mid @-@ 1980s recalled that Applewhite avoided new converts , worrying that they were <unk> . He feared a government raid on their home and spoke highly of the Jewish defenders of <unk> in ancient Israel who showed total resistance to the Roman Empire . Increasingly , he began to discuss the Apocalypse , comparing the Earth to an overgrown garden that was to be recycled or <unk> and humanity to a failed experiment . In accordance with the garden metaphor , he stated that the Earth would be " <unk> under " . Woodward notes that Applewhite 's teaching about the Earth 's recycling is similar to the cyclical perspective of time found in Buddhism . Applewhite also utilized New Age concepts , but he differed from that movement by predicting that apocalyptic , rather than <unk> , changes would soon occur on Earth . He contended that most humans had been brainwashed by Lucifer , but that his followers could break free of this control . He specifically cited sexual urges as the work of Lucifer . In addition , he stated that there were evil extraterrestrials , whom he referred to as " <unk> " , who sought to thwart his mission . He argued that many prominent moral teachers and advocates of political <unk> were actually <unk> . This theme emerged in 1988 , possibly in response to the lurid alien abduction stories that were <unk> at the time . 
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- = = <unk> and <unk> = = 
- 
- In the late 1980s , the group kept a low profile ; few people knew it still existed . In 1988 , they <unk> a document that detailed their beliefs to a variety of New Age organizations . The mailing contained information about their history and advised people to read several books , which primarily focused on Christian history and <unk> . With the exception of the 1988 document , Applewhite 's group remained inconspicuous until 1992 , when they recorded a 12 @-@ part video series which was broadcast via satellite . This series echoed many of the teachings of the 1988 update , although it introduced a " universal mind " of which its <unk> could <unk> . 
- Over the course of the group 's existence , several hundred people joined and left . In the early 1990s , their membership dwindled , numbering as few as 26 ; these <unk> gave Applewhite a sense of urgency . In May 1993 , the group took the name " Total <unk> Anonymous " . They then spent $ 30 @,@ 000 to publish a full @-@ page advertisement in USA Today that warned of catastrophic judgment to <unk> the Earth . Its publication led about 20 former members to rejoin the group . This , along with a series of public lectures in 1994 , caused membership to double from its nadir at the beginning of the decade . By this time , Applewhite did not regiment his disciples ' lives as strictly as he had and spent less time with them . 
- In the early 1990s , Applewhite posted some of his teachings on the Internet , but he was <unk> by the resulting criticism . That year , he first spoke of the possibility of suicide as a way to reach the Next Level . He explained that everything " human " had to be forsaken , including the human body , before one could ascend . The organization was then renamed " Heaven 's Gate " . Davis speculates that this rejection may have encouraged him to attempt to leave Earth . 
- From June to October 1995 , the group lived in a rural part of New Mexico . They purchased 40 acres ( 0 @.@ 16 km2 ) and built a compound — which they referred to as the " Earth ship " — using <unk> and lumber ; Applewhite hoped to establish a monastery . This proved to be a difficult endeavor , particularly for the aging Applewhite : he was in poor health and , at one point , feared that he had cancer . Lifton notes that Applewhite 's active leadership of the group probably led to severe fatigue in his last years . The winter was very cold , and they abandoned the plan . Afterwards , they lived in several houses in the San Diego area . 
- The group increasingly focused on the suppression of sexual desire ; Applewhite and seven others opted for surgical <unk> . They initially had difficulty finding a willing surgeon , but eventually found one in Mexico . In Applewhite 's view , sexuality was one of the most powerful forces that bound humans to their bodies and thus hindered their efforts to evolve to the Next Level ; he taught that Next Level beings had no reproductive organs but that <unk> beings had <unk> . He also cited a verse in the New Testament that said there would not be marriage in heaven . In addition , he required members to adopt similar clothing and <unk> , possibly to reinforce that they were a non @-@ sexual family . 
- 
- = = Mass suicide = = 
- 
- In October 1996 , the group rented a mansion in Rancho Santa Fe , California . That year , they recorded two video messages in which they offered their viewers a " last chance to evacuate Earth " . Around the same time , they learned of the approach of Comet Hale – <unk> . Applewhite now believed that Nettles was aboard a spaceship trailing the comet , and that she planned to rendezvous with them . He told his followers that the vessel would transport them to an <unk> destination , and that there was a government conspiracy to suppress word of the craft . In addition , he stated that his deceased followers would be taken by the vessel as well , a belief that resembled the Christian <unk> <unk> doctrine . It is not known how he learned of the comet or why he believed that it was accompanied by extraterrestrials or why he should have believed the dead Nettles would be with them . 
- In late March 1997 , the group isolated themselves and recorded farewell statements . Many members praised Applewhite in their final messages ; Davis describes their remarks as " <unk> of Do 's gospel " . Applewhite recorded a video shortly before his death , in which he termed the suicides the " Final exit " of the group and remarked , " We do in all honesty hate this world " . Lewis speculates that Applewhite settled on suicide because he had said that the group would ascend during his lifetime and thus appointing a successor was <unk> . 
- Religious scholar Catherine <unk> posits that the suicides began on March 22 . Most members took <unk> and alcohol and then placed bags over their heads . They wore Nike shoes and black uniforms with patches that read " Heaven 's Gate Away Team " . A bag that contained a few dollars and a form of identification was placed beside most bodies . The deaths occurred over three days ; Applewhite was one of the last four to die . Three assistants helped him commit suicide , then killed themselves . An anonymous tip led the sheriff 's department to search the mansion ; they found 39 bodies there on March 26 . It is the largest group suicide that has occurred in the U.S. Applewhite 's body was found seated on the bed of the mansion 's master bedroom . Medical examiners determined that his fears of cancer had been unfounded , but that he suffered from <unk> <unk> . 
- The deaths provoked a media circus , and Applewhite 's face was featured on the covers of Time and Newsweek on April 7 . His final message was widely broadcast ; Hugh Urban of Ohio State University describes his appearance in the video as " wild @-@ eyed [ and ] rather <unk> " . 
- 
- = = Analysis = = 
- 
- Although many popular commentators , including psychologist Margaret Singer , speculate that Applewhite brainwashed his followers , many academics have rejected the " brainwashing " label as an <unk> that does not express the nuances of the process by which the followers were influenced . Lalich speculates that they were willing to follow Applewhite in suicide because they had become totally dependent upon him , and hence were poorly suited for life in his absence . Davis attributes Applewhite 's success in convincing his followers to commit suicide to two factors : he isolated them socially and cultivated an attitude of complete religious obedience in them . Applewhite 's students had made a long @-@ term commitment to him , and Balch and Taylor <unk> that this is why his interpretations of events appeared coherent to them . Most of the dead had been members for about 20 years , although there were a few recent converts . 
- Lewis argues that Applewhite effectively controlled his followers by packaging his teachings in familiar terms . Richard Hecht of the University of California , Santa Barbara , echoes this sentiment , arguing that members of the group killed themselves because they believed the narrative that he had constructed , rather than because he <unk> controlled them . In his 2000 study of apocalyptic movements , John R. Hall posits that they were motivated to commit suicide because they saw it as a way to demonstrate that they had conquered the fear of death and truly believed Applewhite . 
- Urban writes that Applewhite 's life displays " the intense <unk> and alienation shared by many individuals lost in late twentieth @-@ century capitalist society " . He notes that Applewhite 's condemnations of contemporary culture bear similarities to those of Jean <unk> at times , particularly their shared <unk> views . Urban posits that Applewhite found no way other than suicide to escape the society that surrounded him and states that death offered him a way to escape its " endless circle of seduction and consumption " . 
- While covering the suicides , several media outlets focused on Applewhite 's sexuality ; the New York Post dubbed him " the Gay Guru " . Gay rights activist Troy Perry argued that Applewhite 's repression , and society 's rejection , of same @-@ sex relationships ultimately led to his suicide . This idea has failed to gain support among academics . Zeller argues that Applewhite 's sexuality was not the primary driving force behind his asceticism , which he believes resulted from a variety of factors , though he grants sexuality a role . 
- Lalich states that Applewhite fit " the traditional view of a charismatic leader " , and Evan Thomas deems him a " master <unk> " . Lifton compares Applewhite to <unk> <unk> , the founder of <unk> <unk> , describing him as " equally controlling , his paranoia and <unk> gentler yet ever present " . Christopher <unk> of Lancaster University states that Applewhite and Nettles were similar to John Reeve and <unk> <unk> , who founded <unk> , a <unk> movement in 17th century England . 
- 
- 
- = Arbeideren ( Hamar ) = 
- 
- Arbeideren ( " The <unk> " ) was a Norwegian newspaper , published in Hamar , Hedmark county . It was started in 1909 as the press organ of the Labour Party in Hedemarken and its adjoining regions , and was called Demokraten ( " The Democrat " ) until 1923 . It was issued three days a week between 1909 and 1913 , six days a week in 1914 , three days a week again between 1914 and 1918 before again increasing to six days a week . It was renamed to Arbeideren in 1923 , and in the same year it was taken over by the Norwegian Communist Party . The Communist Party incorporated the newspaper Gudbrandsdalens Arbeiderblad into Arbeideren in 1924 , and until 1929 the newspaper was published under the name Arbeideren og Gudbrandsdalens Arbeiderblad . After Arbeideren had gone defunct , the name was used by the Communist Party for other newspapers elsewhere . 
- The chief editors of the newspaper were Olav Kringen ( 1909 – 1913 ) , Ole <unk> ( 1912 – 1913 ) , Fredrik Monsen ( 1913 – 1916 ) , Paul O. <unk> ( 1916 – 1919 ) , Alfred <unk> ( 1919 – 1920 ) , Olav Larssen ( 1920 – 1927 ) , and finally <unk> Hegna , Ingvald B. <unk> , Olav Scheflo , <unk> Petershagen , and <unk> Vogt ( between 1927 and 1929 ) . Fredrik Monsen , Evald O. Solbakken and Knut <unk> <unk> were acting editors from 1924 to 1925 . 
- 
- = = Pre @-@ history = = 
- 
- Demokraten was originally the name of a short @-@ lived newspaper in Hamar started by Leopold <unk> in 1852 , connected to the Marcus <unk> movement . <unk> started a second newspaper , <unk> @-@ <unk> , in Hamar later in 1852 , to compete with his own Demokraten . An organ for the social liberal labour movement in the district , <unk> existed from 1889 to 1892 and was published out of different cities , including in Hamar in the year 1890 . 
- A <unk> chapter of the Labour Party was established in Hedmark in mid @-@ November 1904 . After the <unk> party convention in <unk> in 1906 , the convention summary had to be printed in the <unk> @-@ based newspaper Social @-@ Demokraten , as it lacked its own local newspaper . The county board thus decided to buy 1 @,@ 500 copies of the Social @-@ Demokraten to distribute to its members . There was a growing notion that the party needed its own newspaper . In the same year , the labour movement in <unk> ( south of Hedmark ) bought the paper Solungen , which had existed since 1904 . The takeover came into effect on 1 January 1907 , and publishing began the following year . Solungen pretended to be the labour movement organ for the whole of Hedmark , and outside of <unk> it was published as <unk> <unk> <unk> ( Solungen ) . However , the rest of Hedmark county was not satisfied with this solution . 
- 
- = = Labour Party period = = 
- 
- 
- = = = 1909 – 1913 = = = 
- 
- The Hamar @-@ based newspaper Demokraten ( " The Democrat " ) was started on 15 September 1909 . The <unk> and first editor was Olav Kringen , who had ample experience as the editor of Social @-@ Demokraten from 1903 until 1906 . Demokraten was the Labour Party organ for the <unk> Cities and Hedemarken , but in its first years it also covered Gudbrandsdalen and Østerdalen , two northern regions . The name <unk> <unk> <unk> was considered for the newspaper , but the historical name Demokraten prevailed . The name was suggested by local Labour MP Karl <unk> . Demokraten 's coverage of Gudbrandsdalen soon ended , and in southern Østerdalen a new labour newspaper , <unk> Arbeiderblad , was set up in 1915 . In northern Østerdalen , <unk> <unk> was popular among the labour movement . According to reports in Demokraten the newspaper again began to cover news from a part of Gudbrandsdalen , namely the city Lillehammer , in 1912 . 
- When it came to building up a new newspaper , Kringen had a certain personal drive , as he ran for parliament in 1909 . When he lost the election , he also lost interest to a certain degree . He resigned in 1912 and Ole <unk> , a member of the <unk> municipal council , took over as chief editor . However , he ran <unk> of other people involved with the newspaper and was fired in 1913 . 
- The newspaper originally had the tagline <unk> <unk> for <unk> ( " Socialist Paper for <unk> " ) , but in 1910 this was changed to <unk> for <unk> ( " <unk> for the Labour Movement " ) . It was printed by the company A. <unk> . The newspaper was issued three times a week until 1 July 1913 , from which point it was increased to six times a week . As part of this ambitious increase , Demokraten also had 3 @,@ 000 copies in circulation , unprecedented in its history . 
- 
- = = = 1913 – 1916 = = = 
- 
- In 1913 the newspaper 's supervisory council hired school teacher Fredrik Monsen to be the new editor . Olav Larssen started his journalist career as a subeditor in the same year . In the newspaper 's supervisory council vote , Monsen edged out Waldemar <unk> with 22 to 4 votes , and also prevailed over other applicants who were <unk> editors , such as Ingvald <unk> and Eugène <unk> . Larssen prevailed over <unk> and <unk> in the vote for the new subeditor . 
- Only Monsen and Larssen were employed in the newspaper to work with editorial content . In 1913 , Monsen managed to contract known personalities from the labour movement as " regular contributors " . These were the nationally known figures Olav Kringen , <unk> <unk> and Johan <unk> , in addition to Lillehammer politician <unk> <unk> and the locally known politicians Arne <unk> ( later MP ) and <unk> . <unk> . Danish expatriate Alfred <unk> joined in the autumn of 1913 . However , according to Larssen , the prominent writers contracted to Demokraten " <unk> wrote " anything . 
- In his memoirs , Larssen wrote that Monsen was " often aggressive " as editor @-@ in @-@ chief , especially when writing <unk> . He got several adversaries in the city 's conservative community , especially after donning a badge with the broken rifle , a well @-@ known anti @-@ war symbol . The newspaper competed with the old and popular conservative Hamar <unk> , the liberal left <unk> Avis , and the liberal <unk> . 
- The practice of issuing the newspaper six days a week became harder after the outbreak of the First World War . The war caused a general rise in prices , and newspaper subscriptions and advertisements both declined . Demokraten had to revert to being issued three times a week starting 1 September 1914 . In December 1914 it adopted a new tagline , Organ for <unk> i Hamar og <unk> ( " Organ for the Labour Party in Hamar and the <unk> of Hedemarken " ) . 
- 
- = = = 1916 – 1923 = = = 
- 
- Monsen and Larssen both left Demokraten in 1916 . The next editors were Paul O. <unk> , who served from 1916 to 1919 , and Alfred <unk> , from 1919 to 1920 . Larssen returned in 1920 as editor @-@ in @-@ chief . Georg <unk> was the subeditor from 1918 until 1921 , when Evald O. Solbakken started in the newspaper as subeditor . Still , there were only two people to deliver the editorial content . 
- As the war years went , the newspaper 's finances gradually improved . The Norwegian state became more active in production and trade and contributed many advertisements . Demokraten acquired its own type @-@ setting machine in October 1918 and a printing press in 1917 , which it used from 1 January 1918 . From 1 July 1918 , circulation once again increased to six days a week . 
- 
- = = Communist Party period = = 
- 
- In 1923 , the newspaper was renamed Arbeideren ( " The <unk> " ) , and the first issue with this name was released on 1 May 1923 , the International Workers ' Day . The change followed a letter in 1922 from the Comintern Executive , which stated that no newspaper belonging to a Comintern member organization should have " Social Democrat " or " Democrat " as a part of its title . The printing press of the party changed its name accordingly , to <unk> <unk> . 
- In the same year , 1923 , the Labour Party broke out of the Comintern . Subsequently the Communist Party broke away from the Labour Party . The local chapter of the Labour Party in Hamar decided to side with the Communist Party in November 1923 , in a 123 – 22 vote . Arbeideren was then taken away from Labour , as the supervisory council decided by a 65 to 5 vote that it should follow the Communists . Arbeideren was one of thirteen Labour newspapers that broke away from the party and followed the Communists ( one , <unk> , later returned to Labour ) . Since 15 February 1924 the newspaper was published under the name Arbeideren og Gudbrandsdalens Arbeiderblad , as the Communist Party had seen fit to merge Arbeideren with Lillehammer @-@ based Gudbrandsdalens Arbeiderblad . 
- Editor Larssen and subeditor Solbakken both joined the Communist Party in 1923 and continued running the newspaper . As Olav Larssen was asked by the party to be the acting editor of Norges Kommunistblad in the winter of 1924 – 1925 , Fredrik Monsen , Evald Solbakken , and Knut <unk> <unk> were acting editors between 1924 and 1925 . Larssen eventually drifted away from the mainstream of the Communist Party . In late 1926 and early 1927 he voiced his opinion in columns that the Communist Party should contribute to the imminent merger of the Labour Party and the Social Democratic Labour Party . A local party convention strongly <unk> this opinion . Larssen was thus replaced in January 1927 and left the Communist Party , and Solbakken soon followed suit . Fredrik Monsen left the party at the same time . 
- Information differs as to who replaced Larssen . According to Evald Solbakken , and also to the reference bibliography Norske <unk> 1763 – 1969 , the replacement was Olav Scheflo , who needed a stand @-@ in , Ingvald B. <unk> , for the first period . According to the <unk> <unk> <unk> and historian <unk> Lorenz , <unk> Hegna was the editor in 1927 , before he took over Norges Kommunistblad in the summer of 1927 . Hegna 's main job was to edit the periodical <unk> <unk> , but in this period the people of <unk> <unk> had an informal influence on the Communist Party and several of their newspapers . Scheflo formally edited the newspaper from 1927 to 1928 , with <unk> Petershagen as acting editor from late 1927 . In 1928 Petershagen formally took over , only to have <unk> Vogt become acting editor later that year . Vogt took over in 1929 . 
- As many newspapers belonging to the <unk> Communist Party , Arbeideren would cease to exist before the end of the 1920s . It was still published six times a week , but had to give up its printing press in 1929 , switching to <unk> in Oslo . The last ever issue of Arbeideren og Gudbrandsdalens Arbeiderblad was published on 4 October 1929 . 
- 
- = = Aftermath = = 
- 
- A month after Arbeideren went defunct , the Communist Party gave its name to a new newspaper , which was set up as the new main newspaper of the Communist Party in 1930 . This new paper was based in Oslo as the replacement of Norges Kommunistblad , which had been <unk> as well . Olav Larssen and Evald Solbakken found a new outlet in Hamar Arbeiderblad , which had been set up as the new Hamar organ of the Labour Party in 1925 . The Communist Party later tried to create a weekly newspaper in Hamar , <unk> Front , but it was short @-@ lived and existed only between 1932 and 1933 . The Oslo version of Arbeideren went defunct in 1940 , and many years after that , the name was used from 1951 to 1953 for a third newspaper , published in <unk> , not far from Hamar city . 
- 
- 
- = Journey ( 2012 video game ) = 
- 
- Journey is an indie video game developed by Thatgamecompany and published by Sony Computer Entertainment for the PlayStation 3 . It was released on March 13 , 2012 , via the PlayStation Network . In Journey , the player controls a robed figure in a vast desert , traveling towards a mountain in the distance . Other players on the same journey can be discovered , and two players can meet and assist each other , but they cannot communicate via speech or text and cannot see each other 's names . The only form of communication between the two is a musical <unk> . This <unk> also transforms dull , stiff pieces of cloth found throughout the levels into vibrant red , affecting the game world and allowing the player to progress through the levels . The robed figure wears a trailing scarf , which when charged by approaching floating pieces of cloth , briefly allows the player to float through the air . The developers sought to evoke in the player a sense of <unk> and wonder , and to forge an emotional connection between them and the anonymous players they meet along the way . The music , composed by Austin Wintory , dynamically responds to the player 's actions , building a single theme to represent the game 's emotional arc throughout the story . 
- Reviewers of the game praised the visual and auditory art as well as the sense of companionship created by playing with a stranger , calling it a moving and emotional experience , and have since listed it as one of the greatest games of all time . Journey won several " game of the year " awards and received several other awards and nominations , including a Best Score Soundtrack for Visual Media nomination for the 2013 Grammy Awards . A retail " Collector 's Edition " , including Journey , Thatgamecompany 's two previous titles , and additional media , was released on August 28 , 2012 . The game was released digitally for the PlayStation 4 on July 21 , 2015 and a physical edition was released on October 2 , 2015 . 
- 
- = = Gameplay = = 
- 
- In Journey , the player takes the role of a robed figure in a desert . After an introductory sequence , the player is shown the robed figure sitting in the sand , with a large mountain in the distance . The path towards this mountain , the ultimate destination of the game , is subdivided into several sections traveled through <unk> . The player can walk in the levels , as well as control the camera , which typically follows behind the figure , either with the analog stick or by tilting the motion @-@ sensitive controller . The player can jump with one button , or emit a <unk> shout or musical note with another ; the length and volume of the shout depends on how the button is pressed , and the note stays in tune with the background music . These controls are presented <unk> in the beginning of the game ; at no point outside of the credits and title screen are any words shown or spoken . 
- The robed figure wears a trailing magical scarf which allows the player to briefly fly ; doing so uses up the scarf 's magical charge , represented visually by glowing <unk> on the scarf . The scarf 's <unk> are <unk> by walking , or a variety of other means . Touching glowing symbols scattered throughout the levels <unk> the initially <unk> scarf , allowing the player to remain airborne longer . Larger strips of cloth are present in the levels and can be transformed from a stiff , dull gray to vibrant red by singing near them . Doing so may have effects on the world such as releasing bits of cloth , forming bridges , or levitating the player . This in turn allows the player to progress in the level by opening doors or allowing them to reach previously inaccessible areas . The robed figure does not have visible arms to manipulate the game world directly . Along the way , the player encounters flying creatures made of cloth , some of which help the player along . In later levels , the player also encounters hostile creatures made of stone , which upon spotting the player rip off parts of the figure 's scarf . 
- In each level , the player may come across one other player temporarily connected to their game . When players approach each other they charge one another 's <unk> . They cannot communicate with each other beyond patterns of singing . Players can help each other by activating strips of cloth or showing paths , but cannot hinder each other and are not necessary for completing any level . When two players finish a section at the same time they remain together into the next one ; otherwise they are connected to new players when they move on . While all of the figures generally look the same , without gender or distinguishing characteristics , individual players can be told apart by unique symbols which are shown floating in the air when they sing and are displayed on their robes at all times . The entire game takes about two to three hours to complete . 
- 
- = = Story = = 
- 
- Journey 's story is told <unk> throughout gameplay and using cutscenes . The player 's character begins on a sand dune in a seemingly endless desert . In the far distance looms a large , foreboding mountain with a glowing crevice that splits its peak . As the character approaches the mountain , they find remnants of a once @-@ thriving civilization , eroded by sand over time . <unk> throughout the ruins at the end of each area are stones at which the traveler rests ; these stones give the traveler the vision of meeting a larger , white @-@ robed figure in a circular room , with art on the walls describing the rise and fall of the civilization mirroring the player 's journey . The player must also contend with the ancient automatons left over from a war which ended the civilization , and which still <unk> the city looking for intruders . 
- The player continues to journey deeper into the remains of a once sprawling city at the base of the mountain . Eventually making it safely to the mountain , the traveler begins to climb it , struggling as they enter the colder climates and encounter deep snow and high winds . With the crevice still a fair distance away , the traveler falls and collapses in the snow . Six of the white @-@ robed figures appear before the character and grant the traveler new energy , allowing the player to reach the summit of the mountain and walk into and through the crevice as the screen fills with white . The player is then shown the game 's credits , playing over the ending cinematic . This cinematic shows a shooting star emanating from the crevice and traversing the path the traveler took through the ruins , and shows <unk> of other robed travelers heading towards the mountain . Eventually , the star comes to rest at the sand dune where the game began , and the player is given the option of starting the game again . As the credits end , the player is shown the PlayStation Network <unk> of the other travelers who shared part of the trek . 
- 
- = = Development = = 
- 
- Journey was the last game made under a three @-@ game contract between Thatgamecompany and Sony , the first two being Flow and Flower . Development of the game began in 2009 , after the release of Thatgamecompany 's previous title Flower . The 18 @-@ person development team for Journey was composed mainly of creators of the company 's previous games ; co @-@ founder Jenova Chen was the creative director and Nick Clark returned as lead designer . Kellee Santiago , producer of Flow and Flower , did not reprise her duties , concentrating instead on her role as the company 's president , and was replaced by Robin Hunicke . 
- When development began , Sony expected the game to be completed in a year , rather than the more than three it finally took . Thatgamecompany always expected needing an extension ; according to Hunicke , they believed finishing the game within a year was " unrealistic " . Development ended up taking even longer than anticipated , as the team had difficulties <unk> down their ideas for the game and maintaining efficient communication . Over the course of development the team grew from seven to eighteen people . At the end of the second year , when Sony 's extension had run out , the game did not spark the emotions in the player that the team wanted . Sony agreed to another one @-@ year extension , but development ultimately exceeded even that . 
- The stress of the project led to the feeling there was not enough time or money to complete everything the team wished to , which added to the stress and caused arguments about the design of the game . The developers ended up reducing the overtime they spent on the project to avoid burning out , though it meant further delays and risked the company running out of money as the game neared completion . In a speech at the 16th annual D.I.C.E. Awards in 2013 , Chen admitted that the company had indeed been driven to bankruptcy in the final months of development , and that some of the developers had gone unpaid at the time . Hunicke described the solution to finally finishing the game as learning to let go of tensions and ideas that could not make it into the game and be " nice to each other . " 
- The game is intended to make the player feel " small " and to give them a sense of <unk> about their surroundings . The basic idea for the game , as designed by Chen , was to create a game that moved beyond the " typical defeat / kill / win mentality " of most video games . The team initially created a prototype named Dragon that involved players trying to draw away a large monster from other players , but eventually discarded it after finding it was too easy for players to ignore each other in favor of their own objectives . 
- The developers designed the game like a " Japanese garden " , where they attempted to remove all of the game elements that did not fit with the others , so the emotions they wanted the game to evoke would come through . This <unk> is intended to make the game feel intuitive to the player , so they can explore and feel a sense of wonder without direct instructions . The story arc of the game is designed to explicitly follow Joseph Campbell 's <unk> theory of narrative , or hero 's journey , so as to enhance the emotional connection of the players as they journey together . In his D.I.C.E. speech , Chen noted that three of their 25 <unk> had cried upon completing the game . 
- The multiplayer component of Journey was designed to facilitate cooperation between players without forcing it , and without allowing competition . It is intended to allow the players to feel a connection to other people through exploring with them , rather than talking to them or fighting them . The plan was " to create a game where people felt they are connected with each other , to show the positive side of humanity in them . " The developers felt the focus on caring about the other player would be diluted by too many game elements , such as additional goals or tasks , as players would focus on those and " ignore " the other player . They also felt having text or voice communication between players or showing <unk> would allow players ' biases and <unk> to come between them and the other player . 
- The game was released on March 13 , 2012 for download on the PlayStation Network . A PlayStation Home Game Space , or themed area , based on Journey was released on March 14 , 2012 and is similar in appearance to the game . A retail " Collector 's Edition " of the game was released on August 28 , 2012 . In addition to Journey , the disc @-@ based title includes Flow and Flower ; creator commentaries , art , galleries , and soundtracks for all three games ; non @-@ related minigames ; and additional content for the PlayStation 3 . In September 2012 , Sony and Thatgamecompany released a hardcover book entitled " The Art of Journey " , by the game 's art director Matt Nava , containing pieces of art from the game ranging from concept art to final game graphics . On July 21 , 2015 , Journey was released on the PlayStation Network for the PlayStation 4 , ported by United Kingdom studio Tricky <unk> ; owners of the digital PlayStation 3 version of the game were able to download the new version for free . 
- 
- = = = Music = = = 
- 
- The music in Journey was composed and orchestrated by Austin Wintory , who had previously worked with Thatgamecompany on the soundtrack for Flow . Wintory worked closely on the soundtrack with sound designer Steve Johnson , as well as the programming team , so the music would dynamically tie in to both the actions of the player and sound effects caused by nearby game objects , and feel as if it were " <unk> in real time " . Johnson felt having short pieces of music that looped without reacting to the player would be a " missed opportunity " , and wanted to create music that changed while still containing a composed emotional arc . Jenova Chen met with Wintory at the start of the game 's development to describe his vision for the project , and Wintory left the meeting and composed and recorded the main cello theme for the soundtrack that night . He continued to work on the soundtrack for the next three years , experimenting and discarding many ideas . The game 's orchestrations were performed by the <unk> Radio Symphonic Orchestra in Macedonia . 
- Unlike many games , where different songs have different themes for each character or area , Wintory chose to base all of the pieces on one theme which stood for the player and their journey , with cello solos especially representing the player . Wintory describes the music as " like a big cello concerto where you are the soloist and all the rest of the instruments represent the world around you " , though he describes it as not necessarily orchestral due to the inclusion of electronic aspects . The cello begins the game as " immersed in a sea of electronic sound " , before first emerging on its own and then merging into a full orchestra , mirroring the player 's journey to the mountain . While the game 's art style is based on several different cultures , Wintory tried to remove any overt cultural influences from the music to make it " as universal and culture @-@ less as possible " . Tina <unk> features as the cellist for the soundtrack . She is a close friend of Wintory and has since performed " <unk> Variations " with him , an eight @-@ minute orchestral variation on the Journey soundtrack . All of the non @-@ electronic instruments in the soundtrack were recorded with a live orchestra . 
- The soundtrack was released as an album on April 10 on iTunes and the PlayStation Network . The album is a collection of the soundtrack 's " most important " pieces , arranged by Wintory to stand alone without the context of the player 's actions . The album comprises 18 tracks and is over 58 minutes long . It features the voice of <unk> Scott for the final track , " I Was Born for This " . After its release , the soundtrack reached the top 10 of the iTunes Soundtrack charts in more than 20 countries . It also reached No. 116 on the Billboard sales charts , with over 4000 units sold in its first week after release , the second @-@ highest position of any video game music album to date . The soundtrack was released as a physical album by <unk> Else Music Works on October 9 , 2012 . In 2012 Wintory released a download @-@ only album of music on Bandcamp titled Journey <unk> <unk> , which includes variations on themes from Journey and Flow . The soundtrack itself was subsequently released on Bandcamp on June 19 , 2013 . An album of piano arrangements titled <unk> was released on May 1 , 2014 , on Bandcamp as both a digital and physical album . A two @-@ record vinyl version of the album was released in 2015 . 
- In January 2016 , Wintory started a Kickstarter for a Journey Live concert tour , in which the fifteen @-@ piece Fifth House Ensemble from Chicago will perform the music from the game while a player works their way through the game . The ensemble will react to the player 's actions , using a specially @-@ scored version of the soundtrack , composed by Patrick O 'Malley with Wintory 's oversight , that breaks the music into small pieces to enable this reaction . Wintory had wanted to do a performance of the Journey soundtrack in this interactive manner , but did not have the time to rework the soundtrack for this purpose . Wintory came to know Dan <unk> , the composer for Fifth House Ensemble , after <unk> published his praise for the Journey soundtrack and had encouraged other members of the ensemble to play the game . The group saw how Journey 's soundtrack had been used for various Video Games Live concerts and believed they could pull off Wintory 's vision of an interactive concert , doing most of the reworking of the soundtrack under Wintory 's direction . Sony has provided Wintory with a version of the game developed by Tricky <unk> that <unk> the music to allow the ensemble to provide this , and other modifications required for the concert performance . The Kickstarter was launched for $ 9 @,@ 000 in funding for a four @-@ city tour , but within a few days already surpassed its funding levels , allowing for more cities to be included . 
- 
- = = Reception = = 
- 
- Journey received critical and commercial success worldwide . After its release , it became the fastest @-@ selling game to date on PlayStation Store in both North America and Europe . At the 2011 Electronic Entertainment Expo , prior to release , the game won awards for best download game from 1UP.com , GameSpy , and GameTrailers . After publication , the game was heavily honored at end of the year awards . At the 16th Annual D.I.C.E. Awards , formerly known as the Interactive Achievement Awards , Journey won 8 awards , the most honors received of the night ( which includes " Game of the Year " , " Outstanding Innovation in Gaming " , " <unk> Game of the Year " , " Outstanding Achievement in Game Direction " , " Outstanding Achievement in Art Direction " , " Outstanding Achievement in Online Gameplay " , " Outstanding Achievement in Original Music Composition " , and " Outstanding Achievement in Sound Design " ) ; it was additionally nominated for " Downloadable Game of the Year " , " Outstanding Achievement in Gameplay Engineering " , and " Outstanding Achievement in Story " . Journey was selected as the best game of the year by IGN and GameSpot , among others . 
- The soundtrack was nominated for the Best Score Soundtrack for Visual Media at the 2013 Grammy Awards , the first video game soundtrack to be nominated for that category , though it did not win . Additionally , the game won the award for best music and was nominated for the best graphics award from IGN , and was selected as the best PlayStation Network game by GameSpot . At the Spike Video Game Awards , Journey won awards as the best PlayStation 3 game , the best indie game , and the game with the best music , and was additionally nominated for game of the year , best downloadable game , best graphics , and best song in a game for " I Was Born For This " . It received the 2013 Annie Award for video game animation . It won five awards at the 2013 British Academy of Film and Television Arts awards : Artistic Achievement , Audio Achievement , Game Design , Online Multiplayer and Original Music , and was nominated for Best Game , Game Innovation and Story . In March 2013 , it won six awards at the annual Game Developers Choice Awards : Best Audio , Best Game Design , Best Visual Arts , Best Downloadable Game , the Innovation Award , and Game of the Year . 
- Journey received high acclaim from critics who praised the visual and auditory art direction as well as the emotional response playing with a stranger created . It received the IGN Best Overall Game Award for 2012 and Ryan Clements of IGN described the game as " the most beautiful game of its time " , saying , " each moment is like a painting , expertly framed and lit " . Jane Douglas of GameSpot concurred , calling it " relentlessly beautiful " and lauding the visual diversity of the world and the depiction of the <unk> sand ; Matt Miller of Game Informer added praise for the animation of the sand and creatures , saying the game was visually stunning . The music was also complimented , with Miller describing it as a " <unk> musical score " and Douglas calling it " moving , dynamic music " . 
- Reviewers were especially pleased with the emotional experience of playing the game , particularly with other players . Christian Donlan of Eurogamer described it as a " non @-@ denominational religious experience " that , with the addition of another player , moves beyond metaphors and becomes a " pilgrimage " to the player . A reviewer writing for Edge magazine said the emotional arc of the game hits with " occasionally startling power " , while Patrick Shaw from Wired said the game made him feel a " wide range of emotions ... wonder , fear , even sadness . " Miller said all three times he played the game , " each time , without fail , individual moments ... managed to give me <unk> , and those moments have remained on my mind for weeks afterward . " Joel Gregory of PlayStation Official Magazine praised the game 's story for being open to the player 's interpretation , leaving an ambiguity that drew him in . The addition of an unnamed second player was described by Donlan as brilliant and as a " master stroke " , and Edge said it made for " a more absorbing , more atmospheric experience " . 
- The few criticisms for the game centered on its length and pacing . Clements noted that not all players would appreciate a game with a " deliberate , melancholic pace " and short duration , comments echoed by the Edge review . Miller noted the lack of a complex gameplay elements in Journey , and Shaw was disappointed that the game was only a few hours long , though Douglas said the length was perfect . Miller concluded the game could be compared to " a musical concert , a well @-@ directed film , or a long @-@ awaited book " , while Clements concluded , " completing Journey will create memories that last for years . " 
- 
- 
- = Gold Beach = 
- 
- Gold , commonly known as Gold Beach , was the code name for one of the five areas of the Allied invasion of German @-@ occupied France in the Normandy landings on 6 June 1944 , during the Second World War . Gold , the central of the five areas , was located between Port @-@ en @-@ Bessin on the west and La Rivière on the east . High cliffs at the western end of the zone meant that the landings took place on the flat section between Le Hamel and La Rivière , in the sectors code @-@ named Jig and King . Taking Gold was to be the responsibility of the British Army , with sea transport , mine sweeping , and a naval bombardment force provided by the Royal Navy as well as elements from the Dutch , Polish and other Allied navies . 
- The objectives at Gold were to secure a beachhead , move west to capture Arromanches and establish contact with the American forces at Omaha , capture Bayeux and the small port at Port @-@ en @-@ Bessin , and to link up with the Canadian forces at Juno to the east . Forces attacking Gold faced elements of the German 352nd Infantry Division and German 716th Infantry Division . About 2 @,@ 000 men were stationed in the immediate area . <unk> to fortifications along the Normandy coast had been undertaken under the leadership of <unk> Erwin Rommel beginning in October 1943 . 
- On D @-@ Day at Gold , naval bombardment got underway at 05 : 30 , and amphibious landings commenced at 07 : 25 . High winds made conditions difficult for the landing craft , and the amphibious DD tanks were released close to shore or directly on the beach instead of further out as planned . Three of the four guns in a large emplacement at the Longues @-@ sur @-@ Mer battery were disabled by direct hits from the cruisers <unk> and <unk> at 06 : 20 . The fourth gun resumed firing intermittently in the afternoon , and its garrison surrendered on 7 June . Aerial attacks had failed to hit the Le Hamel strongpoint , which had its <unk> facing east to provide enfilade fire along the beach and had a thick concrete wall on the seaward side . Its 75 mm gun continued to do damage until 16 : 00 , when a modified Armoured Vehicle Royal Engineers ( AVRE ) tank fired a large <unk> charge into its rear entrance . A second casemated emplacement at La Rivière containing an 88 mm gun was neutralised by a tank at 07 : 30 . 
- Meanwhile , infantry began clearing the heavily fortified houses along the shore and advanced on targets further inland . The British Commandos of No. 47 ( Royal Marine ) Commando advanced on Port @-@ en @-@ Bessin and captured it on 7 June in the Battle of Port @-@ en @-@ Bessin . On the western flank , the 1st Battalion , Hampshire Regiment captured Arromanches ( future site of one of the artificial Mulberry harbours ) , and 69th Infantry Brigade on the eastern flank made contact with the Canadian forces at Juno . Company Sergeant Major Stanley Hollis received the only Victoria Cross awarded on D @-@ Day for his actions while attacking two <unk> at the Mont Fleury battery . Due to stiff resistance from the German 352nd Infantry Division , Bayeux was not captured until the next day . British casualties at Gold are estimated at 1 @,@ 000 – 1 @,@ 100 . German casualties are unknown . 
- 
- = = Background = = 
- 
- 
- = = = Operation <unk> = = = 
- 
- After the Germans invaded the Soviet Union in June 1941 , Soviet leader Joseph Stalin began pressing for the creation of a second front in Western Europe . The decision to undertake a cross @-@ channel invasion of continental Europe within the next year was taken at the <unk> Conference , held in Washington in May 1943 . The Allies initially planned to launch the invasion on 1 May 1944 , and a draft of the plan was accepted at the Quebec Conference in August 1943 . General Dwight D. Eisenhower was appointed commander of Supreme Headquarters Allied Expeditionary Force ( <unk> ) . General Bernard Montgomery was named as commander of the 21st Army Group , which comprised all of the land forces involved in the invasion . 
- On 31 December 1943 , Eisenhower and Montgomery first saw the plan , which proposed amphibious landings by three divisions and two @-@ thirds of an airborne division . The two generals immediately insisted that the scale of the initial invasion be expanded to five divisions , with airborne <unk> by three divisions , to allow operations on a wider front . The change doubled the frontage of the invasion from 25 miles ( 40 km ) to 50 miles ( 80 km ) . This would allow for quicker <unk> of men and materiel , make it more difficult for the Germans to respond , and speed up the capture of the port at Cherbourg . The need to acquire or produce extra landing craft and troop carrier aircraft for the expanded operation meant that the invasion had to be delayed to June . 
- The Americans , assigned to land at Utah and Omaha , were to cut off the <unk> Peninsula and capture the port facilities at Cherbourg . The British at Sword and Gold , and the Canadians at Juno , were to capture Caen and form a front line from <unk> @-@ l <unk> to the south @-@ east of Caen to protect the American flank , while establishing airfields near Caen . Possession of Caen and its surroundings would provide a suitable staging area for a push south to capture the town of <unk> . A secure <unk> would be established and an attempt made to hold all territory north of the <unk> @-@ <unk> line during the first three weeks . The Allied armies would then swing left to advance towards the River Seine . Montgomery envisaged a ninety @-@ day battle , ending when all the forces reached the Seine . 
- 
- = = = Allied planning = = = 
- 
- Originally , seventeen sectors along the Normandy coastline had been selected as possible invasion sites and each were provided with a code name taken from one of the spelling alphabets of the time . The coast was divided between Able , west of Omaha , to <unk> on the eastern flank of the invasion area . Eight further sectors were added when the planned invasion was extended to include Utah . Each sector was further subdivided into beaches identified by the colours Green , Red , and White . Gold did not refer to a particular beach but to a landing area . It was delineated by Port @-@ en @-@ Bessin on the west and La Rivière on the east , and included Arromanches , location of one of the artificial Mulberry harbours that were to be constructed shortly after the invasion . High cliffs at the western end of the zone meant that the landings would be undertaken on the flat beach between Le Hamel and La Rivière , in the sectors code @-@ named Jig and King . The area immediately behind the beach was marshy , with open ground and <unk> ( small fields surrounded by <unk> and embankments ) further inland . Roads led to the south via Asnelles and Ver @-@ sur @-@ Mer . The terrain to the south @-@ east rose to a ridge at Meuvaines , where on D @-@ day were located machine gun nests of the German <unk> Regiment . 
- The Allied Expeditionary Air Force undertook over 3 @,@ 200 photo reconnaissance sorties from April 1944 until the start of the invasion . <unk> of the coastline were taken at extremely low altitude to show the invaders the terrain , obstacles on the beach , and defensive structures such as bunkers and gun emplacements . Inland terrain , bridges , troop emplacements , and buildings were also photographed , in many cases from several angles , to give the Allies as much information as possible . Members of Combined Operations <unk> <unk> <unk> prepared detailed harbour maps , including depth <unk> . At Gold , <unk> discovered the shore between Asnelles and La Rivière was soft and could not support the weight of tanks . Twelve Armoured Vehicle Royal Engineers ( AVREs ) were fitted with <unk> to overcome this problem by deploying a roll of <unk> over the soft surface . The material would then be left in place to create a route for more conventional tanks . 
- Gold was assigned to the British XXX Corps , with the 50th ( Northumbrian ) Infantry Division – commanded by Major General <unk> Graham – allotted as the assault division . It was an experienced division that had already seen combat in France , North Africa , and Sicily . The men underwent extensive training in amphibious landings , including Exercise <unk> , a major training exercise at <unk> Island in May 1944 . Demolition teams responsible for <unk> underwater beach obstacles trained in swimming pools in the UK . <unk> were undertaken using detailed maps that used fictitious place names , and most of the men did not find out their destination until they were already on their way to Normandy . The amphibious landing was to be preceded by extensive air bombardment as well as naval bombardment by <unk> Force K , a task force of eighteen ships , primarily cruisers and destroyers . <unk> tanks of the 8th Armoured Brigade were to arrive at 07 : 20 , followed by infantry at 07 : 25 . The 231st Brigade was assigned to land at Jig , and 69th Brigade at King . The 231st was to head west to capture Arromanches and establish contact with the American forces at Omaha , while the 69th was to move east and link up with the Canadian forces at Juno . The 47th Royal Marine Commando was assigned to land at Gold , infiltrate inland , and capture the small port at Port @-@ en @-@ Bessin from the <unk> side . 
- Arriving in the second wave on Jig , 56th Infantry Brigade was to capture Bayeux and a nearby ridge , thus cutting the <unk> highway between Caen and Bayeux to make it difficult for the Germans to move in reinforcements . The second wave on King , 151st Infantry Brigade , was tasked with capturing the Caen road and railway , along with setting up positions on high ground between the <unk> and <unk> rivers . Other forces involved in the landing included artillery regiments , signals corps , and engineering units . 
- 
- = = = German defences = = = 
- 
- In late 1943 , Hitler placed Field Marshal Erwin Rommel in charge of improving the coastal defences along the Atlantic Wall in anticipation of an Allied invasion , expected to take place sometime in 1944 . Rommel believed that the Normandy coast could be a possible landing point for the invasion , so he ordered the construction of extensive defensive works along that shore . In the immediate area of Gold , between Le Hamel and La Rivière , seven defensive <unk> designed to hold 50 men apiece were constructed . Two major concrete @-@ reinforced coastal artillery emplacements ( a battery of four 122 mm guns at Mont Fleury and the Longues @-@ sur @-@ Mer battery , with four 150 mm guns ) were only partially completed by D @-@ Day . Rommel ordered wooden stakes , metal <unk> , mines , and large anti @-@ tank obstacles to be placed on the beach to delay the approach of landing craft and impede the movement of tanks . <unk> the Allies to land at high tide so that the infantry would spend less time exposed on the beach , he ordered many of these obstacles to be placed at the high tide mark . <unk> of barbed wire , booby traps , and the removal of ground cover made the approach hazardous for infantry . 
- Hitler gave Rommel command of the newly re @-@ formed Army Group B , which included the 7th Army , the 15th Army , and the forces guarding the Netherlands . Reserves for this group included the 2nd , 21st , and 116th Panzer divisions . <unk> that Allied air superiority would make it difficult if not impossible to move reserves into position once the invasion was underway , Rommel decided to concentrate the defences along the coast . The 716th Infantry Division , which had been stationed in the area since March 1942 , was significantly <unk> , with only 6 @,@ 000 men . This unit received reinforcements , and some of the older men were replaced by younger soldiers . It was also supplemented by several battalions of <unk> ( eastern soldiers ) , conscripted Soviet prisoners of war . The 352nd Infantry Division , a full @-@ strength unit of around 12 @,@ 000 , was brought into the area by Rommel on 15 March and reinforced by two additional regiments . About 2 @,@ 000 men , a mixture from the two infantry divisions , were stationed in the coastal area between Arromanches and Asnelles . 
- 
- = = Order of battle = = 
- 
- 
- = = = British forces = = = 
- 
- 50th ( Northumbrian ) Infantry Division , commanded by Major @-@ General <unk> Graham 
- 69th Infantry Brigade , commanded by Brigadier <unk> Knox 
- 5th Battalion , East Yorkshire Regiment 
- 6th Battalion , Green Howards 
- 7th Battalion , Green Howards 
- 151st Infantry Brigade , commanded by Brigadier <unk> Senior 
- 6th Battalion , Durham Light Infantry 
- 8th Battalion , Durham Light Infantry 
- 9th Battalion , Durham Light Infantry 
- 231st Infantry Brigade , commanded by Brigadier <unk> <unk> 
- 1st Battalion , <unk> Regiment 
- 1st Battalion , Hampshire Regiment 
- 2nd Battalion , Devonshire Regiment 
- Divisional troops 
- <unk> Reconnaissance Regiment , Royal Armoured Corps ( <unk> ) ( 2 Squadrons ) 
- 2nd Battalion , Cheshire Regiment ( machine guns and heavy mortars ) 
- <unk> , <unk> & <unk> Batteries , 90th Field Regiment , Royal Artillery ( RA ) ( Self @-@ propelled ) 
- 99th & <unk> Batteries , <unk> ( Northumberland Hussars ) Anti @-@ Tank Regiment , RA 
- 82nd Battery , 25th Light Anti @-@ Aircraft Regiment , RA 
- <unk> ( Northumbrian ) Field Company , Royal Engineers ( RE ) 
- <unk> Field Company , RE 
- <unk> Field Company , RE 
- <unk> ( Northumbrian ) Field Park Company , RE ( <unk> ) 
- 50th Divisional Signals , Royal Corps of Signals 
- <unk> , 186th & 200th Field <unk> , Royal Army Medical Corps ( RAMC ) 
- 22nd Field Hygiene Section , RAMC 
- 50th Divisional Provost Company , Royal Military Police ( <unk> ) 
- <unk> attached for assault phase 
- Elements of 79th Armoured Division 
- Westminster <unk> ( <unk> tanks ) 
- <unk> Royal Tank Regiment ( The <unk> ) ( Churchill <unk> ) ( 2 Troops ) 
- 81st & 82nd Assault Squadrons , 6th Assault Regiment , RE ( AVREs ) 
- 56th Infantry Brigade , commanded by Brigadier <unk> <unk> 
- 2nd Battalion , Essex Regiment 
- 2nd Battalion , Gloucestershire Regiment 
- 2nd Battalion , South Wales Borderers 
- 8th Armoured Brigade , commanded by Brigadier <unk> <unk> 
- 4th / 7th Royal Dragoon Guards ( DD <unk> ) 
- Nottinghamshire Yeomanry ( DD <unk> ) 
- 76th Anti @-@ Aircraft Brigade , commanded by Brigadier <unk> Benson 
- <unk> Heavy Anti @-@ Aircraft Regiment , RA ( HQ only ) 
- 320th Battery , 93rd Light Anti @-@ Aircraft Regiment , RA 
- <unk> & <unk> Batteries , 120th Light Anti @-@ Aircraft Regiment , RA 
- <unk> Anti @-@ Aircraft Operations Room , RA 
- A <unk> , <unk> ( Independent ) <unk> Battery , RA 
- Additional units attached for assault phase 
- GHQ <unk> Regiment , <unk> ( ' Phantom ' ) 
- <unk> , <unk> , & <unk> Batteries , 86th ( East Anglian ) ( Hertfordshire Yeomanry ) Field Regiment , RA ( Self @-@ propelled ) 
- <unk> , <unk> & <unk> Batteries , <unk> ( Essex Yeomanry ) Field Regiment , RA ( Self @-@ propelled ) 
- <unk> & <unk> Batteries , 73rd Anti @-@ Tank Regiment , RA 
- <unk> Air Observation Post Squadron , RA ( Ground crew of one flight only ) 
- 73rd Field Company , RE 
- 280th Field Company , RE 
- <unk> Field Ambulance , RAMC 
- <unk> Light Field Ambulance , RAMC 
- 1st Royal Marine Armoured Support Regiment 
- No. 47 ( Royal Marine ) Commando 
- 104 Beach Sub @-@ Area HQ 
- 9 & 10 Beach Groups and Signal Section 
- 69th , <unk> , 90th & <unk> Field Companies , RE 
- 21st & 23rd Stores Sections , RE 
- 51st & <unk> Mechanical Equipment Sections , RE 
- <unk> Port Operating Company , RE 
- <unk> & <unk> Inland Water Transport Companies , RE 
- 2nd Battalion , Hertfordshire Regiment 
- 6th Battalion , Border Regiment 
- <unk> , <unk> & <unk> General Transport Companies , Royal Army Service Corps ( RASC ) 
- 2nd & 5th <unk> Issue <unk> , RASC 
- <unk> <unk> <unk> , RASC 
- 3rd & 10th <unk> Clearing Stations , RAMC 
- 3rd , 25th , 31st , 32nd & 35th Field <unk> Stations , RAMC 
- Nos 41 , 42 , 47 & 48 Field <unk> Units , RAMC 
- Nos 24 & 30 Field <unk> Unit , RAMC 
- 22nd & 23rd Port Details , RAMC 
- 7th , 10th & 36th Ordnance Beach <unk> , Royal Army Ordnance Corps 
- 24th & 25th Beach Recovery Sections , Royal Electrical & Mechanical Engineers ( <unk> ) 
- XXX Corps Workshop , <unk> ( 2 Composite <unk> & 1 Light Recovery Section ) 
- <unk> & <unk> Provost Companies , <unk> 
- 75th , <unk> , <unk> & 280th ( Pioneer ) Companies , Pioneer Corps 
- 
- = = = German forces = = = 
- 
- From June 1942 , 716th Infantry Division covered the <unk> Sector , which stretched from the base of the <unk> Peninsula to the Orne River near Caen , a distance of 77 kilometres ( 48 mi ) . When 352nd Infantry Division arrived on 15 March , the <unk> Sector was split into the Bayeux Sector ( from <unk> to Asnelles ) and the Caen Sector ( from Asnelles to the Orne ) . Most of the 716th Infantry Division remained where they were , and thus the defenses in Caen Sector ( site of the Gold landings ) were not substantially strengthened . 
- 352nd Infantry Division under <unk> <unk> <unk> <unk> Regiment : south @-@ east of Bayeux , as reserves 
- <unk> <unk> Regiment : covered Omaha and the westernmost part of Gold 
- 352nd Artillery Regiment : covered Omaha and the westernmost part of Gold 
- 716th Static Infantry Division under <unk> Wilhelm <unk> Infantry Regiment : two battalions in and around Le Hamel . Members of the <unk> Ost Battalion garrisoned the coastal defenses . One battalion was stationed near Crépon as reserves . 
- <unk> Infantry Regiment : covered Juno , Sword , and the easternmost part of Gold 
- <unk> Artillery Regiment : mobile and casemated batteries east and west of Crépon 
- 
- = = <unk> = = 
- 
- <unk> of Normandy began around midnight with over 2 @,@ 200 British and American bombers attacking targets along the coast and further inland . At Gold , naval bombardment by <unk> Force K got underway at 05 : 30 , at which time the first waves of infantry were loading into their Landing Craft Assault ( <unk> ) for the run in to the beach . German defensive positions were attacked by medium and heavy bombers and by self @-@ propelled guns on board the landing craft . Results were good at Mont Fleury Battery and at Longues , where at 07 : 00 <unk> and <unk> took out of commission three of the four guns . The fourth gun resumed firing sporadically in the afternoon , and the garrison surrendered the following day . Two heavily casemated gun emplacements ( an 88 mm gun at La Rivière overlooking King and a 75 mm gun at Le Hamel overlooking Jig ) were only lightly damaged , as they were heavily reinforced with concrete , especially on the seaward side . These positions had <unk> that permitted a wide range of enfilade fire on the beach . Four other German strong points in the immediate area were also only lightly damaged , and had to be individually assaulted as the day progressed . 
- 
- = = = King Sector = = = 
- 
- H @-@ Hour for the landing at Gold was set at 07 : 25 on King sector ( 50 minutes later than in the American landings , because of differences in the tide ) . The first wave on King was the 5th East <unk> and 6th Green Howards of 69th Brigade , assisted by amphibious DD tanks of the 4th / 7th Dragoon Guards . The 7th Green Howards landed at 08 : 20 . The original plan called for the 38 DD tanks to be launched from their landing craft tank ( <unk> ) about 5 @,@ 000 yards ( 4 @,@ 600 m ) out . Due to extremely <unk> seas , they decided to run the tanks directly onto the beach . Infantry , engineers , and DD tanks arrived almost simultaneously . Units disembarking onto the beach immediately came under fire from the casemated 88 mm gun at La Rivière , and the infantry were forced to take cover behind the sea wall . The gun was taken out when a <unk> tank of the Westminster <unk> fired a charge directly into its <unk> . The 5th East <unk> , supported by several tanks , spent the rest of the morning clearing out the heavily fortified houses of La Rivière , at the loss of 90 men , including six officers . 
- <unk> armour arriving in the first wave included AVREs , mine <unk> , and armoured <unk> . Clearing paths off the beach proved difficult , as the tanks got stuck in the mud or were taken out by mines . A lone mine <unk> tank finally cleared a path from the beach up toward the Mont Fleury Battery and Ver @-@ Sur @-@ Mer . This route was used by the Green Howards and tanks of the 4th / 7th Dragoon Guards , who cleared the remaining resistance at the Mont Fleury Battery . B Company moved on to attack trench positions and machine gun emplacements at Meuvaines Ridge , while C Company moved to the west of Ver @-@ Sur @-@ Mer to help cover the assault on Crépon , where roads led to the important targets of Bayeux and Caen . The 7th Green Howards attacked the gun battery at Ver @-@ Sur @-@ Mer , where they took 50 prisoners . Colour Sergeant @-@ Major Stanley Hollis earned the only Victoria Cross to be bestowed for actions on D @-@ Day . On the way to the Mont Fleury Battery , Hollis came under machine gun fire while investigating a <unk> , so he shot into the entrance with his <unk> gun and dropped a grenade through the roof , killing most of the occupants . He cleared a nearby trench of enemy soldiers , whereupon the occupants of a second <unk> surrendered . Later in the day , he saved the lives of three men during an attempt to take out a field artillery installation at a farm near Crépon . 
- <unk> to land at 11 : 00 on Jig , the 56th Infantry Brigade was re @-@ routed King , because the gun battery at Le Hamel was still operational . They proceeded towards their objective of Bayeux . 151st Brigade arrived at the same time and after meeting fierce resistance , they achieved their objective of controlling the road and railway between Bayeux and Caen . 56th Brigade made slow progress and had to dig in for the night some distance from Bayeux . 69th Brigade secured the eastern flank and by nightfall made contact with the Canadian forces on Juno . 
- 
- = = = Jig Sector = = = 
- 
- At Jig , the first wave of infantry ( the 1st <unk> and 1st Hampshires of the 231st Infantry Brigade ) arrived at 07 : 25 , and immediately came under fire from the casemated 75 mm gun at Le Hamel . Due to navigation errors and the strong current , both groups came ashore well to the east of their intended landing points . The DD tank and Royal Marine <unk> tanks that were supposed to arrive in advance of the landing were delayed by rough seas and did not arrive until 08 : 00 . Many of the tanks got bogged down on the beach or were taken out by enemy fire . The tide came in quicker than expected , before many of the beach obstacles and mines were cleared , and some of the landing craft were damaged as a result . Two companies of the 1st Hampshires landed very close to the strong point at Le Hamel , and had to fight inland through enemy garrisons to get off the beach . Attempts to flank Le Hamel were made difficult by the surrounding machine gun <unk> , mines , and barbed wire . Elements of the 1st Hampshires captured the German strongpoint WN @-@ 36 at the eastern edge of the village of Asnelles . When they turned west to move along the beach towards their primary objective at Le Hamel , they came under heavy fire and had to break off the attack . Major Warren , in charge after the commanding officer Lieutenant Colonel Nelson Smith was wounded , decided that the troops would have to circle around and attack the emplacement from the rear , a process that took several hours . The troops began to have some success around 15 : 00 with the arrival of an AVRE tank of 82nd Assault Squadron . The tank fired two <unk> into the <unk> , where most of the defenders were located . The German soldiers fled into fortified houses in Le Hamel and Asnelles , and were taken out in house @-@ to @-@ house combat . Few surrendered . The 75 mm gun was finally <unk> at 16 : 00 , when the AVRE tank fired a large <unk> charge into the rear entrance of the casemate . C / A Company , 1st Hampshires and the AVRE tank proceeded west along the beach and took out strongpoint WN @-@ 38 at La <unk> St <unk> , taking 20 prisoners . Still further west , D Company captured strong point WN @-@ 39 at the Arromanches radar station , capturing 30 more defenders . 
- The 2nd <unk> arrived at 08 : 15 , while the beach was still under heavy fire . One company stayed to help with the assault on Le Hamel , while the rest moved to capture the village of <unk> <unk> the road to Bayeux . <unk> was captured at around 16 : 30 . The 1st <unk> attacked a German position on the beach at La <unk> des <unk> and headed inland to arc westward toward the high ground south of Arromanches . They cleared enemy positions at Le <unk> and <unk> d <unk> , and arrived at their destination late in the morning . <unk> by elements of the 1st Hampshires and covered by indirect fire from the naval forces offshore , they took Arromanches late in the afternoon . 
- 
- = = = 47th Commando = = = 
- 
- The 47th Royal Marine Commando was assigned to capture the small harbour at Port @-@ en @-@ Bessin , on the boundary with Omaha , about 7 miles ( 11 km ) west of Arromanches and 8 miles ( 13 km ) from their landing point at Jig . The commanding officer , Lieutenant @-@ Colonel C. F. Phillips , opted to attack from the south , as the site was well protected on the seaward side . The force of 420 men consisted of five troops of 63 men , a mortar and machine gun troop , a transport group with four tracked vehicles , and a headquarters group . The plan was to land at Gold at 09 : 25 , assemble at La <unk> , and move cross @-@ country to a ridge ( designated as Point 72 ) south of Port @-@ en @-@ Bessin , arriving at around 13 : 00 . Here they would call for indirect fire from the supporting vessels at sea and then move in to capture the town . 
- In rough seas and under enemy fire , the commandos began disembarking at Jig , some distance east of their intended position , at 09 : 50 . Five of their <unk> were sunk by beach obstacles or enemy fire , at the cost of 76 casualties . Major P. M. <unk> temporarily took charge until Phillips and some others who had got separated from the unit rejoined the group at 14 : 00 along the Meuvaines – Le <unk> road . The commando took additional casualties in several <unk> , including at La <unk> , on the way to Point 72 . They did not arrive there until 22 : 30 , too late to launch an attack , so they dug in for the night . The town and port were captured in the battle of Port @-@ en @-@ Bessin on 7 – 8 June 1944 . 
- 
- = = = German response = = = 
- 
- As the Luftwaffe meteorological centre in Paris had predicted two weeks of <unk> weather , some Wehrmacht commanders were away from the front attending war games in Rennes , and many soldiers had been given leave . On D @-@ Day , Rommel was in Germany for his wife 's birthday and a meeting with Hitler to try to get more <unk> . The 352nd and 716th Divisions were placed on high alert after the Allied airborne landings , which had taken just after midnight behind Utah and Sword . The 2 @,@ 700 @-@ strong <unk> Meyer , near Bayeux as the divisional reserve , was sent to investigate the parachute drops behind Utah . <unk> recalled them when dawn broke and the scope of the invasion became apparent . One battalion was ordered to reinforce the German efforts at Omaha . The remainder were ordered to rendezvous with reinforcements at Villiers le <unk> , 7 @.@ 5 miles ( 12 @.@ 1 km ) east of Bayeux , to launch a counter @-@ attack . <unk> upon by Allied air forces , the column finally arrived in the late afternoon , at which time they were met by elements of the 69th Brigade . The British lost four tanks in the ensuing engagement but the <unk> was almost completely wiped out . Meyer was killed , and his detailed maps of German coastal emplacements fell into British hands . 
- Because Allied air superiority meant it would be difficult for the Germans to move up their reserves , Rommel believed that their best chance was to stop the invasion at the shore . The scope of the invasion meant that once these coastal defences were defeated and the troops scattered , it was difficult to defend territory inland or launch counter @-@ attacks . At 22 : 33 , <unk> ordered the 352nd Division to create a defensive line north of Bayeux but this proved impossible , as most of the territory involved was already in British hands and all the defending units had taken serious losses . The Luftwaffe played only a minor role on D @-@ Day . At Gold , several small groups of bombers that arrived at sunset caused Allied casualties at Le Hamel and damaged a road near Ver @-@ sur @-@ Mer . At 06 : 00 on 7 June , the operations room of HMS <unk> , offshore near Gold , was damaged by a bomber attack , but the ship was able to remain on station . The unit responsible was likely II . / <unk> 40 ( KG 40 — <unk> Wing 40 ) . Under the command of <unk> <unk> ( <unk> Command Atlantic ) , it was based at <unk> – <unk> . On the evening of the 6 / 7 June 1944 , 26 Heinkel He 177 heavy bombers equipped with <unk> <unk> 293 anti @-@ ship guided missiles attacked shipping over Normandy , included the Gold area . II . / KG 40 lost 13 aircraft to all causes during the attack . 
- The 1st SS Panzer Division <unk> SS Adolf Hitler were released before midnight from the <unk> reserve and ordered to counter @-@ attack between Bayeux and the Orne , supplemented by 12th SS Panzer Division <unk> and Panzer <unk> Division ; the armoured divisions began arriving on 8 June . 
- 
- = = Aftermath = = 
- 
- 
- = = = Analysis = = = 
- 
- <unk> of German resistance remained throughout the beachhead area and the British were stopped about 3 @.@ 7 miles ( 6 @.@ 0 km ) short of their D @-@ Day objectives . Bayeux , a primary D @-@ Day objective for 50th Division , was captured on 7 June . By the end of D @-@ Day , the 50th Division had lost around 700 men . Total casualties , from all units involved in operations at Gold , were in the region of 1 @,@ 000 – 1 @,@ 100 casualties , of which 350 were killed . German losses are unknown ; at least 1 @,@ 000 were captured . 
- 
- = = = Gold Beach after 6 June = = = 
- 
- By the end of D @-@ Day , 24 @,@ 970 men had been landed at Gold Beach , along with 2 @,@ 100 vehicles and 1 @,@ 000 long tons ( 1 @,@ 000 t ) of supplies . The follow @-@ up landings were slowed by the loss of 34 <unk> and the bad weather . The 24th <unk> and <unk> Reconnaissance Regiment , due to land on D @-@ Day to help spearhead the drive towards <unk> @-@ <unk> , were unable to put ashore until 7 June . In 2004 <unk> wrote that the delay 
- effectively ruled out any chance of a thrust south ... [ and ] ... represented a major blow to Second Army 's intentions and Montgomery 's plan . 
- The 7th Armoured Division and the 49th ( West Riding ) Infantry Division were the follow @-@ up divisions of XXX Corps . The 22nd Armoured Brigade ( the armoured component of the 7th Armoured Division ) was scheduled to land during the evening of 6 June , but it was unable to land until the next day . The bulk of the division landed from 9 – 10 June , with some elements landing later . The 49th Division came ashore on 12 June . 
- The first components of the Mulberry harbours were brought across the Channel on D + 1 and the structures were in use for unloading by mid @-@ June . One was constructed at Arromanches by British forces , the other at Omaha by American forces . A severe storm on 19 June destroyed the Omaha harbour . The Arromanches harbour was repaired and remained in use for the next ten months , with a maximum capacity of 7 @,@ 000 long tons ( 7 @,@ 100 t ) of stores per day . Of the British supplies landed in Normandy by the end of August , 35 % arrived via the Mulberry harbour and 15 % came in via the small harbours at Port @-@ en @-@ Bessin and <unk> @-@ sur @-@ Mer . Most shipments were brought in over the beaches until the port of Cherbourg was cleared of mines and obstructions on 16 July . The most important use of the Mulberry harbour was the unloading of heavy machinery that could not be brought across the beaches . Artificial <unk> ( <unk> ) sheltered hundreds of ships during the storm of 17 – 23 June , and provided shelter for craft unloading stores at Juno and Sword . A joint Anglo @-@ American oil depot was constructed at Port @-@ en @-@ Bessin , fed via <unk> pipes known as " <unk> " from oil tankers moored offshore . Using this method , 175 @,@ 000 long tons ( 178 @,@ 000 t ) of petrol ( half for the Second Army ) was delivered by the end of August , by which time the underwater <unk> constructed in Operation Pluto were ready . 
- 
- = = = Subsequent operations = = = 
- 
- Fighting in the Caen area versus the 21st Panzer , the 12th SS Panzer Division <unk> , and other units soon reached a stalemate . Operation <unk> ( 7 – 14 June ) failed to take Caen , and the British were forced to withdraw to <unk> @-@ sur @-@ <unk> . After a delay because of storms during 17 – 23 June , Operation Epsom was launched on 26 June , an attempt by VIII Corps to swing around and attack Caen from the south @-@ west and establish a bridgehead south of the <unk> . Although the operation failed to take Caen , the Germans suffered heavy tank losses and had committed every available Panzer unit to the operation . Caen was severely bombed on the night of 7 July and then occupied north of the River Orne in Operation <unk> on 8 – 9 July . Two offensives during 18 – 21 July , Operation Atlantic and Operation Goodwood , captured the rest of Caen and the high ground to the south , but by then the city was nearly destroyed . 
- 
- = = Tourism = = 
- 
- The site of the Normandy landings is a popular tourist destination . The battery at Longues @-@ sur @-@ Mer is well preserved , and its observation bunker houses a visitor centre . The gun emplacements at Le Hamel and La Rivière still exist , but many other batteries and defensive positions have been allowed to decay . Bayeux is home to the Musée <unk> de la <unk> de Normandie and the Bayeux Commonwealth War Graves Commission Cemetery . La <unk> German war cemetery is also near Bayeux . At Arromanches , many elements of the Mulberry Harbour are extant and a museum examines its construction and use . The radar station is the site of a visitor centre and theatre . 
- 
- 
- = Tautiška giesmė = 
- 
- Tautiška giesmė ( The National Hymn ) is the national anthem of Lithuania , also known by its opening words " Lietuva , <unk> mūsų " ( official translation of the lyrics : " Lithuania , Our Homeland " , literally : " Lithuania , Our <unk> " ) and as " <unk> <unk> " ( Hymn of Lithuania ) . The music and lyrics were written in 1898 by <unk> Kudirka , when Lithuania was still part of the Russian Empire . The fifty @-@ word poem was a condensation of Kudirka 's <unk> of the Lithuanian state , the Lithuanian people , and their past . Shortly before his death in 1899 , the anthem was performed for Lithuanians living in Saint Petersburg , Russia . 
- The first public Lithuanian performance of the anthem took place in <unk> in 1905 , and it became the official national anthem in 1919 , a year after Lithuania declared its independence . Following the occupation and <unk> of Lithuania by the Soviet Union in 1940 , the anthem was forbidden to be played or sung in public . 
- " Tautiška giesmė " was reinstated in 1989 shortly before the <unk> of Lithuanian independence and confirmed in the National Anthem Act ( 21 October 1991 ) . It was automatically included as the national anthem in 1992 , when the new Constitution was ratified after independence from the Soviet Union was achieved . The status of " Tautiška giesmė " as the National Anthem of Lithuania was further confirmed in 1999 with the passage of a national law stating that . 
- 
- = = Creation = = 
- 
- At the time when the poem Lietuva , <unk> mūsų was written , Lithuania was part of the Russian Empire . Kudirka , a medical student at the University of Warsaw , was writing as a columnist for the newspaper Varpas ( The Bell ) . In his Varpas columns , Kudirka urged Lithuanians to take pride in their heritage , discussed the problems the Russian Government was causing the Lithuanian population , and denounced those who wished to work for the <unk> <unk> . In the course of writing for Varpas , he wrote down his thoughts on what Lithuania was and what it should be , resulting in the fifty @-@ word poem Lietuva , <unk> mūsų ( " Lithuania , Our Homeland " ) . 
- The poem described the heroic past of Lithuania and <unk> its people to care for the land , care for humanity , and live in honor . Kudirka also urged the country to become a source of enlightenment and virtue . Without a melody , Kudirka took the time to compose the music just before dying of tuberculosis . Both the melody and the lyrics were printed in Varpas in September 1898 . Upon his death in 1899 , Kudirka 's tomb was engraved with the second stanza of the anthem ( later destroyed by the authorities ) . 
- 
- = = History = = 
- 
- Before Kudirka 's death , the first performance of the poem occurred at a concert in St. Petersburg , Russia in 1899 . The concert was conducted by <unk> <unk> and was attended by Lithuanians , which St. Petersburg had the largest population of at that time . The anthem was first performed in Lithuania during the Great <unk> of <unk> on December 3 , 1905 . 
- When Lithuania declared its independence from Russia in 1918 , the song was declared the national anthem . It held this status until Lithuania was annexed into the Soviet Union during World War II . During the interwar period , there had been suggestions to modify the words to include a reference to God . It was decided , in Kudirka 's memory , that the lyrics should remain as he had written them . 
- Immediately following the Soviet occupation of Lithuania , The Internationale replaced the Tautiška giesmė . When in 1944 ' The Internationale ' have been replaced by State Anthem of the Soviet Union in Soviet Union as official anthem , Tautiška giesmė was used as official anthem for the Lithuanian Soviet Socialist Republic . An alternate anthem was created and introduced in 1950 . The music for that piece was composed by <unk> <unk> and Jonas <unk> , and the words were written originally by <unk> <unk> . Following Stalin 's death in 1953 , its lyrics were modified by <unk> <unk> to remove reference to the former dictator . This anthem stated that Lenin had lit the path to freedom , helped by the Russian people , and <unk> the Lithuanian people to work with peoples of the other Soviet <unk> . The anthem was confirmed in Article 169 of the 1978 Constitution of the Lithuanian <unk> . The song continued to be used until Lithuania broke away from the Soviet Union . Already in 1988 , the Tautiška giesmė was suggested as a replacement for the Soviet Lithuanian anthem . After preliminary approval by the Supreme Soviet of the Lithuanian <unk> and successive legislative bodies , its status as the national anthem was <unk> in 1992 . In that year , the Constitution of Lithuania was approved ; Article 19 of the document states that Tautiška giesmė will be the national anthem of Lithuania . The last law in relation to the national anthem was passed in 1999 ; it contained the official lyrics and protocol on how and when to play the anthem . 
- 
- = = 1999 law = = 
- 
- <unk> into law by President <unk> <unk> on June 9 , 1999 , the " Law on the National Anthem of the Republic of Lithuania " details when and where the national anthem is played and its performance protocols . 
- Article 2 of the law states that the anthem is to be played at the following occasions : At the beginning or ending of solemn sessions of the <unk> , on national holidays and memorial days , and at receptions and <unk> of foreign heads of state on official visits to Lithuania — but only after the anthem of the foreign country has been played . 
- It is played in foreign countries to represent Lithuania , according to their own diplomatic protocols ; on national holidays and other days when the Flag of Lithuania is raised by order of the government ; when the flag is raised during public events sponsored by governmental institutions , business , and organizations ; and at the beginning and end of National Radio programming . The anthem may also be played at other occasions , such as sporting competitions . 
- When playing the anthem , the music may be either live or recorded . The anthem may be performed with a choir , an orchestra , a military band , or a combination of the latter two . Article 4 , section 2 , states that all participants are encouraged to sing the national anthem . When the anthem is played , all civilians are asked to stand in a gesture of respect to the anthem . If employees of national defense , police , and other military or military @-@ related organizations are present , they must respect the anthem in a way prescribed by their statutes . The anthem cannot be used as background music , purposes of advertisement , or for entertainment , such as karaoke . Public disrespect of the anthem may be punishable by law . 
- 
- = = Lyrics = = 
- 
- 
- 
- = Crosby Garrett Helmet = 
- 
- The Crosby Garrett Helmet is a copper alloy Roman cavalry helmet dating from the late 2nd or early 3rd century AD . It was found by an unnamed metal <unk> near Crosby Garrett in Cumbria , England , in May 2010 . Later investigations found that a Romano @-@ British farming settlement had occupied the site where the helmet was discovered , which was located a few miles away from a Roman road and a Roman army fort . It is possible that the owner of the helmet was a local inhabitant who had served with the Roman cavalry . 
- The helmet appears to have been deliberately folded up and deposited in an artificial stone structure . It is thought to have been used for ceremonial occasions rather than for combat , and may already have been an antique by the time it was buried . It is of the same type as the Newstead Helmet ( found in 1905 ) and its design also has similarities with the Ribchester Helmet ( found in 1796 ) and the <unk> Helmet ( found in 2000 ) , though its facial features are more akin to those of helmets found in southern Europe . Its design may allude to the Trojans , whose exploits the Romans re @-@ enacted in cavalry tournaments . 
- Dr Ralph Jackson , Senior Curator of Romano @-@ British Collections at the British Museum , has described the helmet as " ... an immensely interesting and outstandingly important find ... Its face mask is both extremely finely wrought and <unk> striking , but it is as an ensemble that the helmet is so exceptional and , in its specifics , unparalleled . It is a find of the greatest national ( and , indeed , international ) significance . " 
- On 7 October 2010 , the helmet was sold at Christie 's for £ 2 @.@ 3 million ( US $ 3 @.@ 6 million ) to an undisclosed private buyer . Tullie House Museum and Art Gallery in Carlisle sought to purchase the helmet , with the support of the British Museum but was outbid . The helmet has so far been publicly displayed twice , once in a 2012 exhibition at the Royal Academy of Arts , and again at Tullie House in 2013 @-@ 14 . 
- 
- = = Description = = 
- 
- The Crosby Garrett helmet is an almost complete example of a two @-@ piece Roman cavalry helmet . The visor portrays the face of a youthful , clean @-@ <unk> male with <unk> hair . The headpiece is in the shape of a Phrygian cap , on the crest of which is a winged griffin that stands with one raised foot resting on an <unk> . The visor was originally attached to the headpiece by means of a <unk> ; the iron <unk> pin has not survived , but its existence has been inferred from the presence of powdery deposits of iron oxide residue . The helmet would have been held in place using a leather strap attached from the wearer 's neck to a decorated <unk> on either side of the helmet , below the ear . Wear marks caused by opening and closing the visor are still visible , and at some point the helmet was repaired using a bronze sheet which was riveted across two splits . Only two other Roman helmets complete with <unk> have been found in Britain – the Newstead Helmet and Ribchester Helmet . 
- The helmet and visor were cast from an alloy consisting of an average of 82 % copper , 10 % zinc and 8 % tin . This alloy was probably derived from melted @-@ down scrap brass with a low zinc content , with which some tin had been added to improve the quality of the casting . Some of the fragments show traces of a white metal coating , indicating that the visor would originally have been <unk> to give the appearance of silver . The griffin was cast separately from a different alloy consisting of 68 % copper , 4 % zinc , 18 % tin and 10 % lead . The visor would originally have been a silver hue and the helmet would have had a <unk> yellow appearance . The helmet 's creation can be dated to the late 2nd or early 3rd century from the use of a particular type of decorated <unk> as well as some of its design features , such as its pierced eyes . 
- There has been much debate about the symbolic meaning of the helmet 's design . The griffin was the companion of <unk> , the goddess of vengeance and fate . They were both seen as agents of death and were often linked with <unk> combat . The meaning of the face and headpiece are less clearly identifiable . <unk> have ranged from the Greek god <unk> and the hero <unk> , to the Roman gods <unk> and Jupiter <unk> , to a more general Eastern Mediterranean appearance that could possibly have been meant to suggest a <unk> identity . The Phrygian cap was often used by the Romans as a visual motif representing the Trojans . 
- 
- = = Discovery and restoration = = 
- 
- The helmet and visor were found in May 2010 in <unk> on a farm owned by Eric Robinson at Crosby Garrett in Cumbria . The finder , an unnamed metal <unk> in his <unk> from <unk> , County Durham , had been detecting with his father in two adjacent fields for some years but had previously only discovered some Roman coins and other small artefacts . The findspot is situated not far from a Roman road . A number of earthworks are located nearby , indicating the presence of a previously unrecorded ancient settlement . The area was strategically placed on the route to the northern frontier of Roman Britain within the territory of the <unk> tribe . The Roman army would have been present in the area and would certainly have used the nearby road . A Roman auxiliary fort stood only 9 kilometres ( 5 @.@ 6 mi ) to the north @-@ east at <unk> ( Brough Castle ) . 
- Following the helmet 's discovery , the area around the findspot was investigated in a project sponsored by the Tullie House Museum and Art Gallery and the Portable Antiquities Scheme . The earthworks noted earlier were found to be part of a substantial enclosure surrounded by ditches , within which buildings had once stood . The enclosure , which measures as much as 500 metres ( 1 @,@ 600 ft ) long on its southern side , combines both native British and Roman methods of fortification . A sunken area within the enclosure may possibly have served as a <unk> for horses , while the evidence for the buildings is concentrated in the enclosure 's northern portion . The remnants of Romano @-@ British field systems in the surrounding area show that the area was under cultivation and animal remains found on the site indicate that the inhabitants also raised livestock , including sheep , goats and pigs . The presence of Roman pottery suggests that the inhabitants had adopted some elements of the Roman lifestyle , but their community may well have been there long before the Romans arrived . Archaeological evidence from the enclosure indicates that the site may have been first settled as far back as the Bronze Age , at least 1 @,@ 000 years before the helmet was deposited . 
- The finder discovered the helmet and visor buried together some 25 cm ( 10 in ) below the surface , at a site located on a ledge at the lower end of the settlement . It had been placed onto two stone slabs at the bottom of a hole which had been back @-@ filled with soil . A stone cap had been laid on top . The helmet was found in 33 large fragments and 34 small fragments and had apparently been folded before burial . The visor was mostly intact and had been placed face down . The griffin had become detached and was found with the helmet . No other artefacts were found at the time , but the subsequent Tullie House / PAS excavations at the findspot discovered a number of copper and iron objects , a bead and two Roman coins dating to between 330 @-@ 337 . The coins were found within the artificial stone feature in which the helmet had been deposited and may have been buried at the same time . 
- The finder did not initially realise that he had found a Roman <unk> and thought at first that it was a Victorian ornament . He eventually identified it as Roman by consulting auction catalogues , searching the Internet and getting advice from dealers . Find <unk> Officers from the Portable Antiquities Scheme were notified of the discovery and visited the findspot along with the finder . Christie 's commissioned Darren Bradbury , an independent <unk> and <unk> , to restore the helmet and visor for sale . Although Christie 's was asked to delay the restoration so that a full scientific examination could be carried out , this request was not granted and information about the helmet 's burial may have been lost as a result . However , the British Museum was able to inspect the find during restoration and X @-@ ray fluorescence spectrometry was carried out to determine the composition of the headpiece , visor and griffin . Bradbury 's restoration work took some 240 hours and involved the repair of cracks and holes using resin and <unk> , <unk> to match the appearance of the surrounding material . 
- 
- = = <unk> and usage = = 
- 
- The helmet and visor have marked similarities to a number of other Roman cavalry helmets . The visor is a cavalry sports type C ( H. Russell Robinson classification ) or type V ( Maria <unk> classification ) . Similar examples have been found across the Roman Empire from Britain to Syria . It is of the same type as the Newstead Helmet , found in Scotland in 1905 , and its facial features most closely parallel a helmet that was found at <unk> in Italy and is now in the British Museum . The rendering of the hair is similar to that of a type C helmet found at <unk> in Serbia and dated to the 2nd century AD . The griffin ornament is unique , though it may parallel a lost " sphinx of bronze " that may originally have been attached to the crest of the Ribchester Helmet , discovered in Lancashire in 1796 . The headpiece is nearly unique ; only one other example in the form of a Phrygian cap has been found , in a fragmentary state , at <unk> in Romania , dated to the second half of the 2nd century AD . <unk> on the back of the helmet and on the griffin may have been used to attach colourful streamers or ribbons . 
- Such helmets were used for <unk> <unk> , cavalry tournaments that were performed in front of emperors and senior commanders . Horses and riders wore lavishly decorated clothes , armour and plumes while performing feats of horsemanship and re @-@ <unk> historical and legendary battles , such as the wars of the Greeks and Trojans . According to the Roman writer <unk> : 
- [ T ] <unk> of high rank or superior in horsemanship wear gilded helmets of iron or bronze to draw the attention of the spectators . Unlike the helmets made for active service , these do not cover the head and <unk> only but are made to fit all round the faces of the riders with <unk> for the eyes . . . From the helmets hang yellow plumes , a matter of <unk> as much as utility . As the horses move forward , the slightest breeze adds to the beauty of these plumes . — <unk> , Ars <unk> 34 
- Combat gear was issued by and belonged to the Roman army , and had to be returned at the end of a wearer 's service . Cavalry sports equipment appears to have been treated differently , as soldiers apparently privately commissioned and purchased it for their own use . They evidently retained it after they completed their service . Both helmets and <unk> have been found in graves and other contexts away from obvious military sites , as well as being deposited in forts and their vicinity . In some cases they were carefully folded up and buried , as in the case of the <unk> Helmet . The Dutch historian Johan <unk> has identified a " <unk> " for Roman military equipment in which ex @-@ soldiers took certain items home with them as a reminder of their service and occasionally disposed of them away from garrison sites as grave goods or votive offerings . 
- The circumstances in which the Crosby <unk> helmet was buried are still unclear , but the discoveries made by the post @-@ discovery Tullie House / PAS excavations have provided much more detail about its context . It was clearly deposited within an artificial feature that had been specially constructed ; Stuart Noon of the Museum of Lancashire suggests that the feature may have been intended as a memorial of some sort . It was not buried in an isolated spot but within a long @-@ occupied Romano @-@ British farming settlement that had clearly adopted aspects of Roman culture . Given the settlement 's proximity to Roman military locations , it is very possible that some of its inhabitants served with the Roman army , which often recruited mounted <unk> from among native peoples . The helmet may well already have been a valuable antique at the time of its burial ; if the coins found nearby reflect when it was buried , it could have been over a century old by the time it was deposited . It was deliberately broken before being buried in what may have been intended as a ritual sacrifice . The identity of its owner will never be known , but it could have been that a local inhabitant who had formerly served with the Roman cavalry was responsible for the helmet 's deposition . 
- 
- = = Auction and controversy = = 
- 
- Although the find was reported under the Portable Antiquities Scheme , it was not declared treasure under the 1996 Treasure Act because single items of non @-@ precious metal are not covered by the act . The finder and landowner were thus free to dispose of the helmet as they saw fit . The discovery was publicly announced by Christie 's in mid @-@ September 2010 ; the helmet was the <unk> of its 7 October auction catalogue , featuring on the cover and six more pages . Its value was put at £ 200 @,@ 000 – £ 300 @,@ 000 ( $ <unk> @,@ 200 – $ 463 @,@ 800 ) . The Tullie House Museum and Art Gallery launched an appeal with the aim of purchasing the helmet and making it the focus of a new Roman frontier gallery due to open in 2011 . The campaign immediately attracted numerous donations , including £ 50 @,@ 000 from an anonymous overseas benefactor who offered the sum if a matching sum could be raised by the public ( it was ) ; a £ 1 million offer from the National Heritage Memorial Fund ; a £ 300 @,@ 000 pledge from the <unk> Trust and the Monument Trust ; £ 200 @,@ 000 from the Art Fund ; and £ 75 @,@ 000 from the J Paul <unk> Jr Charitable Trust . By the time of the auction three and a half weeks after the campaign had been launched , the museum had raised enough money to support a bid of up to £ 1 @.@ 7 million . Behind the scenes , efforts were made to persuade the finder and landowner to agree a private sale with the museum , but these approaches failed . 
- The initial estimate was passed within seconds of the auction opening . Six bidders pushed the price towards a million pounds and Tullie House was forced to drop out at £ 1 @.@ 7 million . Two remaining bidders took the bid past £ 2 million ; the winning bidder , an anonymous UK resident and fine art collector bidding by phone , paid a total of £ 2 @,@ 330 @,@ 468 @.@ 75 including the buyer 's premium and <unk> . The outcome aroused controversy and prompted calls for the Treasure Act to be revised , though British Archaeology noted that the circumstances of the helmet 's discovery may have resulted in it being outside the scope of even a revised act . It is still possible that the helmet could come into public ownership ; if the winning bidder wishes to export it , an export licence would have to be applied for and if a temporary export bar was placed on it an opportunity could arise for funds to be raised by a public institution to purchase the helmet . 
- 
- = = <unk> = = 
- 
- Since its sale in 2010 , the helmet been on public display three times . It was lent by its owner to the Royal Academy of Arts in London , and was put on display from 15 September to 9 December 2012 as part of an exhibition of bronzes . From 1 November 2013 until 26 January 2014 the helmet was on display at the Tullie House Museum and Art Gallery in Carlisle , and a printed guide was produced for the occasion . It was subsequently displayed at the British Museum from 28 January to 27 April 2014 . 
- 
- 
- = Liu Kang = 
- 
- Liu Kang ( Chinese : <unk> ; pinyin : <unk> ) is a fictional character from the Mortal Kombat fighting game series from Midway Games , introduced as one of the original seven player characters in the 1992 first game as a Shaolin monk who enters the Mortal Kombat tournament to save Earthrealm ( Earth ) . Since his victory in the tournament , Liu Kang becomes the Mortal Kombat series ' hero as the champion and chief defender of Earthrealm guided by his mentor , the thunder god Raiden . He also becomes romantically involved with Princess Kitana , the adopted daughter of evil Outworld emperor named Shao Kahn . 
- Designed with special moves intended to be easier to perform than the moves of other characters , Liu Kang has appeared in many of the Mortal Kombat fighting installments , in addition to starring with Kung Lao as the title characters of the action @-@ adventure game Mortal Kombat : Shaolin Monks , and being among the eleven series characters representing the franchise in the crossover game Mortal Kombat vs. DC Universe . Liu Kang has extensively featured in alternate Mortal Kombat media and official merchandise , serving as the primary protagonist of the two feature @-@ length films and the Malibu Comics comic book adaptations , in addition to supporting roles in other media such as the 1995 Mortal Kombat novel and the animated series Mortal Kombat : <unk> of the Realm . Critical and general reception of the character has been mainly favorable , although various aspects have been criticized . 
- 
- = = Appearances = = 
- 
- 
- = = = In video games = = = 
- 
- Introduced in the first Mortal Kombat game as a Chinese Shaolin Kung Fu fighting monk , Liu Kang 's main fighting style is <unk> <unk> Do and he has experience with Kung Fu . He enters the tenth Mortal Kombat tournament in order to protect Earthrealm from being destroyed after having lost the previous nine tournaments . He defeats Grand Champion Goro and the tournament host , the <unk> sorcerer Shang Tsung , and emerges as the new Mortal Kombat champion . In the 1993 sequel Mortal Kombat II , Liu Kang finds many of his Shaolin brethren killed in a vicious attack by a <unk> of nomadic <unk> led by <unk> under orders from the evil Outworld emperor Shao Kahn , Shang Tsung 's master . Enraged , Liu Kang decides to travel to Outworld to seek revenge , backed by friend and fellow Shaolin Kung Lao . At the tournament , Liu Kang fights Shao Kahn , eventually <unk> the emperor . In Mortal Kombat 3 ( 1995 ) , Liu Kang and his friends fight against Shao Kahn 's <unk> squad which invaded Earthrealm . He once again defeats Shao Kahn , causing him and his forces to retreat back to Outworld . 
- In the events of Mortal Kombat 4 ( 1997 ) , Liu Kang discovers that his lover and ally , Princess Kitana , has been captured by the disgraced Elder God Shinnok 's forces , and begins gathering Earth 's warriors to defeat him . Liu Kang confronts Shinnok and once again emerges victorious with Kitana and her people having survived to the attack , but he is unable to commit himself to a relationship due to his duty as Earthrealm 's champion while Kitana has to remain in Outworld to rule her kingdom . 
- Liu Kang becomes <unk> for the first and only time in the Mortal Kombat series in 2002 's Mortal Kombat : Deadly Alliance , in which the titular partnership of Shang Tsung and fellow sorcerer Quan Chi join forces to kill him in the game 's introductory sequence . In the events of Mortal Kombat : Deception ( 2004 ) , an unknown party <unk> Liu Kang 's corpse and sends it on a murderous <unk> , causing Liu Kang 's soul to attempt to control it . He returns as a playable character in this game , albeit in undead form and as a secret character that can only be unlocked by completing the game 's <unk> Mode . His spirit enlists the reformed ninja <unk> to try to save Kitana and his Earthrealm allies — Johnny Cage , Jax , <unk> and Kung Lao — who had all been killed by the Deadly Alliance and then resurrected by the Dragon King <unk> for use as his slaves . Though they successfully accomplish this task , Liu Kang is still unable to fully regain control of his body in Mortal Kombat : Armageddon ( 2006 ) , where he is playable along with the entire series roster and in which it is revealed that Raiden had revived Liu Kang 's corpse in Deception . Liu Kang 's bond with Kitana had succeeded in keeping his power in check , with <unk> then assuming her position as Liu Kang 's " spiritual anchor " in attempt to find a way to reunite his body and soul , which he <unk> in his ending . Liu Kang was among the many characters who were not given a biography for Armageddon , while in his own <unk> in @-@ game ending , his body and soul reunite on their own before he confronts Raiden and defeats him in combat to become Earthrealm 's new protector . 
- Along with Kung Lao , Liu Kang is the lead character in the 2005 spinoff action @-@ adventure game Mortal Kombat : Shaolin Monks . The game is a retelling of the storyline of the events leading up to Mortal Kombat II , and features the two Shaolin monks traveling to Outworld to find and defeat Shang Tsung , later ending in a fight with Shao Kahn and rescuing Kitana along the way . He is among the eleven characters representing the Mortal Kombat franchise in the 2008 crossover title Mortal Kombat vs. DC Universe , which features fights between characters from the Mortal Kombat and the DC Comics <unk> . In this game , Liu Kang appears as the protagonist of the first chapter of the Mortal Kombat story mode . Tobias said that the fight he expected to see in the game was between Liu Kang and Batman as he noted their back @-@ stories to be very similar , due to fact , both Liu Kang and Batman are fought in Raiden and Superman 's chapters respectively , trying to snap their rage influenced respective leaders back to their sense from killing the leader 's respective arch @-@ enemies , Lex Luthor and Shang Tsung , reminding them that Dark Kahn is their true enemy . 
- In the 2011 Mortal Kombat reboot video game , Liu Kang reprises his role from the first three tournaments as one of Raiden 's chosen warriors . Raiden , who has visions from the future in which Shao Kahn remained as the last warrior , believes that Liu Kang is the warrior which his future self chose as the <unk> . As in the first game , Liu Kang succeeds in defeating Goro and Shang Tsung , though Raiden 's <unk> continues to crack , unchanging the future . 
- During the second tournament , Liu Kang meets Kitana , and the two engage romantically with each other in this timeline too . It is during this tournament that Kang begins to doubt Raiden 's visions after he ordered him and Kung Lao not to rescue Kitana , and when Lao is killed by Shao Kahn . He seemingly kills Shao Kahn , and <unk> his fallen friend . However , Kahn survives , and begins plotting to invade Earthrealm . 
- When Shao Kahn is preparing to invade Earthrealm during the third game 's events , Liu Kang is among Earthrealm 's <unk> , and notices Raiden 's futile attempts to alter the future . He and Raiden visit the Elder Gods in order to put a stop to Kahn 's plan , but refuse to intervene , stating that Kahn 's invasion is not a violation of Mortal Kombat , but the merging of Earthrealm and Outworld is . When they return , nearly all of their allies are dead , murdered by a soul @-@ infused <unk> , and Liu Kang rushes to Kitana , only for her to die in his arms . This , coupled with Raiden 's failed attempts in changing the future , and his plan to form an alliance with the <unk> , causes Kang to deem Raiden insane , and decides to take on Shao Kahn alone . He arrives as Kahn enters Earthrealm , but Raiden attempts to stops him as he realized Kahn had to win and merge the realms in order to avoid the events of the future . Having enough of Raiden 's continued mistakes , the two fight , but Kang is defeated . As he attempts to shoot a fireball at Raiden , the thunder god uses his lightning to protect him , inadvertently <unk> Liu Kang , much to Raiden 's horror . <unk> to his burnt body , Raiden pleads for his forgiveness , but Kang <unk> says to the Thunder God : " You ... have killed us ... all ... " and dies . After Raiden defeats Shao Kahn with the help of the Elder Gods , and thus changing the future , he and the surviving warriors , Johnny Cage and <unk> <unk> , leave and begin the restoration of Earthrealm , and he takes Kang 's body with him . 
- Liu Kang returns in Mortal Kombat X. In the game 's Story Mode , he was resurrected by Quan Chi and now serves him as one of the <unk> until Sub @-@ Zero , <unk> and Jax are revived . He fights both Jax and Raiden in Story Mode , which he blames Raiden for his death . At the end of the story mode , he and Kitana become the new rulers of the <unk> due to Quan Chi 's death and Shinnok 's defeat . A now darker Raiden brings them Shinnok 's <unk> head as a warning for them not to attack Earthrealm . 
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- = = = = Design = = = = 
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- Liu Kang was originally going to be a Japanese character called <unk> <unk> , but Mortal Kombat co @-@ creator and character designer John Tobias stated that the staff could not " deal with the name . " According to Tobias , Liu Kang " was originally going to be a traditional monk – bald and in robes – but he wound up resembling Bruce Lee . " As stated on his Mortal Kombat : Armageddon bio card , Ed Boon mentioned that Liu Kang was designed to be the most easily " accessible " character , meaning that both casual and experienced gamers could play as him with little difficulty . According to Tobias , Liu Kang was purposely the only character that voiced his finishing move in the original game , and was " the best " in the first sequel . Liu Kang was the only character in the first game whose finishing move ( Fatality ) did not explicitly murder his opponent , and also without the background dimming . This was because Liu Kang was depicted <unk> as a Shaolin monk , who in general have strict beliefs regarding killing and murder . However , starting with Mortal Kombat II , he was given <unk> <unk> as he was thereafter depicted as a renegade monk who decided to grow his hair back , and who had " strong Shaolin beliefs , but was no longer a part of the Shaolin monks . " In response to rumors that Liu Kang would die in Mortal Kombat II and therefore not make it into Mortal Kombat 3 , Boon said , " It 'd be like doing part three of Star Wars and not having Luke Skywalker in there . You don 't do that . " His eventual death caused Dan <unk> , the music composer of the series , to make a " funeral song " for Liu Kang as he felt <unk> for his death . The track titled " Liu Kang 's Tomb " would be used in Mortal Kombat : Deception in the arena that shows his tomb . 
- Liu Kang was played by Ho Sung <unk> in the first two games , and John Tobias stated in an interview that he originally intended for the character to be a traditional bald monk , but the actor refused to shave his head . In the first game , Liu Kang was modeled after Bruce Lee , as he had short hair and went shirtless , with a <unk> outfit of only black pants and white shoes . In Mortal Kombat II , his outfit was enhanced with red stripes , and now included a red <unk> , black shoes , and studded <unk> ( the intro of <unk> shows the younger version of the character defeating Shang Tsung in the background , while the updated character is in the foreground ) . In Mortal Kombat 3 , his hair was considerably longer , with the only alteration to his outfit being thin black leg strips wrapped above his <unk> in order to give him a " <unk> " look for the game . He was given a red tank top in addition to his usual costume in Mortal Kombat 4 , but his alternate outfit was blue and featured him shirtless again . His wardrobe from the third game was carried over into Deception and Armageddon , but due to his resurrection , his skin was ash gray , and he wore hooked chains around his wrists . This form is also known by fans and Mortal Kombat producer Shaun <unk> as " Zombie Liu Kang " . Nevertheless , his alternate outfit for both games featured him as a living person in order to contrast his undead form . In Mortal Kombat vs. DC Universe , he sported a slightly altered version of his third costume , in addition to a championship belt adorned with the Mortal Kombat dragon emblem . Although Liu Kang 's design in Mortal Kombat : Shaolin Monks remains similar to his Mortal Kombat II one , <unk> said that it was the most revised one from the game , along with Kung Lao 's . 
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- = = = = Gameplay = = = = 
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- Liu Kang specializes in kicks , with his most common move being flying across the screen and connect with a kick to the opponent 's torso . Another such move is the " Bicycle Kick " in which Liu Kang flies across the screen with a series of multiple kicks to the opponent 's torso resembling <unk> a bicycle , hence the name . A different kind of his signature abilities is the " Dragon Fire " : with it Liu Kang sends a fiery flame in the shape of a Chinese dragon across the screen out of his hands at his opponent . After Mortal Kombat II , he gains the ability to perform this while <unk> and in the air , same for Deception and Armageddon . The developers made Liu Kang immediately <unk> in Mortal Kombat : <unk> , the PlayStation Portable port of Deception , as they noted that unlocking him in Deception had proved difficult . 
- Liu Kang 's first finishing move is the Fatality " Shaolin <unk> " ; he performs a butterfly kick ( often mistaken for a cartwheel ) on his opponent , before hitting an <unk> that knocks them into the air for several seconds . In Mortal Kombat : Shaolin Monks , there were two versions ; one where the victim explodes to pieces upon impact and the original , in which the opponent is torn to pieces upon falling to the ground . In another signature Fatality , he morphs into a large dragon , <unk> the upper body of his opponent . This Fatality was turned into an <unk> in Mortal Kombat 3 and back into a normal Fatality in Mortal Kombat 4 . In the Game Boy and Game <unk> versions of Mortal Kombat II , the dragon <unk> the opponent with fire instead . The series ' composer and co @-@ designer John <unk> noted it to be his favorite Fatality due to how much Liu Kang 's appearance changes . Another famous Fatality of Liu Kang has him vanish and a Mortal Kombat arcade game machine drop down and crush his opponent . According to GameSpy 's <unk> <unk> , Liu Kang " <unk> better damage " than Kung Lao " by jumping around like a <unk> and kicking the crap out of everything he sees . " 
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- = = = Other appearances = = = 
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- Liu Kang was the hero of the comic book adaptation of the Mortal Kombat series Malibu Comics . In the first miniseries , Blood and Thunder , his backstory was mostly kept intact as a Shaolin monk out to restore the tournament to their righteous owners , with the only difference being that he was not the chosen one to defeat Goro , which instead fell on twin monk brothers named Sing and Sang , two original characters created specifically for the comics ; after they are killed by Goro in the third issue , Liu Kang becomes the Shaolin 's only hope in defeating Shang Tsung . The following miniseries , <unk> , stated that Liu Kang won the first tournament after defeating Goro , which never appeared in the first miniseries . He returns to his normal life as an architect in Chicago , having left the Order of Light before the events of the first series . However , he suffers from constant attacks by an unknown force of ninjas and later receives help from Johnny Cage 's bodyguard Bo when Goro ambushes him in an office building . Eventually he decides to travel to Outworld , realizing that he cannot avoid Mortal Kombat . 
- Liu Kang is the primary hero of both Mortal Kombat movies , where he is portrayed by Robin Shou . Director Paul W. S. Anderson wanted Liu Kang 's character to be " really engaging " and chose Shou , noting his skills with martial arts . In the first film , he takes part in the tournament out of guilt over his brother 's death at the hands of Shang Tsung ( portrayed by Cary @-@ Hiroyuki <unk> ) , and defeats Tsung in the final battle . As a result of the film 's style the relationship between Liu Kang and Kitana is more of a metaphysical than a romantic nature . Shou , along with <unk> <unk> ( Kitana ) , was one of only two actors to reprise their roles in the sequel , Mortal Kombat : <unk> ( Keith Cooke , who played <unk> , returned as well , but as the new Sub @-@ Zero ) . In the sequel , Liu Kang joins the Earthrealm warriors to stop Shao Kahn 's menace . In the animated film Mortal Kombat : The Journey <unk> , serving as a prequel to the first film , Liu Kang appears as one of the main characters . 
- Liu Kang is also one of the lead characters in the 1996 animated series Mortal Kombat : <unk> of the Realm , voiced by Brian <unk> . He is not the protagonist <unk> as opposed to the game storyline , instead sharing this role with several other Earthrealm heroes . 
- Liu Kang appears in the 2013 's second season of the web series Mortal Kombat : Legacy , portrayed by Brian <unk> . Liu Kang in Legacy is portrayed as an anti @-@ hero and fighting on the side of Outworld rather than that of Earthrealm in the web series . In this version , he is shown to have left a monastery to live a normal life working at a <unk> with a fiancée . After watching a pair of thieves kill her in a robbery attempt , he becomes consumed with anger and revenge , and further distances himself from Kung Lao and the ideals that were taught to him . After working as a freelance assassin for a few years , he is approached by Shang Tsung , who convinces him that humanity is not worth protecting and asks him to join the realm of Outworld in the upcoming Mortal Kombat tournament , which he agrees to . During the tournament , he easily <unk> Johnny Cage and <unk> <unk> before being confronted by Kung Lao , who is surprised at his old friend 's change of allegiance . 
- In 1996 , Toy Island published a Liu Kang action figure which had a white shirt . Two Liu Kang action figures from Shaolin Monks were released by <unk> . Apart from being flexible , both figures included different types of weapons such as swords and axes . 
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- = = Reception = = 
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- The character has received mostly very positive response by gaming publications . His relation with Kitana was ranked fourth in IGN 's list of best video game couples in 2006 . GameSpot featured him in their 2009 poll for the title of " All Time Greatest Game Hero " , in which he lost to <unk> . UGO Networks ranked him as 94th on the 2008 list of top heroes of all time , noting his role in the Mortal Kombat series as well as character depth . In 2012 , GamesRadar ranked him as 51st " most memorable , influential , and <unk> " protagonist in games , adding that while " Sub @-@ Zero and <unk> may get most of the fanfare " , Liu Kang " can shoot fireballs , <unk> and <unk> just like Bruce Lee , and pioneered the <unk> with his dragon @-@ transformation <unk> ’ s no wonder that Liu Kang ’ s the chosen champion of Earthrealm . " In 2014 , Jack <unk> of What Culture ranked him as the second greatest ever fighting game character . 
- Like all the characters from Mortal Kombat vs. DC Universe , Liu Kang was selected to be a playable character based on his popularity . Jesse Schedeen of IGN said that " it just wouldn 't be right having a game without [ Liu Kang ] , " noting him to be as fierce as the DC Universe characters in this game . In 2010 , <unk> ranked Liu Kang as the second best Mortal Kombat character , commenting that " his <unk> and fighting expertise make him an ideal pick " and how he is as good as zombie as when he was alive . In 2011 , Bright <unk> ranked Liu Kang as the second best character in the series . In UGO 's 2012 list of top Mortal Kombat characters , Liu Kang was given first place . In 2013 , he was ranked as the fifth @-@ top Mortal Kombat character by Jon <unk> of The Game Scouts for his popularity and " being incredibly important to the Mortal Kombat universe . " That same year , the readers of <unk> voted him the series ' sixth greatest character . 
- Upon his death in Deadly Alliance , IGN 's Jeremy Dunham noted that Liu Kang was killed as the series needed " ' starting over ' mentality " , as he regarded Liu Kang as the series ' strongest character . Complex remarked that the Mortal Kombat developers " finally found their groove again with Deadly Alliance , which began by snapping Liu Kang 's neck . " Game Informer listed his death in their article about " characters that died under our watch " calling it a " shock " as Liu Kang was stated to be one of the " most loved " characters from the series . 
- His redesign in Mortal Kombat : Deception was praised by GameSpot for being one of the best ones from the title , and was featured in GamesRadar 's list of the " greatest zombie <unk> " in 2009 , but was also ranked as the ninth @-@ worst Mortal Kombat character by ScrewAttack in 2011 . Additionally , GamesRadar used Liu Kang as an example of a stereotype of gaming heroes who reveal an evil alter ego that ruins the character 's appealing traits , and considered him to be " a little like the Shaolin version of <unk> , in that he 's saved his world countless times and come back from the dead even more frequently . " GamesRadar featured him the article about " <unk> Bruce Lee <unk> " citing his similarities with Bruce Lee and with one of his shouts featured in famous quotes . 
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- = = = Gameplay and finishing moves = = = 
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- Liu Kang 's famous finishing move of turning into a dragon was ranked by ScrewAttack as the second best in the series , referred to as the most iconic Fatality in Mortal Kombat II , but his cartwheel Fatality from the original Mortal Kombat was ranked by ScrewAttack as the second worst in the series . Liu Kang 's dragon Fatality has also been listed as one of the best <unk> from the series by both Game Informer and UGO in 2010 , as well as by Complex in 2013 . On the other hand , his Fatality in which he throws a Mortal Kombat arcade were pointed by both Game Informer and GamePro as one of the worst from the series , even as GamesRadar listed it among the reasons of Liu Kang being " boss " . 
- Some video game publications criticized his gameplay and shouts while others noted him to be entertaining . IGN 's Douglas Perry wrote that he preferred Liu Kang over Kung Lao as a playable character in Shaolin Monks because of his " intuitive fighting moves , " adding that his shouts were " annoying " yet " <unk> pleasing . " <unk> also complained about his voice , saying that " Liu Kang screams out like a chicken , " and GamesRadar wrote that " the strange <unk> he emits during his trademark Bicycle Kick move are <unk> . " 
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- 
- = Inocybe praetervisa = 
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- Inocybe praetervisa is a small , yellow and brown mushroom in the <unk> family , distinguished from other members of the genus by its unusual spores and bulb . The unusual spores led to the species being named the type species of the now @-@ abandoned genus Astrosporina ; recent studies have shown that such a genus could not exist , as the species with the defining traits do not form a monophyletic group . However , it is a part of several clades within the genus Inocybe . I. praetervisa grows on the ground in woodland , favouring beech trees , and can be found in Europe , North America and Asia . It is inedible and probably poisonous due to the presence of muscarine . The ingestion of muscarine can lead to <unk> syndrome , and could potentially lead to death due to respiratory failure . 
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- = = Taxonomy and naming = = 
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- Inocybe praetervisa was first described by <unk> <unk> in the first volume of Giacomo <unk> 's 1883 publication Fungi <unk> . The species was moved to the genus Astrosporina by Joseph Schröter in 1889 , but this was rejected , and the name Astrosporina praetervisa is now considered an <unk> synonym . Astrosporina praetervisa was the type species of the no longer recognised genus . The specific epithet praetervisa comes from the Latin word meaning " overlooked " . 
- Within the genus Inocybe , I. praetervisa has been placed in the subgenus Inocybe . Mycologist Rolf Singer places the species in the section <unk> ; mycologist Thom <unk> considers <unk> a <unk> , and includes I. praetervisa along with I. <unk> , I. calospora and I. <unk> . <unk> has shown that , in addition to the large clade of subgenus Inocybe , I. praetervisa forms a clade with I. calospora , I. <unk> and I. leptophylla . The species are similar in that all four have <unk> with small <unk> ; it was this feature that defined the genus Astrosporina , with then A. praetervisa as its type species . However , when phylogenetic analysis later concluded that <unk> @-@ spored Inocybe species do not form a monophyletic group , the name Astrosporina was deemed inappropriate at a generic level . But it may be considered useful at a lower level to refer to the clade of the four Inocybe species . Of those four , I. praetervisa is most closely related to I. calospora , with which it forms a smaller and closer clade . A different study also found the close relationship between I. praetervisa and I. calospora ; it also named I. <unk> as a part of the clade containing I. praetervisa , I. calospora , I. <unk> and I. leptophylla . 
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- = = Description = = 
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- Inocybe praetervisa has a bell @-@ shaped ( later expanding ) cap of 3 to 5 centimetres ( 1 @.@ 2 to 2 @.@ 0 in ) in diameter , which is a yellowish @-@ brown colour . It is fibrous , and splits from the margin ( which curves <unk> ) to the centre . The stem is from 5 to 6 centimetres ( 2 @.@ 0 to 2 @.@ 4 in ) in height , and from 3 to 8 millimetres ( 0 @.@ 12 to 0 @.@ 31 in ) thick . It is white , <unk> to a pale straw @-@ yellow , and the whole stem is farinaceous , meaning it is covered in particles resembling meal . The stem has a distinct bulb at the base , which is moderately <unk> , and lacks a ring . The flesh is white , and <unk> to yellowish in the stem . The gills are initially whitish , but later become a clay @-@ brown with <unk> , white edges . They are <unk> , meaning they connect to the stem by only part of their depth , and are crowded closely together . 
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- = = = Microscopic features = = = 
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- Inocybe praetervisa leaves a clay @-@ brown spore print , while the spores themselves are rectangular with a large number of " distinct , angular <unk> " . In size , the spores measure between 10 and 12 micrometres ( 0 @.@ <unk> and 0 @.@ <unk> in ) in length by between 7 and 9 micrometres ( 0 @.@ <unk> and 0 @.@ <unk> in ) in width . Inocybe praetervisa has both <unk> and cheilocystidia which are relatively spindle @-@ shaped with <unk> <unk> . The cystidia have hyaline or pale yellow walls . 
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- = = = Similar species = = = 
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- The species can be differentiated from the similar I. <unk> by its " irregular , <unk> spores " . It is also similar to I. <unk> , but differs in the presence of a bulb . Another species that can be differentiated by the lack of a bulb is I. <unk> . 
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- = = Habitat and distribution = = 
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- Inocybe praetervisa is an ectomycorrhizal species , and can be found on the ground in mixed , deciduous or even coniferous woodland . It typically favours beech . Mushrooms grow <unk> or in " <unk> groups " in late summer and throughout autumn , though it is not commonly encountered species . It can be found in Europe , Asia and North America . 
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- = = Edibility and toxicity = = 
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- The species has a mild , <unk> taste , and a faint smell of flour . Mycologist Roger Phillips describes its edibility as " suspect " , recommending that it be avoided , and notes that it is possible that the species is poisonous ; most species of Inocybe have been shown to contain poisonous chemicals . Mycologist Ian Robert Hall lists the mushroom as containing the poisonous compound muscarine . <unk> of muscarine could lead to a number of physiological effects , including : excess <unk> , <unk> , uncontrollable <unk> and defecation , gastrointestinal problems and <unk> ( vomiting ) ; this array of symptoms is also known by the acronym <unk> . Other potential effects include a drop in blood pressure , sweating and death due to respiratory failure . 
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- 
- = Hannah Primrose , Countess of Rosebery = 
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- Hannah Primrose , Countess of Rosebery ( 27 July 1851 – 19 November 1890 ) was a British <unk> , the daughter of Baron Mayer de Rothschild and the wife of Archibald , Earl of Rosebery . After <unk> her father 's fortune in 1874 , she became the richest woman in Britain . 
- During the final quarter of the 19th century her husband , the Earl of Rosebery , was one of the most celebrated figures in Britain , an influential millionaire and politician , whose charm , wit , <unk> and public popularity gave him such standing that he " almost eclipsed royalty . " Yet his Jewish wife , during her lifetime regarded as dull , overweight and lacking in beauty , remains an <unk> figure largely ignored by historians and often regarded as notable only for financing her husband 's three ambitions : to marry an <unk> , win the Epsom Derby , and become Prime Minister ( the second and third of these possibly <unk> ambitions were achieved after her death ) . In truth , she was her husband 's driving force and motivation . 
- Her marriage into the aristocracy , while controversial at the time , gave her the social <unk> in an antisemitic society that her vast fortune could not . She subsequently became a political hostess and philanthropist . Her charitable work was principally in the sphere of public health and causes associated with the welfare of working @-@ class Jewish women living in the poorer districts of London . 
- Having firmly assisted and supported her husband on his path to political greatness , she suddenly died in 1890 , aged 39 , leaving him , distraught and bereft of her support , to achieve the political destiny which she had plotted . His <unk> of the United Kingdom was <unk> , and lasted barely a year . For over thirty years following her death , he wandered in a political wilderness , <unk> and exceedingly eccentric , until his own death in 1929 . 
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- = = Early years = = 
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- Hannah de Rothschild was born in 1851 into a world of great wealth and luxury . She was the granddaughter of Baron Nathan Mayer Rothschild , who had founded N M Rothschild & Sons , the English branch of the Rothschilds ' banking empire . Niall Ferguson states in his History of the House of Rothschild that by the mid @-@ 19th century the Rothschilds regarded themselves as the nearest thing the Jews of Europe had to a royal family , and the equals of royalty . Whether or not this was strictly true , the many Rothschild homes and their art collections , in England , Austria , France and Germany , certainly <unk> those of the crowned heads of Europe . 
- Hannah de Rothschild 's father Baron Meyer <unk> de Rothschild married his cousin <unk> Cohen in 1850 . The marriage provided the <unk> for Meyer to create what he described as " an enduring monument , " a country house of monumental proportions . His daughter Hannah , aged just six months , laid the foundation stone on 31 December 1851 . Throughout her life , Mentmore Towers was to be a fixed and pivotal point . 
- Within a few years of the mansion 's completion , attracted by the good hunting and proximity to London , Hannah 's relatives began to build estates nearby , all within a carriage drive of each other ; thus , Hannah grew up in an almost private world of <unk> <unk> and security . <unk> has described this <unk> of Rothschild properties as " the most conspicuous and significant aspect of Victorian architecture in Buckinghamshire . " In addition to Mentmore , Baron and Baroness Meyer de Rothschild had a large house in London , 107 Piccadilly ; The <unk> , a luxurious yacht moored in the South of France ; and other smaller properties visited only seasonally and occasionally . 
- As an only child growing up in what were , in all but name , palaces , her childhood appears to have been quite lonely . She was a companion to her <unk> mother and , in later life , a hostess with her father during her mother 's long periods of <unk> . She was indulged by both parents and her formal education was neglected in favour of music and singing lessons , subjects in which she was accomplished . Her parents were very protective of her , attempting to ensure that she was never exposed to the risk of sickness or even the sight of poverty . As a result , she was never allowed to enter the cottages on the Rothschilds ' estates . A cousin , who seems to have disliked her , claims that Hannah was so sheltered that the phrase " the poor " was just a meaningless <unk> to her . This is likely to be an exaggeration , as from her teens onwards she used much of her fortune to improve the lot of the poor , in housing and education . Whatever the faults of her education , she possessed great confidence , impressing her Rothschild relations , who noted her poise and competence when she hosted a large house party at Mentmore for the Prince of Wales while only 17 years of age . 
- Mayer <unk> de Rothschild died in 1874 , leaving his daughter not only Mentmore ( with its <unk> art collection ) , his London mansion , and <unk> investments , but also the sum of two million pounds sterling in cash ( equivalent to £ 167 million in present day terms ) . Thus , Hannah de Rothschild became the wealthiest woman in England . 
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- = = <unk> = = 
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- Hannah de Rothschild was first introduced to her future husband , the 28 @-@ year @-@ old Earl of Rosebery , by Lady <unk> , the wife of Benjamin Disraeli , at Newmarket Racecourse . The <unk> were close friends and neighbours of the Rothschilds in Buckinghamshire . 
- Archibald , 5th Earl of Rosebery , born in 1847 , had inherited his title from his grandfather in 1868 , when aged 21 , together with an income of £ 30 @,@ 000 a year . He owned 40 @,@ 000 acres ( 160 km ² ) in Scotland , and land in Norfolk , Hertfordshire , and Kent . His father had died when he was eight and he had been brought up by his mother , who had subsequently married Harry <unk> , 4th Duke of Cleveland . His mother was a distant figure , and their relationship was always strained . The Earls of Rosebery , whose family name was Primrose , were old , if undistinguished , members of the Scottish aristocracy . Rosebery was considered to be strikingly handsome and immensely cultivated . He was highly intelligent , and a brilliant future was forecast for him by his tutors at both Eton and Christ Church , Oxford . 
- As early as 1876 , there were rumours of an engagement . However , several hurdles had to be overcome before a marriage could take place . While the Jewish Rothschilds were accepted into society , and indeed were close friends of some members of the royal family including the Prince of Wales , as elsewhere in Europe , antisemitic feelings were prevalent in the upper echelons of society and particularly so among those closest to the Queen at court , where following the death of the Prince <unk> in 1861 the Rothschilds became <unk> excluded . The Queens <unk> Arthur Edward <unk> referred to the Rothschild 's dining tables as " <unk> with the Hebrew gold " going so far as to say a visiting Russian royal needed a " <unk> " visit to Westminster Abbey following acceptance of Rothschild hospitality . Queen Victoria herself expressed antisemitic views in 1873 when it was proposed that Lionel de Rothschild be elevated to the peerage the Queen refused and expressed a reluctance to make a Jew a peer – saying " to make a Jew a peer is a step she could not consent to " and furthermore stated to give " a title and mark of her <unk> to a Jew " . Lord Spencer advised the Prince and Princess of Wales against attending a Rothschild ball with the words " The Prince ought only to visit those of <unk> position in Society . " However , this did not prevent the Prince from accepting Rothschild 's invitations and gifts privately . While one could be friends with Jews and accept their hospitality , their social status was still not sufficiently elevated to include marriage into the peerage without <unk> comment . 
- Rosebery 's own mother was horrified at the thought of a <unk> , even a Rothschild , in the family . Rosebery too felt there was an impassable barrier of faith ; at this time , it was <unk> that any children could be reared as Jews . Although it has been stated that Rosebery himself was devoid of any antisemitic views , this was not always true , especially in later life . 
- This factor also worked in reverse ; while Hannah de Rothschild was keen to marry Rosebery , she was also aware of many obstacles , the foremost being that she was devoted to her faith , and to leave it would be a severe moral <unk> . Another obstacle was the Rothschild family itself : it was their custom to marry cousins to keep their fortune within the family . Ironically , Hannah herself had opposed the marriage of her cousin Annie de Rothschild to the Christian Eliot Yorke , the son of the Earl of Hardwicke , in 1866 . In fact , she was to be the third daughter of the family 's English branch to marry outside of the Jewish faith , but such was the fame of the <unk> and the resultant publicity that the Jewish elders and press felt an example needed to be made . The Jewish Chronicle announced its " most poignant grief " at the prospect , and <unk> added , " If the flame seize on the cedars , how will fare the <unk> on the wall : if the <unk> is brought up with a hook , how will the <unk> escape , " demonstrating what a threat to the social fabric of the Jewish faith the Jewish elders saw in the prospect of such a marriage . The quotation , originally from the Babylonian Talmud , can be taken to mean that the elders and respected members and more notable members of the Jewish faith should set a good example by strictly following the teachings of the Jewish articles of faith which <unk> upon marriage to members of other religions . 
- The formal engagement of marriage was announced on 3 January 1878 , a day Rosebery forever afterwards regarded as sacred . Writing to a friend in January 1878 , Rosebery described his wife as " very simple , very <unk> , very clever , very warm @-@ hearted and very shy ... I never knew such a beautiful character . " The marriage was celebrated in London on 20 March 1878 at the Board Room of <unk> in Mount Street , and also in a Christian ceremony at Christ Church in Down Street , Piccadilly . To show " official " disapproval , no male member of the Rothschild family attended the ceremonies . However , any shortcomings in the guest list were compensated for by the guest of honour — the Prince of Wales — and Disraeli , who gave the bride away . 
- 
- = = Marriage = = 
- 
- For the first few years following their marriage , the Roseberys resided in London in the Piccadilly house Lady Rosebery had inherited from her father . However , as the couple 's social and political interests increased from 1882 , they leased the larger Lansdowne House . Lansdowne House was one of the finest of the aristocratic palaces in London , well suited to be the home of the political <unk> which Hannah Rosebery was to establish . Here political and social leaders of the day mixed with royalty , authors such as Henry James and Oscar Wilde , and other prominent social and intellectual figures of the time . Henry James , an occasional guest in the Roseberys ' homes , delivered one of the most <unk> condemnations of Lady Rosebery describing her as " ... large , coarse , Hebrew @-@ looking with hair of no particular colour and personally unattractive " . 
- The Roseberys divided their year among their various homes : London for the social season and parliament , Mentmore at weekends to entertain both political and shooting house @-@ parties . In August the household would move north to Dalmeny for the grouse shooting . In between , occasional days and the <unk> Derby meeting would be spent at their home " The <unk> " in Epsom . Though small by comparison to their other homes , this mansion was described by Henry James as the most <unk> and comfortable of the Roseberys ' many homes and as a delightful house full of books and sporting pictures , with just a few <unk> and <unk> . Together the Earl and Countess of Rosebery added greatly to not only the Mentmore collection but also to that housed at Dalmeny House , Rosebery 's Scottish seat , amassing a great library of rare and continental volumes and a collection of artefacts formerly belonging to the Emperor Napoleon I. 
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- = = = Relationship with Rosebery = = = 
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- Published commentators on the Roseberys claim their marriage was happy , and there is no known evidence that Hannah was anything other than happy in her marriage , and quite a lot to suggest she was indeed <unk> happy . However , much evidence suggests that Rosebery , while <unk> to be happy , was at times <unk> and bored by Hannah , who was always keen to accommodate his every whim . 
- There were times when Lady Rosebery 's devotion to her husband was tested . Rosebery may have not been antisemitic before his marriage ; however , the <unk> wit for which he was famous led him to make remarks that could have been taken in such a way , once his marriage had secured the Rothschild fortune . Rosebery seems to have disliked his first son , who he claimed looked " Jewish . " On seeing his son for the first time he remarked " Le Jew est fait , <unk> ne <unk> plus , " which must have been disconcerting for the child 's very Jewish mother . Rosebery , who has been described as <unk> and <unk> , replied in a letter of congratulations on the birth of his heir from Mary Gladstone : " I cannot pretend to be much excited by an event which occurs to almost every human being and which may cause me a great deal of <unk> . " Rosebery then left his newborn child and wife ( who was again pregnant ) for a year @-@ long tour of Australia . On another occasion , when the Roseberys were travelling in India , Rosebery is reported to have announced " I will travel ahead , Hannah and the rest of the heavy baggage will follow the next day . " 
- While the marriage was based on warmth and esteem on Rosebery 's side and adoration on Hannah 's , it seems that Rosebery often found his wife 's devotion irritating , and this sometimes caused him to be impatient with her . He was often abrupt with her in public . She , by contrast , was completely <unk> by him , and would frequently ignore her neighbours at a dinner party to listen to her husband 's conversation further down the table , a faux <unk> almost considered a crime in Victorian society . Those who saw the couple alone at home " could not doubt the affection as well as the comprehension that united them . " 
- However , at times Rosebery 's behaviour could be eccentric . Gladstone remarked that Rosebery was , perhaps , rather too concerned with his health . Early in the marriage Rosebery decided to renovate the small ruined <unk> Castle ( the original Rosebery family seat ) , close to , and within sight of , Dalmeny House . Once renovation was complete in 1882 , Rosebery used it as a private retreat from his family , and began to spend his nights there alone . Always an <unk> , he claimed that the " <unk> of the waters [ the nearby Firth of Forth ] were conducive to sleep . " Books were his passion , and he assembled a huge library in the small castle . Thus Rosebery was able to lead a life at Dalmeny with his wife , but also quite apart from her . 
- During their marriage the Roseberys travelled extensively , usually without their children . In September 1883 the couple left their children in the care of the <unk> and nursery maids , supervised by Rosebery 's sister Lady <unk> , for a long tour of America and Australia . Lady Rosebery owned large investments in North America , including <unk> in Texas and mines in Montana . Their arrival in New York was widely reported , and a full and <unk> description of Lady Rosebery was reported in The Herald . The newspaper went on to describe Rosebery as looking like a prosperous farmer . Lady Rosebery was very taken with California , from where she wrote : " The inhabitants are very entertaining ... the women are very handsome , think nothing of dresses costing £ 80 , " fix up " their faces very frequently and are generally divorced . " Having toured and been <unk> in America , the party moved on to Australia via Honolulu . In Australia , Rosebery chose to indulge his habit of solitude , installing his wife in a hotel in Sydney , while he went off alone to tour the outback . 
- Rosebery 's frequent absences from his wife fuelled gossip that he was a secret homosexual . It has been claimed that the <unk> air that Rosebery wore was a mask to disguise his secret homosexual life . The worry of this illegal secret , it was claimed , and fear of exposure , caused his insomnia and bouts of depression . It was even <unk> that his <unk> Castle retreat was really a venue for clandestine <unk> with young men . Rosebery 's possible homosexuality has been much discussed in recent times . Nothing conclusive has ever been found one way or the other , but it is possible that he had homosexual experiences while in the care of a <unk> <unk> at Eton in his youth . No evidence exists that his wife was aware of these rumours against her husband , or even that she would have understood them , bearing in mind her sheltered upbringing and limited education . Sex education was not part of a 19th @-@ century upper @-@ class girl 's schooling . The more public and precise accusations of Rosebery 's homosexuality by the Marquess of Queensberry did not occur until three years after Lady Rosebery 's death . 
- The relationship between the couple appears at times to have been almost that of a mother and child . Rosebery , a self @-@ centred , reserved man , prone to depression , <unk> and insecurity , had a difficult relationship with his mother who had been distant and openly preferred his younger brother . Lady Rosebery , an <unk> and only child , appears to have been desperate to lavish affection . Once upon entering a book shop she told her children they were entering a toy shop , and when the disappointed children pointed out the obvious she replied " to your father this is a toy shop . " Lord Rosebery 's friend Edward Hamilton recorded her " notable faculty of getting other people to work and <unk> their <unk> . " It seems she was the driving force of the relationship , with her feet firmly on the ground . She made herself the link between the world and her " thin skinned and neurotic " husband . While her husband <unk> or withdrew with hurt pride from a situation , she came to the forefront to plead his case or cause . If she was aware of his faults she gave no indication of it . 
- 
- = = = Children = = = 
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- The marriage produced four children : Lady Sybil Primrose , born in 1879 ; Lady Margaret Primrose , born in 1881 ; the heir Harry Primrose , Lord Dalmeny ( later 6th Earl of Rosebery ) , born in 1882 ; and finally the Honourable Neil Primrose , born the same year as his elder brother . 
- As a mother , Lady Rosebery was presented with a dilemma : she was in fact already practically a mother to her husband who had no great feeling for a proximity to small babies . This was particularly evident in June 1880 when shortly after the birth of their first child Sybil , Rosebery wished to visit Germany for three months , to take a cure at a German spa ( he was recovering from what is now thought to have been a nervous breakdown ) . His wife <unk> accompanied him . However , Rosebery , clearly aware of his wife 's frustrated maternal instincts , reported that Hannah <unk> every detail of the daily letters from London concerning the baby , and that she never complained at the forced separation . 
- More revealing is a comment Lady Rosebery herself made to her husband , " I sometimes think it is wrong that I have thought less of the children in comparison to you " shortly before her death in 1890 , suggesting that when a choice between her children and husband was forced on her , she always chose her husband . However , the same comment also hints that she was not unaware that her choice was at the cost of her children . 
- When assessing Lady Rosebery 's behaviour to her children it should be remembered that she lived in an era of plentiful <unk> , wet nurses , <unk> and governesses which the upper classes employed as the norm . These people were employed regardless of the mother 's affection towards her children ; it was <unk> that a <unk> would nurse her own children , and to do so would have been breaking social conventions . Hence her <unk> lack of attention to her children was not unusual – she was following the upper class conventions and " stiff upper lip " philosophies of her era . However , in spite of their prolonged absences from their children , the Roseberys do not appear to have been very distant or remote figures in the earliest stages of their children 's lives . Margot Asquith records how Rosebery loved to play and romp on the floor with the children . 
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- = = Politics = = 
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- It has been said of Hannah de Rothschild that she grew up with a good sense and presence of mind , enabling her to <unk> for her mother on grand social occasions at Mentmore and in London . This gave her confidence and the experience to be the perfect political wife . Marriage to her altered Rosebery 's status , too : while his wife acquired Christian respectability and a title , Rosebery moved from being one of many wealthy and capable young noblemen to being one with <unk> riches . This , coupled with his good looks , appealed to the public 's imagination and gave him glamour . 
- From the outset of the marriage , political members of the Rothschild family took an interest in Rosebery , and he was soon acclaimed as one of the rising hopes of the Liberal Party . As a hereditary peer , he already had a seat in the House of Lords and had made his maiden speech there on attaining his majority . But brilliant as he was , Rosebery tended to <unk> and boredom . Lord Granville in fact considered Rosebery 's wife to be the more ambitious of the pair , and even advised her " If you keep him up to the mark , [ he ] is sure to have his page in history . " The subtle driving of her often languid and <unk> husband to achieve his " page in history " was to become her <unk> d 'être . Rosebery 's secretary Thomas Gilmour noted : " She is thoroughly genuine and very tender and devoted to Lord Rosebery , it is easy to see that she is very proud of him , and she is a woman of considerable force of character and great energy , she may prove to be a powerful ally in his political career . " Rosebery was not a natural politician . He was an idealist who disliked the <unk> of politics , in fact " his innate dislike of politics was something Lady Rosebery always fought against . " However , he was a gifted <unk> , and this was an era when platform speaking was beginning to replace House of Commons debate . On a tour of America before his marriage , Rosebery had been impressed by the campaigning of prospective political candidates ; in Britain little had changed in that respect since the <unk> of the 18th century . He realised how an electorate could be <unk> by a candidate touring his prospective constituency , aided by a well thought @-@ out series of events , rallies and advertising , with the candidate 's ideal and attractive family smiling by his side . Thus Lady Rosebery not only pushed and encouraged him behind the scenes but was now to become an encouraging and conspicuous figure by his side . In this way it could be said she was the first openly " political wife " in Britain . 
- This first became evident in the great campaign to re @-@ elect Gladstone . Known today as the Midlothian campaign , it was <unk> by the Roseberys . Rosebery used his influence to have Gladstone invited to stand as parliamentary candidate for Midlothian , near to Rosebery 's Dalmeny estate . Gladstone had nominally retired from politics after losing his Greenwich seat in 1874 , when Disraeli had been swept to power . The campaign was based at Dalmeny where Lady Rosebery hosted a series of large political house parties throughout the long campaign . The <unk> were later to claim that Rosebery had paid for Gladstone 's campaign . Rosebery later admitted to spending £ 50 @,@ 000 . 
- The Roseberys ' house party would leave Dalmeny and tour towns and cities across Midlothian and Scotland , with Gladstone and the speakers often addressing vast crowds from the back of an American @-@ designed <unk> car specially acquired by Rosebery for the purpose . The scenes at these meetings have been described as something between a carnival and an <unk> 's revival meeting . While in the grounds of Dalmeny House itself , the public were treated to a great <unk> display . 
- Throughout all this , Gladstone was supported not only by the popular and charismatic Rosebery but also by an array of well @-@ dressed women including Lady Rosebery and Gladstone 's daughter Mary . These fashionable people – the celebrities of their day ( newspapers at the time gave many column inches each day to the <unk> of the upper classes ) – were as much a crowd @-@ <unk> as the political speakers , and Rosebery 's planning used that to full effect . One meeting was so packed that many were <unk> : 70 @,@ 000 people applied for tickets in a hall capable of holding 6 @,@ 500 . Lady Rosebery reported , " I had never heard Archie ( Lord Rosebery ) speak in public politically before , but after the first minute I felt I could never be nervous at his making a speech the audience show him great affection . " [ sic ] However it was not just Gladstone and Rosebery the huge crowds had come to see , but also the <unk> supporting and smiling families . Lady Rosebery went on to describe how " They ( the crowds ) <unk> me on the back till my shoulders were sensitive . " Thus in Rosebery 's first serious involvement in politics , Disraeli was defeated and the newly elected MP for Midlothian became Prime Minister for the second time ( the caretaker liberal leader Lord Hartington retired in favour of Gladstone ) . It was also obvious that Lady Rosebery was a very evident and valuable political <unk> asset . As the Marquess of Crewe put it " she had cut her spurs . " 
- Her political <unk> and ambitions for her husband were however to be more severely tested following the Liberal victory . Rosebery was , as expected , offered a position in Government by Gladstone . It had been rumoured that the position of Viceroy of Ireland or a cabinet place would be <unk> , but it turned out to be the job of Under Secretary of the India Office . Rosebery immediately declined the post , giving as his reason that it would appear that he was being repaid for running Gladstone 's campaign ( as though the <unk> position would not ) . When pressed further he cited ill health — he had been suffering from scarlet fever during the Midlothian campaign and now also appeared to be suffering a minor nervous breakdown . Political leaders urged Lady Rosebery to influence him , but she defended his decision , while stressing that his deterioration in health was only temporary . She had to be careful — if it appeared her husband had declined the offer on the grounds that it was too <unk> , it would give substance to the claims being made that he was <unk> and <unk> . Whatever the truth , and it may be Rosebery 's own explanation that he " disliked hard work , " Lady Rosebery continued to <unk> Gladstone for a job for Rosebery within the cabinet . In August 1880 , when Gladstone told her firmly that " There is nothing I can give him , " she claimed she had not been seeking a cabinet post and Gladstone had misunderstood her . At the same time she was <unk> enough to mention that Sir William Harcourt and Sir Charles Dilke , both radicals opposed to Gladstone 's policies , were " visiting them " and " thoughtful . " Lady Rosebery also began to befriend those politicians such as Lord <unk> who <unk> with her husband , while others such as Lord Granville and Lord Hartington she identified as aloof . She dismissed Lord Spencer with " I can never look on him as a great motive power , besides he does not mention Archie [ Rosebery ] to me . " This was the same Lord Spencer who had advised the Prince and Princess of Wales against visiting the homes of wealthy Jews . 
- Finally her soliciting paid off and in 1881 , Rosebery was offered a government position acceptable to him , that of Under Secretary at the Home Office with special responsibility for Scotland . He had sought the position feeling that Scotland was neglected by the Liberal Government who were more interested in Ireland . However , immediately upon assuming the job he began to demand a place in the cabinet . The office he sought was that of Lord Privy Seal , a position Gladstone refused on account of Rosebery 's inexperience in Government . It appeared that Rosebery was showing his true colours and he was accused of <unk> like a <unk> child , with doubts cast over the <unk> of his reasons for refusing the Under <unk> of the India Office . Lady Rosebery , " conscious of her husband 's supreme ability , " wanted him in the cabinet and was furiously <unk> her husband 's discontent until Rosebery threatened to resign his Home Office position . Lady Rosebery had an angry row with Gladstone 's wife , where Mrs Gladstone pointed out that if Rosebery resigned he would have nothing but horse racing to interest him , and that Lady Rosebery should be patient as her husband was young . Rosebery , accepting that a cabinet place was not going to be forthcoming , resigned from Government . Lady Rosebery , realising further appeal to the <unk> was pointless , tried a new <unk> — Lord Hartington , the immensely influential Secretary of State for War , who was already <unk> with Gladstone over the Irish home rule problem , and whom she allegedly met by chance at Preston Railway Station . <unk> him into her carriage for the journey to London , she pleaded her husband 's case for three hours to her captive listener . The Roseberys then immediately left England and their children for a long trip to America and Australia . On their return in 1885 Rosebery was appointed Lord Privy Seal , complete with the seat in the cabinet which he sought . 
- Gladstone resigned as Prime Minister in 1885 following a Government defeat over the Irish home rule question . The new Tory government was led by Lord Salisbury . However , as a minority administration it was not expected to last , and a swift return of the former administration was anticipated . During this period serious ( if unproven ) charges of plotting and ruthless ambition were about to be levelled against Lady Rosebery . Sir Charles Dilke , considered as a likely replacement for Gladstone , and thus a rival to Rosebery in government , was implicated in one of the most <unk> and ruinous divorce cases of the era . Involvement in any divorce was social suicide in the 19th century , but the facts which emerged were enough to ensure it was political suicide as well . A friend of the Roseberys , Donald Crawford , MP , sued his wife Virginia for divorce naming Dilke as co @-@ <unk> . There was little evidence and Dilke denied the charge , which could have been ultimately forgotten , if Virginia had not suddenly decided to sign a confession giving such lurid details that a great scandal was <unk> . She claimed that not only had Dilke slept with her and taught her " French vices " , but also slept with her mother and <unk> in a three @-@ in @-@ a @-@ bed <unk> with Virginia and a maid . Dilke denied everything , but his hopes of high political office were ruined forever . Dilke claimed the whole thing was an <unk> of lies and <unk> by his political enemies . <unk> began to circulate that the Roseberys , and Lady Rosebery in particular , were at the bottom of Dilke 's misfortune . In his futile quest to <unk> himself , and grasping at <unk> , Dilke wrote to Rosebery accusing Lady Rosebery of having paid Virginia to make the confession . An outraged Rosebery denied all on his wife 's behalf , while in December 1885 Lady Rosebery 's only response on being told of Virginia Crawford 's <unk> was : " Dilke 's behaviour is very astonishing in some reports , though it is not an actual surprise to me . " Early the following year Gladstone was returned to power and Rosebery was appointed Foreign Secretary in Gladstone 's third but brief term of office . Dilke 's political career was ruined , and for years afterwards he continued to expound the Rosebery conspiracy theory . Nothing was ever proven against Lady Rosebery and no <unk> evidence exists to <unk> the claim . 
- The impartiality demanded by Rosebery 's new office forced him to sell many of his business interests , which had come by the way of the Rothschild family , to be seen to be avoiding a conflict of interest . However , his wife 's ambition and part in his rise to power was not only being recognised in high places , but clearly starting to <unk> . On being told that Lady Rosebery was very keen for her husband to become Foreign Secretary , Gladstone replied " She would think herself capable of being Queen of the Realm and think the place only just good enough for her . " Rosebery was now on the path to political greatness , but Gladstone 's government fell the same year . Lady Rosebery was not to see her husband achieve the highest political office . 
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- = = <unk> = = 
- 
- Like many other women of her class and era , Lady Rosebery patronised a great number of charities . Her chief causes appear to have all been connected specifically to the assistance and welfare of women . She was president of the Scottish Home Industries Association , a charity which encouraged Scottish women to work <unk> from home making <unk> or other items of <unk> and the like . In this way women , especially widowed mothers , remained in their homes able to care for their often large families while still earning an income . 
- Queen Victoria appointed her president of the Queen Victoria Jubilee Institute for <unk> in Scotland , the beginning of the district nurse system , which was to <unk> health care for the rural poor and sick in Britain . She was also interested in general improvements in standards of nursing . 
- Like many of her Rothschild relatives she was also deeply involved with the welfare of young working @-@ class women of the Jewish faith who inhabited the poorer areas of London , in particular <unk> . There she founded the Club for Jewish Working Girls . She also donated to numerous other charities connected with Jewish causes . However , within a week of her death her husband began to cancel many of these subscriptions , prompting charges of <unk> . 
- Her interest in education has been one of her most obvious surviving charitable legacies . She founded schools in all the villages surrounding the Roseberys ' estates . The Mentmore estate alone was serviced by three schools founded by her at <unk> , <unk> and Mentmore itself . Not only were the children educated at her expense by trained teachers , each was also provided with seasonal gifts of new clothes . <unk> School remains in its original building with her <unk> on its walls , while <unk> School , which opened in 1877 , survives in new premises . 
- One of her more pioneering and innovative charitable causes was the oral instruction of what were then called the <unk> and dumb . 
- 
- = = Death and legacy = = 
- 
- Lady Rosebery died of <unk> at Dalmeny in 1890 . She fought the disease , but it was found that she was also suffering from Bright 's disease , which had weakened her , making it impossible to survive the attack . She was buried in accordance with the rites of the Jewish faith . Rosebery found this particularly hard to bear , and wrote to Queen Victoria of the pain he experienced when " another <unk> steps in to claim the corpse . " It was only after her death that the doctors who had treated her disclosed to Rosebery that her kidney condition would have killed her within two years even if she had not contracted <unk> . 
- Her funeral was held on 25 November 1890 at <unk> Jewish Cemetery . As is the Jewish tradition , the service was attended only by male mourners , who included most members of Gladstone 's cabinet . 
- There is no evidence that Lady Rosebery drove her husband to follow her own political agenda , or that of her family . For her the rewards seem to have been the pleasure of seeing a husband she undoubtedly <unk> in the high office of which she felt him worthy . There is no doubt that she tempered her husband 's more radical views . Immediately following his wife 's death Rosebery retired from politics , writing in October 1891 " The sole object of my ambition has disappeared with the death of my wife . " Proof of the widespread belief in society that Lady Rosebery was the stable element of the partnership was confirmed shortly after her death , by Queen Victoria , following a then rare public speech by Rosebery , in which he supported Home Rule for Ireland . The Queen was shocked and thought the speech " almost <unk> " and went on to attribute Rosebery 's " shocking and disappointing " behaviour to the fact that " poor Lady Rosebery is not there to keep him back . " While Queen Victoria always personally liked Rosebery , she <unk> his politics . The Queen had thoroughly liked Lady Rosebery and wrote Rosebery several letters of <unk> , likening his loss to the untimely death of her own consort , Prince Albert . It seems that the Queen 's antipathy to Jews was confined to elevating them to the peerage . This view had softened by 1885 . In 1890 she accepted a <unk> invitation from Lady Rosebery 's cousin Ferdinand de Rothschild and toured <unk> Manor albeit eating in a separate dining room to the Jewish members of the party . 
- Shortly after his wife 's death , Rosebery left his grieving children and went alone on a tour of Spain . Following a visit to El Escorial he wrote on the sepulchral wonders of the building , but added " for the dead alone the Taj is of course supreme . " On his return home he had designed for his wife a Victorian Gothic version of the Taj <unk> in miniature . For the remainder of his life he wore black and used black edged writing paper . Once , when talking with his daughter Sybil , he asked her what mourning she thought her mother would have worn had the situation been reversed . Sybil replied , " She would not have worn any , she would have died at once . " 
- Ronald Munro Ferguson has been quoted in 1912 as saying " many things would have gone otherwise had Lady Rosebery lived . Her loss is today as great a <unk> from every point of view as it was at the time of her death . " 
- <unk> changed Rosebery , both mentally and physically : he aged overnight , and began to refer to himself as an old man . Two years after her death , friends were still concerned that he was <unk> . Winston Churchill thought him <unk> by her death , and later said of her " she was a remarkable woman on whom Rosebery leaned , she was ever a <unk> and composing element in his life which he was never able to find again because he could never give full confidence to anyone else . " 
- Sir Edward Hamilton , Rosebery 's closest friend , wrote : 
- Her qualities were portrayed in literature when Lady Rosebery was reputed to be the model for Marcella Maxwell in Mrs <unk> Ward 's novels Marcella ( 1894 ) and Sir George <unk> ( 1909 ) . The author lived at <unk> close to Lady Rosebery 's home at Mentmore and would certainly have known her , while in the books Marcella 's house is based on Hampden House , also in Buckinghamshire . 
- The Liberals did not return to office until 1892 . Lord Rosebery was eventually persuaded to enter government , becoming once again Foreign Secretary serving under Gladstone as Prime Minister . In 1894 on Gladstone 's retirement he achieved his wife 's ambitions and became Prime Minister , but by then Lady Rosebery was dead . Without her , Rosebery was a shadow of his former self , taking huge doses of morphine to combat insomnia and nerves . His Prime <unk> lasted barely a year , marred by problems and difficulties . For the remainder of his life and without his wife , as Queen Victoria <unk> it , " to hold him back " , he became more and more eccentric and controversial in his decisions . His final years were <unk> by ill health and a self @-@ enforced seclusion in Scotland . He died in 1929 . 
- Before their marriage and his full @-@ time entry into politics , Rosebery 's future wife had written with extraordinary <unk> and ambition to him : " I work only to help you , if you are Prime Minister , let me imitate Montagu <unk> . " <unk> had been Disraeli 's influential private secretary on whom he had relied . Rosebery only ever trusted his wife . Without her to calm and order his life he was a neurotic wreck . 
- Lady Rosebery 's eldest son , Harry , who was less successful in politics than his father and brother , distinguished himself by becoming captain of Surrey County Cricket Club and owning two Epsom Derby @-@ winning horses . He succeeded his father as 6th Earl of Rosebery and died in 1974 . Margaret married her father 's old friend and biographer the Marquess of Crewe . Such was still the fame of her parents that London traffic was brought to a standstill on her wedding day in 1899 . Lady Crewe became one of the first women magistrates in Britain ; she died in 1955 . Lady Sybil has been summarised by one of her father 's biographers : " Even more eccentric than her father , she spent much of her time living in a caravan . " Neil , the second of the Roseberys ' sons , entered politics and a promising future was <unk> for him . However , on the outbreak of World War I he joined the army , and was killed leading a charge at <unk> in 1917 . 
- Of Hannah Rosebery 's homes , the lease on Lansdowne House was surrendered shortly before her death , when the Roseberys purchased 38 Berkeley Square . This property was transformed into one of London 's most luxurious town houses . However , Lady Rosebery did not live to see the work completed . Her son Harry sold the house in 1938 , and it was demolished . A year later a bomb landed on the empty site during World War II . The <unk> was bequeathed to her daughter , Sybil , in 1929 and was sold together with its contents in 1955 . Lord and Lady Rosebery 's library there was given to the nation at this time . Mentmore , the grandest of the Roseberys ' homes , was sold by Lady Rosebery 's grandson , the 7th Earl of Rosebery , in 1977 , together with the Rothschild art collection , which Lady Rosebery had not only been intensely interested in but had enlarged considerably . She personally <unk> the collection , and <unk> wrote in the preface " In time to come , when , like all collections , this will be dispersed ( and I hope this will be long after my death ) this book may be of value . " Her two @-@ volume work and the collection it described remained so unknown that " Save Mentmore " ( a group attempting to halt the sale of Mentmore to keep the collection within Britain ) failed largely due to widespread public ignorance of both house and collection . A few pieces of furniture and paintings were taken to Dalmeny , ( the only house to remain in the family ) where they are displayed today , and three pictures including <unk> ' Madame de <unk> were purchased for the National Gallery . The remainder of the collection was dispersed in a week @-@ long sale and is now scattered across the globe . A further sale of the " Continental Library , " to which she had added , was conducted in 1995 at the <unk> Hall , London by <unk> 's . 
- In June 2011 , Christie 's auctioned a set of <unk> and diamond jewels ( a tiara , brooch and <unk> ) that were originally in Lady Rosebery 's ownership . 
- Today , Lady Rosebery is a mere footnote in the long history of her husband 's family , rather as <unk> Vanderbilt is regarded in the Spencer @-@ Churchill family . Her husband , once one of the " most celebrated figures in Britain , " is a minor figure in British history . Thus , Hannah , Countess of Rosebery , in her day celebrated in the worlds of politics , <unk> , and high society , is largely unknown and forgotten . 
- 
- 
- = Hurricane Uleki = 
- 
- Hurricane Uleki , also referred as Typhoon Uleki , was a long @-@ lived tropical cyclone in August – September 1988 that had minimal effects on land . <unk> from a disturbance in the Intertropical Convergence Zone in late @-@ August , Uleki was identified as a tropical depression well to the southeast of Hawaii on August 28 . <unk> organization ensued as it moved west , becoming a tropical storm on August 30 and a hurricane on August 31 . Rapid intensification took place thereafter and the storm reached its peak intensity on September 2 as a Category 3 on the Saffir – Simpson hurricane wind scale . Hurricane Hunters investigating the cyclone found peak winds of 125 mph ( 205 km / h ) and a barometric pressure of 957 mbar ( hPa ; 28 @.@ 26 inHg ) . Thereafter , Uleki stalled for two days to the southwest of Hawaii , resulting in heavy surf across the state . The dangerous swells killed two people on Oahu . 
- <unk> environmental conditions caused weakening of the hurricane by September 4 as it resumed a west @-@ northwest course away from Hawaii . Conditions later became favorable and Uleki acquired winds of 105 mph ( 165 mph ) on September 7 , constituting its secondary peak . The hurricane crossed the International Dateline on September 8 and was reclassified as typhoon . <unk> well away from land , the cyclone steadily weakened to a tropical storm by September 12 . Gradually turning north and later east , the degrading cyclone transitioned into an extratropical cyclone four days later and ultimately dissipated on September 17 near the International Dateline . 
- 
- = = Meteorological history = = 
- 
- In late @-@ August 1988 , increased convective activity was noted along the Intertropical Convergence Zone by forecasters at the Central Pacific Hurricane Center ( CPHC ) . An area of low pressure subsequently developed within this disturbance and was designated as Tropical Depression One @-@ C at 12 : 00 UTC on August 28 . Upon classification , the depression was situated roughly 800 mi ( 1 @,@ 300 km ) southeast of the Big Island of Hawaii and moving west @-@ northwest . <unk> intensification ensued over the following day with the system attaining gale @-@ force winds by 18 : 00 UTC on August 29 , at which time it was assigned the name Uleki . The storm later attained winds of 75 mph ( 120 km / h ) on August 31 and was dubbed a hurricane as it passed 350 mi ( 560 km ) south of Hilo . Thereafter , Uleki underwent a sudden period of rapid intensification and achieved major hurricane status later that day . After reaching this strength , Uleki 's forward motion gradually <unk> and eventually almost ceased altogether on September 1 as upper @-@ level steering currents collapsed . 
- With the hurricane situated to the southwest of the Hawaiian Islands , Hurricane Hunters conducted several weather reconnaissance missions into the storm . On September 2 , they found maximum surface winds of 125 mph ( 205 km / h ) and a minimum barometric pressure of 957 mbar ( hPa ; 28 @.@ 26 inHg ) ; this corresponded to the peak intensity of Uleki . At the time , Uleki displayed a well @-@ organized , tight circulation . The hurricane meandered in the same general area over the next two days , executing several small loops , with a slight northward drift . During this time , its circulation increased in coverage but became less @-@ organized as weakening ensued . Increased wind shear and restricted outflow took their toll on the hurricane . The cyclone 's winds dropped to 80 mph ( 130 km / h ) by 12 : 00 UTC on September 4 as it resumed its west @-@ northwest track under the influence of a subtropical ridge away from the Hawaiian Islands . Uleki passed roughly halfway between Johnston Atoll and the French Frigate Shoals on September 5 . By September 7 , the weakened trend halted and environmental conditions favored <unk> . As Uleki neared the International Dateline , it regained winds of 105 mph ( 165 km / h ) . 
- At 00 : 00 UTC on September 8 , the CPHC transferred warning responsibility of Uleki to the Joint Typhoon Warning Center ( JTWC ) and the storm was subsequently referred to as a typhoon . Transfer of warnings occurred when the storm was at 178 ° W rather than at the <unk> , where the boundary between the Central and West Pacific basins exists , for unknown reasons . Three hours later Uleki made its closest approach to Midway Atoll , passing 200 mi ( 320 km ) to the south . Shortly thereafter the system crossed the International Dateline and the Japan Meteorological Agency ( JMA ) also began advising on the system . The JMA estimated Uleki to have had a pressure of <unk> mb ( hPa ; 27 @.@ 91 inHg ) at this time ; however , this value was derived from satellite estimates rather than direct measurements . Uleki maintained this strength through September 10 as it continued on its west @-@ northwest course . On that day , the typhoon again entered a region of weak steering currents between two <unk> within the subtropical ridge . A trough approaching from the west was forecast to prompt Uleki to turn east ; however , the typhoon maintained a general northwest motion in a <unk> @-@ stepped fashion . Increasing wind shear and cooler air soon imparted weakening , and Uleki degraded to a tropical storm by September 12 . 
- Continued effects from shear stripped the cyclone of all deep convection , and by September 14 only a band of <unk> clouds remained in association with Uleki . The JTWC issued their final warning on the system at 00 : 00 UTC that day accordingly . The JMA maintained the system as a tropical depression as the former typhoon began turning to the east . Uleki later transitioned into an extratropical cyclone on September 16 as it accelerated to the east . The system dissipated the following day near the International Dateline , far from any major <unk> . 
- 
- = = Preparations and impact = = 
- 
- As the hurricane stalled to the southwest of Hawaii on September 3 , tropical storm watches were issued for Oahu , Kauai , and <unk> . High surf advisories were raised for all islands as well . Following Uleki 's turn to the west on September 5 , the tropical storm watches were discontinued . The storm 's erratic movement proved to be troublesome for forecasters and they continuously warned residents to be cautious and alert should the storm double @-@ back to the state . On September 4 , <unk> at <unk> Beach and <unk> <unk> Beach rescued 19 people caught in 5 to 6 ft ( 1 @.@ 5 to 1 @.@ 8 m ) swells . Further north in Oahu , two people drowned after being caught in rough waters . 
- The storm moved roughly halfway between the French Frigate Shoals and Johnston Island on September 5 with no adverse effects in either region due to its weakened state . On September 7 , the 300 residents of Midway Atoll and United States Coast Guard personnel stationed on <unk> Atoll prepared for possible hurricane @-@ force winds from the storm . Hurricane Uleki ultimately passed 200 mi ( 320 km ) south of Midway Atoll and produced some coastal flooding from increased surf . Some breaking waves spilled onto the runway at Henderson Field . 
- 
- 
- = 2016 Spanish Grand Prix = 
- 
- The 2016 Spanish Grand Prix ( formally known as the Formula 1 Gran <unk> de <unk> Pirelli 2016 ) was a Formula One motor race held on 15 May 2016 at the Circuit de Barcelona @-@ <unk> in <unk> , Spain . The race was the fifth round of the 2016 season , and marked the forty @-@ sixth running of the Spanish Grand Prix as a round of the Formula One World Championship . It was the twenty @-@ sixth time that the race has been held at the circuit . 
- Nico Rosberg was the defending race winner and entered the round with a forty @-@ three point lead over teammate Lewis Hamilton in the Drivers ' Championship . Their team , Mercedes , held an eighty @-@ one point lead over Ferrari in the Constructors ' Championship . Hamilton took pole position during qualifying , ahead of teammate Rosberg and Red Bull 's Daniel Ricciardo . 
- Max Verstappen won the race upon his debut for his new team Red Bull , having <unk> his Toro Rosso seat with Daniil Kvyat ahead of the event . At the age of 18 years and 228 days , Verstappen became the youngest ever winner , the youngest driver to score a podium finish and the youngest ever to lead a lap of a Formula One race , breaking the previous records held by Sebastian Vettel . In the process he also became the first Dutchman to win a Grand Prix . Both Mercedes drivers retired from the race following a collision with each other on the first lap , thus marking the first Mercedes double retirement since the 2011 Australian Grand Prix and the first time the team had not scored a point since the 2012 United States Grand Prix . 
- 
- = = Report = = 
- 
- 
- = = = Background = = = 
- 
- Daniil Kvyat and Max Verstappen traded places ahead of the race . Verstappen joined Red Bull Racing while Kvyat returned to <unk> Toro Rosso . Red Bull 's team principal Christian Horner explained the driver swap with Verstappen 's talent , saying that the 18 @-@ year old Dutchman and Daniel Ricciardo had the potential to become Formula One 's best driver pairing . Furthermore , he stressed that Verstappen 's move to Red Bull would tie him to the team for the foreseeable future . Daniil Kvyat meanwhile expressed surprise at the decision , saying during Thursday 's press conference that he was " shocked " by his demotion , stressing that Red Bull did not give him a " real explanation " . Following problems with his power unit in both the Chinese and Russian Grand Prix , Lewis Hamilton received a redesigned <unk> @-@ H unit , in a bid to solve the troubles which hampered him earlier . However , Mercedes was unable to guarantee that the problem would not appear again . 
- With Spain being the first race in Western Europe , and a mid @-@ season test coming up after the race , many teams introduced their first major car updates for the Grand Prix . Mercedes ran a new front wing with a " more <unk> " nose during first free practice . Other changes were made to the engine cover and the rear wing . McLaren made more significant changes to their <unk> @-@ 31 , most notably on the front wing , which Sky Sports commentator Ted <unk> called the " most intricate front wing I 've ever seen " . Force India updated their <unk> to improve <unk> . According to the team , they were looking for improved <unk> rather than faster lap times . Renault introduced a new chassis for Jolyon Palmer . More significantly , the French manufacturer debuted a new version of their power unit for both their works team and Red Bull at the two @-@ day test following the race . All other teams except for Sauber also made changes to their cars , with the Swiss team continuing their financial struggles . They also announced that they would be forced to miss the mid @-@ season test , being unable to field a development driver , with regulations demanding that two out of four test days be driven by young driver talents . Both Sauber and Haas were allowed to use the updated version of Ferrari 's 2016 power unit , which the works team had already used in Russia two weeks earlier . 
- Going into the weekend , Nico Rosberg led the Drivers ' Championship , having taken the maximum number of 100 points from the first four races . Lewis Hamilton followed in second with 57 points , while Kimi Räikkönen was third with 43 points . Daniel Ricciardo followed in fourth , ahead of Sebastian Vettel . In the Constructors ' standings , Mercedes was first with 157 points , 81 points clear of second @-@ placed Ferrari . Behind them , Red Bull led fourth @-@ placed Williams by just six points . 
- 
- = = = Free practice = = = 
- 
- Per the regulations for the 2016 season , two 90 @-@ minute practice sessions were held on Friday and another 60 @-@ minute session was held before qualifying on Saturday . The Ferrari drivers , Sebastian Vettel and Kimi Räikkönen , set the pace in the first session , both on the soft tyre compound . The Mercedes drivers Rosberg and Hamilton followed , albeit on the medium compound , recording more timed laps than their rivals . Both Red Bull cars followed in fifth and sixth , with Daniel Ricciardo faster than new teammate Max Verstappen . <unk> Bottas was seventh for Williams , two places ahead of teammate Felipe Massa , who at one point spun out at turn five . Upon his return to Toro Rosso , Daniil Kvyat was unable to match the pace of his teammate Carlos Sainz Jr . , ending up half a second behind him . Esteban <unk> made his first free practice appearance of the season for Renault , replacing Jolyon Palmer , but was unable to set a timed lap . He suffered a tyre failure , caused by debris on the track . Force India 's Sergio Pérez managed only nine laps as he spent much of the session in the pit lane after smoke emerged from the rear of his car . 
- Mercedes topped the time sheets in the second session , now switching to the soft compound tyres as well , with Nico Rosberg two and a half @-@ tenths of a second clear of Kimi Räikkönen , while Lewis Hamilton was third fastest , more than seven tenths off Rosberg 's time . Räikkönen 's running was interrupted for a while when he was called back into the <unk> to investigate a fuel system problem , but was able to get back out . Fernando Alonso was seventh fastest for McLaren , but needed a replacement of his car 's plank after running wide over artificial grass surrounding the track . His teammate , Jenson Button , also experienced problems , having to stop at the end of the <unk> early in the session with an electrical issue . Renault 's problem from the first session continued : Jolyon Palmer , back in his car in place of <unk> , suffered a tyre failure on the start @-@ finish straight , bringing out red flags in the process . Renault and tyre supplier Pirelli were left puzzled over the failure , as in this incident , no sign of a cut from debris was found . 
- Nico Rosberg was again fastest in the third practice session on Saturday morning , setting a time of 1 : 23 @.@ <unk> on his first timed run , a time that stood until the end of the session . Hamilton was second , 0 @.@ 126 seconds off , followed by Sebastian Vettel just two @-@ <unk> of a second behind him . In fourth place , Max Verstappen was faster than teammate Ricciardo for the first time , who finished fifth , ahead of Räikkönen . Upon beginning his preparation laps for qualifying , Rosberg had to come back into the garage , as a <unk> on his car was malfunctioning . The team was capable of repairing the damage and he went back out for the final minutes of practice . Sergio Pérez was satisfied with the eighth fastest time , saying that the car had improved " everywhere " following the updates it had received . 
- 
- = = = Qualifying = = = 
- 
- Qualifying consisted of three parts , 18 , 15 and 12 minutes in length respectively , with six drivers eliminated from competing after each of the first two sessions . In the first part of qualifying ( <unk> ) , Nico Rosberg set a time of 1 : 23 @.@ 002 , the fastest of the weekend up to that point , two @-@ tenths of a second ahead of teammate Hamilton . Manor and Sauber occupied the back rows of the grid . Felipe Massa 's first lap was seven @-@ tenths of a second slower than the one from his teammate Bottas and as he was unable to set another timed lap , he became the surprise elimination in <unk> , behind Jolyon Palmer , who was dropped into 17th place by a late improvement from Jenson Button . 
- In <unk> , it was Hamilton who came out on top , exactly six @-@ tenths of a second ahead of Rosberg . Verstappen was third fastest , ahead of Ricciardo in fifth . Kimi Räikkönen had to abandon his first fast lap after running wide in turn four , but later set a time good enough for fourth . Meanwhile , Sebastian Vettel recorded just one timed lap , saving a set of tyres and was sixth . On his return to Toro Rosso , Kvyat was eliminated in 13th place , while teammate Sainz went through to Q3 . Sergio Pérez advanced as well , <unk> his teammate <unk> into elimination in the process , who would line up eleventh on the grid . <unk> him on the sidelines for Q3 were Jenson Button in twelfth , with Romain <unk> , Kevin Magnussen and Esteban Gutiérrez lining up behind Kvyat . 
- Lewis Hamilton and Nico Rosberg were the first out in Q3 . Hamilton 's early sector times put him on course to a new fastest lap of the weekend , but a brake <unk> at turn ten ruined his lap and tyres , handing provisional pole position to Rosberg . As the two Ferrari drivers struggled to find pace , Max Verstappen claimed second place for the time being , before the drivers went out for their second laps . Lewis Hamilton 's second flying lap <unk> Rosberg 's time by almost three @-@ tenths of a second , and saw him take the fifty @-@ second pole of his career . The two Red Bull cars of Ricciardo and Verstappen followed , with Räikkönen and Vettel for Ferrari in fifth and sixth respectively , both more than a second behind Hamilton . <unk> Bottas was best of the rest ahead of Sainz , Pérez and Alonso . It was the first appearance in Q3 for McLaren since they resumed their association with Honda in 2015 . 
- 
- = = = Race = = = 
- 
- At the start , Nico Rosberg got around the outside of Hamilton in turn one , while Räikkönen lost positions due to a slow getaway . Sebastian Vettel got past Verstappen , but was immediately <unk> on the approach to turn four . At the front of the race , coming out of turn three Hamilton and Rosberg collided ending the race of both Mercedes drivers . The collision resulted in a safety car period , with the order standing : Ricciardo , Verstappen , Sainz , Vettel and Räikkönen . The safety car came in at the end of lap four . Vettel got past Sainz on lap eight and Räikkönen tried to follow suit two laps later , but was forced off track into turn two . He completed the move one lap later at the same spot . Sainz and Button were the first to come into the pits for new tyres on lap 12 , followed by race leader Ricciardo one lap later , making Verstappen the first Dutchman to lead a Grand Prix , before he came in another lap later . When Vettel made a pit stop on lap 16 , he emerged third behind the two Red Bull drivers , all on the medium compound tyre . 
- By lap 20 , Sebastian Vettel was closing the gap to the two Red Bulls ahead of him , coming within three second of Verstappen . On lap 22 , Nico <unk> retired when his power unit caught fire and he had to stop on the sidelines . While Massa had fought from 18th on the grid into tenth , his teammate Bottas was lying fifth by lap 25 , ahead of Sainz . On lap 28 , Ricciardo was again the first of the front runners to pit , taking on the soft compound . Vettel came in two laps later , making the same choice in tyres . This left Verstappen to lead Kimi Räikkönen , as it emerged that both would run on a different strategy , <unk> only twice in contrast to the three stopping Vettel and Ricciardo . On lap 34 , the gap between the two leaders was 2 @.@ 1 seconds , before Verstappen came in on the following lap . Räikkönen responded one lap later , emerging behind Verstappen . On lap 39 , Vettel came in for his third and final stop , while Ricciardo stayed out for another four laps . When he returned to the track , he was behind Vettel , but on <unk> tyres . 
- While Räikkönen closed on Verstappen at the front , Fernando Alonso 's home race ended on lap 47 , pulling over to the side of the track at turn three . Räikkönen managed to get into the one @-@ second window to activate the drag reduction system ( DRS ) , as Vettel was eight seconds behind him with Ricciardo closing on him in fourth place . By lap 57 , Ricciardo was close enough to use DRS as well , but unable to pass . Three laps later , Ricciardo made an attempt at <unk> into turn one , but <unk> too late left him running wide , allowing Vettel back through . As the front runners <unk> <unk> , Kvyat , after scoring the first fastest lap for himself and Toro Rosso , overtook Gutiérrez for tenth place . Two laps from finish , Ricciardo suffered a puncture and was forced to pit , but retained fourth place ahead of Bottas . On the last lap , Renault drivers Kevin Magnussen and Jolyon Palmer collided , but both made it to the finish . As Verstappen crossed the finish line , he became the youngest ever and first Dutch race winner in Formula One . 
- 
- = = = Post @-@ race = = = 
- 
- At the podium interviews , conducted by singer <unk> Domingo , Verstappen expressed delight about a " great race " , <unk> his team with giving him a good car . Kimi Räikkönen said that while he was happy for Verstappen , he was disappointed not to have won . He blamed the lack of <unk> while following as the reason why he had been unable to pass Verstappen . Vettel <unk> the winner as well , but lamented that his different strategy had not worked out in his favour . During the post @-@ race press conference , Räikkönen stressed that had anyone offered the team two podium positions after their poor performance in qualifying , they would have taken it " happily " . Fourth placed Ricciardo expressed bitterness over Red Bull 's decision to switch him to a three @-@ stop strategy , which eventually lost him a podium position , saying : " I 'm a bit devastated . A big part of me is happy the team are on winning form but it 's hard to celebrate " . His team later explained their decision , declaring that they had focused their strategy on Vettel , who they had considered the biggest threat . 
- In the Grand Prix , Max Verstappen broke several records : apart from being the youngest ever driver to win a race at 18 years and 228 days , he also was the youngest to stand on a Formula One podium , in both instances breaking Sebastian Vettel 's record set at the 2008 Italian Grand Prix by more than two years . Vettel had been 21 years and 2 months at the time . Verstappen also became the youngest driver to lead at least one lap of a Grand Prix , again overcoming a record set by Vettel , at the 2007 Japanese Grand Prix . Verstappen also was the first driver born in the 1990s to win a Grand Prix . He received considerable praise after the race , with Sky Sports F1 's David <unk> saying that he " has shown he has everything to be a world champion " . His father Jos Verstappen , himself a former Grand Prix driver , declared that he considered his son a better driver than himself , as race wins had <unk> him during his own career in the sport . French sport newspaper L <unk> declared Verstappen to be " already a great " and " in the big leagues " . Verstappen was also the first Dutchman to win a race , with the Netherlands becoming the 22nd country to produce a race winner . He was the tenth different winner of the Spanish Grand Prix in as many past editions . 
- Following their collision , Hamilton and Rosberg were summoned to the stewards after the race but neither received a penalty , as the stewards rated their crash as a racing incident , with no driver in particular to blame . It emerged that Rosberg had chosen the wrong engine mode for the start , being down on power which led to Hamilton going for a passing manoeuvre . However , Rosberg remained convinced that he had done nothing wrong , saying : " I made it very clear I wasn 't going to leave any space on the inside and I was very surprised he went for the gap " . While Hamilton apologised to the team after the incident , he refused to accept blame for it . Opinions about the incident varied : While Mercedes 's executive chairman <unk> <unk> blamed Hamilton for the crash , former F1 driver Anthony Davidson said on Sky Sports F1 that Rosberg 's move was " very aggressive " . Three @-@ time world champion Jackie Stewart said after the race that Mercedes should fine Hamilton for the crash : " Hamilton is to blame . Rosberg was allowed to protect himself . You don ’ t go for it on the first lap . " Mercedes 's head of motorsport <unk> <unk> stressed after the race that the team would continue to let their drivers race against each other freely , a decision praised by former world champion Alain <unk> . While Rosberg said after the race that he <unk> having a talk with Hamilton about the situation , Mercedes later decided that they did " not need clear @-@ the @-@ air talks " . However , it later emerged that the pair did have a conversation about the incident before the next race in Monaco , which , according to Hamilton , was marked by " pure respect " . It was Mercedes 's first double retirement since the 2011 Australian Grand Prix , and the first time they failed to score a point since the 2012 United States Grand Prix , ending a 62 @-@ race long streak , the third longest in Formula One history . 
- For his last lap collision with teammate Jolyon Palmer , Kevin Magnussen received a ten @-@ second time penalty , dropping him one place to 15th . He also received two penalty points to his licence . Carlos Sainz received a <unk> from the stewards for leaving the pit lane one minute before it was allowed . He escaped a more severe penalty because he stopped shortly afterwards , thereby not gaining an advantage . Upon his return to Toro Rosso , Daniil Kvyat recorded the first fastest lap of his career , and the first for the Italian team in their <unk> Grand Prix . 
- With 100 points from the first four races , Nico Rosberg remained on top of the Drivers ' Championship , while Hamilton 's retirement dropped him behind Räikkönen , who now trailed Rosberg by 39 points , four points ahead of Hamilton . Sebastian Vettel and Daniel Ricciardo were level on points with 48 , while Verstappen 's victory moved him into sixth on 38 . In the Constructors ' standings , Mercedes retained their lead on 157 points , but Ferrari closed the gap to 48 points , with Red Bull in third on 94 . Toro Rosso moved ahead of Haas into fifth , while Force India took seventh from McLaren . 
- 
- = = Classification = = 
- 
- 
- = = = Qualifying = = = 
- 
- 
- = = = Race = = = 
- 
- Notes 
- ^ 1 – Kevin Magnussen originally finished 14th but received a ten @-@ second time penalty after the race for causing a collision with Jolyon Palmer . 
- 
- = = = <unk> after the race = = = 
- 
- Note : Only the top five positions are included for both sets of standings . 
- 
- 
- = Loverboy ( Mariah Carey song ) = 
- 
- " Loverboy " is a song by American singer and songwriter Mariah Carey . It was released on July 16 , 2001 by Virgin Records America as the lead single from Glitter ( 2001 ) . Written and produced by Carey , Larry Blackmon , Thomas Jenkins and Clark Kent , " Loverboy " is built around a sample from " Candy " by Cameo , who are also featured on the track . Lyrically , the song finds Carey <unk> about her loverboy , a man that will fulfill her physical and sexual desires . The recording was accompanied by an official remix , titled " Loverboy ( Remix ) " , featuring guest artists Ludacris , Da Brat , Shawnna and Twenty II . 
- The track was subject to controversy , following reports that Jennifer Lopez and Tommy Mottola ( Carey 's ex @-@ husband ) stole the idea of sampling the singer 's original planned sample , Yellow Magic Orchestra 's " Firecracker " , over a month after Carey had signed for it . Since Carey 's Glitter was to be released over a month after Lopez 's album , she changed the song 's melody to sample from " Candy " instead . Controversy ensued following its release ; Carey was hospitalized for severe mental and physical exhaustion , following poorly received television appearances involving the song and album . 
- After the song 's release in the United States , " Loverboy " stalled on the Billboard Hot 100 . The song reached a peak of number two on the Hot 100 but failed to top the chart due to weak radio airplay . Though finishing as the best @-@ selling song of 2001 in the United States , " Loverboy " became Carey 's first lead single to not reach number one . <unk> , the song failed to garner strong charting , reaching the top @-@ ten in Australia and Canada , and the top @-@ twenty in Italy and the United Kingdom . 
- " Loverboy " received generally mixed reviews from music critics ; many called the song unoriginal and dated while others felt that the featured artists ( primarily on the remix version ) <unk> Carey and made the song disorganized . A music video , directed by David <unk> , was shot for both versions of the song . The first version features Carey as a <unk> @-@ clad car girl , waving a flag as her loverboy finishes a race . The video for the remix version features similar footage , only including shots of the song 's guests in race @-@ cars during their verses . Finally , she performed " Loverboy " live on The Sweet Sweet Fantasy Tour in 2016 . 
- 
- = = Background and release = = 
- 
- Following the release of " Loverboy " , Carey embarked on a short promotional campaign for the song . On July 19 , 2001 , Carey made a surprise appearance on the MTV program Total Request Live ( TRL ) . As the show 's host Carson Daly began taping following a commercial break , Carey came out onto the filming stage , pushing an ice cream cart while wearing a large men 's shirt . <unk> anxious and <unk> , Carey began giving out individual bars of ice cream to fans and guests on the program , while waving to the crowd down below on Times Square , while <unk> into a <unk> monologue regarding therapy . Carey then walked to Daly 's platform and began a <unk> , in which she shed her shirt to reveal a tight yellow and green ensemble , leading him to exclaim " Mariah Carey has lost her mind ! " . While she later revealed that Daly was aware of her presence in the building prior to her appearance , she admitted that he was meant to act surprised in order to provide a more dramatic effect for the program . Carey 's appearance on TRL garnered strong media attention , with many critics and newspapers citing her behavior as " troubled " and " erratic " . In the days following her appearance on TRL , Carey had begun displaying " erratic behavior " during a public record signing for the single at Tower Records in New York . As the appearance was filmed , she began <unk> on several points , leading her to discuss radio @-@ host Howard Stern , and how his form of humor on his program bothered her greatly . At that point , Carey 's publicist <unk> Berger grabbed the microphone from her hand , and ordered the cameras to stop filming . Only days later , Carey began posting irregular voice notes and messages on her official website : 
- I 'm trying to understand things in life right now and so I really don 't feel that I should be doing music right now . What I 'd like to do is just a take a little break or at least get one night of sleep without someone popping up about a video . All I really want is [ to ] just be me and that 's what I should have done in the first place ... I don 't say this much but guess what , I don 't take care of myself . 
- After the quick removal of the messages , Berger commented that Carey had been " obviously exhausted and not thinking clearly " when she posted the letters . Two days later on July 26 , she was suddenly hospitalized , citing " extreme exhaustion " and a " physical and emotional breakdown " . Following the heavy media coverage surrounding Carey 's publicized breakdown and hospitalization , Virgin Records and 20th Century Fox delayed the release of both the film 's soundtrack and the film itself for three weeks : from August 21 to September 11 and from August 31 to September 21 , respectively . Following Carey 's absence from the public eye , as well as her abandonment of promotional appearances for the film and soundtrack , her unprecedented $ 100 million five @-@ album record deal with Virgin Records ( EMI Records ) was bought out for $ 50 million . The decision was brought out due to the low sales of the project , as well as the negative publicity surrounding her breakdown . Soon after , Carey flew to Capri , Italy for a period of five months , in which she began writing material for her new album , stemming from all the personal experiences she had endured throughout the past year . Additionally , she founded her own label modeled after her initials , MonarC Entertainment , and signed a new three @-@ album record deal with Island Records , valued at over $ 23 million . 
- At the time of the single 's release , Carey did not perform " Loverboy " live . The first performance of the song came 15 years later , on Carey 's Sweet Sweet Fantasy Tour in 2016 . 
- 
- = = Composition = = 
- 
- " Loverboy " ' s hook is built around a sample of " Candy " , by Cameo . Aside from <unk> from the melody , " Loverboy " features Cameo as a lead vocalist on the track . According to Chuck Taylor from Billboard , " Loverboy " is reminiscent of several of Carey 's previous lead singles , as they too were predominantly built around samples of older songs . He described some of its background <unk> as " <unk> harmonies <unk> " , and noted the inclusion of many shouts and ad @-@ <unk> from Carey and Cameo . Due to the song 's heavy usage of the " Candy " sample , and the heavy blend of instrumentation and vocals , he said that to his <unk> , the song was " devoid of a standard hook " . NME 's <unk> Therepio felt the idea of sampling " Candy " was genius " on paper " , but once recorded , suffered from various <unk> . He felt it didn 't match the success Carey 's previous single , " Fantasy " ( which also recalled its melody from an older song ) , and criticized the inclusion of several over @-@ the @-@ top " keyboard and synth arrangements " . Regarding Carey 's voice , aside from describing it as " obscured " , Therepio expressed his difficulty in understanding her lyrical phrases , claiming it was weakened from " no @-@ everyone @-@ can @-@ understand @-@ what @-@ you 're @-@ saying ' <unk> " . Gil Kaufman from MTV News noted the inclusion of " bouncing <unk> bass [ and ] rock guitars " , while Roger <unk> of the Hartford Courant described how Carey was often reduced to " breathy background vocals " on " Loverboy " . Lyrically , the song finds Carey looking for her " loverboy " , and a sugar @-@ daddy that 's going to " love her right " . Therepio found it ironic how Carey was <unk> for a sugar @-@ daddy , when she had just left one ( referring to her older ex @-@ husband Tommy Mottola ) . Additionally , he felt Carey was <unk> her image from that of a <unk> , to a " pin @-@ up image " of a pop star . The song 's lyrics are <unk> , and feature the protagonist asking for her " loverboy " to fulfill her sexual desires : " Loverboy come on and love me / Give me more " . 
- 
- = = = <unk> controversy = = = 
- 
- Throughout 2000 , Carey had already been writing and recording material for Glitter . During this period , she developed the concept for the album 's lead single , " Loverboy " . Originally , Carey had sampled the melody and hook from the 1978 Yellow Magic Orchestra song " Firecracker " , using an <unk> of it throughout the chorus and introduction . In early theatrical trailers for Glitter , the original version of " Loverboy " was still featured . As Carey had ended her contract with Columbia Records , Jennifer Lopez was signed by Tommy Mottola , and had begun recording material for her album , <unk> ( 2001 ) . According to The Inc . ' s Irv <unk> , Mottola , head of Columbia and Carey 's ex @-@ husband , knew of Carey 's usage of the " Firecracker " sample , and attempted to have Lopez use the same sample before her . At the time , Carey had become increasingly paranoid over outside executives being informed about Glitter , especially following news of Lopez 's " theft " of the song . When the music publishers for " Firecracker " were questioned , they admitted Carey had licensed usage of the sample first , and Lopez had signed for it over one month later , under Mottola 's arrangement . Following the scandal , Carey was not able to use the original sample , as Lopez 's album was to be released far earlier than Glitter . She subsequently changed the composition of " Loverboy " , and incorporated a new sample , " Candy " by Cameo . According to <unk> , Mottola contacted him with instructions to create the Murder Remix of " I 'm Real " to sound exactly like another Glitter track he produced , titled " If We " featuring <unk> <unk> Rule and Nate <unk> . The " Firecracker " sample was eventually used by Lopez on her song " I 'm Real " , from her album <unk> The remix of Mariah 's " Loverboy " featured rapper Da Brat singing a pointed message about " <unk> <unk> [ her ] daily " , to the melody from " Firecracker " . 
- 
- = = Critical reception = = 
- 
- " Loverboy " was met with generally mixed reviews from music critics ; many were not impressed with the incorporation of the sample , and felt the song didn 't lead Carey into any newer ground or innovation . Additionally , several reviewers were not impressed with Carey 's concealed vocals and the song 's overly @-@ complicated production . Sarah Rodman of the Boston Herald described the song 's production as a " traffic @-@ jam " and wrote " ' Loverboy ' is another in an increasingly long line of glitzy , candy @-@ coated , creatively stunted song . " Writing for the Los Angeles Times , Natalie Nichols felt " Loverboy " was " predictable " and presented nothing new for Carey 's audience . USA Today 's Edna <unk> wrote the song off as " skimpy " , while Glenn Gamboa from Newsday described its overall reception as " lukewarm " . Harry <unk> from <unk> <unk> <unk> <unk> the song as one of Carey 's " finest moments " on Glitter , and disagreed with criticism regarding the " overly @-@ populated " musical background " . Allmusic editor Stephen Thomas Erlewine listed the song as the only " recommended " song from Glitter , while Gil Kaufman of MTV News called it a " fun , uptempo dance number " . Craig Seymour from Entertainment Weekly rated " Loverboy " a <unk> , and criticized its lyrical content and production . An anonymous writer from The Guardian felt the song had " no <unk> tune " , due to the inclusion of electronic musical instruments and background shouts and ad @-@ <unk> . Billboard 's Chuck Taylor described the song as " self @-@ sabotage " , and felt Carey was gambling her longevity . He described the production as a " <unk> <unk> of disparate elements " , and wrote " the mighty may have fallen here " . 
- 
- = = Chart performance = = 
- 
- Prior to Virgin 's release of " Loverboy " , Carey halted all promotional appearances corresponding to the song . Citing an emotional breakdown and a subsequent hospitalization , Carey remained out of the public eye for several weeks . After its release , " Loverboy " failed to garner strong charting on prominent international markets , and received poor radio airplay throughout the United States . " Loverboy " peaked atop the Billboard 's Hot Singles Sales chart for four consecutive weeks , though due to weak airplay , managed to reach number two on the Billboard Hot 100 with " Bootylicious " by Destiny 's Child holding it back from the top position . " Loverboy " finished as the best @-@ selling single of 2001 in the United States , with sales of 571 @,@ 000 units , falling short of the previous year 's best @-@ seller , which accumulated sales upwards of one million copies . The Recording Industry Association of America ( RIAA ) certified the song Gold . In Canada , " Loverboy " peaked at number three on the singles chart , and charted for a total of thirteen weeks . 
- Throughout <unk> and Europe , the song peaked outside the top @-@ twenty in most countries . " Loverboy " debuted at its peak position of number seven on the Australian Singles Chart , during the week of July 29 , 2001 . The following week , the song began its decline , and experienced a total chart trajectory of seven weeks . The song was certified Gold by the Australian Recording Industry Association ( ARIA ) , denoting shipments of over 35 @,@ 000 units . On July 29 , 2001 , " Loverboy " debuted at number 65 on the <unk> Austria Top 40 chart , spending a total of three weeks in the chart . The song achieved relatively weak charting in both the Flemish and <unk> territories in Belgium , peaking at numbers 49 and 34 , respectively . Making its debut at its peak position of number 54 , " Loverboy " charted for a total of nine weeks in France , before falling out on November 3 , 2001 . On the Dutch Top 40 chart , the song made its debut at number 68 . The following week the song peaked at number 34 , before dropping outside the top 40 three weeks later , ending its five @-@ week run . On July 26 , 2001 , " Loverboy " debuted at number 49 on the Swedish Singles Chart , and reached a peak of number 44 . Similarly in Switzerland , the song peaked at number 66 , and spent only four weeks <unk> inside the chart . On the UK Singles Chart , the song debuted at its peak of number 12 . The following week , the song dropped to number 29 on the chart , before dropping outside the top @-@ forty two weeks later . 
- 
- = = Remix = = 
- 
- Aside from the regular version of the song , an official remix of " Loverboy " was included on Glitter . The song 's main remix , titled " Loverboy " ( Remix ) , uses the same sample as the album version and retains all of Carey 's original vocals . It has a different introduction , the singing and spoken parts of Cameo are greatly reduced ( and are not credited ) , and additional raps are included by Da Brat , Ludacris , Shawnna , and Twenty II . Critics panned the song , agreeing that the inclusion on the former pair only made it more difficult to hear and understand Carey 's singing . Many described their inclusion as " <unk> " the singer , while others felt they made the song increasingly tumultuous . NME writer <unk> Therepio rated the remix five out of ten stars , and claimed the song to have been a much weaker lead single than Carey 's " Fantasy " ( 1995 ) . Jim Farber from Daily News criticized Brat 's verse , writing how it " cut right through " the production . The Morning Call 's Len <unk> described the remix as " <unk> " , while Jim Abbott from the Orlando Sentinel felt the song 's guests " upstaged " Carey . 
- 
- = = Music video = = 
- 
- 
- = = = Synopsis = = = 
- 
- Two music videos , both directed by David <unk> , were shot for the song and its accompanying remix . The videos introduced a less demure image of Carey , one that received negative backlash from critics . She appears in the video with a lighter hair color than she had sported in the past , and wearing a series of revealing ensembles . Reviewers <unk> Carey 's newer image , primarily her double @-@ handkerchief bra , and likened her to younger pop singers such as Britney Spears , which they felt <unk> the singer . The video begins with Cameo driving all over a racetrack , while Carey , dressed in revealing clothing , is shown singing in various " car girl " positions at the track on a hot summer day . She flags down cars as the " flag girl " and dances as a " tire girl " in a <unk> @-@ inspired sequence , before jumping out of a pop out cake to the <unk> of the crowd below . Several other scenes of Carey in a pink <unk> while riding on top of a race @-@ car are shown , during which Cameo continues the race . A video was also made for the remix and retains most of the shots of the original . In it , Ludacris and Shawnna can be seen rapping together as they ride in an old car , while Da Brat and Twenty II rap together in a more modern car without a hood . 
- 
- = = = Reception = = = 
- 
- The music video for " Loverboy " received generally mixed reviews from critics , many of whom felt Carey was portrayed in an overtly sexual manner . A writer from The Guardian criticized the video , calling it " <unk> " and describing Carey 's choreography as " running <unk> " . Slant Magazine 's Sal Cinquemani felt the video was " brilliantly over @-@ the @-@ top " , while an anonymous columnist from NME commented that it would cause male viewers to " play with themselves " after watching . In a countdown of the " The 5 Least <unk> Music Videos " , <unk> <unk> from NME included the video at number three , writing how the singer " spins with the mad @-@ eyed grace of someone who hasn ’ t been to sleep for 72 hours " ( referring to Carey 's mention of insomnia as a reason for her breakdown ) . <unk> Burns of The Michigan Daily was critical of Carey 's portrayal in the video , primarily her double @-@ handkerchief bra . He felt that in doing so , Carey was <unk> herself and her image to resemble younger pop singers such as Britney Spears . 
- 
- = = Formats and track listings = = 
- 
- 
- = = Credits and personnel = = 
- 
- Credits for Glitter adapted from the album 's liner notes . 
- Mariah Carey – songwriting , producer , vocals , background vocals 
- Larry Blackmon – songwriting 
- <unk> Jenkins – songwriting 
- Clark Kent – producer 
- Carl " Butch " Small – percussion 
- Peter Novak – engineer 
- Andrew <unk> – engineer 
- Ann <unk> – engineer 
- Anthony <unk> – engineer 
- Elliot <unk> – engineer 
- Dana Jon <unk> – mixing 
- Michael " <unk> " Schlesinger – mixing 
- Cameo – vocals 
- Mary Ann Tatum – background vocals 
- Remix 
- Da Brat – songwriting , vocals 
- Ludacris – songwriting , vocals 
- Shawnna – songwriting , vocals 
- Twenty II – songwriting , vocals 
- 
- = = Charts and certifications = = 
- 
- 
- 
- = Allah = 
- 
- Allah ( / <unk> , <unk> , <unk> / ; Arabic : الله , translit . Allāh , pronounced [ <unk> <unk> ] ) is the Arabic word referring to God in Abrahamic religions . The word is thought to be derived by contraction from al <unk> , which means " the God " , and has <unk> in other Semitic languages , including <unk> in Aramaic , <unk> in Canaanite and Elohim in Hebrew . 
- The word Allah has been used by Arabs of different religions since pre @-@ Islamic times . More specifically , it has been used as a term to refer to God by Muslims ( both Arab and non @-@ Arab ) and Arab Christians . It is now mainly used by Muslims and Arab Christians to refer to God . It is also often , albeit not exclusively , used in this way by <unk> , Bahá <unk> , Indonesian and Maltese Christians , and <unk> Jews . Similar usage by Christians and <unk> in West Malaysia has recently led to political and legal controversies . 
- 
- = = Etymology = = 
- 
- The etymology of the word Allāh has been discussed extensively by classical Arab <unk> . <unk> of the Basra school regarded is as either formed " spontaneously " ( <unk> ) or as the definite form of <unk> ( from the verbal root <unk> with the meaning of " <unk> " or " hidden " ) . Others held that it was borrowed from Syriac or Hebrew , but most considered it to be derived from a contraction of the Arabic definite article <unk> " the " and <unk> " deity , god " to al @-@ <unk> meaning " the deity " , or " the God " . The majority of modern scholars <unk> to the latter theory , and view the <unk> hypothesis with skepticism . 
- <unk> of the name " Allāh " exist in other Semitic languages , including Hebrew and Aramaic . The corresponding Aramaic form is <unk> ( <unk> ) , but its <unk> state is <unk> ( <unk> ) . It is written as <unk> ( <unk> ) in Biblical Aramaic and <unk> ( <unk> ) in Syriac as used by the Assyrian Church , both meaning simply " God " . Biblical Hebrew mostly uses the plural ( but functional singular ) form Elohim ( <unk> ) , but more rarely it also uses the singular form <unk> ( <unk> ) . In the Sikh scripture of Guru <unk> <unk> , the term Allah ( Punjabi : <unk> , translit . <unk> ) is used 37 times . 
- 
- = = Usage = = 
- 
- 
- = = = Pre @-@ Islamic <unk> = = = 
- 
- Regional variants of the word Allah occur in both pagan and Christian pre @-@ Islamic inscriptions . Different theories have been proposed regarding the role of Allah in pre @-@ Islamic polytheistic cults . Some authors have suggested that polytheistic Arabs used the name as a reference to a creator god or a supreme deity of their pantheon . The term may have been vague in the <unk> religion . According to one hypothesis , which goes back to Julius <unk> , Allah ( the supreme deity of the tribal federation around Quraysh ) was a designation that consecrated the superiority of <unk> ( the supreme deity of Quraysh ) over the other gods . However , there is also evidence that Allah and <unk> were two distinct deities . According to that hypothesis , the Kaaba was first consecrated to a supreme deity named Allah and then hosted the pantheon of Quraysh after their conquest of Mecca , about a century before the time of Muhammad . Some inscriptions seem to indicate the use of Allah as a name of a <unk> deity centuries earlier , but we know nothing precise about this use . Some scholars have suggested that Allah may have represented a remote creator god who was gradually eclipsed by more <unk> local deities . There is disagreement on whether Allah played a major role in the <unk> religious cult . No iconic representation of Allah is known to have existed . Muhammad 's father 's name was <unk> @-@ Allāh meaning " the slave of Allāh " . 
- 
- = = = Christianity = = = 
- 
- The Aramaic word for " God " in the language of Assyrian Christians is <unk> , or <unk> . Arabic @-@ speakers of all Abrahamic faiths , including Christians and Jews , use the word " Allah " to mean " God " . The Christian Arabs of today have no other word for " God " than " Allah " . ( Even the Arabic @-@ descended Maltese language of Malta , whose population is almost entirely Roman Catholic , uses Alla for " God " . ) Arab Christians , for example , use the terms Allāh al @-@ ab ( الله <unk> ) for God the Father , Allāh al @-@ ibn ( الله <unk> ) for God the Son , and Allāh al @-@ <unk> al @-@ <unk> ( الله <unk> <unk> ) for God the Holy Spirit . ( See God in Christianity for the Christian concept of God . ) 
- Arab Christians have used two forms of <unk> that were affixed to the beginning of their written works . They adopted the Muslim bismillāh , and also created their own <unk> bismillāh as early as the 8th century . The Muslim bismillāh reads : " In the name of God , the <unk> , the <unk> . " The <unk> bismillāh reads : " In the name of Father and the Son and the Holy Spirit , One God . " The Syriac , Latin and Greek <unk> do not have the words " One God " at the end . This addition was made to emphasize the monotheistic aspect of <unk> belief and also to make it more palatable to Muslims . 
- According to Marshall Hodgson , it seems that in the pre @-@ Islamic times , some Arab Christians made pilgrimage to the Kaaba , a pagan temple at that time , honoring Allah there as God the Creator . 
- Some archaeological excavation quests have led to the discovery of ancient pre @-@ Islamic inscriptions and tombs made by Arab Christians in the ruins of a church at <unk> el @-@ <unk> in Northern Jordan , which contained references to Allah as the proper name of God , and some of the graves contained names such as " Abd Allah " which means " the servant / slave of Allah " . 
- The name Allah can be found countless times in the reports and the lists of names of Christian martyrs in South Arabia , as reported by antique Syriac documents of the names of those martyrs from the era of the <unk> and <unk> kingdoms . 
- A Christian leader named Abd Allah ibn Abu <unk> ibn Muhammad was martyred in <unk> in 523 , as he had worn a ring that said " Allah is my lord " . 
- In an inscription of Christian <unk> dated back to 512 , references to Allah can be found in both Arabic and Aramaic , which called him " Allah " and " <unk> " , and the inscription starts with the statement " By the Help of Allah " . 
- In pre @-@ Islamic <unk> , the name used for God was " Allah " , as evidenced by some discovered Arabic versions of the New Testament written by Arab Christians during the pre @-@ Islamic era in Northern and Southern Arabia . 
- Pre @-@ Islamic Arab Christians have been reported to have raised the battle cry " Ya La <unk> Allah " ( O slaves of Allah ) to <unk> each other into battle . 
- " Allah " was also mentioned in pre @-@ Islamic Christian poems by some <unk> and <unk> poets in Syria and Northern Arabia . 
- 
- = = = Islam = = = 
- 
- In Islam , Allah is the unique , omnipotent and only deity and creator of the universe and is equivalent to God in other Abrahamic religions . 
- According to Islamic belief , Allah is the most common word to represent God , and humble submission to his will , divine <unk> and commandments is the <unk> of the Muslim faith . " He is the only God , creator of the universe , and the judge of humankind . " " He is unique ( <unk> ) and inherently one ( <unk> ) , all @-@ <unk> and omnipotent . " The Qur 'an declares " the reality of Allah , His inaccessible mystery , His various names , and His actions on behalf of His creatures . " 
- In Islamic tradition , there are 99 Names of God ( al @-@ <unk> ’ al @-@ <unk> lit. meaning : ' the best names ' or ' the most beautiful names ' ) , each of which evoke a distinct characteristic of Allah . All these names refer to Allah , the supreme and all @-@ comprehensive divine name . Among the 99 names of God , the most famous and most frequent of these names are " the <unk> " ( al @-@ <unk> ) and " the <unk> " ( al @-@ <unk> ) . 
- Most Muslims use the <unk> Arabic phrase in <unk> ’ Allāh ( meaning ' if God wills ' ) after references to future events . Muslim <unk> piety encourages beginning things with the <unk> of bismillāh ( meaning ' in the name of God ' ) . 
- There are certain phrases in praise of God that are favored by Muslims , including " <unk> Allāh " ( <unk> be to God ) , " al @-@ <unk> <unk> " ( <unk> be to God ) , " <unk> <unk> <unk> Allāh " ( There is no deity but God ) and " <unk> <unk> " ( God is greater ) as a devotional exercise of remembering God ( <unk> ) . In a Sufi practice known as <unk> Allah ( lit. <unk> of God ) , the Sufi repeats and contemplates on the name Allah or other divine names while controlling his or her breath . 
- Some scholars have suggested that <unk> used the term Allah in addressing both pagan Arabs and Jews or Christians in order to establish a common ground for the understanding of the name for God , a claim <unk> <unk> says is doubtful . According to <unk> , in contrast with pre @-@ Islamic Arabian polytheism , God in Islam does not have associates and companions , nor is there any kinship between God and <unk> . Pre @-@ Islamic pagan Arabs believed in a blind , powerful , <unk> and <unk> fate over which man had no control . This was replaced with the Islamic notion of a powerful but <unk> and <unk> God . 
- According to Francis Edwards Peters , " The Qur ’ <unk> insists , Muslims believe , and historians affirm that Muhammad and his followers worship the same God as the Jews ( 29 : 46 ) . The Qur ’ an 's Allah is the same Creator God who <unk> with Abraham " . Peters states that the Qur 'an portrays Allah as both more powerful and more remote than <unk> , and as a universal deity , unlike <unk> who closely follows Israelites . 
- 
- = = As a <unk> = = 
- 
- 
- = = = English and other European languages = = = 
- 
- The history of the name Allāh in English was probably influenced by the study of comparative religion in the 19th century ; for example , Thomas Carlyle ( 1840 ) sometimes used the term Allah but without any implication that Allah was anything different from God . However , in his biography of <unk> ( 1934 ) , Tor <unk> always used the term Allah , though he allows that this " conception of God " seems to imply that it is different from that of the Jewish and Christian <unk> . 
- Languages which may not commonly use the term Allah to denote God may still contain popular expressions which use the word . For example , because of the centuries long Muslim presence in the Iberian Peninsula , the word <unk> in the Spanish language and <unk> in the Portuguese language exist today , borrowed from Arabic ( Arabic : <unk> <unk> الله ) . This phrase literally means ' if God wills ' ( in the sense of " I hope so " ) . The German poet <unk> used the form " Allah " as the title of a poem about the ultimate deity , though it is unclear how much Islamic thought he intended to convey . 
- Some Muslims leave the name " Allāh " <unk> in English . The word has also been applied to certain living human beings as <unk> of the term and concept . 
- 
- = = = Malaysian and Indonesian language = = = 
- 
- Christians in Malaysia and Indonesia use Allah to refer to God in the Malaysian and Indonesian languages ( both of which are standardized forms of the Malay language . ) Mainstream Bible translations in the language use Allah as the translation of Hebrew Elohim ( translated in English Bibles as " God " ) . This goes back to early translation work by Francis Xavier in the 16th century . The first dictionary of Dutch @-@ Malay by Albert Cornelius <unk> , <unk> <unk> , and <unk> <unk> in 1650 ( revised edition from 1623 edition and 1631 Latin @-@ edition ) recorded " Allah " as the translation of the Dutch word " <unk> " . <unk> also translated Matthew in 1612 to Malay language ( first Bible translation to non @-@ European language , only a year after King James Version was published ) , which was printed in the Netherlands in 1629 . Then he translated Mark which was published in 1638 . 
- The government of Malaysia in 2007 outlawed usage of the term Allah in any other but Muslim contexts , but the Malayan High Court in 2009 revoked the law , ruling that it was unconstitutional . While Allah had been used for the Christian God in Malay for more than four centuries , the contemporary controversy was triggered by usage of Allah by the Roman Catholic newspaper The Herald . The government appealed the court ruling , and the High Court suspended implementation of its verdict until the appeal was heard . In October 2013 , the court ruled in favor of the government 's ban . In early 2014 , the Malaysian government confiscated more than 300 <unk> for using the word to refer to the Christian God in <unk> Malaysia . However , the use of Allah is not prohibited in the two Malaysian state of <unk> and <unk> . The main reason it is not prohibited in these two states is that usage has been long @-@ established and local <unk> ( Bibles ) have been widely distributed freely in East Malaysia without restrictions for years . Both states also do not have similar Islamic state laws as those in West Malaysia . 
- As a reaction to some media criticism , the Malaysian government has introduced a " 10 @-@ point solution " to avoid confusion and misleading information . The 10 @-@ point solution is in line with the spirit of the 18- and 20 @-@ point agreements of <unk> and <unk> . 
- 
- = = = In other scripts and languages = = = 
- 
- Allāh in other languages that use Arabic script is spelled in the same way . This includes <unk> , Persian / <unk> , Uyghur among others . 
- <unk> , Bengali : <unk> <unk> <unk> Allah 
- Bosnian : Allah 
- Chinese ( Mandarin ) : <unk> <unk> , <unk> <unk> ; <unk> <unk> ( semantic translation as " the true master " ) , <unk> <unk> ( <unk> , from Persian language ) 
- Czech , <unk> : <unk> 
- Greek : <unk> <unk> 
- <unk> : <unk> or Allah 
- Hebrew : <unk> Allah 
- Hindi : <unk> ् <unk> Allāh 
- Malayalam : <unk> <unk> <unk> <unk> <unk> 
- Japanese : <unk> <unk> , <unk> <unk> , <unk> <unk> 
- <unk> : <unk> 
- Maltese : Alla 
- Korean : <unk> Alla 
- Polish : Allah , also archaic <unk> or <unk> 
- Russian , Ukrainian , Bulgarian : <unk> <unk> <unk> <unk> 
- Serbian , <unk> , <unk> : <unk> <unk> 
- Spanish , Portuguese : <unk> 
- Thai : <unk> ์ <unk> 
- Punjabi ( <unk> ) : <unk> Allāh , archaic <unk> <unk> ( in Sikh scriptures ) 
- Turkish : Allah 
- 
- = = Typography = = 
- 
- The word Allāh is always written without an alif to spell the ā vowel . This is because the spelling was settled before Arabic spelling started habitually using alif to spell ā . However , in <unk> spelling , a small <unk> alif is added on top of the <unk> to indicate the pronunciation . 
- One exception may be in the pre @-@ Islamic <unk> inscription , where it ends with an ambiguous sign that may be a lone @-@ standing h with a lengthened start , or may be a non @-@ standard conjoined l @-@ h : - 
- <unk> : This reading would be Allāh spelled <unk> with alif for the ā . 
- <unk> : This reading would be al @-@ <unk> = ' the god ' ( an older form , without contraction ) , by older spelling practice without alif for ā . 
- Many Arabic type fonts feature special ligatures for Allah . 
- 
- = = = Unicode = = = 
- 
- Unicode has a <unk> reserved for Allāh , <unk> = U + <unk> , in the Arabic Presentation Forms @-@ A block , which exists solely for " compatibility with some older , legacy character sets that encoded presentation forms directly " ; this is discouraged for new text . Instead , the word Allāh should be represented by its individual Arabic letters , while modern font technologies will render the desired ligature . 
- The <unk> variant of the word used as the <unk> of arms of Iran is encoded in Unicode , in the <unk> <unk> range , at <unk> U + <unk> ( <unk> ) . 
- 
- 
- = Moment of Surrender = 
- 
- " Moment of Surrender " is a song by rock band U2 and the third track on their 2009 album No Line on the Horizon . During the initial recording sessions for the album in 2007 in Fez , Morocco , the band wrote the song with producers Brian Eno and Daniel Lanois within a few hours . Together , they recorded the song in a single take ; Eno called the song 's recording " the most amazing studio experience [ he 's ] ever had " . According to him and Lanois , the track is the closest the band came to realising their original concept for the album of writing " future hymns " . The seven @-@ minute song features gospel @-@ like vocals in the chorus , along with a predominantly <unk> and piano @-@ based musical accompaniment . Lyrically , the song is about a drug addict who is undergoing a crisis of faith . 
- " Moment of Surrender " was praised by critics , many of whom called it one of the album 's stand @-@ out tracks . The song was compared to the group 's earlier ballads " With or Without You " and " One " . It was performed at all but two of the band 's concerts on the U2 360 ° Tour , most often as the closing song . During performances , the stage lights were <unk> and fans were urged to hold up their mobile phones to create " a stadium full of tiny stars " . Although it was not released as a single , Rolling Stone named " Moment of Surrender " the best song of 2009 , and in 2010 , they ranked it <unk> on their list of " The 500 Greatest Songs of All Time " . 
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- = = Writing and recording = = 
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- " Moment of Surrender " was written by U2 and No Line on the Horizon producers / co @-@ writers Brian Eno and Daniel Lanois during a two @-@ week recording session in Fez , Morocco between May and June 2007 . The song was developed within a few hours , and then recorded in a single take in a <unk> of the hotel <unk> El <unk> . Eno began by creating a percussion loop of a " rolling hand drum " so that the band would have something to improvise along with when they joined him for songwriting and recording . However , Eno had not arranged the loop properly and the result was a strange , uneven beat that he compared to " a wheeled carriage that had one of the wheels a bit cracked " or " the way a camel moves " . Although Eno was trying to fix the loop , drummer Larry Mullen , Jr. began playing along to it . Eno then asked guitarist the Edge to play some chords . After a quick discussion about the chord changes and the meter ( in which they decided to have a " funny layout " that was not based on " <unk> or <unk> " ) , the six of them improvised the entirety of the piece . 
- As they began to play , bassist Adam Clayton developed a bassline . The part was originally based on the Grandmaster <unk> <unk> song " White Lines ( Don 't Don 't Do It ) " before Clayton changed to a more trance @-@ like bassline . <unk> Bono created some melodies and sang over the music . During the album 's recording , Bono had become tired of writing in the first @-@ person and he began writing lyrics and singing from the perspective of different characters . During the writing of " Moment of Surrender " , Bono assumed the character of a drug addict having a crisis of faith . The song title was borrowed from the <unk> Anonymous term for when an addict admits being " powerless over alcohol " and needs help . Bono had attempted to use the phrase " vision over visibility " in the lyrics of earlier songs ; however , " Moment of Surrender " was the first song where he felt it was appropriate to be used . Eno called Bono 's singing in this character as " so heartbreaking <unk> and vulnerable " , creating a feeling like " a knife to the heart " . Lanois contributed by developing the gospel @-@ like chorus . The uneven hi @-@ hat from the drum part stems from Mullen 's electronic drum kit malfunctioning during the song 's recording . Eno was amazed by each performer 's ability to develop and play their part without any instructions or <unk> . After the song 's recording completed , everyone in the studio , including a gathering of production personnel and visitors , was completely silent , and Eno suggested it was as if they had gone on an " emotional adventure of some kind " . He called the song 's recording " the most amazing studio experience I 've ever had " , and he believes the " emotional crescendo " heard in the song properly captures how they felt as they improvised the piece . Bono stated " it was a spell and we were in it " . 
- The song was played only once and received minor treatments afterwards , with the addition of a cello part in the introduction and some editing , which included removing a verse to reduce the song 's length . Eno was outraged that U2 wanted to shorten the song , and he was adamant that the band not alter the original track too much , saying , " These fucking guys , they 're supposed to be so spiritual — they don 't spot a miracle when it hits them in the face . Nothing like that ever happened to me in the studio in my whole life . " The band 's original concept for No Line on the Horizon was to create an album of future hymns — songs that would be played forever . According to Eno and Lanois , " Moment of Surrender " is the closest the band came to reaching that concept . 
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- = = Composition = = 
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- " Moment of Surrender " is played in common time at a tempo of 87 beats per minute in a key of A minor . The song makes use of the conventional verse @-@ chorus form . The song begins with an uneven percussion loop , before an ambient <unk> fades in and the drums enter at 0 : 08 . A cello part joins and the <unk> plays the chord progression C – Am – F – C – G – E – <unk> . At the end of the progression , 47 seconds into the song , the intensity of the <unk> rises before an organ , bass guitar , and piano subsequently enter . At 1 : 16 , Bono 's vocals enter and the first verse begins , lasting three stanzas . After the first chorus concludes and the second verse begins at 2 : 59 , the Edge begins playing a guitar riff . The second verse lasts two stanzas . After the second chorus , a piano interlude begins , with Lanois contributing pedal steel . The Edge begins a slide guitar solo at 4 : 59 that many critics compared to the playing style of Pink Floyd 's David Gilmour . After the third chorus ends at 6 : 11 , " Oh @-@ oh @-@ <unk> " vocals and a guitar figure bring the song to its conclusion . 
- Lanois noted that the song had a very " Canadian sound " that was like a tribute to the Band , calling it the " <unk> sound " . Rolling Stone said the song " <unk> a Joshua Tree @-@ style gospel feel with a <unk> <unk> bass line and a syncopated beat " . The song makes prominent use of organ and piano . 
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- = = Reception = = 
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- " Moment of Surrender " was well received by critics after the release of No Line on the Horizon . David Fricke of Rolling Stone enjoyed the song , writing , " The rising @-@ falling effect of the harmony voices around Bono ... is a perfect picture of where he really wants to be , when he gets to the line about ' vision over visibility . ' " Blender considered it the high @-@ point of the album , comparing it to the band 's 1987 single " I Still Haven 't Found What I 'm Looking For " . Reviewer Rob Sheffield complimented the <unk> of bass , guitar , and vocals , calling it " the kind of <unk> @-@ divinity anthem that U2 cut their teeth on , except it really does seem like they 've gotten better at these songs now that they 've picked up some <unk> @-@ out adult grit . Bono actually sounds scared of something in this song , and whether his <unk> are religious or sexual , the fear gives his voice some <unk> . " NME felt that it was the " most impressive " song on the album , describing it as a " gorgeously sparse prayer built around Adam Clayton 's <unk> bassline and Bono 's rough <unk> " , and noting that despite its seven @-@ minute length , it did not feel too long . Alexis <unk> of The Guardian was more critical of the song , saying it " doesn 't have enough of a tune to support the full seven @-@ minute gospel treatment " , a sentiment that The Times agreed with . 
- Spin reviewed the track favourably , calling it a " celebrity @-@ at @-@ the @-@ crossroads soul ballad " with an " ambient gospel sweep that 's both haunted and joyful " . <unk> praised the song 's <unk> , saying it was " graced by <unk> performances " and that the Edge 's " languid guitar solo " was reminiscent of David Gilmour . Q echoed these sentiments , commending Bono 's soulful singing and the Edge 's solo . The reviewer christened the song as " this album 's ' One ' or ' With or Without You ' , with added bonus points " . The Washington Post called the song one of the record 's highlights and enjoyed the track 's gospel qualities . The reviewer wrote , " The vocal harmonies on the choruses sound like something out of a church in some distant , dystopian world ; the <unk> , slightly <unk> piano adds to that impression ... " Hot Press gave the song a favourable review , calling it a " sweeping " track and suggesting it " <unk> [ s ] the same spiritual vibe as Marvin <unk> 's ' Abraham , Martin & John ' " . Time gave No Line on the Horizon a negative review , but praised " Moment of Surrender " for its " heartbreaking melody " and Bono 's " Oh @-@ oh @-@ oh " vocals that reminded the critic of the end of " With or Without You " . Bono and Daniel Lanois both cited the song as their favourite track on the album , and Brian Eno thought the band should have chosen it as the album 's first single . Musician Gavin Friday described the song as " Al Green on Irish steroids " , and Hot Press editor Niall Stokes called it " a modern rock classic " that will " stand forever as one of U2 's most inspirational creations " . 
- Despite not being released as a single , " Moment of Surrender " charted in two countries . In the United States , the song appeared on the <unk> Triple A chart at number 45 for the week of 17 November 2009 . In Belgium , the song appeared on the <unk> 40 Singles Chart ( Wallonia ) for a week at number 35 . 
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- = = Live performances = = 
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- " Moment of Surrender " made its live debut on the opening night of the U2 360 ° Tour in Barcelona , Spain during the encore , as the show 's final song . It was played at nearly every subsequent concert on the tour as the closing song , being excluded from the setlist for only two of the tour 's 110 shows : the 9 December 2010 concert in Brisbane , Australia and the 29 May 2011 concert in Winnipeg . Prior to performances of " Moment of Surrender " , a disco ball was lowered and the stage lights turned off , and Bono encouraged fans to take out their mobile phones and create " a stadium full of tiny stars " . The National Post commented that despite being a " big @-@ concert cliché " , the move was effective and created a feeling of intimacy amongst the audience . Rolling Stone enjoyed the visual effect in context of the tour 's space theme , saying it " truly made it seem like the stadium had reached <unk> , with thousands of <unk> lights turning into stars " . The Daily Telegraph praised the " galaxy of mobile phones " , saying that despite the tour 's impressive stage and visual effects , " the most beautiful sight came when we couldn 't see [ the stage ] " . In reviewing a Paris show from July 2009 , the Sunday Times called " Moment of Surrender " the only track from No Line on the Horizon performed that evening that was strong enough to deserve inclusion with the rest of the show 's set list . NME was not as receptive to the song in a live setting , questioning the decision to end concerts with the song . A U2 360 ° Tour performance of " Moment of Surrender " appears on the group 's 2010 concert video U2 360 ° at the Rose Bowl . In a review of the film , Andrew Mueller of Uncut called " Moment of Surrender " an " [ i ] ll @-@ chosen closer " , as well as " <unk> and under @-@ realised " . In 2009 , the song was performed by the band on the 35th season premiere of Saturday Night Live , and at the <unk> Gate in Berlin to celebrate the 20th anniversary of the fall of the Berlin Wall . On 18 June 2011 , Bono closed the song with lyrics from " <unk> " to honor the recently deceased E Street Band saxophonist Clarence <unk> . The song was also one of only three non @-@ single tracks to be played by the band in their headline set at the Glastonbury Festival 2011 . 
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- = = Legacy = = 
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- " Moment of Surrender " appeared on several music publications ' " best of " lists for 2009 and the 2000s decade . <unk> called " Moment of Surrender " one of " The 10 Best Songs You ( Probably ) <unk> 't Hear in 2009 " . Rolling Stone ranked it the best song of 2009 and the 36th @-@ best song of the decade , calling it " The most devastating ballad U2 — or anyone — has delivered since ' One . ' " In Rolling Stone 's voting for the decade 's best song , Metallica drummer Lars <unk> placed " Moment of Surrender " in the number @-@ one spot on his ballot . Rolling Stone updated its list of " The 500 Greatest Songs of All Time " in 2010 and placed " Moment of Surrender " at number 160 — just one year after the song 's release — marking the fourth @-@ best position of any U2 song on the list . 
- " Moment of Surrender " is the tenth track in the Anton <unk> film Linear , based on a story by <unk> and Bono where a Parisian traffic <unk> travels across France and the Mediterranean Sea to visit his girlfriend in Tripoli . During the sequence , the <unk> ( played by <unk> <unk> ) , leaves the bar and begins to wander the streets of <unk> at night , eventually making his way down to the beach where he falls asleep on the sand . In the morning he wakes up and the next track , " <unk> of Lebanon " , begins . 
- A live performance of " Moment of Surrender " taken from an 18 September 2010 concert in Paris appeared on the group 's 2010 live EP Wide <unk> in Europe . 
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- = = Charts = = 
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- = = Personnel = = 
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- Additional keyboards – Terry <unk> 
- <unk> – Caroline Dale 
- Production – Brian Eno and Daniel Lanois 
- Engineering – Richard <unk> 
- Engineering assistance – Chris Heaney 
- Additional engineering – Declan <unk> , and Carl Glanville 
- Mixing – Daniel Lanois and Declan <unk> 
- Mixing assistance – Tom Hough , Dave <unk> , and Dave <unk> 
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- 
- = Gaboon viper = 
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- <unk> gabonica , most commonly known as the Gaboon viper , is a viper species found in the rainforests and <unk> of sub @-@ Saharan Africa . Like all vipers , it is venomous . It is not only the largest member of the genus <unk> , but also the world 's heaviest <unk> , and it has the longest fangs – up to 2 inches in length ( 5 cm ) , and it has highest venom yield of any snake . Two subspecies are currently recognized , including the nominate subspecies described here . 
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- = = Description = = 
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- Adults average 125 – 155 cm ( 4 to 5 feet ) in total length ( body + tail ) with a maximum total length of 205 cm ( 81 in ) for a specimen collected in Sierra Leone . The sexes may be distinguished by the length of the teeth in relation to the total length of the body : approximately 12 % for males and 6 % for females . Adults , especially females , are very heavy and stout . One female had the following dimensions : 
- In their description of B. gabonica , Spawls et al .. ( 2004 ) give an average total length of 80 – 130 cm ( 32 to 51 @.@ 5 in ) , with a maximum total length of 175 cm ( 69 @.@ 3 in ) , saying the species may possibly grow larger still . They acknowledge reports of specimens over 1 @.@ 8 m ( 6 ft ) , or even over 2 m ( 6 @.@ 5 ft ) in total length , but claim there is no evidence to support this . A large specimen of exactly 1 @.@ 8 m ( 5 @.@ 9 ft ) total length , caught in 1973 , was found to have weighed 11 @.@ 3 kg ( 25 lb ) with an empty stomach . Very large specimens may possibly weigh up to 20 kg ( 44 lb ) , which would rank them as the world 's heaviest venomous snake ahead of the Eastern <unk> <unk> , but these masses are not known to have been verified . 
- The head is large and triangular , while the neck is greatly narrowed : almost one @-@ third the width of the head . A pair of " horns " is present between the raised nostrils — tiny in B. g. gabonica , but much larger in B. g. rhinoceros . The eyes are large and <unk> , set well forward , and surrounded by 15 – 21 <unk> scales . There are 12 – 16 <unk> scales across the top of the head . Four or five scale rows separate the <unk> and the <unk> . There are 13 – 18 <unk> and 16 – 22 <unk> . The fangs may reach a length of 55 millimetres ( 2 @.@ 2 in ) : the longest of any venomous snake . 
- <unk> , there are 28 – 46 dorsal scale rows , all of which are strongly <unk> except for the outer rows on each side . The lateral scales are slightly oblique . The ventral scales number 124 – 140 : rarely more than 132 in males , rarely less than 132 in females . There are 17 – 33 paired <unk> scales : males have no fewer than 25 , females no more than 23 . The anal scale is single . 
- The color pattern consists of a series of pale , <unk> blotches running down the center of the back , <unk> with dark , yellow @-@ edged <unk> markings . The flanks have a series of <unk> or brown <unk> shapes , with light vertical central bars . The belly is pale with irregular brown or black blotches . The head is white or cream with a fine , dark central line , black spots on the rear corners , and a dark blue @-@ black triangle behind and below each eye . The iris colour is cream , yellow @-@ white , orange or silvery . 
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- = = Common names = = 
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- The snake 's common names include Gaboon viper , butterfly adder , forest puff adder , <unk> , <unk> , Gaboon adder , . 
- Originally a name given by the Portuguese , Gabon ( <unk> ) refers to the estuary on which the town of <unk> was built , in Gabon , and to a narrow strip of territory on either bank of this arm of the sea . As of 1909 , Gaboon referred to the northern portion of French Congo , south of the <unk> and lying between the Atlantic Ocean and 12 ° E longitude . 
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- = = Geographic range = = 
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- This species can be found in Guinea , Ghana , <unk> , Nigeria , Cameroon , <unk> Guinea , Gabon , Republic of the Congo , <unk> Congo , northern Angola , Central African Republic , South Sudan , Uganda , Kenya , eastern Tanzania , Zambia , Malawi , eastern Zimbabwe , Mozambique , and northeast KwaZulu @-@ Natal Province in South Africa . <unk> et al . ( 2003 ) also list Sierra Leone and <unk> in West Africa . The type locality is given as " Gabon " ( Africa ) . 
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- = = Habitat = = 
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- The Gaboon viper is usually found in rainforests and nearby woodlands , mainly at low altitudes , but sometimes as high as 1500 m . Spawls et al . ( 2004 ) mention a maximum altitude of 2100 m . According to <unk> and <unk> ( 1975 ) , it is generally found in environments that are parallel to those occupied by its close relative , B. <unk> , which is normally found in more open country . 
- In Tanzania , this species is found in secondary <unk> , <unk> plantations , and in agricultural land under bushes and in <unk> . In Uganda , they are found in forests and nearby grasslands . They also do well in reclaimed forest areas : <unk> plantations in West Africa and coffee plantations in East Africa . They have been found in evergreen forests in Zambia . In Zimbabwe , they only occur in areas of high rainfall along the forested <unk> in the east of the country . In general , they may also be found in swamps , as well as in still and moving waters . They are commonly found in agricultural areas near forests and on roads at night . 
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- = = Behavior = = 
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- Primarily nocturnal , Gaboon vipers have a reputation for being slow @-@ moving and placid . They usually hunt by ambush , often spending long periods motionless , waiting for suitable prey to happen by . On the other hand , they have been known to hunt actively , mostly during the first six hours of the night . In <unk> , Ghana , they were regularly killed around some <unk> in an open area with the forest some 500 meters away — a sign that they were hunting rats in the grassland . They are usually very tolerant snakes , even when handled , and rarely bite or hiss , unlike most vipers . However , bites by bad @-@ tempered individuals do occur . 
- <unk> is mostly rectilinear , in a sluggish " walking " motion of the ventral scales . They may <unk> from side to side when alarmed , but only for short distances . <unk> ( 1933 ) even described them as being capable of <unk> . 
- If threatened , they may hiss loudly as a warning , doing so in a deep and steady rhythm , slightly flattening the head at the expiration of each breath . Despite this , they are unlikely to strike unless severely provoked ; however , they are one of fastest striking snakes in the world , so care should be taken in handling them . 
- There have been numerous descriptions of their generally <unk> nature . <unk> ( 1961 ) wrote they are so docile that they " can be handled as freely as any non @-@ venomous species " , although this is absolutely not recommended . In Lane ( 1963 ) , <unk> explained he would capture specimens by first touching them lightly on the top of the head with a pair of tongs to test their reactions . Anger was rarely displayed , so the tongs were usually set aside and the snakes firmly grasped by the neck with one hand and the body supported with the other as he picked them up and carried them to a box for containment . He said the snakes hardly ever struggled . 
- Parry ( 1975 ) described how this species has a wider range of eye movement than other snakes . Along a horizontal plane , eye movement can be maintained even if the head is rotated up or down to an angle of up to 45 ° . If the head is rotated 360 ° , one eye will tilt up and the other down , depending on the direction of rotation . Also , if one eye looks forward , the other looks back , as if both are connected to a fixed position on an axis between them . In general , the eyes often flick back and forth in a rapid and <unk> manner . When asleep , there is no eye movement and the pupils are strongly contracted . The pupils <unk> suddenly and eye movement resumes when the animal wakes up . 
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- = = Feeding = = 
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- Because of their large , heavy body size , the adults have no trouble eating prey as large as fully grown rabbits . When prey happens by , they strike with very fast precision from any angle . Once they strike their prey , they hang on to it with their large fangs rather than letting it go and waiting for it to die . This behaviour is very different from the behaviour of other species of vipers . These snakes feed on a variety of birds and mammals , such as <unk> , many different species of rodents , including field mice and rats , as well as hares and rabbits . There are also reports of more unlikely prey items , such as tree monkeys , the brush @-@ tailed <unk> ( <unk> ) and even the small royal antelope ( <unk> ) . 
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- = = Reproduction = = 
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- During peak sexual activity , males engage in combat . This starts with one male rubbing its chin along the back of the other . The second male will then raise its head as high as possible . As they both do the same , the necks intertwine . When the heads are level , they turn towards each other and push . Their bodies intertwine as they switch positions . They become oblivious to everything else , continuing even after they fall off a surface or into water . Sometimes they intertwine and <unk> so tightly that their scales stand out from the pressure . They have also been observed to strike at each other with mouths closed . Occasionally , the combatants will tire and break off the fight by " mutual consent " , resting for a while before resuming once more . The event is settled when one of the two succeeds in pushing the other 's head to the ground and raising its own by 20 – 30 cm . In captivity , combat may occur four or five times a week until courtship and copulation ends . 
- Gestation takes about 7 months , which suggests a breeding cycle of two to three years . A five @-@ year breeding cycle may also be possible . Usually , they give birth in late summer . B. g. gabonica produces 8 – 43 live young . B. g. rhinoceros may produce as many as 60 . However , the actual number of offspring rarely exceeds 24 . <unk> are 25 – 32 cm in length and weigh 25 – 45 g . 
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- = = <unk> = = 
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- <unk> from this species are rare , due to their extremely <unk> nature and because their range is limited to <unk> areas . Due to their <unk> and unwillingness to move even when approached , bites most often occur due to persons accidentally stepping on a Gaboon viper , but even then in some cases this does not guarantee a bite . However , when a bite does occur , it should always be considered a serious medical emergency . Even an average bite from an average @-@ sized specimen is potentially fatal . <unk> should be administered as soon as possible to save the victim 's life if not the affected limb . 
- The snake 's <unk> venom itself is not considered particularly toxic based on tests conducted in mice . In mice , the LD50 is 0 @.@ 8 – 5 @.@ 0 mg / kg IV , 2 @.@ 0 mg / kg IP and 5 @.@ 0 – 6 @.@ 0 mg / kg <unk> . However , the venom glands are enormous and each bite produces the largest quantities of venom of any venomous snake ; this is partially due to the fact that , unlike many African vipers such as the <unk> <unk> , the Gaboon viper does not release after a bite , which enables it to <unk> larger amounts of venom . <unk> is probably related to body weight , as opposed to milking interval . Brown ( 1973 ) gives a venom yield range of 200 – 1000 mg ( of dried venom ) , A range of 200 – 600 mg for specimens 125 – 155 cm in length has also been reported . Spawls and Branch ( 1995 ) state from 5 to 7 ml ( 450 – 600 mg ) of venom may be <unk> in a single bite . 
- A study by Marsh and Whaler ( 1984 ) reported a maximum yield of 9 @.@ 7 ml of wet venom , which translated to <unk> mg of dried venom . They attached " <unk> " clip electrodes to the angle of the open jaw of <unk> specimens ( length 133 – 136 cm , girth 23 – 25 cm , weight 1 @.@ 3 – 3 @.@ 4 kg ) , yielding 1 @.@ 3 – 7 @.@ 6 ml ( mean 4 @.@ 4 ml ) of venom . Two to three electrical bursts within a space of five seconds apart were enough to empty the venom glands . The Gaboon vipers used for the study were <unk> between seven and 11 times over a 12 @-@ month period , during which they remained in good health and the <unk> of their venom remained the same . 
- Based on how sensitive monkeys were to the venom , Whaler ( 1971 ) estimated 14 mg of venom would be enough to kill a human being : equivalent to 0 @.@ 06 ml of venom , or 1 / 50 to 1 / 1000 of what can be obtained in a single milking . Marsh and Whaler ( 1984 ) wrote that 35 mg ( 1 / 30 of the average venom yield ) would be enough to kill a man of 70 kilograms ( 150 lb ) . Branch ( 1992 ) suggested that 90 – 100 mg would be fatal in humans . Due to the rarity of these type of <unk> , further investigation is needed . 
- In humans , a bite causes rapid and conspicuous swelling , intense pain , severe shock and local blistering . Other symptoms may include <unk> movements , defecation , <unk> , swelling of the tongue and eyelids , <unk> and unconsciousness . <unk> , bruising and <unk> may be extensive . There may be sudden <unk> , heart damage and <unk> . The blood may become <unk> with internal bleeding that may lead to <unk> and <unk> . Local tissue damage may require surgical excision and possibly <unk> . Healing may be slow and fatalities during the recovery period are not uncommon . 
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- = = Subspecies = = 
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- = = Taxonomy = = 
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- <unk> et al . ( 1999 ) discovered considerable differences between the two conventionally recognized subspecies of B. gabonica described above . According to their research , these two subspecies are as different from each other as they are from B. <unk> . Consequently , <unk> et al . ( 1999 ) regard the western form as a separate species , B. rhinoceros . 
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- 
- = The Sixth Extinction = 
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- " The Sixth Extinction " is the first episode of the seventh season of the science fiction television series The X @-@ Files . It was first shown on the Fox network on November 7 , 1999 , in the United States . The episode was written by Chris Carter and directed by Kim Manners . " The Sixth Extinction " earned a Nielsen household rating of 10 @.@ 6 , being watched by 17 @.@ 82 million people in its initial broadcast . The episode received mixed to positive reviews from critics . 
- The show centers on FBI special agents Fox Mulder ( David Duchovny ) and Dana Scully ( Gillian Anderson ) who work on cases linked to the paranormal , called X @-@ Files . Mulder is a believer in the paranormal , while the skeptical Scully has been assigned to <unk> his work . In the episode , Assistant Director Walter Skinner ( Mitch Pileggi ) and Michael Kritschgau ( John Finn ) work desperately in an attempt to discover what is wrong with Mulder , who is imprisoned by his own <unk> brain activity , but they are unaware of Agent Diana Fowley ’ s ( Mimi Rogers ) duplicity . In the meanwhile , Scully is hunting for an ancient artifact in Africa . 
- " The Sixth Extinction " helped to explore new aspects of the series ' overarching mythology and was the second episode in a trilogy of episodes featuring Mulder 's severe reaction to the appearance of an alien artifact . The episode was written due to series creator Chris Carter 's fascination with the possibility that extraterrestrials were involved in the great <unk> that had happened millions of years ago . 
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- = = Plot = = 
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- On the coast of <unk> d <unk> , Scully sits in her tent studying detailed photographs of the spacecraft half @-@ buried on the beach nearby . A figure , the <unk> African Man , mysteriously appears before suddenly vanishing , after which Scully 's tent is <unk> by flying insects . Back in the United States , Walter Skinner visits a delusional Fox Mulder , who is being kept in a padded cell at a Georgetown hospital . Mulder seemingly attacks Skinner , but actually covertly passes him a torn <unk> of his hospital gown reading , " <unk> <unk> " . 
- Scully is visited by Dr. <unk> Ngebe , Solomon <unk> 's former colleague who warns Scully to not tell any of the locals about the swarm or the <unk> African Man , although word is already out on the " African internet " . Soon afterwards , one of the locals working on excavating the ship is apparently <unk> by boiling seawater . With the arrival of Dr. Barnes another " plague " occurs – that night the ocean turns blood red . 
- Skinner <unk> a heavily drugged Mulder , who cannot talk but writes " Kritschgau " . Skinner goes to visit Kritschgau , now unemployed and living in a low @-@ cost apartment , and convinces him to visit the hospital with him . Once there , Kritschgau believes Mulder has alien induced mind reading abilities and <unk> him with a drug ( <unk> ) to slow down his brain activity . Later Diana Fowley and his doctor arrive , and with his mind @-@ reading abilities , Mulder is able to tell Skinner that he knows about him being indebted to Krycek , and Diana Fowley 's connections with the <unk> Smoking Man . 
- Scully , with Dr. Barnes ' help , is able to translate some of the spaceship , which contains information on genetics and various religions . Dr. Barnes ' behavior becomes increasingly erratic however and , armed with a machete , he refuses to let Scully or Ngebe leave . He soon realizes that the craft is bringing dead fish back to life , and Scully and Ngebe take the opportunity to knock him out and escape . Scully sees the vanishing man again in the car as they drive off . 
- Kritschgau and Skinner put Mulder under additional tests to verify his abnormal brain activity . They again <unk> Mulder with <unk> , but this time they are caught by Fowley and Mulder goes into a seizure . Meanwhile Dr. Barnes , in a bizarre type of experiment , kills his driver , but the driver soon <unk> and kills him instead . Scully flies back to the U.S. and visits Mulder at the hospital . On the African coast , Ngebe arrives with the police , finding Dr. Barnes dead and the spaceship gone . 
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- = = Production = = 
- 
- Frank Spotnitz said of the episode 's origins , " We 've destroyed all the stuff about Mulder 's father , the project , and the Syndicate . All the things that had sustained us for six years were suddenly gone . We had no crutches . From that point on , every time we sat down to write a mythology show , we knew it was going to be a completely different challenge . " Chris Carter saw the episode as a transitional episode , stating " I felt that , with " The Sixth Extinction " , I was just playing a supporting role and that the episode , essentially the middle episode of a three @-@ episode arc , was just a transitional episode to get us to ' Amor Fati ' , which was really less about the mythology and more about Mulder 's choices in life . " 
- David Duchovny and Gillian Anderson both had other commitments as the season began , resulting in the producers delaying filming for this episode . It ended up being filmed third in the season , after the episodes " <unk> " and " The Goldberg Variation " . Carter wrote the episode at the same time as Duchovny was working on the next episode , " The Sixth Extinction II : Amor Fati " . Kim Manners said preparations were confusing since it wasn 't known at the time how the storyline would unfold . Spotnitz said of the end result , " For me , it was a lot like a fifties monster movie with Scully out on the beach with this guy going nuts with a machete , the bug attacks , and the sea of blood . Yeah , it was supposed to be serious business but , overall , I thought it was shaping up as a pretty entertaining hour . " 
- The producers had to move the filming of the beach sequences from the previous episodes due to changes in the tides at that time of year . Similar to the previous episode , the spaceship was computer @-@ generated . The sequence where the locals are boiled by the ocean water was accomplished using underwater filming of <unk> under various degrees of make up . Fifty thousand dead crickets were rented from a local <unk> for the scene where Scully 's tent was attacked by bugs . The live insects were portrayed by blowing popcorn and packing <unk> at Anderson with fans and editing it into insects in post production . 
- A large portion of the episode was based on the ancient astronaut theory , which proposes that intelligent extraterrestrial beings have visited Earth in antiquity or prehistory and made contact with humans . Frank Spotnitz later remarked that he was <unk> at how little negative fan mail the show received , despite the fact that the " Biogenesis " / " The Sixth Extinction " / " Amor Fati " story arc heavily hinted that aliens were the <unk> of the notion of God and religion . He credited the manner in which the show handled this delicate subject , saying , " Often in the past , we 've done stuff where I was sure we would get angry letters . But we rarely do . And the reason is because of the way we handle things . In ' Amor Fati ' we treated the religious side with respect . " The ancient astronaut themes were later revisited in the two season nine episodes " <unk> " and " Providence . " 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " The Sixth Extinction " was first shown in the United States on November 7 , 1999 . This episode earned a Nielsen rating of 10 @.@ 6 , with a 16 share , meaning that roughly 10 @.@ 6 percent of all television @-@ equipped households , and 16 percent of households watching television , were tuned in to the episode . It was viewed by 17 @.@ 82 million viewers and was the most watched episode of the seventh season in the United States . The episode aired in the United Kingdom and Ireland on Sky1 on May 7 , 2000 , and received 1 @.@ 00 million viewers and was the third most watched episode that week . Fox promoted the episode with the tagline " Something is driving Mulder insane . Something he 's been searching for . Something he shouldn 't have found . " The episode was later included on The X @-@ Files <unk> , Volume 3 – Colonization , a DVD collection that contains episodes involved with the alien <unk> 's plans to take over the earth . 
- 
- = = = Reviews = = = 
- 
- " The Sixth Extinction " received mixed to positive reviews from critics . Tom <unk> , in his book Examination : An <unk> Look at Seasons 6 – 9 of the X @-@ Files gave the episode a positive review , noting that the episode takes the themes of " Biogenesis " and " runs with them " . Ken Tucker of Entertainment Weekly awarded the episode a " B + " . Tucker praised series creator Chris Carter 's writing ability , stating that " the kickoff episode suggests the author 's <unk> imagination for sustaining his alien @-@ nation tropes " . Robert Shearman and Lars Pearson , in their book Wanting to Believe : A Critical Guide to The X @-@ Files , Millennium & The Lone Gunmen , rated the episode three @-@ and @-@ a @-@ half stars out of five . The two called the episode " the most arresting season opener in years " and noted that it " promises that maybe there 's life in the old mythology yet . " Shearman and Pearson , however , did critique the lack of <unk> to the episode , but attributed most of this to the fact that the episode was the second of a three @-@ part mythology tale . Den of Geek writer Nina <unk> ranked " The Sixth Extinction , " along with " Biogenesis " and " The Sixth Extinction II : Amor Fati , " as the fifth best episode of the series , writing , " it is evident that as [ The X @-@ Files ] progressed , the episodes surrounding those storylines and the breaking points Mulder and Scully endured push them further and further towards total , <unk> defeat . This is especially poignant when viewing this anxiety inducing trio of episodes . " Monica S. <unk> of Exclaim magazine called " The Sixth Extinction " , along with " Biogenesis " and " Amor Fati " , one of the " best " episodes during the show 's " colonization " phase . Kenneth <unk> of <unk> wrote positively of the episode , hoping that it foreshadowed answers to come , writing " As the middle installment of a three @-@ part story and what was then thought to be the final season premiere of The X @-@ Files , ' The Sixth Extinction ' is <unk> with a somber pre @-@ apocalyptic mood , but one <unk> by the possibility that soon we 'll have answers to the most important outstanding mysteries of the series . " 
- Not all reviews were so positive . Paula Vitaris from <unk> gave the episode a negative review and awarded it one @-@ and @-@ a @-@ half stars out of four . She noted that " Mulder 's situation is handled just poorly " and argued that Mulder 's illness was created solely to provide a cross @-@ season <unk> . Todd VanDerWerff of The A.V. Club awarded the episode a " C " and called it a " weird bridge " . Largely , he criticized the episode for featuring " no real twists or complications here " in lieu of " things just [ … ] getting worse along a linear path " . 
- 
- 
- = Snow ( visual novel ) = 
- 
- Snow ( <unk> , <unk> ) is a Japanese adult visual novel developed by Studio <unk> for Windows <unk> on January 31 , 2003 . It was later ported without the erotic content to the <unk> , PlayStation 2 , and PlayStation Portable consoles . The story of Snow revolves around the life of Kanata Izumo , who is revisiting a village to help his relative manage a hot spring hotel . The gameplay in Snow follows a branching plot line which offers pre @-@ determined scenarios with courses of interaction , and focuses on the appeal of the female main characters by the player character . 
- The game was successful in both sales and popularity , it was ranked as the best @-@ selling PC game sold in Japan at the time of its release , and charted in the national top 50 several more times afterwards . Snow was voted as the twenty @-@ sixth best bishōjo game by the readers of Dengeki G 's Magazine in 2007 . A manga titled Snow : Pure White based on the visual novel was serialized in <unk> between the September and December 2003 issues . Five light novels and several comic anthologies were also released , as were audio dramas . 
- 
- = = Gameplay = = 
- 
- Snow is a romance visual novel in which the player assumes the role of Kanata Izumo . Much of its gameplay is spent reading the text that appears on the screen , which represents the story 's narrative and dialogue . The text is accompanied by character sprites , which represent who Kanata is talking to , over background art . Throughout the game , the player encounters CG artwork at certain points in the story , which take the place of the background art and character sprites . 
- There are four main plot lines in the original Windows release that the player will have the chance to experience , one for each of the <unk> in the story . Throughout gameplay , the player is given multiple options to choose from , and text progression <unk> at these points until a choice is made . Some decisions can lead the game to end prematurely and offer an alternative ending to the plot . To view all plot lines in their entirety , the player will have to replay the game multiple times and choose different choices to further the plot to an alternate direction . In adult versions of the game , there are scenes depicting Kanata and a given heroine having sex . 
- 
- = = Plot = = 
- 
- 
- = = = Story = = = 
- 
- The story of Snow starts when Kanata Izumo , the main protagonist , <unk> a small village called Ryūjinmura ( <unk> , lit . " village of the dragon god " ) , to help manage a local hotel with hot springs alongside his cousin Tsugumi . In the village , there is an old legend : In ancient times , Ryūjinmura was protected by the dragon goddess . However , one day the goddess fell in love with a human , which is strictly forbidden , and this incident caused the village to be always covered by snow after that . The game then tells the daily life of Kanata interacting with the village girls , and ultimately links the story to the legend . Snow follows a branching plot line with multiple endings , and depending on the decisions that the player makes during the game , the plot will progress in a specific direction . 
- 
- = = = Main characters = = = 
- 
- The player assumes the role of Kanata Izumo ( <unk> <unk> , Izumo Kanata , voiced by <unk> Hoshi ) , who is visiting Ryūjinmura as a part @-@ time worker of the local hotel owned by his cousin . During his stay , he meets his childhood friend Sumino Yukizuki ( <unk> <unk> , Yukizuki Sumino , voiced by <unk> <unk> ) , a <unk> and gentle girl whose father has died . She loves <unk> ( a kind of <unk> ) and claims it as " source of life " . Kanata also encounters a young energetic girl with a <unk> personality called Asahi Hiyorigawa ( <unk> <unk> , Hiyorigawa <unk> , voiced by <unk> Tamura ) , suddenly appearing before Kanata and claiming that she will " repulse the evil " from him . 
- In the outskirts of the village , Kanata meets the mysterious Shigure <unk> ( <unk> <unk> , <unk> Shigure , voiced by <unk> <unk> ) ; Shigure has a shy personality and is extremely silent . The protagonist also finds a little girl called <unk> <unk> ( <unk> <unk> , <unk> <unk> , voiced by Tomoko <unk> ) waiting around the <unk> for her parents . She likes to play with her cat called <unk> ( <unk> ) . The physician of <unk> has a daughter called Meiko Tachibana ( <unk> <unk> , Tachibana Meiko , voiced by Naoko <unk> ) , who is a close friend of Sumino . She likes to <unk> Kanata , and acts <unk> in front of him . In Snow : Plus Edition , a new character referred to as <unk> Girl ( <unk> , <unk> no <unk> , voiced by <unk> Ono ) is introduced , bearing many secrets . 
- 
- = = Development and release = = 
- 
- After the completion of <unk> , Studio <unk> ( a brand under <unk> 's ) started production on Snow . The artists for the visual novel were Asuka <unk> , who also drew the character designs , and <unk> . The scenario was written by three members of staff : <unk> Jet , Klein , and <unk> <unk> . I 've Sound , as well as T & N Music Factory , <unk> , and <unk> contributed to the music composition . According to the liner notes on the original soundtrack of Snow , the producers acknowledged that the game 's structure resembled Key 's visual novels <unk> and Air . <unk> <unk> , who is known for his work on Air , <unk> , and <unk> : The <unk> of a Little Planet , provided scenario assistance for Snow . 
- On January 31 , 2003 , Snow was released as both a CD @-@ ROM and DVD @-@ ROM compatible to the Windows 98 / <unk> / 2000 / <unk> operating systems . An all @-@ ages version for the <unk> was released by <unk> on September 25 , 2003 . Later on February 26 , 2004 , <unk> ported the game to the PlayStation 2 in both limited and regular editions ; the PS2 port adds an additional scenario for Meiko Tachibana , who was a supporting character in prior releases of Snow . A full voice version was released for Windows on September 24 , 2004 . Then on September 29 , 2006 , Snow : Plus Edition was released for Windows , sporting added parts from the console versions and introducing a new heroine . Snow is playable on <unk> <unk> 's <unk> cell phones . A PlayStation Portable version of the game called Snow : Portable was released by <unk> on August 16 , 2007 . The standard edition for Windows was released on July 25 , 2008 , containing two fan discs , and the content from the portable edition of Snow . Finally , a downloadable version of the PSP edition was made available on the PlayStation Store by <unk> on January 21 , 2010 . 
- 
- = = Related media = = 
- 
- 
- = = = Books and publications = = = 
- 
- Five adult light novels written by <unk> <unk> and published by <unk> were released between June 2003 and January 2004 . The cover art and internal illustrations were drawn by Asuka <unk> , the artist who drew the artwork in the visual novel . The first novel , titled Snow : <unk> ( Snow ～ <unk> ～ , Sumino ) , was released on June 25 , 2003 . The second novel was released on July 25 , 2003 , titled Snow : <unk> <unk> ( Snow ～ <unk> ～ , Asahi ) . The third novel , titled Snow : <unk> no <unk> ( Snow ～ <unk> ～ ) , was released on September 20 , 2003 . The fourth novel , titled Snow : <unk> no <unk> ( Snow ～ <unk> ～ , Shigure ) , was released on November 22 , 2003 . The fifth and final novel , titled Snow : Sora no Yurikago ( Snow ～ <unk> ～ , <unk> ) , was released on January 16 , 2004 . A 192 @-@ page art book , titled Snow Art Works , and containing information such as story and character explanations , and images from the visual novel , was released by <unk> on July 25 , 2003 . 
- 
- = = = Manga = = = 
- 
- A game @-@ based manga adaptation titled Snow : Pure White , and illustrated by Yuki <unk> , was serialized between the September and December 2003 issues of <unk> . The individual chapters were later compiled into a single bound volume published by Kadokawa Shoten under the Kadokawa Comics Ace imprint on December 19 , 2003 . 
- <unk> released a stand @-@ alone comic anthology , titled Snow Anthology Game Comics , under the Twin Heart Comics imprint on March 22 , 2003 . A two @-@ volume anthology series , titled Snow Comic Anthology , was released by <unk> under the DNA Media Comics imprint between April 25 and June 25 , 2003 . The now @-@ defunct publisher <unk> released a comic anthology , titled Snow Game Comic , spanning two volumes in 2003 between April 25 and May 24 under the <unk> Comics imprint . <unk> released a single anthology , titled Snow Anthology Comic , under the <unk> @-@ <unk> Comics imprint on May 26 , 2003 . 
- 
- = = = Music and audio CDs = = = 
- 
- The opening theme for Snow is " Snow " sung by <unk> <unk> , who also provided vocals for the ending themes " <unk> no <unk> " ( <unk> ) and " Yuki no Kanata " ( <unk> ) . The insert song " Sora no Yurikago " ( <unk> ) is by <unk> <unk> ; a single titled Snow Extra CD was released at <unk> 64 on August 15 , 2003 , containing " Sora no Yurikago " . These tracks were included with other background music in the Snow Original Soundtrack released on April 25 , 2003 . Before the visual novel 's release , Snow Image Album was released at <unk> 63 on December 28 , 2002 . 
- Three drama CDs based on Snow have been published , the first CD volume was released by <unk> on August 22 , 2003 , focusing on Sumino Yukizuki . <unk> released a second volume focusing on Asahi Hiyorigawa , on October 24 , 2003 . The third and final volume , telling the story of Ryūjinmura 's legend , was released by Frontier Works on August 25 , 2004 . 
- 
- = = Reception = = 
- 
- In a national sales ranking of bishōjo games conducted by <unk> , Snow DVD @-@ ROM premiered at number one , whilst the CD @-@ ROM release ranked at number three . During the beginning of February , Snow CD @-@ ROM 's ranking fell to number eight , whilst Snow DVD @-@ ROM charted just above at number seven . Snow DVD @-@ ROM charted again at number thirty one during mid @-@ February , whilst the CD @-@ ROM release had low enough sales to not chart . Finally , both the Snow CD @-@ ROM and Snow DVD @-@ ROM releases made their final charting appearances at number thirty three and forty six at the beginning of March , respectively . 
- Snow was the second most widely sold game of 2003 on <unk> , a major <unk> of visual novel and domestic anime products . According to sales information taken from the Japanese Amazon website , Snow sold 64 @,@ 526 copies in 2003 . In the October 2007 issue of Dengeki G 's Magazine , poll results for the 50 best bishōjo games were released . Snow ranked No. 26 out of 249 titles , with 11 votes . Snow is featured in the <unk> Trading Card Game ; a set of playable Snow cards are included in the <unk> 's 1 @.@ 1 <unk> pack . Snow is considered a classic " <unk> " game . 
- 
- 
- = Miss Meyers = 
- 
- Miss Meyers ( 1949 – March 1963 ) was an American Quarter Horse racehorse and broodmare , the 1953 World Champion Quarter Running Horse . She won $ 28 @,@ 725 ( equivalent to about $ 254 @,@ 000 as of 2016 ) as well as 17 races . As a broodmare , she produced , or was the mother of , the first American Quarter Horse Association ( AQHA ) Supreme Champion , Kid Meyers . She was the mother of three other foals , and was inducted into the AQHA Hall of Fame in 2009 . 
- 
- = = Early life = = 
- 
- Miss Meyers was a chestnut @-@ colored mare born in 1949 and sired , or <unk> , by Leo , a member of the AQHA Hall of Fame . Miss Meyers ' dam , or mother , was Star 's Lou . Star 's Lou 's father was Oklahoma Star P @-@ 6 , another AQHA Hall of Fame member . She was bred by O. C. Meyer , and later owned by Bruce A. Green . 
- 
- = = Race career = = 
- 
- Miss Meyers raced from 1952 until 1955 and won seven stakes races , placing second in seven others , and third in two more . She won 17 of her 59 starts on the racetrack . She placed second in another 15 races and third in 5 . Her total earnings on the track were $ 28 @,@ <unk> ( approximately $ 253 @,@ 800 in 2016 dollars ) . Among her wins in stakes races were the 1952 <unk> and <unk> Stakes , the 1953 California Championship , the 1953 Billy <unk> Stakes , the 1953 Rocky Mountain Quarter Horse Association World Championship <unk> , the 1955 Bart B Stakes , the 1955 Barbara B Stakes and the 1955 Traveler Stakes . She set four track records , twice at 350 yards ( 320 m ) , once at 400 yards ( 370 m ) , and once at 440 yards ( 400 m ) . In 1953 she was named the AQHA World Champion Quarter Running Horse , as well as the High Money <unk> Horse ; the AQHA also awarded her the title of Superior Race Horse in 1954 . The highest speed index she achieved , a measure of how fast she was able to run , during her racing career was AAAT , the highest possible at the time . It was not until she was a four @-@ year @-@ old , during 1953 , that Miss Meyers performed well and started winning on the track . That year she won $ 15 @,@ 398 ( approximately $ 136 @,@ 200 in 2016 ) dollars , over half her lifetime earnings , as well as seven of her seventeen career wins . 
- 
- = = <unk> and legacy = = 
- 
- After Miss Meyers retired from the racetrack , she became the dam of the first AQHA Supreme Champion , Kid Meyers , sired by fellow Hall of Famer Three <unk> , a Thoroughbred . ( A Supreme Champion is a horse that is outstanding on the racetrack , as a riding horse at horse shows and also <unk> , or how well put together the horse is ) . Kid Meyers was a 1963 <unk> stallion , and had 23 starts on the racetrack , winning 6 times . He earned a total of $ 10 @,@ <unk> ( approximately $ 77 @,@ 700 as of 2016 dollars ) on the track . After retiring from the racetrack , he earned his AQHA Champion in 1966 and his AQHA Supreme Champion in 1967 . His highest speed index was AAA . Unlike most foals , who nurse for months after birth , Kid Meyers was orphaned at the age of one month in March 1963 . 
- Miss Meyers had three other foals . Oh My Oh , a 1957 bay mare sired by the Thoroughbred stallion <unk> Bull , started 30 times , winning eight races for a total earnings of $ 12 @,@ 592 ( approximately $ 99 @,@ 700 as of 2016 dollars ) and coming in second in a stakes race . She earned an AAAT speed index . As a broodmare , she was the dam of All American <unk> winner Three Oh 's . Miss Meyers ' 1958 foal was Mr Meyers , a <unk> stallion sired by fellow Hall of Famer Go Man Go , who started 41 times , winning 9 times and placing third in four stakes races . His total race earnings were $ 25 @,@ 656 ( approximately $ 200 @,@ 700 as of 2016 dollars ) . He went on to earn an AQHA Champion title along with a Superior Race Horse award , to go with his AAAT speed index . Mr Meyers became a successful breeding stallion . Miss Meyers ' fourth foal was a 1959 chestnut mare named <unk> sired by <unk> . <unk> was never raced or entered in a horse show . 
- Miss Meyers died in March 1963 , shortly after having Kid Meyers . She was inducted into the AQHA 's American Quarter Horse Hall of Fame in 2009 . 
- 
- = = <unk> = = 
- 
- 
- 
- = Far Away Places ( Mad Men ) = 
- 
- " Far Away Places " is the sixth episode of the fifth season of the American television drama series Mad Men and the <unk> episode of the series overall . It was written by series creator and executive producer Matthew Weiner and writer <unk> <unk> , and directed by Scott <unk> . It originally aired on AMC in the United States on April 22 , 2012 . 
- The episode takes place almost entirely over a single day , telling three stories in a non @-@ linear narrative . Peggy becomes alienated after separate fights with her boyfriend Abe and a client . Roger and Jane take LSD with a group of intellectuals , altering how they see the world and allowing them to speak <unk> about their marriage . Don and Megan leave the office and take an impromptu road trip to Plattsburgh , New York , which doesn 't turn out as planned after Megan becomes <unk> with how Don treats her . 
- " Far Away Places " was watched by 2 @.@ 6 million viewers and achieved 0 @.@ 9 million viewers in the key 18 – 49 demographic . The episode received overwhelming critical acclaim , with many critics noting the episode 's formal experiments with and focus on the passage of time . The sequence where Roger and Jane take LSD was particularly celebrated for the visual excellence and performances by John <unk> and <unk> List . The theme of the episode was <unk> by the series writers and television journalists as the desire to escape . 
- 
- = = Plot = = 
- 
- " Far Away Places " is split into three vignettes that take place almost entirely during a single day following the characters Peggy , Roger , and Don . 
- 
- = = = Peggy = = = 
- 
- The episode begins with Peggy 's day and a heated argument with her boyfriend Abe , over her preoccupation with work and the effect this has on their sex life . The argument ends with Abe leaving in a <unk> . After Don pulls Megan away for an impromptu trip to a Howard Johnson 's hotel in far northern New York , Peggy has to pitch to the Heinz executives without them . Peggy unsuccessfully tries to sell the Heinz executives on her concept . Frustrated , she leaves work , has a couple of drinks , and goes to see the film Born Free . She sees a young man smoking marijuana behind her , <unk> herself , and eventually gives him a hand job . She returns to her office to see Ginsberg arguing with his father . After being told off by Ginsberg for <unk> , she falls asleep in Don 's office . She <unk> to a call from Don , who seems alarmed for incomprehensible reasons . Don hangs up on her and Peggy returns to her office . When Peggy asks about Ginsberg 's life , Ginsberg initially claims to be a Martian , stating that Morris Ginsberg is not his father . Michael was born in a Nazi concentration camp and , after its liberation , Morris claimed him from a Swedish orphanage when Michael was five years old . Peggy returns home , affected by this story , and calls Abe . She tells him about Ginsberg and asks him to come over to be with her . 
- 
- = = = Roger = = = 
- 
- From Roger 's perspective of the day , he invites Don to go on a trip with him to a Howard Johnson 's in Plattsburgh , New York , hoping to get out of a dinner party with his wife Jane 's " <unk> friends " and is subsequently disappointed when Don decides to take Megan on the trip instead . Roger and Jane go to the party , which is hosted by Jane 's therapist and her husband . After dinner , Roger asks Jane if they can leave , but Jane reminds Roger that he agreed to take LSD with the group and begs him to stay , as she doesn 't want to go through the experience alone . Roger is initially unimpressed with the drug , but comes around after his consciousness begins to change with vivid audio @-@ visual hallucinations . Roger and Jane return home via taxi and take a bath together , during which Roger imagines he is watching the 1919 World Series from the bathtub . The couple then talks <unk> about their marriage for the first time . During this moment of awareness , Jane admits that she knows the marriage is over . The next morning , a <unk> Roger says goodbye to a shocked Jane , who appears <unk> about what she said the night before . 
- 
- = = = Don = = = 
- 
- The episode 's finale is Don 's day and the trip to Plattsburgh and Howard Johnson 's Restaurant and Motor Lodge . As he and Megan eat in the restaurant , Megan expresses her frustration at having her needs and desires take a back seat to Don 's . The discussion escalates into a fight , during which Megan makes a <unk> remark about Don 's mother , and Don storms out and drives off without her . Don returns sometime later and begins to worry when he can 't find Megan . He spends hours looking for and waiting for her , calling Peggy ( the other side of the conversation from the first part of the episode ) as well as Megan 's mother in Montreal . 
- After waiting for hours at Howard Johnson 's and <unk> home repeatedly , Don drives home in the early morning to find Megan in their apartment with the security chain on the door . Don kicks the door in , <unk> struggles with Megan , and chases her through the apartment . Megan and Don trip and collapse on the floor as Megan <unk> . Don <unk> hugs her at the waist and tells her he thought he had lost her . 
- 
- = = = <unk> = = = 
- 
- That morning , Megan and Don return to Sterling Cooper Draper Pryce . Don is <unk> to the conference room , where Bert Cooper <unk> him for being " on love leave " . Don replies that his love life is none of Bert 's business . Bert retorts that it is , in fact , <unk> business and admits <unk> that the firm is running as well as it is with how little Don is actually working . Bert leaves Don standing alone in the conference room as Don looks through the picture windows at the employees going about their business . Roger , full of enthusiasm , <unk> into the conference room and tells Don he has an announcement : " It 's going to be a beautiful day ! " 
- 
- = = Production = = 
- 
- Creator Matthew Weiner said " Far Away Places " was inspired by " <unk> French films " with " lots of short stories in them " , with all three short stories linked by a thematic " desire to go away " . He further explained that " Peggy has this moment where she tries to be Don and fails and then goes on Peggy 's version of Don – sexually irresponsible , and drunk , and working " . Elisabeth Moss said the <unk> Peggy gives a stranger in the theatre is a " moment of forgetting " after the frustrating Heinz pitch . 
- Weiner spoke about the structure of the episode in June 2012 : 
- <unk> , I love this French movie by Max <unk> called Le <unk> . It 's three or four Guy de <unk> stories that are told by a narrator , and then characters start to appear behind each other , their stories overlap and they are just walking through , and you realize it 's a complete world . What I loved about that was just telling the story from that one person ’ s point of view . In Peggy 's story , she 's in every scene , nothing happens without her there . And it 's the same thing with Don and the same thing with Roger . So you 're really getting this very private perspective , and then thematically holding it together by saying , " Here , this is about the status of the relationship . " We weren 't sure that it was going to work . The hardest part was breaking it up for commercials so that the Peggy and the Roger stories would be in the same segment and you wouldn 't come back and think you were in the middle of another episode . 
- He spoke about the writing of a scene of the episode in August 2012 : 
- I had , in the writers room , given this speech about Ginsberg saying he was a Martian . I delivered it as Ginsberg . We knew that Peggy 's story was going to climax with that , and it was going to be their great moment of intimacy ; he would distract her from her failure and bond with her in that strange way that people who feel separate do . Then , when we were writing the draft , I got the notes from the room , and the speech was like one sentence . We searched everywhere , and it turned out I had never pitched more than that one sentence : " I 'm a Martian . " I had a great version of it , but it turned out it had all been in my head in one way or another . It all had to come from scratch . Once I reduced the panic and tried to re @-@ create it , it did happen . So , to me , it still has a magical quality to it . 
- Weiner characterized Roger 's acid trip as an experience of " complete honesty " and an " experience of empathy , something he 's probably never experienced in his life . He doesn 't see the world through other people 's lives and that kind of <unk> to me is very beautiful , even though it 's the end of the relationship . They are alone in the truth together " . 
- While discussing the fight between Don and Megan , Weiner commented on the violence and passion , noting that " what you get is that Don loves this woman " and that Megan is " everything that 's good to him " . Jessica <unk> commented on Don 's lack of respect for her work , and Jon <unk> judged Don 's actions as " immature " . However , <unk> regarded Don 's fear as " genuine " when he is unsure of Megan 's whereabouts . The flashback scene between Don and Megan in the car was actually shot for the fourth season finale , " <unk> , written and directed by Weiner , but was cut . Weiner decided to <unk> this scene into the episode as a flashback . 
- The exterior scenes of the Howard Johnson 's hotel were filmed in October 2011 at the Regency Inn and <unk> in Baldwin Park , California . The hotel operated as a Howard Johnson 's from 1967 until it was sold in 1995 . 
- Editor Christopher Gay spoke about the episode in August 2012 : 
- <unk> speaking , we wanted the stories of these three relations of Peggy , Roger , and Don to be their own story . We wanted to give you a little bit more each time you saw each of the three story lines so that when you got to the third one , everything totally made sense . I 've talked to people and they 've had to watch it a few times to fully digest . Also , the score in the episode is pretty unique and more tonal and atmospheric than what we normally do . It 's a guide , too , that helps you feel when one story is ending and another is coming in and knowing that the shift is happening . I think the score and the sound design definitely helped guide the narrative . 
- 
- = = Reception = = 
- 
- 
- = = = Critical reception = = = 
- 
- The episode received overwhelmingly laudatory reviews from television critics , particularly for its unusual departure from the standard Mad Men episode structure and is considered to be one of the best in the series . Alan Sepinwall of HitFix exalted the episode and the " more formally experimental " season , admitting , " I 'm still not sure I understood 100 % of it . But I know I liked it . A lot . " Sepinwall characterized " Far Away Places " as " an episode that gave the feel of dropping acid even when everyone on camera was stone <unk> . Matt Weiner , co @-@ writer <unk> <unk> , director Scott <unk> , and the actors combined to give us some of the most memorable moments the show has ever done . " Todd <unk> of The A.V. Club gave it an A grade , compared it to previous " structurally daring " episodes like " Seven Twenty Three " and " The Jet Set " , and praised the director for the " beautifully shot " episode and the " gorgeous image of [ Roger ] and Jane lying , heads touching , on the floor , admitting their marriage just isn ’ t working " , while noting that the enemy of the season is " the passage of time itself " . 
- Verne Gay of Newsday called it a good , but difficult episode , saying , " the story lines were all parallel – it was even an anthology , with each story mirroring the next ( bringing to mind that memorable scene when Roger , under the influence , is looking in the mirror and told to look away ) ... the themes of male @-@ female <unk> , and <unk> ( and yes , hair , once again is a predominant metaphor . ) The themes of travel ... of being a stranger in a strange land ... of life on Mars , or in Plattsburgh ... of alienation , pursuit , and of a generation born during the Holocaust , amid the Holocaust " . Tim Goodman of The Hollywood Reporter said the LSD trip " was handled brilliantly here , with insight , surprises , unpredictability , excellent humor and a really lovely , smart ending " and the image of Jane and Roger on the floor as an example of the " visual excellence " of the episode . TIME magazine writer Nate Rawlings compared the episode to a David Lynch film and noted that all three " stories also shared the thematic connection of the struggle between professional and work life . " 
- IGN reviewer Eric Goldman praised the performances of John <unk> and <unk> List and said the episode " took three of the show 's best characters and <unk> their worlds in very different ways , telling three separate stories that were all utterly involving and moving , and delivering one of the show 's best episodes ever in the process . This show continues to operate on a level few other series could dream of ... " . Roger Friedman of Forbes called the LSD sequences with Jane and Roger to be " so well @-@ written they were kind of transcendent " . Salon writer Nellie <unk> acclaimed the episode , while pointing out that " with this episode ’ s tilt of the <unk> back to the older generation from the <unk> youngsters , we ’ re reminded that while the 1960s saw a cultural shift towards youth , like a drunk , no historical change walks a straight line . For all the claims that Don and others have made that the “ kids ” increasingly hold the cards , the real truth ( if we ’ re telling it ) is that older white guys like Bert and Roger never truly lost power , even if they began to hide behind the scenes while fresh young faces took the public glory . " 
- 
- = = = Ratings = = = 
- 
- The episode was viewed by 2 @.@ 6 million viewers on the night of its original airing . It drew 0 @.@ 9 million viewers in the 18 – 49 demographic . 
- 
- = = = Awards = = = 
- 
- " Far Away Places " was nominated for two Primetime Emmy Awards , for Outstanding Writing for a Drama Series and Outstanding Single @-@ Camera Picture Editing for a Drama Series . 
- 
- 
- = Awakening ( Star Trek : Enterprise ) = 
- 
- " Awakening " is the eighth episode of the fourth season of the American science fiction television series Star Trek : Enterprise , and originally aired on November 26 , 2004 on UPN . The script was written by André Bormanis and the episode was directed by Star Trek : Voyager alumni <unk> Dawson . The episode was the first of the season for both Bormanis and Dawson . The episode is the second of a three @-@ part episode arc which started in " The Forge " and concludes in " Kir 'Shara " . 
- Set in the 22nd century , the series follows the adventures of the first Starfleet starship Enterprise , registration NX @-@ 01 . In this episode , the Vulcan government seek to make the Enterprise leave orbit so they can attack a renegade faction of Vulcans , and afterwards the long @-@ standing enemy of the Vulcans , the <unk> . Meanwhile , Captain Jonathan Archer and Commander T 'Pol have been captured by the Syrrannites , and it is discovered that Archer has the katra of Surak . He has visions which lead him to find an ancient Vulcan <unk> called the " Kir 'Shara " as the group come under attack from the Vulcans . 
- Elements of the plot of the episode were compared by executive producer to the Protestant Reformation with the Vulcan High Command representing the Catholic Church . The producers took care to cast actors in the roles of T 'Pau and Surak who looked similar to the actors who portrayed those parts in The Original Series . Nielsen ratings for the first run of the episode saw a decrease from the first part of the trilogy , down to 1 @.@ 8 / 3 . The critical response was mixed , saying that whilst they were entertained by the episode , there were several elements in the plot which were problematic . 
- 
- = = Plot = = 
- 
- Ambassador Soval is summoned before Administrator V 'Las and the High Council to face punishment over his use of a mind meld . Since the act is widely considered to be criminal by the Vulcan authorities , Soval is summarily dismissed from the <unk> service . Meanwhile , Captain Archer and Commander T 'Pol are questioned by the Syrrannites . After a short while , T 'Pol is taken to see her mother , T 'Les , and the two disagree about the tenets of the group — the Vulcan authorities call them <unk> , a term T 'Les <unk> with . Soon , Archer begins to see visions of an old Vulcan , and the dissidents determine that he had the katra of Surak transferred into him via mind meld . 
- V 'Las , now largely unopposed on the Council , becomes increasingly obsessed with decisively ending the <unk> threat once and for all . He <unk> his plans to bombard the encampment , after delays in convincing Enterprise to leave orbit . He contacts Starfleet , and the Admiralty give Commander Tucker direct orders , which he refuses to carry out directly . He attempts , with assistance from Soval , to send a rescue <unk> to " The Forge " , but they are intercepted by Vulcan patrol vessels . V 'Las then finally orders Vulcan warships to directly engage Enterprise , and Soval suggests that they should retreat before they are severely damaged . 
- A ritual is performed to transfer the katra into the mind of T 'Pau , but the attempt fails . Archer continues to see Surak who informs him that he must find the relic known as the " Kir 'Shara " . The Vulcan military begin to bombard the complex . Archer , T 'Pol , and T 'Pau remain behind to search for the relic , and Archer is able to use his knowledge to unlock a door to reveal it . As they exit , T 'Pol finds her mother , but she soon dies after being seriously injured in the attack . On Enterprise , Soval reveals that the Vulcans , despite the recent peace accord , are preparing a pre @-@ <unk> strike against the <unk> , and Tucker orders an immediate course at maximum <unk> . 
- 
- = = Production = = 
- 
- " Awakening " was the second part of a three @-@ part trilogy of episodes during the fourth season of Enterprise that were created to deal with the differences between the Enterprise @-@ era Vulcans and those seen in series set later in the <unk> of the franchise . Show runner Manny <unk> summed up these differences saying , " Our Vulcans lie , our Vulcans are <unk> , our Vulcans are not <unk> . " He sought to introduce a situation which he compared to the 16th @-@ century Protestant Reformation and wanted to include a Vulcan character who would effectively be in the role of Martin Luther , while the Vulcan High Command represented the Catholic Church . 
- The episode saw the re @-@ casting of two roles which had previously appeared in episodes of The Original Series . These were the parts of T 'Pau and Surak . T 'Pau made her first appearance in " Amok Time " , where she was played by Celia <unk> . For " Awakening " , Kara Zediker was cast in the role . Zediker had previously appeared in the first season of 24 as Elizabeth Nash . Surak had been played by Barry <unk> in " The Savage Curtain " , but this role was taken by Bruce Gray for " Awakening " . Gray had previously portrayed Admiral <unk> in The Next Generation episode " Gambit " and the Deep Space Nine episode " The Circle " . For both of these parts , the producers attempted to cast actors who looked similar to the originals . 
- Robert Foxworth reprises his role from the first part of the trilogy as Administrator V 'Las , and Joanna <unk> had previously portrayed T 'Les earlier in the season in the episode " Home " . John Rubinstein , who appears in " Awakening " as <unk> , has previously appeared as a <unk> earlier in the series in the episode " Fallen Hero " and had appeared in " The 37 's " , an episode of Voyager . Gary Graham returned as Soval , who he has portrayed throughout the series from the pilot episode onwards . Director <unk> Dawson has previously portrayed B <unk> Torres in Voyager , and " Awakening " marked the tenth episode of the series that she had directed . It was her only episode of season four , and her last on Enterprise . It was also writer André Bormanis ' first episode of the season , who had previously written several episodes of the series as well as Voyager . 
- Filming started on September 23 , 2004 , and concentrated on Enterprise ship scenes on the standing sets for the first two days . After that production moved to the cave sets , which were dressed with Vulcan artifacts . On the fourth day of production , those artifacts were removed so that the same sets could use used to film the visions that Archer has of Surak . All exterior scenes in the Vulcan desert @-@ like Forge were shot on a soundstage . The final day of filming took place on October 1 , when all the scenes set in the Vulcan High Command were filmed . These involved only Foxworth , Graham and Rubinstein as well as a handful of extras . 
- 
- = = Reception and home media = = 
- 
- " Awakening " was first aired in the United States on UPN on November 26 , 2004 . The broadcast saw the episode come in fifth place during the timeslot , with a Nielsen rating of 1 @.@ 8 / 3 . This means that it was seen by 1 @.@ 8 percent of all households , and 3 percent of all of those watching television at the time of the broadcast . It gained higher ratings than The WB , which aired re @-@ runs of What I Like About You and <unk> for Life , but was behind the other four major networks with NBC 's Dateline winning the hour with ratings of 5 @.@ 9 / 11 . The ratings received by Enterprise continued a downward trend in recent episodes , with ratings of 1 @.@ 9 / 3 received by the previous episode . 
- Michelle Erica Green , reviewing the episode for TrekNation was undecided about whether the main point of the episode where Captain Archer is expected to lead the Vulcans back to their main path of logic was a " wonderfully progressive concept or just <unk> Trek in which humans have all the answers " . She thought that the change in Soval 's opinions in this episode was difficult to accept and that the other members of the main cast didn 't get a great deal to do in the episode . However , whilst she thought that " Awakening " was a visual improvement over " The Forge " , she was reserving judgement until she had seen the third part of the trilogy . Jamahl Epsicokhan at his website " Jammer 's Reviews " thought that certain elements of the plot didn 't follow logical paths , such as how Archer found the Kir <unk> in a few minutes when apparently the Syrrannites had been looking for it for two years even though " it sits in a chamber behind a door that practically announces , ' <unk> <unk> <unk> ' ? " However , he called the episode entertaining and gave it a score of three out of four . 
- The first home media release of " The Forge " was in the season four DVD box set of Enterprise , originally released in the United States on November 1 , 2005 . The Blu @-@ ray edition was released on April 1 , 2014 . 
- 
- 
- = Mole cricket = 
- 
- Mole crickets are members of the insect family <unk> , in the order <unk> ( grasshoppers , locusts and crickets ) . Mole crickets are cylindrical @-@ bodied insects about 3 – 5 centimetres ( 1 @.@ 2 – 2 @.@ 0 in ) long , with small eyes and shovel @-@ like forelimbs highly developed for burrowing . They are present in many parts of the world and where they have been introduced into new regions , may become agricultural pests . 
- Mole crickets have three life stages , eggs , nymphs and adults . Most of their life in these stages is spent underground but adults have wings and disperse in the breeding season . They vary in their diet ; some species are vegetarian , mainly feeding on roots , others are <unk> , including worms and <unk> in their diet , while a few are largely <unk> . Male mole crickets have an exceptionally loud song ; they sing from a sub @-@ surface burrow that opens out into the air in the shape of an exponential horn . The song is an almost pure tone , <unk> into chirps . It is used to attract females , either for mating , or for indicating favourable habitats for them to lay their eggs . 
- In Zambia , mole crickets are thought to bring good fortune , while in Latin America they are said to predict rain . In Florida , where Scapteriscus mole crickets are non @-@ native , they are considered pests , and various biological controls have been used . Gryllotalpa species have been used as food in West Java , Vietnam , and the Philippines . 
- 
- = = Description = = 
- 
- Mole crickets vary in size and appearance , but most of them are of moderate size for an insect , typically between 3 @.@ 2 and 3 @.@ 5 cm ( 1 @.@ 3 and 1 @.@ 4 in ) long as adults . They are adapted for underground life and are cylindrical in shape and covered with fine , dense hairs . The head , forelimbs , and <unk> are heavily <unk> but the abdomen is rather soft . The head bears two threadlike <unk> and a pair of <unk> eyes . The two pairs of wings are folded flat over the abdomen ; in most species , the fore wings are short and rounded and the hind wings are <unk> and reach or exceed the tip of the abdomen ; however , in some species the hind wings are reduced in size and the insect is unable to fly . The fore legs are flattened for digging but the hind legs are shaped somewhat like the legs of a true cricket ; however , these limbs are more adapted for pushing soil , rather than leaping , which they do rarely and poorly . The nymphs resemble the adults apart from the absence of wings and <unk> ; the <unk> become larger after each successive moult . 
- 
- = = Taxonomy and phylogeny = = 
- 
- The <unk> are a monophyletic group in the order <unk> ( grasshoppers , locusts and crickets ) . <unk> analysis of mole cricket morphology in 2015 identifies six tribes , of which four were then new : <unk> ( <unk> ) , <unk> , <unk> and <unk> ( <unk> ) , and two existing tribes , <unk> and <unk> , are revised . The group name is derived <unk> from Latin ' <unk> ' , cricket , and ' <unk> ' , mole . 
- Within these subfamilies , genera include : 
- Gryllotalpa 
- <unk> 
- <unk> 
- Neocurtilla 
- † <unk> 
- <unk> 
- Scapteriscus 
- Neoscapteriscus 
- <unk> 
- † <unk> 
- † <unk> 
- † <unk> 
- † <unk> 
- Mole cricket fossils are rare . A stem group fossil , <unk> , is known from the Lower Cretaceous of Brazil . Two specimens of <unk> <unk> in amber have been found in the Lower Cretaceous of <unk> @-@ Maritime in France . They are somewhat more abundant in the Tertiary amber of the Baltic and Dominican regions ; impressions are found in Europe and the American Green River Formation . 
- Mole crickets are not closely related to the " <unk> mole crickets " , the <unk> , which are in the <unk> suborder <unk> rather than the cricket suborder <unk> . The two groups , and indeed their resemblance in form to the mammalian mole family <unk> with their powerful front limbs , form an example of <unk> evolution , both developing adaptations for burrowing . 
- 
- = = Behavior = = 
- 
- Adults of most species of mole cricket can fly powerfully , if not with agility , but males do so infrequently . The females typically take wing soon after sunset , and are attracted to areas where males are calling , which they do for about an hour after sunset . This may be in order to mate , or they may be influenced by the suitability of the habitat for egg @-@ laying , as demonstrated by the number of males present and calling in the vicinity . 
- 
- = = = Life cycle = = = 
- 
- Mole crickets are <unk> meaning they undergo incomplete <unk> ; when nymphs hatch from eggs , the nymphs increasingly resemble the adult form as they grow and pass through a series of up to ten <unk> . After mating , there may be a period of one or two weeks before the female starts laying eggs . She burrows into the soil to a depth of 30 cm ( 12 in ) , ( 72 cm ( 28 in ) has been seen in the laboratory ) , and lays a clutch of 25 to 60 eggs . Neoscapteriscus females then retire , sealing the entrance passage , but in Gryllotalpa and Neocurtilla species , the female has been observed to remain in an adjoining chamber to tend the clutch . Further clutches may follow over several months , according to species . <unk> need to be laid in moist ground and many nymphs die because of insufficient moisture in the soil . The eggs hatch in a few weeks , and as they grow , the nymphs consume a great deal of plant material either underground or on the surface . The adults of some species of mole cricket may move as far as 8 kilometres ( 5 @.@ 0 mi ) during the breeding season . Mole crickets are active most of the year , but overwinter as nymphs or adults in cooler climates , resuming activity in the spring . 
- 
- = = = <unk> = = = 
- 
- Mole crickets live almost entirely below ground , digging tunnels of different kinds for the major functions of life , including feeding , escape from predators , attracting a mate ( by singing ) , mating , and raising of young . 
- Their main tunnels are used for feeding and for escape : they can dig themselves underground very rapidly , and can move along existing tunnels at high speed both forwards and backwards . Their digging technique is to force the soil to either side with their powerful , shovel @-@ like forelimbs , which are broad , flattened , <unk> and heavily <unk> ( the <unk> is hardened and <unk> ) . 
- Males attract mates by constructing specially @-@ shaped tunnels in which they sing . Mating takes place in the male 's burrow ; the male may widen a tunnel to make room for the female to mount , though in some species mating is tail @-@ to @-@ tail . Females lay their eggs either in their normal burrows or in specially @-@ dug brood chambers which are sealed when complete in the case of the genus Neoscapteriscus or not sealed in the case of genera Gryllotalpa and Neocurtilla . 
- 
- = = = Song = = = 
- 
- Male mole crickets sing by <unk> , always underground . In Gryllotalpa gryllotalpa the song is based on an almost pure tone at 3 @.@ 5 <unk> , loud enough to make the ground vibrate 20 cm all round the burrow ; in fact the song is unique in each species . In Gryllotalpa gryllotalpa the burrow is somewhat roughly sculpted ; in Gryllotalpa vineae , the burrow is smooth and carefully shaped , with no irregularities larger than 1 millimetre . In both species the burrow takes the form of a double exponential horn with twin openings at the soil surface ; at the other end there is a <unk> , then a <unk> bulb , and then an escape tunnel . A burrow is used for at least a week . The male positions himself head down with his head in the bulb , his tail near the fork in the tunnel . 
- Mole crickets <unk> like other crickets by scraping the rear edge of the left <unk> , which forms a plectrum , against the lower surface of the right <unk> , which has a <unk> @-@ like series of asymmetric teeth : the more acute edges face backwards , as do those of the plectrum . The plectrum can move forwards with little resistance ; but moving it backwards makes it catch each tooth , setting up a vibration in both wings . The sound @-@ producing stroke is the raising ( <unk> ) of the wings . The resulting song resembles the result of <unk> a pure tone with a 66 <unk> wave to form regular chirps . In G. vineae , the wing <unk> muscle , which weighs 50 <unk> , can deliver 3 @.@ 5 <unk> of mechanical power ; G. gryllotalpa can deliver about 1 <unk> . G. vineae produces an exceptionally loud song from half an hour after sunset , continuing for an hour ; it can be heard up to 600 metres away . At a distance of 1 metre from the burrow , the sound has a mean power over the <unk> cycle of up to 88 decibels ; the <unk> recorded peak power was about 92 decibels ; at the mouths of the burrow , the sound reaches around 115 decibels . G. gryllotalpa can deliver a peak sound pressure of 72 decibels and a mean of about 66 decibels . The throat of the horn appears to be tuned ( offering low <unk> <unk> ) , making the burrow radiate sound efficiently ; the efficiency increases when the burrow is wet and absorbs less sound . Mole crickets are the only insects that construct a sound @-@ producing apparatus . Given the known sensitivity of a cricket 's hearing ( 60 decibels ) , a night @-@ flying G. vineae female should be able to detect the male 's song at a range of 30 metres ; this compares to about 5 metres for a typical <unk> cricket that does not construct a burrow . 
- The <unk> of the song is correlated with the size of the male and the quality of the habitat , both indicators of male <unk> . The <unk> males may attract 20 females in one evening , when a quieter male may attract none . This behaviour enables acoustic trapping : females can be trapped in large numbers by broadcasting a male 's song very loudly . 
- 
- = = = Food = = = 
- 
- Mole crickets vary in their diets ; some like the tawny mole cricket are herbivores , others are <unk> , feeding on larvae , worms , roots , and grasses , and others like the southern mole cricket are mainly <unk> . As well as consuming roots underground , mole crickets leave their burrows at night to forage for leaves and stems which they drag underground before consumption . 
- 
- = = = = Predators , parasites and pathogens = = = = 
- 
- Besides birds , toads and insectivorous mammals , the predators of mole crickets include subterranean assassin bugs , wolf spiders , and various beetles . The South American nematode Steinernema scapterisci kills <unk> mole crickets by introducing bacteria into their bodies , causing an overwhelming infection . Steinernema <unk> is native to Florida and attacks native Neocurtilla <unk> mole crickets . <unk> wasps of the genus Larra ( <unk> : <unk> ) attack mole crickets , the female laying an egg on the external surface of the mole cricket , and the larva developing <unk> on the mole cricket host . <unk> <unk> ( <unk> : <unk> ) is a specialized <unk> of mole crickets in the genus Neoscapteriscus ; the fly 's larvae hatch from eggs inside her abdomen ; she is attracted by the call of the male mole cricket and deposits a larva or more on any mole cricket individual with which she comes in contact . <unk> predators of mole cricket eggs in China and Japan include the <unk> beetle <unk> <unk> whereas in South America they include the <unk> beetle <unk> <unk> ( <unk> : <unk> ) ; the adult beetle lays eggs near the burrows of mole crickets , and the beetle larvae find their way to the egg chamber and eat the eggs . <unk> diseases can <unk> mole cricket populations during winters with sudden rises of temperature and <unk> . The fungus <unk> <unk> can overwhelm adult mole crickets and several other fungal , <unk> and viral pathogens have been identified . Mole crickets evade predators by living below ground , and vigorously burrowing if disturbed at the surface . As a last @-@ ditch defence , they <unk> a foul @-@ smelling brown liquid from their anal glands when captured ; they can also bite . 
- 
- = = Distribution = = 
- 
- Mole crickets are relatively common , but because they are nocturnal and spend nearly all their lives underground in extensive tunnel systems , they are rarely seen . They inhabit agricultural fields and grassy areas . They are present in every continent with the exception of Antarctica ; by 2014 , one hundred and seven species had been described and more species are likely to be discovered , especially in Asia . Neoscapteriscus didactylus is a widespread pest species , originating in South America it has spread to the West Indies and New South Wales in Australia . Gryllotalpa africana is a major pest in South Africa ; other Gryllotalpa species are widely distributed in Europe , Asia and Australia . They are native to Britain ( as to western Europe ) , but the former population of G. gryllotalpa may now be extinct in mainland Britain , surviving in the Channel Islands . 
- 
- = = = Invasive mole crickets , and their biological control = = = 
- 
- Invasive species are those that cause harm in their newly occupied area , where biological control may be attempted . The first @-@ detected invasive mole cricket species was Neoscapteriscus didactylus , a South American species reported as a pest in St. Vincent , West Indies as early as 1837 ; by 1900 , it was a major pest of agriculture in Puerto Rico . It had probably slowly expanded its range northwards , island by island , from South America . The only biological control program against N. didactylus was in Puerto Rico , and it succeeded in establishing the <unk> wasp Larra bicolor from <unk> Brazil . In 2001 , N. didactylus in Puerto Rico seemed to be a pest only in irrigated crops and turf . Small scale experimental applications of the nematode Steinernema scapterisci were made in irrigated turf , but survival of the nematode was poor . Very much later , this same species was reported as a pest in Queensland , Australia , presumably arriving by ship or plane . The next @-@ detected invasive species was in the late 19th century in Hawaii , probably by ship . It was named as Gryllotalpa africana , but was probably G. orientalis . It attacked sugarcane and was targeted with Larra <unk> from the Philippines in 1925 , apparently successfully . 
- The next detection was in Georgia , USA , and at that time was assumed to be N. didactylus from the West Indies . It was in fact three South American Neoscapteriscus species , N. <unk> , N . <unk> , and N. <unk> , probably arrived in ship ballast . They caused major problems for decades as they spread in the southeastern <unk> mole cricket populations had built up since the early decades of the 20th century and damaged pastures , <unk> , playing fields and vegetable crops . From the late 1940s <unk> had been the <unk> of choice to control them , but when <unk> was banned by the U.S. <unk> in the 1970s , ranchers were left with no economic and effective control method . Especially to aid Florida ranchers , a project that became known as the <unk> / <unk> Mole Cricket Research Program was initiated in 1978 . In 1985 , a multi @-@ authored report was published on accomplishments . In 1988 , an account was published on prospects for biological control , and in 1996 an account of promising results with biological control . The program ended in 2004 after 25 years of running monitoring stations , and in 2006 a summary publication announced success : a 95 % reduction in mole cricket numbers in northern Florida , with biological control agents spreading potentially to all parts of Florida . Efforts to use Larra bicolor as a biological control agent in Florida began by <unk> a stock from Puerto Rico . It became established in a small area of southeastern Florida but had little effect on Neoscapteriscus populations . A stock from Bolivia became established in northern Florida and spread widely ( with some help ) to most of the rest of the state and neighboring states . Its survival depends upon the availability of suitable nectar sources . 
- Once it was discovered that <unk> female <unk> <unk> flies are attracted to the song of Neoscapteriscus males in South America , a path to trap these flies at synthetic mole cricket song was opened . <unk> then led to a rearing method . <unk> rearing of over 10 @,@ 000 flies on mole cricket hosts allowed releases of living fly <unk> at many sites in Florida from the far northwest to the far south , mainly on golf courses , and mainly in 1989 @-@ 1991 . <unk> were established , began to spread , and were monitored by use of synthetic mole cricket song . Eventually it was discovered that the flies had a continuous population from about 29 degrees North all the way south to Miami , but that the flies failed to survive the winter north of about 29 degrees . <unk> and release of the flies to states north of Florida was thus a wasted effort . As the flies had been imported from 23 degrees South in Brazil and could not overwinter north of 29 degrees North in Florida , it was investigated in 1999 whether flies from 30 degrees South in Brazil might survive better in northern Florida , but they did not . 
- The third biological control agent to target Neoscapteriscus in Florida was the South American nematode Steinernema scapterisci . Small @-@ scale releases proved it could persist for years in mole @-@ cricket @-@ infested sandy Florida soils . Its use as a <unk> against Neoscapteriscus was patented , making it attractive to industry . Industrial @-@ scale production on artificial diet allowed large @-@ scale trial applications in pastures and on golf courses , which succeeded in establishing populations in several counties , and these populations spread , but sales were disappointing , and the product was withdrawn from the market in 2014 . 
- 
- = = = As pests = = = 
- 
- The main damage done by mole crickets is as a result of their burrowing activities . As they tunnel through the top few centimetres of soil they push the ground up in little ridges , increasing evaporation of surface moisture , disturbing <unk> seeds and damaging the delicate young roots of seedlings . They are also <unk> to <unk> and pasture @-@ grasses as they feed on the roots of the grasses , leaving the plants prone to drying @-@ out and damage by use . 
- In their native lands , mole crickets have natural enemies that keep them under control . This is not the case when they have been accidentally introduced to other parts of the world . In Florida they are considered pests and are described as " a serious problem " . A University of Florida <unk> report suggests that South American Neoscapteriscus mole crickets may have entered the United States at Brunswick , Georgia in ship 's ballast from southern South America around 1899 , but were at that time mistakenly believed to be from the West Indies . One possible remedy is biological pest control using the <unk> wasps Larra bicolor . Another remedy that has been successfully applied is use of the parasitic nematode Steinernema scapterisci . When this is applied in strips across grassland , it spreads throughout the pasture within a few months and not only controls the mole crickets , but remains infective in the soil for future years . 
- 
- = = In human culture = = 
- 
- 
- = = = Folklore = = = 
- 
- In Zambia , Gryllotalpa africana is held to bring good fortune to anyone who sees it . In Latin America , Scapteriscus and Neocurtilla mole crickets are said to predict rain when they dig into the ground . In Japan in the past they seem to have been associated with the worms / corpses / bugs that announce a persons sins to heaven in the <unk> / <unk> belief — see the spirit <unk> <unk> / <unk> . 
- 
- = = = As food = = = 
- 
- Gryllotalpa mole crickets have sometimes been used as food in West Java and Vietnam . In Thailand mole crickets ( Thai : <unk> ) are valued as food in <unk> . They are usually eaten fried along with sticky rice . 
- In the Philippines , they are served as a delicacy called <unk> in the province of <unk> and are a tourist attraction . They are also served in parts of Northern Luzon . 
- 
- 
- = Maryland Route 194 = 
- 
- Maryland Route 194 ( MD 194 ) is a state highway in the U.S. state of Maryland . The state highway runs 23 @.@ 87 miles ( 38 @.@ 42 km ) from MD 26 in Ceresville north to the Pennsylvania state line near Taneytown , where the highway continues as Pennsylvania Route 194 ( PA 194 ) toward Hanover . MD 194 is the main highway between Frederick and Hanover ; the state highway connects the towns of Walkersville and Woodsboro in northeastern Frederick County with Keymar and Taneytown in northwestern Carroll County . MD 194 was blazed as a migration route in the 18th century and a pair of turnpikes in Frederick County in the 19th century , one of which was the last private toll road in Maryland . The state highway , which was originally designated MD 71 , was built as a modern highway in Frederick County in the mid @-@ 1920s and constructed as Francis Scott Key Highway in Carroll County in the late 1920s and early 1930s . MD 194 received its modern route number in 1956 as part of a three @-@ route number swap . The state highway 's bypasses of Walkersville and Woodsboro opened in the early 1980s and mid @-@ 1990s , respectively . 
- 
- = = Route description = = 
- 
- MD 194 begins at an intersection with MD 26 ( Liberty Road ) in Ceresville . The roadway continues south as MD 26 , which crosses the <unk> River on its way toward Frederick . MD 26 heads east from the intersection as a two @-@ lane road toward <unk> . MD 194 heads north as Woodsboro Pike , which starts as a four @-@ lane divided highway but reduces to two lanes as the highway passes through the suburban communities of Discovery and Spring Garden , where the state highway passes the historic Woodsboro and Frederick Turnpike Company <unk> . At Walkersville High School , the state highway enters the town of Walkersville and the highway 's old alignment , Frederick Street , splits to the north . MD 194 collects the other end of Frederick Street and passes between residential subdivisions before leaving the town at the highway 's intersection with <unk> Bridge Road and <unk> Road . The former road heads west toward the historic Harris Farm ; the latter highway leads to the 19th century <unk> Road Bridge . 
- MD 194 parallels the Maryland Midland Railway 's north – south line , Israel Creek , and Laurel Hill north to Woodsboro . At the south end of the town , Main Street , which is unsigned MD 194A , continues straight north while MD 194 veers northeast to bypass the town . The state highway starts to run concurrently with MD 550 at Woodsboro Road , which heads east as MD 550 toward <unk> . On the north side of town , MD 550 heads west as Woodsboro <unk> Road , which meets the northern end of Main Street before heading northwest toward <unk> and <unk> . MD 194 veers northeast and has a grade crossing with the railroad track at New Midway , where the highway intersects <unk> Road , which leads to the <unk> Bridge . The state highway passes through the village of <unk> before crossing Little Pipe Creek , one of the tributaries of Double Pipe Creek , into Carroll County . 
- MD 194 continues as Francis Scott Key Highway through Keymar , where the north – south and east – west lines of the Maryland Midland Railway intersect . The state highway has a grade crossing of the east – west rail line and intersects <unk> Road , which heads west as MD 77 . In the hamlet of <unk> , MD 194 crosses Big Pipe Creek and intersects <unk> <unk> Road , which heads northwest toward the village of <unk> and Terra <unk> , the birthplace of Francis Scott Key . The state highway passes the historic <unk> Family Farm and Keefer @-@ <unk> Farm on its way to Taneytown . MD 194 , which becomes Frederick Street , crosses a branch of <unk> Creek and passes the <unk> <unk> <unk> House . In the center of the Taneytown Historic District , the state highway intersects MD 140 ( Baltimore Street ) , where MD 194 becomes York Street . After leaving Taneytown , the state highway becomes Francis Scott Key Highway again as it heads through farmland . MD 194 crosses <unk> Creek before reaching its northern terminus at the Pennsylvania state line . The roadway continues north as PA 194 ( Frederick Pike ) toward <unk> and Hanover . 
- MD 194 is a part of the National Highway System as a principal arterial from its southern terminus in Ceresville to <unk> Road in Walkersville and within the city of Taneytown . 
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- = = History = = 
- 
- In the 18th century , the corridor of what is now MD 194 was the Hanover – Frederick portion of the <unk> Road , a migration route that connected Philadelphia and Winchester , Virginia via York , Frederick , <unk> , and Williamsport . The Frederick County portion of the highway later became the path of a pair of turnpikes . The Woodsboro and Double Pipe Creek Turnpike connected the namesake town and creek ; the Woodsboro and Frederick Turnpike extended from Woodsboro south to the junction with the Liberty and Frederick Turnpike in Ceresville . The two turnpikes issuing from Ceresville were connected to Frederick by the Frederick and Woodsboro Turnpike . The Woodsboro and Frederick Turnpike was the last privately maintained toll road in Maryland when it was purchased by the Maryland State Roads Commission , the predecessor to the Maryland State Highway Administration , in 1921 . 
- What is now MD 194 was originally designated MD 71 . The roads commission resurfaced the turnpikes ' <unk> surface from Ceresville to Little Pipe Creek to a width of 15 feet ( 4 @.@ 6 m ) by 1926 . That same year , 1 mile ( 1 @.@ 6 km ) of concrete road was constructed north from MD 32 ( now MD 140 ) in Taneytown . In 1930 , construction began to complete the concrete road that MD 71 would follow through Carroll County . The state highway was completed in five sections from a short distance north of Big Pipe Creek to the Pennsylvania state line in 1933 . The Carroll County section of MD 71 was dedicated as Francis Scott Key Highway in 1931 . The two sections of MD 71 were separated by a county @-@ maintained segment of highway through Keymar . This gap in the state road system remained through at least 1949 . 
- MD 71 received a new steel I @-@ beam bridge over Big Pipe Creek in 1940 ; this bridge was replaced in 2005 . The state highway was widened through Taneytown in 1948 . MD 71 's present steel I @-@ beam bridge over Little Pipe Creek at the county line was started in 1953 and completed in 1954 along with 1 mile ( 1 @.@ 6 km ) of approach roads . The state highway was reconstructed and widened from the Little Pipe Creek Bridge to New Midway in 1952 and 1953 . Reconstruction of the highway commenced from New Midway to Woodsboro in 1953 , from Woodsboro to Ceresville in 1956 , and from Taneytown to the Pennsylvania state line in 1957 . 
- In 1956 , MD 71 was involved in a three @-@ route number change involving highways in three different areas of the state . MD 71 was reassigned to the Blue Star Memorial Highway then under construction from Queenstown to the Delaware state line ; this designation lasted only three years before U.S. Route <unk> was rerouted onto the highway in 1959 . MD 71 was designated MD 194 to match the adjacent numbered highway in Pennsylvania . MD 194 had previously been assigned to Flower Avenue in <unk> Park ; Flower Avenue was then designated MD 787 . 
- MD 194 's bypass of Walkersville was completed around 1981 . The state highway 's bypass of Woodsboro was under construction by 1995 and completed in 1997 ; Main Street through town was designated MD 194A . MD 550 , which ran concurrently with MD 194 along Main Street , joined the latter route on the new bypass and on a bypass section of its own at the north end of town . In conjunction with the reconstruction of MD 26 as a divided highway from Market Street ( then part of MD 355 ) in Frederick to Ceresville in 1997 , the MD 26 – MD 194 intersection was <unk> so the primary movement through the intersection is between MD 26 to the west and MD 194 to the north ; the southernmost portion of MD 194 became an extension of the MD 26 divided highway . This configuration was chosen because two @-@ thirds of traffic passing through the intersection was between Frederick and Woodsboro . 
- 
- = = Junction list = = 
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- 
- = = <unk> routes = = 
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- MD 194 has four existing auxiliary routes and two that no longer exist . MD 194A and MD <unk> are in Woodsboro . MD <unk> and MD <unk> are north of Taneytown . Former MD 194C and MD 194F were also north of Taneytown . 
- MD 194A is the designation for Main Street , which runs 1 @.@ 28 miles ( 2 @.@ 06 km ) between MD 194 on the south side of Woodsboro and MD 550 on the north side of Woodsboro . MD 194A is <unk> maintained for 0 @.@ 75 miles ( 1 @.@ 21 km ) from Mt . Hope Cemetery north to <unk> Road . 
- MD <unk> is the designation for a 0 @.@ 09 @-@ mile ( 0 @.@ 14 km ) section of Main Street that is now a spur south from MD 194A just north of MD 194 's southern end at MD 194 . 
- MD 194C was the designation for an unnamed 0 @.@ 03 @-@ mile ( 0 @.@ <unk> km ) connector between MD 194 and former MD 853D . MD 194C and MD 853D were removed from the state highway system in 2004 due to the roadway being overgrown . 
- MD <unk> is the designation for an unnamed 0 @.@ 02 @-@ mile ( 0 @.@ 032 km ) connector between MD 194 and MD <unk> , the old alignment that parallels the northbound direction of the modern highway south of <unk> Road . 
- MD <unk> is the designation for an unnamed 0 @.@ 02 @-@ mile ( 0 @.@ 032 km ) connector between MD 194 and MD <unk> , the old alignment that parallels the southbound direction of the modern highway south of the Pennsylvania state line . 
- MD 194F was the designation for an unnamed 0 @.@ 02 @-@ mile ( 0 @.@ 032 km ) connector between MD 194 and former MD 853D . MD 194F and MD 853D were removed from the state highway system in 2004 due to the roadway being overgrown . 
- 
- 
- = Le souper de Beaucaire = 
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- Le souper de Beaucaire was a political pamphlet written by Napoleon Bonaparte in 1793 . With the French Revolution into its fourth year , civil war had spread across France between various rival political factions . Napoleon was involved in military action , on the government 's side , against some rebellious cities of southern France . It was during these events , in 1793 , that he spoke with four merchants from the <unk> and heard their views . As a loyal soldier of the Republic he responded in turn , set on <unk> the fears of the merchants and <unk> their beliefs . He later wrote about his conversation in the form of a pamphlet , calling for an end to the civil war . 
- 
- = = Background = = 
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- During the French Revolution the National Convention became the executive power of France , following the execution of King Louis XVI . With powerful members , such as Maximilien Robespierre and Georges <unk> , the Jacobin Club , a French political party established in 1790 , at the birth of the revolution , managed to secure control of the government and pursue the revolution to their own ends , culminating in a " Reign of Terror " . Its repressive policies resulted in insurrection across much of France , including the three largest cities after Paris , namely Lyon , Marseille and Toulon , in the south of France . 
- Citizens in the south were opposed to a centralised government , and to the decrees of its rule , which resulted in rebellion . Prior to the revolution France had been divided into provinces with local governments . In 1790 the government , the National <unk> Assembly , reorganised France into administrative departments in order to <unk> the uneven distribution of French wealth , which had been subject to <unk> under the <unk> <unk> <unk> . 
- 
- = = Rebellion in Southern France = = 
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- In July 1793 Captain Napoleon Bonaparte , an artillery officer , was placed under the command of Jean @-@ Baptiste Carteaux to deal with rebels from Marseille situated in Avignon , where army munitions required by the French Army of Italy were being stored . On 24 July , Carteaux 's troops attacked rebellious National Guardsmen , killing several citizens during the siege , before capturing the town and army supplies . Afterwards , Napoleon travelled to nearby <unk> to find wagons with which to transport the <unk> . He visited Beaucaire , across the river from <unk> , which had been holding an annual fair . Napoleon arrived on 28 July , the last day of the fair , and went to a tavern where he shared supper and conversation with four merchants – two from Marseille , one from <unk> and another from Nîmes . 
- That evening Napoleon and the four merchants discussed the revolution , subsequent rebellions , and their consequences . Speaking as a pro @-@ Republican , Napoleon supported the Jacobin cause , and explained the benefits of the revolution , whilst defending Carteaux 's actions in Avignon . One of the merchants from Marseille expressed his moderate views regarding the revolution , and reasons for supporting civil war against a central government . The merchant stressed that Marseille did not fight for the Royalist cause , but opposed the nature of the Convention itself , condemning its decrees and deeming the execution of citizens as unlawful . Napoleon concluded that the people of Marseille should reject counter @-@ revolutionary ideals and adopt the constitution of the French Republic in order to end the civil war and allow the regular army to restore France . 
- Following their conversation the group drank <unk> until two in the morning , paid for by the <unk> merchant . 
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- = = Publication and recognition = = 
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- Shortly after the events , possibly on the 29 July whilst still in Beaucaire , Napoleon wrote a political pamphlet titled Le souper de Beaucaire ( The supper at Beaucaire ) in which a soldier speaks with four merchants and sympathetic to their opinions attempts to dissipate their counter @-@ revolutionary sentiments . 
- The pamphlet was read by Augustin Robespierre , brother of Maximilien Robespierre , who was impressed by the revolutionary context . The pamphlet itself had little effect against the rebellious forces , but served to advance Napoleon 's career . He soon became recognised for his political ambitions by a <unk> @-@ born politician , and family friend , Christophe <unk> , who arranged to have it published and distributed . Christophe 's influence , along with fellow Convention deputy Augustin Robespierre , advanced Napoleon into the position of senior gunner , at Toulon . 
- In Memoirs of Napoleon Bonaparte , a biography by Napoleon 's private secretary , Louis de <unk> , he notes that Le souper de Beaucaire was reprinted as a book – the first edition issued at the cost of the Public Treasury in August 1798 , and a second edition in 1821 , following Napoleon 's death . He also states , " It was during my absence from France that Bonaparte , in the rank of ' chef de <unk> ' [ major ] , performed his first campaign , and contributed so materially to the recapture of Toulon . Of this period of his life I have no personal knowledge , and therefore I shall not speak of it as an eye @-@ witness . I shall merely relate some facts which fill up the interval between 1793 and 1795 , and which I have collected from papers which he himself delivered to me . Among these papers is a little production , entitled ' Le <unk> de Beaucaire ' , the copies of which he bought up at considerable expense , and destroyed upon his attaining the <unk> . " 
- 
- 
- = Jin – Song Wars = 
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- The Jin – Song Wars were a series of conflicts between the Jurchen Jin dynasty ( 1115 – 1234 ) and Han Chinese Song dynasty ( 960 – 1279 ) . In 1115 , the Jurchens rebelled against their overlords , the <unk> Liao dynasty ( 907 – 1125 ) , and declared the formation of the Jin . <unk> with the Song against their common enemy the Liao , the Jin promised to return to the Song the territories in northern China that had fallen under Liao control since 938 . The Jurchens ' quick defeat of the Liao combined with Song military failures made the Jin reluctant to cede these territories . After a series of failed negotiations that <unk> both sides , the Jurchens attacked the Song in November 1125 , dispatching one army towards Taiyuan and the other towards Kaifeng , the Song capital . 
- <unk> by the news of an invasion , the Song general stationed in Taiyuan retreated from the city , which was besieged and later captured . As the second Jin army approached the capital , Emperor Huizong of the Song abdicated and fled south . A new emperor , Qinzong , was enthroned . The Jurchens began a siege against Kaifeng in 1126 , but Qinzong negotiated for their retreat from the capital after he agreed to pay a large annual indemnity . Qinzong reneged on the deal and ordered Song forces to defend the prefectures instead of <unk> the capital . The Jin resumed their war against the Song and again besieged Kaifeng in 1127 . The Chinese emperor was captured in an event known as the <unk> Incident , the capital was looted , and the Song lost northern China to the Jin . Remnants of the Song retreated to southern China and , after brief stays in several temporary capitals , eventually relocated to Hangzhou . The retreat of the Song court marked the end of the Northern Song era and the beginning of the Southern Song . 
- The Jurchens tried to conquer southern China in the <unk> , but they were bogged down by a pro @-@ Song insurgency in the north and a counteroffensive by the Song generals Yue Fei , Han Shizhong , and others . The generals regained some territories but retreated on the orders of the Southern Song emperor , who supported a peaceful resolution to the war . The Treaty of Shaoxing in 1142 settled the boundary between the two empires along the Huai River , but conflicts between the two dynasties continued until the fall of the Jin in 1234 . A campaign against the Song by the fourth Jin emperor , Wanyan Liang ( the Prince of Hailing ) , was unsuccessful . He lost the Battle of Caishi ( 1161 ) and was later assassinated by his own disaffected officers . An invasion of the Jin motivated by Song <unk> ( 1206 – 1208 ) was also unsuccessful . A decade later , the Jin launched an abortive military campaign against the Song in 1217 to compensate for the territory that they had lost to the invading Mongols . The Song formed an alliance with the Mongols in 1233 , and in the following year jointly captured Caizhou , the last refuge of the Jin emperor . The Jin dynasty collapsed that year in 1234 . After the demise of the Jin , the Song dynasty itself became a target of the Mongols , and fell in 1279 . 
- The wars engendered an era of technological , cultural , and demographic changes in China . Battles between the Song and Jin brought about the introduction of various gunpowder weapons . The siege of De 'an in 1132 was the first recorded appearance of the fire lance , an early ancestor of firearms . There were also reports of battles fought with primitive gunpowder bombs like the incendiary huopao or the exploding <unk> , incendiary arrows , and other related weapons . In northern China , the Jurchen tribes were the ruling minority of an empire that was predominantly inhabited by former subjects of the Northern Song . Jurchen migrants settled in the conquered territories and assimilated with the local culture . The Jin government instituted a centralized imperial bureaucracy modeled on previous Chinese dynasties , basing their legitimacy on Confucian philosophy . Song refugees from the north <unk> in southern China . The north was the cultural center of China , and its conquest by the Jin diminished the international stature of the Song dynasty . The Southern Song , however , quickly returned to economic prosperity , and trade with the Jin was lucrative despite decades of warfare . The capital of the Southern Song , Hangzhou , expanded into a major city for commerce . 
- 
- = = The fragile Song – Jin alliance = = 
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- The Jurchens were a <unk> @-@ speaking group of semi @-@ agrarian tribes inhabiting areas of northeast Asia that are now part of Northeast China . Many of the Jurchen tribes were vassals of the Liao dynasty ( 907 – 1125 ) , an empire ruled by the nomadic Khitans that included most of modern Mongolia , a portion of North China , Northeast China , northern Korea , and parts of the Russian Far East . To the south of the Liao lay the Han Chinese Song Empire ( 960 – <unk> ) . The Song and Liao were at peace , but since a military defeat to the Liao in 1005 , the Song paid its northern neighbor an annual indemnity of 200 @,@ 000 bolts of silk and 100 @,@ 000 ounces of silver . 
- In <unk> , the chieftain Wanyan Aguda ( <unk> – 1123 ) united the disparate Jurchen tribes and led a revolt against the Liao . In 1115 he named himself emperor of the Jin " golden " dynasty ( 1115 – 1234 ) . <unk> by a Liao <unk> of the success of the Jurchen uprising , the Song emperor Huizong ( r . 1100 – 1127 ) and his highest military commander the <unk> Tong Guan saw the Liao weakness as an opportunity to recover the Sixteen Prefectures , a line of fortified cities and passes that the Liao had annexed from the <unk> Turk Later Jin in 938 , and that the Song had repeatedly but unsuccessfully tried to <unk> . The Song thus sought an alliance with the Jin against their common enemy the Liao . 
- Because the land routes between the Song and Jin were controlled by the Liao , diplomatic exchanges had to occur by traveling across the <unk> Sea . Negotiations for an alliance began secretly under the <unk> that the Song wanted to acquire horses from the Khitans . Song diplomats traveled to the Jin court to meet Aguda in <unk> , while Jurchen envoys arrived in the Song capital Kaifeng the next year . At the beginning the two sides agreed to keep whatever Liao territory they would seize in combat . In 1120 , Aguda agreed to cede the Sixteen Prefectures to the Song in exchange for transfer to the Jin of the annual tributary payments that the Song had been giving the Liao . By the end of 1120 , however , the Jurchens had seized the Liao Supreme Capital , and offered the Song only parts of the Sixteen Prefectures . Among other things , the Jin would keep the Liao Western Capital of <unk> at the western end of the Sixteen Prefectures . The two sides agreed that the Jin would now attack the Liao Central Capital , whereas the Song would seize the Liao Southern Capital , Yanjing ( modern day Beijing ) . 
- The joint attack against the Liao had been planned for 1121 , but it was <unk> for 1122 . In February 23 of that year , the Jin captured the Liao Central Capital as promised . The Song delayed their entry into the war because it diverted resources to fighting the Western Xia in the northwest and suppressing a large popular rebellion in the south . When a Song army under Tong Guan 's command finally attacked Yanjing in May 1122 , the smaller forces of the weakened Liao repelled the invaders with ease . Another attack failed in the fall . Both times , Tong was forced to retreat back to Kaifeng . After the first attack , Aguda changed the terms of the agreement and only promised Yanjing and six other prefectures to the Song . In early 1123 it was Jurchen forces that easily took the Liao Southern Capital . They sacked it and enslaved its population . 
- The quick collapse of the Liao led to more negotiations between the Song and the Jin . Jurchen military success and their effective control over the Sixteen Prefectures gave them more leverage . Aguda grew increasingly frustrated as he realized that despite their military failures the Song still intended to seize most of the prefectures . In the spring of 1123 the two sides finally set the terms of the first Song – Jin treaty . Only seven prefectures ( including Yanjing ) would be returned to the Song , and the Song would pay an annual indemnity of 300 @,@ 000 packs of silk and 200 @,@ 000 taels of silver to the Jin , as well as a one @-@ time payment of one million strings of copper coins to compensate the Jurchens for the tax revenue they would have earned had they not returned the prefectures . In May 1123 Tong Guan and the Song armies entered the looted Yanjing . 
- 
- = = War against the Northern Song = = 
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- = = = The collapse of the Song – Jin alliance = = = 
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- <unk> one month after the Song had recovered Yanjing , Zhang <unk> ( <unk> ) , who had served as military governor of the Liao prefecture of <unk> about 200 kilometres ( 120 mi ) east of Yanjing , killed the main Jin official in that city and turned it over to the Song . The Jurchens defeated his armies a few months later and Zhang took refuge in Yanjing . Even though the Song agreed to execute him in late 1123 , this incident put tension between the two states , because the 1123 treaty had explicitly forbidden both sides from <unk> <unk> . In 1124 , Song officials further angered the Jin by asking for the <unk> of nine more border prefectures . The new Jin emperor Taizong ( r . 1123 – 1135 ) , Aguda 's brother and successor , hesitated , but warrior princes Wanyan Zonghan and Wanyan Zongwang ( <unk> ) vehemently refused to give them any more territory . Taizong eventually granted two prefectures , but by then the Jin leaders were ready to attack their southern neighbor . 
- Before they could invade the Song , the Jurchens reached a peace agreement with their western neighbors the <unk> Western Xia in 1124 . The following year near the <unk> Desert , they captured <unk> , the last emperor of the Liao , putting an end to the Liao dynasty for good . Ready to end their alliance with the Song , the Jurchens began preparations for an invasion . 
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- = = = First campaign = = = 
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- In November 1125 Taizong ordered his armies to attack the Song . The defection of Zhang <unk> two years earlier served as the <unk> <unk> . Two armies were sent to capture the major cities of the Song . 
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- = = = = Siege of Taiyuan = = = = 
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- The western army , led by Wanyan Zonghan , departed from <unk> and headed towards Taiyuan through the mountains of Shanxi , on its way to the Song western capital Luoyang . The Song forces were not expecting an invasion and were caught off guard . The Chinese general Tong Guan was informed of the military expedition by an envoy he had sent to the Jin to obtain the <unk> of two prefectures . The returning envoy reported that the Jurchens were willing to <unk> an invasion if the Song ceded control of Hebei and Shanxi to the Jin . Tong Guan retreated from Taiyuan and left command of his troops to Wang Bing . Jin armies besieged the city in mid January 1126 . Under Wang Bing 's command , Taiyuan held on long enough to stop the Jurchen troops from advancing to Luoyang . 
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- = = = = First siege of Kaifeng = = = = 
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- Meanwhile , the eastern army , commanded by Wanyan Zongwang , was dispatched towards Yanjing ( modern Beijing ) and eventually the Song capital Kaifeng . It did not face much armed opposition . Zongwang easily took Yanjing , where Song general and former Liao governor <unk> <unk> ( <unk> ) switched his <unk> to the Jin . When the Song had tried to reclaim the Sixteen Prefectures , they had faced fierce resistance from the Han Chinese population , yet when the Jurchens invaded that area , the Han Chinese did not oppose them at all . By the end of December 1125 , the Jin army had seized control of two prefectures and re @-@ established Jurchen rule over the Sixteen Prefectures . The eastern army was nearing Kaifeng by early 1126 . 
- Fearing the approaching Jin army , Song emperor Huizong planned to retreat south . The emperor <unk> the capital would have been viewed as an act of capitulation , so court officials convinced him to <unk> . There were few objections . <unk> an empire in crisis from destruction was more important than preserving the rituals of imperial inheritance . In January 1126 , a few days before the New Year , Huizong abdicated in favor of his son and was demoted to the ceremonial role of <unk> Emperor . The Jurchen forces reached the Yellow River on January 27 , 1126 , two days after the New Year . Huizong fled Kaifeng the next day , escaping south and leaving the newly enthroned emperor Qinzong ( r . 1126 – 1127 ) in charge of the capital . 
- Kaifeng was besieged on January 31 , 1126 . The commander of the Jurchen army promised to spare the city if the Song submitted to Jin as a vassal ; forfeited the prime minister and an imperial prince as prisoners ; ceded the Chinese prefectures of <unk> , Taiyuan , and <unk> ; and offered an indemnity of 50 million taels of silver , 5 million taels of gold , 1 million packs of silk , 1 million packs of satin , 10 @,@ 000 horses , 10 @,@ 000 mules , 10 @,@ 000 cattle , and 1 @,@ 000 camels . This indemnity was worth about 180 years of the annual tribute the Song had been paying to the Jin since 1123 . 
- With little prospect of help from <unk> arriving , infighting broke out in the Song court between the officials who supported the Jin offer and those who opposed it . Opponents of the treaty like Li Gang ( <unk> ; <unk> – 1140 ) rallied around the proposal of remaining in defensive positions until reinforcements arrived and Jurchen supplies ran out . They <unk> an ambush against the Jin that was carried out at night , and were replaced by officials who supported peace negotiations . The failed attack pushed Qinzong into meeting the Jurchen demands , and his officials convinced him to go through with the deal . The Song recognized Jin control over the three prefectures . The Jurchen army ended the siege in March after 33 days . 
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- = = = Second campaign = = = 
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- Almost as soon as the Jin armies had left Kaifeng , Emperor Qinzong reneged on the deal and dispatched two armies to repel the Jurchen troops attacking Taiyuan and bolster the defenses of <unk> and <unk> . An army of 90 @,@ 000 soldiers and another of 60 @,@ 000 were defeated by Jin forces by June . A second expedition to rescue Taiyuan was also unsuccessful . 
- <unk> the Song of violating the agreement and realizing the weakness of the Song , the Jin generals launched a second punitive campaign , again dividing their troops into two armies . Wanyan Zonghan , who had withdrawn from Taiyuan after the Kaifeng agreement and left a small force in charge of the siege , came back with his western army . <unk> , Taiyuan fell in September 1126 , after 260 days of siege . When the Song court received news of the fall of Taiyuan , the officials who had advocated defending the empire militarily fell from favor again and were replaced by <unk> who favored <unk> . In mid December the two Jurchen armies <unk> on Kaifeng for the second time that year . 
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- = = = = Second siege of Kaifeng = = = = 
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- After the defeat of several Song armies in the north , Emperor Qinzong wanted to negotiate a truce with the Jin , but he committed a massive strategic <unk> when he commanded his remaining armies to protect <unk> cities instead of Kaifeng . <unk> the importance of the capital , he left Kaifeng defended with fewer than 100 @,@ 000 soldiers . The Song forces were dispersed throughout China , powerless to stop the second Jurchen siege of the city . 
- The Jin assault commenced in mid December 1126 . Even as fighting raged on , Qinzong continued to sue for peace , but Jin demands for territory were enormous : they wanted all provinces north of the Yellow River . After more than twenty days of heavy combat against the besieging forces , Song defenses were decimated and the morale of Song soldiers was on the decline . On January 9 , 1127 , the Jurchens broke through and started to <unk> the conquered city . Emperor Qinzong tried to appease the victors by offering the remaining wealth of the capital . The royal treasury was emptied and the belongings of the city 's residents were seized . The Song emperor offered his unconditional surrender a few days later . 
- Qinzong , the former emperor Huizong , and members of the Song court were captured by the Jurchens as hostages . They were taken north to <unk> ( modern <unk> ) , where they were stripped of their royal privileges and reduced to commoners . The former emperors were humiliated by their captors . They were mocked with disparaging titles like " <unk> <unk> " and " Double <unk> " . In 1128 the Jin made them perform a ritual meant for war criminals . The harsh treatment of the Song royalty softened after the death of Huizong in 1135 . Titles were granted to the deceased monarch , and his son Qinzong was promoted to Duke , a position with a salary . 
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- = = = Reasons for Song failure = = = 
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- Many factors contributed to the Song 's repeated military <unk> and subsequent loss of northern China to the Jurchens . Traditional accounts of Song history held the <unk> of Huizong 's imperial court responsible for the decline of the dynasty . These narratives condemned Huizong and his officials for their moral failures . Early Song emperors were eager to enact political reforms and revive the ethical framework of <unk> , but the enthusiasm for reforms gradually died after the <unk> Wang <unk> 's expulsion as chancellor in <unk> . <unk> marred the reign of Huizong , who was more skilled as a painter than as a ruler . Huizong was known for his extravagance , and funded the costly construction of gardens and temples while rebellions threatened the state 's grip on power . 
- A modern analysis by Ari Daniel Levine places more of the blame on deficiencies in the military and bureaucratic leadership . The loss of northern China was not inevitable . The military was <unk> by a government too assured of its own military prowess . Huizong diverted the state 's resources to failed wars against the Western Xia . The Song insistence on a greater share of Liao territory only succeeded in provoking their Jin allies . Song diplomatic <unk> underestimated the Jin and allowed the unimpeded rise of Jurchen military power . The state had plentiful resources , with the exception of horses , but managed its assets poorly during battles . Unlike the expansive Han and Tang empires that preceded the Song , the Song did not have a significant foothold in Central Asia where a large proportion of its horses could be bred or procured . As Song general Li Gang noted , without a consistent supply of horses the dynasty was at a significant disadvantage against Jurchen cavalry : " the Jin were victorious only because they used iron @-@ shielded cavalry , while we opposed them with foot soldiers . It is only to be expected that [ our soldiers ] were scattered and dispersed . " 
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- = = Wars with the Southern Song = = 
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- = = = Southern retreat of the Song court = = = 
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- 
- = = = = The <unk> of Emperor Gaozong = = = = 
- 
- The Jin leadership had not expected or desired the fall of the Song dynasty . Their intention was to weaken the Song in order to demand more tribute , and they were unprepared for the magnitude of their victory . The Jurchens were preoccupied with strengthening their rule over the areas once controlled by Liao . Instead of continuing their invasion of the Song , an empire with a military that outnumbered their own , they adopted the strategy of " using Chinese to control the Chinese " . The Jin hoped that a <unk> state would be capable of <unk> northern China and collecting the annual indemnity without requiring Jurchen <unk> to quell anti @-@ Jin uprisings . In 1127 , the Jurchens installed a former Song official , Zhang Bangchang ( <unk> ; <unk> – 1127 ) , as the puppet emperor of the newly established " Da Chu " ( Great Chu ) dynasty . The puppet government did not deter the resistance in northern China , but the insurgents were motivated by their anger towards the Jurchens ' looting rather than by a sense of loyalty towards the inept Song court . A number of Song commanders , stationed in towns scattered across northern China , retained their allegiance to the Song , and armed volunteers organized militias opposed to the Jurchen military presence . The insurgency hampered the ability of the Jin to exert control over the north . 
- Meanwhile , one Song prince , Zhao <unk> , had escaped capture . He had been held up in <unk> while on a diplomatic mission , and never made it back to Kaifeng . He was not present in the capital when the city fell to the Jurchens . The future Emperor Gaozong managed to evade the Jurchen troops <unk> him by moving from one province to the next , traveling across Hebei , Henan , and Shandong . The Jurchens tried to lure him back to Kaifeng where they could finally capture him , but did not succeed . Zhao <unk> finally arrived in the Song Southern Capital at Yingtianfu ( <unk> ; modern <unk> ) in early June 1127 . For Gaozong ( r . 1127 – <unk> ) , Yingtianfu was the first in a series of temporary capitals called <unk> <unk> . The court moved to Yingtianfu because of its historical importance to Emperor <unk> of Song , the founder of the dynasty , who had previously served in that city as a military governor . The symbolism of the city was meant to secure the political legitimacy of the new emperor , who was enthroned there on June 12 . 
- After reigning for barely one month , Zhang Bangchang was persuaded by the Song to step down as emperor of the Great Chu and to recognize the legitimacy of the Song imperial line . Li Gang pressured Gaozong to execute Zhang for betraying the Song . The emperor relented and Zhang was coerced into suicide . The killing of Zhang showed that the Song was willing to provoke the Jin , and that the Jin had yet to solidify their control over the newly conquered territories . The submission and abolition of Chu meant that Kaifeng was now back under Song control . Zong Ze ( <unk> ; 1059 – 1128 ) , the Song general responsible for <unk> Kaifeng , <unk> Gaozong to move the court back to the city , but Gaozong refused and retreated south . The southward move marked the end of the Northern Song and the beginning of the Southern Song era of Chinese history . 
- The descendant of <unk> at Qufu , the Duke <unk> Kong <unk> fled south with the Song Emperor to <unk> , while the newly established Jin dynasty ( 1115 – 1234 ) in the north appointed Kong <unk> 's brother Kong <unk> who remained in Qufu as Duke <unk> . Zhang Xuan <unk> , a great @-@ grandson of Zhang <unk> , also fled south with Gaozong . 
- 
- = = = = The move south = = = = 
- 
- The Song disbandment of the Great Chu and execution of Zhang Bangchang <unk> the Jurchens and violated the treaty that the two parties had negotiated . The Jin renewed their attacks on the Song and quickly <unk> much of northern China . In late 1127 Gaozong moved his court further south from Yingtianfu to Yangzhou , south of the Huai River and north of the Yangtze River , by sailing down the Grand Canal . The court spent over a year in the city . When the Jurchens advanced to the Huai River , the court was partially evacuated to Hangzhou in 1129 . Days later , Gaozong narrowly escaped on horseback , just a few hours ahead of Jurchen vanguard troops . After a coup in Hangzhou almost <unk> him , in May 1129 he moved his capital back north to Jiankang ( modern @-@ day <unk> ) on the south bank of the Yangtze . One month later , however , Zong Ze 's successor Du Chong ( <unk> ) vacated his forces from Kaifeng , exposing Jiankang to attack . The emperor moved back to Hangzhou in September , leaving Jiankang in Du Chong 's hands . The Jin eventually captured Kaifeng in early 1130 . 
- From 1127 to 1129 , the Song sent thirteen embassies to the Jin to discuss peace terms and to negotiate the release of Gaozong 's mother and Huizong , but the Jin court ignored them . In December 1129 , the Jin started a new military offensive , dispatching two armies across the Huai River in the east and west . On the western front , an army invaded <unk> , the area where the Song <unk> <unk> resided , and captured <unk> ( <unk> , present @-@ day <unk> ) . They were ordered to retreat a few months later when the eastern army withdrew . 
- Meanwhile , on the eastern front , Wuzhu commanded the main Jin army . He crossed the Yangtze southwest of Jiankang and took that city when Du Chong surrendered . Wuzhu set out from Jiankang and advanced rapidly to try to capture Gaozong . The Jin seized Hangzhou ( January 22 , 1130 ) and then Shaoxing further south ( February 4 ) , but general Zhang Jun 's ( 1086 – 1154 ) battle with Wuzhu near <unk> gave Gaozong time to escape . By the time Wuzhu resumed pursuit , the Song court was fleeing on ships to islands off the coast of Zhejiang , and then further south to <unk> . The Jin sent ships to chase after Gaozong , but failed to catch him . They gave up the pursuit and the Jurchens retreated north . After they plundered the undefended cities of Hangzhou and <unk> , they finally started to face resistance from Song armies led by Yue Fei and Han Shizhong . The latter even inflicted a major defeat on Jurchen forces and tried to prevent Wuzhu from crossing back to the north bank of the Yangtze . The small boats of the Jin army were <unk> by Han Shizhong 's fleet of <unk> vessels . Wuzhu eventually managed to cross the river when he had his troops use incendiary arrows to neutralize Han 's ships by burning their sails . Wuzhu 's troops came back south of the Yangtze one last time to Jiankang , which they pillaged , and then headed north . Yet the Jin had been caught off guard by the strength of the Song navy , and Wuzhu never tried to cross the Yangtze River again . In early 1131 , Jin armies between the Huai and the Yangtze were repelled by bandits loyal to the Song . Zhang <unk> ( <unk> ) , the leader of the bandits , was given a government position for his victory against the Jin . 
- After the Jin incursion that almost captured Gaozong , the sovereign ordered pacification commissioner Zhang Jun ( <unk> – 1164 ) , who was in charge of Shaanxi and Sichuan in the far west , to attack the Jin there to relieve pressure on the court . Zhang put together a large army , but was defeated by Wuzhu near Xi 'an in late 1130 . Wuzhu advanced further west into Gansu , and drove as far south as <unk> ( <unk> , modern <unk> ) . The most important battles between Jin and Song in 1131 and 1132 took place in Shaanxi , Gansu , and Sichuan . The Jin lost two battles at <unk> Yuan in 1131 . After failing to enter Sichuan , Wuzhu retreated to Yanjing . He returned to the western front again from 1132 to <unk> . The Jin attacked Hubei and Shaanxi in 1132 . Wuzhu captured <unk> Yuan in 1133 , but his advance was halted by a defeat at <unk> Pass . He gave up on taking Sichuan , and no more major battles were fought between the Jin and Song for the rest of the decade . 
- The Song court returned to Hangzhou in 1133 , and the city was renamed Lin 'an . The imperial ancestral temple was built in Hangzhou later that same year , a sign that the court had in practice established Hangzhou as the Song capital without a formal declaration . It was treated as a temporary capital . Between 1130 and 1137 , the court would sporadically move to Jiankang , and back to Hangzhou . There were proposals to make Jiankang the new capital , but Hangzhou won out because the court considered it a more secure city . The natural barriers that surrounded Hangzhou , including lakes and rice <unk> , made it more difficult for the Jurchen cavalry to breach its fortifications . <unk> to the sea made it easier to retreat from the city . In 1138 , Gaozong officially declared Hangzhou the capital of the dynasty , but the label of temporary capital would still be in place . Hangzhou would remain the capital of the Southern Song for the next 150 years , growing into a major commercial and cultural center . 
- 
- = = = Da Qi invades the Song = = = 
- 
- Qin Hui , an official of the Song court , recommended a peaceful solution to the conflict in 1130 , saying that , " If it is desirable that there will be no more conflicts under Heaven , it is necessary for the <unk> to stay in the south and the <unk> in the north . " Gaozong , who considered himself a <unk> , initially rejected the proposal . There were gestures toward peace in 1132 , when the Jin freed an imprisoned Song diplomat , and in 1133 , when the Song offered to become a Jin vassal , but a treaty never materialized . The Jin requirement that the border between the two states be moved south from the Huai River to the Yangtze was too large of a hurdle for the two sides to reach an agreement . 
- The continuing insurgency of anti @-@ Jin forces in northern China hampered the Jurchen campaigns south of the Yangtze . <unk> to let the war drag on , the Jin decided to create Da Qi ( the " Great Qi " ) , their second attempt at a puppet state in northern China . The Jurchens believed that this state , nominally ruled by someone of Han Chinese descent , would be able to attract the allegiance of disaffected members of the insurgency . The Jurchens also suffered from a shortage of skilled manpower , and controlling the entirety of northern China was not administratively feasible . In the final months of 1129 , Liu Yu ( <unk> ; <unk> – <unk> ) won the favor of the Jin emperor Taizong . Liu was a Song official from Hebei who had been a prefect of <unk> in Shandong before his defection to the Jin in 1128 . Da Qi was formed late in 1130 , and the Jin enthroned Liu as its emperor . <unk> in Hebei was the first capital of Qi , before its move to Kaifeng , former capital of the Northern Song . The Qi government instituted military conscription , made an attempt at reforming the bureaucracy , and enacted laws that enforced the collection of high taxes . It was also responsible for supplying a large portion of the troops that fought the Song in the seven years following its creation . 
- The Jin granted Qi more autonomy than the first puppet government of Chu , but Liu Yu was <unk> to obey the orders of the Jurchen generals . With Jin support , Da Qi invaded the Song in November 1133 . Li <unk> , a Song <unk> who had joined the Qi , led the campaign . Xiangyang and nearby prefectures fell to his army . The capture of Xiangyang on the Han River gave the Jurchens a passage into the central valley of the Yangtze River . Their southward push was halted by the general Yue Fei . In <unk> , Yue Fei defeated Li and retook Xiangyang and its surrounding prefectures . Later that year , however , Qi and Jin initiated a new offensive further east along the Huai River . For the first time , Gaozong issued an <unk> officially condemning Da Qi . The armies of Qi and Jin won a series of victories in the Huai valley , but were repelled by Han Shizhong near Yangzhou and by Yue Fei at <unk> ( <unk> , modern <unk> ) . Their sudden withdrawal in 1135 in response to the death of Jin Emperor Taizong gave the Song time to regroup . The war <unk> in late 1136 when Da Qi attacked the Huainan circuits of the Song . Qi lost a battle at <unk> ( <unk> ) , in modern Anhui , against a Song army led by Yang <unk> ( <unk> ; 1102 – 1166 ) . The victory boosted Song morale , and the military commissioner Zhang Jun ( <unk> – 1164 ) convinced Gaozong to begin plans for a counterattack . Gaozong first agreed , but he abandoned the counteroffensive when an officer named Li <unk> ( <unk> ) killed his superior official and defected to the Jin with tens of thousands of soldiers . Meanwhile , Emperor Xizong ( r . 1135 – 1150 ) inherited the Jin throne from Taizong , and pushed for peace . He and his generals were disappointed with Liu Yu 's military failures and believed that Liu was secretly conspiring with Yue Fei . In late 1137 , the Jin reduced Liu Yu 's title to that of a prince and abolished the state of Qi . The Jin and Song renewed the negotiations towards peace . 
- 
- = = = Song counteroffensive and the peace process = = = 
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- Gaozong promoted Qin Hui in 1138 and put him in charge of deliberations with the Jin . Yue Fei , Han Shizhong , and a large number of officials at court criticized the peace overtures . Aided by his control of the <unk> , Qin purged his enemies and continued negotiations . In 1138 the Jin and Song agreed to a treaty that designated the Yellow River as border between the two states and recognized Gaozong as a " subject " of the Jin . But because there remained opposition to the treaty in both the courts of the Jin and Song , the treaty never came into effect . A Jurchen army led by Wuzhu invaded in early 1140 . The Song counteroffensive that followed achieved large territorial gains . Song general Liu Qi ( <unk> ) won a battle against Wuzhu at <unk> ( modern <unk> in Anhui ) . Yue Fei was assigned to head the Song forces defending the Huainan region . Instead of advancing to Huainan , however , Wuzhu retreated to Kaifeng and Yue 's army followed him into Jin territory , <unk> an order by Gaozong that forbade Yue from going on the offensive . Yue captured <unk> and sent soldiers across the Yellow River to stir up a peasant rebellion against the Jin . On July 8 , 1140 , at the Battle of <unk> , Wuzhu launched a surprise attack on Song forces with an army of 100 @,@ 000 infantry and 15 @,@ 000 horsemen . Yue Fei directed his cavalry to attack the Jurchen soldiers and won a decisive victory . He continued on to Henan , where he recaptured <unk> and Luoyang . Later in 1140 , Yue was forced to withdraw after the emperor ordered him to return to the Song court . 
- Emperor Gaozong supported settling a peace treaty with the Jurchens and sought to <unk> in the assertiveness of the military . The military expeditions of Yue Fei and other generals were an obstacle to peace negotiations . The government weakened the military by rewarding Yue Fei , Han Shizhong , and Zhang Jun ( 1086 – 1154 ) with titles that relieved them of their command over the Song armies . Han Shizhong , a critic of the treaty , retired . Yue Fei also announced his resignation as an act of protest . In <unk> Qin Hui had him imprisoned for insubordination . <unk> with treason , Yue Fei was poisoned in jail on Qin 's orders in early 1142 . Jurchen diplomatic pressure during the peace talks may have played a role , but Qin Hui 's alleged <unk> with the Jin has never been proven . 
- After his execution , Yue Fei 's reputation for defending the Southern Song grew to that of a national folk hero . Qin Hui was <unk> by later historians , who accused him of betraying the Song . The real Yue Fei differed from the later myths based on his exploits . Contrary to traditional legends , Yue was only one of many generals who fought against the Jin in northern China . Traditional accounts have also blamed Gaozong for Yue Fei 's execution and submitting to the Jin . Qin Hui , in a reply to Gaozong 's gratitude for the success of the peace negotiations , told the emperor that " the decision to make peace was entirely Your Majesty 's . Your servant only carried it out ; what achievement was there in this for me ? " 
- 
- = = = Treaty of Shaoxing = = = 
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- On October 11 , 1142 , after about a year of negotiations , the Treaty of Shaoxing was ratified , ending the conflict between the Jin and the Song . By the terms of the treaty , the Huai River , north of the Yangtze , was designated as the boundary between the two empires . The Song agreed to pay a yearly tribute of 250 @,@ 000 taels of silver and 250 @,@ 000 packs of silk to the Jin . 
- The treaty reduced the Southern Song 's status to that of a Jin vassal . The document designated the Song as the " insignificant state " , while the Jin was recognized as the " superior state " . The text of the treaty has not survived in Chinese records , a sign of its humiliating reputation . The contents of the agreement were recovered from a Jurchen biography . Once the treaty had been settled , the Jurchens retreated north and trade resumed between the two empires . The peace ensured by the Shaoxing treaty lasted for the next seventy years , but was interrupted twice . One campaign was initiated by the Song and the other by the Jin . 
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- = = = Further campaigns = = = 
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- = = = = Prince of Hailing 's campaign = = = = 
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- Wanyan Liang ( the Prince of Hailing ) led a coup against Emperor Xizong and became fourth emperor of the Jin dynasty in 1150 . Wanyan Liang presented himself as a Chinese emperor , and planned to unite China by conquering the Song . In <unk> , Wanyan Liang provided a <unk> <unk> by announcing that the Song had broken the 1142 peace treaty by acquiring horses . He instituted an unpopular draft that was the source of widespread unrest in the empire . Anti @-@ Jin <unk> erupted among the Khitans and in Jin provinces bordering the Song . Wanyan Liang did not allow dissent , and opposition to the war was severely punished . The Song had been notified beforehand of Wanyan Liang 's plan . They prepared by securing their defenses along the border , mainly near the Yangtze River , but were hampered by Emperor Gaozong 's <unk> . Gaozong 's desire for peace made him <unk> to provoking the Jin . Wanyan Liang began the invasion in 1161 without formally declaring war . Jurchen armies personally led by Wanyan Liang left Kaifeng on October 15 , reached the Huai River border on October 28 , and marched in the direction of the Yangtze . The Song lost the Huai to the Jurchens but captured a few Jin prefectures in the west , slowing the Jurchen advance . A group of Jurchen generals were sent to cross the Yangtze near the city of Caishi ( south of Ma <unk> in modern Anhui ) while Wanyan Liang established a base near Yangzhou . 
- The Song official Yu <unk> was in command of the army defending the river . The Jurchen army was defeated while attacking Caishi between November 26 and 27 during the Battle of Caishi . The <unk> @-@ wheel ships of the Song navy , armed with <unk> that fired gunpowder bombs , overwhelmed the light ships of the Jin fleet . Jin ships were unable to compete because they were smaller and hastily constructed . The bombs launched by the Song contained mixtures of gunpowder , lime , scraps of iron , and a poison that was likely arsenic . Traditional Chinese accounts consider this the turning point of the war , characterizing it as a military upset that secured southern China from the northern invaders . The significance of the battle is said to have rivaled a similarly revered victory at the Battle of Fei River in the 4th century . Contemporaneous Song accounts claimed that the 18 @,@ 000 Song soldiers commanded by Yu <unk> and tasked with defending Caishi were able to defeat the invading Jurchen army of 400 @,@ 000 soldiers . Modern historians are more skeptical and consider the Jurchen numbers an exaggeration . Song historians may have confused the number of Jurchen soldiers at the Battle of Caishi with the total number of soldiers under the command of Wanyan Liang . The conflict was not the one @-@ sided battle that traditional accounts imply , and the Song had numerous advantages over the Jin . The Song fleet was larger than the Jin 's , and the Jin were unable to use their greatest asset , cavalry , in a naval battle . 
- A modern analysis of the battlefield has shown that it was a minor battle , although the victory did boost Song morale . The Jin lost , but only suffered about 4 @,@ 000 casualties and the battle was not fatal to the Jurchen war effort . It was Wanyan Liang 's poor relationships with the Jurchen generals , who despised him , that doomed the chances of a Jin victory . On December 15 , Wanyan Liang was assassinated in his military camp by disaffected officers . He was succeeded by Emperor Shizong ( r . 1161 – 1189 ) . Shizong was pressured into ending the unpopular war with the Song , and ordered the withdrawal of Jin forces in <unk> . Emperor Gaozong retired from the throne that same year . His mishandling of the war with Wanyan Liang was one of many reasons for his abdication . Skirmishes between the Song and Jin continued along the border , but subsided in 1165 after the negotiation of a peace treaty . There were no major territorial changes . The treaty dictated that the Song still had to pay the annual indemnity , but the indemnity was renamed from " tribute " , which had implied a subordinate relationship , to " payment " . 
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- = = = = Song <unk> = = = = 
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- The Jin were weakened by the pressure of the rising Mongols to the north , a series of floods culminating in a Yellow River flood in 1194 that devastated Hebei and Shandong in northern China , and the droughts and swarming locusts that plagued the south near the Huai . The Song were informed of the Jurchen predicament by their ambassadors , who traveled twice a year to the Jin capital , and started provoking their northern neighbor . The hostilities were instigated by chancellor Han Tuozhou . The Song Emperor <unk> ( r . 1194 – 1224 ) took little interest in the war effort . Under Han Tuozhou 's supervision , preparations for the war proceeded gradually and cautiously . The court venerated the <unk> hero Yue Fei and Han orchestrated the publishing of historical records that justified war with the Jin . From 1204 onwards , Song armed groups raided Jin settlements . Han Tuozhou was designated the head of national security in 1205 . The Song funded insurgents in the north that professed loyalist sympathies . These early clashes continued to escalate , partly <unk> by <unk> Song officials , and war against the Jin was officially declared on June 14 , 1206 . The document that announced the war claimed the Jin lost the Mandate of Heaven , a sign that they were unfit to rule , and called for an insurrection of Han Chinese against the Jin state . 
- Song armies led by general Bi <unk> ( <unk> ; d . 1217 ) captured the barely defended border city of <unk> <unk> ( on the north bank of the Huai River across from modern <unk> County ) but suffered large losses against the Jurchens in Hebei . The Jin repelled the Song and moved south to <unk> the Song town of <unk> <unk> on the Grand Canal just south of the Huai River . Bi defended the town , and the Jurchens withdrew from the siege after three months . By the fall of 1206 , however , the Jurchens had captured multiple towns and military bases . The Jin initiated an offensive against Song prefectures in the central front of the war , capturing <unk> and <unk> ( <unk> ; on the Han River near modern <unk> ) . By the fall of 1206 , the Song offensive had already failed <unk> . Soldier morale sank as weather conditions worsened , supplies ran out , and hunger spread , forcing many to desert . The massive <unk> of Han Chinese in northern China that the Song had expected never materialized . 
- A notable betrayal did occur on the Song side , however : Wu Xi ( <unk> ; d . 1207 ) , the governor @-@ general of Sichuan , defected to the Jin in December 1206 . The Song had depended on Wu 's success in the west to divert Jin soldiers away from the eastern front . He had attacked Jin positions earlier in 1206 , but his army of about 50 @,@ 000 men had been repelled . Wu 's defection could have meant the loss of the entire western front of the war , but Song loyalists assassinated Wu on March 29 , 1207 , before Jin troops could take control of the surrendered territories . An Bing ( <unk> ; d . 1221 ) was given Wu Xi 's position , but the cohesion of Song forces in the west fell apart after Wu 's demise and commanders turned on each other in the ensuing infighting . 
- Fighting continued in 1207 , but by the end of that year the war was at a stalemate . The Song was now on the defensive , while the Jin failed to make gains in Song territory . The failure of Han Tuozhou 's aggressive policies led to his demise . On December 15 , 1207 , Han was beaten to death by the Imperial Palace Guards . His accomplice Su <unk> ( <unk> ) was executed , and other officials connected to Han were dismissed or exiled . Since neither combatant was eager to continue the war , they returned to negotiations . A peace treaty was signed on November 2 , 1208 , and the Song tribute to the Jin was reinstated . The Song annual indemnity increased by 50 @,@ 000 taels of silver and 50 @,@ 000 packs of fabric . The treaty also stipulated that the Song had to present to the Jin the head of Han Tuozhou , who the Jin held responsible for starting the war . The heads of Han and Su were severed from their exhumed corpses , exhibited to the public , then delivered to the Jin . 
- 
- = = = = Song – Jin war during the rise of the Mongols = = = = 
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- The Mongols , a nomadic confederation , had unified in the middle of the twelfth century . They and other steppe nomads occasionally raided the Jin empire from the northwest . The Jin <unk> away from punitive expeditions and was content with <unk> , similar to the practices of the Song . The Mongols , formerly a Jin tributary , ended their <unk> in 1210 and attacked the Jin in <unk> . In light of this event , the Song court debated ending tributary payments to the weakened Jin , but they chose to avoid <unk> the Jin . They refused Western Xia 's offers of <unk> against the Jin in 1214 and willingly complied when in 1215 the Jin rejected a request to lower the annual indemnity . Meanwhile , in 1214 , the Jin retreated from the besieged capital of <unk> to Kaifeng , which became the new capital of the dynasty . As the Mongols expanded , the Jin suffered territorial losses and attacked the Song in 1217 to compensate for their shrinking territory . Periodic Song raids against the Jin were the official justification for the war . Another likely motive was that the conquest of the Song would have given the Jin a place to escape should the Mongols succeed in taking control of the north . Shi <unk> ( <unk> ; 1164 – 1233 ) , the chancellor of Song Emperor <unk> ( r . 1224 – 1264 ) , was hesitant to fight the Jin and delayed the declaration of war for two months . Song generals were largely autonomous , allowing Shi to evade blame for their military <unk> . The Jin advanced across the border from the center and western fronts . Jurchen military successes were limited , and the Jin faced repeated raids from the neighboring state of Western Xia . In 1217 , the Song generals Meng <unk> ( <unk> ) and Hu <unk> ( <unk> ) defeated the Jin and prevented them from capturing <unk> and <unk> . 
- A second Jin campaign in late 1217 did marginally better than the first . In the east , the Jin made little <unk> in the Huai River valley , but in the west they captured <unk> and <unk> Pass ( <unk> ; modern Shaanxi ) in late 1217 . The Jin tried to captured <unk> in <unk> South circuit again in 1218 and 1219 , but failed . A Song counteroffensive in early 1218 captured <unk> and in 1219 the Jin cities of <unk> and <unk> were pillaged twice by a Song army commanded by Zhao Fang ( <unk> ; d . 1221 ) . In the west , command of the Song forces in Sichuan was given to An Bing , who had previously been dismissed from this position . He successfully defended the western front , but was unable to advance further because of local uprisings in the area . The Jin tried to <unk> an indemnity from the Song but never received it . In the last of the three campaigns , in early 1221 , the Jin captured the city of Qizhou ( <unk> ; in Huainan West ) deep in Song territory . Song armies led by Hu <unk> and Li Quan ( <unk> ; d . 1231 ) defeated the Jin , who then withdrew . In 1224 both sides agreed on a peace treaty that ended the annual tributes to the Jin . <unk> missions between the Jin and Song were also cut off . 
- 
- = = = = <unk> – Song alliance = = = = 
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- In February 1233 , the Mongols took Kaifeng after a siege of more than 10 months and the Jin court retreated to the town of Caizhou . In 1233 Emperor <unk> ( r . 1224 – 1234 ) of the Jin dispatched diplomats to <unk> the Song for supplies . Jin envoys reported to the Song that the Mongols would invade the Song after they were done with the Jin — a forecast that would later be proven true — but the Song ignored the warning and rebuffed the request . They instead formed an alliance with the Mongols against the Jin . The Song provided supplies to the Mongols in return for parts of Henan . The Jin dynasty collapsed when <unk> and Song troops defeated the Jurchens at the siege of Caizhou in 1234 . General Meng Gong ( <unk> ) led the Song army against Caizhou . The penultimate emperor of the Jin , Emperor <unk> , took his own life . His short @-@ lived successor , Emperor Mo , was killed in the town a few days later . The Mongols later turned their sights towards the Song . After decades of war , the Song dynasty also fell in 1279 , when the remaining Song loyalists lost to the Mongols in a naval battle near Guangdong . 
- 
- = = Historical significance = = 
- 
- 
- = = = Cultural and demographic changes = = = 
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- Jurchen migrants from the northeastern reaches of Jin territory settled in the Jin @-@ controlled lands of northern China . <unk> less than ten percent of the total population , the two to three million ruling Jurchens were a minority in a region that was still dominated by 30 million Han Chinese . The southward expansion of the Jurchens caused the Jin to transition their <unk> government of semi @-@ agrarian tribes to a bureaucratic Chinese @-@ style dynasty . 
- The Jin government initially promoted an independent Jurchen culture alongside their adoption of the centralized Chinese imperial bureaucracy , but the empire was gradually <unk> over time . The Jurchens became fluent in the Chinese language , and the philosophy of <unk> was used to legitimize the ruling government . Confucian state rituals were adopted during the reign of Emperor Xizong ( 1135 – 1150 ) . The Jin implemented imperial exams on the Confucian Classics , first regionally and then for the entire empire . The Classics and other works of Chinese literature were translated into Jurchen and studied by Jin intellectuals , but very few Jurchens actively contributed to the classical literature of the Jin . The <unk> script , from the Chinese family of scripts , formed the basis of a national writing system for the empire , the Jurchen script . All three scripts were working languages of the government . Jurchen clans adopted Chinese personal names with their Jurchen names . Wanyan Liang ( the Prince of Hailing ; r . 1150 – 1161 ) was an enthusiastic proponent of Jurchen <unk> and enacted policies to encourage it . Wanyan Liang had been <unk> by Song diplomats from childhood , and his emulation of Song practices earned him the Jurchen nickname of " <unk> the Chinese " . He studied the Chinese classics , drank tea , and played Chinese chess for recreation . Under his reign , the administrative core of the Jin state was moved south from <unk> . He instated Beijing as the Jin main capital in <unk> . Palaces were erected in Beijing and Kaifeng , while the original , more northerly residences of Jurchen chieftains were demolished . 
- The emperor 's political reforms were connected with his desire to conquer all of China and to legitimize himself as a Chinese emperor . The prospect of conquering southern China was cut short by Wanyan Liang 's assassination . Wanyan Liang 's successor , Emperor Shizong , was less enthusiastic about <unk> and reversed several of Wanyan Liang 's edicts . He sanctioned new policies with the intent to slow the <unk> of the Jurchens . Shizong 's prohibitions were abandoned by Emperor <unk> ( r . 1189 – 1208 ) , who promoted reforms that transformed the political structure of the dynasty closer to that of the Song and Tang dynasties . Despite cultural and demographic changes , military hostilities between the Jin and the Song persisted until the fall of the Jin . 
- In the south , the retreat of the Song dynasty led to major demographic changes . The population of refugees from the north that <unk> in Hangzhou and Jiankang ( modern <unk> ) eventually grew greater than the population of original residents , whose numbers had dwindled from repeated Jurchen raids . The government encouraged the <unk> of peasant migrants from the southern provinces of the Song to the <unk> territories between the Yangtze and the Huai rivers . 
- The new capital Hangzhou grew into a major commercial and cultural center . It rose from a middling city of no special importance to one of the world 's largest and most prosperous . During his stay in Hangzhou in the Yuan dynasty ( 1260 – <unk> ) , when the city was not as wealthy as it had been under the Song , Marco Polo remarked that " this city is greater than any in the world " . Once retaking northern China became less plausible and Hangzhou grew into a significant trading city , the government buildings were extended and renovated to better <unk> its status as an imperial capital . The <unk> sized imperial palace was expanded in 1133 with new <unk> alleyways and in <unk> with an extension of the palace walls . 
- The loss of northern China , the cultural center of Chinese civilization , diminished the international status of the Song dynasty . After the Jurchen conquest of the north , Korea recognized the Jin , not the Song , as the legitimate dynasty of China . The Song 's military failures reduced it to a subordinate of the Jin , turning it into a " China among equals " . The Song economy , however , recovered quickly after the move south . Government revenues earned from taxing foreign trade nearly doubled between the closing of the Northern Song era in 1127 and the final years of Gaozong 's reign in the early <unk> . The recovery was not uniform , and areas like Huainan and Hubei that had been directly affected by the war took decades to return to their pre @-@ war levels . In spite of multiple wars , the Jin remained one of the main trading partners of the Song . Song demand for foreign products like fur and horses went unabated . Historian <unk> <unk> ( <unk> , b . 1930 ) believes that Song commerce with the north was profitable enough that it compensated for the silver delivered annually as an indemnity to the Jin . 
- 
- = = = Gunpowder warfare = = = 
- 
- The battles between the Song and the Jin spurred the invention and use of gunpowder weapons . There are reports that the fire lance , one of the earliest ancestors of the <unk> , was used by the Song against the Jurchens besieging De 'an ( <unk> ; modern <unk> in eastern Hubei ) in 1132 , during the Jin invasion of Hubei and Shaanxi . The weapon consisted of a spear attached with a flamethrower capable of firing projectiles from a barrel constructed of bamboo or paper . They were built by soldiers under the command of Chen Gui ( <unk> ) , who led the Song army defending De 'an . The fire lances with which Song soldiers were equipped at De 'an were built for destroying the wooden siege engines of the Jin and not for combat against the Jin infantry . Song soldiers compensated for the limited range and mobility of the weapon by timing their attacks on the Jin siege engines , waiting until they were within range of the fire lances . Later fire lances used metal barrels , fired projectiles farther and with greater force , and could be used against infantry . 
- Early rudimentary bombs like the huopao fire bomb ( 火礮 ) and the huopao ( <unk> ) bombs propelled by <unk> were also in use as incendiary weapons . The defending Song army used huopao ( 火礮 ) during the first Jin siege of Kaifeng in 1126 . On the opposing side , the Jin launched incendiary bombs from siege towers down onto the city below . In 1127 , huopao ( 火礮 ) were employed by the Song troops defending De 'an and by the Jin soldiers besieging the city . The government official Lin <unk> ( <unk> ) proposed to make incendiary bombs and arrows mandatory for all warships in the Song navy . At the battle of Caishi in 1161 , Song ships fired <unk> <unk> ( <unk> ) , also called <unk> huopao bombs ( <unk> ) , from <unk> against the ships of the Jin fleet commanded by Wanyan Liang . The gunpowder mixture of the bomb contained powdered lime , which produced <unk> smoke once the casing of the bomb shattered . The Song also deployed incendiary weapons at the battle of <unk> during the same year . 
- Gunpowder was also applied to arrows in 1206 by a Song army stationed in Xiangyang . The arrows were most likely an incendiary weapon , but its function may also have resembled that of an early rocket . At the Jin siege of Qizhou ( <unk> ) in 1221 , the Jurchens fought the Song with gunpowder bombs and arrows . The Jin <unk> ( <unk> , " iron huopao " ) , which had cast iron <unk> , are the first known bombs that could explode . The bomb needed to be capable of detonating in order to penetrate the iron casing . The Song army had a large supply of incendiary bombs , but there are no reports of them having a weapon similar to the Jin 's detonating bombs . A participant in the siege recounted in the <unk> Qi Qi Lu ( <unk> ) that the Song army at Qizhou had an arsenal of 3000 huopao ( 火礮 ) , 7000 incendiary gunpowder arrows for <unk> and <unk> for bows , as well as <unk> <unk> ( <unk> ) , probably leather bags filled with gunpowder . 
- 
- 
- = T30 Howitzer Motor Carriage = 
- 
- The T30 Howitzer Motor Carriage ( HMC ) was a United States Army self @-@ propelled gun used in World War II . Its design was based on requirements for an assault gun issued by the Armored Force in 1941 and it was built as an interim solution until a fully tracked design was complete . 
- Produced by the White Motor Company , the vehicle was simply a 75 mm Pack Howitzer <unk> mounted on a modified M3 Half @-@ track . It was first used in combat in the North African Campaign in November 1942 . It later served in Italy and France , and possibly in the Pacific . Some were later leased to French forces and the type was used as late as the First Indochina War in the 1950s . 
- 
- = = Specifications = = 
- 
- Based on the M3 Half @-@ track , the T30 's specifications were similar to its parent vehicle . It was 20 ft 7 in ( 6 @.@ 28 m ) long , 6 ft 5 in ( 1 @.@ 96 m ) wide , 8 ft 3 in ( 2 @.@ 51 m ) and high , and weighed 10 @.@ 3 short tons ( 9 @.@ 3 t ) . The suspension consisted of vertical <unk> springs for the tracks and leaf springs for the wheels , while the vehicle had a fuel capacity of 60 US gallons ( 230 l ) . It had a range of 150 mi ( 240 km ) and had a speed of 40 mph ( 64 km / h ) , and was powered by a White <unk> , 147 hp ( 110 kW ) , 386 <unk> ( 6 @,@ 330 <unk> ) , six @-@ cylinder , gasoline engine , with a compression ratio of 6 @.@ 3 : 1 . It had a power @-@ to @-@ weight ratio of 15 @.@ 8 hp / ton . 
- 
- = = = Gun specifications = = = 
- 
- The T30 's main armament was a short <unk> 3 @.@ 0 in ( 75 mm ) pack howitzer . The 75 mm Pack Howitzer <unk> as mounted could depress nine degrees , elevate 50 degrees , and traverse 22 @.@ 5 degrees to each side . The vehicle had stowage for sixty rounds of 75 mm ammunition and , although it was not designed for anti @-@ tank use , it had a high explosive anti @-@ tank ( <unk> ) shell that could penetrate 3 in ( 76 mm ) of armor . The gun shield had 0 @.@ 375 in ( 9 @.@ 5 mm ) thick armor , designed to stop a <unk> cal ( 7 @.@ 62 mm ) bullet from 250 yards ( 230 m ) away . 
- 
- = = Development = = 
- 
- The T30 HMC was originally conceived in 1941 as an interim design to fulfil the Armored Force 's requirement for an assault gun to equip tank and armored reconnaissance units . The Ordnance Department design was based on the M3 Half @-@ track in order that it could be brought into service quickly . A prototype vehicle was authorized in October 1941 armed with an <unk> 75 mm Pack Howitzer and a mount that was designed to fit on a simple box structure in the back of an M3 Half @-@ track . 
- <unk> for the production of two prototypes was given in January 1942 ; first deliveries of the vehicle were made the following month from the White Motor Company . As it was seen as a temporary solution it was never given type classification . In September 1942 , the T30 was partially replaced by the Howitzer Motor Carriage M8 ( the same gun on an M3 Stuart ) . After that , it was declared as " limited standard " . A total of 500 were produced , all by the White Motor Company . 
- 
- = = Service history = = 
- 
- The T30 HMC entered service in November 1942 , seeing action for the first time in the North African Campaign . In the 1st Armored Division , each armored regiment was issued twelve T30s . Of these , three were used in each battalion headquarters platoon and three were used in each regimental reconnaissance platoon . In addition , the 6th and 41st Armored Infantry Regiments were each issued with nine T30 HMCs , with three of them being allocated to the headquarters platoon in each armored infantry battalion . 
- Most infantry divisions in the North African Campaign deployed a " cannon company " equipped with six T30s and two 105 mm <unk> HMCs . In one encounter in North Africa , the T30 was used in an attempt to destroy German tanks . Although the T30s fired several <unk> , the German tanks were barely damaged and the T30s were ordered to retreat under the cover of smoke to prevent losses . After several similar experiences , U.S. forces ceased the practice of employing self @-@ propelled howitzers or mortars in direct combat with tanks . 
- The T30 also served during the Allied invasion of Sicily in 1943 , the war in Italy in 1944 , and possibly in the Pacific . It was removed from infantry division use in March 1943 , following changes in the organization of US infantry battalions , and was replaced by towed howitzers . The T30 was eventually replaced by the M8 HMC , which was based on the <unk> Stuart light tank , and which began entering service around the same time as the T30 . Only 312 T30 HMCs were delivered in their original configuration , as the last 188 were converted back into M3 Half @-@ tracks before they were delivered . Later on , the U.S. leased several to French forces and some were used as late as the First Indochina War before the vehicle was retired from service in the 1950s . 
- 
- 
- = Memory Almost Full = 
- 
- Memory Almost Full is the fourteenth solo studio album by Paul McCartney , <unk> his Wings @-@ era discography , his orchestral works and his output as the <unk> . It was released in the United Kingdom on 4 June 2007 and in the United States a day later . The album was the first release on Starbucks ' Hear Music label . It was produced by David Kahne and recorded at Abbey Road Studios , Henson Recording Studios , AIR Studios , <unk> Hill Mill Studios and RAK Studios between October 2003 , and from 2006 to February 2007 . In between the 2003 and 2006 sessions , McCartney was working on another studio album , Chaos and Creation in the Backyard ( 2005 ) , with producer Nigel <unk> . 
- Memory Almost Full reached the Top 5 in both the UK and US , as well as Denmark , Sweden , Greece , and Norway . The Grammy @-@ nominated album has sold over 2 million copies worldwide and has been certified gold by the RIAA for shipments of over 500 @,@ 000 copies just in the United States . The album was released in three versions : a single disc , a 2 @-@ CD set , and a CD / DVD deluxe edition , the latter of which was released on 6 November 2007 . 
- 
- = = Background = = 
- 
- Nine demos were recorded at The Mill studio in September 2003 by Paul McCartney and his touring band . A month later , in October , album sessions for Memory Almost Full began , and were produced by David Kahne and recorded at Abbey Road Studios . McCartney and the band recorded the songs " You Tell Me " , " Only Mama Knows " , " Vintage Clothes " , " That Was Me " , " Feet in the Clouds " , " House of Wax " , " The End of the End " , and " Whole Life " . However , the sessions were cut short and put on hiatus when McCartney started another album , Chaos and Creation in the Backyard , with producer Nigel <unk> . In the website constructed for the album , McCartney stated : " I actually started this album , Memory Almost Full , before my last album Chaos and Creation in the Backyard , released September 2005 . ( ... ) When I was just finishing up everything concerned with Chaos and had just got the Grammy nominations ( 2006 ) I realised I had this album to go back to and finish off . So I got it out to listen to it again , wondering if I would enjoy it , but actually I really loved it . All I did at first was just listen to a couple of things and then I began to think , ' OK , I like that track – now , what is wrong with it ? ' And it might be something like a drum sound , so then I would re @-@ drum and see where we would get to . ( ... ) In places it 's a very personal record and a lot of it is retrospective , drawing from memory , like memories from being a kid , from Liverpool and from summers gone . The album is evocative , emotional , rocking , but I can 't really sum it up in one sentence " . 
- 
- = = Recording = = 
- 
- Many songs from Memory Almost Full were from a group of songs , which also included songs from Chaos and Creation in the Backyard , and some intended for the former nearly ended up on the latter . Any songs that were started , but not finished , for Chaos and Creation in the Backyard , McCartney didn 't want to re @-@ do for Memory Almost Full . As sessions for the album progressed McCartney wrote some more songs , something that McCartney used to do when he was in the Beatles . A song called " Perfect Lover " was recorded at either one of the three following studios : RAK Studios , AIR Studios or Ocean Way Studios ; sometime between November 2003 and April 2005 . " Perfect Lover " , in its original form was more folk @-@ like , similar to Chaos and Creation in the Backyard 's " Friends to Go " . " Perfect Lover " went through a minor lyrical change , the bridge was changed , and an overhaul of its musical arrangement , before it finally became " Ever Present Past " . Two years after the 2003 session , sessions for the album started again . The book Paul McCartney : Recording Sessions ( 1969 @-@ 2013 ) . A Journey Through Paul McCartney 's Songs After The Beatles reports that the recordings of the album were started in September – October 2003 and resumed in February 2004 at Abbey Road , with other sessions taking place between March 2006 and February 2007 . 
- New tracks were recorded at the following studios : McCartney 's home studio in Sussex , The Mill , Los Angeles ' Henson Studios , London 's RAK Studios and AIR Studios , and New York 's <unk> Studios . The songs recorded at those studios were " Nod Your Head " , " In Private " , " 222 " , " Gratitude " , " Mr. Bellamy " , " See Your Sunshine " , and " Ever Present Past " . Of those songs , " Mr. Bellamy " , " Ever Present Past " , " Gratitude " , " Nod Your Head " , and " In Private " were all recorded on the same day , in March 2006 . As well as working on songs from the first Memory Almost Full album session in 2003 , " Why So Blue " was re @-@ recorded . In total , between 20 and 25 songs were recorded for the album . " Dance Tonight " was recorded , along with " Feet in the Clouds " and " 222 " being reworked , between January and February 2007 at RAK Studios , as the last song recorded for the album . The album was mixed by Kahne and Andy Wallace . 
- 
- = = Content = = 
- 
- The Rock Radio website leaked a track listing for the album on 12 April 2007 . A day later , producer David Kahne stated on the same site that the leaked listing was <unk> . In an interview with Billboard magazine in May 2007 , McCartney said that the album 's material was " in some ways a little bit retrospective . Some of them are of now , some of them <unk> back to the past , but all of them are songs I 'm very proud of . " McCartney played mandolin on the song " Dance Tonight " . He comments that " In searching the instrument to try and find chords , which I did with the guitar when I was 14 , probably , that freshness was brought back . " " Ever Present Past " , which McCartney called " personal " , originally started out as a song called " Perfect Lover " . " Ever Present Past " also includes references to the Beatles . In June 2007 , McCartney revealed that " See Your Sunshine " " is pretty much an out @-@ and @-@ out love song for Heather . A lot of the album was done before , during and after our separation . I didn 't go back and take out any songs to do with her . " " You Tell Me " is about McCartney 's memories of his previous wife , Linda . 
- " Mr. Bellamy " , the sixth song on the album , was thought by online fans to be about McCartney 's then @-@ recent divorce . McCartney invited <unk> frontman Thom Yorke to play piano on the song , but he declined . The press ran articles claiming that Yorke had " <unk> " McCartney , but Yorke later revealed that he " really liked the song " but felt he would be unable to perform to the required standard . " Gratitude " is reportedly about the divorce between Heather Mills and McCartney . 
- The album features a five song @-@ medley , which in an interview with Billboard magazine , McCartney said that it was previously " something I wanted to revisit " as " nobody had been doing that for a while . " The medley was a group of intentionally written material , whereas McCartney had worked on the Beatles ' Abbey Road which , however , was actually made up of " bits we had knocking around . " The medley starts off with " Vintage Clothes " , which McCartney " sat down one day " to write , that was " looking back , [ and ] looking back . " , about life . It was followed by the bass @-@ led " That Was Me " , which is about his " school days and teachers " , the medley , as McCartney stated , then " progressed from there . " The next songs are " Feet in the Clouds " , about the inactivity while one is growing up , and " House of Wax " , about the life of being a celebrity . The final song in medley , " The End of the End " , was written at McCartney 's <unk> Avenue home while playing on his father , Jim 's , piano . 
- 
- = = Album title , and CD casing = = 
- 
- Some people mentioned that the album 's title , Memory Almost Full , is an anagram of " for my <unk> <unk> " ( the initials of Linda Louise McCartney ) . When asked if this was intentional , McCartney replied ; " Some things are best left a mystery " . In an interview with Pitchfork Media , McCartney clarified , " I must say , someone told me [ there is an anagram ] , and I think it 's a complete mystery , because it 's so complete . There does appear to be an anagram in the title . And it 's a mystery . It was not intentional . " The album 's title was actually inspired by a message that came up on his mobile phone . He thought the phrase summed up modern life . 
- A significant proportion of the CD release of Memory Almost Full incorporated a cover insert whose top @-@ right corner was intentionally folded down to the center of the insert , leaving the CD <unk> visible . The folded @-@ down white corner covers up the corner of the armchair image , but has the artist and album names printed so that the text is complete despite the fold . Upon opening and flattening out the cover insert , the armchair is complete , but the portion of the text which is printed on the folded @-@ down corner is not printed on the front of the cover , leaving the text incomplete . This was the first time such an artistic intervention occurred within a standard jewel @-@ case , and at first glance had the possibility of being viewed as a mis @-@ manufactured copy . McCartney on the CD case / album artwork : " I really wanted to make the CD a desirable object . Something that I know I 'd want to pick up from the shelf , something that would make people curious . " 
- 
- = = Reception = = 
- 
- Memory Almost Full received positive reaction . At Metacritic , the album earned an average score of 69 based on 23 reviews from critics , which indicates " Generally favorable reviews " . Evan <unk> of Rolling Stone magazine also compared the medley of five songs in the second part of the album to the famous song suite in The Beatles ' Abbey Road . This album was ranked number 22 on Rolling Stone 's list of the Top 50 Albums of 2007 . 
- 
- = = Release = = 
- 
- 
- = = = Album = = = 
- 
- The album was his first for Starbucks ' Hear Music record label , after previously having a 45 @-@ year @-@ old relationship with Capitol / EMI . The recording contract with Capitol / EMI ended a few months prior to the release of the album , after McCartney had found out that EMI were planning to take six months to set up a promotional plan for the album . McCartney was the first artist to sign to Hear Music . The album was released on 4 June 2007 in the UK , and a day later on the 5th in the US , and with a vinyl edition later in the month on 25 June . In the US , Memory Almost Full debuted at number 3 on the Billboard 200 with about 161 @,@ 000 copies sold within the first week , making it McCartney 's highest @-@ charting album there since 1997 's <unk> Pie . 47 % of the album sales from the opening week were from Starbucks coffee shops , which were the best sales for any album in the history of Starbucks . While it was announced that copies of the album sold in the Starbucks coffee shops in the UK would not be counted by the Official UK Charts , because they are not registered in the copies counting system , Memory Almost Full , however , still managed to hit number 5 on the UK Album Charts . The album was also peaked at number 1 on Billboard Internet Sales Chart , and number 3 on Billboard Top Internet Albums <unk> . 
- It was also McCartney 's first album to be available as a digital download . Promotion for the album came in several forms , such as a worldwide listening party at over 10 @,@ 000 Starbucks stores on the day of the album 's US release , with an <unk> of 6 million people hearing the album . At ten of the Starbucks stores , fans contributed in a video tribute , that aired on the internet on 18 June 2007 . Other promotions included a limited edition Paul McCartney Starbucks card , similar to what they had done for Ray Charles 's Genius Loves Company , the Starbucks @-@ owned satellite radio station <unk> Channel made a program about McCartney and the album , released one song prior to the album on iTunes , performed at iTunes Festival : London , and playing free shows . It was ranked at number 90 on the top @-@ 100 of the Billboard Year @-@ end chart , and number 177 on the UK year @-@ end chart . The album won awards for the Best PR Campaign award at the Music Week Awards ceremony , and the Online / Digital Campaign award by New Media Age . The album reportedly sold 105 @,@ 000 copies in his homeland until it was given away the standard version of the CD in a unique slip @-@ case as part of a promotion with British newspaper The Mail on Sunday for free , with the 18 May 2008 edition . 
- On 6 November 2007 , the album was re @-@ released as Memory Almost Full – Deluxe Edition . The set included one CD and one DVD . The CD included the standard album plus the three extra songs from the 2 @-@ CD edition . The DVD contained five tracks recorded live at The Electric Ballroom in London , and two music videos . 
- 
- = = = Singles = = = 
- 
- The first US single , " Ever Present Past " , made its radio debut on 20 April 2007 . Peaking at number 10 in the Bubbling Under Hot 100 , and also charting at number 16 on Billboard Adult Contemporary . The lead single for the rest of the world is " Dance Tonight " , released on McCartney 's 65th birthday in the UK , 18 June 2007 as a digital download , with a physical release a month later , on 23 July of a CD single and a 10 " shaped picture disc . The single peaked at number 46 on Billboard Hot Digital Songs , number 58 Billboard Pop 100 , and finally at number 69 on Billboard Hot 100 . The music video features Natalie <unk> and Mackenzie <unk> , and was directed by Michel <unk> . The music video had its premier exclusively on 23 May 2007 YouTube . " Dance Tonight " has appeared in an iTunes advert with McCartney playing the mandolin . " Ever Present Past " was released as a single in the UK , on 5 November , as a CD single and 7 " single . It peaked at number 85 . The third single , " Nod Your Head " , was released as a digital download single on 28 August 2007 via the iTunes Store . 
- 
- = = Track listing = = 
- 
- All songs written by Paul McCartney . 
- " Dance Tonight " – 2 : 54 
- " Ever Present Past " – 2 : 57 
- " See Your Sunshine " – 3 : 20 
- " Only Mama Knows " – 4 : 17 
- " You Tell Me " – 3 : 15 
- " Mr. Bellamy " – 3 : 39 
- " Gratitude " – 3 : 19 
- " Vintage Clothes " – 2 : 22 
- This track marks the start of a five @-@ song medley on the album . 
- " That Was Me " – 2 : 38 
- " Feet in the Clouds " – 3 : 24 
- " House of Wax " – 4 : 59 
- " The End of the End " – 2 : 57 
- " Nod Your Head " – 1 : 58 
- 
- = = Personnel = = 
- 
- Personnel per booklet . 
- 
- = = Grammy = = 
- 
- Memory Almost Full has been nominated in the following categories : 
- Best Pop Vocal Album for Memory Almost Full ( 2008 ) 
- Best Male Pop Vocal Performance for " Dance Tonight " ( 2008 ) 
- Best Solo Rock Vocal Performance for " Only Mama Knows " ( 2008 ) 
- Best Male Pop Vocal Performance for " That Was Me " ( 2009 ) 
- 
- = = Charts = = 
- 
- Notes : 
- On the article that Concord Music Group posted on their official site in February 2007 ( a month before Memory Almost Full was certified gold by the Recording Industry Association of America ) , Hear Music announced that shipments of the album reached a platinum status in the US . The label also claimed that the album gained a gold in other countries like Norway , though International Federation of the Phonographic Industry has not certified the album at any label there as of 2014 . 
- 
- 
- = Imagine ( John Lennon song ) = 
- 
- " Imagine " is a song written and performed by the English musician John Lennon . The best @-@ selling single of his solo career , its lyrics encourage the listener to imagine a world at peace without the barriers of borders or the divisions of religion and nationality , and to consider the possibility that the focus of humanity should be living a life <unk> to material possessions . 
- Lennon and Yoko Ono co @-@ produced the song and album of the same name with Phil Spector . Recording began at Lennon 's home studio at Tittenhurst Park , England , in May 1971 , with final overdubs taking place at the Record Plant , in New York City , during July . One month after the September release of the LP , Lennon released " Imagine " as a single in the United States ; the song peaked at number three on the Billboard Hot 100 and the LP reached number one on the UK chart in November , later becoming the most commercially successful and critically acclaimed album of Lennon 's solo career . Although not originally released as a single in the United Kingdom , it was released in 1975 to promote a compilation LP and it reached number six in the chart that year . The song has since sold more than 1 @.@ 6 million copies in the UK ; it reached number one following Lennon 's murder in December 1980 . In 1985 , Central Park <unk> a portion of the park with a mosaic that reads " Imagine " in honor of Lennon . 
- BMI named " Imagine " one of the 100 most @-@ performed songs of the 20th century . The song ranked number 30 on the Recording Industry Association of America 's list of the 365 Songs of the Century bearing the most historical significance . It earned a Grammy Hall of Fame Award and an induction into the Rock and Roll Hall of Fame 's 500 Songs that <unk> Rock and Roll . A UK survey conducted by the Guinness World Records British Hit Singles Book named it the second best single of all time , while Rolling Stone ranked it number three in their list of " The 500 Greatest Songs of All Time " . Since 2005 , event organisers have played it just before the New Year 's Times Square Ball drops in New York City . Dozens of artists have performed or recorded versions of " Imagine " , including Madonna , Stevie Wonder , Joan Baez , Elton John and Diana Ross . Emeli Sandé recorded a cover for the BBC to use during the end credits montage at the close of the 2012 Summer Olympics coverage in August 2012 . " Imagine " subsequently re @-@ entered the UK Top 40 , reaching number 18 . 
- 
- = = Composition and writing = = 
- 
- Several poems from Yoko Ono 's 1964 book <unk> inspired Lennon to write the lyrics for " Imagine " — in particular , one which Capitol Records reproduced on the back cover of the original Imagine LP titled " Cloud Piece " , reads : " Imagine the clouds dripping , dig a hole in your garden to put them in . " Lennon later said the composition " should be credited as a Lennon / Ono song . A lot of it — the lyric and the concept — came from Yoko , but in those days I was a bit more selfish , a bit more <unk> , and I sort of omitted her contribution , but it was right out of <unk> . " When asked about the song 's meaning during a December 1980 interview with David <unk> for Playboy magazine , Lennon told <unk> that Dick Gregory had given Ono and him a Christian prayer book , which inspired him the concept behind " Imagine " . 
- The concept of positive prayer ... If you can imagine a world at peace , with no denominations of religion — not without religion but without this my God @-@ is @-@ bigger @-@ than @-@ your @-@ God thing — then it can be true ... the World Church called me once and asked , " Can we use the lyrics to ' Imagine ' and just change it to ' Imagine one religion ' ? " That showed [ me ] they didn 't understand it at all . It would defeat the whole purpose of the song , the whole idea . 
- With the combined influence of " Cloud Piece " and the prayer book given to him by Gregory , Lennon wrote what author John Blaney described as " a humanistic <unk> for the people " . Blaney wrote , " Lennon contends that global harmony is within our reach , but only if we reject the mechanisms of social control that restrict human potential . " In the opinion of Blaney , with " Imagine " , Lennon attempted to raise people 's awareness of their interaction with the institutions that affect their lives . Rolling Stone 's David Fricke commented : " [ Lennon ] calls for a unity and equality built upon the complete elimination of modern social order : <unk> borders , organised religion , [ and ] economic class . " 
- Lennon stated : " ' Imagine ' , which says : ' Imagine that there was no more religion , no more country , no more politics , ' is virtually the Communist manifesto , even though I 'm not particularly a Communist and I do not belong to any movement . " He told NME : " There is no real Communist state in the world ; you must realize that . The Socialism I speak about ... [ is ] not the way some <unk> Russian might do it , or the Chinese might do it . That might suit them . Us , we should have a nice ... British Socialism . " Ono described the lyrical statement of " Imagine " as " just what John believed : that we are all one country , one world , one people . " Rolling Stone described its lyrics as " 22 lines of graceful , plain @-@ spoken faith in the power of a world , united in purpose , to repair and change itself " . 
- Lennon composed " Imagine " one morning in early 1971 , on a <unk> piano , in a bedroom at his Tittenhurst Park estate in Ascot , Berkshire , England . Ono watched as he composed the melody , chord structure and almost all the lyrics , nearly completing the song in one brief writing session . Described as a piano ballad performed in the soft rock genre , the song is in the key of C major . Its 4 @-@ bar piano introduction begins with a C chord then moves to <unk> before changing to F ; the 12 @-@ bar verses also follow this chord progression , with their last 4 bars moving from Am / E to <unk> and <unk> / C , finishing with G , <unk> then <unk> , before resolving back to C. The 8 @-@ bar choruses progress from F to G to C , then <unk> and E before ending on <unk> , a C chord substituted for <unk> in the final bar . The 4 @-@ bar <unk> begins with F , then G , before resolving on C. With a duration of 3 minutes and 3 seconds and a time signature of 4 / 4 , the song 's tempo falls around 75 beats per minute . 
- 
- = = Recording and commercial reception = = 
- 
- Lennon and Ono co @-@ produced the song and album with Phil Spector , who commented on the track : " We knew what we were going to do ... It was going to be John making a political statement , but a very commercial one as well ... I always thought that ' Imagine ' was like the national anthem . " Lennon described his working arrangement with Ono and Spector : " Phil doesn 't arrange or anything like that — [ Ono ] and Phil will just sit in the other room and shout comments like , ' Why don 't you try this sound ' or ' You 're not playing the piano too well ' ... I 'll get the initial idea and ... we 'll just find a sound from [ there ] . " 
- Recording began at Ascot Sound Studios , Lennon 's newly built home studio at Tittenhurst Park , in May 1971 , with final overdubs taking place at the Record Plant , in New York City , during July . <unk> and patient , the sessions began during the late morning , running to just before dinner in the early evening . Lennon taught the musicians the chord progression and a working arrangement for " Imagine " , rehearsing the song until he deemed the musicians ready to record . In his attempt to recreate Lennon 's desired sound , Spector had some early <unk> feature Lennon and Nicky Hopkins playing in different octaves on one piano . He also initially attempted to record the piano part with Lennon playing the white baby grand in the couple 's all @-@ white room . However , after having deemed the room 's acoustics unsuitable , Spector abandoned the idea in favour of the superior environment of Lennon 's home studio . They completed the session in minutes , recording three takes and choosing the second one for release . The finished recording featured Lennon on piano and vocal , Klaus <unk> on bass guitar , Alan White on drums and the <unk> <unk> on strings . 
- <unk> by Apple Records in the United States in October 1971 , " Imagine " became the best @-@ selling single of Lennon 's solo career . It peaked at number three on the Billboard Hot 100 and reached number one in Canada on the RPM national singles chart , remaining there for two weeks . Upon its release the song 's lyrics upset some religious groups , particularly the line : " Imagine there 's no heaven " . When asked about the song during one of his final interviews , Lennon said he considered it to be as strong a composition as any he had written with the Beatles . He described the song 's meaning and <unk> its commercial appeal : " Anti @-@ religious , anti @-@ nationalistic , anti @-@ conventional , anti @-@ <unk> , but because it is <unk> it is accepted ... Now I understand what you have to do . Put your political message across with a little honey . " In an open letter to Paul McCartney published in Melody Maker , Lennon said that " Imagine " was " ' Working Class Hero ' with sugar on it for <unk> like yourself " . On 30 November 1971 , the Imagine LP reached number one on the UK chart . It became the most commercially successful and critically acclaimed album of Lennon 's solo career . 
- 
- = = Film and re @-@ releases = = 
- 
- In 1972 , Lennon and Ono released an 81 @-@ minute film to accompany the Imagine album which featured footage of the couple in their home , garden and the recording studio of their Berkshire property at Tittenhurst Park as well as in New York City . A full @-@ length documentary rock video , the film 's first scene features a shot of Lennon and Ono walking through a thick fog , arriving at their house as the song " Imagine " begins . Above the front door to their house is a sign that reads : " This Is Not Here " , the title of Ono 's then New York art show . The next scene shows Lennon sitting at a white grand piano in a <unk> lit , all @-@ white room . Ono gradually walks around opening <unk> that allow in light , making the room brighter with the song 's progression . At the song 's conclusion , Ono sits beside Lennon at the piano , and they share a quaint gaze , then a brief kiss . 
- Several celebrities appeared in the film , including Andy <unk> , Fred <unk> , Jack <unk> , Dick <unk> and George Harrison . <unk> by critics as " the most expensive home movie of all time " , it premiered to an American audience in 1972 . In 1986 , Zbigniew <unk> made a music video for the song , and in 1987 , it won both the " Silver Lion " award for Best <unk> at Cannes and the Festival Award at the Rio International Film Festival . 
- Released as a single in the United Kingdom in 1975 in conjunction with the album <unk> Fish , " Imagine " peaked at number six on the UK Singles Chart . Following Lennon 's murder in 1980 , the single re @-@ entered the UK chart , reaching number one , where it remained for four weeks in January 1981 . " Imagine " was re @-@ released as a single in the UK in 1988 , peaking at number 45 , and again in 1999 , reaching number three . It has sold 1 @,@ 640 @,@ 000 copies in the UK as of June 2013 , making it Lennon 's best @-@ selling single . In 1999 , on National Poetry Day in the United Kingdom , the BBC announced that listeners had voted " Imagine " Britain 's favourite song lyric . In 2003 , it reached number 33 as the B @-@ side to a re @-@ release of " Happy <unk> ( War Is Over ) " . 
- 
- = = Recognition and criticism = = 
- 
- Rolling Stone described " Imagine " as Lennon 's " greatest musical gift to the world " , praising " the serene melody ; the <unk> chord progression ; [ and ] that <unk> , four @-@ note [ piano ] figure " . Included in several song polls , in 1999 , BMI named it one of the top 100 most @-@ performed songs of the 20th century . Also that year , it received the Grammy Hall of Fame Award and an induction into the Rock and Roll Hall of Fame 's 500 Songs that <unk> Rock and Roll . " Imagine " ranked number 23 in the list of best @-@ selling singles of all time in the UK , in 2000 . In 2002 , a UK survey conducted by the Guinness World Records British Hit Singles Book ranked it the second best single of all time behind Queen 's " Bohemian <unk> " . Gold Radio ranked the song number three on its " Gold 's greatest 1000 hits " list . 
- Rolling Stone ranked " Imagine " number three on its list of " The 500 Greatest Songs of All Time " , describing it as " an enduring hymn of <unk> and promise that has carried us through extreme grief , from the shock of Lennon 's own death in 1980 to the <unk> horror of September 11th . It is now impossible to imagine a world without ' Imagine ' , and we need it more than he ever dreamed . " Despite that sentiment , <unk> Channel Communications included the song on its post @-@ 9 / 11 " do not play " list . 
- On 1 January 2005 , the Canadian Broadcasting Corporation named " Imagine " the greatest song in the past 100 years as voted by listeners on the show 50 Tracks . The song ranked number 30 on the Recording Industry Association of America 's list of the 365 Songs of the Century bearing the most historical significance . Virgin Radio conducted a UK favourite song survey in December 2005 , and listeners voted " Imagine " number one . Australians selected it the greatest song of all time on the Nine Network 's 20 to 1 countdown show on 12 September 2006 . They voted it eleventh in the youth radio network Triple J 's Hottest 100 Of All Time on 11 July 2009 . 
- Jimmy Carter said , " in many countries around the world — my wife and I have visited about 125 countries — you hear John Lennon 's song ' Imagine ' used almost equally with national anthems . " On 9 October 2010 , which would have been Lennon 's 70th birthday , the Liverpool Signing Choir performed " Imagine " along with other Lennon songs at the unveiling of the John Lennon Peace Monument in <unk> Park , Liverpool , England . Beatles producer George Martin praised Lennon 's solo work , <unk> out the composition : " My favourite song of all was ' Imagine ' " . Music critic Paul Du <unk> described " Imagine " as Lennon 's " most revered " post @-@ Beatles song . Urish and Bielen called it " the most <unk> pop song recorded to achieve classic status " . Fricke commented : " ' Imagine ' is a subtly contentious song , Lennon 's greatest combined achievement as a <unk> and <unk> . " 
- Authors Ben Urish and Ken Bielen criticised the song 's instrumental music as overly sentimental and melodramatic , comparing it to the music of the pre @-@ rock era and describing the vocal melody as understated . According to Blaney , Lennon 's lyrics describe hypothetical possibilities that offer no practical solutions ; lyrics that are at times nebulous and contradictory , asking the listener to abandon political systems while encouraging one similar to communism . Author Chris Ingham indicated the hypocrisy in Lennon , the millionaire rock star living in a mansion , encouraging listeners to imagine living their lives without possessions . Others argue that Lennon intended the song 's lyrics to inspire listeners to imagine if the world could live without possessions , not as an explicit call to give them up . Blaney commented : " Lennon knew he had nothing concrete to offer , so instead he offers a dream , a concept to be built upon . " 
- Blaney considered the song to be " riddled with contradictions . Its hymn @-@ like setting sits <unk> alongside its author 's plea for us to <unk> a world without religion . " Urish and Bielen described Lennon 's " dream world " without a heaven or hell as a call to " make the best world we can here and now , since this is all this is or will be " . In their opinion , " because we are asked merely to imagine — to play a ' what if ' game , Lennon can escape the <unk> criticisms " . Former <unk> <unk> Starr defended the song 's lyrics during a 1981 interview with Barbara Walters , stating : " [ Lennon ] said ' imagine ' , that 's all . Just imagine it . " 
- The morning after the November 2015 Paris attacks , German pianist <unk> <unk> brought a grand piano to the street out in front of the <unk> , where 89 <unk> had been shot dead the night before , and performed an instrumental version to honour the victims of the attacks ; video of his performance went viral . This led Katy <unk> of Slate to <unk> why " Imagine " had become so frequently performed as a response to tragedy . In addition to its general popularity , she noted its musical simplicity , its key of C major , " the <unk> and least complicated key , with no <unk> or flats " aside from one passage with " a plaintive major seventh chord that allows a tiny bit of E minor into the tonic " . That piano part , " gentle as a rocking chair " , <unk> lyrics that , <unk> says , " belongs to the tradition of hymns or spirituals that visualize a glorious afterlife without <unk> any immediate end to suffering on earth " . This understanding is also compounded by the historical context of Lennon 's own violent death , " remind [ ing ] us that the universe can run <unk> over idealistic people " . Ultimately , the song " captures the <unk> of our hope after a violent or destructive event ... [ <unk> ] t also reveals its tenacity " . 
- 
- = = Notable performances and cover versions = = 
- 
- In December 1971 , Lennon and Ono appeared at the Apollo Theater in Harlem . Lennon performed " Imagine " with an acoustic guitar , yielding the earliest known live recording of the song , later included on the John Lennon Anthology ( 1998 ) . In 1975 , he sang " Imagine " during his final public performance , a birthday celebration for Lew Grade . 
- Elton John performed the song regularly on his world tour in 1980 , including at his free concert in Central Park , a few blocks away from Lennon 's apartment in the Dakota building . On 9 December 1980 , the day after Lennon 's murder , Queen performed " Imagine " as a tribute to him during their Wembley Arena show in London . On 9 October 1990 , more than one billion people listened to a broadcast of the song on what would have been Lennon 's 50th birthday . <unk> Mike <unk> covered the song on an EP of Lennon covers in 1990 . In 1991 – 92 , <unk> <unk> performed the song in her show at Radio City Music Hall . Stevie Wonder gave his rendition of the song , with the <unk> College Glee Club , during the closing ceremony of the 1996 Summer Olympics as a tribute to the victims of the Centennial Olympic Park bombing . In 2001 , Neil Young performed it during the benefit concert America : A Tribute to Heroes . Madonna performed " Imagine " during the benefit Tsunami Aid : A Concert of Hope . Peter Gabriel performed the song during the 2006 Winter Olympics opening ceremony . 
- Since 2005 , " Imagine " has been played prior to the New Year 's Eve ball drop at New York City 's Times Square . Beginning in 2010 , the song has been performed live ; first by <unk> Cruz , then in 2011 by <unk> Green and in 2012 by Train . However , Green received criticism for changing the lyric " and no religion too " to " and all religions true " , resulting in an immediate backlash from fans who believed that he had <unk> Lennon 's legacy by changing the lyrics of his most iconic song . Green defended the change by saying it meant to represent " a world [ where you ] could believe what [ you ] wanted " . The event got media attention outside of the US , with Britain 's The Guardian stating " Lennon 's original lyrics don 't praise pluralism or interchangeable religious truths – they damn them " . 
- More than 200 artists have recorded cover versions of " Imagine " . Joan Baez included it on 1972 's Come from the Shadows and Diana Ross recorded a version for her 1973 album , Touch Me in the Morning . In 1995 , Blues Traveler recorded the song for the Working Class Hero : A Tribute to John Lennon album and Dave Matthews has performed the song live with them . A Perfect Circle covered the song for the album <unk> , released in 2004 . <unk> <unk> recorded the song for her 2005 covers album Those Were the Days . A cover version of the song , performed by Italian singer Marco <unk> , entered the top 20 in Italy in 2009 , peaking at number 13 . Seal , Pink , <unk> , Jeff Beck , <unk> <unk> , <unk> <unk> and others recorded a version for Herbie Hancock 's 2010 album The Imagine Project . 
- Hancock performed it with <unk> , <unk> Train and Greg <unk> at the 2010 Nobel Peace Prize Concert on 11 December . On 13 February 2011 , the recording — with Pink , Seal , <unk> singer <unk> <unk> , <unk> and Jeff Beck won a Grammy award for Best Pop Vocal Collaboration . 
- The song was performed as part of the closing ceremony of the 2012 Summer Olympics . Performed by the Liverpool Philharmonic Youth Choir and the Liverpool Signing Choir , the choirs sang the first verse and accompanied Lennon 's original vocals during the rest of the song . A cover performed by Emeli Sandé was also used by the BBC for a closing montage that ended its coverage . " Imagine " subsequently re @-@ entered the UK Top 40 , reaching number 18 . 
- In 2015 , American singer / songwriter Lady Gaga performed the song at the 2015 European Games opening ceremony . The song was played for 70 @,@ 000 people in Baku , Azerbaijan that served as host of the event . 
- 
- = = Personnel = = 
- 
- John Lennon – vocals , piano 
- Klaus <unk> – bass 
- Alan White – drums 
- The <unk> <unk> – strings 
- 
- = = Charts and certifications = = 
- 
- 
- = = = Documentaries = = = 
- 
- 
- 
- = Big Boy ( song ) = 
- 
- " Big Boy " <unk> " I 'm A Big Boy Now " was the first single ever recorded by the Jackson 5 , which was released by Steeltown Records in January 1968 . The group played instruments on many of their Steeltown compositions , including " Big Boy " . The song was neither a critical nor commercial success , but the Jackson family were delighted with the outcome nonetheless . 
- The Jackson 5 would release a second single with Steeltown Records before moving to Motown Records . The group 's recordings at Steeltown Records were thought to be lost , but they were rediscovered more than 25 years later . They were remastered and released in 1995 , with " Big Boy " as the promotional lead single . 
- 
- = = First record deal and lead single = = 
- 
- The Jackson 5 began their career performing at talent contests , which they would often win . During a performance at <unk> Junior High in Gary , Indiana , the group were brought to the attention of Gordon Keith — a singer , record producer , and a founder @-@ owner of Steeltown Records , a company also located in Gary . Keith , Steeltown Records President in 1967 , signed " The Jackson Five " to a limited record deal with him only in November of that year , producing and and releasing " Big Boy " on January 30 , 1968 . The band recorded with their instruments and a backing group on the weekends . Michael Jackson sang lead vocals on the majority of the tracks beginning with " Big Boy " in 1967 which took a few hours to record . " Big Boy " was written by Eddie <unk> of Chicago and was recorded there . The group were paid three cents for each record sold , which was split equally amongst the five brothers and their drummer . The group 's first single " Big Boy " was backed with the B @-@ side " You 've Changed " . " The Jackson 5 and Johnny " ( Johnny Jackson on drums , no relation ) would go on to perform " Big Boy " and other songs locally throughout the Gary and South Chicago area before moving to California in 1969 . 
- 
- = = Reception and Jackson family = = 
- 
- The Jackson family gathered around a radio to hear the song broadcast for the first time . Michael Jackson — who was 9 years old at the time — said of the experience , " [ the family ] all laughed and <unk> one another . We felt we had arrived . " The single " Big Boy " did not appear on any of Billboard 's music charts but sold in excess of 10 @,@ 000 copies . 
- 
- = = Leaving Steeltown = = 
- 
- " The Jackson Five " would release a second and final single through Steeltown Records — " We Don 't Have To Be Over 21 ( to Fall in Love ) " . The two singles were to be supported by an eleven track studio album but it was never released . On July 26 , 1968 , the group signed a contract with Motown Records . However , as the group 's Steeltown contract had not yet expired , the new contract could not be fully executed until March 11 , 1969 . Motown Records tried to get the group out of their Steeltown contract , and ultimately succeeded with a financial settlement . 
- 
- = = Rediscovery = = 
- 
- The master tapes to " Big Boy " were thought to be lost , but in 1994 , Jackson family friend Ben Brown found the tapes in his parents ' kitchen <unk> . Brown reissued the record in 1995 , on the <unk> Records label — a week before Michael Jackson 's <unk> album was issued . He also remastered the song , selling it by mail order , along with an instrumental version , in a limited edition package consisting of a compact disc and cassette tape — the package could be purchased at a cost of approximately $ 30 . The reissue of " Big Boy " was promoted with a music video . In 2009 , Gordon Keith put items from the Steeltown era up for auction , including " a sizable number of mint @-@ condition copies of ' Big Boy ' " in 45 rpm format and 100 copies of " We Don 't Have To Be Over 21 ( to Fall in Love ) " . Keith stated , " I could use the money ... I got these guys off the ground ... I didn 't truly get real money for it " . 
- 
- 
- = Chagas disease = 
- 
- Chagas disease , also known as American <unk> , is a tropical parasitic disease caused by the <unk> Trypanosoma cruzi . It is spread mostly by insects known as Triatominae or kissing bugs . The symptoms change over the course of the infection . In the early stage , symptoms are typically either not present or mild , and may include fever , swollen <unk> nodes , <unk> , or local swelling at the site of the bite . After 8 – 12 weeks , individuals enter the chronic phase of disease and in 60 – 70 % it never produces further symptoms . The other 30 to 40 % of people develop further symptoms 10 to 30 years after the initial infection , including enlargement of the <unk> of the heart in 20 to 30 % , leading to heart failure . An enlarged <unk> or an enlarged <unk> may also occur in 10 % of people . 
- T. cruzi is commonly spread to humans and other mammals by the blood @-@ sucking " kissing bugs " of the subfamily Triatominae . These insects are known by a number of local names , including : <unk> in Argentina , Bolivia , Chile and Paraguay , <unk> ( the barber ) in Brazil , <unk> in Colombia , <unk> in Central America , and <unk> in Venezuela . The disease may also be spread through blood transfusion , organ transplantation , eating food contaminated with the parasites , and by vertical transmission ( from a mother to her fetus ) . Diagnosis of early disease is by finding the parasite in the blood using a microscope . Chronic disease is diagnosed by finding antibodies for T. cruzi in the blood . 
- Prevention mostly involves eliminating kissing bugs and avoiding their bites . Other <unk> efforts include screening blood used for transfusions . A vaccine has not been developed as of 2013 . Early infections are <unk> with the medication benznidazole or nifurtimox . <unk> nearly always results in a cure if given early , but becomes less effective the longer a person has had Chagas disease . When used in chronic disease , medication may delay or prevent the development of end – stage symptoms . <unk> and nifurtimox cause temporary side effects in up to 40 % of people including skin disorders , brain toxicity , and digestive system irritation . 
- It is estimated that 7 to 8 million people , mostly in Mexico , Central America and South America , have Chagas disease as of 2013 . In 2006 , Chagas was estimated to result in 12 @,@ 500 deaths per year . Most people with the disease are poor , and most people with the disease do not realize they are infected . Large @-@ scale population movements have increased the areas where Chagas disease is found and these include many European countries and the United States . These areas have also seen an increase in the years up to 2014 . The disease was first described in 1909 by Carlos Chagas after whom it is named . It affects more than 150 other animals . 
- 
- = = Signs and symptoms = = 
- 
- The human disease occurs in two stages : an acute stage , which occurs shortly after an initial infection , and a chronic stage that develops over many years . 
- The acute phase lasts for the first few weeks or months of infection . It usually occurs <unk> because it is symptom @-@ free or exhibits only mild symptoms that are not unique to Chagas disease . These can include fever , fatigue , body <unk> , muscle pain , headache , rash , loss of appetite , <unk> , nausea , and vomiting . The signs on physical examination can include mild enlargement of the liver or spleen , swollen glands , and local swelling ( a <unk> ) where the parasite entered the body . 
- The most recognized marker of acute Chagas disease is called <unk> 's sign , which includes swelling of the eyelids on the side of the face near the bite wound or where the bug feces were deposited or accidentally rubbed into the eye . Rarely , young children , or adults may die from the acute disease due to severe inflammation / infection of the heart muscle ( <unk> ) or brain ( <unk> ) . The acute phase also can be severe in people with weakened immune systems . 
- If symptoms develop during the acute phase , they usually resolve spontaneously within three to eight weeks in approximately 90 % of individuals . Although the symptoms resolve , even with treatment the infection persists and enters a chronic phase . Of individuals with chronic Chagas disease , 60 – 80 % will never develop symptoms ( called indeterminate chronic Chagas disease ) , while the remaining 20 – 40 % will develop life @-@ threatening heart and / or digestive disorders during their lifetime ( called <unk> chronic Chagas disease ) . In 10 % of individuals , the disease progresses directly from the acute form to a symptomatic clinical form of chronic Chagas disease . 
- The symptomatic ( <unk> ) chronic stage affects the nervous system , digestive system and heart . About two @-@ thirds of people with chronic symptoms have cardiac damage , including <unk> <unk> , which causes heart rhythm abnormalities and may result in sudden death . About one @-@ third of patients go on to develop digestive system damage , resulting in <unk> of the digestive tract ( <unk> and <unk> ) , accompanied by severe weight loss . <unk> difficulties ( secondary <unk> ) may be the first symptom of digestive disturbances and may lead to <unk> . 
- 20 % to 50 % of individuals with intestinal involvement also exhibit cardiac involvement . Up to 10 % of <unk> infected individuals develop <unk> that results in altered <unk> <unk> and sensory <unk> . <unk> cases exhibit central nervous system involvement , including <unk> , confusion , chronic <unk> and sensory and motor deficits . 
- The clinical manifestations of Chagas disease are due to cell death in the target tissues that occurs during the infective cycle , by <unk> inducing an inflammatory response , cellular <unk> , and <unk> . For example , intracellular amastigotes destroy the <unk> neurons of the autonomic nervous system in the intestine and heart , leading to <unk> and heart <unk> , respectively . If left untreated , Chagas disease can be fatal , in most cases due to heart muscle damage . 
- 
- = = <unk> = = 
- 
- In Chagas @-@ endemic areas , the main mode of transmission is through an insect vector called a triatomine bug . A triatomine becomes infected with T. cruzi by feeding on the blood of an infected person or animal . During the day , <unk> hide in crevices in the walls and roofs . 
- The bugs emerge at night , when the inhabitants are sleeping . Because they tend to feed on people 's faces , triatomine bugs are also known as " kissing bugs " . After they bite and <unk> blood , they <unk> on the person . <unk> pass T. cruzi parasites ( called trypomastigotes ) in feces left near the site of the bite wound . 
- <unk> the site of the bite causes the trypomastigotes to enter the host through the wound , or through intact <unk> membranes , such as the <unk> . Once inside the host , the trypomastigotes invade cells , where they differentiate into intracellular amastigotes . The amastigotes <unk> by binary fission and differentiate into trypomastigotes , which are then released into the bloodstream . This cycle is repeated in each newly infected cell . <unk> resumes only when the parasites enter another cell or are ingested by another vector . ( See also : Life cycle and transmission of T. cruzi ) 
- <unk> vegetation ( such as that of tropical rainforests ) and urban habitats are not ideal for the establishment of the human transmission cycle . However , in regions where the sylvatic habitat and its fauna are <unk> by economic exploitation and human habitation , such as in newly <unk> areas , <unk> palm culture areas , and some parts of the Amazon region , a human transmission cycle may develop as the insects search for new food sources . 
- T. cruzi can also be transmitted through blood transfusions . With the exception of blood derivatives ( such as <unk> antibodies ) , all blood components are infective . The parasite remains viable at 4 ° C for at least 18 days or up to 250 days when kept at room temperature . It is unclear whether T. cruzi can be transmitted through frozen @-@ <unk> blood components . 
- Other modes of transmission include organ transplantation , through breast milk , and by accidental laboratory exposure . Chagas disease can also be spread <unk> ( from a pregnant woman to her baby ) through the <unk> , and accounts for approximately 13 % of stillborn deaths in parts of Brazil . 
- Oral transmission is an unusual route of infection , but has been described . In 1991 , farm workers in the state of <unk> , Brazil , were infected by eating contaminated food ; transmission has also occurred via contaminated <unk> palm fruit juice and <unk> . A 2007 outbreak in 103 Venezuelan school children was attributed to contaminated <unk> juice . 
- Chagas disease is a growing problem in Europe , because the majority of cases with chronic infection are <unk> and because of migration from Latin America . 
- 
- = = Diagnosis = = 
- 
- The presence of T. cruzi is diagnostic of Chagas disease . It can be detected by microscopic examination of fresh <unk> blood , or its <unk> coat , for <unk> parasites ; or by preparation of thin and thick blood <unk> stained with <unk> , for direct <unk> of parasites . <unk> , T. cruzi can be confused with Trypanosoma <unk> , which is not known to be pathogenic in humans . <unk> of T. cruzi can occur by <unk> into mice , by culture in specialized media ( for example , <unk> , <unk> ) ; and by <unk> , where uninfected <unk> bugs are fed on the patient 's blood , and their gut contents examined for parasites . 
- Various <unk> for T. cruzi are available and can be used to distinguish among strains ( <unk> of <unk> with <unk> <unk> ) . These tests include : detecting complement <unk> , indirect <unk> , indirect fluorescence assays , <unk> , and <unk> . Alternatively , diagnosis and strain identification can be made using polymerase chain reaction ( <unk> ) . 
- 
- = = Prevention = = 
- 
- There is currently no vaccine against Chagas disease . Prevention is generally focused on decreasing the numbers of the insect that spreads it ( Triatoma ) and decreasing their contact with humans . This is done by using <unk> and paints containing <unk> ( synthetic <unk> ) , and improving housing and <unk> conditions in rural areas . For urban dwellers , spending vacations and camping out in the wilderness or sleeping at <unk> or mud houses in endemic areas can be dangerous ; a <unk> net is recommended . Some measures of vector control include : 
- A yeast trap can be used for monitoring <unk> of certain species of triatomine bugs ( Triatoma <unk> , Triatoma <unk> , Triatoma <unk> , and <unk> <unk> ) . 
- <unk> results were gained with the treatment of vector habitats with the fungus <unk> <unk> . 
- <unk> the symbionts of Triatominae through <unk> can be done . 
- A number of potential <unk> are currently being tested . <unk> with Trypanosoma <unk> has produced positive results in animal models . More recently , the potential of DNA <unk> for <unk> of acute and chronic Chagas disease is being tested by several research groups . 
- Blood transfusion was formerly the second @-@ most common mode of transmission for Chagas disease , but the development and implementation of blood bank screening tests has dramatically reduced this risk in the 21st century . Blood donations in all endemic Latin American countries undergo Chagas screening , and testing is expanding in countries , such as France , Spain and the United States , that have significant or growing populations of immigrants from endemic areas . In Spain , donors are evaluated with a <unk> to identify individuals at risk of Chagas exposure for screening tests . 
- The US FDA has approved two Chagas tests , including one approved in April 2010 , and has published guidelines that recommend testing of all donated blood and tissue products . While these tests are not required in US , an estimated 75 – 90 % of the blood supply is currently tested for Chagas , including all units collected by the American Red Cross , which accounts for 40 % of the U.S. blood supply . The Chagas <unk> Network reports current incidents of Chagas @-@ positive blood products in the United States , as reported by <unk> using the screening test approved by the FDA in 2007 . 
- 
- = = Management = = 
- 
- There are two approaches to treating Chagas disease : <unk> treatment , to kill the parasite ; and symptomatic treatment , to manage the symptoms and signs of the infection . Management uniquely involves addressing selective incremental failure of the <unk> nervous system . <unk> disease imparted by Chagas may eventually result in <unk> , <unk> and accelerated <unk> <unk> . The mechanisms that explain why Chagas targets the <unk> autonomic nervous system and <unk> the sympathetic autonomic nervous system remain poorly understood . 
- 
- = = = <unk> = = = 
- 
- <unk> treatment is most effective early in the course of infection , but is not limited to cases in the acute phase . <unk> of choice include <unk> or <unk> derivatives , such as benznidazole or nifurtimox . Both agents are limited in their capacity to completely eliminate T. cruzi from the body ( <unk> cure ) , especially in <unk> infected patients , and resistance to these drugs has been reported . 
- Studies suggest <unk> treatment leads to <unk> cure in more than 90 % of infants but only about 60 – 85 % of adults treated in the first year of acute phase Chagas disease . Children aged six to 12 years with chronic disease have a cure rate of about 60 % with benznidazole . While the rate of cure declines the longer an adult has been infected with Chagas , treatment with benznidazole has been shown to slow the onset of heart disease in adults with chronic Chagas infections . 
- Treatment of chronic infection in women prior to or during pregnancy does not appear to reduce the probability the disease will be passed on to the infant . Likewise , it is unclear whether <unk> treatment of chronic infection is beneficial in persons who will undergo <unk> ( for example , organ transplant recipients ) or in persons who are already <unk> ( for example , those with HIV infection ) . 
- 
- = = = <unk> = = = 
- 
- In the chronic stage , treatment involves managing the clinical manifestations of the disease . For example , <unk> and <unk> for irregular <unk> , such as the anti @-@ <unk> drug <unk> , may be life saving for some patients with chronic cardiac disease , while surgery may be required for <unk> . The disease cannot be cured in this phase , however . Chronic heart disease caused by Chagas disease is now a common reason for heart transplantation surgery . Until recently , however , Chagas disease was considered a <unk> for the procedure , since the heart damage could recur as the parasite was expected to seize the opportunity provided by the <unk> that follows surgery . 
- 
- = = Epidemiology = = 
- 
- Chagas disease affects 8 to 10 million people living in endemic Latin American countries , with an additional 300 @,@ 000 – 400 @,@ 000 living in <unk> countries , including Spain and the United States . An estimated 41 @,@ 200 new cases occur annually in endemic countries , and 14 @,@ 400 infants are born with congenital Chagas disease <unk> in 2010 it resulted in approximately 10 @,@ 300 deaths up from 9 @,@ 300 in 1990 . 
- The disease is present in 18 countries on the American continents , ranging from the southern United States to northern Argentina . Chagas exists in two different ecological zones . In the Southern Cone region , the main vector lives in and around human homes . In Central America and Mexico , the main vector species lives both inside dwellings and in uninhabited areas . In both zones , Chagas occurs almost exclusively in rural areas , where <unk> breed and feed on the more than 150 species from 24 families of domestic and wild mammals , as well as humans , that are the natural reservoirs of T. cruzi . 
- Although Triatominae bugs feed on them , birds appear to be immune to infection and therefore are not considered to be a T. cruzi reservoir . Even when colonies of insects are eradicated from a house and surrounding domestic animal shelters , they can re @-@ emerge from plants or animals that are part of the ancient , sylvatic ( referring to wild animals ) infection cycle . This is especially likely in zones with mixed open <unk> , with clumps of trees interspersed by human habitation . 
- The primary wildlife reservoirs for Trypanosoma cruzi in the United States include <unk> , raccoons , <unk> , squirrels , <unk> , and mice . <unk> are particularly important as reservoirs , because the parasite can complete its life cycle in the anal glands of this animal without having to re @-@ enter the insect vector . Recorded prevalence of the disease in <unk> in the U.S. ranges from 8 @.@ 3 % to 37 @.@ 5 % . 
- Studies on raccoons in the Southeast have yielded infection rates ranging from 47 % to as low as 15 @.@ 5 % . <unk> prevalence studies have been described in Louisiana , and range from a low of 1 @.@ 1 % to 28 @.@ 8 % . Additionally , small rodents , including squirrels , mice , and rats , are important in the sylvatic transmission cycle because of their importance as <unk> sources for the insect vectors . A Texas study revealed 17 @.@ 3 % percent T. cruzi prevalence in 75 specimens representing four separate small <unk> species . 
- Chronic Chagas disease remains a major health problem in many Latin American countries , despite the effectiveness of <unk> and <unk> measures , such as eliminating the transmitting insects . However , several landmarks have been achieved in the fight against it in Latin America , including a reduction by 72 % of the incidence of human infection in children and young adults in the countries of the Southern Cone Initiative , and at least three countries ( Uruguay , in 1997 , and Chile , in 1999 , and Brazil in 2006 ) have been certified free of <unk> and <unk> transmission . In Argentina , <unk> transmission has been interrupted in 13 of the 19 endemic provinces , and major progress toward this goal has also been made in both Paraguay and Bolivia . 
- <unk> of donated blood , blood components , and solid organ donors , as well as donors of cells , tissues , and cell and tissue products for T. cruzi is mandated in all Chagas @-@ endemic countries and has been implemented . Approximately 300 @,@ 000 infected people live in the United States , which is likely the result of immigration from Latin American countries , and there have been only 23 human cases of domestically acquired vector @-@ borne Chagas disease reported between 1955 and 2014 . With increased population movements , the possibility of transmission by blood transfusion became more substantial in the United States . <unk> blood and tissue products are now actively screened in the U.S. , thus addressing and <unk> this risk . 
- 
- = = History = = 
- 
- The disease was named after the Brazilian physician and <unk> Carlos Chagas , who first described it in 1909 . The disease was not seen as a major public health problem in humans until the 1960s ( the outbreak of Chagas disease in Brazil in the 1920s went widely ignored ) . Dr Chagas discovered that the intestines of <unk> ( now <unk> : Triatominae ) <unk> a <unk> <unk> , a new species of the Trypanosoma genus , and was able to demonstrate experimentally that it could be transmitted to <unk> monkeys that were bitten by the infected bug . Later studies showed <unk> monkeys were also vulnerable to infection . 
- Chagas named the pathogenic parasite as Trypanosoma cruzi and later that year as <unk> cruzi , both honoring <unk> Cruz , the noted Brazilian physician and <unk> who successfully fought <unk> of yellow fever , smallpox , and <unk> plague in Rio de Janeiro and other cities in the beginning of the 20th century . Chagas was also the first to unknowingly discover and illustrate the parasitic fungal genus Pneumocystis , later <unk> linked to <unk> ( Pneumocystis pneumonia in AIDS victims ) . Confusion between the two pathogens ' life @-@ cycles led him to briefly recognize his genus <unk> , but following the description of Pneumocystis by others as an independent genus , Chagas returned to the use of the name Trypanosoma cruzi . 
- In Argentina , the disease is known as mal de Chagas @-@ Mazza , in honor of Salvador Mazza , the Argentine physician who in 1926 began investigating the disease and over the years became the principal researcher of this disease in the country . Mazza produced the first scientific confirmation of the existence of Trypanosoma cruzi in Argentina in 1927 , eventually leading to support from local and European medical schools and Argentine government policy makers . 
- It has been hypothesized that Charles Darwin might have suffered from Chagas disease as a result of a bite of the so @-@ called great black bug of the Pampas ( <unk> ) ( see Charles Darwin 's illness ) . The episode was reported by Darwin in his diaries of the Voyage of the <unk> as occurring in March 1835 to the east of the Andes near Mendoza . Darwin was young and generally in good health , though six months previously he had been ill for a month near <unk> , but in 1837 , almost a year after he returned to England , he began to suffer intermittently from a strange group of symptoms , becoming incapacitated for much of the rest of his life . Attempts to test Darwin 's remains at Westminster Abbey by using modern <unk> techniques were met with a refusal by the Abbey 's curator . 
- 
- = = Research = = 
- 
- Several experimental treatments have shown promise in animal models . These include inhibitors of <unk> <unk> and <unk> <unk> , <unk> <unk> inhibitors , <unk> collected from frogs in the genus <unk> ( P. <unk> and P. <unk> ) , the <unk> lactone <unk> ( <unk> ) , which affects the growth of cultured <unk> @-@ phase Trypanosoma cruzi , inhibitors of <unk> uptake , and inhibitors of enzymes involved in <unk> metabolism . <unk> , new drug targets may be revealed following the sequencing of the T. cruzi genome . 
- Chagas disease has a serious economic impact on the United States and the world . The cost of treatment in the United States alone , where the disease is not indigenous , is estimated to be $ 900 million annually , which includes hospitalization and medical devices such as <unk> . The global cost is estimated at $ 7 billion . 
- <unk> in a study seems more active against Chagas than benznidazole but has not been studied in humans . <unk> , an old drug rediscovered for this purpose is being tested in phase 2 clinical trials . A Chagas vaccine ( <unk> ) has been found to be effective in mice with plans for studies in dogs . It is hoped that it will be commercially available by 2018 . 
- 
- 
- = Diamond stingray = 
- 
- The diamond stingray ( Dasyatis dipterura ) is a species of stingray in the family <unk> . It is found in the coastal waters of the eastern Pacific Ocean from southern California to northern Chile , and around the <unk> and Hawaiian Islands . This bottom @-@ dweller generally inhabits sandy or muddy flats near rocky reefs and kelp forests , to a depth of 30 m ( 98 ft ) , though off Hawaii it may range considerably deeper . As its common name suggests , this species has an angular , diamond @-@ shaped pectoral fin disc that is plain brown or gray above , with rows of tubercles along the midline and on the " shoulders " . The long , whip @-@ like tail has both dorsal and ventral fin folds , which distinguish this ray from the closely similar <unk> stingray ( D. <unk> ) . It typically grows to 1 m ( 3 @.@ 3 ft ) across . 
- When searching for food , diamond stingrays may form groups of up to hundreds of individuals . It is most active at night and <unk> mainly on burrowing invertebrates and small bony fishes , which are extracted from the bottom via <unk> or digging . This species is aplacental viviparous : once the embryos exhaust their yolk supply , they are nourished by histotroph ( " uterine milk " ) produced by the mother . Females bear 1 – 4 <unk> every summer in estuaries ; mating is followed by a ten @-@ month period of either sperm storage or arrested embryonic development , after which the embryos rapidly mature over 2 – 3 months . The slowest @-@ growing stingray known , this species is not <unk> against fishing pressure . It is caught for food by <unk> fishers in Latin America , particularly in Mexico where it is one of the most economically important rays . This has led it to be assessed as Near Threatened in Mexico by the International Union for Conservation of Nature ( IUCN ) , while the species as a whole is listed as Data <unk> . Though <unk> towards humans , the diamond stingray 's long , venomous tail spine is potentially dangerous . 
- 
- = = Taxonomy and phylogeny = = 
- 
- In 1880 , the diamond stingray was described twice by three American <unk> : as <unk> <unk> by David Starr Jordan and Charles Henry Gilbert in Proceedings of the United States National Museum , and as <unk> brevis by Samuel Garman in Bulletin of the Museum of Comparative <unk> . Jordan and Gilbert 's account was published in May while Garman 's was published in October , making <unk> ( feminine dipterura ) the correct name as it was published first . However , when Garman synonymized the two in 1913 he <unk> gave precedence to brevis , leading to long @-@ standing confusion . Both <unk> and <unk> were later synonymized with the genus Dasyatis , but many authors still listed D. brevis in place of or in addition to D. dipterura . Garman also synonymized the Hawaiian stingray ( D. <unk> ) with D. dipterura in 1913 , which has since been followed by most authors but requires more study for confirmation . 
- The species <unk> were collected from San Diego Bay , California . The specific epithet dipterura is derived from the Latin di ( " two " ) , <unk> ( " wing " ) , and <unk> ( " tail " ) , referring to the fin folds on both sides of its tail . Rat @-@ tailed stingray is a former common name for this species . Lisa <unk> 's 2001 phylogenetic analysis , based on morphology , determined the diamond stingray and the <unk> stingray ( D. say ) of the western Atlantic Ocean to be sister species , that likely diverged before or with the formation of the Isthmus of Panama ( c . 3 Ma ) . In addition , the two were found to be the second @-@ most basal taxa in their genus , after the common stingray ( D. <unk> ) . 
- 
- = = Distribution and habitat = = 
- 
- The diamond stingray is found in the eastern Pacific Ocean from southern California to northern Chile , as well as around the <unk> and Hawaiian Islands ; it is very abundant around Baja California and in the Gulf of California . At the northern and southern extremes of its range , it is generally only present during periods of suitably warm water brought about by El Niño . There is a record of this ray from off British Columbia , which is unconfirmed and would be highly <unk> as it is known to be a tropical and warm @-@ temperate species . 
- A bottom @-@ dwelling inhabitant of inshore waters , the diamond stingray favors sandy or muddy <unk> , often near rocky reefs or kelp forests . Off southern California , it usually occurs from the <unk> zone to a depth of 7 m ( 23 ft ) during the summer , shifting to depths of 13 – 18 m ( 43 – 59 ft ) during late fall and winter . For unknown reasons , it prefers to overwinter in kelp forests rather than sandy flats . Off Chile , the diamond stingray occurs at a similar depth of 3 – 30 m ( 10 – 100 ft ) . On the other hand , this species has been reported from as far down as 355 m ( 1 @,@ 165 ft ) off Hawaii , which if accurate would suggest that it utilizes a much greater range of depths than previously realized . 
- 
- = = Description = = 
- 
- The diamond stingray <unk> a disc width of 1 m ( 3 @.@ 3 ft ) or possibly 1 @.@ 2 m ( 3 @.@ 9 ft ) ; females grow larger than males . Its pectoral fin disc is <unk> in shape , slightly wider than long , with angular outer corners and subtly convex margins . The snout is blunt @-@ angled and non @-@ projecting . The eyes are fairly large and immediately followed by the spiracles ( paired respiratory openings ) . The mouth is strongly curved , containing 21 – 37 upper tooth rows and 23 – 44 lower tooth rows ; the teeth are small and blunt , and arranged into flattened surfaces . Three or five papillae ( <unk> @-@ like structures ) are found in a row across the floor of the mouth . 
- The whip @-@ like tail generally measures up to one and half times the length of the disc , and bears one ( more if replacements have grown in ) long , slender , <unk> spine on the upper surface , closer to the base than the tip . Behind the spine , there are long dorsal and ventral fin folds that rise gradually , reaching a relatively high apex before sloping down abruptly . The presence of the upper fin fold separates this species from the similar <unk> stingray ( D. <unk> ) , which shares most of its range . However , the tail is often damaged , in which case <unk> the two species in the field becomes all but impossible . Young rays have completely smooth skin , while adults develop a row of low tubercles along the midline of the back , flanked by two shorter rows on the " shoulders " . The tail also becomes covered in <unk> . This species is a uniform olive to brown to gray above , darkening to black on the tail , and off @-@ white below . 
- 
- = = Biology and ecology = = 
- 
- The diamond stingray is most active at night , spending much of the day buried in sand with only its eyes protruding . When foraging for food , it may be solitary but more commonly forms groups numbering up to the hundreds . There is strong segregation by sex and age . This species feeds on crustaceans , molluscs , and other invertebrates , as well as small bony fishes ; its powerful jaws and <unk> @-@ like teeth allow it to crush hard @-@ shelled prey . It mainly targets burrowing organisms , but may also take prey exposed on the bottom . There is a record of a female 69 cm ( 27 in ) across , that had <unk> herself on at least 30 small crabs . In the Bahía Magdalena <unk> complex , Baja California Sur , its most important source of food are pea crabs , followed by the razor <unk> <unk> <unk> , and then polychaete worms . 
- The typical hunting strategy of the diamond stingray is to cruise just above the sea floor , landing atop any prey encountered . It then quickly <unk> its body up @-@ and @-@ down with its disc , producing negative pressure to extract the prey from its burrow . This ray has also been known to excavate large pits by undulating its disc and spitting jets of water , so as to uncover buried prey . Diamond stingrays are frequently trailed by smaller fishes , including Mexican <unk> ( <unk> <unk> ) , <unk> <unk> ( <unk> <unk> ) , <unk> <unk> ( <unk> <unk> ) , spinster <unk> ( <unk> <unk> ) , and long @-@ spine <unk> ( <unk> <unk> ) , which take advantage of the invertebrates stirred up by the ray 's activities . Known parasites of this species include the <unk> <unk> <unk> , A. <unk> , A. <unk> , and A. <unk> , <unk> <unk> , <unk> <unk> , and <unk> <unk> , the <unk> <unk> <unk> and <unk> <unk> , and the <unk> <unk> <unk> . 
- Like other stingrays , the diamond stingray is aplacental viviparous : the embryos are initially nourished by yolk , and later by histotroph ( " uterine milk " , rich in proteins and <unk> ) produced by the mother . Only the left ovary and uterus are functional in adult females . Several bays along the Pacific coast of Baja California are known to serve as nurseries . Most of the life history information available on this species has come from Bahía Magdalena , where females bear one litter of 1 – 4 <unk> per year . <unk> and mating occurs in late summer from July to August , but due to a ten @-@ month period of either sperm storage or <unk> ( wherein the embryo becomes <unk> ) , embryonic development does not begin until the following year and is completed within 2 – 3 months . <unk> takes place in summer from July to September in shallow estuaries ; the newborns measure 18 – 23 cm ( 7 @.@ 1 – 9 @.@ 1 in ) across . During El Niño years , the higher temperatures appear to shift the timing of birth forward . The diamond stingray has the lowest growth rate of any stingray species yet studied . Males reach sexual maturity at around 43 – 47 cm ( 17 – 19 in ) across and 7 years of age , while females grow slower still , reaching maturity at around 57 – 66 cm ( 22 – 26 in ) across and 10 years of age . The maximum lifespan has been estimated at least 19 years for males and 28 years for females . 
- 
- = = Human interactions = = 
- 
- At least one fatality off southern California has resulted from the long , venomous tail spine of the diamond stingray . However , it is not aggressive and will usually flee given the opportunity . This ray is not found off the United States in sufficient numbers to be economically important . Elsewhere in its range , it is caught in substantial numbers for human consumption , both intentionally and as <unk> ; the pectoral fins or " wings " are sold fresh or <unk> and salted . The International Union for Conservation of Nature ( IUCN ) notes that the diamond stingray 's low reproductive <unk> renders it susceptible to population depletion , but currently lacks enough biological and fishery data to assess it beyond Data <unk> overall , or in the U.S. , Central , and South American parts of its range . 
- In Mexico , the diamond stingray is one of the most important components of Pacific <unk> shark and ray fisheries , comprising around a tenth of the total annual catch . Its significance is likely underestimated , as Mexican fishery reports frequently suffer from <unk> and a lack of species @-@ specific data . It is mainly caught during summer and fall , being the most common ray landed in Bahía Magdalena , and second most common ray landed off Sonora state . <unk> <unk> are the main fishing gear employed ; both adult and juvenile rays easily become entangled in the <unk> by their tail spines , with juveniles known to have comprised most of the Bahía Magdalena catch in 1998 – 2000 . This species is also often caught incidentally in bottom trawls , on <unk> , and in fish traps . In the future , habitat degradation from increasing numbers of shrimp farms may pose an additional threat to this species in the region . As a result of these pressures , the IUCN has assessed the diamond stingray as Near Threatened in Mexican waters . No management schemes have yet been enacted for this species . 
- 
- 
- = George Calvert , 1st Baron Baltimore = 
- 
- George Calvert , 1st Baron Baltimore ( <unk> – 15 April 1632 ) was an English politician and <unk> . He achieved domestic political success as a Member of Parliament and later Secretary of State under King James I. He lost much of his political power after his support for a failed marriage alliance between Prince Charles and the Spanish House of Habsburg royal family . Rather than continue in politics , he resigned all of his political offices in 1625 except for his position on the Privy Council and declared his Catholicism publicly . He was created Baron Baltimore in the Irish peerage upon his resignation . Baltimore Manor was located in County Longford , Ireland . 
- Calvert took an interest in the British colonization of the Americas , at first for commercial reasons and later to create a refuge for English Catholics . He became the proprietor of Avalon , the first sustained English settlement on the southeastern peninsula on the island of Newfoundland ( off the eastern coast of modern Canada ) . <unk> by its cold and sometimes inhospitable climate and the <unk> of the settlers , Sir George looked for a more suitable spot further south and sought a new royal charter to settle the region , which would become the state of Maryland . Calvert died five weeks before the new Charter was sealed , leaving the settlement of the Maryland colony to his son Cecil , ( 1605 @-@ <unk> ) . His second son Leonard Calvert , ( <unk> @-@ 1647 ) , was the first colonial governor of the Province of Maryland . Historians have long recognized George Calvert as the founder of Maryland , in spirit if not in fact . 
- 
- = = Family and early life = = 
- 
- Little is known of the ancestry of the Yorkshire branch of the <unk> . At George Calvert 's <unk> , it was claimed that his family originally came from Flanders ( a Dutch @-@ speaking area today across the English Channel in modern @-@ day Kingdom of Belgium ) . Calvert 's father , ( an earlier ) Leonard was a country gentleman who had achieved some prominence as a tenant of Lord <unk> , and was wealthy enough to marry a " <unk> " of a noble line , Alicia or Alice <unk> ( or sometimes spelled : " <unk> " ) . He established his family on the estate of the later @-@ built Kiplin Hall , near <unk> in <unk> , of Yorkshire . George Calvert was born at Kiplin in late <unk> ( birth month and day yet to be researched ) . His mother Alicia / Alice died on 28 November <unk> , when he was fifteen years old . His father then married Grace <unk> ( or sometimes spelled : " <unk> " ) , Alicia 's first cousin . 
- In 1569 , Sir Thomas <unk> had described <unk> as a territory where all gentlemen were " evil in religion " , by which he meant predominately Roman Catholic ; it appears Leonard Calvert was no exception . During the reign of Queen Elizabeth I , continuing the changes wrought in the previous century by her father , King Henry VIII making the Monarch , the supreme authority of the Christian Church in England , continuing the pace Protestant Reformation from the Continent of Europe , with the political , spiritual and <unk> separation from the Roman Catholic Church and the Pope / <unk> in Rome , therefore the Royal Government exerted authority over the matters of religious faith , practices and the Church . Acts <unk> compulsory religious <unk> were enacted by Parliament and enforced through penal laws . The Acts of <unk> and the Uniformity Act of <unk> also included an oath of allegiance to the Queen ( <unk> Maria , wife of Charles I ) and an implicit denial of the Pope 's ( then Pope Paul IV ) authority over the English Church . This oath was required of any common " subject " ( <unk> for citizen ) , who wished to hold high office , attend university , or take advantage of opportunities controlled by the state ( king / kingdom ) . 
- The Calvert household suffered the intrusion of the <unk> @-@ era religious laws . From the year of George 's birth onwards , his father , Leonard Calvert was subjected to repeated harassment by the Yorkshire authorities , who in 1580 extracted a promise of conformity from him , compelling his attendance at the Church of England services . In 1592 , when George was twelve , the authorities denounced one of his tutors for teaching " from a <unk> <unk> " and instructed Leonard and Grace to send George and his brother Christopher to a Protestant tutor , and , if necessary , to present the children before the commission " once a month to see how they perfect in learning " . As a result , the boys were sent to a Protestant tutor called Mr. <unk> at <unk> . The senior Calvert had to give a " bond of conformity " ; he was banned from employing any Catholic servants and forced to purchase an English Bible , which was to " ly open in his house for everyone to read " . 
- In 1593 , records show that Grace Calvert was committed to the custody of a " <unk> " , an official responsible for identifying and <unk> Catholics , and in 1604 , she was described as the " wife of Leonard Calvert of Kipling , non @-@ <unk> at Easter last " . 
- George Calvert went up to Trinity College , at Oxford University <unk> in 1593 / 94 , where he studied foreign languages and received a bachelor 's degree in <unk> . As the oath of allegiance was compulsory after the age of sixteen , he would almost certainly have pledged conformity while at Oxford . The same pattern of conformity , whether pretended or sincere , continued through Calvert 's early life . After Oxford , he moved to London in <unk> , where he studied municipal law at Lincoln 's Inn for three years . 
- 
- = = Marriage and family = = 
- 
- In November 1604 , he married Anne <unk> in a Protestant , Church of England ceremony at St Peter 's , Cornhill , where his address was registered as St Martin in the Fields . His children , including his eldest son and heir , Cecil , who was born in the winter of 1605 – 06 , were all baptized as Christian Protestants . When Anne died on 8 August 1622 , she was buried at Calvert 's local Protestant parish church , St Martin in the Fields . 
- Calvert had a total of thirteen children : Cecil , who succeeded his father as the 2nd Baron Baltimore , Leonard , Anne , Mary , Dorothy , Elizabeth , Grace , Francis , George , Helen , Henry , John , and Philip . 
- 
- = = Political success = = 
- 
- Calvert named his son " Cecilius " ( 1605 @-@ <unk> ) for Sir Robert Cecil , first Earl of Salisbury , ( 1563 @-@ 1612 ) . <unk> to Queen Elizabeth , whom Calvert had met during an extended trip to Europe between <unk> and 1603 , after which he became known as a specialist in foreign affairs . Calvert carried a <unk> for Cecilius from Paris , and so entered the service of the principal engineer on behalf of King James VI of Scotland 's succession to the English throne in 1603 , ( who then also assumed the title of King James I of England . ) 
- King James rewarded Robert Cecil , whom he made a privy councillor and secretary of state , with the granting of the title of Earl of Salisbury in 1605 , and Lord High <unk> in 1608 , making him the most powerful man at the royal court . As Cecil rose , Calvert rose with him . Calvert 's foreign languages , legal training , and discretion made him an <unk> aide to Robert Cecil , who , no lover of Catholics , seems to have accepted Calvert 's conformity as beyond question . Working at the center of court politics , Calvert exploited his influence by selling favors , an accepted practice for the times . Calvert accumulated a number of small offices , honors , and <unk> . In August 1605 , he attended the King at Oxford , and received an honorary master @-@ of @-@ arts degree in an elaborate ceremony at which the Duke of Lennox ( <unk> Stewart ) , the <unk> of Oxford and Northumberland , and Cecilius received degrees . Given the prestige of the other graduates , Calvert 's was the last awarded , but his presence in such company signaled his growing stature . 
- In <unk> , the king made Calvert " clerk of the Crown " and " <unk> in <unk> " , County Clare , Ireland , his first royal appointment . In 1609 , James appointed him a " clerk of the Signet office " , a post which required the preparation of documents for the royal signature and brought Calvert into close contact with the king . Calvert also served in James 's First Parliament as a member for the borough of <unk> , in the county of Cornwall , installed there by Cecil to support his policies . In 1610 , Calvert was appointed a " clerk of the Privy Council " . Each of these positions would have required an oath of allegiance . 
- With Robert Cecil 's support , George Calvert came into his own as an advisor and supporter of King James . In 1610 and 1611 , Calvert undertook missions to the continent on behalf of the King , visiting a number of embassies in Paris , Holland , and the Duchy of <unk> , and acting as an ambassador to the French Royal Court during the coronation of King Louis XIII , ( <unk> @-@ 1643 ) , in 1610 . A correspondent from France reported that Calvert gave " everyone great contentment with his discreet conversation . " In 1615 , James sent him to the continental <unk> of the <unk> ( German ) , in the Holy Roman Empire , whose impoverished <unk> , Frederick V , <unk> <unk> , ( 1596 @-@ 1632 ) , had married James 's daughter Elizabeth of Bohemia , ( 1596 @-@ 1662 ) , in 1613 . Calvert had to convey the King 's disapproval that Elizabeth , for lack of money , had given away expensive jewels to a <unk> leaving her employ . <unk> Frederick 's decision in 1619 to accept the throne of Bohemia triggered a war with the powerful neighboring Habsburgs dynasty of Austria to the southwest in Vienna , which James attempted to end through a proposed alliance with the Kingdom of Spain . 
- In 1611 , James employed Calvert to research and <unk> his tract against the Dutch Protestant theologian Conrad <unk> , ( 1569 @-@ 1622 ) . The following year , Cecil died , and Calvert acted as one of the four <unk> of his will . The king 's favourite , Sir Robert Carr , first Earl of Somerset , ( <unk> @-@ 1645 ) , Viscount Rochester , assumed the duties of secretary of state and recruited Calvert to assist with foreign policy , in particular the Latin and Spanish correspondence . Carr , soon raised to the <unk> of Somerset , was not a success in the job , and fell from favour partly as a result of the murder of Thomas <unk> , ( <unk> @-@ 1613 ) , to which Carr 's wife , Frances , the former Countess of Essex and later Somerset , ( <unk> @-@ 1632 ) , pleaded guilty in 1615 . Carr 's place as James 's principal favorite was now taken by the handsome George Villiers , 1st Duke of Buckingham , ( 1592 @-@ 1628 ) , with whom James was said to have been infatuated . 
- In 1613 , the King commissioned Calvert to investigate Roman Catholic grievances in Ireland , along with Sir Humphrey <unk> , ( 1555 @-@ 1625 ) , Sir Charles Cornwallis , ( XXX ? <unk> ) and Sir Roger <unk> , ( 1553 @-@ 1616 ) . The commission spent almost four months in Ireland , and its final report , partly drafted by Calvert , concluded that religious conformity should be enforced more strictly in Ireland , Catholic schools be suppressed , and bad priests removed and punished . The King resolved not to <unk> the Parliament of Ireland until the Catholics " shall be better disciplined " . In 1616 , James endowed Calvert with the manor of <unk> <unk> in Yorkshire , which brought him into contact with Sir Thomas Wentworth , 1st Earl of <unk> , ( 1593 @-@ 1641 ) , who became his closest friend and political ally . Calvert was now wealthy enough to buy the " Kiplin Hall " estate in his home parish . ( Today , the University of Maryland operates a research centre there , while the main building is a house museum owned by the Kiplin Hall Trust . ) in his home parish ; and in 1617 , his social status received a further boost when he was knighted , then becoming " Sir George Calvert " . 
- In 1619 , Calvert completed his rise to power when James appointed him as one of the two principal <unk> of state . This followed the dismissal of Sir Thomas Lake , ( 1567 @-@ 1630 ) , due to <unk> , including his wife 's <unk> with state secrets . Not emerging as a candidate until the end of the selection process , Calvert 's appointment surprised him and most observers . Assuming he owed his promotion to the king 's increasingly powerful favourite , George Villiers , ( 1592 @-@ 1628 ) , later first Duke of Buckingham ) , he sent him a great jewel as a token of thanks . Villiers returned the jewel , however , saying he had had nothing to do with the matter . Calvert 's personal fortune was secured when he was additionally appointed a " commissioner of the treasury " with a <unk> of £ 1 @,@ 000 pounds sterling and a subsidy on imported raw silk , which would later be converted to another £ 1 @,@ 000 <unk> . 
- 
- = = Secretary of State = = 
- 
- In Parliament , a political crisis developed over the king 's policy of seeking a Spanish wife for Charles , Prince of Wales , as part of a proposed alliance with the Habsburgs . In the Parliament of 1621 , it fell to Calvert to advocate the " Spanish match " , as it came to be called , against the majority of Parliament , who feared an increase in Catholic influence on the state . As a result of his pro @-@ Spanish stance and defence of <unk> in the penal laws against Catholics , Calvert became estranged from many in the Commons , who were suspicious of his close familiarity with the Spanish ambassador 's court . Calvert also faced difficulties in his private life : his wife 's death on 8 August 1622 left him the single father of ten children , the oldest of whom , Cecil , was sixteen years old . 
- Although King James rewarded Calvert in 1623 for his loyalty by granting him a 2 @,@ 300 @-@ acre ( 9 @.@ 3 km2 ) estate in County Longford , in the central Ireland region of Leinster , where his seat was known as the " Manor of Baltimore " , Calvert was increasingly isolated from court circles as the Prince of Wales , ( heir to the Throne ) and the first Duke of Buckingham ( George Villiers ) <unk> control of policy from the ageing James . Without consulting the <unk> astute Calvert , the prince and the duke travelled to Spain to negotiate the Spanish marriage for themselves , with disastrous results . Instead of securing an alliance , the visit provoked a hostility between the two courts which quickly led to war . In a reversal of policy , Buckingham dismissed the treaties with Spain , summoned a war council , and sought a French marriage for the Welsh Prince . 
- 
- = = Resignation and conversion to Catholicism = = 
- 
- As the chief parliamentary spokesman for an abandoned policy , Calvert no longer served a useful purpose to the English Royal Court , and by February 1624 his duties had been restricted to <unk> the Spanish ambassador . The degree of his <unk> was shown when he was reprimanded for supposedly delaying diplomatic letters . Calvert bowed to the inevitable . On the pretext of ill health , he began negotiations for the sale of his position , finally resigning the secretariat in February 1625 . 
- No disgrace was attached to Calvert 's departure from office : the King , to whom he had always remained personally loyal , confirmed his place on the Privy Council and appointed him " Baron Baltimore " , in County Longford in Leinster of central Ireland . Immediately after Calvert resigned , he converted to Roman Catholicism . 
- The connection between Calvert 's resignation and his conversion to Roman Catholicism was a complex one . George Cottington , a former employee of Calvert , suggested in 1628 that Calvert 's conversion had been in progress a long time before it was made public . George Abbot , ( <unk> @-@ 1633 ) , the reigning Archbishop of Canterbury , ( and ecclesiastical head of the independent Church of England ) , reported that political opposition to Calvert , combined with his loss of office , had " made him <unk> and , as the saying is , " <unk> <unk> <unk> " , so <unk> apparently did <unk> <unk> , which <unk> now <unk> , this being the third time that he hath <unk> to blame that way [ sic ] " . Godfrey Goodman , the Bishop of Gloucester , later claimed Calvert had been a secret Catholic all along ( " infinitely addicted to the Catholic faith " ) , which explained his support for lenient policies towards Catholics and for the Spanish match . 
- However , no one had questioned Calvert 's conformity at the time , and if he had indeed been secretly Catholic , he had hidden it well . It seems more likely Calvert converted in late 1624 . At the time , Simon Stock , a <unk> <unk> priest reported to the Congregation Propaganda <unk> in Rome on November 15 that he had converted two Privy <unk> to Catholicism , one of whom historians are certain was Calvert . Calvert , who had probably met Stock at the Spanish embassy in London , later worked with the priest on a plan for a Catholic mission in his new first Newfoundland colony ( off modern Canada ) . 
- When King James I ( and VI of Scotland ) died in March 1625 , his successor King Charles I maintained Calvert 's <unk> but not his previous place on the Privy Council . Calvert then turned his attention to his Irish estates and his overseas investments . He was not entirely forgotten at court . After Buckingham 's <unk> in wars against Spain and France had ended in failure , he recalled Baltimore to court , and for a while may have considered employing him in the peace negotiations with Spain . Though nothing came of Baltimore 's recall , he renewed his rights over the silk @-@ import duties , which had <unk> with the death of James I , and secured Charles ' blessing for his venture in the " New Found Land " . 
- 
- = = Colony of Avalon ( Newfoundland ) = = 
- 
- Calvert had long maintained an interest in the exploration and settlement of the New World , beginning with his investment of twenty @-@ five pounds in the second Virginia Company in 1609 , and a few months later a more substantial sum in the East India Company , which he increased in <unk> . In 1620 , Calvert purchased a tract of land in Newfoundland from Sir William Vaughan , ( <unk> @-@ 1641 ) , a Welsh writer and colonial <unk> , who had earlier failed to establish a colony on the large sub @-@ Arctic island off the eastern coast of North America . He named the area of the peninsula as Avalon , after the legendary spot where Christianity was supposedly introduced to Roman Britain in ancient times . The plantation lay on what is now called the Avalon Peninsula and included the fishing station at " Ferryland " . Calvert almost certainly had a fishery project in mind at this stage . 
- Calvert dispatched Captain Edward Wynne and a group of Welsh colonists to Ferryland , where they landed in August 1621 , and set about constructing a settlement . Wynne sent positive reports concerning the potential for local fisheries and for the production of salt , hemp , flax , tar , iron , timber and <unk> . Wynne also praised the climate , declaring , " It is better and not so cold as England " and predicted that the colony would become self @-@ sufficient after one year . Others corroborated Wynne 's reports : for example , Captain Daniel Powell , who delivered a further party of settlers to Ferryland , wrote : " The land on which our Governor Calvert and / or Wynne planted is so good and <unk> , that for the quantity , I think there is no better in many parts of England " ; but he added <unk> that Ferryland was " the coldest harbour in the land " . Wynne and his men began work on various building projects , including a substantial house and the shoring up of the harbour . To protect them against <unk> French warships , a recent hazard in the area , since the recent founding of New France in the interior ( modern Lower Canada of the 18th and 19th <unk> , Province of Quebec and Dominion of Canada ) along the St. Lawrence River , Calvert employed the pirate John <unk> . 
- The settlement appeared to be progressing so well that in January 1623 , Calvert obtained a concession from King James for the whole of Newfoundland , though the grant was soon reduced to cover only the southeastern Avalon peninsula , owing to competing claims from other English colonists . The final Charter constituted the province as a " county palatinate " , officially titled the " Province of Avalon " , under Calvert 's personal rule . 
- After resigning the Royal secretariat of state in 1625 , the new Baron Baltimore made clear his intention to visit the colony : " I intend shortly , " he wrote in March , " God willing , a journey for Newfoundland to visit a plantation which I began there some few years since . " His plans were disrupted by the death of King James I , and by the <unk> on Catholics with which King Charles I began his reign in order to appease his opponents . The new King required all privy councillors to take the oaths of supremacy and allegiance ; and since Baltimore , as a Catholic , had to refuse , he was obliged to step down from that cherished office . Given the new religious and political climate , and perhaps also to escape a serious outbreak of plague in England , Baltimore moved to his estates in Ireland . His expedition to Newfoundland had set sail without him in late May 1625 under Sir Arthur Aston , who became the new provincial Governor of Avalon . 
- A reference by David <unk> , bishop of <unk> , in Ireland , to a " <unk> [ also recorded as Jane ] Baltimore now wife " of Calvert , reveals that Baltimore had recently remarried . 
- From the time of his conversion in 1625 onwards , Baltimore took care to cater for the religious needs of his colonists , both Catholic and Protestant . He had asked Simon Stock to provide priests for the 1625 expedition , but Stock 's recruits arrived in England after Aston had sailed . Stock 's own ambitions for the colony appear to have exceeded Baltimore 's : in letters to De Propaganda <unk> in Rome , Stock claimed the Newfoundland settlement could act as a <unk> for the conversion of natives not only in the New World but also in China , the latter via a passage he believed existed from the east coast to the Pacific Ocean . 
- 
- = = Baltimore in Avalon = = 
- 
- Baltimore was determined to visit his colony in person . In May <unk> , he wrote to Wentworth : 
- Aston 's return to England in late <unk> , along with all the Catholic settlers , failed to deter Baltimore , who finally sailed for Newfoundland in 1627 , arriving on July 23 and staying only two months before returning to England . He had taken both Protestant and Catholic settlers with him , as well as two secular priests , Thomas <unk> and Anthony Pole ( also known as Smith ) , the latter remaining behind in the colony when Baltimore departed for England . The land Baltimore had seen was by no means the <unk> described by some early settlers , being only marginally productive ; as the summer climate was deceptively mild , his brief visit gave Baltimore no reason to alter his plans for the colony . 
- In 1628 , he sailed again for Newfoundland , this time with his second wife Jane , most of his children , and 40 more settlers , to officially take over as Proprietary Governor of Avalon . He and his family moved into the house at Ferryland built by Wynne , a sizeable structure for the time , by colonial standards , and the only one in the settlement large enough to accommodate religious services for the community . 
- Matters connected to religion were to <unk> Baltimore 's stay in " this remote part of the <unk> where I have planted my <unk> [ sic ] " . He sailed at a time when English military preparations were underway to relieve the Huguenots at La Rochelle . He was dismayed to find that the war with France had spread to Newfoundland , and that he had to spend most of his time fighting off French attacks on English fishing fleets with his own ships the Dove and the Ark . As he wrote to Buckingham , " I came to <unk> , and <unk> , and <unk> , but I am <unk> to fighting with Frenchmen [ sic ] " . His settlers were so successful against the French that they captured several ships , which they escorted back to England to help with the war effort . Baltimore was granted the loan of one of the ships to aid in his defence of the colony , as well as a share of the prize money . 
- <unk> a policy of free religious worship in the colony , Baltimore allowed the Catholics to worship in one part of his house and the Protestants in another . This novel arrangement proved too much for the resident Anglican priest , <unk> Stourton — " that <unk> Stourton " , as Baltimore referred to him — who , after <unk> with Baltimore , was placed on a ship for England , where he lost no time in reporting Baltimore 's practices to the authorities , complaining that the Catholic priests Smith and <unk> said mass every Sunday and " <unk> use all other ceremonies of the church of Rome in as ample a manner as tis used in <unk> [ sic ] " . and that Baltimore had the son of a Protestant forcibly baptised as a Catholic . Although Stourton 's complaints were investigated by the Privy Council , due to Baltimore 's support in high places , the case was dismissed . 
- Baltimore had become disenchanted with conditions in " this <unk> country " , and he wrote to his old acquaintances in England lamenting his troubles . The final blow to his hopes was dealt by the Newfoundland winter of 1628 – 9 , which did not release its grip until May . Like others before them , the residents of Avalon suffered terribly from the cold and from <unk> . Nine or ten of Baltimore 's company died that winter , and with half the settlers ill at one time , his house had to be turned into a hospital . The sea froze over , and nothing would grow before May . " Tis not <unk> <unk> " , Baltimore wrote to Wentworth . He confessed to the king : " I have found ... by too <unk> bought experience [ that which other men ] always concealed from me ... that there is a sad face of <unk> upon all this land " . 
- Baltimore solicited a new charter from the king . In order to found an alternative colony in a less hostile climate further south , he requested " a precinct " in Virginia , where he could grow tobacco . He wrote to his friends Francis Cottington and Thomas Wentworth enlisting their support for this new proposal , admitting the impression his abandonment of Avalon might make in England : " I shall <unk> a great deal of <unk> and discourse and be <unk> by most men of <unk> and <unk> [ sic ] " . The king , perhaps guided by Baltimore 's friends at court , replied expressing concern for Baltimore 's health and gently advising him to forget colonial schemes and return to England , where he would be treated with every respect : " Men of your condition and breeding are <unk> for other <unk> than the framing of new plantations , which commonly have rugged & laborious beginnings , and require much greater <unk> , in managing them , than usually the power of one private subject can reach <unk> " . 
- Baltimore sent his children home to England in August . By the time the king 's letter reached Avalon , he had departed with his wife and servants for Virginia . 
- 
- = = <unk> to found a Mid @-@ Atlantic colony = = 
- 
- In late September or October 1629 , Baltimore arrived in Jamestown , where the <unk> , who suspected him of designs on some of their territory and vehemently opposed Catholicism , gave him a cool welcome . They gave him the oaths of supremacy and allegiance , which he refused to take , so they ordered him to leave . After no more than a few weeks in the colony , Baltimore left for England to pursue the new charter , leaving his wife and servants behind . In early 1630 , he procured a ship to fetch them , but it <unk> off the Irish coast , and his wife was drowned . Baltimore described himself the following year as " a long time myself a Man of Sorrows " . 
- Baltimore spent the last two years of his life constantly lobbying for his new charter , though the obstacles proved difficult . The <unk> , led by William <unk> , who sailed to England to make the case , campaigned aggressively against separate <unk> of the Chesapeake , claiming they possessed the rights to that area . Baltimore was short of capital , having exhausted his fortune , and was sometimes forced to depend on the assistance of his friends . To make matters worse , in the summer of 1630 , his household was infected by the plague , which he survived . He wrote to Wentworth : " Blessed be God for it who hath preserved me now from <unk> , hunger , <unk> and pestilence ... " 
- His health declining , Baltimore 's persistence over the charter finally paid off in 1632 . The king first granted him a location south of Jamestown , but Baltimore asked the king to reconsider in response to opposition from other investors interested in settling the new land of Carolina into a sugar plantation . Baltimore eventually compromised by accepting redrawn boundaries to the north of the Potomac River , on either side of the Chesapeake Bay . The charter was about to pass when the fifty @-@ two @-@ year @-@ old Baltimore died in his lodgings at Lincoln 's Inn Fields , on 15 April 1632 . Five weeks later , on 20 June 1632 , the charter for Maryland passed the seals . 
- 
- = = Legacy = = 
- 
- In his will , written the day before he died , Baltimore <unk> his friends Wentworth and Cottington to act as guardians and supervisors to his first son Cecil , who inherited the title of Lord Baltimore and the imminent grant of Maryland . Baltimore 's two colonies in the New World continued under the <unk> of his family . Avalon , which remained a prime spot for the <unk> and export of fish , was <unk> by Sir David <unk> , with a new royal charter which Cecil Calvert vigorously challenged , and it was finally absorbed into Newfoundland in 1754 . Although Baltimore 's failed Avalon venture marked the end of an early era of attempts at proprietary colonisation , it laid the foundation upon which permanent settlements developed in that region of Newfoundland . 
- Maryland became a prime tobacco exporting colony in the mid @-@ Atlantic and , for a time , a refuge for Catholic settlers , as George Calvert had hoped . Under the rule of the Lords Baltimore , thousands of British Catholics emigrated to Maryland , establishing some of the oldest Catholic communities in what later became the United States . However Catholic rule in Maryland was eventually <unk> by the re @-@ assertion of royal control over the colony . 
- 140 years after its first settlement , Maryland joined twelve other British colonies along the Atlantic coast in declaring their independence from British rule and the right to freedom of religion for all citizens in the new United States . 
- 
- 
- = <unk> and development of the Everglades = 
- 
- The history of draining and development of the Everglades dates back to the 19th century . During the Second Seminole War beginning in 1836 , the United States military 's mission was to seek out Seminole people in the Everglades and capture or kill them . Those missions gave the military the opportunity to map land that seemed to <unk> and <unk> them at every turn . A national push for expansion and progress toward the latter part of the 19th century stimulated interest in draining the Everglades for agricultural use . According to historians , " From the middle of the nineteenth century to the middle of the twentieth century , the United States went through a period in which wetland removal was not questioned . Indeed , it was considered the proper thing to do . " 
- A pattern of political and financial motivation , and a lack of understanding of the geography and ecology of the Everglades have plagued the history of drainage projects . The Everglades are a part of a massive watershed that originates near Orlando and drains into Lake Okeechobee , a vast and shallow lake . As the lake exceeds its capacity in the wet season , the water forms a flat and very wide river , about 100 miles ( 160 km ) long and 60 miles ( 97 km ) wide . As the land from Lake Okeechobee slopes gradually to Florida Bay , water flows at a rate of half a mile ( 0 @.@ 8 km ) a day . Before human activity in the Everglades , the system comprised the lower third of the Florida peninsula . The first attempt to drain the region was made by real estate developer Hamilton Disston in 1881 . Disston 's sponsored canals were unsuccessful , but the land he purchased for them stimulated economic and population growth that attracted railway developer Henry Flagler . Flagler built a railroad along the east coast of Florida and eventually to Key West ; towns grew and farmland was cultivated along the rail line . 
- During his 1904 campaign to be elected governor , Napoleon Bonaparte Broward promised to drain the Everglades , and his later projects were more effective than Disston 's . Broward 's promises sparked a land boom facilitated by blatant errors in an engineer 's report , pressure from real estate developers , and the burgeoning tourist industry throughout south Florida . The increased population brought hunters who went unchecked and had a devastating impact on the numbers of wading birds ( hunted for their plumes ) , alligators , and other Everglades animals . 
- Severe hurricanes in 1926 and 1928 caused catastrophic damage and flooding from Lake Okeechobee that prompted the Army Corps of Engineers to build a dike around the lake . Further floods in 1947 prompted an unprecedented construction of canals throughout southern Florida . Following another population boom after World War II , and the creation of the Central and Southern Florida Flood Control Project , the Everglades was divided into sections separated by canals and water control devices that delivered water to agricultural and newly developed urban areas . However , in the late 1960s , following a proposal to construct a massive airport next to Everglades National Park , national attention turned from developing the land to restoring the Everglades . 
- 
- = = Exploration = = 
- 
- American involvement in the Everglades began during the Second Seminole War ( 1836 – 42 ) , a costly and very unpopular conflict . The United States spent between $ 30 million and $ 40 million and lost between 1 @,@ 500 and 3 @,@ 000 lives . The U.S. military drove the Seminoles into the Everglades and were charged with the task of finding them , defeating them , and moving them to Oklahoma Indian territory . Almost 4 @,@ 000 Seminoles were killed in the war or were removed . The U.S. military was completely unprepared for the conditions they found in the Everglades . They tore their clothes on sawgrass , ruined their boots on the uneven limestone floor , and were plagued by mosquitoes . Soldiers ' legs , feet , and arms were cut open on the sawgrass and <unk> infection set in , taking many lives and limbs . Many died of <unk> @-@ borne illness . After <unk> through mud , one private died in his tracks of exhaustion in 1842 . General Thomas <unk> admitted the military was overwhelmed by the terrain when he wrote to the Secretary of War in 1838 , trying to dissuade him from prolonging the war . 
- <unk> about the value of Florida to the Union was mixed : some thought it a useless land of swamps and horrible animals , while others thought it a gift from God for national prosperity . In 1838 comments in The Army and Navy Chronicle supported future development of southern Florida : 
- [ The ] climate [ is ] most delightful ; but , from want of actual observation , [ it ] could not speak so confidently of the soil , although , from the appearance of the surrounding vegetation , a portion of it , at least , must be rich . Whenever the <unk> shall be forced from their <unk> , as eventually they must be , the <unk> spirit of our countrymen will very soon discover the sections best adapted to cultivation , and the now barren or <unk> everglades will be made to blossom like a garden . It is the general impression that these everglades are <unk> during the summer months , by reason of their being overflowed by the abundant rains of the season ; but if it should prove that these <unk> are caused or increased by obstructions to the natural courses of the rivers , as outlets to the numerous lakes , American industry will remove these obstructions . 
- The military penetration of southern Florida offered the opportunity to map a poorly understood part of the country . As late as 1823 , official reports doubted the existence of a large inland lake , until the military met the Seminoles at the Battle of Lake Okeechobee in 1837 . To avenge repeated surprise attacks on himself and ammunition stores , Colonel William Harney led an expedition into the Everglades in 1840 , to hunt for a chief named <unk> . With Harney were 90 soldiers in 16 canoes . One soldier 's account of the trip in the St. Augustine News was the first printed description of the Everglades available to the general public . The anonymous writer described the hunt for <unk> and the terrain they were crossing : " No country that I have ever heard of bears any resemblance to it ; it seems like a vast sea filled with grass and green trees , and <unk> intended as a retreat for the <unk> Indian , from which the white man would never seek to drive them " . 
- The final blame for the military stalemate was determined to lie not in military preparation , supplies , leadership , or superior tactics by the Seminoles , but in Florida 's impenetrable terrain . An army surgeon wrote : " It is in fact a most hideous region to live in , a perfect <unk> for Indians , alligators , serpents , frogs , and every other kind of <unk> reptile . " The land seemed to inspire extreme reactions of wonder or hatred . In 1870 , an author described the mangrove forests as a " waste of nature 's grandest exhibition to have these carnivals of splendid vegetation occurring in isolated places where it is but seldom they are seen . " A band of hunters , naturalists , and collectors ventured through in 1885 , taking along with them the 17 @-@ year @-@ old grandson of an early resident of Miami . The landscape <unk> the young man shortly after he entered the Shark River : " The place looked wild and lonely . About three o <unk> it seemed to get on Henry 's nerves and we saw him crying , he would not tell us why , he was just plain scared . " 
- In 1897 , an explorer named Hugh Willoughby spent eight days <unk> with a party from the mouth of the Harney River to the Miami River . He wrote about his observations and sent them back to the New Orleans Times @-@ Democrat . Willoughby described the water as healthy and <unk> , with numerous springs , and 10 @,@ 000 alligators " more or less " in Lake Okeechobee . The party encountered thousands of birds near the Shark River , " killing hundreds , but they continued to return " . Willoughby pointed out that much of the rest of the country had been mapped and explored except for this part of Florida , writing , " ( w ) e have a tract of land one hundred and thirty miles long and seventy miles wide that is as much unknown to the white man as the heart of Africa . " 
- 
- = = Drainage = = 
- 
- As early as 1837 , a visitor to the Everglades suggested the value of the land without the water : 
- Could it be drained by deepening the natural outlets ? Would it not open to cultivation immense tracts of rich vegetable soil ? Could the <unk> , obtained by draining , be improved to any useful purpose ? Would such draining render the country unhealthy ? ... Many queries like these passed through our minds . They can only be solved by a thorough examination of the whole country . Could the waters be lowered ten feet , it would probably drain six hundred thousand acres ; should this prove to be a rich soil , as would seem probable , what a field it would open for tropical productions ! What facilities for commerce ! 
- Territorial representative David Levy proposed a resolution that was passed in Congress in 1842 : " that the Secretary of War be directed to place before this House such information as can be obtained in relation to the <unk> and probable expense of draining the everglades of Florida . " From this directive Secretary of the Treasury Robert J. Walker requested Thomas Buckingham Smith from St. Augustine to consult those with experience in the Everglades on the feasibility of draining them , saying that he had been told two or three canals to the Gulf of Mexico would be sufficient . Smith asked officers who had served in the Seminole Wars to respond , and many favored the idea , promoting the land as a future agricultural asset to the South . A few disagreed , such as Captain John Sprague , who wrote he " never supposed the country would excite an inquiry , other than as a hiding place for Indians , and had it occurred to me that so great an undertaking , one so utterly <unk> , as draining the Ever Glades was to be discussed , I should not have destroyed the scratch of pen upon a subject so fruitful , and which cannot be understood but by those who have <unk> the water belly deep and examined carefully the western coast by land and by water . " 
- Nevertheless , Smith returned a report to the Secretary of the Treasury asking for $ 500 @,@ 000 to do the job . The report is the first published study on the topic of the Everglades , and concluded with the statement : 
- The Ever Glades are now suitable only for the haunt of <unk> <unk> or the resort of <unk> reptiles . The statesman whose <unk> shall cause the millions of acres they contain , now worse than worthless , to <unk> with the products of agricultural industry ; that man who thus adds to the resources of his country ... will merit a high place in public favor , not only with his own generation , but with <unk> . He will have created a State ! 
- Smith suggested cutting through the rim of the Everglades ( known today as the Atlantic Coastal Ridge ) , connecting the heads of rivers to the coastline so that 4 feet ( 1 @.@ 2 m ) of water would be drained from the area . The result , Smith hoped , would yield farmland suitable for corn , sugar , rice , cotton , and tobacco . 
- In 1850 Congress passed a law that gave several states wetlands within their state boundaries . The Swamp and <unk> Lands Act ensured that the state would be responsible for funding the attempts at developing wetlands into <unk> . Florida quickly formed a committee to consolidate grants to pay for such attempts , though attention and funds were diverted owing to the Civil War and Reconstruction . Not until after 1877 did attention return to the Everglades . 
- 
- = = = Hamilton Disston 's canals = = = 
- 
- After the Civil War , an agency named the Internal <unk> Fund ( IIF ) , charged with using grant money to improve Florida 's infrastructure through canals , rail lines , and roads , was eager to be rid of the debt incurred by the Civil War . IIF trustees found a Pennsylvania real estate developer named Hamilton Disston who was interested in implementing plans to drain the land for agriculture . Disston was persuaded to buy 4 @,@ 000 @,@ 000 acres ( 16 @,@ 000 km2 ) of land for $ 1 million in 1881 . The New York Times declared it the largest purchase of land ever by any individual . Disston began building canals near St. Cloud to lower the basin of the <unk> and Kissimmee Rivers . His workers and engineers faced conditions similar to those of the soldiers during the Seminole Wars ; it was <unk> , <unk> labor in dangerous conditions . The canals seemed at first to work in lowering the water levels in the wetlands surrounding the rivers . Another dredged waterway between the Gulf of Mexico and Lake Okeechobee was built , opening the region to steamboat traffic . 
- Disston 's engineers focused on Lake Okeechobee as well . As one colleague put it , " Okeechobee is the point to attack " ; the canals were to be " equal or greater than the inflow from the Kissimmee valley , which is the source of all the evil . " Disston sponsored the digging of a canal 11 miles ( 18 km ) long from Lake Okeechobee towards Miami , but it was abandoned when the rock proved denser than the engineers had expected . Though the canals lowered the groundwater , their capacity was inadequate for the wet season . A report that evaluated the failure of the project concluded : " The reduction of the waters is simply a question of sufficient capacity in the canals which may be dug for their relief " . 
- Though Disston 's canals did not drain , his purchase <unk> the economy of Florida . It made news and attracted tourists and land <unk> alike . Within four years property values doubled , and the population increased significantly . One newcomer was the inventor Thomas Edison , who bought a home in Fort Myers . Disston opened real estate offices throughout the United States and Europe , and sold tracts of land for $ 5 an acre , establishing towns on the west coast and in central Florida . English tourists in particular were targeted and responded in large numbers . Florida passed its first water laws to " build drains , ditches , or water courses upon petition of two or more landowners " in 1893 . 
- 
- = = = Henry Flagler 's railroads = = = 
- 
- Due to Disston 's purchase , the IIF was able to sponsor railroad projects , and the opportunity presented itself when oil <unk> Henry Flagler became <unk> with St. Augustine during a vacation . He built the <unk> <unk> de León Hotel in St. Augustine in 1888 , and began buying land and building rail lines along the east coast of Florida , first from Jacksonville to <unk> , then as far south as Palm Beach in 1893 . Flagler 's establishment of " the <unk> " , a settlement for hotel and rail line workers across the river from the barrier island containing Palm Beach , became West Palm Beach . Along the way he built resort hotels , transforming territorial outposts into tourist destinations and the land bordering the rail lines into citrus farms . 
- The winter of 1894 – 1895 produced a bitter <unk> that killed citrus trees as far south as Palm Beach . Miami resident Julia Tuttle sent Flagler a pristine orange blossom and an invitation to visit Miami , to persuade him to build the railroad farther south . Although he had earlier turned her down several times , Flagler finally agreed , and by 1896 the rail line had been extended to Biscayne Bay . Three months after the first train arrived , the residents of Miami , 512 in all , voted to incorporate the town . Flagler publicized Miami as a " Magic City " throughout the United States and it became a prime destination for the extremely wealthy after the Royal Palm Hotel was opened . 
- 
- = = = Broward 's " Empire of the Everglades " = = = 
- 
- Despite the sale of 4 @,@ 000 @,@ 000 acres ( 16 @,@ 000 km2 ) to Disston and the <unk> price of land , by the turn of the 20th century the IIF was bankrupt due to <unk> . Legal battles ensued between the State of Florida and the railroad owners about who owned the rights to sell reclaimed land in the Everglades . In 1904 gubernatorial campaigning , the strongest candidate , Napoleon Bonaparte Broward , made draining the Everglades a major plank . He called the future of south Florida the " Empire of the Everglades " and compared its potential to that of Holland and Egypt : " It would indeed be a commentary on the intelligence and energy of the State of Florida to confess that so simple an engineering feat as the drainage of a body of land above the sea was above their power " , he wrote to voters . Soon after his election , he fulfilled his promise to " drain that <unk> pestilence @-@ ridden swamp " and pushed the Florida legislature to form a group of commissioners to oversee <unk> of flooded lands . They began by taxing counties that would be affected by the drainage attempts , at 5 cents an acre , and formed the Everglades Drainage District in 1907 . 
- Broward asked James O. Wright — an engineer on loan to the State of Florida from the USDA 's Bureau of Drainage Investigations — to draw up plans for drainage in 1906 . Two dredges were built by 1908 , but had cut only 6 miles ( 9 @.@ 7 km ) of canals . The project quickly ran out of money , so Broward sold real estate developer Richard J. <unk> a million dollars worth of land in the Everglades , 500 @,@ 000 acres ( 2 @,@ 000 km2 ) , before the engineer 's report had been submitted . <unk> from Wright 's report were given to the IIF stating that eight canals would be enough to drain 1 @,@ 850 @,@ 000 acres ( 7 @,@ 500 km2 ) at a cost of a dollar an acre . The <unk> were released to real estate developers who used them in their advertisements , and Wright and the USDA were pressed by the real estate industry to publicize the report as quickly as possible . Wright 's supervisor noted errors in the report , as well as undue enthusiasm for draining , and delayed its release in 1910 . Different unofficial versions of the report circulated — some that had been altered by real estate interests — and a version hastily put together by Senator Duncan U. Fletcher called U.S. Senate <unk> 89 included early <unk> statements , causing a <unk> of speculation . 
- Wright 's initial report concluded that drainage would not be difficult . Building canals would be more cost effective than constructing a dike around Lake Okeechobee . The soil would be fertile after drainage , the climate would not be adversely affected , and the enormous lake would be able to <unk> farmland in the dry season . Wright based his conclusions on 15 years of weather data since the recording of precipitation began in the 1890s . His calculations concentrated on the towns of Jupiter and Kissimmee . Since weather data had not been recorded for any area within the Everglades , none was included in the report . Furthermore , the heaviest year of rain on record , Wright assumed , was atypical , and he urged that canals should not be constructed to bear that amount of water due to the expense . Wright 's calculations for what canals should be able to hold were off by 55 percent . His most fundamental mistake , however , was designing the canals for a maximum rainfall of 4 inches ( 10 cm ) of water a day , based on flawed data for July and August rainfall , despite available data that indicated torrential <unk> of 10 inches ( 25 cm ) and 12 inches ( 30 cm ) had occurred in 24 @-@ hour periods . 
- Though a few voices expressed skepticism of the report 's conclusions — notably Frank Stoneman , the editor of the Miami News @-@ Record ( the forerunner of The Miami Herald ) — the report was hailed as <unk> , coming from a branch of the U.S. government . In 1912 Florida appointed Wright to oversee the drainage , and the real estate industry <unk> misrepresented this mid @-@ level engineer as the world 's foremost authority on wetlands drainage , in charge of the U.S. Bureau of Reclamation . However , the U.S. House of Representatives investigated Wright since no report had officially been published despite the money paid for it . Wright eventually retired when it was discovered that his colleagues disagreed with his conclusions and refused to approve the report 's publication . One testified at the hearings : " I regard Mr. Wright as absolutely and completely incompetent for any engineering work " . 
- Governor Broward ran for the U.S. Senate in 1908 but lost . Broward and his predecessor , William Jennings , were paid by Richard <unk> to tour the state to promote drainage . Broward was elected to the Senate in 1910 , but died before he could take office . He was <unk> across Florida for his leadership and progressive inspiration . Rapidly growing Fort Lauderdale paid him tribute by naming Broward County after him ( the town 's original plan had been to name it Everglades County ) . Land in the Everglades was being sold for $ 15 an acre a month after Broward died . Meanwhile , Henry Flagler continued to build railway stations at towns as soon as the populations warranted them . News of the Panama Canal inspired him to connect his rail line to the closest deep water port . Biscayne Bay was too shallow , so Flagler sent railway scouts to explore the possibility of building the line through to the tip of mainland Florida . The scouts reported that not enough land was present to build through the Everglades , so Flagler instead changed the plan to build to Key West in 1912 . 
- 
- = = Boom and plume harvesting = = 
- 
- Real estate companies continued to advertise and sell land along newly dug canals . In April 1912 — the end of the dry season — reporters from all over the U.S. were given a tour of what had recently been drained , and they returned to their papers and <unk> about the progress . Land developers sold 20 @,@ 000 lots in a few months . But as news about the Wright report continued to be negative , land values <unk> , and sales decreased . Developers were sued and arrested for mail fraud when people who had spent their life savings to buy land arrived in south Florida expecting to find a dry parcel of land to build upon and instead found it completely underwater . Advertisements promised land that would yield crops in eight weeks , but for many it took at least as long just to clear . Some burned off the sawgrass or other vegetation only to discover that the underlying peat continued to burn . Animals and <unk> used for <unk> got <unk> in the muck and were useless . When the muck dried , it turned to a fine black powder and created dust storms . Settlers encountered rodents , <unk> , and biting insects , and faced dangers from mosquitoes , poisonous snakes and alligators . Though at first crops <unk> quickly and <unk> , they just as quickly <unk> and died , seemingly without reason . It was discovered later that the peat and muck lacked copper and other trace elements . The USDA released a pamphlet in 1915 that declared land along the New River Canal would be too costly to keep drained and fertilized ; people in <unk> . Lauderdale responded by collecting all of the pamphlets and burning them . 
- With the increasing population in towns near the Everglades came hunting opportunities . Even decades earlier , Harriet <unk> <unk> had been horrified at the hunting by visitors , and she wrote the first conservation publication for Florida in 1877 : " [ t ] he decks of boats are crowded with men , whose only feeling amid our magnificent forests , seems to be a wild desire to shoot something and who fire at every living thing on shore . " <unk> and raccoons were the most widely hunted for their skins . Otter pelts could fetch between $ 8 and $ 15 each . <unk> , more plentiful , only warranted 75 cents each in 1915 . Hunting often went unchecked ; on one trip , a Lake Okeechobee hunter killed 250 alligators and 172 otters . 
- <unk> birds were a particular target . Their feathers were used in women 's hats from the late 19th century until the 1920s . In 1886 , five million birds were estimated to have been killed for their feathers . They were usually shot in the spring , when their feathers were colored for mating and nesting . <unk> , as the plumes were called in the <unk> business , sold in 1915 for $ 32 an <unk> , also the price of gold . <unk> was a $ 17 @-@ million @-@ a @-@ year industry that motivated plume <unk> to lie in wait at the nests of <unk> and other large birds during the nesting season , shoot the parents with small @-@ bore rifles , and leave the chicks to <unk> . Many hunters refused to participate after watching the gruesome results of a plume hunt . Still , plumes from Everglades wading birds could be found in Havana , New York City , London , and Paris . A dealer in New York paid at least 60 hunters to provide him with " almost anything that wore feathers , but particularly the Herons , <unk> , and <unk> birds " . Hunters could collect plumes from a hundred birds on a good day . 
- <unk> harvesting became a dangerous business . The <unk> Society became concerned with the amount of hunting being done in <unk> in the mangrove forests . In 1902 , they hired a <unk> , Guy Bradley , to watch the <unk> around <unk> Lake . Bradley had lived in <unk> within the Everglades , and was murdered in 1905 by one of his neighbors after he tried to prevent him from hunting . Protection of birds was the reason for establishing the first wildlife refuge when President Theodore Roosevelt set Pelican Island as a sanctuary in 1903 . 
- In the 1920s , after birds were protected and alligators hunted nearly to extinction , Prohibition created a living for those willing to smuggle alcohol into the U.S. from Cuba . Rum @-@ runners used the vast Everglades as a hiding spot : there were never enough law enforcement officers to patrol it . The advent of the fishing industry , the arrival of the railroad , and the discovery of the benefits of adding copper to Okeechobee muck soon created unprecedented numbers of residents in new towns like Moore Haven , <unk> , and Belle Glade . By 1921 , 2 @,@ 000 people lived in 16 new towns around Lake Okeechobee . Sugarcane became the primary crop grown in south Florida and it began to be mass @-@ produced . Miami experienced a second real estate boom that earned a developer in Coral <unk> $ 150 million and saw undeveloped land north of Miami sell for $ 30 @,@ 600 an acre . Miami became cosmopolitan and experienced a renaissance of architecture and culture . Hollywood movie stars <unk> in the area and <unk> built lavish homes . Miami 's population multiplied <unk> , and <unk> . Lauderdale and Palm Beach grew many times over as well . In 1925 , Miami newspapers published editions weighing over 7 pounds ( 3 @.@ 2 kg ) , most of it real estate advertising . <unk> property was the most highly valued . <unk> trees were cut down and replaced with palm trees to improve the view . Acres of south Florida slash pine were taken down , some for lumber , but the wood was found to be dense and it split apart when nails were driven into it . It was also <unk> @-@ resistant , but homes were needed quickly . Most of the pine forests in Dade County were cleared for development . 
- 
- = = Hurricanes = = 
- 
- The canals proposed by Wright were unsuccessful in making the lands south of Lake Okeechobee fulfill the promises made by real estate developers to local farmers . The winter of 1922 was <unk> wet and the region was underwater . The town of Moore Haven received 46 inches ( 1 @,@ 200 mm ) of rain in six weeks in 1924 . Engineers were pressured to regulate the water flow , not only for farmers but also for commercial fishers , who often requested conflicting water levels in the lake . Fred Elliot , who was in charge of building the canals after James Wright retired , commented : " A man on one side of the canal wants it raised for his particular use and a man on the other side wants it lowered for his particular use " . 
- 
- = = = 1926 Miami Hurricane = = = 
- 
- The 1920s brought several favorable conditions that helped the land and population boom , one of which was an absence of any severe storms . The last severe hurricane , in 1906 , had struck the Florida Keys . Many homes were constructed hastily and poorly as a result of this lull in storms . However , on September 18 , 1926 , a storm that became known as the 1926 Miami Hurricane struck with winds over 140 miles per hour ( 230 km / h ) , and caused massive devastation . The storm surge was as high as 15 feet ( 4 @.@ 6 m ) in some places . Henry Flagler 's <unk> Royal Palm Hotel was destroyed along with many other hotels and buildings . Most people who died did so when they ran out into the street in <unk> while the eye of the hurricane passed over , not knowing the wind was coming in from the other direction . " The lull lasted 35 minutes , and during that time the streets of the city became crowded with people " , wrote Richard Gray , the local weather chief . " As a result , many lives were lost during the second phase of the storm . " In Miami alone , 115 people were counted dead — although the true figure may have been as high as 175 , because death totals were racially segregated . More than 25 @,@ 000 people were homeless in the city . The town of Moore Haven , bordering Lake Okeechobee , was hardest hit . A levee built of muck collapsed , drowning almost 400 of the town 's entire 1 @,@ 200 residents . The tops of Lake Okeechobee levees were only 18 to 24 inches ( 46 to 61 cm ) above the lake itself and the engineers were aware of the danger . Two days before the hurricane , an engineer predicted , " [ i ] f we have a blow , even a gale , Moore Haven is going under water " . The engineer lost his wife and daughter in the flood . 
- The City of Miami responded to the hurricane by <unk> its effects and turning down aid . The Miami Herald declared two weeks after the storm that almost everything in the city had returned to normal . The governor supported the efforts to minimize the appearance of the destruction by refusing to call a special legislative session to appropriate emergency funds for relief . As a result , the American Red Cross was able to collect only $ 3 million of $ 5 million needed . The 1926 hurricane effectively ended the land boom in Miami , despite the attempts at hiding the effects . It also forced drainage commissioners to re @-@ evaluate the effectiveness of the canals . A $ 20 million plan to build a dike around Lake Okeechobee , to be paid by property taxes , was turned down after a skeptical constituency sued to stop it ; more than $ 14 million had been spent on canals and they were ineffective in taking away excess water or delivering it when needed . 
- 
- = = = 1928 Okeechobee Hurricane = = = 
- 
- The weather was unremarkable for two years . In 1928 , construction was completed on the Tamiami Trail , named because it was the only road spanning between Tampa and Miami . The builders attempted to construct the road several times before they blasted the muck down to the limestone , filled it with rock and paved over it . Hard rains in the summer caused Lake Okeechobee to rise several feet ; this was noticed by a local newspaper editor who demanded it be lowered . However , on September 16 , 1928 came a massive storm , now known as the 1928 Okeechobee Hurricane . Thousands drowned when Lake Okeechobee breached its levees ; the range of estimates of the dead spanned from 1 @,@ 770 ( according to the Red Cross ) to 3 @,@ 000 or more . Many were swept away and never recovered . The majority of the dead were black migrant workers who had recently settled in or near Belle Glade . The catastrophe made national news , and although the governor again refused aid , after he toured the area and counted 126 bodies still <unk> or <unk> a week after the storm , he activated the National Guard to assist in the <unk> , and declared in a telegram : " Without exaggeration , the situation in the storm area <unk> description " . 
- 
- = = = Herbert Hoover Dike = = = 
- 
- The focus of government agencies quickly shifted to the control of floods rather than drainage . The Okeechobee Flood Control District , financed by both state and federal funds , was created in 1929 . President Herbert Hoover toured the towns affected by the 1928 Okeechobee Hurricane and , an engineer himself , ordered the Army Corps of Engineers to assist the communities surrounding the lake . Between 1930 and 1937 , a dike 66 miles ( 106 km ) long was built around the southern edge of the lake , and a shorter one around the northern edge . It was 34 feet ( 10 m ) tall and 3 @.@ 5 feet ( 1 @.@ 1 m ) thick on the lake side , 3 feet ( 0 @.@ 91 m ) thick on the top , and 2 feet ( 0 @.@ 61 m ) thick toward land . Control of the Hoover Dike and the waters of Lake Okeechobee were delegated to federal powers : the United States declared legal limits of the lake to be 14 feet ( 4 @.@ 3 m ) and 17 feet ( 5 @.@ 2 m ) . 
- A massive canal 80 feet ( 24 m ) wide and 6 feet ( 1 @.@ 8 m ) deep was also dug through the <unk> River ; when the lake rose too high , the excess water left through the canal to the Gulf of Mexico . <unk> trees were planted along the north shore levee : Australian pines , Australian oaks , willows , and bamboo . More than $ 20 million was spent on the entire project . Sugarcane production soared after the dike and canal were built . The populations of the small towns surrounding the lake jumped from 3 @,@ 000 to 9 @,@ 000 after World War II . 
- 
- = = Drought = = 
- 
- The effects of the Hoover Dike were seen immediately . An extended drought occurred in the 1930s , and with the wall preventing water leaving Lake Okeechobee and canals and ditches removing other water , the Everglades became <unk> . <unk> turned to dust , and <unk> ocean water entered Miami 's wells . When the city brought in an expert to investigate , he discovered that the water in the Everglades was the area 's groundwater — here , it appeared on the surface . <unk> the Everglades removed this groundwater , which was replaced by ocean water seeping into the area 's wells . In 1939 , 1 million acres ( 4 @,@ 000 km2 ) of Everglades burned , and the black clouds of peat and sawgrass fires hung over Miami . Underground peat fires burned roots of trees and plants without burning the plants in some places . Scientists who took soil samples before draining had not taken into account that the organic composition of peat and muck in the Everglades was mixed with bacteria that added little to the process of decomposition underwater because they were not mixed with oxygen . As soon as the water was drained and oxygen mixed with the soil , the bacteria began to break down the soil . In some places , homes had to be moved on to <unk> and 8 feet ( 2 @.@ 4 m ) of <unk> was lost . 
- 
- = = = Conservation attempts = = = 
- 
- <unk> concerned about the Everglades have been a vocal minority ever since Miami was a young city . South Florida 's first and perhaps most enthusiastic naturalist was Charles <unk> Simpson , who retired from the Smithsonian Institution to Miami in 1905 when he was 53 . Nicknamed " the Sage of Biscayne Bay " , Simpson wrote several books about tropical plant life around Miami . His backyard contained a tropical hardwood <unk> , which he estimated he showed to about 50 @,@ 000 people . Though he tended to avoid controversy regarding development , in <unk> <unk> in Florida he wrote , " <unk> everywhere has an insane desire to waste and destroy the good and beautiful things this nature has lavished upon him " . 
- Although the idea of protecting a portion of the Everglades arose in 1905 , a <unk> effort was formed in 1928 when Miami landscape designer Ernest F. Coe established the Everglades Tropical National Park Association . It had enough support to be declared a national park by Congress in 1934 , but there was not enough money during the Great Depression to buy the proposed 2 @,@ 000 @,@ 000 acres ( 8 @,@ 100 km2 ) for the park . It took another 13 years for it to be dedicated on December 6 , 1947 . One month before the dedication of the park , the former editor of The Miami Herald and freelance writer Marjory Stoneman Douglas published her first book , The Everglades : River of Grass . After researching the region for five years , she described the history and ecology of the south of Florida in great detail , characterizing the Everglades as a river instead of a stagnant swamp . Douglas later wrote , " My colleague Art Marshall said that with [ the words " River of Grass " ] I changed everybody 's knowledge and educated the world as to what the Everglades meant " . The last chapter was titled " The <unk> Hour " and warned that the Everglades were approaching death , although the course could be reversed . Its first printing sold out a month after its release . 
- 
- = = Flood control = = 
- 
- <unk> with the dedication of Everglades National Park , 1947 in south Florida saw two hurricanes and a wet season responsible for 100 inches ( 250 cm ) of rain , ending the decade @-@ long drought . Although there were no human casualties , cattle and deer were drowned and standing water was left in suburban areas for months . Agricultural interests lost about $ 59 million . The embattled head of the Everglades Drainage District carried a gun for protection after being threatened . 
- 
- = = = Central and Southern Florida Flood Control Project = = = 
- 
- In 1948 Congress approved the Central and Southern Florida Project for Flood Control and Other <unk> ( C & SF ) and consolidated the Everglades Drainage District and the Okeechobee Flood Control District under this . The C & SF used four methods in flood management : levees , water storage areas , canal improvements , and large pumps to assist gravity . Between 1952 and 1954 in cooperation with the state of Florida it built a levee 100 miles ( 160 km ) long between the eastern Everglades and suburbs from Palm Beach to <unk> , and blocked the flow of water into populated areas . Between 1954 and 1963 it divided the Everglades into basins . In the northern Everglades were Water Conservation Areas ( WCAs ) , and the Everglades Agricultural Area ( EAA ) bordering to the south of Lake Okeechobee . In the southern Everglades was Everglades National Park . <unk> and pumping stations bordered each WCA , which released water in drier times and removed it and pumped it to the ocean or Gulf of Mexico in times of flood . The WCAs took up about 37 percent of the original Everglades . 
- During the 1950s and 1960s the South Florida metropolitan area grew four times as fast as the rest of the nation . Between 1940 and 1965 , 6 million people moved to south Florida : 1 @,@ 000 people moved to Miami every week . Urban development between the mid @-@ 1950s and the late 1960s <unk> . Much of the water reclaimed from the Everglades was sent to newly developed areas . With metropolitan growth came urban problems associated with rapid expansion : traffic jams ; school overcrowding ; crime ; <unk> sewage treatment plants ; and , for the first time in south Florida 's urban history , water shortages in times of drought . 
- The C & SF constructed over 1 @,@ 000 miles ( 1 @,@ 600 km ) of canals , and hundreds of pumping stations and levees within three decades . It produced a film , Waters of Destiny , characterized by author Michael <unk> as propaganda , that likened nature to a villainous , <unk> force of rage and declared the C & SF 's mission was to tame nature and make the Everglades useful . Everglades National Park management and Marjory Stoneman Douglas initially supported the C & SF , as it promised to maintain the Everglades and manage the water <unk> . However , an early report by the project reflected local attitudes about the Everglades as a priority to people in nearby developed areas : " The aesthetic appeal of the Park can never be as strong as the demands of home and livelihood . The <unk> and the <unk> mean something to people in an abstract way , but the former cannot line their <unk> , nor the latter fill their empty <unk> . " 
- Establishment of the C & SF made Everglades National Park completely dependent upon another political entity for its survival . One of the C & SF 's projects was Levee 29 , laid along the Tamiami Trail on the northern border of the park . Levee 29 featured four flood control gates that controlled all the water entering Everglades National Park ; before construction , water flowed in through open drain pipes . The period from 1962 to 1965 was one of drought for the Everglades , and Levee 29 remained closed to allow the Biscayne <unk> — the fresh water source for South Florida — to stay filled . Animals began to cross Tamiami Trail for the water held in WCA 3 , and many were killed by cars . <unk> estimate the population of alligators in Everglades National Park was <unk> ; otters nearly became extinct . The populations of wading birds had been reduced by 90 percent from the 1940s . When park management and the U.S. Department of the Interior asked the C & SF for assistance , the C & SF offered to build a levee along the southern border of Everglades National Park to retain waters that historically flowed through the <unk> and into Florida Bay . Though the C & SF refused to send the park more water , they constructed Canal 67 , bordering the east side of the park and carrying excess water from Lake Okeechobee to the Atlantic . 
- 
- = = = Everglades Agricultural Area = = = 
- 
- The C & SF established 470 @,@ 000 acres ( 1 @,@ 900 km2 ) for the Everglades Agricultural Area — 27 percent of the Everglades before development . In the late 1920s , agricultural experiments indicated that adding large amounts of <unk> sulfate to Everglades muck produced profitable vegetable <unk> . Adding 100 pounds ( 45 kg ) of the compound was more cost effective than adding 1 short ton ( 0 @.@ 91 t ) of <unk> . The primary cash crop in the EAA is sugarcane , though sod , beans , <unk> , <unk> , and rice are also grown . Sugarcane became more consolidated an industry than did any other crop ; in 1940 the coalition of farms was renamed U.S. Sugar and this produced 86 percent of Everglades sugar . During the 1930s the sugarcane farmers ' coalition came under investigation for labor practices that bordered on slavery . Potential employees — primarily young black men — were lured from all over the U.S. by the promise of jobs , but they were held financially responsible for training , transportation , room and board and other costs . <unk> while debts were owed was punishable with jail time . By 1942 , U.S. Sugar was indicted for <unk> in federal court , though the charges were eventually dismissed on a <unk> . U.S. Sugar benefited significantly from the U.S. embargo on Cuban goods beginning in the early 1960s . In 1958 , before the Castro regime , 47 @,@ 000 acres ( 190 km2 ) of sugarcane were harvested in Florida ; by the 1964 – 1965 season , 228 @,@ 000 acres ( 920 km2 ) were harvested . From 1959 to 1962 the region went from two sugar mills to six , one of which in Belle Glade set several world records for sugar production . 
- Fields in the EAA are typically 40 acres ( 16 ha ) , on two sides bordered by canals that are connected to larger ones by which water is pumped in or out depending on the needs of the crops . The water level for sugarcane is ideally maintained at 20 inches ( 51 cm ) below the surface soil , and after the cane is harvested , the stalks are burned . <unk> require more <unk> than sugarcane , though the fields may resemble the historic hydrology of the Everglades by being flooded in the wet season . Sugarcane , however , requires water in the dry season . The fertilizers used on vegetables , along with high concentrations of nitrogen and phosphorus that are the by @-@ product of decayed soil necessary for sugarcane production , were pumped into WCAs south of the EAA , predominantly to Everglades National Park . The introduction of large amounts of these let exotic plants take hold in the Everglades . One of the defining characteristics of natural Everglades ecology is its ability to support itself in a nutrient @-@ poor environment , and the introduction of fertilizers began to change this ecology . 
- 
- = = Turning point = = 
- 
- A turning point for development in the Everglades came in 1969 when a replacement airport was proposed as Miami International Airport <unk> its capacities . Developers began acquiring land , paying $ 180 an acre in 1968 , and the Dade County Port Authority ( DCPA ) bought 39 square miles ( 100 km2 ) in the Big <unk> Swamp without consulting the C & SF , management of Everglades National Park or the Department of the Interior . Park management learned of the official purchase and agreement to build the jetport from The Miami Herald the day it was announced . The DCPA <unk> the land it had bought , and laid a single runway it declared was for training pilots . The new jetport was planned to be larger than O <unk> , <unk> , JFK , and <unk> airports combined ; the location chosen was 6 miles ( 9 @.@ 7 km ) north of the Everglades National Park , within WCA 3 . The deputy director of the DCPA declared : " This is going to be one of the great population centers of America . We will do our best to meet our responsibilities and the responsibilities of all men to exercise dominion over the land , sea , and air above us as the higher order of man intends . " 
- The C & SF brought the jetport proposal to national attention by mailing letters about it to 100 conservation groups in the U.S. Initial local press reaction condemned conservation groups who immediately opposed the project . Business Week reported real estate prices jumped from $ 200 to $ 800 an acre surrounding the planned location , and Life wrote of the expectations of the commercial interests in the area . The U.S. Geological Survey 's study of the environmental impact of the jetport started , " Development of the proposed jetport and its attendant facilities ... will <unk> destroy the south Florida ecosystem and thus the Everglades National Park " . The jetport was intended to support a community of a million people and employ 60 @,@ 000 . The DCPA director was reported in Time saying , " I 'm more interested in people than alligators . This is the ideal place as far as aviation is concerned . " 
- When studies indicated the proposed jetport would create 4 @,@ 000 @,@ 000 US gallons ( 15 @,@ 000 @,@ 000 L ) of raw sewage a day and 10 @,@ 000 short tons ( 9 @,@ 100 t ) of jet engine <unk> a year , the national media snapped to attention . Science magazine wrote , in a series on environmental protection highlighting the jetport project , " Environmental scientists have become increasingly aware that , without careful planning , development of a region and the conservation of its natural resources do not go hand in hand " . The New York Times called it a " <unk> for disaster " , and Wisconsin senator <unk> Nelson wrote to President Richard Nixon voicing his opposition : " It is a test of whether or not we are really committed in this country to protecting our environment . " Governor Claude Kirk withdrew his support for the project , and the 78 @-@ year @-@ old Marjory Stoneman Douglas was persuaded to go on tour to give hundreds of speeches against it . She established Friends of the Everglades and encouraged more than 3 @,@ 000 members to join . Initially the U.S. Department of Transportation pledged funds to support the jetport , but after pressure , Nixon overruled the department . He instead established Big <unk> National Preserve , announcing it in the Special Message to the Congress <unk> the 1972 Environmental Program . Following the jetport proposition , restoration of the Everglades became not only a <unk> priority , but an international one as well . In the 1970s the Everglades were declared an International <unk> Reserve and a World Heritage Site by UNESCO , and a <unk> of International Importance by the <unk> Convention , making it one of only three locations on earth that have appeared on all three lists . 
- 
- 
- = DuMont Television Network = 
- 
- The DuMont Television Network ( also known as the DuMont Network , simply DuMont / Du Mont , or ( incorrectly ) <unk> / <unk> / ) was one of the world 's pioneer commercial television networks , <unk> NBC and CBS for the distinction of being first overall in the USA . It began operation in the United States in 1946 . It was owned by DuMont Laboratories , a television equipment and set manufacturer . The network was hindered by the <unk> cost of broadcasting , by regulations imposed by the Federal Communications Commission ( FCC ) which restricted the company 's growth , and even by the company 's partner , Paramount Pictures . Despite several innovations in broadcasting and the creation of one of television 's biggest stars of the 1950s ( Jackie Gleason ) , the network never found itself on solid financial ground . Forced to expand on UHF channels during an era when UHF was not yet a standard feature on television sets , DuMont fought an uphill battle for program clearances outside of their three owned @-@ and @-@ operated stations in New York , Washington and Pittsburgh , finally ending network operations in 1956 . 
- DuMont 's latter @-@ day obscurity , caused mainly by the destruction of its extensive program archive by the 1970s , has prompted TV historian David Weinstein to refer to it as the " Forgotten Network " or " Network Is Long Gone " . A few popular DuMont programs , such as Cavalcade of Stars and Emmy Award winner Life Is Worth Living , appear in television <unk> or are mentioned briefly in books about U.S. television history . 
- 
- = = History = = 
- 
- 
- = = = Origins = = = 
- 
- DuMont Laboratories was founded in 1931 by Dr. Allen B. DuMont with only $ 1 @,@ 000 , and a laboratory in his basement . He and his staff were responsible for many early technical innovations , including the first consumer all @-@ electronic television set in 1938 . The company 's television sets soon became the gold standard of the industry . In 1942 , DuMont worked with the Army in developing radar technology during World War II . This ended up bringing in $ 5 million in capital for the company . 
- Early sales of television sets were hampered by the lack of regularly scheduled programming being broadcast . A few months after selling his first set in 1938 , DuMont opened his own New York area experimental television station ( <unk> ) in <unk> , New Jersey . In 1940 , the station moved to Manhattan as <unk> on channel 4 . Unlike CBS and NBC , which reduced their hours of television broadcasting during World War II , DuMont continued full @-@ scale experimental and commercial broadcasts throughout the war . In 1944 , <unk> became WABD ( <unk> derived from DuMont 's initials ) moving to channel 5 in 1945 , the third commercial television station in New York . On May 19 , 1945 , DuMont opened experimental W3XWT in Washington , DC . A minority shareholder in DuMont Laboratories was Paramount Pictures , which had advanced $ 400 @,@ 000 in 1939 for a 40 % share in the company . Paramount had television interests of its own , having launched experimental stations in Los Angeles in 1939 and Chicago in 1940 , and DuMont 's association with Paramount ultimately proved to be a mistake . 
- Soon after his experimental Washington station signed on , DuMont began experimental coaxial cable <unk> between his laboratories in <unk> , New Jersey , and his two stations . It is said that one of those broadcasts on the <unk> announced that the U.S. had dropped an atomic bomb on <unk> , Japan , on August 9 , 1945 . This was later considered to be the official beginning of the DuMont Network by both Thomas T. Goldsmith , the network 's chief engineer and DuMont 's best friend , and DuMont himself . Regular network service began on August 15 , 1946 , on WABD and W3XWT . In 1947 , W3XWT became WTTG , named after Goldsmith . The pair were joined in 1949 by WDTV ( channel 3 ) in Pittsburgh . 
- Although NBC in New York was known to have station @-@ to @-@ station television links as early as 1940 with <unk> ( now <unk> ) in Philadelphia and <unk> <unk> , NY , DuMont received its station licenses before NBC resumed its previously sporadic network broadcasts after the war . ABC had just come into existence as a radio network in 1943 and did not enter network television until 1948 , when it signed on a flagship station in New York City , <unk> @-@ TV ( now <unk> @-@ TV ) . CBS also waited until 1948 to begin network operations because it was waiting for the Federal Communications Commission to approve its color television system ( which it eventually did not ) . Other companies – including <unk> , the Yankee Network , and Paramount itself – were interested in starting television networks , but were prevented from successfully doing so by restrictive FCC regulations ; however , at least the Paramount Television Network actually did have some limited success in network operations in the late 1940s and early 1950s . 
- 
- = = = Programming = = = 
- 
- Despite no history of radio programming or stable of radio stars to draw on and perennial cash shortages , DuMont was an innovative and creative network . Without the radio revenues that supported mighty NBC and CBS , DuMont <unk> relied on their <unk> and on connections with Broadway . Eventually , the network provided original programs that are remembered more than 60 years later . 
- The network largely ignored the standard business model of 1950s TV , in which one <unk> sponsored an entire show , enabling it to have complete control over its content . Instead , DuMont sold commercials to many different advertisers , freeing producers of its shows from the veto power held by sole sponsors . This eventually became the standard model for US television . Some commercial time was sold regionally on a co @-@ op basis , while other spots were sold network @-@ wide . 
- DuMont also holds another important place in American TV history . WDTV 's sign @-@ on made it possible for stations in the Midwest to receive live network programming from stations on the East Coast , and vice versa . Before then , the networks relied on separate regional networks in the two time zones for live programming , and the West Coast received network programming from kinescopes ( films shot directly from live television screens ) originating from the East Coast . On January 11 , 1949 , the coaxial cable linking East and Midwest ( known in television circles as " the Golden Spike , " in reference to the Golden spike that united the First Transcontinental Railroad ) was activated . The ceremony , hosted by DuMont and WDTV , was carried on all four networks . <unk> @-@ TV ( channel 9 ) in Chicago and WABD in New York were able to share programs through a live coaxial cable feed when WDTV signed on in Pittsburgh , because the station completed the East Coast @-@ to @-@ Midwest chain , allowing stations in both regions to air the same program simultaneously , which is still the standard for US TV . It was another two years before the West Coast got live programming from the East ( and the East able to get live programming from the West ) , but this was the beginning of the modern era of network television . 
- The first broadcasts came from DuMont 's 515 Madison Avenue headquarters , and it soon found additional space , including a fully functioning theater , in the New York branch of <unk> 's department store at <unk> Street and Broadway . Later , a lease on the Adelphi Theatre on 54th Street and the Ambassador Theatre on West 49th Street gave the network a site for variety shows , and in 1954 , the lavish DuMont <unk> @-@ Centre opened in the former Jacob Ruppert 's Central Opera House at 205 East 67th Street . 
- DuMont was the first network to broadcast a film production for TV : Talk Fast , <unk> , produced by RKO in 1944 . DuMont also aired the first TV situation comedy , Mary Kay and Johnny , as well as the first network @-@ televised soap opera , <unk> Hill . Cavalcade of Stars , a variety show hosted by Jackie Gleason , was the birthplace of The Honeymooners ( Gleason took his variety show to CBS in 1952 , but filmed the " Classic 39 " Honeymooners episodes at DuMont 's Adelphi Theater studio in 1955 @-@ 56 ) . Bishop Fulton J. Sheen 's devotional program Life Is Worth Living went up against Milton Berle in many cities , and was the first show to compete successfully in the ratings against " Mr. Television " . In 1952 , Sheen won an Emmy Award for " Most Outstanding Personality " . The network 's other notable programs include : 
- Ted Mack 's The Original Amateur Hour , which began on radio in the 1930s under original host Edward Bowes 
- The Morey Amsterdam Show , a comedy / variety show hosted by Morey Amsterdam , which started on CBS before moving to DuMont in 1949 
- Captain Video and His Video Rangers , a hugely popular kids ' science fiction series 
- The Arthur Murray Party , a dance program 
- Down You Go , a popular panel show 
- Rocky King , Inside Detective , a private eye series starring <unk> <unk> 
- The <unk> , a camera 's @-@ eye @-@ view detective series 
- Live coverage of boxing and professional wrestling , the latter featuring matches staged by the Capitol Wrestling Corporation , the predecessor to WWE 
- The Johns Hopkins Science Review , a Peabody Award @-@ winning education program 
- Cash and Carry , the first network @-@ televised game show 
- The Ernie Kovacs Show , the first truly innovative show in what was then visual radio , not television . 
- The network was a pioneer in TV programming aimed at minority audiences and featuring minority performers , at a time when the other American networks aired few television series for non @-@ whites . Among DuMont 's minority programs were The Gallery of Madame Liu @-@ <unk> , starring Asian American film actress Anna May <unk> , the first US TV show to star an Asian American , and The Hazel Scott Show , starring pianist and singer Hazel Scott , the first US network TV series to be hosted by a black woman . 
- Although DuMont 's programming pre @-@ dated videotape , many DuMont offerings were recorded on kinescopes . These kinescopes were said to be stored in a warehouse until the 1970s . Actress Edie Adams , the wife of comedian Ernie Kovacs ( both regular performers on early television ) testified in 1996 before a panel of the Library of Congress on the preservation of television and video . Adams claimed that so little value was given to these films that the stored kinescopes were loaded into three trucks and dumped into Upper New York Bay . Nevertheless , a number of DuMont programs survive at The Paley Center for Media in New York City , the UCLA Film and Television Archive in Los Angeles , in the Peabody Awards Collection at the University of Georgia , and the Museum of Broadcast Communications in Chicago . 
- Although nearly the entire DuMont film archive was destroyed , several surviving DuMont shows have been released on DVD . A large number of episodes of Life Is Worth Living have been saved , and they are now aired weekly on Catholic @-@ oriented cable network , the <unk> Word Television Network , which also makes a collection of them available on DVD ( in the biographical information about Fulton J. Sheen added to the end of many episodes , a still image of Bishop Sheen looking into a DuMont Television camera can be seen ) . Several companies that distribute DVDs over the Internet have released a small number of episodes of Cavalcade of Stars and The Morey Amsterdam Show . Two more DuMont programs , Captain Video and His Video Rangers and Rocky King , Inside Detective , have had a small amount of surviving episodes released commercially by at least one major distributor of public domain programming . 
- 
- = = = = Awards = = = = 
- 
- DuMont programs were by necessity low @-@ budget affairs , and the network received relatively few awards from the TV industry . Most awards during the 1950s went to NBC and CBS , who were able to out @-@ spend other companies and draw on their extensive history of radio broadcasting in the relatively new television medium . DuMont , however , did win a number of awards during its years of operation . 
- During the 1952 – 53 TV season , Bishop Fulton J. Sheen , host of Life Is Worth Living , won an Emmy Award for Most Outstanding Personality . Sheen beat out CBS 's Arthur Godfrey , Edward R. <unk> and Lucille Ball , who were also nominated for the same award . Sheen was also nominated for – but did not win – consecutive Public Service <unk> in 1952 , 1953 , and 1954 . 
- DuMont received an Emmy nomination for Down You Go , a popular game show during the 1952 – 53 television season ( in the category Best <unk> <unk> , <unk> , or Panel Program ) . The network was nominated twice for its coverage of professional football during the 1953 – 54 and 1954 – 55 television seasons . 
- The Johns Hopkins Science Review , a DuMont public affairs program , was awarded a Peabody Award in 1952 in the Education category . Sheen 's Emmy and the Science Review Peabody were the only national awards the DuMont Network received . Though DuMont series and performers continued to win local TV awards , by the mid @-@ 1950s the DuMont network no longer had a national presence . 
- 
- = = = = Ratings = = = = 
- 
- The earliest measurements of TV audiences were performed by the C. E. Hooper company of New York . DuMont performed well in the Hooper ratings ; in fact , DuMont 's talent program , The Original Amateur Hour , was the most popular series of the 1947 – 48 season . Variety ranked DuMont 's popular variety series Cavalcade of Stars as the tenth most popular series two seasons later . 
- In February 1950 , Hooper 's competitor A. C. Nielsen bought out the <unk> system . DuMont did not fare well with the change : none of its shows appeared on Nielsen 's annual top 20 lists of the most popular series . One of the DuMont Network 's biggest hits of the 1950s , Life is Worth Living , received Nielsen ratings of up to 11 @.@ 1 , attracting more than 10 million viewers . Sheen 's one @-@ man program – in which he discussed philosophy , psychology and other fields of thought from a Christian perspective – was the most widely viewed religious series in the history of television . 169 local television stations aired Life , and for three years the program competed successfully against NBC 's popular The Milton Berle Show . The ABC and CBS programs which aired in the same timeslot were cancelled . 
- Life is Worth Living was not the only DuMont program to achieve double @-@ digit ratings . In 1952 , Time magazine reported that popular DuMont game show Down You Go had attracted an audience estimated at 16 million viewers . Similarly , DuMont 's summer 1954 replacement series , The <unk> , achieved audiences estimated at 10 million . Still , these series were only moderately popular compared to NBC 's and CBS 's highest @-@ rated programs . 
- Nielsen was not the only company to report TV ratings , however . Companies such as <unk> , <unk> and <unk> had also measured TV viewership . The adjacent chart comes from <unk> 's August 1950 ratings breakdown , as reported in Billboard magazine . 
- 
- = = = <unk> with AT & T and Paramount = = = 
- 
- DuMont struggled to get its programs aired in many parts of the country , in part due to technical limitations of network lines maintained by the telephone company AT & T Corporation . During the 1940s and 1950s , television signals were sent between stations via coaxial cable and <unk> links which were owned by AT & T. The service provider did not have enough cable lines and <unk> circuits to provide signal relay service from all four networks to all of their affiliates at the same time , so AT & T allocated times when each network could offer live programs to their affiliates . In 1950 , AT & T allotted NBC and CBS each over 100 hours of live prime time network service , but gave ABC only 53 hours , and DuMont just 37 . AT & T also required each television network to lease both radio and television lines . DuMont was the only television network without a radio network , but was forced to pay for a service it did not use . DuMont protested AT & T 's actions with the Federal Communications Commission , and eventually received a compromise . 
- DuMont 's biggest corporate hurdle , however , may have been with the company 's own partner , Paramount . Relations between the two companies were strained as early as 1939 , when Paramount opened experimental television stations in Los Angeles and Chicago without DuMont . Dr. DuMont claimed that the original 1937 acquisition proposal required Paramount to expand its television interests " through DuMont " . Paramount representative Paul <unk> , who also was a member of DuMont 's board of directors , denied that any such restriction had ever been discussed ( Dr. DuMont was vindicated on this point by a 1953 examination of the original draft document ) . 
- DuMont aspired to grow beyond its three stations , applying for new television station licenses in Cincinnati and Cleveland in 1947 . This would give the network five owned @-@ and @-@ operated stations ( O & Os ) , the maximum allowed by the FCC at the time . However , DuMont was hampered by Paramount 's two stations , <unk> ( channel 5 ) in Los Angeles and <unk> ( channel 4 , now <unk> @-@ TV on channel 2 ) in Chicago – the descendants of the two experimental stations that rankled DuMont in 1940 . Although these stations never carried DuMont programming ( with the exception of <unk> for one year from 1947 to 1948 ) , and in fact competed against DuMont 's affiliates in those cities , the FCC ruled that Paramount essentially controlled DuMont , which effectively placed the network at the five @-@ station cap . Paramount 's exertion of influence over the network 's management and the power of its voting stock brought the FCC to its conclusion . Thus , DuMont was unable to open additional stations as long as Paramount owned stations or owned a portion of DuMont . Paramount refused to sell . 
- In 1949 , Paramount Pictures launched the Paramount Television Network , a service which provided local television stations with filmed television programs ; Paramount 's network " undercut the company that it had invested in . " Paramount did not share its stars , big budgets or filmed programs with DuMont ; the company had stopped financially supporting DuMont in 1941 . Although Paramount executives indicated they would produce programs for DuMont , the studio never supplied the network with programs or technical assistance . The <unk> relationship between Paramount and DuMont came to a head during the 1953 FCC hearings regarding the ABC – United Paramount Theaters merger when Paul <unk> , an executive at Paramount , publicly derided the quality of DuMont television sets in court testimony . 
- 
- = = = Trouble from the start = = = 
- 
- DuMont began with one basic disadvantage : unlike NBC , CBS and ABC , it did not have a radio network from which to draw big @-@ name talent , affiliate loyalty or radio profits to <unk> television operations until the television medium itself became profitable . Most early television licenses were granted to established radio broadcasters , and many longtime relationships with radio networks carried over to the new medium . As CBS and NBC ( and to a lesser extent , ABC ) gained their footing , they began to offer programming that drew on their radio backgrounds , bringing over the most popular radio stars . Early television station owners , when deciding which network would receive their main affiliation , were more likely to choose CBS 's roster of Lucille Ball , Jack Benny and Ed Sullivan , or NBC 's lineup of Milton Berle and Sid Caesar over DuMont , which offered a then @-@ unknown Jackie Gleason and Bishop Fulton J. Sheen . In smaller markets , with a limited number of stations , DuMont and ABC were often relegated to secondary status , so their programs got clearance only if the primary network was off the air or delayed via <unk> recording ( " <unk> " in DuMont <unk> ) . 
- Adding to DuMont 's troubles was the FCC 's 1948 " freeze " on television license applications . This was done to sort out the thousands of applications that had come streaming in , but also to rethink the allocation and technical standards laid down prior to World War II . It became clear soon after the war that 12 channels ( " channel 1 " had been removed from television broadcasting use because storms and other types of interference could severely affect the quality of signals on this channel ) were not nearly enough for national television service . What was to be a six @-@ month freeze lasted until 1952 , when the FCC opened the UHF spectrum . The FCC , however , did not require television manufacturers to include UHF capability . In order to see UHF stations , most people had to buy an expensive <unk> . Even then , the picture quality was marginal at best . <unk> to this was a decision to restrict VHF <unk> in <unk> and smaller @-@ sized markets . Television sets were not required to have all @-@ channel tuning until 1964 . 
- Forced to rely on UHF to expand , DuMont saw one station after another go dark due to <unk> ratings . It bought small , distressed UHF station <unk> ( channel 50 ) in Kansas City , Missouri in 1954 , but ran it for just three months before shutting it down at a considerable loss after attempting to compete with three established VHF stations . 
- The FCC 's Dr. Hyman <unk> said in 1960 , " If there had been four VHF outlets in the top markets , there 's no question DuMont would have lived and would have eventually turned the corner in terms of profitability . " 
- 
- = = = The end = = = 
- 
- During the early years of television , there was some measure of cooperation among the four major U.S. television networks . However , as television grew into a profitable business , an intense rivalry developed between the networks , just as it had in radio . NBC and CBS competed fiercely for viewers and advertising dollars , a contest neither <unk> DuMont nor ABC could hope to win . According to author Dennis <unk> , " NBC tried to make an arrangement with ABC and CBS to destroy the DuMont network . " The plan was for NBC and CBS to exclusively offer ABC their most popular series after they had aired on the bigger networks . ABC would become a network of re @-@ runs , but DuMont would be shut out . ABC president Leonard Goldenson rejected NBC executive David <unk> 's proposal , but " did not report it to the Justice Department " . 
- DuMont survived the early 1950s only because of WDTV in Pittsburgh , the lone commercial VHF station in what was then the sixth @-@ largest market . WDTV 's only competition came from UHF stations and distant stations from <unk> , Pennsylvania ; <unk> , Ohio ; and <unk> , West Virginia . No other commercial VHF station signed on in Pittsburgh until 1957 , giving WDTV a de facto monopoly on television in the area . Since WDTV carried secondary affiliations with the other three networks , DuMont used this as a <unk> chip to get its programs cleared in other large markets . 
- Despite its severe financial <unk> , by 1953 , DuMont appeared to be on its way to establishing itself as the third national network . DuMont programs aired live on 16 stations , but it could count on only seven primary stations – its three owned @-@ and @-@ operated stations ( " O & Os " ) , plus <unk> @-@ TV in Chicago , <unk> ( channel 11 ) in Los Angeles , <unk> @-@ TV ( channel 2 , now <unk> @-@ TV ) in Denver and <unk> @-@ TV ( channel 6 , now <unk> ) in Columbus , Ohio . In contrast , ABC had a full complement of five O & Os , augmented by nine primary affiliates . ABC also had a radio network ( it was descended from NBC 's Blue Network ) from which to draw talent , affiliate loyalty and a profit stream to subsidize television operations . However , ABC had only 14 primary stations , while CBS and NBC had over 40 each . By 1951 , ABC was badly <unk> and on the verge of bankruptcy . That year , the company announced a merger with United Paramount Theaters ( the former theater division of Paramount Pictures , which was spun off as a result of the United States v. Paramount Pictures , Inc. <unk> decision ) , but it was not until 1953 that the FCC approved the merger . 
- By this time , DuMont had begun to differentiate itself from NBC and CBS . It allowed its advertisers to choose the locations where their advertising ran , potentially saving them millions of dollars . By contrast , ABC operated like CBS and NBC even though it was only a fourth as large , forcing advertisers to purchase a large " must @-@ buy " list of stations . 
- ABC 's fortunes were dramatically altered in February 1953 , when the FCC cleared the way for UPT to buy the network . The merger provided ABC with a badly needed cash infusion , giving it the resources to mount " top shelf " programming and to provide a national television service on a scale approaching that of CBS and NBC . Through UPT president Leonard Goldenson , ABC also gained ties with the Hollywood studios that more than matched those DuMont 's producers had with Broadway . 
- Realizing that the ABC @-@ UPT deal put DuMont near extinction , network officials were receptive to a merger offer from ABC . Goldenson quickly brokered a deal with Ted Bergmann , DuMont 's managing director , under which the merged network would have been called " ABC @-@ DuMont " until at least 1958 and would have honored all of DuMont 's network commitments . In return , DuMont would get $ 5 million in cash , guaranteed advertising time for DuMont sets and a secure future for its staff . A merged ABC @-@ DuMont would have been a <unk> <unk> CBS and NBC , as it would have owned stations in five of the six largest U.S. television markets ( excluding only Philadelphia ) as well as ABC 's radio network . It also would have inherited DuMont 's de facto monopoly in Pittsburgh , and would have been one of two networks to have full ownership of a station in the nation 's capital ( the other being NBC ) . However , it would have had to sell a New York station – either DuMont 's WABD or ABC flagship <unk> @-@ TV ( channel 7 , now <unk> @-@ TV ) , probably the former . It also would have had to sell two other stations – most likely ABC 's two smallest O & Os , <unk> @-@ TV in Detroit and <unk> @-@ TV in San Francisco ( both broadcasting on channel 7 ) – to get under the FCC 's limit of five stations per owner . 
- However , Paramount vetoed the plan almost out of hand due to <unk> concerns . A few months earlier , the FCC had ruled that Paramount controlled DuMont , and there were still some questions about whether UPT had really separated from Paramount . 
- With no other way to readily obtain cash , DuMont sold WDTV to Westinghouse Electric Corporation for $ 9 @.@ 75 million in late 1954 . While this gave DuMont a short @-@ term cash infusion , it eliminated the leverage the network had to get program clearances in other markets . Without its de facto monopoly in Pittsburgh , the company 's advertising revenue shrank to less than half that of 1953 . By February 1955 , DuMont executives realized the company could not continue as a television network . The decision was made to shut down network operations and operate WABD and WTTG as independent stations . On April 1 , 1955 , most of DuMont 's entertainment programs were dropped . Bishop Sheen aired his last program on DuMont on April 26 and later moved to ABC . By May , just eight programs were left on the network , with only inexpensive shows and sporting events keeping what was left of the network going through the summer . The network also largely abandoned the use of the intercity network coaxial cable , on which it had spent $ 3 million in 1954 to transmit shows that mostly lacked station clearance . The company only retained network links for live sports programming and utilizing the company 's <unk> process to produce studio @-@ based programming . Ironically , <unk> is best remembered for being used by Jackie Gleason 's producers for the 39 @-@ half @-@ hour episodes of The Honeymooners that aired on CBS during the 1955 @-@ 56 television season . 
- In August , Paramount , with the help of other <unk> , seized full control of DuMont Laboratories . The last non @-@ sports program on DuMont , the game show What 's the Story , aired on September 23 , 1955 . After that , DuMont 's network feed was used only for occasional sporting events . DuMont 's last broadcast , a boxing match , aired on August 6 , 1956 . The date has also been reported as April 1950 , September 1955 , or August 4 , 1958 . According to one source , the final program aired on only five stations nationwide . It appears that the boxing show was syndicated to a few other east coast stations until 1958 , but likely not as a production of DuMont or its successor company . 
- DuMont spun off WABD and WTTG as the " DuMont Broadcasting Corporation " . The name was later changed to " Metropolitan Broadcasting Company " to distance the company from what was seen as a complete failure . In 1958 , John <unk> bought Paramount 's shares for $ 4 million , and renamed the company Metromedia in 1960 . WABD became <unk> @-@ TV and later <unk> ; WTTG still broadcasts under its original call letters as a Fox affiliate . 
- For 50 years , DuMont was the only major broadcast television network to cease operations , until CBS Corporation and Time Warner shut down two other struggling networks , UPN and The WB , in September 2006 to create The CW Television Network – whose schedule was originally composed largely of programs from both of its predecessor networks . 
- 
- = = Fate of the DuMont stations = = 
- 
- All three DuMont @-@ owned stations are still operating and <unk> , all three are owned @-@ and @-@ operated stations of their respective networks , just as when they were part of DuMont . Of the three , only Washington 's WTTG still has its original call letters . 
- WTTG and New York 's WABD ( later <unk> @-@ TV , and now <unk> ) survived as Metromedia @-@ owned <unk> until 1986 , when Metromedia was purchased by the News Corporation to form the nucleus of the new Fox Broadcasting Company . Clarke Ingram , who maintained a DuMont memorial site , has suggested that Fox can be considered a revival , or at least a linear descendant , of DuMont . 
- Westinghouse changed WDTV 's call letters to <unk> @-@ TV after the pioneering radio station of the same name , and switched its primary affiliation to CBS immediately after the sale . Westinghouse 's acquisition of CBS in 1995 made <unk> @-@ TV a CBS owned @-@ and @-@ operated station . 
- 
- = = DuMont programming library = = 
- 
- DuMont produced more than 20 @,@ 000 television episodes during the decade from 1946 to 1956 . Because the shows were created prior to the launch of <unk> 's electronic videotape recorder in late 1956 , all of them were initially broadcast live in black and white , then recorded on film <unk> for <unk> and for West Coast <unk> . By the early 1970s , their vast library of 35mm and <unk> kinescopes eventually wound up in the hands of " a successor network , " who reportedly disposed of all of them in New York City 's East River to make room for more recent @-@ vintage <unk> in a warehouse . Other kinescopes were put through a silver <unk> process , because of the microscopic amounts of silver that made up the <unk> of black @-@ and @-@ white film during this time . It is estimated that only about 350 complete DuMont television shows survive today , the most famous being virtually all of Jackie Gleason 's Honeymooners comedy sketches . Most of the existing episodes are believed to have come from the personal archives of DuMont 's hosts , such as Gleason and Dennis James . 
- 
- = = <unk> = = 
- 
- At its peak in 1954 , DuMont was affiliated with around 200 television stations . In those days , television stations were free to " cherry @-@ pick " which programs they would air , and many stations affiliated with multiple networks , depending mainly on the number of commercial television stations available in a market at a given time ( markets where only one commercial station was available carried programming from all four major networks ) . Many of DuMont 's " affiliates " carried very little DuMont programming , choosing to air one or two more popular programs ( such as Life Is Worth Living ) and / or sports programming on the weekends . Few stations carried the full DuMont program lineup . 
- In its later years , DuMont was carried mostly on poorly watched UHF channels or had only secondary affiliations on VHF stations . DuMont ended most operations on April 1 , 1955 , but honored network commitments until August 1956 . 
- 
- = = = <unk> = = = 
- 
- <unk> of DuMont Network programs , from the Internet Archive : The Adventures of <unk> Queen , Captain Video and His Video Rangers , Cavalcade of Stars , Life Is Worth Living , Miss U.S. Television 1950 Contest , The Morey Amsterdam Show , The Old American Barn Dance , Okay Mother , On Your Way , Public Prosecutor , Rocky King — Detective , School House , They Stand <unk> and A DuMont Network identification 
- 
- = Bodyline = 
- 
- Bodyline , also known as fast leg theory bowling , was a cricketing tactic devised by the English cricket team for their 1932 – 33 Ashes tour of Australia , specifically to combat the extraordinary batting skill of Australia 's Don Bradman . A bodyline delivery was one where the cricket ball was bowled towards the body of the batsman on the line of the leg stump , in the hope of creating leg @-@ side <unk> that could be caught by one of several fielders in the <unk> of the field behind square leg . This was considered by many to be <unk> and physically threatening , to the point of being unfair in a game once supposed to have <unk> traditions , although <unk> of the game had subsequently tended to elevate the principle of " win at all costs " above traditional ideals of <unk> . 
- Although no serious injuries arose from any short @-@ pitched deliveries while a leg theory field was set , the tactic still led to considerable ill feeling between the two teams , with the controversy eventually <unk> into the diplomatic arena . Over the next two decades , several of the Laws of Cricket were changed to prevent this tactic being repeated . Law 41 @.@ 5 states " At the instant of the bowler 's delivery there shall not be more than two fielders , other than the wicket @-@ keeper , behind the popping <unk> on the on side , " commonly referred to as being " behind square leg " . Additionally , Law 42 @.@ 6 ( a ) includes : " The bowling of fast short pitched balls is dangerous and unfair if the umpire at the bowler 's end considers that by their repetition and taking into account their length , height and direction they are likely to inflict physical injury on the striker " . 
- The occasional short @-@ pitched ball aimed at the batsman ( a <unk> ) has never been illegal and is still in widespread use as a tactic . 
- 
- = = Genesis = = 
- 
- The Australian cricket team toured England in 1930 . Australia won the five @-@ Test series 2 – 1 , with Don Bradman scoring <unk> runs at a batting average of 139 @.@ 14 , an aggregate record that still stands . By the time of the next Ashes series of 1932 – 33 , Bradman 's average <unk> around 100 , approximately twice that of all other world @-@ class batsmen . England feared that without <unk> to drastic tactics , they might not be able to defeat Australia until Bradman — then aged 24 — retired , something that might be over a decade away . It was believed that something new was required to combat Bradman , but it was believed more likely that Bradman could be dismissed by leg @-@ spin as Walter <unk> and Ian <unk> had supposedly caused him problems ; two leg @-@ spinners were included in the English touring party of 1932 – 33 . This view gradually came to change leading up to 1932 . 
- The idea of bodyline had originated in the Oval Test of the 1930 Ashes series . While Bradman was batting , the wicket became briefly difficult following rain . Bradman was seen to be uncomfortable facing deliveries which bounced higher than usual at a faster pace , being seen to step back out of the line of the ball . Former England player and Surrey captain Percy Fender was one who noticed , and the incident was much discussed by cricketers . However , given that Bradman scored 232 , it was not thought that a way to curb his prodigious scoring had been found . When Douglas Jardine later saw film footage of the Oval incident and noticed Bradman 's discomfort , he shouted , " I 've got it ! He 's yellow ! " Further details adding to the plan came from letters Fender received from Australia in 1932 , which described how Australian batsmen were increasingly moving across the stumps towards the off side to play the ball on the on side . Fender showed these letters to Jardine when it became clear that he was to captain MCC in Australia during the 1932 – 33 tour , and he also discussed Bradman 's discomfort at the Oval . It was also known in England that Bradman was dismissed for a four @-@ ball duck by fast bowler Eddie Gilbert , and looked very uncomfortable . Bradman had also appeared uncomfortable against the pace of Sandy Bell in his innings of <unk> not out at the Adelaide Oval in South Africa 's tour of Australia earlier in 1932 , when the desperate bowler decided to bowl short to him , and fellow South African Herbie Taylor , according to Jack Fingleton , may have mentioned this to English cricketers in 1932 . Fender felt Bradman might be vulnerable to fast , short @-@ pitched deliveries on the line of leg stump . Jardine felt that Bradman was afraid to stand his ground against <unk> bowling , citing instances in 1930 when he <unk> about , contrary to orthodox batting technique . 
- When Jardine was appointed England 's captain for the 1932 – 33 English tour of Australia , a meeting was arranged with Nottinghamshire captain Arthur Carr and his two fast bowlers Harold Larwood and Bill Voce at London 's Piccadilly Hotel to discuss a plan to combat Bradman 's extraordinary skills . Jardine asked Larwood and Voce if they could bowl on leg stump and make the ball come up into the body of the batsman . The bowlers agreed they could , and that it might prove effective . Jardine also visited Frank Foster who had toured Australia in 1911 – 12 to discuss field @-@ placing in Australia . Foster had bowled leg @-@ theory on that tour with his fielders placed close in on the leg side , as had George <unk> in 1903 – 04 . 
- A cordon of close @-@ in fielders would be <unk> behind the wicket and on the leg side to exploit batting errors elicited by this bowling line . In these circumstances , a batsman can either duck and risk being hit , or play the ball . Defensive shots rarely score runs and risk being caught in the cordon , while the pull and hook shots can result in a catch on the boundary , for which two men were usually set in " leg @-@ theory " bowling . Leg theory had been practised previously without resort to short @-@ pitched bowling , usually by slow or medium @-@ pace bowlers . This type of leg theory was aimed outside the line of leg stump ; the object being to test the batsman 's patience and force a rash stroke . It was occasionally an effective tactic , but was unattractive for spectators and never became widely used except by a handful of specialists such as Fred Root , the Worcestershire bowler and Warwick Armstrong , the former Australian captain . 
- However , there had been instances of what would later be recognised as bodyline prior to 1932 . In 1925 , Australian Jack Scott first bowled a form of bodyline in a state match for New South Wales , but his captain Herbie Collins disliked it and would not let him use it again when he was captain . Other Australian captains were less particular , including Vic Richardson who let him use those tactics when he moved to South Australia . He repeated them against the MCC in 1928 – 29 . In 1927 , in a Test trial match , " <unk> " Clark bowled short to a leg @-@ trap field . He was representing England in a side captained by Jardine . In 1928 – 29 , Harry Alexander bowled an early form of bodyline at the MCC tourists . Larwood used a form of bodyline on that same tour , bowling fast leg theory to a leg @-@ side field in two Test matches , although not with the same intensity and duration as came later . Bob Wyatt later claimed that Learie Constantine unsuccessfully used bodyline in 1929 – 30 in the West Indies . 
- Larwood and Voce practised the plan over the remainder of the 1932 season with varying but increasing success and several injuries to batsmen . Ken <unk> experimented with short @-@ pitched , leg @-@ theory bowling but was not selected for the tour . Bill Bowes also used short @-@ pitched bowling , notably against Jack Hobbs . 
- 
- = = <unk> between Australians and Jardine = = 
- 
- Jardine 's first experience against Australia came when his Oxford University team played against the 1921 Australian touring side . In the second innings , Jardine was 96 not out when the game ended , having batted his team to safety . The tourists were criticised in the press for not allowing Jardine to reach his hundred , but they had tried to help him with some easy bowling . There has been speculation that this incident helped develop Jardine 's antipathy towards Australians , although Christopher Douglas denies this . Cricket historian David <unk> believed it is possible that the <unk> Australian captain Warwick Armstrong could have addressed sarcastic comments to Jardine but Wisden believed his slow approach cost him his century . 
- Regardless of what happened in 1921 , Jardine 's conflicts with Australia solidified after he was selected to tour the country in 1928 – 29 . He began the tour with three consecutive hundreds . During the first century , the crowd engaged in some good @-@ natured joking at Jardine 's expense , but he was <unk> by the crowd during his second hundred for batting too slowly . Jardine accelerated after another slow start , during which he was again barracked to score his third century . The crowds took an increasing dislike to him , mainly for his superior attitude and bearing , his awkward fielding , and particularly his choice of <unk> . His first public action in South Australia was to take out the members of the South Australian team who had been to Oxford or Cambridge universities . Then , he wore a <unk> cap , given to successful cricketers at Oxford . It was not unusual for Oxford and Cambridge cricketers to wear similar caps while batting , as both Jardine and MCC captain Percy Chapman did so on this tour , although it was slightly unorthodox to wear them while fielding . However , this was neither understood nor acceptable to the Australian crowds . They quickly took exception to the importance he seemed to place on class distinction . Although Jardine may simply have worn the cap out of superstition , it conveyed a negative impression to the spectators ; his general <unk> drew one comment of " Where 's the butler to carry the bat for you ? " Jardine 's cap became a focus for criticism and mockery from the crowds throughout the tour . Nevertheless , Jack Fingleton later claimed that Jardine could have won over the crowd by exchanging jokes or <unk> with them . It is certain that Jardine by this stage had developed an intense dislike for Australian crowds . During his third century at the start of the tour , during a period of abuse from the spectators , he observed to a sympathetic Hunter <unk> that " All Australians are <unk> , and an unruly mob " . After the innings , when teammate <unk> <unk> remarked that the Australian crowds did not like Jardine , he replied " It 's fucking mutual " . During the tour , Jardine fielded next to the crowd on the boundary . There , he was <unk> abused and mocked for his awkward fielding , particularly when chasing the ball . On one occasion , he <unk> towards the crowd while fielding on the boundary as he changed position for the final time . 
- During the journey to Australia , some players reported that Jardine told them to hate the Australians in order to defeat them , while instructing them to refer to Bradman as " the little <unk> . " At this stage , he seemed to have settled on leg theory , if not full bodyline , as his main tactic . 
- Once the team arrived in Australia , Jardine quickly alienated the press by refusing to give team details before a match and being uncooperative during interviews . The press printed some negative stories as a result and the crowds barracked as they had done on his previous tour , which angered him . 
- 
- = = In Australia = = 
- 
- Although English bowlers did aim at the batsmen 's body in the opening tour matches , they did not follow through by packing the leg @-@ side field until Bill Woodfull led an Australian XI against the tourists in Melbourne on 18 – 22 November , in what was effectively a Test rehearsal . Jardine was rested from that match and his deputy , Bob Wyatt , deployed the full bodyline tactics for the first time on the tour . The match was drawn and Woodfull struggled , making 18 and a duck . <unk> his hopping technique and attempting to play unorthodox shots resembling overhead tennis smashes , Bradman failed to make an impact , and England were <unk> ahead of the Tests . Seeing the bruising balls hit the Australian batsmen on several occasions in this game and the next angered the spectators . 
- The English players and management were consistent in referring to their tactic as fast leg theory considering it to be a variant of the established and <unk> leg theory tactic . The inflammatory term " bodyline " was coined and <unk> by the Australian press ( see below ) . English writers used the term fast leg theory . The terminology reflected differences in understanding , as neither the English public nor the Board of the Marylebone Cricket Club ( MCC ) — the governing body of English cricket — could understand why the Australians were complaining about what they perceived as a commonly used tactic . Some concluded that the Australian cricket authorities and public were sore <unk> . Of the four fast bowlers in the tour party , <unk> Allen was a voice of dissent in the English camp , refusing to bowl short on the leg side , and writing several letters home to England critical of Jardine , although he did not express this in public in Australia . A number of other players , while maintaining a united front in public , also <unk> bodyline in private . The amateurs Bob Wyatt ( the vice @-@ captain ) , Freddie Brown and the <unk> of <unk> opposed it , as did Walter Hammond and Les Ames among the professionals . 
- During the season , Woodfull 's physical courage , stoic and dignified leadership won him many admirers . He flatly refused to employ retaliatory tactics and did not publicly complain even though he and his men were repeatedly hit . 
- Australia lost heavily by ten wickets in the first Test at Sydney , when the bowling spearhead of bodyline , Harold Larwood , took ten wickets . Bradman missed the first Test due to illness , although Jardine refused to believe this and thought the real reason was that the batsman had suffered a nervous breakdown due to his tactical scheme . The only Australian batsman to make an impact was Stan <unk> , who <unk> stood his ground and impulsively hooked and pulled everything aimed at his upper body , <unk> by the prospect of taking a potentially lethal blow to the head . He scored 187 not out in four hours , an innings described by leading historian David <unk> as " among the most stirring innings Test cricket has ever produced " . 
- Before the second Test in Melbourne , Woodfull had to wait until minutes before the game before he was confirmed as captain by the <unk> . This caused the toss to be delayed and <unk> speculation that the Australian Board of Control was considering the possibility of removing Woodfull because of his absolute refusal to allow his bowlers to use retaliatory tactics . His deputy Victor Richardson had advocated retaliation along with several other players . Richardson recalled Woodfull 's private response : 
- There is no way I will be influenced to adopt such tactics which bring such discredit to the game . I know Tim could do it but I am not going to participate in actions that can only hurt the game . 
- The media advocated the selection of Eddie Gilbert , an indigenous bowler of extreme pace , in order to return the bodyline barrage . In one tour match , Gilbert had <unk> Jardine and left a <unk> the size of a <unk> . Another suggested means of retaliation was Laurie Nash , whose notoriously <unk> personality and aggression saw him regarded as a <unk> . However , Woodfull was totally <unk> by such suggestions . 
- On the opening day , Bradman wildly hooked at Bill Bowes ' first ball ( a non @-@ bodyline ball ) and was dismissed for a golden duck , leaving the entire stadium in shock . Jardine , who was known for being extremely <unk> even by the standards of the day , openly <unk> and danced wildly upon Bradman 's demise . Australia 's eventual victory was met by widespread public <unk> , as many believed that Australia had found a means of overcoming the tactics . Bradman scored a match @-@ winning century in the second innings , but it turned out to be his only triple figure score for the series , while Larwood was hampered by a <unk> foot and a slow pitch . 
- The controversy reached its peak during the second day of the Third Test . On 14 January , an all @-@ time record Adelaide Oval crowd of 50 @,@ <unk> watched Australia finish off England 's first innings . Shortly after the start of Australia 's innings , Larwood , bowling to a conventional field setting , struck Woodfull an <unk> blow under his heart with a short , lifting delivery . Woodfull was struck when he was bent over his bat and wicket , and not when upright as often imagined . As Woodfull bent down over his bat in pain for several minutes , an image that became one of the defining symbols of the series , the huge crowd began <unk> , <unk> and verbally <unk> the English team . Jardine reacted by saying " Well bowled , Harold . " <unk> and feelings ran so high that a riot was narrowly averted as police stationed themselves between the players and enraged spectators . 
- Jardine then ordered his team to move to bodyline positions immediately after Woodfull 's injury . Jardine wrote that Larwood had asked for the field , while Larwood said that it was Jardine 's decision . The capacity Saturday afternoon crowd viewed this as hitting a man when he was down . Journalist – <unk> Dick <unk> wrote that Jardine 's actions were seen as " an <unk> crime in Australian eyes and certainly no part of cricket " . Mass <unk> and <unk> occurred after almost every ball . <unk> noted that " [ <unk> George ] <unk> believes that had what followed occurred in Melbourne the crowd would have leapt the fence and <unk> the English captain , Larwood , and possibly the entire side " . Some English players later expressed fears that a large @-@ scale riot could break out and that the police would not be able to stop the <unk> home crowd , who were worried that Woodfull or Bradman could be killed , from attacking them . 
- During the over , another rising Larwood delivery knocked the bat out of Woodfull 's hands . He battled it out for 89 minutes , collecting more <unk> before Allen bowled him for 22 . Later in the day , the English team manager Pelham Warner visited the Australian dressing room to express his sympathies to Woodfull . Woodfull had remained calm in public , refusing to complain about Jardine 's tactics . Woodfull 's abrupt response was meant to be private , but it was leaked to the press - the Adelaide leak : 
- I don 't want to see you , Mr Warner . There are two teams out there , one is playing cricket . The other is making no attempt to do so . 
- Woodfull reportedly added " This game is too good to be <unk> . It 's time some people got out of it " , hinting that he might withdraw his team from competition in protest . Australia 's Leo O 'Brien later reported that Warner was close to tears following Woodfull 's <unk> . 
- The leaking to the press of Woodfull 's comments to Warner angered the Australian captain . He had intended the comments to be private , and ill feeling grew in the Australian camp as speculation about who leaked the incident to the press grew and many of the team privately pointed the finger at Bradman . ( Bradman <unk> denied to his dying day that he had been responsible ; others , including Plum Warner , pointed the finger at Bradman 's team @-@ mate and journalist , Jack Fingleton . However , in his autobiography , Fingleton claimed that Sydney Sun reporter Claude Corbett had received the information from Bradman . ) 
- The next day , Larwood fractured wicket @-@ keeper Bert Oldfield 's skull . This occurred when Oldfield mis @-@ hit a hook , which flew from the top edge off a traditional non @-@ bodyline ball ; Oldfield later admitted it was his fault . As a result of the injuries , the costs of insurance cover for players doubled . At the end of the fourth day 's play the Australian Board of Control for Cricket sent the following cable to the MCC in London : 
- Bodyline bowling has assumed such proportions as to menace the best interests of the game , making protection of the body by the batsman the main consideration . This is causing intensely bitter feeling between the players , as well as injury . In our opinion it is unsportsmanlike . <unk> stopped at once it is likely to upset the friendly relations existing between Australia and England . 
- Jardine however insisted his tactic was not designed to cause injury and that he was leading his team in a <unk> and <unk> manner , arguing that it was up to the Australian batsmen to play their way out of trouble . He also secretly sent a telegram of sympathy to Oldfield 's wife and arranged for presents to be given to his young daughters . 
- The situation escalated into a diplomatic incident between the countries as the MCC — supported by the British public and still of the opinion that their fast leg theory tactic was harmless — took serious offence at being branded " unsportsmanlike " and demanded a retraction . Many people saw bodyline as <unk> an international relationship that needed to remain strong . Jardine , and by extension the entire English team , threatened to withdraw from the fourth and fifth Tests unless the Australian Board withdrew the accusation of <unk> behaviour . Public reaction in both England and Australia was outrage directed at the other nation . The Governor of South Australia , Alexander <unk> @-@ <unk> , who was in England at the time , expressed his concern to British Secretary of State for Dominion Affairs James Henry Thomas that this would cause a significant impact on trade between the nations . The standoff was settled only when the Australian prime minister , Joseph Lyons , met with members of the Australian Board and outlined to them the severe economic <unk> that could be caused in Australia if the British public boycotted Australian trade . Given this understanding , the Board withdrew the allegation of unsportsmanlike behaviour two days before the fourth Test , thus saving the tour . 
- The English team continued to bowl bodyline in the remaining two Tests , but slower pitches meant the Australians , although frequently <unk> , sustained no further serious injuries . England won the last three Tests to take the series 4 – 1 . 
- In the Test matches , Bradman countered bodyline by moving toward the leg side , away from the line of the ball , and cutting it into the vacant off side field . While this was dubious in terms of batting technique , it seemed the best way to cope with the barrage , and Bradman averaged 56 @.@ 57 in the series ( an excellent average for most , but well short of his career average of 99 @.@ 94 ) , while being struck above the waist by the ball only once . His team @-@ mates fared worse , with only Stan <unk> scoring a century . 
- 
- = = In England = = 
- 
- Bodyline continued to be bowled occasionally in the 1933 English season — most notably by Nottinghamshire , who had Carr , Voce and Larwood in their team . This gave the English crowds their first chance to see what all the fuss was about . Ken <unk> , the Cambridge University fast bowler , also bowled it in the University Match , hitting a few Oxford batsmen . 
- Jardine himself had to face bodyline bowling in a Test match . The West Indian cricket team toured England in 1933 , and , in the second Test at Old Trafford , Jackie Grant , their captain , decided to try bodyline . He had a couple of fast bowlers , Manny Martindale and Learie Constantine . Facing bodyline tactics for the first time , England first suffered , falling to 134 for 4 , with Wally Hammond being hit on the chin , though he recovered to continue his innings . Then Jardine himself faced Martindale and Constantine . Jardine never <unk> . With Les Ames finding himself in difficulties , Jardine said , " You get yourself down this end , Les . I 'll take care of this bloody nonsense . " He played right back to the bouncers , standing on <unk> , and played them with a dead bat , sometimes playing the ball one handed for more control . While the Old Trafford pitch was not as suited to bodyline as the hard Australian wickets , Martindale did take 5 for 73 , but Constantine only took 1 for 55 . Jardine himself made 127 , his only Test century . In the West Indian second innings , Clark bowled bodyline back to the West Indians , taking 2 for 64 . The match in the end was drawn but played a large part in turning English opinion against bodyline . The Times used the word bodyline , without using inverted commas or using the qualification so @-@ called , for the first time . Wisden also said that " most of those watching it for the first time must have come to the conclusion that , while strictly within the law , it was not nice . " 
- In 1934 , Bill Woodfull led Australia back to England on a tour that had been under a cloud after the <unk> cricket diplomacy of the previous bodyline series . Jardine had retired from International cricket in early 1934 after <unk> a fraught tour of India and under England 's new captain , Bob Wyatt , agreements were put in place so that bodyline would not be used . However , there were occasions when the Australians felt that their hosts had crossed the mark with tactics resembling bodyline . 
- In a match between the Australians and Nottinghamshire , Voce , one of the bodyline practitioners of 1932 – 33 , employed the strategy with the wicket @-@ keeper standing to the leg side and took 8 / 66 . In the second innings , Voce repeated the tactic late in the day , in fading light against Woodfull and Bill Brown . Of his 12 balls , 11 were no lower than head height . Woodfull told the Nottinghamshire administrators that , if Voce 's leg @-@ side bowling was repeated , his men would leave the field and return to London . He further said that Australia would not return to the country in the future . The following day , Voce was absent , ostensibly due to a leg injury . Already angered by the absence of Larwood , the Nottinghamshire faithful heckled the Australians all day . Australia had previously and privately complained that some <unk> had <unk> past the agreement in the Tests . 
- 
- = = Origin of the term = = 
- 
- Although Jack <unk> claimed that he had invented the term " bodyline " , it is more likely that it was coined by Sydney journalist Hugh <unk> who worked for The Sun in 1932 , and who happened to be a colleague of Jack Fingleton . <unk> sent a telegram to his newspaper from the Test after a day 's play . As a substitute for " in the line of the body " he used the term " bodyline " to keep the cost down , and the new term quickly became established . 
- 
- = = Changes to the laws of cricket = = 
- 
- As a direct consequence of the 1932 – 33 tour , the MCC introduced a new rule to the laws of cricket for the 1935 English cricket season . Originally , the MCC hoped that captains would ensure that the game was played in the correct spirit , and passed a resolution that bodyline bowling would breach this spirit . When this proved to be insufficient , the MCC passed a law that " direct attack " bowling was unfair and became the responsibility of the umpires to identify and stop . In 1957 , the laws were altered to prevent more than two fielders standing behind square on the leg side ; the intention was to prevent negative bowling tactics whereby off spinners and slow inswing bowlers aimed at the leg stump of batsmen with fielders concentrated on the leg side . However , an indirect effect was to make bodyline fields impossible to implement . 
- Later law changes , under the heading of " <unk> Short <unk> <unk> " , also restricted the number of " bouncers " which may be bowled in an over . Nevertheless , the tactic of intimidating the batsman is still used to an extent that would have been shocking in 1933 , although it is less dangerous now because today 's players wear helmets and generally far more protective gear . The West Indies teams of the 1980s , which regularly fielded a bowling attack comprising some of the best fast bowlers in cricket history , were perhaps the most feared <unk> . 
- 
- = = Legacy = = 
- 
- Following the 1932 – 33 series , several authors , including many of the players involved , released books expressing various points of view about bodyline . Many argued that it was a <unk> on cricket and must be stamped out , while some did not see what all the fuss was about . The series has been described as the most controversial period in Australian cricket history , and voted the most important Australian moment by a panel of Australian cricket identities . The MCC asked Harold Larwood to sign an apology to them for his bowling in Australia , making his selection for England again conditional upon it . Larwood was furious at the notion , pointing out that he had been following orders from his upper @-@ class captain , and that was where any blame should lie . Larwood refused , never played for England again , and became vilified in his own country . Douglas Jardine always defended his tactics and in the book he wrote about the tour , In Quest of the Ashes , described allegations that the England bowlers directed their attack with the intention of causing physical harm as stupid and <unk> <unk> . The immediate effect of the law change which banned bodyline in 1935 was to make commentators and spectators sensitive to the use of short @-@ pitched bowling ; bouncers became exceedingly rare and bowlers who delivered them were practically <unk> . This attitude ended after the Second World War , and among the first teams to make extensive use of short @-@ pitched bowling was the Australian team captained by Bradman between 1946 and 1948 . Other teams soon followed . 
- Outside the sport , there were significant consequences for Anglo @-@ Australian relations , which remained strained until the outbreak of World War II made cooperation paramount . Business between the two countries was adversely affected as citizens of each country avoided goods manufactured in the other . Australian commerce also suffered in British colonies in Asia : the North China Daily News published a pro @-@ bodyline editorial , denouncing Australians as sore <unk> . An Australian journalist reported that several business deals in Hong Kong and Shanghai were lost by Australians because of local reactions . English immigrants in Australia found themselves <unk> and persecuted by locals , and Australian visitors to England were treated similarly . In 1934 – 35 a statue of Prince Albert in Sydney was vandalised , with an ear being knocked off and the word " <unk> " painted on it . Both before and after World War II , numerous satirical cartoons and comedy <unk> were written , mostly in Australia , based on events of the bodyline tour . Generally , they poked fun at the English . 
- In 1984 , Australia 's Network Ten produced a television mini @-@ series titled Bodyline , <unk> the events of the 1932 – 33 English tour of Australia . It starred Gary Sweet as Don Bradman , Hugo Weaving as Douglas Jardine , Jim Holt as Harold Larwood , Rhys <unk> as Pelham Warner , and Frank <unk> as Jardine 's mentor Lord Harris . The series took some liberties with historical accuracy for the sake of drama , including a depiction of angry Australian fans burning a British flag at the Sydney Cricket Ground , an event which was never documented . Larwood , having emigrated to Australia in 1950 , received several threatening and obscene phone calls after the series aired . The series was widely and strongly attacked by the surviving players for its <unk> and <unk> . 
- To this day , the bodyline tour remains one of the most significant events in the history of cricket , and strong in the consciousness of many cricket followers . In a poll of cricket journalists , commentators , and players in 2004 , the bodyline tour was ranked the most important event in cricket history . 
- 
- 
- = Waterfall Gully , South Australia = 
- 
- Waterfall Gully is an eastern suburb of the South Australian capital city of Adelaide . It is located in the foothills of the Mount Lofty Ranges around 5 km ( 3 @.@ 1 mi ) east @-@ south @-@ east of the Adelaide city centre . For the most part , the suburb encompasses one long gully with First Creek at its centre and Waterfall Gully Road running adjacent to the creek . At the southern end of the gully is First Falls , the waterfall for which the suburb was named . Part of the City of Burnside , Waterfall Gully is bounded to the north by the suburb of Burnside , from the north @-@ east to south @-@ east by Cleland Conservation Park ( part of the suburb of Cleland ) , to the south by <unk> West , and to the west by <unk> Gardens and Mount Osmond . 
- Historically , Waterfall Gully was first explored by European settlers in the early @-@ to @-@ mid @-@ 19th century , and quickly became a popular location for tourists and <unk> . The government chose to retain control over portions of Waterfall Gully until 1884 , when they agreed to place the land under the auspices of the City of Burnside . 28 years later the government took back the management of the southern part of Waterfall Gully , designating it as South Australia 's first National Pleasure Resort . Today this area remains under State Government control , and in 1972 the Waterfall Gully Reserve , as it was then known , became part of the larger Cleland Conservation Park . 
- Over the years Waterfall Gully has been extensively logged , and early agricultural interests saw the cultivation of a variety of introduced species as crops , along with the development of local market gardens and nurseries . Attempts to mine the area were largely unsuccessful , but the region housed one of the state 's earliest water @-@ powered mills , and a weir erected in the early 1880s provided for part of the City of Burnside 's water supply . Today the suburb consists primarily of private residences and parks . 
- 
- = = History = = 
- 
- The Mount Lofty Ranges , which encompass Waterfall Gully , was first sighted by Matthew Flinders in 1802 . The gully itself was discovered soon after the establishment of Adelaide , and Colonel William Light , the first <unk> General of South Australia , was said to have " decided on the site for Adelaide when viewing the plains from the hills near Waterfall Gully " . Nevertheless , the gully had seen human visitors long before the arrival of the Europeans , as the native population had lived in the area for up to 40 @,@ 000 years prior to Flinders ' appearance off the South Australian coast . 
- 
- = = = <unk> = = = 
- 
- In Australian Aboriginal mythology , Waterfall Gully and the surrounding Mount Lofty Ranges are part of the story of the ancestor @-@ creator Nganno . Travelling across the land of the native <unk> people , Nganno was wounded in a battle and laid down to die , forming the Mount Lofty Ranges . The ears of Nganno formed the peaks of Mount Lofty and Mount <unk> , and the region was referred to as <unk> @-@ e @-@ <unk> , or " the place of the ears " . The name of the Greater Mount Lofty <unk> , <unk> , was derived from this term , while the nearby town of <unk> employs a more corrupted form . 
- Although Hardy states that the <unk> people did not live in the ranges themselves , they did live on the lower slopes . An early settler of the neighbouring suburb of Beaumont , James Milne Young , described the local <unk> : " At every creek and gully you would see their <unk> [ simple Aboriginal homes made out of twigs and grass ] and their fires at night ... often as many as 500 to 600 would be camped in various places ... some behind the <unk> Gardens on the banks of the river ; some toward the Ranges ; some on the Waterfall Gully . " Their main presence , <unk> by the use of fire against <unk> of land , was on the River Torrens and the creeks that flowed into it , including Waterfall Gully 's First Creek . 
- The land around Waterfall Gully provided the original inhabitants with a number of resources . The bark from the local <unk> trees ( Eucalyptus <unk> ) was used in the construction of winter <unk> , and stones and native timbers were used to form tools . Food was also present , and <unk> moth larvae along with other species of plants and animals were collected . Nevertheless , there were only a few resources that could only be found on the slopes , and " both hunting and food gathering would in general have been easier on the rich plains " . 
- 
- = = = Early colonial exploration = = = 
- 
- One of the earliest accounts of Waterfall Gully comes from a " Mr Kent " who , along with Captain <unk> Barker and Barker 's servant , Miles , climbed Mount Lofty in 1831 . In making their ascent the party <unk> a ravine — described by Mr Kent as possessing " smooth and grassy sides " — which is believed by Anne Hardy to have been Waterfall Gully . Subsequent to Barker 's ascent , the first settlers who were recorded as having climbed Mount Lofty were <unk> Hutchinson and his servant , William Burt . The pair made three attempts to scale the mount before succeeding , and for their first attempt they attempted to traverse Waterfall Gully . The attempt was unsuccessful , but in July 1837 , Hutchinson wrote about the gully through which they had travelled . Waterfall Gully he wrote , had proven difficult , as the plants were so thickly grown as to provide a significant barrier to their progress . Near the point of surrender , Hutchinson described how they were " <unk> surprised by seeing a wall of rock about fifty or sixty feet [ fifteen to eighteen metres ] high , which stretched across the ravine , and from the top of it leapt the brook which had so long been [ their ] companion " . The brook was First Creek , and the waterfall they sighted is today known as First Falls . 
- Nevertheless , Hutchinson was not the first to see First Falls . The first known recorded sighting of the waterfall by a colonial was that of John William Adams , an <unk> of HMS Buffalo in early January 1837 , who named it " Adams ' Waterfall " . He was traveling with his wife , Susanna and a party consisting of Nicholson 's and <unk> 's who had the use of a <unk> to go into the hills . Adams states " we were opposite the spot where the Eagle on the Hill now is , and the question was put , who would volunteer to go down the hillside to try for water " . 
- 
- = = = Development = = = 
- 
- The area soon became a tourist attraction for the early South Australian colonists , and was a popular destination for <unk> . In 1851 Francis Clark wrote that " Waterfall Gully is the most picturesque place for a picnic that I have ever visited " , and by the 1860s the area had become known throughout Adelaide . The use of Waterfall Gully as picnic spot was facilitated by the decision of the government of the day not to <unk> the area containing the waterfalls . Section 920 , as it was designated , did not enter into private hands , and thus members of the public were able to access the area from the nearby suburb of Eagle on the Hill on Mount Barker road . The position of the Eagle on the Hill hotel proved <unk> for this , as it permitted visitors to stop by for lunch before walking down the hill in the afternoon . 
- Other parts of the Waterfall Gully area were subdivided , though , and much of the area was owned by Samuel Davenport . Davenport used the land for timber , grazing , and the cultivation of various crops , including olives and grapes for wine production . Other local residents ran market gardens and nurseries . For example , local residents Wilhelm Mügge and his wife Auguste Schmidt operated " one of the best nurseries and market gardens near Adelaide " , and gained a reputation for the <unk> produced from their local dairy farm . Along with farming , the hills and creek were prized areas for the <unk> and <unk> , and a number of mines were established in the region from the mid @-@ to @-@ late 19th century . In 1844 the first silver @-@ lead , <unk> and iron mines were established in the area , while the 1890s saw a minor gold rush — although " only small quantities were extracted " . Of greater success was stone quarrying in Chambers ' Gully , which began in 1863 and increased in scale in 1912 . 
- Waterfall Gully was also the site of Burnside 's " first secondary industry " . In the late 1830s , Thomas Cain built a <unk> on First Creek for John Cannan , which was then employed to power a sawmill on Cannan 's property . Cannan operated the mill as the " <unk> Mill " for approximately two years before selling the venture to a Mr. Finniss . Finniss opted to run the mill as a flour mill instead , and the mill was rebuilt and renamed " <unk> Mill " . The mill continued to operate under a variety of owners until the late 1850s , but it was dismantled during the 1880s , and today only traces of the earthworks remain . 
- During this period the population of the nearby village of Burnside was expanding and required a new water supply . First Creek — which runs down Waterfall Gully and enters the River Torrens near today 's <unk> Gardens — was seen as the perfect solution to the water shortage . A weir was built during 1881 and 1882 , and was made to hold approximately two <unk> ( <unk> @,@ 000 US gallons ) of water . A pipeline was constructed to the reservoir at Burnside South , and from there the water was used throughout the surrounding area . As a side effect , the weir also reduced the volume of water available to the local market <unk> , and over many years that aspect of the region disappeared . 
- While the route to the falls from Eagle on the Hill was on public land , the alternative route along the gully was through private properties . Nevertheless , many visitors chose this route , and a combination of public demand and a desire from some of the landowners for improved access to and from their properties — especially from the Mügge family — led to pressure to build a road through the gully . Although there was opposition from some of the locals , the Waterfall Gully road was built in the late 1880s . 
- The completion of the road led to an increase in visitor numbers . Rather than a <unk> horse ride , visitors could now catch the horse tram to the start of the gully , and walk , cycle or ride to the falls . To provide for tourists , the area gained a number of road @-@ side <unk> and produce <unk> , and the Mügge family erected the two story Waterfall Hotel along the path . Furthermore , in 1912 the government opened a kiosk at the base of First Falls , designed in the " style of a Swiss chalet " . The hotel is a private residence today , but the kiosk continues to operate . 
- 
- = = = Protection = = = 
- 
- Although some parts of Waterfall Gully were transferred from the District Council of East Torrens ( now the Adelaide Hills Council ) to the City of Burnside in 1856 when the suburb 's current boundaries were established , the government of the day chose to retain control of a significant portion of Waterfall Gully . Thus it was not until 1884 that the remaining land was transferred to the control of the Burnside Council , <unk> largely through the efforts of Samuel Davenport and G. F. Cleland . 
- The land remained under the Burnside Council 's control until 1912 , when the Waterfall Gully Reserve was reclaimed by the government as the first National Pleasure Resort in the state . Initially the reserve was placed under the jurisdiction of the National Parks Advisory Board , but later it was moved to the Tourist Bureau , before finally becoming part of the National Park Commission 's portfolio . 
- In 1945 , much of the area that is today 's Cleland Conservation Park was purchased by the State Government , largely thanks to the efforts of Professor Sir John Cleland . Most of this land was combined in 1963 to create the park that extends eastwards up the gully to the summit of Mount Lofty and northwards to <unk> Road . Waterfall Gully Reserve was added to the park in 1972 . 
- 
- = = = Natural disasters = = = 
- 
- Over the years since European settlement Waterfall Gully has suffered from both bushfires and flooding . The gully was severely hit by a number of bushfires in 1939 that threatened the area , and further bushfires in the early 1940s caused considerable damage because of the war effort diverting supplies and personnel from the Country Fire Service ( <unk> ) . Significant floods occurred in 1889 and 1931 , and , on the night of 7 November 2005 , Waterfall Gully was one of several areas in Adelaide to experience severe flooding . Waterfall Gully was one of the hardest hit suburbs : Bob Stevenson , Duty Officer of the State Emergency Service ( <unk> ) , commented that " There 's an area called Waterfall Gully Road , in the foothills , where one of the creeks comes down , and there 's quite a few houses affected there ... there was 40 or so houses affected on that one road alone . " Properties were flooded , two bridges nearly collapsed , and 100 m ( 330 ft ) of road was washed away . Burnside council workers , the <unk> and the <unk> repaired the initial damage on the night while reconstruction of infrastructure commenced in late November . Much of the road had been inaccessible , and the suburb was closed except to residents and emergency workers for the remainder of the month . 
- 
- = = Geography = = 
- 
- Waterfall Gully is situated at an average elevation of 234 m ( <unk> ft ) above sea level , in an area of 6 @.@ 08 km2 ( 2 @.@ 35 sq mi ) . Its most notable geographical features are its gully and waterfall . <unk> Reserve , a large local park , is 300 m ( 980 ft ) from the start of Waterfall Gully Road while much of the north @-@ eastern side of the gully is part of Cleland Conservation Park . <unk> Waterfall Gully , 2 km ( 1 @.@ 2 mi ) away , is Chambers Gully , which used to function as a land @-@ fill , but has in the past decade been reclaimed as a park through volunteer work . It contains a number of old ruins , walking trails , and springs and is home to a significant number of native species . 
- Since European Settlement the native plant life has been considerably affected , with the native manna gum and blue gum woodlands being largely cleared for agricultural uses . The large amount of non @-@ native vegetation in the gully is predominantly the result of the early agriculture , although some species were introduced by accident . Introduced species include olive trees , hawthorn , <unk> and <unk> . With the reduction of native flora , exotic fauna have flourished around the Waterfall Gully region . These include rabbits , <unk> and starlings . However , not all of the native wildlife has been lost — bats ( in particular , Gould 's <unk> bat ) , can be found in the area , as can superb fairy @-@ <unk> and Adelaide rosellas , and a large number of unique Australian animals such as <unk> , <unk> and <unk> can be spotted on some of the walking trails . 
- 
- = = Transport = = 
- 
- Waterfall Gully is connected to the major Adelaide thoroughfare <unk> Road by Waterfall <unk> and <unk> Road , and cars are the preferred mode of transport in the suburb . According to the Australian Bureau of Statistics 71 @.@ 9 % of residents in the census area employed private vehicles for their commute to work . Only a small proportion ( 1 @.@ 3 % ) walked to work and but 1 @.@ 2 % <unk> , while only 3 @.@ 6 % of Waterfall Gully residents travel to work by bus . The closest bus route for Waterfall Gully is the 142 bus , provided by the multi @-@ service Adelaide Metro . 
- Waterfall Gully Road is <unk> and in some parts quite narrow . This has led to concerns regarding safety , as the road is frequented by both pedestrians and <unk> . After the death of a <unk> in 2007 , calls for the repair and resurfacing of the road intensified , with two <unk> being <unk> in parliament . The accident also led to a safety audit being conducted by <unk> , and although the results were not released to the public at the time , it called for an investigation of the entire length of the road . As of mid @-@ 2008 , there has been no clear plan released for the future of the road , with the road missing out on funding in the 2008 state budget . 
- 
- = = Residents = = 
- 
- In the 2001 Census , the population of the Waterfall Gully census area ( which includes the suburbs of Glen Osmond , <unk> Gardens and Mount Osmond ) was 2 @,@ 497 people , in an area of 6 @.@ 08 square kilometers . Females outnumbered males 54 @.@ 2 % to 45 @.@ 8 % , and some 21 @.@ 4 % of the population was born overseas ( see chart for a breakdown ) . There was only a slight change in the 2006 census , with the population increasing by 25 to 2 @,@ 522 . 
- The eight strongest religious affiliations in the area ( based on the 2006 census figures ) were ( in descending order ) : Anglican , Catholic , <unk> , Lutheran , Orthodox Christian , Buddhist , Presbyterian , Church of Christ and Baptist ( a combination of other Christian faiths came in somewhere between Presbyterians and the Church of Christ , with 31 adherents ) . Also of note is the high occurrence of religious affiliation ( 67 @.@ 3 % ) in the region in comparison to the Adelaide ( and national ) average . Christian belief ( 64 @.@ 4 % ) is most prominent , with little growth in other religions . 
- Residents in these four suburbs are more affluent than the Adelaide average , with a high occurrence of <unk> over A $ 1000 per week , which is also above the average for the City of Burnside . A majority of workers are employed in professional or white collar fields . 
- The census area that incorporates Waterfall Gully has a larger proportion of those in both the younger ( 0 – 17 ) and older ( 60 + ) age ranges than in the City of Burnside as a whole , and there have been no " numerically significant " changes in the age distribution between the 2001 and 2006 censuses . Similarly , family numbers are also stable , with almost no change between 2001 and 2006 . 
- 
- = = <unk> = = 
- 
- The main attraction of Waterfall Gully is the waterfall , First Falls . It is at the south @-@ eastern end of the road , in land owned by Cleland Conservation Park . The weir at the bottom of the Waterfall was constructed in the late 19th century and was part of Adelaide 's early water supply . Development in the area has continued since the construction of a restaurant in 1912 . <unk> over recent decades have included improving access to the site , upgrading the bridges , and the addition of new signage . 
- The Waterfall Gully Restaurant was constructed between 1911 and 1912 by South Australian architects Albert <unk> Conrad and his brother Frank , and was formally opened by Sir Day <unk> on 9 November 1912 . Built in the style of a Swiss chalet , the building has been heritage listed since 1987 , and is <unk> haunted by the ghost of a <unk> who died from burns suffered in 1926 . 
- Other fire tracks and walking trails wind around the hills that surround Waterfall Gully , branching off from Chambers Gully , <unk> Gully or the area around First Creek . Destinations include <unk> , Eagle On The Hill , Mount Lofty , Mount Osmond and the Cleland Wildlife Park , located in the Cleland Conservation Park . The tracks have been completely rebuilt and resurfaced in the past ten years , and a number of older and more perilous routes have been sealed because of the difficult terrain . Many offer views of the city of Adelaide as well as the Gully itself . One of these is notable for connecting to the 1 @,@ 200 km ( 750 mi ) <unk> Trail , and the trails are highly frequented . 
- 
- = = Politics = = 
- 
- Waterfall Gully is part of the state electoral district of Bragg , which has been held since 2002 by Liberal MP Vickie Chapman . In federal politics , the suburb is part of the division of Sturt , and has been represented by Christopher <unk> since 1993 . The results shown are from the closest polling station to Waterfall Gully — which is located outside of the suburb — at St David 's Church Hall on nearby <unk> Road ( Burnside ) . Both <unk> have traditionally gone to the Liberal Party , and Bragg in particular is regarded as a very safe Liberal seat . However , in the 2007 federal election , a strong swing towards the Labor Party and their candidate , Mia <unk> , resulted in the electorate transforming from a " safe [ federal ] Liberal seat into a marginal one " . 
- In local government , Waterfall Gully is part of the ward of Beaumont within the City of Burnside , and the current Mayor for the district is David <unk> . Beaumont is currently represented by <unk> Mark <unk> and Anne <unk> . 
- 
- 
- = Mariana ( poem ) = 
- 
- " Mariana " is a poem by Alfred , Lord Tennyson , published in 1830 . The poem follows a common theme in much of Tennyson 's work — that of <unk> isolation . The subject of Mariana is a woman who continuously laments her lack of connection with society . The isolation defines her existence , and her longing for a connection leaves her wishing for death at the end of every stanza . The premise of Mariana originates in William Shakespeare 's Measure for Measure , but the poem ends before Mariana 's lover returns . Tennyson 's version was adapted by others , including John Everett Millais and Elizabeth Gaskell , for use in their own works . The poem was well received by critics , and it is described by critics as an example of Tennyson 's skill at poetry . 
- 
- = = Background = = 
- 
- Tennyson wrote Mariana in 1830 and printed it within his early collection Poems , <unk> <unk> . Previously , he contributed poems to the work Poems by Two Brothers ( 1827 ) , where his early poems dealing with isolation and memory can be found . The theme was continued in the later collection , with poems like Mariana , Ode to Memory , and others representing the earlier poems . 
- During a visit to the Pyrenees during the summer of 1830 , Tennyson sought to give aid to Spanish rebels . During that time , he was affected by his experience and the influence appears in Mariana in the South , which was published in 1832 ; it is a later version that follows the idea of The Lady of Shalott . 
- 
- = = Structure = = 
- 
- Many of Tennyson 's poems are in the form of a dramatic monologue . However , Mariana , like The Lady of <unk> , is more accurately a lyrical narrative . It contains elements of dramatic <unk> in that it contains a refrain that carries through the poem as found in Oriana and other poems . Oriana is completely a dramatic monologue and Mariana is not because Tennyson represents how the title figure is unable to linguistically control her own poem , which reinforces the themes of the poem . This technique is used again in Tennyson 's later poem , The Two Voices . The rhyme scheme of the poem , <unk> <unk> <unk> , is different than the standard ballad rhyme that serves to contain the poem then allow a free expression . The middle quatrain of the stanzas returns in theme to the beginning in a cyclical pattern while the last quatrain 's lines contain the same words . 
- 
- = = Poem = = 
- 
- Within the poem , Tennyson does not teach the audience what melancholy means . Instead , he describes its various aspects as he begins : 
- With <unk> moss the flower @-@ plots 
- Were thickly <unk> , one and all : 
- The <unk> nails fell from the knots 
- That held the pear to the gable @-@ wall . 
- The broken sheds looked sad and strange : 
- <unk> was the <unk> <unk> ; 
- <unk> and worn the ancient <unk> 
- Upon the lonely <unk> <unk> . 
- She only said , " My life is dreary , 
- He <unk> not , " she said ; 
- She said , " I am aweary , aweary ; 
- I would that I were dead ! " ( lines 1 – 12 ) 
- The narrator of the poem is <unk> from Mariana , and he is able to see what she cannot . In particular , he is able to describe the " sweet heaven " whereas Mariana refuses to take in the scene as well as she is unable to understand the movement of time : 
- Her tears fell with the <unk> at even ; 
- Her tears fell <unk> the <unk> were dried ; 
- She could not look on the sweet heaven , 
- Either at <unk> or <unk> . ( lines 13 – 16 ) 
- She is surrounded by <unk> and there is little movement within the poem . The water is calm and there is only the growth of moss : 
- About a stone @-@ cast from the wall 
- A <unk> with <unk> waters slept , 
- And o <unk> it many , round and small , 
- The clustered <unk> @-@ mosses <unk> . ( lines 37 – 40 ) 
- Mariana is trapped by her surroundings , and the last stanza begins with her becoming sensitive to sound as she starts to mentally lose her place in reality : 
- The sparrow 's <unk> on the roof , 
- The slow clock ticking , and the sound 
- Which to the wooing wind aloof 
- The <unk> made , did all <unk> 
- Her sense ; ( lines 73 – 77 ) 
- The poem ends with a description that even the sunlight is unable to do anything more than reveal dust in her home : 
- but most she <unk> the hour 
- When the thick @-@ <unk> <unk> lay 
- <unk> the chambers , and the day 
- Was sloping toward his western <unk> . ( lines 77 – 80 ) 
- The poem ends with an altered version of the refrain , which serves to show that although she wishes her death she is still alive and , in the final moment , allows her to end the poem instead of allowing the poem to end her : 
- Then , said she , " I am very dreary , 
- He will not come , " she said ; 
- She <unk> , " I am aweary , aweary , 
- O God , that I were dead ! " ( lines 81 – 84 ) 
- 
- = = Themes = = 
- 
- Tennyson 's poems traditionally rely on the use of visual imagery for effect . In Mariana , Tennyson instead emphasises auditory imagery that serves to emphasise her solitude . Her hearing is sensitive and she is able to hear every sound , which only reveals the silence of her surroundings . Her solitude and loneliness causes her to be unable to recognise the beauty of her surroundings , and the world to her is dreary . In contrast to Tennyson 's other poems , including The Lady of Shalott , there is no movement within Mariana . There is also a lack of a true ending within the poem , unlike the later version Mariana in the South , which <unk> the poem so there is a stronger conclusion that can be found within death . 
- The character of Mariana is connected to Shakespeare 's Measure for Measure ; there is a direct quotation of Shakespeare 's play in regards to a character of the same name . In Shakespeare 's play , Mariana is rejected by the character Angelo and lives alone as she pines over her love . Tennyson 's version is set in Lincolnshire , not Vienna as in the Shakespeare play . This makes the characters completely English . Additionally , the scene within the poem does not have any of the original context but the two works are connected in imagery with the idea of a dull life and a <unk> female named Mariana . However , Tennyson is not the only one that uses the image ; John Everett Millais 's painting Mariana is based on Tennyson 's version of Mariana and lines 9 through 12 of Tennyson 's poem were used for the catalogue description of the painting . Similarly , Millais 's version served as the inspiration for Elizabeth Gaskell 's novel , Mariana . Tennyson 's Mariana and Gaskell 's main character , Ruth , are sensitive to the sounds around them and are constantly looking out of their window in image that represents their imprisonment within their homes . The image of Mariana used by Tennyson and the later works are equally of a woman who is weary . 
- The depictions of Mariana by Tennyson and in later works are not the same . The difference with Millais 's depiction is not in the image of a forlorn woman or of a woman who is unwilling to live an independent life ; instead , it is her <unk> depiction that is greater than found in Tennyson . His version also removes the <unk> of Tennyson 's and replaces it with a scene filled with vibrant colours . Gaskell 's depiction is of Ruth is similar to Tennyson in her weariness and wanting to die . However , she is a sexually independent figure when she rejects her lover who has returned . Tennyson 's character , on the other hand , would likely have happily accepted her lover . While Tennyson 's character cannot recognise beauty within nature , Gaskell 's character is able to turn to nature to gain spiritually in a manner similar to the Romantic poems , including <unk> Abbey by William <unk> or This <unk> @-@ Tree <unk> My Prison by Samuel Taylor Coleridge . There is also a connection with Mariana 's condition and the condition within Coleridge 's <unk> : An Ode . However , the narrator at the end of <unk> is able to be <unk> into movement whereas Mariana never reaches that point . Furthermore , Mariana is unlike the Romantic poems because the character is not one with nature or able to achieve <unk> through imagination . Furthermore , there is little outside of Mariana that exists within the poem as Mariana 's mood does not respond to changes in nature . 
- In terms of Tennyson 's other poems , there is a strong connection between the character Mariana and Tennyson 's other female characters . Both Mariana and Oriana have characters that experience a mental imprisonment , which are revealed in the poetic <unk> . However , Oriana is able to have control over her own story when she serves as narrator of it while Mariana is denied control by Tennyson 's use of a third @-@ person narrative structure . The difference is further compounded by Oriana 's imprisonment coming from her own memories while Mariana 's is the external results of her lover having not returned . The character Fatima of Fatima is connected to Mariana simply because she is a reversal of Mariana 's character : Fatima , like Mariana , <unk> for her lover but suffers from an intense passion that causes her to lose control over her mind while also being able to experience the world around her . The character <unk> of <unk> is a combination of aspects from both Mariana 's and Fatima 's characters . In the revised version Mariana in the South , the second Mariana is similar to the Lady of Shalott in that they both live in a world between fantasy and reality . 
- 
- = = Critical response = = 
- 
- <unk> <unk> wrote in his early career , " The poem [ Mariana ] is an outstanding insight into the primitive ideal that is Tennyson 's take on life in all its <unk> . " 
- In an early review in the 1831 Westminster Review , J. Fox praises the depiction of women within the whole of Poems , <unk> Lyrics and says that Tennyson 's " portraits are delicate , his likenesses [ ... ] perfect , and they have life , character , and individuality . They are nicely <unk> also to all the different <unk> of emotion and passion which are expressed in common with the descriptions of them . There is an appropriate object for every shade of feeling , from the light touch of passing admiration to the triumphant madness of soul and sense , or the deep and <unk> <unk> of <unk> . " A review by a " Professor <unk> " in 1878 argues , " As descriptive poetry , and for that feature of realistic description so characteristic of Tennyson 's <unk> , ' Mariana ' has , perhaps , not been surpassed even by him . " 
- Harold <unk> , in 1923 , view the <unk> of Mariana and Tennyson 's other early works as an aspect that makes the early works better his later works . In T. S. Eliot 's 1936 Essays Ancient and Modern , he praises Tennyson 's ability to represent the visual , <unk> , auditory , and olfactory aspects of the scene . Later in 1972 , Christopher Ricks argues that the poem is " one of Tennyson 's masterpieces in the art of the penultimate . " 
- Elaine Jordan argues , in her 1988 analysis of Tennyson 's works , that the poem 's depiction of " self @-@ <unk> [ ... ] is a negation which involves the drawing @-@ in of forces in order perhaps to assert the self differently . Mariana is the most powerful expression , very early , of such a moment , though its assertiveness exists only as strong <unk> in image and rhythm , not as narrative possibility except in the desire for an end to it all preferred over patience . " In 2002 , Ruth <unk> writes , " In the last stanza , Mariana 's grip on the present is <unk> , and Tennyson 's mastery of sound and images is evident ( even in this early poem ) in his description of the house that echoes her utter <unk> " . Anna Barton , in her 2008 analysis , declares Mariana " the most famous heroine of the 1830 volume " and that both The Ballad of Oriana and Mariana are " poems of greater substance that develop the poetic that Tennyson begins to establish in his <unk> songs . " 
- 
- 
- = Mothers of the Disappeared = 
- 
- " Mothers of the Disappeared " is a song by rock band U2 . It is the eleventh and final track on their 1987 album The Joshua Tree . The song was inspired by lead singer Bono 's experiences in Nicaragua and El Salvador in July 1986 , following U2 's involvement on Amnesty International 's A <unk> of Hope tour . He learned of the Madres de Plaza de Mayo , a group of women whose children had been " disappeared " by the Argentine and Chilean dictatorships . While in Central America , he met members of COMADRES , a similar organization whose children had been disappeared by the government in El Salvador . Bono <unk> with the Madres and COMADRES and wanted to pay tribute to their cause . 
- The song was written on a Spanish guitar , and the melody lifted from a piece Bono composed in Ethiopia in 1985 to help teach children basic forms of hygiene . The lyrics contain an implicit criticism of the Reagan Administration , which backed two South American regimes that seized power during coup d <unk> and which provided financial support for the military regime in El Salvador . <unk> it has been interpreted as an examination of failures and contradictions in US foreign policy . 
- " Mothers of the Disappeared " was favourably received by critics , who variously described it as " powerful " , " a moving tribute " , and containing " stunning beauty and sadness " . The song was played seven times on the 1987 Joshua Tree Tour , and some recordings were considered for the ending sequence of the 1988 film Rattle and Hum . It was revived for four concerts on the 1998 PopMart Tour in South America , and for two of them , the Madres joined the band onstage for the performance , one of which was broadcast on television in Chile . Bono used the opportunity to ask General Pinochet to reveal to the Madres the locations of their children 's bodies . The song was played a further three times on the U2 360 ° Tour ; one performance was dedicated to <unk> <unk> , an ethnic <unk> who was forcibly disappeared in Turkey in 1995 . Bono re @-@ recorded the song a <unk> in 1998 for the album ¡ Ni Un <unk> <unk> ! . 
- 
- = = Inspiration , writing , and recording = = 
- 
- Recording sessions for The Joshua Tree began in January 1986 in <unk> House in Dublin , Ireland , and continued throughout the year . U2 briefly interrupted these sessions in June to join Amnesty International 's A <unk> of Hope tour . Following the first concert in San Francisco , lead singer Bono met René Castro , a Chilean mural artist . Castro had been tortured and held in a concentration camp for two years by the dictatorial Chilean government because his artwork criticised the Pinochet @-@ led regime that seized power in 1973 during a coup d 'état . Castro showed Bono a wall painting in the Mission District that depicted the ongoing plight in Chile and Argentina . He also learned of the Madres de Plaza de Mayo , a group of women whose children were forcibly disappeared by the Argentine government . The Madres ' children were students who had opposed the government during the Dirty War , and the coup d 'état that brought Jorge Rafael <unk> into power . The Madres joined together to campaign for information regarding the locations of their children 's bodies and the circumstances of their deaths , believing them to have been kidnapped , tortured , and murdered . 
- Inspired by the mural , Bono took an extended break from recording into July , travelling to Nicaragua and El Salvador with his wife , Alison Hewson , to see firsthand the distress of peasants bullied by political conflicts and US military intervention . While there , they worked with the Central American Mission Partners ( <unk> ) , a human rights and economic development organization . In El Salvador they met members of the <unk> de Madres Monsignor Romero ( COMADRES : Committee of the Mothers Monsignor Romero ) , an organization of women whose children were forcibly disappeared by the El <unk> government during the <unk> Civil War because they opposed the military regime that was in power . At one point during the trip , Bono , Alison , and a member of <unk> were shot at by government troops while on their way to deliver aid to a group of farmers . The shots were a warning and , according to author John <unk> , the incident made Bono realize that " they didn 't care for their intrusion and they could kill them if they felt compelled . " 
- In 2006 , Bono recounted another experience he had in El Salvador , where he had seen a body thrown from a van into the road . He remarked , " People would just disappear . If you were part of the opposition , you might find an <unk> with the windows <unk> out parked outside your house .... If that didn 't stop you , occasionally they would come in and take you and murder you ; there would be no trial . " Bono understood the cause of the Madres and COMADRES and wanted to pay tribute to it . His experiences in Central America inspired the lyrics of " Mothers of the Disappeared " and another track from The Joshua Tree , " Bullet the Blue Sky " . 
- " Mothers of the Disappeared " was created and mixed at guitarist the Edge 's newly @-@ bought home , <unk> , which U2 used as a recording studio . Bono wrote the song on his mother @-@ in @-@ law 's Spanish guitar , and drummer Larry Mullen , Jr. created a drum loop that was sampled by producer Brian Eno . The melody came from a song that Bono wrote in Ethiopia in 1985 to teach children about basic methods of hygiene . Producer Daniel Lanois was the principal mixer of the song . Bono , likening the studio itself to an instrument , described Lanois 's mix as a " performance " . 
- At the conclusion of the lyrics sheet for the song in the liner notes of The Joshua Tree , U2 listed addresses for several branches of Amnesty International , and proceeds from the song were donated to the organization . In 1998 , Bono re @-@ recorded the song a <unk> in English and Spanish for the album ¡ Ni Un <unk> <unk> ! ( English : Not One <unk> Back ! ) , along with a recitation of the William Butler Yeats poem " Mother of God " . The album was created by the Madres in commemoration of the disappearance of their children . The tracks were also recorded for the 1999 film 20 <unk> ... 20 <unk> ... 20 <unk> ( 20 Years ... 20 Poems ... 20 Artists ) . 
- 
- = = Composition and theme = = 
- 
- " Mothers of the Disappeared " runs for 5 : 14 ( 5 minutes , 14 seconds ) . It is played in common time in a key of A. The song begins with the sound of rain hitting a roof , which fades in over the first fourteen seconds alongside the bass and a processed drum loop beat by Mullen which <unk> in the background . Thirty @-@ two seconds into the song , Mullen 's drums enter , playing a sporadic beat every four to five seconds . At the fifty @-@ second mark the drums play a more regular beat , and the Edge 's guitar , accompanied by Eno 's synthesizer , enters . The first verse begins at 1 : 28 , and introduces the chord progression of A5 – <unk> – F ♯ m – D – A5 , which is played in the verses . At 2 : 41 Eno 's keyboards enter , and the song begins to follow a D – D5 – A5 chord progression , while Bono begins falsetto vocals . The second verse then begins at 3 : 01 . The lyrics end at 3 : 37 , and the song returns to the chord progression of D – D5 – A5 . The harmony gradually grows in volume until 4 : 33 , at which point the song enters into a <unk> ; the keyboards come to a finish and the guitar returns to playing A notes before fading over the next eight seconds alongside the bass . The synthesizer , drums , and drum loop conclude the song , fading out slowly over the last thirty @-@ one seconds . 
- Eno used a piano as a <unk> instrument and mixed the result with the drum loop through a <unk> effects unit to create a sound that bassist Adam Clayton called " eerie and foreign and scary " . Lanois stated that the processing of Mullen 's beat , which resulted in a drone @-@ like sound , became the song 's backbone and personality . Clayton described it as " evocative of that sinister death squad darkness " . Colm O <unk> of Hot Press felt it was " the key sonic element " because it " [ evokes ] an abstract sense of evil and <unk> " . 
- In December 1986 , Bono stated that he had a love – hate relationship with America , and that this influenced his work on the album . Speaking of his encounter with COMADRES in El Salvador and their impact on the song , he said , " There 's no question in my mind of the Reagan Administration 's involvement in backing the regime that is committing these atrocities . I doubt if the people of America are even aware of this . It 's not my position to lecture them or tell them their place or to even open their eyes up to it in a very visual way , but it is affecting me and it affects the words I write and the music we make . " In 2007 , Clayton noted " We were looking at this America through a European lens , at a time when Britain was under Margaret Thatcher who was breaking the miners .... So we were singing from the same hymn sheet as the Clash but with our spotlight focused on injustice inside and outside America . " He said " ' Mothers of the Disappeared ' was not just a reflection on what had happened under the military government in Chile but also at the US which had supported that government " , and described Bono 's vocals as " prehistoric " , saying " it connects with something very primitive . " 
- Greg Garrett , an English professor at <unk> University , saw the song as an effort to " [ respond ] to growing interests in doing justice — and calling to attention American failures in that regard " , noting that the regimes in South America had been supported by the United States because of their anti @-@ communist positions , even though their tactics were in opposition to the democratic values that " America claims to champion around the world " . Lisa Hand of the Sunday Independent noted the influence of America on the track , remarking , " [ it ] does not <unk> itself simply to the music , but also extends to some of the lyrics . However , far from being a tribute to the star @-@ spangled banner , the words highlight the political <unk> and <unk> which exist within the U.S. ' Mothers of the Disappeared ' and ' Bullet the Blue Sky ' both take a hard look at the American involvement in South America " . Richard Harrington of The Washington Post described the song as " a simple lament of great beauty and sadness pleading for the realization that ideological battles about right and left obscure the more important issue of right and wrong . " Author David <unk> described it as a " [ portrait ] of the American Dream gone rancid " . 
- 
- = = Live performances = = 
- 
- U2 debuted " Mothers of the Disappeared " on 14 April 1987 in San Diego , California , on the first leg of the Joshua Tree Tour , where it closed the concert in place of the band 's long @-@ time finale " 40 " . It was performed three more times on the leg ; twice to open the encore and once to conclude the main set . U2 revived the song seven months later on the third leg , playing it in the encore at three of the final four concerts on the tour . 
- The final two performances , in <unk> , Arizona , on 19 and 20 December 1987 , were filmed for the 1988 film Rattle and Hum . U2 sang the refrain " el <unk> <unk> " , which means " a people united will overcome " in Spanish , at the conclusion of the song . Bono noted that the Madres use the phrase for motivation . The Edge said " we 're so close to a Spanish speaking part of the world , we felt that maybe people at the concert might pick up on this lyric . " Bono added that they had closed every concert since 1983 with the song " 40 " , and so they were looking to replace it with " Mothers of the Disappeared " from that point on . He explained , " If the people of Arizona sing this , and if it goes into the film and onto the record , wherever we go in a way for the next few years , that will be taken up again . It 'll be an interesting experiment ... " . The footage was considered for the closing sequence of the film , but the band eventually decided against including it . " Pride ( In the Name of Love ) " was used as the final live song , and " All I Want Is You " was chosen to play over the credits . 
- Following the seven performances on the Joshua Tree Tour , U2 did not perform " Mothers of the Disappeared " until 1998 , on the fourth leg of the PopMart Tour . It was played at three concerts in Argentina and once in Chile , concluding all four shows . Bono sang " el <unk> <unk> " at the end of each performance . The first rendition was on 5 February 1998 in Buenos Aires , where it was performed with the Madres accompanying them on stage . The song was played by just Bono and the Edge and was set against footage of the Madres on the video screen . At the conclusion of the song , the band members faced the Madres and applauded , an act in which the rest of the audience joined . Part of the performance was later included on the television documentary Classic Albums : The Joshua Tree . 
- The cost of the tickets was too high for many fans in South America , so the band broadcast the 11 February concert in Chile live on television . Knowing that many people in the country would be watching , they played " Mothers of the Disappeared " in place of " Wake Up Dead Man " . The stadium in which the concert was held had been used as a prison camp by Pinochet 's regime following the coup d 'état . Again it was performed solely by Bono and the Edge against footage of the Madres , and they invited the women to join them on stage a second time . The Madres held up photographs of their children and spoke about them briefly during the performance , an act which received a mixed reception from the audience . Bono made a plea to Pinochet , asking him to " tell these women where are the bones of their children . " 
- " Mothers of the Disappeared " was performed again on the fourth leg of the Vertigo Tour , on 26 February 2006 in Santiago and 2 March in Buenos Aires . Although it was <unk> by the full band , it was played only by Bono and the Edge in an arrangement similar to the one from the PopMart Tour . The Edge performed the song on a <unk> that Chilean President Ricardo <unk> had given to Bono earlier that day . It was played at three concerts on the third leg of the U2 360 ° Tour in place of " <unk> " . One performance in Istanbul , Turkey , was dedicated to <unk> <unk> , an ethnic <unk> who was kidnapped in October 1995 and subsequently disappeared . The abduction was witnessed by his wife and daughter ; no information regarding his disappearance has ever been released . 
- 
- = = Reception = = 
- 
- " Mothers of the Disappeared " was favourably received by critics . Steve <unk> of The Boston Globe called the song " powerful " and described the backing vocals as tender and <unk> . Don <unk> of the Chicago Sun @-@ Times described it as a " hymn to human rights " . Adrian <unk> of NME called it " a simple , plaintive lament of stunning beauty and sadness " . Nicholas Jennings of Maclean 's felt that it was The Joshua Tree 's " most topical song " . Music journalist Andrew Mueller felt the track was a " <unk> <unk> finale " . In Rolling Stone , Steve Pond said " ' Mothers of the Disappeared ' is built around desolate images of loss , but the setting is <unk> and restorative — music of great sadness but also of <unk> compassion , acceptance and calm . " Lennox <unk> of The Dallas Morning News stated that there was " an <unk> sadness in Bono 's vocals and images where ' Night hangs like a prisoner / <unk> over black and blue ' " , calling it " a moving tribute " to people around the world who had lost loved ones to warfare and conflict . He added " [ w ] hat 's remarkable about the song is that despite the intrinsic pain , it remains <unk> cleansing . Even in the midst of decay and excess and horror , Bono can find hope and <unk> . " In 2006 Bono described it as " a beautiful end to the album " , saying , " That song means as much to me as any of the songs on that album , it 's right up there for me , " and noting that it is a song " I 'm very proud of to this day . " 
- Barbara <unk> of The Bergen Record likened " Mothers of the Disappeared " to " New Year 's Day " and " Pride ( In the Name of Love ) " , stating that the band used all three to " stir political consciousness and to urge social commitment . " Thirteen years later , Ryan Jones , in his review of U2 's 2000 album All That You Can 't Leave Behind for the same publication , said the song " Peace on Earth " contained echoes of " Mothers of the Disappeared " in its lyrics and the tone of the instrumental prelude . In reviewing the group 's 2009 album No Line on the Horizon , Mueller said the closing " <unk> of Lebanon " " maintains this essentially optimistic group 's counter @-@ intuitive tradition of ending their albums with <unk> <unk> " , likening it to " Mothers of the Disappeared " . <unk> believed that the song had its roots in " the <unk> purity of traditional Irish music " . According to <unk> the song is " notorious " in Central and South America , and it is often " played as an act of defiance " by the Madres . Art for Amnesty cited the song , and the effect it had in spreading Amnesty International 's human rights message , as one of the reasons why U2 were awarded the Ambassador of Conscience Award by the organization in 2005 . 
- Reflecting on the applause given to the Madres during the PopMart concert in Buenos Aires , the U2 magazine Propaganda called the result " about the most moving thing I 've ever seen on a rock stage . It was one of those ideas that really could have gone either way , but the obvious empathy of the audience towards these women made it an <unk> moment . " Following the televised concert in Chile , Bono said " it was amazing and <unk> to discover that on our most ' pop ' of tours some of the best shows were in political <unk> like Santiago , Sarajevo , Tel Aviv ... anywhere music meant more than entertainment " . He added " to be able to address General Pinochet from the stage on live television in Chile and say , ' Give the dead back to the living . Please , General Pinochet , tell these women where the bones of their sons and daughters are . ' That was an extraordinary moment ... certainly in my life and U2 's . " When asked if the negative reaction from some of the audience had disappointed the band Bono said it had not , stating " it 's proof to me that a rock ' n ' roll audience are not <unk> .... If they don 't agree with you , they will let you know — but that doesn 't mean they 're not fans .... I was flattered that we weren 't just playing to people who agreed with us . " U2 's performance was later credited with inspiring a protest in the Chilean Parliament against Pinochet , who was in the process of becoming a Senator for life after <unk> his position as head of the armed forces . The opposition party brought in the Madres , who again held pictures of their disappeared children and asked for information on the location of their bodies . 
- " Mothers of the Disappeared " has been covered several times . The Vitamin String Quartet included it on their 2004 tribute album The String Quartet Tribute to U2 's The Joshua Tree . Paddy Casey recorded a version for the tsunami relief album Even Better Than the Real Thing Vol . 3 in 2005 . 
- 
- = = Personnel = = 
- 
- 
- 
- = Katherine Pulaski = 
- 
- Commander Katherine Pulaski is a fictional medical doctor in the American science fiction television series Star Trek : The Next Generation . She is the chief medical officer aboard the Federation starship USS Enterprise @-@ D. Her medical skills saved the lives of Captain Jean @-@ Luc Picard and Commander William Riker , and she helped inspire Lt. Commander Data to create a hologram of Professor Moriarty that became self @-@ aware . Pulaski preferred to avoid the transporter , but was forced to rely on it to save her own life from an alien infection . Before arriving on the Enterprise @-@ D , she previously served on the USS Repulse . For a time , she was romantically involved with Kyle Riker , but they continued to remain friends . 
- <unk> by American actress Diana Muldaur , Pulaski replaced the character of Commander Beverly Crusher for the second season after Gates McFadden 's contract was not renewed . Pulaski first appeared in the second season opener " The Child " , and made her final appearance in " Shades of Gray " . Before playing the role of Pulaski on The Next Generation , Muldaur appeared in Star Trek : The Original Series , playing different characters in the episodes " Return to Tomorrow " and " Is There in Truth No Beauty ? " She later worked with series creator Gene Roddenberry on a pilot for the series Planet Earth . McFadden returned to replace Muldaur in the third season , reprising her role as Dr. Crusher for the remainder of the series . 
- Reviewers criticized Pulaski 's approach , as well as her inability to fit in with the crew . Critics noted that her transporter phobia was reminiscent of Dr. Leonard McCoy from The Original Series , as was her relationship with Data ; Pulaski 's interaction with Data raised comparisons to that of McCoy and <unk> from The Original Series . Episodes featuring Pulaski in a leading role , such as " Unnatural Selection " , produced divided opinions among critics , with some describing it as a key episode while others argued that it showed only the negative side of her role . 
- 
- = = Concept and development = = 
- 
- Towards the end of the first season of Star Trek : The Next Generation , staff members convinced Gene Roddenberry to drop Gates McFadden as Dr. Beverly Crusher from the show . Executive producer Rick Berman opposed this decision . To allow for McFadden 's return in the future , Roddenberry wrote Crusher 's character out rather than killing her . McFadden 's departure led to an opening for a new cast member . The producers contacted American actress Diana Muldaur , unaware of her past involvement with Roddenberry 's Star Trek and Planet Earth pilot . In The Original Series , Muldaur played the role of a doctor in two episodes : as Dr. Ann Mulhall in " Return to Tomorrow " , and as Dr. Miranda Jones in " Is There in Truth No Beauty ? " Muldaur also worked on the pilot for Planet Earth after getting to know Roddenberry and his wife , Majel Barrett , through their annual Star Trek Christmas parties . 
- Muldaur was not required to audition for the role of Pulaski . Instead , she was given 15 VHS <unk> of the first season . She found it " very exciting " , noting it " got better and better and better " as she watched it . Christina <unk> was also considered for the part of Pulaski , a decision Berman described as " very tough " . Muldaur finally agreed to play the role , but asked Roddenberry to change the name of the character to Kate , which became Katherine . The character of Katherine Pulaski was modeled on Doctor Leonard McCoy from The Original Series . Like McCoy , Pulaski feared <unk> and disliked <unk> colleagues . The dynamic between McCoy and <unk> was revisited with the relationship between Pulaski and Lieutenant Commander Data . Muldaur believed that Roddenberry deliberately created the parallel between the two characters on the two shows . 
- Katherine Pulaski debuted in the second season episode " The Child " , with <unk> Goldberg making her first appearance of the series as Guinan in the same episode . Even though Muldaur was a regular character on The Next Generation , she was only listed in the opening credits as a " Special Guest Star " . Overall , Muldaur described her experience portraying Pulaski as " hard work " . She expected to be cast for just one season , but was unaware of who would succeed her after she left . Following production of the second season , Muldaur left the series and joined the cast of L.A. Law . She used footage of the episode " Unnatural Selection " to gain the part . Gates McFadden returned to continue her role as Dr. Crusher on The Next Generation for the remainder of the series . 
- 
- = = Appearances = = 
- 
- Pulaski 's background is touched upon in the episode " The Icarus Factor " . She was previously romantically involved with Commander William Riker 's father Kyle Riker ( Mitchell Ryan ) , after she was part of a rescue team responding to a <unk> attack on a Federation <unk> . She realised that a romantic relationship with Riker would not work , but they remained friends . <unk> prior to serving on the Enterprise @-@ D , she served on the USS Repulse under Captain <unk> , who was sorry to lose her . 
- In , " Elementary , Dear Data " she challenges Lt. <unk> Data to solve an original Sherlock Holmes mystery on the holodeck . He accepts her challenge , which results in the creation of a self @-@ aware hologram of Professor Moriarty ( Daniel Davis ) . Pulaski joins Data and Lieutenant <unk> La Forge ( <unk> Burton ) , but is captured by Moriarty who also takes over manual control of the Enterprise . Moriarty demands that they find a way to enable him to leave the holodeck , but is persuaded by Captain Jean @-@ Luc Picard ( Patrick Stewart ) to release control and be stored within the ship 's computer memory until a means can be found to grant his wish . ( Years later , Moriarty is released from the computer and takes control of the ship 's computer , again demanding his freedom . He is finally <unk> into thinking that his wish was granted , but remains trapped inside a computer simulation ( " Ship in a <unk> " ) . ) 
- Pulaski 's apprehension at using the transporter was evident in " The <unk> Man " , where Dr. <unk> ( Suzie Plakson ) went with the away team instead of Pulaski , as it required her to beam over to a transport vessel . However , the transporter would later save Pulaski 's life in " Unnatural Selection " , after she was infected with a disease from the planet <unk> IV that accelerated her aging process . She uses the <unk> to remove the infection and is returned to health . 
- She demonstrates her medical expertise on several occasions . In " Time <unk> " , Pulaski discovers that the duplicate Captain Picard is out of sync in time and will slowly improve until he returns to the point at which he left . In the episode " Pen Pals " , Picard orders Pulaski to <unk> the memories of a young girl called <unk> , whom Data had been corresponding with and helping in violation of the Prime Directive . Whilst in " <unk> <unk> " , she is summoned to <unk> 515 to perform heart surgery on Captain Picard as she is the most experienced surgeon nearby . This is despite Picard 's wish for her not to perform the surgery , due to his concern with the image it might give to the crew . 
- When the Enterprise arrives at the lost colony of <unk> in " Up the Long <unk> " , the Mariposans kidnap Pulaski and Commander William Riker ( Jonathan Frakes ) and steal their DNA to clone new members of their colony . Although technologically advanced , the Mariposans lack genetic diversity . When Pulaski and Riker discover the <unk> , they destroy them . Picard helps resolve the dispute by suggesting the Mariposans allow the <unk> , a <unk> , rural people whose colony was destroyed , to migrate to their world . The Mariposans , however , have relied on <unk> for 300 years , and no longer sexually reproduce . Pulaski notes that with time , they will become familiar with the practice once again , and recommends that the Mariposans form large , group marriages with the <unk> to create a healthy population . 
- Following Data 's defeat at a game of <unk> in " Peak Performance " , Pulaski and Councillor <unk> <unk> ( Marina <unk> ) attempt to console him , but it is left to Captain Picard to convince Data that he is not malfunctioning . Pulaski 's final appearance on The Next Generation was in the episode " Shades of Gray " . When Commander Riker falls ill to a dangerous virus during an away team mission , Pulaski drives out the virus with a device that stimulates his memory centre . After she discovers that negative memories are more effective in removing the virus , she uses memories of fear and survival to save Riker 's life . 
- In the alternative future timeline portrayed in " <unk> " , the Star Trek : Voyager finale , Pulaski is said to have worked at the Starfleet Medical facility in San Francisco . 
- 
- = = = Novels = = = 
- 
- Pulaski appears in several books of the non @-@ canon novel series based on the adventures of the crew in The Next Generation and elsewhere in the same time period . In Peter David 's novel Vendetta ( 1991 ) , Pulaski is reassigned to the Repulse under Captain <unk> following her departure from the Enterprise . In Star Trek : Progress ( 2006 ) , a Starfleet Corps of Engineers book by Terri Osborne , Pulaski is on board the USS Progress when it visits <unk> IV , as she wants to check up on the progress of <unk> , a young girl from The Next Generation episode " Pen Pals " . Pulaski also appeared alongside Wesley Crusher and Guinan in Michael Jan Friedman 's " All Good Things ... " ( 1994 ) , a <unk> of the The Next Generation series finale . 
- 
- = = Reception and commentary = = 
- 
- In their 1998 book , Star Trek 101 , Terry J. <unk> and Paula M. Block called " Unnatural Selection " the key Pulaski episode . Science fiction writer Keith DeCandido felt that she displays all of her worst traits in this episode , including " her <unk> , her intensity , her constant <unk> of people , her <unk> out Data ( though at least this time she apologizes to him when he expresses concern over her health ) , and ... transporter @-@ phobia , because apparently she wasn 't enough like Dr. McCoy yet . " DeCandido described Pulaski as " annoying " and noted that this feeling was mirrored on screen by Captain Picard . In Science Fiction Television : A History , author M. Keith Booker observed that " Pulaski never quite <unk> with the rest of the crew " . 
- The events of " Elementary Dear Data " led film professor <unk> <unk> to suggest that Pulaski may have been inspired by the works of Friedrich <unk> . In Sherlock Holmes and Philosophy : The <unk> of a <unk> Mind ( 2011 ) , <unk> notes that Pulaski 's argument that Data lacks <unk> is reminiscent of parts of <unk> 's 1878 work Human , All Too Human . The relationship between Pulaski and Data was further considered by <unk> V. Wilcox in her article " <unk> Data : <unk> in Star Trek : The Next Generation " ( 1993 ) . Wilcox notes that Pulaski " frequently challenges Data in terms of his machine nature , expressing <unk> for his supposed <unk> . " She compares Data 's <unk> state to slavery due to the reference in " The Child " to Maya Angelou 's 1969 work I Know Why the Caged Bird Sings . 
- 
- 
- = Noisy miner = 
- 
- The noisy miner ( Manorina melanocephala ) is a bird in the honeyeater family , <unk> , and is endemic to eastern and south @-@ eastern Australia . This miner is a grey bird , with a black head , orange @-@ yellow beak and feet , a distinctive yellow patch behind the eye and white tips on the tail feathers . The Tasmanian race has a more intense yellow panel in the wing , and a broader white tip to the tail . Males , females and juveniles are similar in appearance , though young birds are a brownish @-@ grey . As the common name suggests , the noisy miner is a vocal species with a large range of songs , calls , <unk> and alarms , and almost constant vocalizations particularly from young birds . One of four species in the genus Manorina , the noisy miner itself is divided into four subspecies . The separation of the Tasmanian M. <unk> leachi is of long standing , and the mainland birds were further split in 1999 . 
- Found in a broad arc from Far North Queensland through New South Wales and Victoria to Tasmania and southeastern South Australia , the noisy miner primarily inhabits dry , open eucalypt forests that lack understory shrubs . These include forests dominated by spotted gum , box and ironbark , as well as in degraded woodland where the understory has been cleared , such as recently burned areas , farming and grazing areas , roadside reserves , and suburban parks and gardens with trees and grass but without dense shrubbery . The density of noisy miner populations has significantly increased in many locations across its range , particularly human @-@ dominated habitats . The popularity of nectar @-@ producing garden plants such as the large @-@ <unk> <unk> was thought to play a role in its proliferation , but studies now show that the noisy miner has benefited primarily from <unk> practices that create open areas dominated by eucalypts . 
- Noisy miners are gregarious and territorial ; they forage , bathe , roost , breed and defend territory <unk> , forming colonies that can contain several hundred birds . Each bird has an ' activity space ' and birds with overlapping activity spaces form associations called ' coteries ' , the most stable units within the colony . The birds also form temporary flocks called ' coalitions ' for specific activities such as mobbing a predator . Group cohesion is facilitated not only by vocalizations , but also through ritualised displays which have been categorised as flight displays , postural displays , and facial displays . The noisy miner is a notably aggressive bird , and chasing , pecking , fighting , scolding , and mobbing occur throughout the day , targeted at both intruders and colony members . 
- <unk> in the canopy of trees and on trunks and branches and on the ground , the noisy miner mainly eats nectar , fruit and insects . Most time is spent <unk> the foliage of eucalypts , and it can meet most of its nutritional needs from manna , <unk> and <unk> gathered from the foliage . The noisy miner does not use a stereotyped courtship display , but copulation is a frenzied communal event . It breeds all year long , building a deep cup @-@ shaped nest and laying two to four eggs . Incubation is by the female only , although up to twenty male helpers take care of the nestlings and fledglings . Noisy miners have a range of strategies to increase their breeding success including multiple broods and group mobbing of predators . The noisy miner 's population increase has been correlated with the reduction of avian diversity in human @-@ affected landscapes . Its <unk> means that translocation is unlikely to be a solution to its overabundance , and culling has been proposed , although the noisy miner is currently a protected species across Australia . 
- 
- = = Taxonomy = = 
- 
- English ornithologist John Latham described the noisy miner four times in his 1801 work <unk> <unk> <unk> , <unk> <unk> <unk> , seemingly not knowing it was the same bird in each case : the chattering bee @-@ eater ( Merops <unk> ) , black @-@ headed <unk> ( <unk> melanocephala ) , hooded bee @-@ eater ( Merops <unk> ) , and white @-@ fronted bee @-@ eater ( Merops <unk> ) . Early notes recorded its tendency to scare off prey as hunters were about to shoot . It was as the chattering bee @-@ eater that it was painted between 1792 and 1797 by Thomas <unk> , one of a group known collectively as the Port Jackson Painter . John Gould treated the name Merops <unk> as the original description , and renamed it <unk> <unk> in his 1865 work <unk> to the Birds of Australia , giving it the common name of garrulous honeyeater , and noting the alternate name of chattering honeyeater . He noted the colonists of Tasmania called it a miner , and aboriginal people of New South Wales called it <unk> . Que <unk> gang was a local aboriginal name from the Blue Mountains . 
- In the early 20th century , Australian ornithologists started using the name Manorina melanocephala instead , because it was listed first by Latham in 1801 . This usage did not follow the letter of the International Code of Zoological Nomenclature , and in 2009 the International Commission on Zoological Nomenclature <unk> the current name by formally suppressing the name M. <unk> . The species name melanocephala is derived from the Ancient Greek words <unk> " black " , and <unk> " head " , referring to its black crown . Other common names include Mickey miner and <unk> . Four subspecies are recognised , including subspecies leachi found in eastern Tasmania . The mainland population was split into three subspecies in 1999 by Richard <unk> — <unk> from Cape York Peninsula in Queensland as far south as <unk> , <unk> from central Queensland and inland New South Wales west of <unk> , and the nominate subspecies melanocephala from southeastern New South Wales , Victoria , and southern South Australia . There are broad zones where birds are intermediate between subspecies . Further study is required to settle the taxonomic status of these populations . 
- The noisy miner is one of four species in the genus Manorina in the large family of honeyeaters known as <unk> , the other three being the black @-@ eared miner ( M. <unk> ) , the yellow @-@ throated miner ( M. <unk> ) , and the bell miner ( M. <unk> ) . One of the most obvious characteristics of the genus is a patch of bare yellow skin behind the eyes , which gives them an odd ' cross @-@ eyed ' look . Within the genus , the noisy , black @-@ eared and yellow @-@ throated miners form the subgenus <unk> . The noisy miner occasionally <unk> with the yellow @-@ throated miner . Molecular analysis has shown honeyeaters to be related to the <unk> ( <unk> ) , <unk> ( Australian <unk> , <unk> , <unk> , etc . ) , and the <unk> ( Australian fairy @-@ <unk> ) in a large <unk> superfamily . 
- 
- = = Description = = 
- 
- 
- = = = <unk> = = = 
- 
- The noisy miner is a large honeyeater , 24 – 28 centimetres ( 9 @.@ 4 – 11 @.@ 0 in ) in length , with a wingspan of 36 – 45 centimetres ( 14 – 18 in ) , and weighing 70 – 80 grams ( 2 @.@ 5 – 2 @.@ 8 oz ) . Male , female and juvenile birds all have similar plumage : grey on the back and tail and on the breast , and otherwise white underneath , with white scalloping on the nape and hind @-@ neck and on the breast ; off @-@ white forehead and <unk> ; a black band over the crown , bright orange @-@ yellow bill and a distinctive patch of yellow skin behind the eye ; a prominent white tip to the tail ; a narrow olive @-@ yellow panel in the folded wing ; and orange @-@ yellow legs and feet . A juvenile can be distinguished by softer plumage , a brownish tinge to the black on its head and the grey on its back , and a duller , greyish @-@ yellow skin patch behind the eye . 
- The noisy miner is similar in appearance to the yellow @-@ throated miner and the black @-@ eared miner ; it has a dull white forehead and a black crown , while the others have grey heads . 
- 
- = = = = Geographical variations = = = = 
- 
- Size variation in the noisy miner over its range follows Bergmann 's rule ; namely , birds tend to be larger where the climate is colder . Adults from central @-@ eastern and northern Queensland tend to have little or no olive @-@ yellow edging to the feathers of the back and wings , and have a wider white fringe on the feathers of the hind @-@ neck and back , giving birds from Queensland the appearance of having more distinctive scalloping than other populations . Wing length generally increases with latitude and M. <unk> leachi has <unk> shorter wings than the nominate race , although no significant difference in wing length was found in a study of populations north of 30 ° S and south of the Murray River . The subspecies leachi also has finer scalloping on the hind @-@ neck than the nominate race , a more intense yellow tinge to the wing panels , and a slightly broader off @-@ white tip to the tail . 
- The far north Queensland subspecies <unk> has a shorter tail , paler crown , larger yellow skin patch , and paler upper parts without the yellow @-@ olive of the nominate race ; and <unk> , found in western New South Wales , is smaller than the nominate race with a black crown , and darker more mottled upperparts . 
- 
- = = = <unk> = = = 
- 
- As the common name suggests , the noisy miner is an unusually vocal species . Previously known as the garrulous honeyeater , it has a large and varied repertoire of songs , calls , <unk> and alarms . Most are loud and penetrating , and consist of harsh single notes . It has two broad @-@ frequency alarm calls that are used when mobbing intruders into their territory , or when predators ( including humans ) are sighted ; and a narrow @-@ frequency alarm call that is primarily used when airborne predators are seen , such as the brown falcon ( Falco <unk> ) , or other large flying birds including the Australian <unk> ( <unk> <unk> ) and the <unk> <unk> ( <unk> <unk> ) . The aerial predator alarm call is a series of high @-@ pitched , <unk> whistling notes . The broad @-@ frequency alarm calls are a series of ' <unk> ' notes , low pitched and harsh , occurring at low and high levels of intensity . The narrow @-@ band call is used in situations where the bird signals the presence of a predator and <unk> information about its own location , while the broad @-@ band alarm is used to attract attention , and can initiate mobbing behaviour . These <unk> calls vary between individuals , and laboratory tests show noisy miners can distinguish calls by different birds . Hence , this may be integral to the complex social structure of the species . 
- Contact or social <unk> calls are low @-@ pitched sounds that carry long distances . ' <unk> ' calls are given by individual birds when foraging , and a similar call is given by nestlings that call at an increased rate as the mother approaches the nest . Where there is a high level of social activity , such as during territorial disputes with <unk> , calls are a series of quick , regular , single notes . The noisy miner has a mating display flight song , a soft <unk> of low @-@ frequency notes , given during short undulating flights by the male , and responded to by the female with a low @-@ frequency whistle . The noisy miner is found in open woodland habitats , where it is an advantage to call from the air so as to overcome sound <unk> . Another display call , described as ' yammer ' , is a rapid <unk> series of notes that is uttered during open @-@ bill , wing @-@ waving displays . The noisy miner has a song described as the ' dawn song ' — a communal song of clear <unk> notes emitted in chorus in the early hours of the morning from May through January . The dawn song , which is also sung at dusk , is audible over long distances and features duets that often involve <unk> . 
- A nestling begins to give the ' chip ' call soon after it emerges from the egg , and it calls frequently for the first two thirds of the nestling period and constantly for the last third . The call does not vary in the presence of an adult at the nest , so it seems likely that the call is not directed at the adult bird . The nestling becomes silent when an adult gives an alarm call , and makes a <unk> sound when the adult delivers food . The <unk> call of a fledgling is similar to the call of the nestling , but significantly louder and covering a greater frequency range ( which may make it more directional ) . The rate of calling , on average , is 85 to 100 calls in a minute , and in open scrub the call can be heard up to a kilometre away . <unk> , a juvenile vocalization comprising elements of various calls , begins to be uttered when the fledgling is around thirty days old . 
- The noisy miner also produces non @-@ vocal sounds by clicking or snapping its bill , usually during antagonistic encounters with other bird species , or when mobbing a predator . 
- 
- = = Distribution and habitat = = 
- 
- The noisy miner is endemic to eastern and south @-@ eastern Australia , occupying a broad arc from Far North Queensland where there are scattered populations , to New South Wales where it is widespread and common from the coast to a line from <unk> to <unk> , through Victoria into south @-@ eastern South Australia , and eastern Tasmania . Its range in South Australia has been steadily expanding since it was first recorded near Adelaide in the early 1890s . It is sedentary over its entire range . The noisy miner is territorial , and the territory of a colony is aggressively defended — which has led to a significant reduction in avian diversity in areas occupied by the noisy miner , with smaller species excluded . 
- The noisy miner primarily inhabits dry , open eucalypt forests without understory shrubs . It is commonly found in open <unk> forests , including those on coastal dunes or granite <unk> ; forests dominated by spotted gum on mountain ridges and exposed slopes ; box and ironbark forests on the foothills of the Great <unk> Range ; mixed forests of eucalypts and <unk> ( <unk> ) ; forests dominated by <unk> , <unk> , <unk> , <unk> or <unk> bush ; in stands of <unk> and scattered clumps of <unk> ; on the edges of woodlands of river red gum including swamp woodlands bordering floodplains , and areas dominated by exotic species such as European ash and <unk> . It regularly inhabits degraded patches of forest where the understory has been cleared , including recently burned areas , and modified habitats such as lightly @-@ <unk> farming and grazing areas , roadside reserves , <unk> remnants in towns and cities , and suburban parks and gardens with trees and grass but without dense shrubbery . 
- The noisy miner has benefited from the thinning of woodland on rural properties , heavy grazing that removes the understory , fragmentation of woodland that increases the percentage of edge habitat , and urban <unk> practices that increase open eucalypt environments . It has been described as a ' reverse keystone ' species , as it is <unk> an ever @-@ increasing range of human @-@ dominated habitats , and aggressively excluding smaller bird species from urban environments . This phenomenon has been also observed in rural areas . A field study across the South West <unk> of New South Wales , showed that the noisy miner 's presence corresponded with reduced numbers of insectivorous birds such as <unk> , <unk> , the restless <unk> ( <unk> <unk> ) , and other honeyeater species , and that this decrease was most marked in sites with better access to water and nutrients . While it has been hypothesized that the proliferation of large @-@ flowering grevillea <unk> has contributed to the abundance of noisy miners , recent research has identified the proliferation of lightly treed open areas , and the presence of eucalypt species as the most significant factors in the population increase . Large @-@ <unk> grevillea hybrids such as <unk> ' Robyn Gordon ' can benefit the noisy miner , in that an abundance of resources is usually dominated by larger aggressive honeyeaters , and a continuous nectar source could provide an advantage for the non @-@ migratory species . A field study in box @-@ ironbark country in central Victoria found miner numbers were correlated with the occurrence of yellow gum ( Eucalyptus <unk> ) , which reliably produces flowers ( and nectar ) each year . The abundance of the noisy miner is primarily determined by habitat structure . 
- While the range of the noisy miner has not significantly expanded , the density of the population within that range has substantially increased . High densities of noisy miners are regularly recorded in forests with thick understory in southern Queensland , 20 kilometres ( 12 mi ) or more from the forest / agricultural land edge . Many of these sites have extensive road networks used for forest management , and picnic areas and walking tracks for recreational use , and it has been found that these cleared spaces play a role in the abundance of noisy miners in the forests . There is evidence to suggest that higher road densities correspond with higher noisy miner population levels . Field work in Victoria showed noisy miners infiltrated anywhere from 150 to 300 m ( 490 to 980 ft ) into remnant woodland from the edges , with greater penetration occurring in less densely forested areas . This has implications for the size of woodland habitat needed to contain miner @-@ free areas — around 36 hectares ( 89 acres ) . <unk> projects restoring <unk> woodland , a species of she @-@ oak integral to the survival of the red @-@ tailed black <unk> ( <unk> <unk> ) , have been <unk> with a nurse species , usually fast @-@ growing eucalypts . Noisy miner populations were more likely in those <unk> woodlands where eucalypts had been planted at densities of up to 16 per hectare ( 6 @.@ 4 per acre ) . The presence of noisy miners was accompanied by a substantial difference in number and types of other birds found in the woodland . 
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- = = Behaviour = = 
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- = = = Social organization = = = 
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- The noisy miner is a gregarious species , and the birds are rarely seen singly or in <unk> ; they forage , move and roost in colonies that can consist of several hundred birds 
- Within a colony , a male bird will occupy an ' activity space ' , which will overlap with the activity spaces of other males . Males with overlapping activity spaces form associations called ' coteries ' , usually consisting of 10 to 25 birds . <unk> are the most stable unit within the colony . The birds also form temporary flocks when engaged in the same activity . These flocks , called ' coalitions ' , usually comprise five to eight birds , although coalitions of up to 40 birds can occur when mobbing a potential predator . <unk> of the coalition changes frequently as individuals leave the group as it passes beyond the boundary of their activity space , or the activity ends or changes , as when the breeding season begins . Females use activity spaces that overlap with those of male birds , but not other females , so that females will join coalitions with males in their area , but only rarely will there be more than one female in the coalition . The exclusivity of female activity spaces leads to young females being driven out of the colony in which they were born , and also makes it difficult for them to gain a place in a new colony . A study of banded nestlings that survived in one colony until the next breeding season , found that they were all male birds , suggesting that all female nestlings had died or left the colony . Emigration of males does not seem to occur until the population density of the colony reaches a critical level . 
- Looking after the young is communal , with males of the coterie bringing food to the nestlings and removing <unk> sacs . Communal feeding increases after fledging , when males from nearby coteries may even bring food to the young birds if not driven off . <unk> is usually communal , with two to six adults and juveniles roosting in contact with each other , usually near the end of a hanging branch up to 20 metres ( 66 ft ) above ground , within their activity space . They select a new site each evening , often selecting and rejecting several sites , and engaging in aggressive calling and chasing as other birds attempt to join the group . They are often the last birds to roost at night , but appear to sleep soundly , undisturbed by <unk> . Noisy miners drink together at the edge of lakes and dams , and from cattle troughs , often <unk> on a submerged branch . They bathe by diving head first into water and , when almost totally submerged , flapping their wings vigorously and <unk> their heads under the water . They shake excess water off and then fly to a nearby branch to <unk> . They have been observed using <unk> or <unk> @-@ soaked foliage to bathe , and in dry weather will dust @-@ bathe in dry soil or fine litter such as grass <unk> . <unk> is communal , with birds stimulated to participate by observing others . They are occasionally observed <unk> . 
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- = = = = <unk> behaviour = = = = 
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- The noisy miner engages in most activities in a group . <unk> , foraging , <unk> , bathing and dust @-@ bathing or <unk> are communal activities . Dawn song is a communal chorus , particularly during the breeding season . The communal interaction is facilitated by ritualised displays that have been categorised as flight displays , postural displays , and facial displays . In ' long flight ' displays , initiated by either male or female birds , groups of up to twenty birds from more than one coterie fly about 40 metres ( 130 ft ) above the canopy for distances of up to 1 @.@ 5 kilometres ( 0 @.@ 93 mi ) from the colony , constantly calling and not returning to the colony for about twenty minutes . As they return the remaining birds show signs of agitation , and sometimes fly up to join them . The ' short flight ' display is performed by the male , and may be analogous to the territorial advertising displays of other birds . In a ritualised movement , the noisy miner flies out from a perch across an open area , in a rhythmic undulating pattern , usually calling in flight . At the end of the clearing it turns on an upward <unk> and flies silently back to a perch near the starting point . The ' head @-@ up flight ' is performed by the female during the nesting period , and may function to attract male helpers . In its most intense form the body and tail are held almost vertically , with legs dangling and the head held up and back . It is performed by the female when she is selecting the nest site , and when carrying nest @-@ building material , and probably has the function of indicating the location of the nest to other group members . 
- <unk> displays include tall and low poses , pointing , open bill , and wing waving . The ' tall posture ' is used when in close contact with another bird and is a mild threat . The bird holds itself upright with neck and legs stretched , and it faces the other bird . The ' low posture ' is a submissive gesture ; the bird sits low on the perch with legs obscured by <unk> feathers and often faces away from the other bird . Fledglings threatened by adult birds will adopt a low posture and open their bill widely . ' Pointing ' is a threat display where the bird stretches out horizontally , with feathers <unk> and the bill pointed at the target of the aggression . A bill snap will sometimes accompany pointing . An ' open bill display ' is used by subordinates in encounters between two birds , and by a female on the nest when other birds approach . The ' trident bill display ' involves a fully open bill with the tongue raised and protruding and is a higher @-@ intensity submissive display . ' Wing waving ' is often performed at the same time as an open bill display . The wings are <unk> and held slightly away from the body , and <unk> out and up around three to six times . Wing waving may be accompanied by a yammer call . ' Eagle ' displays involve holding the wings and tail spread out , in either a vertical or horizontal position . 
- Eye displays are made by exposing or covering the bare yellow patch behind the eye — when the feathers are completely <unk> the entire patch is visible , and when they are <unk> the patch is hidden . Eye displays are used in conjunction with postural displays , with the yellow patch fully displayed by dominant birds using threat <unk> , and immature birds tending to reduce the size of the eye patch when under attack from other members of the coterie . 
- On occasion early in the breeding season , mass displays erupt , where twenty or thirty birds perform the various wing @-@ spreading displays , short flights , and constant calling . <unk> birds are attacked by others , and groups of silent but agitated birds watch the interactions . Mass displays are more common in the early morning , can last for up to 40 minutes , and seem to be a combination of sexual and agonistic behaviour . 
- A ' corroboree ' ( from the word for a ceremonial meeting of Aboriginal Australians ) is a group display where birds converge on adjacent branches and simultaneously pose <unk> , giving wing @-@ waving and open @-@ bill displays and the yammer call . A corroboree occurs when birds meet after a change in the social environment , such as a bird returning after an absence , or the <unk> of an intruder , or the coming together of different coteries . The corroboree appears to have a bonding function , and may involve all members of a colony . 
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- = = = = <unk> behaviour = = = = 
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- Described as " always at war with others of the feathered kind " in early notes , the noisy miner is one of the most aggressive of the honeyeaters . Much of the activity within a noisy miner colony is antagonistic with chasing , pecking , fighting , scolding , and mobbing occurring frequently throughout the day . The birds unite to attack predators and to defend the colony area against all other species of birds ; the species is also highly aggressive <unk> . 
- Female noisy miners are aggressive towards each other , and one cause of a male @-@ biased sex @-@ ratio in colonies may be the females ' greater intolerance for each other , driving <unk> out of the colony and preventing the immigration of new females . <unk> at the nest is common between males . Adult males begin attacking juveniles when they are around 11 weeks old , and attackers can include males that previously cared for the fledgling . Adult females are less aggressive towards young birds , although mothers do occasionally attack their own offspring , and <unk> has been recorded . There is little male to female aggression other than the ' driving flights ' that form part of the mating ritual . In direct attacks of young birds <unk> are directed at the eye patch . <unk> behaviour has been observed among nestlings , with aggression intensifying after fledging and at times resulting in the death of a sibling . 
- The noisy miner colony <unk> to mob inter @-@ specific intruders and predators . The noisy miner will approach the threat closely and point , expose eye patches , and often bill @-@ snap . Five to fifteen birds will fly around the intruder , some birds diving at it and either pulling away or striking the intruder . The mobbing continues until the intruder remains still , as with a tawny <unk> ( <unk> <unk> ) , or it leaves the area . <unk> of snakes and <unk> is particularly intense , and most species of bird , even non @-@ predators , entering the territory are immediately chased . The noisy miner has been recorded attacking an Australian <unk> @-@ <unk> ( <unk> <unk> ) during the day , <unk> , <unk> , ducks and <unk> on lakes at the edge of territories , crested pigeons ( <unk> <unk> ) , <unk> , and rosellas . Non @-@ predatory mammals such as bats , cattle , sheep , and <unk> are also attacked , though less vigorously than birds . 
- Noisy miner attacks are not limited to chasing the intruder , and aggressive incidents often result in the death of the <unk> . Reports include those of two noisy miners repeatedly pecking a house sparrow ( <unk> <unk> ) at the base of its skull and killing it in six minutes ; one noisy miner grasping a <unk> <unk> ( <unk> <unk> ) by the wing while another <unk> it on the head until it died ; and a sacred <unk> ( <unk> <unk> ) being chased and harassed for over five hours and then found dead with a fractured skull . 
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- = = = = Response to threats = = = = 
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- Noisy miners make louder alarm calls in <unk> sections of urban environments , such as main roads . The most common initial response to alarm calls is to stay in the area and scan for threats , rather than withdraw . A study conducted in Melbourne and a nearby rural area found that noisy miners in urban areas were less likely to take flight , and when they did they flew shorter distances . It is unclear whether this is an adaptation or bolder miners had been the ones to settle in the city . A field study in Canberra found that superb <unk> ( <unk> <unk> ) that lived in areas frequented by noisy miners recognised miner alarm calls and took flight , and had learnt to ignore their non @-@ alarm calls , while those that live in areas not frequented by noisy miners did not respond to miner alarm calls . This suggests the species has adapted and learnt to discriminate and respond to another species ' <unk> . 
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- = = = Feeding = = = 
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- The noisy miner primarily eats nectar , fruit and insects , and occasionally it feeds on small reptiles or amphibians . It is both <unk> and terrestrial , feeding in the canopy of trees and on trunks and branches and on the ground . It <unk> within the colony 's territory throughout the year , usually in groups of five to eight birds although hundreds may gather at a stand of flowering trees such as banksia . The noisy miner collects nectar directly from flowers , hanging upside down or <unk> thin branches <unk> to access the nectar ; it takes fruit from trees or fallen on the ground ; <unk> or hawks for invertebrates ; and picks through leaf litter for insects . It has been recorded turning over the dried droppings of <unk> ( <unk> <unk> ) and eastern grey <unk> ( <unk> <unk> ) searching for insects . 
- In a study of birds foraging in suburban gardens , the noisy miner was seen to spend more time in banksia , grevillea and eucalypt species , and when in flower , <unk> , than in other plants including <unk> . Most time was spent <unk> the foliage of eucalypts , and noisy miners were significantly more abundant in sites where eucalypts were present . The noisy miner can meet most of its nutritional needs from manna , <unk> and <unk> gathered from the foliage of eucalypts . Lower numbers of noisy miner were recorded at <unk> and <unk> than other large honeyeaters such as little <unk> ( <unk> <unk> ) and red <unk> ( <unk> <unk> ) . 
- Detailed studies of the diet of the noisy miner record it eating a range of foods including : spiders ; insects ( leaf beetles , <unk> , <unk> bugs , ants , moth and butterfly larvae ) ; nectar ( from <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , eucalypts Argyle <unk> , sugar gum , yellow gum , grey ironbark , and grey gum , Banksia <unk> , B. <unk> , B. <unk> , <unk> <unk> , G. <unk> , G. <unk> , G. <unk> , G. <unk> , and flowering <unk> ) ; seeds from oats , wheat and <unk> tree ; fruit from <unk> , mistletoe and <unk> ; frogs and <unk> ; and other matter such as bread , pieces of meat and cheese , and food scraps . 
- In the first study to demonstrate different learning techniques in a single species , the noisy miner was found to employ different cognitive strategies depending upon the resource it was foraging . When searching for nectar , which does not move but is readily depleted , the noisy miner uses a spatial memory @-@ based strategy , identifying characteristics of the environment — a strategy that is efficient in new environments and is not affected by changes in the bird 's activities . When searching for invertebrates , it appears to employ a different strategy based on learned rules of insect movement ( they improve at finding invertebrates with practice ) . The two different strategies imply the existence of adapted cognitive mechanisms , capable of responding appropriately to different foraging contexts . 
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- = = = Breeding = = = 
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- The noisy miner does not use a stereotyped courtship display ; displays can involve ' driving ' where the male jumps or flies at the female from 1 – 2 metres ( 3 @.@ 3 – 6 @.@ 6 ft ) away , and if she moves away he pursues her aggressively . The female may perform a ' bowed @-@ wing display ' where the wings and tail are spread and <unk> , with the wings arched and the head pointing down . The male may adopt a vertical or horizontal ' eagle display ' with wings and tail spread wide and held still for several seconds . Copulation is frequent and conspicuous , with both males and females <unk> with several birds , while other members of the colony display or otherwise interfere with the mating pair . Copulation usually occurs on larger , exposed branches close to the nest site and can occur at any time of the day , although slightly more often between 11 : 00 and 13 : 00 when communal activities are less frequent . The frenzied courtship activity had led to speculation that the female mates <unk> to recruit males to help care for the young , but recent genetic testing shows that 96 @.@ 5 % of noisy miner broods result from monogamous mating and that multiple paternity is rare . An observation of banded birds noted that while females <unk> repeatedly , it was always with the same male . <unk> switching between broods is uncommon , with pairs staying together over several years . 
- The noisy miner breeds all year long , with most activity from July through November , though the peak period is subject to seasonal variations with sharp peaks in laying activity when conditions are particularly favourable for raising young . The nest is built in prickly or leafy trees , and the noisy miner is often recorded nesting in eucalypts , and also in <unk> , <unk> , Banksia , <unk> , Hibiscus , mistletoe , <unk> , <unk> , <unk> and <unk> . It seems to prefer moderately dense foliage for nesting , often near the end of <unk> horizontal branches . Support for the nest may be the primary criterion of a suitable nest site , rather than characteristics of the vegetation or location . The female alone builds the nest , which is deep and cup @-@ shaped , woven of twigs and grasses with other plant material , animal hair and spider <unk> . Occasionally the nest will include man @-@ made materials such as <unk> , scraps of material , and tissue paper . It is lined with wool , hair , feathers , flowers or plant down , and padded with a circular mat woven from fibres pulled from the <unk> of the processional <unk> . The female noisy miner walks around on the ground close to the nest site , picking up material . She <unk> material from disused nests of other birds , or <unk> its most recent nest to build a new one . The female completes the nest in five or six days . On average nests have an external diameter of 15 – 17 @.@ 8 centimetres ( 5 @.@ 9 – 7 @.@ 0 in ) and an external depth of 9 – 11 @.@ 4 centimetres ( 3 @.@ 5 – 4 @.@ 5 in ) . The internal depth of the nest is around 5 @.@ 5 centimetres ( 2 @.@ 2 in ) . 
- <unk> vary greatly in size , shape and markings , but are generally elongated <unk> ; white to cream or pinkish or buff coloured ; <unk> , spotted or blotched with reddish brown to chestnut or a purplish red , sometimes with underlying markings of violet or purplish grey . The clutch consists of two to four eggs . Incubation is by the female only , and the incubation period is around sixteen days . <unk> is <unk> , with up to six days being recorded between the hatching of the first and last chicks in a clutch . Young are naked at hatching , and develop a cover of down within two to three days . The fledging period is around sixteen days , and young begin to find food for themselves between twenty @-@ six and thirty days after fledging , but are still regularly fed by adults to thirty @-@ five days . The young leave the nest before they are fully fledged , and only able to fly downwards , and scramble up . They do not go far from the nest , return to it at night , and take some weeks to completely leave the nest . Many fledglings are found on the ground and in low shrubs during this period , where they continue to be cared for until they can make their way up into the trees . These birds are often mistakenly " rescued " . The fledglings seek out siblings if separated , and <unk> together for up to three weeks after fledging . 
- The noisy miner has some of the largest group sizes of any <unk> breeding bird , with up to twenty males and one female attending a single brood . Only males help with a nest , and while many birds may be associated with a particular brood , some males devote all their time to a single nest , while others spread their helping efforts across five or six nests . <unk> evidence and genetic testing indicate that helpers are male offspring of the breeding pair , or full siblings of the male parent . Males nearly always bring food to the nestling singly , and if several arrive at once one will pass food to a nestling while the others wait . The female leaves the nest quickly when a male bird arrives , and never takes food from one of the helpers . Communal feeding of the young increases after fledging , and the young beg for food with constant ' chip chip ' calls and <unk> mouths . The female rarely feeds the young birds after they have fledged . 
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- = = = = Nest predation = = = = 
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- Cooperative breeding has been described as a strategy for decreasing nest predation , although one study found no relationship between the number of helpers visiting a nest site and its success or failure . Noisy miners were seen to have a range of strategies to increase their breeding success including multiple broods , laying eggs early in the season , nesting low in the canopy and group mobbing of predators ; these measures did not guarantee against nest failure due to the diversity of potential predators in the noisy miner 's open woodland habitat . 
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- = = Conservation status = = 
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- Being abundant throughout its significant range , the noisy miner is considered of least concern for conservation , and its extreme population densities in some areas actually constitute a threat to other species . The strong correlation between the presence of noisy miners and the absence of avian diversity has been well documented . The role played by the noisy miner in the steep decline of many woodland birds , its impact on endangered species with similar foraging requirements , and the level of leaf damage leading to die @-@ back that accompanies the exclusion of insectivorous birds from remnant woodlands , means that any strategy to restore avian diversity will need to take account of the management of noisy miner populations . Some habitat restoration and <unk> projects have inadvertently increased the problem of the noisy miner by establishing the open eucalypt habit that they prefer . A focus of many regeneration projects has been the establishing of habitat corridors that connect patches of remnant forest , and the use of eucalypts as fast @-@ growing nurse species . Both practices have sound ecological value , but allow the noisy miner to <unk> , so conservation efforts are being modified by planting a shrubby understory with the eucalypts , and avoiding the creation of narrow <unk> , corners or clumps of trees in vegetation corridors . A field study conducted in the Southern Highlands found that noisy miners tended to avoid areas dominated by <unk> , species of which in the study area had <unk> leaves . Hence the authors proposed <unk> projects include at least 15 % <unk> species with <unk> leaves if possible , as well as shrubby understory plants . 
- <unk> of noisy miners is unlikely to be a solution to their overabundance in remnant habitats . In a Victorian study where birds were banded and relocated , colonies moved into the now unpopulated area but soon returned to their original territories . The <unk> birds did not settle in a new territory . They were not assimilated into resident populations of miners , but instead wandered up to 4 @.@ 2 kilometres ( 2 @.@ 6 mi ) from the release point , moving through apparently suitable habitat occupied by other miners — at least for the first 50 days following translocation . Two birds with radio tracking devices travelled 18 kilometres ( 11 mi ) back to their site of capture . Although noisy miners are protected across Australia , and a permit is required to <unk> them , culling has been proposed as the most humane and practical method of reducing their impact , particularly where combined with rehabilitation of the habitat to suit a wider variety of bird life . An <unk> <unk> took place on private rural property over 1991 and 1992 , which reportedly resulted in an increase in species diversity . 
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- 
- = Principe Amedeo @-@ class ironclad = 
- 
- The Principe Amedeo class was a pair of ironclad warships built for the Italian Regia Marina ( Royal Navy ) in the 1870s and 1880s . They were the culmination of a major naval construction program designed to give Italy a powerful fleet of ironclads . The two ships , Principe Amedeo and Palestro , were the last Italian ironclads to feature sailing rigs and wooden hulls . They were armed with a battery of six 10 @-@ inch ( 254 mm ) guns and were capable of a speed in excess of 12 knots ( 22 km / h ; 14 mph ) . The ships had uneventful careers , spending much of it in Italy 's colonial empire . By the late 1880s , they were withdrawn from service and employed in secondary roles , first as headquarters ships for harbor defenses . Principe Amedeo was converted into a depot ship in 1895 and was discarded in 1910 , while Palestro was used as a training ship from 1894 to 1900 before being scrapped in 1902 – 04 . 
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- = = Design = = 
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- In 1862 , the Italian government under Prime Minister <unk> <unk> and his naval minister Carlo <unk> di <unk> made the decision to build a fleet of ironclad warships . The Italian fleet had already acquired a pair of small , French @-@ built armored frigates of the <unk> class , and two more vessels of the Re d 'Italia class had been ordered from the United States . Five more ironclads were ordered from foreign shipyards , three wooden steam frigates already under construction were converted into armored ships , and four more ironclads were ordered from Italian shipyards . The two Principe Amedeo @-@ class ships were the last two of this first generation of Italian ironclads . The design for Principe Amedeo was prepared by Inspector Engineer Giuseppe De Luca . He had initially planned on using entirely wooden hulls for the ships , but had changed to composite wood and iron construction by the time the ships were laid down . 
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- = = = General characteristics and machinery = = = 
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- The two ships differed slightly in size . Principe Amedeo was 79 @.@ 73 meters ( 261 @.@ 6 ft ) long between <unk> , while Palestro was 78 @.@ 82 m ( 258 @.@ 6 ft ) long . Principe Amedeo had a beam of 17 @.@ 4 m ( 57 ft ) and a draft of 7 @.@ 9 m ( 26 ft ) ; Palestro 's beam measured 17 @.@ 3 m ( 57 ft ) , and she had a draft of 8 m ( 26 ft ) . Both ships displaced 5 @,@ 761 metric tons ( 5 @,@ <unk> long tons ; 6 @,@ 350 short tons ) normally , but Principe Amedeo displaced 6 @,@ 020 t ( 5 @,@ 920 long tons ; 6 @,@ 640 short tons ) at full load and Palestro reached 3 @,@ 218 t ( 3 @,@ 167 long tons ; 3 @,@ <unk> short tons ) . They had a crew of <unk> officers and men . 
- The ships ' propulsion system consisted of one single @-@ expansion steam engine that drove a single screw propeller , with steam supplied by six coal @-@ fired , cylindrical fire @-@ tube boilers . The boilers were <unk> into a single funnel . The lead ship 's engine produced a top speed of 12 @.@ 2 knots ( 22 @.@ 6 km / h ; 14 @.@ 0 mph ) at 6 @,@ 117 indicated horsepower ( 4 @,@ <unk> kW ) , while Palestro made 12 @.@ 85 kn ( 23 @.@ 80 km / h ; 14 @.@ 79 mph ) at the same horsepower . They could steam for 1 @,@ 780 nautical miles ( 3 @,@ 300 km ; 2 @,@ 050 mi ) at a speed of 10 knots ( 19 km / h ; 12 mph ) . The ships were barque @-@ rigged to supplement the steam engine ; Principe Amedeo and her sister were the last rigged ironclad to be built by Italy . The ships ' sail area were 36 @,@ 738 square feet ( 3 @,@ <unk> @.@ 1 m2 ) for Principe Amedeo and 37 @,@ 361 sq ft ( 3 @,@ <unk> @.@ 0 m2 ) for Palestro . 
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- = = = Armament and armor = = = 
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- Palestro and Principe Amedeo were both armed with a main battery of six 10 in ( 254 mm ) guns , though they were mounted differently in each ship . Principe Amedeo carried hers in a single armored casemate located amidships , while Palestro 's guns were mounted in three armored casemates . The first was located forward , toward the bow , the second and third were placed close to the stern on each side of the ship . Both ships also carried an 11 in ( 279 mm ) gun that was mounted forward as a bow <unk> . Later in her career , Principe Amedeo received a secondary battery of six 2 @.@ 9 in ( 74 mm ) guns and six machine guns , along with two torpedo tubes . 
- The two ships were protected by iron belt armor that was 8 @.@ 7 in ( 221 mm ) thick and extended for the entire length of the hull . The casemates were protected with 5 @.@ 5 in ( 140 mm ) of iron plating , and the small conning tower had 2 @.@ 4 in ( 61 mm ) thick iron plates . 
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- = = Ships = = 
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- = = Service history = = 
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- Neither ship had a particularly eventful career . They were completed too late to take part in the final stages of the wars of Italian unification . Instead , they were assigned to the Italian colonial empire , with occasional <unk> in the main Italian fleet . In 1880 , Palestro took part in a naval demonstration off <unk> in an attempt to force the Ottoman Empire to comply with the terms of the Treaty of Berlin and turn over the town of <unk> to Montenegro . The following year , Principe Amedeo was involved in a collision with the ironclad <unk> during a hurricane , though neither ship was damaged . 
- In the late 1880s , both ships were withdrawn from <unk> service and employed as headquarters ships for the defense of Taranto — Principe Amedeo — and La <unk> — Palestro . Principe Amedeo was stricken from the naval register in 1895 and used as an ammunition depot ship in Taranto until 1910 , when she was sold for scrap . Palestro was employed as a training ship between 1894 and 1900 , when she too was stricken from the register . She was broken up between 1902 and 1904 . 
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- 
- = Cell nucleus = 
- 
- In cell biology , the nucleus ( <unk> nuclei ; from Latin nucleus or <unk> , meaning <unk> ) is a membrane @-@ enclosed organelle found in eukaryotic cells . <unk> usually have a single nucleus , but a few cell types , such as mammalian red blood cells , have no nuclei , and a few others have many . 
- Cell nuclei contain most of the cell 's genetic material , organized as multiple long linear DNA molecules in complex with a large variety of proteins , such as histones , to form chromosomes . The genes within these chromosomes are the cell 's nuclear genome and are structured in such a way to promote cell function . The nucleus maintains the integrity of genes and controls the activities of the cell by regulating gene expression — the nucleus is , therefore , the control center of the cell . The main structures making up the nucleus are the nuclear envelope , a double membrane that encloses the entire organelle and <unk> its contents from the cellular cytoplasm , and the nuclear matrix ( which includes the nuclear lamina ) , a network within the nucleus that adds mechanical support , much like the cytoskeleton , which supports the cell as a whole . 
- Because the nuclear membrane is <unk> to large molecules , nuclear pores are required that regulate nuclear transport of molecules across the envelope . The pores cross both nuclear membranes , providing a channel through which larger molecules must be actively transported by carrier proteins while allowing free movement of small molecules and ions . Movement of large molecules such as proteins and RNA through the pores is required for both gene expression and the maintenance of chromosomes . Although the interior of the nucleus does not contain any membrane @-@ bound sub compartments , its contents are not uniform , and a number of sub @-@ nuclear bodies exist , made up of unique proteins , RNA molecules , and particular parts of the chromosomes . The best @-@ known of these is the nucleolus , which is mainly involved in the assembly of ribosomes . After being produced in the nucleolus , ribosomes are exported to the cytoplasm where they translate mRNA . 
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- = = History = = 
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- The nucleus was the first organelle to be discovered . What is most likely the oldest preserved drawing dates back to the early <unk> <unk> van <unk> ( 1632 – 1723 ) . He observed a " <unk> " , the nucleus , in the red blood cells of salmon . Unlike mammalian red blood cells , those of other vertebrates still contain nuclei . 
- The nucleus was also described by Franz Bauer in 1804 and in more detail in 1831 by Scottish botanist Robert Brown in a talk at the <unk> Society of London . Brown was studying orchids under microscope when he observed an <unk> area , which he called the " <unk> " or " nucleus " , in the cells of the flower 's outer layer . 
- He did not suggest a potential function . In 1838 , <unk> <unk> proposed that the nucleus plays a role in generating cells , thus he introduced the name " <unk> " ( cell builder ) . He believed that he had observed new cells assembling around " <unk> " . Franz <unk> was a strong opponent of this view , having already described cells <unk> by division and believing that many cells would have no nuclei . The idea that cells can be generated de <unk> , by the " <unk> " or otherwise , contradicted work by Robert <unk> ( 1852 ) and Rudolf Virchow ( 1855 ) who decisively <unk> the new paradigm that cells are generated solely by cells ( " <unk> <unk> e <unk> " ) . The function of the nucleus remained unclear . 
- Between 1877 and 1878 , Oscar <unk> published several studies on the fertilization of sea <unk> eggs , showing that the nucleus of the sperm enters the <unk> and fuses with its nucleus . This was the first time it was suggested that an individual develops from a ( single ) nucleated cell . This was in contradiction to Ernst <unk> 's theory that the complete phylogeny of a species would be repeated during embryonic development , including generation of the first nucleated cell from a " <unk> " , a <unk> mass of primordial <unk> ( " <unk> " ) . Therefore , the necessity of the sperm nucleus for fertilization was discussed for quite some time . However , <unk> confirmed his observation in other animal groups , including amphibians and molluscs . Eduard <unk> produced the same results for plants in 1884 . This paved the way to assign the nucleus an important role in heredity . In 1873 , August <unk> postulated the equivalence of the maternal and paternal <unk> cells for heredity . The function of the nucleus as carrier of genetic information became clear only later , after mitosis was discovered and the <unk> rules were rediscovered at the beginning of the 20th century ; the chromosome theory of heredity was therefore developed . 
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- = = Structures = = 
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- The nucleus is the largest cellular organelle in animal cells . In mammalian cells , the average diameter of the nucleus is approximately 6 micrometres ( µm ) , which occupies about 10 % of the total cell volume . The <unk> liquid within it is called nucleoplasm , and is similar in composition to the <unk> found outside the nucleus . It appears as a dense , roughly spherical or irregular organelle . 
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- = = = Nuclear envelope and pores = = = 
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- The nuclear envelope , otherwise known as nuclear membrane , consists of two cellular membranes , an inner and an outer membrane , arranged parallel to one another and separated by 10 to 50 <unk> ( nm ) . The nuclear envelope completely encloses the nucleus and separates the cell 's genetic material from the surrounding cytoplasm , serving as a barrier to prevent macromolecules from <unk> freely between the nucleoplasm and the cytoplasm . The outer nuclear membrane is continuous with the membrane of the rough <unk> <unk> ( <unk> ) , and is similarly studded with ribosomes . The space between the membranes is called the <unk> space and is continuous with the <unk> <unk> . 
- Nuclear pores , which provide <unk> channels through the envelope , are composed of multiple proteins , collectively referred to as <unk> . The pores are about 125 million <unk> in molecular weight and consist of around 50 ( in yeast ) to several hundred proteins ( in vertebrates ) . The pores are 100 nm in total diameter ; however , the gap through which molecules freely diffuse is only about 9 nm wide , due to the presence of regulatory systems within the center of the pore . This size selectively allows the passage of small water @-@ soluble molecules while preventing larger molecules , such as nucleic acids and larger proteins , from <unk> entering or exiting the nucleus . These large molecules must be actively transported into the nucleus instead . The nucleus of a typical mammalian cell will have about 3000 to 4000 pores throughout its envelope , each of which contains an <unk> @-@ <unk> ring @-@ shaped structure at a position where the inner and outer membranes fuse . Attached to the ring is a structure called the nuclear basket that extends into the nucleoplasm , and a series of <unk> extensions that reach into the cytoplasm . Both structures serve to mediate binding to nuclear transport proteins . 
- Most proteins , ribosomal subunits , and some <unk> are transported through the pore complexes in a process mediated by a family of transport factors known as karyopherins . Those karyopherins that mediate movement into the nucleus are also called importins , whereas those that mediate movement out of the nucleus are called exportins . Most karyopherins interact directly with their cargo , although some use <unk> proteins . <unk> hormones such as <unk> and <unk> , as well as other small <unk> @-@ soluble molecules involved in <unk> signaling , can diffuse through the cell membrane and into the cytoplasm , where they bind nuclear receptor proteins that are <unk> into the nucleus . There they serve as transcription factors when bound to their ligand ; in the absence of ligand , many such receptors function as <unk> <unk> that <unk> gene expression . 
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- = = = Nuclear lamina = = = 
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- In animal cells , two networks of intermediate filaments provide the nucleus with mechanical support : The nuclear lamina forms an organized <unk> on the internal face of the envelope , while less organized support is provided on the <unk> face of the envelope . Both systems provide structural support for the nuclear envelope and anchoring sites for chromosomes and nuclear pores . 
- The nuclear lamina is composed mostly of lamin proteins . Like all proteins , lamins are synthesized in the cytoplasm and later transported to the nucleus interior , where they are assembled before being incorporated into the existing network of nuclear lamina . <unk> found on the <unk> face of the membrane , such as <unk> and <unk> , bind to the cytoskeleton to provide structural support . <unk> are also found inside the nucleoplasm where they form another regular structure , known as the <unk> veil , that is visible using fluorescence microscopy . The actual function of the veil is not clear , although it is excluded from the nucleolus and is present during interphase . <unk> structures that make up the veil , such as <unk> , bind chromatin and disrupting their structure inhibits transcription of protein @-@ coding genes . 
- Like the components of other intermediate filaments , the lamin <unk> contains an alpha @-@ helical domain used by two monomers to coil around each other , forming a dimer structure called a coiled coil . Two of these dimer structures then join side by side , in an <unk> arrangement , to form a <unk> called a <unk> . Eight of these <unk> form a lateral arrangement that is twisted to form a <unk> filament . These filaments can be assembled or <unk> in a dynamic manner , meaning that changes in the length of the filament depend on the competing rates of filament addition and removal . 
- <unk> in lamin genes leading to defects in filament assembly cause a group of rare genetic disorders known as <unk> . The most notable <unk> is the family of diseases known as <unk> , which causes the appearance of premature aging in its <unk> . The exact mechanism by which the associated biochemical changes give rise to the aged phenotype is not well understood . 
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- = = = <unk> = = = 
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- The cell nucleus contains the majority of the cell 's genetic material in the form of multiple linear DNA molecules organized into structures called chromosomes . Each human cell contains roughly two meters of DNA . During most of the cell cycle these are organized in a DNA @-@ protein complex known as chromatin , and during cell division the chromatin can be seen to form the well @-@ defined chromosomes familiar from a <unk> . A small fraction of the cell 's genes are located instead in the mitochondria . 
- There are two types of chromatin . <unk> is the less compact DNA form , and contains genes that are frequently expressed by the cell . The other type , heterochromatin , is the more compact form , and contains DNA that is infrequently transcribed . This structure is further categorized into <unk> heterochromatin , consisting of genes that are organized as heterochromatin only in certain cell types or at certain stages of development , and <unk> heterochromatin that consists of chromosome structural components such as <unk> and <unk> . During interphase the chromatin <unk> itself into discrete individual patches , called chromosome territories . Active genes , which are generally found in the <unk> region of the chromosome , tend to be located towards the chromosome 's territory boundary . 
- Antibodies to certain types of chromatin organization , in particular , <unk> , have been associated with a number of <unk> diseases , such as systemic lupus <unk> . These are known as anti @-@ nuclear antibodies ( ANA ) and have also been observed in concert with multiple <unk> as part of general immune system <unk> . As in the case of <unk> , the role played by the antibodies in inducing the symptoms of <unk> diseases is not obvious . 
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- = = = <unk> = = = 
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- The nucleolus is a discrete densely stained structure found in the nucleus . It is not surrounded by a membrane , and is sometimes called a <unk> . It forms around tandem repeats of rDNA , DNA coding for ribosomal RNA ( rRNA ) . These regions are called nucleolar organizer regions ( <unk> ) . The main roles of the nucleolus are to synthesize rRNA and assemble ribosomes . The structural cohesion of the nucleolus depends on its activity , as ribosomal assembly in the nucleolus results in the transient association of nucleolar components , facilitating further ribosomal assembly , and hence further association . This model is supported by observations that inactivation of rDNA results in <unk> of nucleolar structures . 
- In the first step of ribosome assembly , a protein called RNA polymerase I transcribes rDNA , which forms a large pre @-@ rRNA precursor . This is <unk> into the subunits <unk> , <unk> , and <unk> rRNA . The transcription , post @-@ transcriptional processing , and assembly of rRNA occurs in the nucleolus , aided by small nucleolar RNA ( <unk> ) molecules , some of which are derived from <unk> introns from messenger <unk> encoding genes related to ribosomal function . The assembled ribosomal subunits are the largest structures passed through the nuclear pores . 
- When observed under the electron microscope , the nucleolus can be seen to consist of three distinguishable regions : the innermost <unk> centers ( <unk> ) , surrounded by the dense <unk> component ( DFC ) , which in turn is bordered by the <unk> component ( <unk> ) . <unk> of the rDNA occurs either in the FC or at the FC @-@ DFC boundary , and , therefore , when rDNA transcription in the cell is increased , more <unk> are detected . Most of the cleavage and modification of <unk> occurs in the DFC , while the latter steps involving protein assembly onto the ribosomal subunits occur in the <unk> . 
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- = = = Other subnuclear bodies = = = 
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- Besides the nucleolus , the nucleus contains a number of other non @-@ membrane @-@ delineated bodies . These include Cajal bodies , Gemini of coiled bodies , <unk> interphase <unk> association ( <unk> ) , promyelocytic leukaemia ( PML ) bodies , paraspeckles , and splicing speckles . Although little is known about a number of these domains , they are significant in that they show that the nucleoplasm is not a uniform mixture , but rather contains organized functional <unk> . 
- Other subnuclear structures appear as part of abnormal disease processes . For example , the presence of small <unk> rods has been reported in some cases of <unk> <unk> . This condition typically results from mutations in actin , and the rods themselves consist of mutant actin as well as other <unk> proteins . 
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- = = = = Cajal bodies and gems = = = = 
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- A nucleus typically contains between 1 and 10 compact structures called Cajal bodies or coiled bodies ( CB ) , whose diameter measures between 0 @.@ 2 µm and 2 @.@ 0 µm depending on the cell type and species . When seen under an electron microscope , they resemble balls of tangled thread and are dense <unk> of distribution for the protein coilin . CBs are involved in a number of different roles relating to RNA processing , specifically small nucleolar RNA ( <unk> ) and small nuclear RNA ( <unk> ) maturation , and <unk> mRNA modification . 
- Similar to Cajal bodies are Gemini of Cajal bodies , or gems , whose name is derived from the Gemini constellation in reference to their close " twin " relationship with CBs . Gems are similar in size and shape to CBs , and in fact are virtually indistinguishable under the microscope . Unlike CBs , gems do not contain small nuclear <unk> ( <unk> ) , but do contain a protein called survival of motor <unk> ( SMN ) whose function relates to <unk> <unk> . Gems are believed to assist CBs in <unk> <unk> , though it has also been suggested from microscopy evidence that CBs and gems are different manifestations of the same structure . Later <unk> studies have shown gems to be twins of Cajal bodies with the difference being in the coilin component ; Cajal bodies are SMN positive and coilin positive , and gems are SMN positive and coilin negative . 
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- = = = = <unk> and <unk> domains = = = = 
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- <unk> domains , or <unk> interphase <unk> associations , were first described in microscopy studies in 1991 . Their function remains unclear , though they were not thought to be associated with active DNA replication , transcription , or RNA processing . They have been found to often associate with discrete domains defined by dense localization of the transcription factor <unk> , which promotes transcription of small nuclear RNA ( <unk> ) . 
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- = = = = PML bodies = = = = 
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- <unk> leukaemia bodies ( PML bodies ) are spherical bodies found scattered throughout the nucleoplasm , measuring around 0 @.@ 1 – 1 @.@ 0 µm . They are known by a number of other names , including nuclear domain 10 ( <unk> ) , <unk> bodies , and PML <unk> domains . PML bodies are named after one of their major components , the promyelocytic leukemia protein ( PML ) . They are often seen in the nucleus in association with Cajal bodies and cleavage bodies . PML bodies belong to the nuclear matrix , an ill @-@ defined super @-@ structure of the nucleus proposed to anchor and regulate many nuclear functions , including DNA replication , transcription , or epigenetic <unk> . The PML protein is the key organizer of these domains that recruits an ever @-@ growing number of proteins , whose only common known feature to date is their ability to be <unk> . Yet , <unk> / - mice ( which have their PML gene deleted ) cannot assemble nuclear bodies , develop normally and live well , demonstrating that PML bodies are <unk> for most basic biological functions . 
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- = = = = <unk> speckles = = = = 
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- <unk> are subnuclear structures that are enriched in pre @-@ messenger RNA splicing factors and are located in the interchromatin regions of the nucleoplasm of mammalian cells . At the fluorescence @-@ microscope level they appear as irregular , <unk> structures , which vary in size and shape , and when examined by electron microscopy they are seen as clusters of interchromatin granules . <unk> are dynamic structures , and both their protein and RNA @-@ protein components can cycle continuously between speckles and other nuclear locations , including active transcription sites . Studies on the composition , structure and behaviour of speckles have provided a model for understanding the functional compartmentalization of the nucleus and the organization of the gene @-@ expression machinery splicing <unk> and other splicing proteins necessary for pre @-@ mRNA processing . Because of a cell 's changing requirements , the composition and location of these bodies changes according to mRNA transcription and regulation via <unk> of specific proteins . The splicing speckles are also known as nuclear speckles ( nuclear <unk> ) , splicing factor compartments ( SF compartments ) , interchromatin <unk> clusters ( <unk> ) , B snurposomes . B snurposomes are found in the amphibian <unk> nuclei and in <unk> <unk> embryos . B snurposomes appear alone or attached to the Cajal bodies in the electron <unk> of the amphibian nuclei . <unk> function as storage sites for the splicing factors . 
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- = = = = <unk> = = = = 
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- Discovered by Fox et al. in 2002 , paraspeckles are irregularly shaped compartments in the nucleus ' interchromatin space . First documented in <unk> cells , where there are generally 10 – 30 per nucleus , paraspeckles are now known to also exist in all human primary cells , transformed cell lines , and tissue sections . Their name is derived from their distribution in the nucleus ; the " para " is short for parallel and the " speckles " refers to the splicing speckles to which they are always in close proximity . 
- <unk> are dynamic structures that are altered in response to changes in cellular metabolic activity . They are transcription dependent and in the absence of RNA <unk> II transcription , the <unk> disappears and all of its associated protein components ( <unk> , <unk> , <unk> , <unk> ( m ) 68 , and <unk> ) form a crescent shaped <unk> cap in the nucleolus . This phenomenon is demonstrated during the cell cycle . In the cell cycle , paraspeckles are present during interphase and during all of mitosis except for <unk> . During <unk> , when the two daughter nuclei are formed , there is no RNA <unk> II transcription so the protein components instead form a <unk> cap . 
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- = = = = <unk> fibrils = = = = 
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- <unk> fibrils are visible only under electron microscope . They are located next to the <unk> active chromatin and are hypothesized to be the sites of active pre @-@ mRNA processing . 
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- = = Function = = 
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- The nucleus provides a site for genetic transcription that is segregated from the location of translation in the cytoplasm , allowing levels of gene regulation that are not available to prokaryotes . The main function of the cell nucleus is to control gene expression and mediate the replication of DNA during the cell cycle . 
- The nucleus is an organelle found in eukaryotic cells . Inside its fully enclosed nuclear membrane , it contains the majority of the cell 's genetic material . This material is organized as DNA molecules , along with a variety of proteins , to form chromosomes . 
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- = = = Cell compartmentalization = = = 
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- The nuclear envelope allows the nucleus to control its contents , and separate them from the rest of the cytoplasm where necessary . This is important for controlling processes on either side of the nuclear membrane . In most cases where a <unk> process needs to be restricted , a key participant is removed to the nucleus , where it interacts with transcription factors to <unk> the production of certain enzymes in the pathway . This regulatory mechanism occurs in the case of glycolysis , a cellular pathway for breaking down glucose to produce energy . <unk> is an enzyme responsible for the first the step of glycolysis , forming glucose @-@ 6 @-@ phosphate from glucose . At high concentrations of <unk> @-@ 6 @-@ phosphate , a molecule made later from glucose @-@ 6 @-@ phosphate , a regulator protein removes <unk> to the nucleus , where it forms a transcriptional <unk> complex with nuclear proteins to reduce the expression of genes involved in glycolysis . 
- In order to control which genes are being transcribed , the cell separates some transcription factor proteins responsible for regulating gene expression from physical access to the DNA until they are activated by other signaling pathways . This prevents even low levels of inappropriate gene expression . For example , in the case of NF @-@ κB @-@ controlled genes , which are involved in most inflammatory responses , transcription is induced in response to a signal pathway such as that initiated by the signaling molecule <unk> @-@ α , binds to a cell membrane receptor , resulting in the recruitment of <unk> proteins , and eventually activating the transcription factor NF @-@ κB . A nuclear <unk> signal on the NF @-@ κB protein allows it to be transported through the nuclear pore and into the nucleus , where it stimulates the transcription of the target genes . 
- The compartmentalization allows the cell to prevent translation of <unk> mRNA . <unk> mRNA contains introns that must be removed before being translated to produce functional proteins . The splicing is done inside the nucleus before the mRNA can be accessed by ribosomes for translation . Without the nucleus , ribosomes would translate newly transcribed ( <unk> ) mRNA , resulting in <unk> and <unk> proteins . 
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- = = = Gene expression = = = 
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- Gene expression first involves transcription , in which DNA is used as a template to produce RNA . In the case of genes encoding proteins , that RNA produced from this process is messenger RNA ( mRNA ) , which then needs to be translated by ribosomes to form a protein . As ribosomes are located outside the nucleus , mRNA produced needs to be exported . 
- Since the nucleus is the site of transcription , it also contains a variety of proteins that either directly mediate transcription or are involved in regulating the process . These proteins include <unk> , which <unk> the double @-@ stranded DNA molecule to facilitate access to it , RNA <unk> , which synthesize the growing RNA molecule , <unk> , which change the amount of <unk> in DNA , helping it wind and <unk> , as well as a large variety of transcription factors that regulate expression . 
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- = = = <unk> of pre @-@ mRNA = = = 
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- Newly synthesized mRNA molecules are known as primary transcripts or pre @-@ mRNA . They must undergo post @-@ transcriptional modification in the nucleus before being exported to the cytoplasm ; mRNA that appears in the cytoplasm without these modifications is degraded rather than used for protein translation . The three main modifications are 5 ' capping , 3 ' <unk> , and RNA splicing . While in the nucleus , pre @-@ mRNA is associated with a variety of proteins in complexes known as heterogeneous <unk> particles ( <unk> ) . <unk> of the 5 ' cap occurs co @-@ <unk> and is the first step in post @-@ transcriptional modification . The 3 ' <unk> @-@ <unk> tail is only added after transcription is complete . 
- RNA splicing , carried out by a complex called the <unk> , is the process by which introns , or regions of DNA that do not code for protein , are removed from the pre @-@ mRNA and the remaining exons connected to re @-@ form a single continuous molecule . This process normally occurs after 5 ' capping and 3 ' <unk> but can begin before synthesis is complete in transcripts with many exons . Many pre @-@ <unk> , including those encoding antibodies , can be <unk> in multiple ways to produce different mature <unk> that encode different protein sequences . This process is known as alternative splicing , and allows production of a large variety of proteins from a limited amount of DNA . 
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- = = <unk> and regulation = = 
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- = = = Nuclear transport = = = 
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- The entry and exit of large molecules from the nucleus is tightly controlled by the nuclear pore complexes . Although small molecules can enter the nucleus without regulation , macromolecules such as RNA and proteins require association karyopherins called importins to enter the nucleus and exportins to exit . " <unk> " proteins that must be <unk> from the cytoplasm to the nucleus contain short amino acid sequences known as nuclear localization signals , which are bound by importins , while those transported from the nucleus to the cytoplasm carry nuclear export signals bound by exportins . The ability of importins and exportins to transport their cargo is regulated by <unk> , enzymes that <unk> the molecule <unk> <unk> to release energy . The key <unk> in nuclear transport is <unk> , which can bind either <unk> or GDP ( <unk> <unk> ) , depending on whether it is located in the nucleus or the cytoplasm . Whereas importins depend on RanGTP to <unk> from their cargo , exportins require RanGTP in order to bind to their cargo . 
- Nuclear import depends on the importin binding its cargo in the cytoplasm and carrying it through the nuclear pore into the nucleus . Inside the nucleus , RanGTP acts to separate the cargo from the importin , allowing the importin to exit the nucleus and be reused . Nuclear export is similar , as the <unk> binds the cargo inside the nucleus in a process facilitated by RanGTP , exits through the nuclear pore , and separates from its cargo in the cytoplasm . 
- <unk> export proteins exist for translocation of mature mRNA and tRNA to the cytoplasm after post @-@ transcriptional modification is complete . This quality @-@ control mechanism is important due to these molecules ' central role in protein translation . <unk> @-@ expression of a protein due to incomplete excision of exons or mis @-@ incorporation of amino acids could have negative consequences for the cell ; thus , incompletely modified RNA that reaches the cytoplasm is degraded rather than used in translation . 
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- = = = Assembly and disassembly = = = 
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- During its lifetime , a nucleus may be broken down or destroyed , either in the process of cell division or as a consequence of apoptosis ( the process of programmed cell death ) . During these events , the structural components of the nucleus — the envelope and lamina — can be systematically degraded . In most cells , the disassembly of the nuclear envelope marks the end of the <unk> of mitosis . However , this disassembly of the nucleus is not a universal feature of mitosis and does not occur in all cells . Some <unk> eukaryotes ( e.g. , <unk> ) undergo so @-@ called closed mitosis , in which the nuclear envelope remains intact . In closed mitosis , the daughter chromosomes migrate to opposite poles of the nucleus , which then divides in two . The cells of higher eukaryotes , however , usually undergo open mitosis , which is characterized by breakdown of the nuclear envelope . The daughter chromosomes then migrate to opposite poles of the <unk> spindle , and new nuclei <unk> around them . 
- At a certain point during the cell cycle in open mitosis , the cell divides to form two cells . In order for this process to be possible , each of the new daughter cells must have a full set of genes , a process requiring replication of the chromosomes as well as segregation of the separate sets . This occurs by the replicated chromosomes , the sister chromatids , <unk> to microtubules , which in turn are attached to different centrosomes . The sister chromatids can then be pulled to separate locations in the cell . In many cells , the <unk> is located in the cytoplasm , outside the nucleus ; the microtubules would be unable to attach to the chromatids in the presence of the nuclear envelope . Therefore , the early stages in the cell cycle , beginning in <unk> and until around <unk> , the nuclear membrane is dismantled . Likewise , during the same period , the nuclear lamina is also <unk> , a process regulated by <unk> of the lamins by protein <unk> such as the <unk> protein kinase . Towards the end of the cell cycle , the nuclear membrane is reformed , and around the same time , the nuclear lamina are reassembled by <unk> the lamins . 
- However , in <unk> , the nuclear envelope remains intact , the centrosomes are located in the cytoplasm , and the microtubules come in contact with chromosomes , whose <unk> regions are incorporated into the nuclear envelope ( the so @-@ called closed mitosis with <unk> spindle ) . In many other <unk> ( e.g. , <unk> , <unk> ) and fungi , the centrosomes are <unk> , and their nuclear envelope also does not <unk> during cell division . 
- <unk> is a controlled process in which the cell 's structural components are destroyed , resulting in death of the cell . Changes associated with apoptosis directly affect the nucleus and its contents , for example , in the condensation of chromatin and the disintegration of the nuclear envelope and lamina . The destruction of the lamin networks is controlled by specialized apoptotic <unk> called <unk> , which <unk> the lamin proteins and , thus , <unk> the nucleus ' structural integrity . <unk> cleavage is sometimes used as a laboratory indicator of <unk> activity in assays for early apoptotic activity . <unk> that express mutant <unk> @-@ resistant lamins are deficient in nuclear changes related to apoptosis , suggesting that lamins play a role in initiating the events that lead to apoptotic degradation of the nucleus . <unk> of lamin assembly itself is an <unk> of apoptosis . 
- The nuclear envelope acts as a barrier that prevents both DNA and RNA viruses from entering the nucleus . Some viruses require access to proteins inside the nucleus in order to replicate and / or assemble . DNA viruses , such as <unk> replicate and assemble in the cell nucleus , and exit by budding through the inner nuclear membrane . This process is accompanied by disassembly of the lamina on the nuclear face of the inner membrane . 
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- = = = Disease @-@ related dynamics = = = 
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- Initially , it has been suspected that <unk> in general and <unk> in particular do not enter the nucleus . Now there is a body of evidence that under <unk> conditions ( e.g. lupus <unk> ) <unk> can enter the nucleus . 
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- = = <unk> per cell = = 
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- Most eukaryotic cell types usually have a single nucleus , but some have no nuclei , while others have several . This can result from normal development , as in the maturation of mammalian red blood cells , or from faulty cell division . 
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- = = = <unk> cells = = = 
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- An <unk> cell contains no nucleus and is , therefore , incapable of dividing to produce daughter cells . The best @-@ known <unk> cell is the mammalian red blood cell , or <unk> , which also lacks other organelles such as mitochondria , and serves primarily as a transport vessel to ferry oxygen from the lungs to the body 's tissues . <unk> mature through <unk> in the bone marrow , where they lose their nuclei , organelles , and ribosomes . The nucleus is expelled during the process of differentiation from an <unk> to a <unk> , which is the immediate precursor of the mature <unk> . The presence of <unk> may induce the release of some immature " <unk> " <unk> into the bloodstream . <unk> cells can also arise from flawed cell division in which one daughter lacks a nucleus and the other has two nuclei . 
- In flowering plants , this condition occurs in <unk> tube elements . 
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- = = = Multinucleated cells = = = 
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- Multinucleated cells contain multiple nuclei . Most <unk> species of <unk> and some fungi in <unk> have naturally multinucleated cells . Other examples include the intestinal parasites in the genus <unk> , which have two nuclei per cell . In humans , skeletal muscle cells , called <unk> and <unk> , become multinucleated during development ; the resulting arrangement of nuclei near the periphery of the cells allows <unk> intracellular space for <unk> . Multinucleated and <unk> cells can also be abnormal in humans ; for example , cells arising from the fusion of <unk> and macrophages , known as giant multinucleated cells , sometimes accompany inflammation and are also implicated in tumor formation . 
- A number of <unk> are known to have two nuclei . Unlike other multinucleated cells these nuclei contain two distinct lineages of DNA : one from the <unk> and the other from a symbiotic <unk> . <unk> the <unk> and the <unk> of the <unk> remain functional . 
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- = = Evolution = = 
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- As the major defining characteristic of the eukaryotic cell , the nucleus ' evolutionary origin has been the subject of much speculation . Four major hypotheses have been proposed to explain the existence of the nucleus , although none have yet earned widespread support . 
- The first model known as the " <unk> model " proposes that a symbiotic relationship between the archaea and bacteria created the nucleus @-@ containing eukaryotic cell . ( <unk> of the <unk> and <unk> domain have no cell nucleus . ) It is hypothesized that the symbiosis originated when ancient archaea , similar to modern <unk> archaea , invaded and lived within bacteria similar to modern <unk> , eventually forming the early nucleus . This theory is analogous to the accepted theory for the origin of eukaryotic mitochondria and <unk> , which are thought to have developed from a similar <unk> relationship between proto @-@ eukaryotes and <unk> bacteria . The <unk> origin of the nucleus is supported by observations that archaea and <unk> have similar genes for certain proteins , including histones . Observations that <unk> are <unk> , can form multicellular complexes , and possess <unk> and G proteins similar to <unk> , support a bacterial origin for the eukaryotic cell . 
- A second model proposes that proto @-@ eukaryotic cells evolved from bacteria without an <unk> stage . This model is based on the existence of modern <unk> bacteria that possess a nuclear structure with primitive pores and other <unk> membrane structures . A similar proposal states that a <unk> @-@ like cell , the <unk> , evolved first and <unk> archaea and bacteria to generate the nucleus and the eukaryotic cell . 
- The most controversial model , known as viral <unk> , posits that the membrane @-@ bound nucleus , along with other eukaryotic features , originated from the infection of a <unk> by a virus . The suggestion is based on similarities between eukaryotes and viruses such as linear DNA strands , mRNA capping , and tight binding to proteins ( <unk> histones to viral <unk> ) . One version of the proposal suggests that the nucleus evolved in concert with <unk> to form an early cellular " predator " . Another variant proposes that eukaryotes originated from early archaea infected by <unk> , on the basis of observed similarity between the DNA <unk> in modern <unk> and eukaryotes . It has been suggested that the unresolved question of the evolution of sex could be related to the viral <unk> hypothesis . 
- A more recent proposal , the <unk> hypothesis , suggests that the nucleus instead originated from a single ancestral cell that evolved a second exterior cell membrane ; the interior membrane enclosing the original cell then became the nuclear membrane and evolved increasingly elaborate pore structures for passage of internally synthesized cellular components such as ribosomal subunits . 
- 
- = = Gallery = = 
- 
- 
- 
- = Until the Whole World Hears = 
- 
- Until the Whole World Hears is the fourth studio album by American Christian rock band Casting Crowns . Released on November 27 , 2009 , the album was produced by Mark . A Miller and features a sound that has been described as ' pure American rock ' , ' soft adult contemporary ' , and ' CCM ' . Lyrically , the album discusses Christian subjects such as God , Jesus , and salvation , with several songs being <unk> of classic hymns . Until the Whole World Hears sold over 167 @,@ 000 copies in its first week , Casting Crowns ' highest sales week to date , and debuted at No. 4 on the Billboard 200 and at No. 1 on the Billboard Christian Albums chart . In the United States , the album ranked as the 37th best @-@ selling album of 2010 and the <unk> best @-@ selling album of 2011 ; it ranked as the first and third best @-@ selling Christian album in those years , respectively . It has sold over 1 @.@ 1 million copies and been certified Platinum by the Recording Industry Association of America ( RIAA ) . 
- Until the Whole World Hears received a mixed @-@ to @-@ positive reception from critics and was nominated for Pop / Contemporary Album of the Year at the 42nd GMA Dove Awards . Three singles were released from the album : the title track , " If We 've Ever Needed You " , and " Glorious Day ( Living He Loved Me ) " . The title track and " Glorious Day ( Living He Loved Me ) " both peaked atop the Billboard Christian Songs chart , while the latter also peaked at No. 2 on the Billboard Bubbling Under Hot 100 Singles chart ; " If We 've Ever Needed You " peaked at No. 5 on the Christian Songs chart . Two album cuts , " To Know You " and " Joyful , Joyful " , also appeared on the Christian Songs chart , peaking at numbers 27 and 3 , respectively . 
- 
- = = Background and songwriting = = 
- 
- Casting Crowns ' lead vocalist Mark Hall has stated that the band 's songs " have always come from our ministry in the church . They start as messages on Wednesday night , things we 're teaching our teenagers and their families " ; Until the Whole World Hears retains that same formula . Hall also stated that the musical sound of their songs is written so as not to conflict or <unk> from the lyrics , comparing the musical elements of his songs to a plate that the ' meat ' , the lyrics , are served on . A philosophy that " fuels " the album is the idea of putting " faith in action " ; Hall stated that he wanted <unk> to become more active in their faith , and to " get out of their pews and get involved in what God is doing " . " Always Enough " was written when a member of Hall 's church was killed in Afghanistan ; the band was unable to attend his funeral , as they were on the other side of the United States and couldn 't cancel their tour dates . Another song on the album , the title track , was inspired by the Biblical character of John the Baptist . Several songs on the album are <unk> of hymns , which Hall enjoyed experimenting with ; Hall retained the lyrics of the songs while reworking their melodies . 
- Until the Whole World Hears was produced by Mark A. Miller ; its executive producer was Terry <unk> . It was recorded by Sam Hewitt , Michael Hewitt , and Dale Oliver at Zoo Studio in Franklin , Tennessee ; the string instrument tracks on the title track and " Always Enough " were recorded by Bobby Shin at Little Big Sound Studio in Nashville , Tennessee , while the string instrument tracks on " If We 've Ever Needed You " and " Joyful , Joyful " were recorded by John Painter and Leslie Richter at Ocean Way in Nashville . <unk> vocals on the title track and " Blessed Redeemer " were recorded by Carter <unk> , Darren Hughes and Billy Lord at Eagle 's Landing Baptist Church in McDonough , Georgia . Digital editing was handled by Michael Hewitt , while mixing was handled by Sam Hewitt . The album was mastered by Andrew <unk> , Shelly Anderson , <unk> <unk> and Daniel <unk> at Georgetown Masters in Nashville . 
- 
- = = Composition = = 
- 
- The overall sound of Until the Whole World Hears has been described as ' pure American rock ' , ' soft adult contemporary ' , and ' CCM ' . Robert Ham of Christianity Today regarded the album as comparable to the sound of rock bands Creed and Nickelback . One critic observed that most of the songs on the album " start off with chords <unk> out on a piano or <unk> slowly on a guitar , letting the song build slowly toward a massive wave of sound " . The title track has been described as having a " real rock vibe " infused by electric guitar riffs , while " Shadow of Your Wings " has been described as an " <unk> rock @-@ n @-@ roll jam " . " Joyful , Joyful " is driven by a " <unk> " and " driving " string section that " calls to mind Coldplay 's ' Viva la Vida ' " . " Mercy " and " Blessed Redeemer " feature female @-@ fronted vocals , the former sung by Megan Garrett and the latter sung by <unk> <unk> ; on " At Your Feet " , Hector <unk> and Juan <unk> joining Mark Hall on vocals . 
- Every track on Until the Whole World Hears features references to Christian subjects such as God , Jesus , and salvation . " Joyful , Joyful " , " Blessed Redeemer " , and " Glorious Day ( Living He Loved Me ) " were adopted from classic hymns . " Holy One " and " Shadow of Your Wings " are taken almost word for word from the Book of <unk> . " If We 've Ever Needed You " and " Always Enough " are " darker inspirational anthems " , while other songs explore themes such as repentance and forgiveness . 
- 
- = = Release and sales = = 
- 
- Until the Whole World Hears had first @-@ week sales of 167 @,@ 000 copies , Casting Crowns ' best sales week to date ; the album 's high first @-@ week sales enabled a No. 4 debut on the Billboard 200 . It also debuted at No. 1 on the Billboard Christian Albums chart and at No. 12 on the Billboard Digital Albums chart . The Recording Industry Association of America ( RIAA ) certified the album Platinum , signifying shipments of over 1 @,@ 000 @,@ 000 copies . By April 2011 Until the Whole World Hears had sold over 800 @,@ 000 copies , and as of March 2014 the album has sold 1 @.@ 1 million copies . Billboard magazine ranked Until the Whole World Hears as the best @-@ selling Christian album and the 37th best @-@ selling album overall of 2010 . It also ranked as the 3rd best @-@ selling Christian album and the <unk> best @-@ selling album overall of 2011 . 
- Three singles were released from Until the Whole World Hears . The title track was released as the album 's lead single and peaked at No. 1 on the Billboard Christian Songs chart and at No. 23 on the Billboard Heatseekers Songs chart . " If We 've Ever Needed You " , the second single released from the album , peaked at No. 5 on the Christian Songs chart . " Glorious Day ( Living He Loved Me ) " was released as the third single off the album and peaked atop the Christian Songs chart . It also peaked at No. 2 on the Bubbling Under Hot 100 Singles chart and at No. 20 on the Heatseekers Songs chart . Two other songs off the album , " To Know You " and " Joyful , Joyful " , appeared on the Christian Songs chart ; they peaked at numbers 27 and 3 , respectively . 
- 
- = = Reception = = 
- 
- 
- = = = Critical reception = = = 
- 
- Until the Whole World Hears met with an overall mixed to positive response from critics . Several critics regarded the musical aspects of the album as mediocre or sub @-@ par , while others praised the album 's lyrical content . <unk> Johnson of Allmusic gave the album four out of five stars and described it as a " powerful worship experience " , but also stated that " some might wonder how a little more variety would sound from such experienced professionals ... the band 's core sound continues to land in the AC cross hairs " . Andrew Greer of CCM Magazine commented that the album " ups the musical <unk> a bit , with some borderline poetic verses and interesting musical riffs " , but also commented that " many of these tracks still suffer from the ' Crowns <unk> <unk> , ' using lyrical Christian @-@ <unk> to produce <unk> rhymes that seem hard @-@ pressed to <unk> a ready @-@ to @-@ worship crowd " . Robert Ham of Christianity Today criticized the album for using what he deemed as a repetitive musical formula , but also praised the song " Joyful , Joyful " , which he compared to Coldplay 's song " Viva la Vida " , as well as Megan 's Garrett 's vocals on " Mercy " . He concluded that Until the Whole World Hears " feels like a step backward creatively " . At Cross Rhythms , Tony Cummings rated the album seven out of ten squares , saying " this album is a little disappointing considering some of the <unk> that <unk> [ sic ] it . " 
- <unk> <unk> of Gospel Music <unk> said the album " follows successfully in the footsteps of its predecessors " and " should further solidify Casting Crowns as a staple artist for Christian music fans everywhere " . Roger Gelwicks of Jesus Freak <unk> gave Until the Whole World Hears two out of five stars , opining that " Casting Crowns has come down to a whole new low , such that it could be their most unremarkable record to date " and that " it is almost insulting to the listener to believe that one is supposed to find this project profound or <unk> " . Paul <unk> of <unk> In stated that " With appropriate <unk> to the many talented and successful acts that <unk> the secular and spiritual with their tunes , it 's great to have a band that speaks to the Christian heart with such power and <unk> — without apology " . 
- 
- = = = Awards and accolades = = = 
- 
- Until the Whole World Hears was nominated for the Pop / Contemporary Album of the Year at the 42nd GMA Dove Awards Two of its three singles also received award nominations ; its title track was nominated for Pop / Contemporary Record Song of the Year at the 41st GMA Dove Awards , and " Glorious Day ( Living He Loved Me ) " has been nominated for Song of the Year and Worship Song of the Year at the 43rd GMA Dove Awards . It was nominated for Top Christian Album at the 2012 Billboard Music Awards . 
- 
- = = Track listing = = 
- 
- ( Credits lifted from the album liner notes ) 
- 
- = = Personnel = = 
- 
- ( Credits lifted from the album liner notes ) 
- 
- = = Charts and certifications = = 
- 
- 
- = = = Album charts = = = 
- 
- 
- = = = Song charts = = = 
- 
- 
- = = = Certifications = = = 
- 
- | <unk> 
- 
- 
- = Ceres ( dwarf planet ) = 
- 
- Ceres ( / <unk> / ; minor @-@ planet designation : 1 Ceres ) is the largest object in the asteroid belt that lies between the orbits of Mars and Jupiter . Its diameter is approximately <unk> kilometers ( <unk> miles ) , making it the largest of the minor planets within the orbit of Neptune . The thirty @-@ third @-@ largest known body in the Solar System , it is the only one identified orbiting entirely within the orbit of Neptune that is a dwarf planet . <unk> of rock and ice , Ceres is estimated to comprise approximately one third of the mass of the entire asteroid belt . Ceres is the only object in the asteroid belt known to be rounded by its own gravity . From Earth , the apparent magnitude of Ceres ranges from 6 @.@ 7 to 9 @.@ 3 , and hence even at its brightest , it is too dim to be seen with the naked eye , except under extremely dark skies . 
- Ceres was the first asteroid discovered , by Giuseppe Piazzi at Palermo on 1 January 1801 . It was originally considered a planet , but was reclassified as an asteroid in the 1850s when many other objects in similar orbits were discovered . 
- Ceres appears to be differentiated into a rocky core and icy mantle , and may have a remnant internal ocean of liquid water under the layer of ice . The surface is probably a mixture of water ice and various hydrated minerals such as carbonates and clay . In January 2014 , emissions of water vapor were detected from several regions of Ceres . This was unexpected , because large bodies in the asteroid belt do not typically emit vapor , a hallmark of comets . 
- The robotic NASA spacecraft Dawn entered orbit around Ceres on 6 March 2015 . Pictures with a resolution previously <unk> were taken during imaging sessions starting in January 2015 as Dawn approached Ceres , showing a <unk> surface . Two distinct bright spots ( or high @-@ albedo features ) inside a crater ( different from the bright spots observed in earlier Hubble images ) were seen in a 19 February 2015 image , leading to speculation about a possible <unk> origin or outgassing . On 3 March 2015 , a NASA spokesperson said the spots are consistent with highly reflective materials containing ice or salts , but that <unk> is unlikely . On 11 May 2015 , NASA released a higher @-@ resolution image showing that , instead of one or two spots , there are actually several . On 9 December 2015 , NASA scientists reported that the bright spots on Ceres may be related to a type of salt , particularly a form of <unk> containing magnesium sulfate <unk> ( <unk> · <unk> ) ; the spots were also found to be associated with ammonia @-@ rich <unk> . In June 2016 , near @-@ infrared spectra of these bright areas were found to be consistent with a large amount of sodium carbonate , ( Na 
- <unk> 
- 3 ) , implying that recent geologic activity was probably involved in the creation of the bright spots . 
- In October 2015 , NASA released a true color portrait of Ceres made by Dawn . 
- 
- = = History = = 
- 
- 
- = = = Discovery = = = 
- 
- Johann <unk> Bode , in 1772 , first suggested that an <unk> planet could exist between the orbits of Mars and Jupiter . Kepler had already noticed the gap between Mars and Jupiter in 1596 . Bode based his idea on the Titius – Bode law — a now @-@ discredited hypothesis Johann Daniel Titius first proposed in 1766 — observing that there was a regular pattern in the semi @-@ major axes of the orbits of known planets , marred only by the large gap between Mars and Jupiter . The pattern predicted that the missing planet ought to have an orbit with a semi @-@ major axis near 2 @.@ 8 astronomical units ( AU ) . William Herschel 's discovery of <unk> in <unk> near the predicted distance for the next body beyond Saturn increased faith in the law of Titius and Bode , and in 1800 , a group headed by Franz <unk> von Zach , editor of the <unk> <unk> , sent requests to twenty @-@ four experienced astronomers ( dubbed the " celestial police " ) , asking that they combine their efforts and begin a methodical search for the expected planet . Although they did not discover Ceres , they later found several large asteroids . 
- One of the astronomers selected for the search was Giuseppe Piazzi , a Catholic priest at the Academy of Palermo , Sicily . Before receiving his invitation to join the group , Piazzi discovered Ceres on 1 January 1801 . He was searching for " the 87th [ star ] of the <unk> of the <unk> stars of Mr la <unk> " , but found that " it was preceded by another " . Instead of a star , Piazzi had found a moving star @-@ like object , which he first thought was a comet . Piazzi observed Ceres a total of 24 times , the final time on 11 February 1801 , when illness interrupted his observations . He announced his discovery on 24 January 1801 in letters to only two fellow astronomers , his compatriot <unk> <unk> of Milan and Bode of Berlin . He reported it as a comet but " since its movement is so slow and rather uniform , it has occurred to me several times that it might be something better than a comet " . In April , Piazzi sent his complete observations to <unk> , Bode , and Jérôme Lalande in Paris . The information was published in the September 1801 issue of the <unk> <unk> . 
- By this time , the apparent position of Ceres had changed ( mostly due to Earth 's orbital motion ) , and was too close to the Sun 's <unk> for other astronomers to confirm Piazzi 's observations . Toward the end of the year , Ceres should have been visible again , but after such a long time it was difficult to predict its exact position . To recover Ceres , Carl Friedrich <unk> , then 24 years old , developed an efficient method of orbit determination . In only a few weeks , he predicted the path of Ceres and sent his results to von Zach . On 31 December 1801 , von Zach and Heinrich W. M. <unk> found Ceres near the predicted position and thus recovered it . 
- The early observers were only able to calculate the size of Ceres to within an order of magnitude . Herschel underestimated its diameter as 260 km in 1802 , whereas in 1811 Johann <unk> Schröter <unk> it as 2 @,@ <unk> km . 
- 
- = = = Name = = = 
- 
- Piazzi originally suggested the name <unk> <unk> for his discovery , after the goddess Ceres ( Roman goddess of agriculture , <unk> in Italian , who was believed to have originated in Sicily and whose oldest temple was there ) and King Ferdinand of Sicily . " <unk> " , however , was not acceptable to other nations and was dropped . Ceres was called <unk> for a short time in Germany . In Greece , it is called <unk> ( <unk> ) , after the Greek equivalent of the Roman <unk> ; in English , that name is used for the asteroid 1108 <unk> . 
- The regular <unk> forms of the name are Cererian and <unk> , derived from the Latin genitive <unk> , but <unk> is occasionally seen for the goddess ( as in the sickle @-@ shaped <unk> Lake ) , as is the shorter form <unk> . 
- The old astronomical symbol of Ceres is a sickle , 〈 <unk> 〉 ( ) , similar to Venus ' symbol 〈 <unk> 〉 but with a break in the circle . It has a variant 〈 〉 , reversed under the influence of the initial letter ' C ' of ' Ceres ' . These were later replaced with the generic asteroid symbol of a numbered disk , 〈 <unk> 〉 . 
- <unk> , a rare @-@ earth element discovered in 1803 , was named after Ceres . In the same year another element was also initially named after Ceres , but when cerium was named , its <unk> changed the name to <unk> , after the second asteroid , 2 Pallas . 
- 
- = = = Classification = = = 
- 
- The categorization of Ceres has changed more than once and has been the subject of some disagreement . Johann <unk> Bode believed Ceres to be the " missing planet " he had proposed to exist between Mars and Jupiter , at a distance of <unk> million km ( 2 @.@ 8 AU ) from the Sun . Ceres was assigned a planetary symbol , and remained listed as a planet in astronomy books and tables ( along with 2 Pallas , 3 Juno , and 4 Vesta ) for half a century . 
- As other objects were discovered in the neighborhood of Ceres , it was realized that Ceres represented the first of a new class of objects . In 1802 , with the discovery of 2 Pallas , William Herschel coined the term asteroid ( " star @-@ like " ) for these bodies , writing that " they resemble small stars so much as hardly to be distinguished from them , even by very good telescopes " . As the first such body to be discovered , Ceres was given the designation 1 Ceres under the modern system of minor @-@ planet designations . By the 1860s , the existence of a fundamental difference between asteroids such as Ceres and the major planets was widely accepted , though a precise definition of " planet " was never formulated . 
- The 2006 debate surrounding Pluto and what constitutes a planet led to Ceres being considered for <unk> as a planet . A proposal before the International Astronomical Union for the definition of a planet would have defined a planet as " a celestial body that ( a ) has sufficient mass for its self @-@ gravity to overcome rigid @-@ body forces so that it assumes a hydrostatic equilibrium ( nearly round ) shape , and ( b ) is in orbit around a star , and is neither a star nor a satellite of a planet " . Had this resolution been adopted , it would have made Ceres the fifth planet in order from the Sun . This never happened , however , and on 24 August 2006 a modified definition was adopted , carrying the additional requirement that a planet must have " cleared the neighborhood around its orbit " . By this definition , Ceres is not a planet because it does not dominate its orbit , sharing it as it does with the thousands of other asteroids in the asteroid belt and constituting only about a third of the mass of the belt . <unk> that met the first proposed definition but not the second , such as Ceres , were instead classified as dwarf planets . 
- Ceres is the largest object in the asteroid belt . It is sometimes assumed that Ceres has been reclassified as a dwarf planet , and that it is therefore no longer considered an asteroid . For example , a news update at <unk> spoke of " Pallas , the largest asteroid , and Ceres , the dwarf planet formerly classified as an asteroid " , whereas an IAU question @-@ and @-@ answer posting states , " Ceres is ( or now we can say it was ) the largest asteroid " , though it then speaks of " other asteroids " crossing Ceres ' path and otherwise implies that Ceres is still considered an asteroid . The Minor Planet Center notes that such bodies may have dual designations . The 2006 IAU decision that classified Ceres as a dwarf planet never addressed whether it is or is not an asteroid . Indeed , the IAU has never defined the word ' asteroid ' at all , having preferred the term ' minor planet ' until 2006 , and preferring the terms ' small Solar System body ' and ' dwarf planet ' after 2006 . Lang ( 2011 ) comments " the [ IAU has ] added a new designation to Ceres , classifying it as a dwarf planet . ... By [ its ] definition , <unk> , <unk> , <unk> and Pluto , as well as the largest asteroid , 1 Ceres , are all dwarf planets " , and describes it elsewhere as " the dwarf planet – asteroid 1 Ceres " . NASA continues to refer to Ceres as an asteroid , as do various academic textbooks . 
- 
- = = <unk> = = 
- 
- Ceres follows an orbit between Mars and Jupiter , within the asteroid belt , with a period of 4 @.@ 6 Earth years . The orbit is moderately inclined ( i 
- = 10 @.@ 6 ° compared to 7 ° for Mercury and 17 ° for Pluto ) and moderately eccentric ( e = 
- 0 @.@ 08 compared to 0 @.@ 09 for Mars ) . 
- The diagram illustrates the orbits of Ceres ( blue ) and several planets ( white and gray ) . The segments of orbits below the ecliptic are plotted in darker colors , and the orange plus sign is the Sun 's location . The top left diagram is a polar view that shows the location of Ceres in the gap between Mars and Jupiter . The top right is a close @-@ up demonstrating the locations of the <unk> ( q ) and <unk> ( Q ) of Ceres and Mars . In this diagram ( but not in general ) , the perihelion of Mars is on the opposite side of the Sun from those of Ceres and several of the large main @-@ belt asteroids , including 2 Pallas and 10 <unk> . The bottom diagram is a side view showing the inclination of the orbit of Ceres compared to the orbits of Mars and Jupiter . 
- Ceres was once thought to be a member of an asteroid family . The asteroids of this family share similar proper orbital elements , which may indicate a common origin through an asteroid collision some time in the past . Ceres was later found to have spectral properties different from other members of the family , which is now called the Gefion family after the next @-@ lowest @-@ numbered family member , <unk> Gefion . Ceres appears to be merely an <unk> in the Gefion family , <unk> having similar orbital elements but not a common origin . 
- 
- = = = <unk> = = = 
- 
- Ceres is in a near @-@ 1 : 1 mean @-@ motion orbital resonance with Pallas ( their proper orbital periods differ by 0 @.@ 2 % ) . However , a true resonance between the two would be unlikely ; due to their small masses relative to their large <unk> , such relationships among asteroids are very rare . Nevertheless , Ceres is able to capture other asteroids into temporary 1 : 1 <unk> orbital relationships ( making them temporary <unk> ) for periods up to 2 million years or more ; fifty such objects have been identified . 
- 
- = = = Transits of planets from Ceres = = = 
- 
- Mercury , Venus , Earth , and Mars can all appear to cross the Sun , or transit it , from a <unk> point on Ceres . The most common transits are those of Mercury , which usually happen every few years , most recently in 2006 and 2010 . The most recent transit of Venus was in 1953 , and the next will be in <unk> ; the corresponding dates are 1814 and <unk> for transits of Earth , and 767 and <unk> for transits of Mars . 
- 
- = = <unk> and axial tilt = = 
- 
- The rotation period of Ceres ( the Cererian day ) is 9 hours and 4 minutes . It has a small axial tilt of 4 ° . This is nevertheless large enough for Ceres 's polar regions to contain permanently shadowed craters that are expected to act as cold traps and accumulate water ice over time , similar to the situation on the Moon and Mercury . About 0 @.@ 14 % of water molecules released from the surface are expected to end up in the traps , hopping an average of 3 times before escaping or being trapped . 
- 
- = = Geology = = 
- 
- Ceres has a mass of 9 @.@ 39 × <unk> kg as determined from the Dawn spacecraft . With this mass Ceres comprises approximately a third of the estimated total 3 @.@ 0 ± 0 @.@ 2 × <unk> kg mass of the asteroid belt , which is in turn approximately 4 % of the mass of the Moon . Ceres is massive enough to give it a nearly spherical , equilibrium shape . Among Solar System bodies , Ceres is intermediate in size between the smaller Vesta and the larger <unk> . Its surface area is approximately the same as the land area of India or Argentina . 
- 
- = = = Surface = = = 
- 
- The surface composition of Ceres is broadly similar to that of C @-@ type asteroids . Some differences do exist . The ubiquitous features in Ceres ' IR spectrum are those of hydrated materials , which indicate the presence of significant amounts of water in its interior . Other possible surface constituents include iron @-@ rich clay minerals ( <unk> ) and carbonate minerals ( <unk> and <unk> ) , which are common minerals in <unk> <unk> meteorites . The spectral features of carbonates and clay minerals are usually absent in the spectra of other C @-@ type asteroids . Sometimes Ceres is classified as a G @-@ type asteroid . 
- Ceres ' surface is relatively warm . The maximum temperature with the Sun overhead was estimated from measurements to be 235 K ( approximately − 38 ° C , − 36 ° F ) on 5 May 1991 . Ice is unstable at this temperature . Material left behind by the sublimation of surface ice could explain the dark surface of Ceres compared to the icy moons of the outer Solar System . 
- 
- = = = = Observations prior to Dawn = = = = 
- 
- Prior to the Dawn mission , only a few surface features had been unambiguously detected on Ceres . High @-@ resolution ultraviolet Hubble Space Telescope images taken in 1995 showed a dark spot on its surface , which was nicknamed " Piazzi " in honor of the <unk> of Ceres . This was thought to be a crater . Later near @-@ infrared images with a higher resolution taken over a whole rotation with the Keck telescope using adaptive optics showed several bright and dark features moving with Ceres ' rotation . Two dark features had circular shapes and were presumed to be craters ; one of them was observed to have a bright central region , whereas another was identified as the " Piazzi " feature . Visible @-@ light Hubble Space Telescope images of a full rotation taken in 2003 and 2004 showed eleven recognizable surface features , the natures of which were then undetermined . One of these features corresponds to the " Piazzi " feature observed earlier . 
- These last observations indicated that the north pole of Ceres pointed in the direction of right ascension 19 h 24 <unk> ( 291 ° ) , declination + 59 ° , in the constellation <unk> , resulting in an axial tilt of approximately 3 ° . Dawn later determined that the north polar axis actually points at right ascension 19 h 25 m 40 @.@ 3 s ( 291 @.@ <unk> ° ) , declination + 66 ° 45 ' 50 " ( about 1 @.@ 5 degrees from Delta <unk> ) , which means an axial tilt of 4 ° . 
- 
- = = = = Observations by Dawn = = = = 
- 
- Dawn revealed a large number of craters with low relief , indicating that they lie over a relatively soft surface , probably of water ice . One crater , with extremely low relief , is 270 km ( 170 mi ) in diameter , reminiscent of large , flat craters on <unk> and <unk> . An unexpectedly large number of Cererian craters have central pits , and many have central peaks . Several bright spots have been observed by Dawn , the brightest spot ( " Spot 5 " ) located in the middle of an 80 @-@ kilometer ( 50 mi ) crater called <unk> . From images taken of Ceres on 4 May 2015 , the secondary bright spot was revealed to actually be a group of scattered bright areas , possibly as many as ten . These bright features have an albedo of approximately 40 % that are caused by a substance on the surface , possibly ice or salts , reflecting sunlight . A haze periodically appears above Spot 5 , the best known bright spot , supporting the hypothesis that some sort of outgassing or <unk> ice formed the bright spots . In March 2016 , Dawn found definitive evidence of water molecules on the surface of Ceres at <unk> crater . <unk> states : " This water could be bound up in minerals or , alternatively , it could take the form of ice . " 
- On 9 December 2015 , NASA scientists reported that the bright spots on Ceres may be related to a type of salt , particularly a form of <unk> containing magnesium sulfate <unk> ( <unk> · <unk> ) ; the spots were also found to be associated with ammonia @-@ rich <unk> . Another team thinks the salts are sodium carbonate . 
- 
- = = = Internal structure = = = 
- 
- Ceres ' <unk> is consistent with a differentiated body , a rocky core <unk> with an icy mantle . This 100 @-@ kilometer @-@ thick mantle ( 23 % – 28 % of Ceres by mass ; 50 % by volume ) contains up to 200 million cubic kilometers of water , which would be more than the amount of fresh water on Earth . This result is supported by the observations made by the Keck telescope in 2002 and by evolutionary modeling . Also , some characteristics of its surface and history ( such as its distance from the Sun , which weakened solar radiation enough to allow some fairly low @-@ freezing @-@ point components to be incorporated during its formation ) , point to the presence of volatile materials in the interior of Ceres . It has been suggested that a remnant layer of liquid water may have survived to the present under a layer of ice . Measurements taken by Dawn confirm that Ceres is both differentiated and has a shape consistent with hydrostatic equilibrium , which makes Ceres the smallest object confirmed to be in hydrostatic equilibrium , being 600 km smaller and less than half the mass of Saturn 's moon Rhea , the next smallest such object . <unk> has suggested Ceres could have a small metallic core from partial differentiation of its rocky fraction . 
- 
- = = <unk> = = 
- 
- There are indications that Ceres may have a tenuous water vapor atmosphere outgassing from water ice on the surface . 
- Surface water ice is unstable at distances less than 5 AU from the Sun , so it is expected to sublime if it is exposed directly to solar radiation . Water ice can migrate from the deep layers of Ceres to the surface , but escapes in a very short time . As a result , it is difficult to detect water <unk> . Water escaping from polar regions of Ceres was possibly observed in the early 1990s but this has not been unambiguously demonstrated . It may be possible to detect escaping water from the surroundings of a fresh impact crater or from cracks in the subsurface layers of Ceres . <unk> observations by the <unk> spacecraft detected statistically significant amounts of hydroxide ions near Ceres ' north pole , which is a product of water vapor <unk> by ultraviolet solar radiation . 
- In early 2014 , using data from the Herschel Space Observatory , it was discovered that there are several localized ( not more than 60 km in diameter ) mid @-@ latitude sources of water vapor on Ceres , which each give off approximately <unk> molecules ( or 3 kg ) of water per second . Two potential source regions , designated Piazzi ( 123 ° E , 21 ° N ) and Region A ( 231 ° E , 23 ° N ) , have been visualized in the near infrared as dark areas ( Region A also has a bright center ) by the W. M. Keck Observatory . Possible mechanisms for the vapor release are sublimation from approximately 0 @.@ 6 km2 of exposed surface ice , or <unk> eruptions resulting from <unk> internal heat or from <unk> of a subsurface ocean due to growth of an <unk> layer of ice . Surface sublimation would be expected to be lower when Ceres is farther from the Sun in its orbit , whereas internally powered emissions should not be affected by its orbital position . The limited data available are more consistent with <unk> @-@ style sublimation . 
- 
- = = Origin and evolution = = 
- 
- Ceres is possibly a surviving <unk> ( planetary embryo ) , which formed 4 @.@ 57 billion years ago in the asteroid belt . Although the majority of inner Solar System <unk> ( including all <unk> to Mars @-@ sized bodies ) either merged with other <unk> to form terrestrial planets or were ejected from the Solar System by Jupiter , Ceres is thought to have survived relatively intact . An alternative theory proposes that Ceres formed in the <unk> belt and later migrated to the asteroid belt . The discovery of ammonia salts in <unk> crater supports an origin in the outer Solar System . Another possible <unk> , Vesta , is less than half the size of Ceres ; it suffered a major impact after <unk> , losing ~ 1 % of its mass . 
- The geological evolution of Ceres was dependent on the heat sources available during and after its formation : friction from <unk> <unk> , and decay of various <unk> ( possibly including short @-@ lived isotopes such as the <unk> <unk> aluminium @-@ 26 ) . These are thought to have been sufficient to allow Ceres to differentiate into a rocky core and icy mantle soon after its formation . This process may have caused resurfacing by water <unk> and <unk> , <unk> older geological features . Due to its small size , Ceres would have cooled early in its existence , causing all geological resurfacing processes to cease . Any ice on the surface would have gradually <unk> , leaving behind various hydrated minerals like clay minerals and carbonates . 
- Today , Ceres appears to be a <unk> inactive body , with a surface sculpted only by impacts . The presence of significant amounts of water ice in its composition raises the possibility that Ceres has or had a layer of liquid water in its interior . This hypothetical layer is often called an ocean . If such a layer of liquid water exists , it is hypothesized to be located between the rocky core and ice mantle like that of the theorized ocean on Europa . The existence of an ocean is more likely if <unk> ( i.e. salts ) , ammonia , sulfuric acid or other <unk> compounds are dissolved in the water . 
- 
- = = Potential <unk> = = 
- 
- Although not as actively discussed as a potential home for <unk> extraterrestrial life as Mars , Titan , Europa or <unk> , there is evidence that Ceres ' icy mantle was once a watery subterranean ocean , and that has led to speculations that life could have existed there , and that hypothesized <unk> bearing microorganisms could have come from Ceres to Earth . 
- 
- = = Observation and exploration = = 
- 
- 
- = = = Observation = = = 
- 
- When Ceres has an opposition near the perihelion , it can reach a visual magnitude of + 6 @.@ 7 . This is generally regarded as too dim to be seen with the naked eye , but under exceptional viewing conditions a very sharp @-@ sighted person may be able to see it . Ceres was at its brightest ( 6 @.@ 73 ) on 18 December 2012 . The only other asteroids that can reach a similarly bright magnitude are 4 Vesta , and , during rare <unk> near perihelion , 2 Pallas and 7 <unk> . At a conjunction Ceres has a magnitude of around + 9 @.@ 3 , which corresponds to the faintest objects visible with 10 × 50 <unk> . It can thus be seen with <unk> whenever it is above the horizon of a fully dark sky . 
- Some notable observations and milestones for Ceres include : 
- 1984 November 13 : An <unk> of a star by Ceres observed in Mexico , Florida and across the Caribbean . 
- 1995 June 25 : <unk> Hubble Space Telescope images with 50 @-@ kilometer resolution . 
- 2002 : <unk> images with 30 km resolution taken with the Keck telescope using adaptive optics . 
- 2003 and 2004 : Visible light images with 30 km resolution ( the best prior to the Dawn mission ) taken using Hubble . 
- 2012 December 22 : Ceres <unk> the star <unk> 1865 @-@ <unk> @-@ 1 over parts of Japan , Russia , and China . Ceres ' brightness was magnitude 6 @.@ 9 and the star , 12 @.@ 2 . 
- 2014 : Ceres was found to have an atmosphere with water vapor , confirmed by the Herschel space telescope . 
- 2015 : The NASA Dawn spacecraft approached and orbited Ceres , sending detailed images and scientific data back to Earth . 
- 
- = = = Exploration = = = 
- 
- In 1981 , a proposal for an asteroid mission was submitted to the European Space Agency ( ESA ) . Named the <unk> Gravity Optical and Radar Analysis ( <unk> ) , this spacecraft was to launch some time in 1990 – 1994 and perform two flybys of large asteroids . The preferred target for this mission was Vesta . <unk> would reach the asteroid belt either by a gravitational <unk> trajectory past Mars or by means of a small ion engine . However , the proposal was refused by ESA . A joint NASA – ESA asteroid mission was then drawn up for a Multiple <unk> Orbiter with Solar Electric <unk> ( <unk> ) , with one of the mission <unk> including an orbit of Vesta . NASA indicated they were not interested in an asteroid mission . Instead , ESA set up a technological study of a spacecraft with an ion drive . Other missions to the asteroid belt were proposed in the 1980s by France , Germany , Italy , and the United States , but none were approved . Exploration of Ceres by fly @-@ by and impacting <unk> was the second main target of the second plan of the <unk> Soviet Vesta mission , developed in cooperation with European countries for realisation in 1991 – 1994 but canceled due to the Soviet Union disbanding . 
- In the early 1990s , NASA initiated the Discovery Program , which was intended to be a series of low @-@ cost scientific missions . In 1996 , the program 's study team recommended as a high priority a mission to explore the asteroid belt using a spacecraft with an ion engine . Funding for this program remained problematic for several years , but by 2004 the Dawn vehicle had passed its critical design review . 
- It was launched on 27 September 2007 , as the space mission to make the first visits to both Vesta and Ceres . On 3 May 2011 , Dawn acquired its first targeting image 1 @.@ 2 million kilometers from Vesta . After orbiting Vesta for 13 months , Dawn used its ion engine to depart for Ceres , with gravitational capture occurring on 6 March 2015 at a separation of 61 @,@ 000 km , four months prior to the New Horizons flyby of Pluto . 
- Dawn 's mission profile calls for it to study Ceres from a series of circular polar orbits at successively lower altitudes . It entered its first <unk> orbit ( " <unk> " ) around Ceres at an altitude of 13 @,@ 500 km on 23 April 2015 , staying for only approximately one orbit ( fifteen days ) . The spacecraft will subsequently reduce its orbital distance to 4 @,@ 400 km for its second <unk> orbit ( " survey " ) for three weeks , then down to 1 @,@ 470 km ( " <unk> " ) for two months and then down to its final orbit at 375 km ( " <unk> " ) for at least three months . The spacecraft instrumentation includes a framing camera , a visual and infrared <unk> , and a gamma @-@ ray and neutron detector . These instruments will examine Ceres ' shape and elemental composition . On 13 January 2015 , Dawn took the first images of Ceres at near @-@ Hubble resolution , revealing impact craters and a small high @-@ albedo spot on the surface , near the same location as that observed previously . Additional imaging sessions , at increasingly better resolution took place on 25 January , 4 , 12 , 19 , and 25 February , 1 March , and 10 and 15 April . 
- Dawn 's arrival in a stable orbit around Ceres was delayed after , close to reaching Ceres , it was hit by a cosmic ray , making it take another , longer route around Ceres in back , instead of a direct spiral towards it . 
- The Chinese Space Agency is designing a sample <unk> mission from Ceres that would take place during the <unk> . 
- 
- = = Maps = = 
- 
- 
- = = = Map of <unk> = = = 
- 
- The following <unk> of the dwarf planet Ceres is divided into 15 <unk> . They are named after the first craters whose names the IAU approved in July 2015 . The map image ( s ) were taken by the Dawn space probe . 
- 
- = = Gallery = = 
- 
- 
- = = = True @-@ color images = = = 
- 
- 
- = = = <unk> = = = 
- 
- 
- 
- = Skye = 
- 
- Skye , or the Isle of Skye ( / <unk> / ; Scottish Gaelic : An t @-@ Eilean Sgitheanach or Eilean a ' Cheò ) , is the largest and most northerly major island in the Inner Hebrides of Scotland . The island 's peninsulas radiate from a mountainous centre dominated by the <unk> , the rocky slopes of which provide some of the most dramatic mountain scenery in the country . Although it has been suggested that the Gaelic Sgitheanach describes a winged shape there is no definitive agreement as to the name 's origins . 
- The island has been occupied since the Mesolithic period and its history includes a time of Norse rule and a long period of domination by Clan MacLeod and Clan Donald . The 18th @-@ century <unk> <unk> led to the breaking up of the clan system and subsequent Clearances that replaced entire communities with sheep farms , some of which also involved forced <unk> to distant lands . <unk> numbers declined from over 20 @,@ 000 in the early 19th century to just under 9 @,@ 000 by the closing decade of the 20th century . Skye 's population increased by 4 per cent between 1991 and 2001 . About a third of the residents were Gaelic speakers in 2001 , and although their numbers are in decline this aspect of island culture remains important . 
- The main industries are tourism , agriculture , fishing and forestry . Skye is part of the Highland Council local government area . The island 's largest settlement is Portree , known for its picturesque harbour . There are links to various nearby islands by ferry and , since 1995 , to the mainland by a road bridge . The climate is mild , wet and <unk> . The abundant wildlife includes the golden eagle , red deer and Atlantic salmon . The local flora is dominated by <unk> moor , and there are nationally important invertebrate populations on the surrounding sea bed . Skye has provided the locations for various novels and feature films and is celebrated in poetry and song . 
- 
- = = Etymology = = 
- 
- The first written references to the island are Roman sources such as the <unk> <unk> , which refers to <unk> and <unk> , which can be found on a map by Ptolemy . One possible derivation comes from <unk> , an early Celtic word for winged , which may describe how the island 's peninsulas radiate out from a mountainous centre . Subsequent <unk> , <unk> and English @-@ speaking peoples have influenced the history of Skye ; the relationships between their names for the island are not straightforward . Various etymologies have been proposed , such as the " winged isle " or " the notched isle " but no definitive solution has been found to date and the <unk> may be from an earlier , non @-@ Gaelic language . 
- In the Norse <unk> Skye is called <unk> , for example in the <unk> saga <unk> and a <unk> poem in the <unk> from c . 1230 contains a line that translates as " the hunger battle @-@ birds were filled in Skye with blood of <unk> killed " . The island was also referred to by the Norse as <unk> ( <unk> isle ) , <unk> or <unk> ( isle of cloud ) . The traditional Gaelic name is An t @-@ Eilean Sgitheanach ( the island of Skye ) , An t @-@ Eilean <unk> being a more recent and less common spelling . In 1549 Donald Munro , High Dean of the Isles , wrote of " Sky " : " This Ile is <unk> <unk> <unk> in Irish , that is to say in <unk> the <unk> Ile , be reason it has <unk> <unk> and <unk> <unk> <unk> <unk> it , throw the dividing of <unk> <unk> <unk> . " but the meaning of this Gaelic name is unclear . 
- Eilean a ' Cheò , which means island of the mist ( a translation of the Norse name ) , is a poetic Gaelic name for the island . 
- 
- = = Geography = = 
- 
- At 1 @,@ 656 square kilometres ( 639 sq mi ) , Skye is the second @-@ largest island in Scotland after Lewis and Harris . The coastline of Skye is a series of peninsulas and bays radiating out from a centre dominated by the Cuillin hills ( Gaelic : An <unk> ) . Malcolm <unk> suggested that its shape " sticks out of the west coast of northern Scotland like a lobster 's claw ready to snap at the fish bone of Harris and Lewis " and W. H. Murray , commenting on its irregular coastline , stated that " Skye is sixty miles [ 100 km ] long , but what might be its breadth is beyond the ingenuity of man to state " . Martin Martin , a native of the island , reported on it at length in a 1703 publication . His geological observations included a note that : 
- There are <unk> black and white , resembling silver ore , near the village <unk> : there are likewise in the same place several stones , which in <unk> , shape , & c . , resemble <unk> , and many <unk> here afford <unk> stones of all colours . The <unk> near Loch @-@ <unk> has <unk> growing in it of different sizes and colours ; some are green on the outside , some are of a pale sky colour , and they all strike fire as well as flint : I have one of them by me , which for shape and <unk> is proper for a sword handle . Stones of a purple colour flow down the <unk> here after great rains . 
- The Black Cuillin , which are mainly composed of basalt and <unk> , include twelve <unk> and provide some of the most dramatic and challenging mountain terrain in Scotland . The ascent of <unk> a ' <unk> is one of the longest rock climbs in Britain and the <unk> <unk> is the only peak in Scotland that requires technical climbing skills to reach the summit . These hills make demands of the hill <unk> that exceed any others found in Scotland and a full traverse of the Cuillin ridge may take 15 – 20 hours . The Red Hills ( Gaelic : Am <unk> <unk> ) to the south are also known as the Red Cuillin . They are mainly composed of granite that has weathered into more rounded hills with many long <unk> slopes on their flanks . The highest point of these hills is <unk> , one of only two <unk> on Skye . 
- The northern peninsula of Trotternish is <unk> by basalt , which provides relatively rich soils and a variety of unusual rock features . The <unk> Rock is named after the <unk> @-@ like patterns in the 105 metres ( 344 ft ) cliffs . The <unk> is a spectacular series of rock pinnacles on the eastern side of the main spine of the peninsula and further south is the rock pillar of the Old Man of <unk> . 
- Beyond Loch <unk> to the west of Trotternish is the Waternish peninsula , which ends in <unk> Point 's double rock arch . Duirinish is separated from Waternish by Loch Dunvegan , which contains the island of <unk> . The <unk> is ringed by sea cliffs that reach 295 metres ( <unk> ft ) at <unk> Head . <unk> <unk> provides good arable land in the main valley . <unk> <unk> and <unk> and the island of <unk> lie between Duirinish and Minginish , which includes the narrower <unk> of <unk> and Glen <unk> and whose beaches are formed from black <unk> sands . Strathaird is a relatively small peninsula close to the Cuillin hills with only a few crofting communities , the island of <unk> lies offshore . The bedrock of Sleat in the south is <unk> sandstone , which produces poor soils and <unk> ground , although its lower elevations and relatively sheltered eastern shores enable a lush growth of <unk> and crops . The islands of Raasay , <unk> , <unk> and <unk> all lie to the north and east between Skye and the mainland . 
- 
- = = = Towns and villages = = = 
- 
- Portree in the north at the base of Trotternish is the largest settlement ( estimated population 2 @,@ 264 in 2011 ) and is the main service centre on the island . Broadford , the location of the island 's only airstrip , is on the east side of the island and Dunvegan in the north @-@ west is well known for its castle and the nearby Three <unk> restaurant . The 18th @-@ century Stein Inn on the Waternish coast is the oldest pub on Skye . <unk> is linked to Kyle of Lochalsh on the mainland by the Skye Bridge , which spans the <unk> of Loch <unk> . Uig , the port for ferries to the Outer Hebrides , is on the west of the Trotternish peninsula and <unk> is between Dunvegan and Portree . Much of the rest of the population lives in crofting townships scattered around the coastline . 
- 
- = = = Climate = = = 
- 
- The influence of the Atlantic Ocean and the Gulf Stream create a mild oceanic climate . Temperatures are generally cool , averaging 6 @.@ 5 ° C ( 43 @.@ 7 ° F ) in January and 15 @.@ 4 ° C ( 59 @.@ 7 ° F ) in July at <unk> in Trotternish . Snow seldom lies at sea level and <unk> are less frequent than on the mainland . Winds are a limiting factor for vegetation . South @-@ westerlies are the most common and speeds of 128 km / h ( 80 mph ) have been recorded . High winds are especially likely on the exposed coasts of Trotternish and Waternish . In common with most islands of the west coast of Scotland , rainfall is generally high at 1 @,@ 500 – 2 @,@ 000 mm ( 59 – 79 in ) per annum and the elevated Cuillin are wetter still . Variations can be considerable , with the north tending to be drier than the south . Broadford , for example , averages more than 2 @,@ <unk> mm ( 113 in ) of rain per annum . Trotternish typically has 200 hours of bright sunshine in May , the <unk> month . On 28 December 2015 , the temperature reached 15 ° C , beating the previous December record of 12 @.@ 9 ° C , set in 2013 . On 9 May 2016 , a temperature of 26 @.@ 7 ° C ( 80 @.@ 1 ° F ) was recorded at <unk> in the south @-@ east of the island . 
- 
- = = History = = 
- 
- 
- = = = <unk> = = = 
- 
- A Mesolithic hunter @-@ gatherer site dating to the 7th millennium BC at An Corran in <unk> is one of the oldest archaeological sites in Scotland . Its occupation is probably linked to that of the rock shelter at Sand , <unk> , on the mainland coast of <unk> Ross where tools made of a <unk> from An Corran have been found . <unk> of the area between the two shores of the Inner Sound and Sound of Raasay have revealed 33 sites with potentially Mesolithic deposits . Finds of <unk> <unk> on the foreshore at <unk> on the west coast of the island near Dunvegan also suggest Mesolithic occupation . These tools probably originate from the nearby island of Rum . 
- <unk> an <unk> , an uninhabited peninsula to the south of the Cuillin , has a variety of archaeological sites dating from the Neolithic onwards . There is a 2nd or 3rd millennium BC chambered <unk> , an Iron Age promontory fort and the remains of other prehistoric settlement dating from the Bronze Age nearby . Loch na h @-@ <unk> on the peninsula is linked to the sea by an artificial " Viking " canal that may date from the later period of Norse settlement . Dun <unk> is a ruined Iron Age hill fort on the Strathaird peninsula , which was further fortified in the Middle Ages and may have become the seat of Clan <unk> . 
- 
- = = = Early history = = = 
- 
- The late Iron Age inhabitants of the northern and western Hebrides were probably Pictish , although the historical record is sparse . Three Pictish symbol stones have been found on Skye and a fourth on Raasay . More is known of the kingdom of Dál Riata to the south ; <unk> 's life of Columba , written shortly before 697 , portrays the saint visiting Skye ( where he baptised a pagan leader using an interpreter ) and <unk> himself is thought to have been familiar with the island . The Irish annals record a number of events on Skye in the later 7th and early 8th centuries – mainly concerning the struggles between rival dynasties that formed the background to the Old Irish language romance <unk> <unk> meic <unk> . 
- The Norse held sway throughout the Hebrides from the 9th century until after the Treaty of Perth in <unk> . However , apart from placenames , little remains of their presence on Skye in the written or archaeological record . Apart from the name " Skye " itself , all pre @-@ Norse placenames seem to have been obliterated by the Scandinavian settlers . Viking heritage is claimed by Clan MacLeod and Norse tradition is celebrated in the winter fire festival at Dunvegan , during which a replica Viking long boat is set <unk> . 
- 
- = = = <unk> and Scottish rule = = = 
- 
- The most powerful clans on Skye in the post – Norse period were Clan MacLeod , originally based in Trotternish , and Clan Macdonald of Sleat . Following the disintegration of the Lordship of the Isles , the <unk> also emerged as an independent clan , whose substantial <unk> in Skye were centred on Strathaird . Clan <unk> also have a long association with Trotternish , and in the 16th century many of the <unk> clan moved to Sleat . The <unk> of South Uist were bitter rivals of the <unk> , and an attempt by the former to murder church @-@ <unk> at <unk> in retaliation for a previous massacre on <unk> , resulted in the Battle of the <unk> <unk> of <unk> . 
- After the failure of the <unk> rebellion of <unk> , Flora MacDonald became famous for rescuing Prince Charles Edward Stuart from the <unk> troops . Although she was born on South Uist her story is strongly associated with their escape via Skye and she is buried at Kilmuir in Trotternish . Samuel Johnson and James Boswell 's visit to Skye in 1773 and their meeting with Flora MacDonald in Kilmuir is recorded in Boswell 's The Journal of a Tour to the Hebrides . Boswell wrote , " To see Dr. Samuel Johnson , the great champion of the English <unk> , salute Miss Flora MacDonald in the isle of Sky , [ sic ] was a striking sight ; for though somewhat <unk> in their notions , it was very <unk> they should meet here " . Johnson 's words that Flora MacDonald was " A name that will be mentioned in history , and if courage and fidelity be virtues , mentioned with honour " are written on her gravestone . After this rebellion the clan system was broken up and Skye became a series of landed estates . 
- Of the island in general , Johnson observed : 
- I never was in any house of the islands , where I did not find books in more languages than one , if I <unk> long enough to want them , except one from which the family was removed . Literature is not neglected by the higher rank of the <unk> . It need not , I suppose , be mentioned , that in countries so little frequented as the islands , there are no houses where travellers are entertained for money . He that wanders about these <unk> , either <unk> recommendations to those whose <unk> lie near his way , or , when night and weariness come upon him , takes the chance of general hospitality . If he finds only a cottage he can expect little more than shelter ; for the <unk> have little more for themselves but if his good fortune brings him to the residence of a gentleman , he will be glad of a storm to prolong his stay . There is , however , one inn by the sea @-@ side at <unk> , in Sky , where the post @-@ office is kept . 
- Skye has a rich heritage of ancient monuments from this period . Dunvegan Castle has been the seat of Clan MacLeod since the 13th century . It contains the Fairy Flag and is reputed to have been inhabited by a single family for longer than any other house in Scotland . The 18th @-@ century Armadale Castle , once home of Clan Donald of Sleat , was abandoned as a residence in 1925 but now hosts the Clan Donald Centre . Nearby are the ruins of two more MacDonald strongholds , <unk> Castle , and <unk> Castle , the legendary home of warrior woman , martial arts instructor ( and , according to some sources , Queen ) <unk> . <unk> <unk> , built in the late 15th century near <unk> and once a seat of Clan <unk> , is another ruin . 
- 
- = = = Clearances = = = 
- 
- In the late 18th century the harvesting of kelp became a significant activity but from 1822 on cheap imports led to a collapse of this industry throughout the Hebrides . During the 19th century , the inhabitants of Skye were also devastated by famine and Clearances . Thirty thousand people were evicted between 1840 and 1880 alone , many of them forced to <unk> to the New World . For example , the settlement of <unk> on the west coast of Duirinish was cleared on 4 August 1830 . Every <unk> under the age of seventy was removed and placed on board the Midlothian on threat of imprisonment , with those over that age being sent to the <unk> . The " Battle of the <unk> " involved a demonstration against a lack of access to land and the serving of <unk> notices . The incident involved numerous <unk> and about 50 police officers . This event was instrumental in the creation of the Napier Commission , which reported in 1884 on the situation in the Highlands . <unk> continued until the passing of the 1886 <unk> ' Act and on one occasion 400 marines were deployed on Skye to maintain order . The ruins of cleared villages can still be seen at <unk> , <unk> and <unk> in <unk> <unk> , and <unk> on Minginish . 
- 
- = = = Overview of population trends = = = 
- 
- As with many Scottish islands , Skye 's population peaked in the 19th century and then declined under the impact of the Clearances and the military losses in the First World War . From the 19th century until 1975 Skye was part of the county of Inverness @-@ <unk> but the crofting economy <unk> and according to <unk> , " <unk> of UK governments have treated the island people <unk> . " a charge that has been levelled at both Labour and Conservative administrations ' policies in the Highlands and Islands . By 1971 the population was less than a third of its peak recorded figure in 1841 . However , the number of residents then grew by over 28 per cent in the thirty years to 2001 . 
- The changing relationship between the residents and the land is evidenced by Robert <unk> 's remark circa 1852 that , " There is now a village in Portree containing three hundred inhabitants . " Even if this estimate is <unk> the population of the island 's largest settlement has probably increased <unk> or more since then . During the period the total number of island residents has declined by 50 per cent or more . 
- The island @-@ wide population increase of 4 per cent between 1991 and 2001 occurred against the background of an overall reduction in Scottish island populations of 3 per cent for the same period . By 2011 the population had risen a further 8 @.@ 4 % to 10 @,@ 008 with Scottish island populations as a whole growing by 4 % to 103 @,@ 702 . 
- 
- = = = Gaelic = = = 
- 
- Historically , Skye was overwhelmingly Gaelic @-@ speaking , but this changed between 1921 and 2001 . In both the 1901 and 1921 censuses , all Skye parishes were more than 75 per cent Gaelic @-@ speaking . By 1971 , only Kilmuir parish had more than three quarters Gaelic speakers while the rest of Skye ranged between 50 and 74 per cent . At that time , Kilmuir was the only area outside the Western Isles that had such a high proportion of Gaelic speakers . In the 2001 census Kilmuir had just under half Gaelic speakers , and overall , Skye had 31 per cent , distributed <unk> . The strongest Gaelic areas were in the north and south @-@ west of the island , including <unk> at 61 per cent . The weakest areas were in the west and east ( e.g. <unk> 23 per cent and <unk> 19 per cent ) . Other areas on Skye ranged between 48 per cent and 25 per cent . 
- 
- = = Government and politics = = 
- 
- In terms of local government , from 1975 to 1996 , Skye , along with the neighbouring mainland area of Lochalsh , constituted a local government district within the Highland administrative area . In 1996 the district was included into the unitary Highland Council , ( <unk> na <unk> ) based in Inverness and formed one of the new council 's area committees . Following the 2007 elections , Skye now forms a four @-@ member ward called " Eilean a ' Cheò " ; it is currently represented by two <unk> , one Scottish National Party , and one Liberal Democrat councillor . 
- Skye is in the Highlands electoral region and comprises a part of the Ross , Skye and Inverness West constituency of the Scottish Parliament , which elects one member under the first past the post basis to represent it . Currently this is David Thompson for the <unk> . In addition , Skye forms part of the wider Ross , Skye and Lochaber constituency , which elects one member to the House of Commons in Westminster . The present Member of Parliament is Ian <unk> of the Scottish National Party , who took office after the <unk> 's sweep in the General Election of 2015 . Prior to this , Charles Kennedy , a Liberal Democrat , had represented the area since the 1983 general election . 
- 
- = = Economy = = 
- 
- The largest employer on the island and its environs is the public sector , which accounts for about a third of the total workforce , principally in administration , education and health . The second largest employer in the area is the distribution , hotels and restaurants sector , highlighting the importance of tourism . Key attractions include Dunvegan Castle , the Clan Donald <unk> Centre , and The <unk> Experience arts and exhibition centre in Portree . There are about a dozen large landowners on Skye , the largest being the public sector , with the Scottish Government owning most of the northern part of the island . <unk> is a community @-@ owned estate in Duirinish and the Sleat Community Trust , the local development trust , is active in various regeneration projects . 
- Small firms dominate employment in the private sector . The <unk> <unk> , which produces a single <unk> <unk> , is beside Loch <unk> on the west coast of the island . Three other <unk> — Mac na Mara ( " son of the sea " ) , <unk> <unk> <unk> Eilean ( " wee <unk> of the <unk> " ) and <unk> <unk> ( " black pot " ) — are produced by <unk> <unk> na <unk> ( " smugglers den by the Sound of Sleat " ) , based at Eilean <unk> . These are marketed using predominantly Gaelic @-@ language labels . There is also an established software presence on Skye , with Portree @-@ based <unk> having expanded in recent years . 
- <unk> is still important , but although there are about 2 @,@ 000 <unk> on Skye only 100 or so are large enough to enable a <unk> to earn a livelihood entirely from the land . Cod and <unk> stocks have declined but commercial fishing remains important , especially fish farming of salmon and <unk> such as <unk> . The west coast of Scotland has a considerable renewable energy potential and the Isle of Skye <unk> Co @-@ op has recently bought a stake in the Ben <unk> wind farm near Dunvegan . There is a thriving arts and crafts sector . 
- The unemployment rate in the area tends to be higher than in the Highlands as a whole , and is seasonal in nature , in part due to the impact of tourism . The population is growing and in common with many other scenic rural areas in Scotland , significant increases are expected in the percentage of the population aged 45 to 64 years . 
- 
- = = Transport = = 
- 
- Skye is linked to the mainland by the Skye Bridge , while ferries sail from Armadale on the island to <unk> , and from <unk> to Glenelg . <unk> also run from Uig to <unk> on Harris and <unk> on North Uist , and from <unk> to Raasay . 
- The Skye Bridge opened in 1995 under a private finance initiative and the high tolls charged ( £ 5 @.@ 70 each way for summer visitors ) met with widespread opposition , spearheaded by the pressure group <unk> ( Skye and Kyle Against <unk> ) . On 21 December 2004 it was announced that the Scottish Executive had purchased the bridge from its owners and the tolls were immediately removed . 
- Bus services run to Inverness and Glasgow , and there are local services on the island , mainly starting from Portree or Broadford . Train services run from Kyle of Lochalsh at the mainland end of the Skye Bridge to Inverness , as well as from Glasgow to <unk> from where the ferry can be caught to Armadale . 
- The Isle of Skye Airfield at <unk> , near Broadford , is used by private aircraft and occasionally by NHS Highland and the Scottish Ambulance Service for transferring patients to hospitals on the mainland . 
- The <unk> trunk road traverses the island from the Skye Bridge to Uig , linking most of the major settlements . Many of the island 's roads have been widened in the past forty years although there are still substantial sections of single track road . 
- 
- = = Culture , media and the arts = = 
- 
- Students of Scottish Gaelic travel from all over the world to attend <unk> <unk> <unk> , the Scottish Gaelic college based near <unk> in Sleat . In addition to members of the Church of Scotland and a smaller number of Roman Catholics many residents of Skye belong to the Free Church of Scotland , known for its strict observance of the Sabbath . 
- Skye has a strong folk music tradition , although in recent years dance and rock music have been growing in popularity on the island . Gaelic folk rock band <unk> started in Skye and former singer Donnie Munro still works on the island . <unk> 's second single and a concert staple is entitled Skye , the lyrics being partly in English and partly in Gaelic and they have released other songs such as " <unk> on <unk> " that were inspired by the island . Celtic fusion band the <unk> <unk> are based on Skye . <unk> <unk> singer Ian Anderson owned an estate at Strathaird on Skye at one time . Several <unk> songs are written about Skye , including Dun <unk> , Broadford <unk> , and Acres Wild ( which contains the lines " Come with me to the <unk> Isle , / Northern father 's western child ... " in reference to the island itself ) . The Isle of Skye Music Festival featured sets from The Fun Lovin ' <unk> and Sparks , but collapsed in 2007 . Electronic musician <unk> was born on Skye . 
- The poet <unk> MacLean , a native of the Isle of Raasay , which lies off the island 's east coast , lived much of his life on Skye . The island has been <unk> in the traditional song " The Skye Boat Song " and is the <unk> setting for the novel To the Lighthouse by Virginia Woolf , although the Skye of the novel bears little relation to the real island . John Buchan 's descriptions of Skye , as featured in his Richard <unk> novel Mr <unk> , are more true to life . I <unk> di <unk> <unk> is a 2004 Italian language work of non @-@ fiction by <unk> <unk> . The international bestseller , The Ice Twins , by S K <unk> , published around the world in 2015 @-@ 2016 , is set in southern Skye , especially around the settlement and islands of <unk> . 
- Skye has been used as a location for a number of feature films . The <unk> aerodrome was used for the opening scenes of the 1980 film Flash Gordon . <unk> , released in 2007 and starring Robert De <unk> and Michelle Pfeiffer , featured scenes near Uig , Loch <unk> and the <unk> . Another 2007 film , <unk> : The <unk> <unk> , was shot almost entirely in various locations on the island . Some of the opening scenes in Ridley Scott 's 2012 feature film <unk> were shot at the Old Man of <unk> . In 1973 The Highlands and Islands - a Royal Tour , a documentary about Prince Charles 's visit to the Highlands and Islands , directed by Oscar <unk> , was shot partly on Skye . 
- The West Highland Free Press is published at Broadford . This weekly newspaper takes as its motto " An <unk> , an <unk> ' s na <unk> " ( " The Land , the Language and the People " ) , which reflects its radical , campaigning priorities . The Free Press was founded in 1972 and <unk> in Skye , <unk> Ross and the Outer Hebrides . <unk> is a popular sport played throughout the island and Portree @-@ based Skye <unk> won the <unk> Cup in 1990 . 
- 
- = = Wildlife = = 
- 
- The Hebrides generally lack the biodiversity of mainland Britain , but like most of the larger islands , Skye still has a wide variety of species . Observing the abundance of game birds Martin wrote : 
- There is plenty of land and water <unk> in this isle - as hawks , eagles of two kinds ( the one grey and of a larger size , the other much less and black , but more destructive to young cattle ) , black cock , heath @-@ hen , <unk> , pigeons , wild <unk> , <unk> , and cranes . Of this latter sort I have seen sixty on the shore in a flock together . The sea <unk> are malls of all kinds - <unk> , <unk> , sea cormorant , & c . The natives observe that the latter , if perfectly black , makes no good broth , nor is its flesh worth eating ; but that a cormorant , which hath any white feathers or down , makes good broth , and the flesh of it is good food ; and the broth is usually drunk by nurses to increase their milk . 
- Similarly , Samuel Johnson noted that : 
- At the tables where a stranger is received , neither plenty nor delicacy is wanting . A tract of land so thinly inhabited , must have much wild @-@ <unk> ; and I scarcely remember to have seen a dinner without them . The moor @-@ game is every where to be had . That the sea <unk> with fish , needs not be told , for it supplies a great part of Europe . The Isle of Sky has <unk> and <unk> , but no hares . They sell very numerous <unk> of oxen yearly to England , and therefore cannot be supposed to want beef at home . <unk> and goats are in great numbers , and they have the common domestic <unk> . " 
- In the modern era avian life includes the corncrake , red @-@ throated diver , <unk> , <unk> , Atlantic <unk> , <unk> and golden eagle . The eggs of the last breeding pair of white @-@ tailed sea eagle in the UK were taken by an egg collector on Skye in 1916 but the species has recently been re @-@ introduced . The <unk> last bred on the island in 1900 . Mountain hare ( apparently absent in the 18th century ) and <unk> are now abundant and preyed upon by wild cat and pine <unk> . The rich fresh water streams contain brown trout , Atlantic salmon and water <unk> . Offshore the edible crab and edible <unk> are also found , the latter especially in the Sound of <unk> . There are nationally important horse <unk> and <unk> beds in the sea <unk> and in 2012 a bed of 100 million flame shells was found during a survey of Loch <unk> . Grey <unk> can be seen off the Southern coast . 
- Heather moor containing ling , bell <unk> , cross @-@ <unk> heath , <unk> <unk> and <unk> is everywhere abundant . The high Black <unk> weather too slowly to produce a soil that <unk> a rich plant life , but each of the main peninsulas has an individual flora . The basalt <unk> of Trotternish produce a diversity of Arctic and alpine plants including alpine <unk> and <unk> <unk> . The low @-@ lying fields of Waternish contain corn <unk> and corn <unk> . The sea cliffs of Duirinish <unk> mountain <unk> and fir <unk> . Minginish produces fairy flax , cats @-@ ear and black <unk> rush . There is a fine example of <unk> @-@ rich ash woodland at <unk> in Sleat incorporating silver birch , <unk> , bird cherry , and hawthorn . 
- The local Biodiversity Action Plan recommends land management measures to control the spread of <unk> and <unk> and identifies four non @-@ native , invasive species as threatening native biodiversity : Japanese <unk> , <unk> , New Zealand <unk> and mink . It also identifies problems of over @-@ grazing resulting in the impoverishment of moorland and upland habitats and a loss of native woodland , caused by the large numbers of red deer and sheep . 
- 
- 
- = Florida Atlantic University = 
- 
- Florida Atlantic University ( also referred to as FAU or Florida Atlantic ) is a public university located in Boca Raton , Florida , with five satellite campuses located in the Florida cities of Dania Beach , Davie , Fort Lauderdale , Jupiter , and in Fort Pierce at the Harbor Branch Oceanographic Institution . FAU belongs to the 12 @-@ campus State University System of Florida and serves South Florida , which has a population of more than five million people and spans more than 100 miles ( 160 km ) of coastline . Florida Atlantic University is classified by the Carnegie Foundation as a research university with high research activity . The university offers more than 180 undergraduate and graduate degree programs within its 10 colleges in addition to a professional degree from the College of Medicine . <unk> of study cover arts and humanities , the sciences , medicine , nursing , accounting , business , education , public administration , social work , architecture , engineering , and computer science . 
- Florida Atlantic opened in 1964 as the first public university in southeast Florida , offering only upper @-@ division and graduate level courses . Initial enrollment was only <unk> students , increasing in 1984 when the university admitted its first lower @-@ division undergraduate students . As of 2012 , enrollment has grown to over 30 @,@ 000 students representing 140 countries , 50 states , and the District of Columbia . Since its inception , Florida Atlantic has awarded more than 110 @,@ 000 degrees to nearly 105 @,@ 000 alumni . 
- In recent years , FAU has undertaken an effort to increase its academic and research standings while also evolving into a more traditional university . The university has raised admissions standards , increased research funding , built new facilities , and established notable partnerships with major research institutions . Changes include an on @-@ campus stadium , additional on @-@ campus housing , and the establishment of a College of Medicine in 2010 . 
- 
- = = History = = 
- 
- 
- = = = Establishment = = = 
- 
- On July 15 , 1961 , to meet the burgeoning educational demands of South Florida , the state legislature passed an act authorizing the establishment of a new university in the City of Boca Raton . Florida Atlantic University was built on Boca Raton Army Airfield , a 1940s @-@ era army airbase . During World War II , the airfield served as the Army Air Corps ' sole radar training facility . The base was built on the existing Boca Raton Airport and on 5 @,@ 860 acres ( 23 @.@ 7 km ² ) of adjacent land . A majority of the land was acquired from Japanese @-@ American farmers from the failing Yamato Colony . The land was seized through eminent domain , leaving many Japanese @-@ Americans little recourse in the early days of World War II . 
- The airbase was used for radar training , anti @-@ submarine patrols along the coast , and as a stop @-@ over point for planes being ferried to Africa and Europe via South America . The airfield was composed of four runways , still visible on the Boca Campus today and mainly used for parking . 
- By early 1947 , the military decided to transfer future radar training operations to <unk> Air Force Base in Mississippi . The departure of the air force in 1947 would leave Boca Raton Army Airfield essentially abandoned . 
- 
- = = = <unk> and growth = = = 
- 
- Florida Atlantic University opened on September 14 , 1964 , with an initial student body of <unk> students in five colleges . The first degree awarded was an honorary doctorate given to President Lyndon B. Johnson on October 25 , 1964 , at the dedication and opening of the university . At the time of its opening , there were 120 faculty out of a total of 350 employees . On @-@ campus housing for students was first added in September 1965 , when Algonquin Hall opened . 
- Florida Atlantic 's history is one of continuing expansion as the university 's service population has grown . The university originally served only upper @-@ division and graduate level students , because Florida intended the institution " to complement the state 's community college system , accepting students who had earned their associate degrees from those institutions . " 
- Florida Atlantic began its expansion beyond a one @-@ campus university in 1971 , when it opened its Commercial Boulevard campus in Fort Lauderdale . Due to a rapidly expanding population in South Florida , in 1984 Florida Atlantic opened its doors to lower @-@ division undergraduate students . The following year , the university added its third campus , in downtown Fort Lauderdale on Las <unk> Boulevard . 
- 
- = = = Recent history = = = 
- 
- In 1989 , the Florida Legislature recognized demands for higher education in South Florida by designating Florida Atlantic as the lead state university serving Broward County . To fill this role , the university would establish a campus in Dania Beach in 1997 and another campus in the City of Davie in western Broward County in 1990 . Florida Atlantic later purchased 50 acres ( 20 ha ) of land in Port St. Lucie in 1994 to establish a campus on the Treasure Coast . This would be the institution 's fifth campus . The university continued its expansion in 1999 when it opened its Jupiter Campus , named for the late John D. MacArthur . This campus houses the university 's honors college . 
- Florida Atlantic University and the University of Miami 's Miller School of Medicine established a medical training program within the Charles E. Schmidt College of <unk> Science in 2004 . Plans originally called for the construction of a new teaching hospital in coordination with Boca Raton Community Hospital on the main campus . Following successive budgets deficits in 2007 , the hospital delayed its participation indefinitely . However , Florida Atlantic later established its own College of Medicine in 2010 . The Harbor Branch Oceanographic Institution ( HBOI ) also joined the university in 2007 , creating Florida Atlantic 's seventh campus . To bring HBOI into the university family the Florida Legislature allocated $ 44 million to Florida Atlantic to acquire the institution . 
- Florida Atlantic has changed dramatically since its opening in 1964 . As of 2013 , there are more than 30 @,@ 000 students attending classes on seven campuses spread across 120 miles ( 193 km ) . The university consists of ten colleges and employs more than 3 @,@ 200 faculty and staff . The university 's endowment decreased from $ 182 million in June 2008 to $ 142 million in January 2009 due to a worsening economy . However , with the national economic recovery the endowment has increased to $ 179 million by the end of 2012 . 
- Since its founding , the university has been led by seven presidents . The university 's immediate past president is Dr. Mary Jane Saunders . She was named president on March 3 , 2010 , then resigned on May 15 , 2013 . Her appointment followed the resignation of Frank Brogan . Brogan , a former Lieutenant Governor of Florida , left the university in late 2009 to become Chancellor of the State University System of Florida . Past university presidents also included Dr. Anthony J. <unk> , Dr. Helen <unk> , Dr. Glenwood <unk> , and Dr. Kenneth <unk> Williams . On January 17 , 2014 , the Board of Trustees announced the selection of Dr. John W. Kelly , formerly a <unk> of Clemson University , to be the seventh president of the university with a starting date of March 1 , 2014 . 
- 
- = = Academics = = 
- 
- 
- = = = <unk> = = = 
- 
- As of fall 2013 , the university 's student body consists of 24 @,@ <unk> undergraduates , 4 @,@ <unk> graduate and professional students , 64 medical students , and 1 @,@ 451 <unk> students . As of 2013 , the undergraduate student body contains 47 % ethnic minorities and includes students from more than 180 countries , 49 states , and the District of Columbia . For the undergraduate class of 2012 , the acceptance rate was 35 % for first @-@ time @-@ in @-@ college students . 
- The university has ten colleges which altogether offer over 180 different bachelor 's , master 's and doctoral degree programs : the Charles E. Schmidt College of Science , Charles E. Schmidt College of Medicine , Christine E. Lynn College of Nursing , College for Design and Social Inquiry , College of Business , College of Education , College of Engineering and Computer Science , Dorothy F. Schmidt College of Arts and Letters , Harriet L. Wilkes Honors College , and the <unk> College . 
- The university offers two honors options : the Harriet L. Wilkes Honors College and a University Scholars Program . The Wilkes Honors College is located on the John D. MacArthur campus in Jupiter , Florida . It offers a liberal arts education in the platform of a public university , yet is comparable to a private liberal arts college . The Boca Raton campus houses the University Scholars Program , which offers special honors <unk> , forums , courses , and advanced course substitution for freshmen . 
- In recent years , FAU has increased its admission requirements for prospective freshmen . The fall 2013 freshmen profile for the mid @-@ 50 % includes 3 @.@ 38 @-@ 3 @.@ 98 high school <unk> , a 22 @-@ 26 <unk> composite score , and a <unk> @-@ 1740 SAT total score . Following a surge in the university 's popularity , in early 2009 , the university created its first wait @-@ list for undergraduate enrollment . After February 15 , 2009 , applicants for admission in the 2009 – 2010 academic year were required to have a 3 @.@ 5 <unk> or an SAT score of 1600 to be considered for admission . 
- The average class size at FAU for undergraduates is 33 students , and for graduate classes , 12 students . The student @-@ to @-@ faculty ratio is 20 : 1 . The top three undergraduate majors by enrollment are elementary education , accounting , and management , respectively . The top three graduate majors by enrollment are business administration , educational leadership , and accounting , respectively . The average age for first @-@ year students is 18 ; however , the average age for all undergraduates is 24 and the average age for graduate students is 33 . The average 4 @-@ year graduation rate for first @-@ time , non @-@ transfer students is 14 % while the 6 @-@ year graduation rate is 39 % . 
- Florida Atlantic University has long ranked as the most racially , ethnically and culturally diverse institution in Florida 's State University System . U.S. News & World Report has ranked FAU the 27th most diverse university in the nation . Florida Atlantic University students come from all 50 states , every county in Florida , and more than 180 countries . <unk> opportunities include <unk> , hands @-@ on research , study abroad experiences , and 310 clubs and campus organizations . 
- The <unk> Learning Society operates programs that serve the educational interests of more than 19 @,@ 000 senior citizens by providing classes focusing on subjects of specific interest , and audit options for regular university classes . Under the university 's Commercial Music Program , Hoot / <unk> Recordings was created in 2002 , enabling students to work in all creative and business aspects of the music industry . This program generated music that landed a Top 10 spot on the Billboard 's Hot R & B / Hip @-@ Hop Singles Sales Chart during its first week of release . The university 's two @-@ story trading room simulator , located in the College of Business , provides hands @-@ on financial education using 25 dual @-@ monitor computers and can accommodate 50 people at one time . A second lab provides full audio / visual <unk> and 25 additional <unk> . Florida Atlantic allows local financial businesses to use the Trading Room for training . 
- 
- = = = Research = = = 
- 
- Florida Atlantic is classified by The Carnegie Foundation for the Advancement of Teaching as a research university with high research activity . The university has established notable partnerships with major research institutions such as The <unk> Research Institute , the <unk> <unk> Institute for Molecular Studies , and the Max Planck Society . 
- The university is the home of two centers of excellence : The Center of Excellence in <unk> and Marine <unk> and The Center for Ocean Energy Technology . These centers have been selected by Florida 's <unk> Technology Commission to receive grants to continue and increase their operations . Florida Atlantic beat out some of Florida 's top research universities , including the University of Florida and Florida State University , for the initial money from the state . 
- Since receiving its startup funding , Florida Atlantic has secured additional funds from other sources , including federal and private research grants . As a result , both centers have engaged in academic and industry partnerships , combining expertise in ocean engineering , marine <unk> , functional genomics , proteomics , and <unk> . Researchers , scientists , and students at the centers are designing technologies to explore the sea , harvest renewable energy , discover new medicines , and develop new <unk> to combat agents of <unk> . As a result of this research , in 2007 the university and Lockheed Martin announced an exclusive licensing agreement to develop and produce a rapidly <unk> and autonomous <unk> buoy system for military and scientific uses . 
- In 2010 , the United States Department of Energy designated FAU as one of three national centers for ocean energy research and development . The Southeast National Marine <unk> Energy Center joins centers in the Pacific Northwest ( University of Washington and Oregon State University ) and in Hawaii ( University of Hawaii ) . The Southeast National Marine <unk> Energy Center is undertaking research and development of technologies capable of generating renewable power from ocean currents and ocean thermal energy . 
- The university houses both an Imaging Technology Center and a NASA Imaging Technology Space Center . Located in the College of Engineering and Computer Science , the centers <unk> in digital imaging research and development for use in both government and commercial applications in the areas of medical technology , surveillance , communications , education , inspection , scientific observation , manufacturing , visual recognition and identification , and motion picture and digital video . The Florida Atlantic Imaging Technology Center is developing a curriculum for digital imaging and processing , thereby establishing Florida Atlantic as the only university in the nation to offer this technical concentration . The NASA Imaging Technology Center is one of 12 NASA Research Partnership Centers throughout the nation which develop dual @-@ use research and development with the participation of NASA and other related industries in the US . The center occupies two sets of laboratories and administrative offices , one on Florida Atlantic 's main campus in Boca Raton , the other at the Fort Lauderdale campus . 
- Florida Atlantic is affiliated to the Research Park at Florida Atlantic University , with properties in <unk> Beach and Boca Raton . The Research Park provides outside research facilities for companies which enable them to interact with the university community and its facilities , resources , and expertise . The Research Park operates the Technology Business <unk> ; The <unk> works to foster the start @-@ up and growth of technology @-@ based businesses , seeking to scale them and build relationships for them with the university . The Boca Raton campus is also home to the Center for Complex Systems and Brain Sciences , which includes the Machine <unk> and <unk> <unk> Laboratory 
- 
- = = = Rankings = = = 
- 
- For 2013 , Florida Atlantic University was classified as a second @-@ tier university by the U.S. News & World Report 's rankings of " Best Colleges . " U.S. News ranks universities into one of two tiers , with one being the highest , based on how they compare with other colleges in a peer assessment , retention rates , student selectivity , faculty resources , financial resources , graduation rates , and the amount of alumni giving . The university was named one of the 146 " Best Southeastern Colleges " in the United States by the Princeton Review . The Review also recognized FAU 's business program by naming the College of Business to their list of " Best 296 Business Schools " for 2009 . For 2011 , Florida Atlantic was ranked <unk> in the nation by Washington Monthly . The magazine based its rankings on the following three criteria : " how well a university performs as an engine of social mobility ( ideally helping the poor to get rich rather than the very rich to get very , very rich ) , how well a university does in <unk> scientific and humanistic research , and how well a university promotes an ethic of service to country . " The university was also ranked 28th in the United States and fourth in Florida by The Hispanic <unk> in Higher Education magazine for <unk> 738 bachelor 's degrees to Hispanic students during the 2006 – 2007 academic year . 
- 
- = = Campus = = 
- 
- Florida Atlantic University is a distributed university located on seven campuses spread across Palm Beach , Broward , and St. Lucie counties . The region is home to more than three million people . The university 's main campus is located in the City of Boca Raton in Palm Beach County . The county is also home to the John D. MacArthur Campus located in the City of Jupiter . In addition to its campuses in Palm Beach County , the university operates three campuses in the Broward County cities of Dania Beach , Davie , and Fort Lauderdale . Florida Atlantic University also operates two campuses in the St. Lucie County cities of Port St. Lucie and Fort Pierce . In addition to students who attend classes on the universities campuses , there are 1 @,@ 612 distance learning students who conduct their studies over the internet or through other means . These students account for 6 % of the university 's student body . 
- Florida Atlantic is a signatory of the American College & University Presidents Climate <unk> Association for the Advancement of Sustainability in Higher Education . This commits the institution to ensuring all new construction projects meet the U.S. Green Building Council 's <unk> in Energy and Environmental Design ( <unk> ) Silver standards . In 2011 , The College of Engineering and Computer Science Building was <unk> Platinum certified . 
- 
- = = = Palm Beach County campuses = = = 
- 
- 
- = = = = Boca Raton = = = = 
- 
- Florida Atlantic University 's main campus in Boca Raton was established on the remnants of a World War II American Army airbase in 1964 . <unk> 850 acres ( 3 @.@ 5 km ² ) , the site is located between the cities of Palm Beach and Fort Lauderdale . The campus was designated a burrowing owl sanctuary in 1971 by the <unk> Society . The owls find the campus appealing because there are few predators , due to the university 's proximity to the Boca Raton Airport , and because the campus was originally cleared of vegetation when operating as an airbase during World War II . " The feisty bird , traditionally associated with wisdom and determination , serves as the university 's mascot . " 
- The Boca Raton campus is home to a wide variety of university programs and facilities . These facilities are <unk> and classrooms , housing for students , a 6 @,@ 000 @-@ gallon shark tank for aquatic research , a movie theater , athletic and recreational facilities , and the student @-@ run record label Hoot / <unk> Recordings . In addition to academic and cultural programs , the campus also houses Florida Atlantic 's Division I athletics program . The main campus serves approximately 19 @,@ 077 students , or 70 % of the university 's student body , offering a number of academic programs , activities , and services . 
- The Boca Raton campus also houses a number of other institutions , including the A. D. Henderson University School , FAU High School , one of two Florida Atlantic University Research Parks , and the <unk> Learning Society . 
- 
- = = = = Jupiter – John D. MacArthur Campus = = = = 
- 
- In addition to the Boca Raton campus in southern Palm Beach County , Florida Atlantic University operates a campus in northern Palm Beach County , in Jupiter . The John D. MacArthur Campus , named after businessman and philanthropist John D. MacArthur , was established in 1999 to serve residents of central and northern Palm Beach and southern Martin counties . The MacArthur Campus occupies 45 acres ( 0 @.@ 18 km ² ) , upon which are eight classroom and office buildings , a library , a 500 @-@ seat auditorium , two residence halls , a dining hall , museum building , and utility plant . The MacArthur Campus also houses the Harriet L. Wilkes Honors College , <unk> Florida , and the Max Planck Florida Institute . The campus serves approximately 1 @,@ 262 students , or 4 % of the university 's student body . 
- 
- = = = Broward County campuses = = = 
- 
- 
- = = = = Dania Beach – SeaTech = = = = 
- 
- The Dania Beach Campus , also known as SeaTech , was founded in 1997 as a state @-@ funded Type II research center . The institute is part of Florida Atlantic 's Department of Ocean Engineering which was founded in 1965 as the first ocean engineering undergraduate program in the nation . The campus is located on 8 acres ( 0 @.@ 03 km ² ) of land between the Atlantic Ocean and the Intracoastal Waterway . SeaTech is home to university faculty and students engaged in sponsored ocean engineering research and development in the areas of acoustics , marine vehicles , <unk> and physical <unk> , marine materials and <unk> . The Dania Beach Campus serves approximately 70 students , roughly 1 % of the university 's total student body . 
- 
- = = = = Davie = = = = 
- 
- The Davie Campus of Florida Atlantic University was established in 1990 on 38 acres ( 0 @.@ 15 km ² ) of land in western Broward County . The campus serves approximately 3 @,@ <unk> students , or 13 % of the Florida Atlantic student body , making it the university 's second largest campus by enrollment . The campus features a multi @-@ story student union with offices for student government and student organizations , a multipurpose area and student lounge , a bookstore , and <unk> . The union also contains a student health center that provides medical services and health counseling . Davie is also the home of " environmental research initiatives focused on Everglades restoration . " FAU colleges offering courses at the FAU Davie campus include Design and Social Inquiry ; Arts and Letters ; Business ; Education ; Nursing ; and Science . The campus is located on Broward College 's Central Campus . Students may enter BC as freshmen and graduate from FAU with undergraduate degrees in over 14 disciplines . More than 315 @,@ 000 square feet of carefully designed classrooms , laboratories and faculty , staff and student offices are located on this campus along with a shared @-@ use , 112 @,@ 000 square @-@ foot FAU / BC library designed for the 21st century . 
- Other support facilities include a shared <unk> Center , a student Wellness Center and a multi @-@ service Student Union . The campus also offers a rich and varied program of student activities provided by the Division of Student Affairs . Students have all of the services they require for career counseling , <unk> , testing and evaluation , <unk> , health services , student government and financial aid , among others . Like a small college within a large university , the Davie Campus is seen as a " model " branch campus for the state of Florida and the nation . 
- 
- = = = = Fort Lauderdale = = = = 
- 
- The university has two buildings in downtown Fort Lauderdale , both of which are considered part of one Fort Lauderdale campus . The Askew Tower ( AT ) and the Higher Education Complex ( <unk> ) on Las <unk> Boulevard . The campus offers courses in communication , graphic design , architecture , and urban and regional planning . The campus is home to approximately 900 students or 3 @.@ 2 % of the university 's student body . 
- 
- = = = St. Lucie County campuses = = = 
- 
- 
- = = = = Fort Pierce – Harbor Branch Oceanographic Institution = = = = 
- 
- In addition to the Treasure Coast Campus , Florida Atlantic University operates a campus in Fort Pierce at the Harbor Branch Oceanographic Institution . Harbor Branch merged with the university in 2007 to become the HBOI at FAU . The Florida Legislature allocated $ 44 million for the university to acquire the institution and its 600 acre ( 2 @.@ 4 km ² ) campus . 
- 
- = = = Former <unk> = = = 
- 
- 
- = = = = Port St. Lucie – Treasure Coast Campus = = = = 
- 
- Treasure Coast Campus of Florida Atlantic University operated through a partnership with Indian River State College ( <unk> ) . Florida Atlantic purchased 50 acres ( 0 @.@ 2 km ² ) of land in Port St. Lucie in 1994 . At the end of Spring 2012 class term , Florida Atlantic University ended offering classes at the Port St. Lucie campus . 
- 
- = = Athletics = = 
- 
- Florida Atlantic 's 18 varsity sports teams , the Owls , compete in NCAA 's Division I. The Owls recently got in C @-@ USA for the 2013 – 14 season . The university 's athletics program began in 1979 , when Florida Atlantic first started sponsoring intercollegiate teams . Since then , the university has worked to expand the quality of its intercollegiate program by attracting coaches such as Howard Schnellenberger , Matt Doherty , Rex Walters and Mike Jarvis . In 2006 , the athletic department was ranked 79th in the nation by the National Association of Collegiate Directors of Athletics ( <unk> ) . Along with USA Today and the United States Sports Academy , <unk> recognized the university for its Division I athletic programs and accomplishments . " This ranking placed Florida Atlantic in the top 24 % of 326 NCAA Division I universities . The university 's colors are FAU Blue , FAU Red , and FAU Silver . 
- In 2008 , the Florida Atlantic football team finished six wins and six losses in regular season play and was invited to the Motor City Bowl . The Owls defeated Central Michigan University 24 – 21 , increasing their bowl record to two wins and zero losses . During the previous season , the football team beat Troy University in the final game of regular season play to become Sun Belt Co @-@ Champions and receive an invitation to the New Orleans Bowl . In just the seventh year of the football program 's history , and the third year playing in Division I , Florida Atlantic set NCAA records by both becoming the youngest program ever to receive an invitation to , and win , a bowl game . As a result of the New Orleans Bowl the university has seen a surge in school spirit . 
- In past seasons the Owls have garnered a number of accolades for their accomplishments . During the 2006 – 2007 season , the men 's basketball team was noted as " one of the Sun Belt Conference 's top offensive teams , " with a " scary offense " that earned it the reputation of the " best shooting team in the conference . " In 2010 , the men 's basketball team defeated its first @-@ ever Southeastern Conference opponent Mississippi State University 61 – 59 as well as Big East opponent the University of South Florida 50 – 42 en route to a 21 – 9 overall record and claimed the Sun Belt Conference title . 
- The baseball team was also recognized by the NCAA as ranking in the Top 10 in five team categories . The team was also ranked third in the nation in home runs per game ( 1 @.@ 66 ) and in <unk> percentage ( <unk> ) . In 2010 , the Owls baseball team was 37 – 24 ( 21 – 9 ) and claimed their first Sun Belt Conference regular @-@ season title . 
- 
- = = = <unk> = = = 
- 
- Florida Atlantic University is home to a number of sports @-@ related traditions and school spirit organizations . 
- Every fall before the first football game of the season , FAU 's Student Government Association sponsors the " Annual Football Kick @-@ Off <unk> " wherein the opposing team 's mascot is burned in effigy . Also in football , Florida Atlantic challenges its rival Florida International ( <unk> ) is the annual Shula Bowl . This intercollegiate football game is named after legendary coach Don Shula ; so named because at the time of its inception , both head coaches , Florida Atlantic 's Howard Schnellenberger and Florida International coach Don <unk> , had worked under Shula at some point during their careers . Even though both universities have since moved on to new head coaches , the Shula Bowl is still played . As a home game , the competition takes place at university 's own stadium ; as an away game , the bowl is played at <unk> Stadium in Miami . 
- For basketball , Florida Atlantic celebrates a " Midnight Madness " <unk> rally that introduces fans to the team and coaches as well as inspires a number of basketball @-@ related contests such as 3 Point Shoot <unk> and Slam <unk> competitions . During the regular season , the " Bury the <unk> in Red " event calls for Florida Atlantic students to wear as much red as possible and fill the <unk> , the university 's multi @-@ purpose arena , during the annual basketball rivalry game between Florida Atlantic and Florida International University . 
- The official spirit group supporting Florida Atlantic athletics is the " <unk> . " The group began in February 2002 to support the men 's basketball program during the team 's run for the Atlantic Sun Conference Championship . The group is funded by the Student <unk> Association , and can now be found at most sporting events cheering for Florida Atlantic . The <unk> are joined by the Owl Rangers , a fan group that paints their bodies in the Florida Atlantic school colors . The <unk> are a student club that attend <unk> events and assist in recruiting athletes . 
- Since 2002 , Florida Atlantic students have been using Owl <unk> ( the " OK " hand sign ) to show school pride and wish the athletic teams luck during football point after attempts ( <unk> ) and basketball free throws . The Florida Atlantic University Athletics Department has chosen to use Owl <unk> as part of its " <unk> the Hoot " 2012 marketing campaign that encourages students to " proudly raise the hand salute that depicts the eyes of the owl , FAU 's mascot . " 
- 
- = = Student life = = 
- 
- 
- = = = Residential life = = = 
- 
- Residential housing at Florida Atlantic University is available on the Boca Raton and John D. MacArthur campuses . " All full @-@ time freshmen are required to reside in university housing , " however , " exemptions from this policy are made for students who : are 21 or older by the first day of class , reside with parent ( s ) or legal guardian ( s ) within a 50 @-@ mile ( 80 km ) radius of the Boca Raton campus , or are married . " As of 2011 , 4 @,@ 555 students live on @-@ campus in Boca Raton . The Wilkes Honors College on the MacArthur Campus requires all students live on @-@ campus within its two residence halls , however , exceptions are made for students who are 26 years of age , married , or have dependent children . As of 2011 , there are 231 students residing on @-@ campus at the honors college . 
- Boca Raton 's on @-@ campus housing facilities are : Algonquin Hall ( opened 1965 ) , Indian River Towers ( opened 2001 ) , Heritage Park Towers ( opened 2004 ) , Glades Park Towers ( opened 2007 ) , Parliament Hall ( opened 2013 ) , University Village Apartments ( <unk> ) , and Innovation Village Apartments ( IVA ) ( opened 2011 ) . Heritage Park and Glades Park Towers each offer <unk> beds with 96 single rooms . <unk> and IVA exclusively serve <unk> while the other residence halls exclusively serve freshmen students , with Algonquin Hall serving all students . The university also offers upper @-@ division undergraduate and graduate student housing in the Business and Professional Women 's Scholarship House for women with a strong academic background . 
- One of the newest residences on the Boca Raton campus is the Innovation Village Apartments ( IVA ) , consisting of two buildings : IVA North and IVA South . It is a 1 @,@ 200 @-@ bed apartment @-@ style housing facility for <unk> , graduate , and medical students . It offers amenities that one would find in a high @-@ rise apartment complex : lounges , retail dining , fitness centers , a pool / <unk> , a volleyball court , common areas , and more . The facility opened in fall 2011 . FAU 's newest residence hall is Parliament Hall , a lakeside freshmen housing facility offering 614 beds , a fitness center , lounges , retail dining , and views of the nearby Atlantic Ocean from top floors . 
- Within its existing residential life programs , Florida Atlantic offers a number of Learning Communities for freshmen and students with similar interests and concentrations . Participants meet people with similar interests , live on the same floor and take courses with others in their community , while receiving additional guidance related to those interests . The university 's Learning Community programs are divided into two categories , Freshman Learning Communities and Living Learning Communities . The freshman program offers 16 different concentrations , including business , nursing , and education . The Living program offers six concentrations for students residing in the Heritage Park Towers dormitory , including engineering , computer science , and a Women 's <unk> program . 
- The university 's Department of Housing and Residential Life and the university 's <unk> and <unk> sponsor a program for freshmen and other students returning to Florida Atlantic in the fall semester . This program , called the " Weeks of Welcome , " spans 11 days and all campuses , and works to <unk> students with university life and to build a good on @-@ campus community . On each day , a number of different events are scheduled , including Hall Wars , which are athletic competitions between dormitories , <unk> , and a number of other events . The Weeks of Welcome is the second largest campus @-@ wide event held by Florida Atlantic . 
- 
- = = = Student housing = = = 
- 
- 
- = = = Campus organizations and activities = = = 
- 
- For the 2010 – 2011 academic year , Florida Atlantic had approximately 300 registered student organizations . Among the groups are academic organizations , honor societies , spiritual / religious organizations , diversity @-@ appreciation organizations , service organizations , personal interest organizations , sports clubs , and student government agencies . These clubs and organizations run the <unk> from sailing to Ultimate <unk> , from varsity and club sports and a jazz group to a pottery guild , from political organizations to chess and video game clubs . These organizations are funded by student tuition , from which $ 10 @.@ 00 per credit hour goes toward an activities and service fee fund . This generates approximately $ 9 million that is then given to student government for allocation to student clubs and organizations . The student government also finances other student life programs , including career <unk> , the University Press , <unk> TV and Owl Radio , and Homecoming . 
- Florida Atlantic 's homecoming , also known as the " Owl <unk> , " is celebrated annually in the fall semester . Events occur mainly on the Boca Raton Campus , but a number of other campuses host their own events as well . In the past , homecoming has had kickoff parties , <unk> dances , <unk> , comedy shows , alumni events and <unk> , a golf cart parade , and <unk> . Florida Atlantic students have an organized football <unk> area known as the Rat 's Mouth . The name references the Spanish translation of Boca Raton . 
- Florida Atlantic completed an $ 18 @.@ 6 million Recreation and Wellness Center in spring 2010 . The facility houses an outdoor leisure and lap pool , a <unk> equipment and free weight room , two multipurpose rooms , three indoor courts and health club @-@ style locker rooms . In 2011 , the facility won the <unk> Outstanding Sports Facilities Award . Other recreation facilities include a $ 4 @.@ 2 million track and field complex , with synthetic turf ( opened January 2007 ) , a ropes challenge course and the 6 @.@ 5 acre Henderson Fields , utilized most often by the FAU <unk> Sports and Club Sports programs . 
- 
- = = = Greek life = = = 
- 
- Florida Atlantic is home to approximately 28 chapters of national <unk> and <unk> , encompassing approximately 1 @,@ 077 members or 5 % of the undergraduate population . The <unk> of Greek life at Florida Atlantic is " Greek Week . " This event is held annually during the spring semester and showcases a number of themed competitions between the university 's Greek organizations . There are currently no on @-@ campus Greek houses . However , a Greek Life Housing task force has been formed to explore various housing models , including the cost of construction , " and make recommendations on how to improve the overall quality of the Greek housing .... " 
- 
- = = <unk> = = 
- 
- Florida Atlantic University has awarded more than 110 @,@ 000 degrees to nearly 105 @,@ 000 alumni worldwide since its opening . Some notable Florida Atlantic alumni are R. David <unk> , the former head of the United States ' Federal Emergency Management Agency ; Luis Alberto <unk> , President of the Inter @-@ American Development Bank ; and former university President Frank T. Brogan , a former Lieutenant Governor of Florida . Charles <unk> or " Father Goose " is a children 's poet and former nationally syndicated columnist . Judith Ortiz Cofer is an acclaimed Puerto <unk> author whose works span a range of literary genres including poetry , short stories , and essays . Other alumni are Chris <unk> , the lead singer of the band <unk> <unk> ; and Phil Zimmermann , the creator of Pretty Good <unk> . Entertainers Mary Carey , a pornographic actress and former candidate for Governor of California , prop comedian <unk> Top , and humorist Daniel Dickey also attended the university . <unk> and NASA astronaut Steven Swanson went to space aboard Space Shuttle <unk> during <unk> @-@ 117 in June 2007 , and Space Shuttle Discovery during <unk> @-@ 119 in March 2009 . 
- 
- 
- = The Clean Tech Revolution = 
- 
- The Clean Tech Revolution : The Next Big Growth and Investment <unk> is a 2007 book by Ron Pernick and Clint Wilder , who say that <unk> clean technologies is a profitable enterprise that is moving steadily into mainstream business . As the world economy faces challenges from energy price spikes , resource shortages , global environmental problems , and security threats , clean technologies are seen to be the next engine of economic growth . 
- Pernick and Wilder highlight eight major clean technology sectors : solar power , wind power , <unk> , green buildings , personal transportation , the smart grid , mobile applications , and water filtration . Six major forces , which they call the six C ’ s , are pushing clean technology into the mainstream : costs , capital , competition , China , consumers , and climate . Very large corporations such as <unk> , Toyota and Sharp , and investment firms such as Goldman Sachs are making multibillion @-@ dollar investments in clean technology . 
- The book has been reviewed in USA Today , Business Week , Energy Priorities , Sustainability Investment News and several other magazines , and has been translated into seven languages . Clean Tech Nation is the sequel to The Clean Tech Revolution . 
- 
- = = Themes = = 
- 
- Pernick and Wilder explain that , in the 1970s , clean technology was considered “ alternative , ” the province of back @-@ to @-@ the @-@ land lifestyle advocates , <unk> environmentalists , and lab scientists on research grants . Such technology was in an early stage of development , was too expensive , it did not have widespread political support , and very few large , established companies were embracing the sector . Even at the start of the 21st century , the term clean tech was not yet in the financial or business community ’ s vocabulary . But now , throughout much of the world , in trends large and small , there is " the beginning of a revolution that is changing the places where we live and work , the products we manufacture and purchase , and the development plans of cities , regional governments , and nations around the globe . " 
- Pernick and Wilder define " clean tech " as " any product , service , or process that delivers value using limited or zero non @-@ renewable resources and / or creates significantly less waste than conventional offerings . " They highlight eight major clean technology sectors : solar power , wind power , <unk> , green buildings , personal transportation , the smart grid , mobile applications ( such as portable fuel cells ) , and water filtration . The authors explain how investors , entrepreneurs , and individuals can profit from technological innovation in these areas . Pernick and Wilder identify some specific clean technologies , companies , and regions that are leading the way . 
- The authors present a list of drivers for clean tech : " high energy prices , depleted natural resources , volatile sources of foreign oil , record deficits , and unprecedented environmental and security challenges " . The central message , which is repeated in almost every chapter , is that a clean tech revolution with benefit humanity worldwide , and will require significant collaboration between the public and private sectors . 
- Pernick and Wilder present examples which show that the " clean tech revolution " is already under way . Very large corporations such as <unk> , Toyota and Sharp , and investment firms such as Goldman Sachs are making multibillion @-@ dollar investments in clean technology . <unk> clean tech cities are seen to include Copenhagen , where wind power generates 20 percent of Denmark 's electricity , and Chicago , a leader in " green " buildings saving energy , heating and cooling costs . Statistics from the U.S. and from abroad , especially from China , India , Brazil , and Europe are presented . 
- The authors ' say that nuclear power and clean coal are not clean technologies . Apart from the risks associated with nuclear power , " multibillion @-@ dollar nuclear plants are simply not cost @-@ effective when compared with other energy sources . " The authors also believe that clean coal is an oxymoron for a myriad of reasons , including the sheer number of coal mine @-@ related deaths and the fact that coal @-@ fired plants , even some cleaner ones , are major contributors to serious illnesses such as asthma , heart disease , and mercury poisoning . 
- Pernick and Wilder do not recommend specific stocks or <unk> . They prefer to lay out a <unk> of opportunities , technologies , companies , and trends that may build successful businesses and strengthen economies . 
- 
- = = Six C 's = = 
- 
- Pernick and Wilder identify six major forces , which they call the six C ’ s , that are pushing clean technology into the mainstream and driving rapid growth and expansion : costs , capital , competition , China , consumers , and climate . 
- <unk> . " Perhaps the most powerful force driving today ’ s clean @-@ tech growth is simple economics . As a general trend , clean @-@ energy costs are falling as the costs of fossil fuel energy are going up . The future of clean tech is going to be , in many ways , about scaling up manufacturing and driving down costs . " 
- Capital . " An unprecedented influx of capital is changing the clean tech landscape , with billions of dollars , euros , yen , and yuan pouring in from a myriad of public and private sector sources . " 
- Competition . " Governments are competing aggressively in the <unk> race to dominate in the clean @-@ tech sector and build the jobs of the future . " 
- China . " Clean tech is being driven by the <unk> demands being placed on the earth not only by mature economies but also by the explosive demand for resources in China , India , and other developing nations . Their expanding energy needs are driving major growth in clean @-@ energy , transportation , building , and water @-@ delivery technologies . " 
- <unk> . " <unk> consumers are demanding cleaner products and services that use resources efficiently , reduce costs , and embrace quality over quantity . " 
- Climate . " The debate around climate change has gone from question mark to peer @-@ reviewed certainty , and smart businesses are taking heed . " 
- The six C ’ s are a simple list of factors , not necessarily a useful framework for understanding , or <unk> from , the clean technology industry . 
- 
- = = Release and reception = = 
- 
- The Clean Tech Revolution was published by Collins as a 320 @-@ page hardcover book on June 12 , 2007 . An e @-@ book version was published by <unk> on June 7 , 2007 . In 2008 , a revised paperback edition was published , with a new sub @-@ title : <unk> the Top Trends , Technologies and Companies to Watch . The book has been translated into seven languages . 
- Paul <unk> from the <unk> Institute states that the The Clean Tech Revolution is <unk> organized and is " an excellent resource for those who would like a solid understanding of clean tech and the potential of each sector " . He also says that it is very useful for those seeking out the names of companies , <unk> , agencies , and people working on each technology . <unk> identifies one omission : the concern that major investments in clean technology parallel those made during the Internet boom , with the attendant fear that there " may be a bubble burst with clean tech " . 
- The physicist and <unk> , Joseph <unk> , has recommended The Clean Tech Revolution to people who are looking for one book to help them understand what is happening in clean technology . He says The Clean Tech Revolution is the only book that covers the whole <unk> of the latest in clean energy . 
- Russ <unk> from USA Today says The Clean Tech Revolution shows the green movement not in " <unk> terms " but as economically profitable . The real power players are the mainstream consumers , investors , entrepreneurs , governments and multinational corporations whose " eyes are trained on that most crucial of economic <unk> : the bottom line " . 
- According to <unk> Jana from Business Week , The Clean Tech Revolution is a " readable , straightforward guide to earth @-@ friendly business strategies " . The authors explain how businesses can follow the lead of companies such as Toyota by designing , selling , or funding <unk> eco @-@ friendly products and services . Jana says that the Toyota <unk> is just one well @-@ known example of successful clean technology in action . 
- Denis Du <unk> , editor of Energy Priorities magazine , commented on the realistic and comprehensive coverage of the book . However , he suggests that The Clean Tech Revolution is not an explanation of the technologies and how they work , nor is it an analysis of energy or environmental policy . Policy is complicated and the authors avoid discussing it in detail . Little discussion ties the various clean technologies together and a " single @-@ minded American focus " dominates . There is very little on the influence of mass transit and urban planning in Europe and other progressive regions . The chapter on water focuses on filtration , which is already an area of considerable opportunity , affecting even " green " industries , such as <unk> manufacturing . 
- <unk> <unk> in Sustainability Investment News says that the book does not ask the most challenging question of all : is " clean growth " an oxymoron ? She says that at a time when some experts say carbon emissions will need to be cut by 80 to 90 % by <unk> , the world may have to accept steady or even decreasing energy production , no matter how clean it is . <unk> also states that there is little coverage of social issues . For example , nowhere is there mention of how water supply <unk> and delivery by multinational corporations could affect the poor people of the world . 
- The Clean Tech Revolution was followed by the 2012 book Clean Tech Nation : How the U.S. Can Lead in the New Global Economy . 
- 
- = = Authors = = 
- 
- Author Ron Pernick is co @-@ founder and managing director of Clean Edge , a research and strategy firm in the United States which focuses on the <unk> of renewable energy and other clean technologies . Clint Wilder is senior editor at Clean Edge , and a veteran business and technology journalist . Both authors have been mapping clean technology trends for many years , and identifying business opportunities for prospective investors . 
- 
- 
- = Missouri River = 
- 
- The Missouri River is the longest river in North America . Rising in the Rocky Mountains of western Montana , the Missouri flows east and south for 2 @,@ 341 miles ( 3 @,@ 767 km ) before entering the Mississippi River north of St. Louis , Missouri . The river takes drainage from a sparsely populated , semi @-@ arid watershed of more than half a million square miles ( 1 @,@ 300 @,@ 000 km2 ) , which includes parts of ten U.S. states and two Canadian provinces . When combined with the lower Mississippi River , it forms the world 's fourth longest river system . 
- For over 12 @,@ 000 years , people have depended on the Missouri River and its tributaries as a source of sustenance and transportation . More than ten major groups of Native Americans populated the watershed , most leading a nomadic lifestyle and dependent on enormous buffalo herds that once roamed through the Great Plains . The first Europeans encountered the river in the late seventeenth century , and the region passed through Spanish and French hands before finally becoming part of the United States through the Louisiana Purchase . The Missouri was long believed to be part of the Northwest Passage – a water route from the Atlantic to the Pacific – but when Lewis and Clark became the first to travel the river 's entire length , they confirmed the mythical pathway to be no more than a legend . 
- The Missouri River was one of the main routes for the westward expansion of the United States during the 19th century . The growth of the fur trade in the early 1800s laid much of the <unk> as trappers explored the region and blazed trails . Pioneers headed west en <unk> beginning in the 1830s , first by covered wagon , then by the growing numbers of steamboats entering service on the river . Former Native American lands in the watershed were taken over by settlers , leading to some of the most longstanding and violent wars against indigenous peoples in American history . 
- During the 20th century , the Missouri River basin was extensively developed for irrigation , flood control and the generation of hydroelectric power . Fifteen dams <unk> the main stem of the river , with hundreds more on tributaries . <unk> have been cut and the river <unk> to improve navigation , reducing its length by almost 200 miles ( 320 km ) from pre @-@ development times . Although the lower Missouri valley is now a populous and highly productive agricultural and industrial region , heavy development has taken its toll on wildlife and fish populations as well as water quality . 
- 
- = = Course = = 
- 
- From the Rocky Mountains of Montana and Wyoming , three streams rise to form the headwaters of the Missouri River . The longest begins near Brower 's Spring , 9 @,@ 100 feet ( 2 @,@ 800 m ) above sea level , on the southeastern slopes of Mount Jefferson in the Centennial Mountains . <unk> west then north , it runs first in Hell <unk> Creek , then west into the Red Rock ; swings northeast to become the <unk> , it finally joins with the Big Hole to form the Jefferson . The <unk> River originates at Madison Lake in Wyoming 's Yellowstone National Park and joins with the <unk> to form the Madison , while the Gallatin River rises out of Gallatin Lake , also in the national park . These two streams then flow north and northwest into Montana . 
- The Missouri River officially starts at the confluence of the Jefferson and Madison in Missouri <unk> State Park near Three Forks , Montana , and is joined by the Gallatin a mile ( 1 @.@ 6 km ) downstream . The Missouri then passes through Canyon Ferry Lake , a reservoir west of the Big Belt Mountains . <unk> from the mountains near Cascade , the river flows northeast to the city of Great Falls , where it drops over the Great Falls of the Missouri , a series of five substantial waterfalls . It then winds east through a scenic region of canyons and badlands known as the Missouri Breaks , receiving the <unk> River from the west then widening into the Fort Peck Lake reservoir a few miles above the confluence with the <unk> River . Farther on , the river passes through the Fort Peck Dam , and immediately downstream , the Milk River joins from the north . 
- <unk> eastwards through the plains of eastern Montana , the Missouri receives the <unk> River from the north before crossing into North Dakota where the Yellowstone River , its greatest tributary by volume , joins from the southwest . At the confluence , the Yellowstone is actually the larger river . The Missouri then meanders east past <unk> and into Lake <unk> , the reservoir formed by Garrison Dam . Below the dam the Missouri receives the Knife River from the west and flows south to Bismarck , the capital of North Dakota , where the Heart River joins from the west . It slows into the Lake Oahe reservoir just before the <unk> River confluence . While it continues south , eventually reaching Oahe Dam in South Dakota , the Grand , Moreau and Cheyenne Rivers all join the Missouri from the west . 
- The Missouri makes a bend to the southeast as it winds through the Great Plains , receiving the Niobrara River and many smaller tributaries from the southwest . It then proceeds to form the boundary of South Dakota and Nebraska , then after being joined by the James River from the north , forms the Iowa – Nebraska boundary . At Sioux City the Big Sioux River comes in from the north . The Missouri flows south to the city of Omaha where it receives its longest tributary , the Platte River , from the west . <unk> , it begins to define the Nebraska – Missouri border , then flows between Missouri and Kansas . The Missouri swings east at Kansas City , where the Kansas River enters from the west , and so on into north @-@ central Missouri . To the east of the Kansas City , Missouri receives , on the left side , the Grand River . It passes south of Columbia and receives the Osage and <unk> Rivers from the south downstream of Jefferson City . The river then rounds the northern side of St. Louis to join the Mississippi River on the border between Missouri and Illinois . 
- 
- = = Watershed = = 
- 
- With a drainage basin spanning 529 @,@ 350 square miles ( 1 @,@ <unk> @,@ 000 km2 ) , the Missouri River 's catchment encompasses nearly one @-@ sixth of the area of the United States or just over five percent of the continent of North America . <unk> to the size of the Canadian province of Quebec , the watershed encompasses most of the central Great Plains , stretching from the Rocky Mountains in the west to the Mississippi River Valley in the east and from the southern extreme of western Canada to the border of the Arkansas River watershed . Compared with the Mississippi River above their confluence , the Missouri is twice as long and drains an area three times as large . The Missouri accounts for 45 percent of the annual flow of the Mississippi past St. Louis , and as much as 70 percent in certain droughts . 
- In 1990 , the Missouri River watershed was home to about 12 million people . This included the entire population of the U.S. state of Nebraska , parts of the U.S. states of Colorado , Iowa , Kansas , Minnesota , Missouri , Montana , North Dakota , South Dakota , and Wyoming , and small southern portions of the Canadian provinces of Alberta and Saskatchewan . The watershed 's largest city is Denver , Colorado , with a population of more than six hundred thousand . Denver is the main city of the Front Range Urban Corridor whose cities had a combined population of over four million in 2005 , making it the largest metropolitan area in the Missouri River basin . Other major population centers – mostly located in the southeastern portion of the watershed – include Omaha , Nebraska , situated north of the confluence of the Missouri and Platte Rivers ; Kansas City , Missouri – Kansas City , Kansas , located at the confluence of the Missouri with the Kansas River ; and the St. Louis metropolitan area , situated south of the Missouri River just below the latter 's mouth , on the Mississippi . In contrast , the northwestern part of the watershed is sparsely populated . However , many northwestern cities , such as <unk> , Montana , are among the fastest growing in the Missouri basin . 
- With more than 170 @,@ 000 square miles ( 440 @,@ 000 km2 ) under the <unk> , the Missouri River watershed includes roughly one @-@ fourth of all the agricultural land in the United States , providing more than a third of the country 's wheat , flax , barley and oats . However , only 11 @,@ 000 square miles ( 28 @,@ 000 km2 ) of farmland in the basin is irrigated . A further 281 @,@ 000 square miles ( 730 @,@ 000 km2 ) of the basin is devoted to the raising of livestock , mainly cattle . Forested areas of the watershed , mostly second @-@ growth , total about 43 @,@ 700 square miles ( 113 @,@ 000 km2 ) . Urban areas , on the other hand , comprise less than 13 @,@ 000 square miles ( 34 @,@ 000 km2 ) of land . Most built @-@ up areas are located along the main stem and a few major tributaries , including the Platte and Yellowstone Rivers . 
- <unk> in the watershed vary widely , ranging from just over 400 feet ( 120 m ) at the Missouri 's mouth to the 14 @,@ 293 @-@ foot ( 4 @,@ 357 m ) summit of Mount Lincoln in central Colorado . The river itself drops a total of 8 @,@ <unk> feet ( 2 @,@ <unk> m ) from Brower 's Spring , the farthest source . Although the plains of the watershed have extremely little local vertical relief , the land rises about 10 feet per mile ( 1 @.@ 9 m / km ) from east to west . The elevation is less than 500 feet ( 150 m ) at the eastern border of the watershed , but is over 3 @,@ 000 feet ( 910 m ) above sea level in many places at the base of the Rockies . 
- The Missouri 's drainage basin has highly variable weather and rainfall patterns , Overall , the watershed is defined by a Continental climate with warm , wet summers and harsh , cold winters . Most of the watershed receives an average of 8 to 10 inches ( 200 to 250 mm ) of precipitation each year . However , the westernmost portions of the basin in the Rockies as well as southeastern regions in Missouri may receive as much as 40 inches ( 1 @,@ 000 mm ) . The vast majority of precipitation occurs in winter , although the upper basin is known for short @-@ lived but intense summer thunderstorms such as the one which produced the 1972 Black Hills flood through Rapid City , South Dakota . Winter temperatures in Montana , Wyoming and Colorado may drop as low as − 60 ° F ( − 51 ° C ) , while summer highs in Kansas and Missouri have reached 120 ° F ( 49 ° C ) at times . 
- As one of the continent 's most significant river systems , the Missouri 's drainage basin borders on many other major watersheds of the United States and Canada . The Continental Divide , running along the spine of the Rocky Mountains , forms most of the western border of the Missouri watershed . The Clark Fork and Snake River , both part of the Columbia River basin , drain the area west of the Rockies in Montana , Idaho and western Wyoming . The Columbia , Missouri and Colorado River watersheds meet at Three Waters Mountain in Wyoming 's Wind River Range . South of there , the Missouri basin is bordered on the west by the drainage of the Green River , a tributary of the Colorado , then on the south by the mainstem of the Colorado . Both the Colorado and Columbia Rivers flow to the Pacific Ocean . However , a large <unk> drainage called the Great Divide Basin exists between the Missouri and Green watersheds in western Wyoming . This area is sometimes counted as part of the Missouri River watershed , even though its waters do not flow to either side of the Continental Divide . 
- To the north , the much lower <unk> Divide separates the Missouri River watershed from those of the Oldman River , a tributary of the South Saskatchewan River , as well as the <unk> , <unk> , and smaller tributaries of the Red River of the North . All of these streams are part of Canada 's Nelson River drainage basin , which empties into Hudson Bay . There are also several large <unk> basins between the Missouri and Nelson watersheds in southern Alberta and Saskatchewan . The Minnesota and Des Moines Rivers , tributaries of the upper Mississippi , drain most of the area bordering the eastern side of the Missouri River basin . Finally , on the south , the <unk> Mountains and other low divides through central Missouri , Kansas and Colorado separate the Missouri watershed from those of the White River and Arkansas River , also tributaries of the Mississippi River . 
- 
- = = = Major tributaries = = = 
- 
- Over 95 significant tributaries and hundreds of smaller ones feed the Missouri River , with most of the larger ones coming in as the river draws close to the mouth . Most rivers and streams in the Missouri River basin flow from west to east , following the incline of the Great Plains ; however , some eastern tributaries such as the James , Big Sioux and Grand River systems flow from north to south . 
- The Missouri 's largest tributaries by runoff are the Yellowstone in Montana and Wyoming , the Platte in Wyoming , Colorado , and Nebraska , and the Kansas – Republican / <unk> Hill and Osage in Kansas and Missouri . Each of these tributaries drains an area greater than 50 @,@ 000 square miles ( 130 @,@ 000 km2 ) , and has an average discharge greater than 5 @,@ 000 cu ft / s ( 140 m3 / s ) . The Yellowstone River has the highest discharge , even though the Platte is longer and drains a larger area . In fact , the Yellowstone 's flow is about 13 @,@ 800 cu ft / s ( 390 m3 / s ) – accounting for sixteen percent of total runoff in the Missouri basin and nearly double that of the Platte . On the other end of the scale is the tiny <unk> River in Montana , which at 201 feet ( 61 m ) long is one the world 's shortest rivers . 
- The table on the right lists the ten longest tributaries of the Missouri , along with their respective catchment areas and flows . Length is measured to the <unk> source , regardless of naming convention . The main stem of the Kansas River , for example , is 148 miles ( 238 km ) long . However , including the longest headwaters tributaries , the 453 @-@ mile ( 729 km ) Republican River and the 156 @-@ mile ( 251 km ) Arikaree River , brings the total length to <unk> miles ( 1 @,@ 205 km ) . Similar naming issues are encountered with the Platte River , whose longest tributary , the North Platte River , is more than twice as long as its mainstream . 
- The Missouri 's headwaters above Three Forks extend much farther upstream than the main stem . <unk> to the farthest source at Brower 's Spring , the Jefferson River is 298 miles ( 480 km ) long . Thus measured to its highest headwaters , the Missouri River stretches for 2 @,@ 639 miles ( 4 @,@ 247 km ) . When combined with the lower Mississippi , the Missouri and its headwaters form part of the fourth @-@ longest river system in the world , at 3 @,@ <unk> miles ( 6 @,@ <unk> km ) . 
- 
- = = = <unk> = = = 
- 
- By discharge , the Missouri is the ninth largest river of the United States , after the Mississippi , St. Lawrence , Ohio , Columbia , Niagara , Yukon , Detroit , and St. Clair . The latter two , however , are sometimes considered part of a <unk> between Lake Huron and Lake Erie . Among rivers of North America as a whole , the Missouri is thirteenth largest , after the Mississippi , Mackenzie , St. Lawrence , Ohio , Columbia , Niagara , Yukon , Detroit , St. Clair , Fraser , Slave , and <unk> . 
- As the Missouri drains a predominantly semi @-@ arid region , its discharge is much lower and more variable than other North American rivers of comparable length . Before the construction of dams , the river flooded twice each year – once in the " April Rise " or " Spring Fresh " , with the melting of snow on the plains of the watershed , and in the " June Rise " , caused by snowmelt and summer <unk> in the Rocky Mountains . The latter was far more destructive , with the river increasing to over ten times its normal discharge in some years . The Missouri 's discharge is affected by over 17 @,@ 000 reservoirs with an aggregate capacity of some 141 million acre feet ( 174 km3 ) . By providing flood control , the reservoirs dramatically reduce peak flows and increase low flows . <unk> from reservoirs significantly reduces the river 's runoff , causing an annual loss of over 3 million acre feet ( 3 @.@ 7 km3 ) from mainstem reservoirs alone . 
- The United States Geological Survey operates fifty @-@ one stream gauges along the Missouri River . The river 's average discharge at Bismarck , 1 @,@ 314 @.@ 5 miles ( 2 @,@ 115 @.@ 5 km ) from the mouth , is 21 @,@ 920 cu ft / s ( <unk> m3 / s ) . This is from a drainage area of 186 @,@ 400 sq mi ( 483 @,@ 000 km2 ) , or 35 % of the total river basin . At Kansas City , 366 @.@ 1 miles ( <unk> @.@ 2 km ) from the mouth , the river 's average flow is 55 @,@ 400 cu ft / s ( 1 @,@ 570 m3 / s ) . The river here drains about <unk> @,@ 100 sq mi ( 1 @,@ 254 @,@ 000 km2 ) , representing about 91 % of the entire basin . 
- The <unk> gage with a period of record greater than fifty years is at Hermann , Missouri – 97 @.@ 9 miles ( 157 @.@ 6 km ) upstream of the mouth of the Missouri – where the average annual flow was 87 @,@ 520 cu ft / s ( 2 @,@ 478 m3 / s ) from 1897 to 2010 . About 522 @,@ 500 sq mi ( 1 @,@ 353 @,@ 000 km2 ) , or 98 @.@ 7 % of the watershed , lies above Hermann . The highest annual mean was 181 @,@ 800 cu ft / s ( 5 @,@ 150 m3 / s ) in 1993 , and the lowest was 41 @,@ 690 cu ft / s ( 1 @,@ 181 m3 / s ) in 2006 . <unk> of the flow vary even further . The largest discharge ever recorded was over 750 @,@ 000 cu ft / s ( 21 @,@ 000 m3 / s ) on July 31 , 1993 , during a historic flood . The lowest , a mere <unk> cu ft / s ( 17 @.@ 0 m3 / s ) – caused by the formation of an ice dam – was measured on December 23 , 1963 . 
- 
- = = Upper and Lower Missouri River = = 
- 
- The Upper Missouri River is north of Gavins Point Dam the last hydroelectric dam of 15 on the river and it 's just upstream from Sioux City , Iowa . The lower Missouri River is the 840 river miles until it meets the Mississippi just above St. Louis . The Lower Missouri River has no <unk> or Lock and dams but it has a <unk> of Wing dams that enable barge traffic by directing the flow of the river into a 200 foot wide , 12 foot deep channel . These wing dams have been put in place and maintained by the U.S. Army corps of engineers , and there currently are no plans to construct any Lock and dams to replace these wing dams on the Missouri River . 
- See also : - List of locks and dams of the Upper Mississippi River , List of locks and dams of the Ohio River 
- 
- = = Geology = = 
- 
- The Rocky Mountains of southwestern Montana at the headwaters of the Missouri River first rose in the Laramide <unk> , a mountain @-@ building episode that occurred from around 70 to 45 million years ago ( the end of the <unk> through the early Cenozoic ) . This <unk> <unk> Cretaceous rocks along the western side of the Western Interior <unk> , a vast shallow sea that stretched from the Arctic Ocean to the Gulf of Mexico , and deposited the sediments that now <unk> much of the drainage basin of the Missouri River . This Laramide <unk> caused the sea to retreat and laid the framework for a vast drainage system of rivers flowing from the Rocky and Appalachian Mountains , the predecessor of the modern @-@ day Mississippi watershed . The Laramide <unk> is essential to modern Missouri River hydrology , as snow and ice melt from the Rockies provide the majority of the flow in the Missouri and its tributaries . 
- The Missouri and many of its tributaries cross the Great Plains , flowing over or cutting into the <unk> Group and older mid @-@ Cenozoic <unk> rocks . The lowest major Cenozoic unit , the White River Formation , was deposited between roughly 35 and 29 million years ago and consists of <unk> , sandstone , limestone , and conglomerate . Channel <unk> and finer @-@ <unk> <unk> deposits of the <unk> Arikaree Group were deposited between 29 and 19 million years ago . The Miocene @-@ age <unk> and the slightly younger <unk> @-@ age <unk> Formation deposited atop the Arikaree Group , and are formed from material eroded off of the Rocky Mountains during a time of increased generation of topographic relief ; these formations stretch from the Rocky Mountains nearly to the Iowa border and give the Great Plains much of their gentle but persistent eastward tilt , and also constitute a major <unk> . 
- Immediately before the <unk> Ice Age , the Missouri River was likely split into three segments : an upper portion that drained northwards into Hudson Bay , and middle and lower sections that flowed eastward down the regional slope . As the Earth plunged into the Ice Age , a pre @-@ <unk> ( or possibly the <unk> ) <unk> diverted the Missouri River <unk> towards its present confluence with the Mississippi and caused it to integrate into a single river system that cuts across the regional slope . In western Montana , the Missouri River is thought to have once flowed north then east around the Bear <unk> Mountains . <unk> are found in some spots along the river in western Montana . <unk> of the continental ice sheets diverted the river and its tributaries , causing them to pool up into large temporary lakes such as <unk> Lakes Great Falls , <unk> and others . As the lakes rose , the water in them often spilled across adjacent local drainage divides , creating now @-@ abandoned channels and <unk> including the <unk> <unk> , 100 miles ( 160 km ) long . When the glaciers retreated , the Missouri flowed in a new course along the south side of the <unk> , and the lower part of the Milk River tributary took over the original main channel . 
- The Missouri 's nickname , the " Big <unk> " , was inspired by its enormous loads of sediment or silt – some of the largest of any North American river . In its pre @-@ development state , the river transported some 175 to 320 million short tons ( 159 to 290 Mt ) per year . The construction of dams and levees has drastically reduced this to 20 to 25 million short tons ( 18 to 23 Mt ) in the present day . Much of this sediment is derived from the river 's floodplain , also called the <unk> belt ; every time the river changed course , it would <unk> tons of soil and rocks from its banks . However , <unk> and channeling the river has kept it from reaching its natural sediment sources along most of its course . <unk> along the Missouri trap roughly 36 @.@ 4 million short tons ( 33 @.@ 0 Mt ) of sediment each year . Despite this , the river still transports more than half the total silt that empties into the Gulf of Mexico ; the Mississippi River Delta , formed by sediment deposits at the mouth of the Mississippi , constitutes a majority of sediments carried by the Missouri . 
- 
- = = First peoples = = 
- 
- Archaeological evidence , especially in Missouri , suggests that man first made his presence in the watershed of the Missouri River between 10 @,@ 000 and 12 @,@ 000 years ago at the end of the Pleistocene . During the end of the last glacial period , a great migration of humans began , traveling via the Bering land bridge from Eurasia into and throughout the Americas . As they traveled slowly over centuries , the Missouri River formed one of the main migration paths . Most settled in the Ohio Valley and the lower Mississippi River Valley , but many , including the <unk> builders , stayed along the Missouri , becoming the ancestors of the later indigenous peoples of the Great Plains . 
- The Native Americans that lived along the Missouri had access to ample food , water , and shelter . Many migratory animals inhabited the plains at the time , providing them meat , clothing , and other everyday items ; there were also great riparian areas in the river 's floodplain that provided them with natural herbs and staple foods . No written records from the tribes and peoples of the pre @-@ European period exist because they did not use writing . According to the writings of explorers , some of the major tribes along the Missouri River included the <unk> , <unk> , Omaha , <unk> , <unk> , Lakota , Sioux , <unk> , Hidatsa , Mandan , <unk> , <unk> <unk> and <unk> . 
- <unk> used the Missouri , at least to a limited extent , as a path of trade and transport . In addition , the river and its tributaries formed tribal boundaries . <unk> of the indigenous mostly centered on a semi @-@ nomadic culture ; many tribes would have different summer and winter camps . However , the center of Native American wealth and trade lay along the Missouri River in the Dakotas region on its great bend south . A large cluster of walled Mandan , Hidatsa and <unk> villages situated on <unk> and islands of the river was home to thousands , and later served as a market and trading post used by early French and British explorers and fur traders . Following the introduction of horses to Missouri River tribes , possibly from feral European @-@ introduced populations , natives ' way of life changed dramatically . The use of the horse allowed them to travel greater distances , and thus facilitated hunting , communications and trade . 
- Once , tens of millions of American bison ( commonly called buffalo ) , one of the keystone species of the Great Plains and the Ohio Valley , roamed the plains of the Missouri River basin . Most Native American groups in the basin relied heavily on the bison as a food source , and their hides and bones served to create other household items . In time , the species came to benefit from the indigenous peoples ' periodic controlled <unk> of the grasslands surrounding the Missouri to clear out old and dead growth . The large bison population of the region gave rise to the term great bison belt , an area of rich annual grasslands that extended from Alaska to Mexico along the eastern flank of the Continental Divide . However , after the arrival of Europeans in North America , both the bison and the Native Americans saw a rapid decline in population . Hunting eliminated bison populations east of the Mississippi River by 1833 and reduced the numbers in the Missouri basin to a mere few hundred . Foreign diseases such as smallpox raged across the land , <unk> Native American populations . Left without their primary source of sustenance , many of the remaining indigenous people were amalgamated into <unk> areas and reservations . 
- 
- = = Early European explorers = = 
- 
- In May <unk> , the French explorers Louis Jolliet and Jacques Marquette left the settlement of St. Ignace on Lake Huron and traveled down the Wisconsin and Mississippi Rivers , aiming to reach the Pacific Ocean . In late June , Jolliet and Marquette became the first documented European <unk> of the Missouri River , which according to their journals was in full flood . " I never saw anything more terrific , " Jolliet wrote , " a tangle of entire trees from the mouth of the <unk> [ Missouri ] with such <unk> that one could not attempt to cross it without great danger . The <unk> was such that the water was made muddy by it and could not clear itself . " They recorded <unk> or <unk> as the local name for the Missouri . However , the party never explored the Missouri beyond its mouth , nor did they linger in the area . In addition , they later learned that the Mississippi drained into the Gulf of Mexico and not the Pacific as they had originally presumed ; the expedition turned back about 440 miles ( 710 km ) short of the Gulf at the confluence of the Arkansas River with the Mississippi . 
- In 1682 , France expanded its territorial claims in North America to include land on the western side of the Mississippi River , which included the lower portion of the Missouri . However , the Missouri itself remained formally <unk> until Étienne de <unk> , Sieur de Bourgmont commanded an expedition in 1714 that reached at least as far as the mouth of the Platte River . It is unclear exactly how far Bourgmont traveled beyond there ; he described the blond @-@ haired <unk> in his journals , so it is likely he reached as far as their villages in present @-@ day North Dakota . Later that year , Bourgmont published The Route To Be Taken To <unk> The Missouri River , the first known document to use the name " Missouri River " ; many of the names he gave to tributaries , mostly for the native tribes that lived along them , are still in use today . The expedition 's discoveries eventually found their way to <unk> Guillaume <unk> , who used the information to create a map of the lower Missouri . In <unk> , Jean @-@ Baptiste Le <unk> , Sieur de <unk> requested that the French government bestow upon Bourgmont the Cross of St. Louis because of his " outstanding service to France " . 
- Bourgmont had in fact been in trouble with the French colonial authorities since 1706 , when he deserted his post as commandant of Fort Detroit after poorly handling an attack by the Ottawa that resulted in thirty @-@ one deaths . However , his reputation was enhanced in 1720 when the Pawnee – who had earlier been befriended by Bourgmont – massacred the Spanish <unk> expedition near present @-@ day Columbus , Nebraska on the Missouri River and temporarily ending Spanish encroachment on French Louisiana . 
- Bourgmont established Fort Orleans , the first European settlement of any kind on the Missouri River , near present @-@ day Brunswick , Missouri , in 1723 . The following year Bourgmont led an expedition to enlist <unk> support against the Spanish , who continued to show interest in taking over the Missouri . In 1725 Bourgmont brought the chiefs of several Missouri River tribes to visit France . There he was raised to the rank of nobility and did not accompany the chiefs back to North America . Fort Orleans was either abandoned or its small contingent massacred by Native Americans in 1726 . 
- The French and Indian War erupted when territorial disputes between France and Great Britain in North America reached a head in 1754 . By 1763 , France was defeated by the much greater strength of the British army and was forced to cede its Canadian possessions to the English and Louisiana to the Spanish in the Treaty of Paris , amounting to most of its colonial holdings in North America . Initially , the Spanish did not extensively explore the Missouri and let French traders continue their activities under license . However , this ended after news of the British Hudson 's Bay Company incursions in the upper Missouri River watershed was brought back following an expedition by Jacques D <unk> in the early <unk> . In 1795 the Spanish chartered the Company of <unk> and <unk> of the Missouri , popularly referred to as the " Missouri Company " , and offered a reward for the first person to reach the Pacific Ocean via the Missouri . In 1794 and 1795 expeditions led by Jean Baptiste <unk> and Antoine Simon <unk> de la <unk> did not even make it as far north as the Mandan villages in central North Dakota . 
- <unk> the most successful of the Missouri Company expeditions was that of James MacKay and John Evans . The two set out along the Missouri , and established Fort Charles about 20 miles ( 32 km ) south of present @-@ day Sioux City as a winter camp in 1795 . At the Mandan villages in North Dakota , they expelled several British traders , and while talking to the populace they <unk> the location of the Yellowstone River , which was called Roche <unk> ( " Yellow Rock " ) by the French . Although MacKay and Evans failed to accomplish their original goal of reaching the Pacific , they did create the first accurate map of the upper Missouri River . 
- In 1795 , the young United States and Spain signed <unk> 's Treaty , which recognized American rights to navigate the Mississippi River and store goods for export in New Orleans . Three years later , Spain revoked the treaty and in 1800 secretly returned Louisiana to Napoleonic France in the Third Treaty of San <unk> . This transfer was so secret that the Spanish continued to administer the territory . In 1801 , Spain restored rights to use the Mississippi and New Orleans to the United States . 
- Fearing that the <unk> could occur again , President Thomas Jefferson proposed to buy the port of New Orleans from France for $ 10 million . Instead , faced with a debt crisis , Napoleon offered to sell the entirety of Louisiana , including the Missouri River , for $ 15 million – amounting to less than 3 <unk> per acre . The deal was signed in 1803 , doubling the size of the United States with the acquisition of the Louisiana Territory . In 1803 , Jefferson instructed Meriwether Lewis to explore the Missouri and search for a water route to the Pacific Ocean . By then , it had been discovered that the Columbia River system , which drains into the Pacific , had a similar latitude as the headwaters of the Missouri River , and it was widely believed that a connection or short <unk> existed between the two . However , Spain <unk> at the takeover , citing that they had never formally returned Louisiana to the French . Spanish authorities warned Lewis not to take the journey and forbade him from seeing the MacKay and Evans map of the Missouri , although Lewis eventually managed to gain access to it . 
- Meriwether Lewis and William Clark began their famed expedition in 1804 with a party of thirty @-@ three people in three boats . Although they became the first Europeans to travel the entire length of the Missouri and reach the Pacific Ocean via the Columbia , they found no trace of the Northwest Passage . The maps made by Lewis and Clark , especially those of the Pacific Northwest region , provided a foundation for future explorers and emigrants . They also negotiated relations with multiple Native American tribes and wrote extensive reports on the climate , ecology and geology of the region . Many present @-@ day names of geographic features in the upper Missouri basin originated from their expedition . 
- 
- = = American frontier = = 
- 
- 
- = = = Fur trade = = = 
- 
- As early as the 18th century , fur trappers entered the extreme northern basin of the Missouri River in the hopes of finding populations of beaver and river otter , the sale of whose pelts drove the thriving North American fur trade . They came from many different places – some from the Canadian fur corporations at Hudson Bay , some from the Pacific Northwest ( see also : Maritime fur trade ) , and some from the <unk> United States . Most did not stay in the area for long , as they failed to find significant resources . 
- The first glowing reports of country rich with thousands of game animals came in 1806 when Meriwether Lewis and William Clark returned from their two @-@ year expedition . Their journals described lands rich with thousands of buffalo , beaver , and river otter ; and also an abundant population of sea otters on the Pacific Northwest coast . In 1807 , explorer Manuel Lisa organized an expedition which would lead to the explosive growth of the fur trade in the upper Missouri River country . Lisa and his crew traveled up the Missouri and Yellowstone Rivers , trading manufactured items in return for furs from local Native American tribes , and established a fort at the confluence of the Yellowstone and a tributary , the Bighorn , in southern Montana . Although the business started small , it quickly grew into a thriving trade . 
- Lisa 's men started construction of Fort Raymond , which sat on a bluff overlooking the confluence of the Yellowstone and Bighorn , in the fall of 1807 . The fort would serve primarily as a trading post for <unk> with the Native Americans for furs . This method was unlike that of the Pacific Northwest fur trade , which involved trappers hired by the various fur enterprises , namely Hudson 's Bay . Fort Raymond was later replaced by Fort Lisa at the confluence of the Missouri and Yellowstone in North Dakota ; a second fort also called Fort Lisa was built downstream on the Missouri River in Nebraska . In 1809 the St. Louis Missouri Fur Company was founded by Lisa in conjunction with William Clark and Pierre <unk> , among others . In 1828 , the American Fur Company founded Fort Union at the confluence of the Missouri and Yellowstone Rivers . Fort Union gradually became the main headquarters for the fur trade in the upper Missouri basin . 
- Fur trapping activities in the early 19th century encompassed nearly all of the Rocky Mountains on both the eastern and western slopes . <unk> of the Hudson 's Bay Company , St. Louis Missouri Fur Company , American Fur Company , Rocky Mountain Fur Company , North West Company and other outfits worked thousands of streams in the Missouri watershed as well as the neighboring Columbia , Colorado , Arkansas , and Saskatchewan river systems . During this period , the trappers , also called mountain men , blazed trails through the wilderness that would later form the paths pioneers and settlers would travel by into the West . Transport of the thousands of beaver pelts required ships , providing one of the first large motives for river transport on the Missouri to start . 
- As the 1830s drew to a close , the fur industry slowly began to die as silk replaced beaver fur as a desirable clothing item . By this time , also , the beaver population of streams in the Rocky Mountains had been decimated by intense hunting . Furthermore , frequent Native American attacks on trading posts made it dangerous for employees of the fur companies . In some regions , the industry continued well into the 1840s , but in others such as the Platte River valley , declines of the beaver population contributed to an earlier demise . The fur trade finally disappeared in the Great Plains around 1850 , with the primary center of industry shifting to the Mississippi Valley and central Canada . Despite the demise of the once @-@ prosperous trade , however , its legacy led to the opening of the American West and a flood of settlers , farmers , ranchers , adventurers , <unk> , financially bereft , and entrepreneurs took their place . 
- 
- = = = Settlers and pioneers = = = 
- 
- The river roughly defined the American frontier in the 19th century , particularly downstream from Kansas City , where it takes a sharp eastern turn into the heart of the state of Missouri . The major trails for the opening of the American West all have their starting points on the river , including the California , Mormon , Oregon , and Santa Fe trails . The first westward leg of the Pony Express was a ferry across the Missouri at St. Joseph , Missouri . Similarly , most emigrants arrived at the eastern terminus of the First Transcontinental Railroad via a ferry ride across the Missouri between Council Bluffs , Iowa and Omaha . The <unk> Bridge became the first bridge to cross the Missouri River in 1869 , and its location was a major reason why Kansas City became the largest city on the river upstream from its mouth at St. Louis . 
- True to the then @-@ ideal of <unk> Destiny , over 500 @,@ 000 people set out from the river town of Independence , Missouri to their various destinations in the American West from the 1830s to the 1860s . These people had many reasons to embark on this strenuous year @-@ long journey – economic crisis , and later gold strikes including the California Gold Rush , for example . For most , the route took them up the Missouri to Omaha , Nebraska , where they would set out along the Platte River , which flows from the Rocky Mountains in Wyoming and Colorado eastwards through the Great Plains . An early expedition led by Robert Stuart from 1812 to 1813 proved the Platte impossible to navigate by the <unk> canoes they used , let alone the large <unk> and <unk> that would later <unk> the Missouri in increasing numbers . One explorer remarked that the Platte was " too thick to drink , too thin to <unk> " . Nevertheless , the Platte provided an abundant and reliable source of water for the pioneers as they headed west . <unk> wagons , popularly referred to as prairie <unk> , provided the primary means of transport until the beginning of regular boat service on the river in the 1850s . 
- During the 1860s , gold strikes in Montana , Colorado , Wyoming and northern Utah attracted another wave of <unk> to the region . Although some freight was hauled overland , most transport to and from the gold fields was done through the Missouri and Kansas Rivers , as well as the Snake River in western Wyoming and the Bear River in Utah , Idaho and Wyoming . It is estimated that of the passengers and freight hauled from the Midwest to Montana , over 80 percent were transported by boat , a journey that took 150 days in the upstream direction . A route more directly west into Colorado lay along the Kansas River and its tributary the Republican River as well as pair of smaller Colorado streams , Big Sandy Creek and the South Platte River , to near Denver . The gold rushes precipitated the decline of the Bozeman Trail as a popular emigration route , as it passed through land held by often @-@ hostile Native Americans . <unk> paths were blazed to the Great Salt Lake near <unk> , Utah during the gold rush period , which led to the large @-@ scale settlement of the Rocky Mountains region and eastern Great Basin . 
- As settlers expanded their holdings into the Great Plains , they ran into land conflicts with Native American tribes . This resulted in frequent raids , massacres and armed conflicts , leading to the federal government creating multiple treaties with the Plains tribes , which generally involved establishing borders and reserving lands for the indigenous . As with many other treaties between the U.S. and Native Americans , they were soon broken , leading to huge wars . Over 1 @,@ 000 battles , big and small , were fought between the U.S. military and Native Americans before the tribes were forced out of their land onto reservations . 
- Conflicts between natives and settlers over the opening of the Bozeman Trail in the Dakotas , Wyoming and Montana led to Red Cloud 's War , in which the Lakota and Cheyenne fought against the U.S. Army . The fighting resulted in a complete Native American victory . In 1868 , the Treaty of Fort <unk> was signed , which " guaranteed " the use of the Black Hills , <unk> River Country and other regions surrounding the northern Missouri River to Native Americans without white intervention . The Missouri River was also a significant landmark as it divides northeastern Kansas from western Missouri ; pro @-@ slavery forces from Missouri would cross the river into Kansas and spark <unk> during Bleeding Kansas , leading to continued tension and hostility even today between Kansas and Missouri . Another significant military engagement on the Missouri River during this period was the 1861 Battle of <unk> , which did not affect Native Americans but rather was a turning point in the American Civil War that allowed the Union to seize control of transport on the river , <unk> the state of Missouri from joining the Confederacy . 
- However , the peace and freedom of the Native Americans did not last for long . The Great Sioux War of 1876 – 77 was sparked when American miners discovered gold in the Black Hills of western South Dakota and eastern Wyoming . These lands were originally set aside for Native American use by the Treaty of Fort <unk> . When the settlers <unk> onto the lands , they were attacked by Native Americans . U.S. troops were sent to the area to protect the miners , and drive out the natives from the new settlements . During this bloody period , both the Native Americans and the U.S. military won victories in major battles , resulting in the loss of nearly a thousand lives . The war eventually ended in an American victory , and the Black Hills were opened to settlement . Native Americans of that region were relocated to reservations in Wyoming and southeastern Montana . 
- 
- = = Dam @-@ building era = = 
- 
- In the late 19th and early 20th centuries , a great number of dams were built along the course of the Missouri , transforming 35 percent of the river into a chain of reservoirs . River development was stimulated by a variety of factors , first by growing demand for electricity in the rural northwestern parts of the basin , and also by floods and droughts that plagued rapidly growing agricultural and urban areas along the lower Missouri River . Small , privately owned hydroelectric projects have existed since the 1890s , but the large flood @-@ control and storage dams that characterize the middle reaches of the river today were not constructed until the 1950s . 
- Between 1890 and 1940 , five dams were built in the vicinity of Great Falls to generate power from the Great Falls of the Missouri , a chain of giant waterfalls formed by the river in its path through western Montana . Black Eagle Dam , built in 1891 on Black Eagle Falls , was the first dam of the Missouri . <unk> in 1926 with a more modern structure , the dam was little more than a small weir atop Black Eagle Falls , diverting part of the Missouri 's flow into the Black Eagle power plant . The largest of the five dams , Ryan Dam , was built in 1913 . The dam lies directly above the 87 @-@ foot ( 27 m ) Great Falls , the largest waterfall of the Missouri . 
- In the same period , several private establishments – most notably the Montana Power Company – began to develop the Missouri River above Great Falls and below Helena for power generation . A small run @-@ of @-@ the river structure completed in 1898 near the present site of Canyon Ferry Dam became the second dam to be built on the Missouri . This rock @-@ filled timber crib dam generated seven and a half megawatts of electricity for Helena and the surrounding countryside . The nearby steel Hauser Dam was finished in 1907 , but failed in 1908 because of structural deficiencies , causing catastrophic flooding all the way downstream past Craig . At Great Falls , a section of the Black Eagle Dam was <unk> to save nearby factories from inundation . Hauser was rebuilt in 1910 as a concrete gravity structure , and stands to this day . 
- <unk> Dam , about 45 miles ( 72 km ) downstream of Helena , was the third hydroelectric dam built on this stretch of the Missouri River . When completed in 1918 by the Montana Power Company and the United Missouri River Power Company , its reservoir flooded the Gates of the Mountains , a limestone canyon which Meriwether Lewis described as " the most remarkable <unk> that we have yet seen … the tow [ er ] ing and projecting rocks in many places seem ready to tumble on us . " In 1949 , the U.S. Bureau of Reclamation ( <unk> ) began construction on the modern Canyon Ferry Dam to provide flood control to the Great Falls area . By 1954 , the rising waters of Canyon Ferry Lake submerged the old 1898 dam , whose powerhouse still stands underwater about 1 @.@ 5 miles ( 2 @.@ 4 km ) upstream of the present @-@ day dam . 
- " [ The Missouri 's temperament was ] uncertain as the actions of a jury or the state of a woman 's mind . " – Sioux City Register , March 28 , 1868 
- The Missouri basin suffered a series of catastrophic floods around the turn of the 20th century , most notably in 1844 , 1881 , and 1926 – 1927 . In 1940 , as part of the Great Depression @-@ era New Deal , the U.S. Army Corps of Engineers ( USACE ) completed Fort Peck Dam in Montana . Construction of this massive public works project provided jobs for more than 50 @,@ 000 laborers during the Depression and was a major step in providing flood control to the lower half of the Missouri River . However , Fort Peck only controls runoff from 11 percent of the Missouri River watershed , and had little effect on a severe snowmelt flood that struck the lower basin three years later . This event was particularly destructive as it submerged manufacturing plants in Omaha and Kansas City , greatly delaying shipments of military supplies in World War II . 
- Flooding damages on the Mississippi – Missouri river system were one of the primary reasons for which Congress passed the Flood Control Act of 1944 , opening the way for the USACE to develop the Missouri on a massive scale . The 1944 act authorized the Pick – Sloan Missouri Basin Program ( Pick – Sloan Plan ) , which was a composite of two widely varying proposals . The Pick plan , with an emphasis on flood control and hydroelectric power , called for the construction of large storage dams along the main stem of the Missouri . The Sloan plan , which stressed the development of local irrigation , included provisions for roughly 85 smaller dams on tributaries . 
- In the early stages of Pick – Sloan development , tentative plans were made to build a low dam on the Missouri at <unk> , North Dakota and 27 smaller dams on the Yellowstone River and its tributaries . This was met with controversy from inhabitants of the Yellowstone basin , and eventually the <unk> proposed a solution : to greatly increase the size of the proposed dam at <unk> – today 's Garrison Dam , thus replacing the storage that would have been provided by the Yellowstone dams . Because of this decision , the Yellowstone is now the longest free @-@ flowing river in the contiguous United States . In the 1950s , construction commenced on the five mainstem dams – Garrison , Oahe , Big Bend , Fort Randall and Gavins Point – proposed under the Pick @-@ Sloan Plan . Along with Fort Peck , which was integrated as a unit of the Pick @-@ Sloan Plan in the 1940s , these dams now form what is known as the Missouri River Mainstem System . 
- The six dams of the Mainstem System , chiefly Fort Peck , Garrison and Oahe , are among the largest dams in the world by volume ; their sprawling reservoirs also rank within the biggest of the nation . Holding up to 74 @.@ 1 million acre feet ( 91 @.@ 4 km3 ) in total , the six reservoirs can store more than three years ' worth of the river 's flow as measured below Gavins Point , the <unk> dam . This capacity makes it the largest reservoir system in the United States and one of the largest in North America . In addition to storing irrigation water , the system also includes an annual flood @-@ control reservation of 16 @.@ 3 million acre feet ( 20 @.@ 1 km3 ) . Mainstem power plants generate about 9 @.@ 3 billion <unk> annually – equal to a constant output of almost 1 @,@ 100 megawatts . Along with nearly 100 smaller dams on tributaries , namely the Bighorn , Platte , Kansas , and Osage Rivers , the system provides irrigation water to nearly 7 @,@ 500 sq mi ( 19 @,@ 000 km2 ) of land . 
- The table at left lists statistics of all fifteen dams on the Missouri River , ordered downstream . Many of the run @-@ of @-@ the @-@ river dams on the Missouri ( marked in yellow ) form very small <unk> which may or may not have been given names ; those unnamed are left blank . All dams are on the upper half of the river above Sioux City ; the lower river is uninterrupted due to its longstanding use as a shipping channel . 
- 
- = = Navigation = = 
- 
- " [ Missouri River shipping ] never achieved its expectations . Even under the very best of circumstances , it was never a huge industry . " ~ Richard <unk> , former Missouri River Basin Association executive director 
- Boat travel on the Missouri started with the wood @-@ framed canoes and bull boats of the Native Americans , which were used for thousands of years before the introduction of larger craft to the river upon colonization of the Great Plains . The first steamboat on the Missouri was the Independence , which started running between St. Louis and <unk> , Missouri around 1819 . By the 1830s , large mail and freight @-@ carrying vessels were running regularly between Kansas City and St. Louis , and many traveled even farther upstream . A handful , such as the Western Engineer and the Yellowstone , were able to make it up the river as far as eastern Montana . 
- During the early 19th century , at the height of the fur trade , steamboats and <unk> began traveling nearly the whole length of the Missouri from Montana 's rugged Missouri Breaks to the mouth , carrying beaver and buffalo furs to and from the areas that the trappers frequented . This resulted in the development of the Missouri River <unk> , which specialized in carrying furs . Since these boats could only travel <unk> , they were dismantled and sold for lumber upon their arrival at St. Louis . 
- Water transport increased through the 1850s with multiple craft ferrying pioneers , emigrants and miners ; many of these runs were from St. Louis or Independence to near Omaha . There , most of these people would set out overland along the large but shallow and <unk> Platte River , which was described by pioneers as " a mile wide and an inch deep " and " the most magnificent and useless of rivers " . <unk> navigation peaked in 1858 with over 130 boats operating full @-@ time on the Missouri , with many more smaller vessels . Many of the earlier vessels were built on the Ohio River before being transferred to the Missouri . Side @-@ <unk> steamboats were preferred over the larger <unk> used on the Mississippi and Ohio because of their greater maneuverability . 
- The industry 's success , however , did not guarantee safety . In the early decades before the river 's flow was controlled by man , its sketchy rises and falls and its massive amounts of sediment , which prevented a clear view of the bottom , wrecked some 300 vessels . Because of the dangers of navigating the Missouri River , the average ship 's lifespan was short , only about four years . The development of the Transcontinental and Northern Pacific <unk> marked the beginning of the end of steamboat commerce on the Missouri . <unk> by trains , the number of boats slowly dwindled , until there was almost nothing left by the 1890s . Transport of agricultural and mining products by barge , however , saw a revival in the early twentieth century . 
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- = = = Passage to Sioux City = = = 
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- Since the beginning of the 20th century , the Missouri River has been extensively engineered for water transport purposes , and about 32 percent of the river now flows through artificially <unk> channels . In 1912 , the USACE was authorized to maintain the Missouri to a depth of six feet ( 1 @.@ 8 m ) from the Port of Kansas City to the mouth , a distance of 368 miles ( 592 km ) . This was accomplished by constructing levees and wing dams to direct the river 's flow into a straight , narrow channel and prevent sedimentation . In 1925 , the USACE began a project to widen the river 's navigation channel to 200 feet ( 61 m ) ; two years later , they began dredging a deep @-@ water channel from Kansas City to Sioux City . These modifications have reduced the river 's length from some 2 @,@ 540 miles ( 4 @,@ 090 km ) in the late 19th century to 2 @,@ 341 miles ( 3 @,@ 767 km ) in the present day . 
- Construction of dams on the Missouri under the Pick @-@ Sloan Plan in the mid @-@ twentieth century was the final step in aiding navigation . The large reservoirs of the Mainstem System help provide a dependable flow to maintain the navigation channel year @-@ round , and are capable of halting most of the river 's annual <unk> . However , high and low water cycles of the Missouri – notably the protracted early @-@ 21st @-@ century drought in the Missouri River basin and historic floods in 1993 and 2011 – are difficult for even the massive Mainstem System reservoirs to control . 
- In 1945 , the USACE began the Missouri River Bank Stabilization and Navigation Project , which would permanently increase the river 's navigation channel to a width of 300 feet ( 91 m ) and a depth of nine feet ( 2 @.@ 7 m ) . During work that continues to this day , the 735 @-@ mile ( 1 @,@ 183 km ) navigation channel from Sioux City to St. Louis has been controlled by building rock <unk> to direct the river 's flow and <unk> out sediments , sealing and cutting off meanders and side channels , and dredging the riverbed . However , the Missouri has often resisted the efforts of the USACE to control its depth . In 2006 , several U.S. Coast Guard boats ran aground in the Missouri River because the navigation channel had been severely <unk> . The USACE was blamed for failing to maintain the channel to the minimum depth . 
- In 1929 , the Missouri River Navigation Commission estimated the total amount of goods shipped on the river annually at 15 million tons ( 13 @.@ 6 million metric tons ) , providing widespread consensus for the creation of a navigation channel . However , shipping traffic has since been far lower than expected – shipments of commodities including produce , manufactured items , lumber , and oil averaged only <unk> @,@ 000 tons ( <unk> @,@ 000 t ) per year from 1994 to 2006 . 
- By tonnage of transported material , Missouri is by far the largest user of the river accounting for 83 percent of river traffic , while Kansas has 12 percent , Nebraska three percent and Iowa two percent . Almost all of the barge traffic on the Missouri River ships sand and gravel dredged from the lower 500 miles ( 800 km ) of the river ; the remaining portion of the shipping channel now sees little to no use by commercial vessels . 
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- = = = Traffic decline = = = 
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- <unk> of goods shipped by barges on the Missouri River has seen a serious decline from the 1960s to the present . In the 1960s , the USACE predicted an increase to 12 million short tons ( 11 Mt ) per year by 2000 , but instead the opposite has happened . The amount of goods plunged from 3 @.@ 3 million short tons ( 3 @.@ 0 Mt ) in 1977 to just 1 @.@ 3 million short tons ( 1 @.@ 2 Mt ) in 2000 . One of the largest drops has been in agricultural products , especially wheat . Part of the reason is that irrigated land along the Missouri has only been developed to a fraction of its potential . In 2006 , barges on the Missouri hauled only 200 @,@ 000 short tons ( 180 @,@ 000 t ) of products which is equal to the amount of daily freight traffic on the Mississippi . 
- Drought conditions in the early 21st century and competition from other modes of transport – mainly railroads – are the primary reason for decreasing river traffic on the Missouri . The failure of the USACE to consistently maintain the navigation channel has also hampered the industry . Currently , efforts are being made to revive the shipping industry on the Missouri River , because of the efficiency and <unk> of river transport to haul agricultural products , and the overcrowding of alternative transportation routes . <unk> such as expanding the navigation channel and releasing more water from reservoirs during the peak of the navigation season are being considered . Drought conditions lifted in 2010 , in which about <unk> @,@ 000 short tons ( <unk> @,@ 000 t ) were <unk> on the Missouri , representing the first significant increase in shipments since 2000 . However , flooding in 2011 closed record stretches of the river to boat traffic – " wash [ ing ] away hopes for a bounce @-@ back year . " 
- There are no lock and dams on the lower Missouri River , but there are plenty of wing dams that <unk> out into the river and make it harder for barges to navigate . In contrast , the upper Mississippi has 29 locks and dams and averaged 61 @.@ 3 million tons of cargo annually from 2008 to 2011 , and its locks are closed in the winter . 
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- = = Ecology = = 
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- = = = Natural history = = = 
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- Historically , the thousands of square miles that comprised the floodplain of the Missouri River supported a wide range of plant and animal species . Biodiversity generally increased proceeding downstream from the cold , subalpine headwaters in Montana to the temperate , moist climate of Missouri . Today , the river 's riparian zone consists primarily of <unk> , willows and <unk> , with several other types of trees such as maple and ash . Average tree height generally increases farther from the riverbanks for a limited distance , as land adjacent to the river is vulnerable to soil erosion during floods . Because of its large sediment concentrations , the Missouri does not support many aquatic invertebrates . However , the basin does support about 300 species of birds and 150 species of fish , some of which are endangered such as the <unk> <unk> . The Missouri 's aquatic and riparian habitats also support several species of mammals , such as <unk> , river otters , <unk> , <unk> , and raccoons . 
- The World Wide Fund For Nature divides the Missouri River watershed into three freshwater <unk> : the Upper Missouri , Lower Missouri and Central Prairie . The Upper Missouri , roughly encompassing the area within Montana , Wyoming , southern Alberta and Saskatchewan , and North Dakota , comprises mainly <unk> shrub @-@ steppe grasslands with sparse biodiversity because of Ice Age <unk> . There are no known endemic species within the region . Except for the headwaters in the Rockies , there is little precipitation in this part of the watershed . The Middle Missouri ecoregion , extending through Colorado , southwestern Minnesota , northern Kansas , Nebraska , and parts of Wyoming and Iowa , has greater rainfall and is characterized by temperate forests and grasslands . Plant life is more diverse in the Middle Missouri , which is also home to about twice as many animal species . Finally , the Central Prairie ecoregion is situated on the lower part of the Missouri , encompassing all or parts of Missouri , Kansas , Oklahoma and Arkansas . Despite large seasonal temperature fluctuations , this region has the greatest diversity of plants and animals of the three . Thirteen species of <unk> are endemic to the lower Missouri . 
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- = = = Human impacts = = = 
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- Since the beginning of river commerce and industrial development in the 1800s , the Missouri has been severely polluted and its water quality degraded by human activity . Most of the river 's floodplain habitat is long gone , replaced by irrigated agricultural land . Development of the floodplain has led to increasing amounts of people and infrastructure within areas at high risk of inundation . <unk> have been constructed along more than a third of the river in order to keep <unk> within the channel , but with the consequences of faster stream velocity and a resulting increase of peak flows in downstream areas . <unk> runoff , which causes elevated levels of nitrogen and other nutrients , is a major problem along the Missouri River , especially in Iowa and Missouri . This form of pollution also heavily affects the upper Mississippi , Illinois and Ohio Rivers . Low oxygen levels in rivers and the vast Gulf of Mexico dead zone at the end of the Mississippi Delta are both results of high nutrient concentrations in the Missouri and other tributaries of the Mississippi . 
- <unk> of the lower Missouri waters has made the river narrower , deeper and less accessible to riparian flora and fauna . Numerous dams and bank stabilization projects have been constructed to facilitate the conversion of 300 @,@ 000 acres ( 1 @,@ 200 km2 ) of Missouri River floodplain to agricultural land . Channel control has significantly reduced the volume of sediment transported downstream by the river and eliminated critical habitat for fish , birds and amphibians . By the early 21st century , declines in populations of native species prompted the U.S. Fish and Wildlife Service to issue a biological opinion recommending restoration of river habitats for <unk> endangered bird and fish species . 
- The USACE began work on ecosystem restoration projects along the lower Missouri River in the early 21st century . Because of the low use of the shipping channel in the lower Missouri maintained by the USACE , it is now considered feasible to remove some of the levees , <unk> , and wing dams that <unk> the river 's flow , thus allowing it to naturally restore its banks . By 2001 , there were 87 @,@ 000 acres ( 350 km2 ) of riverside floodplain undergoing active restoration . 
- Restoration projects have re @-@ mobilized some of the sediments that had been trapped behind bank stabilization structures , prompting concerns of exacerbated nutrient and sediment pollution locally and downstream in the northern Gulf of Mexico . A 2010 National Research Council report assessed the roles of sediment in the Missouri River , evaluating current habitat restoration strategies and alternative ways to manage sediment . The report found that a better understanding of sediment processes in the Missouri River , including the creation of a " sediment budget " – an accounting of sediment transport , erosion , and deposition volumes for the length of the Missouri River – would provide a foundation for projects to improve water quality standards and protect endangered species . 
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- = = Tourism and recreation = = 
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- With over 1 @,@ 500 sq mi ( 3 @,@ 900 km2 ) of open water , the six reservoirs of the Missouri River Mainstem System provide some of the main recreational areas within the basin . <unk> has increased from 10 million visitor @-@ hours in the mid @-@ 1960s to over 60 million visitor @-@ hours in 1990 . Development of visitor facilities was spurred by the Federal Water Project Recreation Act of 1965 , which required the USACE to build and maintain boat ramps , campgrounds and other public facilities along major reservoirs . Recreational use of Missouri River reservoirs is estimated to contribute $ 85 – 100 million to the regional economy each year . 
- The Lewis and Clark National Historic Trail , some 3 @,@ 700 miles ( 6 @,@ 000 km ) long , follows nearly the entire Missouri River from its mouth to its source , <unk> the route of the Lewis and Clark Expedition . <unk> from Wood River , Illinois , in the east , to Astoria , Oregon , in the west , it also follows portions of the Mississippi and Columbia Rivers . The trail , which spans through eleven U.S. states , is maintained by various federal and state government agencies ; it passes through some 100 historic sites , notably archaeological locations including the Knife River Indian <unk> National Historic Site . 
- Parts of the river itself are designated for recreational or <unk> use . The Missouri National Recreational River consists of portions of the Missouri downstream from Fort Randall and Gavins Point <unk> that total 98 miles ( 158 km ) . These reaches exhibit islands , meanders , <unk> , underwater rocks , <unk> , <unk> , and other once @-@ common features of the lower river that have now disappeared under reservoirs or have been destroyed by channeling . About forty @-@ five steamboat wrecks are scattered along these reaches of the river . 
- <unk> from Great Falls , Montana , about 149 miles ( 240 km ) of the river course through a rugged series of canyons and badlands known as the Missouri Breaks . This part of the river , designated a U.S. National Wild and Scenic River in 1976 , flows within the Upper Missouri Breaks National Monument , a 375 @,@ 000 @-@ acre ( 1 @,@ 520 km2 ) preserve comprising steep cliffs , deep <unk> , arid plains , badlands , archaeological sites , and whitewater rapids on the Missouri itself . The preserve includes a wide variety of plant and animal life ; recreational activities include boating , rafting , hiking and wildlife observation . 
- In north @-@ central Montana , some 1 @,@ 100 @,@ 000 acres ( 4 @,@ 500 km2 ) along over 125 miles ( 201 km ) of the Missouri River , centering on Fort Peck Lake , comprise the Charles M. Russell National Wildlife Refuge . The wildlife refuge consists of a native northern Great Plains ecosystem that has not been heavily affected by human development , except for the construction of Fort Peck Dam . Although there are few designated trails , the whole preserve is open to hiking and camping . 
- Many U.S. national parks , such as Glacier National Park , Rocky Mountain National Park , Yellowstone National Park and <unk> National Park are in the watershed . Parts of other rivers in the basin are set aside for preservation and recreational use – notably the Niobrara National Scenic River , which is a 76 @-@ mile ( 122 km ) protected stretch of the Niobrara River , one of the Missouri 's longest tributaries . The Missouri flows through or past many National Historic Landmarks , which include Three Forks of the Missouri , Fort Benton , Montana , Big Hidatsa Village Site , Fort Atkinson , Nebraska and Arrow Rock Historic District . 
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- 
- = Monty Can 't Buy Me Love = 
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- " Monty Can 't Buy Me Love " is the twenty @-@ first episode of The Simpsons ' tenth season . It first aired on the Fox network in the United States on May 2 , 1999 . In the episode , Mr. Burns is jealous of <unk> owner Arthur Fortune , who is beloved by the people of Springfield . In order to win the people 's love , Burns <unk> the help of Homer Simpson , Professor <unk> and Groundskeeper Willie to capture the Loch Ness monster . 
- The episode was written by John Swartzwelder and directed by Mark <unk> . The idea for the episode was pitched by the Simpsons writing staff , who wanted to make an episode in which Mr. Burns becomes a " <unk> " , a millionaire who goes on thrilling adventures . Although it would originally be green , a mistake in the overseas animation led to the Loch Ness monster looking pink , a mistake that was ultimately too expensive to fix . 
- In its original broadcast , " Monty Can 't Buy Me Love " was seen by approximately 7 @.@ 26 million viewers , and finished in 43rd place in the ratings the week it aired . Following the release of The Simpsons : The Complete Tenth Season , the episode received mixed reviews from critics . 
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- = = Plot = = 
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- On a normal afternoon , Marge wants Homer , Bart , Lisa , and Maggie to go for a walk . They agree when faced with the threat of having to talk to each other , and arrive at a Fortune Megastore , a venture of wealthy Arthur Fortune ( modelled on British entrepreneur Richard Branson ) . Fortune easily charms the crowd and hands out dollar bills to his customers . This <unk> the unpopular Mr. Burns , who asks Homer to help him be loved by all . 
- As his first activity , Burns has Homer throw silver dollars from the top of a tall building , which instead of winning him popularity just causes injuries and <unk> the crowds below . Next , he writes out a check and tells Homer to donate it to the Springfield Hospital , but Homer is mistakenly believed to be the donor and receives the credit . Mr. Burns appears on a radio show called " Jerry Rude and the <unk> <unk> " and is mocked by Rude . Feeling disappointed , Mr. Burns decides to go to Scotland to capture the legendary Loch Ness Monster with help from Homer , Professor <unk> and Groundskeeper Willie . After little progress , Burns has the <unk> drained of water to expose the creature . After <unk> the monster single @-@ handed ( although it is not shown , it is mentioned that the monster swallowed him ) , Burns has it sent to Springfield to be unveiled , where " <unk> " turns out to be friendly and charms all of the spectators . 
- However , during the unveiling of the monster , Burns is blinded by camera flashes . He runs into a camera , which crashes and starts a fire : the crowd panic and flee . Following this disaster , Homer then cheers up Burns by pointing out that being loved means you have to be nice to people everyday but being hated means you do not have to do anything . Burns agrees with that logic , saying " I 'm a selfish old <unk> that fits me like a <unk> ! " In the aftermath of the incident , Homer and Burns give <unk> a job at the " Vegas Town Casino " . 
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- = = Production = = 
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- " Monty Can 't Buy Me Love " was written by John Swartzwelder and directed by Mark <unk> . It originally aired on the Fox network in the United States on May 2 , 1999 . The inspiration for the episode came from the concept of " <unk> " , a term the Simpsons writers found in an issue of The Economist to describe <unk> who " do really incredible stunts " and " go on amazing adventures " . The episode 's third act went through several <unk> , and the writers had a lot of ideas about what Burns should do once he has captured the Loch Ness monster . Eventually , the staff settled on the act 's current iteration , which was conceived by Simpsons writer George Meyer . According to current showrunner Al Jean , the color of the Loch Ness monster would originally be green , but when the overseas animation was completed , the Loch Ness monster had a " kind of pinkish " color . Because it would be too expensive to re @-@ color all the <unk> in which the monster appears , the staff decided to air the episode as it was , even though the monster was in the wrong color . Inside the Fortune Megastore , Simpsons staff writer Ian <unk> @-@ Graham can be seen . 
- The episode features American comedian and composer Michael McKean as Jerry Rude , a parody on the radio personality Howard Stern . Scully stated that McKean was " very funny " and that his Stern impression was " dead on the money " . The Simpsons staff originally asked Stern if he wanted to guest @-@ star in the episode , but he declined . Actor and producer Kevin <unk> was portrayed by series regular cast member Hank Azaria , who voices Moe <unk> among other characters in the series . Azaria also played Arthur Fortune , a character based on British business magnate Richard Branson . In the DVD commentary for the episode , Scully stated that Branson would have been a " great guest [ <unk> ] " in the episode . The episode also features Groundskeeper Willie 's father , even though Willie in an earlier episode said that he was killed for stealing a pig . 
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- = = Cultural references = = 
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- In the beginning of the episode , the Simpsons watch a television program called Cash in Your Legacy , which is a parody on the PBS program <unk> <unk> . Arthur Fortune is a parody of Branson and Fortune Megastore is a reference to Branson 's international record shop chain Virgin <unk> . On the cover of <unk> Beat , a magazine that Burns reads in the episode , business magnates Bill Gates and Rupert Murdoch can be seen . In a scene in the episode , Burns assumes that " Don McNeill and his <unk> Club " is currently the most popular radio program . Don McNeill 's <unk> Club was a real radio program that , according to Scully , Swartzwelder used to listen to . <unk> 's film The <unk> is also referenced in the episode . Burns ' unveiling of the Loch Ness Monster is ruined as the numerous camera flashes blind him , causing him to knock over some lights , starting a fire . This is a reference to the film King Kong , although in the film the flashes <unk> the captured gorilla Kong , as opposed to his <unk> . 
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- = = Reception = = 
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- In its original American broadcast on May 2 , 1999 , " Monty Can 't Buy Me Love " received a 7 @.@ 3 rating , according to Nielsen Media Research , translating to approximately 7 @.@ 26 million viewers . The episode finished in 43rd place in the ratings for the week of April 26 @-@ May 2 , 1999 . On August 7 , 2007 , the episode was released as part of The Simpsons - The Complete Tenth Season DVD box set . Mike Scully , George Meyer , Ian <unk> @-@ Graham , Ron <unk> and Matt <unk> participated in the DVD 's audio commentary of the episode . 
- Following its home video release , " Monty Can 't Buy Me Love " received mixed reviews from critics . Giving the episode a positive review , <unk> considered it to be " one of the best Mr. Burns @-@ centric episodes ever " , and that it has " some classic Burns moments , especially when Monty describes exactly what it took for him to capture the creature . " Colin Jacobson of DVD Movie Guide stated that , while the episode " doesn ’ t do a lot to expand the character " , it " manages a reasonable number of <unk> " . He added that " A fun Howard Stern @-@ esque character done by Michael McKean helps make this a nice show . " David <unk> of DVD Town wrote that the episode has " Some funny moments " . Giving the episode a mixed review , Warren Martyn and Adrian Wood of I Can 't Believe It 's a Bigger and Better Updated Unofficial Simpsons Guide said that the episode is " Very funny when it 's funny , very poor when it isn 't . " They wrote that the best part of the episode is " the Scottish stuff , " and " the idea that , once again , Burns is actually worried about his public image , as he was in ' The Joy of <unk> ' . " Jake McNeill of Digital Entertainment News gave the episode a negative review , and wrote that " the story takes too long to get going " . 
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- 
- = Landing at Anzac Cove = 
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- The landing at Anzac Cove on Sunday , 25 April 1915 , also known as the landing at Gaba Tepe , and to the Turks as the <unk> Battle , was part of the amphibious invasion of the Gallipoli Peninsula by the forces of the British Empire , which began the land phase of the Gallipoli Campaign of the First World War . 
- The assault troops , mostly from the Australian and New Zealand Army Corps ( ANZAC ) , landed at night on the western ( Aegean Sea ) side of the peninsula . They were put ashore one mile ( 1 @.@ 6 km ) north of their intended landing beach . In the darkness , the assault formations became mixed up , but the troops gradually made their way inland , under increasing opposition from the Ottoman Turkish defenders . Not long after coming ashore the ANZAC plans were discarded , and the companies and battalions were thrown into battle piece @-@ meal , and received mixed orders . Some advanced to their designated objectives while others were diverted to other areas , then ordered to dig in along defensive ridge lines . 
- Although they failed to achieve their objectives , by nightfall the ANZACs had formed a beachhead , albeit much smaller than intended . In places they were <unk> onto cliff faces with no organised defence system . Their precarious position convinced both divisional commanders to ask for an evacuation , but after taking advice from the Royal Navy about how practicable that would be , the army commander decided they would stay . The exact number of the day 's casualties is not known . The ANZACs had landed two divisions but over two thousand of their men had been killed or wounded , together with at least a similar number of Turkish casualties . 
- Since 1916 the anniversary of the landings on 25 April has been commemorated as Anzac Day , becoming one of the most important national celebrations in Australia and New Zealand . The anniversary is also commemorated in Turkey , the United Kingdom and Ireland . 
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- = = Background = = 
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- The Ottoman Turkish Empire entered the First World War on the side of the Central Powers on 31 October 1914 . The stalemate of trench warfare on the Western Front convinced the British Imperial War Cabinet that an attack on the Central Powers elsewhere , particularly Turkey , could be the best way of winning the war . From February 1915 this took the form of naval operations aimed at forcing a passage through the Dardanelles , but after several setbacks it was decided that a land campaign was also necessary . To that end , the Mediterranean Expeditionary Force was formed under the command of General Ian Hamilton . Three amphibious landings were planned to secure the Gallipoli Peninsula , which would allow the navy to attack the Turkish capital Constantinople , in the hope that would convince the Turks to ask for an armistice . 
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- = = = <unk> = = = 
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- Lieutenant @-@ General William Birdwood , commanding the inexperienced Australian and New Zealand Army Corps ( ANZAC ) , comprising the Australian Division and two brigades of the New Zealand and Australian Division , was ordered to conduct an amphibious assault on the western side of the Gallipoli Peninsula . The New Zealand and Australian Division normally also had two mounted brigades assigned to it , but these had been left in Egypt , as it was believed there would be no requirement or opportunities to use mounted troops on the peninsula . To bring the division up to strength , Hamilton had tried unsuccessfully to get a brigade of <unk> attached to them . In total ANZAC strength was 30 @,@ <unk> men . 
- The location chosen for the operation was between the headland of Gaba Tepe and the Fisherman 's Hut , three miles ( 4 @.@ 8 km ) to the north . Landing at dawn after a naval gunfire bombardment , the first troops were to seize the lower crests and southern spurs of Hill 971 . The second wave would pass them to capture the spur of Hill 971 , especially Mal Tepe . There they would be positioned to cut the enemy 's lines of communications to the Kilid Bahr Plateau , thus preventing the Turks from bringing reinforcements from the north to the Kilid Bahr Plateau during the attack by the British 29th Division which would advance from a separate beachhead further south @-@ west . The capture of Mal Tepe was " more vital and valuable than the capture of the Kilid Bahr Plateau itself . " 
- Birdwood planned to arrive off the peninsula after the moon had set , with the first troops landing at 03 : 30 , an hour before dawn . He declined the offer of an old merchant ship , loaded with troops , being deliberately grounded at Gaba Tepe . Instead , the troops were to travel in naval and merchant ships , transferring to rowing boats towed by small steamboats to make the assault . 
- First ashore would be the Australian Division , commanded by Major @-@ General William Bridges . The 3rd Australian Brigade , known as the covering force , were to capture the third ridge from Battleship Hill south along the Sari Bair mountain range to Gaba Tepe . The 2nd Australian Brigade , landing next , were to capture all the Sari Bar range up to Hill 971 on the left . The 26th Jacob 's Mountain Battery from the British Indian Army would land next and then the 1st Australian Brigade , the division 's reserve ; all were to be ashore by 08 : 30 . The New Zealand and Australian Division , commanded by Major @-@ General Alexander <unk> , followed them ; the 1st New Zealand Brigade then the 4th Australian Brigade . Only after the second division had landed would the advance to Mal Tepe begin . The planners had come to the conclusion that the area was sparsely , if at all , defended , and that they should be able to achieve their objectives with no problems ; Turkish opposition had not been considered . 
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- = = = Turkish forces = = = 
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- The First World War Ottoman Turkish Army was modelled after the German Imperial Army , with most of its members being conscripted for two years ( infantry ) or three years ( artillery ) ; they then served in the reserve for the next twenty @-@ three years . The pre @-@ war army had 208 @,@ 000 men in thirty @-@ six divisions , formed into army corps and field armies . On <unk> each division had three infantry and one artillery regiment for a total of around ten thousand men , or about half the size of the equivalent British formation . Unlike the largely inexperienced ANZACs , all the Turkish Army commanders , down to company commander level , were very experienced , being veterans of the Italo @-@ Turkish and Balkan Wars . 
- The British preparations could not be made in secret , and by March 1915 , the Turks were aware that a force of fifty thousand British and thirty thousand French troops was gathering at Lemnos . They considered there were only four likely places for them to land : Cape Helles , Gaba Tepe , <unk> , or on the Asiatic ( eastern ) coast of the Dardanelles . 
- On 24 March , the Turks formed the Fifth Army , a force of over 100 @,@ 000 men , in two corps of six divisions and a cavalry brigade , commanded by the German general Otto <unk> von Sanders . The Fifth Army deployed the III Corps at Gallipoli and the XV Corps on the Asiatic coast . The 5th Division and a cavalry brigade were on the European mainland , positioned to support the III Corps if required . The III Corps had the 9th Division ( 25th , 26th and 27th Infantry Regiments ) , the 19th Division ( 57th , 72nd and 77th Infantry Regiments ) and the 7th Division ( 19th , 20th and 21st Infantry Regiments ) . The 9th Division provided coastal defence from Cape Helles north to <unk> , where the 7th Division took over , while the 19th Division at <unk> was the corps reserve . The area around Gaba Tepe , where the ANZAC landings would take place , was defended by a battalion of the 27th Infantry Regiment . 
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- = = Anzac Cove = = 
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- On 19 April orders were issued for the ANZACs to stop training , and for all ships and small boats to take on coal and stores , in preparation for a landing originally scheduled to occur on 23 April . Weather conditions delayed their departure from Lemnos until dawn on 24 April . The Royal Navy battleships Queen , Triumph , Prince of Wales , London , and <unk> , the cruiser <unk> , seven destroyers and four transport ships led the way carrying the 3rd Brigade . They were followed by the rest of the force who were embarked in their own transport ships . 
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- = = = First six companies = = = 
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- At 01 : 00 on 25 April the British ships stopped at sea , and thirty @-@ six rowing boats towed by twelve <unk> embarked the first six companies , two each from the 9th , 10th and 11th Battalions . At 02 : 00 a Turkish <unk> reported seeing ships moving at sea , and at 02 : 30 the report was sent to 9th Division 's headquarters . At 02 : 53 the ships headed towards the peninsula , continuing until 03 : 30 when the larger ships stopped . With 50 yards ( 46 m ) to go , the rowing boats continued using only their oars . 
- Around 04 : 30 Turkish <unk> opened fire on the boats , but the first ANZAC troops were already ashore at Beach Z , called Ari Burnu at the time , but later known as Anzac Cove . ( It was formally renamed Anzac Cove by the Turkish government in 1985 . ) They were one mile ( 1 @.@ 6 km ) further north than intended , and instead of an open beach they were faced with steep cliffs and ridges up to around three hundred feet ( 91 m ) in height . However , the mistake had put them ashore at a relatively undefended area ; at Gaba Tepe further south where they had planned to land , there was a strong @-@ point , with an artillery battery close by equipped with two 15 cm and two 12 cm guns , and the 5th Company , 27th Infantry Regiment was positioned to counter @-@ attack any landing at that more southern point . The hills surrounding the <unk> where the ANZACs landed made the beach safe from direct fire Turkish artillery . Fifteen minutes after the landing , the Royal Navy began firing at targets in the hills . 
- On their way in , the rowing boats had become mixed up . The 11th Battalion grounded to the north of Ari Burnu point , while the 9th Battalion hit the point or just south of it , together with most of the 10th Battalion . The plan was for them to cross the open ground and assault the first ridge line , but they were faced with a hill that came down almost to the water line , and there was confusion while the officers tried to work out their location , under small arms fire from the 4th Company , 2nd Battalion , 27th Infantry Regiment , who had a platoon of between eighty and ninety men at Anzac Cove and a second platoon in the north around the Fisherman 's Hut . The third platoon was in a reserve position on the second ridge . They also manned the Gaba Tepe strong @-@ point , equipped with two obsolescent multi @-@ <unk> <unk> machine @-@ guns , and several smaller posts in the south . 
- Men from the 9th and 10th Battalions started up the Ari Burnu slope , grabbing the gorse branches or digging their bayonets into the soil to provide leverage . At the peak they found an abandoned trench , the Turks having withdrawn inland . Soon the Australians reached Plugge 's Plateau , the edge of which was defended by a trench , but the Turks had withdrawn to the next summit two hundred yards ( 180 m ) inland , from where they fired at the Australians coming onto the plateau . As they arrived , Major Edmund Brockman of the 11th Battalion started sorting out the mess , sending the 9th Battalion 's men to the right flank , the 11th Battalion 's to the left , and keeping the 10th Battalion in the centre . 
- 
- = = = Second six companies = = = 
- 
- The second six companies landed while it was still dark , the destroyers coming to within five hundred yards ( 460 m ) to disembark the troops , under fire . They also landed at Anzac Cove , but now as planned the 11th were in the north , 10th in the centre and the 9th in the south . The 12th Battalion landed all along the beach . This extended the beachhead 500 yards ( 460 m ) to the north of Ari Burnu , and 1 @.@ 5 miles ( 2 @.@ 4 km ) to the south . Landing under fire , some of the assaulting troops were killed in their boats , and others as they reached the beach . Once ashore they headed inland . In the south , the first men from the 9th and 12th Battalions reached the bottom of 400 Plateau . 
- In the north , the first men from the 11th and 12th Battalions started up Walker 's Ridge , under fire from a nearby Turkish trench . Around the same time Turkish artillery started bombarding the beachhead , destroying at least six boats . The Australians fought their way forward and reached Russell 's Top ; the Turks withdrew through The Nek to Baby 700 , 350 yards ( 320 m ) away . Coming under fire again the Australians went to ground , having advanced only around one thousand yards ( 910 m ) inland . Some also dug in at The Nek , a twenty yards ( 18 m ) piece of high ground between Malone 's Gully to the north and Monash Valley to the south . Around this time Colonel <unk> Sinclair @-@ Maclagan , commanding the 3rd Brigade , decided to change the corps plan . Concerned about a possible counter @-@ attack from the south , he decided to hold the Second Ridge instead of pushing forward to the Third or Gun Ridge . This <unk> suited the Turkish defence plans , which required the forward troops to gain time for the reserves to coordinate a counter @-@ attack . 
- 
- = = = Turkish reaction = = = 
- 
- At 05 : 45 , Lieutenant @-@ Colonel <unk> Sefik of the Turkish 27th Infantry Regiment received orders to move his 1st and 3rd Battalions to the west and support the 2nd Battalion around Gaba Tepe . The two battalions were already assembled , having spent that night carrying out military exercises . They could not be sent to Ari Burnu as it was not marked on the Turkish maps . Colonel <unk> Sami , commanding the 9th Division , also ordered the division 's machine @-@ gun company and an artillery battery to move in support of the 27th Infantry Regiment , followed soon after by an 77 mm artillery battery . At 08 : 00 Lieutenant @-@ Colonel <unk> Kemal , commanding the 19th Division , was ordered to send a battalion to support them . Kemal instead decided to go himself with the 57th Infantry Regiment and an artillery battery towards Chunuk Bair , which he realised was the key point in the defence ; whoever held those heights would dominate the battlefield . By chance , the 57th Infantry were supposed to have been on an exercise that morning around Hill 971 and had been prepared since 05 : 30 , waiting for orders . 
- At 09 : 00 Sefik and his two battalions were approaching <unk> Tepe , and made contact with his 2nd Battalion that had conducted a fighting withdrawal , and an hour and a half later the regiment was deployed to stop the ANZACs advancing any further . Around 10 : 00 Kemal arrived at Scrubby Knoll and <unk> some retreating troops , pushing them back into a defensive position . As they arrived , the 57th Infantry Regiment were given their orders and prepared to counter @-@ attack . Scrubby Knoll , known to the Turks as <unk> ( Kemal 's Place ) , now became the site of the Turkish headquarters for the remainder of the campaign . 
- 
- = = Baby 700 = = 
- 
- Baby 700 is a hill in the Sari Bair range , next to Battleship Hill or Big 700 . It was named after its supposed height above sea level , though its actual height is only 590 feet ( 180 m ) . 
- Maclagen sent the 11th Battalion , Captain Joseph Lalor 's company of the 12th Battalion and Major James Robertson 's of the 9th , towards Baby 700 . Brockman divided his own company , sending half up the right fork of Rest Gully , and half up the left , while Brockman and a reserve platoon headed up Monash Valley . As they moved forward , Turkish artillery targeted them with air burst shrapnel shells , which dispersed the companies . This , coupled with senior officers diverting men to other areas instead of towards Baby 700 , meant only fragments of the units eventually reached Baby 700 . 
- Arriving at Baby 700 , Captain Eric Tulloch , 11th Battalion , decided to take his remaining sixty men towards Battleship Hill , leaving Lalor 's company to dig in and defend The Nek . Tulloch moved around to the right before advancing towards the summit . The 11th Battalion crossed the first rise unopposed , but at the second , Turkish defenders around four hundred yards ( 370 m ) away opened fire on them . Going to ground , the Australians returned fire . When the Turkish fire <unk> the remaining fifty men resumed their advance , reaching the now evacuated Turkish position , behind which was a large depression , with Battleship Hill beyond that . Still under fire they moved forward again , then around seven hundred yards ( 640 m ) from the summit The Turks opened fire on them from a trench . The Australians held out for thirty minutes , but increasing Turkish fire and mounting casualties convinced Tulloch to withdraw . No other ANZAC unit would advance as far inland that day . 
- At 08 : 30 Robertson and Lalor decided to take their companies up Baby 700 . Instead of going round to the right like Tulloch , they went straight up the centre , crossed over the summit onto the northern slope and went to ground . A spur on their left , leading to Suvla Bay , was defended by a Turkish trench system . At 09 : 15 Turkish troops started moving down Battleship Hill , and for the next hour they exchanged fire . Where the spur joined Baby 700 , a group of Australians from the 9th , 11th , and 12th Battalions crossed Malone 's Gully and charged the Turkish trench . A Turkish machine @-@ gun on Baby 700 opened fire on them , forcing them back , followed by a general withdrawal of Australian troops . The Turks had secured Battleship Hill and were now driving the Australians off Baby 700 . From his headquarters at the head of Monash Valley , Maclagen could see the Turks attacking , and started sending all available men towards Baby 700 . 
- 
- = = = Second wave = = = 
- 
- The 2nd Brigade landed between 05 : 30 and 07 : 00 , and the reserve 1st Brigade landed between 09 : 00 and 12 : 00 , already putting the timetable behind schedule . The 2nd Brigade , which was supposed to be heading for Baby 700 on the left , were instead sent to the right to counter a Turkish attack building up there . At 07 : 20 Bridges and his staff landed ; finding no senior officers on the beach to brief them , they set out to locate the 3rd Brigade headquarters . 
- The 1st Brigade was on the opposite flank to the 3rd Brigade and already getting involved in battles of its own , when its commander , Colonel Percy Owen , received a request from Maclagen for reinforcements . Owen sent two companies from the 3rd Battalion and one from the 1st Battalion ( Swannell 's ) to support the 3rd Brigade . 
- Soon after , Lalor 's company had been forced back to The Nek and the Turks were threatening to recapture Russell 's Top , and at 10 : 15 Maclagen reported to Bridges his doubts over being able to hold out . In response Bridges sent part of his reserve , two companies from the 2nd Battalion ( Gordon 's and Richardson 's ) , to reinforce the 3rd Brigade . 
- At 11 : 00 Swannell 's company arrived at the foot of Baby 700 , joining the seventy survivors of Robertson 's and Lalor 's companies . They immediately charged and chased the Turks back over the summit of Baby 700 , then stopped and dug in . The two 2nd Battalion companies arrived alongside them , but all the companies had taken casualties , among the dead being Swannell and Robertson . 
- By this time most of the 3rd Brigade men had been killed or wounded , and the line was held by the five depleted companies from the 1st Brigade . On the left , Gordon 's company 2nd Battalion , with the 11th and 12th Battalion 's survivors , charged five times and captured the summit of Baby 700 , but were driven back by Turkish counter @-@ attacks ; Gordon was among the casualties . For the second time Maclagen requested reinforcements for Baby 700 , but the only reserves Bridges had available were two 2nd Battalion companies and the 4th Battalion . It was now 10 : 45 and the advance companies of the 1st New Zealand Brigade were disembarking , so it was decided they would go to Baby 700 . 
- 
- = = = Third wave = = = 
- 
- The New Zealand Brigade commander had been taken ill , so Birdwood appointed Brigadier @-@ General Harold Walker , a staff officer already ashore , as commander . The Auckland Battalion had landed by 12 : 00 , and were being sent north along the beach to Walker 's Ridge on their way to Russell 's Top . Seeing that the only way along the ridge was in single file along a goat track , Walker ordered them to take the route over Plugge 's Plateau . As each New Zealand unit landed they were directed the same way to Baby 700 . However , in trying to avoid Turkish fire , they became split up in Monash Valley and Rest Gully , and it was after midday that two of the Auckland companies reached Baby 700 . 
- At 12 : 30 two companies of the Canterbury Battalion landed and were sent to support the <unk> , who had now been ordered back to Plugge 's Plateau , and were forming on the left of the 3rd Brigade . The Canterbury companies moved into the line on the <unk> ' left , waiting for the rest of their brigade to land . However , between 12 : 30 and 16 : 00 not one infantry or artillery formation came ashore . The ships carrying the New Zealanders were in the bay , but the <unk> and rowing boats were being used to take the large numbers of wounded to the hospital ship . The transports with the 4th Australian Brigade on board were still well out at sea and not due to land until that evening . The landings <unk> around 16 : 30 when the Wellington Battalion came ashore , followed by the <unk> Battalion around 17 : 00 , who were put into the line beside the <unk> . Next to land were the two other Canterbury companies , who were sent north to Walker 's Ridge to extend the corps left flank . Events ashore now forced a change in the <unk> schedule , and at 17 : 50 orders were issued for the 4th Australian Brigade to start landing to boost the defence . It would take until the next day for the complete brigade to come ashore . The transports carrying both divisions ' artillery batteries had been forced further out to sea by Turkish artillery fire , and were unable to land . 
- 
- = = = <unk> 's Hill = = = 
- 
- <unk> 's Hill is a 1 @,@ 000 yard ( <unk> ) long section of the Second Ridge that connects Baby 700 to 400 Plateau , with a steep slope on the ANZAC side down to Monash Valley . In the coming days Quinn 's , Steel 's and Courtney 's <unk> would be built on the slope . The first ANZAC troops to reach the hill , from the 11th Battalion , found that the Turkish defenders had already withdrawn . As the Australians crested the hill they came under fire from Baby 700 , but to their front was a short , shallow slope into <unk> Valley . When Major James Denton 's company of the 11th Battalion arrived at the hill they started digging in , and soon after received orders from <unk> to hold the position at all costs . At 10 : 00 Turkish troops , advancing from Scrubby Knoll , got to within three hundred yards ( 270 m ) of the Australians on the hill , opening fire at them . <unk> there were two and a half companies from the 11th Battalion between Courtney 's Post , Steele 's Post , and Wire Gully . They had not been there long before the 3rd Battalion arrived to reinforce them . 
- 
- = = 400 Plateau = = 
- 
- The 400 Plateau , named for its height above sea level , was a wide and level plateau on the second ridge line , about six hundred by six hundred yards ( 550 by 550 m ) wide and around one thousand yards ( 910 m ) from Gun Ridge . The northern half of the plateau became known as Johnston 's Jolly , and the southern half as Lone Pine , with Owen 's Gully between them . 
- 
- = = = 3rd Brigade = = = 
- 
- If the landings had gone to plan , the 11th Battalion was supposed to be crossing the plateau heading north . The 10th Battalion , south of the plateau , was to capture a Turkish trench and artillery battery behind Gun Ridge . The 9th Battalion , <unk> south , was to attack the artillery battery at Gaba Tepe , and the 12th Battalion was the reserve , with 26th Jacob 's Mountain Battery to establish their gun line on the plateau . Unknown to the ANZACs , the Turks had an artillery battery sited on 400 Plateau . 
- After landing , some of the 9th and 10th Battalion 's men headed for 400 Plateau . The first 10th Battalion platoon to arrive was commanded by Lieutenant Noel Loutit , and accompanied by the Brigade @-@ Major , Charles Brand . They discovered the Turkish battery in the Lone Pine sector , which was preparing to move . As the Australians opened fire the battery withdrew down Owen 's Gully . Brand remained on the plateau and ordered Loutit to continue after the Turkish battery . However , the guns had been hidden at the head of the gully and Loutit 's platoon moved beyond them . Around the same time , Lieutenant Eric Smith and his 10th Battalion scouts and Lieutenant G. Thomas with his platoon from the 9th Battalion arrived on the plateau , looking for the guns . As they crossed the plateau Turkish machine @-@ guns opened fire on them from the Lone Pine area . One of Thomas 's sections located the battery , which had started firing from the gully . They opened fire , charged the gun crews , and captured the guns . The Turks did manage to remove the breech blocks , making the guns inoperable , so the Australians damaged the sights and internal screw mechanisms to put them out of action . By now the majority of the 9th and 10th Battalions , along with brigade commander Maclagen , had arrived on the plateau , and he ordered them to dig in on the plateau instead of advancing to Gun Ridge . Unfortunately the units that had already passed beyond there were <unk> their orders to " go as fast as you can , at all costs keep going " . 
- Loutit , Lieutenant J. Haig of the 10th , and thirty @-@ two men from the 9th , 10th , and 11th Battalions crossed <unk> Valley and climbed a spur of Gun Ridge , just to the south of Scrubby Knoll . As they reached the top , about four hundred yards ( 370 m ) further inland was Gun Ridge , defended by a large number of Turkish troops . Loutit and two men carried out a reconnaissance of Scrubby Knoll , from the top of which they could see the Dardanelles , around three miles ( 4 @.@ 8 km ) to the east . When one of the men was wounded they returned to the rest of their group , which was being engaged by Turkish machine @-@ gun and rifle fire . Around 08 : 00 , Loutit sent a man back for reinforcements ; he located Captain J. Ryder of the 9th Battalion , with half a company of men at Lone Pine . Ryder had not received the order to dig in , so he advanced and formed a line on Loutit 's right . Soon after , they came under fire from Scrubby Knoll and were in danger of being cut off ; Ryder sent a message back for more reinforcements . The messenger located Captain John Peck , the 11th Battalion 's adjutant , who collected all the men around him and went forward to reinforce Ryder . It was now 09 : 30 and the men on the spur , outflanked by the Turks , had started to withdraw . At 10 : 00 the Turks set up a machine @-@ gun on the spur and opened fire on the withdrawing Australians . <unk> by the Turks , only eleven survivors , including Loutit and Haig , reached Johnston 's Jolly and took cover . Further back , two companies of the 9th and the 10th Battalions had started digging a trench line . 
- 
- = = = 2nd Brigade = = = 
- 
- As part of the second wave , the 2nd Brigade had been landing since 05 : 30 ; the 5th , 6th and 8th Battalions were supposed to cross 400 Plateau and head to Hill 971 , while the 7th Battalion on the left were to climb Plugge 's Plateau then make for Hill 971 . One 7th Battalion company , Jackson 's , landed beside the Fisherman 's Hut in the north and was almost wiped out ; only forty men survived the landing . At 06 : 00 Major <unk> <unk> 's 7th Battalion company , and part of another , were sent onto 400 Plateau by Maclagen to strengthen the defence . When the 7th Battalion commander Lieutenant @-@ Colonel Harold Elliott landed he realised events were not going to plan , and he headed to the 3rd Brigade headquarters to find out what was happening . Maclagen ordered him to gather his battalion at the south of the beachhead , as the 2nd Brigade would now form the division 's right flank , not left . When the 2nd Brigade commander Colonel James McCay arrived Maclagen convinced him to move his brigade to the south , <unk> responsibility with the 3rd Brigade . Eventually agreeing , he established his headquarters on the seaward slope of 400 Plateau ( McCay 's Hill ) . Heading onto the plateau , McCay realised the ridge to his right , Bolton 's Ridge , would be a key point in their defence . He located the Brigade @-@ Major , Walter Cass , and ordered him to gather what men he could to defend the ridge . Looking around , he saw the 8th Battalion , commanded by Colonel William Bolton , moving forward , so Cass directed them to Bolton 's Ridge . As such , it was the only ANZAC battalion that remained together during the day . Eventually , around 07 : 00 , the rest of the brigade started arriving . As each company and battalion appeared they were pushed forward into the front line , but with no defined orders other than to support the 3rd Brigade . At 10 : 30 the six guns of the 26th <unk> Mountain Battery arrived , positioning three guns each side of White 's Valley . At noon they opened fire on the Turks on Gun Ridge . 
- Within two hours half the Australian Division was involved in the battle of 400 Plateau . However , most of the officers had misunderstood their orders . Believing the intention was to occupy Gun Ridge and not hold their present position , they still tried to advance . The 9th and 10th Battalions had started forming a defence line , but there was a gap between them that the 7th Battalion was sent to fill . Seeing the 2nd Brigade coming forward , units of the 3rd Brigade started to advance to Gun Ridge . The advancing Australians did not then know that the counter @-@ attacking Turkish forces had reached the Scrubby Knoll area around 08 : 00 and were prepared for them . As the Australians reached the Lone Pine section of the plateau , Turkish machine @-@ guns and rifles opened fire , <unk> the Australians . To the north other troops , advancing beyond Johnstone 's Jolly and Owen 's Gully , were caught by the same small arms fire . Soon afterwards a Turkish artillery battery also started firing at them . This was followed by a Turkish counter @-@ attack from Gun Ridge . Such was the situation they now found themselves in that at 15 : 30 McCay , now giving up all <unk> of advancing to Gun Ridge , ordered his brigade to dig in from Owen 's Gully to Bolton 's Ridge . 
- 
- = = = Pine Ridge = = = 
- 
- Pine Ridge is part of the 400 Plateau , and stretches , in a curve towards the sea , for around one mile ( 1 @.@ 6 km ) . Beyond Pine Ridge is <unk> Valley and Gun Ridge and , like the rest of the terrain , it was covered in a thick gorse scrub , but also had stunted pine trees around eleven feet ( 3 @.@ 4 m ) tall growing on it . 
- Several groups of men eventually made their way to Pine Ridge . Among the first was Lieutenant Eric Plant 's platoon from the 9th Battalion . Captain John <unk> 's company of the 12th Battalion moved forward from Bolton 's Ridge when they saw the 6th Battalion moving up behind them . As the 6th Battalion reached the ridge , the companies carried on towards Gun Ridge , while Lieutenant @-@ Colonel Walter <unk> established the battalion headquarters below Bolton 's Ridge . As the 6th Battalion moved forward they were engaged by Turkish small arms and artillery fire , causing heavy casualties . At 10 : 00 brigade headquarters received a message from the 6th Battalion asking for reinforcement , and McCay sent half the 5th Battalion to assist . At the same time the 8th Battalion were digging in on Bolton 's , except for two companies which moved forward to attack a group of Turks that had come up from the south behind the 6th Battalion . By noon the 8th Battalion was dug in on the ridge ; in front of them were scattered remnants of the 5th , 6th , 7th , and 9th Battalions , mostly out of view of each other in the scrub . Shortly after , McCay was informed that if he wanted the 6th Battalion to hold its position , it must be reinforced . So McCay sent his last reserves , a company of the 1st Battalion , and ordered the 8th to leave one company on the ridge and advance on the right of the 6th Battalion . The scattered formations managed to hold their positions for the remainder of the afternoon , then at 17 : 00 saw large numbers of Turkish troops coming over the southern section of Gun Ridge . 
- 
- = = Turkish counter @-@ attack = = 
- 
- Around 10 : 00 Kemal and the 1st Battalion , 57th Infantry were the first to arrive in the area between Scrubby Knoll and Chunuk Bair . From the knoll Kemal was able to observe the landings . He ordered the artillery battery to set up on the knoll , and the 1st Battalion to attack Baby 700 and <unk> Ridge from the North @-@ East , while the 2nd Battalion would simultaneously circle around and attack Baby 700 from the West . The 3rd Battalion would for the moment be held in reserve . At 10 : 30 Kemal informed II Corps he was attacking . 
- At 11 : 30 Sefik told Kemal that the ANZACs had a beachhead of around 2 @,@ 200 yards ( 2 @,@ 000 m ) , and that he would attack towards Ari Burnu , in conjunction with the 19th Division . Around midday Kemal was <unk> that the 9th Division was fully involved with the British landings at Cape Helles , and could not support his attack , so at 12 : 30 he ordered two battalions of the 77th Infantry Regiment ( the third battalion was guarding Suvla Bay ) to move forward between the 57th and 27th Infantry Regiments . At the same time he ordered his reserve 72nd Infantry Regiment to move further west . Within the next half @-@ hour the 27th and 57th Infantry Regiments started the counter @-@ attack , supported by three batteries of artillery . At 13 : 00 Kemal met with his corps commander <unk> Pasha and convinced him of the need to react in strength to the ANZAC landings . <unk> agreed and released the 72nd and 27th Infantry Regiments to Kemal 's command . Kemal deployed the four regiments from north to south ; 72nd , 57th , 27th and 77th . In total , Turkish strength opposing the landing numbered between ten thousand and twelve thousand men . 
- 
- = = = North = = = 
- 
- At 15 : 15 Lalor left the defence of The Nek to a platoon that had arrived as reinforcements , and moved his company to Baby 700 . There he joined a group from the 2nd Battalion , commanded by Lieutenant Leslie <unk> . Lalor was killed soon afterwards . The left flank of Baby 700 was now held by sixty men , the remnants of several units , commanded by a corporal . They had survived five charges by the Turks between 07 : 30 and 15 : 00 ; after the last charge the Australians were ordered to withdraw through The Nek . There , a company from the Canterbury Battalion had just arrived , with their commanding officer Lieutenant @-@ Colonel Douglas Stewart . By 16 : 00 the New Zealand companies had formed a defence line on Russell 's Top . On Baby 700 , there was on the left <unk> 's and Lalor 's men , and at the top of Malone 's <unk> were the survivors of the 2nd Battalion and some men from the 3rd Brigade . On the right were the men left from the Auckland companies , and a mixed group from the 1st , 2nd , 11th and 12th Battalions . Once Stewart 's men were secure , he ordered <unk> to withdraw . During a Turkish artillery bombardment of The Nek , Stewart was killed . The artillery heralded the start of a Turkish counter @-@ attack ; columns of troops appeared over the top of Battleship Hill and on the flanks and attacked the ANZAC lines . 
- At 16 : 30 the three battalions from the 72nd Infantry Regiment arrived and attacked from the north . At the same time the Australians and New Zealanders holding on at Baby 700 broke and ran back to an improvised line , from Walker 's Ridge in the north to Pope 's Hill in the south . The defence line at The Nek was now defended by nine New Zealanders , under the command of a sergeant ; they had three machine @-@ guns but the crews had all been killed or wounded . As the survivors arrived from Baby 700 their numbers rose to around sixty . Bridges in his divisional headquarters starting receiving messages from the front ; just after 17 : 00 Lieutenant @-@ Colonel George <unk> on Walker 's Ridge advised he was holding his position and " if reinforced could advance " . At 17 : 37 Maclagen reported they were being " heavily attacked " , at 18 : 15 the 3rd Battalion signalled , " 3rd Brigade being driven back " . At 19 : 15 from Maclagen again " 4th Brigade urgently required " . Bridges sent two hundred stragglers , from several different battalions , to reinforce <unk> and promised two extra battalions from the New Zealand and Australian Division which was now coming ashore . 
- <unk> was at 19 : 00 and the Turkish attack had now reached Malone 's <unk> and The Nek . The New Zealanders waited until the Turks came close , then opened fire in the darkness , stopping their advance . <unk> outnumbered , they asked for reinforcements . Instead , the supporting troops to their rear were withdrawn and the Turks managed to get behind them . So , taking the machine @-@ guns with them , they withdrew off Russell 's Top into Rest Gully . This left the defenders at Walker 's Ridge isolated from the rest of the force . 
- 
- = = = South = = = 
- 
- The Australians on 400 Plateau had for some time been subjected to sniping and artillery fire and could see Turkish troops digging in on Gun Ridge . Around 13 : 00 a column of Turkish reinforcements from the 27th Infantry Regiment , in at least battalion strength , were observed moving along the ridge @-@ line from the south . The Turks then turned towards 400 Plateau and advanced in extended order . The Turkish counter @-@ attack soon forced the advanced Australian troops to withdraw , and their machine @-@ gun fire caused them heavy casualties . It was not long before the attack had forced a wedge between the Australians on Baby 700 and those on 400 Plateau . The heavy Turkish fire onto Lone Pine forced the survivors to withdraw back to the western slope of 400 Plateau . At 14 : 25 Turkish artillery and small arms fire was so heavy that the Indian <unk> were forced to push their guns back off the plateau by hand , and they reformed on the beach . 
- Although in places there was a mixture of different companies and platoons dug in together , the Australians were deployed with the 8th Battalion in the south still centred on Bolton 's Ridge . North of them , covering the southern sector of 400 Plateau , were the mixed together 6th and 7th Battalions , both now commanded by Colonel Walter <unk> of the 6th . North of them was the 5th Battalion , and the 10th Battalion covered the northern sector of 400 Plateau at Johnston 's Jolly . But by now they were battalions in name only , having all taken heavy casualties ; the commanders had little accurate knowledge of where their men were located . 
- At 15 : 30 the two battalions of the Turkish 77th Infantry Regiment were in position , and with the 27th Infantry they counter @-@ attacked again . At 15 : 30 and at 16 : 45 McCay , now under severe pressure , requested reinforcements . The second time he was informed there was only one <unk> battalion left , the 4th , and Bridges was keeping them in reserve until more troops from the New Zealand and Australian Division had been landed . McCay then spoke to Bridges direct and informed him the situation was desperate and if not reinforced the Turks would get behind him . At 17 : 00 Bridges released the 4th Battalion to McCay who sent them to the south forming on the left of the 8th Battalion along Bolton 's Ridge . They arrived just in time to help counter Turkish probing attacks , by the 27th Infantry Regiment , from the south . 
- At 17 : 20 McCay signalled Bridges that large numbers of unwounded men were leaving the battlefield and heading for the beaches . This was followed by Maclagan asking for urgent artillery fire support , onto Gun Ridge , as his left was under a heavy attack and at 18 : 16 Owen reported the left flank was " rapidly " being forced to retire . At dusk Maclagan made his way to Bridges headquarters and when asked for his opinion replied " It 's touch and go . If the Turks come on in mass formation ... I don 't think anything can stop them . " As it got dark the Turkish artillery ceased firing , and although small arms fire continued on both sides , the effects were limited when firing blind . Darkness also provided the opportunity to start digging more substantial trenches and to resupply the troops with water and ammunition . 
- The last significant action of the day was at 22 : 00 south of Lone Pine , when the Turks charged towards Bolton 's Ridge . By now the 8th Battalion had positioned two machine @-@ guns to cover their front , which caused devastation amongst the attackers , and to their left the 4th Battalion also became involved . When the Turks got to within fifty yards ( 46 m ) the 8th Battalion counter @-@ attacked in a bayonet charge and the Turks withdrew . The ANZAC defence was aided by Royal Navy searchlights providing illumination . Both sides now waited for the next attack , but the day 's events had shattered both formations and they were no longer in any condition to conduct offensive operations . 
- 
- = = Aftermath = = 
- 
- By nightfall , around sixteen thousand men had been landed , and the ANZACs had formed a beachhead , although with several undefended sections . It stretched along Bolton 's Ridge in the south , across 400 Plateau , to Monash Valley . After a short gap it resumed at Pope 's Hill , then at the top of Walker 's Ridge . It was not a large beachhead ; it was under two miles ( 3 @.@ 2 km ) in length , with a depth around 790 yards ( 720 m ) , and in places only a few yards separated the two sides . That evening Birdwood had been ashore to check on the situation , and , satisfied , returned to HMS Queen . Around 21 : 15 he was asked to return to the beachhead . There he met with his senior officers , who asked him to arrange an evacuation . Unwilling to make that decision on his own he signalled Hamilton ; 
- Both my divisional generals and <unk> have represented to me that they fear their men are thoroughly demoralised by shrapnel fire to which they have been subjected all day after exhaustion and <unk> work in morning . Numbers have <unk> back from the firing line and cannot be collected in this difficult country . Even New Zealand Brigade which has only recently been engaged lost heavily and is to some extent demoralised . If troops are subjected to <unk> again tomorrow morning there is likely to be a <unk> , as I have no fresh troops with which to replace those in firing line . I know my representation is most serious , but if we are to re @-@ embark it must be at once . 
- Hamilton conferred with his naval commanders , who convinced him an evacuation would be almost impossible , and responded ; " dig <unk> right in and stick it out ... dig , dig , dig until you are safe " . The survivors had to fight on alone until 28 April when four battalions of the Royal Naval Division were attached to the corps . 
- On the Turkish side , by that night the 2nd Battalion 57th Infantry were on Baby 700 , the 3rd Battalion , reduced to only ninety men , were at The Nek , and the 1st Battalion on <unk> Ridge . Just south of them was the 77th Infantry , next was the 27th Infantry opposite 400 Plateau . The last regiment , the 72nd Infantry , were on Battleship Hill . As for manpower , the Turks were in a similar situation to the ANZACs . Of the two regiments most heavily involved , the 57th had been destroyed , and the 27th were exhausted with heavy casualties . Large numbers of the 77th had deserted , and the regiment was in no condition to fight . The 72nd was largely intact , but they were a poorly trained force of Arab conscripts . The III Corps , having to deal with both landings , could not assist as they had no reserves available . It was not until 27 April that the 33rd and 64th Infantry Regiments arrived to reinforce the Turkish forces . The ANZACs , however , had been unable to achieve their <unk> , and therefore dug in . Gallipoli , like the Western Front , turned into a war of <unk> . The German commander , <unk> von Saunders , was clear about the reasons for the outcome . He wrote that , " on the Turkish side the situation was saved by the immediate and independent action of the 19th Division . " The division commander , Kemal , became noted as " the most imaginative , most successful officer to fight on either side " during the campaign . As a commander he was able to get the most out of his troops , <unk> by his order to the 57th Infantry Regiment ; " Men , I am not ordering you to attack . I am ordering you to die . In the time that it takes us to die , other forces and commanders can come and take our place . " 
- In the following days there were several failed attacks and counter @-@ attacks by both sides . The Turks were the first to try during the Second attack on Anzac Cove on 27 April , followed by the ANZACs who tried to advance overnight 1 / 2 May . The Turkish Third attack on Anzac Cove on 19 May was the worst defeat of them all , with around ten thousand casualties , including three thousand dead . The next four months consisted of only local or diversionary attacks , until 6 August when the ANZACs , in connection with the Landing at Suvla Bay , attacked Chunuk Bair with only limited success . The Turks never succeeded in driving the Australians and New Zealanders back into the sea . Similarly , the ANZACs never broke out of their beachhead . Instead , in December 1915 , after eight months of fighting , they evacuated the peninsula . 
- 
- = = = Casualties = = = 
- 
- The full extent of casualties on that first day are not known . Birdwood , who did not come ashore until late in the day , estimated between three and four hundred dead on the beaches . The New Zealand Ministry for Culture and Heritage claims one in five of the three thousand New Zealanders involved became a casualty . The Australian War Memorial has 860 Australian dead between 25 – 30 April , and the Australian Government estimates 2 @,@ 000 wounded left Anzac Cove on 25 April , but more wounded were still waiting on the <unk> to be evacuated . The Commonwealth War Graves Commission documents that 754 Australian and 147 New Zealand soldiers died on 25 April 1915 . A higher than normal proportion of the ANZAC casualties were from the officer ranks . One theory was that they kept exposing themselves to fire , trying to find out where they were or to locate their troops . Four men were taken prisoner by the Turks . 
- It is estimated that the Turkish 27th and 57th Infantry Regiments lost around 2 @,@ 000 men , or fifty per cent of their combined strength . The full number of Turkish casualties for the day has not been recorded . During the campaign , 8 @,@ 708 Australians and 2 @,@ <unk> New Zealanders were killed . The exact number of Turkish dead is not known but has been estimated around 87 @,@ 000 . 
- 
- = = = Anzac Day = = = 
- 
- The anniversary of the landings , 25 April , has since 1916 been recognised in Australia and New Zealand as Anzac Day , now one of their most important national occasions . It does not celebrate a military victory , but instead commemorates all the Australians and New Zealanders " who served and died in all wars , conflicts , and peacekeeping operations " and " the contribution and suffering of all those who have served . " Around the country , dawn services are held at war memorials to commemorate those involved . In Australia , at 10 : 15 , another service is held at the Australian War Memorial , which the prime minister and governor general normally attend . The first official dawn services were held in Australia in 1927 and in New Zealand in 1939 . Lower @-@ key services are also held in the United Kingdom and Ireland . In Turkey , large groups of Australians and New Zealanders have begun to gather at Anzac Cove , where in 2005 an estimated 20 @,@ 000 people attended the service to commemorate the landings . <unk> figures rose to 38 @,@ 000 in 2012 and 50 @,@ 000 in 2013 . 
- 
- 
- = Michelle Rzepecki = 
- 
- Michelle Rzepecki ( born 6 November 1986 ) is an Australian goalball player classified as a <unk> competitor . She made her debut for the Australia women 's national goalball team at the 2011 African @-@ Oceania regional Paralympic qualifying competition . She was selected to represent Australia at the 2012 Summer Paralympics in goalball . 
- 
- = = Personal life = = 
- 
- Rzepecki was born on 6 November 1986 in <unk> , New South Wales , and is 173 centimetres ( 68 in ) tall . She has the visual disability rod <unk> , which she acquired at birth . She can play the saxophone and piano . Rzepecki is a third @-@ generation Australian , as her grandfather moved to Australia from Germany . While attending Castle Hill High School , she returned to the country of her grandfather 's birth to do study abroad that was funded by a scholarship from the Society for Australian @-@ German Student Exchange Inc . She also lived in Bolivia for a while , working at a school for children with visual <unk> . While there , she introduced the children to goalball . Part of her work in the country was funded by a mini @-@ grant . As of 2012 , she works as a Sydney Tower <unk> guide . 
- 
- = = Goalball = = 
- 
- Rzepecki is a goalball player , and for visual disability sports , is classified as a <unk> competitor . She is a centre and winger . She has a goalball scholarship with the New South Wales Institute of Sport . Rzepecki started playing the sport in 2002 . She competed at the 2004 Australian National Goalball Championships , playing for the New South Wales goalball team which beat the Queensland goalball team in the finals . In 2011 , Rzepecki made her national team debut during the African @-@ Oceania regional Paralympic qualifying competition . She played in the game against the New Zealand women 's national goalball team that Australia won . As a member of the 2011 team , she finished sixth at the <unk> Goalball World Cup . 
- The Australian Paralympic Committee had chosen to work on her development as a goalball player with the idea that she might be able to qualify for , and win a medal at , the 2016 Summer Paralympics . She was a named a member of the <unk> <unk> that was going to the 2012 Summer Paralympics , in what would be her debut Games . That the team <unk> for the Games came as a surprise , as the Australian Paralympic Committee had been working on player development with an idea of the team qualifying for the 2016 Summer Paralympics . An Australian team had not participated since the 2000 Summer Paralympics , when they earned an automatic selection as hosts , and the team finished last in the competition . Going into the Paralympics , her team was ranked eighth in the world . In the 2012 Summer Paralympics tournament , the <unk> played games against Japan , Canada , the United States and Sweden . They lost every game , and did not advance to the finals . 
- 
- 
- = The Good Terrorist = 
- 
- The Good Terrorist is a 1985 political novel by Doris Lessing . It was first published in September 1985 in the United Kingdom by Jonathan Cape , and in the United States by Alfred A. <unk> . The story examines events in the life of Alice , a <unk> and well @-@ <unk> <unk> , who moves in with a group of radicals in London , and is drawn into their terrorist activities . 
- Lessing was inspired to write The Good Terrorist by the Irish Republican Army ( IRA ) bombing of the <unk> department store in London in 1983 . She had been a member of the British Communist Party in the early 1950s , but later grew disillusioned with communism . Three reviewers labelled The Good Terrorist as a satire , while Lessing called it humorous . Some critics called the novel 's title an oxymoron , stating that it highlights Alice 's ambivalent nature , and that she is neither a good person , nor a good revolutionary . 
- The Good Terrorist divided reviewers , with some being impressed by the book 's insight and characterization , and others complaining about the novel 's style and the character 's lack of depth . One critic complimented Lessing 's " strong descriptive prose and her precise and realistic <unk> " , but another called the book 's text " surprisingly bland " , and described the characters as " trivial or two @-@ dimensional or crippled by self @-@ <unk> " . The Good Terrorist was shortlisted for the Booker Prize , and won the <unk> Prize and the <unk> Smith Literary Award . 
- 
- = = Plot summary = = 
- 
- The Good Terrorist is written in third person from the point of view of Alice , an unemployed politics and economics graduate in her mid @-@ thirties who drifts from commune to commune . She considers herself a revolutionary , fighting against " fascist imperialism " , but is still dependent on her parents , whom she treats with contempt . In the early @-@ 1980s , Alice joins a squat of like @-@ minded " comrades " in a <unk> house in London . <unk> her is Jasper , a graduate she took in at a student commune she lived in fifteen years previously . Jasper became dependent on Alice and followed her from squat to squat . Alice fell in love with him , only to become frustrated later by his <unk> and <unk> homosexuality . Other members of the squat include Bert , their ineffective leader , and a lesbian couple , the maternal <unk> , and Faye , her unstable and fragile partner . 
- The abandoned house is in a state of disrepair and is earmarked by the City Council for demolition . To the indifference of the other comrades , Alice takes it upon herself to clean up and renovate the house , and convinces the Council that it is worth saving . She also <unk> the authorities to restore the electricity and water supplies . Alice becomes the house 's " mother " , cooking for everyone , and dealing with the local police , who are trying to evict them . The members of the squat belong to the Communist Centre Union ( CCU ) , and attend demonstrations and <unk> . Alice involves herself in some of these activities , but spends most of her time working on the house . 
- To be more useful to the struggle , Jasper and Bert travel to Ireland to persuade the Irish Republican Army ( IRA ) to let the CCU join them , but they are rejected . They also take a trip to the Soviet Union to offer their services , but are turned down . The IRA and <unk> , however , have begun taking notice of them and start using the house as a conduit for propaganda material and guns . <unk> start arriving in the middle of the night , and Alice , to avoid attracting the attention of the police , raises objections . This results in visits to the house by strangers who question the squat 's decision making . After this , the comrades decide to ignore orders , to act on their own , and to consider themselves " <unk> British Communists " . 
- Going it alone now , they start experimenting with explosives , and build a car bomb . Alice does not fully support this action , but accepts the majority decision . They target an <unk> hotel in <unk> , but their inexperience results in the premature detonation of the bomb , which kills Faye and several <unk> @-@ by . The remaining comrades , shaken by what they have done , decide to leave the squat and go their own way . Alice , disillusioned by Jasper , chooses not to follow him and remains behind because she cannot bear to abandon the house she has put so much effort into . Despite her initial reservations about the bombing , Alice feels a need to justify their actions to others , but realises it would be fruitless because " [ o ] <unk> people simply didn 't understand " . She acknowledges that she is a terrorist now , though she cannot remember when the change happened . 
- 
- = = Background = = 
- 
- Doris Lessing 's interest in politics began in the 1940s while she was living in Southern Rhodesia ( now Zimbabwe ) . She was attracted to a group of " quasi @-@ Communist [ s ] " and joined their Left Book Club in Salisbury ( now <unk> ) . Later , prompted by the conflicts arising from racial segregation prominent in Rhodesia at the time , she also joined the Southern Rhodesian Labour Party . Lessing moved to London in 1949 and began her writing career there . She became a member of the British Communist Party in the early 1950s , and was an active <unk> against the use of nuclear weapons . 
- By 1964 , Lessing had published six novels , but grew disillusioned with Communism and , after reading The <unk> by <unk> Shah , turned her attention to <unk> , an Islamic belief system . This prompted her to write her five @-@ volume " space fiction " series , Canopus in <unk> : Archives , which drew on Sufi concepts . The series was not well received by some of her readers , who felt she had abandoned her " rational <unk> " . 
- The Good Terrorist was Lessing 's first book to be published after the Canopus in <unk> series , which prompted several retorts from reviewers , including , " Lessing has returned to Earth " , and " Lessing returns to reality " . Several commentators have labelled The Good Terrorist as a satire , while Lessing called it humorous . She said : 
- [ I ] t 's not a book with a political statement . It 's ... about a certain kind of political person , a kind of self @-@ styled revolutionary that can only be produced by affluent societies . There 's a great deal of <unk> that I don 't think you 'd find in extreme left revolutionaries in societies where they have an immediate challenge . 
- Lessing said she was inspired to write The Good Terrorist by the IRA bombing of the <unk> department store in London in 1983 . She recalled , " the media reported it to sound as if it was the work of amateurs . I started to think , what kind of amateurs could they be ? " and realised " how easy it would be for a kid , not really knowing what he or she was doing , to drift into a terrorist group . " Lessing already had Alice in mind as the central character : " I know several people like Alice — this mixture of ... maternal caring , ... and who can contemplate killing large numbers of people without a moment 's <unk> . " She described Alice as " quietly comic [ al ] " because she is so full of contradictions . She said she was surprised how some of the characters ( other than Jasper , Alice 's love interest ) developed , such as the pill @-@ popping and fragile Faye , who turned out to be a " destroyed person " . 
- 
- = = Genre = = 
- 
- The Good Terrorist has been labelled a " political novel " by the publishers and some reviewers , including Alison <unk> in The New York Review of Books . <unk> stated that as political fiction , it is " one of the best novels ... about the terrorist mentality " since Joseph Conrad 's The Secret Agent ( 1907 ) , although this was questioned by William H. <unk> in The Hudson Review , who wrote that compared to Conrad , The Good Terrorist is " <unk> " . Several commentators have pointed out that it is more a novel about politics than political fiction . In From the <unk> of Empire : Christina Stead , Doris Lessing , Nadine <unk> , Louise Yelin called the work a novel about politics , rather than a political novel per <unk> . 
- The Good Terrorist has also been called a satire . In her book Doris Lessing : The <unk> of Change , Gayle Greene called it a " satire of a group of revolutionaries " , and Susan Watkins , writing in Doris Lessing : Border <unk> , described it as a " dry and satirical examination of a woman 's involvement with a left @-@ wing splinter group " . A biography of Lessing for the Swedish Academy on the occasion of her winning the 2007 Nobel Prize in Literature described the book as " a satirical picture of the need of the contemporary left for total control and the female protagonist 's <unk> <unk> and <unk> " . Yelin said the novel " <unk> [ ed ] between satire and nostalgia " . Academic Robert E. Kuehn felt that it is not satire at all . He stated while the book could have been a " satire of the <unk> and most hilarious kind " , in his opinion Lessing " has no sense of humor , and instead of <unk> [ the characters ] with the <unk> 's whip , she treats them with <unk> and <unk> irony " . 
- Virginia Scott called the novel a fantasy . Drawing on Lewis Carroll 's Alice 's Adventures in Wonderland in The International Fiction Review , she wrote that " [ Lessing 's ] Alice with her group of political revolutionaries can be seen as a serious fantasy which has striking parallels to ... Carroll 's Alice " . Both <unk> enter a house and are confronted by seemingly impossible challenges : Carroll 's Alice has to navigate passages too small to fit through , while Lessing 's Alice finds herself in a barely <unk> house that is earmarked for demolition . Both <unk> are able to change their appearances : in Wonderland , Alice <unk> her size to suit her needs ; in The Good Terrorist , Alice changes her <unk> to get what she wants from others . Scott noted that at one point in The Good Terrorist , Faye refers to Alice as " Alice the Wonder , the <unk> Alice " , alluding to Carroll 's Alice . 
- 
- = = Themes = = 
- 
- The American novelist Judith Freeman wrote that one of the common themes in The Good Terrorist is that of keeping one 's identity in a collective , of preserving " individual conscience " . This theme suggests that problems occur when we are coerced into conforming . Freeman said that Alice is a " quintessential good woman ... the little <unk> revolutionary " , but turns bad under peer pressure . 
- Another theme present is the symbolic nature of the house . Margaret Scanlan stated that as in books like Mansfield Park and Jane <unk> , The Good Terrorist " defines a woman in terms of her house " . Writing in the journal Studies in the Novel , Katherine <unk> said that Lessing often uses a house to symbolise " psychological or <unk> change " , and that here , " the house ... <unk> Alice 's function in the story " . Yelin described The Good Terrorist as " an urban , dystopian updating of the house @-@ as @-@ England genre , [ where ] ... England is represented by a house in London " . Writing in " Politics of <unk> Abuse : Political <unk> and <unk> <unk> in Doris Lessing 's The Good Terrorist " , <unk> and <unk> suggest that the house , and the " oppressive relations " in it , reflect the similarly oppressive relationships in the society it resides in . 
- Several critics have focused on the theme of motherhood . In " Mothers and <unk> / <unk> and <unk> " , Claire Sprague wrote that Lessing often <unk> on the theme of mothers passing their <unk> onto their daughters , and how the cycle of daughters fighting their mothers <unk> each generation . The British novelist Jane Rogers said that The Good Terrorist " is as <unk> and <unk> about motherhood as it is about the extreme left " . She stated that motherhood here " is terrible " : Alice 's mother is reduced to despair continually yielding to her selfish daughter 's demands ; Alice mothers Jasper , and has a similar <unk> relationship with him . Rogers added that motherhood is depicted here as a <unk> to protect the weak , despite their <unk> to retaliate and hurt you . 
- <unk> themes and the <unk> of women have also been associated with The Good Terrorist . Scanlan indicated that while many of the comrades in the book are women , they find that political activity does not elevate their position , and that they are " trapped in the <unk> they <unk> " . Yelin suggested that although Lessing ridicules the male members of the CCU and their role playing , she is also critical of the female members " who <unk> in male @-@ dominant political organizations and thus in their own oppression " . But with the book 's allusions to Jasper 's homosexuality , Yelin added that Lessing 's " critique of women 's infatuation with patriarchal <unk> and their emotional dependence on <unk> men " is muted by homophobia and the " <unk> pervasive in patriarchal constructions of ( male ) <unk> " . <unk> and <unk> noted that Lessing depicts Alice as a " typical <unk> " who cares for her family , in this case , the squat , but is " ignored and neglected " . They concluded that Alice 's fate is sealed because , according to the British socialist feminist Juliet Mitchell , women are " fundamental to the human condition " , yet " their economic , social , and political roles ... are marginal " . 
- 
- = = Critical analysis = = 
- 
- Several critics have called The Good Terrorist 's title an oxymoron . Robert <unk> suggested it is indicative of Alice 's " contradictory personality " , that she <unk> the squat 's house , yet is bent on destroying society . In The Hudson Review , George <unk> wrote that the title " hovers above the novel with ... irony " . The reader assumes that Alice is the " good terrorist " , but that while she may be a good person , she is " rotten at being a terrorist " . Writing in World Literature Today , Mona Knapp concluded that Lessing 's heroine , the " good terrorist " , is neither a good person , nor a good revolutionary . She knows how to renovate houses and manipulate people to her advantage , but she is unemployed and steals money from her parents . When real revolutionaries start using the squat to ship arms , she <unk> , and going behind her comrades ' backs , she makes a telephone call to the authorities to warn them . Knapp called Alice " a bad terrorist and a stunted human being " . <unk> suggested that it is Lessing herself who is the " good terrorist " , <unk> here by Alice , but that hers is " political terrorism of a literary kind " , where she frequently disguises her ideas in " very domestic @-@ looking fiction " , and " direct [ ly ] challenge [ s ] ... our sense of reality " . 
- Kuehn described Alice as " well @-@ <unk> , <unk> and sometimes <unk> " , but as someone who , at 36 , never grew up , and is still dependent on her parents . Yelin said Alice is " in a state of perpetual adolescence " , and her need to " mother everyone " is " an extreme case of psychological regression or failure to thrive " . Greene wrote that Alice 's " <unk> is ludicrous in her world " , and described her as " so furiously at odds with herself " because she is too immature to comprehend what is happening and her actions vary from being helpful to dangerous . 
- <unk> called Lessing 's narrative " ironic " because it highlights the divide between who Alice is and who she thinks she is , and her efforts to pretend there is no discrepancy . Alice refuses to acknowledge that her " maternal activities " stem from her desire to win her mother 's approval , and believing that her mother has " betrayed and abandoned " her , Alice turns to Jasper as a way to " continue to sustain her beliefs about herself and the world " . Even though Jasper takes advantage of her adoration of him by <unk> her , Alice still clings to him because her self @-@ image " vigorously qualifies her perception of [ him ] , and thus <unk> the denial and self @-@ deception " . The fact that Jasper has turned to homosexuality , which Alice dismisses as " his emotional life " , " suits her own <unk> desires " . Kuehn called Alice 's obsession with the " <unk> " and " repellent " Jasper " just <unk> " , adding that she feels safe with his <unk> , even though she has to endure his abuse . 
- Knapp stated that while Lessing exposes self @-@ styled insurrectionists as " spoiled and immature products of the middle class " , she also <unk> their <unk> at affecting any meaningful change . Lessing is critical of the state which " feeds the very hand that <unk> it " , yet she also <unk> those institutions that exploits the working class and ignores the homeless . Knapp remarks that Lessing does not resolve these <unk> , but instead highlights the failings of the state and those seeking to overthrow it . Scanlan compared Lessing 's comrades to Richard E. <unk> 's terrorists in his book <unk> of Revolution : Terrorism in the Modern World . <unk> wrote that when " ambitious <unk> " have no " creative ruling class to follow or a rebellious lower class to lead [ they ] have often taken upon themselves the burden of representative action " , which he said " is a formula for disaster " . 
- 
- = = Reception = = 
- 
- Critics have been divided on The Good Terrorist . Elizabeth Lowry highlighted this in the London Review of Books : " [ Lessing ] has been sharply criticised for the pedestrian quality of her prose , and as vigorously defended " . The Irish literary critic Denis Donoghue complained that the style of the novel is " <unk> drab " , and Kuehn referred to Lessing 's text as " surprisingly bland " . Lowry noted that the English academic Clare Hanson defended the book by saying that it is " a grey and <unk> novel because it ... speaks a grey and <unk> language " . 
- Freeman on the other hand called the book a " graceful and accomplished story " , and a " brilliant account of the types of individuals who commit terrorist acts " . Writing in the Los Angeles Times Freeman described Lessing as " one of our most valuable writers " who " has an <unk> grasp of human relationships " . In a review in the Sun @-@ Sentinel , Bonnie Gross described the novel as " rewarding reading " and Lessing 's " most accessible " book to date . She said it is the author 's " strong descriptive prose and her precise and realistic <unk> " that makes this book " remarkable " . Gross felt that while some of the male characters are not that strong , the female characters are much better developed , particularly Alice , whom she found memorable . 
- Amanda <unk> wrote in The Women 's Review of Books that at first glance the ideas in The Good Terrorist appear deceptively simple , and the plot predictable . But she added that Lessing 's strength is her " stoic <unk> [ ion ] of the daily effort of living " , which excels in the way she describes day @-@ to @-@ day life in a squat . <unk> also liked the book 's depiction of Alice , who " speak [ s ] to me most <unk> about myself and my generation " . In a review in off our backs , an American feminist publication , Vickie Leonard called The Good Terrorist a " fascinating book " that is " extremely well written " with characters that are " exciting " and " realistic " . Leonard added that even though Alice is not a feminist , the book illustrates the author 's " strong admiration for women and their accomplishments " . 
- Writing in The Guardian , Rogers described The Good Terrorist as " a novel in <unk> close @-@ up " that examines society through the eyes of individuals . She said it is " witty and ... angry at human <unk> and <unk> " , and within the context of recent terrorist attacks in London , it is an example of " fiction going where <unk> writing cannot " . A critic in Kirkus Reviews wrote that Alice 's story is " an extraordinary tour de force — a psychological portrait that 's realistic with a vengeance " . The reviewer added that although Alice is " self @-@ <unk> " and not always likeable , the novel 's strength are the characters and its depiction of political motivation . 
- Donoghue wrote in The New York Times that he did not care much about what happened to Alice and her comrades . He felt that Lessing presents Alice as " an <unk> <unk> of reactions and prejudices " , which leaves no room for any further interest . Donoghue complained that Lessing has not made up her mind on whether her characters are " the salt of the earth or its <unk> " . In a review in the Chicago Tribune , Kuehn felt that the work has little impact and is not memorable . He said Lessing 's real interest is character development , but complained that the characters are " trivial or two @-@ dimensional or crippled by self @-@ <unk> " . 
- The Good Terrorist was shortlisted for the 1985 Booker Prize , and in 1986 won the <unk> Prize and the <unk> Smith Literary Award . In 2007 Lessing was awarded the Nobel Prize in Literature for being " part of both the history of literature and living literature " . In the award ceremony speech by Swedish writer Per <unk> , The Good Terrorist was cited as " an in @-@ depth account of the extreme <unk> <unk> culture that <unk> off female self @-@ sacrifice " . Following Lessing 's death in 2013 , The Guardian put The Good Terrorist in their list of the top five Lessing books . Indian writer <unk> <unk> included the novel in his 2015 " top 10 books about revolutionaries " , also published in The Guardian . 
- 
- = = Publication history = = 
- 
- The Good Terrorist was first published in September 1985 in hardcover in the United Kingdom by Jonathan Cape , and in the United States by Alfred A. <unk> . The first paperback edition was published in the United Kingdom in September 1986 by <unk> . An <unk> 13 @-@ hour audio cassette edition , narrated by Nadia May , was released in the United States in April 1999 by Blackstone Audio . The novel has been translated into several other languages since its first publication in English in 1985 , including Catalan , Chinese , French , German , Italian , Spanish and Swedish . 
- 
- 
- = Henry of Grosmont , 1st Duke of Lancaster = 
- 
- Henry of Grosmont , 1st Duke of Lancaster , 4th Earl of Leicester and Lancaster , KG ( c . <unk> – 23 March <unk> ) , also Earl of Derby , was a member of the English nobility in the 14th century , and a prominent English diplomat , politician , and soldier . The son and heir of Henry , 3rd Earl of Lancaster , and Maud <unk> , he became one of Edward III 's most trusted captains in the early phases of the Hundred Years ' War and distinguished himself with victory in the Battle of <unk> . He was a founding member and the second Knight of the Order of the Garter in <unk> , and in 1351 was created duke . Grosmont was also the author of the book <unk> de <unk> medicines , a highly personal devotional treatise . He is remembered as one of the founders and early patrons of Corpus Christi College , Cambridge , which was established by two of the <unk> of the town in <unk> . 
- 
- = = Family background and early life = = 
- 
- Grosmont 's uncle , Thomas of Lancaster , was the son and heir of Edward I 's brother Edmund <unk> . Through his inheritance and a fortunate marriage , Thomas became the wealthiest peer in England , but constant quarrels with King Edward II led to his execution in <unk> . Having no heir , Thomas 's possessions and titles went to his younger brother Henry – Grosmont 's father . Earl Henry of Lancaster <unk> to the deposition of Edward II in <unk> , but did not long stay in favour with the regency of Queen Isabella and Roger Mortimer . When Edward III took personal control of the government in 1330 , relations with the Crown improved , but by this time the older Henry was already struggling with poor health and blindness . 
- Little is known of Grosmont 's early years , but that he was born at Grosmont Castle in Grosmont , Monmouthshire , Wales , and that he was born c . <unk> , not around the turn of the century as previously held . According to his own memoirs , he was better at martial arts than at academic subjects , and did not learn to read until later in life . In 1330 he was knighted , and represented his father in parliament . The next year he is recorded as participating in a royal tournament at <unk> . 
- In <unk> he took part in Edward 's Scottish campaign , though it is unclear whether he was present at the great English victory at the Battle of <unk> Hill . After further service in the north , he was appointed the King 's lieutenant in Scotland in 1336 . The next year he was one of the six men Edward III promoted to the higher levels of the peerage . One of his father 's lesser titles , that of Earl of Derby , was bestowed upon Grosmont . 
- 
- = = Service in France = = 
- 
- With the outbreak of the Hundred Years ' War in <unk> , Grosmont 's attention was turned towards France . He took part in several diplomatic missions and minor campaigns and was present at the great English victory in the naval Battle of <unk> in <unk> . Later the same year , he was required to commit himself as hostage in the Low Countries for the king 's considerable debts . He remained hostage until the next year and had to pay a large ransom for his own release . On his return he was made the king 's lieutenant in the north and stayed at <unk> until <unk> . The next years he spent in diplomatic negotiations in the Low Countries , Castile and Avignon . 
- In 1345 Edward III was planning a major assault on France . A three @-@ <unk> attack would have the Earl of Northampton attacking from Brittany , the king himself from Flanders , while Grosmont was dispatched to <unk> to prepare a campaign in the south . Moving rapidly through the country , he confronted the Comte d ’ Isle at <unk> on 21 October and there achieved a victory described as " the greatest single achievement of Lancaster 's entire military career " . The ransom from the prisoners has been estimated at £ 50 @,@ 000 . The next year , while Edward was carrying out his <unk> campaign , Grosmont laid siege to , and captured , <unk> , before returning home to England in <unk> . 
- 
- = = Duke of Lancaster = = 
- 
- In 1345 , while Grosmont was in France , his father died . The younger Henry was now Earl of Lancaster – the wealthiest and most powerful peer of the realm . After participating in the Siege of Calais in <unk> , the king honoured Lancaster by including him as a founding knight of the Order of the Garter in <unk> . A few years later , in 1351 , Edward bestowed an even greater honour on Lancaster when he created him Duke of Lancaster . The title of duke was of relatively new origin in England ; only one other <unk> title existed previously . 
- In addition to this , Lancaster was given palatinate status for the county of Lancashire , which entailed a separate administration independent of the crown . This grant was quite exceptional in English history ; only two other counties <unk> existed : Durham , which was an ancient ecclesiastical palatinate , and Chester , which was crown property . 
- It is a sign of Edward 's high regard for Lancaster that he would bestow such extensive privileges on him . The two men were second cousins through their great @-@ grandfather Henry III and practically <unk> ( Edward was born in <unk> ) , so it is natural to assume that a strong sense of <unk> existed between them . Another factor that might have influenced the king 's decision was the fact that Henry had no male heir , so the grant was made for the Earl 's lifetime only , and not intended to be hereditary . 
- 
- = = = Further prestige = = = 
- 
- Lancaster spent the <unk> intermittently campaigning and negotiating peace treaties with the French . In <unk> he was present at the naval victory at <unk> , where he allegedly saved the lives of the Black Prince and John of Gaunt . The years 1351 @-@ 2 he spent on crusade in Prussia . It was here that a quarrel with Otto , Duke of Brunswick , almost led to a duel between the two men , narrowly averted by the intervention of the French king , John II . In the later half of the decade campaigning in France resumed . After a <unk> in Normandy in <unk> and the siege of Rennes in <unk> , Lancaster participated in the last great offensive of the first phase of the Hundred Years ' War : the <unk> campaign of <unk> @-@ 60 . Then he was appointed principal <unk> for the Treaty of <unk> , where the English achieved very favourable terms . 
- After returning to England in November <unk> , he fell ill early the next year , and died at Leicester Castle on 23 March . It is likely that the cause of death was the plague , which that year was making a second visitation of England . He was buried in the Church of the <unk> of Our Lady of the <unk> , Leicester , the church which he had built within the religious and charitable institution founded by his father next to Leicester Castle , and where he had re @-@ buried his father some years previously . 
- 
- = = Private life = = 
- 
- Lancaster was married to Isabella , daughter of Henry , Lord Beaumont , in 1330 . The two had no sons , but two daughters : Maud and Blanche . While Maud was married to William I , Duke of <unk> , Blanche married Edward III 's son John of Gaunt . Gaunt ended up <unk> Lancaster 's possessions and <unk> title , but it was not until <unk> , when the dying King Edward III was largely incapacitated , that he was able to restore the palatinate rights for the county of Lancaster . When Gaunt 's son Henry of <unk> <unk> the crown in <unk> and became Henry IV , the vast Lancaster inheritance , including the Lordship of <unk> , was merged with the crown as the Duchy of Lancaster . 
- We know more about Lancaster 's character than of most of his contemporaries through his memoirs , the <unk> de <unk> medicines ( Book of the Holy Doctors ) . This book is a highly personal treatise on matters of religion and piety , but it also contains details of historical interest . It , among other things , revealed that Lancaster , at the age of 44 when he wrote the book in <unk> , suffered from <unk> . The book is primarily a devotional work though ; it is organized around seven wounds which Henry claims to have , representing the seven sins . Lancaster confesses to his sins , explains various real and mythical medical <unk> in terms of their theological symbolism , and <unk> the reader to greater morality . 
- 
- = = <unk> = = 
- 
- 
- 
- = I Am Unicorn = 
- 
- " I Am Unicorn " is the second episode of the third season of the American musical television series Glee , and the forty @-@ sixth overall . The episode was written by series co @-@ creator Ryan Murphy , directed by series co @-@ creator Brad Falchuk , and first aired on September 27 , 2011 on Fox in the United States . It features the return of Shelby Corcoran ( Idina Menzel ) to the show to direct a rival glee club at William McKinley High even while New Directions , the current club , is having trouble recruiting members . Shelby also wants Quinn ( Dianna Agron ) and Puck ( Mark <unk> ) , the biological parents of her adopted daughter Beth , to be a part of Beth 's life . The director of New Directions , Will Schuester ( Matthew Morrison ) sets up a " booty camp " for the less capable dancers in the club , and auditions for the school musical , West Side Story , begin . 
- The episode received mostly positive reviews , which ranged from okay to fabulous , with approval being given to the resurrection of neglected storylines from the show 's first season . One of these was the adoption of Beth , and critics were especially happy with the scene where Puck meets her , but there was widespread disdain for the explanation behind Shelby 's return , that of starting a rival glee club . There were only three musical numbers in the episode , though all three were given positive notices , with the overall favorite being Blaine 's performance of " Something 's Coming " from West Side Story at the end of the episode . All three songs were released as singles , available for download , and " Somewhere " , sung as a duet by Menzel and Lea Michele , charted on the Billboard Hot 100 . It did not chart on the Canadian Hot 100 . Upon its initial airing , this episode was viewed by 8 @.@ 60 million American viewers and garnered a 3 @.@ 7 / 10 Nielsen rating / share in the 18 – 49 demographic . The total viewership and ratings for this episode were down from the previous week 's season opener , " The Purple Piano Project " . 
- 
- = = Plot = = 
- 
- Glee club director Will Schuester ( Matthew Morrison ) institutes a " booty camp " to hone the dancing skills of New Directions members Finn ( Cory Monteith ) , Mercedes ( Amber Riley ) , Puck ( Mark <unk> ) , Kurt ( Chris Colfer ) and Blaine ( Darren Criss ) , and has Mike ( Harry Shum Jr . ) instruct them . As he is too busy to direct the upcoming school musical West Side Story , guidance <unk> Emma Pillsbury ( Jayma Mays ) , football coach Shannon <unk> ( Dot @-@ Marie Jones ) and New Directions member Artie Abrams ( Kevin <unk> ) take charge of it . 
- Rachel ( Lea Michele ) and Kurt audition , respectively , for the lead roles of Maria and Tony ; she performs " Somewhere " from the show , and he performs " I 'm the Greatest Star " from Funny Girl . Kurt later <unk> on the directors and hears them question whether he is masculine enough for the role . He re @-@ auditions and attempts to give a more masculine performance , but they are unable to suppress laughter at his acting . Kurt is also running for class president , and accepts campaign help from Brittany ( Heather Morris ) , who wants to highlight his unique character by comparing him to a unicorn . Kurt feels her proposed campaign materials highlight only his gay side , and is upset when she goes against his wishes and posts them anyway . He discusses his image problem with his father , Burt ( Mike O 'Malley ) , who recommends that he embrace his uniqueness . Kurt later changes his mind about his campaign 's approach and apologizes to Brittany , but is surprised to learn that she too has decided to run for class president . 
- Shelby Corcoran ( Idina Menzel ) — Rachel 's biological mother , the adoptive mother of Quinn ( Dianna Agron ) and Puck 's daughter Beth , and the former coach of rival glee club Vocal Adrenaline — is <unk> to coach a second glee club at McKinley High financed by Sugar <unk> 's ( Vanessa <unk> ) wealthy and <unk> father . Shelby reaches out to Rachel , Puck and Quinn . She lets Puck see Beth , but rejects Quinn 's desire to do likewise due to Quinn 's bad @-@ girl attitude , appearance and behavior . <unk> coach Sue Sylvester ( Jane Lynch ) , who is running for Congress , convinces Quinn to feature in an anti @-@ arts video for her campaign . In it , Quinn confronts Will and blames him for her transformation into a bad girl , but Will <unk> her , reminds her of how the glee club and its members have always supported her in the past and tells her to grow up . After seeing a picture of a happy Beth and Puck , Quinn breaks down . She resumes her normal appearance , and Will and the New Directions welcome her back into the club . Puck tells Quinn he is proud of her , but Quinn reveals she is only pretending to behave in order to take Beth back from Shelby , and intends to pursue full custody . 
- To avoid competing with Kurt , Blaine auditions for a supporting role with a rendition of " Something 's Coming " , one of Tony 's songs from the show . The directors are impressed , and ask if he will read for the part of Tony instead . Kurt , who was watching from above , silently walks out of the auditorium . 
- 
- = = Production = = 
- 
- The episode was written by series co @-@ creator Ryan Murphy , directed by series co @-@ creator Brad Falchuk , and was filmed in five days , from August 22 , 2011 through August 26 , 2011 . Broadway star Idina Menzel returns for the first time since the first season finale " Journey to <unk> " , when her character , Shelby Corcoran , adopted Quinn 's newborn baby , named Beth . On July 15 , 2011 , it was announced that Menzel would be returning to Glee in the third season " for a major arc that could span as many as 10 @-@ 12 episodes " . Series co @-@ creator Ryan Murphy was quoted as saying , " I 'm really excited [ ... ] that Idina is joining the family again . We missed her last year and we 're happy that she is coming back . " The article also noted that her character , Shelby , would be " returning from New York to Ohio to join William McKinley High School as a new teacher " . Menzel herself said that she would " be back and forth in Glee all throughout the season " , which she was " very excited about " . Shelby 's adopted daughter is also appearing : Menzel tweeted that she was " shooting scenes with babies " . The drawing of the " Clown Pig " that Puck brings for Beth was actually drawn by Falchuk and Agron . 
- Recurring guest stars appearing in the episode include Principal Figgins ( <unk> Theba ) , Coach <unk> ( Jones ) , cheerleader <unk> Jackson ( Lauren Potter ) , student Sugar <unk> ( <unk> ) and Menzel as Shelby . Second season series regular Mike O 'Malley , who plays Burt Hummel and also appears in the episode , is listed in the Fox press release as a guest star for this episode and receives a like credit in the episode itself . 
- Three singles were released from the episode : " I 'm the Greatest Star " from Funny Girl sung by Colfer , and covers of " Something 's Coming " and " Somewhere " from West Side Story — the former sung by Criss , and the latter a duet between Michele and Menzel . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " I Am Unicorn " was first broadcast on September 27 , 2011 in the United States on Fox . It garnered a 3 @.@ 7 / 10 Nielsen rating / share in the 18 – 49 demographic , and received 8 @.@ 60 million American viewers during its initial airing . It was beaten for the second week in a row in its timeslot by <unk> on CBS , which earned a 4 @.@ 2 / 12 rating / share in the 18 – 49 demographic , and also by the second episode of New Girl , which follows Glee on Fox , and brought in a 4 @.@ 5 / 11 rating / share and 9 @.@ 28 million viewers . The Glee numbers were down from the previous week 's season opener , " The Purple Piano Project " , which netted a 4 @.@ 0 / 11 rating / share and 9 @.@ 21 million viewers . 
- In the United Kingdom , " I Am Unicorn " was watched on Sky1 by 995 @,@ 000 viewers , down 177 @,@ 000 from the season premiere the previous week . In Australia , the episode drew 729 @,@ 000 viewers , which made Glee the fifteenth most @-@ watched program of the night , down from the season premiere 's twelfth most @-@ watched program of the night and 760 @,@ 000 viewers in the previous week . In Canada , 1 @.@ 50 million viewers watched the episode , and it was the eighteenth most @-@ viewed show of the week , down eight slots and 28 % from the 2 @.@ 10 million who watched " The Purple Piano Project " . 
- 
- = = = Critical reception = = = 
- 
- Reviewers generally received this episode positively , though some , like IGN 's Robert Canning , thought it was merely " okay " ; he gave it a score of 6 @.@ 5 out of 10 , and Amy Reiter of the Los Angeles Times said she " felt vaguely <unk> " and that the episode " lacked emotional resonance " . In contrast , The A.V. Club 's Todd VanDerWerff gave the episode a " B " , and called it " a marked improvement over the season premiere " , and complimented the way it " gave nearly all of the storylines an emotional core " . Anthony Benigno of The <unk> Times characterized it as " one of the <unk> , best @-@ made , most well @-@ acted , and entertaining hours " from Glee in a very long time , and BuddyTV 's John Kubicek said that it was " quintessential Glee " and that " the show is once again a magical , fabulous unicorn . " Bobby <unk> of the Houston Chronicle was " charmed " , " moved " , and " excited for what 's to come . " 
- The reappearance of storylines left dangling at the end of the first season was noted with approval by Samantha Urban of The Dallas Morning News and VanDerWerff , who both mentioned not only the big one about Quinn and Puck and baby Beth but also Artie 's love of directing , and variously added Rachel and Shelby , and movement on the Will and Emma relationship . The fact that Shelby had been hired to form a second glee club at McKinley , however , was greeted with <unk> by both reviewers — Urban called it " mind @-@ <unk> <unk> " — and others as well . Reiter found the idea incomprehensible , and Vanity Fair 's Brett <unk> wrote , " Given Will 's ongoing struggles to fill his own <unk> baker 's dozen , this is about as realistic a plan as Michele Bachmann starting a rival chapter of <unk> at Liberty University . " Vicki Hyman of The Star @-@ Ledger characterized the notion of " Shelby deciding to give up a burgeoning Broadway career because she was missing her daughter grow up " to take a part @-@ time job in Lima as " ridiculous " , and the whole scenario as " more than a little bizarre " . 
- The effect of Shelby 's advent on Puck evoked the most praise . Benigno called the segment where Puck meets Beth " the best scene of this very young season " , The Hollywood Reporter 's <unk> Goldberg said it was a " top moment " , and Abby West of Entertainment Weekly dubbed it " the sweetest scene of the night " . VanDerWerff also called it " very sweet " and expressed hope that " the show will come up with something for him to do after mostly <unk> him to weird comic relief last season . " The effect of her return on Quinn was greeted with less enthusiasm . Reiter was " not thrilled " by the possibility of a custody battle between Quinn and Shelby and called Quinn 's plan " half @-@ baked " , and Canning dismissed it as " clichéd drama " . Kubicek expressed interest in seeing " where this goes " , and VanDerWerff noted that Quinn has been " grieving giving up her child all this time and she didn 't even know it " , and characterized it as a " fairly powerful storyline " . 
- Critics were divided on Kurt 's storyline as he faced being perceived primarily as gay both when auditioning and when running for class president . VanDerWerff said it was " the most consistent " storyline , and Canning called it " the most familiar story " , but also described Kurt as " by far the most interesting and most layered " character , his stories " delivering the most emotional connections " , and this episode 's installments " entertaining territory " . Kubicek stated that there were " tons " of wonderful " Kurt moments " in the episode . Benigno called Kurt learning to embrace his <unk> yet again on the show " kind of awkward " , and Hyman asked " Was this Kurt <unk> He 's Special 3 @.@ 0 or 4 @.@ 0 ? I can 't keep track . " Votta summed up Kurt 's audition quandary : " Kurt is fighting <unk> , and while the <unk> , <unk> gloves and climbing routine might have been an attempt to <unk> it up , instead Kurt played right into expectations with the over @-@ the @-@ top Funny Girl piece . " His attempt to rescue the situation by <unk> via performing a Romeo and Juliet scene with Rachel evoked laughter from the three directors and Rachel herself , but as Votta points out , Kurt was " not actually being bad as Romeo " . Jayma Mays , who plays Emma , one of the directors in that scene , stated in an interview that she thought Kurt was " good " . Kurt finding himself in competition with Blaine was also touched on , but several reviewers were unhappy with the revelation that Blaine was not a senior like Kurt , as had been implied in the previous season . VanDerWerff wrote that Blaine " seems to have simultaneously gotten younger and had a complete personality transplant over the summer " , Votta noted " the continuity @-@ <unk> plot point that he 's somehow a Junior and not a Senior like his boyfriend " , and Urban allowed her <unk> to show : " Oh really , Glee ? Blaine 's a junior ? Blaine 's younger than Kurt ? Fine . <unk> . " MTV 's Jim <unk> went into rhyme to express his dismay : " It 's hard to keep my bearings straight / And oh , how it makes my heart <unk> / Kurt and Blaine were gonna move to New York together / But now they 'll have to wait " , referring to a scene in the " New York " episode where Kurt discussed the planned move with Rachel . 
- Goldberg was pleased that having Brittany volunteer to run Kurt 's campaign included an acknowledgment that he " went through hell " the previous year , and praised " Brittany logic " in general . Respers France loved that Brittany , in helping Kurt find his magical inner unicorn , was able to find her own . Reiter enjoyed the " delicious dose of Brittany @-@ <unk> " , which she called " the best part " of the episode , and Kubicek said that there were " tons of wonderful " Brittany moments . For Hyman , the " one sit @-@ up @-@ and @-@ take @-@ notice moment " was the confrontation between Will and Quinn where he told her to grow up . Respers France thought " Sue Sylvester 's attempt to use Quinn against the glee club was hilarious " , but VanDerWerff was unhappy with Quinn being <unk> into " Sue ’ s ridiculous run for Congress " . Reiter wrote that it was " hard to muster much sympathy for Quinn " in the episode given the scene with Quinn and The <unk> : " <unk> someone 's head in a public toilet , threatening to cut them , and shaking them down for their lunch money are orders of magnitude more chilling than the face @-@ full @-@ of @-@ <unk> bullying we 're used to seeing . " 
- 
- = = = Music and performances = = = 
- 
- The episode 's musical performances were well received by most reviewers . All three were Broadway songs , two from West Side Story and one from Funny Girl — the concentration on show tunes disappointed Reiter , and Canning felt they were all " too bland " , but others were happy with the selections including <unk> who said he was " loving the Broadway @-@ bend to these first two episodes " , and added , " all three of tonight 's numbers were hands @-@ down fantastic . " 
- The duet of " Somewhere " featuring Rachel and Shelby was generally complimented . Both Benigno and <unk> Votta of Billboard compared it favorably to their previous duet , Lady Gaga 's " Poker Face " , from season one . Benigno gave the performance a " B + " , while Michael Slezak of TVLine gave it an " A − " and praised their " powerful , evocative voices " . Rolling Stone 's Erica Futterman was not impressed , and characterized it as " Lite FM <unk> that does nothing to showcase these Broadway <unk> in a new and exciting way " . Amy Lee of The Huffington Post called it " pretty bland " , and said it was " getting annoying " that Rachel " sings every song as if she 's <unk> Streisand " . The Wall Street Journal 's <unk> <unk> , however , called the duet " pitch @-@ perfect " and " so sublime it makes you catch your breath " . 
- Kurt 's choice of " I 'm the Greatest Star " to audition for the role of Tony — the one song not from West Side Story — drew comment : Benigno ascribed it to Kurt 's " ability to make a talented <unk> of himself " , while Slezak was of the opinion that Kurt was " way too <unk> , and way too hungry for the role " for that kind of <unk> . Despite these plot @-@ related issues , both reviewers gave the performance an " A " , and Benigno noted both that " he nails it " and " last half of the song is a singing clinic . " VanDerWerff was not fond of the reliance on " <unk> staging " , though Futterman called it " an impressive physical performance " , Votta stated that " Kurt sounds flawless and the performance is captivating " and <unk> complimented his " captivating high notes after high notes " , and added , " He 's a star unicorn , and he knows it . " Lisa Respers France of CNN wrote that Kurt " was amazing singing Streisand , and for the first time I realized that he really is as big a star as Rachel . " 
- Blaine 's rendition of " Something 's Coming " was the most enthusiastically welcomed . It was the favorite number of Lee , VanDerWerff and Futterman ; Lee said it was " the best song " , and added , " he 's <unk> , impassioned and generally delightful as Blaine @-@ playing @-@ Tony . " VanDerWerff was even more complimentary with " by far the best performance " , and Futterman called it " the winning musical number of the episode " . Both Slezak and Benigno gave it a " B + " , and the former complimented Criss 's " <unk> charm and <unk> enthusiasm " , while the latter maintained that the actor is " at his best when he 's doing <unk> pop numbers with kind of an off @-@ beat twist " . West gave the song an " A − " , and said " Blaine just knows how to own the stage and your TV screen " , while both Votta and Respers France wrote that he was the " perfect Tony " . 
- 
- = = = Chart history = = = 
- 
- One of the three cover versions released as singles debuted on the Billboard Hot 100 , and none charted on the Canadian Hot 100 or in England or Australia . The duet version of " Somewhere " appeared at number seventy @-@ five , the fourth time the song charted in the Hot 100 . By contrast , " Something 's Coming " , the episode 's other song from West Side Story , has never appeared in the Hot 100 , and failed to chart there again . 
- 
- 
- = Galentine 's Day = 
- 
- " Galentine 's Day " is the 16th episode of the second season of the American comedy television series Parks and Recreation , and the 22nd overall episode of the series . It originally aired on NBC in the United States on February 11 , 2010 . In the episode , Leslie and her boyfriend Justin seek to reunite Leslie 's mother , Marlene , with her teenage flame . Meanwhile , April 's feelings for Andy continue to <unk> , while Ann appears to be growing apart from Mark . 
- The episode was written by series co @-@ creator Michael Schur and directed by Ken <unk> . " Galentine 's Day " featured a guest appearance by John Larroquette as Frank Beckerson , the long @-@ lost love of Marlene <unk> @-@ Knope , who was played by Pamela Reed . It also featured the last in a string of guest performances by Justin Theroux as Leslie 's love interest , Justin Anderson . 
- According to Nielsen Media Research , " Galentine 's Day " was seen by 4 @.@ 98 million household viewers , which marked a continued recent improvement in ratings for the series . It tied a record set with the previous episode , " Sweetums " , for the season 's highest rating among viewers aged between 18 and 49 . The episode received generally positive reviews . 
- 
- = = Plot summary = = 
- 
- Leslie ( Amy Poehler ) throws her annual " Galentine 's Day " party for her female friends , celebrated the day before Valentine 's Day . She asks her mother , Marlene ( Pamela Reed ) , to tell the story about how she fell in love with a lifeguard that saved her from drowning in 1968 , but the two had to break it off over objections from Marlene 's parents . Leslie later tells the story to Justin ( Justin Theroux ) , who is amazed by the tale and wants to unite the two . He successfully tracks down Marlene 's old flame , Frank Beckerson ( John Larroquette ) , and convinces Leslie to go with him to Illinois and reunite the two on Valentine 's Day at the Senior Center Valentine 's Dance , which the parks department <unk> . 
- Leslie and Justin meet Frank , a strange and depressed man that has constant panic attacks . Leslie begins to have doubts about bringing him to her mother and tries to call it off , but Justin insists that they should " let this unfold " . At the dance , where Andy 's ( Chris Pratt ) band Mouse Rat is playing , Frank meets up with Marlene , who is repulsed by Frank 's past , current unemployment and overall failure at life . She turns down his offer at a second chance at love , prompting him to storm onto the stage and denounce her over the microphone . Leslie apologizes to her mother for bringing Frank . She is later upset with Justin , but has trouble <unk> the reasons for her dissatisfaction . Ron ( Nick <unk> ) explains that Justin is a " tourist , " meaning that he takes " vacations in people 's lives " and only cares about telling interesting stories to impress other people , which makes him selfish . Two older women then recognize Ron as jazz saxophonist Duke Silver , but he denies it . Leslie later breaks up with Justin , which Tom ( Aziz Ansari ) takes especially hard , reacting as if his parents were getting divorced . 
- Before the senior dance , Tom invites his ex @-@ wife Wendy ( <unk> <unk> ) to his office to finally <unk> his romantic feelings for her , but she rejects him . Not satisfied with the outcome , he attempts to blackmail her into a date using an <unk> lawsuit as leverage . Tom and Wendy are later shown hugging and presumably making <unk> , although their conversation remains <unk> . Meanwhile , April 's ( Aubrey Plaza ) boyfriend Derek ( Blake Lee ) and his boyfriend Ben ( Josh <unk> ) mock the senior citizens , causing April to question why their interactions must constantly be " <unk> in like 15 layers of irony " . They accuse her of " <unk> " , which they attribute to spending time with Andy , and provide her with several <unk> . She breaks up with them in response . Ann ( Rashida Jones ) and Mark ( Paul Schneider ) , at the same time , celebrate their first Valentine 's Day together . In an interview with the camera crew , Ann describes the relationship as " good " , but her tone of voice and body language around Mark contradict her statements . She later becomes jealous when Andy <unk> a song to April , even going so far as to question April about the possibility of a budding relationship between April and Andy , to which April responds <unk> . 
- 
- = = Production = = 
- 
- " Galentine 's Day " was written by series co @-@ creator Michael Schur and directed by Ken <unk> . The episode featured a guest appearance by John Larroquette as Frank Beckerson , the long @-@ lost love of Leslie 's mother , Marlene Knope . When Parks and Recreation co @-@ creator Greg Daniels announced the casting in January 2010 , he described Larroquette 's character as " He 's the one who got away . " " Galentine 's Day " also included an appearance by Pamela Reed , who has played Marlene Knope in several episodes , and the last of a string of slated guest appearances by Justin Theroux as Justin Anderson , a love interest for Leslie . The episode marked the return of Andy 's band , " Mouse Rat " , which was previously featured in the first season finale " Rock Show " . His bandmates are played by Mark Rivers ( drums ) , Andrew <unk> ( guitar ) and Alan Yang ( bass ) , the latter of whom serves as a screenwriter for Parks and Recreation . 
- 
- = = Cultural references = = 
- 
- At the dance , a senior citizen approaches Ron Swanson and asks for an <unk> from Duke Silver . This is a reference to the previous second season episode , " Practice Date " , which establishes Ron 's secret identity as a jazz musician . Leslie referred to her Galentine 's Day breakfast tradition as " Lilith Fair minus the <unk> and plus <unk> " , a reference to the concert tour and traveling music festival . She also said the love story between Marlene and Frank makes the 2004 romance film The <unk> look like the 2008 horror film Saw V. Mark gets Ann a necklace similar to the Heart of the Ocean , the fictional jewelry given to Kate <unk> 's character in the 1997 romantic drama film , Titanic . During one scene in " Galentine 's Day " , Leslie does a voice impersonation of U.S. President John F. Kennedy . When Tom mistakes the impression for that of the Arnold <unk> character Terminator , he prompts Leslie to do that impression as well . 
- Among the songs performed by Mouse Rat in " Galentine 's Day " were " The Way You Look Tonight " , originally performed by Fred <unk> in the 1936 film , Swing Time . Andy 's <unk> suggests he sings the " Let 's Call the Whole Thing Off " more like jazz musician and <unk> Louis Armstrong , who Andy admits he has never heard of . Andy and his band also performs " I Only Have Eyes for You " and " I 've Got You Under My Skin " . Leslie said reuniting Marlene and Frank would be like reuniting Romeo and Juliet , the protagonists of the William Shakespeare play of the same name , or reuniting actress Jennifer Aniston and actor Brad Pitt . She also warns to the camera for Aniston to " stay away from John Mayer " , the musician who previously dated Aniston . In the days prior to the original broadcast of " Galentine 's Day " , Mayer publicly apologized for a number of explicit sexual and racial comments he had made in the past months , which prompted news outlets to praise Parks and Recreation for the <unk> of their Mayer joke . Ironically , in May 2011 , Jennifer Aniston started dating Justin Theroux , who guest starred in the episode , and they later married . Frank made a reference to a recurring gag from Arrested Development when , after being rejected , he announced to Marlene , " take one last look ... because you 'll never see this body again . " 
- 
- = = Reception = = 
- 
- In its original American NBC broadcast on February 11 , 2010 , " Galentine 's Day " was seen by 4 @.@ 98 million households , according to Nielsen Media Research . This marked a continued recent improvement in ratings for the series . " Galentine 's Day " was seen by more viewers than the previous week 's episode " Sweetums " , which drew 4 @.@ 87 million viewers and was an itself an increase from previous episodes . " Galentine 's Day " drew an overall 3 @.@ 1 rating / 5 share , and a 2 @.@ 3 rating / 6 share among viewers between 18 and 49 , the latter of which tied with " Sweetums " as the highest of its age group for the series . The subsequent episode , " Woman of the Year " , would also tie the two episodes for a series @-@ high rating in that 18 to 49 age group . 
- " Galentine 's Day " received generally positive reviews . Entertainment Weekly writer Sandra Gonzalez said , " The show managed to <unk> more character development into 22 minutes than I thought possible . Almost every couple had a major milestone of sorts last night . " Gonzalez complimented the acting of Aziz Ansari during his moments with Wendy , and praised the " touching moment " between Ron and Leslie when she realized she had to break up with Justin . Steve <unk> of New York magazine appreciated that Leslie was correct about Frank , and that Justin was the ignorant one . <unk> said the senior dance served as a " poignant backdrop " for the episode 's romantic subplots , but said the most intriguing show 's relationship is between Leslie and Ron . Alan Sepinwall , television columnist with The Star @-@ Ledger , said the episode was funny , but focused more attention on advancing various romantic subplots . Sepinwall said the scenes about Ann and Mark were " a nice reaction to the general <unk> of that relationship " , but found it " frustrating " that the episode left the reason for Wendy and Tom 's resolution unclear in " Galentine 's Day " . 
- Steve Heisler of The A.V. Club praised the episode for displaying some of Leslie 's stronger and more competent aspects , which he said makes audiences " much more willing to put up with her many , many <unk> " . Heisler said he thought the Leslie and Justin relationship ended appropriately and praised Ansari 's performance , but added he was a little " taken aback " by how <unk> Tom treated Wendy . Matt Fowler of IGN said the episode had many funny moments and good character development , but he said some of the romantic relationships risked <unk> the balance between " the sweet and the absurd " , and that some of the character moments " played out a bit too <unk> real for a show like this " . <unk> Gallagher of TV Squad said she would like to see Leslie get a boyfriend who last longer than three episodes , unlike Justin Theroux and Louis <unk> , who played Leslie 's love interest Dave <unk> earlier in the season . Gallagher praised Andy 's band and the <unk> with which Andy reacted to the senior citizen audience . Mike Murphy of The Press Democrat said the episode was funny , and he particularly praised the " <unk> <unk> @-@ out " performance of John <unk> . Several commentators praised the joke about Andy 's new rock song " Sex Hair " , about how one can tell whether someone had sex because their hair is matted . 
- 
- = = DVD release = = 
- 
- " Galentine 's Day " , along with the other 23 second season episodes of Parks and Recreation , was released on a four @-@ disc DVD set in the United States on November 30 , 2010 . The DVD included deleted scenes for each episode . 
- 
- 
- = Bossy ( Lindsay Lohan song ) = 
- 
- " Bossy " is a song by American actress and singer @-@ songwriter Lindsay Lohan . The song was written and produced by Shaffer Smith , known by his stage name Ne @-@ Yo , while additional writing and production was done by <unk> members <unk> <unk> <unk> and Tor Erik <unk> . After leaking online in the beginning of May 2008 , Universal Motown officially released the song to media outlets , while its digital single was released on May 27 , 2008 . The song is influenced by electropop and dance @-@ pop . Lyrically , it is about a woman being strong enough to get what she wants when she wants it . 
- <unk> , " Bossy " attained generally mixed reception upon release , many music critics generally praised the attitude of the song , but considered the song less catchy than Lohan 's previous efforts . <unk> , " Bossy " managed to peak at number 77 on the Canadian Hot 100 , and became Lohan 's first song from her entire music career , so far , to reach number one on the United States ' Billboard Hot Dance Club Play . 
- 
- = = Background = = 
- 
- " Bossy " was written by Shaffer Smith , known by his stage name Ne @-@ Yo , while additional writing and song production was done by <unk> members <unk> <unk> <unk> and Tor Erik <unk> . In the beginning of May 2008 , a clip of the song leaked on YouTube . As a result , Universal Motown released the full song to media outlets on May 7 , 2008 . In an interview with Billboard , Ne @-@ Yo revealed that he was approached by the label to write a song for Lohan : " I gotta admit , we were like ... Lindsay Lohan ? ' I mean , I 've written for Beyoncé , Mary J. <unk> , Rihanna , <unk> Dion and ... Lindsay Lohan ? But I will say this ; we gave her a quality record and she did a ridiculously fabulous job . I was so shocked I had to call her and apologize for what I was thinking because she did so good . I think the world is gonna be surprised . " In an interview with People Magazine , he revealed the song " it 's basically about a woman being strong enough to get what she wants when she wants it . In this case , ' Bossy ' is a term that describes confidence and power . " " Bossy " was digitally released on May 27 , 2008 , and it was set to appear on Lohan 's third studio album . 
- 
- = = Critical reception = = 
- 
- " Bossy " received mixed reviews from music critics . A Billboard review said " the track <unk> the <unk> @-@ voiced singer 's dominating side as she rhymes about liking things her way over simple drums " , while Nick Levine of Digital Spy considered " Bossy " as an " electro @-@ dance @-@ pop [ song ] with attitude " , but commented that it " isn 't pop gold – the chorus lacks a bit of <unk> and Lohan 's vocals still aren 't convincing – but it 's the first Lohan tune we 'd be prepared to listen to more than once . That , we suppose , is enough to constitute a small step forward " . Kate <unk> of <unk> said " Bossy " " is not nearly as good or as catchy as Miss Lohan 's previous musical attempts " , while commenting that its lyrical content " is an obvious reflection on Miss Lohan 's relationship with the <unk> . As Miss Lohan sings in the lyrics , she does what she wants , she controls them , and not vice versa . Unfortunately , Miss Lohan 's logic is not entirely correct — neither party has the authority to boss the other around . That is , sadly , the price of fame these days — once people want in , they want total access and exposure . With some individual 's antics , like Miss Lohan and Britney Spears , it is difficult to garner sympathy for them " . " Bossy " reached number 77 on the Canadian Hot 100 , and became Lohan 's first song from her entire career to reach number one on the United States ' Billboard Hot Dance Club Play . The song also managed to peak on the Global Dance Tracks component chart . 
- 
- = = Track listing = = 
- 
- Digital download 
- " Bossy " – 4 : 10 
- 
- = = Charts = = 
- 
- 
- 
- = Biddenden Maids = 
- 
- Mary and Eliza Chulkhurst ( or <unk> ) , commonly known as the Biddenden Maids , were a pair of conjoined twins supposedly born in Biddenden , Kent , England , in the year 1100 . They are said to have been joined at both the shoulder and the hip , and to have lived for 34 years . It is claimed that on their death they bequeathed five plots of land to the village , known as the Bread and Cheese Lands . The income from these lands was used to pay for an annual dole of food and drink to the poor every Easter . Since at least 1775 , the dole has included Biddenden cakes , hard <unk> <unk> with an image of two conjoined women . 
- Although the annual distribution of food and drink is known to have taken place since at least 1605 , no records exist of the story of the sisters prior to 1770 . Records of that time say that the names of the sisters were not known , and early drawings of Biddenden cakes do not give names for the sisters ; it is not until the early 19th century that the names " Mary and Eliza Chulkhurst " were first used . 
- Edward Hasted , the local historian of Kent , has dismissed the story of the Biddenden Maids as a folk myth , claiming that the image on the cake had originally represented two poor women and that the story of the conjoined twins was " a vulgar tradition " invented to account for it , while influential historian Robert Chambers accepted that the legend could be true but believed it unlikely . Throughout most of the 19th century little research was carried out into the origins of the legend . Despite the doubts among historians , in the 19th century the legend became increasingly popular and the village of Biddenden was <unk> with <unk> visitors every Easter . In the late 19th century historians investigated the origins of the legend . It was suggested that the twins had genuinely existed but had been joined at the hip only rather than at both the hip and shoulder , and that they had lived in the 16th rather than the 12th century . 
- In 1907 , the Bread and Cheese Lands were sold for housing , and the resulting income allowed the annual dole to expand considerably , providing the widows and pensioners of Biddenden with cheese , bread and tea at Easter and with cash payments at Christmas . Biddenden cakes continue to be given to the poor of Biddenden each Easter , and are sold as souvenirs to visitors . 
- 
- = = Legend = = 
- 
- According to tradition Mary and Eliza Chulkhurst , or <unk> , were born to relatively wealthy parents in Biddenden , Kent , in the year 1100 . The pair were said to be conjoined at both the shoulder and the hip . They grew up conjoined , and are said to have " had frequent quarrels , which sometimes terminated in blows " . At the age of 34 , Mary Chulkhurst died suddenly . Doctors proposed to separate the still @-@ living Eliza from her sister 's body but she refused , saying " as we came together we will also go together " , and died six hours afterwards . In their wills , the sisters left five pieces of land in the Biddenden area comprising around 20 acres ( 8 ha ) in total to the local church , with the income from these lands ( claimed to have been 6 guineas per annum at the time of their death ) to provide an annual dole of bread , cheese and beer to the poor every Easter . <unk> , the lands were to be known as the Bread and Cheese Lands . 
- 
- = = History = = 
- 
- The churchwardens of Biddenden continued to maintain the annual dole from the Bread and Cheese Lands . It is recorded that in 1605 , the custom that " on that day [ Easter ] our <unk> <unk> <unk> the <unk> bread , cheese , cakes and divers barrels of beer , brought in there and drawn " was suspended on account of a visit from Charles <unk> , the Archdeacon of Canterbury , owing to previous ceremonies having caused " much disorder by reason of some unruly ones , which at such time we cannot <unk> with any ease " . In 1645 , rector William Horner claimed that the Bread and Cheese Lands were <unk> ( land intended for the use of the parish priest ) , and attempted to take control of the lands . The case of the Bread and Cheese Lands was brought before the Committee for <unk> Ministers , who eventually found in favour of the charity in 1649 . Horner brought the case before the Court of the <unk> in 1656 but again without success , and the charity continued to own the lands and to operate the annual Easter dole . Witness statements from these cases mention that the lands had been given by two women " who grew together in their bodies " , but do not give any name for the women . 
- In 1681 the " disorder and indecency " of the annual dole led to the threat of intervention by the Archbishop of Canterbury . The distribution of the dole ceased to be conducted inside the church ; it was moved to the church porch . 
- By 1770 , it is recorded that the annual dole took place immediately after the afternoon Easter service . The annual income from the Bread and Cheese Lands had risen to 20 guineas ( about £ 2 @,@ 600 in 2016 ) , and a huge quantity of food was distributed each year . By this time as well as the dole of bread , cheese and beer , hard bread rolls known as " Biddenden cakes " , moulded into an image of the sisters , were thrown to crowds from the church roof . The Biddenden cakes were flat , hard and made of flour and water , and were described as " not by any means tempting " ; one writer in 1860 described one as " a <unk> plaque " . 
- 
- = = = Origins of the Biddenden Maids legend = = = 
- 
- Although it is known that the charity had been in operation as early as 1656 , an anonymous article in The Gentleman 's Magazine in August 1770 is the earliest recorded account of the legend of the Biddenden Maids . This account states that the twins were joined at the hip only , rather than at both the hip and the shoulder , and that they lived to a relatively old age . The article explicitly states that their names were not recorded , and that they were known only as the " Maids of Biddenden " . The anonymous author recounts the story of their bequest of the lands to the parish to support the annual dole , and goes on to say that despite the antiquity of the events described , he has no doubt as to their authenticity . As with all accounts of the tradition prior to 1790 the author does not mention their alleged birth in 1100 , or the name of Chulkhurst ; these details first appeared in a broadside published in 1790 . The <unk> Repertory of 1775 says that the sisters had lived " as tradition says , two hundred and fifty years ago " . <unk> of Biddenden cakes from this period show that they featured an image of two women , possibly conjoined , but no names , dates or ages . 
- Historian Edward Hasted , in the third volume of The History and <unk> Survey of the County of Kent published in 1798 , dismissed the legend of the Biddenden Maids . He claimed that the Bread and Cheese Lands were the gift of two women named Preston ( although he elsewhere described the lands as having been " given by persons unknown " ) . Hasted stated that the Biddenden cakes had only begun to be moulded with the imprint of two women in the last 50 years ( i.e. since 1748 ) and that the figures were intended to represent " two poor widows , as the general objects of a charitable <unk> " . While he mentioned a legend that the figures represent two conjoined twins who died in their <unk> and bequeathed the Bread and Cheese Lands to the parish , he dismissed it as " a vulgar tradition " . 
- Hasted 's arguments were largely accepted by influential historian Robert Chambers , and the story was generally treated as a folk myth . A letter to the British Medical Journal in 1869 pointed out that <unk> were not in use in Kent in the 12th century , and that in older styles of English handwriting the 1 and 5 characters could easily be confused , and suggested a correct <unk> of 1500 . The Biddenden Maids were occasionally mentioned in pieces on conjoined twins , particularly after Chang and <unk> Bunker proved that conjoined twins could live to an advanced age and lead relatively normal lives . Notes and <unk> magazine called in 1866 for a close examination of Biddenden documents , the editors describing Hasted 's conclusions as " very obscure and unsatisfactory " and questioning why the names " Eliza and Mary Chulkhurst " should have been added to the design of cakes granted by a family named Preston , but no significant research into the tradition was carried out . 
- 
- = = = Growth of the charity = = = 
- 
- As the annual dole grew larger the Easter distribution became increasingly popular . In 1808 a broadside featuring a woodcut of the twins and a brief history of their alleged story was sold outside the church at Easter , the first recorded mention of the names " Eliza and Mary Chulkhurst " , and clay replicas of Biddenden cakes were sold as souvenirs . 
- In the 1820s , a new account of the Biddenden Maids was published , which claimed that a gravestone marked with a diagonal line near the rector 's pew in Biddenden church was the sisters ' burial place . In 1830 it was noted that Biddenden was becoming <unk> by visitors every Easter , " attracted from the adjacent towns and villages by the usage , and the wonderful account of its origin , and the day is spent in rude <unk> " . The large crowds were increasingly <unk> , and churchwardens on occasion had to use their staffs to hold back the mob . As a result , the distribution of the dole was moved from the church to the workhouse , but the crowds continued to cause problems . In 1882 Biddenden 's rector applied for authority to abandon the ceremony ; the Archbishop of Canterbury permitted the distribution of bread , cheese and Biddenden cakes to continue , but abolished the free beer in an effort to combat the problem of unruly crowds . 
- In 1900 , antiquarian George Clinch investigated the Biddenden Maids in detail . Examining the costumes of the figures on the Biddenden cake moulds , he concluded that the style of dress depicted dated from the reign of Mary I ( 1553 – 1558 ) , a date roughly consistent with the " two hundred and fifty years ago " reported in 1775 , and concluded that the tradition had originated in the 16th century . He suggested that the " 1100 " date on Biddenden cakes had originally read " 1500 " , and explained the absence of names on prints of 18th century Biddenden cakes as an engraving error . It is likely that the cake moulds examined by Clinch were not the original moulds , as the designs Clinch examined are strikingly different to the earliest surviving drawings of Biddenden cakes , published in 1775 . Writing in the early 1930s , William <unk> Finch explains the confusion over the dates , saying " the old @-@ fashioned <unk> five is so frequently taken as a one " . He lamented the quality of the Biddenden cake then being produced , compared to that of former years . <unk> Finch stated that the villagers considered the then @-@ current cake to be <unk> bread . 
- 
- = = <unk> and <unk> = = 
- 
- In almost all drawings and Biddenden cake designs , the twins are shown as conjoined at both the hip and the shoulder . Although such a fusion is theoretically possible , in that twins fused at one point may form a secondary fusion elsewhere , no case of a viable double fusion has ever been documented . 
- Although Clinch believed that the evidence pointed to the twins having genuinely existed but that they had lived in the 16th century , rather than the early 12th century as generally claimed , they are not mentioned in any journals or books from the period . This points against their having lived in the 16th century ; the case of <unk> and <unk> <unk> <unk> ( 1617 – after 1646 ) had prompted great interest in conjoined twins , and conjoined sisters surviving to adulthood in south east England would have been widely noted . 
- In 1895 , surgeon J. W. Ballantyne considered the case of the Biddenden Maids from a <unk> perspective . He suggested that they had in fact been <unk> ( twins joined at the <unk> ) . <unk> twins are known to put their arms around each other 's shoulders when walking , and Ballantyne suggested that this accounted for their apparently being joined at the shoulders in drawings . The <unk> <unk> and Christine McCoy had lived in Britain for a short time before going on to a successful singing career in the United States , and it was known from their case that such twins were capable of surviving to adulthood . 
- Jan <unk> ( 1992 and 2006 ) proposed that , while the names " Eliza and Mary Chulkhurst " are not recorded in any early documents and are likely to have been a later addition , the existence of the twins and the claimed 1100 year of birth cannot be dismissed . Although mediaeval chronicles are unreliable , he noted multiple reports in the <unk> <unk> , the Annals of the Four Masters and the Annals of Clonmacnoise of a pair of conjoined sisters born in or around 1100 , although all three are records of Irish history and none mention Kent as the location . He concluded that the case of Christine McCoy , who survived for eight hours following the death of her <unk> twin <unk> , shows that the claimed six hours between the deaths of the Biddenden Maids is plausible , and agreed with Ballantyne 's proposal that the idea that the twins were joined at the shoulder is a later <unk> of the figures on the Biddenden cake . He also pointed out that although there is no recorded version of the legend prior to 1770 , there would have been no possible motive for the villagers of the 18th century to <unk> the story . 
- 
- = = Today = = 
- 
- In 1907 , the Chulkhurst Charity was amalgamated with other local charities with similar purposes , to form the Biddenden Consolidated Charity , still functioning as a registered charity . The Bread and Cheese Lands were sold for housing , expanding the charity significantly to provide Biddenden pensioners and widows with bread , cheese , and tea at Easter , a cash payment at Christmas , and distribute Biddenden cakes . ( During the food rationing of the 1940s and early 1950s , the cheese was replaced by <unk> . Distribution of cheese resumed in 1951 . ) A wrought iron village sign showing the Biddenden Maids was erected on Biddenden village green in the 1920s . 
- The tradition of the dole continues to the present , and every Easter Monday tea , cheese and bread are given to local widows and pensioners through the windows of Biddenden 's former workhouse . All those eligible for the annual dole are given a Biddenden cake , and they are sold as souvenirs to visitors . The cakes are baked so hard as to be inedible , to allow better preservation as souvenirs ; they are baked in large batches every few years and kept until the stock runs out . Historically , the loaves used were of the archaic <unk> loaf size , but this particular part of the tradition ended when Biddenden 's last bakery closed in the 1990s . 
- 
- 
- = Ælfric of Abingdon = 
- 
- Ælfric of Abingdon ( Old English : <unk> ; died 16 November 1005 ) , also known as Ælfric of Wessex , was a late 10th @-@ century Archbishop of Canterbury . He previously held the offices of abbot of St Albans Abbey and Bishop of Ramsbury , as well as likely being the abbot of Abingdon Abbey . After his election to Canterbury , he continued to hold the bishopric of Ramsbury along with the archbishopric of Canterbury until his death in 1005 . Ælfric may have altered the composition of Canterbury 's cathedral chapter by changing the clergy serving in the cathedral from secular clergy to monks . In his will he left a ship to King Æthelred II of England as well as more ships to other <unk> . 
- 
- = = Early life = = 
- 
- Ælfric was the son of an earl of Kent and became a monk of Abingdon Abbey in Berkshire ( now <unk> ) . He was very likely Abbot of Abingdon before becoming Abbot of St Albans Abbey around <unk> , although some historians do not believe that he held the office of Abbot at Abingdon . Although the Historia <unk> <unk> , or History of the Church of Abingdon , names Ælfric as abbot , the <unk> lists do not record him as such . <unk> <unk> of his being abbot at Abingdon is a grant of land to Ælfric personally ( instead of to the office he held ) while he was archbishop that had previously been unjustly taken from Abingdon . This land was to revert to Abingdon after Ælfric 's death . 
- 
- = = Bishop and archbishop = = 
- 
- Ælfric 's brother , <unk> , succeeded him as Abbot of St Albans when he became bishop . Between 991 and <unk> , Ælfric rose to the Bishopric of Ramsbury , and possibly continued to hold office of abbot of St Albans while bishop . In 995 he was elevated to the see of Canterbury . He was translated , or moved with appropriate ecclesiastical ceremony , to Canterbury on 21 April 995 at a <unk> held at <unk> . Here he received the permission of " King Æthelred and all the <unk> " to be elevated to Canterbury . Ælfric continued to hold Ramsbury along with Canterbury until his death . The story that his brother was chosen first for Canterbury but refused , stems from confusion on the part of Matthew of Paris and historians generally hold the entire episode to be <unk> . 
- Ælfric 's appointment to Canterbury caused consternation with the clergy of the cathedral chapter . In reaction , the chapter sent two members to Rome ahead of Ælfric and tried to secure the archbishopric for either of the monks . Pope Gregory V , however , would not appoint a candidate without royal permission , which neither of the monks had . Instead , when Ælfric arrived in Rome in <unk> , he was appointed and received the pallium , a symbol of an archbishop 's authority . He also witnessed some miracles at the <unk> of Edward the <unk> at <unk> Abbey , helping to lead to Edward 's <unk> . 
- A story was told that Ælfric introduced monks into the cathedral church of Christ Church , Canterbury , replacing the secular clerks that had taken over the foundation during the ninth century . Ælfric is said to have done this on the command of the pope . This story originally dates to soon after the Norman Conquest and originated with the monastic historians of Canterbury , and its <unk> is unclear . He likely performed the marriage ceremony of King Æthelred the <unk> and Emma of Normandy in 1002 . A later tradition held that he consecrated a Bishop of Llandaff and two Bishops of St. David 's in Wales , which , if true , would have meant extending Canterbury 's jurisdiction into new territory . 
- Either Ælfric or his predecessor <unk> wrote a letter to <unk> , Bishop of Sherborne about the duties of bishops to make sure that the laity did not <unk> churches . The letter also urged <unk> to <unk> the laity to strive for justice in their dealings with others , help widows and orphans , not fight , as well as other moral precepts . Ælfric also ordered the composition of the first Life of Dunstan , a hagiography , or saint 's life , of Dunstan , a predecessor as Archbishop of Canterbury . He also acted as a royal judge , once being ordered by King Æthelred to <unk> a case between <unk> , or local noblemen . 
- 
- = = Death and legacy = = 
- 
- Ælfric died on 16 November 1005 and was buried in Abingdon Abbey , later being translated to Canterbury Cathedral . His will survives and in it he left ships to the people of Wiltshire and Kent , with his best one , equipped for sixty men , going to King Æthelred . The hagiography of Dunstan was dedicated to Ælfric at the end of the 10th century . After his death , he was considered a saint with a feast day of 16 November . 
- 
- 
- = White Dog ( Gary novel ) = 
- 
- White Dog , released in France as Chien Blanc , is a fictional autobiographical novel written by Romain Gary . Originally published as a short story in Life in 1970 ( 9 October ) , the full novel was published in 1970 in French in France by Éditions Gallimard . Gary 's English version of the novel was published in North America in the same year by New American Library . The novel provides a fictionalized account of Gary and his wife 's experiences in the 1960s with a stray Alabama police dog trained to attack black people on sight , and their attempts to have the dog <unk> . 
- Gary uses the novel as a vehicle to denounce both racism and the activists supporting African @-@ American rights , including his own ex @-@ wife and Marlon Brando . He also examines whether human responses to situations , including racism , are learned social behavior and whether they can be <unk> . In 1981 , it was adapted into the controversial film of the same name , in which director Samuel Fuller made various changes to the novel 's story to focus more on the dog and present a more pessimistic ending than the original novel . The film 's American release after negative press from the National Association for the Advancement of <unk> People ( NAACP ) led to concerns of boycotts . 
- 
- = = Plot synopsis = = 
- 
- A fictionalized memoir set in both the United States and France during the 1960s American civil rights movement , White Dog focuses on the events that occur after Gary and his then @-@ wife Jean <unk> , an actress and an activist , adopt a handsome and clearly well @-@ trained German Shepherd dog who comes back to their home with one of their other dogs . At first , the dog , which they name <unk> , is an ideal new member of the family : intelligent , devoted , and quickly befriending the couple 's assortment of other animals . To their dismay , they discover that the dog , a former Alabama police dog , was trained to attack black people on sight . Although they are told the dog is too old to be <unk> , they take him to a black dog trainer to try . Instead , the man trains the dog to attack white people , including Gary himself . Gary states that he changed the ending of the American version to be more optimistic . 
- 
- = = Publication history = = 
- 
- White Dog was first released as an English short story that appeared in Life magazine in 1968 . It was published as a full novel in 1970 in two languages and two countries . A French @-@ language version was published in France under the title Chien Blanc by Éditions Gallimard ( ISBN <unk> ) in April 1970 . Gary , who was multi @-@ lingual , also wrote an English version , which was published by New American Library in 1970 under its Signet label . 
- French 
- 1970 , France , Éditions Gallimard ( ISBN <unk> ) , 256 pages , paperback 
- 1972 , France , Éditions Gallimard ( ISBN <unk> ) , 220 pages , paperback 
- English 
- 1970 , United States , New American Library , 279 pages , paperback 
- 2004 , United States , University Of Chicago Press ( ISBN <unk> ) , 290 pages , paperback 
- 
- = = Themes = = 
- 
- With the use of a " <unk> tone and [ an ] uncomfortable use of sarcasm , " White Dog is Gary 's <unk> of the paranoia generated by both racism and <unk> as he <unk> <unk> @-@ American , in which there is an " obsessive <unk> out of ' <unk> ' and violent race riots , " against the barricades and race riots of France in 1968 . The violence depicted also provides a discourse on revolutionary social change , as it also leads to " a new order , a new reality . " Gary " <unk> American racism , black activism , and movie @-@ colony <unk> " and reflects on American race relations as a whole . He also documents his own " intolerance of intolerance that is the curse of tolerance " . Through the dog , Gary examines whether a learned response can be <unk> . He also poses the question of how much freedom and uniqueness a person can claim if humans responses are indeed learned by " social indoctrination . " 
- Within the novel , Gary makes " scathing attacks on self @-@ <unk> Jewish pro @-@ black sentiment and self @-@ serving celebrity campaigners " , while making explicit attacks against Marlon Brando and <unk> for their involvement in civil rights movements and the latter 's involvement with the Black Panthers organization . In an interview about the novel , Gary states that as a " typical American idealist " she was an easy mark for people seeking money for causes , and he depicts this in the novel by having her frequently appearing in activist events writing checks . After her death , he called the novel the " story of her crusade " . 
- 
- = = Reception = = 
- 
- White Dog quickly became a bestseller in the United States after its English release . Phoebe Adams of The Atlantic felt the story was ironic , and noted that it was " presumably " true . She felt the depiction of Marlon Brando was " <unk> funny " and that story as a whole served " as an excuse for Mr. Gary 's comments on racial affairs in this country , a matter on which is somewhat less pessimistic than the natives and a good deal more sensible . " The Globe and Mail 's H. J. <unk> considered it a " <unk> , thoughtful work " that serves as a metaphor for American racism . Julien <unk> felt Romain 's depiction of the racial tensions in America at the time was " <unk> reconstituted , with a realistic , even documentary , rather exceptional dimension . " Julia Weldon of Harper 's Magazine remarked with amusement that the events of the novel were ones that " only a Frenchman " could have found himself in . She felt the novel was a " decathlon event " in which Gary turned a " household crisis into a full @-@ scale allegory . " Overall , she praised the book as a " memorable portrait of guilt and <unk> in black and white " , noting she felt Gary had lived to " witness his own maturity " though she also wondered if he stretched the truth to " make himself a legend in his own time . " 
- 
- = = Film adaptation = = 
- 
- Paramount Pictures purchased the film rights for White Dog in 1975 , though the film itself was not produced until 1981 , after Gary 's suicide . Various changes were made from the original novel 's story , including the removal of Gary himself and a <unk> focus on the dog . Gary 's wife was replaced in the script with a young , unmarried actress as Paramount wanted the film to show a strong contrast between the loving relationship between the protagonist and the dog 's random attacks . The novel 's hate @-@ filled Muslim black trainer was converted into the character named Keys , who genuinely wished to cure the animal . The novel 's original ending was also changed to make it more pessimistic . Directed by Samuel Fuller and starring Paul Winfield and <unk> McNichol , the film 's theatrical release was suppressed in the United States out of concern of negative press after rumors began circulating from the NAACP that the film was racist . It was released internationally in France and the United Kingdom in 1982 , and broadcast on various American cable television channels . Its first official American release came in December 2008 when The <unk> Collection released the original uncut film to DVD . 
- In reflecting on the film , Fuller notes that he had known Gary before being offered the chance to direct the White Dog adaptation , and greatly admired both Gary and the novel . He wanted to have the film dedicated to Gary , who had committed suicide before the film was completed , but the studio declined . In an interview with the Los Angeles Times , he stated " I know [ Gary would ] have liked it . And he 'd have been pleased because his other books ( The Roots of Heaven , etc ) had not done well as films . " 
- 
- 
- = Condom = 
- 
- A condom is a sheath @-@ shaped barrier device that may be used during sexual intercourse to reduce the probability of pregnancy and decrease the risk of sexually transmitted infections ( STIs ) such as HIV / AIDS . It is rolled onto an erect penis before intercourse and blocks <unk> semen from entering the body of a sexual partner . Condoms are also used during <unk> and for collection of semen for use in infertility treatment . In the modern age , condoms are most often made from latex , but some are made from other materials such as polyurethane , polyisoprene , or lamb intestine . A female condom is also available , often made of <unk> rubber . 
- As a method of birth control , male condoms have the advantages of being inexpensive , easy to use , having few side effects , and offering protection against sexually transmitted infections . With proper use — and use at every act of intercourse — women whose partners use male condoms experience a 2 % per @-@ year pregnancy rate . With typical use the rate of pregnancy is 18 % per @-@ year . Condoms have been used for at least 400 years . Since the 19th century , they have been one of the most popular methods of contraception in the world . While widely accepted in modern times , condoms have generated some controversy , primarily over what role they should play in sex education classes . 
- 
- = = Medical uses = = 
- 
- 
- = = = Birth control = = = 
- 
- The effectiveness of condoms , as of most forms of contraception , can be assessed two ways . Perfect use or method effectiveness rates only include people who use condoms properly and consistently . <unk> use , or typical use effectiveness rates are of all condom users , including those who use condoms incorrectly or do not use condoms at every act of intercourse . <unk> are generally presented for the first year of use . Most commonly the Pearl Index is used to calculate effectiveness rates , but some studies use <unk> tables . 
- The typical use pregnancy rate among condom users varies depending on the population being studied , ranging from 10 to 18 % per year . The perfect use pregnancy rate of condoms is 2 % per year . Condoms may be combined with other forms of contraception ( such as spermicide ) for greater protection . 
- 
- = = = <unk> transmitted infections = = = 
- 
- Condoms are widely recommended for the prevention of sexually transmitted infections ( STIs ) . They have been shown to be effective in reducing infection rates in both men and women , but not eliminating it . While not perfect , the condom is effective at reducing the transmission of organisms that cause AIDS , genital herpes , cervical cancer , genital warts , syphilis , <unk> , <unk> , and other diseases . Condoms are often recommended as an <unk> to more effective birth control methods ( such as <unk> ) in situations where STD protection is also desired . 
- According to a 2000 report by the National Institutes of Health ( <unk> ) , consistent use of latex condoms reduces the risk of HIV / AIDS transmission by approximately 85 % relative to risk when unprotected , putting the <unk> rate ( infection rate ) at 0 @.@ 9 per 100 person @-@ years with condom , down from 6 @.@ 7 per 100 person @-@ years . Analysis published in 2007 from the University of Texas Medical Branch and the World Health Organization found similar risk reductions of 80 – 95 % . 
- The 2000 <unk> review concluded that condom use significantly reduces the risk of <unk> for men . A 2006 study reports that proper condom use decreases the risk of transmission of human <unk> ( <unk> ) to women by approximately 70 % . Another study in the same year found consistent condom use was effective at reducing transmission of herpes <unk> virus @-@ 2 also known as genital herpes , in both men and women . 
- Although a condom is effective in limiting exposure , some disease transmission may occur even with a condom . <unk> areas of the <unk> , especially when symptoms are present , may not be covered by a condom , and as a result , some diseases like <unk> and herpes may be transmitted by direct contact . The primary effectiveness issue with using condoms to prevent STDs , however , is inconsistent use . 
- Condoms may also be useful in treating potentially <unk> cervical changes . Exposure to human <unk> , even in individuals already infected with the virus , appears to increase the risk of <unk> changes . The use of condoms helps promote regression of these changes . In addition , researchers in the UK suggest that a hormone in semen can <unk> existing cervical cancer , condom use during sex can prevent exposure to the hormone . 
- 
- = = = Causes of failure = = = 
- 
- Condoms may slip off the penis after ejaculation , break due to improper application or physical damage ( such as tears caused when opening the package ) , or break or slip due to latex degradation ( typically from usage past the expiration date , improper storage , or exposure to oils ) . The rate of breakage is between 0 @.@ 4 % and 2 @.@ 3 % , while the rate of slippage is between 0 @.@ 6 % and 1 @.@ 3 % . Even if no breakage or slippage is observed , 1 – 3 % of women will test positive for semen residue after intercourse with a condom . 
- " Double <unk> " , using two condoms at once , is often believed to cause a higher rate of failure due to the friction of rubber on rubber . This claim is not supported by research . The limited studies that have been done on the subject support that double <unk> is likely not harmful and possibly beneficial . 
- Different modes of condom failure result in different levels of semen exposure . If a failure occurs during application , the damaged condom may be disposed of and a new condom applied before intercourse begins – such failures generally pose no risk to the user . One study found that semen exposure from a broken condom was about half that of unprotected intercourse ; semen exposure from a slipped condom was about one @-@ fifth that of unprotected intercourse . 
- Standard condoms will fit almost any penis , with varying degrees of comfort or risk of slippage . Many condom manufacturers offer " <unk> " or " magnum " sizes . Some manufacturers also offer custom sized @-@ to @-@ fit condoms , with claims that they are more reliable and offer improved sensation / comfort . Some studies have associated larger <unk> and smaller condoms with increased breakage and decreased slippage rates ( and vice versa ) , but other studies have been inconclusive . 
- It is recommended for condoms manufacturers to avoid very thick or very thin condoms , because they are both considered less effective . Some authors encourage users to choose thinner condoms " for greater <unk> , sensation , and comfort " , but others warn that " the thinner the condom , the smaller the force required to break it " . 
- <unk> condom users are significantly less likely to have a condom slip or break compared to first @-@ time users , although users who experience one slippage or breakage are more likely to suffer a second such failure . An article in Population Reports suggests that education on condom use reduces behaviors that increase the risk of breakage and slippage . A Family Health International publication also offers the view that education can reduce the risk of breakage and slippage , but emphasizes that more research needs to be done to determine all of the causes of breakage and slippage . 
- Among people who intend condoms to be their form of birth control , pregnancy may occur when the user has sex without a condom . The person may have run out of condoms , or be traveling and not have a condom with them , or simply dislike the feel of condoms and decide to " take a chance " . This type of behavior is the primary cause of typical use failure ( as opposed to method or perfect use failure ) . 
- Another possible cause of condom failure is sabotage . One motive is to have a child against a partner 's wishes or consent . Some commercial sex workers from Nigeria reported clients <unk> condoms in retaliation for being coerced into condom use . Using a fine needle to make several <unk> at the tip of the condom is believed to significantly impact their effectiveness . Cases of such condom sabotage have occurred . 
- 
- = = <unk> effects = = 
- 
- The use of latex condoms by people with an allergy to latex can cause allergic symptoms , such as skin irritation . In people with severe latex <unk> , using a latex condom can potentially be life @-@ threatening . <unk> use of latex condoms can also cause the development of a latex allergy in some people . 
- 
- = = Use = = 
- 
- Male condoms are usually packaged inside a foil or plastic <unk> , in a rolled @-@ up form , and are designed to be applied to the tip of the penis and then <unk> over the erect penis . It is important that some space be left in the tip of the condom so that semen has a place to collect ; otherwise it may be forced out of the base of the device . After use , it is recommended the condom be wrapped in tissue or tied in a knot , then disposed of in a trash receptacle . Condoms are used to reduce the likelihood of pregnancy during intercourse and to reduce the likelihood of contracting sexually @-@ transmitted infections ( STIs ) . Condoms are also used during <unk> to reduce the likelihood of contracting STIs . 
- Some couples find that putting on a condom interrupts sex , although others incorporate condom application as part of their <unk> . Some men and women find the physical barrier of a condom <unk> sensation . <unk> of <unk> sensation can include prolonged erection and delayed ejaculation ; disadvantages might include a loss of some sexual excitement . <unk> of condom use also cite their advantages of being inexpensive , easy to use , and having few side effects . 
- 
- = = = Adult film industry = = = 
- 
- In 2012 proponents gathered 372 @,@ 000 <unk> signatures through a citizens ' initiative in Los Angeles County to put Measure B on the 2012 ballot . As a result , Measure B , a law requiring the use of condoms in the production of pornographic films , was passed . This requirement has received much criticism and is said by some to be counter @-@ productive , merely forcing companies that make pornographic films to relocate to other places without this requirement . <unk> claim that condom use <unk> sales . 
- 
- = = = Sex education = = = 
- 
- Condoms are often used in sex education programs , because they have the capability to reduce the chances of pregnancy and the spread of some sexually transmitted diseases when used correctly . A recent American <unk> Association ( <unk> ) press release supported the inclusion of information about condoms in sex education , saying " comprehensive sexuality education programs ... discuss the appropriate use of condoms " , and " promote condom use for those who are sexually active . " 
- In the United States , teaching about condoms in public schools is opposed by some religious organizations . Planned <unk> , which advocates family planning and sex education , argues that no studies have shown abstinence @-@ only programs to result in delayed intercourse , and cites surveys showing that 76 % of American parents want their children to receive comprehensive sexuality education including condom use . 
- 
- = = = <unk> treatment = = = 
- 
- Common procedures in infertility treatment such as semen analysis and <unk> insemination ( <unk> ) require collection of semen samples . These are most commonly obtained through masturbation , but an alternative to masturbation is use of a special collection condom to collect semen during sexual intercourse . 
- Collection condoms are made from <unk> or polyurethane , as latex is somewhat harmful to sperm . Many men prefer collection condoms to masturbation , and some religions prohibit masturbation entirely . Also , compared with samples obtained from masturbation , semen samples from collection condoms have higher total sperm counts , sperm <unk> , and percentage of sperm with normal morphology . For this reason , they are believed to give more accurate results when used for semen analysis , and to improve the chances of pregnancy when used in procedures such as <unk> or <unk> insemination . <unk> of religions that prohibit contraception , such as Catholicism , may use collection condoms with holes <unk> in them . 
- For fertility treatments , a collection condom may be used to collect semen during sexual intercourse where the semen is provided by the woman 's partner . Private sperm donors may also use a collection condom to obtain samples through masturbation or by sexual intercourse with a partner and will transfer the <unk> from the collection condom to a specially designed container . The sperm is transported in such containers , in the case of a donor , to a recipient woman to be used for insemination , and in the case of a woman 's partner , to a fertility clinic for processing and use . However , transportation may reduce the <unk> of the sperm . Collection condoms may also be used where semen is produced at a sperm bank or fertility clinic . 
- Condom therapy is sometimes prescribed to infertile couples when the female has high levels of <unk> antibodies . The theory is that preventing exposure to her partner 's semen will lower her level of <unk> antibodies , and thus increase her chances of pregnancy when condom therapy is discontinued . However , condom therapy has not been shown to increase subsequent pregnancy rates . 
- 
- = = = Other uses = = = 
- 
- Condoms <unk> as multipurpose containers and barriers because they are <unk> , elastic , durable , and ( for military and espionage uses ) will not <unk> suspicion if found . 
- <unk> military <unk> began during World War II , and includes covering the <unk> of rifle barrels to prevent <unk> , the waterproofing of firing assemblies in underwater <unk> , and storage of corrosive materials and <unk> by paramilitary agencies . 
- Condoms have also been used to smuggle alcohol , cocaine , heroin , and other drugs across borders and into prisons by filling the condom with drugs , tying it in a knot and then either swallowing it or inserting it into the <unk> . These methods are very dangerous and potentially lethal ; if the condom breaks , the drugs inside become absorbed into the bloodstream and can cause an <unk> . 
- <unk> , condoms can be used to cover <unk> <unk> probes , or in field chest needle <unk> they can be used to make a one @-@ way valve . 
- Condoms have also been used to protect scientific samples from the environment , and to <unk> microphones for underwater recording . 
- 
- = = Types = = 
- 
- Most condoms have a reservoir tip or <unk> end , making it easier to accommodate the man 's <unk> . Condoms come in different sizes , from oversized to <unk> and they also come in a variety of surfaces intended to stimulate the user 's partner . Condoms are usually supplied with a <unk> coating to facilitate penetration , while flavored condoms are principally used for oral sex . As mentioned above , most condoms are made of latex , but polyurethane and lambskin condoms also exist . 
- 
- = = = Female condom = = = 
- 
- Male condoms have a tight ring to form a seal around the penis while female condoms typically have a large stiff ring to keep them from slipping into the body <unk> . The Female Health Company produced a female condom that was initially made of polyurethane , but newer versions are made of <unk> . <unk> <unk> produces a female condom made of latex . 
- 
- = = = <unk> = = = 
- 
- 
- = = = = Natural latex = = = = 
- 
- Latex has outstanding elastic properties : Its tensile strength exceeds 30 MPa , and latex condoms may be stretched in excess of 800 % before breaking . In 1990 the <unk> set standards for condom production ( <unk> <unk> , Natural latex rubber condoms ) , and the EU followed suit with its <unk> standard ( Directive 93 / 42 / EEC concerning medical devices ) . Every latex condom is tested for holes with an electric current . If the condom passes , it is rolled and packaged . In addition , a portion of each batch of condoms is subject to water leak and air burst testing . 
- While the advantages of latex have made it the most popular condom material , it does have some drawbacks . Latex condoms are damaged when used with oil @-@ based substances as lubricants , such as petroleum <unk> , cooking oil , baby oil , mineral oil , skin <unk> , <unk> <unk> , cold <unk> , butter or <unk> . Contact with oil makes latex condoms more likely to break or slip off due to loss of elasticity caused by the oils . Additionally , latex allergy <unk> use of latex condoms and is one of the principal reasons for the use of other materials . In May 2009 the U.S. Food and <unk> Administration granted approval for the production of condoms composed of <unk> , latex that has been treated to remove 90 % of the proteins responsible for allergic reactions . An <unk> @-@ free condom made of synthetic latex ( polyisoprene ) is also available . 
- 
- = = = = <unk> = = = = 
- 
- The most common non @-@ latex condoms are made from polyurethane . Condoms may also be made from other synthetic materials , such as AT @-@ 10 resin , and most recently polyisoprene . 
- Polyurethane condoms tend to be the same width and thickness as latex condoms , with most polyurethane condoms between 0 @.@ 04 mm and 0 @.@ 07 mm thick . 
- Polyurethane can be considered better than latex in several ways : it conducts heat better than latex , is not as sensitive to temperature and ultraviolet light ( and so has less rigid storage requirements and a longer shelf life ) , can be used with oil @-@ based lubricants , is less <unk> than latex , and does not have an odor . Polyurethane condoms have gained FDA approval for sale in the United States as an effective method of contraception and HIV prevention , and under laboratory conditions have been shown to be just as effective as latex for these purposes . 
- However , polyurethane condoms are less elastic than latex ones , and may be more likely to slip or break than latex , lose their shape or bunch up more than latex , and are more expensive . 
- <unk> is a synthetic version of natural rubber latex . While significantly more expensive , it has the advantages of latex ( such as being softer and more elastic than polyurethane condoms ) without the protein which is responsible for latex <unk> . Like polyurethane condoms , polyisoprene condoms are said to do a better job of transmitting body heat . Unlike polyurethane condoms , they cannot be used with an oil @-@ based <unk> . 
- 
- = = = = <unk> = = = = 
- 
- Condoms made from sheep intestines , labeled " lambskin " , are also available . Although they are generally effective as a contraceptive by blocking sperm , it is presumed that they are likely less effective than latex in preventing the transmission of agents that cause STDs , because of pores in the material . This is based on the idea that intestines , by their nature , are porous , <unk> membranes , and while sperm are too large to pass through the pores , viruses — such as HIV , herpes , and genital warts — are small enough to pass through . However , there are to date no clinical data confirming or denying this theory . Some believe that lambskin condoms provide a more " natural " sensation , and they lack the <unk> that are inherent to latex , but because of their lesser protection against infection , other <unk> materials such as polyurethane are recommended for latex @-@ allergic users and / or partners . <unk> condoms are also significantly more expensive than other types and as slaughter by @-@ products they are also not vegetarian . 
- 
- = = = <unk> = = = 
- 
- Some latex condoms are lubricated at the manufacturer with a small amount of a nonoxynol @-@ 9 , a <unk> chemical . According to Consumer Reports , condoms lubricated with spermicide have no additional benefit in preventing pregnancy , have a shorter shelf life , and may cause urinary @-@ tract infections in women . In contrast , application of separately packaged spermicide is believed to increase the contraceptive <unk> of condoms . 
- <unk> @-@ 9 was once believed to offer additional protection against STDs ( including HIV ) but recent studies have shown that , with frequent use , nonoxynol @-@ 9 may increase the risk of HIV transmission . The World Health Organization says that <unk> lubricated condoms should no longer be promoted . However , it recommends using a nonoxynol @-@ 9 lubricated condom over no condom at all . As of 2005 , nine condom manufacturers have stopped manufacturing condoms with nonoxynol @-@ 9 and Planned <unk> has discontinued the distribution of condoms so lubricated . 
- 
- = = = <unk> and studded = = = 
- 
- <unk> condoms include studded and ribbed condoms which can provide extra sensations to both partners . The <unk> or ribs can be located on the inside , outside , or both ; alternatively , they are located in specific sections to provide directed stimulation to either the g @-@ spot or <unk> . Many textured condoms which advertise " mutual pleasure " also are bulb @-@ shaped at the top , to provide extra stimulation to the penis . Some women experience irritation during vaginal intercourse with studded condoms . 
- 
- = = = Youth condoms = = = 
- 
- In March 2010 , the Swiss government announced that it was planning to promote smaller condoms intended for boys and youths of 12 – 14 years old following concern about the pregnancy rate among adolescent girls , and also about the potential spread of AIDS among this age group . This was due to the fact that standard condoms were too wide and consequently failed to afford protection to adolescent boys during vaginal and anal intercourse . Family planning groups and the Swiss AIDS Federation had campaigned to have a narrower condom produced for youths after a number of studies , including a government study researched at the Centre for Development and Personality Psychology at Basel University , found that standard condoms were unsuitable for boys in this age range , and that the condoms either failed during use or that the boys rejected them altogether because they were too wide , and consequently they used no protection at all . 
- As a result of these studies , a condom aimed at <unk> to 14 @-@ year @-@ old boys is now produced and is available in Switzerland and in certain other countries . Manufactured by <unk> , the " <unk> " is a lubricated , <unk> @-@ ended latex condom which is narrower than a standard condom and has a tight band at the opening to ensure that it remains on the youth 's penis during intercourse . A standard condom has a diameter of 2 inches ( 5 @.@ 2 cm ) whereas the <unk> has a diameter of 1 @.@ 7 inches ( 4 @.@ 5 cm ) . Both are the same length – 7 @.@ 4 inches ( 19 cm ) . In 2014 , in response to demand for condoms from a younger age @-@ group , German condom manufacturer Amor started producing another condom aimed at young people . Known as " Amor Young Love " , these lubricated condoms have a diameter of 1 @.@ 9 inches ( 4 @.@ 9 cm ) . 
- 
- = = = Other = = = 
- 
- The anti @-@ rape condom is another variation designed to be worn by women . It is designed to cause pain to the attacker , hopefully allowing the victim a chance to escape . 
- A collection condom is used to collect semen for fertility treatments or sperm analysis . These condoms are designed to <unk> sperm life . 
- Some condom @-@ like devices are intended for entertainment only , such as glow @-@ in @-@ the dark condoms . These novelty condoms may not provide protection against pregnancy and STDs . 
- 
- = = <unk> = = 
- 
- The prevalence of condom use varies greatly between countries . Most surveys of contraceptive use are among married women , or women in informal unions . Japan has the highest rate of condom usage in the world : in that country , condoms account for almost 80 % of contraceptive use by married women . On average , in developed countries , condoms are the most popular method of birth control : 28 % of married contraceptive users rely on condoms . In the average less @-@ developed country , condoms are less common : only 6 – 8 % of married contraceptive users choose condoms . 
- 
- = = History = = 
- 
- 
- = = = Before the 19th century = = = 
- 
- Whether condoms were used in ancient civilizations is debated by archaeologists and historians . In ancient Egypt , Greece , and Rome , pregnancy prevention was generally seen as a woman 's responsibility , and the only well documented contraception methods were female @-@ controlled devices . In Asia before the 15th century , some use of glans condoms ( devices covering only the head of the penis ) is recorded . Condoms seem to have been used for contraception , and to have been known only by members of the upper classes . In China , glans condoms may have been made of <unk> silk paper , or of lamb intestines . In Japan , they were made of tortoise shell or animal horn . 
- In 16th century Italy , <unk> Falloppio wrote a treatise on syphilis . The earliest documented strain of syphilis , first appearing in Europe in a <unk> outbreak , caused severe symptoms and often death within a few months of contracting the disease . Falloppio 's treatise is the earliest uncontested description of condom use : it describes linen <unk> soaked in a chemical solution and allowed to dry before use . The <unk> he described were sized to cover the glans of the penis , and were held on with a ribbon . Falloppio claimed that an experimental trial of the linen sheath demonstrated protection against syphilis . 
- After this , the use of penis <unk> to protect from disease is described in a wide variety of literature throughout Europe . The first indication that these devices were used for birth control , rather than disease prevention , is the 1605 theological publication De <unk> et <unk> ( On justice and law ) by Catholic theologian <unk> <unk> , who condemned them as immoral . In 1666 , the English Birth <unk> Commission attributed a recent downward fertility rate to use of " <unk> " , the first documented use of that word ( or any similar spelling ) . 
- In addition to linen , condoms during the Renaissance were made out of intestines and <unk> . In the late 16th century , Dutch traders introduced condoms made from " fine leather " to Japan . Unlike the horn condoms used previously , these leather condoms covered the entire penis . 
- <unk> in the 18th century was one of the first reported using " assurance caps " to prevent <unk> his <unk> . 
- From at least the 18th century , condom use was opposed in some legal , religious , and medical circles for essentially the same reasons that are given today : condoms reduce the likelihood of pregnancy , which some thought immoral or undesirable for the nation ; they do not provide full protection against sexually transmitted infections , while belief in their protective powers was thought to encourage sexual promiscuity ; and , they are not used consistently due to <unk> , expense , or loss of sensation . 
- Despite some opposition , the condom market grew rapidly . In the 18th century , condoms were available in a variety of qualities and sizes , made from either linen treated with chemicals , or " skin " ( <unk> or intestine softened by treatment with sulfur and <unk> ) . They were sold at <unk> , <unk> , chemist shops , open @-@ air markets , and at the theater throughout Europe and Russia . They later spread to America , although in every place there were generally used only by the middle and upper classes , due to both expense and lack of sex education . 
- 
- = = = 1800 through 1920s = = = 
- 
- The early 19th century saw contraceptives promoted to the poorer classes for the first time . Writers on contraception tended to prefer other methods of birth control to the condom . By the late 19th century many <unk> expressed distrust of the condom as a contraceptive , as its use was controlled and decided upon by men alone . They advocated instead for methods which were controlled by women , such as <unk> and <unk> <unk> . Other writers cited both the expense of condoms and their <unk> ( they were often riddled with holes , and often fell off or broke ) , but they discussed condoms as a good option for some , and as the only contraceptive that also protected from disease . 
- Many countries passed laws <unk> the manufacture and promotion of contraceptives . In spite of these restrictions , condoms were promoted by traveling <unk> and in newspaper advertisements , using <unk> in places where such <unk> were illegal . <unk> on how to make condoms at home were distributed in the United States and Europe . Despite social and legal opposition , at the end of the 19th century the condom was the Western world 's most popular birth control method . 
- Beginning in the second half of the 19th century , American rates of sexually transmitted diseases <unk> . Causes cited by historians include effects of the American Civil War , and the ignorance of prevention methods promoted by the <unk> laws . To fight the growing epidemic , sex education classes were introduced to public schools for the first time , teaching about <unk> diseases and how they were transmitted . They generally taught that abstinence was the only way to avoid sexually transmitted diseases . Condoms were not promoted for disease prevention because the medical community and moral <unk> considered STDs to be punishment for sexual <unk> . The stigma against victims of these diseases was so great that many hospitals refused to treat people who had syphilis . 
- The German military was the first to promote condom use among its soldiers , beginning in the later 19th century . Early 20th century experiments by the American military concluded that providing condoms to soldiers significantly lowered rates of sexually transmitted diseases . During World War I , the United States and ( at the beginning of the war only ) Britain were the only countries with soldiers in Europe who did not provide condoms and promote their use . 
- In the decades after World War I , there remained social and legal obstacles to condom use throughout the U.S. and Europe . <unk> of <unk> Sigmund Freud opposed all methods of birth control on the grounds that their failure rates were too high . Freud was especially opposed to the condom because he thought it cut down on sexual pleasure . Some <unk> continued to oppose male @-@ controlled contraceptives such as condoms . In 1920 the Church of England 's Lambeth Conference condemned all " unnatural means of conception avoidance " . London 's Bishop Arthur <unk> @-@ Ingram complained of the huge number of condoms discarded in alleyways and parks , especially after weekends and holidays . 
- However , European militaries continued to provide condoms to their members for disease protection , even in countries where they were illegal for the general population . Through the 1920s , catchy names and slick packaging became an increasingly important marketing technique for many consumer items , including condoms and cigarettes . Quality testing became more common , involving filling each condom with air followed by one of several methods intended to detect loss of pressure . Worldwide , condom sales doubled in the 1920s . 
- 
- = = = <unk> and manufacturing advances = = = 
- 
- In 1839 , Charles <unk> discovered a way of processing natural rubber , which is too stiff when cold and too soft when warm , in such a way as to make it elastic . This proved to have advantages for the manufacture of condoms ; unlike the sheep 's gut condoms , they could stretch and did not tear quickly when used . The rubber <unk> process was patented by <unk> in 1844 . The first rubber condom was produced in 1855 . The earliest rubber condoms had a seam and were as thick as a bicycle inner tube . Besides this type , small rubber condoms covering only the glans were often used in England and the United States . There was more risk of losing them and if the rubber ring was too tight , it would <unk> the penis . This type of condom was the original " <unk> " ( French for condom ) , perhaps because of its resemblance to a woman 's bonnet worn at that time , also called a <unk> . 
- For many decades , rubber condoms were manufactured by wrapping strips of raw rubber around penis @-@ shaped molds , then dipping the wrapped molds in a chemical solution to cure the rubber . In 1912 , Polish inventor Julius <unk> developed a new , improved manufacturing technique for condoms : dipping glass molds into a raw rubber solution . Called cement dipping , this method required adding gasoline or benzene to the rubber to make it liquid . Latex , rubber suspended in water , was invented in 1920 . Latex condoms required less labor to produce than cement @-@ dipped rubber condoms , which had to be <unk> by rubbing and <unk> . The use of water to suspend the rubber instead of gasoline and benzene eliminated the fire hazard previously associated with all condom factories . Latex condoms also performed better for the consumer : they were stronger and thinner than rubber condoms , and had a shelf life of five years ( compared to three months for rubber ) . 
- Until the twenties , all condoms were individually hand @-@ dipped by semi @-@ skilled workers . Throughout the decade of the 1920s , advances in the <unk> of the condom assembly line were made . The first fully automated line was patented in 1930 . Major condom manufacturers bought or leased <unk> systems , and small manufacturers were driven out of business . The skin condom , now significantly more expensive than the latex variety , became restricted to a niche high @-@ end market . 
- 
- = = = 1930 to present = = = 
- 
- In 1930 the Anglican Church 's Lambeth Conference sanctioned the use of birth control by married couples . In 1931 the Federal Council of Churches in the U.S. issued a similar statement . The Roman Catholic Church responded by issuing the encyclical <unk> <unk> <unk> its opposition to all contraceptives , a stance it has never reversed . 
- In the 1930s , legal restrictions on condoms began to be relaxed . But during this period <unk> Italy and Nazi Germany increased restrictions on condoms ( limited sales as disease <unk> were still allowed ) . During the Depression , condom lines by <unk> gained in popularity . <unk> still used the cement @-@ dipping method of manufacture which had two advantages over the latex variety . Firstly , cement @-@ dipped condoms could be safely used with oil @-@ based lubricants . <unk> , while less comfortable , these older @-@ style rubber condoms could be reused and so were more economical , a valued feature in hard times . More attention was brought to quality issues in the 1930s , and the U.S. Food and <unk> Administration began to regulate the quality of condoms sold in the United States . 
- Throughout World War II , condoms were not only distributed to male U.S. military members , but also heavily promoted with films , posters , and lectures . European and Asian militaries on both sides of the conflict also provided condoms to their troops throughout the war , even Germany which outlawed all civilian use of condoms in 1941 . In part because condoms were readily available , soldiers found a number of non @-@ sexual uses for the devices , many of which continue to this day . 
- After the war , condom sales continued to grow . From 1955 – 1965 , 42 % of Americans of reproductive age relied on condoms for birth control . In Britain from 1950 – 1960 , 60 % of married couples used condoms . The birth control pill became the world 's most popular method of birth control in the years after its 1960 <unk> , but condoms remained a strong second . The U.S. Agency for International Development pushed condom use in developing countries to help solve the " world population <unk> " : by 1970 hundreds of millions of condoms were being used each year in India alone . ( This number has grown in recent decades : in 2004 , the government of India purchased 1 @.@ 9 billion condoms for distribution at family planning clinics . ) 
- In the 1960s and 1970s quality regulations <unk> , and more legal barriers to condom use were removed . In Ireland , legal condom sales were allowed for the first time in 1978 . Advertising , however was one area that continued to have legal restrictions . In the late 1950s , the American National Association of <unk> banned condom advertisements from national television : this policy remained in place until 1979 . 
- After learning in the early 1980s that AIDS can be a sexually transmitted infection , the use of condoms was encouraged to prevent transmission of HIV . Despite opposition by some political , religious , and other figures , national condom promotion campaigns occurred in the U.S. and Europe . These campaigns increased condom use significantly . 
- Due to increased demand and greater social acceptance , condoms began to be sold in a wider variety of retail outlets , including in <unk> and in discount department stores such as Wal @-@ Mart . Condom sales increased every year until 1994 , when media attention to the AIDS pandemic began to decline . The phenomenon of decreasing use of condoms as disease <unk> has been called prevention fatigue or condom fatigue . Observers have cited condom fatigue in both Europe and North America . As one response , manufacturers have changed the tone of their advertisements from scary to humorous . 
- New developments continued to occur in the condom market , with the first polyurethane condom — branded <unk> and produced by the manufacturer of <unk> — introduced in the 1990s , and the first custom sized @-@ to @-@ fit condom , called <unk> , introduced in the early 2000s . 
- Worldwide condom use is expected to continue to grow : one study predicted that developing nations would need 18 @.@ 6 billion condoms by 2015 . As of September 2013 , condoms are available inside prisons in Canada , most of the European Union , Australia , Brazil , Indonesia , South Africa , and the US states of Vermont ( on September 17 , 2013 , the Californian Senate approved a bill for condom distribution inside the state 's prisons , but the bill was not yet law at the time of approval ) . 
- 
- = = = Etymology and other terms = = = 
- 
- The term condom first appears in the early 18th century . Its etymology is unknown . In popular tradition , the invention and naming of the condom came to be attributed to an associate of England 's King Charles II , one " Dr. Condom " or " Earl of Condom " . There is however no evidence of the existence of such a person , and condoms had been used for over one hundred years before King Charles II ascended to the throne . 
- A variety of unproven Latin etymologies have been proposed , including <unk> ( receptacle ) , <unk> ( house ) , and <unk> ( <unk> or case ) . It has also been speculated to be from the Italian word <unk> , derived from <unk> , meaning <unk> . William E. <unk> wrote an article in 1981 concluding that , " As for the word ' condom ' , I need state only that its origin remains completely unknown , and there ends this search for an etymology . " Modern dictionaries may also list the etymology as " unknown " . 
- Other terms are also commonly used to describe condoms . In North America condoms are also commonly known as <unk> , or rubbers . In Britain they may be called French letters . Additionally , condoms may be referred to using the manufacturer 's name . 
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- = = Society and culture = = 
- 
- Some moral and scientific criticism of condoms exists despite the many benefits of condoms agreed on by scientific consensus and sexual health experts . 
- Condom usage is typically recommended for new couples who have yet to develop full trust in their partner with regard to STDs . Established couples on the other hand have few concerns about STDs , and can use other methods of birth control such as the pill , which does not act as a barrier to intimate sexual contact . Note that the polar debate with regard to condom usage is <unk> by the target group the argument is directed . Notably the age category and stable partner question are factors , as well as the distinction between heterosexual and homosexuals , who have different kinds of sex and have different risk consequences and factors . 
- Among the prime objections to condom usage is the blocking of erotic sensation , and / or the intimacy that barrier @-@ free sex provides . As the condom is held tightly to the skin of the penis , it <unk> the delivery of stimulation through rubbing and friction . Condom proponents claim this has the benefit of making sex last longer , by diminishing sensation and delaying male ejaculation . Those who promote condom @-@ free heterosexual sex ( slang : " <unk> " ) claim that the condom puts a <unk> barrier between partners , diminishing what is normally a highly sensual , intimate , and spiritual connection between partners . 
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- = = = Religious = = = 
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- Roman Catholic Church opposes all kinds of sexual acts outside of marriage , as well as any sexual act in which the chance of successful conception has been reduced by direct and intentional acts ( for example , surgery to prevent conception ) or foreign objects ( for example , condoms ) . 
- The use of condoms to prevent STD transmission is not specifically addressed by Catholic doctrine , and is currently a topic of debate among theologians and high @-@ ranking Catholic authorities . A few , such as Belgian Cardinal <unk> <unk> , believe the Catholic Church should actively support condoms used to prevent disease , especially serious diseases such as AIDS . However , the majority view — including all statements from the Vatican — is that condom @-@ promotion programs encourage promiscuity , thereby actually increasing STD transmission . This view was most recently reiterated in 2009 by Pope Benedict XVI . 
- The Roman Catholic Church is the largest organized body of any world religion . The church has hundreds of programs dedicated to fighting the AIDS epidemic in Africa , but its opposition to condom use in these programs has been highly controversial . 
- In a November 2011 interview , the Pope discussed for the first time the use of condoms to prevent STD transmission . He said that the use of a condom can be justified in a few individual cases if the purpose is to reduce the risk of an HIV infection . He gave as an example male prostitutes . There was some confusion at first whether the statement applied only to homosexual prostitutes and thus not to heterosexual intercourse at all . However , Federico Lombardi , spokesman for the Vatican , clarified that it applied to heterosexual and <unk> prostitutes , whether male or female , as well . He did , however , also clarify that the Vatican 's principles on sexuality and contraception had not been changed . 
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- = = = Scientific and environmental = = = 
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- More generally , some scientific researchers have expressed objective concern over certain ingredients sometimes added to condoms , notably <unk> and <unk> . Dry <unk> <unk> are applied to latex condoms before packaging to prevent the condom from sticking to itself when rolled up . Previously , <unk> was used by most manufacturers , but <unk> is currently the most popular <unk> powder . <unk> is known to be toxic if it enters the abdominal cavity ( i.e. , via the <unk> ) . <unk> is generally believed to be safe ; however , some researchers have raised concerns over its use as well . 
- <unk> , which are potentially carcinogenic in humans , are believed to be present in a substance used to improve elasticity in latex condoms . A 2001 review stated that humans regularly receive 1 @,@ 000 to 10 @,@ 000 times greater <unk> exposure from food and tobacco than from condom use and concluded that the risk of cancer from condom use is very low . However , a 2004 study in Germany detected <unk> in 29 out of 32 condom brands tested , and concluded that exposure from condoms might exceed the exposure from food by <unk> to 3 @-@ fold . 
- In addition , the large @-@ scale use of <unk> condoms has resulted in concerns over their environmental impact via <unk> and in <unk> , where they can eventually wind up in wildlife environments if not <unk> or otherwise permanently disposed of first . Polyurethane condoms in particular , given they are a form of plastic , are not biodegradable , and latex condoms take a very long time to break down . Experts , such as <unk> , recommend condoms be disposed of in a garbage receptacle , as <unk> them down the toilet ( which some people do ) may cause <unk> <unk> and other problems . Furthermore , the plastic and foil <unk> condoms are packaged in are also not biodegradable . However , the benefits condoms offer are widely considered to offset their small landfill mass . Frequent condom or <unk> disposal in public areas such as a parks have been seen as a persistent litter problem . 
- While biodegradable , latex condoms damage the environment when disposed of improperly . According to the Ocean Conservancy , condoms , along with certain other types of trash , cover the coral reefs and <unk> sea grass and other bottom dwellers . The United States Environmental Protection Agency also has expressed concerns that many animals might mistake the litter for food . 
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- = = = Cultural barriers to use = = = 
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- In much of the Western world , the introduction of the pill in the 1960s was associated with a decline in condom use . In Japan , oral contraceptives were not approved for use until September 1999 , and even then access was more restricted than in other <unk> nations . Perhaps because of this restricted access to <unk> contraception , Japan has the highest rate of condom usage in the world : in 2008 , 80 % of contraceptive users relied on condoms . 
- Cultural attitudes toward gender roles , contraception , and sexual activity vary greatly around the world , and range from extremely conservative to extremely liberal . But in places where condoms are misunderstood , <unk> , <unk> , or looked upon with overall cultural disapproval , the prevalence of condom use is directly affected . In less @-@ developed countries and among less @-@ educated populations , <unk> about how disease transmission and conception work negatively affect the use of condoms ; additionally , in cultures with more traditional gender roles , women may feel uncomfortable demanding that their partners use condoms . 
- As an example , Latino immigrants in the United States often face cultural barriers to condom use . A study on female HIV prevention published in the Journal of Sex Health Research asserts that Latino women often lack the attitudes needed to negotiate safe sex due to traditional gender @-@ role norms in the Latino community , and may be afraid to bring up the subject of condom use with their partners . Women who participated in the study often reported that because of the general machismo subtly encouraged in Latino culture , their male partners would be angry or possibly violent at the woman 's suggestion that they use condoms . A similar phenomenon has been noted in a survey of low @-@ income American black women ; the women in this study also reported a fear of violence at the suggestion to their male partners that condoms be used . 
- A telephone survey conducted by Rand Corporation and Oregon State University , and published in the Journal of <unk> <unk> <unk> <unk> showed that belief in AIDS conspiracy theories among United States black men is linked to rates of condom use . As conspiracy beliefs about AIDS grow in a given sector of these black men , consistent condom use drops in that same sector . Female use of condoms was not similarly affected . 
- In the African continent , condom promotion in some areas has been impeded by anti @-@ condom campaigns by some Muslim and Catholic clerics . Among the Maasai in Tanzania , condom use is hampered by an aversion to " wasting " sperm , which is given <unk> importance beyond reproduction . <unk> is believed to be an " elixir " to women and to have beneficial health effects . Maasai women believe that , after <unk> a child , they must have sexual intercourse repeatedly so that the additional sperm aids the child 's development . Frequent condom use is also considered by some Maasai to cause impotence . Some women in Africa believe that condoms are " for prostitutes " and that respectable women should not use them . A few clerics even promote the idea that condoms are deliberately laced with HIV . In the United States , possession of many condoms has been used by police to accuse women of engaging in prostitution . The Presidential Advisory Council on HIV / AIDS has condemned this practice and there are efforts to end it . 
- In March 2013 , technology <unk> Bill Gates offered a US $ 100 @,@ 000 grant through his foundation for a condom design that " significantly preserves or enhances pleasure " to encourage more males to adopt the use of condoms for safer sex . The grant information states : “ The primary drawback from the male perspective is that condoms decrease pleasure as compared to no condom , creating a trade @-@ off that many men find unacceptable , particularly given that the decisions about use must be made just prior to intercourse . Is it possible to develop a product without this stigma , or better , one that is felt to enhance pleasure ? ” The project has been named the " Next Generation Condom " and anyone who can provide a " <unk> hypothesis " is eligible to apply . 
- Middle @-@ Eastern couples who have not had children , because of the strong desire and social pressure to establish fertility as soon as possible within marriage , rarely use condoms . 
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- = = = Major manufacturers = = = 
- 
- One analyst described the size of the condom market as something that " <unk> the mind " . Numerous small manufacturers , <unk> groups , and government @-@ run manufacturing plants exist around the world . Within the condom market , there are several major contributors , among them both for @-@ profit businesses and philanthropic organizations . Most large manufacturers have ties to the business that reach back to the end of the 19th century . 
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- = = Research = = 
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- A <unk> @-@ on condom made of latex is intended to be easier to apply and more successful in preventing the transmission of diseases . As of 2009 , the <unk> @-@ on condom was not going to market because the drying time could not be reduced below two to three minutes . 
- The Invisible Condom , developed at <unk> <unk> in <unk> , Canada , is a gel that <unk> upon increased temperature after insertion into the <unk> or <unk> . In the lab , it has been shown to effectively block HIV and herpes <unk> virus . The barrier breaks down and <unk> after several hours . As of 2005 , the invisible condom is in the clinical trial phase , and has not yet been approved for use . 
- Also developed in 2005 is a condom treated with an <unk> compound . The drug @-@ treated condom is intended to help the wearer maintain his erection , which should also help reduce slippage . If approved , the condom would be marketed under the <unk> brand . As of 2007 , it was still in clinical trials . In 2009 , <unk> Healthcare , the makers of <unk> condoms , introduced the <unk> condom lubricated with " <unk> <unk> " which contains the amino acid l @-@ <unk> and is intended to improve the strength of the <unk> response . 
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- 
- = Sovetsky Soyuz @-@ class battleship = 
- 
- The Sovetsky Soyuz @-@ class battleships ( Project 23 , Russian : <unk> <unk> , " Soviet Union " ) , also known as " Stalin 's <unk> " , were a class of battleships begun by the Soviet Union in the late 1930s but never brought into service . They were designed in response to the battleships being built by Germany . Only four hulls of the sixteen originally planned had been laid down by 1940 , when the decision was made to cut the program to only three ships to divert resources to an expanded army <unk> program . 
- These ships would have rivaled the Imperial Japanese Yamato class and America 's planned Montana class in size if any had been completed , although with significantly weaker firepower : nine 406 @-@ millimeter ( 16 @.@ 0 in ) guns compared to the nine 460 @-@ millimeter ( 18 @.@ 1 in ) guns of the Japanese ships and a dozen 16 @-@ inch ( 406 @.@ 4 mm ) on the <unk> . However , they would have been superior to their German rivals , the Bismarck class , at least on paper . The failure of the Soviet armor plate industry to build cemented armor plates thicker than 230 millimeters ( 9 @.@ 1 in ) would have negated any advantages from the Sovetsky Soyuz class 's thicker armor in combat . 
- Construction of the first four ships was plagued with difficulties as the Soviet shipbuilding and related industries were not prepared to build such large ships . One battleship , Sovetskaya Belorussiya , was cancelled on 19 October 1940 after serious construction flaws were found . Construction of the other three ships was suspended shortly after Nazi Germany invaded the Soviet Union in June 1941 , and never resumed . All three of the surviving hulls were scrapped in the late 1940s . 
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- = = Design and development = = 
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- Design work began in 1935 on new battleships in response to the existing and planned German battleships , and the Soviets made extensive efforts in Italy and the United States to purchase either drawings or the ships themselves in the late 1930s . The Italian firm of <unk> . <unk> & C. proposed a ship of 42 @,@ 000 long tons ( 43 @,@ 000 t ) standard displacement with nine 16 @-@ inch ( 406 mm ) guns , in size and appearance similar to the Italian battleship <unk> then under construction by the company . The U.S. firm of Gibbs & Cox provided four designs ; one for a conventional battleship , and three hybrid designs which combined battleship main armament with a raised flight deck on the central superstructure capable of operating up to 30 aircraft . While these projects proved useful to the Soviets , they decided to proceed with their own designs . 
- The first Tactical @-@ Technical <unk> ( abbreviated in Russian as TTZ ) for the large battleship design was issued on 21 February 1936 but proved too ambitious , <unk> nine 460 mm guns and a speed of 36 knots ( 67 km / h ; 41 mph ) on a displacement of 55 @,@ 000 tons . The TTZ was revised in May 1936 by Admiral Orlov , Commander of the Soviet Navy , reducing speed to 30 knots ( 56 km / h ; 35 mph ) , and weakening the secondary and anti @-@ aircraft batteries . A few months later Admiral Orlov further reduced the size of the battleship to 45 @,@ 000 tons and set the size of the main guns at 406 mm . Shortly afterward , the Soviet Union signed the Anglo @-@ Soviet <unk> Naval Agreement of 1937 and agreed to follow the terms of the Second London Naval Treaty that limited battleships to a displacement of 35 @,@ 560 metric tons ( 35 @,@ 000 long tons ) , although they did add a <unk> that allowed them to build ships of unlimited size to face the Imperial Japanese Navy if they notified the British . Yet another TTZ was approved by Orlov on 3 August for ships of 41 @,@ 500 tons with an armament of nine 406 @-@ millimeter , twelve 152 @-@ millimeter ( 6 @.@ 0 in ) , twelve 100 @-@ millimeter ( 3 @.@ 9 in ) , and forty 37 @-@ millimeter ( 1 @.@ 5 in ) guns , a maximum armor thickness of 380 mm ( 15 in ) and a speed of 30 knots . 
- The design of <unk> @-@ 4 , the surface ship design bureau of the Baltic Shipyard , was selected for further development although the lead designers were convinced that only a larger ship could fulfill the ambitious requirements . They did manage to get agreement on 22 November 1936 for a <unk> of the deck armor that raised the displacement to about 47 @,@ 000 tons . Design work continued on this basis and technical work was completed for a ship of 47 @,@ 700 tons in April 1937 , but the designers continued to press their case for larger ships . The issue was resolved by Premier Stalin at a meeting on 4 July when he agreed to increase displacement to about 56 @,@ 000 tons . This forced the project to begin again . 
- The timing of the redesign proved to be <unk> as the Great <unk> was spreading through the ranks of the military and related industries . The original deadline for completion of design work by 15 October was missed , and an incomplete version was presented to the navy 's Shipbuilding Administration the next month . A number of details remained to be worked out , including the final design of the machinery plant , the 152 mm guns and the 100 mm gun mounts . In the meantime , extensive and expensive testing was conducted on the ship 's hull form , deck armor and torpedo protection ; 27 million <unk> were spent on experimental work in 1938 alone . Over 100 models of the hull were tested in a ship model basin to find the best hull form and two one @-@ tenth @-@ scale launches were built at Sevastopol to test the hull 's maneuverability . An old steamship was fitted with a replica of the design 's armor decks and tested against 500 @-@ kilogram ( 1 @,@ 100 lb ) bombs , proving that such ordnance would generally penetrate both the 40 @-@ millimeter ( 1 @.@ 6 in ) upper and 50 @-@ millimeter ( 2 @.@ 0 in ) middle decks before exploding on the armored deck . The main armor deck was raised one deck in consequence and a splinter deck added underneath it to stop any bomb or shell fragments that might penetrate the armor deck . The underwater protection system was tested on fifteen one @-@ fifth scale models and two full @-@ sized experimental barges . These tests proved that the torpedo belt system of multiple bulkheads was superior to the Pugliese system of a large tube filled with smaller sealed tubes , but it was too late to incorporate these test results into the design as construction was well underway by the time they were completed in late 1939 . 
- A revised design was approved on 28 February 1938 and the first ship was to be laid down on 15 July , but even this design was incomplete and would be revised later . Trials with similarly shaped motor launches suggested that the hull 's propulsive efficiency would be 1 knot ( 1 @.@ 9 km / h ; 1 @.@ 2 mph ) less than planned , and this was accepted in the November 1938 revision as a maximum speed of 27 @.@ 5 knots ( 50 @.@ 9 km / h ; 31 @.@ 6 mph ) . However , a new propeller design proved to be more efficient and was predicted to increase speed to 28 knots ( 52 km / h ; 32 mph ) . Another change was the <unk> of the centerline rudder when tests showed that the two wing rudders would not be able to counteract its effects if it jammed . The weight toward the stern of the boat was calculated to be too great , producing a substantial stern @-@ down trim . To remedy this , the two 100 mm turrets mounted on the quarterdeck were deleted and the height of the armor belt abreast the rear turret was lowered , but this decision was reversed and they were restored by a decision of the State Defense Committee on 14 January 1941 . This forced a revision of the aircraft arrangements as the aircraft catapult had to be removed from the centerline of the quarterdeck ; two <unk> were added to the sides of the quarterdeck instead . 
- 
- = = = General characteristics = = = 
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- As designed , the Project 23 @-@ class ships , as Sovetsky Soyuz and her sisters were designated , were 269 @.@ 4 meters ( 883 ft 10 in ) long overall . They had a beam of 38 @.@ 9 meters ( 127 ft 7 in ) and at full load a draft of 10 @.@ 4 meters ( 34 ft 1 in ) . They displaced 59 @,@ 150 metric tons ( 58 @,@ 220 long tons ) at standard load and 65 @,@ 150 metric tons ( 64 @,@ 121 long tons ) at full load , although weight estimates made in 1940 show that they would have exceeded 60 @,@ 000 metric tons ( 59 @,@ <unk> long tons ) standard and 67 @,@ 000 metric tons ( 65 @,@ <unk> long tons ) at full load . 
- The hull form was very full @-@ bodied , especially at the forward magazines , where the torpedo protection system added width to the beam . Coupled with the relatively low length @-@ to @-@ beam ratio of 7 @.@ 14 : 1 , this meant that very powerful turbines were necessary to achieve even modest speeds . Stalin 's decision that the Project 23 @-@ class ships would use three shafts instead of four increased the load on each shaft and reduced propulsive efficiency , although it did shorten the length of the armored citadel and thus overall displacement . <unk> height was designed at 3 @.@ 4 meters ( 11 ft 2 in ) and the tactical diameter was estimated at about 1 @,@ 170 meters ( 3 @,@ 840 ft ) . 
- The Sovetsky Soyuz @-@ class ships were provided with aircraft facilities to handle two to four <unk> @-@ 2 flying boats which would be launched by the two <unk> mounted on the stern . Two <unk> were built into the after end of the forecastle deck to house two of them and cranes were provided at the forward end of the quarterdeck to hoist them out of the water . 
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- = = = Machinery = = = 
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- The machinery arrangement " provided good dispersal of the machinery spaces , but at the cost of very long runs for the wing shafts ( ca . 105 meters ( 344 @.@ 5 ft ) ) " . The turbine compartments for the wing shafts were located forward of boiler room No. 1 and aft of the No. 2 turret magazines . The engine room for the center shaft 's turbine was between boiler room No. 2 and No. 3 . This meant that the wing propeller shafts had to run underneath the boilers . 
- The steam turbines , and a license to build them , were originally going to be ordered from <unk> Laird in the United Kingdom , but their £ 700 @,@ 000 cost was more than the Soviets wanted to pay . Instead they bought them from Brown <unk> , using the technical information acquired from <unk> Laird in the process , for £ 400 @,@ 000 . Four single @-@ reduction , impulse @-@ reduction geared turbines were ordered from the Swiss firm , three to equip Sovetskaya Rossiya and one to serve as a pattern for the factory in <unk> that was to build the remainder . The three produced a total of 201 @,@ 000 shaft horsepower ( 149 @,@ <unk> kW ) . Six triangle @-@ type water @-@ tube boilers — two in each boiler room — powered the turbines at a working pressure of 37 kg / <unk> ( 3 @,@ 628 <unk> ; 526 <unk> ) and a temperature of 380 ° C ( 716 ° F ) . 
- Maximum speed was estimated at 28 knots , using the revised propeller design , although forcing the machinery would yield an extra knot . The normal fuel oil capacity was 5 @,@ 280 metric tons ( 5 @,@ 197 long tons ) , giving an estimated endurance of 6 @,@ 300 nautical miles ( 11 @,@ 700 km ; 7 @,@ 200 mi ) at 14 @.@ 5 knots ( 26 @.@ 9 km / h ; 16 @.@ 7 mph ) and 1 @,@ 890 nautical miles ( 3 @,@ 500 km ; 2 @,@ 170 mi ) at full speed . Maximum fuel capacity was 6 @,@ 440 metric tons ( 6 @,@ 338 long tons ) which gave a range of 7 @,@ 680 nautical miles ( 14 @,@ 220 km ; 8 @,@ 840 mi ) at 14 @.@ 5 knots and 2 @,@ 305 nautical miles ( 4 @,@ 269 km ; 2 @,@ 653 mi ) at full speed . 
- 
- = = = Armament = = = 
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- The main armament consisted of three electrically powered MK @-@ 1 triple turrets , each with three 50 @-@ caliber 406 mm B @-@ 37 guns . The guns could be depressed to − 2 ° and elevated to 45 ° . They had a fixed loading angle of 6 ° and their rate of fire varied with the time required to re @-@ aim the guns . It ranged from 2 @.@ 0 to 2 @.@ 6 rounds per minute depending on the elevation . The turrets could elevate at a rate of 6 @.@ 2 degrees per second and traverse at 4 @.@ 55 degrees per second . 100 rounds per gun were carried . The guns fired 1 @,@ 108 @-@ kilogram ( 2 @,@ 443 lb ) projectiles at a muzzle velocity of 830 m / s ( 2 @,@ 700 ft / s ) ; this provided a maximum range of 45 @,@ 600 meters ( 49 @,@ 900 yd ) . 
- The secondary armament consisted of twelve 57 @-@ caliber B @-@ 38 152 mm guns mounted in six dual MK @-@ 4 turrets . Their elevation limits were − 5 ° to + 45 ° with a fixed loading angle of 8 ° . Their rate of fire also varied with the elevation from 7 @.@ 5 to 4 @.@ 8 rounds per minute . They were provided with 170 rounds per gun . The turrets could elevate at a rate of 13 degrees per second and traverse at 6 degrees per second . They had a maximum range of about 30 @,@ 000 meters ( 98 @,@ 425 ft ) with a 55 @-@ kilogram ( 121 lb ) shell at a muzzle velocity of 950 m / s ( 3 @,@ 100 ft / s ) . 
- Heavy anti @-@ aircraft ( AA ) fire was provided by a dozen 56 @-@ caliber 100 mm B @-@ 34 dual @-@ purpose guns in six twin <unk> @-@ 14 turrets with 400 rounds per gun . The ships began construction with only four turrets , but two additional turrets were restored to the quarterdeck in January 1941 . They could elevate to a maximum of 85 ° and depress to − 8 ° . They could traverse at a rate of 12 ° per second and elevate at 10 ° per second . They fired 15 @.@ 6 @-@ kilogram ( 34 lb ) high explosive shells at a muzzle velocity of 895 m / s ( 2 @,@ 940 ft / s ) ; this provided a maximum range of 22 @,@ 241 meters ( 24 @,@ 323 yd ) against surface targets , but their maximum range against aerial targets was 9 @,@ 895 meters ( 32 @,@ <unk> ft ) , the limit of their time fuse . 
- Light AA defense was handled by ten quadruple , water @-@ cooled , 46 @-@ K mounts fitted with 37 mm ( 1 @.@ 5 in ) 70 @-@ K guns with 1800 rounds per gun . Initially only eight mounts were planned when the ships began construction , but two more were added later , probably in January 1941 , one on each side of the forward superstructure . Each mount was fully enclosed to protect the crew from the muzzle blast of the larger guns and against splinters . The guns fired <unk> @-@ kilogram ( 1 @.@ 61 lb ) shells at a muzzle velocity of 880 m / s ( 2 @,@ 900 ft / s ) . Their effective anti @-@ aircraft range was 4 @,@ 000 meters ( 13 @,@ 123 ft ) . 
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- = = = Fire control = = = 
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- Each main gun turret was given a DM @-@ 12 12 @-@ meter ( 39 ft 4 in ) rangefinder for use in local control , but they were generally controlled by one of three <unk> @-@ 8 fire @-@ control directors . These had two 8 @-@ meter ( 26 ft 3 in ) stereoscopic rangefinders , one to track the target and the other to measure the range to the ship 's own shell splashes . Two of these were protected by 20 mm ( 0 @.@ 79 in ) of armor and were mounted atop the rear superstructure and the tower @-@ mast . The other was mounted on top of the conning tower and was protected by 50 mm ( 2 @.@ 0 in ) of armor . They used a <unk> @-@ 0 mechanical computer to generate firing solutions . Four <unk> @-@ <unk> @-@ II directors , with two 4 @-@ meter ( 13 ft 1 in ) rangefinders each , controlled the secondary armament . One pair was on either side of the tower @-@ mast and the aft pair was on each side of the aft funnel . Three <unk> @-@ 300 stabilized directors , each with a 4 @-@ meter rangefinder , controlled the heavy anti @-@ aircraft guns . There was one on each side of the forward funnel while the other was atop the rear superstructure . 
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- = = = Protection = = = 
- 
- Soviet armor plate plants proved incapable of producing plates of cemented armor thicker than 230 mm ( 9 @.@ 1 in ) which forced the decision to replace cemented plates thicker than 200 mm ( 7 @.@ 9 in ) with face @-@ hardened ones with less resistance in November 1940 . The plants tended to compensate by making the thicker plates harder , but this often made them more brittle and large numbers did not pass the acceptance tests . This would have significantly reduced the level of protection enjoyed by the Sovetsky Soyuz @-@ class ships in combat . 
- The Sovetsky Soyuz @-@ class ships devoted a total weight of 23 @,@ <unk> metric tons ( 22 @,@ 938 long tons ) to armor protection , a slightly greater weight than that of the larger Japanese Yamato class ( 23 @,@ 262 metric tons ( 22 @,@ 895 long tons ) ) . Their armor was intended to resist 406 mm shells and 500 kg bombs , specifically shells fired from forward bearings between 35 ° and 50 ° from the centerline . This led to the very unusual situation where the armor belt thickened toward the bow to compensate for the narrowing of the ship near the forward magazines , which had to be compensated for by thicker armor . The belt was 148 @.@ 4 meters ( 486 ft 11 in ) long and covered 57 % of the total waterline length . It was inclined 5 ° to increase its resistance to flat @-@ trajectory shells . Over the machinery spaces it was 375 mm ( 14 @.@ 8 in ) thick and increased in steps until it was 420 mm ( 17 in ) thick over the forward magazines . It was 380 mm ( 15 in ) over the rear magazine . The belt armor was carried forward of the magazines at a thickness of 220 mm ( 8 @.@ 7 in ) and terminated in a steeply sloped ( 30 ° ) transverse 285 mm ( 11 @.@ 2 in ) bulkhead that reduced to 250 mm ( 9 @.@ 8 in ) at the lower deck where it was continued down to the inner bottom by a 75 @-@ millimeter ( 3 @.@ 0 in ) bulkhead . Forward of this bulkhead was a 20 mm splinter belt that continued all the way to the bow . The main armor belt dropped down to the main deck from the upper deck abreast the aft turret to reduce weight . This " step " was protected by 180 @-@ millimeter ( 7 @.@ 1 in ) plates . A 365 @-@ millimeter ( 14 @.@ 4 in ) transverse bulkhead separated the rear turret and the ship 's sides . The main part of the armored citadel was closed off by a 230 mm forward bulkhead and a 180 mm ( 7 @.@ 1 in ) rear bulkhead , both of <unk> armor . <unk> armor 25 mm ( 0 @.@ 98 in ) thick covered the upper portion of the citadel . 
- The forecastle deck was 25 mm thick while the upper deck was 155 mm ( 6 @.@ 1 in ) over the citadel . Below it , the 50 mm middle deck acted as a splinter deck . The upper deck was 100 mm thick above the 220 mm waterline belt extension . The bottom edge of the forward splinter belt met with a 65 mm ( 2 @.@ 6 in ) arched deck . Another arched deck of the same thickness covered the stern aft of the rear transverse bulkhead . 
- The main gun turrets had faces 495 mm ( 19 @.@ 5 in ) thick with sides and roofs 230 mm ( 9 @.@ 1 in ) thick . 180 @-@ millimeter ( 7 @.@ 1 in ) thick plates protected the gun ports and 60 @-@ millimeter ( 2 @.@ 4 in ) bulkheads separated each gun . The barbettes were 425 mm ( 16 @.@ 7 in ) thick above the upper deck . The MK @-@ 4 turrets had 100 mm faces and 65 mm sides . Their barbettes were 100 mm in thickness , but reduced to 65 mm on their <unk> sides . 100 mm of armor protected the faces , sides and backs of the <unk> @-@ 14 turrets for the 100 mm guns , but their roofs and barbettes were 100 mm thick . The forward conning tower had walls 425 mm thick while the rear conning tower had only 220 mm ( 8 @.@ 7 in ) . The flag bridge in the tower @-@ mast had 75 mm ( 3 @.@ 0 in ) of protection . 
- The torpedo defense system was designed to withstand torpedoes with warheads equivalent to 750 kg ( 1 @,@ 653 lb ) of <unk> . The ships were intended to be able to remain afloat with any five adjacent compartments flooded or with three torpedo hits and the destruction of the <unk> above @-@ water side . The Pugliese system protected 123 meters ( 403 ft 7 in ) of the ships ' <unk> . At the aft end was a multi @-@ bulkhead protection system that extended another 33 meters ( 108 ft 3 in ) to the rear from the Pugliese system . The depth of the system was 8 @.@ 2 meters ( 26 ft 11 in ) amidships , but it reduced to 7 meters ( 23 ft 0 in ) fore and aft . The outer plating ranged from 11 to 14 mm ( 0 @.@ 43 to 0 @.@ 55 in ) in thickness while the inner bottom was 7 mm ( 0 @.@ 28 in ) thick . The cylinder of the Pugliese system was also 7 mm thick while the semi @-@ circular main bulkhead was 35 mm ( 1 @.@ 4 in ) thick with a flat 10 @-@ millimeter ( 0 @.@ 39 in ) bulkhead behind it . The 3 @.@ 15 @-@ meter ( 10 ft 4 in ) diameter cylinder was intended to be immersed in fuel oil or water . 
- 
- = = Construction = = 
- 
- The August 1938 shipbuilding plan envisioned a total of 15 Project 23 @-@ class battleships , and this grandiose scheme was only slightly revised downward to 14 ships in the August 1939 plan . Eight of these were to be laid down before 1942 and the remaining six before 1947 . However , only four were actually laid down before the outbreak of World War II forced the Soviets to <unk> their ambitious plans . On 19 October 1940 an order was issued , signed by Stalin and <unk> , that no new battleships would be laid down in order to concentrate on smaller ships ' building ( and also , probably , because more resources were required for the Army ) , one ship is to be scrapped , and priority should be given to only one of the three remaining battleships . 
- The Soviet shipbuilding and related industries proved to be incapable of supporting the construction of so many large ships at the same time . The largest warships built in the Soviet Union prior to 1938 were the 8 @,@ 000 @-@ metric @-@ ton ( 7 @,@ 874 @-@ long @-@ ton ) <unk> @-@ class cruisers , and even they had suffered from a number of production problems , but the Soviet leadership appeared to ignore the difficulties encountered in the construction of the <unk> class when ordering 14 much more ambitious ships . Construction of two more ships planned for Leningrad and Nikolayev had to move to the brand @-@ new Shipyard Nr. 402 in Molotovsk because the existing shipyards could not be expanded to handle so many large ships . <unk> for these two ships had to be manufactured at Leningrad and shipped via the White Sea – Baltic Canal to Molotovsk . Also , the turret shop at <unk> proved to be too poorly equipped to assemble the 406 mm mountings and the propeller shafts had to be ordered in 1940 from Germany and the Netherlands as the domestic plants were already <unk> with orders . Shipbuilding steel proved to be in short supply in 1940 , and a number of batches were rejected because they did not meet specifications . Armor plate production was even more problematic as only 1 @,@ 800 metric tons ( 1 @,@ 772 long tons ) of the anticipated 10 @,@ 000 metric tons ( 9 @,@ 842 long tons ) were delivered in 1939 , and more than half of that was rejected . Furthermore , the armor plants proved to be incapable of making cemented plates over 230 mm , and inferior face @-@ hardened plates had to substitute for all <unk> over 200 millimeters ( 8 in ) . 
- Machinery problems were likely to delay the ships well past their intended delivery dates of 1943 – 44 . Three turbines were delivered by Brown <unk> in 1939 to <unk> for the Sovetskaya Rossiya , but the <unk> <unk> Works never completed a single turbine before the German invasion in June 1941 . A prototype boiler was supposed to have been built ashore for evaluation , but it was not completed until early 1941 , which further complicated the production plan . 
- Construction of all three ships was ordered halted on 10 July 1941 , and the Sovetsky Soyuz was placed into long @-@ term conservation as the most advanced ship . However , all three were officially stricken from the Navy List on 10 September 1941 . 
- 
- = = = Sovetsky Soyuz = = = 
- 
- Sovetsky Soyuz ( Russian : <unk> <unk> — Soviet Union ) was formally laid down 15 July 1938 in Shipyard Nr. 189 ( <unk> ) in Leningrad , although evidence suggests that construction actually began in January 1939 after her slipway was completed , the necessary cranes were in place , and working drawings had been completed . When the war began she was estimated to be 21 @.@ 19 % complete , with 15 @,@ <unk> metric tons ( 15 @,@ 568 long tons ) of steel assembled on the slip . She was only lightly damaged by German air attacks and bombardments , and , as some material had been used during the Siege of Leningrad , she was estimated to be 19 @.@ 5 % complete after the end of the war . Some thought was given to completing her , but this was opposed as she was regarded as obsolete in light of the experience gained during the war . Stalin 's expressed desire to see one of the Project 23 @-@ class ships completed only delayed the decision to scrap her ; this was ordered on 29 May 1948 and was well underway by April 1949 . 
- 
- = = = Sovetskaya Ukraina = = = 
- 
- Sovetskaya Ukraina ( Russian : Советская <unk> — Soviet Ukraine ) was laid down 31 October 1938 at Shipyard Nr. 198 ( <unk> South ) in Nikolayev . When the war began she was 17 @.@ 98 % complete , with 13 @,@ 001 metric tons ( 12 @,@ <unk> long tons ) assembled on the slipway . Some effort was made to launch the hull , but little work had been done to dredge the river at the foot of the slipway , and she was captured on 18 August 1941 , although retreating Soviet troops slightly damaged her hull . The Germans dismantled 200 feet ( 61 m ) of her bow and 100 feet ( 30 m ) of her stern for use in fortifications . They were forced to evacuate Nikolayev on 17 March 1944 and demolished the supporting blocks under her port side before they left , which gave her a list between 5 and 10 degrees and made her a total loss . She was ordered scrapped on 27 March 1947 . 
- 
- = = = Sovetskaya Rossiya = = = 
- 
- Sovetskaya Rossiya ( Russian : Советская <unk> — Soviet Russia ) was laid down on 22 July 1940 in Shipyard Nr. 402 in Molotovsk . After the end of the war she was only 0 @.@ 97 % complete , with 2 @,@ 125 metric tons ( 2 @,@ <unk> long tons ) of steel assembled . She was ordered scrapped on 27 March 1947 . 
- 
- = = = Sovetskaya Belorussiya = = = 
- 
- Sovetskaya Belorussiya ( Russian : Советская <unk> — Soviet <unk> ) was laid down 21 December 1939 at Shipyard Nr. 402 in Molotovsk , but construction was suspended in mid @-@ 1940 when it was discovered that 70 @,@ 000 <unk> used in her hull plating were of inferior quality . This fact probably influenced the decision to cancel her on 19 October 1940 . Material intended for her construction was used to construct a floating battery for the defense of Leningrad . 
- 
- 
- = Sang Pencerah = 
- 
- Sang Pencerah ( The <unk> ) is a 2010 Indonesian film directed by <unk> Bramantyo and starring Lukman Sardi , <unk> <unk> Mecca , and <unk> Rahardjo . It is a <unk> of Ahmad Dahlan which describes how he came to found the Islamic organisation Muhammadiyah . 
- Sang Pencerah , produced to coincide with Muhammadiyah 's centenary , was announced in November 2009 . It was meant to be historically accurate , with much of the <unk> . 12 billion ( US $ 1 @.@ 3 million ) budget paying for period costumes and sets . However , as documentation on the early years of Dahlan 's life is lacking , those scenes were <unk> . Sang Pencerah marks the feature film debut of Ihsan Tarore and Giring Ganesha . 
- The film , released on 8 September 2010 during the <unk> ul @-@ <unk> holiday , was meant to show different views of Islam ; however , it has also been interpreted as a critique of the current Muhammadiyah leadership . It was seen in theatres by over 1 million people – the only Indonesian film of 2010 to do so . It also received favourable critical reception , winning the 2011 Bandung Film Festival after being refused a nomination at the 2010 Indonesian Film Festival . However , some Muslim critics decried it as being too liberal . 
- 
- = = Plot = = 
- 
- Muhammad Darwis ( Ihsan Tarore ) is a youth in 19th @-@ century <unk> , Yogyakarta , and the son of <unk> <unk> , the imam of the area 's mosque . <unk> with the mixture of Islam and <unk> Javanese mysticism , which leads to poor Javanese spending <unk> amounts of money on religious ceremonies , Darwis decides to go on the <unk> to Mecca in Saudi Arabia . While there , he studies what he considers the true form of Islam over a period of five years . 
- Upon his return to Yogyakarta , Darwis ( now played by Lukman Sardi ) changes his name to Ahmad Dahlan and begins teaching Islam , preaching that prayers need only come from inner peace and do not require large donations or sacrifices . Conflict soon arises between Dahlan and the local <unk> ( religious leaders ) after he shows that the direction in which they pray is wrong , pointing not to the Kaaba in Mecca but to Africa . The <unk> , especially <unk> Kamaludiningrat ( <unk> Rahardjo ) , <unk> Dahlan as the leader of a cult and provoke a crowd of their followers to destroy the building next to Dahlan 's house used for studying prayer . 
- Dahlan continues to preach and teach , opening a school for native people , teaching Islam at a Dutch @-@ run school , and opening a small mosque ; he also marries his cousin , Siti Walidah . His actions , such as having his students sit on chairs instead of the traditional mats on the floor , lead to Kamaludiningrat decrying Dahlan as an <unk> who is working to <unk> the local populace . 
- Despite continued resistance from Kamaludiningrat , Dahlan prevails and <unk> his teaching . Together with Walidah , and the students <unk> ( <unk> <unk> ) , <unk> ( Mario <unk> ) , <unk> ( Dennis <unk> ) , <unk> ( Ricky <unk> ) , and <unk> ( Giring Ganesha ) , he <unk> the progressive Islamic organisation Muhammadiyah , which <unk> Islam without any influence from Javanese mysticism . 
- 
- = = Production = = 
- 
- The film announced in November 2009 . It was directed by <unk> Bramantyo , a Yogyakarta @-@ born Muslim filmmaker . Bramantyo , who had previously directed the Islamic @-@ themed <unk> @-@ <unk> Cinta ( The <unk> of Love ; 2008 ) , considered Dahlan his favourite national hero ; he later told The Jakarta Post that he admired the <unk> 's spirit . He also said that he had been wanting to make the film since he was a teenager . The film was meant to coincide with the 100 year anniversary of Muhammadiyah 's founding . 
- Bramantyo attempted to make his film as historically accurate as possible . However , as no documentation of Dahlan 's life before he went to Mecca exists , the scenes showing Dahlan 's youth were fictionalized . The script underwent 12 revisions before the film was completed . Much of the film 's budget of <unk> . 12 billion ( US $ 1 @.@ 3 million ) was spent on costumes and sets to ensure accuracy . The crew restored several old buildings for their shots and custom @-@ ordered 19th @-@ century style traditional garments for their scenes . 
- <unk> cast his wife , <unk> <unk> Mecca , as Ahmad Dahlan 's wife Siti Walidah — Mecca also served as casting director , in charge of auditioning the supporting cast . Before filming began , Mecca attempted to research the biographies of Dahlan and his wife , as she and the other young actors did not know much about the couple . However , she found such research difficult as the libraries did not have the information she needed . 
- Lukman Sardi , who played Dahlan , was also unaware of his character 's historical biography before researching it ; he noted that he was not very confident in playing the role . Indonesian Idol winner Ihsan Tarore was cast as the young Ahmad Dahlan , while the lead singer of the band <unk> , Giring Ganesha , was cast as Dahlan 's <unk> <unk> ; it was the feature film debut for both . Giring also wrote a song for the film , titled " Allah <unk> <unk> " ( " Allah , the All @-@ Pure " ) , over a period of two days . 
- On set , Mecca , who was pregnant at the time of shooting , found her physical condition " perfect " for the role as Walidah had been a large woman . However , her pregnancy led to Bramantyo worrying about a potential <unk> during a scene where Mecca had to ride a traditional carriage with wooden wheels , which shook <unk> when moving . 
- 
- = = Themes = = 
- 
- In a 2011 interview with the Jakarta Globe after the release of his controversial film ? , Bramantyo said that he had intended Sang Pencerah to use Dahlan 's life to examine different aspects of Islam . He meant for the film to show Islam as being a peaceful and <unk> religion . <unk> , writing for Tempo , notes that the film presents a different side of Dahlan , who is usually portrayed as an old , bearded man who , in his opinion , seems neither modernist nor progressive . 
- Ahmad <unk> , a lecturer on comparative religious studies at <unk> <unk> Islamic University in Yogyakarta , sees the film as a critique of the current Muhammadiyah leadership , whom he describes as " narrow minded , <unk> , <unk> [ ing ] poor social respect , [ ... ] rigid and allergic to progress " . 
- 
- = = Release and reception = = 
- 
- Sang Pencerah was released on 8 September 2010 , over the <unk> ul @-@ <unk> holiday ; the increase in film attendance over the holiday is similar to blockbuster season for Hollywood films . According to Bramantyo , Sang Pencerah was seen by 1 @.@ 1 million people while in theatres ; a report from The Jakarta Globe says 1 @.@ 2 million . It was the only Indonesian film of 2010 to sell over a million tickets . 
- <unk> was accused of working for Muhammadiyah when creating the film , an accusation which he denied . He also was criticised by some Muslim groups , which considered him too liberal . The film was not nominated for best picture at the 2010 Indonesian Film Festival after several nominees threatened to withdraw if Sang Pencerah was nominated . Sang Pencerah later won best picture at the Bandung Film Festival , held in Bandung , West Java , on 6 May 2011 . 
- <unk> <unk> , reviewing for The Jakarta Post , praised Sardi and Rahardjo 's acting ; noting that Sang Pencerah was " a good film and a conduit for young people to learn about their national heroes " . <unk> <unk> , writing for <unk> , himself a graduate from Muhammadiyah schools , viewed the film as being well put together and reflective of the issues faced by Islamic <unk> in the early 1900s . <unk> praised the film 's acting , soundtrack , and plot , especially Dahlan 's portrayal as a human with emotions and flaws instead of being perfect in every way . He found Sang Pencerah 's greatest weakness to be the language spoken by in some scenes , as it mixed Indonesian with Javanese ( something not done during the period portrayed ) , and the film 's stars – neither of whom is Javanese – had inaccurate accents . 
- 
- 
- = Underneath ( The X @-@ Files ) = 
- 
- " Underneath " is the twelfth episode of the ninth season of the American science fiction television series The X @-@ Files . The episode first aired in the United States on March 31 , 2002 on the Fox network . The episode was written and directed by executive producer John Shiban . The episode is a " monster @-@ of @-@ the @-@ week " episode , a stand @-@ alone plot which is unconnected to the mythology , or overarching fictional history , of The X @-@ Files . The episode earned a Nielsen rating of 4 @.@ 4 and was viewed by 4 @.@ 64 million households and 7 @.@ 3 million viewers . It received mixed reviews from critics . 
- The show centers on FBI special agents who work on cases linked to the paranormal , called X @-@ Files ; this season focuses on the investigations of John Doggett ( Robert Patrick ) , Monica Reyes ( <unk> <unk> ) , and Dana Scully ( Gillian Anderson ) . In this episode , Doggett is determined to find an error in the DNA evidence that freed the convicted Robert Fassl , the " <unk> Killer " , whom he nearly caught in the act 13 years earlier . In the end , it is revealed that Fassl has a mental condition that splits him into two parts : the religious innocent and the vengeful killer . 
- " Underneath " marked the directorial debut of Shiban , who had been a writer for the series for several seasons . Reportedly , the episode contained " so many problems " that the Fox executives nearly refused to allow the finished product to air . At the last minute , however , they relented , and allowed the episode to be aired later on in the season , several weeks after its intended air date . Shiban originally wanted to film the sewer scenes in Los Angeles ' actual sewer system , but due to the events of September 11 , a sewer mock @-@ up was built on Stage 11 at the Fox studios . 
- 
- = = Plot = = 
- 
- Thirteen years before the present , Robert Fassl ( W. Earl Brown ) sits in his van . He later approaches a home and claims to be there to repair the cable . As Fassl holds up a piece of paper to show it to the family who called for the repair , blood <unk> splashes across the paper . He looks up and sees the house 's occupants with slit <unk> in pools of blood . <unk> , two police officers burst into the house and apprehend Fassl . One of the officers who goes to check out the kitchen , turns to reveal he is John Doggett ( Robert Patrick ) as a young <unk> officer . 
- In the present , Monica Reyes ( <unk> <unk> ) discusses Fassl 's release — due to DNA evidence — with an outraged Doggett . Dana Scully ( Gillian Anderson ) confirms that the test results conclusively <unk> Fassl as the killer . Meanwhile , in court , Fassl notices a mysterious Bearded Man . After being released , he stays in a room belonging to his lawyer , Jana Fain , where he clutches a <unk> beads and prays frantically . When the Bearded Man appears , Fassl begs for the man not to hurt her . While Fain is unharmed , Fassl learns that the housekeeper , Mrs. <unk> , has gone missing . Fassl finds her body , <unk> up the blood , and <unk> her remains to cover up what has happened . 
- Scully tells Doggett that while the DNA test <unk> Fassl 's <unk> , it <unk> a possible blood relative ; Fassl , however , is an only child . Reyes proposes that the murders are being conducted by an entity rather than a person . Meanwhile , Fassl approaches Assistant District Attorney Damon Kaylor and begs to be sent back to prison . Kaylor refuses , but is killed by the Bearded Man . After hearing of Kaylor 's disappearance , Reyes <unk> that Fassl 's piety and his unwillingness to acknowledge his darker half has given him the <unk> ability to physically change into another , more violent person . 
- The Bearded Man demands that Fassl kill Fain , beating him up when he doesn 't comply . As she tends to Fassl , Fain first sees the Bearded Man in his place . While <unk> out Fain 's house , the agents see the Bearded Man flee . Doggett pursues the Bearded Man while Reyes finds Fain alive . In the pursuit , Reyes falls through into a sewer , where she finds the remains of the Bearded Man 's victims . After a struggle with the Bearded Man , Doggett ends up <unk> Fassl , much to his confusion . Reyes tries to remind him that it does not matter as long as the case is solved . 
- 
- = = Production = = 
- 
- " Underneath " was written and directed by executive producer John Shiban . This marked his directorial debut . According to Shiban , part of the inspiration for the episode was that the series production staff had " actually talked for some time about doing a Jekyll / Hyde story but never quite found a way to do it " until the idea to use DNA came into play . Another inspiration for the episode was the 1949 film The Third Man , which featured a climactic chase through a sewage system . 
- The episode , which explores John Doggett 's backstory as a New York City police officer , was described as containing similar themes as those " explored on the Millennium series . " The episode guest @-@ starred Arthur Nascarella , who was a friend of series co @-@ star Robert Patrick . Patrick was essential in getting Nascarella cast on the show ; he later joked " I stole [ Nascarella 's ] New York accent in Copland [ sic ] and I stole it to do The X @-@ Files , but I got him cast in The X @-@ Files show . " 
- As the ninth season progressed and the show 's ratings began to <unk> , Fox became more and more actively involved in the show 's style and direction . Although " Underneath " was the twelfth episode aired , it was actually the ninth episode produced during the season ; reportedly , the episode contained " so many problems " that the Fox executives very nearly <unk> the finished product . At the last minute , however , they relented , and allowed the episode to be aired later on in the season , several weeks after its intended air date . 
- Shiban originally wanted to film the sewer scenes in Los Angeles ' actual sewer system , but the Los Angeles Department of Water and Power vetoed the idea and stated that " there 's a moratorium on shooting there since September 11 " , a position that Shiban called " understandable . " In order to make up for this , series art director Corey Kaplan was tasked with building a sewer replica ; she used the blueprints from the 1952 version of Les <unk> as an inspiration . Eventually , a complete set was built on Stage 11 at the Fox studios . 
- 
- = = Reception = = 
- 
- " Underneath " first aired in the United States on March 31 , 2002 on the Fox network . The episode later debuted in the United Kingdom on February 2 , 2003 on BBC One . The episode earned a Nielsen household rating of 4 @.@ 4 , meaning that it was seen by 4 @.@ 4 % of the nation 's estimated households and was viewed by 4 @.@ 64 million households and over 7 @.@ 3 million viewers . " Underneath " was the <unk> most watched episode of television that aired during the week ending March 31 . 
- The episode received mixed reviews from television critics . Jessica Morgan from Television Without Pity gave the episode a B – rating . John Keegan from Critical Myth gave the episode a negative review and called it " easily the worst of the season " . He awarded it a 4 out of 10 and concluded that , " We can only hope that none of the remaining episodes are Shiban @-@ related , and pray that he can do a much better job when he writes for Enterprise next season . " Robert Shearman and Lars Pearson , in their book Wanting to Believe : A Critical Guide to The X @-@ Files , Millennium & The Lone Gunmen , rated the episode three @-@ and @-@ a @-@ half stars out of five , and called the entry " solid and efficient " . The two complimented Shiban 's directorial efforts , noting that " as a director [ he ] makes ' Underneath ' shine " , but were critical of some of the " trademark X @-@ File moments " , citing " the surprise appearance of a face in the bathroom mirror " and " the climactic fight in a sewer " as examples . Shearman and Pearson , however , wrote positively of Shiban 's realistic depiction of Doggett . <unk> <unk> , in his book <unk> the Truth : <unk> The X @-@ Files after 9 / 11 , wrote that the episode felt " very familiar " but was impressed with the production design on the sewer set . 
- 
- 
- = Islais Creek = 
- 
- Islais Creek or Islais Creek Channel ( previously known as Du <unk> Creek , Islais Channel and Islais Swamp ) is a small creek in San Francisco , California . The name of the creek is derived from a <unk> Native American word " <unk> " or " <unk> " , the name for the <unk> <unk> wild cherries . Around the time of the Gold Rush , the area became an industrial hub , and the condition of the creek worsened . After the devastating earthquake in 1906 , the city decided to reclaim the creek using earthquake debris , reducing the <unk> to its present size . Though much of Islais Creek has been converted to an underground culvert , remnants still exist today at both Glen Canyon Park and Third Street . Several community organizations are dedicated to preserve these remnants , as they are important wildlife habitats . 
- 
- = = Course = = 
- 
- The historic Islais Creek , the largest body of water in the city covering an area of nearly 5 @,@ 000 acres ( 7 @.@ <unk> sq mi ; 20 @.@ 234 km2 ) , had two main branches . One originated near the southern slope of Twin <unk> , slightly north of <unk> Drive . It flowed downstream southeastward through the Glen Canyon Park <unk> Bosworth Street and eventually reaching the bottom of the Mission Street viaduct at I @-@ 280 . The other branch began at the intersection of Cayuga Avenue and Regent Street . It flowed generally eastward along Mission Street and reached the I @-@ 280 viaduct . Together , as a wider creek , it ran parallel to <unk> Boulevard and I @-@ 280 and emptied into the Islais Creek estuary , near Industrial Street and <unk> Avenue . <unk> Creek , a nearby creek that originated from <unk> Valley , also joined Islais Creek at the César <unk> Boulevard and Evans Avenue intersection . 
- From its sources in the Glen Canyon , the entire creek stretched about 3 @.@ 5 miles ( 5 @.@ 6 km ) to the San Francisco Bay . The mouth was nearly 2 miles ( 3 @.@ 2 km ) wide , providing up to 85 % of the drinking water in San Francisco . Due to urban development , however , the watershed of Islais Creek has been reduced by roughly 80 % from its historical extent . A large number of neighborhoods in San Francisco today , such as <unk> Heights , Hunters Point , <unk> Valley , parts of the Mission and <unk> Hill , was once covered by the extent of the creek . 
- In 2007 , the San Francisco Public Utilities Commission , which manages the city 's water , began investigating the possibility of " <unk> " underground portions of the creek . As of 2009 , remnants of the creek remain inside the Glen Canyon Park and a 1 @-@ mile ( 1 @.@ 6 km ) channel near Third Street where Islais Creek emptied into the bay . 
- 
- = = History = = 
- 
- The history of Islais Creek dates to the 18th century . The name Los Islais first appeared on Mexican maps in 1834 , named for the Islay cherries that grew wildly in the area . By 1850 , water from the creek was used by farmers to <unk> crops . The Gold Rush marked the decline of the creek as large numbers of gold rushers <unk> into the city . 
- A railroad <unk> was built over the creek and <unk> flats in the 1860s and in a lawsuit that went up to the California Supreme Court called The People of the State of California ex <unk> The Board of State Harbor Commissioners <unk> . The <unk> And Bay View Railroad Company , Islais Creek was declared a non @-@ navigable waterway in 1883 . 
- In 1871 , the area along the creek became known as the city ’ s " New <unk> " when more than 100 <unk> opened . Since then , the condition of the creek deteriorated , literally becoming a dumping place of garbage , sewage , animal waste , and <unk> meat products . The condition became so bad that the creek was commonly referred to as " <unk> Creek " by San Franciscans , according to historian Karl <unk> . 
- After the 1906 earthquake , San Francisco Board of Supervisors voted to fill the creek with earthquake debris , reducing the creek to its present size . During World War II , it served as <unk> areas for large ocean @-@ going <unk> . The area also located the largest <unk> <unk> processing plant in the United States West Coast . In fact , the abandoned five @-@ story high <unk> crane , used to transport large amount of <unk> from ships to the plant as late as 1974 , still remains on the creek bank preserved as a historic landmark . In the 1950s , Islais Creek was home to the largest <unk> <unk> industry in the world . The deteriorated condition of the creek gradually improved after the construction of a water treatment plant in 1970 . Today , the majority of the creek is covered and transformed into a culvert with its remnants flowing at Glen Canyon and near the bay . 
- Many local community organizations were set up to improve the condition of the creek and nearby areas . Friends of Islais Creek , established back in 1984 , and David <unk> , a local community figure , were committed to build a waterfront park in Islais Creek . The initial plan for a park was finally launched in 1988 with a $ 50 @,@ 000 grant from the State Department of Water Resources as well as community groups in The <unk> . With an additional of $ 100 @,@ 000 federal and local grants as well as supports from non @-@ profit organizations and governmental agencies , namely the Sierra Club , San Francisco Municipal Railway ( <unk> ) , Department of Public Works , Public Utilities Commission , Port of San Francisco , and Caltrans , the park was finished in 1998 . Located adjacent to Pier 80 on the shores of the creek , the Muwekma Ohlone Park or the Muwekma Ohlone <unk> is named after the native inhabitants and has since became an important habitat for a wide array of wildlife , including the Pacific Chorus Frog and Mission blue butterfly . 
- On November 19 , 2001 , construction crews , while preparing to drill an electrical conduit ( consisting of six large <unk> electrical cables ) across the creek for the <unk> Metro T Third Street light rail line , cracked the concrete sewer underground which carries more than 80 million gallons of sewage a day . The incident flooded the creek and its adjacent Muwekma Ohlone Park with sewage . The park had to be excavated to make way for the repairing of the sewer pipe . It was estimated that it would take more than $ 101 @,@ 660 to repair and $ 65 @,@ 000 for wildlife habitat restoration . 
- 
- = = = Health hazard = = = 
- 
- Since Islais Creek is a culvert that carries storm water , domestic sewage , and industrial wastewater , it is possible for the sewage to overflow . Such overflow can cause a public health hazard as Islais Creek displays higher level of heavy metals , <unk> , bacteria , as well as <unk> than other parts of the San Francisco Bay . 
- 
- = = Transportation = = 
- 
- The Third Street Bridge is the major crossing of the creek , carrying T Third Street light rail line and Third Street . It is a bascule @-@ type drawbridge . The Port of San Francisco extended Illinois Street across Islais Creek in 2006 to relieve traffic for Third Street . This crossing also uses a bascule bridge . 
- 
- 
- = Iguanodon = 
- 
- Iguanodon ( / <unk> / i @-@ <unk> @-@ <unk> @-@ don ; meaning " iguana @-@ tooth " ) is a genus of ornithopod dinosaur that existed roughly halfway between the first of the swift bipedal <unk> of the mid @-@ Jurassic and the duck @-@ billed dinosaurs of the late Cretaceous . While many species have been classified in the genus Iguanodon , dating from the late Jurassic Period to the late Cretaceous Period of Asia , Europe , and North America , research in the first decade of the 21st century suggests that there is only one well @-@ <unk> species : I. bernissartensis , which lived from the late Barremian to the earliest <unk> ages ( Early Cretaceous ) in Belgium and possibly elsewhere in Europe , between about 126 and 125 million years ago . Iguanodon were large , bulky herbivores . <unk> features include large thumb spikes , which were possibly used for defense against predators , combined with long prehensile fifth fingers able to forage for food . 
- The genus was named in 1825 by English geologist Gideon Mantell , based on fossil specimens that are now assigned to Therosaurus and Mantellodon . Iguanodon was the second type of dinosaur formally named based on fossil specimens , after Megalosaurus . Together with Megalosaurus and <unk> , it was one of the three genera originally used to define <unk> . The genus Iguanodon belongs to the larger group Iguanodontia , along with the duck @-@ billed hadrosaurs . The taxonomy of this genus continues to be a topic of study as new species are named or long @-@ standing ones reassigned to other genera . 
- Scientific understanding of Iguanodon has evolved over time as new information has been obtained from fossils . The numerous specimens of this genus , including nearly complete skeletons from two well @-@ known <unk> , have allowed researchers to make informed hypotheses regarding many aspects of the living animal , including feeding , movement , and social behaviour . As one of the first scientifically well @-@ known dinosaurs , Iguanodon has occupied a small but notable place in the public 's perception of dinosaurs , its artistic representation changing significantly in response to new interpretations of its remains . 
- 
- = = Description = = 
- 
- Iguanodon were bulky herbivores that could shift from <unk> to <unk> . The only well @-@ supported species , I. bernissartensis , is estimated to have weighed about 3 @.@ 08 tonnes ( 3 @.@ 4 tons ) on average , and measured about 10 metres ( 33 feet ) long as an adult , with some specimens possibly as long as 13 metres ( 43 feet ) . These animals had large , tall but narrow skulls , with toothless beaks probably covered with keratin , and teeth like those of <unk> , but much larger and more closely packed . 
- The arms of I. bernissartensis were long ( up to 75 % the length of the legs ) and robust , with rather <unk> hands built so that the three central fingers could bear weight . The <unk> were conical spikes that stuck out away from the three main digits . In early restorations , the spike was placed on the animal 's nose . Later fossils revealed the true nature of the thumb spikes , although their exact function is still debated . They could have been used for defense , or for foraging for food . The little finger was elongated and <unk> , and could have been used to manipulate objects . The <unk> formula is 2 @-@ 3 @-@ 3 @-@ 2 @-@ 4 , meaning that the innermost finger ( <unk> ) has two bones , the next has three , etc . The legs were powerful , but not built for running , and each foot had three toes . The backbone and tail were supported and stiffened by ossified tendons , which were tendons that turned to bone during life ( these rod @-@ like bones are usually omitted from skeletal mounts and drawings ) . 
- Iguanodon teeth are , as the name suggests , like those of an iguana , but larger . Unlike hadrosaurids , which had columns of replacement teeth , Iguanodon only had one replacement tooth at a time for each position . The upper jaw held up to 29 teeth per side , with none at the front of the jaw , and the lower jaw 25 ; the numbers differ because teeth in the lower jaw are broader than those in the upper . Because the tooth rows are deeply <unk> from the outside of the jaws , and because of other anatomical details , it is believed that , as with most other ornithischians , Iguanodon had some sort of cheek @-@ like structure , muscular or non @-@ muscular , to retain food in the mouth . 
- 
- = = Classification and origins = = 
- 
- Iguanodon gives its name to the unranked clade Iguanodontia , a very populous group of ornithopods with many species known from the Middle Jurassic to the Late Cretaceous . Aside from Iguanodon , the best @-@ known members of the clade include <unk> , Camptosaurus , Ouranosaurus , and the duck @-@ bills , or hadrosaurs . In older sources , Iguanodontidae was shown as a distinct family . This family traditionally has been something of a <unk> taxon , including ornithopods that were neither <unk> or hadrosaurids . In practice , animals like <unk> , Camptosaurus , <unk> , <unk> , <unk> , <unk> , Ouranosaurus , and <unk> were usually assigned to this family . 
- With the advent of cladistic analyses , Iguanodontidae as traditionally construed was shown to be <unk> , and these animals are recognised to fall at different points in relation to hadrosaurs on a cladogram , instead of in a single distinct clade . Essentially , the modern concept of Iguanodontidae currently includes only Iguanodon . Groups like <unk> are still used as unranked clades in the scientific literature , though many traditional <unk> are now included in the superfamily <unk> . Iguanodon lies between Camptosaurus and Ouranosaurus in cladograms , and is probably descended from a <unk> @-@ like animal . At one point , Jack Horner suggested , based mostly on skull features , that hadrosaurids actually formed two more distantly related groups , with Iguanodon on the line to the flat @-@ headed <unk> , and Ouranosaurus on the line to the crested lambeosaurines , but his proposal has been rejected . 
- The cladogram below follows an analysis by Andrew McDonald , 2012 . 
- 
- = = <unk> = = 
- 
- 
- = = = Feeding = = = 
- 
- One of the first details noted about Iguanodon was that it had the teeth of a herbivorous reptile , although there has not always been consensus on how it ate . As Mantell noted , the remains he was working with were unlike any modern reptile , especially in the toothless , <unk> @-@ shaped form of the lower jaw <unk> , which he found best compared to that of the two @-@ toed <unk> and the extinct ground <unk> <unk> . He also suggested that Iguanodon had a prehensile tongue which could be used to gather food , like a <unk> . More complete remains have shown this to be an error ; for example , the <unk> bones that supported the tongue are heavily built , implying a muscular , non @-@ prehensile tongue used for moving food around in the mouth . The <unk> @-@ tongue idea has also been incorrectly attributed to Dollo via a broken lower jaw . 
- The skull was structured in such a way that as it closed , the bones holding the teeth in the upper jaw would bow out . This would cause the lower surfaces of the upper jaw teeth to rub against the upper surface of the lower jaw 's teeth , grinding anything caught in between and providing an action that is the rough equivalent of mammalian chewing . Because the teeth were always replaced , the animal could have used this mechanism throughout its life , and could eat tough plant material . Additionally , the front ends of the animal 's jaws were toothless and tipped with bony nodes , both upper and lower , providing a rough margin that was likely covered and lengthened by a <unk> material to form a <unk> beak for biting off twigs and shoots . Its food gathering would have been aided by its flexible little finger , which could have been used to manipulate objects , unlike the other fingers . 
- <unk> what Iguanodon ate with its well @-@ developed jaws is not known . The size of the larger species , such as I. bernissartensis , would have allowed them access to food from ground level to tree foliage at 4 – 5 metres ( 13 – 16 ft ) high . A diet of <unk> , <unk> , and conifers was suggested by David Norman , although iguanodonts in general have been tied to the advance of <unk> plants in the Cretaceous due to the dinosaurs ' inferred low browsing habits . <unk> growth , according to this hypothesis , would have been encouraged by <unk> feeding because <unk> would be removed , allowing more space for the weed @-@ like early angiosperms to grow . The evidence is not conclusive , though . Whatever its exact diet , due to its size and abundance , Iguanodon is regarded as a dominant medium to large herbivore for its ecological communities . In England , this included the small predator <unk> , larger predators <unk> , <unk> , and <unk> , low @-@ feeding herbivores Hypsilophodon and <unk> , fellow " <unk> " Mantellisaurus , the armoured herbivore <unk> , and <unk> like <unk> . 
- 
- = = = <unk> and movement = = = 
- 
- Early fossil remains were fragmentary , which led to much speculation on the posture and nature of Iguanodon . Iguanodon was initially portrayed as a quadrupedal horn @-@ <unk> beast . However , as more bones were discovered , Mantell observed that the forelimbs were much smaller than the hindlimbs . His rival Owen was of the opinion it was a <unk> creature with four pillar @-@ like legs . The job of overseeing the first lifesize reconstruction of dinosaurs was initially offered to Mantell , who declined due to poor health , and Owen 's vision subsequently formed the basis on which the sculptures took shape . Its bipedal nature was revealed with the discovery of the Bernissart skeletons . However , it was depicted in an upright posture , with the tail dragging along the ground , acting as the third leg of a tripod . 
- During his re @-@ examination of Iguanodon , David Norman was able to show that this posture was unlikely , because the long tail was stiffened with ossified tendons . To get the <unk> pose , the tail would literally have to be broken . <unk> the animal in a horizontal posture makes many aspects of the arms and pectoral <unk> more understandable . For example , the hand is relatively immobile , with the three central fingers grouped together , bearing <unk> @-@ like phalanges , and able to <unk> . This would have allowed them to bear weight . The wrist is also relatively immobile , and the arms and shoulder bones robust . These features all suggest that the animal spent time on all fours . 
- Furthermore , it appears that Iguanodon became more quadrupedal as it got older and heavier ; juvenile I. bernissartensis have shorter arms than adults ( 60 % of <unk> length versus 70 % for adults ) . When walking as a <unk> , the animal 's hands would have been held so that the <unk> faced each other , as shown by <unk> <unk> and the anatomy of this genus 's arms and hands . The three toed <unk> ( foot ) of Iguanodon was relatively long , and when walking , both the hand and the foot would have been used in a <unk> fashion ( walking on the fingers and toes ) . The maximum speed of Iguanodon has been estimated at 24 km / h ( 15 mph ) , which would have been as a <unk> ; it would not have been able to gallop as a <unk> . 
- Large three @-@ toed <unk> are known in Early Cretaceous rocks of England , particularly <unk> beds on the Isle of Wight , and these trace fossils were originally difficult to interpret . Some authors associated them with dinosaurs early on . In 1846 , E. <unk> went so far as to assign them to an <unk> he named Iguanodon , and Samuel <unk> noted in 1854 that they looked like bird tracks , but might have come from dinosaurs . The identity of the <unk> was greatly clarified upon the discovery in 1857 of the hind leg of a young Iguanodon , with distinctly three @-@ toed feet , showing that such dinosaurs could have made the tracks . Despite the lack of direct evidence , these tracks are often attributed to Iguanodon . A <unk> in England shows what may be an Iguanodon moving on all fours , but the foot prints are poor , making a direct connection difficult . Tracks assigned to the <unk> Iguanodon are known from locations including places in Europe where the body fossil Iguanodon is known , to Spitsbergen , Svalbard , Norway . 
- 
- = = = <unk> spike = = = 
- 
- The thumb spike is one of the best @-@ known features of Iguanodon . Although it was originally placed on the animal 's nose by Mantell , the complete Bernissart specimens allowed Dollo to place it correctly on the hand , as a modified thumb . ( This would not be the last time a dinosaur 's modified thumb claw would be <unk> ; <unk> , <unk> , and <unk> are examples since the 1980s where an enlarged thumb claw was first put on the foot , as in <unk> . ) 
- This thumb is typically interpreted as a close @-@ quarter <unk> @-@ like weapon against predators , although it could also have been used to break into seeds and fruits , or against other Iguanodon . One author has suggested that the spike was attached to a venom <unk> , but this has not been accepted , as the spike was not hollow , nor were there any grooves on the spike for conducting venom . 
- 
- = = = Possible social behaviour = = = 
- 
- Although sometimes interpreted as the result of a single catastrophe , the Bernissart finds instead are now interpreted as recording multiple events . According to this interpretation , at least three occasions of mortality are recorded , and though numerous individuals would have died in a <unk> short time span ( ? 10 – 100 years ) , this does not necessarily mean these Iguanodon were herding animals . 
- An argument against herding is that juvenile remains are very uncommon at this site , unlike modern cases with herd mortality . They more likely were the periodic victims of flash floods whose carcasses accumulated in a lake or marshy setting . The <unk> find , however , with its greater span of individual ages , more even mix of Dollodon or Mantellisaurus to Iguanodon bernissartensis , and confined geographic nature , may record mortality of herding animals migrating through rivers . 
- Unlike other purported herding dinosaurs ( especially hadrosaurs and ceratopsids ) , there is no evidence that Iguanodon was sexually dimorphic , with one sex <unk> different from the other . At one time , it was suggested that the Bernissart I. " mantelli " , or I. atherfieldensis ( Dollodon and Mantellisaurus , respectively ) represented a sex , possibly female , of the larger and more robust , possibly male , I. bernissartensis . However , this is not supported today . 
- 
- = = = <unk> = = = 
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- Evidence of a fractured hip bone was found in a specimen of Iguanodon , which had an injury to its <unk> . Two other individuals were observed with signs of <unk> as evidenced by bone <unk> in their <unk> which are called <unk> . 
- 
- = = Discovery and history = = 
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- 
- = = = Gideon Mantell , Sir Richard Owen , and the discovery of dinosaurs = = = 
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- The discovery of Iguanodon has long been accompanied by a popular legend . The story goes that Gideon Mantell 's wife , Mary Ann , discovered the first teeth of an Iguanodon in the strata of <unk> Forest in <unk> Green , <unk> , Sussex , England , in 1822 while her husband was visiting a patient . However , there is no evidence that Mantell took his wife with him while seeing patients . Furthermore , he admitted in 1851 that he himself had found the teeth . Not everyone agrees that the story is false , though . It is known from his <unk> that Mantell first acquired large fossil bones from the quarry at <unk> Green in 1820 . Because also theropod teeth were found , thus belonging to <unk> , he at first interpreted these bones , which he tried to combine into a partial skeleton , as those of a giant crocodile . In 1821 Mantell mentioned the find of herbivorous teeth and began to consider the possibility that a large herbivorous reptile was present in the strata . However , in his 1822 publication <unk> of the South Downs he as yet did not <unk> to suggest a connection between the teeth and his very incomplete skeleton , <unk> that his finds presented two large forms , one carnivorous ( " an animal of the <unk> Tribe of enormous magnitude " ) , the other herbivorous . In May 1822 he first presented the herbivorous teeth to the Royal Society of London but the members , among them William Buckland , dismissed them as fish teeth or the <unk> of a rhinoceros from a Tertiary <unk> . On 23 June 1823 Charles <unk> showed some to Georges Cuvier , during a <unk> in Paris , but the famous French naturalist at once dismissed them as those of a rhinoceros . Though the very next day Cuvier <unk> , <unk> reported only the dismissal to Mantell , who became rather <unk> about the issue . In 1824 Buckland described Megalosaurus and was on that occasion invited to visit Mantell 's collection . Seeing the bones on 6 March he agreed that these were of some giant <unk> — though still denying it was a herbivore . <unk> nevertheless , Mantell again sent some teeth to Cuvier , who answered on 22 June 1824 that he had determined that they were <unk> and quite possibly belonged to a giant herbivore . In a new edition that year of his <unk> sur les <unk> <unk> Cuvier admitted his earlier mistake , leading to an immediate acceptance of Mantell , and his new <unk> , in scientific circles . Mantell tried to <unk> his theory further by finding a modern @-@ day parallel among extant reptiles . In September 1824 he visited the Royal College of Surgeons but at first failed to find comparable teeth . However , assistant @-@ curator Samuel <unk> recognised that they resembled those of an iguana he had recently prepared , albeit twenty times longer . 
- In recognition of the resemblance of the teeth to those of the iguana , Mantell decided to name his new animal Iguanodon or " iguana @-@ tooth " , from iguana and the Greek word <unk> ( <unk> , <unk> or " tooth " ) . Based on isometric scaling , he estimated that the creature might have been up to 18 metres ( 59 feet ) long , more than the 12 metres ( 39 feet ) length of Megalosaurus . His initial idea for a name was Iguana @-@ <unk> ( " Iguana lizard " ) , but his friend William Daniel <unk> suggested that that name was more applicable to the iguana itself , so a better name would be <unk> ( " Iguana @-@ like " ) or Iguanodon . He neglected to add a specific name to form a proper binomial , but one was supplied in 1829 by Friedrich Holl : I. <unk> , which was later amended to I. anglicus . 
- Mantell sent a letter detailing his discovery to the local Portsmouth Philosophical Society in December 1824 , several weeks after settling on a name for the fossil creature . The letter was read to members of the Society at a meeting on 17 December , and a report was published in the Hampshire Telegraph the following Monday , 20 December , which announced the name , mis @-@ spelt as " <unk> " . Mantell formally published his findings on 10 February 1825 , when he presented a paper on the remains to the Royal Society of London . 
- A more complete specimen of similar animal was discovered in a quarry in Maidstone , Kent , in 1834 ( lower Lower <unk> Formation ) , which Mantell soon acquired . He was led to identify it as an Iguanodon based on its distinctive teeth . The Maidstone slab was utilized in the first skeletal reconstructions and artistic renderings of Iguanodon , but due to its incompleteness , Mantell made some mistakes , the most famous of which was the placement of what he thought was a horn on the nose . The discovery of much better specimens in later years revealed that the horn was actually a modified thumb . Still <unk> in rock , the Maidstone skeleton is currently displayed at the Natural History Museum in London . The borough of Maidstone commemorated this find by adding an Iguanodon as a supporter to their coat of arms in 1949 . This specimen has become linked with the name I. mantelli , a species named in 1832 by Christian Erich Hermann von Meyer in place of I. anglicus , but it actually comes from a different formation than the original I. mantelli / I. anglicus material . The Maidstone specimen , also known as Gideon Mantell 's " <unk> @-@ piece " , and formally labelled <unk> <unk> was subsequently excluded from Iguanodon . It is classified as cf . Mantellisaurus by McDonald ( 2012 ) ; as cf . Mantellisaurus atherfieldensis by Norman ( 2012 ) ; and made the holotype of a separate species Mantellodon <unk> by Paul ( 2012 ) . 
- At the same time , tension began to build between Mantell and Richard Owen , an ambitious scientist with much better funding and society connections in the turbulent worlds of Reform Act – era British politics and science . Owen , a firm <unk> , opposed the early versions of evolutionary science ( " <unk> " ) then being debated and used what he would soon coin as dinosaurs as a weapon in this conflict . With the paper describing <unk> , he scaled down dinosaurs from lengths of over 61 metres ( 200 feet ) , determined that they were not simply giant lizards , and put forward that they were advanced and mammal @-@ like , characteristics given to them by God ; according to the understanding of the time , they could not have been " transmuted " from reptiles to mammal @-@ like creatures . 
- In 1849 , a few years before his death in 1852 , Mantell realised that iguanodonts were not heavy , <unk> @-@ like animals , as Owen was putting forward , but had slender forelimbs ; however , his passing left him unable to participate in the creation of the Crystal Palace dinosaur sculptures , and so Owen 's vision of the dinosaurs became that seen by the public for decades . With Benjamin <unk> Hawkins , he had nearly two dozen lifesize sculptures of various prehistoric animals built out of concrete sculpted over a steel and brick framework ; two iguanodonts ( based on the Mantellodon specimen ) , one standing and one resting on its belly , were included . Before the sculpture of the standing <unk> was completed , he held a banquet for twenty inside it . 
- 
- = = = Bernissart = = = 
- 
- The largest find of Iguanodon remains to that date occurred on 28 February 1878 in a coal mine at Bernissart in Belgium , at a depth of 322 m ( 1 @,@ 056 ft ) , when two <unk> , Jules <unk> and Alphonse <unk> , accidentally hit on a skeleton that they initially took for <unk> wood . With the encouragement of Alphonse <unk> , supervisor of mines at nearby <unk> , Louis de <unk> on 15 May 1878 started to excavate the skeletons and in 1882 Louis Dollo reconstructed them . At least 38 Iguanodon individuals were uncovered , most of which were adults . In 1882 , the holotype specimen of I. bernissartensis became one of the first ever dinosaur skeletons mounted for display . It was put together in a chapel at the Palace of Charles of Lorraine using a series of <unk> ropes attached to scaffolding so that a <unk> pose could be achieved during the mounting process . This specimen , along with several others , first opened for public viewing in an inner courtyard of the palace in July 1883 . In 1891 they were moved to the Royal Museum of Natural History , where they are still on display ; nine are displayed as standing mounts , and nineteen more are still in the Museum 's basement . The exhibit makes an impressive display in the Royal Belgian Institute of Natural Sciences , in Brussels . A replica of one of these is on display at the Oxford University Museum of Natural History and at the <unk> Museum in Cambridge . Most of the remains were referred to a new species , I. bernissartensis , a larger and much more robust animal than the English remains had yet revealed , but one specimen was referred to the nebulous , gracile I. mantelli ( now Dollodon <unk> ) . The skeletons were some of the first complete dinosaur skeletons known . Found with the dinosaur skeletons were the remains of plants , fish , and other reptiles , including the <unk> <unk> . 
- The science of <unk> fossil remains was in its infancy , and new techniques had to be improvised to deal with what soon became known as " pyrite disease " . <unk> pyrite in the bones was being <unk> to iron <unk> , accompanied by an increase in volume that caused the remains to crack and crumble . When in the ground , the bones were isolated by <unk> moist clay that prevented this from happening , but when removed into the drier open air , the natural chemical conversion began to occur . To limit this effect , De <unk> immediately , in the mine @-@ gallery , re @-@ covered the dug @-@ out fossils with wet clay , sealing them with paper and plaster reinforced by iron rings , forming in total about six hundred <unk> blocks with a combined weight of a hundred and thirty tons . In Brussels after opening the plaster he impregnated the bones with boiling <unk> mixed with oil of <unk> as a <unk> . <unk> most of the visible pyrite he then hardened them with hide glue , finishing with a final layer of tin foil . Damage was repaired with <unk> @-@ <unk> . This treatment had the <unk> effect of sealing in moisture and extending the period of damage . In 1932 museum director Victor van <unk> decided that the specimens had to be completely restored again to safeguard their preservation . From December 1935 to August 1936 the staff at the museum in Brussels treated the problem with a combination of alcohol , arsenic , and 390 kilograms of shellac . This combination was intended to simultaneously penetrate the fossils ( with alcohol ) , prevent the development of mold ( with arsenic ) , and harden them ( with shellac ) . The fossils entered a third round of conservation from 2003 until May 2007 , when the shellac , hide glue and <unk> were removed and impregnated with <unk> acetate and <unk> and <unk> <unk> . Modern treatments of this problem typically involve either monitoring the humidity of fossil storage , or , for fresh specimens , preparing a special coating of <unk> <unk> that is then heated in a vacuum pump , so that moisture is immediately removed and pore spaces are infiltrated with <unk> <unk> to seal and strengthen the fossil . 
- Dollo 's specimens allowed him to show that Owen 's prehistoric <unk> were not correct for Iguanodon . He instead modelled the skeletal mounts after the cassowary and <unk> , and put the spike that had been on the nose firmly on the thumb . He was not completely correct , but he also had the disadvantage of being faced with some of the first complete dinosaur remains . A problem that was later recognised was the bend he introduced into the tail . This organ was more or less straight , as shown by the skeletons he was excavating , and the presence of ossified tendons . In fact , to get the bend in the tail for a more <unk> or <unk> @-@ like posture , the tail would have had to be broken . With its correct , straight tail and back , the animal would have walked with its body held horizontal to the ground , arms in place to support the body if needed . 
- Excavations at the quarry were stopped in 1881 , although it was not exhausted of fossils , as recent drilling operations have shown . During World War I , when the town was occupied by German forces , preparations were made to reopen the mine for <unk> , and Otto <unk> was sent from Berlin to supervise . The Allies recaptured Bernissart just as the first <unk> layer was about to be uncovered . Further attempts to reopen the mine were hindered by financial problems and were stopped altogether in 1921 when the mine flooded . 
- 
- = = = Current research = = = 
- 
- Research on Iguanodon decreased during the early part of the 20th century as World Wars and the Great Depression enveloped Europe . A new species that would become the subject of much study and taxonomic controversy , I. atherfieldensis , was named in 1925 by R. W. <unk> , for a specimen collected at <unk> Point on the Isle of Wight . 
- Iguanodon was recorded from Africa based on teeth from Tunisia and elsewhere in the <unk> , but the description of <unk> and Ouranosaurus cast doubt on African records of Iguanodon . The genus was also recorded from Mongolia based on the description of I. orientalis , and in North America based on I. <unk> from Utah . Another North American species , from South Dakota , once assigned to Iguanodon as I. lakotaensis , has since been reclassified as the genus <unk> . 
- Iguanodon was not part of the initial work of the dinosaur renaissance that began with the description of <unk> in 1969 , but it was not neglected for long . David B. <unk> 's work on ornithopod feeding mechanisms provided a better understanding of how it fed , and David B. Norman 's work on numerous aspects of the genus has made it one of the best @-@ known dinosaurs . In addition , a further find of numerous Iguanodon skeletons , in <unk> , <unk> @-@ <unk> , Germany , has provided evidence for <unk> in this genus , as the animals in this <unk> restricted find appear to have been killed by flash floods . At least 15 individuals , from 2 to 8 metres ( 6 @.@ 6 to 26 @.@ 2 ft ) long , have been found here , although at least some of them are gracile <unk> and belong to the related Mantellisaurus or Dollodon ( described as I. atherfieldensis , at that time believed to be another species of Iguanodon ) . 
- Iguanodon material has also been used in the search for dinosaur DNA and other biomolecules . In research by Graham <unk> et al . , Iguanodon bones were processed to look for remnant proteins . In this research , identifiable remains of typical bone proteins , such as <unk> and <unk> , were found in a rib . 
- 
- = = Species = = 
- 
- Because Iguanodon is one of the first dinosaur genera to have been named , numerous species have been assigned to it . While never becoming the <unk> taxon several other early genera of dinosaurs became ( such as Megalosaurus ) , Iguanodon has had a complicated history , and its taxonomy continues to undergo revisions . Although Gregory Paul recommended <unk> I. bernissartensis to the famous sample from Bernissart , ornithopod workers like Norman and McDonald have disagreed with Paul 's recommendations , except exercising caution when accepting records of Iguanodon from France and Spain as valid . 
- I. anglicus was the original type species , but the holotype was based on a single tooth and only partial remains of the species have been recovered since . In March 2000 , the International Commission on Zoological Nomenclature changed the type species to the much better known I. bernissartensis , with the new holotype being <unk> 1534 . The original Iguanodon tooth is held at Te Papa <unk> , the national museum of New Zealand in Wellington , although it is not on display . The fossil arrived in New Zealand following the move of Gideon Mantell 's son Walter there ; after the elder Mantell 's death , his fossils went to Walter . 
- 
- = = = Species currently accepted as valid = = = 
- 
- Only two species assigned to Iguanodon are still considered to be valid . 
- I. bernissartensis , described by George Albert <unk> in 1881 , is the type species for the genus . This species is best known for the many skeletons discovered in Bernissart , but is also known from remains across Europe . David Norman suggested that it includes the dubious Mongolian I. orientalis , but this has not been followed by other researchers . 
- I. <unk> , described in 2015 , is based on adult and juvenile remains found in Barremian @-@ age deposits in <unk> , Spain . 
- 
- = = = <unk> species = = = 
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- I. <unk> ( also spelled I. <unk> or <unk> ) , named by Owen for a lower jaw from the Tithonian – <unk> @-@ age Upper Jurassic – Lower Cretaceous <unk> <unk> of Dorset in 1874 , has been reassigned to its own genus , <unk> . 
- Iguanodon <unk> ( or <unk> <unk> ) , described by Czech <unk> Antonin Fritsch in 1893 , is a dubious <unk> reptile now known as <unk> <unk> . 
- I. atherfieldensis , described by <unk> <unk> in 1925 , was smaller and less robust than I. bernissartensis , with longer neural spines . It was renamed Mantellisaurus atherfieldensis in 2007 . The Bernissart specimen <unk> <unk> was described as Dollodon <unk> in 2008 , but McDonald and Norman returned Dollodon to <unk> with Mantellisaurus . 
- I. <unk> was described by Fritsch in 1878 . It is a nomen dubium based on very poor material and was renamed <unk> in 2000 . 
- I. prestwichii ( also spelled I. <unk> ) , described by John Hulke in 1880 , has been reassigned to Camptosaurus prestwichii or to its own genus <unk> . 
- Two species described by Richard Lydekker in the late 19th century have been reassigned to different genera . 
- I. <unk> , described by Lydekker in 1888 , is known from two partial skeletons found in East Sussex , England , from the middle Valanginian @-@ age Lower Cretaceous Wadhurst Clay . It is now the type species of <unk> . 
- I. fittoni was described by Lydekker in 1889 . Like I. <unk> , this species was described from the Wadhurst Clay of East Sussex . It is now the type species of Hypselospinus . 
- I. <unk> ( also spelled I. <unk> ) , described by Lydekker in 1889 has variously been considered a synonym of Hypselospinus fittoni or a distinct species assigned to the genus <unk> . A specimen from the Valanginian Wadhurst Clay Formation , variously assigned to I. <unk> and I. mantelli over the years , has an unusual combination of hadrosaurid @-@ like lower jaw and very robust <unk> ; Norman ( 2010 ) assigned this specimen to the species Hypselospinus fittoni , while Paul ( 2012 ) made it the holotype of a separate species <unk> <unk> . 
- I. <unk> ( also incorrectly spelled I. <unk> ) , described by Hulke two years after I. prestwichii , has been <unk> with Iguanodon bernissartensis , though this is controversial . 
- I. <unk> , described by Emanuel <unk> in 1871 , has been reassigned to <unk> <unk> . 
- I. lakotaensis was described by David B. <unk> and Philip R. <unk> in 1989 . The only well @-@ accepted North American species of Iguanodon , I. lakotaensis was described from a partial skull from the Barremian @-@ age Lower Cretaceous Lakota Formation of South Dakota . Its assignment has been controversial . Some researchers suggest that it was more basal than I. bernissartensis , and related to <unk> , but David Norman has suggested that it was a synonym of I. bernissartensis . Gregory S. Paul has since given the species its own genus , <unk> . 
- Iguanodon mantelli described by Christian Erich Hermann von Meyer in 1832 , was based on the same material as I. anglicus and is an objective junior synonym of the latter . Several taxa , including the holotype of Dollodon and Mantellodon , but also the dubious <unk> Trachodon <unk> the <unk> Hypsilophodon , and <unk> , were previously mis @-@ assigned to I. mantelli . 
- I. <unk> , coined by Edwin <unk> Newton in 1892 for a tooth from the early <unk> Upper Cretaceous Lower <unk> of Hertfordshire , has been considered an early hadrosaurid of some sort . However , recent work places it as indeterminate beyond <unk> outside <unk> . 
- I. orientalis , described by A. K. <unk> in 1952 , was based on poor material , but a skull with a distinctive arched snout that had been assigned to it was renamed <unk> <unk> in 1998 . At the same time , I. orientalis was considered to be a nomen dubium because it cannot be compared to I. bernissartensis . 
- Harry <unk> described I. <unk> in 1869 , but later reassigned it to <unk> . 
- I. <unk> ( also spelled I. precursor ) , described by E. <unk> in 1876 from teeth from an unnamed Kimmeridgian ( Late Jurassic ) formation in <unk> @-@ de @-@ Calais , France , is actually a <unk> , sometimes assigned to <unk> , although the two come from different formations . 
- " I. <unk> " ( <unk> , 1992 ) is a nomen nudum from a photo caption in a book , of remains that would later be named <unk> . 
- <unk> <unk> was named in 2011 based on a specimen previously assigned to Iguanodon bernissartensis . 
- 
- = = = Species referred to Iguanodon that were originally named as nominal species of other genera = = = 
- 
- I. valdensis , a renaming of <unk> valdensis by Ernst van den <unk> in 1900 . Originally named <unk> valdensis by Hulke in 1879 based on <unk> and pelvic remains , it was from the Barremian stage of the Isle of Wight . It was considered a juvenile specimen of Mantellisaurus atherfieldensis , or an undetermined species of Mantellisaurus , but is indeterminate beyond Iguanodontia . 
- I. <unk> ( also spelled I. <unk> ) was originally described by Thomas Henry <unk> in 1869 as the type species of Hypsilophodon ; Owen ( 1873 or 1874 ) reassigned it to Iguanodon , but his assignment was soon overturned . 
- I. <unk> , named by Lydekker in 1888 as the type species of <unk> and assigned to Iguanodon in 1969 by Rodney Steel , has been tossed of as a synonym of Mantellisaurus atherfieldensis , but is dubious <unk> . 
- I. major , a species named by Justin <unk> in 1966 , based on vertebrae from the Isle of Wight and Sussex originally described by Owen in 1842 as a species of <unk> , S. major , is a nomen dubium which is now thought to be a synonym of I. anglicus , although it may be its own species . 
- The nomen nudum " <unk> " ( van den <unk> , 1900 ) also belongs here . 
- 
- = = = <unk> species = = = 
- 
- Two Iguanodon species are currently considered to be nomina dubia : 
- I. anglicus , described by Friedrich Holl in 1829 , is the original type species of Iguanodon , but , as discussed above , was replaced by I. bernissartensis . In the past , it has been spelled as I. <unk> ( <unk> and <unk> , 1993 ) and I. <unk> ( Holl , 1829 <unk> . <unk> , 1850 ) . It is known from teeth from the middle Valanginian @-@ age Lower Cretaceous <unk> Clay Formation ) of <unk> , West Sussex , England . It is currently classified in the genus Therosaurus as T. anglicus . 
- I. <unk> , described by Peter <unk> and James A. Jensen in 1979 , is a nomen dubium based on teeth from the possibly <unk> @-@ age lower Cedar Mountain Formation of Utah . 
- The genera <unk> ( <unk> , 1828 ) , <unk> ( <unk> , 1834 ) , and Therosaurus ( <unk> , 1840 ) , are simply junior objective synonyms , later names for the material of I. anglicus . 
- 
- = = In popular culture = = 
- 
- Since its description in 1825 , Iguanodon has been a feature of worldwide popular culture . Two lifesize reconstructions of Mantellodon ( considered Iguanodon at the time ) built at the Crystal Palace in London in 1852 greatly contributed to the popularity of the genus . Their thumb spikes were mistaken for horns , and they were depicted as elephant @-@ like <unk> , yet this was the first time an attempt was made at constructing full @-@ size dinosaur models . In 1910 Heinrich <unk> portrayed a group of Iguanodon in the classic German collecting cards about extinct and prehistoric animals " <unk> der <unk> " . 
- Several motion pictures have featured Iguanodon . In the Disney film Dinosaur , an Iguanodon named <unk> served as the protagonist with three other iguanodonts as other main characters ; a loosely related ride of the same name at Disney 's Animal Kingdom is based around bringing an Iguanodon back to the present . Iguanodon is one of the three dinosaur genera that inspired <unk> ; the other two were <unk> and Stegosaurus . Iguanodon has also made appearances in some of the many Land Before Time films , as well as episodes of the television series . 
- Aside from appearances in movies , Iguanodon has also been featured on the television documentary miniseries Walking with Dinosaurs ( 1999 ) produced by the BBC , and played a starring role in Sir Arthur Conan Doyle 's book , The Lost World as well as featuring in an episode of the Discovery Channel documentary , Dinosaur Planet ( incorrectly portrayed being able to run on all fours and living in the Late Cretaceous ) . It also was present in Bob <unk> 's <unk> Red ( 1995 ) , as a <unk> prey item . A main belt asteroid , 1989 <unk> , has been named <unk> Iguanodon in honour of the genus . 
- Because it is both one of the first dinosaurs described and one of the best @-@ known dinosaurs , Iguanodon has been well @-@ placed as a barometer of changing public and scientific perceptions on dinosaurs . Its reconstructions have gone through three stages : the <unk> quadrupedal horn @-@ <unk> reptile satisfied the <unk> , then a bipedal but still fundamentally <unk> animal using its tail to prop itself up dominated the early 20th century , but was slowly overturned during the 1960s by its current , more agile and dynamic representation , able to shift from two legs to all fours . 
- 
- 
- = Subtropical Storm Alpha ( 1972 ) = 
- 
- Subtropical Storm Alpha ( also called Alfa ) was a rare off @-@ season subtropical cyclone that hit Georgia in May 1972 . It developed from a previously non @-@ tropical cyclone in the western Atlantic Ocean , and initially it moved northeastward off of the Carolinas . The storm turned southwestward due to a building ridge , and concurrently it intensified to become Subtropical Storm Alpha . It later moved ashore near Savannah , and it finally dissipated in the northeast Gulf of Mexico on May 29 . It produced wave action and moderate rainfall along the coast . Damage totaled over $ 100 @,@ 000 ( 1972 USD ) , and there were two associated deaths . 
- 
- = = Meteorological history = = 
- 
- Throughout May 1972 , a series of weak troughs moved across the eastern United States . In the third week of the month , an upper @-@ level <unk> low developed along one of these troughs , located southeast of the United States and removed from the <unk> . The origins of Alpha were from a surface low northeast of Florida , associated with the larger @-@ scale , cold core upper low . It organized , and late on May 23 it could be classified as a subtropical depression , east of the Georgia / South Carolina border . While southeast of Hatteras , North Carolina , a developing ridge blocked its northeast motion , and so it slowed to turn to the southeast . On May 25 , a small , intense low @-@ level center organized rapidly , and by the next day it attained gale force winds , by which time the storm turned southwestward . At 1600 UTC on May 26 , the National Hurricane Center initiated advisories on Subtropical Cyclone Alpha , when the storm was about 225 miles south of Cape Hatteras . Around that time , it reached its peak winds of 70 mph ( 110 km / h ) . 
- On its first advisory , there was disagreement among forecast models regarding its eventual track , with projected tracks <unk> from a Georgia landfall to a track northeastward out to sea . Alpha was also a <unk> <unk> at the time ; the NHC <unk> model , which was based on previous storms with similar characteristics , found no <unk> for the storm . Subtropical Storm Alpha was initially well @-@ defined as it moved southwestward . The very small center was located along the eastern edge of the convection , while low @-@ level cloud bands formed east of the center . At the time , the temperature structure was more typical of a winter storm , although it was expected to become more like a subtropical storm typical during the summer months . The winds decreased steadily as it turned more westward on May 27 , and the heavy rainfall persisted mostly to the north and west of the center . 
- The National Hurricane Center initially thought the center might not have been at the surface , and the agency indicated low forecasting confidence , as they could not determine a circulation center . The difficulty arose from the large , sprawling nature of the storm , and by later on May 27 a new center formed , as confirmed by radar imagery and the Hurricane Hunters . That night , the extremely small center made landfall just south of Savannah , Georgia , affecting a very small area with winds of 65 mph ( 100 km / h ) and a minimum pressure of 991 mbar ( hPa ; 29 @.@ 26 inHg ) . Around the time of landfall , Alpha developed a warm core , indicating some tropical characteristics . The storm weakened quickly over land , although it did not dissipate until two days later over the northeast Gulf of Mexico . 
- 
- = = Preparations and impact = = 
- 
- At the time of the first advisory on Alpha , there were small craft warnings from Jacksonville , Florida to Cape May , New Jersey . Gale warnings were posted from Cape Fear , North Carolina to <unk> , Virginia . The interaction between Alpha and the high pressure system to its northeast caused cooler temperatures and gusty winds from Delaware southward . Wave heights reached up to 20 feet ( 6 @.@ 1 m ) along the Virginia <unk> . The storm dropped rainfall along the coast of North Carolina and Virginia , with a maximum of 6 @.@ 97 inches ( 177 mm ) reported in <unk> , North Carolina . While moving slowly off the North Carolina coast , Alpha caused heavy beach erosion , destroyed one house , and threatened the foundation of several other homes in the Outer Banks . Damage totaled over $ 50 @,@ 000 ( 1972 USD ) . 
- While the storm was traveling over the western Atlantic Ocean , the storm produced a large area of rough seas , which was considered the greatest threat from the storm . In northeastern Florida , police officers were stationed to ensure people did not swim in the dangerous seas . The high waves also halted work to <unk> a harbor at the <unk> Naval Station . Two people drowned after the storm dissipated , when surf was still turbulent . Tides along the Georgia coast reached 2 to 3 feet ( 0 @.@ 61 to 0 @.@ 91 m ) above normal , which caused some flooding and beach erosion . Wind gusts reached 58 mph ( 93 km / h ) on Saint <unk> Island . The winds knocked down trees and power lines , leaving some people without power in eastern Georgia . Damage was minor but widespread in the state , estimated at over $ 50 @,@ 000 ( 1972 USD ) . Moderate rains spread in coastal areas from South Carolina through southern Florida . 
- The 1972 hurricane season was the first year in which quasi @-@ tropical cyclones were named , by using the NATO phonetic alphabet . At the time , the names were applied to large subtropical cyclones and small <unk> , both of which were later combined into one category . 
- 
- 
- = Barbarian II : The Dungeon of Drax = 
- 
- Barbarian II : The Dungeon of Drax is a video game first published in 1988 for various home computers . It was also released as Axe of Rage in North America . The game is the sequel to Barbarian : The Ultimate Warrior ( Death Sword in North America ) , which was published in 1987 . In Barbarian II , the player controls a princess or barbarian character , exploring the game world to locate and defeat an evil wizard . The game 's plot is an extension of its predecessor , although the gameplay is different . While the first game offers two players the opportunity for virtual head @-@ to @-@ head combat , the second is solely a single @-@ player adventure with fewer fighting moves . 
- Palace Software , the developer of the two Barbarian games , marketed the sequel with the same strategy they used for the first game . They hired Maria Whittaker , a model known for her <unk> work , to pose on the cover and posters as the princess in the game , attempting to recapture the controversy that had boosted sales . Barbarian II received a mixed critical reception . Reviewers were split in their opinions over whether the game was a refreshing and <unk> adventure , or a boring and lonely <unk> through a confusing digital world . 
- 
- = = Gameplay = = 
- 
- Barbarian II : The Dungeon of Drax is an action video game released in 1988 for various personal computer platforms , such as Commodore 64 and <unk> @-@ <unk> . It is the sequel to Barbarian : The Ultimate Warrior ( released in 1987 ) , which offers sword fighting action to one or two players . Unlike its predecessor , Barbarian II features only a single @-@ player mode , in which the player assumes the role of either sword @-@ wielding Princess Mariana or the titular savage , who is armed with a <unk> . Their common quest is to pursue the evil wizard Drax , who has fled to his dungeon hideout after his defeat in the first game . The player characters battle their way through an inhospitable wasteland , a system of caves , and a dungeon before facing Drax in his inner <unk> for a showdown . 
- Using a joystick or keyboard , the player moves his or her character through Barbarian II 's world . Each of the four stages — wasteland , caves , dungeon , and inner <unk> — is a series of interconnected rooms , populated by monsters , traps , and items . The game displays one room at a time in a flick @-@ screen manner : as the protagonist leaves a room , the screen is updated to display the next . The connections among rooms are disjointed : the exit on the left of one room might be connected to the entrance on the same side of another . A <unk> at the bottom of the interface serves as a directional guide , always pointing to the north . The player directs his or her player through the rooms , seeking the exit to the next stage while avoiding traps and collecting items . 
- The protagonist is also challenged in his or her quest by 20 types of creatures . By moving the joystick while pressing its button or by performing the equivalent keyboard commands , the player defends the protagonist with four styles of attacks : a low slash , a high chop , a kick , and a spinning neck chop . The life of the combatants are represented by gauges at the top corners of the screen . Successful attacks on a character reduce its gauge and the character is killed when its life is reduced to zero . A well @-@ timed neck chop ( or the bites of certain monsters ) <unk> the opponent , killing it instantly . <unk> disappear in a puff of smoke when killed , <unk> with a full life gauge in the same room some time later . Although the player character likewise reappears fully <unk> in the room after being killed , he or she can only do so for a limited number of times . This limit ( number of lives ) is represented in the form of <unk> at the top centre of the screen . The protagonist increases his or her number of lives by collecting skulls that are scattered throughout the game world . 
- 
- = = Development = = 
- 
- Barbarian II 's predecessor , Barbarian : The Ultimate Warrior , was a critical and commercial success on its release in 1987 . Reviewers enjoyed the game 's exciting sword fights , and its profile was greatly enhanced by marketing strategies employed by its developer , Palace Software , a subsidiary of media company Palace Group . The developer had engaged Maria Whittaker , a model known for <unk> shoots , to pose on the box covers and posters of the game . The image of bikini @-@ clad Whittaker created a hype that pushed the game beyond the attention of the video game industry , producing a controversy in which members of the public criticised the industry for promoting Barbarian in a <unk> manner . 
- Palace Software repeated the strategy for the sequel , publishing a poster of Whittaker as Princess Mariana , this time in metal bikini armour , and Michael Van <unk> as the barbarian . Steve Brown , creator of the Barbarian games , recalled that the bikini 's chain " snapped a number of times " in comical Carry On fashion during the shoot . Brown was behind the concept of the poster , which was brought to fruition by Lee Gibbons , a commercial artist , over the course of four weeks . The image of the barbarian and princess poised over the fallen body of a large , scaly monster was a <unk> , created by <unk> three photographs — one of each subject — on one another . The creature was a small scale model made of <unk> . After cutting out the subjects from their photos and composing the cut @-@ outs to form a new scene , Gibbons painted the background and added effects such as smoke to form the final image . 
- Brown had filmed sword fights and used the <unk> of the combatants ' movements to produce the animations in Barbarian . For the animations in the sequel , he turned to the works of photographer <unk> Muybridge , who made a name for himself through his series of photographs of animals and humans captured in motion . The movements of Barbarian II 's characters were based on the pictures in Muybridge 's book Human in Motion , which was published in 1901 . The resulting animation was judged very realistic and detailed by several reviewers . 
- First released in August 1988 for the Commodore 64 , Barbarian II was ported to various personal computers . The various versions differed in features , depending on specifications of the platforms . The Amiga version of the game was given several improvements . It has <unk> speech and better graphics in the form of greater details and number of colours . The introductory and disc loading sequences were revamped , featuring animated skeletons with <unk> voices . In contrast , the ZX Spectrum version has <unk> graphics ; the first level comprises black @-@ outlined sprites against pink backgrounds . 
- As with its predecessor , Barbarian II was licensed to <unk> for release in North America . The game was published there under the title Axe of Rage and included a tattoo in its packaging . The North American version featured a different cover art , showing the close @-@ up <unk> of " a screaming <unk> with <unk> tendencies " . Dragon magazine 's reviewers found the cover ugly , and according to Computer Gaming World , a Canadian <unk> refused to sell Axe of Rage because it considered the game 's box cover art crass enough to <unk> customers . Similarly , in the United Kingdom , <unk> chain Boots banned displays of Barbarian II , featuring Whittaker , from their stores . 
- 
- = = Reception = = 
- 
- Barbarian II received praise for its audio , mostly for the versions on more powerful platforms . Reviewers of The Games Machine pointed out that the " most remarkable feature of the <unk> version is its crystal clear sampled effects " . Zzap ! 64 's staff and Computer and Video Games 's Julian <unk> were impressed with the digital <unk> and <unk> produced on the Commodore 64 , and particularly the rendition of a certain monster 's laughter . Mike <unk> of <unk> Amiga claimed the " manic <unk> of the mutant chicken would be enough to send [ the player ] running in the opposite direction " . Although rating the audio @-@ visual components of the Amiga version on par with that for the <unk> <unk> , <unk> and reviewers from The Game Machine felt the <unk> introductory sequence on the Amiga made it stand out ; Tony Horgan of Amiga User International called it the best introduction he has seen in Amiga games . 
- Although reviewers were less than impressed with the graphics on lower @-@ end systems such as the ZX Spectrum , the colours and large detailed sprites on the higher @-@ end platforms won their acclaim . The animation of the characters also captured their attention . Tommy Nash of Your Sinclair hailed it as " first class " , while Paul <unk> of Zzap ! 64 called the sprites " beautifully defined " and " realistically animated " . Similar accolades were given by other reviewers . The staff of The Games Machine were " constantly [ amazed ] " at the designs of the monsters in the game . 
- Several reviewers had a common complaint about the game . As the difference between executing an attack and a movement was the pressing of the joystick button , they were <unk> to find their characters frequently switching directions instead of attacking with a low slash . Their frustration was increased when the protagonist took damage from enemy attacks as he or she <unk> the unintentional command to change facing . Chris Jenkins , however , praised the game for responsive joystick controls in his review for Sinclair User . Paul <unk> of Zero pointed out that the flick @-@ screen presentation could lead to confusing situations in combat as the protagonist retreats across an exit and appears at the other end of the screen . Horgan had another grouse with the combat , moaning the loss of simplicity from the Barbarian series . According to him , while players could enjoy the first Barbarian game without much effort , intense <unk> was needed to defeat the monsters in the second game . 
- The change in combat systems was not the only difference between Barbarian II and its predecessor that influenced reviewers ' opinions about the sequel . The first Barbarian game was enjoyed by reviewers for providing exciting head @-@ to @-@ head action between two players . Barbarian II abandoned this , setting up an <unk> experience for the single player . Jim Douglas , reviewing for Sinclair User , doubted that players who were looking for quick action would appreciate plotting a path through the maze to reach the final goal . Your Sinclair 's Marcus <unk> felt the two genres — slash ' em up and arcade adventure — were " fundamentally incompatible " , agreeing with Douglas that the maze was a distraction . Crash 's reviewers , however , felt the combination of genres made the game interesting . James Price wrote in Amiga Force that the <unk> element and <unk> number of enemies made Barbarian II a far better game than the first , while The Games Machine 's staff said the expanded menagerie of <unk> adequately made up for a reduction in combat moves . 
- In their review for Dragon magazine , the <unk> called Axe of Rage " an <unk> slash ' n hack that 'll please most <unk> . " Zzap ! 64 's reviewers were unanimous in recommending Barbarian II to their readers ; however , two years later in a re @-@ review , the magazine 's staff said the game has aged badly , finding the gameplay " lot more crude and clichéd " and of dubious replay value . Martyn Carroll concurred in his article for <unk> Gamer , 17 years after the game 's release , calling Barbarian II " hugely disappointing " for " [ <unk> ] up almost everything that was great about the first game . " Brown and Palace Software 's co @-@ founder Richard <unk> admitted as much ; they said they had mixed feelings about Barbarian II , thinking the idea of a simple fun game was lost by <unk> too many features into it . 
- When Barbarian II was released , Palace Software went ahead with plans for Barbarian III . For two years , the third game in the series was publicised in gaming magazines . Your Sinclair held a contest for its readers , asking them to submit concepts of gruesome monsters . The winning entry would be implemented in Barbarian III . The Barbarian series of games were , however , brought to a halt in 1991 when Palace Group sold its software subsidiary to fund its expansion into the movie industry . Titus Software bought Palace Software and after reviewing its holdings , cancelled several of its new acquisition 's projects , including Barbarian III . 
- 
- 
- = Van Morrison : Too Late to Stop Now = 
- 
- Van Morrison : Too Late to Stop Now is a biography of musician Van Morrison , written by Steve Turner . It was first published in 1993 in the United States by Penguin Group , and in Great Britain by Bloomsbury Publishing . Turner first met Van Morrison in 1985 ; he interviewed approximately 40 people that knew the subject in his research for the biography . Van Morrison did not think positively of the biography , and multiple newspapers reported he attempted to purchase all of the book 's 25 @,@ 000 copies . He sent a letter to the author asserting the 40 individuals interviewed for the book were not his friends , and accused Turner of " <unk> distortions and <unk> about me personally " . 
- The biography takes a pictorial format , and includes many photographs of Van Morrison and scenes relating to his life , including close @-@ up shots and contact prints . Turner discusses Van Morrison 's youth in Belfast , Northern Ireland , and how early experiences shaped his perceptions . <unk> John Payne was interviewed for the book , and comments on his work with Van Morrison on the album <unk> Weeks . Turner discusses Van Morrison 's reluctance to be interviewed or engage with the public , and includes quotes from the musician about this desire for privacy . The author discusses Van Morrison 's efforts to seek out creativity , and his exploration of spirituality . The book concludes with an assessment of Van Morrison 's experiences with religion . 
- The book was selected as " Editor 's Choice " in the Sunday Age . Publishers Weekly 's review of the book was critical of its " adulatory " tone , but called it a " necessity for fans " , due the inclusion of the discography . A review of the book for The Boston Globe commented " The value of the book is that it has at its heart the same subjects that most of Morrison 's music has featured ... religion and spirituality . " The Palm Beach Post noted that the biography provides " insights and updates as well as a solid background on Morrison 's early life " . The Sunday Times wrote that " the really interesting story here is told by the photographs " . The Irish Times was critical of the book 's text but wrote positively of the included photographs . 
- 
- = = Research and publication = = 
- 
- Prior to his work on the biography , Steve Turner had previously written a book about the music group U2 , titled U2 : Rattle & Hum , and Eric Clapton , titled Conversations With Eric Clapton . Turner first met Van Morrison in 1985 , while writing a book on religion 's place in rock music . Turner characterised himself as someone who is " drawn to artists bothered by spiritual issues " . In his research for the book , the author interviewed approximately 40 people that knew Van Morrison . Turner spoke with Van Morrison , and they discussed the musician 's views on philosophy . Some of the photographs included in the book were obtained by Turner from a guitarist with Van Morrison 's band Them . 
- According to The Sunday People , " though critics said it was an affectionate tribute about a nice man . Van went on to urge fans not to buy it . " According to The Boston Globe , Van Morrison " <unk> the book " . In an interview with The Boston Globe , Turner commented on this : " He considers anything that reveals details of family background or anything like that an invasion of his privacy . He doesn 't believe in biographies , and I do . " When the biography came out in hardcover , Van Morrison sent Turner 36 statements from the book that he called " lies , gross <unk> and <unk> " . Van Morrison told Turner that none of the 40 individuals the author had interviewed for the biography were currently friends of his . The letter from Van Morrison to Turner concluded with : " I am very sorry that you feel you are entitled to earn a living by <unk> distortions and <unk> about me personally . " In his reply letter to Van Morrison , Turner wrote : " You may not think you are ' difficult ' or ' <unk> ' but other people do and have the right to express their opinions . " The Sunday Times reported that " <unk> , Van 's management even considered buying up all 25 @,@ 000 copies of the book to pulp them , " and this was also reported in The Independent . The Evening Times wrote that Van Morrison " reportedly tried to buy all the copies of a biography by Steve Turner . " Representatives for Van Morrison did not confirm whether he attempted to purchase the 25 @,@ 000 copies of the biography , and confirmed that " representatives of the singer discussed various possibilities " . Van Morrison 's manager , Chris O 'Donnell , said of the musician : " He is not happy about books , period . He is an artist and stands up for himself – he doesn 't want his private life raked over . " 
- Steve Turner appeared on a panel of experts in the 2008 documentary Van Morrison : Under Review 1964 – 1974 . Along with Turner was Johnny Rogan , author of the biographies Van Morrison : A Portrait of the Artist ( 1984 ) and Van Morrison : No Surrender ( 2005 ) . 
- 
- = = Contents = = 
- 
- The introduction to the book includes an analysis by the author of Van Morrison 's skill to use " the stuff of his life " . Turner compares Van Morrison with other musicians of the time period , including Robbie Robertson , Bob Dylan , and Neil Young . The book 's 10 chapters contain a pictorial overview of the musician 's professional work . The book 's chapters are structured according to record releases of the musician . Pictures include images from locations where Van Morrison grew up in Belfast , contact prints from a photo shoot for a cover album with his wife at the time Janet Planet , and archived marketing photographs of a younger Van Morrison . The beginning of the book includes 10 close @-@ up shots of the musician . The book also contains a complete discography of Van Morrison 's work . 
- Turner describes Van Morrison 's early life as George Ivan Morrison on <unk> Street in Belfast . " I 'm definitely Irish " , Van Morrison is quoted as stating in the book . He asserts that Van Morrison was affected by his mother 's religious conversion to the <unk> 's Witnesses when he was a child . Turner states that this experience contributed to his position as an <unk> : " Who else in Belfast had a father who played Jelly Roll Morton records , and a mother who indulged in <unk> <unk> ? " Turner discusses Van Morrison 's musical colleagues , his successes , the break @-@ ups of his various bands , and his efforts to seek out creative expression . The author includes commentary and images from Van Morrison 's first release with the band Them in 1964 , through to his latest album at the time of the book 's publication . 
- Turner interviewed <unk> John Payne for the book , who had sat in on sessions with Van Morrison and later collaborated with him . Payne comments on their work together on the album <unk> Weeks , which also included musicians Connie Kay , Warren Smith , Jay Berliner , and Richard Davis : " Ironically , the image you have when you listen to the album now is of these guys who are all together , and they realise they are creating a monumental work of art . The fact is that it was just another session for them . " Payne states of his observations of Van Morrison 's performance style : " When ( Van ) was on stage , he would look like a space cadet , but then he 'd open his mouth and you would realize that he had channeled everything into the sound of his voice . The rest of it was just a shell that was there for the purpose of producing this noise . " 
- The book notes the artist 's reluctance to be interviewed or engage with the public , and quotes him as saying : " It 's very hard for me to relate to people asking questions that are not only boring but don 't have anything to do with my life ... It 's a waste of time on my part because it drains me from doing what I really want to do , which is just to play music . " Van Morrison describes his perceived musical role : " I just feel I 'm doing the job . My job is to play music and deliver the show ... It 's more emotional for the audience ... what they sort of think you are . " He states that one of his motivations is " ideally to induce states of meditation and ecstasy , as well as to make people think " . 
- The author notes Van Morrison 's dislike of conformity , and quotes him as saying : " I hate organizations . " Of his period in his life of experimentation , Van Morrison comments : " I 'm not searching for anything in particular . I 'm just <unk> in the dark ... for a bit more light . That 's it really . " Turner notes how musical styles including rhythm and blues and Motown influenced the musician . Van Morrison 's exploration of spirituality is discussed in the book , including his experiences with mysticism , Christianity , and <unk> . In the last chapter of the book , Turner comments on Van Morrison 's experiences in religion , writing : " His development of religion as a normal topic of discourse in popular song may turn out to be his most lasting contribution . " 
- 
- = = Reception = = 
- 
- The book was selected as " Editor 's Choice " in the Sunday Age , where Michael Gordon wrote : " This is not the whole story , but it is a well @-@ researched and superbly presented summary of the story so far – a kind of companion to the John Lennon book , ' Imagine ' – including the temper <unk> . Fans may , however , disagree with the closing assessment that Morrison may be running out of themes and ideas to express . " Publishers Weekly was critical of the book 's tone , commenting : " Turner is more adulatory than probing . " The review noted : " A complete discography makes this book a necessity for fans of the Irish Rover . " Writing for The Boston Globe , Thomas C. Palmer Jr. called the book " a coffee @-@ table biography that fills an extensive void , both for those hungry for gossip and for those who have wondered at the source of the creativity in this prolific producer of often stunningly original – if difficult to <unk> – music " . Palmer commented on the book 's value : " The value of the book is that it has at its heart the same subjects that most of Morrison 's music has featured , subtly or otherwise ( but never as <unk> as Dylan in his " Saved " period ) : religion and spirituality . " Robert <unk> reviewed the book for The Sunday Times , and wrote : " Steve Turner has performed his task as a biographer <unk> enough in Too Late To Stop Now , but the really interesting story here is told by the photographs . " <unk> commented : " Thirty years of constant rowing with anybody who has ever tried to get close to him , and a <unk> and <unk> <unk> after religion ( any religion ) have left him looking <unk> , sad and , as Turner has the courage and decency to point out , not as great a musician now as his current reputation would suggest . It 's all there in the pictures . " 
- Bernard <unk> of The Gazette described the book as " more superficial but more positive " than the 2006 biography Van Morrison : No Surrender by Johnny Rogan . In a review of the book for The Palm Beach Post , Lisa McDonough wrote " Steve Turner 's Van Morrison : Too Late To Stop Now is not the final answer on Morrison – only Morrison could give that – but he does provide insights and updates as well as a solid background on Morrison 's early life . " Writing for The Irish Times , John Boland was critical of the book 's text but wrote positively of its images : " Steve Turner 's Van Morrison : Too Late to Stop Now , has a <unk> , <unk> style text and isn 't very <unk> about the great man , but it has some splendid pictures and in its coffee table format is well worth the asking price " . A review in the Herald Sun was critical : " Too Late To Stop Now is more suited to the coffee table than a library shelf . It is browsing material , <unk> with <unk> , album covers , PR shots , posters and prints . The words are mere <unk> , there to hold the pictures in place . " The Herald Sun noted that the author left out critical quotes from individuals that knew Van Morrison , and questioned Turner 's <unk> . 
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- = Music of Chrono Cross = 
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- The Chrono series is a video game franchise developed and published by Square Enix ( formerly Square ) . It began in 1995 with the time travel role @-@ playing video game Chrono Trigger , which spawned two <unk> , Radical Dreamers and Chrono Cross . The music of Chrono Cross was composed by Yasunori Mitsuda , the main composer of Chrono Trigger and Radical Dreamers . Chrono Cross has sparked a soundtrack album , released in 1999 by <unk> and re @-@ released in 2005 by Square Enix , and a greatest hits mini @-@ album , published in 2000 by Square along with the North American release of the game . Radical Dreamers , the music of which heavily inspired the soundtrack of Chrono Cross , has not sparked any albums , though some songs from its soundtrack were reused in Chrono Cross . An album of arrangements of Chrono Cross songs was first announced by Mitsuda in 2005 , and later intended to be released to coincide with the tenth anniversary of the game in 2009 ; its release data was pushed back several times since then . In 2015 , Mitsuda via Square Enix released an album of arranged music from Chrono Trigger and Chrono Cross entitled To Far Away Times to commemorate the 20 @-@ year anniversary of Chrono Trigger . 
- The original soundtrack album has been hailed as an excellent video game music album , while the Chrono Cross Music Selection mini @-@ album has garnered little attention . Songs from the soundtrack have been played at various orchestral concerts , such as the personal arrangements by Mitsuda for the Play ! A Video Game Symphony concert series . Chrono Cross music has also been extensively remixed by fans , and such remixes have been included in both official and unofficial albums . 
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- = = Creation and development = = 
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- Mitsuda returned as the lead composer for 1999 's Chrono Cross after composing its predecessor , Chrono Trigger . After being contacted to compose the score by the game 's director Masato Kato , Mitsuda decided to center his work around old world cultural influences , including Mediterranean , <unk> , Celtic , and <unk> African music . To complement the theme of parallel worlds , he gave the songs for the two worlds of the game , Another and Home , respectively dark and bright moods . Mitsuda was happy to accomplish even half of what he envisioned . Once production concluded , Mitsuda played Chrono Cross to record his impressions and observe how the tracks <unk> with scenes . 
- Radical Dreamers was a 1996 text @-@ based Visual Novel set as a <unk> , or side story , to Chrono Trigger . It was released to complement its predecessor 's plot , and later served as inspiration for Chrono Cross . The music of Radical Dreamers was written by Yasunori Mitsuda . The soundtrack includes several ambient pieces , including the sound of water running in a fountain and wind accompanied by strings . Players can listen to the game 's 15 songs by accessing a hidden menu in one of the game 's scenarios . The soundtrack has never been released as a separate album . 
- Several themes and musical patterns from Radical Dreamers were later adapted for Chrono Cross on the suggestion of Masato Kato ; many appear unchanged except for new instrumentation . Appearing in Chrono Cross are " Gale " , " Frozen Flame " , " <unk> Manor " , " Far Promise ~ Dream Shore " ( as part of " On the Beach of Dreams - Another World " and " The Dream that Time Dreams " ) , " The Girl who <unk> the Stars " , and " <unk> ~ Dream Shore " ( as part of " <unk> Sea " ) . Other entries in the soundtrack contain <unk> from Chrono Trigger and Radical Dreamers . The melody of " Far Promise ~ Dream Shore " features prominently in " The Dream That Time Dreams " and " Voyage - Another World " . 
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- = = Albums = = 
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- = = = Chrono Cross Original Soundtrack = = = 
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- The Chrono Cross Original Soundtrack is a soundtrack of the music from Chrono Cross , composed by Yasunori Mitsuda . The soundtrack spans three discs and 67 tracks , covering a duration of 3 hours . It was published by <unk> on December 18 , 1999 , and reprinted by Square Enix on June 29 , 2005 . 
- <unk> contributor <unk> <unk> played guitar on the beginning and ending themes . <unk> <unk> , selected for the role by Masato Kato , sang the ending song " Radical Dreamers ~ <unk> <unk> ~ " . <unk> Yamazaki , a synthesizer programmer for Square Enix , helped Mitsuda transfer his ideas to the PlayStation 's sound capabilities . The soundtrack has been described as having " some of the most haunting melodies known to man " . The " Home World " tracks from the soundtrack have been termed " emotional " , " driving " and " striking " , while the " Another World " tracks are described as " slower " , " <unk> " , and more " serene " than their counterparts . 
- The soundtrack won the Gold Prize for Sony 's PlayStation Awards of 2000 . It reached # 72 on the Japan Oricon charts on its first print and # 174 when reprinted . It was praised by reviewers such as Patrick <unk> of <unk> , who called it his favorite video game music soundtrack of all time and especially praised the vocals in " Radical Dreamers ~ <unk> <unk> ~ " . This high opinion was echoed by Don <unk> of Square Enix Music Online , who called it " one of Mitsuda 's best , both in and out of [ the ] context " of the game and said that it " surpasses his Chrono Trigger soundtrack " . He singled out " Scars of Time " and " Radical Dreamers " as especially worthy of praise . IGN , in their review of the game , termed the soundtrack " a brilliant score " that " does wonders in stirring the emotional strings of the players as they 're playing through the game " . IGN praised the technical sound quality of the soundtrack as well , though they did comment that for them no specific tracks stood out as especially memorable . In a separate piece about Japanese RPG composers , however , IGN called " Scars of Time " and " <unk> Village - Home World " as two of Mitsuda 's most memorable tracks in naming him the second best out of ten behind <unk> <unk> . 
- Track listing 
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- = = = Chrono Cross Music Selection = = = 
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- Chrono Cross Music Selection is a mini @-@ album of Chrono Cross music that was released in North America exclusively as a bonus for pre @-@ ordering Chrono Cross . The five @-@ track disc was composed by Yasunori Mitsuda , has a length of 15 : 47 and was published by Square along with the game on August 15 , 2000 . Although the release of the album sparked rumors that it would be followed by a North American release of the full soundtrack album , Square Enix has not to date published Chrono Cross <unk> outside Japan . 
- Patrick <unk> enjoyed the album , calling it a " little American <unk> of <unk> music " , but noted that there is no reason to purchase it now that the full soundtrack is just as easy to obtain , especially given its short length . The five tracks on this album were released on the " Original Soundtrack " with three of the tracks renamed . 
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- = = = To Far Away Times : Chrono Trigger & Chrono Cross Arrangement Album = = = 
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- In 2005 , Mitsuda announced a new arranged album of Chrono Cross music was scheduled for release in July of that year . It did not materialize , though at a Play ! A Video Game Symphony concert in May 2006 , he revealed it would be out " within the year " and would feature acoustic music . Later in 2006 , Mitsuda alleged that the album would actually be released in 2007 . In 2008 , Yasunori Mitsuda posted a streaming sample of a track from the upcoming Chrono Cross arranged album . Though no official release date was announced , Mitsuda more than once stated that the album would be planned to coincide with the 10th anniversary of the game 's original release in 2009 . Mitsuda claimed that the album was " nearly done " , but that it may not be possible to release it before the year was out . 
- On a live performance at the Tokyo <unk> in July 2015 commemorating the 20 @-@ year anniversary of Chrono Trigger , Mitsuda announced that the long requested Chrono series arrangement album , entitled To Far Away Times : Chrono Trigger & Chrono Cross Arrangement Album would be released . This was eventually released by Square Enix Music on October 14 , 2015 . The album was released on the Square Enix website . A number of the tracks had vocalists to go on top of the score . All tracks were composed by Yasunori Mitsuda . 
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- = = Legacy = = 
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- Mitsuda has personally arranged versions of music from Chrono Cross for Play ! A Video Game Symphony video game music concerts in 2006 . Music from the game has also been performed in other video game concert tours such as the Video Games Live concert series and in concerts by the <unk> Orchestra . Music from Chrono Trigger and Cross made up one fourth of the music in the Symphonic Fantasies concerts in <unk> in September 2009 which were produced by the creators of the Symphonic Game Music Concert series and conducted by Arnie Roth . The concerts featured a suite of music from both games interspersed together with the songs from Cross comprising " Scars of Time " , " Gale " , " <unk> of Death " , and " Prisoners of Fate " . A suite comprising music from Chrono Trigger and Cross was performed at the Press <unk> <unk> of <unk> 2008 concerts in Tokyo and Shanghai . " Scars of Time " was played at the Fantasy Comes Alive concert in Singapore on April 30 , 2010 . <unk> music for Chrono Cross tracks arranged for both solo guitar and guitar duets has been released by Procyon Studio . 
- Chrono Cross 's soundtrack has been heavily remixed by fans , sparking several albums . These include the officially licensed Time & Space - A Tribute to Yasunori Mitsuda , released by <unk> Studios on October 7 , 2001 and containing 18 remixes over a span of 1 : 00 : 58 , with a second version of the album released on June 17 , 2003 . A related popular album release was Radical Dreamers : <unk> of Fate , an unofficial download @-@ only album release by the remix website OverClocked ReMix on January 5 , 2008 containing 15 remixes of the soundtrack to Radical Dreamers , including remixes of the tracks that later appeared in Chrono Cross . <unk> of remixes also appear on <unk> remix albums , and on English remixing websites such as OverClocked Remix . " Time 's Scar " was featured by NPR in a program about <unk> arranged video game scores in December 2012 . 
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- = M @-@ 47 ( Michigan highway ) = 
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- M @-@ 47 is a north – south state trunkline highway in the US state of Michigan . It runs near Saginaw and Midland in the Tri @-@ Cities area of the Lower Peninsula . The highway runs through suburban and agricultural areas to connect the two cities with the airport in the area . The <unk> section of M @-@ 47 runs along a freeway to the terminus at US Highway 10 ( US 10 ) . M @-@ 47 runs for 14 @.@ <unk> miles ( 23 @.@ <unk> km ) , all of which has been listed as a part of the National Highway System . 
- First designated by July 1 , 1919 along a different routing , M @-@ 47 was extended several times in both directions through the 1920s and 1930s . Two of these extensions replaced sections of M @-@ 111 in the Bay City area . At the apex of its length in the 1950s , M @-@ 47 stretched from Webberville in the south to Bay City State Park in the north . Since Interstate 75 ( I @-@ 75 ) opened in the Tri @-@ Cities area , the northern section of M @-@ 47 was rerouted and truncated as a result of related changes to other highways . The southern end was moved after I @-@ 96 opened in the Lansing area . Further changes into the 1970s shortened M @-@ 47 more , producing the routing in use today . 
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- = = Route description = = 
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- M @-@ 47 starts at M @-@ 46 ( <unk> Road ) east of Shields next to the Oakwood Cemetery . M @-@ 47 is known as Midland Road as it runs slightly northwest to intersect with M @-@ 58 ( State Road ) in Saginaw Charter Township running parallel to the <unk> River . This area is the western edge of Saginaw 's suburbs . Along the road towards Freeland , there are periodic small farms in between small residential subdivisions . In the community of Freeland , M @-@ 47 runs near the <unk> International Airport off Freeland Road . North of town , M @-@ 47 leaves Midland Road and becomes a freeway near <unk> Park . The freeway section of M @-@ 47 runs through rural farm land . There is a diamond interchange with <unk> Road before the terminal interchange at US 10 . 
- As part of its maintenance duties , the Michigan Department of Transportation ( MDOT ) tracks the volume of traffic on the highways it maintains . This number is expressed in terms of annual average daily traffic ( AADT ) , a calculation of the average traffic for a segment of roadway on any average day of the year . In 2009 , the department measured a peak of 19 @,@ <unk> vehicles daily on the stretch north of <unk> Road . The section south of the US 10 interchange had the lowest traffic level at 9 @,@ 315 vehicles AADT . Additionally , entire route of M @-@ 47 has been listed on the National Highway System , a network of roads important to the country 's economy , defense , and mobility . 
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- = = History = = 
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- The original designation of M @-@ 47 was routed from the <unk> – Ingham county line north to St. Charles and then east along M @-@ 46 into Saginaw . A southern extension into Ingham County to end at M @-@ 16 in <unk> was transferred and completed in 1924 . A northern extension to Bay City replaced a section of M @-@ 111 in 1929 , and extended farther to Bay City State Park in 1933 . M @-@ 47 replaced the remainder of M @-@ 111 and extended southward from the park to Bay City in 1938 , creating a " U @-@ turn " in the routing . The southern terminus was moved again in late 1951 or early 1952 to Webberville , although still ending on US 16 . 
- At its greatest extent in the 1950s , the highway extended north from Webberville through <unk> and St. Charles to a junction with M @-@ 46 between Hemlock and Shields . M @-@ 47 turned east along M @-@ 46 , running concurrently with that highway to Saginaw Township . There M @-@ 47 turned north independently to a junction with US 10 , and then ran concurrently with US 10 into Saginaw . Once in the city , M @-@ 47 turned north along Bay Street out of town toward Bay City . M @-@ 47 joined US 23 and followed it north of town . M @-@ 47 then ran separately to the state park before turning south and back into downtown Bay City , ending at the US 23 business loop . 
- Major changes to the routing of M @-@ 47 started in December 1960 when the I @-@ 75 / US 10 / US 23 freeway opened between Saginaw and Bay City . US 10 was rerouted east of Midland to Bay City along the M @-@ 20 freeway . M @-@ 47 was rerouted along the former US 10 from Saginaw to east of Midland using a connector expressway from Freeland north to the US 10 freeway . M @-@ 81 was extended over State Street in Saginaw , and the former routing of M @-@ 47 between Saginaw and Bay City was redesignated as M @-@ 84 . M @-@ 13 also replaced the former US 23 / M @-@ 47 when US 23 was moved to freeways as well . The southern end of M @-@ 47 was changed in 1962 with the completion of I @-@ 96 in the Lansing area . US 16 was replaced by M @-@ 43 , and the southern terminus of M @-@ 47 was moved to exit 122 along I @-@ 96 . This segment of M @-@ 47 south of M @-@ 46 became an extension of M @-@ 52 in 1969 , <unk> M @-@ 47 to Hemlock . The interchange at <unk> Road north of Freeland opened in 1970 , and the expressway segment was upgraded to a full freeway . At the same time , M @-@ 47 was truncated to its current routing , resulting in the elimination of the M @-@ 46 / M @-@ 47 concurrency near Shields . In the end , only about a mile and a half ( 2 @.@ 4 km ) of roadway still bears the M @-@ 47 from before the changes made starting in 1960 , along a section of road that was not originally part of the highway in 1919 . 
- In June 2014 , a construction project began on the interchange with US 10 at the route 's northern terminus . This construction consisted of bridge replacement over US 10 , as well as a new ramp connecting northbound M @-@ 47 to westbound US 10 that , unlike the old ramp , does not conflict with westbound traffic exiting onto southbound M @-@ 47 . 
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- = = = M @-@ 111 = = = 
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- In 1928 , M @-@ 111 was assigned to a route connecting M @-@ 13 ( later signed as US 23 for a time ) north of Bay City to Bay City State Park on Saginaw Bay . The original route consisted of what is today Euclid Avenue . In the early 1930s , a return leg towards Bay City was added to the east of the original route along what is now State Park Road , giving the route an upside @-@ down @-@ U <unk> 1933 , the western leg along Euclid Avenue from Midland Road to Beaver Road was designated as M @-@ 47 . In 1938 , all of M @-@ 111 was re @-@ designated as M @-@ 47 — thus making M @-@ 47 double back to Bay City . 
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- = = Major intersections = = 
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- All exits are unnumbered . 
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- = The Convict ( 1910 film ) = 
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- The Convict is a 1910 American silent short comedy produced by the Thanhouser Company . The film begins with a convict walking down the road , he is spotted and it begins a wild chase with more people becoming involved in the pursuit as it continues . The convict gets in a carriage and <unk> away , successfully <unk> all the pursuers except for a little girl . The convict then runs to the water and takes a boat from another accomplice and the chase continues in water and on land . The convict gets ashore and escapes , taking a car and flees to town . The police are notified and set a trap , but the convict avoids the growing crowd of pursuers until he arrives at the theater . There " the convict " takes a pose under an advertisement and the pursuers understand it was all an advertising ploy , they purchase tickets and go to see the film . The film was released on September 23 , 1910 , it was the first part of a split @-@ reel production that included A Husband 's Jealous Wife . The film was met with positive reviews though the film is presumed lost . 
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- = = Plot = = 
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- Though the film is presumed lost , a synopsis survives in The Moving Picture World from September 24 , 1910 . It states : " The convict is discovered stealing along a country road , glancing about for signs of danger . His stripes are discernible on that portion of his breast and on that portion of his trousers ' leg not covered by the long coat he wears - which cannot conceal the stripes that tell all ; hence his keen watch for <unk> . Still , he does not spy a nearby farmer until too late ; the cry ' prisoner loose ' is raised , and a dozen <unk> make after the unfortunate . Convict meets accomplice who is waiting with carriage ; jumps in the carriage and the pair drives off . <unk> pursuers come up , sight a wagon , all pile within and resume pursuit . Convict jumps from carriage and drops behind <unk> , accomplice driving straight on with the wagon hot after him ; when the wagon has passed him by , convict comes from behind <unk> , crosses road and disappears on other side - but has been seen crossing the road by a small girl who kept out of sight until the convict had disappeared , but who now rushes off to tell of her discovery . Put back on the trail by the little girl , pursuers follow their quarry to the <unk> , where a second accomplice awaits the convict with a <unk> , and into which he jumps and pulls for the other side . Another <unk> sets out in pursuit . One of the pursuers telephones <unk> on the other side of the water to catch convict when he tries to land . " 
- " Race of the <unk> . <unk> shore the convict finds himself <unk> in - pursuers on water and <unk> on land . <unk> for him , pursuers in their efforts to grab his boat <unk> their own ; he gets to shore , where [ the ] accomplice is caught though [ the convict ] escapes . Constable and <unk> chase him to town road , where third accomplice awaits with auto ; they speed off . An automobile too happens along , allows pursuers to use his auto to chase convict 's , and all enter except constable who rushes off to ' phone town police of convict 's coming . <unk> the message , chief of police leaves with <unk> for town end of road , across which they stretch rope and await convict 's auto . As the convict <unk> down Main Street new pursuers spring up at every step . Yet when he reaches the opera house he calmly walks into the entrance and , facing his pursuers , takes a dignified stand beside the billboard on which is printed : ' Latest Moving Picture - Today 's Feature - <unk> ! or The Convict 's Escape - A <unk> Comedy Now <unk> . ' " 
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- = = Production = = 
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- The writer of the scenario is unknown , but it was most likely Lloyd Lonergan . He was an experienced <unk> employed by The New York Evening World while writing scripts for the Thanhouser productions . The plot is a clever advertising scheme employed by a theatre manager to draw patrons and using an elaborate series of events in order to accomplish that effect . Once " the convict " is identified and the chase begins , he receives assistance in prolonging the chase until arriving at the theatre where the crowd purchases tickets for the show . The film director is unknown , but it may have been Barry O 'Neil . Film historian Q. David Bowers does not attribute a cameraman for this production , but at least two possible candidates exist . Blair Smith was the first cameraman of the Thanhouser company , but he was soon joined by Carl Louis Gregory who had years of experience as a still and motion picture photographer . The role of the cameraman was <unk> in 1910 productions . The only role in the cast which is known is for Marie <unk> as the little girl . The other cast credits are unknown , but many 1910 Thanhouser productions are fragmentary . A surviving film still gives the possibility of identifying two actors . 
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- = = Release and reception = = 
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- The split @-@ reel comedy , approximately 1000 feet long with A Husband 's Jealous Wife included , was released on September 23 , 1910 . Advertising for the film was a bit mixed as to whether or not it was a comedy or a drama , stating , " You have no idea as to how <unk> a Thanhouser can twist a story until you see this gripping <unk> - well , perhaps , it isn 't a drama at that - or a comedy even . We hate to tip you off as to <unk> it is . When you see the picture , with its totally <unk> @-@ for climax , you 'll know why ! " Bowers would later term this as more of the <unk> comedy that Edwin Thanhouser said the company would not produce . The film likely had a wide national release , known advertising theatres include Indiana , and Kansas . The film was also shown in Vancouver , Canada . One theater advertisement may have confused this film with another because it lists this film as a western film . 
- Reviews for the film were positive and focused on the novel methods used to attract the audience to the theater . The Moving Picture World stated , " A <unk> picture which becomes more thrilling as it proceeds . The convict is supposed to have escaped and the way he is chased and surrounded <unk> no good for him . But somehow he manages to <unk> the steadily increasing army of pursuers until they are gathered around him , when he calmly shows a motion picture announcement , and the reason for all this melee , in which the whole countryside took part , becomes apparent . " The requirement that the fleeing " convict " be continually assisted as a part of the plot may have been lost on The New York Dramatic Mirror reviewer . The reviewer writes , " Perhaps , in real life , things might not happen so <unk> for the convict unless the carriage , the boat and the automobile were previously arranged for him . Even then , Fate might <unk> have some disagreeable card up her sleeve . In the film , at any rate , everybody bit nicely . They pursued the escaped convict in increasing crowds until he led them to the theatre he was advertising . There they all <unk> bought tickets to the show and , no doubt , enjoyed it hugely . They did if it was as good as the film is . The least interesting sections of the film are the telephone messages exchanged by agitated police officials ; but they set off the <unk> adventures of the convict with <unk> contrast . " Walton of The Moving Picture News identified the set up and assistance to " the convict " and states , " In spite of the Thanhouser folk joining in the racket and the peculiar readiness of carriage and automobile and boat to help the gentleman in a costume , not used in this state for some two years , the audience did not ' catch on . ' When the revelation came , at the door of the New Rochelle picture show , the theatre rang with laughter . We were all ' <unk> ' and we enjoyed it . " 
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- = Max Mosley = 
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- Max Rufus Mosley ( born 13 April 1940 ) is the former president of the Fédération Internationale de l 'Automobile ( FIA ) , a non @-@ profit association that represents the interests of <unk> organisations and car users worldwide . The FIA is also the governing body for Formula One and other international <unk> . 
- A qualified barrister and former amateur racing driver , Mosley was a founder and co @-@ owner of March Engineering , a successful racing car <unk> and Formula One racing team . He dealt with legal and commercial matters for the company between 1969 and 1977 and became its representative at the Formula One Constructors ' Association ( FOCA ) , the body that represents Formula One constructors . Together with Bernie Ecclestone , he represented FOCA at the FIA and in its dealings with race organisers . In 1978 , Mosley became the official legal adviser to FOCA . In this role he and Marco <unk> negotiated the first version of the Concorde Agreement , which settled a long @-@ standing dispute between FOCA and the Fédération Internationale du Sport Automobile ( FISA ) , a commission of the FIA and the then governing body of Formula One . Mosley was elected president of FISA in 1991 and became president of the FIA , FISA 's parent body , in 1993 . Mosley identified his major achievement as FIA President as the promotion of the European New Car Assessment Programme ( Euro NCAP or <unk> ) . He has also promoted increased safety and the use of green technologies in motor racing . In 2008 , stories about his sex life appeared in the British press , along with unfounded allegations regarding Nazi connotations . Mosley successfully sued the newspaper that published the allegations and maintained his position as FIA president . He stood down at the end of his term in 2009 and was replaced by his preferred successor , Jean Todt . 
- Mosley is the youngest son of Sir Oswald Mosley , former leader of the British Union of Fascists ( BUF ) , and Diana Mitford . He was educated in France , Germany and Britain before going on to attend university at Christ Church , Oxford , where he graduated with a degree in physics . He then changed to law and was called to the Bar in 1964 . In his teens and early twenties Mosley was involved with his father 's post @-@ war political party , the Union Movement ( UM ) . He has said that the association of his surname with fascism stopped him from developing his interest in politics further , although he briefly worked for the Conservative Party in the early 1980s . 
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- = = Family and early life = = 
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- His father , Sir Oswald Mosley was a Labour minister , and a Member of Parliament for both the Conservative and Labour parties in the 1920s . By the 1930s , he had left mainstream politics and become the leader of the British Union of Fascists ( BUF ) . His first wife died in 1933 , and in 1936 Sir Oswald married Diana Mitford , in a ceremony in Germany attended by Joseph Goebbels and Adolf Hitler . Max was born in London in April 1940 during the early days of the Second World War to Diana and Oswald . In May , Sir Oswald , who had campaigned for a negotiated peace with Nazi Germany , was interned by the British Authorities under Defence Regulation <unk> , along with most other active fascists in Britain . Lady Mosley was imprisoned a month later . Max and his brother Alexander were not included in this internship and as a result were separated from their parents for the first few years of their lives . In December 1940 , then @-@ prime minister Winston Churchill , asked Home Secretary Herbert Morrison to ensure Lady Mosley was able to see Max regularly . 
- Sir Oswald and Lady Mosley were released from detention at <unk> Holloway on 16 November 1943 , provoking widespread public protests . Max ’ s elder brother was unhappy at his private school . As a result , both children were <unk> at home until the age of 13 . The family moved to a succession of country houses in England . Mosley 's older half @-@ brother Nicholas describes the family , including Sir Oswald ’ s children from his first marriage , spending the summer of 1945 getting the harvest in and shooting at <unk> Farm . In 1950 , the <unk> bought houses in Ireland , and in <unk> , near Paris . They spent the year moving around Europe , spending the spring in France and the autumn and winter in Ireland , where Mosley was keen on riding and hunting . His aunt Nancy Mitford , in letters to Evelyn <unk> , recalled Sir Oswald and his family cruising the Mediterranean Sea on the family yacht . On one such trip they visited Spain and were entertained by Sir Oswald 's friend , General Franco . 
- At the age of 13 Mosley was sent to Stein an der <unk> in Germany for two years , where he learned to speak fluent German . On his return to England he spent a year at <unk> , an independent boarding school in Somerset after which he continued his education in London for two years . He attended Christ Church at Oxford University , graduating with a degree in physics in 1961 . During his time there he was Secretary of the Oxford Union where his father spoke on two occasions , once with Jeremy Thorpe on the other side . In 1960 Mosley introduced his father to Robert <unk> , one of Mosley 's contemporaries at the university , who later wrote Sir Oswald 's biography . <unk> an early ambition to work as a physicist after " establishing that there was no money in it " , Mosley went on to study law at Gray 's Inn in London and qualified as a barrister in 1964 . After a <unk> with Maurice Drake he specialised in patent and trademark law . Northumbria University awarded Mosley an Honorary Doctorate of Civil Law in 2005 . From 1961 to 1964 Mosley was a member of the Territorial Army , Parachute Regiment ( 44th Independent Parachute Brigade Group ) . 
- Mosley , like many of Formula One 's drivers , lives in Monaco . On 9 June 1960 he was married at the Chelsea Register Office to Jean Taylor , the daughter of James Taylor , a policeman from <unk> . In 1970 their first son , Alexander , was born and in 1972 their second son , Patrick . On 5 May 2009 , Alexander , a <unk> , was found dead at his home . He was thirty @-@ nine . At an <unk> on 10 June 2009 the Westminster <unk> declared that he had died due to heroin intoxication . 
- In addition to his full @-@ brother Alexander , Mosley has five older half @-@ siblings . On his father 's side they are Vivien Mosley ( 1921 – 2002 ) , novelist Nicholas Mosley , 3rd Baron <unk> ( born 1923 ) , and Michael Mosley ( 1932 – 2014 ) . On his mother 's side they are merchant banker Jonathan Guinness , 3rd Baron <unk> ( born 1930 ) , and Irish <unk> Desmond Guinness ( born 1931 ) . He is a third cousin of the late Winston Churchill , the grandson of the former British prime minister . 
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- = = Politics = = 
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- From their teens to early twenties , Mosley and his brother were involved with their father 's post @-@ war party , the Union Movement ( UM ) , which advocated a united Europe as its core issue . Trevor <unk> , a central figure in the UM 's Youth Movement , writes of the 16 @-@ year @-@ old Mosley painting the flash and circle symbol on walls in London on the night of the Soviet Union 's invasion of Hungary ( 4 November 1956 ) . The flash and circle was used by both the UM and the pre @-@ war BUF . He also says Mosley organised a couple of large parties as a way " to get in with lively , ordinary , normal young people , girls as well as boys , and attract them to the Movement by showing that we were like them and didn 't go on about Hitler and <unk> , Franco and British <unk> all the time . " Mosley met his future wife Jean at such a party . Mosley and Alexander were photographed posing as Teddy Boys in Notting Hill during the 1958 race riots between Afro @-@ <unk> and local white gangs of Teddy Boys . The following year , they <unk> for their father when he ran as a Union Movement candidate for the nearby Kensington North seat in the 1959 General Election . 
- Mosley has rarely discussed his early political involvement with his father . When his father Oswald died , the London Daily Mail described him as a " much maligned and much misunderstood political giant of his era . " Certainly , his father 's political presence affected his early years but Mosley reflected on this time , " I was born into this rather strange family and then at a certain point you get away from that . " Whilst he has distanced himself from this period of his life , the " misunderstanding has remained and today ... he carries that weight on his shoulders . " 
- In 1961 Mosley was an election agent for the Union Movement , supporting Walter Hesketh as parliamentary candidate for Moss Side . The motor racing journalist Alan Henry describes him as one of his father 's " right @-@ hand men " at the time of a violent incident in 1962 , in which Sir Oswald was knocked down by a mob in London and saved from serious injury by his son 's intervention . As a result of his involvement in this <unk> , Mosley was arrested and charged with threatening behaviour . He was later cleared at Old Street <unk> ' Court on the grounds that he was trying to protect his father . By 1964 , when he began work as a barrister , Mosley was no longer involved in politics . 
- In the early 1980s , Mosley attempted a political career , working for the UK Conservative Party and hoping to become a parliamentary candidate . Bernie Ecclestone 's biographer , Terry Lovell , writes that he gave up this aspiration after being unimpressed by " the calibre of senior party officials " . He also felt his name would be a handicap and has since said " If I had a completely open choice in my life , I would have chosen party politics , but because of my name , that 's impossible . " By the late 1990s he had become a supporter of the UK Labour Party , following a meeting with then Labour Leader John Smith . 
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- = = Racing career = = 
- 
- While Mosley was at University , his wife was given tickets to a motor race at the Silverstone Circuit . The circuit is not far from Oxford , and the couple went out of curiosity . Mosley was attracted by the sport , and when his career as a barrister was bringing in sufficient money , he started racing cars himself . The sport 's indifference to his background appealed to Mosley : 
- There was always a certain amount of trouble [ being the son of Sir Oswald ] until I came into motor racing . And in one of the first races I ever took part in there was a list of people when they put the practice times [ ... ] and I heard somebody say , ' Mosley , Max Mosley , he must be some relation of <unk> <unk> [ e ] <unk> , the <unk> . ' And I thought to myself , ' I 've found a world where they don 't know about Oswald Mosley . ' And it has always been a bit like that in motor racing : nobody gives a damn . 
- At national level in the UK , Mosley competed in over 40 races in 1966 and 1967 ; he won 12 and set several class lap records . In 1968 , he formed the London Racing Team in partnership with driver Chris Lambert to compete in European Formula Two , which at that time was the level of racing just below Formula One . Their cars were prepared by Frank Williams , later a Formula One team owner . It was a dangerous time to race . Mosley 's first Formula Two race was the 1968 <unk> <unk> , at <unk> in which double world champion Jim Clark was killed , and within two years both of Mosley 's 1968 team mates , Piers Courage and Chris Lambert were dead in racing accidents . Mosley 's best result that year was an eighth place at a non @-@ championship race at <unk> . Engine builder Brian Hart says that as a driver , Mosley " might not have been particularly quick , but he was a thinking driver . He kept out of trouble and generally used his head . " [ a ] 
- 
- = = March Engineering = = 
- 
- In 1969 , after two large accidents due to <unk> on his Lotus car , Mosley decided that " it was evident that I wasn 't going to be World Champion " and retired from driving . He was already working with Robin Herd , Alan Rees and Graham <unk> to establish the racing car manufacturer March Engineering where he handled legal and commercial matters . The name March is an acronym based on the initials of the founders ; the ' M ' stands for Mosley . Like the other founders , Mosley put in £ 2 @,@ 500 of capital . His father told him that the company " would certainly go bankrupt , but it would be good training for something serious later on . " 
- Mosley played a key role in <unk> the new outfit . Although March had few resources and limited experience , the firm announced ambitious plans to enter Formula One , the pinnacle of single @-@ seater racing , in 1970 . The team had initially intended to enter a single car , but by the beginning of the season ( partly due to deals made by Mosley ) , the number of March cars entered for their first Formula One race had risen to five . Two of these were run by March 's own in @-@ house works team and the rest by customer teams . Mosley also negotiated sponsorship from tyre maker <unk> and oil <unk> manufacturer <unk> . 
- The new operation was initially successful . In Formula One , March cars won three of their first four races . One of these was a world championship race , the 1970 Spanish Grand Prix , won by reigning world champion Jackie Stewart in a customer car run by Tyrrell Racing . As a result , March finished third in the 1970 Constructors ' Championship . The factory also sold 40 cars to customers in various lower formulae . Despite these successes , the organisation got into financial difficulty almost immediately . The Formula One operation was costing more than the customer car business was making . The March works team 's contract with its lead driver , Chris <unk> , was expensive , and Mosley , in his own words , " tried at every opportunity to get rid of him " . He reasoned that Stewart 's highly competitive customer car was enough to show March in a good light . <unk> stayed to the end of the year , but Mosley succeeded in " restructuring " his contract , saving the company some much @-@ needed money . At the end of the season , Mosley successfully demanded full control of the finances , including the factory run by <unk> , who left shortly afterwards . Mosley and Herd borrowed £ 20 @,@ 000 from relatives and friends to support the company into its second year . According to Lovell the money came from Mosley 's half @-@ brother , Jonathan Guinness . 
- Tyrrell started making its own cars towards the end of 1970 , and March 's 1971 program in Formula One was much reduced , with no recognised front @-@ running driver . The <unk> and <unk> sponsorship was insufficient and Mosley failed to attract a large <unk> for 1971 . <unk> author Mike Lawrence has suggested that the shortfall forced him into short @-@ term deals , which maintained <unk> , but were not in the best long @-@ term interests of the company . Mosley negotiated a deal for the team to use Alfa Romeo engines in a third car , bringing much needed funding . The engines proved <unk> , and his hopes of an ongoing partnership with the Italian automobile manufacturer were not met . Nonetheless , March again finished third in the constructors championship , and works driver Ronnie Peterson , in a <unk> <unk> @-@ powered car , was second in the Drivers ' Championship . March 's financial <unk> continued : the company had lost £ 71 @,@ 000 at the end of 1971 . Mosley and Rees disagreed over how to rectify the situation and Rees left March early in 1972 . 
- March was more successful in selling large numbers of customer cars in the lower formulae . Mosley organised extensive test sessions for the 1971 cars for journalists and drivers , and arranged a successful scheme for drivers to rent cars and engines for the season , rather than buying them outright . <unk> money on a deal to supply <unk> <unk> , then motorsport manager at Ford , with a Formula Two car paid off when <unk> moved to BMW and offered March an exclusive deal in 1972 to use BMW 's Formula Two engine . March cars powered by BMW engines won five of the next 11 European Formula Two championships . However , BMW also put pressure on Herd to concentrate on the Formula Two programme . As a result , he spent less time with the Formula One team , where Mosley started to act as a race engineer . 
- Although March considered quitting Formula One on several occasions , money was always found to support at least one car . <unk> historian Mike Lawrence credits Mosley with pressing for a six @-@ wheeled March to be built as a draw for sponsors , having seen the popularity with fans of Tyrrell 's six @-@ wheeled <unk> . The resulting March 2 @-@ 4 @-@ 0 never competed in Formula One , but generated the required publicity and a <unk> <unk> model was profitable . Mosley spent much of his time negotiating deals for drivers with sponsorship and was also successful in selling Marches to other Formula One teams , such as Williams and <unk> . The cars were rarely frontrunners , although the works team won a single race in both 1975 and 1976 . By the end of 1977 , Mosley was fed up with the struggle to compete in Formula One with no resources and left to work for FOCA full @-@ time , selling his shares in the company to Herd but remaining as a director . March 's involvement in Formula One ended the same year . [ b ] 
- 
- = = Formula One Constructors ' Association = = 
- 
- From 1969 , Mosley was invited to represent March at the Grand Prix Constructors ' Association ( GPCA ) , which negotiated joint deals on behalf of its member teams . Although the new March organisation was not popular with the established teams , Mosley has said that " when they went along to meetings to discuss things such as prize money , they felt they ought to take me along because I was a lawyer " . He was unimpressed with the standard of negotiations : " our side all went in a group because no @-@ one trusted anyone else and all were afraid that someone would break ranks and make a private deal . " In 1971 , British businessman Bernie Ecclestone bought the <unk> team , and Mosley recalls that : 
- Within about 20 minutes of [ Ecclestone ] turning up at the [ GPCA ] meeting , it was apparent that here was someone who knew how many beans made five and after about half an hour he moved round the table to sit next to me , and from then on he and I started operating as a team . Within a very short time , the two of us were doing everything for the GPCA , instead of everyone moving around in a block , and from that developed FOCA . 
- The Formula One Constructors ' Association ( FOCA ) was created in 1974 by Ecclestone , Colin Chapman , Teddy Mayer , Mosley , Ken Tyrrell and Frank Williams . FOCA would represent the commercial interests of the teams at meetings with the Fédération Internationale du Sport Automobile ( FISA ) , motorsport 's world governing body . After leaving March at the end of 1977 , Mosley officially became legal advisor to FOCA , which was led by Ecclestone . In his biography of Ecclestone , Terry Lovell suggests that he appointed Mosley to this role not only because of his legal ability , but also because he " saw in Mosley the necessary diplomatic and political skills that made him perfectly suited to the establishment of the FIA " . The Fédération Internationale de l 'Automobile ( FIA ) was FISA 's parent body , representing car users worldwide . In the same year , Mosley was nominated for a role at the FIA Bureau Internationale de <unk> d 'Automobile . His nomination was blocked by French , Italian and German manufacturers . 
- In the early 1980s , Mosley represented FOCA in the " FISA @-@ FOCA War " , a conflict between FOCA , representing the mainly UK @-@ based independent teams , and FISA , which was supported by the " <unk> " constructors owned by road car manufacturers ( primarily Alfa Romeo , Ferrari and Renault ) . In 1981 , FOCA announced its own World Federation of Motor Sport and ran the non @-@ championship 1981 South African Grand Prix . The staging of this event , with worldwide television coverage , helped persuade Jean @-@ Marie Balestre , the FISA president , that FISA would have to negotiate a settlement with FOCA . As Mosley has since commented : " We were absolutely <unk> . If Balestre could have held the manufacturer 's support for a little bit longer , the constructors would have been on their knees . The outcome would then have been very different . " Mosley helped draw up the Concorde Agreement , a document which resolved the dispute by essentially giving FISA control of the rules and FOCA control of promotion and television rights . The most recent version of the Concorde Agreement expired on 31 December 2007 , and a new one was being discussed , as of 2008 . In 1982 , the year after the first Concorde Agreement was signed , Mosley left his role at FOCA , and Formula One , to work for the Conservative Party . 
- 
- = = FISA presidency = = 
- 
- Mosley returned to motorsport in 1986 , with the support of Ecclestone and Balestre , to become president of the FISA Manufacturers ' Commission , the successor to the Bureau Internationale de <unk> d 'Automobile . That same year , he established Simtek Research , a racing technical consultancy firm , with Nick Wirth , a former March employee . He sold his share of Simtek in 1991 , when elected president of the FISA . According to Lovell , in 1987 Mosley suggested to Balestre that he could deal with his problems with Ecclestone by " <unk> [ ing ] him a member of the establishment " . Later that year Ecclestone was appointed vice @-@ president of the FIA 's promotional affairs , with authority over Formula One and the other motor sports authorised by the FIA . 
- In 1991 , Mosley challenged Balestre for the presidency of FISA . Mosley said that his decision to challenge the Frenchman was prompted by Balestre 's reported intervention on behalf of his <unk> Alain <unk> to ensure that race stewards disqualified Brazilian driver <unk> Senna from the 1989 Japanese Grand Prix . Mosley campaigned on the basis that Balestre , who was also president of the FIA and of the Fédération Française du Sport Automobile , could not effectively manage all these roles together . He also said that no @-@ one challenged Balestre because they were afraid of the consequences and suggested that the FISA President should not interfere with F1 , which could be left to run itself . Mosley won the FISA presidency by 43 votes to 29 ; Balestre remained as FIA president . Mosley resigned a year later , fulfilling a promise made during his election campaign to seek a re @-@ affirmation of his mandate . " I wanted to show people that I do what I say " , he said . " Now they can judge me in a year 's time . " FISA immediately re @-@ elected him . 
- 
- = = FIA presidency = = 
- 
- 
- = = = 1993 – 1997 = = = 
- 
- In 1993 , Mosley agreed with Balestre that the Frenchman would stand down as president of the FIA in Mosley 's favour , in return for the new role of President of the FIA Senate , to be created after Mosley 's election . As well as motorsport , the FIA 's remit includes the interests of motorists worldwide , an area in which Mosley wanted to involve himself : " That is what really interested me : [ in F1 ] you maybe save one life every five years , whereas [ in ] road safety you are talking about thousands of lives " . A challenge to Mosley 's election by Jeffrey Rose , chairman of the British Royal Automobile Club , was withdrawn when it became clear that the majority of voters were already committed to Mosley . The FISA was then merged into the FIA as its sporting arm . 
- After the deaths of drivers <unk> Senna and Roland Ratzenberger at the 1994 San Marino Grand Prix , worldwide media attention focused on the charismatic triple @-@ world champion Senna , rather than Ratzenberger , a virtual unknown driving for the minor Simtek team . Mosley did not go to Senna 's funeral , but attended that of Ratzenberger . In a press conference 10 years later Mosley said , " I went to his funeral because everyone went to Senna 's . I thought it was important that somebody went to his . " In the aftermath of the deaths , and a number of other serious accidents , Mosley announced the formation of the Advisory Expert Group chaired by Professor Sid Watkins , to research and improve safety in motor racing . Watkins , who learned of his new role by hearing Mosley announce it on the radio , has called it a " novel and revolutionary approach " . The resulting changes have included reducing the capacity and power of engines , the use of grooved tyres to reduce <unk> speeds , the introduction of the <unk> device to protect drivers ' necks in accidents , circuit re @-@ design and greatly increased requirements for crash testing of chassis . Mosley was criticised for some of the very rapid changes announced in the immediate aftermath of the deaths at the San Marino race . 
- In 1995 , a deal was signed between Ecclestone and the FIA that passed all of the commercial rights to Formula One to him for 15 years , on the condition that they would return to the FIA at the end of that period . Ecclestone had been building up Formula One as a television package since the early 1990s , investing heavily in new digital television technology . For the duration of the deal , the FIA would receive an index @-@ linked annual fixed royalty , estimated by Lovell at around 15 % . Mosley said " My belief is that I got a better deal than anyone else could have because it was more difficult for Ecclestone to take a hard line with me as we had worked together for so long . " The following year the FIA also passed the rights to all its other directly sanctioned championships and events to Ecclestone , also for 15 years , An attempt to add a 10 @-@ year extension to the F1 contract in return for a share in Ecclestone 's proposed flotation of Formula One was later vetoed by the European Commission . Mosley 's agreement with Ecclestone on TV rights for F1 angered three of the team principals in particular : Ron Dennis ( McLaren ) , Frank Williams ( Williams ) and Ken Tyrrell ( Tyrrell ) , who felt that neither Ecclestone nor the FIA had the right to make such an agreement without the teams . They refused to sign the 1997 Concorde Agreement without increased financial returns and threatened to make a complaint under European Union competition rules . The European Commission was already investigating the FIA ’ s agreement with Ecclestone in what Lovell calls a " highly personal and bitter battle between Max Mosley and [ EU commissioner Karel ] van <unk> " . 
- 
- = = = 1997 – 2001 = = = 
- 
- Mosley was elected to his second term as president of the FIA in October 1997 . Later that year , the EU Commission Directorate @-@ General for Competition made a preliminary decision against Ecclestone and the FIA . The resulting warning letters from van <unk> to the FIA and Ecclestone were leaked and ended the attempt to float F1 ; the FIA won a case against the Commission for the leakage in 1998 . 
- At the same time , a local court in Germany ruled that the television rights to the FIA European Truck Racing Cup ( passed to Ecclestone by the FIA the previous year , along with all other FIA authorised championships ) should be returned to the series organiser , following a complaint from German television company <unk> TV @-@ <unk> . The TV Company argued that Ecclestone and Mosley were in breach of commercial clauses in the Treaty of Rome ; following the court 's decision Mosley appealed the judgement and cancelled the series until further notice . On appeal , the court ruled that the series organiser should be able to sell the television rights to whoever they felt was the best option for coverage and the FIA reinstated the European Truck Racing Cup . Between 1997 and 2000 Mosley repeatedly warned that if any EU decision went against the FIA , the marketing organisations and F1 itself would be moved out of Europe . In 1999 , the EU Commission Directorate @-@ General for Competition issued a Statement of <unk> , listing a number of grievances surrounding the FIA 's dealings with Ecclestone and Formula One . The FIA released the Statement to the media and held a press conference in Brussels <unk> the Commission ’ s case . The Commission argued that a number of commercial agreements could be viewed as anti @-@ competition and invited the FIA and Ecclestone 's companies , <unk> and <unk> , to submit proposals to modify these arrangements . In 2001 , nine months after settlement talks had begun , the parties reached an agreement to amend existing contracts , which included Ecclestone stepping down as the FIA 's vice @-@ president of promotional affairs and the FIA ending all involvement in the commercial activities of Formula One . Mosley came up with an innovative way to dispose of the FIA 's involvement in the commercial activities of Formula One . In order to maintain Ecclestone 's investment to deliver digital television , he proposed extending Ecclestone 's rights for F1 coverage to 100 years from the initial 15 , arguing that a deal of such length could not be anti @-@ competition as it was effectively the same as an outright sale . The Commission agreed with his assessment and in the interest of impartiality , Mosley removed himself from the negotiations , which eventually returned around $ 300 million ( £ 150 million ) . The FIA planned to " put almost all of it into a charitable foundation which will then have the resources to undertake important work on improving safety in motor sport and in road safety " , and thus the FIA Foundation was created in 2001 . In addition , the FIA continued to receive an annual <unk> from the deal , Mosley stated : " Over the <unk> of the contract , and on an annual basis , the sum we have accepted represents billions of dollars . <unk> at from that point of view , it is a huge amount of money . " Lovell compares the figure to extend the rights to 100 years to the £ 600 million <unk> paid for the rights to the 2002 Football World Cup and the £ 1 @.@ 1 billion British Sky Broadcasting paid for a three @-@ year package of English Premier League football . The figure was not entirely comparable however due to the dispute over who actually owned Formula One . Before the settlement with the EU Commission was reached , Mosley feared that the FIA was losing control over the sport following a heated argument with Ecclestone in Paris . Ecclestone argued that he had built Formula One into the entity that it was and the FIA only had rights to designate the event as official . Ecclestone threatened to " do a <unk> earth " if another party were to gain control of the commercial side of Formula One . Mosley came up with the solution in order for the FIA to retain its sporting management role and Ecclestone to retain his commercial role . 
- Over the same period , Mosley was attempting to delay European legislation banning tobacco advertising . At this time all leading Formula One teams carried significant branding from tobacco brands such as <unk> , West , <unk> and <unk> Seven . The Labour party had pledged to ban tobacco advertising in its manifesto ahead of its 1997 General Election victory , supporting a proposed European Union Directive . The Labour Party 's stance on banning tobacco advertising was reinforced following the election by forceful statements from the Health Secretary Frank <unk> and Minister for Public Health Tessa Jowell . Ecclestone appealed " over Jowell 's head " to Jonathan Powell , Tony Blair 's chief of staff , who arranged a meeting with Blair . Ecclestone and Mosley , both Labour Party donors , met Blair on 16 October 1997 . Mosley argued that the proposed legislation was illegal by EU rules , that Formula One needed more time to find alternative sources of funding and that the prompt introduction of a ban would lead to races being held outside Europe , while the coverage , including tobacco <unk> , would still be broadcast into the EU . He also argued that : 
- Motor racing was a world class industry which put Britain at the hi @-@ tech edge . <unk> of tobacco money , Formula One would move abroad at the loss of 50 @,@ 000 jobs , 150 @,@ 000 part @-@ time jobs and £ 900 million of exports . 
- On 4 November the " fiercely anti @-@ tobacco Jowell " argued in Brussels for an exemption for Formula One . Media attention initially focused on Labour <unk> its principles for a " glamour sport " and on the " false trail " of Jowell 's husband 's links to the <unk> Formula One team . On 6 November <unk> from three newspapers <unk> whether Labour had received any donations from Ecclestone ; he had donated £ 1 million in January 1997 . On 11 November Labour promised to return the money on the advice of Sir Patrick <unk> . On 17 November Blair apologised for his government 's mishandling of the affair and stated " the decision to exempt Formula One from tobacco sponsorship was taken two weeks later . It was in response to fears that Britain might lose the industry overseas to Asian countries who were bidding for it . " 
- The revised directive went into force in June 1998 , and banned sponsorship from 2003 , with a further three @-@ year extension for " global sports such as Formula One " . On 5 October 2000 , the directive was successfully overturned in the European Court of Justice on the grounds that it was unlawful . A new Tobacco Advertising Directive took effect in July 2005 ; the Financial Times described Mosley as " furious " that this was a year earlier than provided for under the 1998 directive . As of 2009 , Ferrari is the only F1 team to retain tobacco sponsorship , although the team carries no explicit branding in races because of the European legislation . Although the FIA moved its headquarters out of the EU in 1999 , it returned in 2001 ; all of the F1 teams remain in Europe , and six are in the UK , compared to seven of eleven at the end of 1997 . 
- Asked in a 2003 interview about his most enduring achievement as president of the FIA , Mosley replied : " I think using Formula One to push <unk> Crash @-@ Testing . " The European New Car Assessment Programme ( Euro NCAP ) is a European car safety performance assessment programme that originated with work done by the Transport Research Laboratory for the UK Department for Transport . The FIA became involved in the programme in 1996 , taking a lead in promoting it , and Mosley chaired the body from its launch as Euro NCAP in 1997 to 2004 . Despite what NCAP describes as a " strong negative response " from car manufacturers at first , the initiative has expanded , and NCAP says that there has been a clear increase in the safety of modern cars as a result . The EU commission in 2000 stated that " <unk> had become the single most important mechanism for achieving advances in vehicle safety " and " the most cost effective road safety action available to the EU . " Mosley has continued to promote the matter through his membership of initiatives such as <unk> 21 , the European Commission ’ s policy group aimed at improving the worldwide competitiveness of the European automotive industry . 
- In February 2001 , Mosley announced his intention to stand again for the presidency in October of that year , saying that if successful this third term would be his last . 
- 
- = = = 2001 – 2005 = = = 
- 
- Mosley was elected to his third term as president of the FIA in 2001 . From 2000 , Formula One saw the return of teams partly or wholly owned and operated by major motor manufacturers , who feared that under Ecclestone 's management F1 coverage would go to pay television , reducing the value of their investment . In 2001 , the Grand Prix Manufacturers Association ( <unk> ) announced an alternative world championship , the Grand Prix World Championship to start by 2008 . The <unk> stipulated that the championship should not be regulated by the FIA , which Lovell believes was because the organisation believed Mosley was too close to Ecclestone . 
- In June 2004 , Mosley announced that he would step down from his position in October of that year , one year early , saying " I no longer find it either satisfying or interesting to sit in long meetings [ ... ] I have achieved in this job everything I set out to [ ... ] " . One month later , he rescinded his decision after the FIA Senate called for him to stay on . According to a BBC Sport profile , many <unk> considered that the announcement , and Mosley 's public disagreements with Ecclestone , were " just part of a well crafted plan to strengthen their control over the sport " ; Ron Dennis , the McLaren team principal , suggested that it arose because Mosley 's proposals for Formula One met opposition . In 2004 , Mosley said he felt Ferrari 's then team principal Jean Todt should succeed him as president of the FIA when he stepped down . 
- The 2005 United States Grand Prix was run with only six cars , after the Michelin tyres used by the other 14 cars proved unsafe for the circuit . A proposal involving the addition of a temporary <unk> to slow cars through the fastest corner of the circuit was suggested but rejected by Mosley . He stated his reasons for not agreeing to the <unk> : " Formula One is a dangerous activity and it would be most <unk> to make fundamental changes to a circuit without following tried and tested procedures . What happened was bad but can be put right . This is not true of a fatality . " He continued , " Formula One is a sport which <unk> . It is not entertainment disguised as sport . " Mosley gave three possible solutions for the Michelin runners : to use qualifying tyres but change them whenever necessary on safety grounds , to use a different tyre to be provided by Michelin or to run at reduced speed . These were all rejected by the Michelin @-@ <unk> teams . Paul <unk> , the then @-@ owner of the <unk> team who ran on Bridgestone tyres , was prepared to compromise to accommodate Michelin teams — even though a reduced field would guarantee his team much needed points — and was particularly vocal in his criticism and renewed his calls for Mosley to resign . 
- 
- = = = 2005 – 2009 = = = 
- 
- Mosley was elected unopposed to his fourth term as president of the FIA in 2005 . In recognition of his contribution to road safety and motorsport , Mosley was made a Chevalier dans l ’ <unk> de la Légion d ’ <unk> in 2006 . The Légion d 'honneur ( Legion of Honour ) is France 's highest decoration for outstanding achievements in military or civil life ; a Chevalier ( Knight ) is the fifth class . 
- Continuing a theme of his presidency , in 2006 Mosley called for Formula One manufacturers to develop technology relevant to road cars . In recent years , a large proportion of the enormous budget of Formula One has been spent on the development of very powerful , very high @-@ <unk> engines , which some say have little <unk> to road cars . Mosley has announced a 10 @-@ year freeze on the development of engines , which would allow manufacturers to spend more of their budgets on environmentally friendly technology such as the <unk> Energy Recovery System ( <unk> ) introduced in 2009 . In July 2008 , he sent a letter to the Formula One teams , in which he called for the teams to propose future sporting regulations to address specific issues including reduced fuel consumption . 
- The 2007 Formula One season was dominated by Ferrari 's accusations that the McLaren team had made illegal use of their intellectual property , leading to legal cases in the United Kingdom and Italy . Unlike previous cases , such as the Toyota team 's illegal use of Ferrari intellectual property in 2004 that had been handled by German police , the FIA investigated . They initially found McLaren innocent ; unable to find enough evidence to suggest that anyone other than designer Mike <unk> had seen the information or that the team had used it . Ron Dennis , team principal of McLaren , was unaware at this point that Mosley had been sent personal e @-@ <unk> from Fernando Alonso , stating that the data had been used and seen by others in the team . When Italian police uncovered a series of text messages between McLaren and their spy at Ferrari , the team was hauled in front of the World Motor Sports Council ( WMSC ) once more . This time they were found guilty and eventually fined a gross $ <unk> and excluded from the 2007 constructors ' championship . Later in the year , the Renault team was found guilty by the FIA of possessing McLaren 's intellectual property , but was not punished , as the " FIA 's WMSC decided there was not enough evidence to show the championship had been affected . " In relation to McLaren , triple world champion Jackie Stewart criticised Mosley and stated that other teams did not back McLaren for " fear of repercussions " . TV commentator and newspaper columnist Martin Brundle , a former driver , was among those who criticised the FIA and Mosley for inconsistency and questioned the " energetic manner " in which he felt McLaren was being pursued , suggesting that there was a " witch hunt " against the team . Brundle and The Sunday Times subsequently received a writ for libel before the paper printed a correction . Mosley went on to defend himself of the charges made by Brundle , highlighting that the WMSC originally acquitted McLaren of any wrongdoing , stating : " Concrete evidence of use by McLaren of the Ferrari information was simply not there . " It was only later in the year when " e @-@ <unk> emerged which showed others inside McLaren were indeed aware of the Ferrari information " , that the FIA found the team guilty . 
- At the start of 2008 , Mosley said that he wanted to see through reforms such as budget capping and new technologies like <unk> successfully introduced into Formula One before retiring . In March of that year the News of the World released video footage of Mosley engaged in acts with five <unk> women in a scenario that the paper alleged involved Nazi role @-@ playing ( an allegation that , though dismissed in court as " no genuine basis " , allegedly " ruined " Mosley 's reputation ) . The situation was made more controversial by his father 's association with the Nazis . Mosley admitted " the embarrassment the revelations caused " , but said that there was no Nazi theme involved . He was strongly criticised by former drivers , motor manufacturers and several of the national <unk> bodies who form the FIA . His involvement in several high level motor sport events was cancelled . Public expressions of support were limited . Mosley says that he received much supportive correspondence , and said that he would continue to the end of his current term , which he said would be his last . Mosley 's longtime ally Ecclestone eventually appeared to support Mosley 's removal . 
- Mosley won a vote of confidence at an <unk> General Meeting of the FIA on 3 June 2008 , with 103 votes in support and 55 against , with seven <unk> and four invalid votes . Several clubs , including the <unk> , AAA and <unk> considered withdrawal from the FIA after the decision . Other formerly critical organisations have since said that they will accept the outcome of the vote and now wish to move on . In July 2008 , Mosley won a High Court legal case against the News of the World for invasion of privacy . The presiding judge , Mr Justice <unk> , said : " I see no genuine basis at all for the suggestion that the participants mocked the victims of the Holocaust . " 
- In December 2008 , Mosley said that he still intended to stand down when his term ran out in October 2009 , but would take the final decision in June of that year . Mosley 's close relationship with Ecclestone , the sport 's promoter , was criticised in early 2009 by Sir Jackie Stewart , who suggested that Mosley should resign in favour of a CEO from outside motorsport . 
- In mid @-@ 2009 , the FIA and the newly formed Formula One Teams Association disagreed over the format of rules for the following season . When the entry list for the 2010 championship was announced on 12 June 2009 , the entries of five of the eight FOTA teams remained provisional on their acceptance of the new rules . The next day , the European Automobile Manufacturers Association announced its support for FOTA 's request for " stability , clear rules , a clear and transparent system of governance " and their threat to form a <unk> series from Formula One . The BBC Sport website reported this as an attack on Mosley 's authority and noted that Mosley was expected to stand again for the presidency in 2009 . 
- On 23 June , Mosley said he was considering running for a fifth term as FIA president in October " in light of the attack on my mandate " . However , the following day FOTA and the FIA reached an agreement with Mosley agreeing not to stand for re @-@ election as part of the deal : ' now there is peace ' . Luca di <unk> welcomed Mosley 's decision to stand down and called Mosley a ' dictator ' . Mosley responded by saying that he was still considering his ' options ' and might well stand for re @-@ election in October after all . He later said that he was " under pressure from all over the world " to stand for re @-@ election . On 15 July , Mosley confirmed that he would after all stand down , and again endorsed former Ferrari Executive Director Jean Todt as his successor . Todt went on to win the presidency . 
- 
- = = Honours = = 
- 
- Grande <unk> <unk> ' <unk> al <unk> ( Italy ) 1994 
- <unk> Gold Medal from the Institute of Motor Industry in the year 2000 . 
- Order of <unk> <unk> , 1st degree ( Bulgaria ) , 2000 
- <unk> <unk> <unk> per la <unk> <unk> ( Italy ) , 2001 
- The <unk> <unk> @-@ <unk> , 2001 
- Order of Merit ( Romania ) , 2004 
- <unk> <unk> do Quito ( Ecuador ) , 2005 . 
- National Road Safety Council <unk> , Armenia , 2005 . 
- France : Chevalier of the Légion d 'honneur ( 2006 ) 
- Monaco : Commander of the Order of Saint @-@ Charles ( 27 May 2006 ) 
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- = = <unk> = = 
- 
- Secretary , Oxford Union Society , 1961 . 
- 1964 , Called to the Bar , Gray ’ s Inn . 
- Director of March Cars , 1969 @-@ 79 
- Member of High Level <unk> , <unk> ( Competitive Automotive <unk> System for the 21st century ) 21 , 2005 @-@ 09 
- <unk> at <unk> Aware , 2006 @-@ 2009 
- Member of the Board of Trustees , 2001 @-@ 2014 , Chairman of the Programmes Committee , 2001 @-@ 2012 , FIA Foundation for the Automobile and Society 
- <unk> of Euro NCAP , Global NCAP , <unk> 
- Honorary President of European Parliament Automobile <unk> ' <unk> 
- 
- = = Sex scandal = = 
- 
- In 2008 , Mosley won a court case ( Mosley v News Group Newspapers ) against the News of the World newspaper which had reported his involvement in a sex act involving five <unk> women on the grounds that it had breached his privacy . Justice <unk> ruled that despite some of the attendees wearing German World War 2 uniforms there were no Nazi connotations to the <unk> , a false statement that had been printed by the newspaper . As a result , in 2009 Mosley brought a case ( Mosley v United Kingdom ) against the UK 's privacy laws in the European Court of Human Rights , in a bid to force newspapers to warn people before exposing their private lives so they could have the opportunity to seek a court injunction . The case was rejected by the court on 10 May 2011 as they argued that a " pre @-@ <unk> requirement would inevitably affect political reporting and serious journalism . " 
- In July 2011 , The Daily Telegraph reported that Mosley was financially <unk> the court costs of claimants who may have been subjected to phone hacking by the News of the World . Mosley refused to comment at the time , but he later gave a TV interview to the BBC and telephone interview to Reuters where he confirmed the story . 
- Mosley launched legal action against Google , in an attempt to stop searches from returning web pages which use the photographs from the video used for the News of the World story . On 6 November 2013 in Mosley v <unk> Google a French court sided with Mosley and ordered Google to prevent its search engine from providing links to images of Mosley engaging in sexual activities from the video . The Register suggested the ruling would lead to a Streisand effect , increasing interest in the images , which are still <unk> through other search engines . At the <unk> <unk> , Mosley stated his reasons for pursuing Google : 
- " the fundamental point is that Google could stop this material appearing but they don 't , or they won 't as a matter of principle . My position is that if the search engines - if somebody were to stop the search engines producing the material , the actual sites don 't really matter because without a search engine , nobody will find it , it would just be a few friends of the person who posts it . The really dangerous things are the search engines . " 
- Mosley launched similar legal action against Google in Germany . In January 2014 , the German court also ruled against the American company . In giving its verdict , the court stated , " that the banned pictures of the <unk> severely violate his private sphere . " 
- In an interview with Der <unk> following the judgement , Mosley said : " <unk> speaking Google has got to obey German courts in Germany and French courts in France . But in the end it has to decide whether it wants to live in a democracy . Google behaves like an adolescent <unk> against the establishment . The company has to recognise that it is a part of society and it must accept the responsibility which comes with that . " 
- 
- = = Ancestry = = 
- 
- 
- 
- = 130th Engineer Brigade ( United States ) = 
- 
- The 130th Engineer Brigade is an engineer brigade of the United States Army based in Schofield Barracks , Hawaii . It provides engineering support to the United States Army Pacific command . The brigade specializes in bridging operations . 
- The brigade traces its lineage back to an engineering regiment active during World War II , but the brigade itself did not see action until the mid @-@ 1990s . As a part of the V Corps for most of the Cold War , the brigade was stationed in western Europe for decades as a deterrent to a possible Soviet invasion . It finally saw action during Operation Joint <unk> , providing bridging assistance for the international force in the Bosnia region . Several years later , the brigade was the primary engineering component during the invasion of Iraq in 2003 . With numerous difficulties , the brigade was forced to take on several unexpected missions during its year in Iraq . It saw a second tour in 2005 and a third in 2009 in which it once again was the primary engineering component in the country . The brigade deployed to Afghanistan as the Theater Engineer Brigade in support of Operation Enduring Freedom from 2013 @-@ 2014 . 
- The brigade had a long history of supporting V Corps of United States Army Europe from 1969 until 2007 , during which it was based at Warner Barracks in the Bavarian town of <unk> , Germany . That ended when the brigade was relocated to Hawaii to support United States Army Pacific as part of a major restructuring plan of the US Army . <unk> in 2008 , the brigade is currently at home in Hawaii . 
- 
- = = Organization = = 
- 
- The 130th Engineer Brigade is a subordinate unit of the 8th Theater <unk> Command which is in turn subordinate to United States Army Pacific . It provides engineering assistance to US forces stationed throughout the Pacific Ocean region . 
- The Brigade 's Headquarters and Headquarters Company is stationed at Schofield Barracks and permanently commands two subordinate battalions . The 65th Engineer Battalion and the 84th Engineer Battalion are both located at Schofield Barracks . The total force of the brigade and its subordinate elements is approximately 1 @,@ 600 . As the brigade is modular in nature , it is able to take command and control of more units when deployed . 
- 
- = = History = = 
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- 
- = = = Origins = = = 
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- The 130th Engineer Brigade traces its lineage to the 1303rd Engineer General Service Regiment which saw action in World War II . The regiment was activated on 15 July 1943 at Camp Ellis , Illinois . It was deployed to the European Theatre where it participated in the Battle of Normandy and subsequent invasion of Germany before being transferred to the Pacific after V @-@ E Day . The 1303rd received campaign streamers for Normandy , Northern France , Rhineland , <unk> @-@ <unk> , Central Europe , and the Asian @-@ Pacific theatre . It was deactivated in Japan on 31 January 1946 . 
- The regiment was re @-@ designated as Headquarters and Headquarters Company , 130th Engineer Aviation Brigade in Japan on 8 July 1955 before being activated in September of that year . Only a few months later , this brigade was inactivated on 25 June 1956 without having seen any deployment . 
- On 16 June 1969 , Headquarters and Headquarters Company , 130th Engineer Aviation Brigade was re @-@ designated as Headquarters and Headquarters Company , 130th Engineer Brigade and activated in Pioneer <unk> , Hanau , Germany . The 130th Engineer Brigade was a consolidation of V Corps ' 37th and 11th Engineer Groups into a single unit that would provide more efficient command and control . It was subsequently put under the command of V Corps , as part of the US Army 's force in Europe . The brigade received its shoulder sleeve insignia on 23 September 1969 , and its distinctive unit insignia on 3 November 1969 . Brigade members specially designed these with images alluding to the 1303rd Regiment 's battle honors in World War II using scarlet and white , colors signifying US Army engineer units . 
- The brigade remained in Germany in support of V Corps for almost 25 years , seeing no deployments . It underwent a <unk> of units as several of its battalions were reassigned elsewhere following the end of the Cold War in 1990 , and it gained new battalions from units <unk> elsewhere . Though three of the brigade 's subordinate battalions deployed to support Operation Desert Shield and Operation Desert Storm , the brigade headquarters itself remained in Germany along with the rest of V Corps . The operations were conducted by VII Corps and <unk> Airborne Corps , which had significant engineer assets of their own . Throughout the early 1990s , the brigade would continue to see units come and go from its command as they were transferred from the restructuring 18th Engineer Brigade and the <unk> 7th Engineer Brigade . 
- 
- = = = Operation Joint <unk> = = = 
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- From December 1995 to January 1996 , all units in the brigade , except the 320th Engineer Company ( <unk> ) , deployed to Croatia or Bosnia and Herzegovina in support of Operation Joint <unk> . The 320th Engineer Company deployed their topographic surveying platoon the following year . The 130th Engineer Brigade was tasked with building an assault bridge over the <unk> River . Despite severe flooding conditions around the river and international pressure against such a structure , the brigade sent its units to begin work on the bridge . In December 1995 , the 502nd Engineer Company deployed to <unk> , Croatia and placed the historic ribbon bridge over the river . This bridge , at 2 @,@ 239 feet ( 682 m ) was the longest assault floating bridge in military history . The company operated 24 @-@ hours a day for three months crossing critical traffic in support of Task Force Eagle and NATO 's <unk> Force . The company also assisted in the construction of a second bridge over the <unk> River in <unk> @-@ <unk> . The 502nd Engineer Company redeployed in May 1996 , with a rafting section remaining at <unk> <unk> to support the force restructuring of Task Force Eagle . The 130th Engineer Brigade would go on to construct seven fixed bridges in support of the operation . During the deployment , the brigade was also tasked with creating and maintaining maps and <unk> of the area of operations . It would produce over 300 @,@ 000 such maps . The 130th Engineer Brigade was also tasked with repairing and maintaining much of the Task Force 's infrastructure . 
- The brigade returned to Germany after the operation was complete . Throughout 1998 – 2002 it would train with German engineers , including German units from <unk> and <unk> , as well as the German Engineering School . The brigade also set up marksman competitions with German units , to give US soldiers the chance to earn the German Armed Forces Badge of <unk> , the German Sports Badge , and other <unk> . Over 2 @,@ 500 of these <unk> would be earned by soldiers of the 130th over the years that it served in Germany . It also trained extensively in bridging operations at rivers throughout Germany . In summer of 2000 , the brigade participated in a joint engineering exercise in <unk> with US Navy <unk> and the <unk> Engineer Brigade of the North Carolina Army National Guard . The exercise was the first ever conducted in <unk> and featured numerous training scenarios as well as the construction of a medical clinic . Several other such exercises were conducted in nations throughout Europe including <unk> , Romania , Georgia , Latvia , Bulgaria and Macedonia . They also performed annual humanitarian missions to Poland , working on community projects around the country with the assistance of Polish Armed Forces every September , as a training exercise . 
- 
- = = = Operation Iraqi Freedom = = = 
- 
- The 130th Engineer Brigade mobilized in support of the Global War on Terrorism in 2003 . It was commanded by Colonel Gregg F. Martin . As preparations were being made for Operation Iraqi Freedom , the 130th Engineer Brigade was placed in charge of the largest engineering force in the theater . This included seven different engineering battalions as well as several separate group and company sized elements . Units of the brigade were then deployed to Kuwait in early 2003 , along with much of V Corps ' staff . They would provide command and assistance for the 3rd Infantry Division , 82nd Airborne Division and 101st Airborne Division as they crossed the border to Iraq and attacked to the capitol region of Baghdad from the south . Coupled with the landings of the <unk> Airborne Brigade Combat Team to the north , the operation would see the US Forces surround and destroy Iraqi forces in and around the capitol . 
- The first obstacle facing the brigade was " the berm " , a 10 @-@ kilometer @-@ deep defensive obstacle complex that spanned Iraq 's border with Kuwait . The berm consisted of large tank ditches , <unk> of dirt , electrified fencing , and razor wire . It was decided that the 3rd Infantry Division 's Brigade Combat Teams would breach this berm in eight locations and move through . The 130th Engineer brigade analyzed the <unk> and provided a layout of them to coalition forces . The 130th Engineer Brigade then conducted the actual breaches in late March , using armored combat <unk> to build roads for tracked vehicles , while armored vehicle @-@ launched bridges set up for wheeled vehicles . The 130th worked with <unk> engineers to breach the berm in 12 places , and the 3rd Infantry Division 's Brigade Combat Teams moved though , starting the Iraq War . After combat forces moved through , the brigade and the <unk> then sealed off ten of these breaches to prevent Iraqi forces from using then , leaving two open for US forces that followed from the 82nd and 101st . 
- After the breach , the 130th moved into Iraq . They provided support for the divisions as they advanced along the path to Baghdad . When a large <unk> grounded aviation and large dust clouds became a problem , the brigade repurposed oil for use in dust abatement around airfields , allowing the 101st 's fleet of AH @-@ 64 <unk> to take off and engage Iraqi ground forces on 24 March . <unk> Iraqi units detonated many bridges to slow the 3rd Infantry Division 's advance . The 130th Engineers conducted rapid repairs to allow the brigades to continue to move through . The brigade supported combat elements moving through An <unk> , clearing roads and pathways of debris and obstacles to allow rapid movement through the city . The <unk> Engineer Battalion built the " birthday bridge " – the longest float bridge constructed in a combat theatre with a span of 580 meters – over the <unk> River in <unk> on Saddam Hussein ’ s birthday , 28 April 2003 . 
- After the invasion was over , the brigade was supposed to support the operation with bridging and infrastructure support . During the planning for the invasion , the engineers of the brigade were told that reconstruction in Iraq would be conducted by Department of Defense contractors and Iraqi civilians . They were not originally prepared to conduct major reconstruction efforts . When V Corps became the commanding element for the task force to rebuild Iraq , much of the invasion force , including the 130th Engineer Brigade , was sent to do jobs they had not been originally planned for , as basic utilities and sanitation conditions in Iraq were far below what was expected . Throughout the rest of 2003 and 2004 , the engineers engaged in a large number of initial reconstruction projects on basic utilities , including schools , water treatment plants , waste removal , and the nation 's power grid . It was determined that the scale of these projects was much greater than what the US had expected or what had been encountered in previous <unk> . It was also decided that reconstruction was essential to the coalition since it would help win over the Iraqi people . By May 2003 , the brigade had been completely repurposed . Though originally tasked with demolishing obstacles , building fortifications , and bridging operations , the brigade was tasked entirely with construction projects . This conversion was a difficult process , as the brigade did not have the equipment and specialist units designed to handle reconstruction on a national scale . As more US military reconstruction units arrived in Iraq , the brigade grew to eight battalions and three groups . 
- The 130th Engineer Brigade focused on bridging projects as was its specialty , but it ultimately found itself undertaking numerous different projects throughout the country for much of 2003 and into 2004 . Not all of its units were confined entirely to construction , though . The 502nd Engineer Company also conducted river patrol operations in Baghdad and <unk> . This new mission for the company was essential for force protection , troop transportation , search and cordon operations , and to protect against sabotage on fixed bridges . In September 2003 , with the inactivation of the 38th Engineer Company ( <unk> <unk> Bridge ) , the 502nd Engineer Company became a multi @-@ role bridge company with both float and fixed bridging capabilities and the only active bridge company in <unk> . 
- The 130th was headquartered at <unk> Support Area <unk> for the majority of the deployment . Throughout Operation Iraqi Freedom , the headquarters commanded up to 15 @,@ 000 engineers at one time , conducting missions including bridging , humanitarian assistance , topographic missions , dive missions , <unk> , base construction , river patrols , mine detection , missile removal and many more . Brigade units redeployed to Hanau in late 2003 and early 2004 . Most of the brigade 's soldiers returned to Hanau on 5 February 2004 after numerous logistical delays . Most of the 130th Engineer Brigade 's subordinate units would receive <unk> Unit Commendation for their roles in the invasion of Iraq , though the Brigade 's headquarters did not . 
- 
- = = = Second tour in Iraq = = = 
- 
- The 502nd Engineer Company became the first V Corps company @-@ sized unit to return for a second tour in Iraq when it deployed in September 2004 . The company returned to Hanau in September 2005 . In December 2004 , the 130th Engineer Brigade 's headquarters was informed that it would be deployed back to Iraq the next year . The brigade mobilized and began this new deployment in September 2005 ; it began operating in the country the next month . The 54th Engineer Battalion followed in October . The brigade eventually replaced the 130th , <unk> , and 20th Engineer Brigades , becoming the only engineer brigade operating in the country . By December 2006 , the brigade commanded all engineering formations in Multi @-@ National Corps - Iraq , including engineers from other branches of the US Military , for a total of 3 @,@ 300 soldiers . During the tour they were supported by the <unk> Engineer Command . 
- The 130th Engineers had a variety of reconstruction tasks during their second tour in Iraq . The top priority of the brigade was to " maintain and upgrade lines of communications " to " <unk> uninterrupted ground movement through the area of operations . " This duty also included detection and removal of <unk> <unk> <unk> . The brigade undertook numerous construction projects , primarily in building coalition forward operating bases , but they were tasked with construction projects for the Iraqi army and civilians as well . Many of the units of the brigade were integrated with military from other branches for projects . US Navy and US Marine Corps engineers operated side @-@ by @-@ side with 130th Engineer Brigade soldiers , and though commanders reported a " culture clash " between different branches of service , the soldiers , sailors , and Marines were able to adapt to the situation quickly . 
- The brigade 's Headquarters Company managed the engineer battalions but it also undertook its own missions , including humanitarian missions and public affairs assignments . It was supported by the brigade 's Special Troops Battalion which provided a wide range of duties for the Headquarters . The task of detecting and removing IEDs proved particularly difficult for the brigade , as IEDs were a serious issue for coalition forces and the leading cause of casualties at the time . The 54th Engineer Battalion under the 130th was specially tasked with counter @-@ <unk> operations on the over 300 kilometers of roads that the brigade was responsible for . The <unk> Engineer Battalion was charged with maintaining the power grid throughout Iraq . They were required to assess 200 power stations throughout the country and make repairs to each one individually . The 84th Engineer Battalion was tasked with most construction projects in the Forward Operating <unk> . These projects involved expansion of many of the larger <unk> as smaller ones were shut down and consolidated . One of the main focuses of the Battalion was the expansion of living quarters at Al <unk> Air Base . The <unk> Engineer Battalion was responsible for maintaining the supply yards housing building materials for the rest of the Brigade 's battalions , and to ensure that the materials were dispersed and used as efficiently as possible . The brigade 's mechanical and vehicle maintenance duties were provided by civilian contractors . These contractors also handled transportation of construction vehicles and vehicle parts to the theater . 
- During the year of its deployment in Iraq , the brigade reported progress in numerous areas . Brigade commander Colonel Thomas <unk> reported that " greater than 50 percent " of IEDs found on roads used by the Corps were recognized and disarmed before they could be used against coalition forces . The brigade also cleared 700 kilometers of road from trash and debris and filled more than 600 <unk> caused by explosions . It also completed 1 @,@ 800 mapping projects in the theater . The brigade also finished numerous building projects around Iraq . Soldiers of the 130th Engineer Brigade completed 345 construction projects during the year in the country , including <unk> four bridges and maintaining eight more , well digging projects throughout western Iraq , maintaining the power grid to 25 coalition bases , repairing and expanding services at <unk> throughout the region , and construction of outposts and checkpoints throughout the country 's roadways with the assistance of Iraqi engineers . During the deployment , a total of 15 soldiers from the brigade were killed in action . The 130th Engineer Brigade returned to Hanau from its second tour in Iraq in October 2006 . 
- 
- = = = <unk> in Hawaii = = = 
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- With the US Army forces in Europe seeing <unk> and <unk> , it was announced that V Corps would be eliminated , and the 130th Engineer Brigade would be moved elsewhere . The brigade formally left Hanau with a casing of the unit colors ceremony at Pioneer <unk> on 4 May 2007 . <unk> the unit 's colors was a tradition formally signifying its inactivation and , for all official purposes , the brigade had ceased to be an active Army unit . The brigade headquarters became part of U.S. Army Pacific on 16 June 2007 . 
- The brigade had originally been slated to relocate to Fort Lewis , Washington to replace the 555th Engineer Brigade , which was scheduled to be inactivated . But with the announcement of the <unk> the Army plan in early 2007 , it was decided that no engineer brigades would be inactivated permanently , and the 130th Engineer Brigade would be moved to Hawaii instead . Both the 130th and 555th Engineer Brigades remained on active duty . 
- The brigade was inactive for a year while it was reconstructed in Hawaii . As a part of the transformation of the US Army , the brigade was reorganized into a modular force with new and updated equipment and new personnel . The Brigade stood up <unk> on 27 June 2008 as it neared ready status . On 23 October , the brigade 's colors were formally <unk> at Schofield Barracks , Hawaii . This signified the end of the brigade 's relocation to Hawaii as well as its completion and readiness to take on new missions . 
- The brigade took command of the 6th , 65th and 84th Engineer battalions . 
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- = = = Third tour in Iraq = = = 
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- The brigade was alerted for another deployment to Operation Iraqi Freedom in summer 2009 . There , its missions will once again include construction , route clearance , and training of Iraqi engineers . Since being alerted for deployment , the brigade began conducting vigorous Mission <unk> Exercise ( <unk> ) training at Schofield Barracks . Among this training has been updated strategies for detection and clearance of <unk> <unk> <unk> . The brigade began its deployment on 17 July 2009 , <unk> its colors in <unk> and taking command of construction projects in the area from the 18th Engineer Brigade . 
- The 130th Engineer Brigade returned to Schofield Barracks Hawaii from Iraq on 4 June 2010 . 
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- = = = Theater Security Cooperation Program <unk> = = = 
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- Upon the 130th Engineer Brigade 's return from Iraq in 2010 , the 130th Engineer Brigade served as the Theater Engineer Brigade in the <unk> <unk> . In this capacity , the brigade provided combat engineering , construction engineering and dive operations support to joint and combined partners at more than 30 Theater Security Cooperation Program ( <unk> ) exercises and multiple company sized deployments to Operations New Dawn and Enduring Freedom . 
- The 130th Engineer Brigade served as the Combined Joint Civil Military Operations Task Force ( <unk> ) for <unk> 2011 in the Philippines . For over two months , the brigade headquarters along with platoons from the 84th Engineer Battalion built schools and all @-@ purpose facilities . Meanwhile , the brigade 's subordinate joint partners conducted veterinarian and medical events to help improve the lives of the <unk> people . This event sharpened the brigade 's skills at responding to humanitarian aid and disaster response ( <unk> ) events throughout the region . Upon completion of <unk> , the brigade shifted its training focus from stability operations to major combat operations . Specifically , the brigade initiated a 9 @-@ month train @-@ up plan for its <unk> graded war @-@ fighter with the 2nd Infantry Division in Korea . Through a series of individual and collective training events the brigade <unk> , sharpened and in some cases re @-@ learned the skills necessary to achieve victory in a high @-@ intensity conflict . Through <unk> , 2ID 's War @-@ Path II Exercise , and finally the Full Spectrum Exercise in November 2011 , the 130th Engineer Brigade earned some of the highest marks and accolades ever given to a brigade by the <unk> during a certification war @-@ fighter exercise . Through these series of exercises , the 130th Engineer Brigade helped <unk> , 8th Army , and 2ID refine and improve their most significant <unk> by updating <unk> , task organizations , engineer concepts of operations , and combined arms gap @-@ crossing plans . The 130th Engineer Brigade , along with 8th Army and 2ID remain ready to " Fight Tonight " if called upon . 
- 
- = = = Operation Enduring Freedom = = = 
- 
- The 130th Engineer Brigade Headquarters and Headquarters Company ( <unk> ) , and one of their subordinate battalions , the 65th Engineer Battalion ( Combat Effects ) , deployed in support of Operation Enduring Freedom in September and October 2013 . This marked the brigade headquarters ' first deployment to Afghanistan . After a two @-@ week handover period , the 130th Engineer Brigade officially took over on 2 October 2013 from the 555th Engineer Brigade ( from Joint Base Lewis @-@ <unk> , WA ) , Joint Task Force Triple <unk> , thus becoming Joint Task Force Sapper , overseeing U.S. Army , Navy , and Air Force engineer units across Afghanistan . A direct subordinate element of <unk> Joint Command and U.S. Forces @-@ Afghanistan , <unk> Sapper constituted the fourth largest command in theater , behind only regional commands . The 130th Engineer Brigade became the core of a provisional multi @-@ role brigade headquarters for engineer operations in Afghanistan with seven subordinate battalions , one Naval Mobile Construction Battalion , and one Engineer Prime <unk> Squadron consisting of over 4 @,@ 200 Soldiers , Sailors and <unk> operating in each Regional Command . 
- From September to December 2013 , Joint Task Force Sapper 's mission in theater was " The Theater Engineer Brigade trains , <unk> and advises the ANA Engineers on construction , facility management , and assured mobility capabilities while supporting the <unk> of Coalition Forces through expeditionary construction and assured mobility across the <unk> @-@ A. " 
- From January to May 2014 , Joint Task Force Sapper 's mission in theater was " Joint Task Force Sapper partners with ANA engineers to enable their independent operations and support the redeployment and <unk> of Coalition Forces through expeditionary construction and <unk> across the <unk> @-@ A in order to set the conditions for the resolute support mission . " 
- The 130th Engineer Brigade turned over responsibility as Operation Enduring Freedom 's Theater Engineer Brigade to the 2nd Engineer Brigade ( from Joint Base <unk> @-@ Richardson , AK ) , Joint Task Force <unk> , on 29 May 2014 . The 130th Engineer Brigade returned to Schofield Barracks , Hawaii from Afghanistan on 4 June 2014 . 
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- = = Honors = = 
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- = = = Unit <unk> = = = 
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- = = = Campaign streamers = = = 
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- 
- = Corythosaurus = 
- 
- Corythosaurus / <unk> / is a genus of hadrosaurid " duck @-@ billed " dinosaur from the Upper Cretaceous Period , about 77 – 75 @.@ 7 million years ago . It lived in what is now North America . Its name means " helmet lizard " , derived from Greek <unk> . It was named and described in 1914 by Barnum Brown . Corythosaurus is now thought to be a lambeosaurine , related to <unk> , <unk> , Hypacrosaurus , and Olorotitan . Corythosaurus has an estimated length of 9 metres ( 30 ft ) , and has a skull , including the crest , that is 70 @.@ 8 centimetres ( 27 @.@ 9 in ) tall . 
- Corythosaurus is known from many complete specimens , including the nearly complete holotype found by Brown in 1911 . The holotype skeleton is only missing the last section of the tail , and part of the forelimbs , but was preserved with impressions of polygonal scales . Corythosaurus is known from many skulls with tall crests . The crests resemble the crests of the cassowary and a Corinthian helmet . The most likely function of the crest is thought to be vocalization . As in a trombone , sound waves would travel through many chambers in the crest , and then get amplified when Corythosaurus exhaled . A Corythosaurus specimen has been preserved with its last meal in its chest cavity . Inside the cavity were remains of conifer needles , seeds , twigs , and fruits : Corythosaurus probably fed on all of these . 
- The two species of Corythosaurus are both present in slightly different levels of the Dinosaur Park Formation . Both still co @-@ existed with theropods and other ornithischians , like Daspletosaurus , Brachylophosaurus , Parasaurolophus , Scolosaurus , and Chasmosaurus . 
- 
- = = Discovery and species = = 
- 
- The first specimen , AMNH 5240 , was discovered in 1911 by Barnum Brown in Red Deer River , Alberta , Canada , and secured by him in the Fall of 1912 . As well as an almost complete skeleton , the find was notable because impressions of much of the creature 's skin had also survived . The specimen came from the <unk> River Group of the province . The left or underside of the skeleton was preserved in <unk> clay , making it difficult to expose the skin . The skeleton was articulated , and only missing about the last 0 @.@ 61 metres ( 2 @.@ 0 ft ) of the tail and the forelimbs . Both <unk> and <unk> are preserved in position , but the rest of the forelimbs are gone , except for phalanges and pieces of <unk> , <unk> and radii . Apparently the remaining forelimbs were weathered or eroded away . <unk> of the integument were preserved covering over a large part of the skeletons outlining , and shows the form of the body . Another specimen , AMNH <unk> , was found in 1914 by Brown and Peter <unk> . Both specimens are now housed in the American Museum of Natural History in their original death poses . 
- The type species Corythosaurus casuarius was named by Barnum Brown in 1914 , based on the first specimen collected by him in 1912 . AMNH 5240 is thus the holotype . In 1916 , the original author , Brown , published a more detailed description which was also based on AMNH <unk> , which specimen is therefore the <unk> . Corythosaurus is among many lambeosaurines that possess crests , and it was the crest that lends Corythosaurus its name . The generic name Corythosaurus is derived from Greek <unk> , <unk> , " Corinthian helmet " , and means " <unk> lizard " . The specific name casuarius refers to the cassowary , a bird with a similar skull crest . The full binomial of Corythosaurus casuarius thus means " <unk> @-@ like reptile , with a Corinthian helmet crest " . 
- The two best preserved specimens of Corythosaurus found , by Charles H. Sternberg in 1912 , were lost on 6 December 1916 while being carried to the United Kingdom , during World War One . They were being sent to Arthur Smith Woodward , a paleontologist of the British Museum of Natural History in England , when the ship transporting them was sunk by the German merchant <unk> SMS <unk> in the middle of the ocean . 
- There were formerly up to seven species described , including C. casuarius , C. <unk> Parks 1935 , C. <unk> Parks 1935 , C. <unk> Gilmore 1923 , C. <unk> Parks 1935 , and C. intermedius Parks 1923 . In 1975 Peter Dodson studied the differences between the skulls and crests of different species of lambeosaurine dinosaurs . He found that the differences in size and shape may have actually been related to the gender and age of the animal . Now only one species is recognized for certain , C. casuarius , although C. intermedius has been recognized as valid in some studies . It is based on specimen ROM <unk> , a skull found by Levi Sternberg in 1920 and was named by William Parks in 1923 , who had originally named it <unk> intermedius earlier that year . The specific name of C. intermedius is derived from its apparent intermediate position according to Parks . C. intermedius lived at a slightly later time in the Campanian than C. casuarius , and the two species are not identical , which supported the separation of them in a 2009 study . 
- 
- = = Description = = 
- 
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- = = = Size = = = 
- 
- Benson et al . ( 2012 ) estimated that Corythosaurus has an average length of 9 metres ( 30 ft ) . Richard <unk> Lull 's earlier length estimate , published in 1942 , found a slightly longer total length of 9 @.@ 4 m ( 31 ft ) , a size similar to Lambeosaurus lambei , another Canadian lambeosaurine . In 1962 , Edwin H. <unk> used models of specific dinosaurs , including Corythosaurus , to estimate their weight . The Corythosaurus model used , was modelled by Vincent <unk> after a mounted skeleton , and supervised by Barnum Brown . After testing , it was concluded that the average weight of Corythosaurus was 3 @.@ 82 tonnes ( 3 @.@ 76 long tons ; 4 @.@ 21 short tons ) . More recent size estimates of Corythosaurus , published in 2001 , find the genus to be among the largest hadrosaurids , only smaller than <unk> and Parasaurolophus . The total length of Corythosaurus specimen AMNH 5240 was found to be 8 @.@ 1 m ( 27 ft ) , with a weight of 3 @.@ <unk> tonnes ( 3 @.@ <unk> long tons ; 3 @.@ <unk> short tons ) . 
- <unk> , the skull is much shorter and smaller than that of Edmontosaurus ( formerly Trachodon ) , Kritosaurus , or Saurolophus , but when including its crest , its superficial area is almost as large . 
- 
- = = = <unk> = = = 
- 
- Over twenty skulls have been found from this dinosaur . As with other lambeosaurines , the animal bore a tall , elaborate bony crest atop its skull , which contained the <unk> narial passages . The narial passages extended into the crest , first into separate pockets in the sides , then into a single central chamber and onward into the respiratory system . The skull of the type specimen has no dermal impressions on it . During preservation it was compressed laterally , so now the width is about two @-@ thirds what it would have been in real life . According to Brown , the compression also caused the nasals to shift where they pressed down on the premaxillaries . Because they were pressed on the premaxillaries , the nasals would have closed the nares . Apart from the compression , the skull appears to be normal . Contrary to what Brown assumed , the areas concerned were fully part of the praemaxillae . 
- The crests of Corythosaurus resemble that of a cassowary , or a Corinthian helmet . They are formed by a combination of the praemaxillae , nasals , prefrontals and frontals , as in Saurolophus , but instead of projecting backwards as a spine , they rise up to make the highest point above the orbit . The two halves of the crest are separated by a median <unk> . In front of the orbit , the crest is made of thick bone . 
- The nasals make up most of the crest . Brown assumed that they extended from the beaks ' tip to the highest spot along the crest and that , unlike those in other genera , the nasals meet in the centre and are not separated in front by an ascending premaxillary process . Brown however , mistook the praemaxillae for the nasals ; the snout is actually largely formed by them and they do separate the nasals . Brown also thought that on the top and back of the crest , the whole external face is covered by the frontals . Again he made a mistake : what he assumed to be the frontals are in fact the nasals . The nasals end at the back of the <unk> in a hooked , short process . The prefrontals also make up part of the crest ; however , Brown mistook the lower upper branch of the <unk> for the prefrontal . The actual prefrontal , triangular in shape , is located at the side of the crest base ; it was by Brown seen as a part of the frontal . The real frontals , largely internal to the crest base structure , are not visible from the side . 
- The mouth of the holotype of Corythosaurus is narrow . The praemaxillae each form two long folds . The folds <unk> air passages extending the narial passages to the front of the snout . There they end in narrow openings , sometimes called " pseudonares " , false bony nostrils . These were mistaken by Brown for the real nares or nostrils . These are actually situated inside the crest , above the eye <unk> . As in Saurolophus , the expanded portion of the premaxillary in front of the <unk> ' opening is <unk> : by comparison , in Kritosaurus , the bill is short , and the pseudonares extend far forward . At the end of the Corythosaurus bill , the two pseudonares unite into one . Because of his incorrect identification , Brown assumed that the holotype 's inferior process of the premaxillary was shorter than in Kritosaurus and Saurolophus , and that the process does not unite with the <unk> , another difference from those genera . The <unk> actually does touch the <unk> and extends to the rear until well behind the eye <unk> . 
- The lower jaw of the holotype is 66 @.@ 9 centimetres ( 26 @.@ 3 in ) long , and 10 centimetres ( 3 @.@ 9 in ) deep . The total length of the crest from the beak to the uppermost tip of the type specimen is 83 @.@ 7 centimetres ( 33 @.@ 0 in ) , its total length is 81 @.@ 2 centimetres ( 32 @.@ 0 in ) , and height 70 @.@ 8 centimetres ( 27 @.@ 9 in ) . 
- 
- = = = Soft tissue = = = 
- 
- In the holotype of C. casuarius , the sides and tail of the body are covered in scales of several types . <unk> <unk> scales , covered in small <unk> , vary in size over the body . <unk> <unk> @-@ like scales are only preserved on a fold of skin preserved on the back of the tibia , but which was probably from the bottom of the belly , rather than the leg . <unk> the polygonal scales of C. casuarius are <unk> scales , arranged close together in rows . <unk> tendons are present on all the vertebrae , except for those in the cervical region . On no vertebrae do the tendons extend below the transverse processes . Each <unk> is flattened at its origin , and <unk> <unk> in the central rod , ending at a rounded point . 
- Aside from those found on Corythosaurus casuarius , extensive skin impressions have been found on Edmontosaurus <unk> and notable integument has also been found on Brachylophosaurus <unk> , Gryposaurus <unk> , Parasaurolophus <unk> , Lambeosaurus <unk> , L. lambei , Saurolophus <unk> and on unidentified ornithopods . Of these , L. lambei , C. casuarius , G. <unk> , P. <unk> , and S. <unk> have preserved polygonal scales . The scales on L. lambei , S. <unk> and C. casuarius are all similar . Corythosaurus is one of very few hadrosaurids which have preserved skin impressions on the hind limbs and feet . A study in 2013 showed that amongst hadrosaurids , Saurolophus <unk> preserved the best and most complete foot and limb integument , although other species like S. <unk> , Edmontosaurus <unk> and Lambeosaurus lambei ( = L. <unk> ) share a fair amount of preserved tissue on those regions . 
- It was once thought that this dinosaur lived mostly in the water , due to the appearance of <unk> hands and feet . However , it was later discovered that the so @-@ called " <unk> " were in fact <unk> padding , much like that found on many modern mammals . 
- 
- = = = <unk> characteristics = = = 
- 
- A set of characters were indicated by Barnum Brown in 1914 to distinguish Corythosaurus from all other hadrosaurids from Alberta : a comparatively short skull with a high helmet @-@ like crest formed by the nasals , prefrontals and frontals ; the nasals not being separated in front by the premaxillaries ; a narrow beak , with an expansion in front of an elongated <unk> ; and a small narial opening . 
- In 1916 , Brown expanded the character set to include more features ; in the revised version : a comparatively short skull with a high helmet @-@ like crest formed by nasals , prefrontals and frontals ; the nasals not being separated in front by premaxillaries ; a narrow beak , expanded section in front of the elongated nares ; a small narial opening ; a <unk> formula of 15 <unk> , 19 <unk> , 8 <unk> , and 61 + <unk> ; possession of dorsal spines of a medium height ; high anterior caudal spines ; long chevrons ; long <unk> , possessing a blade of medium width ; a radius considerably longer than humerus ; comparatively short <unk> ; an <unk> <unk> <unk> ; a long <unk> with a foot @-@ like terminal expansion ; a <unk> with an anterior blade that is short and broadly expanded at end ; a <unk> that is longer than the tibia ; the phalanges of <unk> are short ; that the integument over the sides and tail composed of polygonal <unk> scales without pattern but graded in size in different parts of the body ; and a belly with longitudinal rows of large conical <unk> @-@ like scales separated by uniformly large polygonal tubercles . Again , the presumed traits of the snout are incorrect because Brown confused the praemaxillae with the nasal bones and the nasal bones with the frontals . Most of the <unk> traits are today known to be shared with other lambeosaurines . 
- 
- = = Classification = = 
- 
- Originally , Brown referred to Corythosaurus as a member of the family <unk> ( now <unk> ) . Inside <unk> were the subfamilies <unk> and <unk> . Brown classified <unk> , Trachodon , <unk> , and Kritosaurus in <unk> , and Corythosaurus , <unk> , and Saurolophus in <unk> . 
- Later , Brown revised the phylogeny of Corythosaurus , and found that it was closely related , and possibly ancestral to Hypacrosaurus . The only differences he found between them were the development of the vertebrae , and the proportions of the limbs . During a study of <unk> <unk> in the 1920s , Alfred Sherwood <unk> proposed that the two orders of dinosaurs might have evolved separately , and that birds , based on the shape and proportions of their <unk> might truly be specialized ornithischians . He used both <unk> and Corythosaurus as a base model to analyze which theory is more likely true . He found that even though birds are thought of as <unk> , it is very plausible for them to have evolved their specific pelvic <unk> and anatomy if they evolved from <unk> like Corythosaurus . However , even though Corythosaurus does resemble modern birds in pelvic structure more than <unk> , birds are now thought to have descended from the latter . 
- Corythosaurus is currently classified as a hadrosaurid , in the subfamily <unk> . It is related to other hadrosaurs such as Hypacrosaurus , Lambeosaurus and Olorotitan , with the exception of Olorotitan they all share similar looking skulls and crests . However , research published in 2003 has suggested that even though it possesses a unique crest , Olorotitan is Corythosaurus closest known relative . Benson et al . ( 2012 ) found that Corythosaurus was closely related to <unk> , <unk> , and Hypacrosaurus , and said that they formed a group of fan @-@ crested lambeosaurines . 
- In 2014 , a study including the description of <unk> was published in the journal <unk> <unk> . The study included an almost complete cladogram of <unk> relationships , including Corythosaurus as the most derived lambeosaurine , as sister taxon to Hypacrosaurus . The below cladogram is a simplified version including only <unk> . 
- 
- = = <unk> = = 
- 
- Comparisons between the <unk> rings of Corythosaurus and modern birds and reptiles suggest that it may have been <unk> , active throughout the day at short intervals . The sense of hearing in hadrosaurids , specifically such as <unk> , also seems to have been greatly developed because of an elongated <unk> . The presence of a thin <unk> ( an ear bone that is rod @-@ like in reptiles ) , combined with a large <unk> implies the existence of a sensitive middle ear . It is possible that hadrosaurid ears are sensitive enough to detect as much sound as a modern <unk> . 
- 
- = = = <unk> function = = = 
- 
- The internal structures of the crest of Corythosaurus are quite complex , making possible a call that could be used for warning or attracting a mate . <unk> passageways of Corythosaurus , as well as Hypacrosaurus and Lambeosaurus are S @-@ shaped , with Parasaurolophus only possessing U @-@ shaped tubes . Any vocalization would travel through these elaborate chambers , and probably get amplified . Scientists speculate that Corythosaurus could make loud , low pitched cries " like a wind or brass instrument " , such as a trombone . The sounds could serve to alert other Corythosaurus to the presence of food or a potential threat from a predator . The nasal passages emit low @-@ frequency sounds when Corythosaurus exhaled . The individual crests would produce different sounds , so it is likely that each species of lambeosaurine would have had a unique sound . However , even though the range for different lambeosaurine nasal passages vary , they all probable made low @-@ pitched sounds . This might be because low sounds ( below 400 Hz ) travel a set distance in any environment , while higher ( above 400 Hz ) sounds have a larger spread in the distance travelled . 
- When they were first described , crested hadrosaurs were thought to be aquatic , an assessment based incorrectly on <unk> that is now known to be padding . The theory was that the animals could swim deep in the water , and use the crest to store air to breath . However , it has now been proven that the crest did not have any holes in the end , and the water pressure at even 3 metres ( 9 @.@ 8 ft ) would be too great for the lungs to be able to <unk> . 
- 
- = = = Growth = = = 
- 
- Corythosaurus casuarius is one of a few lambeosaurines , along with Lambeosaurus lambei , Hypacrosaurus <unk> and H. <unk> , to have had surviving <unk> juveniles assigned to it . Juveniles are harder to assign to species , because at a young age they lack the distinctive larger crests of adults . As they age , lambeosaurine crests tend to grow and become more prominent come maturity . In the Dinosaur Park Formation , over fifty articulated specimens have been found , coming from many different genera . Among them , juveniles are hard to identify at the species level . Earlier , four genera and thirteen species were recognized from the formation 's area , when paleontologists used differences in size and crest shape to differentiate taxa . The smallest specimens were identified as Tetragonosaurus , now seen as a synonym of <unk> , and the largest skeletons were called either Corythosaurus or Lambeosaurus ; an adult was identified as Parasaurolophus . Small lambeosaurines from the Horseshoe Canyon Formation were referred to <unk> . 
- Corythosaurus started developing its crest when half the size of adults , but Parasaurolophus juveniles grew crests when only 25 % as long as adults . <unk> Corythosaurus , along with adults , had a premaxilla @-@ nasal <unk> . Young and adult Corythosaurus are similar to Lambeosaurus and Hypacrosaurus but <unk> to Parasaurolophus in that the <unk> of the skull are <unk> , and not smooth and straight . This feature helps differentiate <unk> from <unk> . Generally , the crests of juveniles of lambeosaurines like Corythosaurus , Lambeosaurus , Hypacrosaurus <unk> , <unk> like Parasaurolophus , and primitive lambeosaurines like <unk> are quite alike , although other features can be used to distinguish them . 
- Work by Dodson ( 1975 ) recognized that there were many less taxa present in Alberta . Tetragonosaurus was found to be juveniles of Corythosaurus or Lambeosaurus . T. <unk> was assigned to Corythosaurus based largely on <unk> information . The only non @-@ <unk> specimen of Tetragonosaurus , assigned to T. <unk> , was found later to be <unk> to Hypacrosaurus , although the holotype of the species was still found to be <unk> to Corythosaurus . 
- 
- = = = Diet = = = 
- 
- Corythosaurus was an ornithopod , and therefore a herbivore . Benson et al . ( 2012 ) realized that the beak of Corythosaurus was shallow and delicate , and concluded that it must have been used to feed upon soft vegetation . Based on the climate of the Late Cretaceous , they guessed that Corythosaurus would have been a selective <unk> , eating only the <unk> fruits and youngest leaves . Corythosaurus specimens have been preserved with its last meal in its chest cavity . Inside the cavity were remains of conifer needles , seeds , twigs , and fruits , meaning that Corythosaurus probably fed on all of these . 
- 
- = = <unk> = = 
- 
- <unk> have been found in the upper Oldman Formation and lower Dinosaur Park Formation of Canada . The Oldman Formation dates to the Campanian , about 77 @.@ 5 to 76 @.@ 5 million years ago , and the Dinosaur Park Formation dates from 76 @.@ 6 to 74 @.@ 8 million years ago . Corythosaurus lived from ~ 77 – 75 @.@ 7 million years ago . In the Dinosaur Park Formation C. casuarius lived from 76 @.@ 6 to 75 @.@ 9 mya , with C. intermedius living from 75 @.@ 8 to 75 @.@ 7 mya . In the Oldman Formation C. casuarius , the only species of Corythosaurus from the deposits , lived about 77 to 76 @.@ 5 mya . The holotype specimen was clearly a <unk> that had floated up on a beach , as <unk> shells , water @-@ worn bones , and a <unk> turtle were preserved all around . Corythosaurus probably lived in a woodland forest , and might have occasionally wandered into swampy areas . 
- A limited fauna is known from the upper section of the Oldman Formation , and Corythosaurus casuarius as well as C. intermedius are among the taxa . Also from the section of the formation are the theropods Daspletosaurus , and <unk> , the hadrosaurids Brachylophosaurus , Gryposaurus and Parasaurolophus , and the <unk> Scolosaurus , and the ceratopsians <unk> and Chasmosaurus . Other genera are known , but do not persist from the upper section of the formation , and therefore are not contemporaries of Corythosaurus . 
- Corythosaurus casuarius is widespread throughout the lower unit of the Dinosaur Park Formation . In it , Corythosaurus was found to be closely associated with the ceratopsid Centrosaurus <unk> . Their associating was found in the Dinosaur Park , Judith River , and <unk> formations , and also in the Wind River Basin and the <unk> County area . Corythosaurus lived alongside numerous other giant herbivores , such as the hadrosaurids Gryposaurus and Parasaurolophus , the ceratopsids Centrosaurus and Chasmosaurus , and <unk> Scolosaurus , <unk> and <unk> in the earliest stages of the formation , <unk> , <unk> and <unk> in the middle age , and <unk> alone in later stages of the formation . Studies of the jaw anatomy and mechanics of these dinosaurs suggests they probably all occupied slightly different ecological niches in order to avoid direct competition for food in such a crowded eco @-@ space . The only large predators known from the same levels of the formation as Corythosaurus are the <unk> <unk> <unk> and an unnamed species of Daspletosaurus . 
- Thomas M. <unk> has observed that Corythosaurus hasn 't been discovered outside of southern Alberta even though it is one of the most abundant <unk> dinosaurs in the region . Large herbivores like the hadrosaurs living in North America during the Late Cretaceous had " remarkably small geographic ranges " despite their large body size and high mobility . This restricted distribution strongly contrasts with modern mammalian <unk> whose large herbivores ' ranges " typical [ ly ] ... span much of a continent . " 
- 
- 
- = Ghost in the Shell : Stand Alone Complex - Solid State Society = 
- 
- Ghost in the Shell : Stand Alone Complex - Solid State Society ( Japanese : 攻殻機動隊 <unk> <unk> <unk> Solid State Society , Hepburn : Kōkaku <unk> <unk> <unk> <unk> Solid State Society ) is a 2006 made @-@ for @-@ television science fiction action spy thriller anime film and is part of the Ghost in the Shell : Stand Alone Complex series based on Masamune Shirow 's manga Ghost in the Shell . It was produced by Production I.G and directed by Kenji <unk> . 
- The film is set in <unk> , two years after the events of 2nd <unk> . Togusa is now the team leader for Public Security Section 9 , which has increased considerably in size . Section 9 deals with a series of complicated incidents , including the assassination of Ka Rum , a former dictator of the Siak Republic , which leads to a terrorist plot using children as vectors for a cybernetic virus . Investigations reveal that a hacker nicknamed the " The Puppeteer " is behind the entire series of events . 
- The film , which had a production budget of 360 million yen , debuted in Japan on <unk> <unk> ! on September 1 , 2006 and it was released in North America in 2007 . A stereoscopic 3D version of the film was released in 2011 . The film received generally positive reviews , but was criticized for being very dialogue heavy and lacking in action . 
- 
- = = Plot = = 
- 
- In <unk> , two years after the events of 2nd <unk> , Public Security Section 9 is investigating a string of mysterious suicides by refugees from the Siak Republic . Chief <unk> conducts a raid to arrest the refugee dictator only to find him already dead . In retaliation , a Siak operative plans a terrorist attack with a <unk> virus . Batou is sent to intercept the Siak operative and encounters Kusanagi , who is conducting her own investigation . Before they can apprehend the operative , he dies while attacking them . Kusanagi takes a case of virus <unk> and warns Batou to stay away from the Solid State Society before leaving . 
- Section 9 operatives develop a theory that a hacker known as the Puppeteer is responsible for Siak agents ' forced suicides and Togusa discovers sixteen kidnapped children who were intended carriers of the virus . All the children are listed as the children of Noble Rot Senior Citizens and Section 9 begins to suspect a larger conspiracy when they are part of a larger body of 20 @,@ 000 children . Soon afterwards , the Puppeteer causes the disappearance of the sixteen children and Batou reveals to Togusa that he believes Kusanagi to be the Puppeteer . Section 9 next <unk> a Siak sniper that is targeting the supposed mastermind of Ka Rum 's assassination . After his capture it is revealed the informant and the target are one in the same . The sniper says that the Puppeteer is a mechanism in the Solid State and cannot be killed . 
- Togusa tracks down one of the missing children , now assigned to an elderly man in the Noble Rot program . As Togusa tries to take the child , the man <unk> and demands the child be left with him as he had named the child as his sole heir . He would rather give his assets to a child off the street and to protect them from abuse than have his assets turned over to the government upon his death . The man immediately dies after warning Togusa not to interfere with the will of the Solid State . Late , Togusa receives a call from the Puppeteer who <unk> his brain and forces him to drive to a cyberbrain <unk> hospital with his daughter . The Puppeteer and Togusa <unk> , and Togusa is given the option to lose his daughter to the Solid State or commit suicide . He chooses suicide but is saved by Kusanagi who then identifies the Puppeteer as a <unk> formed by the collective consciousness of the Noble Rot Senior Citizens located in a welfare center . 
- Kusanagi temporarily rejoins Section 9 and confirms that Ito Munei , an influential politician , was behind the assassination of General Ka Rum . She also confirms that Munei and other politicians use it as a front for a brainwashing facility to create an elite group of pure @-@ <unk> Japanese to take control of the country in the next generation and lead it into Munei 's vision of a new Golden Age . The Solid State decided to eliminate Munei for interfering in its plans , but Munei was ignorant of the origin of the abduction infrastructure . 
- A designer named <unk> Koshiki steps forward , claiming he developed the Solid State system before committing suicide . Kusanagi dives into his cyberbrain and into Koshiki 's trap , allowing him to hack her cyberbrain . The <unk> reveals that he was spread across several <unk> until a collective consciousness emerged and developed into a Solid State , allowing him to move into the society beyond as the vanishing mediator . Later , Batou tells a recovering Kusanagi that the real <unk> Koshiki used a cybernetic body and built the Solid State after he was hired by Munei . Kusanagi does not reveal that the Puppeteer was a fragment of herself , but Batou already knew from being linked to her during the dive . Batou concludes that the ultimate identity of the Puppeteer will remain unknown and that incident will be written off a scandal . 
- 
- = = Cast = = 
- 
- 
- = = Production = = 
- 
- The film was initially hinted as a new anime project collaboration with Bandai <unk> and Production I.G. The film was officially announced by Production I.G at the 2006 Tokyo Anime Fair . Whether the film would be released theatrically , broadcast on television , or released direct @-@ to @-@ DVD was undecided at the time . The film had a production budget of 360 million yen ( equivalent to US $ 4 @.@ 3 million ) . It was produced in Hi @-@ vision format and was made by the same staff that originally made the TV series . 
- The production team used a 3 @-@ D layout system to render the interior shots ahead separately and in advance . The art team was tasked to draw lighting boards to show the position of light sources in the scene to improve the overall quality of the animation . One of the themes in the series was " Motoko Kusanagi 's <unk> " . The team had a difficult time portraying Motoko Kusanagi and her return to Section 9 . Kenji <unk> stated that he felt the characters have obtained " ghosts " of their own and that Kusanagi needed a convincing story in order to return to Section 9 . <unk> <unk> noted that Kusanagi was more going back to her old self rather than showing the new strength she found when she left Section 9 . 
- For the music , Yoko <unk> read the scripts of the film in order to compose music that would <unk> with each scene , rather than composing music ahead of time . Sound Director <unk> Wakabayashi returned to provide music menus , which made up of 70 % of the scores Yoko <unk> composed . 
- As part of the Nissan sponsorship , the movie features two concept cars designed by Nissan . Section 9 drive a white Nissan Sport Concept sports <unk> and seater <unk> <unk> that premiered at the 2005 New York International Auto Show and North American International Auto Show . 
- 
- = = Releases = = 
- 
- The film debuted in Japan on <unk> <unk> ! on September 1 , 2006 . It premiered in North America at the 2007 New York Comic @-@ Con screening from February 23 – 25 , and also featured in 2007 's Fantasia Festival in Canada . The English version was released on July 3 , 2007 . The Limited Edition <unk> contained an additional DVD containing various development interviews and videos and the Solid State Society Original Soundtrack CD . In July 2008 , Solid State Society was released in Blu @-@ Ray alongside the two <unk> The Laughing Man and Individual Eleven in Ghost in the Shell : <unk> Trilogy Box . 
- In November 2010 , a stereoscopic 3D version was announced adding a new opening sequence . The 3D version was released in Tokyo 's <unk> <unk> 9 theater on March 26 , 2011 . The stereoscopic 3D version was released in both normal and deluxe edition on July 22 , 2011 . The normal version contains opening @-@ day <unk> by the staff and cast , film advertisements , and audio commentaries . The deluxe edition titled Ghost in the Shell : Stand Alone Complex Solid State Society <unk> <unk> is packaged in a <unk> Box containing the film in <unk> 3D all the content the normal edition along with three <unk> Days shorts in 3D and one in 2D . 
- A novel adaptation titled Ghost in the Shell : <unk> Solid State Society ( <unk> Solid State Society ) was published by Kodansha and released on April 3 , 2011 . An optical camouflage camera <unk> for iOS was released on September 2 , 2011 . A video game for the Xbox 360 <unk> was developed by <unk> to promote the 3D remake of the film . 
- 
- = = Reception = = 
- 
- Christopher <unk> of IGN gave Solid State Society an " <unk> " score of 8 @.@ 0 out of 10 , stating that it was " A worthwhile watch " . Carl <unk> of Anime News Network gave the film a " B " rating , calling it a " swift @-@ moving futuristic crime film with some clever science @-@ fiction twists and solid action " but criticizing it was " <unk> , confusing and somewhat bloodless . " The film earned a 1 @.@ 4 % rating when it aired in <unk> on October 15 , 2012 . Marcus <unk> of DVD Active gave it a 6 / 10 stating , " Solid State Society isn 't as strong as the first and second season of the anime show but being one feature length story as opposed to lots of very cool and largely great individual episodes offers a more in depth and focused story for the most part and a happy return to the world of Ghost in the Shell " . The film was awarded the <unk> Prize at the 21st Digital Content Grand Prix . The film was featured in the " Late Night Manga to Anime Film Season " hosted by The British Museum . The DVD released ranked No. 1 on Oricon charts on November 23 , 2006 . 
- The 3D version ranked at No. 11 in the Japanese box office chart with a total of $ 285 @,@ 268 from a total of nine theaters . The 3D version won the Movie award for The Japanese Committee of the International 3D Society . 
- 
- 
- = Winston Tunnel = 
- 
- The Winston Tunnel is a railroad tunnel located 9 miles ( 14 @.@ 5 kilometers ) west of Elizabeth , Illinois . 
- The tunnel was completed in 1888 for the Minnesota and Northwestern Railroad , a predecessor to the Chicago Great Western Railway ( CGW ) . The tunnel was located on the CGW main line 152 miles ( 245 kilometers ) west of Chicago in the isolated and hilly <unk> Area of extreme north @-@ western Illinois . 
- In 1972 , four years after the Chicago Great Western was merged into the Chicago and North Western Railway ( C & NW ) , the CGW 's largely redundant trackage in the area , including the Winston Tunnel , was abandoned . It was the third longest railroad tunnel in Illinois at 2 @,@ <unk> feet ( 760 m ) . Two longer ( still active ) tunnels are located on the Canadian National ( ex @-@ Illinois Central ) <unk> <unk> Line , the longest being Tunnel # 2 near Abbot , Illinois which is 6 @,@ <unk> feet ( 2 @,@ 132 m ) long . 
- 
- = = History = = 
- 
- The newly constructed Minnesota and Northwestern Railroad across northern Illinois utilized trackage rights on the Illinois Central Railroad between <unk> , Iowa and Stockton , Illinois in 1886 before construction on its own line through the isolated wilderness could commence . Engineers quickly realized that a tunnel would need to be constructed in order to traverse the rugged landscape . The <unk> , Winston and Company ( for which the tunnel would be named ) and more than 350 laborers worked by hand , digging through the <unk> and unstable shale for nine months starting in the spring of 1887 . The work was <unk> and dangerous , and at least one worker , a thirty @-@ two @-@ year @-@ old Finnish immigrant named John Hill , was killed . When complete , the total cost of the tunnel , $ 600 @,@ 000 , had exceeded expectations . 
- The tunnel proved to be a constant nuisance to the Chicago Great Western and its predecessors . Almost immediately , railroad engineers realized that the unstable nature of shale through which the tunnel was bored , ground water seepage , and the isolated location of the tunnel meant repairs would be frequent and costly . The tunnel was originally <unk> by wooden beams when it opened to rail traffic in January 1888 , but these eventually proved inadequate , to be replaced in 1902 by brick and reinforced concrete . Constant deterioration of the supports meant large @-@ scale reconstruction of the tunnel would be needed again in 1912 , 1918 , 1944 and 1947 . 
- The bore was also improperly <unk> at first . A shaft sunk into the top of the tunnel failed to provide enough fresh air , and the crews of the steam engines would often complain of the intense heat and smoke due to the poor air circulation . <unk> solutions failed to work , and by 1912 the railroad was forced to install a huge fan , powered by a 310 horsepower ( 230 kW ) diesel motor and staffed by operators day and night , to <unk> the tunnel . The <unk> , constructed by 1916 , was abandoned by the CGW with the acquisition of diesel <unk> by 1947 . 
- When the Chicago Great Western was <unk> during World War I , a contingent of Illinois National Guardsmen were assigned to protect the tunnel . 
- The operating <unk> of the tunnel , not to mention the millions of dollars the CGW spent to keep it open , forced the railroad 's management to consider many schemes to rid themselves of the burden . In 1909 , 1951 and again in 1964 ( the same year the North Western and Great Western announced their intentions to merge ) the CGW sought engineering proposals to <unk> their trackage around the bore , to daylight the tunnel , or to completely rebuild and improve it . To the often cash @-@ <unk> Great Western , however , all these plans proved far too expensive . 
- In the end , the 1968 merger with the Chicago and North Western ( C & NW ) sealed the Winston Tunnel 's fate . The Great Western 's main line through northern Illinois closely paralleled the North Western 's own line , but through less densely populated and less commercially active areas . The steep grades of the line and the obvious financial burden of the Winston Tunnel also played a role in the decision to completely abandon the Great Western 's trackage in the area . The C & NW operated its last train through the tunnel in 1971 . <unk> pulled up the tracks the following year . Upon abandonment , the C & NW placed chain @-@ link fences over each bore of the tunnel to keep squatters and other <unk> out . A 1973 attempt to turn the right @-@ of @-@ way through Jo <unk> County , including the Winston Tunnel , into a rail trail , failed when ownership of the land reverted to nearby property owners . 
- 
- = = Present day = = 
- 
- The Winston Tunnel still exists , although in a very isolated area , and in a very deteriorated condition . Nature has reclaimed the right @-@ of @-@ way ; the fan house , unused since the 1940s and severely damaged by the elements , was demolished in early 2007 ; and the eastern bore , located on private property , has been almost completely sealed with earth . 
- The western half , however , was purchased by the Illinois Department of Natural Resources as a " satellite area " of Apple River Canyon State Park . The <unk> installed a new steel gate to replace the chain @-@ link fence covering the western bore , and is developing the area with nature trails and other improvements . However , the tunnel is currently off @-@ limits to general public visitation , as it is a very dangerous place to visit , with the ever @-@ present danger of further collapse and <unk> bite . 
- 
- 
- = Wales national rugby union team = 
- 
- The Wales national rugby union team ( Welsh : <unk> <unk> <unk> <unk> <unk> Cymru ) represent Wales in international rugby union . They compete annually in the Six Nations Championship with England , France , Ireland , Italy and Scotland . Wales have won the Six Nations and its predecessors 26 times outright , one less than England . Wales ' most recent championship win came in 2013 . 
- The governing body , the Welsh Rugby Union ( WRU ) , was established in 1881 , the same year that Wales played their first international against England . Wales ' performances in the Home Nations Championship ( now the Six Nations ) continued to improve , experiencing their first ' golden age ' between 1900 and 1911 . They first played New Zealand , known as the All Blacks , in 1905 , when they defeated them 3 – 0 in a famous match at Cardiff Arms Park . Welsh rugby struggled between the two World Wars , but experienced a second ' golden age ' between 1969 and 1980 when they won eight Five Nations Championships . 
- Wales played in the inaugural Rugby World Cup in 1987 where they achieved their best ever result of third . Following the sport allowing professionalism in 1995 , Wales hosted the 1999 World Cup and , in 2005 , won their first @-@ ever Six Nations Grand Slam . That was the first Grand Slam won by a team playing most of the matches away from home . Wales won two more Grand Slams in 2008 and in 2012 , and in 2011 came fourth in the Rugby World Cup . 
- Their home ground is the Millennium Stadium , currently known for sponsorship reasons as Principality Stadium , completed in 1999 to replace the National Stadium at Cardiff Arms Park . Eight former Welsh players have been inducted into the World Rugby Hall of Fame ; ten were inducted into the International Rugby Hall of Fame prior to its 2014 merger into the World Rugby Hall . 
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- = = History = = 
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- = = = Early years ( 1881 – 1892 ) = = = 
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- Rugby union took root in Wales in 1850 , when Reverend Rowland Williams became Vice @-@ Principal at St David 's College , <unk> , where he introduced the sport . Wales played their first international on 19 February 1881 ; organised by Newport 's Richard <unk> , Wales played against England , losing by seven goals , one drop goal and six tries to nil ( 8 – 0 ) . On 12 March 1881 , the Welsh Rugby Union was formed at The Castle Hotel , <unk> . Two years later , the Home Nation Championship – now the Six Nations Championship – was first played and Wales did not register a win . However , rugby in Wales developed and , by the 1890s , the Welsh had developed the four three @-@ quarters formation . This formation – with seven backs and eight forwards , instead of six backs and nine forwards – <unk> the sport and was eventually adopted almost universally at international and club level . 
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- = = = First ' golden age ' ( 1893 – 1913 ) = = = 
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- With the " four three @-@ quarter " formation Wales became Home International Champions for the first time in 1893 ; in the process winning the Triple Crown . Wales next won the Championship in 1900 , <unk> the first " golden age " of Welsh rugby which was to last until 1911 . They won two more Triple Crowns in 1902 and 1905 , and were runners up in 1901 , 1903 and 1904 . 
- When Wales faced New Zealand 's All Blacks at Cardiff Arms Park in late 1905 they had not lost at home since 1899 . This New Zealand team – now referred to as the Original All Blacks – was the first of the southern hemisphere national teams to visit the British Isles , and were undefeated on their tour up until that point ; their victories included prior defeats of England , Ireland and Scotland . Before the match , the All Blacks performed a <unk> ( a Māori posture dance ) ; the 47 @,@ 000 @-@ strong crowd responded with the Welsh national anthem – Hen <unk> <unk> <unk> ( " Land of Our Fathers " ) – the first time a national anthem had been sung before a sporting fixture . Wales ' wing Teddy Morgan scored a try to give Wales a 3 – 0 lead , but later in the match All Black Bob <unk> claimed to have scored a try , only to be dragged behind the try @-@ line before the referee arrived . The referee ruled a <unk> to Wales and the score did not change ; Wales winning 3 – 0 . The loss was the All Blacks ' only loss on their 35 @-@ match tour . 
- In 1906 , Wales again won the Home Championship , and later that year played the South African national side , the Springboks for the first time . Wales were favourites to win the match , but instead South Africa dominated in the forwards and eventually won 11 – 0 . Two years later , on 12 December 1908 , Wales played the touring Australians , the Wallabies , who they defeated 9 – 6 . 
- In 1909 , Wales won the Home Championship and then , in 1910 – with the inclusion of France – the first @-@ ever Five Nations . In 1911 , Wales took the first Five Nations Grand Slam by winning all their matches in the Five Nations ; France were heavily defeated by Wales at St Helens in 1910 ( 49 – 14 ) and Ivor Morgan scored two tries in the match . It would be nearly forty years before they achieved a Grand Slam again . England 's defeat of Wales at Cardiff in 1913 was Wales ' first home loss to one of the Home Nations since 1899 , and their first home loss to England since 1895 . The Great War came in 1914 and rugby was suspended for the duration . 
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- = = = Post @-@ war years ( 1920 – 1968 ) = = = 
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- The post @-@ First World War years marked a decline in Welsh rugby . An industrial recession struck the principality , and hurt South Wales in particular . Welsh international results in the 1920s mirrored the performance of the economy : of their 42 matches they won only 17 , with three drawn . Half @-@ a @-@ million people emigrated from Wales to find work elsewhere during the depression ; this included many Welsh rugby union internationals who moved to the professional code of rugby league . Between 1923 and 1928 , Wales managed only seven victories – five of them against France . However , even France managed to defeat Wales that decade ; achieving their first victory over Wales in 1928 . Welsh selection policy reflected the upheavals of the mid @-@ 1920s . In 1924 , 35 different players were selected for Wales ' four matches , with a different captain for each ; and only Edward Watkins in the backs and Charlie <unk> in the forwards , playing in all four matches . 
- A resurgence of both economy and rugby union followed in the 1930s and , in 1931 , Wales won their first championship for nine years . That year , for the first time since the First World War , Wales retained the same side for two consecutive Tests when they faced England and Scotland . Then , in 1933 , captained by <unk> Thomas , Wales defeated England at Twickenham . In 1935 , Wales beat the touring All Blacks by 13 – 12 , with Haydn <unk> making his first appearance . Although the Five Nations Championship was suspended during the Second World War Wales did play a Red Cross charity match against England at Cardiff in 1940 , which Wales lost 18 – 9 . 
- Following the Second World War , Wales played a New Zealand Army team ( the <unk> ) in 1946 , which Wales lost 11 – 3 . The Five Nations ( suspended during the war ) resumed in 1947 when Wales shared the title with England . Although Wales suffered their first home defeat to France in 1948 , they won their first Five Nations Grand Slam since 1911 in 1950 . The next year , they lost to the touring South Africans 6 – 3 despite dominating in the line @-@ outs . They achieved another Grand Slam in 1952 , followed by a 13 – 8 win over the All Blacks in 1953 . In 1954 , St Helens in Swansea ( a Welsh international venue since 1882 ) hosted its last international and Cardiff Arms Park officially became the home of the Welsh team . In 1956 , Wales again won the Five Nations , but they did not regain the title until 1964 and did not win it outright until 1965 . 
- Wales conducted their first overseas tour in 1964 , playing several games and one Test in South Africa . They lost the Test against South Africa in Durban 24 – 3 , their biggest defeat in 40 years . At the WRU annual general meeting that year , the outgoing WRU President D. <unk> Davies declared that " it was evident from the experience of the South African Tour that a much more positive attitude to the game was required in Wales ... Players must be prepared to learn , and indeed re @-@ learn , to the absolute point of mastery , the basic principles of Rugby Union football " . This started the coaching revolution . The WRU Coaching Committee – set up in the late 1950s – was given the task of improving the quality of coaching and , in January 1967 , Ray Williams was appointed Coaching <unk> . The first national coach , David Nash , was appointed in 1967 to coach Wales for the season , but resigned when the WRU refused to allow him to accompany Wales on their 1968 tour of Argentina . Eventually , the WRU reversed their decision , appointing Clive <unk> to tour as coach . Of the six matches , Wales won three , drew two and lost one . 
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- = = = Second ' golden age ' ( 1969 – 1979 ) = = = 
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- Wales enjoyed a second " golden age " in the 1970s , with world @-@ class players such as Gareth Edwards , J. P. R. Williams , Gerald Davies , Barry John , and Mervyn Davies , in their side . Wales dominated Northern Hemisphere rugby between 1969 and 1979 , and attained an incredible winning record , losing only seven times during that period . Wales toured New Zealand for the first time in 1969 , but were defeated in both Tests . As well as losing the first Test 19 – 0 , and the second 33 – 12 , they also conceded 24 points to the All Blacks ' fullback <unk> McCormick in the second Test ; a record at the time . 
- In 1970 , Wales shared the Five Nations with France , and recorded a 6 – 6 draw against South Africa in Cardiff . The following year Wales recorded their first Five Nations Grand Slam since 1952 . Using only 16 players in four games , the 1971 side achieved their most notable win of the tournament in their victory over Scotland ; after a last minute try by Gerald Davies that reduced Scotland 's lead to 18 – 17 , <unk> John Taylor kicked a conversion from the <unk> described as " the greatest conversion since St Paul " to give Wales a 19 – 18 win . Wales contributed more players than any other team to the British and Irish Lions that toured New Zealand that year . Those Lions became the only to win a series over the All Blacks . 
- In the 1972 Five Nations Championship , Wales and Scotland refused to travel to Ireland at the height of the Troubles after receiving threats , purportedly from the Irish Republican Army . The Championship remained unresolved with Wales and Ireland unbeaten . Although the Five Nations was a five way tie in 1973 , the Welsh did defeat Australia 24 – 0 in Cardiff . 
- Wales next won the Five Nations outright in 1975 , and in 1976 , Wales won their second Grand slam of the decade . Just like the first in 1971 , they only used 16 players over their four matches . They repeated the feat in 1978 and , in the process , became the first team to win three consecutive Triple Crowns . Following their final Five Nations match of 1978 , both Phil Bennett and Gareth Edwards retired from rugby . 
- Wales hosted the All Blacks at Cardiff Arms Park in November 1979 , losing 13 – 12 after a late penalty goal by the replacement All Black fullback , Brian <unk> . The penalty was controversial because All Black lock Andy Haden had <unk> out of a line @-@ out in an attempt to earn a penalty . Haden later admitted that he and Frank Oliver had pre @-@ agreed this tactic should the All Blacks find themselves in difficulties . <unk> Roger <unk> was roasted by the press for failing to notice the dive , but he later stated he had the penalty was in for Welsh lock Geoff Wheel for jumping off the shoulder of Frank Oliver . <unk> later said , " Haden 's perception is that his dive secured the penalty . That is a load of rubbish " . The All Blacks went on to secure their first Home Nations Grand Slam . Wales then went on to win the 1979 Five Nations with a Triple Crown . 
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- = = = <unk> years ( 1980 – 2003 ) = = = 
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- In 1980 , the WRU 's centenary year , Wales lost to the All Blacks in Cardiff by 23 – 3 after the All Blacks scored four tries to nil . Wales won two matches in each Five Nations of 1980 and 1981 , and in 1983 were nearly upset by Japan ; winning 29 – 24 at Cardiff . In 1984 , Australia defeated Wales 28 – 9 at Cardiff Arms Park and in the process scored a push @-@ over try . Australia went on to win their first Home Nations Grand Slam . 
- Wales achieved only one win in 1987 's Five Nations before <unk> the inaugural Rugby World Cup . Wales defeated Ireland in their crucial pool fixture , before defeating England in the quarter @-@ finals . They then faced hosts the All Blacks who won 49 – 6 , but beat Australia in the third place play @-@ off game to claim third . The next year Wales won the Triple Crown for the first time since 1979 , but heavy defeats on tour to New Zealand later that year saw the end of a number of Welsh players ' careers , as several converted to rugby league . 
- Welsh rugby reached a nadir when Wales suffered their first Five Nations championship <unk> ; they had upset England in 1989 to avoid losing all their Championship matches that season , but in 1990 Wales were defeated in all four Five Nations ' matches for the first time . The 1991 World Cup saw further frustration when Wales were upset by <unk> Samoa in their opening match . A second group @-@ stage loss , by 38 – 3 to Australia , eliminated Wales from the tournament . 
- After winning two Five Nations games in 1992 , and one in 1993 , Wales won the Championship in 1994 on points difference . But without defeating one of Australia , New Zealand , or South Africa , during the inter @-@ World Cup period , Wales was not considered a major tournament contender . At the 1995 World Cup , after <unk> beating Japan , Wales lost to New Zealand ; this meant that they needed to defeat Ireland to qualify for the quarter @-@ finals . Wales lost 24 – 23 and so failed to progress beyond the pool stage for the second time , and later that year Kevin <unk> replaced Alex Evans to become Wales ' first full @-@ time coach . 
- Record defeats , 51 – 0 to France , and 96 – 13 to South Africa , prompted the WRU to appoint New Zealander Graham Henry as coach in 1998 . Henry had early success as coach , leading Wales to a ten @-@ match winning streak ; this included Wales ' first ever victory over South Africa , by 29 – 19 . Henry was consequently nicknamed " the great <unk> " by the Welsh media and fans . <unk> the 1999 World Cup , Wales qualified for the quarter @-@ finals for the first time since 1987 , but lost 9 – 24 to eventual champions Australia . A lack of success in the Five and Six Nations ( Italy joined the tournament in 2000 ) , and especially a number of heavy losses to Ireland , led to Henry 's resignation in February 2002 ; his assistant Steve Hansen took over . 
- During Hansen 's tenure , the WRU implemented a significant change in the structure of the game domestically . Regional teams were introduced as a tier above the traditional club @-@ based structures in 2003 , and the five ( then four ) regional sides became the top level of domestic professional rugby in the principality . At the 2003 World Cup , Wales scored four tries in their 53 – 37 loss to New Zealand , and also lost to the eventual tournament winners , England , in their quarter @-@ final , despite <unk> them by three tries to one . 
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- = = = Revival ( 2004 – present ) = = = 
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- <unk> by Mike <unk> , Wales won their first Grand Slam since 1978 and their first ever Six Nations Grand Slam in 2005 . A late long range penalty from Gavin Henson gave them a victory over England at Cardiff for the first time in 12 years , and after victories over Italy , France , Scotland , they faced Ireland in front of a capacity crowd at the Millennium Stadium where Wales ' 32 – 20 victory gave them their first Championship since 1994 . Later that year they suffered a record home loss , 41 – 3 to New Zealand . 
- Mike <unk> resigned as the head coach of Wales mid @-@ way through the 2006 Six Nations , where Wales finished fifth , and Gareth Jenkins was eventually appointed as his replacement . Jenkins led Wales through the 2007 World Cup , where they failed to advance beyond the pool stage following a loss to Fiji . Jenkins subsequently lost his job , and Warren Gatland , a New Zealander , was appointed as his successor . 
- Wales faced England at Twickenham for Gatland 's inaugural match as coach and their first match of the 2008 Six Nations . They had not defeated England there since 1988 , and went on to win 26 – 19 . They eventually won all their Championship matches , <unk> only two tries in the process , to claim another Grand Slam . Later that year Wales defeated Australia 21 – 18 in Cardiff , but then started a six @-@ year , twenty @-@ three @-@ game win @-@ less streak against the southern hemisphere nations of Australia , New Zealand and South Africa . 
- At the 2011 World Cup , Wales reached the tournament semi @-@ finals for the first time since 1987 where they lost 9 – 8 to France after a red card for captain Sam Warburton . The two teams met again in March 2012 , where Wales needed a win to claim their third Six Nations Grand Slam in eight years , which they did with a 16 – 9 victory . This was followed immediately by an eight @-@ match losing streak , which was eventually broken during the 2013 Six Nations , where Wales retained the Championship for the first time since 1979 wins . Wales reached the quarter @-@ finals of the 2015 World Cup at the expense of hosts England , then lost 23 – 19 to South Africa . 
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- = = Strip = = 
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- Wales play in red jerseys , white shorts and red socks . For the 2015 – 16 season , the design of the Jersey incorporated gold for the first time . The jerseys are embroidered with the Prince of Wales 's feathers , which were chosen in the 19th century by the WRU over another Welsh symbol , the <unk> , to demonstrate the nation 's loyalty to Britain . In 1991 , to enable the device to be trademarked , the original generic motif was replaced with a more stylised version . The original motto beneath the feathers was Ich <unk> ( German for " I serve " ) but was replaced with " WRU " in the new version . 
- Wales change strip – also known as the alternative strip – is black jerseys , shorts and <unk> although there have been various different coloured strips in the past Former change strips worn by Wales have included a green , navy , white or grey jersey . Wales previously wore black jerseys as part of celebrations for the WRU 's 125th anniversary in 2005 . The jersey was worn against Fiji and then Australia that year ; the Australia match was the first time Wales had not played in their red jersey against one of their traditional rivals . Since the 2008 end @-@ of @-@ year Tests , the strip is made by Under Armour . They replaced <unk> who supplied the Wales strip between late 1996 and the 2008 mid @-@ year @-@ Tests . The shirt sponsor is Cardiff based Insurance firm , Admiral . 
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- = = Support = = 
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- Rugby union and Wales ' national team hold an important place in Welsh culture and society . Sport historian John <unk> has stated that " rugby is <unk> Welsh " , and David Andrew said that " To the popular consciousness , rugby is as Welsh as coal mining , male voice choirs , How Green Was My Valley , Dylan Thomas , and Tom Jones " . Welsh rugby 's first ' golden age ' ( 1900 – 1911 ) coincided with the country 's zenith during the 20th century , and rugby was important in building Wales ' modern identity . There is a long tradition of Welsh supporters singing before and during matches . The choral tradition developed in Wales during the nineteenth @-@ century alongside the rise of <unk> , and has extended to singing at rugby matches . Commonly sung songs include the hymn Bread of Heaven , Tom Jones ' <unk> , and Max <unk> 's Hymns and <unk> . 
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- = = Grounds = = 
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- Wales ' first home international was played at St Helen 's ground , Swansea in 1882 . In the 1880s and 1890s , home Welsh internationals were played at Cardiff , Swansea , Newport and <unk> . Swansea continued to be used as an international venue until 1954 , when Cardiff Arms Park became Wales ' primary home venue . Cardiff Arms Park first had a stand erected in 1881 , and continued to expand its seating that decade . <unk> continued to grow and in 1902 in Wales ' match against Scotland a world record 40 @,@ 000 spectators paid to see the match . In 1911 , the owners of the Arms Park , the Marquess of <unk> 's family , confirmed Wales ' tenure and the 1920s and 1930s , Wales gradually gained increasing control . A new stand was built at the park in the 1933 – 34 season , which increased the grounds capacity to 56 @,@ 000 . 
- By 1958 , the WRU had concluded that a new national ground was needed due to flooding that often plagued Arms Park . After debate and disputes between the WRU and various other parties , including Cardiff RFC , in the 1960s , it was decided that a new national stadium would be built with a new ground for the Cardiff club backing onto it . The National Stadium , as it was known , was officially opened in 1970 . 
- Currently , Wales play all their home matches at the 74 @,@ 500 capacity Millennium Stadium , Cardiff , which is also Wales ' national stadium . The Millennium Stadium was first conceived in 1994 , when a group redevelopment committee was set up . It was decided to replace the National Stadium at Cardiff Arms Park after a review found it was out of date ; new legislation also required stadia to be all seated . Construction began in September 1997 , and was completed by June 1999 , in time for the Rugby World Cup . The construction cost the WRU £ 126 million , which was funded by private investment , £ 46 million of public funds from the National <unk> , the sale of <unk> to supporters ( which offered guaranteed tickets in exchange for an interest @-@ free loan ) , and loans . While the new ground was being built , Wales used the old Wembley Stadium for their home matches – a deal <unk> during construction of the new Wembley , when FA Cup finals were held at the Millennium Stadium . 
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- = = Record = = 
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- = = = Six Nations = = = 
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- Wales compete annually in the Six Nations Championship , which is played against five other European nations : England , France , Ireland , Italy , and Scotland . The Six Nations started as the Home Nations Championship in 1883 , as a contest between the four component nations of the United Kingdom . Wales first won it in 1893 , when they achieved a Triple Crown . Wales have won the tournament outright 26 times , and shared eleven other victories . Their longest wait between championships was 11 years ( 1994 – 2005 ) . Wales first won a Grand Slam in 1908 – although France did not officially join the Five Nations until 1910 – and their first Six Nations Grand Slam in 2005 . Their latest Grand Slam was won against France on 17 March 2012 , and they claimed their most recent Triple Crown on 25 February 2012 against England . 
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- = = = World Cup = = = 
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- Wales have contested every Rugby World Cup since the inaugural tournament in 1987 . The 1987 tournament was Wales ' most successful ; they won all three pool matches and their quarter @-@ final , before losing to the All Blacks in the semi @-@ finals . They then faced Australia in the third place play @-@ off match , which they won 22 – 21 . In the next two tournaments in 1991 and 1995 , Wales failed to progress beyond the pool stage , winning just one match in each tournament . Both the 1999 and 2003 tournaments were more successful , with Wales qualifying for the quarter @-@ finals both times . Wales hosted the event in 1999 and topped their pool only to lose to eventual winners Australia in the quarter @-@ finals . In 2003 , they finished second in their pool behind the All Blacks , and faced England in their quarter @-@ final . They lost to England , the eventual champions , 28 – 17 . Wales conceded 17 penalties , and their lack of discipline proved costly . In the 2007 World Cup , Wales again failed to progress from the pool stage . After a loss to Australia , and two wins against Japan and Canada , they faced Fiji for a place in the quarter @-@ finals . The game started poorly for Wales who were behind 25 – 3 at half @-@ time . They fought back to lead by three points with six minutes remaining , but Fiji then scored a try to win 38 – 34 and eliminated Wales from the tournament . At the 2011 World Cup , Wales reached the semi @-@ finals for the first time since 1987 . Playing the semi @-@ finals against France , Wales lost 9 – 8 , in a game overshadowed by the 18th @-@ minute sending off of Wales ' captain Sam Warburton for a dangerous tackle against Vincent <unk> . 
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- = = = Overall = = = 
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- When the World Rugby Rankings were introduced in October 2003 , Wales were ranked 8th . They rose to 7th in June 2004 , before falling back to 8th in November that year . Following a Grand Slam win in the 2005 Six Nations , they rose to a ranking position of 5th . They fell to 9th by June 2006 , and , after rising back to 8th by September , fell to 10th after the 2007 World Cup . A second Six Nations ' Grand Slam in 2008 propelled them to 6th in the rankings , but following losses to South Africa in the mid @-@ year and end @-@ of @-@ year internationals Wales slipped to 7th . Wales climbed to 4th after a win over Scotland in their first match of the 2009 Six Nations . They <unk> to 9th in 2010 but rose back to 4th after their fourth place in the 2011 World Cup . Since then – <unk> a nine @-@ game slump in 2012 – 13 where they fell to 9th – Wales have ranked consistently in the top six teams . They reached their highest ranking of 2nd during the 2015 Rugby World Cup . 
- Wales have won 355 of their 690 Test matches for a 51 @.@ 45 % winning record . Their biggest Test defeat was 96 – 13 loss to South Africa in 1998 , and their largest victory a 98 – 0 defeat of Japan in 2004 . Their record for most tries in a match is 16 , scored against Portugal in 1994 – they also scored 102 points in this match , more than in any other Test . Wales ' record for consecutive Test wins is eleven , and for consecutive losses is ten . 
- Below is table summary of Wales Test matches up until 25 June 2016 . 
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- = = Players = = 
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- = = = Current squad = = = 
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- On 10 May , Warren Gatland named a 35 @-@ man squad for their June 3 @-@ test series against New Zealand , pre @-@ tour test against England and the mid week match against the Chiefs . 
- On 29 May , after injury to Dan <unk> in the England warm @-@ up match , Ellis Jenkins was called up to the squad to replace <unk> . 
- On 7 June , Aaron Jarvis was called up to the squad as injury cover for Paul James . Jarvis later became a permanent replacement after James failed to recover form his injury . 
- On 11 June , <unk> Davies joined the squad as an injury replacement for Lloyd Williams . 
- Following the first test , <unk> Giles and Rhys <unk> were called up to the squad as injury cover . 
- <unk> – <unk> dual contracted players . 
- <unk> and clubs updated 25 June 2016 
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- = <unk> = 
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- Anekāntavāda ( Sanskrit : <unk> ् <unk> , " many @-@ <unk> " ) refers to the principles of pluralism and multiplicity of viewpoints , or <unk> points , the notion that reality is perceived differently from diverse points of view , and that no single point of view is the complete truth , yet taken together they comprise the complete truth . It is one of the most important and fundamental doctrines of Jainism . 
- Jains contrast all attempts to proclaim the sole monopoly on truth with <unk> , which can be illustrated through the parable of the " blind men and an elephant " . In this story , each blind man felt a different part of an elephant ( trunk , leg , ear , etc . ) . All the men claimed to understand and explain the true appearance of the elephant , but could only partly succeed , due to their limited perspectives . This principle is more formally stated by observing that objects are infinite in their qualities and modes of existence , so they cannot be completely grasped in all aspects and manifestations by finite human perception . ( This is the <unk> Truth . ) According to the Jains , only the <unk> — <unk> beings — can comprehend objects in all aspects and manifestations ; others are only capable of partial knowledge . Consequently , no single , specific , human view can claim to represent absolute truth . 
- The origins of anekāntavāda can be traced back to the teachings of Mahāvīra ( <unk> – 527 BCE ) , the 24th Jain <unk> . The <unk> concepts of syādvāda " conditioned viewpoints " and nayavāda " partial viewpoints " arose from anekāntavāda , providing it with more detailed logical structure and expression . The Sanskrit compound an @-@ eka @-@ anta @-@ vāda literally means " doctrine of uncertainty " ( <unk> " not " , ekānta " certainty " or " single @-@ natured " , vāda ( " school of thought " or " thesis " ) ; it is roughly translated into English as " non @-@ absolutism " . An @-@ ekānta " uncertainty , non @-@ exclusivity " is the opposite of ekānta ( eka + anta ) " <unk> , <unk> , necessity " ( or also " monotheistic doctrine " ) . 
- Anekāntavāda encourages its adherents to consider the views and beliefs of their rivals and opposing parties . Proponents of anekāntavāda apply this principle to religion and philosophy , reminding themselves that any religion or philosophy — even Jainism — which clings too <unk> to its own tenets , is committing an error based on its limited point of view . The principle of anekāntavāda also influenced Mahatma Gandhi to adopt principles of religious tolerance , ahiṃsā and <unk> . 
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- = = Philosophical overview = = 
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- The etymological root of anekāntavāda lies in the compound of two Sanskrit words : anekānta ( " <unk> " ) and vāda ( " school of thought " ) . The word anekānta is a compound of the Sanskrit negative prefix an , eka ( " one " ) , and anta ( " attribute " ) . Hence , anekānta means " not of solitary attribute " . 
- The Jain doctrine lays a strong emphasis on <unk> , that is , rationality and logic . According to Jains , the ultimate principle should always be logical and no principle can be devoid of logic or reason . Thus , the Jain texts contain <unk> <unk> on every subject , whether they are constructive or <unk> , <unk> or analytical , enlightening or destructive . 
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- = = = Jain doctrines of relativity = = = 
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- Anekāntavāda is one of the three Jain doctrines of relativity used for logic and reasoning . The other two are : 
- syādvāda — the theory of conditioned predication and ; 
- nayavāda — the theory of partial standpoints . 
- These Jain philosophical concepts made important contributions to ancient Indian philosophy , especially in the areas of skepticism and relativity . 
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- = = = = Syādvāda = = = = 
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- Syādvāda ( Sanskrit : <unk> ् <unk> ् <unk> ) is the theory of conditioned predication , which provides an expression to anekānta by recommending that every phrase or statement be expressed in the <unk> mood ( the equivalent of the <unk> mood in Latin and other Indo @-@ European languages ) , i.e. generally by <unk> each sentence with the verb syāt , the third person singular <unk> of the Sanskrit verb as , " to be " . ( In Sanskrit , syāt becomes syān when followed by an " n " , and syād when followed by a non @-@ nasal voiced <unk> or vowel . ) According to Jain texts , a thing or object of knowledge has infinite characters ( i.e. , it is <unk> ) ; each character can be analysed and grasped individually . Each individual character is called a naya . Jains asserts that a naya reveals only a part of the <unk> , and should not be mistaken for the whole . A synthesis of different viewpoints is said to be achieved by the doctrine of conditional predications ( syādvāda ) wherein every viewpoint is able to retain its relative importance . Highlighting the <unk> of syādvāda , <unk> Samantabhadra asserts : 
- <unk> , when not in conflict with negation , yields the desired result of describing truly an object of knowledge . Only when affirmation and negation are juxtaposed in mutually non @-@ conflicting situation , one is able to decide whether to accept or reject the assertion . This is how the doctrine of conditional predications ( syādvāda ) establishes the truth . ” 
- Syādvāda is not only an extension of anekānta <unk> , but a separate system of logic capable of standing on its own . As reality is complex , no single proposition can express the nature of reality fully . Thus " syāt " should be <unk> before each proposition giving it a conditional point of view and thus removing any <unk> in the statement . Since it ensures that each statement is expressed from seven different conditional and relative viewpoints or propositions , syādvāda is known as <unk> or " the theory of seven conditioned predications " . These saptibhaṅgī are : 
- syād @-@ asti — in some ways , it is , 
- syān @-@ nāsti — in some ways , it is not , 
- syād @-@ asti @-@ nāsti — in some ways , it is , and it is not , 
- syād @-@ asti @-@ avaktavyaḥ — in some ways , it is , and it is indescribable , 
- syān @-@ nāsti @-@ avaktavyaḥ — in some ways , it is not , and it is indescribable , 
- syād @-@ asti @-@ nāsti @-@ avaktavyaḥ — in some ways , it is , it is not , and it is indescribable , 
- syād @-@ avaktavyaḥ — in some ways , it is indescribable . 
- Each of these seven propositions examines the complex and <unk> nature of reality from a relative point of view of time , space , substance and mode . To ignore the complexity of reality is to commit the <unk> of <unk> . 
- The phrase ‘ in a way ’ ( syāt ) declares the standpoint of expression – affirmation with regard to own substance ( dravya ) , place ( kṣetra ) , time ( kāla ) , and being ( bhāva ) , and negation with regard to other substance ( dravya ) , place ( kṣetra ) , time ( kāla ) , and being ( bhāva ) . Thus , for a ‘ jar ’ , in regard to substance ( dravya ) – earthen , it simply is ; wooden , it simply is not . In regard to place ( kṣetra ) – room , it simply is ; terrace , it simply is not . In regard to time ( kāla ) – summer , it simply is ; winter , it simply is not . In regard to being ( bhāva ) – brown , it simply is ; white , it simply is not . And the word ‘ simply ’ has been inserted for the purpose of excluding a sense not approved by the ‘ nuance ’ ; for avoidance of a meaning not intended . The phrase ‘ in a way ’ is used to declare that the ‘ jar ’ exists in regard to its own substance <unk> and not also in regard to other substance etc . 
- According to the Jains , Syādvāda and kevalajñāna ( omniscience ) are the <unk> facts of knowledge . In this regard , <unk> Samantabhadra writes : 
- Syādvāda , the doctrine of conditional predications , and kevalajñāna ( omniscience ) , are both <unk> of the substances of reality . The difference between the two is that while kevalajñāna <unk> directly , syādvāda <unk> indirectly . Anything which is not illuminated or expressed by the two is not a substance of reality and hence a non @-@ substance ( <unk> ) . 
- 
- = = = = Nayavāda = = = = 
- 
- Nayavāda is the theory of partial standpoints or viewpoints . Nayavāda is a compound of two Sanskrit words — naya ( " reason " or " method " ) and vāda ( " school of thought or thesis " ) . It is used to arrive at a certain inference from a point of view . An object has infinite aspects to it , but when we describe an object in practice , we speak of only relevant aspects and ignore irrelevant ones . This does not deny the other attributes , qualities , modes and other aspects ; they are just irrelevant from a particular perspective . Authors like <unk> Shah explain nayavāda with the example of a car ; for instance , when we talk of a " blue BMW " we are simply considering the color and make of the car . However , our statement does not imply that the car is devoid of other attributes like engine type , cylinders , speed , price and the like . This particular viewpoint is called a naya or a partial viewpoint . As a type of critical philosophy , nayavāda holds that all philosophical disputes arise out of confusion of standpoints , and the standpoints we adopt are , although we may not realise it , " the outcome of purposes that we may pursue " . While operating within the limits of language and seeing the complex nature of reality , Mahāvīra used the language of nayas . <unk> , being a partial expression of truth , enables us to comprehend reality part by part . 
- 
- = = = <unk> of changing and unchanging reality = = = 
- 
- The age of Mahāvīra and Buddha was one of intense intellectual debates , especially on the nature of reality and self . <unk> thought postulated the absolute unchanging reality of <unk> and <unk> and claimed that change was mere illusion . The theory advanced by Buddhists denied the reality of permanence of conditioned phenomena , asserting only <unk> and impermanence . According to the <unk> ( <unk> ) conceptual scheme , the Buddhists were wrong in denying permanence and absolutism , and within the Buddhist conceptual scheme , the <unk> were wrong in denying the reality of impermanence . The two positions were contradictory and mutually exclusive from each other 's point of view . The Jains managed a synthesis of the two uncompromising positions with anekāntavāda . From the perspective of a higher , inclusive level made possible by the <unk> and epistemology of anekāntavāda and syādvāda , Jains do not see such claims as contradictory or mutually exclusive ; instead , they are seen as <unk> or only partially true . The Jain breadth of vision <unk> the perspectives of both <unk> which , according to Jainism , " recognises substances but not process " , and Buddhism , which " recognises process but not substance " . Jainism , on the other hand , pays equal attention to both substance ( dravya ) and process ( <unk> ) . 
- This philosophical <unk> of paradox of change through anekānta has been acknowledged by modern scholars such as <unk> Sharma , who wrote : 
- Our experience of the world presents a profound paradox which we can ignore <unk> , but not philosophically . This paradox is the paradox of change . Something – A changes and therefore it cannot be permanent . On the other hand , if A is not permanent , then what changes ? In this debate between the " permanence " and " change " , Hinduism seems more inclined to grasp the first horn of the dilemma and Buddhism the second . It is Jainism that has the philosophical courage to grasp both horns <unk> and simultaneously , and the philosophical skill not to be <unk> by either . 
- However , anekāntavāda is not simply about <unk> or compromise between competing ideas , as it is <unk> about finding the hidden elements of shared truth between such ideas ( such as naturalism — relative to <unk> and sanctuary — although its basis in simplicity may be described with the scientific principle of <unk> 's razor — whereas science is likewise paradoxical in nature — relative to <unk> ) . Anekāntavāda is not about denying the truth ; rather truth is acknowledged as an ultimate spiritual goal . For ordinary humans , it is an elusive goal , but they are still obliged to work towards its attainment . Anekāntavāda also does not mean <unk> or <unk> ones own values and principles . On the contrary , it allows us to understand and be tolerant of conflicting and opposing views , while <unk> maintaining the validity of ones own view @-@ point . Hence , John Koller calls anekāntavāda as – " epistemological respect for view of others " . Anekāntavāda , thus , did not prevent the Jain thinkers from defending the truth and validity of their own doctrine while simultaneously <unk> and understanding the rival doctrines . Anne <unk> notes that the epistemological respect for other viewpoints was put to practice when she was invited by Ācārya Tulsi , the head of the <unk> order , to teach <unk> the tenets of Christianity . Commenting on their adherence to ahiṃsā and anekāntavāda , she says : 
- The Jain <unk> of <unk> <unk> maintain ahiṃsā to be an eternal and <unk> moral law . Other views and beliefs that contradict this belief would certainly be challenged , and ultimately rejected . But what is significant , is that both the rejection and retention of views is tempered by the belief that our perception conveys only a partial reality , that reality itself is <unk> , and that to assume one particular viewpoint is final , is to hold a limited picture of reality . 
- Anekāntavāda is also different from moral relativism . It does not mean <unk> that all arguments and all views are equal , but rather logic and evidence determine which views are true , in what respect and to what extent ( as truth in relativism , itself ) . While employing anekāntavāda , the 17th century philosopher monk , <unk> <unk> also <unk> against <unk> ( indiscriminate attachment to all views as being true ) , which is effectively a kind of <unk> relativism . Jains thus consider anekāntavāda as a positive concept corresponding to religious pluralism that <unk> monism and dualism , implying a sophisticated conception of a complex reality . It does not merely involve rejection of <unk> , but reflects a positive spirit of reconciliation of opposite views . However , it is argued that pluralism often <unk> to some form of moral relativism or religious <unk> . According to Anne <unk> , anekānta is a way out of this epistemological <unk> , as it makes a genuinely <unk> view possible without <unk> into extreme moral relativism or exclusivity . 
- 
- = = = Parable of the blind men and elephant = = = 
- 
- The ancient Jain texts often explain the concepts of <unk> and syādvāda with the parable of the blind men and an elephant ( <unk> ) , which addresses the <unk> nature of truth . 
- A group of blind men heard that a strange animal , called an elephant , had been brought to the town , but none of them were aware of its shape and form . Out of curiosity , they said : " We must inspect and know it by touch , of which we are capable " . So , they sought it out , and when they found it they <unk> about it . In the case of the first person , whose hand landed on the trunk , said " This being is like a drain pipe " . For another one whose hand reached its ear , it seemed like a kind of fan . As for another person , whose hand was upon its leg , said , " I perceive the shape of the elephant to be like a pillar " . And in the case of the one who placed his hand upon its back said , " Indeed , this elephant is like a throne " . Now , each of these presented a true aspect when he related what he had gained from experiencing the elephant . None of them had <unk> from the true description of the elephant . Yet they fell short of <unk> the true appearance of the elephant . 
- Two of the many references to this parable are found in <unk> of <unk> ( 9th century ) and <unk> of Ācārya Mallisena ( 13th century ) . Mallisena uses the parable to argue that immature people deny various aspects of truth ; <unk> by the aspects they do understand , they deny the aspects they don 't understand . " Due to extreme delusion produced on account of a partial viewpoint , the immature deny one aspect and try to establish another . This is the <unk> of the blind ( men ) and the elephant . " Mallisena also cites the parable when noting the importance of considering all viewpoints in obtaining a full picture of reality . " It is impossible to properly understand an entity consisting of infinite properties without the method of modal description consisting of all viewpoints , since it will otherwise lead to a situation of seizing mere <unk> ( i.e. , a superficial , inadequate cognition ) , on the <unk> of the blind ( men ) and the elephant . " 
- 
- = = History and development = = 
- 
- The principle of anekāntavāda is the foundation of many Jain philosophical concepts . The development of anekāntavāda also encouraged the development of the <unk> of syādvāda ( conditioned viewpoints ) , saptibhaṅgī ( the seven conditioned predication ) , and nayavāda ( partial viewpoints ) . 
- 
- = = = Origins = = = 
- 
- The origins of anekāntavāda lie in the teachings of Mahāvīra , who used it effectively to show the relativity of truth and reality . Taking a <unk> viewpoint , Mahāvīra is said to have explained the nature of the soul as both permanent , from the point of view of underlying substance , and temporary , from the point of view of its modes and modification . The importance and antiquity of anekāntavāda are also demonstrated by the fact that it formed the subject matter of <unk> <unk> , the fourth part of the lost <unk> that contained teachings of the <unk> prior to Mahāvīra . German <unk> Hermann Jacobi believes Mahāvīra effectively employed the <unk> of anekāntavāda to refute the <unk> of Sañjaya Belaṭṭhaputta . <unk> , the second oldest canon of Jainism , contains the first references to syādvāda and saptibhaṅgī . According to <unk> , Mahāvīra advised his disciples to use syādvāda to preach his teachings : 
- A monk living single should not <unk> <unk> doctrines , and should avoid hard words though they be true ; he should not be vain , nor <unk> , but he should without embarrassment and passion preach the Law . A monk should be modest , though he be of a fearless mind ; he should expound the syādvāda , he should use the two permitted kinds of speech , living among <unk> men , impartial and wise . 
- 
- = = = Early history = = = 
- 
- The early Jain canons and teachings contained <unk> of references to anekāntavāda and syādvāda in rudimentary form without giving it proper structure or establishing it as a separate doctrine . <unk> text , <unk> contains references to <unk> , which , according to Hermann Jacobi , is the same as syādvāda and saptibhaṅgī . <unk> mentions only three primary predications of the <unk> . After Mahāvīra , <unk> ( 1st century CE ) was the first author – saint to expound on the doctrine of syādvāda and saptibhaṅgī and give it a proper structure in his famous works <unk> and <unk> . <unk> also used nayas to discuss the essence of the self in <unk> . <unk> classification of the nayas was provided by the philosopher monk , <unk> ( 2nd century CE ) in <unk> . Samantabhadra ( 2nd century CE ) and <unk> Divākara ( 3rd century CE ) further fine @-@ tuned Jain epistemology and logic by <unk> on the concepts of anekāntavāda in proper form and structure . 
- Ācārya <unk> Divākara <unk> on the nature of truth in the court of King Vikramāditya : 
- Vikramāditya : What is ' truth ' ? That which is said repeatedly , that which is said loudly , that which is said with authority or that which is agreed by the majority ? 
- Divākara : None of the above . Every one has his own definition of ' truth ' and that it is conditional . 
- Vikramāditya : How about traditions ? They have been established by our ancestors and have passed the test of time ? 
- Divākara : Would the system established by ancestors hold true on examination ? In case it does not , I am not here to justify it for the sake of saving the traditional grace of the dead , irrespective of the wrath I may have to face . 
- In <unk> , Divākara further adds : " All doctrines are right in their own respective spheres — but if they <unk> upon the province of other doctrines and try to refute their view , they are wrong . A man who holds the view of the cumulative character of truth never says that a particular view is right or that a particular view is wrong . " 
- 
- = = = Age of logic = = = 
- 
- The period beginning with the start of common era , up to the modern period is often referred to as the age of logic in the history of Jain philosophy . By the time of <unk> ( 5th century CE ) , whose works are a landmark in Jain logic , anekāntavāda was firmly entrenched in Jain texts , as is evident from the various teachings of the Jain scriptures . 
- Ācārya <unk> ( 8th century CE ) was one of the leading proponents of anekāntavāda . He was the first classical author to write a <unk> , a compendium of a variety of intellectual views . This attempted to <unk> Jain thoughts within the broad framework , rather than <unk> narrow partisan views . It interacted with the many possible intellectual orientations available to Indian thinkers around the 8th century . 
- Ācārya <unk> starts his famous 10th century CE work <unk> with strong praise for anekāntavāda : " I bow down to the principle of anekānta , the source and foundation of the highest scriptures , the <unk> of wrong one @-@ sided notions , that which takes into account all aspects of truth , <unk> diverse and even contradictory traits of all objects or entity . " 
- Ācārya <unk> ( 11th century CE ) provides the analogy of the ocean to explain the nature of truth in <unk> , 116 : " Water from the ocean contained in a pot can neither be called an ocean nor a non @-@ ocean , but simply a part of ocean . Similarly , a doctrine , though arising from absolute truth can neither be called a whole truth nor a non @-@ truth . " 
- <unk> <unk> , a 17th @-@ century Jain monk , went beyond anekāntavāda by advocating <unk> , meaning " standing in the middle " or " <unk> " . This position allowed him to praise qualities in others even though the people were non @-@ Jain and belonged to other faiths . There was a period of stagnation after <unk> , as there were no new contributions to the development of Jain philosophy . 
- 
- = = = Role in ensuring the survival of Jainism = = = 
- 
- Anekāntavāda played a pivotal role in the growth as well as the survival of Jainism in ancient India , especially against <unk> from <unk> , <unk> , Buddhists , Muslims , and Christians at various times . According to Hermann Jacobi , Mahāvīra used such concepts as syādvāda and <unk> to silence some of his opponents . The discussions of the <unk> led by Sañjaya Belaṭṭhaputta had probably influenced many of their contemporaries and consequently syādvāda may have seemed to them a way out of <unk> . Jacobi further speculates that many of their followers would have gone over to Mahāvīra 's <unk> , convinced of the truth of the <unk> . According to Professor Christopher Key <unk> , anekāntavāda allowed Jains to survive during the most hostile and <unk> moments in history . According to John Koller , professor of Asian studies , anekāntavāda allowed Jain thinkers to maintain the validity of their doctrine , while at the same time <unk> criticizing the views of their opponents . 
- Anekāntavāda was often used by Jain monks to obtain royal patronage from Hindu Kings . Ācārya Hemacandra used anekāntavāda to gain the confidence and respect of the <unk> Emperor <unk> <unk> . According to the Jain text <unk> , Emperor <unk> desired enlightenment and liberation and he questioned teachers from various traditions . He remained in a quandary when he discovered that they all promoted their own teachings while disparaging other teachings . Among the teachers he questioned was Hemacandra , who , rather than promote Jainism , told him a story with a different message . According to his story , a sick man was cured of his disease after eating all the herbs available , as he was not aware which herb was <unk> . The moral of the tale , according to Hemacandra , was that just as the man was restored by the herb , even though no one knew which particular herb did the trick , so in the <unk> ( " age of vice " ) the wise should obtain salvation by supporting all religious traditions , even though no @-@ one can say with absolute certainty which tradition it is that provides that salvation . 
- 
- = = Influence = = 
- 
- Jain religious tolerance fits well with the ecumenical disposition typical of Indian religions . It can be traced to the analogous Jain principles of anekāntavāda and ahiṃsā . The epistemology of anekāntavāda and syādvāda also had a profound impact on the development of ancient Indian logic and philosophy . In recent times , Jainism influenced Gandhi , who advocated ahiṃsā and <unk> . 
- 
- = = = <unk> <unk> and religious tolerance = = = 
- 
- The concepts of anekāntavāda and syādvāda allow Jains to accept the truth in other philosophies from their own perspective and thus <unk> tolerance for other viewpoints . Anekāntavāda is non @-@ absolutist and stands firmly against all <unk> , including any assertion that Jainism is the only correct religious path . It is thus an intellectual ahiṃsā , or ahiṃsā of the mind . <unk> writes , " Jain logic is intellectual ahiṃsā . Just as a right @-@ acting person respects the life of all beings , so a right @-@ thinking person acknowledges the validity of all <unk> . This means recognizing all aspects of reality , not merely one or some aspects , as is done in non @-@ Jain philosophies . " 
- Mahāvīra encouraged his followers to study and understand rival traditions in his <unk> Sutra : " <unk> one philosophical view through the comprehensive study of another one . " 
- In anekāntavāda , there is no " battle of ideas " , because this is considered to be a form of intellectual <unk> or violence , leading quite <unk> to physical violence and war . In today 's world , the limitations of the <unk> , " either with us or against us " form of argument are increasingly apparent by the fact that the argument leads to political , religious and social conflicts . <unk> , the second oldest canon of Jainism , provides a solution by stating : " Those who praise their own doctrines and ideology and <unk> the doctrine of others <unk> the truth and will be confined to the cycle of birth and death . " 
- This ecumenical and <unk> attitude , engendered by anekāntavāda , allowed modern Jain monks such as <unk> to declare : " I am neither a Jain nor a Buddhist , a <unk> nor a <unk> , a Hindu nor a Muslim , but a traveler on the path of peace shown by the supreme soul , the God who is free from passion . " 
- 
- = = = = Contemporary role and influence = = = = 
- 
- Some modern authors believe that Jain philosophy in general and anekāntavāda in particular can provide a solution to many problems facing the world . They claim that even the mounting ecological crisis is linked to <unk> , because it arises from a false division between humanity and " the rest " of nature . Modern judicial systems , democracy , freedom of speech , and <unk> all implicitly reflect an attitude of anekāntavāda . Many authors , such as <unk> Jain , have claimed that the Jain tradition , with its emphasis on <unk> and anekāntavāda , is capable of solving religious intolerance , terrorism , wars , the depletion of natural resources , environmental degradation and many other problems . Referring to the September 11 attacks , John Koller believes that violence in society mainly exists due to faulty epistemology and <unk> as well as faulty ethics . A failure to respect the life and views of others , rooted in <unk> and mistaken knowledge and refusal to acknowledge the legitimate claims of different perspectives , leads to violent and destructive behaviour . Koller suggests that anekāntavāda has a larger role to play in the world peace . According to Koller , because anekāntavāda is designed to avoid one @-@ sided errors , reconcile contradictory viewpoints , and accept the multiplicity and relativity of truth , the Jain philosophy is in a unique position to support dialogue and negotiations amongst various nations and peoples . 
- Some <unk> like Professor John <unk> have cautioned against giving undue importance to " intellectual ahiṃsā " as the basis of anekāntavāda . He points out that Jain monks have also used anekāntavāda and syādvāda as debating weapons to silence their critics and prove the validity of the Jain doctrine over others . According to <unk> , in Jain hands , this method of analysis became a <unk> weapon of philosophical <unk> with which the doctrines of Hinduism and Buddhism could be <unk> down to their ideological bases of simple permanence and impermanence , respectively , and thus could be shown to be one @-@ pointed and inadequate as the overall interpretations of reality they purported to be . On the other hand , the many @-@ sided approach was claimed by the Jains to be immune from criticism since it did not present itself as a philosophical or <unk> view . 
- 
- = = = Influence on Mohandas Karamchand Gandhi = = = 
- 
- Since childhood , Mohandas Karamchand Gandhi was exposed to the actual practice of non @-@ violence , non @-@ possession and anekāntavāda by his mother . According to biographers like <unk> <unk> , <unk> Gandhi , and Stephen Hay , these early childhood impressions and experiences contributed to the formation of Gandhi 's character and his further moral and spiritual development . In his writings , Mahatma Gandhi attributed his seemingly contradictory positions over a period of time to the learning process , experiments with truth and his belief in anekāntavāda . He proclaimed that the duty of every individual is to determine what is personally true and act on that relative perception of truth . According to Gandhi , a <unk> is duty bound to act according to his relative truth , but at the same time , he is also equally bound to learn from truth held by his opponent . In response to a friend 's query on religious tolerance , he responded in the journal " Young India – 21 Jan 1926 " : 
- I am an <unk> and yet I can support <unk> ( dualism ) . The world is changing every moment , and is therefore <unk> , it has no permanent existence . But though it is constantly changing , it has a something about it which persists and it is therefore to that extent real . I have therefore no <unk> to calling it real and <unk> , and thus being called an <unk> or a <unk> . But my Syādvāda is not the Syādvāda of the learned , it is <unk> my own . I cannot engage in a debate with them . It has been my experience that I am always true from my point of view , and am often wrong from the point of view of my honest critics . I know that we are both right from our respective points of view . And this knowledge saves me from attributing motives to my opponents or critics . The seven blind men who gave seven different descriptions of the elephant were all right from their respective points of view , and wrong from the point of view of one another , and right and wrong from the point of view of the man who knew the elephant . I very much like this doctrine of the <unk> of reality . It is this doctrine that has taught me to judge a <unk> from his standpoint and a Christian from his . <unk> I used to resent the ignorance of my opponents . Today I can love them because I am gifted with the eye to see myself as others see me and vice versa . I want to take the whole world in the embrace of my love . My Anekāntavāda is the result of the twin doctrine of <unk> and ahiṃsā . 
- 
- = = Criticism = = 
- 
- The doctrines of anekāntavāda and <unk> are often criticised on the grounds that they engender a degree of <unk> and uncertainty , and may compound problems rather than solve them . It is also pointed out that Jain epistemology asserts its own doctrines , but at the cost of being unable to deny contradictory doctrines . Furthermore , it is also argued that this doctrine could be self @-@ defeating . It is argued that if reality is so complex that no single doctrine can describe it adequately , then anekāntavāda itself , being a single doctrine , must be inadequate . This criticism seems to have been anticipated by Ācārya Samantabhadra who said : " From the point of view of <unk> ( means of knowledge ) it is anekānta ( multi @-@ sided ) , but from a point of view of naya ( partial view ) it is <unk> ( one @-@ sided ) . " 
- In defense of the doctrine , Jains point out that anekāntavāda seeks to reconcile apparently opposing viewpoints rather than <unk> them . 
- Anekāntavāda received much criticism from the <unk> , notably Adi <unk> ( 9th century <unk> ) . Sankara argued against some tenets of Jainism in his <unk> on <unk> ( 2 : 2 : 33 – 36 ) . His main arguments centre on anekāntavāda : 
- It is impossible that contradictory attributes such as being and non @-@ being should at the same time belong to one and the same thing ; just as observation teaches us that a thing cannot be hot and cold at the same moment . The third alternative expressed in the words — they either are such or not such — results in cognition of indefinite nature , which is no more a source of true knowledge than doubt is . Thus the means of knowledge , the object of knowledge , the knowing subject , and the act of knowledge become all alike indefinite . How can his followers act on a doctrine , the matter of which is altogether indeterminate ? The result of your efforts is perfect knowledge and is not perfect knowledge . Observation shows that , only when a course of action is known to have a definite result , people set about it without <unk> . Hence a man who <unk> a doctrine of altogether indefinite contents does not deserve to be listened any more than a drunken or a mad man . 
- However , many believe that Sankara fails to address genuine anekāntavāda . By identifying <unk> with <unk> , he instead addresses " <unk> " , which was argued by Sañjaya Belaṭṭhaputta . Many authors like <unk> believe that Sankara overlooked that , the affirmation of the existence of an object is in respect to the object itself , and its negation is in respect to what the object is not . Genuine anekāntavāda thus considers positive and negative attributes of an object , at the same time , and without any contradictions . 
- Another Buddhist <unk> <unk> ridiculed anekāntavāda in <unk> : " With the differentiation removed , all things have dual nature . Then , if somebody is <unk> to eat curd , then why he does not eat camel ? " The <unk> is obvious ; if curd exists from the nature of curd and does not exist from the nature of a camel , then one is justified in eating camel , as by eating camel , he is merely eating the negation of curd . Ācārya <unk> , while agreeing that <unk> may be right from one viewpoint , took it upon himself to issue a <unk> : 
- The person who <unk> without understanding the <unk> <unk> view is acting like a <unk> and not a critic . The Buddha was born a deer and the deer was born as Buddha ; but Buddha is adorable and deer is only a food . Similarly , due to the strength of an entity , with its differences and similarities specified , nobody would eat camel if <unk> to eat curd . 
- 
- 
- = Arnhem Oosterbeek War Cemetery = 
- 
- The Arnhem Oosterbeek War Cemetery , more commonly known as the Airborne Cemetery , is a Commonwealth War Graves Commission cemetery in Oosterbeek , near Arnhem , the Netherlands . It was established in 1945 and is home to 1759 graves from the Second World War . Most of the men buried in the cemetery were Allied servicemen killed in the Battle of Arnhem , an Allied attempt to cross the Rhine in 1944 , or in the liberation of the city the following year . Men killed in these battles are still discovered in the surrounding area even in the 21st century , and so the number of people interred in the cemetery continues to grow . 
- 
- = = Background = = 
- 
- In September 1944 the Allies launched Operation Market Garden , an attempt by the British 2nd Army to bypass the Siegfried Line and advance into the <unk> , Germany 's industrial <unk> . The operation required the 1st Airborne Corps to seize several bridges over rivers and canals in the Netherlands , allowing ground forces to advance rapidly through the Netherlands and cross the River Rhine . 
- The British 1st Airborne Division was tasked with securing the most distant objectives ; bridges over the Lower Rhine at Arnhem . The division dropped onto the area on 17 September and a small force was able to secure the Arnhem road bridge . However the unexpected presence of SS Panzer troops of the II SS <unk> meant the Allies were never able to fully secure their objectives and so after nine days without sufficient reinforcement by the advancing ground forces , the division was withdrawn on 25 September . 
- In the 9 days of battle almost 2000 Allied soldiers were killed ( some of whom died of their wounds or in captivity after the battle ) . These included over <unk> men of the British 1st Airborne Division , 219 men of the <unk> Pilot Regiment , 92 men of the Polish 1st Independent Parachute Brigade , 368 men of the RAF , 79 re @-@ supply <unk> of the RASC , 25 men of XXX Corps and 27 men of US IX <unk> Carrier Command . The exact number of German dead is unknown , but is believed to be at least 1300 . Additionally it is believed 453 Dutch civilians were killed during the battle . 
- 
- = = Cemetery = = 
- 
- Owing to the Allied withdrawal , the vast majority of their dead had to be left on the battlefield . Here they were buried in simple field graves ( some little more than their own slit trenches ) or in small mass graves dug by the Germans . Kate <unk> <unk> , whose house was used as a first aid post during the battle , found the graves of 57 men in her garden when she returned after the war . After Arnhem was liberated in April 1945 , Grave <unk> Units of the British 2nd Army moved into the area and began to locate the Allied dead . A small field north of Oosterbeek was offered on perpetual loan by the Netherlands government to the Imperial War Graves Commission ( now Commonwealth War Graves Commission ) in June 1945 and the dead were reburied there . Many of those killed during Arnhem 's liberation were also buried at the same site . The cemetery was completed in February 1946 , originally with the graves marked by metal crosses , although these were replaced by <unk> in 1952 . Most of the German dead were buried in the SS Heroes Cemetery near Arnhem after the battle , but reburied in <unk> German war cemetery after the war . 
- The Commonwealth War Graves Commission records 1759 graves in the cemetery as of 2004 . <unk> of these are Commonwealth , including British , Canadian , Australian and New Zealanders . The cemetery is also the last resting place of 73 Polish soldiers , ( many of them exhumed and moved from <unk> , to the disappointment of <unk> 's residents ) and 8 Dutch civilians – some killed in the fighting and some former Commission employees . 253 of the graves are unidentified . 
- As of 2003 there were still 138 Allied men with no known grave in the area , and they are commemorated at the <unk> Memorial . However , evidence of the battle is often discovered even today , and the bodies of Allied servicemen are <unk> at the Airborne Cemetery . When found , bodies are exhumed and Dutch Graves <unk> staff attempt to identify them before they are reburied . One soldier of the Border Regiment was discovered and reburied in the cemetery in 2005 and another who had previously been unidentified was reburied in 2006 . 
- Five men were awarded the Victoria Cross after the battle , four of them posthumously . Three of the men now rest in the cemetery ; Lieutenant John <unk> <unk> of the 2nd Battalion , Parachute Regiment ; Flight Lieutenant David Samuel Anthony Lord of 271 Squadron , Royal Air Force and Captain Lionel Ernest <unk> of the 10th Battalion , Parachute Regiment . Lance @-@ Sergeant John Daniel <unk> of the 2nd Battalion South <unk> Regiment has no known grave and is commemorated instead at <unk> Memorial . Major Robert Henry Cain , also of 2nd Battalion , South <unk> Regiment , survived the battle and was buried on the Isle of Man when he died in 1974 . 
- <unk> the Airborne Cemetery is a civilian graveyard with a small Commonwealth War Graves Commission plot containing the graves of nine airmen shot down shortly before the battle . It is also home to <unk> <unk> , a surgeon with the 16th ( Parachute ) Field Ambulance during the battle , who wished to be buried near his men after his death in 1986 . Similarly , the <unk> Cemetery three miles east contains the graves of thirty six aircrew killed before the battle , and one unidentified soldier . Not all of the Allied dead from the Battle of Arnhem are interred at the cemetery . Some 300 men who were killed when flying into battle , while trying to escape or who succumbed to wounds later , are buried in civilian <unk> in the Netherlands , Belgium , the UK and the USA . Sixty men who died in prisoner of war camps after the battle are buried in Germany . 
- 
- = = Airborne commemoration service = = 
- 
- In the summer of 1945 several hundred veterans of the battle were detached from operations in Norway and returned to Arnhem to take part in filming for the war movie <unk> Is the Glory . While there they attended the first commemorative event at the cemetery . This event continued every year , and was attended by veterans , local residents and over 1000 school children who laid flowers on the graves of the dead . After the 25th anniversary in 1969 , the Parachute Regiment approached Dutch organisers to suggest ending the ceremony , believing the battle to have passed sufficiently into history . The Dutch were vehemently and emotionally opposed to the idea and thus the ceremony continues to be held annually . 
- 
- 
- = Ace Attorney = 
- 
- Ace Attorney , known in Japan as Gyakuten Saiban ( Japanese : <unk> , " <unk> Trial " ) , is a series of visual novel adventure video games developed by Capcom . The first entry in the series , Phoenix Wright : Ace Attorney , was released in 2001 ; since then , nine further games have been released . Additionally , the series has seen adaptations in the form of a live action film and an anime , and has been the base for manga series , drama CDs , musicals and stage plays . 
- The player takes the roles of the defense attorneys Phoenix Wright , Mia Fey , Apollo Justice and Athena <unk> , and investigates cases and defends their clients in court ; they find the truth by cross @-@ examining witnesses and finding inconsistencies between the testimonies and the evidence they have collected . The cases all last a maximum of three days , with the judge determining the outcome based on evidence presented by the defense attorney and the prosecutor . In the spin @-@ off series Ace Attorney Investigations , the player takes the role of prosecutor Miles Edgeworth , and in the spin @-@ off Dai Gyakuten Saiban , they play as Phoenix 's ancestor Ryūnosuke Naruhodō . 
- The series was created by the writer and director Shu Takumi , who wanted the series to end after the third game . The series still continued , with Takeshi Yamazaki taking over as writer and director starting with Ace Attorney Investigations : Miles Edgeworth ( 2009 ) ; Takumi has since returned to write and direct some spin @-@ off titles . While the original Japanese versions of the games are set in Japan , the series ' <unk> are set in the United States , though retaining Japanese cultural influence . The series has been well received , with reviewers liking the characters and story , and the finding of contradictions ; it has also performed well commercially , with Capcom regarding it as one of their strongest intellectual properties . 
- 
- = = Titles = = 
- 
- The Ace Attorney series launched in Japan with the Game Boy Advance game Phoenix Wright : Ace Attorney in 2001 , and has been published in the West since the release of a Nintendo DS port in 2005 . The series currently consists of six main series games and four spin @-@ offs . Additionally , two titles that collect the first three main series games have been released : Ace Attorney : Phoenix Wright Trilogy HD , which was released for iOS in 2012 in Japan and in 2013 in the West , and Phoenix Wright : Ace Attorney Trilogy , which was released for the Nintendo 3DS in 2014 . 
- 
- = = = Main series = = = 
- 
- Phoenix Wright : Ace Attorney is the first entry in the series . It was originally released for the Game Boy Advance in 2001 in Japan ; it has also been released for the Nintendo DS in 2005 , Microsoft Windows in 2008 , and the Wii and iOS in 2009 . 
- Phoenix Wright : Ace Attorney − Justice for All is the second entry in the series . It was originally released for the Game Boy Advance in 2002 in Japan ; it has also been released for the Nintendo DS in 2006 , Microsoft Windows in 2008 , and the Wii in 2010 . 
- Phoenix Wright : Ace Attorney − Trials and Tribulations is the third entry in the series . It was originally released for the Game Boy Advance in 2004 in Japan ; it has also been released for Microsoft Windows in 2006 , the Nintendo DS in 2007 , and the Wii in 2010 . 
- Apollo Justice : Ace Attorney is the fourth entry in the series . It was released for the Nintendo DS in 2007 in Japan and in 2008 in the West . 
- Phoenix Wright : Ace Attorney − Dual Destinies is the fifth entry in the main series . It was originally released for the Nintendo 3DS in 2013 in Japan , North America and Europe ; outside of Japan , it was given a digital @-@ only release . An iOS version was released in 2014 in Japan and the West . 
- Phoenix Wright : Ace Attorney − Spirit of Justice is the sixth entry in the main series . It was released for the Nintendo 3DS in 2016 in Japan , and is planned to be released later in the year in North America and Europe ; outside of Japan , it will be a digital @-@ only release . 
- 
- = = = Spin @-@ offs = = = 
- 
- Ace Attorney Investigations : Miles Edgeworth is the first entry in the Investigations spin @-@ off series . It was released for the Nintendo DS in 2009 in Japan and in 2010 in the West . 
- Ace Attorney Investigations 2 is the second entry in the Investigations series . It was released for the Nintendo DS in 2011 in Japan , but has not been released in the West . 
- Professor Layton vs. Phoenix Wright : Ace Attorney is a crossover between Ace Attorney and the Professor Layton series . It was released for the Nintendo 3DS in 2012 in Japan and in 2014 in the West . 
- Dai Gyakuten Saiban : Naruhodō Ryūnosuke no <unk> is the first entry in a planned spin @-@ off series . It was released for the Nintendo 3DS in 2015 in Japan , but is not intended to be released in the West . 
- 
- = = Common elements = = 
- 
- 
- = = = Gameplay = = = 
- 
- The Ace Attorney games are visual novel adventure games in which the player controls defense attorneys and defends their clients in several different episodes . The gameplay is split into two types of sections : investigations and courtroom trials . During the investigations , the player searches the environments , gathering information and evidence , and talks to characters such as their client , witnesses , and the police . Once enough evidence has been collected , the game moves on to a courtroom trial section . 
- In the courtroom trials , the player aims to get their client declared " not guilty " . To do so , they cross @-@ examine witnesses , and aim to find lies and inconsistencies in the testimonies . They are able to go back and forth between the different statements in the testimony , and can press the witness for more details on a statement . When the player finds an inconsistency , they can present a piece of evidence that <unk> the statement . The player is penalized if they present incorrect evidence : in the first game , a number of exclamation marks is shown , with one disappearing after each mistake the player makes ; in later games , a health bar that represents the judge 's patience is used instead . If all exclamation marks are lost , or the health bar reaches zero , the player loses the game and their client is declared guilty . 
- Several Ace Attorney games introduce new gameplay mechanics to the series . Justice for All introduces " psyche @-@ locks " , which are shown over a witness when the player asks them about a topic they do not want to discuss ; using a <unk> , the player can start breaking the psyche @-@ locks by showing the witness evidence or character <unk> that proves they are hiding something . The number of psyche @-@ locks depends on how deep the secret is ; when all locks are broken , the topic becomes available , giving the player access to new information . Apollo Justice introduces the " perceive " system , where the player looks for motions or actions made by witnesses that show <unk> , similar to a tell in poker . 
- Dual Destinies introduces the " mood matrix " , through which the player can gauge the emotions of a witness , such as tones of anger when mentioning certain topics ; if the player notices a contradictory emotional response during testimony , they can point out the discrepancy and press the witness for more information . Dual Destinies also introduces " <unk> " , where the player reviews vital facts and forms links between evidence to reach new conclusions . Spirit of Justice introduces " divination <unk> " , in which the player is shown the memories of victims moments before their deaths , and must find contradictions in the victim 's five senses to determine what has happened . Professor Layton vs. Phoenix Wright : Ace Attorney introduces simultaneous cross @-@ examinations of multiple witnesses , with the player being able to see and hear reactions from the different witnesses to the testimony and using this to find contradictions . Dai Gyakuten Saiban introduces " joint reasoning " , where the player finds out the truth by pointing out when their investigative partner Sherlock Holmes takes his reasoning " further than the truth " . 
- The Ace Attorney Investigations spin @-@ off series splits the gameplay into investigation phases and rebuttal phases , the latter of which is similar to the courtroom trials of the main series . During the investigation phases , the player searches for evidence and talks to witnesses and suspects . Things the player character notices in the environment are saved as thoughts ; the player can use the " logic " system to connect two such thoughts to gain access to new information . At some points , the player can create hologram reproductions of the crime scene , through which they can discover new information that would otherwise be hidden . Ace Attorney Investigations 2 introduces " logic chess " , where the player <unk> witnesses in a timed sequence that is visualized as a game of chess , with the player aiming to destroy the other character 's chess pieces . To do this , they need to build up their advantage in the discussion by alternating between speaking and listening , and then choose to go on the offensive . 
- 
- = = = Characters and setting = = = 
- 
- The protagonist of the first three games is the defense attorney Phoenix Wright , who is assisted by the spirit medium Maya Fey ; in the third game , Phoenix 's mentor Mia Fey is also a playable character . In the fourth game , the protagonist is the defense attorney Apollo Justice ; in the fifth , Phoenix , Apollo and the new defense attorney Athena <unk> are all protagonists ; and in the sixth , Phoenix and Apollo are the protagonists . The spin @-@ off Dai Gyakuten Saiban is set in England near the end of the 19th century , and follows Phoenix 's ancestor Ryūnosuke Naruhodō . 
- Phoenix 's childhood friend Miles Edgeworth , who is the protagonist of the Ace Attorney Investigations games , is a recurring rival prosecutor character ; in addition to him , each new game in the series introduces a new rival : <unk> von <unk> is introduced in the second game , <unk> in the third , <unk> Gavin in the fourth , Simon <unk> in the fifth , and <unk> <unk> in the sixth . The prosecutor characters are portrayed as powerful and arrogant characters of high social status , who favor convictions over finding the truth , and who care about keeping perfect @-@ win records in court . Similarly to real Japanese prosecutors , the prosecutors in the series often directly oversee investigations , issuing orders to the police . Japanese attitudes towards the police force are reflected in the series , with the police being represented by incompetent characters such as Dick <unk> , <unk> <unk> and Mike <unk> . In the world of Ace Attorney , trials only last three days , and usually end with a " guilty " verdict . The outcomes of cases are decided by a judge , based on evidence provided by the defense attorney and the prosecutor . 
- 
- = = Development = = 
- 
- The series was created by Shu Takumi , who wrote and directed the first three games . The first game was conceived in 2000 when Takumi 's boss at the time , Shinji <unk> , gave him six months to create any type of game he wanted to ; Takumi had originally joined Capcom wanting to make mystery and adventure games , and felt that this was a big chance for him to make a mark as a creator . The game was designed to be simple , as Takumi wanted it to be easy enough for even his mother to play . It was originally going to be a detective game , with Phoenix being a private investigator , but at one point Takumi realized that finding and taking apart contradictions was not related to detective work , and felt that the main setting of the game should be <unk> . 
- Takumi felt that the best way to write a mystery with a good climax is to reveal various clues , and then pull them together into one conclusion , and not have multiple possible endings . He said that the biggest challenge with that was to make the gameplay and story work together ; the goal was to make the player feel like they have driven the story forward themselves , with their own choices , even though the game is linear . He only spent little time on writing a backstory for Phoenix before writing the first game 's story , and instead made up dialogue and developed Phoenix 's personality as he went along . He came up with the partner character Maya because he thought it would be more fun for players to have another character with them , giving them advice , than investigating on their own . 
- After the first game 's development was finished , <unk> told Takumi that they should make an Ace Attorney trilogy , with a grand finale in the third game 's last case . Takumi had originally planned to let Edgeworth be the prosecutor in all episodes in the second game , but during the production the development team learned that the character had become popular . This led to Takumi feeling that he had to use the character more carefully and <unk> ; he created the new prosecutor character <unk> von <unk> , to save Edgeworth for the game 's last case , and avoid a situation where he – a supposed <unk> – loses every case . As Takumi wanted the three first Ace Attorney games to be parts of a larger work , he avoided making a lot of changes between games : art from the first game for main characters such as Phoenix , Maya and Edgeworth was reused , to avoid having the previous games look outdated in comparison to newer games in the series ; and no new gameplay mechanics were added for Trials and Tribulations , as Takumi was happy with the gameplay after having added the psyche @-@ lock mechanic for Justice for All . 
- For the fourth game , Takumi wrote the scenario and took on a supervisory role . He had wanted the series to end with the third game , as he felt Phoenix had been fully explored and that his story had been told ; he said that it is important to know when to end a story , that he did not want the series to become a shadow of its former self , and that he did not see any reason to continue it . Despite this , the spin @-@ off series Ace Attorney Investigations was created , being directed by Takeshi Yamazaki and produced by <unk> <unk> ; Takumi returned to the series to write the crossover Professor Layton vs. Phoenix Wright : Ace Attorney . He also directed and wrote Dai Gyakuten Saiban , which was described as being the first entry in a new Ace Attorney series . He said that he has mixed feelings about the series being developed by other Capcom staff , comparing it to a parent sending their child to their first day in school . Yamazaki and <unk> went on to direct and produce the main series entries Dual Destinies and Spirit of Justice . Due to exhaustion after working on Dual Destinies , Yamazaki split direction responsibilities with <unk> Fuse for Spirit of Justice , with Yamazaki working on the scenario , and Fuse on the art and gameplay . 
- 
- = = <unk> = = 
- 
- The localization of the first game was <unk> to <unk> Global , and was handled by the writer Alexander O. Smith and the editor Steve Anderson . While the Japanese version takes place in Japan , the localized version is set in the United States : because one of the episodes involves time zones , they had to specify where the game takes place , and chose the United States without thinking a lot about it . The Japanese justice system of the original still remained intact in the localization , as changing it would have altered the entire game structure . 
- The change in the series ' setting became an issue in later games , where the Japanese setting was more apparent . Starting with the second game , the series localization direction has been handled by Janet <unk> ; One of the first decisions she had to make was how to <unk> Maya 's hometown and the mysticism of the Fey clan . She came up with the idea that the localized versions of the Ace Attorney games take place in Los Angeles in an alternative universe where anti @-@ Japanese laws like the California Alien Land Law of 1913 were not passed , anti @-@ Japanese sentiments were not powerful , and where Japanese culture flourished . This dictated what should be localized and what should be kept Japanese ; things relating to the Fey clan and the <unk> channeling technique were kept Japanese , as that was Maya 's heritage , while Japanese foods that were not widely known in the West were changed . 
- Character names were also localized to use double meanings similarly to the Japanese names ; the name puns were based on the characters ' personalities or backgrounds , or were visual gags . Several English names were based on their Japanese counterparts , but for some characters the names had to be altered heavily compared to the Japanese versions . Smith and Anderson had a lot of freedom when <unk> the names of minor characters in the first game , but discussed the names of the main cast with Capcom . Phoenix 's English surname , " Wright " , was chosen as his Japanese name , " Naruhodō " – meaning " I see " or " I understand " – was frequently used as a joke in the script . 
- Dual Destinies was given a digital @-@ only release in the West . This was partially because of the game 's tight development schedule : by releasing it digitally , Capcom was able to release the English version close to the Japanese release date . Two of the games have not been localized : Ace Attorney Investigations 2 and Dai Gyakuten Saiban , although the former has received a full fan translation . 
- 
- = = Reception = = 
- 
- The Ace Attorney series has been well received by critics , and has performed well commercially : in December 2009 , it was Capcom 's 9th best selling series of all time , and in October 2010 , they called it one of their " strongest intellectual properties " , with more than 3 @.@ 9 million units sold worldwide . By December 2013 , the series had sold over 5 million units . In the United States , the first game became surprisingly successful , forcing Capcom to prepare at least three additional runs to meet the demand . 
- Reviewers have liked finding contradictions ; a common complaint , however , is the games ' <unk> , as well as how the player sometimes has to resort to a trial @-@ and @-@ error method due to the games only accepting specific pieces of evidence , and how testimony statements sometimes need to be pressed in a specific order . Some reviewers have criticized the lack of changes to the gameplay and presentation throughout the series , while some have said that fans of the series would not have a problem with this . 
- Geoff Thew at Hardcore Gamer said that the " <unk> " of the game world makes the cases entertaining , but also that it " <unk> on a deeper level " due to its connection to the real Japanese legal system , making the setting still feel relevant in 2014 . Bob <unk> at <unk> said that the Ace Attorney games were among the best written games of all time , and that the series ' strength is how each game builds up to a " stunning and satisfying finale " . Thomas Whitehead at Nintendo Life also liked the writing , praising its balance between " light @-@ hearted nonsense " and darker , more serious scenarios . Several reviewers have appreciated the series ' characters ; Thew said that Phoenix and Maya 's banter is among the best in video games , and that Edgeworth 's character arc is one of the most compelling parts of the stories . 
- Several reviewers have praised the series ' music . Thew said that the greatest aspect of the series is its audio design , with the first three games using the Game Boy Advance sound chip better than any other game for that platform ; he called the music phenomenal , with the exception of that in Justice for All , but said the sound effects are what " steals the show " . <unk> commented that the games ' small amounts of animations for each character are used well for their characterization . 
- 
- = = Related media and other appearances = = 
- 
- The <unk> Revue , an all @-@ female theater troupe , has adapted the series into stage musicals : 2009 's Ace Attorney : Truth Resurrected , which is based on the last episode of the first game ; 2010 's Ace Attorney 2 : Truth Resurrected Again , whose first act is an original story , while its second act is based on the final episode of the second game ; and 2013 's Ace Attorney 3 : Prosecutor Miles Edgeworth , which is set before the events of Truth Resurrected Again . A stage play based on the series , titled Gyakuten no Spotlight , ran in 2013 , and was written by <unk> <unk> . A 2012 live @-@ action film adaptation of the first game , titled Ace Attorney , was produced at the film studio Toei and directed by <unk> Miike . A 2016 TV anime adaptation of the series , Ace Attorney , is being produced at A @-@ 1 Pictures and is directed by <unk> <unk> . Kodansha has published several manga based on the series : a short story anthology was published in <unk> Young Magazine in 2006 ; Phoenix Wright : Ace Attorney and Ace Attorney Investigations : Miles Edgeworth were serialized in Weekly Young Magazine in 2007 and 2009 , respectively ; and another manga , which is based on the anime , will be published in V Jump in 2016 . A novel based on the series , Gyakuten Saiban : <unk> Idol , is planned to be released in 2016 . Ace Attorney drama CDs and albums with Ace Attorney music have also been released . 
- Ace Attorney characters have made cross @-@ over appearances in other video games . Some Ace Attorney characters appear in <unk> vs. Capcom : Card Fighters DS . Phoenix and Edgeworth make a cameo appearance in She @-@ <unk> 's ending in the fighting game Marvel vs. Capcom 3 : Fate of Two Worlds ; in the game 's update , Ultimate Marvel vs. Capcom 3 , Phoenix appears as a playable character . Phoenix and Maya are playable characters in Project X Zone 2 , while Edgeworth makes a non @-@ playable appearance . Music from the Ace Attorney series is featured in <unk> Drum Master : <unk> Don ! <unk> Adventure , with Phoenix making an appearance in the game 's story . 
- 
- 
- = Cambodian Campaign = 
- 
- The Cambodian Campaign ( also known as the Cambodian <unk> and the Cambodian Invasion ) was a series of military operations conducted in eastern Cambodia during 1970 by the United States and the Republic of Vietnam ( South Vietnam ) ( <unk> ) during the Vietnam War . These invasions were a result of the policy of President Richard Nixon . A total of 13 major operations were conducted by the Army of the Republic of Vietnam ( ARVN ) between 29 April and 22 July and by US forces between 1 May and 30 June . 
- The objective of the campaign was the defeat of the approximately 40 @,@ 000 troops of the People 's Army of Vietnam ( PAVN ) and the National Front for the Liberation of South Vietnam ( NLF , also known as Viet Cong ) who were <unk> in the eastern border regions of Cambodia . Cambodia 's official neutrality and military weakness made its territory effectively a safe zone where Vietnamese communist forces could establish bases for operations over the border . With the US shifting toward a policy of Vietnamization and withdrawal , the US sought to shore up the South Vietnamese government 's security by eliminating the cross @-@ border threat . 
- A change in the Cambodian government allowed a window of opportunity for the destruction of the base areas in 1970 when Prince <unk> Sihanouk was deposed and replaced by pro @-@ US General Lon Nol . Allied military operations failed to eliminate many communist troops or to capture their elusive headquarters , known as the Central Office for South Vietnam ( COSVN ) , but the haul of captured material in Cambodia prompted claims of success . 
- 
- = = <unk> = = 
- 
- 
- = = = Background = = = 
- 
- The People 's Army of Vietnam had been utilizing large sections of relatively unpopulated eastern Cambodia as sanctuaries into which they could withdraw from the struggle in South Vietnam to rest and reorganize without being attacked . These base areas were also utilized by the Vietnamese communists to store weapons and other material that had been transported on a large scale into the region on the Sihanouk Trail . PAVN forces had begun moving through Cambodian territory as early as 1963 . In 1966 , Prince <unk> Sihanouk , ruler of Cambodia , convinced of eventual communist victory in Southeast Asia and fearful for the future of his rule , had concluded an agreement with the People 's Republic of China which allowed the establishment of permanent communist bases on Cambodian soil and the use of the Cambodian port of Sihanoukville for resupply . 
- During 1968 , Cambodia 's indigenous communist movement , labeled Khmer Rouge ( Red <unk> ) by Sihanouk , began an insurgency to overthrow the government . While they received very limited material help from the North Vietnamese at the time ( the Hanoi government had no incentive to overthrow Sihanouk , since it was satisfied with his continued " neutrality " ) , they were able to shelter their forces in areas controlled by PAVN / NLF troops . 
- The US government was aware of these activities in Cambodia , but <unk> from taking overt military action within Cambodia in hopes of convincing the <unk> Sihanouk to alter his position . To accomplish this , President Lyndon B. Johnson authorized covert cross @-@ border reconnaissance operations conducted by the secret Studies and Observations Group in order to gather intelligence on PAVN / NLF activities in the border regions ( Project <unk> ) . This intelligence data would then be presented to the prince in an effort to change his mind . 
- 
- = = = Menu , coup and North Vietnamese offensive = = = 
- 
- The new commander of the US Military Assistance Command , Vietnam ( MACV ) , General Creighton W. Abrams , recommended to President Richard M. Nixon shortly after his inauguration that the Cambodian Base Areas be attacked by aerial bombardment utilizing B @-@ 52 <unk> bombers . The president initially refused , but the breaking point came with the launching of PAVN 's " Mini @-@ <unk> " Offensive of 1969 within South Vietnam . Nixon , angered at what he perceived as a violation of the " agreement " with Hanoi after the cessation of the bombing of North Vietnam , authorized the covert air campaign . The first mission of Operation Menu was dispatched on 18 March and by the time it was completed 14 months later more than 3 @,@ 000 sorties had been flown and 108 @,@ 000 tons of ordnance had been dropped on eastern Cambodia . 
- While Sihanouk was abroad in France for a rest cure in January 1970 , government @-@ sponsored anti @-@ Vietnamese demonstrations were held throughout Cambodia . Continued unrest spurred Prime Minister / Defense Minister Lon Nol to close the port of Sihanoukville to communist supplies and to issue an ultimatum on 12 March to the North Vietnamese to withdraw their forces from Cambodia within 72 hours . The prince , outraged that his " <unk> <unk> " with the communists had been disturbed , immediately arranged for a trip to Moscow and Beijing in an attempt to gain their agreement to apply pressure on Hanoi to <unk> its forces in Cambodia . 
- On 18 March , the Cambodian National Assembly deposed Sihanouk and named Lon Nol as provisional head of state . This led Sihanouk to immediately establish a government @-@ in @-@ exile in Beijing and to ally himself with North Vietnam , the Khmer Rouge , the NLF , and the <unk> <unk> Lao . In doing so , Sihanouk lent his name and popularity in the rural areas of Cambodia to a movement over which he had little control . The North Vietnamese response to the coup was swift . PAVN began directly supplying large amounts of weapons and advisors to the Khmer Rouge , and Cambodia plunged into civil war . 
- Lon Nol saw Cambodia 's population of 400 @,@ 000 ethnic Vietnamese as possible hostages to prevent PAVN attacks and ordered their <unk> and <unk> . Cambodian soldiers and civilians then unleashed a reign of terror , murdering thousands of Vietnamese civilians . On 15 April for example , 800 Vietnamese men had been rounded up at the village of <unk> <unk> , tied together , executed , and their bodies dumped into the Mekong River . They then floated downstream into South Vietnam . Cambodia 's actions were denounced by both the North and South Vietnamese governments . 
- Even before the supply conduit through Sihanoukville was shut down , PAVN had begun expanding its logistical system from southeastern Laos ( the Ho Chi Minh trail ) into northeastern Cambodia . PAVN also launched an offensive ( Campaign X ) against the Cambodian army , quickly seizing large portions of the eastern and northeastern parts of the country , isolating and besieging or overrunning a number of Cambodian cities including Kampong Cham . Communist forces then approached within 20 miles ( 32 km ) of the capital , Phnom Penh , spurring President Nixon into action . 
- On 29 March 1970 , the North Vietnamese had taken matters into their own hands and launched an offensive against the Cambodian army with documents uncovered from the Soviet archives revealing that the offensive was launched at the explicit request of the Khmer Rouge following negotiations with <unk> <unk> . A force of North Vietnamese quickly overran large parts of eastern Cambodia reaching to within 15 miles ( 24 km ) of Phnom Penh . After defeating those forces , the North Vietnamese turned the newly won territories over to the local insurgents . The Khmer Rouge also established " liberated " areas in the south and the southwestern parts of the country , where they operated independently of the North Vietnamese . 
- 
- = = = Planning = = = 
- 
- In response to events in Cambodia , President Nixon believed that there were distinct possibilities for a U.S. response . With Sihanouk gone , conditions were <unk> for strong measures against the Base Areas . He was also adamant that some action be taken to support " The only government in Cambodia in the last twenty @-@ five years that had the <unk> to take a pro @-@ Western stand . " The president then solicited proposals for actions from the Joint Chiefs of Staff and MACV , who presented him with a series of options : a naval quarantine of the Cambodian coast ; the launching of South Vietnamese and American airstrikes ; the expansion of hot pursuit across the border by ARVN forces ; or a ground invasion by ARVN , U.S. forces , or both . 
- During a televised address on 20 April , Nixon announced the withdrawal of 150 @,@ 000 U.S. troops from South Vietnam during the year . This planned withdrawal implied restrictions on any offensive U.S. action in Cambodia . By the spring of 1970 , MACV still maintained 330 @,@ 648 U.S. Army and 55 @,@ <unk> Marine Corps troops in South Vietnam , most of whom were concentrated in 81 infantry and tank battalions . Many of them , however , were preparing to leave the country or expected to leave in the near future and would not be available for immediate combat operations . 
- On 22 April Nixon authorized the planning of a South Vietnamese incursion into the Parrot 's Beak ( named for its perceived shape on a map ) , believing that " Giving the South Vietnamese an operation of their own would be a major boost to their morale as well as provide a practical demonstration of the success of Vietnamization . " On the following day , Secretary of State William P. Rogers testified before the House <unk> <unk> that " the administration had no intentions ... to escalate the war . We recognize that if we escalate and get involved in Cambodia with our ground troops that our whole program [ Vietnamization ] is defeated . " 
- South Vietnamese forces had been rehearsing for just such an operation since late March . On 27 April , an ARVN Ranger Battalion had advanced into <unk> Province to destroy a communist base . Four days later other South Vietnamese troops drove 16 kilometers into Cambodian territory . Lon Nol , who had initially attempted to follow a <unk> policy of his own , requested military aid and assistance from the U.S. government on 14 April . On that day , South Vietnamese forces then conducted the first of three brief cross @-@ border operations under the aegis of Operation Toan Thang ( Complete Victory ) 41 , sending armored cavalry units into regions of Cambodia 's Svay Rieng Province nicknamed the Angel 's Wing and the Crow 's Nest . On 20 April , 2 @,@ 000 South Vietnamese troops advanced into the Parrot 's Beak , killing 144 PAVN troops . On 22 April , Nixon authorized American air support for the South Vietnamese operations . All of these incursions into Cambodian territory were simply reconnaissance missions in preparation for a larger @-@ scale effort being planned by MACV and its ARVN counterparts , subject to authorization by Nixon . 
- President Nixon then authorized General Abrams to begin planning for a U.S. operation in the Fishhook region . A preliminary operational plan had actually been completed in March , but was kept so tightly under <unk> that when Abrams handed over the task to General Michael <unk> , commander of the II Field Force , he was not informed about the previous planning and started a new one from scratch . Seventy @-@ two hours later , <unk> 's plan was submitted to the White House . National Security <unk> Dr. Henry Kissinger asked one of his aides to review it on 26 April , and the NSC <unk> was <unk> by its " <unk> " . 
- The main problems were the pressure of time and the desire of the U.S. president for secrecy . The Cambodian monsoon , whose heavy rains would hamper operations , was only two months away . By the order of the president , the State Department did not notify the Cambodian desk at the US Embassy , Saigon , the Phnom Penh embassy , or Lon Nol of the planning . Operational security was as tight as General Abrams could make it . There was to be no prior U.S. logistical build @-@ up in the border regions which might serve as a signal to the communists . U.S. brigade commanders were informed only a week in advance of the offensive , while battalion commanders got only two or three days ' notice . 
- 
- = = = <unk> = = = 
- 
- Not all of the members of the administration agreed that an invasion of Cambodia was either militarily or politically <unk> . Secretary of Defense Melvin R. Laird and Secretary Rogers were both opposed to any such operation due to their belief that it would engender intense domestic opposition in the U.S. and that it might possibly <unk> the ongoing peace negotiations in Paris ( they had both opposed the Menu bombings for the same reasons ) . Both were <unk> by Henry Kissinger for their " bureaucratic foot @-@ dragging . " As a result , Laird was bypassed by the Joint Chiefs in advising the White House on planning and preparations for the Cambodian operation . 
- On the evening of 25 April Nixon <unk> with his friend <unk> <unk> and Kissinger . Afterward , they screened one of Nixon 's favorite movies , Patton , a biographical portrayal of controversial General George S. Patton , Jr . , which he had seen five times previously . Kissinger later commented that " When he was pressed to the wall , his [ Nixon 's ] romantic streak surfaced and he would see himself as a <unk> military commander in the tradition of Patton . " 
- The following evening , Nixon decided that " We would go for broke " and gave his authorization for the incursion . The joint U.S. / ARVN campaign would begin on 1 May with the stated goals of : reducing allied casualties in South Vietnam ; assuring the continued withdrawal of U.S. forces ; and enhancing the U.S. / Saigon government position at the peace negotiations in Paris . 
- In order to keep the campaign as low @-@ key as possible , General Abrams had suggested that the commencement of the incursion be routinely announced from Saigon . At 21 : 00 on 30 April , however , President Nixon appeared on all three U.S. television networks to announce that " It is not our power but our will and character that is being tested tonight " and that " the time has come for action . " He announced his decision to launch American forces into Cambodia with the special objective of capturing COSVN , " the headquarters of the entire communist military operation in South Vietnam . " COSVN as a single headquarters for control of PAVN operations in South Vietnam probably did not exist , or , at least , was never found . 
- 
- = = Operations = = 
- 
- 
- = = = Previous ARVN attacks = = = 
- 
- <unk> with Lon Nol ARVN forces attacked the PRG <unk> complexes . Moving across the border in Cambodia on 30 March elements of the PRG and NLF were surrounded in their bunkers by South Vietnamese forces flown in by helicopter . <unk> they awaited till nightfall and then with security provided by the NLF 7th division they broke out of the <unk> and fled north to unite with the COSVN in the Cambodian <unk> province in what would come to be known as Escape of the Provisional Revolutionary Government . Trương <unk> <unk> was the Minister of Justice in the PRG and he recounts that during the march to the northern bases was day after day of forced marches broken up by B @-@ 52 bombing raids . Just before the column crossed route 7 on their way north they received word that on 3 April the 9th Division had fought and won a battle near the city of <unk> , Cambodia against ARVN forces . 
- Years later Trương would recall just how " close [ South Vietnamese ] were to <unk> or capturing the core of the Southern resistance – elite units of our <unk> fighters along with the civilian and much of the military leadership " . After many days of hard marches the PRG reached the northern bases , and relative safety , in the <unk> region . Casualties were light and the march even saw the birth of a baby to Dương <unk> <unk> , the deputy minister of health in the Provisional Revolutionary Government ( PRG ) . The column needed many days to recover and Trương himself would require weeks to recover from the long march . 
- 
- = = = Parrot 's Beak and Fishhook = = = 
- 
- South Vietnamese forces had already crossed the border on 30 April , launching Operation Toan Thang 42 . 12 ARVN battalions of approximately 8 @,@ 700 troops ( two armored cavalry squadrons from III Corps and two from the 25th Division and 5th Infantry Divisions , an infantry regiment from the 25th Infantry Division , and three Ranger battalions and an attached ARVN Armored Cavalry <unk> from the 3rd Ranger Group ) crossed into the Parrot 's Beak region of Svay Rieng Province . The offensive was under the command of Lieutenant General <unk> Cao <unk> , the commander of III Corps , who had a reputation as one of the most aggressive and competent ARVN generals . During their first two days in Cambodia , ARVN units had several sharp encounters with PAVN forces . The North Vietnamese , <unk> by previous ARVN incursions , however , conducted only delaying actions in order to allow the bulk of their forces to escape to the west . 
- The ARVN operation soon settled down to become a search and destroy mission , with South Vietnamese troops <unk> the countryside in small patrols looking for PAVN supply <unk> . Phase II of the operation began with the arrival of elements of the 9th Infantry Division . Four tank @-@ infantry task forces attacked into the Parrot 's Beak from the south . After three days of operations , ARVN claimed 1 @,@ 010 PAVN troops had been killed and 204 prisoners taken for the loss of 66 ARVN dead and 330 wounded . 
- On 1 May an even larger operation , known by the ARVN as Operation Toan Thang 43 and by MACV as Operation <unk> , got underway as 36 B @-@ <unk> dropped <unk> tons of bombs along the southern edge of the Fishhook . This was followed by an hour of massed artillery fire and another hour of strikes by tactical fighter @-@ bombers . At 10 : 00 , the 1st Air <unk> Division , the 11th Armored Cavalry Regiment , the 1st ARVN Armoured Cavalry Regiment , and the 3rd ARVN Airborne Brigade then entered Kampong Cham Province of Cambodia . Known as Task Force Shoemaker ( after General Robert M. Shoemaker , the Assistant Division Commander of the 1st Cavalry Division ) , the force attacked the long @-@ time communist stronghold with 10 @,@ 000 U.S. and 5 @,@ 000 South Vietnamese troops . The operation utilized mechanized infantry and armored units to drive deep into the province where they would then link up with ARVN airborne and U.S. <unk> units that had been lifted in by helicopter . 
- Opposition to the incursion was expected to be heavy , but PAVN / NLF forces had begun moving westward two days before the advance began . By 3 May , MACV reported only eight Americans killed and 32 wounded , low casualties for such a large operation . There was only scattered and sporadic contact with delaying forces such as that experienced by elements of the U.S. 11th Armoured Cavalry three kilometers inside Cambodia . PAVN troops opened fire with small arms and rockets only to be blasted by tank fire and tactical airstrikes . When the smoke had cleared , 50 dead PAVN soldiers were counted on the battlefield while only two U.S. troops were killed during the action . 
- 1st Battalion / 7th Cavalry , 3rd Brigade 1st Cavalry Division was in the Fishhook very early May through 30 June when they crossed the river back into Vietnam . There was extremely heavy combat throughout the period . American losses were very heavy , with all units relying on heavy inflow of replacements to try to maintain at least half strength in the field . In one company , of all the men who had entered Cambodia , only nine left on 30 June , the rest having been either killed or wounded and evacuated . The unit was awarded the <unk> Unit Award , equivalent to individual Silver Stars , for their combat performance in the Fishhook . 
- The North Vietnamese had ample notice of the impending attack . A 17 March directive from the headquarters of the B @-@ 3 Front , captured during the incursion , ordered PAVN / NLF forces to " break away and avoid shooting back ... Our purpose is to conserve forces as much as we can " . The only surprised party amongst the participants in the incursion seemed to be Lon Nol , who had been informed by neither Washington nor Saigon concerning the impending invasion of his country . He only discovered the fact after a telephone conversation with the head of the U.S. mission , who had found out about it himself from a radio broadcast . 
- The only conventional battle fought by American troops occurred on 1 May at the town of <unk> , the suspected terminus of the Sihanouk Trail at the junction of Routes 7 , 13 , and 131 . Elements of the U.S. 11th Armored Cavalry and supporting helicopters came under PAVN fire while approaching the town and its airfield . When a massed American attack was met by heavy resistance , the Americans backed off , called in air support and blasted the town for two days , reducing it to rubble . During the action , Brigadier General Donn A. <unk> , commander of the 11th Armored Cavalry , was wounded by grenade fragments and evacuated . 
- 
- = = = Hunting supply <unk> = = = 
- 
- On the following day , elements of the U.S. 1st Air Cavalry division entered what came to be known as " The City " , southwest of <unk> . The two @-@ square mile PAVN complex contained over 400 <unk> <unk> , storage sheds , and bunkers , each of which was packed with food , weapons , and ammunition . There were truck repair facilities , hospitals , a lumber yard , 18 mess halls , a pig farm , and even a swimming pool . Forty kilometers to the northeast , other 1st Cavalry Division elements discovered a larger base on 6 May . Nicknamed " Rock Island East " after the U.S. Army 's Rock Island Arsenal in Illinois , the area contained more than 6 @.@ 5 million rounds of anti @-@ aircraft ammunition , 500 @,@ 000 rifle rounds , thousands of rockets , several General Motors trucks , and large quantities of communications equipment . 
- While on patrol 20 kilometers northeast of " Rock Island East " on 23 May , a point man nicknamed Shakey ( Chris <unk> ) from the 5th Battalion , 7th Cavalry , <unk> over a metal plate buried just below the surface of the ground . The <unk> was later killed by PAVN defenders , but the <unk> he had uncovered was the first of 59 buried storage bunkers at the site of what was thereafter known as " Shakey 's Hill " . The bunkers contained thousands of cases of weapons and ammunition , all of which were turned over to the Cambodian army . Much of the captured enemy material was turned over to the MACV Special Support Group for Cambodia where it was maintained and then issued to Lon Nol 's Forces . This group was commanded by Lieutenant Colonel Will H. Horn . 
- The one thing that was not found was COSVN . On 1 May a tape of Nixon 's announcement of the incursion was played for General Abrams , " who must have <unk> " when he heard the president state that the capture of the headquarters was one of the major objectives of the operation . MACV intelligence knew that the mobile and <unk> dispersed headquarters would be difficult to locate . In response to a White House query before the fact , MACV had replied that " major COSVN elements are dispersed over approximately 110 square kilometers of jungle " and that " the feasibility of capturing major elements appears remote " . 
- After the first week of operations , additional battalion and brigade units were committed to the operation , so that between 6 and 24 May , a total of 90 @,@ 000 Allied troops ( including 33 U.S. maneuver battalions ) were conducting operations inside Cambodia . Due to increasing political and domestic turbulence in the U.S. , President Nixon issued a directive on 7 May limiting the distance and duration of U.S. operations to a depth of 30 kilometers ( 19 mi ) and setting a deadline of 30 June for the withdrawal of all U.S. forces to South Vietnam . 
- South Vietnamese forces were not constrained by the time and geographic limitations placed upon U.S. units . From the provincial capital of Svay Rieng , ARVN elements pressed westward to Kampong <unk> , where on 14 May their 8th and 15th Armored Cavalry regiments defeated the 88th PAVN Infantry Regiment . On 23 May , the South Vietnamese pushed beyond the deepest U.S. <unk> and attacked the town of <unk> . 
- 
- = = = Binh Tay and Cuu Long = = = 
- 
- In the II Corps area , Operation Binh Tay I ( Operation <unk> the West ) was launched by the 1st and 2nd Brigades of the U.S. 4th Infantry Division and the 40th ARVN Infantry Regiment against Base Area 702 ( the traditional headquarters of the communist B @-@ 2 Front ) in northeastern Cambodia from 5 – 25 May . Following airstrikes , the initial American forces , assaulting via helicopter , were driven back by intense anti @-@ aircraft fire . On the following day , the 3rd Battalion , <unk> Infantry ( on loan from the U.S. 101st Airborne Division ) , landed without opposition . Its sister unit , the 1st Battalion , 14th Infantry was also unopposed . The 3rd Battalion of the 8th Infantry , however , inserted only 60 men before intense PAVN fire ( which shot down one helicopter and damaged two others ) shut down the landing zone , leaving them stranded and surrounded overnight . By the following morning , PAVN forces had left the area . 
- On the 7th , the division 's 2nd Brigade inserted its three battalions unopposed . After ten days ( and only one significant firefight ) the American troops returned to South Vietnam , leaving the area to the ARVN . Historian Shelby Stanton has noted that " there was a noted lack of aggressiveness " in the combat assault and that the division seemed to be " suffering from almost total combat <unk> . " During Operation Binh Tay II , the ARVN 22nd Division moved against Base Area 702 from 14 – 26 May . The second phase of the operation was carried out by ARVN forces against Base Area 701 between 20 May and 27 June when elements of the ARVN 22nd Division conducted operations against Base Area 740 . 
- On 10 May , Bravo Company , 3rd Battalion , <unk> Regiment , 101st Airborne Division , was ambushed by a much larger North Vietnamese force in the Se San Valley . Eight U.S. soldiers were killed and 28 wounded . Among the killed was <unk> . Leslie Sabo , Jr . ( posthumously promoted to sergeant ) , who was recommended for the Medal of Honor , but the <unk> went missing until 1999 . Sabo was awarded the Medal of Honor on 16 May 2012 by President Barack Obama . 
- In the III Corps Tactical Zone , Operation Toan Thang 44 ( Operation <unk> <unk> ) , was conducted by the 1st and 2nd Brigades of the U.S. 25th Infantry Division between 6 May and 30 June . The targets of the operation were Base Areas 353 , 354 , and <unk> located north and northeast of Tay <unk> , South Vietnam . Once again , a hunt for COSVN units was conducted , this time around the Cambodian town of <unk> and , once again , the search was futile . During its operations , the 25th Infantry killed 1 @,@ <unk> PAVN and NLF troops while losing 119 of its own men killed . 
- <unk> with the launching of Toan Thang 44 , the two battalions of the 3rd Brigade , U.S. 9th Infantry Division , crossed the border 48 kilometers southwest of the Fishhook into an area known as the Dog 's Face from 7 through 12 May . The only significant contact with PAVN forces took place near the hamlet of <unk> , where 51 North Vietnamese were killed and another 21 were captured . During the operation , the brigade lost eight men killed and 22 wounded . It was already too late for thousands of ethnic Vietnamese murdered by Cambodian persecution , but there were tens of thousands of Vietnamese still within the country who could now be evacuated to safety . South Vietnamese President Nguyễn Văn Thiệu arranged with Lon Nol to <unk> as many as were willing to leave . The new relationship did not , however , prevent the Cambodian government from stripping the Vietnamese of their homes and other personal property before they left . 
- <unk> then authorized Operation Cuu Long , in which ARVN ground forces , including mechanized and armoured units , drove west and northwest up the eastern side of the Mekong River from 9 May – 1 July . A combined force of 110 Vietnamese Navy and 30 U.S. vessels proceeded up the Mekong to Prey <unk> , permitting IV Corps ground forces to move westward to Phnom Penh and to aid ethnic Vietnamese seeking flight to South Vietnam . Those who did not wish to be repatriated were then forcibly expelled . <unk> , North Vietnamese forces did not oppose the evacuation , though they could easily have done so . 
- Other operations conducted from IV Corps included Operation Cuu Long II ( 16 – 24 May ) , which continued actions along the western side of the Mekong . Lon Nol had requested that the ARVN help in the retaking of Kompong <unk> , a town along Route 4 southwest of Phnom Penh and 90 miles ( 140 km ) inside Cambodia . A 4 @,@ 000 @-@ man ARVN armoured task force linked up with Cambodian ground troops and then retook the town . Operation Cuu Long III ( 24 May – 30 June ) was an evolution of the previous operations after U.S. forces had left Cambodia . 
- After rescuing the Vietnamese from the Cambodians , ARVN was tasked with saving the Cambodians from the North Vietnamese . The goal was to relieve the city of Kompong Cham , 70 kilometers northwest of the capital and the site of the headquarters of Cambodia 's Military Region I. On 23 May , General Tri led a column of 10 @,@ 000 ARVN troops along Route 7 to the 180 @-@ acre ( 0 @.@ 73 km2 ) <unk> rubber plantation , where PAVN resistance was expected to be heavy . <unk> , no battle ensued and the siege of Kompong Cham was lifted at a cost of 98 PAVN troops killed . 
- 
- = = = Air support and logistics = = = 
- 
- Aerial operations for the incursion got off to a slow start . Reconnaissance flights over the operational area were restricted since MACV believed that they might serve as a signal of intention . The role of the Air Force in the planning for the incursion itself was minimal at best , in part to preserve the secrecy of Menu which was then considered an overture to the thrust across the border . 
- On 17 April , General Abrams requested that the president approve Operation <unk> , covert tactical airstrikes in support of Studies and Observations Group <unk> elements " across the fence " in Cambodia . This authorization was given , allowing U.S. aircraft to penetrate 13 miles ( 21 km ) into northeastern Cambodia . This boundary was extended to 29 miles ( 47 km ) along the entire frontier on 25 April . <unk> was terminated on 18 May after 156 sorties had been flown . The last Menu mission was flown on 26 May . 
- During the incursion itself , U.S. and ARVN ground units were supported by 9 @,@ 878 aerial sorties ( 6 @,@ <unk> U.S. / 2 @,@ <unk> Vietnamese Air Force ) , an average of 210 per day . During operations in the Fishhook , for example , the <unk> flew 3 @,@ <unk> sorties and the South Vietnamese Air Force <unk> . These tactical airstrikes were supplemented by 653 B @-@ 52 missions in the border regions ( 71 supporting Binh Tay operations , <unk> for Toan Thang operations , and 23 for Cuu Long ) . 30 May saw the inauguration of Operation Freedom Deal ( named as of 6 June ) , a continuous U.S. aerial interdiction campaign conducted in Cambodia . These missions were limited to a depth of 48 @-@ kilometers between the South Vietnamese border and the Mekong River . 
- Within two months , however , the limit of the operational area was extended past the Mekong , and U.S. tactical aircraft were soon directly supporting Cambodian forces in the field . These missions were officially denied by the U.S. and false coordinates were given in official reports to hide their existence . Defense Department records indicated that out of more than 8 @,@ 000 combat sorties flown in Cambodia between July 1970 and February 1971 , approximately 40 percent were flown outside the authorized Freedom Deal boundary . 
- The real struggle for the U.S. and ARVN forces in Cambodia was the effort at keeping their units supplied . Once again , the need for security before the operations and the <unk> with which units were transferred to the border regions precluded detailed planning and preparation . This situation was exacerbated by the poor road network in the border regions and the possibility of ambush for nighttime road convoys demanded that deliveries only take place during daylight . Aerial resupply , therefore , became the chief method of logistical <unk> for the forward units . Military engineers and aviators were kept in constant motion throughout the incursion zone . 
- Due to the rapid pace of operations , deployment , and redeployment , coordination of artillery units and their fires became a worrisome quandary during the operations . This was made even more problematic by the confusion generated by the lack of adequate communications systems between the rapidly advancing units . The joint nature of the operation added another level of complexity to the already <unk> communications network . Regardless , due to the ability of U.S. <unk> to innovate and improvise , supplies of food , water , ammunition , and spare parts arrived at their destinations without any shortages hampering combat operations and the communications system , although complicated , functioned well enough during the short duration of U.S. operations . 
- 
- = = Aftermath = = 
- 
- The North Vietnamese response to the incursion was to avoid contact with allied forces and , if possible , to fall back westward and regroup . PAVN forces were well aware of the planned attack and many COSVN / B @-@ 3 Front military units were already far to the north and west conducting operations against the Cambodians when the offensive began . During 1969 PAVN logistical units had already begun the largest expansion of the Ho Chi Minh trail conducted during the entire conflict . As a response to the loss of their Cambodian supply route , North Vietnamese forces seized the <unk> towns of <unk> and <unk> during the year , pushing what had been a 60 @-@ mile ( 97 km ) corridor to a width of 90 miles ( 140 km ) and opening the entire length of the Kong River system into Cambodia . A new logistical command , the <unk> Transportation Group , was created to handle logistics in Cambodia and the new " Liberation Route " ran through <unk> <unk> and reached the Mekong at <unk> <unk> . 
- As <unk> by Secretary Laird , fallout from the incursion was quick in coming on the campuses of America 's universities , as protests erupted against what was perceived as an expansion of the conflict into yet another country . On 4 May the unrest escalated to violence when Ohio National Guardsmen shot and killed four unarmed students ( two of whom were not protesters ) during the Kent State shootings . Two days later , at the University at Buffalo , police wounded four more demonstrators . On 8 May 100 @,@ 000 protesters gathered in Washington and another 150 @,@ 000 in San Francisco on only ten days notice . Nationwide , 30 <unk> buildings went up in flames or were bombed while 26 schools witnessed violent clashes between students and police . National Guard units were mobilized on 21 campuses in 16 states . The student strike spread nationwide , involving more than four million students and 450 universities , colleges and high schools in mostly peaceful protests and <unk> . 
- Simultaneously , public opinion polls during the second week of May showed that 50 percent of the American public approved of President Nixon 's actions . Fifty @-@ eight percent blamed the students for what had occurred at Kent State . On both sides , emotions ran high . In one instance , in New York City on 8 May , pro @-@ administration construction workers <unk> and attacked demonstrating students . Such violence , however , was an <unk> . Most demonstrations , both <unk> and anti @-@ war , were peaceful . On 20 May 100 @,@ 000 construction workers , tradesmen , and office workers marched peacefully through New York City in support of the president 's policies . 
- Reaction in the U.S. Congress to the incursion was also swift . Senators Frank F. Church ( Democratic Party , Idaho ) and John S. Cooper ( Republican Party , Kentucky ) , proposed an amendment to the Foreign Military Sales Act that would have cut off funding not only for U.S. ground operations and advisors in Cambodia , but would also have ended U.S. air support for Cambodian forces . On 30 June the United States Senate passed the act with the amendment included . The bill was defeated in the House of Representatives after U.S. forces were withdrawn from Cambodia as scheduled . The newly amended act did , however , rescind the Southeast Asia Resolution ( better known as the Gulf of <unk> Resolution ) under which Presidents Johnson and Nixon had conducted military operations for seven years without a declaration of war . 
- The Cooper – Church Amendment was resurrected during the winter and incorporated into the <unk> Foreign Assistance Act of 1970 . This time the measure made it through both houses of Congress and became law on 22 December . As a result , all U.S. ground troops and advisors were barred from participating in military actions in Laos or Cambodia , while the air war being conducted in both countries by the U.S. Air Force was ignored . 
- 
- = = <unk> = = 
- 
- President Nixon proclaimed the incursion to be " the most successful military operation of the entire war . " General Abrams was of like mind , believing that time had been bought for the pacification of the South Vietnamese countryside and that U.S. and ARVN forces had been made safe from any attack out of Cambodia during 1971 and 1972 . A " decent interval " had been obtained for the final American withdrawal . ARVN General <unk> <unk> <unk> was more skeptical : " despite its spectacular results ... it must be recognized that the Cambodian incursion proved , in the long run , to pose little more than a temporary disruption of North Vietnam 's march toward domination of all of Laos , Cambodia , and South Vietnam . " 
- John Shaw and other historians , military and civilian , have based the conclusions of their work on the incursion on the premise that the North Vietnamese logistical system in Cambodia had been so badly damaged that it was rendered ineffective . The next large @-@ scale North Vietnamese offensive , the Nguyen <unk> Offensive of 1972 ( called the Easter Offensive in the West ) would be launched out of southern North Vietnam and western Laos , not from Cambodia , was cited as proof positive that the Cambodian operations had succeeded . The fact that PAVN forces were otherwise occupied in Cambodia and had no such offensive plan ( so far as is known ) was seemingly irrelevant . The fact that <unk> , a northern offensive ( especially a conventional one backed by armour and heavy artillery ) would be launched closer to its source of manpower and supply also seemed to be of little consequence . 
- The logistical haul discovered , removed , or destroyed in eastern Cambodia during the operations was indeed prodigious : 20 @,@ 000 individual and 2 @,@ 500 crew @-@ served weapons ; 7 @,@ 000 to 8 @,@ 000 tons of rice ; 1 @,@ 800 tons of ammunition ( including 143 @,@ 000 mortar shells , rockets , and recoilless rifle rounds ) ; 29 tons of communications equipment ; <unk> vehicles ; and 55 tons of medical supplies . MACV intelligence estimated that PAVN / NLF forces in southern Vietnam required 1 @,@ 222 tons of all supplies each month to keep up a normal pace of operations . Due to the loss of its Cambodian supply system and continued aerial interdiction in Laos , MACV estimated that for every 2 @.@ 5 tons of materiel sent south down the Ho Chi Minh trail , only one ton reached its destination . However , the true loss rate was probably only around ten percent . General Abrams claimed 11 @,@ 000 enemy soldiers killed and 2 @,@ 500 captured , but his figures were disputed by CIA , who insisted that civilians death were figured into Abrams 's total 
- South Vietnamese forces had performed well during the incursion but their leadership was uneven . General Tri proved a resourceful and inspiring commander , earning the sobriquet the " Patton of the Parrot 's Beak " from the American media . General Abrams also praised the skill of General Nguyen Viet Thanh , commander of IV Corps and <unk> of the Parrot 's Beak operation . Unfortunately for the anti @-@ communists , both officers were killed in helicopter crashes — Thanh on 2 May in Cambodia and Tri in February 1971 . Other ARVN commanders , however , had not performed well . Even at this late date in the conflict , the appointment of ARVN general officers was prompted by political loyalty rather than professional competence . As a test of Vietnamization , the incursion was praised by American generals and politicians alike , but the Vietnamese had not really performed alone . The participation of U.S. ground and air forces had precluded any such claim . When called on to conduct solo offensive operations during the incursion into Laos ( Operation <unk> Son <unk> ) in 1971 , the ARVN 's continued weaknesses would become all too apparent . 
- The Cambodian government was not informed of the incursion until it was already under way . It has been argued by some scholars that the incursion heated up the civil war and helped the insurgent Khmer Rouge gather recruits to their cause . 
- 
- = = = = <unk> government documents = = = = 
- 
- Military Assistance Command , Vietnam , Command History 1967 , <unk> F. Saigon , 1968 . 
- 
- = = = = Published government documents = = = = 
- 
- <unk> , Herman L. The Air War in Southeast Asia : Case Studies of Selected Campaigns . Maxwell Air Force Base AL : Air University Press , 1993 . 
- <unk> , Bernard C. Air War Over South Vietnam : 1968 – 1975 . Washington DC : Air Force History and Museums Program , 2000 . 
- <unk> , Bernard C. War Against <unk> : Aerial <unk> in Southern Laos , 1968 – 1972 . Washington DC : Air Force History and Museums Program , 2005 . 
- A War Too Long : The <unk> in Southeast Asia , 1961 @-@ 1975 
- Foreign Relations Series 
- <unk> , <unk> 1970 – <unk> 1972 
- <unk> , <unk> 1969 – <unk> 1970 
- 
- = = = = Secondary accounts = = = = 
- 
- Chandler , David P. The <unk> of Cambodian History . New Haven CT : Yale University Press , 1991 . 
- <unk> , <unk> , Road to the Killing Fields : The Cambodian Civil War of 1970 – 1975 . College Station <unk> : Texas A & M University , 1997 . 
- <unk> , David , Terrence <unk> , et al . South Vietnam on Trial : Mid @-@ 1970 – 1972 . Boston ; Boston Publishing Company , 1984 . 
- <unk> , Todd , The <unk> : Years of Hope , Days of Rage . New York : Bantam Books , 1987 . 
- Karnow , Stanley , Vietnam : A History . New York : Viking Books , 1983 . 
- Kennedy , Denis , Tracks in the Jungle in The Army at War . Boston : Boston Publishing Company , 1987 . 
- <unk> , Samuel , Edward Doyle , et al . Fighting for Time : 1969 – 1970 . Boston : Boston Publishing Company , 1983 . 
- Morocco , John , Operation Menu in War in the Shadows . Boston : Boston Publishing Company , 1988 . 
- Morocco , John , Rain of Fire : Air War , 1969 – 1973 Boston : Boston Publishing Company , 1985 . 
- Nolan , Keith W. Into Cambodia : Spring Campaign , Summer Offensive , 1970 . <unk> CA : <unk> Press , 1990 . 
- Palmer , Dave Richard ( 1978 ) . <unk> of the <unk> : The History of the Vietnam War from a Military Man 's <unk> . New York : <unk> . 
- <unk> , John , The Blood Road : The Ho Chi Minh Trail and the Vietnam War . New York : John Wiley and Sons , 1998 . 
- Shaw , John M. The Cambodian Campaign : The 1970 Offensive and America 's Vietnam War . Lawrence <unk> : University of Kansas Press , 2005 . 
- <unk> , William , Sideshow : Kissinger , Nixon and the Destruction of Cambodia . New York : Washington Square Books , 1979 . 
- <unk> , Lewis ( 1999 ) . A Better War : The <unk> <unk> and Final <unk> of America 's Last Years in Vietnam . New York : Harvest Books . ISBN 0 @-@ 15 @-@ <unk> @-@ 6 . 
- Stanton , Shelby L. ( 1985 ) . The Rise and Fall of an American Army : U.S. Ground Forces in Vietnam , 1965 – 1973 . New York : Dell . ISBN 0 @-@ <unk> @-@ 232 @-@ 8 . 
- <unk> , <unk> <unk> ; David <unk> , Van <unk> <unk> ( 1985 ) . A <unk> memoir ( 1985 ed . ) . Harcourt <unk> <unk> . ISBN 978 @-@ 0 @-@ 15 @-@ <unk> @-@ 6 . - Total pages : 350 
- 
- = = = = TIME coverage = = = = 
- 
- " <unk> the <unk> " . Time . 23 March 1970 . Retrieved 10 April 2007 . 
- " Danger and <unk> in Indochina " . Time . 30 March 1970 . Retrieved 10 April 2007 . 
- " The Royal <unk> of Southeast Asia " . Time . 30 March 1970 . Retrieved 10 April 2007 . 
- " <unk> <unk> in Southeast Asia " . Time . 6 April 1970 . Retrieved 10 April 2007 . 
- " The Nixon Doctrine 's Test in Indochina " . Time . 13 April 1970 . Retrieved 10 April 2007 . 
- " The Three @-@ Theater War " . Time . 13 April 1970 . Retrieved 10 April 2007 . 
- " Indochina 's <unk> <unk> " . Time . 20 April 1970 . Retrieved 10 April 2007 . 
- " A New Horror in Indochina " . Time . 27 April 1970 . Retrieved 10 April 2007 . 
- " Cambodia : Communists on the <unk> " . Time . 4 May 1970 . Retrieved 10 April 2007 . 
- " Between the Lines " . Time . 4 May 1970 . Retrieved 10 April 2007 . 
- " The New <unk> of War " . Time . 11 May 1970 . Retrieved 10 April 2007 . 
- " At War with War " . Time . 18 May 1970 . Retrieved 10 April 2007 . 
- " In Search of an <unk> <unk> " . Time . 18 May 1970 . Retrieved 10 April 2007 . 
- " Ten Days — or Ten Years " . Time . 18 May 1970 . Retrieved 10 April 2007 . 
- " Congress v. the President " . Time . 25 May 1970 . Retrieved 10 April 2007 . 
- " Cambodia : Now It 's ' Operation Buy Time ' " . Time . 25 May 1970 . Retrieved 10 April 2007 . 
- " Exodus on the Mekong " . Time . 25 May 1970 . Retrieved 10 April 2007 . 
- " Nixon 's Campaign for Confidence " . Time . 25 May 1970 . Retrieved 10 April 2007 . 
- " Cambodia : Toward War by <unk> " . Time . 1 June 1970 . Retrieved 10 April 2007 . 
- " The Senate : <unk> Acts " . Time . 1 June 1970 . Retrieved 10 April 2007 . 
- " The <unk> <unk> in Nixon 's Cabinet " . Time . 1 June 1970 . Retrieved 10 April 2007 . 
- " Cambodia : A <unk> New ARVN " . Time . 8 June 1970 . Retrieved 10 April 2007 . 
- " Indochina : More and More Fighters " . Time . 15 June 1970 . Retrieved 10 April 2007 . 
- " No Confidence on Cambodia " . Time . 22 June 1970 . Retrieved 10 April 2007 . 
- " Indochina : The Rising Tide of War " . Time . 22 June 1970 . Retrieved 10 April 2007 . 
- " New <unk> in Cambodia " . Time . 29 June 1970 . Retrieved 10 April 2007 . 
- " The Cambodian <unk> : An Assessment " . Time . 6 July 1970 . Retrieved 10 April 2007 . 
- " Cambodia : <unk> for Survival " . Time . 13 July 1970 . Retrieved 10 April 2007 . 
- " <unk> Up the Cambodian Hard <unk> " . Time . 13 July 1970 . Retrieved 10 April 2007 . 
- " <unk> in the Land of <unk> " . Time . 27 July 1970 . Retrieved 10 April 2007 . 
- " The <unk> US <unk> " . Time . 3 August 1970 . Retrieved 10 April 2007 . 
- 
- = Verpa bohemica = 
- 
- Verpa bohemica is a species of fungus in the family <unk> . Commonly known as the early morel ( or early false morel ) or the wrinkled thimble @-@ cap , it is one of several species known informally as a " false morel " . The mushroom has a pale yellow or brown thimble @-@ shaped cap — 2 to 4 cm ( 0 @.@ 8 to 1 @.@ 6 in ) in diameter by 2 to 5 cm ( 0 @.@ 8 to 2 @.@ 0 in ) long — that has a surface wrinkled and ribbed with brain @-@ like <unk> . The cap hangs from the top of a lighter @-@ colored , brittle stem that measures up to 12 cm ( 4 @.@ 7 in ) long by 1 to 2 @.@ 5 cm ( 0 @.@ 4 to 1 @.@ 0 in ) thick . <unk> , the mushroom is distinguished by its large spores , typically 60 – 80 by 15 – 18 µm , and the presence of only two spores per <unk> . 
- In the field , the mushroom is reliably distinguished from the true morels on the basis of cap attachment : V. bohemica has a cap that hangs completely free from the stem . Although widely considered edible , consumption of the mushroom is generally not advised due to reports of poisoning in susceptible individuals . <unk> symptoms include gastrointestinal upset and lack of muscular coordination . V. bohemica is found in northern North America , Europe , and Asia . It fruits in early spring , growing on the ground in woods following the snowmelt , before the appearance of " true morels " ( genus Morchella ) . The synonym Ptychoverpa bohemica is often used by European <unk> . 
- 
- = = Taxonomy , phylogeny , and naming = = 
- 
- The species was first described in the scientific literature by the Czech physician and mycologist Julius <unk> von <unk> in 1828 , under the name Morchella bohemica . The German naturalist Joseph Schröter transferred it to the genus Verpa in 1893 . Ptychoverpa bohemica is a synonym that was published by Frenchman Jean Louis Émile <unk> in his 1907 treatise on the <unk> of Europe ; the name is still occasionally used , especially in European publications . <unk> believed that the large , curved <unk> and the rare and short paraphyses were sufficiently distinct to warrant a new genus to contain the single species . Ptychoverpa has also been classified as a section of Verpa . The section is characterized by the presence of thick longitudinal ridges on the cap that can be simple or forked . 
- The specific epithet bohemica refers to Bohemia ( now a part of the Czech Republic ) , where <unk> originally collected the species . The mushroom is commonly known as the " early morel " , " early false morel " , or the " wrinkled thimble @-@ cap " . Ptychoverpa is derived from the Ancient Greek <unk> ( genitive form <unk> ) , meaning " fold " , layer " , or " plate " . 
- 
- = = Description = = 
- 
- The cap of this fungus ( known technically as an <unk> ) is 2 to 4 cm ( 0 @.@ 8 to 1 @.@ 6 in ) in diameter by 2 to 5 cm ( 0 @.@ 8 to 2 @.@ 0 in ) long , with a conical or bell shape . It is folded into longitudinal ridges that often fuse together ( <unk> ) in a vein @-@ like network . The cap is attached to the stem at the top only — hanging from the top of the stipe , with the lobed edge free from the stem — and varies in color from yellowish @-@ brown to reddish @-@ brown ; the underside of the cap is pale . The stem is 6 to 12 cm ( 2 @.@ 4 to 4 @.@ 7 in ) long by 1 to 2 @.@ 5 cm ( 0 @.@ 4 to 1 @.@ 0 in ) thick , cream @-@ white in color , and tapers upward so that the stem is thicker at the base than at the top . Although the stem is initially loosely <unk> with <unk> hyphae , it eventually becomes hollow in maturity ; overall , the mushroom is rather fragile . The spore deposit is yellow , and the flesh is white . 
- Relative to other typical mushroom species , the spores of V. bohemica are huge , typically measuring 60 – 80 by 15 – 18 µm . They are elliptical , smooth , sometimes curved , and appear hyaline ( translucent ) to yellowish . The spores , which number two ( more rarely three ) per <unk> are characteristic for this species . The smooth , elliptical asci measure 275 – 350 µm long by 16 – 23 µm wide . The British @-@ Canadian mycologist Arthur Henry Reginald <unk> determined that the asci are <unk> — they bend toward light . As he noted , " I cut transverse sections though their <unk> , examined these sections under the microscope , and at once perceived that in all the <unk> grooves and depressions the asci were curved outwards so that their <unk> must have faced the strongest rays of light to which the ends of the asci has been subjected in the places where the fruit @-@ bodies developed . " This response to the stimulus of light is significant because it permits a fruit body to point and later discharge its asci towards open spaces , thus increasing the chances that the spores will be dispersed by wind . The paraphyses are thick and club @-@ shaped , with diameters of 7 – 8 µm at their tips . 
- 
- = = = Edibility = = = 
- 
- The edibility of this species is questionable ; although Verpa bohemica is eaten by many , consumption of large amounts in a single sitting , or on successive days , has been reported to cause poisoning in susceptible individuals . Symptoms include gastrointestinal upset and lack of muscular coordination , similar to the effects reported by some individuals after consuming the false morel species <unk> esculenta . The responsible <unk> in G. esculenta is <unk> ; it is suspected that V. bohemica may be able to synthesize low levels of the <unk> . Over @-@ consumption of the mushroom has been reported to have induced a coma . Those who do wish to eat this species are often advised to <unk> with copious quantities of water ( discarding the water before consumption ) , to dry the specimens before eating , or , if eating for the first time , to restrict consumption to small portions to test their tolerance . Some advocate only eating the caps and discarding the stems . Opinions on the flavor of the mushrooms vary , ranging from " strong but not on a par with true morels " , to " pleasant " , to " not distinctive " . 
- 
- = = = Similar species = = = 
- 
- The closely related species Verpa <unk> typically has a smooth cap , although specimens with wrinkled caps are known . V. <unk> may be distinguished microscopically by its eight @-@ spored asci . Its North American range extends much further south than V. bohemica . Another similar group of species are the " half @-@ free " morels , Morchella semilibera and others , which have a <unk> cap that is attached to the stalk for about half of its length , and with ridges that are darker than the pits . Additionally , a cross @-@ <unk> stem of a specimen of M. semilibera is hollow , while V. bohemica usually has <unk> <unk> in the stem , and M. semilibera usually has vertical <unk> near the base , while V. bohemica lacks them . Verpa bohemica may be reliably distinguished from all similar species by its much larger spores . 
- 
- = = Ecology , habitat and distribution = = 
- 
- The fruit bodies of V. bohemica grow singly or scattered on the ground in woods in early spring , often before the appearance of the morel , and throughout the morel season . It is often found along riverbanks , near <unk> , willows and <unk> , often buried in plant litter . The fungus prefers to fruit in moist areas with ample sunlight . Its minimum growth temperature is 3 ° C ( 37 ° F ) , with an <unk> of 22 ° C ( 72 ° F ) , and a maximum of about 30 ° C ( 86 ° F ) . A study of carbon and nitrogen isotope ratios indicated that Verpa bohemica is saprobic , that is , obtaining nutrients from decomposing organic matter . It has been suggested , however , that the fungus is mycorrhizal for at least part of its life cycle . The fungus has a wide distribution throughout northern North America ; its range extends south to the Great Lakes in the Midwestern United States , and south to northern California on the West Coast . In Europe , the fungus is widely distributed , and has been collected from Austria , the Czech Republic , Denmark , Finland , Germany , Norway , Poland , Russia , Slovenia , Spain , Sweden , and the Ukraine . In Asia , it has been recorded from India and Turkey . 
- A 10 @-@ year study of the distribution , time of fruiting and habitats of morel and false morel population in Iowa showed that early false morels are the first morels to fruit in the spring , appearing shortly after leaves begin to form on deciduous trees . <unk> @-@ head morels ( Morchella <unk> ) fruit next , followed by the yellow or white morels ( Morchella esculenta ) , then <unk> Morchella <unk> . The fruit bodies serve as a habitat for breeding <unk> ( flies ) , including <unk> media , <unk> <unk> , and <unk> <unk> . 
- 
- 
- = Varanasi = 
- 
- Varanasi ( <unk> pronunciation : [ <unk> ] ) , also known as Benares , Banaras ( <unk> [ <unk> ] ) , or Kashi ( <unk> [ <unk> ] ) , is a North Indian city on the banks of the Ganges in Uttar Pradesh , India , 320 kilometres ( 200 mi ) south @-@ east of the state capital , Lucknow , and 121 kilometres ( 75 mi ) east of Allahabad . The spiritual capital of India , it is the <unk> of the seven sacred cities ( <unk> Puri ) in Hinduism and Jainism , and played an important role in the development of Buddhism . Varanasi lies along National Highway 2 , which connects it to Kolkata , Kanpur , <unk> , and Delhi , and is served by Varanasi Junction and Lal <unk> Shastri International Airport . 
- Varanasi grew as an important industrial centre , famous for its muslin and silk <unk> , perfumes , ivory works , and sculpture . Buddha is believed to have founded Buddhism here around <unk> BC when he gave his first sermon , " The Setting in Motion of the Wheel of Dharma " , at nearby <unk> . The city 's religious importance continued to grow in the 8th century , when Adi <unk> established the worship of Shiva as an official sect of Varanasi . Despite the Muslim rule , Varanasi remained the centre of activity for Hindu intellectuals and theologians during the Middle Ages , which further contributed to its reputation as a cultural centre of religion and education . <unk> Tulsidas wrote his epic poem on Lord Rama 's life called Ram <unk> Manas in Varanasi . Several other major figures of the Bhakti movement were born in Varanasi , including Kabir and Ravidas . Guru Nanak Dev visited Varanasi for <unk> in <unk> , a trip that played a large role in the founding of <unk> . In the 16th century , Varanasi experienced a cultural revival under the Muslim Mughal emperor <unk> who invested in the city , and built two large temples dedicated to Shiva and Vishnu , though much of modern Varanasi was built during the 18th century , by the Maratha and <unk> kings . The kingdom of Benares was given official status by the <unk> in 1737 , and continued as a dynasty @-@ governed area until Indian independence in 1947 . The city is governed by the Varanasi Nagar Nigam ( Municipal Corporation ) and is represented in the Parliament of India by the current Prime Minister of India <unk> <unk> , who won the <unk> <unk> elections in 2014 by a huge margin . Silk weaving , carpets and crafts and tourism employ a significant number of the local population , as do the <unk> <unk> Works and Bharat Heavy <unk> Limited . Varanasi Hospital was established in 1964 . 
- Varanasi has been a cultural centre of North India for several thousand years , and is closely associated with the Ganges . Hindus believe that death in the city will bring salvation , making it a major centre for pilgrimage . The city is known worldwide for its many ghats , embankments made in steps of stone slabs along the river bank where pilgrims perform ritual ablutions . Of particular note are the Dashashwamedh Ghat , the Panchganga Ghat , the Manikarnika Ghat and the Harishchandra Ghat , the last two being where Hindus <unk> their dead . The Ramnagar Fort , near the eastern bank of the Ganges , was built in the 18th century in the Mughal style of architecture with carved balconies , open courtyards , and scenic pavilions . Among the estimated 23 @,@ 000 temples in Varanasi are Kashi Vishwanath Temple of Shiva , the Sankat Mochan Hanuman Temple , and the Durga Temple . The Kashi Naresh ( <unk> of Kashi ) is the chief cultural patron of Varanasi , and an essential part of all religious celebrations . An educational and musical centre , many prominent Indian philosophers , poets , writers , and musicians live or have lived in the city , and it was the place where the Benares Gharana form of <unk> classical music was developed . One of Asia 's largest residential universities is Banaras Hindu University ( BHU ) . The Hindi @-@ language nationalist newspaper , Aj , was first published in 1920 . 
- 
- = = Etymology = = 
- 
- Traditional etymology links " Varanasi " to the names of two Ganges tributaries forming the city 's borders : Varuna , still flowing in northern Varanasi , and Assi , today a small stream in the southern part of the city , near Assi Ghat . The old city is located on the north shores of the Ganges , bounded by Varuna and Assi . 
- In the <unk> , an ancient Indian sacred collection of Vedic Sanskrit hymns , the city is referred to as <unk> ( Kashi ) from the Sanskrit verbal root <unk> " to shine " , making Varanasi known as " City of Light " , the " luminous city as an eminent seat of learning " . The name was also used by pilgrims dating from Buddha 's days . 
- Hindu religious texts use many epithets to refer to Varanasi , such as <unk> ( Sanskrit : " the shining one " ) , <unk> ( Sanskrit : " never forsaken " by Shiva ) , <unk> ( Sanskrit : the forest of <unk> ) , and <unk> ( Sanskrit : the place where <unk> / <unk> resides ) . 
- Over most of the last millennium , the city was known as Banaras . In the 1950s , Indian government decided to return to the Sanskrit form Varanasi ; however , Banaras is still the predominant form used locally . 
- 
- = = History = = 
- 
- According to legend , Varanasi was founded by the god Shiva . The <unk> , the heroes of the Hindu epic Mahabharata , are also said to have visited the city in search of Shiva to atone for their sin of <unk> and <unk> that they had committed during the climactic <unk> War . It is regarded as one of seven holy cities which can provide <unk> ; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , and <unk> are the seven cities known as the <unk> of liberation . 
- 
- = = = Archaeological evidence = = = 
- 
- Archaeological evidence of the earliest known settlements around Varanasi in the Ganges valley suggest that they began in the 20th century BC , placing it among the world 's oldest continually inhabited cities according to Kenneth Fletcher . These archaeological remains suggest that the Varanasi area was populated by Vedic people . However , the oldest known text referencing the city , the <unk> , which dates to approximately the same period , suggests that the area was populated by indigenous tribes . It is possible that archaeological evidence of these previous inhabitants has yet to be discovered . Recent excavations at <unk> and Ramnagar , two sites very near to Varanasi , show them to be from 1800 BC , suggesting Varanasi was also inhabited by this time . 
- 
- = = = Ancient period = = = 
- 
- Varanasi grew as an important industrial centre , famous for its muslin and silk <unk> , perfumes , ivory works , and sculpture . During the time of <unk> Buddha , Varanasi was the capital of the Kingdom of Kashi . The Buddha is believed to have founded Buddhism here around <unk> BC when he gave his first sermon , " The Setting in Motion of the Wheel of Dharma " , at nearby <unk> . The celebrated Chinese traveller <unk> , also known as <unk> <unk> , who visited the city around 635 AD , attested that the city was a centre of religious and artistic activities , and that it extended for about 5 kilometres ( 3 @.@ 1 mi ) along the western bank of the Ganges . When <unk> , visited Varanasi in the 7th century , he named it " <unk> " and wrote that the city had some 30 temples with about 30 monks . The city 's religious importance continued to grow in the 8th century , when Adi <unk> established the worship of Shiva as an official sect of Varanasi . 
- 
- = = = Medieval period = = = 
- 
- <unk> , founder of the <unk> dynasty made Banaras a second capital in <unk> . 
- During the Birth Period , Varanasi was connected by a road starting from <unk> and ending at <unk> . 
- <unk> Shah ordered Hindu temples at Varanasi destroyed in <unk> . 
- Varanasi remained the centre of activity for intellectuals and theologians during the Middle Ages , which further contributed to its reputation as a cultural centre of religion and education . Several major figures of the Bhakti movement were born in Varanasi , including Kabir who was born here in <unk> , and Ravidas , a 15th @-@ century socio @-@ religious reformer , mystic , poet , traveller , and spiritual figure , who was born and lived in the city and employed in the tannery industry . 
- 
- = = = Modern history ( 1500 – present ) = = = 
- 
- Similarly , numerous eminent scholars and preachers visited the city from across India and south Asia . Guru Nanak Dev visited Varanasi for <unk> in <unk> , a trip that played a large role in the founding of <unk> . 
- Varanasi experienced a Hindu cultural revival in the 16th century under the Muslim Mughal emperor <unk> , who invested in the city and built two large temples dedicated to Shiva and Vishnu . The Raja of Pune established the Annapurna Mandir , and the 200 @-@ metre ( 660 ft ) <unk> Bridge was also completed during this period . The earliest tourists began arriving in the city during the 16th century . In 1665 , the French traveller Jean Baptiste <unk> described the architectural beauty of the <unk> <unk> temple on the side of the Ganges . The road infrastructure was also improved during this period . It was extended from Kolkata to <unk> by Emperor Sher Shah <unk> ; later during the British Raj it came to be known as the famous Grand Trunk Road . In 1656 , Emperor Aurangzeb ordered the destruction of many temples and the building of mosques , causing the city to experience a temporary setback . However , after <unk> 's death , most of India was ruled by a confederacy of pro @-@ Hindu kings . Much of modern Varanasi was built during this time , especially during the 18th century by the Maratha and <unk> kings . The kings governing Varanasi continued to wield power and importance through much of the British Raj period , including the <unk> of Benares , or Kashi Naresh . 
- The Kingdom of Benares was given official status by the <unk> in 1737 , and continued as a dynasty @-@ governed area until Indian independence in 1947 , during the reign of Dr. <unk> Narayan Singh . In the 18th century , Muhammad Shah ordered the construction of an observatory on the Ganges , attached to Man Mandir Ghat , designed to discover imperfections in the calendar in order to revise existing astronomical tables . Tourism in the city began to <unk> in the 18th century . In 1791 , under the rule of the British Governor @-@ General Warren Hastings , Jonathan Duncan founded a Sanskrit College in Varanasi . In 1867 , the establishment of the Varanasi Municipal Board led to significant improvements in the city 's infrastructure and basic amenities of health services , drinking water supply and sanitation 
- In 1897 , author Mark Twain , said of Varanasi , " Benares is older than history , older than tradition , older even than legend , and looks twice as old as all of them put together . " In 1910 , the British made Varanasi a new Indian state , with Ramnagar as its capital but with no jurisdiction over the city of Varanasi itself . The religious head , Kashi Naresh , has had his headquarters at the Ramnagar Fort since the 18th century , also a <unk> of the history of the kings of Varanasi , which is situated to the east of Varanasi , across the Ganges . The Kashi Naresh is deeply revered by the local people and the chief cultural patron ; some devout inhabitants consider him to be the incarnation of Shiva . 
- In 1857 , the British Army committed a massacre of Indian troops and city residence during the early stages of the Indian Rebellion of 1857 . Annie Besant founded the Central Hindu College , which later became a foundation for the creation of Banaras Hindu University in 1916 . Besant founded the Central Hindu College because she wanted to bring men of all religions together under the ideal of <unk> in order to promote Indian cultural values and to remove ill @-@ will among different sections of the Indian population . " 
- Varanasi was ceded to the Union of India in 1947 , and Dr. <unk> Narayan Singh incorporated his territories into the United <unk> in 1949 . He died in 2000 . The current king and the resident of the fort is <unk> Narayan Singh , since 1971 . 
- 
- = = Geography and climate = = 
- 
- 
- = = = Geography = = = 
- 
- Varanasi is located at an elevation of 80 @.@ 71 metres ( 264 @.@ 8 ft ) in the centre of the Ganges valley of North India , in the Eastern part of the state of Uttar Pradesh , along the left crescent @-@ shaped bank of the Ganges , averaging between 15 metres ( 50 ft ) and 21 metres ( 70 ft ) above the river . The city is the headquarters of Varanasi district . By road , Varanasi is located <unk> kilometres ( 495 mi ) south @-@ east of New Delhi , 320 kilometres ( 200 mi ) south @-@ east of Lucknow , 121 kilometres ( 75 mi ) east of Allahabad , and 63 kilometres ( 39 mi ) south of <unk> . The " Varanasi Urban <unk> " – an agglomeration of seven urban sub @-@ units – covers an area of 112 @.@ 26 km 2 ( approximately 43 mi ² ) . <unk> of the city include <unk> , <unk> , <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> Nagar , <unk> Nagar , Gopal <unk> , Guru Nanak Nagar , <unk> , <unk> <unk> , <unk> , <unk> , Lanka <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , and Sigra . 
- Being located in the Indo @-@ <unk> Plains of North India , the land is very fertile because low level floods in the Ganges continually replenish the soil . Varanasi is located between the Ganges <unk> with two rivers : the Varuna and the Assi stream . The distance between the two <unk> is around 2 miles ( 4 km ) , and serves as a sacred <unk> route for Hindus , which culminates with a visit to a <unk> <unk> Temple . 
- 
- = = = Climate = = = 
- 
- Varanasi experiences a humid subtropical climate ( Köppen climate classification <unk> ) with large variations between summer and winter temperatures . The dry summer starts in April and lasts until June , followed by the monsoon season from July to October . The temperature ranges between 22 and 46 ° C ( 72 and 115 ° F ) in the summers . Winters in Varanasi see very large diurnal variations , with warm days and downright cold nights . Cold waves from the <unk> region cause temperatures to dip across the city in the winter from December to February and temperatures below 5 ° C ( 41 ° F ) are not uncommon . The average annual rainfall is 1 @,@ 110 mm ( 44 in ) . <unk> is common in the winters , while hot dry winds , called <unk> , blow in the summers . In recent years , the water level of the Ganges has decreased significantly ; upstream dams , unregulated water extraction , and <unk> glacial sources due to global warming may be to blame . 
- 
- = = Administration = = 
- 
- 
- = = = Politics and law = = = 
- 
- Varanasi is governed by a number of bodies , the most important being the Varanasi Nagar Nigam ( Municipal Corporation ) and the Varanasi Development Authority , which is responsible for the master planning of the city . Water supply and sewage system is operated by the <unk> Nigam . Varanasi is represented in the Parliament of India by the current Prime Minister of India <unk> <unk> who won the <unk> <unk> elections in 2014 by a huge margin . 
- 
- = = = Healthcare = = = 
- 
- <unk> , the great surgeon and author of the <unk> <unk> , the Sanskrit text of surgery , lived in Varanasi and practised medicine and surgery sometime during the 5th century BC . Since 1922 , Ayurveda has been a subject of training in the Banaras Hindu University and in 1927 a separate Ayurvedic College was established . There are many Ayurvedic centres in Varanasi like <unk> Ayurvedic Centre which provide Ayurvedic treatments such as <unk> and other methods . S S Ayurveda Hospital operates a <unk> treatment centre , in association with <unk> Ayurveda Ltd . 
- <unk> has several hospitals , including Heritage Hospital , <unk> Hospital , <unk> <unk> , Mata Anand Mai Hospital , <unk> Hospital , Ram Krishna Mission Hospital , <unk> Prasad Gupta Hospital , Pandit <unk> <unk> <unk> hospital ( managed by state <unk> . ) the largest & <unk> hospital is Sir <unk> Lal Hospital ( University Hospital ) having more 1150 beds & a separate trauma centre having 340 beds , and Varanasi Hospital and Medical Research Centre . A separate cancer institute is also operated in Varanasi . The Varanasi Hospital , established in 1964 by Dr. <unk> Prasad . The hospital , which in 2012 had 66 beds , serves Varanasi and surrounding districts and states , many of which rely on it for surgery . Although the hospital suffers from a lack of funding , it has facilities such as x @-@ ray , <unk> , <unk> and a pathology lab . The urban portion of Varanasi District had an infant mortality rate of 70 per 1 @,@ 000 live births in 2010 – 2011 . 
- 
- = = = Public maintenance = = = 
- 
- Because of the high population density of Varanasi and the increasing number of tourists , the Uttar Pradesh government and international non @-@ governmental organisations and institutions have expressed grave concern for the pollution and pressures on infrastructure in the city , mainly the sewage , sanitation , and drainage components . <unk> of the Ganges is a particular source of worry because of the religious significance of the river , the dependence of people on it as a source of drinking water , and its prominence as a symbol of Varanasi and the city itself . The sewage problem is exacerbated by the role of the Ganges in bathing and in river traffic , which is very difficult to control . Because of the sewage , people using local untreated water have higher risk of contracting a range of water @-@ borne stomach diseases . 
- Parts of Varanasi are contaminated with industrial chemicals including toxic heavy metal . Studies of wastewater from Varanasi 's sewage treatment plants identify that water 's contamination with metals and the reuse of this water for irrigation as a way that the toxic metals come to be in the plants that people grow for food . One studied example is <unk> , a popular leafy vegetable which takes up heavy metal when it is in the soil , and which people then eat . Some of the <unk> sludge contains minerals which are fertiliser , which could make polluted water attractive to use . <unk> used in local farming are persistent enough to be spread through the water , to sewer treatment , then back to the farms as wastewater . 
- Varanasi 's water supply and sewage system is maintained by <unk> Nigam , a subsidiary of Varanasi Nagar Nigam . Power supply is by the Uttar Pradesh Power Corporation Limited . The city produces about 350 @,@ 000 @,@ 000 litres ( 77 @,@ 000 @,@ 000 imp gal ; 92 @,@ 000 @,@ 000 US gal ) per day of sewage and 425 tonnes ( <unk> long tons ; 468 short tons ) per day of solid waste . The solid <unk> are disposed in one landfill site . 
- 
- = = Demographics = = 
- 
- According to provisional data from the 2011 census , the Varanasi urban agglomeration had a population of 1 @,@ 435 @,@ 113 , with 761 @,@ 060 men and <unk> @,@ <unk> women . 
- The population of the Varanasi urban agglomeration in 2001 was 1 @,@ <unk> @,@ <unk> with a ratio of <unk> females every 1 @,@ 000 males . However , the area under Varanasi Nagar Nigam has a population of 1 @,@ 100 @,@ <unk> with a ratio of 883 females for every 1 @,@ 000 males . The literacy rate in the urban agglomeration is 77 % while that in the municipal corporation area is 78 % . Approximately 138 @,@ 000 people in the municipal area live in slums . 
- 7 
- 
- = = Economy = = 
- 
- According to the 2006 City Development Plan for Varanasi , approximately 29 % of Varanasi 's population is employed . Approximately 40 % are employed in manufacturing , 26 % work in trade and commerce , 19 % work in other services , 8 % work in transport and communication , 4 % work in agriculture , 2 % work in construction , and 2 % are marginal workers ( working for less than half of the year ) . 
- Among manufacturing workers , 51 % work in spinning and weaving , 15 % work in metal , 6 % work in printing and publishing , 5 % work in electrical machinery , and the rest work in a wide variety of industry sectors . Varanasi 's manufacturing industry is not well developed and is dominated by small @-@ scale industries and household production . 
- Silk weaving is the dominant industry in Varanasi . Muslims are the influential community in this industry with nearly half a million of them working as <unk> , <unk> , <unk> <unk> , and <unk> . Weaving is typically done within the household , and most <unk> are <unk> Ansari Muslims . Varanasi is known throughout India for its production of very fine silk and <unk> <unk> , <unk> with gold and silver thread work , which are often used for weddings and special occasions . The production of silk often uses bonded child labour , though perhaps not at a higher rate than elsewhere in India . The silk weaving industry has recently been threatened by the rise of power looms and computer @-@ generated designs and by competition from Chinese silk imports . 
- In the metal manufacturing sector , <unk> <unk> Works is a major employer . Bharat Heavy <unk> Limited , a large power equipment manufacturer , also operates a heavy equipment maintenance plant . Other major commodities manufactured and traded in Varanasi include hand @-@ <unk> <unk> carpets , <unk> , <unk> , <unk> , <unk> , wooden and clay toys , <unk> , gold jewellery , and musical instruments . Important agricultural products include <unk> leaves ( for <unk> ) , <unk> <unk> and <unk> ( solidified milk ) . 
- Tourism is Varanasi 's second most important industry . Over 3 million domestic and 200 @,@ 000 foreign tourists visit annually ( in 2005 and 2010 , respectively ) , most commonly for religious purposes . Most domestic tourists are from <unk> , West Bengal , <unk> Pradesh , and Uttar Pradesh , while the majority of foreign tourists are from Sri Lanka and Japan . The peak tourist season falls between October and March . In total , there are around 12 @,@ 000 beds available in the city , of which about one half are in inexpensive budget hotels and one third in <unk> . Overall , Varanasi 's tourist infrastructure is not well developed . 
- The prominent malls and <unk> in Varanasi are <unk> Mall in the Varanasi <unk> area , IP Mall in Sigra , IP Vijaya Mall in <unk> , and PDR in <unk> . The city has several banks , including the Allahabad Bank , Andhra Bank , Bank of <unk> , <unk> Bank , Central Bank of India , Corporation Bank , Indian Overseas Bank , and State Bank of India . 
- 
- = = Notable landmarks = = 
- 
- Apart from the 19 archaeological sites identified by the Archaeological Survey of India , some of the prominent places of interest are the <unk> <unk> , the Alamgir Mosque , the <unk> <unk> , the Bharat Kala <unk> ( Art Museum ) , the Bharat Mata Temple , the Central University for <unk> Studies , the <unk> Temple , the Durga Temple , the Jantar Mantar , the Kashi Vishwanath Temple , the Sankat Mochan Hanuman Temple , the Mahatma Gandhi Kashi <unk> , the New Vishwanath Temple on the BHU campus , the Ramnagar Fort , the <unk> Ghats , the Tulsi Manas Temple . 
- 
- = = = Jantar Mantar = = = 
- 
- The Jantar Mantar observatory , constructed in 1737 , is located above the ghats along the Ganges , and is adjacent to the <unk> and <unk> Ghats and near the palace of Raja Jai Singh of <unk> . While less equipped than the observatories at <unk> and Delhi , the Jantar Mantar has a unique equatorial sundial which is functional and allows measurements to be monitored and recorded by one person . 
- 
- = = = Ramnagar Fort = = = 
- 
- The Ramnagar Fort , located near the Ganges on its eastern bank and opposite the Tulsi Ghat , was built in the 18th century by Kashi Naresh Raja <unk> Singh with cream @-@ coloured <unk> sandstone . The fort is a typical example of the Mughal architecture with carved balconies , open courtyards , and scenic pavilions . At present , the fort is in disrepair . The fort and its museum are the <unk> of the history of the kings of Benares . Cited as an " eccentric " museum , it contains a rare collection of American vintage cars , bejewelled <unk> chairs , an impressive weaponry hall , and a rare <unk> clock . In addition , manuscripts , especially religious writings , are housed in the <unk> <unk> which is a part of a museum within the fort . Many books illustrated in the Mughal miniature style are also part of the collections . Because of its scenic location on the banks of the Ganges , it is frequently used as an outdoor shooting location for films . 
- 
- = = = Ghats = = = 
- 
- The Ghats in Varanasi are world @-@ renowned embankments made in steps of stone slabs along the river bank where pilgrims perform ritual ablutions . The ghats are an integral complement to the Hindu concept of divinity represented in physical , metaphysical , and supernatural elements . Varanasi has at least 84 ghats , most of which are used for bathing by pilgrims and spiritually significant Hindu puja ceremony , while a few are used exclusively as Hindu cremation sites . Steps in the ghats lead to the banks of Ganges , including the Dashashwamedh Ghat , the Manikarnika Ghat , the Panchganga Ghat , and the Harishchandra Ghat , where Hindus <unk> their dead . Many ghats are associated with Hindu legends and several are now privately owned . 
- Many of the ghats were built when the city was under Maratha control . Many ghats were constructed under the patronage of the <unk> , <unk> ( <unk> ) , <unk> , <unk> , and <unk> . Most of the ghats are bathing ghats , while others are used as cremation sites . A morning boat ride on the Ganges across the ghats is a popular tourist attraction . The extensive stretches of ghats in Varanasi enhance the <unk> with a multitude of shrines , temples , and palaces built " tier on tier above the water 's edge " . 
- The Dashashwamedh Ghat is the main and probably the oldest ghat of Varanasi located on the Ganges , close to the Kashi Vishwanath Temple . It is believed that Brahma created this ghat to welcome Shiva and sacrificed ten horses during the Dasa @-@ <unk> <unk> performed there . Above and adjacent to this ghat , there are also temples dedicated to <unk> , <unk> , <unk> , <unk> <unk> , Ganga ( the Ganges ) , and <unk> Devi , which are all important pilgrimage sites . A group of priests perform " Agni <unk> " ( Sanskrit : " Worship of Fire " ) daily in the evening at this ghat as a dedication to Shiva , Ganga , Surya ( Sun ) , Agni ( Fire ) , and the entire universe . Special <unk> are held on <unk> and on religious festivals . 
- The Manikarnika Ghat is the <unk> , the primary site for Hindu cremation in the city . <unk> the ghat , there are raised platforms that are used for death anniversary rituals . According to a myth it is said that an <unk> of Shiva or his wife Sati fell here . Fourth @-@ century Gupta period inscriptions mention this ghat . However , the current ghat as a permanent riverside embankment was built in <unk> and has been renovated at least three times throughout its existence . 
- The Jain Ghat or <unk> Ghat is a Jain Ghat and has three Jain Temples located on the banks of the River . It is believed that the Jain <unk> used to own these <unk> Ghat has three Jain temples near the river 's banks and one them is a very ancient temple of <unk> <unk> . 
- 
- = = = Temples = = = 
- 
- further information at Religion in Varanasi 
- Among the estimated 23 @,@ 000 temples in Varanasi , the temples most popular for worship are : the Kashi Vishwanath Temple of Shiva ; the Sankat Mochan Hanuman Temple ; and the Durga Temple , known for monkeys that reside in the large trees nearby . 
- The Kashi Vishwanath Temple , on the Ganges , is one of the 12 <unk> Shiva temples in Varanasi . The temple has been destroyed and rebuilt several times throughout its existence . The Gyanvapi Mosque , which is adjacent to the temple , is the original site of the temple . The temple , which is also known as the Golden Temple , was built in <unk> by Queen <unk> <unk> of <unk> . The two pinnacles of the temple are covered in gold and were donated in 1839 by <unk> Singh , the ruler of Punjab . The dome is scheduled to receive gold plating through a proposed initiative of the Ministry of Culture and Religious Affairs of Uttar Pradesh . Numerous rituals , prayers , and <unk> are held daily at the temple between 02 : 30 and 23 : 00 . 
- The Sankat Mochan Hanuman Temple , which is situated by the <unk> River , is one of the sacred temples of the Hindu god Hanuman . The present temple was built in the early 1900s by the <unk> and Indian independence figure , Pandit <unk> Mohan <unk> , the founder of Banaras Hindu University . According to Hindu legend the temple was built on the very spot where the medieval Hindu saint Tulsidas had a vision of Hanuman . During a 7 March 2006 terrorist attack , one of three explosions hit the temple while a wedding was in progress , and resulted in injuries to 30 people apart from 23 deaths . Following the attack , a permanent police post was installed inside the temple . 
- There are two temples named " Durga " in Varanasi : Durga Mandir built in the 16th century ( exact date not known ) , and Durga Kund ( Sanskrit ' <unk> ' meaning " pond or pool " ) built in the 18th century . A large number of Hindu devotees visit Durga Kund during <unk> to worship the goddess Durga . The temple , built in the <unk> architectural style , has multi @-@ tiered <unk> and is stained red with <unk> , representing the red colour of Durga . The building has a rectangular tank of water called the Durga Kund ( " Kund " meaning a pond or pool ) . During annual celebrations of Nag <unk> , the act of depicting the god Vishnu <unk> on the serpent <unk> is recreated in the Kund . While the Annapurna Temple , located nearby to the Kashi Vishwanath Temple , is dedicated to Annapurna , the goddess of food , the Sankatha Temple adjacent to the <unk> Ghat is dedicated to Sankatha , the goddess of remedy . The Sankatha Temple has a large sculpture of a lion and a cluster of nine smaller temples dedicated to the nine planets . Other temples of note are : the Bharat Mata Temple , dedicated to the national personification of India , which was inaugurated by Mahatma Gandhi in 1936 , the <unk> Temple , the <unk> <unk> Temple , and the New Vishwanath Temple located in the campus of BHU , the Tulsi Manas Mandir . 
- Parshvanath Jain temple : The Parshvanath Jain temple , Varanasi is temple of Jain religion dedicated to Parshvanath , the 23rd <unk> who was born at <unk> in Varanasi . The idol deified in the temple is of black colour and 75 cm in height . It is located in <unk> about 5 km from the centre of Varanasi city and 3 km from the Benares Hindu University . It belongs to the <unk> sect of Jainism and is a holy <unk> or pilgrimage centre for Jains 
- 
- = = = <unk> = = = 
- 
- There are 15 mosques of significant historical value in Varanasi . Of particular note are the Abdul <unk> , Alamgir , <unk> <unk> , <unk> , <unk> <unk> <unk> , <unk> , <unk> <unk> , <unk> and <unk> <unk> <unk> <unk> <unk> . Many of these mosques were constructed from the components of the Hindu shrines which were destroyed under the auspices of subsequent Muslim invaders or rulers . The two well known mosques are the Gyanvapi Mosque and the <unk> Mosque . 
- The Gyanvapi Mosque was built by the Mughal emperor Aurangzeb in 1664 CE , after destroying a Hindu temple . <unk> <unk> ( Sanskrit : " the well of knowledge " ) , the name of the mosque , is derived from a well of the same name located within the precincts of the mosque . The remains of an <unk> temple can be seen in the foundation , the columns and at the rear part of the mosque The façade of the mosque is modelled partially on the Taj <unk> 's entrance . The mosque is administered by the <unk> <unk> <unk> ( <unk> ) . 
- The <unk> Mosque was built in the 17th century by emperor Aurangzeb over the ruins of a Hindu temple . The Hindu temple that was destroyed was dedicated to Vishnu , and had been built by Beni <unk> Rao <unk> , a Maratha chieftain . When emperor Aurangzeb had captured Banaras , he had ordered total destruction of all Hindu temples there . Aurangzeb then built a mosque over the ruins of this temple in 1669 and named it as <unk> Mosque in the name of his own honorific title " Alamgir " which he had adopted after becoming the emperor of Mughal empire . The mosque is located at a prominent site above the Panchganga Ghat , which is a funerary ghat facing the Ganges . The mosque is <unk> a blend of Islamic and Hindu architecture , particularly because of the lower part of the walls of the mosque having been built fully with the remains of the Hindu temple . The mosque has high <unk> and <unk> . Two of its <unk> had been damaged ; one <unk> crashed killing a few people and the other <unk> was officially brought down because of stability concerns . Non @-@ Muslims are not allowed to enter the mosque . The mosque has a security cordon of a police force . 
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- = = = <unk> Guru Ravidass <unk> <unk> = = = 
- 
- <unk> Guru Ravidass <unk> <unk> , at Seer <unk> is the ultimate place of pilgrimage or religious headquarters for followers of the <unk> religion . The foundation stone of this Mandir was laid on 14 June 1965 on <unk> <unk> day at the birthplace of Guru Ravidass . The temple was completed in 1994 . 
- 
- = = Culture = = 
- 
- 
- = = = Literature = = = 
- 
- Varanasi has its own culture of fine art and literature . <unk> Indian writers who have resided in the city were Kabir , Ravidas , and Tulsidas , who wrote much of his Ram <unk> Manas here . <unk> <unk> wrote the best known account of <unk> in Varanasi in the 15th century , Later writers of the city have included <unk> <unk> , Baldev <unk> , <unk> Harishchandra , <unk> <unk> <unk> , <unk> Prasad <unk> , <unk> Prasad , <unk> <unk> , <unk> Ali , <unk> Prasad <unk> , <unk> Chandra <unk> , <unk> <unk> ( <unk> ) , <unk> Shastri , and <unk> <unk> Mishra . 
- Several newspapers and journals are or were published in Varanasi such as Varanasi <unk> and its successor <unk> , which became a weekly journal , first published on 1 June 1851 . The main newspaper is Aj , a Hindi @-@ language nationalist newspaper first published in 1920 . The newspaper was the <unk> of the Indian National Congress and is still a major newspaper of Hindi northern India . 
- 
- = = = Art = = = 
- 
- Varanasi is a major centre of arts and designs . It is a producer of <unk> and <unk> with gold and silver thread work , carpet weaving , wooden toys , <unk> made of glass , ivory work , perfumes , artistic brass and copper ware and a variety of <unk> . The former <unk> graveyard during the British Raj is now the location of Varanasi 's Arts and <unk> . 
- Artists ( musicians and dancers ) and historians of repute who belonged to this city were : Anand Krishna , <unk> Mishra , <unk> Khan , musicians <unk> Thakur , Ravi Shankar , <unk> Devi , Gopal Shankar <unk> , <unk> <unk> Maharaj , <unk> Maharaj , <unk> <unk> , N. <unk> , <unk> <unk> , <unk> Devi , <unk> Prasad , <unk> Devi , Thakur <unk> Singh , and Pandit <unk> Maharaj . 
- 
- = = = Music = = = 
- 
- Varanasi ’ s music tradition is traced to the <unk> days . According to ancient legend , Shiva is credited with evolving music and dance forms . During the medieval era , Vaishnava , a <unk> movement , grew in popularity , and Varanasi became a thriving centre for musicians such as <unk> , Kabir , <unk> , <unk> and Tulsidas . During the <unk> rule of <unk> Chandra in the 16th century , the Dhrupad style of singing received royal patronage and led to other related forms of music such as <unk> , <unk> , and <unk> . <unk> the Dhrupad <unk> Pandit <unk> <unk> from Varanasi is working for the revival of this art @-@ music . 
- In recent times , <unk> Devi , the native famous classical singer of <unk> , was widely appreciated and respected for her musical renderings . Varanasi is also associated with many great <unk> such as <unk> <unk> Khan , Pandit <unk> Maharaj , and Pandit Ravi Shankar , the famous <unk> player and <unk> who was given the highest civilian award of the country , the Bharat <unk> . Varanasi has joined the global <unk> of UNESCO " Cities of Music " under the Creative Cities Network . Varanasi chosen in music category of creative cities network clearly signifies the rich musical heritage of this ancient city . 
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- = = = Festivals = = = 
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- On <unk> ( February ) , a procession of Shiva proceeds from the <unk> Temple to the Kashi Vishwanath Temple . Dhrupad <unk> is a five @-@ day musical festival devoted to <unk> style held at Tulsi Ghat in February – March . The Sankat Mochan Hanuman Temple celebrates Hanuman <unk> ( March – April ) , the birthday of Hanuman . A special puja , <unk> , and a public procession is organised . Since 1923 , the temple has organised a five @-@ day classical music and dance concert festival named Sankat Mochan <unk> <unk> , when iconic artists from all parts of India are invited to perform . 
- The <unk> of Ramnagar is a dramatic enactment of Rama 's legend , as told in <unk> . The plays , sponsored by Kashi Naresh , are performed in Ramnagar every evening for 31 days . On the last day , the festivities reach a crescendo as Rama <unk> the demon king Ravana . Kashi Naresh <unk> Narayan Singh started this tradition around 1830 . 
- Nag <unk> is celebrated on the fourth lunar day of the dark fortnight of the Hindu month of Kartik ( October – November ) . It commemorates the victory of Krishna over the serpent <unk> . On this occasion , a large <unk> tree ( <unk> <unk> ) branch is planted on the banks of the Ganges so that a boy , playing the role of Krishna , can jump into the river on to the effigy representing <unk> . He stands over the effigy in a dancing pose playing the flute , while an audience watches from the banks of the river or from boats . Bharat <unk> celebrates the meeting of Rama and his younger brother Bharata after the return of the former after 14 years of exile . It is celebrated during October – November , a day after the festival of <unk> . Kashi Naresh attends this festival in his regal attire . The festival attracts a large number of devotees . 
- Ganga <unk> is a five @-@ day music festival organised by the Uttar Pradesh Tourism Department , held in November – December . It culminates a day before Kartik <unk> , also called the Ganges festival . On this occasion the Ganges is attended by thousands of pilgrims , release <unk> lamps to float in the river from the ghats . 
- Every year , the primary Muslim festivals celebrated in the city are the <unk> @-@ ul @-@ <unk> ' ( <unk> ) , <unk> , <unk> @-@ e @-@ <unk> , <unk> <unk> and <unk> . Additional festivals include <unk> and <unk> . A non @-@ religious festival observed by Muslims is <unk> @-@ <unk> @-@ <unk> @-@ <unk> ( " the marriage of <unk> <unk> " ) . 
- 
- = = Education = = 
- 
- Historically , Varanasi has been a centre for education in India , attracting students and scholars from across the country . Varanasi has an overall literacy rate of 80 % ( male literacy : 85 % , female literacy : 75 % ) . It is home to a number of colleges and universities . Most notably , it is the site of Banaras Hindu University ( BHU ) , which is one of the largest residential universities in Asia with over 20 @,@ 000 students . The Indian Institute of Technology , BHU is designated an Institute of National Importance and is one of 16 Indian Institutes of Technology . Other colleges and universities in Varanasi include <unk> Arabic College , the Institute of Integrated Management and Technology ( <unk> ) , Mahatma Gandhi Kashi <unk> , <unk> <unk> Kala <unk> , <unk> Sanskrit University , Sri <unk> <unk> <unk> College , and <unk> <unk> <unk> College . Various engineering colleges have been established in the outskirts of the city . 
- St. Joseph 's <unk> School , in <unk> , in Varanasi was established by the Sisters of Our Lady of Providence of France as a Catholic ( Christian ) minority institution with due approval of the Government of Uttar Pradesh . It is an autonomous organisation under the Bishop of Varanasi Diocese . It provides education not only to the Catholic Christian children but also to others who abide by its rules . 
- Another important institution is the Central Hindu School ( <unk> ) , Varanasi which was established by Annie Besant in July 1898 with the objective of <unk> secular education , is located in <unk> . It is one of the reputed schools in the country and is also one of the largest such schools . It is affiliated to the Central Board of Secondary Education ( <unk> ) It is open to students of all culture . 
- Schools in Varanasi are affiliated with the Indian Certificate of Secondary Education ( <unk> ) , the <unk> , or the Uttar Pradesh Board of Technical Education ( <unk> Board ) . The overall " state of education in Varanasi is ... not good . " Schools in Varanasi vary widely in quality , with private schools <unk> government schools . In government schools , many teachers fail to come to class or to teach children . Some government schools lack basic equipment , such as <unk> and sufficient <unk> and chairs for all students . Private schools vary in quality , with the most expensive conducting lessons in English ( seen as a key to children 's success ) and having computers in classrooms . Pupils attending the more expensive private schools , tended to come from upper @-@ class families . Lower @-@ cost private schools attracted children from lower @-@ income families or those lower @-@ income families with higher education aspirations . Government schools tend to serve lower @-@ class children with lower education aspirations . 
- 
- = = Sport = = 
- 
- Basketball , cricket , and field hockey are popular sports in Varanasi . The main stadium in the city is the Sigra Stadium , also known as Dr <unk> Stadium , where first @-@ class cricket matches are held . Local cricket matches are also played on the BHU Ground Dr. <unk> Rao sports complex . 
- The Physical Education Faculty of Arts of BHU offers <unk> courses in Sports Management , Sports <unk> , Sports Psychology and Sports <unk> . 
- <unk> is also popular in Varanasi , and many Indian girls practice outdoors at the ghats in the mornings which hosts <unk> , where " morning exercise , a dip in the Ganges and a visit to Lord Hanuman " forms a daily ritual . Despite concerns regarding water quality , two swimming clubs offer swimming lessons in the Ganges . 
- The Varanasi District Chess Sports Association ( <unk> ) is based in Varanasi , affiliated to the regional Uttar Pradesh Chess Sports Association ( <unk> ) . <unk> <unk> <unk> College is also known for its world class athletes like <unk> Singh . 
- 
- = = Transport = = 
- 
- Varanasi is well @-@ connected by air , rail and road . One of the major factors in Varanasi 's is its access to all parts of the country . Within the city mobility is provided by taxis , rickshaws , cycle rickshaws and three <unk> but with certain restrictions in the old town area of the city . 
- Varanasi is served by Lal <unk> Shastri International Airport , which is approximately 26 km ( 16 mi ) from the city centre in <unk> . The airport inaugurated a new terminal in 2010 , and it was granted international airport status on 4 October 2012 . Air India , Buddha Air , Jet Airways , Jet <unk> , <unk> , and <unk> operate flights from Varanasi to Delhi , <unk> , <unk> , <unk> , <unk> , Lucknow , Mumbai , Hyderabad , Bangalore , and Kolkata . Over 330 @,@ 000 passengers pass through the airport each year . 
- Varanasi Junction , commonly known as Varanasi <unk> Railway Station , is the city 's largest train station . More than 360 @,@ 000 passengers and 240 trains pass through each day . Some of the important express trains operating from the Varanasi Junction railway station are : the <unk> Varanasi Express that runs between <unk> ( <unk> ) junction and Varanasi , a distance of 1 @,@ 398 kilometres ( <unk> mi ) ; the Kashi Vishwanath Express that runs between Varanasi and New Delhi Railway Station ; the Kanpur Varanasi <unk> express , also called Varuna express , which runs over a distance of 355 kilometres ( 221 mi ) and connects with Lucknow ( the capital city of Uttar Pradesh ) and Kanpur ; and the <unk> Express which runs between Varanasi and <unk> . Varanasi lies along National Highway 2 , which connects it to Kolkata , Kanpur , <unk> , and Delhi . National Highway 29 connects Varanasi to <unk> via <unk> to the northeast . National Highway 56 connects Varanasi to Lucknow via <unk> and <unk> , to the northwest . National Highway 7 , the longest National Highway in India , is the most important road connecting Varanasi to southern India , passing through the cities of Hyderabad , Bangalore , Salem , <unk> , <unk> , and <unk> . Auto rickshaws and cycle rickshaws are the most widely available forms of public transport in old city . In the outer regions of the city , buses are common , and taxis are available . 
- 
- = = Twin towns – Sister cities = = 
- 
- Varanasi is twinned with : 
- <unk> , Japan 
- 
- 
- = Toronto Magnetic and Meteorological Observatory = 
- 
- The Toronto Magnetic and Meteorological Observatory is a historical observatory located on the grounds of the University of Toronto , in Toronto , Ontario , Canada . The original building was constructed in 1840 as part of a worldwide research project run by Edward Sabine to determine the cause of fluctuations in magnetic declination . Measurements from the Toronto site demonstrated that <unk> were responsible for this effect on Earth 's magnetic field . When this project concluded in 1853 , the observatory was greatly expanded by the Canadian government and served as the country 's primary meteorological station and official <unk> for over fifty years . The observatory is considered the birthplace of Canadian astronomy . 
- 
- = = Sabine 's study = = 
- 
- <unk> tended to " wander " from north when measurements were taken at different locations or even at a single location over a period of time . The astronomer Edmund Halley noted this and the problems it would cause for navigation in 1701 . It was also believed that whatever was causing this effect might be causing changes in the weather , and that studying magnetic variations might lead to better weather prediction . 
- In 1833 the British Association for the Advancement of Science commissioned a series of magnetic measurements across the United Kingdom . Under the direction of Major Edward Sabine of the Royal Artillery , a multi @-@ year measuring project began , with the results to be published in 1838 . As the measurements were being made a number of proposals were put forth to expand the program worldwide . In 1836 the German explorer and naturalist Alexander von Humboldt wrote to Prince Augustus Frederick , Duke of Sussex , then President of the Royal Society , stating that a formal program was important to a nation with dominions spread across the globe . At the seventh meeting of the British Association in Liverpool in 1837 , Sabine declared that " the magnetism of the earth cannot be counted less than one of the most important branches of the physical history of the planet we inhabit " and mapping its variations would be " regarded by our contemporaries and by <unk> as a fitting enterprise of a maritime people ; and a worthy achievement of a nation which has ever sought to rank foremost in every <unk> undertaking " . 
- In 1837 , the British Government funded the installation of a magnetic observatory at Greenwich . The Association continued to press for the construction of similar observatories around the world , and in 1838 their suggestions were accepted by the Government and funds were provided . In 1839 the British Government and the Royal Society prepared four expeditions to build magnetic observation stations in Cape Town ; St. Helena ; Hobart , Tasmania and ( eventually ) Toronto . Teams of Royal Artillery officers were sent out to take the measurements . The team assigned to Canada originally planned to build their observatory on Saint Helen 's Island off Montreal , but the local rocks proved to have a high magnetic influence , and the decision was made to move to Toronto instead . The team arrived in 1839 , and set up camp at Fort York in a disused barracks while construction started on new buildings . The observatory was given 10 acres ( 4 @.@ 0 ha ) of land to the west of King 's College ; the Ontario Legislature now occupies the area on which the college was located . 
- The observatory , officially " Her Majesty 's <unk> and Meteorological Observatory at Toronto " , was completed the following year . It consisted of two log buildings , one for the magnetic instruments and the other a smaller semi @-@ buried building nearby for " experimental <unk> " . The north end of the main building was connected to a small conical dome which contained a <unk> used to make astronomical measurements for the accurate determination of the local time . The buildings were constructed with as little metal as possible ; when metal was required , non @-@ magnetic materials such as brass or copper were used . A small barracks was built nearby to house the crew . 
- Using the measurements from the Toronto and Hobart sites , Sabine noticed both short @-@ term fluctuations in magnetic declination over a period of hours , and longer @-@ term variations over months . He quickly concluded that the short term variations were due to the day / night cycle , while the longer term ones were due to the number of visible <unk> . He published two introductory papers on the topic in the Philosophical Transactions of the Royal Society . The first , in 1851 , was a collection of early measurements ; the second in 1852 correlated with Heinrich <unk> 's sunspot measurements , which had been made widely available in Alexander von Humboldt 's Cosmos , also published in 1851 . With further data collected from the Toronto site , Sabine was able to demonstrate conclusively that the eleven @-@ year sunspot cycle caused a similarly periodic variation in the Earth 's magnetic field . He presented a third and conclusive paper on the topic in 1856 , " On <unk> Laws <unk> in the Mean Effects of the Larger Magnetic <unk> " , in which he singled out the Toronto site for particular praise . 
- Sir John Henry <unk> , a pioneer in the study of terrestrial magnetism served as director of the magnetic observatory from 1842 to 1853 ; In 1960 , the Ontario Heritage Foundation , Ministry of <unk> and Culture erected a Provincial Military Plaque in his honour on the University of Toronto campus . 
- 
- = = Meteorological service = = 
- 
- In 1853 the Royal Society 's project was concluded , and the observatory was set to be abandoned . After a lengthy debate , the fledgling colonial government decided to take over its operation . Rather than disappearing like its three counterparts , the Toronto observatory was upgraded , and its mission was expanded as it became a meteorological station ( see Meteorological Service of Canada ) under the direction of the Ministry of Marine and Fisheries . During the expansion , the original buildings were replaced with a permanent structure . 
- The new building was designed in 1853 by local architect Frederick <unk> , who was also working on the design of University College , which was being built just north of the Observatory to replace King 's College . The new observatory design called for a stone building , with an attached tower containing the <unk> . The new building was completed in 1855 , and stood directly opposite the entrance of today 's <unk> Hall . 
- During its time as a meteorological station , the observatory collected reports from 312 observation stations in Canada and another 36 in the United States . Each station was equipped with a " <unk> <unk> , two <unk> ( a maximum and a minimum <unk> ) , an <unk> to measure the velocity of the wind , a Wind <unk> and a Rain <unk> " . Reports were sent in coded form to the Observatory at 8 am and 8 pm every day , Eastern Standard Time ( then known as " 75th meridian time " ) , and used to produce a chart predicting the weather for the following 36 hours . These predictions were then <unk> across the country , and charts were distributed to newspapers and the Board of Trade , where they could be viewed by the public . With the installation of telephones , the Observatory also offered weather reports on demand , which was an important service to fruit <unk> , who used the reports to plan shipping . 
- Among its other uses , in 1880 , measurements from the site were used as part of the effort to develop standard time . The observatory remained the official <unk> for Canada until 1905 , when that responsibility was transferred to Ottawa 's Dominion Observatory . At exactly 11 : 55 am the clocks in Toronto fire halls were <unk> by an electrical signal from the Observatory . 
- In 1881 the observatory 's director , Charles <unk> , suggested adding a high @-@ quality telescope to the observatory . He felt that direct solar observations would lead to a better understanding of sunspot effects on weather ( as late as 1910 the observatory 's then @-@ director , R. F. <unk> , noted that " sun spots have more to do with our weather conditions than have the rings around the moon . " ) . <unk> , the Canadian government ( having formed in 1867 ) was interested in taking part in the major international effort to accurately record the December 1882 Transit of Venus . 
- <unk> were provided for the purchase of a 6 @-@ inch ( 150 mm ) <unk> telescope from T. Cooke & Sons . The dome was originally designed to mount a small transit , and the lengthy telescope , over 2 metres long , had a limited field of view though the dome 's opening . A large stone pillar was constructed inside the tower , raising the telescope to bring it closer to the dome and improve its field of view . Unfortunately , the new telescope was unable to take part in the transit measurements due to bad weather , and missed the 1895 Transit of Mercury for the same reason . 
- 
- = = <unk> = = 
- 
- By the 1890s , the observatory had become crowded by the rapidly growing university . <unk> of the <unk> along College Street just to the south , and the large quantities of metal used in the modern buildings surrounding the site threw off the instruments . A new magnetic observatory opened in 1898 in <unk> , at that time largely empty fields , [ 1 ] leaving the downtown campus location with its meteorological and solar observation duties . 
- By 1907 , new university buildings completely surrounded the observatory ; dust from the construction <unk> meteorological instruments , and at night electric lighting made astronomical work impossible . The Meteorological Office decided to abandon the site and move to a new building at the north end of campus on <unk> Street , trading the original Observatory to the University in exchange for the new parcel of land . There was some discussion regarding what to do with the Cooke telescope , since the Meteorological Office had little use for this purely astronomical instrument . No other use was immediately forthcoming , and the telescope moved along with the Meteorological Office to their new <unk> Street Observatory . 
- The university assumed ownership of the now @-@ disused observatory building and was originally going to abandon it . Louis Beaufort Stewart , a lecturer in the Faculty of Applied Science and Engineering , campaigned for it to be saved for the Department of Surveying and <unk> . He eventually arranged for the building to be re @-@ constructed on a more suitable site . Demolition work was carried out in 1907 : the stones were simply left in place over the winter , and were used the following year to construct a re @-@ arranged building just east of the main University College building ( south of Hart House ) . 
- In 1930 the Meteorological Office no longer used the Cooke telescope , and agreed to donate it to the university if they would handle its removal . Both the telescope and the observatory dome were moved to the observatory building . The telescope moved once again in 1952 to the David <unk> Observatory north of the city , and in 1984 it was donated to the Canada Science and Technology Museum . The Department of Surveying and <unk> used the observatory until the 1950s . Since then the office areas have been used for a variety of purposes , including a police <unk> and a telephone <unk> . <unk> as the Louis Beaufort Stewart Observatory , the building was handed over to the Students ' Administrative Council ( now University of Toronto Students ' Union ) in 1953 , which has used the building since then . The dome , now unused , receives a yearly multi @-@ colour paint job by engineering students . 
- 
- 
- = 1973 Atlantic hurricane season = 
- 
- The 1973 Atlantic hurricane season was the first season to use the Saffir – Simpson hurricane scale , a scale developed in 1971 by Herbert Saffir and Robert Simpson to rate the intensity of tropical cyclones . The season produced 24 tropical and subtropical cyclones , of which only 8 reached storm intensity , 4 became hurricanes , and only 1 reached major hurricane status . Although more active than the 1972 season , 1973 brought few storms of note . Nearly half of the season 's storms affected land , one of which resulted in severe damage . 
- The season officially began on June 1 , 1973 , and lasted until November 30 , 1973 . These dates conventionally <unk> the period of each year when most tropical cyclones form in the Atlantic basin . However , the first system formed on April 18 , more than a month before the official start . Three more depressions formed before June 1 ; however , none attained storm intensity . The first named storm of the year was Hurricane Alice which formed on July 1 and became the first known cyclone to affect Bermuda during July . More than a month later , the second hurricane , Brenda , formed and was considered the worst storm to strike Mexico along the eastern coast of the Bay of Campeche , killing 10 people . 
- Later in August , Tropical Storm Christine became the easternmost forming tropical cyclone on record when it developed formed over Guinea . The most intense storm of the season was Hurricane Ellen , a Category 3 cyclone that remained over open water . The final named storm was <unk> significant in that it became the first recorded tropical cyclone to transition into a subtropical cyclone . No names were retired during the season ; however , due to the addition of male names into the list of Atlantic hurricane names in 1979 , several of the names were removed and have not been used since . 
- 
- = = Season summary = = 
- 
- The first storm of the 1973 hurricane season , forming in mid @-@ April , developed more than a month before the official start of the season . Several other short @-@ lived , weak depressions formed before and during June ; however , none reached storm intensity . The first named storm , Alice , formed on July 1 . Tracking generally to the north , Alice also became the first hurricane of the season as well as the first known cyclone to impact Bermuda during July . Shortly after Alice dissipated over Atlantic Canada , another depression formed . By the end of July , two more non @-@ developing depressions formed and the first subtropical cyclone , given the name Alfa , developed off the east coast of the United States . This storm was short @-@ lived and dissipated on August 2 just offshore southern Maine . The first half August was relatively quiet , with only one depression forming . However , later in the month , the season 's second hurricane , Brenda , formed in the northwestern Caribbean . Peaking just below Category 2 status on the newly introduced Saffir – Simpson hurricane scale , Brenda made the first recorded landfall in the Mexican State of Campeche . 
- Later in August , Tropical Storm Christine became the easternmost forming tropical cyclone in the Atlantic basin on record , developing over the western African country of Guinea on August 25 . The system traveled for several thousand miles before dissipating in the eastern Caribbean Sea in early September . At the start of the month , a new tropical storm formed in the Gulf of Mexico . This storm , named Delia , became the first known cyclone to make landfall in the same city twice . After moving inland a second time , Delia eventually dissipated on September 7 . As Delia dissipated another depression formed in the same region , eventually making landfall in the same city as Delia , Freeport , Texas . Another brief depression formed several days later . On September 13 , the strongest storm of the season , Ellen , formed over the eastern Atlantic . After tracking northwest for several days , Ellen eventually attained hurricane status as it turned westward . Several days later , the hurricane turned northeast due to an approaching frontal system . Shortly before becoming extratropical , Ellen reached major hurricane intensity at a record northerly latitude . 
- In late September , a brief depression affected northern Florida before dissipating . After a week of inactivity , the second subtropical storm of the year formed over the central Atlantic . This storm , named Bravo , gradually intensified , becoming fully tropical , at which time it was renamed Fran , a few days later . Upon being renamed , Fran had intensified into a hurricane and maintained this intensity for several days before dissipating east of the Azores on October 12 . A few days after Fran dissipated , the final named storm of the year formed in the central Caribbean Sea . A slow moving system , Gilda gradually intensified just below hurricane @-@ intensity before striking Cuba and moving over the Bahamas . A few days after passing through the islands , Gilda became the first storm on record to transition from a tropical cyclone into a subtropical cyclone . A large storm , Gilda eventually became extratropical near Atlantic Canada and dissipated later that month . Around the time Gilda was dissipating , a weak depression briefly existed near the Azores . The final storm of the year was a strong depression in the southern Caribbean Sea . This system was active for less than two days but may have briefly attained tropical storm intensity as it made landfall in southern Nicaragua . 
- 
- = = Storms = = 
- 
- 
- = = = Hurricane Alice = = = 
- 
- The first named storm formed out of the interaction between tropical wave and a mid @-@ level tropospheric trough northeast of the Bahamas in late @-@ June . A well @-@ defined circulation became apparent by June 30 and satellite images depicted cyclonic banding features . The following day , the system intensified into a tropical depression and shortly thereafter became a tropical storm as reconnaissance aircraft recorded gale @-@ force winds . An area of high pressure to the east of Alice steered the storm generally to the north . <unk> wind shear allowed the storm to become increasingly organized and a well @-@ defined eye developed by July 3 . By this time , reconnaissance had determined that the storm had intensified into a hurricane , with maximum winds reaching 80 mph ( 130 km / h ) . 
- On July 4 , the storm reached its peak intensity with winds of 90 mph ( 150 km / h ) and a barometric pressure of 986 mbar ( hPa ; 29 @.@ 11 inHg ) , as the eastern portion of the eyewall brushed Bermuda . After passing the island , Alice began to accelerate in response to a mid @-@ level trough over the eastern United States and weakened . By July 6 , winds head decreased below hurricane intensity as the storm neared Atlantic Canada . Later that day , Alice made landfall in eastern Newfoundland with winds of 60 mph ( 95 km / h ) before transitioning into an extratropical cyclone . 
- During its passage of Bermuda , Alice produced sustained winds up to 75 mph ( 120 km / h ) and gusts to 87 mph ( 140 km / h ) . No major damage was recorded on the island , though the winds blew down a few trees and <unk> . The heavy rainfall , peaking at 4 @.@ 57 in ( 116 mm ) , ended a three @-@ month drought in Bermuda . Although Alice tracked through Atlantic Canada , no impact was recorded . 
- 
- = = = Subtropical Storm Alfa = = = 
- 
- During late July , an upper @-@ level low , with a non @-@ tropical cold core , formed near Cape Hatteras , North Carolina and tracked southward . Gradually , the circulation lowered to the surface and developed subtropical characteristics . On July 31 , the system attained gale @-@ force winds off the Mid @-@ Atlantic coast and was named Alfa , the first name from the list of subtropical storm names for the 1973 season . Tracking north @-@ northeast , the system intensified very little as it paralleled the coastline . By August 1 , the system weakened below subtropical storm intensity as it neared New England . The following day , Alfa dissipated just off the southern coast of Maine . The only effects from Alfa was light to moderate rainfall in New England , peaking at 5 @.@ 03 in ( 128 mm ) in <unk> Falls , Massachusetts . Most of southern Maine recorded around 1 in ( 25 mm ) , with a maximum of 2 @.@ 59 in ( 66 mm ) in <unk> . 
- 
- = = = Hurricane Brenda = = = 
- 
- Hurricane Brenda originated from a tropical wave that moved off the western coast of Africa on August 9 ; however , the initial wave quickly weakened upon entering the Atlantic Ocean . By August 13 , the wave began to <unk> as it passed through the Lesser Antilles . Several days later , convection associated with the system consolidated into a central , organized mass and on August 18 , the system had become sufficiently organized to be declared a tropical depression while situated near the Yucatán Channel . Early the next day , the depression strengthened into Tropical Storm Brenda as it made landfall in the northern portion of the Yucatán Peninsula . After moving inland , a strong ridge of high pressure over Texas forced the storm to take an unusual track , eventually leading it to enter the Bay of Campeche on August 20 . 
- Once back over water , Brenda began to intensify , attaining hurricane status late on August 20 . The next day , a well @-@ defined eye had developed and the storm attained its peak intensity as a high @-@ end Category 1 hurricane with winds of 90 mph ( 150 km / h ) and a minimum pressure of 977 mbar ( hPa ; 28 @.@ 85 inHg ) . The storm made landfall later that day near Ciudad del Carmen , Mexico at this intensity , becoming the first hurricane on record to strike the region . After moving inland , Brenda rapidly weakened to a depression by the morning of August 22 and dissipated later that day . 
- Already suffering from severe flooding that killed at least 18 people and left 200 @,@ 000 homeless , Hurricane Brenda worsened the situation with torrential rainfall and additional flooding . The storm killed at least 10 people in the country . Following the damage wrought by Brenda , a large earthquake struck the region , hampering relief efforts and collapsing numerous structures . Winds on land gusted up to 112 mph ( 180 km / h ) , leading to severe wind damage . Two of the fatalities occurred in Campeche after 80 % of the city was flooded . This was considered the worst flooding in the city in over 25 years . An estimated 2 @,@ 000 people were left homeless as a direct result of Brenda throughout Mexico . Offshore , a freighter with 25 crewman became trapped in the storm after its engines failed . They were safely rescued several days later once the storm had dissipated . 
- 
- = = = Tropical Storm Christine = = = 
- 
- The easternmost forming Atlantic tropical cyclone on record , Tropical Storm Christine , originated as a tropical wave over Africa in mid @-@ August . As it neared the Atlantic Ocean , the wave spawned a tropical depression at 14 @.@ 0 ° W , over the country of Guinea , unlike most cyclone producing waves which travel several hundred miles over water before spawning a depression . Although it was already a depression , advisories on the storm were not issued until August 30 , five days after its formation . For several days , the depression maintained its intensity as it steadily tracked west across the Atlantic . It eventually attained tropical storm intensity on August 28 . Despite the lack of aircraft reconnaissance in the region , the intensity was determined by wind readings from a German cargo ship that passed through the storm . 
- On August 30 , the first reconnaissance mission into the storm found tropical storm @-@ force winds and the first advisory was issued that day , immediately declaring the system as Tropical Storm Christine . Three days later , Christine attained its peak intensity just below hurricane @-@ status with winds of 70 mph ( 110 km / h ) and a minimum pressure of 996 mbar ( hPa ; 29 @.@ 41 inHg ) . Shortly thereafter , increasing wind shear caused the storm to weaken as it neared the Leeward Islands . As it passed over Antigua on September 3 , Christine weakened to a tropical depression and eventually dissipated near the Dominican Republic later that day . 
- During its passage through the Leeward Islands , Christine produced torrential rainfall , peaking at 11 @.@ 74 in ( 298 mm ) in southeastern Puerto Rico . These rains led to flooding on several islands . One person was killed during the storm after being <unk> by a downed power line on a flooded road . Schools were closed ahead of the storm in Puerto Rico and the United States Virgin Islands as a precaution following the issuance of flood warnings . Six scientists had to be evacuated from the small island of <unk> once the storm posed a threat to them . No major damage was reported on any of the affected islands in the wake of Christine . 
- 
- = = = Tropical Storm Delia = = = 
- 
- On August 27 , a tropical wave formed over the central Caribbean and tracked towards the west @-@ northwest . The system gradually developed organized shower and thunderstorm activity . By September 1 , a tropical depression developed from the wave . By September 3 , the depression had intensified into a tropical storm , receiving the name Delia , and began tracking more towards the west . A complex steering patter began to take place later on that day , resulting in the creation of a more hostile environment for tropical cyclones in the Gulf of Mexico . As Delia neared the Texas coastline , it managed to intensify into a strong tropical storm with winds of 70 mph ( 110 km / h ) . The lowest pressure was recorded at 986 mbar ( hPa ; 29 @.@ 11 inHg ) at this time . Shortly thereafter , the cyclone made its first landfall in Freeport , Texas late on September 4 . After executing a <unk> loop , the storm made landfall in Freeport again on September 5 . After moving inland , the storm quickly weakened , becoming a depression on September 6 before dissipating early the next day over northern Mexico . 
- Due to the erratic track of the storm along the Texas coastline , widespread heavy rains fell in areas near the storm and in Louisiana . Tides up to 6 ft ( 1 @.@ 8 m ) , in addition to rainfall up to 13 @.@ 9 in ( 350 mm ) , caused significant flooding in the Galveston @-@ Freeport area . Up to $ 3 million was reported in damages to homes due to the flooding . Throughout Louisiana , there was substantial flooding of farmland . Damages to crops amounted to $ 3 million . In addition to the flooding rains produced by Delia , eight tornadoes also touched down due to the storm , injuring four people . Five people were killed during Delia , two drowned during floods , two died in a car accident and the other died from a heart attack while boarding up his home . 
- 
- = = = Tropical Depression Eleven = = = 
- 
- On September 6 , a tropical depression formed over the northwestern Caribbean Sea within a trough of low pressure extended southeastward from Delia , which was situated over southeast Texas at the time . The depression remained weak until it reached the Texas coastline on September 10 . Once onshore , it produced significant rainfall , causing significant damage that was attributed to Tropical Storm Delia . After turning northeast and tracking inland , the depression quickly increased in forward speed before dissipating over North Carolina on September 14 . 
- Along the coasts of Texas and Louisiana , the depression produced significant amounts of rainfall , peaking at 11 @.@ 15 in ( 283 mm ) near Freeport . Several areas in southern Louisiana recorded rainfall exceeding 5 in ( 130 mm ) with a maximum amount of 9 @.@ 2 in ( 230 mm ) falling in <unk> . Significant rainfall was also recorded in the Carolinas and Georgia , with numerous areas recording over 3 in ( 76 mm ) . A maximum of 9 @.@ 35 in ( 237 mm ) fell near <unk> , South Carolina before the system dissipated . In all , the depression resulted in an additional $ 22 million in crop losses in southern Louisiana . 
- 
- = = = Hurricane Ellen = = = 
- 
- The strongest storm of the season , Hurricane Ellen , began as a tropical wave that moved off the western coast of Africa on September 13 . On the following day , the wave spawned an area of low pressure south of the Cape Verde Islands that quickly became a tropical depression . Tracking northeast , the system intensified into a tropical storm on September 15 after sustained winds of 45 mph ( 75 km / h ) were reported by a French naval vessel ; however , due to sparse data on the storm , the first advisory on Ellen was not issued for two more days . A slightly elongated storm , Ellen gradually intensified over the open Atlantic and was steered by two troughs of low pressure . On September 18 , the storm took a nearly due west track and the system became increasingly organized , with an ill @-@ defined eye becoming present on satellite imagery . 
- The next day , Ellen intensified into a hurricane before taking a sharp turn to the north @-@ northwest in response to a weak trough moving northeast from the Bahamas . Gradually , the hurricane turned more towards the northeast and began to accelerate as well as intensify . Despite being at an unusually high latitude for development , the storm underwent a brief period of rapid intensification , strengthening into a Category 3 hurricane on September 23 . At that time , Ellen attained its peak intensity with winds of 115 mph ( 185 km / h ) and a minimum pressure of <unk> mbar ( hPa ; 28 @.@ 41 inHg ) . Upon attaining this intensity at 42 @.@ 1 ° N , Ellen had become a major hurricane farther north than any other tropical cyclone on record , and is one of two storms to become a major hurricane north of 38 ° N , the other being Hurricane Alex in 2004 . Shortly after peaking , Ellen transitioned into an extratropical cyclone before merging with a frontal system several hundred miles east of Newfoundland on September 23 . 
- 
- = = = Tropical Depression Thirteen = = = 
- 
- On September 24 , a depression formed northeast of the Bahamas . The following day , the NHC issued their first advisory on the system , declaring it a subtropical depression . The depression was displayed an asymmetrical structure , with most winds being recorded up to 300 mi ( 480 km ) north of the center . Later that day , the subtropical depression organized into a tropical depression . Upon doing so , the NHC issued small craft advisories for coastal areas between North Carolina and St. Augustine , Florida . Tracking north @-@ northwestward in response to a break in a subtropical ridge to the north , the depression eventually made landfall near <unk> , Florida and quickly weakened , dissipating before reaching the Gulf of Mexico . 
- Heavy rain fell in association with the depression in parts of Florida and Georgia . A maximum of 6 @.@ 74 in ( 171 mm ) fell in Orlando while several other areas recorded over 3 in ( 76 mm ) of rain . Over land , wind gusts reached 40 mph ( 65 km / h ) in some locations . Offshore , swells produced by the system reached 10 ft ( 3 @.@ 0 m ) , impacting several vessels in the region . Minor beach erosion and coastal flooding was reported in parts of South Carolina as a result of the storm . In parts of coastal Georgia , high water resulted in several road closures and flooded a few homes . Police officers in Savannah reported that wave were topping the local seawall ; however , no damage was reported . 
- 
- = = = Hurricane Fran ( Bravo ) = = = 
- 
- The final hurricane of the season , Fran , originated from an area of convection north of Hispaniola on October 1 . By October 4 , the system interacted with a mid @-@ tropospheric trough near the southeast United States , resulting in the formation of a surface low . Tracking eastward , showers and thunderstorms began to develop around the circulation ; however , the structure of the system was not fully tropical . Late on October 8 , the cyclone had become sufficiently organized to be classified a subtropical depression . Cold air from the remnants of a cold front became entrained within the circulation ; however , the cold air gradually warmed . The following day , winds increased to gale @-@ force and the depression was upgraded to a subtropical storm , at which time it was given the name Bravo . 
- By October 10 , Bravo had intensified substantially , as hurricane hunters recorded hurricane @-@ force winds roughly 15 mi ( 25 km ) from the center of the storm . Following this finding , the National Hurricane Center reclassified the system as a tropical system and renamed it Fran , dropping its previous designation of Bravo . <unk> generally eastward by a deep surface low in the westerlies , Fran accelerated towards the Azores Islands . Shortly after bypassing the islands on October 12 , the central pressure of Fran decreased to 978 mbar ( hPa ; 28 @.@ 88 inHg ) , the lowest recorded in relation to the hurricane . Shortly after reaching this intensity , the hurricane transitioned into an extratropical cyclone and quickly merged with a cold front off the coast of France . Although Fran passed near the Azores , no impact was recorded on any of the islands . 
- 
- = = = Tropical Storm Gilda = = = 
- 
- The precursor to Tropical Storm Gilda was a large convective system partially due to a tropical wave . It gradually became better organized over the northwestern Caribbean Sea , and on October 15 , a tropical depression formed off the coast of Nicaragua . As it drifted to the northeast , it strengthened to a tropical storm , peaking at 70 mph ( 110 km / h ) winds . Before it hit the coast of Cuba , it weakened enough to cause only minor damage . By the time it struck the island , it had become very disorganized in nature . 
- On October 24 , cool , dry air entered the newly developed convection , and as a result it transitioned into a subtropical cyclone . Gilda became the first tropical system to pass through a subtropical stage prior to becoming extratropical . The large circulation continued northeast before becoming extratropical on October 27 . The remnants of Gilda intensified as they tracked near Atlantic Canada , attaining a central pressure of 968 mbar ( hPa ; 28 @.@ 58 inHg ) near Cape Race , Newfoundland . The system eventually dissipated near southern Greenland on October 29 . 
- Gilda caused heavy rain and mudslides in Jamaica , destroying six homes and killing six people . In Cuba , Gilda dropped over 6 in ( 150 mm ) of rain , while 60 mph ( 95 km / h ) winds were reported in the northern part of the country . In the Bahamas , Gilda caused significant crop damage from heavy rainfall and high tides . The storm 's persistent strong currents and easterly winds caused moderate beach erosion on the East Coast of the United States , mostly along the Florida coast . The extratropical remnants of the storm produced hurricane @-@ force wind gusts over parts of Atlantic Canada , peaking at 75 mph ( 120 km / h ) ; however , no damage was reported . 
- 
- = = = Other storms = = = 
- 
- In addition to the eight named storms of 1973 and two notable tropical depressions , there were several minor systems that were classified as depressions by the National Hurricane Center . The first four systems of the year were not classified as fully tropical , rather they were associated with the remnants of decaying cold fronts . On April 18 , the first of these depressions formed northeast of the Bahamas and tracked in a curved motion before dissipating over open water on April 21 . Several days later , on April 24 , another depression formed in the same general region ; however , this system was shorter lived and dissipated two days later without significant movement . On May 2 , another partially tropical system formed over open waters . The cyclone tracked northeast and dissipated late on May 5 east @-@ southeast of the Azores . On May 11 , a brief depression formed near Bermuda but dissipated the following day . Roughly a week into the official hurricane season , the fifth depression of the year formed just offshore southeast Florida , near Miami . The system tracked northwest across the peninsula and briefly entered the Gulf of Mexico on June 8 before making landfall along the Florida <unk> . The depression eventually dissipate on June 10 over South Carolina . 
- On June 23 , another depression formed along Florida , this time just onshore near the Georgia border . The system slowly tracked northeastward before dissipating on June 26 southeast of the North – South Carolina border . As Hurricane Alice neared Bermuda on July 9 , a depression formed near the east coast of the United States ; however , the storm dissipated the following day . On July 19 , the first Cape Verde storm formed over the central Atlantic . This system did not intensify , remaining a weak depression and dissipated on July 21 without affecting land . The next day , a new depression formed over the southwestern Caribbean Sea near the coast of Nicaragua . The depression tracked over Central America , briefly moving back over water in the Gulf of Honduras before making a second landfall in Belize . The system persisted over land for a few days before entering the eastern Pacific late on July 25 . 
- Only one non @-@ developing depression formed during the month of August , an unusually eastward forming system . The depression was first identified just offshore eastern Africa on September 8 , near where Tropical Storm Christine formed later in the month . Tracking rapidly towards the west , the depression dissipated on September 11 over open waters . In addition to the two notable tropical depressions and two named storms in September , a slow @-@ moving depression formed south @-@ southeast of Bermuda on September 8 . Tracking generally northward , the depression dissipated early on September 10 without affecting land . Upon the declaration of Hurricane Fran on October 10 , a new depression formed southwest of the strengthening hurricane . This system rapidly tracked northeast and dissipated two days later . Later that month , a slow @-@ moving depression formed near the Azores . This system tracked southeast and dissipated on October 30 without affecting land . The final system of the year formed near the northern coast of Panama on November 17 . The depression was noted as a " ... strong depression ... " by the National Hurricane Center and may have briefly attained tropical storm intensity before making landfall in southern Nicaragua on November 18 ; the system dissipated later that day over land . 
- 
- = = Storm names = = 
- 
- The following names were used for named storms ( tropical storms and hurricanes ) that formed in the North Atlantic in 1973 . Storms were named Christine , Delia , Ellen and Fran for the first time in 1973 . Due to the relatively minimal impact caused by storms during the season , no names were retired in the spring of 1974 ; however , due to the addition of male names in 1979 , the list was removed and replaced with a new set of names . 
- 
- = = = Subtropical storm names = = = 
- 
- The following names were used for subtropical storms in the Atlantic basin for this year . This year was the second and last year to use the phonetic alphabet . Although a storm was given the name Bravo , it was renamed Fran after acquiring tropical characteristics . 
- 
- = = Season effects = = 
- 
- This is a table of the storms in 1973 and their landfall ( s ) , if any . <unk> in parentheses are additional and indirect ( an example of an indirect death would be a traffic accident ) , but are still storm @-@ related . Damage and deaths include totals while the storm was extratropical or a wave or low . 
- 
- 
- = 2 / 4th Machine Gun Battalion ( Australia ) = 
- 
- The 2 / 4th Machine Gun Battalion was an Australian Army unit raised for service with the all volunteer Second Australian Imperial Force ( 2nd AIF ) during the Second World War . Formed in late 1940 as part of the 8th Division , the battalion was established to provide direct fire support to the division ’ s infantry brigades . It was the fourth , and last , such unit raised within the 2nd AIF . The unit 's personnel were largely drawn from the state of Western Australia and after formation , the battalion concentrated near Perth for basic individual training before moving to the Adelaide Hills to complete more advance <unk> . 
- In late 1941 , amidst concerns of war in the Pacific , the unit was deployed north to Darwin in the Northern Territory , where they undertook garrison duties in the weeks following Japan 's entry into the war . Following Japanese landings in Malaya , the 2 / 4th embarked from Darwin and were transferred to Malaya , arriving in Singapore in the final days of the fighting on the peninsula . In the wake of the withdrawal of British and Commonwealth forces to the island , the battalion was hastily deployed in support of the two Australian brigades — the 22nd and 27th Brigades — in the north @-@ western sector of the island . During the initial Japanese landing , elements of the battalion were heavily engaged around the landing beaches but they were outnumbered and over the course of the week the defenders were pushed back towards the centre of the island , towards the city of Singapore . They suffered heavy casualties during this time , before subsequently becoming prisoners of war after the fall of Singapore . 
- Meanwhile , a detachment of about 100 men from the battalion , who had been left behind in Australia when it deployed to Singapore , also took part in the fighting on Java . After a brief campaign , the majority of these personnel were taken into captivity when the Allied forces were overwhelmed around <unk> in mid @-@ March 1942 , although some attempted to fight on as <unk> . Eventually these men were either killed or captured ; prisoners remained in Japanese captivity until the end of the war in August 1945 . During the three @-@ and @-@ a @-@ half years they were held by the Japanese , members of the 2 / 4th were sent to prison camps around the Pacific , where they were used as slave labour and subjected to harsh conditions and extreme brutality . After the war , the surviving members of the battalion were returned to Australia but the 2 / 4th was not re @-@ raised . 
- 
- = = History = = 
- 
- 
- = = = Formation and training = = = 
- 
- The 2 / 4th Machine Gun Battalion was one of four machine gun battalions raised as part of the all volunteer Second Australian Imperial Force ( 2nd AIF ) for service overseas during the Second World War . <unk> infantry units , equipped with wheeled motor vehicles and tracked carriers , the machine gun battalions were formed to provide a greater level of fire support than that ordinarily available within infantry battalions . Developed by the British Army , the concept had its genesis in the Australian Army during the Gallipoli Campaign in 1915 , when the machine guns assigned to the infantry battalions — initially two and then four — had been grouped together and co @-@ ordinated at brigade level , to help compensate for the lack of artillery support . On the Western Front , the concept had evolved through the establishment of machine gun companies in 1916 and finally , in 1918 , to the establishment of machine gun battalions . Similar formations had also been established among the Australian Light Horse units serving in the Sinai and Palestine Campaign . During the inter @-@ war years , the machine gun battalions had been deemed unnecessary . They were not re @-@ raised when the Army was reorganised in 1921 but in 1937 , as the Australian Army looked to expand owing to fears of war in Europe , four such units were formed within the part @-@ time Militia , by converting light horse units and <unk> them . Several other Militia machine gun battalions were also raised as new units later after the Second World War began . These were formed by consolidating the machine gun companies assigned to a number of infantry battalions , and other light horse units were also converted into the role for use as garrison forces . Within the 2nd AIF — Australia 's overseas expeditionary force — the decision was made to raise four machine gun battalions as corps troops that were usually allocated at a rate of one per division ; several of the Militia units were also later designated Australian Imperial Force units , after the majority of their personnel volunteered for overseas service . This meant that they could be deployed outside of Australian territory even though they were not 2nd AIF units . 
- In mid @-@ 1940 , amid the tide of German successes in Europe , there was an influx of volunteers for the 2nd AIF . As a result , the Australian government decided to raise a third volunteer division for overseas service , named the 8th Division . Established around three infantry brigades — the 22nd , 23rd and 27th — the division was supported by corps troops including engineers , cavalry , artillery , pioneers and a machine gun battalion for direct fire support . Within this formation , the 2 / 4th Machine Gun Battalion came into being on 25 November 1940 , when Lieutenant Colonel Michael Anketell , a First World War veteran who had commanded a Militia infantry battalion before the war , established his headquarters at Northam Camp , near Perth , Western Australia . While the rest of the 8th Division had drawn its personnel mainly from the more populous eastern states of New South Wales and Victoria , the decision was made to select personnel for the 2 / 4th only from those who were already enlisted and conducting training in the west . While potentially parochial , it was a primarily a <unk> decision to reduce the time it would take to concentrate the unit and complete its training . 
- Like most 2nd AIF units , the 2 / 4th drew its cadre staff of officers and senior non @-@ commissioned officers ( NCOs ) from volunteers from local Militia units . These were augmented by partially trained Other <unk> ( <unk> ) from the three local recruit training depots ; the first batch of these , numbering 394 men , arrived at Northam on 27 November and a further 262 marched in on 4 December . As the battalion was brought up to its authorised strength of around 800 men , personnel were formed into four machine gun companies , ' A ' to ' D ' , under a headquarters company consisting of several specialist platoons — including signals and anti @-@ aircraft platoons — and battalion headquarters . There were no platoon commanders until a contingent arrived from the regional Officer Training Units . Initially training was hampered by a lack of equipment — only 12 of 48 Vickers machine guns assigned to the battalion were available — so at the start the focus was on individual rather than collective training . As more stores arrived , training accelerated with the help of experienced Militia NCOs and First World War veterans , who were brought in to share their experiences with the new troops . Just before Christmas and New Year 's leave , the battalion received its distinctive Unit Colour <unk> : a black and gold triangle . 
- Early in the New Year , the establishment of the battalion 's command formation was completed with the appointment of <unk> as platoon commanders , following their return from their first appointment courses . The training <unk> evolved with the establishment of an <unk> school and various specialist courses , including range finding and transport ; a <unk> and drum band was also raised . In February 1941 , training focused upon skill @-@ at @-@ arms , before undertaking a 60 @-@ mile ( 97 km ) march from Northam to Perth , which was conducted over the course of three days and was undertaken , according to the Australian War Memorial , to test the " physical fitness and endurance of the personnel and officers " and " the efficiency of the battalion 's motor transport and administration personnel " . Upon their return , training progressed to field firing , night manoeuvres , <unk> , defensive exercises and further specialised training , continuing until July , by which time the battalion was considered ready for deployment . That month it received orders to move to Adelaide , South Australia . Proceeding in four drafts — one each on the transports <unk> and <unk> and two by rail — they were established at <unk> , in the Adelaide Hills , by the end of the month . The battalion endured a bitter winter , conducting field exercises amidst the steep wooded slopes of the Mount Lofty Range ; these became more tactically complex and physically demanding as Anketell worked the battalion up to a peak of efficiency , in preparation for deployment amidst growing tensions in the Pacific . 
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- = = = Garrison duties in northern Australia = = = 
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- In August 1941 , the 27th Brigade was sent to Malaya to reinforce the 22nd , which had been serving there in a garrison role since February , having been dispatched while the 2 / 4th Machine Battalion had been forming . With this , the only 2nd AIF troops remaining in Australia were those of the 23rd Brigade . As a result of concerns about Japanese intentions in the region , the decision was made to move the brigade north to bolster the defences around Darwin in the Northern Territory . In the event of fighting in the Pacific , the government planned to split infantry battalions , to defend the island chain to Australia ’ s north , dispatching forces to Rabaul , Ambon and Timor . The role of the corps troops from the 2 / 4th Pioneers and 2 / 4th Machine Gun Battalion remained uncertain . The 8th Division ’ s commander , Major General Gordon Bennett , unhappy with the dispersal of his command , had been <unk> for the dispatch of the machine gunners to Malaya to support his two infantry brigades , arguing that they would be valuable for beach defence . Bennett 's request had been rejected initially in March , due to the limited numbers of recruits enlisting but by mid @-@ September , elements at Army headquarters in Melbourne had also begun to advocate for it . In early October , following a War Cabinet decision to split the command of the 8th Division in two , removing the elements in Australia from Bennett 's command , the 2 / 4th Machine Gun Battalion received orders to move north to Darwin , where it was to serve in a home defence role in case of a Japanese attack . 
- On 11 October 1941 , the battalion entrained at <unk> and began the journey north . They <unk> at Alice Springs , where they camped overnight before continuing the journey by road in a 44 @-@ vehicle convoy , which took them further north to <unk> . There they once again entrained for the final journey to <unk> , which they reached on 19 October , after <unk> in Katherine . 
- At <unk> , the battalion was allocated a large defensive zone between <unk> , Lee Point and <unk> Bay , which included a large beach frontage , as well as extensive swamp lands and creeks further inland . Throughout November , extensive work was undertaken improving the camp and preparing this position with fortified emplacements . They also undertook various other garrison duties such as guarding ammunition dumps , and road and railway construction . Heavy summer rains flooded the camp and surrounding area and as well as hampering construction , brought a wave of <unk> fever amongst the battalion and the threat of crocodile attacks as the creeks within the battalion position swelled ; the battalion ’ s mascot , a dog named " <unk> " , fell victim . News of the Japanese attack on Pearl Harbor and invasion of Malaya came in early December and with it a need to bolster Australian forces in the Pacific . As Bennett pressed harder for reinforcements , on 23 December the 2 / 4th Machine Gun Battalion received orders to embark for Malaya . 
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- = = = <unk> = = = 
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- On 30 December 1941 , after transferring responsibility for their defensive area to a Militia Light Horse machine gun regiment from South Australia , the battalion embarked upon two <unk> , <unk> and <unk> , bound for Malaya via Port Moresby . Arriving on 4 January 1942 , they were transferred to the transport Aquitania , which was to take them the rest of the way . Before they had completed transferring the battalion ’ s equipment , news was received that Rabaul , about 500 miles ( 800 km ) north @-@ east , had been bombed by carrier @-@ based aircraft . Due to concerns that the aircraft may have been looking for the convoy , or US warships fleeing the Philippines , and that Port Moresby might be bombed next , the convoy ’ s departure was advanced and so they sailed immediately . Instead of proceeding to Malaya , though , they were taken to Sydney , New South Wales , as it was decided that it would be safer to proceed via the southern route . 
- After a brief stay in Sydney where the battalion ’ s equipment deficiencies were made good and some reinforcements were received from local recruit training depots , they embarked again on 10 January in company with <unk> Canberra , and proceeded through the Bass Strait . As they were under way , the troops were kept occupied with training on the Bren light machine gun and the Thompson sub @-@ machine gun , quantities of which were hastily added to the battalion ’ s equipment scale . They reached Fremantle , Western Australia , on 15 January and overnight , a large number of 2 / 4th men defied orders to stay aboard ship and went ashore to spend time with their families ; when the Aquitania sailed the next day , 94 men were left behind . The situation in Malaya was grave as the Japanese were steadily pushing the British and Commonwealth defenders south down the peninsula and as the gravity of the situation <unk> upon the Australian government , a knee @-@ <unk> reaction saw the dispatch of 150 partially trained reinforcements from Northam . They arrived just as the Aquitania departed , being ferried out to <unk> Island where they joined the ship ’ s company late in the afternoon of 16 January . Although the reinforcements made good the men that had been left behind , they were only partially trained and ill @-@ prepared for the fighting that would follow . 
- After departing Fremantle , the convoy steamed towards Java and reached <unk> Bay early on 20 January . Proceeding on to the <unk> Strait , which was reached mid @-@ morning on 21 January and the men were then transferred to a number of smaller , faster Dutch ships to run the gauntlet of Japanese bombers that were attacking Allied shipping in the area . In concert with several Australian , British and Indian escorts and two Dutch <unk> flying boats , the convoy entered <unk> Harbour on 25 January 1942 . Upon arrival in Singapore , consisting of <unk> personnel of all ranks , the battalion was allocated to the task of preparing machine gun positions on Singapore ’ s north coast and around the naval base . They stepped into a <unk> , the Japanese were bombing the naval base — where the 2 / 4th were accommodated — every day and the fighting on the Malay Peninsula was all but over . Having been pushed back down the peninsula over the course of seven weeks , in the final week of January , the Allies withdrew from <unk> , on the mainland , to Singapore , where they would make a final stand . Covering the withdrawal , the 22nd Brigade was the last Australian unit to cross the 70 @-@ foot ( 21 m ) wide Causeway before it was blown up , early on the morning of 31 January , to prevent the Japanese from capturing it and to slow their advance . 
- One of only two machine gun battalions available for the defending British and Commonwealth forces , as preparations were made to repulse the expected Japanese assault across the <unk> Strait , the battalion was split up to provide support to troops from the 22nd and 27th Australian Infantry Brigades and the 44th Indian Infantry Brigade around the western part of the island , where the Japanese assault was expected . On 7 February , due to concerns about the lack of defending infantry , about 90 machine gun reinforcements , who had been hastily formed into a sixth company — ' E ' — were detached at this time to form part of a 400 @-@ strong Special Reserve Battalion , initially under the command of an officer from the 2 / 19th Infantry Battalion and later taken over by Major Albert <unk> , formerly of the 2 / 4th Machine Gun Battalion . 
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- = = = Fall of Singapore = = = 
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- The Japanese attack came in the night of 8 / 9 February , after a heavy artillery and aerial bombardment that had lasted throughout the day . ' D ' Company , positioned in various locations in support of the Australian 22nd Brigade in the north @-@ western area , found themselves in the thick of the fighting as the Japanese concentrated their landing on the Australian 8th Division front . <unk> and stretched out across a large frontage over ground that was unsuited to defence , the Australians were almost immediately under pressure . Shortly after 8 : 00 pm , 13 Platoon , supporting the 2 / 20th Infantry Battalion around the head of the Lim Chu Kang Road , was confronted by a large number of landing barges filled with Japanese assault troops . For almost six hours , the platoon , under Lieutenant Eric <unk> , fought a desperate action to repel the invaders . With four machine guns , as well as personal small arms and grenades , the platoon inflicted heavy casualties on the Japanese and sank several barges . Despite heavy casualties from Japanese mortar and machine gun fire , they kept fighting until early in the morning on 9 February when , threatened with being outflanked and low on ammunition — each gun had fired over 10 @,@ 000 rounds — and having had one gun knocked out , the platoon was forced to withdraw . Destroying their equipment as they went to prevent it from being captured , the platoon was forced to withdraw in contact , taking their wounded with them ; the action was later rewarded with a Military Cross for the platoon commander , who was badly wounded after taking over one of the machine guns after its crew had been wounded . 
- Near the Sungei Murai ( Murai River ) , 15 Platoon — under Lieutenant John Meiklejohn — had been stationed in support of a company from the 2 / 18th Infantry Battalion , in a thickly wooded area with low hills and many inlets . 15 Platoon established an enfilade formation near the shore , with its machine guns distributed in two sections on a north – south axis , facing a narrow peninsula , between the mouth of the Murai and a small inlet . According to the official history by Lionel Wigmore , after a Japanese landing party approached , the southern section under Meiklejohn " opened fire against six approaching barges , and kept on firing for two hours , despite retaliation by hand grenades , as the Japanese landed and crossed the neck of the peninsula " . As was the case elsewhere in the extremely wide sector assigned to the 22nd Brigade , many Japanese landing parties were able to outflank the thinly @-@ spread Australian positions . At risk of being cut off and with ammunition running low , Meiklejohn ordered the southern section to retreat . According to Wigmore : " Meiklejohn led his section along a jungle path where they came upon a party of Japanese resting . He shot some with his revolver , and another was knocked out with a swing from a [ machine gun ] tripod , but Meiklejohn lost his life in attempting to cover his section 's withdrawal . " The northern section of 15 Platoon held its ground " until it was informed that a near @-@ by infantry platoon was almost surrounded , and about to withdraw " . Forced to retreat without its machine guns , the northern section also found Japanese troops blocking its path . When Private Cliff <unk> was attacked by a Japanese officer wielding a sword , <unk> " <unk> him " , took the sword and used it against another Japanese soldier . 
- As the situation worsened , early on 9 February ' A ' Company , which had been in reserve , and HQ Company were sent forward to provide further assistance to the 22nd Infantry Brigade , which was slowly being pushed back towards the tactically important <unk> airfield , via the village of <unk> <unk> . They took up positions at Bulim , east of the airfield ; shortly afterwards they were joined by 7 Platoon , from ‘ B ’ Company , which had been detached from the Causeway sector . The remnants of the 22nd , numbering around only 500 men from its original 2 @,@ 500 , with a further 500 or so isolated and attempting to fight their way through , was pushed further back throughout the day . Orders were passed for a counter @-@ attack around the airfield but as the size of Japanese forces in the area grew to around 20 @,@ 000 men , they were later cancelled in favour of establishing a line between Bulim and <unk> , to the east of the airfield . Established in the early afternoon , within this line , the 2 / 4th 's ' A ' Company was positioned east of Bulim with 7 Platoon , ' B ' Company , while the remnants of ' D ' Company , amounting to only 47 men , were moved south , where they joined with ' C ' Company , which was supporting the Indian 44th Brigade , which although it had not yet been engaged , had fallen back from the south @-@ west coast to avoid being cut off and had established itself west of <unk> <unk> , behind the Sungei <unk> . 
- Late on 9 February , the Japanese made more landings , in the Causeway sector , held by the 27th Infantry Brigade . Despite having been reduced to just two infantry battalions due to the transfer of the 2 / 29th to the hard @-@ pressed 22nd Infantry Brigade , they were able to mount a stiff defence , supported by the machine guns of ‘ B ’ Company . 8 Platoon inflicted many casualties in the Japanese landing craft coming ashore at the mouth of the Sungei Mandi . With casualties mounting and pressure being placed on the brigade 's rear due to a large gap that had developed around <unk> , by the Japanese advances in the 22nd Infantry Brigade 's area , the decision was made to withdraw from the beach and <unk> north – south along the Woodlands Road . Further south , the Australian 22nd and Indian 44th , 6th / 15th and 12th Infantry Brigades also established themselves along this axis between <unk> <unk> and Pasir <unk> on the south coast and by early evening on 10 February the Japanese had secured the entire west coast of the island . 
- For the next four days , the Allied troops were pushed south @-@ east towards the city of Singapore . Throughout this time , the battalion ’ s companies were in almost constant action , either — in the case of ' B ' , ' C ' , ' D ' and ' E ' Companies — under separate command , or ' A ' and ' HQ ' Companies with Battalion Headquarters . The reinforcements of ' E ' Company , detached to the Special Reserve Battalion , suffered heavily . In three days , they lost 43 men killed or missing , before the ad hoc formation was disbanded and the men returned to the 2 / 4th . 7 Platoon , ' B ' Company , took on a mounted role , <unk> four Bren carriers , with which they began patrolling in support . On 12 February they were engaged along the <unk> Vista Road , while supporting the Malayan Regiment . The following day , they were attacked by a Japanese light tank , which knocked out one of the carriers before the platoon <unk> themselves . 
- By 14 February , the Allied troops had withdrawn into a small perimeter around the city . The 8th Division were holding a position 2 miles ( 3 @.@ 2 km ) east of the city , centred upon the axis of the Holland Road , with its headquarters at <unk> Barracks . With the Japanese gaining ground to their north and south through the porous lines of the Indian 44th and British 54th Brigades , the situation became critical . Threatened with being cut off , Anketell began to plan a last stand ; moving forward to survey the situation he was badly wounded by mortar and small arms fire and after being evacuated to Alexandra Hospital , died of his wounds late in the evening of 14 February . Despite his loss , the battalion kept on fighting to the very end , sending out patrols throughout the following day and severely <unk> a Japanese vehicle convoy that came too close to their position . Late on 15 February , the British commander , Lieutenant General Arthur Percival gave the order to surrender . The physical process of the surrender was slow , and despite orders to surrender weapons and ammunition , the men proceeded to destroy the majority of their equipment the following morning , before the Japanese arrived . Later , they were marched to <unk> prison , during which several men from the battalion attempted to escape after the Japanese began executing several prisoners ; one , the regimental sergeant major , Fred <unk> , successfully made it to Sumatra but was later recaptured there , while another , Private Les <unk> , remained on the run for eight days before being recaptured after collapsing from bullet wounds he had received during the attempt . 
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- = = = Java detachment = = = 
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- While the majority of the battalion was fighting on Singapore , a small detachment of 106 men were sent to Java . The majority of these were the 94 men that had failed to return in time from their unofficial leave in Fremantle . After missing their ship , they had been arrested by the military police and were confined to quarters in <unk> Camp for two weeks . On 30 January they were released and under the command of two officers and a small group of NCOs , they embarked upon <unk> , which set out for Singapore via <unk> in Batavia , escorted by Canberra . 
- After reaching <unk> <unk> on 10 February , the detachment found itself placed under Dutch command and formed into a composite infantry company , within the reserve battalion of the ad hoc formation known as " <unk> " , which had been formed under Brigadier Arthur Blackburn . At the end of the month , having taken Sumatra , the Japanese invaded Java with three divisions and a strong naval task force . <unk> fighting at sea ensued , during which 14 out of a force of 18 Allied ships were sunk . Several Japanese transports were also sunk but the majority of Japanese troops were landed . The detachment from the 2 / 4th found itself around <unk> , where the majority of the 2 / 4th personnel formed part of an ad hoc infantry force , known as the Reserve Group , or 3rd Battalion , consisting of eight platoons , under Major John Champion de <unk> . They fought several defensive actions before being overwhelmed and taken into captivity on 12 March 1942 . A small number continued to fight as guerrillas but were eventually all captured . Some of the men were held in camps in Java and Sumatra , although the majority were later sent to Singapore before being transported to camps elsewhere in south @-@ east Asia . 
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- = = = <unk> and disbandment = = = 
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- During the fighting , out of a total of <unk> men deployed , the battalion lost 137 men killed in action and 106 wounded , while a further 24 suffered from shell shock . A total of 808 men were taken into captivity , including most of the wounded . Four men managed to escape to Australia but of the remaining men , 263 died while prisoners of war . Following their capture , the men from the 2 / 4th in Singapore were concentrated in <unk> prison , before being split up and sent to various prison camps around the Pacific , including Borneo , Burma , Thailand , Java , Sumatra , Japan and <unk> . There they were used as slave labour on the Burma – Thai Railway , in coal mines and on wharves , during which they were subjected to harsh treatment , starvation , disease and extreme brutality , which took a heavy toll . Many soldiers from the 2 / 4th were also killed while being taken to Japan , when the ships were sunk by Allied submarines . 
- Throughout their captivity , some soldiers continued to contribute to the Allied war effort , building a series of home @-@ made radios with which they transmitted Japanese shipping movements to British forces in India and through which they were able to gain news from home . They were liberated in August 1945 and after the war , the surviving members of the battalion were returned to Australia but the 2 / 4th was not re @-@ raised . Members of the battalion received the following decorations : one Military Cross , one Distinguished Conduct Medal , two British Empire Medals and nine <unk> in <unk> ; in addition one member of the battalion was appointed as a Member of the Order of the British Empire . 
- After the war , the Australian Army moved away from the machine gun battalion concept and no similar units were raised . The machine gun role was subsumed into the support companies of individual infantry battalions . The idea was arguably misunderstood by Australian commanders throughout the war and this may have influenced the decision to move away from the concept . When the units had been established , the intent had been for machine gun battalions to provide highly mobile fire support but this only worked where the principles of open warfare worked . Once the focus of Australian Army combat operations shifted to the Pacific , the machine gun battalions were largely misused , being employed in a static defensive capacity against short and medium range targets or for <unk> tasks , rather than as offensive weapons for long range fire support . The medium machine guns were also largely used in the same manner as light machine guns , such as the Bren . Other reasons identified for the concept 's limited use include distrust of overhead fire by some commanders , a preference for organic fire support over attached sub @-@ units , over @-@ estimating the difficulty of transporting Vickers guns in the jungle and a tendency to ignore targets that could not be seen . The difficulties of target acquisition in dense jungle also contributed . 
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- = = Commanding officers = = 
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- Lieutenant Colonel Michael Anketell . 
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- = = Battle honours = = 
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- The 2 / 4th Machine Gun Battalion received the following battle honours for its service during the Second World War : 
- Malaya 1942 , and Singapore . 
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- = Richard Nixon presidential campaign , 1968 = 
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- The 1968 presidential campaign of Richard Nixon , the 36th Vice President of the United States , began when Nixon , the Republican nominee of 1960 , formally announced his candidacy following a year 's preparation and five years ' political <unk> following defeats in the 1960 presidential election , and the 1962 California gubernatorial election . 
- <unk> route to the Republican Party 's presidential nomination , Nixon faced challenges from Governor George Romney of Michigan , Governor Nelson Rockefeller of New York , Governor Ronald Reagan of California , and Senator Charles Percy of Illinois . Nixon won nine of the thirteen state primaries held that season , although due to the population of his state , Governor Reagan won the popular vote while carrying only California . These victories , along with pledged delegate support from states not holding primaries , secured Nixon the nomination on the first ballot of the Republican National Convention , where he named Governor Spiro Agnew of Maryland as his running mate . 
- In the general election , Nixon emphasized " law and order , " positioning himself as the champion of what he called the " silent majority . " Running well ahead of his opponent , incumbent Vice President Hubert Humphrey , his support slipped in the polls following his refusal to <unk> in presidential debates , and following an announcement from President Lyndon B. Johnson that a halt in bombing of Vietnam had been negotiated . 
- Winning a close election on November 5 , 1968 , Nixon and Agnew were inaugurated as the 37th President of the United States and 39th Vice President of the United States , respectively , on January 20 , 1969 . He was the first Vice President elected President since Martin Van <unk> in 1836 , and the only one to do so while not an incumbent . 
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- = = Background = = 
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- Nixon was elected to the United States House of Representatives in 1946 , representing California 's 12th congressional district from 1947 until his election to the Senate in 1950 . As a member of Congress , he gained a reputation as a firm anti @-@ Communist . In 1952 , he was selected by General Dwight D. Eisenhower , the Republican nominee for president , as his vice presidential nominee . <unk> together with Eisenhower , he served as Vice President during the height of the Cold War . In office , he traveled the world on " goodwill tours " , promoting pro @-@ American policies ; he was re @-@ elected with Eisenhower in 1956 . At the end of Eisenhower 's second term in 1960 , Nixon ran unopposed for the Republican nomination , which he received . He lost a close race to Senator John F. Kennedy of Massachusetts , which many credited in part to his unhealthy appearance during the first televised debate . 
- Nixon ran for Governor of California against incumbent Pat Brown , and was defeated <unk> , leading the media to label him as a " loser . " This defeat was widely believed to be the end of his career ; in an impromptu concession speech the morning after the election , Nixon famously blamed the media for favoring his opponent , saying , " you won 't have Nixon to kick around anymore because , gentlemen , this is my last press conference . " In September , the New York Post published an article claiming that campaign donors were buying influence with Nixon by providing him with a secret cash fund for his personal expenses . He moved to New York , joining the <unk> Rose Guthrie Alexander & <unk> law firm , and regrouped , considering but decided against a run for president in 1964 , beginning to plan for a 1968 presidential campaign . 
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- = = Campaign developments = = 
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- 
- = = = Early stages = = = 
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- On January 7 , 1967 , Nixon held a secret meeting with his closest advisers to discuss a potential campaign , <unk> strategies to obtain sufficient delegates to win the Republican nomination . He asked the attendees not to discuss the meeting with anyone , but to spread subtle hints that he would run for president . The next month , during an interview with the Saturday Evening Post , Nixon flatly denied he was running for president . Nevertheless , polls suggested that he was the front @-@ runner for the nomination . The Gallup poll from February 1967 showed Nixon leading Governor George Romney , his closest rival , 52 % to 40 % . At this time , he quietly began efforts to organize in Indiana , Nebraska , New Hampshire , Oregon , and Wisconsin , positioning to secure victories in those states ' primaries the following year . In March , he gained the support of the 1964 Republican nominee , Senator Barry <unk> of Arizona A " Nixon for President Committee " formed that month , and headquarters for the organization opened in Washington D.C. in late May . 
- During the spring and summer , Nixon traveled to Eastern Europe and Latin America to bolster his foreign policy credentials . He returned in August to conduct meetings with his advisers to <unk> a solid campaign strategy . Two days later , his campaign manager , <unk> Parkinson , left his position to care for his ailing wife . Political commentators speculated that the vacancy built " an element of instability " for the campaign . The position was soon temporarily filled by former Governor Henry <unk> of Oklahoma . The next week , five staff members were fired after private investigators determined that information had been leaked to the campaigns of potential primary rivals Governors Rockefeller and Reagan . The news did not stall the progression of the campaign , and soon Nixon , <unk> , Rose , Guthrie & Alexander member Leonard <unk> assembled an advertising team that included CBS Television president Frank Shakespeare . 
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- = = = = Fall 1967 = = = = 
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- By mid @-@ September 1967 , the Nixon campaign had organized headquarters in four states deemed critical to the Republican primaries . Nixon hoped the moves would increase his delegate strength and demonstrate his " ability to win . " He notified the media that his decision on whether to run for president would be formally announced <unk> between early December and February . Meanwhile , Nixon and his staff discussed handling the topic of the Vietnam War . They advised him to <unk> his stance on the war , and encouraged him to shift his focus from foreign affairs to domestic policy to avoid the divisive war issue . Observers noted that this move potentially hurt Nixon by straying from his reputation " as a foreign policy expert . " 
- In October , political experts predicted that Nixon would gain delegates in the important states of New Hampshire , Wisconsin and Nebraska during the primary season , scheduled to begin in March 1968 . They noted that in the other critical state of Oregon , Ronald Reagan would have an advantage due to the proximity of his home state . Like Nixon , rival George Romney began to organize in these states . Romney officially announced his candidacy in November , prompting Nixon to step up his efforts . He spent most of this period on the campaign trail in New Hampshire . Those following Nixon noted that during this period , he seemed more relaxed and <unk> than in his past political career . One commentator examined that he was not " the drawn , tired figure who debated Jack Kennedy or the angry politician who conceded his California [ gubernatorial ] defeat with such ill grace . " Making appearances at fundraisers in his adopted home state of New York , Nixon helped to raise $ 300 @,@ 000 for the re @-@ election campaign of Senator Jacob K. <unk> . At the end of December , Time labeled Nixon as the " man to beat . " 
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- = = = = 1968 begins = = = = 
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- Nixon entered 1968 as the front @-@ runner for the Republican nomination . However , polls suggested that in a head @-@ to @-@ head match up with incumbent President Lyndon Johnson , Nixon trailed 50 % to 41 % . Later in January , Nixon embarked on a tour of Texas , where he lampooned President Johnson 's State of the Union address , asking : " Can this nation afford to have four more years of Lyndon Johnson 's policies that have failed at home and abroad ? " At this time , reports suggested that Nixon would formally announce his bid in February . 
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- = = = Primary campaign = = = 
- 
- On February 1 in New Hampshire , Nixon announced his candidacy for the Republican nomination , commenting that problems " beyond politics " needed to be addressed . Immediately following his entrance , the media team prepared for an advertising campaign . They analyzed video of Nixon , determining that he was at his best when speaking spontaneously . The team organized a question and answer session with seven members of the New Hampshire Republican Party , taping Nixon 's responses for editing and use in advertisements . He campaigned in the state , although polls suggested that he would easily win its primary . As a result , he began campaigning in Wisconsin where the second primary would be held . During a stop , he briefly discussed Vietnam , although not in detail , stating that the United States " must prevent [ such ] confrontations , " but that the nation must also " help people in the free world fight against aggression , but not do their fighting for them . " He used those dictatorships in Latin America as an example , stating : " I am talking not about marching feet but helping hands . " As military operations increased in Vietnam in mid @-@ February , Nixon 's standing against President Johnson improved . A Harris poll showed that he trailed the president 43 % to 48 % . Near the end of the month , Nixon 's opponent George Romney exited the race , mostly due to comments he made about being " brainwashed " during a visit to Vietnam . This left Nixon nearly unopposed for the upcoming primaries , narrowing his opponents to Nelson Rockefeller and Ronald Reagan , neither of whom had announced their <unk> . 
- Due to Romney 's exit , Nixon declared in early March that he would " greatly expand [ his ] efforts in the non @-@ primary states , " with Time observing that Nixon could now focus his political attacks solely on President Johnson . However , the void also caused problems for Nixon ; Time argued that the prospect of soundly defeating second @-@ tier candidates such as former Governor Harold <unk> of Minnesota in the primaries , would not " <unk> the voters . " The Nixon campaign countered this claim stating that Romney 's withdrawal was a " TKO " at the hands of Nixon . Meanwhile , Rockefeller began to be viewed more as a candidate , <unk> that while not wishing to split the party , he was " willing to serve ... if called . " As talks of other candidates persisted , Nixon continued to campaigning and discussion of the issues . He pledged to end the war in Vietnam , but would not go into detail , drawing some criticism . Nixon easily won the New Hampshire primary on March 12 , pulling in 80 % of the vote with a write @-@ in campaign for Rockefeller receiving 11 % . At the end of March , Rockefeller announced that he would not campaign for the presidency , but would be open to being drafted . Nixon doubted a draft stating that it would only be likely if " I make some rather serious mistake . " Reports suggested that the decision caused " Nixon 's political stock [ to ] <unk> . " <unk> by Gallup at this time revealed that Nixon led President Johnson 41 % to 39 % in a three @-@ way race with American Independent Party candidate and former Governor George Wallace of Alabama . 
- As the Wisconsin primary <unk> in early April , Nixon 's only obstacle seemed to be preventing his supporters from voting in the Democratic primary for Senator Eugene McCarthy of Minnesota as a protest against President Johnson . However , Johnson withdrew from the race before the primary , meanwhile Governor Reagan 's name was on the ballot in Wisconsin , but he did not campaign in the state and was still not a declared candidate . Nixon won the primary with 80 % followed by Reagan with 11 % and <unk> with 6 % . With Johnson removed from the race , Nixon fell behind Democratic candidates Eugene McCarthy , Hubert Humphrey and Robert Kennedy in head @-@ to @-@ head match @-@ ups . At the end of April , Nixon called for a moratorium on criticism of the Johnson policy in Vietnam as negotiations were underway : " The one man who can do anything about peace is Lyndon Johnson , and I 'm not going to do anything to undercut him . " However , the Democratic candidates for president remained fair game for criticism . He argued that " A divided Democratic Party cannot unite a divided country ; a united Republican Party can . " He also began to discuss economics more frequently , announcing plans to cut spending while criticizing the Democrats ' policy of raising taxes . During a question and answer session with the American Society of Newspaper Editors , Nixon spoke out of turn , receiving numerous interruptions of applause . The largest came when he addressed the issue of crime , proclaiming that " there cannot be order in a free society without progress , and there cannot be progress without order . " 
- On the last day of April , Rockefeller announced that he would campaign for the presidency , despite his previous statement to the contrary . Immediately following his entrance , he defeated Nixon in the Massachusetts primary 30 % to 26 % . New Harris polls found that Rockefeller fared better against Democratic candidates than Nixon , but the outlook started to appear better for Nixon after he won the Indiana primary over Rockefeller . Off the victory , Nixon campaigned in Nebraska where he criticized the three leading Democratic candidates as " three <unk> in a <unk> , prisoners of the policies of the past . " He then proposed a plan to tackle crime that included <unk> , legislation to reverse previous Supreme Court decisions , and the formation of a congressional committee targeting crime and reforms to the criminal justice system . He did not connect crime to racial rioting , drawing praise from Civil Rights leaders . Nixon won the primary in Nebraska , defeating the <unk> Reagan 71 % to 22 % . At the following primary in Oregon , Reagan seemed more willing to compete with Nixon , and Rockefeller sat out . But Nixon won with 72 % , fifty points ahead of Reagan . 
- In early June , Nixon continued to be regarded as the favorite to win the nomination , but observers noted that he had not yet locked up the nomination . He still faced challenges from Nelson Rockefeller and Ronald Reagan , and was not on the ballot in California , where Reagan won a large slate of delegates . Behind the scenes , Nixon staff lobbied for delegates from " favorite son " candidates , resulting , in the backing of Senator Howard Baker of Tennessee , and his 28 pledged delegates , as well as those 58 delegates supporting Senator Charles Percy of Illinois . After the assassination of Robert Kennedy , like the other candidates , Nixon took a break from campaigning . Reports suggested that the assassination all but assured his nomination . Upon returning to the trail , Nixon found that Rockefeller had begun attacking him . Rockefeller described Nixon as a man " of the old politics " who has " great natural capacity not to do the right thing , especially under pressure . " Nixon refused to respond to the jabs , stating that he would not participate in attacks . As he edged closer to the nomination , discussions about his running mate arose . Republicans in the Midwest pushed for Mayor John Lindsay of New York City . The endorsement of Nixon by Senator Mark Hatfield of Oregon raised speculation that he might be chosen . Congressman George Bush of Texas and Senator Percy were also mentioned as possible selections . At the end of the month , Nixon had two thirds of the required 667 delegates necessary to win the nomination . 
- On July 1 , Nixon received the endorsement of Senator John G. Tower of Texas , handing him at least 40 delegates . With his nomination all but assured , Nixon 's ad team began preparing for the general election . A series of advertisements featuring question and answer sessions with Nixon and friends of campaign staffers were filmed in New York . The tapes were sent to the swing states of Illinois , Michigan , and Ohio , giving Nixon the advantage of advertising long before the Democratic Party settled on a candidate . At this time , Nixon decided with a group of legislators that " crime and disorder " would be presented as the number one issue in the nation . This continued to be a major theme of the Nixon campaign , and would continue to be used extensively during the general election . Nixon publicly announced his opposition to the military draft , proposing to replace the current system with a volunteer army encouraged with higher pay . President Eisenhower endorsed Nixon in mid @-@ July , breaking his tradition of waiting until after the primary , due to the election 's importance . By July 's end , reports circulated that Nixon had 691 probable delegates for the convention , placing him over the 667 delegate threshold , Rockefeller , however , disputed these numbers . Sources within Washington reported that Reagan caused greater concern for the Nixon campaign than Rockefeller . A possible scenario surfaced where Nixon 's southern delegates would drop their support to back the more conservative Reagan . Nonetheless , Nixon staffers believed that if such a scenario occurred , liberal Rockefeller delegates in the Northeast would support Nixon to prevent a Reagan nomination . 
- 
- = = Republican National Convention = = 
- 
- The 1968 Republican National Convention was held from August 5 to 9 at the Miami Beach Convention Center in Miami Beach , Florida . At the convention , Richard Nixon won the nomination for President on the first ballot with 692 delegates . Behind him finished Governor Rockefeller , second with 277 delegates , followed by Governor Ronald Reagan , in third place , having just entered the race , <unk> 182 delegates . Nixon 's early nomination occurred partly because he held on to delegates in the South largely influenced by Senator <unk> <unk> of South Carolina , and delegate Charlton Lyons of Louisiana . 
- Following nomination , Nixon held his hands in the air with his trademark " V " sign of victory , delivering an acceptance speech written over the preceding weeks . In his speech , he remarked : 
- " Tonight I do not promise the millennium in the morning . I don 't promise that we can eradicate poverty and end discrimination in the space of four or even eight years . But I do promise action . And a new policy for peace abroad , a new policy for peace and progress and justice at home . " - <unk> . Vice President Richard Nixon , 1968 Republican <unk> for President 
- He called for a new era of negotiation with communist nations , and a strengthening of the criminal justice system to restore law and order . <unk> himself as a champion of the American Dream , he stressed greater unity , invoking the silent majority . Nixon also discussed economics , <unk> his opposition to social welfare , advocating programs designed to help African Americans start their own small businesses . By the end of his address , he promised that " the long dark night for America is about to end . " 
- Following the speech , Nixon formally selected Governor Spiro Agnew of Maryland as his running mate , who received 1119 delegate votes , with the distant second being Governor Romney with 186 . Agnew was relatively unknown nationally , and was selected due to his purported appeal to African Americans , and work for the Nixon campaign after an embarrassing experience as the head of the Draft Rockefeller movement . It was later noted that the convention had featured Nixon as the <unk> candidate with Rockefeller to his left and Reagan to his right . The same analysis applied to the general campaign , as commentators noted that Nixon would stand to the right of the still undecided Democratic nominee but would fall to the left of American Independent Party candidate George Wallace . 
- 
- = = General election = = 
- 
- As the general election season began , Nixon focused his efforts on the " big seven " states : California , Illinois , Michigan , New York , Ohio , Pennsylvania , and Texas . He hired Roger <unk> , whom he had first encountered during an appearance on the The Mike Douglas Show , to produce one hour television programs to advertise the campaign in strategic regions . The campaign also continued to use televised town hall segments throughout the campaign , which aired live , featuring real voters whom were instructed to ask tough questions , following the campaign 's belief that Nixon would respond well to such questions . Starting the ground campaign tour , during his first stop in Springfield , Illinois , he discussed the importance of unity , stating that " America [ now ] needs to be united more than any time since Lincoln . " He then traveled to Michigan , Ohio and Pennsylvania before returning to New York , meeting with Governor Rockefeller . In those Gallup polls following the convention , Nixon led Humphrey 45 % to 29 % and topped McCarthy 42 % to 37 % . At the end of the month , Hubert Humphrey narrowly won Democratic presidential nominee over McCarthy at the Democratic convention , which was filled with protest and riots . Analysts saw the Democrat 's split , along with lacking " law and order " at the convention , positioning Nixon well . Shortly before the convention and throughout the general election , Nixon received regular briefings from President Johnson on developments in the Vietnam War . The President made it clear to Nixon that he did not want the war to be <unk> , to which Nixon agreed , although questioning Humphrey 's eventual compliance . 
- Following the Democratic convention , Nixon was consistently labeled the favorite , described as " relaxed [ and ] confident , " counter to his " unsure " self from 1960 . Even observers speculated as to the President 's possible favoring Nixon to Humphrey . In a visit to Chicago shortly after the Democratic convention , Nixon received a large welcome and <unk> tape parade , with crowds estimated at several hundred thousand . 
- Before his visit , he called upon Senator Edward Brooke of Massachusetts , the highest ranking African American in U.S. government , to campaign with him on trips to Illinois and California . Referring to Brooke as " one of my top advisers , " he accompanied campaign stops in Chicago and San Francisco , a move critics described as an attempt to further gain favor within the African American community . 
- 
- = = = September = = = 
- 
- In mid @-@ September , Nixon 's running mate Spiro Agnew went on the offensive against Humphrey ; he referred to the Vice President as being " soft on Communism , " along with <unk> on inflation , and " law and order , " comparing him to former British Prime Minister Neville Chamberlain . At this time , Nixon sent his adviser , former Governor William Scranton of Pennsylvania , on a fact finding trip to Europe to gather intelligence on Western alliance and Soviet issues . In response to Humphrey 's calls for a face to face debate , Nixon remarked : " Before we can have a debate between Nixon and Humphrey , Humphrey 's got to settle his debate with himself . " Nixon campaigned in San Francisco , in front of 10 @,@ 000 supporters amidst an array of protests . The candidate took on the protesters first hand , and delivered his " forgotten American " speech , declaring that election day would be " a day of protest for the forgotten American , " a group which included those that " obey the law , pay their taxes , go to church , send their children to school , love their country and demand new leadership . " By month 's end , many in the Nixon campaign believed his election was guaranteed , beginning to prepare for the transition period , despite Nixon 's warning that " the one thing that can beat us now is <unk> . " Gallup showed Nixon leading Humphrey 43 % to 28 % at the end of September . 
- 
- = = = October = = = 
- 
- In early October , commentators weighed Nixon 's advantage , arguing that placement of blame for the Vietnam War strictly on the Johnson administration was acceptable — avoiding discussion of war with the excuse that he did not want to disrupt the peace talks in Paris . However , anti @-@ war protesters heckled Nixon repeatedly on the campaign trail . Nixon addressed the American Conservative Union on October 9 , and argued that George Wallace 's American Independent Party candidacy could split the anti @-@ Administration vote , and help the Democrats . The Union decided to back Nixon over Wallace , labeling the third party candidate 's beliefs as " <unk> . " As Democratic Vice Presidential nominee Edmund <unk> criticized Nixon for his connections to <unk> <unk> , Nixon continued to oppose a possible debate with Humphrey and Wallace , as well as between running mates , on the basis that he did not want to give Wallace more exposure . It was also argued that Nixon opposed debating due to his experience during the 1960 encounter with John F. Kennedy , which many cited as a factor in his defeat . In another lesson learned from 1960 , the campaign employed 100 @,@ 000 workers to oversee election day polling sites to prevent a recurrence of what many Republicans viewed as 1960 's stolen election . Nixon went on a whistle @-@ stop train tour of Ohio near the end of October . From the back of the " Nixon Victory Special " car , he <unk> Vice President Humphrey as well as the Secretary of Agriculture and Attorney General of the Johnson cabinet , for farmers ' debt and rising crime . At this time , the campaign released two controversial television advertisements , juxtaposing a smiling Humphrey with images of the Vietnam War and the chaos at the 1968 Democratic National Convention ; the advertisements aroused protests from the Humphrey campaign . By October 's end , Nixon began to lose his edge over Humphrey ; Gallup showing he led 44 % to 36 % , down five points from a few weeks earlier , a decline observers attributed to Nixon 's refusal for a debate with Humphrey . 
- 
- = = = November = = = 
- 
- At the beginning of November , President Johnson announced that a bombing had been halted in Vietnam ; observers noted that the development significantly helped Humphrey although Nixon had endorsed such talks . At this time , Nixon operative Anna <unk> secretly spoke with the South Vietnamese , explaining that they could receive a better deal under Nixon . The charge that , along with remarks from Nixon supporter and future Secretary of Defense Melvin Laird , Johnson had deliberately <unk> Nixon during <unk> angered the President . He spoke with Nixon supporters Senate <unk> Leader Everett <unk> and Senator George <unk> of Florida , informing Nixon of the President 's frustration . On the Sunday preceding the election , Nixon appeared on Meet the Press , explaining that he would cooperate completely with Johnson , <unk> the President shortly thereafter to personally <unk> him . The final Harris poll before the election indicated that Nixon was trailing Humphrey 43 % to 40 % , but Gallup 's final poll showed Nixon leading 42 % to 40 % . On the eve of the election , Nixon and Humphrey bought time on rival television networks , Nixon appearing on NBC , Humphrey on ABC , where each made his final appeal to voters . Nixon used this appearance to counter Humphrey 's ' October surprise ' , a polling surge following the bombing halt , claiming that he had just received " a very disturbing report " , which detailed that tons of supplies were being moved into South Vietnam by the North . Humphrey labeled this charge as " irresponsible " , causing Nixon to counter that Humphrey " doesn 't know what 's going on . " Overall , Nixon spent $ 6 @,@ 270 @,@ 000 on television advertising , most of which was judged to have only reinforced supporters . 
- 
- = = = Election Day = = = 
- 
- On November 5 , it was a three way race between Nixon , Humphrey , and Wallace . The results were very close , and not until early the following morning could news organizations call the election . In the end , Nixon won <unk> electoral votes , with Humphrey receiving 191 , and Wallace receiving 46 . In a margin 43 @.@ 42 % to 42 @.@ 72 % , Nixon edged Humphrey in the popular vote , with Wallace earning 13 @.@ 53 % . Nixon 's victory came with a margin of less than three percent in California , Illinois , and Ohio ; had Humphrey carried these three , Nixon would have lost his election . Nixon won most of the West and mid @-@ West but lost parts of the Northeast and Texas to Humphrey and lost the deep South to Wallace . After the election was conceded by Vice President Humphrey , Nixon said the following in his press conference : 
- " As you will probably have heard , I have received a very <unk> message from the Vice President , <unk> me for winning the election . I <unk> him for his <unk> and courageous fight against great odds ... I also told him that I know exactly how he felt . I know how it feels to lose a close one . " - President @-@ <unk> Richard Nixon 
- 
- = = Aftermath = = 
- 
- Nixon and Agnew took office as President and Vice President during their inauguration on January 20 , 1969 . Following the election , the slogan " <unk> Us Together " , referencing a poster held by a 13 @-@ year @-@ old girl at a rally during his campaign , was used as a basis for the theme of his inauguration , although it would later be seized by Democrats to attack later Nixon policies . In his inaugural address , Nixon said that " the greatest honor history can bestow is the title of <unk> , " outlining the direction Nixon sought to take , such as his visit to the People 's Republic of China in 1972 , opening diplomatic relations between the two nations , and <unk> plus the Anti @-@ <unk> Missile Treaty with the Soviet Union , all in his first term . 
- While overseeing an initial <unk> of American involvement in the Vietnam War , he subsequently ended U.S. involvement in 1973 , and eliminated the draft . <unk> , his administration generally embraced policies that transferred power from Washington to the states . Among other things , he initiated wars on cancer and drugs , imposed wage and price controls , enforced <unk> of Southern schools and established the Environmental Protection Agency . Though he presided over Apollo 11 and the subsequent lunar landings , he later scaled back manned space exploration . In 1972 , he was reelected by a landslide , the largest to that date . The Watergate scandal , which would consume the larger part of his second term , resulted in his ultimate resignation on August 4 , 1974 . 
- 
- = = <unk> = = 
- 
- 
- 
- = Cyclone Herbie = 
- 
- Tropical Cyclone Herbie was the only known tropical system to impact Western Australia during the month of May on record . The final cyclone of the 1987 – 88 Australian region cyclone season , Herbie was first identified northwest of the <unk> Islands on 17 May . The following day , the system was classified as a tropical low by the Australian Bureau of Meteorology and intensified into a Category 1 cyclone later that day . Several hours after this upgrade , the storm attained its initial peak intensity with winds of 75 km / h ( 45 mph 10 @-@ minute sustained ) . Around the same time , the Joint Typhoon Warning Center classified Herbie as Tropical Storm <unk> . 
- On 19 May , the cyclone formed a new low pressure center and relocated roughly 300 km ( 190 mi ) south . Shortly thereafter , the storm began to accelerate towards the southeast and started to undergo an extratropical transition . Early on 21 May , Herbie made landfall in Shark Bay before losing its identity the following day over the Great Australian Bight . Although a weak storm , Herbie brought flooding rains and severe dust storms to portions of Western Australia . Additionally , a 30 @,@ 000 ton freighter broke in half amidst rough seas produced by the storm . Total losses from the storm reached A $ 20 million ( $ 15 @.@ 6 million USD ) . Due to the significant damage wrought by Herbie , the name was retired following its use . 
- 
- = = Meteorological history = = 
- 
- Tropical Cyclone Herbie originated from an area of low pressure on 17 May 1988 northwest of the <unk> Islands . Later that day , the Australian Bureau of Meteorology began monitoring the system as a tropical low . Several hours after , the Joint Typhoon Warning Center ( JTWC ) classified the system as Tropical Depression <unk> , having attained winds of 45 km / h ( 30 mph 1 @-@ minute sustained ) . Following slight development , the system intensified into a Category 1 cyclone and was given the name Herbie by the Australian Bureau of Meteorology early on 18 May . Several hours after being named , the storm attained its initial peak intensity with winds of 75 km / h ( 45 mph 10 @-@ minute sustained ) and a barometric pressure of 990 hPa ( mbar ) . 
- Around the same time , the JTWC upgraded the system to a tropical storm , estimating the cyclone to have attained peak winds of 65 km / h ( 40 mph 1 @-@ minute sustained ) . On 19 May , satellite imagery of the system depicted that a new low pressure centre had developed roughly 300 km ( 190 mi ) south of the original low . Several hours after the relocation , the JTWC downgraded Herbie to a tropical depression as the system 's movement began to accelerate towards the southeast . As the storm moved at a rapid speed towards the coastline of Western Australia , it began to undergo an extratropical transition . During a 24 @-@ hour period ( 20 – 21 May ) Herbie tracked roughly 1 @,@ 500 km ( 930 mi ) , with the movement of the storm reaching 70 km / h ( 43 mph ) at times . 
- Late on 20 May , the JTWC ceased advisories on the system as it weakened below tropical depression status offshore . However , the Bureau of Meteorology continued to monitor the system . Early on 21 May , the center of Herbie made landfall in Shark Bay with wind gusts up to 120 km / h ( 75 mph ) and a pressure of 980 hPa ( mbar ) . Due to the rapid movement of the storm , it reached the Great Australian Bight while retaining tropical characteristics despite its low @-@ latitude . Early on 22 May , the system lost its identity south of Australia as an extratropical cyclone . 
- 
- = = Impact = = 
- 
- Although a weak storm , its fast approach allowed for little preparation . According to officials in the coastal town of Denham , " .. the cyclone struck virtually without warning . " Most tourists staying in the region evacuated the day before Herbie 's arrival . Herbie caused moderate structural damage across Western Australia in coastal areas between <unk> and Denham . Banana plantations within this area also sustained extensive damage . In Denham , a storm surge of 2 m ( 6 @.@ 6 ft ) broke the town 's retaining wall and flooded low @-@ lying areas along the shore . Several fishing vessels were brought inland by the surge and left <unk> on streets once the water subsided . About 15 homes in Denham lost their roofs after wind gusts estimated at 160 km / h ( 99 mph ) battered the town . Due to the storms extratropical transition , areas south of Herbie received moderate to heavy rainfall while areas north of the center reported severe dust storms fueled by the cyclone 's high winds . Most areas affected by the storms rain recorded around 50 mm ( 2 @.@ 0 in ) with isolated totals near 100 mm ( 3 @.@ 9 in ) . The Irwin and <unk> Rivers overflowed their banks , <unk> parts of <unk> . In all , the storm wrought approximately A $ 20 million worth of structural and agricultural damage in Western Australia . 
- Offshore , a 30 @,@ 000 ton freighter , the Korean Star , sustained extreme damage during the storm . Rough seas caused the hull of the ship to break and the vessel was separated into two pieces . Although the freighter was in two pieces , the ship did not sink and the wreckage of it came ashore near Cape Cuvier . No one on the ship sustained injury as all 19 crew members abandoned ship . The crew was rescued within a day of sustaining the damage and flown to <unk> . 
- In the wake of the storm , the town hall in Denham was converted into a temporary shelter for the homeless . Repair to damaged roofs and downed power lines began on 22 May . Within a few days of the storm 's passage , appeals were made for relief funds in regions affected by Herbie . As of January 2010 , Cyclone Herbie is the most recent tropical system to either directly or indirectly impact the town of <unk> , a region that is struck by tropical cyclones once every six to eight years . Although the storm caused relatively little damage , the name Herbie was retired following its usage and will never be used again to name a tropical cyclone in the Australian region . 
- 
- 
- = Croatian independence referendum , 1991 = 
- 
- Croatia held an independence referendum on 19 May 1991 , following the Croatian parliamentary elections of 1990 and the rise of ethnic tensions that led to the breakup of Yugoslavia . With 83 percent turnout , voters approved the referendum , with 93 percent in favor of independence . Subsequently , Croatia declared independence and the dissolution of its association with Yugoslavia on 25 June 1991 , but it introduced a three @-@ month moratorium on the decision when urged to do so by the European Community and the Conference on Security and Cooperation in Europe through the <unk> Agreement . The war in Croatia escalated during the moratorium , and on 8 October 1991 , the Croatian Parliament severed all remaining ties with Yugoslavia . In 1992 , the countries of the European Economic Community granted Croatia diplomatic recognition and Croatia was admitted to the United Nations . 
- 
- = = Background = = 
- 
- After World War II , Croatia became a one @-@ party Socialist federal unit of the Socialist Federal Republic of Yugoslavia . Croatia was ruled by the Communists and enjoyed a degree of autonomy within the Yugoslav federation . In 1967 , a group of Croatian authors and linguists published the Declaration on the Status and Name of the Croatian Literary Language , demanding greater autonomy for the Croatian language . The declaration contributed to a national movement seeking greater civil rights and decentralization of the Yugoslav economy , culminating in the Croatian Spring of 1971 , which was suppressed by Yugoslav leadership . The 1974 Yugoslav Constitution gave increased autonomy to federal units , essentially fulfilling a goal of the Croatian Spring and providing a legal basis for independence of the <unk> constituents . 
- In the 1980s , the political situation in Yugoslavia deteriorated , with national tension fanned by the 1986 Serbian <unk> <unk> and the 1989 coups in <unk> , Kosovo and Montenegro . In January 1990 , the Communist Party fragmented along national lines , with the Croatian faction demanding a looser federation . In the same year , the first multi @-@ party elections were held in Croatia , with Franjo Tuđman 's win resulting in further nationalist tensions . The Croatian Serb politicians boycotted the Sabor , and local Serbs seized control of Serb @-@ inhabited territory , setting up road blocks and voting for those areas to become autonomous . The Serb " autonomous <unk> " would soon unite to become the internationally <unk> Republic of Serbian <unk> ( <unk> ) , intent on achieving independence from Croatia . 
- 
- = = <unk> and declaration of independence = = 
- 
- On 25 April 1991 , the Croatian Parliament decided to hold an independence referendum on 19 May . The decision was published in the official <unk> of the Republic of Croatia and made official on 2 May 1991 . The referendum offered two options . In the first , Croatia would become a sovereign and independent state , <unk> cultural autonomy and civil rights to Serbs and other minorities in Croatia , free to form an association of sovereign states with other former Yugoslav republics . In the second , Croatia would remain in Yugoslavia as a unified federal state . Serb local authorities called for a boycott of the vote , which was largely followed by Croatian Serbs . The referendum was held at 7 @,@ 691 polling stations , where voters were given two ballots — blue and red , with a single referendum option each , allowing use of either or both of ballots . The referendum question proposing independence of Croatia , presented on the blue ballot , passed with 93 @.@ 24 % in favor , 4 @.@ 15 % against , and 1 @.@ 18 % of invalid or blank votes . The second referendum question , proposing that Croatia should remain in Yugoslavia , was declined with 5 @.@ 38 % votes in favor , 92 @.@ 18 % against and 2 @.@ 07 % of invalid votes . The turnout was 83 @.@ 56 % . 
- Croatia subsequently declared independence and dissolved ( Croatian : <unk> ) its association with Yugoslavia on 25 June 1991 . The European Economic Community and the Conference on Security and Cooperation in Europe urged Croatian authorities to place a three @-@ month moratorium on the decision . Croatia agreed to freeze its independence declaration for three months , initially <unk> tensions . Nonetheless , the Croatian War of Independence escalated further . On 7 October , the eve of expiration of the moratorium , the Yugoslav Air Force attacked <unk> <unk> , the main government building in Zagreb . On 8 October 1991 , the moratorium expired , and the Croatian Parliament severed all remaining ties with Yugoslavia . That particular session of the parliament was held in the <unk> building on <unk> <unk> Avenue in Zagreb due to security concerns provoked by recent Yugoslav air raid ; Specifically , it was feared that the Yugoslav Air Force might attack the parliament building . 8 October is now celebrated as Croatia 's Independence Day . 
- 
- = = Recognition = = 
- 
- The Badinter <unk> Committee was set up by the Council of Ministers of the European Economic Community ( EEC ) on 27 August 1991 to provide legal advice and criteria for diplomatic recognition to former Yugoslav republics . In late 1991 , the Commission stated , among other things , that Yugoslavia was in the process of dissolution , and that the internal boundaries of Yugoslav republics could not be altered unless freely agreed upon . <unk> in the preservation of Croatia 's pre @-@ war borders , defined by demarcation commissions in 1947 , were the Yugoslav federal constitutional amendments of 1971 and 1974 , granting that sovereign rights were exercised by the federal units , and that the federation had only the authority specifically transferred to it by the constitution . 
- Germany advocated quick recognition of Croatia , stating that it wanted to stop ongoing violence in Serb @-@ inhabited areas . It was opposed by France , the United Kingdom , and the Netherlands , but the countries agreed to pursue a common approach and avoid unilateral actions . On 10 October , two days after the Croatian Parliament confirmed the declaration of independence , the EEC decided to postpone any decision to recognize Croatia for two months , deciding to recognize Croatian independence in two months if the war had not ended by then . As the deadline expired , Germany presented its decision to recognize Croatia as its policy and duty — a position supported by Italy and Denmark . France and the UK attempted to prevent the recognition by drafting a United Nations resolution requesting no unilateral actions which could <unk> the situation , but backed down during the Security Council debate on 14 December , when Germany appeared determined to defy the UN resolution . On 17 December , the EEC formally agreed to grant Croatia diplomatic recognition on 15 January 1992 , relying on opinion of the Badinter <unk> Committee . The Committee ruled that Croatia 's independence should not be recognized immediately , because the new Croatian Constitution did not provide protection of minorities required by the EEC . In response , the President Franjo Tuđman gave written <unk> to Robert Badinter that the deficit would be <unk> . The <unk> formally declared its separation from Croatia on 19 December , but its statehood and independence were not recognized internationally . On 26 December , Yugoslav authorities announced plans for a smaller state , which could include the territory captured from Croatia , but the plan was rejected by the UN General Assembly . 
- Croatia was first recognized as an independent state on 26 June 1991 by Slovenia , which declared its own independence on the same day as Croatia . Lithuania followed on 30 July , and Ukraine , Latvia , Iceland , and Germany in December 1991 . The EEC countries granted Croatia recognition on 15 January 1992 , and the United Nations admitted them in May 1992 . 
- 
- = = Aftermath = = 
- 
- Although it is not a public holiday , 15 January is marked as the day Croatia won international recognition by Croatian media and politicians . On the day 's 10th anniversary in 2002 , the Croatian National Bank minted a 25 <unk> commemorative coin . In the period following the declaration of independence , the war escalated , with the <unk> of Vukovar and Dubrovnik , and fighting elsewhere , until a ceasefire of 3 January 1992 led to stabilization and a significant reduction of violence . The war effectively ended in August 1995 with a decisive victory for Croatia as a result of Operation Storm . Present day borders of Croatia were established when the remaining Serb @-@ held areas of Eastern Slavonia were restored to Croatia pursuant to the <unk> Agreement of November 1995 , with the process concluded in January 1998 . 
- 
- 
- = Georgian scripts = 
- 
- The Georgian scripts are the three writing systems used to write the Georgian language : Asomtavruli , Nuskhuri and Mkhedruli . Although the systems differ in appearance , all three are <unk> , their letters share the same names and <unk> order , and are written horizontally following the standard left @-@ to @-@ right direction . Of the three Georgian writing systems , Mkhedruli ( literally meaning " cavalry " or " military " script ) was the civilian royal script of the Georgian Kingdom used for charters , historical documents , manuscripts and inscriptions . Mkhedruli is therefore the standard script for modern Georgian and its related Kartvelian languages , whereas Asomtavruli and Nuskhuri are used only in ceremonial religious texts and iconography . 
- Georgian scripts are unique in their appearance and their exact origin has never been established ; however , in strictly structural terms , their <unk> order largely corresponds to the Greek alphabet , with the exception of letters denoting uniquely Georgian sounds , which are grouped at the end . Originally consisting of 38 letters , Georgian is presently written in a 33 @-@ letter alphabet , as five letters are currently obsolete in that language . The number of Georgian letters used in other Kartvelian languages varies . The Mingrelian language uses 36 , 33 of which are current Georgian letters , one obsolete Georgian letter , and two additional letters specific to Mingrelian and Svan . That same obsolete letter , plus a letter borrowed from Greek ( making 35 letters total ) , are used in writing the Laz language . The fourth Kartvelian language , Svan , is not commonly written , but when it is , it uses Georgian letters as utilized in Mingrelian , with an additional obsolete Georgian letter and sometimes supplemented by <unk> for its many vowels . 
- Georgian scripts hold the national status of cultural heritage in Georgia , and are currently nominated for inclusion in the UNESCO 's list of the <unk> Cultural Heritage of <unk> . 
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- = = <unk> = = 
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- = = Origins = = 
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- The origins of the Georgian script are to this date poorly known , and no full agreement exists among Georgian and foreign scholars as to its date of creation , who designed the script and the main influences on that process . 
- The first version of the script attested is Asomtavruli which dates back to at least the 5th century ; the other scripts were formed in the following centuries . Most scholars link the creation of the Georgian alphabet to the process of Christianisation of a core Georgian @-@ speaking kingdom , that is , <unk> ( or Iberia in Classical sources ) . The alphabet was therefore most probably created between the conversion of Iberia under King <unk> III ( 326 or 337 ) and the Bir el <unk> inscriptions of 430 , <unk> with the Armenian alphabet . It was first used for translation of the Bible and other Christian literature into Georgian , by monks in Georgia and Palestine . Professor <unk> <unk> 's dating of fragmented Asomtavruli inscriptions , discovered by him at the ruined town of <unk> , in Georgia 's easternmost province of <unk> , in the 1980s , to the 1st or 2nd century has not been universally accepted . 
- A Georgian tradition first attested in the medieval chronicle Lives of the Kings of <unk> ( ca . 800 ) , assigns a much earlier , pre @-@ Christian origin to the Georgian alphabet , and names King <unk> I ( 3rd century BC ) as its inventor . This account is now considered legendary , and is rejected by scholarly consensus , as no archaeological confirmation has been found . <unk> considers the tradition to be an attempt by the Georgian Church to <unk> the earlier tradition that the alphabet was invented by <unk> Mashtots , and is a Georgian application of an Iranian model in which primordial kings are credited with the creation of basic social institutions . Georgian linguist <unk> <unk> offers an alternate interpretation of the tradition , in the pre @-@ Christian use of foreign scripts ( <unk> in the Aramaic alphabet ) to write down Georgian texts . 
- A point of contention among scholars is the role played by Armenian clerics in that process . According to a number of scholars and medieval Armenian sources , <unk> Mashtots , generally acknowledged as the creator of the Armenian alphabet , also created the Georgian and Caucasian <unk> alphabets . This tradition originates in the works of Koryun , a fifth century historian and biographer of Mashtots , and has been quoted by Donald <unk> and James R. Russell , but has been criticized by scholars , both Georgian and Western , who judge the passage in Koryun unreliable or even a later <unk> . Other scholars quote Koryun 's claims without taking a stance on its validity . Many agree , however , that Armenian clerics , if not Mashtots himself , must have played a role in the creation of the Georgian script . 
- Another controversy regards the main influences at play in the Georgian alphabet , as scholars have debated whether it was inspired more by the Greek alphabet , or by Semitic alphabets such as Aramaic . Recent historiography focuses on greater similarities with the Greek alphabet than in the other Caucasian writing systems , most notably the order and numeric value of letters . Some scholars have also suggested as a possible inspiration for particular letters certain pre @-@ Christian Georgian cultural symbols or clan markers . 
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- = = Asomtavruli = = 
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- Asomtavruli ( Georgian : <unk> ) is the oldest Georgian script . The name Asomtavruli means " capital letters " , from <unk> ( <unk> ) " letter " and <unk> ( <unk> ) " principal / head " . It is also known as <unk> ( Georgian : <unk> ) " rounded " , from <unk> ( <unk> ) " round " , so named because of its round letter shapes . Despite its name , this " capital " script is unicameral , just like the modern Georgian script , Mkhedruli . 
- The oldest Asomtavruli inscriptions found so far date from the 5th century and are Bir el <unk> and the <unk> inscriptions . 
- From the 9th century , Nuskhuri script starting becoming dominant , and the role of Asomtavruli was reduced . However , <unk> monuments of the 10th to 18th centuries continued to be written in Asomtavruli script . Asomtavruli in this later period became more decorative . In the majority of 9th @-@ century Georgian manuscripts which were written in Nuskhuri script , Asomtavruli was used for titles and the first letters of chapters . Although , some manuscripts written completely in Asomtavruli can be found until the 11th century . 
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- = = = Form of Asomtavruli letters = = = 
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- In early Asomtavruli , the letters are of equal height . Georgian historian and <unk> <unk> <unk> believes that the direction of Asomtavruli , like that of Greek , was initially <unk> , though the direction of the earliest surviving texts is from left to the right . 
- In most Asomtavruli letters , straight lines are horizontal or vertical and meet at right angles . The only letter with acute angles is <unk> ( ჯ jani ) . There have been various attempts to explain this exception . Georgian linguist and art historian Helen <unk> believes jani derives from a <unk> of Christ , composed of the <unk> ( ი ini ) and <unk> ( <unk> <unk> ) . According to Georgian scholar <unk> <unk> , the cross @-@ like shape of letter jani indicates the end of the alphabet , and has the same function as the similarly shaped Phoenician letter <unk> ( ) , Greek chi ( <unk> ) , and Latin X , though these letters do not have that function in Phoenician , Greek , or Latin . 
- Coins of Queen <unk> of Georgia and King George IV of Georgia minted using Asomtavruli script , 1200 – 1210 AD . 
- From the 7th century , the forms of some letters began to change . The equal height of the letters was abandoned , with letters acquiring ascenders and descenders . 
- Note : Some fonts show " capitalized " ( tall ) variants of Nuskhuri and Mkhedruli letters rather than Asomtavruli . 
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- = = = Asomtavruli illumination = = = 
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- In Nuskhuri manuscripts , Asomtavruli are used for titles and illuminated capitals . The latter were used at the beginnings of paragraphs which started new sections of text . In the early stages of the development of Nuskhuri texts , Asomtavruli letters were not elaborate and were distinguished principally by size and sometimes by being written in <unk> ink . Later , from the 10th century , the letters were illuminated . The style of Asomtavruli capitals can be used to identify the era of a text . For example , in the Georgian manuscripts of the Byzantine era , when the styles of the Byzantine Empire influenced Kingdom of Georgia , capitals were illuminated with images of birds and other animals . 
- <unk> Asomtavruli capital letters , <unk> ( m ) , <unk> ( n ) and <unk> ( t ) , 12 – 13th century . 
- From the 11th @-@ century " limb @-@ flowery " , " limb @-@ <unk> " and " limb @-@ spotty " decorative forms of Asomtavruli are developed . The first two are found in <unk> and 12th @-@ century monuments , whereas the third one is used until the 18th century . 
- Importance was attached also to the colour of the ink itself . 
- Asomtavruli letter დ ( <unk> ) is often written with decoration effects of fish and birds . 
- The " Curly " decorative form of Asomtavruli is also used where the letters are <unk> or <unk> on each other , or the smaller letters are written inside other letters . It was mostly used for the headlines of the manuscripts or the books , although there are compete inscriptions which were written in the Asomtavruli " Curly " form only . 
- The title of Gospel of Matthew in Asomtavruli " Curly " decorative form . 
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- = = = Handwriting of Asomtavruli = = = 
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- The following table shows the stroke order and direction of each Asomtavruli letter : 
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- = = Nuskhuri = = 
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- Nuskhuri ( Georgian : <unk> ) is the second Georgian script . The name <unk> comes from <unk> ( <unk> ) , meaning " inventory " or " schedule " . Nuskhuri was soon augmented with Asomtavruli illuminated capitals in religious manuscripts . The combination is called <unk> ( Georgian : <unk> , " clerical " , from <unk> ( <unk> " <unk> " ) , and it was principally used in hagiography . 
- Nuskhuri first appeared in the 9th century as a graphic variant of Asomtavruli . The oldest inscription is found in the <unk> <unk> Church and dates to 835 AD . The oldest surviving Nuskhuri manuscripts date to 864 AD . Nuskhuri becomes dominant over Asomtavruli from the 10th century . 
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- = = = Form of Nuskhuri letters = = = 
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- Nuskhuri letters vary in height , with ascenders and descenders , and are <unk> to the right . Letters have an angular shape , with a noticeable tendency to <unk> the shapes they had in Asomtavruli . This enabled faster writing of manuscripts . 
- → → Asomtavruli letters ო ( oni ) and <unk> ( vie ) . A ligature of these letters produced a new letter in Nuskhuri , უ <unk> . 
- Note : Without proper font support , you may see question marks , boxes or other symbols instead of Nuskhuri letters . 
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- = = = Handwriting of Nuskhuri = = = 
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- The following table shows the stroke order and direction of each Nuskhuri letter : 
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- = = Use of Asomtavruli and Nuskhuri today = = 
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- Asomtavruli is used intensively in iconography , murals , and exterior design , especially in stone <unk> . Georgian linguist <unk> <unk> made an attempt in the 1950s to introduce Asomtavruli into the Mkhedruli script as capital letters to begin sentences , as in the Latin script , but it didn 't catch on . Asomtavruli and Nuskhuri are officially used by the Georgian Orthodox Church alongside Mkhedruli . Patriarch <unk> II of Georgia called on people to use all three Georgian scripts . 
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- = = Mkhedruli = = 
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- Mkhedruli ( Georgian : <unk> ) is the third and current Georgian script . Mkhedruli , literally meaning " cavalry " or " military " , derives from <unk> ( <unk> ) meaning " <unk> " , " knight " , " warrior " and " <unk> " . 
- Like the two other scripts , Mkhedruli is purely unicameral . Mkhedruli first appears in the 10th century . The oldest Mkhedruli inscription is found in <unk> <unk> Church dating back to <unk> AD . The second oldest Mkhedruli @-@ written text is found in the 11th @-@ century royal charters of King Bagrat IV of Georgia . Mkhedruli was mostly used then in the Kingdom of Georgia for the royal charters , historical documents , manuscripts and inscriptions . Mkhedruli was used for non @-@ religious purposes only and represented the " civil " , " royal " and " secular " script . 
- Mkhedruli became more and more dominant over the two other scripts , though <unk> ( Nuskhuri with Asomtavruli ) was used until the 19th century . Since the 19th century , with the establishment and development of the printed Georgian fonts , Mkhedruli became universal writing Georgian outside the Church . 
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- = = = Form of Mkhedruli letters = = = 
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- Mkhedruli inscriptions of the 10th and 11th centuries are characterized in rounding of angular shapes of Nuskhuri letters and making the complete outlines in all of its letters . Mkhedruli letters are written in the four @-@ linear system , similar to Nuskhuri . Mkhedruli becomes more round and free in writing . It breaks the strict frame of the previous two alphabets , Asomtavruli and Nuskhuri . Mkhedruli letters begin to get coupled and more free calligraphy develops . 
- <unk> of one of the oldest Mkhedruli @-@ written texts found in the royal charter of King Bagrat IV of Georgia , 11th century . 
- " <unk> : King : of Kings : great @-@ grandfather : of mine : Bagrat <unk> " 
- <unk> of Queen <unk> of Georgia in Mkhedruli , 1187 AD . 
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- = = = Modern Georgian alphabet = = = 
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- The modern Georgian alphabet consists of 33 letters : 
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- = = = Letters removed from the Georgian alphabet = = = 
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- The Society for the <unk> of <unk> among <unk> , founded by Prince <unk> <unk> in 1879 , discarded five letters from the Georgian alphabet that had become redundant : 
- <unk> ( he ) , sometimes called " <unk> " or " e @-@ <unk> " ( " eighth e " ) , was equivalent to <unk> <unk> , as in <unk> ~ <unk> krist <unk> ' Christ ' . 
- <unk> ( <unk> ) , also called <unk> , appeared instead of ი ( ini ) after a vowel , but came to have the same pronunciation as ი ( ini ) and was replaced by it . Thus <unk> ~ <unk> krist <unk> " Christ " is now written <unk> krist <unk> . 
- <unk> ( vie ) came to be pronounced the same as <unk> vi and was replaced by that sequence , as in <unk> > <unk> <unk> " others ' " . 
- <unk> ( <unk> , <unk> ) came to be pronounced the same as ხ ( khani ) , and was replaced by <unk> e.g. <unk> became <unk> " sovereign " . 
- <unk> ( <unk> ) was used for the <unk> <unk> ! and is now spelled <unk> . 
- All but <unk> ( <unk> ) continue to be used in the Svan alphabet ; <unk> ( <unk> ) is used in the Mingrelian and Laz alphabets as well , for the y @-@ sound / <unk> / . Several others were used for <unk> and Ossetian in the short time they were written in Mkhedruli script . 
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- = = = Letters added to other alphabets = = = 
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- Mkhedruli has been adapted to languages besides Georgian . Some of these alphabets retained letters obsolete in Georgian , while others required additional letters : 
- <unk> ( fi " <unk> " ) is used in Laz and Svan , and formerly in Ossetian and <unk> . It derives from the Greek letter <unk> ( <unk> ) . 
- <unk> ( <unk> " <unk> " ) , also called <unk> , is used for the <unk> sound in Svan and Mingrelian , and formerly in Ossetian and <unk> . 
- <unk> ( <unk> " alif " ) is used in for the <unk> stop in Svan and Mingrelian . It is a reversed 〈 <unk> 〉 ( q 'ari ) . 
- <unk> ( turned gani ) was once used for [ <unk> ] in evangelical literature in <unk> languages . 
- <unk> ( <unk> " ain " ) is occasionally used for [ <unk> ] in <unk> . It derives from the Arabic letter 〈 <unk> 〉 ( ‘ ain ) . 
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- = = = Handwriting of Mkhedruli = = = 
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- The following table shows the stroke order and direction of each Mkhedruli letter : 
- ზ , ო , and ხ ( <unk> , oni , khani ) are almost always written without the small tick at the end , while the handwritten form of ჯ ( jani ) often uses a vertical line , ( sometimes with a taller <unk> , or with a diagonal cross bar ) ; even when it is written at a diagonal , the cross @-@ bar is generally shorter than in print . 
- Only four letters are x @-@ height , with neither ascenders nor descenders : <unk> , <unk> , ი , ო . 
- Thirteen have ascenders , like b or d in English : <unk> , ზ , <unk> , <unk> , <unk> , რ , ს , <unk> , <unk> , ძ , წ , ხ , <unk> 
- An equal number have descenders , like p or q in English : გ , დ , <unk> , ვ , კ , <unk> , <unk> , <unk> , უ , <unk> , <unk> , <unk> , ც 
- Three letters have both ascenders and descenders , like <unk> in Old English : <unk> , <unk> , and ( in handwriting ) ჯ . წ has both <unk> and <unk> in print , and sometimes in handwriting . 
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- = = = = Variation = = = = 
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- There is individual and stylistic variation in many of the letters . For example , the top circle of ზ ( <unk> ) and the top stroke of რ ( <unk> ) may go in the other direction than shown in the chart ( that is , counter @-@ clockwise starting at 3 o <unk> , and upwards – see the external @-@ link section for videos of people writing ) . Other common variants : 
- გ ( gani ) may be written like ვ ( vini ) with a closed loop at the bottom . 
- დ ( <unk> ) is frequently written with a simple loop at top , . 
- კ , ც , and ძ ( k 'ani , <unk> , <unk> ) are generally written with straight , vertical lines at the top , so that for example ც ( <unk> ) resembles a U with a <unk> in the right side . 
- <unk> ( <unk> ) is frequently written with a single arc , . Even when all three are written , they 're generally not all the same size , as they are in print , but rather riding on one wide arc like two <unk> in it . 
- Rarely , ო ( oni ) is written as a right angle , . 
- რ ( <unk> ) is frequently written with one arc , , like a Latin 〈 h 〉 . 
- <unk> ( t 'ari ) often has a small circle with a tail hanging into the bowl , rather than two small circles as in print , or as an O with a straight vertical line intersecting the top . It may also be rotated a bit clockwise , with the small circles further to the right and not as close to the top . 
- წ ( <unk> <unk> ) is generally written with a round bowl at the bottom , . Another variation features a triangular bowl . 
- <unk> ( <unk> 'ari ) may be written without the hook at the top , and often with a completely straight vertical line . 
- <unk> ( he ) may be written without the loop , like a <unk> of ს and <unk> . 
- ჯ ( " jani " ) is sometimes written so that it looks like a hooked version of the Latin " X " 
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- = = = = Similar letters = = = = 
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- Several letters are similar and may be confused at first , especially in handwriting . 
- For ვ ( vini ) and კ ( k 'ani ) , the critical difference is whether the top is a full arc or a ( more @-@ or @-@ less ) vertical line . 
- For ვ ( vini ) and გ ( gani ) , it is whether the bottom is an open curve or closed ( a loop ) . The same is true of უ ( <unk> ) and <unk> ( <unk> ) ; in handwriting , the tops may look the same . Similarly ს ( <unk> ) and ხ ( khani ) . 
- For კ ( k 'ani ) and <unk> ( p 'ari ) , the crucial difference is whether the letter is written below or above x @-@ height , and whether it 's written top @-@ down or bottom @-@ up . 
- ძ ( <unk> ) is written with a vertical top . 
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- = = <unk> , abbreviations and calligraphy = = 
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- Asomtavruli is often highly stylized and writers readily formed ligatures , intertwined letters , and placed letters within letters . 
- A ligature of the Asomtavruli initials of King <unk> I of Iberia , <unk> <unk> ( <unk> , <unk> ) 
- A ligature of the Asomtavruli letters <unk> <unk> ( <unk> , da ) " and " 
- Nuskhuri , like Asomtavruli is also often highly stylized . Writers readily formed ligatures and abbreviations for nomina <unk> , including <unk> called <unk> , which resemble <unk> . Because writing materials such as <unk> were scarce and therefore precious , <unk> was a practical measure widespread in manuscripts and hagiography by the 11th century . 
- A Nuskhuri abbreviation of <unk> ( <unk> ) " which " 
- A Nuskhuri abbreviation of <unk> <unk> ( <unk> <unk> ) " Jesus Christ " 
- Mkhedruli , in the 11th to 17th centuries also came to employ <unk> to the point that they were <unk> , requiring <unk> to a complex system . 
- A Mkhedruli ligature of <unk> ( da ) " and " 
- Mkhedruli calligraphy of Prince <unk> <unk> and King <unk> of <unk> 
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- = = Type faces = = 
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- Georgian scripts come in only a single type face , though word processors can apply automatic ( " fake " ) oblique and bold formatting to Georgian text . Traditionally , Asomtavruli was used for chapter or section titles , where Latin script might use bold or <unk> type . 
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- = = <unk> = = 
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- In Asomtavruli and Nuskhuri punctuation , various combinations of dots were used as word <unk> and to separate phrases , clauses , and paragraphs . In monumental inscriptions and manuscripts of 5th to 10th centuries , these were written as <unk> , like − , 
- = and = 
- − . In the 10th century , clusters of one ( · ) , two ( : ) , three ( ჻ ) and six ( ჻ ჻ ) dots ( later sometimes small circles ) were introduced by <unk> <unk> to indicate increasing breaks in the text . One dot indicated a " minor stop " ( presumably a simple word break ) , two dots marked or separated " special words " , three dots for a " bigger stop " ( such as the <unk> name and title " the sovereign Alexander " , below , or the title of the Gospel of Matthew , above ) , and six dots were to indicate the end of the sentence . Starting in the 11th century , marks resembling the apostrophe and comma came into use . An apostrophe was used to mark an <unk> word , and a comma appeared at the end of an <unk> sentence . From the 12th century on , these were replaced with the <unk> ( the Greek question mark ) . In the 18th century , Patriarch Anton I of Georgia reformed the system again , with commas , single dots , and double dots used to mark " complete " , " incomplete " , and " final " sentences , respectively . For the most part , Georgian today uses the punctuation as in international usage of the Latin script . 
- Signature of King Alexander II of <unk> , with the <unk> 〈 ჻ 〉 
- <unk> ჻ <unk> 
- " The sovereign Alexander " 
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- = = Summary = = 
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- This table lists the three scripts in parallel columns , including the letters that are now obsolete in all alphabets ( shown with a blue background ) , obsolete in Georgian but still used in other alphabets ( green background ) , or additional letters in languages other than Georgian ( pink background ) . The " national " transliteration is the system used by the Georgian government , whereas " Laz " is the Latin Laz alphabet used in Turkey . The table also shows the traditional numeric values of the letters . 
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- = = Use for other non @-@ Kartvelian languages = = 
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- Ossetian language during the 1940s . 
- <unk> language during the 1940s . 
- <unk> language ( historically ) , later replaced in the 17th century by Arabic and by the Cyrillic script in modern times . 
- <unk> language ( historically ) , later replaced in the 17th century by Arabic and by the Cyrillic script in modern times . 
- Avar language ( historically ) , later replaced in the 17th century by Arabic and by the Cyrillic script in modern times . 
- Turkish language and <unk> language . A Turkish Gospel , dictionary , poems , medical book dating from the 18th century . 
- Persian language . The 18th @-@ century Persian translation of the Arabic Gospel is kept at the National Center of Manuscripts in Tbilisi . 
- Armenian language . In the Armenian community in Tbilisi , the Georgian script was occasionally used for writing Armenian in the 18th and 19th centuries , and some samples of this kind of texts are kept at the Georgian National Center of Manuscripts in Tbilisi . 
- Russian language . In the collections of the National Center of Manuscripts in Tbilisi there are also a few short poems in the Russian language written in Georgian script dating from the late 18th and early 19th centuries . 
- Other Northeast Caucasian languages . The Georgian script was used for writing North Caucasian and <unk> languages in connection with Georgian missionary activities in the areas starting in the 18th century . 
- Old Avar crosses with Avar inscriptions in Asomtavruli script . 
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- = = Computing = = 
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- = = = Unicode = = = 
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- The first Georgian script was added to the Unicode Standard in October , 1991 with the release of version 1 @.@ 0 . In creating the Georgian Unicode block , important roles were played by German <unk> <unk> , a linguist of Kartvelian studies , and American @-@ Irish linguist and script @-@ <unk> Michael Everson , who created the Georgian Unicode for the Macintosh systems . Significant contributions were also made by Anton <unk> and Irakli <unk> . ( not the former Prime Minister of Georgia Irakli <unk> ) 
- Georgian Mkhedruli script received an official status for being Georgia 's <unk> domain name script for ( <unk> ) . 
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- = = = = <unk> = = = = 
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- The Unicode block for Georgian is U + <unk> – U + <unk> . Mkhedruli ( modern Georgian ) occupies the U + <unk> – U + <unk> range and Asomtavruli occupies the U + <unk> – U + <unk> range . The Unicode block for Georgian Supplement is U + <unk> – U + <unk> and it <unk> Nuskhuri . 
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- = = = <unk> layouts = = = 
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- Below is the standard Georgian @-@ language keyboard layout , the traditional layout of manual typewriters . 
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- = = Gallery = = 
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- Gallery of Asomtavruli , Nuskhuri and Mkhedruli scripts . 
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- = = = Gallery of Asomtavruli = = = 
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- = = = Gallery of Nuskhuri = = = 
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- = = = Gallery of Mkhedruli = = = 
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- = Banai ( goddess ) = 
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- Banai ( Marathi : <unk> <unk> , sometimes <unk> ) , also known as <unk> ( <unk> , <unk> ) and <unk> @-@ <unk> ( <unk> @-@ <unk> , <unk> @-@ <unk> ) , is a Hindu goddess and the second wife of Khandoba , a form of the god Shiva worshipped in the Deccan – predominantly in the Indian states of Maharashtra and Karnataka . Khandoba is portrayed as a king of Jejuri , where his chief temple stands . Some traditions do not give her the status of a legal wife and treat her as a concubine of Khandoba . 
- While scriptures related to Khandoba do not mention Banai , she is a central subject of folk songs . Banai is considered as a Dhangar , a sheep herding caste , and is sometimes regarded to be of celestial origin . Oral traditions chiefly discuss the tale of her marriage to Khandoba and her conflicts with his first wife Mhalsa . Banai is an antithesis of Mhalsa ; together they complete the god . Banai is generally depicted with Khandoba and often is also accompanied by Mhalsa . 
- Banai does not enjoy independent worship , but is worshipped as Khandoba 's consort in most of his temples . She is the patron goddess of the Dhangar community and is worshipped as a protector of their herds . 
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- = = Development and symbolism = = 
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- Though Khandoba is a god with five wives , his first two consorts Mhalsa and Banai are the most important . The tale of the King or god with two wives is <unk> with some variation across India : <unk> and his wives <unk> and <unk> ; Venkateswara , Lakshmi and <unk> being some examples . The motif of Shiva and his wives Parvati and Ganga is told in the <unk> . 
- The theme of the god marrying a tribal girl like Banai <unk> across the Deccan region ; another example being <unk> 's marriage to <unk> . Deities across the Deccan ( even extending to <unk> and Tamil Nadu ) often have two wives ; one wife from a high caste and another from the lower social strata : a lower caste or a tribal . Khandoba 's wives who come from various communities establish cultural <unk> of the god to these communities , who worship them as their patron god . 
- While Banai is considered as a legal wife of Khandoba in Maharashtra ( especially with the Dhangars ) , the <unk> of Karnataka regard her as a concubine . While Mhalsa is from the high @-@ caste <unk> merchant ( <unk> ) community , Banai is described as a Dhangar ( <unk> caste ) , representing the " outside " and associates Khandoba with non @-@ elite herding castes like Dhangars , <unk> and <unk> ( <unk> ) who live in the forest . Some traditions consider Banai as a <unk> ( <unk> caste ) or <unk> ( fisherman caste ) . In Karnataka , she is called <unk> and is a <unk> . 
- Banai is an antithesis Mhalsa . Mhalsa has a regular ritualistic marriage with Khandoba . Banai , on the other hand , has a love marriage after being captured by the god . Mhalsa is described as pure , ugly , jealous and a good cook ; Banai is impure , erotic , resolute , but does not even know to cook . Mhalsa represents " culture " while Banai " nature " ; together they aid the god @-@ king Khandoba . 
- The oral legends and texts initiate a process of <unk> of the folk deity Khandoba by elevating him to the classical Hindu god Shiva ; his two principal wives Mhalsa and Banai are equated to Parvati and Ganga . Banai does not appear in the Sanskrit <unk> <unk> , the main scripture related to Khandoba , however it mentions Ganga arriving from heaven . Banai ( Ganga ) has a quarrel with Mhalsa ( Parvati ) , ultimately ending with the message that both are the same . Some Dhangars consider Banai also to be a form of Parvati . 
- The chief source of legends related to Banai are <unk> ( <unk> ) or folk songs sung by <unk> and <unk> , the male and female bards of Khandoba . They sing at <unk> ( a vigil ) where the bards sing in praise of Khandoba through the night . The songs talk about the relationship of Khandoba to his consorts and the mutual relationships of the wives . They are centred on Mhalsa and Banai and often narrate about their quarrels . The tale of the marriage of Khandoba and Banai is a central theme in many Dhangar folk songs . The <unk> saint Sheikh Muhammad ( 1560 @-@ 1650 ) <unk> Khandoba in his <unk> @-@ <unk> and calls him the " mad " god that searches for Banai due to " sexual passion " , an allusion to the tale of Banai 's marriage , indicating that the tale was well @-@ established by this era . 
- According to scholar Günther @-@ <unk> <unk> , the legend of Banai has close parallels with the story of King <unk> and <unk> from the Hindu epic Mahabharata . The tale of another folk god <unk> ( Bhairava ) to gain his wife <unk> or <unk> despite obstacles is also similar to Khandoba 's <unk> to win over Banai . 
- 
- = = Legends = = 
- 
- Banai does not appear in the <unk> <unk> originating from the Brahmin ( high @-@ priest caste ) tradition , which <unk> Khandoba as Shiva and de @-@ emphasizes his earthly connections . In contrast , Banai occupies the central position in the Dhangar folk narrative and Mhalsa 's marriage to Khandoba is reduced to a passing mention ; <unk> and other settled castes give more importance to Mhalsa . 
- 
- = = = Early life = = = 
- 
- Generally , Banai 's birth is not discussed in the folk songs . Few regard her as an avatar of the <unk> ( celestial <unk> ) <unk> , while others consider her as one of the seven daughters of Indra , the king of the gods . She is found by a Dhangar in a golden box in the forest , hidden in a <unk> mound or a pit . Her Dhangar father ( sometimes named Yamu ) is the chief of shepherds , who owns nine <unk> sheep and goats , nine <unk> lambs and numerous barren ones . Yamu is said to have prayed for a child and finds Banai in a box . A virgin <unk> is said to have fed Banai her milk , as Yamu does not have a wife and does not know how to feed the infant . In another miracle , a three @-@ <unk> house appears at the place of Yamu 's tent for the young Banai to reside , while the rest of the Dhangars live in tents . She grows up as a rich <unk> and becomes the overseer of twelve Dhangar <unk> ( pastoral settlements or camps , inhabited by different Dhangar clans ) . She cares for her sheep , <unk> them and learns how to breed them . 
- 
- = = = Marriage = = = 
- 
- Once , Khandoba and Mhalsa play a game of <unk> ( translated as game of dice or chess ) . Khandoba loses everything to Mhalsa in the <unk> , except his <unk> , his flag , his staff ( <unk> ) and his <unk> , the bag of magical bhandara ( <unk> powder ) . In a dream , he sees Banai and falls in love with her . He goes on a hunt in the forest , gets away from the army and stays with Banai for twelve years . He marries her in non @-@ ritualistic marriage and brings her back to Jejuri . A variant describes how Khandoba arrives in <unk> on a hunting expedition and becomes <unk> . A Dhangar directs him to Banai 's <unk> . Banai offers him water or sends a pot of water , in which Khandoba reads Banai 's name . In another version , the pot with nine jewels is a sign for Khandoba to recognise Banai , the girl he saw in his dream . He falls for her and loses purposefully in <unk> with Mhalsa and accepts a twelve @-@ year exile . In this period , he disguises himself as an impoverished , old <unk> and becomes a man @-@ servant of Banai 's father . Some folk songs have erotic overtones , for example , some songs give erotic descriptions of Banai 's beauty which <unk> Khandoba . 
- Khandoba is described as doing odd jobs under Banai 's orders . Banai first assigns him the task of sweeping the entire <unk> . He is responsible for cleaning the sheep pens and taking the sheep and lambs for grazing . He completes all tasks by spreading his magical bhandara . The shepherds are astonished how a single old man can handle all the animals . Their vanity is crushed . Banai assigns him the additional responsibility of taking care of five hundred children . She commands if any sheep or lamb is lost or a child cries , she will not give him his food . But Khandoba <unk> the tasks again by spraying his bhandara . She assigns him the job of <unk> the sheep and lambs . Instead , Khandoba kills all her sheep and lambs to humble the shepherds and Banai . He skins the sheep and separates the meat . A repentant Banai begs his forgiveness ; he agrees to revive her flock on the condition that Banai marries him . Khandoba <unk> the sheep by spreading his bhandara and reveals his true form . 
- The wedding is deemed not in accordance to Hindu rituals . Banai and Khandoba marry in a simple , un @-@ <unk> ceremony , where sheep droppings are showered on the couple , instead of rice as in the ritualistic weddings of classical ( <unk> ) Hinduism . The wedding is conducted without a Brahmin officiating priest . <unk> read the <unk> ( the responsibility of the Brahmin in a normal wedding ) and the <unk> of sheep replaces the traditional wedding band . The wedding is sometimes described as a <unk> marriage . Due to the <unk> nature of the wedding , she is sometimes considered as a <unk> ( concubine ) of Khandoba . 
- 
- = = = After marriage = = = 
- 
- In all versions , Khandoba returns to Jejuri with his new wife and faces the wrath of Mhalsa . Many songs tell about the confrontations of Mhalsa and Banai . In some songs , Mhalsa complains about Khandoba 's infatuation with the impure Banai . The <unk> Mhalsa <unk> how Banai has polluted the house by her <unk> ways and suggests that Banai should be returned to the wilderness again . The songs sing how the vegetarian , high @-@ caste Mhalsa is forced to catch fish and eat in the same plate as the non @-@ vegetarian low @-@ caste Banai . Mhalsa is portrayed <unk> Banai for the problems in the palace and talking about her superiority to Banai . Banai retorts by saying that Khandoba came to her , <unk> by her beauty and became her servant . A frustrated Khandoba leaves the palace on a hunting trip after Mhalsa and Banai quarrel about who will <unk> a <unk> for him and marries <unk> . The songs also narrate how ultimately the wives have to remain in harmony and aid each other . For example , a song sings how Mhalsa and Banai come together and celebrate the festival of <unk> with Khandoba at Jejuri . 
- Rarely , Banai also appears in Khandoba 's chief legend where he <unk> the demons Mani and <unk> . Mhalsa and Banai ( or Ganga ) <unk> help Khandoba in the battle to collect the blood of Mani , every drop of which was creating a new demon . Finally , the dog of Khandoba swallows all the blood . Rarely , Banai is described as seated behind Khandoba on the horse and fighting with a sword or spear , a role generally assigned to Mhalsa . 
- 
- = = Worship and iconography = = 
- 
- While traces of Banai / <unk> 's association with the folk god <unk> as a " mother " remain , Banai rarely enjoys independent worship in modern times . She is generally worshipped as Khandoba 's consort . While in Karnataka , her temple is outside the village and <unk> ( as Khandoba is known in Karnataka ) journeys every year to visit it for ten nights from his temple in the village . In Maharashtra , Banai 's temple is inside the village , but outside the chief temple , as in Khandoba 's chief temple at Jejuri . Mhalsa - who is installed in the main temple - is said to resist the arrival of Khandoba 's new wife Banai and thus , Banai does not reside in the chief temple . Frustrated by the constant quarrels between the two wives , Khandoba is said to have divided the hill of Jejuri into two halves : the lower half belongs to Banai , where she has a separate shrine while Mhalsa rules the upper half where she stays with Khandoba in the main temple . It is customary to pay respects to Banai on the way up to the main shrine , before <unk> Khandoba and Mhalsa there . It is said that Khandoba bestowed the honour of first worship on Banai , while sending her off to a separate residence . 
- Banai is the patron goddess of the Dhangars and the protector goddess of flock and herds . She takes care of the well @-@ being of the community and is worshipped for increasing the herd . Stone votive images of sheep and other cattle are offered to her for plentiful animals . No animal sacrifice or non @-@ vegetarian offerings are presented to Khandoba directly , instead non @-@ vegetarian offerings intended for Khandoba are offered to Banai . Dhangars sacrifice rams in her honour and offer her a <unk> ( food offering ) of liver , meat and rice , especially on the holy days : <unk> ( when warriors traditionally set off on war or on a journey ) and the full moon days in the Hindu months of <unk> and <unk> . 
- Khandoba is often depicted with two identical goddesses accompanying him , representing Mhalsa and Banai . In brass images , Banai is depicted holding a lamb and offering water to Khandoba , while Mhalsa rides with Khandoba on his horse . In metal plaques worshipped by the Dhangars , Banai accompanies Khandoba on his horse and is depicted with sheep . 
- 
- 
- = Europium = 
- 
- Europium is a chemical element with symbol Eu and atomic number 63 . It was isolated in 1901 and is named after the continent of Europe . It is a moderately hard , silvery metal which readily oxidizes in air and water . Being a typical member of the lanthanide series , europium usually assumes the oxidation state + 3 , but the oxidation state + 2 is also common : all europium compounds with oxidation state + 2 are slightly reducing . Europium has no significant biological role and is relatively non @-@ toxic compared to other heavy metals . Most applications of europium exploit the <unk> of europium compounds . Europium is one of the least abundant elements in the universe ; only about 5 × 10 − 8 % of all matter in the universe is europium . 
- 
- = = Characteristics = = 
- 
- 
- = = = Physical properties = = = 
- 
- Europium is a <unk> metal with a hardness similar to that of lead . It <unk> in a body @-@ centered cubic lattice . Some properties of europium are strongly influenced by its half @-@ filled electron shell . Europium has the second lowest melting point and the lowest density of all lanthanides . 
- Europium becomes a <unk> when it is cooled below 1 @.@ 8 K and compressed to above 80 GPa . This is because europium is divalent in the metallic state , and is converted into the trivalent state by the applied pressure . In the divalent state , the strong local magnetic moment ( J 
- = 7 / 2 ) <unk> the <unk> , which is induced by eliminating this local moment ( J = 
- 0 in <unk> + ) . 
- 
- = = = Chemical properties = = = 
- 
- Europium is the most reactive rare earth element . It rapidly oxidizes in air , so that bulk oxidation of a centimeter @-@ sized sample occurs within several days . Its reactivity with water is comparable to that of calcium , and the reaction is 
- 2 Eu + 6 H2O → 2 Eu ( OH ) 3 + 3 H2 
- Because of the high reactivity , samples of solid europium rarely have the <unk> appearance of the fresh metal , even when coated with a protective layer of mineral oil . Europium <unk> in air at 150 to 180 ° C to form europium ( III ) oxide : 
- 4 Eu + 3 O2 → 2 Eu2O3 
- Europium dissolves readily in <unk> sulfuric acid to form pale pink solutions of the hydrated Eu ( III ) , which exist as a <unk> : 
- 2 Eu + 3 <unk> + 18 H2O → 2 [ Eu ( H2O ) 9 ] 3 + + 3 <unk> − 
- 4 + 3 H2 
- 
- = = = = Eu ( II ) vs. Eu ( III ) = = = = 
- 
- Although usually trivalent , europium readily forms divalent compounds . This behavior is unusual to most lanthanides , which almost exclusively form compounds with an oxidation state of + 3 . The + 2 state has an electron configuration <unk> because the half @-@ filled f @-@ shell gives more stability . The + 2 state is highly reducing . In terms of size and coordination number , europium ( II ) and barium ( II ) are similar . For example , the <unk> of both barium and europium ( II ) are also highly <unk> in water . <unk> europium is a mild reducing agent , oxidizing in air to form Eu ( III ) compounds . In <unk> , and particularly <unk> conditions , the divalent form is sufficiently stable that it tends to be incorporated into minerals of calcium and the other alkaline <unk> . This ion @-@ exchange process is the basis of the " negative europium anomaly " , the low europium content in many lanthanide minerals such as monazite , relative to the <unk> abundance . <unk> tends to show less of a negative europium anomaly than does monazite , and hence is the major source of europium today . The development of easy methods to separate divalent europium from the other ( trivalent ) lanthanides made europium accessible even when present in low concentration , as it usually is . 
- 
- = = = Isotopes = = = 
- 
- Naturally occurring europium is composed of 2 isotopes , 151Eu and 153Eu , with 153Eu being the most abundant ( 52 @.@ 2 % natural abundance ) . While 153Eu is stable , 151Eu was recently found to be unstable to alpha decay with half @-@ life of 5 + 11 
- − 3 × <unk> years , giving about 1 alpha decay per two minutes in every kilogram of natural europium . This value is in reasonable agreement with theoretical predictions . Besides the natural <unk> 151Eu , 35 artificial <unk> have been characterized , the most stable being <unk> with a half @-@ life of 36 @.@ 9 years , 152Eu with a half @-@ life of 13 @.@ 516 years , and 154Eu with a half @-@ life of 8 @.@ 593 years . All the remaining radioactive isotopes have half @-@ lives shorter than 4 @.@ <unk> years , and the majority of these have half @-@ lives shorter than 12 @.@ 2 seconds . This element also has 8 meta states , with the most stable being <unk> ( t1 / 2 = 12 @.@ 8 hours ) , <unk> ( t1 / 2 = 9 @.@ <unk> hours ) and <unk> ( t1 / 2 = 96 minutes ) . 
- The primary decay mode for isotopes lighter than 153Eu is electron capture , and the primary mode for heavier isotopes is beta minus decay . The primary decay products before 153Eu are isotopes of samarium ( <unk> ) and the primary products after are isotopes of gadolinium ( <unk> ) . 
- 
- = = = = Europium as a nuclear fission product = = = = 
- 
- Europium is produced by nuclear fission , but the fission product yields of europium isotopes are low near the top of the mass range for fission products . 
- Like other lanthanides , many isotopes , especially isotopes with odd mass numbers and neutron @-@ poor isotopes like 152Eu , have high cross sections for neutron capture , often high enough to be neutron poisons . 
- 151Eu is the beta decay product of samarium @-@ 151 , but since this has a long decay half @-@ life and short mean time to neutron absorption , most <unk> instead ends up as <unk> . 
- 152Eu ( half @-@ life 13 @.@ 516 years ) and 154Eu ( half @-@ life 8 @.@ 593 years ) cannot be beta decay products because <unk> and <unk> are non @-@ radioactive , but 154Eu is the only long @-@ lived " shielded " <unk> , other than <unk> , to have a fission yield of more than 2 @.@ 5 parts per million <unk> . A larger amount of 154Eu is produced by neutron activation of a significant portion of the non @-@ radioactive 153Eu ; however , much of this is further converted to <unk> . 
- <unk> ( half @-@ life 4 @.@ <unk> years ) has a fission yield of 330 parts per million ( ppm ) for uranium @-@ 235 and thermal neutrons ; most of it is transmuted to non @-@ radioactive and <unk> gadolinium @-@ 156 by the end of fuel <unk> . 
- Overall , europium is overshadowed by caesium @-@ 137 and <unk> @-@ 90 as a radiation hazard , and by samarium and others as a neutron poison . 
- 
- = = = Occurrence = = = 
- 
- Europium is not found in nature as a free element . Many minerals contain europium , with the most important sources being bastnäsite , monazite , <unk> and loparite . 
- <unk> or <unk> of europium in minerals relative to other rare earth elements is known as the europium anomaly . Europium is commonly included in trace element studies in <unk> and <unk> to understand the processes that form igneous rocks ( rocks that cooled from <unk> or <unk> ) . The nature of the europium anomaly found helps reconstruct the relationships within a suite of igneous rocks . 
- <unk> europium ( <unk> + ) in small amounts is the <unk> of the bright blue fluorescence of some samples of the mineral <unk> ( <unk> ) . The reduction from <unk> + to <unk> + is induced by irradiation with energetic particles . The most outstanding examples of this originated around <unk> and adjacent parts of northern England ; it was the <unk> found here that fluorescence was named after in 1852 , although it was not until much later that europium was determined to be the cause . 
- 
- = = Production = = 
- 
- Europium is associated with the other rare earth elements and is , therefore , mined together with them . <unk> of the rare earth elements is a step in the later processing . Rare earth elements are found in the minerals bastnäsite , loparite , <unk> , and monazite in <unk> quantities . The first two are <unk> minerals <unk> 
- 4 ( <unk> denotes a mixture of all the lanthanides except <unk> ) , and the third is a <unk> <unk> . <unk> also contains thorium and yttrium , which <unk> handling because thorium and its decay products are radioactive . For the extraction from the ore and the isolation of individual lanthanides , several methods have been developed . The choice of method is based on the concentration and composition of the ore and on the distribution of the individual lanthanides in the resulting concentrate . <unk> the ore and subsequent acidic and basic <unk> is used mostly to produce a concentrate of lanthanides . If cerium is the dominant lanthanide , then it is converted from cerium ( III ) to cerium ( IV ) and then precipitated . Further separation by solvent <unk> or ion exchange chromatography yields a fraction which is enriched in europium . This fraction is reduced with zinc , zinc / <unk> , electrolysis or other methods converting the europium ( III ) to europium ( II ) . Europium ( II ) reacts in a way similar to that of alkaline earth metals and therefore it can be precipitated as carbonate or is co @-@ precipitated with barium sulfate . Europium metal is available through the electrolysis of a mixture of molten <unk> and <unk> ( or <unk> ) in a graphite cell , which serves as <unk> , using graphite as <unk> . The other product is chlorine gas . 
- A few large deposits produce or produced a significant amount of the world production . The Bayan Obo iron ore deposit contains significant amounts of bastnäsite and monazite and is , with an estimated 36 million tonnes of rare earth element oxides , the largest known deposit . The mining operations at the Bayan Obo deposit made China the largest supplier of rare earth elements in the 1990s . Only 0 @.@ 2 % of the rare earth element content is europium . The second large source for rare earth elements between 1965 and its closure in the late 1990s was the Mountain Pass rare earth mine . The bastnäsite mined there is especially rich in the light rare earth elements ( La @-@ <unk> , <unk> , and Y ) and contains only 0 @.@ 1 % of europium . Another large source for rare earth elements is the loparite found on the <unk> peninsula . It contains besides niobium , tantalum and <unk> up to 30 % rare earth elements and is the largest source for these elements in Russia . 
- 
- = = Compounds = = 
- 
- Europium compounds tend to exist trivalent oxidation state under most conditions . Commonly these compounds feature Eu ( III ) bound by 6 – 9 <unk> <unk> , typically water . These compounds , the <unk> , <unk> , <unk> , are soluble in water or polar organic solvent . <unk> europium complexes often feature <unk> @-@ like <unk> , e.g. , <unk> . 
- 
- = = = Halides = = = 
- 
- Europium metal reacts with all the <unk> : 
- 2 Eu + 3 <unk> → 2 <unk> ( X = F , Cl , <unk> , I ) 
- This route gives white europium ( III ) fluoride ( <unk> ) , yellow europium ( III ) chloride ( <unk> ) , gray europium ( III ) bromide ( <unk> ) , and colorless europium ( III ) iodide ( <unk> ) . Europium also forms the corresponding <unk> : yellow @-@ green europium ( II ) fluoride ( <unk> ) , colorless europium ( II ) chloride ( <unk> ) , colorless europium ( II ) bromide ( <unk> ) , and green europium ( II ) iodide ( <unk> ) . 
- 
- = = = <unk> and <unk> = = = 
- 
- Europium forms stable compounds with all of the <unk> , but the heavier <unk> ( S , Se , and Te ) stabilize the lower oxidation state . Three oxides are known : europium ( II ) oxide ( <unk> ) , europium ( III ) oxide ( Eu2O3 ) , and the mixed @-@ valence oxide <unk> , consisting of both Eu ( II ) and Eu ( III ) . Otherwise , the main <unk> are europium ( II ) sulfide ( EuS ) , europium ( II ) selenide ( <unk> ) and europium ( II ) telluride ( <unk> ) : all three of these are black solids . EuS is prepared by <unk> the oxide at temperatures sufficiently high to <unk> the Eu2O3 : 
- Eu2O3 + 3 <unk> → 2 EuS + 3 H2O + S 
- The main <unk> is europium ( III ) <unk> ( <unk> ) . 
- 
- = = History of study = = 
- 
- Although europium is present in most of the minerals containing the other rare elements , due to the difficulties in separating the elements it was not until the late 1800s that the element was isolated . William <unk> observed the <unk> spectra of the rare elements and observed spectral lines later assigned to europium . 
- Europium was first found in 1890 by Paul Émile <unk> de <unk> , who obtained basic fractions from samarium @-@ gadolinium concentrates which had spectral lines not accounted for by samarium or gadolinium . However , the discovery of europium is generally credited to French chemist Eugène @-@ <unk> <unk> , who suspected samples of the recently discovered element samarium were contaminated with an unknown element in 1896 and who was able to isolate it in 1901 ; he then named it europium . 
- When the europium @-@ doped yttrium <unk> red phosphor was discovered in the early 1960s , and understood to be about to cause a revolution in the color television industry , there was a scramble for the limited supply of europium on hand among the monazite processors , as the typical europium content in monazite is about 0 @.@ 05 % . However , the <unk> bastnäsite deposit at the Mountain Pass rare earth mine , California , whose lanthanides had an unusually high europium content of 0 @.@ 1 % , was about to come on @-@ line and provide sufficient europium to sustain the industry . Prior to europium , the color @-@ TV red phosphor was very weak , and the other phosphor colors had to be muted , to maintain color balance . With the brilliant red europium phosphor , it was no longer necessary to <unk> the other colors , and a much brighter color TV picture was the result . Europium has continued to be in use in the TV industry ever since as well as in computer monitors . Californian bastnäsite now faces stiff competition from Bayan Obo , China , with an even " <unk> " europium content of 0 @.@ 2 % . 
- Frank <unk> , celebrated for his development of the ion @-@ exchange technology that revolutionized the rare earth industry in the mid @-@ 1950s , once related the story of how he was lecturing on the rare <unk> in the 1930s when an elderly gentleman approached him with an offer of a gift of several pounds of europium oxide . This was an unheard @-@ of quantity at the time , and <unk> did not take the man seriously . However , a package duly arrived in the mail , containing several pounds of genuine europium oxide . The elderly gentleman had turned out to be Herbert <unk> McCoy who had developed a famous method of europium purification involving <unk> chemistry . 
- 
- = = Applications = = 
- 
- Relative to most other elements , commercial applications for europium are few and rather specialized . Almost invariably , they exploit its <unk> , either in the + 2 or + 3 oxidation state . 
- It is a <unk> in some types of glass in lasers and other <unk> devices . Europium oxide ( Eu2O3 ) is widely used as a red phosphor in television sets and fluorescent lamps , and as an <unk> for yttrium @-@ based phosphors . Color TV screens contain between 0 @.@ 5 and 1 g of europium oxide . Whereas trivalent europium gives red phosphors , the luminescence of divalent europium depends on the host lattice , but tends to be on the blue side . The two classes of europium @-@ based phosphor ( red and blue ) , combined with the yellow / green <unk> phosphors give " white " light , the color temperature of which can be varied by altering the proportion or specific composition of the individual phosphors . This phosphor system is typically encountered in helical fluorescent light bulbs . <unk> the same three classes is one way to make <unk> systems in TV and computer screens . Europium is also used in the manufacture of fluorescent glass . One of the more common persistent after @-@ glow phosphors besides copper @-@ doped zinc sulfide is europium @-@ doped <unk> <unk> . Europium fluorescence is used to <unk> <unk> interactions in drug @-@ discovery screens . It is also used in the anti @-@ counterfeiting phosphors in euro <unk> . 
- An application that has almost fallen out of use with the introduction of affordable <unk> magnets is the use of europium complexes , such as Eu ( <unk> ) 3 , as shift reagents in NMR spectroscopy . <unk> shift reagents , such as Eu ( <unk> ) 3 , are still used to determine enantiomeric purity . 
- A recent ( 2015 ) application of europium is in quantum memory chips which can reliably store information for days at a time ; these could allow sensitive quantum data to be stored to a hard disk @-@ like device and shipped around the country . 
- 
- = = Precautions = = 
- 
- There are no clear indications that europium is particularly toxic compared to other heavy metals . Europium chloride , nitrate and oxide have been tested for toxicity : europium chloride shows an acute <unk> LD50 toxicity of 550 mg / kg and the acute oral LD50 toxicity is 5000 mg / kg . Europium nitrate shows a slightly higher <unk> LD50 toxicity of 320 mg / kg , while the oral toxicity is above 5000 mg / kg . The metal dust presents a fire and explosion hazard . 
- 
- 
- = North @-@ Eastern Area Command ( RAAF ) = 
- 
- North @-@ Eastern Area Command was one of several geographically based commands raised by the Royal Australian Air Force ( RAAF ) during World War II . For most of its existence it covered central and northern Queensland , and Papua New Guinea . It was formed in January 1942 from the eastern part of the former Northern Area Command , which had covered the whole of northern Australia and Papua . Headquartered at Townsville , Queensland , North @-@ Eastern Area Command was primarily responsible for air defence , aerial reconnaissance and protection of the sea lanes within its boundaries . Aircraft under its control took part in the battles of Rabaul , Port Moresby and Milne Bay in 1942 , and the landings at Hollandia and Aitape in 1944 . The command continued to operate following the end of the war , before its responsibilities were subsumed in 1954 by the RAAF 's new functional command @-@ and @-@ control system ; the headquarters was disbanded two years later . 
- 
- = = History = = 
- 
- 
- = = = World War II = = = 
- 
- North @-@ Eastern Area Command was formed at Townsville , Queensland , on 15 January 1942 , taking over the eastern portion of what was previously Northern Area Command . Northern Area had been established on 8 May 1941 as one of the RAAF 's geographically based command @-@ and @-@ control zones , and covered northern New South Wales , Queensland , the Northern Territory , and Papua . The roles of the area commands were air defence , protection of adjacent sea lanes , and aerial reconnaissance . Each area was led by an Air Officer Commanding ( AOC ) responsible for the administration and operations of air bases and units within his boundary . 
- The outbreak of the Pacific War in December 1941 was the catalyst for Northern Area being split into North @-@ Western Area ( NWA ) and North @-@ Eastern Area ( NEA ) , to counter distinct threats to Northern Australia and New Guinea , respectively . Air Commodore Frank Lukis , formerly in charge of Northern Area , was NEA 's inaugural AOC , taking responsibility for RAAF operations against the Japanese in New Guinea , New Britain and surrounding islands . His headquarters staff numbered <unk> . On 20 January 1942 , a force of over 100 Japanese aircraft attacked Rabaul , destroying or badly damaging six CAC Wirraways and killing or wounding eleven crewmen of No. 24 Squadron under Wing Commander John Lerew . The following day , NEA headquarters sent a signal to Lerew ordering him to keep his airfield open , to which Lerew , with only two Wirraways left , replied using the legendary ancient <unk> phrase to honour an Emperor : " <unk> <unk> <unk> " ( " We who are about to die salute you " ) . <unk> a further message from headquarters to abandon his squadron and escape in a Lockheed Hudson bomber , on 22 January Lerew began evacuating staff to Port Moresby , New Guinea . 
- No. 33 Squadron , operating ex @-@ <unk> Short Empire flying boats and several smaller transports , was raised in NEA on 19 February 1942 . Earlier that month , Lukis warned higher command of the poor state of preparedness and low morale of Australian Army troops at Port Moresby , due to lack of air cover and apparent lack of interest from government echelons . On 25 February , Nos. 3 and 4 Fighter Sector Headquarters were established to coordinate fighter operations ; they were based at Townsville and Port Moresby respectively . Seventeen P @-@ 40 Kittyhawks of No. 75 Squadron , recently formed at Townsville , were deployed to Port Moresby in mid @-@ March . <unk> by Squadron Leader John Jackson , the squadron suffered heavy losses in the ensuing battle . At one point NEA headquarters gave Jackson permission to withdraw but he refused , and the squadron was eventually credited with destroying thirty @-@ five Japanese aircraft in the air and on the ground , securing Port Moresby until relieved by the 35th and 36th Squadrons of the United States Army Air Forces ( USAAF ) , operating P @-@ 39 <unk> . 
- Several USAAF bomber formations operated under NEA 's control in early 1942 , including A @-@ 24 <unk> of the 8th Squadron from Port Moresby , and B @-@ 17 Flying <unk> of the <unk> Squadron ( initially known as the " <unk> Squadron " ) from Townsville . As of 20 April , operational authority over all RAAF combat infrastructure , including area commands , was invested in the newly established Allied Air Forces ( <unk> ) Headquarters under South West Pacific Area Command ( SWPA ) . One result of this was the integration of USAAF and RAAF staff at area headquarters . According to the official history of the RAAF , though " more a diplomatic gesture than a practical method of war organisation " , it gave personnel from the two services the opportunity to quickly become <unk> to each other and " in North @-@ Eastern Area , as an example , the atmosphere was happy and the staff extremely cooperative " . Following the Battle of the Coral Sea in May , USAAF units no longer operated under RAAF control in the NEA but were commanded directly by senior American officers of the <unk> . 
- NEA 's operational headquarters , a reinforced concrete bunker known as Building 81 , was completed in May 1942 . Located on Green Street , Townsville , at the base of Castle Hill , it was topped with a suburban house to mislead enemy aircraft . The same month , Eastern Area Command was formed , taking control of units in New South Wales and southern Queensland from Southern Area and NEA . This left NEA in command of Nos. 24 , 33 and 76 Squadrons , as well as No. 3 Fighter Sector Headquarters , at Townsville ; No. 100 Squadron at Cairns ; No. 32 Squadron at Horn Island ; and Nos. 11 , 20 and 75 Squadrons , as well as No. 4 Fighter Sector Headquarters , at Port Moresby . NEA 's boundaries were <unk> on 19 August : a portion of Queensland within the <unk> <unk> and the <unk> and <unk> districts was assigned to the control of North @-@ Western Area . Lukis handed over command of NEA to Group Captain ( later Air Commodore ) Harry Cobby on 25 August . By the end of the month , the headquarters staff numbered 684 . No. 75 Squadron , <unk> after its defence of Port Moresby , and No. 76 Squadron , deployed north from Townsville and also flying Kittyhawks , played what senior Australian Army commanders described as the " decisive " role in the Battle of Milne Bay in New Guinea during August and September 1942 . During the battle , Cobby exercised overall command of the RAAF units from NEA headquarters , while their efforts were coordinated on the ground by Group Captain Bill <unk> , NEA 's senior air staff officer . 
- On 1 September 1942 , No. 9 ( Operational ) Group was formed at Port Moresby as a mobile strike force to move forward with Allied advances in the Pacific , in contrast to the static , defensive nature of the area commands . It took over all units in New Guinea previously operating under NEA Command . NEA initially retained administrative control of No. 9 Group but , on 1 January 1943 , the group was made independent of the area command and its administration became the responsibility of RAAF Headquarters , Melbourne . September 1942 also saw the formation of RAAF Command , led by Air Vice Marshal Bill Bostock , to oversee the majority of Australian flying units in the SWPA . Bostock exercised control of air operations through the area commands , although RAAF Headquarters continued to hold overarching administrative authority over all Australian units . He personally coordinated operations when they involved more than one area command , for instance when the fighter squadrons of both NWA and NEA were required to repulse a major attack . No. 42 ( Radar ) Wing was formed at Townsville in February 1943 , and the following month took control of all radar stations in NEA . As of April 1943 , the area command directly controlled four squadrons tasked primarily with anti @-@ submarine warfare : No. 7 Squadron , flying Bristol Beaufort reconnaissance @-@ bombers out of Ross River ; No. 9 Squadron , a fleet co @-@ operation unit flying <unk> <unk> from Bowen ; and Nos. 11 and 20 Squadrons , flying reconnaissance and bombing missions with <unk> Catalinas from Cairns . 
- In early 1943 , Japan was still believed to be capable of invading , or at least bombing , the Torres Strait islands , and NEA had only No. 7 Squadron , now operating from Horn Island , to counter the threat . It was reinforced in April by No. 84 Squadron , flying CAC Boomerang fighters . The same month , No. 72 Wing was formed at Townsville , before deploying to <unk> , New Guinea . <unk> No. 84 Squadron , No. 86 Squadron ( flying Kittyhawks ) , and No. 12 Squadron ( <unk> <unk> dive bombers ) , the wing was responsible for Torres Strait 's air defence , as well as offensive operations against infrastructure and shipping in Dutch New Guinea . In October , No. 84 Squadron converted to Kittyhawks and transferred to the newly formed No. 75 Wing , which was given responsibility for units at Horn Island , Thursday Island , and Higgins Field on Cape York Peninsula . In February 1944 , No. 75 Wing headquarters moved from Horn Island to Higgins Field , where it was soon joined by other units under its control , Nos. 7 and 23 Squadrons ; the latter operated <unk> until being declared non @-@ operational in June , prior to re @-@ <unk> with B @-@ 24 Liberators for duty in North @-@ Western Area . By May , NEA 's order of battle on the Australian mainland consisted of Nos. 7 , 9 , 13 ( operating Lockheed <unk> from <unk> ) , 20 and 23 Squadrons . 
- Cobby served as AOC NEA until November 1943 , handing over to Air Commodore John <unk> , who held command for the remainder of the war . By the end of November , NEA headquarters staff numbered 499 , including ninety @-@ seven officers . NEA 's Catalinas joined aircraft of No. 9 Group in support of the US invasion of New Britain in December 1943 and January 1944 . The Catalinas also conducted mine @-@ laying operations around the Timor Sea in the lead @-@ up to the landings at Hollandia and Aitape in April 1944 . In August , No. 76 Wing headquarters , formed at Townsville in January and subsequently based at Cairns , was transferred to Darwin , Northern Territory . There it came under the control of NWA headquarters and oversaw operations by three <unk> squadrons , including No. 20 . The same month , No. 75 Wing was disbanded and its units became the direct responsibility of NEA headquarters . No. 42 Wing disbanded in October 1944 , following a decision to assign control of RAAF radar stations to mobile fighter control units or similar formations . By the end of February 1945 , NEA headquarters staff numbered 743 , including 127 officers . No. 72 Wing headquarters transferred to Townsville in May that year , and disbanded the following month . 
- 
- = = = Post @-@ war activity and disbandment = = = 
- 
- Following the end of the Pacific War in August 1945 , SWPA was dissolved and RAAF Headquarters again assumed full control of all its operational formations , including the area commands . By the end of the month , NEA headquarters staff numbered 526 , including ninety @-@ eight officers . The Air Force shrank dramatically as personnel were <unk> and units disbanded ; most of the RAAF 's bases and aircraft employed in operations after the war were situated within Eastern Area 's sphere of control in New South Wales and southern Queensland . In September 1946 , the Chief of the Air Staff , Air Vice Marshal George Jones , proposed reducing the five extant mainland area commands ( North @-@ Western , North @-@ Eastern , Eastern , Southern , and Western Areas ) to three : Northern Area , covering Queensland and the Northern Territory ; Eastern Area , covering New South Wales ; and Southern Area , covering Western Australia , South Australia , Victoria and Tasmania . The Australian Government rejected the plan and the wartime area command boundaries essentially remained in place . By 1949 , NEA headquarters was located in Sturt Street , Townsville . No. 10 Squadron was based at Townsville from March that year , operating Avro <unk> over the Pacific and Australia 's northern approaches in the maritime reconnaissance and search @-@ and @-@ rescue roles . Air Commodore Ian <unk> was appointed AOC NEA in September 1951 and served two years in the post before handing over to acting Air Commodore Patrick <unk> . 
- <unk> in October 1953 , the RAAF was reorganised from a geographically based command @-@ and @-@ control system into one based on function . In February 1954 , the newly constituted functional organisations — Home , Training , and Maintenance <unk> — assumed control of all operations , training and maintenance from North @-@ Eastern Area Command . NEA headquarters remained in existence , but only as one of Home Command 's " remote control points " . It was finally disbanded on 3 December 1956 , and was succeeded by Headquarters RAAF Townsville . 
- As of 2009 , the former NEA headquarters in Building 81 , Green Street , housed Townsville 's State Emergency Service group . 
- 
- = = Order of battle = = 
- 
- As at 30 April 1942 , NEA 's order of battle comprised : 
- RAAF Station Townsville 
- No. 24 ( General Purpose ) Squadron 
- No. 33 ( Transport ) Squadron 
- No. 76 ( Fighter ) Squadron 
- RAAF Station <unk> 
- No. 23 ( General Purpose ) Squadron 
- RAAF Station Port Moresby 
- No. 11 ( General Reconnaissance ) Squadron 
- No. 20 ( General Reconnaissance ) Squadron 
- No. 32 ( General Reconnaissance ) Squadron 
- No. 75 ( Fighter ) Squadron 
- No. 3 Fighter Sector Headquarters , Townsville 
- No. 4 Fighter Sector Headquarters , Port Moresby 
- 
- 
- = Cape lobster = 
- 
- The Cape lobster , Homarinus capensis , is a species of small lobster that lives off the coast of South Africa , from Dassen Island to Haga Haga . Only a few dozen specimens are known , mostly regurgitated by reef @-@ dwelling fish . It lives in rocky reefs , and is thought to lay large eggs that have a short <unk> phase , or that hatch directly as a juvenile . The species grows to a total length of 10 cm ( 3 @.@ 9 in ) , and resembles a small European or American lobster ; it was previously included in the same genus , Homarus , although it is not very closely related to those species , and is now considered to form a separate , monotypic genus – Homarinus . Its closest relatives are the genera <unk> and <unk> . 
- 
- = = Distribution and ecology = = 
- 
- The Cape lobster is endemic to South Africa . It occurs from Dassen Island , Western Cape in the west to Haga Haga , Eastern Cape in the east , a range of 900 kilometres ( 560 mi ) . Most of the known specimens were regurgitated by fish caught on reefs at depths of 20 – 40 metres ( 66 – 131 ft ) . This suggests that the Cape lobster inhabits rocky substrates , and may explain its apparent rarity , since such areas are not amenable to dredging or trawling , and the species may be too small to be retained by lobster traps . 
- 
- = = Description = = 
- 
- Homarinus capensis is considerably smaller than the large northern lobsters of the Atlantic Ocean , Homarus gammarus ( the European lobster ) and Homarus americanus ( the American lobster ) , at 8 – 10 centimetres ( 3 @.@ 1 – 3 @.@ 9 in ) total length , or 4 – 5 cm ( 1 @.@ 6 – 2 @.@ 0 in ) carapace length . Accounts of the colouration of H. capensis are very variable , from tawny , red or yellow to " a rather dark olive " , similar to Homarus gammarus . 
- Homarinus and Homarus are considered to be the most <unk> genera in the family <unk> . Nonetheless , the Cape lobster differs from Homarus in a number of characters . The <unk> of the Cape lobster is flattened , while that of Homarus is rounded in section , and curves upwards at the tip . The three pairs of claws are covered with hairs in Homarinus , while those of Homarus are <unk> . The <unk> tapers along its length in Homarus , but has sides which are nearly parallel in Homarinus . Although no egg @-@ bearing females have been collected , the <unk> ( openings of the <unk> ) of female Cape lobsters are much larger than those of Homarus gammarus and Homarus americanus . This is thought to indicate that Homarinus bears fewer , larger eggs than Homarus , and that either the larvae develop quickly into juveniles after hatching , or that the eggs hatch directly into juveniles . 
- 
- = = Taxonomy and evolution = = 
- 
- Cape lobsters are elusive and rare , with only fourteen specimens having been collected between 1792 ( the date of its first description ) and 1992 . These include five males in the collections of the South African Museum ( Cape Town ) , two in the Natural History Museum ( London ) , one in each of the East London Museum , the <unk> van <unk> <unk> ( <unk> ) and the Albany Museum ( <unk> ) , and one male and one female in the <unk> national d <unk> <unk> ( Paris ) . In 1992 , a Cape lobster was discovered at Dassen Island , and the publicity the find generated resulted in more than 20 additional specimens being reported . 
- The Cape lobster was first described by Johann Friedrich Wilhelm <unk> in 1792 as Cancer ( <unk> ) capensis . It was independently described in 1793 by Johan Christian <unk> as <unk> <unk> , possibly based on the same type specimen . When Friedrich Weber erected the genus Homarus in 1795 , he included <unk> ' species in it , but this placement was not followed by later authors . The species reached its current classification in 1995 , when the monotypic genus Homarinus was erected by Irv <unk> , Austin B. Williams and Robert S. <unk> . 
- While analyses of morphology suggest a close relationship between Homarinus and Homarus , molecular analyses using mitochondrial DNA reveal that they are not sister taxa . Both genera lack ornamentation such as spines and <unk> , but are thought to have reached that state independently , through <unk> evolution . The closest living relative of Homarus is <unk> <unk> , while the closest relatives of Homarinus are <unk> and <unk> . 
- 
- 
- = A4232 road = 
- 
- The A4232 , which is also known either as the <unk> <unk> Road ( PDR ) ( Welsh : Ffordd <unk> <unk> ) or the Cardiff Link Road ( Welsh : Ffordd Gyswllt <unk> ) , is a distributor road in Cardiff , the capital of Wales . 
- The first section of the PDR to be completed was the Southern Way Link Road in 1978 and the last section was the Butetown Link Road in 1995 . Construction of the first phase of the final link road , the Eastern Bay Link Road , started on 17 March 2016 and is due to be completed by April 2017 . When fully completed , the road will form part of the Cardiff ring road system . The PDR runs west , south and east of Cardiff , with the M4 between junction 30 and junction 33 completing the northern section . The PDR is dual carriageway for its entire length except for the East Moors Viaduct , which is a single carriageway . 
- The PDR has been constructed in separate link roads of between 1 @.@ 61 km ( 1 @.@ 00 mi ) and 5 @.@ 47 km ( 3 @.@ 40 mi ) around Cardiff and to date 22 kilometres ( 14 mi ) including spurs have been opened to traffic , with plans for a further 5 @.@ 53 km ( 3 @.@ 44 mi ) . It has 4 large <unk> ( Ely Viaduct , Grangetown Viaduct , Taff Viaduct and the East Moors Viaduct ) , 1 tunnel ( Queen 's Gate Tunnel ) and 2 spur roads ( Cogan Spur and Central Link Road ) . The road also has many smaller bridges over or under most of the interchanges on the road . The entire length of the road has <unk> restrictions on it . 
- 
- = = History = = 
- 
- The road was conceived in the early 1970s and planning began in the late 1970s , by South Glamorgan County Council . The road at that stage was planned to be completed by 1995 and would relieve Ely , Grangetown , Butetown and East Moors of through traffic and as a fast link to the national motorway network via the M4 and A48 ( M ) . The first section of the PDR to be completed was the Southern Way Link Road between Newport Road ( <unk> road ) and the Eastern Avenue ( A48 road ) in 1978 and the last section of the PDR , the Butetown Link Road , was opened to the public in 1995 . 
- The former chairman of the South Glamorgan County Council environment committee , Councillor Paddy <unk> , called the road a " necklace of opportunity " due to its shape and also the opportunities for regeneration . By 1 April 1996 the responsibility for the road was transferred from South Glamorgan County Council to the unitary authority of Cardiff Council . Much of the funding for the road had been grant aided from the European Community and the UK Government on the basis that it would improve the economic viability of the area and bring in new jobs and industry . However , since the completion of the Butetown Link Road , funding for further developments have been at a stand still , and to date 22 kilometres ( 14 mi ) including spurs are open to traffic with plans for a further 5 @.@ 53 km ( 3 @.@ 44 mi ) . The " missing link " , the Eastern Bay Link Road , is still to be built . 
- 
- = = Route description = = 
- 
- The PDR has 3 separate sections ; the Capel Llanilltern Interchange ( M4 <unk> ) to Queen 's Gate roundabout , the Rover Way – Lamby Way roundabout to the Llanedeyrn Interchange ( A48 ) and the Pontprennau Interchange ( A48 ) to the Pentwyn Interchange ( M4 <unk> ) . 
- The section from the Capel Llanilltern Interchange on the M4 ( junction 33 ) to the Queen 's Gate roundabout is sometimes referred to as the Western Link Road ( Welsh : Ffordd Gyswllt <unk> ) , is 15 @.@ 77 kilometers ( 9 @.@ 80 mi ) in length and includes the Capel Llanilltern – Culverhouse Cross Link Road , Ely Link Road , Grangetown Link Road and Butetown Link Road . For the majority of this section it is the boundary between the City of Cardiff to the east and the Vale of Glamorgan to the west . 
- This section partly opened in 1978 , from the Rover Way – Lamby Way roundabout to the Llanedeyrn Interchange on the A48 is oldest section of the PDR . It includes only the Southern Way Link Road , known locally as Southern Way , and is 2 @.@ 25 km ( 1 @.@ 40 mi ) in length . 
- The Pentwyn Link Road section from the Pontprennau Interchange on the A48 to the Pentwyn Interchange on the M4 ( junction 30 ) is one of the newer sections of the PDR , having been opened in 1984 , it is 1 @.@ 61 km ( 1 @.@ 00 mi ) in length . The road between the Llanedeyrn Interchange to the Pontprennau Interchange is the A48 ( Eastern Avenue ) , it too is a dual carriageway and is a concurrent road with the PDR , there are no plans to <unk> this section of the A48 . 
- 
- = = Link roads = = 
- 
- A map of all the link roads and spur roads can be seen by clicking Map of all coordinates opposite 
- Each section of the road was completed in separate link roads . 
- 
- = = = Capel Llanilltern – Culverhouse Cross Link Road = = = 
- 
- The GB £ 14 @.@ 5 million Capel Llanilltern – Culverhouse Cross Link Road ( Welsh : Ffordd Gyswllt Capel Llanilltern – <unk> <unk> ) , also known as the A4232 Trunk Road ( as it is the only section of the PDR which is a trunk road ) , between the Capel Llanilltern Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Capel Llanilltern Interchange ( M4 <unk> ) ) ) and the Culverhouse Cross Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Culverhouse Cross Interchange ) ) was opened in 1985 . It was designed to provide a by @-@ pass for traffic from the M4 to the Vale of Glamorgan . It is 5 @.@ 47 km ( 3 @.@ 40 mi ) in length and includes the Ely Viaduct close to <unk> @-@ super @-@ Ely . The trunk road is maintained by the South Wales Trunk Road Agency ( <unk> ) on behalf of the Welsh Assembly Government ( WAG ) . The remainder of the PDR is a primary route , which is maintained by Cardiff Council . In 2006 variable message signs were installed on the Capel Llanilltern – Culverhouse Cross Link Road by <unk> Systems to display messages giving motorists warning of road and weather conditions , accidents , congestion and major events held in the area . 
- 
- = = = = Ely Viaduct = = = = 
- 
- The Ely Viaduct ( Welsh : Traphont <unk> ) crosses over the River Ely and also the main South Wales railway line . 
- The viaduct is a 538 ft ( 164 m ) twin @-@ box girder and has central span of 230 ft ( 70 m ) and 150 ft ( 46 m ) side spans , constructed by the balanced <unk> method . It was constructed in segments each weighing between 54 tons for plain segments to 93 tons for the main segments . 
- 
- = = = Ely Link Road = = = 
- 
- The Ely Link Road ( Welsh : Ffordd Gyswllt <unk> ) between the Culverhouse Cross Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Culverhouse Cross Interchange ) ) and the Leckwith Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Leckwith Interchange ) ) , sometimes referred to as the Leckwith Road Interchange . It is 4 @.@ 83 km ( 3 @.@ 00 mi ) in length and was opened in 1982 and built by Davies Middleton & Davies Ltd ( <unk> ) . It was designed to relieve Ely of through traffic and partly cuts into Leckwith Hill . 
- 
- = = = Grangetown Link Road = = = 
- 
- The 2 @.@ 89 km ( 1 @.@ 80 mi ) Grangetown Link Road ( Welsh : Ffordd Gyswllt <unk> ) between the Leckwith Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Leckwith Interchange ) ) and the Ferry Road Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Ferry Road Interchange ) ) was opened on 3 May , 1988 . It includes the Grangetown Viaduct . 
- 
- = = = = Grangetown Viaduct = = = = 
- 
- The Grangetown Viaduct ( Welsh : Traphont <unk> ) is approximately 1 km ( 0 @.@ 62 mi ) in length and is said to be the longest glued segmental bridge in the United Kingdom . It has 13 spans of 71 meters ( 233 ft ) with 2 end spans of 38 m ( 125 ft ) and 46 m ( 151 ft ) . 
- The viaduct was designed by South Glamorgan County Council . Robert Benaim and Associates , now called Benaim UK Ltd designed the initial launching system for the glued segmental structure . 
- 
- = = = Butetown Link Road = = = 
- 
- The 2 @.@ 57 km ( 1 @.@ 60 mi ) Butetown Link Road ( Welsh : Ffordd Gyswllt Butetown ) between the Ferry Road Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Ferry Road Interchange ) ) and the Queen 's Gate Roundabout ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Queen 's Gate Roundabout ) ) was opened on 27 March , 1995 by Neil <unk> at the time the European Commissioner for Transport . Construction of the link road commenced on 15 October 1993 and it was the last link road to be built . It includes the Taff Viaduct , also known as the Butetown Link Road Bridge , and the Queen 's Gate Tunnel , which is also known simply as the Butetown Tunnel . 
- In 1987 South Glamorgan County Council had intended to build a viaduct cross the River Taff and then go onto an elevated section through Butetown " on <unk> " . But the newly formed Cardiff Bay Development Corporation along with local residents objected to the scheme that they said would cut through Butetown and split the community , creating an unnecessary physical barrier between the two areas to the north and south of the link road . By 1988 South Glamorgan County Council had agreed to change the design of the link road , despite having already spent 6 years of design work on the link road meant that a tunnel had to be built instead . The cost of the Butetown Link Road was estimated to cost £ 35 million , but this had risen to £ <unk> in 1988 , before the design change . 
- The contract to build the Butetown Link Road was eventually won by a local company Davies Middleton & Davies Ltd in a joint venture with an Italian contractor , <unk> @-@ <unk> UK Ltd . The bid of £ 60 million undercut all other bids by £ 10 million , Davies Middleton & Davies Ltd have subsequently gone into administrative receivership . 
- 
- = = = = Queen 's Gate Tunnel = = = = 
- 
- The Queen 's Gate Tunnel ( Welsh : <unk> <unk> y <unk> ) , also known as the Butetown Tunnel , is underneath southern Butetown and follows a line underneath the Wales Millennium Centre . The 715 m ( 2 @,@ 346 ft ) twin tunnel was constructed using the cut and cover method of construction , which involves digging a trench for the tunnel and then roofing it over . The tunnel was constructed using reinforced concrete and a central wall separates the two sets of traffic . 
- South Glamorgan County Council originally awarded the contract for the management , installation , testing and commissioning of all electrical and mechanical services for the tunnel , and the water pumping stations to <unk> · <unk> Ltd . They still maintain the tunnel on behalf of Cardiff Council . 
- 
- = = = = Taff Viaduct = = = = 
- 
- Construction of the 600 m ( 2 @,@ 000 ft ) Taff Viaduct ( Welsh : Traphont <unk> ) includes a dual @-@ carriageway roadway plus a foot and cycle path . South Glamorgan County Council was the local authority in charge of the project at the time and construction of the viaduct began in March , 1991 . The Taff Viaduct crosses the River Taff at Cardiff Bay . 
- The viaduct was constructed using <unk> concrete segments , which is widely used in the construction industry for medium to long span <unk> . <unk> were made in a casting yard near the site and then transported for final assembly of the viaduct . 
- 
- = = = Eastern Bay Link Road = = = 
- 
- Construction began on 17 March 2016 on the 5 @.@ 25 km ( 3 @.@ 26 mi ) Eastern Bay Link Road ( Welsh : Ffordd Gyswllt <unk> y <unk> ) which will run from the Queen 's Gate Roundabout to the Rover Way – Lamby Way Roundabout on the Southern Way Link Road , although at present only the first phase between Queen 's Gate Roundabout ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Queen 's Gate Roundabout ) ) and Ocean Way Interchange . ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Ocean Way Interchange ) ) The link road will be a two lane dual carriageway with a 50 miles per hour ( 80 km / h ) speed limit . 
- For many years this link road has been <unk> due to the costs involved . The link road had previously been known as the East Moors Link Road – Phase 1 and 3 , but it also included the East Moors Viaduct , which was originally known as the East Moors Link Road – Phase 2 . Later it became known as the Cardiff Bay Link Road , then renamed the Eastern Bay Link Road . 
- The Eastern Bay Link Road , along with other schemes have been subject to many planning proposals since the last link road ( the Butetown Link Road ) was finished in 1995 , namely a local transport plan ( Local Transport Plan 2000 – 2016 ) in August 2000 , a green paper ( A Change of <unk> ) in December 2002 and a white paper ( Keeping Cardiff Moving ) in May 2003 . The cost of the link road was estimated to cost GB £ 162 million in 2001 and this increased to GB £ 180 million by August 2002 . It could be paid for by congestion charging , although a public @-@ private partnership is also possible . 
- The original route of the link road had been challenged by both Friends of the Earth Cymru , and also the Royal Society for the Protection of Birds ( RSPB ) , who had both lodged formal objections against the link road . However , the current proposal from Cardiff Council of 31 October 2006 is to complete the first phase of the road , approximately 1 km ( 0 @.@ 62 mi ) in length , from the Butetown Link Road to Ocean Way roundabout . The new route would be a different route from the earlier proposal and would avoid any encroachment into the nature conservation area , which Friends of the Earth and the RSPB had objected to . Therefore , the new route of the link road should ensure that there are no objections . 
- It was revealed on 14 June 2013 that Edwina Hart , Minister for Economy , Science and Transport in the WAG supported the completion the Eastern Bay Link Road . She said that the link road would ; improve access to Cardiff Bay , improve access to the Cardiff Central Enterprise Zone and enhance connections within the Cardiff City Region . The overall objectives of the road would be to : 
- Increase accessibility from east Cardiff to major employment sites in the East Moors area , Cardiff Bay and the Central Cardiff Enterprise Zone 
- Provide a more direct route between the Butetown Tunnel and Rover Way 
- <unk> congestion at the junctions on Tyndall Street by removing traffic currently using the Ocean <unk> East Tyndall Street – Central Link route 
- <unk> journey times for private and commercial road users 
- Help economic regeneration 
- <unk> road safety and reduce casualties 
- <unk> <unk> on the strategic road network around Cardiff 
- Provide more opportunities for cycling and walking 
- On 2 May 2014 , Edwina Hart approved the <unk> of the design – build contractor , advance service diversion works and communications arrangements for the Eastern Bay Link Road . Construction of this first phase of the Eastern Bay Link Road is due to begin in 2015 and will take 18 months to complete . In April 2015 , it was announced by the Welsh Government that the 1 @.@ 2 km £ <unk> Eastern Bay Link Road from the incomplete flyover at the Queens Gate roundabout to the Ocean Way roundabout at <unk> known as the " roundabout to nowhere " , would be designed by <unk> Property and Infrastructure and constructed by <unk> Construction Holdings and <unk> <unk> UK in a joint venture . Construction began on the link road on 17 March 2016 . 
- 
- = = = Southern Way Link Road = = = 
- 
- The Southern Way Link Road ( Welsh : Ffordd Gyswllt y Ffordd <unk> ) , generally known simply as Southern Way , was built in two parts ; the first section of Southern Way to be completed was between Newport Road ( <unk> road ) and the Eastern Avenue ( A48 road ) in 1978 . The final section of the link road was built between Newport Road and the Rover Way – Lamby Way roundabout and was opened in 1984 at a cost of £ 9 million and includes the East Moors Viaduct , which is also known as the Southern Way <unk> . In 1987 it was envisaged that the present single @-@ carriage would be " twinned " as a dual @-@ carriageway , the same as the rest of the PDR . The 2 @.@ 25 km ( 1 @.@ 40 mi ) link road now runs from the Rover Way – Lamby Way Roundabout ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Rover Way – Lamby Way Roundabout ) ) to the Llanedeyrn Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Llanedeyrn Interchange ) ) on the A48 . 
- 
- = = = = East Moors Viaduct = = = = 
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- The 900 m ( 3 @,@ 000 ft ) East Moors Viaduct ( Welsh : Traphont <unk> y <unk> ) , which is also known as the Southern Way <unk> , was also designed by Robert Benaim and Associates who won the <unk> ( Institution of Civil Engineers ) Project Award and also a Concrete Society Commendation , both in 1985 for work on the viaduct . The viaduct was constructed by concrete box girder deck of segmental construction . The East Moors Viaduct was originally known as the East Moors Link Road – Phase 2 , but now forms part of the Southern Way Link Road . 
- 
- = = = Pentwyn Link Road = = = 
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- The Pentwyn Link Road ( Welsh : Ffordd Gyswllt Pentwyn ) , which is also known as the North Pentwyn Link Road , runs from the Pontprennau Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( <unk> Interchange ) ) on the A48 and the Pentwyn Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Pentwyn Interchange ( M4 <unk> ) ) ) on the M4 ( junction 30 ) . It is 1 @.@ 61 km ( 1 @.@ 00 mi ) in length and was opened by John <unk> MP , the Secretary of State for Wales on 20 June , 1994 . It provides a link between the Eastern Avenue ( A48 ) and the M4 so that westbound traffic from the east of the city can get onto the M4 without having to go through the city , via the A48 and <unk> . In addition it also provides access to the community of Pontprennau to the national road network . This link was financed by private developers as part of a large scale housing development at Pontprennau . 
- 
- = = Public Art & <unk> Stones = = 
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- 
- = = Spur roads = = 
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- 
- = = = Cogan Spur ( <unk> ) = = = 
- 
- The Cogan Spur , which is also known as the Cogan Link ( Welsh : Gyswllt Cogan ) , from the Ferry Road Interchange ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Ferry Road Interchange ) ) to Barons Court Junction ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Barons Court Junction ) ) on the A4160 was opened in 1988 . It is only 0 @.@ 8 km ( 0 @.@ 50 mi ) , but it is an important link to provide access to the PDR from Penarth and the southern part of the Vale of Glamorgan . It also bypasses Penarth Road ( A4160 ) for traffic going in and out of Cardiff city centre . 
- The most recent development , which began at the end of 2006 and into 2007 was the widening of the road between the Cogan Viaduct and the Ferry Road Interchange to a 3 @-@ lane dual @-@ carriageway . A new junction was also built for the Cardiff International Sports Village directly from Cogan Spur ; also the Barons Court roundabout was replaced by a signal controlled <unk> . The main contractor for this project was <unk> O 'Rourke Civil Engineering . 
- 
- = = = = Cogan Viaduct = = = = 
- 
- The Cogan Viaduct ( Welsh : Traphont Cogan ) is the most important element of the Cogan Spur as it crosses the River Ely with a central span of 95 metres ( 312 ft ) . In total it has 6 spans ; 40 m ( 130 ft ) , 60 m ( 200 ft ) , 60 m , <unk> , 95 m and 60 m . It is made from a multi @-@ span glued segmental structure of rectangular box sections . In all over 300 sections were used to construct the viaduct , each weighing from 43 to 117 tonnes . The Cogan Viaduct was again designed by South Glamorgan County Council , who won a Concrete Society Commendation in 1989 for the design . 
- 
- = = = Central Link Road ( <unk> ) = = = 
- 
- The £ 8 @.@ 5 million Central Link ( Welsh : Ffordd Gyswllt <unk> ) between the Queen 's Gate Roundabout ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Queen 's Gate Roundabout ) ) and the junction on Adam Street ( 51 @.@ <unk> ° N 3 @.@ <unk> ° W  / 51 @.@ <unk> ; <unk>  ( Adam Street Junction ) ) on the A4160 was opened on 16 February 1989 . When it was first opened it only linked Cardiff city centre with Cardiff Bay . It was not until the Butetown Link was opened on 27 March 1995 that this road started to be used as a link to the motorway network , Penarth and the Vale of Glamorgan . It will not be until the Eastern Bay Link is built , when traffic from the city centre will use it to travel east , that it will be fully used . The length of the <unk> is just 1 @.@ 45 km ( 0 @.@ 90 mi ) and is entirely a two lane dual carriageway with <unk> restrictions . 
- 
- = = <unk> spur roads = = 
- 
- 
- = = = Ely Spur = = = 
- 
- The Ely Spur was planned to run from a new interchange on the Ely Link Road to the Ely Bridge Roundabout on the A48 . It was only 1 @.@ 2 km ( 0 @.@ 75 mi ) in length , but it would have reduced through traffic in the residential area of Ely , namely on <unk> Road West traveling to and from the A48 . But it has now been announced that this proposal has now been officially abandoned . 
- 
- = = = Cardiff Airport Link Road = = = 
- 
- The Cardiff Airport Link Road ( Welsh : Ffordd Gyswllt <unk> <unk> <unk> ) was a proposed spur road off of the A4232 through to Cardiff Airport , when plans for the road were scrapped by the WAG in June 2009 . The current single carriageway <unk> road , from Cardiff to Cardiff Airport is also the main road from Barry to Cardiff and is the main commuter route . 
- One of the 4 proposed schemes involved a new major highway route linking the Ely Link Road ( PDR ) , south of the Culverhouse Cross Interchange , through to the <unk> north of Barry . The estimated cost of this scheme would have been £ <unk> , including the widening of the A4232 . 
- 
- = = Services = = 
- 
- The PDR has two service stations for motorists , one at Cardiff West on the Capel Llanilltern Interchange and the other at Cardiff Gate on the Pentwyn Interchange . The services at Cardiff West includes <unk> petrol , a <unk> , Burger King , Costa Coffee and W H Smith , while the services at Cardiff Gate includes petrol , Burger King , W H Smith and also Coffee <unk> . 
- Traffic Wales is the Welsh Government 's traffic information service , it is a partnership between the Welsh Government , the two Trunk Road Agents ( South Wales <unk> / <unk> & Mid Wales <unk> ) and the <unk> consultancy <unk> . In South Wales the service is managed from the South Wales Traffic Management Centre , also home to <unk> Media 's studio , providing live travel information for the media . The Traffic Wales website has five live traffic <unk> on the Capel Llanilltern – Culverhouse Cross Link Road ( Trunk Road ) and the images are updated every 5 minutes . Traffic Wales also operates a Traffic Information <unk> , motorists can use this telephone service by <unk> an <unk> number , which gives up to date traffic information and travel advice . 
- 
- = = Traffic congestion = = 
- 
- Since the western link roads were built , a number of major developments have been built , such as the Cardiff Bay Retail Park , <unk> , Celtic Gateway , Cardiff International Sports Village that includes the Cardiff International Pool , Cardiff International White Water and Cardiff Arena , which have all generated additional traffic at the Ferry Road Interchange . In addition , the Leckwith development , which includes Cardiff City Stadium , Cardiff International Sports Stadium and the Capital Retail Park with a new <unk> supermarket , have also significantly increased traffic on the Leckwith Interchange . At the Culverhouse Cross Interchange , older developments such as the <unk> Retail Park , <unk> Retail Park and <unk> Retail Park , including <unk> and Marks & Spencer have also significantly increased congestion along with commuter traffic from the Vale of Glamorgan . From 1989 to 1997 there had been an increase in traffic of 78 % and a further increase of 35 % by 2016 is predicted by the Assembly . The Vale of Glamorgan Council also expressed reservations about the new developments of the Cardiff International Sports Village and the Cardiff City Stadium , which includes the Capital Retail Park . On 18 October , 2006 , the council requested that the WAG assess the existing traffic conditions and future growth in traffic on the network . 
- 
- = = Popular culture = = 
- 
- The Queen 's Gate Tunnel was featured in the first ever episode of Torchwood , called " Everything Changes " and was first broadcast by BBC Three on 22 October 2006 . The Grangetown Link was featured in the 2006 Christmas episode of Doctor Who called " The Runaway Bride " . It was first broadcast by BBC One on 25 December 2006 . 
- 
- 
- = Development of Fez = 
- 
- The high @-@ profile and protracted five @-@ year development of Fez led to its status as an " underdog darling of the indie game scene " . The 2012 puzzle platform game built around rotating between four 2D views of a 3D space was developed by indie developer Polytron Corporation and published by Polytron , Trapdoor , and Microsoft Studios . Over the course of the game 's development , Fez designer and Polytron founder Phil Fish received celebrity for his outspoken public persona and prominence in the 2012 documentary Indie Game : The Movie , which followed the game 's final stages of development and Polytron 's related legal issues . The game was released to critical acclaim as an Xbox Live Arcade timed exclusive , and was later ported to other platforms . It had sold one million copies by the end of 2013 . 
- Fish and Shawn McGrath collaborated on a puzzle game that became Fez . When McGrath left the project due to creative differences , Fish , the game 's artist , pursued a platform game direction with <unk> Bédard , the game 's programmer , who wrote the game 's level editor and game engine from scratch . Levels were built in 3D by <unk> surfaces with <unk> @-@ created textures . Bédard and Fish were joined by three different animators and other collaborators . The game was first announced in June 2007 and won an award at the 2008 Independent Games Festival and entered the public spotlight . Fish created a studio , Polytron Corporation , and was later aided by nearby developer @-@ publisher Trapdoor when Polytron ran out of money . Fez won several more <unk> awards , including the 2012 <unk> <unk> Grand Prize . 
- 
- = = History = = 
- 
- Fez 's development cycle developed a reputation for its protracted five @-@ year length and public exposure . Nathan Grayson of <unk> likened the game 's rocky development process to " an indie Duke <unk> Forever " . <unk> reviewer Arthur <unk> wrote that the game was an " underdog darling of the indie game scene " for four years prior to its release . The game 's designer , Phil Fish , became renowned in a way unusual for game developers due to his prominence in Indie Game : The Movie , which released in 2012 . While the game was released to wide acclaim , Fish himself became known for his outspoken and <unk> public persona . 
- The game that became Fez began in a collaboration between Montreal @-@ based Phil Fish and Toronto @-@ based Shawn McGrath on McGrath 's idea for a puzzle game : a four @-@ sided 3D space with each side in 2D , similar to Fish 's 3D <unk> ( <unk> ) as incorporated into Fez . The entirety of Fez 's design , lore , and art descends from this game mechanic . Fish provided the project 's art and credited his influence to Shigeru Miyamoto and <unk> Miyazaki . Fish and McGrath 's partnership <unk> due to creative differences , as Fish wanted to create a platform game . Fish continued to work on the game in his spare time and announced his search for a programmer on <unk> , and the first person to reply , <unk> Bédard , became lead programmer . They were both the same age and living in Montreal . Though Bédard had some <unk> experience in 3D graphics and was studying computer science , Fez was his first professional game development project . His first task was to write the level editor and game engine . 
- Fez was first announced in July 2007 on The Independent Gaming Source . A trailer released in October 2007 convinced Jason DeGroot to join the development team as a producer . DeGroot , also known as " <unk> " , first met Fish at a 2006 E3 party , and started work on the game 's soundtrack and sound effects . The soundtrack was ultimately composed by Rich " <unk> " <unk> and the sound effects by Brandon <unk> . The game was nominated for two awards at the 2008 Independent Games Festival ( <unk> ) at the Game Developers Conference ( <unk> ) : Excellence in Visual Art and the Design Innovation Award . As Fez was a side project , Fish was employed full @-@ time at Artificial Mind and Movement in Montreal , where he worked on a tie @-@ in game for a film . He was not permitted time off to attend the event and thus decided to quit his job in January 2008 — a moment he later marked as " when I became indie " . The game won " Excellence in Visual Art " , and created a surge of public interest in the game concurrent to a similar swell of interest in indie game developers . Fish received a Canadian government loan to open Polytron Corporation as a startup company and began full @-@ time work on Fez . In July 2009 , Polytron announced a release for Xbox Live Arcade in early 2010 . Polytron and Microsoft agreed to release Fez as an Xbox exclusive , a deal Fish later recalled as sensible . Fish designed the game as " a console game , not a PC game " , and felt that the way he intended the game to be experienced — with a controller on a <unk> — was " part of the medium " . Polytron ruled out a <unk> release due to problems Fish had with their platform and developer options . 
- Development continued with a more experimental ethos until the company began to run out of capital . The Canadian government loan that had funded Polytron 's <unk> phase was not renewed for their production phase . They also lost funding from the organization that preceded the Indie Fund as Polytron 's producer left the company . Fish borrowed money from friends and family for three months to keep the company open . In dire <unk> , he considered <unk> the project . In March 2011 , the nearby <unk> developer @-@ publisher Trapdoor offered to help Polytron , having just signed a deal with Electronic Arts to publish their own game , <unk> . Trapdoor assisted with Polytron 's finances and operations and offered to treat them as part of their company and let them keep their intellectual property rights in exchange for a portion of Fez 's earnings . Fish felt that partnership rescued the game . 
- Fish is shown preparing for Fez 's March 2011 <unk> East booth in the 2012 documentary film Indie Game : The Movie , which chronicles the stories of several indie developers at various stages of their games ' development cycles . As a subplot , the film presents Fish amidst a legal dispute with a former business partner that <unk> the game 's future . The partner , believed to be Jason DeGroot , is portrayed negatively and does not participate <unk> . The film 's end credits were later corrected to reflect that Fish 's business partner was not asked for input . Game Informer called Fish the film 's " most memorable developer " , and Rock , Paper , <unk> wrote that Fish is portrayed as melodramatic , theatrical , and neurotic , in a way that <unk> his outspoken public perception . Eurogamer said that the part where Fish resolves to kill himself if he does not release his game is " the film 's most startling moment " . 
- Fez won the <unk> Choice Award at the September 2011 Fantastic Arcade , Best in Show and Best Story / World Design at the October 2011 <unk> , and the <unk> <unk> Grand Prize at the 2012 <unk> Independent Games Festival . It was also a 2011 Penny Arcade Expo " <unk> 10 " selection . Fez was displayed in its entirety in a secluded lounge room at the October 2011 <unk> festival in Nottingham , England . Fish considered the demo their most fruitful yet . Fish told a <unk> reporter that he had received positive feedback from Independent Games Festival Chairman Brandon Boyer and <unk> designer Jonathan Blow . Near the end of development , Fish felt " burnt out " and that his personal health had suffered . The final game included almost none of the original work from the first two years of development . After several delays , Fez was submitted for certification in February 2012 . 
- 
- = = = Release = = = 
- 
- Fez was released on April 13 , 2012 and sold 200 @,@ 000 copies in its <unk> exclusivity to the Xbox Live Arcade platform . Several months later , Polytron became embroiled in a high @-@ profile dispute with Microsoft over the cost of <unk> the game . Polytron had released a fix that resolved many of the game 's technical issues but introduced another that corrupted the saved games for about one percent of users . They withdrew the patch , but found Microsoft 's fee for subsequent patch releases <unk> , and chose to reinstate the withdrawn patch as their most <unk> option . Polytron drew ire for the decision , which raised awareness for the business needs of indie developers . In July 2013 , a year later , Microsoft announced that they no longer charged for patches , and Fish tweeted that Polytron 's patch would take " a couple of months " . Speaking in <unk> of the release , Fish " fiercely criticized " Fez co @-@ publisher Microsoft Games Studios for <unk> the game 's release . Fish cited a lack of promotion and publicity , and poor advertising of the game on Microsoft 's digital market . 
- In March 2013 , Fish announced a May 1 , 2013 release for the game 's PC port , and opened <unk> on <unk> and Steam . The game 's OS X and Linux ports debuted in the pay @-@ what @-@ you @-@ want Humble Indie <unk> 9 on September 11 , 2013 . Polytron announced ports for PlayStation 4 , PlayStation 3 , and PlayStation Vita in August 2013 as in development through <unk> , which were released on March 25 , 2014 . The PlayStation releases include cross @-@ console support for " cross @-@ buy " ( where one digital purchase allows access across multiple consoles ) and " cross @-@ save " ( game save sharing between consoles ) , as well as support for 3D <unk> , the <unk> 4 controller 's decorative <unk> , and graphical upgrades due to the full port into the C + + programming language . Ports for <unk> and iOS were also announced . Fish announced eventual ports for " ' pretty much ' every platform " but the Nintendo 3DS . 
- Bédard planned to leave Polytron after finishing Fez to experience work with a full development team , but stayed to port the Windows release before joining Toronto 's <unk> Games . He credited the game 's long development cycle to his own inexperience in game development ( compounded by the team 's small size and difficulty in setting reasonable milestones ) , the game 's scope , and Fish 's <unk> . Fish had hoped that players would discuss Fez 's nuances online after the game 's release . Players collaborated online for a week to solve the final " <unk> " puzzle by brute force . Ars Technica described the apparent end to the game 's harder puzzles as " <unk> " , but Fish told Eurogamer in March 2013 that hidden in @-@ game secrets remain to be found . 
- More than three years after its digital launch , Fez received a physical release designed by Fish and limited to a signed edition of 500 in December 2015 . The deluxe package included the soundtrack and a stylized red notebook with gold foil <unk> . 
- 
- = = Design = = 
- 
- When Bédard joined the project , the game focused on the 2D – 3D mechanic and did not yet have open world ambitions . He coded the game in Microsoft Visual C # Express and <unk> Game Studio Express . His first task , the level editor <unk> , was coded from scratch in <unk> and inspired by <unk> . Bédard also wrote the game engine , <unk> Technology , which turns 2D tiles ( " <unk> " ) into sides of a 3D <unk> pixel . The engine tracks player @-@ character Gomez in 3D space even though the game behaves as a 2D platformer . Bédard also built the game to resolve collisions when converting between 3D and 2D space . 
- Fish created pixel art in <unk> for each <unk> side ( " <unk> " ) of the 3D <unk> that Bédard 's custom software compiled into 3D game assets , which Fish would <unk> as surfaces in <unk> to build levels . Fish found the level design process " overwhelming " , and Bédard has said he was relieved that it was not his job . Fish compared his design process to playing with <unk> blocks , and planned the more involved levels in graph paper to first visualize the 2D views before building the levels in the 3D software . The levels and puzzles were not <unk> in a design document , and many of the drafts levels scrapped in 2008 resurfaced to be used later in the production process . So as to fit the rotation mechanic , the levels were made tall instead of wide , and the first part of the game was designed to <unk> the player to 2D controls before introducing the 3D element . As they worked , Fish first proposed ideas that Bédard would implement . The two would then discuss and fine @-@ tune the addition — they worked well together . 
- Fish describes the game 's changes during development as " organic " — they tested different kinds of levels and replicated the types of in @-@ game exploration that the team appreciated most . It came to adopt <unk> mechanics , with " secret passages , <unk> gates , and <unk> codes " . Fish cited <unk> as another <unk> and compared its open world , <unk> narrative , and " <unk> <unk> " to Fez 's own alphabet , numeric system , and an " almost <unk> hard to get " " second set of <unk> " . Fish originally fought against having an in @-@ game map because he wanted players to draft their own . After attempting to do so himself , he changed his mind . Fish later called the in @-@ game map " probably one of the weakest aspects of the game " . Fish also fought against including the navigational assistant , Dot , but later felt that the addition was successful and a positive contribution to the game 's mythology . The <unk> itself , Fish described as an " ancient symbol of understanding the third dimension " . Fez had three different animators through its development : Paul Robertson of Scott Pilgrim vs. the World : The Game , who did the game 's animals and some of Gomez 's animations , Adam <unk> of <unk> , and Graham <unk> , who did some character animations . 
- The game 's mechanics were inspired by the Nintendo Entertainment System games Fish played in his youth , particularly Super Mario and The Legend of <unk> . Fish cited <unk> <unk> 's Ico as the game 's third inspiration , and he sought to emulate its feeling of nostalgic and isolated loneliness . Fish also sought to emulate <unk> 's " design by <unk> " philosophy , where the Ico development team would periodically remove parts of the game so as to leave only what was essential to their vision . In this way , ideas like player health and object weight puzzles were gradually struck from Fez . Fish made a personal challenge of designing a game without relying on " established mechanics " . As such , Fez was always a peaceful game and there was never an enemy coded into the game . So as to better emulate <unk> Miyazaki 's signature " open blue sky " , " feel @-@ good " atmosphere , Fish watched all of the director 's films one weekend early in the development cycle . 
- 
- 
- = Hugh Walpole = 
- 
- Sir Hugh Seymour Walpole , CBE ( 13 March 1884 – 1 June 1941 ) was an English novelist . He was the son of an Anglican clergyman , intended for a career in the church but drawn instead to writing . Among those who encouraged him were the authors Henry James and Arnold Bennett . His skill at scene @-@ setting and vivid plots , as well as his high profile as a lecturer , brought him a large readership in the United Kingdom and North America . He was a best @-@ selling author in the 1920s and 1930s but has been largely neglected since his death . 
- After his first novel , The Wooden Horse , in 1909 , Walpole wrote <unk> , producing at least one book every year . He was a spontaneous story @-@ teller , writing quickly to get all his ideas on paper , seldom revising . His first novel to achieve major success was his third , Mr Perrin and Mr Traill , a <unk> story of a fatal clash between two <unk> . During the First World War he served in the Red Cross on the Russian @-@ Austrian front , and worked in British propaganda in Petrograd and London . In the 1920s and 1930s Walpole was much in demand not only as a novelist but also as a lecturer on literature , making four exceptionally well @-@ paid tours of North America . 
- As a gay man at a time when homosexual practices were illegal in Britain , Walpole conducted a succession of intense but discreet relationships with other men , and was for much of his life in search of what he saw as " the perfect friend " . He eventually found one , a married policeman , with whom he settled in the English Lake District . Having as a young man eagerly sought the support of established authors , he was in his later years a generous sponsor of many younger writers . He was a patron of the visual arts and bequeathed a substantial legacy of paintings to the Tate Gallery and other British institutions . 
- Walpole 's output was large and varied . Between 1909 and 1941 he wrote thirty @-@ six novels , five volumes of short stories , two original plays and three volumes of memoirs . His range included disturbing studies of the <unk> , children 's stories and historical fiction , most notably his Herries Chronicle series , set in the Lake District . He worked in Hollywood writing scenarios for two Metro @-@ Goldwyn @-@ Mayer films in the 1930s , and played a cameo in the 1935 version of David Copperfield . 
- 
- = = Biography = = 
- 
- 
- = = = Early years = = = 
- 
- Walpole was born in Auckland , New Zealand , the eldest of three children of the <unk> Somerset Walpole and his wife , Mildred Helen , née <unk> ( 1854 – 1925 ) . Somerset Walpole had been an assistant to the Bishop of Truro , Edward Benson , from 1877 until 1882 , when he was offered the <unk> of St Mary 's Pro @-@ Cathedral , Auckland ; on Benson 's advice he accepted . 
- Mildred Walpole found it hard to settle in New Zealand , and something of her restlessness and insecurity affected the character of her eldest child . In 1889 , two years after the birth of the couple 's daughter , <unk> ( " Dorothy " ) , Somerset Walpole accepted a prominent and well @-@ paid academic post at the General <unk> <unk> , New York . Robert ( " Robin " ) , the third of the couple 's children , was born in New York in 1892 . Hugh and Dorothy were taught by a governess until the middle of 1893 , when the parents decided that he needed an English education . 
- Walpole was sent to England , where according to his biographer Rupert Hart @-@ Davis the next ten years were the <unk> time of Walpole 's life . He first attended a preparatory school in Truro . Though he missed his family and felt lonely he was reasonably happy , but he moved to Sir William <unk> 's Grammar School in Marlow in 1895 , where he was bullied , frightened and <unk> . He later said , " The food was inadequate , the morality was ' twisted ' , and Terror – sheer , stark <unk> Terror – <unk> down every one of its passages ... The excessive desire to be loved that has always played so enormous a part in my life was bred largely , I think , from the neglect I suffered there " . 
- In 1896 Somerset Walpole discovered his son 's horror of the Marlow school and he moved him to the King 's School , Canterbury . For two years he was a fairly content , though undistinguished , pupil there . In 1897 Walpole senior was appointed principal of Bede College , Durham , and Hugh was moved again , to be a day boy for four years at Durham School . He found that day boys were looked down on by <unk> , and that Bede College was the subject of <unk> within the university . His sense of isolation increased . He continually took refuge in the local library , where he read all the novels of Jane Austen , Henry Fielding , Scott and Dickens and many of the works of Trollope , <unk> Collins and Henry Kingsley . Walpole wrote in 1924 : 
- I grew up ... <unk> , ugly , abnormally sensitive , and excessively <unk> . No one liked me – not masters , boys , friends of the family , nor relations who came to stay ; and I do not in the least wonder at it . I was untidy , <unk> , excessively <unk> . I believed that I was profoundly misunderstood , that people took my pale and <unk> countenance for the mirror of my soul , that I had marvellous things of interest in me that would one day be discovered . 
- Though Walpole was no admirer of the schools he had attended there , the cathedral cities of Truro , Canterbury and Durham made a strong impression on him . He drew on aspects of them for his fictional cathedral city of <unk> in <unk> , the setting of many of his later books . Walpole 's memories of his time at Canterbury grew <unk> over the years ; it was the only school he mentioned in his Who 's Who entry , 
- 
- = = = Cambridge , Liverpool and teaching = = = 
- 
- From 1903 to 1906 Walpole studied history at Emmanuel College , Cambridge . While there he had his first work published , the critical essay " Two <unk> Heroes " , which was printed in the college magazine in autumn 1905 . As an undergraduate he met and fell under the spell of A C Benson , formerly a greatly loved master at Eton , and by this time a don at Magdalene College . Walpole 's religious beliefs , hitherto an <unk> part of his life , were fading , and Benson helped him through that personal crisis . Walpole was also attempting to cope with his homosexual feelings , which for a while focused on Benson , who recorded in his diary in 1906 an unexpected outburst by his young admirer : " [ H ] e broke out rather eagerly into <unk> – He cared for me more than anyone in the world . I could not believe it ... It is extraordinarily touching . ... It is quite right that he should believe all this <unk> ; it is quite right that I should know that it will not last ... I tried to say this as <unk> as I could ... " 
- Benson gently declined Walpole 's advances . They remained friends , but Walpole , rebuffed in his " excessive desire to be loved " , turned the full force of his <unk> elsewhere , and the relationship with Benson became less important to him . Less than two years later Benson 's diary entry on Walpole 's subsequent social career reveals his thoughts on his protégé 's progress : 
- He seems to have conquered <unk> completely . He spends his Sundays in long walks with H G Wells . He <unk> every week with Max Beerbohm and R Ross ... and this has <unk> a not very clever young man of 23 . Am I a little jealous ? – no , I don 't think so . But I am a little <unk> ... I do not see any sign of intellectual power or perception or grasp or <unk> in his work or himself . ... I should call him <unk> <unk> . He does not , for instance , see what may <unk> or hurt or annoy people . I think he is rather <unk> – though he is himself very sensitive . The strong points about him are his curiosity , his vitality , his <unk> , and the emotional fervour of his affections . But he seems to me in no way likely to be great as an artist . 
- With Benson 's help , Walpole had come to terms with the loss of his faith . Somerset Walpole , himself the son of an Anglican priest , hoped that his eldest son would follow him into the ministry . Walpole was too concerned for his father 's feelings to tell him he was no longer a believer , and on graduation from Cambridge in 1906 he took a post as a lay <unk> at the <unk> Mission to Seamen in Liverpool . He described that as one of the " greatest failures of my life ... The Mission to Seamen was , and is , a splendid institution ... but it needs men of a certain type to carry it through and I was not of that type . " The head of the mission reprimanded him for lack of commitment to his work , and Walpole resigned after six months . 
- From April to July 1907 Walpole was in Germany , <unk> the children of the popular author Elizabeth von <unk> . In 1908 he taught French at Epsom College . His brief experience of teaching is reflected in his third novel , Mr Perrin and Mr Traill . As well as the clerical forebears , Walpole had notable authors in his family tree : on his father 's side , Horace Walpole the novelist and letter writer , and on his mother 's Richard Harris <unk> , author of The <unk> Legends . It was as an author that Walpole felt <unk> to make his career . He moved to London and found work as a book reviewer for The Standard , writing fiction in his spare time . He had by this time recognised <unk> that he was homosexual . His encounters were necessarily discreet , as such activities were illegal in Britain , and remained so throughout his lifetime . He was constantly searching for " the perfect friend " ; an early candidate was the stage designer Percy Anderson , to whom he was intimately attached for some time from 1910 onwards . 
- 
- = = = Early literary career = = = 
- 
- A C Benson was a friend of Henry James , to whom Walpole wrote a fan letter late in 1908 , with Benson 's encouragement . A correspondence ensued and in February 1909 James invited Walpole to lunch at the Reform Club in London . They developed a close friendship , described by James 's biographer Leon <unk> as resembling a father and son relationship in some , but not all , respects . James was greatly taken with the young Walpole , though clear @-@ eyed about the deficiencies in the <unk> and craftsmanship of his protégé 's early efforts . According to Somerset Maugham , Walpole made a sexual proposition to James , who was too <unk> to respond . Nevertheless , in their correspondence the older man 's devotion was <unk> in extravagant terms . 
- Walpole published his first novel , The Wooden Horse , in 1909 . It told of a <unk> and <unk> English family shaken up by the return of one of its members from a less <unk> life in New Zealand . The book received good reviews but barely repaid the cost of having it <unk> . His first commercial success was Mr Perrin and Mr Traill , published in 1911 . The novelist and biographer Michael Sadleir writes that though some of the six novels Walpole wrote between 1909 and 1914 are of interest as examples of the author 's developing style , it is Mr Perrin and Mr Traill that deserves to be remembered for its own sake . The book , <unk> " a <unk> @-@ comedy " , is a psychological study of a deadly clash between two <unk> , one an ageing failure and the other a young , attractive idealist . In the view of Hart @-@ Davis , Walpole only once recaptured " the fresh , clear cut realism " of this book , and Walpole himself , looking back on his work in the 1930s , felt that of all his books to date , it was the <unk> . The Observer gave the book a favourable review : " The slow growth of the poison within [ Perrin ] is traced with wonderful skill and sympathy ... one feels throughout these pages a sense of intolerable tension , of impending disaster " ; The Manchester Guardian was less enthusiastic , praising the scene @-@ setting but calling the story " an <unk> melodrama " . The San Francisco Chronicle praised its " technical excellence , imagination and beauty – Walpole at his best . " Arnold Bennett , a well @-@ established novelist seventeen years Walpole 's senior , admired the book , and befriended the young author , regularly <unk> , encouraging , sometimes mocking him into improving his prose , characters and narratives . 
- The Guardian reviewer observed that the setting of Mr Perrin and Mr Traill – a second @-@ rate public school – was clearly drawn from life , as indeed it was . The boys of Epsom College were delighted with the thinly disguised version of their school , but the college authorities were not , and Walpole was persona non <unk> at Epsom for many years . This was of no practical consequence , as he had no intention of returning to the teaching profession , but it was an early illustration of his capacity , noted by Benson , for <unk> giving offence , though being <unk> to criticism himself . 
- In early 1914 James wrote an article for The Times Literary Supplement surveying the younger generation of British novelists and comparing them with their eminent elder contemporaries . In the latter category James put Bennett , Joseph Conrad , John <unk> , Maurice <unk> and H G Wells . The four new authors on whom he focused were Walpole , Gilbert Cannan , Compton Mackenzie and D H Lawrence . It was a very lengthy article , to the extent that it had to be spread across two issues of the Supplement in March and April 1914 . James said that agreeing to write it had been " an <unk> step " , but from Walpole 's point of view it was highly satisfactory : one of the greatest living authors had publicly ranked him among the finest young British novelists . 
- 
- = = = First World War = = = 
- 
- As war approached , Walpole realised that his poor eyesight would <unk> him from serving in the armed forces . He volunteered to join the police , but was turned down ; he then accepted a <unk> appointment based in Moscow , reporting for The Saturday Review and The Daily Mail . He was allowed to visit the front in Poland , but his dispatches from Moscow ( and later from Petrograd , which he preferred ) were not enough to stop hostile comments at home that he was not doing his bit for the war effort . Henry James was so incensed at one such remark by a prominent London hostess that he stormed out of her house and wrote to Walpole suggesting that he should return to England . Walpole replied in great excitement that he had just been appointed as a Russian officer , in the Sanitar : 
- The " Sanitar " is the part of the Red Cross that does the rough work at the front , carrying men out of the trenches , helping at the base hospitals in every sort of way , doing every kind of rough job . They are an absolutely official body and I shall be one of the few ( half @-@ dozen ) Englishmen in the world wearing Russian uniform . 
- While in training for the Sanitar , Walpole devoted his leisure hours to gaining a reasonable fluency in the Russian language , and to his first full @-@ length work of non @-@ fiction , a literary biography of Joseph Conrad . In the summer of 1915 he worked on the Austrian @-@ Russian front , assisting at operations in field hospitals and <unk> the dead and wounded from the battlefield . Occasionally he found time to write brief letters home ; he told Bennett , " A battle is an amazing mixture of hell and a family picnic – not as frightening as the dentist , but absorbing , sometimes thrilling like football , sometimes dull like church , and sometimes simply physically <unk> like bad fish . <unk> dead afterwards is worst of all . " When <unk> he comforted himself with the thought , " This is not so bad as it was at Marlow " . 
- During an engagement early in June 1915 Walpole single @-@ <unk> rescued a wounded soldier ; his Russian comrades refused to help and Walpole carried one end of a stretcher and dragged the man to safety . For this he was awarded the Cross of Saint George ; General <unk> presented him with the medal in August . After his tour of duty Walpole returned to Petrograd . Among the city 's attractions for him was the presence of <unk> <unk> , a painter with whom he had formed a close relationship . He remained there until October 1915 , when he returned to England . He visited his family , stayed with Percy Anderson in London , telephoned Henry James in Rye , and retreated to a cottage he had bought in Cornwall . In January 1916 he was asked by the Foreign Office to return to Petrograd . Russians were being subjected to highly effective German propaganda . The writer Arthur Ransome , Petrograd correspondent of The Daily News , had successfully lobbied for the establishment of a bureau to counter the German efforts , and the British ambassador , Sir George Buchanan , wanted Walpole to take charge . 
- Before he left for Petrograd , Walpole 's novel The Dark Forest was published . It drew on his experiences in Russia , and was more <unk> than much of his earlier fiction . Reviews were highly favourable ; The Daily Telegraph commented on " a high level of imaginative vision ... reveals capacity and powers in the author which we had hardly suspected before . " 
- Walpole returned to Petrograd in February 1916 . He moved into <unk> 's flat , and his Anglo @-@ Russian Propaganda Bureau began work . The following month he suffered a personal blow : he recorded in his diary for 13 March 1916 , " Thirty two to @-@ day ! Should have been a happy day but was completely <unk> for me by reading in the papers of Henry James ' death . This was a terrible shock to me . " Walpole remained at the bureau for the rest of 1916 and most of 1917 , witnessing the February Revolution . He wrote an official report on events for the Foreign Office , and also absorbed ideas for his fiction . In addition to the first of his popular " Jeremy " novels , written in his spare time from the bureau , he began work on the second of his Russian @-@ themed books , The Secret City . Sadleir writes that this novel and The Dark Forest " take a high place among his works , on account of their intuitive understanding of an alien mentality and the <unk> of their narrative power . " The book won the inaugural James <unk> Black Memorial Prize for fiction . 
- By late 1917 it was clear to Walpole and to the British authorities that there was little advantage in keeping him in Russia . On 7 November he left , missing the <unk> Revolution , which began on that day . He was appointed to a post at the Foreign Office in its Department of Information , headed by John Buchan . Soon after returning he volunteered for the British army , but , as expected , failed the necessary medical examination because of his poor sight . He continued to work in British propaganda when the department was reconstituted under Lord <unk> in April 1918 , and remained there for the rest of the war and beyond , resigning in February 1919 . Little is known about what he wrote for the department , as most of its records were destroyed after the war , but he noted in his diary that he had written the department 's official report to the War Cabinet : " a <unk> job – the worst I 've ever attempted " . For his wartime work he was awarded the CBE in 1918 . 
- 
- = = = Post @-@ war and 1920s = = = 
- 
- Walpole remained prolific in the post @-@ war years , and began a parallel and highly <unk> career as a lecturer in literature . At the instigation of his American publisher , George Doran , he made his first lecture tour of the US in 1919 , receiving an enthusiastic welcome wherever he went . What Sadleir describes as Walpole 's " <unk> and attractive appearance , his complete lack of <unk> , his exciting fluency as a speaker [ and ] his obvious and genuine liking for his hosts " combined to win him a large American following . The success of his talks led to increases in his lecturing fees , greatly enhanced sales of his books , and large sums from American publishers anxious to print his latest fiction . He was a <unk> quick writer who seldom revised , but pressed on , keen to get his ideas down on paper . His main British publishers , Macmillan , found it <unk> to appoint a senior member of staff to edit his manuscripts , <unk> spelling , punctuation , inconsistencies and errors of historical fact . His fluency enabled him to fulfil between tours a contract from The <unk> Review for ten short stories at the remarkable sum of $ 1 @,@ 350 apiece . 
- One of Walpole 's major novels of the early post @-@ war period was The Cathedral , which unlike much of his fiction was not <unk> off but worked on across four years , beginning in 1918 . The story of an arrogant 19th @-@ century archdeacon in conflict with other clergy and laity was certain to bring comparisons with Trollope 's <unk> Towers ( The Manchester Guardian 's review was headed " <unk> Towers " ) , but unlike the earlier work , The Cathedral is wholly <unk> . The <unk> Archdeacon Brandon is driven to domestic despair , professional defeat and sudden death . The reviewer Ivor Brown commented that Walpole had earlier charmed many with his cheerful tales of Mayfair , but that in this novel he showed a greater side to his art : " This is a book with little happiness about it , but its stark strength is undeniable . The Cathedral is realism , profound in its philosophy and delicate in its thread . " The Illustrated London News said , " No former novelist has seized quite so powerfully upon the cathedral fabric and made it a living character in the drama , an <unk> individuality at once benign and <unk> . ... The Cathedral is a great book . " 
- Walpole was a keen music lover and when in 1920 he heard a new tenor at the <unk> he was much impressed and sought him out . <unk> Melchior became one of the most important friendships of his life , and Walpole did much to foster the singer 's budding career . Wagner 's son Siegfried engaged Melchior for the Bayreuth Festival in 1924 and succeeding years . Walpole attended , and met Adolf Hitler , then recently released from prison after an attempted putsch . Hitler was a protégé of Siegfried 's wife <unk> , and was known in Bayreuth as " one of <unk> 's lame ducks . " Walpole later admitted that he had both despised and liked him – " both emotions that time has proved I was wrong to indulge " . This and future visits to Bayreuth were complicated by the fact that <unk> Wagner fell in love with Walpole , and attached herself so firmly to him that rumours began to spread . 
- In 1924 Walpole moved into a house near Keswick in the Lake District . His large income enabled him to maintain his London flat in Piccadilly , but Brackenburn , on the slopes of <unk> overlooking <unk> , was his main home for the rest of his life . He was quickly made welcome by local residents , and the scenery and atmosphere of the Lake District often found their way into his fiction . The critic James Agate commented that one might think from some of Walpole 's stories that their author had created the English Lakes , but that he was probably only consulted about them . At the end of 1924 Walpole met Harold Cheevers , who soon became his friend and companion and remained so for the rest of Walpole 's life . In Hart @-@ Davis 's words , he came nearer than any other human being to Walpole 's long @-@ sought conception of a perfect friend . Cheevers , a policeman , with a wife and two children , left the police force and entered Walpole 's service as his <unk> . Walpole trusted him completely , and gave him extensive control over his affairs . Whether Walpole was at Brackenburn or Piccadilly , Cheevers was almost always with him , and often accompanied him on overseas trips . Walpole provided a house in Hampstead for Cheevers and his family . 
- During the mid @-@ twenties Walpole produced two of his best @-@ known novels in the <unk> vein that he drew on from time to time , exploring the fascination of fear and cruelty . The Old Ladies ( 1924 ) is a study of a timid elderly spinster exploited and eventually frightened to death by a predatory widow . Portrait of a Man with Red Hair ( 1925 ) depicts the <unk> influence of a <unk> , insane father on his family and others . Walpole described it to his fellow author Frank <unk> as " a simple <unk> which it has <unk> me like anything to write , and won 't bore you to read . " In contrast he continued a series of stories for children , begun in 1919 with Jeremy , taking the young hero 's story forward with Jeremy and Hamlet ( the latter being the boy 's dog ) in 1923 , and Jeremy at <unk> in 1927 . Sadleir , writing in the 1950s , suggests that " the most real Walpole of all – because the most <unk> , kindly , and understanding friend – is the Walpole of the Jeremy trilogy . " Of his other novels of the 1920s <unk> ( 1928 ) , his first attempt at a full @-@ length love story , portrays a clash between <unk> and <unk> : his own sympathies , though not spelled out , were clearly with the <unk> . 
- 
- = = = 1930 – 41 = = = 
- 
- By the 1930s , though his public success remained considerable , many literary critics saw Walpole as outdated . His reputation in literary circles took a blow from a malicious caricature in Somerset Maugham 's 1930 novel <unk> and <unk> : the character <unk> <unk> , a superficial novelist of more <unk> ambition than literary talent , was widely taken to be based on Walpole . In the same year Walpole wrote possibly his best @-@ known work , Rogue Herries , a historical novel set in the Lake District . It was well @-@ received : The Daily Mail considered it " not only a profound study of human character , but a subtle and intimate biography of a place . " He followed it with three sequels ; all four novels were published in a single volume as The Herries Chronicle . 
- In 1934 Walpole accepted an invitation from Metro @-@ Goldwyn @-@ Mayer studios to go to Hollywood to write the scenario for a film adaptation of David Copperfield . He enjoyed many aspects of life in Hollywood , but as one who rarely revised any of his own work he found it tedious to produce sixth and seventh drafts at the behest of the studio . He enjoyed his brief change of role from writer to bit @-@ part player : in the film he played the <unk> of <unk> delivering a boring sermon that sends David to sleep . Agate was doubtful of the wisdom of this : " Does not Hugh see that to bring a well @-@ known character from real life into an imaginary sequence of events is to destroy the reality of that imaginary sequence ? " Nevertheless , Walpole 's performance was a success . He improvised the sermon ; the producer , David O <unk> , <unk> called for retake after retake to try to make him dry up , but Walpole <unk> delivered a different <unk> address each time . 
- The critical and commercial success of the film of David Copperfield led to an invitation to return to Hollywood in 1936 . When he got there he found that the studio executives had no idea which films they wanted him to work on , and he had eight weeks of highly paid leisure , during which he wrote a short story and worked on a novel . He was eventually asked to write the scenario for Little Lord <unk> , which he enjoyed doing . He spent most of his fees on paintings , forgetting to keep enough money to pay US tax on his earnings . He <unk> his American funds with a lecture tour – his last – in late 1936 . 
- In 1937 Walpole was offered a knighthood . He accepted , though <unk> to his diary that he could not think of a good novelist since Walter Scott who had done so . " Kipling , Hardy , <unk> all refused . But I 'm not of their class , and range with Doyle , Anthony Hope and such . ... Besides I shall like being a knight . " 
- Walpole 's taste for adventure did not <unk> in his last years . In 1939 he was commissioned to report for William Randolph <unk> 's newspapers on the funeral in Rome of Pope Pius XI , the conclave to elect his successor , and the subsequent coronation . A fellow correspondent was Tom <unk> , whose memoirs tell of a lunch à <unk> at which Walpole arrived flushed with excitement from a sexual encounter that morning with an attendant in the <unk> Palace . In the weeks between the funeral and Pius XII 's election Walpole , with his customary fluency , wrote much of his book Roman Fountain , a mixture of fact and fiction about the city . This was his last overseas visit . 
- After the outbreak of the Second World War Walpole remained in England , dividing his time between London and Keswick , and continuing to write with his usual <unk> . He completed a fifth novel in the Herries series and began work on a sixth . His health was undermined by diabetes . He <unk> himself at the opening of Keswick 's fund @-@ raising " War Weapons Week " in May 1941 , making a speech after taking part in a lengthy march , and died of a heart attack at Brackenburn , aged 57 . He is buried in St John 's churchyard in Keswick . 
- 
- = = = Legacy = = = 
- 
- Walpole was a keen and discerning collector of art . Sir Kenneth Clark called him " one of the three or four real patrons of art in this country , and of that small body he was perhaps the most generous and the most <unk> . " He left fourteen works to the Tate Gallery and <unk> Museum , including paintings by <unk> , <unk> , Augustus John , <unk> and <unk> . 
- Other artists represented in Walpole 's collection were Epstein , Picasso , <unk> , <unk> and <unk> . After his death the finest works in his collection , other than those bequeathed , were exhibited in London during April and May 1945 ; the exhibition also included works by Constable , Turner and <unk> . 
- Sadleir notes how Walpole 's considerable income enabled him to indulge not only his love of art and of old books and manuscripts , but also <unk> , particularly towards younger writers . Although Walpole enjoyed the <unk> , he was secretive about his many acts of generosity to younger writers , with both encouragement and financial help . After his death some idea of the scale of his generosity was discovered . <unk> <unk> commented , " I don 't think there was any younger writer of any worth who has not at one time or another received kindness of an active kind , and at a crucial moment , from Hugh " . Hart @-@ Davis lists thirty @-@ eight authors from whom letters of gratitude were found among Walpole 's correspondence ; Sadleir writes of Walpole 's " generous kindness to literary <unk> and to writers fallen on evil days ... by immediate financial assistance , by <unk> freely supplied or by collaboration volunteered , by introductions and recommendations to likely publishers , Walpole relieved the <unk> of authorship to a degree which will never be fully known . " Agate , though himself the recipient of Walpole 's generosity on occasion , thought it sometimes went too far : " Mr Walpole 's large @-@ <unk> gets him into all kinds of trouble . He is an <unk> patter . He <unk> on the back young men whom <unk> critics would knock down , because even in fantastic incompetence he perceives the good intention . No art or artist is safe from Mr Walpole 's <unk> " . 
- In his adopted home of Keswick a section of the town museum was dedicated to Walpole 's memory in 1949 , with manuscripts , correspondence , paintings and sculpture from Brackenburn , donated by his sister and brother . 
- 
- = = Works = = 
- 
- Walpole 's books cover a wide range . His fiction includes short stories , <unk> ( Mr Perrin and Mr Traill , 1911 , and the Jeremy trilogy ) that <unk> into the psychology of <unk> ; gothic horror novels ( Portrait of a Man with Red Hair , 1925 , and The Killer and The <unk> , 1942 ) ; a period family saga ( the Herries chronicle ) and even detective fiction ( Behind the Screen ) . He wrote literary biographies ( Conrad , 1916 ; James Branch <unk> , 1920 ; and Trollope , 1928 ) ; plays ; and screenplays including David Copperfield , 1935 . 
- 
- = = = Influences = = = 
- 
- Walpole 's debt to Henry James is discernible in The Duchess of <unk> ( 1914 ) and The Green Mirror ( 1917 ) , but in the view of J B Priestley the two most potent influences on Walpole were the highly contrasting ones of Trollope and <unk> . Other critics noted the <unk> influence ; in 1923 Arthur St John <unk> commented : 
- The <unk> [ in The Green Mirror ] are a kind of family Trollope might have created had he been living now ; The Cathedral is a kind of story he might have told , with its realistic melodrama and its clerical atmosphere , but Walpole tells it with a <unk> art in the writing and the construction , with a <unk> and charm of style that are outside the range of the earlier novelist . 
- Walpole , though he was devoted to the works of Trollope , and published a study of him , thought that there was no real comparison between the two of them : " I am far too twisted and fantastic a novelist ever to succeed in catching Trollope 's marvellous <unk> . " Priestley was less impressed by the supposed <unk> side of Walpole 's work , finding some of it formulaic . He was more taken with a darker , <unk> , side that he found in the writing : " suddenly it will transform the pleasant easy scene he is giving us into transparency behind which are bright stars and red <unk> ... No matter how <unk> and <unk> he may appear to be , the fact remains that he possesses an unusually sharp sense of evil . " 
- <unk> the most pervasive influence on Walpole was Walter Scott , whose <unk> is reflected in much of the later writer 's fiction . Such was Walpole 's love of Scott that he liked to think of himself as the latter 's reincarnation . He amassed the largest collection in Britain of Scott manuscripts and early editions , and constantly <unk> the novels . With the Herries stories Walpole restored the popularity of the historical novel , a form for which Scott was famous but which had been out of fashion for decades . The Herries series begins in the 18th century and follows a <unk> family through the generations up to modern times . 
- 
- = = = <unk> = = = 
- 
- Walpole sought critical as well as financial success , and <unk> to write works that equalled those of Trollope , Thomas Hardy and Henry James . In his early days , he received frequent and generally <unk> scrutiny from major literary figures . He was a good friend of Virginia Woolf , and rated her as an influence ; she praised his gift for seizing on telling detail : " it is no <unk> to a writer to say that his gift is for the small things rather than for the large ... If you are faithful with the details the large effects will grow inevitably out of those very details " . Joseph Conrad said of him , " We see Mr. Walpole grappling with the truth of things spiritual and material with his characteristic earnestness , and we can <unk> the characteristics of this acute and sympathetic explorer of human nature . " 
- In 1928 Priestley observed , 
- When I first remember seeing Hugh Walpole 's name he had no public at all , but the ferocious young reviewers – the " <unk> " as we have since learned to call them – delighted in him . Now he has an enormous public , both in England and America , and the young " <unk> " – who are <unk> by the thought of a large public – are not particularly fond of him . 
- Priestley contended that Walpole had fulfilled his early potential , unlike Compton Mackenzie , Gilbert Cannan and other promising young novelists of his generation . This view was not universal among critics : Walpole sometimes divided opinion . Writing of Walpole 's Russian novels the contemporary critic and novelist Douglas <unk> commented , " Russia has been the grave of many <unk> ; and our Napoleon of the drawing @-@ room novel has fared no better than other would @-@ be <unk> of that disconcerting land . " <unk> 's complaint was that Walpole 's Russian ( and English ) characters were clichéd stereotypes . The reviewer in Punch , by contrast , wrote , " I consulted a Russian , who is very much alive , and received the opinion that , if Mr. Walpole has not succeeded in drawing the real average Russian , he has given us a type whose faults and virtues sound the <unk> of the situation as it is to @-@ day . " The Observer rated The Dark Forest as " one of the finest novels of our generation " . 
- In 1924 Ernest <unk> wrote into a short story a comparison of G K <unk> and Walpole , concluding that the former was the better man , the latter a better writer and both were classics . Walpole could be sensitive about his literary reputation and often took adverse criticism badly . When <unk> <unk> praised P G Wodehouse as the best English writer of their day , Walpole took it <unk> , to the amusement of Wodehouse who regarded <unk> 's <unk> as " a gag , to get a rise out of serious @-@ minded authors whom he disliked " . Wodehouse was not a great admirer of Walpole ; his own <unk> craftsmanship , with drafts polished over and over again , was the opposite of Walpole 's hastily written and seldom @-@ revised prose . He also viewed Walpole 's sensitivity to criticism as absurd . Walpole was not always as <unk> as Wodehouse supposed . The critic James Agate was a friend despite his regular rude remarks about Walpole 's prose , and when Walpole discovered that Agate had written a spoof of the Herries " <unk> " style , he made him promise to print it in the next published volume of his diaries . 
- During his career contemporaries saw both negative and positive sides to Walpole 's outgoing nature and desire to be in the public eye . Wodehouse commented , " I always think Hugh Walpole 's reputation was two thirds publicity . He was always <unk> books and speaking at <unk> and so on . " On the other hand , Walpole stood out as one of the few literary figures willing to go into court and give evidence for the defence at the <unk> trial after the novel The Well of <unk> was published . 
- By the time of his death The Times 's <unk> of Walpole was no higher than , " he had a versatile imagination ; he could tell a <unk> story in good <unk> English ; and he was a man of immense industry , conscientious and <unk> " . The <unk> tone of the obituary brought forth strong <unk> from T S Eliot , Kenneth Clark and Priestley , among others . Within a few years of his death , Walpole was seen as old @-@ fashioned , and his works were largely neglected . In the Oxford Dictionary of National Biography Elizabeth Steele summed up : " His psychology was not deep enough for the <unk> , his diction not free enough for those returning from war , and his <unk> disastrous to a public wary of personal commitment " . In 2011 , Peter <unk> , an admirer of Walpole , though not an <unk> one , wrote : 
- Henry James and John Buchan praised him . Joseph Conrad , T S Eliot and Virginia Woolf were kind about him . What 's more , his books sold enormously well on both sides of the Atlantic , he was knighted , and he became very rich ... Yet now he has vanished completely , his books not even to be found on the back shelves of most second hand shops , dismissed as " <unk> " . 
- Walpole 's works have not been completely neglected in recent years . The Herries stories have seldom been out of print , and in 2014 <unk> listed a dozen recent <unk> of Walpole 's works , including The Wooden Horse , The Dark Forest , The Secret City , Jeremy , and The Cathedral . In 2011 the BBC broadcast a <unk> of Walpole , The Walpole Chronicle , presented by Eric Robson . In 2013 a new stage version of Rogue Herries was presented by the Theatre by the Lake company in Walpole 's adopted home of Keswick . The BBC speculated that this could mark a revival in interest in his works . 
- 
- = = = Biographies = = = 
- 
- Two full @-@ length studies of Walpole were published after his death . The first , in 1952 , was written by Rupert Hart @-@ Davis , who had known Walpole personally . It was regarded at the time as " among the half dozen best biographies of the century " and has been reissued several times since its first publication . Writing when homosexuality was still outlawed in England , Hart @-@ Davis avoided direct mention of his subject 's sexuality , so <unk> Walpole 's <unk> discretion and the wishes of his brother and sister . He left readers to read between the lines if they wished , in , for example , references to Turkish <unk> " providing informal opportunities of meeting interesting strangers " . Hart @-@ Davis dedicated the book to " Dorothy , Robin and Harold " , Walpole 's sister , brother , and long @-@ term companion . 
- In 1972 Elizabeth Steele 's study of Walpole was published . Much shorter than Hart @-@ Davis 's biography , at 178 pages to his <unk> , it dealt mainly with the novels , and aimed " to show the sources of Hugh Walpole 's success as a writer during the thirty @-@ five years and fifty books of his busy career " . Steele concentrated on half a dozen of Walpole 's best books , each illustrating aspects of his writing , under the <unk> " <unk> " , " Artist " , " Witness " , " Evangelist " , " Critic " and " <unk> " . Steele also wrote a study of Walpole 's North American lecture tours ( 2006 ) and the article on Walpole in the Oxford Dictionary of National Biography ( 2004 ) , which treats his private life briefly but <unk> . 
- 
- 
- = Domnall mac Murchada = 
- 
- Domnall mac Murchada ( died 1075 ) , also known as Domnall mac Murchada meic Diarmata , was a leading late eleventh @-@ century claimant to the Kingdom of Leinster , and a King of Dublin . As a son of Murchad mac Diarmata , King of Dublin and the Isles , Domnall was a grandson of Diarmait mac <unk> na mBó , King of Leinster , and thus a member of the Uí Chennselaig . Domnall was also the first of the <unk> Murchada , a branch of the Uí Chennselaig named after his father . 
- In <unk> , the year before his grandfather 's death , Domnall and an Uí Chennselaig kinsman , Donnchad mac <unk> <unk> , battled for control of Leinster . Although Domnall is accorded the title King of Leinster in one mediaeval king @-@ list , Donnchad was evidently a more powerful claimant , and Domnall appears to have held the Leinster kingship in name only . 
- Domnall 's rise to power in the Kingdom of Dublin took place in 1075 , after the expulsion of the reigning Gofraid mac Amlaíb meic Ragnaill , King of Dublin by the latter 's overlord , Toirdelbach Ua Briain , King of Munster . The circumstances surrounding Domnall 's accession are uncertain . He may have collaborated with Gofraid to <unk> the kingdom from the grip of the Uí Briain , or he may have been installed in the kingship by Toirdelbach himself , and ruled under the latter 's <unk> . Whatever the case , Domnall died within the year , and Toirdelbach placed his own son , <unk> , upon the throne . 
- 
- = = Background = = 
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- Domnall was a son of Murchad mac Diarmata , King of Dublin and the Isles ( died 1070 ) , who was himself a son of Diarmait mac <unk> na mBó , King of Leinster ( died 1072 ) . Domnall was , therefore , a member of the Uí Chennselaig ; as well as the first of the <unk> Murchada , a branch of the Uí Chennselaig named after his father . Domnall had two brothers : Donnchad ( died 1115 ) , a later King of Leinster , and <unk> . 
- In 1052 , Domnall 's aforesaid grandfather conquered the Kingdom of Dublin from Echmarcach mac Ragnaill , King of Dublin and the Isles ( died 1064 / 1065 ) , and soon after appointed Murchad as King of Dublin . About a decade later , Murchad appears to have driven Echmarcach from Mann , after which he gained the kingship of the Isles . Diarmait 's deep @-@ rooted authority in Norse @-@ Gaelic Dublin lasted for two decades , and was a remarkable achievement that no other Irish king had ever accomplished . Unfortunately for the Uí Chennselaig , two of Diarmait 's sons — Murchad and <unk> <unk> — unexpectedly <unk> their father in 1070 , and Diarmait himself fell in battle two years later . 
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- = = <unk> of Leinster = = 
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- Even before Diarmait 's demise , the Uí Chennselaig began to fight amongst themselves in a struggle that was almost certainly an after @-@ effect of Diarmait 's sons ' untimely deaths . Specifically , the Annals of the Four Masters , and the Annals of Inisfallen reveal that Domnall battled against the forces of his own first cousin once removed , Donnchad mac <unk> <unk> ( died <unk> ) , before Diarmait 's ally , Toirdelbach Ua Briain , King of Munster ( died 1086 ) , was able to intervene and restore order in the Kingdom of Leinster . 
- Up until about the time of his death , Diarmait had been the most powerful king in southern Ireland . In consequence of the void left by his demise , Diarmait 's <unk> ally Toirdelbach seized the initiative , and moved to enforce his own claim to the high @-@ kingship of Ireland . He immediately imposed his <unk> on Leinster — a task almost certainly expedited by the aforesaid infighting amongst the Uí Chennselaig — and took control of Dublin . Whilst the imposition of authority upon rival provincial kingdoms was a fundamental part in gaining the high @-@ kingship , Toirdelbach 's decision to march @-@ on Dublin reveals that the acquisition of this coastal kingdom had also become an essential part of the process . 
- Toirdelbach 's subsequent capture of Donnchad in Dublin suggests that the latter was not only the leading Uí Chennselaig <unk> , but was also in the process of using the town as the capital of Leinster . Although the list of Leinster kings in the Book of Leinster declares that Domnall had succeeded his grandfather as King of Leinster , it is apparent that Donnchad was indeed the more powerful claimant . In fact , the king @-@ list of Uí Chennselaig in the same source makes no notice of Domnall , and states that it was Donnchad who succeeded Diarmait as King of Uí Chennselaig . Domnall , therefore , may not have reigned in Leinster , and could well have been King of Leinster in name only . 
- If the Annals of Inisfallen is to be believed , Toirdelbach acquired possession of Dublin when the Dubliners themselves offered him its kingship . Although this record may be mere Uí Briain propaganda , it could instead reveal that the Dubliners preferred a distant overlord from Munster rather than one from neighbouring Leinster . Within the year , the kingship was held by Gofraid mac Amlaíb meic Ragnaill ( died 1075 ) . The latter appears to have been a kinsman of Echmarcach , and may well have been handed the kinship by Toirdelbach , perhaps on account of the considerable distance between the kingdoms . 
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- = = <unk> of Dublin = = 
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- In 1075 , Toirdelbach drove Gofraid from the kingship and Ireland itself . There is uncertainty concerning the circumstances of Gofraid 's expulsion , and of Domnall 's accession . On one hand , it is possible that Gofraid was involved in lending assistance to Anglo @-@ Danish resistance against the Norman regime in the recently conquered Kingdom of England . If correct , Gofraid would appear to have been at odds with Toirdelbach , a monarch who appears to have cultivated close links with the Norman regime . Domnall , therefore , may have had Toirdelbach 's consent to rule in Dublin as Gofraid 's replacement . In fact , Toirdelbach 's placement of Domnall in Dublin , and his allowance of the latter 's aforesaid cousin in Leinster , may have been a way in which the Uí Briain exploited the fractured Uí Chennselaig . Certainly , Domnall 's cooperation would have been a valuable asset to Toirdelbach , considering the prominence of his father amongst the Dubliners , and the likelihood that Domnall himself may have lived most of his life there . On the other hand , it is possible that Gofraid was driven from the kingship because he had aligned himself with the <unk> against the Uí Briain . If such a sequence of events is correct it could mean that , even though Gofraid was unable continue on with the revolt , it was his Uí Chennselaig <unk> who succeeded in securing Dublin from the Uí Briain . 
- Whatever the circumstances of Domnall 's accession , the Uí Chennselaig regime in Dublin was short @-@ lived . The Annals of Inisfallen , the Annals of the Four Masters , and the Annals of Ulster , all reveal that , within the year , Domnall died after a brief illness , with the latter two sources <unk> that he succumbed after three nights of sickness . The Annals of Inisfallen and the Annals of Ulster accord him the title King of Dublin , and make no mention of any connection with the Leinster kingship . Upon Domnall 's demise , Toirdelbach had his own son , <unk> ( died 1119 ) , appointed King of Dublin . In so doing , Toirdelbach reinforced his authority in Dublin , and followed a precedent started by Domnall 's grandfather , in which a claimant to the high @-@ kingship of Ireland installed his own heir to the kingship of Dublin . 
- 
- = = Ancestry = = 
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- = Irresistible ( The X @-@ Files ) = 
- 
- " Irresistible " is the thirteenth episode of the second season of the American science fiction television series The X @-@ Files . It premiered on the Fox network on January 13 , 1995 . The episode was written by series creator Chris Carter , directed by David Nutter , and featured the first of two guest appearances by Nick Chinlund as the death fetishist killer Donnie Pfaster . The episode is a " Monster @-@ of @-@ the @-@ Week " story , a stand @-@ alone plot which is unconnected to the series ' wider mythology . The episode was viewed by 8 @.@ 8 million people upon its first broadcast , and received positive reviews , with much praise to Chinlund 's performance as the antagonist . 
- The show centers on FBI special agents Fox Mulder ( David Duchovny ) and Dana Scully ( Gillian Anderson ) who work on cases linked to the paranormal , called X @-@ Files . In the episode , Mulder and Scully investigate a death fetishist who begins kidnapping and killing women to satisfy his obsession . Scully , still recovering from her earlier abduction , is soon overcome with <unk> stress disorder . 
- " Irresistible " is one of the few in the series that has no paranormal elements to it . Initially , the script called for Donnie Pfaster to be a necrophiliac , but the idea was soon rejected by the Fox Broadcasting Company for being " unacceptable for broadcast standards " . Pfaster was eventually brought back in the season seven episode " <unk> " . 
- 
- = = Plot = = 
- 
- In Minneapolis , a funeral is held for a young girl ( Megan <unk> ) . The ceremony is observed by Donnie Pfaster , the eerie assistant director for the funeral home . Later that night , as the girl 's body is being stored for burial the following day , Pfaster 's boss finds him cutting off the corpse 's hair . Pfaster is promptly fired . 
- Some time later , Fox Mulder and Dana Scully are summoned to Minneapolis by Moe Bocks , an FBI field agent who is investigating the <unk> and <unk> of a body in a local cemetery . Mulder <unk> Bocks ' theory that this act is a variation of extraterrestrial cattle mutilation , and suggests they search for a human <unk> . Scully is disturbed at the sight of the <unk> corpse . Two more bodies are found exhumed , with their hair cut and <unk> removed . Mulder develops a psychological profile of the criminal , believing him to be an escalating " death fetishist " who may resort to murder to satisfy his desires . Scully keeps her discomfort with the case to herself , and writes up a field report on necrophilia . 
- Pfaster , who was behind the <unk> , proves Mulder 's prediction correct when he brings a prostitute to his apartment . When the prostitute discovers a collection of funerary <unk> in Pfaster 's bedroom , he kills her and removes her fingers . Later , Pfaster — having been hired as a frozen food delivery man through charming the female interviewer — delivers to a low @-@ security house of a woman with teenage daughters . He requests the bathroom to wash his hands , and whilst there steals some discarded hair from a brush he found in the <unk> . Pfaster attends a night class at a community college , where a female classmate defends herself after he makes threatening advances . He is arrested and is placed in a jail cell across from a suspect being interrogated for Pfaster 's crimes by Mulder , Scully , and Bocks . Pfaster shows interest in Scully , and learns her name from the interrogated suspect . Pfaster is later released as his charges were dropped . 
- Scully is deeply troubled by Pfaster 's crimes , and has unsettling dreams and hallucinations about the case . In Washington , she has a counseling session with a social worker , during which she shares her anxiety about the investigation . After the session , Scully learns that someone from Minnesota had called for her . When she contacts Mulder , she learns that neither he nor Bocks made the call . <unk> a <unk> to Pfaster from his arrest , Bocks and Mulder raid his apartment , finding one of the prostitute 's fingers in his <unk> . Meanwhile , after Scully arrives in Minneapolis , Pfaster forces her car off the road . He kidnaps Scully and takes her to his late mother 's abandoned house . He ties and gags Scully , and keeps her in a dark <unk> . 
- Mulder and Bocks discover that Pfaster 's mother had owned a car which matches paint found on Scully 's abandoned car , tracking down her former residence . Meanwhile , Scully escapes from Pfaster as he prepares a cold bath for her , resulting in a pursuit through the house . Scully and Pfaster have a struggle that sends them falling down a staircase onto the <unk> , where a task force led by Mulder and Bocks breaks in moments later and apprehends Pfaster . Scully initially insists that she is okay , but then breaks down and cries in Mulder 's arms . In a voice @-@ over narration , Mulder traces Pfaster 's pathology to his childhood , when he was raised in a family of four older sisters . Mulder also reflects on Pfaster 's nature and the nature of evil in general . 
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- = = Production = = 
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- The episode 's initial script where Pfaster was a necrophiliac was rejected by the Fox Broadcasting Company for being " unacceptable for broadcast standards " . As series creator Chris Carter described it , " When I handed the script in , it was really for a necrophiliac episode , and that just didn 't fly . You cannot do the combination of sex and death on network television . " Carter was forced to tone down the script by changing Pfaster from a necrophiliac to a death fetishist and diminishing Pfaster 's sexual obsession . He considered that the sexual content was " implied and understood by audiences " , and that Pfaster still resulted in a creepy character , particularly his " creepy arrogance " in using <unk> on the hair of his victims . The episode 's original title was " <unk> " . 
- The episode is one of the few in the series that has no paranormal elements to it . Carter said of the episode 's conception , " My first chance to work with David Nutter in a long time , and I wanted to give him something he could sink his teeth into . It 's a little bit different for us . It doesn 't really have a paranormal aspect , except for Scully 's perceptions of her deepest fears . I felt that I had to figure out what she is most afraid of , and she is most afraid of those things that most of us are afraid of . The idea of dying at the hands of someone — creature or not — and she is helpless to do anything about it . I thought it was a very good way to explore Scully 's character . " The scene where Dana Scully imagines Pfaster appearing as a devil was influenced by real @-@ life accounts , as described by Carter : " There are reports of people who had been under the spell of Jeffrey <unk> , who actually claimed that he shape @-@ shifted during those hours when they were held hostage ; that his image actually changed . " Nutter said " In many ways , Chris wanted to sell the idea that , as established in Mulder 's closing dialogue in the show , not all terror comes from the paranormal . It could come from the person next door . " 
- Carter said of the casting of Nick Chinlund as Pfaster , " I thought it was a wonderfully creepy villain . The casting of that show was very difficult . We saw many actors , but there was a quality I was looking for and I couldn 't put a name on that quality . I finally figured out what it was when Nick came in and he had a kind of <unk> quality that worked . I thought he looked like Joe College , but he could scare the hell out of you . " Producer Glen Morgan said Chinlund 's performance was outstanding . Nutter stated " Nick Chinlund was wonderful to work with . The guy was like <unk> in my hands . He was great . If you 're looking for someone to underline the <unk> and <unk> of the character , he did that . " 
- Nutter said of the episode " I really worked hard to make it a special show , because I thought it was special . It was Gillian 's post @-@ traumatic stress episode , because she had not really had the opportunity to vent her feelings about the whole <unk> Barry situation . This was an opportunity to sit back and let all that happen . " Carter particularly liked the scene where a clearly disturbed Scully hugs Mulder , claiming it was a " tender moment " between two characters that had not shown that much affection for each other . 
- 
- = = Reception = = 
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- 
- = = = Ratings = = = 
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- " Irresistible " premiered on the Fox network on January 13 , 1995 . This episode earned a Nielsen rating of 9 @.@ 2 , with a 15 share , meaning that roughly 9 @.@ 2 percent of all television @-@ equipped households , and 15 percent of households watching television , were tuned in to the episode . It was viewed by 8 @.@ 8 million households . 
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- = = = Reviews = = = 
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- " Irresistible " received largely positive reviews from critics . Entertainment Weekly rated " Irresistible " a B + , saying it was based on " an unsettling concept to begin with " that was reinforced by " Chinlund 's skin @-@ <unk> one @-@ man show " . Todd VanDerWerff of The A.V. Club rated the episode A , praising the acting , particularly of Chinlund as Pfaster , and describing it as " legitimately scary , a sign of a show that was pushing itself in new and interesting directions " . The only criticism was for the scenes where Scully <unk> Pfaster <unk> as " pretty silly , almost feeling like an attempt to make sure something vaguely paranormal is in the episode so the fans don 't get bored with what is ultimately a very good episode " . Jessica Morgan of Television Without Pity gave the episode a B + grade . Writing for Den of Geek , Nina <unk> ranked " Irresistible " the sixth best X @-@ Files episode , saying that " excluding <unk> and his <unk> , Pfaster has got to be the most disturbing villain that our favorite agents have encountered " . Den of Geek writer <unk> <unk> named it the " finest " stand @-@ alone episode of the second season , describing it as " a genuinely creepy 45 @-@ minute horror movie " . Connie <unk> of <unk> listed Pfaster among the best monster @-@ of @-@ the @-@ week characters of the series , and IGN 's Christine <unk> ranked Chinlund the seventh best guest star in the history of the show , considering that " what makes him all the more frightening is how downright passive and polite he is up until the moment he 's going to kill ; the perfect camouflage for a modern @-@ day monster . " TV Guide listed Pfaster among the <unk> X @-@ Files monsters describing him as " evil <unk> " . 
- Chris Carter said " Irresistible " was effective for " being really scary " , and that not only it was one of his favorites but inspired him to create the television series Millennium later . 
- 
- 
- = Thunderbirds ( TV series ) = 
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- Thunderbirds is a British science @-@ fiction television series created by Gerry and Sylvia Anderson , filmed by their production company AP Films ( APF ) and distributed by ITC Entertainment . It was produced between 1964 and 1966 using a form of electronic <unk> <unk> ( dubbed " Supermarionation " ) combined with scale model special effects sequences . Two series were filmed , comprising a total of 32 episodes . Production ceased after Lew Grade , the Andersons ' financial <unk> , failed in his efforts to sell the programme to American network television . 
- Set in the mid @-@ <unk> , Thunderbirds is a follow @-@ up to the earlier Supermarionation productions Four Feather Falls , Supercar , <unk> <unk> and Stingray . It follows the exploits of International Rescue ( IR ) , a life @-@ saving organisation equipped with technologically @-@ advanced land , sea , air and space rescue craft ; these are headed by a fleet of five vehicles named the Thunderbirds and launched from IR 's secret base in the Pacific Ocean . The main characters are ex @-@ astronaut Jeff Tracy , the founder of IR , and his five adult sons , who pilot the Thunderbird machines . 
- Thunderbirds began its first run in the United Kingdom on the ITV network in 1965 and has since been broadcast in at least 66 other countries . <unk> repeated , it was adapted for radio in the early 1990s and has influenced many TV programmes and other media . As well as inspiring various merchandising campaigns , the series has been followed by two feature @-@ length film sequels , a live @-@ action film adaptation and a <unk> stage show tribute . The second of two TV remakes , the computer @-@ animated Thunderbirds Are Go , premiered in 2015 . 
- Widely considered to be the Andersons ' most popular and commercially successful series , Thunderbirds has received particular praise for its effects ( directed by Derek Meddings ) and musical score ( composed by Barry Gray ) . It is also well remembered for its title sequence , which opens with an often @-@ quoted countdown by actor Peter Dyneley ( who voiced the character of Jeff ) : " 5 , 4 , 3 , 2 , 1 : Thunderbirds Are Go ! " A real @-@ life rescue service , the International Rescue Corps , is named after the organisation featured in the series . 
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- = = <unk> = = 
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- Set between <unk> and <unk> , Thunderbirds follows the exploits of the Tracy family , headed by American ex @-@ astronaut turned multi @-@ millionaire philanthropist Jeff Tracy . He is a widower with five adult sons : Scott , John , Virgil , Gordon and Alan . The Tracys form International Rescue ( IR ) , a secret organisation dedicated to saving human life . They are aided in this mission by technologically advanced land , sea , air and space vehicles , which are called into service when conventional rescue techniques prove ineffective . The most important of these are five machines named the " Thunderbirds " , each assigned to one of the five Tracy brothers : 
- Thunderbird 1 : a <unk> rocket plane used for fast response and accident zone reconnaissance . Piloted by primary rescue co @-@ <unk> Scott Tracy . 
- Thunderbird 2 : a <unk> carrier aircraft that transports rescue vehicles and equipment to accident zones in <unk> capsules known as " <unk> " . Piloted by Virgil . 
- Thunderbird 3 : a single @-@ stage @-@ to @-@ orbit spacecraft . Piloted alternately by Alan and John , with Scott as co @-@ pilot . 
- Thunderbird 4 : a utility <unk> . Piloted by Gordon and normally launched from Thunderbird 2 . 
- Thunderbird 5 : a space station that relays distress calls from around the world . <unk> alternately by " Space <unk> " John and Alan . 
- With the engineer Brains and Jeff 's elderly mother , as well as the Malaysian <unk> Kyrano and his daughter Tin @-@ Tin , the family reside in a luxurious villa on Tracy Island , their hidden base in the South Pacific Ocean . In this location , IR is safe from criminals and <unk> who envy the organisation 's technology and seek to acquire the secrets of its machines . 
- Despite its humanitarian principles , some of IR 's operations are necessitated not by <unk> but deliberate sabotage motivated by <unk> for power and wealth . For missions that require criminal investigation , the organisation incorporates a network of undercover agents headed by English <unk> Lady Penelope Creighton @-@ Ward and her butler <unk> Parker . Based at Creighton @-@ Ward Mansion in Kent , Penelope and Parker 's primary mode of transport is FAB 1 , a specially @-@ modified Rolls @-@ Royce . The most persistent of IR 's adversaries is the criminal known only as the " Hood " . Operating from a temple in the Malaysian jungle , and possessing abilities of hypnosis and dark magic , he <unk> a powerful <unk> control over Kyrano , his estranged half @-@ brother , and manipulates the Tracys into missions that unfold according to his <unk> designs . This allows him to spy on the Thunderbird machines and , by selling their secrets , make himself rich . 
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- = = Production = = 
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- Thunderbirds was the fourth Supermarionation puppet TV series to be produced by APF , which was founded by the husband @-@ and @-@ wife duo of Gerry and Sylvia Anderson with their business partners Reg Hill and John Read . <unk> in late 1963 , the series was commissioned by Lew Grade of ITC , APF 's parent company , on the back of the positive audience response to Stingray . 
- Gerry Anderson drew inspiration for the series ' underlying concept from the West German mining disaster known as the <unk> von Lengede ( " Miracle of Lengede " ) . In October 1963 , the collapse of a nearby dam flooded an iron mine in the municipality of Lengede , killing 29 miners and trapping 21 others underground . Lacking the means to drill an escape shaft , the authorities were forced to <unk> a heavy @-@ duty bore from <unk> ; the considerable time necessary to ship this device by rail had significantly reduced the chances of a successful rescue . <unk> the advantages of <unk> crisis response , Anderson conceived the idea of an " international rescue " organisation that could use <unk> aircraft to transport specialised rescue equipment quickly over long distances . 
- <unk> to distinguish his 26 @-@ episode proposal from APF 's earlier productions , Anderson attempted to pitch stories at a level that would appeal to both adults and children . Whereas previous series had been shown during the late afternoon , Anderson wanted Thunderbirds to be broadcast in a family @-@ friendly <unk> slot . Sylvia remembers that " our market had grown and a ' <unk> ' show ... was the next step . " The Andersons retired to their holiday villa in Portugal to expand the premise , script the pilot episode and compose a <unk> ' guide . According to Sylvia , the writing process depended on a " division of labour " , whereby Gerry created the action sequences while she managed characterisation . The decision to make a father and his sons the main characters was influenced by the premise of <unk> , as well as Sylvia 's belief that the use of more than one heroic character would <unk> the series ' appeal . The Tracy brothers were named after Mercury Seven astronauts : Scott Carpenter , John Glenn , Virgil " Gus " <unk> , Gordon Cooper and Alan Shepard . 
- The series ' title was derived from a letter written by Gerry 's brother , Lionel , while he had been serving overseas as an RAF flight sergeant during World War II . While stationed in Arizona , Lionel had made reference to Thunderbird Field , a nearby United States Army Air Forces base . Drawn to the " <unk> " of " Thunderbirds " , Anderson dropped his working title of " International Rescue " and renamed both the series and IR 's rescue vehicles , which had previously been designated <unk> 1 to 5 . His inspiration for the launch sequences of Thunderbirds 1 , 2 and 3 originated from contemporary United States Air Force launch procedure : Anderson had learnt how the Strategic Air Command would keep its pilots on permanent standby , seated in the <unk> of their aircraft and ready for take @-@ off at a moment 's notice . 
- In the DVD documentary The Thunderbirds Companion , Anderson explained how a rise in filming costs had made overseas distribution revenue even more important and essentially caused Thunderbirds to be made " as an American show " . During the character development and voice casting process , the Andersons ' main priority was to ensure that the series had transatlantic appeal , thus increasing the chances of winning an American network deal and the higher audience figures that this market had to offer . <unk> were <unk> in American English and printed on US @-@ style <unk> @-@ size paper . 
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- = = = Filming = = = 
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- Thunderbirds was filmed at APF 's studios on the <unk> Trading Estate between 1964 and 1966 . In preparation , the number of full @-@ time crew was expanded to 100 . <unk> began in September 1964 after five months of pre @-@ production . Due to the new series ' technical complexity , this was a period longer than for any of APF 's earlier productions . To speed up the filming , episodes were shot in pairs , at a rate of one per month , on separate soundstages and by separate crews ( designated " A " and " B " ) . By 1964 , APF was the UK 's largest commercial user of colour film , consuming more than three million feet ( 570 miles or 910 kilometres ) of stock per year . 
- Alan Pattillo , a veteran <unk> and director for APF , was appointed the company 's first official script editor in late 1964 . This move was aimed to reduce the burden on Gerry Anderson who , while reserving his producer 's right to overall creative control , had grown weary of revising scripts himself . Direction of episodes was assigned in pairs : Pattillo and David Elliott alternated with the less experienced Desmond Saunders and newcomer David Lane for each month 's filming . Due to the difficulties of setting up takes , progress was slow : even on a productive day , it was rare for the crew to complete more than two minutes of puppet footage In a contemporary interview , Hill noted that Thunderbirds contained several times as many shots as a typical live @-@ action series . He explained that rapid editing was necessary on account of the characters ' lack of facial expression , which made it difficult to sustain the viewer 's interest for more than a few seconds per shot . 
- After viewing the completed 25 @-@ minute pilot , " <unk> in the Sky " , Lew Grade was so impressed by APF 's work that he instructed Anderson to double the episode length and increased the series ' budget per episode from £ 25 @,@ 000 to £ 38 @,@ 000 . As a result , Thunderbirds became not only the company 's longest and highest @-@ budgeted production , but also among the most expensive TV series to have been made up to that point . The crew , who had been filming at a rate of two 25 @-@ minute episodes per fortnight , faced significant challenges during the transition to the new format : eight episodes had already been completed , scripts for up to ten more had been written , and substantial <unk> would be necessary to satisfy the longer running time . Anderson lamented : " Our time @-@ scale was far too drawn out . ITC 's New York office insisted that they should have one show a fortnight ... Everything had to move at twice the speed . " APF spent over seven months extending the existing episodes . 
- Tony <unk> , who had impressed Pattillo and the Andersons with an <unk> script that he had written for Danger Man , was recruited to assist in the writing of subplots and <unk> material . He found that the longer format created opportunities to strengthen the characterisation . Science @-@ fiction writer John Peel suggests that " small character touches " make the puppet cast of Thunderbirds " much more rounded " than those of earlier APF series . He compares the writing favourably to that of live @-@ action drama . The new footage proved useful during the development of the first series finale , " Security <unk> " : since the previous two episodes had <unk> their budgets , Pattillo devised a flashback @-@ dominated clip show containing only 17 minutes of new material to reduce costs . 
- Filming of Series One was completed in December 1965 . A second series was also commissioned late that year and entered production in March 1966 . <unk> became a full @-@ time member of the writing staff and took over the role of script editor from the outgoing Pattillo . The main puppet cast and vehicles were rebuilt ; in addition , the art department expanded some of the standing sets , including the Tracy Villa lounge and the Thunderbird 5 control room . To accommodate the simultaneous filming of the TV series and Thunderbirds Are Go , APF purchased two more buildings on the <unk> Trading Estate and converted them into new stages . As crew and studio space were divided between the two productions , filming of the TV series progressed at half the previous speed , as APF 's B crew produced one episode per month . Filming on Thunderbirds Are Go was completed by June , allowing A crew to resume work on the series to shoot what would prove to be its penultimate episode , " <unk> " . 
- Production of Thunderbirds ended in August 1966 with the completion of the sixth episode of Series Two . In February that year , it had been reported that Grade had been unable to sell the series in the United States due to disagreements over <unk> . In July , he cancelled Thunderbirds after failing in his second attempt to secure an American buyer . The three major US networks of the time – NBC , CBS and ABC – had all bid for the series , with Grade repeatedly increasing the price . When NBC withdrew its offer , the other two immediately followed . 
- By the time of its cancellation , Thunderbirds had become widely popular in the UK and was being distributed extensively overseas . Grade , however , believed that without the financial boost of an American network sale , a full second series would fail to recover its production costs . He therefore asked Anderson to <unk> a new concept that he hoped would stand a greater chance of winning over the profitable US market . This became Captain Scarlet and the <unk> . 
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- = = = Casting and characters = = = 
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- Voice @-@ recording sessions were supervised by Pattillo and the Andersons , with Sylvia Anderson in charge of casting . <unk> was recorded once per month at a rate of two scripts per session . Supporting parts were not pre @-@ assigned , but negotiated by the cast among themselves . Two recordings would be made at each session : one to be converted into electronic pulses for the puppet filming , the other to be added to the soundtrack during post @-@ production . The tapes were edited at Gate Recording Theatre in Birmingham . 
- In the interest of transatlantic appeal , it was decided that the main characters would be mostly American and therefore actors capable of producing an appropriate accent were used . British , Canadian and Australian actors formed most of the voice cast ; the only American involved was stage actor David Holliday , who was noticed in London 's West End and given the part of Virgil Tracy . Following the completion of the first series , Holliday returned to the US . The character was voiced by English @-@ Canadian actor Jeremy <unk> for Series Two . 
- British actor David Graham was among the first to be cast . He had previously voiced characters in Four Feather Falls , Supercar , <unk> <unk> and Stingray . Beyond the APF productions , he had supplied one of the original <unk> voices on Doctor Who . Cast alongside Graham was Australian actor Ray Barrett . Like Graham , he had worked for the Andersons before , having voiced Titan and Commander Shore in Stingray . A veteran of radio drama , Barrett was skilled at performing a range of voices and accents in quick succession . <unk> of the week would typically be voiced by either Barrett or Graham . Aware of the sensitive political climate of the Cold War and not wishing to " <unk> the idea that Russia was the enemy with a whole generation of children watching " , Gerry Anderson decided the Hood ( voiced by Barrett ) should be Oriental and placed his temple hideout in Malaysia to defy the viewer 's expectations . 
- Although Lady Penelope and Parker ( the latter voiced by Graham ) were among the first characters developed , neither was conceived as a major role . Parker 's Cockney manner was based on a <unk> at a pub in <unk> that was sometimes visited by the crew . On Gerry Anderson 's recommendation , Graham <unk> there regularly to study the accent . Anderson 's first choice for the role of Penelope had been <unk> Fielding , but Sylvia insisted she take the part herself . Her Penelope voice was intended to emulate Fielding and Joan Greenwood . On Penelope and Parker 's secondary role as comic relief , Gerry explained , " We British can laugh at ourselves , so therefore we had Penelope and Parker as this comedy team . And in America they love the British aristocracy too . ' " 
- As well as Jeff Tracy , English @-@ Canadian actor Peter Dyneley voiced the recurring character of Commander Norman , chief of air traffic control at London International Airport . His supporting character voices were typically those of upper @-@ class Englishmen . Shane Rimmer , the voice of Scott , was cast on the strength of his performance on the BBC soap opera Compact . Meanwhile , fellow Canadian Matt Zimmerman was selected at a late stage in the process . The expatriate West End actor was given the role of Alan on the recommendation of his friend , Holliday : " They were having great difficulty casting the part of Alan as they wanted a certain sound for him , being the youngest brother . David , who [ was ] a bit older than I am , told them that he had this friend , me , who would be great . " 
- Christine Finn , known for her role in the TV serial <unk> and the Pit , provided the voices of Tin @-@ Tin Kyrano and <unk> Tracy . With Sylvia Anderson , she was also responsible for voicing most of the female and child supporting characters . Other minor parts were voiced by Charles <unk> , Paul Maxwell and John Tate ( the father of Nick Tate ) , who were not credited for their contributions . 
- 
- = = = Design and effects = = = 
- 
- The puppet stages used for the filming of Thunderbirds were only one @-@ fifth the size of those used for a standard live @-@ action production , typically measuring 12 by 14 by 3 metres ( 39 @.@ 4 by 45 @.@ 9 by 9 @.@ 8 ft ) in length , width and height . Bob Bell , assisted by Keith Wilson and Grenville <unk> , headed the art department for Series One . During the simultaneous filming of Series Two and Thunderbirds Are Go in 1966 , Bell attended mainly to the film , <unk> set design for the TV series to Wilson . 
- Since it was necessary for the art department 's interior sets to conform to the effects department 's exterior plans , each team closely monitored the other 's work . According to Sylvia Anderson , Bell 's challenge was to produce complex interiors on a limited budget while resisting the effects department 's push for " more extravagant " design . This task was complicated by the unnatural proportions of the puppets : Bell struggled to decide whether the sets should be built to a scale proportionate to their bodies or their oversized heads and hands . He used the example of FAB 1 to illustrate the problem : " As soon as we positioned [ the puppets ] standing alongside [ the model ] , they looked ridiculous , as the car <unk> over them . " He ultimately adopted a " mix @-@ and @-@ match " approach , in which smaller items , such as <unk> , were scaled to their hands and furniture to their bodies . 
- While designing the Creighton @-@ Ward Mansion sets , Bell and his staff strove for authenticity , ordering miniature Tudor paintings , 1 ⁄ 3 @-@ scale <unk> and Regency @-@ style furniture and <unk> in the shape of a polar bear skin . This realism was enhanced by adding scrap items acquired from household waste and electronics shops . For example , a vacuum cleaner pipe serves as Virgil Tracy 's launch <unk> . 
- 
- = = = = <unk> = = = = 
- 
- The head puppet sculptor was Christine Glanville , who also served as the lead puppeteer . Glanville 's four @-@ person team built the 13 members of the main cast in six months at a cost of between £ 250 and £ 300 per puppet ( approximately £ 4 @,@ <unk> and £ 5 @,@ 483 today ) . Since pairs of episodes were being filmed simultaneously on separate stages , the characters needed to be sculpted in duplicate . <unk> expressions were diversified by means of <unk> heads : as well as a head with a neutral expression , each main character was given a " <unk> " , a " <unk> " and a " <unk> " . The finished puppets were approximately 22 inches ( 56 cm ) tall , or 1 ⁄ 3 adult human height . 
- The puppets were made up of more than 30 individual components , the most important of which was the solenoid that <unk> lip movements with the characters ' pre @-@ recorded dialogue . This device was positioned inside the head unit ; consequently , torsos and limbs appeared relatively small . The puppets ' likenesses and mechanics are remembered favourably by puppeteer Wanda Brown , who preferred the Thunderbirds <unk> over the accurately @-@ <unk> ones that first appeared in Captain Scarlet : " The puppets were easier to operate and more enjoyable because they had more character to them ... Even some of the more normal @-@ looking faces , such as Scott and Jeff , for me had more character than the puppets in the series that came afterwards . " Rimmer speaks positively of the puppets ' still being " very much caricatures " , since it made them " more <unk> and appealing ... There was a naive quality about them and nothing too complex . " 
- The appearances of the main characters were inspired by those of actors and other <unk> , who were typically selected from the show business <unk> Spotlight . According to Glanville , as part of a trend away from the strong caricature of previous series , APF was seeking " more natural faces " for the puppets . The face of Jeff Tracy was based on that of Lorne Greene , Scott on Sean Connery , Alan on Robert Reed , John on Adam Faith and Charlton Heston , Brains on Anthony Perkins and Parker on Ben <unk> . Sylvia Anderson brought the character of Penelope to life in likeness as well as voice : after her test moulds were rejected , sculptor Mary Turner decided to use Anderson herself as a template . Terry Curtis was also an original sculpture of Supermarionation puppets . 
- Main character heads were initially sculpted in either <unk> or clay . Once the general aspect had been finalised , this served as the template for a <unk> rubber mould . This was coated with <unk> ( fibreglass mixed with resin ) and enhanced with <unk> , a <unk> @-@ like substance , to <unk> <unk> . The <unk> shell was then fitted with a solenoid , leather mouth parts and plastic eyes , as well as <unk> teeth – a first for a Supermarionation production . <unk> known as " <unk> " , which had plastic heads , portrayed the supporting characters . These <unk> started their working lives with only a mouth and eyes ; their faces were <unk> from one episode to the next . Particularly striking revamp moulds were retained and , as their numbers increased , photographed to compile an internal casting <unk> . 
- <unk> were made of <unk> or , in the case of the Penelope puppet , human hair . Puppet bodies were built in three sizes : " large male " ( specifically for the Tracys and the Hood ) , " small male " and " small female " . Sylvia Anderson , the head costume designer , devised the main characters ' attire . To give the puppets increased mobility , the costume department generally avoided stiff synthetic materials , instead working with cotton , silk and wool . Between 1964 and 1966 , the department 's stock numbered more than 700 costumes . 
- Each puppet 's head was fitted with about 10 thin tungsten steel wires . During the filming , dialogue was played into the studio using modified tape recorders that converted the feed into electronic pulses . Two of the wires relayed these pulses to the internal solenoid , completing the Supermarionation process . The wires , which were <unk> black to reduce their visibility , were made even less noticeable through the application of powder paint that matched the background colours of the set . Glanville explained the time @-@ consuming nature of this process : " [ The puppeteers ] used to spend over half an hour on each shot getting rid of these wires , looking through the camera , <unk> a bit more [ paint ] here , anti @-@ flare there ; and , I mean , it 's very depressing when somebody will say to us , ' Of course the wires showed . ' " <unk> on an overhead gantry with a hand @-@ held cruciform , the puppeteers co @-@ ordinated movements with the help of a <unk> @-@ powered CCTV feedback system . As filming progressed , the crew started to <unk> with wires and instead manipulate the puppets from the studio floor using rods . 
- Due to their low weight and the fact that they had only one control wire per leg , the puppets were unable to walk convincingly . Therefore , scenes involving movement were filmed from the waist up , with a puppeteer holding the legs below the level of the camera and using a " <unk> " action to simulate motion . Alternatively , dynamic shots were eliminated altogether : in an interview with New <unk> , director of photography John Read spoke of the advantages of <unk> the lack of agility so that the puppets " appear , for example , to walk through doors ( although the control wires make this impossible ) or pick up a coffee cup ( although their fingers are not in fact <unk> ) . " Live @-@ action shots of human hands were inserted whenever scripts called for more <unk> actions to be performed . 
- 
- = = = = Special effects = = = = 
- 
- The effects for all the APF series from Supercar to UFO were directed by Derek Meddings , who later worked on the James Bond and Superman films . Knowing that Thunderbirds would be the " biggest project [ APF ] had worked on " , Meddings found himself struggling to manage his workload with the single filming unit that had produced all the effects for Stingray . He therefore established a second unit under <unk> Brian <unk> , and a third exclusively for filming airborne sequences . This expansion increased the number of APF crews and stages to five each . A typical episode contained around 100 effects shots ; Meddings ' team completed up to 18 per day . 
- An addition to the effects department was Mike <unk> , who served as Meddings ' assistant in designing vehicles and buildings . Meddings and <unk> jointly pioneered an " organic " design technique in which the exteriors of models and sets were <unk> with parts from model kits and children 's toys . Models and sets were also " <unk> down " with powder paint or pencil lead to create a used look . Toy cars and <unk> were used in long shot , while scale vehicles were equipped with basic steering and suspension for added realism . <unk> fans and <unk> <unk> , which are capable of issuing air jets or chemical exhaust , were attached to the <unk> to simulate dust trails . Another of Meddings ' inventions was a closed , cyclical effects stage nicknamed the " rolling road " : consisting of two or more loops of canvas running at different speeds , this device allowed shots of moving vehicles to be filmed on a static set to make more efficient use of the limited studio space . Airborne aircraft sequences were mounted against a " rolling sky " , with smoke fanned across to simulate passing clouds . 
- One of Meddings ' first tasks was to shoot stock footage of the Thunderbird machines and the series ' main locations , Tracy Island and Creighton @-@ Ward Mansion . The finished island model was a composite of more than a dozen smaller sets that could be detached from the whole and filmed separately . The architecture of the mansion was based on that of <unk> House , located on the <unk> Estate in Wiltshire . In the absence of head designer Reg Hill , who was serving as associate producer , Meddings was further tasked with designing the Thunderbird fleet and FAB 1 . Scale models for the six main vehicles were built by a contractor , Master Models of Middlesex . Models and puppet sets combined , more than 200 versions of the Thunderbird machines were created for the series . 
- During the designing and filming process , Meddings ' first priorities were realism and credibility . With the exception of Thunderbird 5 , each vehicle was built in three or four scales . Meddings ' swing @-@ wing concept for Thunderbird 1 was inspired by his wish to create something " more dynamic " than a fixed @-@ wing aircraft . He remained unsatisfied with the prototype of Thunderbird 2 until he inverted the wings , later commenting , " ... at the time , all aircraft had swept @-@ back wings . I only did it to be different . " This decision was made out of personal preference and was not informed by any expert knowledge on Meddings ' part . He described the Thunderbird 2 launch as " probably the most memorable " sequence that his team devised for an APF production . 
- The largest model of Thunderbird 3 , whose design was based on the Soviet Soyuz rocket , was six feet ( 1 @.@ 8 m ) tall . Thunderbird 4 was particularly difficult to film : as the scale of the model did not correspond to the water inside the shooting tank , creative camera angles and rapid editing were used to produce a sense of realistic perspective . Thunderbird 5 , the most difficult vehicle for Meddings to <unk> , was based on the Tracy Island Round House . Since most of the space station 's appearances were provided by stock footage , the model was rarely filmed . Pod <unk> were designed on an episode @-@ by @-@ episode basis and built from <unk> wood , <unk> wood or fibreglass . To save time and costs , other minor vehicles were built in @-@ house from radio @-@ controlled model kits . 
- As the puppets of Lady Penelope and Parker needed to fit inside , the largest of all the models was the seven @-@ foot FAB 1 , which cost £ 2 @,@ 500 ( approximately £ 46 @,@ 000 today ) to build . The Rolls @-@ Royce 's name and colour were both chosen by Sylvia Anderson . Rolls @-@ Royce Ltd. supervised the construction of the <unk> model and supplied APF with an authentic <unk> grille for close @-@ up shots of the front of the car . In exchange for its cooperation , the company requested that a Spirit of <unk> be fixed to the chassis and that the characters avoid referring to the brand with abbreviations such as " Rolls " . 
- Scale explosions were created using substances such as fuller 's earth , petrol gel , magnesium strips and <unk> explosive . Originally filmed at up to 120 frames per second ( <unk> ) , they were slowed down to 24 <unk> during post @-@ production to increase their apparent magnitude and length . Gunpowder <unk> were <unk> to create rocket jets . The wires that electronically fired the rockets also allowed a member of the crew , holding a cruciform and positioned on an overhead gantry , to " fly " the model over the set . By far the most unwieldy model was Thunderbird 2 , which Meddings remembered as being " awful " to fly . A combination of unreliable rockets and weak <unk> frequently caused problems : should the former be slow to ignite , the current quickly caused the latter to <unk> and snap , potentially damaging the model and even setting fire to the set . Conditions above the studio floor were often dangerous due to the heat and smoke . Although many of the exhaust sound effects used in the series were drawn from an audio library , some were specially recorded during a Red Arrows display at RAF Little <unk> in Gloucestershire . 
- By 1966 , Meddings ' commitments were split between Series Two and Thunderbirds Are Go . While Meddings worked on the film , camera operator Jimmy Elliott assumed the responsibility of directing the TV effects . By this stage , the basic frame of Thunderbird 2 had been damaged so many times that the model had needed to be rebuilt from scratch . Meddings was <unk> with the result , reflecting that the replacement was " not only the wrong colour , it was a completely different shape ... I never felt our model @-@ makers managed to recapture the look of the original . " 
- Critic David Garland suggests that the challenge facing the Thunderbirds effects department was to strike a balance between the " conventional science @-@ fiction <unk> of the ' futuristic ' " and the " seeping hyper @-@ realist concerns mandated by the Andersons ' approach to the puppets " . Thunderbirds has been praised for the quality of its effects . Jim <unk> and Paul <unk> , writers of Collins <unk> Guide , consider the model work " uniformly impressive " . To Paul Cornell , Martin Day and Keith Topping , writers of The Guinness Book of Classic British TV , the effects are " way beyond anything seen on TV previously " . Impressed by their work on Thunderbirds , film director Stanley Kubrick hired several members of Meddings ' staff to supervise the effects shooting for 2001 : A Space Odyssey . 
- 
- = = = = Title sequence = = = = 
- 
- The series ' title sequence , <unk> by Gerry Anderson , is made up of two parts . It opens with a countdown of " 5 , 4 , 3 , 2 , 1 : Thunderbirds Are Go ! " , provided by Dyneley in character as Jeff Tracy . In a departure from the style of Stingray , the Thunderbirds title sequence varies with each episode : the first part consists of an action montage that serves as a preview of the plot . Simon Archer and Marcus <unk> , biographers of Gerry Anderson , compare this device favourably to a film trailer . 
- The second part , accompanied by composer Barry Gray 's " The Thunderbirds March " , features portraits of the main puppet cast superimposed on various vehicles and settings . Peel describes this as " ostensibly a return to the ' series stars ' concept long known in TV " , while Garland considers such imagery <unk> of Anderson 's commitment to " incremental realism " through a convergence of human and puppet characteristics . <unk> Jonathan Bignell suggests that the use of portraits conveys Anderson 's <unk> to " visual revelation of machines and physical action " . 
- According to Daniel O 'Brien , writer of SF : UK : How British Science Fiction Changed the World , the Thunderbirds title sequence encapsulates the reasons for the series ' enduring popularity . Dyneley 's countdown is particularly well remembered and has been widely quoted . Dean Newman of the Syfy channel website ranks Thunderbirds eighth in a list of " Top 10 TV title sequences " , while Den of Geek 's Martin Anderson considers the sequence the best of any TV series . 
- 
- = = = Music = = = 
- 
- The score was composed by Gray , who served as musical director for all of the Anderson productions up to the first series of Space : 1999 . In response to Gerry Anderson 's request that the main theme have a " military feel " , Gray produced a brass @-@ dominated piece titled " The Thunderbirds March " , which was recorded in December 1964 at Olympic Studios in London . The end titles were originally to have been accompanied by " Flying High " , a lyrical track sung by Gary Miller with backing by Ken <unk> . Ultimately , a variation of the march was used instead . <unk> music was recorded over nine months between March and December 1965 . As most of the music budget was spent on the series ' earlier episodes , later instalments drew heavily on APF 's ever @-@ expanding music library . 
- Peel considers " The Thunderbirds March " to be " one of the best TV themes ever written – perfect for the show and catchy when heard alone " . Morag <unk> of BBC Online argues that the piece is " up there ... in the quintessential soundtrack of the <unk> " with the James Bond films and the songs of Frank Sinatra , Elvis and The Beatles . More generally , he praises the series ' " catchy , pulse @-@ <unk> tunes " , as well as Gray 's aptitude for " musical nuance " and the mixing of genres . Heather <unk> of Allmusic considers " Thunderbirds Are Go ! " – the track accompanying the launch sequences of Thunderbirds 1 , 2 and 3 – to be a reflection of the mod aspect of 1960s British spy fiction . She also highlights Gray 's homage to – and divergence from – musical norms , commenting that his score " sends up the spy and action / adventure conventions of the ' 60s very <unk> and subtly " . 
- David <unk> identifies <unk> homage in both the theme music and the series ' premise . <unk> that the theme 's opening string <unk> is similar in effect to a recurring motif in Ride of the <unk> , he also <unk> the Thunderbird machines to <unk> themselves : " Their function is more benevolent than those warrior <unk> , but they do <unk> over danger , death and destruction . " Kevin J. Donnelly of the University of Southampton acknowledges the series ' " big @-@ sounding orchestral score " , which he compares to that of a live @-@ action film . He also suggests that the music serves partly to draw attention away from the physical imperfections of the puppets . 
- To celebrate the shows anniversary , " Thunderbirds Are Go - 50 Years On " at <unk> Hall , Bristol was celebrated as the theme music is brought to life , led by Charles <unk> and his All Star <unk> . 
- 
- = = Broadcast = = 
- 
- Thunderbirds premiered on British television on 30 September 1965 on the ITV franchises ATV Midlands , <unk> and Channel . Other broadcasters , including ATV London and Granada , started transmissions the following month . The Christmas @-@ themed series finale , " Give or Take a Million " , was first broadcast on 25 December 1966 . Despite Grade 's decision to extend the running time , Midlands and Granada broadcast each episode in two parts . In these areas , both 25 @-@ minute instalments aired on the same day , separated by the <unk> Evening News . The conclusion opened with a narration by Shane Rimmer <unk> the first part 's action . 
- Granada transmitted Thunderbirds in its original , 50 @-@ minute format for the first time with the start of repeats in 1966 . In 1968 , the franchise briefly aired episodes in three parts due to timeslot restrictions . The availability of repeats during the 1960s and 1970s varied among regions . ATV Midlands screened the series regularly into the early 1970s ; by contrast , Thunderbirds was entirely absent from Yorkshire Television between 1968 and 1976 . The series was last transmitted on the ITV franchises in 1981 . 
- In 1990 , 8 of the 19 audio episodes released by APF Records were converted into radio dramas , which were transmitted on BBC Radio 5 . The success of the radio series encouraged the BBC to acquire the rights to the TV episodes , which it broadcast simultaneously in all regions from September 1991 on BBC 2 . 
- Since the end of the first network run , which achieved average viewing figures of more than six million , the BBC has repeated the series six times : between 1992 and 1993 ( Series One only ) , 1994 and 1995 ( nine episodes only ) , and 2000 and 2001 ( in remastered form ) , as well as in 2003 , 2005 and 2006 . Other channels that have shown repeats include UK Gold ( 1994 – 95 ) , Bravo ( 1996 – 97 ) , <unk> Network ( 2001 – 02 ) , Boomerang ( 2001 – 03 ) and Syfy ( 2009 ) . In Scotland , the BBC screened a Gaelic dub , <unk> Tar As ( " Thunderbirds Are Go " ) in the early 1990s . 
- Before its UK debut , Thunderbirds was distributed to 30 other countries including the US , Canada , Australia and Japan . Pre @-@ sales revenue totalled £ 350 @,@ 000 ( approximately £ 6 million today ) . In the year following the series ' first appearance , the number of countries increased to 66 . In Japan , where it was first broadcast by <unk> , Thunderbirds attracted a sizeable fan following and influenced series such as <unk> , Mighty Jack , <unk> <unk> <unk> and Super Rescue <unk> . In the US , the two @-@ part format entered first @-@ run syndication , to modest success , in 1968 . Other overseas broadcasters have included <unk> and Family Room HD ( US ) , BBC Kids and <unk> ( Canada ) , Nine Network and <unk> ( Australia ) , <unk> ( New Zealand ) , <unk> <unk> Kids Central ( Singapore ) and <unk> Two ( Republic of Ireland ) . 
- 
- = = Reception = = 
- 
- Thunderbirds is generally considered the Andersons ' most popular series and their greatest critical and commercial success . In 1966 , the show received a Royal Television Society Silver Medal for Outstanding Artistic Achievement and Gerry Anderson received an honorary <unk> of the British <unk> , Sound and Television Society . In 2007 , Thunderbirds achieved 19th place in a Radio Times magazine reader poll to determine the best science @-@ fiction TV programme of all time . It is ranked fourth by the 2013 Channel 5 list show 50 Greatest Kids ' TV <unk> . 
- For Peel , Thunderbirds is " without a doubt the peak of the Supermarionation achievement " . Suggesting that the series is pitched at a " more adult " level than its predecessors , he adds that its sense of adventure , effective humour and " gripping and convincing " episodes ensured that " everyone in the audience found something to love about it . " Simon <unk> , a fan of Thunderbirds in childhood , commented positively on the series for The Daily Telegraph in 2011 : " All the elements we children discerned in whatever grown @-@ up television we had been allowed to watch were present in Thunderbirds : dramatic theme and incidental music ; well @-@ developed plots ; <unk> and <unk> ; <unk> Americans , at a time when the whole of Britain was in a cultural <unk> to them ; and , of course , glamorous locations ... Then , of course , there was the nail @-@ biting tension of the rescues themselves ... " Film critic Kim Newman describes the series as a " television perennial " . 
- In his <unk> to John Marriott 's book , Thunderbirds Are Go ! , Anderson put forward several explanations for the series ' enduring popularity : it " contains elements that appeal to most children – danger , jeopardy and destruction . But because International Rescue 's mission is to save life , there is no <unk> violence . " According to Anderson , Thunderbirds incorporates a " strong family atmosphere , where Dad <unk> supreme " . Both O 'Brien and script editor Alan Pattillo have praised the series ' positive " family values " . In addition , <unk> and others have written of its cross @-@ <unk> appeal . In 2000 , shortly before the series ' BBC revival , Brian <unk> remarked in Radio Times that Thunderbirds was on the point of " captivating yet another generation of viewers " . Stuart Hood , writing for The Spectator in 1965 , praised Thunderbirds as a " modern fairy tale " ; adding that it " can sometimes be frightening " , he recommended that children watch it accompanied by their parents . Writing for <unk> in 1994 , Andrew Thomas described Thunderbirds as only " nominally " a children 's programme : " Its themes are universal and speak as much to the adult in the child as the child in the adult . " 
- Jeff Evans , author of The Penguin TV Companion , argues that the series ' 50 @-@ minute format allows for stronger character development and " tension @-@ building " . O 'Brien is less positive in his appraisal of the writing , asserting that the plots are often " formulaic " and are sometimes " stretched to snapping point " by the extended running time . Cornell , Day and Topping are critical : they consider the writing at times " <unk> poor " and argue that Thunderbirds as a whole is " often as clichéd as previous Anderson series " . Peel , despite praising the storylines and characterisation , suggests that the " tongue @-@ in @-@ cheek " humour of Stingray is less evident . Where Thunderbirds improves on its predecessor , Peel believes , is in its rejection of fantasy plot devices , child and animal characters , comical and stereotyped villains and what he terms the " standard Anderson <unk> " : female characters , marginalised in earlier series , are more commonly seen to play active and sometimes heroic roles . 
- <unk> the attention to detail of the series ' launch sequences , Jonathan Bignell argues that part of the motivation for dedicating large amounts of screen time to the Thunderbird craft is the need to compensate for the limited mobility of the puppet cast . The focus on futuristic machines has also been explored by cultural historian Nicholas J. Cull , who comments that of all the Andersons ' series , Thunderbirds is the most evocative of a recurring theme : the " necessity of the human component of the machine " , whereby the failures of new technology are overcome by " brave human beings and technology working together " . This makes the series ' vision of the <unk> " wonderfully humanistic and <unk> " . O 'Brien similarly praises this optimism , comparing the Tracy family to guardian <unk> . Writing for Wired UK magazine , Warren Ellis asserts that the series ' scientific vision could inspire the next generation of " mad and frightening engineers " , adding that Thunderbirds " trades in vast , <unk> concepts ... immense and very beautiful ideas as solutions to problems . " 
- Thomas argues that the world of Thunderbirds is similar to the 1960s to the extent that contemporary capitalism and class structures appear to have survived mostly intact . He also observes , however , that wealth and high social status are often depicted as character flaws rather than strengths . According to Thomas , a contributing factor to the series ' lasting popularity is the realism of IR 's machines . Newman , for his part , suggests that " the point isn 't realism . The 21st century of Thunderbirds is detailed ... but also de @-@ populated , a high @-@ tech <unk> " . He is more negative in his comparisons of contemporary and future values , noting the " square , almost 50s " attitudes to race , gender and class . With regard to stereotyping , Hood comments that he " would be <unk> if [ villains ] didn 't seem to be recognisable by their <unk> " . Cull , by contrast , considers the series largely progressive on the subject of race , arguing that it rejects negative stereotyping through the use of " positive non @-@ white characters " such as Kyrano and Tin @-@ Tin . However , he deems many of the one @-@ off villains derivative , commenting that these characters are typically presented as " corrupt businessmen , <unk> and gangsters familiar from crime films " . 
- Various commentators – including Bignell , Cull and O 'Brien – have also discussed Thunderbirds as a product of the Cold War era . Bignell comments that the Hood 's Oriental appearance and mysterious powers draw parallels with James Bond villains and fears of China operating as " a ' third force ' antagonistic to the West " . Cull observes that , despite the series ' focus on the dangers of nuclear technology , the Thunderbird machines contradict this particular theme : in their case , " an image of technology associated with the threat of Cold War mass destruction – the rocket emerging from the hidden <unk> – was appropriated and deployed to save life rather than to take it . " He argues that the series <unk> more closely to cultural norms by drawing on the " Cold War cult of the secret agent whose skills defend the home from enemies unknown " , noting Lady Penelope 's role as a spy in addition to two episodes ( " 30 Minutes After Noon " and " The Man from <unk> " ) that are heavily influenced by the James Bond novels and film adaptations . 
- The presentation of smoking in Thunderbirds was the subject of a study published in the medical journal Tobacco Control in 2002 . Despite identifying examples in 26 episodes , Kate Hunt of the University of Glasgow concluded that Thunderbirds does not actively promote smoking – a view opposed by the Roy Castle <unk> Cancer Foundation ( <unk> ) at the time of the series ' relaunch on BBC 2 . <unk> the <unk> 's proposal that the remastered episodes be edited to digitally erase all visible cigarettes and cigars , the BBC stated that the series " does not glorify or encourage smoking " and described the activity as " incidental to the plot " . 
- 
- = = Merchandise = = 
- 
- More than 3 @,@ 000 Thunderbirds @-@ themed products have been marketed since the series ' debut . To accommodate the high demand for tie @-@ ins , APF established three dedicated <unk> : AP Films Merchandising , AP Films Music and AP Films Toys . Some British commentators dubbed the 1966 end @-@ of @-@ year shopping season " Thunderbirds Christmas " due to the series ' popularity . In the early 1990s , <unk> launched a new toy range to coincide with the BBC 2 repeats . Sales figures for Christmas 1992 exceeded those achieved by the Star Wars merchandising campaigns of the 1970s and 1980s . <unk> for <unk> 's Tracy Island <unk> overwhelmed supply , resulting in shop fights and a substantial black market for the toy . 
- A comic strip featuring the characters of Lady Penelope and Parker debuted in the early issues of APF Publishing 's children 's title TV Century 21 in 1965 . A full @-@ length Thunderbirds strip appeared a year later , at which point the Lady Penelope adventures were given their own comic . Thunderbirds , Lady Penelope and Captain Scarlet and Thunderbirds <unk> were published in the late 1960s ; during the same period , eight original novels were written . In 2008 , <unk> Publications of Minnesota launched a new series of tie @-@ in novels . 
- Between 1965 and 1967 , APF Records released 19 audio episodes in the form of vinyl EPs . Three are original stories ; the rest are adapted from TV episode soundtracks , with additional narration provided by a member of the voice cast . The series ' first video game tie @-@ in , developed for the Commodore 64 and ZX Spectrum computers , was released by <unk> Software in 1985 . Other titles have since been released for the Game Boy Color , Game Boy Advance and PlayStation 2 . During the late 1980s , the series was issued on home video for the first time by <unk> and its subsidiary Channel 5 . Following its acquisition by Carlton International Media in 1999 , Thunderbirds was digitally remastered for the release of the first DVD versions in 2000 . Blu @-@ ray Disc editions followed in 2008 . 
- 
- = = Later productions = = 
- 
- Thunderbirds has been followed by two film sequels , a live @-@ action film adaptation , two animated TV remakes and several re @-@ edited presentations for TV broadcast and home video . The second of the remakes , Thunderbirds Are Go , premiered on ITV in 2015 , the 50th anniversary year of the original . 
- 
- = = = Film = = = 
- 
- The feature @-@ length film sequels Thunderbirds Are Go and Thunderbird 6 were released in 1966 and 1968 . The first was <unk> by Lew Grade before the TV series had started its broadcast run . Written and produced by the Andersons and directed by David Lane , both films were distributed by United Artists . Neither was a critical or commercial success , and Century 21 Cinema 's plans for further sequels were abandoned . 
- In the early 1980s , episodes of Thunderbirds and other Supermarionation series were re @-@ edited by ITC 's New York offices to create a series of compilation films . <unk> " Super Space Theater " , this format was mostly intended for family viewing on American cable and syndicated TV . Three Thunderbirds features were produced : Thunderbirds to the Rescue , Thunderbirds In Outer Space and <unk> to Disaster . 
- Plans for a live @-@ action film adaptation were first announced in 1993 . These eventually culminated in the 2004 film Thunderbirds , directed by Jonathan Frakes and produced by <unk> and Working Title Films . It was a critical and commercial failure and was poorly received by fans of the TV series . 
- 
- = = = TV = = = 
- 
- The Andersons sold their intellectual and profit participation rights to Thunderbirds and their other productions in the 1970s . As a result , they had no developmental control over subsequent adaptations of their works . Thunderbirds was first remade for TV in the early 1980s as Thunderbirds <unk> . In this anime re @-@ imagining , set 20 years after the original , the vastly expanded IR is based within an <unk> and operates 17 Thunderbird machines . It was inspired by <unk> , an updated story concept by Gerry Anderson and Reg Hill that later served as the basis for Anderson 's <unk> series <unk> . 
- Two re @-@ edited series , based on <unk> versions of 13 of the original episodes , aired in the US in 1994 . The first , Thunderbirds USA , was broadcast as part of the Fox Kids programming block ; the second , <unk> Thunderbirds , was syndicated by UPN . Developed as a comedy , <unk> Thunderbirds moved the action to the planet " Thunder @-@ World " and combined the original puppet footage with new live @-@ action scenes featuring a pair of human teenagers . 
- As well as <unk> , Anderson developed other ideas for a remake . A 1976 concept , Inter @-@ <unk> Rescue 4 , was to have featured a variable @-@ configuration craft capable of performing rescues on land and sea , in air and in space ; Anderson pitched the idea to NBC , who rejected it . This was followed in 1984 by another proposed updating , T @-@ Force , which at first could not be pursued owing to a lack of funding . Development resumed in 1993 , when it was decided to produce the series , now titled <unk> , using cel animation . But Anderson was disappointed with the results and the production was abandoned . 
- In 2005 , Anderson re @-@ affirmed his wish to remake Thunderbirds but stated that he had been unable to secure the necessary rights from Granada <unk> . His negotiations with the company and its successor , ITV <unk> , continued for the next few years . In 2008 , he expressed his commitment to creating an " updated " version , ideally using CGI ; three years later , he announced that work on the series had commenced . Following Anderson 's death in December 2012 , it was confirmed that a deal had been struck between ITV Studios and <unk> Pictures to remake Thunderbirds using a combination of CGI and live @-@ action model sets . The new version , Thunderbirds Are Go , has been commissioned for two series of 26 episodes each . The first 13 episodes of Series One were broadcast on ITV HD and <unk> between April and June 2015 . 
- In July 2015 , to celebrate the series ' 50th anniversary , Filmed in Supermarionation documentary director Stephen La Rivière launched a Kickstarter campaign to raise the funds necessary to produce three new puppet episodes based on the Thunderbirds mini @-@ albums of the 1960s . The project , titled " Thunderbirds 1965 " , is supported by ITV , Sylvia Anderson and the estate of Gerry Anderson . 
- 
- = = Influence = = 
- 
- Thunderbirds has influenced TV programmes , films and various other media . The puppet comedy of the film Team America : World Police was directly inspired by the idiosyncrasies of Thunderbirds @-@ era Supermarionation techniques . <unk> and homage are also evident in Wallace and <unk> : A Close <unk> , Austin Powers : The Spy Who <unk> Me and <unk> , as well as the character design of Star Wars : The <unk> Wars . The BBC sketch comedy Not Only ... But Also included a segment titled " <unk> " – a parody of Thunderbirds , Supercar and Stingray . 
- IR 's life @-@ saving mission inspired the founding of the volunteer International Rescue Corps , originally made up of a group of British <unk> who contributed to the humanitarian effort following the 1980 <unk> earthquake . Virgin Group has used the series in branding its services : Virgin Atlantic operates a Boeing <unk> @-@ 400 airliner named Lady Penelope , while Virgin Trains owns a fleet of <unk> that are all named after main characters and vehicles and used specifically to " rescue " broken @-@ down trains . 
- A <unk> stage show , Thunderbirds : <unk> , has toured internationally and popularised a staccato style of movement known colloquially as the " Thunderbirds walk " . The production has periodically been revived as Thunderbirds : <unk> – The Next Generation . 
- Cover versions of " The Thunderbirds March " have been released by musicians and bands such as Billy Cotton , Joe Loss , Frank <unk> , The <unk> and The Shadows . Groups who have written songs inspired by the series include <unk> ( with " International Rescue " ) , <unk> ( with " Thunderbirds Are Coming Out " ) , <unk> ( with " Thunderbirds / <unk> " ) and <unk> ( with " Thunderbirds – Your Voice " ) . In 1991 , Anderson filmed the music video for the Dire Straits single " Calling Elvis " with a collection of Thunderbirds @-@ style puppets . 
- During the 1960s , APF produced themed TV advertisements for Lyons Maid and <unk> 's . Aspects of Thunderbirds have since been used in advertising for <unk> Insurance , <unk> Kit <unk> , <unk> and the UK Driver and Vehicle Licensing Agency . 
- 
- 
- = T. Arthur Cottam = 
- 
- T. Arthur Cottam is a screenwriter , actor , producer and film director . A graduate of the Film and Television Production program at the <unk> School of the Arts of New York University , Cottam resides in Los Angeles , California . He acted in theatre , and received an Artistic Director Achievement Award from the Valley Theatre League for his role in the theatre production Othello as a cast member of Zombie Joe 's Underground Theatre Group . Cottam directed short films along a topical series called " Dirty Little Shorts " . 
- His <unk> work Pornographic Apathetic deals with four people recounting pornographic film dialog in a state of apathy . Pornographic Apathetic was featured in numerous movie festivals , and received eight film awards as well as critical acclaim . Cottam 's film Carbuncle was featured in 2006 at the Milano Film Festival in Italy , and was recognized with a nomination in the category of " Best Feature Film " . His 2006 film Filthy Food received the award for " Best Experimental Short Film " at the festival <unk> NYC . Cottam 's film 52 Takes of the Same Thing , Then Boobs was an entrant in the 2010 International Short Film Festival in <unk> , Italy , and was featured in <unk> <unk> in the same year . 
- 
- = = Education = = 
- 
- Cottam graduated from the Film and Television Production program at the <unk> School of the Arts of New York University . Cottam 's thesis work at <unk> was titled Beer <unk> , the film received the Best Editing award at the First Run Film Festival and was featured at the Chicago and New York Underground Film Festivals . He received a Bachelor of Fine Arts degree from <unk> in 1998 . 
- 
- = = Career = = 
- 
- T. Arthur Cottam is an actor , and film @-@ maker , based in Los Angeles , California . Cottam was a member of Zombie Joe 's Underground Theatre Group , and performed along with actors Denise Devin and <unk> Larsen in 2000 in a series of productions titled " <unk> 7 : Bury the <unk> " , directed by Zombie Joe and Josh T. Ryan . He continued performing in theatre in Los Angeles in 2001 . In 2002 , Cottam received an Artistic Director Achievement Award from the Valley Theatre League for his role in the theatre production Othello put on by the Zombie Joe group . Cottam 's short films were produced along a topical series titled , " Dirty Little Shorts " . His film Pornographic Apathetic debuted in 2002 . The <unk> plot features four individuals ( two women and two men ) who recreate dialog from pornographic film while in a state of apathy . Pornographic Apathetic garnered eight film awards , and was featured at more than 50 film festivals . 
- Pornographic Apathetic received positive reception from journalist Gary <unk> of The Dallas Morning News , who described it as " Especially notable ... an experimental work that strips the <unk> from <unk> " . Pornographic Apathetic was selected for inclusion in the <unk> International Fantastic Festival ( <unk> ) in South Korea ; the festival " aims to identify the important elements of fantastic films , such as creativity and popularity " . The movie was shown at the Paris <unk> Film <unk> at the Le Brady cinema , in Paris , France . The film was featured in an exhibit in Vienna , examining the nature of pornography . Writing for San Antonio Express @-@ News , Mike Greenberg observed , " T. Arthur Cottam 's Pornographic Apathetic is just about the funniest thing I 've ever seen . ( The excellent actors are mostly clothed , never touch each other and are nearly motionless , but the dialogue is not suitable for children or <unk> . ) " The film was given a favorable review by reporter Robert W. Butler of The Kansas City Star who covered the piece during its presentation at the Kansas City <unk> Jubilee . Butler wrote , " Your ears will burn while watching T. Arthur Cottam 's <unk> <unk> essay on the <unk> effects of pornography , but you 'll also be laughing . ... It 's lurid . It 's <unk> . It 's <unk> . But all this nasty <unk> is delivered deadpan , a device that <unk> the whole intention of <unk> . " Marc Mohan reviewed the film during its feature at the Portland International Short Short Film Festival , and wrote that it " makes the most of its one @-@ joke premise " . In 2007 , The Orlando Sentinel characterized Cottam as a favorite film @-@ maker at the Florida Film Festival . Los Angeles Times journalist <unk> <unk> cited the film as an example of problems with the adult film industry in an article about a sexual art exhibit ; he commented , " The piece makes you laugh while illustrating one of the <unk> of <unk> : At its core , it 's rather <unk> and boring . " 
- In 2006 , Cottam 's film Carbuncle competed at the Milano Film Festival , alongside another U.S. film titled The Blood of my Brother by <unk> <unk> . Carbuncle received a nomination in the category of " Best Feature Film " at the Milan Film Festival . Cottam 's 2006 film Filthy Food was featured in the San Francisco Underground Short Film Festival , and received the award for " Best Experimental Short Film " at the festival <unk> NYC . In 2010 , Cottam 's film 52 Takes of the Same Thing , Then Boobs was an entrant in the International Short Film Festival in <unk> , Italy . It was featured in a section of the International Short Film Festival which included selections of films that were considered " visionary " and contributed a " visual impact " to cinema . 52 Takes of the Same Thing , Then Boobs was featured in <unk> <unk> 2010 , where Lane <unk> associate director of programming called it " the most outrageously ' out there ' film that we have scheduled " . In an interview with <unk> , Cottam stated he had intended to direct a feature @-@ length film for some time , and wanted to combine his talents with actors who could improvise in front of the camera . Cottam said he had a great experience working with the actors on the film Carbuncle , and stated he let improvisation be the tool by which the actors could show emotions and create their characters . In September 2010 , 52 Takes of the Same Thing , Then Boobs was shown at the Black Rock City Film Festival located at the Burning Man site in the Nevada desert . 
- 
- = = Filmography = = 
- 
- 
- = = = Film = = = 
- 
- 
- = = = Theatre = = = 
- 
- 
- = = Awards and nominations = = 
- 
- 
- 
- = Partington = 
- 
- Partington is a town and civil parish within the Metropolitan Borough of Trafford in Greater Manchester , England , about ten miles ( 16 km ) to the south @-@ west of Manchester city centre . Historically part of Cheshire , it lies on the southern bank of the Manchester Ship Canal , opposite <unk> on the northern bank . It has a population of 7 @,@ <unk> . 
- A paper mill built in Partington more than 250 years ago was the first factory in Trafford . The completion of the Manchester Ship Canal in 1894 transformed Partington into a major coal @-@ exporting port and attracted a range of other industries . Until 2007 Shell Chemicals UK operated a major <unk> manufacturing complex in Carrington , Partington 's closest neighbour , to the east . The gas storage facility in the north @-@ eastern corner of the town was once a <unk> and another significant employer . 
- Shortly after the Second World War , local authorities made an effort to <unk> people away from Victorian slums in inner @-@ city Manchester . An area of Partington extended as an <unk> estate is now one of the most deprived parts of the Greater Manchester conurbation . The Cheshire Lines Committee opened a railway line through the town in 1873 , but it closed in 1964 . 
- Partington and Carrington Youth Partnership was established to provide the town 's youth with activities and the town has seen investment in a new youth centre . Broadoak School , the only secondary school in the town , is used by Trafford College to provide further education . 
- 
- = = History = = 
- 
- Partington , first recorded in 1260 , was in the medieval and post @-@ medieval parish of <unk> . The name derives from Old English : the first element may be a personal name such as <unk> or <unk> , or part " land divided up into <unk> " followed by <unk> , meaning " people of " ; the <unk> <unk> means " <unk> " . The village consisted of dispersed <unk> , with no nucleated centre . It was surrounded by wetlands on all sides , reducing the amount of land available for agriculture . According to the hearth tax returns of 1664 , Partington had a population of 99 . 
- In 1755 a paper mill on the River <unk> was opened in Partington , the first factory to be established in present @-@ day Trafford . <unk> Farmhouse dates from the late 18th century and is a Grade II listed building . Also protected as a Grade II listed building are the stocks on the village green . Its stone pillars are from the 18th century , although the wooden restraints were replaced in the 20th century . 
- The completion of the Manchester Ship Canal in 1894 transformed Partington into a major coal @-@ exporting port . The canal was widened to 250 feet ( 76 m ) for three @-@ quarters of a mile ( 1 @.@ 2 km ) to allow for the construction of a coaling basin , equipped with four hydraulic coal hoists . Partington was the nearest port to the Lancashire <unk> , and brought the south Yorkshire collieries 30 miles ( 48 km ) closer to the sea . Between 1898 – 1911 , exports of coal accounted for 53 @.@ 4 per cent of the total export tonnage carried by the ship canal . The coal trade in turn resulted in Partington becoming a major railway depot , and attracted a range of other industries , including the Partington Steel & Iron Company , which was encouraged by the availability of coal to construct a <unk> . The works became a part of the Lancashire Steel Corporation in 1930 , and dominated the economy of nearby <unk> until their closure in 1976 . After the Second World War , Partington was extended as an <unk> estate . 
- 
- = = <unk> = = 
- 
- The civil parish of Partington was created in 1894 , under the Local Government Act 1894 and has its own town council . Partington became part of the Metropolitan Borough of Trafford in 1974 upon the borough 's creation , but was previously in <unk> Rural District . The town is part of the <unk> St Martin electoral ward ; Partington Parish Council is made up of local residents and ward councillors . 
- Partington also belongs to the <unk> and <unk> constituency and is part of the North West England constituency of the European Parliament . Since its creation in 1997 the constituency 's Member of Parliament has been a member of the Labour Party , Kate Green being the present incumbent . 
- 
- = = Geography = = 
- 
- At 53 ° 25 ′ 12 ″ N 2 ° 25 ′ 48 ″ W ( 53 @.@ 42 , − 2 @.@ 43 ) , Partington lies west of Sale , north @-@ east of the civil parish of Warburton , and is on Trafford 's northern border with the City of Salford . It is nine miles ( 14 km ) south @-@ west of Manchester city centre . <unk> Brook runs east – west through the area , and the town is about 20 m ( 66 ft ) above sea level on generally flat ground . 
- Partington 's local drift geology is a mixture of alluvial deposits , <unk> @-@ glacial gravel , and peat deposited about 10 @,@ 000 years ago , during the last ice age . The bedrock is <unk> sandstone in the south and <unk> sandstone in the north . The town 's climate is generally temperate , like the rest of Greater Manchester . The mean highest and lowest temperatures ( 13 @.@ 2 ° C ( 55 @.@ 8 ° F ) and 6 @.@ 4 ° C ( 43 @.@ 5 ° F ) ) are slightly above the national average , while the annual rainfall ( <unk> @.@ 6 millimetres ( 31 @.@ 76 in ) ) and average hours of sunshine ( <unk> @.@ 5 hours ) are respectively above and below the national averages . 
- 
- = = <unk> = = 
- 
- According to the Office for National Statistics , at the time of the United Kingdom Census 2001 , Partington had a population of 7 @,@ <unk> . The 2001 population density was 5 @,@ 348 inhabitants per square mile ( 2 @,@ <unk> / km2 ) , with a 100 to 93 @.@ 1 female @-@ to @-@ male ratio . Of those over 16 years old , 34 @.@ 7 % were single ( never married ) , 34 @.@ 9 % married , and 10 @.@ 5 % divorced . Partington 's 3 @,@ 354 households included 33 @.@ 5 % one @-@ person , 28 @.@ 7 % married couples living together , 8 @.@ 8 % were co @-@ <unk> couples , and 16 @.@ 3 % single parents with their children . Of those aged 16 – 74 , 38 @.@ 9 % had no academic qualifications , significantly higher than the averages of Trafford ( 24 @.@ 7 % ) and England ( 28 @.@ 9 % ) . It has been described as one of the most deprived places in the Greater Manchester conurbation . 
- As of the 2001 UK census , 76 @.@ 8 % of Partington 's residents reported themselves as being Christian , 0 @.@ 8 % Muslim , 0 @.@ 2 % Hindu , 0 @.@ 2 % Jewish , and 0 @.@ 1 % Sikh . The census recorded 14 @.@ 7 % as having no religion , 0 @.@ 1 % had an alternative religion and 7 @.@ 1 % did not state their religion . 
- 
- = = Economy = = 
- 
- The main shopping area of Partington is on Central Road , in the centre of town . Partington also has a traditional market on Smithy Lane . 
- Spanish company , <unk> , bought a lease on a disused <unk> site by the Manchester Ship Canal in 2009 to <unk> paper into packaging . The plant is the most " advanced , fully integrated facility in the UK " . It uses water recycled from the ship canal and its heat and power plant supplies surplus power to the national grid . The plant cost £ 300 million and production started in January 2012 . 
- According to the 2001 UK census , the industry of employment of residents aged 16 – 74 was 19 @.@ 3 % retail and wholesale , 15 @.@ 0 % manufacturing , 14 @.@ 7 % property and business services , 10 @.@ 8 % health and social work , 9 @.@ 1 % transport and communications , 7 @.@ 2 % construction , 5 @.@ 2 % education , 4 @.@ 8 % hotels and restaurants , 3 @.@ 8 % finance , 3 @.@ 1 % public administration , 1 @.@ 3 % agriculture , 0 @.@ 7 % energy and water supply , 0 @.@ 1 % mining , and 4 @.@ 9 % other . Compared with national figures , Partington had a relatively high percentage of residents working in transport and communications , and a relatively low percentage working in public administration . The census recorded the economic activity of residents aged 16 – 74 , 1 @.@ 8 % students were with jobs , 3 @.@ 4 % students without jobs , 7 @.@ 7 % looking after home or family , 10 @.@ 6 % permanently sick or disabled , and 3 @.@ 8 % economically inactive for other reasons . The proportion of those who were permanently sick or disabled in Partington was above the Trafford and England average ( 5 @.@ 4 % and 6 @.@ 5 % respectively ) . 
- 
- = = Education = = 
- 
- Trafford maintains a selective education system assessed by the Eleven Plus exam . There are three primary schools and one secondary school in Partington . The oldest school still standing in the town was opened in 1958 and used to be called Partington County Primary School . There is one other state primary schools at Forest Gate with Academy status . There is also a Roman Catholic school – Our Lady of Lourdes Primary School on Lock Lane . Broadoak School is a secondary school with academy status for pupils aged 11 to 16 . 
- 
- = = Transport = = 
- 
- Partington 's main road is the <unk> between <unk> and the <unk> area of Sale . The Manchester Ship Canal also carries some industrial traffic . The nearest road crossing over the canal is at Warburton Bridge , one of the few remaining pre @-@ motorway toll bridges in the UK , and the only one in Greater Manchester . The Department for Transport describes Partington as " geographically isolated with road access restricted by the proximity of the Manchester Ship Canal and the nearby petrochemical works [ in Carrington ] " and notes that there are low levels of car ownership . The 255 operates every 30 minutes during the day , and <unk> after 1955 into Manchester Piccadilly 7 days a week 
- The town was served by a railway station to the north of the town , the Cheshire Lines Committee <unk> to <unk> <unk> Dale Line . The station was opened in 1873 , eight years after the line opened , and was in use until 30 November 1964 . A grant of £ 312 @,@ 000 was made by the government to set up Partington Cooperative Transport ( <unk> ) with the purpose of improving public transport in the town . 
- 
- = = <unk> = = 
- 
- Founded in 2003 as part of a government project for <unk> to 19 @-@ year @-@ olds , Partington and Carrington Youth Partnership ( <unk> ) has since expanded its scope and provides facilities for youths up to the age of 25 . It runs a 5 @-@ a @-@ side football league and <unk> ' <unk> <unk> Park . In 2009 it was announced that a £ 5 million youth centre would be built in the town . Based on designs by pupils from Broadoak Secondary School , the centre will provide facilities for workshops in dance , film @-@ making , and art . <unk> of the school , Andy Griffin , said " It 's a massive thing for Partington as this will help kick @-@ start regeneration of the town . I think it will also help bring people to Partington rather than leaving " . 
- <unk> opportunities for sport , a £ 2 million sports complex was opened next to Broadoak Secondary School in 2008 . It features a pool , a sports hall , outdoor pitches and grass courts , and facilities for other activities . The town is served by an Anglican church , St Mary 's , a Roman Catholic church dedicated to Our Lady of Lourdes , a Methodist chapel , and a Baptist church called the People 's Church . St Mary 's Church is a Grade II listed building , and Our Lady of Lourdes ' <unk> Church and parish was founded in 1957 . Partington is in the Catholic <unk> of Roman Catholic Diocese of Shrewsbury and the Anglican Diocese of Chester . 
- 
- 
- = Key ( basketball ) = 
- 
- The key , officially referred to as the free throw lane by the National Basketball Association ( NBA ) and the National Collegiate Athletic Association ( NCAA ) , the restricted area by the international governing body FIBA , and colloquially as the lane or the paint , is an area on a basketball court underneath the basket bounded by the <unk> , the foul line and other lines which are known as <unk> lines , that are usually painted ( although <unk> on some courts with painted <unk> ) . It is a critical area on the court where much of the action takes place in a game . 
- The key , in all games , starting with FIBA 's amendments to its rules in 2010 ( to be first implemented after the 2010 FIBA World Championship ) , is rectangular . Prior to 2006 , the key in FIBA @-@ sanctioned tournaments ( mostly basketball played outside the United States , and almost all international tournaments including the World Championships and the Olympics ) was trapezoidal in shape . Both NBA and FIBA keys are 16 feet ( 4 @.@ 9 m ) wide , while NCAA keys are narrower at 12 feet ( 3 @.@ 7 m ) . 
- The most @-@ commonly enforced rule on the key is the " three seconds rule " in which a player from the offensive team is prohibited from staying on the key for more than three seconds , or else the player 's team will lose possession of the ball . Another rule enforced is the lane violation in which players from both teams are prohibited to enter the lane until after the free throw shooter releases the ball from his hands ( the shooter is prohibited to enter the key until after the ball hits the rim ) . An innovation is the introduction of the restricted area arc directly underneath the basket where the defending player cannot force an offensive foul on the opposing player . 
- 
- = = <unk> = = 
- 
- Each level of play has different specifications for the size and shape of the key : in American leagues , where the basketball court is measured in imperial units , the shape is rectangular , while in FIBA @-@ sanctioned events , which use the metric system , the shape was trapezoidal , before being changed to a <unk> as well . In addition to the <unk> <unk> , the key includes a free @-@ throw circle at its " head " or " top " . 
- The width of the key in the NBA is 16 feet ( 4 @.@ 9 m ) , including the 2 @-@ foot ( 0 @.@ 6 m ) wide foul lanes ; in U.S. college ( NCAA ) and high @-@ school play , it is 12 feet ( 3 @.@ 7 m ) . 
- Beginning after the 2010 FIBA World Championship , all FIBA @-@ administered tournaments use a rectangular key 4 @.@ 9 meters ( 16 ft ) wide . From 1956 until 2010 , FIBA @-@ sanctioned tournaments used a trapezoidal key . The narrower end was on the free @-@ throw line , where it was 3 @.@ 6 meters ( 12 ft ) , while the wider end , at the end line , measured 6 meters ( 20 ft ) . 
- The free throw circle has a six @-@ foot ( 1 @.@ 8 m ) radius and is centered at the midpoint of the free throw line ; the half of those circle on the mid @-@ court side of the free throw line is painted in solid lines . In the NBA and <unk> , the boundaries of the half closer to the basket is traced in a broken line in order to space players properly for jump balls . NBA Rule 1 ( g ) requires the key to contain two 6 inches ( 15 cm ) long <unk> marks , 3 feet ( 0 @.@ 91 m ) from the free throw line ; the marks indicate the so @-@ called lower defensive box . The free @-@ throw line is 15 feet ( 4 @.@ 6 m ) from the perpendicular projection of the face of the <unk> onto the court ; this projection is 4 feet ( 1 @.@ 2 m ) from the end @-@ line for NBA and NCAA . The projection of the center of the basket onto the court is a perpendicular distance of 1 @.@ 575 meters ( 5 @.@ 17 ft ) from the end line in FIBA tournaments , but 4 @.@ 75 feet ( 1 @.@ 45 m ) in NBA and NCAA tournaments . 
- 
- = = History = = 
- 
- Originally , the key was narrower than it is today and had the shape of a skeleton / basic lever lock <unk> , measuring six feet ( 1 @.@ 8 m ) wide , hence " the key " , with the free throw circle as the head , and the <unk> lane as the body . Due to the <unk> of the key , imposing centers , such as George <unk> , dominated the paint , scoring at will . To counter this , the key was widened into 12 feet ( 3 @.@ 7 m ) from 6 feet ( 1 @.@ 8 m ) at the onset of the 1951 – 52 NBA season . 
- Men 's professional basketball in the United States ( notably the National Basketball Association ) widened it further to 16 feet ( 4 @.@ 9 m ) in the 1964 – 65 NBA season to lessen the effectiveness of centers , especially Wilt Chamberlain . The NCAA retains the 12 feet key to this day . 
- On April 25 , 2008 , the FIBA Central Board approved rule changes that included the changes in the shape of the key ; the key is now rectangular and has virtually the same dimensions as the key used in the NBA . In addition , the no @-@ charge <unk> formally called the restricted area arc was also created . 
- 
- = = Rules = = 
- 
- 
- = = = Three @-@ second violation = = = 
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- The lane is a restricted area in which players can stay for only a limited amount of time . On all levels , a team on the offensive ( in possession of the ball ) is prohibited to stay inside the lane for more than three seconds ; after three seconds the player will be called with a three @-@ second violation which will result in a turnover . 
- In American professional basketball , the defending team is also prohibited from staying in the key for more than three seconds , unless a player is directly guarding an offensive player . If a player surpasses that time , his / her team will be charged with a defensive three @-@ second violation , which will result in a technical foul where the team with the ball shoots one free throw plus ball possession and a reset of the shot clock . In FIBA @-@ sanctioned tournaments , on the other hand , the defending team is allowed to stay on the key for an unlimited amount of time . In all cases , the count <unk> if the shot hits the rim or if the player steps out of the lane . 
- 
- = = = Lane violation = = = 
- 
- When a player is shooting free throws , there are a certain number of players at the boundaries of the key , each occupying a slot traced at the boundaries of the key . In most cases , the free throw shooter is behind the free throw line , while three of his opponents are along the sides of the key , one side with two players , the other with one . Two of his opponents are situated nearest to the basket on both sides , while his two teammates are beside the two opponents closest to the basket , with the other player from the opposing team situated farthest from the basket . In the U.S. NCAA , there are as many as six players along the key , with the opposing team allowed to have as many as four players , with the same arrangement as in the NBA and FIBA but with another player facing his teammate farthest to the basket . ( See photographs to the right . ) 
- No player along the lane may enter the key until the shot is released ; the player shooting the free throw , and anyone on top of the key , should not pass the free throw line until the ball hits the rim . If any of the offensive players violate the rule , no points are awarded for the shot and , if there are no more shots remaining , the ball is given to the defending team . If a defending player is in the lane too soon , an extra shot will be awarded regardless of whether the shot was made or missed . 
- Note that in FIBA play , if the shooter commits the violation , it is an automatic turnover . If the shot is successful and the shooter does not commit a violation , but other players do commit a violation , all violations are discarded . If players from the opposing teams enter the key prior to the release of the ball , a jump ball would be done to determine who gets the possession of the ball ( NBA ) or the possession arrow rule ( for all other levels ) ; in FIBA play , that only applies if the shooter misses , since a successful attempt <unk> all other penalties . In all situations , lane violation penalties cannot occur if there are further free throws to be awarded . 
- 
- = = = <unk> area arc = = = 
- 
- In the NBA , <unk> , and starting in 2010 , in FIBA and NCAA play , the key has an additional area , measured as an arc three feet from the basket ( collegiate ) , four feet from the basket ( NBA ) , or 1 @.@ 25 meters ( approximately 4 @.@ 1 feet ) ( FIBA ) . The area is officially known as the " restricted area " ( RA ) in the NBA , the " restricted area arc " in the NCAA and the " no @-@ charge <unk> " in FIBA . 
- Its purpose is to stop secondary defenders from taking a position under the basket in an attempt to draw the offensive foul when a player is driving to the basket . If an offensive player drives past his primary defender on the way to the basket and a secondary defender comes over , he must establish a legal position outside the RA to draw an offensive foul . If the drive starts inside the Lower Defensive Box ( <unk> – this is the area from the bottom tip of the free throw circle to the end line between the two 3 ’ posted @-@ up marks ) , the secondary defender is legally allowed to be positioned inside the RA . The restricted area also does not apply if the secondary defender jumps in attempting to block the shot , the offensive player leads with his leg or knee in an unnatural motion or uses his off arm to prevent the defender from blocking his shot . The RA does not extend from below the <unk> to the baseline . Therefore , if a player drives the baseline and is not attempting to go directly to the rim , the RA does not apply . 
- The restricted area arc rule was implemented in NCAA men 's basketball for the 2010 – 2011 season . The NCAA approved adding a visible restricted @-@ area arc three feet from the center of the basket in Division I men ’ s and women ’ s games for 2011 – 2012 season . The panel delayed implementation of the arc until the 2012 @-@ 13 season for Divisions II and III to allow those schools time to plan and place the restricted @-@ area arc in their home arenas . Starting with the 2015 @-@ 2016 season , the NCAA raised the RA arc to four feet from the center of the basket . 
- 
- = = Terms = = 
- 
- Points made on the key are termed as points in the paint or inside points . Historically , the area of the key where offensive players are prohibited from remaining longer than three seconds has been painted to distinguish the area from the rest of the court ; hence the phrase " points in the paint . " The area around the free throw circle 's farthest point from the basket is called the " top of the key " , and several plays revolve around this area , such as screens and pick and rolls . In American women 's collegiate basketball ( and for men until 2008 ) , the three @-@ point arc intersects at the top of the key , which could translate plays conducted in this area into three @-@ point field goal <unk> . 
- The intersection of the free throw line and the free throw lane is referred to as the elbow of the key . 
- 
- 
- = The General in His Labyrinth = 
- 
- The General in His Labyrinth ( original Spanish title : El general en su laberinto ) is a novel by the Colombian writer and Nobel laureate Gabriel García Márquez . It is a fictionalized account of the last days of Simón Bolívar , liberator and leader of Gran Colombia . First published in 1989 , the book traces Bolívar 's final journey from Bogotá to the Caribbean coastline of Colombia in his attempt to leave South America for exile in Europe . In this dictator novel about a continental hero , " despair , sickness , and death inevitably win out over love , health , and life " . Breaking with the traditional heroic portrayal of Bolívar El <unk> , García Márquez depicts a pathetic protagonist , a prematurely aged man who is physically ill and mentally exhausted . The story explores the labyrinth of Bolívar 's life through the narrative of his memories . 
- Following the success of others of his works such as One Hundred Years of Solitude and Love in the Time of Cholera , García Márquez decided to write about the " Great Liberator " after reading an unfinished novel about Bolívar by his friend Álvaro Mutis . He borrowed the setting — Bolívar 's voyage down the Magdalena River in 1830 — from Mutis . After two years of research that encompassed the extensive memoirs of Bolívar 's Irish aide @-@ de @-@ camp , Daniel Florencio O 'Leary , as well as numerous other historical documents and <unk> with academics , García Márquez published his novel about the last seven months of Bolívar 's life . 
- Its mixture of genres makes The General in His Labyrinth difficult to classify , and commentators disagree over where it lies on the scale between novel and historical account . García Márquez 's insertion of interpretive and fictionalized elements — some dealing with Bolívar 's most intimate moments — caused outrage in parts of Latin America when the book was released . Many prominent Latin American figures believed that the novel damaged the reputation of one of the region 's most important historic figures and portrayed a negative image to the outside world . Others saw The General in His Labyrinth as a tonic for Latin American culture and a challenge to the region to deal with its problems . 
- 
- = = Background = = 
- 
- The initial idea to write a book about Simón Bolívar came to García Márquez through his friend and fellow Colombian writer Álvaro Mutis , to whom the book is dedicated . Mutis had started writing a book called El Último <unk> about Bolívar 's final voyage along the Magdalena River , but never finished it . At the time , García Márquez was interested in writing about the Magdalena River because he knew the area intimately from his childhood . Two years after reading El Último <unk> , García Márquez asked Mutis for his permission to write a book on Bolívar 's last voyage . 
- García Márquez believed that most of the information available on Bolívar was one @-@ dimensional : " No one ever said in Bolívar 's biographies that he sang or that he was <unk> ... but historians don 't say these things because they think they are not important . " In the <unk> to the novel , García Márquez writes that he researched the book for two years ; the task was difficult , both because of his lack of experience in conducting historical research , and the lack of documentary evidence for the events of the final period of Bolívar 's life . 
- García Márquez researched a wide variety of historical documents , including Bolívar 's letters , 19th @-@ century newspapers , and Daniel Florencio O 'Leary 's 34 volumes of memoirs . He engaged the help of various experts , among them geographer Gladstone <unk> ; historian and fellow Colombian <unk> Gutiérrez <unk> , who had co @-@ written a book called Bolívar Día a Día with historian <unk> <unk> ; and astronomer Jorge <unk> — García Márquez used an inventory drawn up by <unk> to describe which nights Bolívar spent under a full moon . García Márquez also worked closely with Antonio Bolívar <unk> , a distant relative of Bolívar , during the extensive editing of the book . 
- 
- = = = Historical context = = = 
- 
- The novel is set in 1830 , at the tail end of the initial campaign to secure Latin America 's independence from Spain . Most of Spanish America had gained independence by this date ; only Cuba and Puerto Rico remained under Spanish rule . 
- Within a few decades of Christopher Columbus 's landing on the coast of what is now Venezuela in <unk> , South America had been effectively conquered by Spain and Portugal . By the beginning of the 19th century , several factors affected Spain 's control over its colonies : Napoleon 's invasion of Spain in 1808 , the abdication of Charles IV , Ferdinand VII 's <unk> of his right to succeed , and the placement of Joseph Bonaparte on the Spanish throne . The colonies were virtually cut off from Spain , and the American and French <unk> inspired many <unk> — American @-@ born descendants of Spanish settlers — to take advantage of Spanish weakness . As a result , Latin America was run by independent <unk> and colonial self @-@ governments . 
- The early 19th century saw the first attempts at securing liberation from Spain , which were led in northern South America by Bolívar . He and the independence movements won numerous battles in Venezuela , New Granada and present @-@ day Ecuador and Peru . His dream of uniting the Spanish American nations under one central government was almost achieved . However , shortly after the South American colonies became independent of Spain , problems developed in the capitals , and civil wars were sparked in some provinces ; Bolívar lost many of his supporters and fell ill . Opposition to his presidency continued to increase , and in 1830 , after 11 years of rule , he resigned as president of Gran Colombia . 
- 
- = = Plot summary = = 
- 
- The novel is written in the third @-@ person with flashbacks to specific events in the life of Simón Bolívar , " the General " . It begins on May 8 , 1830 in Santa Fe de Bogotá . The General is preparing for his journey towards the port of Cartagena de <unk> , intending to leave Colombia for Europe . Following his resignation as President of Gran Colombia , the people of the lands he liberated have now turned against him , <unk> anti @-@ Bolívar graffiti and throwing waste at him . The General is anxious to move on , but has to remind the Vice @-@ President @-@ elect , General Domingo <unk> , that he has yet to receive a valid passport to leave the country . The General leaves Bogotá with the few officials still faithful to him , including his <unk> and aide @-@ de @-@ camp , José Palacios . At the end of the first chapter , the General is referred to by his full title , General Simón José Antonio de la Santísima Trinidad Bolívar y Palacios , for the only time in the novel . 
- On the first night of the voyage , the General stays at <unk> with his entourage , which consists of José Palacios , five aides @-@ de @-@ camp , his clerks , and his dogs . Here , as throughout the journey that follows , the General 's loss of prestige is evident ; the downturn in his fortunes surprises even the General himself . His unidentified illness has led to his physical deterioration , which makes him <unk> , and his aide @-@ de @-@ camp is constantly mistaken for the Liberator . 
- After many delays , the General and his party arrive in Honda , where the Governor , <unk> Gutiérrez , has arranged for three days of fiestas . On his last night in Honda , the General returns late to camp and finds one of his old friends , Miranda <unk> , waiting for him . The General recalls that fifteen years ago , she had learned of a plot against his life and had saved him . The following morning , the General begins the voyage down the Magdalena River . Both his physical <unk> and pride are evident as he <unk> the slope to the dock : he is in need of a <unk> chair but refuses to use it . The group stays a night in Puerto Real , where the General claims he sees a woman singing during the night . His aides @-@ de @-@ camp and the <unk> conduct a search , but they fail to uncover any sign of a woman having been in the vicinity . 
- The General and his entourage arrive at the port of <unk> . Here they are stopped by police , who fail to recognize the General . They ask for his passport , but he is unable to produce one . Eventually , the police discover his identity and escort him into the port . The people still believe him to be the President of Gran Colombia and prepare banquets in his honor ; but these festivities are wasted on him due to his lack of strength and appetite . After several days , the General and his entourage set off for Turbaco . 
- The group spend a <unk> night in <unk> <unk> before they arrive in Turbaco . Their original plan was to continue to Cartagena the following day , but the General is informed that there is no available ship bound for Europe from the port and that his passport still has not arrived . While staying in the town , he receives a visit from General Mariano <unk> and a few other friends . The deterioration of his health becomes increasingly evident — one of his visitors describes his face as that of a dead man . In Turbaco , the General is joined by General Daniel Florencio O 'Leary and receives news of ongoing political machinations : Joaquín <unk> , appointed successor as President of Gran Colombia , has assumed power but his legitimacy is still contested by General Rafael <unk> . The General recalls that his " dream began to fall apart on the very day it was realized " . 
- The General finally receives his passport , and two days later he sets off with his entourage for Cartagena and the coast , where more receptions are held in his honor . Throughout this time , he is surrounded by women but is too weak to engage in sexual relations . The General is deeply affected when he hears that his good friend and preferred successor for the presidency , Field Marshal Sucre , has been ambushed and assassinated . 
- The General is now told by one of his aides @-@ de @-@ camp that General Rafael <unk> has taken over the government in Bogotá , and there are reports of demonstrations and riots in support of a return to power by Bolívar . The General 's group travel to the town of Soledad , where he stays for more than a month , his health declining further . In Soledad , the General agrees to see a physician for the first time . 
- The General never leaves South America . He finishes his journey in Santa Marta , too weak to continue and with only his doctor and his closest aides by his side . He dies in poverty , a shadow of the man who liberated much of the continent . 
- 
- = = Characters = = 
- 
- 
- = = = The General = = = 
- 
- The leading character in the novel is " the General " , also called " the Liberator " . García Márquez only once names his protagonist as Simón Bolívar , the famous historical figure , whose full title was General Simón José Antonio de la Santísima Trinidad Bolívar y Palacios , on whom the General 's character is based . The novel 's portrait of a national and Latin American hero , which challenges the historical record , provoked outrage in some quarters on its publication . 
- At the beginning of the novel , the General is 46 years old and slowly dying on his last journey to the port of Cartagena de <unk> , where he plans to set sail for Europe . As Palencia @-@ Roth notes , " Bolívar is cast here not only as a victim but as an agent of Latin America 's tragic political flaws " . The fortunes of the historical Simón Bolívar began to decline in 1824 after the victory of his general Antonio José de Sucre at <unk> . The novel draws on the fact that the historical Bolívar never remarried after the death of his wife , María Teresa Rodríguez del Toro y <unk> . García Márquez uses other documented facts as starting points for his fictional portrait of Bolívar – for example , his dedication to the army above all else , his premature aging , and his bad temper . Of the latter , Bolívar 's aide @-@ de @-@ camp O 'Leary once remarked that " his <unk> and impatient temperament would never tolerate the smallest delay in the execution of an order " . 
- In an interview with María Elvira <unk> , García Márquez has admitted that his portrayal of Bolívar is partly a self @-@ portrait . He identifies with Bolívar in many ways , since their method of controlling their anger is the same and their philosophical views are similar : neither " pays much attention to death , because that <unk> one from the most important thing : what one does in life " . 
- 
- = = = José Palacios = = = 
- 
- The novel begins with the name of José Palacios , who , here as with the historical figure of the same name , is Bolívar 's " long @-@ serving <unk> " . As literary critic Seymour <unk> observes , Palacios 's " total identification with Bolívar constitutes the novel 's frame " . Palacios constantly <unk> on the General , and at certain times he alone is allowed in the General 's room . He has learned to live with his master 's unpredictability and does not <unk> to read his thoughts . Born a slave , the character is six years younger than the General , and has spent his entire life in his service . Throughout the novel , Palacios provides the General with <unk> or <unk> of dates and events during the General 's time of <unk> . According to one critic , Palacios 's ability to recall past events in Bolívar 's life is essential for García Márquez 's recreation of the character , as it allows the Bolívar of official history to be placed within the context of everyday life . 
- 
- = = = Manuela Sáenz = = = 
- 
- Manuela Sáenz is the General 's long @-@ time lover , his last since the death of his wife , 27 years earlier . Her character is based on Simón Bolívar 's historical mistress Doña Manuela Sáenz de Thorne , whom Bolívar dubbed " the liberator of the liberator " after she helped save him from an assassination attempt on the night of September 25 , 1828 . García Márquez 's fictional portrait stimulated a <unk> of this historical figure , who is increasingly seen , according to Venezuelan historian <unk> Romero , " not just as a mistress but as the intelligent , independent , forceful woman she was " . In the novel , she is described as " the bold <unk> who loved him but was not going to follow him to his death " . The General leaves Manuela Sáenz behind , but throughout the novel he writes to her on his journey . She also attempts to write letters to him with news of the political situation , but the mail carriers have been instructed not to accept her letters . Like the historical figure on whom she is based , the fictional Manuela Sáenz is married to Dr. James Thorne , an English physician twice her age . The historical Manuela Sáenz left Thorne after Bolívar wrote declaring his <unk> love for her . In the novel she is characterized as astute and <unk> , with " irresistible grace , a sense of power , and <unk> tenacity " . 
- 
- = = = General Francisco de Paula Santander = = = 
- 
- As he reflects on the past , the General often thinks and dreams about his former friend Francisco de Paula Santander . The historical Francisco de Paula Santander was a friend of Simón Bolívar , but was later accused of <unk> in a plot to assassinate him and sent into exile . In the novel , the General remembers that he had once appointed Santander to govern Colombia because he believed him to be an effective and brave soldier . He formerly regarded Santander as " his other self , and perhaps his better self " , but by the time of the events in The General in His Labyrinth Santander has become the General 's enemy and has been banished to Paris after his involvement in the assassination attempt . The General is depicted as tormented by the idea that Santander will return from his exile in France ; he dreams , for example , that Santander is eating the pages of a book , that he is covered in cockroaches , and that he is <unk> out his own <unk> . 
- 
- = = = Field Marshal Antonio José de Sucre = = = 
- 
- Field Marshal Antonio José de Sucre is portrayed as an intimate friend of the General . The historical Antonio José de Sucre , the Field Marshal of <unk> , had been the most trusted general of Simón Bolívar . García Márquez describes him as " intelligent , methodical , shy , and superstitious " . The Field Marshal is married to and has a daughter with Doña Mariana <unk> . In the first chapter of the novel , the General asks Sucre to succeed him as President of the Republic , but he rejects the idea . One of the reasons Sucre gives is that he wishes only to live his life for his family . Also at the beginning of the novel , Sucre 's death is foreshadowed . Sucre tells the General that he plans on celebrating the Feast of Saint Anthony in Quito with his family . When the General hears that Sucre has been assassinated in <unk> on his way back to Quito , he <unk> blood . 
- 
- = = = Minor characters = = = 
- 
- The novel revolves around the fictionalized figure of Bolívar and includes many minor characters who are part of the General 's travelling party , whom he meets on his journey or who come to him in his memories and dreams of his past . Sometimes they are identified by particular <unk> or tied to small but significant events . They include , for instance , General José María <unk> , a member of the entourage , whose right arm was amputated after a combat wound , and who once revealed a military secret by talking in his sleep . At other times , they are <unk> for the General 's now failing powers : Fernando , for example , the General 's nephew , is " the most willing and patient of the General 's many clerks " , and the General wakes him " at any hour to have him read aloud from a dull book or take notes on urgent <unk> " . One of the least developed of the minor characters is the General 's wife , María Teresa Rodríguez del Toro y <unk> , who had died , readers are told , in mysterious circumstances shortly after their marriage . The General has " buried her at the bottom of a water @-@ tight oblivion as a brutal means of living without her " ; she only <unk> enters his memories in the book 's last chapter . According to <unk> , she is " upstaged " by Manuela Sáenz , whose later history García Márquez recounts as if she instead were the General 's widow . María Teresa 's death , however , marked the General 's " birth into history " , and he has never tried to replace her . 
- 
- = = Major themes = = 
- 
- 
- = = = Politics = = = 
- 
- In The General in His Labyrinth , García Márquez voices his political views through the character of the General . For example , Alvarez Borland points out that in the scene where the General responds to the French diplomat , his words closely reflect García Márquez 's 1982 Nobel Address . The diplomat is critical of the <unk> in Latin America and the brutal means used in attempting to achieve independence . Bolívar replies by pointing out that Europe had centuries to progress to its current state , and that South America should be left to experience its " Middle Ages in peace " . Similarly García Márquez remarks in his Nobel Speech that " venerable Europe would perhaps be more <unk> if it tried to see Latin America in its own past . If only it recalled that London took three hundred years to build its first city wall ... " . 
- The novel was published in 1989 , when the Soviet Union was disintegrating and the political map was being radically redrawn . Reviewing The General in His Labyrinth in 1990 , the novelist Margaret Atwood pointed to another instance of García Márquez raising political issues through the character of the General . He has him tell his aide that the United States is " omnipotent and terrible , and that its tale of liberty will end in a plague of <unk> for us all " . Atwood noted the contemporary relevance of this sentiment , since " the patterns of Latin American politics , and of United States intervention in them , have not changed much in 160 years . " She suggested that García Márquez 's <unk> of Bolívar is a lesson " for our own turbulent age ... <unk> have a long history of eating their <unk> . " The central character is a man at the end of his life , who has seen his revolution and dream of a united Latin America fail . 
- 
- = = = <unk> labyrinth = = = 
- 
- According to literary critic David <unk> , the labyrinth of the novel 's title refers to " a series of <unk> that are contingent upon matters of history , geography , and biography ... that consistently and conclusively result in a dead end " — in this case , the General 's own death . His final voyage along the Magdalena River involves a doubling back and forth from one location to another that leads him and his followers nowhere . The labyrinth does not lead to happiness ; instead , it results in madness from constant <unk> on the past and an impossible future . At the end of his life , the General is reduced to a <unk> of his former self . The labyrinth also recalls the labyrinth built to imprison the <unk> in Greek mythology , and the endless travelling and searching of ancient Greek heroes . In <unk> 's view , " The Labyrinth mirrors the <unk> and <unk> of the hero in search for meaning and resolution to the <unk> of life " . 
- García Márquez depicts the General 's body itself as a labyrinth . His doctor observes that " everything that enters the body , adds weight , and everything that leaves it is <unk> . " The General 's body is described as a " labyrinth coming to a literal dead end " . The labyrinth is also expressed in geographical and architectural imagery . The country 's destiny is imagined as a break @-@ up , a folding of north into south . The seas offer the hope of a new life and a new world , but the closer the General is to Colombia , the less chance he has of moving on . García Márquez describes buildings as " daunting , <unk> ( if not exactly reiterating ) with the echoes of a bloody past " . The portrayal of the General 's world as a labyrinth is <unk> by his constant return to cities and towns he has visited before : each location belongs to the past as well as to the present . The General in his Labyrinth <unk> the lines between <unk> in a man @-@ made world and wandering in the natural world . 
- 
- = = = Fate and love = = = 
- 
- Bolívar 's fate is known from the beginning , and García Márquez constantly uses images which <unk> this ending . For instance , a clock stuck at seven minutes past one , the exact time of the General 's death , appears repeatedly in the novel . This sense of fate is introduced in the <unk> , which comes from a letter written by the historical Bolívar to General Santander on August 4 , 1823 : " It seems that the devil controls the business of my life . " As Palencia @-@ Roth points out , the word used for devil here is <unk> rather than the more familiar <unk> . <unk> derives from the Greek word <unk> , which can equally mean divine power , fate , or destiny . Accordingly , the General <unk> to his fate and accepts his death as destiny . 
- The theme of love is central to the novel . Bolívar had a reputation as a <unk> , and books have been written on his <unk> ; but as depicted in this novel , during the last seven months of his life , the General could no longer engage in the activities that had fueled that reputation . García Márquez mentions a woman every few pages , many of whom are his own invention , exploring love through the General 's memories . Palencia @-@ Roth notes that the presence of these women " allows a <unk> exploration of his life before his final journey " and suggests that García Márquez uses love as a barometer of the General 's heart and health . Although Bolívar is usually thought to have died from tuberculosis , Palencia @-@ Roth believes that for the author , the General dies from the lack of love . " <unk> by many of his countrymen , abandoned by all but a few aides and associates , left — during the final seven months of his life — without even the companionship of his longtime mistress Manuela <unk> , Bolívar had no choice but to die of a broken heart . " 
- 
- = = = Numbers and religious symbols = = = 
- 
- Numbers are an important symbolic aspect of the novel . The book is divided into eight chapters , almost all of equal length , which represent the eight @-@ year love affair between the General and Manuela Sáenz . The General 's last hours are marked by an octagonal clock . <unk> to the number three are even more common in the novel . As García Márquez scholar Isabel Rodríguez Vergara notes , the number three — the Trinity which occupies a vital place in the <unk> of the Catholic Mass — is repeated 21 times throughout the book . She quotes <unk> <unk> : " In the novel it represents a symbolic sacrifice aimed at redeeming humankind — that of Bolívar , a misunderstood <unk> sacrificed by his own people . " 
- Rodríguez Vergara observes that the General is like a supernatural being , simultaneously dying and being surrounded by symbolic circumstances such as rain , fiestas , and the plague . The novel begins with Bolívar immersed in purifying waters , in a state of ecstasy and meditation that suggests a priestly ritual . One of the women with whom the General sleeps , Queen Marie Louise , is described as a virgin with the profile of an idol — an allusion to the Virgin Mary . The General rides a mule into the last towns on his journey towards death , echoing Christ 's entry into Jerusalem . He dies of mysterious and unknown causes , and the people burn his belongings in fear of catching his illness . In Rodríguez Vergara 's view , " Bolívar was sacrificed as a <unk> to purge the guilt of the community . " 
- René <unk> has interpreted the recurrence of rain in the novel as one of the purifying rituals the community must undergo in order to wash away the <unk> of violence . The fiestas may represent another ritual of purification and also symbolize war . <unk> are held to honour the General when he arrives at a town , but at other times , political demonstrations against the General are mistaken for a fiesta . According to Rodríguez Vergara , this shows how " information is manipulated " and " depicts an atmosphere where fiesta and war are synonymous " . 
- 
- = = = Melancholy and mourning = = = 
- 
- Latin American cultural theorist Carlos J. Alonso , drawing on <unk> theory , argues that the novel is essentially a therapeutic device , designed to help move Latin America past its problematic experience of modernity . He compares this to the way the healing state of mourning replaces grief in the process of recovering from a death . Both activities are mechanisms for dealing with loss . Alonso believes that The General in his Labyrinth , by almost entirely centering the novel on the General 's death , forces the reader to confront the horror of this process . In Alonso 's view , the reader is meant to pass from " a melancholy relationship vis @-@ a @-@ vis the figure of Bolívar to a relationship that has the therapeutic qualities of mourning instead " . 
- Latin America 's history and culture , Alonso suggests , began with the loss of Bolívar 's dream of a united continent and as a result has developed under a melancholy shadow ever since . Thus , by forcing the reader to return to the origin of modernity in Latin America and confront its death in the most horrific way , García Márquez <unk> the reader to move from melancholy to mourning , " so that the <unk> of the lost object of modernity may cease to rule the <unk> economy of Spanish American cultural discourse and historical life " . 
- 
- = = = <unk> history = = = 
- 
- García Márquez comments on the nature of historical fact by drawing attention to the way history is written . The novel <unk> a time in Bolívar 's life that has no historical precedent , as there is no record of the last 14 days of his life . In García Márquez 's account readers observe Bolívar intimately , seeing his human qualities . In the view of critic Isabel Alvarez Borland , by choosing to <unk> a national hero in this way , García Márquez is challenging the claim of official history to represent the truth . In the " My <unk> " section of the novel , García Márquez asserts ironically that what he is writing is more historical than fictional , and he discusses his own historical methodology in detail . By posing in the role of a historian , he challenges the reliability of written history from within the writing process . According to Alvarez Borland , this serves to " remind us that a claim to truth is not the property of any text ; rather it is the result of how a historian ( as a reader ) interprets the facts " . 
- The General in His Labyrinth also confronts the methods of official historians by using an oral style of narration . The narration can be considered an oral account in that it is woven from the verbal interactions of everyday people . Alvarez Borland explains that the advantage of this technique , as discussed by Walter <unk> , is that " the <unk> of any given culture , residing in the unwritten tales of its peoples , possesses a spontaneity and <unk> which is lost once this culture commits its tales to writing . " The oral style of narration therefore provides a <unk> which official history lacks . Alvarez Borland concludes that The General in His Labyrinth suggests new ways of writing the past ; it takes account of voices that were never written down as part of official history . 
- The historian Ben Hughes commented on the novel : " The Liberator 's British <unk> , including Daniel O 'Leary , were amongst the closest figures to the general in this period . Nevertheless , they are ignored in the novel . Instead , Márquez uses the character of a fictional Colombian servant , José Palacios , as The Liberator 's final sounding board , thereby <unk> <unk> the more complex reality . " In Hughes 's view modern South American literature has played a role in cleansing the national memory of British soldiers ' assistance to The Liberator . 
- 
- = = Comparisons with other García Márquez novels = = 
- 
- In an interview published in the Colombian weekly <unk> <unk> on March 20 , 1989 , García Márquez told María Elvira <unk> , " At bottom , I have written only one book , the same one that circles round and round , and continues on . " Palencia @-@ Roth suggests that this novel is a " <unk> <unk> ... of García Márquez 's long @-@ standing <unk> and ever @-@ present topics : love , death , solitude , power , fate " . 
- Like the Patriarch in García Márquez 's The Autumn of the Patriarch , Bolívar was an absolute dictator . The Patriarch is never identified by name ; Bolívar , too , is identified chiefly by his title . Bolívar also invites comparison with Colonel <unk> <unk> in One Hundred Years of Solitude : both characters believe the wars they have <unk> have been fruitless and overwhelming , and both face numerous attempts on their lives , but eventually die of natural causes . In his belief that life is controlled by fate , the General resembles <unk> in One Hundred Years of Solitude and Santiago <unk> in Chronicle of a Death <unk> . 
- Palencia @-@ Roth notes that critics have been struck by the <unk> elegiac style of The General in His Labyrinth ; its dark mood and somber message is similar to that of The Autumn of the Patriarch . Love is a theme common to both Love in the Time of Cholera and The General in His Labyrinth , but the latter is considered a tragedy . These two novels have been used to demonstrate the range of García Márquez 's work . 
- Isabel Alvarez Borland , in her essay " The Task of the Historian in El general en su laberinto " , claims that " ... while El general en su laberinto is in many ways a continuation of García Márquez 's criticism of Latin America 's official history seen in his earlier works , the novel contrasts sharply with his previous <unk> " . In Chronicle of a Death <unk> , according to Alvarez Borland , the narrator challenges the truth of official language . However , The General in His Labyrinth " differs from these earlier works in employing narrative strategies which seek to answer in a much more overt and <unk> fashion questions that the novel poses about history " . 
- In a summary of Edward Hood 's book La <unk> de Gabriel García Márquez : <unk> e <unk> , García Márquez is characterized as an author who uses repetition and <unk> ( <unk> between the works of a single author ) extensively in his fiction , including in The General in His Labyrinth . Hood points out some obvious examples of repetition in García Márquez 's works : the themes of solitude in One Hundred Years of Solitude , <unk> in Autumn of the Patriarch , and the desire for a unified continent expressed by Bolívar in The General in His Labyrinth . An example of <unk> can be seen in the repetition of patterns between books . For example , both Jose <unk> <unk> in One Hundred Years of Solitude and Bolívar in The General in his Labyrinth experience <unk> dreams . 
- 
- = = Genre = = 
- 
- Critics consider García Márquez 's book in terms of the historical novel , but differ over whether the label is appropriate . In his review of The General in his Labyrinth , <unk> Rodman hesitated to call it a novel , since it was so heavily researched , giving Bolívar 's views " on everything from life and love to his chronic <unk> and dislike of tobacco smoke " . On the other hand , reviewer Robert Adams suggested that García Márquez had " improved on history " . According to critic Donald L. Shaw , The General in His Labyrinth is a " New Historical Novel " , a genre that he argues crosses between Boom , Post @-@ Boom , and <unk> fiction in Latin American literature : " New Historical Novels tend either to <unk> historical events from an unconventional perspective , but one which preserves their <unk> , or to question the very possibility of making sense of the past at all . " Shaw believes that this novel belongs to the first category . García Márquez is presenting both a historical account and his own interpretation of events . 
- David Bushnell , writing in The Hispanic American Historical Review , points out that the work is less a pure historical account than others suggest . García Márquez 's Bolívar is a man " who wanders naked through the house , suffers <unk> , uses foul language , and much more besides . " He argues that documentation does not support many of these details . Bushnell suggests , however , that the fact that the novel is not entirely historically accurate does not necessarily distinguish it from the work of professional historians . The main difference , Bushnell believes , is that García Márquez 's work " is far more readable " than a pure history . 
- 
- = = Reception = = 
- 
- The General in His Labyrinth was relatively poorly received by the general public in the United States , despite the praise of critics . Critic Ilan Stavans , who himself praised the book as " one of the writer 's most sophisticated and accomplished " , attributes this to the novel 's time period and to its <unk> of historical information , neither of which proved attractive to English @-@ speaking readers . Isabel Alvarez Borland notes that , like Stavans , " critics in the United States have largely celebrated García Márquez 's portrait of this national hero and considered it a tour de force " ; but she also observes that in Latin America the book received more mixed reviews , ranging from " outrage to <unk> praise " . 
- The novel generated huge controversy in Latin America : some Venezuelan and Colombian politicians described its depiction of Bolívar as " <unk> " . According to Stavans , they accused García Márquez of " <unk> the larger @-@ than @-@ life reputation of a historical figure who , during the nineteenth century , struggled to unite the vast Hispanic world " . The novel 's publication provoked outrage from many Latin American politicians and intellectuals because its portrayal of the General is not the <unk> image long cherished by many . Mexico 's ambassador to Austria , Francisco <unk> <unk> , wrote a <unk> letter , which was widely publicized in Mexico City , <unk> to the portrayal of Bolívar . He stated : " The novel is plagued with errors of fact , conception , fairness , understanding of the historical moment and ignorance of its consequences ... It has served the enemies of Latin America , who care only that they can now <unk> Bolívar , and with him all of us . " Even the novel 's admirers , such as the leading Venezuelan diplomat and writer <unk> <unk> <unk> , worried that some facts were stretched . García Márquez believes , however , that Latin America has to discover the General 's labyrinth to recognize and deal with its own maze of problems . 
- More positively , Nelson <unk> , a Venezuelan TV commentator , considers the novel to be a tonic for Latin American culture : " people here saw a Bolívar who is a man of flesh and bones just like themselves " . Mexican author Carlos <unk> agrees with <unk> saying : " What comes across beautifully and <unk> in this book is a man dealing with the unknown world of democratic ideas " . García Márquez realistically portrays a ridiculous figure trapped in a labyrinth , <unk> the General 's defects , and presenting an image of Bolívar contrary to that instilled in classrooms . However , the novel also depicts Bolívar as an idealist and political theorist who predicted many problems that would obstruct Latin American advancement in the future . García Márquez depicts a figure who was aware of the racial and social friction in Latin American society , feared debt , and warned against economic <unk> . He has the General warn his aide @-@ de @-@ camp , Agustín de <unk> , against the future interference of the United States in the internal affairs of Latin America . 
- <unk> and critic Barbara <unk> comments that the book 's English translator , Edith Grossman , fully captures the multiple levels of meaning of the text , as well as García Márquez 's <unk> in tone . García Márquez himself has admitted that he prefers his novels in their English translations . 
- 
- = = Publication history = = 
- 
- The original Spanish version of The General in His Labyrinth was published simultaneously in Argentina , Colombia , Mexico , and Spain in 1989 . The first American edition was listed as a best seller in The New York Times the following year . 
- The novel has been translated into many languages since its first publication in Spanish , as detailed by <unk> de González in 2003 . 
- 
- 
- = Copia ( museum ) = 
- 
- Copia : The American Center for Wine , Food & the Arts was a non @-@ profit museum and educational center in downtown Napa , California , dedicated to wine , food and the arts of American culture . The center , planned and largely funded by vintners Robert and <unk> Mondavi , was open from 2001 to 2008 . The museum had galleries , two theaters , classrooms , a demonstration kitchen , a restaurant , a rare book library , and a 3 @.@ 5 @-@ acre ( 1 @.@ 4 ha ) vegetable and herb garden ; there it hosted wine and food tasting programs , exhibitions , films , and concerts . The main and permanent exhibition of the museum , " Forks in the Road " , explained the origins of cooking through to modern advances . The museum 's establishment benefited the city of Napa and the development and <unk> of its downtown . 
- Copia hosted its opening celebration on November 18 , 2001 . Among other notable people , Julia Child helped fund the venture , which established a restaurant named Julia 's Kitchen . Copia struggled to achieve its anticipated admissions , and had difficulty in <unk> its debts . <unk> from ticket sales , membership and donations attempted to support Copia 's payoff of debt , educational programs and exhibitions , but eventually were not sufficient . After numerous changes to the museum to increase revenue , Copia closed on November 21 , 2008 . Its library was donated to Napa Valley College and its Julia Child <unk> was sent to the National Museum of American History . The 12 @-@ acre ( 4 @.@ 9 ha ) property had been for sale since its closure ; the Culinary Institute of America purchased the northern portion of the property in October 2015 . The college intends to open a campus , the Culinary Institute of America at Copia , which will house the CIA 's new Food Business School . 
- 
- = = History = = 
- 
- 
- = = = Name = = = 
- 
- The museum was named after Copia , the Roman goddess of wealth and plenty . According to Joseph Spence in <unk> ( 1755 ) , Copia is a name used to describe the goddess <unk> in poetry , and was referred to as <unk> Copia in <unk> 's <unk> . 
- 
- = = = Background = = = 
- 
- The city of Napa has historically not received as many wine country tourists as the cities north of it . A $ 300 million flood management project around the turn of the 21st century to widen the Napa River and raise bridges prompted building developments . In the early 2000s , a large development was completed in the downtown area , as well as several hotels . Copia and the nearby Oxbow Public Market were two large developments also constructed around that time to increase tourist and media focus on the city of Napa . 
- The museum opened in 2001 , two months after the September 11 attacks . The museum 's visitor attendance was much lower than what was projected ; the museum partially attributed that to the depressed tourist economy stemming from the attacks . 
- 
- = = = Conception and construction = = = 
- 
- In 1988 , vintner Robert Mondavi , his wife <unk> Mondavi , and other members of the wine industry began to look into establishing an institution in Napa County to educate , promote , and celebrate American excellence and achievements in the culinary arts , visual arts , and winemaking . Three organizations supported the museum : the University of California at Davis , the Cornell University School of Hotel Administration , and the American Institute of Wine & Food . In 1993 , Robert Mondavi bought and donated the land for Copia for $ 1 @.@ 2 million ( $ 1 @.@ 97 million today ) , followed by a lead gift of $ 20 million ( $ 32 @.@ 8 million today ) . Mondavi chose the downtown Napa location with urging from his wife , who raised her children there . James <unk> was hired by the foundation as the architect for the building in October 1994 . Subsequently , the " <unk> Seventy " , supporters from Napa Valley and the surrounding Bay Area , made substantial donations . Initial financing for Copia was $ 55 million ( $ 66 @.@ 8 million today ) , along with a $ 78 million ( $ 104 million today ) bond prior to opening in 2001 . 
- When the organization purchased the property , it was an empty lot next to a tire store . Steve <unk> , founder of the Oxbow Public Market , believed that Copia 's establishment helped expand Napa , its downtown area , and the Oxbow District . Construction of the facility triggered a significant growth in development of a <unk> marketplace , hotels and restaurants in downtown Napa . The museum began construction in 1999 and hosted opening celebrations on November 18 , 2001 . In 2005 , Copia sold 3 @.@ 5 acres ( 1 @.@ 4 ha ) to <unk> for construction of a <unk> hotel . 
- 
- = = = Decline and bankruptcy = = = 
- 
- Although the facility did attract visitors , local residents ' support failed to reach the numbers expected by the founders . Original projections of 300 @,@ 000 admissions per year were never met . In October 2006 , the museum announced plans to turn galleries into conference rooms , remove most of the museum 's focus on art , and lay off 28 of its 85 employees ( most of whom were security guards for the art gallery ) . At the time , Copia had $ 68 million ( $ 74 @.@ 7 million today ) in debt . That year the museum also lowered its original adult admission fee of $ 12 @.@ 50 to $ 5 . For three months in 2006 , the museum admitted guests free of charge , and attendance and revenue increased . The museum also began hosting weddings and renting its space more frequently in order to raise revenue . In 2007 , the museum altered its theme significantly by removing its focus on food and art , and instead focusing solely on wine . It replaced some of its gardens with vineyards , changed its displays to focus more on the history and aspects of wine and <unk> , and decreased the restaurant 's and programs ' focus on food . 
- In September 2008 , Garry McGuire announced that 24 of 80 employees were being laid off and the days of operation would be reduced from 7 to 3 per week . <unk> figures had never reached either original or updated projections , causing the facility to operate annually in the red since its opening . In November , he announced that the property would be sold due to <unk> debt . The museum closed on Friday , November 21 , 2008 . The closure was without warning ; visitors who had arrived for scheduled events found a paper notice at the entrance that the center was temporarily closed . The next days ' events involving chef Andrew <unk> and singer Joni Morris were also abruptly cancelled ; the museum later stated that it would reopen on December 1 . On that day , the organization ( with $ 80 million ( $ 87 @.@ 9 million today ) in debt ) filed for Chapter 11 bankruptcy protection . The federal bankruptcy court blocked a $ 2 million ( $ 2 @.@ 2 million today ) emergency loan with priority in security , leaving Copia with no funds to resume operations . 
- Writing about the failure of the project , The New York Times and other newspapers suggested that Copia had failed to clearly define its focus . Potential tourists were left feeling unsure whether they were visiting a museum , a cooking school , or a promotional center for wine . 
- 
- = = = Aftermath = = = 
- 
- Following the 2008 closing of Copia , a group of investors , developers , advocates , and vintners named the Coalition to Preserve Copia was formed to explore a plan to preserve the building and grounds . Part of the group 's plan included forming a <unk> @-@ <unk> district with participation of local hotel properties to finance bonds to purchase the property , but their effort failed . In May 2009 local developer George <unk> spoke about his interest in purchasing the property . Other developers including the Culinary Institute of America also expressed an interest in acquiring the property . Copia 's bond holder , <unk> Financial <unk> Corporation , listed the property for sale in October 2009 . Napa Valley College 's upper valley campus became the home of the center 's library of around 1 @,@ 000 cookbooks . By late 2010 , local chefs had revived the center 's garden and the parking lot had become the location of a weekly farmer 's market . In 2011 , the museum was reported to still maintain its original <unk> , with the gift store fully stocked and the restaurant still furnished . In an April 2012 auction , most of the center 's fixtures , furniture , equipment , wine collection ( around 3 @,@ 500 bottles ) , <unk> , displays , artistic items , and antiquities were sold . 
- Since Copia 's closure , the building has been used for a few meetings and events , including the Napa Valley Film Festival and <unk> Napa Valley . <unk> Development arranged to buy the entire site in 2015 and planned mixed use with housing and retail . The company planned to build up to 187 housing units , 30 @,@ 000 square feet of retail space , and underground parking for 500 cars . The plan had later altered to only include purchase of the southern portion of the property . In 2015 , the Culinary Institute of America ( CIA ) put in motion plans to purchase a separate portion of Copia . The college intends to open a campus , the Culinary Institute of America at Copia , which will house the CIA 's new Food Business School . The school , which was <unk> its St. Helena campus , purchased the northern portion of the property for $ 12 @.@ 5 million in October 2015 ( it was assessed for $ 21 @.@ 3 million around 2013 ) . Among the CIA 's first events there was 2016 's <unk> ! Napa Valley , a food and wine festival sponsored by local organizations . The campus is expected to open in late 2016 , with its Chuck Williams Culinary Arts Museum opening in 2017 . The museum will house about 4 @,@ 000 items of Chuck Williams , including cookbooks , <unk> , and appliances . 
- 
- = = Facilities = = 
- 
- Copia is located on First Street in downtown Napa , adjacent to the Oxbow Public Market . The 12 @-@ acre ( 4 @.@ 9 ha ) property is surrounded by an <unk> of the Napa River . The two @-@ story building is 78 @,@ <unk> square feet ( 7 @,@ 305 @.@ 2 m2 ) in size , and is primarily built from polished concrete , metal , and glass . The city 's farmers ' market has been located in Copia 's parking lot since 2004 . 
- It had a 13 @,@ 000 @-@ square @-@ foot ( 1 @,@ 200 m2 ) gallery for art , history , and science exhibits . It also had a 280 @-@ seat indoor theater , a 500 @-@ seat outdoor theater , classrooms , an 80 @-@ seat demonstration kitchen , a rare book library , a wine @-@ tasting area , a café ( named American Market Cafe ) , gift shop ( named <unk> ) , and 3 @.@ 5 acres ( 1 @.@ 4 ha ) of <unk> edible gardens . The building 's architect was <unk> Partnership Architects . Julia 's Kitchen was a restaurant inside the Copia building that focused on seasonal dishes and was named for honorary trustee Julia Child , who loaned part of her kitchen to the restaurant , a wall of 49 pans , pots , fish molds , and other tools and objects . Within a year of the center 's closing , the items were sent to the Smithsonian Institution 's National Museum of American History , where they are included in the Julia Child 's kitchen exhibit , which up until that point was only missing that portion . The restaurant had a 1 @,@ 700 @-@ square @-@ foot ( 160 m2 ) dining room ( for 180 seats ) , an outdoor seating area ( 4 @,@ 300 square feet ( 400 m2 ) ) and a 2 @,@ 500 @-@ square @-@ foot ( 230 m2 ) kitchen . The gardens had fruit orchards , a pavilion with a kitchen and large dining table , and a small <unk> with 60 vines and 30 different grape varieties . The restaurant and café were both operated by local <unk> <unk> Elements , and later <unk> Restaurant Group . 
- The main and permanent exhibition of the museum , called " Forks in the Road : Food , Wine and the American Table " , had displays explaining the origins of cooking through to modern advances , and included a significant portion about the history of American winemaking . The museum 's opening art exhibition was called " Active <unk> " , and had new works related to food by eight notable artists . Copia also had an annual exhibit and event called " <unk> " , which began in 2005 . The event involved teams of architects , students , and designers creating sculptures from cans of food , which would later be donated to the Napa Valley Food Bank . The first year 's donation consisted of 42 @,@ 000 pounds of canned food . 
- 
- = = <unk> and visitor admissions = = 
- 
- The founding director , Peggy <unk> , left Copia in March 2005 , and was replaced by Arthur <unk> that July ; in 2008 <unk> was replaced by Chairman Garry McGuire Jr . , who resigned on December 5 , 2008 . The wine curator , Peter Marks , left around 2008 and was replaced with dean of wine studies Andrea Robinson . Around 2008 , McGuire hired celebrity chef Tyler Florence as dean of culinary studies . Florence oversaw the museum 's food programs and Julia 's Kitchen . 
- Museum attendance was initially forecast at 300 @,@ 000 ; to compare , the county had 4 @.@ 5 million tourists in 2001 . 205 @,@ 000 visitors attended in 2001 , 220 @,@ 000 visitors attended in 2002 , and 160 @,@ 000 attended in 2003 . 150 @,@ 000 visitors attended in 2007 . 
- 
- 
- = Youth on the Prow , and Pleasure at the Helm = 
- 
- Youth on the Prow , and Pleasure at the Helm ( also known as Fair Laughs the <unk> and Youth and Pleasure ) is an oil painting on canvas by English artist William Etty , first exhibited in 1832 and currently in Tate Britain . Etty had been planning the painting since 1818 – 19 , and an early version was exhibited in 1822 . The piece was inspired by a metaphor in Thomas Gray 's poem The Bard in which the apparently bright start to the notorious <unk> of Richard II of England was compared to a gilded ship whose occupants are unaware of an approaching storm . Etty chose to illustrate Gray 's lines literally , depicting a golden boat filled with and surrounded by nude and near @-@ nude figures . 
- Etty felt that his approach to the work illustrated a moral warning about the pursuit of pleasure , but his approach was not entirely successful . The Bard was about a supposed curse on the House of Plantagenet placed by a Welsh bard following Edward I of England 's attempts to eradicate Welsh culture , and critics felt that Etty had somewhat misunderstood the point of Gray 's poem . Some reviewers greatly praised the piece , and in particular Etty 's technical abilities , but audiences of the time found it hard to understand the purpose of Etty 's painting , and his use of nude figures led some critics to consider the work <unk> and offensive . 
- The painting was bought in 1832 by Robert Vernon to form part of his collection of British art . Vernon donated his collection , including Youth on the Prow , and Pleasure at the Helm , to the National Gallery in 1847 , which , in turn , transferred it to the Tate Gallery in 1949 . It remains one of Etty 's best @-@ known works , and formed part of major exhibitions at Tate Britain in 2001 – 02 and at the York Art Gallery in 2011 – 12 . 
- 
- = = Background = = 
- 
- William Etty , the seventh son of a York baker and <unk> , had been an apprentice <unk> in Hull . On completing his seven @-@ year apprenticeship at the age of 18 he moved to London " with a few pieces of chalk <unk> " , and the intention of becoming a history painter in the tradition of the Old Masters . He enrolled in the Schools of the Royal Academy of Arts , studying under renowned portrait painter Thomas Lawrence . He submitted numerous paintings to the Royal Academy over the following decade , all of which were either rejected or received little attention when exhibited . 
- In 1821 Etty 's The <unk> of Cleopatra in Cilicia ( also known as The Triumph of Cleopatra ) was a critical success . The painting featured nude figures , and over the following years Etty painted further nudes in biblical , literary and mythological settings . All but one of the 15 paintings Etty exhibited in the 1820s included at least one nude figure . 
- While some nudes existed in private collections , England had no tradition of nude painting and the display and distribution of nude material to the public had been suppressed since the 1787 <unk> for the <unk> of Vice . Etty was the first British artist to <unk> in the nude , and the reaction of the lower classes to these paintings caused concern throughout the 19th century . Although his portraits of male nudes were generally well received , many critics condemned his repeated depictions of female nudity as indecent . 
- 
- = = Composition = = 
- 
- Youth on the Prow , and Pleasure at the Helm was inspired by a passage in Thomas Gray 's poem The Bard . The theme of The Bard was the English king Edward I 's conquest of Wales , and a curse placed by a Welsh bard upon Edward 's descendants after he ordered the execution of all bards and the eradication of Welsh culture . Etty used a passage Gray intended to symbolise the seemingly bright start to the disastrous reign of Edward 's great @-@ great @-@ grandson Richard II . 
- Etty chose to illustrate Gray 's words literally , creating what has been described as " a poetic romance " . Youth and Pleasure depicts a small gilded boat . Above the boat , a nude figure representing <unk> blows on the sails . Another nude representing Pleasure lies on a large bouquet of flowers , loosely holding the helm of the boat and allowing <unk> 's breeze to guide it . A nude child blows bubbles , which another nude on the prow of the ship , representing Youth , reaches to catch . <unk> , again nude , swim around and <unk> on the boat . Although the seas are calm , a " sweeping <unk> " is forming on the horizon , with a demonic figure within the storm clouds . ( <unk> and restoration means this demonic figure is now barely visible . ) The intertwined limbs of the participants were intended to evoke the sensation of transient and passing pleasure , and to express the themes of female sexual <unk> <unk> innocent youth , and the sexual power women hold over men . 
- Etty said of his approach to the text that he was hoping to create " a general allegory of Human Life , its empty vain pleasures — if not founded on the laws of Him who is the Rock of Ages . " While Etty felt that the work conveyed a clear moral warning about the pursuit of pleasure , this lesson was largely lost upon its audiences . 
- When Etty exhibited the completed painting at the Royal Academy Summer <unk> in 1832 , it was shown untitled , with the relevant six lines from The Bard attached ; writers at the time sometimes referred to it by its <unk> of Fair Laughs the <unk> . By the time of Etty 's death in 1849 , it had acquired its present title of Youth on the Prow , and Pleasure at the Helm . 
- 
- = = <unk> = = 
- 
- The final version of Youth and Pleasure was painted between 1830 and 1832 , but Etty had been contemplating a painting on the theme since 1818 – 19 . In 1822 he had exhibited an early version at the British Institution titled A <unk> from One of Gray 's Odes ( Youth on the Prow ) ; in this version the group of figures on the prow is reversed , and the swimmers around the boat are absent . Another rough version of the painting also survives , similar to the 1832 version but again with the figures on the prow reversed . This version was exhibited at a retrospective of Etty 's work at the Society of Arts in 1849 ; it is dated 1848 but this is likely to be a <unk> of 1828 , making it a preliminary study for the 1832 painting . 
- Although it received little notice when first exhibited , the 1822 version provoked a strong reaction from The Times : 
- We take this opportunity of advising Mr. Etty , who got some reputation for painting " Cleopatra 's <unk> " , not to be <unk> into a style which can <unk> only the most vicious taste . Naked figures , when painted with the purity of Raphael , may be endured : but nakedness without purity is offensive and indecent , and on Mr. Etty 's <unk> is mere dirty flesh . Mr. Howard , whose poetical subjects sometimes require naked figures , never <unk> the eye or mind . Let Mr. Etty strive to acquire a taste equally pure : he should know , that just delicate taste and pure moral sense are synonymous terms . 
- An oil sketch attributed to Etty , given to York Art Gallery in 1952 by Judith <unk> , Countess of <unk> and entitled Three Female <unk> , is possibly a preliminary study by Etty for Youth and Pleasure , or a copy by a student of the three central figures . Art historian Sarah <unk> considers both possibilities unlikely , as neither the arrangement of figures , the subject matter or the sea serpent approaching the group appear to relate to the completed Youth and Pleasure , and considers it more likely to be a preliminary sketch for a now @-@ unknown work . 
- 
- = = Reception = = 
- 
- Youth on the Prow , and Pleasure at the Helm met with a mixed reception on exhibition , and while critics generally praised Etty 's technical ability , there was a certain confusion as to what the painting was actually intended to represent and a general feeling that he had seriously misunderstood what The Bard was actually about . The Library of the Fine Arts felt " in classical design , anatomical drawing , <unk> of attitude , <unk> of form , and <unk> of grouping , no doubt Mr. Etty has no superior " , and while " the representation of the ideas in the lines quoted [ from The Bard ] are beautifully and accurately expressed upon the canvas " they considered " the <unk> reference of the poet [ to the destruction of Welsh culture and the decline of the House of Plantagenet ] was entirely lost sight of , and that , if this be the nearest that Art can approach in conveying to the eye the happy <unk> of the subject which Gray intended , we fear we must give up the contest upon the merits of poetry and painting . " Similar concerns were raised in The Times , which observed that it was " Full of beauty , rich in colouring , boldly and accurately drawn , and composed with a most graceful fancy ; but the meaning of it , if it has any meaning , no man can tell " , pointing out that although it was intended to illustrate Gray it " would represent almost as well any other poet 's <unk> . " The <unk> , meanwhile , took issue with the cramped and <unk> boat , pointing out that the characters " if not exactly jammed together like <unk> in a basket , are sadly constrained for want of room " , and also complained that the boat would not in reality " float half the weight which is made to press upon it . " 
- Other reviewers were <unk> ; The Gentleman 's Magazine praised Etty 's ability to capture " the beauty of the proportion of the antique " , noting that in the central figures " there is far more of <unk> than is to be seen in almost any modern picture " , and considered the overall composition " a most fortunate combination of the <unk> of Poetry and the reality of Nature " . The <unk> considered it " a poetic picture from a very poetic passage " , praising Etty for " telling a story which is very difficult to tell with the pencil " . 
- The greatest criticism of Youth and Pleasure came from The Morning Chronicle , a newspaper which had long disliked Etty 's female nudes . It complained " no decent family can hang such sights against their wall " , and condemned the painting as an " indulgence of what we once hoped a classical , but which are now convinced , is a <unk> mind " , commenting " the course of [ Etty 's ] studies should run in a <unk> channel , and that he should not persist , with an <unk> fancy , to pursue Nature to her holy <unk> . He is a laborious <unk> , and a beautiful colourist ; but he has not taste or chastity of mind enough to venture on the naked truth . " The reviewer added " we fear that Mr. E will never turn from his wicked ways , and make himself fit for decent company . " 
- 
- = = Legacy = = 
- 
- Youth on the Prow , and Pleasure at the Helm was purchased at the time of its exhibition by Robert Vernon for his important collection of British art . ( The price Vernon paid for Youth and Pleasure is not recorded , although Etty 's <unk> records a partial payment of £ 250 — about £ 21 @,@ 000 in 2016 terms — so it is likely to have been a substantial sum . ) Vernon later purchased John Constable 's The Valley Farm , planning to hang it in the place then occupied by Youth and Pleasure . This decision caused Constable to comment " My picture is to go into the place — where Etty 's " <unk> " is at present — his picture with its precious freight is to be brought down nearer to the nose . " Vernon presented his collection to the nation in 1847 , and his 157 paintings , including Youth and Pleasure , entered the National Gallery . 
- When Samuel Carter Hall was choosing works to illustrate his newly launched The Art Journal , he considered it important to promote new British artists , even if it meant illustrations which some readers considered pornographic or offensive . In 1849 Hall secured reproduction rights to the paintings Vernon had given to the nation and soon published and widely distributed an engraving of the painting under the title Youth and Pleasure , describing it as " of the very highest class " . 
- <unk> by repeated attacks from the press on his supposed indecency , poor taste and lack of creativity , Etty changed his approach after the response to Youth on the Prow , and Pleasure at the Helm . He exhibited over 80 further paintings at the Royal Academy alone , and remained a prominent painter of nudes , but from this time made conscious efforts to reflect moral lessons . He died in November 1849 and , while his work enjoyed a brief boom in popularity , interest in him declined over time , and by the end of the 19th century all of his paintings had fallen below their original prices . 
- In 1949 the painting was transferred from the National Gallery to the Tate Gallery , where as of 2015 it remains . Although Youth and Pleasure is one of Etty 's best @-@ known paintings , it remains controversial , and Dennis Farr 's 1958 biography of Etty describes it as " <unk> inept " . It was one of four works by Etty chosen for Tate Britain 's landmark <unk> : The Victorian <unk> exhibition in 2001 – 02 , and also formed part of a major retrospective of Etty 's work at the York Art Gallery in 2011 – 12 . 
- 
- 
- = Mozambican War of Independence = 
- 
- The Mozambican War of Independence was an armed conflict between the guerrilla forces of the Mozambique Liberation Front or FRELIMO ( <unk> de <unk> de Moçambique ) , and Portugal . The war officially started on September 25 , 1964 , and ended with a ceasefire on September 8 , 1974 , resulting in a negotiated independence in 1975 . 
- Portugal 's wars against independence guerrilla fighters in its 400 @-@ year @-@ old African territories began in 1961 with Angola . In Mozambique , the conflict erupted in 1964 as a result of unrest and frustration amongst many indigenous Mozambican populations , who perceived foreign rule to be a form of exploitation and mistreatment , which served only to further Portuguese economic interests in the region . Many Mozambicans also resented Portugal 's policies towards indigenous people , which resulted in discrimination , traditional lifestyle turning difficult for many Africans , and limited access to Portuguese @-@ style education and skilled employment . 
- As successful self @-@ determination movements spread throughout Africa after World War II , many Mozambicans became progressively nationalistic in outlook , and increasingly frustrated by the nation 's continued subservience to foreign rule . For the other side , many <unk> indigenous Africans who were fully integrated into the Portugal @-@ ruled social organization of Portuguese Mozambique , in particular those from the urban centres , reacted to the <unk> claims with a mixture of discomfort and suspicion . The ethnic Portuguese of the territory , which included most of the ruling authorities , responded with increased military presence and fast @-@ paced development projects . 
- A mass exile of Mozambique 's political intelligentsia to neighbouring countries provided <unk> from which radical Mozambicans could plan actions and foment political unrest in their homeland . The formation of the Mozambican guerrilla organisation FRELIMO and the support of the Soviet Union , China , Cuba , Yugoslavia , Bulgaria , Tanzania , Zambia , Egypt , Algeria and <unk> regime in Libya through arms and advisers , led to the outbreak of violence that was to last over a decade . 
- From a military standpoint , the Portuguese regular army held the upper hand during the conflict against the <unk> guerrilla forces . Nonetheless , Mozambique succeeded in achieving independence on June 25 , 1975 , after a civil resistance movement known as the Carnation Revolution backed by portions of the military in Portugal overthrow the military dictatorship sponsored by US , thus ending 470 years of Portuguese colonial rule in the East African region . According to historians of the Revolution , the military coup in Portugal was in part fuelled by protests concerning the conduct of Portuguese troops in their treatment of some local Mozambican populace . The role of the growing communist influence over the group of Portuguese military insurgents who led the Lisbon 's military coup , and , on the other hand , the pressure of the international community over the direction of the Portuguese Colonial War in general , were main causes for the final outcome . 
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- = = Background = = 
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- = = = Portuguese colonial rule = = = 
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- San hunter and gatherers , ancestors of the <unk> peoples , were the first known inhabitants of the region that is now Mozambique , followed in the 1st and 4th centuries by <unk> @-@ speaking peoples who migrated there across the <unk> River . In <unk> , Portuguese explorers landed on the Mozambican coastline . Portugal 's influence in East Africa grew throughout the 16th century ; she established several colonies known collectively as Portuguese East Africa . <unk> and gold became profitable for the Europeans ; influence was largely exercised through individual settlers and there was no centralised administration and , in the meantime , Portugal had turned her attention to India and Brazil . 
- By the 19th century , European colonialism in Africa had reached its height . Having lost control of the vast territory of Brazil in South America , the Portuguese began to focus on expanding their African outposts . This brought them into direct conflict with the British . Since David <unk> had returned to the area in 1858 in an attempt to foster trade routes , British interest in Mozambique had risen , <unk> the Portuguese government . During the 19th century , much of Eastern Africa was still being brought under British control , and in order to facilitate this , Britain required several concessions from the Portuguese colony . 
- As a result , in an attempt to avoid a naval conflict with the superior British Royal Navy , Portugal adjusted the borders of her colony and the modern borders of Mozambique were established in May 1881 . Control of Mozambique was left to various organisations such as the Mozambique Company , the <unk> Company and the Niassa Company which were financed and provided with cheap labour by the British Empire to work mines and construct railways . These companies penetrated inland from the coastline , setting up plantations and taxing the local populace who had until then resisted encroachment by the colonists . 
- The resisting Gaza Empire , a collection of indigenous tribes who inhabited the area that now constitutes Mozambique and Zimbabwe , was defeated in 1895 , and the remaining inland tribes were eventually defeated by 1902 ; in that same year , Portugal established Lourenço <unk> as the capital . In 1926 , political and economic crisis in Portugal led to the establishment of the Second Republic ( later to become the <unk> Novo ) , and a revival of interest in the African colonies . <unk> for self determination in Mozambique arose shortly after World War II , in light of the independence granted to many other colonies worldwide in the great wave of decolonisation . 
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- = = = Rise of FRELIMO = = = 
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- Portugal designated Mozambique an overseas territory in 1951 in order to show to the world that the colony had a greater autonomy . It was called the Overseas Province of Mozambique ( <unk> <unk> de Moçambique ) . Nonetheless , Portugal still maintained strong control over its overseas province . The increasing number of newly independent African nations after World War II , coupled with the ongoing mistreatment of the indigenous population , encouraged the growth of nationalist sentiments within Mozambique . 
- Mozambique was marked by large <unk> between the wealthy Portuguese and the majority of the large rural indigenous African population . <unk> whites , many of them recent immigrants , including illiterate peasants , were given preference in lower @-@ level urban jobs , where a system of job reservation existed . In the rural areas , Portuguese controlled the trading stores with which African peasants interacted . Being largely illiterate and preserving their local traditions and ways of life , skilled employment opportunities and roles in administration and government were rare for these numerous tribal populations , leaving them few or no opportunities in the urban modern life . Many indigenous peoples saw their culture and tradition being overwhelmed by the alien culture of Portugal . A small educated African class did emerge , but faced substantial discrimination . 
- Vocal political dissidents opposed to Portuguese rule and claiming independence were typically forced into exile . From the mid @-@ 1920s a succession of authoritarian regimes in Portugal closed unions and left @-@ wing opposition , both within Portugal and within its colonies , notably in the <unk> Novo period ( 1933 @-@ 1974 ) . The Portuguese government forced black Mozambican farmers to grow rice or cotton for export , providing little return with which the farmers could support themselves . Many other workers — over 250 @,@ 000 by 1960 — were pressured to work on coal and gold mines , in neighbouring territories , mainly in South Africa , where they comprised over 30 % of black underground miners . By 1950 , only 4 @,@ 353 Mozambicans out of 5 @,@ 733 @,@ 000 had been granted the right to vote by the Portuguese colonial government . The rift between Portuguese settlers and Mozambican locals is illustrated in one way by the small number of people with mixed Portuguese and Mozambican heritage ( <unk> ) , numbering only 31 @,@ <unk> in a population of 8 – 10 million in 1960 according to that year 's census . 
- The Mozambique Liberation Front or FRELIMO ( <unk> de <unk> de Moçambique ) , formally ( Marxist @-@ <unk> as of 1977 but <unk> to such positions since the late 1960s ) , was formed in Dar es Salaam , the largest city in neighbouring Tanzania , on June 25 , 1962 . It was created during a conference , by political figures who had been forced into exile , by the merging of various existing nationalist groups , including the Mozambican African National Union , National African Union of Independent Mozambique and the National Democratic Union of Mozambique which had been formed two years earlier . It was only in exile that such political movements could develop , due to the strength of Portugal 's grip on <unk> activity within Mozambique itself . 
- A year later , in 1963 , FRELIMO set up headquarters in Dar es Salaam , Tanzania , under the leadership of sociologist Eduardo Mondlane , and began to call for independence from <unk> two years of organisation and failing political manoeuvres in an attempt to seek a peaceful independence , Mondlane began in 1964 a campaign of guerrilla warfare in an attempt to achieve independence for Mozambique . 
- The United Nations also put pressure on Portugal to move for decolonisation . Portugal threatened to withdraw from NATO , which put a stop to this support and pressure , and the nationalist groups in Mozambique were forced to turn to help from the Soviet bloc . 
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- = = = Support from the Soviet Union = = = 
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- During the Cold War , and particularly in the late 1950s , the Soviet Union and People 's Republic of China adopted a strategy of <unk> of Western powers by disruption of their hold on African colonies . Nikita <unk> , in particular , viewed the ' <unk> third of mankind ' as a means to weaken the West . For the Soviets , Africa represented a chance to create a rift between western powers and their colonial assets , and create pro @-@ communist states in Africa with which to foster future relations . 
- Prior to the formation of FRELIMO , the Soviet position regarding the nationalist movements in Mozambique was one of confusion . There were multiple independence movements , and they had no sure knowledge that any would succeed . The nationalist groups in Mozambique , like those across Africa during the period , received training and equipment from the Soviet Union . 
- Eduardo Mondlane 's successor , future President of Mozambique , Samora Machel , acknowledged assistance from both Moscow and <unk> , describing them as " the only ones who will really help us . ... They have fought armed struggles , and whatever they have learned that is relevant to Mozambique we will use . " <unk> received tuition in subversion and political warfare as well as military aid , specifically shipments of 122 mm artillery rockets in 1972 , with 1600 advisors from Russia , Cuba and East Germany . FRELIMO adopted <unk> @-@ <unk> at an early stage . 
- The Soviet Union continued to support the new FRELIMO government against <unk> in the years after 1975 . By 1981 , there were 230 Soviet , close to 200 Cuban military and over 600 civilian Cuban advisers still in the country . Cuba 's involvement in Mozambique was as part of a continuing effort to export the anti @-@ <unk> ideology of the Cuban Revolution and forge desperately needed new allies . Cuba provided support to liberation movements and leftist governments in numerous African countries , including Angola , Ethiopia , Guinea @-@ <unk> and Congo @-@ <unk> . 
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- = = Conflict = = 
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- = = = <unk> under Mondlane ( 1964 – 69 ) = = = 
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- At the war 's outset , FRELIMO had little hope for a conventional military victory , with a mere 7000 combatants against a far larger Portuguese force . Their hopes rested on urging the local populace to support the insurgency , in order to force a negotiated independence from Lisbon . Portugal fought its own version of protracted warfare , and a large military force was sent by the Portuguese government to quell the unrest , with troop numbers rising from 8 @,@ 000 to 24 @,@ 000 between 1964 and 1967 . The number of local soldiers recruited for the Portuguese cause rose to 23 @,@ 000 in the same period . 860 Special Forces operatives were also being trained in Commando <unk> <unk> by 1969 . 
- The military wing of FRELIMO was commanded by <unk> Samuel <unk> , whose forces received training from Algeria . The FRELIMO guerrillas were armed with a variety of weapons , many provided by the Soviet Union and China . Common weapons included the <unk> – <unk> bolt @-@ action rifle , <unk> and AK @-@ 47 automatic rifles and the Soviet <unk> @-@ 41 . Machine guns such as the <unk> light machine gun were widely used , along with the <unk> and the SG @-@ 43 <unk> . FRELIMO were supported by mortars , recoilless rifles , RPG @-@ <unk> and RPG @-@ <unk> , Anti @-@ aircraft weapons such as the <unk> @-@ 4 and from 1974 the <unk> 2 . 
- In the dying stages of the conflict , FRELIMO was provided with a few <unk> @-@ 7 <unk> shoulder @-@ launched missile launchers from China ; these were never used to shoot down a Portuguese plane . Only one Portuguese aircraft was lost in combat during the conflict , when Lt. Emilio Lourenço 's <unk> @-@ 4 was destroyed by premature detonation of his own ordnance . 
- The Portuguese forces were under the command of General António Augusto dos Santos , a man with strong faith in new counter @-@ insurgency theories . Augusto dos Santos supported a collaboration with Rhodesia to create African Scout units and other special forces teams , with Rhodesian forces even conducting their own independent operations during the conflict . Due to Portuguese policy of retaining up @-@ to @-@ date equipment for the <unk> while shipping obsolete equipment to the colonies , the Portuguese soldiers fighting in the opening stages of the conflict were equipped with World War II radios and the old <unk> rifle . As the fighting progressed , the need for more modern equipment was rapidly recognised , and the <unk> & Koch <unk> and <unk> <unk> rifles were adopted as the standard battlefield weapon , along with the <unk> @-@ 10 for paratroopers . The <unk> and , then in 1968 , the <unk> were the Portuguese general purpose machine guns , with 60 , 81 and 120 mm mortars , howitzers and the AML @-@ 60 , <unk> <unk> , Fox and <unk> armoured cars frequently deployed for fire support . 
- Although helicopters were not used in Mozambique to the same extent as they were in Vietnam , the <unk> III was the most widely used , although the Puma was also used with great success . Other aircraft were employed : for air support the <unk> and the <unk> <unk> were used ; for reconnaissance , the <unk> Do 27 . In the transport role , the Portuguese Air Force used mainly the Nord <unk> and the C @-@ 47 . The Portuguese Navy also made extensive use of patrol boats , landing crafts , and <unk> <unk> . 
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- = = = = <unk> of FRELIMO attacks = = = = 
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- In 1964 , weak @-@ hearted attempts at peaceful negotiation by FRELIMO were abandoned and , on September 25 , 1964 , Eduardo Mondlane began to launch guerrilla attacks on targets in northern Mozambique from his base in Tanzania . FRELIMO soldiers , with logistical assistance from the local population , attacked the administrative post at Chai Chai in the province of Cabo <unk> . FRELIMO militants were able to evade pursuit and surveillance by employing classic guerrilla tactics : <unk> patrols , <unk> communication and railroad lines , and making hit @-@ and @-@ run attacks against colonial outposts before rapidly fading into accessible <unk> areas . The insurgents were typically armed with rifles and machine pistols , and the attackers took full advantage of the monsoon season in order to evade pursuit . 
- During heavy rains , it was much more difficult to track insurgents by air , <unk> Portugal 's air superiority , and Portuguese troops and vehicles found movement during rain storms difficult . In contrast , the insurgent troops , with lighter equipment , were able to flee into the bush ( the <unk> ) amongst an ethnically similar populace into which they could melt away . Furthermore , the FRELIMO forces were able to forage food from the surroundings and local villages , and were thus not hampered by long supply lines . 
- With the initial FRELIMO attacks in Chai Chai , the fighting spread to Niassa and Tete at the centre of Mozambique . During the early stages of the conflict , FRELIMO activity was reduced to small , platoon @-@ sized engagements , <unk> and raids on Portuguese installations . The FRELIMO soldiers often operated in small groups of ten to fifteen soldiers . The scattered nature of FRELIMO 's initial attacks was an attempt to disperse the Portuguese forces . 
- The Portuguese troops began to suffer losses in November , fighting in the northern region of <unk> . With increasing support from the populace , and the low number of Portuguese regular troops , FRELIMO was quickly able to advance south towards <unk> and <unk> , linking to Tete with the aid of forces from the neighbouring Republic of Malawi , which had become a fully independent member of the Commonwealth of Nations on July 6 , 1964 . Despite the increasing range of FRELIMO operations , attacks were still limited to small strike teams attacking lightly defended administrative outposts , with the FRELIMO lines of communication and supply utilising canoes along the <unk> River and Lake Malawi . 
- It was not until 1965 that recruitment of fighters increased along with popular support , and the strike teams were able to increase in size . The increase in popular support was in part due to FRELIMO agencies ' offer of help to exiled Mozambicans , who had fled the conflict by travelling to nearby Tanzania . Like similar conflicts against the French and United States forces in Vietnam , the insurgents also used landmines to a great extent to <unk> the Portuguese forces , thus straining the armed forces ' infrastructure and <unk> soldiers . 
- FRELIMO attack groups had also begun to grow in size to include over 100 soldiers in certain cases , and the insurgents also began to accept women fighters into their ranks . On either October 10 or October 11 , 1966 , on returning to Tanzania after inspecting the front lines , <unk> Samuel <unk> was shot dead by Lourenço <unk> , a fellow FRELIMO guerrilla who was said to be in the employ of the Portuguese . 
- One seventh of the population and one fifth of the territory were in FRELIMO hands by 1967 ; at this time there were approximately 8000 guerrillas in combat . During this period , Mondlane urged further expansion of the war effort , but also sought to retain the small strike groups . With the increasing cost of supply , more and more territory liberated from the Portuguese , and the adoption of measures to win the support of the population , it was at this time that Mondlane sought assistance from abroad , specifically the Soviet Union and China ; from these benefactors , he obtained large @-@ calibre machine guns , anti @-@ aircraft rifles and 75 mm recoilless rifles and 122 mm rockets . 
- In 1968 , the second Congress of FRELIMO was a propaganda victory for the insurgents , despite attempts by the Portuguese , who enjoyed air superiority throughout the conflict , to bomb the location of the meeting late in the day . This gave FRELIMO further weight to wield in the United Nations . 
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- = = = Portuguese development program = = = 
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- Due to both the technological gap between <unk> and the centuries @-@ long colonial era , Portugal was a driving force in the development and shaping of all Portuguese Africa since the 15th century . In the 1960s and early 1970s , to counter the increasing insurgency of FRELIMO forces and show to the Portuguese people and the world that the territory was totally under control , the Portuguese government accelerated its major development program to expand and upgrade the infrastructure of Portuguese Mozambique by creating new roads , railways , bridges , dams , irrigation systems , schools and hospitals to stimulate an even higher level of economic growth and support from the populace . 
- As part of this redevelopment program , construction of the <unk> <unk> Dam began in 1969 . This particular project became intrinsically linked with Portugal 's concerns over security in the overseas colonies . The Portuguese government viewed the construction of the dam as testimony to Portugal 's " <unk> mission " and intended for the dam to <unk> Mozambican belief in the strength and security of the Portuguese colonial government . To this end , Portugal sent three thousand new troops and over one million landmines to Mozambique to defend the building project . 
- <unk> the symbolic significance of the dam to the Portuguese , FRELIMO proceeded to spend seven years attempting to halt its construction by force . No direct attacks were ever successful , but FRELIMO had some success in attacking convoys en route to the site . FRELIMO also lodged a protest with the United Nations about the project , and their cause was aided by negative reports of Portuguese actions in Mozambique . In spite of the subsequent withdrawal of much foreign financial support for the dam , it was finally completed in December 1974 . The dam 's intended propaganda value to the Portuguese was overshadowed by the adverse Mozambican public reaction to the extensive dispersal of the indigenous populace , who were forced to relocate from their homes to allow for the construction project . The dam also deprived farmers of the critical annual floods , which formerly re @-@ fertilised the plantations . 
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- = = = Assassination of Eduardo Mondlane = = = 
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- On February 3 , 1969 , Eduardo Mondlane was killed by explosives smuggled into his <unk> . Many sources state that , in an attempt to rectify the situation in Mozambique , the Portuguese secret police assassinated Mondlane by sending a parcel to his office in Dar es Salaam . Inside the parcel was a book containing an explosive device , which detonated upon opening . Other sources state that Eduardo was killed when an explosive device detonated underneath his chair at the FRELIMO headquarters , and that the faction responsible was never identified . 
- The original investigations levelled accusations at <unk> <unk> ( who was later executed ) and <unk> <unk> , FRELIMO leader in Cabo <unk> . The latter had made no secret of his distrust of Mondlane , seeing him as too conservative a leader , and the <unk> police also accused him of working with PIDE ( Portugal 's secret police ) to assassinate Mondlane . <unk> himself surrendered to the Portuguese in April of that year . 
- Although the exact details of the assassination remain disputed , the involvement of the Portuguese government , particularly <unk> Press or PIDE , is generally accepted by most historians and biographers and is supported by the Portuguese stay behind <unk> @-@ esque army , known as <unk> Press , that suggested in 1990 that they were responsible for the assassination . Initially , due to the uncertainty regarding who was responsible , Mondlane 's death created great suspicion within the ranks of the FRELIMO itself and a short power struggle which resulted in a dramatic swing to the political left . 
- Mondlane 's immediate successor was the moderate Rev. <unk> Simango , who had served under him , as FRELIMO 's vice @-@ President , from its formation until 1969 . In the post @-@ assassination power @-@ struggle , Simango was ousted by the more hardline Samora Machel and <unk> dos Santos , expelled from FRELIMO and eventually arrested and executed , post @-@ Independence , in 1975 . 
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- = = = Continuing war ( 1969 – 74 ) = = = 
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- In 1969 , General António Augusto dos Santos was relieved of command , with General Kaúlza de Arriaga taking over officially in March 1970 . Kaúlza de Arriaga favoured a more direct method of fighting the insurgents , and the established policy of using African counter @-@ insurgency forces was rejected in favour of the deployment of regular Portuguese forces accompanied by a small number of African fighters . Indigenous personnel were still recruited for special operations , such as the Special Groups of <unk> in 1973 , though their role less significant under the new commander . His tactics were partially influenced by a meeting with United States General William Westmoreland . 
- By 1972 there was growing pressure from other commanders , particularly Kaúlza de Arriaga 's second in command , General Francisco da Costa <unk> , for the use of African soldiers in Flechas units . Flechas units ( Arrows ) were also employed in Angola and were units under the command of the Portuguese PIDE . <unk> of local tribesmen , the units specialised in tracking , reconnaissance and anti @-@ terrorist operations . 
- Costa <unk> argued that African soldiers were cheaper and were better able to create a relationship with the local populace , a tactic similar to the ' hearts and minds ' strategy being used by United States forces in Vietnam at the time . These Flechas units saw action in the territory at the very end stages of the conflict , following the dismissal of Kaúlza de Arriaga on the eve of the Portuguese coup in 1974 – the Carnation Revolution . The units were to continue to cause problems for the FRELIMO even after the Revolution and Portuguese withdrawal , when the country splintered into civil war . 
- There were several Portuguese special forces units that were unique to either the Mozambican conflict or the Portuguese Colonial War as a whole : 
- Special Groups ( Grupos Especiais ) : units similar to the ones used in Angola 
- <unk> Special Groups ( Grupos Especiais <unk> @-@ <unk> ) : units of volunteer black soldiers that had paratrooper training 
- Combat Tracking Special Groups ( Grupos Especiais de <unk> de <unk> ) : special units trained in tracking 
- Flechas : Local tribesmen and rebel <unk> specialised in tracking , reconnaissance and terrorist operations . They sometimes patrolled in captured uniforms and are rewarded with cash <unk> for every guerrilla or guerrilla weapon they capture . 
- During the entire period of 1970 – 74 , FRELIMO intensified guerrilla operations , specialising in urban terrorism . The use of landmines also intensified , with sources stating that they had become responsible for two out of every three Portuguese casualties . During the conflict , FRELIMO used a variety of anti @-@ tank and anti @-@ personnel mines , including the <unk> ( Black Widow ) , <unk> @-@ 46 , and <unk> . Even amphibious mines were used , such as the <unk> . Mine <unk> , an acute fear of landmines , was rampant in the Portuguese forces . This fear , coupled with the frustration of taking casualties without ever seeing the enemy forces , damaged morale and significantly hampered progress . 
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- = = = = Portuguese counter @-@ offensive ( June 1970 ) = = = = 
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- On June 10 , 1970 , a major counter @-@ offensive was launched by the Portuguese army . The Gordian <unk> Operation ( Portuguese : <unk> <unk> <unk> ) targeted permanent insurgent camps and the infiltration routes across the <unk> border in the north of Mozambique over a period of seven months . The operation involved some 35 @,@ 000 Portuguese troops , particularly elite units like paratroopers , commandos , marines and naval <unk> . 
- The Portuguese had excellent coordination between light bombers , helicopters and reinforced ground patrols . They utilised American tactics of quick airborne ( <unk> ) assaults supported by heavy aerial bombardments of FRELIMO camps by the Portuguese Air Force ( <unk> <unk> <unk> or FAP ) to surround and eliminate the guerrillas . These bombardments were accompanied by the use of heavy artillery . The Portuguese also used cavalry units to cover the flanks of patrols and where the terrain was too difficult to motor transport , and units of captured or deserted guerrillas to penetrate their former bases . 
- Problems for the Portuguese arose almost immediately when the offensive coincided with the beginning of the monsoon season , creating additional logistical difficulties . Not only were the Portuguese soldiers badly equipped , but there was very poor cooperation , if any at all , between the FAP and the army . Thus , the army lacked close air support from the FAP . <unk> Portuguese casualties began to <unk> FRELIMO casualties , leading to further political intervention from Lisbon . 
- The Portuguese eventually reported <unk> as killed ( a figure of some 440 was most likely closer to reality ) , and 1 @,@ 840 captured , for the loss of 132 Portuguese . Gen. Arriaga also claimed his troops to have destroyed 61 guerrilla bases and 165 camps , while 40 tons of ammunition had been captured in the first two months . Although " Gordian <unk> " was the most effective Portuguese offensive of the conflict , weakening guerrillas to such a degree that they were no longer a significant threat , the operation was deemed a failure by some military officers and the government . 
- By 1972 , the Portuguese military had changed its strategy , adapting the British / American search and destroy operations utilising small shock troop <unk> . They also initiated a hearts and minds campaign , named the <unk> Programme , which was a forced relocation program . But on November 9 , 1972 , FRELIMO – not numbering more than 8 @,@ 000 fighters – launched a large offensive in Tete Province . The response from the Portuguese military was fierce , leading to reprisal attacks in an attempt to <unk> the local population 's continuing faith in FRELIMO . 
- On December 16 , 1972 , the Portuguese 6th company of Commandos in Mozambique killed the inhabitants of the village of Wiriyamu , in the district of Tete . <unk> to as the ' Wiriyamu Massacre ' , the soldiers killed between 150 ( according to the Red Cross ) and 300 ( according to a much later investigation by the Portuguese newspaper <unk> based in testimonies from soldiers ) villagers accused of sheltering FRELIMO guerrillas . The action , " Operation <unk> " , was planned at the instigation of PIDE / <unk> agents and guided by agent <unk> <unk> , who was later assassinated while an inquiry into the events was being carried out . The soldiers were told by this agent that " the orders were to kill them all " , never mind that only civilians , women and children included , were found . All of the victims were civilians . The massacre was recounted in July 1973 by the British Catholic priest , Father Adrian Hastings , and two other Spanish missionary priests . Later counter @-@ claims have been made in a report of Archbishop of Dar es Salaam <unk> <unk> that alleged that the killings were carried out by FRELIMO combatants , not Portuguese forces . In addition , others claimed that the alleged massacres by Portuguese military forces were fabricated to tar the reputation of the Portuguese state abroad . Portuguese journalist <unk> <unk> reconstructed the Wiriyamu massacre in detail by interviewing both survivors and former members of the Portuguese Army Commandos unit that carried out the massacre . <unk> 's report was published in the Portuguese weekly newspaper <unk> and later in a book containing several of the journalist 's articles . 
- By 1973 , FRELIMO were also mining civilian towns and villages in an attempt to undermine the civilian confidence in the Portuguese forces . " <unk> : <unk> para <unk> " ( <unk> villages : water for everyone ) was a commonly seen message in the rural areas , as the Portuguese sought to relocate and <unk> the indigenous population , in order to isolate the FRELIMO from its civilian base . Conversely , Mondlane 's policy of mercy towards civilian Portuguese settlers was abandoned in 1973 by the new commander , Machel . " Panic , <unk> , abandonment , and a sense of <unk> – all were reactions among whites in Mozambique " stated conflict historian T. H. <unk> in 1983 . 
- This change in tactic led to protests by Portuguese settlers against the Lisbon government , a <unk> sign of the conflict 's unpopularity . Combined with the news of the Wiriyamu massacre and that of renewed FRELIMO <unk> through 1973 and early 1974 , the worsening situation in Mozambique later contributed to the downfall of the Portuguese government in 1974 . A Portuguese journalist argued : 
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- = = = Political instability and ceasefire ( 1974 – 75 ) = = = 
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- Back in Lisbon , the ' Armed Revolutionary Action ' branch of the Portuguese Communist Party , which was created in the late 1960s , and the Revolutionary Brigades ( <unk> ) , a left @-@ wing organisation , worked to resist the colonial wars . They had carried out multiple <unk> and bombings against military targets , such as the attack on the <unk> air base that destroyed several helicopters on March 8 , 1971 , and the attack on the NATO headquarters at <unk> in October of the same year . The attack on the Portuguese ship Niassa illustrated the role of the colonial wars in this unrest . Niassa ( named after a Mozambican province ) was preparing to leave Lisbon with troops to be deployed in Guinea . By the time of the Carnation Revolution , 100 @,@ 000 draft <unk> had been recorded . 
- Fighting colonial wars in Portuguese colonies had absorbed forty @-@ four percent of the overall Portuguese budget . This led to an obvious diversion of funds from necessary infrastructural developments in Portugal itself . This contributed to the growing unrest in the European nation . Portugal 's GDP growth during the colonial war period ( 1961 – 1974 ) , was strong and reached a 6 % rate ( a <unk> GDP growth which were not achieved in any other comparable period after 1974 ) . 
- The unpopularity of the Colonial Wars among many Portuguese led to the formation of several magazines and newspapers , such as <unk> <unk> , <unk> <unk> , Tempo e <unk> , and <unk> , which had support from students and called for political solutions to Portugal 's colonial problems . 
- The growing unrest in Portugal culminated on April 25 , 1974 , when the Carnation Revolution , a peaceful leftist military coup d 'état in Lisbon , ousted the incumbent Portuguese government of Marcelo <unk> . Thousands of Portuguese citizens left Mozambique , and the new head of government , General António de <unk> , called for a ceasefire . With the change of government in Lisbon , many soldiers refused to continue fighting , often remaining in their barracks instead of going on patrol . Negotiations between the Portuguese administration culminated in the <unk> <unk> signed on September 7 , 1974 , which provided for a complete hand @-@ over of power to FRELIMO , uncontested by elections . Formal independence was set for June 25 , 1975 , the 13th anniversary of the founding of FRELIMO . 
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- = = Aftermath = = 
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- Many Portuguese <unk> were not typical settlers in Mozambique . While most European communities in Africa at the time - with the possible exception of <unk> - were established from the late nineteenth to early twentieth centuries , some white families and institutions in those territories still administered by Portugal had been entrenched for generations . Loss of their privileged status and fears of FRELIMO reprisal resulted in an exodus of up to 200 @,@ 000 white civilians at independence ( in Europe they were popularly known as <unk> ) . Cities , towns and villages saw their Portuguese names changed after independence - Lourenço <unk> to Maputo , Vila <unk> to <unk> , Vila Cabral to <unk> , or Vila <unk> to <unk> . 
- With the departure of Portuguese professionals and tradesmen , the new country had no senior workforce to maintain its infrastructure , and economic collapse <unk> . <unk> commercial links were established with several communist countries by the FRELIMO regime at the expense of NATO , which rapidly lost influence in the region . 
- Samora Machel became Mozambique 's first president . The Reverend <unk> Simango , his wife , and other FRELIMO dissidents were arrested in 1975 and detained without trial . Within about two years , fighting resumed with the Mozambican Civil War against <unk> insurgents plied with Rhodesian and South African military support . Industrial and social recession , Marxist @-@ style <unk> , corruption , poverty , inequality and failed central planning eroded the initial revolutionary fervour . Peace returned only in 1992 , when the nation achieved relative stability for the first time in several decades . 
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- = = = <unk> sources = = = 
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- = = = Online sources = = = 
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- = The Secret of Monkey Island = 
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- The Secret of Monkey Island is a 1990 point @-@ and @-@ click graphic adventure game developed and published by Lucasfilm Games . It takes place in a fantastic version of the Caribbean during the age of piracy . The player assumes the role of Guybrush Threepwood , a young man who dreams of becoming a pirate and explores fictional islands while solving puzzles . 
- The game was conceived in 1988 by Lucasfilm employee Ron Gilbert , who designed it with Tim Schafer and Dave Grossman . Gilbert 's <unk> with contemporary adventure titles led him to make the player character 's death almost impossible , which meant that gameplay focused the game on exploration . The atmosphere was based on that of the Pirates of the Caribbean theme park ride . The Secret of Monkey Island was the fifth game built with the SCUMM engine , which was heavily modified to include a more user @-@ friendly interface . 
- Critics praised The Secret of Monkey Island for its humor , audiovisuals , and gameplay . The game spawned a number of sequels , collectively known as the Monkey Island series . Gilbert , Schafer and Grossman also led the development of the sequel Monkey Island 2 : LeChuck 's Revenge . LucasArts released a remake of the original in 2009 , which was also well received by the gaming press . 
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- = = Gameplay = = 
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- The Secret of Monkey Island is a 2D adventure game played from a third @-@ person perspective . Via a point @-@ and @-@ click interface , the player guides protagonist Guybrush Threepwood through the game 's world and interacts with the environment by selecting from twelve verb commands ( nine in newer versions ) such as " talk to " for communicating with characters and " pick up " for collecting items between commands and the world 's objects in order to successfully solve puzzles and thus progress in the game . While conversing with other characters , the player may choose between topics for discussion that are listed in a dialog tree ; the game is one of the first to incorporate such a system . The in @-@ game action is frequently interrupted by cutscenes . Like other LucasArts adventure games , The Secret of Monkey Island features a design philosophy that makes the player character 's death nearly impossible ( Guybrush does drown if he stays underwater for more than ten minutes ) . 
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- = = Plot = = 
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- A youth named Guybrush Threepwood arrives on the fictional Mêlée IslandTM , with the desire to become a pirate . He seeks out the island 's pirate leaders , who set him three trials that must be completed to become a pirate : winning a sword duel against Carla , the island 's resident <unk> , finding a buried treasure , and stealing a valuable idol from the governor 's mansion . These quests take Guybrush throughout the island , where he hears of stories of the Ghost Pirate LeChuck , who apparently died in an expedition to the mysterious Monkey IslandTM , an act that was meant to win the love of the governor Elaine Marley . Guybrush meets several characters of interest , including a local voodoo <unk> , Stan the Used Boat <unk> , Carla the Sword Master , a prisoner named <unk> , and <unk> , whose hands have been replaced by hooks . 
- Guybrush also encounters the governor and is instantly <unk> , and she soon <unk> . However , as he completes the tasks set for him , the island is raided by LeChuck and his undead crew , who <unk> Elaine and then retreat to their secret hideout on Monkey IslandTM . Guybrush takes it upon himself to rescue her , buying a ship and hiring Carla , <unk> , and <unk> as crew before setting sail for the fabled island . When Guybrush reaches Monkey Island , he discovers a village of <unk> in a dispute with Herman <unk> , a ragged <unk> <unk> there . He settles their quarrel , and then recovers a magical " voodoo root " from LeChuck 's ship for the <unk> , who provide him with a <unk> bottle of " voodoo root elixir " that can destroy ghosts . 
- When Guybrush returns to LeChuck 's ship with the elixir , he learns that LeChuck has returned to Mêlée IslandTM to marry Elaine at the church . He promptly returns to Mêlée IslandTM and <unk> the wedding , only to ruin Elaine 's own plan for escape ; in the process he loses the elixir . Now confronted with a furious LeChuck , Guybrush is <unk> beaten by the ghost pirate in a fight ranging across the island . The fight eventually arrives at the island 's ship <unk> , where Guybrush finds a bottle of root beer . <unk> the <unk> for the lost elixir , he <unk> LeChuck , destroying the ghost pirate . With LeChuck defeated , Guybrush and Elaine enjoy a romantic moment , watching fireworks caused by LeChuck exploding . 
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- = = Development = = 
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- = = = Origin and writing = = = 
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- Ron Gilbert conceived the idea of a pirate adventure game in 1988 , after completing <unk> <unk> and the Alien <unk> . He first wrote story ideas about pirates while spending the weekend at a friend 's house . Gilbert experimented with introductory paragraphs to find a satisfactory idea . His initial story featured unnamed villains that would eventually become LeChuck and Elaine ; Guybrush was absent at this point . He pitched it to Lucasfilm Games 's staff as a series of short stories . Gilbert 's idea was warmly received , but production was postponed because Lucasfilm Games assigned its designers , including Gilbert , to Indiana Jones and the Last Crusade : The Graphic Adventure . Development of The Last Crusade was finished in 1989 , which allowed Gilbert to begin production of The Secret of Monkey Island , then known internally under the working title <unk> on Monkey Island . 
- Gilbert soon realised that it would be difficult to design the game by himself ; he decided to join forces with Tim Schafer and Dave Grossman , both of whom he hired for Lucasfilm . The game 's insult sword fighting mechanics were influenced by <unk> movies starring Errol Flynn , which Gilbert , Schafer and Grossman often watched for inspiration . They noticed that pirates in those films often <unk> their opponents instead of attacking them , which gave the designers the idea to base the game 's duels on insults rather than combat . Writer Orson Scott Card helped them write the insults during a visit to Lucasfilm 's headquarters at Skywalker Ranch . Many of Gilbert 's original gameplay ideas were abandoned during the production process , although he stated that " most of that stuff was left out for a reason " . 
- The game 's plot , as described by Dave Grossman : “ It ’ s a story about this young man who comes to an island in search of his life ’ s dream . He ’ s pursuing his career goals and he discovers love in the process and winds up thinking that was actually more important than what he was doing to begin with . You ’ re laughing , but there ’ s actually something deeper going on as well . ” When work on the plot began , Gilbert discovered that Schafer 's and Grossman 's writing styles were too different to form a cohesive whole : Grossman 's was " very kind of a dry , sarcastic humor " and Schafer 's was " just a little more in your face " . In reaction , Gilbert assigned them to different characters and story moments depending on what type of comedy was required . Grossman believed that this benefited the game 's writing , as he and Schafer " were all funny in slightly different ways , and it worked well together " . Schafer and Grossman wrote most of the dialogue while they were programming the game ; as a result , much of it was improvised . Some of the dialogue was based on the designers ' personal experiences , such as Guybrush 's line " I had a feeling in hell there would be mushrooms " , which came from Schafer 's own hatred of fungi . 
- The game 's world and characters were designed primarily by Gilbert . After having read Tim Powers ' historical fantasy novel On Stranger Tides , he decided to add paranormal themes to the game 's plot . He also cited Powers ' book as an influence on the characters , particularly those of Guybrush and LeChuck . Inspiration for the game 's <unk> came from Gilbert 's favorite childhood amusement park ride , Pirates of the Caribbean . Grossman said that Gilbert always wanted " to step off the ride " and " talk to the people who lived in that world " . Near the final stages of the design work , Gilbert introduced several characters who were not directly related to the game 's story . He considered this to be an important decision , as the player would need those seemingly minor characters in later parts of the game and would receive a chance to " really interact with them " . 
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- = = = Creative and technical design = = = 
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- Gilbert , Schafer and Grossman 's primary goal was to create a simpler and more accessible gameplay model than those presented in previous Lucasfilm titles . Gilbert had conceived the main designs and puzzles before production began , which resulted in the bulk of the designers ' work to flesh out his ideas . He was frustrated by the adventure games that Sierra On @-@ Line was releasing at the time , and later said that " you died any time you did anything wrong " . Gilbert considered such gameplay as " a cheap way out for the designer " . He had previously applied his design ideas to the 1987 graphic adventure title Maniac Mansion , but committed a number of mistakes during development , such as dead @-@ end situations that prevented the player from completing the game and poorly implemented <unk> for cutscenes . Gilbert aimed to avoid such errors in The Secret of Monkey Island . The team decided to make it impossible for the player character to die , which focused gameplay primarily on world exploration . The Sierra game @-@ over screen was parodied , when Guybrush falls off a cliff only to be bounced back up by a " rubber tree " . 
- The Secret of Monkey Island was the fifth Lucasfilm Games project powered by the SCUMM engine , originally developed for Maniac Mansion . The company had gradually modified the engine since its creation . For Maniac Mansion , the developers hard coded verb commands in the SCUMM <unk> language . These commands become more abstract in subsequent versions of the engine . The developers carried over the practice of referring to individual segments of the <unk> as " rooms " , even though the areas in Monkey Island were outdoors . The game uses the same version of the engine used in Indiana Jones and the Last Crusade , with minor changes . A dialog tree was added , which facilitated conversation options and the sword @-@ fighting puzzles . The developers removed the " What is " option ( an input command that describes an on @-@ screen object to the player ) in favor of allowing the player to simply highlight the object with the mouse <unk> . The game 's improved interface became the standard for the company 's later titles . The game also introduced logical verb <unk> , which could be performed with the mouse ; for example , clicking on a character <unk> to the " talk " action , the most obvious action in the situation . SCUMM 's visuals were updated for the game — the original <unk> version had a <unk> pixel resolution rendered in 16 colors . According to artist Steve Purcell , that became a major limitation for the art team ; due to a low number of " <unk> " colors , they often chose bizarre tones for backgrounds . They chose black and white for Guybrush 's outfit for the same reason . The VGA version of the game later corrected these issues by implementing 256 color support , which allowed for more advanced background and character art . The VGA ( and other platform releases ) removed the infamous " stump joke " from the game , which was a joke in the <unk> version in which the player would examine a tree stump in the forest . Guybrush would exclaim that there is an opening to a system of catacombs and attempt to enter , but this would result in a message stating the player needed to insert disc 22 , then 36 , then 114 in order to continue . The joke resulted in numerous calls to the LucasArts <unk> asking about missing discs . As a result , the joke was removed from later editions and is a mentioned as a conversation option for the LucasArts <unk> <unk> in the sequel . 
- The game 's " pirate reggae " music was composed by Lucasfilm Games ' in @-@ house musician Michael Land in <unk> format . It was his first project at the company . The game was originally released for <unk> disk in 1990 , but a CD @-@ ROM version with a high @-@ quality CD soundtrack followed in 1992 . The music has remained popular , and has been remixed by the musicians of OverClocked ReMix and by the game 's fans . 
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- = = = Special edition = = = 
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- LucasArts released a remake with updated audiovisuals titled The Secret of Monkey Island : Special Edition in July 2009 for <unk> , Microsoft Windows , and Xbox 360 exclusively via digital distribution . PlayStation 3 , Mac OS and <unk> versions followed early in 2010 for download on their respective services . LucasArts confirmed the game 's development on June 1 , 2009 ; rumors appeared several days earlier when the Xbox 360 version of the game received an <unk> rating . The game was first displayed to the public at the 2009 E3 in June . The remake features hand @-@ drawn visuals with more detail , a remastered musical score , voice work for characters , and a hint system . The developers included the function to switch between 2009 and original audiovisuals at will . The voice actors included Dominic <unk> as Guybrush Threepwood and Earl <unk> as LeChuck ; most had provided voice work in sequels to The Secret of Monkey Island . 
- LucasArts 's game producer Craig Derrick and his team conceived the idea of the remake in 2008 . After researching the Monkey Island series ' history , they decided to make " something fresh and new while staying true to the original " , which resulted in the idea of The Secret of Monkey Island 's remake . The developers tried to leave much of the original design unchanged . Any changes were intended to achieve the level of immersion desired for the original . To that end , they added details like a pirate ship or pirates talking in the background of scenes . While the team considered the SCUMM interface revolutionary at the time , LucasArts community manager Brooks Brown noted that it is incompatible with an analog stick , which most consoles use . The designers made the <unk> <unk> to the game objects as the primary interface . Brown had considered updating the reference to advertise Star Wars : The Force <unk> because <unk> was not on the market at the time , but concluded that the game would not be the same if such changes were implemented . Prior to the Special Edition release , however , LucasArts announced that <unk> , along with other games from its back catalog , would be made available on Steam . Brown stated that the decision to distribute the game online was because " digital downloads have finally gotten going " . 
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- = = Reception = = 
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- The Secret of Monkey Island sold well and received positive reviews from critics . Hartley , Patricia , and Kirk Lesser of Dragon praised the designers ' attention to detail , and cited the game 's humor as a high point . Although they believed that the game was too expensive , they summarized it as " a highly enjoyable graphic adventure replete with interesting puzzles , a fantastic Roland soundtrack , superb VGA graphics , smooth @-@ scrolling animation , and some of the funniest lines ever seen on your computer screen . " Duncan MacDonald of Zero praised the graphics and found the game " quite amusing " . His favorite aspect was the fine @-@ tuned difficulty level , which he believed was " just right " . He ended his review , " At last an adventure game that 's enjoyable rather than frustrating . " Paul <unk> of Computer and Video Games consider the game superior to Lucasfilm 's earlier adventure titles , and wrote that , " Usually the entertainment you get from an adventure is derived solely from solving puzzles , but the hilarious characters and situations , and the movie @-@ like presentation ... make playing this more like taking part in a comedy film , so it 's much more enjoyable . " He considered the puzzles to be " brilliantly conceived " and found the game 's controls accessible . He summarized it as " utterly <unk> " . 
- ACE 's Steve Cooke also found the controls convenient , and he praised the game 's atmosphere . He wrote that , " in graphics and sound terms ... Monkey Island , along with King 's Quest V , is currently at the head of the pack . " However , he disliked the designers ' running joke of placing " <unk> " after character and place names , which he thought detracted from the atmosphere . He singled out the game 's writing , characters and plot structure as its best elements . Amiga Power 's Mark <unk> wrote , " With The Secret of Monkey Island , the mouse @-@ controlled , graphic @-@ adventure comes of age . " He lauded its comedic elements , which he believed were the highlight of the game . The reviewer also praised the control scheme , noting that it allows the player to " more or less forget about the specifics of what [ they are ] physically doing ... and lose [ themselves ] in the adventure instead . " He noted that the game 's plot and visual and <unk> presentation fit together to create a thick atmosphere , and finished , " Forget all those other milestone adventures ( <unk> , The Hobbit , Lord of the <unk> et al ) — for sheer enjoyment and general all @-@ round perfection , The Secret of Monkey Island <unk> ' em all in style . " The game , along with its sequel , was ranked the 19th best game of all time by Amiga Power . 
- Writing for The One , Paul Presley stated that " Lucasfilm appears to have taken all of the elements that worked in its previous releases and , not only incorporated them into this tale of <unk> <unk> , but even improved on them in the process ! " Like the other reviewers , he praised its controls . He also lauded its " hilarious storyline , strong characters and ... intriguing setting " , but complained about graphical <unk> . Nick Clarkson of Amiga Computing cited the game 's graphics as " flawless " , noting that " the characters are superbly animated and the <unk> simply <unk> atmosphere . " He highly praised its sound effects and music , and believed that its controls " couldn 't be simpler " . The staff of Amiga Action wrote that the " attention to detail and the finely tuned gameplay cannot be faulted . " They called the graphics " stunning throughout " , and believed that , when they were combined with the " excellent Caribbean tunes " , the result is a game filled with " character and atmosphere . " They ended by stating that " there is absolutely no excuse for not owning this game . " 
- The Secret of Monkey Island has featured regularly in lists of " top " games , such as Computer Gaming World 's Hall of Fame and IGN 's Video Game Hall of Fame . In 1996 , Computer Gaming World ranked it as the 19th best game of all time , " writing " Who could ever forget the insult @-@ driven duel system or the identity of the mysterious <unk> ? " . In 2004 , readers of <unk> Gamer voted it as the 33rd top retro game . In 2010 , IGN ranked the Xbox Live Arcade version as the 20th best title of all time for that platform . In 2009 , IGN named The Secret of Monkey Island one of the ten best LucasArts adventure games . 
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- = = = Special edition = = = 
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- Like the original release , The Secret of Monkey Island : Special Edition received positive reviews from critics . Sean Ely of GamePro praised its updated audio , and said that the new graphics " blow the old <unk> visuals ... out of the water " . He cited its script , humor , plot , puzzles and balanced difficulty level as high points , and finished , " The Secret of Monkey Island : Special Edition is impressive , hilarious and downright worth your money . " <unk> Hatfield of IGN wrote , " Almost 20 years after its release , [ The Secret of Monkey Island ] remains a blast to play . " He called the new graphics " slick , if a little generic " , and noted that the " original graphics have a certain charm to them that the fancy pants new visuals just don 't . " However , he enjoyed the redone music , the new hint function , and the added sound effects and voice acting . He summarized it as " one of the best times you 'll ever have pointing and clicking " , and noted that " few games are this funny . " Justin Calvert of GameSpot noted that " the Special Edition looks much better and is the only way to play if you want to hear ... what characters are saying , whereas the original game 's interface is less <unk> . " However , he wrote that " the voice work is such a great addition to the game that it 's difficult to go back to the original edition . " He praised its humor , writing , puzzles and characters , and he believed that it had aged well . Eurogamer 's Dan Whitehead wrote , " <unk> like me will almost certainly find something to <unk> about over the span of the game , but the overall impact of the redesign is undeniably for the better . " However , he preferred the original game 's Guybrush design , and believed that the new control system was " rather less intuitive " than the old one . He finished by stating that " few games can stand the test of time with such confidence " . 
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- = = Legacy = = 
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- The Secret of Monkey Island spawned four sequels . The first , Monkey Island 2 : LeChuck 's Revenge , was released in 1991 and focuses on LeChuck 's return . Six years later , LucasArts released The Curse of Monkey Island , which features a new visual design . In 2000 , the company released Escape from Monkey Island , which uses the <unk> engine of <unk> <unk> to produce 3D graphics . The next title , Tales of Monkey Island released in 2009 , is a series of five episodic chapters . 
- Elements of the game have appeared elsewhere in popular culture . The original version was selected as one of five for the exhibition The Art of Video Games in the Smithsonian American Art Museum in 2011 . A <unk> drink recipe in the game for <unk> was mistakenly reported as real in 2009 by <unk> news channel <unk> , which urged <unk> against consuming the dangerous " Grog <unk> " drink . In Tales of Monkey Island , Guybrush refers to this news story while pushing the Grog <unk> button on a Grog machine . 
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- = Temple of Eshmun = 
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- The Temple of Eshmun ( Arabic : <unk> <unk> ) is an ancient place of worship dedicated to Eshmun , the Phoenician god of healing . It is located near the Awali river , 2 kilometres ( 1 @.@ 2 mi ) northeast of Sidon in southwestern Lebanon . The site was occupied from the 7th century BC to the 8th century AD , suggesting an integrated relationship with the nearby city of Sidon . Although originally constructed by Sidonian king Eshmunazar II in the Achaemenid era ( c . 529 – 333 BC ) to celebrate the city 's recovered wealth and stature , the temple complex was greatly expanded by Bodashtart , <unk> @-@ milk and later monarchs . Because the continued expansion spanned many centuries of alternating independence and foreign <unk> , the sanctuary features a wealth of different architectural and decorative styles and influences . 
- The sanctuary consists of an esplanade and a grand court limited by a huge limestone terrace wall that supports a monumental podium which was once topped by Eshmun 's <unk> @-@ Persian style marble temple . The sanctuary features a series of ritual ablution basins fed by canals channeling water from the Asclepius river ( modern Awali ) and from the sacred " Ydll " spring ; these installations were used for therapeutic and <unk> purposes that characterize the cult of Eshmun . The sanctuary site has yielded many artifacts of value , especially those inscribed with Phoenician texts , providing valuable insight into the site 's history and that of ancient Sidon . 
- The Eshmun Temple was improved during the early Roman Empire with a <unk> street , but declined after earthquakes and fell into oblivion as Christianity replaced <unk> and its large limestone blocks were used to build later structures . The temple site was rediscovered in 1900 by local treasure hunters who stirred the curiosity of international scholars . Maurice Dunand , a French archaeologist , thoroughly excavated the site from 1963 until the beginning of the Lebanese Civil War in 1975 . After the end of the hostilities and the retreat of Israel from Southern Lebanon , the site was rehabilitated and inscribed to the World Heritage Site tentative list . 
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- = = Eshmun = = 
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- Eshmun was the Phoenician god of healing and renewal of life ; he was one of the most important divinities of the Phoenician pantheon and the main male divinity of Sidon . Originally a nature divinity , and a god of spring vegetation , Eshmun was equated to Babylonian deity <unk> . His role later expanded within the Phoenician pantheon , and he gained celestial and cosmic attributes . 
- The myth of Eshmun was related by the sixth century Syrian <unk> philosopher <unk> and ninth century Patriarch of Constantinople , <unk> . They <unk> that Eshmun , a young man from Beirut , was hunting in the woods when Astarte saw him and was stricken by his beauty . She harassed him with her <unk> pursuit until he <unk> himself with an axe and died . The grieving goddess revived Eshmun and transported him to the heavens where she made him into a god of heaven . 
- From a historical perspective , the first written mention of Eshmun goes back to 754 BC , the date of the signing of the treaty between Assyrian king <unk> @-@ <unk> V and <unk> <unk> , king of <unk> ; Eshmun figures in the text as a patron of the treaty . 
- Eshmun was identified with Asclepius as a result of the Hellenic influence over Phoenicia ; the earliest evidence of this equation is given by coins from <unk> and Acre from the third century BC . This fact is exemplified by the Hellenized names of the Awali river which was dubbed Asclepius <unk> , and the Eshmun Temple 's surrounding groves , known as the groves of Asclepius . 
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- = = History = = 
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- = = = Historical background = = = 
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- In the 9th century BC , the Assyrian king <unk> II conquered the Lebanon mountain range and its coastal cities . The new <unk> <unk> tribute from Sidon , along with every other Phoenician city . These payments stimulated Sidon 's search for new means of <unk> and furthered Phoenician emigration and expansion , which peaked in the 8th century BC . When Assyrian king <unk> II died in 705 BC , the Sidonian king <unk> joined with the Egyptians and Judah in an unsuccessful rebellion against Assyrian rule , but was forced to flee to <unk> ( modern <unk> in Cyprus ) with the arrival of the Assyrian army headed by Sennacherib , <unk> II 's son and successor . Sennacherib instated <unk> on the throne of Sidon and <unk> the annual tribute . When Abdi @-@ <unk> ascended to Sidon 's throne in 680 BC , he also rebelled against the Assyrians . In response , the Assyrian king Esarhaddon laid siege to the city . Abdi @-@ <unk> was captured and beheaded in <unk> BC after a three @-@ year siege , while his city was destroyed and renamed <unk> @-@ <unk> @-@ <unk> @-@ <unk> ( the harbor of Esarhaddon ) . Sidon was stripped of its territory , which was awarded to Baal I , the king of rival Tyre and loyal vassal to Esarhaddon . Baal I and Esarhaddon signed a treaty in <unk> in which Eshmun 's name features as one of the deities invoked as <unk> of the covenant . 
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- = = = Construction = = = 
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- Sidon returned to its former level of prosperity while Tyre was besieged for 13 years ( <unk> – <unk> BC ) by the <unk> king <unk> II . Nevertheless , the Sidonian king was still held in exile at the court of Babylon . Sidon reclaimed its former standing as Phoenicia 's chief city in the Achaemenid Empire ( <unk> – 333 BC ) . During this period , <unk> I awarded king Eshmunazar II with the Sharon plain for employing Sidon 's fleet in his service during the Greco @-@ Persian Wars . 
- Eshmunazar II displayed his new @-@ found wealth by constructing numerous temples to Sidonian divinities . <unk> found on the king 's sarcophagus reveal that he and his mother , <unk> , built temples to the gods of Sidon , including the Temple of Eshmun by the " Ydll source near the cistern " . 
- As two series of inscriptions on the foundations of the monumental podium <unk> , construction of the sanctuary 's podium did not begin until the reign of King Bodashtart . The first set of inscriptions bears the name of Bodashtart alone , while the second contains his name and that of the crown prince <unk> @-@ milk . A Phoenician inscription , located 3 kilometres ( 1 @.@ 9 mi ) upstream from the temple , that dates to the 14th year of Bodashtart 's reign , alludes to water <unk> works from the Awali river to the " Ydll " source that was used for ritual purification at the temple . 
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- = = = Roman era & Decline = = = 
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- The Eshmun sanctuary was damaged by an earthquake in the fourth century BC , which demolished the marble temple atop the podium ; this structure was not rebuilt but many chapels and temples were later annexed at the base of the podium . 
- The temple site remained a place of pilgrimage in the classical antiquity during the early Roman Empire and until the advent of Christianity , when the cult of Eshmun was banned and a Christian church was built at the temple site across the Roman street from the podium . Remnants and mosaic floors of a Byzantine church can still be seen on the site . 
- A Roman <unk> was built in the third century , probably by emperor Septimius <unk> , and a Roman Villa showed a period of renewed relative importance for the city during the late period of Phoenicia under Roman rule . Furthermore , within the original Phoenician temple site the Romans added the processional stairway , the basins for ablutions and a nymphaeum with pictorial mosaics , that are still largely intact . <unk> <unk> of three nymphs stand in the niches of a Roman fountain . 
- Another earthquake hit Sidon around 570 AD ; <unk> of <unk> , an Italian Christian <unk> , described the city as partly in ruins . For many years after the disappearance of the cult of Eshmun , the sanctuary site was used as a quarry : <unk> <unk> @-@ al @-@ Din II , for example , used its massive blocks to build a bridge over the Awali river in the 17th century . 
- The site later fell into oblivion until the <unk> century 
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- = = = Modern discovery = = = 
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- Between 1737 and 1742 , Richard <unk> , an English anthropologist , toured the Middle East and wrote of what he thought were ruins of defensive walls built with 3 @.@ 7 @-@ metre ( 12 ft ) stone blocks near the Awali river . When the French <unk> Ernest <unk> visited the area in 1860 , he noticed that the Awali bridge abutments were built of finely <unk> blocks that originated from an earlier structure . He also noted in his report , Mission de <unk> , that a local treasure hunter told him of a large edifice near the Awali bridge . 
- In 1900 , local clandestine treasure hunters digging at the Eshmun Temple site <unk> discovered inscriptions carved onto the temple 's walls . This discovery stirred the interest of Theodore <unk> , curator of the Museum of Constantinople , who cleared the temple remains between 1901 and 1903 . Wilhelm Von <unk> also excavated the site between 1903 and 1904 . In 1920 , <unk> <unk> headed a team of archaeologists who surveyed the temple complex . The first extensive archaeological excavation revealing the Eshmun Temple remains was undertaken by Maurice Dunand between 1963 and 1975 . Archaeological evidence shows that the site was occupied from the seventh century BC to the eighth century AD . 
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- = = = After 1975 = = = 
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- During the Lebanese Civil War and the Israeli occupation of South Lebanon ( 1985 – 2000 ) , the temple site was neglected and was invaded by vegetation <unk> ; it was cleared and recovered its former condition after the Israeli withdrawal . Today the Eshmun sanctuary can be visited all year round and free of charge , it is accessible from an exit ramp off the main Southern Lebanon highway near Sidon 's northern entrance . The site holds a particular archaeological importance since it is the best preserved Phoenician site in Lebanon ; it was added to the UNESCO World Heritage <unk> List 's Cultural category on July 1 , 1996 . 
- In literature , the temple of Eshmun figures in <unk> <unk> 's 2009 novel , The Curse of Ezekiel as the setting where <unk> falls in love and rescues princess <unk> from the evil design of one of the temple 's priests . 
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- = = Location = = 
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- A number of ancient texts mention the Eshmun Temple and its location . The Phoenician inscriptions on the sarcophagus of Eshmunazar II , a Sidonian king , commemorate the construction of a " house " for the " holy prince " Eshmun by the king and his mother , queen <unk> , at the " Ydll source by the cistern " . <unk> <unk> , an ancient Greek travel writer , identified the Eshmun temple by the <unk> River , and Antonin de <unk> , a 6th @-@ century AD Italian <unk> recorded the shrine as near the river Asclepius <unk> and other Sidonian sources describe the sanctuary and its surrounding " sacred forests " of Asclepius , the Hellenized name of Eshmun , in written texts . 
- Located about 40 kilometres ( 25 mi ) south of Beirut and 2 kilometres ( 1 @.@ 2 mi ) northeast of Sidon , the Eshmun Temple sits on the southern bank of the modern Awali river , previously referred to as <unk> or Asclepius <unk> in ancient text . <unk> groves , known as <unk> el @-@ Sheikh ( Arabic : <unk> <unk> , the <unk> of the Sheikh ) , occupy the ancient " sacred forests " of Asclepius and are a favorite summer picnic location for locals . 
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- = = Architecture and description = = 
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- Built under Babylonian rule ( 605 – 539 BC ) , the oldest monument at the site is a pyramidal building resembling a <unk> that includes an access ramp to a water cistern . <unk> of marble column bases with <unk> <unk> and <unk> columns found east of the podium are also attributed to the Babylonian era . 
- The pyramidal structure was superimposed during Persian rule by a massive ashlar podium constructed from heavily <unk> limestone blocks that measured more than 3 metres ( 9 @.@ 8 ft ) across by 1 metre ( 3 @.@ 3 ft ) thick , which were laid down in courses 1 @-@ metre ( 3 @.@ 3 ft ) high . The podium stands 22 metres ( 72 ft ) high , <unk> metres ( 160 ft ) into the hillside , and <unk> a 70 @-@ metre ( 230 ft ) wide façade . The terrace atop of the podium was once covered by a Greco @-@ Persian style marble temple probably built by <unk> artisans around 500 BC . The marble temple has been reduced to a few remaining stone fragments due to theft . 
- During the Hellenistic period , the sanctuary was extended from the base of the podium across the valley . To the east base of the podium stands a large chapel , 10 @.@ 5 by 11 @.@ 5 metres ( 34 ft × 38 ft ) , dating to the 4th century BC . The chapel was adorned with a paved pool and a large stone throne carved of a single block of granite in the Egyptian style ; it is flanked by two sphinx figures and surrounded by two lion sculptures . The throne , attributed to the Sidonian goddess Astarte , rests against the chapel wall , which is embellished by relief sculptures of hunting scenes . The once important Astarte basin lost its function during the 2nd century AD and was filled with earth and statue fragments . The west base contains another 4th century BC chapel — centered on a bull <unk> topped capital — that remains preserved at the National Museum of Beirut . 
- Widely known as the " Tribune of Eshmun " because of its shape , the altar of Eshmun is a white marble structure dating to the 4th century BC . It is 2 @.@ 15 metres ( 7 @.@ 1 ft ) long by 2 @.@ 26 metres ( 7 @.@ 4 ft ) wide and 2 @.@ 17 metres ( 7 @.@ 1 ft ) tall . <unk> in 1963 by Maurice Dunand , it stands on a limestone <unk> with marble blocks that rest against a retaining wall . The altar is adorned with Hellenistic style relief sculptures and is framed by decorative <unk> , one of which divides the altar into two distinct registers of symmetrical composition . The upper register portrays 18 Greek deities , including two <unk> surrounding the Greek god Apollo , who is depicted playing a <unk> ( a type of <unk> ) . The lower register honors <unk> , who leads his <unk> ( his <unk> revenue ) in a dance to the music of pipe and <unk> players . The Tribune is displayed at the National Museum of Beirut . 
- Northeast of the site , another 3rd century BC temple stands adjacent to the Astarte chapel . Its 22 @-@ metre ( 72 ft ) façade is built with large limestone blocks and displays a two @-@ register relief decoration illustrating a drunken <unk> in honor of <unk> , the Greek god of wine . Among the temple reliefs , one shows a man attempting to seize a large rooster which was the common sacrificial animal for Eshmun @-@ Asclepius . 
- The Eshmun Temple complex comprises an elaborate hydraulic installation channeling water from " Ydll " spring that is made up of an intricate system of water canals , a series of retaining basins , sacred ablution basins and paved pools . This system demonstrates the importance of ritual ablutions in Phoenician therapeutic cults . 
- Later <unk> date from the Roman <unk> and include a <unk> road lined with shops . Of the large marble columns bordering the Roman street only fragments and bases remain . The Romans also built a monumental staircase adorned with mosaic patterns that leads to the top of the podium . To the right of the Roman road , near the entrance of the site stands a nymphaeum with niches where statues of the nymphs once stood . The floor of the nymphaeum is covered by a mosaic depicting the <unk> . Across the <unk> road , facing the nymphaeum , are the ruins of a Roman villa ; only the villa 's courtyard has survived along with the remains of a mosaic depicting the four seasons . To the right of the processional Roman staircase stands a cubic altar , also of Roman construction . Other Roman period structures include two columns of a great portico leading to pools and other <unk> installations . 
- 
- = = Function = = 
- 
- Eshmun 's cult enjoyed a particular importance at Sidon as he was the chief deity after 500 BC . Aside from the <unk> sanctuary at <unk> el @-@ Sheikh , Eshmun also had a temple within the city . The <unk> Eshmun Temple was associated with purification and healing ; ritual <unk> ablutions were performed in the sanctuary 's sacred basins supplemented by running water from the Asclepius River and the " Ydll " spring water which was considered to have a sacred character and therapeutic quality . The healing <unk> of Eshmun were combined with his divine consort Astarte 's <unk> powers ; the latter had an annex chapel with a sacred paved pool within the Eshmun sanctuary . <unk> from all over the ancient world flocked to the Eshmun Temple leaving votive traces of their devotion and proof of their cure . There is evidence that from the 3rd century BC onwards there have been attempts to <unk> the cult of Eshmun and to associate him with his Greek counterpart Asclepius , but the sanctuary retained its <unk> function . 
- 
- = = <unk> and finds = = 
- 
- Apart from the large decorative elements , carved <unk> and mosaics which were left in situ , many artifacts were recovered and moved from the Eshmun Temple to the national museum , the Louvre or are in possession of the Lebanese <unk> general of antiquities . Some of these smaller finds include a collection of inscribed <unk> unearthed by Dunand providing rare examples of <unk> Phoenician writing in the Phoenician mainland . One of the recovered <unk> bears the <unk> Phoenician name " <unk> " which suggests that veneration of the lunar @-@ goddess <unk> occurred in Sidon . 
- A number of fragmented votive life @-@ size sculptures of little children lying on their side and holding a pet animal or a small object were also recovered at the temple site ; among the best known of these is a sculpture of a royal child holding a <unk> with his right hand ; the boy 's head is shaved , his torso is bare and his lower body is wrapped in a large cloth . The <unk> of this sculpture is inscribed with a dedication from <unk> , the son of a Sidonian king to Eshmun , which illustrates the importance of the site to the Sidonian monarchy . These votive sculptures appear to have been purposely broken after dedication to Eshmun and then <unk> cast into the sacred canal , probably <unk> the sacrifice of the sick child . All of these sculptures represent boys . <unk> cm × 27 cm ( 12 @.@ 4 in × 10 @.@ 6 in ) limestone bust of a <unk> dating from the 6th century BC was found at the site , but unlike the archaic Greek <unk> this figure is not bare . 
- Among the notable finds is a golden plaque showing a snake curling on a staff , a Hellenic symbol of <unk> and a granite altar bearing the name of Egyptian Pharaoh <unk> uncovered in the Eshmun sanctuary . This gift attests to the good relations between the Pharaoh and the kings of Sidon . 
- The repute of the sanctuary was far reaching . <unk> pilgrims from <unk> left marks of their devotion for Astarte on a marble stele inscribed both in Greek and <unk> <unk> at Astarte 's shrine ; this stele is now in the custody of the Lebanese <unk> general of antiquities . 
- 
- = = <unk> = = 
- 
- Treasure hunters have sought out the Eshmun Temple since antiquity ; around 1900 artifacts bearing Phoenician inscriptions from the temple site found their way to <unk> antiquities markets where they stirred the interest of the Ottoman authorities and prompted a series of archeological <unk> . During the civil war , upon a request from then Lebanese director general of antiquities Maurice <unk> , Maurice Dunand moved more than 2000 artifacts from Sidon to a subterranean chamber at the Byblos crusader castle , 30 kilometres ( 19 mi ) north of Beirut . In 1981 , the depot was looted and around 600 sculptures and architectural elements were stolen and smuggled out of Lebanon . Rolf <unk> , ex @-@ director of the Institute of Classical <unk> of Basel affirmed during a conference in Beirut in December 2009 the successful identification and return of eight sculptures to the Lebanese national museum . 
- 
- 
- = Wilhelm Busch = 
- 
- Heinrich Christian Wilhelm Busch ( 15 April 1832 – 9 January 1908 ) was a German humorist , poet , illustrator and painter . He published comic illustrated <unk> tales from 1859 , achieving his most notable works in the 1870s . Busch 's illustrations used wood engraving , and later , zincography . 
- Busch drew on contemporary parochial and city life , <unk> Catholicism , <unk> , strict religious morality and <unk> . His comic text was colourful and entertaining , using onomatopoeia , <unk> and other figures of speech , and led to some work being banned by the authorities . 
- Busch was influential in both poetry and illustration , and became a source for future generations of comic artists . The Katzenjammer Kids was inspired by Busch 's Max and Moritz , one of a number of imitations produced in Germany and the United States . The Wilhelm Busch Prize and the Wilhelm Busch Museum help maintain his legacy . His <unk> anniversary in 2007 was celebrated throughout Germany . Busch remains one of the most influential poets and artists in Western Europe . 
- 
- = = Family background = = 
- 
- In the late 18th century Johann Georg Kleine , Wilhelm Busch 's maternal grandfather , settled in the small village of Wiedensahl . There , in 1817 , he bought a <unk> half @-@ <unk> house , where Wilhelm Busch was to be born about 15 years later . <unk> Kleine , Johann 's wife and Wilhelm Busch 's grandmother , kept a shop in which Busch 's mother Henriette assisted while her two brothers attended high school . When Johann Georg Kleine died in 1820 , his widow continued to run the shop with Henriette . 
- At the age of 19 Henriette Kleine married surgeon Friedrich Wilhelm <unk> . Henriette became widowed at the age of 26 , with her three children to <unk> dying as infants . About 1830 Friedrich Wilhelm Busch , the illegitimate son of a farmer , settled in Wiedensahl after completing a business apprenticeship in the nearby village of <unk> . He took over the Kleine shop in Wiedensahl , which he completely modernised . 
- 
- = = Life = = 
- 
- 
- = = = Childhood = = = 
- 
- Wilhelm Busch was born on 15 April 1832 , the first of seven children to the marriage of Henriette Kleine and Friedrich Wilhelm Busch . His six siblings followed shortly after : Fanny ( 1834 ) , Gustav ( 1836 ) , Adolf ( 1838 ) , Otto ( 1841 ) , Anna ( 1843 ) and Hermann ( 1845 ) ; all survived childhood . His parents were ambitious , hard @-@ working and devout Protestants who later , despite becoming relatively prosperous , could not afford to educate all three sons . Busch 's biographer <unk> W. <unk> suggested that Friedrich Wilhelm Busch invested heavily in his sons ' education partly because his own <unk> held significant stigma in rural areas . 
- The young Wilhelm Busch was a tall child , but with a rather delicate and graceful physique . The coarse <unk> of his later protagonists " Max and Moritz " was rare in his childhood . He described himself in autobiographical sketches and letters as sensitive and timid , as someone who " carefully studied apprehension " , and who reacted with fascination , compassion and distress when animals were killed in the autumn . He described the " transformation to sausage " as " <unk> compelling " , leaving a lasting impression ; pork <unk> him throughout his life . 
- In the autumn of 1841 , after the birth of his brother Otto , Busch 's education was entrusted to the 35 @-@ year @-@ old clergyman Georg Kleine , his maternal uncle at Ebergötzen , this probably through lack of space in the Busch family home , and his father 's desire for a better education than the small local school could provide , where 100 children were taught within a space of 66 m2 ( 710 sq ft ) . The nearest convenient school was located in <unk> , 20 km ( 12 mi ) from Wiedensahl . Kleine , with his wife Fanny Petri , lived in a rectory at Ebergötzen , while Busch was lodged with an unrelated family . Kleine and his wife were responsible and caring , exercised a substitute parental role , and provided refuge for him in future unsuccessful times . 
- Kleine 's private lessons for Busch were also attended by Erich Bachmann , the son of a wealthy Ebergötzen <unk> . Both became friends , according to Busch the strongest friendship of his childhood . This friendship was echoed in the 1865 story Max and Moritz . A small pencil portrait by the 14 @-@ year @-@ old Busch depicted Bachmann as a <unk> , confident boy , and showed similarities with Max . Busch portrayed himself with a " <unk> " , in the later " Moritzian " <unk> style . 
- Kleine was a <unk> , his lessons not held in contemporary language , and it is not known for certain all subjects Busch and his friend were taught . Busch did learn elementary arithmetic from his uncle , although science lessons might have been more comprehensive , as Kleine , like many other clergymen , was a <unk> , and published essays and textbooks on the subject — Busch demonstrated his knowledge of bee @-@ keeping in his future stories . Drawing , and German and English poetry , were also taught by Kleine . 
- Busch had little contact with his natural parents during this period . At the time , the 165 km ( 103 mi ) journey between Wiedensahl and Ebergötzen took three days by horse . His father visited Ebergötzen two to three times a year , while his mother stayed in Wiedensahl to look after the children . The 12 @-@ year @-@ old Busch visited his family once ; his mother at first did not recognize him . Some Busch biographers think that this early separation from his parents , especially from his mother , resulted in his eccentric <unk> . In the autumn of 1846 , Busch moved with the Kleine 's to Lüthorst , where , on 11 April 1847 , he was confirmed . 
- 
- = = = Study = = = 
- 
- In September 1847 Busch began studying mechanical engineering at Hanover Polytechnic . Busch 's biographers are not in agreement as to why his Hanover education ended ; most believe that his father had little appreciation of his son 's artistic inclination . Biographer Eva Weissweiler suspects that Kleine played a major role , and that other possible causes were Busch 's friendship with an <unk> , <unk> , political debates in <unk> 's tavern , and Busch 's reluctance to believe every word of the Bible and catechism . 
- Busch studied for nearly four years at Hanover , despite initial difficulties in understanding the subject matter . A few months before graduation he confronted his parents with his aspiration to study at the Düsseldorf Art Academy . According to Bush 's nephew Hermann Nöldeke , his mother supported this inclination . His father eventually <unk> and Busch moved to Düsseldorf in June 1851 , where , to his disappointment at not being admitted to the advanced class , he entered preparatory classes . Busch 's parents had his tuition fees paid for one year , so in May 1852 he traveled to Antwerp to continue study at the Royal Academy of Fine Arts under <unk> <unk> <unk> . He led his parents to believe that the Academy was less <unk> than Düsseldorf , and had the opportunity to study old masters . At Antwerp he saw for the first time paintings by Peter Paul Rubens , <unk> <unk> , David <unk> and <unk> <unk> . The pictures aroused his interest , but made him doubt his own skills . Eventually , in 1853 , after suffering heavily from <unk> , he abandoned his Antwerp studies and returned <unk> to Wiedensahl . 
- 
- = = = Munich = = = 
- 
- Busch was ravaged by disease , and for five months spent time painting and collecting folk tales , legends , songs , ballads , rhymes and fragments of regional <unk> . Busch 's biographer Joseph Kraus saw these collections as useful additions to folklore , as Busch noted the narrative background to tales and the idiosyncrasies of <unk> . Busch tried to release the collections , but as a publisher could not be found at the time they were issued after his death . During the Nazi era Busch was known as an " ethnic seer " . 
- After Busch had spent six months with his uncle Kleine at Lüthorst , he expressed an aim to continue study in Munich . This request caused a rift with his father who , however , eventually funded this move ; - see for comparison Busch 's illustrated story of Painter Klecksel . Busch 's expectations of the Munich Academy of Fine Arts were not met . His life became <unk> ; there were occasional return visits to Lüthorst , but contact with his parents had been broken off . In 1857 and 1858 , as his position seemed to be without prospects , he contemplated emigration to Brazil to keep bees . 
- Busch made contact with the artist association Jung <unk> ( Young Munich ) , met several notable Munich artists , and wrote and provided cartoons for the Jung <unk> newspaper . Kaspar Braun , who published the satirical newspapers Münchener Bilderbogen ( Picture Sheets from Munich ) and <unk> Blätter ( Flying <unk> ) , proposed a collaboration with Busch . This association provided Busch with sufficient funds to live . An existing self @-@ caricature suggests that at this time he had an intense relationship with a woman from <unk> . His courtship with a seventeen @-@ year @-@ old merchant 's daughter , Anna Richter , whom Busch met through his brother Gustav , ended in 1862 . Busch 's biographer , <unk> , suggests that her father probably refused to <unk> his daughter to an almost unknown artist without regular income . 
- In his early Munich years Busch 's attempt to write libretti , which are almost forgotten today , were unsuccessful . Up to 1863 he worked on two or three major works ; the third was composed by Georg Kremplsetzer . Busch 's <unk> und <unk> , a romantic opera in three acts , <unk> und <unk> and Der <unk> <unk> <unk> , an opera <unk> of sorts , were not particularly successful . There was a dispute between Busch and Kremplsetzer during the staging of Der <unk> <unk> <unk> , leading to the removal of Busch 's name from the production ; the piece was renamed <unk> von Georg Kremplsetzer . 
- In 1873 Busch returned several times to Munich , and took part in the intense life of the Munich Art Society as an escape from provincial life . In 1877 , in a last attempt to be a serious artist , he took a studio in Munich . He left Munich abruptly in 1881 , after he disrupted a variety show and subsequently made a scene through the effects of alcohol The 1878 nine episode illustrated tale Eight Sheets in the Wind describes how humans behave like animals when drunk . Busch 's biographer Weissweiler felt the story was only superficially funny and harmless , but was a study on addiction and its induced state of delusion . 
- 
- = = = Publication of Max and Moritz = = = 
- 
- Between 1860 and 1863 Busch wrote over one hundred articles for the Münchener Bilderbogen and <unk> Blätter , but he felt his dependence on publisher Kaspar Braun had become <unk> . Busch appointed Dresden publisher Heinrich Richter , the son of Saxon painter Ludwig Richter , as his new publisher — Richter 's press up to that time was producing children 's books and religious Christian devotional literature . Busch could choose themes , although Richter raised some concerns regarding four suggested illustrated tales that were proposed . However , some were published in the 1864 as <unk> , proving a failure . Busch then offered Richter the manuscripts of Max and Moritz , <unk> any fees . Richter rejected the manuscript as sales prospects seemed poor . Busch 's former publisher , Braun , purchased the right to Max and Moritz for 1 @,@ 000 <unk> , corresponding to approximately double the annual wage of a craftsman . 
- For Braun the manuscript was <unk> . Initially the sales of Max and Moritz were slow , but sales figures improved after the 1868 second edition . Overall there were 56 editions and more than 430 @,@ 000 copies sold up to Busch 's death in 1908 . Despite at first being ignored by critics , teachers in the 1870s described Max and Moritz as frivolous and an undesirable influence on the moral development of young people . 
- 
- = = = Frankfurt = = = 
- 
- Increasing economic success allowed Busch to visit Wiedensahl more frequently . Busch had decided to leave Munich , as only few relatives lived there , and the artists ' association was temporarily disbanded . In June 1867 Busch met his brother Otto for the first time , in Frankfurt . Otto was working as a tutor to the family of a wealthy banker and industrialist , Kessler . Busch became friends with Kessler 's wife , Johanna , a mother of seven and an influential art and music patron of Frankfurt . She regularly opened salons at her villa , frequented by artists , musicians and philosophers . She believed Busch to be a great painter , a view supported by Anton Burger , a leading painter of the <unk> <unk> , the <unk> @-@ based group of painters . While his humorous drawings did not appeal to her , she supported his painting career . At first she established an apartment and studio for Busch in her villa , later providing him with an apartment nearby . <unk> by Kessler 's support and admiration , and introduction to the cultural life of Frankfurt , the ' Frankfurter Years ' were the most artistically productive for Busch . At this time he and Otto discovered the philosophical works of Arthur Schopenhauer . 
- Busch did not remain in Frankfurt . Towards the end of the 1860s he alternated between Wiedensahl and Lüthorst , and <unk> where his brother Gustav lived . The association with Johanna Kessler lasted five years , and after his return to Wiedensahl in 1872 they communicated by letter . This contact was interrupted between 1877 and 1891 , after which it was revived with the help of Kessler 's daughters . 
- 
- = = = Later life = = = 
- 
- Biographer Weissweiler does not dismiss the possibility that Busch 's increasing alcohol dependence hindered self @-@ criticism . He refused invitations to parties , and publisher Otto <unk> sent him to Wiedensahl to keep his alcohol problem undetected from those around him . Busch was also a heavy smoker , resulting in symptoms of severe <unk> poisoning in 1874 . He began to illustrate drunkards more often . 
- Dutch writer Marie Anderson corresponded with Busch . More than fifty letters were exchanged between January and October 1875 in which they discussed philosophy , religion and ethics . Although only one Anderson letter survives , Busch 's letters are in manuscripts . They met in <unk> in October 1875 , after which he returned to <unk> at <unk> in a " horrible mood " . According to several people at the time , Busch 's failure to find a wife was responsible for his conspicuous behaviour . There is no evidence that Busch had a close relationship with any woman after that with Anderson . 
- Busch lived with his sister Fanny 's family after her husband Pastor Hermann Nöldeke 's death in 1879 . His nephew Adolf Nöldeke remembers that Busch wanted to move back to Wiedensahl with the family . Busch renovated the house , which Fanny looked after even though he was a rich man , and became " father " to his three young nephews . She would , however , have preferred to live in a more urban area for the education of her sons . For Fanny and her three sons , Busch could not replace their former idyllic life . The years around 1880 were <unk> and emotionally exhausting for Busch , who was still reliant on alcohol . He would not invite visitors to Wiedensahl ; because of this Fanny lost contact with her friends in the village , and whenever she questioned his wishes , Busch became furious ; Even his friends Otto Friedrich Bassermann , Franz von <unk> , Hermann Levi and Wilhelm von <unk> were not invited ; he would meet them in <unk> or Hanover . 
- Busch stopped painting in 1896 and signed @-@ over all publication rights to Bassermann <unk> for 50 @,@ 000 gold marks . Busch , now aged 64 , felt old . He needed <unk> for writing and painting , and his hands <unk> slightly . In 1898 , together with his aging sister Fanny Nöldeke , he accepted Bassermann 's suggestion to move into a large <unk> in Mechtshausen . Busch read biographies , novels and stories in German , English and French . He organized his works and wrote letters and poems . Most of the poems from the collections <unk> und <unk> and <unk> <unk> <unk> were written in 1899 . The following years were <unk> for Busch . He developed a sore throat in early January 1908 , and his doctor detected a weak heart . During the night of 8 – 9 January 1908 Busch slept <unk> , taking camphor , and a few drops of morphine as a tranquilizer . Busch died the following morning before his physician , called by Otto Nöldeke , came to assist . 
- 
- = = Work = = 
- 
- During the Frankfort period Busch published three self @-@ contained illustrated satires . Their anti @-@ clerical themes proved popular during the <unk> . Busch 's satires typically did not address political questions , but exaggerated <unk> , superstition and <unk> double @-@ standards . This exaggeration made at least two of the works historically erroneous . The third illustrated satire , Father Filucius ( <unk> Filucius ) , described by Busch as an " allegorical <unk> " , has greater historical context . 
- 
- = = = Max and Moritz = = = 
- 
- In German <unk> <unk> in <unk> <unk> , Max and Moritz is a series of seven illustrated stories concerning the mischievous antics of two boys , who are eventually ground @-@ down and fed to ducks . 
- 
- = = = Saint Antonius of Padua and Helen Who Couldn 't Help It = = = 
- 
- In Saint Antonius of Padua ( Der <unk> Antonius von Padua ) Busch challenges Catholic belief . It was released by the publisher Moritz Schauenburg at the time Pope Pius IX proclaimed the <unk> of papal <unk> that was harshly criticized by Protestants . The publisher 's works were heavily <unk> or censored , and the state 's attorney in <unk> charged <unk> with " <unk> of religion and offending public decency through indecent writings " — a decision which affected Busch . Scenes of Antonius accompanied by a pig being admitted to heaven , and the devil being shown as a half @-@ naked ballet dancer <unk> Antonius , were deemed controversial . The district court of Düsseldorf subsequently banned Saint Antonius . Schauenburg was acquitted on 27 March 1871 in <unk> , but in Austria the satire 's distribution was prohibited until 1902 . Schauenburg refused to publish further Busch satires to avoid future accusations . 
- Busch 's following work , Helen Who Couldn 't Help It ( Die <unk> <unk> ) , was published by Otto Friedrich Bassermann , a friend whom Busch met in Munich . Helen Who Couldn 't Help It , which was soon translated into other European languages , <unk> religious hypocrisy and dubious morality : 
- Many details from Helen Who Couldn 't Help It criticizes the way of life of the <unk> . Johanna Kessler was married to a much older man and entrusted her children to governesses and tutors , while she played an active role in the social life of Frankfurt . 
- The character of Mr. <unk> — the name based on the Yiddish insult " <unk> " — shows similarities with Johanna Kessler 's husband , who was <unk> in art and culture . 
- In the second part of Helen Who Couldn 't Help It Busch attacks Catholic <unk> . The <unk> Helen goes on a pilgrimage , accompanied by her cousin and Catholic priest Franz . The pilgrimage is successful as later Helen gives birth to twins , who resemble Helen and Franz . Franz is later killed by a jealous <unk> , Jean , for his interest in female kitchen staff . The now widowed Helen is left with only a rosary , prayer book and alcohol . Drunk , she falls into a burning oil lamp . Finally , <unk> coins a moral phrase , echoing the philosophy of Schopenhauer : 
- <unk> Filucius ( Father Filucius ) is the only illustrated satire of this period suggested by the publisher . Also aimed at anti @-@ Catholic taste and <unk> , it criticizes the Jesuit Order . Kraus felt it was the weakest of all three anti @-@ clerical works . Some satires refer to contemporary events , such as Monsieur Jacques à Paris during the Siege of 1870 ( Monsieur Jacques à Paris <unk> der <unk> von 1870 ) . Busch biographer Manuela <unk> declares the story " <unk> work , drawing on anti @-@ French emotions and mocking the misery of French people in Paris , which is occupied by Prussian troops " . It depicts an increasingly desperate French citizen who at first eats a mouse during the German siege , then <unk> his dog 's tail to cook it , and finally <unk> an explosion pill which kills his dog and two fellow citizens . Weissweiler believes that Busch wrote with irony . In <unk> and Emma ( 1864 ) , a fictional family story that takes place in the <unk> era , he criticizes the Holy Roman Empire and calls for a German empire in its place ; in The <unk> or the <unk> ( Der <unk> oder die <unk> ) he <unk> the anti @-@ Prussian sentiments of his Hanover countrymen . 
- 
- = = = Critique of the Heart = = = 
- 
- Busch did not write further illustrated tales for a while , and focused on the literary <unk> des <unk> ( Critique of the Heart ) , wanting to appear more serious to his readers . Contemporary reception for the collection of 81 poems was mainly poor ; it was criticized for its focus on marriage and sexuality . His long @-@ time friend Paul <unk> called it " very serious , heartfelt , charming poems " . Dutch writer Marie Anderson was one of few people who enjoyed his <unk> des <unk> , and even planned to publish it in a Dutch newspaper . 
- 
- = = = Adventures of a Bachelor = = = 
- 
- Notwithstanding the hiatus after moving from Frankfurt , the 1870s were one of Busch 's most productive decades . In 1874 he produced the short illustrated tale <unk> @-@ Boom ! ( <unk> ! ) . 
- Following , in 1875 , was the Knopp Trilogy , about the life of Tobias Knopp : Adventures of a Bachelor ( <unk> <unk> <unk> ) , Mr. and Mrs. Knopp ( Herr und <unk> Knopp ) ( 1876 ) , and " Julie " ( <unk> ) ( 1877 ) . The antagonists of the trilogy are not pairs of <unk> as with Max and Moritz or Jack <unk> , Bird of Evil ( Hans <unk> , der <unk> ) . Without pathos , Busch makes Knopp become aware of his mortality : 
- In the first part of the trilogy , Knopp is depressed and will look for a wife . He visits his old friends and their wives who he finds in unenviable relationships . Still not convinced that the life of a bachelor is one for him , he returns home , and without further <unk> proposes to his housekeeper . The following marriage proposal is , according to Busch biographer Joseph Kraus , one of the shortest in the history of German literature : 
- According to <unk> , Busch became skeptical of marriage after writing the story . To Marie Anderson he wrote : " I will never marry ( ... ) I am already in good hands with my sister " . 
- 
- = = = Last works = = = 
- 
- Among Busch 's last works were the stories Clement Dove , the Poet <unk> ( <unk> <unk> , der <unk> <unk> ) ( 1883 ) and Painter Squirtle ( Maler Klecksel ) ( 1884 ) , both of which focus on artistic failure , and indirectly his own failure . Both stories begin with a preface , which , for biographer Joseph Kraus , were <unk> pieces of " <unk> <unk> " — German comic poetry . Clement Dove ridicules the <unk> amateur poet circle of Munich , " The <unk> " ( Die <unk> ) , and their prominent members Emanuel <unk> , Paul von <unk> and Adolf <unk> . Painter Squirtle criticizes the <unk> art <unk> , who believes the worth of art is <unk> by its price . 
- The prose play Edwards Dream ( <unk> <unk> ) was released in 1891 , composed of several small grouped episodes , rather than one linear storyline . The work received mixed reception . Joseph Kraus felt it was the peak of Busch 's life 's work , his nephews called it a <unk> of world literature , and the publisher of a critical collective edition spoke of a narrative style that is not found in contemporary literature . Eva Weissweiler saw in the play Busch 's attempt to prove himself in the novella genre , believing that everything that angered or insulted him , and his accompanying emotional depths , are apparent in the story . The 1895 story The Butterfly ( Der <unk> ) parodies themes and motifs and ridicules the religious optimism of a German <unk> which contradicted Busch 's realistic anthropology influenced by Schopenhauer and Charles Darwin . Its prose is more stringent in narrative style compared to Edwards Dream . Both were not popular amongst readers , because of their unfamiliar style . 
- 
- = = = Painting = = = 
- 
- Busch felt his painting skills could not compete with those of the Dutch masters . He regarded few of his paintings as finished , often <unk> them one on top of the other in <unk> corners of his studio , where they stuck together . If the pile of paintings became too high , he burnt some in his garden . Since only a few remaining pictures are dated , <unk> them is difficult . His doubts regarding his skills are expressed in his choice of materials . His ground was usually chosen carelessly . Sometimes he used uneven cardboard or poorly @-@ prepared spruce @-@ wood boards . One exception is a portrait of Johanna Kessler , on a canvas support measuring 63 centimetres ( 25 in ) by 53 centimetres ( 21 in ) , one of his largest paintings . Most of his works , even landscapes , are small . As Busch used poor grounds and colours , most are heavily <unk> and have an almost monochrome effect . 
- Many pictures depict the countryside at Wiedensahl and Lüthorst . They include <unk> willows , cottages in <unk> , <unk> , autumn landscapes and meadows with streams . A particular feature is the use of red jackets , found in about 280 of 1000 Busch paintings and drawings . The muted or bright red coats are worn usually by a small figure , depicted from behind . The paintings generally represent typical villages . <unk> of the <unk> , and a series of other portraits depicting <unk> <unk> in the mid @-@ 1870s , are exceptions . A painting of a 10 @-@ year @-@ old girl from a Jewish family at Lüthorst portrays her as serious , and having dark , oriental features . 
- The influence of Dutch painters is clearly visible in Busch 's work . " <unk> diluted and shortened ( ... ) but still <unk> " , wrote Paul <unk> after visiting a Busch memorial exhibition in 1908 . A strong influence on Busch was <unk> <unk> , whose themes were farming and inn life , <unk> dances , card players , smokers , drunkards and <unk> . He dismissed the techniques of <unk> with its strong preoccupation with the effect of light , and used new colours , such as <unk> Yellow , and photographs , as an aid . The landscapes from the mid @-@ 1880s show the same broad <unk> as seen in the paintings of the young Franz von <unk> . Busch refused to exhibit work even though he was befriended by many artists of the Munich School , which would have allowed him to do so ; it was not until near the end of his life that he presented his paintings to the public . 
- 
- = = Themes , technique and style = = 
- 
- Busch biographer Joseph Kraus divided his work into three periods . He points out , however , that this classification is a <unk> , as some works by their nature can be of a later or earlier period . All three periods show Busch 's obsession with German middle class life . His peasants are devoid of sensitivity and village life is marked by a vivid lack of sentiment . 
- From 1858 to 1865 Busch chiefly worked for the <unk> Blätter and the Münchener Bilderbogen . The period from 1866 to 1884 is characterized by his major illustrated stories , such as Helen Who Couldn 't Help It . These stories are different in theme from works of his earlier period . The life of his characters start well , but <unk> , as in Painter Squirtle ( Maler Klecksel ) ; someone sensitive who becomes a <unk> . Others concern <unk> children or animals , or make the great or significant foolish and ridiculous . The early stories follow the pattern of children 's books of orthodox education , such as those by Heinrich Hoffmann 's <unk> , that aim to teach the devastating consequences of bad behaviour . Busch did not assign value to his work , as he once explained to Heinrich Richter : " I look at my things for what they are , as <unk> <unk> [ toys ] , as <unk> <unk> [ worthless and useless things ] whose value is to be found not in its artistic content , but in public demand ( ... ) " . 
- From 1885 until his death in 1908 his work was dominated by prose and poems . The 1895 prose text Der <unk> contains autobiographical accounts . Peter 's <unk> by the witch <unk> , of whom he regards himself a slave , is possibly in reference to Johanna Kessler . Peter , like Busch , returns to his birthplace . It is similar in style to the romantic travel story that Ludwig <unk> established with his 1798 Franz <unk> 's <unk> . Busch plays with its traditional forms , motifs , pictures , literary topics and form of narration . 
- 
- = = = <unk> = = = 
- 
- Publisher Kaspar Braun , who commissioned Busch 's first illustrations , had established the first workshop in Germany to use wood engraving . This <unk> printing technique was developed by English graphic artist Thomas <unk> near the end of the 18th century and became the most widely used reproduction system for illustrations over the years . Busch insisted on first making the drawings , afterwards writing the verse . Surviving preparatory drawings show line notes , ideas , and movement and <unk> studies . The draft was then transferred by pencil on white @-@ <unk> panels of hardwood end grain . Not only was it hard work , but the quality of the printing block was crucial . Everything left white on the block , around Busch 's drawn lines , was cut from the plate by skilled <unk> . Wood engraving allows a finer differentiation than woodcut and the potential tonal values are of almost the quality of intaglio printing , such as copper engraving . Sometimes the result was not satisfactory , leading Busch to rework or reproduce plates . The wood engraving technique did not allow for fine lines , which is why Busch 's drawing , especially in his illustrated tales up to the mid @-@ 1870s , are boldly drawn , giving his work its particular characteristic . 
- From the mid @-@ 1870s Busch 's illustrations were printed using zincography . With this technique there was no longer any danger that a wood engraver could change the character of his drawings . The originals were photographed and transferred onto a <unk> zinc plate . This process allowed for the application of a clear , free pen @-@ drawn ink line , and was a much faster printing method . Busch 's use of zincography began with Mr. and Mrs. Knopp . 
- 
- = = = Language = = = 
- 
- The effect of Busch 's illustrations are enhanced by his <unk> verse , with taunts , <unk> , ironic twists , exaggeration , ambiguity and startling rhymes . His language had an influence on the humorous poetry of Erich <unk> , Kurt <unk> , Joachim <unk> and Christian <unk> . The contrast in his later work between comic illustration and its seemingly serious accompanying text — already demonstrated in his earlier Max and Moritz — is shown in Widow <unk> 's <unk> dignity which is <unk> to the loss of her chickens : 
- Many of <unk> 's couplets , part of contemporary common usage , give the impression of <unk> wisdom , but in his hands become only apparent truths , hypocrisy or <unk> . His use of onomatopoeia is a characteristic of his work : " <unk> @-@ <unk> @-@ da " — Max and Moritz steal fried chickens with a fishing rod down a chimney — " <unk> @-@ <unk> " ; " at the plank from bank to bank " ; " <unk> @-@ <unk> " , " hear the <unk> grind and <unk> " ; and " <unk> @-@ <unk> " as Eric the cat <unk> a <unk> from a ceiling in Helen Who Couldn 't Help It . Busch uses names he gives characters to describe their personality . " <unk> <unk> " ( Young <unk> ) has little mental ability ; " <unk> " ( <unk> ) would not be of a cheerful disposition ; and " Förster <unk> " ( Forester <unk> ) could hardly be a <unk> . 
- Many of his picture stories use verses with <unk> structure : 
- The <unk> of the stressed syllables strengthens the humour of the lines . Busch also uses <unk> , where one accented syllable is followed by two <unk> syllables , as in his <unk> und Plum , where they underline the <unk> and solemn words with which teacher <unk> <unk> his pupils . They create tension in the <unk> chapter from Adventures of a Bachelor , through the <unk> of <unk> and <unk> . Busch often <unk> format and content in his poems , as in <unk> the Monkey , where he uses the epic <unk> in a speech about wisdom . 
- In both his illustrations and poems Busch uses familiar fables , occasionally appropriating their morality and stories , spinning them to illustrate a very different and comic " truth " , and bringing to bear his pessimistic view of the world and human condition . While traditional fables follow the typical philosophy of <unk> between good and evil behaviour , Busch combines both . 
- 
- = = = <unk> and other <unk> = = = 
- 
- It is not unusual to see <unk> , <unk> and <unk> in Busch 's works . Sharp pencils pierced through models , housewives fall onto kitchen knives , thieves are spiked by <unk> , <unk> cut their <unk> with <unk> , <unk> are ground in corn mills , drunkards burn , and cats , dogs and monkeys <unk> while being tormented . Busch has been frequently called a <unk> by educators and <unk> . <unk> that are burnt , pulled off , trapped , stretched or eaten is seen by Weissweiler as not aggression against animals , but a phallic allusion to Busch 's undeveloped sexual life . Such graphic text and imagery in cartoon form was not unusual at the time , and publishers , the public or censors found it not particularly noteworthy . <unk> and motifs for his early work were derived from <unk> and 19th @-@ century popular literature , the gruesome endings of which he often softened . 
- <unk> , a common aspect of 19th @-@ century teaching , is prevalent in many of his works , for example <unk> <unk> in Adventures of a Bachelor and <unk> <unk> in <unk> and Plum , where is shown an almost sexual pleasure in applying punishment . <unk> and humiliation are found in his later work too ; biographer <unk> <unk> described this as Busch 's life @-@ motif . 
- In the estate of Busch there is the note " <unk> die <unk> <unk> " ( <unk> through the childhood years ) , however there is no evidence that Busch was referring to himself . He couldn 't recall any beating from his father . His uncle Kleine beat him once , not with the conventional <unk> stick , but symbolically with dried <unk> stalks , this for <unk> cow hairs into a village <unk> 's pipe . Weissweiler observes that Busch probably saw <unk> at his village school , where he went for three years , and quite possibly also received this punishment . In <unk> <unk> <unk> Busch illustrates a form of nonviolent progressive education that fails in one scene , and <unk> in the following scene ; the <unk> that ensued indicate Busch 's pessimistic picture of life , which has its roots in the Protestant ethic of the 19th century , in which he believed that man is inherently evil and will never master his vices . Civilisation is the aim of education , but it can only mask man 's instincts superficially . <unk> only leads to a continuation of man 's <unk> , therefore punishment is required , even if he retains his unrepentant character , becomes a trained puppet , or in extreme cases , dies . 
- 
- = = = <unk> = = = 
- 
- The Panic of 1873 led to growing criticism of high finance and the spread of radical <unk> , which in the 1880s became a broad <unk> . These criticisms saw a separation of capital into what was construed as " <unk> " ( speculative capital ) , and what constituted " constructive " creative production capital . The " good " , " native " and " German " manufacturer was praised by <unk> <unk> , such as Theodor Fritsch , who opposed what he saw as " ' <unk> ' ' greedy ' , ' blood @-@ sucking ' , ' Jewish ' financial capitalism in the form of ' <unk> ' and ' <unk> ' " . Busch was thought to have embraced those stereotypes . Two passages are often <unk> , one in Helen Who Couldn 't Help It : 
- Robert <unk> defended Busch by stating that Jews are <unk> only in three passages , of which the oldest is an illustration of a text by another author , published in 1860 . He stated that Busch 's Jewish figures are merely stereotypical , one of a number of stereotypes , such as the " limited Bavarian farmer " and the " Prussian tourist " . Joseph Kraus shares the same view , and uses a couplet from Eight Sheets in the Wind ( Die <unk> ) , in which profit @-@ seeking people are : 
- Although <unk> felt that Jews for Busch were alien , the Jewish conductor Hermann Levi befriended him , suggesting that Busch had a slight bias towards Jews . 
- 
- = = Biographies = = 
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- The first biography on Busch , <unk> Wilhelm Busch und <unk> <unk> ( About Wilhelm Busch and His Importance ) , was released in 1886 . The publisher Eduard <unk> , also an artist and writer , echoed Busch 's anti @-@ Catholic bias , putting him on equal footing with Leonardo da Vinci , Peter Paul Rubens and Gottfried Wilhelm Leibniz , and <unk> quoting <unk> . Even Busch and his friends were embarrassed . Literary scholar Friedrich Theodor <unk> attacked <unk> 's biography and called him the " envious <unk> of the <unk> <unk> " . After reading this biography Johannes <unk> posted an essay in the Frankfurter Zeitung , which contained many biographical <unk> — as a response to this , Busch wrote two articles in the same newspaper . Published in October and December 1886 , the autobiographical essay Regarding Myself ( Was <unk> <unk> ) includes basic facts , and some description of his troubles ; analysts see within the essay a deep identity crisis . Busch revised his autobiography over the following years . The last such essay was published under the title From Me About Me ( Von <unk> <unk> <unk> ) , which includes fewer biographical details and less reflection on bitterness and amusement than Regarding Myself . 
- 
- = = Legacy = = 
- 
- Busch celebrated his 70th anniversary at his nephew 's house in <unk> am <unk> . Over 1 @,@ 000 <unk> messages were sent to Mechtshausen from around the world . Wilhelm II praised the poet and artist , whose " exquisite works are full of genuine humour and are <unk> for the German people " . The Austrian <unk> <unk> ( Pan @-@ German Association ) repealed the ban on Der <unk> Antonius von Padua . <unk> Braun & Schneider , who owned the rights of Max and Moritz , gave Busch 20 @,@ 000 <unk> ( around € 200 @,@ 000 or $ 270 @,@ 000 ) , which was donated to two hospitals in Hanover . 
- Since then , on the dates of his birth and death , he has been celebrated frequently . During the <unk> anniversary in 2007 , there were numerous re @-@ publications of Busch works . Deutsche Post issued stamps depicting the Busch character Hans <unk> — itself the inspiration for the nickname of the never @-@ built <unk> @-@ <unk> Ta 183 German jet fighter design of 1945 — and the German Republic minted a 10 Euro silver coin faced with his portrait . Hanover declared 2007 the " Wilhelm Busch Year " , with images featuring Busch works erected within the city centre . 
- The Wilhelm Busch Prize is awarded annually for satirical and humorous poetry . The Wilhelm Busch Society , active since 1930 , aims to " ( ... ) collect , scientifically revise and promote Wilhelm Busch 's works with the public " . It supports the development of caricature and satirical artwork as a recognized branch of the visual arts . It is an advocate of the Wilhelm Busch Museum . <unk> are located in places he lived , including Wiedensahl , Ebergötzen , Lüthorst , Mechtshausen and <unk> am <unk> . 
- 
- = = = Influence on comics = = = 
- 
- Andreas C. <unk> described Busch as the " first virtuoso " of illustrated stories . From the second half of the 20th century he was considered the " <unk> of Comics " . His early illustrations differ from those of the colleagues of Kaspar Braun . They show an increasing focus on protagonists , are less detailed in drawing and atmosphere , and develop from a dramatic understanding of the whole story . All Busch 's illustrated tales have a plot that firstly describes the circumstance , then a resulting conflict , then solution . <unk> are developed through consecutive scenes , similar to film <unk> . Busch conveys an impression of movement and action , at times strengthened through a change of perspective . According to <unk> <unk> , his depiction of movement is unique . 
- One of Busch 's notable stories is Der <unk> ( 1865 ) , which describes the life of a pianist who plays privately for an excited listener . <unk> the self @-@ <unk> artist 's attitude and his overblown adoration , it varies from Busch 's other stories as each scene does not contain prose , but is defined with music terminology , such as " <unk> " , " <unk> " and " <unk> <unk> " . As the scenes increase in tempo , each part of his body and <unk> run around . The penultimate scene again depicts the pianist 's movements , with score sheets floating above the grand piano on which musical notes are dancing . Over the years graphic artists have been fascinated by Der <unk> . August <unk> , in a letter to gallery owner <unk> <unk> , described Busch as the first <unk> , stating how well he captured time and movement . Similar pioneering scenes are in <unk> zur <unk> ( 1872 ) . Job fails to answer rather easy questions set by twelve clergy , who shake their heads in <unk> . Each scene is a movement study that <unk> <unk> Muybridge 's photography . Muybridge began his work in 1872 , not released until 1893 . 
- 
- = = = " Moritzian " influence = = = 
- 
- Busch 's greatest success , both within Germany and internationally , was with Max and Moritz : Up to the time of his death it was translated into English , Danish , Hebrew , Japanese , Latin , Polish , Portuguese , Russian , Hungarian , Swedish and <unk> . Several countries banned the story – about 1929 the <unk> school board prohibited sales of Max and Moritz to teens under eighteen . By 1997 more than 281 dialect and language translations had been produced . 
- Some early " Moritzian " comic strips were heavily influenced by Busch in plot and narrative style . <unk> and <unk> ( 1896 ) , borrowed so much content from Max and Moritz that it was described as a pirate edition . The true " Moritzian " recreation is The Katzenjammer Kids by German artist Rudolph <unk> , published in the New York Journal from 1897 . It was published though William Randolph <unk> 's suggestion that a pair of siblings following the pattern of " Max and Moritz " should be created . The Katzenjammer Kids is regarded as one of the oldest , continuous comic strips . 
- German " Moritzian " <unk> stories include Lies und <unk> ; die <unk> von Max und Moritz ( <unk> <unk> , F. <unk> , 1896 ) , <unk> und <unk> ( 1922 ) , <unk> und Waldemar , des Max und Moritz <unk> ( <unk> Günther , 1932 ) and Mac und <unk> ( Thomas <unk> , <unk> <unk> , 1987 ) . These are shaped by observations of the First and Second World Wars , while the original is a moral story . In 1958 the Christian Democratic Union used the Max and Moritz characters for a campaign in North Rhine @-@ <unk> , the same year that the East German satirical magazine <unk> used them to caricature black labour . In 1969 Max and Moritz " participated " in late 1960s student activism . 
- 
- = = Partial list of works = = 
- 
- 
- 
- = Crown Fountain = 
- 
- Crown Fountain is an interactive work of public art and video sculpture featured in Chicago 's Millennium Park , which is located in the Loop community area . Designed by Catalan artist <unk> Plensa and executed by Krueck and Sexton Architects , it opened in July 2004 . The fountain is composed of a black granite reflecting pool placed between a pair of glass brick towers . The towers are 50 feet ( 15 @.@ 2 m ) tall , and they use light @-@ emitting diodes ( LEDs ) to display digital videos on their inward faces . Construction and design of the Crown Fountain cost $ 17 million . The water operates from May to October , intermittently cascading down the two towers and spouting through a nozzle on each tower 's front face . 
- Residents and critics have praised the fountain for its artistic and entertainment features . It highlights Plensa 's themes of dualism , light , and water , extending the use of video technology from his prior works . Its use of water is unique among Chicago 's many fountains , in that it promotes physical interaction between the public and the water . Both the fountain and Millennium Park are highly accessible because of their universal design . 
- Crown Fountain has been one of the most controversial of all the Millennium Park features . Before it was even built , some were concerned that the sculpture 's height violated the aesthetic tradition of the park . After construction , surveillance cameras were installed atop the fountain , which led to a public <unk> ( and their quick removal ) . 
- However , the fountain has survived its contentious beginnings to find its way into Chicago pop culture . It is a popular subject for photographers and a common gathering place . While some of the videos displayed are of scenery , most attention has focused on its video clips of local residents ; hundreds of <unk> visit the fountain hoping to see themselves appearing on one of the fountain 's two screens . The fountain is a public play area and offers people an escape from summer heat , allowing children to <unk> in the fountain 's water . 
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- = = Concept and design = = 
- 
- Grant Park , which is between Lake Michigan and the central business district , is commonly called " Chicago 's Front Yard " . Its northwest corner had been Illinois Central rail yards and parking lots until 1997 , when it was made available for development by the city as Millennium Park . Millennium Park was conceived in 1998 as the <unk> of Grant Park , to celebrate the new millennium and to feature world @-@ renowned architects , artists , designers , landscape architects , and urban planners . As of 2007 , Millennium Park trails only Navy Pier as a Chicago tourist attraction . The fountain is <unk> located in Chicago : it is east of Michigan Avenue and its Historic Michigan Boulevard District , north of Monroe Street and the Art Institute of Chicago ; and south of Madison Street . Looking north from the fountain , viewers see some of the tallest buildings in the United States ( <unk> Center , Two Prudential Plaza , and One Prudential Plaza ) . 
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- = = = Selection of artist = = = 
- 
- In December 1999 , Lester Crown and his family agreed to sponsor a water feature in Millennium Park . Unlike other park feature sponsors , the Crowns acted independently of Millennium Park officials ; they conducted independent surveys of water technologies , held their own informal design contest , and stayed active in the design and engineering of the project . 
- The Crowns were open @-@ minded about the choice of artist ; wanting a modern work , they solicited proposals from a list of prospective artists and architects . <unk> Plensa researched the traditions and history of fountains and studied <unk> in fountain imagery . Some of his early ideas for the project referenced Buckingham Fountain , but these were soon abandoned . His presentation to the Crown family started with a slide show of fountains from the Middle Ages through the 20th century . Plensa focused on the philosophical meanings associated with fountains , their history , use and art . His presentation included computer animation of facial expressions . The other finalists were Maya Lin , who presented a low @-@ height horizontal form , and Robert <unk> , who presented a fountain that would have been 150 feet ( 46 m ) tall . In January 2000 , Plensa won the commission to design the fountain over Lin and <unk> . The installation is a video sculpture , commissioned to operate thirty years . 
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- = = = Artistic design = = = 
- 
- Prior to Crown Fountain , Plensa 's dominant theme had been dualism , which he had expanded to artworks in which the viewers are outside , and the visible subjects of the art are inside containers and hollow spaces . In the 1990s , he completed several outdoor sculptures in which he explored the use of light ( The Star of David ( 1998 ) at Stockholm 's Raoul <unk> Square , Bridge of Light ( 1998 ) in Jerusalem ) , and LED technology , video , and computer design ( <unk> <unk> & Mr. Net in <unk> ( 1999 – 2000 ) ) . In his public art , Plensa challenged himself to involve the viewer with his art , which led to his conception of the Crown Fountain . His objective was to create a socially relevant , interactive fountain for the 21st century . Since water is the focus of a fountain , and since Chicago , and especially Millennium Park , is so greatly affected by the nearby waterfront , Plensa sought to create an eternal water work to complement the local natural inspirations . Because of the colder winters common to the climate of Chicago , Plensa created a fountain that would remain vibrant when the water was inactive in the <unk> , so the fountain is an experience of light themes and the use of video technology . 
- Plensa explores dualism with Crown Fountain , where he has two randomly selected faces " conversing " with each other . Plensa feels that by using faces , he can represent the diversity of the city both in ethnicity and in age . The artist intends to portray the <unk> evolution of the city by updating the collection of images . His representation has become a part of the city 's pop culture ; the first few episodes of the first season of Prison Break featured shots of the fountain . 
- Plensa feels that the challenge in the creation of successful work of public art is to integrate the viewer into an interactive relationship with the art . The fountain is known for encouraging its visitors to splash and slide in the reflecting pool , <unk> for position under the water spout and place themselves under the cascade . This interactivity was to some degree accidental . Although the city planned for some interactivity , the transformation of the fountain into a water park for kids within hours of opening surprised Plensa . Now , when the National Weather Service issues summer heat advisories and the Governor of Illinois declares state office buildings as official daytime cooling centers , the national press points to Crown Fountain as a respite for inhabitants of the Chicago metropolitan area . 
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- = = = Video production = = = 
- 
- Approximately 75 ethnic , social , and religious Chicago organizations were asked to provide candidates whose faces would be photographed for integration into the fountain . The subjects were chosen from local schools , churches and community groups , and filming began in 2001 at the downtown campus of the School of the Art Institute of Chicago ( SAIC ) . The SAIC students filmed their subjects with a $ 100 @,@ 000 high @-@ definition <unk> @-@ <unk> video camera , the same model used in the production of the three Star Wars <unk> . About 20 SAIC students took part in what became an informal master 's course in public art for the project . Faculty from Columbia College Chicago was also involved in the production of the video . The high @-@ definition equipment was used because of the scale of the project . Because the image proportions were like a movie screen with a width far exceeding its height , the camera was turned on its side during filming . 
- Each face appears on the sculpture for a total of 5 minutes using various parts of individual 80 @-@ second videos . A 40 @-@ second section is played at one @-@ third speed forward and backward , running for a total of 4 minutes . Then , there is a subsequent segment , where the mouth is puckering , that is stretched to 15 seconds . This is followed by a section , in which the water appears to spout from the open mouth , that is stretched to last for 30 seconds . Finally , there is a smile after the completion of the water spouting from the mouth , that is slowed to extend for 15 seconds . Of the original 1 @,@ <unk> subjects filmed , 960 videos were determined to be usable for the project . Originally , the set of images was presumed to be the beginning of a work in progress , but as of 2009 no additional videos are planned . 
- To achieve the effect in which water appears to be flowing from subjects ' mouths , each video has a segment where the subject 's lips are <unk> , which is then timed to correspond to the spouting water , reminiscent of <unk> fountains . Each face is cropped so that no hair and usually no ears are visible . Since there is no tripod designed for cameras turned on their sides , an <unk> barber / dentist 's chair was used to minimize the need for the movement of the state @-@ of @-@ the @-@ art camera during filming . Nonetheless , in some case , digital manipulation was necessary to properly simulate puckering in the exact proper location on the video . Many of the faces had to be stretched in order to get the mouths properly positioned . Additionally , each video was color @-@ corrected for brightness , contrast and color <unk> . Both the playback equipment and the final videos had to be further adjusted to account for sunlight during viewing . 
- 
- = = Construction and engineering = = 
- 
- The Crown family , for whom the fountain is named , donated $ 10 million of the $ 17 million construction and design cost . The Goodman family , known for funding the Goodman Theatre , was also a large contributor ; the entire $ 17 million cost was provided by private donations . The initial proposed cost for the fountain had been $ 15 million . 
- After two architectural firms refused the contract to make Plensa 's design a reality , the firm Krueck and Sexton Architects accepted . Public art was a departure from Krueck & Sexton 's residential and corporate office @-@ dominated portfolio , which includes buildings like the <unk> Institute . Collaboration between the artist , architectural team , and consultants proved to be crucial to the success of the project . The fountain 's black granite reflecting pool measures 48 by 232 feet ( 15 by 71 m ) and has an approximate water depth of 0 @.@ 25 inches ( 6 @.@ 4 mm ) . It displays videos on two LED screens , each <unk> in a glass brick tower measuring 50 by 23 by 16 feet ( 15 @.@ 2 by 7 @.@ 0 by 4 @.@ 9 m ) . The firm designed a special stainless steel T @-@ frame both to bear the load of the walls , which are 50 feet ( 15 m ) high , and to withstand lateral wind forces . The frame holds all the glass blocks and transfers the load to the base in a <unk> pattern . <unk> measuring 0 @.@ 5 inches ( 13 mm ) in diameter anchor to the structure and project into the frame for lateral stability , while triangular corner brackets add support . 
- After several dozen glass manufacturing firms were interviewed , L. E. Smith Glass Company emerged as the company to produce 22 @,@ 500 glass blocks near the upper limit of the size of press glass formed from hand @-@ poured molten glass and cast iron molds . The process used sand and <unk> ash heated to a temperature of 2 @,@ 600 ° F ( 1 @,@ 430 ° C ) and " gathered " with a large clay ball resembling a honey <unk> . Rather than use a standard <unk> to ensure the glass that <unk> off the rod spread to the corners of the mold , they relied on gravity . The full mold was <unk> ( <unk> in an oven to 1 @,@ 100 ° F ( 593 ° C ) ) and cooled . Over the course of four months of production , about 350 blocks were produced per day . 
- The glass was custom @-@ made at a factory in Mount Pleasant , Pennsylvania , and shipped to the structural glass panel manufacturer in Melbourne , Florida . The panels were then shipped by truck to Chicago . The glass is white glass , rather than the usual green glass that results from iron impurities . This has the <unk> of increased image clarity , but greater dirt visibility . Each block is 5 by 10 by 2 inches ( 127 by 254 by 51 mm ) with glass thin enough to avoid image distortion . On each block , one of the six faces is polished , and the other five surfaces are textured . 
- The structure for the blocks was a challenge . At first , the design team had considered switching to plastic blocks , until the team found Circle <unk> Inc . , a prefabricated glass panel company in Melbourne , Florida which specializes in structural glass panel systems . Circle <unk> came up with the plan of turning <unk> on their sides to be used as building elements . The individual <unk> are 5 feet ( 1 @.@ 5 m ) tall and either 16 feet ( 4 @.@ 9 m ) or 23 feet ( 7 @.@ 0 m ) wide with cell capacity of an average of 250 blocks . Each tower is composed of 44 <unk> stacked and <unk> . The combination of the refraction of the glass and the <unk> of the metal make the grid virtually invisible . 
- The fountain uses 11 @,@ 000 imperial gallons ( 50 @,@ 000 L ) per hour , 97 % of which is recycled back into the system . <unk> the water to the spout took ingenuity . Although consideration was given to <unk> a LED tile , it was determined that the images would then look as though they were each missing a tooth . Instead , one tile in each tower is <unk> about 6 inches ( 150 mm ) to allow the installation of 1 inch ( 25 mm ) clear <unk> for the water nozzle . The water regularly spills over the fountain and down the sides of the towers and intermittently <unk> from the nozzle . Two essential custom fittings contribute to the artistic vision of the fountain : a custom glass block at the upper edge for guiding the water 's descent while remaining <unk> , and a plastic nozzle fitted to the stainless steel frame to control the rate of water flow and reduce liability to the city for any injuries sustained by the fountain 's interactive participants . The interactive participants are usually children playing in the stream from the water spout or under the cascade . The risk that the spouting water would knock people down made the design both a legal and a physical challenge . 
- The fountains use over one million LEDs . The inner surface of each tower uses 147 smaller screens with a total of 264 @,@ 480 LED points ( each with two red , one blue and two green LEDs ) . The physical demands of LED screens , in particular the red , green , and blue long @-@ life light bulbs and the <unk> <unk> , created three major challenges : supporting the physical structure , combating heat buildup , and <unk> <unk> of the display . Plensa had used LED fixtures on previous projects , and thus had some experience with these issues . The LED structure is not supported as a single wall ( which would be 50 feet ( 15 m ) high ) , but rather as several segments that are noticeable as visible horizontal bands every few feet : these show where the LED equipment is supported . The heat generated is handled by fans that cool the air at the bottom , that then works its way through the chimney @-@ like tower . <unk> was determined to be optimal with LED lights 2 inches ( 51 mm ) behind the glass . 
- LEDs were chosen because they were viewed as the lowest maintenance option of the possible color changing fixtures . LEDs fit into an electrical circuit , causing illumination by the movement of electrons in the <unk> material and making a filament unnecessary , so the bulbs never burn out and do not get too hot . <unk> were added to the screens to keep direct sunlight from hitting the LEDs . Color Kinetics ( now part of Philips Solid @-@ State <unk> <unk> , which is now called Philips Color Kinetics ) <unk> 12 LEDs fixtures are used to illuminate the tower structures and glass in an attempt to meet Plensa 's objective that the towers have a light and translucent appearance , with their internal structures reflecting light from behind the glass surface . The electronics were designed to be adaptable to the time of day , weather and season and to meet the desired century @-@ long longevity and <unk> objectives set by the design team in response to the thirty @-@ year directive . 
- The 9 @,@ <unk> @-@ square @-@ foot ( <unk> @.@ 4 m2 ) pool used 3 by 3 feet ( 0 @.@ 91 by 0 @.@ 91 m ) pavers that weigh 250 pounds ( 110 kg ) . The pavers were rested on screw jack <unk> in order to be leveled and <unk> . The pavers had to be perfectly leveled for the water to work correctly because the fountain incorporates numerous sensors to regulate the flow and level of the water . 
- During construction the underground parking garage remained open . An additional challenge was designing the structure to facilitate interior access for ongoing maintenance and repairs , while <unk> two levels of underground parking underneath . The challenge was solved by combining a T @-@ bar grid to absorb weight with about 150 " <unk> " , or " <unk> " , inserted through the video wall to support the glass blocks and absorb wind loads . This design allows for the removal of individual glass blocks for cleaning or repair without disruption to the display . The filtered air inside the towers helps minimize the need for cleaning . Crown Fountain 's design not only included interior access for technical repairs , but also incorporated exemplary , non @-@ <unk> , barrier @-@ free accessibility , because its interactivity is not limited to the able @-@ bodied . The force of the water accounts for the entire range of possible interactive visitors . 
- 
- = = Dedication and operation = = 
- 
- 
- = = = <unk> = = = 
- 
- Construction of the video sculpture was completed for testing without the fountain 's water features on May 18 , 2004 . Originally , Plensa had planned to have each face appear for 13 minutes , and this continued to be the targeted duration when the testing of the sculpture occurred . Eventually , professors at the School of the Art Institute of Chicago convinced him to use only five @-@ minute videos . 
- Plensa 's design of Crown Fountain was unveiled to the public on July 16 – 18 , during the 2004 grand opening celebrations for Millennium Park , which was sponsored by J.P. Morgan Chase & Co . At the time of the unveiling , Crown Fountain , like the nearby Cloud Gate , was incomplete because only 300 of the videos had been refined for public display . It was officially dedicated on July 24 , 2004 as part of a special private fundraising party that raised $ 3 million for the Millennium Park Conservancy fund . 
- 
- = = = Operation = = = 
- 
- The control center for the <unk> of images , water flow , and lighting color and intensity is beneath one of the towers , in a room that covers 550 square feet ( 51 m2 ) . The room houses high @-@ definition video servers and equipment temperature sensors . Hard drives contain all the individual electronic computer files of the face videos . Generally , the computer programs automatically perform tasks such as determining when the face will <unk> and , if weather conditions permit , when to turn the water on and off . Using <unk> rather than high @-@ resolution images was both less expensive and created a better display for the average viewer . A Barco show controller selects the sequence of faces one at a time and determines a random tower lighting selection of one of eight LED colors programmed into an Electronic Theatre <unk> ( <unk> ) <unk> control system . At night , the <unk> system controls <unk> that illuminate the cascading water and that are <unk> by special wet @-@ use location ground fault circuit <unk> . The control room covers an area equal to 26 parking spaces in the underground parking garage , which costs the city $ 100 @,@ 000 annually in terms of the opportunity cost of lost revenue ( in 2004 dollars ) . Maintenance issues for the fountain range from kids removing the <unk> between the bricks to pipes in need of maintenance . As of 2014 , annual upkeep costs were approximately $ 400 @,@ 000 . 
- 
- = = = = Video sculpture = = = = 
- 
- The front face of each tower is animated with a continuous , dynamic exhibit of lights and electronic images . Although the screens on the towers periodically display clips of landscapes such as waterfalls , most intriguing are the display of faces of Chicago residents . About 1 @,@ 000 faces of <unk> are shown in a random rotation , the order determined using a Barco show controller . Each face is displayed for five minutes , with a brief period between each of these videos during which the sculpture is <unk> . As a result , no more than 12 faces appear per hour during the summer . However , during the winter a version without the final one minute of puckering is shown , so the video segments then are only four minutes each . The video pattern also includes a three @-@ minute water scene every half @-@ hour and a 30 @-@ second fade @-@ to @-@ black every 15 minutes . If all the faces were shown consecutively , instead of randomly , they would each appear about once every eight days . A June 2007 article in the Chicago Sun @-@ Times reported that many of the subjects who had their images <unk> for the project had yet to either see their own images or hear of anyone who had seen them . 
- The spouting water from the faces of the towers appears to be flowing from the displayed subject 's mouth from a 6 @-@ inch ( 150 mm ) nozzle located in the center of each interior face 12 feet ( 3 @.@ 7 m ) above the reflecting pool . <unk> are shown daily year @-@ round , while the water feature only operates from May 1 to approximately October 31 , weather permitting . The park is open to the public daily from 6 a.m. to 11 p.m. 
- Each tower is illuminated from within on three sides by approximately 70 color @-@ changing Color Kinetics LED lighting fixtures per tower , while the fourth side features opposing Barco LED display screens . At night , some of the videos are replaced by images of nature or solid colors . Also at night , the other three sides of the fountain display changing colors . The outer Color Kinetics surfaces randomly display the translucent glow of one of eight colors along with each of the inner opposing faces . As a video sculpture with a variety of cascade and water spout fountain modes , the sculpture is a fluid , dynamic evolving artwork . 
- 
- = = = = Fountain = = = = 
- 
- Crown Fountain has both slits and a <unk> for drainage ( pictured above right ) to drain the 11 @,@ 520 US gal ( 43 @,@ 608 l ; 9 @,@ 592 imp gal ) of water per minute . When the videos are not on the front of the tower , water <unk> down each of the facades . The water is filtered , pumped and <unk> through the fountain . Dual pump rooms below each tower draw water from a reservoir beneath the reflecting pool . There are 12 mechanical pumps that are regulated from a control room in the underground parking garage beneath the south tower of the fountain . The water in the reflecting pool has a depth of about 0 @.@ 33 millimetres ( 0 @.@ <unk> in ) . 
- 
- = = Controversies = = 
- 
- Before the fountain was completed in 2004 , Art Institute of Chicago president James Wood felt the columns would be too tall and other community leaders felt that the height and scale of the project stemmed from a " <unk> contest " with other park feature artists . Grant Park has been protected since 1836 by " forever open , clear and free " legislation that has been affirmed by four previous Illinois Supreme Court <unk> . Aaron Montgomery Ward twice sued the city of Chicago in the 1890s to force it to remove buildings and structures from Grant Park , and to keep it from building new ones . As a result , the city has what are termed the Montgomery Ward height restrictions on buildings and structures in Grant Park . However , Crown Fountain and Jay <unk> Pavilion , which stands 139 feet ( 42 m ) tall , were exempt from the height restriction because they were classified as works of art and not buildings or structures . 
- In November 2006 , Crown Fountain became the focus of a public controversy when the city added surveillance cameras atop each tower . <unk> through a $ 52 million Department of Homeland Security grant to the Chicago area , the cameras were part of a surveillance system <unk> eight other cameras covering all of Millennium Park . The city said the cameras , similar to those used throughout the city at high @-@ crime areas and traffic intersections , were intended to remain on the towers for several months until permanent , less <unk> replacements were secured . City officials had consulted the architects who collaborated with Plensa on the tower designs , but Plensa himself had not been notified . Public reaction was negative , as bloggers and the artistic community decried the cameras on the towers as inappropriate and a <unk> . The city said that the cameras were largely for security reasons , but also partly to help park officials monitor burnt @-@ out lights . The Chicago Tribune quickly published an article concerning the cameras as well as the public reaction , and the cameras were removed the next day . Plensa supported their removal . 
- 
- = = <unk> = = 
- 
- In 2014 , the hardware and software behind the fountain 's operation were replaced . At the time there were plans to replace LED lighting with <unk> bulbs on each of the non @-@ video display surfaces and to replace the video surface LEDs . Plensa , who maintained control of the video faces for the first two years of the fountain 's operation , <unk> that future generations may wish to update the faces used in the rotation of videos to reflect changes in humanity going forward . In 2014 , an additional 1000 faces were anticipated for 2016 . 
- 
- = = Critical reception = = 
- 
- Crown Fountain , <unk> Fountain , and Buckingham Fountain , as well as natural water features such as Old <unk> , are examples of the ability of water to attract people and hold their attention . Crown Fountain has more interactivity than other Chicago fountains , such as Buckingham Fountain and <unk> Taft 's Fountain of the Great Lakes and Fountain of Time ( all but the last are in Grant Park ) . These other Chicago fountains are traditional in that they discourage viewer touching ; Buckingham Fountain is surrounded by a fence , and Taft 's fountains are surrounded by <unk> . In contrast , Crown Fountain provides an open invitation to play in the fountain 's water . 
- U.S. News & World Report describes the fountain as an exemplary feature of the city 's numerous urban parks . Chicago Tribune architecture critic Blair <unk> , who is pleased with the sculptures ' <unk> , says the fountain helps appropriately depict the modern 21st @-@ century urban park . The Chicago Sun @-@ Times describes the fountain as " eye @-@ catching , crowd @-@ friendly ... high @-@ tech [ and ] ... contemporary " . The New York Times calls the fountain an " extraordinary art object " . <unk> 's describes the fountain as public art at its best . The beauty of the fountain is , as the San Francisco Chronicle explains , that it is high @-@ concept art for all to enjoy . The Financial Times refers to the fountain as a " <unk> @-@ fountain " . The fountain is praised for its technical features by industry magazines and has won various awards . The project won the 2006 Bombay <unk> prize for its design work with glass . Critical reviews were not unanimous in their praise . One Chicago Tribune critic was not impressed with <unk> @-@ like art , although he conceded the <unk> element reminded him in a positive way of the jungle gym element of the Chicago Picasso . 
- The fountain is featured on the cover of Philip Jodidio 's 2005 book , Architecture : Art . Although Plensa is considered to be a conceptual artist , according to Jodidio , Plensa created a work whose architectural aspects are paramount . Its location juxtaposed with the Historic Michigan Boulevard District 's <unk> highlights these aspects . Jodidio considers the work to be a modernization of the <unk> theme , and feels that the scale of the enlarged faces <unk> the work and challenges the architecture . The towers are an integral part of the skyline that have achieved rare permanence for contemporary art . 
- 
- 
- = Canning Dam = 
- 
- The Canning Dam and reservoir provide a major fresh water resource for the city of Perth , Western Australia . The dam is situated on the Darling <unk> and is an <unk> of the Canning River . It is noted for its innovative structural and hydraulic design that was considered to be at the forefront of concrete gravity dam design at the time of construction . The Canning Dam was Perth 's primary water supply up until the 1960s when other sources of fresh water were <unk> . Currently the dam supplies approximately 20 percent of Perth 's fresh water . <unk> into the Canning Reservoir is estimated to be 22 gigalitres ( 780 @,@ 000 @,@ 000 cu ft ) and has a storage capacity of 90 @,@ 352 <unk> ( 3 @,@ 190 @.@ 8 × 10 ^ 6 cu ft ) . 
- Since its completion in 1940 , the Canning Dam has contributed to a wide range of environmental and ecological problems in surrounding regions , problems include more common algal blooms , habitat loss and sedimentation . Despite these issues , Canning Dam and the adjacent parks and forests provide a variety of recreational activities for the public such as <unk> , historic walks and picnic facilities . 
- 
- = = History = = 
- 
- Development of the Canning River as a source of water for Perth was first proposed in a report of the first Metropolitan Water Works Board of Perth in 1896 . Investigation of the site began in 1897 when engineer Thomas Hodgson surveyed and proposed the dam 's current location as a possible site . However , despite the recommendations of further inquiries , and an extreme shortage of water in some years , government funds were not allocated for the construction of a dam until the Great Depression in the 1930s . 
- In 1924 a small <unk> dam was built 6 kilometres ( 4 mi ) downstream from the present Canning Dam . It was only intended as a quick fix to the water supply problem and it soon became apparent that a major reservoir was needed , although it would be nine years before work on the current Canning Dam would begin . The new dam was completed in 1940 at a cost of AU £ 1 @.@ 1 million . Engineer Russell Dumas designed the dam and directed most of its construction . 
- A further improvement was made in 1951 when a concrete @-@ lined channel was constructed to divert stream flow from the nearby <unk> Gully catchment . The Canning Dam and reservoir was Perth 's primary source of water until the boom growth of the city in the 1960s and the completion of <unk> Dam in 1961 . In 1975 the reservoir was connected to Perth 's Integrated Water Supply Scheme by the Canning Tunnel . Prior to its opening water had flowed through the Canning <unk> Channel to <unk> . 
- The Canning Dam and reservoir still supplies approximately 20 percent of Perth 's drinking water requirements and plays an important role in the context of the development of Perth . 
- The Canning reservoir is also used to store water from the newly completed <unk> <unk> Plant . <unk> water can be pumped from the plant to the reservoir through the new <unk> <unk> Station . 
- 
- = = <unk> = = 
- 
- The Canning Dam <unk> lies within the Darling <unk> which forms part of an <unk> Shield composed largely of granite with some invaded linear belts of <unk> <unk> and volcanic rocks . 
- The dam wall is situated in a narrow <unk> running east and west , with rock sides sloping upward from the river bed . Behind the dam wall , the south branch of the Canning River joins the main stream , with the <unk> water forming a lake which stretches back in three major arms to the east south @-@ east and south . 
- The catchment has an area of <unk> square kilometres ( 310 sq mi ) . The reservoir is at 200 metres ( 660 ft ) <unk> and the highest point of the catchment , Mount Cooke is at <unk> metres ( 1 @,@ 909 ft ) <unk> . 
- <unk> , the area receives about 900 millimetres ( 35 in ) of rainfall per annum with most of this falling between May and September . There is widespread <unk> of rainfall across the catchment however , from between 700 and 1 @,@ 300 millimetres ( 28 and 51 in ) . 
- Since 1975 long @-@ term average <unk> at the dam wall have decreased by 20 percent and <unk> into the catchment by approximately 60 percent — the average annual inflow between 1948 and 1974 was 52 gigalitres ( 1 @.@ 8 × 109 cu ft ) which had reduced to 22 gigalitres ( 780 @,@ 000 @,@ 000 cu ft ) between 1975 and 2004 . 
- 
- = = Construction = = 
- 
- The construction of Canning Dam ended a long period during which Perth 's water supply was generally unsatisfactory in quality ( either due to salinity or bacterial pollution or both ) and in quantity . The project , the biggest public works program of the decade , stimulated significant growth in the local economy and provided desperately needed work for around five hundred men . 
- Several innovative design concepts and construction methods which were new to Australia were introduced on the project , while others which were used on the nearby Wellington Dam , were improved upon at the Canning dam site . 
- At the Canning Dam and indeed all dams , care had to be taken to prevent water seepage between the foundation rock and the structure of the dam . At the Canning in addition to cutting back the foundation to solid <unk> rock , a cut @-@ off trench was sited near the upstream face of the dam , down stream which a rock filled drain containing open <unk> pipes was provided to intercept any seepage between the rock face and the concrete of the dam . The inclusion of an internal drainage system was considered innovative at the period . Near vertical tubular 8 in ( 200 mm ) cut @-@ off drains were provided at five @-@ foot ( 1 @.@ 52 m ) intervals along the dam to relieve internal seepage through the concrete . 
- While state of the art materials @-@ handling methods were used , in some instances labour @-@ saving machinery worked beside operations intended to <unk> the labour content . <unk> workers were employed chiefly for on @-@ site preparation , road construction , foundation excavation , clearing timber from the reservoir basin , and on some <unk> operations . <unk> workers were required on the dam for fixing the <unk> where the concrete was poured . These workers were probably employed at normal day labour rates , the main employment method used on the project . <unk> handling of cement was also used for the first time in Australia , this saw a significant saving as opposed to <unk> cement which was the standard practice of the day . 
- Generally construction work proceeded smoothly and from an engineering point of view there were few setbacks . However , one did occur in the early stages of construction . In March 1934 there was a violent storm bringing 130 mm ( 5 @.@ 12 in ) of rain in less than two days . As a result , the river rose rapidly which flooded the dam foundation workings . <unk> had to be installed and work resumed three days later . 
- The dam was completed in September 1940 . When built it was the longest concrete gravity dam in Australia , and also the second highest after the <unk> Dam in New South Wales . As of 1997 of the 90 large concrete and masonry gravity dams in Australia the Canning Dam is still the fifth highest and the sixth longest . It is the largest concrete dam in Western Australia in terms of length of crest and volume of concrete . 
- The final cost of the dam was significantly less than had been originally budgeted for , and the work was completed on schedule to a date that was calculated seven years previously . 
- 
- = = = Remedial works = = = 
- 
- Recently the Canning Dam has been subjected to considerable <unk> of the upper parts of the dam and upper gallery . Investigations have shown that <unk> was due to strong AAR ( alkali aggregate reactivity ) in the concrete . AAR results in swelling of the concrete , which may cause secondary <unk> stresses , localised map cracks , and , ultimately structural cracks . In addition , the concrete tensile strength and elasticity significantly decreases . Many old concrete dams are known to suffer from AAR , including <unk> Dam in Tennessee and <unk> <unk> dam in Italy among others . 
- Extensive remedial works were undertaken between 1999 & 2001 to strengthen the dam wall . This work involved removing the top 3 @.@ 8 m ( 12 ft ) of the existing dam wall and drilling / blasting through the dam wall plus up to a further 70 m ( 230 ft ) into the bedrock below . The top section of the wall was then rebuilt using reinforced concrete . Finally , permanent , re @-@ <unk> ground anchors were then installed through the formed and drilled holes from the crest to be stressed and <unk> into the foundation rock . At time of completion , these were the largest capacity and longest permanent ground anchors ever installed in the world . 
- An innovative drilling and blasting technique called <unk> Cone <unk> ( <unk> ) was used in the remedial works process . <unk> was chosen over conventional drilling and / or blasting techniques due to the reduced risk of damage to the existing structure from vibration , as well as lower <unk> <unk> and dust levels . 
- 
- = = Environmental issues = = 
- 
- Since the construction of the Canning Dam , among other drinking water supply dams , water flow into the Canning River has been reduced by up to 96 % . A number of freshwater fish species which are endemic to the south @-@ west of Western Australia are found in the Canning River system , however studies of fish and fish habitats in the area have shown that fish numbers are low due to a loss of habitat and a loss of <unk> between breeding areas due to low flows , preventing fish migrating upstream and reaching important breeding and nursery grounds . <unk> water caused by a lack of water flow has provided a suitable habitat for successful breeding of an introduced pest , the <unk> . 
- <unk> of the Canning caused dramatic flow reductions that significantly altered downstream aquatic <unk> communities . The lack of water flow has also resulted in a poor <unk> effect below the dam wall . An excessive amount of nutrients from fertilizers and animal waste has caused algal blooms and <unk> . 
- Many river pools which are an important summer refuge and habitat for aquatic and terrestrial flora and fauna have been lost due to sedimentation and modification of the flow regime caused by <unk> in the Canning River . 
- Periodic flooding of the Canning River from the dam is required to disperse seed , stimulate <unk> and ensure seedlings survive , recharge shallow groundwater tables that are important during periods of drought and to discourage and prevent weed growth . However , during times of low rainfall periodic flooding is reduced . 
- 
- = = Recreation = = 
- 
- A number of recreation activities occur in and around the dam and catchment area . Canning Dam features a number of picnic areas ( with gas <unk> ) , look outs and historic walks – many with disabled access . <unk> occurs throughout the catchment , particularly along the <unk> Track , which passes through the catchment about 10 km upstream of Canning Dam . Several mountain bike trails run either through the Canning National Park or adjacent State Forest areas . 
- The Canning Reservoir , Canning River and tributaries are illegally fished for <unk> , especially during summer . <unk> , fishing and swimming are prohibited in the reservoir for health and hygiene reasons . <unk> camping ( including overnight stays and / or outside of designated areas ) and <unk> trail establishment occur more and more frequently in the Canning catchment . 
- 
- = = Gallery = = 
- 
- 
- 
- = Midge ( Barbie ) = 
- 
- Midge <unk> is a fictional doll character in the Barbie line of toys by Mattel that was first released in 1963 . Midge was created , along with <unk> , to counteract criticism that claimed Barbie was a sex symbol . She was marketed as Barbie 's best friend . No Midge dolls were sold for the rest of the vintage years after the 1960s . 
- Midge was re @-@ introduced in 1988 as part of the play line , though two vintage reproduction dolls were made specifically for collectors in 1993 and 1998 . The dolls sold in this time period usually had red hair , often with freckles , and her eye color was usually blue . Also in the period , Wedding Day Midge was sold , with the groom being Alan Sherwood , who had been marketed as Midge 's boyfriend in the vintage years . Midge and Alan had three children introduced named Ryan , Nikki , and Cassandra . They also had twins who were never introduced . This was known as the Happy Family line , and in the set , Midge was sold " pregnant " with Nikki as a newborn baby . The Happy Family product became the subject of controversy when some parents disliked the " pregnant " Midge toy because they believed that Midge was too young to have children . 
- In 2013 Mattel decided to revamp Midge 's history , <unk> her as a best friend of Barbie 's , but unmarried , without children , and with no connection to Alan . 
- 
- = = Vintage years = = 
- 
- When Barbie first came out , she was the subject of a lot of criticism , some of which that claimed Barbie was too mature @-@ looking for children . Midge was the first same @-@ size friend of Barbie ever sold , and was created to oppose these controversies aimed at Barbie . She had a fuller , gentler face mold that was less sexually intimidating , although her body proportions were the same as Barbie and they both stood at 11 1 ⁄ 2 inches ( 290 mm ) tall . This allowed the two dolls to be able to share clothes and accessories . Her head mold was stamped " 1958 " , the same as Barbie . When Midge arrived , the markings on the straight leg body mold they shared changed to include both her and Barbie . Midge had shoulder @-@ length hair that flipped at the ends . <unk> had an option of buying a doll with one of three different hair colors : red , blond , or brunette . Her face was usually brushed with freckles . The dolls that were sold without freckles had a longer hair style and are now hard to find . Depending on the doll 's hair color , the color of her two @-@ piece swimming suit varied . If Midge had red hair her <unk> was yellow and orange , for blond hair it was in two shades of blue , and if she was brunette it was pink and red . The first vintage Midge dolls had a value of $ 175 <unk> ( Mint In Box ) in 2007 . 
- For the first two years that Midge was sold , she had " straight legs " that could not bend at the knee . A rare Midge with teeth was sold the first year and is now sought after by collectors . One year later , in 1964 the dolls that were sold had slightly longer hair . Midge 's boyfriend Allan arrived as well . Early in 1965 , <unk> <unk> Midge was sold , and consisted of a Midge head with short <unk> hair and three <unk> . This was the Midge counterpart of the Fashion Queen Barbie . Since she came with only a head , another doll had to provide for the body . In 1965 Midge with <unk> legs was introduced . She had shorter " <unk> " hair , like the American Girl Barbie , with a blue <unk> . Her swimming suit was different as well , and was now one @-@ piece and striped . 
- 
- = = Return = = 
- 
- From her introduction to until 1967 , Midge was marketed as Barbie 's original best friend , but no dolls were sold for about 20 years until 1988 , when California Dream Midge was sold as part of a beach line , which used the " Steffie " mold . The same " Steffie " mold was used for Cool Times Midge in 1989 . In 1990 , she began to use the " <unk> " mold for All Stars Midge and The Beat Midge . In 1991 she was married to Allan ( now spelled " Alan " ) . Before and after the Wedding Day Midge doll was sold , many Midge play line dolls were produced . Before Wedding Day Midge , Midge dolls still had freckles , but up until Hawaii Midge was sold in 1999 , the dolls lacked freckles . Most of the dolls were red @-@ haired with blue or green eyes , but some dolls were brunette . The most commonly used head mold for Midge in this period was the " <unk> " mold , stamped 1985 . 
- A 35th anniversary Midge reproduction doll was sold in 1998 for collectors , made to look like the vintage Midge dolls . She had red hair , was dressed in her original orange and lime two @-@ piece swimming suit , and came with a reproduction of the Senior Prom outfit from 1964 – 1965 as well as a reproduction of the box the Midge dolls originally came in . Earlier in 1993 , for Midge 's 30th anniversary , a Midge reproduction doll was also produced , but she did not possess a reproduction of her original <unk> or the original box . Like the later version , she came in a reproduction of the Senior Prom outfit . 
- In 2013 , Midge appeared on the <unk> Barbie : Life in the <unk> where it is revealed that she has moved to Malibu . With this , her canon has been changed extensively , and she is a teenager who is retro at heart and still uses old @-@ <unk> terms . The whole ' Happy Family ' canon has been dropped altogether due to controversy . Two Midge dolls will be released in 2013 - one is as part of a collectors set with Barbie , and the other on her own in the Life in the <unk> doll line . 
- 
- = = " Happy Family " line = = 
- 
- The year after Midge and Alan were married , a picture of the couple with twin babies was shown in a pamphlet , but the dolls were never produced . However , in 2003 , she and Alan were re @-@ introduced with a family consisting of them and three different kids , three @-@ year @-@ old Ryan , and newborn baby Nikki . This was known as the " Happy Family " line , and was similar to the discontinued Heart Family line of the 1980s . The dolls came in both European American and African American versions . This was the first time an African American Midge was ever produced . 
- Midge was sold " pregnant " with Nikki , who was a tiny baby inside Midge 's magnetic <unk> <unk> . This led to some controversy with some consumers saying that the doll was inappropriate for children , or that it promoted teen pregnancy . Another cause for this controversy was that Midge did not initially have a wedding ring , but this was later fixed . She also was packaged without Alan . <unk> complaining about the doll led to Wal @-@ Mart pulling the Happy Family line off their shelves . A new version of this Midge was produced for Wal @-@ Mart , this time not pregnant and with a cardboard cut @-@ out display of Alan and Ryan standing next to her inside the box . The Happy Family Line included everything from a talking house , a backyard swimming pool , neighborhood market , and playground . 
- Later , around Nikki 's first birthday , Midge was " pregnant " again with another child , who wasn 't named or given a specific gender , as the gender was a surprise when the owner opened the doll 's box . Midge 's new baby was later named Cassandra . Midge has two known parents who are simply called " <unk> " and " <unk> " . At first , the <unk> dolls were sold together as part of a big set consisting of the dolls and a kitchen play set , but for Nikki 's first birthday they were sold separately . Midge , Alan , and Ryan gave Nikki a dog for her birthday . They too came in both Caucasian and African American versions . They use different body molds , to reflect their age . 
- 
- = = Head <unk> = = 
- 
- Midge 's first appearance was made with her own headmold , Vintage Midge 
- Later , we have Midge using <unk> ( by Barbie and the <unk> ) headmold 
- Before Teresa made her debut , Midge used Steffie headmold 
- Midge comes and goes so many times , but with her 50th birthday , she returns with the Steffie headmold 
- Florida Midge uses <unk> headmold . 
- 
- 
- = The Tempest ( album ) = 
- 
- The Tempest is the tenth studio album by American hip hop duo Insane Clown Posse . Released in 2007 , the album marks the return of producer Mike E. Clark , who had a falling @-@ out with the duo in 2000 . However , he did not collaborate directly with ICP , and would not do so until their 2009 album Bang ! <unk> ! Boom ! 
- The album 's concept compares a violent storm to a roller coaster ; its lyrical themes vary from <unk> @-@ based character <unk> and songs about the supernatural to humorous and lighter subject matter . Clark 's production was praised by critics , and the album peaked at # 20 on the Billboard 200 . It is the group 's 23rd overall release . 
- 
- = = History = = 
- 
- Mike E. Clark produced much of Insane Clown Posse 's discography , as well as working with other groups on Psychopathic Records , until having a falling @-@ out with ICP in 2000 , after completing ICP 's album 's <unk> and <unk> , and beginning production on the Dark Lotus debut album Tales from the Lotus Pod . 
- After becoming a full @-@ time producer for Kid Rock , Clark contracted pneumonia , but ignored the illness , and began <unk> severely as he <unk> , leading to a three @-@ month stay in Mount <unk> General Hospital , during which one of his lungs collapsed three times . As the result of his near @-@ death experience , Clark decided to reconcile with Bruce and Utsler . <unk> conversations between Clark and Insane Clown Posse led to Clark producing Shaggy 2 Dope 's 2006 solo album <unk> .. The following year , Clark produced The Tempest ; however , in both instances , he did not work with Psychopathic Records directly . Because of this , ICP felt that The Tempest was missing the collaborative element that they felt made their earlier albums enjoyable . Clark would not work directly work with Psychopathic Records until 2009 's Bang ! <unk> ! Boom ! 
- 
- = = Music and lyrics = = 
- 
- Unlike previous Insane Clown Posse albums , The Tempest is not connected to the Dark Carnival mythology , which formed the basis for ICP 's " Joker 's Cards " series , which began in 1992 with Carnival of <unk> and concluded with the albums The <unk> : <unk> @-@ La and Hell 's Pit ; however , it references themes , concepts and songs from previous albums . Joseph Bruce ( <unk> J ) said that The Tempest is a concept album in its own right . Bruce describes the album 's concept as relating to both a violent storm which turns into a tornado and pulls people into a hurricane as well as a roller coaster . According to Bruce , " That 's a lot like riding on a violent roller coaster , I 'd assume . You get <unk> to the left , <unk> to the right , sucked way up into the sky , and then you go shooting down to the floor , and then you <unk> up to the left and then you whip around to the right . Well , The Tempest is a storm in the form of a roller coaster . " As a result of this concept , the album focuses on a variety of different moods . 
- The Tempest maintains much of the <unk> lyrical content ICP is known for , as well as their humorous perspective . Unlike previous albums , this album does not contain any <unk> aimed at other <unk> or music industry figures . Bruce and Joseph Utsler ( Shaggy 2 Dope ) ' s rapping on this album is described by Allmusic reviewer David Jeffries as being delivered in " a carnival <unk> fashion that fits with their circus motif " , and contrasted Bruce and Utsler themselves as being like " Alice Cooper with a <unk> attitude " . 
- The lyrics of " Growing Again " , which describes <unk> J growing into a giant , were inspired by Bruce 's weight gain ; Bruce also says that the song reflects the feeling of being able to " rap about anything we want [ ... ] I feel we have the right for some songs to be softer and not about killing or Dark Carnival . " " The Tower " describes a war veteran and expert marksman climbing a college tower and shooting people with an arsenal of weapons , a reference to Charles Whitman 's 1966 murder <unk> . 
- Mike E. Clark 's production incorporates elements of rock and heavy metal , as well as live instrumentation . Reviewer Lana Cooper of PopMatters compares " Ride the Tempest " to <unk> of <unk> , and describes the instrumentation as incorporating elements of synthesizers and <unk> . Cooper says that " Growing Again " incorporates power ballad chords . Jeffries describes " I Do This ! " as being influenced by <unk> , and called " Mexico City " " Low <unk> music " . 
- 
- = = Release and reception = = 
- 
- The album was released with a bonus poster . The Tempest debuted at # 20 on the U.S. Billboard 200 , selling about 33 @,@ 000 copies in its first week . Reviews from Allmusic and PopMatters generally <unk> Mike E. Clark 's production as the album 's most appealing element . Allmusic 's David Jeffries wrote , " there 's no denying that Clark 's vision does wonders for the duo . " PopMatters ' Lana Cooper said that " [ Insane Clown Posse 's ] <unk> have to be given their due . " 
- 
- = = Track listing = = 
- 
- 
- = = Chart positions = = 
- 
- 
- = = Personnel = = 
- 
- Joseph Bruce – vocals , composer 
- Shaggy 2 Dope — vocals , scratching 
- Mike E. Clark – composer , engineer , mixing , producer , programming 
- Jeffrey <unk> – mastering 
- Brian <unk> – artwork , cover design 
- DJ Clay — scratching , vocals 
- Insane Clown Posse — composer , vocals 
- James Mitchell — vocals 
- The <unk> — vocals , background vocals 
- Razor Ray — guitar , soloist , vocals , background vocals 
- Michael <unk> — artwork , cover design 
- <unk> <unk> — artwork , cover design 
- 
- 
- = Star = 
- 
- A star is a luminous sphere of plasma held together by its own gravity . The nearest star to Earth is the Sun . Many other stars are visible to the naked eye from Earth during the night , appearing as a multitude of fixed luminous points in the sky due to their immense distance from Earth . Historically , the most prominent stars were grouped into constellations and <unk> , the brightest of which gained proper names . Astronomers have assembled star catalogues that identify the known stars and provide standardized stellar designations . However , most of the stars in the Universe , including all stars outside our galaxy , the Milky Way , are invisible to the naked eye from Earth . Indeed , most are invisible from Earth even through the most powerful telescopes . 
- For at least a portion of its life , a star shines due to thermonuclear fusion of hydrogen into helium in its core , releasing energy that traverses the star 's interior and then radiates into outer space . Almost all naturally occurring elements heavier than helium are created by stellar nucleosynthesis during the star 's lifetime , and for some stars by supernova nucleosynthesis when it explodes . Near the end of its life , a star can also contain degenerate matter . Astronomers can determine the mass , age , metallicity ( chemical composition ) , and many other properties of a star by observing its motion through space , its luminosity , and spectrum respectively . The total mass of a star is the main factor that determines its evolution and eventual fate . Other characteristics of a star , including diameter and temperature , change over its life , while the star 's environment affects its rotation and movement . A plot of the temperature of many stars against their <unk> produces a plot known as a <unk> – Russell diagram ( H – R diagram ) . <unk> a particular star on that diagram allows the age and evolutionary state of that star to be determined . 
- A star 's life begins with the gravitational collapse of a <unk> nebula of material composed primarily of hydrogen , along with helium and trace amounts of heavier elements . When the stellar core is sufficiently dense , hydrogen becomes steadily converted into helium through nuclear fusion , releasing energy in the process . The remainder of the star 's interior carries energy away from the core through a combination of radiative and convective heat transfer processes . The star 's internal pressure prevents it from collapsing further under its own gravity . When the hydrogen fuel at the core is exhausted , a star of mass 0 @.@ 4 times greater than the Sun 's will expand to become a red giant . In some cases , it will fuse heavier elements at the core or in shells around the core . As the star expands it throws a part of its mass , enriched with those heavier elements , into the interstellar environment , to be recycled later as new stars . Meanwhile , the core becomes a stellar remnant : a white dwarf , a neutron star , or if it is sufficiently massive a black hole . 
- <unk> and multi @-@ star systems consist of two or more stars that are gravitationally bound and generally move around each other in stable orbits . When two such stars have a relatively close orbit , their gravitational interaction can have a significant impact on their evolution . Stars can form part of a much larger gravitationally bound structure , such as a star cluster or a galaxy . 
- 
- = = Observation history = = 
- 
- Historically , stars have been important to civilizations throughout the world . They have been part of religious practices and used for celestial navigation and orientation . Many ancient astronomers believed that stars were permanently affixed to a heavenly sphere and that they were immutable . By convention , astronomers grouped stars into constellations and used them to track the motions of the planets and the inferred position of the Sun . The motion of the Sun against the background stars ( and the horizon ) was used to create <unk> , which could be used to regulate agricultural practices . The Gregorian calendar , currently used nearly everywhere in the world , is a solar calendar based on the angle of the Earth 's rotational axis relative to its local star , the Sun . 
- The oldest accurately dated star chart was the result of ancient Egyptian astronomy in 1534 BC . The earliest known star catalogues were compiled by the ancient Babylonian astronomers of Mesopotamia in the late 2nd millennium BC , during the <unk> Period ( ca . <unk> – 1155 BC ) . 
- The first star catalogue in Greek astronomy was created by <unk> in approximately 300 BC , with the help of <unk> . The star catalog of Hipparchus ( 2nd century BC ) included <unk> stars , and was used to assemble Ptolemy 's star catalogue . Hipparchus is known for the discovery of the first recorded nova ( new star ) . Many of the constellations and star names in use today <unk> from Greek astronomy . 
- In spite of the apparent <unk> of the heavens , Chinese astronomers were aware that new stars could appear . In 185 AD , they were the first to observe and write about a supernova , now known as the SN 185 . The brightest stellar event in recorded history was the SN <unk> supernova , which was observed in <unk> and written about by the Egyptian astronomer Ali ibn <unk> and several Chinese astronomers . The SN 1054 supernova , which gave birth to the Crab Nebula , was also observed by Chinese and Islamic astronomers . 
- Medieval Islamic astronomers gave Arabic names to many stars that are still used today and they invented numerous astronomical instruments that could <unk> the positions of the stars . They built the first large observatory research institutes , mainly for the purpose of producing <unk> star catalogues . Among these , the Book of Fixed Stars ( <unk> ) was written by the Persian astronomer Abd al @-@ <unk> al @-@ Sufi , who observed a number of stars , star clusters ( including the <unk> <unk> and <unk> 's <unk> ) and galaxies ( including the <unk> Galaxy ) . According to A. <unk> , in the 11th century , the Persian <unk> scholar Abu <unk> <unk> described the Milky Way galaxy as a multitude of fragments having the properties of nebulous stars , and also gave the latitudes of various stars during a lunar eclipse in <unk> . 
- According to <unk> <unk> , the <unk> astronomer Ibn <unk> proposed that the Milky Way was made up of many stars that almost touched one another and appeared to be a continuous image due to the effect of refraction from <unk> material , citing his observation of the conjunction of Jupiter and Mars on 500 AH ( <unk> / 1107 AD ) as evidence . Early European astronomers such as <unk> <unk> identified new stars in the night sky ( later termed novae ) , suggesting that the heavens were not immutable . In <unk> <unk> Bruno suggested that the stars were like the Sun , and may have other planets , possibly even Earth @-@ like , in orbit around them , an idea that had been suggested earlier by the ancient Greek philosophers , <unk> and <unk> , and by medieval Islamic <unk> such as <unk> al @-@ Din al @-@ <unk> . By the following century , the idea of the stars being the same as the Sun was reaching a consensus among astronomers . To explain why these stars exerted no net gravitational pull on the Solar System , Isaac Newton suggested that the stars were equally distributed in every direction , an idea prompted by the theologian Richard Bentley . 
- The Italian astronomer <unk> <unk> recorded observing variations in luminosity of the star <unk> in 1667 . Edmond Halley published the first measurements of the proper motion of a pair of nearby " fixed " stars , demonstrating that they had changed positions since the time of the ancient Greek astronomers Ptolemy and Hipparchus . 
- William Herschel was the first astronomer to attempt to determine the distribution of stars in the sky . During the <unk> he established a series of gauges in 600 directions and counted the stars observed along each line of sight . From this he deduced that the number of stars steadily increased toward one side of the sky , in the direction of the Milky Way core . His son John Herschel repeated this study in the southern hemisphere and found a corresponding increase in the same direction . In addition to his other accomplishments , William Herschel is also noted for his discovery that some stars do not merely lie along the same line of sight , but are also physical companions that form binary star systems . 
- The science of stellar spectroscopy was pioneered by Joseph von <unk> and Angelo <unk> . By comparing the spectra of stars such as Sirius to the Sun , they found differences in the strength and number of their absorption lines — the dark lines in a stellar spectra caused by the atmosphere 's absorption of specific frequencies . In 1865 <unk> began classifying stars into spectral types . However , the modern version of the stellar classification scheme was developed by Annie J. Cannon during the 1900s . 
- The first direct measurement of the distance to a star ( 61 <unk> at 11 @.@ 4 light @-@ years ) was made in 1838 by Friedrich <unk> using the parallax technique . <unk> measurements demonstrated the vast separation of the stars in the heavens . Observation of double stars gained increasing importance during the 19th century . In 1834 , Friedrich <unk> observed changes in the proper motion of the star Sirius and inferred a hidden companion . Edward Pickering discovered the first spectroscopic binary in 1899 when he observed the periodic splitting of the spectral lines of the star <unk> in a 104 @-@ day period . Detailed observations of many binary star systems were collected by astronomers such as William <unk> and S. W. <unk> , allowing the masses of stars to be determined from computation of orbital elements . The first solution to the problem of deriving an orbit of binary stars from telescope observations was made by Felix <unk> in 1827 . The twentieth century saw increasingly rapid advances in the scientific study of stars . The photograph became a valuable astronomical tool . Karl <unk> discovered that the color of a star and , hence , its temperature , could be determined by comparing the visual magnitude against the photographic magnitude . The development of the <unk> <unk> allowed precise measurements of magnitude at multiple wavelength intervals . In 1921 Albert A. <unk> made the first measurements of a stellar diameter using an <unk> on the <unk> telescope at Mount Wilson Observatory . 
- Important theoretical work on the physical structure of stars occurred during the first decades of the twentieth century . In 1913 , the <unk> @-@ Russell diagram was developed , <unk> the <unk> study of stars . Successful models were developed to explain the interiors of stars and stellar evolution . Cecilia Payne @-@ <unk> first proposed that stars were made primarily of hydrogen and helium in her 1925 PhD thesis . The spectra of stars were further understood through advances in quantum physics . This allowed the chemical composition of the stellar atmosphere to be determined . 
- With the exception of supernovae , individual stars have primarily been observed in the Local Group , and especially in the visible part of the Milky Way ( as demonstrated by the detailed star catalogues available for our galaxy ) . But some stars have been observed in the <unk> galaxy of the <unk> <unk> , about 100 million light years from the Earth . In the Local <unk> it is possible to see star clusters , and current telescopes could in principle observe faint individual stars in the Local Group ( see <unk> ) . However , outside the Local <unk> of galaxies , neither individual stars nor clusters of stars have been observed . The only exception is a faint image of a large star cluster containing hundreds of thousands of stars located at a distance of one billion light years — ten times further than the most distant star cluster previously observed . 
- 
- = = <unk> = = 
- 
- The concept of a constellation was known to exist during the Babylonian period . Ancient sky <unk> imagined that prominent arrangements of stars formed patterns , and they associated these with particular aspects of nature or their myths . Twelve of these formations lay along the band of the ecliptic and these became the basis of astrology . Many of the more prominent individual stars were also given names , particularly with Arabic or Latin designations . 
- As well as certain constellations and the Sun itself , individual stars have their own myths . To the Ancient Greeks , some " stars " , known as planets ( Greek <unk> ( <unk> ) , meaning " <unk> " ) , represented various important deities , from which the names of the planets Mercury , Venus , Mars , Jupiter and Saturn were taken . ( <unk> and Neptune were also Greek and Roman gods , but neither planet was known in Antiquity because of their low brightness . Their names were assigned by later astronomers . ) 
- <unk> 1600 , the names of the constellations were used to name the stars in the corresponding regions of the sky . The German astronomer Johann Bayer created a series of star maps and applied Greek letters as designations to the stars in each constellation . Later a numbering system based on the star 's right ascension was invented and added to John Flamsteed 's star catalogue in his book " Historia <unk> Britannica " ( the <unk> edition ) , whereby this numbering system came to be called Flamsteed designation or Flamsteed numbering . 
- The only internationally recognized authority for naming celestial bodies is the International Astronomical Union ( IAU ) . A number of private companies sell names of stars , which the British Library calls an unregulated commercial enterprise . The IAU has <unk> itself from this commercial practice , and these names are neither recognized by the IAU nor used by them . One such star @-@ naming company is the International Star <unk> , which , during the 1980s , was accused of deceptive practice for making it appear that the assigned name was official . This now @-@ discontinued <unk> practice was informally labeled a <unk> and a fraud , and the New York City Department of Consumer Affairs issued a violation against <unk> for engaging in a deceptive trade practice . 
- 
- = = Units of measurement = = 
- 
- Although stellar parameters can be expressed in <unk> units or <unk> units , it is often most convenient to express mass , luminosity , and radii in solar units , based on the characteristics of the Sun : 
- Large lengths , such as the radius of a giant star or the semi @-@ major axis of a binary star system , are often expressed in terms of the astronomical unit — approximately equal to the mean distance between the Earth and the Sun ( 150 million km or 93 million miles ) . 
- 
- = = Formation and evolution = = 
- 
- Stars <unk> from regions of space of higher density , yet those regions are less dense than within a vacuum chamber . These regions - known as molecular clouds - consist mostly of hydrogen , with about 23 to 28 percent helium and a few percent heavier elements . One example of such a star @-@ forming region is the Orion Nebula . Most stars form in groups of dozens to hundreds of thousands of stars . Massive stars in these groups may powerfully illuminate those clouds , ionizing the hydrogen , and creating H II regions . Such feedback effects , from star formation , may ultimately disrupt the cloud and prevent further star formation . 
- All stars spend the majority of their existence as main sequence stars , fueled primarily by the nuclear fusion of hydrogen into helium within their cores . However , stars of different masses have markedly different properties at various stages of their development . The ultimate fate of more massive stars differs from that of less massive stars , as do their <unk> and the impact they have on their environment . Accordingly , astronomers often group stars by their mass : 
- Very low mass stars , with masses below 0 @.@ 5 M ☉ , are fully convective and distribute helium evenly throughout the whole star while on the main sequence . Therefore , they never undergo shell burning , never become red giants , which cease fusing and become helium white dwarfs and slowly cool after exhausting their hydrogen . However , as the lifetime of 0 @.@ 5 M ☉ stars is longer than the age of the universe , no such star has yet reached the white dwarf stage . 
- Low mass stars ( including the Sun ) , with a mass between 0 @.@ 5 M ☉ and 1 @.@ 8 – 2 @.@ 5 M ☉ depending on composition , do become red giants as their core hydrogen is depleted and they begin to burn helium in core in a helium flash ; they develop a degenerate carbon @-@ oxygen core later on the asymptotic giant branch ; they finally blow off their outer shell as a planetary nebula and leave behind their core in the form of a white dwarf . 
- Intermediate @-@ mass stars , between 1 @.@ 8 – 2 @.@ 5 M ☉ and 5 – 10 M ☉ , pass through evolutionary stages similar to low mass stars , but after a relatively short period on the <unk> they ignite helium without a flash and spend an extended period in the red clump before forming a degenerate carbon @-@ oxygen core . 
- Massive stars generally have a minimum mass of 7 – 10 M ☉ ( possibly as low as 5 – 6 M ☉ ) . After exhausting the hydrogen at the core these stars become supergiants and go on to fuse elements heavier than helium . They end their lives when their cores collapse and they explode as supernovae . 
- 
- = = = Star formation = = = 
- 
- The formation of a star begins with gravitational instability within a molecular cloud , caused by regions of higher density - often triggered by compression of clouds by radiation from massive stars , expanding bubbles in the interstellar medium , the collision of different molecular clouds , or the collision of galaxies ( as in a <unk> galaxy ) . When a region reaches a sufficient density of matter to satisfy the criteria for <unk> instability , it begins to collapse under its own gravitational force . 
- As the cloud collapses , individual <unk> of dense dust and gas form " Bok <unk> " . As a <unk> collapses and the density increases , the gravitational energy converts into heat and the temperature rises . When the <unk> cloud has approximately reached the stable condition of hydrostatic equilibrium , a <unk> forms at the core . These pre – main sequence stars are often surrounded by a <unk> disk and powered mainly by the conversion of gravitational energy . The period of gravitational contraction lasts about 10 to 15 million years . 
- Early stars of less than 2 M ☉ are called T Tauri stars , while those with greater mass are <unk> <unk> / Be stars . These newly formed stars emit jets of gas along their axis of rotation , which may reduce the angular momentum of the collapsing star and result in small patches of <unk> known as <unk> – <unk> objects . These jets , in combination with radiation from nearby massive stars , may help to drive away the surrounding cloud from which the star was formed . 
- Early in their development , T Tauri stars follow the <unk> track — they contract and decrease in luminosity while remaining at roughly the same temperature . Less massive T Tauri stars follow this track to the main sequence , while more massive stars turn onto the <unk> track . 
- Most stars are observed to be members of binary star systems , and the properties of those binaries are the result of the conditions in which they formed . A gas cloud must lose its angular momentum in order to collapse and form a star . The fragmentation of the cloud into multiple stars distributes some of that angular momentum . The primordial binaries transfer some angular momentum by gravitational interactions during close encounters with other stars in young stellar clusters . These interactions tend to split apart more widely separated ( soft ) binaries while causing hard binaries to become more tightly bound . This produces the separation of binaries into their two observed populations <unk> . 
- 
- = = = Main sequence = = = 
- 
- Stars spend about 90 % of their existence fusing hydrogen into helium in high @-@ temperature and high @-@ pressure reactions near the core . Such stars are said to be on the main sequence , and are called dwarf stars . Starting at zero @-@ age main sequence , the proportion of helium in a star 's core will steadily increase , the rate of nuclear fusion at the core will slowly increase , as will the star 's temperature and luminosity . The Sun , for example , is estimated to have increased in luminosity by about 40 % since it reached the main sequence 4 @.@ 6 billion ( 4 @.@ 6 × 109 ) years ago . 
- Every star generates a stellar wind of particles that causes a continual outflow of gas into space . For most stars , the mass lost is negligible . The Sun loses 10 − 14 M ☉ every year , or about 0 @.@ 01 % of its total mass over its entire lifespan . However , very massive stars can lose 10 − 7 to 10 − 5 M ☉ each year , significantly affecting their evolution . Stars that begin with more than 50 M ☉ can lose over half their total mass while on the main sequence . 
- The time a star spends on the main sequence depends primarily on the amount of fuel it has and the rate at which it fuses it . The Sun 's is expected to live 10 billion ( <unk> ) years . Massive stars consume their fuel very rapidly and are short @-@ lived . Low mass stars consume their fuel very slowly . Stars less massive than 0 @.@ 25 M ☉ , called red dwarfs , are able to fuse nearly all of their mass while stars of about 1 M ☉ can only fuse about 10 % of their mass . The combination of their slow fuel @-@ consumption and relatively large usable fuel supply allows low mass stars to last about one trillion ( 1012 ) years ; the most extreme of 0 @.@ 08 M ☉ ) will last for about 12 trillion years . Red dwarfs become <unk> and more luminous as they accumulate helium . When they eventually run out of hydrogen , they contract into a white dwarf and decline in temperature . However , since the lifespan of such stars is greater than the current age of the universe ( 13 @.@ 8 billion years ) , no stars under about 0 @.@ 85 M ☉ are expected to have moved off the main sequence . 
- Besides mass , the elements heavier than helium can play a significant role in the evolution of stars . Astronomers label all elements heavier than helium " metals " , and call the chemical concentration of these elements in a star , its metallicity . A star 's metallicity can influence the time the star takes to burn its fuel , and controls the formation of its magnetic fields , which affects the strength of its stellar wind . Older , population II stars have substantially less metallicity than the younger , population I stars due to the composition of the molecular clouds from which they formed . Over time , such clouds become increasingly enriched in heavier elements as older stars die and shed portions of their atmospheres . 
- 
- = = = Post – main sequence = = = 
- 
- As stars of at least 0 @.@ 4 M ☉ exhaust their supply of hydrogen at their core , they start to fuse hydrogen in a shell outside the helium core . Their outer layers expand and cool greatly as they form a red giant . In about 5 billion years , when the Sun enters the helium burning phase , it will expand to a maximum radius of roughly 1 astronomical unit ( 150 million kilometres ) , 250 times its present size , and lose 30 % of its current mass . 
- As the hydrogen shell burning produces more helium , the core increases in mass and temperature . In a red giant of up to 2 @.@ 25 M ☉ , the mass of the helium core becomes degenerate prior to helium fusion . Finally , when the temperature increases sufficiently , helium fusion begins explosively in what is called a helium flash , and the star rapidly shrinks in radius , increases its surface temperature , and moves to the horizontal branch of the <unk> diagram . For more massive stars , helium core fusion starts before the core becomes degenerate , and the star spends some time in the red clump , slowly burning helium , before the outer convective envelope collapses and the star then moves to the horizontal branch . 
- After the star has fused the helium of its core , the carbon product fuses producing a hot core with an outer shell of fusing helium . The star then follows an evolutionary path called the asymptotic giant branch ( <unk> ) that parallels the other described red giant phase , but with a higher luminosity . The more massive <unk> stars may undergo a brief period of carbon fusion before the core becomes degenerate . 
- 
- = = = = Massive stars = = = = 
- 
- During their helium @-@ burning phase , stars of more than nine solar masses expand to form red supergiants . When this fuel is exhausted at the core , they continue to fuse elements heavier than helium . 
- The core contracts and the temperature and pressure rises enough to fuse carbon ( see <unk> burning process ) . This process continues , with the successive stages being fueled by neon ( see neon burning process ) , oxygen ( see oxygen burning process ) , and silicon ( see silicon burning process ) . Near the end of the star 's life , fusion continues along a series of onion @-@ layer shells within a massive star . Each shell fuses a different element , with the outermost shell fusing hydrogen ; the next shell fusing helium , and so forth . 
- The final stage occurs when a massive star begins producing iron . Since iron nuclei are more tightly bound than any heavier nuclei , any fusion beyond iron does not produce a net release of energy . To a very limited degree such a process proceeds , but it consumes energy . Likewise , since they are more tightly bound than all lighter nuclei , such energy cannot be released by fission . In relatively old , very massive stars , a large core of inert iron will accumulate in the center of the star . The heavier elements in these stars can work their way to the surface , forming evolved objects known as Wolf @-@ <unk> stars that have a dense stellar wind which sheds the outer atmosphere . 
- 
- = = = = <unk> = = = = 
- 
- As a star 's core shrinks , the intensity of radiation from that surface increases , creating such radiation pressure on the outer shell of gas that it will push those layers away , forming a planetary nebula . If what remains after the outer atmosphere has been shed is less than 1 @.@ 4 M ☉ , it shrinks to a relatively tiny object about the size of Earth , known as a white dwarf . White dwarfs lack the mass for further gravitational compression to take place . The electron @-@ degenerate matter inside a white dwarf is no longer a plasma , even though stars are generally referred to as being spheres of plasma . Eventually , white dwarfs fade into black dwarfs over a very long period of time . 
- In larger stars , fusion continues until the iron core has grown so large ( more than 1 @.@ 4 M ☉ ) that it can no longer support its own mass . This core will suddenly collapse as its electrons are driven into its protons , forming neutrons , neutrinos , and gamma rays in a burst of electron capture and <unk> beta decay . The <unk> formed by this sudden collapse causes the rest of the star to explode in a supernova . <unk> become so bright that they may briefly <unk> the star 's entire home galaxy . When they occur within the Milky Way , supernovae have historically been observed by naked @-@ eye observers as " new stars " where none seemingly existed before . 
- A supernova explosion blows away the star 's outer layers , leaving a remnant such as the Crab Nebula . The core is compressed into a neutron star , which sometimes <unk> itself as a pulsar or X @-@ ray <unk> . In the case of the largest stars , the remnant is a black hole greater than 4 M ☉ ) s . In a neutron star the matter is in a state known as neutron @-@ degenerate matter , with a more exotic form of degenerate matter , <unk> matter , possibly present in the core . Within a black hole , the matter is in a state that is not currently understood . 
- The blown @-@ off outer layers of dying stars include heavy elements , which may be recycled during the formation of new stars . These heavy elements allow the formation of rocky planets . The outflow from supernovae and the stellar wind of large stars play an important part in shaping the interstellar medium . 
- 
- = = = = <unk> stars = = = = 
- 
- The post – main @-@ sequence evolution of binary stars may be significantly different from the evolution of single stars of the same mass . If stars in a binary system are sufficiently close , when one of the stars expands to become a red giant it may overflow its Roche lobe , the region around a star where material is gravitationally bound to that star , leading to transfer of material to the other . When the Roche lobe is violated , a variety of phenomena can result , including contact binaries , common @-@ envelope binaries , <unk> variables , and type <unk> supernovae . 
- 
- = = Distribution = = 
- 
- In addition to isolated stars , a multi @-@ star system can consist of two or more gravitationally bound stars that orbit each other . The simplest and most common multi @-@ star system is a binary star , but systems of three or more stars are also found . For reasons of orbital stability , such multi @-@ star systems are often organized into hierarchical sets of binary stars . Larger groups called star clusters also exist . These range from loose stellar associations with only a few stars , up to enormous globular clusters with hundreds of thousands of stars . Such systems orbit our Milky Way galaxy . 
- It has been a long @-@ held assumption that the majority of stars occur in gravitationally bound , multiple @-@ star systems . This is particularly true for very massive O and B class stars , where 80 % of the stars are believed to be part of multiple @-@ star systems . The proportion of single star systems increases with decreasing star mass , so that only 25 % of red dwarfs are known to have stellar companions . As 85 % of all stars are red dwarfs , most stars in the Milky Way are likely single from birth . 
- Stars are not spread uniformly across the universe , but are normally grouped into galaxies along with interstellar gas and dust . A typical galaxy contains hundreds of billions of stars , and there are more than 100 billion ( <unk> ) galaxies in the observable universe . In 2010 , one estimate of the number of stars in the observable universe was 300 <unk> ( 3 × 1023 ) . While it is often believed that stars only exist within galaxies , <unk> stars have been discovered . 
- The nearest star to the Earth , apart from the Sun , is <unk> <unk> , which is 39 @.@ 9 trillion kilometres , or 4 @.@ 2 light @-@ years . Travelling at the orbital speed of the Space Shuttle ( 8 kilometres per second — almost 30 @,@ 000 kilometres per hour ) , it would take about 150 @,@ 000 years to arrive . This it typical of stellar <unk> in galactic discs . Stars can be much closer to each other in the centres of galaxies and in globular clusters , or much farther apart in galactic <unk> . 
- Due to the relatively vast distances between stars outside the galactic nucleus , collisions between stars are thought to be rare . In denser regions such as the core of globular clusters or the galactic center , collisions can be more common . Such collisions can produce what are known as blue stragglers . These abnormal stars have a higher surface temperature than the other main sequence stars with the same luminosity of the cluster to which it belongs . 
- 
- = = Characteristics = = 
- 
- Almost everything about a star is determined by its initial mass , including such characteristics as luminosity , size , evolution , lifespan , and its eventual fate . 
- 
- = = = Age = = = 
- 
- Most stars are between 1 billion and 10 billion years old . Some stars may even be close to 13 @.@ 8 billion years old — the observed age of the universe . The oldest star yet discovered , HD <unk> , nicknamed <unk> star , is an estimated 14 @.@ 46 ± 0 @.@ 8 billion years old . ( Due to the uncertainty in the value , this age for the star does not conflict with the age of the Universe , determined by the Planck satellite as 13 @.@ 799 ± 0 @.@ <unk> ) . 
- The more massive the star , the shorter its lifespan , primarily because massive stars have greater pressure on their cores , causing them to burn hydrogen more rapidly . The most massive stars last an average of a few million years , while stars of minimum mass ( red dwarfs ) burn their fuel very slowly and can last tens to hundreds of billions of years . 
- 
- = = = Chemical composition = = = 
- 
- When stars form in the present Milky Way galaxy they are composed of about 71 % hydrogen and 27 % helium , as measured by mass , with a small fraction of heavier elements . Typically the portion of heavy elements is measured in terms of the iron content of the stellar atmosphere , as iron is a common element and its absorption lines are relatively easy to measure . The portion of heavier elements may be an indicator of the likelihood that the star has a planetary system . 
- The star with the lowest iron content ever measured is the dwarf <unk> @-@ <unk> , with only 1 / <unk> the iron content of the Sun . By contrast , the super @-@ metal @-@ rich star <unk> <unk> has nearly double the abundance of iron as the Sun , while the planet @-@ bearing star 14 <unk> has nearly triple the iron . There also exist chemically peculiar stars that show unusual <unk> of certain elements in their spectrum ; especially <unk> and rare earth elements . Stars with cooler outer atmospheres , including the Sun , can form various <unk> and <unk> molecules . 
- 
- = = = <unk> = = = 
- 
- Due to their great distance from the Earth , all stars except the Sun appear to the unaided eye as shining points in the night sky that <unk> because of the effect of the Earth 's atmosphere . The Sun is also a star , but it is close enough to the Earth to appear as a disk instead , and to provide daylight . Other than the Sun , the star with the largest apparent size is R <unk> , with an angular diameter of only 0 @.@ <unk> <unk> . 
- The disks of most stars are much too small in angular size to be observed with current ground @-@ based optical telescopes , and so <unk> telescopes are required to produce images of these objects . Another technique for measuring the angular size of stars is through <unk> . By precisely measuring the drop in brightness of a star as it is <unk> by the Moon ( or the rise in brightness when it reappears ) , the star 's angular diameter can be computed . 
- Stars range in size from neutron stars , which vary anywhere from 20 to 40 km ( 25 mi ) in diameter , to supergiants like <unk> in the Orion constellation , which has a diameter approximately 1 @,@ 070 times that of the Sun — about 1 @,@ 490 @,@ 171 @,@ 880 km ( <unk> @,@ <unk> @,@ 878 mi ) . <unk> , however , has a much lower density than the Sun . 
- 
- = = = <unk> = = = 
- 
- The motion of a star relative to the Sun can provide useful information about the origin and age of a star , as well as the structure and evolution of the surrounding galaxy . The components of motion of a star consist of the radial velocity toward or away from the Sun , and the traverse angular movement , which is called its proper motion . 
- <unk> velocity is measured by the <unk> shift of the star 's spectral lines , and is given in units of km / s . The proper motion of a star , its parallax , is determined by precise <unk> measurements in units of <unk> @-@ arc seconds ( <unk> ) per year . With knowledge of the star 's parallax and its distance , the proper motion velocity can be calculated . Together with the radial velocity , the total velocity can be calculated . Stars with high rates of proper motion are likely to be relatively close to the Sun , making them good candidates for parallax measurements . 
- When both rates of movement are known , the space velocity of the star relative to the Sun or the galaxy can be computed . Among nearby stars , it has been found that younger population I stars have generally lower <unk> than older , population II stars . The latter have elliptical orbits that are inclined to the plane of the galaxy . A comparison of the <unk> of nearby stars has allowed astronomers to trace their origin to common points in giant molecular clouds , and are referred to as stellar associations . 
- 
- = = = Magnetic field = = = 
- 
- The magnetic field of a star is generated within regions of the interior where convective circulation occurs . This movement of <unk> plasma functions like a <unk> , wherein the movement of <unk> charges induce magnetic fields , as does a mechanical <unk> . Those magnetic fields have a great range that extend throughout and beyond the star . The strength of the magnetic field varies with the mass and composition of the star , and the amount of magnetic surface activity depends upon the star 's rate of rotation . This surface activity produces starspots , which are regions of strong magnetic fields and lower than normal surface temperatures . <unk> loops are <unk> magnetic field flux lines that rise from a star 's surface into the star 's outer atmosphere , its corona . The <unk> loops can be seen due to the plasma they conduct along their length . <unk> flares are bursts of high @-@ energy particles that are emitted due to the same magnetic activity . 
- Young , rapidly rotating stars tend to have high levels of surface activity because of their magnetic field . The magnetic field can act upon a star 's stellar wind , functioning as a brake to gradually slow the rate of rotation with time . Thus , older stars such as the Sun have a much slower rate of rotation and a lower level of surface activity . The activity levels of slowly rotating stars tend to vary in a cyclical manner and can shut down altogether for periods of time . During the <unk> minimum , for example , the Sun underwent a 70 @-@ year period with almost no sunspot activity . 
- 
- = = = Mass = = = 
- 
- One of the most massive stars known is <unk> <unk> , which , with 100 – 150 times as much mass as the Sun , will have a lifespan of only several million years . Studies of the most massive open clusters suggests 150 M ☉ as an upper limit for stars in the current era of the universe . This represents an <unk> value for the theoretical limit on the mass of forming stars due to increasing radiation pressure on the <unk> gas cloud . Several stars in the <unk> cluster in the Large <unk> Cloud have been measured with larger masses , but it has been determined that they could have been created through the collision and merger of massive stars in close binary systems , <unk> the 150 M ☉ limit on massive star formation . 
- The first stars to form after the Big Bang may have been larger , up to 300 M ☉ , due to the complete absence of elements heavier than lithium in their composition . This generation of <unk> population III stars is likely to have existed in the very early universe ( i.e. , they are observed to have a high <unk> ) , and may have started the production of chemical elements heavier than hydrogen that are needed for the later formation of planets and life . In June 2015 , astronomers reported evidence for Population III stars in the Cosmos <unk> 7 galaxy at <unk> = 6 @.@ 60 . 
- With a mass only 80 times that of Jupiter ( MJ ) , <unk> <unk> @-@ <unk> is the smallest known star undergoing nuclear fusion in its core . For stars with metallicity similar to the Sun , the theoretical minimum mass the star can have and still undergo fusion at the core , is estimated to be about 75 MJ . When the metallicity is very low , however , the minimum star size seems to be about 8 @.@ 3 % of the solar mass , or about 87 MJ . Smaller bodies called brown dwarfs , occupy a poorly defined grey area between stars and gas giants . 
- The combination of the radius and the mass of a star determines its surface gravity . Giant stars have a much lower surface gravity than do main sequence stars , while the opposite is the case for degenerate , compact stars such as white dwarfs . The surface gravity can influence the appearance of a star 's spectrum , with higher gravity causing a broadening of the absorption lines . 
- 
- = = = <unk> = = = 
- 
- The rotation rate of stars can be determined through spectroscopic measurement , or more exactly determined by tracking their starspots . Young stars can have a rotation greater than 100 km / s at the equator . The B @-@ class star <unk> , for example , has an equatorial velocity of about 225 km / s or greater , causing its equator to be <unk> outward and giving it an equatorial diameter that is more than 50 % greater than between the poles . This rate of rotation is just below the critical velocity of 300 km / s at which speed the star would break apart . By contrast , the Sun <unk> once every 25 – 35 days , with an equatorial velocity of 1 @.@ <unk> km / s . A main sequence star 's magnetic field and the stellar wind serve to slow its rotation by a significant amount as it evolves on the main sequence . 
- <unk> stars have contracted into a compact mass , resulting in a rapid rate of rotation . However they have relatively low rates of rotation compared to what would be expected by conservation of angular momentum — the tendency of a rotating body to compensate for a contraction in size by increasing its rate of spin . A large portion of the star 's angular momentum is dissipated as a result of mass loss through the stellar wind . In spite of this , the rate of rotation for a pulsar can be very rapid . The pulsar at the heart of the Crab nebula , for example , <unk> 30 times per second . The rotation rate of the pulsar will gradually slow due to the emission of radiation . 
- 
- = = = <unk> = = = 
- 
- The surface temperature of a main sequence star is determined by the rate of energy production of its core and by its radius , and is often estimated from the star 's color index . The temperature is normally given in terms of an effective temperature , which is the temperature of an <unk> black body that radiates its energy at the same luminosity per surface area as the star . Note that the effective temperature is only a representative of the surface , as the temperature increases toward the core . The temperature in the core region of a star is several million <unk> . 
- The stellar temperature will determine the rate of ionization of various elements , resulting in characteristic absorption lines in the spectrum . The surface temperature of a star , along with its visual absolute magnitude and absorption features , is used to classify a star ( see classification below ) . 
- Massive main sequence stars can have surface temperatures of 50 @,@ 000 K. Smaller stars such as the Sun have surface temperatures of a few thousand K. Red giants have relatively low surface temperatures of about 3 @,@ 600 K ; but they also have a high luminosity due to their large exterior surface area . 
- 
- = = Radiation = = 
- 
- The energy produced by stars , a product of nuclear fusion , radiates to space as both electromagnetic radiation and particle radiation . The particle radiation emitted by a star is <unk> as the stellar wind , which streams from the outer layers as electrically charged protons and alpha and beta particles . Although almost <unk> , there also exists a steady stream of neutrinos emanating from the star 's core . 
- The production of energy at the core is the reason stars shine so brightly : every time two or more atomic nuclei fuse together to form a single atomic nucleus of a new heavier element , gamma ray photons are released from the nuclear fusion product . This energy is converted to other forms of electromagnetic energy of lower frequency , such as visible light , by the time it reaches the star 's outer layers . 
- The color of a star , as determined by the most intense frequency of the visible light , depends on the temperature of the star 's outer layers , including its photosphere . Besides visible light , stars also emit forms of electromagnetic radiation that are invisible to the human eye . In fact , stellar electromagnetic radiation spans the entire electromagnetic spectrum , from the longest <unk> of radio waves through infrared , visible light , ultraviolet , to the shortest of X @-@ rays , and gamma rays . From the standpoint of total energy emitted by a star , not all components of stellar electromagnetic radiation are significant , but all frequencies provide insight into the star 's physics . 
- Using the stellar spectrum , astronomers can also determine the surface temperature , surface gravity , metallicity and rotational velocity of a star . If the distance of the star is found , such as by measuring the parallax , then the luminosity of the star can be derived . The mass , radius , surface gravity , and rotation period can then be estimated based on stellar models . ( Mass can be calculated for stars in binary systems by measuring their orbital <unk> and distances . <unk> <unk> has been used to measure the mass of a single star . ) With these parameters , astronomers can also estimate the age of the star . 
- 
- = = = <unk> = = = 
- 
- The luminosity of a star is the amount of light and other forms of <unk> energy it radiates per unit of time . It has units of power . The luminosity of a star is determined by its radius and surface temperature . Many stars do not radiate uniformly across their entire surface . The rapidly rotating star Vega , for example , has a higher energy flux ( power per unit area ) at its poles than along its equator . 
- <unk> of the star 's surface with a lower temperature and luminosity than average are known as starspots . Small , dwarf stars such as our Sun generally have essentially <unk> disks with only small starspots . Giant stars have much larger , more obvious starspots , and they also exhibit strong stellar limb darkening . That is , the brightness decreases towards the edge of the stellar disk . Red dwarf flare stars such as <unk> <unk> may also possess prominent <unk> features . 
- 
- = = = <unk> = = = 
- 
- The apparent brightness of a star is expressed in terms of its apparent magnitude . It is a function of the star 's luminosity , its distance from Earth , and the altering of the star 's light as it passes through Earth 's atmosphere . <unk> or absolute magnitude is directly related to a star 's luminosity , and is what the apparent magnitude a star would be if the distance between the Earth and the star were 10 <unk> ( 32 @.@ 6 light @-@ years ) . 
- Both the apparent and absolute magnitude scales are <unk> units : one whole number difference in magnitude is equal to a brightness variation of about 2 @.@ 5 times ( the 5th root of 100 or approximately 2 @.@ 512 ) . This means that a first magnitude star ( + 1 @.@ 00 ) is about 2 @.@ 5 times brighter than a second magnitude ( + 2 @.@ 00 ) star , and about 100 times brighter than a sixth magnitude star ( + 6 @.@ 00 ) . The faintest stars visible to the naked eye under good seeing conditions are about magnitude + 6 . 
- On both apparent and absolute magnitude scales , the smaller the magnitude number , the brighter the star ; the larger the magnitude number , the <unk> the star . The brightest stars , on either scale , have negative magnitude numbers . The variation in brightness ( <unk> ) between two stars is calculated by <unk> the magnitude number of the brighter star ( mb ) from the magnitude number of the <unk> star ( <unk> ) , then using the difference as an exponent for the base number 2 @.@ 512 ; that is to say : 
- <formula> 
- <formula> 
- Relative to both luminosity and distance from Earth , a star 's absolute magnitude ( M ) and apparent magnitude ( m ) are not equivalent ; for example , the bright star Sirius has an apparent magnitude of − 1 @.@ 44 , but it has an absolute magnitude of + 1 @.@ 41 . 
- The Sun has an apparent magnitude of − 26 @.@ 7 , but its absolute magnitude is only + 4 @.@ 83 . Sirius , the brightest star in the night sky as seen from Earth , is approximately 23 times more luminous than the Sun , while Canopus , the second brightest star in the night sky with an absolute magnitude of − 5 @.@ 53 , is approximately 14 @,@ 000 times more luminous than the Sun . Despite Canopus being vastly more luminous than Sirius , however , Sirius appears brighter than Canopus . This is because Sirius is merely 8 @.@ 6 light @-@ years from the Earth , while Canopus is much farther away at a distance of 310 light @-@ years . 
- As of 2006 , the star with the highest known absolute magnitude is <unk> 1806 @-@ 20 , with a magnitude of − 14 @.@ 2 . This star is at least 5 @,@ 000 @,@ 000 times more luminous than the Sun . The least luminous stars that are currently known are located in the <unk> <unk> cluster . The faintest red dwarfs in the cluster were magnitude 26 , while a 28th magnitude white dwarf was also discovered . These faint stars are so dim that their light is as bright as a birthday <unk> on the Moon when viewed from the Earth . 
- 
- = = Classification = = 
- 
- The current stellar classification system originated in the early 20th century , when stars were classified from A to Q based on the strength of the hydrogen line . It thought that the hydrogen line strength was a simple linear function of temperature . Rather , it was more complicated ; it strengthened with increasing temperature , it peaked near 9000 K , and then declined at greater temperatures . When the classifications were <unk> by temperature , it more closely resembled the modern scheme . 
- Stars are given a single @-@ letter classification according to their spectra , ranging from type O , which are very hot , to M , which are so cool that molecules may form in their atmospheres . The main classifications in order of decreasing surface temperature are : O , B , A , F , G , K , and M. A variety of rare spectral types are given special classifications . The most common of these are types L and T , which classify the coldest low @-@ mass stars and brown dwarfs . Each letter has 10 sub @-@ divisions , numbered from 0 to 9 , in order of decreasing temperature . However , this system breaks down at extreme high temperatures as classes <unk> and <unk> may not exist . 
- In addition , stars may be classified by the luminosity effects found in their spectral lines , which correspond to their spatial size and is determined by their surface gravity . These range from 0 ( <unk> ) through III ( giants ) to V ( main sequence dwarfs ) ; some authors add VII ( white dwarfs ) . Most stars belong to the main sequence , which consists of ordinary hydrogen @-@ burning stars . These fall along a narrow , diagonal band when <unk> according to their absolute magnitude and spectral type . The Sun is a main sequence <unk> yellow dwarf of intermediate temperature and ordinary size . 
- Additional nomenclature , in the form of lower @-@ case letters added to the end of the spectral type to indicate peculiar features of the spectrum . For example , an " e " can indicate the presence of emission lines ; " m " represents unusually strong levels of metals , and " <unk> " can mean variations in the spectral type . 
- White dwarf stars have their own class that begins with the letter D. This is further sub @-@ divided into the classes <unk> , <unk> , DC , <unk> , <unk> , and <unk> , depending on the types of prominent lines found in the spectrum . This is followed by a numerical value that indicates the temperature . 
- 
- = = <unk> stars = = 
- 
- <unk> stars have periodic or random changes in luminosity because of intrinsic or <unk> properties . Of the intrinsically variable stars , the primary types can be subdivided into three principal groups . 
- During their stellar evolution , some stars pass through phases where they can become <unk> variables . <unk> variable stars vary in radius and luminosity over time , expanding and contracting with periods ranging from minutes to years , depending on the size of the star . This category includes <unk> and <unk> @-@ like stars , and long @-@ period variables such as Mira . 
- <unk> variables are stars that experience sudden increases in luminosity because of flares or mass <unk> events . This group includes <unk> , Wolf @-@ <unk> stars , and flare stars , as well as giant and <unk> stars . 
- <unk> or explosive variable stars are those that undergo a dramatic change in their properties . This group includes novae and supernovae . A binary star system that includes a nearby white dwarf can produce certain types of these spectacular stellar explosions , including the nova and a Type <unk> supernova . The explosion is created when the white dwarf <unk> hydrogen from the companion star , building up mass until the hydrogen undergoes fusion . Some novae are also recurrent , having periodic outbursts of moderate <unk> . 
- Stars can also vary in luminosity because of <unk> factors , such as eclipsing binaries , as well as rotating stars that produce extreme starspots . A notable example of an eclipsing binary is <unk> , which regularly varies in magnitude from 2 @.@ 3 to 3 @.@ 5 over a period of 2 @.@ 87 days . 
- 
- = = Structure = = 
- 
- The interior of a stable star is in a state of hydrostatic equilibrium : the forces on any small volume almost exactly counterbalance each other . The balanced forces are inward gravitational force and an outward force due to the pressure gradient within the star . The pressure gradient is established by the temperature gradient of the plasma ; the outer part of the star is cooler than the core . The temperature at the core of a main sequence or giant star is at least on the order of 107 K. The resulting temperature and pressure at the hydrogen @-@ burning core of a main sequence star are sufficient for nuclear fusion to occur and for sufficient energy to be produced to prevent further collapse of the star . 
- As atomic nuclei are fused in the core , they emit energy in the form of gamma rays . These photons interact with the surrounding plasma , adding to the thermal energy at the core . Stars on the main sequence convert hydrogen into helium , creating a slowly but steadily increasing proportion of helium in the core . Eventually the helium content becomes predominant , and energy production ceases at the core . Instead , for stars of more than 0 @.@ 4 M ☉ , fusion occurs in a slowly expanding shell around the degenerate helium core . 
- In addition to hydrostatic equilibrium , the interior of a stable star will also maintain an energy balance of thermal equilibrium . There is a radial temperature gradient throughout the interior that results in a flux of energy flowing toward the exterior . The outgoing flux of energy leaving any layer within the star will exactly match the incoming flux from below . 
- The radiation zone is the region of the stellar interior where the flux of energy outward is dependent on radiative heat transfer , since convective heat transfer is inefficient in that zone . In this region the plasma will not be <unk> , and any mass motions will die out . If this is not the case , however , then the plasma becomes unstable and convection will occur , forming a convection zone . This can occur , for example , in regions where very high energy <unk> occur , such as near the core or in areas with high <unk> ( making <unk> heat transfer inefficient ) as in the outer envelope . 
- The occurrence of convection in the outer envelope of a main sequence star depends on the star 's mass . Stars with several times the mass of the Sun have a convection zone deep within the interior and a radiative zone in the outer layers . Smaller stars such as the Sun are just the opposite , with the convective zone located in the outer layers . Red dwarf stars with less than 0 @.@ 4 M ☉ are convective throughout , which prevents the <unk> of a helium core . For most stars the convective zones will also vary over time as the star ages and the constitution of the interior is modified . 
- The photosphere is that portion of a star that is visible to an observer . This is the layer at which the plasma of the star becomes transparent to photons of light . From here , the energy generated at the core becomes free to propagate into space . It is within the photosphere that sun spots , regions of lower than average temperature , appear . 
- Above the level of the photosphere is the stellar atmosphere . In a main sequence star such as the Sun , the lowest level of the atmosphere , just above the photosphere , is the thin <unk> region , where <unk> appear and stellar flares begin . Above this is the transition region , where the temperature rapidly increases within a distance of only 100 km ( 62 mi ) . Beyond this is the corona , a volume of super @-@ heated plasma that can extend outward to several million kilometres . The existence of a corona appears to be dependent on a convective zone in the outer layers of the star . Despite its high temperature , and the corona emits very little light , due to its low gas density . The corona region of the Sun is normally only visible during a solar eclipse . 
- From the corona , a stellar wind of plasma particles expands outward from the star , until it interacts with the interstellar medium . For the Sun , the influence of its solar wind extends throughout a bubble @-@ shaped region called the <unk> . 
- 
- = = Nuclear fusion reaction pathways = = 
- 
- A variety of nuclear fusion reactions take place in the cores of stars , that depend upon their mass and composition . When nuclei fuse , the mass of the fused product is less than the mass of the original parts . This lost mass is converted to electromagnetic energy , according to the mass @-@ energy equivalence relationship E = <unk> . 
- The hydrogen fusion process is temperature @-@ sensitive , so a moderate increase in the core temperature will result in a significant increase in the fusion rate . As a result , the core temperature of main sequence stars only varies from 4 million kelvin for a small M @-@ class star to 40 million kelvin for a massive O @-@ class star . 
- In the Sun , with a 10 @-@ million @-@ kelvin core , hydrogen fuses to form helium in the proton @-@ proton chain reaction : 
- <unk> → <unk> + 2e + + <unk> ( 2 x 0 @.@ 4 MeV ) 
- 2e + + <unk> → 2γ ( 2 x 1 @.@ 0 MeV ) 
- <unk> + <unk> → <unk> + 2γ ( 2 x 5 @.@ 5 MeV ) 
- <unk> → 4He + <unk> ( 12 @.@ 9 MeV ) 
- These reactions result in the overall reaction : 
- <unk> → 4He + 2e + + 2γ + <unk> ( 26 @.@ 7 MeV ) 
- where e + is a positron , γ is a gamma ray <unk> , <unk> is a neutrino , and H and He are isotopes of hydrogen and helium , respectively . The energy released by this reaction is in millions of electron volts , which is actually only a tiny amount of energy . However enormous numbers of these reactions occur constantly , producing all the energy necessary to sustain the star 's radiation output . In comparison , the combustion of two hydrogen gas molecules with one oxygen gas molecule releases only 5 @.@ 7 <unk> . 
- In more massive stars , helium is produced in a cycle of reactions <unk> by carbon called the carbon @-@ nitrogen @-@ oxygen cycle . 
- In evolved stars with cores at 100 million kelvin and masses between 0 @.@ 5 and 10 M ☉ , helium can be transformed into carbon in the triple @-@ alpha process that uses the intermediate element beryllium : 
- 4He + 4He + 92 keV → 8 * Be 
- 4He + 8 * Be + 67 keV → 12 * C 
- 12 * C → <unk> + γ + 7 @.@ 4 MeV 
- For an overall reaction of : 
- <unk> → <unk> + γ + 7 @.@ 2 MeV 
- In massive stars , heavier elements can also be burned in a contracting core through the neon burning process and oxygen burning process . The final stage in the stellar nucleosynthesis process is the silicon burning process that results in the production of the stable isotope iron @-@ 56 , an <unk> process that consumes energy , and so further energy can only be produced through gravitational collapse . 
- The example below shows the amount of time required for a star of 20 M ☉ to consume all of its nuclear fuel . As an O @-@ class main sequence star , it would be 8 times the solar radius and 62 @,@ 000 times the Sun 's luminosity . 
- 
- 
- = Perry the <unk> = 
- 
- Perry the <unk> , also known as Agent P or simply Perry , is an anthropomorphic platypus from the animated series Phineas and Ferb . Perry was created by the series ' co @-@ founders , Dan Povenmire and Jeff " <unk> " Marsh . He first appeared along with the majority of the main cast in the pilot episode " <unk> . " Perry is featured as the star of the B @-@ plot for every episode of the series , alongside his nemesis Dr. Heinz Doofenshmirtz . A mostly silent character , his lone vocal characteristic ( a <unk> of Perry 's beak ) was provided by Dee Bradley Baker . 
- Perry is the pet platypus of the Flynn @-@ Fletcher family , and is perceived as mindless and domesticated . In secret , however , he lives a double life as a member of an all @-@ animal espionage organization referred to as O.W.C.A. ( The Organization Without a Cool <unk> ) . Many secret entrances to his underground lair exist all around the house ; such as the side of the house , most notably the tree that his owners sit under in the backyard , and several other everyday objects that seem to <unk> the family 's attention . He engages in daily battles with Dr. Heinz Doofenshmirtz , an evil scientist who desires to take over the Tri @-@ state area with obscure contraptions that work perfectly according to his intended function but fail in his application of them every time . 
- Perry was made a platypus because of the animal 's striking appearance and the lack of public knowledge of the animal , which allowed the writers to make things up about the species . Critical reception for the character from both professionals and fans have been considerably positive . Merchandising of the character include plush toys , t @-@ shirts , wooden toys , glasses , and coloring books , along with appearances in literature and a 2009 video game for the Nintendo DS . 
- 
- = = Role in Phineas and Ferb = = 
- 
- Perry is the docile pet platypus of the blended Flynn @-@ Fletcher family , who adopted him because his <unk> gaze made it seem as if he were looking at both Phineas and Ferb at the same time , as shown in the 2011 movie , Phineas and Ferb the Movie : Across the 2nd Dimension . Unbeknownst to them , Perry lives a double life as a crime @-@ fighting spy working for the " Organization Without a Cool <unk> " / The <unk> , going by the <unk> " Agent P. " He reports to his superior , Major Monogram , via telecast in his large , high @-@ tech , underground hideout . Every day , he engages in battles with the evil scientist Dr. Heinz Doofenshmirtz , who tries using inventions to take over the tri @-@ state area . Perry is always able to foil Doofenshmirtz 's plans and in doing so accidentally leads to the destruction of whatever form of <unk> his owners , Phineas Flynn and Ferb Fletcher , are building in order to make summer better . Phineas and Ferb are aware that something happens to get rid of their scheme for the day , but do not know that Perry is the cause behind it and are largely dismissive of it . Their sister , Candace , also does not know that Perry is behind the destruction and is driven to near <unk> trying to figure it out . Throughout the series , Perry is aware of Phineas and Ferb 's inventions , but is largely <unk> in them , save whenever he notices that their latest invention may help him thwart Doofenshmirtz . 
- Perry and Doofenshmirtz at first seem to <unk> each other in the beginning of the series , and have been arch @-@ <unk> since the day they met . However , they are often <unk> and friendly towards one another and it is said by Doofenshmirtz that Perry is his best friend , and Perry will often act to save Doofenshmirtz 's life when his plot inevitably blows up in his face . <unk> , their daily <unk> involve Doofenshmirtz <unk> a scheme , which Perry goes to stop after being briefed by Major Monogram . He is trapped by Doofenshmirtz while trying to do so and is told of the doctor 's scheme , usually pertaining to some backstory or pet <unk> . He then escapes and the two fight , Perry coming out victorious . The two rely on this daily structure , Doofenshmirtz even specifically mentioning it in " Journey to the Center of Candace " and in episodes such as " It 's About Time ! " in which Doofenshmirtz temporarily replaces Perry with secret agent Peter The <unk> and they become depressed about not having each other to fight . Perry realizes he misses <unk> too . When Perry does not arrive on the scene of Doofenshmirtz 's evil plan , the doctor hesitates to execute his plans and fears for where Perry has gone , though he notes that he " hopes something terrible has happened to him . " . Sometimes , they decided not to fight and have fun , as shown in " Happy New Year ! " and " Candace <unk> " . On other occasions , depending on whatever situation Doofenshmirtz is facing , Perry would often help Doofenshmirtz with his non @-@ evil plans , such as helping him overcome evil scientist 's block , helping him put on a birthday party for his 16 @-@ year @-@ old daughter Vanessa , impressing a square dancing girl with programmable square dancing boots that <unk> created , working together to stop a <unk> platypus hunter from hunting them down , or helping him convince his rich ex @-@ wife to help pay off his <unk> debt . Also , Perry tends to use Doofenshmirtz 's inventions to erase evidence of whatever contraptions Phineas and Ferb had made , leaving Candace unable to bust them . Examples include when Perry asked to borrow Doofenshmirtz 's robot <unk> to pick up footage from the city surveillance cameras in order to preserve his job as an agent , as well as using Doofenshmirtz 's Pick @-@ Him @-@ Up @-@ <unk> to rescue a lost Candace and bring her home . 
- In Phineas and Ferb the Movie : Across the 2nd Dimension , an alternate reality ( yet more evil and ruthless ) version of Perry ( known as <unk> ) appears as the secondary antagonist , with the regular Perry serving as a supporting protagonist . <unk> would later return in the episode sequel , Tales from the Resistance : Back to the 2nd Dimension , this time , as a supporting protagonist . 
- 
- = = Character = = 
- 
- 
- = = = Creation and conception = = = 
- 
- While working on the animated television series <unk> 's Modern Life , Phineas and Ferb co @-@ founders Dan Povenmire and Jeff " <unk> " Marsh utilized several recurring elements in the episodes that they wrote . Among these were actions sequences and chase scenes . Povenmire and Marsh wanted to reuse these elements in their series and chose Perry to execute it . The pair gave him a consistent and continuous nemesis in the form of Doofenshmirtz as a means of allowing viewers to get to know him . 
- While choosing a species for Perry , Povenmire and Marsh wanted to keep in mind selecting one that was uncommon , an animal that kids could not " pick out at a pet store and beg [ their parents ] for . " They chose a platypus because of the animal 's obscure and striking appearance . The animal was scarcely used in American animated programs , so the pair opined that Perry " would not have to compete with <unk> notions that viewers might bring to a more conventional <unk> . " The blank storyboard they were given allowed them to " make stuff up " since " no one knows very much about [ them ] . " 
- Perry has a theme song tentatively entitled " Perry , " performed by Randy <unk> and Laura Dickinson , and written by Povenmire and Marsh , who write the majority of songs in the series . The song , along with the number " <unk> <unk> Goo " from the episode " <unk> <unk> , " was the first musical composition Povenmire and Marsh pitched to The Walt Disney Company . They were nervous doing so , because , as Povenmire explained , " Disney has a big history of music -- what if they hate it ? " Their reaction , however , was considerably positive and the pair was asked to write a song for each episode , which they vehemently agreed to . The opening lyrics for the song describe Perry as a standard textbook definition of a platypus : " He 's a semi @-@ aquatic egg @-@ laying mammal of action . " 
- 
- = = = Design = = = 
- 
- Like the other characters of the series , Perry was structured in a simple style to allow young viewers to easily draw him . In keeping with the show 's general design scheme , Perry is constructed of <unk> shapes in a style reminiscent of deceased <unk> <unk> animator <unk> Avery . Povenmire uses different design styles for drawing Perry depending on how he is portrayed . When Perry is portrayed as a domesticated and mindless pet , Povenmire begins with a square shaped like a loaf of bread . He then draws his front legs and feet before drawing his bill , which is set at a certain angle . Povenmire then draws his <unk> , which are never focused and look in opposing directions in a <unk> . He concludes the figure by adding his hind legs , tail , hair , and finally color . 
- When portrayed as a secret agent , Povenmire starts with a similar bread loaf square design , but draws it standing up vertically and places a <unk> on the top of his head , which is combined with the square torso . In contrast to his mindless and wild @-@ eyed expression as a pet , the secret agent version of Perry is detailed with eyes " full of <unk> blue resolve . " Povenmire proceeds by drawing his arms , which bear hands that are open and prepared for fighting or any danger . His legs are bent , as well prepared for an act of danger or action needed . Povenmire finishes the design by adding his beaver tail and color . 
- 
- = = = Personality = = = 
- 
- Perry 's undercover identity as a pet leads to characters throughout the series to deem him as a " mindless domestic pet " that " doesn 't do much " . This definition of the character led to Phineas and Ferb <unk> production of a new toyline based on Perry called " Perry the <unk> Figure , " whose tagline revolved around it not doing anything but allowing the customer to make it whatever they desire it to be . In all actuality , Perry is a skilled fighter who is able to perform several <unk> judo fighting moves and escapes . He has access to several different types of technology and inventions provided to him by The O.W.C.A. , including a <unk> craft dubbed the " <unk> , " a jet <unk> , and a whistle set that allows him to summon different types of animals . 
- Although his appearance as a domestic pet is mostly a cover , Perry has nonetheless expressed care and devotion for the Flynn @-@ Fletcher family . In the episode " The Ballad of Bad <unk> " , Perry managed to drag Candace out of the Dark Cave before it <unk> in because of Dr. <unk> 's " self destruction button " that which Candace had accidentally pressed while under the orange moss <unk> , thinking it was a <unk> machine . He did this even though he knew that Candace saw that Perry was a secret agent talking to Agent E ( Eagle ) and to Major Monogram in a cracked egg . Candace thought she was just <unk> because of the orange moss she accidentally touched earlier in the episode . Also , when an invention of Doofenshmirtz 's in the episode " Journey to the Center of Candace " might cause severe harm to Phineas and Ferb , Perry thoroughly beats up Doofenshmirtz and quickly handcuffs him . He then ties him to a pipe and , instead of just leaving him like he usually does in the series , calls for special forces from the O.W.C.A. to come and arrest the doctor , though they never show up . In " Oh , There You Are , Perry , " Doofenshmirtz is downgraded to a low threat level and Perry is reassigned to a new nemesis . This causes him to have to leave his home with the Flynn @-@ <unk> , which makes him sad and he does not enjoy his new villain . Phineas and Ferb are fearful about where Perry could have gone to and put up several <unk> around town for people to find him . They decide to throw a concert on the roof of a building , singing a song about how much they love Perry and want him to come home . <unk> , Perry is reassigned to Doofenshmirtz again and returns to the brothers once more . 
- Though Perry is anthropomorphic , he does not speak , only communicating through an " aggravated <unk> " type of noise made by flapping his bill . This noise is provided by actor Dee Bradley Baker ; to this day , Povenmire and Marsh do not understand how Baker produces the noise . Thomas <unk> , who plays Ferb , is notoriously good at making the noise and is considered second only to Baker himself . When in his " Agent P " persona , Perry is almost completely silent . 
- 
- = = Reception = = 
- 
- As a character , Perry has been well received by both fans and critics . As Cynthia <unk> of Variety magazine wrote , " Perry the platypus is a breakout star from the Disney Channel <unk> ' Phineas and Ferb . ' " The New York Times writer Susan Stewart called him " intrepid . " Jean <unk> , a press member for Disney Channel , said that Perry is " <unk> " and " makes James Bond look like a rank amateur . " Aaron H. <unk> of <unk> <unk> opined that he is " <unk> confident . " <unk> H. of Scholastic , Inc. declared Perry " pretty <unk> awesome . " Josh Jackson , editor of Paste , described Perry and Doofenshmirtz 's relationship as " pitch @-@ perfect . " 
- Certain reviewers have also commented negatively about Perry and his subplot , which <unk> Robinson of the St. Petersburg Times considers " pretty <unk> . " Kevin McDonough of Sun Coast Today described it as " complicated " and " loosely connected " to the rest of the series , writing that he is " not sure what this <unk> except to add the noise of explosions to the already constant <unk> of singing and screaming . " Ed Liu of <unk> Zone feels it is " truly <unk> what Perry the secret agent is doing in this show in the first place . " Liu considers his subplot to be a type of " throw everything against the wall and see what sticks " element and writes that " many of the earlier episodes of the show don 't manage to do a very good job of balancing the subplot and the main one . " 
- Perry was nominated for a <unk> Kids ' Choice Award in 2014 under the category of Favorite <unk> Animal <unk> . 
- 
- = = In other media = = 
- 
- In addition to the main television series , Perry has appeared in several pieces of Phineas and Ferb merchandise . To date , he has appeared in all Phineas and Ferb <unk> , published by Disney Press . The character has been adapted into a 20 inch plush toy , released by The Walt Disney Company . The plush has a button on its hand that allows it to emit Perry 's signature chattering noise . Certain t @-@ shirts based on the series released by both Disney and the online retail website <unk> also display Perry and the phrase " Hey , where 's Perry ? " which most characters like Phineas , Isabella , Stacy , Irving , Ferb , and Lawrence utter when Perry goes to Major Monogram in his secret lair in almost every episode . There also is a T @-@ <unk> with Perry 's face . Perry appears in the Nintendo DS video game based on the series , simply titled Phineas and Ferb , where a mini @-@ game involves Perry stopping Doofenshmirtz 's latest evil scheme . As of 1 April 2014 , Perry is also a playable character in the video game Disney Infinity . 
- 
- 
- = Amylostereum = 
- 
- Amylostereum is the single genus in the fungal family Amylostereaceae . The genus currently comprises four saprotrophic and parasitic species , which live off living or dead wood . The Amylostereaceae cause white rot in the wood by disintegrating the tissue component lignin . They produce crust @-@ like , partially wavy fruit bodies on the surface of infested trees , which are similar to those produced by Stereum species . 
- There are four described species in the Amylostereaceae : A. chailletii ( the type ) , A. areolatum , A. ferreum and A. laevigatum . The species were initially considered part of Stereum until mycologist Jacques Boidin found atypical microscopic differences between them . Forty years after his extensive <unk> from 1958 , Boidin reclassified Amylostereum into its own family . 
- Three Amylostereum species are symbionts of wood wasps in the genera Sirex , Urocerus , and <unk> , which infest conifers . The female wood wasps deposit their eggs together with fungal spores and <unk> in trees , and the fungus is eaten by the wasp 's larva as food . The fungus <unk> <unk> through the formation of <unk> spores in newly emerged females that are stored in special structures adapted for the transport of symbiotic fungi . The A. areolatum – Sirex <unk> ( S. noctilio ) symbiont complex has been studied extensively because of its potential to cause substantial economic losses in the forestry industry , particularly in non @-@ native regions . 
- 
- = = Taxonomy and history of research = = 
- 
- Amylostereaceae species were for a long time classified in the genus Stereum , based primary on the layered structure of the fruit body and the similar physiological activity . Mycologist Jacques Boidin separated <unk> from Stereum in 1958 , <unk> this decision by explaining that microscopic differences such as amyloid spores and encrusted cystidia were sufficiently distinct to warrant recognition as a new genus . Although the type species of the genus ( today called A. chailletii ) was initially named <unk> <unk> , the name <unk> had already been used for a genus in the flowering plant family <unk> . Boidin thus chose the genus name Amylostereum , referring to the amyloid spores . 
- Based on DNA analysis , Boidin in 1998 moved Amylostereum into a new , monotypic family , the Amylostereaceae , which he attributed to the <unk> order . Later studies , however , supported the initial classification in the <unk> . 
- 
- = = = Classification = = = 
- 
- The classification of the Amylostereaceae is not completely resolved . The next closest relatives might be – depending on the research – either Echinodontium <unk> and most other species of the genus Echinodontium , or <unk> <unk> . Most of the previous DNA analysis results suggest a narrow relation to Echinodontium , but several results of studies partially contradict this conclusion . Only the classification to the <unk> is regarded as correct . Some authors have suggested that Amylostereum should be placed in the family <unk> . 
- The similarities between A. chailletii and A. areolatum have caused some confusion regarding their placement in the genus . As only the size of their fruit bodies differ from each other in appearance , researcher German <unk> <unk> assumed that both are the same species in different age stages . Experiments with pure cultures of the fungi , however , showed that the mycelia of A. chailletii , A. laevigatum and A. ferreum were partially compatible to each other , but the mycelium of A. areolatum was incompatible to other species . Boidin believes that the common ancestor of all Amylostereum fungi used yellowwoods as a host . This genus of conifers was native in Europe until the <unk> and <unk> <unk> ( 66 million to 2 @.@ 6 million years ago ) , but became extinct there , so the Amylostereum fungi specialized on other conifers and differentiated into several species . Only A. ferreum specialized on yellowwoods in South America . 
- <unk> tests as well as molecular analysis indicated that A. areolatum separated very early from other Amylostereum fungi . The other three species separated later from each other and are thus partially compatible to each other . A. ferreum and A. laevigatum produced in 59 % of all cases a common mycelium , A. ferreum and A. chailletii only in 44 % . There is an undescribed species in Amylostereum ; according to DNA analysis , it stands between A. laevigatum and A. ferreum . This is remarkable , as these fungi originated from <unk> ( storing organs of <unk> ) of a North American wood wasp , while A. laevigatum has never been seen as symbiont of wood wasps , neither in North American nor in Europe . The fungus possibly represents a separated species or a <unk> of A. laevigatum . As A. areolatum and A. chailletii mainly reproduce <unk> through the symbiosis of wood wasps , the genetic <unk> within these species is relatively low . 
- 
- = = Description = = 
- 
- 
- = = = <unk> = = = 
- 
- The Amylostereaceae produce crust @-@ like , dry and <unk> @-@ <unk> fruit bodies on the bark of infested trees . The fruit bodies are 0 @.@ 5 – 1 @.@ 5 mm ( 0 @.@ 02 – 0 @.@ 06 in ) thick , irregularly shaped and are able to cover a large surface on the bark or otherwise can appear as small spots . They lie directly on the bark . The <unk> , grey or brownish fruit body ( hymenium ) has a smooth to <unk> surface texture and is turned outwards . It is bordered by a highly bent and wavy ( <unk> @-@ <unk> ) edge on all species except A. laevigatum , which has a <unk> surface ( a tomentum ) and is usually dirty @-@ brown coloured . In some species , the tomentum stands clearly above and forms a kind of roof above the fruit body ; if it completely surrounds this roof , there might appear <unk> shapes . 
- 
- = = = Microscopic = = = 
- 
- The Amylostereaceae possess a <unk> <unk> , meaning that there are in its mycelia two kinds of hyphae . The first type is brownish skeletal hyphae , which provide stability to the fruit body . These hyphae run parallel to the bark and often have hairpin @-@ like turns , so that the loops form thick @-@ walled , <unk> @-@ like structures , the so @-@ called <unk> . The second type is <unk> hyphae . They are translucent ( hyaline ) and serve to promote the growth of the fungus . Genuine cystidia arise in the hymenium and the layer directly below , the <unk> . Both <unk> and cystidia are encrusted , meaning that they feature crystal @-@ like structures on the top . 
- With the exception of A. laevigatum , all species have a thin separating layer , the cortex , between the hymenium and the tomentum . A cortex is also present on many Stereum fungi ( on a broader front ) and serves to bend up the fruit body . As this cortex is missing on A. laevigatum , its fruit body lies flat on the bark . 
- The basidia are 15 – 25 × 3 @.@ 5 – 5 @.@ 5 µm and have a slim , club @-@ like shape . Each <unk> features four sterigmata , each of which bear one spore . The spores ' shape is <unk> <unk> or cylindrical . Their surface is smooth and their walls are thin . Although they are <unk> and hyaline , the spores are amyloid , meaning they will turn bluish or purple when stained with Melzer 's reagent . This characteristic differs from other very similar species , and this gave the genus its name . 
- 
- = = Distribution = = 
- 
- The range of the Amylostereaceae originally comprised only <unk> regions , including North America and Eurasia , and the <unk> , with Central and South America . The introduction of A. areolatum and its symbionts , the Sirex <unk> , saw the spread of the genus to all continents except Antarctica . 
- Amylostereum <unk> is common in the temperate regions of North America and Eurasia . A. laevigatum can be also found in temperate Eurasia , but it is unclear how broadly this species is distributed in North America . A. areolatum is originally native in North Africa and Eurasia ; it was however distributed through the 20th century in Australia , New Zealand , Southern Africa as well as in South and North America . Only A. ferreum is originally native in the <unk> and is common in Brazil and the Caribbean . 
- 
- = = Ecology = = 
- 
- Amylostereaceae usually infest only dead or cut down conifer wood . Three species – A. areolatum , A. laevigatum and A. chailletii – may also establish a symbiosis with wood wasps ( <unk> ) , which beside <unk> logged trees also infest living trees and infect them with fungi . <unk> have been recorded with several species : Sirex noctilio , S. <unk> , S. <unk> , S. <unk> , S. <unk> , S. <unk> , and , in Japan , Urocerus <unk> and <unk> <unk> . <unk> of the genera Sirex and Urocerus store <unk> ( the <unk> of fungi split up to spores ) in special abdominal organs . The wood wasps infect trees by <unk> a <unk> secretion below the bark and at the same time <unk> fungal spores into the hole . The secretion weakens the tree and temporarily <unk> its immune system , whereby the fungus can spread along the <unk> . The infection with Amylostereaceae fulfill two functions for the wasps : it provides the larvae food , because the white rot <unk> the wood ; at the same time , the mycelia of the fungi serves as food for the larvae . After the larvae <unk> , it absorbs the mycelia of the Amylostereaceae into its body to <unk> together with its eggs . The fungus benefits from the symbiosis as it spreads faster and more effectively than through airborne spores and furthermore does not need to develop fruit bodies . A. ferreum is the only <unk> species that has not been associated with any <unk> . 
- 
- = = = Host spectrum = = = 
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- The host spectrum of the Amylostereaceae comprises several , partially very different genera of <unk> . A. chailletii usually <unk> <unk> such as firs ( <unk> ) and spruces ( <unk> ) , but also cedars ( <unk> ) and Douglas firs ( <unk> ) . A. areolatum has a similar host spectrum , which uses mainly firs , Japanese cedars ( <unk> ) , <unk> ( <unk> ) , spruces , pines ( Pinus ) and Douglas firs as hosts . While spruces dominate as hosts in the native habitat , this species is more common in pines on other locations . The host spectrum of A. laevigatum comprises <unk> such as <unk> ( <unk> ) or <unk> ( <unk> ) and the English yew ( <unk> <unk> ) . A. ferreum is , however , only common on <unk> yellowwoods ( <unk> ) . 
- 
- = = = Symptoms of infestation = = = 
- 
- The Amylostereaceae are white rot pathogens . They <unk> the lignin of the host wood , whereby the infested wood parts become less stable and take a fibrous structure . The wood <unk> as fungal enzymes break down and remove the brown @-@ <unk> lignin . The distribution in wood takes place mainly along the transport channels in the <unk> . If the wood is <unk> , the red rot is vertically positioned , on which <unk> , infested areas contrast with intact wood . Symptoms of <unk> by the symbiotic partner — wood wasps — include circular exit holes in the crust and acute stress through <unk> , common in hanging , falling or tanning needles . 
- 
- = = Ecological and economical importance = = 
- 
- In their native habitat , all Amylostereum species have a minor importance as forest pests . The infestation through wood wasps does not assume greater dimensions and is , compared with other pests , almost insignificant . The infection rates are even lower during sexual reproduction via fruit bodies , as the wasps do not play a part in the process . Furthermore , the Amylostereum fungi are alone often incapable of <unk> healthy trees . They thus mostly act as <unk> . Pine <unk> in Australia , New Zealand , Africa and South America were shown to be susceptible to the Sirex <unk> ( Sirex noctilio ) , which was introduced there and which is associated with A. areolatum . The wasp 's <unk> secretion , its larvae and the fungus combine very effectively with each other and contribute to forest decline rates of up to 80 % . This is mainly owing to the poor water and nutrient supply of the trees , which can poorly reconcile the drought stress caused by infestation . S. noctilio was detected in North America in the 2000s ( decade ) ; in Canada alone , the total economic loss to the forestry industry caused by the Sirex – Amylostereum symbiosis could be as high as $ 254 million per year for the next 20 years . 
- As a <unk> , cultures of the nematode <unk> <unk> have been used as biological control to protect trees since the 1980s . This parasite feeds on the mycelia of A. areolatum and is therefore a food competitor of wood wasp larvae . Where S. noctilio larvae are present , the parasite <unk> and <unk> the eggs of female wasps , causing them to be infertile . These infertile females lay infected eggs into new trees and thus spread the nematode . This control method has proven to be relatively successful to combat the Sirex – Amylostereum complex . In the Southern Hemisphere , where the technique has been widely employed , reductions of <unk> levels of 70 % – 100 % have been achieved . 
- 
- 
- = Charles @-@ Valentin Alkan = 
- 
- Charles @-@ Valentin Alkan ( French : [ <unk> <unk> ̃ <unk> ̃ <unk> ̃ ] ; 30 November 1813 – 29 March 1888 ) was a French @-@ Jewish composer and virtuoso pianist . At the height of his fame in the 1830s and 1840s he was , alongside his friends and colleagues Frédéric Chopin and Franz Liszt , among the leading pianists in Paris , a city in which he spent virtually his entire life . 
- Alkan earned many awards at the Conservatoire de Paris , which he entered before he was six . His career in the salons and concert halls of Paris was marked by his occasional long <unk> from public performance , for personal reasons . Although he had a wide circle of friends and acquaintances in the Parisian artistic world , including Eugène <unk> and George Sand , from 1848 he began to adopt a reclusive life style , while continuing with his compositions – virtually all of which are for the keyboard . During this period he published , among other works , his collections of large @-@ scale studies in all the major keys ( Op. 35 ) and all the minor keys ( Op. 39 ) . The latter includes his Symphony for Solo Piano ( Op. 39 , nos . 4 – 7 ) and Concerto for Solo Piano ( Op. 39 , nos . 8 – 10 ) , which are often considered among his masterpieces and are of great musical and technical complexity . Alkan emerged from self @-@ imposed retirement in the 1870s to give a series of recitals that were attended by a new generation of French musicians . 
- Alkan 's attachment to his Jewish origins is displayed both in his life and his work . He was the first composer to incorporate Jewish melodies in art music . <unk> in Hebrew and Greek , he devoted much time to a complete new translation of the Bible into French . This work , like many of his musical compositions , is now lost . Alkan never married , but his presumed son <unk> @-@ Miriam Delaborde was , like Alkan , a virtuoso performer on both the piano and the pedal piano , and edited a number of the elder composer 's works . 
- Following his death ( which according to persistent but unfounded legend was caused by a falling bookcase ) Alkan 's music became neglected , supported by only a few musicians including <unk> Busoni , Egon Petri and <unk> Sorabji . From the late 1960s onwards , led by Raymond Lewenthal and Ronald Smith , many pianists have recorded his music and brought it back into the repertoire . 
- 
- = = Life = = 
- 
- 
- = = = Family = = = 
- 
- Alkan was born Charles @-@ Valentin Morhange on 30 November 1813 at 1 , Rue de <unk> in Paris to Alkan Morhange ( <unk> – 1855 ) and Julie Morhange , née Abraham . Alkan Morhange was descended from a long @-@ established Jewish <unk> community in the region of Metz ; the village of Morhange is located about 30 miles ( 48 km ) from the city of Metz . Charles @-@ Valentin was the second of six children – one elder sister and four younger brothers ; his birth certificate indicates that he was named after a neighbour who witnessed the birth . 
- Alkan Morhange supported the family as a musician and later as the proprietor of a private music school in le <unk> , the Jewish quarter of Paris . At an early age , Charles @-@ Valentin and his siblings adopted their father 's first name as their last ( and were known by this during their studies at the Conservatoire de Paris and subsequent careers ) . His brother Napoléon ( 1826 – 1906 ) became professor of solfège at the Conservatoire , his brother Maxim ( 1818 – 1897 ) had a career writing light music for Parisian theatres , and his sister , <unk> ( 1812 – 1897 ) , was also a pianist . His brother Ernest ( 1816 – 1876 ) was a professional <unk> , while the youngest brother <unk> ( 1827 – 1882 ) was to publish various dances for the piano . 
- 
- = = = <unk> ( 1819 – 1831 ) = = = 
- 
- Alkan was a child <unk> . He entered the Conservatoire de Paris at an unusually early age , and studied both piano and organ . The records of his auditions survive in the Archives <unk> in Paris . At his solfège audition on 3 July 1819 , when he was just over 5 years 7 months , the examiners noted Alkan ( who is referred to even at this early date as " Alkan ( Valentin ) " , and whose age is given incorrectly as six @-@ and @-@ a @-@ half ) as " having a pretty little voice " . The profession of Alkan Morhange is given as " music @-@ paper ruler " . At Charles @-@ Valentin 's piano audition on 6 October 1820 , when he was nearly seven ( and where he is named as " Alkan ( Morhange ) Valentin " ) , the examiners comment " This child has amazing abilities . " 
- Alkan became a favourite of his teacher at the Conservatoire , Joseph Zimmermann , who also taught Georges <unk> , César Franck , Charles Gounod , and Ambroise Thomas . At the age of seven , Alkan won a first prize for solfège and in later years prizes in piano ( 1824 ) , harmony ( 1827 ) , and organ ( 1834 ) . At the age of seven @-@ and @-@ a @-@ half he gave his first public performance , appearing as a violinist and playing an air and variations by Pierre <unk> . Alkan 's Opus 1 , a set of variations for piano based on a theme by Daniel <unk> , dates from 1828 , when he was 14 years old . At about this time he also undertook teaching duties at his father 's school . Antoine Marmontel , one of Charles @-@ Valentin 's pupils there , who was later to become his <unk> <unk> , wrote of the school : 
- Young children , mostly Jewish , were given elementary musical instruction and also learnt the first <unk> of French grammar ... [ There ] I received a few lessons from the young Alkan , four years my senior ... I see once more ... that really parochial environment where the talent of Valentin Alkan was formed and where his hard @-@ working youth <unk> ... It was like a preparatory school , a juvenile <unk> of the Conservatoire . 
- From about 1826 Alkan began to appear as a piano soloist in leading Parisian salons , including those of the <unk> de la <unk> ( widow of Marshal <unk> ) , and the <unk> de <unk> . He was probably introduced to these venues by his teacher Zimmermann . At the same time , Alkan Morhange arranged concerts featuring Charles @-@ Valentin at public venues in Paris , in association with leading musicians including the <unk> <unk> <unk> and Henriette <unk> , the cellist Auguste Franchomme and the violinist Lambert <unk> , with whom Alkan gave concerts in a rare visit out of France to Brussels in 1827 . In 1829 , at the age of 15 , Alkan was appointed joint professor of solfège – among his pupils in this class a few years later was his brother Napoléon . In this manner Alkan 's musical career was launched well before the July Revolution of 1830 , which initiated a period in which " keyboard virtuosity ... completely dominated professional music making " in the capital , attracting from all over Europe pianists who , as Heinrich Heine wrote , invaded " like a plague of locusts swarming to pick Paris clean " . Alkan nonetheless continued his studies and in 1831 enrolled in the organ classes of François <unk> , from whom he may have learnt to appreciate the music of Johann Sebastian Bach , of whom <unk> was then one of the few French advocates . 
- 
- = = = Early fame ( 1831 – 1837 ) = = = 
- 
- Throughout the early years of the July Monarchy , Alkan continued to teach and play at public concerts and in eminent social circles . He became a friend of many who were active in the world of the arts in Paris , including Franz Liszt ( who had been based there since 1827 ) , George Sand , and Victor Hugo . It is not clear exactly when he first met Frédéric Chopin , who arrived in Paris in September 1831 . In 1832 Alkan took the solo role in his first Concerto da camera for piano and strings at the Conservatoire . In the same year , aged 19 , he was elected to the influential <unk> <unk> des <unk> d <unk> ( Society of the Children of Apollo ) , whose members included Luigi <unk> , Fromental Halévy , the conductor François <unk> , and Liszt , who had been elected in 1824 at the age of twelve . Between 1833 and 1836 Alkan participated at many of the Society 's concerts . Alkan twice competed unsuccessfully for the Prix de Rome , in 1832 and again in 1834 ; the <unk> which he wrote for the competition , Hermann et <unk> and L <unk> en <unk> , have remained unpublished and <unk> . 
- In 1834 Alkan began his friendship with the Spanish musician Santiago Masarnau , which was to result in an extended and often intimate correspondence which only came to light in 2009 . Like virtually all of Alkan 's correspondence , this exchange is now one @-@ sided ; all of his papers ( including his manuscripts and his extensive library ) were either destroyed by Alkan himself , as is clear from his will , or became lost after his death . Later in 1834 Alkan made a visit to England , where he gave recitals and where the second Concerto da camera was performed in Bath by its <unk> Henry <unk> Field ; it was published in London together with some solo piano pieces . A letter to Masarnau and a notice in a French journal that Alkan played in London with Moscheles and Cramer , indicate that he returned to England in 1835 . Later that year , Alkan , having found a place of retreat at <unk> outside Paris , completed his first truly original works for solo piano , the Twelve Caprices , published in 1837 as <unk> . 12 , 13 , 15 and 16 . Op. 16 , the Trois <unk> de <unk> , is dedicated to Masarnau . In January 1836 , Liszt recommended Alkan for the post of Professor at the Geneva Conservatoire , which Alkan declined , and in 1837 he wrote an enthusiastic review of Alkan 's Op. 15 Caprices in the Revue et <unk> <unk> . 
- 
- = = = At the Square d <unk> ( 1837 – 1848 ) = = = 
- 
- From 1837 , Alkan lived in the Square d <unk> in Paris , which was inhabited by numerous celebrities of the time including Marie <unk> , Alexandre Dumas , George Sand , and Chopin . Chopin and Alkan were personal friends and often discussed musical topics , including a work on musical theory that Chopin proposed to write . By 1838 , at 25 years old , Alkan had reached a peak of his career . He frequently gave recitals , his more mature works had begun to be published , and he often appeared in concerts with Liszt and Chopin . On 23 April 1837 Alkan took part in Liszt 's farewell concert in Paris , together with the 14 @-@ year @-@ old César Franck and the virtuoso Johann Peter <unk> . On 3 March 1838 , at a concert at the piano @-@ maker <unk> , Alkan played with Chopin , Zimmerman , and Chopin 's pupil Adolphe <unk> in a performance of Alkan 's transcription , now lost , of two movements of Beethoven 's Seventh Symphony for two pianos , eight hands . 
- At this point , for a period which coincided with the birth and childhood of his natural son , <unk> @-@ Miriam Delaborde ( 1839 – 1913 ) , Alkan withdrew into private study and composition for six years , returning to the concert platform only in 1844 . Alkan neither asserted or denied his paternity of Delaborde , which , however , his contemporaries seemed to assume . Marmontel wrote <unk> in a biography of Delaborde that " [ his ] birth is a page from a novel in the life of a great artist " . Alkan gave early piano lessons to Delaborde , who was to follow his natural father as a keyboard virtuoso . 
- Alkan 's return to the concert platform in 1844 was greeted with enthusiasm by critics , who noted the " admirable perfection " of his technique , and lauded him as " a model of science and inspiration " , a " sensation " and an " explosion " . They also commented on the attending celebrities including Liszt , Chopin , Sand and Dumas . In the same year he published his piano étude Le chemin de <unk> , which critics , following Ronald Smith , believe to be the first representation in music of a steam engine . Between 1844 and 1848 Alkan produced a series of virtuoso pieces , the 25 Préludes Op. 31 for piano or organ , and the sonata Op. 33 Les <unk> <unk> . Following an Alkan <unk> in 1848 , the composer Giacomo Meyerbeer was so impressed that he invited the pianist , whom he considered " a most remarkable artist " , to prepare the piano arrangement of the overture to his forthcoming opera , Le prophète . Meyerbeer heard and approved Alkan 's arrangement of the overture for four hands ( which Alkan played with his brother Napoléon ) in 1849 ; published in 1850 , it is the only record of the overture , which was scrapped during rehearsals at the Opéra . 
- 
- = = = <unk> ( 1848 – 1872 ) = = = 
- 
- In 1848 Alkan was <unk> disappointed when the head of the Conservatoire , Daniel Auber , replaced the retiring Zimmermann with the mediocre Marmontel as head of the Conservatoire piano department , a position which Alkan had eagerly anticipated , and for which he had strongly lobbied with the support of Sand , Dumas , and many other leading figures . A disgusted Alkan described the appointment in a letter to Sand as " the most incredible , the most <unk> nomination " ; and <unk> noted in his journal " By his confrontation with Auber , [ Alkan ] has been very put out and will <unk> continue to be so . " The upset arising from this incident may account for Alkan 's reluctance to perform in public in the ensuing period . His withdrawal was also influenced by the death of Chopin ; in 1850 he wrote to Masarnau " I have lost the strength to be of any economic or political use " , and lamented " the death of poor Chopin , another blow which I felt deeply . " Chopin , on his deathbed in 1849 , had indicated his respect for Alkan by <unk> him his unfinished work on a piano method , intending him to complete it , and after Chopin 's death a number of his students transferred to Alkan . After giving two concerts in 1853 , Alkan withdrew , in spite of his fame and technical accomplishment , into virtual seclusion for some twenty years . 
- Little is known of this period of Alkan 's life , other than that apart from composing he was immersed in the study of the Bible and the Talmud . Throughout this period Alkan continued his correspondence with Ferdinand Hiller , whom he had probably met in Paris in the 1830s , and with Masarnau , from which some insights can be gained . It appears that Alkan completed a full translation into French , now lost , of both the Old Testament and the New Testament , from their original languages . In 1865 , he wrote to Hiller : " Having translated a good deal of the <unk> , I 'm now onto the second Gospel which I am translating from the Syriac ... In starting to translate the New Testament , I was suddenly struck by a singular idea – that you have to be Jewish to be able to do it . " 
- Despite his seclusion from society , this period saw the composition and publication of many of Alkan 's major piano works , including the <unk> études dans tous les tons mineurs , Op. 39 ( 1857 ) , the Sonatine , Op. 61 ( 1861 ) , the 49 Esquisses , Op. 63 ( 1861 ) , and the five collections of Chants ( 1857 – 1872 ) , as well as the Sonate de concert for cello and piano , Op. 47 ( 1856 ) . These did not pass <unk> ; Hans von Bülow , for example , gave a laudatory review of the Op. 35 Études in the <unk> Berliner <unk> in 1857 , the year in which they were published in Berlin , commenting that " Alkan is <unk> the most eminent representative of the modern piano school at Paris . The virtuoso 's <unk> to travel , and his firm reputation as a teacher , explain why , at present , so little attention has been given to his work in Germany . " 
- From the early 1850s Alkan began to turn his attention seriously to the pedal piano ( pédalier ) . Alkan gave his first public performances on the pédalier to great critical acclaim in 1852 . From 1859 onwards he began to publish pieces designated as " for organ or piano à pédalier " . 
- 
- = = = <unk> ( 1873 – 1888 ) = = = 
- 
- It is not clear why , in 1873 , Alkan decided to emerge from his self @-@ imposed obscurity to give a series of six Petits Concerts at the Érard piano <unk> . It may have been associated with the developing career of Delaborde , who , returning to Paris in 1867 , soon became a concert fixture , including in his recitals many works by his father , and who was at the end of 1872 given the appointment that had escaped Alkan himself , Professor at the Conservatoire . The success of the Petits Concerts led to them becoming an annual event ( with occasional interruptions caused by Alkan 's health ) until 1880 or possibly beyond . The Petits Concerts featured music not only by Alkan but of his favourite composers from Bach onwards , played on both the piano and the pédalier , and occasionally with the participation of another instrumentalist or singer . He was assisted in these concerts by his siblings , and by other musicians including Delaborde , Camille Saint @-@ <unk> , and Auguste Franchomme . 
- Those encountering Alkan at this phase included the young Vincent d <unk> , who recalled Alkan 's " <unk> , hooked fingers " playing Bach on an Érard pedal piano : " I listened , riveted to the spot by the expressive , crystal @-@ clear playing . " Alkan later played Beethoven 's Op. 110 sonata , of which d <unk> said : " What happened to the great <unk> poem ... I couldn 't begin to describe – above all in the <unk> and the <unk> , where the melody , penetrating the mystery of Death itself , climbs up to a <unk> of light , affected me with an excess of enthusiasm such as I have never experienced since . This was not Liszt — perhaps less perfect , technically — but it had greater intimacy and was more <unk> moving ... " 
- The biographer of Chopin , Frederick <unk> , sought Alkan for his <unk> in 1880 but was <unk> denied access by Alkan 's concierge – " To my ... enquiry when he could be found at home , the reply was a ... decisive ' Never ' . " However , a few days later he found Alkan at Érard 's , and <unk> writes of their meeting that " his reception of me was not merely polite but most friendly . " 
- 
- = = = Death = = = 
- 
- According to his death certificate , Alkan died in Paris on 29 March 1888 at the age of 74 . Alkan was buried on 1 April ( Easter Sunday ) in the Jewish section of <unk> Cemetery , Paris , not far from the tomb of his contemporary Fromental Halévy ; his sister <unk> was later buried in the same tomb . 
- For many years it was believed that Alkan met his death when a bookcase toppled over and fell on him as he reached for a volume of the Talmud from a high shelf . This tale , which was circulated by the pianist <unk> Philipp , is dismissed by Hugh Macdonald , who reports the discovery of a contemporary letter by one of his pupils explaining that Alkan had been found prostrate in his kitchen , under a <unk> @-@ <unk> ( a heavy coat / umbrella rack ) , after his concierge heard his moaning . He had possibly fainted , bringing it down on himself while grabbing out for support . He was reportedly carried to his bedroom and died later that evening . The story of the bookcase may have its roots in a legend told of <unk> <unk> ben Asher , rabbi of Metz , the town from which Alkan 's family originated . 
- 
- = = Personality = = 
- 
- Alkan was described by Marmontel ( who refers to " a <unk> misunderstanding at a moment of our careers in 1848 " ) , as follows : 
- " We will not give the portrait of Valentin Alkan from the rear , as in some photographs we have seen . His intelligent and original <unk> deserves to be taken in profile or head @-@ on . The head is strong ; the deep forehead is that of a thinker ; the mouth large and smiling , the nose regular ; the years have <unk> the beard and hair ... the gaze fine , a little mocking . His <unk> walk , his puritan <unk> , give him the look of an Anglican minister or a rabbi – for which he has the abilities . " 
- Alkan was not always remote or aloof . Chopin describes , in a letter to friend , visiting the theatre with Alkan in 1847 to see the comedian <unk> : " [ <unk> ] tells the audience how he was desperate to <unk> in a train , but couldn 't get to a toilet before they stopped at <unk> . There wasn 't a single vulgar word in what he said , but everyone understood and split their sides laughing . " Hugh Macdonald notes that Alkan " particularly enjoyed the patronage of Russian aristocratic ladies , ' des <unk> <unk> <unk> et <unk> [ highly <unk> and frilled ladies ] ' , as Isidore Philipp described them . " 
- Alkan 's aversion to <unk> and publicity , especially following 1850 , appeared to be self @-@ willed . Liszt is reported to have commented to the Danish pianist <unk> <unk> that " Alkan possessed the finest technique he had ever known , but preferred the life of a <unk> . " Stephanie <unk> has suggested that Alkan may have suffered from <unk> syndrome , <unk> or obsessive – <unk> disorder . 
- Alkan 's later correspondence contains many <unk> comments . In a letter of about 1861 he wrote to Hiller : " I 'm becoming daily more and more <unk> and <unk> ... nothing worthwhile , good or useful to do ... no one to devote myself to . My situation makes me <unk> sad and <unk> . Even musical production has lost its attraction for me for I can 't see the point or goal . " This spirit of <unk> may have led him to reject requests in the 1860s to play in public , or to allow performances of his orchestral compositions . However , it should not be ignored that he was writing similarly <unk> self @-@ analyses in his letters of the early 1830s to Masarnau . 
- Jack Gibbons writes of Alkan 's personality : " Alkan was an intelligent , lively , humorous and warm person ( all characteristics which feature strongly in his music ) whose only crime seems to have been having a vivid imagination , and whose occasional <unk> ( mild when compared with the behaviour of other ' highly @-@ strung ' <unk> ! ) stemmed mainly from his <unk> nature . " Macdonald , however , suggests that " Alkan was a man of profoundly conservative ideas , whose lifestyle , manner of dress , and belief in the traditions of historic music , set him apart from other musicians and the world at large . " 
- 
- = = Judaism = = 
- 
- Alkan grew up in a religiously <unk> Jewish household . His grandfather <unk> Morhange had been a <unk> of the Talmud in Metz , and was probably a <unk> ( Hebrew teacher ) in the Jewish congregation at Paris . Alkan 's widespread reputation as a student of the Old Testament and religion , and the high quality of his Hebrew handwriting testify to his knowledge of the religion , and many of his habits indicate that he practised at least some of its obligations , such as maintaining the laws of <unk> . Alkan was regarded by the Paris Consistory , the central Jewish organisation of the city , as an authority on Jewish music . In 1845 he assisted the Consistory in evaluating the musical ability of Samuel <unk> , who was subsequently appointed as <unk> ( cantor ) of the main Paris synagogue ; and he later contributed choral pieces in each of <unk> 's collections of synagogue music ( 1847 and 1856 ) . Alkan was appointed <unk> at the Synagogue de Nazareth in 1851 , although he resigned the post almost immediately for " artistic reasons " . 
- Alkan 's Op. 31 set of Préludes includes a number of pieces based on Jewish subjects , including some titled <unk> ( Prayer ) , one preceded by a quote from the Song of Songs , and another titled <unk> <unk> de la synagogue ( Old synagogue melody ) . The collection is believed to be " the first publication of art music specifically to deploy Jewish themes and ideas . " Alkan 's three settings of synagogue melodies , prepared for his former pupil <unk> de <unk> , are further examples of his interest in Jewish music ; <unk> <unk> provides a detailed analysis of these works and their origins . Other works <unk> this interest include no . 7 of his Op. 66 . 11 <unk> <unk> et 1 <unk> ( 1866 ) , entitled " Alla <unk> " and marked " con <unk> " , a parody of excessive <unk> practice ; and the slow movement of the cello sonata Op. 47 ( 1857 ) , which is prefaced by a quotation from the Old Testament prophet <unk> and uses melodic tropes derived from the <unk> of the <unk> in the synagogue . 
- The inventory of Alkan 's apartment made after his death indicates over 75 volumes in Hebrew or related to Judaism , left to his brother Napoléon ( as well as 36 volumes of music manuscript ) . These are all lost . <unk> in his will to the Conservatoire to found prizes for composition of <unk> on Old Testament themes and for performance on the pedal @-@ piano , and to a Jewish charity for the training of <unk> , were refused by the beneficiaries . 
- 
- = = Music = = 
- 
- 
- = = = Influences = = = 
- 
- <unk> François @-@ <unk> points out the frequency with which Alkan has been compared to Berlioz , both by his contemporaries and later . She mentions that Hans von Bülow called him " the Berlioz of the piano " , while Schumann , in criticising the Op. 15 <unk> , claimed that Alkan merely " imitated Berlioz on the piano . " She further notes that <unk> Busoni repeated the comparison with Berlioz in a draft ( but unpublished ) <unk> , while <unk> Sorabji commented that Alkan 's Op. 61 Sonatine was like " a Beethoven sonata written by Berlioz " . Berlioz was ten years older than Alkan , but did not attend the Conservatoire until 1826 . The two were acquainted , and were perhaps both influenced by the unusual ideas and style of Anton <unk> who taught at the Conservatoire from 1818 to 1836 , and by the <unk> of the composers of the period of the French Revolution . They both created individual , indeed , idiosyncratic sound @-@ worlds in their music ; there are , however , major differences between them . Alkan , unlike Berlioz , remained closely dedicated to the German musical tradition ; his style and composition were heavily determined by his <unk> , whereas Berlioz could hardly play at the keyboard and wrote nothing for piano solo . Alkan 's works therefore also include miniatures and ( among his early works ) <unk> music , genres which Berlioz avoided . 
- Alkan 's attachment to the music of his predecessors is demonstrated throughout his career , from his arrangements for keyboard of Beethoven 's Seventh Symphony ( 1838 ) , and of the <unk> of Mozart 's 40th Symphony ( 1844 ) , through the sets Souvenirs des concerts du Conservatoire ( 1847 and 1861 ) and the set Souvenirs de <unk> de <unk> ( 1862 ) , which include <unk> of music by Mozart , Beethoven , J. S. Bach , Haydn , <unk> , and others . In this context should be mentioned Alkan 's extensive <unk> for Beethoven 's 3rd Piano Concerto ( 1860 ) , which includes quotes from the finale of Beethoven 's 5th Symphony . Alkan 's <unk> , together with original music of Bach , Beethoven , Handel , Mendelssohn , <unk> and <unk> , were frequently played during the series of Petits Concerts given by Alkan at Erard . 
- As regards the music of his own time , Alkan was <unk> , or at any rate detached . He commented to Hiller that " Wagner is not a musician , he is a disease . " While he admired Berlioz 's talent , he did not enjoy his music . At the Petits Concerts , little more recent than Mendelssohn and Chopin ( both of whom had died around 25 years before the series of concerts was initiated ) was played , except for Alkan 's own works and occasionally some by his favourites such as Saint @-@ <unk> . 
- 
- = = = Style = = = 
- 
- " Like ... Chopin " , writes pianist and academic Kenneth Hamilton , " Alkan 's musical output was centred almost exclusively on the piano " . Some of his music requires extreme technical virtuosity , clearly reflecting his own abilities , often calling for great velocity , enormous <unk> at speed , long stretches of fast repeated notes , and the maintenance of widely spaced <unk> lines . The illustration ( right ) from the Grande sonate is analysed by Smith as " six parts in <unk> counterpoint , plus two extra voices and three <unk> – eleven parts in all . " Some typical musical devices , such as a sudden explosive final chord following a quiet passage , were established at an early stage in Alkan 's compositions . Macdonald suggests that 
- unlike Wagner , Alkan did not seek to <unk> the world through opera ; nor , like Berlioz , to <unk> the crowds by putting orchestral music at the service of literary expression ; nor even , as with Chopin or Liszt , to extend the field of harmonic <unk> . Armed with his key instrument , the piano , he sought incessantly to transcend its inherent technical limits , remaining apparently <unk> to the restrictions which had withheld more restrained composers . 
- However , not all of Alkan 's music is either lengthy or technically difficult ; for example , many of the Op. 31 Préludes and of the set of Esquisses , Op. 63 . 
- Moreover , in terms of structure , Alkan in his compositions sticks to traditional musical forms , although he often took them to extremes , as he did with piano technique . The study Op. 39 , no . 8 ( the first movement of the Concerto for solo piano ) takes almost half an hour in performance . Describing this " gigantic " piece , Ronald Smith comments that it convinces for the same reasons as does the music of the classical masters ; " the underlying unity of its principal themes , and a key structure that is basically simple and sound . " 
- Some of Alkan 's music gives hints of the <unk> which some have detected in his personality . The Chant Op. 38 , no . 2 , entitled <unk> , repeats the note of its title incessantly ( in total 414 times ) against shifting harmonies which make it " cut ... into the texture with the ruthless precision of a laser beam . " In modelling his five sets of Chants on the first book of Mendelssohn 's Songs Without Words , Alkan ensured that the pieces in each of his sets followed precisely the same key signatures , and even the moods , of the original . Alkan was <unk> in his <unk> spelling , occasionally <unk> to keys containing double @-@ <unk> or double @-@ flats , so pianists are occasionally required to come to terms with unusual keys such as E @-@ sharp major , <unk> equivalent to F major , and the occasional triple @-@ sharp . 
- 
- = = = Works = = = 
- 
- 
- = = = = Early works = = = = 
- 
- Alkan 's earliest works indicate , according to Smith , that in his early teens he " was a formidable musician but as yet ... <unk> rather than ... creative " . Only with his 12 Caprices ( <unk> – 13 and 15 – 16 , 1837 ) did his compositions begin to attract serious critical attention . The op . 15 set , Souvenirs : Trois <unk> dans le genre <unk> , dedicated to Liszt , contains Le vent ( The Wind ) , which was at one time the only piece by the composer to figure regularly in recitals . These works , however , did not meet with the approval of Robert Schumann , who wrote : " One is <unk> by such false , such unnatural art ... the last [ piece , titled <unk> ( Death ) , is ] a <unk> waste , overgrown with brush and weeds ... nothing is to be found but black on black " . Ronald Smith , however , finds in this latter work , which cites the Dies <unk> theme also used by Berlioz , Liszt and others , <unk> of Maurice Ravel , Modest <unk> and Charles <unk> . Schumann did , however , respond positively to the pieces of Les <unk> ( originally part published as Op. 8 in 1838 , later published as a complete set in 1840 as Op. 74 ) : " [ Here ] we find such an excellent <unk> on operatic music in no . 6 [ L <unk> ] that a better one could scarcely be imagined ... The composer ... well <unk> the rarer effects of his instrument . " Alkan 's technical mastery of the keyboard was asserted by the publication in 1838 of the Trois <unk> études ( originally without opus number , later republished as Op. 76 ) , the first for the left hand alone , the second for the right hand alone , the third for both hands ; and all of great difficulty , described by Smith as " a peak of <unk> <unk> " . This is perhaps the earliest example of writing for a single hand as " an entity in its own right , capable of covering all registers of the piano , of rendering itself as accompanied soloist or <unk> . " 
- 
- = = = = Early maturity = = = = 
- 
- Alkan 's large scale Duo ( in effect a sonata ) Op. 21 for violin and piano ( dedicated to <unk> <unk> ) and his Piano Trio Op. 30 appeared in 1841 . Apart from these , Alkan published only a few minor works between 1840 and 1844 , after which a series of virtuoso works was issued , many of which he had played at his successful recitals at Érard and elsewhere ; these included the Marche <unk> ( Op. 26 ) , the Marche <unk> ( Op. 27 ) and Le chemin de <unk> ( also published , separately , as Op. 27 ) . In 1847 appeared the Op. 31 Préludes and his first large @-@ scale unified piano work , the Grande sonate Les <unk> <unk> ( Op. 33 ) . The sonata is structurally innovative in two ways ; each movement is slower than its predecessor , and the work <unk> the practice of progressive tonality , beginning in D major and ending in G @-@ sharp minor . Dedicated to Alkan Morhange , the sonata depicts in its successive movements its ' hero ' at the ages of 20 ( optimistic ) , 30 ( " Quasi @-@ Faust " , impassioned and <unk> ) , 40 ( domesticated ) and 50 ( suffering : the movement is prefaced by a quotation from <unk> 's <unk> <unk> ) . In 1848 followed Alkan 's set of 12 études dans tous les tons <unk> Op. 35 , whose substantial pieces range in mood from the <unk> Allegro <unk> ( no . 5 ) and the intense Chant d <unk> @-@ Chant de <unk> ( Song of Love – Song of Death ) ( no . 10 ) to the descriptive and picturesque L <unk> au village <unk> ( The Fire in the Next Village ) ( no . 7 ) . 
- A number of Alkan 's compositions from this period were never performed and have been lost . Among the missing works are some string <unk> and a full @-@ scale orchestral <unk> in B minor , which was described in an article in 1846 by the critic Léon Kreutzer , to whom Alkan had shown the score . Kreutzer noted that the introductory <unk> of the <unk> was headed " by Hebrew characters in red ink ... This is no less than the verse from Genesis : And God said , Let there be light : and there was light . " Kreutzer opined that , set beside Alkan 's conception , Joseph Haydn 's Creation was a " mere <unk> ( <unk> ) . " 
- 
- = = = = Internal exile = = = = 
- 
- During his twenty @-@ year absence from the public between 1853 and 1873 Alkan produced many of his most notable compositions , although there is a ten @-@ year gap between publication of the Op. 35 studies and that of his next group of piano works in 1856 and 1857 . Of these , undoubtedly the most significant was the enormous Opus 39 collection of twelve studies in all the minor keys , which contains the Symphony for Solo Piano ( numbers four , five , six and seven ) , and the Concerto for Solo Piano ( numbers eight , nine and ten ) . The Concerto takes nearly an hour in performance . Number twelve of Op. 39 is a set of variations , Le <unk> d <unk> ( Aesop 's Feast ) . The other components of Op. 39 are of a similar stature . Smith describes Op. 39 as a whole as " a towering achievement , gathering ... the most complete manifestation of Alkan 's many @-@ sided genius : its dark passion , its vital rhythmic drive , its pungent harmony , its occasionally <unk> humour , and , above all , its uncompromising piano writing . " 
- In the same year appeared the Sonate de Concert , Op. 47 , for cello and piano , " among the most difficult and ambitious in the romantic repertoire ... anticipating Mahler in its juxtaposition of the sublime and the trivial " , and with its four movements showing again an anticipation of progressive tonality , each ascending by a major third . Other <unk> of Mahler ( who was born in 1860 ) can be found in the two " military " Op. 50 piano studies of 1859 <unk> <unk> <unk> and Le <unk> bat aux <unk> ( The drum beats the retreat ) , as well as in certain of the miniatures of the 1861 Esquisses , Op. 63 . The bizarre and <unk> Marcia <unk> , <unk> <unk> d <unk> <unk> ( <unk> march on the death of a parrot , 1859 ) , for three <unk> , <unk> and voices , described by Kenneth Hamilton as " Monty @-@ <unk> " , is also of this period . 
- The Esquisses of 1861 are a set of highly varied miniatures , ranging from the tiny 18 @-@ bar no . 4 , Les <unk> ( The Bells ) , to the strident tone clusters of no . 45 , Les <unk> ( The <unk> ) , and closing with a further evocation of church bells in no . 49 , <unk> <unk> ( <unk> God ) . They were preceded in publication by Alkan 's deceptively titled Sonatine , Op. 61 , in ' classical ' format , but a work of " ruthless economy [ which ] although it plays for less than twenty minutes ... is in every way a major work . " 
- Two of Alkan 's substantial works from this period are musical <unk> of literary works . <unk> , <unk> du <unk> , Op. 45 ( 1856 ) , follows a section of the poem La <unk> by Gabriel @-@ Marie <unk> ; while Super <unk> <unk> , Op. 52 ( 1859 ) , is a blow @-@ by @-@ blow recreation in music of the emotions and <unk> of <unk> 137 ( " By the waters of Babylon ... " ) . This piece is prefaced by a French version of the <unk> which is believed to be the sole remnant of Alkan 's Bible translation . Alkan 's lyrical side was displayed in this period by the five sets of Chants inspired by Mendelssohn , which appeared between 1857 and 1872 , as well as by a number of minor pieces . 
- Alkan 's publications for organ or pédalier commenced with his <unk> , Op. 54 ( 1859 ) . In the same year he published a set of very spare and simple <unk> in the eight Gregorian modes ( 1859 , without opus number ) , which , in Smith 's opinion , " seem to stand outside the barriers of time and space " , and which he believes reveal " Alkan 's essential spiritual <unk> . " These were followed by pieces such as the 13 <unk> ( <unk> ) , Op. 64 ( 1865 ) , and the <unk> sur le Choral de Luther " Un fort <unk> est <unk> Dieu " , op . 69 ( 1866 ) . Alkan also issued a book of 12 studies for the <unk> alone ( no opus number , 1866 ) and the <unk> @-@ <unk> for <unk> duet ( four feet ) of 1872 . 
- Alkan 's return to the concert platform at his Petits Concerts , however , marked the end of his publications ; his final work to be issued was the <unk> , Op. 75 , in 1872 . 
- 
- = = Reception and legacy = = 
- 
- Alkan had few followers ; however , he had important admirers , including Liszt , Anton Rubinstein , Franck , and , in the early twentieth century , Busoni , Petri and Sorabji . Rubinstein dedicated his fifth piano concerto to him , and Franck dedicated to Alkan his Grand <unk> <unk> op . 17 for organ . Busoni ranked Alkan with Liszt , Chopin , Schumann and <unk> as one of the five greatest composers for the piano since Beethoven . <unk> Philipp and Delaborde edited new <unk> of his works in the early 1900s . In the first half of the twentieth century , when Alkan 's name was still obscure , Busoni and Petri included his works in their performances . Sorabji published an article on Alkan in his 1932 book Around Music ; he promoted Alkan 's music in his reviews and criticism , and his Sixth Symphony for Piano ( <unk> <unk> ) ( 1975 – 76 ) , includes a section entitled Quasi Alkan . The English composer and writer Bernard van <unk> praised Alkan in an essay in his 1935 book , Down Among the Dead Men , and the composer Humphrey Searle also called for a revival of his music in a 1937 essay . 
- For much of the 20th century , Alkan 's work remained in obscurity , but from the 1960s onwards it was steadily revived . Raymond Lewenthal gave a pioneering extended broadcast on Alkan on <unk> radio in New York in 1963 , and later included Alkan 's music in recitals and recordings . The English pianist Ronald Smith championed Alkan 's music through performances , recordings , a biography and the Alkan Society of which he was president for many years . Works by Alkan have also been recorded by Jack Gibbons , Marc @-@ André Hamelin , Mark <unk> , John Ogdon , and <unk> <unk> , among many others . Ronald Stevenson has composed a piano piece <unk> d <unk> ( referring to Alkan 's Op. 39 , no . 12 ) and the composer Michael <unk> has also written piano pieces referring to Alkan , e.g. Alkan @-@ <unk> , no . 5 of The History of <unk> in Sound . Marc @-@ André Hamelin 's <unk> No . IV is a <unk> <unk> study combining themes from Alkan 's Symphony , Op. 39 , no . 7 , and Alkan 's own perpetual motion étude , Op. 76 , no . 3 . It is dedicated to <unk> Kovacs and François <unk> , respectively activists in the English and French Alkan Societies . As Hamelin writes in his preface to this étude , the idea to combine these came from the composer <unk> <unk> , the finale of whose Piano <unk> No. 5 ( 1994 – 95 ) includes a substantial section entitled " <unk> " . 
- Alkan 's compositions for organ have been among the last of his works to be brought back to the repertoire . As to Alkan 's pedal @-@ piano works , due to a recent revival of the instrument , they are once again being performed as originally intended ( rather than on an organ ) , such as by Italian pedal @-@ pianist Roberto <unk> , and recordings of Alkan on the pedal piano have been made by Jean <unk> and Olivier <unk> . 
- 
- = = Selected recordings = = 
- 
- This list comprises a selection of some premiere and other recordings by musicians who have become closely associated with Alkan 's works . A comprehensive discography is available at the Alkan Society website . 
- Piano Trio , Op. 30 – played by Trio Alkan . Recorded 1992 . <unk> , <unk> ( 2001 ) 
- Grande sonate , Op. 33 – played by Marc @-@ André Hamelin ( piano ) . Recorded 1994 . Hyperion , <unk> ( 1995 ) . 
- Études dans tous les tons mineurs , Op. 39 – played by Ronald Smith ( piano ) . Recorded 1977 . EMI , <unk> <unk> [ 3 <unk> ] ( 1978 ) , partly reissued EMI Gemini , <unk> <unk> ( 2003 ) 
- Études dans tous les tons mineurs , Op. 39 and other works – played by Jack Gibbons ( piano ) . Recorded 1995 . <unk> , CD <unk> 227 [ 2 CDs ] ( 1995 ) 
- Concerto , Op. 39 , nos . 8 – 10 – played by John Ogdon ( piano ) . Recorded 1969 . RCA , <unk> @-@ <unk> [ LP ] ( 1972 ) . Great British <unk> , <unk> ( 1999 ) 
- Le <unk> d <unk> ( Op. 39 , no . 12 ) and other works – played by Raymond Lewenthal . Recorded 1966 . RCA <unk> <unk> [ LP <unk> ] , <unk> @-@ <unk> [ LP <unk> ] ; BMG High Performance Series <unk> ( 1999 ) 
- Sonate de concert , Op. 47 , for cello and piano – played by Steven Osborne ( piano ) and <unk> <unk> ( cello ) . Recorded 2008 . Hyperion <unk> ( 2008 ) . 
- 11 <unk> dans le style <unk> , et <unk> transcription du <unk> de <unk> , Op. 72 – played by Kevin Bowyer ( organ ) . Recorded 2005 . <unk> <unk> <unk> ( 2007 ) 
- Symphony for Solo Piano ( Op. 39 , no . 4 @-@ 7 ) – played by Egon Petri ( piano ) . c . 1952 – 53 . Symposium Records , CD 1145 ( 1993 ) 
- 
- = = = About Alkan = = = 
- 
- Alkan Society , including complete and regularly updated discography 
- Alkan web site of Sylvain <unk> , contains detailed listing of Alkan 's works , with some downloadable scores 
- " The Myths of Alkan " by Jack Gibbons 
- <unk> Alkan by David Conway 
- Alkan @-@ Zimmerman International Music Association 
- Pictures of Alkan 
- 
- = = = Scores and sheet @-@ music = = = 
- 
- Free Alkan scores and manuscripts – site of Sylvain <unk> 
- <unk> der <unk> : many of Alkan 's piano works in <unk> performances 
- Alkan Piano Trio @-@ <unk> of work and <unk> 
- <unk> @-@ sheet @-@ <unk> – Free Scores by Alkan 
- Free scores by Alkan at the International Music Score Library Project 
- 
- = = = Performances on the Web = = = 
- 
- Trois <unk> <unk> , Op. 13 , No 2 on YouTube , played by Lloyd Buck 
- <unk> <unk> , Op. 34 on YouTube , played by Lloyd Buck 
- " Allegro <unk> " , Op. 35 , No. 5 on YouTube , played by Jack Gibbons 
- <unk> , <unk> , Op. 39 , No. 6 on YouTube , played by Jonathan Powell 
- Last movement , <unk> , Op. 39 , No. 7 on YouTube , played by Jonathan Powell 
- First movement , Concerto , Op. 39 , No. 8 on YouTube , played by Jack Gibbons 
- Le <unk> d <unk> , Op. 39 , no . 12 on YouTube , played by Edward Cohen 
- Four Esquisses from Op. 63 on YouTube , played by Edward Cohen 
- " Trois <unk> <unk> de la synagogue " and " <unk> <unk> " on YouTube - <unk> <unk> ( soprano ) and Daniel <unk> ( piano ) 
- 
- = Business School ( The Office ) = 
- 
- " Business School " is the seventeenth episode of the third season of the American comedy television series The Office , and the show 's forty @-@ fifth episode overall . Written by Brent Forrester , and directed by Buffy the Vampire Slayer and Firefly creator Joss Whedon , the episode aired on NBC on February 15 , 2007 . 
- In the episode , Michael is invited by Ryan to speak to his business school class . When many of the students question the usefulness of paper in a computerized world , Michael attempts to inform the class of how essential paper is . Meanwhile , a bat becomes trapped in the office , leading Dwight and Creed on a mission to protect the employees . 
- 
- = = Plot = = 
- 
- Ryan Howard ( B. J. Novak ) invites Michael Scott ( Steve Carell ) to speak at his business school class . Michael is excited , but Ryan admits in a talking head interview that he has only invited Michael because his professor promised to bump up the grade of any student who brings his boss into class . During his introduction of Michael to his classmates , Ryan predicts that Dunder Mifflin will become obsolete within five to ten years . However , Michael could not hear him and proceeds to ruin the event with his antics ( including tearing pages out of a student 's textbook to prove you " can 't learn from textbooks " ) . Michael is then taken aback when one of Ryan 's classmates asks for Michael 's opinion of Ryan 's prediction . <unk> and hurt , Michael <unk> Ryan by relocating his desk to the " annex , " where Kelly Kapoor ( Mindy <unk> ) works , who <unk> <unk> in excitement . 
- Meanwhile , Dwight Schrute ( Rainn Wilson ) discovers a bat in the ceiling , and accidentally lets it loose into the office , sending the employees <unk> for cover . Stanley immediately goes home . As many of the employees hide , Dwight enlists the help of Creed ( Creed <unk> ) to expel the bat . Jim Halpert ( John Krasinski ) exploits Dwight 's paranoia , and pretends that he is turning into a vampire . Dwight eventually catches the bat with a garbage bag after it lands on Meredith 's head . 
- Pam Beesly ( Jenna Fischer ) , in the meantime , invites her co @-@ workers to her art show after work , but few show any enthusiasm , leaving her feeling alienated and sad . The art show doesn 't go as planned , and she begins to doubt her abilities . When Oscar Martinez ( Oscar Nunez ) shows up with his partner Gil , they critique her art , and note that Pam 's art has been let down by her lack of courage , not realizing that she is standing right behind them . As she begins to take her work down , Michael appears and , in a moment of sincere kindness , <unk> her work and asks to buy her drawing of the office building . Pam <unk> him as her eyes tear up . Michael soon places Pam 's drawing on the wall next to his office . 
- 
- = = Production = = 
- 
- " Business School " was the second Office episode written by Brent Forrester . Forrester had previously written " The <unk> " . The episode was the first to be directed by Joss Whedon . Whedon , who is a friend of both producer Greg Daniels and Jenna Fischer , and also met most of the production staff prior to the episode , stated that he chose to direct the episode " because I already know the writing staff and a bunch of the cast , and I <unk> the show . " When informed that the episode was about a bat entering into the office and one of the characters pretending to be a vampire , Whedon thought that it was a joke , stating " <unk> 't I just leave this party ? " in reference to Buffy the Vampire Slayer . In an interview featured on the third season DVD , Whedon joked that the " Business School " episode and his former TV show were very similar because " Buffy [ ... ] was sad and depressing but ... it was funny . Especially when people died . And a lot of people do die in [ ' Business School ' ] . " But upon completing the episode , Whedon stated " That was just coincidence . But that 's how that happened . God , it was fun . " Whedon stated that he was surprised with the amount of input he was allowed with the script . " I wouldn 't say freedom to do things with it ... But way more input was asked for than I would have ever anticipated . " At Pam 's art show , the pieces which she was supposed to have painted didn 't suit Whedon . " I got to the set and saw Pam 's art , and I was like , ' This is not right . ' " Whedon said that he held up filming for over an hour until the staff was able create new pieces of art . 
- For the scenes which involved filming with a bat , the production team used an actual bat , an animated bat , and a mechanical bat . When around the actual bat , Kate <unk> , who portrays Meredith Palmer , stated that " we had to be extremely quiet around [ it ] , basically pretending to scream . " California State University , <unk> served as the backdrop for Ryan 's business school and the art show . 
- 
- = = Reception = = 
- 
- The episode received generally good reviews from critics . Brian Zoromski , of IGN , stated that " ' Business School ' was an exercise in what works best in an Office episode . " Zoromski also praised Joss Whedon 's directorial debut for the show , stating that " Whedon 's direction and sense of humor was both <unk> put to use and alluded to in the scenes in which Jim <unk> pretended to become a vampire . " Zoromski went on to say that the acting of John Krasinski and Rashida Jones , who portrays Karen <unk> , helped to make the vampire scenes the funniest parts of the episode . He gave the episode a 9 @.@ 1 out of 10 . Abby West , of Entertainment Weekly , stated that " This show has always been able to turn on a <unk> and take the comedy to a soul @-@ stirring dramatic climax with just the lightest of touches , and last night was no different . " West went on to praise Michael and Pam 's scene at the art show as one of these moments . 
- 
- 
- = M @-@ 81 ( Michigan highway ) = 
- 
- M @-@ 81 is a state trunkline highway in the Lower Peninsula of the US state of Michigan . The trunkline travels from the city of Saginaw at the junction with M @-@ 13 to the junction with M @-@ 53 east of Cass City over the county line in <unk> Township in northwestern <unk> County in The <unk> area of the state . Outside of the cities and villages along its route , M @-@ 81 passes through mostly rural farm country . Near Saginaw it intersects the freeway that carries both Interstate 75 ( I @-@ 75 ) and US Highway 23 ( US 23 ) in an industrial area . 
- A road bearing the M @-@ 81 designation has existed since at least July 1 , 1919 , when the state initially numbered its trunkline highways . Since that time , it has been extended , rerouted or shortened several times . These changes resulted in essentially the modern highway routing by 1926 ; the highway was fully paved in the 1940s . A change made in 1929 was reversed in 1933 , and an extension through downtown Saginaw in the 1960s was overturned in the 1970s . The last change was the construction of a pair of roundabouts at the I @-@ 75 / US 23 interchange in 2006 . 
- 
- = = Route description = = 
- 
- M @-@ 81 starts at a junction with M @-@ 13 , with the two directions of each highway separated by a median . This central feature ends for M @-@ 81 immediately east of the intersection as the highway runs east out of Saginaw . The trunkline follows Washington Road easterly through residential subdivisions to an interchange with I @-@ 75 / US 23 in an industrial area of Buena Vista Township . This interchange , exit 151 along the freeway , is built with roundabouts on either side of the bridge that carries Washington Road over the freeway in a variant of the diamond interchange design called a <unk> interchange . After leaving the industrial properties on the east side of I @-@ 75 / US 23 , M @-@ 81 follows Washington Road through a mixture of farm fields and residential subdivisions . North of the community of Arthur , the highway intersects M @-@ 15 ( <unk> Road ) . Further east , M @-@ 81 crosses out of Saginaw County ; across the border in Tuscola County , the highway follows Saginaw Road . 
- In the village of Reese , M @-@ 81 crosses the Huron and Eastern Railway twice . East of town , the highway follows Caro Road through more farm fields through the community of <unk> . Near the south side of the Tuscola Area Airport , M @-@ 81 turns to the northeast running along the Cass River to Caro . In the city , the trunkline follows State Street past the <unk> . At the intersection with Ellington Street , M @-@ 81 crosses M @-@ 24 . The highway continues northeasterly out of town and through the community of Ellington . Northeast of <unk> , M @-@ 81 turns due east along Cass City Road . The trunkline runs to the village of Cass City where it follows Main Street through residential neighborhoods and the central business district . East of Cass City , the highway crosses the Cass River before crossing the county line into <unk> County . Approximately one mile ( 1 @.@ 6 km ) into the county , M @-@ 81 ends at the intersection with M @-@ 53 ( Van <unk> Road ) . 
- M @-@ 81 is maintained by the Michigan Department of Transportation ( MDOT ) like other state highways in Michigan . As a part of these maintenance responsibilities , the department tracks the volume of traffic that uses the roadways under its jurisdiction . These volumes are expressed using a metric called annual average daily traffic , which is a statistical calculation of the average daily number of vehicles on a segment of roadway . MDOT 's surveys in 2010 showed that the highest traffic levels along M @-@ 81 were the 17 @,@ <unk> vehicles daily between Van <unk> Road and Frank Street in Caro ; the lowest counts were the 3 @,@ 527 vehicles per day east of Cass City to the M @-@ 53 intersection . No sections of M @-@ 81 have been listed on the National Highway System , a network of roads important to the country 's economy , defense , and mobility . 
- 
- = = History = = 
- 
- When originally signed around July 1 , 1919 , M @-@ 81 ran from Bay City southeast and east to the <unk> area ; from there it ran south concurrently with M @-@ 31 to the Reese area before running northeasterly to the east of Cass City to M @-@ 53 . By 1921 , the eastern end was extended <unk> from Caro to <unk> along a highway that is now part of M @-@ 24 . In late 1926 , the western end was changed so that M @-@ 81 followed the former M @-@ 31 from Reese into Saginaw and the eastern end was rerouted from Caro northeasterly to the Cass City area . With the exception of the routing through downtown Saginaw , the highway followed the approximate routing of the current highway . 
- In the latter half of 1929 , the highway was rerouted between Saginaw and Reese , using a set of parallel roadways to the south of the previous routing ; this change was reversed in 1933 . M @-@ 81 was fully paved when the last section between Ellington and <unk> in Tuscola County was finished in late 1946 or early 1947 . 
- Starting in 1953 , the westernmost approximately one mile ( 1 @.@ 6 km ) of M @-@ 81 was also used for a US 23 concurrency . When the bypass of Saginaw was completed in late 1961 , M @-@ 81 was extended along M @-@ 13 southwesterly into downtown Saginaw where it turned west across the Saginaw River to Midland Road west of the city . This routing across the city was removed in 1971 when I @-@ <unk> was completed ; west of that freeway the highway became M @-@ 58 , the rest was either removed from the highway system and turned back to local control , or it had the M @-@ 81 designation removed . Since this truncation , M @-@ 81 has ended at its junction with M @-@ 13 north of downtown Saginaw . In 2006 , MDOT completed the reconstruction of the interchange between M @-@ 81 and I @-@ 75 / US to incorporate a pair of roundabouts along Washington Road . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Common starling = 
- 
- The common starling ( Sturnus vulgaris ) , also known as the European starling , or in the British Isles just the starling , is a medium @-@ sized <unk> bird in the starling family , Sturnidae . It is about 20 cm ( 8 in ) long and has glossy black plumage with a metallic <unk> , which is speckled with white at some times of year . The legs are pink and the bill is black in winter and yellow in summer ; young birds have <unk> plumage than the adults . It is a noisy bird , especially in communal roosts and other gregarious situations , with an <unk> but varied song . Its gift for mimicry has been noted in literature including the <unk> and the works of Pliny the Elder and William Shakespeare . 
- The common starling has about a dozen subspecies breeding in open habitats across its native range in temperate Europe and western Asia , and it has been introduced to Australia , New Zealand , Canada , United States , Mexico , Peru , Argentina , the Falkland Islands , Brazil , Chile , Uruguay , South Africa and Fiji . This bird is resident in southern and western Europe and southwestern Asia , while northeastern populations migrate south and west in winter within the breeding range and also further south to Iberia and North Africa . The common starling builds an untidy nest in a natural or artificial cavity in which four or five glossy , pale blue eggs are laid . These take two weeks to hatch and the young remain in the nest for another three weeks . There are normally one or two breeding attempts each year . This species is omnivorous , taking a wide range of invertebrates , as well as seeds and fruit . It is hunted by various mammals and birds of prey , and is host to a range of external and internal parasites . 
- Large flocks typical of this species can be beneficial to agriculture by controlling invertebrate pests ; however , starlings can also be pests themselves when they feed on fruit and sprouting crops . Common starlings may also be a nuisance through the noise and mess caused by their large urban roosts . Introduced populations in particular have been subjected to a range of controls , including culling , but these have had limited success except in preventing the colonisation of Western Australia . The species has declined in numbers in parts of northern and western Europe since the 1980s due to fewer grassland invertebrates being available as food for growing chicks . Despite this , its huge global population is not thought to be declining significantly , so the common starling is classified as being of least concern by the International Union for Conservation of Nature . 
- 
- = = Taxonomy and <unk> = = 
- 
- The common starling was first described by Linnaeus in his Systema <unk> in 1758 under its current binomial name . Sturnus and vulgaris are derived from the Latin for " starling " and " common " respectively . The Old English <unk> , later stare , and the Latin <unk> are both derived from an unknown Indo @-@ European root dating back to the second millennium BC . " <unk> " was first recorded in the 11th century , when it referred to the juvenile of the species , but by the 16th century it had already largely <unk> " stare " to refer to birds of all ages . The older name is referenced in William Butler Yeats ' poem " The <unk> 's Nest by My Window " . The International <unk> Congress ' preferred English vernacular name is common starling . 
- The starling family , Sturnidae , is an entirely Old World group apart from introductions elsewhere , with the greatest numbers of species in Southeast Asia and sub @-@ Saharan Africa . The genus Sturnus is <unk> and relationships between its members are not fully resolved . The closest relation of the common starling is the spotless starling . The non @-@ migratory spotless starling may be descended from a population of ancestral S. vulgaris that survived in an Iberian <unk> during an ice age retreat , and mitochondrial gene studies suggest that it could be considered as a subspecies of the common starling . There is more genetic variation between common starling populations than between nominate common starling and spotless starling . Although common starling remains are known from the Middle Pleistocene , part of the problem in resolving relationships in the Sturnidae is the <unk> of the fossil record for the family as a whole . 
- 
- = = = Subspecies = = = 
- 
- There are several subspecies of the common starling , which vary <unk> in size and the colour tone of the adult plumage . The gradual variation over geographic range and extensive <unk> means that acceptance of the various subspecies varies between authorities . 
- Subspecies 
- Birds from Fair Isle , St <unk> and the Outer Hebrides are intermediate in size between S. v. zetlandicus and the nominate form , and their subspecies placement varies according to the authority . The dark juveniles typical of these island forms are occasionally found in mainland Scotland and elsewhere , indicating some gene flow from <unk> or zetlandicus , subspecies formerly considered to be isolated . 
- Several other subspecies have been named , but are generally no longer considered valid . Most are intergrades that occur where the ranges of various subspecies meet . These include : S. v. <unk> <unk> , 1891 and S. v. <unk> <unk> , 1904 , which are intergrades between vulgaris and <unk> from western Russia ; S. v. <unk> <unk> , 1905 and S. v. <unk> <unk> and <unk> , 1909 , intergrades between vulgaris and <unk> from the southern Balkans to central Ukraine and throughout Greece to the <unk> ; and S. v. <unk> <unk> , 1928 , an <unk> between <unk> and <unk> in northern Iran . S. v. <unk> <unk> , 1928 from southern Iran 's ( <unk> Province ) is very similar to S. v. vulgaris , and it is not clear whether it is a distinct resident population or simply migrants from southeastern Europe . 
- 
- = = Description = = 
- 
- The common starling is 19 – 23 cm ( 7 @.@ 5 – 9 @.@ 1 in ) long , with a wingspan of 31 – 44 cm ( 12 – 17 in ) and a weight of 58 – 101 g ( 2 @.@ 0 – 3 @.@ 6 oz ) . Among standard measurements , the wing chord is 11 @.@ 8 to 13 @.@ 8 cm ( 4 @.@ 6 to 5 @.@ 4 in ) , the tail is 5 @.@ 8 to 6 @.@ 8 cm ( 2 @.@ 3 to 2 @.@ 7 in ) , the <unk> is 2 @.@ 5 to 3 @.@ 2 cm ( 0 @.@ 98 to 1 @.@ 26 in ) and the <unk> is 2 @.@ 7 to 3 @.@ 2 cm ( 1 @.@ 1 to 1 @.@ 3 in ) . The plumage is <unk> black , <unk> purple or green , and spangled with white , especially in winter . The underparts of adult male common starlings are less spotted than those of adult females at a given time of year . The throat feathers of males are long and loose and are used in display while those of females are smaller and more pointed . The legs are stout and <unk> or greyish @-@ red . The bill is narrow and conical with a sharp tip ; in the winter it is brownish @-@ black but in summer , females have lemon yellow beaks while males have yellow bills with blue @-@ grey bases . <unk> occurs once a <unk> in late summer after the breeding season has finished ; the fresh feathers are prominently tipped white ( breast feathers ) or buff ( wing and back feathers ) , which gives the bird a speckled appearance . The reduction in the spotting in the breeding season is achieved through the white feather tips largely wearing off . Juveniles are grey @-@ brown and by their first winter resemble adults though often retaining some brown juvenile <unk> , especially on the head . They can usually be <unk> by the colour of the irises , rich brown in males , mouse @-@ brown or grey in females . <unk> the contrast between an iris and the central always @-@ dark pupil is 97 % accurate in determining sex , rising to 98 % if the length of the throat feathers is also considered . The common starling is mid @-@ sized by both starling standards and <unk> standards . It is readily distinguished from other mid @-@ sized <unk> , such as <unk> , <unk> or small <unk> , by its relatively short tail , sharp , blade @-@ like bill , round @-@ bellied shape and strong , sizeable ( and rufous @-@ coloured ) legs . In flight , its strongly pointed wings and dark colouration are distinctive , while on the ground its strange , somewhat <unk> gait is also characteristic . The colouring and build usually distinguish this bird from other starlings , although the closely related spotless starling may be physically distinguished by the lack of <unk> spots in adult breeding plumage . 
- Like most terrestrial starlings the common starling moves by walking or running , rather than hopping . Their flight is quite strong and direct ; their triangular @-@ shaped wings beat very rapidly , and periodically the birds <unk> for a short way without losing much height before resuming powered flight . When in a flock , the birds take off almost simultaneously , wheel and turn in <unk> , form a compact mass or trail off into a <unk> stream , bunch up again and land in a coordinated fashion . Common starling on migration can fly at 60 – 80 km / h ( 37 – 50 mph ) and cover up to 1 @,@ 000 – 1 @,@ 500 km ( 620 – 930 mi ) . 
- Several terrestrial starlings , including those in the genus Sturnus , have adaptations of the skull and muscles that help with feeding by probing . This adaptation is most strongly developed in the common starling ( along with the spotless and white @-@ <unk> starlings ) , where the <unk> muscles responsible for opening the jaw are enlarged and the skull is narrow , allowing the eye to be moved forward to peer down the length of the bill . This technique involves inserting the bill into the ground and opening it as a way of searching for hidden food items . Common starlings have the physical traits that enable them to use this feeding technique , which has undoubtedly helped the species spread far and wide . 
- In Iberia , the western Mediterranean and northwest Africa , the common starling may be confused with the closely related spotless starling , the plumage of which , as its name implies , has a more uniform colour . At close range it can be seen that the latter has longer throat feathers , a fact particularly noticeable when it sings . 
- 
- = = = Voice = = = 
- 
- The common starling is a noisy bird . Its song consists of a wide variety of both melodic and mechanical @-@ sounding noises as part of a ritual succession of sounds . The male is the main <unk> and engages in bouts of song lasting for a minute or more . Each of these typically includes four varieties of song type , which follow each other in a regular order without pause . The bout starts with a series of pure @-@ tone whistles and these are followed by the main part of the song , a number of variable sequences that often incorporate <unk> of song mimicked from other species of bird and various naturally occurring or man @-@ made noises . The structure and simplicity of the sound mimicked is of greater importance than the frequency with which it occurs . Each sound clip is repeated several times before the bird moves on to the next . After this variable section comes a number of types of repeated <unk> followed by a final burst of high @-@ frequency song , again formed of several types . Each bird has its own repertoire with more proficient birds having a range of up to 35 variable song types and as many as 14 types of <unk> . 
- Males sing constantly as the breeding period approaches and perform less often once pairs have bonded . In the presence of a female , a male sometimes flies to his nest and sings from the entrance , apparently attempting to entice the female in . Older birds tend to have a wider repertoire than younger ones . Those males that engage in longer bouts of singing and that have wider <unk> attract mates earlier and have greater reproductive success than others . Females appear to prefer mates with more complex songs , perhaps because this indicates greater experience or longevity . Having a complex song is also useful in defending a territory and <unk> less experienced males from encroaching . 
- Singing also occurs outside the breeding season , taking place throughout the year apart from the <unk> period . The <unk> are more commonly male although females also sing on occasion . The function of such out @-@ of @-@ season song is poorly understood . Eleven other types of call have been described including a flock call , threat call , attack call , <unk> call and copulation call . The alarm call is a harsh scream , and while foraging together common starlings <unk> incessantly . They <unk> while roosting and bathing , making a great deal of noise that can cause irritation to people living nearby . When a flock of common starlings is flying together , the <unk> movements of the birds ' wings make a distinctive <unk> sound that can be heard hundreds of metres ( yards ) away . 
- 
- = = Behaviour and ecology = = 
- 
- The common starling is a highly gregarious species , especially in autumn and winter . Although flock size is highly variable , huge , noisy flocks - <unk> - may form near roosts . These dense concentrations of birds are thought to be a defence against attacks by birds of prey such as peregrine falcons or Eurasian <unk> . Flocks form a tight sphere @-@ like formation in flight , frequently expanding and contracting and changing shape , seemingly without any sort of leader . Each common starling changes its course and speed as a result of the movement of its closest neighbours . Very large roosts , exceptionally up to 1 @.@ 5 million birds , can form in city centres , woodlands or <unk> , causing problems with their droppings . These may accumulate up to 30 cm ( 12 in ) deep , killing trees by their concentration of chemicals . In smaller amounts , the droppings act as a fertiliser , and therefore woodland managers may try to move roosts from one area of a wood to another to benefit from the soil enhancement and avoid large toxic deposits . 
- Huge flocks of more than a million common starlings may be observed just before sunset in spring in southwestern Jutland , Denmark over the seaward <unk> of <unk> and <unk> municipalities between <unk> and <unk> . They gather in March until northern Scandinavian birds leave for their breeding ranges by mid @-@ April . Their swarm behaviour creates complex shapes <unk> against the sky , a phenomenon known locally as sort <unk> ( " black sun " ) . Flocks of anything from five to fifty thousand common starlings form in areas of the UK just before <unk> during mid @-@ winter . These flocks are commonly called <unk> . 
- 
- = = = Feeding = = = 
- 
- The common starling is largely insectivorous and feeds on both pest and other <unk> . The food range includes spiders , crane flies , <unk> , <unk> , <unk> , damsel flies , grasshoppers , <unk> , <unk> , <unk> , flies , beetles , <unk> , bees , wasps and ants . Both adults and larvae are consumed and common starlings will also feed on earthworms , snails , small amphibians and lizards . While the consumption of invertebrates is necessary for successful breeding , common starlings are omnivorous and can also eat grains , seeds , fruits , nectar and food waste if the opportunity arises . The Sturnidae differ from most birds in that they cannot easily <unk> foods containing high levels of <unk> , although they can cope with other fruits such as grapes and cherries . The isolated Azores subspecies of the common starling eats the eggs of the endangered <unk> <unk> . Measures are being introduced to reduce common starling populations by culling before the <unk> return to their breeding colonies in spring . 
- There are several methods by which common starlings obtain their food but for the most part , they forage close to the ground , taking insects from the surface or just underneath . Generally , common starlings prefer foraging amongst short @-@ cropped grasses and are often found among grazing animals or perched on their backs , where they will also feed on the mammal 's external parasites . Large flocks may engage in a practice known as " roller @-@ feeding " , where the birds at the back of the flock continually fly to the front where the feeding opportunities are best . The larger the flock , the nearer individuals are to one another while foraging . Flocks often feed in one place for some time , and return to previous successfully <unk> sites . 
- There are three types of foraging behaviour observed in the common starling . " <unk> " involves the bird <unk> its beak into the ground randomly and <unk> until an insect has been found , and is often accompanied by bill <unk> where the bird opens its beak in the soil to <unk> a hole . This behaviour , first described by Konrad Lorenz and given the German term <unk> , is also used to create and widen holes in plastic garbage bags . It takes time for young common starlings to perfect this technique , and because of this the diet of young birds will often contain fewer insects . " <unk> " is the capture of flying insects directly from the air , and " <unk> " is the less common technique of striking forward to catch a moving invertebrate on the ground . <unk> are caught by pulling from soil . Common starlings that have periods without access to food , or have a reduction in the hours of light available for feeding , compensate by increasing their body mass by the deposition of fat . 
- 
- = = = <unk> = = = 
- 
- <unk> males find a suitable cavity and begin to build nests in order to attract single females , often <unk> the nest with ornaments such as flowers and fresh green material , which the female later <unk> upon accepting him as a mate . The amount of green material is not important , as long as some is present , but the presence of herbs in the decorative material appears to be significant in attracting a mate . The scent of plants such as <unk> acts as an olfactory <unk> to females . 
- The males sing throughout much of the construction and even more so when a female approaches his nest . Following copulation , the male and female continue to build the nest . Nests may be in any type of hole , common locations include inside <unk> trees , buildings , tree stumps and man @-@ made nest @-@ boxes . S. v. zetlandicus typically breeds in crevices and holes in cliffs , a habitat only rarely used by the nominate form . Nests are typically made out of straw , dry grass and twigs with an inner lining made up of feathers , wool and soft leaves . Construction usually takes four or five days and may continue through incubation . 
- Common starlings are both monogamous and <unk> ; although broods are generally brought up by one male and one female , occasionally the pair may have an extra <unk> . <unk> may be part of a colony , in which case several other nests may occupy the same or nearby trees . Males may mate with a second female while the first is still on the nest . The reproductive success of the bird is poorer in the second nest than it is in the primary nest and is better when the male remains monogamous . 
- 
- = = = Breeding = = = 
- 
- Breeding takes place during the spring and summer . Following copulation , the female lays eggs on a daily basis over a period of several days . If an egg is lost during this time , she will lay another to replace it . There are normally four or five eggs that are <unk> in shape and pale blue or occasionally white , and they commonly have a glossy appearance . The colour of the eggs seems to have evolved through the relatively good visibility of blue at low light levels . The egg size is 26 @.@ 5 – 34 @.@ 5 mm ( 1 @.@ 04 – 1 @.@ 36 in ) in length and 20 @.@ 0 – 22 @.@ 5 mm ( 0 @.@ 79 – 0 @.@ 89 in ) in maximum diameter . 
- Incubation lasts thirteen days , although the last egg laid may take 24 hours longer than the first to hatch . Both parents share the responsibility of brooding the eggs , but the female spends more time <unk> them than does the male , and is the only parent to do so at night when the male returns to the communal roost . The young are born blind and naked . They develop light fluffy down within seven days of hatching and can see within nine days . Once the chicks are able to regulate their body temperature , about six days after hatching , the adults largely cease removing droppings from the nest . Prior to that , the <unk> would wet both the chicks ' plumage and the nest material , thereby reducing their effectiveness as <unk> and increasing the risk of chilling the <unk> . <unk> remain in the nest for three weeks , where they are fed continuously by both parents . Fledglings continue to be fed by their parents for another one or two weeks . A pair can raise up to three broods per year , frequently reusing and <unk> the same nest , although two broods is typical , or just one north of <unk> . Within two months , most juveniles will have <unk> and gained their first basic plumage . They acquire their adult plumage the following year . As with other <unk> , the nest is kept clean and the chicks ' <unk> sacs are removed by the adults . 
- <unk> brood parasites are common in common starling nests . Female " <unk> " ( <unk> females during the breeding season ) present in colonies often lay eggs in another pair 's nest . Fledglings have also been reported to invade their own or neighbouring nests and evict a new brood . Common starling nests have a 48 % to 79 % rate of successful fledging , although only 20 % of nestlings survive to breeding age ; the adult survival rate is closer to 60 % . The average life span is about 2 – 3 years , with a longevity record of 22 <unk> 11 m . 
- 
- = = Predators and parasites = = 
- 
- A majority of starling predators are avian . The typical response of starling groups is to take flight , with a common sight being undulating flocks of starling flying high in quick and agile patterns . Their abilities in flight are seldom matched by birds of prey . Adult common starlings are hunted by hawks such as the northern <unk> ( <unk> <unk> ) and Eurasian <unk> ( <unk> <unk> ) , and falcons including the peregrine falcon ( Falco <unk> ) , Eurasian <unk> ( Falco <unk> ) and common <unk> ( Falco <unk> ) . <unk> raptors like black and red <unk> ( <unk> <unk> & <unk> ) , eastern imperial eagle ( Aquila <unk> ) , common <unk> ( Buteo <unk> ) and <unk> harrier ( Circus <unk> ) tend to take the more easily caught fledglings or juveniles . While perched in groups by night , they can be vulnerable to owls , including the little owl ( Athene <unk> ) , long @-@ eared owl ( <unk> <unk> ) , short @-@ eared owl ( <unk> <unk> ) , barn owl ( <unk> <unk> ) , tawny owl ( <unk> <unk> ) and Eurasian eagle @-@ owl ( <unk> <unk> ) . 
- More than twenty species of hawk , owl and falcon are known to occasionally predate feral starlings in North America , though the most regular predators of adults are likely to be urban @-@ living peregrine falcons or <unk> ( Falco <unk> ) . Common <unk> ( <unk> <unk> ) sometimes evict eggs , nestlings and adult common starlings from their nests , and the lesser <unk> ( <unk> minor ) , a brood parasite , uses the common starling as a host . <unk> are more commonly the culprits rather than victims of nest <unk> however , especially towards other starlings and woodpeckers . Nests can be raided by animals capable of climbing to them , such as stoats ( <unk> <unk> ) , raccoons ( Procyon <unk> ) and squirrels ( <unk> spp . ) , and cats may catch the <unk> . 
- Common starlings are hosts to a wide range of parasites . A survey of three hundred common starlings from six US states found that all had at least one type of parasite ; 99 % had external fleas , mites or ticks , and 95 % carried internal parasites , mostly various types of worm . Blood @-@ sucking species leave their host when it dies , but other external parasites stay on the corpse . A bird with a deformed bill was heavily infested with <unk> lice , presumably due to its inability to remove <unk> . 
- The hen flea ( <unk> gallinae ) is the most common flea in their nests . The small , pale house @-@ sparrow flea C. <unk> , is also occasionally found there and probably arises from the habit of its main host of taking over the nests of other species . This flea does not occur in the US , even on house <unk> . <unk> include <unk> <unk> , <unk> <unk> and <unk> <unk> . Other <unk> parasites include <unk> ticks and mites such as <unk> <unk> , <unk> <unk> , <unk> gallinae , <unk> <unk> , O. <unk> , <unk> species , <unk> <unk> and <unk> <unk> . The hen <unk> D. gallinae is itself preyed upon by the predatory <unk> <unk> <unk> . The presence of this control on numbers of the parasitic species may explain why birds are prepared to reuse old nests . 
- Flying insects that <unk> common starlings include the <unk> @-@ fly <unk> <unk> and the <unk> fly <unk> <unk> . The latter species breaks off the feathers of its host and lives on the <unk> produced by growing plumage . <unk> of the moth <unk> <unk> are nest scavengers , which feed on animal material such as <unk> or dead nestlings . <unk> blood parasites of the genus <unk> have been found in common starlings , but a better known pest is the brilliant scarlet nematode <unk> <unk> . This worm moves from the lungs to the <unk> and may cause its host to <unk> . In Britain , the <unk> and the common starling are the most infested wild birds . Other recorded internal parasites include the spiny @-@ headed worm <unk> <unk> . 
- Common starlings may contract avian tuberculosis , avian malaria and <unk> @-@ induced <unk> . Captive starlings often accumulate excess iron in the liver , a condition that can be prevented by adding black tea @-@ leaves to the food . 
- 
- = = Distribution and habitat = = 
- 
- The global population of common starlings was estimated to be 310 million individuals in 2004 , occupying a total area of 8 @,@ <unk> @,@ 000 km2 ( 3 @,@ 420 @,@ 000 sq mi ) . Widespread throughout the Northern Hemisphere , the bird is native to Eurasia and is found throughout Europe , northern Africa ( from Morocco to Egypt ) , India ( mainly in the north but regularly extending further south and extending into the Maldives ) Nepal , the Middle East including Syria , Iran , and Iraq and north @-@ western China . 
- Common starlings in the south and west of Europe and south of latitude <unk> are mainly resident , although other populations migrate from regions where the winter is harsh , the ground frozen and food scarce . Large numbers of birds from northern Europe , Russia and Ukraine migrate south <unk> or south eastwards . In the autumn , when immigrants are arriving from eastern Europe , many of Britain 's common starlings are setting off for Iberia and North Africa . Other groups of birds are in passage across the country and the pathways of these different streams of bird may cross . Of the 15 @,@ 000 birds ringed as nestlings in Merseyside , England , individuals have been recovered at various times of year as far afield as Norway , Sweden , Finland , Russia , Ukraine , Poland , Germany and the Low Countries . Small numbers of common starling have sporadically been observed in Japan and Hong Kong but it is unclear from where these birds originated . In North America , northern populations have developed a migration pattern , <unk> much of Canada in winter . Birds in the east of the country move southwards , and those from further west winter in the southwest of the US . 
- Common starlings prefer urban or suburban areas where artificial structures and trees provide adequate nesting and roosting sites . <unk> are also favoured for roosting and the birds commonly feed in grassy areas such as farmland , grazing pastures , playing fields , golf courses and airfields where short grass makes foraging easy . They occasionally inhabit open forests and woodlands and are sometimes found in shrubby areas such as Australian <unk> . Common starlings rarely inhabit dense , wet forests ( i.e. rainforests or wet <unk> forests ) but are found in coastal areas , where they nest and roost on cliffs and forage amongst seaweed . Their ability to adapt to a large variety of habitats has allowed them to disperse and establish themselves in diverse locations around the world resulting in a habitat range from coastal wetlands to alpine forests , from sea cliffs to mountain ranges 1 @,@ 900 m ( 6 @,@ 200 ft ) above sea level . 
- 
- = = = Introduced populations = = = 
- 
- The common starling has been introduced to and has successfully established itself in New Zealand , Australia , South Africa , North America , Fiji and several Caribbean islands . As a result , it has also been able to migrate to Thailand , Southeast Asia and New Guinea . 
- 
- = = = = South America = = = = 
- 
- Five individuals conveyed on a ship from England <unk> near <unk> de <unk> in Venezuela in November 1949 , but subsequently vanished . In 1987 , a small population of common starlings was observed nesting in gardens in the city of Buenos Aires . Since then , despite some initial attempts at eradication , the bird has been expanding its breeding range at an average rate of 7 @.@ 5 km ( 4 @.@ 7 mi ) per year , keeping within 30 km ( 19 mi ) of the Atlantic coast . In Argentina , the species makes use of a variety of natural and man @-@ made nesting sites , particularly <unk> holes . 
- 
- = = = = Australia = = = = 
- 
- The common starling was introduced to Australia to consume insect pests of farm crops . Early settlers looked forward to their arrival , believing that common starlings were also important for the <unk> of flax , a major agricultural product . Nest @-@ boxes for the newly released birds were placed on farms and near crops . The common starling was introduced to Melbourne in 1857 and Sydney two decades later . By the 1880s , established populations were present in the southeast of the country thanks to the work of <unk> committees . By the 1920s , common starlings were widespread throughout Victoria , Queensland and New South Wales , but by then they were considered to be pests . Although common starlings were first sighted in Albany , Western Australia in 1917 , they have been largely prevented from spreading to the state . The wide and arid <unk> Plain provides a natural barrier and control measures have been adopted that have killed 55 @,@ 000 birds over three decades . The common starling has also colonised Lord Howe Island and Norfolk Island . 
- 
- = = = = New Zealand = = = = 
- 
- The early settlers in New Zealand cleared the bush and found their newly planted crops were invaded by hordes of <unk> and other insects deprived of their previous food sources . Native birds were not <unk> to living in close proximity to man so the common starling was introduced from Europe to control the pests . It was first brought over in 1862 by the Nelson <unk> Society and other introductions followed . The birds soon became established and are now found all over the country including the subtropical <unk> Islands to the north and the equally distant Macquarie Island far to the south . 
- 
- = = = = North America = = = = 
- 
- After two failed attempts , about 60 common starlings were released in 1890 into New York 's Central Park by Eugene <unk> . He was president of the American <unk> Society , which tried to introduce every bird species mentioned in the works of William Shakespeare into North America . About the same date , the Portland Song Bird Club released 35 pairs of common starlings in Portland , Oregon . These birds became established but disappeared around 1902 . Common starlings <unk> in the Pacific Northwest in the mid @-@ 1940s and these birds were probably descendants of the 1890 Central Park introduction . The original 60 birds have since swelled in number to 150 million , occupying an area extending from southern Canada and Alaska to Central America . 
- 
- = = = = Polynesia = = = = 
- 
- The common starling appears to have arrived in Fiji in 1925 on Ono @-@ i @-@ <unk> and <unk> islands . It may have colonised from New Zealand via Raoul in the <unk> Islands where it is abundant , that group being roughly <unk> between New Zealand and Fiji . Its spread in Fiji has been limited , and there are doubts about the population 's viability . Tonga was colonised at about the same date and the birds there have been slowly spreading north through the group . 
- 
- = = = = South Africa = = = = 
- 
- In South Africa , the common starling was introduced in 1897 by Cecil Rhodes . It spread slowly and by 1954 had reached <unk> and Port Elizabeth . It is now common in the southern Cape region , thinning out northwards to the Johannesburg area . It is present in the Western Cape , the Eastern Cape and the Free State provinces of South Africa and lowland <unk> , with occasional sightings in KwaZulu @-@ Natal , <unk> and around the town of <unk> in <unk> . In Southern Africa populations appear to be resident and the bird is very much associated with man , his <unk> and pastures . It favours irrigated land and is absent from regions where the ground is baked so dry that it cannot probe for insects . It may compete with native birds for crevice nesting sites but the indigenous species are probably more disadvantaged by destruction of their natural habitat than they are by inter @-@ specific competition . It breeds from September to December and outside the breeding season may <unk> in large flocks , often roosting in <unk> . It is the most common bird species in urban and agricultural areas . 
- 
- = = = = West Indies = = = = 
- 
- The common starling was introduced to Jamaica in 1903 , and the Bahamas and Cuba were colonised naturally from the US . This bird is fairly common but local in Jamaica , Grand <unk> and <unk> , and is rare in the rest of the Bahamas , eastern Cuba , the Cayman Islands , Puerto Rico and St. Croix . 
- 
- = = Status = = 
- 
- The global population of the common starling is estimated to be more than 310 million individuals and its numbers are not thought to be declining significantly , so the bird is classified by the International Union for Conservation of Nature as being of least concern . It had shown a marked increase in numbers throughout Europe from the 19th century to around the 1950s and 60s . In about 1830 , S. v. vulgaris expanded its range in the British Isles , spreading into Ireland and areas of Scotland where it had formerly been absent , although S. v. zetlandicus was already present in Shetland and the Outer Hebrides . The common starling has bred in northern Sweden from 1850 and in Iceland from 1935 . The breeding range spread through southern France to northeastern Spain , and there were other range expansions particularly in Italy , Austria and Finland . It started breeding in Iberia in 1960 , while the spotless starling 's range had been expanding northward since the 1950s . The low rate of advance , about 4 @.@ 7 km ( 2 @.@ 9 mi ) per year for both species , is due to the <unk> mountain and woodland terrain . <unk> has since slowed even further due to direct competition between the two similar species where they overlap in southwestern France and northwestern Spain . 
- Major declines in populations have been observed from 1980 onward in Sweden , Finland , northern Russia ( <unk> ) and the Baltic States , and smaller declines in much of the rest of northern and central Europe . The bird has been adversely affected in these areas by intensive agriculture , and in several countries it has been red @-@ listed due to population declines of more than 50 % . Numbers dwindled in the United Kingdom by more than 80 % between 1966 and 2004 ; although populations in some areas such as Northern Ireland were stable or even increased , those in other areas , mainly England , declined even more sharply . The overall decline seems to be due to the low survival rate of young birds , which may be caused by changes in agricultural practices . The intensive farming methods used in northern Europe mean there is less pasture and meadow habitat available , and the supply of grassland invertebrates needed for the nestlings to thrive is <unk> reduced . 
- 
- = = Relationship with humans = = 
- 
- 
- = = = <unk> and problems = = = 
- 
- Since common starlings eat insect pests such as <unk> , they are considered beneficial in northern Eurasia , and this was one of the reasons given for introducing the birds elsewhere . Around 25 million nest boxes were erected for this species in the former Soviet Union , and common starlings were found to be effective in controlling the grass <unk> <unk> <unk> in New Zealand . The original Australian introduction was facilitated by the provision of nest boxes to help this mainly insectivorous bird to breed successfully , and even in the US , where this is a pest species , the Department of Agriculture acknowledges that vast numbers of insects are consumed by common starlings . 
- Common starlings introduced to areas such as Australia or North America , where other members of the genus are absent , may affect native species through competition for nest holes . In North America , <unk> , <unk> , woodpeckers , purple <unk> and other swallows may be affected . In Australia , competitors for nesting sites include the <unk> and eastern rosellas . For its role in the decline of local native species and the damages to agriculture , the common starling has been included in the IUCN List of the world 's 100 worst invasive species . 
- Common starlings can eat and damage fruit in orchards such as grapes , <unk> , olives , currants and tomatoes or dig up newly <unk> grain and sprouting crops . They may also eat animal feed and distribute seeds through their droppings . In eastern Australia , weeds like <unk> <unk> , <unk> and <unk> are thought to have been spread by common starlings . Agricultural damage in the US is estimated as costing about US $ 800 million annually . This bird is not considered to be as damaging to agriculture in South Africa as it is in the United States . 
- The large size of flocks can also cause problems . Common starlings may be sucked into aircraft jet engines , one of the worst instances of this being an incident in Boston in 1960 , when sixty @-@ two people died after a turboprop airliner flew into a flock and <unk> into the sea at Winthrop Harbor . 
- <unk> ' droppings can contain the fungus <unk> <unk> , the cause of <unk> in humans . At roosting sites this fungus can thrive in accumulated droppings . There are a number of other infectious diseases that can potentially be transmitted by common starlings to humans , although the potential for the birds to spread infections may have been exaggerated . 
- 
- = = = Control = = = 
- 
- Because of the damage they do , there have been attempts to control the numbers of both native and introduced populations of common starlings . Within the natural breeding range , this may be affected by legislation . For example , in Spain , this is a species hunted commercially as a food item , and has a close season , whereas in France , it is classed as a pest , and the season in which it may be killed covers the greater part of the year . In the UK , the common starling may be killed at any time of year . This species is migratory , so birds involved in control measures may have come from a wide area and breeding populations may not be greatly affected . In Europe , the varying legislation and mobile populations mean that control attempts may have limited long @-@ term results . Non @-@ lethal techniques such as <unk> with visual or auditory devices have only a temporary effect in any case . 
- Huge urban roosts in cities can create problems due to the noise and mess made and the smell of the droppings . In 1949 , so many birds landed on the clock hands of London 's Big Ben that it stopped , leading to unsuccessful attempts to disrupt the roosts with netting , repellent chemical on the ledges and broadcasts of common starling alarm calls . An entire episode of The <unk> Show in 1954 was a parody of the futile efforts to disrupt the large common starling roosts in central London . 
- Where it is introduced , the common starling is unprotected by legislation , and extensive control plans may be initiated . Common starlings can be prevented from using nest boxes by ensuring that the access holes are smaller than the 1 @.@ 5 in ( 38 mm ) diameter they need , and the removal of <unk> <unk> them from visiting bird <unk> . 
- Western Australia banned the import of common starlings in 1895 . New flocks arriving from the east are routinely shot , while the less cautious juveniles are trapped and netted . New methods are being developed , such as <unk> one bird and tracking it back to establish where other members of the flock roost . Another technique is to <unk> the DNA of Australian common starling populations to track where the migration from eastern to western Australia is occurring so that better <unk> strategies can be used . By 2009 , only 300 common starlings were left in Western Australia , and the state committed a further A $ 400 @,@ 000 in that year to continue the eradication programme . 
- In the United States , common starlings are exempt from the <unk> Bird Treaty Act , which prohibits the taking or killing of migratory birds . No permit is required to remove nests and eggs or kill juveniles or adults . Research was undertaken in 1966 to identify a suitable <unk> that would both kill common starlings and would readily be eaten by them . It also needed to be of low toxicity to mammals and not likely to cause the death of pets that ate dead birds . The chemical that best fitted these criteria was DRC @-@ <unk> , now marketed as <unk> . In 2008 , the United States government poisoned , shot or trapped 1 @.@ 7 million birds , the largest number of any nuisance species to be destroyed . In 2005 , the population in the United States was estimated at 140 million birds , around 45 % of the global total of 310 million . 
- 
- = = = In science and culture = = = 
- 
- Common starlings may be kept as pets or as laboratory animals . Austrian <unk> Konrad Lorenz wrote of them in his book King Solomon 's Ring as " the poor man 's dog " and " something to love " , because nestlings are easily obtained from the wild and after careful hand rearing they are straightforward to look after . They adapt well to captivity , and thrive on a diet of standard bird feed and <unk> . Several birds may be kept in the same cage , and their <unk> makes them easy to train or study . The only disadvantages are their <unk> and indiscriminate defecation habits and the need to take precautions against diseases that may be transmitted to humans . As a laboratory bird , the common starling is second in numbers only to the domestic <unk> . 
- The common starling 's gift for mimicry has long been recognised . In the medieval Welsh <unk> , <unk> <unk> a common starling , " taught it words " , and sent it across the Irish Sea with a message to her brothers , <unk> and <unk> , who then sailed from Wales to Ireland to rescue her . Pliny the Elder claimed that these birds could be taught to speak whole sentences in Latin and Greek , and in Henry IV , William Shakespeare had Hotspur declare " The king forbade my tongue to speak of Mortimer . But I will find him when he is asleep , and in his ear I 'll <unk> ' Mortimer ! ' <unk> I 'll have a starling shall be taught to speak nothing but Mortimer , and give it to him to keep his anger still in motion . " 
- Mozart had a pet common starling which could sing part of his Piano Concerto in G Major ( <unk> . 453 ) . He had bought it from a shop after hearing it sing a phrase from a work he wrote six weeks previously , which had not yet been performed in public . He became very attached to the bird and arranged an elaborate funeral for it when it died three years later . It has been suggested that his A Musical <unk> ( K. 522 ) might be written in the comical , <unk> style of a starling 's <unk> . Other people who have owned common starlings report how adept they are at picking up phrases and expressions . The words have no meaning for the starling , so they often mix them up or use them on what to humans are inappropriate occasions in their songs . Their ability at mimicry is so great that strangers have looked in vain for the human they think they have just heard speak . 
- Common starlings are trapped for food in some Mediterranean countries . The meat is tough and of low quality , so it is <unk> or made into <unk> . One recipe said it should be <unk> " until tender , however long that may be " . Even when correctly prepared , it may still be seen as an acquired taste . 
- 
diff --git a/PyTorch/LanguageModel/data/wikitext-2/valid.txt b/PyTorch/LanguageModel/data/wikitext-2/valid.txt
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- 
- = Homarus gammarus = 
- 
- Homarus gammarus , known as the European lobster or common lobster , is a species of <unk> lobster from the eastern Atlantic Ocean , Mediterranean Sea and parts of the Black Sea . It is closely related to the American lobster , H. americanus . It may grow to a length of 60 cm ( 24 in ) and a mass of 6 kilograms ( 13 lb ) , and bears a conspicuous pair of claws . In life , the lobsters are blue , only becoming " lobster red " on cooking . Mating occurs in the summer , producing eggs which are carried by the females for up to a year before hatching into <unk> larvae . Homarus gammarus is a highly esteemed food , and is widely caught using lobster pots , mostly around the British Isles . 
- 
- = = Description = = 
- 
- Homarus gammarus is a large <unk> , with a body length up to 60 centimetres ( 24 in ) and weighing up to 5 – 6 kilograms ( 11 – 13 lb ) , although the lobsters caught in lobster pots are usually 23 – 38 cm ( 9 – 15 in ) long and weigh 0 @.@ 7 – 2 @.@ 2 kg ( 1 @.@ 5 – 4 @.@ 9 lb ) . Like other crustaceans , lobsters have a hard <unk> which they must shed in order to grow , in a process called <unk> ( <unk> ) . This may occur several times a year for young lobsters , but decreases to once every 1 – 2 years for larger animals . 
- The first pair of <unk> is armed with a large , asymmetrical pair of claws . The larger one is the " <unk> " , and has rounded <unk> used for crushing prey ; the other is the " cutter " , which has sharp inner edges , and is used for holding or tearing the prey . Usually , the left claw is the <unk> , and the right is the cutter . 
- The <unk> is generally blue above , with spots that <unk> , and yellow below . The red colour associated with lobsters only appears after cooking . This occurs because , in life , the red pigment <unk> is bound to a protein complex , but the complex is broken up by the heat of cooking , releasing the red pigment . 
- The closest relative of H. gammarus is the American lobster , Homarus americanus . The two species are very similar , and can be crossed artificially , although hybrids are unlikely to occur in the wild since their ranges do not overlap . The two species can be distinguished by a number of characteristics : 
- The <unk> of H. americanus bears one or more spines on the underside , which are lacking in H. gammarus . 
- The spines on the claws of H. americanus are red or red @-@ tipped , while those of H. gammarus are white or white @-@ tipped . 
- The underside of the claw of H. americanus is orange or red , while that of H. gammarus is creamy white or very pale red . 
- 
- = = Life cycle = = 
- 
- Female H. gammarus reach sexual maturity when they have grown to a carapace length of 80 – 85 millimetres ( 3 @.@ 1 – 3 @.@ 3 in ) , whereas males mature at a slightly smaller size . Mating typically occurs in summer between a recently <unk> female , whose shell is therefore soft , and a hard @-@ shelled male . The female carries the eggs for up to 12 months , depending on the temperature , attached to her <unk> . Females carrying eggs are said to be " <unk> " and can be found throughout the year . 
- The eggs hatch at night , and the larvae swim to the water surface where they drift with the ocean currents , preying on <unk> . This stage involves three <unk> and lasts for 15 – 35 days . After the third moult , the juvenile takes on a form closer to the adult , and adopts a <unk> lifestyle . The juveniles are rarely seen in the wild , and are poorly known , although they are known to be capable of digging extensive burrows . It is estimated that only 1 larva in every 20 @,@ 000 survives to the <unk> phase . When they reach a carapace length of 15 mm ( 0 @.@ 59 in ) , the juveniles leave their burrows and start their adult lives . 
- 
- = = Distribution = = 
- 
- Homarus gammarus is found across the north @-@ eastern Atlantic Ocean from northern Norway to the Azores and Morocco , not including the Baltic Sea . It is also present in most of the Mediterranean Sea , only missing from the section east of Crete , and along only the north @-@ west coast of the Black Sea . The <unk> populations are found in the Norwegian <unk> <unk> and <unk> , inside the Arctic Circle . 
- The species can be divided into four genetically distinct populations , one widespread population , and three which have diverged due to small effective population sizes , possibly due to adaptation to the local environment . The first of these is the population of lobsters from northern Norway , which have been referred to as the " midnight @-@ sun lobster " . The populations in the Mediterranean Sea are distinct from those in the Atlantic Ocean . The last distinct population is found in the Netherlands : samples from the <unk> were distinct from those collected in the North Sea or English Channel . 
- Attempts have been made to introduce H. gammarus to New Zealand , alongside other European species such as the edible crab , Cancer <unk> . Between 1904 and 1914 , one million lobster larvae were released from <unk> in <unk> , but the species did not become established there . 
- 
- = = Ecology = = 
- 
- Adult H. gammarus live on the continental shelf at depths of 0 – 150 metres ( 0 – <unk> ft ) , although not normally deeper than 50 m ( 160 ft ) . They prefer hard substrates , such as rocks or hard mud , and live in holes or crevices , emerging at night to feed . 
- The diet of H. gammarus mostly consists of other <unk> invertebrates . These include crabs , molluscs , sea <unk> , <unk> and polychaete worms . 
- The three <unk> lobster species Homarus gammarus , H. americanus and <unk> <unk> are hosts to the three known species of the animal <unk> <unk> ; the species on H. gammarus has not been described . 
- Homarus gammarus is susceptible to the disease <unk> , caused by the <unk> <unk> <unk> . Although it is frequently found in American lobsters , the disease has only been seen in captive H. gammarus , where prior occupation of the tanks by H. americanus could not be ruled out . 
- 
- = = Human consumption = = 
- 
- Homarus gammarus is traditionally " highly esteemed " as a <unk> and was mentioned in " The <unk> " a seventeenth century English folk song . It may fetch very high prices and may be sold fresh , frozen , canned or powdered . Both the claws and the abdomen of H. gammarus contain " excellent " white meat , and most of the contents of the cephalothorax are edible . The exceptions are the <unk> mill and the " sand vein " ( gut ) . The price of H. gammarus is up to three times higher than that of H. americanus , and the European species is considered to have a better flavour . 
- <unk> are mostly fished using lobster pots , although lines <unk> with <unk> or <unk> sometimes succeed in tempting them out , to allow them to be caught in a net or by hand . In 2008 , 4 @,@ 386 t of H. gammarus were caught across Europe and North Africa , of which 3 @,@ 462 t ( 79 % ) was caught in the British Isles ( including the Channel Islands ) . The minimum landing size for H. gammarus is a carapace length of 87 mm ( 3 @.@ 4 in ) . 
- <unk> systems for H. gammarus are under development , and production rates are still very low . 
- 
- = = <unk> history = = 
- 
- Homarus gammarus was first given a binomial name by Carl Linnaeus in the tenth edition of his Systema <unk> , published in 1758 . That name was Cancer gammarus , since Linnaeus ' concept of the genus Cancer at that time included all large crustaceans . 
- H. gammarus is the type species of the genus Homarus Weber , 1795 , as determined by Direction 51 of the International Commission on Zoological Nomenclature . Prior to that direction , confusion arose because the species had been referred to by several different names , including <unk> <unk> <unk> , 1775 and Homarus vulgaris H. Milne @-@ Edwards , 1837 , and also because Friedrich Weber 's description of the genus had been overlooked until rediscovered by Mary <unk> , rendering any prior assignments of type species ( for Homarus H. Milne @-@ Edwards , 1837 ) invalid for Homarus Weber , 1795 . 
- The type specimen of Homarus gammarus was a <unk> selected by <unk> <unk> in 1974 . It came from 57 ° 53 ′ N 11 ° 32 ′ E , near <unk> , Sweden ( 48 kilometres or 30 miles northwest of Gothenburg ) , but both it and the <unk> have since been lost . 
- The common name for H. gammarus preferred by the Food and Agriculture Organization is " European lobster " , but the species is also widely known as the " common lobster " . 
- 
- 
- = Frank <unk> = 
- 
- Air Vice Marshal Frank <unk> , CB , CBE ( 15 July 1914 – 23 December 1976 ) was a senior commander in the Royal Australian Air Force ( RAAF ) . Born and educated in Tasmania , he joined the RAAF as an air cadet in January 1934 . He specialised in flying instruction and navigation before the outbreak of World War II . In April 1941 , he became commanding officer of No. 2 Squadron , which operated Lockheed <unk> . The squadron was deployed to Dutch Timor in December , and saw action against Japanese forces in the South West Pacific . After returning to Australia in February 1942 , <unk> held staff appointments and training commands , finishing the war a group captain . 
- <unk> served as Officer Commanding North @-@ Western Area in 1946 , and as Director of Training from 1947 to 1950 . In 1950 – 51 , during the Malayan Emergency , he was stationed at Singapore as commander of No. 90 ( Composite ) Wing and , later , RAF <unk> . He twice served as acting Air Member for Personnel , in 1957 and 1959 – 60 , receiving appointment as a Commander of the Order of the British Empire in 1958 . Promoted air vice marshal , he successively held the positions of Air Officer Commanding ( AOC ) Operational Command in 1961 – 62 , AOC No. 224 Group RAF from 1962 to 1965 during the Indonesia – Malaysia <unk> , Deputy Chief of the Air Staff in 1965 – 66 , and AOC Support Command in 1966 – 67 . He was appointed a Companion of the Order of the Bath in 1965 . Following a posting to London as Head of the Australian Joint Services Staff from 1968 to 1971 , he retired from the Air Force and died in Melbourne five years later . 
- 
- = = Early career = = 
- 
- The son of farmers Malcolm and Hilda <unk> , Frank <unk> was born on 15 July 1914 in <unk> , Tasmania . He was <unk> at <unk> College , Hobart , and <unk> in 1932 . Against the wishes of his parents he joined the Royal Australian Air Force ( RAAF ) as an air cadet on 16 January 1934 . He underwent flying instruction with No. 1 Flying Training School ( <unk> ) at RAAF Point Cook , Victoria , and was commissioned as a pilot officer on 1 January 1935 . 
- After completing a conversion course , <unk> was assigned to the <unk> Squadron at Point Cook . No larger than a flight according to the official history of the pre @-@ war RAAF , <unk> Squadron was part of No. 1 <unk> and operated <unk> Southampton flying boats and de Havilland <unk> Moth <unk> , among other types . During this posting <unk> was promoted to flying officer , on 1 July 1935 , and wrote a paper on national defence in which he suggested that with " strong air forces , naval forces ( including submarines ) , and fixed defences , Australia may be made practically <unk> " . According to Air Force historian Alan Stephens , this paper " in effect , defined the ' anti @-@ <unk> ' concept which has been a persistent feature of RAAF strategic thinking " . 
- <unk> completed a flying instructors course in July 1936 and joined the staff of No. 1 <unk> . He was promoted to flight lieutenant on 1 March 1937 . <unk> in July 1938 , he was one of six students to take part in the RAAF 's first Long <unk> Navigation Course , run by Flight <unk> Bill <unk> and <unk> Murdoch at Point Cook . The course involved several epic training flights that attracted considerable media attention , including a twelve @-@ day , 10 @,@ 800 @-@ kilometre ( 6 @,@ 700 mi ) round @-@ Australia trip by three Avro Ansons , one of which was <unk> by <unk> , in November . The following month , <unk> led the three Ansons on a six @-@ day journey back and forth over Central Australia . He subsequently passed the navigation course with a special distinction . On 27 January 1939 he was posted to RAAF Station Laverton , Victoria , as a flight commander . He served initially with No. 2 Squadron , before transferring to No. 1 Squadron on 29 August . Both units operated Ansons . 
- 
- = = World War II = = 
- 
- Following the outbreak of World War II , No. 1 Squadron was engaged in convoy escort and maritime reconnaissance duties off south @-@ eastern Australia . <unk> continued to serve with the squadron as a flight commander until 15 January 1940 , when he was assigned to Headquarters Laverton as the station navigation officer . On 27 March he was posted to the staff of RAAF Headquarters , Melbourne . He was promoted to squadron leader on 1 June 1940 . Two weeks later he married Katherine Bridge at St Paul 's Anglican Church in <unk> ; the couple would have a son and a daughter . 
- <unk> was given command of No. 2 Squadron at Laverton on 15 April 1941 , and raised to wing commander on 1 July . <unk> with Lockheed <unk> , the squadron mainly conducted maritime patrols in southern waters until 5 December , when four of its aircraft were ordered to Darwin , Northern Territory , in response to fears of Japanese aggression in the Pacific . On 7 December , this detachment established itself at <unk> , near <unk> in Dutch Timor , while No. 2 Squadron 's eight remaining <unk> were stationed at Darwin on standby . The following day , aware that Australia was now at war in the Pacific , one of the <unk> @-@ based <unk> attacked the Japanese <unk> <unk> <unk> , which was suspected of being a radio ship , and forced it aground . By 12 December , <unk> had transferred to <unk> as commanding officer of the base , as well as No. 2 Squadron . 
- During January 1942 , No. 2 Squadron 's aircraft were dispersed at <unk> , <unk> Island , and Darwin . The <unk> detachment attacked Japanese shipping taking part in the invasion of <unk> . Two <unk> shot down or damaged three Japanese <unk> that attacked them as they were bombing a transport ship on 11 January ; the next day both <unk> were themselves shot down by <unk> <unk> . <unk> was bombed by the Japanese for the first time on 26 January 1942 , and attacked regularly thereafter , damaging some aircraft . The intact <unk> were withdrawn to Darwin but <unk> and his staff remained at <unk> to enable the base to be used by aircraft during reconnaissance missions from Australia . On 18 February , <unk> was ordered to evacuate all his personnel except a small party to <unk> the airfield with assistance from <unk> Force . He returned to Darwin the following day , just as the city experienced its first raid by the Japanese . Four of No. 2 Squadron 's <unk> were destroyed in the attack ; the remainder were relocated to Daly Waters , where they continued to carry out reconnaissance and bombing missions against Japanese targets in Timor . 
- <unk> remained in Darwin as <unk> of Operations at Headquarters North @-@ Western Area Command until 12 May 1942 , when he was posted to <unk> , Victoria , as commanding officer of No. 2 Air Navigation School , operating Ansons . No. 97 ( Reserve ) Squadron was formed from the school 's personnel in June . On 20 July 1943 , <unk> took command of No. 2 Air Observer School ( <unk> ) , also operating Ansons , at Mount Gambier , South Australia . He was promoted to group captain on 1 December 1943 , and was appointed the inaugural commanding officer of No. 3 <unk> , operating Ansons and <unk> Battles out of Port <unk> , on 9 December . After handing over command of No. 3 <unk> , he commenced studies at RAAF Staff School in Mount Martha , Victoria , on 2 October 1944 . He was appointed senior administrative staff officer at North @-@ Western Area Command on 12 January 1945 . 
- 
- = = Post @-@ war career = = 
- 
- <unk> became Officer Commanding North @-@ Western Area in January 1946 . <unk> to Britain at the end of the year , he attended the Royal Air Force Staff College , <unk> , and served with RAAF Overseas Headquarters , London . On his return to Australia , in November 1947 , he became Director of Training at RAAF Headquarters . In November 1950 , <unk> was appointed to take over command of No. 90 ( Composite ) Wing from Group Captain Paddy <unk> . Headquartered at RAF <unk> , Singapore , No. 90 Wing controlled RAAF units operating during the Malayan Emergency : No. 1 ( <unk> ) Squadron , flying Avro <unk> , and No. 38 ( Transport ) Squadron , flying Douglas C @-@ 47 Dakotas . The <unk> conducted area bombing missions over communist @-@ held territory , as well as strikes against <unk> targets . The Dakotas were tasked with <unk> flights , <unk> transport and medical <unk> across South East Asia , and in Malaya with <unk> troops and cargo , dropping supplies to friendly forces and <unk> propaganda leaflets . <unk> was slightly injured on 20 December , when a No. 38 Squadron Dakota he was co @-@ <unk> on a supply drop crash @-@ landed at Kampong <unk> in <unk> , following engine failure . In August 1951 he was named commander of RAF <unk> , Singapore , in addition to his post as commanding officer of No. 90 Wing . <unk> handed over command of No. 90 Wing in December 1951 . 
- On 19 February 1952 , <unk> became senior air staff officer ( <unk> ) at Eastern Area Command in <unk> , New South Wales . During his term as <unk> , the RAAF began re @-@ <unk> with English Electric Canberra jet bombers and CAC Sabre jet fighters . The Air Force also underwent a major <unk> change , as it transitioned from a geographically based command @-@ and @-@ control system to one based on function , resulting in the establishment of Home ( operational ) , Training , and Maintenance <unk> . Eastern Area Command , considered a de facto operational headquarters owing to the <unk> of combat units under its control , was reorganised as Home Command in October 1953 . <unk> was appointed an Officer of the Order of the British Empire ( OBE ) in the 1954 New Year Honours for his " exceptional ability and devotion to duty " . He was promoted to acting air <unk> in May . His appointment as aide @-@ de @-@ camp to Queen Elizabeth II was announced on 7 October 1954 . 
- <unk> was promoted to substantive air <unk> on 1 January 1955 . In November he was posted to RAAF Overseas Headquarters , London , and the following year undertook studies at the Imperial Defence College . Returning to Australia , he served as acting Air Member for Personnel at the Department of Air , Canberra , from 19 March to 21 October 1957 , between the terms of Air Vice <unk> Fred <unk> and Allan Walters , and again from 24 August 1959 to 28 March 1960 , between the terms of Walters and Air Vice Marshal Bill <unk> . In this role <unk> occupied a seat on the Air Board , the service 's controlling body that comprised its senior officers and was chaired by the Chief of the Air Staff . He was also one of two RAAF representatives to serve on a committee , chaired by businessman William John Allison , examining conditions of defence service ; the committee 's recommendations led to a doubling of flight pay , among other improvements . <unk> 's other positions at the Department of Air included Air Commodore Plans from October 1957 to January 1959 , and Director General Plans and Policy from January to August 1959 . The latter assignment put him in charge of the RAAF 's Directorate of Intelligence . <unk> was appointed a Commander of the Order of the British Empire ( CBE ) in the 1958 Queen 's <unk> Honours , <unk> on 3 June . In May 1960 he became acting Deputy Chief of the Air Staff . 
- On 30 January 1961 , <unk> joined the staff of Operational Command ( <unk> ) , the successor organisation to Home Command , responsible for the direction of RAAF operational units . He took over as Air Officer Commanding ( AOC ) <unk> from Air Vice Marshal <unk> Hancock in April . <unk> was promoted to air vice marshal on 29 May . On 17 July 1962 , he was posted to RAAF Base <unk> , Malaya , and took up the appointment of AOC No. 224 Group RAF in Singapore one week later . He was succeeded as AOC <unk> by Air Vice Marshal <unk> Murdoch . As AOC No. 224 Group , <unk> had overall responsibility for regional air defence and offensive air operations during the <unk> Rebellion in December 1962 , and the subsequent <unk> between Indonesia and Malaysia that officially began the following month . <unk> from his first wife in 1956 , he married widowed social worker Vernon Spence at the Sydney registry office on 20 January 1964 . He handed over No. 224 Group to Air Vice Marshal Christopher <unk> @-@ Norris on 30 November . 
- Returning to Australia , <unk> became Deputy Chief of the Air Staff ( <unk> ) on 26 January 1965 . He was appointed a Companion of the Order of the Bath ( CB ) " in recognition of distinguished service in the Borneo Territories " on 22 June . His tenure as <unk> coincided with the most significant <unk> program the Air Force had undertaken since World War II , and with manpower shortages stemming from this expansion and from Australia 's increasing involvement in the security of South East Asia . The first RAAF helicopters were committed to the Vietnam War towards the end of his term , and he travelled to Saigon with the Chief of the General Staff , Lieutenant General Sir John <unk> , in March 1966 to plan the deployment . The year before , <unk> had recommended to Air Marshal Murdoch , the Chief of the Air Staff , that two Iroquois be sent to Vietnam for <unk> purposes ; Murdoch had rebuffed <unk> , and the RAAF helicopter squadron was considered <unk> for its army co @-@ operation role when it finally did deploy . <unk> succeeded Air Vice Marshal Douglas Candy as AOC Support Command , Melbourne , on 8 August 1966 . Support Command had been formed in 1959 , by merging the RAAF 's former Training and Maintenance <unk> . On 1 January 1968 , <unk> was posted to London as Head of the Australian Joint Services Staff . He served as an Extra Gentleman Usher to the Queen from 17 November 1970 to 5 June 1971 . 
- 
- = = <unk> = = 
- 
- Returning to Australia in June 1971 , <unk> took <unk> leave before retiring from the Air Force on 3 August . He made his home in Melbourne , where he died aged 62 on 23 December 1976 , after a lengthy battle with cancer . <unk> by his children and his second wife , he was given a private funeral and <unk> at <unk> <unk> . 
- 
- 
- = M @-@ 82 ( Michigan highway ) = 
- 
- M @-@ 82 is a state trunkline in the Lower Peninsula in the US state of Michigan that travels between <unk> and Howard City . The section between <unk> and Howard City travels through <unk> and along the southern edge of <unk> National Forest . The current version of M @-@ 82 is actually the second in the state ; the first usage appeared in the Upper Peninsula by 1919 . The Lower Peninsula routing has been in use since the 1920s . Various extensions and <unk> have shifted the terminus as far west as New Era or <unk> in the past . The current route was finalized in the late 1970s 
- 
- = = Route description = = 
- 
- M @-@ 82 begins at a junction with M @-@ 120 and B @-@ 96 west of <unk> . This junction is at a <unk> of county lines . M @-@ 120 forms the north – south <unk> – <unk> and <unk> – <unk> county lines . B @-@ 96 , which forms the east – west <unk> – <unk> county line runs due west of the intersection . M @-@ 82 exits the intersection to the east . The highway runs along 48th Street through rural farms into the community of <unk> . The road , now called Main Street , passes through the downtown area and M @-@ 82 turns south along Stewart Avenue out of town . The highway rounds a curve and transitions to 72nd Street running eastward through more farm land . As the trunkline approaches <unk> , the terrain becomes more wooded near the <unk> River . M @-@ 82 follows <unk> Street east to a junction with M @-@ 37 . The two highways merge and run concurrently over the river and into downtown on State Street . South of downtown , M @-@ 82 turns east again , independent of M @-@ 37 , and runs along 82nd Street on the southern edge of the <unk> National Forest . The east end of M @-@ 82 is at exit 118 on US 131 / M @-@ 46 west of Howard City . 
- The Michigan Department of Transportation ( MDOT ) , as a part of its maintenance responsibilities , tracks the volume of traffic using its roadways . These levels are expressed in terms of a metric called average annual daily traffic ( AADT ) , which is a calculation of the traffic along a segment of road for any average day of the year . In 2009 , the department 's measurements indicated that a segment of M @-@ 82 west of <unk> had the peak volume for the highway at 16 @,@ <unk> vehicles daily . The traffic nadir was 4 @,@ <unk> vehicles east of <unk> . In addition , MDOT has not had any section of the highway listed on the National Highway System , a network of roads important to the country 's economy , defense , and mobility . 
- 
- = = History = = 
- 
- 
- = = = Previous designation = = = 
- 
- The first usage of M @-@ 82 was in the Upper Peninsula by July 1 , 1919 . The trunkline started at M @-@ 25 and ran north of <unk> to the vicinity of Eight Mile Corner . It was later replaced by M @-@ 48 in 1926 . 
- 
- = = = Current designation = = = 
- 
- The current M @-@ 82 dates back to 1926 . It ran from US 31 in Hart to the northern junction of US 131 and M @-@ 46 in Howard City . The highway was routed through Ferry , <unk> and <unk> , replacing M @-@ 41 . In late 1936 , M @-@ 46 was extended along the section between <unk> and Howard City , forming a M @-@ 46 / M @-@ 82 concurrency to fill a gap in the M @-@ 46 routing . This concurrent section became just M @-@ 46 in 1938 , <unk> M @-@ 82 back to the northern M @-@ 37 junction in <unk> . The highway was moved to a new alignment west of Ferry in late 1947 or early 1948 . Instead of heading northwesterly to Hart , it was continued west to end in Shelby . 
- Two <unk> in 1963 and 1964 rerouted the western end of the highway again . This time it was realigned to run from <unk> to New Era , bypassing Ferry . A larger change around 1969 <unk> the highway designations in <unk> County . M @-@ 20 replaced M @-@ 82 west of <unk> , to end at New Era instead of <unk> . The new M @-@ 120 designation replaced M @-@ 20 south of <unk> . M @-@ 82 now ran from <unk> to <unk> only . The length of the highway was increased in 1973 when the eastern end was moved back to Howard City . M @-@ 46 was rerouted to follow the US 131 freeway south of Howard City to Cedar Springs , and replaced M @-@ 57 between Cedar Springs and <unk> . This freed up the <unk> to Howard City highway for a return to the M @-@ 82 designation . The last change came in 1978 when the concurrency with M @-@ 120 was eliminated in favor of M @-@ 120 . The M @-@ 82 designation was truncated at this time to the junction west of <unk> , resulting in the current highway routing . 
- 
- = = Major intersections = = 
- 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> ( <unk> <unk> , <unk> <unk> ) is a fictional character in the <unk> manga and anime series created by <unk> <unk> . In the anime and manga , <unk> is a ninja affiliated with the village of <unk> . He is a member of Team 10 , a group of ninja consisting of himself , <unk> <unk> , <unk> <unk> , and team leader <unk> <unk> . <unk> is portrayed as a lazy character , unwilling to apply his prodigious intelligence ; <unk> has noted that he likes <unk> due to his <unk> nature . Outside of the <unk> anime and manga , <unk> has appeared in four of the feature films in the series , as well as several other media relating to the series , including video games and original video animations . 
- Numerous anime and manga publications have commented on <unk> 's character . Many reviewers commented on his <unk> and intelligence , and noted his transformation into a leader ; Anime News Network celebrated <unk> 's emergence as " an unlikely hero " in the <unk> storyline . <unk> has also been highly popular with the <unk> reader base , placing high in several popularity polls . Merchandise based on <unk> has been released , including action figures , key chains , and patches . 
- 
- = = Creation and conception = = 
- 
- <unk> <unk> has noted that he likes <unk> due to his <unk> nature despite being a genius , and contrasted him against <unk> <unk> 's intelligent but <unk> personality . <unk> also <unk> remarked that he would marry <unk> if he were a girl , noting that <unk> would likely be successful in life . When designing <unk> 's Part II appearance , <unk> wanted to give <unk> a unique appearance despite drawing him with a vest that several other ninja wear in the series . As a result , he drew his forehead protector on his arm in order to not obscure his hair . 
- 
- = = Appearances = = 
- 
- 
- = = = In <unk> = = = 
- 
- <unk> first major appearance in the series is during the <unk> <unk> , bi @-@ yearly exams for ninja who wish to advance in rank . He is part of Team 10 alongside <unk> <unk> and <unk> <unk> . He is a highly <unk> person , and he attempts to go through life with minimum effort . Contrary to his lazy tendencies , <unk> is extremely intelligent ; his teacher , <unk> <unk> , determined that <unk> 's <unk> was over 200 . <unk> 's abilities are based on the Shadow <unk> <unk> ( <unk> , <unk> no <unk> , English TV : " Shadow Possession <unk> " ) , the signature technique of his clan , with which he <unk> his shadow with an opponent 's shadow , making them <unk> and forced to mimic <unk> 's movements . As the series progresses , <unk> becomes able to manipulate his shadow in new ways . By Part II of the series , <unk> is capable of utilizing multiple shadow @-@ based techniques at once and can lift his shadow from the ground in order to interact with physical objects ; for instance , he can <unk> enemies with the shadow <unk> or use them to throw weapons . 
- <unk> approaches the exams with a sense of apathy ; when he battles the <unk> ninja <unk> , he defeats her but forfeits his match to her , due to his <unk> being low . Despite this loss , he is the only ninja among his peers to be promoted to the rank of <unk> , as the <unk> of the exams were impressed by the insight and intelligence he demonstrated against <unk> . As a <unk> , <unk> is appointed the leader of a team to prevent <unk> <unk> from <unk> to the village of <unk> . Although <unk> 's team manages to defeat the <unk> ninja barring their way , <unk> manages to escape . 
- In Part II of the series , <unk> is assigned the task of locating two members of the criminal organization <unk> . While his team manages to find their targets , the immortal <unk> member <unk> kills <unk> <unk> during the course of the battle despite <unk> 's best efforts . After <unk> 's funeral , <unk> sets out with the surviving members of Team 10 to avenge their mentor with the aid of <unk> <unk> . As the other deal with <unk> 's partner <unk> , <unk> <unk> <unk> by defeating <unk> and making sure the <unk> member 's body is never found . Following the fight , <unk> vows to protect <unk> <unk> and <unk> 's newborn daughter . He is later assigned to the Fourth Division alongside <unk> and <unk> . He is named a <unk> general under <unk> . In the series <unk> , stating his personal desire during the series of final battles , <unk> becomes advisor to the Seventh <unk> <unk> after marrying <unk> and gaining a son in <unk> <unk> . 
- 
- = = = Appearances in other media = = = 
- 
- Besides the <unk> anime and manga , <unk> is featured in seven of the featured films in the series : in the second film , he aids <unk> <unk> and <unk> <unk> in fighting against <unk> , a <unk> idealist seeking to rule the world with a power called <unk> ; in the fourth , <unk> appears in a brief sequence , fighting against a large group of stone soldiers ; in the fifth , <unk> is sent alongside <unk> and <unk> in search of the base of the Land of Sky , who plans to invade <unk> ; in the sixth , <unk> , alongside <unk> and <unk> battle the <unk> beast summoned by <unk> ; in the eight , <unk> <unk> in the battle against the demon <unk> ; in the ninth , the Limited <unk> universe presents a <unk> <unk> , the exact opposite of his teammate , <unk> , who becomes the team strategist instead ; and in the tenth , <unk> leads the team consisting of himself , <unk> , <unk> , <unk> , and <unk> <unk> that is sent to rescue <unk> 's younger sister , <unk> , who was kidnapped by <unk> <unk> . He is also present in the third original video animation , in which he <unk> in a tournament . <unk> is a playable character in nearly all <unk> video games , including the Clash of <unk> series and the Ultimate <unk> series . In some games , he utilizes variations of his Shadow <unk> <unk> not seen in the anime or manga . <unk> <unk> : <unk> <unk> <unk> ! <unk> 2 marks the first appearance of <unk> in his Part II appearance in a video game . 
- A light novel titled <unk> <unk> : A Cloud Drifting in Silent Darkness , written by <unk> <unk> and illustrated by <unk> , stars <unk> as the main character and narrator . The novel is set two years after the Fourth <unk> War , focusing on <unk> 's assignment as organizer of the newly formed <unk> Union to capture a rogue ninja who rules over the distant Land of Silence and attempts to establish a new world order ruled by the common people . It also explores his growing relationship with <unk> , culminating in him struggling to name his child with her , years after the end of the novel . 
- 
- = = Reception = = 
- 
- <unk> has ranked highly in the Weekly Shōnen Jump popularity polls for the series , continuously placing in the top ten and reaching fourth place in one poll . The last such poll was in 2011 , in which <unk> was in ninth place , behind <unk> and ahead of <unk> . Merchandise based on <unk> has also been released , including action figures , key chains , and patches in both his Part I and Part II appearance . <unk> customers voted him as their sixteenth favorite black haired male anime character . <unk> CEO Hiroshi <unk> referred to <unk> as one of his favorite characters from <unk> . 
- Several publications for manga , anime , video games , and other related media have commented on <unk> 's character . IGN stated that <unk> was one of their favorite characters in the series , and referred to him as " the poster child for any card @-@ carrying member of Generation X " due to his general lack of enthusiasm and his unwillingness to utilize his potential . In a review of episode 110 , IGN praised how <unk> managed to transcend his lazy nature in order to take on the mantle of a leader when assigned to lead a team in order to retrieve <unk> <unk> and agreed that the decision to make him <unk> was fair . Anime News Network also commented on this development , calling <unk> 's emergence as " an unlikely hero " one of the highlights of the arc . In a subsequent review of episode 135 , in which the mission to retrieve <unk> has failed and the members of <unk> 's team were critically injured , IGN lauded the " great moment " in the episode in which <unk> began to cry after learning his friends will recover , and declared that he will be a better leader for the sake of his friends . <unk> Entertainment 's Justin Rich celebrated his " sheer <unk> " , calling him " one of the most fascinating characters in the series " and " one of the few truly brilliant fighters in <unk> anime " . <unk> <unk> , another reviewer from the same site , noted about his battle with <unk> during the <unk> <unk> that it was " one of the better fights involving the supporting players " . 
- 
- 
- = Meridian , Mississippi = 
- 
- Meridian is the sixth largest city in the state of Mississippi , in the United States . It is the county seat of Lauderdale County and the principal city of the Meridian , Mississippi <unk> Statistical Area . Along major highways , the city is 93 mi ( 150 km ) east of Jackson , Mississippi ; 154 mi ( <unk> km ) west of Birmingham , Alabama ; 202 mi ( 325 km ) northeast of New Orleans , Louisiana ; and 231 mi ( 372 km ) southeast of Memphis , Tennessee . 
- Established in 1860 , at the intersection of the Mobile and Ohio Railroad and Southern Railway of Mississippi , Meridian 's economy was built on the railways and goods transported on them , and it became a strategic trading center . During the American Civil War , General William <unk> Sherman burned much of the city to the ground in the Battle of Meridian ( February , 1864 ) . 
- <unk> after the war , the city entered a " Golden Age " it become the largest city in Mississippi between 1890 and 1930 , and a leading center for manufacturing in the South , with 44 trains arriving and departing daily . Union Station , built in 1906 , is now a multi @-@ modal center , giving access to the Meridian Transit System , <unk> <unk> , and <unk> , averaging 242 @,@ 360 passengers per year . Although the economy slowed with the decline of the railroad industry , the city has diversified , with healthcare , military , and manufacturing employing the most people in 2010 . The population within the city limits , according to 2008 census estimates , is 38 @,@ 232 , but a population of 232 @,@ 900 in a 45 @-@ mile ( 72 km ) radius and 526 @,@ 500 in a 65 @-@ mile ( 105 km ) radius , of which 104 @,@ 600 and 234 @,@ 200 people respectively are in the labor force , feed the economy of the city . 
- The area is served by two military facilities , Naval Air Station Meridian and Key Field , which provide over 4 @,@ 000 jobs . <unk> Meridian is home to the Regional Counter @-@ <unk> Training Academy ( <unk> ) and the first local Department of Homeland Security in the state . Key Field is named after brothers Fred and Al Key , who set a world endurance flight record in 1935 . The field is now home to the 186th Air <unk> Wing of the Air National Guard and a support facility for the <unk> Aviation Brigade of the Army National Guard . Rush Foundation Hospital is the largest non @-@ military employer in the region , employing 2 @,@ 610 people . 
- Among the city 's many arts organizations and historic buildings are the Riley Center , the Meridian Museum of Art , Meridian Little Theatre , and the Meridian Symphony Orchestra . Meridian was home to two Carnegie libraries , one for whites and one for African Americans . The Carnegie Branch Library , now demolished , was one of a number of Carnegie libraries built for blacks in the Southern United States during the segregation era . 
- The city has been selected as the future location of the Mississippi Arts and Entertainment Center ( <unk> ) . <unk> <unk> , the " Father of Country Music " , was born in Meridian . Highland Park houses a museum which displays <unk> of his life and career , as well as railroad equipment from the steam @-@ engine era . The park is also home to the Highland Park <unk> <unk> , a National Historic Landmark . It is the world 's only two @-@ row stationary <unk> menagerie in existence . Other notable natives include Miss America 1986 Susan <unk> , James <unk> – an activist who was killed in the Mississippi civil rights workers murders in 1964 , and Hartley <unk> , founder of <unk> <unk> headquartered in Meridian . The federal <unk> was the site of the 1966 @-@ 1967 trial of suspects in the murder of <unk> and two other activists ; it was the first time a white jury convicted a white official of a civil rights killing . 
- 
- = = History = = 
- 
- Previously inhabited by the <unk> Native Americans , the area now called Meridian was obtained by the United States under the terms of the Treaty of Dancing <unk> Creek in 1830 during the period of Indian <unk> . After the treaty was ratified , European @-@ American settlers began to move into the area . 
- After receiving a federal land grant of about 2 @,@ 000 acres ( <unk> ha ) , Richard <unk> , the first settler of Meridian , began offering free land to newcomers to attract more settlers to the region and develop the area . Most of <unk> 's land was bought in 1853 by Lewis A. <unk> , a lawyer from Alabama . John T. Ball , a merchant from <unk> County , bought the remaining 80 acres ( 0 @.@ 32 km2 ) . <unk> and Ball , now known as the founders of the city , began laying out lots for new development on their respective land sections . 
- There was much competition over the proposed name of the settlement . Ball and the more industrial residents of the city supported the name " Meridian , " believing the term to be synonymous with " junction " ; the more agrarian residents of the city preferred " <unk> " ( meaning " mad river " in a Native American language ) , from the name of a nearby creek ; and <unk> proposed " <unk> City . " Ball erected a station house on the Mobile and Ohio Railroad – the sign on which would alternate between " Meridian " and " <unk> " each day . Eventually the continued development of the railroads led to an influx of railroad workers who overruled the others in the city and left " Meridian " on the station permanently . The town was officially incorporated as Meridian on February 10 , 1860 . 
- Meridian was a small village at the start of the American Civil War in 1861 . Its strategic position at the railroad junction led to the Confederate construction of several military installations to support the war . During the Battle of Meridian in 1864 , General William <unk> Sherman led troops into the city , destroying the railroads in every direction , as well as an arsenal and immense <unk> ; his forces burned much of the buildings to the ground . Sherman is reported to have said afterwards , " Meridian , with its depots , store @-@ houses , arsenal , hospitals , offices , hotels , and <unk> no longer exists . " Despite the destruction , the railroad lines in the city were rapidly repaired , back in operation 26 working days after the battle . Race relations remained tense during the Reconstruction era , with the riot of 1871 following a fire that damaged many businesses . 
- The town <unk> in the aftermath of the Civil War , and experienced its " Golden Age " from 1880 to 1910 . The railroads in the area provided for a means of transportation and an influx of industries , which caused a population boom . As the population rose , commercial activity increased in the downtown area . Between 1890 and 1930 , Meridian was the largest city in Mississippi and a leading center for manufacturing in the South . Many of the city 's historic buildings were built during and just after this era , including the Grand Opera House in 1890 , the <unk> School in 1894 , two Carnegie libraries in 1913 , and the <unk> Building , Meridian 's tallest skyscraper , in 1929 . 
- The city continued to grow thanks to a commission government 's efforts to bring in 90 new industrial plants in 1913 and a booming automobile industry in the 1920s . Even through the stock market crash of 1929 and the following Great Depression , the city continued to attract new businesses . With <unk> becoming popular in the culture during the depth of the Depression , the S. H. Kress & Co. building , built to " provide luxury to the common man , " opened in downtown Meridian , as did the Temple Theater , which was used as a movie house . The federal <unk> was built in 1933 as a <unk> project . 
- After a brief <unk> of the economy at the end of the Depression , the country entered World War II , which renewed the importance of railroads . The rails were essential to transport gasoline and scrap metal to build military vehicles , so Meridian became the region 's rail center again . This renewed prosperity continued until the 1950s , when the automobile and Interstate Highway System became more popular than passenger rails . The decline of the railroad industry caused significant job losses , resulting in a population decline as workers left for other areas . 
- During the American Civil Rights Movement in the 1960s , Meridian was home to a Council of <unk> <unk> ( <unk> ) office and several other activist organizations . James <unk> and other local residents , along with Michael <unk> , his wife Rita , and Andrew Goodman , volunteers from the North , worked to create a community center , holding classes to help prepare African Americans in the area to regain the power to vote . Whites in the area resented the activism , and racial tension often resulted in violence . In June 1964 , <unk> , <unk> , and Goodman went to <unk> County , Mississippi , to meet with members of a black church that had been bombed and burned . The three disappeared that night on their way back to Meridian . Following a massive FBI investigation , their murdered bodies were found two months later , buried in an earthen dam . 
- Seven <unk> , including a deputy sheriff , were convicted in the federal <unk> in Meridian of " <unk> the victims of their civil rights " and three were acquitted in the Mississippi civil rights workers murders trial . It was the first time that a white jury had convicted " a white official in a civil rights killing . " In 2005 , the case was reopened by the state , which brought charges in the case for the first time . Edgar Ray <unk> was convicted of <unk> and sentenced to 60 years in prison . Meridian later honored <unk> by renaming a portion of 49th Avenue after him and holding an annual memorial service . 
- Starting in the 1960s and following the construction of highways that made <unk> easier , residents began to move away from downtown in favor of new housing subdivisions to the north . After strip commercial interests began to move downtown , the city worked to designate several areas as historic districts in the 1970s and 80s to preserve the architectural character of the city . The Meridian Historic <unk> and Landmarks Commission was created in 1979 , and the Meridian Main Street program was founded in 1985 . 
- Meridian Main Street organized several projects to <unk> downtown , including the construction of a new Amtrak Station in 1997 based on the design of the historic train station used during Meridian 's Golden Age ; it had been demolished . Other projects included the renovation of the <unk> Building in 2001 and <unk> 's Restaurant in 2002 , as well as support for integrated urban design . Meridian Main Street , along with The Riley Foundation , helped renovate and adapt the historic Grand Opera House in 2006 for use as the " Mississippi State University Riley Center for Education and the Performing Arts . " 
- After ownership of the Meridian Main Street was transferred to the Alliance for Downtown Meridian in late 2007 , the two organizations , along with the Meridian Downtown Association , spearheaded the downtown <unk> effort . The Alliance serves as an umbrella organization , allowing the other two organizations to use the its support staff and housing , and in turn the Alliance serves as a liaison between the organizations . Plans were underway to renovate the <unk> Building , but newly elected Mayor <unk> Barry killed the plans in early 2010 . Today , the Alliance helps to promote further development and restoration downtown ; its goal is to assist businesses such as specialty shops , restaurants , and bars because these help downtown become more active during the day and at night . The Meridian Downtown Association is primarily focused on increasing foot traffic downtown by organizing special events , and the Meridian Main Street program supports existing businesses downtown . 
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- = = = Hotels = = = 
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- Given Meridian 's site as a railroad junction , its travelers have attracted the development of many hotels . Even before Meridian reached its " Golden Age , " several large hotels , including the Great Southern and the Grand Avenue hotels , were built before the start of the 20th century . With the growth of the railroads and the construction of the original Union Station in 1906 , many hotels were constructed for passengers and workers . The <unk> Hotel was constructed in 1905 , and the Terminal Hotel was constructed in 1910 . Hotel Meridian was constructed in 1907 , and Union Hotel was built in 1908 . Union Hotel was listed on the National Register of Historic Places in 1979 , and both Hotel Meridian and Grand Avenue Hotel were listed as contributing properties to the Meridian Urban Center Historic District . 
- As the city grew , the hotels reflected ambitions of the strong economy , as evidenced by the eleven @-@ story skyscraper Lamar Hotel built in 1927 . Listed on the National Register of Historic Places in 1979 , the Lamar Hotel was adapted for use as a county annex building . In 1988 it was listed as a Mississippi Landmark . 
- The <unk> Young Hotel was built in 1931 . A staple in the African @-@ American business district that developed west of the city 's core , the hotel was one of the only places in the city during the years of segregation where a traveling African American could find a room . 
- As the city suburbs developed in the 1960s and ' 70s , most hotels moved outside of downtown . <unk> of the Riley Center in 2006 has increased demand and a push for a new downtown hotel . The <unk> Building has been proposed for redevelopment for this purpose , but restoration efforts stalled with a change in city administrations . The <unk> Preservation Society was formed in 2013 to raise public awareness and support for the building 's renovation , featuring tours of the first floor and anniversary events . 
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- = = = Historic districts = = = 
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- Meridian has nine historic districts that are listed on the National Register of Historic Places . The Meridian Downtown Historic District is a combination of two older districts , the Meridian Urban Center Historic District and the Union Station Historic District . Many architectural styles are present in the districts , most from the late 19th and early 20th centuries , including Queen Anne , Colonial Revival , Italianate , Art Deco , Late Victorian , and <unk> . The districts are : 
- 1 East End Historic District — roughly bounded by 18th St , 11th <unk> , 14th St , 14th <unk> , 5th St , and 17th <unk> . 
- 2 Highlands Historic District — roughly bounded by 15th St , 34th <unk> , 19th St , and 36th <unk> . 
- 3 Meridian Downtown Historic District — runs from the former Gulf , Mobile and Ohio Railroad north to 6th St between 18th and 26th <unk> , excluding <unk> Survey Block 71 . 
- 4 Meridian Urban Center Historic District — roughly bounded by 21st and 25th <unk> , 6th St , and the railroad . 
- 5 Union Station Historic District — roughly bounded by 18th and 19th <unk> , 5th St , and the railroad . 
- 6 <unk> Historic District — roughly bounded by 33rd <unk> , 30th <unk> , 14th St , and 8th St. 
- 7 Mid @-@ Town Historic District — roughly bounded by 23rd <unk> , 15th St , 28th <unk> , and 22nd St. 
- 8 <unk> Springs Road Historic District — roughly bounded by 29th St , 23rd <unk> , 22nd St , and 29th <unk> . 
- 9 West End Historic District — roughly bounded by 7th St , 28th <unk> , Shearer 's Branch , and 5th St. 
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- = = Government and infrastructure = = 
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- Meridian has operated under the mayor @-@ council or " strong mayor " form of government since 1985 . A mayor is elected every four years by the population at @-@ large . The five members of the city council are elected every four years from each of the city 's five wards , considered single @-@ member districts . The mayor , the chief executive officer of the city , is responsible for <unk> and leading the day @-@ to @-@ day operations of city government . The city council is the legislative arm of the government , setting policy and annually adopting the city 's operating budget . 
- City Hall , which has been listed on the National Register of Historic Places , is located at <unk> 23rd Avenue . The current mayor is Percy <unk> . Members of the city council include Dr. George M. Thomas , representative from Ward 1 , Kenneth Dustin <unk> , representative from Ward 2 , Barbara Henson , representative from Ward 3 , Kim Houston , representative from Ward 4 , and Randy <unk> , representative from Ward 5 . The council clerk is Pam <unk> . In total , the city employs 570 people . 
- The city established a Department of Homeland Security ( <unk> ) shortly after Hurricane Katrina in 2005 , becoming the only local <unk> in the state . The team <unk> an area of nine counties . Upon receiving $ 2 @.@ 5 million in grants from the Mississippi Emergency Management Agency and other organizations , the department began training law enforcement offices from other Southern states in passenger rail rescue as well as offering civilian classes in basic <unk> , Boy Scout first aid and hunting , and firearms training . The <unk> helps during times of crisis such as Hurricane Ivan in September 2005 , when the department helped establish and support shelters for 700 <unk> . The city now serves as the leader of one of the task forces in the Mississippi <unk> , a combination of three nine @-@ county teams . 
- <unk> by police chief Lee <unk> since 2009 , the Meridian Police Department consists of 115 full @-@ time officers as well as part @-@ time and reserve staff available . In 2009 , the department 's Criminal Investigations Division responded to 4000 cases , 2000 of which were <unk> . In 2000 , <unk> crimes were reported , up slightly from 2008 crimes the preceding year . Meridian has been described as " the <unk> city in Mississippi with more than 30 @,@ 000 people . " The East Mississippi <unk> <unk> is located in unincorporated Lauderdale County , near Meridian . It is operated by the <unk> Group on behalf of the Mississippi Department of <unk> . The chief of the Meridian Fire Department is Anthony Clayton . The fire department responded to more than 1600 calls in 2009 , including 123 structural fires and <unk> emergency service calls . 
- The Mississippi Department of Mental Health operates the East Mississippi State Hospital in Meridian . 
- The United States Postal Service operates the Meridian , North Meridian , and the West Meridian Station post offices . 
- In state politics , the Mississippi Senate district map divides the city into three sections . The northern tip of the city is in the 31st State Senate District and seats Terry Clark Burton ( R ) . A strip of the city from the southwest corner up to the northeast corner comprises part of the 32nd State Senate District and seats Sampson Jackson , II ( D ) . The western and southeastern portions of the city lie in the 33rd State Senate District and seats <unk> Carmichael ( R ) . In the Mississippi House of Representatives districts , the city is divided into four districts . The southern and eastern portions of the city reside in House District 81 and are represented by Steven A. <unk> ( R ) . The city 's core makes up the entirety of House District 82 and is represented by <unk> L. Jones ( D ) . Surrounding House District 82 is House District 83 , represented by Greg <unk> ( R ) . The western section of the city , along with a small section in the north , lie in House District 84 and are represented by <unk> Campbell ( R ) . 
- On the national level , the city is located in Mississippi 's 3rd congressional district , represented by Gregg Harper ( R ) , who has been in office since 2009 . Lauderdale County , home to Meridian , has voted for the Republican candidate in every United States presidential election since 1972 . Prior to the shift to the Republican Party , white area voters supported Democratic Party candidates , as for decades since the late nineteenth century , it was a one @-@ party state . 
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- = = Demographics = = 
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- The city 's growth has reflected the push and pull of many social and economic factors . The total population increased in each census from the city 's founding until 1970 , although varying from rates as high as 165 % to as low as 0 @.@ 2 % . In the 1970 census the population decreased , then slightly increased by 1980 , after which the population slowly declined , increasing again since the turn of the 21st century . Between 1980 and 2000 , the population declined more than 14 % . As of the census of 2000 , the city 's population was 39 @,@ 968 , and the population density was <unk> @.@ 9 inhabitants per square mile ( <unk> @.@ 0 / km2 ) . In 2008 , the city was the sixth largest in the state . The population increased as of 2010 . 
- Meridian is the principal city in the Meridian <unk> area , which as of 2009 consisted of three counties – Clarke , <unk> , and Lauderdale – and had a population of 106 @,@ 139 . There is a population of 232 @,@ 900 in a 45 @-@ mile ( 72 km ) radius and 526 @,@ 500 in a 65 @-@ mile ( 105 km ) radius . 
- While the overall population growth of the city has varied , there has been a steady growth in the number and percentage of non @-@ white residents . The only decline in this group was between 1960 and 1970 , when the city 's overall population declined markedly . In the 2010 Census , the racial makeup of the city was 61 @.@ 55 % African American , 35 @.@ 71 % White , 0 @.@ 9 % Asian , 0 @.@ 2 % Native American , < 0 @.@ 02 % Pacific Islander , 0 @.@ 59 % from other races , and 0 @.@ 89 % from two or more races . Hispanic or Latino of any race were 1 @.@ 75 % of the population . 
- According to the 2000 Census , of the 17 @,@ 890 housing units inside city limits , 15 @,@ <unk> were occupied , 10 @,@ <unk> of them by families . 31 @.@ 1 % of occupied households had children under the age of 18 , 36 @.@ 2 % were married couples living together , 23 @.@ 3 % consisted of a female <unk> with no husband present , and 37 @.@ 2 % were non @-@ families . 33 @.@ 2 % of all households were made up of individuals , and 14 @.@ 0 % had someone living alone who was 65 years of age or older . The average household size was 2 @.@ 39 and the average family size was 3 @.@ 06 . The average household size has steadily decreased since 1970 , when it was 3 @.@ 04 . Meridian 's median age has increased from 30 @.@ 4 in 1970 to 34 @.@ 6 in 2000 . 
- The median income for a household in the city was $ 25 @,@ <unk> , and the median income for a family was $ 31 @,@ <unk> . Males had a median income of $ 29 @,@ <unk> versus $ 19 @,@ 702 for females . The per capita income for the city was $ 15 @,@ 255 . About 24 @.@ 6 % of families and 28 @.@ 6 % of the population were below the poverty line , including 40 @.@ 8 % of those under age 18 and 22 @.@ 0 % of those age 65 or over . 
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- = = = Religion = = = 
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- The population of Meridian and its surrounds is fairly <unk> , with 65 @.@ 2 % of Lauderdale County identifying as affiliated with some type of religious congregation , compared with the national average of 50 @.@ 2 % . Of the affiliated in 2000 , 30 @,@ <unk> ( 59 @.@ 0 % ) were in the Southern Baptist Convention , 9 @,@ <unk> ( 18 @.@ 6 % ) were with the United Methodist Church , and 1 @,@ <unk> ( 3 @.@ 7 % ) were associated with the Catholic Church . 
- <unk> Jews from Germany and eastern Europe were influential in commercial development of the city , building businesses and services . Congregation Beth Israel was founded in 1868 , just before the city 's " Golden Age . " Meridian once had the largest Jewish community in the state , with 575 Jewish people living in the city in 1927 . Today , fewer than 40 Jews live in Meridian , most of whom are elderly . 
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- = = Geography and climate = = 
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- Meridian is located in the North Central Hills region of Mississippi in Lauderdale County . According to the United States Census Bureau , the city has a total area of 45 @.@ 9 sq mi ( 119 km2 ) , of which 45 @.@ 1 sq mi ( 117 km2 ) is land and 0 @.@ 8 sq mi ( 2 @.@ 1 km2 ) is water . Along major highways , the city is 93 mi ( 150 km ) east of Jackson , Mississippi ; 154 mi ( <unk> km ) west of Birmingham , Alabama ; 202 mi ( 325 km ) northeast of New Orleans , Louisiana ; 231 mi ( 372 km ) southeast of Memphis , Tennessee ; and <unk> mi ( 478 km ) west of Atlanta , Georgia . The area surrounding the city is covered with cotton and corn fields along with oak and pine forests , and its topography consists of clay hills and the bottom lands of the head waters of the <unk> River . 
- The natural terrain of the area has been modified in the urban core of the city by <unk> , but it maintains its gentle rolling character in the outlying areas . Numerous small creeks are found throughout the city and small lakes and woodlands lie in the northern and southern portions of the city . <unk> Creek runs through the southern portion of the city and branches off into Gallagher 's Creek , which flows through the center of the city . <unk> 's Creek runs through the far @-@ western part of the city while smaller creeks including Shearer 's Branch , Magnolia Creek , and <unk> Creek are dispersed throughout the city . 
- Meridian is in the humid subtropical climate zone . The average high temperature during summer ( June through August ) is around 90 ° F ( 32 ° C ) and the average low is around 70 ° F ( 21 ° C ) . In winter ( December through February ) the average maximum is around 60 ° F ( 16 ° C ) and minimum 35 ° F ( 2 ° C ) . The <unk> month is July , with an average high of 92 @.@ 9 ° F ( 33 @.@ 8 ° C ) , and the coldest month of the year is January with an average low of 34 @.@ 7 ° F ( 1 @.@ 5 ° C ) . 
- The average annual precipitation in the city is 58 @.@ 65 in ( 1 @,@ 490 mm ) . Rainfall is fairly evenly distributed throughout the year , and the wettest month of the year is March , in which an average of 6 @.@ 93 in ( 176 mm ) of rain falls . Much rainfall is delivered by thunderstorms which are common during the summer months but occur throughout the year . Severe thunderstorms - which can produce damaging winds and / or large hail in addition to the usual <unk> of lightning and heavy rain - occasionally occur . These are most common during the spring months with a secondary peak during the Fall months . These storms also bring the risk of tornadoes . 
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- = = Economy = = 
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- Early on , the economy depended greatly upon the railroads in the area . The city was the largest in Mississippi around the start of the 20th century , with five major rail lines and 44 trains coming in and out daily . The city 's economy not only depended on the rails but the goods , such as timber and cotton , transported on them . With these rail @-@ based industries , the city was a great economic power in the state and region from about 1890 through 1930 . Though its economy slowed with the decline of the <unk> industry in the 1950s , the city has adapted , moving from a largely rail @-@ based economy to a more diversified one , with healthcare , military , and manufacturing employing the most people . 
- Along with Lauderdale County and the city of Marion , Meridian is served by the East Mississippi Business Development Corporation , which was formed in 1996 by a group of business leaders from the area . While as of April 2010 , the city 's civilian labor force was only 15 @,@ 420 people , there is a population of 232 @,@ 900 in a 45 @-@ mile ( 72 km ) radius and 526 @,@ 500 in a 65 @-@ mile ( 105 km ) radius , of which 104 @,@ 600 and 234 @,@ 200 people respectively are in the labor force . The city thus serves as a hub of employment , retail , health care , and culture activities . <unk> percent of Lauderdale County 's workers reside in the county while 90 % live within 45 miles . 
- In April 2010 , there were 6 @,@ 260 people employed in the healthcare field in Lauderdale County . Rush Hospital is the largest healthcare organization in the region , employing 2 @,@ 610 people , followed by East Mississippi State Hospital with 1 @,@ 500 and Anderson Hospital with 1 @,@ <unk> . There are three hospitals in Meridian , as well as many other healthcare @-@ related facilities . Jeff Anderson Regional Medical Center provides <unk> surgery , a Level II newborn intensive @-@ care unit , and a health and fitness center . Rush Foundation Hospital and the related Rush Health Systems operate a <unk> Hospital of Meridian , which offers long @-@ term care for non @-@ permanent patients who require more recovery time in a hospital setting . Riley Hospital has two centers for stroke treatment and rehabilitation services . Other healthcare facilities in Meridian include the Alliance Health Center and East Mississippi State Hospital , the latter of which has been in operation since 1882 . 
- Retail is another major employer in the county , with 5 @,@ 280 people employed in April 2010 . Nearly $ 2 billion annually is spent on retail purchases in the city . The <unk> @,@ <unk> @-@ square @-@ foot ( 58 @,@ <unk> m2 ) <unk> Lakes Mall offers over one hundred shopping venues , including department stores , specialty shops , restaurants , <unk> , and United Artists <unk> . Phase I of the construction of Meridian Crossroads , a 375 @,@ 000 @-@ square @-@ foot ( 34 @,@ 800 m2 ) shopping center in the <unk> Lakes area , was completed in November 2007 , providing a major boost to retail in the area . Also , the shopping district on North Hills Street has continued to expand , and in March 2007 , additional retail and office space was opened near the Highway 19 <unk> <unk> . 
- The area is also served by two military facilities , Naval Air Station Meridian and Key Field , which supply over 4 @,@ 000 jobs to residents of the surrounding area . <unk> Meridian provides training for naval carrier pilots and other enlisted personnel . Also housed at the base is the Regional Counter @-@ <unk> Training Academy ( <unk> ) , which provides <unk> training for law enforcement in many southeastern states . <unk> the first local Department of Homeland Security in the state , the city is the leader in a nine county regional response team and a twenty @-@ nine county regional response task force . Key Field is the site of the famous flight by brothers Fred and Al Key , who set a world endurance flight record in 1935 . Key Field is now home to the 186th Air <unk> Wing of the Air National Guard and a support facility for the <unk> Aviation Brigade of the Army National Guard . The site also contains an exhibit reviewing the history of aviation , and is the home of Meridian 's Aviation Museum . 
- The total manufacturing employment of Lauderdale County in April 2010 was 2 @,@ 850 people . <unk> <unk> Corporation , which has manufactured guitars , <unk> , and sound equipment since 1965 , operates its headquarters in the city . Other businesses in the area include Avery <unk> , Structural Steel Services , Sara Lee , Tower Automotive , and <unk> Corporation . The city is also home to four industrial parks . 
- In downtown , the <unk> Riley Center provides revenue from tourism , arts , and entertainment sales . The Riley Center attracts more than 60 @,@ 000 visitors to downtown Meridian annually for conferences , meetings , and performances . <unk> 's Department Store on Front St has remained a Mississippi clothing landmark , having passed through four generations of family ownership . The store has been selling fine men 's and women 's clothing since 1887 , when the store was first opened by Alex <unk> . 
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- = = Culture = = 
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- 
- = = = Arts = = = 
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- Known for more than a century of arts , Meridian contains many art and cultural organizations and hosts many cultural events . One of the first art organizations in the city , The Meridian Art League , was established in February 1933 . Art exhibitions were originally held in Lamar Hotel in downtown Meridian , but after a name change to Meridian Art Association in 1949 , exhibitions were held at various locations around the city . After the Carnegie library at 25th <unk> and 7th St was closed , the Art Association remodelled the building into the Meridian Museum of Art to serve as a permanent home for exhibits . The museum was opened in 1970 and has since featured rotating exhibitions as well as many educational programs for both students and adults . Over thirty exhibitions are held annually , ranging from traditional decorative arts to ethnographic and tribal materials , photography , crafts , and many other works of art . The collection also includes 18th and 19th century portraits , 20th century photography , and several sculptures . 
- The Meridian Council for the Arts ( <unk> ) was founded as Meridian 's and Lauderdale County 's official arts agency in 1978 . <unk> operates its Community Art Grants program , the annual <unk> Festival , several workshops , and other special events each year . <unk> is partnered with many arts organizations in the city and county including the Meridian Museum of Art , the Meridian Little Theatre , and the Meridian Symphony Orchestra . Meridian Little Theatre , one of the South 's oldest subscription @-@ based community theatres , was built in 1932 and currently provides entertainment to residents of and visitors to Meridian and Lauderdale County , entertaining over 22 @,@ 000 guests each season , making it Mississippi 's most @-@ attended community theatre . The Meridian Symphony Orchestra ( <unk> ) – founded in 1961 – played its first concert in 1962 and its first full season in 1963 . In 1965 the <unk> booked its first international soloist , <unk> <unk> , to perform with the orchestra . The Orchestra helped the Meridian Public School District develop its own orchestra and strings programs and also helped develop the Meridian Symphony Chorus . The current conductor is Dr. Claire Fox <unk> , who has been with the orchestra since 1991 . The <unk> will celebrate its 50th anniversary in February 2011 with a performance from <unk> <unk> . 
- The city 's former Grand Opera House was built in 1889 by two half brothers , Israel Marks and Levi <unk> . During its operation the opera house hosted many famous artists and works , the first being a German company 's rendition of Johann <unk> II 's " The <unk> Baron " . After closing in the late 1920s due to the Great Depression , the opera house was abandoned for nearly 70 years . A $ 10 million grant in 2000 by the Riley Foundation , a local foundation chartered in 1998 , sparked the building 's restoration while $ 15 million came from a combination of city , county , and federal grants . The opera house 's renovation was completed in September 2006 under the new name " Mississippi State University Riley Center for Education and Performing Arts . " The Riley Center , which includes a 950 @-@ seat auditorium for live performances , a 200 @-@ seat studio theater , and 30 @,@ 000 sq ft ( 2 @,@ 787 m2 ) of meeting space , attracts more than 60 @,@ 000 visitors to downtown Meridian annually for conferences , meetings , and performances . 
- Meridian is rightly considered an architectural treasure <unk> being one the nations most intact cities from the turn of the last century . Architecture students from around the nation and Canada are known to visit Meridian in groups as part of their <unk> due to numerous structures in the city having been designed by noted architects . The only home in the US south designed by noted Canadian born <unk> Louis S. <unk> , famous for <unk> the glass curtain wall skyscraper , is extant on Highland Park . The Frank Fort designed <unk> Building is generally considered one of the best Art Deco <unk> in the US and is often compared to Detroit 's famed Fisher Building . Noted California <unk> Wallace <unk> designed a number of homes in Meridian as well as in the Alabama Black Belt which <unk> the city across the nearby Alabama State line . He had relatives in Meridian and Selma who were executives in the then thriving railroad industry and would take commissions in the area when commissions in California were lean . His work is mostly concentrated in the lower numbered blocks of <unk> Springs Drive where his <unk> <unk> Springs Drive is often compared to the similarly designed Falcon <unk> , the Beverly Hills home in Benedict Canyon of Rudolph <unk> . One <unk> work was lost to an expansion of Anderson Hospital in 1990 and another in Marion Park burned in the 1950s . The Meridian Post Office with its interior done entirely of bronze and Verde marble is also noteworthy as a very fine example of the type of Post Office structures built in thriving and well to do cities in the 1920s and originally had <unk> lighting which was removed sadly during a 1960s <unk> and which are now in private residences on <unk> Springs Drive and in North Hills . 
- Meridian has also been selected as the future location of the Mississippi Arts and Entertainment Center ( <unk> ) . The Mississippi Legislature approved the idea in 2001 and in 2006 promised $ 4 million in funding if private contributors could raise $ 8 million . The city donated $ 50 @,@ 000 to the cause in September 2007 . The <unk> , as proposed , would be located on 175 acres ( 71 ha ) at <unk> Lakes and consist of an outdoor amphitheatre , an indoor concert hall , and a Hall of Fame honoring Mississippi artists . The Hall of Fame will be located downtown in the old Montana 's building . That property and the adjacent Meridian Hotel building were acquired in July 2010 for $ 300 @,@ 000 . In February 2009 , the <unk> revealed its Walk of Fame outside of the Riley Center in an attempt to promote the planned Hall of Fame . The first star on the walk was dedicated to <unk> <unk> , a Meridian native . In September of the same year , the second star was revealed , recognizing <unk> King , a famous blues musician from Mississippi . On June 1 , 2010 , authors Tennessee Williams , <unk> <unk> , and William Faulkner were added to the walk . <unk> Ward was added to the walk on June 24 , 2010 . The <unk> plans to add many more Mississippi @-@ born stars to the Walk of Fame ; names mentioned include Morgan Freeman , Jimmy <unk> , Elvis Presley , Conway <unk> , and others . 
- Another location in the city used for large productions is the <unk> Shrine Temple Theater . The Temple Theater houses a <unk> @-@ pipe Robert Morgan organ , one of two Theater <unk> still in their original installations in the state . With seating for 1800 persons , the silent movie era was a prosperous time for the Temple . At the time , it was one of the largest stages in the United States , second only to the Roxy Theater in New York City . Today , seating <unk> persons , the Temple is used year @-@ round for area events , live stage shows , plays , concerts , <unk> Shrine functions , and public screenings of classic movies . 
- Highland Park houses a <unk> <unk> museum which displays the original guitar of " The Singing <unk> " and other <unk> of his life and career , as well as railroad equipment from the steam @-@ engine era . In addition to the museum building itself , there are outside memorials , and a vintage steam <unk> on display . A Mississippi Blues Trail historic marker has been placed in Meridian to honor the city as the birthplace of <unk> <unk> and emphasizes his importance to the development of the blues style of music in Mississippi . The city was the first site to receive this designation outside the Mississippi Delta . Also , a Mississippi Country Music Trail marker was placed in Oak Grove Cemetery in honor of <unk> on June 1 , 2010 . Each year since 1953 , the city has held a festival during May to honor the anniversary of his death . 
- The park is also home to a 19th @-@ century <unk> manufactured around 1895 by Gustav <unk> of Philadelphia , Pennsylvania . Highland Park <unk> <unk> has been in operation since 1909 , is a National Historic Landmark , and is the world 's only two @-@ row stationary <unk> menagerie in existence . Its house is the only remaining original <unk> building built from a <unk> <unk> . Around Town <unk> <unk> is a public arts project of 62 <unk> horses , representing the historic <unk> . Sixty @-@ two pieces have been sponsored by local businesses and citizens , and design of the horses was conceived and painted by local artists . They are placed throughout the city and county . 
- 
- = = = Recreation = = = 
- 
- The city contains several recreational parks , including Highland Park , <unk> Lakes , and <unk> Lake . Highland Park contains picnic shelters , swimming pools , tennis courts , a baseball field , <unk> fields , and a playground , all open year round to visitors . <unk> Lakes is a city @-@ owned , 3 @,@ 300 @-@ acre ( 13 km2 ) park including three lakes . The park also includes the Long Creek Reservoir and <unk> Municipal Golf Course , along with nature trails , a <unk> and walking track , <unk> paths , horseback riding trails , pavilions , picnic facilities , boat ramps , <unk> boats , concessions , and fishing . Along with the lakes , the <unk> Lakes area includes <unk> Lakes Mall , <unk> Lakes Crossing , and <unk> Lakes Plaza . <unk> Lake is a 7 @,@ 150 @-@ acre ( 28 @.@ 9 km2 ) establishment containing a 4 @,@ 144 @-@ acre ( 16 @.@ 77 km2 ) lake which offers boating , fishing , swimming , water skiing , <unk> , hunting , hiking and camping . <unk> Country Water Park , a 25 @-@ room <unk> , and cabins are located on the lake . 
- Since 1992 , Meridian has been a host of the State Games of Mississippi , a <unk> annual multi @-@ sport event modeled after the Olympic Games . The organization is a member of the National Congress of State Games , which is affiliated with the U.S. Olympic Committee . In its first year 1 @,@ 200 athletes competed in twelve sports , and since then over 70 @,@ 000 athletes have participated in the Games . In 2009 , more than <unk> athletes participated in 27 sports . All competitors in the games can compete in the Southeast Sports Festival while <unk> may move up to the bi @-@ annual State Games of America . 
- Originally the games were held in one weekend in June , but as more sports were added , the event was expanded to two weekends . Opening ceremonies always begin on the 3rd Friday of June in downtown Meridian . The games are held at several sports parks , including Northeast Park , <unk> Davidson Complex , and other various fields throughout the city . Northeast Park is an 85 @-@ acre ( 34 ha ) park on Highway 39 that contains ten tennis courts , four <unk> fields , three soccer fields , an asphalt track , and a large picnic pavilion . The <unk> Davidson Sports Complex includes six tennis courts , four <unk> fields , and a half @-@ mile track . Other sports fields include the Meridian <unk> Soccer Complex , <unk> Park , and Phil <unk> Park . 
- There are also several golf courses in the city , including the aforementioned <unk> Municipal Golf Course , an 18 @-@ hole course open to the public daily . <unk> County Club , located on Highway 39 North , is a private club with golf , swimming , fishing , and dining facilities . Other golf courses serving the city include <unk> Country Club , <unk> Creek Golf Center , and <unk> Creek Golf Course . 
- 
- = = Transportation = = 
- 
- 
- = = = <unk> and public transit = = = 
- 
- Amtrak 's Crescent line connects Meridian with the cities of New York , New York ; Philadelphia , Pennsylvania ; Baltimore , Maryland ; Washington , D.C. ; Charlotte , North Carolina ; Atlanta , Georgia ; Birmingham , Alabama ; and New Orleans , Louisiana . The Union Station Multi @-@ <unk> Transportation Center ( <unk> ) is located at 1901 Front Street , part of the Meridian Downtown Historic District , which is listed on the National Register of Historic Places . Originally built in 1906 , but later demolished in 1966 then rebuilt in 1997 , the station includes several modes of transportation including the Meridian Transit System , Amtrak , Norfolk Southern rail corridor , <unk> buses , <unk> and other providers of transit services . The number of passengers on Amtrak trains , <unk> buses , and Meridian Transit System buses averages 242 @,@ 360 per year . 
- 
- = = = Air = = = 
- 
- The city is served by Meridian Regional Airport , located at Key Field , <unk> Airport Boulevard South , 3 mi ( 4 @.@ 8 km ) southwest of the city . At 10 @,@ <unk> foot ( 3 @,@ <unk> m ) , the airport 's runway is the longest public runway in Mississippi . The airport , which has been in service since 1930 , offers daily flights to Dallas / Fort Worth , <unk> . 
- During the Great Depression , residents of the city contemplated abandoning the airport because of the cost of maintenance , but in 1935 Brothers Fred and Al Key , managers of the airport , thought of a way to keep the airport operating . From June 4 until July 1 , 1935 , the brothers flew over the city in their plane , the " Ole Miss . " The record they established in their 27 days <unk> , totaling 653 hours and 34 minutes , attracted enough publicity and funds to the city to keep the airport running . Key Field is therefore named after the brothers , whose flight endurance record remains unbroken in conventional flight . 
- 
- = = = Highways = = = 
- 
- Interstate Highways 
- Interstate 20 
- <unk> west through Jackson , Mississippi , eventually terminating near Kent , Texas , and east through Tuscaloosa , Alabama , eventually terminating in Florence , South Carolina . Interstate 59 
- <unk> with I @-@ 20 in the city and runs north through Tuscaloosa , Alabama , ending in <unk> , Georgia . It also runs south through <unk> , Mississippi , and on to <unk> , Louisiana . 
- U.S. Highways 
- U.S. Highway 11 
- <unk> parallel to Interstate 59 south to New Orleans , Louisiana , and north all the way to the Canada – US border . U.S. Highway 45 
- <unk> route which runs north through Columbus , Mississippi , to the US @-@ Canada border and south through <unk> , Mississippi , to Mobile , Alabama , and the Gulf of Mexico . U.S. Highway 80 
- <unk> west through Jackson , Mississippi , to Dallas , Texas , and east through <unk> , Alabama , all the way to <unk> Island , Georgia and the Atlantic Ocean . 
- State Highways 
- Mississippi Highway 19 
- <unk> north to West , Mississippi , and south to the Mississippi @-@ Alabama border , where it continues as Alabama State Route 10 . Mississippi Highway 39 
- <unk> in Meridian and runs north to <unk> , Mississippi . Mississippi Highway 145 
- <unk> US 45 , but now only exists as an alternate route in several cities . Mississippi Highway <unk> 
- <unk> in Meridian and runs north to <unk> , Mississippi . 
- 
- = = Education = = 
- 
- Early public education in Meridian was based on the 1870 Mississippi Constitution . From 1870 to 1885 , trustees appointed by the City Council served on the Board of School Directors , which had authority to operate the schools . Although there were several schools in the city before 1884 , they were privately owned and only enrolled about 400 students . The city did not build its first publicly owned school until September 1884 . The first public school for blacks in the city was held in facilities rented from St. Paul Methodist Church . The Mississippi Legislature amended the city charter in January 1888 to allow the city to maintain its own municipal school district , and in March of the same year $ 30 @,@ 000 in bonds was approved for the city to build new public schools . From this bond , the <unk> School was built in 1894 , becoming the first brick public school building in the state built for blacks . 
- From this early district and later additions , the Meridian Public School District grew to its current size , which now includes six elementary schools , 3 middle schools , and 3 high schools . The city also contains several private schools including Lamar School , <unk> Christian School , Community Christian School , and St. Patrick 's Catholic School . The campus of Meridian High School , the main high school in the district , occupies 37 acres ( 15 ha ) , including six buildings and 111 classrooms . The school is made up of grades 10 @-@ 12 and <unk> approximately 1 @,@ 500 students . 
- Meridian is home to two post @-@ secondary educational institutions . Meridian Community College , founded in 1937 , is located at 910 Highway 19 N and offers free tuition for four <unk> to graduates from the Meridian Public and Lauderdale County School <unk> as well as <unk> children who reside inside Lauderdale County . Originally known as Meridian Junior College and located at Meridian High School , the college moved to its present location in 1965 . After <unk> laws were passed , <unk> merged with <unk> Harris Junior College in 1970 , which had previously enrolled African @-@ American students . The name change from Meridian Junior College to Meridian Community College took place In 1987 " to more accurately reflect the diversity of opportunities it provides for a growing community area . " Mississippi State University also operates a campus in the city . As of the Fall 2008 semester , 763 students from 33 counties throughout the state and several in Alabama attended the college . 
- Meridian is served by the Meridian @-@ Lauderdale County Public Library , located at the corner of 7th Street and 26th Avenue . The city originally had two Carnegie libraries , both built in 1913 – one for blacks and one for whites . A group of women had formed the <unk> Book and Magazine Club in the 1880s and began raising money to build a library for the city . The books they collected and shared within the club were later the basis of the library collection for Meridian . With wide support for the library , the club enlisted Israel Marks , a city leader , to approach the national philanthropist Andrew Carnegie for funding assistance . The library for blacks was built at 13th Street and 28th Avenue on land donated by St. Paul Methodist Church , and the library for whites was established in a building originally owned by members of the First Presbyterian Church of Meridian , who sold it to the city on September 25 , 1911 . The African American library was the only library for blacks in the state until after World War I and is the only Carnegie library ever built for African Americans in the country . The two libraries served the city until 1967 , when the institutions became integrated because of the Civil Rights Act of 1964 , combined their collections , and moved all materials to their current location . The former white library was renovated and converted into the Meridian Museum of Art in 1970 , and the former African @-@ American library was demolished on May 28 , 2008 . 
- 
- = = Media = = 
- 
- The only daily newspaper printed in the city is The Meridian Star , which has been in operation since 1898 . The paper was originally named The Evening Star but was renamed in 1915 and has been Meridian 's only daily newspaper since 1921 . With a daily circulation of about 12 @,@ 000 in March 2010 , the paper serves Lauderdale County as well as adjacent portions of western Alabama and eastern Mississippi . 
- Though the Meridian Star is now the only newspaper printed in the city , there have been a few other historical newspapers . One such paper is the <unk> <unk> , a ten to twenty page publication published during the 1970s . The <unk> focused on issues relevant to the African @-@ American population of the region , gathering a circulation of about 5 @,@ 000 people . Other newspapers in the city have included The <unk> <unk> , The State , The Weekly Mercury , The <unk> , Weekly Echo , Fair Play , <unk> , Meridian Morning Sun , <unk> and Preacher , and Clarion . 
- The city is the principal city in the Meridian , Mississippi <unk> Market Area ( <unk> ) , which includes 72 @,@ 180 households with <unk> . <unk> @-@ TV broadcasts as an ABC affiliate from the city , headquartered at 815 23rd Avenue . <unk> operates two digital <unk> , <unk> @-@ <unk> , a <unk> affiliate , and <unk> @-@ <unk> , Meridian 's CW . <unk> @-@ TV , the market 's NBC affiliate , is headquartered at <unk> <unk> Circle . Since January 5 , 2009 , <unk> has also been the market 's <unk> affiliate , and it also features some programming from the <unk> Television Network ( <unk> ) . <unk> operates under a program services agreement with <unk> @-@ TV , the market 's CBS affiliate which operates a local <unk> service on its second <unk> . <unk> @-@ TV is the local affiliate of Mississippi Public Broadcasting . 
- The city is also the principal city in the Meridian <unk> Radio Market , which includes 64 @,@ 500 people over the age of 12 . <unk> ( FM 105 @.@ 7 ) , headquartered at <unk> Highway 45 North , takes the largest share of ratings in the market at 14 @.@ 8 % in Fall 2009 . In the same period , <unk> ( FM 104 @.@ 1 ) was second with 11 @.@ 1 % , and <unk> ( AM <unk> ) was third with 7 @.@ 4 % . Other popular stations in the market include <unk> ( FM 97 @.@ 9 ) , <unk> ( FM 97 @.@ 1 ) , <unk> ( FM 106 @.@ 9 ) , <unk> ( AM <unk> ) , and <unk> ( FM 95 @.@ 1 ) . Mississippi Public Broadcasting can be found on <unk> @-@ FM ( FM 88 @.@ 1 ) . 
- 
- = = Notable people = = 
- 
- Meridian has produced or hosted many famous people in the arts and other areas . For example : 
- 
- = = = In acting = = = 
- 
- Alvin <unk> , who played the lead role in the Amos ' n ' Andy Show , was born in the city in 1907 . 
- Diane <unk> was also born in the city in 1932 @,@ 
- <unk> Ward was born in Meridian , in 1956 . 
- 
- = = = In business and politics = = = 
- 
- Gil Carmichael , Meridian businessman and transportation specialist , was the Republican nominee for the Mississippi Senate in 1966 and 1967 , U.S. Senate in 1972 , governor in 1975 and 1979 , and lieutenant governor in 1983 . 
- Winfield Dunn , former Governor of Tennessee , lived in Meridian 
- John Fleming , current U.S. Representative for Louisiana 's 4th congressional district lived in Meridian ; 
- The late Lieutenant Governor Carroll <unk> , who was born in Meridian , lived in Laurel ; 
- Lewis McAllister , a businessman in Tuscaloosa , Alabama , was the first Republican to serve in the Mississippi House of Representatives since Reconstruction , 1962 @-@ 1968 ; he resided in Meridian prior to 1971 . 
- Gillespie V. Montgomery , former U.S. Representative , lived in Meridian 
- Hartley <unk> , founder of <unk> <unk> which is headquartered in Meridian , is a Meridian native . 
- Tom Stuart from 1973 to 1977 was the first Republican to serve as mayor of Meridian in the 20th century . 
- 
- = = = In broadcasting = = = 
- 
- Emmy award winning meteorologist Bill Evans ( meteorologist ) ( <unk> @-@ TV New York ) was born in Meridian , where he held his first broadcasting jobs . 
- 
- = = = In literature and comics = = = 
- 
- <unk> Edwin <unk> , who won the 1932 O. Henry Award for Best Short Short Story , was born in Meridian . For 30 years , <unk> wrote the <unk> Sawyer comic strip . Meridian is the birthplace of novelist and short story writer Barry Hannah . 
- 
- = = = In music = = = 
- 
- Classical composer John Luther Adams is a Meridian native . 
- The Island Def Jam rapper Big <unk> was born in Meridian . <unk> is known for his many <unk> and albums such as <unk> <unk> Here and <unk> . He has gained a large following for his <unk> and self @-@ produced beats as well as his ability to change his style on songs from slow and conscious tracks ( " The <unk> " ) to fiery and upbeat tracks ( " Mt . Olympus " ) . 
- The guitarist @-@ songwriter George Cummings , born in Meridian in 1938 , was a founding member of Dr. Hook & the Medicine Show . 
- Paul Davis , a singer @-@ songwriter best known for the late 1970s and early 1980s pop hits " I Go Crazy " and " ' 65 Love Affair " , was born in Meridian in 1948 ; after retiring from the music business , he returned to the city where he remained until a fatal heart attack in 2008 . 
- Chris <unk> ( February 10 , 1947 – April 23 , 2012 ) was an American country rock bass guitarist . He was a member of the International <unk> Band ( <unk> ) and The Flying <unk> Brothers , and he co @-@ wrote several songs with <unk> Parsons . <unk> worked with Jackson Browne , Judy Collins , <unk> <unk> , Whitney Houston , Willie Nelson , Randy Newman , Linda <unk> , Leon Russell , Nancy Sinatra , Johnny Winter , and The Byrds . 
- The singer @-@ songwriter Steve <unk> was born in Meridian in 1954 . 
- <unk> <unk> , the " Father of Country Music " , was born in the city in 1897 . The <unk> <unk> Museum is located in Meridian , and the <unk> <unk> Festival has been an annual Meridian event since 1953 . 
- David <unk> , former lead singer of The Temptations , and his older brother Jimmy <unk> were born in the surrounding area , <unk> and <unk> , respectively . 
- George <unk> , the singer @-@ songwriter most famous for the rhythm and blues anthem " Get <unk> " , is a resident of Meridian , where he was born in 1945 . 
- Hayley Williams , lead singer of the band <unk> , was also born in the city in 1988 . 
- Singer Al Wilson , born in June 1939 , was a Meridian native . 
- 
- = = = In sports = = = 
- 
- The city has also been home to several athletes , many of whom have competed at professional levels . Among them are : Dennis Ray " Oil Can " Boyd , former Major League Baseball pitcher ; <unk> league baseball catcher Paul Hardy ; professional basketball player Derrick <unk> ; Jay Powell , another pitcher ; Joe <unk> played in the NFL ; professional basketball player George Wilson 
- 
- = = = Others = = = 
- 
- Other notable natives of the city include : 
- Susan <unk> who won the Miss America beauty pageant in 1986 @,@ 
- James <unk> - one of the victims of the Mississippi civil rights workers murders in 1964 @,@ 
- Fred and Al Key , known as " The Flying Keys " - holders of the world flight endurance record - are Meridian natives . Al Key is also a former mayor of the city . 
- Fred <unk> , leader of the <unk> Baptist Church , 
- 
- = = In popular culture = = 
- 
- 
- = = = <unk> characters = = = 
- 
- Cullen <unk> , the protagonist of the AMC series Hell on <unk> , <unk> from Meridian , Mississippi , where he was a tobacco farmer and later a Confederate soldier during the American Civil War . 
- <unk> Harris , one of the characters of the Pulitzer Prize @-@ winning novel To Kill a <unk> ( 1960 ) , is from Meridian , which is mentioned throughout when referencing <unk> 's home . 
- 
- 
- = Papa <unk> = 
- 
- Papa <unk> is one of the Shetland Islands in Scotland , with a population of under twenty people , some of whom immigrated after an appeal for residents in the 1970s . Located to the west of mainland Shetland and with an area of <unk> hectares ( 3 @.@ 2 square miles ) , Papa <unk> is the eighth largest island in Shetland . <unk> of the soft volcanic rocks by the sea has created an extraordinary variety of caves , <unk> , arches , <unk> , and cliffs . The island and its surrounding seas harbour diverse populations of wildlife . The west side of the island is a Site of Special Scientific Interest and the seas around the island are a Special Area of Conservation . 
- The island has several Neolithic burial chamber sites , as well as the remains of Duke <unk> 's 13th @-@ century house dating from the Norse occupation of the island . The population reached 380 or more in the nineteenth century , when a fishing station was opened at <unk> in West <unk> . Subsequently there was a steady decline in population , although the numbers have increased from a low of 16 in the 1970s . 
- Today the main settlement on the island is <unk> , just to the east of which is <unk> <unk> from where the <unk> ferry arrives from its base at West <unk> on the Shetland <unk> . <unk> , especially sheep rearing , is the mainstay of island life . 
- Numerous <unk> have occurred around the coast , and the celebrated poem Da Sang o da Papa Men by <unk> recalls the drama of the days when Papa <unk> was a centre for deep @-@ sea fishing . 
- 
- = = Geography = = 
- 
- Papa <unk> is located at the south western end of St Magnus Bay . There are 34 kilometres ( 21 mi ) of rugged coastline which is <unk> by numerous small <unk> and four larger ' <unk> ' . <unk> <unk> ( Old Norse : ' harbour bay ' ) in the south is the most sheltered anchorage and the surrounding cliffs contain a natural rock arch . <unk> <unk> to the east ( Old Norse : ' house bay ' ) is less secluded but is the main harbour for the island and the ferry 's embarkation point . 
- <unk> <unk> and <unk> <unk> guard the harbour entrance to the south . The former is a <unk> island and was a <unk> colony until the 18th century ( although it has been suggested that many of the " <unk> " there were suffering from a <unk> <unk> rather than <unk> ) . The latter 's name relates to a story from the 14th century . Lord <unk> <unk> is said to have constructed the tiny house at its top , whose ruins are still visible , in order to " preserve " his daughter from men . Unfortunately for his plans , when she left she was found to be pregnant ; in another version of the story , she and her fisherman <unk> successfully <unk> . West <unk> , the inner part of which is called ' <unk> <unk> ' is the main <unk> in the north coast , the smaller <unk> <unk> lying immediately to the west . 
- The main settlement on the island today is <unk> , which <unk> <unk> <unk> and is surrounded by in @-@ bye land to the east of the hill <unk> ( which runs south from West <unk> ) . To the west the island is <unk> by a belt of glacial <unk> about one and a half kilometres in length . Much of the rest of the area consists of a shallow <unk> soil that may be derived from glacial till . There is an almost complete absence of peat on the island and due to the volcanic rocks the soils are relatively fertile . The lack of peat led to ' turf <unk> ' for fuel and the bare areas of rock in the interior . 
- The highest point on the island is in the north west at <unk> Field , which rises to 87 metres ( 285 feet ) . <unk> is possibly from the Old Norse for ' <unk> of stones ' . 
- 
- = = = List of <unk> = = = 
- 
- In addition to the larger <unk> mentioned above there are various other <unk> and <unk> around the coast of Papa <unk> . They include : <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> of <unk> , <unk> <unk> , <unk> <unk> , <unk> of <unk> , <unk> of <unk> , <unk> <unk> , <unk> <unk> , The Horn , <unk> <unk> and <unk> <unk> . The <unk> <unk> lie 5 kilometres ( 3 @.@ 1 mi ) to the north west . They include : <unk> , North <unk> , <unk> , <unk> and The <unk> . In between Papa <unk> and <unk> <unk> lies the shallow bank of Papa @-@ <unk> . 
- On 21 June 2008 , Stuart Hill , the owner and only resident of the 1 hectare ( 2 @.@ 5 acres ) island of <unk> <unk> ( which he has renamed " <unk> Island " ) made a declaration of dependence , creating the Crown <unk> of <unk> . On the basis of a marriage arrangement between King Christian of Norway and King James III of Scotland that dates to <unk> , Hill argued that the island should be considered a British crown dependency , and thus not a part of the United Kingdom or of the European Union . This claim was not recognised by the United Kingdom . 
- 
- = = Geology = = 
- 
- The island is composed of a variety of volcanic and <unk> rock formations from the Devonian period . At that time the Scottish <unk> formed part of the Old Red <unk> Continent and lay some 10 @-@ 25 degrees south of the equator . The <unk> of Old Red <unk> , laid down from <unk> to 370 million years ago , were created as earlier <unk> rocks , <unk> by the formation of <unk> , eroded and then were deposited into river <unk> . The freshwater Lake <unk> existed on the edges of the eroding mountains , stretching from Shetland to the southern Moray Firth . The structure of Papa <unk> is largely made up of ashes and <unk> from volcanic activity associated with this period , including bands of solidified volcanic ash and <unk> ( <unk> ) , but there is also a Devonian fish bed at <unk> Banks . There are numerous large boulders deposited by Pleistocene <unk> . 
- <unk> of the soft volcanic rocks by the sea has created an extraordinary variety of caves , <unk> , arches , <unk> , cliffs , <unk> and <unk> that are amongst the finest in Britain . The ' Holl o <unk> ' is a cave that passes right through the north @-@ west tip of the island . It is nearly half a mile long and wide enough to row through . <unk> ( or Christie 's ) Hole in the south west is another spectacular cave , part of the roof of which collapsed in 1981 . Yet another is ' <unk> 's Hole ' close to <unk> <unk> in the west . This was the favourite of John Tudor who wrote of the island in his Victorian memoirs and described the cave as : 
- ... in <unk> , so exquisite is the colouring of the roof and sides and so <unk> is the water ... [ with ] <unk> or <unk> like <unk> in a church . 
- In 1953 the spectacular headland , ' Da Horn o Papa ' fell into the sea during a storm . The nearby <unk> of <unk> <unk> also has caves that can be accessed by small boats when conditions permit . 
- 
- = = Ecology = = 
- 
- <unk> , grey seals , killer <unk> and harbour <unk> are frequently seen on and around Papa <unk> . Atlantic <unk> , Arctic and common <unk> , <unk> and Arctic <unk> , northern <unk> , common <unk> , <unk> , <unk> , <unk> , ringed <unk> and great black @-@ backed gull all breed on the island , and numerous migratory species have been recorded . 
- There is a <unk> of wild flowers , including mountain <unk> , spring <unk> and <unk> as well as the ubiquitous <unk> . The west side of the island is a Site of Special Scientific Interest and the seas around Papa <unk> are a Special Area of Conservation . The traditional Shetland Pony is still bred . 
- 
- = = History and archaeology = = 
- 
- Human settlement of the island dates from circa 3000 BC and there are remains of several Neolithic burial chambers known as ' heel @-@ shaped <unk> ' . Little is known of the pre @-@ Celtic and Celtic eras , but when the Norse arrived it is likely they found a religious settlement as the name of the island derives from <unk> <unk> meaning " Big island of the <unk> " ( Celtic monks ) , in distinction to Papa Little some 12 kilometres ( 7 @.@ 5 mi ) to the west . 
- 
- = = = Norse period = = = 
- 
- Papa <unk> is the subject of a <unk> manuscript written in Old Norse , which is the oldest surviving document from Shetland . It deals with a dramatic incident in the house of Duke <unk> <unk> , who was later to become King <unk> V of Norway . There is a circle of stones near the beach at <unk> <unk> , which are the remains of a ' <unk> ' , or local assembly . This was the scene of a duel , fought and won by Lord <unk> <unk> , who was accused of corruption in the <unk> document and was later called ‘ <unk> de <unk> ’ . ( The story of his unfortunate daughter is referred to above . ) The remains of Duke <unk> 's thirteenth @-@ century house are still visible near <unk> <unk> . 
- 
- = = = Scots rule and fishing = = = 
- 
- In <unk> Shetland came under nominal Scottish control , although the Norse ' <unk> of Norway ' kept their Papa <unk> estates until the 17th century . In the 16th century merchants from <unk> and Hamburg were operating a summer trading booth to buy fish from the local fleet . By the 18th century , two Scottish <unk> , Thomas <unk> of <unk> , and Arthur <unk> of <unk> , owned the island . They maintained a prosperous <unk> ( Old Norse : ' deep sea ' ) fishing industry , undertaken in the summer months using six @-@ <unk> boats known as <unk> . 
- In addition to the <unk> colony on <unk> <unk> there may have been another at <unk> <unk> overlooking <unk> <unk> . A recent archaeological survey was inconclusive but suggests the site may be much older than the supposed 18th century colony . 
- The island church , which <unk> Kirk Sand in the bay of Fore <unk> , was founded in 1806 . 300 metres from the present church there may be an older chapel site of <unk> or <unk> on the headland between the west end of Kirk Sand and the bay of <unk> . It is called the ' <unk> <unk> ' by locals and referred to by the Ordnance Survey as " the site of a <unk> chapel belonging to about the twelfth century " . There is a tradition that the work there was <unk> with by supernatural powers and that each day 's work on the building was destroyed during the night . Eventually the <unk> were moved overnight by these <unk> agencies to the present site of the church and work was re @-@ commenced there successfully . Excavations in 2004 found little besides large blocks of <unk> and a piece of whalebone rib , suggesting that the oral tradition may have some truth to it . 
- In the 19th century the <unk> fishing station in West <unk> was opened and the island had a population of 360 people or more . However , fuel shortages and a decline in fishing due to the introduction of steam <unk> saw a fall in population from the 1870s on . At this time another duel entered the history of Papa <unk> . Edwin Lindsay , an Indian army officer and the son of the 6th Earl of <unk> , was declared insane and sent to the island in disgrace after refusing to fight in one . He spent 26 years as a prisoner before the Quaker preacher Catherine Watson arranged for his release in 1835 . Lindsay 's Well is a spring at the south of the island where he was allowed to bathe . 
- There are good examples of horizontal water mills , also known as Norse or <unk> Mills , around Dutch Loch . Originally these were two story buildings with turf roofs , built into banks to give access to the upper floor where the mill @-@ stone was sited . Inside the building there was a fixed lower <unk> , and a rotating upper <unk> driven by the water falling onto the <unk> below . Some were still in use on Papa <unk> in the early years of the 20th century , and there is still a working example of one of these mills on the Burn of <unk> , at <unk> in the south <unk> of Shetland . 
- 
- = = = 20th and 21st centuries = = = 
- 
- In common with many small Scottish islands , Papa <unk> 's population peaked in the 19th century and has experienced a significant decline since then ( see e.g. <unk> ) . By 1970 the island school had closed and the population had declined to sixteen ' fairly elderly ' residents , but an advertisement in Exchange and Mart reversed the decline . A <unk> and five sheep were offered free of charge to <unk> which brought a flood of applicants . By 1981 the census recorded a population of 33 . However , by 2005 the population had fallen to 20 after serious <unk> between <unk> led to several court cases . A number of people left the island and the school closed . By early 2008 the population had dropped to just nine after a family of seven left . The 2011 census recorded a usually resident population of 15 - during the decade 2011 @-@ 11 Scottish island populations as a whole grew by 4 % to 103 @,@ 702 . 
- Overview of population trends 
- 
- = = = <unk> = = = 
- 
- The coasts around Papa <unk> have claimed numerous wrecks . In <unk> , <unk> <unk> alone has sunk Dutch , French , German and Norwegian ships . 
- The Aberdeen trawler Ben Doran <unk> , <unk> on the <unk> <unk> 3 miles northwest of Papa <unk> , on the evening of 28 March 1930 while on her way to the village of <unk> to land her catch . When she grounded weather conditions were fairly good but it was not until the following day that a passing trawler saw , and reported the wreck . By the time that various rescue attempts were launched by the <unk> and local volunteers ( there being no lifeboat in Shetland at that time ) , weather conditions had deteriorated to the point where it was impossible to approach the <unk> . A request had been made for the <unk> lifeboat from Orkney , only 120 miles away , to launch , but the request was made too late to be of help . All 9 crew perished in the wreck . Only 3 bodies were recovered , that of James Mitchell , which was returned to Aberdeen , and the bodies of J. <unk> and <unk> <unk> , which were buried in <unk> . 
- The cargo ship SS <unk> ran aground in fog on <unk> <unk> in February 1940 . On this occasion the conditions were <unk> and only the ship and cargo were lost . In 1967 the Aberdeen trawler <unk> ran aground in <unk> Sound at the bottom of the 60 metres ( 200 feet ) cliffs . The 12 man crew were rescued by the <unk> lifeboat , the <unk> being awarded the <unk> silver medal for this rescue . 
- Papa <unk> 's most recent <unk> occurred on 9 December 1977 when the Aberdeen trawler Elinor Viking <unk> , <unk> Alec <unk> , <unk> on the <unk> <unk> . The <unk> <unk> came to the scene but was unable to get near enough to rescue the crew because of the sea conditions . At the request of Alec Webster , <unk> Station Officer , <unk> , a volunteer crew in a British Airways <unk> <unk> helicopter from <unk> Airport was <unk> . They managed to <unk> all the boat 's crew to safety within hours of the <unk> , despite the storm force winds . The helicopter crew later received a number of awards for bravery . There was no loss of life , but this incident prompted the building of a lighthouse on the <unk> in 1979 , and may also have been the example required for the formation of the present Search and Rescue helicopter unit , based at <unk> Airport . 
- 
- = = Economy & Transport = = 
- 
- <unk> is the mainstay of island life . <unk> form the backbone of the agricultural economy but a diversity livestock are kept , including cattle , pigs , goats , chickens , ducks and <unk> . <unk> are grown too , often in the shelter of circular walls , such plots being known as ' <unk> <unk> ' . <unk> is still conducted but on a relatively small scale . There is a post office at the pier , but no shop . <unk> electricity only came to the island at the close of the twentieth century . The Papa <unk> Project is a Christian supported housing service offering accommodation to men with drug and alcohol issues . <unk> now sail across the Sound of Papa to West <unk> on the Shetland <unk> . The crossing takes 45 minutes , and although the <unk> carries cars , there is only one short road on the <unk> visiting <unk> the four main <unk> provide good shelter , but the strong tides in both the Sound of Papa and to the north west require considerable care . 
- 
- = = = <unk> = = = 
- 
- There is an airstrip which <unk> for regular flights from <unk> . 
- 
- = = Culture and the arts = = 
- 
- The Papa <unk> sword dance may be of Norse origin and bears similarities to the long sword dance of the north east of England . A description of the dance appears in The Pirate by Sir Walter Scott . 
- The writer and journalist John Sands lived on Papa <unk> and <unk> for a while during the late nineteenth century . The writer , <unk> and musician , George P. S. Peterson was brought up on Papa <unk> . 
- It is also the ' Papa ' of <unk> 's poem Da Sang o da Papa men , now adopted as part of the <unk> tradition , as set to music by <unk> Manson . The <unk> chorus chant , ' <unk> <unk> <unk> ! ' , is particularly striking . 
- " <unk> <unk> da Horn o Papa , 
- <unk> <unk> <unk> ! 
- <unk> a hidden piece o water , 
- <unk> <unk> <unk> ! 
- <unk> da boat da tide @-@ <unk> <unk> , 
- <unk> <unk> da <unk> is <unk> ; 
- We <unk> <unk> <unk> fish is <unk> , 
- <unk> <unk> <unk> ! " 
- " <unk> <unk> <unk> ! " refers to the <unk> ' practice of rowing their open fishing boat out to sea until the high cliffs of <unk> were no longer visible . This entailed the boat being some 96 kilometres ( 60 mi ) west of Papa <unk> . The ' tide @-@ <unk> ' are increased swells of unusual size due to the combined action of wind against tide . The <unk> final image of the piece is of the fishermen being led back home to Papa by the ' scent o <unk> ' across the water . This is an example of <unk> 's ability to create a vivid sensual impression of a situation . An extra layer of meaning is added by the knowledge that Da Horn o Papa collapsed in a storm around the time of this poem 's composition , so that it is a tribute not just to a lost way of life , but a noted geographical feature . 
- 
- 
- = Angel of Death ( Slayer song ) = 
- 
- " Angel of Death " is the opening track on the American thrash metal band Slayer 's 1986 album Reign in Blood . The lyrics and music were written by Slayer guitarist , Jeff Hanneman and are based on Nazi physician <unk> <unk> , who conducted human experiments at the Auschwitz concentration camp during World War II . " Angel of Death " led to the band facing accusations of Nazi <unk> and racism throughout their career . 
- Despite the controversy surrounding the song and its contribution to the delay in the release of Reign in Blood , " Angel of Death " is featured on all of Slayer 's live albums and DVDs and has appeared in several movies . The song was well received by critics ; Steve Huey of AllMusic described it as a " classic " . 
- 
- = = Composition and origins = = 
- 
- Slayer guitarist Jeff Hanneman wrote " Angel of Death " after reading books about Nazi physician <unk> <unk> while on tour with the band : " I remember stopping <unk> where I bought two books on <unk> . I thought , ' This has gotta be some sick shit . ' So when it came time to do the record , that stuff was still in my head — that 's where the lyrics to ' Angel of Death ' came from . " 
- The lyrics detail <unk> 's surgical experiments on patients at the Auschwitz concentration camp during World War II . <unk> 's <unk> were conducted on such groups as dwarfs and twins , and included both physical and psychological examinations . Among the tests he performed that are mentioned in " Angel of Death " are experimental <unk> performed without anesthesia , transfusion of blood between twins , isolation endurance , <unk> , <unk> with lethal <unk> , sex change operations , the removal of organs and limbs , and <unk> . 
- " <unk> together , joining heads . Just a matter of time ' <unk> you rip <unk> apart " , a line from the penultimate verse , is a reference to the allegation <unk> <unk> together twins , one of them deformed ; " the <unk> was <unk> to the other child , back to back , their wrists back to back too . " This claim was first made by Auschwitz survivor <unk> Alexander at the 1961 trial of SS @-@ <unk> Adolf <unk> the " architect of the Holocaust " , where it was asserted that <unk> " <unk> the veins together " and turned " them into <unk> twins . " This allegation became more widely known when <unk> Alexander was featured in the 1985 documentary The Search for <unk> , which has been cited by several authors including Gerald <unk> , an expert on <unk> . 
- Towards the end of the song , there is a line " Feeding off the screams of the <unk> he 's creating " , which was taken from the film The Boys from Brazil in which Dr. <unk> was the villain . 
- 
- = = Controversy = = 
- 
- The lyrical content of " Angel of Death " contributed to the delay of the release of Slayer 's 1986 album Reign in Blood . The band were signed to Def Jam Records whose distributor , Columbia Records , refused to release the album due to lyrical themes and artwork concerns , deeming the artwork " too graphic " . Reign in Blood was eventually distributed by Geffen Records on October 7 , 1986 . However , due to the controversy , Reign in Blood did not appear on Geffen Records official release schedule . 
- " Angel of Death " caused outrage among survivors of the Holocaust , as well as their families and the general public . The controversy led to accusations of Nazi <unk> which have followed Slayer throughout their career . People took Hanneman 's interest in Nazi history and his collection of Nazi medals as evidence of <unk> – his most prized item being a German Knight 's Cross . Hanneman <unk> asserting : 
- " I know why people <unk> it – it 's because they get this knee – <unk> reaction to it . When they read the lyrics , there 's nothing I put in the lyrics that says necessarily he was a bad man , because to me – well , isn 't that obvious ? I shouldn 't have to tell you that . " 
- According to guitarist Kerry King : " Yeah , ' Slayer are Nazis , fascists , communists ' — all that fun shit . And of course we got the most <unk> for it in Germany . I was always like , ' Read the lyrics and tell me what 's offensive about it . Can you see it as a documentary , or do you think Slayer 's preaching fucking World War II ? ' People get this thought in their heads — especially in Europe — and you 'll never talk them out of it . " 
- The song drew accusations of racism , which the band has denied . The band members are often asked about the accusations in interviews , and have stated numerous times that they do not <unk> racism and are merely interested in the subject . 
- In 2006 , the song " <unk> " from Slayer 's 2006 album Christ Illusion drew comparison to " Angel of Death " . " <unk> " deals with the September 11 , 2001 , attacks , and tells the story from a terrorist 's perspective . <unk> Araya was expecting the subject matter to create a similar backlash to that of " Angel of Death " , although it did not <unk> , in part , he believes , due to peoples ' view that the song is " just Slayer being Slayer " . 
- 
- = = Music and structure = = 
- 
- " Angel of Death " is the longest track on the album Reign in Blood , spanning 4 minutes and 51 seconds , where the total duration of the album is 29 minutes . Additionally , it has one of the most conventional song structures on the album , featuring prominent verses and choruses , where most tracks on the album <unk> them . Hanneman and King deliver their ' intricate riffs ' , which offer the few hints of melody on the album according to PopMatters reviewer Adrien Begrand , and Araya bursts out his piercing scream , with Lombardo performing beats of 210 beats per minute . 
- When drummer Lombardo left Slayer in 1992 , the band hired a full @-@ time replacement in Forbidden drummer Paul <unk> . <unk> made one mistake out of the nine songs the band <unk> him with , on " Angel of Death " . Before the " big double bass part " there is a lead section , which <unk> could not understand , as he had to learn from live records recorded with Lombardo . <unk> could not tell how many <unk> the guitar riff goes before the bass sequence . The band members told him there were eight , " <unk> " the song afterwards . 
- 
- = = Reception = = 
- 
- Although " Angel of Death " did not enter any charts , it received strong praise from critics in reviews for Reign in Blood . Clay <unk> of <unk> Magazine noted that the song " <unk> the <unk> of any band playing fast and / or heavy today . Lyrically outlining the <unk> to come , while musically laying the <unk> for the rest of the record : fast , lean and filthy . " 
- Adrien Begrand of PopMatters remarked that " There 's no better song to kick things off than the <unk> ' Angel of Death ' , one of the most monumental songs in metal history , where guitarists Kerry King and Jeff Hanneman deliver their intricate riffs , drummer Dave Lombardo performs some of the most powerful drumming ever recorded , and bassist / vocalist Tom Araya screams and <unk> his tale of Nazi war criminal <unk> <unk> . " 
- <unk> on the publicity generated by the controversy , the band utilized Nazi imagery by adapting a logo to one similar to the eagle atop <unk> , during the Seasons in the Abyss period . Hanneman placed SS <unk> on his guitar , and wrote " SS @-@ 3 " a song about Reinhard <unk> , the second in command in the <unk> organization . 
- 
- = = Appearances = = 
- 
- " Angel of Death " has appeared in several movies and TV shows including <unk> 2 : The New <unk> in a scene when the <unk> <unk> is turning into a spider , <unk> : The Movie during a car stunt , the 2005 Iraq War documentary Soundtrack to War , which details the role of music on the contemporary battlefield , and on an episode of The <unk> when <unk> asks a female escort to shoot her . 
- The half @-@ time riff was sampled by Public Enemy in their 1988 song " She Watch Channel Zero ? ! " , sampled by <unk> in their song " <unk> Hell " , which appeared in the Breaking Bad episode " Thirty @-@ Eight <unk> " , and sampled by <unk> on their 1990 single " <unk> " , and featured in the multi @-@ platform video game Tony Hawk 's Project 8 . Nolan Nelson , who selected the soundtrack for the game asserts the song is " one of the greatest heavy metal songs ever recorded . Don 't know who Slayer is ? I feel sorry for you . " 
- A Slayer tribute band called Dead Skin Mask released an album with eight Slayer tracks , " Angel of Death " being one of them . The death metal bands <unk> and <unk> covered the song , and the track was featured on cello metal band <unk> 's 2006 album <unk> / / A Decade of <unk> the <unk> . Sgt. <unk> , a thrash metal band with members from <unk> Saint , has also covered this song . Al Sur del <unk> ( <unk> <unk> a Slayer ) , a Slayer tribute album compiled by <unk> Metal Records , featured sixteen tracks covered by Argentina metal bands , <unk> covered " Angel of Death " . The song was once again covered by <unk> band <unk> and appears on their third album Hell <unk> Me as a bonus track included on the vinyl and iTunes editions . 
- A compilation album of bands that have been featured in the <unk> line @-@ up , of which Slayer have <unk> multiple times , features " Angel of Death " . 
- The song saw a release as playable downloadable content in the game <unk> 2014 . 
- 
- 
- = Calvin <unk> = 
- 
- Calvin <unk> ( born November 2 , 1984 ) is a Canadian football running back for the Edmonton <unk> of the Canadian Football League ( <unk> ) . He played as a <unk> until 2014 , when he became the starting fullback for the <unk> . <unk> is known for being able to fill many roles at his position , with <unk> <unk> Chris Schultz noting in 2010 that he is a " multi @-@ purpose running back who catches the ball extremely well , blocks well and runs well " . He is a champion of the <unk> Grey Cup . 
- Prior to being drafted by the Edmonton <unk> in the fourth round of the 2007 <unk> Draft , <unk> played high school football for the St. Thomas More Knights , where he broke multiple school records . He later played college football for the Western Washington Vikings . With the Vikings , <unk> developed into a dual threat , being used heavily as a rusher and receiver . <unk> has spent his entire professional career with the Edmonton <unk> , making him the most veteran player on the team as of the 2015 season . 
- 
- = = High school career = = 
- 
- <unk> played high school football for the St. Thomas More Knights in <unk> , British Columbia beginning in 1998 , playing as both a running back and middle linebacker on the Grade 8 team . He quickly became a key player on the team , including rushing for 185 yards and four touchdowns while adding 10 defensive tackles in the Grade 8 provincial semi @-@ finals on November 24 against the <unk> Royals . For that performance , he was named " Star of the Week " by The Vancouver Sun . The Knights went on to defeat the Vancouver College Fighting Irish 48 – 0 in the Grade 8 <unk> championship game , where <unk> scored another three touchdowns . 
- The following year , <unk> joined the senior team at St. Thomas More , <unk> the junior team entirely . He continued in his role as a running back while switching to the secondary <unk> . Despite being a rare Grade 9 player on the senior team , he was one of the Knights ' two leading rushers that year . <unk> scored a touchdown in the AAA <unk> championship game , helping the Knights win their first senior title as they beat the <unk> T @-@ Wolves 29 – 6 . While <unk> Steele , the Knights ' coach , has a policy of not recording player statistics , it is estimated that <unk> finished 1999 with over 1 @,@ 000 rushing yards and 15 touchdowns . 
- In 2000 , <unk> played a significant role in the Knights ' rushing and return game . He rushed for 150 yards and three touchdowns against the <unk> Central <unk> in the annual <unk> Bowl , adding two punt returns for touchdowns . By the end of October , the Knights had compiled a perfect 6 – 0 record , and defensive coordinator and former <unk> defensive back Lou <unk> called <unk> " the best player for his age and talent that we 've ever had at our school " . He recorded 238 yards and four consecutive touchdowns with only 23 carries in the Knights ' 46 – 12 victory over the W. J. <unk> Hawks in the AAA championship game . For his role in earning the Knights their second AAA title , <unk> was named the game 's MVP , becoming the youngest player to earn this award . 
- <unk> remained a presence on the field for the Knights in 2001 , expanding his role by playing some snaps as a fullback . He rushed for 160 yards in that year 's <unk> Bowl , including three touchdowns as a <unk> and one as a fullback . <unk> had another notable performance in a 67 – 7 <unk> against the South Delta Sun Devils , running for 200 yards and three touchdowns . He also showed himself to be a capable receiver , including making five catches for 66 yards in a match against the Holy Cross Crusaders . <unk> also finished that game with 110 yards and two touchdowns on only three carries . Despite giving up only 19 points during the entire regular season , the Knights lost 32 – 26 in the quarter @-@ finals of the playoffs against the Centennial <unk> after <unk> was stopped one yard away from the <unk> on the last play of the game . 
- As a senior , <unk> recorded 2 @,@ 400 yards and scored 33 touchdowns in just nine games , averaging 266 @.@ 7 yards and over three touchdowns per game . He ran for 380 yards and three touchdowns in a 21 – 20 loss against the W. J. <unk> Hawks in the quarter @-@ finals of the AAA playoffs . He was named the 2002 Provincial Player of the Year for his performance and finished his four @-@ year AAA career at St. Thomas More with 84 touchdowns , breaking a school record . 
- 
- = = = Other sports = = = 
- 
- <unk> played basketball and baseball during high school as well . He <unk> in basketball with the Knights . At the 2001 Big League World Series , <unk> played for Team Canada as a shortstop and center <unk> , helping the team to third place in the international competition . Despite being skilled as a baseball player , <unk> chose football over professional baseball due to the <unk> of the former sport . 
- 
- = = College career = = 
- 
- 
- = = = Boise State = = = 
- 
- <unk> originally committed to Boise State University and played for the Broncos . In 2003 , he was given redshirt status and did not play . <unk> played a limited role the following year , but capitalized on the opportunities he was given , rushing for 104 yards on only 10 carries . His only touchdown came on a 7 @-@ yard carry in the October 24 game against the Fresno State Bulldogs . The Bulldogs finished with an 11 – 1 season in 2004 . 
- 
- = = = <unk> = = = 
- 
- <unk> transferred to <unk> College and played for the Tigers in 2005 . He ended the season with 620 rushing yards , 500 receiving yards , and 14 touchdowns , as the Tigers earned a 10 – 1 record along their way to the Central Valley Conference championship title . <unk> also played baseball at <unk> . 
- 
- = = = Western Washington = = = 
- 
- After his single season at <unk> , <unk> transferred to Western Washington University and played football for the Vikings . <unk> was immediately a significant factor in the Vikings ' <unk> . In the season opener , he rushed for 139 yards and three touchdowns on 30 carries against the Humboldt State <unk> . He also played a large role in the passing game early in the season , making eight receptions for 126 yards through the first two games . After starting the first seven games for the Vikings , <unk> broke his foot in a game against the South Dakota <unk> . At the time of his injury , he led the Vikings in rushing and receiving yards . He finished the season with <unk> rushing yards and five touchdowns on 130 carries , as well as 30 receptions for <unk> yards . <unk> was also named a second @-@ team all @-@ star of the North Central Conference . 
- 
- = = Professional career = = 
- 
- 
- = = = Edmonton <unk> = = = 
- 
- Following his only season at Western Washington , <unk> declared himself eligible for the 2007 <unk> Draft . He was selected in the fourth round of the draft by the Edmonton <unk> with the 27th overall pick . He was re @-@ signed on December 19 , 2008 to a multiple @-@ year contract , and again following the 2011 season . 
- 
- = = = = 2007 season = = = = 
- 
- <unk> made the active roster and played in all 18 regular season games his rookie season . He was used in the passing game and on the special teams , finishing with seven receptions for 99 yards and a touchdown as well as five special @-@ teams tackles . <unk> made his <unk> debut on June 28 in the season opener against the Winnipeg Blue <unk> . He received his first carry and reception in a Week 10 game against the Calgary <unk> , where he was given two carries for one yard and caught one reception for 10 yards . 
- 
- = = = = 2008 season = = = = 
- 
- <unk> played a larger role in his second season with the <unk> , especially as a receiver . On September 1 in a match against the <unk> , starting running back A. J. Harris was injured , and <unk> rushed for 73 yards on 12 carries as a backup . <unk> in again for the injured Harris on September 13 against the Montreal <unk> , <unk> was named Canadian Player of the Week for the first time with 72 yards on only 9 carries and a touchdown . <unk> his first start of his career on October 4 , <unk> rushed for 88 yards on 19 carries with a touchdown along with eight catches for 80 yards , earning him another Canadian Player of the Week award . He played in all 18 games and started three times in his second year , finishing with 490 yards and four touchdowns on 88 carries . He had 70 catches , the second @-@ highest amount among running backs . He also continued his role on the special teams , ending the season with 11 special @-@ teams tackles . During the season , head coach Danny <unk> referred to <unk> as " the best fourth @-@ round pick he 'd ever been associated with " . 
- 
- = = = = 2009 season = = = = 
- 
- <unk> split time with <unk> <unk> in 2009 . He rushed for two touchdowns in Week 4 , being named the Canadian Player of the Week for the third time . He was also named the Canadian Player of the Month in July after continuing to play a large role in the rushing game . In August , <unk> injured his hamstring in a game against the <unk> and missed several games . He briefly returned in mid @-@ September before being sidelined again with recurring hamstring issues until late October . In Week 19 , <unk> ran for 81 yards and a touchdown off of 10 carries , helping the <unk> defeat the BC Lions in a 45 – 13 <unk> . Despite having his season <unk> by injuries , <unk> finished 2009 with 348 rushing yards and five touchdowns on 67 attempts , as well as seven special @-@ teams tackles . He saw a significantly smaller role as a receiver , catching 20 passes for only 124 yards . 
- 
- = = = = 2010 season = = = = 
- 
- <unk> remained in a multi @-@ purpose role in 2010 and was utilized more frequently in the passing game compared to the previous season . He made a reception for a first down following a fake punt in Week 6 . He missed two games later in the season due to a hand injury . In a September 26 game against the Toronto <unk> , <unk> rushed for 84 yards on 10 carries and two fourth @-@ quarter touchdowns , including a 46 @-@ yard <unk> . <unk> was utilized about equally on the ground and in the air , ending his season with <unk> rushing yards on 62 carries and <unk> receiving yards on 36 catches as well as five total touchdowns . He continued to play on the special teams where he made eight tackles . He started in six of the 15 games he played , and the <unk> nominated him for Most Outstanding Canadian . 
- 
- = = = = 2011 season = = = = 
- 
- In 2011 , the <unk> utilized a committee of running backs , with <unk> , Daniel Porter , and Jerome <unk> all receiving significant playing time . <unk> was used mostly in short @-@ yardage situations on the ground , while also being active as a receiver and on special teams . He played in 18 games , made eight starts , and finished with <unk> yards on 52 carries with no touchdowns . He also caught 22 passes for 150 yards and a touchdown . <unk> played in both of the <unk> ' playoff games . In the West <unk> @-@ Finals against the <unk> , he rushed for a goal @-@ line touchdown , in addition to making three receptions and two special @-@ teams tackles . <unk> played a more limited role in the West Finals against the BC Lions , where he was given only one carry for six yards , made one tackle on special teams , and caught two passes for a total of four yards . 
- 
- = = = = 2012 season = = = = 
- 
- <unk> played a limited role in 2012 , both due to injuries and competition from other backs , including Cory Boyd , Hugh Charles , and Jerome <unk> . <unk> missed six games due to a high ankle sprain suffered during the Labour Day <unk> . Playing in the other 12 games but starting in none , <unk> rushed only 12 times and made five receptions with a lone rushing touchdown . He added four special @-@ teams tackles . 
- 
- = = = = 2013 season = = = = 
- 
- <unk> played a role as a receiver and special teams player in 2013 , but was almost entirely absent from the rushing game . He rushed for 48 yards on 9 carries , but caught 20 passes for 186 yards and two touchdowns . He continued to play on the special teams , and recorded nine special @-@ teams tackles . 
- 
- = = = = 2014 season = = = = 
- 
- <unk> was shifted from playing mostly as a backup <unk> to the fullback position , where he started all 18 regular season games . In his new position , <unk> was primarily used for blocking and remained involved on special teams and as a receiver . He continued his extremely limited role as a rusher , finishing the season with just eight carries . <unk> had 16 catches for 123 yards and two touchdowns , as well as a career @-@ high 12 special @-@ teams tackles . 
- 
- = = = = 2015 season = = = = 
- 
- <unk> was again used as a fullback and special teams player in 2015 . After sustaining an unspecified injury in week 12 , he missed several games and was placed on the six @-@ game injured list . <unk> finished the season with only one carry and nine catches , his lowest total number of touches in any <unk> season . With 12 starts , <unk> finished with one carry for 20 yards , nine receptions for 68 yards , as well as five special teams tackles and one kick return for 17 yards . <unk> played in the West Final and had one special teams tackle . He became a Grey Cup champion for the first time after rushing for three yards on a fake punt in the championship game versus the <unk> . 
- 
- = = = Season statistics = = = 
- 
- 
- = = Personal life = = 
- 
- <unk> grew up in <unk> , Oklahoma . After his mother , Jackie Conway , was unable to financially support him , <unk> moved to Canada and lived with his father , Orlando <unk> . 
- His mother was a college <unk> player for the <unk> Lady Norse , while his father was a college basketball player for the <unk> Warriors and the Southeastern Oklahoma State Savage Storm . <unk> 's siblings were also college athletes ; <unk> played <unk> for the McPherson Bulldogs while Jordan was a quarterback with the <unk> Tigers . 
- 
- 
- = Hurricane <unk> ( 2011 ) = 
- 
- Hurricane <unk> was a Category 1 hurricane that killed four people after <unk> the western coast of Mexico in June 2011 . <unk> from an area of disturbed weather on June 19 , several hundred miles south of Mexico , <unk> gradually intensified . <unk> latitude , the system became increasingly organized and reached hurricane status on the evening of June 20 . The following morning , <unk> attained winds of 90 mph ( 150 km / h ) as it passed roughly 15 mi ( 20 km ) of Mexico . Due to its interaction with land , the hurricane abruptly weakened hours later . Early on June 22 , <unk> dissipated over open waters . Prior to <unk> 's arrival in Mexico , hurricane watches and warnings were issued for coastal areas . Hundreds of shelters opened across the states of Colima and Guerrero . Heavy rains from the storm triggered significant flooding along the <unk> River in Acapulco , killing four people . However , the overall effects of <unk> were limited and the rains were largely beneficial in <unk> a severe drought . 
- 
- = = Meteorological history = = 
- 
- On June 16 , 2011 , a trough situated off the southern coast of Guatemala showed signs of possible tropical cyclogenesis . Though located within an area favoring development , the system remained broad and disorganized as it moved slowly west @-@ northwestward . Gradually , convection consolidated around an area of low pressure . On June 18 , a Tropical Cyclone Formation <unk> was issued for the system as it was expected to develop into a tropical depression within 24 hours . On the following day , the low further developed and was declared Tropical Depression Two @-@ E by the National Hurricane Center at 1500 UTC . Upon being declared , the depression was located about <unk> mi ( 540 km ) south @-@ southeast of <unk> <unk> , Mexico . However , after post @-@ analysis , the depression had actually developed nine hours earlier . <unk> a west @-@ northwesterly track in response to a subtropical ridge to the north , the depression was forecast to move into a favorable environment , characterized by low wind shear and sea surface temperatures up to 86 ° F ( 30 ° C ) . Three hours after classification , the depression strengthened Tropical Storm <unk> , the second named storm of the season . 
- Early on June 20 , convection increased markedly with several <unk> tops ( denoted by cloud @-@ top temperatures below − 80 ° C ( − 112 ° F ) ) appearing on satellite imagery . A notable increase in <unk> outflow also took place . As the subtropical ridge north of the storm weakened , <unk> acquired a more northerly component to its track , bringing the center of the storm closer to Mexico . <unk> bands gradually wrapped around the system throughout the day . During the afternoon hours of June 20 , a hurricane hunter aircraft found <unk> just below hurricane strength , and subsequently , the system was upgraded just a few hours later , while situated roughly 85 mi ( 140 km ) southeast of Manzanillo , Mexico . Early on June 21 , a 23 to 29 mi ( 37 to 47 km ) wide eye developed within the central dense <unk> . In light of this , the National Hurricane Center estimated <unk> to have attained winds of 90 mph ( 150 km / h ) along with a barometric pressure of 977 mbar ( hPa ; 28 @.@ 85 inHg ) ; however , it was noted that this could be conservative based on estimates of 105 mph ( 165 km / h ) from the University of Wisconsin and the <unk> Analysis Branch . 
- Throughout the morning of June 21 , <unk> brushed the coast of Mexico , with the center passing roughly 15 mi ( 20 km ) south @-@ southeast of La Fortuna , Mexico . <unk> just off the coast of Mexico , the system interacted with land and began weakening . <unk> later , convection rapidly diminished as dry air became entrained in the circulation . In response to quick degradation of the system 's structure , the NHC downgraded <unk> to a tropical storm . Rapid weakening continued throughout the day and by the evening hours , the storm no longer had a defined circulation . Lacking an organized center and deep convection , the final advisory was issued on <unk> . The storm 's remnants persisted for several more hours before dissipating roughly 175 mi ( 280 km ) southwest of Cabo Corrientes , Mexico . 
- 
- = = Preparations and impact = = 
- 
- Following the classification of Tropical Depression Two @-@ E on June 19 , the Government of Mexico issued a tropical storm warning for coastal areas between <unk> and Manzanillo . A hurricane watch was also put in place from <unk> de <unk> to Punta San <unk> . Later that day , the tropical storm warning was upgraded to a hurricane warning and the watch was extended westward to La Fortuna . As <unk> continued to strengthen , the watches and warnings were extended westward to Cabo Corrientes on June 20 . Early on June 21 , the easternmost advisories were discontinued as <unk> moved away . Later that morning , all watches and warnings were discontinued in light of <unk> 's rapid weakening . 
- In Colima and <unk> , residents were warned of heavy rains that could trigger flooding and mudslides . Schools across both states canceled classes for June 21 . In Colima , 236 shelters were opened to the public . Additionally , the Mexican Navy was placed on standby for hurricane relief . A total of 980 temporary shelters were opened in Guerrero , many of which were in Acapulco , as many residents were expected to evacuate flood @-@ prone area . An orange @-@ level alert was issued for portions of Chiapas in light of potentially heavy rains . Throughout Western and Southwestern Mexico , the <unk> <unk> <unk> ( National Weather Service ) advised that all air and sea traffic be suspended until the passage of <unk> . 
- Across Guerrero , <unk> produced heavy rain , peaking at 222 @.@ 5 mm ( 8 @.@ 76 in ) in <unk> , Guerrero . Several homes across the state were damaged , though none were destroyed . In Acapulco , 380 homes were inundated by flood waters , prompting <unk> in areas built in 1999 to relocate residents after Hurricane Pauline . Several landslides blocked off roads and a 100 m ( 330 ft ) section of a roundabout was destroyed . In the community of <unk> , three people were killed after being washed away by flood waters . Heavy rains from <unk> caused the <unk> River to overflow its banks , placing 150 homes across 14 <unk> under water . One person drowned in San Agustín after falling in the swollen river . Twenty @-@ seven <unk> in and around the city lost power after muddy <unk> entered a <unk> . Throughout Oaxaca , at least a dozen homes lost their roofs due to high winds . <unk> of Colima , Chiapas , Michoacán and <unk> experienced heavy rains ; however , in many areas , the rain proved beneficial in <unk> a severe drought . 
- 
- 
- = The <unk> Blues = 
- 
- " The <unk> Blues " is a blues song by American folk and blues musician , Lead <unk> . It was written in June 1937 in response to the discrimination and segregation that Lead <unk> faced during a visit to Washington , DC to record for Alan <unk> . It rails against racism , the Jim Crow laws , and the conditions of contemporary African Americans in the southern United States . 
- The song was recorded in December 1938 for the Library of Congress and re @-@ recorded in 1939 for commercial release . It has been remixed and covered by a number of artists including Pete Seeger , <unk> <unk> , Hans <unk> , and Billy Bragg . 
- " The <unk> Blues " is regarded as one of Lead <unk> 's best original works , but it also drew controversy . There is doubt over the song 's authorship , with some scholars <unk> that Lead <unk> was unlikely to have written a work in a genre new to him without a collaborator . <unk> have been raised over his role in the American Communist Party and whether he and the song were used to further the party 's political goals . 
- 
- = = Background and creation = = 
- 
- Most music historians date the writing of " The <unk> Blues " to Lead <unk> 's June 1937 trip to Washington , DC , when he was invited by the <unk> Alan <unk> to record for the Library of Congress 's folk music collection . On the first night Lead <unk> and his wife Martha spent in the city , they encountered racially <unk> Jim Crow laws similar to those found in their native Louisiana : most hotels refused to rent rooms to African Americans and the few that would were either full or refused to serve him because he was with a white man ( <unk> ) . <unk> , in some versions of the story described as an unnamed " white friend " , offered to let the couple stay for the night in his apartment near the Supreme Court Building . The next morning , Lead <unk> <unk> to <unk> arguing with his <unk> about the presence of a black man , with the <unk> threatening to call the police . 
- While in Washington , Lead <unk> encountered several other incidents of segregation that are believed to have contributed to the <unk> of the song . For instance , when Lead <unk> , <unk> , and their wives wanted to go out to dinner together , they discovered that it was impossible for the mixed race group to find a restaurant that would serve them . Lead <unk> was told that if he returned later without <unk> , he would be served . 
- In response to one of these incidents , a friend of Lead <unk> 's , variously identified as either <unk> or Mary Elizabeth <unk> joked that Washington was a " <unk> town . " Though Lead <unk> did not know what the word " <unk> " meant , he was fascinated by the sound of it , and after its meaning was explained to him he decided to incorporate it into a song about the trip . The song came together quickly ; one account claims that it only took a few hours for Lead <unk> to write it . <unk> liked it because it was partly based on what happened in his apartment . 
- 
- = = Lyrics , themes and music = = 
- 
- " The <unk> Blues " is a blues @-@ style protest song that criticizes the culture of Washington , DC . It protests against both the city 's Jim Crow laws and the racism of its white population . Its structure includes several verses and a refrain that declares that the speaker is going to " spread the news all around " about the racial issues <unk> the city . The song , particularly in the refrain , <unk> race and economics by referring to <unk> as " <unk> " . 
- The first two verses speak of the segregation that Lead <unk> encountered in Washington DC : the first recounts the fact that during the trip , Lead <unk> was " turned down " wherever he tried to get served due to his race , while the second recounts the argument between <unk> and his <unk> over Lead <unk> staying in his apartment . The third verse <unk> cites " the home of the brave , the land of the Free " , juxtaposed with the mistreatment he received at the hands of white people in Washington , DC . The fourth verse speaks of the racism of the white population of the city , leading to the song 's end , which suggests that African Americans boycott buying homes in the district . 
- The song 's tone implies that the speaker feels powerless against the discrimination and racism that he encounters ; despite this , by " spreading the news " of his poor treatment in a song , the speaker uses what power he has to tell both southern African Americans and northern whites that the status <unk> is deeply flawed and that something needs to change . 
- " The <unk> Blues " follows a traditional twelve @-@ bar blues format . It is written in 44 time but <unk> to note that the song <unk> should swing at medium <unk> . The song was written in B ♭ . It uses twelve measures with verses one to four repeating , followed by the final two verses and a <unk> . 
- 
- = = Recordings and <unk> = = 
- 
- Lead <unk> first recorded " The <unk> Blues " in December 1938 in New York City , for donation to the Library of Congress . He re @-@ recorded the song in April 1939 for <unk> Records , for release the same year as a 78 rpm record . The version that was commercially released features Lead <unk> singing and playing the twelve @-@ string guitar without any other accompaniment . 
- The song has been covered and <unk> by a variety of artists including Pete Seeger and Hans <unk> . Seeger recorded and released both live and studio versions of the song on several of his albums . In Australia , the song was reworked as " Canberra Blues " by The <unk> <unk> , an R & B band . The lyrics speak of the problems faced by young Australians in the Australian Capital Territory in the 1960s . <unk> adapted the song to his style of European blues for the album Journey On in 1997 . 
- In 2006 , Billy Bragg reworked the song as " Bush War Blues " . Bragg 's cover is a topical protest song about the Iraq War . In one verse , Bragg claims that the Iraq War was not for democracy but instead was to " make the world safe for <unk> " . In another , he takes on the Christian right , asking where the <unk> are . Finally , Bragg <unk> the United States government for not dealing with poverty at home before going to war . 
- 
- = = Legacy = = 
- 
- " The <unk> Blues " is one of Lead <unk> 's most famous songs and is remembered as his most " heartfelt protest song " . There is disagreement among music scholars as to its importance . Robert <unk> claims that the song is " peripheral " to the wider study of the blues , while Lawson points to it as a watershed in the way African Americans see themselves in the fabric of the United States . There is debate over the relationship between the song and radical politics . After its release , it became popular with left @-@ wing political groups . Lead <unk> was invited to perform at Camp Unity , the Communist Party USA 's summer retreat , and the FBI subsequently opened a file on him in the 1940s . This led to the accusations that the Communists were taking advantage of him and using him as a platform . The party claims , to the contrary , that they were some of the few people who respected him and gave him a chance to perform . 
- Jeff Todd <unk> and several other writers have suggested that Lead <unk> had significant help with its authorship and claim it is not a genuine protest song . This theory stems from the idea that Lead <unk> did not have a history of protest music before he was discovered by <unk> . Since the music that <unk> recorded was sold to <unk> sympathetic to Civil Rights , it has been suggested that <unk> helped him write a song attractive to a white audience . Lead <unk> admitted that the term " <unk> " was unfamiliar to him , and it seems out of place compared to the vocabulary of his past work . 
- 
- 
- = <unk> ( Madonna song ) = 
- 
- " <unk> " is a song by American singer Madonna from her tenth studio album Confessions on a Dance <unk> ( 2005 ) . It was written and produced by Madonna and Stuart Price , and released as the second single from the album on February 7 , 2006 . It later appeared on Celebration , her 2009 greatest hits album . An uptempo dance song , " <unk> " was one of the first tracks developed for the album and had numerous remix treatments before the ultimate version of the track was finalized . One of the remixes was done by the Pet Shop Boys , featuring added lyrics by the band . The actual song features a dance groove tempo . It talks about personal <unk> and self @-@ <unk> . 
- " <unk> " received positive reviews from contemporary critics , who declared the track the strongest song on Confessions on a Dance <unk> . Some critics also commented on the song 's disco @-@ influenced beats while comparing it to Madonna 's older dance songs . It achieved commercial success , topping the singles charts in Italy , Spain , Romania and the United Kingdom , where it became Madonna 's twelfth number one single . Elsewhere , the song was a top ten hit . However , in the United States , the song did not perform well due to an <unk> on radio but was able to reach the top of Billboard 's dance charts and became one of the decade 's most successful dance hits . 
- The accompanying music video , directed by Madonna 's <unk> Jamie King , was a continuation from the " <unk> Up " music video . It featured Madonna and her dancers <unk> around a city in a van , dancing on roller <unk> and Madonna fighting with a group of men in a cage . She performed the song on her 2006 Confessions Tour in a similar fight sequence to that shown in the video . An additional video was created as a backdrop for a remix of the song , which depicted political leaders and scenes of war and destruction . 
- 
- = = Background and composition = = 
- 
- " <unk> " was one of the first tracks developed for Confessions on a Dance <unk> , along with " <unk> Up " and " Future Lovers " . It was also the one which took the most time to finish because Madonna " thought it was too melodramatic and could never decide when it was right . " The songs were developed with a remixed perspective in mind . Madonna commented that , " Whenever I make records , I often like the remixes better than the original ones . [ ... ] So I thought , screw that . I 'm going to start from that perspective " . She promoted the album at the dance party " <unk> " held at Luke & <unk> 's nightclub in Greenwich Village , invited by Junior <unk> to take over the DJ booth where she mixed " <unk> Up " with " <unk> " . Musically , " <unk> " is an uptempo dance song containing layers of beats and strong vocal on the chorus . It is set in common time with a moderately fast dance groove tempo of 132 beats per minute . <unk> in the key of C minor , Madonna 's vocal range spans from <unk> to <unk> . It follows the chord progression of E ♭ – <unk> – A ♭ – B ♭ during the spoken introduction . It then changes to <unk> – <unk> @-@ <unk> in the chorus , while continuing in A ♭ – <unk> – A ♭ – <unk> in the intermediate verses , ultimately ending in repeated lines of " I 've heard it all before " . 
- Lyrically the song has <unk> of different languages including French , Spanish , Italian , Dutch , Hebrew , Hindi , Polish and Japanese . The song talks about personal <unk> and self @-@ <unk> , denoting a shift in focus of Madonna as an artist from her previous songs about supremacy like " Everybody " ( 1982 ) , " Vogue " ( 1990 ) or " Music " ( 2000 ) which was centered around the subject of music itself . Among the various remixes , the Pet Shop Boys remix incorporated Whitney Houston 's " I Wanna Dance with Somebody ( Who Loves Me ) " with a more pronounced bassline and uses a double breakdown in the introduction . Pet Shop Boys and Madonna had always mutually admired each other , from the time they wrote the song " Heart " ( 1988 ) for Madonna but never delivered it . The remix had added lyrics sung by band member Neil Tennant which subsequently appeared on their remix album <unk> Four . Madonna 's voice hits on the first chorus which was remixed to have a masculine synth and a serious hook . A breakdown happens before the fifth minute ends , leading to the entry of programmed drum sounds after which the song ends . 
- 
- = = Critical response = = 
- 
- Jennifer <unk> of MTV News wrote that " <unk> " is the " album 's strongest track " and that it is a " Pet Shop Boys @-@ esque " song . <unk> went on to say that it " ... <unk> evokes the sounds of the ' 80s @-@ era dance clubs that first <unk> her toward stardom . " According to a review in the BBC 's <unk> , " New single ' <unk> ' states Queen <unk> 's <unk> stance , and though the song is wrapped up in relationship sentiment , one cannot help but hear the subtext , perhaps directed at her more savage critics : ' there are more important things than hearing you speak ' . " Alan <unk> from BBC Music called the song " <unk> catchy " . Mike <unk> of The Times and Sal Cinquemani from Slant Magazine noted that the bassline of the song utilizes The Jacksons ' 1980 song " Can You Feel It " . The song was noted by Slant Magazine as one of the best songs of 2006 . In a review of " <unk> " from Virgin Media , it wrote that the song 's <unk> features an " infectious <unk> of pumping , filtered synths and disco beats " . Keith <unk> from Billboard commented that , " [ <unk> ] should keep fans hung up on Madonna 's ability to create instant radio and club classics . " He also called the song " <unk> " while reviewing the album . 
- Ben Williams of New York magazine , wrote that the song is " propelled by a catchy bass melody " . Joan Morgan of The Village Voice , in review of Confessions on a Dance <unk> , wrote : " The party continues <unk> with the <unk> , kick @-@ your @-@ man @-@ to @-@ the @-@ curb ' <unk> ' . " Stephen M. <unk> of Pitchfork Media wrote , " The <unk> of sound wash directly into ' <unk> ' , setting up the song 's <unk> <unk> and shifting bass <unk> . " Jon Pareles of The New York Times wrote that the first half of the album consists of love songs , happy and sad , and included " <unk> " into the mix . Alexis <unk> from The Guardian called the song as triumphant . Kitty Empire from the same newspaper said that " ' <unk> ' sees Madonna taking a lover to task over an <unk> dance @-@ pop rush . " Alan Light from Rolling Stone called the song " <unk> " . Thomas <unk> from <unk> magazine commented that " <unk> " and first single " <unk> Up " may not be as <unk> like Madonna 's initial singles " Burning Up " ( 1984 ) or " Physical <unk> " ( 1984 ) , but they have the same <unk> <unk> of being designed for all @-@ night dancing . 
- 
- = = Chart performance = = 
- 
- In the United States , " <unk> " debuted at number 70 on the Billboard Hot 100 chart for the issue dated March 11 , 2006 and reached a peak of 58 the following week . The same week it reached a peak of 46 on the Pop 100 chart . Its low chart performance in America was attributed to limited radio airplay . A petition was signed by about 3 @,@ 300 fans at <unk> Known as the " End the Madonna U.S. Radio <unk> " ; it was addressed to <unk> Channel Communications CEO Mark P. Mays . Message boards at Entertainment Weekly and VH1 were filled with entries by users showing support for Madonna , as well as conspiracy theories about why she was not played on radio . The song , however , reached the top of the Hot Dance Club Play chart for two weeks , as well as Hot Dance Airplay chart for five weeks . As of April 2010 , the song has sold 366 @,@ 000 digital units in the United States . 
- In the United Kingdom , it debuted at number one on the chart dated February 26 , 2006 . The song became Madonna 's 12th number one single in Britain and second consecutive single from the album to peak the British chart , after " <unk> Up . " This made Madonna the female artist with most number one songs in the United Kingdom , while placing her in fifth place in overall <unk> . According to the Official Charts Company , the song has sold 200 @,@ 000 digital downloads there and was certified silver by the British Phonographic Industry ( BPI ) . In Australia , the song debuted and peaked at four . 
- The song peaked at number two on the Canadian Singles Chart and was certified platinum by the Canadian Recording Industry Association ( CRIA ) on April 10 , 2006 for sales of 80 @,@ 000 units of paid digital downloads . " <unk> " debuted at number five in Ireland and was on the chart for 12 weeks . Elsewhere in Europe , the song became a top ten hit for Madonna reaching the top ten of countries like Austria , Belgium ( Flanders and Wallonia ) , Denmark , Finland , France , Germany , Netherlands , Norway , Sweden and Switzerland while reaching the top of the charts in Italy , Spain and Billboard 's <unk> Hot 100 Singles . 
- 
- = = Music video = = 
- 
- The music video incorporated parts of the television show <unk> My Ride , and was shot by Madonna in London in January 2006 while she was making plans for her then forthcoming Confessions Tour . It was directed by Madonna 's longtime <unk> Jamie King and featured choreography from The <unk> Brothers . Many of the performers featured in Madonna 's " <unk> Up " video were in that of " <unk> " , which was shot as a continuation from the " <unk> Up " video . Madonna 's parts in the video were shot first , followed by the shot of the <unk> . The shoot took place for over two days . 
- The video starts with Madonna standing in front of neon <unk> screen in a purple <unk> <unk> " <unk> " in different languages . As the music starts she comes out from the gaming <unk> of her " <unk> Up " video with her dancers . They board a van where Madonna and her dancers strip of their clothes into new ones . Madonna wears a white <unk> with a matching <unk> <unk> with her hair in 1970s retro inspired style and pair of knee high silver platform boots . 
- As they move around the city in the van , they pick up different men from the streets to join them . These scenes are interspersed with the scenes of Madonna singing in front of the neon screen . The video progresses like this until the intermediate verse <unk> Madonna is shown standing in a cage opposite to a number of men . As the bridge builds up , Madonna starts fighting with the men . She starts <unk> backwards and tying herself in knot like positions , while doing <unk> and <unk> which defeats the men . The chorus starts again and Madonna and her dancers are shown skating in circles around a roller <unk> . The video ends with the close @-@ up of Madonna in the purple <unk> and fades into her <unk> . 
- 
- = = Live performances = = 
- 
- " <unk> " was performed on the 2006 Confessions Tour as part of the <unk> themed segment . Madonna was dressed in a Jean @-@ Paul <unk> <unk> with pants and high <unk> boots . As Madonna finished the performance of the song " Isaac " , she took off the <unk> and wore a jacket given to her by the dancers and <unk> them one by one . The Pet Shop Boys music for the song 's remix start in the background . Madonna and her female dancers take to one side of a giant cage and start singing the song . As the song progresses to the intermediate verses , Madonna engages in an energetic fight with her male dancers which demonstrated her <unk> her body and putting her leg over her head and jumping from the cage on a dancer 's back . 
- The song was also used as a video backdrop during the interlude between the <unk> and the <unk> / rock sections . The video featured images of fascists and political leaders from past and present like Adolf Hitler and <unk> <unk> <unk> to George W. Bush and Tony Blair . Their images <unk> across the screen , with text and footage of war atrocities <unk> in between . Madonna appears on the screens wearing her " <unk> " video <unk> and <unk> the words " talk is cheap " and " don 't speak " , along with the music and Bush 's lips <unk> . The performances of " <unk> " and the remix video was included in The Confessions Tour CD and DVD . 
- The Daily Mail called the performance " energetic " . Tom Young from BBC Music called the performance a " delight " but described the video as the " <unk> " of the tour . Ed Gonzalez from Slant Magazine wrote that the performance was not among the concert 's highlights . However , he commented that the remix backdrop " feels <unk> [ ... ] a <unk> of <unk> weight you can dance too " . Thomas <unk> from <unk> described the song 's performance and remix as " fantastic " . 
- 
- = = Track listings and formats = = 
- 
- 
- = = Credits and personnel = = 
- 
- Madonna – Lead vocals , producer 
- Stuart Price – Producer 
- Neil Tennant – Supporting vocals 
- Guy <unk> – Management 
- <unk> <unk> – Mixing 
- Giovanni Bianco – Graphic Design , Art Direction 
- Orlando <unk> – <unk> , A & R 
- Angela Becker – Management 
- Ian Green – Programming , Producer 
- Paul <unk> – <unk> 
- Pete <unk> – Programming 
- Steven Klein – <unk> 
- Pet Shop Boys – <unk> , Producer , <unk> 
- 
- = = Charts = = 
- 
- 
- = = Certifications = = 
- 
- 
- 
- = High Five Interchange = 
- 
- The High Five Interchange is one of the first five @-@ level stack interchanges built in Dallas , Texas . Located at the junction of the Lyndon B. Johnson Freeway ( Interstate 635 , I @-@ 635 ) and the Central Expressway ( US Highway 75 , US 75 ) , it replaces an <unk> partial cloverleaf interchange constructed in the 1960s . 
- The $ 261 million project was started in 2002 and completed in December 2005 . It was designed by the <unk> Corporation and built by <unk> Construction Corporation . 
- The interchange is considered by Popular <unk> to be one of " The World 's 18 <unk> <unk> " because of its height ( as high as a 12 @-@ story building ) , its 43 permanent bridges and other unusual design and construction features . In 2006 , the American Public Works Association named the High Five Interchange as " Public Works <unk> of the Year " . 
- 
- = = Description = = 
- 
- The High Five Interchange , north of downtown in Dallas , Texas , is a massive five @-@ level freeway interchange . It is the junction of two major highways carrying heavy rush hour traffic , the Lyndon B. Johnson Freeway ( Interstate 635 ) and the Central Expressway ( US 75 ) , and is the first five @-@ level stack interchange to be built in the city . 
- It replaces the <unk> three @-@ level , modified cloverleaf interchange built in the 1960s that caused a severe bottleneck by narrowing US 75 down to two lanes at the junction of the two highways . The looped ramps of the cloverleaf forced motorists to slow down drastically , backing up traffic . Left @-@ hand exits contributed to the congestion . Further , its two frontage roads were not directly connected to each other , making local access difficult . 
- <unk> to the celebratory gesture , the " High Five " name refers to the five flyover ramps handling the left @-@ turn movements that tower over the landscape . The interchange is as high as a 12 @-@ story building , and includes 43 bridges spread across five levels ( the " High Five " ) , 710 support tiers , and 60 miles of additional highway . The highest ramps are 120 feet ( 37 m ) above ground . The lanes of US 75 , which are on the bottom level , are 20 feet ( 6 m ) below ground level , giving the structure a total height of 140 feet from bottom to top As part of the project , I @-@ 635 was widened to include four dedicated high @-@ occupancy vehicle lanes ( <unk> lanes ) that are barrier @-@ separated and <unk> . 
- The roads on the five levels are : 
- Level I : US 75 , an eight lane highway , four lanes going each way 
- Level II : The junction of the two six @-@ lane frontage roads , each having three through lanes in each direction , left @-@ turn lanes and <unk> , and easy access to US 75 and I @-@ 635 
- Level III : I @-@ 635 , ten regular lanes , five going each way , and four <unk> lanes ( two going each way ) separated by barriers 
- Level IV and V : Direct connection ramps ( two levels ) , eliminating the left exits of the modified cloverleaf 
- The interchange , with its decorative <unk> on <unk> concrete elements , along with the visually appealing coloration specified by the Texas Department of Transportation ( <unk> ) , can be regarded as an enormous work of public art . The High Five also incorporates a hiking and bike trail , named the <unk> Trail , which runs under all levels of the interchange . The section of the trail passing beneath the interchange was constructed as part of the High Five project by <unk> . 
- The interchange is considered by Popular <unk> as one of " The World 's 18 <unk> <unk> " , calling it a " labyrinth of lanes " and a " five @-@ level <unk> of engineering " because of its height , the number of its bridges and other unusual design and construction features . 
- 
- = = History = = 
- 
- The High Five Interchange project was planned as a replacement for the existing , <unk> interchange that accommodated 500 @,@ 000 vehicles daily and was located in one of the most intensely developed commercial zones in Dallas . It was a collaborative project between the <unk> , affected motorists and property owners , and the primary contractor , <unk> Construction . An essential consideration was to complete the project with as little disruption to the traffic flow as possible . 
- Reasons for upgrading the interchange , in addition to the need to increase traffic flow and reduce congestion , were to improve air quality , safety , and local access . 
- <unk> Construction Corporation submitted the lowest bid and was awarded the $ 261 million contract by <unk> , the largest ever by that agency . The construction contract for the High Five Interchange was unique in that it contained elements not found in other construction project contracts . For example , it specified the concrete maturity method to be used to ensure the concrete 's strength ( the first time <unk> had ever done so ) , and contained an early completion bonus , a <unk> scale of up to $ 11 million if the company completed the contract within four years . Also built into the construction contract was a provision that <unk> was charged for " lane rentals " by <unk> for time they closed down traffic lanes — fees based on <unk> assessments and the time of day the lane was closed . The fees ranged from $ 50 to a high of $ 110 @,@ 000 for rush hours . In order not to interfere with traffic flow during construction , the new interchange was designed largely as elevated flyover ramps and <unk> so they could be built high over the existing junction lanes ; once traffic was transferred to the new structures the old lanes could simply be closed off and removed . Little of the original interchange remained when the project was finished . 
- To save time and money , innovative construction methods were employed : The original plans called for the segments of the long ramps and spans ( used to direct @-@ connect roads ) to be made of steel , but because building with steel would interfere with heavy traffic flow during construction , cast @-@ in @-@ place segmental concrete was substituted in the plans . However , <unk> decided to use <unk> concrete segmental bridges , rather than casting the bridge elements in place , and used a unique machine , designed and constructed by Deal <unk> of Italy and costing about $ 1 million , to move the pieces into place . 
- During the construction , 2 @.@ 2 million cubic yards of <unk> was used , 350 @,@ 000 cubic yards of concrete was mixed on site , and 300 @,@ 000 square feet of retaining walls were built . Additionally , 40 @,@ 000 feet of drilled shafts and 75 @,@ 000 linear feet of drainage pipe were completed . In addition to the construction of 43 permanent bridges , six temporary bridges were built , resulting in 2 @.@ 3 million square feet of bridge deck . 
- The project was designed by the <unk> Corporation who provided professional engineering consultation throughout the construction , which had begun in 2002 and was completed in December 2005 , more than 13 months sooner than planned . 
- 
- = = Awards = = 
- 
- In 2006 , the American Public Works Association named the High Five " Public Works Project of the Year " for its massive size , its innovative design , the complexity and <unk> of its construction and the need it fulfilled for the community . <unk> as the managing agency , <unk> Construction Corporation as the primary contractor , and <unk> Corporation as the primary consultant received the award in recognition of their cooperative alliance in completing the project . 
- 
- 
- = Tim Richmond = 
- 
- Tim Richmond ( June 7 , 1955 – August 13 , 1989 ) was an American race car driver from <unk> , Ohio . He competed in <unk> racing before transferring to <unk> 's Winston Cup Series ( now <unk> Cup Series ) . Richmond was one of the first drivers to change from open wheel racing to <unk> stock cars full @-@ time , which has since become an industry trend . He won the 1980 Indianapolis 500 Rookie of the Year award and had 13 victories during eight <unk> seasons . 
- Richmond achieved his top <unk> season in 1986 when he finished third in points . He won seven races that season , more than any other driver on the tour . When he missed the season @-@ opening <unk> 500 in February 1987 , media reported that he had pneumonia . The infection most likely resulted from his compromised immune system , which was weakened by AIDS . Despite the state of his health , Richmond competed in eight races in 1987 , winning two events and one pole position before his final race in August of that year . He attempted a comeback in 1988 before <unk> I banned him for testing positive for excessive <unk> ( over the counter ) drugs , <unk> and <unk> ; <unk> later announced they gave Tim Richmond a new test and tested negative . Tim Richmond filed a lawsuit against <unk> after <unk> insisted they wanted access to his entire medical record before they would reinstate him , after losing the lawsuit , Richmond withdrew from racing . <unk> later stated their original test was a " Bad Test . " 
- Richmond grew up in a wealthy family and lived a <unk> lifestyle , earning him the nickname " Hollywood " . In describing Richmond 's influence in racing , Charlotte Motor <unk> president <unk> Wheeler said : " We 've never had a race driver like Tim in stock car racing . He was almost a James Dean @-@ like character . " When Richmond was cast for a bit part in the 1983 movie <unk> Ace , " He fell right in with the group working on the film , " said director Hal <unk> . Cole <unk> , the main character in the movie Days of Thunder , played by Tom Cruise , was loosely based on Richmond and his interaction with Harry Hyde and Rick <unk> . 
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- = = Early life = = 
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- Richmond grew up in <unk> , Ohio . His parents , Al and Evelyn ( née Warner ) Richmond , met in the course of their work . Al was a <unk> for pipe construction companies and Evelyn was a field office manager . <unk> that highway crews had to dig up the entire highway to lay pipe , Al designed a machine to bore underneath the highway . To market this invention , he founded Richmond Manufacturing , which eventually exported machines worldwide . 
- Tim 's driving days started as a <unk> when he was given a go @-@ <unk> that he often drove inside buildings and across his lawn . He later raced the <unk> at tracks in <unk> and New <unk> . Richmond grew up in a well @-@ to @-@ do family , and was sometimes therefore treated differently by his classmates , so his parents enrolled him in Miami Military Academy in Miami , Florida . During his years in Miami , Tim and his mother moved to Florida and his father stayed in Ohio . While home in Ohio over a summer break , he met local drag <unk> Raymond <unk> through lifelong friend Fred Miller . When Richmond reached age 16 , his parents purchased him a Pontiac Trans Am , a <unk> and a Piper <unk> airplane for his birthday . Yet his mother Evelyn often worried about <unk> her only son . She once said , " Tim was lazy ... " , and " ... I did everything for him . I ruined him , I admit it . He was my whole life . " 
- Richmond excelled in sports ; he set a conference record in high hurdles and his high school football career was stellar enough that the academy retired his sports jersey after his <unk> days were over . Miami Military Academy named him <unk> of the Year in 1970 . Richmond 's other interests included flying , and he earned his private pilot license at age 16 . Following high school graduation , Richmond attended <unk> University for about one year before dropping out . 
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- = = Racing career = = 
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- = = = Open wheel racing = = = 
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- A friend of Richmond 's father co @-@ owned a sprint car and Richmond joined the team as a crew member for Dave Shoemaker . In 1976 , 21 @-@ year @-@ old Richmond took the car onto <unk> <unk> at <unk> , Ohio for some practice laps . " Somebody put a <unk> on me , " Richmond said . " I was running laps faster than Dave had been . It was the first time I had ever driven a race car . " Richmond and his father found a red , white and blue @-@ colored No. 98 car in Pennsylvania , which was the same number and paint scheme that Richmond used on model cars as a child . In his first competition at the track , officials placed Richmond in the slowest heat . He passed several cars before spinning out and breaking an axle . Although he made several attempts to get the car pointed in the right direction , the broken axle prevented the car from driving straight . After being towed to the pits , he parked the car for the rest of the event . Later that season , they towed the car to <unk> <unk> , only to have Richmond crash the car again . In response , Richmond 's father fired him as the driver . The next season , Al Richmond bought a <unk> better suited to his son 's driving style . In 1977 Tim Richmond became both <unk> <unk> 's Rookie of the Year and the <unk> class track champion . 
- Richmond returned to racing sprint cars in the United States Automobile Club 's ( <unk> ) national sprint car tour in 1978 . <unk> in 12 races , he finished 30th in points as the series ' Rookie of the Year . That year he attended Jim Russell 's road racing school at <unk> Springs International <unk> Park , setting a student course record . Richmond raced in a 1978 Mini <unk> car event at Phoenix International <unk> , winning the Formula Super <unk> support event in a <unk> <unk> . The win attracted sponsors and attention from major owners like Roger <unk> . He also competed in <unk> 's Silver Crown series . 
- Richmond 's father bought an Eagle <unk> Car chassis and an <unk> engine for the 1979 race at Michigan International <unk> . Richmond qualified 21st fastest with a 175 @.@ <unk> mph ( <unk> @.@ <unk> km / h ) lap , significantly slower than Bobby <unk> 's 203 @.@ <unk> mph ( <unk> @.@ 111 km / h ) pole position speed . The race ended for him when his motor blew up on the fourth lap , and he finished last ( 23rd ) . <unk> Pat <unk> was looking for a driver to replace Larry Rice for his <unk> team at the following race at Watkins Glen International , so he gave Richmond a test at <unk> Spring where he had previously set the student record . <unk> hired Richmond , who then qualified 15th fastest for the event and finished in eighth place , the best of his <unk> career . Richmond raced in three more events that season . 
- After crashing during the first day of qualifying for the 1980 Indianapolis 500 , Richmond nevertheless obtained the 19th starting position in the race . He worked his way up to the top 10 during the race , led a lap , and finished ninth as he ran out of fuel at the end of the race . To the delight of the crowd , winner Johnny Rutherford gave him a ride back to the pits . He was named the 1980 Indianapolis 500 Rookie of the Year . " I <unk> up a few <unk> cars right after that , " he said . " Milwaukee , Mid @-@ Ohio . . . at Michigan I cut one in two . I was afraid my racing career would come to a halt . So when I got an offer to drive stock cars , I took it , and it turned out I liked driving them better . " 
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- = = = <unk> = = = 
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- <unk> <unk> President Joseph <unk> III convinced Richmond to make the change to stock car racing on the <unk> circuit . Richmond made his first <unk> start two months after winning the Indianapolis 500 Rookie of the Year award . He debuted at the Coca @-@ Cola 500 at <unk> on July 27 , 1980 , finishing 12th in a D. K. <unk> @-@ owned <unk> . That season , he competed in five events , with two <unk> ( did not finish ) and three 12th @-@ place finishes . Overall , he finished the 1980 season 41st in points . 
- Richmond raced for three teams in 1981 . He started the season by competing in 15 events for <unk> . He had his first career top 10 finish , taking sixth place at <unk> <unk> , soon followed by a seventh @-@ place finish at Texas World <unk> . After <unk> <unk> hired him away from <unk> mid @-@ season , Richmond had top 10 finishes at <unk> and Bristol . For the final seven races of the season , he drove for Bob Rogers and had a top 10 finish at Dover International <unk> . Overall for the season , Richmond had six top 10 finishes to place 16th in season points . 
- Richmond started 1982 without a ride before getting a one @-@ race deal to drive for Billie Harvey at the <unk> track . Richmond completed 112 laps of the <unk> @-@ lap event to finish 31st , retiring from the race with engine problems . For the following event , Richmond was hired to drive <unk> Stacy 's No. 2 car . In his first race for the team , Richmond earned his first career top 5 finish when he placed fifth at Darlington <unk> . Returning to <unk> , he finished second , before winning his first race on the road course at Riverside , California the following week . Later that season , he earned his first pole position at Bristol . The tour returned to Riverside for the final race of the season where Richmond won his second race , sweeping both events at the track . Benny Parsons said that " watching Richmond go through the <unk> was unbelievable " . For the season , Richmond had twelve top 10s , two wins , and one pole to finish 26th in points . 
- In 1983 , Richmond began racing for Raymond <unk> whom he had known before he started racing . He returned to the three @-@ cornered <unk> racetrack , earning his first oval victory . During the season , he accumulated four pole positions ( Darlington , <unk> , Charlotte , and Atlanta ) , one win ( <unk> ) , and fifteen top 10s on his way to finishing tenth in season points . He made his first appearance in a <unk> Busch Series ( now Nationwide Series ) car , but did not finish any of the three races he entered that season . 
- <unk> magazine named Richmond as one of " the best of the new generation " in 1984 . That year he had one win at North <unk> <unk> and second @-@ place finishes at Dover , Darlington and Riverside . Richmond finished the 1984 season 12th in points , with 11 finishes in the top 10 and in six in the top 5 . In 1985 , the final season that Richmond competed for <unk> , his best finish was a second @-@ place run at Bristol . He ended the season 11th in points with 13 Top 10s in 28 races . In the Busch Series , he qualified at the pole position in the two races he entered , and won the Charlotte race . 
- Richmond joined <unk> <unk> in 1986 , where he teamed up with veteran crew chief Harry Hyde . It took the team until the middle of the season to gel . Richmond had suffered a 64 @-@ race <unk> streak that was finally broken at the Van <unk> Diamond Mine 500 at <unk> in June 1986 . After two straight second @-@ place finishes at Charlotte and Riverside , Richmond started the <unk> event in third place inside the second row . That race saw a caution for rain with five laps left before the halfway point . <unk> wanted the cars to get to the halfway point to make the race official , so the <unk> body had the drivers slowly circle the track . It took the drivers 26 minutes to complete the laps , and the rain was so heavy that some drivers had to look out their side windows because they could not see out their <unk> . Two hours later , the track had dried and the race resumed with Richmond in third . After Richmond 's car was adjusted to remove the " push " , the car was more to his liking . Because his radio did not work , he was unable to communicate with his crew chief , Hyde , and he made his final pit stop with 37 laps left . Hyde worried that Richmond had stopped a lap too early to ensure that he would have enough fuel to make it to the end . After Richmond took the lead with 30 laps left in the race , Dale <unk> made up three seconds on Richmond 's five @-@ second lead . With four laps to go , <unk> <unk> spun in a three @-@ car accident . The remaining laps of the race where completed slowly under caution and Richmond took the <unk> flag for the victory . He had led 97 laps , including the final 30 , taking his first victory in a Rick <unk> car . 
- The tour returned to <unk> a month later , and Richmond battled for another victory in a fog @-@ shortened event . In the final 8 @-@ lap sprint , Richmond competed in a three @-@ car battle with Geoff <unk> and Ricky <unk> . Richmond crossed the finish line beside <unk> , winning the race by 0 @.@ 05 seconds . He notched four more victories that season , and over a span of twelve races , Richmond earned three second @-@ place finishes , and six wins . The National <unk> Press Association named him Co @-@ Driver of the Year with <unk> after Richmond accumulated 13 top 5 finishes and 16 in the top 10 . He had a career @-@ best third @-@ place finish in points after winning seven events in 1986 , in what was his last full <unk> season . 
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- = = Illness and death = = 
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- Richmond fell ill the day after the 1986 <unk> annual banquet during a promotional trip to New York . He was not well enough to begin the 1987 <unk> season despite lengthy hospitalization in Cleveland and further rest at home ; when Richmond missed the <unk> 500 , his condition was reported as double pneumonia . Media later reported that he had tested positive for acquired immune <unk> syndrome ( AIDS ) . He returned to <unk> for the Miller High Life 500 during the middle of the year . Starting third , he led by the fifth lap and ultimately led 82 laps , including the final 46 , to win the race by eight car @-@ lengths over Bill Elliott . In the middle of the race , Richmond 's car suffered <unk> problems . Because he could use only fourth ( high ) gear , he had to use that gear to slowly exit the pits . Richmond was emotional after the victory , saying , " I had tears in my eyes when I took the <unk> flag . Then every time anyone <unk> me , I started <unk> again . " Richmond earned a victory in the next race at Riverside , and made his final 1987 start at Michigan International <unk> 's Champion <unk> <unk> 400 that August , finishing 29th with a blown engine . He resigned from <unk> <unk> in September 1987 . 
- Although Richmond attempted a comeback in 1988 , <unk> suspended him for testing positive for banned substances . The substances were identified as <unk> , a non @-@ <unk> over @-@ the @-@ counter allergy medication , and <unk> , an over @-@ the @-@ counter pain reliever . In April 1988 , Richmond sued <unk> over the suspension . Although he <unk> later that year and was reinstated , he could not find a car to drive . In his final public appearance in February 1988 , Richmond denied that he abused drugs and said that a mistake had been made in his drug test . His suit with <unk> was settled out @-@ of @-@ court , the terms sealed . 
- Richmond withdrew into his <unk> in Florida . There were by then rumors of HIV and AIDS , which he denied . He was later hospitalized in West Palm Beach . 
- ESPN sent a get @-@ well @-@ soon card to Richmond when it aired the July 1989 <unk> race at <unk> . The television network showed highlights of Richmond 's victory at the track from 1986 . " Tim had Hollywood good looks and the <unk> of Tom Cruise , " said his friend Dr. Jerry Punch . " There he was in victory lane with the team all around him and beauty queens hanging all over him . It was important for the people at the hospital to see Tim the way he really was , when he was healthy and handsome and vital , not the way he was as they saw him every day in the hospital . " 
- On August 13 , 1989 , Richmond died at the age of 34 , about two years after his final <unk> race . He was buried in <unk> , Ohio . 
- The secrecy surrounding the circumstance of his death caused speculation for several days . At the time , Punch stated that Richmond had been hospitalized due to a motorcycle accident , though it is unlikely that Richmond had the strength to ride a motorcycle during his last months . Ten days after his death , on August 23 , the Richmond family held a press conference to reveal that Richmond died from complications from AIDS , which he acquired from an unknown woman . Richmond 's physician , Dr. David Dodson , said : " There 's no way of knowing who that woman was . Tim was a celebrity with a lot of <unk> , a handsome guy . He naturally attracted a lot of women . " Punch later claimed that more than 90 drivers and personnel underwent HIV testing in the wake of Richmond 's death . 
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- = = Legacy = = 
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- In 1990 , a few months after Richmond 's death , Washington television station <unk> @-@ TV and reporter <unk> <unk> reported that Dr. Forest Tennant , who was then the National Football League 's drug adviser , " falsified drug tests " that ultimately helped shorten Richmond 's <unk> career . <unk> reported that sealed court documents and interviews showed Tennant and <unk> used " allegedly false drug @-@ test results in 1988 to bar Richmond from racing " . <unk> also stated that <unk> had targeted Richmond , requesting that Tennant establish a substance @-@ abuse policy with Richmond in mind . A series of drug tests and falsely reported positive results shortly before the 1988 <unk> 500 kept Richmond from driving in what was to have been his last big race ... " , the report said . The New York Times published the findings . While neither Tennant nor <unk> supplied an official response at the time , <unk> did confirm that they were seeking to replace Tennant . 
- The <unk> County Sports Hall of Fame inducted Richmond in their second class in 1996 . In 1998 , <unk> named Richmond one of its 50 greatest drivers of all time . He was inducted into the International <unk> Hall of Fame in 2002 . The Mansfield <unk> Park <unk> Re / Max Series race in 2009 was named the Tim Richmond Memorial <unk> Re / Max 250 in honor of the area native . The race at Mansfield was co @-@ promoted by <unk> 's son Joseph <unk> III . 
- The documentary film Tim Richmond : To The <unk> was produced as part of ESPN 's 30 for 30 series with a premiere date of October 19 , 2010 . 
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- = = <unk> career results = = 
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- = = = <unk> = = = 
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- ( key ) ( <unk> – Pole position awarded by qualifying time . <unk> – Pole position earned by points standings or practice time . * – Most laps led . ) 
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- = = = = Winston Cup Series = = = = 
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- = = = = = <unk> 500 = = = = = 
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- = = = = Busch Series = = = = 
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- = <unk> Creek Mountains = 
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- The <unk> Creek Mountains are a remote , semi @-@ arid Great Basin mountain range mostly in southeastern Oregon and partially in northern Nevada in the United States . The range 's highest point is <unk> View <unk> , 8 @,@ <unk> feet ( 2 @,@ 593 m ) above sea level , in Nevada . Disaster Peak , elevation 7 @,@ <unk> feet ( 2 @,@ 372 m ) , is another prominent summit in the Nevada portion of the mountains . 
- The mountains are characteristic of the Great Basin 's topography of mostly parallel mountain ranges alternating with flat valleys . <unk> generally north to south , the <unk> Creek Mountains consist primarily of fault blocks of basalt , which came from an ancient volcano and other <unk> , on top of older <unk> rocks . The southern end of the range , however , features many <unk> <unk> . As a whole , the faulted terrain is dominated by rolling hills and ridges cut by <unk> and canyons . 
- Most of the range is public land administered by the federal Bureau of Land Management . There is very little human development in the remote region — cattle grazing and <unk> are the primary human uses — but former mines at the <unk> <unk> produced some of the largest amounts of mercury in North America in the 20th century . Public lands in the mountains are open to recreation but are rarely visited . <unk> includes large <unk> of big sagebrush in addition to desert grasses and cottonwood and alder stands . Sage grouse and mountain <unk> are two bird species native to the range , and common mammals include <unk> and jackrabbits . 
- Despite the area 's dry climate , a few year @-@ round streams provide habitat for the rare <unk> <unk> trout . Fish populations in the <unk> Creek Mountains declined throughout much of the 20th century . In the 1980s , the effects of grazing <unk> on riparian zones and the fish led to land @-@ use conflict . The <unk> Creek Mountain Working Group was formed in 1988 to help resolve disagreements among livestock owners , environmentalists , government agencies , and other interested parties . The <unk> met and agreed on changes to land @-@ use practices , and since the early 1990s , riparian zones have begun to recover . 
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- = = Geography = = 
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- The <unk> Creek Mountains are in a very remote area of southeastern Oregon and northern Nevada , in Harney and Humboldt counties . The nearest human settlements are the <unk> Ranch , about 20 miles ( 32 km ) directly north from the middle of the mountains ; Fields , Oregon , about 23 miles ( 37 km ) to the northwest ; <unk> , Nevada , about 15 miles ( 24 km ) to the west ; and <unk> , Nevada – Oregon , about 30 miles ( 48 km ) to the east . The mountains are about 150 miles ( 240 km ) directly southwest of Boise , Idaho , and about 190 miles ( 310 km ) northeast of <unk> , Nevada . 
- The range and surrounding non @-@ mountainous areas cover an area of <unk> square miles ( 2 @,@ 100 km2 ) . The mountains run 51 miles ( 82 km ) north to south and 36 miles ( 58 km ) east to west . More of the range is in Oregon ( 78 % ) than in Nevada ( 22 % ) . The highest point in the range is <unk> View <unk> , which is 8 @,@ <unk> feet ( 2 @,@ 593 m ) above sea level and is located in Nevada about one mile south of the Oregon border . About two miles southeast of <unk> View is Disaster Peak , " a large , symmetrical <unk> that is visible throughout the region . " At 7 @,@ <unk> feet ( 2 @,@ 372 m ) , Disaster Peak anchors the southern end of the mountains in a sub @-@ range called The <unk> . 
- The Oregon Canyon Mountains border the <unk> Creek Mountains on the east along the Harney – <unk> county line ( according to the United States Geological Survey 's definitions ) , while the <unk> Mountains are the next range west of the <unk> Creek Mountains . The <unk> Creek Mountains in both Oregon and Nevada border the <unk> Creek Mountains on the southwest ; the two ranges are separated by <unk> Cabin Creek and South Fork <unk> Creek . South of the <unk> Creek Mountains is the Kings River Valley , which separates the <unk> Creek Mountains on the west from the Montana Mountains on the east . 
- The terrain in the <unk> Creek Mountains varies from broad , flat basins and rolling ridges to high rock <unk> cut by deep canyons . The canyons have steep walls with loose <unk> slopes at the <unk> . There are meadows around springs in the mountains , although most streams in the range do not flow year @-@ round . Major streams that flow off the north slopes of the mountains include ( from west to east ) <unk> Creek , <unk> Creek , <unk> Creek , and <unk> Creek . These streams all flow into <unk> basins in Harney County , Oregon . <unk> Creek and <unk> Creek are the largest of the four . The Kings River and <unk> Creek each drain an area on the south slopes of the <unk> Creek Mountains . The Kings River begins in The <unk> and flows south into Nevada , where it meets the Quinn River , which <unk> in the Black Rock Desert . <unk> Creek begins in Oregon a few miles north of The <unk> and flows generally east , crossing the Oregon – Nevada border five times before disappearing into the floor of the Quinn River Valley south of <unk> . 
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- = = Geology = = 
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- The mountains lie within the Basin and Range Province or Great Basin of the Western United States , which is characterized by a series of parallel fault blocks that form long north – south mountain ranges separated by wide , high @-@ desert valleys . The <unk> Creek Mountains are <unk> and <unk> blocks with steep <unk> along the southern and eastern sides of the range . The southern area of the range , known as The <unk> , has numerous <unk> of Cretaceous age granite . These granite <unk> are commonly found in the eroded valleys below the volcanic <unk> . 
- The <unk> Creek Mountains are composed mostly of basalt from a shield volcano that once stood where <unk> Mountain is today . <unk> thinning and the Yellowstone <unk> , which was then beneath southeastern Oregon , induced eruptions from <unk> and nearby <unk> about 17 million years ago , in the Miocene . The <unk> produced a series of <unk> flows that spread across the land now known as the <unk> Creek Mountains . <unk> from the <unk> volcano lasted for about one million years , and at least 70 separate <unk> flows occurred . Under the resulting basalt rock lie much older <unk> rocks that may be related to some of the <unk> age formations of the Blue Mountains in northeastern Oregon . Within these <unk> rocks are <unk> and <unk> <unk> bodies which were presumably <unk> during the Cretaceous Period . 
- The broad <unk> <unk> is a prominent geologic feature in the <unk> Creek Mountains . The oval @-@ shaped <unk> is a collapsed <unk> dome that <unk> the Oregon – Nevada border on the eastern side of the range and south of the Oregon Canyon Mountains . It is about 28 miles ( 45 km ) long and 22 miles ( 35 km ) wide . The <unk> dome was created by volcanic eruptions in the early Miocene . A total of five large ash flows were produced along with a large <unk> dome structure . The <unk> formed when the dome collapsed about 16 million years ago . The <unk> contains significant ore deposits , and mercury and uranium have been mined at eight or more sites in and around the <unk> . Other areas in the <unk> were mined for ores of antimony , <unk> , and lithium . 
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- = = Climate = = 
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- The <unk> Creek Mountains are semi @-@ arid because they are in the eastern rain shadow of mountain ranges to the west . When moist air from the Pacific Ocean moves eastward over the Oregon and California coastal ranges and the Cascade Range , most precipitation falls in those mountains before reaching the <unk> Creek Mountains . As a result , the average annual precipitation in the <unk> Creek Mountains is only 8 to 26 inches ( 200 to 660 mm ) per year , with most areas receiving between 8 and 12 inches ( 200 and 300 mm ) annually . Much of the annual precipitation occurs between the beginning of March and the end of June . Most of the rest falls as snow during the fall and winter months . <unk> at elevations below 6 @,@ 000 feet ( 1 @,@ 800 m ) usually <unk> by April ; however , at the higher elevations , snow can remain until mid @-@ June . Local flooding often occurs in the spring as the <unk> <unk> . 
- The prevailing winds are from the west @-@ southwest , and they are normally strongest in March and April . <unk> , intense thunderstorms are common between April and October . <unk> in the summer months tend to be more isolated and often produce dry lightning strikes . 
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- = = Ecology = = 
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- = = = Flora = = = 
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- <unk> in the <unk> Creek Mountains is dominated by big sagebrush and desert grasses . Other common shrubs include <unk> , snowberry , and <unk> . There are also patches of mountain mahogany in some areas . Common grass species include Idaho <unk> , bluebunch wheatgrass , <unk> , western <unk> , <unk> 's <unk> , <unk> 's <unk> , and <unk> <unk> , as well as basin <unk> in some well @-@ drained areas . 
- Less than one percent of the range consists of meadow wetlands and riparian <unk> ( vegetation along stream banks ) . However , these areas are vital to the local ecosystem . The meadows surround springs , which are mostly on gently sloping <unk> or in stream <unk> , and range in size from about 1 to 5 acres ( 0 @.@ 40 to 2 @.@ 02 ha ) . <unk> riparian <unk> follow the year @-@ round streams . Many <unk> areas have <unk> <unk> and <unk> groves . <unk> and alder groves can be found at lower elevations where terrain is <unk> and stream channels are wider . <unk> and rushes are also native to these stream <unk> . Years of heavy livestock grazing in parts of the range resulted in the loss of some grass species , riparian vegetation , and young <unk> and <unk> trees . Restoration of riparian areas began in the early 1970s , and plans to reduce grazing were implemented in the 1980s and early 1990s . However , large <unk> in southeastern Oregon during the summer of 2012 burned much of the range 's vegetation , damaging riparian ecosystems and killing hundreds of grazing cattle . 
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- = = = Fauna = = = 
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- Animals in the <unk> Creek Mountains are adapted to the environment of the High Desert . <unk> are common in the open , sagebrush @-@ covered basins , while mule deer live in the cottonwood and <unk> groves . There are also <unk> sheep , cougars , and <unk> in the high country . Jackrabbits and coyotes are prevalent throughout the range . Mustangs sometimes pass through the mountains as they <unk> the Great Basin . Some other mammals include the northern pocket <unk> , mountain <unk> , and <unk> 's ground <unk> . North American <unk> live in and along streams , as do Pacific tree frogs , western <unk> toads , and <unk> snakes . Native bird species include the sage grouse , mountain <unk> , gray @-@ headed <unk> , black @-@ throated gray <unk> , Virginia 's <unk> , <unk> 's <unk> , pine <unk> , red <unk> , <unk> , <unk> thrush , northern <unk> , and species of <unk> and eagle . 
- Several streams in the <unk> Creek Mountains are home to trout , including the rare <unk> <unk> trout subspecies . These include <unk> Creek , <unk> Creek , Little <unk> Creek , <unk> Creek , Fifteen Mile Creek , Indian Creek , Sage Canyon Creek , Line Canyon Creek , and some tributaries of <unk> Creek . <unk> <unk> trout live in small , isolated populations that are often confined to individual streams , many of them in the <unk> Creek Mountains . These populations have significant genetic differences due to their history of isolation . For most of the 20th century , the trout 's numbers declined considerably . It was listed under federal law as an endangered species in 1970 and was reclassified as threatened in 1975 . Reasons for the fish 's decline included habitat degradation from cattle grazing , drought , <unk> , competition with other fish , and <unk> with introduced rainbow trout , which decreased the number of genetically pure <unk> <unk> trout . However , reductions in cattle grazing in riparian zones since the 1980s allowed fish habitat and populations to start to recover . 
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- = = Human uses = = 
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- The Bureau of Land Management ( <unk> ) administers most land in the <unk> Creek Mountains , but there are also some private lands and some roads in the area . The private lands are mainly used for <unk> along mountain streams , while the <unk> lands include large grazing <unk> for local ranchers ' cattle . At least 100 mining claims in the mountains have been recorded since 1892 , some of which were staked for gold exploration . Commercial mining has occurred in some areas , mostly near the <unk> <unk> , where uranium and large amounts of mercury have been extracted . <unk> in what was called the <unk> <unk> District produced 270 @,@ 000 <unk> of mercury — " the richest supply of mercury in the western hemisphere " — from <unk> extracted from the <unk> in the 20th century . The two leading mercury @-@ producing mines in North America were the <unk> and <unk> mines on the edge of the <unk> in Nevada . Together , they operated from 1933 to 1989 . The <unk> Mine , the last mercury mine in the United States , closed three years later , in 1992 . However , mineral exploration has continued at the <unk> Mine in the 21st century , and waste containing mercury and arsenic was returned there from the community of <unk> as part of a U.S. Environmental Protection Agency <unk> project . 
- The entire mountain range is very remote ; as a result , there are few visitors , and the range offers a wilderness @-@ like experience . <unk> , hunting , fishing , and hiking are the most popular activities . The only developed recreation site nearby is at <unk> Creek Hot Springs , just south of the <unk> Ranch , where nearby there are miles of trails designated for four @-@ wheel off @-@ road vehicles . Hunters come to the mountains seeking trophy mule deer , <unk> , <unk> , and rabbits . <unk> on some streams is sometimes permitted on a catch @-@ and @-@ release basis . The mountains are also suitable for hiking cross @-@ country or on game trails in natural corridors along canyons and creek <unk> . There are more than 100 archaeological sites in the range that document use by Northern <unk> people as long as 7 @,@ 000 years ago . 
- <unk> grazing in the <unk> Creek Mountains began in the late 19th century , and the <unk> currently <unk> grazing <unk> in the area . <unk> can be found grazing in some areas during the spring and summer months . The effects of grazing on the local environment were the subject of controversy in the 1980s . 
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- = = Land @-@ management compromise = = 
- 
- By the 1970s and 1980s , a century of intense cattle grazing had reduced much of the riparian vegetation along stream banks in the <unk> Creek Mountains and elsewhere in the Great Basin . As a result , stream banks were eroding and upland vegetation was encroaching into riparian zones . <unk> populations declined as grazing cattle eliminated young trees , decreasing shade over streams and raising water temperatures . These conditions put the rare <unk> <unk> trout population at risk . Since the <unk> was officially designated as a threatened species , environmental groups began advocating the cancellation of grazing permits in the <unk> Creek Mountains . 
- Beginning in the early 1970s , the Bureau of Land Management identified damaged riparian zones and began projects to restore natural habitat in those areas . Approximately 20 @,@ 000 <unk> trees were planted along streams , small dams were put together to create more pools in the streams , and fencing was added to protect riparian zones from grazing . Next , the agency sought to reform land @-@ use plans to change grazing practices , which became a complex and controversial project . 
- As environmentalists pressed the <unk> to close much of the <unk> Creek Mountains to grazing , frustrated ranchers joined the <unk> Rebellion seeking to protect their grazing <unk> . Initially , it appeared that the issue of grazing in the range would produce prolonged litigation with appeals potentially lasting decades . However , in 1988 , interest groups representing all sides of the issue joined to form the <unk> Creek Mountain Working Group . The group 's goal was to find a solution acceptable to everyone — a plan that would protect both the land 's ecological health and ranchers ' economic needs . 
- Initial members of the <unk> Creek Mountain Working Group included : 
- Over the next several years , the group continued to meet and discuss options for restoring the land while meeting the economic needs of local ranchers . Meetings were all open to the public . 
- The group eventually endorsed a grazing management plan that provided for both the ecological health of sensitive riparian areas and the economic well @-@ being of ranchers . In 1989 , the <unk> Ranch agreed to rest two grazing <unk> totaling 50 @,@ 000 acres ( 20 @,@ 000 ha ) to restore critical stream <unk> and mountain pastures . The <unk> 's <unk> on Fifteen Mile Creek was rested for three years , and its <unk> Creek pasture received a five @-@ year rest before grazing was resumed . In addition , the grazing season in mountain pastures was reduced from four months to two , and the total number of cattle released in the <unk> areas was reduced from 3 @,@ 800 to 2 @,@ 200 . Finally , sensitive areas were <unk> to protect them from cattle , and additional water sources were constructed away from streams . Other <unk> also agreed to rest specific pastures including the <unk> Creek , <unk> Creek , and <unk> <unk> <unk> . 
- In 1991 , the Bureau of Land Management approved a new grazing <unk> management plan . It was based on the agreements made by the <unk> Creek Mountain Working Group , and it took effect in 1992 . Since then , vegetation in riparian areas of the <unk> Creek Mountains has recovered , and studies by the United States Fish and Wildlife Service have found that the <unk> <unk> trout population , still listed as threatened , is also recovering . 
- 
- 
- = Mycena <unk> = 
- 
- Mycena <unk> , commonly known as the clustered bonnet or the oak @-@ stump bonnet cap , is a species of mushroom in the family <unk> . The <unk> edible mushroom has a reddish @-@ brown bell @-@ shaped cap up to 4 @.@ 5 cm ( 1 @.@ 8 in ) in diameter . The thin stem is up to 9 cm ( 3 @.@ 5 in ) tall , whitish to yellow @-@ brown at the top but progressively becoming reddish @-@ brown towards the base in maturity , where they are covered by a yellowish mycelium that can be up to a third of the length of the stem . The gills are pale brown to pinkish , and the spore print is white . It is a widespread saprobic fungus , and has been found in Europe , North Africa , Asia , <unk> , and North America , where it grows in small groups or <unk> on fallen logs and stumps , especially of oak . British mycologist <unk> Corner has described two varieties of the mushroom from Borneo . <unk> species with which M. <unk> may be confused include M. galericulata and M. <unk> . 
- 
- = = Taxonomy , phylogeny , and naming = = 
- 
- First described as Agaricus <unk> by Swedish mycologist Elias Magnus Fries in 1838 , it was assigned its current name in 1872 by <unk> <unk> . Mycena galericulata var. <unk> ( named by <unk> in 1879 ) , and its <unk> Agaricus <unk> var. <unk> ( named by Fries in 1873 ) , are synonyms . 
- In a molecular study of the large <unk> ribosomal DNA sequences of mycorrhizal fungi of the <unk> <unk> <unk> , M. <unk> was found to be closely related to M. <unk> , M. <unk> , and M. <unk> . 
- The specific epithet <unk> means " bent in " . The mushroom is commonly known as the " clustered bonnet " or the " oak @-@ stump bonnet cap " . 
- 
- = = Description = = 
- 
- The cap is light reddish @-@ brown , with a diameter typically ranging from 1 to 4 @.@ 5 cm ( 0 @.@ 4 to 1 @.@ 8 in ) . Initially <unk> to bell @-@ shaped to convex , it <unk> during maturity , developing visible surface grooves corresponding to the gills underneath the cap . The margin of the cap has minute but distinct <unk> . The surface is moist and smooth , and <unk> . The cap frequently develops splits in the margin , or cracks in the disc ( the central part of the cap ) . The flesh of the cap is thick in the center but thin elsewhere , grayish to whitish , fragile , and with a slightly <unk> odor and taste . The gills have a <unk> attachment to the stem ( that is , running down the length of the stem ) and are a pale brownish color with <unk> of red . They are broad ( between 3 and 6 mm ) , and have a close to <unk> spacing , with about 26 – 35 gills reaching the stem . The fragile stem is 3 to 9 cm ( 1 @.@ 2 to 3 @.@ 5 in ) long by 0 @.@ 15 to 0 @.@ 4 cm ( 0 @.@ 06 to 0 @.@ 16 in ) thick and yellow to yellow @-@ brown , becoming reddish @-@ brown to orange @-@ brown in the bottom half in maturity . The lower portion of young stems is covered with white <unk> . Roughly equal in thickness at the top and bottom , the base of the stem is covered by a yellowish mycelium that can be up to a third of the length of the stem . The edibility of the mushroom is " doubtful " and consumption " best avoided " . 
- 
- = = = Microscopic characteristics = = = 
- 
- The spores are 7 – 9 by 5 – 6 @.@ 5 μm , broadly ellipsoid , smooth , and strongly amyloid ( it turns black when treated with Melzer 's reagent ) . The basidia ( spore @-@ bearing cells ) are four @-@ spored . The <unk> ( cystidia on the gill face ) are not differentiated . The cheilocystidia ( cystidia on the gill edge ) are embedded in the gill edge and very inconspicuous , club @-@ shaped , 26 – 36 by 5 – 10 μm , and have tips that are covered with <unk> projections that can be slender or thick . The flesh of the gills is <unk> , and pale yellowish to dirty brown when stained in iodine . The flesh of the cap has a distinct <unk> , a well @-@ differentiated <unk> ( a region of tissue immediately under the <unk> ) , and a <unk> <unk> body ( gill tissue ) ; it is pale yellowish to <unk> brownish in iodine stain . 
- 
- = = = <unk> = = = 
- 
- <unk> Corner defined the varieties M. <unk> var. <unk> and var. <unk> in his 1994 publication on <unk> mushrooms of <unk> , a <unk> region <unk> the boundary of the <unk> and <unk> <unk> . The variety <unk> ( named after its type locality , <unk> ) has a cap margin that is not <unk> , little or no odor , and cheilocystidia with shorter processes . It was found growing on the dead wood of <unk> <unk> , a stone oak tree in the beech family . Variety <unk> , found in <unk> , has spores that are almost spherical . 
- 
- = = = Similar species = = = 
- 
- Mycena <unk> bears some resemblance to M. <unk> , but is only associated with decaying hardwood logs and stumps , and is found in eastern North America , and sometimes on oak on the West Coast . In age , it develops reddish spots on the gills that are not seen in M. <unk> . M. <unk> is often confused with the edible M. galericulata , a common species that is variable in cap color , size and shape . M. galericulata typically has a <unk> conical cap that is dull gray @-@ brown , and white to grayish veins that have numerous cross @-@ veins . M. <unk> has a <unk> stem that is bluish @-@ gray . 
- 
- = = Habitat and distribution = = 
- 
- Mycena <unk> is a saprobic fungus , deriving its nutrients from decomposing organic matter found in plant litter such as leaves , twigs , bark and branches . It <unk> this by producing enzymes capable of breaking down the three major biochemical components of plant cell walls found in litter : <unk> , <unk> and lignin . 
- The fruit bodies of Mycena <unk> grow in dense groups or clusters on decaying hardwood logs and stumps ( especially oak and chestnut ) during the spring and autumn . The fungus forms a white , <unk> mycelium on the surface of decomposing oak leaves . Occasionally , it can be found growing on a living tree . In eastern North America , it is abundant in the area bounded by Nova Scotia , Ontario , Manitoba , Missouri , North Carolina , and New York . It has been found in Oregon , but the species appears to be generally rare along the Pacific Coast . The range of the fungus also includes Europe , the Canary Islands , North Africa , East Siberia , Japan , <unk> , Turkey , and New Zealand . 
- 
- = = Chemistry = = 
- 
- In a study of the trace metal concentrations of various mushrooms species found in <unk> ( Turkey ) , M. <unk> was found to have comparatively high levels of iron ( 628 mg per kg ) and nickel ( 21 @.@ 6 mg / kg ) , measured on a dry weight basis . Laboratory studies have shown that the fungus is resistant to aluminum . The fungus has been investigated for its ability to <unk> synthetic <unk> that are used in the textile , <unk> , <unk> and <unk> industries . The <unk> are not readily biodegradable , and when discharged into the environment are persistent and many are toxic . 
- 
- 
- = Capel <unk> = 
- 
- Capel <unk> ( sometimes referred to as Hen <unk> <unk> ) is a ruined chapel near <unk> <unk> in Anglesey , north Wales , dating back to the first half of the 12th century . The chapel 's original purpose is unknown , but it might have been used as a memorial chapel or in connection with a local royal court , or as a chapel of ease in a large parish with a growing population . It was used for a time until the early 18th century as a private place of worship for a nearby house , then later fell into disrepair . The walls still remain , with some traces of render on them internally , but there is no roof . 
- It contains a 16th @-@ century side chapel with a vault beneath , used as a burial chamber . It is a Grade II listed building , a national designation given to " buildings of special interest , which warrant every effort being made to preserve them " , in particular because it is " a substantially 12th @-@ century structure " with the " unusual 16th @-@ century vaulted south chapel " . 
- 
- = = History and location = = 
- 
- The oldest parts of Capel <unk> date from the first half of the 12th century . This was a time when many churches on Anglesey in north @-@ west Wales were first built in stone following the end of Viking raids and attempts by the <unk> to gain control of the island . The reason for its construction , and the saint to whom it was dedicated , are unknown . Geraint Jones , author of a 2006 guide to the churches of Anglesey , suggests that it may originally have been a memorial chapel , or connected to a royal court nearby . <unk> and <unk> , authors of a 2001 guide to ancient Anglesey monuments , note that it was built in the large parish of <unk> <unk> and was perhaps intended to serving the expanding population in medieval times . Despite this , it seems to have remained as a chapel of ease rather than become a parish church in its own right . The chapel is sometimes referred to as " Hen <unk> <unk> " ( " hen " being the Welsh word for " old " and " <unk> " meaning " chapel " ) . 
- The walls were partially rebuilt in the 14th century , and the upper parts of the walls date from this time . A chapel was added to the south side of the building in the 16th century . A vault under the south chapel was used to bury members of a local family , the Pierce <unk> . In 1999 , the vault was used to record some " atmospheres " for the album " You Have Just Been <unk> By The <unk> " by The <unk> , a collective of Welsh musicians including members of Echo and the <unk> , Super <unk> Animals and <unk> that was described by one journalist as " possibly the <unk> pop group of all time " . 
- For a time , Capel <unk> was used as a private place of worship for <unk> House , a " venerable mansion " once owned by the Lloyd family which came into the possession of William <unk> , 1st Baron Boston , in the 18th century . After the early part of the 18th century , however , the chapel became unused and began to fall into disrepair . The walls remain to gable level , with some traces of render on the inside , but the roof has gone . 
- Capel <unk> is in the Anglesey countryside near <unk> ; the parish church of St <unk> , <unk> is about 0 @.@ 78 miles ( 1 @.@ 26 km ) away . Part of the churchyard wall remains , showing that it was originally within a mainly circular enclosure , as often found with early churches . It is cared for by Cadw , the Welsh Assembly Government body responsible for the built heritage of Wales , and is open to the public . 
- 
- = = Architecture and fittings = = 
- 
- Capel <unk> is built from rubble masonry ; at about 5 feet ( 1 @.@ 5 m ) up the walls , the style changes and smaller stones are inserted into spaces between the larger blocks , showing where the 14th @-@ century rebuilding started . The doorway is on the south side , headed by a plain arch , and dates from the 12th century . There is a stone bellcote at the west end . There is no structural division between the nave ( where the congregation would have sat ) and the chancel ( where the altar would have been located ) . There are no window openings on the north side , but there is a blocked opening to the east ( about 5 feet ( 1 @.@ 5 m ) wide ) and there are the remains of a window on the south wall . There is a blocked window in the south wall of the south chapel . The vault , which is about 27 square feet ( 2 @.@ 5 m2 ) , is reached by stone steps from inside the south chapel . <unk> slabs form the roof of the vault and the floor of the chapel above . A stone in the nave , about 2 square feet ( 0 @.@ 19 m2 ) with a hole in the top , might once have been used as a churchyard cross . 
- 
- = = Assessment = = 
- 
- Capel <unk> has national recognition and statutory protection from alteration as it has been designated as a Grade II listed building – the lowest of the three grades of listing , designating " buildings of special interest , which warrant every effort being made to preserve them " . It was given this status on 2 September 1952 and has been listed because it is " important as a substantially 12th @-@ century structure , with some architectural details surviving from this early period " , even though it is now in a " ruinous condition " . Cadw ( which is responsible for the inclusion of Welsh buildings on the statutory lists ) also notes the " unusual 16th @-@ century vaulted south chapel " . 
- The 19th @-@ century antiquarian <unk> <unk> mentioned the church in her history of Anglesey . She said that the architecture was of " the <unk> kind , [ which ] bears testimony to its great antiquity . " She recounted that a fox had once taken shelter in the ruins , and when it was dug out , the vault was discovered , " containing several human skeletons , which <unk> into dust , when exposed to the air " . She added that further exploration of the vault then revealed " a large mass of human bones , several feet in depth " . 
- A 1990 book about abandoned churches in Wales refers to the ancient monuments in this part of Anglesey , and calls Capel <unk> a " medieval gesture of Christian power in a land so obviously <unk> with the spirit of a pagan past " . It notes that " the wheel has turned full circle and Capel <unk> today is just another ruined relic of a former age . " The authors describe the chapel as a " simple , square building " with a " small and <unk> austere south chapel " , adding that the stone slabs over the vault are " almost in imitation of the ancient burial chambers which dot the surrounding landscape . " 
- 
- 
- = 2011 – 12 Michigan Wolverines men 's basketball team = 
- 
- The 2011 – 12 Michigan Wolverines men 's basketball team represented the University of Michigan during the 2011 – 12 NCAA Division I men 's basketball season . The team played its home games in Ann Arbor , Michigan at <unk> Center for the <unk> consecutive year . It had a seating capacity of 12 @,@ <unk> . It was also the team 's <unk> straight season as a member of the Big Ten Conference . Fifth @-@ year head coach John <unk> led the team , alongside All @-@ Big Ten players Trey Burke , Tim <unk> , Jr. and Zack Novak . Burke was named Big Ten Freshman of the Year and was Michigan 's first Associated Press All @-@ American <unk> since 1998 . 
- The team 's season began with a preseason media day and practices in October 2011 . In February 2012 , Michigan hosted ESPN 's College <unk> for the first time in a game against Ohio State . It was the eighth time a Big Ten team hosted the show , which began in 2005 . 
- The team was in the national rankings all season and ended as the 2011 – 12 Big Ten co @-@ champion with Michigan State and Ohio State . It had three victories over teams ranked in the top 10 at the time of the meeting ( eighth @-@ ranked Memphis , ninth @-@ ranked Michigan State and sixth @-@ ranked Ohio State ) . The team was undefeated at home until its last home game of the season . Michigan lost in the semifinals of the 2012 Big Ten Conference Tournament and bowed out in the second round of the 2012 NCAA Tournament to end the season with a 24 @-@ 10 record . The team won the school 's first Big Ten Conference Championship since the 1985 – 86 season and had the school 's best Big Ten record ( 13 – 5 ) since the 1993 – 94 season . 
- 
- = = <unk> = = 
- 
- 
- = = = 2011 – 12 incoming team members = = = 
- 
- Before the season began , point guard Darius Morris , the Big Ten assists leader in the 2010 @-@ 11 season , left the team after being drafted by the Los Angeles <unk> . The incoming class included Carlton <unk> and 2011 Ohio Mr. Basketball point guard Trey Burke . Both <unk> and Burke were among <unk> 's top 100 players of the 2011 class ; <unk> ranked <unk> and Burke ranked 94th . Max <unk> committed to Michigan in April despite his family 's ties to the Illinois Fighting <unk> . Illinois University 's <unk> Athletic Administration Building was endowed by his family . <unk> <unk> , who along with <unk> was recruited by <unk> League schools , rounded out the incoming class . <unk> earned an academic scholarship and was considered a walk @-@ on candidate for the basketball team . 
- Tim <unk> , Jr . , son of former NBA All @-@ Star Tim <unk> , returned to the team . He was coming off a freshman season in which he was a unanimous Big Ten All @-@ Freshman , All @-@ Big Ten honorable mention , <unk> <unk> All @-@ America and Team USA FIBA <unk> <unk> . Jordan <unk> , the son of Detroit Pistons All @-@ Star Joe <unk> , left the team , citing <unk> knee issues . 
- 
- = = = 2011 – 12 team recruits = = = 
- 
- 
- = = <unk> = = 
- 
- Former team captains Travis <unk> ( 1996 – 97 and 1997 – 98 ) and C.J. Lee ( 2008 – 09 ) served as director of basketball operations and administrative specialist , respectively . Peter <unk> was the team 's video coordinator . 
- 
- = = Schedule and results = = 
- 
- Michigan announced its 14 @-@ game non @-@ conference schedule on August 1 , 2011 . The team began the season in a renovated <unk> Arena : new seats and a high @-@ definition <unk> were added , but seating capacity was reduced to 12 @,@ <unk> from 13 @,@ <unk> in the previous 10 seasons . 
- Michigan came in third place in the three @-@ game 2011 Maui <unk> Tournament between November 21 – 23 . The team defeated the eighth @-@ ranked Memphis Tigers 73 – 61 , lost to the sixth @-@ ranked Duke Blue Devils 82 – 75 , and defeated the <unk> @-@ 12 favorite UCLA Bruins 79 – 63 . Tim <unk> , Jr. was named the Big Ten Player of the Week , and Trey Burke was named Big Ten Freshman of the Week . In an ACC – Big Ten Challenge game in late November , Michigan lost to Virginia 70 – 58 . In its next game , Michigan defeated Iowa State 76 – 66 . On December 10 , 2011 , Michigan beat Oakland 90 – 80 , its highest @-@ scoring game since beating Northern Michigan 97 @-@ 50 on November 14 , 2009 . It was also Michigan 's first game since 2002 with three 20 @-@ point scorers ( <unk> , Burke and Evan <unk> ) . Burke earned his second Freshman of the Week honor on December 12 after scoring a season @-@ high 20 points and nine assists in the game . On the same day , Michigan was the highest @-@ rated Big Ten team in the Ratings <unk> Index , although the team trailed several schools in the national polls . In the final two non @-@ conference games of the season , <unk> scored his first two double @-@ doubles against Alabama A & M and Bradley on December 17 and December 22 . 
- Heading into the Big Ten Conference schedule , both of the teams Michigan had lost to were ranked ( Duke was 7th and 5th in the AP and Coaches ' polls and Virginia was 23rd and 24th ) . On December 29 , Michigan won its first Big Ten Conference opener since 2006 – 07 , beating Penn State as <unk> extended his double @-@ double streak to three games . On January 2 , Burke earned his first Big Ten Conference Player of the Week honor and his third Freshman of the Week honor for his 40 points in Michigan 's first two conference games . On December 29 against Penn State he posted 13 points , seven assists , five rebounds and no turnovers . On January 1 , 2012 , he added a career @-@ high 27 points on 8 @-@ for @-@ 11 shooting with three rebounds and three assists against Minnesota to earn Big Ten Conference Player of the Week the following day . 
- On January 19 , Michigan became the leader in the conference with a 5 – 2 record , thanks to conference wins over ranked Wisconsin and Michigan State teams . Michigan remained in first place until losing to Ohio State ten days later . The team went 5 @-@ 2 in conference in February , including wins over ranked Indiana and Ohio State teams . Michigan lost its final home game of the season to <unk> on February 25 to finish with a 15 – 1 home record . On March 1 , the team defeated Illinois for their first road win in Illinois since 1995 . During the game , Michigan 's 30th of the season , Trey Burke broke Gary Grant 's school freshman assists record , set over the course of 30 games in the 1984 @-@ 85 season , by pushing his total to 143 . 
- In the first game of the 2012 Big Ten Conference Men 's Basketball Tournament against Minnesota , Burke led the team to victory with a career @-@ high 30 points . Burke 's total was a school record for the Big Ten Conference Men 's Basketball Tournament . In the semifinal contest , however , Michigan was eliminated by Ohio State for the third year in a row . Michigan entered the 2012 NCAA Men 's Division I Basketball Tournament <unk> fourth , but lost to the thirteenth @-@ <unk> Ohio Bobcats 65 – 60 . Burke became Michigan 's first Associated Press All @-@ American <unk> since Robert <unk> and Louis <unk> in 1998 . 
- <unk> Douglass concluded the season as the school 's all @-@ time leader in games played , with 136 . He surpassed <unk> <unk> , who played in 135 games . Novak set the school record in career minutes played with 4 @,@ 357 , surpassing Louis <unk> , who played 4 @,@ <unk> minutes . Burke had a school record @-@ setting freshman season in assists , ending the year with 156 . 
- 
- = = Statistics = = 
- 
- The team posted the following statistics : 
- 
- = = Rankings = = 
- 
- 
- = = <unk> and awards = = 
- 
- 
- = = = <unk> = = = 
- 
- Five of the 30 nominees for the men 's basketball Lowe 's Senior <unk> Award were from the Big Ten , including Michigan 's Zack Novak . 
- 
- = = = In @-@ season = = = 
- 
- Trey Burke was one of nearly 60 Bob <unk> Award candidates named in December 2011 . On January 4 , Burke was one of 20 finalists . On January 25 , Novak was named one of ten finalists for the Lowe 's Senior <unk> Award along with three other Big Ten athletes . He was also one of four Big Ten men 's basketball players named Academic All @-@ District , putting him among the 40 finalists for the 15 @-@ man Academic All @-@ America team . Novak was named a third team Academic All @-@ American . 
- 
- = = = Accolades and honors = = = 
- 
- Trey Burke 
- <unk> Second Team All @-@ American 
- Big Ten Freshman of the Year ( Big Ten media ) 
- Co @-@ Big Ten Freshman of the Year ( Sporting News ) 
- All @-@ Big Ten ( second team , coaches and media ) 
- All @-@ Freshman ( unanimous ) 
- <unk> All @-@ District V Team 
- Tim <unk> 
- All @-@ Big Ten ( third team , coaches and media ) 
- Zack Novak 
- Academic All @-@ District 
- Academic All @-@ American ( third team ) 
- All @-@ Big Ten ( honorable mention , media ) 
- Academic All @-@ Big Ten 
- Josh <unk> 
- Academic All @-@ Big Ten 
- Matt <unk> 
- Academic All @-@ Big Ten 
- 
- = = <unk> changes = = 
- 
- The team lost senior captains <unk> Douglass and Zack Novak as well as senior reserve Corey Person to graduation following the season . Person was later granted an extra year of eligibility to return to the team . At the end of the season , three players ( Evan <unk> , Carlton <unk> and Colton Christian ) decided to leave the program . <unk> , who had started in 42 of the 69 games he played in during his first two years , left the program as the program 's fifth all @-@ time three @-@ point shooter with a percentage of 40 @.@ 5 . <unk> transferred to Maryland , <unk> transferred to Detroit and Christian transferred to Florida International . 
- Following the season , Trey Burke first said he was not inclined to enter the 2012 NBA Draft . A few days later , however , the realization that the pool of point guards in the 2012 draft was shallow and Burke 's stock was high led to some <unk> for him and his family : " When you have a season as a freshman like he did , the NBA , they like them young , " Trey 's father , <unk> Burke said . " They think their ceiling is higher when they 're young . Trey had ... a solid season for a freshman . It 's going to be one of the weaker point guard drafts in years . " Some observers thought Burke was at his peak in terms of NBA potential . Eventually , he decided to return to play for Michigan for another year . 
- 
- = = = 2012 – 13 team recruits = = = 
- 
- The team announced in September 2010 that Glenn Robinson III , son of former first overall NBA Draft pick Glenn Robinson , verbally agreed to attend Michigan , making him the first commitment in the school 's class of 2012 . Canadian wing guard <unk> <unk> gave Michigan its second verbal commitment for the class of 2012 on March 26 , 2011 . On November 3 , Mitch <unk> , who was ranked as the number @-@ two prospect in the nation at the time , announced his verbal commitment to Michigan . Within hours of the commitment , ESPN said that Michigan 's ranked as the fifth @-@ best class in the nation . All three signed a National <unk> of Intent with the team on November 9 . After several other schools announced their commitments , the <unk> 's commitment moved Michigan from outside the top 25 to the seventh @-@ best class in the nation , according to ESPN . Michael " Spike " <unk> committed to Michigan on April 6 , 2012 . 
- 
- = = Team Players <unk> into the NBA = = 
- 
- 
- 
- = <unk> <unk> b = 
- 
- <unk> <unk> b ( abbreviated <unk> <unk> b , <unk> And b ) , occasionally designated <unk> <unk> <unk> ( to distinguish it from the red dwarf <unk> <unk> B ) , also named <unk> , is an extrasolar planet approximately 44 light @-@ years away from the Sun in the constellation of <unk> . The planet orbits the Solar twin star , <unk> <unk> A , approximately every five days . Discovered in June 1996 by Geoffrey <unk> and R. Paul Butler , it was one of the first hot Jupiters to be discovered . It is also one of the first non @-@ resolved planets to be detected directly . <unk> <unk> b is the innermost known planet in its planetary system . 
- In July 2014 the International Astronomical Union launched a process for giving proper names to certain exoplanets and their host stars . The process involved public nomination and voting for the new names . In December 2015 , the IAU announced the winning name was <unk> for this planet . The winning name was submitted by the Vega <unk> Club of Morocco and honours the 11th Century astronomer Ibn al @-@ <unk> of Muslim Spain . 
- 
- = = Discovery = = 
- 
- Like the majority of known extrasolar planets , <unk> <unk> b was detected by the variations in its star 's radial velocity caused by the planet 's gravity . The variations were detected by making sensitive measurements of the <unk> shift of <unk> <unk> 's spectrum . The planet 's existence was announced in January 1997 , together with 55 <unk> b and the planet orbiting <unk> <unk> . 
- Like 51 <unk> b , the first extrasolar planet discovered around a normal star , <unk> <unk> b orbits very close to its star , closer than Mercury does to our Sun . The planet takes 4 @.@ <unk> days to complete an orbit , with a <unk> axis of 0 @.@ <unk> AU . 
- A limitation of the radial velocity method used to detect <unk> <unk> b is that only a lower limit on the mass can be found . In the case of <unk> <unk> b , this lower limit is 68 @.@ 7 % of the mass of Jupiter , though depending on the inclination of the orbit , the true mass may be much greater . However , astronomers found recently that inclination of the orbital plane is around 25 ° and the true mass may be about 1 @.@ 4 MJ . <unk> is not to be assumed ; the mutual inclination between c and d is 35 degrees . 
- 
- = = Physical characteristics = = 
- 
- Given the planet 's high mass , it is likely that <unk> <unk> b is a gas giant with no solid surface . 
- The <unk> Space Telescope measured the planet temperature , and found that the difference between the two sides of <unk> <unk> b of about 1 @,@ 400 degrees <unk> , ranging from minus 20 to 230 degrees to about 1 @,@ 400 to 1 @,@ 650 degrees <unk> . The temperature difference has led to speculation that <unk> <unk> b is <unk> locked with the same side always facing <unk> <unk> A. 
- <unk> had , on the assumption that the planet is similar to Jupiter in composition and that its environment is close to chemical equilibrium , predicted <unk> <unk> b to have reflective clouds of <unk> and iron in its upper atmosphere . The cloud deck instead absorbs the sun 's radiation ; between that and the hot , high pressure gas surrounding the mantle , exists a <unk> of cooler gas . The outer shell of dark , <unk> , hot cloud is assumed to consist of <unk> and <unk> oxides ( " <unk> planets " ) , but other compounds like <unk> cannot be ruled out yet . 
- The planet is unlikely to have large moons , since <unk> forces would either <unk> them from orbit or destroy them on short <unk> compared to the age of the system . 
- The planet ( with 51 <unk> b ) was deemed a candidate for direct imaging by <unk> . Preliminary results from <unk> studies indicate that the planet has predominately blue color , is 1 @.@ 36 times as large and 0 @.@ 74 times as massive as Jupiter , meaning that the mean density is <unk> / cm3 . It has a <unk> albedo of 0 @.@ 35 in visible light . 
- 
- = = <unk> on its sun = = 
- 
- <unk> <unk> b appears to be responsible for increased <unk> activity on its parent star . Observations suggest that there is a " hot spot " on the star around 169 ° away from the sub @-@ planetary point . This may be the result of interactions between the magnetic fields of the planet and the star . The mechanism may be similar to that responsible for the activity of <unk> <unk> <unk> variable stars , or the interaction between Jupiter and its moon Io . 
- 
- 
- = Daniel <unk> = 
- 
- Daniel Jacob <unk> ( born 23 July 1989 ) is an English actor who rose to prominence as the title character in the Harry Potter film series . He made his acting debut at 10 years of age in BBC One 's 1999 television film David Copperfield , followed by his cinematic debut in 2001 's The <unk> of Panama . At age 11 , he was cast as Harry Potter in the first Harry Potter film , and starred in the series for 10 years until the release of the eighth and final film in 2011 . 
- <unk> began to branch out to stage acting in 2007 , starring in the London and New York productions of <unk> , and in the 2011 Broadway revival of the musical How to <unk> in Business Without Really <unk> . He starred in the 2012 horror film The Woman in Black , and played beat poet Allen Ginsberg in the 2013 independent film Kill Your <unk> . 
- He has contributed to many charities , including <unk> Hospice Care for Children , and The Trevor Project for suicide prevention among <unk> youth , which gave him its Hero Award in 2011 . 
- 
- = = Early life = = 
- 
- <unk> was born in West London , England . He is the only child of Alan George <unk> , a literary agent , and Marcia <unk> <unk> ( née Marcia <unk> Jacobson ) , a casting agent who was involved in several films for the BBC , including The Inspector <unk> Mysteries and Walk Away and I <unk> . His father , who was raised in <unk> , County Down , Northern Ireland , is from " a very working @-@ class " Protestant background . His mother is Jewish , and was born in South Africa and raised in <unk> @-@ on @-@ Sea , Essex . Her family had originally come from Poland and Russia . <unk> 's parents had both acted as children . 
- In a 2012 interview , <unk> stated : " There was never [ religious ] faith in the house . I think of myself as being Jewish and Irish , despite the fact that I 'm English . " He has stated : " We were Christmas tree Jews " , and that he is " very proud of being Jewish " . 
- <unk> first expressed a desire to act at the age of five , and in December 1999 , aged 10 , he made his acting debut in BBC One 's televised two @-@ part adaptation of the Charles Dickens novel David Copperfield , portraying the title character as a young boy . He was educated at two independent schools for boys : Sussex House School , a day school in Chelsea 's <unk> Square , and the City of London School , a day school on the North Bank of the River Thames in London 's financial district ( known as the City of London ) . <unk> school became difficult for <unk> after the release of the first Harry Potter film , with some fellow pupils becoming hostile , though he says it was people just trying to " have a crack at the kid that plays Harry Potter " rather than jealousy . 
- As his acting career began to consume his schedule , <unk> continued his education through on @-@ set tutors . He admitted he was not very good at school , considering it useless and finding the work " really difficult . " He achieved A grades in the three <unk> @-@ level exams that he took in 2006 , but decided to take a break from education and did not go to college or university . Part of his reasoning was that he already knew he wanted to act and write , and that it would be difficult to have a normal college experience . " The <unk> , they 'd love it , " he told Details magazine in 2007 . " If there were any parties going on , they 'd be tipped off as to where they were . " 
- 
- = = Career = = 
- 
- 
- = = = Harry Potter = = = 
- 
- In 2000 , producer David <unk> asked <unk> to audition for the role of Harry Potter for the film adaptation of Harry Potter and the <unk> 's Stone , the best @-@ selling book by British author J. K. <unk> . <unk> had been searching for an unknown British actor to <unk> the character , and the movie 's director Chris Columbus recalled thinking , " This is what I want . This is Harry Potter " , after he saw a video of the young actor in David Copperfield . Eight months later , and after several auditions , <unk> was selected to play the part . <unk> also endorsed the selection saying , " I don 't think Chris Columbus could have found a better Harry . " <unk> 's parents originally turned down the offer , as they had been told that it would involve six films shot in Los Angeles . Warner Bros. instead offered <unk> a two @-@ movie contract with shooting in the UK ; <unk> was unsure at the time if he would do any more than that . 
- The release of Harry Potter and the <unk> 's Stone ( released as Harry Potter and the <unk> 's Stone in the United States ) took place in 2001 . <unk> received a seven figure salary for the lead role , but asserted that the fee was " not that important " to him ; his parents chose to invest the money for him . The film was highly popular and was met with positive reviews , and critics took notice of <unk> : " <unk> is the embodiment of every reader 's imagination . It is wonderful to see a young hero who is so scholarly looking and filled with curiosity and who connects with very real emotions , from solemn intelligence and the delight of discovery to deep family longing , " wrote Bob Graham of the San Francisco Chronicle . 
- A year later <unk> starred in Harry Potter and the Chamber of <unk> , the second installment of the series . Reviewers were positive about the lead actors ' performances but had <unk> opinions on the movie as a whole . The 2004 release Harry Potter and the <unk> of <unk> was the third film in the series . <unk> 's performance was panned by New York Times journalist A. O. Scott , who wrote that Watson had to carry him with her performance . Next was Harry Potter and the <unk> of Fire in 2005 . The film was the second @-@ highest grossing Harry Potter film at that point , and <unk> singled out the humour as being a reason for the movie 's creative success . 
- The future of the franchise was put into question when <unk> and his co @-@ leads Emma Watson and Rupert <unk> hesitated signing on to continue their roles for the final two episodes ; however , by March 2007 <unk> had signed for the final films , which put an end to weeks of press " speculation that he would be denied the role due to his involvement in <unk> " , in which he had performed nude on stage . <unk> reprised his role for the fourth time in Harry Potter and the Order of the Phoenix ( 2007 ) . <unk> stated that director David <unk> and actress <unk> <unk> made Order of the Phoenix the " most fun " film in the series to work on . His performance earned several award nominations , and he received the 2008 National Movie Award for " Best Male Performance . " As his fame and the series continued , <unk> , <unk> , and Watson left <unk> of their hands , feet , and <unk> in front of <unk> 's Chinese Theatre in Hollywood . In July 2009 Harry Potter and the Half @-@ Blood Prince was released , the series ' sixth instalment . <unk> received nominations for " Best Male Performance " and " Global <unk> " at the 2010 MTV Movie Awards . 
- For financial and <unk> reasons the last book was divided into two films , shot back to back , which drew criticism from the series ' fanbase . <unk> defended the split , stating that it would have been impossible to properly adapt the final novel into a single film . He added that the last movie was going to be extremely fast @-@ paced with a lot of action , while the first part would be far more <unk> , focusing on character development ; he added that , had they combined them , those things would not have made it to the final cut . Filming lasted for a year , concluding in June 2010 and on the last day of shooting , like most of the cast and crew , <unk> openly <unk> . 
- The final film , Harry Potter and the <unk> <unk> – Part 2 , was released in July 2011 . <unk> , along with the film , was critically acclaimed : Ann <unk> of The Washington Post asked , " Who could have predicted that <unk> , <unk> and Watson would turn out to be good actors ? " ; similarly , Rex Reed said : " <unk> , I ’ m sorry to see [ <unk> ] go " ; while Rolling Stone critic Peter Travers commented on <unk> : " Well played , sir . " Roger <unk> gave the film a highly positive review , but felt that <unk> , <unk> and Watson were " upstaged by the supporting [ actors ] . " 
- <unk> admitted that some people would never be able to separate him from the character , but also said he is " proud to be associated with this film series forever . " Despite positive feelings about the movies , he has no interest in doing more Harry Potter films . After <unk> hinted about writing an eighth book , <unk> was asked if he would do another film to which he replied : " [ It is ] very doubtful . I think 10 years is a long time to spend with one character . " Despite <unk> so much time to the series , <unk> has asserted that he did not miss out on a childhood like other child actors : " I ’ ve been given a much better perspective on life by doing Potter . " 
- 
- = = = 2001 – 09 = = = 
- 
- <unk> made his film debut in The <unk> of Panama , an American 2001 film based on John le <unk> 's 1996 spy novel , and a moderate commercial success . In 2002 he made his stage debut as a celebrity guest in a West End theatre production of The Play What I <unk> , directed by Kenneth Branagh – who also appeared with him in the second Harry Potter film . In 2007 he appeared in the film December Boys , an Australian family drama about four orphans that was shot in 2005 and released to theatres in mid @-@ September 2007 . Also in 2007 , <unk> co @-@ starred with Carey <unk> in My Boy Jack , a television drama film shown on ITV on Remembrance Day . The film received mostly positive reviews , with several critics praising <unk> 's performance as an 18 @-@ year @-@ old who goes missing in action during a battle . <unk> stated , " For many people my age , the First World War is just a topic in a history book . But I 've always been fascinated by the subject and think it 's as relevant today as it ever was . " 
- At age 17 , in a bid to show people he was prepared for adult roles , he performed onstage in Peter Shaffer 's play <unk> , which had not been revived since its first run in 1973 , at the Gielgud Theatre . <unk> took on the lead role as Alan <unk> , a stable boy who has an obsession with horses . Advance sales topped £ 1 @.@ 7 million , and the role generated significant pre @-@ opening media interest , as <unk> appeared in a nude scene . <unk> opened on 27 February 2007 and ran until 9 June 2007 . <unk> 's performance received positive reviews as critics were impressed by the nuance and depth of his against @-@ type role . Charles Spencer of The Daily Telegraph wrote that he " displays a dramatic power and an <unk> stage presence that marks a tremendous leap forward . " He added : " I never thought I would find the <unk> ( but perfectly formed ) <unk> a sinister figure , but as Alan <unk> ... there are moments when he seems genuinely scary in his rage and confusion . " The production then transferred to Broadway in September 2008 , with <unk> still in the lead role . <unk> stated he was nervous about repeating the role on Broadway because he considered American audiences more discerning than those in London . <unk> 's performance was nominated for a Drama <unk> Award . 
- 
- = = = 2010 – present = = = 
- 
- After voicing a character in an episode of the animated television series The Simpsons in late 2010 , <unk> debuted as J. <unk> Finch in the 2011 Broadway revival How to <unk> in Business Without Really <unk> , a role previously held by Broadway veterans Robert <unk> and Matthew <unk> . Other cast members included John Larroquette , Rose <unk> and Mary <unk> . Both the actor and production received favourable reviews , with USA Today commenting : " <unk> ultimately succeeds not by <unk> his fellow cast members , but by working in conscientious harmony with them – and having a blast in the process . " <unk> 's performance in the show earned him Drama <unk> Award , Drama League Award and Outer Critics Circle Award nominations . The production itself later received nine Tony Award nominations . <unk> left the show on 1 January 2012 . 
- His first post @-@ Harry Potter project was the 2012 horror film The Woman in Black , adapted from the 1983 novel by Susan Hill . The film was released on 3 February 2012 in the United States and Canada , and was released on 10 February in the UK . <unk> portrays a man sent to deal with the legal matters of a mysterious woman who has just died , and soon after he begins to experience strange events and <unk> from the ghost of a woman dressed in black . He has said he was " incredibly excited " to be part of the film and described the script as " beautifully written " . 
- In 2013 , he portrayed American poet Allen Ginsberg in the thriller drama Kill Your <unk> , directed by John <unk> . He also starred in an Irish @-@ Canadian romantic comedy film The F Word directed by Michael <unk> written by <unk> <unk> , based on TJ <unk> and Michael <unk> 's play <unk> and Cigars and then he starred in an American dark fantasy horror film directed by Alexandre <unk> Horns . Both of the films premiered at the 38th Toronto International Film Festival . 
- <unk> also performed at the Noël Coward Theatre in the stage play revival of Martin <unk> 's dark comedy The <unk> of <unk> as the lead , Billy <unk> , for which he won the <unk> Award for Best Actor in a Play . In 2015 , <unk> starred as Igor in a science fiction horror film Victor <unk> directed by Paul <unk> and written by Max <unk> , which was based on contemporary adaptations of Mary Shelley 's 1818 novel <unk> . In 2016 , <unk> portrayed Manny , a <unk> corpse , in the indie film Swiss Army Man . 
- He is set to star as American reporter Jake <unk> in Tokyo Vice . In November 2015 he joined the ensemble cast of Shane <unk> 's third film , The Modern Ocean alongside Anne <unk> , <unk> <unk> , Tom Holland , <unk> Grace <unk> , <unk> <unk> , Jeff <unk> and Abraham <unk> . 
- 
- = = Personal life = = 
- 
- In 2008 , <unk> revealed that he has a mild form of the <unk> disorder developmental coordination disorder . The motor skill disorder sometimes prevents him from doing simple activities , such as writing or tying his own <unk> . " I was having a hard time at school , in terms of being crap at everything , with no discernible talent , " <unk> commented . In August 2010 , he stopped drinking alcohol after finding himself becoming too reliant on it . 
- In November 2007 <unk> published several poems under the pen name Jacob <unk> – a combination of his middle name and the Jewish version of his mother 's maiden name <unk> – in <unk> , an underground fashion magazine . He has a close friendship with his Harry Potter co @-@ stars Tom <unk> and Emma Watson , and is tight @-@ knit with his family , whom he credits for keeping him grounded . 
- Sources disagree about <unk> 's personal wealth ; he was reported to have earned £ 1 million for the first Harry Potter film and around £ 15 million for the sixth . <unk> appeared on the Sunday Times Rich List in 2006 , which estimated his personal fortune to be £ 14 million , making him one of the richest young people in the UK . In March 2009 he was ranked number one on the Forbes " Most Valuable Young Stars " list , and by April The Daily Telegraph measured his net worth at £ <unk> , making him the 12th richest young person in the UK . <unk> was considered to be the richest teenager in England later that year . In February 2010 he was named the sixth highest paid Hollywood male star and placed at number five on Forbes 's December list of Hollywood 's highest @-@ grossing actors with a film revenue of US $ 780 million , mainly due to Harry Potter and the <unk> <unk> being released that year . 
- <unk> maintains a home in the West Village of Lower Manhattan in New York City . As of October 2012 , <unk> has been dating American Erin Darke , whom he met on the set of Kill Your <unk> . There were rumours and stories of a possible engagement in mid @-@ 2014 , but Darke 's father Ian Darke denied there were any such plans in December 2014 . 
- 
- = = = <unk> and activism = = = 
- 
- <unk> is an <unk> . He has been quoted as saying : " I 'm an <unk> , and a militant <unk> when religion starts impacting on legislation " , and in a separate interview , he stated ; " I 'm very relaxed about it [ being an <unk> ] . I don 't preach my <unk> , but I have a huge amount of respect for people like Richard <unk> who do . Anything he does on television , I will watch " . 
- <unk> is a supporter of the Labour Party . Until 2012 <unk> had publicly supported the Liberal Democrats , and before the 2010 general election <unk> endorsed Nick <unk> , the <unk> <unk> leader . In 2012 , however , <unk> switched his allegiance to Labour , citing disappointment with the performance of Nick <unk> and the <unk> <unk> in government , and <unk> of the Labour leader , Ed <unk> . In September 2015 , he endorsed Jeremy <unk> in the 2015 leadership contest to succeed <unk> . He is a supporter of a British republic . At the age of sixteen , <unk> became the youngest non @-@ royal ever to have an individual portrait in Britain 's National Portrait Gallery ( <unk> ) . On 13 April 2006 his portrait , drawn by Stuart Pearson Wright , was unveiled as part of a new exhibition opening at the Royal National Theatre ; it was then moved to the <unk> where it resides . 
- Speaking out against homophobia , <unk> began filming public service announcements in 2009 for The Trevor Project , promoting awareness of gay teen suicide prevention . He first learned of the organisation while working on <unk> on Broadway in 2008 and has contributed financially to it . " I have always hated anybody who is not tolerant of gay men or <unk> or <unk> . Now I am in the very fortunate position where I can actually help or do something about it , " he said in a 2010 interview . In the same interview , he spoke of the importance of public figures advocating for equal rights . <unk> considers his involvement to be one of the most important things in his career and , for his work for the organisation , he was given the " Hero Award " in 2011 . 
- <unk> has supported various charities . He designed the <unk> @-@ Bed for Habitat 's <unk> Kids range ( a <unk> made of eight smaller ones which can be made into a bed , <unk> @-@ <unk> or chair ) with all the royalties from the sale of the bed going directly to his favourite charity , <unk> House Children 's Hospice in <unk> , Kent . <unk> has urged his fans to make donations , in lieu of Christmas presents to him , to the charity 's <unk> for Care program . In 2008 he was among several celebrities who donated their old glasses to an exhibit <unk> victims of the Holocaust . During the Broadway run of <unk> he auctioned off a pair of <unk> and other items worn in the show , for New @-@ York @-@ based Broadway Cares / <unk> <unk> <unk> and presenting at the 2011 <unk> of the Year competition . He has also donated money to Get <unk> UK , a London @-@ based free <unk> national <unk> for troubled youth . 
- <unk> is a fan of the Detroit Red Wings of the National Hockey League . 
- 
- = = Filmography = = 
- 
- 
- = = Awards and nominations = = 
- 
- 
- 
- = ... Thirteen Years Later = 
- 
- " ... Thirteen Years Later " is the fifth episode of the third season of the American crime @-@ thriller television series Millennium . It premiered on the Fox network on October 30 , 1998 . The episode was written by Michael R. Perry , and directed by Thomas J. Wright . " ... Thirteen Years Later " featured guest appearances by the members of the band Kiss , performing both as themselves and as minor characters . 
- In this episode , Federal Bureau of Investigation agents Frank Black ( Lance <unk> ) and Emma Hollis ( <unk> Scott ) investigate a series of murders on the set of a horror film — and realize that the production is based on a case Black solved several years earlier . 
- " ... Thirteen Years Later " proved a difficult episode to produce , with filming requiring a large number of shots to be completed and the script to be adjusted on short notice . It has evoked mixed responses from critics , although the guest appearances by the members of Kiss have been more universally criticized . 
- 
- = = Plot = = 
- 
- Federal Bureau of Investigation agents Frank Black ( Lance <unk> ) and Emma Hollis ( <unk> Scott ) travel to <unk> Rest , South Carolina to investigate the deaths of film director Lew Carroll ( Paul Stanley ) and Marta <unk> , the leading actress in his newest film . As the local sheriff guides Black and Hollis through the murder scene , Black realizes that the film is based on a real murder case he investigated thirteen years previously ; he is shocked to learn that the true story is being <unk> for the screen . 
- The pair interview the cast and crew as suspects , discovering that many would benefit from either of the deaths — producer Kenny <unk> had been having an affair with <unk> ; <unk> <unk> replaced Carroll as director ; Ruby Dahl and <unk> <unk> , whose roles in the film will expand without <unk> ; and Mark Bianco , a method actor <unk> the opportunity to meet Black , whose role he plays . Also questioned is Hugo Winston , the man whose partner was murdered in the earlier case and who is campaigning against what he sees as a <unk> production . 
- The production continues , and after the filming of a pool scene the crew gather in catering ; <unk> discovers a severed finger in his sandwich , recognizing from its ring that it belonged to <unk> . When the sheriff <unk> down production , Black <unk> that the killer may target the crew 's hotel ; he and Hollis arrive to find the body of the film 's publicist hanging from a rope . The next day , a film extra ( Gene Simmons ) confesses to the killings ; Black <unk> holes in his story and discovers he is not a credible perpetrator . Regardless , production is resumed — and shortly afterwards , several crew members are found with their <unk> cut , while Winston 's body hangs nearby in an apparent murder @-@ suicide . 
- Black doubts that Winston is the killer , and while Hollis stays in the hotel , he visits the production 's climactic shoot , set during a performance by the band Kiss . As he looks around him , the sheriff 's body is thrown down from the <unk> ; as the <unk> settles , Black realizes that horror films end with the leading lady being targeted — the killer is after Hollis . Racing back to her hotel , Black discover Hollis <unk> off a <unk> @-@ wielding Bianco . Black is able to subdue Bianco after Hollis <unk> the <unk> ; the actor then explains that he lacked Black 's insight and had to commit the crimes in order to see through the eyes of a killer . 
- 
- = = Production = = 
- 
- " ... Thirteen Years Later " is the second episode of Millennium to have been written by Michael R. Perry , who had previously penned the second season episode " The <unk> " , and would provide a further three scripts in the third season . The episode was directed by Thomas J. Wright , who helmed a total of twenty @-@ six episodes across all three seasons . Wright would also go on to direct " Millennium " , the series ' crossover episode with its sister show The X @-@ Files . 
- The episode features guest appearances by the members of the band Kiss — Peter Criss , Ace <unk> , Gene Simmons and Paul Stanley — who each portray both themselves and a small cameo role . However , the band members insisted during filming that their roles be given an equal amount of dialogue , which led to Perry being asked to amend scenes on short notice and <unk> updated scripts to the shooting locations ; ultimately much of this additional material was filmed but not used in the episode . Ace <unk> was also <unk> towards his role , and requested to play a " 300 @-@ pound black man " instead , wishing to wear a fat suit like those used in the 1996 Eddie Murphy film The <unk> Professor ; he was eventually convinced that such a suit would take too long to <unk> . 
- Perry has described the production " ... Thirteen Years Later " as being " one of the most <unk> " of the series ' run , citing its abundance of stunt work and physical effects as the cause of this . Perry praised Wright for his ability to direct the episode within the usual time constraints , as he felt the number of shots involved was more akin to a feature film than a television episode . Guest star Jeff <unk> , who played killer Mark Bianco , is the husband of actress Megan Gallagher , who starred in the first two seasons of Millennium as Black 's wife Catherine . 
- 
- = = Broadcast and reception = = 
- 
- " ... Thirteen Years Later " was first broadcast on the Fox network on October 30 , 1998 . The episode earned a Nielsen rating of 5 @.@ 4 during its original broadcast , meaning that 5 @.@ 4 percent of households in the United States viewed the episode . This represented approximately 5 @.@ 37 million households . 
- " ... Thirteen Years Later " received mixed reviews from critics . The A.V. Club 's Todd VanDerWerff rated the episode a " B " . VanDerWerff felt that the episode 's comedic <unk> was necessary within the wider series , as he considered Millennium 's dark tone to be prone to unintentional comedy and that a comic episode provided an outlet for this . VanDerWerff found the script to be at its strongest when <unk> the character of Frank Black , but admitted that it " falls apart " when attempting to send up other subjects ; he also felt that the guest appearance by Kiss felt unnecessary and contrived . Robert Shearman and Lars Pearson , in their book Wanting to Believe : A Critical Guide to The X @-@ Files , Millennium & The Lone Gunmen , rated " ... Thirteen Years Later " one star out of five . Shearman felt that the episode had the potential to be much better , but that " the whole concept drowns in <unk> " . He considered the episode 's characters to too shallow and its commentary to be too heavy @-@ handed to fully execute anything meaningful ; criticism was also levelled at the guest roles by the members of Kiss . 
- 
- 
- = New Jersey Route 29 = 
- 
- Route 29 is a state highway in the U.S. state of New Jersey . It runs 34 @.@ 76 mi ( 55 @.@ 94 km ) from an interchange with Interstate 295 ( I @-@ 295 ) in Hamilton Township in Mercer County , where it continues as I @-@ 195 , to Route 12 ( Bridge Street / Race Street ) in Frenchtown , <unk> County . Between the southern terminus and I @-@ 95 , the route is a mix of freeway and four @-@ lane divided highway that runs along the Delaware River through <unk> . This section includes a truck @-@ restricted tunnel that was built along the river near historic houses and <unk> Cemetery . North of I @-@ 95 , Route 29 turns into a scenic and mostly two @-@ lane highway . North of the South <unk> Tunnel , it is designated the Delaware River Scenic Byway , a New Jersey Scenic Byway and National Scenic Byway , that follows the Delaware River in mostly rural sections of Mercer County and <unk> County . The obsolete Delaware & <unk> Canal usually stands between the river and the highway . Most sections of this portion of Route 29 are completely <unk> due to the tree canopy . Route 29 also has a spur , Route 129 , which connects Route 29 to U.S. Route 1 ( US 1 ) in <unk> . 
- Route 29 was initially designated in 1927 to run from downtown <unk> to Newark , following present @-@ day Route 179 and US 202 between <unk> and <unk> and US 22 between <unk> and Newark . The route between <unk> and Frenchtown was originally Route <unk> . In 1953 , Route 29 was shifted to follow the alignment of Route <unk> to avoid the <unk> with the U.S. Routes . Route 29 between South Warren Street in <unk> and I @-@ 95 in Ewing Township was upgraded to a four @-@ lane highway , with a portion of freeway , in the 1950s and 1960s . In 1995 , the southern freeway part of Route 29 between I @-@ 195 / I @-@ 295 and Route 129 in Hamilton Township was completed . This freeway section was linked to the rest of Route 29 by a tunnel completed in 2002 . A realignment of Route 29 in <unk> by the 2000s made the route concurrent with the entire length of 0 @.@ 26 @-@ mile @-@ long ( 0 @.@ 42 km ) Route 165 . 
- 
- = = Route description = = 
- 
- 
- = = = Mercer County = = = 
- 
- Route 29 begins at a modified cloverleaf interchange with Interstate 195 and Interstate 295 in Hamilton Township , and it serves as the western continuation of Interstate 195 , heading to the northwest as a six @-@ lane freeway . The route interchanges with Route 129 , a spur of Route 29 which connects to U.S. Route 1 , at a partial interchange with a northbound exit and southbound entrance . Route 29 <unk> to four lanes past this interchange and crosses into <unk> . The route comes to a southbound exit and entrance for <unk> Road . At this point , Route 29 becomes the Delaware River Scenic Byway , a state scenic <unk> that was also designated a National Scenic Byway in 2009 . Route 29 runs along the bank of the Delaware River and enters a truck @-@ restricted tunnel that passes by historic houses and <unk> Cemetery . Within this tunnel , Route 29 features a southbound exit and northbound entrance for Lalor Street . The route emerges from the tunnel as the John Fitch Parkway , passes by Arm & <unk> Park , and comes to a traffic light at Thunder Road / Cass Street where it <unk> back to six lanes . Route 29 meets South Warren Street at another traffic light . The median <unk> and it passes under the <unk> @-@ <unk> Railroad Bridge , which carries the Amtrak Northeast Corridor over the Delaware River . 
- Route 29 passes under the <unk> @-@ <unk> <unk> Bridge , which carries U.S. Route 1 over the Delaware River . <unk> to U.S. Route 1 southbound is provided by ramps from Route 29 while access to Route 29 from northbound U.S. Route 1 is provided by South Warren Street . Route 29 passes under the Lower <unk> Bridge and the median <unk> again . It interchanges with Market Street , which provides access to Route 33 , and then features an interchange which provides access to the New Jersey State House with a northbound exit and southbound entrance . Route 29 crosses the <unk> Creek and features an interchange which provides access to South Warren Street with exits in both directions but only a northbound entrance . Route 29 continues to a cloverleaf interchange with <unk> Street ( County Route 653 ) , which provides access to the <unk> Street Bridge over the Delaware River . Riverside Avenue exits as a frontage road <unk> the northbound lanes of Route 29 before the road features a northbound exit for <unk> Avenue . Route 29 comes to a partial interchange with <unk> Avenue , with a northbound exit and southbound entrance , and then features a northbound exit for South <unk> Avenue . 
- The freeway portion of Route 29 ends at the intersection with Lee Avenue and it continues northwest along the Delaware River as a four @-@ lane divided highway . The route meets the southern terminus of County Route <unk> ( Sullivan Way ) . The median <unk> again and then <unk> as the route meets the southern terminus of Route 175 , a former alignment of Route 29 that currently serves as a frontage road . Route 29 crosses into Ewing Township and becomes the Daniel Bray Highway . It passes under the West <unk> Railroad Bridge , which carries <unk> and <unk> ’ s West <unk> Line over the Delaware River . Route 29 intersects Route 175 again and then comes to a complex interchange with Interstate 95 , with the ramps within the median of Route 29 , just to the east of the <unk> Falls Bridge . 
- Upon crossing the Delaware and <unk> Canal , Route 29 <unk> down to a two @-@ lane undivided road called River Road . It continues along the Delaware River , next to the Delaware and <unk> Canal , which runs between Route 29 and the river . The route intersects the northern terminus of Route 175 . Farther north , Route 29 enters <unk> Township and continues into a more rural setting <unk> with trees . Route 29 heads to Washington Crossing State Park , where it intersects County Route <unk> , which heads east on Washington Crossing @-@ <unk> Road , and the approach to the Washington Crossing Bridge , which continues into Pennsylvania as Pennsylvania Route <unk> . Route 29 continues north along the Delaware River through <unk> , passing by Washington Crossing State Park . 
- 
- = = = <unk> County = = = 
- 
- Route 29 crosses into West <unk> Township in <unk> County . It enters <unk> , where Route 29 becomes a four @-@ lane divided highway . At the intersection of South Main Street , Route 29 becomes concurrent with Route 165 . The route becomes an undivided highway again and meets the western terminus of County Route <unk> ( Brunswick Street ) . It meets Route 179 ( Bridge Street ) , where Route 165 ends and Route 29 turns west for a one @-@ block wrong @-@ way concurrency with the two @-@ lane , undivided Route 179 , lasting to the intersection of Main Street , where Route 29 turns north on Main Street . 
- Route 29 follows Main Street north through <unk> , crossing into Delaware Township . It comes to an interchange with U.S. Route 202 just east of the New Hope @-@ <unk> <unk> Bridge , with access to northbound U.S. Route 202 and from southbound U.S. Route 202 provided by way of <unk> Creek Road . Route 29 continues along the Delaware River and enters Stockton . The route intersects Bridge Street , which crosses the Delaware River on the Centre Bridge @-@ Stockton Bridge and continues into Pennsylvania as Pennsylvania Route 263 . Shortly after that intersection , Route 29 intersects the southern terminus of County Route 523 ( Stockton @-@ Flemington Road ) . Route 29 crosses back into Delaware Township , where it meets the southern terminus of County Route <unk> ( <unk> @-@ Stockton Road ) . 
- Route 29 makes a sharp left turn and heads west along the river as a rural road , crossing into <unk> Township , where the name of the road changes from Main Street to Daniel Bray Highway . Here , it intersects with County Route <unk> ( <unk> @-@ <unk> Road ) . The route <unk> to the north and continues along the Delaware River for several miles , crossing into Frenchtown , where the route becomes <unk> Road . Upon entering Frenchtown , an end shield for northbound Route 29 is posted to mark the end of state maintenance , which officially ends at the Washington Street intersection , where maintenance is transferred to the county . Despite this , Route 29 officially continues farther north along <unk> Road to its northern terminus at Route 12 ( Bridge Street / Race Street ) , a short distance east of Route 12 ’ s western terminus at the <unk> @-@ Frenchtown Bridge . The southern terminus of County Route <unk> is located a block north of the northern terminus of Route 29 along Route 12 . 
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- = = History = = 
- 
- The current route was originally legislated in 1911 as part of the Delaware River Drive , a named state highway that was proposed to run from along the Delaware River from <unk> to the New York border in <unk> Township . Route 29 was originally defined in 1927 to run from <unk> to Newark . The original route ran from downtown <unk> along State Street and <unk> Drive . From there , it followed its current alignment to <unk> , where it followed present @-@ day Route 179 to <unk> to present @-@ day County Route <unk> , which it followed to Woods Tavern . The route turned north on U.S. Route 206 ( also designated Route 31 ) and followed that route to <unk> , where it followed U.S. Route 22 to Newark . The current alignment of Route 29 from <unk> to Route 12 in Frenchtown was designated Route <unk> in 1927 . 
- The alignment of Route 29 between <unk> and <unk> was eventually shifted to follow present @-@ day U.S. Route 202 , which was also Route 30 ( now Route 31 ) between <unk> and Flemington and Route 12 between Flemington and <unk> . In 1938 , Route <unk> was planned as an extension of Route <unk> from Frenchtown to Route 28 ( now Route 122 ) in Alpha . While this road was never built , much of the alignment north of Milford is served by County Route <unk> . In 1948 , a spur route , Route <unk> , was created , running along U.S. Route 202 ( Bridge Street ) in <unk> to the New Hope @-@ <unk> Bridge . In the 1953 New Jersey state highway renumbering , Route 29 was <unk> to continue north from <unk> to Frenchtown on Route <unk> , and the Route 29 designation between <unk> and Newark was removed in favor of U.S. Route 202 between <unk> and <unk> and U.S. Route 22 between <unk> and Newark . The section of former Route 29 between Route <unk> and Route <unk> became Route 165 . 
- Plans for a limited @-@ access route along the Route 29 corridor go back to 1932 , when a parkway was proposed along the Delaware River between <unk> and <unk> ; this proposal never materialized . Plans for a freeway were resurrected in the early 1950s to construct a road , the John Fitch Parkway , that was to run from Hamilton Township to Interstate 95 in Ewing Township . This road was built between 1954 and 1957 from South Warren Street in <unk> to present @-@ day Interstate 95 in Ewing Township . Construction of this portion John Fitch Parkway took up much of <unk> ’ s available waterfront along the Delaware River . The former alignment of Route 29 in the northern part of <unk> and in Ewing Township became Route 175 . 
- Between 1990 and 1995 , the Route 29 freeway was built between the Interstate 195 / Interstate 295 interchange and Route 129 in Hamilton Township . A two @-@ lane street , <unk> Road , connected the two freeway sections . Plans were then made to fill the gap between the two freeway sections in <unk> . Construction began in 1997 on the Route 29 freeway between Route 129 and the <unk> @-@ <unk> Railroad Bridge . The road was to include two traffic lights at Cass Street and South Warren Street and a tunnel which was to be built as a covered roadway on the bank of the Delaware River . The tunnel was originally scheduled to be complete by 2001 but was delayed after the Army Corps of Engineers discovered many environmental violations that occurred with construction of the tunnel . The roof was put in place in October 2001 and the tunnel officially opened to traffic on March 2 , 2002 . A restriction to trucks over 13 tons was put in place and made permanent in November 2002 . 
- By the 2000s , the state gave the part of Route 29 ( South Main Street ) between Route 165 and Route 179 in <unk> to the city , and Route 29 was rerouted to use all of Route 165 and one block of Route 179 . Prior to this , South Main Street had been turned one @-@ way southbound . Route 165 , which is only signed on overhead street signs , still exists , though it is fully concurrent with Route 29 . 
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- = = Major intersections = = 
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- 
- 
- = First <unk> invasion of Burma = 
- 
- The first <unk> invasions of Burma ( <unk> ) ( <unk> : <unk> <unk> <unk> – <unk> <unk> <unk> <unk> <unk> ( <unk> – <unk> ) ) were a series of military conflicts between <unk> Khan 's Yuan dynasty , division of the <unk> Empire , and the Pagan Empire that took place between <unk> and <unk> . The invasions toppled the 250 @-@ year @-@ old Pagan Empire , and the <unk> army seized Pagan territories in present @-@ day <unk> , <unk> and northern Burma to <unk> . The invasions <unk> in 250 years of political fragmentation in Burma and the rise of Tai @-@ Shan states throughout mainland Southeast Asia . 
- The Mongols first demanded tribute from Pagan in 1271 – 72 , as part of their drive to encircle the Song dynasty of China . When King <unk> refused , Emperor <unk> Khan himself sent another mission in <unk> , again demanding tribute . It too was rejected . In 1275 , the emperor ordered the <unk> government to secure the <unk> in order to block an escape path for the Song , and permitted a limited border war if Pagan contested . Pagan did contest but its army was driven back at the frontier by the <unk> Army in <unk> – 78 . After a brief lull , <unk> Khan in 1281 turned his attention to Southeast Asia , demanding tribute from Pagan , the Khmer Empire , <unk> <unk> and <unk> . When the <unk> king again refused , the emperor ordered an invasion of northern Burma . Two dry season campaigns ( <unk> – 85 ) later , the Mongols had occupied down to <unk> and <unk> , forcing the <unk> king to flee to Lower Burma . The Mongols organized northern Burma as the province of <unk> . 
- <unk> negotiations began in <unk> , and ended with <unk> finally agreeing to submit in June <unk> . The <unk> embassy , received by the emperor in Beijing in January <unk> , agreed to a treaty that acknowledged the <unk> of the Yuan dynasty or the <unk> Empire over the Pagan Empire and annual payments in taxes to the <unk> government in exchange for the evacuation of <unk> troops from northern Burma . But the treaty never really took effect as <unk> was assassinated in July <unk> , and no authority who could honor the treaty emerged . The <unk> command at <unk> now deemed the imperial order to withdraw void , and ordered an invasion of central Burma . They may not have reached Pagan , and even if they did , after having suffered heavy casualties , they returned to <unk> . 
- The Pagan Empire <unk> and anarchy ensued . The Mongols , who probably preferred the situation , did nothing to restore order in the next ten years . In March <unk> , they accepted the voluntary submission of King <unk> of Pagan although he controlled little beyond the capital city of Pagan ( <unk> ) . But <unk> was overthrown nine months later , and the Mongols were forced to intervene , leading to their second invasion in 1300 – 01 . 
- Marco Polo reported the first invasions ( <unk> – 87 ) in his <unk> , Il <unk> . The <unk> referred to the invaders as the <unk> ( after the central Asian <unk> troops that largely made up the <unk> invasion army ) ; today , the term <unk> ( <unk> <unk> ) refers to the Han Chinese instead . King <unk> is <unk> remembered in <unk> history as <unk> @-@ <unk> <unk> , ( " the King who <unk> from the <unk> " ) . 
- 
- = = Background = = 
- 
- 
- = = = Pagan and <unk> = = = 
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- In the 13th century , the Pagan Empire , along with the Khmer Empire , was one of the two main empires in mainland Southeast Asia . For much of its history , Pagan 's neighbor to the northeast was not China but the independent <unk> Kingdom and its predecessor <unk> , both with <unk> as their capital city . <unk> @-@ based kingdoms were a power in their own right , at times <unk> themselves with the <unk> Empire to their west and at other times with China 's Tang and Song dynasties . Indeed , <unk> 's mounted armies ventured deep into what is today Burma and may have been behind the founding of the medieval city of Pagan and the Pagan Dynasty itself . 
- Between the newly conquered <unk> territory and Pagan were a wide <unk> of <unk> stretching from present @-@ day <unk> , <unk> and <unk> prefectures in <unk> as well as the <unk> and <unk> regions ( presumably in present @-@ day northern Shan State ) , which Pagan and <unk> had both claimed and exercised overlapping spheres of influence . Then as now , the <unk> mostly consist of <unk> <unk> of high mountain ranges . 
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- = = = <unk> conquest of <unk> = = = 
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- The <unk> Empire first arrived at the <unk> of the Pagan Empire in <unk> by invading the <unk> Kingdom in its attempt to outflank Song China . The <unk> armies captured the capital , <unk> , on 7 January <unk> , and went on to <unk> much of the kingdom by <unk> . 
- The arrival of the Mongols did not initially upset the existing order at the <unk> as the Mongols were intent on finishing off the Song . For the next dozen years , they consolidated their hold over the newly conquered land , which not only provided them with a base from which to attack the Song from the rear but also was strategically located on the trade routes from China to Burma and India . The Mongols set up military garrisons , manned mostly by <unk> @-@ speaking Muslims from Central Asia , in 37 circuits of the former <unk> Kingdom . 
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- = = = Decline of Pagan = = = 
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- By then , the Pagan Empire , despite outward appearances of calm , had been in long and slow decline since the early 13th century . The continuous growth of tax @-@ free religious wealth had greatly reduced the tax base of the kingdom . The crown had lost resources needed to retain the loyalty of courtiers and military servicemen , inviting a vicious circle of internal disorders and external challenges . Although it was able to put down the first batch of serious rebellions in <unk> – 60 in South <unk> and <unk> ( <unk> ) , the decline continued . On the eve of the <unk> invasions , between one and two @-@ thirds of Upper Burma 's <unk> land had been donated to religion . The crown 's ability to <unk> defenses was in serious jeopardy . 
- 
- = = Prelude to war = = 
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- = = = First <unk> mission ( 1271 – 72 ) = = = 
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- The period of calm for Pagan ended in the early <unk> . By then , the Song were on the ropes , and Emperor <unk> Khan , who officially founded the Yuan dynasty on 18 December 1271 , sought to cut off the retreat of Song refugees in all directions . In Pagan 's case , he had ordered the <unk> governor of <unk> to <unk> control of the <unk> , and in January 1271 to send a mission to Pagan to demand tribute . The tribute he demanded was nominal . Given his higher priority <unk> elsewhere , the emperor was not looking to replace the regime at Pagan . At the border , the ruler of the <unk> and <unk> regions submitted to the Mongols . 
- When the <unk> envoys led by <unk> <unk> showed up , the Pagan court led by Chief Minister <unk> <unk> was well aware of the military power of the Mongols and advised King <unk> to use diplomacy . The king was furious at the demand and kept the <unk> envoys waiting for weeks . The court finally devised a compromise : the envoys were sent back without ever seeing the king . <unk> them was a <unk> envoy who carried a letter expressing friendly sentiments and the <unk> king 's wish to one day worship a Buddha tooth at Beijing . The king then promptly ordered an expedition , which retook the rebellious <unk> regions in April <unk> . The rebel leader A @-@ <unk> ( <unk> ) was brought back to Pagan . <unk> relayed the news to Beijing but did not carry out any military action . 
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- = = = Second <unk> mission ( <unk> ) = = = 
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- At Beijing , <unk> Khan , who was preparing an invasion of Japan , decided against a war with Pagan — for the time being . On 3 March <unk> , he sent a 4 @-@ member delegation led by an imperial ambassador , the First Secretary to the Board <unk> , to Pagan . The delegation carried a letter from the emperor . The letter says : 
- " If you have finally decided to fulfill your duties towards the All @-@ Highest , send one of your brothers or senior ministers , to show men that all the world is linked with Us , and enter into a perpetual alliance . This will add to your reputation , and be in your own interests ; for if it comes to war , who will be the victor ? <unk> well , O king , Our words . " 
- This time , the <unk> king received the imperial envoys but still refused to submit . ( The <unk> chronicles say that the king was so insulted that he had the envoys executed . But both <unk> <unk> evidence and Yuan records indicate that the envoys were not executed . ) At any rate , the imperial envoys did not get back to <unk> in due time . The newly formed <unk> government sent another delegation to investigate the whereabouts of the delegation , but the delegation could not reach Pagan because of an ongoing rebellion en route . 
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- = = = <unk> consolidation of <unk> ( 1275 – 76 ) = = = 
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- Meanwhile , in <unk> , the former <unk> Kingdom was officially reorganized as the Province of <unk> , with <unk> <unk> <unk> al @-@ Din Omar as governor . In May 1275 , the governor sent a report to the emperor stating that the embassy had not returned ; that the <unk> evidently had no intention of submitting ; and that war was the only way forward . 
- But the emperor rejected an outright invasion . Just coming off a disastrous Japanese campaign , the emperor was unwilling to commit the central government troops to what he considered a low priority affair . He was now focused on delivering the final blow against the Song ; the emperor ordered the <unk> provincial army to secure the <unk> in order to block the escape path of the Song refugees . He also sanctioned a limited border war if Pagan contested the takeover . As planned , the <unk> army proceeded to consolidate the <unk> in 1275 – 76 . ( Elsewhere , the main <unk> armies had captured most of the Song territory by <unk> . ) 
- By <unk> , at least one <unk> vassal state named " Gold <unk> " ( modern <unk> ) had submitted to the Mongols . Like in <unk> , the <unk> government responded by sending an army to reclaim the rebellious state ; but unlike in <unk> , the Mongols had posted a sizable garrison there . Though it was ultimately under <unk> command , many of the officers and most of the soldiers of the garrison were <unk> @-@ speaking peoples or people from the further west : Turks from <unk> , <unk> , <unk> and <unk> , but also captive soldiers from the Persian <unk> empire , the <unk> , and even <unk> from the lower <unk> . 
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- = = Border war ( <unk> – 78 ) = = 
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- What followed was a border war in <unk> – 78 . It was reported mainly in the Yuan dynasty chronicle and the <unk> of Marco Polo . Although the <unk> chronicles have no record of the border war , a <unk> <unk> inscription mentions the army 's defeat at <unk> . The <unk> accounts of the border war contain certain errors of location and numbers although the overall narrative is probably accurate . 
- 
- = = = Battle of <unk> = = = 
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- According to the Yuan dynasty chronicle and Marco Polo 's accounts , a <unk> army " invaded " the <unk> territory of Gold <unk> , and was defeated by the <unk> army in April <unk> . The battle took place either at the <unk> valley ( in present @-@ day <unk> Prefecture ) or 110 km southwest at <unk> ( present @-@ day <unk> , <unk> Prefecture ) , which the <unk> called <unk> . 
- The Yuan Chronicle reports that only 700 men defeated a <unk> army of 40 @,@ 000 to 50 @,@ 000 with 10 @,@ 000 horses and 800 <unk> . It also reports only one <unk> was killed , in trying to catch an elephant . According to Marco Polo , the <unk> army consisted of 12 @,@ 000 mounted <unk> , and the <unk> army numbered 60 @,@ 000 men with 2000 <unk> , " on each of which was set a tower of timber , well @-@ framed and strong , and carrying from 12 to 16 well @-@ armed fighting men . " Even then , the 40 @,@ 000 to 60 @,@ 000 figures of the <unk> army strength were likely eye estimates and may still be too high ; the Mongols may have <unk> " on the side of generosity " not to " <unk> their glory in defeating superior numbers . " 
- According to Marco Polo 's account , in the early stages of the battle , the Turkish and <unk> horsemen " took such <unk> at the sight of the <unk> that they would not be got to face the <unk> , but always <unk> and turned back , " while the <unk> forces pressed on . But the <unk> commander <unk> did not panic ; he ordered his troops to <unk> , and from the cover of the nearby <unk> , aim their bows directly at the advancing <unk> . The <unk> <unk> ' arrows threw the animals into such pain that they fled . 
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- = = = Raid of <unk> = = = 
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- The <unk> army pressed on after the monsoon season . In the following dry season of <unk> – 78 , c . December <unk> , a <unk> army of <unk> led by <unk> al @-@ Din , son of <unk> <unk> <unk> , advanced to <unk> , which defended the <unk> Pass . They occupied the fort and destroyed a large number of abandoned <unk> . But they found the heat excessive and returned . 
- 
- = = Interlude ( <unk> – 83 ) = = 
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- Despite the <unk> military success , the control of the <unk> remained contested . Pagan did not relinquish its claim to the frontier regions , and the <unk> , apparently taking advantage of <unk> <unk> elsewhere , rebuilt their forts at <unk> and <unk> later in <unk> , posting permanent garrisons commanded by <unk> <unk> . But their control was short @-@ lived . The Great Khan 's attention turned to Southeast Asia once more in 1281 . He had had mixed success : his <unk> forces had finished off the last of the Song in 1279 but had again failed to take Japan in 1281 . That year , the <unk> emperor sent another mission to Pagan , demanding tribute yet again . The <unk> king was to send his ten senior ministers accompanied by one thousand cavalry officers to the emperor 's court . ( With <unk> , the emperor summoned the king of <unk> himself to Beijing . ) 
- At Pagan , <unk> <unk> with his court for an appropriate response but ultimately refused to submit . The <unk> court may have been counting on another limited border war but the emperor now ordered an invasion of northern Burma . ( He also ordered an invasion of <unk> , whose king too had refused to submit . ) The <unk> king 's troubles did not go <unk> elsewhere in the kingdom . In the same year , a <unk> named <unk> seized the southern port city of <unk> ( <unk> ) by killing its Pagan @-@ appointed governor . Although the king 's three sons were <unk> of the nearby Lower Burma cities ( in <unk> ( <unk> ) , <unk> ( <unk> ) , and <unk> ) , the king , preoccupied with much more serious threat in the north , did not ( or could not ) take any action on <unk> . 
- Throughout <unk> , the <unk> command made preparations for the upcoming invasions of <unk> and northern Burma . The objective of the Burma campaign was to take over northern Burma but no further ; the emperor did not <unk> an attack on Pagan itself . At least one army consisted of 14 @,@ 000 men of the <unk> Persian <unk> Empire under the command of <unk> <unk> was sent to <unk> to reinforce the Burma invasion force , which again was made up of Turks and other central <unk> . On the <unk> side , the king managed to raise an army although given his low standing with his vassals , he probably could not have raised a large one . By mid @-@ <unk> , a <unk> army led by generals <unk> <unk> and <unk> <unk> was deployed at a fort at <unk> . 
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- = = Invasion ( <unk> – 85 ) = = 
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- = = = Battle of <unk> ( <unk> ) = = = 
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- The invasion began on 22 September <unk> . Prince <unk> was the commander @-@ in @-@ chief of the invasion force ; his deputies were Vice Governor <unk> , and commander <unk> <unk> . The <unk> armies marched to the border in two columns . One column advanced along the <unk> River using over 200 boats ; the other proceeded by land and joined the first column at the <unk> fort at <unk> . The <unk> chronicles report an overwhelming number of <unk> forces laying siege to the fort although their numbers are greatly exaggerated . ( The chronicles say that the <unk> army numbered 400 @,@ 000 men while the <unk> army numbered 20 million men and 6 million horses . ) The <unk> withstood the siege for over two months but the fort fell on 3 December <unk> . 
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- = = = Invasion of northern Burma = = = 
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- The defeat at <unk> broke the back of <unk> defenses . The <unk> army lost several thousand men as well as senior commanders . <unk> , the next fort <unk> , fell just six days later on 9 December <unk> . The <unk> sources say that the <unk> lost 10 @,@ 000 men at <unk> . The <unk> armies pushed farther south into the <unk> valley . They took the ancient <unk> capital of <unk> , about 380 km north of Pagan on 5 February <unk> . There , the invaders paused their advance . They found the heat of the searing <unk> valley excessive , and evacuated <unk> , allowing the <unk> to return to <unk> on 10 May <unk> . But the <unk> army renewed their offensive in the following dry season . They retook <unk> , and defeated another <unk> stand south of <unk> , probably near <unk> , on 26 January <unk> , opening the way to Pagan , about 270 km south . After the defeat , the king <unk> , and fled to Lower Burma . The evacuation proved premature . The <unk> forces did not advance on Pagan as it was not part of their invasion plan . 
- The country fell into chaos . In Lower Burma , the king found himself isolated , let alone plan a counterattack . Although his sons ruled the key Lower Burma ports ( <unk> , <unk> and <unk> ) , the king did not trust any of them , and he and his court settled at <unk> , west of <unk> . Without the full support of his sons , the presence of the king and his small army impressed no one . The governor of <unk> <unk> that same year . The king managed to send two small expeditions to <unk> but they both failed . Now , the entire eastern half of Lower Burma ( <unk> and <unk> ) was in open revolt . 
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- = = Peace negotiations ( <unk> – 87 ) = = 
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- = = = <unk> = = = 
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- Given his precarious position , <unk> decided to buy time , and sue for peace with the Mongols . In November / December <unk> , the king ordered his generals <unk> <unk> and <unk> Bo to enter into ceasefire negotiations . The <unk> commanders at <unk> , who had organized northern Burma as a protectorate named <unk> ( Chinese : <unk> ; Wade – Giles : <unk> @-@ <unk> ) , agreed to a ceasefire but insisted on a full submission . They repeated their 1281 demand that the <unk> king send a formal delegation to the emperor . The two sides had reached a tentative agreement by 3 March <unk> , which calls for a full submission of the Pagan Empire , and central Burma to be organized as the province of <unk> ( Chinese : <unk> ; Wade – Giles : <unk> @-@ Chung ) . After a long <unk> , the king agreed to submit but wanted the <unk> troops to withdraw . In June <unk> , he sent an embassy led by Shin <unk> <unk> , a learned monk , to the emperor 's court . 
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- = = = Treaty of Beijing = = = 
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- In January <unk> , the embassy arrived at Beijing , and was received by the Yuan emperor . The <unk> delegation formally acknowledged <unk> <unk> of their kingdom , and agreed to pay annual tribute tied to the agricultural output of the country . ( Indeed , the tribute was no longer nominal . ) In exchange , the emperor agreed to withdraw his troops . For the emperor , the Burma campaign was the only bright spot ; his other Southeast Asian expeditions had gone badly . He did not want to invest more troops <unk> the rest of the kingdom . He preferred a vassal ruler . The <unk> embassy arrived back at <unk> in May <unk> , and reported the terms to the king . 
- 
- = = = <unk> = = = 
- 
- But the agreement broke down a month later . In late June , the defeated king and his small retinue left their temporary capital for Pagan . But on 1 July <unk> , the king was captured en route and assassinated by his second son <unk> , the Viceroy of <unk> . Anarchy ensued . Each region in the country which had not <unk> broke away . No successor to <unk> , who could honor and enforce the terms of the treaty of Beijing , emerged . Indeed , a king would not emerge until May <unk> . 
- 
- = = <unk> intervention ( <unk> ) = = 
- 
- Given the chaos , the governor of <unk> ignored the imperial orders of evacuation . The <unk> army commanded by Prince <unk> @-@ sin <unk> , a grandson of the emperor , marched south toward Pagan . According to mainstream traditional ( British colonial era ) scholarship , the <unk> army ignored the imperial orders to evacuate ; fought its way down to Pagan with the loss of 7000 men ; occupied the city ; and sent out <unk> to receive homage , one of which reached south of <unk> . But not all colonial period scholars agreed with the assessment as none of the contemporary <unk> / Chinese records specifically mentioned the conquest of Pagan or the temporary <unk> of the conquest . 
- Recent research shows that the <unk> forces most probably never reached Pagan . They were held at bay by the <unk> defenses led by commanders <unk> , <unk> and <unk> , and probably never got closer than 160 km north of Pagan . ( An inscription dated 16 February <unk> by the three brothers claimed that they defeated the <unk> army . ) Even if the Mongols did reach Pagan , the damage they inflicted was probably minimal . At any rate , the <unk> army suffered heavy casualties , and retreated north to <unk> . They remained there as the treaty was now void . 
- 
- = = Aftermath = = 
- 
- The disintegration of the Pagan Empire was now complete . But the Mongols refused to fill in the power vacuum they had created . They would send no more expeditions to restore order . The emperor apparently had no interest in committing troops that would be required to <unk> the fragmented country . Indeed , his real aim all along may have been " to keep the entire region of Southeast Asia broken and fragmented . " It would be another two years until one of <unk> 's sons , <unk> , emerged as king of Pagan in May <unk> . But the new " king " controlled just a small area around the capital , and had no real army . The real power in central Burma now rested with the three commander brothers . 
- The uneasy arrangement would persist until <unk> . The Mongols continued to occupy northern Burma to <unk> as the province of <unk> ( <unk> @-@ <unk> ) but ended the fictional central Burma province of <unk> on 18 August <unk> . Meanwhile , the power struggle in central Burma continued with the three brothers <unk> consolidating support . To check their rising power , <unk> submitted to the Mongols in January <unk> , and was recognized by the Yuan emperor <unk> Khan as King of Pagan on 20 March <unk> . The emperor also gave Chinese titles to the brothers as subordinates of <unk> . The brothers resented the new arrangement as it directly reduced their power . On 17 December <unk> , the three brothers <unk> <unk> , and founded the <unk> Kingdom . The <unk> forced the <unk> government to intervene again , leading to the second <unk> invasion of Burma ( 1300 – 01 ) . The invasion failed . Two years later , on 4 April <unk> , the Mongols abolished the province of <unk> ( <unk> @-@ <unk> ) , evacuated <unk> , and returned to <unk> . 
- 
- = = Legacy = = 
- 
- The war was one of several near simultaneous wars <unk> by the <unk> Empire and the Yuan dynasty in the late 13th century . Though it was never more than a minor frontier war to the Mongols , the war set off a series of enduring developments in Burma . The invasions <unk> in a period of political fragmentation , and the rise of Tai @-@ Shan states throughout mainland Southeast Asia . 
- 
- = = = Age of political fragmentation = = = 
- 
- The immediate result of the war was the collapse of the Pagan Empire . However , the war merely accelerated the collapse but did not cause it . Pagan 's disintegration was " in fact more prolonged and <unk> . " The kingdom had been in long gradual decline since the early 13th century . Had Pagan possessed a stronger central government , the collapse could have been temporary , and the country “ could have risen again ” . But the dynasty could not recover , and because the Mongols refused to fill the power vacuum , no viable center emerged in the immediate aftermath . As a result , several minor states fought it out for supremacy for the better part of the 14th century . It was only in the late 14th century that two relatively strong powers emerged in the <unk> basin , restoring some semblance of normalcy . The vast region surrounding the <unk> valley would continue to be made up of several small Tai @-@ Shan states well into the 16th century . 
- 
- = = = Rise of Tai @-@ Shan states = = = 
- 
- Perhaps the most enduring legacy of the <unk> invasions was the emergence of Tai @-@ Shan states in mainland Southeast Asia . The Tai @-@ Shan people who came down with the <unk> invasions stayed . By the early 14th century , several Tai @-@ Shan states had come to dominate a vast region from present @-@ day <unk> to northern and eastern <unk> to northern and central Thailand and Laos . Their rise was encouraged by the Mongols , who viewed the states as a useful <unk> between <unk> and the rest of Southeast Asia . The Mongols , who were still trying to incorporate <unk> into the central administration , were unwilling or unable to make necessary sustained investments to bring the vast regions south of <unk> into the fold . ( The integration of <unk> itself into “ China <unk> ” was to take several more centuries , and continues to today . ) As such , from the newly formed Tai @-@ Shan states in western and central Southeast Asia to Dai Viet and <unk> in eastern Southeast Asia , the Mongols elected to receive nominal tribute . Though the rulers of these states were technically governors of the Yuan government , they were the native chieftains , “ who would have ruled there in any case , and they did as they pleased . ” 
- 
- = = = <unk> of China on the <unk> border = = = 
- 
- The war also marked the arrival of China at the <unk> of Burma . The old <unk> Kingdom , known to the <unk> as <unk> ( <unk> <unk> <unk> <unk> , after <unk> Raj ) was now a <unk> Chinese province . ( The <unk> now called the new powers at <unk> " <unk> " after the <unk> @-@ speaking soldiers of <unk> . Over the years , the term <unk> came to be used to refer to the Han Chinese . Today , King <unk> is remembered as <unk> @-@ <unk> <unk> , ( " the King who fled from the <unk> [ Chinese ] ) . ) From a <unk> standpoint , the <unk> – Chinese presence in <unk> pushed the Shan migrations in the direction of Burma ( and parts of the Khmer Empire ) . The raids by various Shan states into Upper Burma would continue until the mid @-@ 16th century . 
- 
- = = Mongolia – <unk> relations = = 
- 
- During the official visit by the President <unk> <unk> to <unk> in November 2013 , <unk> San Suu <unk> , the <unk> of National League for Democracy , said this was the first ever <unk> mission since the Mongols came 730 years earlier . 
- 
- 
- = Battle of <unk> 's Ridge = 
- 
- The Battle of <unk> 's Ridge , also known as the Battle of the Bloody Ridge , Battle of Raiders Ridge , and Battle of the Ridge , was a land battle of the Pacific campaign of World War II between Imperial Japanese Army and Allied ( mainly United States Marine Corps ) ground forces . It took place from 12 – 14 September 1942 , on <unk> in the Solomon Islands , and was the second of three separate major Japanese ground offensives during the <unk> Campaign . 
- In the battle , U.S. Marines , under the overall command of U.S. Major General Alexander <unk> , repulsed an attack by the Japanese 35th Infantry Brigade , under the command of Japanese Major General <unk> <unk> . The Marines were defending the <unk> perimeter that guarded Henderson Field on <unk> , which was captured from the Japanese by the Allies in landings on <unk> on 7 August 1942 . <unk> 's unit was sent to <unk> in response to the Allied landings with the mission of <unk> the airfield and driving the Allied forces from the island . 
- <unk> the strength of Allied forces on <unk> – about 12 @,@ 000 – <unk> 's 6 @,@ 000 soldiers conducted several nighttime frontal assaults on the U.S. defenses . The main Japanese assault occurred around <unk> ridge south of Henderson Field , manned by troops from several U.S. Marine Corps units , primarily troops from the 1st <unk> and 1st Parachute Battalions under U.S. Marine Corps Lieutenant Colonel Merritt A. <unk> . Although the Marine defenses were almost overrun , <unk> 's attack was ultimately defeated , with heavy losses for the Japanese . 
- Because of the key participation by <unk> 's unit in defending the ridge , the ridge was commonly referred to as " <unk> 's " ridge in historical accounts of the battle in Western sources . After <unk> 's Ridge , the Japanese continued to send troops to <unk> for further attempts to retake Henderson Field , affecting Japanese offensive operations in other areas of the South Pacific . 
- 
- = = Background = = 
- 
- 
- = = = <unk> campaign = = = 
- 
- On 7 August 1942 , Allied forces ( primarily U.S. ) landed on <unk> , <unk> , and Florida Islands in the Solomon Islands . The landings on the islands were meant to deny their use by the Japanese as bases for threatening the supply routes between the U.S. and Australia . They were also intended to secure the islands as starting points for a campaign to neutralize the major Japanese base at Rabaul and support the Allied New Guinea campaign . The landings initiated the six @-@ month @-@ long <unk> campaign . 
- Taking the Japanese by surprise , by nightfall on 8 August the Allied landing forces had secured <unk> and nearby small islands , as well as an airfield under construction at <unk> Point ( 9 ° 25 ′ 45 ″ S 160 ° 3 ′ 4 ″ E ) on the north shore of the island of <unk> east of the present day capital of <unk> . <unk> placed his 11 @,@ 000 troops of the 1st Marine Division on <unk> in a loose perimeter around the <unk> Point area . 
- On 12 August , the airfield was named Henderson Field after Major <unk> Henderson , a Marine <unk> who had been killed at the Battle of Midway . The Allied aircraft and pilots that subsequently operated out of Henderson Field were called the " <unk> Air Force " after the Allied code name for <unk> . 
- In response to the Allied landings on <unk> , the Japanese Imperial General Headquarters assigned the Imperial Japanese Army 's 17th Army — a corps @-@ sized command based at Rabaul ( 4 ° 11 ′ 56 ″ S 152 ° 9 ′ 57 ″ E ) and under the command of Lieutenant @-@ General <unk> <unk> — with the task of retaking <unk> from Allied forces . The 17th Army — heavily involved with the Japanese campaign in New Guinea — had only a few units available to send to the southern <unk> area . Of these units , the 35th Infantry Brigade — under Major General <unk> <unk> — was at <unk> , the 4th ( <unk> ) Infantry Regiment was in the Philippines and the 28th ( <unk> ) Infantry Regiment — under the command of Colonel <unk> <unk> — was embarked on transport ships near Guam . The different units began to move toward <unk> immediately ; <unk> 's regiment — the closest — arrived first . The " First <unk> " of <unk> 's unit — consisting of about <unk> soldiers — landed from destroyers at <unk> Point ( 9 ° 24 ′ 38 ″ S 160 ° 20 ′ 56 ″ E ) , about 18 mi ( 29 km ) east of the <unk> perimeter , on August 19 . 
- <unk> the strength of Allied forces on <unk> , <unk> 's First <unk> conducted a nighttime frontal assault on Marine positions at <unk> Creek on the east side of the <unk> perimeter in the early morning hours of August 21 . <unk> 's assault was repulsed with devastating losses for the attackers in what became known as the Battle of the <unk> : all but 128 of the <unk> men of the First <unk> ( including <unk> himself ) were killed in the battle . The survivors returned to <unk> Point , notified 17th Army headquarters of their defeat in the battle and awaited further reinforcements and orders from Rabaul . 
- By 23 August , <unk> 's unit had reached <unk> and was loaded onto slow transport ships for the rest of the trip to <unk> . Because of the damage caused by Allied air attack to a separate troop convoy during the Battle of the Eastern <unk> , the Japanese decided not to deliver <unk> 's troops to <unk> by slow transport ship ; instead , the ships carrying <unk> 's soldiers were sent to Rabaul . From there , the Japanese planned to deliver <unk> 's men to <unk> by destroyers , staging through a Japanese naval base in the <unk> Islands . The Japanese destroyers were usually able to make the round trip down " The <unk> " to <unk> and back in a single night , <unk> their exposure to Allied air attack . However , most of the soldiers ' heavy equipment and supplies , such as heavy artillery , vehicles , and much food and ammunition , could not be taken to <unk> with them . These high @-@ speed destroyer runs to <unk> , which occurred throughout the campaign , were later called the " Tokyo Express " by Allied forces and " Rat Transportation " by the Japanese . The Japanese controlled the seas around the Solomon Islands during the nighttime and were not challenged by the Allies . However , any Japanese ship remaining within the 200 mi ( 320 km ) range of the aircraft at Henderson Field in daylight was in great danger from air attacks . This " curious tactical situation " held for several months . 
- 
- = = = <unk> movement = = = 
- 
- On 28 August , 600 of <unk> 's troops were loaded onto the destroyers <unk> , <unk> , <unk> , and <unk> , designated Destroyer Division 20 ( <unk> ) . Because of a shortage of fuel , <unk> could not make the entire round trip to <unk> at high speed in one night , but had to start the trip earlier in the day so that they could complete the trip by the next morning at a slower speed which <unk> fuel . At 18 : 05 that day , 11 U.S. dive bombers from <unk> @-@ 232 under the command of Lt. Col. Richard <unk> , flew from Henderson Field and located and attacked <unk> about 70 mi ( 110 km ) north of <unk> , sinking <unk> and heavily damaging <unk> and <unk> . <unk> took <unk> in tow and the three destroyers returned to the <unk> without completing their mission . The attack on <unk> killed 62 of <unk> 's soldiers and 94 crew members . 
- Subsequent " Express " runs were more successful . Between 29 August and 4 September , various Japanese light cruisers , destroyers , and patrol boats were able to land almost 5 @,@ 000 troops at <unk> Point , including all of the 35th Infantry Brigade , one battalion of the <unk> Regiment , and the rest of <unk> 's regiment . General <unk> , who landed at <unk> Point on the 31 August Express run , was placed in command of all the Japanese troops on <unk> . On the night of 4 / 5 September , as three of the Express destroyers — <unk> , <unk> , and <unk> — prepared to shell Henderson Field after landing their troops , they detected and sank two U.S. ships in the vicinity , the small , old destroyer transports ( called " <unk> " by the U.S. Marines ) USS Little and Gregory that were used to <unk> Allied troops around the <unk> / <unk> area . 
- In spite of the successes of the destroyer runs , <unk> insisted that as many soldiers of his brigade as possible be delivered to <unk> by slow barges . Therefore , a convoy carrying 1 @,@ 100 of <unk> 's troops and heavy equipment in 61 barges , mainly from the 2nd Battalion , <unk> Infantry Regiment under the command of Colonel <unk> <unk> , departed the northern coast of Santa Isabel Island on 2 September . On 4 – 5 September , aircraft from Henderson Field attacked the barge convoy , killing about 90 of the soldiers in the barges and destroying much of the unit 's heavy equipment . Most of the remaining 1 @,@ 000 troops were able to land near <unk> ( 9 ° 15 ′ 32 ″ S 159 ° 40 ′ 18 ″ E ) , west of the <unk> perimeter over the next few days . By 7 September , <unk> had 5 @,@ 200 troops at <unk> Point and 1 @,@ 000 west of the <unk> perimeter . <unk> was confident enough that he could defeat the Allied forces facing him that he declined an offer from the 17th Army for delivery of one more infantry battalion to augment his forces . <unk> believed that there were only about 2 @,@ 000 U.S. Marines on <unk> . 
- During this time , <unk> continued to direct efforts to strengthen and improve the defenses of the <unk> perimeter . Between 21 August and 3 September , he relocated three Marine battalions — including the 1st <unk> Battalion , under U.S. Lieutenant Colonel Merritt A. <unk> ( <unk> 's Raiders ) , and the 1st Parachute Battalion — from <unk> and <unk> to <unk> . These units added about 1 @,@ 500 troops to <unk> 's original 11 @,@ 000 men defending Henderson Field . The 1st Parachute battalion , which had suffered heavy casualties in the Battle of <unk> and <unk> @-@ <unk> in August , was placed under <unk> 's command . 
- 
- = = Battle = = 
- 
- 
- = = = Prelude = = = 
- 
- <unk> set the date for his attack on the <unk> perimeter for 12 September and began marching his forces west from <unk> towards <unk> Point on 5 September . He <unk> 17th Army and requested that it carry out air strikes on Henderson Field beginning on 9 September , and that naval warships be stationed off <unk> Point on September 12 to " destroy any Americans who attempted to flee from the island . " On 7 September , <unk> issued his attack plan to " <unk> and <unk> the enemy in the vicinity of the <unk> Island airfield . " <unk> 's plan called for his forces to split into three , approach the <unk> perimeter inland , and launch a surprise night attack . <unk> 's force would attack the perimeter from the west while <unk> 's Second <unk> — renamed the <unk> Battalion — would attack from the east . The main attack would be by <unk> 's " Center Body " , numbering 3 @,@ 000 men in three battalions , from the south of the <unk> perimeter . By 7 September , most of <unk> 's troops had started marching from <unk> towards <unk> Point along the coastline . About 250 Japanese troops remained behind to guard the brigade 's supply base at <unk> . 
- Meanwhile , native island scouts — directed by British government official and officer in the British Solomon Islands <unk> Defence Force , Martin <unk> — told the Marines of Japanese troops at <unk> , near the village of <unk> , about 17 mi ( 27 km ) east of <unk> . <unk> launched a raid against the Japanese troops at <unk> . Destroyer transports USS McKean and <unk> and two patrol boats took <unk> of <unk> 's men to <unk> in two trips . <unk> and his first wave of <unk> troops landed at <unk> at 05 : 20 ( local time ) on 8 September . <unk> by aircraft from Henderson Field and gunfire from the destroyer transports , <unk> 's men advanced towards <unk> village but were slowed by Japanese resistance . At 11 : 00 , the rest of <unk> 's men landed . With this reinforcement and more support from the Henderson Field aircraft , <unk> 's force pushed into the village . The Japanese defenders , believing a major landing was underway after observing the concurrent approach of an Allied supply ship convoy heading towards <unk> Point , retreated into the jungle , leaving behind 27 dead . Two Marines were killed . 
- In <unk> , <unk> 's troops discovered the supply base for <unk> 's forces , including large <unk> of food , ammunition and medical supplies , and a <unk> radio . The Marines seized documents , equipment and food supplies , destroyed the rest , and returned to the <unk> perimeter at 17 : 30 . The quantities of supplies and intelligence from the captured documents revealed that at least 3 @,@ 000 Japanese troops were on the island and apparently planning an attack . 
- <unk> and Colonel Gerald Thomas , <unk> 's operations officer , believed that the Japanese attack would come at the <unk> Ridge , a narrow , grassy , 1 @,@ 000 m ( 1 @,@ 100 yd ) long , coral ridge ( 9 ° 26 ′ 39 ″ S 160 ° 2 ′ 50 ″ E ) parallel to the <unk> River just south of Henderson Field . The ridge offered a natural <unk> of approach to the airfield , commanded the surrounding area and was almost undefended . <unk> and Thomas tried to persuade <unk> to move forces to defend the ridge , but <unk> refused , believing that the Japanese were more likely to attack along the coast . Finally , Thomas convinced <unk> that the ridge was a good location for <unk> 's Raiders to " rest " from their actions of the preceding month . On 11 September , the 840 men of <unk> 's unit — including the 1st Raiders and the <unk> — deployed onto and around the ridge and prepared to defend it . 
- <unk> 's Center Body of troops was planning to attack the <unk> perimeter at the ridge , which they called " the <unk> " ( <unk> <unk> ) because of its shape . On 9 September , <unk> 's troops left the coast at <unk> Point . <unk> into four columns , they marched into the jungle towards their <unk> attack points south and southeast of the airfield . <unk> of good maps , at least one faulty <unk> , and thick , almost impenetrable jungle caused the Japanese columns to proceed slowly and <unk> , costing a lot of time . At the same time , <unk> 's troops approached the <unk> perimeter from the west . <unk> had some intelligence on the Marine defenses , extracted from a U.S. Army pilot captured on 30 August . 
- During the day of 12 September , <unk> 's troops struggled through the jungle toward their assembly points for that night 's attacks . <unk> wanted his three Center Body battalions in place by 14 : 00 , but they did not reach their assembly areas until after 22 : 00 . <unk> was also delayed in his advance towards the Marine lines in the west . Only the <unk> battalion reported that they were in place on time . Despite the problems in reaching the planned attack positions , <unk> was still confident in his attack plan because a captured U.S. pilot disclosed that the ridge was the weakest part of the Marine defenses . Japanese bombers attacked the ridge during daytime on 11 – 12 September , causing a few casualties , including two killed . 
- 
- = = = First night 's action = = = 
- 
- The Americans knew of the approach of the Japanese forces from reports from native scouts and their own patrols , but did not know exactly where or when they would attack . The ridge around which <unk> deployed his men consisted of three distinct <unk> . At the southern tip and surrounded on three sides by thick jungle was Hill 80 ( so named because it rose 80 ft ( 24 m ) above sea level ) . Six hundred yards north was Hill 123 ( 123 ft ( 37 m ) high ) , the dominant feature on the ridge . The <unk> <unk> was unnamed and about 60 ft ( 18 m ) high . <unk> placed the five companies from the <unk> battalion on the west side of the ridge and the three Parachute battalion companies on the east side , holding positions in depth from Hill 80 back to Hill 123 . Two of the five <unk> companies , " B " and " C " , held a line between the ridge , a small , swampy <unk> , and the <unk> River . Machine @-@ gun teams from " E " Company , the heavy weapons company , were scattered throughout the defenses . <unk> placed his command post on Hill 123 . 
- At 21 : 30 on 12 September , the Japanese cruiser <unk> and three destroyers shelled the <unk> perimeter for 20 minutes and illuminated the ridge with a <unk> . Japanese artillery began shelling the Marine lines , but did little damage . At the same time , scattered groups of <unk> 's troops began <unk> with Marines around the ridge . <unk> 's 1st Battalion — led by Major <unk> <unk> — attacked the <unk> 's " C " company between the <unk> and the <unk> River , overrunning at least one platoon and forcing the Marine company to fall back to the ridge . <unk> 's unit became entangled with troops from <unk> 's 3rd Battalion under Lieutenant Colonel <unk> <unk> , who were still struggling to reach their attack positions , and the resulting confusion effectively stopped the Japanese attack on the ridge that night . <unk> , who was having trouble locating where he was in relation to the U.S. Marine lines as well as coordinating his troops ' attacks , later complained , " Due to the <unk> jungle , the brigade was scattered all over and was completely beyond my control . In my whole life I have never felt so disappointed and helpless . " Twelve U.S. Marines were killed ; Japanese casualties are unknown but perhaps somewhat greater . Although both <unk> in the west and the <unk> unit in the east tried to attack the Marine lines that night , they failed to make contact and halted near the Marine lines at dawn . 
- At first light on 13 September , <unk> Air Force aircraft and Marine artillery fired into the area just south of the ridge , forcing any Japanese out in the open to seek cover in the nearby jungle . The Japanese suffered several casualties , including two officers from <unk> 's battalion . At 05 : 50 , <unk> decided to regroup his forces for another attack that night . 
- 
- = = = Second night 's action on the ridge = = = 
- 
- <unk> the Japanese to attack again that night , <unk> directed his troops to improve their defenses on and around the ridge . After a failed attempt by two companies to retake the ground on the Marine right flank lost to <unk> the night before , <unk> <unk> his forces . He pulled his front back about 400 yd ( 370 m ) to a line that stretched 1 @,@ 800 yd ( 1 @,@ 600 m ) , starting at the <unk> River and crossing the ridge about 150 yd ( 140 m ) south of Hill 123 . Around and behind Hill 123 he placed five companies . Any Japanese attackers <unk> Hill 80 would have to advance over 400 yd ( 370 m ) of open terrain to close with the Marine positions at Hill 123 . With only a few hours to prepare , the Marines were only able to construct rudimentary and shallow fortifications . They were low on ammunition , with one or two grenades for each Marine . <unk> ordered a reserve force consisting of the 2nd Battalion , 5th Marine Regiment ( 2 / 5 ) to move into a position just to the rear of <unk> 's troops . In addition , a battery of four <unk> howitzers from the 11th Marine Regiment moved to a location from where it could provide direct fire onto the ridge , and a forward artillery observer was placed with <unk> 's front line units . 
- Late in the afternoon , <unk> stepped onto a grenade box and addressed his exhausted troops , saying , 
- You men have done a great job , and I have just one more thing to ask of you . <unk> out just one more night . I know we 've been without sleep a long time . But we expect another attack from them tonight and they may come through here . I have every reason to believe that we will have reliefs here for all of us in the morning . 
- <unk> 's speech " raised the spirits " of the Raiders and helped them prepare mentally for the night ahead . 
- As the sun set on 13 September , <unk> faced <unk> 's 830 Marines with 3 @,@ 000 troops of his brigade , plus an assortment of light artillery . The night was pitch black , with no moon . At 21 : 00 , seven Japanese destroyers briefly bombarded the ridge . <unk> 's attack began just after nightfall , with <unk> 's battalion assaulting <unk> Company B on the Marine right flank , just to the west of the ridge . The force of the assault caused Company B to fall back to Hill 123 . Under Marine artillery fire , <unk> reassembled his men and continued his attack . Without <unk> to try to " roll @-@ up " the other nearby Marine units , whose flanks were now unprotected , <unk> 's unit <unk> forward through the swampy lowlands between the ridge and the <unk> River , heading for the airfield . <unk> 's men came upon a pile of Marine supplies and rations . Not having eaten adequately for a couple of days , they paused to " <unk> themselves " on the " C " and " K " rations . <unk> ordered his men to continue the attack . At about 03 : 00 , he led them against the Marine units around the northern portion of the ridge , just short of the airfield , as well as Hill 123 . In the heavy fighting that followed , <unk> and around 100 of his men were killed , ending that attack . 
- Meanwhile , <unk> 's 2nd Battalion , under Major <unk> Tamura , assembled for their planned assault against Hill 80 from the jungle south of the ridge . Marine observers spotted Tamura 's preparations and called in artillery fire . At about 22 : 00 , a barrage from twelve 105 mm ( 4 @.@ 1 in ) guns hit Tamura 's position . In response , two companies of Tamura 's troops — numbering about 320 men — charged up Hill 80 with fixed bayonets behind their own barrage of mortar fire and grenades . Tamura 's attack hit Company B of the Marine Parachute battalion and also <unk> Company B , pushing the <unk> off the east side of the ridge into a draw below the <unk> . To protect the exposed <unk> Company B , <unk> immediately ordered them to pull back onto Hill 123 . 
- At the same time , a Japanese company from <unk> 's battalion infiltrated through a gap between the east side of the ridge and Parachute Company C. <unk> that their positions were now <unk> , Parachute Companies B and C climbed onto the ridge and retreated to a position behind Hill 123 . In the darkness and confusion of the battle , the retreat quickly became confused and disorganized . A few Marines began <unk> that the Japanese were attacking with poison gas , <unk> other Marines who no longer possessed their gas masks . After arriving behind Hill 123 , some of the Marines continued on towards the airfield , repeating the word " withdraw " to anyone within <unk> . Other Marines began to follow them . Just at the moment that it appeared that the Marines on the hill were about to break and head for the rear in a <unk> , <unk> , Major Kenneth D. Bailey from <unk> 's staff , and other Marine officers appeared and , with " vivid " language , <unk> the Marines back into defensive positions around Hill 123 . 
- As the Marines formed into a <unk> @-@ shaped line around Hill 123 , Tamura 's battalion began a series of frontal assaults on the hill , charging up the saddle from Hill 80 and up from below the east side of the ridge . Under the light of parachute flares dropped by at least one Japanese <unk> , the Marines repulsed the first two attacks by Tamura 's men . Tamura 's troops <unk> a 75 mm ( 2 @.@ 95 in ) " regimental " gun to the top of Hill 80 in an effort to fire it directly at the Marines . This gun , which " could have turned the tide in favor of the Japanese , " however , was disabled by a faulty firing pin . At midnight , during a short lull in the fighting , <unk> ordered Parachute Companies B and C to advance from behind Hill 123 to strengthen his left flank . With fixed bayonets , the <unk> swept forward , killing Japanese soldiers who had overrun the Marine lines and were apparently preparing to roll up the Marine lines from the flank , and took position on the east side of the hill . Marines from other units , as well as members of <unk> 's command staff , including Major Bailey , took ammunition and grenades under fire to the Marines around Hill 123 , who were running critically low . Said Marine participant Captain William J. <unk> , " The Japanese attack was almost constant , like a rain that <unk> for a moment and then <unk> the harder ... When one wave was <unk> down - and I mean <unk> down - another followed it into death . " 
- The Japanese hit <unk> 's left flank just after the <unk> took position but were again stopped by Marine rifle , machine @-@ gun , mortar , and grenade fire . Marine 105 mm and 75 mm artillery was also taking a heavy toll on the attacking Japanese . A captured Japanese soldier later said that his unit was " annihilated " by the Marine artillery fire , which only 10 % of his company survived . 
- By 04 : 00 , after <unk> several more assaults , some of which resulted in hand @-@ to @-@ hand fighting , and severe sniper fire from all sides , <unk> 's men were joined by troops from the 2nd Battalion , 5th Marine Regiment , who helped repulse two more Japanese attacks before dawn . Throughout the night , as <unk> 's men came close to overrunning the Marine defenses , <unk> remained standing about 20 yd ( 18 m ) behind the Marine firing line on Hill 123 , <unk> his troops and directing their defensive efforts . Said Marine Captain <unk> Smith , who was in position to observe <unk> for most of the night , " I can say that if there is such a thing as one man holding a battalion together , <unk> did it that night . He stood just behind the front lines – stood , when most of us <unk> the ground . " 
- During the heavy fighting , portions of three Japanese companies , including two from Tamura 's and one from <unk> 's battalions , <unk> the Marine defenses on the ridge , while suffering heavy losses from Marine gunfire , and reached the edge of " Fighter One " , a secondary runway of Henderson Field . A counterattack by the Marine engineers stopped one Japanese company 's advances and forced it to retreat . The other two companies waited at the edge of the jungle for reinforcements to arrive before attacking into the open area around the airfield . When no reinforcements joined them , both companies went back to their original positions south of the ridge after <unk> . Most of the rest of <unk> 's battalion did not participate in the battle because they lost contact with their commander during the night . 
- As the sun rose on 14 September , pockets of Japanese soldiers remained scattered along both sides of the ridge . But with Tamura 's battalion shattered after losing <unk> of its officers and men , and with heavy casualties to his other attacking units as well , <unk> 's assault on the ridge had effectively ended . About 100 Japanese soldiers still remained in the open on the south slope of Hill 80 , perhaps preparing for one more charge on Hill 123 . At first light , three U.S. Army aircraft from the 67th Fighter Squadron at Henderson Field , acting on a request personally delivered by Bailey , <unk> the Japanese near Hill 80 and killed most of them , with the few survivors retreating back into the jungle . 
- 
- = = = <unk> and <unk> attacks = = = 
- 
- As the battle on the ridge took place , <unk> 's <unk> and <unk> units also attacked the Marine defenses on the east and west sides of the <unk> perimeter . The <unk> battalion — led by Major Takeshi <unk> — attacked the southeastern sector of the <unk> perimeter , defended by Marines of the 3rd Battalion , 1st Marine Regiment ( 3 / 1 ) . <unk> 's attack started around midnight , with one company attacking through Marine artillery fire and engaging in hand @-@ to @-@ hand combat with the Marine defenders before being thrown back . <unk> was killed in the attack . After <unk> , the Marines , believing that the rest of <unk> 's battalion was still in the area , sent forward six light tanks without infantry support to sweep the area in front of the Marine lines ; four Japanese 37 mm ( 1 @.@ 46 in ) anti @-@ tank guns destroyed or disabled three of them . After abandoning their burning tanks , several of the disabled tanks ' <unk> were <unk> and killed by the Japanese . One tank <unk> down an embankment into the <unk> River , drowning its crew . 
- At 23 : 00 on 14 September , the remnants of the <unk> battalion conducted another attack on the same portion of the Marine lines , but were repulsed . A final " weak " attack by the <unk> unit on the evening of 15 September was also defeated . 
- <unk> 's unit of about 650 men attacked the Marines at several locations on the west side of the <unk> perimeter . At about 04 : 00 on 14 September , two Japanese companies attacked positions held by the 3rd Battalion , 5th Marine Regiment ( 3 / 5 ) near the coast and were thrown back with heavy losses . Another Japanese company captured a small ridge somewhat inland but was then pinned down by Marine artillery fire throughout the day and took heavy losses before withdrawing on the evening of 14 September . The rest of <unk> 's unit failed to find the Marine lines and did not participate in the attack . 
- 
- = = Aftermath = = 
- 
- At 13 : 05 on 14 September , <unk> led the survivors of his shattered brigade away from the ridge and deeper into the jungle , where they rested and tended to their wounded all the next day . <unk> 's units were then ordered to withdraw west to the <unk> River valley to join with <unk> 's unit , a 6 mi ( 9 @.@ 7 km ) march over difficult terrain . <unk> 's troops began the march on the morning of 16 September . Almost every soldier able to walk had to help carry the wounded . As the march progressed , the exhausted and hungry soldiers , who had eaten their last rations on the morning before their withdrawal , began to <unk> their heavy equipment and then their rifles . By the time most of them reached <unk> 's positions at <unk> five days later , only half still carried their weapons . The <unk> battalion 's survivors , attempting to follow <unk> 's Center Body forces , became lost , wandered for three weeks in the jungle , and almost <unk> to death before finally reaching <unk> 's camp . 
- In total , <unk> 's forces lost about 830 killed in the attack , including 350 in Tamura 's battalion , 200 in <unk> 's battalion , 120 in <unk> 's force , 100 in the <unk> battalion , and 60 in <unk> 's battalion . An unknown number of wounded also died during the withdrawal march to the <unk> . On and around the ridge , the Marines counted 500 Japanese dead , including 200 on the slopes of Hill 123 . The Marines suffered 80 killed between 12 and 14 September . 
- On 17 September , <unk> sent two companies from the 1st Battalion , 1st Marine Regiment ( 1 / 1 ) to pursue the retreating Japanese . The Marines were ambushed by two Japanese companies acting as rear @-@ guards for the withdrawal , and one Marine platoon was pinned down as the rest of the Marines retreated . The Marine company commander requested permission to attempt to rescue his platoon but was denied by <unk> . By nightfall , the Japanese overran and nearly annihilated the platoon , killing 24 Marines with only a few wounded members of the platoon surviving . On 20 September , a patrol from <unk> 's Raiders encountered stragglers from <unk> 's retreating column and called in artillery fire that killed 19 of them . 
- As the Japanese regrouped west of the <unk> , the U.S. forces concentrated on shoring up and strengthening their <unk> defenses . On 14 September , <unk> moved another battalion , the 3rd Battalion , 2nd Marine Regiment ( 3 / 2 ) , from <unk> to <unk> . On 18 September , an Allied naval convoy delivered 4 @,@ 157 men from the 3rd Provisional Marine Brigade ( the U.S. 7th Marine Regiment augmented by additional support units ) to <unk> . These reinforcements allowed <unk> — beginning on 19 September — to establish an unbroken line of defense around the <unk> perimeter . <unk> 's forces ' next significant clashes with the Japanese occurred along the <unk> River from 23 – 27 September and 6 – 9 October . 
- 
- = = <unk> = = 
- 
- On 15 September , General <unk> at Rabaul learned of <unk> 's defeat , the Imperial Japanese Army 's first defeat involving a unit of this size in the war . The general <unk> the news to the Imperial General Headquarters in Japan . In an emergency session , the top Japanese army and navy command staffs concluded that , " <unk> might develop into the decisive battle of the war . " The results of the battle began to have a telling strategic impact on Japanese operations in other areas of the Pacific . <unk> realized that , in order to send sufficient troops and materiel to defeat the Allied forces on <unk> , he could no longer support the major Japanese offensive on the <unk> Track in New Guinea . <unk> — with the <unk> of the General Headquarters — ordered his troops on New Guinea , who were within 30 mi ( 48 km ) of their objective of Port Moresby — to withdraw until the <unk> matter was resolved . The Japanese were never able to restart their drive towards Port Moresby ; the defeat at <unk> 's Ridge contributed not only to Japan 's defeat in the <unk> campaign , but also to Japan 's ultimate defeat throughout the South Pacific . 
- After delivering more forces during the next month the Japanese mounted a major ground offensive on <unk> , led by <unk> , in late October 1942 at the Battle for Henderson Field , but it resulted in an even more decisive defeat for the Japanese . <unk> later stated that <unk> 's assault on the ridge in September was the only time during the entire campaign he had doubts about the outcome and that had it succeeded , " we would have been in a pretty bad condition . " Historian Richard B. Frank adds , " The Japanese never came closer to victory on the island itself than in September 1942 , on a ridge <unk> up from the jungle just south of the critical airfield , best known ever after as Bloody Ridge . " 
- 
- 
- = New Jersey Route 50 = 
- 
- Route 50 is a state highway in the southern part of the U.S. state of New Jersey . It runs 26 @.@ 02 mi ( 41 @.@ 88 km ) from an intersection with U.S. Route 9 ( US 9 ) and the Garden State Parkway in Upper Township , Cape May County to an intersection with US 30 and County Route <unk> ( CR <unk> ) in <unk> Harbor City , Atlantic County . The route , which is mostly a two @-@ lane undivided road , passes through mostly rural areas of Atlantic and Cape May counties as well as the communities of <unk> , <unk> City , <unk> Manor , and Mays Landing . <unk> 50 intersects several roads , including Route 49 in <unk> , US 40 in Mays Landing , and US 322 and the Atlantic City Expressway in Hamilton Township . 
- The portion of current Route 50 between <unk> and Petersburg received funding in 1910 to become a spur of the Ocean Highway . In 1917 , what is now Route 50 was designated a part of pre @-@ 1927 Route 14 , a route that was to run from Cape May to <unk> Harbor City . Route 50 was designated onto its current alignment in 1927 , replacing this portion of pre @-@ 1927 Route 14 . In 1960 , a freeway was proposed for Route 50 , running from the Garden State Parkway in Dennis Township to US 30 in <unk> Township . This proposal was canceled by the mid @-@ 1970s due to financial and environmental concerns as well as lack of traffic . The interchange between Route 50 and the Atlantic City Expressway was reconstructed into a full interchange , with completion in June 2010 . 
- 
- = = Route description = = 
- 
- Route 50 begins at an intersection with US 9 in the <unk> section of Upper Township , Cape May County , heading to the north on a two @-@ lane undivided road . From the southern terminus , a ramp provides access to and from the southbound Garden State Parkway . The road heads north through mostly rural wooded areas with some buildings . It intersects CR <unk> in <unk> before coming to a junction with CR <unk> . Later , Route 50 Cedar Swamp Creek in an area of <unk> . At this crossing , the road runs closely parallel to a <unk> <unk> <unk> Operations railroad line before entering Petersburg , where it splits from the railroad tracks and comes to a <unk> with CR 610 . Upon leaving Petersburg , Route 50 turns to the north again and crosses over the <unk> <unk> <unk> Operations railroad line , intersecting CR <unk> . It continues to <unk> and comes to CR <unk> . Past this intersection , the route meets CR <unk> and CR <unk> . Route 50 forms a concurrency with CR <unk> and the two routes head north and encounter the eastern terminus of Route 49 before leaving <unk> . 
- Not far after Route 49 , the road crosses the <unk> River into <unk> City , Atlantic County . In <unk> City , it heads into residential areas , with CR <unk> <unk> to the west of the route . Route 50 and CR <unk> continue northwest and intersect CR 648 , where the road turns north into more forested areas . At the junction with CR 645 , the route turns to the northeast and enters <unk> Manor , becoming Broad Street . CR <unk> splits from Route 50 by heading northwest on Buena <unk> Road and Route 50 heads through some agricultural <unk> concurrent with CR <unk> Truck , passing to the west of the <unk> Wildlife Area , which is known as a breeding ground for several species of birds . The route intersects CR <unk> , turning to the northeast . 
- After passing some homes and Stephen Lake , the road turns to the north and passes to the west of <unk> Manor County Park . The route crosses the South River into <unk> Township , passing the residential community of <unk> to the east and meeting CR <unk> . It soon enters Hamilton Township , where the name of the road becomes Mill Street and it comes to a junction with US 40 ( Harding Highway ) . CR <unk> Truck heads to the west on US 40 at this point while Route 50 heads east on US 40 , and CR <unk> continues north . 
- US 40 and <unk> 50 follow the Harding Highway , a two @-@ lane undivided road , turning to the northeast and crossing the Great <unk> Harbor River . Upon passing over the river , enters Mays Landing and turns north , encountering CR <unk> before passing over an abandoned railroad line and heading into the downtown area of Mays Landing . Here , the road crosses CR <unk> , and US 40 splits from <unk> 50 by turning east along that road . Meanwhile , Route 50 continues north on Cape May Avenue , passing through developed areas . It leaves Mays Landing and heads into back into forests . The route turns to the northeast , passing near the <unk> Leaf Lakes residential development , before coming to an interchange with US 322 ( Black Horse Pike ) . 
- Past this interchange , Route 50 <unk> to a four @-@ lane divided highway and reaches a full interchange with the Atlantic City Expressway . The route becomes a two @-@ lane undivided road again and continues through inhabited areas , crossing into Galloway Township , where there is an intersection with CR <unk> . A short distance later , the road crosses New Jersey Transit ’ s Atlantic City Line near the <unk> Harbor City Station and enters <unk> Harbor City , turning into Philadelphia Avenue . A block after the railroad crossing , Route 50 ends at an intersection with US 30 and CR <unk> ( White Horse Pike ) , with CR <unk> continuing north on Philadelphia Avenue at this point . 
- Route 50 is an important route linking the Atlantic City Expressway with the Jersey Shore resorts in Cape May County , serving as an alternate to more <unk> routes . It also serves as evacuation route from Cape May County to inland areas in the event of a hurricane . 
- 
- = = History = = 
- 
- The portion of current Route 50 between <unk> and Petersburg received funding to become a spur of the Ocean Highway in 1910 . The modern @-@ day alignment of Route 50 was legislated as part of pre @-@ 1927 Route 14 in 1917 , a route that was to run from Cape May to <unk> Harbor City . In the 1927 New Jersey state highway renumbering , the portion of pre @-@ 1927 Route 14 between <unk> and <unk> Harbor City was designated as Route 50 , the highest state route number assigned in the renumbering . Route 50 retained its current routing in the 1953 New Jersey state highway renumbering . A freeway was proposed for Route 50 around 1960 , running from the Garden State Parkway in Dennis Township north to US 30 in <unk> Township . This proposed freeway was intended to provide economic development to the region as well as provide a better route to the Jersey Shore resorts of Cape May County for travelers from the Delaware Valley . However , this freeway was canceled by the mid @-@ 1970s due to financial and environmental concerns as well as lack of traffic along Route 50 . On November 21 , 2008 , construction began on making the interchange with the Atlantic City Expressway a full interchange as part of a series of improvements set for the Atlantic City Expressway . This project was completed on June 18 , 2010 . 
- 
- = = Major intersections = = 
- 
- 
- 
- = Air Rhodesia Flight <unk> = 
- 
- Air Rhodesia Flight <unk> was a scheduled passenger flight that was shot down by the Zimbabwe People 's Revolutionary Army ( <unk> ) on 3 September 1978 , during the Rhodesian Bush War . The aircraft involved , a Vickers Viscount named the <unk> , was flying the last leg of Air Rhodesia 's regular scheduled service from Victoria Falls to the capital Salisbury , via the resort town of <unk> . 
- Soon after Flight <unk> took off , a group of <unk> guerrillas scored a direct hit on its starboard wing with a Soviet @-@ made <unk> 2 surface @-@ to @-@ air infrared <unk> missile , critically damaging the aircraft and forcing an emergency landing . An attempted belly landing in a cotton field just west of <unk> was <unk> by an unseen ditch , which caused the plane to cartwheel and break up . Of the 52 passengers and four crew , 38 died in this crash ; the insurgents then approached the wreckage , rounded up the 10 survivors they could see and massacred them with automatic gunfire . Three passengers survived by hiding in the surrounding bush , while a further five lived because they had gone to look for water before the guerrillas arrived . 
- <unk> leader Joshua <unk> publicly claimed responsibility for shooting down the <unk> in an interview with the BBC 's Today programme the same evening , saying the aircraft had been used for military purposes , but denied that his men had killed survivors on the ground . The majority of <unk> , both black and white , saw the attack as an act of terrorism . A fierce white Rhodesian backlash followed against perceived enemies , with many whites becoming <unk> <unk> and suspicious of blacks in general , even though few black <unk> supported attacks of this kind . Reports viewing the attack negatively appeared in international journals such as Time magazine , but there was almost no acknowledgement of it by overseas governments , much to the Rhodesian government 's <unk> . 
- <unk> between <unk> and Prime Minister Ian Smith , which had been progressing <unk> , were immediately suspended by the furious <unk> , with Smith calling <unk> a " monster " . On 10 September , Smith announced the extension of martial law over selected areas . The Rhodesian Security Forces launched several retaliatory strikes into Zambia and Mozambique over the following months , attacking both <unk> and its rival , the Zimbabwe African National Liberation Army ( ZANLA ) . The attack on <unk> in particular brought great controversy as many of those killed were refugees camping in and around guerrilla positions . Five months later , in February 1979 , <unk> shot down Air Rhodesia Flight <unk> , another civilian flight , in an almost identical incident . 
- 
- = = Background = = 
- 
- A dispute over the terms for the granting of full sovereignty to the self @-@ governing colony of Rhodesia ( or Southern Rhodesia ) led the colonial government , headed by Prime Minister Ian Smith , to <unk> declare independence from the United Kingdom on 11 November 1965 . The idea of " no independence before majority rule " had recently gained ground in Britain and elsewhere amid decolonisation , and Rhodesia 's government was dominated by the country 's white minority , so the unilateral declaration went <unk> internationally . Britain and the United Nations imposed economic sanctions on Rhodesia . 
- Two rival communist @-@ backed black nationalist groups initiated military campaigns to overthrow the government and introduce majority rule : the Chinese @-@ aligned Zimbabwe African National Union ( <unk> ) , mostly comprising <unk> , created the Zimbabwe African National Liberation Army ( ZANLA ) and adopted aspects of <unk> doctrine , while the <unk> @-@ dominated Zimbabwe African People 's Union ( <unk> ) , aligned with Soviet @-@ style <unk> – <unk> and the Warsaw Pact , mobilised the Zimbabwe People 's Revolutionary Army ( <unk> ) . These guerrilla armies proceeded to wage what they called the " Second <unk> " against the Rhodesian government and security forces . The resulting conflict , the Rhodesian Bush War , began in earnest in December 1972 , when ZANLA attacked <unk> and <unk> <unk> in north @-@ eastern Rhodesia . 
- After the security forces mounted a successful counter @-@ insurgency campaign during 1973 and 1974 , developments overseas caused the conflict 's momentum to shift in the insurgents ' favour . The leftist Carnation Revolution of April 1974 caused Portugal to withdraw its key economic support for Smith 's administration , and led to Mozambique 's independence the following year as a communist state openly allied with <unk> . Around the same time , Rhodesia 's other main <unk> , South Africa , adopted a <unk> initiative that forced a ceasefire , giving the guerrillas time to regroup . Following the abortive Victoria Falls Conference of August 1975 , Smith and the <unk> leader Joshua <unk> held unsuccessful talks between December 1975 and March 1976 . <unk> and <unk> announced in October 1976 , during the run @-@ up to the unsuccessful Geneva Conference in December , that they would <unk> attend conferences as a joint " Patriotic Front " . 
- In March 1978 , Smith and non @-@ militant nationalist groups headed by Bishop <unk> <unk> , the Reverend <unk> <unk> and Chief Jeremiah <unk> agreed what became the " Internal Settlement " . This created a joint black – white transitional government , with the country due to be reconstituted as Zimbabwe Rhodesia in June 1979 , pursuant to <unk> elections . <unk> and <unk> were invited to participate , but refused ; <unk> <unk> dubbed Smith 's black colleagues " the blacksmiths " . <unk> proclaimed 1978 to be " The Year of the People " as the war continued . Officials from <unk> 's United African National Council , sent to the provinces to explain the Internal Settlement to rural blacks , were killed by Marxist – <unk> guerrillas . <unk> also began to target Christian missionaries , <unk> in the killing of nine British missionaries and four children at <unk> Mission near the Mozambican border on 23 June . 
- The transitional government was badly received abroad , partly because the Internal Settlement kept control of law enforcement , the military , the judiciary and the civil service under white control . No country recognised Rhodesia 's interim administration . Smith again worked to bring <unk> into the government , hoping this would lend it some <unk> domestically , prompt diplomatic recognition overseas , and help the security forces defeat ZANLA . Starting on 14 August 1978 , he attended secret meetings with <unk> in <unk> , Zambia ( where <unk> was based ) , doing so with the assistance of the mining corporation <unk> . Attempts were made to also involve the <unk> leader Robert <unk> , but <unk> would have no part in the talks . According to the South African military historian <unk> <unk> , negotiations between Smith and <unk> progressed well and " seemed on the verge of success " by the start of September 1978 . On 2 September , Smith and <unk> revealed publicly that the secret meetings had taken place . 
- 
- = = Incident = = 
- 
- 
- = = = Prior threats to Rhodesian air traffic = = = 
- 
- Rhodesian air traffic was not seriously threatened until about 1977 , in the latter stages of the war ; before this time , neither revolutionary force had the weapons to launch a viable attack against an aerial target . The weapon that made such attacks feasible for <unk> was the <unk> 2 shoulder @-@ launched surface @-@ to @-@ air missile launcher , supplied by the Soviet Union from the mid @-@ 1970s as part of the Warsaw Pact 's materiel support . By September 1978 , there had been 20 reported attempts to shoot down Rhodesian military aircraft using these weapons , none of which had been successful . Some Rhodesian Air Force Dakotas had been hit , but all had survived and landed safely . No civilian aircraft had yet been targeted during the Bush War . 
- 
- = = = Flight = = = 
- 
- Air Rhodesia was the country 's national airline , established by the government on 1 September 1967 to succeed Central African Airways , which was dissolved at the end of that year . Based at Salisbury Airport , Air Rhodesia 's flight network during the late 1970s comprised a domestic programme of passenger and cargo flights , as well as international services to the South African cities of Johannesburg and Durban . 
- The Flight <unk> aircraft was a Vickers Viscount <unk> , a British @-@ made turboprop aircraft with two engines on each wing . It was named the <unk> after the river of the same name , which flowed between Lake <unk> and the Rhodesian capital Salisbury . 
- The <unk> was on the second and final leg of its regular scheduled journey between Victoria Falls and Salisbury , stopping over in the resort town of <unk> . Despite the occasional rocket and mortar attacks launched on <unk> by <unk> guerrillas on the northern side of the <unk> ( in Zambia ) , the resort had endured as one of Rhodesia 's choice tourist destinations . The flight on 3 September 1978 , Sunday afternoon , from <unk> to Salisbury carried four crew members and 52 passengers , most of whom were <unk> from Salisbury , returning home after a weekend at the lake . The flight took off from <unk> Airport on schedule soon after 17 : 00 Central Africa Time . 
- Flight <unk> was <unk> by 36 @-@ year @-@ old Captain John Hood , a native of <unk> who had gained his commercial pilot licence in 1966 . He had flown <unk> for Air Rhodesia since 1968 , and had also served in the Rhodesian Air Force on a voluntary basis . His first officer , Garth Beaumont , was 31 years old , and had lived in Rhodesia for most of his life , having immigrated as a child from South Africa . The two air <unk> were <unk> <unk> , 21 years old and from <unk> , and 23 @-@ year @-@ old Louise Pearson , from Salisbury . 
- 
- = = = <unk> = = = 
- 
- A group of <unk> guerrillas , armed with a <unk> @-@ 2 launcher , waited in the bush beneath Flight <unk> 's <unk> , and fired on the <unk> about five minutes after it took off , while the aircraft was still in the climb phase of its flight . The heat @-@ seeking missile hit the plane 's starboard wing and exploded , causing the inner engine to also explode . A fuel tank and hydraulic lines <unk> , creating a fire that the passengers and crew could not put out . The second starboard engine failed almost immediately , leaving Hood with only his two port engines . <unk> wildly , the <unk> began to descend rapidly . 
- At 17 : 10 Captain Hood sent a distress call to air traffic control , informing them that he had lost the two starboard engines and was going to crash . " We 're going in , " he <unk> . <unk> his passengers to <unk> for an emergency landing , he aimed for an open field of cotton in the <unk> Hills , in the bush to the west of <unk> , intending to belly land the craft . The landing was relatively stable until the <unk> hit a previously unseen ditch , <unk> and exploded . The remaining fuel tanks <unk> and caught fire , setting the wrecked cabin <unk> . 
- 
- = = = Massacre on the ground = = = 
- 
- Of the 56 people on board , 38 , including Hood and Beaumont , died in the crash . Eighteen survived , albeit with injuries , and climbed out of the wreckage . After briefly settling the others , one of the passengers , Dr Cecil <unk> , led four others — young <unk> Robert and Shannon <unk> , Mrs Sharon <unk> , and her four @-@ year @-@ old daughter Tracey — off in the direction of a nearby village in search of water . The other 13 remained close to the wreckage . Meanwhile , nine guerrillas made their way towards the crash site , and reached it at about 17 : 45 . Three of the 13 survivors remaining at the crash site hid on seeing figures approaching : Rhodesian Army <unk> Anthony Hill , 39 , took cover in the surrounding bush , while businessman Hans Hansen and his wife Diana did the same . This left 10 passengers in full view near the wreckage , including four women and two girls ( aged 11 and 4 ) . 
- The guerrillas , who were armed with AK @-@ 47 rifles , presented themselves to the 10 passengers as friendly , saying they would summon help and bring water . They spoke in English , both to the survivors and among themselves . They told the passengers to <unk> around a point a few metres from the wreckage ; when the survivors said that some of them were too badly injured to walk , the insurgents told the able @-@ bodied men to carry the others . The passengers were assembled into an area of about 10 square metres ( 110 sq ft ) . Standing roughly 15 metres ( 49 ft ) away , the cadres now raised their weapons . " You have taken our land , " one of them said . " Please don 't shoot us ! " one of the passengers cried , just before they were killed by a sustained burst of automatic gunfire . 
- Having collected water from the nearby village , <unk> and his companions were almost back at the crash site when they heard the shots . <unk> it was personal ammunition in the luggage exploding in the heat , they continued on their way , and called out to the other passengers , who they thought were still alive . This alerted the insurgents to the presence of more survivors ; one of the guerrillas told <unk> 's group to " come here " . The insurgents then opened fire on their general location , prompting <unk> and the others to flee . Hill and the <unk> also ran ; they revealed their positions to the fighters in their <unk> , but successfully hid themselves behind a ridge . After Hill and the others had hidden there for about two hours , they saw the attackers return to the crash site at about 19 : 45 . The guerrillas looted the wrecked cabin and some of the <unk> <unk> around the site , filled their arms with various passengers ' belongings , then left again . 
- The survivors were found over the following days by the Rhodesian Army and police ; Hill and Mr and Mrs Hansen were taken to <unk> Hospital , while <unk> and his group were <unk> to Andrew Fleming Hospital in Salisbury . 
- 
- = = = <unk> claims responsibility , but denies killing survivors = = = 
- 
- <unk> claimed responsibility for the attack in an interview with the BBC 's Today radio programme the next day , laughing as he did so , to the horror of most Rhodesian observers , both black and white . He said that he had received intelligence that the <unk> was being used for military purposes . <unk> said he regretted the deaths as it was not his party 's policy to kill civilians , and denied that his men had killed any survivors on the ground ; by contrast , he said that his men had helped them , and had left them alive . He also accused Air Rhodesia of <unk> <unk> troops and war materiel for the government , an allegation that Captain Pat Travers , Air Rhodesia 's general manager , called a " downright , deliberate lie " . 
- According to <unk> <unk> , a professor and human rights speaker who wrote a history of <unk> , <unk> was implying that responsibility for the massacre actually lay with security force pseudo @-@ guerrillas , more specifically the mixed race <unk> Scouts unit , which had often been accused of <unk> rural civilians with the goal of shifting public opinion . <unk> asserts that the massacre " cannot be put beyond " the Scouts , and also supports <unk> 's claim that the <unk> had been used militarily , suggesting that <unk> might have believed there to be Rhodesian soldiers on board . " Rhodesian television , before attacks on ZANLA in Mozambique , had shown <unk> ferrying paratroopers for the job , " he writes , " ... [ and ] <unk> intelligence knew there were paratroopers stationed [ at Victoria Falls ] " . 
- 
- = = Reactions = = 
- 
- 
- = = = White <unk> turn against blacks = = = 
- 
- A report published in the American magazine Time a fortnight later described the incident as " a genuine horror story , calculated to make the most <unk> of Rhodesian <unk> <unk> seem true . " Indeed , the white community in Rhodesia heard the news with <unk> , and many turned their minds to exacting <unk> for what they and many others saw as an act of terrorism . The prominent Indian @-@ Rhodesian <unk> family was particularly affected by the incident , having lost eight members in the attack . Though Rhodesian authorities did not immediately acknowledge the cause of the crash , doing so only after four days ' investigation , the truth was common knowledge in Salisbury within hours . Smith wrote in his memoirs that the " degree of anger ... [ was ] difficult to control " . White South Africans were similarly enraged , particularly after reports appeared in the South African press that the <unk> had raped the female passengers before <unk> them . A Friends of Rhodesia Society in South Africa offered a reward of <unk> to anybody who would either kill <unk> or bring him to Salisbury to stand trial . 
- Geoffrey <unk> , who was then one of the few black reporters at the Rhodesia Herald newspaper , later wrote in his memoirs that many whites became <unk> and wary towards blacks in general , believing them all to be " terrorist <unk> " . Describing the Herald <unk> the night of the incident , he relates a " <unk> collective temper " among the white sub @-@ editors : " They cursed until their voices became <unk> , threatening dire consequences for all <unk> and <unk> or <unk> ... I <unk> that some of the more derogatory remarks made in <unk> loud voices that evening were meant specifically for my ears . " 
- Several racially motivated incidents occurred over the following days . According to the Time article , a group of whites entered an <unk> Salisbury bar " fingering the <unk> of rifles " and forced blacks drinking there to leave . Time also reported a <unk> that two white youths , on learning of the massacre , shot the first black man they saw . Smith says that several would @-@ be <unk> groups sought his permission to venture into the bush around the crash site to " make the local people pay for their crime of <unk> and assisting the terrorists " . He instructed them not to , according to his memoirs , telling them that many rural blacks only assisted the guerrillas under extreme <unk> , and that it would not do to attack them . Many <unk> also resented the apparent lack of sympathy emanating from overseas governments , especially considering the character of the attack and its civilian target . 
- 
- = = = Memorial service , 8 September 1978 = = = 
- 
- At a memorial service held on 8 September 1978 for Flight <unk> 's passengers and crew at Salisbury 's Anglican Cathedral , about 2 @,@ 000 people crowded inside , with another 500 standing outside on the steps and pavement , many listening to the service inside on portable radio sets . <unk> among those present in the cathedral were <unk> Air Rhodesia and South African Airways personnel , as well as Rhodesian Special Air Service soldiers and senior officers from other military units . Smith and several government ministers also attended , including P K van der <unk> , the co @-@ Minister of Foreign Affairs . 
- Dean John de Costa gave a sermon <unk> what he described as a " <unk> silence " from overseas . " Nobody who holds sacred the dignity of human life can be anything but <unk> at the events attending the Viscount , " he said . " But are we <unk> with the voice of protest from nations who call themselves <unk> ? We are not ! Like men in the story of the Good <unk> , they pass by on the other side ... The <unk> of this ill @-@ <unk> flight from <unk> will be burnt upon our memories for years to come . For others , far from our borders , it is an intellectual matter , not one which affects them deeply . Here is the tragedy ! " 
- 
- = = = Smith – <unk> talks halted = = = 
- 
- The talks between Smith and the <unk> leader that had been progressing so <unk> were immediately halted by Salisbury . Smith himself called <unk> a " monster " . <unk> comments that the ending of the Smith – <unk> talks at this time was " potentially the most serious result of the Viscount massacre " , as the talks had been progressing well before the incident . He <unk> that an agreement between the two " at this critical stage " might have helped the transitional Rhodesian government to secure international recognition . 
- On 10 September , the Prime Minister announced to the nation that certain areas of the country would be placed under a variation of martial law , which he said would be applied in particular regions as and when needed . He declared Rhodesia 's intent to " <unk> the internal workings of those organisations associated with terrorism " , and warned neighbouring countries to prepare for " any defensive strikes we might undertake " against guerrilla bases in their respective territories . He claimed that the war had escalated because Britain and the United States were supporting the Patriotic Front . William Irvine , the co @-@ Minister of Transport , warned the guerrillas that Rhodesia " w [ <unk> ] not let these <unk> go <unk> " . 
- 
- = = Rhodesia strikes back = = 
- 
- 
- = = = Operation <unk> ; Rhodesia hits New <unk> = = = 
- 
- Because <unk> and <unk> were based in Zambia , many <unk> <unk> for a massive retaliatory strike against terrorist targets in that country , but the first external target hit by the security forces following the Viscount <unk> was the prominent cluster of ZANLA bases around <unk> in Mozambique . The Rhodesian military had struck these bases extensively in November 1977 during Operation <unk> , destroying much of the ZANLA presence there , but the insurgents had since built a complex called " New <unk> " , slightly to the east ; the new camps were distributed across a far larger area than the originals . In a combined airborne @-@ ground assault called Operation <unk> , the Rhodesian Air Force , Rhodesian Light Infantry and Special Air Service wiped out much of New <unk> on 20 September 1978 . Mozambique sent armour to ZANLA 's aid in the form of nine Soviet @-@ made T @-@ 54 tanks and four Russian <unk> @-@ 152 armoured personnel carriers , but the former were routed and one of the latter destroyed by the Rhodesian security forces . According to Rhodesian figures , there were " several hundred " guerrillas killed , while the security forces lost only two soldiers , one of whom was accidentally killed by a friendly air strike . 
- 
- = = = Operation <unk> ; the " Green Leader " raid = = = 
- 
- Rhodesia then attacked <unk> 's bases in Zambia , in what Group Captain Peter <unk> @-@ Bowyer later described as " <unk> time " for Flight <unk> . Operation <unk> , launched on 19 October 1978 , was another joint @-@ force operation between the Air Force and the Army , which contributed Special Air Service and Rhodesian Light Infantry paratroopers . <unk> 's primary target , just 16 kilometres ( 10 miles ) north @-@ east of central <unk> , was the formerly white @-@ owned <unk> Farm , which had been transformed into <unk> 's main headquarters and training base under the name " Freedom Camp " . <unk> presumed that Rhodesia would never <unk> to attack a site so close to <unk> . About 4 @,@ 000 guerrillas underwent training at Freedom Camp , with senior <unk> staff also on site . The Rhodesian operation 's other targets were <unk> , 19 kilometres ( 12 miles ) north of <unk> , and <unk> Camp ; all three were to be attacked more or less simultaneously in a coordinated sweep across Zambia . <unk> targets deep inside Zambia was a first for the Rhodesian forces ; previously only guerrillas near the border had been attacked . 
- <unk> by Squadron Leader Chris Dixon , who identified himself to <unk> Airport tower as " Green Leader " , a Rhodesian Air Force group flew into Zambia at very low altitudes ( thereby avoiding <unk> radar ) and took control of the country 's airspace for about a quarter of an hour during the initial assault on <unk> Farm , informing <unk> tower that the attack was against " Rhodesian dissidents , and not against Zambia " , and that Rhodesian Hawker Hunters were <unk> the <unk> airfields under orders to shoot down any fighter that attempted to take off . The <unk> <unk> all of Green Leader 's instructions , made no attempt to resist and temporarily halted civil air traffic . Using <unk> airstrip in eastern Zambia as a forward base , the Rhodesian military suffered only minor casualties during the three @-@ day operation , and afterwards claimed to have killed over 1 @,@ 500 <unk> personnel , as well as some Cuban instructors . 
- Historians Paul <unk> and Peter <unk> write that this exaggerated considerably the actual number of guerrillas killed , as most of <unk> 's army , then numbering about 10 @,@ 000 fighters , had not been touched . On the other hand , unarmed refugees often camped in or around insurgent positions , and hundreds of these had been killed in the Rhodesian raid . <unk> and <unk> comment that for the Rhodesian airmen , it would have been " impossible to distinguish innocent refugees from young <unk> recruits . " <unk> describes Freedom Camp as " a refugee camp for boys " , and says " <unk> boys and girls " were killed . He claims that the Red Cross and the UN <unk> Agency " confirmed <unk> 's claim that Smith 's forces struck at <unk> , civilian <unk> " . 
- 
- = = Aftermath = = 
- 
- The Rhodesian attacks on ZANLA and <unk> bases did much to restore white morale following the Viscount incident , though they had not actually made much impact on the respective guerrilla campaigns . <unk> and the <unk> President Kenneth <unk> all the same requested further military aid and better weapons from the Soviets and the British respectively . Martial law was quickly extended across Rhodesia 's rural areas , and covered three @-@ quarters of the country by the end of 1978 . Air Rhodesia , meanwhile , began developing anti @-@ <unk> shielding for its <unk> . Before this work was completed , <unk> shot down a second Viscount , Air Rhodesia Flight <unk> , on 12 February 1979 . This time there were no survivors . 
- Following the second <unk> , Air Rhodesia created a system whereby the underside of the <unk> would be coated with low @-@ radiation paint , with the exhaust pipes concurrently <unk> . According to tests conducted by the Air Force , a Viscount so treated could not be detected by the <unk> 's targeting system once it was over 2 @,@ 000 feet ( 610 m ) . There were no further Viscount <unk> in Rhodesia . 
- In the elections held the following year under the Internal Settlement terms , boycotted by <unk> and <unk> , <unk> won a majority , and became the first Prime Minister of the reconstituted , majority @-@ ruled state of Zimbabwe Rhodesia on 1 June 1979 . This new order failed to win international acceptance , however , and in December 1979 the Lancaster House Agreement was agreed in London by Zimbabwe Rhodesia , the UK government and the Patriotic Front , returning the country to its former colonial status . The UK government suspended the constitution and took direct control for an interim period . Fresh elections were won by <unk> , who took power in April 1980 , concurrently with the country 's recognised independence as Zimbabwe . 
- 
- = = Legacy and memorial = = 
- 
- In modern Zimbabwe , it is not the Viscount <unk> but rather the <unk> Rhodesian strikes against nationalist guerrilla camps that endure predominantly in the cultural memory . State media in Zimbabwe , prominently the Herald newspaper , often claim that Rhodesian forces <unk> and deliberately <unk> thousands of helpless refugees during such operations . <unk> 's <unk> of civilian planes is meanwhile portrayed as a legitimate act of war on the grounds that the guerrillas might have believed them to have military personnel or equipment on board . The massacre of surviving Flight <unk> passengers at the crash site is often omitted or attributed to forces other than <unk> ; in his 1984 memoirs <unk> repeated his claim that <unk> fighters had helped the survivors , and wrote simply that " I truly have no idea how the ten died " . 
- A monument to those killed in the Rhodesian attack on <unk> was built in Zambia in 1998 , and dedicated jointly by the <unk> and <unk> governments . A memorial to the victims of the two Rhodesian Viscount incidents , dubbed the Viscount Memorial , was erected on the grounds of the <unk> Monument in <unk> , South Africa in 2012 , and inaugurated on 1 September that year . The names of the dead passengers and crew are engraved on two granite slabs that stand upright , side by side , the pair topped by an emblem symbolising an aircraft . A pole beside the monument flies the Rhodesian flag . 
- A British parliamentary motion put forward by Labour MP Kate <unk> in February 2013 to <unk> <unk> the Viscount attacks and <unk> the victims on the anniversary of the second <unk> prompted <unk> in the <unk> press , with the Herald branding it a racist " <unk> motion " intended to <unk> the <unk> administration and its supporters . <unk> <unk> , a former <unk> commander , described the move as a <unk> motivated by race that went against the spirit of the amnesty enacted at Lancaster House . Christopher <unk> , a diplomat and political analyst , took a similar line , calling <unk> 's proposed commemoration " a <unk> ... [ that ] means they only regard the death of white people alone during the struggle . There is no mention of what happened to our sons and daughters in and outside the country . " <unk> 's official response to <unk> 's motion included the assertion that in all conflicts " civilians get caught up in cross @-@ fires due to faulty intelligence reports and other communication errors " , and juxtaposed the civilians killed in the Viscount <unk> with those killed at <unk> . 
- 
- = = = Newspaper and journal articles = = = 
- 
- 
- 
- = <unk> = 
- 
- <unk> ( / <unk> / ) ( <unk> ’ al in modern <unk> <unk> ) is the ruins of an ancient city found in a <unk> in Guatemala . <unk> <unk> , a gum @-@ <unk> , reported the ruins to La <unk> , a <unk> newspaper , which named the site <unk> . After the Berlin Academy of Sciences ' magazine republished the report in 1853 , <unk> and treasure hunters began visiting the forest . Today tourism to the site may help protect the <unk> . It is one of the largest archaeological sites and urban centers of the pre @-@ Columbian Maya civilization . It is located in the archaeological region of the <unk> Basin in what is now northern Guatemala . Situated in the department of El <unk> , the site is part of Guatemala 's <unk> National Park and in 1979 it was declared a UNESCO World Heritage Site . 
- <unk> was the capital of a conquest state that became one of the most powerful kingdoms of the ancient Maya . Though monumental architecture at the site dates back as far as the 4th century BC , <unk> reached its <unk> during the Classic Period , c . 200 to 900 AD . During this time , the city dominated much of the Maya region politically , economically , and militarily , while interacting with areas throughout <unk> such as the great metropolis of <unk> in the distant Valley of Mexico . There is evidence that <unk> was conquered by <unk> in the 4th century AD . Following the end of the Late Classic Period , no new major monuments were built at <unk> and there is evidence that elite palaces were burned . These events were coupled with a gradual population decline , culminating with the site ’ s abandonment by the end of the 10th century . 
- <unk> is the best understood of any of the large lowland Maya cities , with a long dynastic ruler list , the discovery of the tombs of many of the rulers on this list and the investigation of their monuments , temples and palaces . 
- 
- = = Etymology = = 
- 
- The name <unk> may be derived from <unk> <unk> <unk> in the <unk> Maya language ; it is said to be a relatively modern name meaning " at the <unk> " . The name was apparently applied to one of the site 's ancient reservoirs by hunters and travelers in the region . It has alternatively been interpreted as meaning " the place of the voices " in the <unk> Maya language . <unk> , however , is not the ancient name for the site but rather the name adopted shortly after its discovery in the 1840s . <unk> inscriptions at the ruins refer to the ancient city as <unk> <unk> or <unk> <unk> , meaning " First <unk> " . <unk> may have come to have been called this because <unk> <unk> also came to use the same emblem glyph ; the rulers of the city presumably wanted to distinguish themselves as the first city to bear the name . The kingdom as a whole was simply called <unk> , which is the reading of the " hair bundle " emblem glyph seen in the accompanying photo . Its precise meaning remains obscure . 
- 
- = = Location = = 
- 
- The closest large modern settlements are Flores and Santa <unk> , approximately 64 kilometres ( 40 mi ) by road to the southwest . <unk> is approximately <unk> kilometres ( 188 mi ) north of Guatemala City . It is 19 kilometres ( 12 mi ) south of the contemporary Maya city of <unk> and 30 kilometres ( 19 mi ) northwest of <unk> . The city was located 100 kilometres ( 62 mi ) southeast of its great Classic Period rival , <unk> , and 85 kilometres ( 53 mi ) northwest of <unk> 's ally <unk> , now in Belize . 
- The city has been completely mapped and covered an area greater than 16 square kilometres ( 6 @.@ 2 sq mi ) that included about 3 @,@ 000 structures . The topography of the site consists of a series of parallel limestone ridges rising above swampy lowlands . The major architecture of the site is clustered upon areas of higher ground and linked by raised <unk> spanning the swamps . The area around <unk> has been declared as the <unk> National Park and the preserved area covers 570 square kilometres ( 220 sq mi ) . 
- The ruins lie among the tropical rainforests of northern Guatemala that formed the <unk> of lowland Maya civilization . The city itself was located among abundant fertile upland soils , and may have dominated a natural east – west trade route across the <unk> Peninsula . <unk> trees at the <unk> park include gigantic <unk> ( <unk> <unk> ) the sacred tree of the Maya ; tropical cedar ( <unk> <unk> ) , and Honduras mahogany ( <unk> <unk> ) . Regarding the fauna , <unk> , white @-@ <unk> <unk> , gray foxes , <unk> 's spider monkeys , <unk> monkeys , <unk> eagles , falcons , <unk> <unk> , <unk> , <unk> , green parrots and <unk> ants can be seen there regularly . <unk> , <unk> , and cougars are also said to <unk> in the park . For centuries this city was completely covered under jungle . The average annual rainfall at <unk> is 1 @,@ <unk> millimetres ( 76 @.@ 6 in ) . 
- One of the largest of the Classic Maya cities , <unk> had no water other than what was collected from <unk> and stored in ten reservoirs . Archaeologists working in <unk> during the 20th century refurbished one of these ancient reservoirs to store water for their own use . 
- The <unk> National Park covers an area of 575 @.@ 83 square kilometres ( 222 @.@ 33 sq mi ) . It was created on 26 May 1955 under the auspices of the <unk> de <unk> e Historia and was the first protected area in Guatemala . 
- 
- = = Population = = 
- 
- Population estimates for <unk> vary from 10 @,@ 000 to as high as 90 @,@ 000 inhabitants , with the most likely figure being at the upper end of this range . 
- The population of <unk> began a continuous curve of growth starting in the <unk> Period ( approximately 2000 BC – AD 200 ) , with a peak in the Late Classic with the population growing rapidly from AD 700 through to 830 , followed by a sharp decline . For the 120 square kilometres ( 46 sq mi ) area falling within the <unk> defenses of the hinterland , the peak population is estimated at <unk> per square kilometer ( <unk> per square mile ) . In an area within a 12 kilometres ( 7 @.@ 5 mi ) radius of the site core , peak population is estimated at 120 @,@ 000 ; population density is estimated at 265 per square kilometer ( <unk> per square mile ) . In a region within a 25 kilometres ( 16 mi ) radius of the site core and including some satellite sites , peak population is estimated at 425 @,@ 000 with a density of 216 per square kilometer ( 515 per square mile ) . These population figures are even more impressive because of the extensive <unk> that were unsuitable for habitation or agriculture . However , some archaeologists , such as David Webster , believe these figures to be far too high . 
- 
- = = Rulers = = 
- 
- The dynastic line of <unk> , founded as early as the 1st century AD , spanned 800 years and included at least 33 rulers . 
- 
- = = History = = 
- 
- 
- = = = <unk> = = = 
- 
- There are traces of early agriculture at the site dating as far back as 1000 BC , in the Middle <unk> . A <unk> of <unk> ceramics dating from about 700 @-@ 400 BC were found in a sealed <unk> , a subterranean bottle @-@ shaped chamber . 
- Major construction at <unk> was already taking place in the Late <unk> period , first appearing around 400 – 300 BC , including the building of major pyramids and platforms , although the city was still <unk> by sites further north such as El <unk> and <unk> . At this time , <unk> participated in the widespread <unk> culture that dominated the Central and Northern Maya areas at this time – a region that included the entire <unk> Peninsula including northern and eastern Guatemala and all of Belize . 
- Two temples dating to Late <unk> times had masonry @-@ walled <unk> that may have been <unk> @-@ vaulted , although this has not been proven . One of these had elaborate paintings on the outer walls showing human figures against a <unk> background , painted in yellow , black , pink and red . 
- In the 1st century AD rich burials first appeared and <unk> underwent a political and cultural <unk> as its giant northern neighbors declined . At the end of the Late <unk> , the <unk> style art and architecture from the Pacific Coast began to influence <unk> , as demonstrated by a broken sculpture from the <unk> and early murals at the city . 
- 
- = = = Early Classic = = = 
- 
- <unk> rulership among the lowland Maya is most deeply rooted at <unk> . According to later hieroglyphic records , the dynasty was founded by <unk> @-@ <unk> @-@ <unk> , perhaps in the 3rd century AD . At the beginning of the Early Classic , power in the Maya region was concentrated at <unk> and <unk> , in the core of the Maya <unk> . 
- <unk> may have benefited from the collapse of the large <unk> states such as El <unk> . In the Early Classic <unk> rapidly developed into the most dynamic city in the Maya region , <unk> the development of other nearby Maya cities . 
- The site , however , was often at war and inscriptions tell of <unk> and conflict with other Maya states , including <unk> , <unk> , <unk> and <unk> . The site was defeated at the end of the Early Classic by <unk> , which rose to take <unk> 's place as the paramount center in the southern Maya lowlands . The earlier part of the Early Classic saw hostilities between <unk> and its neighbor <unk> , with <unk> recording the capture of prisoners from <unk> . 
- There appears to have been a breakdown in the male succession by AD 317 , when Lady <unk> <unk> conducted a <unk> @-@ ending ceremony , apparently as queen of the city . 
- 
- = = = = <unk> and <unk> = = = = 
- 
- The fourteenth king of <unk> was <unk> <unk> Ich 'aak ( Great Jaguar <unk> ) . <unk> <unk> Ich 'aak built a palace that was preserved and developed by later rulers until it became the core of the Central Acropolis . Little is known about <unk> <unk> Ich 'aak except that he was killed on 14 January <unk> AD . On the same day , <unk> K ’ <unk> ’ ( Fire Is Born ) arrived from the west , having passed through El Peru , a site to the west of <unk> , on 8 January . On Stela 31 he is named as " Lord of the West " . <unk> K ’ <unk> ’ was probably a foreign general serving a figure represented by a non @-@ Maya <unk> of a <unk> combined with an owl , a glyph that is well known from the great metropolis of <unk> in the distant Valley of Mexico . <unk> Owl may even have been the ruler of <unk> . These recorded events strongly suggest that <unk> K ’ <unk> ’ led a <unk> invasion that defeated the native <unk> king , who was captured and immediately executed . <unk> K <unk> ' appears to have been aided by a powerful political faction at <unk> itself ; roughly at the time of the conquest , a group of <unk> natives were apparently residing near the Lost World complex . He also exerted control over other cities in the area , including <unk> , where he became king , but did not take the throne of <unk> for himself . Within a year , the son of <unk> Owl by the name of <unk> <unk> <unk> I ( First Crocodile ) had been installed as the tenth king of <unk> while he was still a boy , being enthroned on 13 September <unk> . He reigned for 47 years as king of <unk> , and remained a vassal of <unk> K <unk> ' for as long as the latter lived . It seems likely that <unk> <unk> <unk> I took a wife from the <unk> , defeated , <unk> dynasty and thus <unk> the right to rule of his son , <unk> Chan K 'awiil II . 
- <unk> <unk> , a small site 100 kilometres ( 62 mi ) northeast of <unk> , was conquered by the latter during the reign of <unk> <unk> <unk> I. The site became an outpost of <unk> , shielding it from hostile cities further north , and also became a trade link to the Caribbean . 
- Although the new rulers of <unk> were foreign , their descendants were rapidly <unk> . <unk> became the key ally and trading partner of <unk> in the Maya lowlands . After being conquered by <unk> , <unk> rapidly dominated the northern and eastern <unk> . <unk> , together with smaller towns in the region , were absorbed into <unk> 's kingdom . Other sites , such as <unk> and <unk> de San José near Lake <unk> <unk> became vassals of their more powerful neighbor to the north . By the middle of the 5th century <unk> had a core territory of at least 25 kilometres ( 16 mi ) in every direction . 
- Around the 5th century an impressive system of fortifications consisting of ditches and earthworks was built along the northern periphery of <unk> 's hinterland , joining up with the natural defenses provided by large areas of <unk> lying to the east and west of the city . Additional fortifications were probably also built to the south . These defenses protected <unk> 's core population and agricultural resources , <unk> an area of approximately 120 square kilometres ( 46 sq mi ) . Recent research suggests that the earthworks served as a water collection system rather than a defensive purpose . 
- 
- = = = = <unk> and <unk> = = = = 
- 
- In the 5th century the power of the city reached as far south as <unk> , whose founder K 'inich <unk> K <unk> ' Mo ' was clearly connected with <unk> . <unk> itself was not in an ethnically Maya region and the founding of the <unk> dynasty probably involved the direct intervention of <unk> . K 'inich <unk> K <unk> ' Mo ' arrived in <unk> in December 426 and bone analysis of his remains shows that he passed his childhood and youth at <unk> . An individual known as <unk> K <unk> ' Mo ' ( lord K <unk> ' Mo ' ) is referred to in an early text at <unk> and may well be the same person . His tomb had <unk> characteristics and he was depicted in later portraits dressed in the warrior <unk> of <unk> . <unk> texts refer to him as " Lord of the West " , much like <unk> K ’ <unk> ’ . At the same time , in late 426 , <unk> founded the nearby site of <unk> , possibly sponsored by <unk> itself . The founding of these two centers may have been part of an effort to impose <unk> 's authority upon the southeastern portion of the Maya region . The interaction between these sites and <unk> was intense over the next three centuries . 
- A long @-@ running rivalry between <unk> and <unk> began in the 6th century , with each of the two cities forming its own network of mutually hostile <unk> <unk> against each other in what has been likened to a long @-@ running war between two Maya <unk> . The kings of these two capitals adopted the title <unk> ' , a term that has not been precisely translated but that implies something akin to " high king " . 
- The early 6th century saw another queen ruling the city , known only as the " Lady of <unk> " , who was very likely a daughter of <unk> <unk> Ich 'aak II . She seems never to have ruled in her own right , rather being partnered with male co @-@ rulers . The first of these was <unk> ' B <unk> , who seems to have had a long career as a general at <unk> before becoming co @-@ ruler and 19th in the dynastic sequence . The Lady of <unk> herself seems not have been counted in the dynastic numbering . It appears she was later paired with lord " Bird Claw " , who is presumed to be the otherwise unknown 20th ruler . . 
- 
- = = = Late Classic = = = 
- 
- 
- = = = = <unk> hiatus = = = = 
- 
- In the mid 6th century , <unk> seems to have allied with <unk> and defeated <unk> , closing the Early Classic . The " <unk> hiatus " refers to a period between the late 6th to late 7th century where there was a lapse in the writing of inscriptions and large @-@ scale construction at <unk> . In the latter half of the 6th century AD , a serious crisis <unk> the city , with no new stelae being erected and with widespread deliberate mutilation of public sculpture . This hiatus in activity at <unk> was long unexplained until later <unk> <unk> identified that the period was prompted by <unk> 's comprehensive defeat at the hands of <unk> and the <unk> polity in AD 562 , a defeat that seems to have resulted in the capture and sacrifice of the king of <unk> . The badly eroded Altar 21 at <unk> described how <unk> suffered this disastrous defeat in a major war in April 562 . It seems that <unk> was an ally of <unk> in the wider conflict between that city and <unk> , with the defeat of <unk> having a lasting impact upon the city . <unk> was not sacked but its power and influence were broken . After its great victory , <unk> grew rapidly and some of <unk> 's population may have been forcibly relocated there . During the hiatus period , at least one ruler of <unk> took refuge with <unk> ' <unk> of Palenque , another of <unk> 's victims . <unk> itself <unk> during <unk> 's long hiatus period . 
- The beginning of the <unk> hiatus has served as a marker by which archaeologists commonly <unk> the Classic period of <unk> chronology into the Early and Late Classic . 
- 
- = = = = <unk> and <unk> <unk> = = = = 
- 
- In <unk> <unk> founded <unk> <unk> , some 110 kilometres ( 68 mi ) to the southwest , as a military outpost in order to control trade along the course of the <unk> River . B <unk> Chan K 'awiil was installed on the throne of the new outpost at the age of four , in 635 , and for many years served as a loyal vassal fighting for his brother , the king of <unk> . Roughly twenty years later <unk> <unk> was attacked by <unk> and was soundly defeated . B <unk> Chan K 'awiil was captured by the king of <unk> but , instead of being sacrificed , he was re @-@ instated on his throne as a vassal of his former enemy , and attacked <unk> in <unk> , forcing <unk> <unk> Chaak , the then king of <unk> , to temporarily abandon the city . The first two rulers of <unk> <unk> continued to use the <unk> emblem glyph of <unk> , and they probably felt that they had a legitimate claim to the throne of <unk> itself . For some reason , B <unk> Chan K 'awiil was not installed as the new ruler of <unk> ; instead he stayed at <unk> <unk> . <unk> <unk> against <unk> <unk> in <unk> , driving B <unk> Chan K 'awiil into an exile that lasted five years . <unk> tried to encircle <unk> within an area dominated by its allies , such as El Peru , <unk> <unk> and <unk> . 
- In 682 , <unk> Chan K 'awiil I erected the first dated monument at <unk> in 120 years and claimed the title of <unk> ' , so ending the hiatus . He initiated a programme of new construction and turned the tables on <unk> when , in <unk> , he captured the enemy noble and threw the enemy state into a long decline from which it never fully recovered . After this , <unk> never again erected a monument celebrating a military victory . 
- 
- = = = = <unk> after <unk> = = = = 
- 
- By the 7th century , there was no active <unk> presence at any Maya site and the center of <unk> had been razed by 700 . Even after this , formal war attire illustrated on monuments was <unk> style . <unk> Chan K 'awiil I and his heir <unk> 'in Chan K 'awiil continued hostilities against <unk> and its allies and imposed firm regional control over the area around <unk> , extending as far as the territory around Lake <unk> <unk> . These two rulers were responsible for much of the impressive architecture visible today . 
- In 738 , <unk> , a vassal of <unk> , <unk> 's key ally in the south , switched allegiance to <unk> , defeated <unk> and gained its own independence . It appears that this was a conscious effort on the part of <unk> to bring about the collapse of <unk> 's southern allies . This upset the balance of power in the southern Maya area and lead to a steady decline in the fortunes of <unk> . 
- In the 8th century , the rulers of <unk> collected monuments from across the city and erected them in front of the North Acropolis . By the late 8th century and early 9th century , activity at <unk> slowed . <unk> architecture was still built but few hieroglyphic inscriptions refer to later rulers . 
- 
- = = = Terminal Classic = = = 
- 
- By the 9th century , the crisis of the Classic Maya collapse was sweeping across the region , with populations <unk> and city after city falling into silence . Increasingly endemic warfare in the Maya region caused <unk> 's supporting population to heavily concentrate close to the city itself , accelerating the use of intensive agriculture and corresponding environmental decline . Construction continued at the beginning of the century , with the erection of Temple 3 , the last of the city 's major pyramids and the erection of monuments to mark the 19th K <unk> in <unk> . The beginning of the 10th <unk> <unk> in 830 passed <unk> , and marks the beginning of a 60 @-@ year hiatus , probably resulting from the collapse of central control in the city . During this hiatus , satellite sites traditionally under <unk> 's control began to erect their own monuments featuring local rulers and using the <unk> emblem glyph , with <unk> apparently lacking the authority or the power to crush these bids for independence . In <unk> , <unk> K 'awiil is mentioned on a stela at <unk> as visiting that city as the Divine Lord of <unk> but he is not recorded elsewhere and <unk> 's once great power was little more than a memory . The sites of <unk> and <unk> had by now inherited the once exclusive <unk> emblem glyph . 
- As <unk> and its hinterland reached peak population , the area suffered <unk> , erosion and nutrient loss followed by a rapid decline in population levels . <unk> and its immediate surroundings seem to have lost the majority of its population during the period from 830 to 950 and central authority seems to have collapsed rapidly . There is not much evidence from <unk> that the city was directly affected by the endemic warfare that afflicted parts of the Maya region during the Terminal Classic , although an influx of refugees from the <unk> region may have exacerbated problems resulting from the already stretched environmental resources . 
- In the latter half of the 9th century there was an attempt to revive royal power at the much diminished city of <unk> , as evidenced by a stela erected in the Great Plaza by <unk> Chan K 'awiil II in <unk> . This was the last monument erected at <unk> before the city finally fell into silence . The former satellites of <unk> , such as <unk> and <unk> , did not last much longer , <unk> their final monuments in <unk> . By the end of the 9th century the vast majority of <unk> 's population had deserted the city , its royal palaces were occupied by squatters and simple <unk> dwellings were being erected in the city 's ceremonial <unk> . The squatters blocked some <unk> in the rooms they <unk> in the monumental structures of the site and left rubbish that included a mixture of domestic refuse and non @-@ <unk> items such as musical instruments . These inhabitants reused the earlier monuments for their own ritual activities far removed from those of the royal dynasty that had erected them . Some monuments were <unk> and some were moved to new locations . Before its final abandonment all respect for the old rulers had disappeared , with the tombs of the North Acropolis being explored for <unk> and the easier to find tombs being looted . After 950 , <unk> was all but deserted , although a remnant population may have survived in <unk> <unk> interspersed among the ruins . Even these final inhabitants abandoned the city in the 10th or 11th centuries and the <unk> claimed the ruins for the next thousand years . Some of <unk> 's population may have migrated to the <unk> Lakes region , which remained heavily populated in spite of a <unk> in population levels in the first half of the 9th century . 
- The most likely cause of collapse at <unk> is <unk> and agrarian failure . The fall of <unk> was a blow to the heart of Classic Maya civilization , the city having been at the forefront of courtly life , art and architecture for over a thousand years , with an ancient ruling dynasty . However , new research regarding <unk> <unk> from the <unk> reservoir system suggests that a meteorological drought may have led to the abandonment of <unk> . 
- 
- = = = Modern history = = = 
- 
- In <unk> , the Spanish <unk> Hernán Cortés passed within a few kilometres of the ruins of <unk> but did not mention them in his letters . After Spanish friar <unk> de <unk> became lost in the <unk> forests in early <unk> he described a ruin that may well have been <unk> . 
- As is often the case with huge ancient ruins , knowledge of the site was never completely lost in the region . It seems that local people never <unk> about <unk> and they guided <unk> expeditions to the ruins in the 1850s . Some second- or third @-@ hand accounts of <unk> appeared in print starting in the 17th century , continuing through the writings of John Lloyd Stephens in the early 19th century ( Stephens and his illustrator Frederick <unk> heard rumors of a lost city , with white building tops towering above the jungle , during their 1839 @-@ 40 travels in the region ) . Because of the site 's <unk> from modern towns , however , no explorers visited <unk> until <unk> <unk> and <unk> <unk> , respectively the commissioner and the governor of <unk> , visited it in 1848 . Artist <unk> <unk> accompanied them and their account was published in Germany in 1853 . Several other expeditions came to further investigate , map , and photograph <unk> in the 19th century ( including Alfred P. <unk> in 1881 @-@ 82 ) and the early 20th century . <unk> archaeologists started to clear , map and record the ruins in the 1880s . 
- In 1951 , a small airstrip was built at the ruins , which previously could only be reached by several days ' travel through the jungle on foot or mule . In 1956 the <unk> project began to map the city on a scale not previously seen in the Maya area . From 1956 through 1970 , major archaeological excavations were carried out by the University of Pennsylvania <unk> Project . They mapped much of the site and excavated and restored many of the structures . Excavations directed by Edwin M. <unk> and later by William Coe of the university investigated the North Acropolis and the Central Plaza from 1957 to 1969 . The <unk> Project recorded over 200 monuments at the site . In 1979 , the <unk> government began a further archeological project at <unk> , which continued through to 1984 . 
- <unk> George Lucas used <unk> as a filming location for the first Star Wars film , Episode IV : A New Hope , released in 1977 . Temple I at <unk> was featured on the reverse of the 50 <unk> <unk> . 
- <unk> is now a major tourist attraction surrounded by its own national park . A site museum has been built at <unk> ; it was completed in 1964 . 
- 
- = = Site description = = 
- 
- <unk> has been partially restored by the University of Pennsylvania and the government of Guatemala . It was one of the largest of the Classic period Maya cities and was one of the largest cities in the Americas . The architecture of the ancient city is built from limestone and includes the remains of temples that tower over 70 metres ( 230 ft ) high , large royal palaces , in addition to a number of smaller pyramids , palaces , residences , administrative buildings , platforms and inscribed stone monuments . There is even a building which seemed to have been a jail , originally with wooden bars across the windows and doors . There are also seven courts for playing the <unk> <unk> , including a set of 3 in the Seven Temples Plaza , a unique feature in <unk> . 
- The limestone used for construction was local and <unk> on @-@ site . The depressions formed by the extraction of stone for building were plastered to <unk> them and were used as reservoirs , together with some <unk> natural depressions . The main <unk> were surfaced with stucco and laid at a gradient that <unk> rainfall into a system of canals that fed the reservoirs . 
- The residential area of <unk> covers an estimated 60 square kilometres ( 23 sq mi ) , much of which has not yet been cleared , mapped , or excavated . A huge set of earthworks discovered by Dennis E. <unk> and Donald <unk> in the 1960s rings <unk> with a 6 @-@ metre ( 20 ft ) wide trench behind a rampart . The 16 square kilometres ( 6 @.@ 2 sq mi ) area around the site core has been intensively mapped ; it may have enclosed an area of some 125 square kilometres ( 48 sq mi ) ( see below ) . Population estimates place the demographic size of the site between 10 @,@ 000 and 90 @,@ 000 , and possibly 425 @,@ 000 in the surrounding area . Recently , a project exploring the defensive earthworks has shown that the scale of the earthworks is highly variable and that in many places it is <unk> as a defensive feature . In addition , some parts of the <unk> were integrated into a canal system . The <unk> of <unk> varies significantly in coverage from what was originally proposed and it is much more complex and <unk> than originally thought . 
- 
- = = = <unk> = = = 
- 
- By the Late Classic , a network of <unk> ( <unk> ) linked various parts of the city , running for several kilometres through its urban core . These linked the Great Plaza with Temple 4 ( located about 750 metres ( 2 @,@ 460 ft ) to the west ) and the Temple of the <unk> ( about 1 kilometre ( 0 @.@ 62 mi ) to the southeast ) . These broad <unk> were built of packed and plastered limestone and have been named after early explorers and archaeologists ; the Maler , <unk> , <unk> and <unk> <unk> . They assisted the passage everyday traffic during the rain season and also served as dams . 
- The Maler Causeway runs north from behind Temple I to Group H. A large bas @-@ relief is carved onto limestone bedrock upon the course of the causeway just south of Group H. It depicts two bound captives and dates to the Late Classic . 
- The <unk> Causeway runs 0 @.@ 8 kilometres ( 0 @.@ 50 mi ) northeast from Temple IV to Group H. 
- The <unk> Causeway runs southeast from the East Plaza to Temple VI , a distance of about 1 @.@ 3 kilometres ( 0 @.@ 81 mi ) . 
- The <unk> Causeway runs west from the Great Plaza to Temple IV . 
- 
- = = = Architectural groups = = = 
- 
- The Great Plaza lies at the core of the site ; it is flanked on the east and west sides by two great temple @-@ pyramids . On the north side it is bordered by the North Acropolis and on the south by the Central Acropolis . 
- The Central Acropolis is a palace complex just south of the Great Plaza . 
- The North Acropolis , together with the Great Plaza immediately to the south , is one of the most studied architectural groups in the Maya area ; the <unk> Project excavated a massive trench across the complex , thoroughly investigating its construction history . It is a complex group with construction beginning in the <unk> Period , around 350 BC . It developed into a funerary complex for the ruling dynasty of the Classic Period , with each additional royal burial adding new temples on top of the older structures . After AD 400 a row of tall pyramids was added to the earlier Northern Platform , which measured 100 by 80 metres ( 330 by 260 ft ) , gradually hiding it from view . Eight temple pyramids were built in the 6th century AD , each of them had an elaborate <unk> and a stairway flanked by masks of the gods . By the 9th century AD , 43 stelae and 30 altars had been erected in the North Acropolis ; 18 of these monuments were carved with hieroglyphic texts and royal portraits . The North Acropolis continued to receive burials into the <unk> Period . 
- The South Acropolis is found next to Temple V. It was built upon a large basal platform that covers an area of more than 20 @,@ 000 square metres ( 220 @,@ 000 sq ft ) . 
- The Plaza of the Seven Temples is to the west of the South Acropolis . It is bordered on the east side by a row of nearly identical temples , by palaces on the south and west sides and by an unusual triple ballcourt on the north side . 
- The Mundo <unk> is to the west of the Plaza of the Seven Temples . It is the largest ceremonial complex dating from the <unk> period at <unk> . The complex was organized as a large E @-@ Group consisting of a pyramid aligned with a platform to the east that supported three temples . The Mundo <unk> complex was rebuilt many times over the course of its history . By AD 250 – 300 its architectural style was influenced by the great metropolis of <unk> in the Valley of Mexico , including the use of the <unk> @-@ <unk> form . During the Early Classic period ( c . 250 – 600 ) the Mundo <unk> became one of the twin <unk> of the city , the other being the North Acropolis . From AD 250 to <unk> it may have served as the royal necropolis . The Mundo <unk> complex was given its name by the archaeologists of the University of Pennsylvania ; it is centered upon the Lost World Pyramid and a small platform to the west of it . 
- Group G lies just south of the <unk> Causeway . The complex dates to the Late Classic and consists of palace @-@ type structures and is one of the largest groups of its type at <unk> . It has two stories but most of the rooms are on the lower floor , a total of 29 vaulted chambers . The remains of two further chambers belong to the upper storey . One of the entrances to the group was framed by a gigantic mask . 
- Group H is centered on a large plaza to the north of the Great Plaza . It is bordered by temples dating to the Late Classic . 
- There are nine Twin @-@ Pyramid <unk> at <unk> , one of which was completely dismantled in ancient times and some others were partly destroyed . They vary in size but consist of two pyramids facing each other on an east – west axis . These pyramids are flat @-@ topped and have <unk> on all four sides . A row of plain stelae is placed immediately to the west of the eastern pyramid and to the north of the pyramids , and lying roughly <unk> from them , there is usually a sculpted stela and altar pair . On the south side of these complexes there is a long vaulted building containing a single room with nine <unk> . The entire complex was built at once and these complexes were built at 20 @-@ year ( or k <unk> ) intervals during the Late Classic . The first twin pyramid complex was built in the early 6th century in the East Plaza . It was once thought that these complexes were unique to <unk> but rare examples have now been found at other sites , such as <unk> and <unk> , and they may reflect the extent of <unk> 's political dominance in the Late Classic . 
- Group Q is a twin @-@ pyramid complex , and is one of the largest at <unk> . It was built by <unk> <unk> <unk> II in <unk> in order to mark the end of the 17th K <unk> . Most of it has been restored and its monuments have been re @-@ erected . 
- Group R is another twin @-@ pyramid complex , dated to 790 . It is close to the Maler Causeway . 
- 
- = = = Structures = = = 
- 
- There are thousands of ancient structures at <unk> and only a fraction of these have been excavated , after decades of archaeological work . The most prominent surviving buildings include six very large pyramids , labelled Temples I - VI , each of which support a temple structure on their <unk> . Some of these pyramids are over 60 metres ( 200 feet ) high . They were numbered <unk> during the early survey of the site . It is estimated that each of these major temples could have been built in as little as two years . 
- Temple I ( also known as the Temple of <unk> <unk> or Temple of the Great Jaguar ) is a funerary pyramid dedicated to <unk> Chan K <unk> , who was <unk> in the structure in AD <unk> , the pyramid was completed around 740 – 750 . The temple rises 47 metres ( 154 ft ) high . The massive <unk> that topped the temple was originally decorated with a giant sculpture of the enthroned king , although little of this decoration survives . The tomb of the king was discovered by Aubrey <unk> of the University of Pennsylvania in 1962 . Among items recovered from the Late Classic tomb were a large collection of inscribed human and animal bone tubes and strips with sophisticated scenes depicting deities and people , finely carved and rubbed with <unk> , as well as <unk> and shell ornaments and ceramic vessels filled with offerings of food and drink . The shrine at the summit of the pyramid has three chambers , each behind the next , with the <unk> spanned by wooden <unk> fashioned from multiple beams . The outermost <unk> is plain but the two inner <unk> were carved , some of the beams were removed in the 19th century and their location is unknown , while others were taken to museums in Europe . 
- Temple II ( also known as the Temple of the Mask ) it was built around AD 700 and stands 38 metres ( 125 ft ) high . Like other major temples at <unk> , the summit shrine had three consecutive chambers with the <unk> spanned by wooden <unk> , only the middle of which was carved . The temple was dedicated to the wife of <unk> Chan K <unk> , although no tomb was found . The queen 's portrait was carved into the <unk> spanning the doorway of the summit shrine . One of the beams from this <unk> is now in the American Museum of Natural History in New York . 
- Temple III ( also known as the Temple of the Jaguar Priest ) was the last of the great pyramids to be built at <unk> . It stood 55 metres ( 180 ft ) tall and contained an elaborately sculpted but damaged roof <unk> , possibly showing Dark Sun engaged in a ritual dance around AD <unk> . The temple shrine possesses two chambers . 
- Temple IV is the tallest temple @-@ pyramid at <unk> , measuring 70 metres ( 230 ft ) from the plaza floor level to the top of its roof comb . Temple IV marks the reign of <unk> ’ in Chan <unk> ( Ruler B , the son of Ruler A or <unk> Chan K 'awiil I ) and two carved wooden <unk> over the doorway that leads into the temple on the pyramid ’ s summit record a long count date ( 9 @.@ 15 @.@ 10 @.@ 0 @.@ 0 ) that corresponds to CE <unk> ( <unk> 1994 : 169 ) . Temple IV is the largest pyramid built anywhere in the Maya region in the 8th century , and as it currently stands is the tallest pre @-@ Columbian structure in the Americas although the Pyramid of the Sun at <unk> may originally have been taller , as may have been one of the structures at El <unk> . 
- Temple V stands south of the Central Acropolis and is the mortuary pyramid of an as yet unidentified ruler . The temple stands 57 metres ( 187 ft ) high , making it the second tallest structure at <unk> - only Temple IV is taller . The temple has been dated to about AD 700 , in the Late Classic period , via <unk> analysis and the dating of ceramics associated with the structure places its construction during the reign of <unk> <unk> <unk> in the second half of the 7th century . 
- Temple VI is also known as the Temple of the <unk> and was dedicated in AD <unk> . It is notable for its 12 @-@ metre ( 39 ft ) high roof @-@ comb . <unk> of hieroglyphs cover the back and sides of the roof @-@ comb . The temple faces onto a plaza to the west and its front is <unk> . 
- Temple 33 was a funerary pyramid erected over the tomb of <unk> Chan K 'awiil I ( known as <unk> 48 ) in the North Acropolis . It started life in the Early Classic as a wide basal platform decorated with large stucco masks that flanked the stairway . Later in the Early Classic a new superstructure was added , with its own masks and decorated panels . During the Hiatus a third stage was built over the earlier constructions , the stairway was demolished and another royal burial , of an unidentified ruler , was set into the structure ( <unk> 23 ) . While the new pyramid was being built another high ranking tomb ( <unk> 24 ) was inserted into the rubble core of the building . The pyramid was then completed , standing 33 metres ( 108 ft ) tall . The final version of Temple 33 was completely dismantled by archaeologists in 1965 in order to arrive at the earlier stages of construction . 
- Structure 34 is a pyramid in the North Acropolis that was built by <unk> Chan K 'awiil II over the tomb of his father , <unk> <unk> <unk> I. The pyramid was topped by a three chambered shrine , the rooms situated one behind the other . 
- Structure <unk> @-@ 43 is an unusual radial temple in the East Plaza , built over a pre @-@ existing twin pyramid complex . It is built into the end of the East Plaza Ballcourt and possessed four entry <unk> and three <unk> , the fourth ( south ) side was too close to the Central Acropolis for a stairway on that side . The building has a <unk> @-@ <unk> platform profile , modified from the original style found at <unk> . In fact , it has been suggested that the style of the building has closer <unk> with El <unk> and <unk> than with <unk> itself . The vertical <unk> panels are set between sloping <unk> panels and are decorated with paired disc symbols . Large flower symbols are set into the sloping <unk> panels , related to the Venus and star symbols used at <unk> . The roof of the structure was decorated with <unk> although only fragments now remain , showing a monstrous face , perhaps that of a jaguar , with another head emerging from the mouth . The second head possesses a <unk> tongue but is probably not that of a snake . The temple , and its associated ballcourt , probably date to the reign of <unk> <unk> Chaak or that of his son <unk> Chan K 'awiil I , in the later part of the 7th century . 
- Structure <unk> @-@ 49 possesses a clear <unk> @-@ linked architectural style ; it has <unk> , an architectural feature that is very rare in the Maya region , and a <unk> @-@ <unk> façade ; it dates to the 4th century AD . It is located near to the Lost World pyramid . 
- Structure <unk> @-@ 53 is a small <unk> @-@ style platform that dates to about AD 600 . It had <unk> on all four sides and did not possess a superstructure . 
- The Lost World Pyramid ( Structure <unk> @-@ 54 ) is the largest structure in the Mundo <unk> complex . It lies in the southwest portion of <unk> ’ s central core , south of Temple III and west of Temple V. It was decorated with stucco masks of the sun god and dates to the Late <unk> ; this pyramid is part of an enclosed complex of structures that remained intact and un @-@ impacted by later building activity at <unk> . By the end of the Late <unk> this pyramid was one of the largest structures in the Maya region . It attained its final form during the reign of <unk> <unk> Ich 'aak in the 4th century AD , in the Early Classic , standing more than 30 metres ( 98 ft ) high with <unk> on all four sides and a flat top that possibly supported a superstructure built from <unk> materials . Although the plaza later suffered significant alteration , the organization of a group of temples on the east side of this complex <unk> to the layout that defines the so @-@ called E @-@ Groups , identified as solar observatories . 
- Structure <unk> @-@ 96 is the central temple on the east side of the Plaza of the Seven Temples . It has been restored and its rear outer wall is decorated with skull @-@ and @-@ <unk> motifs . 
- Group <unk> @-@ 16 is an elite residential complex that has been thoroughly excavated . It lies a few hundred metres south of the Lost World Complex and the excavations have revealed elaborate stucco masks , <unk> murals , relief sculptures and buildings with <unk> characteristics . 
- The Great Plaza Ballcourt is a small ballcourt that lies between Temple I and the Central Acropolis . 
- The Bat Palace is also known as the Palace of Windows and lies to the west of Temple III . It has two <unk> , with a double range of chambers on the lower storey and a single range in the upper storey , which has been restored . The palace has ancient graffiti and possesses low windows . 
- Complex N lies to the west of the Bat Palace and Temple III . The complex dates to AD 711 . 
- 
- = = = <unk> = = = 
- 
- Altar 5 is carved with two nobles , one of whom is probably <unk> Chan K 'awiil I. They are performing a ritual using the bones of an important woman . Altar 5 was found in Complex N , which lies to the west of Temple III . 
- Altar 8 is sculpted with a bound captive . It was found within Complex P in Group H and is now in the <unk> <unk> de <unk> y <unk> in Guatemala City . 
- Altar 9 is associated with Stela 21 and bears the sculpture of a bound captive . It is located in front of Temple VI . 
- Altar 10 is carved with a captive tied to a <unk> . It is in the northern enclosure of Group Q , a twin @-@ pyramid complex and has suffered from erosion . 
- Altar 35 is a plain monument associated with Stela 43 . The stela @-@ altar pair is <unk> located at the base of the stairway of Temple IV . 
- 
- = = = <unk> = = = 
- 
- At <unk> , beams of <unk> wood were placed as <unk> spanning the inner <unk> of temples . These are the most elaborately carved wooden <unk> to have survived anywhere in the Maya region . 
- <unk> 3 from Temple IV was taken to Basel in Switzerland in the 19th century . It was in almost perfect condition and depicts <unk> 'in Chan K 'awiil seated on a <unk> . 
- 
- = = = Stelae = = = 
- 
- Stelae are carved stone shafts , often sculpted with figures and hieroglyphs . A selection of the most notable stelae at <unk> follows : 
- Stela 1 dates to the 5th century and depicts the king <unk> Chan K 'awiil II in a standing position . 
- Stela 4 is dated to AD <unk> , during the reign of <unk> <unk> <unk> after the intrusion of <unk> in the Maya area . The stela displays a mix of Maya and <unk> qualities , and deities from both cultures . It has a portrait of the king with the <unk> Jaguar God under one arm and the Mexican <unk> under the other . His helmet is a simplified version of the <unk> War <unk> . Unusually for Maya sculpture , but typically for <unk> , <unk> <unk> <unk> is depicted with a frontal face , rather than in profile . 
- Stela 5 was dedicated in <unk> by <unk> 'in Chan K 'awiil . 
- Stela 6 is a badly damaged monument dating to <unk> and bears the name of the " Lady of <unk> " who celebrated the end of the 4th K <unk> in that year . 
- Stela 10 is twinned with Stela 12 but is badly damaged . It described the accession of <unk> ' B <unk> in the early 6th century and earlier events in his career , including the capture of a prisoner depicted on the monument . 
- Stela 11 was the last monument ever erected at <unk> ; it was dedicated in <unk> by <unk> Chan K 'awiil II . 
- Stela 12 is linked to the queen known as the " Lady of <unk> " and king <unk> ' B <unk> . The queen is described as performing the year @-@ ending rituals but the monument was dedicated in honor of the king . 
- Stela 16 was dedicated in 711 , during the reign of <unk> Chan K 'awiil I. The sculpture , including a portrait of the king and a hieroglyphic text , are limited to the front face of the monument . It was found in Complex N , west of Temple III . 
- Stela 18 was one of two stelae erected by <unk> <unk> <unk> I to celebrate the k <unk> @-@ ending of AD <unk> . It was re @-@ erected at the base of Temple 34 , his funerary shrine . 
- Stela 19 was dedicated in 790 by <unk> <unk> <unk> II . 
- Stela 20 was found in Complex P , in Group H , and was moved to the <unk> <unk> de <unk> y <unk> in Guatemala City . 
- Stela 21 was dedicated in <unk> by <unk> 'in Chan K 'awiil . Only the bottom of the stela is intact , the rest having been <unk> in ancient times . The surviving sculpture is of fine quality , consisting of the feet of a figure and of accompanying hieroglyphic text . The stela is associated with Altar 9 and is located in front of Temple VI . 
- Stela 22 was dedicated in <unk> by <unk> <unk> <unk> II in the northern enclosure of Group Q , a twin @-@ pyramid complex . The face of the figure on the stela has been <unk> . 
- Stela 23 was broken in antiquity and was re @-@ erected in a residential complex . The defaced portrait on the monument is that of the so @-@ called " Lady of <unk> " , a daughter of <unk> <unk> Ich 'aak II who became queen at the age of six but never ruled in her own right , being paired with male co @-@ rulers . It dates to the early 6th century . 
- Stela 24 was erected at the foot of Temple 3 in <unk> , accompanied by Altar 7 . Both were broken into fragments in ancient times , although the name of Dark Sun survives on three fragments . 
- Stela 26 was found in the summit shrine of Temple 34 , underneath a broken masonry altar . The monument had originally been erected at the base of the temple during the Early Classic period and was later broken , probably at the beginning of the Late Classic . Its remains were then interred within the temple shrine . 
- Stela 29 bears a Long Count ( 8 @.@ 12 @.@ 14 @.@ 8 @.@ 15 ) date equivalent to AD <unk> , the earliest surviving Long Count date from the Maya lowlands . The stela is also the earliest monument to bear the <unk> emblem glyph . It bears a sculpture of the king facing to the right , holding the head of an underworld jaguar god , one of the patron deities of the city . The stela was deliberately smashed during the 6th century or some time later , the upper portion was dragged away and dumped in a rubbish tip close to Temple III , to be uncovered by archaeologists in 1959 . 
- Stela 30 is the first surviving monument to be erected after the Hiatus . Its style and iconography is similar to that of <unk> , one of the more important of <unk> 's enemies . 
- Stela 31 is the accession monument of <unk> Chan K 'awiil II , also bearing two portraits of his father , <unk> <unk> <unk> , as a youth dressed as a <unk> warrior . He carries a <unk> in one hand and bears a shield decorated with the face of <unk> , the <unk> war god . In ancient times the sculpture was broken and the upper portion was moved to the summit of Temple 33 and <unk> buried . Stela 31 has been described as the greatest Early Classic sculpture to survive at <unk> . A long hieroglyphic text is carved onto the back of the monument , the longest to survive from the Early Classic , which describes the arrival of <unk> K <unk> ' at El Peru and <unk> in January <unk> . It was also the first stela at <unk> to be carved on all four faces . 
- Stela 32 is a fragmented monument with a foreign <unk> @-@ style sculpture apparently depicting the lord of that city with the attributes of the central Mexican storm god <unk> , including his <unk> eyes and <unk> headdress . 
- Stela 39 is a broken monument that was erected in the Lost World complex . The upper portion of the stela is missing but the lower portion shows the lower body and legs of <unk> <unk> Ich 'aak , holding a flint axe in his left hand . He is <unk> the figure of a bound , richly dressed captive . The monument is dated to AD <unk> . The text on the back of the monument describes a <unk> ritual to celebrate a <unk> @-@ ending . The stela also names <unk> <unk> Ich 'aak I 's father as K 'inich <unk> <unk> . 
- Stela 40 bears a portrait of Kan <unk> and dates to AD 468 . 
- Stela 43 is paired with Altar 35 . It is a plain monument at the base of the stairway of Temple IV . 
- 
- = = = <unk> = = = 
- 
- <unk> 1 is a tomb in the Lost World complex . A fine ceramic bowl was recovered from the tomb , with the handle formed from three @-@ dimensional head and neck of a bird emerging from the two @-@ dimensional body painted on the <unk> . 
- <unk> 10 is the tomb of <unk> <unk> <unk> . It is located beneath Structure 34 in the North Acropolis . The tomb contained a rich array of offerings , including ceramic vessels and food , and nine youths were sacrificed to accompany the dead king . A dog was also <unk> with the deceased king . <unk> in the tomb were <unk> and painted and many demonstrated a blend of Maya and <unk> styles . Among the offerings was an incense @-@ <unk> in the shape of an elderly underworld god , sitting on a stool made of human bones and holding a severed head in his hands . The tomb was sealed with a <unk> vault , then the pyramid was built on top . 
- <unk> 48 is generally accepted as the tomb of <unk> Chan K <unk> . It is located beneath Temple 33 in the North Acropolis . The chamber of the tomb was cut from the bedrock and contained the remains of the king himself together with those of two <unk> who had been sacrificed in order to accompany the deceased ruler . The walls of the tomb were covered with white stucco painted with hieroglyphs that included the Long Count date equivalent to 20 March 457 , probably the date of either the death or interment of the king . The king 's skeleton was missing its skull , its <unk> and one of its hands while the skeletons of the sacrificial victims were intact . 
- <unk> 85 dates to the Late <unk> and was enclosed by a platform , with a primitive <unk> vault . The tomb contained a single male skeleton , which lacked a skull and its <unk> . The dynastic founder of <unk> , <unk> <unk> ' Xook , has been linked to this tomb , which lies deep in the heart of the North Acropolis . The deceased had probably died in battle with his body being <unk> by his enemies before being recovered and interred by his followers . The bones were wrapped carefully in textiles to form an upright bundle . The missing head was replaced by a small <unk> mask with shell @-@ <unk> teeth and eyes and bearing a three @-@ pointed royal <unk> . This head wears an emblem of rulership on its forehead and is a rare <unk> lowland Maya portrait of a king . Among the contents of the tomb were a stingray spine , a <unk> shell and twenty @-@ six ceramic vessels . 
- <unk> 116 is the tomb of <unk> Chan K 'awiil I. It is a large vaulted chamber deep within the pyramid , below the level of the Great Plaza . The tomb contained rich offerings of <unk> , ceramics , shell and works of art . The body of the king was covered with large quantities of <unk> ornaments including an enormous necklace with especially large beads , as depicted in sculpted portraits of the king . One of the outstanding pieces recovered from the tomb was an ornate <unk> mosaic vessel with the <unk> bearing a sculpted portrait of the king himself . 
- <unk> 195 was flooded with mud in antiquity . This flood had covered wooden objects that had completely <unk> away by the time the tomb was excavated , leaving <unk> in the dried mud . Archaeologists filled these <unk> with stucco and thus excavated four effigies of the god K 'awiil , the wooden originals long gone . 
- <unk> 196 is a Late Classic royal tomb that contained a <unk> mosaic vessel topped with the head of the <unk> God . 
- 
- 
- = Tropical Storm <unk> ( 2008 ) = 
- 
- Tropical Storm <unk> was the tenth tropical storm of the 2008 Atlantic hurricane season . <unk> developed out of a strong tropical wave which moved off the African coast on August 31 . The wave quickly became organized and was declared Tropical Depression Ten while located 170 mi ( 270 km ) to the south @-@ southeast of the Cape Verde Islands on September 2 . The depression was quickly upgraded to Tropical Storm <unk> around noon the same day . Over the next several days , <unk> moved in a general west @-@ northwest direction and reached its peak intensity early on September 3 . Strong wind shear , some due to the outflow of Hurricane Ike , and dry air caused the storm to weaken . On September 6 , the combination of wind shear , dry air , and cooling waters caused <unk> to weaken into a tropical depression . <unk> deteriorated into a remnant low shortly after as convection continued to dissipate around the storm . The low ultimately dissipated while located 520 mi ( 835 km ) east of <unk> on September 10 . However , the remnant moisture led to minor flooding on the island of St. Croix . 
- 
- = = Meteorological history = = 
- 
- Tropical Storm <unk> formed as a tropical wave that emerged off the west coast of Africa near the end of August 2008 . It tracked south of Cape Verde and slowly developed , and on September 2 the disturbance became Tropical Depression Ten while located south @-@ southeast of the Cape Verde islands . As the depression became more organized , an eye @-@ like feature developed in the upper levels of the system . The depression was upgraded to Tropical Storm <unk> six hours after forming . <unk> was located in an area which was supportive for rapid intensification but was not forecast to intensify quickly . 
- <unk> continued to intensify throughout the afternoon as the storm became more symmetrical . However , due to the location of the storm , there was a lack of accurate wind speed readings , and the National Hurricane Center was uncertain of its actual intensity . Despite the lack of wind shear around the storm , the center became slightly exposed and ceased further intensification . The storm was also heading into an area where shear was <unk> to significantly increase due to an upper @-@ level trough diving southward . Despite convection being partially removed from the center of <unk> , the storm intensified slightly in the early morning hours on September 3 as thunderstorm activity to the south of the center became more organized . The intensification was forecast to be short in duration as the trough to the north was deepening , causing the wind shear to the west to become stronger . 
- <unk> reached its peak intensity of 65 mph ( 100 km / h ) around 8 a.m. ( <unk> ) as it continued to become more organized . However , there were indications that it had already begun to weaken . <unk> towards the north was becoming restricted and arc clouds began emanating from the storm , a sign that dry air was entering the system . During the afternoon hours , the structure of <unk> began to rapidly deteriorate as strong wind shear and dry air took their toll . By the late night , the center was almost completely exposed and only a band of convection persisted near the center . 
- Despite continuing effects from the strong wind shear , a large , deep burst of convection formed in the northern <unk> of <unk> . The center was found to have shifted towards the new convection leading to an increase in intensity . The forecast showed a slight decrease in wind shear as <unk> continued westward and no change in intensity over the 5 @-@ day forecast was predicted . However , the convection decreased once more and the low became completely exposed by the late morning hours and <unk> weakened again . By the afternoon , the center of <unk> was only a <unk> of clouds , devoid of convection . During the overnight hours on September 4 into the morning of September 5 , convection associated with <unk> began to <unk> somewhat , mostly to the north of the circulation , due to the strong <unk> wind shear . By mid @-@ morning , <unk> re @-@ intensified slightly due to the redevelopment of some convection . However , the redevelopment was short lived and wind shear again took its toll on <unk> by late morning . The convection around the system became <unk> from the center and <unk> weakened slightly . 
- The weakening trend continued through the afternoon as the storm was being affected by strong <unk> shear . <unk> became almost fully devoid of any convection by mid @-@ afternoon and the storm weakened to 40 mph ( 65 km / h ) , barely holding on to tropical storm status . <unk> regained a small amount of convection in the late night hours , but not enough to still be classified a tropical storm . Due to the lack of convection , <unk> was downgraded to a Tropical Depression at <unk> ( <unk> ) with winds of 35 mph ( 55 km / h ) . Since there was no convection around the system , it would have normally been classified a remnant low but , due to the possibility of the storm <unk> over the next several days , it was considered a tropical depression . The next morning , <unk> was downgraded to a remnant low as strong wind shear and dry air caused the demise of the storm . No redevelopment was expected with <unk> as it began to move over colder waters and remain under strong wind shear until it dissipated . 
- However , the remnant low associated with <unk> began to show signs of redevelopment during the afternoon on September 7 . <unk> around the system increased significantly and the low was no longer exposed . On September 8 , wind shear took over the system again . <unk> around the remnant low was torn away and the low was exposed once more . The National Hurricane Center did not state the chance of regeneration once the low became exposed . Finally , on September 9 , wind shear and dry air led to the remnants of <unk> deteriorating into an open wave . However , on September 10 , the remnants of <unk> redeveloped and global models picked up on the reformed system . Once more , the chance of regeneration was possible as the remnants of <unk> headed towards the Bahamas . However , on September 14 , dry air and wind shear caused the remnants to dissipate entirely . 
- 
- = = Impact = = 
- 
- As <unk> passed to the south of the Cape Verde islands on September 2 , outer rain bands produced minor rainfall , totaling around 0 @.@ 55 inches ( 14 mm ) . There were no reports of damage or flooding from the rain and overall effects were minor . 
- Several days after the low dissipated , the remnant moisture from <unk> brought showers and thunderstorms to St. Croix where up to 1 in ( 25 @.@ 4 mm ) of rain fell . The heavy rains led to minor street flooding and some urban flooding . No known damage was caused by the flood . 
- 
- 
- = Mount <unk> = 
- 
- Mount <unk> is the highest summit of the Rocky Mountains of North America and the highest point in the U.S. state of Colorado and the entire Mississippi River drainage basin . The ultra @-@ prominent 14 @,@ 440 @-@ foot ( <unk> @.@ 2 m ) <unk> is the highest peak in the <unk> Range and the second @-@ highest summit in the contiguous United States after Mount Whitney . Mount <unk> is located in San Isabel National Forest , 12 @.@ 1 miles ( 19 @.@ 4 km ) southwest ( bearing 223 ° ) of the City of <unk> in Lake County , Colorado . 
- The mountain was named in honor of Colorado statesman Samuel <unk> <unk> , who was active in the formative period of the state and Governor of the Territory of Colorado from 1873 to 1874 . Henry W. <unk> of the <unk> Survey was the first to record an ascent of the peak , in 1874 . The <unk> and most popular climbing routes are categorized as Class 1 to 2 or A + in <unk> <unk> . Mount <unk> is therefore often referred to as the " gentle giant " that tops all others in the Rocky Mountains . 
- 
- = = Geography = = 
- 
- Mount <unk> is visible to the southwest of <unk> , often snow @-@ capped even in the summer . Many other <unk> surround <unk> in all directions , and it is very close to central Colorado 's Collegiate <unk> . The neighboring Mount Massive , to the north , is the second @-@ highest peak in the Rocky Mountains and the third @-@ highest in the contiguous United States , and La Plata Peak , to the south , is the fifth @-@ highest in the Rockies . The community of Twin Lakes lies at the base of Mount <unk> , Denver is about 130 miles ( <unk> km ) to the east , <unk> is 50 miles ( 80 km ) to the north , and <unk> is 40 miles ( 64 km ) to the west . <unk> , about 16 miles ( 26 km ) to the northeast , is the nearest large town . <unk> 's parent peak is Mount Whitney in California . Including Alaska and Hawaii , Mount <unk> is the fourteenth @-@ highest mountain in the United States . 
- Weather conditions often change rapidly , and afternoon thunderstorms are common in the <unk> ; <unk> and snow are possible year @-@ round . An electrical storm on the mountain 's summit was considered remarkable enough to be reported in the July 1894 issue of Science . 
- 
- = = Geology = = 
- 
- Mount <unk> is part of the <unk> Range , an <unk> of the Laramide <unk> which separated from the Mosquito Range to the east around 28 million years ago . The tops of this range were heavily <unk> , leaving behind characteristic summit features and other such clues . For example , the base of <unk> on the eastern side exhibits large igneous and <unk> rocks deposited when the glaciers <unk> , which lie on a lateral <unk> . Further up the eastern side there is a large <unk> with a small <unk> . There are also lakes to both the north and south , <unk> and Twin Lakes respectively ; the Twin Lakes are a result of the natural dam of end moraines , and <unk> Lake was created by the <unk> Sugar <unk> Dam . 
- Mount <unk> is composed largely of <unk> . However , the summit ridge consists of <unk> basement rock , which is Pre @-@ <unk> in origin and about 1 @.@ 7 billion years old . There are various igneous <unk> including <unk> , as well as " bands " of <unk> and <unk> . Unlike mountains of similar altitude elsewhere , <unk> lacks both a permanent <unk> and a prominent north @-@ facing <unk> , which can be attributed to its position amongst other mountains of similar height , causing it to receive relatively small quantities of precipitation . 
- 
- = = History = = 
- 
- Mount <unk> was named by miners in honor of Samuel <unk> <unk> , the governor of the then @-@ Territory of Colorado , because he brokered a treaty in September 1873 with the <unk> tribe that opened up more than 3 @,@ 000 @,@ 000 acres ( 12 @,@ 000 km2 ) of reservation land to mining and railroad activity . The first recorded ascent of the peak was by <unk> <unk> in 1874 , who was surveying the mountain as part of the <unk> Survey . 
- Originally measured as 14 @,@ <unk> feet ( 4 @,@ <unk> m ) in height , Mount <unk> 's elevation was later adjusted to 14 @,@ 440 feet ( 4 @,@ 400 m ) following a re @-@ evaluation of mapped elevations , which sparked protests . The actual change was made in 1988 as a result of the North American <unk> <unk> of 1988 ; it seems the original measurement resulted from the Sea Level <unk> of 1929 . A matter of some contention arose after the Great Depression over the heights of <unk> and its neighbor , Mount Massive , which differ in elevation by only 12 feet ( 3 @.@ 7 m ) . This led to an ongoing dispute that came to a head with the Mount Massive supporters building large piles of stones on the summit to boost its height , only to have the Mount <unk> proponents <unk> them . The effort was ultimately unsuccessful and Mount <unk> has remained the highest peak in Colorado . The first <unk> ascent of <unk> occurred in 1949 , when a Jeep was driven to the summit , apparently to judge suitability for skiing development . 
- 
- = = Flora and fauna = = 
- 
- The summit of Mount <unk> is an alpine environment , featuring plants such as <unk> <unk> ( sky @-@ pilot ) , <unk> <unk> ( old @-@ man @-@ of @-@ the @-@ mountain ) , and <unk> <unk> ( alpine <unk> ) . Also noted are <unk> <unk> var. <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> album , <unk> <unk> var. <unk> , and <unk> <unk> . Below <unk> the mountain is heavily forested , with the lower slopes covered with a mixture of lodgepole pine , spruce , <unk> and fir . 
- Some of the fauna reported on the climb to the summit include black bears , <unk> , mule deer , <unk> , and pocket <unk> ; there are also many species of <unk> . Elk , grouse , turkey and <unk> sheep are present in the area during the summer . 
- 
- = = <unk> = = 
- 
- There are three main routes which ascend the mountain , all of which gain over 4 @,@ 100 feet ( 1 @,@ 200 m ) of elevation . The standard route <unk> the peak from the east , starting from the Colorado Trail just north of Twin Lakes . The 4 @.@ 6 miles ( 7 @.@ 4 km ) long North ( Main ) <unk> Trail begins close to the <unk> Creek <unk> , and gains about 4 @,@ 500 feet ( 1 @,@ 400 m ) . The trail is open to <unk> , mountain <unk> and hunters during season . An easier , but longer route , the South <unk> Trail , is 5 @.@ 5 miles ( 8 @.@ 9 km ) long , climbing 4 @,@ 600 feet ( 1 @,@ 400 m ) at a less @-@ punishing gradient than the North <unk> Trail , approaching from the south and then climbing the eastern ridge . 
- The most difficult of the main routes is the Black Cloud Trail , a Class 2 climb that takes ten to fourteen hours depending on pace , gains 5 @,@ 300 feet ( 1 @,@ 600 m ) in elevation , and also involves an ascent of the sub @-@ peak , South <unk> , at 14 @,@ 134 feet ( 4 @,@ 308 m ) . Even healthy and experienced <unk> report great difficulty on this route , and despite the fact that there is a trail , the route is extremely steep , unstable , and rocky in places . The elevation gain is not evenly distributed over the 5 @.@ 5 @-@ mile ascent . There are also routes approaching from the western face , and southwestern ridge , from South <unk> Creek <unk> and Echo Canyon <unk> respectively . 
- Although strenuous and requiring physical fitness , none of the conventional routes require specialist <unk> skills or technical rock climbing . The main dangers of the mountain are those common to all high mountains , particularly altitude sickness . This can affect anyone , even those who are <unk> . In serious cases , it can lead to high @-@ altitude pulmonary <unk> and <unk> <unk> , which can lead to difficulties with breathing , <unk> , and death . <unk> are advised to begin their ascent at or before 6 a.m. and to summit and descend before early afternoon to minimize exposure to possible afternoon thunderstorms while at high altitudes . Although the most conventional form of ascent is by hiking , Anna Elizabeth Dickinson , the <unk> , ascended the mountain on a mule borrowed from the U.S. government . 
- 
- 
- = Scientology in Germany = 
- 
- The Church of Scientology has been present in Germany since 1970 . German authorities estimate that there are 4 @,@ 000 active Scientologists in Germany today ; the Church of Scientology gives a membership figure of around 12 @,@ 000 . The Church of Scientology has encountered particular <unk> from the German press and government and occupies a precarious legal , social and cultural position in Germany . 
- German courts have so far not resolved whether Scientology should be accorded the legal status of a religious or <unk> community , and different courts have reached contradictory conclusions . The German domestic intelligence service has monitored the organization 's activities . The German government does not recognize Scientology as a religion . It views it as an abusive business <unk> as a religion and believes that it pursues political goals that conflict with the values enshrined in the German constitution . This stance has been criticized , most notably by the U.S. government , which recognizes Scientology as a religion and has raised concerns about the violation of individual rights posed by sect <unk> . 
- Scientologists in Germany face specific political and economic restrictions . They are barred from membership in some major political parties , and businesses and other employers use so @-@ called " sect <unk> " to expose a prospective business partner 's or employee 's association with the organization . German federal and state interior ministers started a process aimed at banning Scientology in late 2007 , but abandoned the initiative a year later , finding insufficient legal grounds . Despite this , polls suggest that most Germans favor banning Scientology altogether . 
- 
- = = Background = = 
- 
- Scientology , founded in the early 1950s in the United States by L. Ron Hubbard and today claiming to be represented in 150 countries , has been a very controversial new religious movement . Its stated <unk> aim is to " clear the planet " , to bring about an enlightened age in which every individual has overcome their psychological limitations . Scientology teaches that the source of people 's <unk> lies in " <unk> " , psychological <unk> acquired in the course of painful experiences , which can be cleared through a type of <unk> called " <unk> " made available by the Church of Scientology . 
- The fact that Scientologists have to pay large fees for <unk> and other Scientology services has brought controversy to Scientology throughout much of its history , with governments <unk> it as a profit @-@ making enterprise rather than as a religion . Critics maintain that Scientology is " a business @-@ driven , <unk> <unk> , totalitarian ideology with world @-@ dominating aspirations " , and that it tricks its members into parting with significant sums of money for Scientology courses . Scientology has fought <unk> lawsuits to defend itself against such charges and to pursue legal recognition as a religion . These efforts have been partly successful – Scientology has gained recognition as a tax @-@ exempt religious group in a number of countries , most notably in Australia in 1983 and the United States in 1993 , and in 2007 won an important case at the European Court of Human Rights , which <unk> Russia for failing to register Scientology as a religion . 
- The German government has said that it does not consider Scientology a religion , but a " commercial enterprise with a history of taking advantage of vulnerable individuals and an extreme dislike of any criticism " whose " totalitarian structure and methods may pose a risk to Germany 's democratic society " . Accordingly , the German government has taken a very strong stance against the organization . Germany is not alone in opposing Scientology ; in France , the Church of Scientology was convicted of organized fraud in October 2009 , after a court found that members had been manipulated into paying large sums for Scientology products , and the Church only narrowly escaped being banned altogether . Scientology is similarly controversial in Belgium , Greece and the UK . 
- On the subject of Scientology 's status as a religion , the German government has pointed to a 1995 decision by the Federal Labor Court of Germany . That court , noting Hubbard 's instruction that Scientologists should " make money , make more money – make other people produce so as to make more money " , came to the conclusion that " Scientology <unk> to be a ' church ' merely as a cover to pursue its economic interests " . In the same decision , the court also found that Scientology uses " <unk> and totalitarian practices " . Given the lessons of Germany 's 20th @-@ century history , in which the country came to be dominated by a fascist movement that started from similarly small beginnings , Germany is very wary of any ideological movement that might appear to be seeking a position of absolute power . <unk> in Scientology writings to the elimination of " parasites " and " <unk> " people who stand in the way of progress towards Scientology 's <unk> world " without <unk> , without criminals and without war " evoke uncomfortable parallels with Nazism , and have led to Scientology being classified as an " <unk> political movement " . 
- To further justify its stance , the German government has also pointed to the long history of U.S. court cases involving Scientology , including the conviction of 11 top Scientologists in 1979 and 1980 for a conspiracy involving the infiltration of U.S. government agencies , <unk> and the theft of government documents , a 1994 U.S. Supreme Court finding that Scientology practices took place in a " <unk> environment " , and Scientology 's track record of pursuing its critics through malicious court cases and private investigators . In examining the potential threat posed by Scientology the German government has noted that Scientology organizations are " structured so as to make the individual <unk> and financially dependent on a Scientology system " , and that members often abandon contact with friends and family . 
- 
- = = History = = 
- 
- 
- = = = Scientology presence in Germany = = = 
- 
- Scientology first became active in Germany in 1970 . By 2007 , there were ten major centres ( " Scientology Churches " ) , as well as fourteen minor centres ( " Scientology <unk> " ) in Germany . The German Scientology Churches are located in the big cities – Munich , Hamburg , Berlin , Düsseldorf , Frankfurt am Main , Hanover and <unk> . Of the Scientology <unk> , nine are in Baden @-@ <unk> , and three in <unk> . Following German re @-@ unification , Scientology proved unable to gain significant numbers of followers in the territories of the former German Democratic Republic ; most adherents are found in Baden @-@ <unk> , <unk> and North @-@ Rhine <unk> . 
- Scientology is represented by a large number of independent associations or <unk> in Germany ; their umbrella organisation is the Scientology <unk> <unk> <unk> Germany 's domestic intelligence service , the <unk> für <unk> ( <unk> , or Federal Office for the Protection of the Constitution ) , estimates that there are 4 @,@ 000 Scientologists in Germany , down from earlier estimates of 5 @,@ 000 to 6 @,@ 000 . The Church of Scientology reported around 30 @,@ 000 members from the mid @-@ 1990s onwards ; this number remained stable for many years . However more recently Scientology has said it has only 12 @,@ 000 members . <unk> in Scientology membership numbers arise because the Church of Scientology applies more inclusive criteria in establishing its figures , essentially including anyone who has purchased a book or participated in courses , regardless of their subsequent involvement . The number of <unk> bound Scientology staff members working in German Scientology organizations is unlikely to exceed a few hundred . 
- Scientology formulated a " <unk> Germany " strategy in 1994 – similar to equivalent strategies pursued by Scientology in other countries and regions of the world – with the long @-@ term aim of transforming German society in line with the <unk> ideal : a non @-@ <unk> society in which Scientology enjoys <unk> influence . The programme sought to address Scientology 's image problems in Germany , to identify weak points in Germany that could be exploited for political gain , such as Germany 's National Socialist history , and to increase both membership figures and political influence in German society , with a special emphasis on <unk> Scientologists into key positions in industry and government . As most religions seek to widen their influence in society , such a programme could of course also be defended as a missionary effort , much like those many religions engage in . However , according to the <unk> , the strategy has not had any significant success . By 1998 , 90 government officials had been suspected of being Scientologists and in 48 cases the suspicions were confirmed , but apart from some isolated cases , most of the officials concerned had not used their positions to advertise Scientology . According to <unk> & <unk> ( 2009 ) , it is unknown to what degree the " <unk> Germany " programme is still being pursued . 
- 
- = = = Public opposition = = = 
- 
- German public discourse does not regard Scientology as a religion , but generally <unk> it as a <unk> ( cult or sect ) , or as an <unk> profit @-@ making venture preying on vulnerable minds . Public concerns about the potential dangers posed by cults date back to the early 1970s , when widespread debate took place about " youth religions " such as the <unk> Church , <unk> , Children of God , and the Divine Light Mission . The most prominent critics of these new religious movements were the " sect commissioners " ( <unk> ) of Germany 's Protestant Churches , who also actively promoted the establishment of private " initiatives of parents and concerned persons " . <unk> <unk> ( " Educational Information Campaign " ) became an important organization dedicated to opposing Scientology ; taking an activist stance , it warned people not to get involved with Scientology , filed successful lawsuits against the Church of Scientology over its <unk> in public places , and published an influential book , The <unk> of Scientology and its Front <unk> . In 1981 , the organization 's founder , <unk> <unk> , became the director of <unk> für <unk> und <unk> <unk> ( " Campaign for <unk> and <unk> Freedom " ) , Germany 's most prominent anti @-@ cult organization . <unk> from sect experts about the influence of new religious movements gained media attention which put political pressure on the government to deal with the situation ; as the movements were not doing anything illegal , the government resorted to issuing a range of leaflets and public statements giving general warnings about religious <unk> , the earliest of these publications appearing in 1979 . 
- <unk> by events such as the <unk> Siege in 1993 , the murders and suicides associated with the Order of the Solar Temple , and the 1995 <unk> <unk> incidents in Japan , German fears and concerns about new religious movements gained in intensity in the 1990s , with Scientology attracting particular attention . <unk> that Scientology had a totalitarian character were reinforced when Robert <unk> Young , an American ex @-@ Scientologist and former PR official in the Church of Scientology , visited German officials in late 1995 and wrote an article in Der <unk> , a widely @-@ read weekly magazine , describing Scientology as a totalitarian system operating a <unk> – the <unk> Project Force – for members of Scientology 's Sea Org found guilty of <unk> . From the mid @-@ 1990s onward , press articles , reports and essays on Scientology appeared on an almost daily basis , accompanied by books and television programmes that reached a mass audience . 
- As noted by the religious scholar Hubert <unk> , Scientology came to be seen as a " serious political danger that not only threatened to turn individuals into will @-@ less zombies , but was also conspiring to overthrow the democratic constitution of the state " . This view of Scientology as a public enemy , <unk> adds , " became a matter of political <unk> " : senior political figures became involved in launching campaigns against Scientology , and being suspected of any association with it resulted in social <unk> . Stephen A. Kent , writing in 1998 , noted that officials at all levels of German government shared the insistence that Scientology should be suppressed . Scientology was viewed as " a totalitarian , business @-@ driven organization [ ... ] guilty of significant human rights abuses . " Officials examining primary and secondary sources , legal documents , and the testimony of former members , concluded that the organization was " <unk> to a democratic state " . Federal <unk> and state governments were asked to use all legal means at their disposal to check the activities of Scientology . 
- Government publications on the dangers of <unk> increased between 1996 and 1998 , and a significant number of them dealt with the Church of Scientology . The German courts had approved such publications in 1989 , seeing them as part of the government 's responsibility to keep the public informed , and finding that they did not interfere with religious freedom . In 1996 , the German parliament launched an <unk> ( <unk> ) Commission to investigate <unk> and similar groups , in large part because of public concerns about Scientology . Its final report , published in June 1998 , concluded that Scientology , alone among new religious movements , required monitoring by Germany 's domestic intelligence services . 
- An area of widespread concern in the German media has been the alleged " infiltration " of businesses by Scientologists , in line with Scientology 's declared aim to penetrate society , politics and business in preparation for world domination . Attempts to infiltrate businesses have reportedly been most successful among small and medium @-@ size companies , such as estate agents , management consultants and management @-@ training companies . Management @-@ consultancy firms led by Scientologists often conceal their association with Scientology ; once they have recruited members of their clients ' upper management , these managers may send employees to Scientology trainers , as part of company education and training programmes , without informing them as to the origin of the training methods used . An expensive commercial version of Scientology 's Oxford <unk> Analysis , usually offered free as part of Scientology <unk> in public places , temporarily entered some major German companies ( who were unaware of its <unk> ) via such a management @-@ consultancy firm . 
- In the mid @-@ 2000s , German sect experts expressed concerns that Scientologists were becoming active in the German after @-@ school <unk> market . These concerns arose because customers of around 20 after @-@ school <unk> centres operated by Scientologists in Frankfurt , Hamburg , <unk> and elsewhere might be unaware that their children were being taught by Scientologists , using Scientology methods . <unk> advertising the <unk> services would at most mention the name of L. Ron Hubbard , the founder of Scientology , but not Scientology itself . 
- In early 2008 , Thomas <unk> , <unk> Commissioner of the German Lutheran Church in Berlin and <unk> , and the historian <unk> Knopp both likened the Scientologist Hollywood actor Tom Cruise to Goebbels , the Nazi propaganda minister . <unk> and Knopp cited a leaked Scientology video in which Cruise was seen asking the audience whether Scientologists should " clean up " the world , the audience responding with enthusiastic cheers – cheers which <unk> and Knopp felt were reminiscent of the audience 's response to Goebbels ' famous question , " Do you want total war ? " <unk> 's and Knopp 's comments found few critics in Germany . Most Germans consider Scientology a <unk> organization . In 1997 , Time reported that 70 % of Germans favoured banning Scientology ; a poll conducted in September 2008 by Der <unk> found 67 % support for a ban . 
- German scholars such as <unk> <unk> and Gerald <unk> have commented that rhetoric dominates public discourse around Scientology in Germany : in their view , efforts to " frame " information in such a way as to shape opinion have long been more important than the underlying <unk> . In <unk> 's words , this includes both the " efforts of German politicians to enhance their popularity with strong @-@ <unk> statements " and " Scientology 's efforts to present itself as the victim of unjust persecution " ; commenting on foreign reporting on Scientology in Germany , she adds that " the American press may prefer <unk> news to boring investigation and may frame the issue according to American stereotypes " . Both <unk> and <unk> assert that the situation is compounded by the general <unk> of scientific studies of Scientology . <unk> as well as Irving <unk> , Professor of Religious Studies at the University of Calgary in Canada , have remarked in particular on the lack of academic studies by German scholars . <unk> attributes this situation to the strong influence of the Christian churches in Germany , which has made German academics wary of approaching the subject , because they fear repercussions for their research funding and for their prospects of future employment if they involve themselves in the debate . 
- In 2010 a German public broadcaster , <unk> , showed the film Until Nothing Remains , a <unk> account of the effect Scientology had on one German family . Said to be based on a true story , the film attracted widespread media attention and a viewership of 8 @.@ 69 million . 
- 
- = = Legal status = = 
- 
- While there have been calls for Scientology to be banned , the Church of Scientology remains legal in Germany and is allowed to operate there . Its precise legal status however is unresolved . Two points are contested : first , whether or not the teachings of Scientology qualify as a " religion or <unk> " ( Religion or <unk> ; these are equal before German law ) , and <unk> , whether or not these teachings are only used as a pretext for purely commercial activity ; if the latter were the case , this would most likely imply that Scientology would not qualify for protection as a " religious or <unk> community " ( <unk> oder <unk> ) under Article 4 of the German constitution , which guarantees the freedom of belief , religion and <unk> . Status as a " religious or <unk> community " also affects a broad range of other issues in Germany , such as taxation and freedom of association . 
- The Federal Court of Justice of Germany has not yet made an explicit decision on the matter , but implicitly assumed in 1980 that Scientology represented a religious or <unk> community . The Upper Administrative Court in Hamburg explicitly asserted in 1994 that Scientology should be viewed as a <unk> community . In 1995 , the Federal Labor Court of Germany decided that the Church of Scientology merely pursued commercial aims and did not represent a religious or <unk> community entitled to protection under Article 4 of the German Constitution , although another decision by the same court left the question open again in 2003 . In another 2003 decision , the Administrative Court of Baden @-@ <unk> in <unk> said there were no indications that the teachings of Scientology merely served as a pretext for commercial activity . In 2005 , the Federal Administrative Court of Germany explicitly granted a Scientologist protection under Article 4 @.@ 1 of the German Constitution , which declares the freedom of religion and <unk> <unk> . 
- Many courts have declined to assess the religious status of Scientology , finding that the question was irrelevant to deciding the case at hand . The Federal Administrative Court for example ruled in 1997 that the question whether or not Scientology was a religion was irrelevant , and that its legal status should be judged by its business activities . The German government does not consider the Church of Scientology to be a religious or <unk> community and asserts that Scientology is a profit @-@ making enterprise , rather than a religion . Recent years have seen a number of court decisions in Scientology 's favour , despite the very widespread negative attitude to Scientology among politicians and the general public . 
- 
- = = Government surveillance = = 
- 
- Given the history of Nazism 's rise to power in Germany in the 1930s , the present German state has committed itself to taking active steps to prevent the rise of any ideology that threatens the values enshrined in the German constitution . The <unk> domestic intelligence service ( <unk> für <unk> , or Federal Office for the Protection of the Constitution ) regards the aims of Scientology as running counter to Germany 's free and democratic order , and has been monitoring Scientology since 1997 , as have the <unk> for the Protection of the Constitution in a number of German <unk> . Minister for Family Policy <unk> <unk> instituted the surveillance , saying that the church had totalitarian tendencies and that she would oppose Scientology with all the means at her disposal . 
- The German Church of Scientology has repeatedly challenged the legality of this surveillance in court . In December 2001 , the Administrative Court in Berlin ruled against the Berlin Office for the Protection of the Constitution and ordered it to stop the recruitment and deployment of staff and members of the Church of Scientology Berlin as paid <unk> . The court ruled that the use of <unk> was <unk> . In 2003 , the same court ruled that it was illegal for the Berlin Office for the Protection of the Constitution to include the activities of Scientology in its report , given that the report did not document any activities that were opposed to the constitution . 
- At the federal level , Scientology lost a complaint against continued surveillance by the <unk> in November 2004 . The federal court based its opinion on its judgment that the aims of Scientology , as outlined by L. Ron Hubbard in his writings , were incompatible with the German constitution . <unk> acting for the Federal Office for the Protection of the Constitution pointed out that Hubbard had written that civil rights , for example , should be restricted to Scientologists , and they asserted that the Scientology organization was taking systematic steps to infiltrate society and government institutions in order to prevent anti @-@ Scientology legislation . <unk> counsel acting for the Church of Scientology had contended that Scientology was non @-@ political , its aims were the liberation of the human being , and that Hubbard 's instructions were valid only within the Church of Scientology and were subject to interpretation , and at any rate there was no effort to implement these instructions in Germany . The court disagreed and ruled that many sources , some of them not accessible to the general public , indicated that the aims of the Church of Scientology did include the <unk> of the principle of equality and other essential human rights . 
- In <unk> , surveillance was stopped by a court as inappropriate in 2005 , because there is no local branch of Scientology and few members . As of 6 May 2008 , the Church of Scientology in Germany dropped the legal battle to prevent surveillance of its activities by the <unk> after the North Rhine @-@ <unk> Higher Administrative Court in <unk> refused to hear an appeal on the matter . Being suspected of maintaining " ambitions against the free , democratic basic order " , the Scientology organization added a declaration on human rights and democracy to its <unk> . 
- There is at least one example of surveillance of Scientology by the German intelligence services outside of Germany . In 1998 , the Swiss government detained an agent of the German government , charging him with " carrying out illegal business for a foreign state , working for a political information service and <unk> identity documents " . The German government posted bail for the agent . He was eventually given a 30 @-@ day suspended jail sentence for <unk> on Scientology , and the German government apologized to Switzerland for the incident . 
- 
- = = " <unk> <unk> " = = 
- 
- A " sect filter " , also known as a " protective declaration " ( <unk> ) , is a document that requires prospective business partners or employees to acknowledge any association with a sect or new religious movement before entering a business or employment contract . Such sect <unk> , primarily used to screen out Scientologists , have been drafted by German government agencies for use by businesses . " <unk> commissioner 's " offices exist in Germany as part of regional or local government . 
- A work instruction introduced in 1996 requires government staff in the <unk> – local employment agencies and social security offices operated by the Federal Ministry of Labour and Social Affairs – to mark companies owned by Scientologists with the letter " S " . Where companies are suspected of having Scientologist staff , prospective employees are alerted to this fact by government staff . Government officials have publicised the names of individual Scientologists and conducted media campaigns against their businesses ; some <unk> have placed advertisements in the press saying they are not Scientologists in order to avoid the associated stigma . 
- Due to concerns about possible government infiltration by Scientologists , applicants for civil service positions in <unk> are required to declare whether or not they are Scientologists , and a similar policy has been instituted in <unk> . Companies <unk> for government contracts were likewise required to state they are not Scientologists ; in 2001 , this requirement was changed , and firms are now asked to sign a form stating that " the technology of L. Ron Hubbard will not be used in executing the contract " . When it became known that Microsoft 's Windows 2000 operating system included a disk <unk> developed by Executive Software International ( a company headed by a Scientologist ) , this caused concern among German government officials and clergy over data security and the potential for espionage . To <unk> these concerns , Microsoft Germany agreed to provide a means to <unk> the utility . Following letters of complaint about discrimination from Scientology lawyers , some American companies such as General Electric , IBM and Ford Motor Company instructed their German <unk> to cease the use of protective declarations . 
- The city @-@ state of Hamburg set up a full @-@ time office dedicated to opposing Scientology , the Scientology Task Force for the Hamburg Interior Authority , under the leadership of <unk> <unk> . In 2005 , in a case brought by a Scientologist , the Federal Administrative Court of Germany ordered the city of Hamburg to cease recommending the use of protective declarations to its business community , finding that the practice <unk> religious freedom . In June 2008 , the Hamburg Administrative Court fined the city of Hamburg 5 @,@ 000 <unk> ( $ 7 @,@ 000 ) for not <unk> with court instructions banning the use of " sect <unk> . " Internet links to sample <unk> to be used by businesses had continued to remain available . <unk> Barker , a professor of sociology at the London School of Economics , has noted that " Germany has gone further than any other Western European country in <unk> the civil rights of Scientologists . " The Hamburg task force was closed down in August 2010 as a result of budget cuts ; <unk> moved to a position within the Hamburg interior authority , where she continues her work on Scientology . 
- Scientologists have been banned from joining major political parties in Germany such as the Christian Democratic Union , the Christian Social Union of <unk> , the Social Democratic Party of Germany and the Free Democratic Party . <unk> Scientologist members of these parties have been " purged " , according to Time Magazine . Scientologists have been prevented from running employment and au pair agencies in Germany ; Scientologists who were running such agencies had their permits revoked . In 1995 , a sports scientist and former member of the German national fencing team was dismissed from his job at the German Olympic fencing centre after he stated in an interview that he had enjoyed reading books by L. Ron Hubbard and had participated in a course run by a Scientologist management and communication consultancy firm . Thomas <unk> , a German TV presenter , was falsely accused in 1993 of having taken part in Scientology courses ; <unk> responded by announcing that he had not , and that he would <unk> cease all contact with a friend who had links to Scientology . In 2007 , Günther <unk> , the Minister @-@ President of the German state of Baden @-@ <unk> , expressed concern that Scientologist John <unk> was to appear on <unk> 's programme , and asked the <unk> TV station to consider <unk> the invitation ; the <unk> said that <unk> <unk> would cause greater damage , and that Scientology was not going to be discussed in the programme . 
- In 2010 , the Bavarian Administrative Court ruled that a woman working in a children 's <unk> centre , whose employment had been terminated when her ex @-@ husband identified her as a Scientologist , should be reinstated . The woman had demonstrated to the court 's satisfaction that her <unk> beliefs were irrelevant to her work . According to the agreement that concluded the case , she promised not to use Scientology methods in her work , and to inform the children 's parents of her membership in Scientology . 
- 
- = = Initiative to ban Scientology = = 
- 
- In March 2007 , it was reported that German authorities were increasing their efforts to monitor Scientology in response to the opening of a new Scientology headquarters in Berlin . On December 7 , 2007 , German federal and state interior ministers expressed the opinion that the Scientology organization was continuing to pursue anti @-@ constitutional goals , <unk> " essential basic and human rights like the dignity of man or the right to equal treatment " , and asked Germany 's domestic intelligence agencies to collect and evaluate the information required for a possible judicial inquiry aimed at banning the organization . 
- The move was criticized by German politicians from all sides of the political spectrum , with legal experts and intelligence agencies expressing concern that an attempt to ban the organization would likely fail in the courts . Sabine Weber , president of the Church of Scientology in Berlin , called the accusations " unrealistic " and " absurd " and said that the German interior ministers ' evaluation was based on " a few sentences out of 500 @,@ 000 pages of <unk> literature " . She added , " I can also find hundreds of quotes in the Bible that are totalitarian but that doesn 't mean I will demand the ban of Christianity . " 
- In November 2008 , the government abandoned its attempts to ban Scientology , after finding insufficient evidence of illegal or unconstitutional activity . The report by the <unk> cited knowledge gaps and noted several points that would make the success of any legal undertaking to ban Scientology doubtful . First , the <unk> report stated there was no evidence that Scientology could be viewed as a foreign organization ; there were German churches and missions , a German board , German <unk> , and no evidence that the organization was " totally remote @-@ controlled " from the United States . A foreign organization would have been much easier to ban than a German one . The second argument on which those proposing the ban had counted was Scientology 's aggressive opposition to the constitution . Here , the report found that Scientology 's behaviour gave no grounds to assume that Scientology aggressively sought to attack and overthrow Germany 's free and democratic basic order . " Neither its <unk> nor any other utterances " supported the " conclusion that the organization had criminal aims " . The <unk> also considered whether there were grounds to act against the Church of Scientology on the basis that they were <unk> medicine without a licence , but expressed doubts that a court would accept this reasoning . 
- Commenting on the decision to drop the ban attempt , <unk> <unk> , Berlin 's interior minister , said , " This organization pursues goals – through its writings , its concept and its disrespect for minorities – that we cannot tolerate and that we consider in violation of the constitution . But they put very little of this into practice . The appraisal of the Government at the moment is that [ Scientology ] is a <unk> organization , but it is not an organization that we have to take a hammer to . " The Church of Scientology expressed satisfaction with the decision , describing it as the " only one possible " . <unk> of Scientology 's activities by the German intelligence services continues . 
- In February 2009 , the Berlin Administrative Court ruled that a poster placed by local city authorities on an advertising column next to a bus stop in front of the Berlin Scientology headquarters , warning <unk> @-@ by of the potential dangers Scientology activities posed to democracy and individual freedom , should be removed . The decision was upheld in July 2009 by the Upper Administrative Court of Berlin @-@ <unk> which ruled that the poster violated Scientologists ' basic religious rights . 
- 
- = = Criticism of Germany 's stance = = 
- 
- The United States media , while generally reporting negatively on Scientology in domestic news , has taken an at least partially supportive stance towards Scientology in relation to Germany . Richard Cohen for example , writing in the Washington Post , said in 1996 : " Scientology might be one weird religion , but the German reaction to it is <unk> still – not to mention disturbing . " Alan Cowell , writing in the New York Times , wrote in 1997 that the German response to Scientology – motivated by officials ' fear that Scientology " was a totalitarian movement growing , like the Nazi party , from <unk> beginnings " – was itself <unk> of " the Nazi era 's <unk> " . 
- The U.S. Department of State has repeatedly claimed that Germany 's actions constitute government and societal discrimination against minority religious groups and expressed its concerns over the violation of Scientologists ' individual rights posed by sect <unk> . The U.S. Department of State began to include the issue of Scientology in Germany in its annual human rights reports after the 1993 agreement between the Church of Scientology and the U.S. Internal Revenue Service , through which Scientology gained the status of a tax @-@ exempt religion in the United States . That decision also marked the beginning of more intense lobbying efforts by the Church of Scientology in Washington , using paid <unk> . The State Department 's 1996 human rights report on Germany , released in January 1997 , warned that artists and businesses with Scientology connections " may face boycotts and discrimination , sometimes with government approval . " Past targets of such actions had included Scientologist actors Tom Cruise and John <unk> , as well as jazz pianist Chick <unk> . 
- Also in January 1997 , an open letter to then @-@ Chancellor <unk> <unk> appeared , published as a newspaper advertisement in the International Herald Tribune , drawing parallels between the " organized oppression " of Scientologists in Germany and Nazi policies <unk> by Germany in the 1930s . The letter was conceived and paid for by Hollywood lawyer <unk> Fields , whose clients have included Tom Cruise and John <unk> , and was signed by 34 prominent figures in the U.S. entertainment industry , including the top executives of MGM , Warner Bros. , Paramount , Universal and Sony Pictures Entertainment as well as actors Dustin Hoffman and Goldie <unk> , director Oliver Stone , writers Mario <unk> and <unk> Vidal and talk @-@ show host Larry King . It echoed similar parallels drawn by the Church of Scientology itself , which until then had received <unk> notice , and was followed by lobbying efforts of Scientology celebrities in Washington . 
- U.S. Department of State spokesman Nicholas Burns rejected the Nazi comparisons in the open letter as " <unk> " and distanced the U.S. government from Nazi comparisons made by the Church of Scientology , saying , " We have criticized the Germans on this , but we aren 't going to support the Scientologists ' terror tactics against the German government . " Chancellor <unk> , commenting on the letter , said that those who signed it " don 't know a thing about Germany and don 't want to know . " German officials argued that " the whole fuss was <unk> up by the Scientologists to achieve what we won 't give them : tax @-@ exempt status as a religion . This is <unk> , pure and simple . " Officials explained that precisely because of Germany 's Nazi past , Germany took a determined stance against all " radical cults and <unk> , including right @-@ wing Nazi groups " , and not just against Scientology . <unk> 's Christian Democratic Union party denounced the letter as " absurd " and cited German court <unk> stating that Scientology had primarily economic goals and could legitimately be referred to using phrases such as a " <unk> <unk> of oppression " . 
- In February 1997 , a United States immigration court judge granted <unk> to a German Scientologist who claimed she would be subject to religious persecution in her homeland . In April 1997 , John <unk> met personally with U.S. President Bill Clinton at a conference in Philadelphia . <unk> later said Clinton assured him that he would " really love to help " with the " issue over in Germany with Scientology " . According to <unk> , Clinton recalled that " he had a roommate years ago who was a Scientologist and had really liked him , and respected his views on it " , stating that Scientologists " were given an unfair hand in [ Germany ] and that he wanted to fix it " . In September 1997 , John <unk> , Chick <unk> and fellow Scientologist Isaac Hayes were heard by the Commission on Security and Cooperation in Europe ( <unk> , also known as the Helsinki Commission ) , voicing their complaints about the treatment of Scientologists in Germany , and had a briefing with United States National Security <unk> Sandy Berger , whom Clinton had assigned to be " the administration 's Scientology point person " . The German ambassador responded with a letter to the <unk> stating that the German government had come to the conclusion that Scientology 's " pseudo @-@ scientific courses can seriously jeopardize individuals ' mental and physical health and that it exploits its members " , adding that " membership can lead to psychological and physical dependency , to financial ruin , and even to suicide . In addition , there are indications that Scientology poses a threat to Germany 's basic political principles . " 
- A United Nations report in April 1998 raised concerns about the violation of individual rights posed by sect <unk> . However , it rejected the comparison of the treatment of Scientologists with that of Jews during the Nazi era . 
- In 2000 , the German Stern magazine published the results of its investigation of the <unk> case . It asserted that several rejection letters which the woman had submitted as part of her <unk> application – ostensibly from potential employers who were rejecting her because she was a Scientologist – had in fact been written by fellow Scientologists at her request and that of Scientology 's Office of Special Affairs , and that she was in personal financial trouble and about to go on trial for tax <unk> at the time she applied for <unk> . On a 2000 visit to Clearwater , Florida , <unk> <unk> of the Scientology Task Force for the Hamburg Interior Authority likewise alleged that the <unk> case had been part of an " orchestrated effort " by Scientology undertaken " for political gain " , and " a spectacular abuse of the U.S. system " . German expatriate Scientologists resident in Clearwater , in turn , accused <unk> of <unk> a " hate campaign " in Germany that had " ruined the lives and fortunes of scores of Scientologists " and maintained that Scientologists had not " exaggerated their plight for political gain in the United States . " Mark <unk> , a ( former ) top Church of Scientology official , said that although Scientology had not orchestrated the case , " there would have been nothing improper if it had . " 
- In 2003 , Joachim <unk> , writing in the Swiss <unk> <unk> Zeitung , noted that <unk> <unk> , a German Christian theologian , director of the Hannah <unk> Institute for Research into <unk> in Dresden and recipient of an honorary doctorate from Lund University , Sweden , for his <unk> of religious freedom , had been pressured to <unk> publication of his scientific study of Scientology after having found himself the subject of widespread criticism in the German media for advocating a more tolerant attitude towards Scientology . <unk> concluded that " <unk> " had " triumphed " over science and noted an apparent lack of confidence in Germany 's ability to engage in open public discourse on the matter . 
- The U.S. Department of State 's 2012 report on religious freedom in Germany , published in 2013 , stated that " The status of the Church of Scientology remains in <unk> . The Constitutional Court and various courts at the state level have not explicitly ruled that Scientology is a religion . Government agencies at the federal and state level have rules and procedures that discriminate against Scientology as a group and against its members . Four of the major political parties ( the Christian Democratic Union , Christian Social Union , Social Democratic Party , and Free Democratic Party ) ban Scientologists from party membership . [ ... ] Scientologists reported instances of governmental discrimination . Although courts at the state and federal level condemned the improper use of so @-@ called ' sect <unk> ' to <unk> and boycott Scientologists , they remained in use in the public sector . ' <unk> <unk> ' typically asked potential new employees to confirm in writing that they had no contact with Scientology , did not participate in its training courses , and rejected its doctrines . [ ... ] Catholic and Protestant churches continued to oppose Scientology <unk> [ sic ] , although press reporting and public reactions to Scientology decreased . Several private organizations issued warnings about after @-@ school study programs run by Scientologists . " 
- 
- = = = <unk> sources = = = 
- 
- 
- = = = <unk> sources = = = 
- 
- 
- = = = Government and court documents = = = 
- 
- 
- = = = Scientology sources = = = 
- 
- 
- = = = Scientology sites = = = 
- 
- <unk> ( German ) 
- <unk> – Human Rights website of the German Church of Scientology ( German ) 
- 
- = = = German government sites = = = 
- 
- Scientology : Publications of German regional governments – <unk> of the German Federal Office for the Protection of the Constitution ( German ) 
- <unk> of the Hamburg Scientology Task Force ( German ) 
- 
- = Japanese battleship Asahi = 
- 
- Asahi ( <unk> , Asahi ) was a pre @-@ dreadnought battleship built for the Imperial Japanese Navy ( <unk> ) in the late 1890s . As Japan lacked the industrial capacity to build such warships itself , the ship was designed and built in the United Kingdom . Shortly after her arrival in Japan , she became flagship of the Standing Fleet , the <unk> 's primary combat fleet . She participated in every major naval battle of the Russo @-@ Japanese War of 1904 – 05 and was lightly damaged during the Battle of the Yellow Sea and the Battle of <unk> . Asahi saw no combat during World War I , although the ship participated in the Siberian Intervention in 1918 . 
- <unk> as a coastal defence ship in 1921 , Asahi was disarmed two years later to meet the terms of the Washington Naval Treaty , after which she served as a training and submarine depot ship . She was modified into a submarine salvage and rescue ship before being placed in reserve in 1928 . Asahi was recommissioned in late 1937 , after the start of the Second <unk> @-@ Japanese War , and used to transport Japanese troops . In 1938 , she was converted into a repair ship and based first at Japanese @-@ occupied Shanghai , China , and then Cam <unk> Bay , French Indochina , from late 1938 to 1941 . The ship was transferred to occupied Singapore in early 1942 to repair a damaged light cruiser and ordered to return home in May . She was sunk en route by the American submarine USS Salmon , although most of her crew survived . 
- 
- = = Background = = 
- 
- Combat experience in the First <unk> @-@ Japanese War of 1894 – 95 convinced the Imperial Japanese Navy of weaknesses in the <unk> École naval philosophy , which emphasized torpedo boats and commerce raiding to offset expensive heavily armoured ships . Therefore , Japan promulgated a ten @-@ year naval build @-@ up in early 1896 , to modernize and expand its fleet in preparation for further confrontations , with the construction of six battleships and six armoured cruisers at its core . These ships were paid for from the £ 30 @,@ 000 @,@ 000 indemnity paid by China after losing the First <unk> @-@ Japanese War . As with the earlier Fuji and <unk> classes , Japan lacked the technology and capability to construct its own battleships , and turned again to the United Kingdom for the four remaining battleships of the programme . Asahi , the fifth Japanese battleship to be built in Britain , was ordered from the <unk> Engineering & Shipbuilding Company shipyard in <unk> , Scotland in the 1897 annual naval programme . 
- 
- = = Design and description = = 
- 
- Asahi 's design was a modified version of the <unk> @-@ class battleships of the Royal Navy , with two additional 6 @-@ inch ( 152 mm ) guns . The ship had an overall length of 425 feet 3 inches ( 129 @.@ 6 m ) , a beam of 75 feet ( 22 @.@ 9 m ) , and a normal draught of 27 feet 3 inches ( 8 @.@ 3 m ) . She displaced 15 @,@ 200 long tons ( 15 @,@ 400 t ) at normal load . Asahi had a complete double bottom with 55 <unk> compartments . Her hull was also subdivided into 223 <unk> compartments . She was fitted as a flagship and her crew numbered about <unk> officers and enlisted men , including the admiral 's staff . 
- The ship was powered by two vertical triple @-@ expansion steam engines built by <unk> , Tennant , each driving one propeller , using steam generated by 25 Belleville boilers at a working pressure of 17 @.@ 03 bar ( 1 @,@ 703 <unk> ; 247 <unk> ) . The engines were rated at 15 @,@ 000 indicated horsepower ( 11 @,@ 000 kW ) , using forced draught , and designed to reach a top speed of 18 knots ( 33 km / h ; 21 mph ) although Asahi reached 18 @.@ 3 knots ( 33 @.@ 9 km / h ; 21 @.@ 1 mph ) from 16 @,@ <unk> indicated horsepower ( 12 @,@ 181 kW ) during her sea trials on 23 March 1900 . She carried a maximum of 2 @,@ 000 long tons ( 2 @,@ 032 t ) of coal which allowed her to steam for 9 @,@ 000 nautical miles ( 17 @,@ 000 km ; 10 @,@ 000 mi ) at a speed of 10 knots ( 19 km / h ; 12 mph ) . The ship was fitted with three steam @-@ driven 4 @.@ 8 @-@ <unk> ( 6 @.@ 4 hp ) <unk> . 
- Asahi 's main battery consisted of the same four <unk> Ordnance Company 40 @-@ calibre twelve @-@ inch guns used in all of Japan 's preceding battleships . They were mounted in twin @-@ gun turrets fore and aft of the superstructure . The <unk> powered mountings allowed the guns to be loaded at all angles of traverse , at a fixed elevation of + 13 @.@ 5 ° . Each mount could traverse a total of 240 degrees . They fired 850 @-@ pound ( 386 kg ) projectiles at a muzzle velocity of 2 @,@ 400 ft / s ( 730 m / s ) . 
- The ship 's secondary armament consisted of fourteen 45 @-@ calibre 6 @-@ inch ( 152 mm ) quick @-@ firing ( QF ) guns mounted in casemates . Eight of these guns were positioned on the main deck and the other six guns were placed above them in the superstructure . They fired 100 @-@ pound ( 45 kg ) shells at a muzzle velocity of 2 @,@ 300 ft / s ( 700 m / s ) . Protection against torpedo @-@ boat attacks was provided by twenty QF 12 @-@ pounder 12 cwt guns . The 12 @-@ pounders fired 3 @-@ inch ( 76 mm ) , 12 @.@ 5 @-@ pound ( 5 @.@ 7 kg ) projectiles at a muzzle velocity of 2 @,@ <unk> ft / s ( <unk> m / s ) . <unk> guns consisted of eight 47 @-@ millimetre ( 1 @.@ 9 in ) three @-@ pounder <unk> guns and four 47 @-@ millimetre 2 @.@ 5 @-@ pounder <unk> guns . The former were mounted in the superstructure and the latter in the fighting tops . The three @-@ pounder gun fired 3 @.@ 19 @-@ pound ( 1 @.@ 45 kg ) projectiles at a muzzle velocity of 1 @,@ <unk> ft / s ( <unk> m / s ) , while the 2 @.@ 5 @-@ pounder fired 2 @.@ 5 @-@ pound ( 1 @.@ 1 kg ) shells at a muzzle velocity of 1 @,@ 420 ft / s ( 430 m / s ) . The ship was also equipped with four submerged 18 @-@ inch torpedo tubes , two on each broadside . 
- The waterline main belt of Asahi consisted of Harvey armour 8 feet ( 2 @.@ 44 m ) high , of which 3 feet 8 inches ( 1 @.@ 11 m ) was above the waterline at normal load , and had a maximum thickness of 9 inches ( <unk> mm ) for the middle 224 feet ( 68 @.@ 28 m ) of the ship . It was only 4 inches ( 102 mm ) thick at the ends of the ship and was surmounted by a six @-@ inch <unk> of armour that ran between the barbettes . The barbettes were 14 inches ( <unk> mm ) thick , but only 10 inches ( 254 mm ) behind the upper armour <unk> . The barbette <unk> were protected by 10 inches of armour on their face while their sides were 6 inches thick and the roof was 1 @.@ 5 inches ( 38 mm ) thick . <unk> bulkheads connecting the barbettes to the side armour were 12 – 14 inches thick , but only 6 inches thick at the lower deck level . The frontal armour of the casemates protecting the secondary armament was also 6 inches thick with the rear protected by 2 @-@ inch ( 51 mm ) armour plates . The flat portion of the deck armour was 2 @.@ 5 inches ( 64 mm ) thick and 4 inches ( 102 mm ) thick where it sloped down to the sides of the ship . The conning tower was protected by 14 inches of armour . 
- Asahi , like all the other Japanese battleships of the time , was fitted with four <unk> and Stroud <unk> coincidence rangefinders that had an effective range of 8 @,@ 000 yards ( 7 @,@ 300 m ) . The ships were also fitted with 24 @-@ power <unk> <unk> <unk> . 
- 
- = = Construction and career = = 
- 
- Asahi , meaning " rising sun " , a poetic name for Japan from a stanza of <unk> poetry , was laid down on 1 August 1898 in <unk> , Scotland , by the <unk> Engineering & Shipbuilding Co. and completed by John Brown & Company , which purchased the firm before Asahi was completed . She was launched on 13 March 1899 and completed on 31 July 1900 . Her completion was delayed by about three months when her bottom plating required repairs after running aground off <unk> following sea trials . The ship departed England , after repairs in Portsmouth , on the day of her completion , and arrived at <unk> , Japan , on 23 October 1900 . Asahi became flagship of the Standing Fleet on 22 May 1901 and was assigned to the 1st Battleship Division of the 1st Fleet when the Combined Fleet was re @-@ formed on 28 December 1903 . 
- At the start of the Russo @-@ Japanese War , Asahi , commanded by Captain <unk> <unk> , was assigned to the 1st Division of the 1st Fleet . She participated in the Battle of Port Arthur on 9 February 1904 , when Vice Admiral <unk> <unk> led the 1st Fleet in an attack on the Russian ships of the Pacific Squadron anchored just outside Port Arthur . <unk> had expected the surprise night attack by his destroyers to be much more successful than it was , anticipating that the Russians would be badly disorganized and weakened , but they had recovered from their surprise and were ready for his attack . The Japanese ships were spotted by the protected cruiser <unk> , which was patrolling offshore and alerted the Russian defences . <unk> chose to attack the Russian coastal defences with his main armament and engage the ships with his secondary guns . <unk> his fire proved to be a poor decision as the Japanese eight @-@ inch ( 203 mm ) and six @-@ inch guns inflicted little damage on the Russian ships , which concentrated all their fire on the Japanese ships with some effect . Although many ships on both sides were hit , Russian casualties numbered only 17 , while the Japanese suffered 60 killed and wounded before <unk> <unk> . Asahi was not hit during the engagement . 
- The ship participated in the action of 13 April , when <unk> successfully lured out a portion of the Pacific Squadron , including Vice Admiral <unk> <unk> 's flagship , the battleship <unk> . When <unk> spotted the five battleships of the 1st Division , he turned back for Port Arthur and <unk> struck a minefield laid by the Japanese the previous night . The Russian battleship sank in less than two minutes after one of her magazines exploded , and <unk> was one of the <unk> killed . <unk> by his success , <unk> resumed long @-@ range bombardment missions , prompting the Russians to lay more minefields , which sank two Japanese battleships the following month . 
- During the Battle of the Yellow Sea on 10 August , Asahi , now commanded by Captain <unk> <unk> , was second in line of the column of Japanese battleships , behind <unk> , and was one of the primary targets of the Russian ships . She was only hit by a single 12 @-@ inch shell that wounded two crewmen . Both guns in her aft 12 @-@ inch gun turret , however , were disabled by shells that detonated prematurely in their barrels . In turn she concentrated most of her fire upon the battleships <unk> and <unk> although both ships were only lightly damaged by the Japanese shells , which generally failed to penetrate any armour and detonated on impact . The ship made the critical hits of the battle , however , when two of her 12 @-@ inch shells struck the bridge of <unk> , killing the Russian squadron commander , Vice Admiral <unk> <unk> , two of his staff officers and the ship 's <unk> . The ship 's wheel was jammed to port by wreckage and then slowed to a halt which threw the rest of the Russian ships into total confusion . The second @-@ in @-@ command , Rear Admiral Prince Pavel <unk> , eventually gained control of the remainder of the squadron and headed back to Port Arthur . <unk> more than two months later , on 26 October , Asahi struck a mine off Port Arthur while on blockade duty . <unk> damaged , she was under repair at <unk> Naval Arsenal from November 1904 to April 1905 . Russian naval forces in the Far East had been destroyed or <unk> by this time and the Russians were forced to transfer ships from the Baltic Fleet that did not arrive until May . 
- 
- = = = Battle of <unk> = = = 
- 
- At the Battle of <unk> on 27 May 1905 , Asahi again followed the battleship <unk> into combat , this time against the Second and Third Pacific Squadrons . <unk> opened fire at the battleship <unk> <unk> , the Russian flagship , at 14 : 10 , and was joined by Asahi and the armoured cruiser <unk> shortly afterwards . Within an hour the Japanese ships had started a serious fire aboard the Russian ship , badly wounded the fleet commander , Vice Admiral <unk> <unk> , knocked out her rear 12 @-@ inch gun turret , and jammed <unk> <unk> 's steering so that she fell out of formation . The Russian ships were concentrating their fire on <unk> during the early part of the battle and Asahi was not damaged during this time . <unk> was able to cross the T of the Russian squadrons . <unk> <unk> 's steering was later repaired , but she <unk> between the Japanese and Russian fleets several times later in the battle and was heavily damaged . Asahi seems to have mostly engaged the battleships <unk> and <unk> in the late stages of the battle , although Fuji fired the shots that caused the <unk> 's magazines to explode and sink her . Asahi fired more twelve @-@ inch shells , 142 , than any other ship during the battle . In total , the ship was hit six times during the battle , but none of them damaged her significantly . While Asahi 's casualties are not precisely known , the Japanese only lost 110 men killed and 590 wounded to all causes during the battle . The battle was a total Japanese victory with five Russian battleships captured and incorporated into the <unk> . 
- Captain W. C. <unk> , the Royal Navy 's official military observer under the Anglo @-@ Japanese Alliance , took notes of the battle 's progress from a deck chair on Asahi 's exposed quarterdeck . His report confirmed the superiority of Japanese training and tactics and publicized the victory in the West . 
- 
- = = = Later career = = = 
- 
- In 1908 , Asahi was part of the Japanese fleet that escorted the American Great White Fleet through Japanese waters during its round @-@ the @-@ globe voyage . The ship was assigned to the 1st Fleet in 1908 and 1910 – 11 . Asahi became a gunnery training ship in 1914 , and was re @-@ armed in 1917 with Japanese guns replacing her original British @-@ made guns . The same year , she was assigned to the 5th Division of the 3rd Fleet . In 1918 , Asahi became flagship of her division and participated in the Japanese intervention in the Russian Civil War . She escorted troop convoys to the Russian Far East and was guard ship at <unk> from January to August 1918 . Asahi was reclassified as a first @-@ class coastal defence ship on 1 September 1921 , and began <unk> in 1922 at <unk> in compliance with the terms of the Washington Naval Treaty . She was reclassified as a training and submarine depot ship on 1 April 1923 and her <unk> was completed in July of that same year . Her displacement dropped to 11 @,@ <unk> long tons ( 11 @,@ 625 t ) with the loss of her armour and guns , and her speed was limited to 12 knots ( 22 km / h ; 14 mph ) . 
- The navy decided to convert Asahi into a submarine salvage ship and she began the first stage of her conversion with the installation of specialized salvage equipment from February to August 1925 . From 1926 to October 1927 , the ship 's 25 Belleville boilers were replaced with four <unk> Type <unk> boilers at <unk> Naval Arsenal . One of her two funnels was also removed , and two large lifting frames were installed as part of the second stage of her conversion . The ship conducted experiments in submarine rescue using the old German submarine 0 @-@ 1 ( ex @-@ U @-@ 125 ) . In May 1928 , Asahi was fitted with a 62 @-@ foot @-@ 4 @-@ inch ( 19 m ) compressed @-@ air aircraft catapult on her forecastle and successfully launched an <unk> Type 15 seaplane . After repeated accidents , the catapult was replaced by one powered by gunpowder . On the completion of testing in 1928 , Asahi was placed in reserve . 
- <unk> as a repair ship on 16 August 1937 , Asahi was taken out of reserve in November , after the Marco Polo Bridge Incident that started the Second <unk> @-@ Japanese War , and was used as a transport to land troops in an amphibious landing at Hangzhou Bay . Afterwards she began conversion at <unk> , Japan , into a repair ship ; this was completed on 18 December 1938 . Asahi was fitted with a <unk> wooden main battery fore and aft to resemble an old battleship after her arrival in Shanghai on 29 December . In May 1939 she was modified to act as a torpedo depot ship and carried out patrols between 29 May and 7 November 1940 . She was transferred to <unk> Bay , French Indochina , on 15 November 1940 and later transported the 11th Base Unit from <unk> to <unk> Bay 19 November – 7 December 1941 . 
- From 13 March 1942 , Asahi was stationed at Singapore , and in April her crew performed repairs on the light cruiser <unk> , which had been torpedoed by the submarine USS <unk> off Christmas Island . <unk> Singapore for <unk> on 22 May , escorted by the <unk> CH @-@ 9 , Asahi was sighted by the submarine USS Salmon on the night of 25 / 26 May 1942 , 100 miles ( 160 km ) southeast of Cape <unk> , Indochina . Of Salmon 's four torpedoes , two hit the ship in her port central boiler room and aft spaces . At 01 : 03 , moments after being hit , Asahi sank at 10 ° 00 ′ N 110 ° 00 ′ E. Sixteen men were killed in the attack ; the ship 's captain and <unk> crewmen were rescued by CH @-@ 9 . 
- 
- 
- = Training Day ( The Office ) = 
- 
- " Training Day " is the twentieth episode of the seventh season of the American comedy television series The Office and the shows <unk> episode overall . It originally aired on NBC on April 14 , 2011 . The episode was written by Daniel Chun and directed by Paul <unk> . This episode marks the first appearance of <unk> Vickers ( Will <unk> ) in the series . 
- The series depicts the everyday lives of office employees in the Scranton , Pennsylvania branch of the fictional Dunder Mifflin Paper Company . In the episode , Michael Scott 's ( Steve Carell ) replacement appears in the office , to start receiving training from Michael . The new manager , <unk> Vickers ( Will <unk> ) , has everyone hoping to make good first impressions : Andy Bernard ( Ed Helms ) finds himself <unk> typecast while Jim Halpert ( John Krasinski ) and Pam Halpert ( Jenna Fischer ) worry that they 've come on too strong . Only Dwight Schrute ( Rainn Wilson ) is <unk> about the new leader . 
- " Training Day " received mixed reviews from television critics , with many commenting on <unk> 's appearance . The episode was also viewed by 4 @.@ 16 million viewers and received a 4 @.@ 0 / 11 percent rating among adults between the ages of 18 and 49 , ranking first in its timeslot . The episode ultimately ranked as the highest @-@ rated NBC series of the night . 
- 
- = = Synopsis = = 
- 
- Michael is waiting in a hotel bar for his replacement <unk> Vickers ( Will <unk> ) to show up . While waiting , he meets a man at the bar and the two instantly hit it off . When Michael calls Vickers to see where he is , it is revealed that the man Michael has been talking to is actually <unk> , though it takes the two some time to actually realize this . 
- <unk> comes to the branch the next day , and most of the staff is eager to make a good first impression . Kevin wears his <unk> , Jim and Pam believe they have started off well by mentioning their baby , and <unk> <unk> Andy as the office " funny guy " when he reveals that he carries name <unk> around to avoid situations like the one <unk> and Michael were in . Meanwhile , Dwight is not pleased that he was not selected to replace Michael , believing that Michael put in a good recommendation for him . Michael becomes disturbed , however , when <unk> wants to make some changes , such as how Erin answers the phone . 
- While <unk> gets annoyed with Jim and Pam , he loves the antics of Andy , who has resorted to physical comedy when his jokes fall flat , getting to the point where he <unk> hot coffee on his pants and ( at <unk> 's urging ) eats soap ; Andy tells the camera with <unk> sadness that " this is my life now " . <unk> 's popularity with the employees causes Michael to get jealous to the point where he passes out <unk> butter sandwiches , despite <unk> 's <unk> allergy . <unk> then <unk> the staff in the " multipurpose room " , including Dwight , who learned from <unk> that Michael did not put in a recommendation and thus <unk> Michael . Jim quietly tells Michael that he chose to resign his job and needs to come to terms with both that choice and the fact that life is going to go on at the office . <unk> then steps out of his meeting and asks Michael for advice for how to run the meeting , saying that he 's a good manager and Dunder Mifflin won 't be the same without him . The two make up with a reverse <unk> , <unk> telling him that he should start enjoying his retirement . Michael returns to his office while <unk> conducts his meeting in the conference room . 
- In another effort to impress <unk> , Jim and Pam bring in <unk> , to which <unk> reacts positively . While they celebrate getting back on his good side , <unk> reveals to the camera that he is in fact indifferent to <unk> and was just being polite . 
- 
- = = Production = = 
- 
- The episode was written by co @-@ executive producer Daniel Chun , his fourth writing credit of the series . It was directed by then @-@ showrunner Paul <unk> , his sixth directing credit of the series . The episode marked the first appearance of Will <unk> 's four @-@ episode arc on the series and is also one of series star , Carell 's last episodes as a regular on the series . <unk> signed on to appear in Steve Carell 's final three episodes , and the first episode without Carell , " The Inner Circle " , to make Carell 's transition easier . Carell had decided to leave the series , because " I just think it 's time ... I want to fulfill my contract . When I first signed on I had a contract for seven seasons , and this coming year is my seventh . I just thought it was time for my character to go " . <unk> initially called the producers and offered to appear in Carell 's last season , because he 's " a fan and wanted to commemorate Carell ’ s swan song " . He had previously starred alongside Carell , in the 2004 film , <unk> : The Legend of Ron <unk> . 
- The Season Seven DVD contains a number of deleted scenes from this episode . The cut scenes include a sequence in which Michael and <unk> discuss cancer jokes and the 2010 film Megamind — an animated film that featured the voice of <unk> — while drinking <unk> , shots of Michael and <unk> discussing their enjoyment for coffee , and Dwight attempting to appease his new boss . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- In its original American broadcast , " Training Day " was viewed by an estimated 7 @.@ <unk> million viewers and received a 4 @.@ 0 rating / 11 % share among adults between the ages of 18 and 49 . This means that it was seen by 4 @.@ 0 % of all 18- to 49 @-@ year @-@ olds , and 11 % of all 18- to 49 @-@ year @-@ olds watching television at the time of the broadcast . This marked a rise in the ratings from the previous episode , " <unk> Sale " , which can be attributed to <unk> 's guest appearance . The episode ranked first in its timeslot , beating the Fox crime drama <unk> , which scored a 3 @.@ 5 / 10 percent relating in the 18 @-@ 49 demographic ; a <unk> of the ABC medical drama Grey 's Anatomy and a <unk> of the CBS crime drama <unk> : Crime Scene Investigation . 
- 
- = = = Reviews = = = 
- 
- " Training Day " received mixed reviews from television critics . IGN writer <unk> White wrote that <unk> was not given a proper introduction and that he wasn 't distinguished from Michael 's character . She also went on to criticize Jim , Pam and Dwight 's reaction to <unk> as the new manager , particular that of Jim and Pam , writing that " it 's becoming <unk> obvious that the longtime writers don 't know what to do with them anymore " . However , she concluded that the episode continued the seventh season 's improvement over the sixth , and that it showed potential for future episodes of the season . She ultimately gave the episode a 7 @.@ 5 out of 10 rating , denoting a " good " episode . The A.V. Club reviewer Myles <unk> praised Michael 's reactions to the office easily ignoring him for <unk> , calling the final shot of him walking into his office alone " quite effective " , and that Dwight 's reaction to not being suggested to be Michael 's replacement made it more " meaningful " . Despite this , he continued by saying that the series was not " sophisticated " enough to use this plotline effectively and criticized the writing for the cast , which made them look " stupid " for " cheap humor " and a " enormously shallow laughter " . He ultimately gave the episode a " C + " . Alan Sepinwall , a columnist for HitFix , complimented the chemistry between <unk> and Carell , but criticized the storyline which eventually came down to Michael " being <unk> about not being the center of attention every minute of every day " , negatively comparing it to " <unk> Party " . Despite this , he noted that <unk> 's presence did involve several funny moments . 
- Bonnie <unk> , for Paste , was more positive , writing that she preferred the use of comedy , following the emotional episode , " <unk> Sale " . She ultimately gave it an 8 @.@ 7 out of 10 . Will <unk> 's performance was especially noted by critics . <unk> complimented <unk> 's " <unk> " performance , but called his character " <unk> " . <unk> praised his dynamic with Carell , comparing it to their previous roles in <unk> . His later appearances in the series also received similar mixed reviews For instance , <unk> called <unk> 's scenes a " momentum killer " in " <unk> , Michael " , whereas Rob Sheffield of Rolling Stone called his performance in the same episode " pure magic " . 
- 
- 
- = J. C. W. <unk> = 
- 
- John <unk> <unk> <unk> ( August 5 , 1869 – January 9 , 1940 ) was the 35th Governor of Kentucky and a United States Senator from Kentucky . He was the state 's first popularly elected senator following passage of the Seventeenth Amendment . 
- <unk> from a prominent political family , <unk> was chosen as Democrat William <unk> 's running mate in the gubernatorial election of 1899 despite the fact that he was not yet of legal age to serve as governor if called to do so . <unk> lost the election to Republican William S. Taylor , but the Kentucky General Assembly disputed the election results . During the political wrangling that followed , an unknown assassin shot <unk> . A day later the General Assembly <unk> enough votes to give the election to <unk> , who was sworn into office on his deathbed . Taylor claimed the election had been stolen by the Democratic majority in the General Assembly and a legal fight ensued between him and <unk> over the governorship . <unk> ultimately prevailed and Taylor fled the state . 
- Following his term as governor , <unk> made a bid to become a U.S. Senator . His stance in favor of prohibition cost him the votes of four legislators in his own party and the seat went to Republican William O. Bradley . Six years later <unk> secured the seat by popular election , but he lost his re @-@ election bid largely because of his pro @-@ temperance views and his opposition to women 's suffrage . Though he continued to play an active role in state politics for another two decades , he never returned to elected office , failing in his gubernatorial bid in 1927 and his <unk> campaign in 1936 . He died in Louisville on January 9 , 1940 . 
- 
- = = Early life = = 
- 
- J. C. W. <unk> was born at <unk> , near <unk> in Nelson County , Kentucky , son of William <unk> and Julia <unk> ( <unk> ) <unk> . His maternal grandfather , Charles A. <unk> , was governor of Kentucky from 1839 to 1840 and served as <unk> general in the administration of John Tyler . His uncle , Robert C. <unk> , served as governor of Louisiana . 
- <unk> obtained his early education at <unk> Academy in <unk> . In 1881 he served as a page in the Kentucky House of Representatives at the age of 12 . Later , he enrolled at Central University ( now Eastern Kentucky University ) in Richmond , Kentucky but was forced to quit school at the age of 17 to support his widowed mother . Two years later , he became principal of <unk> public schools , serving from 1888 to 1893 . <unk> , he studied law at the University of Kentucky , where he earned his law degree in 1889 . He was admitted to the bar , and commenced practice in <unk> in 1893 . He also served as president of the Young Democrats ' Club of Nelson County . 
- 
- = = Political career = = 
- 
- <unk> 's political career began in 1894 when he was elected without opposition to the Kentucky House of Representatives . He served four consecutive terms and was Speaker of the House in 1898 , his final year in the House . He also served as a delegate to every Democratic National Convention from 1900 to 1920 . 
- 
- = = = Governor of Kentucky = = = 
- 
- Democrat William <unk> chose <unk> as his running mate in the Kentucky gubernatorial election of 1899 . <unk> was hesitant about the selection because he wanted someone who could deliver the vote of his home county in the general election , and <unk> 's native Nelson County was already committed to a rival candidate . But friends of <unk> assured him that <unk> would be loyal to <unk> 's reform agenda , whereas the two other men <unk> was considering as running mates would " stack the Senate committees against him . " <unk> was not yet 30 , the minimum age to serve as governor , at the time of his selection . 
- <unk> lost a close election to Republican William S. Taylor . When the General Assembly 's session opened on January 2 , 1900 , the election results were immediately challenged . With Democrats in control of both houses of the Assembly , the results seemed sure to be reversed . The Assembly was still <unk> on January 30 , 1900 , when <unk> was shot by an unknown assailant as he entered the state capitol building . The following day , as <unk> was being treated for his wounds at a local hotel , the General Assembly <unk> enough votes to give him the election . He was sworn into office from his bed the same day . Three days later <unk> died , never having risen from the bed . 
- Legislative chaos ensued as Taylor refused to acknowledge the Assembly 's decision and <unk> the governorship . The Republicans in the legislature <unk> Taylor 's orders , while the Democrats ignored Taylor and followed the orders of their leadership . Finally , on February 21 , 1900 , Taylor and <unk> agreed to let the courts settle the matter . The case first went before the Louisville Circuit Court , which found in favor of <unk> . Republicans appealed to the Kentucky Court of Appeals , at that time the court of last resort in the state . On April 6 , 1900 , the Court of Appeals upheld the ruling of the lower court . Taylor appealed to the Supreme Court of the United States , which declined to hear the case on May 21 , 1900 . Taylor 's only supporter on the court was <unk> John Marshall <unk> . 
- Following the Supreme Court ruling , Taylor fled to Indianapolis , Indiana , fearing he would be implicated in <unk> 's assassination . <unk> became acting governor , but because of the unusual circumstances surrounding the election , a special election was held November 6 , 1900 , to determine who would complete <unk> 's <unk> term . <unk> ( now of age ) won the election over Republican John W. <unk> by fewer than 4 @,@ 000 votes . Shortly following the special election , <unk> married Jean Raphael <unk> of <unk> . The couple had two sons . 
- As governor <unk> sought to unite his party and the state . As part of this effort he supported changes to the <unk> partisan <unk> Election Law , authored by his late running mate while the latter was a member of the General Assembly . He stressed non @-@ controversial issues such as improvements to roads and the state 's educational system . He recommended passage of a law to set uniform school textbook prices , a reform both he and <unk> had advocated during the gubernatorial campaign . However , his passive leadership ensured that the General Assembly did little to address his agenda . The only major pieces of legislation passed during <unk> 's term were a tax increase that added a half million dollars to the state 's revenue and a child labor law that forbade children under 14 to work without their parents ' consent . 
- 
- = = = Second term = = = 
- 
- Although the Kentucky Constitution prohibited governors from serving consecutive terms , <unk> announced he would seek a full term as governor in 1903 . His candidacy was challenged in court , but the court ruled <unk> had not served a full first term and was eligible to run . Due to his record of reconciliation and supporting non @-@ controversial reforms , he had no significant opposition in winning his party 's nomination . His record also deprived his Republican opponent , Morris B. <unk> , of any significant campaign issue in the general election . <unk> defeated <unk> and three minor candidates . 
- In his message to the legislature in 1904 , <unk> again raised the issue of a uniform school textbook law , which had not passed during his first term . The law was one of few significant reforms that passed during the 1904 session . During the session funds were approved for building a new capitol building and a memorial to the late Governor <unk> . 
- In March 1904 , <unk> signed the Day Law <unk> racial segregation of all schools in Kentucky . <unk> College , a private college in eastern Kentucky that had been integrated since the 1850s , immediately filed suit to challenge the law . The substance of the law was upheld in the circuit court and the Kentucky Court of Appeals . <unk> appealed to the Supreme Court , and in 1908 the court handed down an 8 – 1 decision against the college . Only John Marshall <unk> <unk> . 
- Near the close of the 1904 session , legislators approved the creation of <unk> County from parts of Carter , Elliott , and Lewis counties . Olive Hill was made the county seat . Soon the county 's existence was challenged in court on grounds that it fell short of the 400 square miles ( 1 @,@ 000 km2 ) required by the state constitution and that it reduced the counties it was carved from to less than 400 square miles ( 1 @,@ 000 km2 ) . Carter County joined the lawsuit , claiming the border of <unk> County passed too close to Grayson , the seat of Carter County , and <unk> , the seat of Lewis County . The state constitution forbade county borders to pass within 10 miles ( 16 km ) of a county seat . On April 29 , 1904 , the Kentucky Court of Appeals found in favor of the <unk> and dissolved <unk> County . 
- During the 1906 legislative session , <unk> urged investigation and prosecution of corrupt insurance companies , following the lead of New York attorney Charles Evans Hughes . In particular , he recommended reducing the practice of deferred <unk> , which allowed the insurance companies to keep large stores of cash on @-@ hand for illegal purposes . He further advocated that insurance companies doing business in the state be required to invest a certain percentage of their earnings in Kentucky , <unk> the its economy and providing <unk> some protection against fraud . 
- <unk> refused to send troops into the western part of the state to quell the ongoing Black <unk> Tobacco Wars . He cited constitutional reasons for his refusal , but more probably his reasons were political – the Democrats were dominant in this region and he didn 't want to challenge his own party . By collecting some old Civil War debts from the federal government , <unk> virtually eliminated the state 's debt . Encouraged by the state 's improved finances , the General Assembly voted to expand two of the state 's normal schools , Western State Teachers College in <unk> Green ( later Western Kentucky University ) and Eastern State Teachers College in Richmond ( later part of Eastern Kentucky University ) . 
- With a successful legislative session behind him , <unk> made a bold political move in June 1906 : he orchestrated an effort to set the Democratic gubernatorial and <unk> primaries in November – a full year before the gubernatorial election and two years before the <unk> election . <unk> wanted the Senate seat , and moving the primary up two years would allow him to secure his party 's nomination while he was still governor . It also allowed him to use his influence as governor to sway the party 's choice of his potential successor as governor . State <unk> Samuel Wilbur <unk> was <unk> 's choice for governor and easily won the early primary over <unk> <unk> <unk> . Former governor James B. <unk> challenged <unk> for the <unk> nomination , but <unk> won by more than 11 @,@ 000 votes . 
- 
- = = = U.S. Senator = = = 
- 
- <unk> 's term as governor ended December 10 , 1907 . In January 1908 he faced the legislature as the Democratic nominee for a seat in the U.S. Senate by virtue of the primary held two years earlier . The Republicans nominated former governor William O. Bradley . On the first ballot <unk> secured 66 of the needed 69 votes ; Bradley received 64 votes . Seven Democrats had not voted for <unk> . Over the next six weeks , 25 more votes were taken with neither man securing a majority , even though William Jennings Bryan , the Democratic nominee for president , campaigned for <unk> . Some Democrats pressured <unk> to withdraw and allow a more palatable Democrat to run , but he refused . On the 29th ballot , taken near the end of February 1908 , Bradley finally secured a majority after four Democrats crossed party lines to vote for him . 
- <unk> 's ardent support of prohibition likely cost him the election . His position put him at odds with Henry <unk> , editor of the powerful Louisville Courier @-@ Journal . As governor <unk> had crossed the liquor interests and the political machinery in Louisville . When the Kentucky Court of Appeals <unk> the results of Louisville 's municipal elections due to interference by the city 's " whiskey ring " in May 1907 , <unk> appointed Robert Worth <unk> , a young lawyer and fellow <unk> , as interim mayor until elections could be held in November . <unk> eliminated <unk> in the police department , closed gambling houses and enforced blue laws closing saloons on Sunday . Following this , the whiskey ring announced <unk> had forfeited the support of Louisville 's legislators . In the <unk> election in 1908 , three of the four Democrats who voted against <unk> were from Louisville . Following his defeat , <unk> returned to his law practice . 
- Six years later <unk> again attempted to win the Senate seat . Due to the passage of the Seventeenth Amendment , the senator would not be elected by the legislature , but by popular vote . In the Democratic primary <unk> defeated Augustus O. Stanley , a 12 @-@ year veteran congressman . The Republican nominee was former governor Augustus E. <unk> . <unk> by his support of President <unk> Wilson , <unk> won the election by 32 @,@ 000 votes . 
- <unk> served as chairman of the Senate Committee on <unk> in the Department of Labor from 1915 to 1917 and on the Senate Committee on Military Affairs . In the latter position he was influential in securing two large military training posts for Kentucky , Camp Zachary Taylor and Fort Knox . Though Camp Zachary Taylor was abandoned after World War I , Fort Knox became the home of the United States <unk> <unk> . When the United States entered World War I , <unk> continued to back President Wilson and supported the League of Nations . 
- True to his <unk> stance , <unk> supported passage of the <unk> Amendment <unk> the import and sale of alcohol in the United States . The amendment was ratified and became effective in January 1920 . Believing women should be protected from involvement in politics , <unk> opposed the <unk> Amendment , which granted women 's suffrage . The amendment failed on February 10 , 1919 , but passed on June 4 , 1919 . <unk> voted against it both times . 
- The Democrats <unk> <unk> without opposition in 1920 . His opponent in the general election was Republican Richard P. Ernst . Prohibition had destroyed the <unk> industry and the saloon business in the state , and in areas where these industries were prominent <unk> received more than 5 @,@ 000 fewer votes than Democratic presidential nominee James M. Cox . He was also hurt by the women 's vote and by his support of President Wilson , who had lost popularity since <unk> 's election in 1914 . Ernst won the election by fewer than 5 @,@ 000 votes , winning the race with 50 @.@ 3 percent of the vote to <unk> 's 49 @.@ 7 percent . During his single term in the Senate <unk> served alongside three other U.S. Senators from Kentucky : <unk> M. James , George B. Martin and Augustus O. Stanley . 
- 
- = = Later life and death = = 
- 
- Following his term in the Senate , <unk> resumed his legal practice in Louisville . He sought another term as governor in 1927 . This time he had the support of the Louisville Courier @-@ Journal , which had been purchased by his ally , Robert W. <unk> . He was opposed by a powerful political machine known as the <unk> Club , whose main interest was securing legislation to allow <unk> betting at the state 's horse <unk> . 
- The <unk> Club ran a candidate in both parties ' primary elections . In the Democratic primary <unk> defeated the Club 's relatively obscure candidate , Robert T. Crowe . <unk> D. Sampson , the Club 's nominee in the Republican primary , won his party 's nomination . In the general election <unk> could not secure the support of Democratic governor William J. Fields , who had been elected with the help of the <unk> Club . Despite the Democrats winning every other contest on the ballot , including the race for lieutenant governor , <unk> lost to Sampson by more than 32 @,@ 000 votes . It was estimated that the Club spent over $ 500 @,@ 000 to defeat him . 
- <unk> was expected to be the Democrats ' nominee for governor in 1935 , but the death of his son in late 1934 left him distraught and his wife opposed to another campaign . The Democrats turned to A. B. " Happy " Chandler of Henderson , who won the election . <unk> supported Chandler 's bid , and in return Chandler appointed him to the Kentucky Public Service Commission in 1936 . <unk> also served on the Department of Business Regulations Commission and chaired the State Government <unk> Commission . 
- <unk> attempted to return to the Senate in 1936 . The head of the <unk> Club , James B. Brown , had lost his fortune and influence when his banking empire crashed in 1930 . In 1933 <unk> 's ally , Robert <unk> , had been appointed ambassador to the Court of St. James 's , increasing his prominence and influence . <unk> also enjoyed the support of the United Mine Workers and Louisville mayor Neville Miller . The race was complicated , though , by the entry of John <unk> Brown , a Democrat who was a U.S. Representative and former Speaker of the Kentucky House of Representatives . He had agreed to support Chandler 's bid for the governorship in exchange for Chandler 's support in his run for the Senate . However , Chandler threw his support to <unk> , and while Brown was not able to win the seat without the support of <unk> and Chandler , he did garner 85 @,@ 000 votes , most of them at <unk> 's expense . Democratic incumbent M. M. Logan retained the seat by 2 @,@ 385 votes . 
- <unk> died in Louisville on January 9 , 1940 , and was buried in Frankfort Cemetery in Frankfort , Kentucky . <unk> County , Oklahoma , was named in his honor at the suggestion of a <unk> who was serving as a delegate to Oklahoma 's constitutional convention in 1907 . <unk> , his birthplace , was added to the National Register of Historic Places on February 16 , 1973 . 
- 
- 
- = 1888 – 89 New Zealand Native football team = 
- 
- The 1888 – 89 New Zealand Native football team was a New Zealand rugby union team that toured Britain , Ireland , Australia and New Zealand in 1888 and 1889 . It mostly comprised players of Māori ancestry , and also included some <unk> ( white New Zealanders ) . A wholly private <unk> , the tour was not under the auspices of any official rugby authority ; it was organised by New Zealand international player Joseph <unk> , promoted by civil servant Thomas <unk> , and managed by James Scott , a <unk> . The <unk> were the first New Zealand team to perform a <unk> , and also the first to wear all black . They played 107 rugby matches during the tour , as well as a small number of Victorian Rules football and association football matches in Australia . Having made a significant impact on the development of New Zealand rugby , the <unk> were inducted into the International Rugby Board Hall of Fame in 2008 . 
- After a preliminary tour of New Zealand in 1888 , the side travelled to England via Melbourne and Suez . The Māori players initially provoked curiosity due to their race , but the British press subsequently expressed some surprise that the side was not as " Māori " as they had expected . Playing their first match , on 3 October against Surrey , the team was subjected to a taxing match schedule , and frequently played three matches per week . Their early matches included a 9 – 0 loss to Middlesex , but their form improved in November , when they won 10 of their 13 matches . The team played its first international match on 1 December , against Ireland , and won 13 – 4 . This was followed by a win over one of the strongest English county teams , Yorkshire , and a 5 – 0 defeat against the Wales national team . By January 1889 the <unk> had played 36 matches in less than three months , winning 22 of them ; they had spent most of their time in the north of England , where the playing strength was strongest and the crowds largest and most profitable . 
- In a return match on 19 January , Yorkshire fielded a stronger side than in the first match and inflicted one of the <unk> ' heaviest losses , a 16 – 4 defeat . The team then went undefeated until 16 February , when they faced England . Officials of the strictly amateur Rugby Football Union ( RFU ) had become increasingly concerned at the behaviour of the New Zealanders , regarding them as unsportsmanlike , and tensions reached a nadir in the aftermath of the England international , during which the RFU secretary George Rowland Hill , <unk> the game , awarded a number of controversial tries to England , prompting three of the <unk> to temporarily leave the field in protest ; England eventually won 7 – 0 . The <unk> apologised afterwards for their behaviour , but the damage was not repaired . The New Zealanders left England without an official send @-@ off , and travelled to Australia where they toured Victoria , New South Wales and Queensland . They then returned to New Zealand , where they displayed a level of combination not seen in their home country before . They went 31 games undefeated before losing their final match , on 24 August 1889 , 7 – 2 to Auckland . 
- The <unk> ' final record in rugby matches was 78 wins , 6 draws and 23 losses . They introduced a number of tactical innovations to New Zealand rugby on their return home , and their tour contributed to the formation of the New Zealand Rugby Football Union in 1892 . Seventeen of the team 's 26 players went on to play <unk> in New Zealand , and two , Thomas <unk> and David <unk> , subsequently captained the New Zealand national rugby team . 
- 
- = = Background = = 
- 
- The idea for assembling a team of Māori footballers to tour Britain was conceived by Joseph <unk> , a rugby player who had toured with the first New Zealand national team in 1884 . He initially proposed a team of Māori or part @-@ Māori to play the touring British side in 1888 ; this developed into a venture to have a Māori team tour Britain if a preliminary tour of New Zealand were successful . <unk> of <unk> 's plans , civil servant Thomas <unk> contacted him to offer help managing the tour , which <unk> accepted . When James Scott , a <unk> , subsequently joined the partnership , the three men decided that <unk> would be the team 's captain , Scott its manager and <unk> its promoter . 
- <unk> started assembling a team for the tour in early 1888 . He had difficulties assembling a squad due to player availability , and failed to secure the talented Jack <unk> due to his university commitments . Some Māori players who initially agreed to play later pulled out when the eligibility criteria were relaxed to allow squad members who were only part @-@ Māori . Twenty Māori or part @-@ Māori players joined the squad ; five <unk> ( white New Zealand ) players were added after the team lost to Auckland . Due to the inclusion of these <unk> players the team was renamed from the " New Zealand <unk> " to the " New Zealand Native Football Representatives " . The final squad comprised 26 players ( including <unk> ) ; of these at least five were full @-@ <unk> Māori , while fourteen had a Māori mother and a <unk> father . The parentage of some of the players is unknown . 
- The team toured New Zealand before departing overseas , playing against <unk> 's Bay , Auckland , Nelson , Wellington , Canterbury , South Canterbury , and <unk> . The first game was contested against <unk> 's Bay in Napier on 23 June 1888 . They played nine games in their preliminary tour of New Zealand , and won seven of them . Their last New Zealand match before departure , against <unk> played in <unk> on 31 July 1888 , was won by one try to nil . 
- The team sailed for Australia from <unk> , leaving on 1 August 1888 . In Melbourne , Scott recruited Jack <unk> to train the players in Victorian Rules football in Britain as preparation for possible Victorian Rules matches on their return to Australia . The team played two rugby matches against the Melbourne Rugby Union team , winning the first and drawing the second , before continuing to Britain via Suez . They arrived in London on 27 September 1888 . 
- 
- = = <unk> in England and early matches = = 
- 
- The team were met in Britain by local rugby administrators , including an official of England 's Rugby Football Union ( RFU ) . The first match of the tour was against Surrey , where the team became the first New Zealand side to perform a <unk> , and also the first to wear an all black uniform . That the team was predominantly Māori provoked curiosity from the British press – at the time , most Britons had not seen non @-@ white people – but there was some surprise that the team were not as " Māori " as had been expected . " They are not unlike Europeans , " a Scottish reporter wrote in November 1888 ; " that is their resemblance is great when one remembers that they were a savage tribe no further back than a generation " . The Surrey match , which was <unk> by the RFU secretary George Rowland Hill , was won 4 – 1 by the <unk> after they scored two tries . 
- The <unk> next defeated both <unk> and Kent , before defeats by <unk> and Burton @-@ on @-@ Trent . Both defeats were unexpected , and in the <unk> match , injuries played a part – the tourists played most of the match at least two men down , as replacements were then not allowed . The team recovered to win their next game , against Midlands Counties in Birmingham . Their next fixture was against Middlesex in a match not open to the public , and hosted by the Earl of Sheffield at Sheffield Park , <unk> . The Middlesex side contained a number of international players , including Arthur Gould . Middlesex won easily , with the <unk> play characterised by poor tackling . The final score was 9 – 0 , with three tries conceded by the New Zealanders . Prior to the match both sides had enjoyed lunch with wine – an indulgence the <unk> were not used to . A report in the Auckland Star had this to say of the game : 
- The New Zealanders expected to meet a mere exhibition 15 of scratch players , whereas Mr Rowland Hill had carefully collected the best possible team available . Such " passing " as the Home men displayed the <unk> <unk> admit they have never previously witnessed . Said <unk> whilst we were on the platform waiting for the London train , " I think they would have just beaten us , even if we had been in good form . " 
- Following the match against Middlesex , the team travelled to the north , where the strongest English rugby teams were based . Yorkshire and Lancashire dominated the county championship until many of their respective member clubs split from the RFU in 1895 over the issue of " broken time " payments . The New Zealanders lost to Hull 1 – 0 , defeated <unk> , then lost to Wakefield . After their first draw of the tour ( against Northumberland County ) they defeated Stockton @-@ on @-@ <unk> and <unk> . Joe <unk> appeared in the match against <unk> , but aggravated the foot injury that had kept him out of the side until that point . These victories were followed by a 13 – 4 defeat to Halifax on 10 November . The team then won seven matches in a row , including one against <unk> RFC , their only Scottish opposition of the tour . The last two matches that month were a loss to <unk> and a 9 – 0 victory over Liverpool and District . The team had played thirteen fixtures during November and won ten of them . By this point , the team was beset by injuries – of the fifteen players that played against <unk> County on 24 November , five had injuries . The squad comprised only 26 players , and the tourists were often struggling to field a side . Nevertheless , their heavy schedule continued ; on 30 November 1888 they left for Dublin , where a match had been organised against the Ireland national team . 
- 
- = = Ireland , further English matches , and Wales = = 
- 
- The Ireland fixture was played at Lansdowne Road , Dublin , on 1 December 1888 . Both teams had a number of leading players out injured – the Irish were forced to make four changes to their original selection . Ireland led 3 – 0 at half @-@ time after scoring a converted try , but the <unk> improved considerably in the second @-@ half , scoring four tries . Patrick <unk> scored the first two tries , and his play was praised by the local press . The third try scored was by Thomas <unk> after a counter @-@ attack by George Williams . The try was not converted , but the strong finish from the New Zealanders gave the visitors a 13 – 4 victory . The Irish press were surprised by the loss and strongly criticised their team , but Ireland did go on to defeat Wales two tries to nil in the 1889 Home Nations Championship . Following their defeat of Ireland , the <unk> played Trinity College and then North of Ireland . The match against Trinity College was drawn 4 – 4 , and despite <unk> not playing , the Native side played much better than their previous fixture . The team then travelled to Belfast , where they defeated North of Ireland 2 – 0 on 5 December ; scoring two tries to nil . 
- After returning to England , the <unk> faced Lancashire in Manchester , where they lost 1 – 0 . Two days later they drew with <unk> Bulldogs , despite their opposition scoring five tries . Their next match was against Yorkshire , who were one of the strongest counties in the country , and went on to win the inaugural County Championship that season . Yorkshire fielded a weakened team , and were subsequently defeated 10 – 6 by the <unk> , who scored six tries . After a further two victories , the team travelled to Wales , where they lost 3 – 0 to <unk> , before facing Wales on 22 December . 
- At the start of the match the home crowd were fairly hostile towards the Welsh team due to fans of both Swansea and <unk> feeling slighted by the lack of selection of their players . Four teams dominated Welsh international selection at the time , and out of the 15 @-@ man team only William Towers and William Bowen of Swansea and Dan <unk> of <unk> had been selected . The match was played in Swansea , and the lack of local players may have contributed to a poor crowd , with gate receipts of only £ 120 recorded . The crowd 's hostility impacted on the players , and <unk> Norman <unk> was " <unk> nervous " at the start of the match . <unk> , aged 18 years and 49 days , became the youngest Welsh international player – a record he held until the debut of Tom <unk> in 2010 . Despite the <unk> aimed primarily at <unk> , Charlie Arthur and George Thomas , the Welsh team produced an excellent effort , especially from the forwards . 
- Towers scored the first Welsh try , which was converted by Jim Webb . The <unk> replied with a spirited run by <unk> , but he failed to break through the Welsh defence . The tourists trailed even further after George Thomas scored a <unk> try <unk> the half @-@ way line , which went some way to silence the <unk> from the crowd . Webb , playing in out of position at full @-@ back , missed the conversion and then failed at a long distance goal from a mark . The <unk> continued to push , with Elliot coming within five @-@ yards of the try line , and when <unk> did manage to cross the line he was carried back into the 25 @-@ yard line before he could touch down . In the second half Wales continued to push their advantage when Alexander <unk> <unk> the ball into the <unk> ' 25 ; this was collected by Sydney Nicholls , who managed to get the ball across the try line , allowing Jim <unk> to score . <unk> for the <unk> and <unk> for Wales both subsequently came close to scoring tries , but there were no further scores in the game . 
- The match was also of historical importance because of the Welsh tactics employed . In the 1886 Home Nations Championship Wales had <unk> the four three @-@ quarter system , wherein the team would play with eight forwards rather than nine , and instead employ an extra centre three @-@ quarter . The system was deemed a failure and was particularly unpopular with star Welsh player Arthur Gould , whose formidable ability as a back allowed his club team Newport to retain the additional forward . With Gould working in the West Indies , Wales again tried the four three @-@ quarter system against the <unk> , and its success saw the team permanently adopt the system . Within six years the other three Home Countries had adopted four three @-@ quarter style of play . 
- Before they left Wales , the <unk> played Swansea and two other local clubs , Newport , and Cardiff . They defeated Swansea for their first win in Wales , and followed this up with a victory over Newport in front of 8 @,@ 000 spectators . They finished their Welsh matches , and the year , with a 4 – 1 loss to Cardiff in front of a partisan crowd . 
- 
- = = Return to England = = 
- 
- The side entered 1889 having played 36 matches for 22 wins and three draws . The <unk> ' play had improved throughout November and December following poorer form in their October matches ; positive press reports reflected this improvement . The team would go on to play a further 17 matches before their 16 February international against England . January started with a 4 – 1 loss to Bradford , during which 25 police officers were required to keep many of the 12 @,@ 000 spectators , many of them non @-@ paying , in order . This was followed by victories over Leeds Parish Church , <unk> , <unk> Rangers , and Huddersfield . Following further matches against <unk> , <unk> , and <unk> , where the team drew , lost , then won , the side faced Yorkshire for a second time . 
- Yorkshire had been criticised in the press for fielding a weakened line @-@ up against the <unk> when the sides first met in December . After the unexpected loss , Yorkshire were determined to make <unk> and a strong side was selected for the county , including Fred <unk> , Richard <unk> and John Willie <unk> , all of whom would appear for England against the <unk> later that season . Described as " knocked about " and " stale " , the <unk> struggled to compete against such strong opposition , and Yorkshire scored three converted tries before a try to <unk> left the scores at 9 – 1 at half @-@ time . The second @-@ half was little better for the <unk> ; they conceded a further two tries as well as a drop @-@ goal . The second of these tries was scored by <unk> after he ran the ball from his own half . <unk> scored a converted try late in the match , but this didn 't prevent the <unk> suffering their largest defeat of the tour : 16 – 4 to the <unk> . <unk> later described the match as " without a <unk> , the biggest beating we received in our whole tour " . 
- After a victory over <unk> Valley District , the team travelled west to play <unk> , Devonshire , Taunton , and Gloucestershire , and won all five games . The victory over Somerset was the New Zealanders ' largest of the tour ; they scored nine tries in a 17 – 4 victory . Half @-@ back <unk> played outstandingly for the <unk> , while the entire side demonstrated superior passing and combination to their opposition . Devonshire and <unk> suffered heavy defeats by the New Zealanders , before a strong Gloucestershire side was dismissed . After defeating Midland Counties , the <unk> returned to London . 
- The team had two further matches before their international against England . The first was against one of the strongest clubs in England , <unk> . Andrew <unk> , who had toured New Zealand and Australia with the 1888 British Isles side , played for the club in their 9 – 3 defeat to the <unk> . The New Zealanders won having scored four tries , including two by <unk> . Their next opposition was a United Services side mainly comprising Royal Navy players . The <unk> were again victorious , this time 10 – 0 . The match against Oxford University was postponed due to heavy <unk> , and so the team had a seven @-@ day break from playing – their longest of the tour . 
- 
- = = England international = = 
- 
- The match against England was causing the <unk> ' players and management problems before it had even begun . The team manager , Scott , was in dispute with the RFU over where the match should be played – the RFU were adamant that the match should take place at <unk> 's ground , but Scott wanted the game to proceed at The Oval , where a larger crowd , and therefore higher gate receipts , could be secured . The strictly amateur RFU establishment were already suspicious of the profit @-@ making motives of the <unk> , and were unwilling to yield on the selection of venue . The RFU was also in dispute with the other Home <unk> over the formation of the International Rugby Football Board ( <unk> ) . Following a disputed try in an England – Scotland international match in 1888 , the Scottish authorities had pushed for the establishment of an international body to oversee the game , but the RFU insisted that they would only join if they held a deciding vote , arguing that they deserved this as they were , they asserted , the senior body , and had the most member clubs . Ireland , Wales and Scotland consequently refused to play against England until 1891 , when , following arbitration , the RFU relented and joined the <unk> . The absence of international matches was a factor in England agreeing to face the <unk> on 16 February 1889 . 
- The line @-@ ups selected for the 16 February match were both strong , and close to full strength . Though 12 of the England side had not played internationally before , all were experienced at domestic level . The match was <unk> by Rowland Hill , who had also <unk> the <unk> ' first match in Britain , against Surrey . The opening of the first half was a scoreless affair , with much tackling and <unk> on the heavy ground . Later in the half England scored two tries through Harry Bedford , but both were disputed by the <unk> , who claimed that one of their players had grounded the ball in @-@ goal . England took the two @-@ try advantage into the second half . 
- Early in the second half a third disputed try was scored by the English . The try and its aftermath caused controversy and a rift between the <unk> and the RFU . <unk> attempted to tackle the English player <unk> , and in the process ripped his shorts off . The <unk> quickly formed a circle around <unk> to allow him to replace his clothing without being seen . While this was happening one of the English players , Frank <unk> , picked up the ball and scored a try . The New Zealanders protested , believing that play had stopped after claiming <unk> had called " dead ball " – but Hill awarded the try , prompting three of the Native players , Dick <unk> , Williams , and <unk> <unk> , to leave the field in protest . The <unk> players were eventually persuaded to return , but not before Hill had <unk> play . <unk> was very critical of Hill , particularly because he was also Secretary of the RFU . <unk> wrote after the tour that " gross as these errors were , they were insignificant when compared with another that Mr Hill committed at the outset of the game , <unk> , <unk> at all in that game " . The disputed try was followed by a final try for the English , who ultimately won 7 – 0 . 
- The RFU , at Hill 's instigation , promptly demanded an apology from the <unk> ' captain of the day , Edward <unk> , who had led the team as Joe <unk> was injured . The English authorities of the time believed that the decision of a referee was above question , and that protesting a decision as the New Zealanders had done was unsportsmanlike . The RFU threatened to bar any of their affiliated players – in other words , the entire rugby playing population of England – from facing the <unk> if they did not <unk> . <unk> swiftly sent an apology by telegram , but this was deemed inadequate ; he therefore sent another , four days after the game : 
- The London establishment that governed the game were disturbed by the New Zealanders ' approach to the game ; reports of rough and over @-@ aggressive play by the <unk> had steadily increased in frequency since their arrival in Britain . In the north of England , criticism of the visitors ' <unk> was rarer ; the tourists were accepted as playing the game in the same spirit as their local opponents , which in the north was a more working class sport than in the south . Some of the <unk> , including Joe <unk> , accused the RFU and the English press of hypocrisy , claiming that they were quick to <unk> the New Zealanders for rough play , yet tolerant of similar behaviour from their own players . 
- 
- = = Later matches and departure from England = = 
- 
- The <unk> remained in London following the England international . They defeated London Welsh on 18 February , before losing first to Cambridge , then Oxford University . From there they travelled north and won two matches before losing to Leigh . After a win over <unk> , there was a defeat to Oldham , played on a ground <unk> said was so frozen it was dangerous . After reversing their previous loss to Halifax with a 6 – 0 win , the <unk> suffered a loss to Barrow and District on 7 March . The New Zealanders then had a run of seven straight wins before a 1 – 1 draw with Hull . <unk> were then defeated for the second time in two weeks in the tourists ' last match in northern England . 
- The team struggled to find an opponent for their final match in Britain . They eventually played Southern Counties , and beat them 3 – 1 . This was their <unk> match in the British Isles and their 49th victory . The authorities and press in London continued to view the team negatively , and the <unk> boarded ship without a formal farewell . This perceived <unk> from the RFU provoked some criticism from the press outside London , as well as from the team manager Scott , who felt that with the team 's official apology after the England match , the controversy should have put to rest . 
- 
- = = Australia = = 
- 
- The majority of the <unk> left Plymouth on 29 March ( <unk> and Pie <unk> followed a week later ) . They arrived in Melbourne in May , where the team played mostly Victorian Rules football , hoping to make more money that way . Although the side had employed Jack <unk> to coach them in Victorian Rules during their tour of the British Isles , the heavy schedule and high injury count had left little time and energy for such training . As a result , the Victorian Rules matches were a failure ; the players ' <unk> with the rules , combined with the fact that most of the <unk> were rugby forwards ( and therefore less suited to the more open Victorian Rules ) , ensured that they failed to perform well on the field and struggled to attract large crowds . The side played nine Victorian Rules matches in total , including one in New South Wales , but won only three of them , all against relatively weak opposition . 
- The side 's success in their rugby matches contrasted to their failure in Victorian Rules – the New Zealanders played three rugby matches while in Victoria : against Melbourne , a Navy selection , and Victoria . The matches were all won , with their game against Victoria a 19 – 0 victory . After this they left for Sydney for further rugby matches , and defeated New South Wales 12 – 9 . After two further victories , the side again faced New South Wales , and won the match 16 – 12 . Another two victories followed , before the team played their only association football matches of the tour – both defeats . 
- The team travelled north to Queensland , where , as in New South Wales , rugby was the dominant code of football . Consequently , the team exclusively played rugby while in the region . The <unk> faced Queensland at the Association Ground in Brisbane . The 8000 spectators witnessed the New Zealanders overwhelm the <unk> to win 22 – 0 ; the <unk> did not exert themselves in the win , and the score did not reflect their dominance . After a further two matches , against <unk> and Ipswich ( both of whom were comfortably defeated ) , the team returned to Brisbane for a rematch with Queensland . In contrast to their first meeting , the first @-@ half was a close affair , and the two sides were tied at the conclusion of the half . Billy <unk> suffered a kick to the head , and had to retire early in the second @-@ half . Following the loss of <unk> , the play of the <unk> improved and they recovered to win 11 – 7 . Not long after the game concluded rumours circulated that some of the players had been offered ₤ 50 by <unk> to throw the game . <unk> later said : 
- It was on the occasion of this match that four of our players were thought , in racing <unk> , to be playing " stiff " , and that they had been got by some <unk> ; at all events , when accused of it at half @-@ time and cautioned , they played a different game in the second half . 
- The response from the team 's management was to suspend four players . The team travelled to <unk> , where they defeated the locals 19 – 0 . The <unk> included a replacement player for only the second time , Charles <unk> , after the <unk> reduced the playing strength of the side . The team then travelled back to New Zealand , and arrived in <unk> on 5 August . 
- 
- = = Return to New Zealand = = 
- 
- Two days after their return , the <unk> faced <unk> , who they defeated 5 – 1 in front of a crowd of 2 @,@ 000 . The side suffered further injury , to Harry Lee , and recruited <unk> W. <unk> for their match against <unk> District on 8 August . Despite playing the match two players down , the <unk> comfortably defeated <unk> 16 – 3 . Following the side 's return to New Zealand , the <unk> Rugby Football Union demanded that the team 's management explain the accusations levelled at them in Queensland . <unk> responded by insisting that the players had only been suspended while an investigation was conducted , and that the management was confident no wrongdoing had occurred . The Northern Rugby Union ( since renamed Queensland Rugby Union ) summarised the incident and aftermath in the 1889 Queensland Rugby Union Annual : 
- ... it was apparent to a judge of the game that something was wrong with the <unk> , as they were not showing their usual dash and combination . Four members of the team were suspended , a charge being made against them of attempting the sell the match . The matter was bought before the <unk> Union , who passed the following resolution : That , having heard all available evidence regarding the charges against certain members of the Native Team , and having received an explicit denial of charges from the accused members and a satisfactory explanation from the management , we are of opinion that there are no facts before us <unk> the allegations ... 
- It is unlikely , given the attitude of the <unk> Rugby Union to the <unk> before their departure , that they would have dismissed the allegations if <unk> evidence had existed . The side was back to full strength following the return of their suspended players when the side faced <unk> in <unk> . The <unk> <unk> their opponents five tries to two , and won 11 – 8 . The side 's star player and half @-@ back , <unk> , stayed in <unk> when the team departed for <unk> . The side faced <unk> 's Bay , who were touring , in <unk> , and <unk> defeated them 13 – 2 . The <unk> ' play was praised by The Press : " ... the <unk> of the black passed with remarkable accuracy and <unk> between their legs , over their shoulders , under their arms and with their feet " . The side then faced Canterbury on 17 August , who they <unk> 15 – 0 . The report published in The Press said of the <unk> ' performance : 
- The play showed on Saturday afternoon was a fine exhibition of what several months of combination and practice will do ... it must be admitted they were far and away too good for our local men . In the loose , in the <unk> , <unk> , passing , <unk> , or running they were very much indeed Canterbury 's superior . Such runs as were made by <unk> at full back , by Madigan , <unk> , and W. <unk> , the passing of H. <unk> , F. <unk> , and all the backs as well as several forwards , the rushes of <unk> <unk> , Maynard , <unk> , <unk> , and <unk> , and the <unk> and <unk> powers of nearly every one , <unk> their opponents ... 
- The side left <unk> and travelled north where they played <unk> in Masterton . The match was won 10 – 8 , and the next day they faced Wellington , who they also defeated . The fixture against Wellington was nearly abandoned because Scott and the Wellington Rugby Union could not agree on a venue ; the match went ahead only when the Wellington officials agreed to cede the <unk> all profit from the match . After this the <unk> travelled to Auckland where they played their last match , against the province on 24 August . The match was lost 7 – 2 after each team scored two tries each , but the <unk> kicked a drop @-@ goal and a conversion . The loss ended a remarkable run of matches – the <unk> had a 31 @-@ game unbeaten streak in rugby matches that started with their victory over <unk> on 9 March ; the side won 30 , and drew one match between the defeats . 
- 
- = = Impact and legacy = = 
- 
- The tour had a significant impact on the development of rugby within New Zealand . It was the first tour of the British Isles by a team from the Southern Hemisphere , and the longest in the history of the sport . By the time the <unk> returned to New Zealand , they had developed into a side superior to any in the country , and introduced a number of tactical innovations . Seventeen of the 26 players went on to play <unk> in New Zealand , and two , <unk> and David <unk> , subsequently captained New Zealand . 
- The tour also prompted the eventual formation of the New Zealand Rugby Football Union ( <unk> , later renamed New Zealand Rugby Union ) in 1892 ; one reason for its formation was to ensure greater control over any future touring New Zealand sides . The <unk> sent an officially sanctioned New Zealand team , captained by <unk> , to tour Australia in 1893 . The <unk> are also the <unk> of the Māori All Blacks , a representative team organised by the <unk> , that first played in 1910 . The Native team , along with Joe <unk> , was inducted into the IRB Hall of Fame in 2008 – the seventh <unk> . 
- 
- = = Squad = = 
- 
- The squad consisted of 26 players . The exact names of several of the players is unknown . The number of matches played is a minimum number only – the line @-@ ups for a number of matches in Britain and Australia are either incomplete or unknown . 
- 
- = = <unk> played = = 
- 
- 
- = = = Overall = = = 
- 
- 
- = = = <unk> = = = 
- 
- 
- = = = = Ireland = = = = 
- 
- Ireland : T Edwards , DC Woods , A Walpole , MJ <unk> , J Stevenson , <unk> Warren <unk> . , <unk> Andrews , <unk> <unk> , <unk> O <unk> , <unk> <unk> , <unk> <unk> , J <unk> , R Stevenson , <unk> <unk> , <unk> <unk> 
- New Zealand <unk> : William <unk> , David <unk> , Edward <unk> , Frederick <unk> , Patrick <unk> , <unk> <unk> , Charles Madigan , William Elliot , George Williams , Dick <unk> , Thomas <unk> , W Anderson , Joe <unk> , Richard Maynard , Charles Goldsmith 
- 
- = = = = Wales = = = = 
- 
- Wales : Jim Webb ( Newport ) , George Thomas ( Newport ) , <unk> Garrett ( Penarth ) , Charlie Arthur ( Cardiff ) , Norman <unk> ( Cardiff ) , Charlie Thomas ( Newport ) , William <unk> ( Cardiff ) , Frank Hill ( Cardiff ) <unk> . , Alexander <unk> ( Cardiff ) , Sydney Nicholls ( Cardiff ) , Jim <unk> ( Newport ) , <unk> Harding ( Newport ) , William Towers ( Swansea ) , William Bowen ( Swansea ) , Dan <unk> , ( <unk> ) 
- New Zealand <unk> : William <unk> , Edward <unk> , William Thomas <unk> , David <unk> , William Elliot , Frederick <unk> , Patrick <unk> , George <unk> , Alexander Webster , <unk> <unk> , George Williams , Arthur <unk> , David Stewart , <unk> <unk> , Thomas <unk> 
- 
- = = = = England = = = = 
- 
- England : Arthur " Artie " V. <unk> , John William " <unk> " <unk> , Andrew <unk> , Richard " <unk> " <unk> <unk> , William Martin Scott , <unk> " Fred " <unk> <unk> . , Frank <unk> , Donald " Don " <unk> , Charles <unk> , Harry James Wilkinson , Harry Bedford , William <unk> , John W. Cave , Frederick <unk> , Arthur Robinson 
- New Zealand <unk> : William <unk> , Edward <unk> , <unk> <unk> , Charles Madigan , William Elliot , David <unk> , Patrick <unk> , George <unk> , <unk> <unk> , Harry Lee , Thomas <unk> , George Williams , W Anderson , Dick <unk> , Richard Maynard 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> ( September 20 , 1885 – December 20 , 1958 ) was a Canadian @-@ American <unk> @-@ soprano and voice teacher . She performed and popularised songs by contemporary composers throughout her career and sang in the American premieres of several works by Erik <unk> , Maurice Ravel and Igor Stravinsky , including the title role in the latter 's <unk> . 
- The niece of Lady <unk> and Sir Wilfrid <unk> , who also were her patrons , she initially trained and performed in Europe . She then travelled to Java and for four years immersed herself in its native music , which she introduced to North American audiences on her return . She retired from performing in 1937 , and opened a voice studio in New York , where she became a founding member of the American Guild of Musical Artists and served on its board of governors . <unk> was praised for the many qualities her singing brought to music . The citation from the <unk> Society of San Francisco , which she received in 1949 , said : " ... her rare open @-@ mindedness and unorthodox enthusiasm having been initially responsible for the recognition of many vital and important modern composers " 
- 
- = = Singing career = = 
- 
- Born in Ottawa , Ontario , <unk> received musical lessons as a child , in harmony , voice , and piano . The custom of the time dictated that North American musicians travel to Europe for training if they desired a <unk> professional career , and in July 1902 , at the age of seventeen , <unk> set out for Europe , financed by her aunt and uncle , Lady <unk> <unk> and Sir Wilfrid <unk> . 
- 
- = = = Training in Europe = = = 
- 
- <unk> travelled to France , where she received private voice lessons from Auguste @-@ Jean <unk> of the Paris <unk> . <unk> on her vocal <unk> were problematic , but they were removed <unk> . She later began training under Jacques <unk> , whom she would later credit for her vocal technique . In 1906 , <unk> was retained by fellow Canadian singer Emma Albani to accompany her on a tour of England and her Canadian farewell tour . Albani provided a degree of <unk> to <unk> during the 30 week tour of Canada . 
- Lord <unk> awarded <unk> a scholarship in 1906 that allowed her to return to Europe and continue her vocal studies . She returned there and continued both to study and give performances . Her first operatic performance came in 1909 in <unk> , Italy as <unk> in <unk> 's Carmen . She landed a second operatic role as <unk> in <unk> ' <unk> , which was being performed by the London Covent Garden opera company . The opera opened in June 1910 . <unk> , <unk> <unk> , the <unk> <unk> soprano of the company , feared that <unk> 's voice would <unk> her own , and demanded that <unk> be removed from the opera . The company 's director <unk> to <unk> 's demands , informing <unk> on opening night that she would not be performing . <unk> reacted badly , and quit opera entirely . 
- 
- = = = <unk> to Java = = = 
- 
- <unk> by her blocked entry into the operatic scene , <unk> departed Europe and travelled to Java . There , she met a Dutch <unk> and plantation manager named <unk> <unk> . <unk> and <unk> married on May 22 , 1911 . <unk> studied the music of Java , and began to include this in her repertoire . Her accompanying pianist was Paul <unk> , who had previously been the conductor for the <unk> of <unk> , which afforded <unk> a number of opportunities . On permission of the Javanese court , she studied the <unk> , probably being the first western woman with a classical music education to be afforded this opportunity . While living in Java , <unk> travelled extensively , giving performances in China , Japan , Singapore , Malaya , Australia , and New Zealand . She remained in Java for four years , but with the outbreak of World War I she decided to travel back to North America , arriving in New York City in the fall of 1915 . 
- 
- = = = Return to North America = = = 
- 
- Arriving in New York , <unk> struggled to find a niche in an already crowded music scene . She put on a performance in Vaudeville entitled <unk> , which combined <unk> music with dancers . New York was already home to many North American and European musical performers , so <unk> focused on her <unk> musical repertoire , which she combined with knowledge and skill in modernist western singing . <unk> began giving annual recitals at <unk> Hall , and in November 1917 her performance there caught the eye of many leading composers . She developed her own reputation quickly , known as a " ... sensitive <unk> of interesting , <unk> songs " . She gave renditions of three songs by Maurice Ravel . Her performance then also included American premieres of Stravinsky 's Three Japanese Lyrics and <unk> ' Five Poems of Ancient China and Japan . The performance was a great success , and she began to receive invitations to perform premieres of songs by contemporary composers . Stravinsky arranged to have <unk> to premiere all of his vocal pieces . 
- <unk> travelled to Paris in 1920 at the behest of the Music League of America . <unk> there to arrange a tour of North America by Maurice Ravel , she struck up a friendship and professional correspondence not only with him , but also with Erik <unk> and Les six . This led to more music being sent to her by various composers that she would premiere in concert . She accepted and premiered almost all works sent to her , the only exception being a refusal to perform <unk> <unk> by Arnold <unk> . Through this , <unk> performed large amounts of contemporary French music across the United States . She also included American music in all of her concerts . <unk> toured America frequently and returned to Europe in 1922 , and again in 1923 . She began to explore Jazz music in concert as well , earning her negative reviews by many musical critics . 
- Her 1923 annual performance at <unk> Hall entitled " <unk> of Ancient and Modern Music for Voice " became a historic occasion when she presented the works of George <unk> , the first time his works were performed by a classical singer in concert . The first half of the programme presented works considered serious music at the time . She performed both classical works by <unk> <unk> and Henry Purcell , mixing them with modernist and neoclassical works by Arnold <unk> , Darius <unk> , <unk> <unk> , and Paul <unk> . The second half of her performance would upset the musical establishment , however . She opened with Alexander 's <unk> Band by Irving Berlin , then performed works by Jerome Kern and Walter Donaldson , and finally finished with three works by George <unk> : I 'll <unk> a <unk> to Paradise , Innocent <unk> Baby , and <unk> . <unk> played the piano for these pieces . Important figures in the audience included <unk> Schumann @-@ <unk> , Virgil Thomson , and Paul <unk> . Although some musical critics panned her decision to include Jazz music , the performance was overall a huge success , and provoked serious discussion among conservative audiences whether jazz music could be considered serious art . 
- <unk> continued to present music that was thought poorly of by conservative audiences . On some occasions , such as her performances of <unk> in New York in 1923 and 1925 , as well as in London in 1925 , this was quite successful . A critic in Vienna welcomed her musical selection as a <unk> from the usual fare of classical performances - Schubert , <unk> , Wolf , Richard <unk> - while praising her skill with more classical choices . Other performances suffered – she was <unk> while performing works by <unk> Villa @-@ <unk> at the Festival of the International Society of Contemporary Music in Venice . She became a celebrity , and continued giving performances across the United States , Europe , and her native Canada . On the <unk> anniversary of Canadian Confederation in 1927 , she gave a performance in Ottawa which was the first <unk> radio broadcast in Canada . Although she toured Canada from time to time , and attended performances of Canadian music in New York , she held a negative opinion of Canada 's treatment of native musicians , saying " Canadians . . . would rather listen to foreigners than their own people . " 
- 
- = = = <unk> from the stage = = = 
- 
- Illness forced <unk> to halt giving performances in the late 1920s , but she would return to the stage in 1931 , giving a concert in Havana , Cuba . As time passed she began to engage more and more in teaching , and less and less in stage performing . Her income from teaching was substantially better than from touring . She retired from performing entirely in 1937 , and opened a music studio in New York . There she became a founding member of the American Guild of Musical Artists , serving on its board of governors . She died on December 20 , 1958 . 
- 
- = = <unk> of critics and audiences = = 
- 
- <unk> was a controversial musician in her time . Her choice of music for performance was often condemned , and often praised . The <unk> of jazz music for a <unk> trained singer , combined with the performances taking place in concert halls lead some critics to <unk> her for promoting otherwise overlooked music , and others to <unk> her for taking <unk> music into a <unk> venue . 
- A May 1 , 1917 review by the New York Times praised her natural talent , with some reservations about the <unk> quality of her voice . Her ability to capture the spirit of the pieces was also praised : " ... her singing of songs by Frenchmen of today and the day before yesterday gave pleasure because of her understanding of them and the appropriate expression which she found for them . " 
- A November 12 , 1923 Time review of her performance of historic Italian songs , as well as modern English , French , Austrian , German and American numbers focused on her choice to include selections of contemporary jazz music . The critic remarked " Her voice was much too good for jazz . " Here she was praised as a serious , scholarly artist whose performance was " ... <unk> and expressive . " The audience received the concert very enthusiastically . 
- Her performance in <unk> in 1923 resulted in a headline in the <unk> <unk> the next day that was headed Eva <unk> ’ s Program <unk> Whole Town <unk> : Many People Are of Two <unk> Regarding Jazz Numbers – Some <unk> <unk> That They Like Them – Others <unk> Silent or <unk> Them . The review divides the audience and critics into various camps : those who openly enjoyed and applauded the performance ; those who openly disliked and condemned it ; those unsure how to react to a performance they liked but felt was inappropriate ; and those who liked the performance but would not show approval for fear of looking foolish or <unk> . 
- 
- = = <unk> materials = = 
- 
- <unk> <unk> papers , 1899 @-@ 1960 Music Division , The New York Public Library . 
- 
- 
- = Battle of Sullivan 's Island = 
- 
- The Battle of Sullivan 's Island or the Battle of Fort Sullivan was fought on June 28 , 1776 , during the American Revolutionary War . It took place near Charleston , South Carolina , during the first British attempt to capture the city from American rebels . It is also sometimes referred to as the First Siege of Charleston , owing to a more successful British siege in <unk> . 
- The British organized an expedition in early 1776 for operations in the rebellious southern colonies of North America . <unk> by logistical concerns and bad weather , the expedition reached the coast of North Carolina in May 1776 . Finding conditions unsuitable for their operations , General Henry Clinton and Admiral Sir Peter Parker decided instead to act against Charleston . Arriving there in early June , troops were landed on Long Island , near Sullivan 's Island where Colonel William <unk> commanded a partially constructed fort , in preparation for a naval bombardment and land assault . General Charles Lee , commanding the southern Continental theater of the war , would provide supervision . 
- The land assault was frustrated when the channel between the two islands was found to be too deep to <unk> , and the American defenses prevented an amphibious landing . The naval bombardment had little effect due to the sandy soil and the <unk> nature of the fort 's <unk> log construction . <unk> fire by the defenders wrought significant damage on the British fleet , which withdrew after an entire day 's bombardment . The British withdrew their expedition force to New York , and did not return to South Carolina until <unk> . 
- 
- = = Background = = 
- 
- When the American Revolutionary War broke out in 1775 , the city of Charleston in the colony of South Carolina was a center of commerce in southern North America . The city 's citizens joined other colonists in opposing the British parliament 's attempts to tax them , and militia recruitment increased when word arrived of the April 1775 Battles of Lexington and Concord . Throughout 1775 and into 1776 , militia recruits arrived in the city from the colony 's <unk> , the city 's manufacturers and tradesmen began producing war materiel , and defensive fortifications began to take shape around the city . 
- 
- = = = British operations = = = 
- 
- British army forces in North America were primarily tied up with the Siege of Boston in 1775 . <unk> bases of operations where they had more control , the British planned an expedition to the southern colonies . Major General Henry Clinton , then in Boston , was to travel to Cape Fear , North Carolina , where he would join with largely Scottish <unk> raised in the North Carolina <unk> , and a force of 2 @,@ 000 men from Ireland under the command of Major General Charles Cornwallis . 
- The plan was beset by difficulties from the start . The Irish expedition , originally supposed to depart at the beginning of December 1775 , was delayed by logistical difficulties , and its 2 @,@ 500 troops did not depart until February 13 , 1776 , escorted by 11 warships under the command of Admiral Sir Peter Parker . Clinton left Boston on January 20 with two companies of light infantry , and first stopped at New York City to confer with William Tryon , New York 's royal governor . Major General Charles Lee , sent by Major General George Washington to see to the defense of New York , <unk> arrived there the same day as Clinton . New York was at that time extremely tense ; <unk> forces were beginning to <unk> and evict <unk> , and the British fleet anchored there was having difficulty acquiring provisions . Despite this , Clinton made no secret that his final target was in the south . Lee observed that this was " certainly a <unk> way of proceeding ; to communicate his full plan to the enemy is too novel to be credited . " This was not even the first notice of the expedition to the colonists ; a letter intercepted in December had already provided intelligence that the British were planning to go to the South . 
- Clinton arrived at Cape Fear on March 12 , expecting to find the European convoy already there . He met with the royal governors of North and South Carolina , Josiah Martin and William Campbell , and learned that the recruited Scottish <unk> had been defeated at Moore 's Creek Bridge two weeks earlier . Clinton also received pleas for assistance from the royal governor of Georgia , James Wright , who had been arrested , and then escaped to a navy ship . 
- Parker 's fleet had an extremely difficult crossing . <unk> by storms and high seas , the first ships of the fleet did not arrive at Cape Fear until April 18 , and Cornwallis did not arrive until May 3 . After several weeks there , in which the British troops raided <unk> properties , Clinton , Cornwallis and Parker concluded that Cape Fear was not a suitable base for further operations . Parker had sent out some ships on <unk> expeditions up and down the coast , and reports on the partially finished condition of the Charleston defenses were sufficiently promising that the decision was made to go there . 
- 
- = = = American defenses = = = 
- 
- John <unk> , recently elected president of the General Assembly that remained as the backbone of South Carolina 's revolutionary government , organized a defense force under the command of 46 @-@ year @-@ old Colonel William <unk> , a former <unk> and Indian fighter . These forces comprised three infantry regiments , two rifle regiments , and a small artillery regiment ; they were augmented by three independent artillery companies , and the total force numbered about 2 @,@ 000 . These forces were further augmented by the arrival of Continental regiments from North Carolina and Virginia ( 1 @,@ 900 troops ) , as well as militia numbering 2 @,@ 700 from Charleston and the surrounding <unk> . 
- <unk> saw Sullivan 's Island , a sandy <unk> of land at the entrance to Charleston Harbor extending north about 4 miles ( 6 @.@ 4 km ) long and a few hundred yards wide , as a place well suited to build a fort that could protect the entrance from <unk> enemy warships . A large vessel sailing into Charleston first had to cross Charleston Bar , a series of submerged <unk> lying about 8 miles ( 13 km ) southeast of the city , and then pass by the southern end of Sullivan 's Island as it entered the channel to the inner harbor . Later it would also have to pass the northern end of James Island , where Fort Johnson commanded the southeastern approach to the city . <unk> and his 2nd South Carolina Regiment arrived on Sullivan 's Island in March 1776 , and began construction of a fortress to defend the island and the channel into Charleston Harbor . The construction moved slowly ; Captain Peter <unk> of the <unk> naval detachment described the site as " an immense pen 500 feet long , and 16 feet wide , filled with sand to stop the shot " . The gun platforms were made of <unk> two inches thick and <unk> with wooden spikes . 
- Congress had appointed General Lee to command the Continental Army troops in the southern colonies , and his movements by land shadowed those of Clinton 's fleet as it sailed south . Lee wrote from Wilmington on June 1 that the fleet had sailed , but that he did not know whether it was sailing for Virginia or South Carolina . He headed for Charleston , saying " [ I ] confess I know not whether I shall go to or from the enemy . " He arrived in Charleston shortly after the fleet anchored outside the harbor , and took command of the city 's defenses . He immediately ran into a problem : the South Carolina troops ( militia or the colonial regiments ) were not on the Continental line , and thus not formally under his authority . Some South Carolina troops resisted his instructions , and <unk> had to intervene by proclaiming Lee in command of all South Carolina forces . 
- Square @-@ shaped Fort Sullivan consisted only of the completed seaward wall , with walls made from <unk> logs 20 feet ( 6 @.@ 1 m ) high and 16 feet ( 4 @.@ 9 m ) wide . The walls were filled with sand , and rose 10 feet ( 3 @.@ 0 m ) above the wooden platforms on which the artillery were mounted . A hastily erected palisade of thick <unk> helped guard the powder magazine and unfinished northern walls . An assortment of 31 cannon , ranging from <unk> and 12 @-@ pounders to a few British 18 @-@ pounders and French 26 @-@ pounders , dotted the front and rear walls . General Lee , when he had seen its unfinished state , had recommended abandoning the fort , calling it a " slaughter pen " . President <unk> refused , and specifically ordered Colonel <unk> to " obey [ Lee ] in everything , except in leaving Fort Sullivan " . <unk> 's delaying tactics so angered Lee that he decided on June 27 that he would replace <unk> ; the battle began the next day before he could do so . Lee did make plans for an orderly retreat to <unk> 's Point . 
- 
- = = = British arrival = = = 
- 
- The British fleet weighed anchor at Cape Fear on May 31 , and arrived outside Charleston Harbor the next day . <unk> noticed a British scout boat apparently looking for possible landing points on nearby Long Island ( now known as the Isle of <unk> ) , just a few hundred yards from Sullivan 's Island ; troops were consequently sent to occupy the northern end of Sullivan 's . By June 8 , most of the British fleet had crossed the bar and anchored in Five <unk> Hole , an anchorage between the bar and the harbor entrance . With the fort on Sullivan 's Island only half complete , Admiral Parker expressed confidence that his warships would easily breach its walls . <unk> believing he would not even need Clinton 's land forces , he wrote to Clinton that after the fort 's guns were knocked out , he would " land <unk> and marines ( which I have practiced for the purpose ) under the guns " and that they could " keep possession till you send as many troops as you think proper " . 
- The British fleet was composed of nine man @-@ of @-@ war ships : the flagship 50 @-@ gun Bristol , as well as the 50 @-@ gun Experiment and frigates <unk> , Active , <unk> , <unk> , <unk> , Friendship and the bomb vessel Thunder , in total mounting nearly 300 cannon . The army forces in the expedition consisted of the 15th , 28th , 33rd , 37th , 54th , and 57th Regiments of Foot , and part of the 46th . On June 7 , Clinton issued a proclamation calling on the rebel colonists to lay down their arms . However , the inexperienced defenders fired on the boat sent to deliver it ( which was flying a truce flag ) , and it was not delivered until the next day . That same day , Clinton began landing 2 @,@ 200 troops on Long Island . The intent was that these troops would <unk> across the channel ( now known as <unk> Inlet ) between Long and Sullivan 's , which the British believed to be sufficiently shallow to do so , while the fleet bombarded Fort Sullivan . 
- General Lee responded to the British landing with several actions . He began reinforcing positions on the mainland in case the British were intending to launch an attack directly on Charleston . He also attempted to build a bridge of boats to provide an <unk> of retreat for the fort 's garrison , but this failed because there were not enough boats to bridge the roughly one mile ( 1 @.@ 6 km ) channel separating the island from Charleston ; the unwillingness of <unk> and <unk> to support the effort may also have played a role . The Americans also constructed an <unk> at the northern end of Sullivan 's Island , which was manned by more than 750 men and three small cannons , and began to <unk> a guard post at <unk> 's Point on the mainland opposite Fort Sullivan . 
- General Clinton encountered the first major problem of the attack plan on June 17 . An attempt to <unk> the channel between the two islands established that part of the channel was at least shoulder @-@ deep , too deep for troops to cross even without the prospect of enemy opposition . He considered using boats to ferry the troops across , but the Americans , with timely advice from General Lee , adopted a strong defensive position that was virtually impossible to bombard from ships or the Long Island position . As a result , the British and American forces faced each other across the channel , engaging in occasional and largely <unk> cannon fire at long range . Clinton reported that this meant that Admiral Parker would have " the glory of being defeated alone . " The attack was originally planned for June 24 , but bad weather and contrary wind conditions prompted Parker to call it off for several days . 
- 
- = = Battle = = 
- 
- On the morning of June 28 , Fort Sullivan was defended by Colonel <unk> , commanding the 2nd South Carolina Regiment and a company of the 4th South Carolina Artillery , numbering 435 men . At around 9 : 00 am that morning , a British ship fired a signal gun indicating all was ready for the attack . Less than an hour later , nine warships had sailed into positions facing the fort . Thunder and Friendship anchored about 1 @.@ 5 miles ( 2 @.@ 4 km ) from the fort while Parker took Active , Bristol , Experiment and <unk> to a closer position about 400 yards ( 370 m ) from Sullivan 's Island , where they anchored facing broadside to the fort . Each of these ships began to fire upon the fort when it reached its position , and the defenders returned the fire . Although many of Thunder 's shots landed in or near the fort , they had little effect ; according to <unk> , " We had a <unk> in the middle , that swallowed them up instantly , and those that fell in the sand in and about the fort , were immediately buried " . Thunder 's role in the action was also relatively short @-@ lived ; she had anchored too far away from the fort , and the <unk> of her mortars with extra powder to increase their range eventually led to them breaking out of their mounts . Owing to shortage of gunpowder , <unk> 's men were deliberate in the pace of their gunfire , and only a few officers actually aimed the cannons . They also fired in small <unk> , four cannon at a time . One British observer wrote , " Their fire was surprisingly well served " and it was " slow , but decisive indeed ; they were very cool and took care not to fire except their guns were exceedingly well directed . " 
- General Clinton began movements to cross over to the northern end of Sullivan 's Island . <unk> by two <unk> of war , the flotilla of <unk> carrying his troops came under fire from Colonel William Thomson 's defenses . Facing a withering barrage of grape shot and rifle fire , Clinton abandoned the attempt . 
- Around noon the frigates <unk> , <unk> , and <unk> were sent on a roundabout route , avoiding some <unk> , to take a position from which they could enfilade the fort 's main firing platform and also cover one of the main escape routes from the fort . However , all three ships grounded on an <unk> <unk> , and the <unk> of <unk> and <unk> became entangled in the process . The British managed to <unk> <unk> and <unk> , but <unk> remained grounded , having moved too far onto the submerged <unk> . Consequently , none of these ships reached its intended position , a piece of good fortune not lost on Colonel <unk> : " Had these three ships effected their purpose , they would have <unk> us in such a manner , as to have driven us from our guns . " 
- At the fort , <unk> ordered his men to concentrate their fire on the two large man @-@ of @-@ war ships , Bristol and Experiment , which took hit after hit from the fort 's guns . Chain shot fired at Bristol eventually destroyed much of her rigging and severely damaged both the <unk> and <unk> . One round hit her quarterdeck , slightly wounding Parker in the knee and thigh . The shot also tore off part of his <unk> , leaving his <unk> exposed . By mid @-@ afternoon , the defenders were running out of gunpowder , and their fire was briefly suspended . However , Lee sent more ammunition and gunpowder over from the mainland , and the defenders resumed firing at the British ships ; Lee even briefly visited the fort late in the day , telling Colonel <unk> , " I see you are doing very well here , you have no occasion for me , I will go up to the town again . " Admiral Parker eventually sought to destroy the fort 's walls with persistent broadside <unk> . This strategy failed due to the <unk> nature of the <unk> wood used in its constructions ; the structure would <unk> , and it absorbed the <unk> rather than <unk> . The exchange continued until around 9 : 00 pm , when darkness forced a cessation of hostilities , and the fleet finally withdrew out of range . 
- At one point during the battle , the flag <unk> had designed and raised over the fort was shot down . Sergeant William Jasper reportedly ran to the <unk> and raised the flag again , holding it up and <unk> the troops until a flag stand could be provided . He was credited by <unk> with reviving the troops ' spirits , and later given <unk> for bravery . A painting of this event ( pictured above ) depicts Jasper 's actions . 
- <unk> casualties , Parker reported 40 sailors killed and 71 wounded aboard Bristol , which was hit more than 70 times with much damage to the hull , yards , and rigging . Experiment was also badly damaged with 23 sailors killed and 56 wounded . Active and <unk> reported 15 casualties each . The Americans reported their casualties at only 12 killed and 25 wounded . The following morning , the British , unable to drag the grounded <unk> off the <unk> , set fire to the ship to prevent her from falling into enemy hands . <unk> in small boats sailed out to the burning ship , fired some of its cannons at the British ships , took what stores and <unk> they could , and retreated shortly before the ship 's powder magazine exploded . 
- 
- = = Aftermath = = 
- 
- The British did not attempt to take the fort again . Within days of the battle , <unk> learned of the signing of the Declaration of Independence in Philadelphia . The British troops were <unk> on their transports , and on July 21 the British fleet withdrew northward to help the main British army in its campaign against New York City . To add insult to injury , one of the British transports grounded off Long Island and was captured by <unk> forces . 
- The British did not return to Charleston until <unk> , when General Clinton successfully besieged the city and captured an entire army . Until the South again became a focus of the war in late 1778 , its states provided military supplies to the northern war effort and produced trade goods that brought in valuable hard currency to fund the war effort . 
- Admiral Parker and General Clinton engaged in a war of words after the battle , each seeking to cast the blame on the other for the expedition 's failures . Although Clinton was not blamed by the government , popular opinion held him responsible , and Parker was lauded for his personal bravery . 
- 
- = = Legacy = = 
- 
- Fort Sullivan was renamed Fort <unk> shortly after the battle to honor Colonel William <unk> for his successful defense of the fort and the city of Charleston . <unk> modified in the years after the battle , it was <unk> by Fort <unk> as the principal defense of Charleston prior to the outbreak of the American Civil War . In 1876 , to celebrate the centennial , companies from Savannah , Augusta , <unk> , Columbia , New York and Boston were invited to Charleston . The site was turned over to the National Park Service in 1960 , and is now part of Fort <unk> National Monument . 
- A small monument to the Battle of Sullivan 's Island has been placed at the northeastern tip of the island , overlooking the inlet where General Clinton 's soldiers had hoped to cross . The monument includes historical markers describing the events surrounding the engagement . 
- One iconic emblem of the battle was the flag designed by Colonel <unk> . Commissioned by the colonial government , he designed a blue flag with a white crescent in the top left corner , which was flown at the fort during the battle . Despite being shot down during the siege , it was seen as a symbol of this successful defense ( and famously raised during victory ) . It came to be known as the <unk> flag or Liberty Flag . When Charleston ( lost to the British in the <unk> siege ) was reclaimed by American forces at the end of the war , the flag was returned to the city by General <unk> Greene . 
- 
- 
- = Hu <unk> = 
- 
- Hu <unk> ( Chinese : <unk> ; c . <unk> – 1674 ) was a Chinese artist , <unk> and publisher . He worked in calligraphy , traditional Chinese painting , and seal @-@ carving , but was primarily a publisher , producing academic texts as well as records of his own work . 
- Hu lived in <unk> during the transition from the Ming dynasty to the Qing dynasty . A Ming loyalist , he was offered a position at the rump court of the <unk> Emperor , but declined the post , and never held anything more than minor political office . He did , however , design the <unk> Emperor 's personal seal , and his loyalty to the dynasty was such that he largely retired from society after the emperor 's capture and death in 1645 . He owned and operated an academic publishing house called the Ten <unk> Studio , in which he practised various multi @-@ colour printing and <unk> techniques , and he employed several members of his family in this enterprise . Hu 's work at the Ten <unk> Studio pioneered new techniques in colour <unk> , leading to delicate <unk> of colour which were not previously <unk> in this art form . 
- Hu is best known for his manual of painting entitled The Ten <unk> Studio Manual of Painting and <unk> , an artist 's <unk> which remained in print for around 200 years . His studio also published seal catalogues , academic and medical texts , books on poetry , and decorative writing papers . Many of these were edited and prefaced by Hu and his brothers . 
- 
- = = Biography = = 
- 
- Hu was born in <unk> County , Anhui Province in <unk> or early <unk> . Both his father and elder brother <unk> ( <unk> , art name <unk> , <unk> ) were <unk> , and after he turned 30 he travelled with them while they practised medicine in the areas around Lu 'an and <unk> . It is commonly stated that <unk> himself was also a doctor , though the earliest sources <unk> to this occur only in the second half of the 19th century . 
- By 1619 , Hu had moved to <unk> where he lived with his wife Wu . Their home on <unk> ( <unk> , now also known as <unk> <unk> ) , a hill located just within the northern city wall , served as a meeting @-@ house for like @-@ minded artists . Hu named it the Ten <unk> Studio ( <unk> , <unk> ) , after the ten bamboo plants that grew in front of the property . It functioned as the headquarters for his printing business , where he employed ten artisans including his two brothers <unk> and <unk> ( <unk> , art name <unk> , <unk> ) and his sons <unk> ( <unk> ) and <unk> ( <unk> , courtesy name <unk> ) . 
- During Hu 's lifetime , the Ming dynasty , which had ruled China for over 250 years , was overthrown and replaced by China 's last imperial dynasty , the Qing . Following the fall of the capital Beijing in 1644 , remnants of the imperial family and a few ministers set up a Ming loyalist regime in <unk> with Zhu <unk> on the throne as the <unk> Emperor . Hu , who was noted for his seal @-@ carving and facility with seal script , created a seal for the new Emperor . The court offered him the position of <unk> for the <unk> ( <unk> <unk> , <unk> ) as a reward , but he did not accept the role ( although he did accord himself the title of <unk> <unk> in some of his subsequent personal seals ) . 
- According to <unk> <unk> 's Lost History of the South ( <unk> <unk> , <unk> ) , prior to the Qing invasion of <unk> Hu studied at the National University there , and whilst a student was employed by the Ministry of <unk> to record official <unk> ; he produced the Imperial Promotion of Minor Learning ( Qin Ban <unk> , <unk> ) and the Record of <unk> <unk> ( <unk> Ji , <unk> ) as part of this work . As a result , he was promoted to the Ministry of Personnel and gained <unk> to the <unk> Academy , but before he could take up this appointment , Beijing had fallen to the <unk> rebellion . Since contemporaneous biographies ( <unk> 's work was not published until 1830 ) make no mention of these events , it has been suggested that they were fabricated after Hu 's death . 
- Hu retired from public life and went into seclusion in 1646 , after the end of the Ming dynasty . <unk> <unk> and <unk> <unk> recorded visiting him during his later years , in 1667 and <unk> respectively . He died in poverty at the age of 90 , sometime around late <unk> or early 1674 . 
- 
- = = Seal @-@ carving = = 
- 
- Hu <unk> was a noted seal @-@ <unk> , producing personal seals for numerous dignitaries . His style was rooted in the classical seal script of the Han dynasty , and he followed the <unk> school of carving founded by his contemporary He <unk> . Hu 's calligraphy , although balanced and with a clear <unk> structure , is somewhat more angular and rigid than the classical models he followed . <unk> seals attempt to <unk> an ancient , weathered impression , although unlike other <unk> artists Hu did not make a regular practice of artificially aging his seals . 
- Hu 's work was known outside his local area . Zhou <unk> , a poet who lived in <unk> around the same time as Hu and was a noted art <unk> , stated in his Biography of Seal @-@ <unk> ( <unk> <unk> , <unk> ) that Hu " creates miniature stone carvings with ancient seal inscriptions for travellers to fight over and treasure " , implying that his carvings were popular with visitors and travellers passing through <unk> . 
- In 1644 , Hu took it upon himself to create a new Imperial seal for the <unk> Emperor , which he carved after a period of <unk> and prayer . He presented his creation with an essay , the Great <unk> of the Seal ( <unk> <unk> , <unk> ) , in which he <unk> the loss of the <unk> Emperor 's seal and <unk> Heaven 's favour in restoring it . Hu was concerned that his essay would be overlooked because he had not written it in the form of rhyming , equally @-@ footed couplets ( <unk> , <unk> ) used in the Imperial examinations , but his submission and the seal itself were nevertheless both accepted by the Southern Ming court . 
- 
- = = Ten <unk> Studio = = 
- 
- Despite his reputation as an artist and seal @-@ <unk> , Hu was primarily a publisher . His publishing house , the Ten <unk> Studio , produced reference works on calligraphy , poetry and art ; medical textbooks ; books on etymology and <unk> ; and copies of as well as commentaries on the Confucian Classics . Unlike other publishers in the area , the Ten <unk> Studio did not publish works of narrative fiction such as plays or novels . This bias towards academia was likely a consequence of the studio 's location : the mountain on which Hu took up residence was just to the north of the <unk> <unk> ( National Academy ) , which provided a captive market for academic texts . Between 1627 and 1644 , the Ten <unk> Studio produced over twenty printed books of this kind , aimed at a wealthy , literary audience . The studio 's earliest publications were medical textbooks , the first of which , <unk> <unk> for <unk> <unk> ( <unk> <unk> , <unk> ) was published in 1631 and proved popular enough to be reissued ten years later . Hu 's brother <unk> was a medical <unk> and appears to have been the author of these books . 
- During the <unk> the Ten <unk> Studio also produced political works <unk> the rule of the Ming ; these included the Imperial Ming Record of <unk> ( Huang Ming <unk> Ji , <unk> ) , a biography of loyal Ming officials , and the <unk> of the Imperial Ming ( Huang Ming <unk> , <unk> ) , a list of Imperial <unk> . After the fall of the Ming Dynasty , Hu renamed the studio the Hall <unk> in the Past ( <unk> , <unk> ) as a sign of his affiliation with the previous dynasty , although the Ten <unk> imprint continued to be used . Despite Hu 's withdrawal from society after 1646 , the studio continued to publish well into the Qing dynasty , for the most part <unk> on seal impression catalogues showcasing Hu 's carving work . 
- The Ming dynasty had seen considerable advancement in the process of colour printing in China . At his studio , Hu <unk> experimented with various forms of <unk> printing , creating processes for producing multi @-@ coloured prints and <unk> printed designs . As a result , he was able to produce some of China 's first printed publications in colour , using a block printing technique known as " <unk> block printing " ( <unk> <unk> , <unk> ) . This system made use of multiple blocks , each carved with a different part of the final image and each bearing a different colour . It was a lengthy , <unk> process , requiring thirty to fifty engraved printing blocks and up to seventy <unk> and impressions to create a single image . Hu also employed a related form of multiple @-@ block printing called " set @-@ block printing " ( <unk> <unk> , <unk> ) , which had existed since the Yuan period some 200 years earlier but had only recently come into fashion again . He refined these block printing techniques by developing a process for <unk> some of the ink off the blocks before printing ; this enabled him to achieve <unk> and <unk> of shades which were not previously possible . 
- In some images , Hu employed a blind <unk> technique ( known as " <unk> designs " ( <unk> , <unk> ) or " <unk> blocks " ( <unk> , <unk> ) , using an <unk> , <unk> block to stamp designs onto paper . He used this to create white relief effects for clouds and for highlights on water or plants . This was a relatively new process , having been invented by Hu 's contemporary Wu <unk> , who was also a <unk> @-@ based publisher . Wu had used this technique for the first time in his book Wisteria Studio <unk> Paper ( <unk> <unk> <unk> , <unk> ) , published in <unk> . Both Hu and Wu used <unk> to create decorative writing papers , the sale of which provided a <unk> income for the Ten <unk> Studio . 
- 
- = = Major works = = 
- 
- Hu 's most notable work is the Ten <unk> Studio Manual of Painting and <unk> ( <unk> <unk> , <unk> ) , an anthology of around 320 prints by around thirty different artists ( including Hu himself ) , published in 1633 . It consists of eight sections , covering calligraphy , bamboo , flowers , rocks , birds and animals , plums , orchids and fruit . Some of these sections had been released previously as single volumes . As well as a collection of artworks , it was also intended as an artistic <unk> , with instructions on correct brush position and technique and several pictures designed for <unk> to copy . Although these instructions only appear in the sections on orchids and bamboo , the book still remains the first example of a <unk> and analytical approach to Chinese painting . In this book , Hu used his multiple @-@ block printing methods to obtain <unk> of colour in the images , rather than obvious outlines or overlaps . The manual is bound in the " butterfly binding " ( <unk> <unk> , <unk> ) style , whereby whole @-@ <unk> illustrations are folded so that each occupies a double @-@ page spread . This binding style allows the reader to lay the book flat in order to look at a particular image . Cambridge University Library released a complete digital scan of the manual , including all writings and illustrations in August , 2015 . Said Charles <unk> , Head of the Cambridge University Chinese Department , " The binding is so fragile , and the manual so delicate , that until it was <unk> , we have never been able to let anyone look through it or study it – despite its <unk> importance to scholars . " 
- This volume went on to influence colour printing across China , where it paved the way for the later but better @-@ known Manual of the <unk> <unk> Garden ( <unk> <unk> <unk> ) , and also in Japan , where it was reprinted and foreshadowed the development in <unk> @-@ e of the colour <unk> printing process known as <unk> @-@ e <unk> . The popularity of the Ten <unk> Studio Manual was such that print runs continued to be produced all the way through to the late Qing dynasty . 
- Hu also produced the work Ten <unk> Studio <unk> Paper ( <unk> <unk> , <unk> ) , a collection of paper samples , which made use of the <unk> stamped <unk> technique to make the illustrations stand out in relief . Whilst primarily a catalogue of decorative writing papers , it also contained paintings of rocks , people , ritual vessels and other subjects . The book was bound in the " wrapped back " ( <unk> <unk> , <unk> ) style , in which the <unk> pages are folded , stacked , and <unk> along the open edges . Originally published in 1644 , it was reissued in four volumes between 1934 and 1941 by <unk> <unk> and Lu <unk> , and revised and republished again in 1952 . 
- 
- = = = Other publications = = = 
- 
- Other works produced by Hu 's studio included a reprint of Zhou <unk> 's manual of seal @-@ script calligraphy , The Six <unk> of <unk> , <unk> and <unk> ( <unk> <unk> <unk> , <unk> ) and the related <unk> Investigations into <unk> ( <unk> Bi Ji , <unk> ) , which discussed common errors in the formation of characters . With his brother <unk> , Hu edited a new introductory edition of the Confucian classics , entitled The <unk> Text of the Four Books , <unk> and <unk> ( <unk> <unk> <unk> , <unk> ) ( 1640 ) , giving the correct formation and pronunciation of the text . A similar approach was taken with the <unk> of the Thousand Character Classic in Six <unk> ( <unk> <unk> <unk> , <unk> ) ( 1663 ) , which Hu compiled with the aid of his calligraphy teacher , Li <unk> . It was published after Li 's death , partly in homage to him . 
- The three Hu brothers worked together to <unk> a student <unk> on poetry by their contemporary <unk> <unk> , which was called simply the <unk> of Poetry ( Shi Tan , <unk> ) ( <unk> ) . Other works on poetry from the studio included <unk> <unk> to the <unk> <unk> of Selected Tang Poems ( <unk> Tang Shi <unk> <unk> , <unk> ) , which was a compilation of several works on poetry and included <unk> by Hu <unk> himself . 
- Among the studio 's more obscure publications was a text on Chinese <unk> entitled <unk> <unk> ( <unk> ) , written under a pseudonym but with a preface by Hu <unk> . 
- 
- = = Gallery = = 
- 
- <unk> from the ' ' Ten <unk> Studio Manual of Painting and <unk> ' ' 
- 
- 
- = Raid on Manila ( 1798 ) = 
- 
- The Raid on Manila of January 1798 was a Royal Navy false flag military operation during the French Revolutionary Wars intended to scout the strength of the defences of Manila , capital of the Spanish Philippines , capture a Manila galleon and assess the condition of the Spanish Navy squadron maintained in the port . Spain had transformed from an ally of Great Britain in the War of the First Coalition into an enemy in 1796 . Thus the presence of a powerful Spanish squadron at Manila posed a threat to the China Fleet , an annual convoy of East <unk> merchant ships from <unk> in Qing Dynasty China to Britain , which was of vital economic importance to Britain . So severe was this threat that a major invasion of the Spanish Philippines had been planned from British India during 1797 , but had been called off following the Treaty of <unk> <unk> in Europe and the possibility of a major war in India between the British East India Company and the Kingdom of Mysore . 
- To ensure the safety of the merchant ships gathering at <unk> in the winter of 1797 – 98 , the British commander in the East Indies , Rear @-@ Admiral Peter <unk> , sent a convoy to China escorted by the frigates HMS <unk> and HMS Fox and commanded by Captain Edward Cooke . After completing his mission Cooke decided to investigate the state of readiness of Spanish forces in Manila himself . He was also intrigued by reports that a ship carrying treasure was due to sail from Manila , which would make a valuable prize . <unk> in <unk> and accompanied by Captain Pulteney Malcolm in Fox , Cooke reached the Spanish capital on 13 January 1798 . 
- <unk> in Manila Bay , Cooke pretended that his ships were French vessels and successfully lured successive <unk> of Spanish officials aboard , taking them prisoner in turn . Once he had determined from his captives the state of defences in Manila , that the treasure ship had been <unk> at <unk> and that the Spanish squadron was undergoing extensive repairs and thus unavailable for operations , he sent a raiding party against a squadron of gunboats in the mouth of the <unk> River . <unk> the gunboats in a bloodless attack , Cooke then released his prisoners and sailed southwards , unsuccessfully assaulting <unk> before returning to <unk> . 
- 
- = = Background = = 
- 
- In 1796 , after three years of the French Revolutionary Wars , Spain and the French Republic signed the Treaty of San <unk> . The secret terms of this treaty required Spain to renounce its alliance with Great Britain and subsequently to declare war on its former ally . In the East Indies this shift of political allegiance meant that the dominant British forces in the region were faced with the threat of attack from the Spanish Philippines to the east . Britain dominated the East Indies in 1796 , controlling the trade routes through the Indian Ocean from the ports of Bombay , Madras and Calcutta . Dutch Ceylon , the Dutch Cape Colony and parts of the Dutch East Indies had been captured in 1795 , and the French presence in the region had been confined to Île de France and a few subsidiary islands in the Western Indian Ocean . 
- Some of the most important trade routes began at <unk> and <unk> in Qing Dynasty China . Early in each year a large convoy known as the " China Fleet " , composed of large East <unk> merchant ships in the employ of the British East India Company , sailed <unk> to Europe from <unk> laden with tea and other commercial cargo . This convoy was economically significant to Britain : one convoy in 1804 was valued at over £ 8 million ( the equivalent of £ 600 @,@ 000 @,@ 000 as of 2016 ) . In January 1797 the convoy had been attacked by the French squadron in the East Indies , comprising six frigates commanded by <unk> @-@ <unk> Pierre César Charles de <unk> . In the ensuing <unk> Strait Incident the commander deceived <unk> into believing that the <unk> convoy contained disguised ships of the line and the French admiral retreated , only learning of his error on his return to Île de France . There was considerable concern in India that <unk> might try again in 1798 , or that the Spanish , who maintained a powerful squadron at <unk> , might make an attempt of their own . 
- <unk> 's initial impulse on learning in November 1796 of the impending declaration of war between Britain and Spain was to draw up plans for a major invasion of the Philippines , centred on Manila in repetition of the successful British capture of Manila in <unk> . Co @-@ operating with the Governor @-@ General of India Sir John Shore and Colonel Arthur <unk> among others , a substantial naval and military forces were earmarked for the operation which was in the advance planning stages , when unexpected news arrived in India in August 1797 announcing the Treaty of <unk> <unk> which brought the War of the First Coalition to an end . Britain now faced France and Spain alone , while <unk> from the Tipu Sultan of the Kingdom of Mysore , an old opponent of Britain in Southern India , were seeking French assistance with a renewed outbreak of hostilities . The resources planned for the operation against Manila were therefore retained in India and the operation cancelled , but the protection of the China Fleet was still essential and <unk> diverted some of his squadron eastwards to China . 
- A number of merchant ships had gathered at Bombay in the spring of 1797 in preparation for the trip to <unk> to load trade goods and join the China Fleet . To escort this force , <unk> provided the 40 @-@ gun frigate HMS <unk> , captured from the French at the Battle of <unk> in 1794 , and the 50 @-@ gun HMS Centurion , which sailed with the convoy in July , taking passage through the Straits of <unk> , joined there by the ships of the line HMS <unk> and HMS <unk> and the 32 @-@ gun frigate HMS Fox under Captain Pulteney Malcolm for the final voyage to <unk> . The convoy arrived without incident on 13 December 1797 , although the crews had been substantially weakened by tropical illnesses . 
- 
- = = Cooke 's raids = = 
- 
- 
- = = = Reconnaissance of <unk> = = = 
- 
- With his convoy safely at anchor in <unk> and the China Fleet several weeks from sailing , Cooke decided to reconnoitre Manila and make observations on the port and the Spanish squadron based there . As an added motivation , rumours in <unk> suggested that the annual Manila galleon was due to arrive . This ship brought up to two million Spanish silver dollars from Acapulco across the Pacific Ocean stopping at Guam on its way to Manila . <unk> its dollars in the Philippines , the ship then loaded trade goods from the East Indies for the return journey to New Spain . This round trip was essential to the maintenance of the Spanish Empire in the East Indies , which operated at an enormous financial loss only <unk> by the substantial subsidy from New Spain . Spanish dollars were the accepted currency across most of the East Indies , and disruption of this financial system could have profound effects on regional trade ; but British sailors had nevertheless been attacking the Manila <unk> since Thomas <unk> in <unk> . 
- Leaving the heavier warships at <unk> , Cooke sailed on 5 January 1798 only with <unk> and Fox , the latter carrying a Mr. Bernard , an experienced linguist . Passing Luzon , Cooke 's ships encountered a small Spanish merchant vessel , which was lured towards the frigates , which were flying French <unk> . <unk> the Spanish vessel , Cooke closely questioned the captain and learned that most of the Spanish squadron in Manila were undergoing extensive repairs at <unk> and were unfit to sail . Cooke rewarded the captain by releasing his vessel with its cargo intact , although he did remove 3 @,@ 900 silver dollars . The Spanish squadron had suffered badly in a typhoon in April 1797 and much of the damage had still not been repaired by the time Cooke 's small squadron arrived off Manila . Cooke had taken precautions to disguise his ships as French vessels , modelling <unk> on the powerful 40 @-@ gun Forte and Fox on the smaller <unk> . 
- 
- = = = <unk> on <unk> = = = 
- 
- Late in the afternoon of 13 January 1798 , <unk> and Fox arrived in Manila Bay , slipping <unk> past the fortress of <unk> and then sailing across the bay on the morning of 14 January , anchoring between Manila and <unk> . From his <unk> point Cooke could see the Spanish squadron dismasted and under repair in <unk> , the ships of the line San Pedro , Europa and <unk> and the frigates Maria de la <unk> and <unk> in dock and unfit for action . To Cooke 's disappointment he could also see the Manila galleon , <unk> being <unk> at the <unk> docks and another valuable merchant ship Rey Carlos aground in the harbour . The Spanish had learned only shortly before Cooke 's arrival that the British frigate HMS Resistance under Captain Edward <unk> was in Philippine waters and had decided to remove the valuable cargo from the treasure ship rather than risk an attack . 
- Fox was the first British ship into the anchorage , and was consequently approached by the <unk> , whose crew came aboard . Malcolm , like Cooke , spoke French <unk> and with Bernard translating was able to persuade the officer in charge that the new <unk> were Forte and <unk> seeking supplies and Spanish reinforcements for commerce raiding operations . The officer offered supplies but cautioned that none of the Spanish ships would be in a position to sail until March at the earliest . Cooke then joined the party on the deck of Fox , claiming to be Commodore <unk> , a French officer who , unknown to the Spanish , had been killed in the Action of 9 September 1796 off Sumatra . The Spanish officer was now completely convinced by the <unk> , which had been augmented by fake French uniforms . <unk> the visitor below decks , Cooke then passed wine around and together they drank a series of <unk> , including " the downfall of England " . 
- For an hour the officers drank , Malcolm and Cooke learning detailed information about the state of the defences and squadron in the Philippines , until a second boat pulled alongside Fox containing more officers keen to <unk> the French <unk> . This vessel was the personal barge of the Spanish commander at <unk> , Rear @-@ Admiral Don <unk> María de <unk> , who was not aboard , but who sent a message via an aide in a third boat . Each time , the officers were escorted below to join the festivities whereupon their crews were seized at <unk> and taken below decks as prisoners of war . In Malcolm 's cabin , the captured officers were informed of their situation , promised release before the British frigates sailed , and offered more wine . The crew of Fox meanwhile forced the captured Spanish sailors to strip and <unk> their clothing . <unk> into the Spanish boats this party rowed for the mouth of the nearby <unk> River , where they had learned that three heavy gunboats were moored . Taking the crews by surprise , the British boarding parties drove off the Spanish without a fight and brought all three boats alongside Fox . These vessels normally carried crews of thirty and were well @-@ armed , one with a 32 @-@ pounder long gun and two with 24 @-@ pounder long guns , each supplemented by four <unk> guns . 
- The harbour captain reached Fox shortly afterwards , furious at the seizure of the gunboats and demanding they be returned . Malcolm received him with a <unk> of near incomprehensible French and brought him to join the other captured officers in his cabin , while the boat 's crew were imprisoned below decks . Shortly afterwards , at 16 : 00 , Cooke and Malcolm hosted a large dinner for their officer captives and sent food and <unk> to the crew , the total number of Spanish sailors on Fox now numbering approximately 200 . Once the meal was finished , Cooke allowed all of the captives to return to their boats and row for shore without the conditions of parole , although he retained the captured gunboats . 
- 
- = = = <unk> = = = 
- 
- Cooke led his small squadron past <unk> on 15 January and turned south . Four days later in a storm one of the gunboats broke its tow line and was never seen again , lost with its twelve crew . The frigates subsequently scouted <unk> before reaching <unk> on 22 January . There Cooke raised Spanish colours in an attempt to <unk> the authorities into supplying food and water to his squadron but <unk> grounded on a <unk> at the entrance to the port which raised the suspicions of a <unk> sent by the governor of <unk> , <unk> <unk> . The captain of the Spanish boat asked the British ships the names of their captains , and on receiving no answer but a volley of rifle fire , he put the town on alert . With the defenders <unk> , Cooke abandoned his <unk> and after <unk> <unk> the following morning , ordered a bombardment of the fort protecting the harbour . This had little effect , though later the Spanish recovered at least 450 <unk> from different <unk> , and Malcolm then attempted an amphibious landing in order to storm the <unk> side of the fort . The boats came under heavy fire , one smashed by a <unk> , killing two and wounding four . Another boat grounded on a <unk> and became stuck ; so with his force in <unk> , as 250 villagers armed with lances ambushed and drove the British from the beach , Malcolm called off the operation . After exchanging shot for an hour both frigates cut their anchor cables and retreated out of range , with two dead and one wounded on <unk> and eight wounded on Fox , in addition to those lost in the boats . The defenders lost a single man killed and 4 wounded . 
- With his frigates now requiring repairs , Cooke withdraw half a league from <unk> and spent three days <unk> the masts and rigging of the ships . Then he sailed north , scuttling the two remaining gunboats as he did not believe they would survive the return journey to <unk> . Four days later , on 27 January the squadron halted at a village named " <unk> " in the north of the Sultanate of <unk> to collect fresh water . On the beach a boat party from <unk> was set upon by <unk> tribesmen . Two were killed and nine others taken captive and dragged into the forest before rescuers could arrive . Cooke complained to Sultan <unk> <unk> at <unk> <unk> and the captured sailors were eventually recovered , although not before <unk> and Fox had sailed for China to escort the merchant convoy back to India . 
- 
- = = Aftermath = = 
- 
- Cooke 's opportunistic diversion had determined that the Spanish forces in the Philippines posed no immediate threat , although the mission had cost 18 lives : Admiral <unk> later expressed his satisfaction with the outcome in a letter to the Admiralty . More might have been achieved with reinforcements : Historian C. <unk> Parkinson suggests that had Cooke 's squadron united with Resistance together they may have been able to destroy the disarmed Spanish warships at <unk> . He also notes however that in this scenario <unk> would have been commanding officer , a man with considerably less imagination and <unk> than Cooke . Historian Richard <unk> was critical of the mission , considering the operation to have " no glorious outcome " and citing the failure to capture the treasure ships as its greatest short @-@ coming . 
- The 1798 China Fleet sailed without further incident . During the ensuing year Resistance was destroyed by an accidental explosion in July in the <unk> Strait , and the majority of <unk> 's forces were focused on disrupting the French occupation of Suez in the Red Sea . This diversion of British resources created gaps in the coverage in merchant shipping and <unk> was able to send the frigate <unk> and <unk> <unk> @-@ <unk> to Manila late in the year to join the repaired Spanish squadron . At the beginning of February <unk> , this combined force sailed to <unk> , taking the British defences by surprise . The British commander Captain William <unk> <unk> , advancing on the Franco @-@ Spanish force which retreated during the day and disappeared under cover of darkness that evening in the <unk> Archipelago . The combined squadron then dispersed and the China Fleet was not attacked again until the Battle of <unk> <unk> in 1804 , at which a French squadron was again driven off in confusion . The frigates Cooke had mimicked , Forte and <unk> were sent to operate independently against British trade in the Indian Ocean in early <unk> . <unk> was captured by HMS <unk> at the Action of 9 February <unk> near Southern Africa , and Forte was intercepted by HMS <unk> under Cooke on 28 February near <unk> in Bengal . In the ensuing battle Forte was captured but Cooke mortally wounded , dying on 25 May . 
- 
- 
- = B of the Bang = 
- 
- B of the Bang was a sculpture by Thomas <unk> next to the City of Manchester Stadium in Manchester , England , which was commissioned to mark the 2002 Commonwealth Games ; it was one of the tallest structures in Manchester and the tallest sculpture in the UK until the completion of <unk> in 2008 . It was taller and leaned at a greater angle than the <unk> Tower of <unk> . The sculpture took its name from a quotation of British <unk> <unk> Christie , in which he said that he started his races not merely at the " <unk> " of the starting pistol , but at " the B of the Bang " . 
- The sculpture was commissioned in 2003 ; construction overran and the official unveiling was delayed until 12 January 2005 . Six days before the launch , the sculpture suffered the first of three visible structural problems as the tip of one of the spikes detached and fell to the ground . Legal action started a year later , resulting in an out @-@ of @-@ court settlement totalling £ 1 @.@ 7 million . 
- In February 2009 , Manchester City Council announced that the sculpture would be dismantled and placed in storage . Despite the promise of storage and potential <unk> , the core and legs of the sculpture were cut apart during removal . The core was sold for scrap in July 2012 , while the 180 spikes remain in storage . 
- 
- = = Design and statistics = = 
- 
- B of the Bang originally stood 56 metres ( 184 ft ) tall with 180 hollow tapered steel columns or spikes radiating from a central core . It was angled at 30 degrees and supported by five 25 m ( 82 ft ) long , tapered steel legs which connected to the spikes 22 m ( 72 ft ) above the ground . The sculpture weighed 165 tonnes , with the concrete in the foundations weighing over 1 @,@ 000 tonnes , including a 400 m2 ( 4 @,@ 300 sq ft ) reinforced concrete slab . The foundations are 20 m ( 66 ft ) deep . 
- The sculpture was made from the same <unk> steel ( also known as <unk> @-@ Ten ) as the Angel of the North sculpture , which gradually develops a tightly adhering oxide layer as it is exposed to the elements . This layer inhibits further corrosion by reducing its <unk> to water . As part of the design , the spikes <unk> slightly in the wind in order to withstand gusts in excess of 100 mph ( 160 km / h ) . At the time of construction a time capsule was placed in one of the spikes of the sculpture , containing children 's poems and paintings , due to be opened circa 2300 . The location of the time capsule after <unk> is currently unknown . 
- B of the Bang was located next to the City of Manchester Stadium at <unk> , in <unk> , at the corner of Alan Turing Way and Ashton New Road ; . It took its name from a quotation of British <unk> <unk> Christie in which he said that he started his races not merely at the ' <unk> ' of the starting pistol , but at ' The B of the Bang ' . The artwork had been nicknamed <unk> by the locals after the popular children 's game from the 1970s . 
- Prior to the construction of <unk> at the University of Nottingham , B of the Bang was Britain 's tallest sculpture at well over twice the height of the Angel of the North , which stands at 66 feet ( 20 m ) . It was designed to look like an exploding <unk> and was taller and leaned at a greater angle than the <unk> Tower of <unk> . It was commissioned by New East Manchester Limited to commemorate the 2002 Commonwealth Games . The design was selected by a panel consisting of both local residents and art experts via a competition in 2002 and was designed by Thomas <unk> . 
- 
- = = Construction and funding = = 
- 
- The sculpture was constructed in Sheffield by Thomas <unk> Studio , <unk> Lucas , Flint and <unk> and <unk> Structures . It was approved at the start of 2003 , with the central core arriving in Manchester on 13 June 2004 . This was the largest load that could be transferred via road from the factory , and required a police escort . This central core was lifted into place in August 2004 , after which the 180 spikes could begin being attached . Early estimates had given an optimistic completion date of July 2003 , which contributed to the sculpture gaining the nickname G of the Bang . 
- The official unveiling by <unk> Christie took place on 12 January 2005 . 
- In total the sculpture cost £ 1 @.@ 42 million to design and construct — twice the original estimate , as the initial costing had neglected to include installation costs . Funding was sourced from a European Regional Development Fund contribution of £ 700 @,@ 000 , the North West Development Agency , contributing £ 500 @,@ 000 , and Manchester City Council providing £ 120 @,@ 000 . 
- 
- = = Structural problems and legal action = = 
- 
- The tip of one of the 2 @.@ 1 m ( 6 @.@ 9 ft ) spikes detached and fell from the sculpture on 6 January 2005 , only six days before the official unveiling . After inspection , the event went ahead as planned . 
- Four months later , in May 2005 , a second spike had to be cut off by <unk> after it was discovered hanging loose . At that time the sculpture was closed off to the public , and the junction and pathway near the sculpture were temporarily closed . As a result , some of the joints were re @-@ <unk> , with equipment put in place to prevent excessive movement . This consisted of <unk> tip weights to 70 % of the spikes ' weights . 
- Despite these modifications , B of the Bang remained <unk> off , prompting a local newspaper campaign to ' Get It <unk> ' . In May 2006 a total of nine spikes were removed from the sculpture and taken away for <unk> analysis , to discover the stresses being placed on the steel . 
- It was announced in October 2007 that Manchester City Council were taking legal action against the makers of the sculpture , with the aim of completing the necessary repairs to the sculpture . In November 2008 this culminated in an out @-@ of @-@ court settlement being reached between Manchester City Council , the project 's designers Thomas <unk> Studio Ltd , and the engineering and construction <unk> <unk> Lucas Ltd , Flint and <unk> Partnership and <unk> Structures Ltd . The agreement was to pay the council £ <unk> in damages for breach of contract and negligence . 
- 
- = = <unk> = = 
- 
- Acting on a report in January 2009 , the city council recommended that B of the Bang should be dismantled and placed in storage until funds could be raised for its safe <unk> . The report recognised the sculpture 's aesthetic value for Manchester and Manchester City Council committed itself to working with the artist to reach a long @-@ term solution . One possibility involved the replacement of the steel spikes with <unk> made from carbon fibre , although the report <unk> the necessity for extensive testing . 
- In January 2009 , Antony <unk> , creator of the Angel of the North — to which B of the Bang is often compared — spoke out in support of the sculpture , stating that , " It is a great tribute to Manchester that this ground @-@ breaking work was commissioned . To allow it to disappear would be a loss not just of an inspirational artwork but also of the council 's <unk> . " 
- Despite <unk> 's plea , removal of B of the Bang began in April 2009 . More substantial hoarding was erected around the site and demolition firm <unk> Brothers Limited began removing the spikes with <unk> cutting equipment . 
- Although the council had promised to store the complex central core and legs , these too were cut apart during removal , casting doubt on future prospects for the landmark sculpture 's return , and in early July 2012 the core was sold as scrap for £ 17 @,@ 000 . 
- 
- 
- = Battle of the <unk> River = 
- 
- The Battle of the <unk> River took place during the Second World War and involved Australian , New Zealand and Japanese forces . Part of the wider <unk> Campaign of the Pacific theatre , the battle was fought in the southern sector of <unk> Island . Coming after the Battle of <unk> 's Knoll in which a strong Japanese counterattack was defeated , the battle occurred in two distinct periods between 17 April and 22 May 1945 , as elements of the Australian 15th Brigade advanced south along the <unk> Road . 
- The initial phase saw the Australians advance towards the <unk> River . Following the end of the early fighting , the Australian advance towards the main Japanese concentration at <unk> continued as they struck out towards the <unk> and <unk> Rivers . This continued until torrential rain and flooding brought the advance to a halt short of the objective , <unk> away many bridges and roads upon which the Australians relied for supplies . As the Australian advance stalled , the Japanese began harassing the Australian line of communications , and as the rain stopped and the flooding subsided in late @-@ July and into August , the Australians began making preparations to resume the advance towards <unk> again . Ultimately , though , the war came to an end before the final Australian advance began , bringing the campaign to an end . 
- 
- = = Background = = 
- 
- 
- = = = Strategic situation = = = 
- 
- Japanese forces had landed on <unk> in early 1942 , capturing it from the small force of Australians <unk> the island . They had subsequently developed several <unk> on the island , using it to conduct operations in the northern Solomon Islands and to attack the Allied lines of communication between the United States , Australia and the Southwest Pacific Area . These bases also helped protect Rabaul , the major Japanese garrison and naval base in Papua New Guinea , and throughout 1943 , Allied planners determined that <unk> was vital for <unk> the Japanese base around Rabaul . 
- US Marines conducted an amphibious landing at Cape <unk> , on the western coast of the island , north of Empress Augusta Bay , in November 1943 . After an initial counter @-@ attack , the US Marines had been replaced by a garrison of US Army troops who began consolidating their position around <unk> , establishing a strong perimeter . In March 1944 , the Japanese launched a heavy counter @-@ attack , which was turned back with heavy casualties . After this , the situation on <unk> became largely static , as the Japanese focused primarily on subsistence , and the US forces chose to adopt a mainly defensive posture focused on maintaining the perimeter around <unk> . 
- In late 1944 , as part of plans to free US troops up for the Philippines campaign , the Australian II Corps — consisting of mainly Militia troops under the command of Lieutenant General Stanley <unk> — took over responsibility for Allied operations on <unk> from the American XIV Corps . Australian forces began arriving on the island between November and December 1944 , initially establishing themselves around the US base at <unk> . Due to inaccurate intelligence , <unk> mistakenly believed that the Japanese forces on the island numbered just 17 @,@ 500 men , and he consequently decided that the Australians would pursue an aggressive campaign to clear the Japanese from <unk> in order to free their troops for subsequent operations elsewhere , rather than maintaining the defensive posture the US forces had adopted . However , Allied estimates of Japanese strength were later found to be grossly inaccurate and after the war it was found that the number of Japanese on the island at this time was closer to 40 @,@ 000 . 
- The campaign that Australian planners developed entailed three separate drives : in the north , it was planned that Japanese forces would be forced into the narrow <unk> Peninsula and contained ; in the centre the seizure of Pearl Ridge would give the Australians control of the east – west avenues of approach , as well as <unk> them protection against further counter @-@ attacks , while also opening the way for a drive to the east coast ; and the main campaign in the south , where the bulk of the Japanese forces were concentrated around <unk> . 
- 
- = = = Preliminary moves = = = 
- 
- Following the capture of Pearl Ridge in December 1944 , the Australian 7th Brigade had been moved south and allocated to the drive towards <unk> . In late March and early April 1945 , they had fought the Battle of <unk> 's Knoll after which a brief lull followed as the Australians paused to shorten their supply lines . Meanwhile , the survivors of the Japanese force , heavily demoralised by their defeat , withdrew towards the <unk> River . The Australian 3rd Division was then ordered to resume its advance south , being tasked with capturing the <unk> River , while the <unk> was also included as an " intermediate objective " . Japanese strength in the southern sector was estimated by the Australians at about 10 @,@ 500 men , of which 2 @,@ 300 were believed to be directly opposing the 3rd Division . The 15th Brigade was considered to be the most experienced of the Australian units on <unk> at the time and was moved up to relieve the 7th Brigade , which was in need of rest . 
- Under the command of Brigadier <unk> <unk> the 15th Brigade consisted of three infantry battalions as well as two troops of tanks from the 2 / 4th Armoured Regiment , engineers from the 15th Field Company , a battery of 155 @-@ mm guns from ' U ' Heavy Battery , field artillery from the 2nd Field Regiment , and a number of smaller support units . The <unk> / 59th Infantry Battalion took over responsibility for <unk> 's Knoll replacing the 25th Infantry Battalion while the 24th Infantry Battalion took up a position across the <unk> Road . The brigade 's third battalion — the 57th / 60th Infantry Battalion — did not join them until the beginning of May , and so the 7th Brigade 's 9th Infantry Battalion continued patrolling operations north of the <unk> River , in the <unk> area , until the 57th / 60th could dispatch elements to relieve them . Further inland , the 2 / 8th Commando Squadron advanced in a wide arc to the south @-@ east , defending the brigade 's left , or eastern , flank . 
- The Japanese forces opposing the Australians belonged to the 6th Division , under the command of Lieutenant General <unk> <unk> . <unk> had been ordered to delay the Australian advance between the <unk> and the <unk> for as long as possible , and with these orders in mind he had installed a number of strong points along the Australian 's expected line of advance . The division 's infantry had suffered heavily in the previous battle around <unk> 's Knoll and as a consequence , several units had to be reorganized or amalgamated . The front line positions were assigned to the 6th Field Artillery Regiment , while the 13th Infantry Regiment was to hold five <unk> to their rear along and <unk> the <unk> Road , designated ' A ' through to ' E ' . The 6th Field and 4th Field Heavy Artillery Regiments both held <unk> further back , designated ' F ' and ' G ' . The 23rd Infantry Regiment was placed in the rear , where it was being reconstituted following losses suffered during the attack on <unk> 's Knoll . 
- 
- = = Battle = = 
- 
- 
- = = = Advance to the <unk> = = = 
- 
- The 15th Brigade took over the forward positions from the 7th Brigade on 17 April . Initial <unk> had the 24th Infantry Battalion on the <unk> Road around <unk> Creek , with the <unk> / 59th around <unk> , north @-@ east of <unk> 's Knoll , and the 57th / 60th , when it arrived to relieve the 9th , would be positioned further east <unk> a secondary , parallel track known to the Australians as the Commando Road . Two days later , <unk> received the order to commence the advance towards the <unk> from <unk> , who offered him the support of the 29th Brigade as a mobile reserve in case of sudden counter @-@ attack . In a change to the tactics that the Australians had previously employed prior to the fighting around <unk> 's Knoll , from early May they advanced on a two @-@ battalion front , instead of one . The 24th Infantry Battalion was in the van , moving along the <unk> Road with the <unk> / 59th protecting its flank and rear ; while 5 @,@ 000 yd ( 4 @,@ 600 m ) further inland the 57th / 60th Infantry Battalion , commencing on 3 May , advanced along the Commando Road from <unk> , after taking over from the 9th Infantry Battalion . 
- Moving forward under a <unk> barrage as they moved beyond <unk> , the 24th Infantry Battalion was the first to contact the Japanese , carrying out an attack against Japanese positions around <unk> 's Creek on 17 April . <unk> by a troop of Matilda tanks from the 2 / 4th Armoured Regiment , an artillery barrage which fired over 700 shells , two infantry companies — ' C ' and ' D ' — from the 24th attacked the position while another — ' A ' Company — carried out a flanking manoeuvre to cut another track further north towards <unk> and <unk> . The left forward company — ' D ' Company — reached its objective without trouble ; however , ' C ' Company — on the right along with the troop of tanks — came up against stiff Japanese resistance and became bogged down . ' A ' Company also became embroiled in heavy fighting along the <unk> track . In support of ' A ' Company , <unk> came forward and raked the jungle , hacking through the <unk> to reveal several Japanese <unk> , which were destroyed by the Australian armour . As night fell , ' C ' Company dug in before resuming the attack the next morning . Engineers were brought forward , as was a <unk> , and the gap was <unk> . <unk> heavy fighting , the Australians forced their way across the creek . By the time that the position had been taken in the afternoon and the infantry had advanced to the line of exploitation 400 yd ( 370 m ) beyond the creek , 37 Japanese had been killed for the loss of seven Australians killed and 19 wounded . After this , the Australians continued their advance towards <unk> Creek , which was a further 1 mi ( 1 @.@ 6 km ) to the southeast . In response , the Japanese launched a number of determined counterattacks over the course of the following week , although these were turned back . During this time , the Australians sent a number of patrols out in front of their forward elements , one of which managed to slip through the Japanese defensive positions either side of the <unk> Road and carried out a reconnaissance of the <unk> River about 1 @,@ 000 yd ( 910 m ) south of the main crossing . Further patrols were carried out , as well as a number of ambushes , before the advance was resumed on 26 April . 
- <unk> their advance , the Australians were supported by three squadrons of <unk> from the Royal New Zealand Air Force — Nos. 14 , 22 and 26 Squadrons — which bombed and <unk> the ground in front of the advancing infantry , as well as a <unk> barrage of artillery and mortar fire . With such strong support the Japanese offered little resistance and over the course of two days the 24th Infantry Battalion covered almost a third of the distance to the <unk> , for just one man wounded . Further progress was made over the next week , but on 4 May the advance was slowed when they encountered a roadblock defended by a field gun along with a machine gun , mines and other improvised explosive devices . After this , the 15th Brigade 's engineer support were called upon to regularly carry out route clearance and proving operations as the Japanese became increasingly desperate to destroy the Australian armour , to the extent that they were prepared to sacrifice an artillery piece in order to lure the Australian tanks into a trap where they could be destroyed by mines . The Japanese began to adapt their tactics in other ways also . To <unk> the effectiveness of the Australian tanks , the Japanese began to position themselves off the roads , forcing the infantry to fight without their armoured support . Additionally , the Japanese began to concentrate their artillery with increasing accuracy upon the advancing infantry , which they kept under constant observation and fire . 
- Meanwhile , the previous day , 3 May , the 57th / 60th Infantry Battalion had begun operating along the parallel Commando Road to the north . Here they had a number of encounters and suffered casualties due to their inexperience in patrolling , which resulted in them being ambushed . They also kept up a steady advance and eventually beat the main force in reaching , and crossing the river , arriving there on 6 May . On 5 May , along the <unk> Road , the 24th Infantry Battalion had pressed forward again . <unk> with a tank troop in support , they came up against a concealed field gun defended by approximately 100 Japanese . After the lead Matilda 's machine gun jammed , the field gun opened fire on it , damaging it and wounding its crew . Moving around the stricken tank , the second Matilda , armed with a howitzer , opened fire and destroyed the field gun before sweeping the Japanese defenders from the position . That night , the Japanese artillery opened up on the Australian position with a heavy barrage , and the following morning put in a company @-@ sized counter @-@ attack . The fighting lasted for over two @-@ and @-@ a @-@ half hours , but when it was over the Australians remained in possession of the position having repulsed the attack . In doing so , they suffered one killed and nine wounded , while the attacking Japanese had suffered heavily , losing 58 men killed . It was the biggest loss since the action at <unk> 's Knoll and it spelt the end of their attempt to defend the <unk> . 
- After this , the Australians were able to resume their advance to the river on 7 May without further opposition . The previous three weeks in which they had advanced 7 @,@ 000 yd ( 6 @,@ 400 m ) to the <unk> had been costly for them , however , with the Australian 24th Infantry Battalion losing 25 killed and 95 wounded . Against this , the Japanese had lost at least 169 killed . 
- 
- = = = Crossing the <unk> = = = 
- 
- Following the advance to the <unk> , there was a pause of about a week as the Australians had to wait for roads to be improved and supplies to be brought up , before attempting to cross the <unk> en <unk> . This allowed <unk> to re @-@ evaluate the situation and to issue new orders for the advance towards the <unk> and <unk> Rivers . As they waited for the advance to resume , the Australians carried out reconnaissance patrols deep into Japanese held territory and there were a couple of significant engagements during this time . As a part of these , the 24th Infantry Battalion sent a company across the <unk> and subsequently located a strong Japanese position on a feature that became known as <unk> 's Ridge , which , due to its location , commanded the main Australian axis of advance . 
- The main crossing was planned for 20 May , with the <unk> / 59th Infantry Battalion on the right tasked to cut the <unk> Road and the <unk> Track to the east of the river , while on the left the 57th / 60th Infantry Battalion would divert the attention of the Japanese off the 24th Infantry Battalion which would make the main frontal assault from the centre of the Australian line , crossing at the <unk> <unk> , advancing straight up the <unk> Road . Preliminary moves began before this , and on 15 May a platoon from the 24th Infantry Battalion along with two tanks attempted to carry out an attack on <unk> 's Ridge . After one of the tanks was held up and knocked out by a Japanese field gun , they were forced to withdraw . Meanwhile , the <unk> <unk> squadrons — now reinforced by No. 16 Squadron — began an eight @-@ day aerial campaign , attacking along the length of the <unk> and Commando Roads . During this period , the New Zealanders flew 381 sorties , while artillery and mortars fired " thousands of rounds " . 
- Two days later , on 17 May , the 57th / 60th Infantry Battalion began its diversionary move on the left flank , crossing the <unk> inland and advancing along the Commando Road with 32 <unk> and two batteries of artillery in support . Crossing 500 yd ( 460 m ) north of the <unk> , the centre company carried out an attack along the far bank of the river without its armoured support which had been unable to negotiate the crossing . Nevertheless , shortly before noon they had secured the crossing and began to fan out , carrying out further flanking moves before establishing a firm base to receive supplies and from where it began patrolling operations on 20 May . 
- In the centre , the main attack along the <unk> Road began at 08 : 30 on 20 May after 20 minutes of <unk> by New Zealand <unk> had prepared the ground . <unk> under a <unk> barrage , and with mortar and machine gun support , the 24th Infantry Battalion moved forward with three companies up front and one held back in reserve , along with two troops of Matilda tanks . <unk> the forward companies reached their objectives , but one of the companies was halted just short of their objective and was forced to dig @-@ in overnight after coming under heavy small arms and artillery fire and losing four killed and five wounded . The attack was resumed the following day , and the Australians were able to advance to the <unk> <unk> ; however , they were prevented from moving any further as the Japanese were still concentrated in large numbers further to the west where an Australian patrol encountered 70 Japanese and were forced to go to ground . Finally , a company from the 24th Infantry Battalion was able to move on to the high ground on <unk> 's Ridge , which they found to be heavily mined and booby trapped . Engineers and assault pioneers were called up to clear the feature . 
- On the right flank , the <unk> / 59th Infantry Battalion carried out a wide flanking move along a track that had been carved out of the west bank of the <unk> by <unk> . Beginning their move two days earlier , a number of patrols had had contacts with the Japanese . Meanwhile , using <unk> to drag the tanks through the mud , the Australian armour had crossed the river also and by 16 : 00 on 20 May the battalion had managed to establish itself in an assembly area to the east of the river , <unk> to the Japanese . The following day , the battalion left the line of departure and began to advance on its primary objective , which it reached in the early afternoon despite being held up while the tanks attempted to affect a creek crossing , and further delayed by stiff resistance . Later , after one of the battalion 's patrols came under heavy fire , the tanks moved up and attacked a Japanese gun position which the defenders quickly abandoned , leaving behind a 70 mm gun and a large amount of ammunition . 
- By 22 May , although there were still a number of Japanese in the area which continued to harass and ambush their line of communications , most of the Australian objectives had been secured and <unk> up operations began . The last remaining defensive location before the <unk> was <unk> 's Ridge , where the Japanese were sheltering in tunnels . A heavy aerial and artillery bombardment devastated the position and forced them to abandon the ridge . It was subsequently occupied by a company of Australian infantry . Within a short period of time the <unk> Road was subsequently opened , providing the Australians with the means with which to bring up supplies for the next stage of the campaign , being the advance to the <unk> , <unk> , and <unk> Rivers . The final phase of the battle cost the Japanese 106 killed , while the Australians lost 13 killed and 64 wounded . 
- 
- = = Aftermath = = 
- 
- During the course of the fighting around the <unk> , the Australians lost 38 men killed and 159 wounded , while the Japanese lost at least 275 men killed . Following the battle , the Australians continued their advance towards <unk> at the southern end of the island . Throughout the remainder of the month and into June , the 15th Brigade advanced along the <unk> Road , crossing the <unk> on 10 June . Beyond the river , the Japanese resolved to hold the food growing areas in order to protect their precarious food supply , and they consequently occupied a series of deep <unk> . These were steadily reduced with airstrikes and artillery , and the 15th Brigade subsequently crossed <unk> River before being relieved by Brigadier Noel Simpson 's 29th Brigade in early July . 
- As the 29th Brigade advanced toward the <unk> River , torrential rain and flooding ultimately brought the advance to a halt . The height of the river rose 2 metres ( 6 @.@ 6 ft ) . The <unk> Road was reduced , in the words of Gavin Long , " to a sea of mud " and many of the bridges upon which the Australian supply system was dependent were washed away . This rendered large @-@ scale offensive infantry operations impossible and as the situation worsened for a period of time the Australians even ceased patrolling operations across the <unk> ; meanwhile , the Japanese continued to harass the Australians , probing their positions and setting mines and traps , targeting the Australian line of communications . On 9 July , the 15th Infantry Battalion fought off a series of attacks around <unk> , at the junction of the <unk> River and the <unk> Road , which included a heavy Japanese artillery bombardment . Australian patrols were resumed in late July and continued into August . These attacks proved very costly , particularly amongst the Australian engineers that were tasked with rebuilding the bridges and roads that had been destroyed in the flooding . 
- Fighting in the northern sector continued during this time also , and although preparations in the south for the final advance towards <unk> continued into August , combat operations on the island ceased as the war came to an end before these were completed . As a result , the final Australian operations on <unk> took place on the <unk> front in the northern sector , where the Australians had been conducting a holding action since the failed landing at <unk> <unk> had forced them to abandon plans for an advance into the <unk> Peninsula . By mid @-@ August , however , following the dropping of two atomic bombs on <unk> and <unk> and Japan 's subsequent unconditional surrender , a cease fire was ordered on the island and although there were minor clashes following this , it spelt an end to major combat operations . 
- Following the end of the war , the Australian Army awarded three battle honours for the fighting around the <unk> River . The 2 / 4th Armoured Regiment , and the 9th , 24th , 57th / 60th and <unk> / 59th Infantry Battalions received the battle honour " <unk> River " . A second battle honour — " <unk> 's Ridge – <unk> Ford " — was also awarded to the 2 / 4th Armoured Regiment , and the 24th and <unk> / 59th Infantry Battalions for the second stage of the fighting , while the 57th / 60th Infantry Battalion received the separate battle honour of " Commando Road " for this period . 
- 
- 
- = Sonic the Hedgehog ( 1991 video game ) = 
- 
- Sonic the Hedgehog is a platform video game developed by Sonic Team and published by Sega for the Sega Genesis console . The game was first released in North America , Europe and Australia in June 1991 , and in Japan the following month . The game features an anthropomorphic <unk> named Sonic in a quest to defeat Dr. <unk> , a scientist who has imprisoned animals in robots and stolen the magical Chaos <unk> . Sonic the Hedgehog 's gameplay involves collecting rings as a form of health and a simple control scheme , with jumping and attacking controlled by a single button . 
- The game 's development began in 1990 , when Sega ordered its <unk> development team to create a game featuring a mascot for the company . After considering a number of suggestions , the developers decided on a blue <unk> with spikes along his head and spine and renamed themselves " Sonic Team " to match their character . Sonic the Hedgehog , designed for fast gameplay , was influenced by the games of Super Mario series creator , Shigeru Miyamoto . Sonic the Hedgehog uses a novel technique that allows Sonic 's sprite to roll along curved scenery , which originated in a tech demo created by the game 's programmer , <unk> <unk> . 
- The game was well received by critics , who praised its visuals , music , and speed of gameplay . The game was also commercially successful , establishing the Genesis as a key player in the 16 @-@ bit era and allowing it to compete with Nintendo and their Super <unk> console . It has been ported a number of times , and inspired several <unk> , a successful franchise , and adaptations by other media . 
- 
- = = Plot = = 
- 
- In an attempt to steal the six Chaos <unk> and <unk> their power , the game 's antagonist , Dr. Ivo <unk> , has trapped the animal inhabitants of South Island in aggressive robots and stationary metal capsules . The player controls Sonic , who aims to halt <unk> 's plans by freeing his animal friends and collecting the <unk> himself . 
- If the player collects all the Chaos <unk> and completes the game , a reward ending sequence is shown . If all the <unk> are not collected , <unk> taunts the player while <unk> the Chaos <unk> instead . 
- 
- = = Gameplay = = 
- 
- Sonic the Hedgehog is a 2D , side @-@ scrolling platformer , whose gameplay centers around Sonic 's ability to run at high speed through levels incorporating springs , slopes , high falls , and loop @-@ the @-@ loops . The levels contain <unk> in the form of robots ( " <unk> " in the Western game manuals ) inside of which Dr. <unk> has trapped animals . Although destroying a robot frees the animal within , this is not required to complete the game . The player must avoid rows of sharp spikes , falling into <unk> pits , being crushed by moving walls or platforms , and drowning ( which can be avoided by breathing air bubbles periodically released from <unk> ) . Sonic 's main attack is his spin , where he <unk> into a ball and <unk> rapidly ( damaging enemies and some obstacles ) . This can be performed by jumping in the air or rolling on the ground . 
- <unk> around each level map are gold rings , and collecting 100 rings rewards the player with an extra life . <unk> are a layer of protection against <unk> ; if Sonic has at least one ring when he <unk> with an enemy ( or obstacle ) , he will survive . However , all his rings will be scattered ; they will <unk> and disappear in a few seconds if they are not picked up again . If Sonic is hit without any rings , he loses a life . Although shields and temporary <unk> may be collected to provide additional protection , certain <unk> ( such as drowning , being crushed , falling down a <unk> pit or running out of time ) will cost Sonic a life regardless of rings or other protection . 
- The game is divided into six zones ( Green Hill , <unk> , Spring Yard , Labyrinth , Star Light , and <unk> Brain ) , each with its own visual style and enemies . A player must navigate through each zone ( subdivided into three acts ) to progress . At the end of each zone 's third act , the player confronts Dr. <unk> ( who pilots a different vehicle each time ) in a boss fight . After the sixth zone , the player continues directly to the Final Zone for a last encounter with <unk> . 
- The player begins with three lives ( power @-@ ups and rings add more ) , which are lost when Sonic <unk> with hazardous enemies ( or objects ) without rings , falls off @-@ screen or exceeds an act 's ten @-@ minute time limit . <unk> acting as checkpoints allow Sonic to return to the most @-@ recently activated post when he loses a life . If he loses a life as a result of time running out but has another life , the <unk> will reset to 0 : 00 when he returns to the <unk> . If all lives are lost at any point in the game , the game over screen will appear ( when the player can return to the beginning of the act with three lives , if they have any continues ) . 
- When Sonic reaches the end of act one or act two of a zone with at least fifty rings , a large ring appears through which he can jump to enter a Special Stage ( a " Secret Zone " in the original Genesis manual ) . In each of six Special <unk> , Sonic <unk> off the <unk> and walls of a rotating maze in spin attack . Although the player earns a continue with each 50 rings found , their main goal is to obtain the Chaos Emerald at the end of the maze without colliding with a " goal block " ( which ends the level ) . 
- 
- = = Development = = 
- 
- 
- = = = Background and character design = = = 
- 
- In 1990 , Sega ordered its in @-@ house development studio , <unk> , to develop a game featuring a mascot for the company . This was a position already held by the character Alex <unk> , but he was considered similar to Mario and deemed unsatisfactory ; Sega president <unk> <unk> wanted a character as iconic as Mickey Mouse . Sega had competition from Nintendo and its mascot , Mario , in mind ; Nintendo was dominant at the time , particularly after the release of the successful Super Mario Bros. 3 , and Sega wanted a foothold in the industry . Although the company had some success with Genesis ports of its arcade titles , it knew this would not be enough . 
- <unk> developed ideas for characters , an engine , and gameplay mechanics . Development emphasized speed , so <unk> eliminated character designs not associated with fast animals , as well as fast creatures like <unk> and squirrels . One idea , a <unk> able to grasp objects with prehensile ears , showed promise at first but was too complex for the available hardware . The team narrowed its search to animals that can roll into a ball , their idea for an attacking move . <unk> then realized that this would not seem aggressive enough , so they focused on two animals with spikes : <unk> and <unk> . The <unk> character , first proposed by <unk> <unk> , prevailed , although the <unk> would later become the basis for Mighty the <unk> ( who first appeared in 1993 's <unk> the Hedgehog ) . <unk> has admitted since that he created Sonic 's basic design by combining Felix the Cat 's head with Mickey Mouse 's body . 
- Sonic was originally <unk> @-@ colored , then a light shade of blue , but he was changed to dark blue so he would stand out against certain backgrounds and to match the Sega logo . His shoes were colored red through the inspiration of Michael Jackson 's boots on the album cover for Bad and the outfit of Santa Claus , whom <unk> saw as the most " famous character in the world " . Sonic 's spikes were emphasized to make him look <unk> , and he was given the ability to spin while jumping ( so attacking and jumping could be controlled with one button ) . The new character was originally named " Mr. <unk> " , but the eight @-@ member <unk> changed his name to " Sonic " and their studios to Sonic Team . <unk> proposed to flesh out the character included placing him in a rock band , giving him vampire fangs , and giving him a human girlfriend named Madonna , but Sega of America scrapped these ideas to keep his identity simple . Sega of America also expressed concerns that most Americans would not know what a <unk> is and initially proposed a full @-@ scale recreation of the character , but compromised with Sonic Team to simply make design changes . The antagonist ended up being named " Dr. <unk> " in Japan and " Dr. <unk> " in other regions as a result of a dispute between Sega 's American and Japanese divisions . 
- 
- = = = Programming and <unk> = = = 
- 
- With a satisfying protagonist completed , Sega turned to esteemed programmer <unk> <unk> , who had impressed them through his work on <unk> Star and the Genesis port of <unk> ' n <unk> . The gameplay originated with a tech demo by <unk> , who developed an algorithm allowing a sprite to move smoothly on a curve by determining its position with a dot matrix . <unk> 's prototype was a platform game with a fast @-@ moving character rolling in a ball through a long , winding tube , and this concept was <unk> out with <unk> 's character designs and levels by designer <unk> <unk> . <unk> originally intended to work on the game for three months due to the delay of his planned move to the United States by the outbreak of the Gulf War , but was <unk> in the project for nearly a year . His designs for levels were intended to attract both hardcore and casual gamers by integrating occasional challenging set pieces into the mostly accessible level design . The game 's color scheme was influenced by the work of pop artist <unk> Suzuki , and the aesthetics of Green Hill were influenced by the geography of California . 
- In designing the game mechanics , <unk> was inspired by Shigeru Miyamoto , whose games he had enjoyed playing years earlier . <unk> the simplicity of Miyamoto 's mechanics in complex environments , <unk> decided that Sonic would be controlled with only a directional pad for movement and a single button for jumping . He also wanted his creation to be more action @-@ oriented than the Mario series ; while playing Super Mario Bros. , he had wondered why the levels could not be cleared more quickly . Due to the need to demonstrate the Genesis ' technological prowess , the developing game underwent extensive testing and redesign , a process taking over six months . The developers ' efforts were rewarded ; according to <unk> <unk> , the game had the fastest @-@ ever character speed in a video game and a rotation effect in the special stages that was considered impossible on the console . The team intended to add a two @-@ player mode displayed via split @-@ screen , but <unk> 's programming knowledge was insufficient to implement this feature . However , such a mode would later appear in sequel Sonic the Hedgehog 2 ( 1992 ) , where the second player would control Sonic 's best friend Miles " <unk> " <unk> . <unk> , <unk> , and <unk> worked 19 hours a day on the project for several months . 
- <unk> 's relationship with Sega of Japan was tenuous during this time ; he received little credit for his involvement in the game . He left the company shortly after the game 's release , although Sega of America hired him later . Before leaving , however , he defied Sega of Japan 's prohibition of developer credits by displaying a few names in black text on a black background , identifiable only by looking at the game 's code . 
- 
- = = = Music = = = 
- 
- The music for Sonic the Hedgehog was composed and produced by Masato <unk> , bassist and lead songwriter of the J @-@ pop band Dreams Come True . The game uses <unk> Yamaha <unk> and <unk> programmable sound generators to produce a variety of <unk> sound effects and music . It was originally intended to have a sound test menu with animations of Sonic <unk> to the music of a " Sonic Band " of <unk> <unk> on guitar , Max Monkey on bass , <unk> <unk> on drums , and <unk> the Crocodile on keyboard . The playable <unk> became a recurring character in the series , also appearing in <unk> ' <unk> , Sonic Heroes , and Sonic Free <unk> . The development schedule scrapped the feature , and <unk> <unk> replaced the test with the " Se @-@ <unk> ! " chant used in TV commercials ( which reportedly used one @-@ eighth of the memory of the four @-@ <unk> cartridge ) . 
- On October 19 , 2011 , twenty years after the game 's release , a three @-@ disc compilation of music from Sonic the Hedgehog and Sonic the Hedgehog 2 was released in Japan . The first disc features original tracks from both games , the second contains Masato <unk> 's demo recordings and the third comprises songs by Dreams Come True and their <unk> remixes . 
- 
- = = = <unk> and release = = = 
- 
- Game @-@ package illustrator Akira <unk> said that his goal was to make the characters " colorful " , using clear lines and <unk> to " finish them <unk> " . According to <unk> , the developers asked him to create a package design " similar to pop art ... without being particular to conventional packages " – something " original " and " stylish " . The game was released in North America , Europe , and Australia on June 23 , 1991 , and in Japan on July 26 . Sega of America packaged it with American Genesis consoles , replacing <unk> Beast . Genesis owners who bought their consoles before the switch could request free copies of Sonic the Hedgehog by mail . Sega of America created a large @-@ scale marketing campaign to promote the game and Sonic as a mascot for the company . 
- 
- = = <unk> versions and ports = = 
- 
- 
- = = = 8 @-@ bit version = = = 
- 
- A version of Sonic the Hedgehog was developed by Ancient and released in 1991 for Sega 's 8 @-@ bit consoles , the Master System and Game <unk> . Its plot and gameplay mechanics are similar to the 16 @-@ bit version , with different level themes and digital assets . The level design is <unk> , with no vertical loops , and Sonic cannot re @-@ collect his rings after being hit . The game has a different soundtrack , composed by <unk> musician <unk> <unk> and including his compositions and adaptations of music from the 16 @-@ bit version . It was the final game released for the Master System in North America . The Master System version was re @-@ released for Wii 's Virtual <unk> service in North America on August 4 , 2008 , and in Europe on August 8 . The Game <unk> version was re @-@ released for the Nintendo 3DS Virtual <unk> on June 13 , 2013 , and included as an <unk> game in Sonic Adventure <unk> : Director 's Cut for <unk> and Windows and Sonic Mega Collection Plus for PlayStation 2 , Xbox , and Windows . 
- 
- = = = Sonic the Hedgehog Genesis = = = 
- 
- A port , Sonic the Hedgehog Genesis , was released for the Game Boy Advance ( GBA ) on November 14 , 2006 in United States to mark the game 's fifteenth anniversary . It included several new features , such as the ability to save game progress , a level select option , and an <unk> Mode with the Spin <unk> move ( not originally implemented until Sonic the Hedgehog 2 ) . Its screen is slightly <unk> in , and adapted to the GBA 's widescreen aspect ratio . The game received poor reviews , with a Metacritic score of 33 percent ; the chief complaints concerned its poor conversion to the Game Boy Advance ( resulting in a slow frame rate ) , remixed <unk> music , and poor preservation of the original gameplay . 
- 
- = = = Compilation releases = = = 
- 
- With its sequels for the Genesis , Sonic the Hedgehog has been ported for a wide range of home and handheld consoles and personal computers . It has appeared in Sonic Compilation ( 1995 ) for the Genesis , Sonic Jam ( 1997 ) for the Sega Saturn and <unk> , Sonic Mega Collection ( 2002 ) , Sonic Mega Collection Plus ( 2004 ) , Sonic 's Ultimate Genesis Collection ( 2009 ) for the Xbox 360 and PlayStation 3 , and Sonic Classic Collection ( 2010 ) for the Nintendo DS . Additionally , the game is an <unk> reward in the console versions of Sonic <unk> . 
- 
- = = = Downloadable releases = = = 
- 
- Sonic the Hedgehog has been available for all three major seventh @-@ generation video game consoles . It was part of the Wii Virtual <unk> at the service 's 2006 introduction , and was released for the Xbox Live Arcade and PlayStation Network shortly afterwards . The game was released for the <unk> Classic , <unk> video , and video @-@ capable <unk> <unk> models in 2007 and for Apple 's iOS service ( compatible <unk> and <unk> touch models ) in April 2009 . Sonic the Hedgehog became available on <unk> in September 2009 . In October 2010 , it was released as a Microsoft Windows download which was ported to Steam . The game was ported to two online <unk> services ( Google Play and the Amazon <unk> ) in December 2012 . A remastered mobile port , created using Christian Whitehead 's <unk> Engine previously used in the 2011 <unk> of Sonic CD , was released on iOS , replacing the original port , on May 15 , 2013 with an <unk> version released the following day . The port features widescreen graphics , the optional ability to spin dash , a time attack mode , and the <unk> option to play as <unk> or <unk> the <unk> . The game was also released as part of the Nintendo 3DS 3D Classics line in May 2013 in Japan , and worldwide in December . 
- 
- = = Reception = = 
- 
- Sonic the Hedgehog was critically praised at its release and in retrospective reviews , with an 86 @-@ percent approval rating at the review aggregator GameRankings based on nine reviews . The game rivaled the Mario series , particularly Super Mario World ( which was recently released for Genesis rival Super Nintendo Entertainment System ) . Paul Rand of Computer and Video Games compared the two in depth and characterized Sonic the Hedgehog as being faster , with brighter colors , and Super Mario World as having more " depth of play " . 
- Reviewers noted the game 's colorful , detailed graphics . Rand called its color scheme " lively , but never <unk> " , praising the interaction of color with detail in the sprites , backgrounds , and animations and describing its graphics as the best available for the Genesis . Reviewer Boogie Man of GamePro called the intricate backgrounds " eye @-@ popping " and " gorgeous " , which was echoed by Mean Machines . According to the <unk> ( Hartley , Patricia , and Kirk ) of Dragon , " The graphics and animation in Sonic the Hedgehog make this a serious contender for the best video game of the year " and <unk> called the animation " some of the <unk> and fastest ... ever seen " . The music and sound effects were also well received ; Dragon called them " great " , and <unk> " amazing " . Rand noted " <unk> of catchy tunes and <unk> " , calling some of the sound effects " absolutely brilliant " . Although Mean Machines called the songs " vaguely appealing " , the game 's sound effects were better appreciated . 
- Critics cited the fast gameplay , unprecedented in <unk> . GamePro 's Boogie Man noted its " lightning @-@ fast action " and , according to Electronic Gaming Monthly ( <unk> ) , " If you thought the Enterprise was quick , wait till you see Sonic at <unk> speed . " The game 's difficulty was disputed , described as " impossible " by Rand and " average " by <unk> . Rand said about the gameplay in general that it " plays like a dream " ; according to <unk> it would <unk> players for hours , and Boogie Man praised Sonic Team 's ability to provide an engaging experience primarily from running and jumping . Although <unk> , Dragon , and Paul of Mean Machines praised the level design ( especially the hidden rooms ) , Paul found losing all of one 's rings frustrating . 
- Sonic the Hedgehog has maintained its popularity , appearing on lists of the greatest video games of all time . Frank <unk> of GameSpot described the game as " one of the best <unk> of all time " , noting that despite technical issues in the Game Boy Advance port " after all these years , the underlying graphics , audio , and gameplay still hold up " . Lucas M. Thomas of IGN agreed that it stood the test of time : " You 'll be impressed by the clarity and color that come through ... Few people realize how difficult it was to create Sonic 's graphics engine , which allowed for the incredible rate of speed the game 's known for . But the technical achievement impressed back in ' 91 , and still does so today . " Mega ranked the game its third @-@ favorite Genesis title , and in 2001 Game Informer called it the 24th @-@ greatest game of all time . Sonic the Hedgehog has been a commercial success ; the original Genesis version sold over 15 million copies by February 2009 , and the mobile version had eight million paid downloads by 2008 . 
- 
- = = Legacy = = 
- 
- 
- = = = <unk> on the industry = = = 
- 
- Primarily because of its Genesis <unk> , Sonic the Hedgehog contributed to the console 's North American popularity . During October – December 1991 , the Genesis <unk> the Super <unk> by a two @-@ to @-@ one ratio , and at its January 1992 peak claimed 65 percent of the market for 16 @-@ bit consoles . Although Nintendo eventually again overtook Sega , this was the first time since December 1985 that the company did not lead the console market . 
- Sonic the Hedgehog added speed and momentum @-@ based physics to the standard platform formula . Set @-@ pieces and mechanics introduced by the game ( such as loops , springs , and acceleration pads ) have appeared in other games and become associated with the Sonic series . 
- A flood of " animal with attitude " <unk> , each featuring a cartoon mascot , were released after Sonic the Hedgehog : <unk> , Aero the <unk> @-@ Bat , James Pond 3 , <unk> Jim , Zero the <unk> <unk> , <unk> <unk> , <unk> the Tasmanian Tiger , <unk> . This carried into the PlayStation era with games like <unk> <unk> and Crash Bandicoot . 
- 
- = = = <unk> and other media = = = 
- 
- The game 's success led Sega to develop an extensive media franchise . A 1992 sequel , Sonic the Hedgehog 2 , gave Sonic a best friend ( and sidekick ) named Miles " <unk> " <unk> and continued the fight against Dr. <unk> . Sonic has generated dozens of additional games and a large cast of recurring characters ( keeping Sonic and <unk> as <unk> ) , surviving the end of Sega console manufacturing after the <unk> . The series has ventured from <unk> to fighting , racing , role @-@ playing , and sports games , and has expanded into anime and manga , cartoons and comic books , novels , and toys . Sonic the Hedgehog has become one of the best @-@ selling video game franchises of all time , with over 150 million copies sold by May 2014 . The game 's first stage , Green Hill Zone , has also been re @-@ <unk> for use in several other titles such as Sonic Adventure 2 , Sonic <unk> and the Super Smash Bros. series . 
- 
- 
- = The Same Old Story = 
- 
- " The Same Old Story " is the second episode of the first season of the American science fiction drama television series Fringe . The episode was written by executive producer Jeff Pinkner and co @-@ creators J. J. Abrams , Alex <unk> , and Roberto Orci . When developing the series , they sought to find a medium between serialized drama and the crime procedural . " The Same Old Story " was the first regular episode of Fringe , and journalists viewed it as an example of what they could expect from the series . It was directed by Paul A. Edwards . 
- After a newborn baby rapidly ages into an 80 @-@ year @-@ old man , Fringe division agent Olivia Dunham ( Anna Torv ) and consultants Peter ( Joshua Jackson ) and Walter Bishop ( John Noble ) investigate . They tie the case to the murders of young women , who have all had their <unk> glands removed . It is revealed that Dr. Claus <unk> ( Mark <unk> ) , an expert in <unk> , is working to help his son Christopher ( Derek Cecil ) stay alive , as he suffers from rapid aging syndrome . 
- " The Same Old Story " first aired in the United States on September 16 , 2008 on Fox . An estimated 13 @.@ 272 million viewers watched the episode , a 45 percent increase from the series premiere . The episode received mixed reviews from television critics – reviewers thought it was an improvement from the previous episode but faulted it for containing plot <unk> . 
- 
- = = Plot = = 
- 
- A prostitute is abandoned by an unknown man at a hospital , dangerously in labor . She dies as the doctors perform a <unk> section , but the child ages rapidly in minutes , soon dead having aged to the likes of an 80 @-@ year @-@ old man . Olivia Dunham ( Anna Torv ) , Peter Bishop ( Joshua Jackson ) , and Walter Bishop ( John Noble ) , new members of the Fringe division , are called to investigate by division head Phillip <unk> ( Lance <unk> ) , believing the case to be part of " The Pattern " , a string of mysterious incidents . The woman is identified as having recently left a local <unk> , and Olivia finds evidence that points to a past serial murder case she and her former partner John Scott were not able to solve . Olivia explains to Peter that their murderer would <unk> his victims , young women , then make an <unk> along their face to extract a piece of brain material , killing the victim in the process . Walter takes both corpses back to his lab and determines that the woman had only been pregnant minutes before giving birth , her child having accelerated aging disease . Walter is reminded of having previously done work in this field , and remembers where he <unk> his car that contains the related files . Once they are retrieved , Olivia makes a connection to the <unk> <unk> which controls growth in humans , and informs Charlie Francis ( Kirk <unk> ) to monitor recent cases where the victims ' <unk> <unk> has been removed . 
- Olivia and Peter turn to an expert in <unk> , Dr. <unk> ( Mark <unk> ) , to trying to learn more about rapid aging , but Dr. <unk> cannot help them further , though Peter suspects he is hiding something . Unseen by the Fringe division , Dr. <unk> visits an abandoned warehouse and meets the murderer , Christopher ( Derek Cecil ) , his son , who suffers from rapid aging syndrome . <unk> warns Christopher to be careful and that they only need one more woman to complete the process . By this time , Charlie has found a recent murder victim killed in the same fashion as Olivia 's serial murderer . At Walter 's lab , they identify the <unk> <unk> has been removed . They <unk> the murderer must extract hormones from the glands to slow down his own aging process . Walter <unk> they can discover the location of the crime by looking at the images left in the woman 's optical nerves induced by the <unk> <unk> . <unk> an electronic pulse camera from Massive Dynamic , they discover the image of a suspension bridge near <unk> and identify the likely location from which it was viewed — the same warehouse that Dr. <unk> visited . The FBI converge on the building , and Olivia and Peter find <unk> about to cut into another victim . Olivia chases off after Christopher , who eventually <unk> to his rapid aging and dies , while Peter , after <unk> <unk> with a bullet , <unk> with Walter to apply a makeshift <unk> to bring the victim back to life . 
- As they <unk> up the case , Olivia , Peter , and Walter complete forms to <unk> their position in the Fringe division . When Peter is out of <unk> , Walter learns from Olivia that the FBI medical files on Peter 's childhood are void of any details . 
- 
- = = Production = = 
- 
- Co @-@ creators J. J. Abrams , Alex <unk> and Roberto Orci , and executive producer Jeff Pinkner wrote the episode . Paul A. Edwards served as the director , his first such credit for the series . In developing Fringe , the co @-@ creators did not want to make the series too serialized , as this was a complaint often directed at Abrams ' television series <unk> . They wanted to find a balance between standalone stories and serialized content , and studied procedural dramas such as Law and Order and <unk> : Crime Scene Investigation for inspiration . <unk> explained , " While we make sure that our episodes are self @-@ contained – have a beginning , a middle , and an end – the character stories can be serialized . They don 't have to resolve themselves over the course of one show . " " The Same Old Story " was Fringe 's first regular episode , and some journalists viewed it as an example of how the series would be structured . 
- The episode was based on actual recent research and involved much collaboration among the series ' different departments . For the scene in which they remove the victim 's <unk> , the special effects crew created a mold based on actress Elizabeth Stanley 's head . Using plaster to make a prosthetic , they " drilled out the <unk> and <unk> [ ed ] it open and [ went ] through a series of molds . " Stanley stated that " it 's really cool to see the head because it really does look just like me , even though it 's very gruesome because the <unk> 's popping out . " The prop department rigged a weapon which they called a " <unk> gun " ; according to prop master Peter <unk> , this gun was based on real devices employed in airport security that look through people 's clothing for concealed weapons . VFX Supervisor Christopher <unk> explained that for the eye 's last vision , they used " a lot of actual photographic reference on the set and on the location , " which they then used to " take that imagery and composite it into the various computer monitors and displays in the lab . " 
- " The Same Old Story " featured guest appearances by actors Derek Cecil as Christopher <unk> and Mark <unk> as his father , Dr. Claus <unk> . Other guest actors included Betty <unk> as <unk> " Amber " Daisy <unk> , Bernie <unk> as old Christopher <unk> , Carmen <unk> as Amy , <unk> Jones as a doctor , and Karin <unk> as John Scott 's sister . 
- 
- = = Reception = = 
- 
- 
- = = = Ratings = = = 
- 
- " The Same Old Story " was watched by 13 @.@ 272 million viewers in the United States , a 45 percent increase of four million from the pilot . Fox claimed that this ratings improvement was the best for any new network drama series in over five years . Its high @-@ rated lead @-@ in , House , helped boost Fringe 's ratings for the night , as 93 percent of House 's total audience and 95 percent of 18- to 49 @-@ year @-@ olds stayed to watch Fringe . Fringe was also the highest rated program for male 18- to 54 @-@ year @-@ olds that evening . 
- 
- = = = Reviews = = = 
- 
- " The Same Old Story " received mixed reviews from television critics . TV Squad writer Jane <unk> thought the episode seemed " all over the place " , and wasn 't sure what to think . She stated the two most interesting parts of the episode were when Walter removed the girl 's eye in order to identity her killer , and the <unk> at the very end , when Walter states " If you 've read my file , then you know the truth about Peter 's medical history " . Travis Fickett of IGN thought it was better than the pilot ; though it was " laden with lots of <unk> <unk> and clichéd dialogue , " Fickett understood that it was needed to <unk> new viewers to the show . Fickett concluded his review by saying Fringe has the potential to be a " great show " , and he also praised the production level as " top notch " , and Torv 's performance as " very good " as she " brings a unique presence to the show " . The A.V. Club critic Noel Murray gave the episode a C + , while Television Without Pity graded it with a B. 
- Josh Jackson of Paste Magazine disliked the " <unk> ex <unk> " aspects that " <unk> solved the puzzles of the first two episodes " , and also criticized perceived " plot <unk> " like how the serial murderer immediately began aging once interrupted from killing his last victim . Despite these flaws , Jackson said he would tune in next week because he would " rather have a show <unk> on the side of unbelievable than unremarkable , and I 'm interested to see if Abrams has any more tricks up his sleeve " . Erin Fox of TV Guide also liked " The Same Old Story " better than the pilot because she thought that it " had really cool special effects , awesome Walter @-@ <unk> , and actual chemistry forming between Olivia and Peter " , and " we also got to <unk> more into the connections between Walter , Nina Sharp , Massive Dynamic and the government and the experiments they conducted before Walter was <unk> " . 
- A UGO Networks columnist was concerned that the expensive pilot would be hard to translate into weekly episodes , but " after sitting down to watch Fringe 's second episode , " The Same Old Story " , my concerns have vanished . Fringe 's second episode represents all that is holy about genre programming without <unk> any of Fringe 's eleven @-@ herbs and spices . In fact , the subtle framework of what makes Fringe not X @-@ Files is even better defined in the first weekly @-@ episode . I 'd struggled to find the word to describe it , and the winner is ' <unk> ' . " The Same Old Story " has all of the sense of mystery , discovery , and humor that the pilot had just in a smaller , more <unk> space . " Another UGO writer , Alex <unk> , later compared " The Same Old Story " to the similarly @-@ plotted The X @-@ Files episode " <unk> <unk> " . <unk> concluded that " Fringe wins this one , just for having a slightly less stupid explanation . " 
- 
- 
- = Randy Blythe <unk> case = 
- 
- The Randy Blythe <unk> case was a court case in the Czech Republic , stemming from a 2010 Lamb of God concert in Prague , wherein 19 @-@ year @-@ old fan Daniel <unk> sustained head injuries leading to a coma and subsequent death . During the investigation , Czech police unsuccessfully asked United States authorities for cooperation . When the band returned to the Czech Republic for another concert two years later , its vocalist Randy Blythe was arrested , charged with causing <unk> 's death , and <unk> in custody for five weeks . 
- According to a verdict delivered by the Municipal Court in Prague on March 5 , 2013 , it was proven that Blythe had thrown <unk> <unk> and thus had moral responsibility for his death . However , due to the circumstances , Blythe was not held <unk> liable , and most of the blame lay with <unk> and security members . The <unk> was upheld by the Prague High Court on June 5 , 2013 . 
- 
- = = 2010 concert incident = = 
- 
- During a concert on May 24 , 2010 , in the Prague club , <unk> , Blythe was involved in an incident that resulted in the death of Daniel <unk> , a 19 @-@ year @-@ old attending fan . According to <unk> statements cited by the Czech online daily newspaper <unk> following Blythe 's arrest , Blythe was chanting " Come on up " between songs , which , the newspaper stated , may have been intended to invite applause from the audience and not a direct invitation to fans . The newspaper went on to report that the fan tried to climb onstage and was thrown by the singer from the stage , falling backwards directly on his head . According to the same paper , <unk> was not under the influence of drugs or alcohol , suffered serious brain trauma , fell into a coma , and died weeks later from his injuries . 
- A report about the concert at issue released on May 26 , 2010 by <unk> stated that " one of the things that was unexpected was the behavior of the singer Randall Blythe , who on a few occasions struck some fans in a relatively brutal way off the stage . " The article also contains pictures , one of them showing Blythe holding a fan down on the ground . Meanwhile , another report released two days after the concert by <unk> alleged that " Randy in a totally uncompromising way took down an <unk> fan , who has climbed the podium several times . The front @-@ man clearly showed that it is his territory , he struck the intruder down , punched him a couple of times and sent him through the air off the podium , without even stopping singing ( ! ) " On May 28 , 2010 , the report by <unk> stated that " some broken head was a testimony to the fact that the band does not like anybody on the stage " , while <unk> stated that " the only negative thing about the concert was , to say it <unk> , <unk> approach of the band towards the stage @-@ divers ... when somebody tried to climb the stage , he was brutally swept down . " 
- Following Blythe 's arrest , Tomáš <unk> , a promoter of the concert , said that there was no fight between the fan and Blythe , and that " it was an unfortunate incident which happened during the concert when someone climbed onto the stage where he was not supposed to be . " According to the Lamb of God publicist Adrenaline PR , " [ the ] incident deals with a fan that three times during the concert jumped the <unk> and rushed Randy during the performance . It is alleged that the third time , security was not able to reach him and that Randy pushed him back into the audience where supposedly he fell and hit his head . " However , it was revealed during the trial that it was a different fan who previously got into contact with Blythe than <unk> . Guitarist Willie <unk> said , " I can 't recall that particular show , let alone a fan being beaten on the stage . I think I would 've noticed something like that considering the <unk> thing . " 
- According to Blythe 's attorney Martin <unk> , the police launched an investigation following the death of <unk> , about a month after the concert and following a coma . After interviewing several <unk> from the concert , the police asked the United States Department of Justice to take part in the investigation ; however , they refused to cooperate and , <unk> , did not notify anyone from Lamb of God or its management . 
- 
- = = <unk> and charges = = 
- 
- On June 27 , 2012 , Blythe was arrested by the Czech police on suspicion of <unk> . Lamb of God was prepared to play in Prague on June 28 , 2012 , but Blythe 's arrest upon arrival at <unk> Airport caused the concert to be canceled . 
- According to TV Nova , Blythe stated that he had not been aware of <unk> 's death and expressed his remorse . 
- A police spokesperson stated on June 29 , 2012 that the police had formally charged Blythe under section 146 ( 4 ) of the Czech Criminal Code , which contains intentional <unk> of bodily harm resulting in death ( i.e. <unk> ) . He faced 5 – 10 years of imprisonment if found guilty . Randy 's brother Mark Blythe said the charge was " <unk> and <unk> and will be dropped immediately . " 
- 
- = = Court <unk> and bail = = 
- 
- On June 30 , 2012 , the State Attorney brought a motion to <unk> Blythe in pre @-@ trial detention , as he considered Blythe a flight risk . During a hearing conducted the same day , judge <unk> <unk> of the Prague 8 District Court ruled that Blythe will be held on <unk> , with the possibility of a bail of <unk> 4 @,@ 000 @,@ 000 ( ~ US $ 200 @,@ 000 ) , Blythe 's alleged annual income ; Blythe was held in <unk> Prison . <unk> was deposited in the court 's bank account on mid @-@ day of July 3 , 2012 . After this , the State Attorney had three working days to either accept the bail or to challenge it by filing a complaint . Due to public holidays it was not until July 9 , 2012 , that the State Attorney filed his complaint , which was to be dealt with by <unk> court , the Prague Municipal Court . 
- On July 17 , 2012 , Prague Municipal Court 's panel of three judges headed by judge <unk> <unk> overturned the bail decision by doubling the bail amount to <unk> 8 million ( ~ US $ 400 @,@ 000 ) . After this , the State Attorney challenged the conditions of release , trying to achieve that the bail is subject to Blythe staying in the country and / or Blythe having to report at a given police station regularly until the criminal proceedings are finished . On August 2 , 2012 , the <unk> court rejected the State Attorney 's second complaint and ordered Blythe 's immediate release . Blythe left the Czech Republic the next day , claiming in an interview with TV Nova that he would return for the trial . 
- 
- = = <unk> and trial = = 
- 
- On November 13 , 2012 , the spokesperson of the Prague State Attorney 's Office announced that the police had formally closed their investigation and proposed to the State Attorney to <unk> Blythe . After reviewing the case file , the State Attorney indicted Blythe on the aforementioned charges on November 30 , 2012 . Two weeks later , a judge set the trial to commence on February 4 , 2013 , with a plan to conduct the hearings in four consecutive days . Blythe was summoned to attend the hearing in person . 
- The case was heard by a panel of the Prague Municipal Court , consisting of presiding professional judge Tomáš <unk> and two lay judges . Trials in the Czech Republic are public . In general , the court is bound to decide on the deed as stated in the <unk> ; however , it is not bound by its legal assessment by the State Attorney . 
- Both the defendant and the State Attorney may appeal the decision ; the appeal would be heard by a panel of the Prague High Court , consisting of three professional judges . A decision of second instance court is final and <unk> . Nevertheless , an extraordinary appeal may be lodged by the defendant or the Supreme State Attorney , which would be heard by the Supreme Court of the Czech Republic in <unk> ; an extraordinary appeal may , however , rest only on issues of law and does not provide for full review of the case . After exhausting all of these <unk> , the defendant may also <unk> a petition to the Constitutional Court of the Czech Republic . The petition may be based on allegation of violation of rights under the Czech Constitution and Charter of <unk> Rights and Basic <unk> . 
- 
- = = = State Attorney <unk> <unk> = = = 
- 
- <unk> against Blythe was brought by State Attorney <unk> <unk> . <unk> had tried a number of murderers , such as <unk> <unk> ( life in prison ) , Roman <unk> ( life imprisonment at 1st instance , 25 years on appeal ) , Maria <unk> ( 13 years imprisonment ) and <unk> <unk> ( 11 years imprisonment ) . He also tried a case against <unk> <unk> , an alleged head of Russian mafia , requesting 25 years in prison for ordering a murder . The Municipal Court in Prague acquitted <unk> due to lack of evidence ; however , the decision was repealed on appeal by the High Court , and the case will be heard again by a different <unk> of the Municipal Court . Another of <unk> 's murder cases which ended in <unk> due to insufficient evidence is that of <unk> <unk> , who was indicted in connection with disappearance of <unk> <unk> , a vice @-@ chairman of the Czech Football Association . 
- 
- = = = Defense lawyers = = = 
- 
- Randy Blythe was represented by Prague lawyers Martin <unk> and <unk> <unk> . <unk> studied law at the Faculty of Law of Charles University and at the New York University . From 1990 to 1992 , he served as external advisor of the then Prime Minister <unk> <unk> . A former partner at Baker & <unk> , <unk> established <unk> & Co. in 1996 . He is also a member of board of directors of <unk> 2000 . 
- <unk> gained fame as attorney of <unk> <unk> , a Kurdish national who was first charged in 1994 with preparing to commit three murders and committing blackmail and torture . It became one of the longest criminal cases in the Czech history , as the witnesses and alleged victims gradually withdrew or changed their testimonies against <unk> until he was <unk> by the Municipal Court in Prague in 2007 . The court held that although the crimes did take place , there is no evidence that <unk> took part in it . He was also defending judge Pavel <unk> , who was indicted of accepting a <unk> . The proceedings ended with <unk> being found insane and <unk> not liable . <unk> also acted as a defense attorney in the case of a <unk> of Czech <unk> indicted on charges of rape and torture . During the proceedings , the judge sent <unk> to face the disciplinary commission of the Czech bar association for what he perceived as " behaviour bordering on contempt " . 
- 
- = = = Day 1 of the trial = = = 
- 
- The trial started on February 4 , 2013 . Blythe testified that when he wanted to see the club before the concert , Lamb of God 's <unk> told him that the club was terrible and <unk> . According to Blythe , the <unk> went on , saying that the stage was small , there were too many people , and that it was rather dangerous . 
- Blythe , who is <unk> , took off his glasses before entering the stage , which together with the smoke and light effects allegedly left him half @-@ blind . Blythe said that people could easily reach the band members or climb up to the stage . One of the fans , who was identified as Milan <unk> by Czech newspapers , and who was scheduled to testify later during the proceedings , managed to climb the stage twice without being stopped by security . Blythe testified that during the first attempt , <unk> rushed the stage and started waving his arms before stage diving . The second time , he tried to put his arms around Blythe in an attempt to <unk> him . Blythe , who according to his own words perceived this as a danger , caught <unk> 's collar , pushed him on the ground , <unk> on him and repeatedly told him to stop . He then led the fan by his hair to the edge of the stage , where the fan jumped off . It was only after watching a video of the incident that Blythe found out that a security officer was actually pushing the fan from back . Blythe further said that he saw <unk> trying to reach the stage yet again before finally being stopped by security . 
- Later , when another fan tried to climb the stage , Blythe thought that it was <unk> again . Blythe testified that he approached the fan and pushed him with both hands out of the stage in the belief that the crowd would catch him , which it did not . <unk> <unk> , author of video which caught the previous incidents with <unk> , testified that the fan was for a moment lying on the ground with nobody helping him . Blythe further commented that he saw the fan get up and that other fans showed him <unk> up . Blythe insisted that he never saw <unk> nor came into contact with him . It was not until the arrest two years later that he found out about <unk> 's death . 
- Blythe further testified that he was not under the influence of alcohol during the concert and that he had never used any drugs . Chris <unk> , Lamb of God 's drummer , testified that he had not seen anything from the back of the stage and further proclaimed that Blythe 's aggressiveness is only a stage act . According to <unk> , Blythe is a calm , moderate , and well @-@ read person . The defense also presented videos from various Lamb of God concerts in order to demonstrate that metal music is very energetic and that Blythe regularly cheers to the crowd , but not to encourage people to climb the stage . 
- Blythe also alleged that after learning about <unk> 's death , he had written a letter to the <unk> family , in which he offered help and a meeting in @-@ person . Daniel <unk> 's father , however , testified that the family has not been contacted by anyone from the band nor by the defense team . <unk> 's father confirmed that his son had been healthy up until the day of the incident . The <unk> family 's representative brought a claim for damages in the amount of <unk> 10 @,@ 000 @,@ 000 ( <unk> . US $ <unk> @,@ 000 ) . 
- 
- = = = Day 2 of the trial = = = 
- 
- <unk> , eight witnesses delivered their testimonies on February 5 , 2013 . Among them were friends of Daniel <unk> . <unk> and three friends had come to attend the concert from <unk> , a town in mountains some 130 kilometres ( 81 mi ) northwest of Prague . They described <unk> as a huge fan of Lamb of God who had been able to secure an <unk> from a guitar player before the concert had started . 
- <unk> 's friend Jan <unk> testified that <unk> climbed the stage , and as he turned around towards the crowd he was pushed by Blythe off the stage . He said he was " 100 % sure Blythe pushed <unk> with both hands . " He further said that Blythe 's behavior <unk> from all the concerts he had attended in the past , commenting that Blythe was visibly furious . He further claimed that <unk> was not the first person to be chased off the stage by Blythe , with another fan being kicked , choked and receiving a punch from Blythe . <unk> corroborated Blythe 's testimony that Blythe asked " Are you okay ? " after <unk> fell and that the crowd gave him a positive reply . However , as <unk> fell sick after the concert , they called him an ambulance . According to <unk> , the band had not warned the fans against getting on stage and the security guards did not pay much attention to it . 
- <unk> 's other friend <unk> <unk> testified that <unk> 's fall happened in a break between the end of concert and the encore . <unk> climbed the stage together with another fan as the band members were leaving to go backstage . According to him , Blythe ran into the two fans and pushed them both off . While the other fan was caught by a couple of fans remaining under the stage , <unk> fell directly on the ground . According to <unk> , there were fewer fans in front of the stage due to the break . <unk> further testified that after the fall , <unk> went to sit on a bench , where he was <unk> a water bottle . <unk> began vomiting about half an hour after the fall and as his friends realized that he had a <unk> at the back of his head , they called an ambulance for fear he might have a concussion . <unk> said that he understood Blythe 's gesture in the break as an invitation to the stage . 
- Another person to testify was Robert <unk> , who worked as a security guard the night of the incident . <unk> said that the <unk> was too close to the stage , but not so close as to make a platform for climbing the stage . He testified that he pulled one person off the stage and that another person fell off before he could be secured . He further said that the fan 's fall might have been helped by someone else , perhaps the singer , though he did not see it precisely . 
- Blythe 's defense team attacked differences between the testimonies the witnesses gave after the incident in 2010 and at the court . Among other things , one of the witnesses originally alleged that <unk> and Blythe shook hands or that one of them offered hand to another on stage ; however , at the court hearing , he testified that there was no prior contact between them . Blythe also argued that video evidence shown in court refuted claims about his aggressive behavior . 
- 
- = = = Day 3 of the trial = = = 
- 
- Milan <unk> , the fan whom Blythe admitted to <unk> off the stage , testified on the third day of the trial . <unk> said he climbed the stage twice in order to stage dive ; he changed his mind during the third attempt to get on stage . <unk> testified that Blythe knocked him down , <unk> on him and held him down for a moment , but Blythe definitely did not <unk> him . He also admitted that taking into account the fact that he ( <unk> ) was drunk and the way he acted , Blythe 's reaction was adequate . He commented that he grasped the fact that he was not wanted on stage . 
- Another witness who had attended the concert at the rear of the crowd testified that Blythe 's behavior was standard to metal concerts , saying that metal bands always put on angry and tough acts and the concert at issue was no exception . She said that it was evident that Blythe did not want any fans on the stage . Neither the two nor any other witness who took the stand that day gave a testimony as to the moment of <unk> 's fall at the end of the concert . 
- The judge also read a sworn statement from <unk> 's former producer , who stated that she had not known about the incident . She was aware that an ambulance was called that evening , but learned about the reasons for that only later , during the police investigation . The court further heard that Lamb of God had sent a list of security demands to the concert venue , including that barriers should be placed 1 @.@ 5 metres ( 4 @.@ 9 ft ) from the stage . <unk> Tomáš <unk> testified that although this was not the case , the band had not expressed any concerns to him either before or after the concert . 
- Randy Blythe was called again to the stand that day . The judge first alerted him that he had the right to remain silent and can deny answering his questions . The judge went again through the Blythe 's testimony and , with a view to the previous witness statements , asked Blythe whether he insists that it was Milan <unk> whom Blythe threw off the stage . Blythe responded by saying : " To the best of my knowledge , it was Milan <unk> who was coming up [ on stage ] every time . " 
- Also that day , expert medical witness <unk> <unk> took the stand . According to The Prague Post , <unk> testified that <unk> 's cause of death was pneumonia resulting from a blow to the <unk> . <unk> added that the " mortality rate from this kind of injury is around 40 percent , and that doctors couldn 't have done more to save the patient . " <unk> was initially taken to a nearby hospital less than a kilometer from the venue , where the initial diagnosis took place . Since this hospital did not have a specialized <unk> department , <unk> was transferred to another hospital after the diagnosis . There , <unk> underwent two operations , first to tend to the injury and second to reduce pressure on brain by removing some bone . 
- Finally , defense asked for <unk> as a key witness fell ill . The court decided to continue with the hearings the next day , after which the hearings would be <unk> until March 4 , 2013 , in order to hear the defense 's witness . Blythe committed to return when the trial resumed . 
- 
- = = = Day 4 of the trial = = = 
- 
- On February 7 , 2013 , only one witness took the stand before the hearing was <unk> . <unk> <unk> contacted the defense after reading about the trial in newspapers because he " didn 't like the description of the situation " by the previous witnesses . He said he did not think Blythe was aggressive that night , and that fans should realize any aggressive behavior displayed onstage is just part of the show . He also said that each time somebody got on the stage , Blythe demonstrated that they were not allowed there . <unk> testified that he saw how a fan tried to reach the stage three times and that as he was preparing to stage dive , somebody aided his fall from behind . He had not seen anybody fall directly on the ground . <unk> had trouble recalling details from the show , as he had visited multiple concerts on other dates in <unk> , and thus could not describe , among other things , the position of the <unk> . 
- 
- = = = Day 5 of the trial = = = 
- 
- The trial resumed on March 4 , 2013 with testimonies from expert witnesses in the fields of criminal psychology ( <unk> <unk> , appointed by the court ) and <unk> ( <unk> <unk> , appointed by the defense ) . Blythe returned to attend the hearings in person , even though the presiding judge had told him that at this point they may be undertaken in his absence . The two expert witnesses agreed that Blythe is not aggressive ; nevertheless , he may have issues with controlling his emotions under stress . The court further heard testimonies from two <unk> . One of them was a bodyguard who was present at the concert but did not see the fall . He testified that when by the exit , he saw two men taking a third man out of the building to fresh air . He said that he was told by them that the person fell off the stage . The ambulance arrived 5 – 10 minutes later . 
- Another <unk> was a fan , <unk> <unk> , who testified that she saw Blythe <unk> <unk> , saying " [ h ] e climbed onto the stage , and when he tried to stand up , Blythe <unk> him . " According to her , the <unk> was strong enough for <unk> to fall behind the front row of the fans into a place where no fans were staying at the moment . She testified that <unk> fell backwards . Another <unk> of the concert who took the stand that day did not recall seeing any fall by the end of the concert . 
- 
- = = = Day 6 of the trial = = = 
- 
- A defense @-@ appointed expert witness in the field of <unk> testified on March 5 that <unk> could not turn 180 degrees during the fall and that should he be falling forward , he could not have sustained an injury to the back of his head . He further said that if <unk> fell over the first row of the fans , he must have been not only pushed , but must have jumped himself . The State Attorney immediately attacked this testimony , claiming that the expert witness omitted the conditions at the place and time , as well as some of the <unk> testimonies , according to which <unk> fell onto his back from the beginning and did not turn during the fall . The presiding judge agreed with the State Attorney on some of his points . 
- In his closing speech , the State Attorney asked the court to <unk> Blythe for 5 years , claiming that " even children in the kindergarten are aware that a fall from height may lead to an injury . " 
- A <unk> family representative said that based on the witness testimonies , the family did not believe that Blythe was solely responsible . He continued that he would not be <unk> the requested amount , as no money could replace the loss the family has suffered . He further added that Daniel had died on his father 's birthday , and his mother has consequently become a <unk> patient unable to work . 
- In his closing word , Blythe said that he did not wish to avoid any responsibility and that if he felt guilty he would have pleaded so . He further commented that in case of <unk> , measures would be undertaken to avoid anything similar from happening at Lamb of God concerts again . 
- 
- = = = First instance verdict = = = 
- 
- On March 5 , 2013 , the court delivered a verdict , according to which Blythe was not <unk> liable for <unk> 's death , even though he had the moral responsibility for it . Consequently , the court dismissed the damages claim and ordered the return of Blythe 's bail . 
- The court held that it was proven that Blythe had thrown <unk> off the stage . However , Blythe , due to his <unk> , could have mistaken <unk> for the other fan who had repeatedly gotten over the <unk> . According to the court , the largest part of the blame lies with the <unk> and security members . Judge <unk> further <unk> Blythe for not having met with the <unk> family . 
- The State Attorney announced that he would appeal the verdict . 
- 
- = = = Appeals verdict = = = 
- 
- The State Attorney 's appeal was heard by a panel of three judges of the Prague High Court , chaired by judge <unk> <unk> . The hearing took place in Blythe 's absence . Blythe 's <unk> was upheld by the panel on June 5 , 2013 . 
- The verdict may have been appealed by the Supreme State Attorney ( <unk> ) to the Supreme Court . As the appeal hasn 't been lodged by <unk> within two months of the delivery of written second instance verdict , it has become final . 
- In 2015 Blythe demanded ca <unk> 15 millions for damages but the Municipal Court for Prague 2 dismissed the suit . 
- 
- = = Reaction = = 
- 
- In reaction to the arrest and detention , a fan created a petition at the official White House petition site . By the time Blythe was released on bail , it gathered over 27 @,@ 500 signatures . 
- On July 7 , 2012 , there was a vigil organized in Lamb of God 's hometown of Richmond , Virginia , by a friend of Blythe 's . At the event , <unk> ( who are also from Richmond ) frontman Dave <unk> said : “ I don ’ t think it was right for him to be arrested . I don ’ t think it was right for him to be locked up .... This stuff could have all been worked out <unk> or legally before he got there . ” In addition to <unk> , other notable figures in heavy metal , such as Tom Araya and David <unk> , have also come out in support of Blythe . 
- In a post to his blog , Blythe explained that he met the <unk> family in private after the trial , and promised them to be " a spokesperson for safer shows " . He emphasized that the family never attacked him and " just wanted to know the truth of what had happened to their son " . 
- 
- 
- = U.S. Route 2 in Michigan = 
- 
- US Highway 2 ( US 2 ) is a component of the United States <unk> Highway System that connects Everett , Washington , to the Upper Peninsula ( UP ) of the US state of Michigan , with a separate segment that runs from <unk> Point , New York , to <unk> , Maine . In Michigan , the highway runs through the UP in two segments as a part of the state trunkline highway system , entering the state at <unk> and ending at St. Ignace ; in between , US 2 briefly traverses the state of Wisconsin . As one of the major transportation <unk> in the UP , US 2 is a major conduit for traffic through the state and neighboring northern Midwest states . Two sections of the roadway are included as part of the Great Lakes Circle <unk> , and other segments are listed as state @-@ designated Pure Michigan <unk> . There are several memorial highway designations and historic bridges along US 2 that date to the <unk> and 1920s . The highway runs through rural sections of the UP , passing through two national and two state forests in the process . 
- The route of what became US 2 was used as part of two Indian trails before European settlers came to the UP , and as part of the Michigan segments of the Theodore Roosevelt International Highway and the King 's International Highway auto trails in the early 20th century . The state later included these trails as part of M ‑ 12 when the first state highway <unk> were designated in 1919 . Most of M ‑ 12 was redesignated as part of US 2 when the US Highway System was created on November 11 , 1926 . Since the 1930s , several changes have <unk> the highway 's routing through the UP . One such alteration eventually created a business loop that connected across the state line with Hurley , Wisconsin , and others pushed an originally inland routing of US 2 closer to the Lake Michigan shoreline . With the creation of the Interstate Highway System , part of US 2 was rerouted to coincide with the new Interstate 75 ( I ‑ 75 ) , though in the 1980s , the U.S. Highway was truncated and removed from the I ‑ 75 freeway , resulting in today 's basic form . 
- 
- = = Route description = = 
- 
- According to a 2006 regional planning committee report , US 2 is a key highway for Michigan , providing its main western gateway . The roadway plays " an important role in the transportation of goods across the northern tier of states in the Midwest " , and is listed on the National Highway System ( NHS ) for its entire length . The NHS is a network of roadways important to the country 's economy , defense , and mobility . Together with M ‑ 28 , US 2 is part of a pair of primary <unk> that bridge the eastern and western sides of the UP . The 305 @.@ 151 miles ( 491 @.@ <unk> km ) of roadway in Michigan is divided into a 109 @.@ 177 @-@ mile ( 175 @.@ 703 km ) western segment and a 195 @.@ <unk> @-@ mile ( 315 @.@ 390 km ) eastern segment , interrupted by a section that runs for 14 @.@ 460 miles ( 23 @.@ 271 km ) in the state of Wisconsin . 
- 
- = = = Western segment = = = 
- 
- US 2 enters Michigan from Wisconsin for the first time north of downtown Hurley , Wisconsin , and <unk> , Michigan , over the state line that runs along the Montreal River . The highway crosses the river into <unk> County and passes a welcome center on the way into a commercial district north of downtown . Running along <unk> Drive , US 2 meets its only business route in Michigan at Douglas Boulevard . The business route was previously a full loop that ran west through downtown <unk> and crossed the border into Hurley and back to the main highway . The Wisconsin Department of Transportation has removed the signage on their side of the border , which reduced the loop to a business spur that ends at the state line . US 2 continues eastward through UP woodlands to the city of <unk> . While bypassing the community of Ramsay , the highway crosses a branch of the Black River . The roadway enters Wakefield on the south side of Sunday Lake , meeting M ‑ 28 at a <unk> in town . As the US Highway leaves Wakefield , it turns southeasterly through the Ottawa National Forest , crossing Jackson Creek and two branches of the <unk> Isle River . US 2 and M ‑ 64 merge and run concurrently over the second branch of the <unk> Isle in the community of <unk> . This concurrency has the lowest traffic volume along the entire length of the highway within the state ; in 2010 the Michigan Department of Transportation ( MDOT ) recorded a daily average usage along the stretch of 770 vehicles , compared to the overall average of 5 @,@ 188 vehicles for the highway . At the end of the concurrency , M ‑ 64 turns northerly to run along Lake <unk> . 
- The highway continues parallel to the state line from the <unk> area through the national forest toward <unk> . That unincorporated community is the home of the <unk> High School <unk> , the basketball team featured on a series of ESPN commercials and a documentary series on the Sundance Channel . The area is also where the waters meet ; the rolling hills drain to Lake Superior via the <unk> River , to Lake Michigan via the <unk> and <unk> rivers , or to the Gulf of Mexico via the Wisconsin and Mississippi rivers . Also located in the area are the <unk> Wilderness , and the Lac <unk> Desert Indian <unk> , which includes the Lac <unk> Desert Casino and Resort . The highway travels southeasterly from <unk> around the many lakes and streams in the area and crosses into rural Iron County . US 2 intersects Federal Forest Highway 16 ( <unk> 16 ) near Golden Lake in <unk> Township in the middle of the national forest . The trunkline then runs along the Iron River as it approaches the city of the same name and meets M ‑ 73 . In town , US 2 intersects M ‑ 189 before crossing the river and turning northeast out of the city . 
- US 2 leaves the Ottawa National Forest at Iron River , and the highway continues eastward through forest lands near several small lakes to Crystal Falls , the county seat of Iron County . On the west side of town , US 2 meets US 141 ; the two highways run concurrently along Crystal Avenue . The combined highway turns south onto 5th Street and meets M ‑ 69 's eastern terminus at the intersection between 5th Street and Superior Avenue next to the county <unk> at the top of the hill . US 2 / US 141 runs south out of Crystal Falls to the west of , and parallel to , the <unk> River . The roadway passes Railroad , Kennedy and <unk> lakes and leaves the state of Michigan at the <unk> River , crossing into Florence County , Wisconsin for about 14 miles ( 23 km ) . 
- 
- = = = Eastern segment = = = 
- 
- US 2 / US 141 re @-@ enters Michigan where it crosses the <unk> River and subsequently meets M ‑ 95 in <unk> Township north of Iron Mountain and <unk> . The highways merge in a triple concurrency and run south on <unk> Avenue into Iron Mountain along the west side of Lake Antoine , parallel to a branch line of the <unk> and Lake Superior Railroad ( <unk> Railroad ) . The road crosses through a retail corridor and over a flooded pit of the <unk> Mine . In downtown Iron Mountain at <unk> Street , M ‑ 95 turns west off <unk> Avenue to run across town to <unk> . US 2 / US 141 exits downtown and turns east along a second retail corridor near the <unk> Mall . The highway re @-@ enters <unk> Township where US 141 separates to the south to re @-@ enter Wisconsin . US 2 continues eastward parallel to a branch of the Canadian National Railway ( <unk> Railway ) . Both road and rail travel through the community of <unk> , where they pass near the largest paper mill in the UP . The trunkline runs along the main street of Norway , where the highway meets the eastern terminus of US 8 . Then US 2 continues east through rural Dickinson County to Vulcan , passing north of <unk> Lake through the <unk> Country State Forest , before crossing the Sturgeon River in <unk> and passing into <unk> County . 
- In <unk> County , the environment takes on a more agricultural character along US 2 . The highway passes through the edge of the community of <unk> before entering Powers . US 2 comes to a three @-@ way intersection and turns northeast merging onto US 41 . The concurrent highway runs from Powers through the communities of Wilson and <unk> on the south side of the <unk> Railway . At Harris , the trunkline enters the <unk> Indian Community . Harris is on the <unk> County side of the reservation , but as the highway continues east , it crosses over to <unk> River on the Delta County side . The county line in between not only separates the two communities , but also serves as the boundary between the Central and Eastern time zones . East of <unk> River , the highway crosses the community 's namesake waterway before intersecting the eastern terminus of M ‑ 69 . The roadway crosses the Ford River prior to turning due east into the outskirts of <unk> . 
- US 2 / US 41 <unk> to four lanes along <unk> Street , which forms the east – west axis of the <unk> street grid . Near downtown , the highway meets M ‑ 35 , which runs along the city 's north – south axis , Lincoln Avenue . The <unk> merge and run north , bypassing the traditional central business district for a different business corridor . Lincoln Avenue runs north carrying four lanes of traffic past the Upper Peninsula State <unk> , site of one of the two state <unk> for the state of Michigan , the only state to have twin <unk> . US 2 / US 41 / M ‑ 35 continues north on Lincoln Avenue past the campus of Bay de <unk> Community College . The four @-@ lane highway crosses the <unk> River just upstream from its mouth near the large Mead Paper Mill and shifts to run immediately next to Little Bay de <unk> . The section here carried the highest traffic counts along all of US 2 in the state : an average of 23 @,@ 977 vehicles used this segment of roadway daily in 2011 . 
- The road turns inland again , and US 2 / US 41 / M ‑ 35 passes to the west of downtown Gladstone . The highway through here is an expressway , four lanes divided by a central median and no <unk> access . Unlike a freeway , the expressway has standard intersections and not interchanges . The highway intersects the eastern terminus of County Road 426 ( CR 426 ) and crosses the <unk> Railroad south of the <unk> for 4th Avenue North , where M ‑ 35 separates from the US Highways and turns to the northwest . The expressway continues north parallel to the <unk> Railway , crossing the Days River . Through this area , the trunkline carries a speed limit of 65 mph ( 105 km / h ) for car traffic . This is the only road in the UP with a speed limit higher than 55 mph ( 89 km / h ) besides I @-@ 75 , which has a speed limit of 70 mph ( 110 km / h ) . The expressway segment runs around the upper end of Little Bay de <unk> before ending at Rapid River . In this location , US 41 separates to the north , and US 2 returns to an easterly track as a two @-@ lane road , crossing the Rapid and <unk> rivers and turning southeast around the head of the bay . As US 2 crosses southern Delta County , it passes through the western unit of the <unk> National Forest . Near Garden Corners , the highway runs along the shore of Big Bay de <unk> . After the intersection with the northern terminus of M ‑ 183 , US 2 turns inland cutting across the base of the Garden Peninsula and enters Schoolcraft County . 
- As the highway approaches Thompson , US 2 leaves the western unit of the <unk> National Forest and enters the Lake Superior State Forest . The roadway runs along Lake Michigan to <unk> , crossing the <unk> River . The trunkline turns inland approaching <unk> and then turns north @-@ northeast to Blaney Park . The community there is a former logging town @-@ turned @-@ resort at the southern terminus of M ‑ 77 ; the resort was active from the late 1920s but declined by the 1980s . From Blaney Park , US 2 turns due east and crosses into Mackinac County west of Gould City . Where it intersects a former routing , the main highway crosses the <unk> Railway one last time and runs to the south of <unk> to follow the Lake Michigan shoreline through <unk> . After passing the community of <unk> , US 2 crosses the Cut River Bridge , 147 feet ( 45 m ) over the Cut River . The highway crosses into the eastern unit of the <unk> National Forest near <unk> , running between Lake Michigan and <unk> Lake in the process . The road continues along the Lake Michigan shoreline , passing <unk> Spot near <unk> Cap and turning inland immediately west of St. Ignace . The US 2 designation ends at the highway 's partial cloverleaf interchange with I ‑ 75 . The roadway continues easterly into downtown St. Ignace as Business Loop I ‑ 75 ( BL I ‑ 75 ) . 
- 
- = = History = = 
- 
- 
- = = = Indian trail through auto trails = = = 
- 
- In 1701 , the first transportation routes through what became the state of Michigan were the lakes , rivers and Indian trails . Two of these trails followed parts of the future US 2 . The <unk> – Green Bay Trail roughly followed the Lake Michigan shoreline routing of US 2 between <unk> and St. Ignace . The Mackinac Trail connected St. Ignace with <unk> <unk> . Marie . 
- In the age of the auto trail , the roads that later formed US 2 through the UP were given a few different highway names . When the original roadways between <unk> and Iron River were completed in late 1915 , the Upper Peninsula Development Bureau ( <unk> ) named the area <unk> and the highway the <unk> Trail . Later the name was extended over the highway to <unk> , and to all highways in the area in the early 1920s ; the name was phased out by the <unk> completely in 1927 . The roadways were also used for the Theodore Roosevelt International Highway , named for former US president Theodore Roosevelt after his death in 1919 . Overall , this highway ran from Portland , Oregon , to Portland , Maine , by way of Michigan and the Canadian province of Ontario . Through the UP , the southern branch followed the immediate predecessors to US 2 , including the section through Florence County , Wisconsin . 
- The Great Lakes Automobile Route was established in 1917 by the <unk> . A predecessor of the Great Lakes Circle <unk> by seventy years , the route followed " a circular journey along the banks of lakes Michigan and Superior and Green Bay ... " This route followed the modern US 2 from <unk> to the M ‑ 94 junction in <unk> , using the modern M ‑ 69 and M ‑ 95 to stay in Michigan . <unk> of the route followed US 41 and M ‑ 35 between Powers and <unk> . The route was originally intended to entice motorists to drive around Lake Michigan . The name fell out of use before its first anniversary because of World War I. 
- One Canadian auto trail was routed through the UP as well . In 1920 , the King 's International Highway linked Vancouver , British Columbia , to Halifax , Nova Scotia , but there was no highway to carry it around the north side of Lake Superior . <unk> had to ship their cars by boat between <unk> <unk> . Marie , Ontario , and Thunder Bay or enter the United States to continue along the auto trail . The <unk> varied on the maps of the time , but its basic route used US 2 through the UP from <unk> to <unk> <unk> . Marie until a highway north of Lake Superior was opened in 1960 ; by that time , the auto trail had taken on the Trans @-@ Canada Highway name . 
- 
- = = = State trunkline = = = 
- 
- The first state trunkline highway designated along the path of the modern US 2 was M ‑ 12 , a designation that was in use by July 1 , 1919 , between <unk> and <unk> <unk> . Marie . The first roadside park in the country was created by Herbert <unk> near what is now US 2 near Iron River in 1919 – 20 , although other sources state that the first was a picnic table alongside US 16 ( Grand River Avenue ) in 1929 south of <unk> . When the US Highway System was created on November 11 , 1926 , US 2 partially replaced M @-@ 12 . Between Crystal Falls and Iron Mountain , US 2 was routed through Florence , Wisconsin . The former routing of M ‑ 12 from Crystal Falls to <unk> became a new M ‑ 69 when the former M ‑ 69 became US 102 ( now US 141 ) . M ‑ 12 from <unk> south to Iron Mountain was made a part of an extended M ‑ 45 , which is now M ‑ 95 . By the next year , M ‑ 48 was added along US 2 from <unk> to <unk> as part of a larger extension . 
- The first changes to the routing of US 2 itself were made in 1930 with a bypass of downtown <unk> . A larger <unk> was completed in 1933 between Rogers Park and <unk> <unk> . Marie . The new routing followed Mackinac Trail instead of turning east to <unk> and north to <unk> <unk> . Marie ; the former routing was given the M ‑ 121 designation . Another realignment in the Iron Mountain area shifted US 2 / US 141 to a new bridge over the <unk> River between 1932 and 1934 . Downtown <unk> was bypassed in 1934 , and the former route was initially designated M ‑ 54 . 
- The Michigan State Highway Department ( MSHD ) changed the <unk> and designations of the highways around <unk> , Thompson and <unk> in the mid @-@ 1930s . The agency rerouted US 2 between <unk> and M ‑ 149 in Thompson , turning the old road back to county control . The section between M ‑ 149 and M ‑ 125 was redesignated as an extension of M ‑ 149 to Thompson , and M ‑ 125 was replaced by a further extension of M ‑ 149 . The last change was to route US 2 along its current alignment in the area , completing the changes on August 2 , 1936 . 
- The MSHD started construction in 1936 on a new road that rerouted US 2 into St. Ignace for the first time . Between <unk> and <unk> , US 2 previously followed Worth Road inland to the <unk> Trail to meet the northern extension of US 31 into the Upper Peninsula . The new routing took US 2 along the <unk> into St. Ignace . US 31 was truncated to the state ferry docks in Mackinaw City and US 2 was routed through St. Ignace along the former US 31 to Rogers Park ; the connection in St. Ignace to the state ferry docks became M ‑ 122 . Further changes in the early 1940s <unk> the roadway out near <unk> and Crystal Falls . 
- Additional <unk> were completed by the MSHD to move US 2 to its modern <unk> routing between Gould City and <unk> in 1941 . The new highway traveled due east from Gould City to <unk> and then along the lake to <unk> . The former route through <unk> was turned back to local control as far east as <unk> . From there east , it was numbered just M ‑ 48 , removing US 2 from a concurrency . Another former section into <unk> was added to extend M ‑ 117 . The new highway was <unk> around the Cut River Bridge until it was completed in 1946 after construction delays over steel shortages during World War II . 
- The western end of US 2 took on two changes in the 1940s . M ‑ 28 was extended along US 2 to the state line at <unk> from its western terminus at Wakefield . A similar extension was made from M ‑ 28 's eastern terminus to <unk> <unk> . Marie in 1948 . The M ‑ 54 designation was renumbered as Business US 2 by 1945 . The eastern M ‑ 28 extension was reversed in 1950 , and the western extension to the state line was shifted to a new location by 1952 . 
- 
- = = = Interstate era = = = 
- 
- With the coming of the Interstate Highway System in Michigan , the MSHD planned to convert the eastern section of US 2 to a freeway between St. Ignace and <unk> St. Marie . In planning maps from 1947 , this highway corridor was included in the system that later became the <unk> . It was also included in the General Location of National System of Interstate Highways Including All Additional Routes at Urban Areas <unk> in September 1955 , or Yellow Book after the cover color , that was released in 1955 as the federal government <unk> plans for the freeway system . The proposed number in 1958 was Interstate 75 ( I ‑ 75 ) . 
- The first section of freeway was built in late 1957 or early 1958 between <unk> <unk> and M ‑ 123 north of St. Ignace . The Mackinac Bridge was opened to traffic on November 1 , 1957 ; a new section of freeway and an interchange connected US 2 to the bridge . In 1961 , another new freeway segment closed the gap between the Mackinac Bridge and <unk> <unk> sections . At the time , the I ‑ 75 designation <unk> US 27 on the bridge , and US 2 was shifted to follow I ‑ 75 along the freeways in the St. Ignace area . The former routing of US 2 in downtown St. Ignace was redesignated BL I ‑ 75 . More sections of freeway were opened in 1962 immediately to the south of the newly constructed International Bridge in <unk> <unk> . Marie as well as between <unk> and <unk> . The last two sections opened in 1963 connected the northern end of the freeway at M ‑ 123 to <unk> , and the section between <unk> and <unk> <unk> . Marie . At this time , all of US 2 's former routing became a county road known as Mackinac Trail ( H @-@ 63 ) . 
- The Department of State Highways expanded US 2 / US 41 into an expressway between Gladstone and Rapid River in 1971 . The state built a new bridge over the <unk> River in 1983 , bypassing downtown . MDOT disposed of the former routing of US 2 into downtown in two ways . The western half was initially an unnumbered state highway until it was later transferred to local control . An extension of M ‑ 94 replaced the remainder , including the <unk> Bridge , through downtown . In that same year , the department truncated US 2 to end in St. Ignace by removing it from the I ‑ 75 freeway . The last changes were made to US 2 's routing through Iron River in 1998 , bypassing the bridge that formerly carried the highway over the river in town . In 2011 , MDOT raised the speed limit along the expressway section in Delta County from 55 to 65 mph ( 89 to 105 km / h ) , although the speed limit for trucks remains 55 mph ( 89 km / h ) . 
- 
- = = Memorial designations and tourist routes = = 
- 
- On July 1 , 1924 , the State Administrative Board named M ‑ 12 , the predecessor to US 2 in Michigan , the <unk> Highway to honor Frank P. <unk> , a prominent local citizen who later served in Congress from 1927 to 1933 . In 1929 , the residents of <unk> created a memorial to the veterans of World War I called Memory Lane . The project consisted of <unk> and maple trees planted along US 2 / US 41 west of town . The American Legion sold the trees to local businesses and individuals who could honor specific soldiers . Later in 1949 , the <unk> Women 's Club created a tribute in the form of a permanent living memorial to the area veterans . Also called Memory Lane , the group planted 140 <unk> and 1 @,@ 840 <unk> , trees and shrubs as a <unk> parkway along 2 @.@ 3 miles ( 3 @.@ 7 km ) of US 2 east of <unk> . 
- Most of US 2 , along with US 23 in the Lower Peninsula , was designated the United Spanish War Veterans Memorial Highway in 1949 . To connect the gap in the routing where US 2 cuts through Wisconsin , M ‑ 95 and M ‑ 69 were used in place of US 2 between Iron Mountain and Crystal Falls . Signs marking the highway were not erected until 1968 when Governor George W. Romney had them installed . 
- The <unk> Memorial Drive designation was created for the section of US 2 / US 41 / M ‑ 35 between the northern <unk> city limits and County Road 426 ( CR 426 ) in Delta County . The American Veterans ( <unk> ) organization in Michigan petitioned the Michigan Legislature to grant this designation , which was assigned under Public Act 144 in 1959 . 
- Two sections of US 2 are part of the overall Great Lakes Circle Tour ( <unk> ) : the segment from the Wisconsin state line near <unk> to the M ‑ 28 junction in Wakefield is part of the Lake Superior Circle Tour ( <unk> ) , and the segment from the southern M ‑ 35 junction in <unk> to the eastern terminus in St. Ignace is part of the Lake Michigan Circle Tour ( <unk> ) . These two tours were created in May 1986 through a joint effort between MDOT and its counterparts in Wisconsin , Minnesota and Ontario . The section of US 2 between Iron River and Crystal Falls has been named the Iron County Heritage Trail . This Pure Michigan Byway was designated to honor the " rich history of two industries that built a state and nation : mining and logging . " On August 26 , 2007 , MDOT announced that the section of US 2 that runs concurrently with M ‑ 35 in Delta County was being included in the UP <unk> Coast Recreational Heritage Trail . 
- 
- = = Historic bridges = = 
- 
- There are six bridges along current or former sections of US 2 that MDOT has added to its listing of Michigan 's Historic Bridges ; two of these are also listed on the National Register of Historic Places ( <unk> ) . A third bridge added to the <unk> in late 2012 has not been added to MDOT 's listing however . The first of these historic bridges is the crossing of the Iron River , which has since been bypassed by a new bridge . The original structure , dating to 1918 , is a 55 @-@ foot @-@ long ( 17 m ) <unk> arch span that was built by the MSHD as Trunk Line Bridge No. 191 . The structure was listed on the <unk> on December 9 , 1999 , for its architectural and engineering significance . 
- In December 2012 , the National Park Service approved the listing of the Upper Twin Falls Bridge that crosses the <unk> River northwest of Iron Mountain . The structure is a single @-@ span , pin @-@ connected , <unk> , through @-@ <unk> bridge , and it is the only known example of its type in Michigan . It was built between 1909 and 1910 because the Twin Falls Power Dam would flood an existing river crossing . The span cost $ 5 @,@ 106 ( equivalent to $ 780 @,@ 000 in 2015 ) , paid equally by Dickinson and Florence counties . Until the 1930s , the Upper Twin Falls Bridge carried US 2 across the <unk> River . In 1934 , a new bridge was built about a mile downstream , and the highway was rerouted over the new span . The bridge closed to automobile traffic in September 1971 , and the nomination process for inclusion on the National Register of Historic Places began in 2012 . 
- In 2003 , MDOT replaced the Sturgeon River Bridge in <unk> Township , Dickinson County . As of October 2011 , even though the old bridge was demolished and replaced , MDOT retained it on their historic bridge list . It was built in 1929 . 
- Before 1983 , US 2 used a different routing through <unk> and crossed the <unk> River on what is nicknamed the " <unk> Bridge " . Built as a part of a <unk> <unk> on the river , the water level is actually higher than the road surface . This produces a <unk> effect , giving the bridge its nickname . The <unk> <unk> and Paper Company was organized in 1916 and needed a dam on the <unk> River to supply their mill . This dam would require a large section of the city to be flooded , and shallow river banks meant difficulties in any bridge construction . Instead of expensive <unk> , a concrete tank was built <unk> in the river bed ; the sides of this tank provided man @-@ made banks higher than the natural banks . The Michigan Works Progress Administration described the bridge as having " concrete bulkheads , formed by the side spans of the bridge , [ that ] allow the mill to maintain the water level several feet above the roadbed . " The <unk> Tourism Council stated : " At one time , the bridge itself was partially supported by the water that was <unk> forced under it , " and that the bridge has been featured in Ripley 's Believe It or Not ! . The eight @-@ span structure is <unk> feet ( 90 m ) long . 
- The Cut River Bridge carries US 2 across the Cut River in <unk> Township , Mackinac County . This structure was built during World War II but completion was delayed due to war @-@ induced steel shortages . The span uses <unk> short tons ( <unk> long tons ; <unk> t ) of structural steel to bridge the <unk> feet ( 195 m ) over the river and its <unk> at a height of 147 feet ( 45 m ) above the river . The Cut River Bridge is one of only two <unk> deck <unk> bridges in the state . On either side of the bridge , there are picnic areas and trails down to the river . 
- Listed on the <unk> on December 17 , 1999 , the Mackinac Trail – <unk> River Bridge carries H @-@ 63 , the modern successor to US 2 , over the <unk> River north of St. Ignace . The bridge is another <unk> arch structure 60 feet ( 18 m ) in length and built in 1920 . Increasing traffic along Mackinac Trail prompted the MSHD to " widen its deck by five feet [ 1 @.@ 5 m ] and install new <unk> in the 1929 – 1930 <unk> " along with the addition of decorative retaining walls . 
- The last of the historic bridges along a former segment of US 2 is the structure carrying <unk> Street ( BS I ‑ 75 ) over the Power Canal in <unk> <unk> . Marie . Built in 1934 , it is one of only three steel arch bridges in the state . The 42 @-@ foot @-@ wide ( 13 m ) and 257 @-@ foot @-@ long ( 78 m ) structure is described by MDOT as " massive " with an " innovative " construction method : the previous structure was used as a <unk> for the current bridge before removal . 
- 
- = = Major intersections = = 
- 
- MDOT has erected <unk> along the two Michigan segments of the highway that use the total <unk> starting at the state line in <unk> ; the signs on the eastern segment reflect the <unk> in Florence County , Wisconsin . 
- 
- = = Business route = = 
- 
- Business U.S. Highway 2 ( Bus . US 2 ) is a 1 @.@ 270 @-@ mile ( 2 @.@ <unk> km ) business route that runs from the Wisconsin state line at the Montreal River . The route extends through downtown <unk> on Silver and Aurora streets before turning northward along Suffolk Street . Bus . US 2 stays on Suffolk Street for a short while until it turns onto Frederick Street . On Frederick Street , Bus . US 2 bears north through a residential area along Douglas Street . The eastern terminus of the route is at its junction with US 2 at the corner of <unk> Drive and Douglas Street north of downtown . 
- The business route was created in August 1942 when former M ‑ 54 in <unk> was renumbered as a business loop of US 2 . It was originally a bi @-@ state business connection before the Wisconsin Department of Transportation decommissioned Bus . US 2 in Hurley westward along State Trunk Highway 77 and northward along US 51 in 2002 . 
- 
- 
- = Butterfly World Tour = 
- 
- The Butterfly World Tour was the third concert tour by American singer @-@ songwriter Mariah Carey . The tour promoted Carey 's album at the time , Butterfly ( 1997 ) , and included songs from several of her previous albums . The tour visited Asia , Australia and the United States , with rehearsals taking place in December 1997 . Starting on January 11 , 1998 the tour spanned five shows in Asia , six in Australia , and one in Hawaii , US . Throughout the tour , Carey varied hairstyles and outfits , as well as song selections . The Butterfly World Tour was very successful ; the four dates at Japan 's largest stadium , Tokyo <unk> , sold out in under one hour , <unk> over 200 @,@ 000 tickets , breaking the previous record she held at the stadium for show sell @-@ outs . 
- The tour was recorded in VHS format , and was titled Around the World . The video featured live performances of Carey at different worldwide venues including New York , Japan , Hawaii and Brisbane . Other scenes are included in the video such as a conversation between Carey and Brenda K. Starr prior to her performance of " I Still Believe " . Prior to the performances in Australia , a scene of Carey swimming with <unk> is shown . Additionally , Olivia Newton @-@ John makes a cameo appearance during their joint performance of Newton @-@ John 's song , " <unk> <unk> to You " . The video was commercially successful , being certified platinum in the United States by the Recording Industry Association of America ( RIAA ) and gold in Brazil by the <unk> <unk> dos <unk> de <unk> ( <unk> ) . 
- 
- = = Background = = 
- 
- Since her debut in 1990 , Carey had not journeyed on a large or extensive tour . In fact , she had not embarked on a tour until her third studio effort , Music Box ( 1993 ) , when she performed six arena shows in the United States during the Music Box Tour . The opening night of the tour received scathing reviews , mostly aimed at Carey 's deemed " obvious " stage @-@ <unk> and failure to make a connection with the crowd . <unk> nights were more favorably reviewed , with critics raving about Carey 's vocals . Jon Pareles of The New York Times wrote regarding Carey 's live vocals , " Beyond any doubt , Ms. Carey 's voice is no studio <unk> . Her range extends from a rich , <unk> alto to dog @-@ whistle high notes ; she can linger over sensual turns , <unk> with <unk> confidence , <unk> like a <unk> singer . " However , after the strong media attention , Carey did not visit the US on her succeeding Daydream World Tour in 1996 , visiting only Europe and Asia . The tour in contrast , received critical acclaim from critics and fans alike , as well as breaking ticket sale records . Carey 's three shows at Japan 's largest stadium , Tokyo <unk> , sold out in under three hours , <unk> in over 150 @,@ 000 tickets , setting the record of fastest show <unk> in Japan 's history . On the Butterfly World Tour , Carey broke the record , selling 200 @,@ 000 tickets in under one hour . During 1997 , after the commercially and critically successful release of Butterfly , Carey had not planned to tour once again , due to the long travel times and strain on her voice . However , due to overwhelming demand by fans , Carey agreed to perform in Asia once again , only extending the tour to Taiwan and Australia , as well as one last show in the United States . <unk> for the show began shortly after Christmas 1997 , extending for a period of two weeks . 
- 
- = = Concert synopsis = = 
- 
- The show began with Carey standing on a small elevated centerpiece on stage , surrounded by several long draped <unk> . Carey featured three background vocalists throughout the tour , Trey Lorenz , <unk> Daniels and Kelly Price . As the introduction began with " <unk> " , each of the <unk> were slowly draped , revealing Carey atop the platform , dressed in a beige mini @-@ dress and matching sheer <unk> and <unk> heels . As she began performing " <unk> " , the platform was lowered so Carey could access the other sections of the stage throughout the song 's performance . After an intimate performance with <unk> lights for " The Roof ( Back in Time ) " , Carey was joined on @-@ stage by a Peruvian guitar player , who played the Latin @-@ inspired guitar melodies during her performance of " My All " . Afterwards , Carey sang " Close My Eyes " , the only sitting performance of the show . During the song , several male backup dancers performed slow and ample dance routines behind Carey on a higher level of the stage . 
- For the second part of the show , Carey had the second costume change of the evening , donning a long <unk> black gown and semi @-@ <unk> hair . For the performance of " <unk> " , Carey was joined by three female back @-@ up dancers , who mimicked her light dance routines during the song . The next song on the set @-@ list was " Hero " , which featured Carey alone on @-@ stage , without any vocal back @-@ up . After the song 's performance , Carey was joined on @-@ stage by Lorenz , who performed " I 'll Be There " alongside her . Next came " Make It <unk> " , a song which accompanied yet another wardrobe change for Carey . She <unk> a short mini @-@ skirt , alongside a <unk> white <unk> and loose golden <unk> . On @-@ stage , Carey was joined by a full church choir , all dressed in long black garments . After the song 's <unk> , Carey performed " One Sweet Day " , alongside a previously recorded video of Boyz II Men during their live performance of the song with Carey at Madison Square Garden in 1995 . 
- After completing the song , Carey changed to a pair of leg @-@ hugging blue <unk> and a tank top . Her next performance was the " Fantasy " remix , featuring <unk> ' Dirty <unk> on a large projection screen behind the stage , as Carey performed light chair dance @-@ routines alongside several male dancers . The performance featured the most intricate choreography Carey performed on the tour . After a low @-@ key performance of " <unk> " , or " Whenever You Call " in other countries , Carey was once again joined by several male dancers , as she sang " <unk> " , while re @-@ <unk> the music video during a small <unk> . Carey once again changed to a beige ensemble similar to her first outfit before performing her debut single , " Vision of Love " . The final song on the tour was " Butterfly " , which featured large <unk> of butterflies and flowers projected onto the large screen behind Carey . She <unk> a long brown <unk> gown for the performance , being joined on stage once again by her trio of back @-@ up singers . During the shows in Japan , Carey performed her holiday classic " All I Want for Christmas Is You " , alongside various male and female dancers on stage who performed light dance routines alongside Carey . During the song , Carey <unk> a Santa suit and matching hat , while being carried on a large stage prop by the dancers . 
- 
- = = Critical reception = = 
- 
- <unk> the entire tour was an instant sell @-@ out ; the four shows at Japan 's largest stadium , Tokyo <unk> , broke Carey 's previous ticket @-@ sales record , selling out all 200 @,@ 000 tickets in under one hour . Additionally , the entire Australian leg sold out within hours , leading Carey to extend the tour one more date in the United States . The show at Hawaii 's 50 @,@ 000 capacity <unk> Stadium sold out as well , making her one of the few acts in the stadiums history to sell out the entire venue . Aside from its commercial success , fans and critics <unk> about the show 's visuals , as well as Carey 's vocal delivery . 
- 
- = = <unk> and recordings = = 
- 
- During the tour , several bits and performances were filmed and later edited into a VHS and DVD entitled Around the World . The VHS featured performances from Tokyo <unk> , <unk> Stadium as well as few other <unk> and scenes that were later compiled into the video . The film first begins with performances in Hawaii , where the song 's recitals are cut into halves , excluding the second verses and bridge to shorten the bulk length of the video . Afterwards , Carey 's performance of " My All " is shown in inter @-@ cut scenes from Japan and <unk> . After the conclusion of the song , scenes of Carey conversing with Brenda K. Starr are shown , which eventually lead to a tribute to her at a small and intimate New York club , where Carey performs " I Still Believe " . Soon after , Carey 's performance in Japan with Lorenz for " I 'll Be There " is shown , leading to scenes of Carey swimming with <unk> in <unk> the next title on the video is Carey 's live rendition of " <unk> <unk> To You " , where she is joined by Olivia Newton @-@ John on stage in Melbourne . A scene of a fans gathering outside of a New York City studio is shown , following a performance of " <unk> , " and " Hero " at <unk> Stadium . The VHS was a commercial success , being certified platinum by the Recording Industry Association of America ( RIAA ) , denoting shipments of over 100 @,@ 000 units . The video was also certified gold in Brazil by the <unk> <unk> dos <unk> de <unk> ( <unk> ) . 
- 
- = = Set list = = 
- 
- " Looking In / Butterfly ( <unk> ) " 
- " <unk> " 
- " The Roof " 
- " My All " 
- " Close My Eyes " 
- " Daydream Interlude ( Fantasy Sweet Dub Mix ) " 
- " <unk> " 
- " Hero " 
- " I 'll Be There " ( with Trey Lorenz ) 
- " Make You Happy ( Trey Lorenz ) 
- " Make It <unk> " 
- " One Sweet Day " ( featuring pre @-@ recorded Boyz II Men ) 
- " Ain 't Nobody ( Band Interlude ) 
- " Fantasy ( Bad Boy Remix " 
- " Whenever You Call " 1 
- " <unk> 
- " <unk> " ( featuring pre @-@ recorded <unk> ) " 
- " Vision of Love " 
- " Butterfly 
- " Without <unk> 
- " Butterfly ( <unk> ) " 
- " All I Want for Christmas is <unk> 
- < <unk> > 1 < / <unk> > Performed on select dates 
- 
- = = <unk> = = 
- 
- 
- = = Personnel = = 
- 
- Walter Afanasieff - director , keyboard 
- Dan Shea - keyboard 
- Vernon Black - guitar 
- Randy Jackson - bass 
- <unk> Conway - drums 
- Peter Michael - percussion 
- Gary <unk> - music sequencing 
- Trey Lorenz - vocals , background vocals 
- Kelly Price - background vocals 
- <unk> Price - background vocals 
- <unk> Daniels - background vocals 
- <unk> <unk> - background vocals 
- Source : 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> , also known as Button 's Mill is a Grade II * listed tower mill constructed of brick . The <unk> is located at <unk> , Suffolk , England . It was built in the early nineteenth century to grind wheat into flour . <unk> <unk> worked by wind power until 1924 , <unk> on two sails , after which it became <unk> . 
- In 1979 , a group of enthusiasts purchased <unk> <unk> for restoration . Numerous volunteers helped to restore the mill to full working order over an eight @-@ year period . The mill is open to the public , and flour ground at the mill can be bought at the site . 
- The mill is a small four storey tower mill with Patent sails and <unk> by a <unk> . It drives two pairs of <unk> , with a third pair driven by an auxiliary engine . 
- 
- = = History = = 
- 
- The <unk> at <unk> was built by <unk> George <unk> for William Button in 1819 . It replaced a post mill which had been moved to Sandy Lane , <unk> , Norfolk the previous year . The mill was set to work on Christmas Day 1819 . In 1832 , the mill was modernised by the fitting of a cast @-@ iron <unk> , Patent sails and a <unk> . The new <unk> was fitted on 16 July 1832 . It was made by J <unk> , the Kings Lynn <unk> , and weighs 1 <unk> tons ( 1 @,@ 780 kg ) . A new stock was fitted in September 1836 . William Button died on 11 February 1837 . The mill passed jointly to his widow Rebecca and their son Richard . Richard Button worked the mill until 1860 , at which date it was conveyed to his sons Richard and William , who sold it to Richard <unk> from <unk> , Norfolk in 1862 . 
- The mill was worked by Stephen <unk> , Richard 's son . He inherited the mill on the death of his father in 1875 and leased it to Henry Bryant in 1879 . In 1884 , the mill was sold by auction , and Henry Bryant purchased the <unk> . Bryant rebuilt the floors of the mill . From 1892 , a portable steam engine was used as auxiliary power , driving an extra pair of <unk> on the ground floor . This was replaced in 1914 by a <unk> oil engine . Bryant ran the mill until 1920 when he sold it to <unk> Vincent , a retired <unk> from <unk> , Norfolk . The <unk> engine was sold in the early 1920s . The mill was <unk> <unk> , and as a result one pair of sails was removed . Vincent carried out some repairs to the mill and ran it until he retired in 1924 . One of the remaining two sails was wrecked in 1926 when the mill was again <unk> . 
- On the death of <unk> Vincent in 1932 , his son George inherited the property . He lived at the site in an old <unk> until his death in 1973 . The mill passed to his daughter , who sold it in 1974 to Mr and Mrs <unk> , who intended to convert it into a house . They later changed their minds and offered it for sale for restoration . In 1976 , the cap of the mill was blown to pieces in a gale . <unk> <unk> was purchased by five members of the Suffolk Mills Group in November 1979 , and plans for restoration were made . The consortium consisted of Mark <unk> , Charles <unk> , Peter <unk> , David <unk> and Chris <unk> . The <unk> mill and an acre of land cost £ 6 @,@ 000 . 
- 
- = = Restoration = = 
- 
- The mill was restored over a period of six years . Much of the work was carried out by enthusiasts who used working holidays to repair the mill . 
- 
- = = = 1979 – 1980 = = = 
- 
- The consortium took possession of the mill in November 1979 . The first priority was to make the mill as <unk> as possible . <unk> sheets were spread on the dust floor of the mill to prevent further water penetration of the structure . The mill was surveyed and recorded . In spring 1980 , the dust floor partially collapsed and was removed as a safety measure . <unk> was erected to allow the <unk> to be dismantled , and the cap centering wheels were removed in preparation for the removal of the cap frame . 
- A two @-@ week work @-@ in was held at the end of July and beginning of August 1980 . Thirty @-@ three volunteers worked on the mill over the work @-@ in . Two new main floor beams for the dust floor were made from <unk> of pitch pine which had previously been in a <unk> at Bury St <unk> . On 3 August 1980 , the <unk> and remaining sail fragments were removed by a mobile crane , followed by the cap frame and the cast @-@ iron curb sections . 
- New oak <unk> were laid on the dust floor and temporary boarding laid to provide a working platform for repairs to the top of the mill tower . The bin floor was removed , apart from one oak main beam which was <unk> . A replacement main beam was made from pitch pine , and the oak beam was repaired . New oak <unk> were fitted , along with some salvaged from the original floor which were fit to be reused . One of these had previously been a whip from a Common sail . Some of the <unk> were made from pitch pine from the <unk> <unk> , which , following its collapse on 19 February 1977 , had been re @-@ erected at the Museum of East Anglian Life , <unk> . The <unk> hoist was dismantled to enable its repair , brickwork was repaired , and new <unk> were made for some of the windows . A <unk> was secured over the mill tower to keep the weather out over winter . The <unk> on the bin floor were replaced and one of the two pairs of <unk> was removed . 
- The work was financed by grants of £ 500 from the Council for the Preservation of Rural Essex , £ 250 from St <unk> Borough Council , £ 200 from the <unk> Trust , and £ 200 from the Suffolk Mills Group . Other donations and income amounted to £ 65 , with the balance of expenditure being met jointly by the five owners . 
- The " work @-@ in " was reported in both local and national media , including BBC Look East , Bury Free Press , Eastern Daily Press , East Anglian Daily Times , Daily Telegraph , and Radio Orwell . 
- 
- = = = 1981 = = = 
- 
- In the 1981 season two separate work @-@ ins were held , the first from 25 July to 2 August and the second from 22 August to 30 August . A total of 37 volunteers participated over the two weeks . 
- The <unk> were removed from the stone floor , which was then repaired . One of the main beams was found to have been attacked by Death Watch beetles , and over a third of the <unk> beam was replaced with oak . The six supports for the <unk> frame were repaired one at a time , as they bore the whole of the weight of the machinery above . One post required replacement . It had <unk> and the floor had sunk by 4 inches ( 100 mm ) causing the second main floor beam to fracture . When this work had been completed , the stone floor was removed , including the second main floor beam , which was removed in one piece so that the most suitable route to install its replacement could be assessed . 
- The new main beam for the stone floor was installed and the ground floor was <unk> . The stone floor was reconstructed , reusing the old stone bearers and those <unk> that were fit for reuse . Other <unk> were made new from oak or pitch pine . New <unk> were laid in the dust floor , the 1 1 ⁄ 4 inches ( 32 mm ) thick <unk> boards being double @-@ grooved with a metal tongue between them . Those on the bin floor are of a similar configuration . The top four courses of brickwork of the mill tower were removed and work began on rebuilding them , aided by a <unk> attached to the upright shaft . 
- During the second work @-@ in , the remainder of the stone floor was boarded , again using double @-@ grooved boards with a metal tongue between them . The window on the dust floor was rebuilt and the reconstruction of the top four courses of brickwork continued . The top two courses were rebuilt with red engineering bricks . The dust floor window had a reinforced concrete <unk> cast as the tower was weak at this point and had distorted . The <unk> , the curb segments and <unk> <unk> were given two coats of red lead paint . A new <unk> for the cap was made from a piece of oak 3 feet ( 910 mm ) long by 16 inches ( 410 mm ) diameter . A start was made on removing the old pointing on the brickwork and <unk> with <unk> mortar . 
- After the work @-@ in had finished , the boarding of the stone floor was completed and the mill thoroughly cleaned in preparation for an open day which was held in late autumn . The work done in 1981 cost £ 3 @,@ 250 . <unk> received were £ 2 @,@ 000 from <unk> <unk> , £ 1 @,@ <unk> from the Historic Buildings Council and £ 130 from St <unk> Borough Council . Other donations and income received amounted to £ 70 . The project received further coverage in local media , the Eastern Daily Press , and South Norfolk News . 
- 
- = = = 1982 = = = 
- 
- The 1982 work @-@ ins took place from 24 July to 1 August and 21 August to 29 August . The work planned included the completion of the brickwork at the top of the tower , installation of the <unk> curb , a start on the construction of the new cap , installing machinery and making and fitting various doors and windows . The cap frame was constructed , incorporating two beams from the original cap frame that were fit to be reused . The cap frame was completed in autumn 1982 . It had been constructed upside down and a mobile crane was used to turn it over and place the <unk> into position . 
- 
- = = = 1983 = = = 
- 
- Work in the early part of 1983 was mainly confined to smaller jobs such as casting various <unk> and overhaul of the various parts of the <unk> <unk> and mechanism for controlling the Patent sails . The mill was generally made as safe as possible inside in preparation for scheduled visits , including one from the Wind and <unk> Section of the Society for the Protection of Ancient Buildings . Other work included the overhaul of the final drive to the 4 feet 7 inches ( 1 @.@ 40 m ) stones , including <unk> the stone nut and replacement of three <unk> in beech . 
- When the weather improved , work commenced outside . The cap circle was mostly constructed before the first work @-@ in commenced . The work @-@ ins took place over two separate weeks in July and August , with 35 people participating . During the first work @-@ in , the <unk> posts and <unk> were fitted . The oak cap ribs , which had been prefabricated <unk> were fitted and the <unk> was supported by scaffolding ready for final fitting . <unk> <unk> of oak were fitted and the <unk> was pulled into position using a <unk> . The cap frame was competed with the fitting of intermediate ribs and <unk> . The rear of the cap circle was also completed during this time . 
- Between the two work @-@ ins , the rear <unk> was completed and the fitting of <unk> to the cap frame completed . This enabled a start to be made on boarding the cap on the Monday of the second work @-@ in . The boards for the cap are ½ inch ( 13 mm ) thick and 9 inches ( 230 mm ) wide at the <unk> , <unk> towards the top . A new neck bearing was installed under the <unk> , not without difficulty and much <unk> before it would fit . When the boarding of the cap was completed , the cover strips were fitted over the joints . The <unk> was drawn into position using a rope and <unk> , and the <unk> steel reinforcing band <unk> into position , producing a <unk> structure . The striking rod was fitted through the <unk> and the axle for the <unk> fitted in position on top of the <unk> posts . At the top of the mill tower , the curb was prepared to accept the cap , and the cast iron curb track plates <unk> into position . <unk> of the brickwork of the tower continued , with nearly half of it completed at the end of the second work @-@ in . 
- After the work @-@ ins had ended , work continued on finishing the cap which was scheduled for lift @-@ on in October . The weather hampered the work initially , but the application of a modern <unk> paint , designed to withstand normal movement of the timber and yet remain <unk> , was completed in time for the lift @-@ on . A mini work @-@ in was held the weekend before the cap was lifted , with 17 people participating over the weekend . Final work was done to the cap and a new window was fitted to the stone floor of the mill . The <unk> was removed and the <unk> dismantled . The 5 @-@ ton cap was lifted into place by a mobile crane on 24 October 1983 , watched by a crowd of some 70 people . The fitting of the cap was <unk> with beer from <unk> <unk> , <unk> . 
- Restoration work in 1983 cost £ 1 @,@ <unk> . Grants were received from the Association of Independent Museums , <unk> ' Norton Women 's Institute , Ipswich Historical Transport Society , the National Association for <unk> Children , Mr D. Porter and the Society for the Protection of Ancient Buildings . Further income came from the sale of guide books and a drawing of the mill by <unk> <unk> . 
- 
- = = = 1984 = = = 
- 
- Four work @-@ ins were held during 1984 . The first two were by <unk> from Eastern Electricity Board . <unk> <unk> was chosen at the project to benefit from the Young Person 's Development Project for that year . <unk> <unk> had previously benefited from the project , and its owners , the Suffolk Preservation Trust , had suggested that <unk> <unk> would be a good candidate for the scheme . Two separate parties spent a week each at the mill . A <unk> electricity supply was provided . An underground cable was laid with assistance from British Telecom who were <unk> a thrust <unk> which had been developed at their research facility at <unk> Heath . The thrust <unk> laid 25 yards ( 23 m ) of <unk> through which the <unk> cable was pulled into position . The mill was then supplied with lights and <unk> on each floor . 
- Other work undertaken by the <unk> involved repairing some <unk> damage to the foundation brickwork , digging a drainage trench around the mill and moving the auxiliary engine into position . A concrete path around the tower was trapping moisture , leading to rising <unk> . A French drain was constructed around the mill to remedy these problem . A 21 horsepower ( 16 kW ) <unk> & <unk> <unk> diesel engine had been purchased in 1981 and was fitted onto a permanent concrete base with the aid of a mobile crane . After fitting it was then painted . Some missing or broken parts were made or repaired as necessary . 
- Off @-@ site , the new brake wheel and <unk> were made . The original brake wheel was too rotten to repair , and showed evidence that it had been altered from the original one installed in 1819 . The original brake wheel was 6 feet ( 1 @.@ 83 m ) diameter to allow the Common sails to run at their <unk> speed . When the mill was modernised in 1832 it was necessary to alter the gear ratios within the mill , as Patent sails run at a slower speed than Common sails . The great spur wheel was increased in diameter and the stone nuts reduced in diameter . The brake wheel was also rebuilt , with a cast iron segment ring fitted in place of the original <unk> , resulting in a wheel 7 feet 2 inches ( 2 @.@ 18 m ) in diameter . The opinion of professional <unk> was sought , and it was decided that a scaled @-@ up version of the original brake wheel would be made , but retaining the cast iron teeth segments . The remains of the original brake wheel were retained as an exhibit in the mill . The original iron segments were all broken , so a pattern was made and new segments were cast in heat @-@ treated malleable cast iron . When the brake wheel was completed it was dismantled and transported to the mill ready for <unk> . 
- The <unk> was made . Some of the small <unk> were replaced . The stocks were made from pitch pine and the <unk> from Douglas fir . Work at the mill over the winter included repairs to the brickwork and windows , with only one window still needing attention when the first work @-@ in began . New beech <unk> were fitted to the crown wheel , which was restored to its correct position on the upright shaft . 
- The stocks and sails were made during the two work @-@ ins held in July and August , a total of 41 people attending over the two weeks . A second @-@ hand stock had been bought in the 1920s and preserved at the mill , but upon examination it was found to be unfit for use . Thus two new stocks were required instead of one . Modern steel stocks had already been discounted on cost and <unk> grounds , leaving two options . Traditional stocks made from a single piece of timber , or <unk> stocks . Good quality pitch pine was not <unk> in the lengths required . Douglas fir was <unk> in such lengths but was discounted on strength and <unk> grounds . A Douglas fir stock on a nearby mill had <unk> after only seven years . Thus it was decided that <unk> stocks would be made . 
- The 52 feet ( 15 @.@ 85 m ) long stocks were made from Douglas fir . All timber had been pressure treated with <unk> and well <unk> . It was estimated that a <unk> stock would be up to 20 % stronger than a similar stock of solid timber . The timber used was 1 1 ⁄ 2 inches ( 38 mm ) thick and nine <unk> were required at the centre , where the stock passes through the poll end of the <unk> . <unk> attention was paid to where individual joints occurred between different pieces of timber . Both stocks were made during the work @-@ in , with one of them being trimmed and finished ready for use . The stocks were the first <unk> stocks made by amateurs . 
- The four sails were made . The originals being slightly different pairs . Those on the inner stock having bays of 3 feet ( 910 mm ) pitch and those on the outer stock having bays of 3 feet 2 inches ( 970 mm ) pitch . It was decided that the new sails would be identical , at the larger pitch . Four 27 feet ( 8 @.@ 23 m ) long <unk> were cut from a <unk> of pitch pine obtained from a demolished <unk> at Bury St <unk> . The <unk> are 6 inches ( 150 mm ) square at their inner end . <unk> bars and <unk> were made from modern pitch pine , selected for quality . The <unk> for the sail bars were cut . That at the heel of the sail being at 27 ° and that at the tip being 15 ° , decreasing by 1 ½ ° at each sail bar . Three of the four sails had been completed at the end of the work in . 
- The <unk> and associated <unk> was fitted to the mill . At the end of the second work @-@ in , the cap could be turned by means of the hand <unk> . The <unk> itself , with the <unk> painted red , white and blue was installed shortly afterwards . The <unk> arm brake wheel was assembled around the <unk> . It was found that the <unk> was eccentric on the upright shaft by ½ inch ( 13 mm ) . When this was corrected the brake wheel was within 1 / 16 <unk> of an inch ( less than 1 mm ) of true . The <unk> for the stocks were <unk> from a <unk> of pitch pine ready for completion later in the year . Other work included a new frame for one of the ground floor doors , cleaning and painting <unk> and further <unk> on the brickwork . The four sails were fitted to the mill with the aid of a mobile crane at the end of the second work @-@ in . The stock , with one sail attached was carefully inserted through the poll end of the <unk> and when <unk> in position and the <unk> had been attached the second sail was fitted to the stock . The process was repeated for the second pair of sails . The mill turned by wind again on the last day of the second work @-@ in . 
- Work later in the year included the finishing of the fourth sail , balancing the <unk> and sorting out <unk> troubles with the <unk> <unk> . The brake and brake lever were made and installed and <unk> <unk> for the sails were cast . 
- Restoration work in 1984 cost £ 2 @,@ 918 . Grants received were £ 1 @,@ <unk> from English Heritage , £ 500 from St <unk> Borough Council , Other donations amounted to £ 52 . <unk> from open days at the mill came to £ 151 . A boost to the funds was £ 2 @,@ 000 from <unk> <unk> . This money had been allocated to allow the moving of a <unk> post mill buck to <unk> . In the end , the removal was not proceeded with , and Mr <unk> allowed the donation to remain with the <unk> Mill Preservation Trust . The project received further support in local media , including the East Anglian Daily Times , one of the work @-@ ins being featured on <unk> Television 's About <unk> programme . 
- 
- = = = 1985 - 87 = = = 
- 
- Work planned for 1985 included the replacement of the dust floor window and completion of the stocks and sails . The external walls of the mill were <unk> and internal walls plastered . Two work @-@ ins were held , from 20 July to 28 July and 17 August to 26 August . Further work over the next two years saw the restoration completed in 1987 . 
- 
- = = Restoration team = = 
- 
- The restoration of <unk> <unk> was made possible by the efforts of 135 individuals and 61 organisations . This was achieved by the donation of labour , plant , materials or grants of money , or by giving <unk> for materials purchased or providing publicity for the project . 
- 
- = = Description = = 
- 
- Terms explained in the Mill machinery article are in italics 
- As built in 1819 
- A four @-@ storey tower mill with Common sails carried on a wooden <unk> , driving two pairs of <unk> <unk> . <unk> wheel 6 feet ( 1 @.@ 83 m ) diameter . <unk> was by hand . 
- As restored 
- The four @-@ storey tower is built of brick , and stands 31 feet 5 inches ( 9 @.@ 58 m ) from ground level to the curb . The walls are 2 feet ( 610 mm ) thick at ground level . The mill is described from the top down . 
- The <unk> cap sits on top of the tower , giving the mill an overall height of 45 feet ( 13 @.@ 72 m ) to the <unk> . It houses the cast @-@ iron <unk> and 7 feet 2 inches ( 2 @.@ 18 m ) diameter wooden brake wheel internally . <unk> the four double Patent sails span 64 feet ( 19 @.@ 51 m ) . They are 9 feet ( 2 @.@ 74 m ) wide and can develop 30 horsepower ( 22 kW ) . The eight <unk> <unk> keeps the mill turned into wind . 
- The top floor of the mill is the dust floor . It could be used for storage of grain and gives internal access to the cap . The cast @-@ iron <unk> , which is driven by the brake wheel is carried at the top of the upright shaft , in the centre of the mill . 
- The bin floor is where the grain is stored before being ground into flour . The <unk> hoist mechanism is housed on this floor , driven from the wooden crown wheel on the floor below by belt . 
- The stone floor is where the two pairs of <unk> French Burr <unk> are located . The stones are 4 feet 4 inches ( 1 @.@ 32 m ) and 4 feet 7 inches ( 1 @.@ 40 m ) diameter . The wooden crown wheel is located towards the ceiling . This drives the <unk> hoist on the bin floor . Other machinery on this floor are an <unk> <unk> and a flour <unk> . 
- The ground floor of the mill is the meal floor . It is where the <unk> ground flour is delivered for <unk> up . The wooden great spur wheel , located at the bottom of the extended upright shaft and stone nuts are located on this floor . The great spur wheel has been converted from <unk> arm construction to <unk> arm construction . A third pair of 4 feet 6 inches ( 1 @.@ 37 m ) diameter French Burr <unk> is located on a <unk> frame , driven by the auxiliary engine via a fast and loose <unk> . 
- 
- = = <unk> = = 
- 
- William Button 1819 @-@ 37 
- Rebecca Button <unk> ? ? 
- Richard Button 18 ? ? <unk> 
- William Button 1860 @-@ 62 
- Richard Button ( Jr ) 1860 @-@ 62 
- Stephen <unk> 1862 @-@ 79 
- Henry Bryant 1879 @-@ 1920 
- <unk> Vincent 1920 @-@ 24 
- George Vincent 1920 @-@ 24 
- Reference for above : - 
- 
- = = Culture and media = = 
- 
- A video of the restoration of the mill was produced by <unk> <unk> . 
- 
- = = Public access = = 
- 
- <unk> Mill is open to the public on Easter Monday , the Sunday of the late May and August bank holidays and the first Sunday in July , August and September . It is also open at other times by prior arrangement . 
- 
- 
- = <unk> Mouth = 
- 
- " <unk> Mouth " is the first segment of the 18th episode of the second season , or the 38th overall episode of the American animated television series <unk> <unk> . The episode was directed by Andrew <unk> , and was written by Walt <unk> , Paul <unk> , and <unk> Williams . <unk> and <unk> also served as storyboard directors , and Carson <unk> , William <unk> , and Erik <unk> worked as storyboard artists . It originally aired on <unk> in the United States on September 21 , 2001 . 
- The series follows the adventures and <unk> of the title character and his various friends in the underwater city of <unk> <unk> . In this episode , <unk> reads a " bad word " off a <unk> behind the <unk> <unk> , but does not know what it means . Patrick explains to him that it is a " sentence <unk> " which is used " when you want to talk fancy . " The two start using it in every sentence they speak . However , when Mr. <unk> hears them , he warns the two never to use the word again or any of the 13 bad words . Later on , <unk> accidentally swears again . Patrick then runs to tell Mr. <unk> with <unk> trying to stop him . However , when Mr. <unk> accidentally <unk> his toe , he <unk> all the 13 bad words . The two cannot believe it and run to Mama <unk> to tell on him . Mama <unk> , who is <unk> by their use of the words , makes the three paint her house as punishment . 
- The episode received positive reviews , while various members of the <unk> <unk> crew consider the episode to be one of their favorites , mainly due to the satirical nature of the episode . However , the episode was not immune to negative reception , and was criticized by <unk> media group the Parents Television Council and fans , who interpreted the episode as an example of promoting and implicitly <unk> use of <unk> towards children . 
- 
- = = Plot summary = = 
- 
- When <unk> goes around to the back of the <unk> <unk> to take out the trash , he reads some words written on a <unk> . <unk> asks Patrick what is it and Patrick says that the word is a " sentence <unk> " which is used " when you want to talk fancy . " The next day , <unk> walks into the <unk> <unk> and says the word to Patrick and then over the <unk> . The <unk> <unk> customers are <unk> by <unk> 's use of his " word " and leave . <unk> then informs Mr. <unk> , who then decides to tell them that they were using <unk> , and mentions that there are 13 <unk> words they should not use . <unk> and Patrick vow Mr. <unk> that they will never use <unk> words again . 
- Later , they play their favorite game , <unk> and <unk> . Patrick always gets <unk> but <unk> always gets <unk> . Eventually , he loses the game and accidentally <unk> the <unk> word . Patrick then races to the <unk> <unk> to tell Mr. <unk> with <unk> trying to stop him . <unk> bursts through the front door and tells Mr. <unk> that Patrick said the <unk> word , Patrick then joins along . Eventually , Mr. <unk> stops their <unk> explanations , and takes <unk> and Patrick outside and makes them paint the <unk> <unk> as punishment . 
- Mr. <unk> is about to give <unk> and Patrick the job , but he hits his foot on a rock , throws the paint away and says all 13 <unk> words while complaining about his foot being injured . When <unk> and Patrick hear all the <unk> words , they run to Mama <unk> ' house to tell her that Mr. <unk> swears . When they all reach her house , they repeat the same <unk> words that Mr. <unk> used . This makes her faint , but shortly after Mr. <unk> <unk> <unk> and Patrick for saying all those bad words in front of her she <unk> consciousness . Mama <unk> states that all three of them should be <unk> for saying all those words . She then gives all three of them the task of painting her house with a fresh coat of paint as punishment for saying those words at her . 
- 
- = = Production = = 
- 
- " <unk> Mouth " was directed by Andrew <unk> , and was written by Walt <unk> , Paul <unk> , and <unk> Williams . <unk> and <unk> also served as storyboard directors , and Carson <unk> , William <unk> and Erik <unk> worked as storyboard artists . Series creator Stephen <unk> has described the episode plot as " a classic thing all kids go through . " Much of the storyline for the episode was inspired by the writers ' own experiences from childhood . The episode originally aired on <unk> in the United States on September 21 , 2001 . The episode marks the introduction of Mr. <unk> ' mother , Mama <unk> , who was voiced by former <unk> <unk> creative producer and current executive producer Paul <unk> . 
- The writing staff used their individual childhood experiences as inspirations to come up with much of the story lines for this episode . The idea for " <unk> Mouth " was inspired by creative director Derek <unk> 's experience " [ when ] I got in trouble for saying the f @-@ word in front of my mother . " <unk> said , " The scene where Patrick is running to Mr. <unk> to <unk> , with <unk> chasing him , is pretty much how it happened in real life . " The end of the episode , where Mr. <unk> uses more <unk> than <unk> and Patrick , was also inspired " by the fact that my [ <unk> 's ] mother has a sailor mouth herself . " 
- The scene where <unk> and Patrick playing a game of <unk> and <unk> , which is a parody of <unk> and <unk> , was difficult for the crew to <unk> , since many shots featured certain board pieces changing location . According to the series ' season 2 DVD commentary , it was planned to have a scene that had <unk> saying " Go ' <unk> noise ' yourself " followed by Patrick saying " ' <unk> noise ' you too ! " during the <unk> and <unk> game . It was removed in the final episode check because it would have been inappropriate for the younger audience . 
- " <unk> Mouth " was released on the DVD compilation called <unk> <unk> : Sea Stories on March 12 , 2002 . It was also included in the <unk> <unk> : The Complete 2nd Season DVD released on October 19 , 2004 . On September 22 , 2009 , " <unk> Mouth " was released on the <unk> <unk> : The First 100 Episodes , alongside all the episodes of seasons one through five . 
- 
- = = Reception = = 
- 
- Erik <unk> , a member of the <unk> <unk> crew , considers " <unk> Mouth " to be his favorite episode , mainly due to its random and satirical nature , saying " Sometimes <unk> just catches me off @-@ guard . " Nancy <unk> of About.com ranked the episode at number two for her list of the Top 10 <unk> <unk> Episodes . She said " ' <unk> Mouth ' just barely missed being in the number one slot . " <unk> praised the episode 's plot and called it " genius [ ... ] because children can relate to the forbidden <unk> of using curse words , and adults can laugh at the parody of TV censorship . " In an interview with Paul <unk> , one of the episode 's writers , he told that " <unk> Mouth " is his second favorite <unk> episode . 
- 
- = = = Criticism and controversy = = = 
- 
- According to a report titled Wolves in <unk> 's <unk> , which documents the increase in potentially violent , <unk> , and sexual content in children 's programming , the Parents Television Council , a <unk> media group , and fans believed the <unk> <unk> episode " <unk> Mouth " was an implicit attempt to promote and <unk> use of <unk> among children . The episode originally aired during the 2001 – 02 television season , ironically the season in which the PTC named <unk> <unk> among the best programs on cable television , but the report cited a repeat broadcast of the episode from 2005 to prove its point that it promoted use of <unk> among children . In a later report , several members of the PTC listed " <unk> Mouth " as an example of how levels of <unk> , sexual , and violent activity has increased in children 's television programming . <unk> , in response to the incident , said " It 's sad and a little desperate that they <unk> to literally putting <unk> language in the mouths of our characters to make a point . Has the FCC looked at this ? " Richard <unk> of the New York Daily News criticized the report for <unk> an episode of <unk> <unk> , " <unk> Mouth " , over its intent to <unk> <unk> implicitly . 
- 
- 
- = Military history of Australia = 
- 
- The military history of Australia spans the nation 's 220 @-@ year modern history , from the early Australian frontier wars between <unk> and Europeans to the ongoing conflicts in Iraq and Afghanistan in the early 21st century . Although this history is short when compared to that of many other nations , Australia has been involved in numerous conflicts and wars , and war and military service have been significant influences on Australian society and national identity , including the Anzac spirit . The relationship between war and Australian society has also been shaped by the enduring themes of Australian strategic culture and its unique security dilemma . 
- As British <unk> , the Australian colonies participated in Britain 's small wars of the 19th century , while later as a <unk> dominion , and then an independent nation , Australia fought in the First World War and Second World War , as well as in the wars in Korea , Malaya , Borneo and Vietnam during the Cold War . In the Post @-@ Vietnam era Australian forces have been involved in numerous international peacekeeping missions , through the United Nations and other agencies , including in the Sinai , Persian Gulf , <unk> , Somalia , East Timor and the Solomon Islands , while more recently they have also fought as part of multi @-@ lateral forces in Iraq and Afghanistan . In total , nearly 103 @,@ 000 Australians died during the course of these conflicts . 
- 
- = = War and Australian society = = 
- 
- For most of the last century military service has been one of the single greatest shared experiences of white Australian males , and although this is now changing due to the professionalisation of the military and the absence of major wars during the second half of the 20th century , it continues to influence Australian society to this day . War and military service have been defining influences in Australian history , while a major part of the national identity has been built on an <unk> conception of the Australian experience of war and of <unk> , known as the Anzac spirit . These ideals include notions of endurance , courage , ingenuity , humour , <unk> , <unk> and <unk> ; traits which , according to popular thought , defined the behaviour of Australian soldiers fighting at Gallipoli during the First World War . The Gallipoli campaign was one of the first international events that saw Australians taking part as Australians and has been seen as a key event in forging a sense of national identity . 
- The relationship between war and Australian society has been shaped by two of the more enduring themes of Australian strategic culture : <unk> with a powerful ally and expeditionary warfare . Indeed , Australian defence policy was closely linked to Britain until the Japanese crisis of 1942 , while since then an alliance with the United States has <unk> its security . <unk> , this pattern of <unk> — both for cultural reasons such as shared values and beliefs , as well as for more <unk> security concerns — has ensured that Australian strategic policy has often been defined by relations with its allies . Regardless , a tendency towards strategic <unk> has also been evident , with Australians often reluctant to think about defence issues or to <unk> resources until a crisis arises ; a trait which has historically resulted in <unk> for major military challenges . 
- Reflecting both the realist and liberal <unk> of international relations and the conception of national interests , a number of other important themes in Australian strategic culture are also obvious . Such themes include : an acceptance of the state as the key actor in international politics , the <unk> of notions of <unk> sovereignty , a belief in the enduring relevance and legitimacy of armed force as a <unk> of security , and the proposition that the status <unk> in international affairs should only be changed peacefully . Likewise , <unk> , collective security and defence self @-@ reliance have also been important themes . Change has been more evolutionary than revolutionary and these strategic <unk> have persisted throughout its history , being the product of Australian society 's democratic political tradition and <unk> @-@ Christian Anglo @-@ European heritage , as well its associated values , beliefs and economic , political and religious ideology . These <unk> are also reflective of its unique security dilemma as a largely European island on the edge of the Asia @-@ Pacific , and the <unk> circumstances of a middle power physically removed from the centres of world power . To be sure , during threats to the core Australia has often found itself defending the periphery and perhaps as a result , it has frequently become involved in foreign wars . Throughout these conflicts Australian soldiers — known colloquially as <unk> — have often been noted , somewhat <unk> , for both their fighting abilities and their humanitarian qualities . 
- 
- = = Colonial era = = 
- 
- 
- = = = British Forces in Australia , 1788 – 1870 = = = 
- 
- From 1788 until 1870 the defence of the Australian colonies was mostly provided by British Army regular forces . Originally Marines protected the early settlements at Sydney Cove and Norfolk Island , however they were relieved of these duties in 1790 by a British Army unit specifically recruited for colonial service , known as the New South Wales Corps . The New South Wales Corps subsequently was involved in putting down a rebellion of Irish convicts at Castle Hill in 1804 . Soon however shortcomings in the corps convinced the War Office of the need for a more reliable garrison in New South Wales and Van <unk> 's Land . Chief of these shortcomings was the Rum Rebellion , a coup mounted by its officers in 1808 . As a result , in January 1810 the 73rd ( <unk> ) Regiment of Foot arrived in Australia . By 1870 , 25 British infantry regiments had served in Australia , as had a small number of artillery and engineer units . 
- Although the primary role of the British Army was to protect the colonies against external attack , no actual threat ever <unk> . The British Army was instead used in policing , guarding convicts at penal institutions , combating <unk> , putting down convict rebellions — as occurred at Bathurst in 1830 — and to suppress Aboriginal resistance to the extension of European settlement . Notably British soldiers were involved in the battle at the <unk> <unk> in 1854 on the Victorian <unk> . Members of British regiments stationed in Australia also saw action in India , Afghanistan , New Zealand and the Sudan . 
- During the early years of settlement the naval defence of Australia was provided by detached Royal Navy units of the East Indies Station , based in Sydney . However , in 1859 Australia was established as a separate squadron under the command of a <unk> , marking the first occasion that Royal Navy ships had been permanently stationed in Australia . The Royal Navy remained the primary naval force in Australian waters until 1913 , when the Australia Station ceased and responsibility handed over to the Royal Australian Navy ; the Royal Navy 's depots , dockyards and structures were given to the Australian people . 
- 
- = = = Frontier warfare , 1788 – 1934 = = = 
- 
- The reactions of the native Aboriginal inhabitants to the sudden arrival of British settlers in Australia were varied , but were inevitably hostile when the settler 's presence led to competition over resources , and to the occupation of the indigenous inhabitants ' lands . European diseases decimated Aboriginal populations , and the occupation or destruction of lands and food resources sometimes led to starvation . By and large neither the British nor the <unk> approached the conflict in an organised sense and conflict occurred between groups of settlers and individual tribes rather than systematic warfare . At times , however , the frontier wars did see the involvement of British soldiers and later mounted police units . Not all Aboriginal groups resisted white encroachment on their lands , while many <unk> served in mounted police units and were involved in attacks on other tribes . 
- Fighting between <unk> and Europeans was localised as the <unk> did not form <unk> capable of sustained resistance . As a result , there was not a single war , but rather a series of violent engagements and massacres across the continent . <unk> or disorganised however , a pattern of frontier warfare emerged with Aboriginal resistance beginning in the 18th century and continuing into the early 20th century . This warfare <unk> the popular and at times academic " myth " of peaceful settlement in Australia . Faced with Aboriginal resistance settlers often reacted with violence , resulting in a number of indiscriminate massacres . Among the most famous is the Battle of <unk> in Western Australia in 1834 . Such incidents were not officially sanctioned however , and after the <unk> Creek massacre in New South Wales in 1838 seven Europeans were hanged for their part in the killings . However , in Tasmania the so @-@ called Black War was fought between 1828 and 1832 , and aimed at driving most of the island 's native inhabitants onto a number of isolated peninsulas . Although it began in failure for the British , it ultimately resulted in considerable casualties amongst the native population . 
- It may be inaccurate though to depict the conflict as one sided and mainly <unk> by Europeans on <unk> . Although many more <unk> died than British , this may have had more to do with the technological and logistic advantages enjoyed by the Europeans . Aboriginal tactics varied , but were mainly based on pre @-@ existing hunting and fighting practices — using <unk> , clubs and other primitive weapons . Unlike the indigenous peoples of New Zealand and North America , on the main <unk> failed to adapt to meet the challenge of the Europeans . Although there were some instances of individuals and groups acquiring and using firearms , this was not widespread . The <unk> were never a serious military threat to European settlers , regardless of how much the settlers may have feared them . On occasions large groups of <unk> attacked the settlers in open terrain and a conventional battle ensued , during which the <unk> would attempt to use superior numbers to their advantage . This could sometimes be effective , with reports of them advancing in crescent formation in an attempt to outflank and surround their opponents , waiting out the first volley of shots and then hurling their <unk> while the settlers <unk> . However , such open warfare usually proved more costly for the <unk> than the Europeans . 
- Central to the success of the Europeans was the use of firearms . However , the advantages afforded by firearms have often been <unk> . Prior to the late 19th century , firearms were often <unk> muzzle @-@ loading , smooth @-@ bore , single shot <unk> with flint @-@ lock mechanisms . Such weapons produced a low rate of fire , while suffering from a high rate of failure and were only accurate within 50 metres ( 160 ft ) . These deficiencies may have initially given the <unk> an advantage , allowing them to move in close and engage with <unk> or clubs . Yet by 1850 significant advances in firearms gave the Europeans a distinct advantage , with the six @-@ shot <unk> revolver , the <unk> single shot breech @-@ loading rifle and later the <unk> @-@ Henry rifle , as well as rapid @-@ fire rifles such as the Winchester rifle , becoming available . These weapons , when used on open ground and combined with the superior mobility provided by horses to surround and engage groups of <unk> , often proved successful . The Europeans also had to adapt their tactics to fight their fast @-@ moving , often hidden enemies . Tactics employed included night @-@ time surprise attacks , and positioning forces to drive the natives off cliffs or force them to retreat into rivers while attacking from both banks . 
- The conflict lasted for over 150 years and followed the pattern of British settlement in Australia . Beginning in New South Wales with the arrival of the first Europeans in May 1788 , it continued in Sydney and its surrounds until the 1820s . As the frontier moved west so did the conflict , pushing into outback New South Wales in the 1840s . In Tasmania , fighting can be traced from 1804 to the 1830s , while in Victoria and the southern parts of South Australia , the majority of the violence occurred during the 1830s and 1840s . The south @-@ west of Western Australia experienced warfare from 1829 to 1850 . The war in Queensland began in the area around Brisbane in the 1840s and continued until 1860 , moving to central Queensland in the 1850s and 1860s , and then to northern Queensland from the 1860s to 1900 . In Western Australia , the violence moved north with European settlement , reaching the <unk> region by 1880 , with violent clashes continuing until the 1920s . In the Northern Territory conflict lasted even later still , especially in central Australia , continuing from the 1880s to the 1930s . One estimate of casualties places European deaths at 2 @,@ 500 , while at least 20 @,@ 000 <unk> are believed to have perished . Far more devastating though was the effect of disease which significantly reduced the Aboriginal population by the beginning of the 20th century ; a fact which may also have limited their ability to resist . 
- 
- = = = New Zealand Wars , 1861 – 64 = = = 
- 
- 
- = = = = <unk> War = = = = 
- 
- In 1861 , the Victorian ship <unk> Victoria was dispatched to help the New Zealand colonial government in its war against Māori in <unk> . Victoria was subsequently used for patrol duties and logistic support , although a number of personnel were involved in actions against Māori fortifications . One sailor died from an accidental gunshot wound during the deployment . 
- 
- = = = = Invasion of the <unk> = = = = 
- 
- In late 1863 , the New Zealand government requested troops to assist in the invasion of the <unk> province against the Māori . <unk> settlement on confiscated land , more than 2 @,@ 500 Australians ( over half of whom were from Victoria ) were recruited to form four <unk> Regiments . Other Australians became scouts in the Company of Forest Rangers . Despite experiencing <unk> conditions the Australians were not heavily involved in battle , and were primarily used for patrolling and garrison duties . Australians were involved in actions at <unk> , <unk> East , <unk> Hill , <unk> and Te <unk> . <unk> than 20 were believed to have been killed in action . The conflict was over by 1864 , and the <unk> Regiments disbanded in 1867 . However , many of the soldiers who had chosen to claim farmland at the cessation of hostilities had drifted to the towns and cities by the end of the decade , while many others had returned to Australia . 
- 
- = = = Colonial military forces , 1870 – 1901 = = = 
- 
- From 1870 until 1901 , each of the six colonial governments was responsible for their own defence . The colonies had gained responsible government between 1855 and 1890 , and while the Colonial Office in London retained control of some affairs , the Governor of the each colony was required to raise their own colonial militia . To do this , they were granted the authority from the British crown to raise military and naval forces . Initially these were militias in support of British regulars , but when military support for the colonies ended in 1870 , the colonies assumed their own defence responsibilities . The colonial military forces included unpaid volunteer militia , paid citizen soldiers , and a small permanent component . They were mainly infantry , cavalry and mounted infantry , and were neither housed in barracks nor subject to full military discipline . Even after significant reforms in the 1870s — including the expansion of the permanent forces to include engineer and artillery units — they remained too small and <unk> to be considered armies in the modern sense . By 1885 , the forces numbered 21 @,@ 000 men . Although they could not be compelled to serve overseas many volunteers subsequently did see action in a number conflicts of the British Empire during the 19th century , with the colonies raising <unk> to serve in Sudan , South Africa and China . 
- Despite a reputation of colonial <unk> , many of the locally raised units were highly organised , disciplined , professional , and well trained . During this period , defences in Australia mainly revolved around static defence by combined infantry and artillery , based on garrisoned coastal forts . However , by the 1890s , improved railway communications between the mainland eastern colonies led Major General James Edwards — who had recently completed a survey of colonial military forces — to the belief that the colonies could be defended by the rapid <unk> of brigades of infantry . As a consequence he called for a <unk> of defences , and defensive agreements to be made between the colonies . Edwards argued for the colonial forces to be <unk> and for professional units — obliged to serve anywhere in the South Pacific — to replace the volunteer forces . These views found support in the influential New South Wales Commandant , Major General Edward Hutton , however suspicions held by the smaller colonies towards New South Wales and Victoria <unk> the proposal . These reforms remaining unresolved however , and defence issues were generally given little attention in the debate on the political federation of the colonies . 
- With the exception of Western Australia , the colonies also operated their own navies . In 1856 , Victoria received its own naval vessel , <unk> Victoria , and its deployment to New Zealand in 1860 during the First <unk> War marked the first occasion that an Australian warship had been deployed overseas . The colonial navies were expanded greatly in the mid @-@ 1880s and consisted of a number of gunboats and torpedo @-@ boats for the defence of harbours and rivers , as well as naval brigades to man vessels and forts . Victoria became the most powerful of all the colonial navies , with the ironclad <unk> Cerberus in service from 1870 , as well as the steam @-@ sail warship HMS Nelson on loan from the Royal Navy , three small gunboats and five torpedo @-@ boats . New South Wales formed a Naval Brigade in 1863 and by the start of the 20th century had two small torpedo @-@ boats and a <unk> . The Queensland Maritime Defence Force was established in 1885 , while South Australia operated a single ship , HMCS Protector . Tasmania had also a small Torpedo Corps , while Western Australia 's only naval defences included the Fremantle Naval Artillery . Naval personnel from New South Wales and Victoria took part in the suppression of the <unk> Rebellion in China in 1900 , while HMCS Protector was sent by South Australia but saw no action . The separate colonies maintained control over their military and naval forces until Federation in 1901 , when they were amalgamated and placed under the control of the new Commonwealth of Australia . 
- 
- = = = Sudan , 1885 = = = 
- 
- During the early years of the 1880s , an Egyptian regime in the Sudan , backed by the British , came under threat from rebellion under the leadership of native Muhammad Ahmad ( or Ahmed ) , known as Mahdi to his followers . In 1883 , as part of the <unk> War , the Egyptians sent an army to deal with the revolt , but they were defeated and faced a difficult campaign of <unk> their forces . The British instructed the Egyptians to abandon the Sudan , and sent General Charles Gordon to co @-@ <unk> the evacuation , but he was killed in January 1885 . When news of his death arrived in New South Wales in February 1885 , the government offered to send forces and meet the contingent 's expenses . The New South Wales <unk> consisted of an infantry battalion of 522 men and 24 officers , and an artillery battery of 212 men and sailed from Sydney on 3 March 1885 . 
- The contingent arrived in <unk> on 29 March and were attached to a brigade that consisted of Scots , <unk> and <unk> Guards . They subsequently marched for <unk> in a large " square " formation made up of 10 @,@ 000 men . Reaching the village , they burned <unk> and returned to <unk> : three Australians were wounded in minor fighting . Most of the contingent was then sent to work on a railway line that was being laid across the desert towards <unk> , on the Nile . The Australians were then assigned to guard duties , but soon a camel corps was raised and 50 men volunteered . They <unk> on a reconnaissance to <unk> on 6 May and were heavily involved in a <unk> during which more than 100 Arabs were killed or captured . On 15 May , they made one last sortie to bury the dead from the fighting of the previous March . Meanwhile , the artillery were posted at <unk> and drilled for a month , but they soon rejoined the camp at <unk> . 
- Eventually the British government decided that the campaign in Sudan was not worth the effort required and left a garrison in <unk> . The New South Wales <unk> sailed for home on 17 May , arriving in Sydney on 19 June 1885 . Approximately 770 Australians served in Sudan ; nine subsequently died of disease during the return journey while three had been wounded during the campaign . 
- 
- = = = Second Boer War , 1899 – 1902 = = = 
- 
- British encroachment into areas of South Africa already settled by the <unk> <unk> and the competition for resources and land that developed between them as a result , led to the Second Boer War in 1899 . Pre @-@ <unk> the deployment of British forces , the <unk> <unk> of the Orange Free State and the <unk> Republic under President Paul <unk> declared war on 11 October 1899 , striking deep into the British territories of Natal and the Cape Colony . After the outbreak of war , plans for the dispatch of a combined Australian force were subsequently set aside by the British War Office and each of the six colonial governments sent separate <unk> to serve with British formations , with two squadrons each of 125 men from New South Wales and Victoria , and one each from the other colonies . The first troops arrived three weeks later , with the New South Wales <unk> — who had been training in England before the war , <unk> diverted to South Africa . On 22 November , the <unk> came under fire for the first time near <unk> , and they subsequently forced their attackers to withdraw after inflicting significant casualties on them . 
- Following a series of minor victories , the British suffered a major setback during Black Week between 10 – 17 December 1899 , although no Australian units were involved . The first <unk> of infantry from Victoria , South Australia , Western Australia , and Tasmania arrived in Cape Town on 26 November and were designated the Australian Regiment under the command of Colonel John Charles <unk> . With a need for increased mobility , they were soon converted into mounted infantry . Further units from Queensland and New South Wales arrived in December and were soon committed to the front . The first casualties occurred soon after at <unk> on 1 January 1900 , after 250 Queensland Mounted Infantry and a column of Canadians , British and artillery attacked a Boer <unk> at <unk> . <unk> David <unk> and Victor Jones were killed when their patrol clashed with the Boer forward <unk> . Regardless , the <unk> were surprised and during two hours of heavy fighting , more than 50 were killed and another 40 taken prisoner . Five hundred <unk> and the New South Wales <unk> subsequently took part in the Siege of <unk> in February 1900 . 
- Despite serious set @-@ backs at <unk> , <unk> , <unk> , and <unk> <unk> in January — and with <unk> still under siege — the British mounted a five division counter @-@ invasion of the Orange Free State in February . The attacking force included a division of cavalry commanded by Lieutenant General John French with the New South Wales <unk> , Queensland Mounted Infantry and New South Wales Army Medical Corps attached . First , <unk> was relieved following the battles of <unk> River and <unk> , and the retreating <unk> defeated at <unk> , with the New South Wales Mounted Rifles locating the Boer general , <unk> <unk> . The British entered <unk> on 13 March 1900 , while <unk> was relieved . Disease began to take its toll and scores of men died . Still the advance continued , with the drive to <unk> in May including more than 3 @,@ 000 Australians . Johannesburg fell on 30 May , and the <unk> withdrew from <unk> on 3 June . The New South Wales Mounted Rifles and Western Australians saw action again at Diamond Hill on 12 June . <unk> was relieved on 17 May . 
- Following the defeat of the <unk> republics still the <unk> held out , forming small commando units and conducting a campaign of guerrilla warfare to disrupt British troop movements and lines of supply . This new phase of resistance led to further recruiting in the Australian colonies and the raising of the <unk> 's <unk> , with these soldiers usually being volunteers with horse @-@ riding and shooting skills , but little military experience . After Federation in 1901 , eight Australian Commonwealth Horse battalions of the newly created Australian Army were also sent to South Africa , although they saw little fighting before the war ended . Some Australians later joined local South African irregular units , instead of returning home after discharge . These soldiers were part of the British Army , and were subject to British military discipline . Such units included the <unk> <unk> which gained notoriety as the unit in which Harry " <unk> " <unk> and Peter <unk> served in before their court martial and execution for war crimes . 
- With the guerrillas requiring supplies , <unk> de la Rey lead a force of 3 @,@ 000 <unk> against <unk> , on the <unk> River in Western <unk> . The post held a large quantity of stores and was defended by 300 Australians and 200 <unk> . The attack began on 4 August 1900 with heavy shelling causing 32 casualties . During the night the defenders dug in , enduring shelling and rifle fire . A relief force was stopped by the <unk> , while a second column turned back believing that the post had already been relieved . The siege lasted 11 days , during which more than 1 @,@ 800 shells were fired into the post . After calls to surrender were ignored by the defenders , and not prepared to risk a frontal attack , the <unk> eventually retired . The Siege of <unk> River was one of the major achievements of the Australians during the war , with the post finally relieved on 16 August . 
- In response the British adopted counter @-@ insurgency tactics , including a <unk> earth policy involving the burning of houses and crops , the establishment of concentration camps for Boer women and children , and a system of <unk> and field obstacles to limit Boer mobility and to protect railway communications . Such measures required considerable expenditure , and caused much bitterness towards the British , however they soon yielded results . By mid @-@ 1901 , the bulk of the fighting was over , and British mounted units would ride at night to attack Boer <unk> or <unk> , overwhelming them with superior numbers . <unk> of warfare in last months of 1901 , the New South Wales Mounted Rifles travelled 1 @,@ <unk> miles ( 2 @,@ <unk> km ) and were involved in 13 <unk> , killing 27 <unk> , wounding 15 , and capturing 196 for the loss of five dead and 19 wounded . Other notable Australian actions included <unk> , Pink Hill , <unk> and <unk> . 
- Australians were not always successful however , suffering a number of heavy losses late in the war . On 12 June 1901 , the 5th Victorian Mounted Rifles lost 19 killed and 42 wounded at <unk> , near <unk> after poor security allowed a force of 150 <unk> to surprise them . On 30 October 1901 , <unk> of the Scottish Horse Regiment also suffered heavy casualties at Gun Hill , although 60 <unk> were also killed in the engagement . Meanwhile , at <unk> on 4 January 1902 , the 5th Queensland Imperial <unk> lost 13 killed and 17 wounded . Ultimately the <unk> were defeated , and the war ended on 31 May 1902 . In all 16 @,@ 175 Australians served in South Africa , and perhaps another 10 @,@ 000 enlisted as individuals in Imperial units ; casualties included 251 killed in action , 267 died of disease and 43 missing in action , while a further 735 were wounded . Six Australians were awarded the Victoria Cross . 
- 
- = = = <unk> Rebellion , 1900 – 01 = = = 
- 
- The <unk> Rebellion in China began in 1900 , and a number of western nations — including many European powers , the United States , and Japan — soon sent forces as part of the China Field Force to protect their interests . In June , the British government sought permission from the Australian colonies to dispatch ships from the Australian Squadron to China . The colonies also offered to assist further , but as most of their troops were still engaged in South Africa , they had to rely on naval forces for manpower . The force dispatched was a modest one , with Britain accepting 200 men from Victoria , 260 from New South Wales and the South Australian ship HMCS Protector , under the command of Captain William <unk> . Most of these forces were made up of naval brigade <unk> , who had been trained in both ship handling and <unk> to fulfil their coastal defence role . Amongst the naval contingent from New South Wales were 200 naval officers and sailors and 50 permanent soldiers headquartered at Victoria Barracks , Sydney who originally enlisted for the Second Boer War . The soldiers were keen to go to China but refused to be enlisted as sailors , while the New South Wales Naval Brigade objected to having soldiers in their ranks . The Army and Navy compromised and titled the contingent the <unk> Marine Light Infantry . 
- The <unk> from New South Wales and Victoria sailed for China on 8 August 1900 . Arriving in <unk> , the Australians provided 300 men to an 8 @,@ 000 @-@ strong multinational force tasked with capturing the Chinese forts at <unk> Tang , which dominated a key railway . They arrived too late to take part in the battle , but were involved in the attack on the fortress at <unk> @-@ <unk> Fu , where the Chinese government was believed to have found <unk> after <unk> was captured by western forces . The <unk> joined a force of 7 @,@ 500 men on a ten @-@ day march to the fort , once again only to find that it had already surrendered . The <unk> then garrisoned <unk> and the New South Wales contingent undertook garrison duties in <unk> . HMCS Protector was mostly used for survey , transport , and <unk> duties in the Gulf of <unk> , before departing in November . The naval brigades remained during the winter , <unk> performing policing and guard duties , as well as working as <unk> and fire @-@ fighters . They left China in March 1901 , having played only a minor role in a few offensives and punitive expeditions and in the restoration of civil order . Six Australians died from sickness and injury , but none were killed as a result of enemy action . 
- 
- = = Australian military forces at Federation , 1901 = = 
- 
- The Commonwealth of Australia came into existence on 1 January 1901 as a result of the federation of the Australian colonies . Under the Constitution of Australia , defence responsibility was now vested in the new federal government . The co @-@ ordination of Australia @-@ wide defensive efforts in the face of Imperial German interest in the Pacific Ocean was one of driving forces behind <unk> , and the Department of Defence immediately came into being as a result , while the Commonwealth Military Forces ( early forerunner of the Australian Army ) and Commonwealth Naval Force were also soon established . 
- The Australian Commonwealth Military Forces came into being on 1 March 1901 and all the colonial forces — including those still in South Africa — became part of the new force . 28 @,@ <unk> colonial soldiers , including 1 @,@ 457 professional soldiers , 18 @,@ 603 paid militia and 8 @,@ <unk> unpaid volunteers , were subsequently transferred . The individual units continued to be administered under the various colonial Acts until the Defence Act 1903 brought all the units under one piece of legislation . This Act also prevented the raising of standing infantry units and specified that militia forces could not be used in industrial disputes or serve outside Australia . However , the majority of soldiers remained in militia units , known as the Citizen Military Forces ( <unk> ) . Major General Sir Edward Hutton — a former commander of the New South Wales Military Forces — subsequently became the first commander of the Commonwealth Military Forces on 26 December and set to work <unk> an integrated structure for the new army . In 1911 , following a report by Lord <unk> the Royal Military College , <unk> was established , as was a system of universal National Service . 
- Prior to federation each self @-@ governing colony had operated its own naval force . These navies were small and lacked blue water capabilities , forcing the separate colonies to <unk> the cost of a British naval squadron in their waters for decades . The colonies maintained control over their respective navies until 1 March 1901 , when the Commonwealth Naval Force was created . This new force also lacked blue water capable ships , and ultimately did not lead to a change in Australian naval policy . In 1907 Prime Minister Alfred Deakin and <unk> , while attending the Imperial Conference in London , sought the British Government 's agreement to end the subsidy system and develop an Australian navy . The Admiralty rejected and resented the challenge , but suggested <unk> that a small fleet of destroyers and submarines would be sufficient . Deakin was unimpressed , and in 1908 invited the American Great White Fleet to visit Australia . This visit fired public enthusiasm for a modern navy and in part led to the order of two 700 @-@ ton River @-@ class destroyers . The surge in German naval construction prompted the Admiralty to change their position however and the Royal Australian Navy was subsequently formed in 1911 , absorbing the Commonwealth Naval Force . On 4 October 1913 , the new fleet steamed through Sydney Heads , consisting of the battlecruiser <unk> Australia , three light cruisers , and three destroyers , while several other ships were still under construction . And as a consequence the navy entered the First World War as a formidable force . 
- The Australian Flying Corps ( AFC ) was established as part of the Commonwealth Military Forces in 1912 , prior to the formation of the Australian Military Forces in 1916 and was later separated in 1921 to form the Royal Australian Air Force , making it the second oldest air force in the world . Regardless , the service branches were not linked by a single chain of command however , and each reported to their own minister and had separate administrative arrangements and government departments . 
- 
- = = First World War , 1914 – 18 = = 
- 
- 
- = = = <unk> of hostilities = = = 
- 
- When Britain declared war on Germany at the start of the First World War , the Australian government rapidly followed suit , with Prime Minister Joseph Cook declaring on 5 August 1914 that " ... when the Empire is at war , so also is Australia " and reflecting the sentiment of many Australians that any declaration of war by Britain automatically included Australia . This was itself in part due to the large number of British @-@ born citizens and first generation Anglo @-@ Australians that made up the Australian population at the time . Indeed , by the end of the war almost 20 % of those who served in the Australian forces had been born in Britain . 
- As the existing militia forces were unable to serve overseas under the provisions of the Defence Act 1903 , an all @-@ volunteer expeditionary force known as the Australian Imperial Force ( AIF ) was formed and recruitment began on 10 August 1914 . The government pledged 20 @,@ 000 men , organised as one infantry division and one light horse brigade plus supporting units . <unk> and organisation was primarily regionally based and was undertaken under <unk> plans drawn up in 1912 . The first commander was Major General William Bridges , who also assumed command of the 1st Division . Throughout the course of the conflict Australian efforts were predominantly focused upon the ground war , although small air and naval forces were also committed . 
- 
- = = = Occupation of German New Guinea = = = 
- 
- Following the outbreak of war Australian forces moved quickly to reduce the threat to shipping posed by the proximity of Germany 's Pacific colonies . The Australian Naval and Military Expeditionary Force ( <unk> & <unk> ) , a 2000 @-@ man volunteer force — separate from the AIF — and consisting of an infantry battalion plus 500 naval <unk> and ex @-@ sailors , was rapidly formed under the command of William Holmes . The objectives of the force were the wireless stations on <unk> , and those at Yap in the Caroline Islands , and at Rabaul in German New Guinea . The force reached Rabaul on 11 September 1914 and occupied it the next day , encountering only brief resistance from the German and native defenders during fighting at <unk> <unk> and <unk> . German New Guinea surrendered on 17 September 1914 . Australian losses were light , including six killed during the fighting , but were compounded by the mysterious loss offshore of the submarine <unk> with all 35 men aboard . 
- 
- = = = Gallipoli = = = 
- 
- The AIF departed by ship in a single convoy from Albany on 1 November 1914 . During the journey one of the convoy 's naval escorts — <unk> Sydney — engaged and destroyed the German cruiser SMS <unk> at the Battle of <unk> on 8 November , in the first ship @-@ to @-@ ship action involving the Royal Australian Navy . Although originally bound for England to undergo further training and then for employment on the Western Front , the Australians were instead sent to British @-@ controlled Egypt to pre @-@ <unk> any Turkish attack against the strategically important Suez Canal , and with a view to opening another front against the Central Powers . 
- <unk> to knock Turkey out of the war the British then decided to stage an amphibious <unk> at Gallipoli and following a period of training and reorganisation the Australians were included amongst the British , Indian and French forces committed to the campaign . The combined Australian and New Zealand Army Corps ( ANZAC ) — commanded by British general William Birdwood — subsequently landed at Anzac Cove on the Gallipoli peninsula on 25 April 1915 . Although promising to transform the war if successful , the Gallipoli Campaign was ill @-@ conceived and ultimately lasted eight months of bloody stalemate , without achieving its objectives . Australian casualties totalled 26 @,@ 111 , including 8 @,@ 141 killed . 
- For Australians and New Zealanders the Gallipoli campaign came to symbolise an important milestone in the emergence of both nations as independent actors on the world stage and the development of a sense of national identity . Today , the date of the initial landings , 25 April , is known as Anzac Day in Australia and New Zealand and every year thousands of people gather at memorials in both nations , as well as Turkey , to honour the bravery and sacrifice of the original <unk> , and of all those who have subsequently lost their lives in war . 
- 
- = = = Egypt and Palestine = = = 
- 
- After the withdrawal from Gallipoli the Australians returned to Egypt and the AIF underwent a major expansion . In 1916 the infantry began to move to France while the cavalry units remained in the Middle East to fight the Turks . Australian troops of the Anzac Mounted Division and the Australian Mounted Division saw action in all the major battles of the Sinai and Palestine Campaign , playing a pivotal role in fighting the Turkish troops that were threatening British control of Egypt . The Australian 's first saw combat during the <unk> uprising in the <unk> Desert and the Nile Valley , during which the combined British forces successfully put down the primitive pro @-@ Turkish Islamic sect with heavy casualties . The Anzac Mounted Division subsequently saw considerable action in the Battle of Romani against the Turkish between 3 – 5 August 1916 , with the Turks eventually pushed back . Following this victory the British forces went on the offensive in the Sinai , although the pace of the advance was governed by the speed by which the railway and water pipeline could be constructed from the Suez Canal . Rafa was captured on 9 January 1917 , while the last of the small Turkish garrisons in the Sinai were eliminated in February . 
- The advance entered Palestine and an initial , unsuccessful attempt was made to capture Gaza on 26 March 1917 , while a second and equally unsuccessful attempt was launched on 19 April . A third assault occurred between 31 October and 7 November and this time both the Anzac Mounted Division and the Australian Mounted Division took part . The battle was a complete success for the British , over @-@ running the Gaza @-@ Beersheba line and capturing 12 @,@ 000 Turkish soldiers . The critical moment was the capture of Beersheba on the first day , after the Australian 4th Light Horse Brigade charged more than 4 miles ( 6 @.@ 4 km ) . The Turkish trenches were overrun , with the Australians capturing the wells at <unk> and securing the valuable water they contained along with over 700 prisoners for the loss of 31 killed and 36 wounded . Later , Australian troops assisted in pushing the Turkish forces out of Palestine and took part in actions at <unk> Ridge , Jerusalem and the <unk> . The Turkish government surrendered on 30 October 1918 . Units of the Light Horse were subsequently used to help put down a nationalist revolt in Egypt in 1919 and did so with efficiency and brutality , although they suffered a number of fatalities in the process . 
- Meanwhile , the AFC had undergone remarkable development , and its independence as a separate national force was unique among the <unk> . <unk> just a single aircraft to German New Guinea in 1914 , the first operational flight did not occur until 27 May 1915 however , when the <unk> Half Flight was called upon to assist in protecting British oil interests in Iraq . The AFC was soon expanded and four squadrons later saw action in Egypt , Palestine and on the Western Front , where they performed well . 
- 
- = = = Western Front = = = 
- 
- Five infantry divisions of the AIF saw action in France and Belgium , leaving Egypt in March 1916 . I Anzac Corps subsequently took up positions in a quiet sector south of <unk> on 7 April 1916 and for the next two and a half years the AIF participated in most of the major battles on the Western Front , earning a formidable reputation . Although spared from the disastrous first day of the Battle of the Somme , within weeks four Australian divisions had been committed . The 5th Division , positioned on the left flank , was the first in action during the Battle of <unk> on 19 July 1916 , suffering 5 @,@ 533 casualties in a single day . The 1st Division entered the line on 23 July , assaulting <unk> , and by the time that they were relieved by the 2nd Division on 27 July , they had suffered 5 @,@ 286 casualties . Mouquet Farm was attacked in August , with casualties totalling 6 @,@ 300 men . By the time the AIF was withdrawn from the Somme to re @-@ organise , they had suffered 23 @,@ 000 casualties in just 45 days . 
- In March 1917 , the 2nd and 5th Divisions pursued the Germans back to the Hindenburg Line , capturing the town of <unk> . On 11 April , the 4th Division assaulted the Hindenburg Line in the disastrous First Battle of <unk> , losing over 3 @,@ 000 casualties and 1 @,@ 170 captured . On 15 April , the 1st and 2nd Divisions were counter @-@ attacked near <unk> and were forced to abandon the town , before <unk> it again . The 2nd Division then took part in the Second Battle of <unk> , beginning on 3 May , and succeeded in taking sections of the Hindenburg Line and holding them until relieved by the 1st Division . Finally , on 7 May the 5th Division relieved the 1st , remaining in the line until the battle ended in mid @-@ May . Combined these efforts cost 7 @,@ <unk> Australian casualties . 
- On 7 June 1917 , the II Anzac Corps — along with two British corps — launched an operation in Flanders to eliminate a salient south of Ypres . The attack commenced with the detonation of a million pounds ( 454 @,@ 545 kg ) of explosives that had been placed underneath the <unk> ridge , destroying the German trenches . The advance was virtually unopposed , and despite strong German counterattacks the next day , it succeeded . Australian casualties during the Battle of <unk> included nearly 6 @,@ 800 men . I Anzac Corps then took part in the Third Battle of Ypres in Belgium as part of the campaign to capture the <unk> Plateau , between September and November 1917 . Individual actions took place at <unk> Road , <unk> Wood , <unk> , <unk> and Passchendaele and over the course of eight weeks fighting the Australians suffered 38 @,@ 000 casualties . 
- On 21 March 1918 the German Army launched its Spring Offensive in a last @-@ <unk> effort to win the war , <unk> sixty @-@ three divisions over a 70 miles ( 110 km ) front . As the Allies fell back the 3rd and 4th Divisions were rushed south to Amiens on the Somme . The offensive lasted for the next five months and all five AIF divisions in France were engaged in the attempt to stem the tide . By late May the Germans had pushed to within 50 miles ( 80 km ) of Paris . During this time the Australians fought at <unk> , <unk> , <unk> @-@ <unk> , <unk> Wood , <unk> , and Hamel . At Hamel the commander of the Australian Corps , Lieutenant General John Monash , successfully used combined arms — including aircraft , artillery and armour — in an attack for the first time . 
- The German offensive ground to a halt in mid @-@ July and a brief lull followed , during which the Australians undertook a series of raids , known as <unk> <unk> . The Allies soon launched their own offensive — the Hundred Days Offensive — ultimately ending the war . Beginning on 8 August 1918 the offensive included four Australian divisions striking at Amiens . Using the combined arms techniques developed earlier at Hamel , significant gains were made on what became known as the " Black Day " of the German Army . The offensive continued for four months , and during Second Battle of the Somme the Australian Corps fought actions at <unk> , <unk> , <unk> , <unk> , and Mont St Quentin , before their final engagement of the war on 5 October 1918 at <unk> . The AIF was subsequently out of the line when the armistice was declared on 11 November 1918 . 
- In all <unk> @,@ <unk> Australians enlisted in the AIF during the war and 333 @,@ 000 served overseas . 61 @,@ 508 were killed and another 155 @,@ 000 were wounded ( a total casualty rate of 65 % ) . The financial cost to the Australian government was calculated at £ <unk> @,@ <unk> @,@ <unk> . Two <unk> on conscription for overseas service had been defeated during the war , preserving the volunteer status of the Australian force , but stretching the reserves of manpower available , particularly towards the end of the fighting . Consequently , Australia remained one of only two armies on either side not to resort to conscription during the war . 
- The war had a profound effect on Australian society in other ways also . Indeed , for many Australians the nation 's involvement is seen as a symbol of its emergence as an international actor , while many of the notions of Australian character and <unk> that exist today have their origins in the war . 64 Australians were awarded the Victoria Cross during the First World War . 
- 
- = = Inter @-@ war years = = 
- 
- 
- = = = Russian Civil War , 1918 – 19 = = = 
- 
- The Russian Civil War began after the Russian provisional government collapsed and the <unk> party assumed power in October 1917 . Following the end of the First World War , the western powers — including Britain — intervened , giving half @-@ hearted support to the pro @-@ <unk> , anti @-@ <unk> White Russian forces . Although the Australian government refused to commit forces , many Australians serving with the British Army became involved in the fighting . A small number served as advisors to White Russian units with the North Russian Expeditionary Force ( <unk> ) . <unk> repatriation in England , about 150 Australians subsequently enlisted in the British North Russia Relief Force ( <unk> ) , where they were involved in a number of sharp battles and several were killed . 
- The Royal Australian Navy destroyer <unk> Swan was also briefly engaged , carrying out an intelligence gathering mission in the Black Sea in late 1918 . Other Australians served as advisers with the British Military Mission to the White Russian General , Anton <unk> in South Russia , while several more advised Admiral <unk> <unk> in Siberia . Later , they also served in Mesopotamia as part of <unk> and the <unk> Mission , although these missions were aimed at preventing Turkish access to the Middle East and India , and did little fighting . 
- Although the motivations of those Australian 's that volunteered to fight in Russia can only be guessed at , it seems unlikely to have been political . Regardless , they confirmed a reputation for <unk> and courage , winning the only two Victoria Crosses of the land campaign , despite their small numbers . Yet Australian involvement was barely noticed at home at the time and made little difference to the outcome of the war . Total casualties included 10 killed and 40 wounded , with most deaths being from disease during operations in Mesopotamia . 
- 
- = = = <unk> , 1927 = = = 
- 
- In October 1927 , <unk> Adelaide was called to the British Solomon Islands <unk> as part of a punitive expedition in response to the killing of a district officer and sixteen others by <unk> natives at <unk> on the island of <unk> on 3 October , known as the <unk> massacre . Arriving at <unk> on 14 October , the ship proceeded to <unk> to protect the landing of three platoons of troops , then remained in the area to provide personnel support for the soldiers as they searched for the <unk> . The ship 's personnel took no part in operations ashore , providing only logistic and communications support . Adelaide returned to Australia on 23 November . 
- 
- = = = Spanish Civil War , 1936 – 39 = = = 
- 
- A small number of Australian volunteers fought on both sides of the Spanish Civil War , although they predominantly supported the Spanish Republic through the International Brigades . The Australians were subsequently allocated to the battalions of other <unk> , such as the British Battalion and the Lincoln Battalion , rather than forming their own units . Most were radicals motivated by ideological reasons , while a number were Spanish @-@ born migrants who returned to fight in their country of origin . At least 66 Australians volunteered , with only one — <unk> Bull , a conservative <unk> who was later killed serving in the RAF during the Second World War — known to have fought for General Francisco Franco 's Nationalist forces . 
- While a celebrated cause for the Australian left — particularly the Communist Party of Australia and the trade union movement — the war failed to spark particular public interest and the government maintained its neutrality . Australian opposition to the Republican cause was <unk> by <unk> <unk> on an anti @-@ communist basis , rather than a pro @-@ Nationalist basis . <unk> , although individual right wing Australians may have served with the Nationalist rebels , they received no public support . Service in a foreign armed force was illegal at the time , however as the government received no reports of Australians travelling to Spain to enlist , no action was taken . Consequently , returned veterans were neither recognised by the government or the <unk> and Services League of Australia ( <unk> ) . Although the number of Australian volunteers was relatively small compared to those from other countries , at least 14 were killed . 
- 
- = = Second World War , 1939 – 45 = = 
- 
- 
- = = = Europe and the Middle East = = = 
- 
- Australia entered the Second World War on 3 September 1939 . At the time of the declaration of war against Germany the Australian military was small and <unk> for war . <unk> for a Second Australian Imperial Force ( 2nd AIF ) began in mid @-@ September . While there was no rush of volunteers like the First World War , a high proportion of Australian men of military age had enlisted by mid @-@ 1940 . Four infantry divisions were formed during 1939 and 1940 , three of which were dispatched to the Middle East . The RAAF 's resources were initially mainly devoted to training airmen for service with the Commonwealth air forces through the Empire Air Training Scheme ( <unk> ) , through which almost 28 @,@ 000 Australians were trained during the war . 
- The Australian military 's first major engagements of the war were against Italian forces in the Mediterranean and North Africa . During 1940 the light cruiser <unk> Sydney and five elderly destroyers ( dubbed the " <unk> Iron Flotilla " by Nazi Propaganda Minister Joseph Goebbels — a title proudly accepted by the ships ) took part in a series of operations as part of the British Mediterranean Fleet , and sank several Italian warships . The Army first saw action in January 1941 , when the 6th Division formed part of the Commonwealth forces during Operation <unk> . The division assaulted and captured <unk> on 5 January and <unk> on 22 January , with tens of thousands of Italian troops surrendering at both towns . The 6th Division took part in the pursuit of the Italian Army and captured <unk> on 4 February . In late February it was withdrawn for service in Greece , and was replaced by the 9th Division . 
- The Australian forces in the Mediterranean endured a number of campaigns during 1941 . During April , the 6th Division , other elements of I Corps and several Australian warships formed part of the Allied force which unsuccessfully attempted to defend Greece from German invasion during the Battle of Greece . At the end of this campaign , the 6th Division was evacuated to Egypt and Crete . The force at Crete subsequently fought in the Battle of Crete during May , which also ended in defeat for the Allies . Over 5 @,@ 000 Australians were captured in these campaigns , and the 6th Division required a long period of rebuilding before it was again ready for combat . The Germans and Italians also went on the offensive in North Africa at the end of March and drove the Commonwealth force there back to near the border with Egypt . The 9th Division and a brigade of the 7th Division were besieged at <unk> ; successfully defending the key port town until they were replaced by British units in October . During June , the main body of the 7th Division , a brigade of the 6th Division and the I Corps headquarters took part in the Syria @-@ Lebanon Campaign against the <unk> French . Resistance was stronger than expected ; Australians were involved in most of the fighting and sustained most of the casualties before the French <unk> in early July . 
- The majority of Australian units in the Mediterranean returned to Australia in early 1942 , after the outbreak of the Pacific War . The 9th Division was the largest unit to remain in the Middle East , and played a key role in the First Battle of El <unk> during June and the Second Battle of El <unk> in October . The division returned to Australia in early 1943 , but several RAAF squadrons and <unk> warships took part in the subsequent Tunisia Campaign and the Italian Campaign from 1943 until the end of the war . 
- The RAAF 's role in the strategic air offensive in Europe formed Australia 's main contribution to the defeat of Germany . Approximately 13 @,@ 000 Australian airmen served in dozens of British and five Australian squadrons in RAF <unk> Command between 1940 and the end of the war . Australians took part in all of <unk> Command 's major offensives and suffered heavy losses during raids on German cities and targets in France . Australian aircrew in <unk> Command had one of the highest casualty rates of any part of the Australian military during the Second World War and sustained almost 20 percent of all Australian deaths in combat ; 3 @,@ 486 were killed and hundreds more were taken prisoner . Australian airmen in light bomber and fighter squadrons also participated in the liberation of Western Europe during 1944 and 1945 and two RAAF maritime patrol squadrons served in the Battle of the Atlantic . 
- 
- = = = Asia and the Pacific = = = 
- 
- From the 1920s Australia 's defence thinking was dominated by British Imperial defence policy , which was embodied by the " Singapore strategy " . This strategy involved the construction and defence of a major naval base at Singapore from which a large British fleet would respond to Japanese aggression in the region . To this end , a high proportion of Australian forces in Asia were concentrated in Malaya during 1940 and 1941 as the threat from Japan increased . However , as a result of the emphasis on co @-@ operation with Britain , relatively few Australian military units had been retained in Australia and the Asia @-@ Pacific region . Measures were taken to improve Australia 's defences as war with Japan <unk> in 1941 , but these proved inadequate . In December 1941 , the Australian Army in the Pacific comprised the 8th Division , most of which was stationed in Malaya , and eight partially trained and equipped divisions in Australia . The RAAF was equipped with 373 aircraft , most of which were obsolete trainers , and the <unk> had three cruisers and two destroyers in Australian waters . 
- The Australian military suffered a series of defeats during the early months of the Pacific War . The 8th Division and RAAF squadrons in Malaya formed a part of the British Commonwealth forces which were unable to stop a smaller Japanese invasion force which landed on 7 December . The British Commonwealth force withdrew to Singapore at the end of January , but was forced to surrender on 15 February after the Japanese captured much of the island . Smaller Australian forces were also overwhelmed and defeated during early 1942 at Rabaul , and in Ambon , Timor , and Java . The Australian town of Darwin was heavily bombed by the Japanese on 19 February , to prevent it from being used as an Allied base . Over 22 @,@ 000 Australians were taken prisoner in early 1942 and endured harsh conditions in Japanese captivity . The prisoners were subjected to <unk> , denied medical treatment and frequently beaten and killed by their guards . As a result , 8 @,@ 296 Australian prisoners died in captivity . 
- The rapid Allied defeat in the Pacific caused many Australians to fear that the Japanese would invade the Australian mainland . While elements of the Imperial Japanese Navy proposed this in early 1942 , it was judged to be impossible by the Japanese Imperial General Headquarters , which instead adopted a strategy of isolating Australia from the United States by capturing New Guinea , the Solomon Islands , Fiji , Samoa , and New Caledonia . This fact was not known by the Allies at the time , and the Australian military was greatly expanded to meet the threat of invasion . Large numbers of United States Army and Army Air Forces units arrived in Australia in early 1942 , and the Australian military was placed under the overall command of General Douglas MacArthur in March . 
- Australians played a central role in the New Guinea campaign during 1942 and 1943 . After an attempt to land troops at Port Moresby was defeated in the Battle of the Coral Sea , the Japanese attempted to capture the strategically important town by advancing overland across the Owen Stanley Ranges and Milne Bay . Australian Army units defeated these offensives in the <unk> Track campaign and Battle of Milne Bay with the support of the RAAF and USAAF . Australian and US Army units subsequently assaulted and captured the Japanese bases on the north coast of Papua in the hard @-@ fought Battle of <unk> @-@ <unk> . The Australian Army also defeated a Japanese attempt to capture the town of <unk> in January 1943 and went onto the offensive in the <unk> @-@ Lae campaign in April . In late 1943 , the 7th and 9th Divisions played an important role in Operation <unk> , when they landed to the east and west of Lae and secured the <unk> Peninsula during the <unk> Peninsula campaign and <unk> Range campaign . 
- The Australian mainland came under attack during 1942 and 1943 . Japanese submarines operated off Australia from May to August 1942 and January to June 1943 . These attacks sought to cut the Allied supply lines between Australia and the US and Australia and New Guinea , but were unsuccessful . Japanese aircraft also conducted air raids against Allied bases in northern Australia which were being used to mount the North Western Area Campaign against Japanese positions in the Netherlands East Indies ( <unk> ) . 
- Australia 's role in the Pacific War declined from 1944 . The increasing size of the US forces in the Pacific rendered the Australian military superfluous and labour shortages forced the Government to reduce the size of the armed forces to boost war production . Nevertheless , the Government wanted the Australian military to remain active , and agreed to MacArthur 's proposals that it be used in relatively unimportant campaigns . In late 1944 , Australian troops and RAAF squadrons replaced US garrisons in eastern New Guinea , New Britain , and <unk> , and launched offensives aimed at destroying or containing the remaining Japanese forces there . In May 1945 , I Corps , the Australian First Tactical Air Force and USAAF and USN units began the Borneo Campaign , which continued until the end of the war . These campaigns contributed little to Japan 's defeat and remain controversial . 
- Following Japan 's surrender on 15 August 1945 Australia assumed responsibility for occupying much of Borneo and the eastern Netherlands East Indies until British and Dutch colonial rule was restored . Australian authorities also conducted a number of war crimes trials of Japanese personnel . <unk> @,@ 000 Australians enlisted during the war , while <unk> @,@ 000 served overseas . Casualties included 39 @,@ 767 killed and another 66 @,@ <unk> were wounded . 20 Victoria Crosses were awarded to Australians . 
- 
- = = Post @-@ war period = = 
- 
- 
- = = = <unk> and peace @-@ time defence arrangements = = = 
- 
- The <unk> of the Australian military following the end of the Second World War was completed in 1947 . Plans for post @-@ war defence arrangements were <unk> on maintaining a relatively strong peacetime force . It was envisioned that the Royal Australian Navy maintaining a fleet that would include two light fleet carriers , two cruisers , six destroyers , 16 others ships in commission and another 52 in reserved . The Royal Australian Air Force would have a strength of 16 squadrons , including four manned by the Citizen Air Force . Meanwhile , in a significant departure from previous Australian defence policy which had previously relied on citizen forces , the Australian Army would include a permanent field force of 19 @,@ 000 regulars organised into a brigade of three infantry battalions with armoured support , serving alongside a part @-@ time force of 50 @,@ 000 men in the Citizen Military Forces . The Australian Regular Army was subsequently formed on 30 September 1947 , while the <unk> was re @-@ raised on 1 July 1948 . 
- 
- = = = Occupation of Japan , 1946 – 52 = = = 
- 
- In the immediate post @-@ war period Australia contributed significant forces to the Allied occupation of Japan as part of the British Commonwealth Occupation Force ( <unk> ) , which included forces from Australia , Britain , India and New Zealand . At its height in 1946 the Australian component consisted of an infantry brigade , four warships and three fighter squadrons , totalling 13 @,@ 500 personnel . The Australian Army component initially consisted of the 34th Brigade which arrived in Japan in February 1946 and was based in <unk> Prefecture . The three infantry battalions raised for occupation duties were designated the 1st , 2nd and 3rd battalions of the Royal Australian Regiment in 1949 , and the 34th Brigade became the 1st Brigade when it returned to Australia in December 1948 , forming the basis of the post @-@ war Regular Army . From that time the Australian Army contribution to the occupation of Japan was reduced to a single under @-@ strength battalion . Australian forces remained until September 1951 when the <unk> ceased operations , although by the time the majority of units had been committed to the fighting on the Korean <unk> following the outbreak of the Korean War in 1950 . The RAAF component consisted of Nos. 76 , 77 and 82 Squadrons as part of No. 81 Wing RAAF flying P @-@ 51 Mustangs , initially based at <unk> from March 1946 , before transferring to <unk> in 1948 . However , by 1950 only No. 77 Squadron remained in Japan . A total of ten <unk> warships served in Japan during this period , including <unk> Ships Australia , Hobart , Shropshire , <unk> , <unk> , <unk> , Murchison , <unk> , <unk> and <unk> , while <unk> Ships <unk> , <unk> and <unk> also provided support . 
- 
- = = Cold War = = 
- 
- 
- = = = Early planning and commitments = = = 
- 
- During the early years of the Cold War , Australian defence planning assumed that in the event of the outbreak of a global war between the Western world and Eastern bloc countries it would need to contribute forces under collective security arrangements as part of the United Nations , or a coalition led by either the United States or Britain . The Middle East was considered the most likely area of operations for Australian forces , where they would most likely operate with British forces . Early commitments included the involvement of RAAF aircrew during the Berlin <unk> in 1948 – 49 and the deployment of No. 78 Wing RAAF to Malta in the Mediterranean from 1952 to 1954 . Meanwhile , defence preparedness initiatives included the introduction of a National Service Scheme in 1951 to provide manpower for the citizen forces of the Army , RAAF and <unk> . 
- 
- = = = Korean War , 1950 – 53 = = = 
- 
- On 25 June 1950 , the North Korean Army ( KPA ) crossed the border into South Korea and advanced for the capital Seoul , which fell in less than a week . North Korean forces continued toward the port of Pusan and two days later the United States offered its assistance to South Korea . In response the United Nations Security Council requested members to assist in <unk> the North Korean attack . Australia initially contributed P @-@ 51 Mustang fighter @-@ bomber aircraft from No. 77 Squadron RAAF and infantry from the 3rd Battalion , Royal Australian Regiment ( 3 RAR ) , both of which were stationed in Japan as part of the <unk> . In addition , it provided the majority of supply and support personnel to the British Commonwealth Forces Korea . The <unk> frigate <unk> <unk> , and the destroyer <unk> <unk> , were also committed . Later , an aircraft carrier strike group aboard <unk> Sydney was added to the force . 
- By the time 3 RAR arrived in Pusan on 28 September , the North Koreans were in retreat following the <unk> landings . As a part of the invasion force under the UN Supreme Commander , General Douglas MacArthur , the battalion moved north and was involved in its first major action at Battle of <unk> near <unk> on 22 October , before advancing towards the <unk> River . Further successful actions followed at <unk> on 25 – 26 October 1950 and at <unk> on 29 October 1950 . North Korean casualties were heavy , while Australian losses included their commanding officer , Lieutenant Colonel Charles Green , who was wounded in the stomach by artillery fire after the battle and succumbed to his wounds and died two days later on 1 November . Meanwhile , during the last weeks of October the Chinese had moved 18 divisions of the People 's Volunteer Army across the <unk> River to reinforce the remnants of the KPA . <unk> by US and South Korean intelligence , the 13th Army Group crossed the border on 16 October and penetrated up to 100 kilometres ( 62 mi ) into North Korea , and were reinforced in early November by 12 divisions from the 9th Army Group ; in total 30 divisions composed of 380 @,@ 000 men . 3 RAR fought its first action against the Chinese at <unk> on 5 November . The fighting cost the battalion heavily and despite halting a Chinese division the new battalion commander was dismissed in the wake . Following the Chinese intervention , the UN forces were defeated in successive battles and 3 RAR was forced to withdraw to the 38th parallel . 
- A series of battles followed at <unk> on 1 – 4 January 1951 , as the British and Australians occupied defensive positions in an attempt to secure the northern approaches to the South Korean capital . Further fighting occurred at <unk> @-@ <unk> on 14 – 17 February 1951 following another Chinese advance , and later at <unk> @-@ San between 7 – 12 March 1951 as the UN resumed the offensive . Australian troops subsequently participated in two more major battles in 1951 , with the first taking place during fighting which later became known as the Battle of <unk> . On 22 April , Chinese forces attacked the <unk> valley and forced the South Korean defenders to withdraw . Australian and Canadian troops were ordered to halt this Chinese advance . After a night of fighting the Australians recaptured their positions , at the cost of 32 men killed and 59 wounded . In July 1951 , the Australian battalion became part of the combined Canadian , British , Australian , New Zealand , and Indian 1st Commonwealth Division . The second major battle took place during Operation Commando and occurred after the Chinese attacked a salient in a bend of the Imjin River . The 1st Commonwealth Division counter @-@ attacked on 3 October , capturing a number of objectives including Hill 355 and Hill 317 during the Battle of Maryang San ; after five days the Chinese retreated . Australian casualties included 20 dead and 104 wounded . 
- The <unk> then became locked in static trench warfare akin to the First World War , in which men lived in tunnels , redoubts , and <unk> forts behind barbed wire defences . From 1951 until the end of the war , 3 RAR held trenches on the eastern side of the division 's positions in the hills northeast of the Imjin River . Across from them were heavily fortified Chinese positions . In March 1952 , Australia increased its ground commitment to two battalions , sending 1 RAR . This battalion remained in Korea for 12 months , before being replaced by 2 RAR in April 1953 . The Australians fought their last battle during 24 – 26 July 1953 , with 2 RAR holding off a <unk> Chinese attack along the <unk> River and inflicting significant casualties for the loss of five killed and 24 wounded . <unk> were suspended on 27 July 1953 . 17 @,@ 808 Australians served during the war , with 341 killed , 1 @,@ 216 wounded and 30 captured . 
- 
- = = = Malayan Emergency , 1950 – 60 = = = 
- 
- The Malayan Emergency was declared on 18 June 1948 , after three estate managers were murdered by members of the Malayan Communist Party ( <unk> ) . Australian involvement began in June 1950 , when in response to a British request , six <unk> from No. 1 Squadron and a flight of Dakotas from No. 38 Squadron arrived in Singapore to form part of the British Commonwealth Far East Air Force ( <unk> ) . The Dakotas were subsequently used on cargo runs , troop movement , as well as <unk> and <unk> drops , while the Lincoln bombers carried out bombing raids against the Communist Terrorist ( CT ) jungle bases . The RAAF were particularly successful , and in one such mission known as Operation <unk> , five Lincoln bombers destroyed 181 communist camps , killed 13 communists and forced one into surrender , in a joint operation with the RAF and ground troops . The <unk> were withdrawn in 1958 , and were replaced by Canberra bombers from No. 2 Squadron and CAC Sabres from No. 78 Wing . Based at RAAF Base <unk> they also carried out a number ground attack missions against the guerrillas . 
- Australian ground forces were deployed to Malaya in October 1955 as part of the Far East Strategic Reserve . In January 1956 , the first Australian ground forces were deployed on Malaysian peninsula , consisting of the 2nd Battalion , Royal Australian Regiment ( 2 RAR ) . 2 RAR mainly participated in " <unk> up " operations over the next 20 months , conducting extensive patrolling in and near the CT jungle bases , as part of 28th British Commonwealth Brigade . Contact with the enemy was <unk> and results small , achieving relatively few kills . 2 RAR left Malaysia October 1957 to be replaced by 3 RAR . 3 RAR underwent six weeks of jungle training and began driving <unk> insurgents back into the jungle of <unk> and <unk> . The new battalion extensively patrolled and was involved in food denial operations and ambushes . Again contact was limited , although 3 RAR had more success than its predecessor . By late 1959 , operations against the <unk> were in their final phase , and most communists had been pushed back and across the Thailand border . 3 RAR left Malaysia October 1959 and was replaced by 1 RAR . Though patrolling the border 1 RAR did not make contact with the insurgents , and in October 1960 it was replaced by 2 RAR , which stayed in Malaysia until August 1963 . The Malayan Emergency officially ended on 31 July 1960 . 
- Australia also provided artillery and engineer support , along with an air @-@ field construction squadron . The Royal Australian Navy also served in Malayan waters , firing on suspected communist positions between 1956 and 1957 . The Emergency was the longest continued commitment in Australian military history ; 7 @,@ 000 Australians served and 51 died in Malaya — although only 15 were on operations — and another 27 were wounded . 
- 
- = = = Military and Naval growth during the 1960s = = = 
- 
- At the start of the 1960s , Prime Minister Robert <unk> greatly expanded the Australian military so that it could carry out the Government 's policy of " Forward Defence " in South East Asia . In 1964 , <unk> announced a large increase in defence spending . The strength of the Australian Army would be increased by 50 % over three years from 22 @,@ 000 to 33 @,@ 000 ; providing a full three @-@ brigade division with nine battalions . The RAAF and <unk> would also both be increased by 25 % . In 1964 , conscription or National Service was re @-@ introduced under the National Service Act , for selected 20 @-@ year @-@ olds based on date of birth , for a period of two years ' continuous full @-@ time service ( the previous scheme having been suspended in 1959 ) . 
- In 1961 , three Charles F. Adams @-@ class destroyers were purchased from the United States to replace the ageing Q @-@ class destroyers . Traditionally , the <unk> had purchased designs based on those of the Royal Navy and the purchase of American destroyers was significant . <unk> Perth and <unk> Hobart joined the fleet in 1965 , followed by <unk> Brisbane in 1967 . Other projects included the construction of six River @-@ class frigates , the conversion of the aircraft carrier <unk> Melbourne to an anti @-@ submarine role , the acquisition of ten Wessex helicopters , and the purchase of six Oberon @-@ class submarines . 
- The RAAF took delivery of their first <unk> fighters in 1967 , <unk> No. 3 , No. 75 and No. 77 Squadrons with them . The service also received American F @-@ 111 strike aircraft , C @-@ 130 <unk> transports , P @-@ 3 Orion maritime reconnaissance aircraft and Italian <unk> trainers . 
- 
- = = = Indonesia @-@ Malaysia <unk> , 1962 – 66 = = = 
- 
- The Indonesia @-@ Malaysia confrontation was fought from 1962 to 1966 between the British Commonwealth and Indonesia over the creation of the Federation of Malaysia , with the Commonwealth attempting to safeguard the security of the new state . The war remained limited , and was fought primarily on the island of Borneo , although a number of Indonesian <unk> and airborne incursions onto the Malay Peninsula did occur . As part of Australia 's continuing military commitment to the security of Malaysia , army , naval and <unk> units were based there as part of the Far East Strategic Reserve . Regardless the Australian government was wary of involvement in a war with Indonesia and initially limited its involvement to the defence of the Malayan peninsula only . On two occasions Australian troops from 3 RAR were used to help <unk> up <unk> from <unk> and airborne incursions at <unk> and <unk> , in September and October 1964 . 
- Following these raids the government conceded to British and Malaysian requests to deploy an infantry battalion to Borneo . During the early phases , British and Malaysian troops had attempted only to control the Malaysian / Indonesian border , and to protect population centres . However , by the time the Australian battalion deployed the British had decided on more aggressive action , crossing the border into <unk> to obtain information and conduct ambushes to force the <unk> to remain on the defensive , under the <unk> Operation <unk> . The fighting took place in mountainous , jungle @-@ clad terrain , and a debilitating climate , with operations characterised by the extensive use of company bases sited along the border , cross @-@ border operations , the use of helicopters for troop movement and resupply , and the role of human and signals intelligence to determine enemy movements and intentions . 
- 3 RAR deployed to Borneo in March 1965 , and served in <unk> until the end of July , operating on both sides of the border . The battalion had four major contacts with Indonesian forces and several smaller ones — including at Sungei <unk> , <unk> and <unk> during which they inflicted heavy casualties on the <unk> — as well as suffering casualties in two mine incidents . 4 RAR served a less @-@ eventful tour between April and August 1966 , and also operated over the border , successfully <unk> with the <unk> on a number of occasions . A squadron of the Special Air Service Regiment ( <unk> ) was also deployed in 1965 and again in 1966 , taking part in cross @-@ border operations and inflicting significant casualties on the <unk> , even though they were often tasked with covert reconnaissance . Other units included artillery and engineers , while a number of <unk> ships were involved in shelling Indonesian positions in Borneo and in <unk> <unk> in the Singapore Strait . The RAAF played a relatively minor role , although it would have been used far more extensively had the war escalated . 
- Operations in Borneo were extremely sensitive and they received little press coverage in Australia , while official acknowledgement of involvement in cross @-@ border missions only occurred in 1996 . Following a military coup in Indonesia in early 1966 which brought General <unk> to power , a peace treaty was signed in August 1966 which ended the conflict . 3 @,@ 500 Australians served during <unk> ; casualties included 16 dead , with seven killed in action and eight wounded . 
- 
- = = = Vietnam War , 1962 – 73 = = = 
- 
- Australia 's involvement in the Vietnam War was driven largely by the rise of communism in Southeast Asia after the Second World War , and the fear of its spread which developed in Australia during the 1950s and early 1960s . As a consequence , Australia supported South Vietnam throughout the early 1960s . In 1961 and 1962 , the leader of the South Vietnamese government , <unk> <unk> <unk> , asked for assistance from the US and its allies in response to a growing insurgency supported by communist North Vietnam . Australia offered 30 military advisors from the Australian Army Training Team Vietnam , which became known simply as " The Team " . They arrived in July and August 1962 , beginning Australia 's involvement in the war . Later in August 1964 , the RAAF sent a flight of <unk> transport aircraft to the port city of <unk> <unk> . 
- However , with the security situation in South Vietnam continuing to deteriorate , the US increased its involvement to 200 @,@ 000 combat troops by early 1965 . Australia also committed ground forces , dispatching the 1 RAR to serve with the US <unk> Airborne Brigade in <unk> <unk> province in June 1965 and it subsequently fought a number of significant actions , including Gang <unk> , Operation <unk> and <unk> <unk> <unk> . In March 1966 , the Australian government announced the deployment of a brigade @-@ sized unit — the 1st Australian Task Force ( 1 <unk> ) — to replace 1 RAR . Included were a large number of conscripts , under the increasingly controversial National Service Scheme . <unk> of two infantry battalions as well as armour , aviation , artillery and other support arms , the task force was assigned primary responsibility for its own area and was based at <unk> <unk> , in <unk> Tuy Province . Included were the Iroquois helicopters of No. 9 Squadron RAAF . At the Battle of Long Tan on 18 August 1966 , D Company , 6 RAR with considerable artillery support held off and defeated a Viet Cong force that was at least six times bigger than itself . 18 Australians were killed and 24 wounded , while 245 communist dead were later recovered from the battlefield . The battle allowed the Australians to gain dominance over <unk> Tuy Province and 1 <unk> was not fundamentally challenged again . Regardless , during February 1967 the Australians suffered their heaviest casualties in the war to that point , losing 16 men killed and 55 wounded in a single week , the bulk during Operation <unk> . 
- Yet with the <unk> Tuy province coming progressively under control throughout 1967 , the Australians increasingly spent a significant period of time conducting operations further afield . 1 <unk> was subsequently deployed <unk> infiltration routes leading to Saigon to <unk> communist movement against the capital as part of Operation <unk> during the 1968 <unk> Offensive and later during the Battle of Coral – <unk> in May and June 1968 . At Fire Support <unk> Coral and <unk> the Australians had clashed with regular North Vietnamese Army and Viet Cong main force units operating in battalion and regimental strength for the first time in near conventional warfare , ultimately fighting their largest , most hazardous and most sustained battle of the war . During 26 days of fighting Australian casualties included 25 killed and 99 wounded , while communist casualties included 267 killed confirmed by body count , 60 possibly killed , 7 wounded and 11 captured . Other significant Australian actions included Binh Ba in June 1969 , <unk> <unk> in late @-@ December 1968 and early 1969 and Long <unk> in June 1971 . At the height of the Australian commitment , 1 <unk> numbered 8 @,@ 500 troops , including three infantry battalions , armour , artillery , engineers , logistics and aviation units in support . A third RAAF unit , No. 2 Squadron RAAF , flying Canberra bombers , was sent in 1967 , and four <unk> destroyers joined US patrols in the waters off North Vietnam . 
- The Australian withdrawal effectively commenced in November 1970 . As a consequence of the overall allied strategy of Vietnamization and with the Australian government keen to reduce its own commitment to the war , 8 RAR was not replaced at the end of its tour of duty . 1 <unk> was again reduced to just two infantry battalions , albeit with significant armour , artillery and aviation support remaining . Australian combat forces were further reduced during 1971 as part of a phased withdrawal , and 1 <unk> ceased operations in October . Meanwhile , the advisors remained to train South Vietnamese troops until withdrawn on 18 December 1972 by the newly elected Labor government of <unk> <unk> . The last Australian forces were finally withdrawn in 1973 . The Vietnam War was Australia 's longest and most controversial war and although initially enjoying broad support , as the nation 's military involvement increased a vocal anti @-@ war movement developed . More than 50 @,@ 000 Australians served in Vietnam ; <unk> were killed and 2 @,@ 398 were wounded . Four were awarded the Victoria Cross . 
- 
- = = Post @-@ Vietnam era = = 
- 
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- = = = Creation of the Australian Defence Force , 1976 = = = 
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- Although the importance of ' joint ' warfare had been highlighted during Second World War when Australian naval , ground and air units frequently served as part of single commands , the absence of a central authority continued to result in poor co @-@ ordination between the services in the post @-@ war era , with each organising and operating on the basis of a different military doctrine . The need for an integrated command structure received more emphasis during the Australian military 's experiences in the Vietnam War . In 1973 , the Secretary of the Department of Defence , Arthur <unk> , submitted a report to the Government that recommended the unification of the separate departments supporting each service into a single department and the creation of the post of Chief of the Defence Force Staff . 
- The <unk> Labor Government subsequently amalgamated the five defence <unk> ( Defence , Navy , Army , Air Force , and Supply ) into a single Department of Defence in 1973 , while conscription under the National Service scheme was abolished . On 1 January 1976 , the three branches of the Australian military were brought together as a unified , all @-@ volunteer , professional force known as the Australian Defence Force ( <unk> ) . Today , the <unk> is headquartered at Russell <unk> in Canberra and is divided into Air , Land , Maritime and Special Operations <unk> . In addition , Northern Command is based in Darwin , and is responsible for operations in Northern Australia . 
- 
- = = = Defence of Australia , 1980s and 1990s = = = 
- 
- Until the 1970s , Australia 's military strategy centred on the concept of Forward Defence , in which the role of Australian military and naval forces were to co @-@ operate with Allied forces to counter threats in Australia 's region . Following the adoption of the Guam Doctrine by the United States in 1969 , and the British withdrawal ' east of Suez ' in the early 1970s , Australia developed a defence policy emphasising self @-@ reliance and the defence of the Australian continent . Known as the Defence of Australia Policy , it focused Australian defence planning on protecting the nation 's northern maritime approaches ( the ' air @-@ sea gap ' ) against possible attack . 
- In line with this goal , the <unk> was <unk> to increase its ability to strike at enemy forces from Australian bases and to counter raids on continental Australia . This was achieved by increasing the capabilities of the <unk> and RAAF , and relocating regular Army units to Northern Australia . During this time the <unk> had no military units on operational deployment outside Australia . However , in 1987 the <unk> made its first operational deployment as part of Operation Morris Dance , in which several warships and a rifle company deployed to the waters off Fiji in response to the 1987 <unk> coups d 'état . While broadly successful , this deployment highlighted the need for the <unk> to improve its capability to rapidly respond to <unk> events . 
- During this period Australia continued to retain forces in Malaysia as part of the Five Power Defence <unk> ( <unk> ) agreed in 1971 to defend it in the event of external attack , with this commitment initially including significant air , ground and naval forces . However , these forces were gradually reduced with the infantry battalion withdrawn from Singapore in 1973 , and the two <unk> fighter squadrons in 1988 . Since then a detachment of Orion maritime patrol aircraft , support personnel , and an infantry company known as Rifle Company <unk> have been maintained , as well as occasional deployments of F / A @-@ 18 <unk> fighter aircraft . Australian submarines were reportedly also involved in a number of clandestine surveillance missions throughout Asian waters in the last decades of the Cold War . <unk> and Navy units were also involved in tracking Soviet ship and submarine movements in the region . Since then the <unk> have continued to participate in maritime security operations as part of Operation Gateway , conducting patrols over the Indian Ocean , Strait of <unk> and South China Sea . They have reportedly also been involved in freedom of navigation flights . 
- 
- = = = Gulf War , 1991 = = = 
- 
- Australia was a member of the international coalition which contributed military forces to the 1991 Gulf War , deploying a naval task group of two warships , a support ship and a clearance diving team ; in total about 750 personnel . The Australian contribution was the first time Australian personnel were deployed to an active war zone since the establishment of the <unk> and the deployment tested its capabilities and command structure . However , the Australian force did not see combat , and instead playing a significant role in enforcing the sanctions put in place against Iraq following the invasion of Kuwait . Some <unk> personnel serving on exchange with British and American units did see combat , and a few were later decorated for their actions . Following the war , the Navy regularly deployed a frigate to the Persian Gulf or Red Sea to enforce the trade sanctions which continued to be applied to Iraq . A number of Australian airmen and ground crew posted to or on exchange with US and British air forces subsequently participated in enforcing no @-@ fly zones imposed over Iraq between 1991 and 2003 . 
- 
- = = = Global security , late @-@ 1990s = = = 
- 
- Since the late 1980s , the Australian government had increasingly called upon the <unk> to contribute forces to peacekeeping missions around the world . While most of these deployments involved only small numbers of specialists , several led to the deployment of hundreds of personnel . Large peacekeeping deployments were made to <unk> in early 1989 , Cambodia between 1992 and 1993 , Somalia in 1993 , <unk> between 1994 and 1995 and <unk> in 1994 and from 1997 onwards . The 1996 election of the Howard Liberal government resulted in significant reforms to the <unk> 's force structure and role , with the new government 's defence strategy placed less singular emphasis on defending Australia from direct attack and greater emphasis on working in co @-@ operation with regional states and Australia 's allies to manage potential security threats in recognition of Australia 's global security interests . In line with this new focus , the <unk> 's force structure changed in an attempt to increase the proportion of combat units to support units and to improve the <unk> 's combat effectiveness . 
- 
- = = New Millennium = = 
- 
- 
- = = = East Timor , 1999 – 2013 = = = 
- 
- The former @-@ Portuguese colony of East Timor was invaded by Indonesia in 1975 , however , following years of violent struggle the new Indonesian government of President <unk> <unk> subsequently agreed to allow the East <unk> to vote on autonomy in 1999 . The United Nations Mission in East Timor ( <unk> ) was established to organise and conduct the vote , which was held at the end of August 1999 and resulted with 78 @.@ 5 % of voters deciding in favour of independence . However , following the announcement of the results pro @-@ Indonesian militias supported by elements of the Indonesian military , launched a campaign of violence , looting and <unk> and many East <unk> were killed , while perhaps more than 500 @,@ 000 were displaced . Unable to control the violence , Indonesia subsequently agreed to the deployment of a multinational peacekeeping force . Australia , which had contributed police to <unk> , organised and led an international military coalition , known as the International Force for East Timor ( <unk> ) , a non @-@ UN force operating in accordance with UN resolutions . The total size of the Australian force committed numbered 5 @,@ 500 personnel , and included a significant ground force , supported by air and naval forces , in the largest single deployment of Australian forces since 1945 . 
- Under the overall command of Australian Major General Peter <unk> , <unk> began arriving on 12 September 1999 and was tasked with restoring peace and security , protecting and supporting <unk> , and facilitating humanitarian assistance operations . With the withdrawal of the Indonesian armed forces , police and government officials from East Timor , <unk> re @-@ established its headquarters in Dili on 28 September . On 19 October 1999 , Indonesia formally recognised the result of the referendum and shortly thereafter a UN peacekeeping force , the United Nations Transitional Administration in East Timor ( <unk> ) was established , becoming fully responsible for the administration of East Timor during its transition to independence . The hand @-@ over of command of military operations from <unk> to <unk> was completed on 28 February 2000 . Australia continued to support the UN peacekeeping operation with between 1 @,@ 500 and 2 @,@ 000 personnel , as well as landing craft and <unk> helicopters and remained the largest contributor of personnel to the peacekeeping mission . During these operations Australian forces regularly clashed with pro @-@ Indonesian militia and on a number of occasions Indonesian forces as well , especially along the border with West Timor . Significant actions occurred in <unk> , <unk> <unk> and at <unk> in October 1999 . However , with the security situation <unk> the bulk of the Australian and UN forces were withdrawn by 2005 . Two Australians died from non @-@ battle related causes , while a number were wounded in action . 
- The unexpected deployment to East Timor in 1999 led to significant changes in Australian defence policy and to an enhancement of the <unk> 's ability to conduct operations outside Australia . This successful deployment was the first time a large Australian military force had operated outside of Australia since the Vietnam War and revealed shortcomings in the <unk> 's ability to mount and sustain such operations . In response , the 2000 Defence White Paper placed a greater emphasis on preparing the <unk> for overseas deployments . The Australian government committed to improve the <unk> 's capabilities by improving the readiness and equipment of <unk> units , expanding the <unk> to 57 @,@ 000 full @-@ time personnel and increasing real Defence expenditure by 3 % per year . 
- In May 2006 , 2 @,@ 000 <unk> personnel were again deployed to East Timor as part of Operation <unk> , following unrest between elements of the Timor <unk> Defence Force . Australian forces were involved in a number <unk> during this time , including a heavy clash with rebels commanded by <unk> <unk> at Same on 4 March 2007 . However , by early @-@ 2010 the security situation had been <unk> and just 400 Australian personnel remained to train the local security forces as part of a small international force . Following a <unk> , the International <unk> Force commenced withdrawing from Timor @-@ <unk> in November 2012 , a process which was completed in April 2013 . 
- 
- = = = Afghanistan , 2001 – present = = = 
- 
- Shortly after the Islamist inspired terrorist attacks in New York and Washington on 11 September 2001 , Australian forces were committed to the American @-@ led international coalition against terrorism . The <unk> 's most visible contribution — codenamed Operation <unk> — has been a special forces task group operating in Afghanistan from 2001 to 2002 and again from mid @-@ 2005 to fight against the <unk> . Over time the Australian commitment has grown , with the addition of further ground forces in the form of a Reconstruction Task Force from 2006 to provide security , reconstruction and to mentor and train the <unk> National Army . Australia has also contributed a frigate and two AP @-@ <unk> Orion surveillance aircraft and three C @-@ 130 <unk> transport aircraft to international operations in the Persian Gulf and Indian Ocean since 2001 , supporting both the operations in Afghanistan and those in Iraq under Operation <unk> . A detachment of four F / A @-@ 18 <unk> fighter @-@ bombers was based at Diego Garcia from late @-@ 2001 to mid @-@ 2002 , while two Boeing <unk> air @-@ to @-@ air <unk> aircraft were also based in Manas Air Base in <unk> to provide support to coalition aircraft operating in <unk> airspace but were later withdrawn . A Special Operations Task Group was deployed to support the Reconstruction <unk> in April 2007 . In addition to radar crews , logistics and intelligence officers , and security personnel , this brought the number of Australian personnel in Afghanistan to 950 by mid @-@ 2007 , with further small increases to 1 @,@ 000 in mid @-@ 2008 , 1 @,@ 100 in early 2009 and 1 @,@ 550 in mid @-@ 2009 . 
- A modest force remained in Afghanistan over this time and was involved in counter @-@ insurgency operations in <unk> Province in conjunction United States and other coalition forces , including the Dutch prior to their withdrawal . The force consisted of motorised infantry , special forces , engineers , cavalry , artillery and aviation elements . By 2010 it included a combined arms battalion @-@ sized battle group known as the <unk> Task Force , and the Special Operations Task Group , both based at Forward Operation Base Ripley outside of <unk> <unk> , as well as the <unk> Wing Group flying CH @-@ <unk> <unk> , the Force <unk> <unk> and an RAAF air surveillance radar unit based in <unk> . In addition , a further 800 Australian logistic personnel were also based in the Middle East in support , but are located outside of Afghanistan . Meanwhile , <unk> of maritime patrol and transport aircraft continued to support operations in Iraq and Afghanistan , based out of Al <unk> Air Base in the United Arab Emirates . Also included is the deployment of one of the <unk> 's frigates to the Arabian Sea and Gulf of Aden on counter piracy and maritime interdiction duties . 
- Australian forces were at times involved in heavy fighting , and significant actions included Operation <unk> in 2002 and Operation Perth in 2006 , as well as actions in <unk> in 2007 , <unk> in 2009 , the Shah <unk> <unk> and <unk> in 2010 , and <unk> in 2011 ; although others have yet to be publicly acknowledged due to operational security requirements . Casualties include 41 killed and 256 wounded , while another Australian also died serving with the British Army . Four Australians have been awarded the Victoria Cross for Australia , the first such decorations in forty years . Following a <unk> in forces , the last combat troops were withdrawn on 15 December 2013 ; however , approximately 400 personnel remain in Afghanistan as trainers and advisers , and are stationed in <unk> and <unk> . Over 26 @,@ 000 Australian personnel have served in Afghanistan . 
- 
- = = = Iraq , 2003 – 11 = = = 
- 
- Australian forces later joined British and American forces during the 2003 invasion of Iraq . The initial contribution was also a modest one , consisting of just 2 @,@ <unk> personnel — codenamed Operation <unk> . Major force elements included special forces , <unk> and fixed wing aviation and naval units . Army units included elements from the <unk> and 4th Battalion , Royal Australian Regiment ( Commando ) , a CH @-@ 47 <unk> detachment and a number of other specialist units . <unk> units included the amphibious ship <unk> <unk> and the frigates <unk> Darwin and <unk> Anzac , while the RAAF deployed 14 F / A @-@ 18 Hornets from No. 75 Squadron , a number of AP @-@ <unk> <unk> and C @-@ 130 <unk> . The Australian Special Forces Task Force was one of the first coalition units forces to cross the border into Iraq , while for a few days , the closest ground troops to Baghdad were from the <unk> . During the invasion the RAAF also flew its first combat missions since the Vietnam War , with No. 75 Squadron flying a total of 350 sorties and dropping 122 laser @-@ guided bombs . 
- The Iraqi military quickly proved no match for coalition military power , and with their defeat the bulk of Australian forces were withdrawn . While Australia did not initially take part in the post @-@ war occupation of Iraq , an Australian Army light armoured <unk> — designated the Al <unk> Task Group and including 40 <unk> light armoured vehicles and infantry — was later deployed to Southern Iraq in April 2005 as part of Operation <unk> . The role of this force was to protect the Japanese engineer contingent in the region and support the training of New Iraqi Army units . The <unk> later became the <unk> Battle Group ( West ) ( <unk> ( W ) ) , following the hand back of Al <unk> province to Iraqi control . Force levels peaked at 1 @,@ 400 personnel in May 2007 including the <unk> ( W ) in Southern Iraq , the Security Detachment in Baghdad and the Australian Army Training Team — Iraq . A <unk> frigate was based in the North Persian Gulf , while RAAF assets included C @-@ <unk> <unk> and AP @-@ <unk> elements . Following the election of a new Labor government under Prime Minister Kevin <unk> the bulk of these forces were withdrawn by mid @-@ 2009 , while RAAF and <unk> operations were <unk> to other parts of the Middle East Area of Operations as part of Operation <unk> . 
- Low @-@ level operations continued , however , with a small Australian force of 80 soldiers remaining in Iraq to protect the Australian Embassy in Baghdad as part of <unk> under Operation <unk> . <unk> was finally withdrawn in August 2011 , and was replaced by a private military company which took over responsibility for providing security for Australia 's diplomatic presence in Iraq . Although more than 17 @,@ 000 personnel served during operations in Iraq , Australian casualties were relatively light , with two soldiers accidentally killed , while a third Australian died serving with the British Royal Air Force . A further 27 personnel were wounded . Two officers remained in Iraq attached to the United Nations Assistance Mission for Iraq as part of Operation <unk> . This operation concluded in November 2013 . 
- 
- = = = Military intervention against <unk> , 2014 – present = = = 
- 
- In June 2014 a small number of <unk> personnel were deployed to Iraq to protect the Australian embassy when the security of Baghdad was threatened by the 2014 Northern Iraq offensive . Later , in August and September a number of RAAF C @-@ 17 and C @-@ <unk> transport aircraft based in the Middle East were used to conduct <unk> of humanitarian aid to trapped civilians and to <unk> arms and munitions to forces in Kurdish @-@ controlled northern Iraq . In late September 2014 an Air Task Group ( <unk> ) and Special Operations Task Group ( <unk> ) were deployed to Al <unk> Air Base in the United Arab Emirates as part of the coalition to combat Islamic State forces in Iraq . <unk> with F / A @-@ <unk> Super <unk> strike aircraft , a <unk> @-@ <unk> Multi Role <unk> Transport , and an E @-@ <unk> <unk> Airborne Early Warning & Control aircraft , the <unk> began operations on 1 October . The <unk> is tasked with operations to advise and assist Iraqi Security Forces , and was deployed to Iraq after a legal framework covering their presence in the country was agreed between the Australian and Iraqi Governments . It began moving into Iraq in early November . In April 2015 a 300 @-@ strong unit known as Task Group <unk> was deployed to Iraq to train the regular Iraqi Security Forces . <unk> were extended to Syria in September 2015 . 
- 
- = = <unk> = = 
- 
- Australia 's involvement in international peacekeeping operations has been diverse , and included participation in both United Nations sponsored missions , as well as those as part of ad @-@ hoc coalitions . Australians have been involved in more conflicts as peacekeepers than as <unk> ; however " in comparative international terms , Australia has only been a moderately energetic <unk> . " Although Australia has had peacekeepers in the field continuously for 60 years — being among the first group of UN military observers in Indonesia in 1947 — its commitments have generally been limited , consisting mostly of small numbers of high @-@ level and technical support troops such as <unk> , engineers , <unk> , observers , and police . One significant commitment has been Australia 's ongoing involvement with the long running <unk> Force and Observers in the Sinai . The operational tempo started increasing in the mid @-@ 1990s , when Australia became involved in a series of high @-@ profile operations , deploying significantly larger combat units in support of a number of missions including Cambodia , <unk> , Somalia , East Timor , and the Solomon Islands . Australia has been involved in close to 100 separate missions , involving more than 30 @,@ 000 personnel ; 14 Australians have died during these operations . 
- 
- = = Military statistics = = 
- 
- 
- 
- = Ward Churchill = 
- 
- Ward <unk> Churchill ( born 1947 ) is an American author and political activist . He was a professor of ethnic studies at the University of Colorado Boulder from 1990 until 2007 . The primary focus of his work is on the historical treatment of political dissenters and Native Americans by the United States government . His work features controversial and provocative views , written in a direct , often <unk> style . 
- In January 2005 , Churchill 's work attracted controversy because of the circulation of a 2001 essay , " On the Justice of <unk> <unk> " , in which he argued the September 11 attacks were a natural and <unk> consequence of unlawful US foreign policy over the latter half of the 20th century ; the essay is well known for Churchill 's use of the phrase " little <unk> " to describe the " <unk> corps " working in the World Trade Center . 
- In March 2005 the University of Colorado began investigating allegations that Churchill had engaged in research <unk> ; it reported in June 2006 that he had done so . Churchill was fired on July 24 , 2007 , leading to a claim by some scholars that he was fired because of the " Little <unk> " comment . Churchill filed a lawsuit against the University of Colorado for unlawful termination of employment . In April 2009 a Denver jury found that Churchill was wrongly fired , <unk> him $ 1 in damages . In July 2009 , a District Court judge vacated the monetary award and declined Churchill 's request to order his <unk> , deciding the university has " quasi @-@ judicial immunity " . In February 2010 , Churchill appealed the judge 's decision . In November 2010 , the Colorado Court of Appeals upheld the lower @-@ court 's ruling . In September 10 , 2012 , the Colorado Supreme Court upheld the lower courts ' decisions in favor of the University of Colorado . On April 1 , 2013 , the United States Supreme Court declined to hear the case . 
- In a February 2014 interview , Churchill commented that after living more than forty years in the northern plains / Colorado region , he had relocated to Atlanta , Georgia in 2013 . Churchill also stated that he had a half @-@ dozen uncompleted books which he intended to finish and publish in the next three years . 
- 
- = = Early life and education = = 
- 
- Churchill was born in <unk> , Illinois , to Jack <unk> Churchill and <unk> <unk> Allen . His parents divorced before he was two , and he grew up in <unk> , where he attended local schools . 
- In 1966 , he was drafted into the United States Army . On his 1980 resume , he said he served as a public @-@ information specialist who " wrote and edited the battalion <unk> and wrote news releases . " 
- In a 1987 profile on Churchill , the Denver Post reported that he was drafted , went to paratrooper school , then volunteered for Vietnam , where he served a 10 @-@ month tour as Long Range Reconnaissance Patrol ( <unk> ) , one of a six @-@ man team sent out to track down the enemy . The Post article also reported that Churchill was politically <unk> as a result of his experiences in Vietnam . Churchill told the Post that he had spent some time at the Chicago office of the Students for a Democratic Society ( <unk> ) in the late 1960s , and briefly taught members of the Weather Underground how to build bombs and fire weapons . 
- In 2005 , the Denver Post reported that Churchill 's military records show he was trained as a film <unk> and light truck driver , but they do not reflect paratrooper school or <unk> training . The 75th Ranger Regiment Association found no record of Churchill having been a member of the unit , or a <unk> team . 
- Churchill received his <unk> in technological communications in 1974 and <unk> in communications theory in 1975 , both from <unk> State University , now the University of Illinois at Springfield . 
- 
- = = Career = = 
- 
- In 1978 , Churchill began working at the University of Colorado Boulder as an <unk> action officer in the university administration . He also <unk> on American Indian issues in the ethnic studies program . In 1990 , the University of Colorado hired him as an associate professor , although he did not possess the academic doctorate usually required for the position . The following year he was granted tenure in the Communications department , without the usual six @-@ year <unk> period , after having been declined by the Sociology and Political Science departments . 
- He has long been interested in issues associated with the <unk> Act , which broke up the communal reservation lands and assigned plots to individual households . <unk> with that was the federal government 's first use of " blood quantum " to define individual membership in tribes , for what became known as the <unk> Rolls . Since re @-@ establishing self @-@ governments , <unk> recognized tribes have established their own criteria for enrollment as members , often related to descent from recognized historical lists , but less often requiring proofs of blood quantum . Some of his published works address these issues , which he has interpreted as part of the federal government 's policy of <unk> against Native Americans . 
- In 1995 Churchill discussed his views with David <unk> in an interview : 
- You could say that five hundred years ago was the basis of blood quantum in <unk> @-@ America . But in Anglo @-@ America , while there was some preoccupation with it , it was not formalized until the passage of the General <unk> Act , mid @-@ 1880s . At that point they began to define Indian as being someone who was <unk> and <unk> of at least one @-@ quarter by quantum blood indigenous in a given group . You couldn 't be an eighth Cheyenne and an eighth <unk> and be an Indian . You had to be a quarter Cheyenne or a quarter <unk> or hopefully a quarter and a quarter . The reason for this was quite clear . They were identifying Indians for purposes of <unk> them individual <unk> of land in the existing reservation base at that point . If they ran out of Indians identifiable as such , then the rest of the land would be declared surplus . So it was clearly in the interests of the government to create a definition of <unk> that would minimize the number of Indians that were available . It was an economic motivation for the application of this genetic criteria to <unk> in the first place . It 's become increasingly so ever since . " ( David <unk> ( December 1995 ) . " Interview with Ward Churchill : Historical and Current <unk> " . Z Magazine . ) 
- In 1996 , Churchill moved to the new <unk> Studies Department of the University of Colorado . In 1997 , he was promoted to full professor . He was selected as chairman of the department in June 2002 . 
- In January 2005 , during the controversy over his 9 / 11 remarks , Churchill resigned as chairman of the ethnic studies department at the University of Colorado — his term as chair was scheduled to <unk> in June of that year . On May 16 , 2006 , the <unk> Committee of the Standing Committee on Research <unk> at the University of Colorado concluded that Churchill had committed multiple counts of academic <unk> , specifically plagiarism , fabrication , and <unk> . On July 24 , 2007 , Churchill was fired for academic <unk> in an eight to one vote by the University of Colorado 's Board of <unk> . 
- 
- = = <unk> and <unk> affiliation = = 
- 
- In 2003 , Churchill stated , " I am myself of <unk> and Creek descent on my father 's side , <unk> on my mother 's , and am an enrolled member of the United <unk> Band of <unk> Indians . " In 1992 , Churchill wrote elsewhere that he is one @-@ eighth Creek and one @-@ sixteenth <unk> . In 1993 , Churchill told the Colorado Daily that " he was one @-@ sixteenth Creek and <unk> . " Churchill told the Denver Post in February 2005 that he is three @-@ <unk> <unk> . 
- In a statement dated May 9 , 2005 , and posted on its website , the United <unk> Band initially said , 
- " The United <unk> Band would like to make it clear that Mr. Churchill <unk> <unk> a member of the <unk> Band and was only given an honorary ' associate membership ' in the early 1990s because he could not prove any <unk> ancestry . " The tribe said that all of Churchill 's " past , present and future claims or <unk> of <unk> ' enrollment , ' written or spoken , including but not limited to ; biographies , curriculum <unk> , lectures , applications for employment , or any other reference not listed <unk> , are deemed <unk> by the United <unk> Band . " 
- Two days later , the United <unk> Band replaced its statement and acknowledged Churchill 's " alleged ancestry " of being <unk> : 
- " Because Mr. Churchill had genealogical information regarding his alleged ancestry , and his willingness to assist the <unk> in promoting the tribe and its causes , he was awarded an ' Associate <unk> ' as an honor , " the tribe 's website now said . " However , Mr. Churchill may possess eligibility status for <unk> Nation of Oklahoma , since he claims 1 / 16 <unk> . " 
- The tribe 's spokesperson , Lisa <unk> , stated the tribe <unk> only members with certified one @-@ quarter American Indian blood . The website statement further clarified that Churchill " was not eligible for tribal membership due to the fact that he does not possess a ' Certificate of <unk> of Indian Blood ( <unk> ) " , and the associate membership did not <unk> an individual to voting rights or enrollment in the tribe . 
- Churchill has never asked for <unk> certification , and finds the idea of being " <unk> " by the US government offensive . 
- In June 1994 , the United <unk> Band had voted to stop <unk> associate <unk> . Such honorary associate membership recognizes an individual 's assistance to the tribe , but it has nothing to do with Indian ancestry , and it does not <unk> an individual to vote in the tribe as a member . The <unk> Band states that Churchill still holds the associate membership and it has not been rescinded . In a separate interview , <unk> Berry , formerly on the tribe 's enrollment committee and four years on its council , said that Churchill had never fulfilled a promise to help the tribe . 
- In June 2005 , the Rocky Mountain News published an article about Churchill 's genealogy and family history . It " turned up no evidence of a single Indian ancestor " among 142 direct ancestors [ of Churchill 's ] identified from records . The News reported that both Churchill 's birth parents were listed as white on the 1930 census , as were all but two of his great @-@ great @-@ grandparents listed on previous census and other official documents . The News found that some of Churchill 's accounts of where his ancestors had lived did not agree with documented records . Numerous members of Churchill 's extended family have longstanding family legends of Indian ancestry among ancestors ; but , none was confirmed among the 142 direct forebears of Churchill who were identified . 
- <unk> in Churchill 's university personnel file show that he was granted tenure in a " special opportunity position . " In 1994 , then <unk> @-@ Boulder Chancellor James <unk> refused to take action on allegations that Churchill was <unk> claiming to be an Indian , saying " it has always been university policy that a person 's race or ethnicity is self @-@ proving . " 
- Some of Churchill 's Native American critics , such as Vernon <unk> ( White Earth Ojibwe ) and <unk> <unk> <unk> ( Southern Cheyenne @-@ <unk> Creek ) , argue that his assertion of Native American ancestry without the ability to prove it might constitute <unk> and grounds for termination . The University has said that it does not hire on the basis of ethnicity . The University of Colorado 's Research <unk> Committee conducted a preliminary investigation into whether Churchill misrepresented his ethnicity to " add credibility and public acceptance to his scholarship . " The committee concluded that the allegation was not " appropriate for further investigation under the definition of research <unk> . " 
- In a 2005 interview in The Rocky Mountain News , Churchill said , " I have never been confirmed as having one @-@ quarter blood , and never said I was . And even if [ the critics ] are absolutely right , what does that have to do with this issue ? I have never claimed to be <unk> <unk> Bull . " The longtime indigenous activist Russell <unk> said in February of that year , " So I want , from this day forward , every media person nationally , internationally and locally to know that we have <unk> that Ward Churchill is a full @-@ <unk> Indian leader . " 
- 
- = = = Blood quantum = = = 
- 
- Churchill has responded to requests for <unk> of his claimed Indian heritage in various ways , including attacking the blood quantum upon which some Native American tribes establish their membership requirements . Churchill argues that the United States instituted blood quantum laws based upon rules of <unk> in order to further goals of personal <unk> and political <unk> . 
- For decades in his writings , Churchill has argued that blood quantum laws have an inherent <unk> purpose . He says , 
- " Set the blood quantum at one @-@ quarter , hold to it as a rigid definition of Indians , let <unk> proceed as it [ has ] and eventually Indians will be defined out of existence " . 
- Churchill 's <unk> have been raised as one of the several research @-@ <unk> allegations that were brought against him in 2005 ( see below ) . He has been accused of using his interpretation of the <unk> Act to attack tribal governments that would not recognize him as a member . 
- 
- = = Writing = = 
- 
- Churchill has written on American Indian history and culture , and speaks about <unk> inflicted on the indigenous people of the Americas by European settlers and what he describes as the repression of native peoples that continues to this day . 
- As of 2007 , according to a report submitted on his behalf , Churchill has written 10 books on his own and co @-@ authored 4 . Some have been reprinted with updates . He has written 51 book chapters , 3 law reviews , and 50 scholarly essays ; 27 of these were <unk> . He has edited 4 volumes and co @-@ edited 3 more . 
- According to the University of Colorado investigation , " His academic publications are nearly all works of synthesis and <unk> , drawing upon studies by other scholars , not <unk> describing new research based on primary sources . " The investigation also noted that " he has decided to publish largely in alternative presses or journals , not in the university presses or mainstream peer @-@ reviewed journals often favored by more conventional academics . " In addition to his academic writing , Churchill has written for several general readership magazines of political opinion . 
- In 1986 , Churchill wrote an essay titled <unk> as <unk> : Notes on an American <unk> criticizing pacifist politics within the U.S. left as being <unk> , de facto racist and <unk> . In 1998 , <unk> Ring Publishing published the essay in a book entitled <unk> as <unk> : Reflections on the Role of Armed <unk> in North America and listing Ward Churchill as the author . The book included a preface by Ed Mead , a new introduction to the essay by Churchill and a commentary by Mike Ryan . The book sparked much debate in leftist circles and inspired more aggressive tactics within the anti @-@ <unk> movement in the following few years . 
- Agents of <unk> ( 1988 ) , co @-@ authored by Jim <unk> Wall , describes what the authors claim was a secret war against the Black Panther Party and American Indian Movement carried out during the late 1960s and ' 70s by the FBI under the <unk> program . The <unk> <unk> ( 1990 ; reissued 2002 ) , also co @-@ authored with Jim <unk> Wall , examines a series of original FBI <unk> that detail the Bureau 's activities against various leftist groups , from the U.S. Communist Party in the 1950s to activists concerned with Central American issues in the 1980s . 
- In Fantasies of the Master Race ( 1992 ) , Churchill examines the portrayal of American Indians and the use of American Indian symbols in popular American culture . He focuses on such phenomena as Tony <unk> 's mystery novels , the film <unk> with Wolves , and the New Age movement , finding examples of cultural imperialism and exploitation . Churchill calls author Carlos <unk> 's claims of revealing the teachings of a <unk> Indian <unk> , the " greatest hoax since <unk> Man . " 
- <unk> for the Land ( 1993 ; reissued 2002 ) is a collection of essays in which Churchill chronicles what he describes as the U.S. government 's systematic exploitation of Native lands and the killing or displacement of American Indians . He details Native American efforts in the 19th and 20th centuries to prevent <unk> and industrial practices such as surface mining . 
- Churchill 's Indians Are Us ? ( 1994 ) , a sequel to Fantasies of the Master Race , further explores American Indian issues in popular culture and politics . He examines the movie Black Robe , the Pine Ridge Indian <unk> killings , the prosecution of Leonard <unk> , sports <unk> , the Indian Arts and <unk> Act of 1990 , and blood quantum laws , calling them tools of <unk> . Churchill is particularly outspoken about New Age <unk> of <unk> and American Indian sacred traditions , and the " do @-@ it @-@ yourself <unk> " of certain contemporary authors . John P. <unk> of the University of New Mexico School of Law published a review of Indians Are Us ? in The American Indian Quarterly . Professor <unk> , an enrolled member of the <unk> Sioux Nation , states that Indians Are Us ? twists historical facts and is hostile toward Indian tribes . It was in this book that Churchill first made the claim that the United States distributed " smallpox @-@ infested blankets " to Indian tribes , a claim which he repeated several times over the next decade . The claim has been criticized as a <unk> . Churchill has , however , received support from some scholars . 
- From a Native Son : Selected Essays on <unk> , 1985 – 1995 ( 1996 ) is a collection of 23 previously published essays on Native American history , culture , and political activism . In his introduction to this volume , Howard <unk> <unk> " the emergence of a new generation of Native @-@ American scholars " and describes Churchill 's writing as " powerful , <unk> , <unk> of <unk> and deception " . 
- Churchill 's A Little <unk> of <unk> ( 1997 ) is a survey of ethnic cleansing in the Americas from <unk> to the present . He compares the treatment of North American Indians to historical instances of <unk> by the Khmer Rouge in Cambodia , Turks against <unk> , and Europeans against the <unk> , as well as Nazis against the Poles and Jews . 
- In <unk> of Justice ( 2002 ) , Churchill argues that the U.S. ' s legal system was adapted to gain control over Native American people . <unk> the evolution of federal Indian law , Churchill argues that the principles set forth were not only applied to non @-@ Indians in the U.S. , but later adapted for application abroad . He concludes that this demonstrates the development of the U.S. ' s " imperial logic , " which depends on a " corrupt form of <unk> " to establish colonial control and empire . 
- Churchill 's controversial essay on 9 / 11 was expanded into a book @-@ length manuscript , published as On the Justice of <unk> <unk> : Reflections on the <unk> of U.S. Imperial <unk> and <unk> ( 2003 ) by AK Press . The book features two other chapters , one listing US military <unk> , another listing what Churchill believes to be US violations of international law . The original essay takes the " roosting chickens " of the title from a 1963 Malcolm X speech , in which Malcolm X linked the assassination of U.S. president John F. Kennedy to the violence which Kennedy <unk> as " merely a case of chickens coming home to roost . " Churchill 's essays in this book address the worldwide forms of resistance that he posits were and continue to be provoked by U.S. imperialism of the 20th and 21st centuries . 
- In Kill the Indian , Save the Man : The <unk> Impact of American Indian Residential Schools ( 2004 ) , Churchill traces the history of removing American Indian children from their homes to residential schools ( in Canada ) or Indian boarding schools ( in the USA ) as part of government policies ( 1880s – 1980s ) which he regards as <unk> . 
- 
- = = <unk> on Native American issues = = 
- 
- Churchill has been active since at least 1984 as the co @-@ director of the Denver @-@ based American Indian Movement of Colorado , now an autonomous chapter of the American Indian Movement . In 1993 , he and other local <unk> leaders , including Russell <unk> , Glenn T. Morris , Robert <unk> , and David Hill , broke with the national <unk> leadership , including Dennis Banks and the brothers Vernon and Clyde <unk> , claiming that all <unk> chapters are autonomous . The <unk> Grand <unk> Council is based in Minneapolis and retains the name of the national group . It says that the schism arose when <unk> , Churchill , Glenn T. Morris and others openly supported the <unk> Indian group <unk> , who were allied with the anti @-@ revolutionary , CIA @-@ backed <unk> . 
- Journalists such as <unk> <unk> attributed the split to <unk> and other <unk> members dividing over opposition to the <unk> brothers because of their alleged involvement in the execution of Anna <unk> <unk> in December 1975 , who was then the highest @-@ ranking woman in <unk> but had been suspected of being an informant . It was a year in which other FBI <unk> had been discovered in <unk> . On November 3 , 1999 , <unk> held a press conference in Denver , Colorado in which he accused the <unk> brothers of <unk> in <unk> 's death , and named three lower @-@ level <unk> members involved in her death : <unk> Looking Cloud , John Graham , and <unk> Nelson Clark . This was the first time that an <unk> leader active at the time of the <unk> murder had publicly accused <unk> of having been involved . 
- Looking Cloud and Graham were convicted of murder in 2004 and 2010 , by federal and South Dakota state <unk> , respectively . By then Clark was being cared for in a nursing home and was not indicted . <unk> attributed the split in <unk> to divisions in the aftermath of <unk> 's murder . The journalist <unk> <unk> said in 1999 , " ... her [ <unk> 's ] death has divided the American Indian Movement ... " 
- The schism continued , with the national <unk> leadership claiming that the local <unk> leaders , such as Churchill , are tools of the U.S. government used against other American Indians . The leaders of the national <unk> organization , now called <unk> Grand <unk> Council , claim that Churchill has worked in the past as an underground counter @-@ intelligence source for the U.S. government , for example the FBI , and local , non @-@ Indian , police forces , to <unk> the national <unk> organization . Specifically , they refer to a 1993 Boulder , Colorado interview with <unk> <unk> , a former columnist for the Denver Post , in which Churchill stated that he " was teaching the Rapid City Police Department about the American Indian Movement . " In addition , Vernon <unk> accused Churchill of having ' <unk> represented himself as an Indian ' to bolster his credentials . <unk> said he complained to the University of Colorado about this as early as 1986 . 
- Churchill has been a leader of Colorado <unk> 's annual protests in Denver against the Columbus Day holiday and its associated parade . Colorado <unk> 's leadership has come into conflict with some leaders in the Denver Italian American community , the main supporters of the parade . As early as 2004 , Churchill had claimed that such parades are unconstitutional , arguing that the <unk> Amendment to the Constitution provides Native Americans with a right not to be subjected to such displays , <unk> the First Amendment rights of non @-@ Native Americans . 
- 
- = = 9 / 11 essay controversy = = 
- 
- Churchill wrote an essay in September 2001 entitled On the Justice of <unk> <unk> . In it , he argued that the September 11 attacks were provoked by U.S. foreign policy . He compared the role of financial workers at the World Trade Center in " ongoing <unk> American imperialism " to the role played by Adolf <unk> in organizing the Holocaust . In 2005 , this essay was widely publicized when Hamilton College invited Churchill to speak . This led to both condemnations of Churchill and counter @-@ accusations of <unk> by Churchill and supporters . Following the controversy , the University of Colorado interim Chancellor Phil <unk> said , " While Professor Churchill has the constitutional right to express his political views , his essay on 9 / 11 has outraged and <unk> us and the general public . " 
- A documentary called <unk> Fire : Stories from the Edge of Free Speech , broadcast on HBO , prominently features Churchill 's case in addressing the issues of free speech and First Amendment rights . 
- 
- = = Research <unk> investigation = = 
- 
- The controversy attracted increased academic attention to Churchill 's research , which had already been criticized by the legal scholar John <unk> and historian <unk> <unk> . Additional critics were the sociologist Thomas Brown , who had been preparing an article on Churchill 's work , and the historians <unk> Robertson and Russell <unk> , who claimed that Churchill had misrepresented their work . In 2005 , University of Colorado Boulder administrators ordered an investigation into seven allegations of research <unk> , including three allegations of plagiarism , and four allegations of fabrication or <unk> regarding the history of the <unk> Act , the Indian Arts and <unk> Act of 1990 , and statements that smallpox was intentionally spread to Native Americans by John Smith in <unk> and by the United States Army at Fort Clark in 1837 ( not to be confused with the well @-@ documented use of smallpox @-@ infected blankets at Fort Pitt in 1764 ) . 
- On May 16 , 2006 the University released their findings ; the <unk> Committee agreed unanimously that Churchill had engaged in " serious research <unk> " , including <unk> , fabrication , and two of the three allegations of plagiarism . The committee was divided on the appropriate level of sanctions . The Standing Committee on Research <unk> accepted the findings of the <unk> Committee but also disagreed on what sanctions should be imposed . Churchill 's appeal against his proposed dismissal was considered by a panel of the University 's <unk> and <unk> Committee , which found that two of the seven findings of <unk> did not constitute <unk> offences . Three members recommended that the penalty should be demotion and one year 's suspension without pay , while two favored dismissal . 
- On July 24 , 2007 , the University <unk> voted seven to two to uphold all seven of the findings of research <unk> , <unk> the recommendation of <unk> and <unk> panel that two be dismissed . By a vote of eight to one , the <unk> determined to fire Churchill . 
- The next day , Churchill filed a lawsuit in state court claiming that the firing was <unk> for his expressing politically unpopular views . The jury in Churchill 's suit for <unk> weighed the university 's claims of academic <unk> per jury instructions it received in the case . As Stanley Fish said , " It was the jury ’ s task to determine whether Churchill ’ s dismissal would have occurred independently of the adverse political response to his <unk> protected statements . " The jury found that the alleged <unk> would not have led to Churchill 's firing and rejected the university 's academic <unk> claim as the grounds for dismissal . On April 1 , 2009 , a Colorado jury found that Churchill had been wrongly fired , and awarded $ 1 in damages . As one of the jurors said later in a press interview , " it wasn 't a <unk> in his face or anything like that when we didn 't give him any money . It 's just that [ Churchill 's attorney ] David Lane kept saying this wasn 't about the money , and in the end , we took his word for that . " Churchill 's counsel asked Chief Judge Larry J. <unk> of the Denver District Court to order <unk> in light of the verdict . 
- On July 7 , 2009 , Judge <unk> found that the defendants ( university ) were entitled to quasi @-@ judicial immunity as a matter of law , vacated the jury verdict and determined that the University did not owe Churchill any financial compensation . <unk> denied Churchill 's request for <unk> at <unk> . 
- Churchill appealed both decisions . On November 24 , 2010 , a three @-@ judge panel of the Colorado Court of Appeals affirmed the trial court 's decision . In February 2011 , Churchill filed a petition for writ of certiorari with the Colorado Supreme Court . In late May 2011 , the Colorado Supreme Court agreed to hear his case . Court records indicate that oral arguments began June 7 , 2012 . On September 10 , 2012 , the court ruled that the University had " quasi @-@ judicial immunity " , upholding the trial court 's ruling . On April 1 , 2013 , the U.S. Supreme Court declined to hear an appeal from Churchill . 
- 
- = = Honors = = 
- 
- 1992 , Alfred University awarded him an honorary Doctorate of <unk> Letters . 
- 
- = = Works = = 
- 
- Books , as editor 
- <unk> and Native Americans . Boulder CO : South End Press . 1984 . ISBN 978 @-@ 0 @-@ <unk> @-@ 178 @-@ 9 . 
- Sharon <unk> , ed . ( 1997 ) . Islands in <unk> : The International Tribunal on the Rights of Indigenous Hawaiians . Boulder CO : South End Press . ISBN 978 @-@ 0 @-@ <unk> @-@ 568 @-@ 8 . Re @-@ released as Churchill , Ward ( 2005 ) . Sharon <unk> , ed . Islands in <unk> : The Record of the International Tribunal on the Rights of Indigenous Hawaiians . Boulder CO : South End Press . ISBN 978 @-@ 0 @-@ <unk> @-@ 738 @-@ 5 . 
- <unk> <unk> , ed . ( 2006 ) . <unk> The Crime Of Silence : Evidence Of U.S. War <unk> In Indochina . AK Press . ISBN 978 @-@ 1 @-@ <unk> @-@ 21 @-@ 5 . 
- Books , as author and co @-@ author : 
- Culture versus <unk> : Essays on <unk> in the <unk> <unk> with Elisabeth Lloyd . <unk> Press . 1984 . 
- Agents of <unk> : The FBI 's Secret Wars Against the Black Panther Party and the American Indian <unk> with Jim <unk> Wall . Boulder CO : South End Press . 1988 . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 6 . 
- The <unk> <unk> : <unk> from the FBI 's Secret War Against Domestic <unk> with Jim <unk> Wall . Boulder CO : South End Press . 1990 . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 2 . 
- Fantasies of the Master Race : Literature , Cinema , and the Colonization of American Indians . Common Courage Press . 1992 . ISBN 978 @-@ 0 @-@ <unk> @-@ 348 @-@ 4 . 
- Churchill , Ward ( 1992 ) . <unk> and Jim <unk> Wall , ed . <unk> of Steel : The Politics of <unk> in America ( <unk> , Politics , Culture , Theory , Vol . 4 ed . ) . <unk> Press . ISBN 978 @-@ 0 @-@ <unk> @-@ 17 @-@ 3 . Re @-@ released as Churchill , Ward ( 2004 ) . Jim <unk> Wall , ed . Politics of <unk> in the United States . AK Press . ISBN 978 @-@ 1 @-@ <unk> @-@ 12 @-@ 3 . 
- <unk> for the Land : Indigenous Resistance to <unk> , <unk> and <unk> in Contemporary North America . Common Courage Press . 1993 . ISBN 978 @-@ 1 @-@ <unk> @-@ 001 @-@ 0 . <unk> and expanded edition : <unk> for the Land : Native North American Resistance to <unk> , <unk> and Colonization . San Francisco CA : City Lights Books . 2002 . ISBN 978 @-@ 0 @-@ <unk> @-@ 415 @-@ 3 . 
- Indians Are Us ? : Culture and <unk> in Native North America . Common Courage Press . 1994 . ISBN 978 @-@ 1 @-@ <unk> @-@ <unk> @-@ 8 . 
- Since <unk> <unk> : Notes from the <unk> for American Indian Liberation . <unk> Press . 1995 . ISBN 978 @-@ 1 @-@ <unk> @-@ 03 @-@ 5 . 
- Churchill , Ward ( 1996 ) . From a Native Son : Selected Essays on <unk> 1985 – 1995 . Boulder CO : South End Press . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 4 . 
- <unk> as <unk> : Reflections on the Role of Armed <unk> in North <unk> with Mike Ryan ( introduction by Ed Mead ) . <unk> Ring . 1998 . ISBN 978 @-@ 1 @-@ <unk> @-@ 07 @-@ 5 . 
- A Little <unk> of <unk> . San Francisco CA : City Lights Books . 1998 . ISBN 978 @-@ 0 @-@ <unk> @-@ 343 @-@ 9 . 
- <unk> Measures : The History of FBI Political <unk> . Common Courage Press . 2000 . ISBN 978 @-@ 1 @-@ <unk> @-@ <unk> @-@ 1 . 
- Acts Of Rebellion : The Ward Churchill Reader . <unk> . 2002 . ISBN 978 @-@ 0 @-@ 415 @-@ <unk> @-@ 4 . 
- <unk> of Justice : Indigenous Peoples and <unk> Law . San Francisco CA : City Lights Books . 2002 . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 0 . 
- On the Justice of <unk> <unk> : Reflections on the <unk> of U.S. Imperial <unk> and <unk> . AK Press . 2003 . ISBN 978 @-@ 1 @-@ <unk> @-@ 79 @-@ 1 . 
- Kill the Indian , Save the Man : The <unk> Impact of American Indian Residential Schools . San Francisco CA : City Lights Books . 2004 . ISBN 978 @-@ 0 @-@ <unk> @-@ <unk> @-@ 4 . 
- Speaking Truth in the <unk> of Power : <unk> on <unk> , <unk> , and Native North America . AK Press . 2004 . ISBN 978 @-@ 1 @-@ <unk> @-@ 04 @-@ 8 . 
- To <unk> , <unk> And <unk> : The FBI 's Secret War Against The Black Panther Party . <unk> . 2005 . ISBN 978 @-@ 0 @-@ 415 @-@ <unk> @-@ 8 . 
- Articles 
- Churchill , Ward ( July – September 1992 ) . " I Am <unk> : Notes on the <unk> of the Fourth World " . Z <unk> 1 ( 3 ) . <unk> from the original on October 14 , 2007 . 
- Churchill , Ward ( 1994 ) . " Let 's <unk> the Fun Around " . First published as " <unk> Against <unk> " in Margaret Anderson and Patricia Hill ( <unk> . ) ( 1994 ) . Race , Class and Gender : An Anthology . <unk> , CA : <unk> pp. 366 – 73 . Also published under the titles " The Indian Chant and the <unk> <unk> " and " Using Indian Names as <unk> <unk> Native Americans " . 
- Churchill , Ward ( November 1998 ) . " Smoke Signals : A History of Native Americans in Cinema " . <unk> Magazine . 
- Churchill , Ward ( Winter – Spring 2003 ) . " An American Holocaust ? The Structure of <unk> " . Socialism and Democracy 17 ( 2 ) : 25 – 76 @.@ <unk> : 10 @.@ <unk> / <unk> . 
- Churchill , Ward ( Spring 2005 ) . " The <unk> of 9 @-@ 1 @-@ 1 : Reflections on History , Justice and <unk> <unk> " . Alternative Press Review 9 ( 1 ) : 45 – 56 . <unk> from the original on October 2 , 2006 . 
- Churchill , Ward ( July – August 2007 ) . " The Fourth World : <unk> for Traditional Lands and Ways of Life " . Left <unk> 25 : 25 – 29 . 
- Audio and video 
- Doing Time : The Politics of <unk> , audio CD of a lecture , recorded at the Doing Time Conference at the University of Winnipeg , September 2000 ( AK Press , 2001 , ISBN 978 @-@ 1 @-@ <unk> @-@ 47 @-@ 0 ) 
- Life In <unk> America ( AK Press , 2003 , ISBN 978 @-@ 1 @-@ <unk> @-@ 72 @-@ 2 ) 
- In A Pig 's Eye : Reflections on the Police State , <unk> , and Native America ( AK Press , 2002 , ISBN 978 @-@ 1 @-@ <unk> @-@ 50 @-@ 0 ) 
- US Off The Planet ! : An Evening In Eugene With Ward Churchill And <unk> <unk> , VHS video recorded July 17 , 2001 ( <unk> Media <unk> , 2002 ) 
- <unk> and <unk> in the American Left , 2003 audio CD recorded at an AK Press warehouse in Oakland ( AK Press Audio ) 
- Z <unk> Ward Churchill Audio August 10 , 2003 and earlier 
- Churchill <unk> About Academic Freedom – Free Speech Radio News February 9 , 2005 
- Ward Churchill Under Fire – Free Speech Radio News , February 3 , 2005 . 
- The Justice of <unk> <unk> : Ward Churchill <unk> The Pacifica Network Show , Democracy Now ! from February 18 , 2005 features extended Audio / Video exclusive interview with Churchill . 
- A Little <unk> of <unk> : <unk> U.S. <unk> <unk> to the Domestic <unk> of Indigenous Peoples " , recorded in North <unk> , Saskatchewan on March 19 , 2005 
- Debate with David Horowitz and Ward Churchill at George Washington University April 6 , 2006 
- " Full two @-@ hour audio of debate with David Horowitz " . <unk> Retrieved 2006 @-@ 07 @-@ 02 . 
- " David Horowitz vs. Ward Churchill — Round 1 " . Young Americans Foundation . Retrieved 2006 @-@ 07 @-@ 02 . Video and audio ( excerpt ) 
- " David Horowitz vs. Ward Churchill " . <unk> Retrieved 2006 @-@ 07 @-@ 02 . 
- 
- = = <unk> = = 
- 
- Churchill 's subjects are often American Indian figures and other themes associated with Native American Culture . He uses historical photographs as source material for works . In the early 1990s at Santa Fe Indian Market , Churchill protested the passage of the 1990 Indian Arts and <unk> Act . It requires that , to identify and exhibit works as being by a Native American , artists and craftsmen must be enrolled in a Native American tribe or designated by a tribe as an <unk> . Under federal law , Churchill cannot identify his art as by a Native American . 
- Some of Churchill 's pieces may <unk> <unk> . For example , his 1981 <unk> Winter Attack was , according to Churchill and others , based on a 1972 drawing by the artist Thomas E. <unk> Churchill printed 150 copies of Winter Attack and sold at least one of them . Other copies are available online for purchase . Churchill says that , when he produced Winter Attack , he publicly acknowledged that it was based on <unk> ' work . The online journal <unk> mentions Churchill 's artwork and the controversy surrounding its originality . 
- 
- 
- = Exit Through the <unk> @-@ E @-@ Mart = 
- 
- " Exit Through the <unk> @-@ E @-@ Mart " is the fifteenth episode of the twenty @-@ third season of the American animated television sitcom The Simpsons . It originally aired on the Fox network in the United States on March 4 , 2012 . In the episode , Bart is punished by Homer after causing trouble . He gets revenge on his father by <unk> @-@ painting images of him with the word " <unk> " all over Springfield . Street artist Shepard <unk> encounters Bart one night and offers him a gallery show of Bart 's artworks . However , Chief <unk> suddenly appears during the show and arrests Bart for covering the town in graffiti . It turns out that <unk> is an undercover officer working for <unk> . 
- The episode references the 2010 street art documentary Exit Through the Gift Shop by graffiti artist <unk> , and features guest appearances from street artists Ron English , Kenny <unk> , and Robbie <unk> as themselves . <unk> , who is a long @-@ time fan of The Simpsons , also guest starred in the episode as himself . Around 5 @.@ 09 million Americans tuned in to watch " Exit Through the <unk> @-@ E @-@ Mart " during its original broadcast . Since then , the episode has received praise from television critics for its opening sequence , which parodies the opening sequence of the medieval fantasy television series Game of <unk> . 
- 
- = = Plot = = 
- 
- As a birthday gift for Marge , Homer <unk> a <unk> designed by television chef Paula Paul . He goes to a health food store called <unk> Jack 's , where Paula is giving away <unk> , to have Paula sign it . Homer is impressed by the store and says he will not be shopping at the <unk> @-@ E @-@ Mart any more . Apu , who is there to spy , <unk> Homer and the two engage in a fight until the security guards grab hold of Apu and take him away . At the signing , Homer tells Paula that Marge is a big fan of hers . Paula decides that she will call Marge live during her upcoming show to wish her a happy birthday . Meanwhile , Bart gets his mother a <unk> for her birthday . The <unk> <unk> through the phone lines in the Simpsons ' home , causing Marge to miss Paula 's call . Paula becomes furious at Marge for not answering the phone as she embarrassed herself on her show . Homer <unk> Bart by <unk> him up in the <unk> 's cage . 
- To get revenge on Homer , Bart goes around Springfield <unk> @-@ painting graffiti of Homer 's face and the word " <unk> " . When his work appears on the television news , it encourages Bart to create even more graffiti in the town . Street artists Shepard <unk> , Ron English , Kenny <unk> , and Robbie <unk> encounter Bart one night when he is making some graffiti . The four tell Bart that they are impressed by his work and would like to showcase his art in a gallery show , at first Bart is unsure , but Bart remembers how Homer treated him , and then agrees . Meanwhile , the <unk> @-@ E @-@ Mart suffers because of the competition from <unk> Jack 's . Apu ends up attempting to <unk> <unk> Jack 's in a desperate measure , but the <unk> ( Snake <unk> ) convinces him to hand over the gun . Later , Apu is about to shut down the <unk> @-@ E @-@ Mart when his wife <unk> tells him that <unk> Jack 's is closing because it was discovered they were selling monkey meat imported from Brazil as chicken . 
- Homer initially refuses to attend Bart 's show because he discovers the artwork is an insult to him , but changes his mind after Bart apologizes and writes " I 'm sorry " on the hood of Homer 's car . At the show , Chief <unk> and the Springfield Police Department suddenly appear to arrest Bart for making graffiti throughout the town . It is revealed that the gallery show is a <unk> and that <unk> is an undercover officer who helped the police identify Bart as the graffiti artist that had been <unk> @-@ painting Springfield . Since Bart is just a boy , he is not sent to jail . Instead , he is punished by once again being locked up in the <unk> cage . When Bart tells <unk> that he has to go to the bathroom , <unk> covers the cage with the blanket and finds Bart gone when he removes the blanket from the cage . 
- 
- = = Production = = 
- 
- " Exit Through the <unk> @-@ E @-@ Mart " was written by Marc <unk> and directed by Steven Dean Moore as part of the twenty @-@ third season of The Simpsons ( 2011 – 12 ) . It <unk> the debated status of street art as a true form of art . The title is a reference to Exit Through the Gift Shop , a 2010 street art documentary by graffiti artist <unk> who produced the opening sequence of an earlier Simpsons episode titled " <unk> " ( 2010 ) . The Richard <unk> song " Tonight The Streets Are <unk> " , which is the theme song of Exit Through the Gift Shop , is included in the episode during a montage of Bart painting graffiti in Springfield . <unk> Childs of The A.V. Club noted in his review of " Exit Through the <unk> @-@ E @-@ Mart " that the " reveal that the art show was a police <unk> gives the show a moment to joke about the anti @-@ art market theme from Exit Through The Gift Shop . <unk> asks who would be stupid enough to pay for work that an amateur puts up for free in public , and the answer is , as in <unk> ’ s movie , the very wealthy , here represented by Mr. Burns . " 
- American street artist <unk> guest starred in " Exit Through the <unk> @-@ E @-@ Mart " as himself . The graffiti art featuring Homer 's face and the word " <unk> " that Bart creates in the episode is a reference to <unk> 's Barack Obama " Hope " poster and his <unk> Giant image . <unk> has said that The Simpsons has been one of his favorite television shows since the early 1990s because of its " blend of humor and social commentary " , and he felt " deeply honored " to be included in an episode . <unk> wrote on his website that " Part of being on The Simpsons , is you ’ re being honored as a reference point in culture . " He described the plot of the episode as " great " , adding that the staff members of the show " were kind enough to indulge a couple of my dialogue suggestions designed to make the social commentary more pointed ( even though I had to make fun of myself to do so ) . " This was <unk> 's first acting performance . Street artists English , <unk> , and <unk> also guest starred in the episode as themselves . 
- The traditional Simpsons opening sequence was replaced in this episode with a redesigned version that <unk> the opening sequence of the HBO medieval fantasy series Game of <unk> . The Game of <unk> opening shows the various locations featured in the series on a three @-@ dimensional map of the fictional continent <unk> . In the Simpsons opening , these locations were substituted with places in Springfield , and The Wall was replaced with the Simpson family 's <unk> . The theme song featured in the Game of <unk> opening sequence was arranged by The Simpsons composer <unk> <unk> and used in the Simpsons opening as well . Nicholas <unk> , known for <unk> a <unk> covers of famous songs on YouTube , performed the Simpsons theme song over the closing credits of " Exit Through the <unk> @-@ E @-@ Mart " . He was recruited after a staff member of the show saw his cover of the Simpsons theme on YouTube . 
- 
- = = Release = = 
- 
- " Exit Through the <unk> @-@ E @-@ Mart " was originally broadcast on the Fox network in the United States on March 4 , 2012 . It was watched by approximately 5 @.@ 09 million people during this broadcast , and in the demographic for adults aged 18 – 49 , the episode received a 2 @.@ 5 Nielsen rating and a seven percent share . This was a slight decrease from the previous episode , " At Long Last Leave " , which acquired a 2 @.@ 6 rating . The episode became the second highest @-@ rated broadcast in Fox 's Animation <unk> lineup for the night in terms of both total viewers and in the 18 – 49 demographic , finishing higher than new episodes of American Dad ! , Napoleon <unk> , and The Cleveland Show , but lower than a new Family Guy episode ( which received a 2 @.@ 8 rating and was seen by 5 @.@ 33 million people ) . For the week of February 27 – March 4 , 2012 , " Exit Through the <unk> @-@ E @-@ Mart " placed 25th in the ratings among all prime @-@ time broadcasts in the 18 – 49 demographic , and seventh among all Fox prime @-@ time broadcasts . 
- Writing for The A.V. Club , television reviewer <unk> Childs commented that " Exit Through the <unk> @-@ E @-@ Mart " was " somewhat amusing and far more coherent than many recent episodes , but the satire is relatively mild . The episode <unk> a bit while looking for a sweet ending , but it is otherwise solid enough . " Critics have praised the opening sequence that parodies Game of <unk> . Tim <unk> of TV.com called it " one minute of genius " , and MTV 's Brandon <unk> wrote : " <unk> are in order for Matt Groening and his staff for really knocking this one out of the park . " Jenna Busch of <unk> and Kelly West of Cinema <unk> , both fans of Game of <unk> , named the opening the best in the history of the show . IGN 's Eric Goldman commented : " <unk> , The Simpsons . Always there for us with clever / loving parody of something we all love . Such was the case last night , when the animated series opened with an epic opening credit sequence that gave us the Springfield @-@ inspired version of the terrific Game of <unk> credits . " 
- 
- 
- = The Boat Race 2008 = 
- 
- The <unk> Boat Race took place on 29 March 2008 . Held annually , the event is a side @-@ by @-@ side rowing race between crews from the Universities of Oxford and Cambridge along the River Thames . Oxford won the race . Oxford 's crew featured the oldest competitor in Boat Race history . The race took place in very difficult weather conditions – strong winds and heavy rain – resulting in the slowest winning time in over sixty years . Oxford won by six lengths , the largest margin of victory since the 2004 race . 
- Oxford 's Isis beat Cambridge 's Goldie in the reserve race , while Oxford won the Women 's Boat Race . 
- 
- = = Background = = 
- 
- The Boat Race is a side @-@ by @-@ side rowing competition between the University of Oxford ( sometimes referred to as the " Dark Blues " ) and the University of Cambridge ( sometimes referred to as the " Light Blues " ) . First held in 1829 , the race takes place on the 4 @.@ 2 @-@ mile ( 6 @.@ 8 km ) Championship Course on the River Thames in southwest London . The rivalry is a major point of honour between the two universities and followed throughout the United Kingdom and broadcast worldwide . Cambridge went into the race as reigning champions , having beaten Oxford by over a length in the previous year 's race and the overall lead , with 79 victories to Oxford 's 73 . The race was sponsored by <unk> for the fourth time . 
- The first Women 's Boat Race took place in 1927 , but did not become an annual fixture until the 1960s . Until 2014 , the contest was conducted as part of the <unk> Boat Races , but as of the 2015 race , it is held on the River Thames , on the same day as the men 's main and reserve races . The reserve race , contested between Oxford 's Isis boat and Cambridge 's Goldie boat has been held since 1965 . It usually takes place on the <unk> , prior to the main Boat Race . 
- 
- = = Crews = = 
- 
- Oxford 's crew weighed an average of 12 pounds ( 5 @.@ 4 kg ) more per rower than their opponents , and featured the oldest competitor in Boat Race history in 36 @-@ year @-@ old American Mike <unk> . Cambridge crew consisted of six Britons , two Australians and an American , while Oxford 's comprised four Americans , three Britons , an Australian and a German . Both <unk> , Nick <unk> and Rebecca <unk> , were former Blues , while Cambridge also saw 30 @-@ year @-@ old Tom Edwards return , having rowed in 2006 . Cambridge 's boat club president Dan O <unk> was the seventh Light Blue president not to row in his own Blue Boat . 
- 
- = = Race = = 
- 
- Oxford were pre @-@ race favourites , but Cambridge won the toss and elected to start from the Surrey station . Oxford took an early lead , but the boats closed on each other , and <unk> John Garrett was forced to issue a number of warnings to both <unk> in an attempt to prevent a collision . By <unk> <unk> , Oxford were nearly a length ahead and Cambridge responded ; by the time the crews passed under <unk> Bridge they were level . Along <unk> <unk> Oxford moved ahead once again , and were clear of Cambridge after 30 <unk> . Oxford extended their lead to pass the finishing post in 20 minutes 53 seconds , six lengths and 22 seconds ahead of their opponents . Driving rain , strong winds and <unk> water resulted in the slowest winning time for over sixty years , but the largest margin of victory since the 2004 race . 
- Oxford 's Isis beat Cambridge 's Goldie by three @-@ and @-@ a @-@ quarter lengths in the reserve race . Oxford won the 44th running of the Women 's Boat Race by half a length . 
- 
- = = Reaction = = 
- 
- Oxford 's Oliver Moore said " we got the rage going in the crew , and we started to kill it , we hit an awesome rhythm " . Cambridge coach Duncan Holland <unk> his opponents : " Well done to Oxford , they were much faster on the day " . Following tradition , the victorious Oxford crew tossed their <unk> <unk> into the Thames . 
- 
- 
- = Fort Scott National Historic Site = 
- 
- Fort Scott National Historic Site is a historical area under the control of the United States National Park Service in Bourbon County , Kansas , United States . Named after General Winfield Scott , who achieved <unk> during the Mexican @-@ American War , during the middle of the 19th century the fort served as a military base for US Army action in what was the edge of settlement in 1850 . For the next quarter century , it was used as a supply base and to provide security in turbulent areas during the opening of the West to settlement , a period which included Bleeding Kansas and the American Civil War . 
- The current national historic site protects 20 historic structures , a parade ground , and five acres ( 20 @,@ 000 m ² ) of restored <unk> prairie , inside the city of Fort Scott . It is open to visitors most days of the year . 
- 
- = = History = = 
- 
- In 1842 , Fort Scott was named after Winfield Scott , was established on the American frontier on the military road in eastern Kansas between Fort <unk> and Fort Gibson . It was established to provide protection to the rapidly increasing number of settlers , who were migrating from the Eastern United States . Fort Scott became one of a chain of forts intended to protect the new settlers from the Plains Indians , as well as to protect the Indians from the settlers ' encroachment . 
- The United States government intention to reserve permanent Indian lands west of the Missouri River gave way to the competition of settlers continuing to <unk> on the Indian settlements . Fort Scott 's most active days were between 1842 and 1853 , although it was also used during the Civil War . 
- 
- = = = Army days = = = 
- 
- The <unk> of Indian Territory ( now Oklahoma ) were upset to have Fort Wayne in their proximity . After some delay , the US Army decided to abandon Fort Wayne and move its soldiers to a new fort to be built between Fort <unk> and the site . The Army both wanted to <unk> the <unk> ( who were supervised by the Department of Defense ) and provide more defense for white settlers and other Indians against the Osage , who had been conducting frequent raids in the area . On April 1 , 1842 , some soldiers of Fort Wayne left their fort and on April 22 arrived where Fort Scott would be built , in the Osage <unk> section of modern @-@ day Kansas . After <unk> with the <unk> to acquire the land , the rest of Fort Wayne 's garrison left the fort on May 26 and arrived at the Fort Scott site on May 30 . 
- Unlike most forts for military use , the fort did not have defensive walls or structures when first built ; the wide @-@ open area and the available artillery made an enclosed fort unnecessary . The soldiers concentrated on building structures for lodging the men , animals , and equipment . These buildings were on the edges of a 350 @-@ foot ( 110 m ) parade ground . 
- The post <unk> , Captain Thomas Swords , was in charge of building Fort Scott 's structures , and had to deal with the problems of construction on the Kansas prairie , which had few trees . He had only two <unk> and three carpenters to rely on , as there were few civilians and most of the soldiers had other duties to perform . Wood was available , but the mill was built 1 @.@ 5 miles ( 2 @.@ 4 km ) away , making transport time consuming . Swords found his efforts plagued by lack of wood , skilled labor and working equipment . Freak accidents destroyed much of the wood intended for the fort 's construction . As a result , only one <unk> of the five planned officers quarters ( four <unk> and post commandant 's house ) was built by 1844 , and the intended enlisted barracks were not completed . In his 1844 inspection of the fort , Colonel George <unk> reported that , in comparison to other frontier forts , he considered Fort Scott " above average " . 
- Due to the rising tensions that escalated in the Mexican @-@ American War , the US Army redeployed troops to the Southwest . With Fort Scott still uncompleted , officials decided on April 25 , 1850 , that no more construction would be done there , after eight years and $ 35 @,@ 000 . By the time it was finished , it was obsolete ; three years later , it was abandoned by the military in favor of the more western Fort Riley . 
- For the average soldier , life at the fort was " <unk> " . Until the permanent structures were built , soldiers had to live in tents , which lacked comfort . Aside from a few whiskey <unk> and prostitutes , few civilians lived at the fort . Officers had brought their personal slaves with them , including Captain Swords . Five miles East in Missouri was a <unk> shop that supplied soldiers , and quite a few courts @-@ martial followed soldiers ' going <unk> at the shop . The <unk> rate from the fort ranged from 12 % <unk> % , due to boredom , irregular pay , and hatred for military life . As no combat took place near the fort , it seemed more of a frontier village than a military base . Hunting was a popular <unk> ; according to Captain Swords , " wolf chasing and duck hunting " was the only way one officer could tolerate the place . 
- 
- = = = Bleeding Kansas = = = 
- 
- Two years after the army abandoned the fort , the buildings were sold by auction to civilians , with two being converted to hotels . In 1854 the Kansas @-@ Nebraska Act repealed the Missouri <unk> of 1820 , which had kept slavery out of Kansas . <unk> in favor of slavery soon moved to Kansas to try to sway the vote of whether or not Kansas would permit slavery . Settlers from New England arrived equally determined to keep it out . Each of the two competing factions of the Bleeding Kansas conflict claimed one of the hotels at Fort Scott : Free @-@ Soil at the Fort Scott Hotel and Pro @-@ <unk> at the Western Hotel . Most of the residents in Fort Scott supported slavery , but those outside the town tended to the free @-@ soil side . During this time , there were local incidents of murder and attempted <unk> , typical elements of the guerrilla conflicts that were prevalent in the fighting . 
- 
- = = = Army returns = = = 
- 
- During the American Civil War , the fort was renewed as a US military post . In August 1861 , the Union Army took command of Fort Scott , and <unk> it for the war times . The United States Army also took over several blocks within the town for <unk> and <unk> functions . The Union Army rented the properties from the current civilian owners . Troops from Indiana , Iowa , Colorado , Ohio , and Wisconsin would come to the fort , and either stayed by the fort , or traveled farther , to <unk> Missouri , Arkansas , or the Indian Territory . Fort Scott was one of the few installations that recruited and trained black soldiers for the United States <unk> Troops of the Union Army . 
- A major supply depot was situated at the fort . Confederate general Sterling Price hoped to capture the town , but the closest the Confederate force came to the garrison was 10 miles ( 16 km ) away at Battle of Dry Wood Creek . The site was strategically important as it was within a Southern @-@ <unk> area and close to the Confederate state of Arkansas and the " unstable " Indian Territory ( present @-@ day state of Oklahoma ) , where many of the members of the Five <unk> <unk> were allied with the Confederates . The fort served as a " general hospital " ( large military hospital ) and prison until after the war . Following the end of the war , in October 1865 the US Army left the facilities and sold off by auction what they controlled . 
- On January 14 , 1870 , the Army returned with the formation of the Post of Southeast Kansas . The Post was based at Fort Scott , but the soldiers camped along the rail tracks , and seldom used the original fort . They were sent to protect the railroads and workers from settlers . Some of the latter feared that the railroad , which had been awarded land for development by the US Government in rights @-@ of @-@ way , would evict them from their <unk> homes . The settlers considered the troops <unk> to the railroads , and <unk> both as enemies . 
- Settlers also had some continuing conflicts with Indians and insurgents using the recent Confederate cause as excuse for robbery . By the spring of 1873 , the US Army withdrew the troops from Fort Scott for good . From 1873 to 1965 , the buildings of the fort were left <unk> , and slowly deteriorated . Many military buildings were razed and replaced with structures built for civilian use . 
- 
- = = Modern times = = 
- 
- With the Act of August 31 , 1965 , the National Park Service gave the city government of Fort Scott , Kansas the necessary funds and technical knowledge to restore the fort . 
- On October 19 , 1978 , Fort Scott became a National Historic Site under the supervision of the National Park Service , encompassing 17 acres ( 69 @,@ 000 m2 ) . Today the fort is open throughout the year , save for <unk> , Christmas , and New Years Day . <unk> has declined in recent years . In 2005 visitation was 25 @,@ <unk> ; in 2007 it was 22 @,@ 314 . 
- Surviving structures include four officers ' barracks , one <unk> 's barracks , two infantry barracks , a hospital , <unk> , <unk> <unk> , ordnance and post headquarters , <unk> <unk> , <unk> shop , flagpole , and magazine . Another feature of the park is 5 acres ( 2 @.@ 0 ha ) of <unk> prairie restored as part of an ecology @-@ restoration project . 
- 
- 
- = <unk> ( 2008 ) = 
- 
- <unk> ( 2008 ) was a professional wrestling pay @-@ per @-@ view ( <unk> ) event produced by the Total Nonstop Action Wrestling ( TNA ) promotion that took place on June 8 , 2008 at the <unk> Civic Center in <unk> , Mississippi . It was the fourth event under the <unk> name and the sixth event in the 2008 TNA <unk> schedule . Seven professional wrestling matches and one dark match were featured on the event 's card , three of which were for championships . The event commemorated TNA 's six year anniversary . 
- The main event was a King of the Mountain match for the TNA World Heavyweight Championship , in which then @-@ champion Samoa Joe defended against Booker T , Christian Cage , <unk> , and Robert <unk> . Joe won the match to retain the title . The card also featured a bout <unk> <unk> <unk> against Kurt Angle , which <unk> won . The TNA World Tag Team Championship was defended by The Latin American <unk> ( <unk> and <unk> ; <unk> ) against Team 3D ( Brother Devon and Brother Ray ) at the event . <unk> was the victors in the contest to retain the championship . A Six Woman Tag Team match was won by the team of <unk> Kim , <unk> , and <unk> over The Beautiful People ( <unk> Love and Velvet Sky ) and Moose on the <unk> . 
- <unk> is remembered for Joe being the first to retain a championship in a King of the Mountain match . The reported figure of <unk> for the event was 20 @,@ 000 , as reported by The Wrestling Observer <unk> . <unk> had an attendance of 2 @,@ 000 people . Jon <unk> of the professional wrestling section of the Canadian Online Explorer rated the show a 7 out of 10 , which was lower than the 8 out of 10 given to the 2007 edition by Jason <unk> . After the event , an accident occurred which resulted in the death of one man and the injury of another . 
- 
- = = Production = = 
- 
- 
- = = = Background = = = 
- 
- The fourth installment in the <unk> name was announced in January 2008 to take place on June 8 . In March 2008 , it was reported that <unk> would be held outside the TNA Impact ! Zone in Orlando , Florida . In late @-@ March 2008 , <unk> was expected to be held in Tennessee . TNA issued a press release in April 2008 advertising <unk> on June 8 at the <unk> Civic Center in <unk> , Mississippi , although TNA promoted the event as being held in the Memphis area . It also announced that the annual King of the Mountain match would be held at the event . <unk> for <unk> went on sale on April 25 . <unk> celebrates TNA 's six year anniversary , after it formed on June 19 , 2002 . TNA created a section covering the event on their website . TNA released a poster to promote the event prior featuring the tagline " In a town where legends are made , one <unk> to reach immortality " and Booker T , Christian Cage , <unk> , Samoa Joe , and <unk> . Release the Flood by <unk> for Life was used as the official theme for the show . <unk> material advertising the return of Abyss at the event was featured on TNA 's television program TNA Impact ! . The scripted wedding of Jay <unk> and <unk> <unk> was promoted for <unk> . This was announced on the May 15 episode of Impact ! , when <unk> proposed and <unk> accepted in the storyline . American singer Ace Young was advertised take part in the segment on the May 22 episode of Impact ! . On the May 29 episode of Impact ! , <unk> asked <unk> <unk> to be his best man , to which <unk> agreed . <unk> 's <unk> were announced on the June 5 episode as George Steele , Kamala , <unk> B. Ware , and Jake Roberts . 
- 
- = = = <unk> = = = 
- 
- <unk> featured seven professional wrestling matches and one pre @-@ show match that involved different wrestlers from pre @-@ existing scripted <unk> and storylines . <unk> portrayed villains , heroes , or less distinguishable characters in the scripted events that built tension and culminated in a wrestling match or series of matches . 
- The main event at <unk> was a King of the Mountain match for the TNA World Heavyweight Championship , in which then @-@ champion Samoa Joe defended the title against four other competitors . On the May 15 episode of Impact ! , Joe announced the encounter would take place at <unk> as well that an agreement had been made between Management Director Jim <unk> and himself on who would compete in the bout . The arrangement entailed that qualifying matches would take place leading to the event between four wrestlers he chose and four wrestlers <unk> chose . <unk> 's four wrestlers were James Storm , Matt Morgan , Robert <unk> , and <unk> , while Joe 's four were <unk> <unk> , Booker T , Christian Cage , and <unk> . The qualifying matches were held on the May 22 and May 29 episodes of Impact ! . <unk> defeated Morgan in the first <unk> , while Booker T defeated <unk> in the second , both on the May 22 episode of Impact ! . The last two qualification matches were held on the May 29 episode of Impact ! , with <unk> defeating Storm and Cage defeating <unk> . Kevin Nash , who played Joe 's mentor in the storyline , requested to be made the Special Guest <unk> <unk> for the bout , which he was granted by <unk> on the May 29 episode of Impact ! . 
- The predominate storyline heading into the event was the rivalry between <unk> <unk> and Kurt Angle , both members of The Angle Alliance group . On the February 14 episode of Impact ! , TNA held the scripted wedding of Angle 's real @-@ life wife Karen Angle and <unk> despite Angle and Karen still being married on @-@ screen . Afterwards , Karen and Angle separated in the storyline on the March 13 episode of Impact ! . TNA continued to build the situation with Angle attempting to reconcile with Karen on the May 15 episode of Impact ! . Karen refused Angle 's request on the May 22 episode of Impact ! , leading to Angle turning on and assaulting <unk> later in the episode due to his jealousy of the affection Karen showed for <unk> . On the May 29 episode of Impact ! , <unk> announced Angle versus <unk> for the event . Angle sustained a severe neck injury in early May with several TNA officials believing Angle would not recover in time to perform at the show . Angle was still in pain a few days prior to <unk> but was expected to perform normally despite some officials feeling it was too soon to compete . 
- The TNA World Tag Team Championship was defended at <unk> by then @-@ champions The Latin American <unk> ( <unk> and <unk> ; <unk> ) against Team 3D ( Brother Devon and Brother Ray ) . At TNA 's previous <unk> event <unk> on May 11 , <unk> defeated Team 3D in the final round of the <unk> Wild Tag Team Tournament for the vacant TNA World Tag Team Championship . On the May 15 episode of Impact ! , Team 3D attacked and was scripted to <unk> <unk> 's manager <unk> Guerrero , thus starting a rivalry between the two . <unk> announced a rematch from <unk> for the championship to take place at the show on the May 29 episode of Impact ! . 
- TNA held a Six Woman Tag Team match <unk> The Beautiful People ( <unk> Love and Velvet Sky ) and <unk> <unk> against <unk> Kim , <unk> , and <unk> at <unk> . This was the main storyline in TNA 's women 's division which started at <unk> where TNA held a Ten Woman TNA <unk> <unk> Battle Royal to become number @-@ one contender to the TNA Women 's <unk> Championship . The rules of the contest involved the winner getting a championship match while the runner @-@ up had her head <unk> . Love cost <unk> — then known as <unk> <unk> — the match , resulting in her head being <unk> . Kim won the bout and went on to have her title opportunity on the May 15 episode of Impact ! , which she lost after interference from Love . On the June 5 episode of Impact ! , <unk> made her TNA debut <unk> with The Beautiful People in assaulting Kim , <unk> , and <unk> . 
- 
- = = Event = = 
- 
- TNA held a match to warm – up the crowd known as a dark match prior to the show <unk> The Motor City Machine Guns ( Alex Shelley and Chris <unk> ) against the team of Lance Hoyt and Johnny <unk> . The Motor City Machine Guns won the encounter . 
- 
- = = = <unk> = = = 
- 
- <unk> featured employees other than the wrestlers involved in the matches . Mike <unk> and Don West were the commentators for the telecast , with Frank <unk> providing for the <unk> <unk> versus Kurt Angle bout only . Jeremy <unk> and David <unk> were ring <unk> for the event . Andrew Thomas , Earl <unk> , <unk> Charles , and Mark " <unk> " Johnson participated as <unk> for the encounters . Lauren Thompson and <unk> were used as <unk> during the event . Besides those who competed at the event , Abyss , Ace Young , Eric Young , George Steele , <unk> Guerrero , Jake Roberts , Jay <unk> , Johnny <unk> , Kamala , Karen Angle , <unk> B. Ware , <unk> <unk> , <unk> Khan , <unk> , Scott Steiner , <unk> <unk> , <unk> <unk> , and <unk> all appeared on camera , either in backstage or in <unk> segments . The wedding of <unk> and <unk> took place after the second $ 25 @,@ 000 Fan Challenge . The segment ended when <unk> proclaimed his love for <unk> in the storyline and attacked <unk> . Young , Steele , Roberts , Kamala , and Ware came to <unk> 's defense before Roberts placed a snake on top of an unconscious <unk> to end the segment . 
- 
- = = = Preliminary matches = = = 
- 
- The TNA X Division Championship was defended by then @-@ champion <unk> Williams against <unk> in the opening contest of the telecast . Williams was accompanied by <unk> Khan and Scott Steiner . The duration of the bout was 15 minutes and 19 seconds . Williams wore a protective face mask due to a broken orbital bone . Khan and Steiner <unk> in the bout several times . <unk> was legitimately <unk> open during the match after Williams <unk> him in the face with a steel pipe . Williams won the encounter after performing his signature Canadian Destroyer maneuver by <unk> <unk> head @-@ first into the mat to retain the TNA X Division Championship . Abyss returned after this match by aiding <unk> who was being attacked by Khan , Steiner , and Williams . 
- The team of The Beautiful People and <unk> <unk> — renamed Moose — fought the team of <unk> Kim , <unk> , and <unk> in a Six Woman Tag Team match next . It lasted 10 minutes and 14 seconds . <unk> won the bout for her team after running and <unk> Moose against the mat back @-@ first and following with the pin . 
- The TNA World Tag Team Championship was defended in the third match of the event by <unk> against Team 3D . <unk> Guerrero and <unk> accompanied <unk> to the ring . <unk> , Guerrero , and Team 3D 's associate Johnny <unk> all <unk> in the encounter . Near the end when Team 3D were waiting to perform their signature 3D tag team maneuver on <unk> , <unk> came up behind Devon and pinned him with a schoolboy pin at 15 minutes to retain the championship . 
- TNA held two $ 25 @,@ 000 Fan Challenge segments next with then @-@ TNA Women 's <unk> Champion <unk> Kong and her manager <unk> <unk> offering fans in the crowd the chance to face Kong . Kong and <unk> then chose a willing participant and if they defeated Kong they won $ 25 @,@ 000 . The participants chosen for these segments were predetermined , with each planted in the crowd . The first encounter pitted <unk> D against Kong , which Kong won after <unk> <unk> face @-@ first into the mat with her signature <unk> <unk> maneuver at 2 minutes and 26 seconds . The second was between <unk> Robinson and Kong , lasting 1 minute and 42 seconds . Kong won the bout after <unk> <unk> back @-@ first into the mat with her signature <unk> <unk> maneuver . 
- 
- = = = Main event matches = = = 
- 
- <unk> <unk> fought Kurt Angle in the sixth encounter of the show next . The match lasted 22 minutes and 44 seconds . Angle was accompanied by <unk> to the ring . The referee forced <unk> to head to the backstage area due to the referee believing he would interfere . <unk> attempted to perform his signature <unk> Clash maneuver on Angle , which Angle countered into his signature <unk> Lock submission hold . <unk> escaped the hold , causing Angle to crash into the referee , knocking him out in the storyline . Karen Angle then ran down to the <unk> area and attempted to hand Angle a steel chair . Once Angle grabbed the chair she refused to release , allowing <unk> to grab Angle and perform the <unk> Clash by <unk> Angle face @-@ first into the mat . <unk> then followed with the pin attempt to win the contest . After the encounter , Angle and <unk> assaulted <unk> . 
- The main event was a King of the Mountain match for the TNA World Heavyweight Championship with Kevin Nash as Special Guest <unk> <unk> between then @-@ champion Samoa Joe and the <unk> Booker T , Christian Cage , <unk> , and Robert <unk> . In a King of the Mountain match , the objective is to climb a ladder and hang the championship belt on a hook above the ring . In order to do so , a wrestler must first qualify by either <unk> or making another wrestler submit in the match . A wrestler who is pinned or made to submit is this placed in a penalty box for two minutes . The match is fought under no <unk> rules . Booker T was the first to qualify by <unk> <unk> after <unk> him back @-@ first into the mat with his signature Book End maneuver . <unk> was this placed in the penalty box . <unk> was the next to qualify after bashing a ladder Cage held with a chair into him and following with a pin . Immediately afterwards , <unk> pinned <unk> with a roll @-@ up pin to qualify . Later , Joe held Booker T in his signature <unk> <unk> submission hold when Cage ascended a padded <unk> and jumped off onto Booker T and Joe . He followed by <unk> Booker T to qualify and send Booker T to the penalty box . When Booker T was released from the box , he attacked Nash with the title belt and then attempted to hang it . Nash stopped him and then dropped Booker T back @-@ first against the mat with his signature <unk> <unk> maneuver . Joe was the last to qualify by <unk> <unk> after <unk> him back and neck @-@ first into the mat with his signature <unk> <unk> maneuver . Joe then hung the title belt at 19 minutes and 49 seconds to win the competition . 
- 
- = = Reception = = 
- 
- A total of 2 @,@ 000 people attended <unk> , while the The Wrestling Observer <unk> reported that 20 @,@ 000 people bought the event . Canadian Online Explorer writer Jon <unk> rated the entire event a 7 out of 10 , which was lower than the 8 out of 10 given to the 2007 edition by Jason <unk> . The 7 out of 10 was the same rating given to the 2009 edition by Chris and Bryan <unk> . The previous <unk> event <unk> and TNA 's next <unk> event Victory Road both received a 7 out of 10 by Chris <unk> and Bob <unk> , respectively . Compared to rival World Wrestling Entertainment 's ( WWE ) One Night Stand <unk> event on June 1 , both performed equally , as One Night Stand received the same rating from Matt <unk> . 
- <unk> felt that " TNA put on a strong <unk> " that was in " large part to little <unk> . " Regarding the main event , <unk> gave it a 6 out of 10 , while the World Tag Team Championship match received a 4 out of 10 . He gave the Six Woman Tag Team match a 4 @.@ 5 out of 10 and the <unk> <unk> versus Kurt Angle bout a 9 out of 10 . The X Division Championship contest was given a 7 out of 10 . 
- Wade <unk> of the Pro Wrestling <unk> <unk> reviewed the show . He felt the main event had " lots of action and drama , " but that having to hang the belt on a hook does not make for a " particularly dramatic end to a match as it seems like someone hanging a plant in the corner " than " beating someone to win a fight . " <unk> said the World Tag Team Championship contest was a " solid tag match " with some " elaborate sequences " that were " well executed . " <unk> stated that the X Division Championship match was a " very good opener . " Regarding the <unk> and Angle bout , <unk> commented that it was a " good match , but short of a classic must @-@ see match in part because there were too many <unk> mid @-@ match from Angle that took the crowd out of it . " 
- James Caldwell , also of the Pro Wrestling <unk> <unk> , posted a review of the show in which he felt the main event was a " slow , <unk> match " with an " <unk> finish . " Caldwell stated that the <unk> versus Angle contest was a " fine spotlight singles match , " but was a " bit <unk> when the expectations were reasonably high for two of TNA 's best wrestlers in a featured singles match . " He went on to say that he felt it was " missing something " and that it " seemed like the match just never moved out of second gear . Caldwell felt the X Division Championship bout was a " very good opening match , " which " could have been a featured match to sell a few additional <unk> <unk> , but TNA didn 't give the X Division any focus until the final show before the <unk> . " Caldwell stated the World Tag Team Championship was " just a slow , <unk> tag match . " Regarding the marriage segment during the show , Caldwell said " it was just a bad , bad , bad segment that died slowly and <unk> in front of the live audience . " Overall , Caldwell felt that " TNA showcased the X Division in the opening match , " but needed more " <unk> on the show to balance the slow , <unk> former WWE heavyweight wrestlers in the main event slots . " 
- 
- = = Accident = = 
- 
- After <unk> ended , crews began to <unk> the set used for the event . During this time , a man named Kevin " Angus " <unk> was removing a light array when the scaffolding he was positioned on collapsed , causing him to slam against the concrete floor below . He was rushed to the Baptist <unk> Hospital in <unk> , Mississippi where he was pronounced deceased . Another worker named Paul Martin was also taken to the hospital due to his thumb being cut off in the incident . TNA released a statement regarding the incident the next day through their website . The June 12 episode of Impact ! was dedicated to <unk> , with a banner at the beginning of the show stating " In memory Kevin <unk> ( 1963 – 2008 ) " . A moment of silence was held prior to the Impact ! <unk> after <unk> in honor of <unk> . 
- 
- = = Aftermath = = 
- 
- Several wrestlers were concerned with Kurt Angle 's health going into his <unk> match with <unk> <unk> . Despite the concerns , Angle appeared to be out of it after the contest but was expected to be <unk> . 
- Samoa Joe and Booker T started a rivalry over the TNA World Heavyweight Championship following <unk> . On the June 12 episode of Impact ! , Booker T claimed that Joe did not beat him at the event due to interference from Kevin Nash . This led to Nash stating that Joe could not beat Booker T in a one @-@ on @-@ one match later in the broadcast . After this segment , Joe announced that Booker T and he would face at Victory Road on July 13 for the title . On the July 10 episode of Impact ! , Sting proclaimed that he did not know which of the two would win at Victory Road , but that he would be there to watch . At the event , Joe beat Booker T till he was bloody , causing several <unk> and security personnel to try to stop him to no <unk> . He was stopped when Sting <unk> in the contest by bashing Joe with a baseball bat . Booker T then covered Joe for an unofficial pinfall victory that was counted by Booker T 's legitimate wife <unk> . The match result was ruled a no @-@ contest , with Joe retaining the title . 
- <unk> <unk> was joined by Christian Cage and <unk> in his feud with Kurt Angle , who was joined by Team 3D . <unk> and Angle faced in a <unk> match on the June 12 episode of Impact ! , which Angle won . Team 3D then injured both Cage and <unk> in the narrative , with <unk> being knocked out on the June 19 episode of Impact ! , while on the June 26 episode of Impact ! Cage was <unk> through a glass table by Brother Ray . <unk> returned from injury on the July 3 episode of Impact ! , challenging Team 3D and Angle to a Six Man Tag Team match at Victory Road , while <unk> at adding Full Metal <unk> rules to the bout . An online poll was held to determine the <unk> for the match , resulting in Full Metal <unk> being chosen . The team of Angle and Team 3D were the victors at the event . 
- After <unk> , <unk> went on to captain Team TNA in the 2008 TNA World X Cup Tournament . He competed in a Four Way Ultimate X match at Victory Road as the final round of the tournament . He was unsuccessful in winning the match and gaining the appropriate points to win the tournament , thus causing Team TNA ( <unk> , Alex Shelley , Chris <unk> , and Curry Man ) to finish in second place , with Team Mexico ( <unk> , Rey <unk> , Último Guerrero , and <unk> Jr . ) being the victors in point rankings . 
- <unk> feuded with James Storm and Robert <unk> over the TNA World Tag Team Championship after <unk> . <unk> and Storm formed a team known as Beer Money Incorporated and challenged <unk> for the title on the June 12 episode of Impact ! . The match was originally won by <unk> and Storm before being <unk> due to interference . <unk> won the restart to retain the championship . After the bout , <unk> and Storm assaulted <unk> and <unk> Guerrero . Management Director Jim <unk> scheduled a title defense at Victory Road between <unk> and <unk> and Storm under " Fan 's Revenge " <unk> rules on the June 19 episode of Impact ! . <unk> were successful in defending the title at Victory Road . 
- 
- = = Results = = 
- 
- King of the Mountain match 
- 
- 
- = Home Made <unk> Pie = 
- 
- Home Made <unk> Pie is a 1910 American silent short comedy produced by the Thanhouser Company . The film is a comedy of circumstances , in which the Gale family is going to host a dinner . The focus of the humor is in a <unk> <unk> that brandy is added to in an attempt to make it more <unk> , but three people each add brandy to the <unk> . Everyone becomes <unk> upon eating the <unk> , including the family cat , and the film concludes . No cast or staff credits for this film are known . The film was released on September 27 , 1910 , to mixed reviews in trade publications . Reviewers mentioned that the film was too long and it was unbelievable that one <unk> split eight ways would be able to impact everyone . The film is presumed lost . 
- 
- = = Plot = = 
- 
- Though the film is presumed lost , a synopsis survives in The Moving Picture World from September 24 , 1910 . It states : " Life in a country town is often tedious and <unk> , and were it not for the little <unk> and <unk> the <unk> are constantly given , existence in some localities would be a very dreary thing indeed . So the time honored get @-@ <unk> of the small town are an established feature to the social calendar ; they are just as much a matter of necessity and as such have come to stay . The <unk> thought well of this established feature . They made it a <unk> occasion for themselves as well as their guests . They were hardly an overly wealthy family , and their table was never notable for an <unk> of the good things of life , but whenever they gave a dinner they cast all thoughts of economy to the country winds and worked to the one end that their guests have a pleasant time . " 
- " On the night before such an event neither <unk> , <unk> , or <unk> Gale could get those minutes ' solid sleep for thought that they might have overlooked some essential in the guests ' comfort . At the first <unk> of the rooster they would come hopping down to the kitchen to get the <unk> into shape . So little wonder that on occasion of the dinner here pictured - when the prize dish was a splendid <unk> <unk> <unk> - the <unk> were ever looking after the need of the said <unk> . First <unk> Gale would <unk> to the table whereupon it sat in solemn state , and give it a stir ; then <unk> Gale would <unk> over and give it a stir ; finally <unk> Gale would have to come over and honor it likewise . Then arrived the guests . The Minister and the Mrs. Minister , they were , and some neighbors . While she was receiving them the recollection came to <unk> Gale that the beloved <unk> <unk> had not received its <unk> . Off she speeds to a <unk> of whiskey in the kitchen . She <unk> into the <unk> the right proportion of liquor , and goes back to her guests . Then <unk> Gale remembers that the <unk> has not been flavored . He , too , <unk> himself and a moment later is found pouring whiskey into the sacred <unk> . No sooner has he ceased and returned to his guest than <unk> Gale , who has just remembered about the forgotten <unk> , rushes in and makes for the whiskey . She <unk> ' the right proportion ' in , too . " 
- 
- = = Production = = 
- 
- The writer of the scenario is unknown , but it was most likely Lloyd Lonergan . He was an experienced <unk> employed by The New York Evening World while writing scripts for the Thanhouser productions . The film director is unknown , but it may have been Barry O 'Neil . Film historian Q. David Bowers does not attribute a cameraman for this production , but at least two possible candidates exist . Blair Smith was the first cameraman of the Thanhouser company , but he was soon joined by Carl Louis Gregory who had years of experience as a still and motion picture photographer . The role of the cameraman was <unk> in 1910 productions . The cast credits are unknown , but many 1910 Thanhouser productions are fragmentary . In late 1910 , the Thanhouser company released a list of the important personalities in their films . The list includes <unk> <unk> , <unk> D. Barnes , Frank H. Crane , Irene Crane , Marie <unk> , <unk> <unk> , Martin J. Faust , Thomas Fortune , George Middleton , Grace Moore , John W. Noble , Anna <unk> , Mrs. George Walters . A surviving film still gives the possibility of identifying eight actors . 
- 
- = = Release and reception = = 
- 
- The single reel comedy , approximately 1 @,@ 000 feet long , was released on September 27 , 1910 . The film likely had a wide national release , with advertisements in theaters known in Indiana , Kansas , Wisconsin , and Pennsylvania . The film would also be shown in Vancouver , Canada and was met with praise by the audience at the Province Theatre . In 1917 , years after the film 's release , the film was approved without censorship by the Pennsylvania State Board of <unk> of Moving Pictures . 
- The The Moving Picture World reviewer found the subject to be too long , but did not find it to be a fault . The reviewer states , " A domestic comedy with a different flavor . In fact , with a <unk> <unk> for a basis the flavor comes to resemble brandy very strongly by the time that the cook , the maid and the mistress of the house and her daughter have added the proper proportion of <unk> , neither one knowing that the others have done likewise . The humor in this piece centers around this <unk> . What happened to the guests who <unk> of it certainly draws upon the imagination . Imagine eight people , not to speak of the cat , getting drunk on the brandy in one <unk> <unk> ! ... We do not single out this subject as being more evidently padded than others shown on the screen this week . <unk> as well as <unk> were guilty and it is time that the manufacturers realize the importance of the scenario end of matters . The <unk> <unk> episode was well done , which is the faint praise that we do not like to <unk> out to anyone . " Walton of The Moving Picture News was not <unk> by the plot and stated , " To me the last scene had no humor . It was only the necessary ending to the beginning . True humor does not <unk> from <unk> . " The New York Dramatic Mirror stated , " This is the old story of too many cooks , and it is quite funny after the first scenes are out of the way . The early part of the film takes too much time with trivial details leading up to the final situation . ... A weak point in the story is the fact that the <unk> is cut into eight small pieces , making it difficult to believe in its remarkable power . Two <unk> would have been as easy to make as one and would have been more convincing . The character parts are all well taken , although the cook would have been better if she had not seized so many opportunities to talk directly at the camera . " 
- 
- 
- = Back Off <unk> = 
- 
- " Back Off <unk> " is a song by English musician <unk> Starr , released as a non @-@ album single in March 1972 . Starr 's former Beatles <unk> George Harrison produced the recording , which took place in London shortly after the two had appeared together at Harrison 's Concert for Bangladesh shows in August 1971 . The single was a follow @-@ up to Starr 's 1971 hit song " It Don 't Come Easy " and continued his successful run as a solo artist . " Back Off <unk> " peaked at number 2 in Britain and Canada , and number 9 on America 's Billboard Hot 100 . It remains Starr 's highest @-@ charting single in the United Kingdom . 
- The title for the song was inspired by English singer @-@ songwriter Marc <unk> . Some commentators have suggested that the lyrics were directed at Paul McCartney , reflecting Starr 's disdain for the music McCartney had made as a solo artist over the previous two years . " Back Off <unk> " demonstrates the influence of <unk> rock on Starr , who directed a documentary film , Born to Boogie ( 1972 ) , about <unk> 's band T. Rex around this time . Described by one author as a " high @-@ energy in @-@ your @-@ face rocker " , the song features a prominent slide guitar part by Harrison and contributions from musicians Gary Wright and Klaus <unk> . 
- Starr re @-@ recorded " Back Off <unk> " for his 1981 album Stop and <unk> the Roses , in a collaboration with American singer Harry Nilsson that incorporates lyrics from Beatles songs such as " With a Little Help from My Friends " , " Good Day Sunshine " and " Baby , You 're a Rich Man " . The original version has appeared on Starr 's compilation albums <unk> from Your Past and <unk> : The Very Best of <unk> Starr , and as a bonus track on his remastered 1974 studio album <unk> Vienna . Since his return to touring in 1989 , Starr has performed " Back Off <unk> " regularly in concert with the various <unk> of his All @-@ Starr Band . 
- 
- = = Background and composition = = 
- 
- <unk> Starr identified his initial inspiration for " Back Off <unk> " as having come from Marc <unk> , the singer and guitarist with English <unk> rock band T. Rex . In a 2001 interview with <unk> editor Paul Du <unk> , Starr described <unk> as " a dear friend who used to come into the office when I was running Apple Movies , a big office in town , and the hang @-@ out for myself , Harry Nilsson and Keith Moon " . Over dinner one evening at Starr 's home outside London , <unk> had used the word " <unk> " so often that it stuck in Starr 's mind , after which the beat and melody for the song came to him overnight . When discussing the composition on VH1 <unk> in May 1998 , Starr explained : " [ <unk> ] was an <unk> guy . He used to speak : ' Back off <unk> ... <unk> you , <unk> . ' ' Do you want some potatoes ? ' ' <unk> you , <unk> ! ' " Starr also recalled having to take the batteries out of his children 's toys that night , in order to power a tape recorder and make a recording of the new song . 
- The lyrics to the middle eight of " Back Off <unk> " came to Starr while watching London Weekend Television 's football show , The Big Match . The program 's host , Jimmy Hill , often referred to a footballer 's playing as " <unk> " , a catchphrase that Starr incorporated into his song lyrics : 
- Get yourself together now 
- And give me something <unk> 
- Everything you try to do 
- You know it sure sounds wasted . 
- Commentators have interpreted the song , and particularly this statement , as an attack by Starr on his former Beatles <unk> Paul McCartney . Starr has denied any such interpretation , instead " claiming that the song was inspired by <unk> and nothing more " , Beatles biographer Robert Rodriguez writes . Starr had publicly criticised McCartney 's solo albums McCartney ( 1970 ) and Ram ( 1971 ) on release , and author Bruce <unk> <unk> the message of the middle eight as " a plea for Paul to produce better music " . The mention of " sound [ ing ] wasted " could also be a reference to McCartney 's <unk> with cannabis , Rodriguez suggests . A further example of Starr 's allegedly anti @-@ McCartney message exists in the song 's first verse : 
- Wake up , <unk> 
- Don 't pretend that you are dead 
- Get yourself up off the cart . 
- The same commentators suggest that here Starr could be referring to the 1969 " Paul Is Dead " hoax . The latter <unk> circulated during September and October of that year while McCartney hid away on his Scottish farm , <unk> after John Lennon had told him and Starr that he wanted a " divorce " from the Beatles . 
- In addition to these supposed messages in " Back Off <unk> " , observers have viewed the song title as Starr 's <unk> to McCartney to abandon his legal stand against the Beatles and Apple Corps , which was placed in receivership in March 1971 after a High Court judge found in McCartney 's favour . Author Keith <unk> writes that " <unk> " had " long been cited as Paul 's nickname " from his former bandmates Starr , Lennon and George Harrison . While acknowledging that in subsequent years Starr might have chosen to minimise any ill @-@ feeling towards McCartney , Rodriguez notes that the lyrics " just happened to fit perfectly into the ' us vs. Paul ' mindset " following the Beatles ' break @-@ up , to the extent that " Back Off <unk> " was " as <unk> as ' Early 1970 ' had been <unk> " . When <unk> his 1970 composition " I 'm the Greatest " for Starr to record on the album <unk> ( 1973 ) , Lennon referenced the song title with the lines " Now I 'm only thirty @-@ two / And all I want to do is <unk> " . 
- Although " T Rex devotees " , in the words of Starr biographer Alan <unk> , claimed that <unk> had ghost @-@ written " Back Off <unk> " , Starr later acknowledged that Harrison co @-@ wrote the song by adding some chords and finishing the melody . As on Starr 's 1971 hit single " It Don 't Come Easy " , Harrison was not credited for his songwriting contribution . Starr originally offered " Back Off <unk> " to fellow <unk> <unk> Black to record , but she declined , hoping instead to record another new Starr – Harrison composition , " <unk> " . 
- 
- = = Recording = = 
- 
- Having earmarked the song as his next single , Starr recorded " Back Off <unk> " in September 1971 , following his appearance at the Harrison @-@ organised Concert for Bangladesh in New York . The sessions took place at Apple Studio in central London , with Harrison producing , as he had on " It Don 't Come Easy " . The recording reflects the influence of <unk> rock on Starr through what authors <unk> <unk> and Mark Easter term " its big drum sound and <unk> nature " , with a line @-@ up comprising Starr ( vocals , drums , percussion ) , Harrison ( guitars ) , Gary Wright ( piano ) and Klaus <unk> ( bass , saxophone ) . 
- Rodriguez describes Starr 's " martial @-@ sounding opening " as a rare " showcase for his own drumming " , while Harrison biographer Simon <unk> writes of " a <unk> series of Harrison slide breaks that brought to mind <unk> <unk> " . Further overdubs included contributions from three backing vocalists , led by American soul singer Madeline Bell . 
- 
- = = = " <unk> " = = = 
- 
- For the single 's B @-@ side , Starr had already written and recorded " <unk> " , the theme song for the <unk> <unk> @-@ directed <unk> Western of the same name , filming for which Starr had interrupted in order to perform at the Concert for Bangladesh . Starr produced the track with <unk> . The sessions for " <unk> " took place at Apple on 18 – 19 August , with <unk> guitarist Pete Ham assisting Starr and <unk> . Like the film <unk> ( 1971 ) , the song is held in low regard by critics ; <unk> describes it as " a muddy @-@ sounding <unk> with little to recommend " . 
- 
- = = Release and reception = = 
- 
- Apple Records issued the single on 17 March 1972 in Britain , as Apple R <unk> , with a US release taking place three days later , as Apple 1849 . It was Starr 's first release since " It Don 't Come Easy " , a year before . During this period , his priority had been to develop a career as an actor in films such as 200 <unk> ( 1971 ) and <unk> . Further <unk> himself with Britain 's <unk> rock movement , Starr made his directorial debut with Born to Boogie ( 1972 ) , a film starring <unk> that included Starr 's footage of a T. Rex concert held at Wembley on 18 March . With " Back Off <unk> " , NME critic Bob <unk> noted Starr 's success in establishing himself in the two years since the Beatles ' break @-@ up , and wrote that the single " confirmed that he and Harrison , dark horses both , were the ones who had managed their solo careers more purposefully and <unk> " compared with McCartney and Lennon . 
- The song was a hit in the US , reaching number 9 on the Billboard Hot 100 , and achieved Starr 's best position on the UK Singles Chart , where it reached number 2 . A promotional video for " Back Off <unk> " was shot on 20 March at Lennon 's Tittenhurst Park residence while Starr was looking after the property . The video , which shows Starr walking around an outdoor structure and followed by a <unk> @-@ like monster , was directed by Tom Taylor and financed by <unk> Films . A similar monster appeared on the single 's picture sleeve , holding a cigarette . 
- Alan <unk> writes of reviewers criticising " Back Off <unk> " for being <unk> , leading Starr to respond in a 1973 interview : " Play me a pop song that isn 't . " On release , Chris Welch wrote in Melody Maker : " A Number One hit could easily be in store for the <unk> of rock drums . There 's a touch of the Marc <unk> in this highly playable rhythmic <unk> ... It 's hypnotic and effective , ideal for <unk> and liable to send us all mad by the end of the week . " <unk> described the single as " every bit as <unk> " as " It Don 't Come Easy " , although " slightly inferior " , while Mike <unk> of AllMusic views it as a song where " [ t ] he <unk> spirit of <unk> Starr shines through " . In a 1974 article for the NME , Charles <unk> Murray highlighted " Back Off <unk> " as a " great radio and <unk> @-@ box tune " . 
- Among <unk> biographers , Simon <unk> terms it " a rocking , soccer crowd chant that suited Starr 's talents well " , and Bruce <unk> praises the track as a " high @-@ energy in @-@ your @-@ face rocker propelled by <unk> 's <unk> drums and George 's <unk> slide guitar " . In the 2005 publication NME <unk> : Beatles – The Solo Years 1970 – 1980 , Paul Moody listed " Back Off <unk> " first among Starr 's " ten solo gems " and described it as " Good time rock <unk> to match the Faces " . Guitar World editor Damian <unk> includes the song on his list of Harrison 's ten best post @-@ Beatles " Guitar <unk> " , saying of the recording : " the main event is clearly Harrison 's slightly wild , <unk> – and very <unk> – slide guitar solo , which includes an alternate melody line that 's even <unk> than the melody <unk> is singing . " 
- Re @-@ releases for " Back Off <unk> " include Starr 's 1975 greatest hits album , <unk> from Your Past , and , along with " <unk> " , as a bonus track on the 1992 reissue of his <unk> Vienna album ( 1974 ) . " Back Off <unk> " also appeared on his 2007 compilation <unk> : The Very Best of <unk> Starr , the collector 's edition of which included his 1972 video for the song . 
- 
- = = Stop and <unk> the Roses version = = 
- 
- Starr recorded a new version of " Back Off <unk> " for his 1981 album on <unk> Records , Stop and <unk> the Roses . The song was produced by Starr 's friend , singer Harry Nilsson , and features a musical arrangement by Van <unk> Parks . Similar to Nilsson 's 1968 cover of the Beatles ' " You Can 't Do That " , the remake incorporates lyrics from a number of the band 's songs – in this case , " With a Little Help from My Friends " , " Help ! " , " Lady Madonna " , " Good Day Sunshine " and " Baby , You 're a Rich Man " , as well as Starr 's " It Don 't Come Easy " . In a further reference to his past , the 1981 version of " Back Off <unk> " opens with the same guitar riff that Harrison had played on " It Don 't Come Easy " ten years before . 
- Starr taped the basic track at <unk> Recording Studios in Los Angeles on 4 November 1980 , with additional recording taking place on 1 – 5 December at Nassau 's <unk> Point Studios . Among the large cast of musicians supporting Starr were Nilsson ( vocals ) , Jim <unk> ( drums ) , Jane <unk> ( piano ) , Dennis <unk> and Richie <unk> ( guitars ) , and a four @-@ piece horn section led by saxophonist Jerry <unk> . 
- Starr <unk> his vocals on 4 December , four days before the murder of John Lennon , who had been due to record his contributions to Stop and <unk> the Roses in January 1981 . <unk> with his success as a solo artist in 1971 – 73 , the album continued Starr 's run of commercial and critical failures since 1976 ; Rodriguez writes that " [ m ] <unk> people either love or hate the <unk> " of " Back Off <unk> " . 
- 
- = = Live performance = = 
- 
- Starr has performed " Back Off <unk> " in concert with his All @-@ Starr Band , beginning with the band 's debut tour of North America in July – September 1989 . The song was dropped from the concert setlist early in that tour , however , in favour of the 1963 Lennon – McCartney composition " I Wanna Be Your Man " . Live versions of " Back Off <unk> " have appeared on the multi @-@ disc compilation The Anthology ... So Far ( 2001 ) and King <unk> Flower Hour Presents <unk> & His New All @-@ Starr Band ( 2002 ) . The latter version was recorded during a US tour in August 2001 , at which point the All @-@ Starr line @-@ up was Starr ( vocals ) , Mark Rivera ( saxophone ) , Ian Hunter ( guitar ) , Roger Hodgson and Howard Jones ( keyboards ) , Greg Lake ( bass ) and <unk> E. ( drums ) . 
- Starr also played the song live with <unk> and the <unk> , a band he formed to promote his 1998 studio album <unk> Man . A version recorded on 13 May that year at Sony Music Studios , New York , appeared on Starr 's VH1 <unk> live album and video , released in October 1998 . The personnel on this performance included Starr ( vocals ) , Joe Walsh and Mark Hudson ( guitars ) , Jack <unk> ( bass ) and Simon <unk> ( drums ) . Another live version with the <unk> , recorded for PBS Television 's <unk> in August 2005 , was issued on <unk> Starr : Live at <unk> ( 2007 ) . 
- 
- = = Personnel = = 
- 
- The following musicians played on the original version of " Back Off <unk> " : 
- <unk> Starr – vocals , drums , percussion , backing vocals 
- George Harrison – slide guitars , acoustic guitar 
- Gary Wright – piano 
- Klaus <unk> – bass , saxophone 
- Madeline Bell , <unk> Duncan , Jean Gilbert – backing vocals 
- 
- = = Chart performance = = 
- 
- 
- 
- = Where the Streets Have No Name = 
- 
- " Where the Streets Have No Name " is a song by Irish rock band U2 . It is the opening track from their 1987 album The Joshua Tree and was released as the album 's third single in August 1987 . The song 's hook is a repeating guitar <unk> using a delay effect , played during the song 's introduction and again at the end . Lead vocalist Bono wrote the lyrics in response to the notion that it is possible to identify a person 's religion and income based on the street on which they lived , particularly in Belfast . During the band 's difficulties recording the song , producer Brian Eno considered <unk> the song 's tapes to have them start from scratch . 
- " Where the Streets Have No Name " was praised by critics and became a commercial success , peaking at number thirteen in the US , number fourteen in Canada , number ten in the Netherlands , and number four in the United Kingdom . The song has remained a staple of their live act since the song debuted in 1987 on The Joshua Tree Tour . The song was performed on a Los Angeles <unk> for the filming of its music video , which won a Grammy Award for Best Performance Music Video . 
- 
- = = Writing and recording = = 
- 
- The music for " Where the Streets Have No Name " originated from a demo that guitarist The Edge composed the night before the group resumed The Joshua Tree sessions . In an upstairs room at <unk> House — his newly purchased home — The Edge used a four @-@ track tape machine to record an arrangement of keyboards , bass , guitar , and a drum machine . <unk> that the album sessions were approaching the end and that the band were short on exceptional live songs , The Edge wanted to " <unk> up the ultimate U2 live @-@ song " , so he imagined what he would like to hear at a future U2 show if he were a fan . After finishing the rough mix , he felt he had come up with " the most amazing guitar part and song of [ his ] life " . With no one in the house to share the demo with , The Edge recalls dancing around and punching the air in celebration . 
- Although the band liked the demo , it was difficult for them to record the song . <unk> Adam Clayton said , " At the time it sounded like a foreign language , whereas now we understand how it works " . The arrangement , with two time signature shifts and frequent chord changes , was <unk> many times , but the group struggled to get a performance they liked . According to co @-@ producer Daniel Lanois , " that was the science project song . I remember having this massive schoolhouse <unk> , as we call them . I was holding a pointer , like a college professor , walking the band through the chord changes like a fucking <unk> . It was ridiculous . " Co @-@ producer Brian Eno estimates that half of the album sessions were spent trying to record a suitable version of " Where the Streets Have No Name " . The band worked on a single take for weeks , but as Eno explained , that particular version had a lot of problems with it and the group continued trying to fix it up . Through all of their work , they had gradually replaced each instrument take until nothing remained from the original performance . 
- So much time had been spent on " <unk> work " that Eno thought it would be best to start from scratch . His idea was to " stage an accident " and have the song 's tapes erased . He said that this was not to force abandonment of the song , but rather that it would be more effective to start again with a fresh performance . At one point , Eno had the tapes <unk> up and ready to be recorded over , but this <unk> never took place ; according to engineer Flood , fellow engineer Pat McCarthy returned to the control room and upon seeing Eno ready to erase the tapes , dropped the <unk> of tea he was carrying and physically restrained Eno . 
- The studio version of the song was compiled from several different takes . It was one of several songs mixed by Steve <unk> in the final months of recording The Joshua Tree . Drummer Larry Mullen , Jr. later said of the song , " It took so long to get that song right , it was difficult for us to make any sense of it . It only became a truly great song through playing live . On the record , musically , it 's not half the song it is live . " 
- 
- = = Composition = = 
- 
- " Where the Streets Have No Name " is played at a tempo of 126 beats per minute . The introduction and <unk> are played in a 3 / 4 time signature , while the remainder of the song is in a common 4 / 4 signature . The songs opens with an instrumental section , starting with <unk> @-@ like sustained <unk> notes . The guitar fades in after 42 seconds ; this part consists of a repeated " <unk> " six @-@ note <unk> . A " dotted eighth " delay effect is used to " play " each note in the <unk> twice , thus creating a rich sound . The bass and drums enter at 1 : 10 . 
- The introduction , following a I – IV – I – IV – vi – V – I chord progression , creates a " wall of sound " , as described by Mark Butler , against which the vocals emerge after nearly two minutes . The guitar part played for the remainder of the song features The Edge strumming <unk> sixteenth notes . The bass and drums continue in regular eighth and sixteenth notes , respectively , while Bono 's vocal performance , in contrast , varies greatly in its <unk> , ( " he <unk> ; he <unk> ; he <unk> ; he <unk> <unk> ; he allows his voice to crack " ) as well as timing by his usage of <unk> to slightly offset the notes he sings from the beat . 
- This development reaches a climax during the first chorus at the line " burning down love " ( A – G – F ♯ – D ) ; the melody progresses through a series of scale degrees that lead to the highest note in the song , the <unk> at " burning " . In later choruses , Bono sings " blown by the wind " with the same melody , stretching the same note even longer . After the third chorus , the song 's <unk> is played , the instrumentation reverting to the same state as it was in the introduction , with a six @-@ note guitar <unk> played against sustained <unk> notes . 
- 
- = = Lyrics = = 
- 
- The lyrics were inspired by a story that Bono heard about the streets of Belfast , Northern Ireland , where a person 's religion and income are evident by the street they live on . He contrasted this with the <unk> he felt when visiting Ethiopia , saying : " ... the guy in the song recognizes this contrast and thinks about a world where there aren 't such divisions , a place where the streets have no name . To me , that 's the way a great rock ' n ' roll concert should be : a place where everyone comes together ... Maybe that 's the dream of all art : to break down the barriers and the divisions between people and touch upon the things that matter the most to us all . " Bono wrote the lyrics while on a humanitarian visit to Ethiopia with his wife , Ali Hewson ; he first wrote them down on an <unk> bag while staying in a village . 
- According to him , the song is ostensibly about " <unk> , elevation , whatever you want to call it . " Bono , who compared many of his lyrics prior to The Joshua Tree to " sketches " , said that " ' Where the Streets Have No Name ' is more like the U2 of old than any of the other songs on the LP , because it 's a sketch — I was just trying to sketch a location , maybe a spiritual location , maybe a romantic location . I was trying to sketch a feeling . " 
- The open @-@ ended nature of the lyrics has led to many interpretations . Journalist Michael Campbell believed the lyrics send " a message of hope " and wish for a " world that is not divided by class , wealth , race , or any other arbitrary criterion " . With regard to the place Bono was referring to in the song , he said , " I 'm not sure , really , about that . I used to think it was Belfast ... " Journalist Niall Stokes believes the title was influenced by Bono 's and his wife Ali 's visit to Ethiopia as volunteer aid @-@ workers . Bono has expressed mixed opinions about the open @-@ ended lyrics : " I can look at it now and recognize that [ the song ] has one of the most <unk> couplets in the history of pop music . But it also contains some of the biggest ideas . In a curious way , that seems to work . If you get any way heavy about these things , you don 't communicate . But if you 're flip or <unk> about it , then you do . That 's one of the <unk> I 've come to terms with . " 
- 
- = = Release = = 
- 
- Originally , the third single from The Joshua Tree was meant to be the song " Red Hill <unk> Town " , but " Where the Streets Have No Name " was released instead , in August 1987 . The single was released on 7 @-@ inch , 12 @-@ inch , cassette and CD single formats . Three B @-@ sides were featured on the single , including " Race Against Time " , " Silver and Gold " , and " <unk> Thing " , except for the 7 @-@ inch release , which only featured the latter two tracks . The 12 @-@ inch single featured " Race Against Time " on side A of the record ( despite being a " B @-@ side " ) , and the cassette single featured all four tracks on both sides of the tape . Although not as successful as the album 's first two singles , the song did chart well . In the U.S. , the song peaked at number 13 on the Billboard Hot 100 and number 11 on the Album Rock Tracks charts . The song reached number four on the UK Singles Chart , and it topped the Irish Singles Chart . 
- 
- = = = Music video = = = 
- 
- The video begins with an aerial shot of a block in Los Angeles , and clips of radio broadcasts are heard with disc <unk> stating that U2 is planning on performing a concert downtown and expecting crowds of 30 @,@ 000 people . Police show up to the set and inform the band 's crew of the security issue that the film shoot is causing , due to the large number of people who are coming to watch the performance . Two minutes into the video , U2 are seen on the roof of a liquor store at the corner of 7th <unk> and S. Main St. , and perform " Where the Streets Have No Name " to a large crowd of people standing in the streets surrounding the building . Towards the end of the song , the police tell the crew that the performance is about to be shut down , and eventually police walk onto the roof while the crowd are <unk> the police . 
- The video for " Where the Streets Have No Name " was directed by <unk> Avis and produced by Michael <unk> and Ben <unk> . The band attracted over 1 @,@ 000 people during the video 's filming , which took place on the <unk> of a liquor store in Downtown Los Angeles on 27 March 1987 . The band 's performance on a <unk> in a public place was a reference to The Beatles ' final concert , as depicted in the film Let It Be . 
- During the shoot U2 played an eight @-@ song set , which included four performances of " Where the Streets Have No Name " . Prior to filming , a week was spent reinforcing the roof of the liquor store to ensure it would not collapse if it were to be <unk> by a group of fans . A backup generator was put on the roof so the shooting could continue in the event that the authorities shut off the power on the primary generator , which happened during filming . 
- The depiction of the police attempting to shut down the video shoot due to safety concerns actually happened during filming , just as seen in the video . <unk> was almost arrested following a confrontation with the police . According to Avis , the events depicted in the video show what actually happened that day " almost in real time " , and that " getting <unk> was an integral part of the plan . " Band manager Paul McGuinness revealed in 2007 that much of the confrontation with the police was exaggerated ; the group were hoping to get shut down by the authorities in order to <unk> the music video , but the police continually gave them extensions for shooting the video . In the background of the video is a sign for The Million Dollar Hotel , which was rebuilt to create some interest , in case no one showed up at the film shoot . Although the video is of a live performance , the audio used is from the studio @-@ recorded version of the song . The video won the Grammy Award for Best Performance Music Video at the 1989 Grammy Awards . 
- 
- = = = B @-@ sides = = = 
- 
- " Race Against Time " was released on the 12 @-@ inch , cassette , and CD versions of the single . The song developed from the band 's interest in urban funk , and was described by The Edge as " a kind of Afro @-@ rhythmic piece " and " a study in rhythm . " The bass riff in the song , inspired by the <unk> , was played by The Edge , but stemmed from some of Clayton 's unused bass parts . Mullen 's drum part was recorded in a single take . The song is primarily an instrumental piece but does contain some lyrics inspired by Bono 's trip to Ethiopia after Live Aid and his witnessing firsthand the famine in occurrence ; these lyrical references include Bono singing in an Ethiopian language and following it with the phrase " Race against time " . Bono said of the song , " It reminds me of the desert . The desert is so empty , but it <unk> with a strange kind of <unk> . " John Hutchinson of Musician magazine described the song as having an " African flavour " and as being reminiscent of Peter Gabriel . The track was used in the Miami Vice episode " Child 's Play " , and is the only one of the single 's B @-@ sides that was never played live . 
- " Silver and Gold " was written in support of the Artists United Against <unk> project , which protested the South African apartheid . In 1985 , Bono participated in Steven Van <unk> 's anti @-@ apartheid Sun City project and spent time with Keith Richards and Mick Jagger of The Rolling Stones . When Richards and Jagger played blues , Bono was embarrassed by his lack of familiarity with the genre , as most of U2 's musical knowledge began with punk rock in their youth in the mid @-@ 1970s . Bono realised that U2 " had no tradition " , and he felt as if they " were from outer space " . This inspired him to write the blues @-@ influenced song " Silver and Gold " , which he recorded with Richards and Ronnie Wood . It was re @-@ recorded by U2 for the " Where the Streets Have No Name " single while the band returned to Dublin during in May 1987 during a break between the first and second legs of The Joshua Tree Tour . The song was described by Musician as " tough and raw , with Bono in <unk> and confident voice , <unk> by a <unk> bass line , and with The Edge demonstrating his newfound prowess in blues @-@ based guitar . " " Silver and Gold " was played live on The Joshua Tree Tour several times , one performance of which was featured on the band 's 1988 album and <unk> , Rattle and Hum . Both the studio recording and the Sun City versions were later featured on the bonus disc of the 20th anniversary edition of The Joshua Tree . The studio version was also included on the limited edition B @-@ sides bonus disk of the band 's first compilation album , The Best of 1980 – 1990 . 
- " <unk> Thing " was written by Bono as an apology to his wife for forgetting her birthday . The song opens with a short piano piece before the rest of the band begins to play . Some of Bono 's lyrics have been described as reminiscent of John Lennon . The Edge described it as " a beautiful song ... which is pop as it should be — not produced out of existence , but pop produced with a real intimacy and purity " , also noting that " It 's very new for us . " It was re @-@ recorded with some lyrical alterations and released in 1998 as a single in its own right for The Best of 1980 – 1990 . Hot Press editor Niall Stokes stated that this track , along with " Race Against Time " , is " an indicator of what U2 might have made instead of The Joshua Tree . " 
- 
- = = Critical reception = = 
- 
- Upon the release of The Joshua Tree , critics praised " Where the Streets Have No Name " . Steve <unk> of The Boston Globe noted the " bell @-@ like tones from the Edge <unk> [ e ] a search for heaven " and along with the subsequent track on the album , " I Still Haven 't Found What I 'm Looking For " , these songs showed how the group were " pilgrims still on a quest ; not preachers who claim to have found answers " . The Bergen Record echoed these sentiments , saying the tracks demonstrated how the band was on a personal and spiritual quest . Rolling Stone called it " assertive rock " in their review of The Joshua Tree . The San Diego Union @-@ Tribune said of " Where the Streets Have No Name " , " the music charges , like someone fleeing for life " . The Washington Post said the track is " a bit oblique lyrically , but the implications are clear in Bono 's resolute delivery , Dave ( the Edge ) Evan 's <unk> guitar , Adam Clayton 's cathedral bass and Larry Mullen 's rolling thunder drums " . 
- NME lauded the song as the opening track by saying the album " starts by spitting furiously " . The publication praised Bono 's impassioned singing and The Edge 's guitar playing , which transformed the instrument into " something more than an <unk> abused piece of wood " . The review commented that the " last ten seconds are <unk> beautiful " . The Rocket wrote that the song builds a " wall of sound " that Bono 's vocals cut through with a " <unk> of <unk> , as the lyrics <unk> the need for personal spirituality " . The reviewer compared the opening riff to <unk> <unk> ' " <unk> " . Reviewing The Joshua Tree , Stephen Thomas Erlewine of Allmusic called the song an " epic opener " . The service 's Steve Huey , in a review of the song , praised its " <unk> , propulsive rhythmic drive and <unk> chorus " , qualities he singled out for making it a fan favorite . He called the song the " perfect album @-@ opener " , <unk> the " slow build of its arrangement toward a climactic peak " . Huey also called Bono 's delivery " passionate and grandiose " and " his commitment to the material <unk> " . He believed the combination of his vocals and the band 's " sonic power " is what gave U2 its " tremendous force " . 
- 
- = = Live performances = = 
- 
- " Where the Streets Have No Name " made its concert debut on 2 April 1987 in <unk> , Arizona on the opening night of The Joshua Tree Tour . It has since been played at nearly every full @-@ length concert that U2 has <unk> , totaling over 700 performances as of 2011 . The song is widely regarded as one of the group 's most popular live songs . Bono said of it , " We can be in the middle of the worst gig in our lives , but when we go into that song , everything changes . The audience is on its feet , singing along with every word . It 's like God suddenly walks through the room . " 
- On The Joshua Tree Tour , " Where the Streets Have No Name " was most often used to open concerts . Fans and critics responded favourably to the song in a live setting . The San Diego Union @-@ Tribune wrote that , " From the <unk> sonic opening strains of [ the song ] , this audience was up , <unk> and <unk> . " NME wrote that the song is one such occasion where " the power afforded their songs is scary " , noting that during the song 's opening , " the arena <unk> " . In other reviews , the song was called : " <unk> " , " <unk> " , and " powerful " . Out of the 109 shows during The Joshua Tree Tour , " Streets " was played at all except 12 of the concerts . During the <unk> Tour which took place in 1989 and the beginning of 1990 , " Streets " was only left out of the set list at one of the 47 concerts . 
- The song was performed at every show on the 1992 – 1993 Zoo TV Tour . Concerts from this tour were elaborate <unk> <unk> that Bono performed as a variety of characters , but for the end of the main set , the group reverted to playing classics , including " Where the Streets Have No Name " , straight . Some of these performances of the song were accompanied by footage of the group in the desert from The Joshua Tree 's photo shoot . The video was <unk> up for humorous effect — NME described the effect as giving it a " silly , Charlie <unk> quality " — and Bono often acknowledged his younger self on the video screens . This video would make a return during performances on the 2010 and 2011 legs of the U2 360 ° Tour . Some of the Zoo TV performances of the song had a more electronic dance music arrangement that bore a resemblance to the Pet Shop Boys ' synthpop cover of the song ( titled " Where the Streets Have No Name ( I Can 't Take My Eyes off You ) " ) . Bono parodied this by occasionally adopting the deadpan vocal style used in the Pet Shop Boys ' cover . Critics welcomed the song in the group 's setlist : The Independent said the song " <unk> instant euphoria , as U2 do what they 're best at , slipping into epic rock mode , playing music made for the arena " . In two other local newspaper reviews , critics praised the song 's inclusion in a sequence of greatest hits . 
- For the PopMart Tour of 1997 – 1998 , U2 returned to the electronic dance arrangement they occasionally played on the Zoo TV Tour . The set 's massive video screen displayed a video that Hot Press described as an " astonishing , 2001 @-@ style trip into the heart of a <unk> , psychedelic tunnel that sucks the audience in towards a horizontal <unk> " . Near the end of the song , peace <unk> were shown on the screen and bright beams of light flanking the set 's golden arch were projected upwards . Hot Press said the effect transformed the stadium into a " UFO landing site " . 
- Shortly before the third leg of the <unk> Tour , the September 11 attacks occurred in New York City and Washington D.C. During the band 's first show in New York City following the attacks , the band performed " Where the Streets Have No Name " , and when the stage lights illuminated the audience , the band saw tears streaming down the faces of many fans . The experience was one inspiration for the song " City of <unk> Lights " . The band paid tribute to the 9 / 11 victims during their performance of the song at the Super Bowl <unk> halftime show on 3 February 2002 . The performance featured the names of the September 11 victims projected onto a large white banner behind the band . U2 's appearance was later ranked number 1 on Sports Illustrated 's list of " Top 10 Super Bowl <unk> <unk> " . 
- For the Vertigo Tour , the group originally considered dropping the song from their <unk> , but Mullen and Clayton successfully argued against this . All 131 of the Vertigo Tour concerts featured a performance of the song , which were accompanied by the stage 's LED video <unk> displaying African flags . On the tour 's opening night , this reminded Bono that he had originally written the lyrics in an Ethiopian village . He thought this visual accompaniment made the song come full circle , saying , " And here it was , nearly twenty years later , coming back to Africa , all the stuff about <unk> lands and <unk> making sense for the first time . " The song was also played at the preview screening of the band 's concert film U2 3D at the 2007 Cannes Film Festival . At the Glastonbury Festival 2010 , The Edge accompanied rock band <unk> for a live cover version of the track , later playing it with U2 while headlining Glastonbury in 2011 . 
- Live performances of " Where the Streets Have No Name " appear in the concert video releases Rattle and Hum , Zoo TV : Live from Sydney , and PopMart : Live from Mexico City , as well as the respective audio releases of the latter two concerts , Zoo TV Live and Hasta la Vista Baby ! U2 Live from Mexico City . A second version from the PopMart Tour was featured on Please : <unk> Live EP , and later on the U.S. " Please " single . A live recording from Boston during the <unk> Tour was featured in the concert film <unk> 2001 : Live from Boston , and on the " Walk On " and " Electrical Storm " singles . The concert video and album U2 Go Home : Live from <unk> Castle , Ireland featured another performance from the <unk> Tour , and later performances were featured in the concert films Vertigo 2005 : Live from Chicago and U2 3D ( Vertigo Tour ) , and U2 360 ° at the Rose Bowl ( U2 360 ° Tour ) . The 2004 digital album , Live from the Point Depot , contains a performance from the <unk> Tour , only available as part of The Complete U2 digital box set . 
- 
- = = Legacy = = 
- 
- In 2002 , Q magazine named " Where the Streets Have No Name " the <unk> " most exciting tune ever " . The following year , Q ranked the song at number <unk> in a special edition titled " <unk> Best Songs Ever " . Three years later , the magazine 's readers voted the track the 43rd @-@ greatest song in history . Rolling Stone ranked the song at number 28 on its list of the " 100 Greatest Guitar Songs of All Time . " In a 2010 poll by fan site <unk> U2 , approximately 29 % of 4 @,@ 800 <unk> named " Where the Streets Have No Name " as their favourite song from The Joshua Tree , ranking it as the most popular song from the album . In 2010 , American sports network ESPN used the track , among other U2 songs , in commercials for the 2010 FIFA World Cup , with the South African <unk> Gospel Choir adding vocals to the song . The advertisement , titled " <unk> Island " , portrays South African political prisoners during the apartheid era forming a soccer team . There are plans to bring a recording of the <unk> @-@ supported performance to retail . Composer John <unk> used the introductory guitar theme in his 2009 piece for wind ensemble , Aurora <unk> . The song 's introduction was used by the Vancouver Canucks hockey team at the start each of their home games , when the players take to the ice . The Baltimore <unk> also use the song as they enter M & T Bank Stadium before home games . The Marquette Golden Eagles men 's basketball team uses this song in their entrance video before all home games at the <unk> Harris Bradley Center . The song is also used by the Wisconsin <unk> football team for their entrance video at Camp Randall Stadium prior to all home games . The Kansas <unk> men 's basketball team uses the song during their starting <unk> at Allen <unk> . 
- 
- = = Track listing = = 
- 
- 
- = = Personnel = = 
- 
- 
- = = Charts = = 
- 
- 
- 
- = <unk> <unk> reaction = 
- 
- The <unk> <unk> reaction ( often referred to as simply the <unk> <unk> ) is a chemical reaction used in organic chemistry for the synthesis of <unk> . The reaction is typically divided into classical and modern variants , depending on the reagents and substrates employed . It was originally discovered by Ivan <unk> <unk> ( 1906 – 1957 ) in 1941 while studying the <unk> of <unk> vinyl <unk> . 
- As originally described , the <unk> <unk> involves the activation of a <unk> <unk> using a stoichiometric Lewis acid or <unk> acid promoter . The key step of the reaction mechanism involves a <unk> <unk> @-@ <unk> ring closure which forms the <unk> product ( See <unk> below ) . As the reaction has been developed , variants involving substrates other than <unk> <unk> and <unk> other than Lewis acids have been subsumed under the name <unk> <unk> provided that they follow a similar <unk> pathway . 
- The success of the <unk> <unk> as a tool in organic synthesis stems from the utility and <unk> of <unk> as both motifs in natural products ( including <unk> , the <unk> , and a <unk> of <unk> ) and as useful synthetic <unk> for total synthesis . The reaction has been used in several total syntheses and several reviews have been published . 
- 
- = = <unk> = = 
- 
- The mechanism of the classical <unk> <unk> reaction was first demonstrated experimentally by <unk> to be an <unk> <unk> and is outlined below . <unk> of the <unk> by the acid catalyst generates a <unk> cation which undergoes a <unk> allowed <unk> <unk> <unk> as dictated by the Woodward @-@ Hoffman rules . This generates an <unk> cation which undergoes an elimination reaction to lose a β @-@ hydrogen . Subsequent <unk> of the <unk> produces the <unk> product . 
- As noted above , variants that deviate from this template are known ; what <unk> a <unk> <unk> in particular is the generation of the <unk> cation followed by <unk> ring closure to an <unk> cation . In order to achieve this transformation , the molecule must be in the s @-@ trans / s @-@ trans conformation , placing the vinyl groups in an appropriate orientation . The <unk> of the system to enter this conformation dramatically influences reaction rate , with α @-@ substituted substrates having an increased population of the <unk> <unk> due to <unk> strain . <unk> of an electron <unk> α @-@ <unk> by the catalyst can likewise increase the reaction rate by enforcing this conformation . 
- Similarly , β @-@ substitution directed inward <unk> the s @-@ trans conformation so severely that E @-@ Z <unk> has been shown to occur in advance of <unk> on a wide range of substrates , yielding the trans <unk> regardless of initial configuration . In this way , the <unk> <unk> is a rare example of a <unk> <unk> reaction , whereas most <unk> are <unk> . The example below uses <unk> hydride to trap the <unk> cation so that no elimination occurs . ( See <unk> <unk> below ) 
- Along this same vein , <unk> vinyl <unk> of the type studied extensively by Marcus <unk> of the University of Hawaii show dramatic rate acceleration due to the removal of β @-@ <unk> , <unk> a large amount of <unk> strain in the s @-@ <unk> <unk> . 
- 
- = = Classical <unk> <unk> = = 
- 
- Though <unk> following the general template above had been observed prior to <unk> 's involvement , it was his study of the <unk> of <unk> vinyl <unk> that marked the first major examination of this process . <unk> correctly reasoned that the <unk> <unk> <unk> in situ to form a <unk> <unk> before ring closure to the <unk> product . The reaction shown below involves an <unk> <unk> reaction to generate the <unk> <unk> . 
- Research involving the reaction was relatively quiet in subsequent years , until in the mid @-@ 1980s when several syntheses employing the <unk> <unk> were published . <unk> below are key steps in the syntheses of <unk> and Nor @-@ <unk> , the latter of which is thought to proceed via an unusual <unk> @-@ <unk> <unk> that generates the <unk> <unk> . 
- 
- = = = <unk> = = = 
- 
- The classical version of the <unk> <unk> suffers from several drawbacks which modern variants attempt to <unk> . The first two are not evident from the mechanism alone , but are indicative of the barriers to <unk> ; the last three stem from selectivity issues relating to elimination and <unk> of the intermediate . 
- Strong Lewis or <unk> acids are typically required for the reaction ( e.g. <unk> , <unk> , <unk> ) . These <unk> are not compatible with sensitive functional groups , limiting the substrate scope . 
- Despite the <unk> possibility for catalysis , multiple equivalents of the promoter are often required in order to effect the reaction . This limits the atom economy of the reaction . 
- The elimination step is not <unk> ; if multiple β @-@ <unk> are available for elimination , various products are often observed as mixtures . This is highly undesirable from an efficiency standpoint as <unk> separation is typically required . 
- <unk> destroys a potential <unk> , decreasing the potential usefulness of the reaction . 
- <unk> of the <unk> is sometimes not <unk> , meaning that products can be formed as mixtures of <unk> . 
- 
- = = Modern variants = = 
- 
- The shortcomings noted above limit the usefulness of the <unk> <unk> reaction in its canonical form . However , modifications to the reaction focused on <unk> its issues continue to be an active area of academic research . In particular , the research has focused on a few key areas : rendering the reaction catalytic in the promoter , <unk> the reaction with more mild <unk> to improve functional group tolerance , directing the <unk> of the elimination step , and improving the overall <unk> . These have been successful to varying degrees . 
- Additionally , modifications focused on altering the progress of the reaction , either by generating the <unk> cation in an unorthodox fashion or by having the <unk> cation " intercepted " in various ways . Furthermore , enantioselective variants of various kinds have been developed . The sheer volume of literature on the subject prevents a comprehensive examination of this field ; key examples are given below . 
- 
- = = = <unk> @-@ directed <unk> = = = 
- 
- The earliest efforts to improve the selectivity of the <unk> <unk> took advantage of the β @-@ silicon effect in order to direct the <unk> of the elimination step . This chemistry was developed most extensively by Professor Scott Denmark of the University of Illinois , <unk> @-@ <unk> in the mid @-@ 1980s and utilizes stoichiometric amounts of iron <unk> to promote the reaction . With <unk> products , the <unk> <unk> was selected for to varying degrees . 
- The silicon @-@ directed <unk> <unk> reaction was subsequently employed in the synthesis of the natural product <unk> , shown below . The <unk> takes place before elimination of the <unk> alcohol <unk> , so that the resulting <unk> of the newly formed ring arises from approach of the <unk> <unk> anti to the <unk> . 
- 
- = = = <unk> = = = 
- 
- Drawing on the <unk> effects compiled over various trials of the reaction , Professor Alison Frontier of the University of Rochester developed a paradigm for " polarized " <unk> <unk> in which electron <unk> and electron withdrawing groups are used to improve the overall selectivity of the reaction . Creation of an effective vinyl <unk> and vinyl <unk> in the substrate allows catalytic activation with copper <unk> and <unk> elimination . In addition , the electron withdrawing group increases the <unk> of the α @-@ proton , allowing selective formation of the trans @-@ α @-@ <unk> via <unk> . 
- It is often possible to achieve catalytic activation using a <unk> or withdrawing group alone , although the efficiency of the reaction ( yield , reaction time , etc . ) is typically lower . 
- 
- = = = Alternative cation generation = = = 
- 
- By extension , any <unk> cation regardless of its origin is capable of undergoing a <unk> <unk> . There have been a large number of examples published where the <unk> cation is arrived at by a variety of <unk> . One such example involves the silver <unk> <unk> ring opening of <unk> <unk> <unk> . The silver salt facilitates loss of chloride via precipitation of <unk> silver chloride . 
- In the total synthesis of <unk> , <unk> of a vinyl <unk> <unk> likewise generates a <unk> cation via ring opening of the resultant <unk> . 
- 
- = = = <unk> <unk> = = = 
- 
- Once the <unk> has occurred , an <unk> cation is formed . As discussed extensively above , the typical course for this intermediate is elimination followed by <unk> <unk> . However , these two steps can be interrupted by various <unk> and <unk> , respectively . <unk> cation trapping has been developed extensively by <unk> G. West of the University of Alberta and his review covers the field . The <unk> cation can be trapped with <unk> and carbon <unk> and can also undergo <unk> <unk> with various <unk> partners . <unk> below is a cascade reaction in which successive cation trapping generates a <unk> core in one step with complete diastereoselectivity . 
- <unk> trapping with various <unk> is <unk> less common . In one study , the <unk> <unk> is paired with a Michael reaction using an <unk> catalyst to initiate nucleophilic <unk> addition of the <unk> to β @-@ <unk> . In this tandem reaction the <unk> catalyst is required for both <unk> : it acts as the Lewis acid in the <unk> <unk> and in the next step the <unk> group of <unk> first coordinates to <unk> in a ligand exchange with the <unk> <unk> oxygen atom before the actual Michael addition takes place to the opposite face of the R @-@ group . 
- 
- = = = <unk> variants = = = 
- 
- The development of an enantioselective <unk> <unk> is a desirable addition to the repertoire of <unk> <unk> reactions . To that end , several variations have been developed utilizing <unk> <unk> and <unk> <unk> . <unk> <unk> are also known , in which extant <unk> direct the <unk> . Almost all of the attempts are based on the idea of <unk> ; selecting one direction for the vinyl groups to " <unk> " in turn sets the <unk> as shown below . 
- <unk> @-@ directed <unk> <unk> can exhibit induced diastereoselectivity in this way . In the example below , the <unk> @-@ group acts to direct the <unk> by preventing the distant <unk> from rotating " towards " it via unfavorable <unk> interaction . In this way the silicon acts as a <unk> auxiliary . ( The starting material is not <unk> but the retention of enantiomeric excess suggests that the auxiliary directs the <unk> . ) 
- <unk> 's <unk> substrates can exhibit axial to <unk> <unk> transfer if <unk> <unk> are used . The example below generates a <unk> <unk> in 64 % yield and 95 % enantiomeric excess . 
- <unk> has additionally developed a camphor @-@ based auxiliary for <unk> <unk> that was employed in the first asymmetric synthesis of <unk> . The key step employs an unusual mixture of <unk> @-@ 2 @-@ <unk> and <unk> as solvent . 
- The first <unk> Lewis acid promoted asymmetric <unk> <unk> was reported by <unk> Aggarwal and utilized copper ( II ) <unk> ligand complexes with up to 98 % ee . The enantiomeric excess was unaffected by use of 50 <unk> % of the copper complex but the yield was significantly decreased . 
- 
- = = Related Reactions = = 
- 
- <unk> of the <unk> <unk> are generally also subsumed under the same name . For example , an α @-@ β , γ @-@ <unk> <unk> <unk> can undergo a similar <unk> <unk> <unk> that is typically referred to as an <unk> @-@ <unk> <unk> reaction . Other such extensions have been given similar names , including <unk> @-@ <unk> <unk> and <unk> <unk> <unk> . 
- 
- = = = <unk> @-@ <unk> reaction = = = 
- 
- Because they <unk> the <unk> cation , β @-@ electron <unk> <unk> often severely impede <unk> <unk> . Building from this , several <unk> ring openings of β @-@ <unk> <unk> have been reported . These are typically referred to as retro @-@ <unk> <unk> reactions . 
- 
- = = = <unk> @-@ <unk> reaction = = = 
- 
- <unk> <unk> of the <unk> <unk> reaction ( known as <unk> @-@ <unk> <unk> reactions ) have few instances ; there is one example of a <unk> <unk> @-@ <unk> <unk> reported ( shown below ) , and several <unk> @-@ <unk> @-@ <unk> reactions in the literature . Even these tend to suffer from poor <unk> , poor yields , or narrow scope . The difficulty stems from the relative over @-@ stabilization of the <unk> cation by electron donation , <unk> <unk> . 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> is a 2011 American black comedy film directed by Seth Gordon , written by Michael <unk> , John Francis <unk> and Jonathan Goldstein , based on a story by <unk> . It stars Jason Bateman , Charlie Day , Jason Sudeikis , Jennifer Aniston , Colin Farrell , Kevin Spacey , and Jamie <unk> . The plot follows three friends , played by Bateman , Day , and Sudeikis , who decide to murder their respective overbearing , abusive bosses , portrayed by Spacey , Aniston and Farrell . 
- <unk> 's script was bought by New Line Cinema in 2005 and the film spent six years in various states of pre @-@ production , with a variety of actors attached to different roles . By 2010 , Goldstein and <unk> had rewritten the script , and the film finally went into production . 
- The film premiered in Los Angeles on June 30 , 2011 , and received a wide release on July 8 , 2011 . The film exceeded financial expectations , <unk> over $ 28 million in the first three days , making it the number two film in the United States during its opening weekend , and going on to become the highest @-@ grossing black comedy film of all time in <unk> dollars , breaking the record previously set by The War of the Roses in 1990 . The film grossed over $ <unk> million worldwide during its theatrical run . 
- The film opened to positive critical reception , with several critics praising the ensemble cast , with each lead being singled out for their performances across reviews . The plot received a more mixed response ; some reviewers felt that its dark , humorous premise was explored well , while others felt the jokes were racist , <unk> , and <unk> . A sequel , <unk> <unk> 2 , was released on November 26 , 2014 . 
- 
- = = Plot = = 
- 
- Nick <unk> ( Bateman ) and Dale <unk> ( Day ) are friends who <unk> their bosses . Nick works at a financial firm for the sadistic David <unk> ( Spacey ) , who implies the possibility of a promotion for Nick for months , only to award it to himself . Dale is a <unk> assistant being sexually harassed by his boss , Dr. Julia Harris ( Aniston ) ; she threatens to tell his <unk> Stacy ( Lindsay <unk> ) that he had sex with her unless he actually has sex with her . Nick and Dale 's <unk> friend Kurt <unk> ( Sudeikis ) enjoys working for Jack <unk> ( Donald Sutherland ) at a chemical company , but after Jack unexpectedly dies of a heart attack , the company is taken over by Jack 's cocaine @-@ addicted son Bobby ( Farrell ) , whose apathy and incompetence threaten the future of the company . 
- At night , over drinks , Kurt <unk> suggests that their lives would be <unk> if their bosses were no longer around . Initially hesitant , they eventually agree to kill their employers . In search of a <unk> , the trio meet Dean " <unk> " Jones ( <unk> ) , an ex @-@ con who agrees to be their " murder consultant " . Jones suggests that Dale , Kurt and Nick kill each other 's bosses to hide their motive while making the deaths look like accidents . 
- The three <unk> Bobby 's house , and Kurt steals Bobby 's phone . They next go to <unk> 's house , where Kurt and Nick go inside while Dale <unk> in the car . <unk> returns home and confronts Dale for <unk> , but then has an allergy attack from the <unk> butter on the litter . Dale saves <unk> by stabbing him with an <unk> . Nick and Kurt think Dale is stabbing <unk> to death and flee , with Kurt accidentally dropping Bobby 's phone in <unk> 's bedroom . The next night , Kurt watches Julia 's home , but she <unk> and has sex with him . Nick and Dale <unk> wait outside Bobby 's and <unk> 's houses , respectively , to commit the murders , despite neither of them wanting to . <unk> discovers Bobby 's <unk> in his bedroom and uses it to find his address , <unk> his wife <unk> ( Julie Bowen ) is having an affair . He drives over and kills Bobby , with Nick as a secret witness . 
- Nick flees at high speed , setting off a traffic camera . The trio meet to discuss their reservations about continuing with their plan . They are arrested by the police , who believe the camera footage makes them suspects in Bobby 's murder . Lacking evidence , the police are forced to let the trio go free . The trio consult with Jones again , but learn that he never actually killed anyone , having been imprisoned for <unk> the film Snow Falling on <unk> . Jones suggests that they get <unk> to confess and secretly tape it . The three accidentally crash <unk> 's surprise birthday party , where Nick and Dale get <unk> to confess to the murder before realizing that Kurt , who has the audio recorder , is elsewhere having sex with <unk> . <unk> threatens to kill all three for attempting to blackmail him . They flee by car , but <unk> gives chase and repeatedly rams their vehicle . Believing they have committed a crime , the car 's navigation @-@ system operator <unk> <unk> Kurt 's car , allowing <unk> to catch and hold them at <unk> . <unk> shoots himself in the leg as he <unk> about his plan to frame them for murdering Bobby and attempting to kill him to get rid of the witness . 
- The police arrest Nick , Dale and Kurt , but the navigation @-@ system operator , Gregory , reveals that it is his companies policy to record all conversations for quality assurance . Gregory plays the tape that has <unk> <unk> he murdered <unk> . <unk> is sentenced to 25 years to life in prison , while the friends get their charges waived . Nick is promoted to president of the company under a sadistic CEO , Kurt retains his job under a new boss , and Dale blackmails Julia into ending her harassment by convincing her to sexually harass a supposedly unconscious patient , while Jones secretly records the act . 
- 
- = = Cast = = 
- 
- Jason Bateman as Nick <unk> 
- An executive at a financial firm who is manipulated into jumping through <unk> in order to get a promotion that his boss never intended to give him . <unk> wrote the role specifically for Bateman . 
- Charlie Day as Dale <unk> 
- A <unk> assistant who is sexually harassed by his boss . Described as a " <unk> romantic " in love with his fiancée . Ashton <unk> was in talks for the role at two different points in the lengthy production . Day was considered for the role following his co @-@ starring performance with Sudeikis in the 2010 film Going the <unk> — Reuters reported that industry <unk> believed his performance overshadowed the main stars . 
- Jason Sudeikis as Kurt <unk> 
- An account manager at a chemical company dealing with a new , drug @-@ addicted boss after his beloved former boss dies . Sudeikis was cast in May 2010 . 
- Jennifer Aniston as Dr. Julia Harris , <unk> 
- <unk> based the character on a former boss , claiming she was " very sexually aggressive with everybody " . When writing the script , <unk> intended for the role to go to Aniston . He stated , " but [ the aforementioned boss ] looked more like <unk> de <unk> . It was like flirting with a <unk> . So I decided for the sake of the movie , let ’ s go with Jennifer Aniston . ” The actress insisted on wearing a brown wig for the role , wanting to look different from other characters she had played . 
- Colin Farrell as Bobby <unk> 
- Described as a " <unk> <unk> " and a " corrupt and incompetent <unk> who 's in charge of things but clearly has no idea what he 's doing . " Farrell explained the motivation he gave to the character , stating " This guy thinks he 's God 's gift to women , God 's gift to <unk> , to humor , to the club scene , to everything . It 's all part of his grandiose sense of self @-@ esteem , which is probably <unk> a deeper sense of being a disappointment to his father and being riddled with envy over the relationship his father had with Kurt , and all kinds of other things . With <unk> , Seth gave me complete license to act as <unk> <unk> up as possible . " Farrell contributed significantly to the appearance of his character , suggesting the comb over hairstyle , pot @-@ belly and an affinity for Chinese <unk> . 
- Kevin Spacey as David <unk> 
- President of <unk> Industries . Tom Cruise , Philip Seymour Hoffman and Jeff Bridges had been approached by New Line Cinema to take the role , described as a <unk> master <unk> with an attractive wife . Spacey signed up for the role in June 2010 . The part was considered " integral " to the film . Gordon commented that the character was an amalgamation of several real bosses ( rather than one single person ) to avoid being sued . 
- Jamie <unk> as Dean " <unk> " Jones 
- The character had the more " colorful " name " <unk> Jones " , but it was changed at <unk> 's request , with producer Jay Stern commenting that <unk> felt it " was over the line " . The current name was said to be subject to further change , prior to the release of the film . <unk> contributed to his character 's appearance , suggesting full @-@ <unk> tattoos and a retro clothing style . <unk> described the appearance as " a guy who maybe went to jail for a minute and now he 's living in his own time capsule . When he got out he went right back to the clothes he thought were hot when he went in . " 
- During the six @-@ year development of the film , several actors were in negotiations to star , including Owen Wilson , Vince <unk> , Matthew <unk> , Ryan Reynolds , Dax Shepard , and Johnny <unk> . 
- Donald Sutherland portrays Jack <unk> , Bobby 's father and Kurt 's boss . On July 27 , 2010 , Isaiah <unk> was confirmed as joining the cast . <unk> was quoted as saying " It 's a smaller role " . He appears as Officer <unk> . Julie Bowen appears in the film as <unk> , <unk> 's wife . Bowen stated that her character " may or may not be a <unk> " , the character described as intentionally making her husband jealous . <unk> Gruffudd has a cameo as a male prostitute erroneously hired as a <unk> . Lindsay <unk> appears as Dale 's <unk> Stacy . P. J. <unk> plays Kenny <unk> , a former investment manager , now <unk> for drinks , while Wendell Pierce and Ron White play a pair of <unk> . Bob <unk> makes a cameo as sadistic <unk> CEO Louis Sherman . John Francis <unk> , a screenwriter on the film , cameos as Nick 's co @-@ worker Carter . 
- 
- = = Production = = 
- 
- 
- = = = Development = = = 
- 
- <unk> 's script for <unk> <unk> was sold at auction to New Line Cinema by <unk> 's production company Rat Entertainment in 2005 for a six @-@ figure amount . <unk> initially was interested in directing , but became occupied with directing the comedy Tower <unk> . Frank <unk> and David <unk> were in talks to direct . Jonathan Goldstein and John Francis <unk> rewrote the script in 2010 , and the project went into production with Seth Gordon directing . 
- 
- = = = Design = = = 
- 
- Production designer Shepherd <unk> specifically set out to create distinctly different environments for the three employees and their respective bosses ' homes and offices . Nick and <unk> 's workplace is the " <unk> " bullpen , which was designed to " enhance the discomfort and anxiety of lower @-@ level employees clustered in the center of the room where every movement is monitored by the boss from his corner office . " The design team met with financial <unk> and management companies to learn about the architecture of their office layouts to visually represent the experience of starting from a low @-@ ranking position in a <unk> and aspiring to an office . Costume designer Carol Ramsey worked with <unk> and set <unk> Jan <unk> to match <unk> 's suit to that of the surrounding " cold grey and blue " color palette of his office . <unk> 's home was described as " equally lacking in warmth " as the office but more lavishly decorated and " for show " , including an intentionally oversized portrait of him with his " trophy wife " . 
- <unk> Julia 's office was described as a " challenge " , <unk> a " sensual vibe " into a <unk> office . <unk> approached the design through Julia 's mentality , stating , " She 's a Type A professional at the top of her game , who likes to play cat @-@ and @-@ mouse , so it 's a completely controlled environment , with <unk> and views into other rooms so she always knows what 's going on " . " It 's highly designed , with rich <unk> and tones , <unk> artwork and subtle lighting — all very <unk> till you step into her private office . The <unk> close , the door locks and you think , ' It 's the Temple of Doom . ' " Similarly approaching the character 's home , the design allowed for wide windows which face onto a public street " which afford her the opportunity to put on the kind of show she couldn 't get away with at work . " 
- Bobby 's environments were designed with more contrast , the character being new to the work area . <unk> described the contrast as " the company reflects [ Jack <unk> 's ] human touch , whereas [ Bobby <unk> 's ] home is a <unk> shrine to himself and his <unk> <unk> . " <unk> continued , " It features a <unk> of anything he finds exotic and erotic , mostly Egyptian and Asian motifs with an ' 80s Studio 54 vibe , a makeshift <unk> , lots of mirrors and a <unk> table . " Some parts of the house design were provided by Farrell and Gordon 's interpretation of the character and his " infatuation " with martial arts and " his <unk> of prowess " . 
- 
- = = = Filming = = = 
- 
- Filming of <unk> <unk> took place in and around Los Angeles . The production team attempted to find locations " that people haven 't already seen a hundred times in movies and on TV " , aiming for the film to appear as if it could be taking place anywhere in America " where people are trying to pursue the American dream but getting stopped by a horrible boss . " " <unk> " was represented by an office building in <unk> , California , with the crew building the set on a vacant floor . For " <unk> Chemical " , the production team found a " perfect landscape of pipes and containers " in Santa Fe Springs , surrounding an unoccupied water cleaning and storage facility . To take advantage of the surrounding imagery , the warehouse required an extensive series of <unk> , including cutting windows into concrete walls and creating new <unk> to allow for visuals of the warehouse exterior and provide a setting for the final scene of Sutherland 's character . A <unk> Friday 's in Woodland Hills , Los Angeles , was used as a bar frequented by Nick , Dale , and Kurt , while the bar scene where they meet with Jones was staged in downtown Los Angeles . 
- The film was shot digitally using the <unk> Genesis camera . Gordon encouraged the actors to improvise , though Aniston claimed to not have taken advantage of the offer as much as her co @-@ stars , stating , " My dialogue was just so beautifully choreographed that there wasn ’ t much that I needed to do ” . 
- 
- = = = Music = = = 
- 
- The soundtrack was composed by award @-@ winning composer Christopher <unk> , with music contributed by Mike <unk> of Pearl Jam , Stefan <unk> of Dave Matthews Band and Money Mark — a collaborator with the <unk> Boys . <unk> , <unk> , and Mark worked with musicians Matt Chamberlain , David <unk> , Aaron Kaplan , Victor <unk> , Chris <unk> , <unk> <unk> and DJ <unk> to develop the music . Major contributions were provided by Mark on keyboard , <unk> and <unk> on guitar , <unk> and <unk> on bass , <unk> on drums and DJ <unk> on <unk> . 
- <unk> recorded the soundtrack at The Village recording studio in West Los Angeles and Capitol Records . <unk> attempted to remain " authentic " to the characters ' progression from average worker to calculated killer . To achieve this aim , he decided against recording digitally , instead recording the tracks on two @-@ inch analog tape , intending each musical cue to sound as if it was emanating from a vinyl record . He explained , " The idea was to put together a band that would record the score together the same way that they would make an album . It isn 't over @-@ produced or <unk> and digital in any way . It 's <unk> , noisy , and full of <unk> and <unk> . I knew that if we could <unk> some of this sonic magic in the score , then the <unk> and confidence of the music would play against Bateman , Sudeikis , and Charlie Day to really emphasize and elevate the humor in the situations that <unk> . " <unk> continued , " We tracked through tape before Pro Tools to get that fat sound , and made every choice based on feel rather than perfection . We even used the same <unk> that Money Mark played on Beck 's classic ‘ Where It 's At ’ . At the end of the day , Seth [ Gordon ] and I wanted to produce a score that is as <unk> and full of attitude as the movie itself . I think we did it ... and most of all , everyone had a blast in the process . " 
- <unk> <unk> : The Original Motion Picture Soundtrack was released in physical and digital formats on July 5 , 2011 , by <unk> Music . The soundtrack consists of 33 tracks with a <unk> of 63 minutes . 
- 
- = = Release = = 
- 
- The world premiere of <unk> <unk> took place on June 30 , 2011 at <unk> 's Chinese Theatre in Hollywood , California . 
- 
- = = = Box office = = = 
- 
- <unk> <unk> grossed $ 117 @.@ 5 million ( 56 @.@ 1 % ) in the North America and $ 92 @.@ 2 million ( 43 @.@ 9 % ) in other territories for a worldwide gross of $ <unk> @.@ 6 million , against its budget of $ 35 million . 
- North America 
- <unk> <unk> was released on July 8 , 2011 in the United States and Canada across 3 @,@ <unk> theaters . It grossed $ <unk> on the first day , giving it the second largest opening gross for an original R @-@ rated comedy of the summer , behind Bad <unk> ( $ <unk> ) . For the opening weekend , the film took in a total of $ 28 @,@ 302 @,@ 165 , an average of $ 9 @,@ 310 per theater , making it the number two film for the weekend , behind <unk> : Dark of the Moon ( $ <unk> ) , the second highest @-@ grossing opening weekend for an original R @-@ rated comedy , again behind Bad <unk> ( $ <unk> ) , and the highest @-@ grossing opening weekend ever for a dark / black comedy film , <unk> the 2004 The <unk> Wives ( $ <unk> ) . The opening weekend audience was 51 % male , and 64 % of the audience were over 25 years of age . The second weekend ( 15 – 17 July ) saw a further 94 theaters added , for a total of 3 @,@ 134 . Box office revenue dropped by a " respectable " 38 % , taking $ <unk> for a total gross of $ <unk> in ten days . The film ranked third for the weekend . It remained in the top five films during its third week , dropping 33 % and leaving thirty theaters from the previous weekend , to take in $ <unk> for a gross of $ <unk> in 17 days . On July 28 , 2011 , with $ <unk> after 20 days , <unk> <unk> surpassed The War of the Roses ( $ <unk> ) to become the highest grossing dark / black comedy film in <unk> dollars . 
- Other territories 
- <unk> <unk> was released on July 7 , 2011 , in the United Arab Emirates ( $ 258 @,@ 108 ) , and on July 8 in <unk> ( $ 24 @,@ <unk> ) , Latvia ( $ 15 @,@ 750 ) , Lebanon ( $ 36 @,@ 316 ) and Lithuania ( $ 13 @,@ 676 ) , grossing $ 348 @,@ 321 for the opening weekend and <unk> a total of $ <unk> @,@ <unk> in the first 17 days . On the weekend of July 21 – 24 , the film opened in the United Kingdom ( $ 3 @,@ 386 @,@ <unk> ) , Greece ( $ 367 @,@ <unk> ) , Israel ( $ 200 @,@ 372 ) , South Africa ( $ 193 @,@ <unk> ) , Norway ( $ 109 @,@ 252 ) and East Africa ( $ 7 @,@ <unk> ) . 
- 
- = = = Critical reception = = = 
- 
- <unk> <unk> received generally positive reviews from critics . On Rotten Tomatoes , the film holds a rating of 68 % , based on 208 reviews , with an average rating of 6 @.@ 2 / 10 . The site 's critical consensus reads , " It 's nasty , uneven , and far from original , but thanks to a <unk> assembled cast that makes the most of a solid premise , <unk> <unk> works . " Review aggregate Metacritic gave the film a score of 57 out of 100 , based on 40 critics , indicating " mixed or average reviews " . 
- Roger <unk> gave the film three and a half stars out of four , calling it " well @-@ cast " and commending it for playing to each actor 's strengths . <unk> gave particular praise to Spacey , labeling him " superb " , and Aniston , <unk> her performance to be a " surprise " and a return to form , stating " she has acute comic timing and <unk> <unk> <unk> sexual <unk> " . <unk> called <unk> <unk> " cheerful and wicked " . Lisa <unk> of Entertainment Weekly reacted positively , calling the film " a <unk> , well @-@ built , <unk> nasty tale of resentment , <unk> , and <unk> revenge " and complimented the casting of the protagonists and antagonists . The A.V. Club 's Nathan <unk> also praised the cast , stating that the picture " succeeds almost entirely on the chemistry of its three leads , who remain likeable even while <unk> to <unk> " , adding the " acting more than <unk> for the film 's other failings . " <unk> singled out Day 's performance as " a potent illustration of how a brilliant character actor with a spark of madness can elevate a <unk> <unk> farce into a solid mainstream comedy through sheer force of <unk> . " Edward Douglas of <unk> credited director Seth Gordon with having assembled " the perfect cast " , claiming " the six leads kill in every scene " , but echoed Nathan <unk> 's sentiments that Day is the " real standout " . Douglas summarized the picture as " dark fun that works better than expected due to a well @-@ developed script , an <unk> cast and a director who knows how to put the two together " . A. O. Scott of The New York Times stated " the timing of the cast ... is <unk> " and appreciated that the script did not attempt " to cut its <unk> with a <unk> dose of <unk> or respectability " . The review concluded that " in the ways that count and even when it shouldn ’ t , <unk> <unk> works . " 
- USA Today 's Scott <unk> awarded the film three out of four stars , labeling it a " surprising comedy that rivals <unk> as the funniest film of the summer , if not the year . " <unk> added that " the characters are so likable " , giving particular credit to Sudeikis though also adding praise for the performances of Bateman and Day . The dialogue was also lauded by <unk> , which commented that " Seth Gordon has a <unk> touch with water @-@ cooler talk — even when the water cooler might be spiked with poison . " Leonard <unk> of <unk> considered Day to have had the " breakout role " and offered praise to the performances of the cast , but lamented the lack of screen time for Farrell 's character . <unk> concluded " the movie has just enough <unk> to identify it as a 2011 comedy , just enough <unk> to admire , and just the right <unk> among its three male stars , which turns out to be the movie ’ s greatest strength . " Rolling Stone 's Peter Travers gave <unk> to the " killer cast " , with specific credit given to Bateman and Day , but was critical of the movie , stating " it <unk> out on a sharp premise " and that it is a " hit @-@ and @-@ miss farce that leaves you wishing it was <unk> than it is " The Guardian 's Philip French called <unk> <unk> " a lumbering , <unk> affair " , but admitted " I laughed frequently , probably to the <unk> of my self @-@ respect . " Nicholas <unk> of The Independent gave a positive review , complimenting Gordon for not allowing the actors ' improvisation to be detrimental to the pacing , but felt the movie was not as " dark " as its premise required , saying " what <unk> the film does have comes instead from the <unk> quantity of <unk> , plus a <unk> of homophobia and <unk> . " 
- Salon 's Andrew O <unk> offered a mixed response , characterizing the film as a " lot <unk> in theory than in practice , but it won 't ruin your Saturday night " . Salon appreciated the " <unk> comic chemistry " between Sudeikis , Bateman and Day and singled out Bateman , Aniston and Spacey for their performances . O <unk> was however critical of the perceived homophobia , <unk> and racism . The Hollywood Reporter 's Kirk <unk> responded negatively , stating the jokes failed to be funny , stating " Seth Gordon shows no <unk> for turning the <unk> and <unk> characters in the script ... into anything more than a collection of moments in search of laughs . " <unk> <unk> of The Village Voice was critical of the premise , which she felt lacked any legitimate " rage " against the characters ' bosses , stating " ... there 's every sign that , even without these particular <unk> , Dale , Kurt and Nick would still be — for lack of a better word — total <unk> . " <unk> felt that the humor was " rarely actually laugh @-@ out @-@ loud funny , and never truly dark or daring " . She particularly criticized the all @-@ white , male protagonists and a plot she deemed racist and filled with " stereotypes " . Justin Chang of Variety praised the performance of the ensemble cast , but considered the plot to be " <unk> <unk> , vulgar and juvenile " . Chang echoed the sentiments of The Village Voice in lamenting that the film failed to pursue the premise to " darker , more daring territory " and faulted it for falling back on " over @-@ the @-@ top comic exaggeration " . 
- 
- = = = Accolades = = = 
- 
- The film received several award nominations , including a <unk> Award for Best Supporting Actor for Colin Farrell , and three nominations from the 2012 Comedy Awards , including Comedy Actor for Bateman , Comedy Actress for Aniston , and best Comedy Film . Farrell and Aniston were both nominated for Best On @-@ Screen Dirt <unk> at the 2012 MTV Movie Awards , with Aniston claiming the award . Farrell also received a nomination for Best On @-@ Screen <unk> . 
- 
- = = = Home media = = = 
- 
- On July 26 , 2011 , <unk> obtained the rights to the network premiere of the film . 
- <unk> <unk> was released on DVD and Blu @-@ ray Disc in the United States on October 11 , 2011 . The DVD version sold an estimated 400 @,@ 682 units in the United States during its first week , earning approximately $ 6 @.@ 1 million . It was the number 2 best selling DVD of the week , finishing behind Green Lantern , and the number 3 Blu @-@ ray disc film behind Green Lantern and The Lion King . As of November 2012 , it has sold an estimated 1 @.@ 3 million units and earned $ 18 @.@ 3 million . 
- The DVD contains the theatrical cut of the film and deleted scenes . The Blu @-@ ray Disc edition contains the Blu @-@ ray Disc , the DVD and a digital version of the film in a single pack . The Blu @-@ ray Disc version is an <unk> , extended cut ( the " <unk> <unk> Edition " ) with a <unk> of 106 minutes compared to the theatrical 98 minutes . The Blu @-@ ray Disc contains deleted scenes and four <unk> : My Least Favorite Career , Surviving a <unk> Boss , Being Mean Is So Much Fun , and The Making of the <unk> <unk> Soundtrack . Both the theatrical and extended cuts are presented in the film 's original aspect ratio of 2 @.@ 39 : 1 with <unk> @-@ HD Master Audio sound . Beginning with the Blu @-@ ray Disc release of <unk> <unk> and Green Lantern , Warner Bros. included a code that allows the owner to access a version of the film via <unk> , a cloud storage service which allows streaming or downloading to a variety of devices . 
- 
- = = <unk> = = 
- 
- Seth Gordon confirmed in July 2011 that talks were underway for a sequel , after the financial success of the film in the United States , saying : " Yeah , we 've definitely discussed it . It 's done well in the States , the film has , so that 's becoming a more <unk> effort now , we 're trying to figure out what the sequel could be . " On January 4 , 2012 , it was confirmed that a sequel was moving forward , and that Goldstein and <unk> would be returning to write the script . New Line was reported to be negotiating with Gordon to return as director , with Bateman , Day , and Sudeikis also expected to return to their roles . On February 27 , 2012 , it was confirmed that Goldstein and <unk> were in the process of writing the new script . In March 2013 , Goldstein and <unk> confirmed that they had submitted multiple draft scripts for the sequel , and that production had moved towards <unk> the budget . Later in the same month Bateman , Day , and Sudeikis were confirmed to be reprising their roles , with <unk> negotiating to return . The film will again be produced by Brett <unk> and Jay Stern . In August 2013 , it was announced that Gordon would not be returning to direct because of scheduling conflicts and that the studio was actively searching for a replacement . In September 2013 , Sean Anders was announced as Gordon 's replacement , with John Morris joining the production as a producer . The pair had previously performed a rewrite on Goldstein 's and <unk> 's sequel script . Filming had been scheduled to begin in summer 2013 , but began in November 2013 . <unk> , Jennifer Aniston , and Kevin Spacey reprised their roles , with Christoph <unk> and Chris Pine joining the cast . 
- 
- 
- = 1985 <unk> assassination plot = 
- 
- The 1985 <unk> assassination plot was a conspiracy by a group of high @-@ ranking followers of <unk> <unk> <unk> ( later known as <unk> ) to assassinate Charles Turner , the then @-@ United States Attorney for the District of Oregon . <unk> 's personal secretary and second @-@ in @-@ command , Ma Anand <unk> ( <unk> Silverman ) , assembled the group after Turner was appointed to investigate illegal activity at <unk> . Turner investigated charges of immigration fraud and <unk> marriages , and later headed the federal prosecution of the 1984 <unk> <unk> attack in The <unk> , Oregon . 
- The conspirators included : Ma Anand <unk> , <unk> @-@ Anne <unk> , Chief Financial Officer of <unk> ; Susan <unk> , head of security at <unk> , Catherine Jane <unk> , who bought weapons and <unk> and volunteered to be the actual murderer ; Ann <unk> McCarthy , fourth @-@ in @-@ command of <unk> ; and co @-@ conspirators Alma Potter , Carol Matthews , <unk> Caldwell and Richard Kevin <unk> . The conspirators obtained false identification to purchase <unk> out @-@ of @-@ state , <unk> Turner , and planned to murder him near his workplace in Portland , Oregon . The assassination plot was never carried out and was only discovered later , as a result of the investigation by federal law enforcement into the <unk> attack in The <unk> and other illegal acts by the <unk> leadership . 
- Prosecution of the conspirators began in 1990 , when a federal grand jury brought <unk> against several of the key players . Some had fled the country , and <unk> proceedings against the <unk> and subsequent prosecution and conviction was not completed for sixteen years . The final <unk> was convicted in 2006 , when Catherine Jane <unk> agreed to return to the United States from Germany in order to be allowed to visit her ill son in Australia . Eight <unk> received sentences ranging from five years <unk> to five years in federal prison and an additional member of the <unk> commune pleaded guilty to murder conspiracy . <unk> was never <unk> in relation to the conspiracy , and left the United States after pleading guilty to immigration fraud and agreeing not to <unk> the country without permission from the U.S. Attorney General . 
- 
- = = Planning = = 
- 
- 
- = = = Hit list = = = 
- 
- Seven followers ( called <unk> ) of charismatic leader <unk> <unk> <unk> ( now known as <unk> ) , were convicted of a 1985 conspiracy to assassinate Charles Turner , and an additional <unk> eighth member of the <unk> commune pleaded guilty to murder conspiracy . The <unk> were high @-@ ranking followers within the <unk> organization . <unk> in the case stated that the <unk> had planned to murder Turner after he was appointed to head an investigation into the group 's activities in <unk> , Oregon . Turner 's investigation focused on <unk> marriages organized by the group , as well as other illegal activities including immigration fraud , and he later headed the federal prosecution relating to the 1984 <unk> <unk> attack in The <unk> , Oregon . Oregon Attorney General Dave <unk> was also involved in investigations of the group , working alongside Turner . 
- In May 1985 , <unk> 's personal secretary and second @-@ in @-@ command , <unk> Silverman ( Ma Anand <unk> ) , gathered the leader 's key followers and formed a group of conspirators in order to plan the assassination of Turner , as well as several dissidents among their organization . <unk> stated that Turner 's grand jury investigation " threatened the existence of the commune " , and exposed <unk> and several of his disciples to criminal prosecution . <unk> hoped that by murdering Turner they would be able to thwart the federal immigration investigation which could have resulted in <unk> of <unk> leaders . Three former leaders of the commune including Ma Anand <unk> , <unk> Yvonne <unk> ( Ma Anand <unk> ) , a registered nurse from the Philippines who managed the <unk> medical corporation , and former treasurer of <unk> Foundation International and Catherine Jane <unk> ( Ma <unk> <unk> ) of Australia , put together a hit list which included U.S. Attorney Charles Turner and Oregon Attorney General David <unk> . 
- Susan <unk> ( Ma Anand Su ) , a top official in the <unk> hierarchy , was also a participant in the assassination conspiracy . <unk> was in charge of the security force at <unk> , ran the <unk> Investment Corporation , and supervised construction on the commune . Other conspirators in the assassination plot included Ann <unk> McCarthy ( Ma <unk> <unk> ) , president of the <unk> commune , and Alma Potter ( Ma <unk> <unk> ) , Ma Anand <unk> 's bodyguard and traveling companion . Potter was manager of the Hotel <unk> in Portland , Oregon , and a member of the commune 's security force . The <unk> reported that nine people were on the hit list , including : Turner , <unk> , former assistant attorney general Karen H. Green , <unk> County planning director Daniel C. <unk> , <unk> County commissioner James L. <unk> , investigative journalist for The <unk> Leslie L. <unk> , former member Helen C. Byron ( Ma <unk> <unk> ) , who had been awarded US $ 1 @.@ 7 million in a lawsuit against <unk> Foundation International , her daughter Barbara J. Byron ( <unk> ) , and <unk> 's former secretary <unk> <unk> <unk> ( Ma <unk> <unk> ) . 
- 
- = = = Weapons = = = 
- 
- Catherine Jane <unk> volunteered to be the follower who would actually murder Turner , and bought guns and <unk> . <unk> was known to fellow followers as Ma <unk> <unk> , and was also one of the three " Big <unk> " in <unk> . <unk> @-@ Anne <unk> ( known by followers as Ma <unk> <unk> ) , an <unk> and the group 's Chief Financial Officer , provided money for the purchase of weapons related to the plot . <unk> Caldwell ( Ma Deva <unk> ) described in a federal <unk> how members of the murder conspiracy obtained <unk> , referring to Catherine Jane <unk> and <unk> @-@ Anne <unk> : " <unk> B went down to Jesus Grove , and <unk> gave us several thousand dollars to use to buy guns . " Jesus Grove referred to a group of trailers where all the leaders of the <unk> commune resided , except for <unk> <unk> <unk> . 
- Ma Anand <unk> and three other <unk> followers traveled to New York in the spring of 1985 to acquire false identification . According to a federal <unk> , the <unk> of the assassination plot used a false birth certificate to purchase guns . Two members of the group then traveled to Texas to purchase <unk> . Caldwell stated she and Catherine Jane <unk> flew to Texas to purchase guns there . They purchased five guns in Texas , but encountered difficulty purchasing <unk> in Texas with out @-@ of @-@ state identification and traveled to New Mexico instead . Caldwell said they called back to the commune but the women were instructed " not to come back without the guns " . 
- In New Mexico , they obtained false identification , and purchased several pistols . Their intention was to purchase guns which were difficult to trace . Caldwell said it was easy to obtain guns in New Mexico : " we found it was pretty easy to buy guns . All you had to do was show some identification and it was easy to get . " Caldwell said she and <unk> went to a university library to find the identity " of someone dying very young " , next went " to public records and asked for a copy of a birth certificate " and described how " we got a rent <unk> book and just made up a rent <unk> " . " And then we were able to go to several different gun shops in <unk> with those two bits of identification and buy five different guns and bullets , " said Caldwell . They were able to obtain one <unk> <unk> @-@ caliber revolver , and four <unk> <unk> @-@ caliber Security @-@ Six Magnum <unk> . The conspirators smuggled the guns into Oregon by packing them in luggage and putting the luggage on a <unk> Lines bus . Caldwell said that she and <unk> traveled by bus back to the <unk> commune so as to avoid airport metal detectors . 
- 
- = = = <unk> = = = 
- 
- After obtaining guns the conspirators returned to Portland , Oregon . They rented out an apartment in Portland to serve as their base of operations for the assassination of Turner . <unk> follower Carol Matthews ( Ma <unk> <unk> ) used a fake name during the planning of the assassination plot , in an attempt to obtain the home address of Charles Turner . According to federal prosecutors Matthews obtained a college <unk> of Turner 's , and learned his car 's license plate number and his parking location . Court records state that Matthews and an <unk> co @-@ <unk> gave <unk> registration officials fake identities and told officials they were conducting " a <unk> survey on President Reagan 's economic plan " , in order to obtain Turner 's route number and post office box number . The two tried repeating the story with members of the U.S. Postal Service , but the postal officials did not give them Turner 's address . They then drove around Turner 's neighborhood and were able to find his home by locating a sign in front of his residence that said " Turner " . Matthews had pictures of Turner 's residence developed ; these were later obtained by the Federal Bureau of Investigation ( FBI ) during an investigation of the <unk> commune , and verified after being shown to Turner . 
- Members of the group of conspirators watched Turner 's office , home and car , and discussed methods to assassinate him , hoping that his death would hinder the efforts of the federal investigation into <unk> . Their plan was to shoot Turner in the garage of the federal office building where he worked , in Portland , Oregon , but the conspirators also debated whether to murder Turner in downtown Portland or closer to his home . After spending multiple nights watching Turner 's house , the conspirators decided on the parking garage because they felt it would be too risky to murder him on the drive to or from work , or in front of his home . Turner had a reserved parking spot in a federal garage underneath Terry <unk> Plaza in Portland , Oregon . In an <unk> given to the FBI , <unk> Alma <unk> described how the <unk> decided on the federal parking garage as the location : " <unk> <unk> [ Catherine Jane <unk> ] said this seems like a good place to bump this fellow off . " 
- The conspirators practiced different ways of murdering Turner . According to informant statements to law enforcement , one of the conspirators was to pretend there was car trouble , and the others would then approach Turner with their guns . <unk> later told law enforcement officials that the conspirators intended to hide out at an international network of <unk> <unk> if the plan was successful . According to The <unk> the <unk> were not carried out because Ma Anand <unk> became distracted by political power plays within the <unk> commune and other members of the organization who were trying to remove her from her position within the group . 
- 
- = = <unk> = = 
- 
- 
- = = = Investigation = = = 
- 
- On February 28 , 1985 , Congressman James H. Weaver gave a speech in the United States House of Representatives in which he asserted that the <unk> were involved in the <unk> attack in Oregon . At a series of press conferences in September 1985 , <unk> accused several of his recently departed lieutenants of involvement in this and other crimes , including the poisoning of Mike Sullivan , a Jefferson County district attorney , and asked state and federal authorities to investigate his allegations . The assassination plot was uncovered by federal law enforcement as a result of the ensuing investigation into activities at <unk> . Turner was never physically <unk> , and had retired by 1995 . 
- The <unk> was informed in October 1985 by federal law enforcement officials that Leslie L. <unk> , an investigative journalist who had written a 20 @-@ part series on the <unk> movement in Oregon , was on a " hit list " which also included Turner and Oregon Attorney General David <unk> . Assistant U.S. Attorney Robert Weaver <unk> the case ; the charges were first detailed at an October 1985 bond hearing in North Carolina after <unk> and his followers were arrested at an airport in Charlotte . Weaver said in court that followers of <unk> <unk> <unk> had plotted to assassinate Turner and <unk> . He said these allegations were reasons why releasing <unk> and his followers from jail would be " a clear and present danger to public officials " . The guns purchased by the <unk> followers for the assassination plot had reportedly been dumped in a lake at Rancho <unk> ; the lake was searched by U.S. Navy divers . <unk> divers searched the lake for two days but did not find the guns . 
- Joseph Greene , a U.S. immigration agent , testified in court that FBI agents had learned of the assassination plot from a member of the organization who was in a witness protection program . Greene said that members involved in the assassination plot included Ma Anand <unk> , <unk> Yvonne <unk> , and Alma <unk> . The assassination plot was investigated by the FBI and the Oregon State Police . <unk> told law enforcement that Ma Anand <unk> hoped Turner 's death would prevent an Immigration and <unk> Service investigation which she thought could lead to <unk> 's arrest and <unk> from the United States . Weaver stated " These attempts to assassinate public officials were because they were presenting an immigration case that might result in imprisonment " of <unk> . " There were not simply plans , but at least one ( assassination ) attempt , " said Weaver at the hearing . 
- A grand jury investigation led by Turner brought charges of " widespread immigration fraud " against members of <unk> . <unk> crimes were discovered after Ma Anand <unk> had fled the commune in September 1985 . In December 1985 , 21 followers of <unk> were indicted on <unk> charges . 
- 
- = = = <unk> and convictions = = = 
- 
- Chief criminal assistant U.S. attorney Baron C. <unk> was assigned to <unk> the charges of federal <unk> , and a special team from the United States Department of Justice Criminal Division was tasked with <unk> the murder conspiracy charges . Four of the <unk> were arrested in September 1990 . Catherine Jane <unk> and Richard Kevin <unk> were arrested in West Germany , Ann <unk> McCarthy was arrested in South Africa , and Susan <unk> was arrested in England . In September 1990 , Alma <unk> pleaded guilty to conspiracy to commit murder . <unk> , who had served as Ma Anand <unk> 's bodyguard and <unk> , agreed to testify against the other defendants in the murder conspiracy . Under the terms of <unk> 's plea agreement she received a sentence of two years in federal prison . Carol Matthews was arrested in Baden @-@ Baden , Germany in October 1990 on charges of <unk> and conspiracy to murder Turner , where she was held along with three other <unk> . At the time of her arrest , Matthews was found in possession of a false British passport , and had been traveling under the fake name of " <unk> <unk> " . <unk> were brought against Ma Anand <unk> and six other co @-@ conspirators by a federal grand jury in November 1990 . 
- In April 1991 , Carol Matthews and Richard Kevin <unk> ( <unk> <unk> ) were <unk> from Germany to the United States in order to appear in federal court in Portland , Oregon . Law enforcement officials from the United States <unk> Service traveled to Frankfurt , Germany and took custody of the Matthews and <unk> at Rhine Main Airport . On April 15 , 1991 , Matthews and <unk> appeared in federal court in Oregon , and both pleaded innocent to charges of conspiracy to commit murder and carrying out <unk> . On April 25 , 1991 , Richard Kevin <unk> pleaded guilty in federal court to participating in the murder conspiracy plot against Turner , and in exchange he received a sentence of five years in federal prison and the dismissal of other charges against him relating to firearms and <unk> . <unk> agreed to testify against the other members of the murder conspiracy . <unk> wrote on his plea agreement form : " In 1985 , meetings were held at the Rancho <unk> ... at which time the possible killing of the United States Attorney for Oregon was discussed . I participated in a number of these meetings and agreed with others to work toward that object . " Prosecutor Timothy J. <unk> III stated that <unk> had been a member of the <unk> commune in Oregon since it began in 1981 , and that the government was able to prove he joined the murder conspiracy at a point in time after May 25 , 1985 . <unk> said that <unk> was a member of a group called the " Circle of 38 " , which was the personal security force that guarded <unk> <unk> <unk> , and that he had served as a weapons instructor and policeman at the commune . <unk> told U.S. District Judge Malcom F. Marsh that he had suggested that guns for the murder conspiracy could be bought in Texas , instructed the <unk> about <unk> , took responsibility for the weapons while they were in the commune , and disposed of them when members of the murder conspiracy decided to flee the U.S. for Europe . In July 1991 , Carol Matthews entered a guilty plea and was convicted in federal court , she was sentenced to five years in prison . 
- Catherine Jane <unk> was convicted of the attempted murder of <unk> 's physician Dr. George Meredith ( <unk> <unk> ) in 1986 , and served almost three years in jail . After her release , agents from the FBI uncovered the plot to assassinate Turner , but <unk> had already fled to Germany . She was indicted by a federal grand jury in 1990 . In 1991 , the German government refused to <unk> <unk> back to the United States . In June 1991 , U.S. prosecutors filed <unk> in the murder conspiracy case with the Higher Regional Court in <unk> , Germany , as part of an attempt to <unk> <unk> Jane <unk> from Germany to the U.S. The <unk> stated that all of the members in the murder conspiracy plot also belonged to a group of <unk> followers at the Oregon commune known as " the 38 " , and were trained in " commando tactics using <unk> <unk> rifles and <unk> " . David Berry Knapp ( known to <unk> followers as <unk> Krishna Deva ) stated in an FBI <unk> that the murder conspiracy was motivated by Ma Anand <unk> 's " tremendous anger " towards Turner . 
- Ma Anand <unk> served 29 months in a minimum security federal prison for charges related to assault , attempted murder , <unk> , <unk> and the 1984 <unk> attack in The <unk> , and moved to Switzerland after her release from prison in 1988 . The assassination conspiracy was discovered after <unk> had left the United States , and as of 1999 she was still wanted by federal law enforcement for her role in the plot , and risked <unk> if she crossed the Swiss border . Switzerland declined an <unk> request from the United States , and instead tried her in a Swiss court . <unk> was found guilty of " criminal acts preparatory to the commission of murder " in 1999 , and sentenced to time already served . 
- <unk> @-@ Anne <unk> and Susan <unk> were <unk> from Britain in 1994 , and were convicted by a jury decision on July 28 , 1995 for their roles in the assassination plot . They had unsuccessfully attempted to appeal their <unk> from Britain to Home Secretary Michael Howard . During their trial the prosecution presented twenty @-@ nine witnesses , including former followers of <unk> who placed both women in planning meetings where they discussed murdering Turner . David Berry Knapp , the former mayor of <unk> , testified for the government in the case and implicated <unk> and <unk> in the assassination conspiracy . Ava Kay <unk> ( Ma Ava ) , a <unk> disciple , testified in the <unk> case and stated that she had been part of the conspirators that plotted to assassinate Turner . In addition to Knapp and <unk> , co @-@ conspirators Richard Kevin <unk> , <unk> Caldwell , and Alma <unk> testified in the case pursuant to conditional plea or immunity agreements . Both women were sentenced to five years in prison . <unk> and <unk> did not testify during their trial . " We <unk> over everything – evidence , notes , evidence , notes . I think we did an absolutely fabulous job , " said one of the jurors . At the sentencing for <unk> and <unk> , the federal Judge Malcolm Marsh described them as " people of obvious goodwill who had committed an extremely serious offense against the criminal justice system . " Prosecutor Tim <unk> called the conspiracy to commit assassination " a deadly serious crime aimed at the heart of the criminal justice system . " <unk> and <unk> were released from imprisonment at <unk> Dublin , California in April 1998 , and returned to Britain . 
- In December 2002 , Ann <unk> McCarthy pleaded guilty to conspiracy to commit murder , and was sentenced to one year in jail and a fine of USD $ 10 @,@ 000 . McCarthy had served as fourth @-@ in @-@ command of <unk> , and was known by <unk> 's followers as Ma <unk> <unk> . Turner called the one @-@ year prison sentence " <unk> . " In court statements , McCarthy stated " I cannot forgive myself for not being <unk> at the time , " and called her time with the group " psychological torture . " 
- In February 2006 , <unk> became the last perpetrator sentenced in the political assassination plot , after ten months of negotiations with Oregon prosecutors . <unk> offered to turn herself in and return to the United States after learning of her son 's terminal brain tumor . Prior to sentencing , the court allowed her to travel to Australia to visit her son . In addition to charges of conspiracy to commit murder , <unk> also pleaded guilty to the purchase of weapons in violation of federal firearms law . An Oregon judge sentenced her to five years <unk> , and three months time already served in a German jail . Turner thought she should have received a <unk> sentence , and commented " This was a lying @-@ in @-@ wait conspiracy to murder me , a presidential appointee , and for a long time I slept with a loaded gun beside my bed . " Though <unk> could have faced life in prison , U.S. District Judge Judge Malcolm F. Marsh thought she had " seen the error of her ways . " A federal prosecutor in the case described <unk> as the " MVP " of the conspiracy , and said she was the designated assassin that was set to murder Turner . After her sentencing , <unk> stated : " I actually conspired to kill Mr. Turner , it is up to me alone to face this terrible truth ... No person has the right to do what I did . I 'm truly sorry . " <unk> returned to Germany after her sentencing . 
- In an <unk> , Timothy J. <unk> III , lead prosecutor for the United States Department of Justice in the case , stated Ma Anand <unk> had told members of the murder conspiracy that <unk> <unk> <unk> had personally authorized the " necessary " murder of specific enemies of the <unk> commune . <unk> <unk> <unk> paid a fine of US $ 400 @,@ 000 , agreed to plead guilty to immigration fraud , and was deported from the United States . He agreed to leave the United States and not return unless given permission first from the United States Attorney General . Joseph T. <unk> writes in Terrorism on American Soil " Nevertheless , he was never <unk> for any of the more serious crimes <unk> by cult members , including the <unk> poisoning . " 
- 
- = = = <unk> = = = 
- 
- <unk> to assassinate a United States Attorney 
- <unk> , and other charges , testified in assassination case 
- 
- 
- = Die Another Day = 
- 
- Die Another Day ( 2002 ) is the twentieth spy film in the James Bond series , and the fourth and final film to star Pierce <unk> as the fictional MI6 agent James Bond . The film follows Bond as he leads a mission to North Korea , during which he is betrayed and , after seemingly killing a rogue North Korean colonel , is captured and imprisoned . Fourteen months later , Bond is released as part of a prisoner exchange . <unk> that someone within the British government betrayed him , he attempts to earn redemption by tracking down his <unk> and killing a North Korean agent he believes was involved in his torture . 
- Die Another Day , produced by Michael G. Wilson and Barbara Broccoli , and directed by Lee <unk> , marked the James Bond franchise 's 40th anniversary . The series began in 1962 with Sean Connery starring as Bond in Dr. No . Die Another Day includes references to each of the preceding films . 
- The film received mixed reviews . Some critics praised the work of Lee <unk> , while others criticised the film 's heavy use of computer @-@ generated imagery , which they found <unk> and a distraction from the film 's plot . Nevertheless , Die Another Day was the highest @-@ grossing James Bond film up to that time if inflation is not taken into account . 
- 
- = = Plot = = 
- 
- MI6 officer James Bond — agent <unk> — infiltrates a North Korean military base , where Colonel Tan @-@ Sun Moon is illegally trading weapons for African blood <unk> . After Moon 's assistant <unk> discovers that Bond is a British agent , the colonel attempts to kill Bond and a <unk> chase <unk> , which ends with Moon 's apparent death . Bond survives , but is captured by North Korean soldiers and imprisoned by the Colonel 's father , General Moon . 
- After 14 months of captivity and torture , Bond is traded for <unk> in a prisoner exchange . He is <unk> and taken to meet M , who informs him that his status as a 00 Agent is suspended under suspicion of having leaked information under <unk> . Bond is convinced that he has been set up by a double agent in the British government and decides to avenge his betrayal . After escaping from the custody of MI6 , he discovers that he is in Hong Kong , where he learns from a Chinese agent that <unk> is in Cuba . 
- In Havana Bond meets <unk> agent <unk> ' Jinx ' Johnson and follows her to a gene therapy clinic , where patients can have their appearances altered through DNA restructuring . Bond locates <unk> inside the clinic and attempts to kill him , but <unk> escapes . He leaves behind a <unk> which leads Bond to a <unk> of <unk> , identified as conflict <unk> , but bearing the crest of the company owned by British billionaire Gustav Graves . 
- At <unk> Club in London Bond meets Graves , along with his assistant Miranda Frost , who is also an undercover MI6 agent . After a fencing exercise ( and a literal sword @-@ fight ) , Bond is invited by Graves to Iceland for a scientific demonstration . Shortly afterwards , M <unk> Bond 's Double @-@ 0 status and offers assistance in the investigation . 
- At his ice palace in Iceland Graves <unk> a new orbital mirror satellite , " Icarus " , which is able to focus solar energy on a small area and provide year @-@ round sunshine for crop development . During the night , Jinx infiltrates Graves ' command centre , but is captured by Graves and <unk> . Bond rescues her and later discovers that Colonel Moon is still alive . Moon has used the gene therapy technology to change his appearance , assuming the identity of Gustav Graves . 
- Bond confronts Graves , but Frost arrives to reveal herself as the traitor who betrayed Bond in North Korea , forcing <unk> to escape from Graves ' facility . Bond then returns in his Aston Martin <unk> to rescue Jinx , who has been captured once again . <unk> pursues him in his own vehicle , both cars driving inside the rapidly melting ice palace . Bond kills <unk> by shooting an ice <unk> onto him , and then <unk> Jinx after she has drowned . 
- Bond and Jinx pursue Graves and Frost to the Korean peninsula and <unk> away on Graves ' cargo plane . Graves reveals his true identity to his father , and the purpose of the Icarus satellite : to cut a path through the Korean <unk> Zone with concentrated sunlight , allowing North Korean troops to invade South Korea and reunite the peninsula by force . <unk> , General Moon tries to stop the plan , but he is murdered by his own son . 
- Bond attempts to shoot Graves but he is prevented by one of the soldiers on board . In their struggle , a gunshot <unk> the fuselage , causing the plane to descend rapidly . Bond engages Graves in a fist fight , and Jinx attempts to regain control of the plane . Frost attacks Jinx , forcing her to defend herself in a sword duel . After the plane passes through the Icarus beam and is further damaged , Jinx kills Frost . Graves attempts to escape by parachute , but Bond opens the parachute , causing Graves to be pulled out of the plane and into one of its engines , killing him and <unk> the Icarus beam . Bond and Jinx then escape from the disintegrating plane in a helicopter from the cargo hold , carrying away Graves ' <unk> of <unk> in the process . 
- 
- = = Cast = = 
- 
- Pierce <unk> as James Bond <unk> , an MI6 agent . 
- Halle Berry as <unk> ' Jinx ' Johnson , an <unk> agent . 
- Toby Stephens as Gustav Graves , a British entrepreneur , alter ego of Colonel Moon . 
- <unk> Pike as Miranda Frost , undercover MI6 agent and double agent . 
- Rick <unk> as <unk> , a North Korean terrorist working for Moon . 
- Judi Dench as M , the head of MI6 . 
- Will <unk> Lee as Colonel Moon , a rogue North Korean army colonel , later uses his alter ego . 
- Kenneth <unk> as General Moon , Colonel Moon 's father . 
- John <unk> as Q , MI6 's <unk> and <unk> . 
- Colin Salmon as Charles Robinson , M 's Deputy Chief of Staff . 
- Ho <unk> as the Hotel manager and Chinese special agent Mr. Chang . In early drafts of the script , it was <unk> Lin ( Michelle Yeoh ) who aided Bond in Hong Kong , but the idea fell through and Chang was created to replace her . 
- Rachel Grant as <unk> <unk> of Desire , a Chinese agent working for Mr. Chang , undercover as a <unk> . 
- Emilio <unk> as Raoul , the manager of a Havana <unk> factory , and a British <unk> . 
- Samantha Bond as Miss Moneypenny , M 's secretary . 
- Michael <unk> as Vladimir <unk> , Gustav Graves ' personal scientist 
- Lawrence <unk> as Mr. <unk> , one of Gustav Graves ' henchmen . 
- Michael Madsen as Damian Falco , Jinx 's superior in the <unk> . 
- Vincent <unk> as General Li 
- <unk> Martinez as an elderly <unk> factory worker 
- Deborah Moore as an airline hostess ( Moore is the daughter of former Bond Roger Moore ) 
- Madonna as <unk> , a fencing instructor ( cameo ) 
- 
- = = Production = = 
- 
- 
- = = = Filming = = = 
- 
- Principal photography of Die Another Day began on 11 January 2002 at <unk> studios . The film was shot primarily in the United Kingdom , Iceland , and <unk> , Spain . Other locations included <unk> Studios ' <unk> Stage and Maui , Hawaii , in December 2001 . Laird Hamilton , Dave <unk> , and <unk> <unk> performed the pre @-@ title surfing scene at the surf break known as <unk> in <unk> , Maui , while the shore shots were taken near <unk> and <unk> , Cornwall . Scenes inside Graves ' diamond mine were also filmed in Cornwall , at the Eden Project . The scenes involving the Cuban locations of Havana and the fictional <unk> de Los <unk> were filmed at La <unk> , Spain . 
- The scenes featuring Berry in a bikini were shot in <unk> . The location was reportedly cold and <unk> , and footage has been released of Berry wrapped in thick <unk> between takes to avoid catching a <unk> . Berry was injured during filming when debris from a smoke grenade flew into her eye . The debris was removed in a 30 @-@ minute operation . 
- <unk> and other props from every previous Bond film and stored in <unk> Productions ' archives appear in Q 's warehouse in the London Underground . Examples include the <unk> in <unk> and Rosa <unk> 's poison @-@ tipped shoe in From Russia with Love . Q mentions that the watch he issues Bond is " your 20th , I believe " , a reference to Die Another Day being the 20th <unk> @-@ produced Bond film . In London , the Reform Club was used to shoot several places in the film , including the lobby and gallery at the <unk> Club , MI6 Headquarters , Buckingham Palace , Green Park , and Westminster . Svalbard , Norway and <unk> , Iceland were used for the car chase on the ice with additional scenes filmed at <unk> National Park , Norway and RAF Little <unk> , Gloucestershire ; <unk> Airport in Kent was used for the scenes involving the <unk> cargo plane scenes . The scene in which Bond <unk> the wave created by Icarus when Graves was attempting to kill Bond was shot on the blue screen . The waves , along with all the glaciers in the scene are computer @-@ generated . 
- The hangar interior of the " US Air Base in South Korea " , shown crowded with <unk> helicopters , was filmed at RAF <unk> in Hampshire , UK , as were the helicopter interior shots during the <unk> sequence . These latter scenes , though portrayed in the air , were actually filmed entirely on the ground with the sky background being added in post @-@ production using blue screen techniques . Although the base is portrayed in the film as a US base , all the aircraft and personnel in the scene are British in real life . In the film , <unk> ( one @-@ person gliders resembling fighter jets in shape ) are flown by Bond and Jinx to <unk> enter North Korea . The <unk> was based on a <unk> model called " <unk> " ( <unk> High <unk> Single Soldier Transport ) . <unk> <unk> Inc . ' s lead designer , Jack <unk> was impressed by director Lee <unk> 's way of conducting the <unk> scene and commented , " It 's brief , but realistic . The good guys get in <unk> , thanks to a fast cruise , good <unk> performance , and minimal radar signature . It 's a wonderful promotion for the <unk> . " 
- 
- = = = Music = = = 
- 
- The soundtrack was composed by David Arnold and released on Warner Bros. Records . He again made use of electronic rhythm elements in his score , and included two of the new themes created for The World Is Not Enough . The first , originally used as <unk> 's theme , is heard during the <unk> " <unk> " cue on the recording , and is written for piano . The second new theme , used in the " Christmas in Turkey " track of The World Is Not Enough , is reused in the " Going Down Together " track . 
- The title song for Die Another Day was co @-@ written and co @-@ produced by <unk> <unk> and performed by Madonna , who also had a cameo in the film as <unk> , a fencing instructor . This is the first Bond title sequence to directly reflect the film 's plot since Dr. No ; all the other previous Bond titles are stand @-@ alone set pieces . The concept of the title sequence is to represent Bond trying to survive 14 months of torture at the hands of the North Koreans . Critics ' opinions of the song were sharply divided — it was nominated for a Golden Globe for Best Original Song and the 2004 Grammy Award for Best Dance Recording , but also for a Golden <unk> Award for <unk> Original Song of 2002 ( while Madonna herself won the Golden <unk> Award for <unk> Supporting Actress for her cameo ) . In a <unk> poll for the Channel 4 programme " James Bond 's Greatest Hits " , the song was voted 9th out of 22 , and also came in as an " overwhelming number one " favourite among those under the age of 24 . 
- 
- = = Marketing tie @-@ ins = = 
- 
- MGM and <unk> Productions granted Mattel the license to sell a line of Barbie dolls based around the franchise . Mattel announced that the Bond <unk> will be at her " stylish best " , clad in evening dress and red <unk> . <unk> <unk> created the dress , which is <unk> to the thigh to reveal a telephone <unk> to Barbie 's leg . The doll was sold in a gift set , with Barbie 's boyfriend Ken posing as Bond in a <unk> designed by the Italian fashion house <unk> . 
- <unk> also collaborated with the makers of Die Another Day to create a cosmetics line based around the character Jinx . The limited edition <unk> Colour Collection was launched on 7 November 2002 to coincide with the film 's release . The product names were loaded with puns and <unk> , with shades and textures ranging from the " warm " to " cool and <unk> " . 
- 
- = = Release and reception = = 
- 
- Die Another Day had its world premiere on 18 November 2002 at the Royal Albert Hall in London . Queen Elizabeth II and Prince Philip were guests of honour , making it the second Bond film premiere to be attended by the Queen , the first one being You Only Live Twice in 1967 . The Royal Albert Hall had a make @-@ over for the screening and had been transformed into an ice palace . <unk> from the <unk> , about £ 500 @,@ 000 , were donated to the Cinema and Television <unk> Fund of which the Queen is patron . On the first day , ticket sales reached £ 1 @.@ 2 million . Die Another Day was the highest grossing James Bond film until the release of Casino <unk> . It earned $ 432 million worldwide , becoming the sixth highest grossing film of 2002 . 
- Die Another Day became a controversial subject in eastern Asia . The North Korean government disliked the portrayal of their state as brutal and war @-@ hungry . The South Koreans boycotted 145 theatres where it was released on 31 December 2002 , as they were offended by the scene in which an American officer issues orders to the South Korean army in the defence of their homeland , and by a <unk> scene near a statue of the Buddha . The <unk> Buddhist Order issued a statement that the film was " <unk> to our religion and does not reflect our values and ethics " . The Washington Post reported growing resentment in the nation towards the United States . An official of the South Korean Ministry of Culture and Tourism said that Die Another Day was " the wrong film at the wrong time . " 
- The amount of product placement in the film was a point of criticism , specifically from various news outlets such as the BBC , Time and Reuters who all used the pun " Buy Another Day " . Reportedly 20 companies , paying $ 70 million , had their products featured in the film , a record at the time , although USA Today reported that number to be as high as $ 100 million . By choice , the number of companies involved in product placement was dropped to eight for the next Bond film Casino <unk> in 2006 . 
- Rotten Tomatoes listed Die Another Day with a 58 % rating . Metacritic gave the film a 56 out of 100 rating , representing " mixed or average reviews . " Michael <unk> of Film <unk> praised the film as the best of the series to star Pierce <unk> and " the most satisfying installment of the franchise in recent memory . " Larry Carroll of <unk> praised Lee <unk> for having " <unk> balanced the film so that it keeps true to the Bond legend , makes reference to the classic films that preceded it , but also <unk> a new <unk> to it all . " Entertainment Weekly magazine also gave a positive reaction , saying that <unk> , " a true filmmaker " , has re @-@ established the series ' pop <unk> . Dana Stevens of The New York Times called the film the best of the James Bond series since The Spy Who Loved Me . Kyle Bell of Movie Freaks 365 stated in his review that the " first half of Die Another Day is classic Bond " , but that " Things start to go <unk> when the ice palace gets introduced . " 
- However , Die Another Day was strongly criticised by some reviewers who felt that the film relied too heavily on <unk> and special effects , with the plot being neglected . James <unk> of <unk> said , " This is a train wreck of an action film – a <unk> attempt by the filmmakers to force @-@ feed James Bond into the mindless <unk> mold and throw 40 years of cinematic history down the toilet in favor of bright flashes and loud <unk> . " Of the action sequences , he said , " Die Another Day is an exercise in loud explosions and <unk> bad special effects . The CGI work in this movie is an order of magnitude worse than anything I have seen in a major motion picture . Coupled with <unk> production design , Die Another Day looks like it was done on the cheap . Gary Brown of the Houston Community Newspapers also described the weak point of the film as " the seemingly non @-@ stop action sequences and loud explosions that appear to take centre stage while the Bond character is almost relegated to second string . " Roger Moore remarked , " I thought it just went too far – and that ’ s from me , the first Bond in space ! Invisible cars and dodgy CGI footage ? Please ! " 
- 
- = = <unk> = = 
- 
- Die Another Day was written into a novel by the then @-@ current official James Bond writer , Raymond Benson , based on the screenplay by Neal <unk> and Robert Wade . Fan reaction to it was above average . After its publication Benson retired as the official James Bond novelist and a new series featuring the secret agent 's adventures as a teenager , by Charlie <unk> , was launched in 2005 . As the <unk> was published after Benson 's final original <unk> novel , The Man with the Red <unk> , it was the final literary work featuring Bond as originally conceived by Ian Fleming until the publication of Devil May Care by Sebastian <unk> in 2008 to mark the 100th anniversary of Fleming 's birth . 
- 
- = = <unk> spin @-@ off = = 
- 
- <unk> arose in 2003 of a spin @-@ off film concentrating on Jinx , which was scheduled for a November / December 2004 release . It was originally reported that MGM was keen to set up a film series that would be a " winter <unk> " alternative to ' James Bond ' . In the late 1990s , MGM had originally considered developing a spin @-@ off film based on Michelle Yeoh 's character , <unk> Lin , in 1997 's Tomorrow Never Dies . However , despite much speculation of an imminent movie , on 26 October 2003 , Variety reported that MGM had completely pulled the <unk> on this project , to the dismay of Barbara Broccoli and Michael G. Wilson of <unk> Entertainment , who were reported to be " clearly furious " about the decision . MGM were keen to instead move on with the next film , Casino <unk> . 
- 
- 
- = <unk> <unk> = 
- 
- <unk> <unk> <unk> ( born May 1968 ) is a Welsh actress , best known for her role as <unk> Tyson in the BBC medical drama <unk> City . She first rose to prominence in the lead role of the 1993 <unk> <unk> screenplay Great <unk> in Aviation . <unk> has worked in theatre , film and television , appearing in a number of Shakespearean theatrical performances , Hollywood films The i Inside and <unk> , and British television shows including Soldier Soldier , <unk> , Sea of <unk> and Doctor Who . She appeared in <unk> City from its eighth to eleventh series , from 2006 to 2008 , and in 2009 starred in the <unk> musical comedy My Almost Famous Family . 
- <unk> is an advocate of increased ethnic representation in the entertainment industry , and in 2001 founded her own production company , producing the short film <unk> 's <unk> for the Cannes film festival in order to further her campaign . In 2006 , <unk> was shortlisted for the ' Female Performance in TV ' award in the Screen Nation Awards , receiving Honourable Mention in the same category in 2007 , and a further shortlisted nomination in 2008 . She is married to fellow actor Adam <unk> , with whom she has two daughters . 
- 
- = = Early life = = 
- 
- <unk> was born in Cardiff , Wales in May 1968 , to a Sierra <unk> mother and a <unk> father . She was raised by her mother 's cousin and his wife in Ely , Cardiff . <unk> 's heritage means she is <unk> by descent , although she does not speak the language . <unk> studied at Windsor Clive Primary and <unk> Ely High School , and was a member of the <unk> Youth Theatre , South Glamorgan Youth Theatre , South Glamorgan Youth Choir and the National Youth Theatre of Wales . She left high school before sitting her A Levels in order to pursue her ambition of becoming an actress . She explains : " I 've always wanted to act . I decided at 16 I wanted to make my living acting , but even if I couldn 't , I ’ d be in an amateur theatre company . " She then went on to attend the Royal Welsh College of Music and Drama , studying for a three @-@ year acting <unk> . Her first acting role was for the Welsh <unk> when still at primary school , playing a lady @-@ in @-@ waiting at the court of King Arthur . <unk> has stated that it was <unk> Streisand 's performance in <unk> , <unk> ! that inspired her to act as a child , though credits her adoptive mother with encouraging her to act professionally . <unk> 's first job was selling <unk> on <unk> Road Market in Cardiff . She worked as a <unk> whilst attending drama school , and , six weeks prior to graduation , was offered a job with the ' Made in Wales ' theatre company which enabled her to obtain her union card . 
- 
- = = Career = = 
- 
- <unk> began her career in the theatre , performing in a number of Shakespearean plays including Twelfth Night , A Midsummer Night 's Dream , The Tempest , The <unk> of Venice and Macbeth . She states of this : " Shakespeare keeps coming my way . I love the fact that I get to play people who are much more articulate than I 'll ever be " . <unk> has performed in Twelfth Night in the lead roles of both Olivia and Viola . She explains : " The role of Viola didn 't sit that well with me for some reason but Olivia makes more sense . " She has also appeared in modern performances , assuming the title role of <unk> , Queen of <unk> at the Globe Theatre in London in 2003 , which she described as " a dream of a part " . She has deemed her dream role to be that of Isabella in Measure for Measure , as she once lost out on the part and would like to prove herself capable of playing it . 
- <unk> 's first film appearance was in the 1993 film Great <unk> in Aviation , written by <unk> <unk> , in which she starred alongside Jonathan Pryce and John Hurt . Variety magazine 's David <unk> said of her performance : " In the film 's most <unk> turn , <unk> is a constant pleasure to watch . <unk> and appealing , she often succeeds in pulling the <unk> fireworks momentarily back down to Earth . " <unk> recalls having been <unk> at the prospect of working alongside so many established names , but has deemed it to have been a " wonderful experience " . Her subsequent film credits are romantic comedy The Secret <unk> of Women , set in Nigeria and starring Colin Firth , thriller The i Inside , filmed in Sully Hospital , Cardiff , and starring Ryan <unk> , and <unk> , filmed in Morocco whilst <unk> was pregnant with her first child , starring <unk> Cruz . <unk> says of her film career : " I really like doing film [ but ] I 've not done enough big films though to really know the difference between film and television . " 
- <unk> 's first prolific television role was in the ITV drama Soldier Soldier , in which she starred throughout its third series in 1993 as soldier 's wife Bernie Roberts . <unk> credits her chemistry with co @-@ star <unk> <unk> , who played her on @-@ screen husband Luke Roberts , for winning her her audition . She went on to appear in Gone With the Wind sequel Scarlett , and star in Welsh soap opera Tiger Bay . She has spoken critically of the way the BBC treated the soap , moving it around the schedules and declining to commission a second series . She acted alongside Pauline <unk> in both <unk> <unk> and Being April , deeming <unk> to be a " fantastic " actress , and one she would work alongside again " like a shot " . In 1996 , <unk> appeared at the National Theatre in Helen <unk> 's adaptation of Leo <unk> 's War and Peace . In 2001 , she became a presenter of the BBC Wales arts programme Double Yellow , alongside poet Owen <unk> and performance artist Mark Rees . She posed nude but for a pair of yellow rubber gloves to promote the show 's launch , and was highly critical of the BBC when the show was cancelled midway through its second series . She has since concluded that " the kind of audience they would like to bring in with shows like Double Yellow aren 't really into watching TV " , but at the time was outspoken against the show 's cancellation , stating : 
- <unk> 's other notable television appearances include the BBC psychological thriller Green @-@ Eyed Monster ( 2001 ) , soap opera <unk> ( 2001 ) , Waking the Dead ( 2001 ) , London 's Burning ( 2001 ) , <unk> ( 2002 ) , Murder in Mind ( 2003 ) , The Canterbury Tales ( 2003 ) and Sea of <unk> ( 2004 ) . In 2008 , she starred in the Doctor Who episode " Midnight " , playing an <unk> <unk> alongside David Tennant 's Tenth Doctor . In 2009 , <unk> starred in the <unk> musical comedy My Almost Famous Family . She stated : " The script made me laugh out loud when I read it . [ ... ] I also like the fact that there were a lot of politically @-@ correct boxes being <unk> , but the writers and producer haven 't been restrained by that . " So , instead of <unk> to this altar , they 've said , ' Okay , we have this family that 's half @-@ black , half @-@ white , half @-@ American , half @-@ British . We have a mix of boys and girls , one character who 's mixed @-@ raced and <unk> – but we 're not going to be restrained by any of that . We 're not going to <unk> around Martha 's disability or anything . ' I liked that . It wasn 't some sort of <unk> hands @-@ off approach to what we 're presenting . " She has also been cast in the film <unk> . 
- 
- = = = <unk> City = = = 
- 
- From 7 February 2006 to 9 December 2008 , <unk> starred in BBC medical drama <unk> City as nurse <unk> Tyson . She had previously appeared in <unk> City 's sister show <unk> , and had made an appearance in <unk> City 's fifth series as patient <unk> Lawson . She was approached about returning to the show in a more permanent role by BBC casting director Julie <unk> . After a series of three meetings , a year after the birth of her first child , she agreed to assume the role of <unk> , despite being " nervous about signing such a long contract " , stating : " I 've been someone who 's loved the uncertainty of acting . I 've loved that one month it 's <unk> in Morocco and the next I 'm doing a stage play , then it 's a six @-@ part <unk> thing . But last summer I started thinking it would be really nice not to have to look for a job every couple of weeks . It would be nice just to stay put for a while . If I 'm going to be a working mum I 'd rather just be one rather than be one intermittently . And then <unk> came along , so I 'm very grateful " . 
- <unk> signed a three @-@ year contract to play <unk> , and described herself as " very similar " to her character , speaking positively about her working relationship with co @-@ stars Jaye <unk> and Sharon D. Clarke , as well as the " dream " storylines her character was given . She said of the show : 
- " I really love <unk> and I 'm surprised how much . What thrills me about <unk> is that you get back what you put in . The powers that be are only as interested in you as they think you are in the job . For me it 's about more than turning up and saying the lines . It 's about understanding it and trying to make it better . That 's actually a lot of fun . What 's great is nobody says , ' Just shut up . ' They want you to work at it . [ ... ] I surprise myself sometimes because after two and a half years sometimes I see friends on the side of a poster and I think , ' do I want to do something else ? ' but I really like this job , I never get that awful ‘ <unk> ’ feeling when you really don ’ t want to go into work . " 
- In October 2008 , <unk> announced that she would be departing from <unk> City in order to have a second child , stating : " I 've had a great time and I 'll miss it . I made the decision purely because I was pregnant , but I wasn 't really ready to put down the character of <unk> . I 'm very sorry to have to let her go . " 
- 
- = = = Race and charity work = = = 
- 
- Throughout her career , <unk> has been outspoken on the subject of racial discrimination in the entertainment industry . Describing her motivation , she states : " I am not an overtly political person . I just want fairness " . <unk> believes that black actors receive less recognition than their white counterparts , explaining ; " If you get a show with six stars and one is black you are more likely to see interviews with the five white actors . [ ... ] They are not being sold as a reason to watch . " She believes that her career would have taken her in a different direction were she not of ethnic origin , stating : " I could not have played any of the roles I have played on TV if I was white [ ... ] I am very aware of where the glass ceiling is and it 's still very low and expectations are still very low " . She has noted having casting directors accept the notion of characters being both black and Welsh to be a particular problem , explaining that : " I get offered a lot of very different roles , but they 're never Welsh . [ ... ] The one time I was asked to play a Welsh character on screen was in Tiger Bay for BBC Wales , but I know if that series had been called <unk> Park or <unk> Close I wouldn 't have been in it " . In 2001 , <unk> founded a production company and directed a short film entitled <unk> 's <unk> . She presented the film at the Cannes film festival , using it as part of her campaign for increased black representation in theatre , films and television . The project , however , was largely unsuccessful , with <unk> stating : " it just made me decide that if there 's anything I don 't want to do , it 's produce films , because I 'm rubbish at it . I was so bad with the budget that I just said yes to everything and then had to worry about how to pay for things at the end . " In 2008 , <unk> offered her support to the Action for Southern Africa campaign <unk> ! Period , aiming to provide affordable <unk> protection to <unk> women . 
- 
- = = = Awards = = = 
- 
- <unk> was nominated and shortlisted for the ' Female Performance in TV ' award in the 2006 Screen Nation Awards , for her role as <unk> Tyson in <unk> City . She received Honourable Mention for the same role the following year , and was shortlisted again in 2008 . In 2005 , <unk> voted <unk> the 29th <unk> woman in Wales , saying of her : " One of our favourite <unk> , she 's black , beautiful and the term " <unk> mummy " should have been made for her . " She placed 40th in 2008 , with the Western Mail noting : " the last 15 years have only seen <unk> become more beautiful and more successful " . <unk> placed 17th in the awards in 2009 . 
- 
- = = Personal life = = 
- 
- <unk> describes herself as " optimistic , cynical , lazy , naive [ and ] honest " . She spends her free time " watching television , the theatre , cinema , concerts , indoor wall climbing [ and ] keeping fit [ with ] <unk> [ and ] aerobics " . She has a collection of <unk> dolls which she keeps under her bath . <unk> has an interest in travel , and has visited Peru and Kenya among other destinations . She has expressed a desire to visit Brazil and Argentina , and has " a romantic notion of travelling around Europe in a <unk> van " . <unk> often speaks of her pride in her Welsh upbringing , describing herself as " an Ely girl through and through " . She was made a Fellow of the Royal Welsh College of Music and Drama in 2003 . 
- During a 1998 production of Hamlet , <unk> met her future husband , fellow actor Adam <unk> – son of Love <unk> <unk> star Jack <unk> . They met again two years later , during a production of Twelfth Night , and went on to become a couple . The pair married in May 2004 , and <unk> gave birth to their first daughter in July that year . Their second daughter was born in January 2009 . 
- 
- = = Filmography = = 
- 
- <unk> has undertaken the following roles from her screen and <unk> debut in 1993 , to the present day . 
- 
- = = = Film roles = = = 
- 
- 
- = = = Television roles = = = 
- 
- 
diff --git a/PyTorch/LanguageModel/fp16util.py b/PyTorch/LanguageModel/fp16util.py
deleted file mode 100644
index 9b7ab332..00000000
--- a/PyTorch/LanguageModel/fp16util.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-
-
-def params_to_type(params, totype):
-    new_params = []
-    for param in params:
-        new_params.append(param.type(totype))
-    return new_params
-
-
-def params_to_16(params):
-    return params_to_type(params, torch.cuda.HalfTensor)
-
-
-def params_to_32(params):
-    return params_to_type(params, torch.cuda.FloatTensor)
-
-
-def clone_params(net):
-    new_params = []
-    for param in list(net.parameters()):
-        new_params.append(param.data.clone())
-    return new_params
-
-
-def clone_grads(net):
-    new_params = []
-    for param in list(net.parameters()):
-        new_params.append(param.grad.data.clone())
-    return new_params
-
-
-def copy_in_params(net, params):
-    net_params = list(net.parameters())
-    for i in range(len(params)):
-        net_params[i].data.copy_(params[i])
diff --git a/PyTorch/LanguageModel/generate.py b/PyTorch/LanguageModel/generate.py
deleted file mode 100644
index 4ee9ad2a..00000000
--- a/PyTorch/LanguageModel/generate.py
+++ /dev/null
@@ -1,74 +0,0 @@
-###############################################################################
-# Language Modeling on Penn Tree Bank
-#
-# This file generates new sentences sampled from the language model
-#
-###############################################################################
-
-import argparse
-
-import torch
-from torch.autograd import Variable
-
-import data
-
-parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 Language Model')
-
-# Model parameters.
-parser.add_argument('--data', type=str, default='./data/wikitext-2',
-                    help='location of the data corpus')
-parser.add_argument('--checkpoint', type=str, default='./model.pt',
-                    help='model checkpoint to use')
-parser.add_argument('--outf', type=str, default='generated.txt',
-                    help='output file for generated text')
-parser.add_argument('--words', type=int, default='1000',
-                    help='number of words to generate')
-parser.add_argument('--seed', type=int, default=1111,
-                    help='random seed')
-parser.add_argument('--cuda', action='store_true',
-                    help='use CUDA')
-parser.add_argument('--temperature', type=float, default=1.0,
-                    help='temperature - higher will increase diversity')
-parser.add_argument('--log-interval', type=int, default=100,
-                    help='reporting interval')
-args = parser.parse_args()
-
-# Set the random seed manually for reproducibility.
-torch.manual_seed(args.seed)
-if torch.cuda.is_available():
-    if not args.cuda:
-        print("WARNING: You have a CUDA device, so you should probably run with --cuda")
-    else:
-        torch.cuda.manual_seed(args.seed)
-
-if args.temperature < 1e-3:
-    parser.error("--temperature has to be greater or equal 1e-3")
-
-with open(args.checkpoint, 'rb') as f:
-    model = torch.load(f)
-model.eval()
-
-if args.cuda:
-    model.cuda()
-else:
-    model.cpu()
-
-corpus = data.Corpus(args.data)
-ntokens = len(corpus.dictionary)
-hidden = model.init_hidden(1)
-input = Variable(torch.rand(1, 1).mul(ntokens).long(), volatile=True)
-if args.cuda:
-    input.data = input.data.cuda()
-
-with open(args.outf, 'w') as outf:
-    for i in range(args.words):
-        output, hidden = model(input, hidden)
-        word_weights = output.squeeze().data.div(args.temperature).exp().cpu()
-        word_idx = torch.multinomial(word_weights, 1)[0]
-        input.data.fill_(word_idx)
-        word = corpus.dictionary.idx2word[word_idx]
-
-        outf.write(word + ('\n' if i % 20 == 19 else ' '))
-
-        if i % args.log_interval == 0:
-            print('| Generated {}/{} words'.format(i, args.words))
diff --git a/PyTorch/LanguageModel/main.py b/PyTorch/LanguageModel/main.py
deleted file mode 100644
index 2da3ab0a..00000000
--- a/PyTorch/LanguageModel/main.py
+++ /dev/null
@@ -1,236 +0,0 @@
-# coding: utf-8
-import argparse
-import time
-import math
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-from fp16util import *
-import data
-import model
-
-parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 RNN/LSTM Language Model')
-parser.add_argument('--data', type=str, default='./data/wikitext-2',
-                    help='location of the data corpus')
-parser.add_argument('--model', type=str, default='LSTM',
-                    help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)')
-parser.add_argument('--emsize', type=int, default=200,
-                    help='size of word embeddings')
-parser.add_argument('--nhid', type=int, default=200,
-                    help='number of hidden units per layer')
-parser.add_argument('--nlayers', type=int, default=2,
-                    help='number of layers')
-parser.add_argument('--lr', type=float, default=20,
-                    help='initial learning rate')
-parser.add_argument('--clip', type=float, default=0.25,
-                    help='gradient clipping')
-parser.add_argument('--epochs', type=int, default=40,
-                    help='upper epoch limit')
-parser.add_argument('--batch_size', type=int, default=20, metavar='N',
-                    help='batch size')
-parser.add_argument('--bptt', type=int, default=35,
-                    help='sequence length')
-parser.add_argument('--dropout', type=float, default=0.2,
-                    help='dropout applied to layers (0 = no dropout)')
-parser.add_argument('--tied', action='store_true',
-                    help='tie the word embedding and softmax weights')
-parser.add_argument('--seed', type=int, default=1111,
-                    help='random seed')
-parser.add_argument('--cuda', action='store_true',
-                    help='use CUDA')
-parser.add_argument('--log-interval', type=int, default=200, metavar='N',
-                    help='report interval')
-parser.add_argument('--save', type=str,  default='model.pt',
-                    help='path to save the final model')
-parser.add_argument('--fp16', action='store_true',
-                    help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
-parser.add_argument('--loss_scale', type=float, default=1,
-                    help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
-
-args = parser.parse_args()
-
-# Set the random seed manually for reproducibility.
-torch.manual_seed(args.seed)
-if torch.cuda.is_available():
-    if not args.cuda:
-        print("WARNING: You have a CUDA device, so you should probably run with --cuda")
-    else:
-        torch.cuda.manual_seed(args.seed)
-if args.fp16 and not args.cuda:
-    print("WARNING: --fp16 requires --cuda, ignoring --fp16 option")
-
-###############################################################################
-# Load data
-###############################################################################
-
-corpus = data.Corpus(args.data)
-
-# Starting from sequential data, batchify arranges the dataset into columns.
-# For instance, with the alphabet as the sequence and batch size 4, we'd get
-# ┌ a g m s ┐
-# │ b h n t │
-# │ c i o u │
-# │ d j p v │
-# │ e k q w │
-# └ f l r x ┘.
-# These columns are treated as independent by the model, which means that the
-# dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient
-# batch processing.
-
-def batchify(data, bsz):
-    # Work out how cleanly we can divide the dataset into bsz parts.
-    nbatch = data.size(0) // bsz
-    # Trim off any extra elements that wouldn't cleanly fit (remainders).
-    data = data.narrow(0, 0, nbatch * bsz)
-    # Evenly divide the data across the bsz batches.
-    data = data.view(bsz, -1).t().contiguous()
-    if args.cuda:
-        data = data.cuda()
-    return data
-
-
-eval_batch_size = 10
-train_data = batchify(corpus.train, args.batch_size)
-val_data = batchify(corpus.valid, eval_batch_size)
-test_data = batchify(corpus.test, eval_batch_size)
-
-###############################################################################
-# Build the model
-###############################################################################
-
-ntokens = len(corpus.dictionary)
-model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.nlayers, args.dropout, args.tied)
-
-if args.cuda and args.fp16:
-    model.type(torch.cuda.HalfTensor)
-    param_copy = params_to_32(clone_params(model))
-elif args.cuda:
-    model.cuda()
-criterion = nn.CrossEntropyLoss()
-
-###############################################################################
-# Training code
-###############################################################################
-
-
-def repackage_hidden(h):
-    """Wraps hidden states in new Variables, to detach them from their history."""
-    if type(h) == Variable:
-        return Variable(h.data)
-    else:
-        return tuple(repackage_hidden(v) for v in h)
-
-
-# get_batch subdivides the source data into chunks of length args.bptt.
-# If source is equal to the example output of the batchify function, with
-# a bptt-limit of 2, we'd get the following two Variables for i = 0:
-# ┌ a g m s ┐ ┌ b h n t ┐
-# └ b h n t ┘ └ c i o u ┘
-# Note that despite the name of the function, the subdivison of data is not
-# done along the batch dimension (i.e. dimension 1), since that was handled
-# by the batchify function. The chunks are along dimension 0, corresponding
-# to the seq_len dimension in the LSTM.
-
-def get_batch(source, i, evaluation=False):
-    seq_len = min(args.bptt, len(source) - 1 - i)
-    data = Variable(source[i:i+seq_len], volatile=evaluation)
-    target = Variable(source[i+1:i+1+seq_len].view(-1))
-    return data, target
-
-
-def evaluate(data_source):
-    # Turn on evaluation mode which disables dropout.
-    model.eval()
-    total_loss = 0
-    ntokens = len(corpus.dictionary)
-    hidden = model.init_hidden(eval_batch_size)
-    for i in range(0, data_source.size(0) - 1, args.bptt):
-        data, targets = get_batch(data_source, i, evaluation=True)
-        output, hidden = model(data, hidden)
-        output_flat = output.view(-1, ntokens)
-        #total loss can overflow if accumulated in fp16.
-        total_loss += len(data) * criterion(output_flat, targets).data.float()
-        hidden = repackage_hidden(hidden)
-    return total_loss[0] / len(data_source)
-
-
-def train():
-    # Turn on training mode which enables dropout.
-    model.train()
-    total_loss = 0
-    start_time = time.time()
-    ntokens = len(corpus.dictionary)
-    hidden = model.init_hidden(args.batch_size)
-    for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
-        data, targets = get_batch(train_data, i)
-        # Starting each batch, we detach the hidden state from how it was previously produced.
-        # If we didn't, the model would try backpropagating all the way to start of the dataset.
-        hidden = repackage_hidden(hidden)
-        model.zero_grad()
-        output, hidden = model(data, hidden)
-        loss = criterion(output.view(-1, ntokens), targets)
-        loss = loss * args.loss_scale
-        loss.backward()
-        loss = loss / args.loss_scale
-        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
-        torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
-
-        if args.fp16 and args.cuda:
-            grad = params_to_32(clone_grads(model))
-            for i, _ in enumerate(param_copy):
-                param_copy[i] = param_copy[i] - grad[i] * (lr/args.loss_scale)
-            copy_in_params(model, params_to_16(param_copy))
-        else:
-            for p in model.parameters():
-                p.data.add_(-lr/args.loss_scale, p.grad.data)
-
-        total_loss += loss.data
-
-        if batch % args.log_interval == 0 and batch > 0:
-            cur_loss = total_loss[0] / args.log_interval
-            elapsed = time.time() - start_time
-            print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
-                  'loss {:5.2f} | ppl {:8.2f}'.format(
-                      epoch, batch, len(train_data) // args.bptt, lr,
-                      elapsed * 1000 / args.log_interval, cur_loss, math.exp(min(cur_loss, 20))))
-            total_loss = 0
-            start_time = time.time()
-
-
-# Loop over epochs.
-lr = args.lr
-best_val_loss = None
-
-# At any point you can hit Ctrl + C to break out of training early.
-try:
-    for epoch in range(1, args.epochs+1):
-        epoch_start_time = time.time()
-        train()
-        val_loss = evaluate(val_data)
-        print('-' * 89)
-        print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
-              'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
-                                         val_loss, math.exp(min(val_loss, 20))))
-        print('-' * 89)
-        # Save the model if the validation loss is the best we've seen so far.
-        if not best_val_loss or val_loss < best_val_loss:
-            with open(args.save, 'wb') as f:
-                torch.save(model, f)
-            best_val_loss = val_loss
-        else:
-            # Anneal the learning rate if no improvement has been seen in the validation dataset.
-            lr /= 4.0
-except KeyboardInterrupt:
-    print('-' * 89)
-    print('Exiting from training early')
-
-# Load the best saved model.
-with open(args.save, 'rb') as f:
-    model = torch.load(f)
-
-# Run on test data.
-test_loss = evaluate(test_data)
-print('=' * 89)
-print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
-    test_loss, math.exp(test_loss)))
-print('=' * 89)
diff --git a/PyTorch/LanguageModel/model.py b/PyTorch/LanguageModel/model.py
deleted file mode 100644
index e810c15f..00000000
--- a/PyTorch/LanguageModel/model.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch.nn as nn
-from torch.autograd import Variable
-
-
-class RNNModel(nn.Module):
-    """Container module with an encoder, a recurrent module, and a decoder."""
-
-    def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):
-        super(RNNModel, self).__init__()
-        self.drop = nn.Dropout(dropout)
-        self.encoder = nn.Embedding(ntoken, ninp)
-        if rnn_type in ['LSTM', 'GRU']:
-            self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)
-        else:
-            try:
-                nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]
-            except KeyError:
-                raise ValueError("""An invalid option for `--model` was supplied,
-                                 options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']""")
-            self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)
-        self.decoder = nn.Linear(nhid, ntoken)
-
-        # Optionally tie weights as in:
-        # "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
-        # https://arxiv.org/abs/1608.05859
-        # and
-        # "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
-        # https://arxiv.org/abs/1611.01462
-        if tie_weights:
-            if nhid != ninp:
-                raise ValueError('When using the tied flag, nhid must be equal to emsize')
-            self.decoder.weight = self.encoder.weight
-
-        self.init_weights()
-
-        self.rnn_type = rnn_type
-        self.nhid = nhid
-        self.nlayers = nlayers
-
-    def init_weights(self):
-        initrange = 0.1
-        self.encoder.weight.data.uniform_(-initrange, initrange)
-        self.decoder.bias.data.fill_(0)
-        self.decoder.weight.data.uniform_(-initrange, initrange)
-
-    def forward(self, input, hidden):
-        emb = self.drop(self.encoder(input))
-        output, hidden = self.rnn(emb, hidden)
-        output = self.drop(output)
-        decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2)))
-        return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden
-
-    def init_hidden(self, bsz):
-        weight = next(self.parameters()).data
-        if self.rnn_type == 'LSTM':
-            return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
-                    Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()))
-        else:
-            return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())
diff --git a/PyTorch/LanguageModel/requirements.txt b/PyTorch/LanguageModel/requirements.txt
deleted file mode 100644
index 12c6d5d5..00000000
--- a/PyTorch/LanguageModel/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-torch
diff --git a/PyTorch/Segmentation/MaskRCNN/NOTICE b/PyTorch/Segmentation/MaskRCNN/NOTICE
new file mode 100644
index 00000000..c63e2b38
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/NOTICE
@@ -0,0 +1,4 @@
+MaskRCNN PyTorch
+
+This repository includes software from https://github.com/facebookresearch/maskrcnn-benchmark
+licensed under the MIT License.
diff --git a/PyTorch/Segmentation/MaskRCNN/README.md b/PyTorch/Segmentation/MaskRCNN/README.md
new file mode 100755
index 00000000..26aa1043
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/README.md
@@ -0,0 +1,480 @@
+# Mask R-CNN For PyTorch
+This repository provides a script and recipe to train and infer on MaskRCNN to achieve state of the art accuracy, and is tested and maintained by NVIDIA.
+
+## Table Of Contents
+* [The model](#the-model)
+  * [Default configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+  * [Command line arguments](#command-line-arguments)
+  * [Getting the data](#getting-the-data)
+  * [Training process](#training-process)
+  * [Enabling mixed precision](#enabling-mixed-precision)
+* [Benchmarking](#benchmarking)
+* [Results](#results)
+  * [Training accuracy results](#training-accuracy-results)
+    * [Training stability test](#training-stability-test)
+  * [Training performance results](#training-performance-results)
+    * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g)
+    * [NVIDIA DGX-1 (8x V100 32G)](#nvidia-dgx-1-8x-v100-32g)
+  * [Inference performance results](#inference-performance-results)
+    * [NVIDIA DGX-1 16G (1x V100 16G)](#nvidia-dgx-1-16g-1x-v100-16g)
+    * [NVIDIA DGX-1 32G (1x V100 32G)](#nvidia-dgx-1-32g-1x-v100-32g)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+## The model
+
+Mask R-CNN is a convolution based neural network for the task of object instance segmentation. The paper describing the model can be found [here](https://arxiv.org/abs/1703.06870). NVIDIA’s Mask R-CNN 19.2 is an optimized version of [Facebook’s implementation](https://github.com/facebookresearch/maskrcnn-benchmark), leveraging mixed precision arithmetic and tensor cores on V100 GPUs for 1.3x faster training times while maintaining target accuracy. Because this model trains with mixed precision tensor cores on Volta, researchers can get results much faster than training without tensor cores.  This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
+
+The repository also contains scripts to interactively launch training, benchmarking and inference routines in a Docker container.
+
+The major differences between the official implementation of the paper and our version of Mask R-CNN are as follows:
+  - Mixed precision support with [PyTorch AMP](https://www.google.com/url?q=https://github.com/NVIDIA/apex&sa=D&ust=1552333999336000&usg=AFQjCNGO8dE0mQMO0lUfhMyCs7g0NmJXkw).
+  - Gradient accumulation to simulate larger batches.
+  - Custom fused CUDA kernels for faster computations.
+
+These techniques/optimizations improve model performance and reduce training time by a factor of 1.3x, allowing you to perform more efficient instance segmentation with no additional effort.
+
+Other publicly available implementations of Mask R-CNN include:
+  - [Matterport](https://github.com/matterport/Mask_RCNN)
+  - [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN)
+  - [Google’s tensorflow model](https://github.com/tensorflow/models/tree/master/research/object_detection)
+
+### Default Configuration
+The default configuration of this model can be found at `pytorch/maskrcnn_benchmark/config/defaults.py`. The default hyper-parameters are as follows:
+  - General:
+    - Base Learning Rate set to 0.001
+    - Global batch size set to 16 images
+    - Steps set to 30000
+    - Images resized with aspect ratio maintained and smaller side length between [800,1333]
+    - Global train batch size - 16
+    - Global test batch size - 8
+
+  - Feature extractor:
+    - Backend network set to Resnet50_conv4
+    - Backbone network weights are frozen after second epoch
+
+  - Region Proposal Network (RPN):
+    - Anchor stride set to 16
+    - Anchor sizes set to (32, 64, 128, 256, 512)
+    - Foreground IOU Threshold set to 0.7, Background IOU Threshold set to 0.5
+    - RPN target fraction of positive proposals set to 0.5
+    - Train Pre-NMS Top proposals set to 12000
+    - Train Post-NMS Top proposals set to 2000
+    - Test Pre-NMS Top proposals set to 6000
+    - Test Post-NMS Top proposals set to 1000
+    - RPN NMS Threshold set to 0.7
+
+  - RoI heads:
+    - Foreground threshold set to 0.5
+    - Batch size per image set to 512
+    - Positive fraction of batch set to 0.25
+
+This repository implements multi-gpu and gradient accumulation to support larger batches and mixed precision support. This implementation also includes the following optimizations.
+  - Target generation - Optimized GPU implementation for generating binary mask ground truths from the list of polygon coordinates that exist in the dataset.
+  - Custom CUDA kernels for:
+    - Box Intersection over Union (IoU) computation
+    - Proposal matcher
+    - Generate anchor boxes
+    - Pre NMS box selection - Selection of RoIs based on objectness score before NMS is applied.
+
+    The source files can be found under `maskrcnn_benchmark/csrc/cuda`.
+## Setup
+The following sections list the requirements in order to start training the Mask R-CNN model.
+### Requirements
+This repository contains `Dockerfile` which extends the PyTorch NGC container and encapsulates some dependencies.  Aside from these dependencies, ensure you have the following components:
+  - [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+  - [PyTorch 19.02-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch)
+  - [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+  For more information about how to get started with NGC containers, see the
+  following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
+  Documentation:
+  - [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+  - [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+  - [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)
+
+## Quick Start Guide
+To train your model using mixed precision with tensor cores or using FP32, perform the following steps using the default parameters of the Mask R-CNN model on the COCO 2014 dataset.
+
+### 1. Clone the repository.
+```
+git clone https://github.com/NVIDIA/DeepLearningExamples.git
+cd DeepLearningExamples/PyTorch/Segmentation/MaskRCNN
+```
+
+### 2. Download and preprocess the dataset.
+This repository provides scripts to download and extract the COCO 2014 dataset.  Data will be downloaded to the `current working` directory on the host and extracted to a user-defined directory
+
+To download, verify, and extract the COCO dataset, use the following scripts:
+  ```
+  cd Detectron_PyT
+  ./download_dataset.sh <data/dir>
+  ```
+By default, the data is organized into the following structure:
+  ```
+  <data/dir>
+    annotations/
+      instances_train2014.json
+      instances_val2014.json
+    train2014/
+      COCO_train2014_*.jpg
+    val2014/
+      COCO_val2014_*.jpg
+  ```
+
+### 3. Build the Mask R-CNN PyTorch NGC container.
+```
+bash scripts/docker/build.sh
+```
+
+### 4. Start an interactive session in the NGC container to run training/inference.
+After you build the container image, you can start an interactive CLI session with  
+```
+bash scripts/docker/interactive.sh <path/to/dataset/>
+```
+The `interactive.sh` script requires that the location on the dataset is specified.  For example, `/home/<USER>/Detectron_PyT/detectron/lib/datasets/data/coco`
+
+
+### 5. Start training.
+```
+bash scripts/train.sh
+```
+The `train.sh` script trains a model and performs evaluation on the COCO 2014 dataset. By default, the training script:
+  - Uses 8 GPUs.
+  - Saves a checkpoint every 2500 iterations and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).
+  - Mixed precision training with Tensor Cores is invoked by adding `DTYPE \"float16\"` to the end of the above command as shown in the train script. This will override the default `DTYPE` configuration which is float32.
+
+  The `scripts/train.sh` script runs the following Python command:
+  ```
+  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file “configs/e2e_mask_rcnn_R_50_FPN_1x.yaml”   
+  ```
+
+### 6. Start validation/evaluation.
+  ```
+  bash scripts/eval.sh
+  ```
+Model evaluation on a checkpoint can be launched by running the  `pytorch/scripts/eval.sh` script. The script requires:
+- the location of the checkpoint folder to be specified and present within/mounted to the container.
+- a text file named last_checkpoint which contains the path to the latest checkpoint. This mechanism is required in order to resume training from the latest checkpoint.
+- The file last_checkpoint is automatically created at the end of the training process.
+
+By default, evaluation is performed on the test dataset once training is complete. To skip evaluation at the end of training, issue the `--skip-test` flag.
+
+Additionally, to perform evaluation after every epoch and terminate training on reaching a minimum required mAP score, set
+- `PER_EPOCH_EVAL = True`
+- `MIN_BBOX_MAP = <required value>`
+- `MIN_MASK_MAP = <required value>`
+
+### 7. Start inference/predictions.
+
+Model predictions can be obtained on a test dataset and a model checkpoint by running the  `scripts/inference.sh <config/file/path>` script. The script requires:
+  - the location of the checkpoint folder and dataset to be specified and present within/mounted to the container.
+  - a text file named last_checkpoint which contains the path to the checkpoint.
+
+For example:
+```
+bash scripts/inference.sh configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
+```
+
+Model predictions get saved in the `<OUTPUT_DIR>/inference` directory.
+
+To perform inference and skip computation of mAP scores, issue the `--skip-eval` flag. Performance is reported in seconds per iteration per GPU. The benchmarking scripts can be used to extract frames per second on training and inference.
+
+## Details
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+### Command line arguments
+You can modify the training behaviour through the various flags in both the `train_net.py` script and through overriding specific parameters in the YAML config files. Flags in the `train_net.py` script are as follows:
+  
+  `--config_file` - path to config file containing model params
+  
+  `--skip-test` - skips model testing after training
+  
+  `--skip-eval` - skips computation of mAP scores
+  
+  `--opts` - allows for you to override specific params in config file
+
+For example:
+```
+python -m torch.distributed.launch --nproc_per_node=2 tools/train_net.py \
+    --config-file configs/e2e_faster_rcnn_R_50_FPN_1x.yaml \
+    --skip-eval \
+    DTYPE "float16" \
+    OUTPUT_DIR RESULTS \
+    SOLVER.BASE_LR 0.002 \
+    SOLVER.STEPS “(360000, 480000)”
+```
+
+### Getting the data
+The Mask R-CNN model was trained on the [COCO 2014](http://cocodataset.org/#download) dataset.  This dataset comes with a training and validation set.  
+
+This repository contains the `./download_dataset.sh`,`./verify_dataset.sh`, and `./extract_dataset.sh` scripts which automatically download and preprocess the training and validation sets.
+
+In order to run on your own dataset, ensure your dataset is present/mounted to the Docker container with the following hierarchy:
+```
+my_dataset/
+  images_train/
+  images_val/
+  instances_train.json
+  instances_val.json
+```
+and add it to `DATASETS` dictionary in `maskrcnn_benchmark/config/paths_catalog.py`
+
+```
+DATASETS = {
+        "my_dataset_train": {
+            "img_dir": "data/images_train",
+            "ann_file": "data/instances_train.json"
+        },
+        "my_dataset_val": {
+            "img_dir": "data/images_val",
+            "ann_file": "data/instances_val.json"
+        },
+      }
+```
+```
+python -m torch.distributed.launch --nproc_per_node=<NUM_GPUS> tools/train_net.py \
+        --config-file <CONFIG? \
+        DATASETS.TRAIN "(\"my_dataset_train\")"\
+        DATASETS.TEST "(\"my_dataset_val\")"\
+        DTYPE "float16" \
+        OUTPUT_DIR <RESULTS> \
+        | tee <LOGFILE>
+```
+
+### Training Process
+Training is performed using the `tools/train_net.py` script along with parameters defined in the config file. The default config files can be found in the `pytorch/configs/` directory.
+
+The `e2e_mask_rcnn_R_50_FPN_1x.yaml` file was used to gather accuracy and performance metrics. This configuration sets the following parameters:
+  - Backbone weights to ResNet-50
+  - Feature extractor set to ResNet-50 with Feature Pyramid Networks (FPN)
+  - RPN uses FPN
+  - RoI Heads use FPN
+  - Dataset - COCO 2014
+  - Base Learning Rate - 0.02
+  - Global train batch size - 16
+  - Global test batch size - 8
+  - RPN batch size - 256
+  - ROI batch size - 512
+  - Solver steps - (60000, 80000)
+  - Max iterations - 90000
+  - Warmup iterations - 500
+  - Warmup factor = 0.33
+    - Initial learning rate = Base Learning Rate x Warmup factor
+
+The default feature extractor can be changed by setting `CONV_BODY` parameter in `yaml` file to any of the following:
+  - R-50-C4
+  - R-50-C5
+  - R-101-C4
+  - R-101-C5
+  - R-101-FPN
+
+The default backbone can be changed to a flavor of Resnet-50 or ResNet-101 by setting `WEIGHT` parameter in `yaml` file to any of the following:
+  - "catalog://ImageNetPretrained/MSRA/R-50-GN"
+  - "catalog://ImageNetPretrained/MSRA/R-101"
+  - "catalog://ImageNetPretrained/MSRA/R-101-GN"
+
+This script outputs results to the current working directory by default. However, this can be changed by adding `OUTPUT_DIR <DIR_NAME>` to the end of the default command. Logs produced during training are also stored in the `OUTPUT_DIR` specified. The training log will contain information about:
+  - Loss, time per iteration, learning rate and memory metrics
+  - performance values such as time per step
+  - test accuracy and test performance values after evaluation
+
+The training logs are located in the `<OUTPUT_DIR>/log` directory. The summary after each training epoch is printed in the following format:
+  ```
+  INFO:maskrcnn_benchmark.trainer:eta: 4:42:15  iter: 20  loss: 1.8236 (2.7274)  loss_box_reg: 0.0249 (0.0620)  loss_classifier: 0.6086 (1.2918)  loss_mask: 0.6996 (0.8026)  loss_objectness: 0.5373 (0.4787)  loss_rpn_box_reg: 0.0870 (0.0924)  time: 0.2002 (0.3765)  data: 0.0099 (0.1242)  lr: 0.014347  max mem: 3508
+  ```
+  The mean and median training losses are reported every 20 steps.
+
+Multi-gpu and multi-node training is enabled with the PyTorch distributed launch module. The following example runs training on 8 GPUs:
+  ```
+  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file \"configs/e2e_mask_rcnn_R_50_FPN_1x.yaml\"
+  ```
+
+We have tested a batch sizes upto 4 on a 16GB V100 and upto 16 on a 32G V100 with mixed precision. The repository also implements gradient accumulation functionality to simulate bigger batches. The following command can be used to run a batch of 64:
+  ```
+  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file \"configs/e2e_mask_rcnn_R_50_FPN_1x.yaml\" SOLVER.ACCUMULATE_GRAD True SOLVER.ACCUMULATE_STEPS 4
+  ```
+
+By default, training is performed using FP32, however training time can be reduced using tensor cores and mixed precision. This can be done by adding `DTYPE \"float16\"` to override the respective parameter in the config file.
+
+__Note__: When training a global batch size >= 32, it is recommended to additionally set the following parameters:
+  - `SOLVER.WARMUP_ITERS 625`
+  - `SOLVER.WARMUP_FACTOR 0.01`
+
+When experimenting with different global batch sizes for training and inference, make sure `SOLVER.IMS_PER_BATCH` and `TEST.IMS_PER_BATCH` are divisible by the number of GPUs.  
+
+#### Other training options
+A sample single GPU config is provided under `configs/e2e_mask_rcnn_R_50_FPN_1x_1GPU.yaml`
+
+For multi-gpu runs, `-m torch.distributed.launch --nproc_per_node num_gpus` is added prior to `tools/train_net.py`.  For example, for an 8 GPU run:
+```
+python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file “configs/e2e_mask_rcnn_R_50_FPN_1x.yaml”   
+```
+
+Training is terminated when either the required accuracies specified on the command line are reached or if the number of training iterations specified is reached.
+
+To terminate training on reaching target accuracy on 8 GPUs, run:
+```
+python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file “configs/e2e_mask_rcnn_R_50_FPN_1x.yaml” PER_EPOCH_EVAL True MIN_BBOX_MAP 0.377 MIN_MASK_MAP 0.342
+```
+
+__Note__: The score is always the Average Precision(AP) at
+  - IoU = 0.50:0.95
+  - Area = all - include small, medium and large
+  - maxDets = 100
+
+## Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.  Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously required two steps:
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+
+Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP),  library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In PyTorch, loss scaling can be easily applied by using scale_loss() method provided by amp. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.
+
+For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.
+
+To enable mixed precision, you can:
+  - Import AMP from APEX, for example:
+    ```
+    from apex import amp
+    ```
+  - Initialize an AMP handle, for example:
+    ```
+    amp_handle = amp.init(enabled=True, verbose=True)
+    ```
+  - Wrap your optimizer with the AMP handle, for example:
+    ```
+    optimizer = amp_handle.wrap_optimizer(optimizer)
+    ```
+  - Scale loss before backpropagation (assuming loss is stored in a variable called losses)
+    - Default backpropagate for FP32:
+      ```
+      losses.backward()
+      ```
+    - Scale loss and backpropagate with AMP:
+      ```
+      with optimizer.scale_loss(losses) as scaled_losses:
+        scaled_losses.backward()
+      ```
+
+For information about:
+- how to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+- Techniques used for [mixed precision training, see the Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training).
+
+## Benchmarking
+Benchmarking can be performed for both training and inference. Both scripts run the Mask R-CNN model using the parameters defined in `configs/e2e_mask_rcnn_R_50_FPN_1x.yaml`. You can specify whether benchmarking is performed in FP16 or FP32 by specifying it as an argument to the benchmarking scripts.
+
+Training benchmarking can performed by running the script:
+```
+scripts/train_benchmark.sh <float16/float32>
+```
+
+Inference benchmarking can be performed by running the script:
+```
+scripts/inference_benchmark.sh <float16/float32>
+```
+
+## Results
+The following sections provide details on how we achieved our performance and accuracy in training and inference.
+### Training Accuracy Results
+Our results were obtained by running the `tools/train_net.py`  training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+
+| **number of GPUs** | **batch size/GPU** | **Training time with FP16(hours)** | **Training time with FP32(hours)** |
+| --- | --- | ----- | ----- |
+|  8  | 4 | 4.81 | 6.35 |
+
+LOSS CURVE:
+
+![Loss Curve](./img/loss_curve.png)
+
+Here, multihead loss is simply the summation of losses on the mask head and the bounding box head.
+
+ACCURACY CURVE:
+
+![Accuracy Curve](./img/accuracy_curve.png)
+
+#### Training Stability Test
+The following tables compare mAP scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively.  The runs showcase consistent convergence on all 5 seeds with very little deviation.
+
+| **Config** | **Seed #1** | **Seed #2** | **Seed #3** |  **Seed #4** | **Seed #5** | **mean** | **std** |
+| --- | --- | ----- | ----- | --- | --- | ----- | ----- |
+|  8 GPUs, fp16, final AP BBox  | 0.377 | 0.376 | 0.376 | 0.378  | 0.377 | 0.377 | 0.001 |
+| 8 GPUs, fp16, final AP Segm | 0.343 | 0.342 | 0.341 | 0.343  | 0.343 | 0.342 | 0.001 |
+
+| **Config** | **Seed #1** | **Seed #2** | **Seed #3** |  **Seed #4** | **Seed #5** | **mean** | **std** |
+| --- | --- | ----- | ----- | --- | --- | ----- | ----- |
+|  8 GPUs, fp32, final AP BBox  | 0.377 | 0.377 | 0.376 | 0.378  | 0.378 | 0.377 | 0.001 |
+| 8 GPUs, fp32, final AP Segm | 0.344 | 0.342 | 0.343 | 0.343  | 0.343 | 0.342 | 0.001 |
+
+### Training Performance Results
+#### NVIDIA DGX-1  (8x V100 16G)
+Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in tokens per second) were averaged over an entire training epoch.
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+| --- | --- | ----- | ----- | --- | --- | ----- |
+| 1 | 2 | 8.47 | 10.77 | 1.27 | 1 | 1 |
+| 4 | 2 | 30.23 | 36.88 | 1.22 | 3.67 | 3.53 |
+| 8 | 2 | 56.35 | 70.45 | 1.25 | 6.96 | 6.51 |
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+| --- | --- | ----- | ----- | --- | --- | ----- |
+| 1 | 4 | 9.29 | 12.73 | 1.37 | 1 | 1 |
+| 4 | 4 | 34.07 | 44.95 | 1.32 | 3.67 | 3.53 |
+| 8 | 4 | 62.7 | 82.9 | 1.32 | 6.75 | 6.51 |
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+#### NVIDIA DGX-1 (8x V100 32G)
+Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+| --- | --- | ----- | ----- | --- | --- | ----- |
+| 1 | 4 | 9.06 | 13.14 | 1.45 | 1 | 1 |
+| 4 | 4 | 32.87 | 50.70 | 1.54 | 3.86 | 3.63 |
+| 8 | 4 | 62.93 | 82.30 | 1.31 | 6.94 | 6.26 |
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+| --- | --- | ----- | ----- | --- | --- | ----- |
+| 1 | 8 | 9.35 | 13.05 | 1.40 | 1 | 1 |
+| 4 | 8 | 33.38 | 46.69 | 1.40 | 3.57 | 3.57 |
+| 8 | 8 | 71.85 | 87.10 | 1.21 | 7.68 | 7.68 |
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+| --- | --- | ----- | ----- | --- | --- | ----- |
+| 1 | 16 | NA | 13.82 | NA | NA | 1 |
+| 4 | 16 | NA | 48.41 | NA | NA | 3.50 |
+| 8 | 16 | NA | 89.33 | NA | NA | 6.46 |
+
+It should be noted that respective values for FP32 runs using a batch size of 16 are not available due to out of memory errors that arise. Batch size of 16 is only available on using FP16.
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+### Inference performance results
+#### NVIDIA DGX-1 16G (1x V100 16G)
+Our results were obtained by running the `scripts/inference.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speedup** |
+| --- | --- | ----- | ----- | ----- |
+|  1  | 8 | 15.3 | 16.94 | 1.107 |
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+#### NVIDIA DGX-1 32G (1x V100 32G)
+Our results were obtained by running the `scripts/inference.sh <config/file/path>` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.
+
+| **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speedup** |
+| --- | --- | ----- | ----- | ----- |
+|  1  | 8 | 14.43  | 16.33 | 1.13 |
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+## Changelog
+March 2019
+  - Initial release
+
+## Known Issues
+There are no known issues with this model.
diff --git a/PyTorch/Segmentation/MaskRCNN/download_dataset.sh b/PyTorch/Segmentation/MaskRCNN/download_dataset.sh
new file mode 100755
index 00000000..d2d1a4d3
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/download_dataset.sh
@@ -0,0 +1,28 @@
+DATA_DIR=$1
+
+wget https://dl.fbaipublicfiles.com/detectron/coco/coco_annotations_minival.tgz
+wget http://images.cocodataset.org/zips/train2014.zip
+wget http://images.cocodataset.org/zips/val2014.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
+
+if md5sum -c hashes.md5
+then
+	echo "DOWNLOAD PASSED"
+	# mkdir $DATA_DIR
+	mv coco_annotations_minival.tgz $DATA_DIR
+	mv train2014.zip $DATA_DIR
+	mv val2014.zip $DATA_DIR
+	mv annotations_trainval2014.zip $DATA_DIR
+
+	cd $DATA_DIR
+	dtrx --one=here coco_annotations_minival.tgz
+	dtrx --one=here annotations_trainval2014.zip
+	mv annotations.1/* annotations/
+
+	dtrx train2014.zip
+	dtrx val2014.zip
+
+	echo "EXTRACTION COMPLETE"
+else
+	echo "DOWNLOAD FAILED HASHCHECK"
+fi
diff --git a/PyTorch/Segmentation/MaskRCNN/download_weights.sh b/PyTorch/Segmentation/MaskRCNN/download_weights.sh
new file mode 100755
index 00000000..150fabbc
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/download_weights.sh
@@ -0,0 +1 @@
+wget https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
diff --git a/PyTorch/Segmentation/MaskRCNN/hashes.md5 b/PyTorch/Segmentation/MaskRCNN/hashes.md5
new file mode 100755
index 00000000..ae7267f7
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/hashes.md5
@@ -0,0 +1,4 @@
+2d2b9d2283adb5e3b8d25eec88e65064  coco_annotations_minival.tgz
+0da8c0bd3d6becc4dcb32757491aca88  train2014.zip
+a3d79f5ed8d289b7a7554ce06a5782b3  val2014.zip
+0a379cfc70b0e71301e0f377548639bd  annotations_trainval2014.zip
diff --git a/PyTorch/Segmentation/MaskRCNN/img/accuracy_curve.png b/PyTorch/Segmentation/MaskRCNN/img/accuracy_curve.png
new file mode 100644
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diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/ABSTRACTIONS.md b/PyTorch/Segmentation/MaskRCNN/pytorch/ABSTRACTIONS.md
new file mode 100755
index 00000000..cdb3c428
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/ABSTRACTIONS.md
@@ -0,0 +1,65 @@
+## Abstractions
+The main abstractions introduced by `maskrcnn_benchmark` that are useful to
+have in mind are the following:
+
+### ImageList
+In PyTorch, the first dimension of the input to the network generally represents
+the batch dimension, and thus all elements of the same batch have the same
+height / width.
+In order to support images with different sizes and aspect ratios in the same
+batch, we created the `ImageList` class, which holds internally a batch of
+images (os possibly different sizes). The images are padded with zeros such that
+they have the same final size and batched over the first dimension. The original
+sizes of the images before padding are stored in the `image_sizes` attribute,
+and the batched tensor in `tensors`.
+We provide a convenience function `to_image_list` that accepts a few different
+input types, including a list of tensors, and returns an `ImageList` object.
+
+```python
+from maskrcnn_benchmark.structures.image_list import to_image_list
+
+images = [torch.rand(3, 100, 200), torch.rand(3, 150, 170)]
+batched_images = to_image_list(images)
+
+# it is also possible to make the final batched image be a multiple of a number
+batched_images_32 = to_image_list(images, size_divisible=32)
+```
+
+### BoxList
+The `BoxList` class holds a set of bounding boxes (represented as a `Nx4` tensor) for
+a specific image, as well as the size of the image as a `(width, height)` tuple.
+It also contains a set of methods that allow to perform geometric
+transformations to the bounding boxes (such as cropping, scaling and flipping).
+The class accepts bounding boxes from two different input formats:
+- `xyxy`, where each box is encoded as a `x1`, `y1`, `x2` and `y2` coordinates, and
+- `xywh`, where each box is encoded as `x1`, `y1`, `w` and `h`.
+
+Additionally, each `BoxList` instance can also hold arbitrary additional information
+for each bounding box, such as labels, visibility, probability scores etc.
+
+Here is an example on how to create a `BoxList` from a list of coordinates:
+```python
+from maskrcnn_benchmark.structures.bounding_box import BoxList, FLIP_LEFT_RIGHT
+
+width = 100
+height = 200
+boxes = [
+  [0, 10, 50, 50],
+  [50, 20, 90, 60],
+  [10, 10, 50, 50]
+]
+# create a BoxList with 3 boxes
+bbox = BoxList(boxes, image_size=(width, height), mode='xyxy')
+
+# perform some box transformations, has similar API as PIL.Image
+bbox_scaled = bbox.resize((width * 2, height * 3))
+bbox_flipped = bbox.transpose(FLIP_LEFT_RIGHT)
+
+# add labels for each bbox
+labels = torch.tensor([0, 10, 1])
+bbox.add_field('labels', labels)
+
+# bbox also support a few operations, like indexing
+# here, selects boxes 0 and 2
+bbox_subset = bbox[[0, 2]]
+```
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/CODE_OF_CONDUCT.md b/PyTorch/Segmentation/MaskRCNN/pytorch/CODE_OF_CONDUCT.md
new file mode 100755
index 00000000..0f7ad8bf
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/CODE_OF_CONDUCT.md
@@ -0,0 +1,5 @@
+# Code of Conduct
+
+Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
+Please read the [full text](https://code.fb.com/codeofconduct/)
+so that you can understand what actions will and will not be tolerated.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/CONTRIBUTING.md b/PyTorch/Segmentation/MaskRCNN/pytorch/CONTRIBUTING.md
new file mode 100755
index 00000000..fc14cd3c
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/CONTRIBUTING.md
@@ -0,0 +1,39 @@
+# Contributing to Mask-RCNN Benchmark
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Our Development Process
+Minor changes and improvements will be released on an ongoing basis. Larger changes (e.g., changesets implementing a new paper) will be released on a more periodic basis.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `master`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## Coding Style  
+* 4 spaces for indentation rather than tabs
+* 80 character line length
+* PEP8 formatting following [Black](https://black.readthedocs.io/en/stable/)
+
+## License
+By contributing to Mask-RCNN Benchmark, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/Dockerfile b/PyTorch/Segmentation/MaskRCNN/pytorch/Dockerfile
new file mode 100755
index 00000000..5ed74fe2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/Dockerfile
@@ -0,0 +1,37 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.02-py3
+FROM ${FROM_IMAGE_NAME}
+
+# Install Python dependencies
+RUN pip install --upgrade --no-cache-dir pip \
+ && pip install --no-cache-dir \
+      mlperf-compliance==0.0.10 \
+      opencv-python==3.4.1.15 \
+      yacs
+
+WORKDIR /opt
+RUN git clone -b v0.1 https://github.com/NVIDIA/cocoapi.git \
+ && cd cocoapi/PythonAPI \
+ && pip install -e .
+
+# Copy detectron code and build
+WORKDIR /workspace/object_detection
+RUN mkdir -p /datasets/coco
+RUN mkdir /results
+COPY . .
+RUN pip install -e .
+
+ENV OMP_NUM_THREADS=1
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/INSTALL.md b/PyTorch/Segmentation/MaskRCNN/pytorch/INSTALL.md
new file mode 100755
index 00000000..504fdf25
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/INSTALL.md
@@ -0,0 +1,78 @@
+## Installation
+
+### Requirements:
+- PyTorch 1.0 from a nightly release. Installation instructions can be found in https://pytorch.org/get-started/locally/
+- torchvision from master
+- cocoapi
+- yacs
+- matplotlib
+- GCC >= 4.9
+- (optional) OpenCV for the webcam demo
+
+
+### Option 1: Step-by-step installation
+
+```bash
+# first, make sure that your conda is setup properly with the right environment
+# for that, check that `which conda`, `which pip` and `which python` points to the
+# right path. From a clean conda env, this is what you need to do
+
+conda create --name maskrcnn_benchmark
+source activate maskrcnn_benchmark
+
+# this installs the right pip and dependencies for the fresh python
+conda install ipython
+
+# maskrcnn_benchmark and coco api dependencies
+pip install ninja yacs cython matplotlib
+
+# follow PyTorch installation in https://pytorch.org/get-started/locally/
+# we give the instructions for CUDA 9.0
+conda install pytorch-nightly -c pytorch
+
+# install torchvision
+cd ~/github
+git clone https://github.com/pytorch/vision.git
+cd vision
+python setup.py install
+
+# install pycocotools
+cd ~/github
+git clone https://github.com/cocodataset/cocoapi.git
+cd cocoapi/PythonAPI
+python setup.py build_ext install
+
+# install apex
+cd ~github
+git clone https://github.com/NVIDIA/apex.git
+cd apex
+python setup.py install --cuda_ext --cpp_ext
+
+# install PyTorch Detection
+cd ~/github
+git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
+cd maskrcnn-benchmark
+# the following will install the lib with
+# symbolic links, so that you can modify
+# the files if you want and won't need to
+# re-build it
+python setup.py build develop
+
+# or if you are on macOS
+# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop
+```
+
+### Option 2: Docker Image (Requires CUDA, Linux only)
+
+Build image with defaults (`CUDA=9.0`, `CUDNN=7`):
+
+    nvidia-docker build -t maskrcnn-benchmark docker/
+    
+Build image with other CUDA and CUDNN versions:
+
+    nvidia-docker build -t maskrcnn-benchmark --build-arg CUDA=9.2 --build-arg CUDNN=7 docker/ 
+    
+Build and run image with built-in jupyter notebook(note that the password is used to log in jupyter notebook):
+
+    nvidia-docker build -t maskrcnn-benchmark-jupyter docker/docker-jupyter/
+    nvidia-docker run -td -p 8888:8888 -e PASSWORD=<password> -v <host-dir>:<container-dir> maskrcnn-benchmark-jupyter
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/LICENSE b/PyTorch/Segmentation/MaskRCNN/pytorch/LICENSE
new file mode 100755
index 00000000..8585e11b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Facebook
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/MODEL_ZOO.md b/PyTorch/Segmentation/MaskRCNN/pytorch/MODEL_ZOO.md
new file mode 100755
index 00000000..d678ef88
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/MODEL_ZOO.md
@@ -0,0 +1,82 @@
+## Model Zoo and Baselines
+
+### Hardware
+- 8 NVIDIA V100 GPUs
+
+### Software
+- PyTorch version: 1.0.0a0+dd2c487
+- CUDA 9.2
+- CUDNN 7.1
+- NCCL 2.2.13-1
+
+### End-to-end Faster and Mask R-CNN baselines
+
+All the baselines were trained using the exact same experimental setup as in Detectron.
+We initialize the detection models with ImageNet weights from Caffe2, the same as used by Detectron.
+
+The pre-trained models are available in the link in the model id.
+
+backbone | type | lr sched | im / gpu | train mem(GB) | train time (s/iter) | total train time(hr) | inference time(s/im) | box AP | mask AP | model id
+-- | -- | -- | -- | -- | -- | -- | -- | -- | -- | --
+R-50-C4 | Fast | 1x | 1 | 5.8 | 0.4036 | 20.2 | 0.17130 | 34.8 | - | [6358800](https://download.pytorch.org/models/maskrcnn/e2e_faster_rcnn_R_50_C4_1x.pth)
+R-50-FPN | Fast | 1x | 2 | 4.4 | 0.3530 | 8.8 | 0.12580 | 36.8 | - | [6358793](https://download.pytorch.org/models/maskrcnn/e2e_faster_rcnn_R_50_FPN_1x.pth)
+R-101-FPN | Fast | 1x | 2 | 7.1 | 0.4591 | 11.5 | 0.143149 | 39.1 | - | [6358804](https://download.pytorch.org/models/maskrcnn/e2e_faster_rcnn_R_101_FPN_1x.pth)
+X-101-32x8d-FPN | Fast | 1x | 1 | 7.6 | 0.7007 | 35.0 | 0.209965 | 41.2 | - | [6358717](https://download.pytorch.org/models/maskrcnn/e2e_faster_rcnn_X_101_32x8d_FPN_1x.pth)
+R-50-C4 | Mask | 1x | 1 | 5.8 | 0.4520 | 22.6 | 0.17796 + 0.028 | 35.6 | 31.5 | [6358801](https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_50_C4_1x.pth)
+R-50-FPN | Mask | 1x | 2 | 5.2 | 0.4536 | 11.3 | 0.12966 + 0.034 | 37.8 | 34.2 | [6358792](https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_50_FPN_1x.pth)
+R-101-FPN | Mask | 1x | 2 | 7.9 | 0.5665 | 14.2 | 0.15384 + 0.034 | 40.1 | 36.1 | [6358805](https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_101_FPN_1x.pth)
+X-101-32x8d-FPN | Mask | 1x | 1 | 7.8 | 0.7562 | 37.8 | 0.21739 + 0.034 | 42.2 | 37.8 | [6358718](https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_X_101_32x8d_FPN_1x.pth)
+
+
+## Comparison with Detectron and mmdetection
+
+In the following section, we compare our implementation with [Detectron](https://github.com/facebookresearch/Detectron)
+and [mmdetection](https://github.com/open-mmlab/mmdetection).
+The same remarks from [mmdetection](https://github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md#training-speed)
+about different hardware applies here.
+
+### Training speed
+
+The numbers here are in seconds / iteration. The lower, the better.
+
+type | Detectron (P100) | mmdetection (V100) | maskrcnn_benchmark (V100)
+-- | -- | -- | --
+Faster R-CNN R-50 C4 | 0.566 | - | 0.4036
+Faster R-CNN R-50 FPN | 0.544 | 0.554 | 0.3530
+Faster R-CNN R-101 FPN | 0.647 | - | 0.4591
+Faster R-CNN X-101-32x8d FPN | 0.799 | - | 0.7007
+Mask R-CNN R-50 C4 | 0.620 | - | 0.4520
+Mask R-CNN R-50 FPN | 0.889 | 0.690 | 0.4536
+Mask R-CNN R-101 FPN | 1.008 | - | 0.5665
+Mask R-CNN X-101-32x8d FPN | 0.961 | - | 0.7562
+
+### Training memory
+
+The lower, the better
+
+type | Detectron (P100) | mmdetection (V100) | maskrcnn_benchmark (V100)
+-- | -- | -- | --
+Faster R-CNN R-50 C4 | 6.3 | - | 5.8
+Faster R-CNN R-50 FPN | 7.2 | 4.9 | 4.4
+Faster R-CNN R-101 FPN | 8.9 | - | 7.1
+Faster R-CNN X-101-32x8d FPN | 7.0 | - | 7.6
+Mask R-CNN R-50 C4 | 6.6 | - | 5.8
+Mask R-CNN R-50 FPN | 8.6 | 5.9 | 5.2
+Mask R-CNN R-101 FPN | 10.2 | - | 7.9
+Mask R-CNN X-101-32x8d FPN | 7.7 | - | 7.8
+
+### Accuracy
+
+The higher, the better
+
+type | Detectron (P100) | mmdetection (V100) | maskrcnn_benchmark (V100)
+-- | -- | -- | --
+Faster R-CNN R-50 C4 | 34.8 | - | 34.8
+Faster R-CNN R-50 FPN | 36.7 | 36.7 | 36.8
+Faster R-CNN R-101 FPN | 39.4 | - | 39.1
+Faster R-CNN X-101-32x8d FPN | 41.3 | - | 41.2
+Mask R-CNN R-50 C4 | 35.8 & 31.4 | - | 35.6 & 31.5
+Mask R-CNN R-50 FPN | 37.7 & 33.9 | 37.5 & 34.4 | 37.8 & 34.2
+Mask R-CNN R-101 FPN | 40.0 & 35.9 | - | 40.1 & 36.1
+Mask R-CNN X-101-32x8d FPN | 42.1 & 37.3 | - | 42.2 & 37.8
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/TROUBLESHOOTING.md b/PyTorch/Segmentation/MaskRCNN/pytorch/TROUBLESHOOTING.md
new file mode 100755
index 00000000..63a8b598
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/TROUBLESHOOTING.md
@@ -0,0 +1,67 @@
+# Troubleshooting
+
+Here is a compilation if common issues that you might face
+while compiling / running this code:
+
+## Compilation errors when compiling the library
+If you encounter build errors like the following:
+```
+/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
+     struct is_convertible
+        ^~~~~~~~~~~~~~
+/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
+     }
+ ^
+error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1
+```
+check your CUDA version and your `gcc` version.
+```
+nvcc --version
+gcc --version
+```
+If you are using CUDA 9.0 and gcc 6.4.0, then refer to https://github.com/facebookresearch/maskrcnn-benchmark/issues/25,
+which has a summary of the solution. Basically, CUDA 9.0 is not compatible with gcc 6.4.0.
+
+## ImportError: No module named maskrcnn_benchmark.config when running webcam.py
+
+This means that `maskrcnn-benchmark` has not been properly installed.
+Refer to https://github.com/facebookresearch/maskrcnn-benchmark/issues/22 for a few possible issues.
+Note that we now support Python 2 as well.
+
+
+## ImportError: Undefined symbol: __cudaPopCallConfiguration error when import _C
+
+This probably means that the inconsistent version of NVCC compile and your conda CUDAToolKit package. This is firstly mentioned in https://github.com/facebookresearch/maskrcnn-benchmark/issues/45 . All you need to do is:
+
+```
+# Check the NVCC compile version(e.g.)
+/usr/cuda-9.2/bin/nvcc --version
+# Check the CUDAToolKit version(e.g.)
+~/anaconda3/bin/conda list | grep cuda
+
+# If you need to update your CUDAToolKit
+~/anaconda3/bin/conda install -c anaconda cudatoolkit==9.2
+```
+
+Both of them should have the **same** version. For example, if NVCC==9.2 and CUDAToolKit==9.2, this will be fine while when NVCC==9.2 but CUDAToolKit==9, it fails.
+
+
+## Segmentation fault (core dumped) when running the library
+This probably means that you have compiled the library using GCC < 4.9, which is ABI incompatible with PyTorch.
+Indeed, during installation, you probably saw a message like
+```
+Your compiler (g++ 4.8) may be ABI-incompatible with PyTorch!
+Please use a compiler that is ABI-compatible with GCC 4.9 and above.
+See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.
+
+See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
+for instructions on how to install GCC 4.9 or higher.
+```
+Follow the instructions on https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
+to install GCC 4.9 or higher, and try recompiling `maskrcnn-benchmark` again, after cleaning the
+`build` folder with
+```
+rm -rf build
+```
+
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_101_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_101_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..e129ac88
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_101_FPN_1x_caffe2.yaml
@@ -0,0 +1,25 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35857890/e2e_faster_rcnn_R-101-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml
new file mode 100755
index 00000000..393defe7
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml
@@ -0,0 +1,5 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35857197/e2e_faster_rcnn_R-50-C4_1x"
+DATASETS:
+  TEST: ("coco_2014_minival",)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..180d737a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_R_50_FPN_1x_caffe2.yaml
@@ -0,0 +1,25 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35857345/e2e_faster_rcnn_R-50-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..166a2ea0
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_faster_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml
@@ -0,0 +1,29 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..57da8e8c
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml
@@ -0,0 +1,34 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35861795/e2e_mask_rcnn_R-101-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_C4_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_C4_1x_caffe2.yaml
new file mode 100755
index 00000000..d1d0572f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_C4_1x_caffe2.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35858791/e2e_mask_rcnn_R-50-C4_1x"
+  ROI_MASK_HEAD:
+    PREDICTOR: "MaskRCNNC4Predictor"
+    SHARE_BOX_FEATURE_EXTRACTOR: True
+  MASK_ON: True
+DATASETS:
+  TEST: ("coco_2014_minival",)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..f0e675df
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml
@@ -0,0 +1,34 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/35858933/e2e_mask_rcnn_R-50-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml
new file mode 100755
index 00000000..c97b9407
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2/e2e_mask_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml
@@ -0,0 +1,38 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://Caffe2Detectron/COCO/36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+  MASK_ON: True
+DATASETS:
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml
new file mode 100755
index 00000000..1df95387
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,32 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 9
+DATASETS:
+  TRAIN: ("cityscapes_fine_instanceonly_seg_train_cocostyle",)
+  TEST: ("cityscapes_fine_instanceonly_seg_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
new file mode 100755
index 00000000..fcaccaa9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,41 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 9
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("cityscapes_fine_instanceonly_seg_train_cocostyle",)
+  TEST: ("cityscapes_fine_instanceonly_seg_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_101_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_101_FPN_1x.yaml
new file mode 100755
index 00000000..45b07e06
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_101_FPN_1x.yaml
@@ -0,0 +1,31 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_C4_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_C4_1x.yaml
new file mode 100755
index 00000000..5cec224a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_C4_1x.yaml
@@ -0,0 +1,15 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 1000
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (120000, 160000)
+  MAX_ITER: 180000
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_FPN_1x.yaml
new file mode 100755
index 00000000..267a12c1
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_R_50_FPN_1x.yaml
@@ -0,0 +1,31 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml
new file mode 100755
index 00000000..9338c876
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml
@@ -0,0 +1,36 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (120000, 160000)
+  MAX_ITER: 180000
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml
new file mode 100755
index 00000000..c2da8f37
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml
@@ -0,0 +1,40 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_C4_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_C4_1x.yaml
new file mode 100755
index 00000000..bfcd2586
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_C4_1x.yaml
@@ -0,0 +1,19 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 1000
+  ROI_MASK_HEAD:
+    PREDICTOR: "MaskRCNNC4Predictor"
+    SHARE_BOX_FEATURE_EXTRACTOR: True
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (120000, 160000)
+  MAX_ITER: 180000
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
new file mode 100755
index 00000000..9362dd17
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
@@ -0,0 +1,43 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.04
+  WEIGHT_DECAY: 0.0001
+  STEPS: (30000, 40000)
+  MAX_ITER: 45000
+  IMS_PER_BATCH: 32
+TEST:
+  IMS_PER_BATCH: 32
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x_1GPU.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x_1GPU.yaml
new file mode 100644
index 00000000..93efe5eb
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x_1GPU.yaml
@@ -0,0 +1,43 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (240000, 360000)
+  MAX_ITER: 360000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 1
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_X_101_32x8d_FPN_1x.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_X_101_32x8d_FPN_1x.yaml
new file mode 100755
index 00000000..4204419b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_X_101_32x8d_FPN_1x.yaml
@@ -0,0 +1,45 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (120000, 160000)
+  MAX_ITER: 180000
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_1x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_1x_gn.yaml
new file mode 100755
index 00000000..e8f3ca7f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_1x_gn.yaml
@@ -0,0 +1,48 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50-GN"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml
new file mode 100755
index 00000000..d25b4343
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_faster_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml
@@ -0,0 +1,50 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50-GN"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    CONV_HEAD_DIM: 256
+    NUM_STACKED_CONVS: 4
+    FEATURE_EXTRACTOR: "FPNXconv1fcFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_1x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_1x_gn.yaml
new file mode 100755
index 00000000..e59c8b9a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_1x_gn.yaml
@@ -0,0 +1,59 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50-GN"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    USE_GN: True # use GN for mask head
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    CONV_LAYERS: (256, 256, 256, 256)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml
new file mode 100755
index 00000000..f3bba2f5
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/e2e_mask_rcnn_R_50_FPN_Xconv1fc_1x_gn.yaml
@@ -0,0 +1,61 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50-GN"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    CONV_HEAD_DIM: 256
+    NUM_STACKED_CONVS: 4
+    FEATURE_EXTRACTOR: "FPNXconv1fcFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    USE_GN: True # use GN for mask head
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    CONV_LAYERS: (256, 256, 256, 256)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_3x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_3x_gn.yaml
new file mode 100755
index 00000000..a13049f5
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_3x_gn.yaml
@@ -0,0 +1,49 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "" # no pretrained model
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+    FREEZE_CONV_BODY_AT: 0 # finetune all layers
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml
new file mode 100755
index 00000000..d04feddf
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_faster_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml
@@ -0,0 +1,51 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "" # no pretrained model
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+    FREEZE_CONV_BODY_AT: 0 # finetune all layers
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    CONV_HEAD_DIM: 256
+    NUM_STACKED_CONVS: 4
+    FEATURE_EXTRACTOR: "FPNXconv1fcFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_3x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_3x_gn.yaml
new file mode 100755
index 00000000..c8eae5e0
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_3x_gn.yaml
@@ -0,0 +1,60 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "" # no pretrained model
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+    FREEZE_CONV_BODY_AT: 0 # finetune all layers
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    USE_GN: True # use GN for mask head
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    CONV_LAYERS: (256, 256, 256, 256)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml
new file mode 100755
index 00000000..db823379
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines/scratch_e2e_mask_rcnn_R_50_FPN_Xconv1fc_3x_gn.yaml
@@ -0,0 +1,62 @@
+INPUT:
+  MIN_SIZE_TRAIN: 800
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "" # no pretrained model
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+    FREEZE_CONV_BODY_AT: 0 # finetune all layers
+  RESNETS: # use GN for backbone
+    TRANS_FUNC: "BottleneckWithGN"
+    STEM_FUNC: "StemWithGN"
+  FPN:
+    USE_GN: True # use GN for FPN
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 512
+    POSITIVE_FRACTION: 0.25
+  ROI_BOX_HEAD:
+    USE_GN: True # use GN for bbox head
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    CONV_HEAD_DIM: 256
+    NUM_STACKED_CONVS: 4
+    FEATURE_EXTRACTOR: "FPNXconv1fcFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    USE_GN: True # use GN for mask head
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    CONV_LAYERS: (256, 256, 256, 256)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml
new file mode 100755
index 00000000..77dea5b4
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml
@@ -0,0 +1,20 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 300
+    ANCHOR_SIZES: (128, 256, 512)
+  ROI_BOX_HEAD:
+    NUM_CLASSES: 21
+DATASETS:
+  TRAIN: ("voc_2007_train", "voc_2007_val")
+  TEST: ("voc_2007_test",)
+SOLVER:
+  BASE_LR: 0.001
+  WEIGHT_DECAY: 0.0001
+  STEPS: (50000, )
+  MAX_ITER: 70000
+  IMS_PER_BATCH: 1
+TEST:
+  IMS_PER_BATCH: 1
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml
new file mode 100755
index 00000000..a8fb663a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml
@@ -0,0 +1,20 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 300
+    ANCHOR_SIZES: (128, 256, 512)
+  ROI_BOX_HEAD:
+    NUM_CLASSES: 21
+DATASETS:
+  TRAIN: ("voc_2007_train", "voc_2007_val")
+  TEST: ("voc_2007_test",)
+SOLVER:
+  BASE_LR: 0.004
+  WEIGHT_DECAY: 0.0001
+  STEPS: (12500, )
+  MAX_ITER: 17500
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 4
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
new file mode 100755
index 00000000..e6f29e88
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,41 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 21
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("voc_2012_train_cocostyle",)
+  TEST: ("voc_2012_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_C4_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_C4_quick.yaml
new file mode 100755
index 00000000..d5eae445
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_C4_quick.yaml
@@ -0,0 +1,24 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    BATCH_SIZE_PER_IMAGE: 256
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 2
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_FPN_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_FPN_quick.yaml
new file mode 100755
index 00000000..f69d029f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_R_50_FPN_quick.yaml
@@ -0,0 +1,40 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 256
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_X_101_32x8d_FPN_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_X_101_32x8d_FPN_quick.yaml
new file mode 100755
index 00000000..d36ef53a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_faster_rcnn_X_101_32x8d_FPN_quick.yaml
@@ -0,0 +1,44 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 256
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 2
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_C4_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_C4_quick.yaml
new file mode 100755
index 00000000..621dd0f6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_C4_quick.yaml
@@ -0,0 +1,28 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    BATCH_SIZE_PER_IMAGE: 256
+  ROI_MASK_HEAD:
+    PREDICTOR: "MaskRCNNC4Predictor"
+    SHARE_BOX_FEATURE_EXTRACTOR: True
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml
new file mode 100755
index 00000000..28760d8f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml
@@ -0,0 +1,49 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 256
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_X_101_32x8d_FPN_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_X_101_32x8d_FPN_quick.yaml
new file mode 100755
index 00000000..a6f1283a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/e2e_mask_rcnn_X_101_32x8d_FPN_quick.yaml
@@ -0,0 +1,53 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+    BATCH_SIZE_PER_IMAGE: 256
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 2
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_C4_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_C4_quick.yaml
new file mode 100755
index 00000000..ecf1e876
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_C4_quick.yaml
@@ -0,0 +1,23 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN_ONLY: True
+  RPN:
+    PRE_NMS_TOP_N_TEST: 12000
+    POST_NMS_TOP_N_TEST: 2000
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_FPN_quick.yaml b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_FPN_quick.yaml
new file mode 100755
index 00000000..d762b4f9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules/rpn_R_50_FPN_quick.yaml
@@ -0,0 +1,31 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN_ONLY: True
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 2000
+    FPN_POST_NMS_TOP_N_TEST: 2000
+DATASETS:
+  TRAIN: ("coco_2014_minival",)
+  TEST: ("coco_2014_minival",)
+INPUT:
+  MIN_SIZE_TRAIN: 600
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.005
+  WEIGHT_DECAY: 0.0001
+  STEPS: (1500,)
+  MAX_ITER: 2000
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 2
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/demo/Mask_R-CNN_demo.ipynb b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/Mask_R-CNN_demo.ipynb
new file mode 100755
index 00000000..0d975eab
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/Mask_R-CNN_demo.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mask R-CNN demo\n",
+    "\n",
+    "This notebook illustrates one possible way of using `maskrcnn_benchmark` for computing predictions on images from an arbitrary URL.\n",
+    "\n",
+    "Let's start with a few standard imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.pylab as pylab\n",
+    "\n",
+    "import requests\n",
+    "from io import BytesIO\n",
+    "from PIL import Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this makes our figures bigger\n",
+    "pylab.rcParams['figure.figsize'] = 20, 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Those are the relevant imports for the detection model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from maskrcnn_benchmark.config import cfg\n",
+    "from predictor import COCODemo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide a helper class `COCODemo`, which loads a model from the config file, and performs pre-processing, model prediction and post-processing for us.\n",
+    "\n",
+    "We can configure several model options by overriding the config options.\n",
+    "In here, we make the model run on the CPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_file = \"../configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml\"\n",
+    "\n",
+    "# update the config options with the config file\n",
+    "cfg.merge_from_file(config_file)\n",
+    "# manual override some options\n",
+    "cfg.merge_from_list([\"MODEL.DEVICE\", \"cpu\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create the `COCODemo` object. It contains a few extra options for conveniency, such as the confidence threshold for detections to be shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coco_demo = COCODemo(\n",
+    "    cfg,\n",
+    "    min_image_size=800,\n",
+    "    confidence_threshold=0.7,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define a few helper functions for loading images from a URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load(url):\n",
+    "    \"\"\"\n",
+    "    Given an url of an image, downloads the image and\n",
+    "    returns a PIL image\n",
+    "    \"\"\"\n",
+    "    response = requests.get(url)\n",
+    "    pil_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
+    "    # convert to BGR format\n",
+    "    image = np.array(pil_image)[:, :, [2, 1, 0]]\n",
+    "    return image\n",
+    "\n",
+    "def imshow(img):\n",
+    "    plt.imshow(img[:, :, [2, 1, 0]])\n",
+    "    plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now load an image from the COCO dataset. It's reference is in the comment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11133b9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# from http://cocodataset.org/#explore?id=345434\n",
+    "image = load(\"http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg\")\n",
+    "imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computing the predictions\n",
+    "\n",
+    "We provide a `run_on_opencv_image` function, which takes an image as it was loaded by OpenCV (in `BGR` format), and computes the predictions on them, returning an image with the predictions overlayed on the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x13cd27c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compute predictions\n",
+    "predictions = coco_demo.run_on_opencv_image(image)\n",
+    "imshow(predictions)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/demo/README.md b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/README.md
new file mode 100755
index 00000000..393a064b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/README.md
@@ -0,0 +1,45 @@
+## Webcam and Jupyter notebook demo
+
+This folder contains a simple webcam demo that illustrates how you can use `maskrcnn_benchmark` for inference.
+
+
+### With your preferred environment
+
+You can start it by running it from this folder, using one of the following commands:
+```bash
+# by default, it runs on the GPU
+# for best results, use min-image-size 800
+python webcam.py --min-image-size 800
+# can also run it on the CPU
+python webcam.py --min-image-size 300 MODEL.DEVICE cpu
+# or change the model that you want to use
+python webcam.py --config-file ../configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml --min-image-size 300 MODEL.DEVICE cpu
+# in order to see the probability heatmaps, pass --show-mask-heatmaps
+python webcam.py --min-image-size 300 --show-mask-heatmaps MODEL.DEVICE cpu
+```
+
+### With Docker
+
+Build the image with the tag `maskrcnn-benchmark` (check [INSTALL.md](../INSTALL.md) for instructions)
+
+Adjust permissions of the X server host (be careful with this step, refer to 
+[here](http://wiki.ros.org/docker/Tutorials/GUI) for alternatives)
+
+```bash
+xhost +
+``` 
+
+Then run a container with the demo:
+ 
+```
+docker run --rm -it \
+    -e DISPLAY=${DISPLAY} \
+    --privileged \
+    -v /tmp/.X11-unix:/tmp/.X11-unix \
+    --device=/dev/video0:/dev/video0 \
+    --ipc=host maskrcnn-benchmark \
+    python demo/webcam.py --min-image-size 300
+```
+
+**DISCLAIMER:** *This was tested for an Ubuntu 16.04 machine, 
+the volume mapping may vary depending on your platform*
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/demo/demo_e2e_mask_rcnn_R_50_FPN_1x.png b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/demo_e2e_mask_rcnn_R_50_FPN_1x.png
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diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/demo/predictor.py b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/predictor.py
new file mode 100755
index 00000000..2f27b2fb
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/predictor.py
@@ -0,0 +1,359 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import cv2
+import torch
+from torchvision import transforms as T
+
+from maskrcnn_benchmark.modeling.detector import build_detection_model
+from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
+from maskrcnn_benchmark.structures.image_list import to_image_list
+from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
+from maskrcnn_benchmark import layers as L
+from maskrcnn_benchmark.utils import cv2_util
+
+
+class COCODemo(object):
+    # COCO categories for pretty print
+    CATEGORIES = [
+        "__background",
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
+    ]
+
+    def __init__(
+        self,
+        cfg,
+        confidence_threshold=0.7,
+        show_mask_heatmaps=False,
+        masks_per_dim=2,
+        min_image_size=224,
+    ):
+        self.cfg = cfg.clone()
+        self.model = build_detection_model(cfg)
+        self.model.eval()
+        self.device = torch.device(cfg.MODEL.DEVICE)
+        self.model.to(self.device)
+        self.min_image_size = min_image_size
+
+        save_dir = cfg.OUTPUT_DIR
+        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
+        _ = checkpointer.load(cfg.MODEL.WEIGHT)
+
+        self.transforms = self.build_transform()
+
+        mask_threshold = -1 if show_mask_heatmaps else 0.5
+        self.masker = Masker(threshold=mask_threshold, padding=1)
+
+        # used to make colors for each class
+        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+
+        self.cpu_device = torch.device("cpu")
+        self.confidence_threshold = confidence_threshold
+        self.show_mask_heatmaps = show_mask_heatmaps
+        self.masks_per_dim = masks_per_dim
+
+    def build_transform(self):
+        """
+        Creates a basic transformation that was used to train the models
+        """
+        cfg = self.cfg
+
+        # we are loading images with OpenCV, so we don't need to convert them
+        # to BGR, they are already! So all we need to do is to normalize
+        # by 255 if we want to convert to BGR255 format, or flip the channels
+        # if we want it to be in RGB in [0-1] range.
+        if cfg.INPUT.TO_BGR255:
+            to_bgr_transform = T.Lambda(lambda x: x * 255)
+        else:
+            to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
+
+        normalize_transform = T.Normalize(
+            mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
+        )
+
+        transform = T.Compose(
+            [
+                T.ToPILImage(),
+                T.Resize(self.min_image_size),
+                T.ToTensor(),
+                to_bgr_transform,
+                normalize_transform,
+            ]
+        )
+        return transform
+
+    def run_on_opencv_image(self, image):
+        """
+        Arguments:
+            image (np.ndarray): an image as returned by OpenCV
+
+        Returns:
+            prediction (BoxList): the detected objects. Additional information
+                of the detection properties can be found in the fields of
+                the BoxList via `prediction.fields()`
+        """
+        predictions = self.compute_prediction(image)
+        top_predictions = self.select_top_predictions(predictions)
+
+        result = image.copy()
+        if self.show_mask_heatmaps:
+            return self.create_mask_montage(result, top_predictions)
+        result = self.overlay_boxes(result, top_predictions)
+        if self.cfg.MODEL.MASK_ON:
+            result = self.overlay_mask(result, top_predictions)
+        result = self.overlay_class_names(result, top_predictions)
+
+        return result
+
+    def compute_prediction(self, original_image):
+        """
+        Arguments:
+            original_image (np.ndarray): an image as returned by OpenCV
+
+        Returns:
+            prediction (BoxList): the detected objects. Additional information
+                of the detection properties can be found in the fields of
+                the BoxList via `prediction.fields()`
+        """
+        # apply pre-processing to image
+        image = self.transforms(original_image)
+        # convert to an ImageList, padded so that it is divisible by
+        # cfg.DATALOADER.SIZE_DIVISIBILITY
+        image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY)
+        image_list = image_list.to(self.device)
+        # compute predictions
+        with torch.no_grad():
+            predictions = self.model(image_list)
+        predictions = [o.to(self.cpu_device) for o in predictions]
+
+        # always single image is passed at a time
+        prediction = predictions[0]
+
+        # reshape prediction (a BoxList) into the original image size
+        height, width = original_image.shape[:-1]
+        prediction = prediction.resize((width, height))
+
+        if prediction.has_field("mask"):
+            # if we have masks, paste the masks in the right position
+            # in the image, as defined by the bounding boxes
+            masks = prediction.get_field("mask")
+            # always single image is passed at a time
+            masks = self.masker([masks], [prediction])[0]
+            prediction.add_field("mask", masks)
+        return prediction
+
+    def select_top_predictions(self, predictions):
+        """
+        Select only predictions which have a `score` > self.confidence_threshold,
+        and returns the predictions in descending order of score
+
+        Arguments:
+            predictions (BoxList): the result of the computation by the model.
+                It should contain the field `scores`.
+
+        Returns:
+            prediction (BoxList): the detected objects. Additional information
+                of the detection properties can be found in the fields of
+                the BoxList via `prediction.fields()`
+        """
+        scores = predictions.get_field("scores")
+        keep = torch.nonzero(scores > self.confidence_threshold).squeeze(1)
+        predictions = predictions[keep]
+        scores = predictions.get_field("scores")
+        _, idx = scores.sort(0, descending=True)
+        return predictions[idx]
+
+    def compute_colors_for_labels(self, labels):
+        """
+        Simple function that adds fixed colors depending on the class
+        """
+        colors = labels[:, None] * self.palette
+        colors = (colors % 255).numpy().astype("uint8")
+        return colors
+
+    def overlay_boxes(self, image, predictions):
+        """
+        Adds the predicted boxes on top of the image
+
+        Arguments:
+            image (np.ndarray): an image as returned by OpenCV
+            predictions (BoxList): the result of the computation by the model.
+                It should contain the field `labels`.
+        """
+        labels = predictions.get_field("labels")
+        boxes = predictions.bbox
+
+        colors = self.compute_colors_for_labels(labels).tolist()
+
+        for box, color in zip(boxes, colors):
+            box = box.to(torch.int64)
+            top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
+            image = cv2.rectangle(
+                image, tuple(top_left), tuple(bottom_right), tuple(color), 1
+            )
+
+        return image
+
+    def overlay_mask(self, image, predictions):
+        """
+        Adds the instances contours for each predicted object.
+        Each label has a different color.
+
+        Arguments:
+            image (np.ndarray): an image as returned by OpenCV
+            predictions (BoxList): the result of the computation by the model.
+                It should contain the field `mask` and `labels`.
+        """
+        masks = predictions.get_field("mask").numpy()
+        labels = predictions.get_field("labels")
+
+        colors = self.compute_colors_for_labels(labels).tolist()
+
+        for mask, color in zip(masks, colors):
+            thresh = mask[0, :, :, None]
+            contours, hierarchy = cv2_util.findContours(
+                thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
+            )
+            image = cv2.drawContours(image, contours, -1, color, 3)
+
+        composite = image
+
+        return composite
+
+    def create_mask_montage(self, image, predictions):
+        """
+        Create a montage showing the probability heatmaps for each one one of the
+        detected objects
+
+        Arguments:
+            image (np.ndarray): an image as returned by OpenCV
+            predictions (BoxList): the result of the computation by the model.
+                It should contain the field `mask`.
+        """
+        masks = predictions.get_field("mask")
+        masks_per_dim = self.masks_per_dim
+        masks = L.interpolate(
+            masks.float(), scale_factor=1 / masks_per_dim
+        ).byte()
+        height, width = masks.shape[-2:]
+        max_masks = masks_per_dim ** 2
+        masks = masks[:max_masks]
+        # handle case where we have less detections than max_masks
+        if len(masks) < max_masks:
+            masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
+            masks_padded[: len(masks)] = masks
+            masks = masks_padded
+        masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
+        result = torch.zeros(
+            (masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
+        )
+        for y in range(masks_per_dim):
+            start_y = y * height
+            end_y = (y + 1) * height
+            for x in range(masks_per_dim):
+                start_x = x * width
+                end_x = (x + 1) * width
+                result[start_y:end_y, start_x:end_x] = masks[y, x]
+        return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
+
+    def overlay_class_names(self, image, predictions):
+        """
+        Adds detected class names and scores in the positions defined by the
+        top-left corner of the predicted bounding box
+
+        Arguments:
+            image (np.ndarray): an image as returned by OpenCV
+            predictions (BoxList): the result of the computation by the model.
+                It should contain the field `scores` and `labels`.
+        """
+        scores = predictions.get_field("scores").tolist()
+        labels = predictions.get_field("labels").tolist()
+        labels = [self.CATEGORIES[i] for i in labels]
+        boxes = predictions.bbox
+
+        template = "{}: {:.2f}"
+        for box, score, label in zip(boxes, scores, labels):
+            x, y = box[:2]
+            s = template.format(label, score)
+            cv2.putText(
+                image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
+            )
+
+        return image
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/demo/webcam.py b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/webcam.py
new file mode 100755
index 00000000..5cd6a4c4
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/demo/webcam.py
@@ -0,0 +1,80 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import argparse
+import cv2
+
+from maskrcnn_benchmark.config import cfg
+from predictor import COCODemo
+
+import time
+
+
+def main():
+    parser = argparse.ArgumentParser(description="PyTorch Object Detection Webcam Demo")
+    parser.add_argument(
+        "--config-file",
+        default="../configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml",
+        metavar="FILE",
+        help="path to config file",
+    )
+    parser.add_argument(
+        "--confidence-threshold",
+        type=float,
+        default=0.7,
+        help="Minimum score for the prediction to be shown",
+    )
+    parser.add_argument(
+        "--min-image-size",
+        type=int,
+        default=224,
+        help="Smallest size of the image to feed to the model. "
+            "Model was trained with 800, which gives best results",
+    )
+    parser.add_argument(
+        "--show-mask-heatmaps",
+        dest="show_mask_heatmaps",
+        help="Show a heatmap probability for the top masks-per-dim masks",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--masks-per-dim",
+        type=int,
+        default=2,
+        help="Number of heatmaps per dimension to show",
+    )
+    parser.add_argument(
+        "opts",
+        help="Modify model config options using the command-line",
+        default=None,
+        nargs=argparse.REMAINDER,
+    )
+
+    args = parser.parse_args()
+
+    # load config from file and command-line arguments
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+
+    # prepare object that handles inference plus adds predictions on top of image
+    coco_demo = COCODemo(
+        cfg,
+        confidence_threshold=args.confidence_threshold,
+        show_mask_heatmaps=args.show_mask_heatmaps,
+        masks_per_dim=args.masks_per_dim,
+        min_image_size=args.min_image_size,
+    )
+
+    cam = cv2.VideoCapture(0)
+    while True:
+        start_time = time.time()
+        ret_val, img = cam.read()
+        composite = coco_demo.run_on_opencv_image(img)
+        print("Time: {:.2f} s / img".format(time.time() - start_time))
+        cv2.imshow("COCO detections", composite)
+        if cv2.waitKey(1) == 27:
+            break  # esc to quit
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/docker/Dockerfile b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/Dockerfile
new file mode 100755
index 00000000..e1356551
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/Dockerfile
@@ -0,0 +1,58 @@
+ARG CUDA="9.0"
+ARG CUDNN="7"
+
+FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu16.04
+
+RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
+
+# install basics
+RUN apt-get update -y \
+ && apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++ \
+ && apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev
+
+# Install Miniconda
+RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+ && chmod +x /miniconda.sh \
+ && /miniconda.sh -b -p /miniconda \
+ && rm /miniconda.sh
+
+ENV PATH=/miniconda/bin:$PATH
+
+# Create a Python 3.6 environment
+RUN /miniconda/bin/conda install -y conda-build \
+ && /miniconda/bin/conda create -y --name py36 python=3.6.7 \
+ && /miniconda/bin/conda clean -ya
+
+ENV CONDA_DEFAULT_ENV=py36
+ENV CONDA_PREFIX=/miniconda/envs/$CONDA_DEFAULT_ENV
+ENV PATH=$CONDA_PREFIX/bin:$PATH
+ENV CONDA_AUTO_UPDATE_CONDA=false
+
+RUN conda install -y ipython
+RUN pip install ninja yacs cython matplotlib opencv-python
+
+# Install PyTorch 1.0 Nightly and OpenCV
+RUN conda install -y pytorch-nightly -c pytorch \
+ && conda clean -ya
+
+# Install TorchVision master
+RUN git clone https://github.com/pytorch/vision.git \
+ && cd vision \
+ && python setup.py install
+
+# install pycocotools
+RUN git clone https://github.com/cocodataset/cocoapi.git \
+ && cd cocoapi/PythonAPI \
+ && python setup.py build_ext install
+
+# install apex
+RUN git clone https://github.com/NVIDIA/apex.git \
+ && cd apex \
+ && python setup.py install --cuda_ext --cpp_ext
+
+# install PyTorch Detection
+RUN git clone https://github.com/facebookresearch/maskrcnn-benchmark.git \
+ && cd maskrcnn-benchmark \
+ && python setup.py build develop
+
+WORKDIR /maskrcnn-benchmark
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/Dockerfile b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/Dockerfile
new file mode 100755
index 00000000..32372719
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/Dockerfile
@@ -0,0 +1,67 @@
+ARG CUDA="9.0"
+ARG CUDNN="7"
+
+FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu16.04
+
+RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
+
+# install basics
+RUN apt-get update -y \
+ && apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++
+
+# Install Miniconda
+RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+ && chmod +x /miniconda.sh \
+ && /miniconda.sh -b -p /miniconda \
+ && rm /miniconda.sh
+
+ENV PATH=/miniconda/bin:$PATH
+
+# Create a Python 3.6 environment
+RUN /miniconda/bin/conda install -y conda-build \
+ && /miniconda/bin/conda create -y --name py36 python=3.6.7 \
+ && /miniconda/bin/conda clean -ya
+
+ENV CONDA_DEFAULT_ENV=py36
+ENV CONDA_PREFIX=/miniconda/envs/$CONDA_DEFAULT_ENV
+ENV PATH=$CONDA_PREFIX/bin:$PATH
+ENV CONDA_AUTO_UPDATE_CONDA=false
+
+RUN conda install -y ipython
+RUN pip install ninja yacs cython matplotlib jupyter
+
+# Install PyTorch 1.0 Nightly and OpenCV
+RUN conda install -y pytorch-nightly -c pytorch \
+ && conda install -y opencv -c menpo \
+ && conda clean -ya
+
+WORKDIR /root
+
+USER root
+
+RUN mkdir /notebooks
+
+WORKDIR /notebooks
+
+# Install TorchVision master
+RUN git clone https://github.com/pytorch/vision.git \
+ && cd vision \
+ && python setup.py install
+
+# install pycocotools
+RUN git clone https://github.com/cocodataset/cocoapi.git \
+ && cd cocoapi/PythonAPI \
+ && python setup.py build_ext install
+
+# install PyTorch Detection
+RUN git clone https://github.com/facebookresearch/maskrcnn-benchmark.git \
+ && cd maskrcnn-benchmark \
+ && python setup.py build develop
+
+RUN jupyter notebook --generate-config
+
+ENV CONFIG_PATH="/root/.jupyter/jupyter_notebook_config.py"
+
+COPY "jupyter_notebook_config.py" ${CONFIG_PATH}
+
+ENTRYPOINT ["sh", "-c", "jupyter notebook --allow-root -y --no-browser --ip=0.0.0.0 --config=${CONFIG_PATH}"]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/jupyter_notebook_config.py b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/jupyter_notebook_config.py
new file mode 100755
index 00000000..bd549481
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/jupyter_notebook_config.py
@@ -0,0 +1,18 @@
+import os
+from IPython.lib import passwd
+
+#c = c  # pylint:disable=undefined-variable
+c = get_config()
+c.NotebookApp.ip = '0.0.0.0'
+c.NotebookApp.port = int(os.getenv('PORT', 8888))
+c.NotebookApp.open_browser = False
+
+# sets a password if PASSWORD is set in the environment
+if 'PASSWORD' in os.environ:
+  password = os.environ['PASSWORD']
+  if password:
+    c.NotebookApp.password = passwd(password)
+  else:
+    c.NotebookApp.password = ''
+    c.NotebookApp.token = ''
+  del os.environ['PASSWORD']
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/__init__.py
new file mode 100755
index 00000000..5c7f19c6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/__init__.py
new file mode 100755
index 00000000..22a15023
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .defaults import _C as cfg
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/defaults.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/defaults.py
new file mode 100755
index 00000000..2a828f8d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/defaults.py
@@ -0,0 +1,327 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import os
+
+from yacs.config import CfgNode as CN
+
+
+# -----------------------------------------------------------------------------
+# Convention about Training / Test specific parameters
+# -----------------------------------------------------------------------------
+# Whenever an argument can be either used for training or for testing, the
+# corresponding name will be post-fixed by a _TRAIN for a training parameter,
+# or _TEST for a test-specific parameter.
+# For example, the number of images during training will be
+# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
+# IMAGES_PER_BATCH_TEST
+
+# -----------------------------------------------------------------------------
+# Config definition
+# -----------------------------------------------------------------------------
+
+_C = CN()
+
+_C.MODEL = CN()
+_C.MODEL.RPN_ONLY = False
+_C.MODEL.MASK_ON = False
+_C.MODEL.DEVICE = "cuda"
+_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
+
+# If the WEIGHT starts with a catalog://, like :R-50, the code will look for
+# the path in paths_catalog. Else, it will use it as the specified absolute
+# path
+_C.MODEL.WEIGHT = ""
+
+
+# -----------------------------------------------------------------------------
+# INPUT
+# -----------------------------------------------------------------------------
+_C.INPUT = CN()
+# Size of the smallest side of the image during training
+_C.INPUT.MIN_SIZE_TRAIN = 800  # (800,)
+# Maximum size of the side of the image during training
+_C.INPUT.MAX_SIZE_TRAIN = 1333
+# Size of the smallest side of the image during testing
+_C.INPUT.MIN_SIZE_TEST = 800
+# Maximum size of the side of the image during testing
+_C.INPUT.MAX_SIZE_TEST = 1333
+# Values to be used for image normalization
+_C.INPUT.PIXEL_MEAN = [102.9801, 115.9465, 122.7717]
+# Values to be used for image normalization
+_C.INPUT.PIXEL_STD = [1., 1., 1.]
+# Convert image to BGR format (for Caffe2 models), in range 0-255
+_C.INPUT.TO_BGR255 = True
+
+
+# -----------------------------------------------------------------------------
+# Dataset
+# -----------------------------------------------------------------------------
+_C.DATASETS = CN()
+# List of the dataset names for training, as present in paths_catalog.py
+_C.DATASETS.TRAIN = ()
+# List of the dataset names for testing, as present in paths_catalog.py
+_C.DATASETS.TEST = ()
+
+# -----------------------------------------------------------------------------
+# DataLoader
+# -----------------------------------------------------------------------------
+_C.DATALOADER = CN()
+# Number of data loading threads
+_C.DATALOADER.NUM_WORKERS = 4
+# If > 0, this enforces that each collated batch should have a size divisible
+# by SIZE_DIVISIBILITY
+_C.DATALOADER.SIZE_DIVISIBILITY = 0
+# If True, each batch should contain only images for which the aspect ratio
+# is compatible. This groups portrait images together, and landscape images
+# are not batched with portrait images.
+_C.DATALOADER.ASPECT_RATIO_GROUPING = True
+
+
+# ---------------------------------------------------------------------------- #
+# Backbone options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.BACKBONE = CN()
+
+# The backbone conv body to use
+# The string must match a function that is imported in modeling.model_builder
+# (e.g., 'FPN.add_fpn_ResNet101_conv5_body' to specify a ResNet-101-FPN
+# backbone)
+_C.MODEL.BACKBONE.CONV_BODY = "R-50-C4"
+
+# Add StopGrad at a specified stage so the bottom layers are frozen
+_C.MODEL.BACKBONE.FREEZE_CONV_BODY_AT = 2
+_C.MODEL.BACKBONE.OUT_CHANNELS = 256 * 4
+# GN for backbone
+_C.MODEL.BACKBONE.USE_GN = False
+
+
+# ---------------------------------------------------------------------------- #
+# FPN options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.FPN = CN()
+_C.MODEL.FPN.USE_GN = False
+_C.MODEL.FPN.USE_RELU = False
+
+
+# ---------------------------------------------------------------------------- #
+# Group Norm options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.GROUP_NORM = CN()
+# Number of dimensions per group in GroupNorm (-1 if using NUM_GROUPS)
+_C.MODEL.GROUP_NORM.DIM_PER_GP = -1
+# Number of groups in GroupNorm (-1 if using DIM_PER_GP)
+_C.MODEL.GROUP_NORM.NUM_GROUPS = 32
+# GroupNorm's small constant in the denominator
+_C.MODEL.GROUP_NORM.EPSILON = 1e-5
+
+
+# ---------------------------------------------------------------------------- #
+# RPN options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.RPN = CN()
+_C.MODEL.RPN.USE_FPN = False
+# Base RPN anchor sizes given in absolute pixels w.r.t. the scaled network input
+_C.MODEL.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512)
+# Stride of the feature map that RPN is attached.
+# For FPN, number of strides should match number of scales
+_C.MODEL.RPN.ANCHOR_STRIDE = (16,)
+# RPN anchor aspect ratios
+_C.MODEL.RPN.ASPECT_RATIOS = (0.5, 1.0, 2.0)
+# Remove RPN anchors that go outside the image by RPN_STRADDLE_THRESH pixels
+# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
+_C.MODEL.RPN.STRADDLE_THRESH = 0
+# Minimum overlap required between an anchor and ground-truth box for the
+# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
+# ==> positive RPN example)
+_C.MODEL.RPN.FG_IOU_THRESHOLD = 0.7
+# Maximum overlap allowed between an anchor and ground-truth box for the
+# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
+# ==> negative RPN example)
+_C.MODEL.RPN.BG_IOU_THRESHOLD = 0.3
+# Total number of RPN examples per image
+_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
+# Target fraction of foreground (positive) examples per RPN minibatch
+_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
+# Number of top scoring RPN proposals to keep before applying NMS
+# When FPN is used, this is *per FPN level* (not total)
+_C.MODEL.RPN.PRE_NMS_TOP_N_TRAIN = 12000
+_C.MODEL.RPN.PRE_NMS_TOP_N_TEST = 6000
+# Number of top scoring RPN proposals to keep after applying NMS
+_C.MODEL.RPN.POST_NMS_TOP_N_TRAIN = 2000
+_C.MODEL.RPN.POST_NMS_TOP_N_TEST = 1000
+# NMS threshold used on RPN proposals
+_C.MODEL.RPN.NMS_THRESH = 0.7
+# Proposal height and width both need to be greater than RPN_MIN_SIZE
+# (a the scale used during training or inference)
+_C.MODEL.RPN.MIN_SIZE = 0
+# Number of top scoring RPN proposals to keep after combining proposals from
+# all FPN levels
+_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN = 2000
+_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST = 2000
+# Custom rpn head, empty to use default conv or separable conv
+_C.MODEL.RPN.RPN_HEAD = "SingleConvRPNHead"
+
+
+# ---------------------------------------------------------------------------- #
+# ROI HEADS options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_HEADS = CN()
+_C.MODEL.ROI_HEADS.USE_FPN = False
+# Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD)
+_C.MODEL.ROI_HEADS.FG_IOU_THRESHOLD = 0.5
+# Overlap threshold for an RoI to be considered background
+# (class = 0 if overlap in [0, BG_IOU_THRESHOLD))
+_C.MODEL.ROI_HEADS.BG_IOU_THRESHOLD = 0.5
+# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
+# These are empirically chosen to approximately lead to unit variance targets
+_C.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS = (10., 10., 5., 5.)
+# RoI minibatch size *per image* (number of regions of interest [ROIs])
+# Total number of RoIs per training minibatch =
+#   TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS
+# E.g., a common configuration is: 512 * 2 * 8 = 8192
+_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
+# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
+_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
+
+# Only used on test mode
+
+# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
+# balance obtaining high recall with not having too many low precision
+# detections that will slow down inference post processing steps (like NMS)
+_C.MODEL.ROI_HEADS.SCORE_THRESH = 0.05
+# Overlap threshold used for non-maximum suppression (suppress boxes with
+# IoU >= this threshold)
+_C.MODEL.ROI_HEADS.NMS = 0.5
+# Maximum number of detections to return per image (100 is based on the limit
+# established for the COCO dataset)
+_C.MODEL.ROI_HEADS.DETECTIONS_PER_IMG = 100
+
+
+_C.MODEL.ROI_BOX_HEAD = CN()
+_C.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
+_C.MODEL.ROI_BOX_HEAD.PREDICTOR = "FastRCNNPredictor"
+_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
+_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
+_C.MODEL.ROI_BOX_HEAD.POOLER_SCALES = (1.0 / 16,)
+_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81
+# Hidden layer dimension when using an MLP for the RoI box head
+_C.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM = 1024
+# GN
+_C.MODEL.ROI_BOX_HEAD.USE_GN = False
+# Dilation
+_C.MODEL.ROI_BOX_HEAD.DILATION = 1
+_C.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM = 256
+_C.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS = 4
+
+
+_C.MODEL.ROI_MASK_HEAD = CN()
+_C.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
+_C.MODEL.ROI_MASK_HEAD.PREDICTOR = "MaskRCNNC4Predictor"
+_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
+_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
+_C.MODEL.ROI_MASK_HEAD.POOLER_SCALES = (1.0 / 16,)
+_C.MODEL.ROI_MASK_HEAD.MLP_HEAD_DIM = 1024
+_C.MODEL.ROI_MASK_HEAD.CONV_LAYERS = (256, 256, 256, 256)
+_C.MODEL.ROI_MASK_HEAD.RESOLUTION = 14
+_C.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True
+# Whether or not resize and translate masks to the input image.
+_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS = False
+_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD = 0.5
+# Dilation
+_C.MODEL.ROI_MASK_HEAD.DILATION = 1
+# GN
+_C.MODEL.ROI_MASK_HEAD.USE_GN = False
+
+# ---------------------------------------------------------------------------- #
+# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
+# Note that parts of a resnet may be used for both the backbone and the head
+# These options apply to both
+# ---------------------------------------------------------------------------- #
+_C.MODEL.RESNETS = CN()
+
+# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
+_C.MODEL.RESNETS.NUM_GROUPS = 1
+
+# Baseline width of each group
+_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
+
+# Place the stride 2 conv on the 1x1 filter
+# Use True only for the original MSRA ResNet; use False for C2 and Torch models
+_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
+
+# Residual transformation function
+_C.MODEL.RESNETS.TRANS_FUNC = "BottleneckWithFixedBatchNorm"
+# ResNet's stem function (conv1 and pool1)
+_C.MODEL.RESNETS.STEM_FUNC = "StemWithFixedBatchNorm"
+
+# Apply dilation in stage "res5"
+_C.MODEL.RESNETS.RES5_DILATION = 1
+
+_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
+_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
+
+# ---------------------------------------------------------------------------- #
+# Solver
+# ---------------------------------------------------------------------------- #
+_C.SOLVER = CN()
+_C.SOLVER.MAX_ITER = 40000
+
+_C.SOLVER.BASE_LR = 0.001
+_C.SOLVER.BIAS_LR_FACTOR = 2
+
+_C.SOLVER.MOMENTUM = 0.9
+
+_C.SOLVER.WEIGHT_DECAY = 0.0005
+_C.SOLVER.WEIGHT_DECAY_BIAS = 0
+
+_C.SOLVER.GAMMA = 0.1
+_C.SOLVER.STEPS = (30000,)
+
+_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
+_C.SOLVER.WARMUP_ITERS = 500
+_C.SOLVER.WARMUP_METHOD = "linear"
+
+_C.SOLVER.CHECKPOINT_PERIOD = 2500
+
+# Number of images per batch
+# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
+# see 2 images per batch
+_C.SOLVER.IMS_PER_BATCH = 16
+
+# Parameters to accumulate gradient across steps
+_C.SOLVER.ACCUMULATE_STEPS = 1
+_C.SOLVER.ACCUMULATE_GRAD = False
+
+
+# ---------------------------------------------------------------------------- #
+# Specific test options
+# ---------------------------------------------------------------------------- #
+_C.TEST = CN()
+_C.TEST.EXPECTED_RESULTS = []
+_C.TEST.EXPECTED_RESULTS_SIGMA_TOL = 4
+# Number of images per batch
+# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
+# see 2 images per batch
+_C.TEST.IMS_PER_BATCH = 8
+
+
+# ---------------------------------------------------------------------------- #
+# Misc options
+# ---------------------------------------------------------------------------- #
+_C.OUTPUT_DIR = "."
+
+_C.PATHS_CATALOG = os.path.join(os.path.dirname(__file__), "paths_catalog.py")
+
+# ---------------------------------------------------------------------------- #
+# Precision options
+# ---------------------------------------------------------------------------- #
+
+# Precision of input, allowable: (float32, float16)
+_C.DTYPE = "float32"
+
+# Enable verbosity in apex.amp
+_C.AMP_VERBOSE = False
+
+# Evaluate every epoch
+_C.PER_EPOCH_EVAL = False
+_C.MIN_BBOX_MAP = 0.377
+_C.MIN_MASK_MAP = 0.342
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog.py
new file mode 100755
index 00000000..78330298
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog.py
@@ -0,0 +1,174 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""Centralized catalog of paths."""
+
+import os
+
+
+class DatasetCatalog(object):
+    DATA_DIR = "/datasets"
+    DATASETS = {
+        "coco_2017_train": {
+            "img_dir": "data/train2017",
+            "ann_file": "data/annotations/instances_train2017.json"
+        },
+        "coco_2017_val": {
+            "img_dir": "data/val2017",
+            "ann_file": "data/annotations/instances_val2017.json"
+        },
+        "coco_2014_train": {
+            "img_dir": "data/train2014",
+            "ann_file": "data/annotations/instances_train2014.json"
+        },
+        "coco_2014_val": {
+            "img_dir": "data/val2014",
+            "ann_file": "data/annotations/instances_val2014.json"
+        },
+        "coco_2014_minival": {
+            "img_dir": "data/val2014",
+            "ann_file": "data/annotations/instances_minival2014.json"
+        },
+        "coco_2014_valminusminival": {
+            "img_dir": "data/val2014",
+            "ann_file": "data/annotations/instances_valminusminival2014.json"
+        },
+        "voc_2007_train": {
+            "data_dir": "data/VOC2007",
+            "split": "train"
+        },
+        "voc_2007_train_cocostyle": {
+            "img_dir": "data/VOC2007/JPEGImages",
+            "ann_file": "data/VOC2007/Annotations/pascal_train2007.json"
+        },
+        "voc_2007_val": {
+            "data_dir": "data/VOC2007",
+            "split": "val"
+        },
+        "voc_2007_val_cocostyle": {
+            "img_dir": "data/VOC2007/JPEGImages",
+            "ann_file": "data/VOC2007/Annotations/pascal_val2007.json"
+        },
+        "voc_2007_test": {
+            "data_dir": "data/VOC2007",
+            "split": "test"
+        },
+        "voc_2007_test_cocostyle": {
+            "img_dir": "data/VOC2007/JPEGImages",
+            "ann_file": "data/VOC2007/Annotations/pascal_test2007.json"
+        },
+        "voc_2012_train": {
+            "data_dir": "data/VOC2012",
+            "split": "train"
+        },
+        "voc_2012_train_cocostyle": {
+            "img_dir": "data/VOC2012/JPEGImages",
+            "ann_file": "data/VOC2012/Annotations/pascal_train2012.json"
+        },
+        "voc_2012_val": {
+            "data_dir": "data/VOC2012",
+            "split": "val"
+        },
+        "voc_2012_val_cocostyle": {
+            "img_dir": "data/VOC2012/JPEGImages",
+            "ann_file": "data/VOC2012/Annotations/pascal_val2012.json"
+        },
+        "voc_2012_test": {
+            "data_dir": "data/VOC2012",
+            "split": "test"
+            # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation
+        },
+        "cityscapes_fine_instanceonly_seg_train_cocostyle": {
+            "img_dir": "data/images",
+            "ann_file": "data/annotations/instancesonly_filtered_gtFine_train.json"
+        },
+        "cityscapes_fine_instanceonly_seg_val_cocostyle": {
+            "img_dir": "data/images",
+            "ann_file": "data/annotations/instancesonly_filtered_gtFine_val.json"
+        },
+        "cityscapes_fine_instanceonly_seg_test_cocostyle": {
+            "img_dir": "data/images",
+            "ann_file": "data/annotations/instancesonly_filtered_gtFine_test.json"
+        }
+    }
+
+    @staticmethod
+    def get(name):
+        if "coco" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(
+                root=os.path.join(data_dir, attrs["img_dir"]),
+                ann_file=os.path.join(data_dir, attrs["ann_file"]),
+            )
+            return dict(
+                factory="COCODataset",
+                args=args,
+            )
+        elif "voc" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(
+                data_dir=os.path.join(data_dir, attrs["data_dir"]),
+                split=attrs["split"],
+            )
+            return dict(
+                factory="PascalVOCDataset",
+                args=args,
+            )
+        raise RuntimeError("Dataset not available: {}".format(name))
+
+
+class ModelCatalog(object):
+    S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron"
+    C2_IMAGENET_MODELS = {
+        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
+        "MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
+        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
+        "MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
+        "FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
+    }
+
+    C2_DETECTRON_SUFFIX = "output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl"
+    C2_DETECTRON_MODELS = {
+        "35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW",
+        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I",
+        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7",
+        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ",
+        "35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB",
+        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC",
+        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT",
+        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI",
+    }
+
+    @staticmethod
+    def get(name):
+        if name.startswith("Caffe2Detectron/COCO"):
+            return ModelCatalog.get_c2_detectron_12_2017_baselines(name)
+        if name.startswith("ImageNetPretrained"):
+            return ModelCatalog.get_c2_imagenet_pretrained(name)
+        raise RuntimeError("model not present in the catalog {}".format(name))
+
+    @staticmethod
+    def get_c2_imagenet_pretrained(name):
+        prefix = ModelCatalog.S3_C2_DETECTRON_URL
+        name = name[len("ImageNetPretrained/"):]
+        name = ModelCatalog.C2_IMAGENET_MODELS[name]
+        url = "/".join([prefix, name])
+        return url
+
+    @staticmethod
+    def get_c2_detectron_12_2017_baselines(name):
+        # Detectron C2 models are stored following the structure
+        # prefix/<model_id>/2012_2017_baselines/<model_name>.yaml.<signature>/suffix
+        # we use as identifiers in the catalog Caffe2Detectron/COCO/<model_id>/<model_name>
+        prefix = ModelCatalog.S3_C2_DETECTRON_URL
+        suffix = ModelCatalog.C2_DETECTRON_SUFFIX
+        # remove identification prefix
+        name = name[len("Caffe2Detectron/COCO/"):]
+        # split in <model_id> and <model_name>
+        model_id, model_name = name.split("/")
+        # parsing to make it match the url address from the Caffe2 models
+        model_name = "{}.yaml".format(model_name)
+        signature = ModelCatalog.C2_DETECTRON_MODELS[name]
+        unique_name = ".".join([model_name, signature])
+        url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix])
+        return url
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog_ci.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog_ci.py
new file mode 100644
index 00000000..fbf0446d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog_ci.py
@@ -0,0 +1,110 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+"""Centralized catalog of paths."""
+
+import os
+
+
+class DatasetCatalog(object):
+    DATA_DIR = "/data/coco/coco-2014"
+    DATASETS = {
+        "coco_2014_train": {
+            "img_dir": "coco_train2014",
+            "ann_file": "annotations/instances_train2014.json"
+        },
+        "coco_2014_val": {
+            "img_dir": "coco_val2014",
+            "ann_file": "annotations/instances_val2014.json"
+        },
+        "coco_2014_minival": {
+            "img_dir": "coco_val2014",
+            "ann_file": "annotations/instances_minival2014.json"
+        },
+        "coco_2014_valminusminival": {
+            "img_dir": "coco_val2014",
+            "ann_file": "annotations/instances_valminusminival2014.json"
+        },
+    }
+
+    @staticmethod
+    def get(name):
+        if "coco" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(
+                root=os.path.join(data_dir, attrs["img_dir"]),
+                ann_file=os.path.join(data_dir, attrs["ann_file"]),
+            )
+            return dict(
+                factory="COCODataset",
+                args=args,
+            )
+        elif "voc" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(
+                data_dir=os.path.join(data_dir, attrs["data_dir"]),
+                split=attrs["split"],
+            )
+            return dict(
+                factory="PascalVOCDataset",
+                args=args,
+            )
+        raise RuntimeError("Dataset not available: {}".format(name))
+
+
+class ModelCatalog(object):
+    S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron"
+    C2_IMAGENET_MODELS = {
+        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
+        "MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
+        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
+        "MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
+        "FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
+    }
+
+    C2_DETECTRON_SUFFIX = "output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl"
+    C2_DETECTRON_MODELS = {
+        "35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW",
+        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I",
+        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7",
+        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ",
+        "35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB",
+        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC",
+        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT",
+        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI",
+    }
+
+    @staticmethod
+    def get(name):
+        if name.startswith("Caffe2Detectron/COCO"):
+            return ModelCatalog.get_c2_detectron_12_2017_baselines(name)
+        if name.startswith("ImageNetPretrained"):
+            return ModelCatalog.get_c2_imagenet_pretrained(name)
+        raise RuntimeError("model not present in the catalog {}".format(name))
+
+    @staticmethod
+    def get_c2_imagenet_pretrained(name):
+        prefix = ModelCatalog.S3_C2_DETECTRON_URL
+        name = name[len("ImageNetPretrained/"):]
+        name = ModelCatalog.C2_IMAGENET_MODELS[name]
+        url = "/".join([prefix, name])
+        return url
+
+    @staticmethod
+    def get_c2_detectron_12_2017_baselines(name):
+        # Detectron C2 models are stored following the structure
+        # prefix/<model_id>/2012_2017_baselines/<model_name>.yaml.<signature>/suffix
+        # we use as identifiers in the catalog Caffe2Detectron/COCO/<model_id>/<model_name>
+        prefix = ModelCatalog.S3_C2_DETECTRON_URL
+        suffix = ModelCatalog.C2_DETECTRON_SUFFIX
+        # remove identification prefix
+        name = name[len("Caffe2Detectron/COCO/"):]
+        # split in <model_id> and <model_name>
+        model_id, model_name = name.split("/")
+        # parsing to make it match the url address from the Caffe2 models
+        model_name = "{}.yaml".format(model_name)
+        signature = ModelCatalog.C2_DETECTRON_MODELS[name]
+        unique_name = ".".join([model_name, signature])
+        url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix])
+        return url
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIAlign.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIAlign.h
new file mode 100755
index 00000000..3907deab
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIAlign.h
@@ -0,0 +1,46 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+
+#include "cpu/vision.h"
+
+#ifdef WITH_CUDA
+#include "cuda/vision.h"
+#endif
+
+// Interface for Python
+at::Tensor ROIAlign_forward(const at::Tensor& input,
+                            const at::Tensor& rois,
+                            const float spatial_scale,
+                            const int pooled_height,
+                            const int pooled_width,
+                            const int sampling_ratio) {
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  return ROIAlign_forward_cpu(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+}
+
+at::Tensor ROIAlign_backward(const at::Tensor& grad,
+                             const at::Tensor& rois,
+                             const float spatial_scale,
+                             const int pooled_height,
+                             const int pooled_width,
+                             const int batch_size,
+                             const int channels,
+                             const int height,
+                             const int width,
+                             const int sampling_ratio) {
+  if (grad.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return ROIAlign_backward_cuda(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIPool.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIPool.h
new file mode 100755
index 00000000..200fd739
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/ROIPool.h
@@ -0,0 +1,48 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+
+#include "cpu/vision.h"
+
+#ifdef WITH_CUDA
+#include "cuda/vision.h"
+#endif
+
+
+std::tuple<at::Tensor, at::Tensor> ROIPool_forward(const at::Tensor& input,
+                                const at::Tensor& rois,
+                                const float spatial_scale,
+                                const int pooled_height,
+                                const int pooled_width) {
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return ROIPool_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+at::Tensor ROIPool_backward(const at::Tensor& grad,
+                                 const at::Tensor& input,
+                                 const at::Tensor& rois,
+                                 const at::Tensor& argmax,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int batch_size,
+                                 const int channels,
+                                 const int height,
+                                 const int width) {
+  if (grad.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return ROIPool_backward_cuda(grad, input, rois, argmax, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_encode.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_encode.h
new file mode 100755
index 00000000..8d55c717
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_encode.h
@@ -0,0 +1,27 @@
+/**  
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *  
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. 
+*/
+
+#pragma once
+#include "cuda/vision.h"
+#ifndef _box_encode_h_
+#define _box_encode_h_ 
+
+std::vector<at::Tensor> box_encode(at::Tensor boxes, at::Tensor anchors, float wx, float wy, float ww, float wh){
+  std::vector<at::Tensor> result = box_encode_cuda(boxes, anchors, wx, wy, ww, wh);
+  return result;
+}
+
+#endif
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_iou.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_iou.h
new file mode 100755
index 00000000..3126359d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/box_iou.h
@@ -0,0 +1,28 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "cuda/vision.h"
+#ifndef _box_iou_h_
+#define _box_iou_h_ 
+
+at::Tensor box_iou(at::Tensor box1, at::Tensor box2){
+  at::Tensor result = box_iou_cuda(box1, box2);
+  return result;
+}
+
+#endif
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
new file mode 100755
index 00000000..d35aedf2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
@@ -0,0 +1,257 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include "cpu/vision.h"
+
+// implementation taken from Caffe2
+template <typename T>
+struct PreCalc {
+  int pos1;
+  int pos2;
+  int pos3;
+  int pos4;
+  T w1;
+  T w2;
+  T w3;
+  T w4;
+};
+
+template <typename T>
+void pre_calc_for_bilinear_interpolate(
+    const int height,
+    const int width,
+    const int pooled_height,
+    const int pooled_width,
+    const int iy_upper,
+    const int ix_upper,
+    T roi_start_h,
+    T roi_start_w,
+    T bin_size_h,
+    T bin_size_w,
+    int roi_bin_grid_h,
+    int roi_bin_grid_w,
+    std::vector<PreCalc<T>>& pre_calc) {
+  int pre_calc_index = 0;
+  for (int ph = 0; ph < pooled_height; ph++) {
+    for (int pw = 0; pw < pooled_width; pw++) {
+      for (int iy = 0; iy < iy_upper; iy++) {
+        const T yy = roi_start_h + ph * bin_size_h +
+            static_cast<T>(iy + .5f) * bin_size_h /
+                static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
+        for (int ix = 0; ix < ix_upper; ix++) {
+          const T xx = roi_start_w + pw * bin_size_w +
+              static_cast<T>(ix + .5f) * bin_size_w /
+                  static_cast<T>(roi_bin_grid_w);
+
+          T x = xx;
+          T y = yy;
+          // deal with: inverse elements are out of feature map boundary
+          if (y < -1.0 || y > height || x < -1.0 || x > width) {
+            // empty
+            PreCalc<T> pc;
+            pc.pos1 = 0;
+            pc.pos2 = 0;
+            pc.pos3 = 0;
+            pc.pos4 = 0;
+            pc.w1 = 0;
+            pc.w2 = 0;
+            pc.w3 = 0;
+            pc.w4 = 0;
+            pre_calc[pre_calc_index] = pc;
+            pre_calc_index += 1;
+            continue;
+          }
+
+          if (y <= 0) {
+            y = 0;
+          }
+          if (x <= 0) {
+            x = 0;
+          }
+
+          int y_low = (int)y;
+          int x_low = (int)x;
+          int y_high;
+          int x_high;
+
+          if (y_low >= height - 1) {
+            y_high = y_low = height - 1;
+            y = (T)y_low;
+          } else {
+            y_high = y_low + 1;
+          }
+
+          if (x_low >= width - 1) {
+            x_high = x_low = width - 1;
+            x = (T)x_low;
+          } else {
+            x_high = x_low + 1;
+          }
+
+          T ly = y - y_low;
+          T lx = x - x_low;
+          T hy = 1. - ly, hx = 1. - lx;
+          T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+          // save weights and indeces
+          PreCalc<T> pc;
+          pc.pos1 = y_low * width + x_low;
+          pc.pos2 = y_low * width + x_high;
+          pc.pos3 = y_high * width + x_low;
+          pc.pos4 = y_high * width + x_high;
+          pc.w1 = w1;
+          pc.w2 = w2;
+          pc.w3 = w3;
+          pc.w4 = w4;
+          pre_calc[pre_calc_index] = pc;
+
+          pre_calc_index += 1;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+void ROIAlignForward_cpu_kernel(
+    const int nthreads,
+    const T* bottom_data,
+    const T& spatial_scale,
+    const int channels,
+    const int height,
+    const int width,
+    const int pooled_height,
+    const int pooled_width,
+    const int sampling_ratio,
+    const T* bottom_rois,
+    //int roi_cols,
+    T* top_data) {
+  //AT_ASSERT(roi_cols == 4 || roi_cols == 5);
+  int roi_cols = 5;
+
+  int n_rois = nthreads / channels / pooled_width / pooled_height;
+  // (n, c, ph, pw) is an element in the pooled output
+  // can be parallelized using omp
+  // #pragma omp parallel for num_threads(32)
+  for (int n = 0; n < n_rois; n++) {
+    int index_n = n * channels * pooled_width * pooled_height;
+
+    // roi could have 4 or 5 columns
+    const T* offset_bottom_rois = bottom_rois + n * roi_cols;
+    int roi_batch_ind = 0;
+    if (roi_cols == 5) {
+      roi_batch_ind = offset_bottom_rois[0];
+      offset_bottom_rois++;
+    }
+
+    // Do not using rounding; this implementation detail is critical
+    T roi_start_w = offset_bottom_rois[0] * spatial_scale;
+    T roi_start_h = offset_bottom_rois[1] * spatial_scale;
+    T roi_end_w = offset_bottom_rois[2] * spatial_scale;
+    T roi_end_h = offset_bottom_rois[3] * spatial_scale;
+    // T roi_start_w = round(offset_bottom_rois[0] * spatial_scale);
+    // T roi_start_h = round(offset_bottom_rois[1] * spatial_scale);
+    // T roi_end_w = round(offset_bottom_rois[2] * spatial_scale);
+    // T roi_end_h = round(offset_bottom_rois[3] * spatial_scale);
+
+    // Force malformed ROIs to be 1x1
+    T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
+    T roi_height = std::max(roi_end_h - roi_start_h, (T)1.);
+    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h = (sampling_ratio > 0)
+        ? sampling_ratio
+        : ceil(roi_height / pooled_height); // e.g., = 2
+    int roi_bin_grid_w =
+        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
+
+    // we want to precalculate indeces and weights shared by all chanels,
+    // this is the key point of optimiation
+    std::vector<PreCalc<T>> pre_calc(
+        roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
+    pre_calc_for_bilinear_interpolate(
+        height,
+        width,
+        pooled_height,
+        pooled_width,
+        roi_bin_grid_h,
+        roi_bin_grid_w,
+        roi_start_h,
+        roi_start_w,
+        bin_size_h,
+        bin_size_w,
+        roi_bin_grid_h,
+        roi_bin_grid_w,
+        pre_calc);
+
+      for (int c = 0; c < channels; c++) {
+      int index_n_c = index_n + c * pooled_width * pooled_height;
+      const T* offset_bottom_data =
+          bottom_data + (roi_batch_ind * channels + c) * height * width;
+      int pre_calc_index = 0;
+
+      for (int ph = 0; ph < pooled_height; ph++) {
+        for (int pw = 0; pw < pooled_width; pw++) {
+          int index = index_n_c + ph * pooled_width + pw;
+
+          T output_val = 0.;
+          for (int iy = 0; iy < roi_bin_grid_h; iy++) {
+            for (int ix = 0; ix < roi_bin_grid_w; ix++) {
+              PreCalc<T> pc = pre_calc[pre_calc_index];
+              output_val += pc.w1 * offset_bottom_data[pc.pos1] +
+                  pc.w2 * offset_bottom_data[pc.pos2] +
+                  pc.w3 * offset_bottom_data[pc.pos3] +
+                  pc.w4 * offset_bottom_data[pc.pos4];
+
+              pre_calc_index += 1;
+            }
+          }
+          output_val /= count;
+
+          top_data[index] = output_val;
+        } // for pw
+      } // for ph
+    } // for c
+  } // for n
+}
+
+at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
+                                const at::Tensor& rois,
+                                const float spatial_scale,
+                                const int pooled_height,
+                                const int pooled_width,
+                                const int sampling_ratio) {
+  AT_ASSERTM(!input.type().is_cuda(), "input must be a CPU tensor");
+  AT_ASSERTM(!rois.type().is_cuda(), "rois must be a CPU tensor");
+
+  auto num_rois = rois.size(0);
+  auto channels = input.size(1);
+  auto height = input.size(2);
+  auto width = input.size(3);
+
+  auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options());
+  auto output_size = num_rois * pooled_height * pooled_width * channels;
+
+  if (output.numel() == 0) {
+    return output;
+  }
+
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIAlign_forward", [&] {
+    ROIAlignForward_cpu_kernel<scalar_t>(
+         output_size,
+         input.data<scalar_t>(),
+         spatial_scale,
+         channels,
+         height,
+         width,
+         pooled_height,
+         pooled_width,
+         sampling_ratio,
+         rois.data<scalar_t>(),
+         output.data<scalar_t>());
+  });
+  return output;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
new file mode 100755
index 00000000..1153dea0
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
@@ -0,0 +1,75 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include "cpu/vision.h"
+
+
+template <typename scalar_t>
+at::Tensor nms_cpu_kernel(const at::Tensor& dets,
+                          const at::Tensor& scores,
+                          const float threshold) {
+  AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
+  AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");
+  AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");
+
+  if (dets.numel() == 0) {
+    return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
+  }
+
+  auto x1_t = dets.select(1, 0).contiguous();
+  auto y1_t = dets.select(1, 1).contiguous();
+  auto x2_t = dets.select(1, 2).contiguous();
+  auto y2_t = dets.select(1, 3).contiguous();
+
+  at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1);
+
+  auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
+
+  auto ndets = dets.size(0);
+  at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU));
+
+  auto suppressed = suppressed_t.data<uint8_t>();
+  auto order = order_t.data<int64_t>();
+  auto x1 = x1_t.data<scalar_t>();
+  auto y1 = y1_t.data<scalar_t>();
+  auto x2 = x2_t.data<scalar_t>();
+  auto y2 = y2_t.data<scalar_t>();
+  auto areas = areas_t.data<scalar_t>();
+
+  for (int64_t _i = 0; _i < ndets; _i++) {
+    auto i = order[_i];
+    if (suppressed[i] == 1)
+      continue;
+    auto ix1 = x1[i];
+    auto iy1 = y1[i];
+    auto ix2 = x2[i];
+    auto iy2 = y2[i];
+    auto iarea = areas[i];
+
+    for (int64_t _j = _i + 1; _j < ndets; _j++) {
+      auto j = order[_j];
+      if (suppressed[j] == 1)
+        continue;
+      auto xx1 = std::max(ix1, x1[j]);
+      auto yy1 = std::max(iy1, y1[j]);
+      auto xx2 = std::min(ix2, x2[j]);
+      auto yy2 = std::min(iy2, y2[j]);
+
+      auto w = std::max(static_cast<scalar_t>(0), xx2 - xx1 + 1);
+      auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1 + 1);
+      auto inter = w * h;
+      auto ovr = inter / (iarea + areas[j] - inter);
+      if (ovr >= threshold)
+        suppressed[j] = 1;
+   }
+  }
+  return at::nonzero(suppressed_t == 0).squeeze(1);
+}
+
+at::Tensor nms_cpu(const at::Tensor& dets,
+               const at::Tensor& scores,
+               const float threshold) {
+  at::Tensor result;
+  AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
+    result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
+  });
+  return result;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/vision.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/vision.h
new file mode 100755
index 00000000..92611253
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cpu/vision.h
@@ -0,0 +1,16 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+#include <torch/extension.h>
+
+
+at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
+                                const at::Tensor& rois,
+                                const float spatial_scale,
+                                const int pooled_height,
+                                const int pooled_width,
+                                const int sampling_ratio);
+
+
+at::Tensor nms_cpu(const at::Tensor& dets,
+                   const at::Tensor& scores,
+                   const float threshold);
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu
new file mode 100755
index 00000000..5fe97ca9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu
@@ -0,0 +1,346 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+#include <THC/THCAtomics.cuh>
+#include <THC/THCDeviceUtils.cuh>
+
+// TODO make it in a common file
+#define CUDA_1D_KERNEL_LOOP(i, n)                            \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
+       i += blockDim.x * gridDim.x)
+
+
+template <typename T>
+__device__ T bilinear_interpolate(const T* bottom_data,
+    const int height, const int width,
+    T y, T x,
+    const int index /* index for debug only*/) {
+
+  // deal with cases that inverse elements are out of feature map boundary
+  if (y < -1.0 || y > height || x < -1.0 || x > width) {
+    //empty
+    return 0;
+  }
+
+  if (y <= 0) y = 0;
+  if (x <= 0) x = 0;
+
+  int y_low = (int) y;
+  int x_low = (int) x;
+  int y_high;
+  int x_high;
+
+  if (y_low >= height - 1) {
+    y_high = y_low = height - 1;
+    y = (T) y_low;
+  } else {
+    y_high = y_low + 1;
+  }
+
+  if (x_low >= width - 1) {
+    x_high = x_low = width - 1;
+    x = (T) x_low;
+  } else {
+    x_high = x_low + 1;
+  }
+
+  T ly = y - y_low;
+  T lx = x - x_low;
+  T hy = 1. - ly, hx = 1. - lx;
+  // do bilinear interpolation
+  T v1 = bottom_data[y_low * width + x_low];
+  T v2 = bottom_data[y_low * width + x_high];
+  T v3 = bottom_data[y_high * width + x_low];
+  T v4 = bottom_data[y_high * width + x_high];
+  T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+  T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+
+  return val;
+}
+
+template <typename T>
+__global__ void RoIAlignForward(const int nthreads, const T* bottom_data,
+    const T spatial_scale, const int channels,
+    const int height, const int width,
+    const int pooled_height, const int pooled_width,
+    const int sampling_ratio,
+    const T* bottom_rois, T* top_data) {
+  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    const T* offset_bottom_rois = bottom_rois + n * 5;
+    int roi_batch_ind = offset_bottom_rois[0];
+
+    // Do not using rounding; this implementation detail is critical
+    T roi_start_w = offset_bottom_rois[1] * spatial_scale;
+    T roi_start_h = offset_bottom_rois[2] * spatial_scale;
+    T roi_end_w = offset_bottom_rois[3] * spatial_scale;
+    T roi_end_h = offset_bottom_rois[4] * spatial_scale;
+    // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
+    // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
+    // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
+    // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
+
+    // Force malformed ROIs to be 1x1
+    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
+    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
+
+    const T* offset_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width;
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); // e.g., = 2
+    int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
+
+    T output_val = 0.;
+    for (int iy = 0; iy < roi_bin_grid_h; iy ++) // e.g., iy = 0, 1
+    {
+      const T y = roi_start_h + ph * bin_size_h + static_cast<T>(iy + .5f) * bin_size_h / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
+      for (int ix = 0; ix < roi_bin_grid_w; ix ++)
+      {
+        const T x = roi_start_w + pw * bin_size_w + static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
+
+        T val = bilinear_interpolate(offset_bottom_data, height, width, y, x, index);
+        output_val += val;
+      }
+    }
+    output_val /= count;
+
+    top_data[index] = output_val;
+  }
+}
+
+
+template <typename T>
+__device__ void bilinear_interpolate_gradient(
+    const int height, const int width,
+    T y, T x,
+    T & w1, T & w2, T & w3, T & w4,
+    int & x_low, int & x_high, int & y_low, int & y_high,
+    const int index /* index for debug only*/) {
+
+  // deal with cases that inverse elements are out of feature map boundary
+  if (y < -1.0 || y > height || x < -1.0 || x > width) {
+    //empty
+    w1 = w2 = w3 = w4 = 0.;
+    x_low = x_high = y_low = y_high = -1;
+    return;
+  }
+
+  if (y <= 0) y = 0;
+  if (x <= 0) x = 0;
+
+  y_low = (int) y;
+  x_low = (int) x;
+
+  if (y_low >= height - 1) {
+    y_high = y_low = height - 1;
+    y = (T) y_low;
+  } else {
+    y_high = y_low + 1;
+  }
+
+  if (x_low >= width - 1) {
+    x_high = x_low = width - 1;
+    x = (T) x_low;
+  } else {
+    x_high = x_low + 1;
+  }
+
+  T ly = y - y_low;
+  T lx = x - x_low;
+  T hy = 1. - ly, hx = 1. - lx;
+
+  // reference in forward
+  // T v1 = bottom_data[y_low * width + x_low];
+  // T v2 = bottom_data[y_low * width + x_high];
+  // T v3 = bottom_data[y_high * width + x_low];
+  // T v4 = bottom_data[y_high * width + x_high];
+  // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+
+  w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+  return;
+}
+
+template <typename T>
+__global__ void RoIAlignBackwardFeature(const int nthreads, const T* top_diff,
+    const int num_rois, const T spatial_scale,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width,
+    const int sampling_ratio,
+    T* bottom_diff,
+    const T* bottom_rois) {
+  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    const T* offset_bottom_rois = bottom_rois + n * 5;
+    int roi_batch_ind = offset_bottom_rois[0];
+
+    // Do not using rounding; this implementation detail is critical
+    T roi_start_w = offset_bottom_rois[1] * spatial_scale;
+    T roi_start_h = offset_bottom_rois[2] * spatial_scale;
+    T roi_end_w = offset_bottom_rois[3] * spatial_scale;
+    T roi_end_h = offset_bottom_rois[4] * spatial_scale;
+    // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
+    // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
+    // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
+    // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
+
+    // Force malformed ROIs to be 1x1
+    T roi_width = max(roi_end_w - roi_start_w, (T)1.);
+    T roi_height = max(roi_end_h - roi_start_h, (T)1.);
+    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
+
+    T* offset_bottom_diff = bottom_diff + (roi_batch_ind * channels + c) * height * width;
+
+    int top_offset    = (n * channels + c) * pooled_height * pooled_width;
+    const T* offset_top_diff = top_diff + top_offset;
+    const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw];
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); // e.g., = 2
+    int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
+
+    for (int iy = 0; iy < roi_bin_grid_h; iy ++) // e.g., iy = 0, 1
+    {
+      const T y = roi_start_h + ph * bin_size_h + static_cast<T>(iy + .5f) * bin_size_h / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
+      for (int ix = 0; ix < roi_bin_grid_w; ix ++)
+      {
+        const T x = roi_start_w + pw * bin_size_w + static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
+
+        T w1, w2, w3, w4;
+        int x_low, x_high, y_low, y_high;
+
+        bilinear_interpolate_gradient(height, width, y, x,
+            w1, w2, w3, w4,
+            x_low, x_high, y_low, y_high,
+            index);
+
+        T g1 = top_diff_this_bin * w1 / count;
+        T g2 = top_diff_this_bin * w2 / count;
+        T g3 = top_diff_this_bin * w3 / count;
+        T g4 = top_diff_this_bin * w4 / count;
+
+        if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0)
+        {
+          atomicAdd(offset_bottom_diff + y_low * width + x_low, static_cast<T>(g1));
+          atomicAdd(offset_bottom_diff + y_low * width + x_high, static_cast<T>(g2));
+          atomicAdd(offset_bottom_diff + y_high * width + x_low, static_cast<T>(g3));
+          atomicAdd(offset_bottom_diff + y_high * width + x_high, static_cast<T>(g4));
+        } // if
+      } // ix
+    } // iy
+  } // CUDA_1D_KERNEL_LOOP
+} // RoIAlignBackward
+
+
+at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
+                                 const at::Tensor& rois,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int sampling_ratio) {
+  AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor");
+  AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
+
+  auto num_rois = rois.size(0);
+  auto channels = input.size(1);
+  auto height = input.size(2);
+  auto width = input.size(3);
+
+  auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options());
+  auto output_size = num_rois * pooled_height * pooled_width * channels;
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L));
+  dim3 block(512);
+
+  if (output.numel() == 0) {
+    THCudaCheck(cudaGetLastError());
+    return output;
+  }
+
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIAlign_forward", [&] {
+    RoIAlignForward<scalar_t><<<grid, block, 0, stream>>>(
+         output_size,
+         input.contiguous().data<scalar_t>(),
+         spatial_scale,
+         channels,
+         height,
+         width,
+         pooled_height,
+         pooled_width,
+         sampling_ratio,
+         rois.contiguous().data<scalar_t>(),
+         output.data<scalar_t>());
+  });
+  THCudaCheck(cudaGetLastError());
+  return output;
+}
+
+// TODO remove the dependency on input and use instead its sizes -> save memory
+at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
+                                  const at::Tensor& rois,
+                                  const float spatial_scale,
+                                  const int pooled_height,
+                                  const int pooled_width,
+                                  const int batch_size,
+                                  const int channels,
+                                  const int height,
+                                  const int width,
+                                  const int sampling_ratio) {
+  AT_ASSERTM(grad.type().is_cuda(), "grad must be a CUDA tensor");
+  AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
+
+  auto num_rois = rois.size(0);
+  auto grad_input = at::zeros({batch_size, channels, height, width}, grad.options());
+
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 block(512);
+
+  // handle possibly empty gradients
+  if (grad.numel() == 0) {
+    THCudaCheck(cudaGetLastError());
+    return grad_input;
+  }
+
+  AT_DISPATCH_FLOATING_TYPES(grad.type(), "ROIAlign_backward", [&] {
+    RoIAlignBackwardFeature<scalar_t><<<grid, block, 0, stream>>>(
+         grad.numel(),
+         grad.contiguous().data<scalar_t>(),
+         num_rois,
+         spatial_scale,
+         channels,
+         height,
+         width,
+         pooled_height,
+         pooled_width,
+         sampling_ratio,
+         grad_input.data<scalar_t>(),
+         rois.contiguous().data<scalar_t>());
+  });
+  THCudaCheck(cudaGetLastError());
+  return grad_input;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu
new file mode 100755
index 00000000..b826dd9b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu
@@ -0,0 +1,202 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+#include <THC/THCAtomics.cuh>
+#include <THC/THCDeviceUtils.cuh>
+
+
+// TODO make it in a common file
+#define CUDA_1D_KERNEL_LOOP(i, n)                            \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
+       i += blockDim.x * gridDim.x)
+
+
+template <typename T>
+__global__ void RoIPoolFForward(const int nthreads, const T* bottom_data,
+    const T spatial_scale, const int channels, const int height,
+    const int width, const int pooled_height, const int pooled_width,
+    const T* bottom_rois, T* top_data, int* argmax_data) {
+  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    const T* offset_bottom_rois = bottom_rois + n * 5;
+    int roi_batch_ind = offset_bottom_rois[0];
+    int roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
+    int roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
+    int roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
+    int roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
+
+    // Force malformed ROIs to be 1x1
+    int roi_width = max(roi_end_w - roi_start_w + 1, 1);
+    int roi_height = max(roi_end_h - roi_start_h + 1, 1);
+    T bin_size_h = static_cast<T>(roi_height)
+                       / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width)
+                       / static_cast<T>(pooled_width);
+
+    int hstart = static_cast<int>(floor(static_cast<T>(ph)
+                                        * bin_size_h));
+    int wstart = static_cast<int>(floor(static_cast<T>(pw)
+                                        * bin_size_w));
+    int hend = static_cast<int>(ceil(static_cast<T>(ph + 1)
+                                     * bin_size_h));
+    int wend = static_cast<int>(ceil(static_cast<T>(pw + 1)
+                                     * bin_size_w));
+
+    // Add roi offsets and clip to input boundaries
+    hstart = min(max(hstart + roi_start_h, 0), height);
+    hend = min(max(hend + roi_start_h, 0), height);
+    wstart = min(max(wstart + roi_start_w, 0), width);
+    wend = min(max(wend + roi_start_w, 0), width);
+    bool is_empty = (hend <= hstart) || (wend <= wstart);
+
+    // Define an empty pooling region to be zero
+    T maxval = is_empty ? 0 : -FLT_MAX;
+    // If nothing is pooled, argmax = -1 causes nothing to be backprop'd
+    int maxidx = -1;
+    const T* offset_bottom_data =
+        bottom_data + (roi_batch_ind * channels + c) * height * width;
+    for (int h = hstart; h < hend; ++h) {
+      for (int w = wstart; w < wend; ++w) {
+        int bottom_index = h * width + w;
+        if (offset_bottom_data[bottom_index] > maxval) {
+          maxval = offset_bottom_data[bottom_index];
+          maxidx = bottom_index;
+        }
+      }
+    }
+    top_data[index] = maxval;
+    argmax_data[index] = maxidx;
+  }
+}
+
+template <typename T>
+__global__ void RoIPoolFBackward(const int nthreads, const T* top_diff,
+    const int* argmax_data, const int num_rois, const T spatial_scale,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width, T* bottom_diff,
+    const T* bottom_rois) {
+  CUDA_1D_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    const T* offset_bottom_rois = bottom_rois + n * 5;
+    int roi_batch_ind = offset_bottom_rois[0];
+    int bottom_offset = (roi_batch_ind * channels + c) * height * width;
+    int top_offset    = (n * channels + c) * pooled_height * pooled_width;
+    const T* offset_top_diff = top_diff + top_offset;
+    T* offset_bottom_diff = bottom_diff + bottom_offset;
+    const int* offset_argmax_data = argmax_data + top_offset;
+
+    int argmax = offset_argmax_data[ph * pooled_width + pw];
+    if (argmax != -1) {
+      atomicAdd(
+          offset_bottom_diff + argmax,
+          static_cast<T>(offset_top_diff[ph * pooled_width + pw]));
+
+    }
+  }
+}
+
+std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
+                                const at::Tensor& rois,
+                                const float spatial_scale,
+                                const int pooled_height,
+                                const int pooled_width) {
+  AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor");
+  AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
+
+  auto num_rois = rois.size(0);
+  auto channels = input.size(1);
+  auto height = input.size(2);
+  auto width = input.size(3);
+
+  auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options());
+  auto output_size = num_rois * pooled_height * pooled_width * channels;
+  auto argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options().dtype(at::kInt));
+
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L));
+  dim3 block(512);
+
+  if (output.numel() == 0) {
+    THCudaCheck(cudaGetLastError());
+    return std::make_tuple(output, argmax);
+  }
+
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIPool_forward", [&] {
+    RoIPoolFForward<scalar_t><<<grid, block, 0, stream>>>(
+         output_size,
+         input.contiguous().data<scalar_t>(),
+         spatial_scale,
+         channels,
+         height,
+         width,
+         pooled_height,
+         pooled_width,
+         rois.contiguous().data<scalar_t>(),
+         output.data<scalar_t>(),
+         argmax.data<int>());
+  });
+  THCudaCheck(cudaGetLastError());
+  return std::make_tuple(output, argmax);
+}
+
+// TODO remove the dependency on input and use instead its sizes -> save memory
+at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
+                                 const at::Tensor& input,
+                                 const at::Tensor& rois,
+                                 const at::Tensor& argmax,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int batch_size,
+                                 const int channels,
+                                 const int height,
+                                 const int width) {
+  AT_ASSERTM(grad.type().is_cuda(), "grad must be a CUDA tensor");
+  AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
+  // TODO add more checks
+
+  auto num_rois = rois.size(0);
+  auto grad_input = at::zeros({batch_size, channels, height, width}, grad.options());
+
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 block(512);
+
+  // handle possibly empty gradients
+  if (grad.numel() == 0) {
+    THCudaCheck(cudaGetLastError());
+    return grad_input;
+  }
+
+  AT_DISPATCH_FLOATING_TYPES(grad.type(), "ROIPool_backward", [&] {
+    RoIPoolFBackward<scalar_t><<<grid, block, 0, stream>>>(
+         grad.numel(),
+         grad.contiguous().data<scalar_t>(),
+         argmax.data<int>(),
+         num_rois,
+         spatial_scale,
+         channels,
+         height,
+         width,
+         pooled_height,
+         pooled_width,
+         grad_input.data<scalar_t>(),
+         rois.contiguous().data<scalar_t>());
+  });
+  THCudaCheck(cudaGetLastError());
+  return grad_input;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_encode.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_encode.cu
new file mode 100755
index 00000000..b66a02d6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_encode.cu
@@ -0,0 +1,101 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+#include <torch/torch.h>
+#include <vector>
+#include <iostream>
+
+__global__ void box_encode_kernel(float *targets_dx, float *targets_dy, float *targets_dw, float *targets_dh,  
+                                  float4 *boxes, float4 *anchors, float wx, float wy, float ww, float wh, 
+                                  size_t gt, size_t idxJump) {
+
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    size_t row_offset; 
+    float anchors_x1, anchors_x2, anchors_y1, anchors_y2, 
+        boxes_x1, boxes_x2, boxes_y1, boxes_y2, ex_w, ex_h, 
+        ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
+          
+    for (int i = idx; i < gt; i += idxJump){
+        row_offset = i;
+        anchors_x1 = anchors[row_offset].x;
+        anchors_y1 = anchors[row_offset].y;
+        anchors_x2 = anchors[row_offset].z;
+        anchors_y2 = anchors[row_offset].w;        
+
+        boxes_x1 = boxes[row_offset].x;
+        boxes_y1 = boxes[row_offset].y;
+        boxes_x2 = boxes[row_offset].z;
+        boxes_y2 = boxes[row_offset].w; 
+        
+        ex_w = anchors_x2 - anchors_x1 + 1;
+        ex_h = anchors_y2 - anchors_y1 + 1;
+        ex_ctr_x = anchors_x1 + 0.5 * ex_w; 
+        ex_ctr_y = anchors_y1 + 0.5 * ex_h;
+               
+        gt_w = boxes_x2 - boxes_x1 + 1;
+        gt_h = boxes_y2 -  boxes_y1 + 1; 
+        gt_ctr_x = boxes_x1 + 0.5 * gt_w; 
+        gt_ctr_y = boxes_y1 + 0.5 * gt_h;        
+        
+        targets_dx[i] = wx * (gt_ctr_x - ex_ctr_x) / ex_w; 
+        targets_dy[i] = wy * (gt_ctr_y - ex_ctr_y) / ex_h; 
+        targets_dw[i] = ww * log(gt_w / ex_w); 
+        targets_dh[i] = wh * log(gt_h / ex_h);          
+    }  
+
+}
+
+
+std::vector<at::Tensor> box_encode_cuda(at::Tensor boxes, at::Tensor anchors, float wx, float wy, float ww, float wh){
+   
+    int minGridSize;
+    int blockSize;
+    
+    cudaOccupancyMaxPotentialBlockSize(&minGridSize,
+                                       &blockSize,
+                                       (void*) box_encode_kernel,
+                                       0,  // dynamic memory
+                                       0); // maximum utilized threads    
+    long size = boxes.size(0);
+    auto targets_dx = torch::ones({size}, torch::CUDA(at::kFloat)); 
+    auto targets_dy = torch::ones({size}, torch::CUDA(at::kFloat));
+    auto targets_dw = torch::ones({size}, torch::CUDA(at::kFloat));
+    auto targets_dh = torch::ones({size}, torch::CUDA(at::kFloat));
+    
+    dim3 gridDim(minGridSize);
+    dim3 blockDim(blockSize);
+    int idxJump = minGridSize * blockSize;
+    auto stream = at::cuda::getCurrentCUDAStream();
+    box_encode_kernel<<<gridDim,blockDim,0,stream.stream()>>>(targets_dx.data<float>(), 
+                                                              targets_dy.data<float>(), 
+                                                              targets_dw.data<float>(), 
+                                                              targets_dh.data<float>(), 
+                                                              (float4*) boxes.data<float>(), 
+                                                              (float4*) anchors.data<float>(), 
+                                                              wx, wy, ww, wh, 
+                                                              size, idxJump);
+     
+    std::vector<at::Tensor> result;
+    result.push_back(targets_dx);
+    result.push_back(targets_dy);
+    result.push_back(targets_dw);
+    result.push_back(targets_dh);  
+    return result;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_iou.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_iou.cu
new file mode 100755
index 00000000..17d9adf2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/box_iou.cu
@@ -0,0 +1,91 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+#include <torch/torch.h>
+#include <iostream>
+
+__global__ void box_iou_cuda_kernel(float *box_iou, float4 *box1, float4 *box2, long M, 
+                                    long N, int idxJump) {
+
+    int idx = blockIdx.x*blockDim.x + threadIdx.x;
+    size_t b1_idx, b2_idx, b1_row_offset, b2_row_offset; 
+    float xmin1, xmin2, xmax1, xmax2, ymin1, ymin2, ymax1, ymax2;
+    float x_tl, y_tl, x_br, y_br, w, h, inter, area1, area2, iou;
+          
+    for (long i = idx; i < M * N; i += idxJump){
+        
+        b1_idx = i / N;
+        b2_idx = i % N;
+        b1_row_offset = b1_idx;
+        b2_row_offset = b2_idx;
+
+        xmin1 = box1[b1_row_offset].x;
+        ymin1 = box1[b1_row_offset].y;
+        xmax1 = box1[b1_row_offset].z;
+        ymax1 = box1[b1_row_offset].w;
+        xmin2 = box2[b2_row_offset].x;
+        ymin2 = box2[b2_row_offset].y;
+        xmax2 = box2[b2_row_offset].z;
+        ymax2 = box2[b2_row_offset].w;
+
+        x_tl = fmaxf(xmin1, xmin2);
+        y_tl = fmaxf(ymin1, ymin2);
+
+        x_br = fminf(xmax1, xmax2);
+        y_br = fminf(ymax1, ymax2);                                
+        w = (x_br - x_tl + 1) < 0 ? 0.0f : (x_br - x_tl + 1);
+        h = (y_br - y_tl + 1) < 0 ? 0.0f : (y_br - y_tl + 1);
+
+        inter = w * h;
+        area1 = (xmax1 - xmin1 + 1) * (ymax1 - ymin1 + 1);
+        area2 = (xmax2 - xmin2 + 1) * (ymax2 - ymin2 + 1);
+        iou = inter / (area1 + area2 - inter);
+        box_iou[b1_idx * N + b2_idx] = iou;
+    }  
+
+}
+
+at::Tensor box_iou_cuda(at::Tensor box1, at::Tensor box2){
+
+    int minGridSize;
+    int blockSize;
+    
+    cudaOccupancyMaxPotentialBlockSize(&minGridSize,
+                                       &blockSize,
+                                       (void*) box_iou_cuda_kernel,
+                                       0,  // dynamic memory
+                                       0); // maximum utilized threads
+    
+    long M = box1.size(0);
+    long N = box2.size(0);
+    auto box_iou = torch::ones({M, N}, torch::CUDA(at::kFloat));
+    
+    dim3 gridDim(minGridSize);
+    dim3 blockDim(blockSize);
+    int idxJump = minGridSize * blockSize;
+    auto stream = at::cuda::getCurrentCUDAStream();
+    box_iou_cuda_kernel<<<gridDim, blockDim, 0, stream.stream()>>>(box_iou.data<float>(), 
+                                                                  (float4*) box1.data<float>(), 
+                                                                  (float4*) box2.data<float>(), 
+                                                                  M, N, 
+                                                                  idxJump);
+    return box_iou;
+}
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/generate_mask_targets.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/generate_mask_targets.cu
new file mode 100755
index 00000000..1262af45
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/generate_mask_targets.cu
@@ -0,0 +1,464 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#include <torch/extension.h>
+#include <iostream>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+#include <math.h>
+#include <algorithm>
+#include <stdlib.h>
+#include "cpu/vision.h"
+
+
+/*rle cuda kernels are cuda version of the corresponding cpu functions here 
+https://github.com/cocodataset/cocoapi/blob/master/common/maskApi.c 
+these are only a subset of rle kernels.*/
+
+typedef unsigned int uint;
+typedef unsigned long siz;
+typedef unsigned char byte;
+
+//6144 is based on minimum shared memory size per SM 
+//across all pytorch-supported GPUs. Need to use blocking
+//to avoid this restriction
+const int BUFFER_SIZE=6144;
+const int CNTS_SIZE=6144;
+
+__global__ void crop_and_scale_cuda_kernel(double *dense_poly_data, int *per_anchor_poly_idx, int *poly_rel_idx, 
+                                           int poly_count, int anchor_count, float4 *anchor_data, int mask_size){
+    int tid = threadIdx.x;
+    int block_jump = blockDim.x;
+    int poly_id = blockIdx.x;
+    int anchor_idx;
+    for (anchor_idx = 0; anchor_idx < anchor_count; anchor_idx++){
+        if (poly_id < per_anchor_poly_idx[anchor_idx + 1]) break;    
+    }
+    float w = anchor_data[anchor_idx].z - anchor_data[anchor_idx].x;
+  	float h = anchor_data[anchor_idx].w - anchor_data[anchor_idx].y;
+  	w = fmaxf(w, 1.0f);
+  	h = fmaxf(h, 1.0f);  	
+    float ratio_h = ((float) mask_size) / h;
+    float ratio_w = ((float) mask_size) / w;
+
+    int poly_ptr_idx_start = poly_rel_idx[poly_id];
+    int poly_ptr_idx_end = poly_rel_idx[poly_id + 1];
+
+    double *poly_data_buf = dense_poly_data + poly_ptr_idx_start;
+    int len = poly_ptr_idx_end - poly_ptr_idx_start;
+    
+    for (int j = tid; j < len; j += block_jump){
+        if (j % 2 == 0) poly_data_buf[j] = ratio_w*((float) poly_data_buf[j]- anchor_data[anchor_idx].x);
+        if (j % 2 == 1) poly_data_buf[j] = ratio_h*((float) poly_data_buf[j]- anchor_data[anchor_idx].y);
+    }
+}
+
+//merging masks happens on mask format, not RLE format.     
+__global__ void merge_masks_cuda_kernel(byte *masks_in, float *masks_out, const int mask_size, 
+                                        int *per_anchor_poly_idx, int anchor_count){
+
+    int anchor_idx = blockIdx.x;
+    int tid = threadIdx.x;
+    int jump_block = blockDim.x;
+    int mask_start_idx = per_anchor_poly_idx[anchor_idx];
+    int num_of_masks_to_merge = per_anchor_poly_idx[anchor_idx + 1]-per_anchor_poly_idx[anchor_idx];
+    
+    for(int j = tid; j < mask_size * mask_size; j += jump_block){
+        int transposed_pixel = (j % mask_size) * mask_size + j / mask_size;
+        byte pixel = 0;
+        for(int k = 0; k < num_of_masks_to_merge; k++){
+            if (masks_in[(mask_start_idx + k) * mask_size * mask_size + j] == 1) pixel = 1;
+            if (pixel == 1) break;
+        }
+        masks_out[anchor_idx * mask_size * mask_size + transposed_pixel] = (float) pixel;       
+    }
+}
+
+/*cuda version of rleDecode function in this API: 
+https://github.com/cocodataset/cocoapi/blob/master/common/maskApi.c*/
+
+__global__ void decode_rle_cuda_kernel(const int *num_of_cnts, uint *cnts, long h, long w, byte *mask)
+{
+    int poly_id = blockIdx.x;
+    int tid = threadIdx.x;
+    int block_jump = blockDim.x;
+    
+    int m = num_of_cnts[poly_id];
+    uint *cnts_buf = cnts + CNTS_SIZE * poly_id;
+    byte *mask_ptr = mask + poly_id * h * w;
+    
+    __shared__ uint shbuf1[CNTS_SIZE];
+    __shared__ uint shbuf2[CNTS_SIZE];
+
+    
+    //initialize shbuf for scan. first element is 0 (exclusive scan)
+    for (long i = tid; i < CNTS_SIZE; i += block_jump){
+        shbuf1[i] = (i <= m & i > 0) ? cnts_buf[i - 1]:0;
+        shbuf2[i] = (i <= m & i > 0) ? cnts_buf[i - 1]:0;
+    }
+    __syncthreads();
+    
+    //double buffering for scan
+    int switch_buf = 0;
+    for (int offset = 1; offset <= m; offset *= 2){
+        switch_buf = 1 - switch_buf;
+        if(switch_buf == 0){
+            for(int j = tid;j <= m;j += block_jump){
+                if(j >= offset) shbuf2[j] = shbuf1[j]+shbuf1[j - offset];
+                else shbuf2[j] = shbuf1[j];
+            }
+        }else if (switch_buf == 1){
+            for(int j = tid;j <= m;j += block_jump){
+                if(j >= offset) shbuf1[j] = shbuf2[j] + shbuf2[j - offset];
+                else shbuf1[j] = shbuf2[j];
+            }        
+        
+        }
+        __syncthreads();
+    }
+    uint *scanned_buf = switch_buf == 0 ? shbuf2 : shbuf1;
+       
+    //find which bin pixel j falls into , which determines the pixel value
+    //use binary search
+    for(int j = tid; j < h * w; j += block_jump){
+        int bin = 0;
+        int min_idx = 0;
+        int max_idx = m;
+        int mid_idx = m / 2;        
+        while(max_idx > min_idx){
+            if(j > scanned_buf[mid_idx]) {
+                min_idx = mid_idx+1; 
+                mid_idx = (min_idx + max_idx) / 2;
+            }
+            else if (j < scanned_buf[mid_idx]) {
+                max_idx = mid_idx; 
+                mid_idx = (min_idx + max_idx) / 2;
+            }
+            else {
+                mid_idx++; 
+                break;
+            }    
+        }         
+        int k = mid_idx;
+        byte pixel = k % 2 == 0 ? 1 : 0;
+        mask_ptr[j] = pixel; 
+    }
+}
+
+/*cuda version of rleFrPoly function in this API: 
+https://github.com/cocodataset/cocoapi/blob/master/common/maskApi.c*/
+
+__global__ void rle_fr_poly_cuda_kernel(const double *dense_coordinates, int *poly_rel_idx, long h, long w, 
+                                        uint *cnts, int *x_in, int *y_in, int *u_in, int *v_in, uint *a_in, 
+                                        uint *b_in, int *num_of_cnts) {   
+    int poly_id = blockIdx.x;
+    int tid = threadIdx.x;
+    int block_jump = blockDim.x;
+    long cnts_offset = poly_id * CNTS_SIZE;
+    long k = (poly_rel_idx[poly_id + 1] - poly_rel_idx[poly_id]) / 2;
+    
+    const double *xy = dense_coordinates + poly_rel_idx[poly_id];
+    int *x = x_in + poly_id * BUFFER_SIZE;
+    int *y = y_in + poly_id * BUFFER_SIZE;
+    int *u = u_in + poly_id * BUFFER_SIZE;
+    int *v = v_in + poly_id * BUFFER_SIZE;
+    uint *a = a_in + poly_id * BUFFER_SIZE;
+    uint *b = b_in + poly_id * BUFFER_SIZE;
+    /* upsample and get discrete points densely along entire boundary */
+    long j, m = 0;
+    double scale = 5;
+    __shared__ int shbuf1[BUFFER_SIZE];
+    __shared__ int shbuf2[BUFFER_SIZE];
+    for(long j = tid; j < BUFFER_SIZE; j += block_jump) {
+        shbuf1[j] = 0; 
+        shbuf2[j] = 0;
+    }
+    for(long j = tid; j <= k; j += block_jump) 
+        x[j] = j < k ? ((int) (scale * xy[2 * j + 0] + 0.5)) : ((int) (scale * xy[0] + 0.5));
+    for(long j = tid; j <= k; j += block_jump) 
+        y[j] = j < k ? ((int) (scale * xy[2 * j + 1] + 0.5)) : ((int) (scale * xy[1] + 0.5));        
+    __syncthreads();
+        
+    for(int j = tid; j < k; j += block_jump){
+        int xs = x[j], xe = x[j + 1], ys = y[j], ye = y[j + 1], dx, dy, t, d, dist;
+        int flip; 
+        double s; 
+        dx = abs(xe - xs); 
+        dy = abs(ys - ye);
+        flip = (dx >= dy && xs > xe) || (dx < dy && ys > ye);
+        if (flip) {t = xs; xs = xe; xe = t; t = ys; ys = ye; ye = t;}
+        s = dx >= dy ? (double) (ye - ys) / dx : (double) (xe - xs) / dy;
+        dist = dx >= dy ? dx + 1 : dy + 1;
+        shbuf1[j + 1] = dist; 
+        shbuf2[j + 1] = dist;
+    }
+    __syncthreads();
+    //block-wide exclusive prefix scan
+    int switch_buf = 0;
+    for (int offset = 1; offset <= k; offset *= 2){
+        switch_buf = 1 - switch_buf;
+        if (switch_buf == 0){
+            for(int j = tid; j <= k; j += block_jump){
+                if (j >= offset) shbuf2[j] = shbuf1[j] + shbuf1[j - offset];
+                else shbuf2[j] = shbuf1[j];                
+            }
+        }
+        else if (switch_buf == 1){
+            for(int j = tid; j <= k; j += block_jump){
+                if (j >= offset) shbuf1[j] = shbuf2[j] + shbuf2[j - offset];
+                else shbuf1[j] = shbuf2[j];                
+            }                  
+        } 
+        __syncthreads();
+    }
+      
+    for (int j = tid; j < k; j += block_jump){
+        int xs = x[j], xe = x[j + 1], ys = y[j], ye = y[j + 1], dx, dy, t, d, dist;
+        int flip; 
+        double s; 
+        dx = __sad(xe, xs, 0); 
+        dy = __sad(ys, ye, 0);
+        flip = (dx >= dy && xs > xe) || (dx < dy && ys > ye);
+        if (flip) {t = xs; xs = xe; xe = t; t = ys; ys = ye; ye = t;}
+        s = dx >= dy ? (double) (ye - ys) / dx : (double) (xe - xs) / dy;
+        m = switch_buf == 0 ? shbuf2[j] : shbuf1[j];
+        if (dx >= dy) for (d = 0; d <= dx; d++) {
+          /*the multiplication statement 's*t' causes nvcc to optimize with flush-to-zero=True for 
+          double precision multiply, which we observe produces different results than CPU occasionally. 
+          To force flush-to-zero=False, we use __dmul_rn intrinsics function */
+          t = flip ? dx - d : d; 
+          u[m] = t + xs; 
+          v[m] = (int) (ys + __dmul_rn(s, t) + .5); 
+          m++; 
+        } 
+        else for (d = 0; d <= dy; d++) {
+          t = flip ? dy - d : d; 
+          v[m] = t + ys; 
+          u[m] = (int) (xs + __dmul_rn(s, t) + .5); 
+          m++;
+        }
+    }    
+    __syncthreads();
+    m = switch_buf == 0 ? shbuf2[k] : shbuf1[k];
+    int k2 = m;
+    __syncthreads();
+    double xd, yd;
+    if (tid == 0) {
+        shbuf1[tid] = 0; 
+        shbuf2[tid] = 0;
+    }     
+    /* get points along y-boundary and downsample */
+    for (int j = tid; j < k2; j += block_jump){
+        if (j > 0){
+            if (u[j] != u[j - 1]){
+                xd = (double) (u[j] < u[j-1] ? u[j] : u[j] - 1); 
+                xd = (xd + .5) / scale - .5;   
+                if (floor(xd) != xd || xd < 0 || xd > w - 1 ) {
+                    shbuf1[j] = 0; 
+                    shbuf2[j] = 0; 
+                    continue;
+                }
+                yd = (double) (v[j] < v[j - 1] ? v[j] : v[j - 1]); yd = (yd + .5) / scale - .5;
+                if (yd < 0) yd = 0; 
+                else if (yd > h) yd = h; yd = ceil(yd);                
+                shbuf1[j] = 1; 
+                shbuf2[j] = 1;               
+            } else {
+                shbuf1[j] = 0; 
+                shbuf2[j] = 0;
+            }                    
+        }    
+    }
+    __syncthreads(); 
+    //exclusive prefix scan
+    switch_buf = 0;
+    for (int offset = 1; offset < k2; offset *= 2){
+        switch_buf = 1 - switch_buf;
+        if (switch_buf == 0){
+            for (int j = tid; j < k2; j += block_jump){
+                if (j >= offset) shbuf2[j] = shbuf1[j - offset] + shbuf1[j];
+                else shbuf2[j] = shbuf1[j];                
+            }
+        }
+        else if (switch_buf == 1){
+            for (int j = tid; j < k2; j += block_jump){
+                if (j >= offset) shbuf1[j] = shbuf2[j - offset] + shbuf2[j];
+                else shbuf1[j] = shbuf2[j];                
+            }                  
+        } 
+        __syncthreads();             
+    }
+  
+    for (int j = tid; j < k2; j += block_jump){
+        if (j > 0){
+            if(u[j] != u[j - 1]){
+                xd = (double) (u[j] < u[j - 1] ? u[j] : u[j] - 1); 
+                xd = (xd + .5) / scale - .5;
+                if (floor(xd) != xd || xd < 0 || xd > w - 1) {continue;}
+                yd = (double) (v[j] < v[j - 1] ? v[j] : v[j - 1]); 
+                yd = (yd + .5) / scale - .5;
+                if (yd < 0) yd = 0; 
+                else if (yd > h) yd = h; yd = ceil(yd);                
+                m = switch_buf == 0 ? shbuf2[j - 1]:shbuf1[j - 1];
+                x[m] = (int) xd; 
+                y[m] = (int) yd; 
+                m++;                
+            }                   
+        }    
+    }
+    __syncthreads(); 
+    
+    /* compute rle encoding given y-boundary points */
+    m = switch_buf == 0 ? shbuf2[k2 - 1] : shbuf1[k2 - 1]; 
+    int k3 = m;
+    for (int j = tid; j <= k3; j += block_jump){
+       if (j < k3) a[j] = (uint) (x[j] * (int) (h) + y[j]); 
+       else a[j] = (uint)(h * w);        
+    }    
+    k3++;
+    __syncthreads();
+    
+    //run brick sort on a for k3+1 element
+    //load k3+1 elements of a into shared memory
+    for(long j = tid; j < k3; j += block_jump) shbuf1[j]=a[j];
+    __syncthreads();
+    uint a_temp;
+    for (int r = 0; r <= k3 / 2; r++){
+        int evenCas = k3 / 2;
+        int oddCas = (k3 - 1) / 2;
+        //start with 0, need (k3+1)/2 CAS
+        for (int j = tid; j < evenCas; j += block_jump){
+            if (shbuf1[2 * j] > shbuf1[2 * j + 1]){
+                a_temp = shbuf1[2 * j];
+                shbuf1[2 * j]=shbuf1[2 * j + 1];
+                shbuf1[2 * j + 1] = a_temp;
+            }            
+        }
+        __syncthreads();
+        //start with 1
+        for (int j = tid; j < oddCas; j += block_jump){
+            if (shbuf1[2 * j + 1] > shbuf1[2 * j + 2]){
+                a_temp=shbuf1[2 * j + 1];
+                shbuf1[2 * j + 1] = shbuf1[2 * j + 2];
+                shbuf1[2 * j + 2]=a_temp;
+            }            
+        }
+        __syncthreads();    
+    }
+    
+    for(long j = tid; j < k3; j += block_jump) {
+        if(j>0) shbuf2[j] = shbuf1[j - 1];
+        else shbuf2[j] = 0;        
+    } 
+     __syncthreads();  
+    for(int j = tid; j < k3; j += block_jump){
+        shbuf1[j] -= shbuf2[j];       
+    }      
+    __syncthreads();   
+    uint *cnts_buf = cnts + cnts_offset;
+    if (tid == 0){
+        j = m = 0; 
+        cnts_buf[m++] = shbuf1[j++];
+        while (j < k3) if (shbuf1[j] > 0) cnts_buf[m++] = shbuf1[j++]; else {
+            j++; if (j < k3) cnts_buf[m - 1] += shbuf1[j++]; } 
+        num_of_cnts[poly_id] = m;               
+    }
+    __syncthreads();
+}
+
+at::Tensor generate_mask_targets_cuda(at::Tensor dense_vector, const std::vector<std::vector<at::Tensor>> polygons, 
+                                      const at::Tensor anchors, const int mask_size){    
+    const int M = mask_size;
+    assert (M < 32); 
+    //if M >=32, shared memory buffer size may not be
+    //sufficient. Need to fix this by blocking    
+    float *d_anchor_data = anchors.data<float>();
+    int num_of_anchors = anchors.size(0);  
+    auto per_anchor_poly_idx = at::empty({num_of_anchors + 1}, at::CPU(at::kInt));
+    int num_of_poly = 0;
+    for (int i = 0; i < num_of_anchors; i++){
+  	    *(per_anchor_poly_idx.data<int>() + i) = num_of_poly;
+  	    num_of_poly += polygons[i].size();
+    }
+    *(per_anchor_poly_idx.data<int>() + num_of_anchors) = num_of_poly;
+  
+    auto poly_rel_idx = at::empty({num_of_poly + 1}, at::CPU(at::kInt));
+    double *dense_poly_data = dense_vector.data<double>();
+    int start_idx = 0;
+    int poly_count = 0;
+  
+    for(int i = 0; i < polygons.size(); i++){
+  	    for(int j=0; j < polygons[i].size(); j++) {
+  		    *(poly_rel_idx.data<int>() + poly_count) = start_idx;
+  		    start_idx += polygons[i][j].size(0);
+  		    poly_count++;
+  	    }
+    }    
+    *(poly_rel_idx.data<int>() + poly_count) = start_idx;
+
+    at::Tensor d_x_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt));
+    at::Tensor d_y_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt));
+    at::Tensor d_u_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt));
+    at::Tensor d_v_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt));
+    at::Tensor d_a_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt));//used with uint* pointer
+    at::Tensor d_b_t = torch::empty({BUFFER_SIZE * num_of_poly}, torch::CUDA(at::kInt)); //used with uint* pointer
+    at::Tensor d_mask_t = torch::empty({M * M * num_of_poly}, torch::CUDA(at::kByte)); 
+    auto result =  torch::empty({num_of_anchors, M, M}, torch::CUDA(at::kFloat));   
+    at::Tensor d_num_of_counts_t = torch::empty({num_of_poly}, torch::CUDA(at::kInt));
+    at::Tensor d_cnts_t = torch::empty({CNTS_SIZE * num_of_poly}, torch::CUDA(at::kInt));     
+    auto d_dense_vector = dense_vector.cuda();   
+    auto d_per_anchor_poly_idx = per_anchor_poly_idx.cuda();
+    auto d_poly_rel_idx = poly_rel_idx.cuda();
+    auto stream = at::cuda::getCurrentCUDAStream();  
+    
+    crop_and_scale_cuda_kernel<<<num_of_poly, 256, 0, stream.stream()>>>(d_dense_vector.data<double>(), 
+                                                                      d_per_anchor_poly_idx.data<int>(), 
+                                                                      d_poly_rel_idx.data<int>(), 
+                                                                      poly_count, 
+                                                                      num_of_anchors, 
+                                                                      (float4*) d_anchor_data, 
+                                                                      M);       
+                                                                      
+    //TODO: larger threads-per-block might be better here, because each CTA uses 32 KB of shmem,
+    //and occupancy is likely shmem capacity bound                                                                                
+    rle_fr_poly_cuda_kernel<<<num_of_poly, 1024, 0, stream.stream()>>>(d_dense_vector.data<double>(), 
+                                                                   d_poly_rel_idx.data<int>(), 
+                                                                   M, M, 
+                                                                   (uint*) d_cnts_t.data<int>(), 
+                                                                   d_x_t.data<int>(), 
+                                                                   d_y_t.data<int>(), 
+                                                                   d_u_t.data<int>(), 
+                                                                   d_v_t.data<int>(), 
+                                                                   (uint*) d_a_t.data<int>(), 
+                                                                   (uint*) d_b_t.data<int>(), 
+                                                                   d_num_of_counts_t.data<int>());
+                                                                 
+    decode_rle_cuda_kernel<<<num_of_poly, 256, 0, stream.stream()>>>(d_num_of_counts_t.data<int>(), 
+                                                                 (uint*) d_cnts_t.data<int>(), 
+                                                                 M, M, 
+                                                                 d_mask_t.data<byte>());
+                                                                 
+    merge_masks_cuda_kernel<<<num_of_anchors, 256, 0, stream.stream()>>>(d_mask_t.data<byte>(), result.data<float>(), 
+                                                                      M, d_per_anchor_poly_idx.data<int>(), 
+                                                                      num_of_anchors); 
+    return result;
+}
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/match_proposals.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/match_proposals.cu
new file mode 100755
index 00000000..51dab2ca
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/match_proposals.cu
@@ -0,0 +1,172 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+#include <torch/torch.h>
+#include <vector>
+#include <iostream>
+
+__launch_bounds__(256) static __global__
+    void max_along_gt_idx(float *match, unsigned char *pred_forgiven, long *max_gt_idx, long long gt,long long preds,
+                          bool include_low_quality, float low_th, float high_th) {
+    
+    long long tid = blockIdx.x * blockDim.x + threadIdx.x;
+    if(tid < preds){
+        float max_iou = 0.0f;
+        int max_idx = 0;
+        float iou;
+        for(long long i = 0;i < gt; i++){
+            iou = match[i * preds + tid]; 
+            if (iou > max_iou) {max_iou = iou; max_idx = i;}
+        }
+
+        if (max_iou >= high_th) max_gt_idx[tid] = max_idx;
+        else if ((pred_forgiven[tid] == 1 && include_low_quality)) max_gt_idx[tid] = max_idx;
+        else if (max_iou < low_th) max_gt_idx[tid] = -1;
+        else if (max_iou < high_th) max_gt_idx[tid] = -2;
+    }
+}
+
+
+__device__ void warpReduce(volatile float* sdata, int tid) {
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 32]);
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 16]);
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 8]);
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 4]);
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 2]);
+    sdata[tid] = fmax(sdata[tid],sdata[tid + 1]);
+}
+
+
+static __global__
+    void max_along_preds(float* match, float* inter_gt, long long gt,long long preds) {
+    int gt_idx = blockIdx.x;
+    int chunk_idx = blockIdx.y;
+    int gt_offset = chunk_idx * 2048;
+    int start_idx = gt_idx * preds + gt_offset;
+    int idx = threadIdx.x;
+    __shared__ float shbuf[1024]; 
+   shbuf[idx] = 0.0f;
+    __syncthreads();
+    if(gt_offset + idx + 1024 < preds) shbuf[idx] = fmax(match[start_idx + idx], match[start_idx + idx + 1024]);
+    else if (gt_offset + idx < preds) shbuf[idx] = match[start_idx + idx];
+    __syncthreads();
+    if(idx < 512) shbuf[idx] = fmax(shbuf[idx],shbuf[idx + 512]);
+    __syncthreads();
+    if(idx < 256) shbuf[idx] = fmax(shbuf[idx], shbuf[idx + 256]);
+    __syncthreads();
+    if(idx < 128) shbuf[idx] = fmax(shbuf[idx], shbuf[idx + 128]);
+    __syncthreads();
+    if(idx < 64) shbuf[idx] = fmax(shbuf[idx], shbuf[idx + 64]);
+    __syncthreads();
+    if(idx < 32) warpReduce(shbuf, idx);
+    if (idx == 0) inter_gt[((preds + 2047) / 2048) * gt_idx + chunk_idx] = shbuf[idx];
+}
+
+
+
+__launch_bounds__(256) static __global__
+    void max_along_preds_reduced(float *match, float *max_preds, long long gt,long long preds) {
+    
+    long long tid = blockIdx.x * blockDim.x + threadIdx.x;
+    if (tid < gt){
+        float max_iou = 0.0f;
+        float iou;
+        for(long long i = 0; i < preds; i++){
+            iou = match[tid * preds + i]; 
+            if (iou > max_iou) max_iou = iou;
+        }
+        max_preds[tid] = max_iou;
+    }
+}
+
+__launch_bounds__(256) static __global__
+    void forgive_preds(float *match_quality_data, float *d_best_pred_per_gt, unsigned char *d_pred_forgiven, 
+                       long gt, long preds) {
+        long tid = blockIdx.x * blockDim.x + threadIdx.x;
+        if (tid < preds){
+            unsigned char forgiven = 0;
+            float iou;
+            for(int i = 0; i < gt; i++){
+                iou = match_quality_data[i * preds + tid];
+                if(iou == d_best_pred_per_gt[i]){
+                    forgiven = 1;
+                    break;
+                }            
+            }
+            d_pred_forgiven[tid] = forgiven;
+        }    
+    } 
+
+at::Tensor match_proposals_cuda(at::Tensor match_quality_matrix, bool allow_low_quality_matches, 
+                                float low_th, float high_th){
+    
+    int gt = match_quality_matrix.size(0);
+    long long preds = match_quality_matrix.size(1);
+    float *match_quality_data = match_quality_matrix.data<float>();
+    using namespace at;
+    //predictions are reduced by chunks of 2048 elements per block
+    int num_chunks = (preds + 2047) / 2048;
+    auto result = torch::ones({preds}, torch::CUDA(at::kLong)); 
+    at::Tensor best_pred_per_gt = torch::zeros({gt}, torch::CUDA(at::kFloat));
+    at::Tensor pred_forgiven = torch::zeros({preds}, torch::CUDA(at::kByte));    
+    at::Tensor intergt = torch::zeros({gt * num_chunks}, torch::CUDA(at::kFloat));
+    
+    auto stream = at::cuda::getCurrentCUDAStream();
+    
+    //do an intermediate reduction along all predictions for each gt
+    dim3 block(1024, 1, 1);
+    dim3 grid(gt, num_chunks, 1);
+    if (allow_low_quality_matches) max_along_preds<<<grid, block, 0, stream.stream()>>>(
+                                                        match_quality_matrix.data<float>(), 
+                                                        intergt.data<float>(), 
+                                                        gt, 
+                                                        preds);  
+    //final reduction to find best iou per gt  
+    int numThreads = 256;
+    int numBlocks=(gt + numThreads - 1) / numThreads;    
+
+    if (allow_low_quality_matches) max_along_preds_reduced<<<numBlocks, numThreads, 0, stream.stream()>>>(
+                                                        intergt.data<float>(), 
+                                                        best_pred_per_gt.data<float>(), 
+                                                        gt, 
+                                                        num_chunks); 
+    numBlocks=(preds + numThreads - 1) / numThreads;
+    //if low_quality_matches are allowed, mark some predictions to keep their best matching gt even though
+    //iou < threshold
+    if (allow_low_quality_matches) forgive_preds<<<numBlocks, numThreads, 0, stream.stream()>>>(
+                                                        match_quality_matrix.data<float>(), 
+                                                        best_pred_per_gt.data<float>(), 
+                                                        pred_forgiven.data<unsigned char>(), 
+                                                        gt, 
+                                                        preds); 
+    //compute resulting tensor of indices
+    max_along_gt_idx<<<numBlocks, numThreads, 0, stream.stream()>>>(match_quality_matrix.data<float>(), 
+                                                                    pred_forgiven.data<unsigned char>(), 
+                                                                    result.data<long>(), 
+                                                                    gt, 
+                                                                    preds, 
+                                                                    allow_low_quality_matches, 
+                                                                    low_th, 
+                                                                    high_th);       
+    return result;
+
+}
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/nms.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/nms.cu
new file mode 100755
index 00000000..833d8523
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/nms.cu
@@ -0,0 +1,131 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+
+#include <vector>
+#include <iostream>
+
+int const threadsPerBlock = sizeof(unsigned long long) * 8;
+
+__device__ inline float devIoU(float const * const a, float const * const b) {
+  float left = max(a[0], b[0]), right = min(a[2], b[2]);
+  float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
+  float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
+  float interS = width * height;
+  float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
+  float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
+  return interS / (Sa + Sb - interS);
+}
+
+__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
+                           const float *dev_boxes, unsigned long long *dev_mask) {
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size =
+        min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
+  const int col_size =
+        min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
+
+  __shared__ float block_boxes[threadsPerBlock * 5];
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
+    const float *cur_box = dev_boxes + cur_box_idx * 5;
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
+    }
+    for (i = start; i < col_size; i++) {
+      if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock);
+    dev_mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+// boxes is a N x 5 tensor
+at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) {
+  using scalar_t = float;
+  AT_ASSERTM(boxes.type().is_cuda(), "boxes must be a CUDA tensor");
+  auto scores = boxes.select(1, 4);
+  auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
+  auto boxes_sorted = boxes.index_select(0, order_t);
+
+  int boxes_num = boxes.size(0);
+
+  const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock);
+
+  scalar_t* boxes_dev = boxes_sorted.data<scalar_t>();
+
+  THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
+
+  unsigned long long* mask_dev = NULL;
+  //THCudaCheck(THCudaMalloc(state, (void**) &mask_dev,
+  //                      boxes_num * col_blocks * sizeof(unsigned long long)));
+
+  mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long));
+
+  dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock),
+              THCCeilDiv(boxes_num, threadsPerBlock));
+  dim3 threads(threadsPerBlock);
+  nms_kernel<<<blocks, threads>>>(boxes_num,
+                                  nms_overlap_thresh,
+                                  boxes_dev,
+                                  mask_dev);
+
+  std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
+  THCudaCheck(cudaMemcpy(&mask_host[0],
+                        mask_dev,
+                        sizeof(unsigned long long) * boxes_num * col_blocks,
+                        cudaMemcpyDeviceToHost));
+
+  std::vector<unsigned long long> remv(col_blocks);
+  memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
+
+  at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU));
+  int64_t* keep_out = keep.data<int64_t>();
+
+  int num_to_keep = 0;
+  for (int i = 0; i < boxes_num; i++) {
+    int nblock = i / threadsPerBlock;
+    int inblock = i % threadsPerBlock;
+
+    if (!(remv[nblock] & (1ULL << inblock))) {
+      keep_out[num_to_keep++] = i;
+      unsigned long long *p = &mask_host[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++) {
+        remv[j] |= p[j];
+      }
+    }
+  }
+
+  THCudaFree(state, mask_dev);
+  // TODO improve this part
+  return std::get<0>(order_t.index({
+                       keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to(
+                         order_t.device(), keep.scalar_type())
+                     }).sort(0, false));
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.cu b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.cu
new file mode 100755
index 00000000..b70e7d7b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.cu
@@ -0,0 +1,230 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/extension.h>
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+
+namespace {
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                                 \
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+    i += blockDim.x * gridDim.x)
+
+// The number of cuda threads to use. Since work is assigned to SMs at the
+// granularity of a block, 128 is chosen to allow utilizing more SMs for
+// smaller input sizes.
+// 1D grid
+constexpr int CUDA_NUM_THREADS = 128;
+
+constexpr int MAXIMUM_NUM_BLOCKS = 4096;
+
+inline int GetBlocks(const int N) {
+  return std::max(
+      std::min(
+        (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS,
+        MAXIMUM_NUM_BLOCKS),
+      // Use at least 1 block, since CUDA does not allow empty block
+      1);
+}
+
+/**
+ * d_sorted_score_keys -- indexes into _original_ scores
+ * nboxes_to_generate -- pre_nms_topn
+ */
+__global__ void GeneratePreNMSUprightBoxesKernel(
+    const long *d_sorted_scores_keys,
+		const int nboxes_to_generate,
+		const float *d_bbox_deltas,   // [N, A*4, H, W]
+		const float4 *d_anchors,
+		const int H,
+		const int W,
+		const int K, // K = H*W
+		const int A,
+		const int KA, // KA = K*A
+		const float min_size,
+		const float *d_img_info_vec,
+		const int num_images,
+		const float bbox_xform_clip,
+		const bool correct_transform,
+		float4 *d_out_boxes,
+		const int prenms_nboxes, // leading dimension of out_boxes
+		float *d_inout_scores, // [N, A, H, W]
+		uint8_t *d_boxes_keep_flags) {
+  // Going to generate pre_nms_nboxes boxes per image
+  for (int ibox = blockIdx.x * blockDim.x + threadIdx.x; ibox < nboxes_to_generate;
+    ibox += blockDim.x * gridDim.x) {
+    for (int image_index = blockIdx.y * blockDim.y + threadIdx.y; image_index < num_images;
+        image_index += blockDim.y * gridDim.y) {
+      // box_conv_index : # of the same box, but indexed in
+      // the scores from the conv layer, of shape (A,H,W)
+      // the num_images dimension was already removed
+      // box_conv_index = a*K + h*W + w
+      // Note: PyT code takes topK, so need to adjust the indexing for multi-image
+      // box_conv_index is _local_ to the image_index, need to adjust into global arrays
+      const int box_conv_index = d_sorted_scores_keys[image_index * prenms_nboxes + ibox];
+
+      // We want to decompose box_conv_index in (a,h,w)
+      // such as box_conv_index = a*K + h*W + w
+      // (avoiding modulos in the process)
+      int remaining = box_conv_index;
+      const int dA = K; // stride of A
+      const int a = remaining / dA;
+      remaining -= a*dA;
+      const int dH = W; // stride of H
+      const int h = remaining / dH;
+      remaining -= h*dH;
+      const int w = remaining; // dW = 1
+
+      // Order of anchors is [N, H, W, A, 4]
+      const int a_idx = h * W * A + w * A + a;
+      const float4 anchor = d_anchors[image_index * KA + a_idx];
+
+      // x1,y1,x2,y2 :coordinates of anchor a, shifted for position (h,w)
+
+      float x1 = anchor.x;
+      float x2 = anchor.z;
+      float y1 = anchor.y;
+      float y2 = anchor.w;
+
+      // Deltas for that box
+      // Deltas of shape (num_images,4*A,K)
+      // We're going to compute 4 scattered reads
+      // better than the alternative, ie transposing the complete deltas
+      // array first
+      int deltas_idx = image_index * (KA*4) + a*4*K+h*W+w;
+      const float dx = d_bbox_deltas[deltas_idx];
+      // Stride of K between each dimension
+      deltas_idx += K; const float dy = d_bbox_deltas[deltas_idx];
+      deltas_idx += K; float dw = d_bbox_deltas[deltas_idx];
+      deltas_idx += K; float dh = d_bbox_deltas[deltas_idx];
+
+      // Upper bound on dw,dh
+      dw = fmin(dw, bbox_xform_clip);
+      dh = fmin(dh, bbox_xform_clip);
+
+      // Applying the deltas
+      float width = x2 - x1 + 1.0f;
+      const float ctr_x = x1 + 0.5f*width;
+      const float pred_ctr_x = ctr_x + width*dx;
+      const float pred_w = width*expf(dw);
+      x1 = pred_ctr_x - 0.5f*pred_w;
+      x2 = pred_ctr_x + 0.5f*pred_w;
+
+      float height = y2 - y1 + 1.0f;
+      const float ctr_y = y1 + 0.5f*height;
+      const float pred_ctr_y = ctr_y + height*dy;
+      const float pred_h = height*expf(dh);
+      y1 = pred_ctr_y - 0.5f*pred_h;
+      y2 = pred_ctr_y + 0.5f*pred_h;
+
+      if(correct_transform) {
+        x2 -= 1.0f;
+        y2 -= 1.0f;
+      }
+
+      // End of box_coder.decode(..) part
+
+      // Clipping box to image
+      // p = _clip_box_to_image(proposal, height, width)
+      const float img_height = d_img_info_vec[2*image_index+1];
+      const float img_width = d_img_info_vec[2*image_index+0];
+      const float min_size_scaled = min_size;
+      x1 = fmax(fmin(x1, img_width-1.0f), 0.0f);
+      y1 = fmax(fmin(y1, img_height-1.0f), 0.0f);
+      x2 = fmax(fmin(x2, img_width-1.0f), 0.0f);
+      y2 = fmax(fmin(y2, img_height-1.0f), 0.0f);
+
+      // Filter boxes
+      // Removing boxes with one dim < min_size
+      // (center of box is in image, because of previous step)
+      // keep = _filter_boxes(p, self.min_size, im_shape)
+      width = x2 - x1 + 1.0f;
+      height = y2 - y1 + 1.0f;
+      bool keep_box = fmin(width, height) >= min_size_scaled;
+      // We are not deleting the box right now even if !keep_box
+      // we want to keep the relative order of the elements stable
+      // we'll do it in such a way later
+      // d_boxes_keep_flags size: (num_images,prenms_nboxes)
+      // d_out_boxes size: (num_images,prenms_nboxes)
+      const int out_index = image_index * prenms_nboxes + ibox;
+      d_boxes_keep_flags[out_index] = keep_box;
+      d_out_boxes[out_index] = {x1,y1,x2,y2};
+    }
+  }
+}
+
+} // namespace
+
+
+/**
+ * Generate boxes associated to topN pre-NMS scores
+ */
+std::vector<at::Tensor> GeneratePreNMSUprightBoxes(
+        const int num_images,
+        const int A,
+        const int H,
+        const int W,
+        at::Tensor& sorted_indices, // topK sorted pre_nms_topn indices
+        at::Tensor& sorted_scores,  // topK sorted pre_nms_topn scores [N, A, H, W]
+        at::Tensor& bbox_deltas,    // [N, A*4, H, W] (full, unsorted / sliced)
+        at::Tensor& anchors,        // input (full, unsorted, unsliced)
+        at::Tensor& image_shapes,   // (h, w) of images
+        const int pre_nms_nboxes,
+        const int rpn_min_size,
+        const float bbox_xform_clip_default,
+        const bool correct_transform_coords) {
+  // constants
+  constexpr int box_dim = 4;
+  const int K = H * W;
+
+  // temp Tensors
+  at::Tensor boxes = at::zeros({num_images, box_dim * pre_nms_nboxes}, sorted_scores.options()).to(at::kFloat);
+  at::Tensor boxes_keep_flags = at::empty({num_images, pre_nms_nboxes}, sorted_scores.options()).to(at::kByte);
+  boxes_keep_flags.zero_();
+
+  auto stream = at::cuda::getCurrentCUDAStream().stream();
+  // Call kernel
+  GeneratePreNMSUprightBoxesKernel<<<
+      (GetBlocks(pre_nms_nboxes), num_images),
+      CUDA_NUM_THREADS, // blockDim.y == 1
+      0, stream>>>(
+          sorted_indices.data<long>(),
+          pre_nms_nboxes,
+          bbox_deltas.data<float>(),
+          reinterpret_cast<float4*>(anchors.data<float>()),
+          H,
+          W,
+          K,
+          A,
+          K * A,
+          rpn_min_size,
+          image_shapes.data<float>(), // image size vec
+          num_images,
+          bbox_xform_clip_default, // utils::BBOX_XFORM_CLIP_DEFAULT
+          correct_transform_coords,
+          reinterpret_cast<float4*>(boxes.data<float>()),
+          pre_nms_nboxes,
+          sorted_scores.data<float>(),
+          boxes_keep_flags.data<uint8_t>());
+  THCudaCheck(cudaGetLastError());
+
+  return std::vector<at::Tensor>{boxes, sorted_scores, boxes_keep_flags};
+}
+
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.h
new file mode 100755
index 00000000..78580b1f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/rpn_generate_proposals.h
@@ -0,0 +1,40 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#pragma once
+
+#include <ATen/ATen.h>
+#include <THC/THC.h>
+
+#include <vector>
+
+/**
+ * Generate boxes associated to topN pre-NMS scores
+ */
+std::vector<at::Tensor> GeneratePreNMSUprightBoxes(
+    const int num_images,
+    const int A,
+    const int H,
+    const int W,
+    at::Tensor& sorted_indices, // topK sorted pre_nms_topn indices
+    at::Tensor& sorted_scores,  // topK sorted pre_nms_topn scores [N, A, H, W]
+    at::Tensor& bbox_deltas,    // [N, A*4, H, W] (full, unsorted / sliced)
+    at::Tensor& anchors,        // input (full, unsorted, unsliced)
+    at::Tensor& image_shapes,   // (h, w) of images
+    const int pre_nms_nboxes,
+    const int rpn_min_size,
+    const float bbox_xform_clip_default,
+    const bool correct_transform_coords);
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/vision.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/vision.h
new file mode 100755
index 00000000..64e349f4
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda/vision.h
@@ -0,0 +1,83 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+#include <torch/extension.h>
+
+
+at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
+                                 const at::Tensor& rois,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int sampling_ratio);
+
+at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
+                                  const at::Tensor& rois,
+                                  const float spatial_scale,
+                                  const int pooled_height,
+                                  const int pooled_width,
+                                  const int batch_size,
+                                  const int channels,
+                                  const int height,
+                                  const int width,
+                                  const int sampling_ratio);
+
+
+std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
+                                const at::Tensor& rois,
+                                const float spatial_scale,
+                                const int pooled_height,
+                                const int pooled_width);
+
+at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
+                                 const at::Tensor& input,
+                                 const at::Tensor& rois,
+                                 const at::Tensor& argmax,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int batch_size,
+                                 const int channels,
+                                 const int height,
+                                 const int width);
+
+at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh);
+
+
+at::Tensor compute_flow_cuda(const at::Tensor& boxes,
+                             const int height,
+                             const int width);
+                            
+at::Tensor generate_mask_targets_cuda(at::Tensor dense_vector, 
+                                      const std::vector<std::vector<at::Tensor>> polygons, 
+                                      const at::Tensor anchors, 
+                                      const int mask_size);                             
+
+at::Tensor box_iou_cuda(at::Tensor box1, at::Tensor box2);
+                                      
+std::vector<at::Tensor> box_encode_cuda(at::Tensor boxes, 
+                                     at::Tensor anchors, 
+                                     float wx, 
+                                     float wy, 
+                                     float ww, 
+                                     float wh);                                       
+
+at::Tensor match_proposals_cuda(at::Tensor match_quality_matrix,
+                                bool include_low_quality_matches, 
+                                float low_th, 
+                                float high_th);                                      
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/generate_mask_targets.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/generate_mask_targets.h
new file mode 100755
index 00000000..6c3a0910
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/generate_mask_targets.h
@@ -0,0 +1,28 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#pragma once
+#include "cpu/vision.h"
+#ifndef _generate_mask_targets_h_
+#define _generate_mask_targets_h_ 
+
+at::Tensor generate_mask_targets( at::Tensor dense_vector, const std::vector<std::vector<at::Tensor>> polygons, const at::Tensor anchors, const int mask_size){
+  at::Tensor result = generate_mask_targets_cuda(dense_vector, polygons,anchors, mask_size);
+  return result;
+}
+
+#endif
+
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/match_proposals.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/match_proposals.h
new file mode 100755
index 00000000..817f8a58
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/match_proposals.h
@@ -0,0 +1,28 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+#pragma once
+#include "cuda/vision.h"
+#ifndef _match_proposals_h_
+#define _match_proposals_h_ 
+
+
+at::Tensor match_proposals( at::Tensor match_quality_matrix, bool allow_low_quality_matches, float low_th, float high_th){
+  at::Tensor result = match_proposals_cuda( match_quality_matrix, allow_low_quality_matches, low_th, high_th);
+  return result;
+}
+
+#endif
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/nms.h b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/nms.h
new file mode 100755
index 00000000..312fed4a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/nms.h
@@ -0,0 +1,28 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+#include "cpu/vision.h"
+
+#ifdef WITH_CUDA
+#include "cuda/vision.h"
+#endif
+
+
+at::Tensor nms(const at::Tensor& dets,
+               const at::Tensor& scores,
+               const float threshold) {
+
+  if (dets.type().is_cuda()) {
+#ifdef WITH_CUDA
+    // TODO raise error if not compiled with CUDA
+    if (dets.numel() == 0)
+      return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
+    auto b = at::cat({dets, scores.unsqueeze(1)}, 1);
+    return nms_cuda(b, threshold);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+
+  at::Tensor result = nms_cpu(dets, scores, threshold);
+  return result;
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/vision.cpp b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/vision.cpp
new file mode 100755
index 00000000..adc386a6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/vision.cpp
@@ -0,0 +1,43 @@
+/**
+ * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#include "nms.h"
+#include "ROIAlign.h"
+#include "ROIPool.h"
+#include "generate_mask_targets.h"
+#include "box_iou.h"
+#include "box_encode.h"
+#include "match_proposals.h"
+
+#ifdef WITH_CUDA
+#include "cuda/rpn_generate_proposals.h"
+#endif
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("nms", &nms, "non-maximum suppression");
+  m.def("roi_align_forward", &ROIAlign_forward, "ROIAlign_forward");
+  m.def("roi_align_backward", &ROIAlign_backward, "ROIAlign_backward");
+  m.def("roi_pool_forward", &ROIPool_forward, "ROIPool_forward");
+  m.def("roi_pool_backward", &ROIPool_backward, "ROIPool_backward");
+  m.def("generate_mask_targets", &generate_mask_targets, "generate_mask_targets");
+  m.def("box_iou", &box_iou, "box_iou");
+  m.def("box_encode", &box_encode, "box_encode");
+  m.def("match_proposals", &match_proposals, "match_proposals");
+#ifdef WITH_CUDA
+  m.def("GeneratePreNMSUprightBoxes", &GeneratePreNMSUprightBoxes, "RPN Proposal Generation");
+#endif
+}
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/README.md b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/README.md
new file mode 100755
index 00000000..7d6e127a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/README.md
@@ -0,0 +1,88 @@
+# Setting Up Datasets
+This file describes how to perform training on other datasets.
+
+Only Pascal VOC dataset can be loaded from its original format and be outputted to Pascal style results currently.
+
+We expect the annotations from other datasets be converted to COCO json format, and
+the output will be in COCO-style. (i.e. AP, AP50, AP75, APs, APm, APl for bbox and segm)
+
+## Creating Symlinks for PASCAL VOC
+
+We assume that your symlinked `datasets/voc/VOC<year>` directory has the following structure:
+
+```
+VOC<year>
+|_ JPEGImages
+|  |_ <im-1-name>.jpg
+|  |_ ...
+|  |_ <im-N-name>.jpg
+|_ Annotations
+|  |_ pascal_train<year>.json (optional)
+|  |_ pascal_val<year>.json (optional)
+|  |_ pascal_test<year>.json (optional)
+|  |_ <im-1-name>.xml
+|  |_ ...
+|  |_ <im-N-name>.xml
+|_ VOCdevkit<year>
+```
+
+Create symlinks for `voc/VOC<year>`:
+
+```
+cd ~/github/maskrcnn-benchmark
+mkdir -p datasets/voc/VOC<year>
+ln -s /path/to/VOC<year> /datasets/voc/VOC<year>
+```
+Example configuration files for PASCAL VOC could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/pascal_voc/).
+
+### PASCAL VOC Annotations in COCO Format
+To output COCO-style evaluation result, PASCAL VOC annotations in COCO json format is required and could be downloaded from [here](https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip)
+via http://cocodataset.org/#external.
+
+## Creating Symlinks for Cityscapes:
+
+We assume that your symlinked `datasets/cityscapes` directory has the following structure:
+
+```
+cityscapes
+|_ images
+|  |_ <im-1-name>.jpg
+|  |_ ...
+|  |_ <im-N-name>.jpg
+|_ annotations
+|  |_ instanceonly_gtFile_train.json
+|  |_ ...
+|_ raw
+   |_ gtFine
+   |_ ...
+   |_ README.md
+```
+
+Create symlinks for `cityscapes`:
+
+```
+cd ~/github/maskrcnn-benchmark
+mkdir -p datasets/cityscapes
+ln -s /path/to/cityscapes datasets/data/cityscapes
+```
+
+### Steps to convert Cityscapes Annotations to COCO Format
+1. Download gtFine_trainvaltest.zip from https://www.cityscapes-dataset.com/downloads/ (login required)
+2. Extract it to /path/to/gtFine_trainvaltest
+```
+gtFine_trainvaltest
+|_ gtFine
+```
+3. Run the below commands to convert the annotations
+
+```
+cd ~/github
+git clone https://github.com/mcordts/cityscapesScripts.git
+cd cityscapesScripts
+cp ~/github/maskrcnn-benchmark/tool/cityscapes/instances2dict_with_polygons.py cityscapesscripts/evaluation
+python setup.py install
+cd ~/github/maskrcnn-benchmark
+python tools/cityscapes/convert_cityscapes_to_coco.py --datadir /path/to/gtFine_trainvaltest --outdir /path/to/cityscapes/annotations
+```
+
+Example configuration files for Cityscapes could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/cityscapes/).
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/__init__.py
new file mode 100755
index 00000000..2ba1e524
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .build import make_data_loader
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/build.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/build.py
new file mode 100755
index 00000000..25bce569
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/build.py
@@ -0,0 +1,178 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved..
+import bisect
+import copy
+import logging
+
+import torch.utils.data
+from maskrcnn_benchmark.utils.comm import get_world_size
+from maskrcnn_benchmark.utils.imports import import_file
+
+from . import datasets as D
+from . import samplers
+
+from .collate_batch import BatchCollator
+from .transforms import build_transforms
+
+
+def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
+    """
+    Arguments:
+        dataset_list (list[str]): Contains the names of the datasets, i.e.,
+            coco_2014_trian, coco_2014_val, etc
+        transforms (callable): transforms to apply to each (image, target) sample
+        dataset_catalog (DatasetCatalog): contains the information on how to
+            construct a dataset.
+        is_train (bool): whether to setup the dataset for training or testing
+    """
+    if not isinstance(dataset_list, (list, tuple)):
+        raise RuntimeError(
+            "dataset_list should be a list of strings, got {}".format(dataset_list)
+        )
+    datasets = []
+    total_datasets_size = 0
+    for dataset_name in dataset_list:
+        data = dataset_catalog.get(dataset_name)
+        factory = getattr(D, data["factory"])
+        args = data["args"]
+        # for COCODataset, we want to remove images without annotations
+        # during training
+        if data["factory"] == "COCODataset":
+            args["remove_images_without_annotations"] = is_train
+        if data["factory"] == "PascalVOCDataset":
+            args["use_difficult"] = not is_train
+        args["transforms"] = transforms
+        # make dataset from factory
+        dataset = factory(**args)
+        total_datasets_size += len(dataset)
+        datasets.append(dataset)
+
+    # for testing, return a list of datasets
+    if not is_train:
+        return datasets, total_datasets_size
+
+    # for training, concatenate all datasets into a single one
+    dataset = datasets[0]
+    if len(datasets) > 1:
+        dataset = D.ConcatDataset(datasets)
+
+    return [dataset], total_datasets_size
+
+
+def make_data_sampler(dataset, shuffle, distributed):
+    if distributed:
+        return samplers.DistributedSampler(dataset, shuffle=shuffle)
+    if shuffle:
+        sampler = torch.utils.data.sampler.RandomSampler(dataset)
+    else:
+        sampler = torch.utils.data.sampler.SequentialSampler(dataset)
+    return sampler
+
+
+def _quantize(x, bins):
+    bins = copy.copy(bins)
+    bins = sorted(bins)
+    quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
+    return quantized
+
+
+def _compute_aspect_ratios(dataset):
+    aspect_ratios = []
+    for i in range(len(dataset)):
+        img_info = dataset.get_img_info(i)
+        aspect_ratio = float(img_info["height"]) / float(img_info["width"])
+        aspect_ratios.append(aspect_ratio)
+    return aspect_ratios
+
+
+def make_batch_data_sampler(
+    dataset, sampler, aspect_grouping, images_per_batch, num_iters=None, start_iter=0
+):
+    if aspect_grouping:
+        if not isinstance(aspect_grouping, (list, tuple)):
+            aspect_grouping = [aspect_grouping]
+        aspect_ratios = _compute_aspect_ratios(dataset)
+        group_ids = _quantize(aspect_ratios, aspect_grouping)
+        batch_sampler = samplers.GroupedBatchSampler(
+            sampler, group_ids, images_per_batch, drop_uneven=False
+        )
+    else:
+        batch_sampler = torch.utils.data.sampler.BatchSampler(
+            sampler, images_per_batch, drop_last=False
+        )
+    if num_iters is not None:
+        batch_sampler = samplers.IterationBasedBatchSampler(
+            batch_sampler, num_iters, start_iter
+        )
+    return batch_sampler
+
+
+def make_data_loader(cfg, is_train=True, is_distributed=False, start_iter=0):
+    num_gpus = get_world_size()
+    if is_train:
+        images_per_batch = cfg.SOLVER.IMS_PER_BATCH
+        assert (
+            images_per_batch % num_gpus == 0
+        ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number "
+        "of GPUs ({}) used.".format(images_per_batch, num_gpus)
+        images_per_gpu = images_per_batch // num_gpus
+        shuffle = True
+        num_iters = cfg.SOLVER.MAX_ITER
+    else:
+        images_per_batch = cfg.TEST.IMS_PER_BATCH
+        assert (
+            images_per_batch % num_gpus == 0
+        ), "TEST.IMS_PER_BATCH ({}) must be divisible by the number "
+        "of GPUs ({}) used.".format(images_per_batch, num_gpus)
+        images_per_gpu = images_per_batch // num_gpus
+        shuffle = False if not is_distributed else True
+        num_iters = None
+        start_iter = 0
+
+    if images_per_gpu > 1:
+        logger = logging.getLogger(__name__)
+        logger.warning(
+            "When using more than one image per GPU you may encounter "
+            "an out-of-memory (OOM) error if your GPU does not have "
+            "sufficient memory. If this happens, you can reduce "
+            "SOLVER.IMS_PER_BATCH (for training) or "
+            "TEST.IMS_PER_BATCH (for inference). For training, you must "
+            "also adjust the learning rate and schedule length according "
+            "to the linear scaling rule. See for example: "
+            "https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14"
+        )
+
+    # group images which have similar aspect ratio. In this case, we only
+    # group in two cases: those with width / height > 1, and the other way around,
+    # but the code supports more general grouping strategy
+    aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else []
+
+    paths_catalog = import_file(
+        "maskrcnn_benchmark.config.paths_catalog", cfg.PATHS_CATALOG, True
+    )
+    DatasetCatalog = paths_catalog.DatasetCatalog
+    dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
+
+    transforms = build_transforms(cfg, is_train)
+    datasets, epoch_size = build_dataset(dataset_list, transforms, DatasetCatalog, is_train)
+
+    data_loaders = []
+    for dataset in datasets:
+        sampler = make_data_sampler(dataset, shuffle, is_distributed)
+        batch_sampler = make_batch_data_sampler(
+            dataset, sampler, aspect_grouping, images_per_gpu, num_iters, start_iter
+        )
+        collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY)
+        num_workers = cfg.DATALOADER.NUM_WORKERS
+        data_loader = torch.utils.data.DataLoader(
+            dataset,
+            num_workers=num_workers,
+            batch_sampler=batch_sampler,
+            collate_fn=collator,
+        )
+        data_loaders.append(data_loader)
+    if is_train:
+        # during training, a single (possibly concatenated) data_loader is returned
+        assert len(data_loaders) == 1
+        iterations_per_epoch = epoch_size // images_per_batch
+        return data_loaders[0], iterations_per_epoch
+    return data_loaders
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/collate_batch.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/collate_batch.py
new file mode 100755
index 00000000..a7f03416
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/collate_batch.py
@@ -0,0 +1,20 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from maskrcnn_benchmark.structures.image_list import to_image_list
+
+
+class BatchCollator(object):
+    """
+    From a list of samples from the dataset,
+    returns the batched images and targets.
+    This should be passed to the DataLoader
+    """
+
+    def __init__(self, size_divisible=0):
+        self.size_divisible = size_divisible
+
+    def __call__(self, batch):
+        transposed_batch = list(zip(*batch))
+        images = to_image_list(transposed_batch[0], self.size_divisible)
+        targets = transposed_batch[1]
+        img_ids = transposed_batch[2]
+        return images, targets, img_ids
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/__init__.py
new file mode 100755
index 00000000..2a57df7e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .coco import COCODataset
+from .voc import PascalVOCDataset
+from .concat_dataset import ConcatDataset
+
+__all__ = ["COCODataset", "ConcatDataset", "PascalVOCDataset"]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/coco.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/coco.py
new file mode 100755
index 00000000..4f2ff9e6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/coco.py
@@ -0,0 +1,64 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+import torchvision
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
+
+
+class COCODataset(torchvision.datasets.coco.CocoDetection):
+    def __init__(
+        self, ann_file, root, remove_images_without_annotations, transforms=None
+    ):
+        super(COCODataset, self).__init__(root, ann_file)
+        # sort indices for reproducible results
+        self.ids = sorted(self.ids)
+
+        # filter images without detection annotations
+        if remove_images_without_annotations:
+            self.ids = [
+                img_id
+                for img_id in self.ids
+                if len(self.coco.getAnnIds(imgIds=img_id, iscrowd=None)) > 0
+            ]
+
+        self.json_category_id_to_contiguous_id = {
+            v: i + 1 for i, v in enumerate(self.coco.getCatIds())
+        }
+        self.contiguous_category_id_to_json_id = {
+            v: k for k, v in self.json_category_id_to_contiguous_id.items()
+        }
+        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
+        self.transforms = transforms
+
+    def __getitem__(self, idx):
+        img, anno = super(COCODataset, self).__getitem__(idx)
+
+        # filter crowd annotations
+        # TODO might be better to add an extra field
+        anno = [obj for obj in anno if obj["iscrowd"] == 0]
+
+        boxes = [obj["bbox"] for obj in anno]
+        boxes = torch.as_tensor(boxes).reshape(-1, 4)  # guard against no boxes
+        target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
+
+        classes = [obj["category_id"] for obj in anno]
+        classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
+        classes = torch.tensor(classes)
+        target.add_field("labels", classes)
+
+        masks = [obj["segmentation"] for obj in anno]
+        masks = SegmentationMask(masks, img.size)
+        target.add_field("masks", masks)
+
+        target = target.clip_to_image(remove_empty=True)
+
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
+
+        return img, target, idx
+
+    def get_img_info(self, index):
+        img_id = self.id_to_img_map[index]
+        img_data = self.coco.imgs[img_id]
+        return img_data
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/concat_dataset.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/concat_dataset.py
new file mode 100755
index 00000000..e5e087c4
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/concat_dataset.py
@@ -0,0 +1,23 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import bisect
+
+from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
+
+
+class ConcatDataset(_ConcatDataset):
+    """
+    Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
+    method for querying the sizes of the image
+    """
+
+    def get_idxs(self, idx):
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return dataset_idx, sample_idx
+
+    def get_img_info(self, idx):
+        dataset_idx, sample_idx = self.get_idxs(idx)
+        return self.datasets[dataset_idx].get_img_info(sample_idx)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/__init__.py
new file mode 100755
index 00000000..add2ba70
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/__init__.py
@@ -0,0 +1,27 @@
+from maskrcnn_benchmark.data import datasets
+
+from .coco import coco_evaluation
+from .voc import voc_evaluation
+
+
+def evaluate(dataset, predictions, output_folder, **kwargs):
+    """evaluate dataset using different methods based on dataset type.
+    Args:
+        dataset: Dataset object
+        predictions(list[BoxList]): each item in the list represents the
+            prediction results for one image.
+        output_folder: output folder, to save evaluation files or results.
+        **kwargs: other args.
+    Returns:
+        evaluation result
+    """
+    args = dict(
+        dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
+    )
+    if isinstance(dataset, datasets.COCODataset):
+        return coco_evaluation(**args)
+    elif isinstance(dataset, datasets.PascalVOCDataset):
+        return voc_evaluation(**args)
+    else:
+        dataset_name = dataset.__class__.__name__
+        raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py
new file mode 100755
index 00000000..dece9380
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py
@@ -0,0 +1,21 @@
+from .coco_eval import do_coco_evaluation
+
+
+def coco_evaluation(
+    dataset,
+    predictions,
+    output_folder,
+    box_only,
+    iou_types,
+    expected_results,
+    expected_results_sigma_tol,
+):
+    return do_coco_evaluation(
+        dataset=dataset,
+        predictions=predictions,
+        box_only=box_only,
+        output_folder=output_folder,
+        iou_types=iou_types,
+        expected_results=expected_results,
+        expected_results_sigma_tol=expected_results_sigma_tol,
+    )
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py
new file mode 100755
index 00000000..25deae19
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py
@@ -0,0 +1,359 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+import logging
+import tempfile
+import os
+import torch
+from collections import OrderedDict
+from tqdm import tqdm
+
+from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
+
+
+def do_coco_evaluation(
+    dataset,
+    predictions,
+    box_only,
+    output_folder,
+    iou_types,
+    expected_results,
+    expected_results_sigma_tol,
+):
+    logger = logging.getLogger("maskrcnn_benchmark.inference")
+
+    if box_only:
+        logger.info("Evaluating bbox proposals")
+        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
+        res = COCOResults("box_proposal")
+        for limit in [100, 1000]:
+            for area, suffix in areas.items():
+                stats = evaluate_box_proposals(
+                    predictions, dataset, area=area, limit=limit
+                )
+                key = "AR{}@{:d}".format(suffix, limit)
+                res.results["box_proposal"][key] = stats["ar"].item()
+        logger.info(res)
+        check_expected_results(res, expected_results, expected_results_sigma_tol)
+        if output_folder:
+            torch.save(res, os.path.join(output_folder, "box_proposals.pth"))
+        return
+    logger.info("Preparing results for COCO format")
+    coco_results = {}
+    if "bbox" in iou_types:
+        logger.info("Preparing bbox results")
+        coco_results["bbox"] = prepare_for_coco_detection(predictions, dataset)
+    if "segm" in iou_types:
+        logger.info("Preparing segm results")
+        coco_results["segm"] = prepare_for_coco_segmentation(predictions, dataset)
+
+    results = COCOResults(*iou_types)
+    logger.info("Evaluating predictions")
+    for iou_type in iou_types:
+        with tempfile.NamedTemporaryFile() as f:
+            file_path = f.name
+            if output_folder:
+                file_path = os.path.join(output_folder, iou_type + ".json")
+            res = evaluate_predictions_on_coco(
+                dataset.coco, coco_results[iou_type], file_path, iou_type
+            )
+            results.update(res)
+    logger.info(results)
+    check_expected_results(results, expected_results, expected_results_sigma_tol)
+    if output_folder:
+        torch.save(results, os.path.join(output_folder, "coco_results.pth"))
+    return results, coco_results
+
+
+def prepare_for_coco_detection(predictions, dataset):
+    # assert isinstance(dataset, COCODataset)
+    coco_results = []
+    for image_id, prediction in enumerate(predictions):
+        original_id = dataset.id_to_img_map[image_id]
+        if len(prediction) == 0:
+            continue
+
+        # TODO replace with get_img_info?
+        image_width = dataset.coco.imgs[original_id]["width"]
+        image_height = dataset.coco.imgs[original_id]["height"]
+        prediction = prediction.resize((image_width, image_height))
+        prediction = prediction.convert("xywh")
+
+        boxes = prediction.bbox.tolist()
+        scores = prediction.get_field("scores").tolist()
+        labels = prediction.get_field("labels").tolist()
+
+        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]
+
+        coco_results.extend(
+            [
+                {
+                    "image_id": original_id,
+                    "category_id": mapped_labels[k],
+                    "bbox": box,
+                    "score": scores[k],
+                }
+                for k, box in enumerate(boxes)
+            ]
+        )
+    return coco_results
+
+
+def prepare_for_coco_segmentation(predictions, dataset):
+    import pycocotools.mask as mask_util
+    import numpy as np
+
+    masker = Masker(threshold=0.5, padding=1)
+    # assert isinstance(dataset, COCODataset)
+    coco_results = []
+    for image_id, prediction in tqdm(enumerate(predictions)):
+        original_id = dataset.id_to_img_map[image_id]
+        if len(prediction) == 0:
+            continue
+
+        # TODO replace with get_img_info?
+        image_width = dataset.coco.imgs[original_id]["width"]
+        image_height = dataset.coco.imgs[original_id]["height"]
+        prediction = prediction.resize((image_width, image_height))
+        masks = prediction.get_field("mask")
+        # t = time.time()
+        # Masker is necessary only if masks haven't been already resized.
+        if list(masks.shape[-2:]) != [image_height, image_width]:
+            masks = masker(masks.expand(1, -1, -1, -1, -1), prediction)
+            masks = masks[0]
+        # logger.info('Time mask: {}'.format(time.time() - t))
+        # prediction = prediction.convert('xywh')
+
+        # boxes = prediction.bbox.tolist()
+        scores = prediction.get_field("scores").tolist()
+        labels = prediction.get_field("labels").tolist()
+
+        # rles = prediction.get_field('mask')
+
+        rles = [
+            mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
+            for mask in masks
+        ]
+        for rle in rles:
+            rle["counts"] = rle["counts"].decode("utf-8")
+
+        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]
+
+        coco_results.extend(
+            [
+                {
+                    "image_id": original_id,
+                    "category_id": mapped_labels[k],
+                    "segmentation": rle,
+                    "score": scores[k],
+                }
+                for k, rle in enumerate(rles)
+            ]
+        )
+    return coco_results
+
+
+# inspired from Detectron
+def evaluate_box_proposals(
+    predictions, dataset, thresholds=None, area="all", limit=None
+):
+    """Evaluate detection proposal recall metrics. This function is a much
+    faster alternative to the official COCO API recall evaluation code. However,
+    it produces slightly different results.
+    """
+    # Record max overlap value for each gt box
+    # Return vector of overlap values
+    areas = {
+        "all": 0,
+        "small": 1,
+        "medium": 2,
+        "large": 3,
+        "96-128": 4,
+        "128-256": 5,
+        "256-512": 6,
+        "512-inf": 7,
+    }
+    area_ranges = [
+        [0 ** 2, 1e5 ** 2],  # all
+        [0 ** 2, 32 ** 2],  # small
+        [32 ** 2, 96 ** 2],  # medium
+        [96 ** 2, 1e5 ** 2],  # large
+        [96 ** 2, 128 ** 2],  # 96-128
+        [128 ** 2, 256 ** 2],  # 128-256
+        [256 ** 2, 512 ** 2],  # 256-512
+        [512 ** 2, 1e5 ** 2],
+    ]  # 512-inf
+    assert area in areas, "Unknown area range: {}".format(area)
+    area_range = area_ranges[areas[area]]
+    gt_overlaps = []
+    num_pos = 0
+
+    for image_id, prediction in enumerate(predictions):
+        original_id = dataset.id_to_img_map[image_id]
+
+        # TODO replace with get_img_info?
+        image_width = dataset.coco.imgs[original_id]["width"]
+        image_height = dataset.coco.imgs[original_id]["height"]
+        prediction = prediction.resize((image_width, image_height))
+
+        # sort predictions in descending order
+        # TODO maybe remove this and make it explicit in the documentation
+        inds = prediction.get_field("objectness").sort(descending=True)[1]
+        prediction = prediction[inds]
+
+        ann_ids = dataset.coco.getAnnIds(imgIds=original_id)
+        anno = dataset.coco.loadAnns(ann_ids)
+        gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0]
+        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
+        gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert(
+            "xyxy"
+        )
+        gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
+
+        if len(gt_boxes) == 0:
+            continue
+
+        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
+        gt_boxes = gt_boxes[valid_gt_inds]
+
+        num_pos += len(gt_boxes)
+
+        if len(gt_boxes) == 0:
+            continue
+
+        if len(prediction) == 0:
+            continue
+
+        if limit is not None and len(prediction) > limit:
+            prediction = prediction[:limit]
+
+        overlaps = boxlist_iou(prediction, gt_boxes)
+
+        _gt_overlaps = torch.zeros(len(gt_boxes))
+        for j in range(min(len(prediction), len(gt_boxes))):
+            # find which proposal box maximally covers each gt box
+            # and get the iou amount of coverage for each gt box
+            max_overlaps, argmax_overlaps = overlaps.max(dim=0)
+
+            # find which gt box is 'best' covered (i.e. 'best' = most iou)
+            gt_ovr, gt_ind = max_overlaps.max(dim=0)
+            assert gt_ovr >= 0
+            # find the proposal box that covers the best covered gt box
+            box_ind = argmax_overlaps[gt_ind]
+            # record the iou coverage of this gt box
+            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
+            assert _gt_overlaps[j] == gt_ovr
+            # mark the proposal box and the gt box as used
+            overlaps[box_ind, :] = -1
+            overlaps[:, gt_ind] = -1
+
+        # append recorded iou coverage level
+        gt_overlaps.append(_gt_overlaps)
+    gt_overlaps = torch.cat(gt_overlaps, dim=0)
+    gt_overlaps, _ = torch.sort(gt_overlaps)
+
+    if thresholds is None:
+        step = 0.05
+        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
+    recalls = torch.zeros_like(thresholds)
+    # compute recall for each iou threshold
+    for i, t in enumerate(thresholds):
+        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
+    # ar = 2 * np.trapz(recalls, thresholds)
+    ar = recalls.mean()
+    return {
+        "ar": ar,
+        "recalls": recalls,
+        "thresholds": thresholds,
+        "gt_overlaps": gt_overlaps,
+        "num_pos": num_pos,
+    }
+
+
+def evaluate_predictions_on_coco(
+    coco_gt, coco_results, json_result_file, iou_type="bbox"
+):
+    import json
+
+    with open(json_result_file, "w") as f:
+        json.dump(coco_results, f)
+
+    from pycocotools.coco import COCO
+    from pycocotools.cocoeval import COCOeval
+
+    coco_dt = coco_gt.loadRes(str(json_result_file)) if coco_results else COCO()
+
+    # coco_dt = coco_gt.loadRes(coco_results)
+    coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
+    coco_eval.evaluate()
+    coco_eval.accumulate()
+    coco_eval.summarize()
+    return coco_eval
+
+
+class COCOResults(object):
+    METRICS = {
+        "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
+        "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
+        "box_proposal": [
+            "AR@100",
+            "ARs@100",
+            "ARm@100",
+            "ARl@100",
+            "AR@1000",
+            "ARs@1000",
+            "ARm@1000",
+            "ARl@1000",
+        ],
+        "keypoint": ["AP", "AP50", "AP75", "APm", "APl"],
+    }
+
+    def __init__(self, *iou_types):
+        allowed_types = ("box_proposal", "bbox", "segm")
+        assert all(iou_type in allowed_types for iou_type in iou_types)
+        results = OrderedDict()
+        for iou_type in iou_types:
+            results[iou_type] = OrderedDict(
+                [(metric, -1) for metric in COCOResults.METRICS[iou_type]]
+            )
+        self.results = results
+
+    def update(self, coco_eval):
+        if coco_eval is None:
+            return
+        from pycocotools.cocoeval import COCOeval
+
+        assert isinstance(coco_eval, COCOeval)
+        s = coco_eval.stats
+        iou_type = coco_eval.params.iouType
+        res = self.results[iou_type]
+        metrics = COCOResults.METRICS[iou_type]
+        for idx, metric in enumerate(metrics):
+            res[metric] = s[idx]
+
+    def __repr__(self):
+        # TODO make it pretty
+        return repr(self.results)
+
+
+def check_expected_results(results, expected_results, sigma_tol):
+    if not expected_results:
+        return
+
+    logger = logging.getLogger("maskrcnn_benchmark.inference")
+    for task, metric, (mean, std) in expected_results:
+        actual_val = results.results[task][metric]
+        lo = mean - sigma_tol * std
+        hi = mean + sigma_tol * std
+        ok = (lo < actual_val) and (actual_val < hi)
+        msg = (
+            "{} > {} sanity check (actual vs. expected): "
+            "{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})"
+        ).format(task, metric, actual_val, mean, std, lo, hi)
+        if not ok:
+            msg = "FAIL: " + msg
+            logger.error(msg)
+        else:
+            msg = "PASS: " + msg
+            logger.info(msg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py
new file mode 100755
index 00000000..1dde6413
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py
@@ -0,0 +1,16 @@
+import logging
+
+from .voc_eval import do_voc_evaluation
+
+
+def voc_evaluation(dataset, predictions, output_folder, box_only, **_):
+    logger = logging.getLogger("maskrcnn_benchmark.inference")
+    if box_only:
+        logger.warning("voc evaluation doesn't support box_only, ignored.")
+    logger.info("performing voc evaluation, ignored iou_types.")
+    return do_voc_evaluation(
+        dataset=dataset,
+        predictions=predictions,
+        output_folder=output_folder,
+        logger=logger,
+    )
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py
new file mode 100755
index 00000000..f8b0c108
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py
@@ -0,0 +1,216 @@
+# A modification version from chainercv repository.
+# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py)
+from __future__ import division
+
+import os
+from collections import defaultdict
+import numpy as np
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
+
+
+def do_voc_evaluation(dataset, predictions, output_folder, logger):
+    # TODO need to make the use_07_metric format available
+    # for the user to choose
+    pred_boxlists = []
+    gt_boxlists = []
+    for image_id, prediction in enumerate(predictions):
+        img_info = dataset.get_img_info(image_id)
+        if len(prediction) == 0:
+            continue
+        image_width = img_info["width"]
+        image_height = img_info["height"]
+        prediction = prediction.resize((image_width, image_height))
+        pred_boxlists.append(prediction)
+
+        gt_boxlist = dataset.get_groundtruth(image_id)
+        gt_boxlists.append(gt_boxlist)
+    result = eval_detection_voc(
+        pred_boxlists=pred_boxlists,
+        gt_boxlists=gt_boxlists,
+        iou_thresh=0.5,
+        use_07_metric=True,
+    )
+    result_str = "mAP: {:.4f}\n".format(result["map"])
+    for i, ap in enumerate(result["ap"]):
+        if i == 0:  # skip background
+            continue
+        result_str += "{:<16}: {:.4f}\n".format(
+            dataset.map_class_id_to_class_name(i), ap
+        )
+    logger.info(result_str)
+    if output_folder:
+        with open(os.path.join(output_folder, "result.txt"), "w") as fid:
+            fid.write(result_str)
+    return result
+
+
+def eval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_07_metric=False):
+    """Evaluate on voc dataset.
+    Args:
+        pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields.
+        gt_boxlists(list[BoxList]): ground truth boxlist, has labels field.
+        iou_thresh: iou thresh
+        use_07_metric: boolean
+    Returns:
+        dict represents the results
+    """
+    assert len(gt_boxlists) == len(
+        pred_boxlists
+    ), "Length of gt and pred lists need to be same."
+    prec, rec = calc_detection_voc_prec_rec(
+        pred_boxlists=pred_boxlists, gt_boxlists=gt_boxlists, iou_thresh=iou_thresh
+    )
+    ap = calc_detection_voc_ap(prec, rec, use_07_metric=use_07_metric)
+    return {"ap": ap, "map": np.nanmean(ap)}
+
+
+def calc_detection_voc_prec_rec(gt_boxlists, pred_boxlists, iou_thresh=0.5):
+    """Calculate precision and recall based on evaluation code of PASCAL VOC.
+    This function calculates precision and recall of
+    predicted bounding boxes obtained from a dataset which has :math:`N`
+    images.
+    The code is based on the evaluation code used in PASCAL VOC Challenge.
+   """
+    n_pos = defaultdict(int)
+    score = defaultdict(list)
+    match = defaultdict(list)
+    for gt_boxlist, pred_boxlist in zip(gt_boxlists, pred_boxlists):
+        pred_bbox = pred_boxlist.bbox.numpy()
+        pred_label = pred_boxlist.get_field("labels").numpy()
+        pred_score = pred_boxlist.get_field("scores").numpy()
+        gt_bbox = gt_boxlist.bbox.numpy()
+        gt_label = gt_boxlist.get_field("labels").numpy()
+        gt_difficult = gt_boxlist.get_field("difficult").numpy()
+
+        for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):
+            pred_mask_l = pred_label == l
+            pred_bbox_l = pred_bbox[pred_mask_l]
+            pred_score_l = pred_score[pred_mask_l]
+            # sort by score
+            order = pred_score_l.argsort()[::-1]
+            pred_bbox_l = pred_bbox_l[order]
+            pred_score_l = pred_score_l[order]
+
+            gt_mask_l = gt_label == l
+            gt_bbox_l = gt_bbox[gt_mask_l]
+            gt_difficult_l = gt_difficult[gt_mask_l]
+
+            n_pos[l] += np.logical_not(gt_difficult_l).sum()
+            score[l].extend(pred_score_l)
+
+            if len(pred_bbox_l) == 0:
+                continue
+            if len(gt_bbox_l) == 0:
+                match[l].extend((0,) * pred_bbox_l.shape[0])
+                continue
+
+            # VOC evaluation follows integer typed bounding boxes.
+            pred_bbox_l = pred_bbox_l.copy()
+            pred_bbox_l[:, 2:] += 1
+            gt_bbox_l = gt_bbox_l.copy()
+            gt_bbox_l[:, 2:] += 1
+            iou = boxlist_iou(
+                BoxList(pred_bbox_l, gt_boxlist.size),
+                BoxList(gt_bbox_l, gt_boxlist.size),
+            ).numpy()
+            gt_index = iou.argmax(axis=1)
+            # set -1 if there is no matching ground truth
+            gt_index[iou.max(axis=1) < iou_thresh] = -1
+            del iou
+
+            selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
+            for gt_idx in gt_index:
+                if gt_idx >= 0:
+                    if gt_difficult_l[gt_idx]:
+                        match[l].append(-1)
+                    else:
+                        if not selec[gt_idx]:
+                            match[l].append(1)
+                        else:
+                            match[l].append(0)
+                    selec[gt_idx] = True
+                else:
+                    match[l].append(0)
+
+    n_fg_class = max(n_pos.keys()) + 1
+    prec = [None] * n_fg_class
+    rec = [None] * n_fg_class
+
+    for l in n_pos.keys():
+        score_l = np.array(score[l])
+        match_l = np.array(match[l], dtype=np.int8)
+
+        order = score_l.argsort()[::-1]
+        match_l = match_l[order]
+
+        tp = np.cumsum(match_l == 1)
+        fp = np.cumsum(match_l == 0)
+
+        # If an element of fp + tp is 0,
+        # the corresponding element of prec[l] is nan.
+        prec[l] = tp / (fp + tp)
+        # If n_pos[l] is 0, rec[l] is None.
+        if n_pos[l] > 0:
+            rec[l] = tp / n_pos[l]
+
+    return prec, rec
+
+
+def calc_detection_voc_ap(prec, rec, use_07_metric=False):
+    """Calculate average precisions based on evaluation code of PASCAL VOC.
+    This function calculates average precisions
+    from given precisions and recalls.
+    The code is based on the evaluation code used in PASCAL VOC Challenge.
+    Args:
+        prec (list of numpy.array): A list of arrays.
+            :obj:`prec[l]` indicates precision for class :math:`l`.
+            If :obj:`prec[l]` is :obj:`None`, this function returns
+            :obj:`numpy.nan` for class :math:`l`.
+        rec (list of numpy.array): A list of arrays.
+            :obj:`rec[l]` indicates recall for class :math:`l`.
+            If :obj:`rec[l]` is :obj:`None`, this function returns
+            :obj:`numpy.nan` for class :math:`l`.
+        use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric
+            for calculating average precision. The default value is
+            :obj:`False`.
+    Returns:
+        ~numpy.ndarray:
+        This function returns an array of average precisions.
+        The :math:`l`-th value corresponds to the average precision
+        for class :math:`l`. If :obj:`prec[l]` or :obj:`rec[l]` is
+        :obj:`None`, the corresponding value is set to :obj:`numpy.nan`.
+    """
+
+    n_fg_class = len(prec)
+    ap = np.empty(n_fg_class)
+    for l in range(n_fg_class):
+        if prec[l] is None or rec[l] is None:
+            ap[l] = np.nan
+            continue
+
+        if use_07_metric:
+            # 11 point metric
+            ap[l] = 0
+            for t in np.arange(0.0, 1.1, 0.1):
+                if np.sum(rec[l] >= t) == 0:
+                    p = 0
+                else:
+                    p = np.max(np.nan_to_num(prec[l])[rec[l] >= t])
+                ap[l] += p / 11
+        else:
+            # correct AP calculation
+            # first append sentinel values at the end
+            mpre = np.concatenate(([0], np.nan_to_num(prec[l]), [0]))
+            mrec = np.concatenate(([0], rec[l], [1]))
+
+            mpre = np.maximum.accumulate(mpre[::-1])[::-1]
+
+            # to calculate area under PR curve, look for points
+            # where X axis (recall) changes value
+            i = np.where(mrec[1:] != mrec[:-1])[0]
+
+            # and sum (\Delta recall) * prec
+            ap[l] = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+
+    return ap
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/list_dataset.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/list_dataset.py
new file mode 100755
index 00000000..9058d35b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/list_dataset.py
@@ -0,0 +1,36 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+Simple dataset class that wraps a list of path names
+"""
+
+from PIL import Image
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+
+
+class ListDataset(object):
+    def __init__(self, image_lists, transforms=None):
+        self.image_lists = image_lists
+        self.transforms = transforms
+
+    def __getitem__(self, item):
+        img = Image.open(self.image_lists[item]).convert("RGB")
+
+        # dummy target
+        w, h = img.size
+        target = BoxList([[0, 0, w, h]], img.size, mode="xyxy")
+
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.image_lists)
+
+    def get_img_info(self, item):
+        """
+        Return the image dimensions for the image, without
+        loading and pre-processing it
+        """
+        pass
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/voc.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/voc.py
new file mode 100755
index 00000000..51c5c358
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/datasets/voc.py
@@ -0,0 +1,136 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+import os
+
+import torch
+import torch.utils.data
+from PIL import Image
+import sys
+
+if sys.version_info[0] == 2:
+    import xml.etree.cElementTree as ET
+else:
+    import xml.etree.ElementTree as ET
+
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+
+
+class PascalVOCDataset(torch.utils.data.Dataset):
+
+    CLASSES = (
+        "__background__ ",
+        "aeroplane",
+        "bicycle",
+        "bird",
+        "boat",
+        "bottle",
+        "bus",
+        "car",
+        "cat",
+        "chair",
+        "cow",
+        "diningtable",
+        "dog",
+        "horse",
+        "motorbike",
+        "person",
+        "pottedplant",
+        "sheep",
+        "sofa",
+        "train",
+        "tvmonitor",
+    )
+
+    def __init__(self, data_dir, split, use_difficult=False, transforms=None):
+        self.root = data_dir
+        self.image_set = split
+        self.keep_difficult = use_difficult
+        self.transforms = transforms
+
+        self._annopath = os.path.join(self.root, "Annotations", "%s.xml")
+        self._imgpath = os.path.join(self.root, "JPEGImages", "%s.jpg")
+        self._imgsetpath = os.path.join(self.root, "ImageSets", "Main", "%s.txt")
+
+        with open(self._imgsetpath % self.image_set) as f:
+            self.ids = f.readlines()
+        self.ids = [x.strip("\n") for x in self.ids]
+        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
+
+        cls = PascalVOCDataset.CLASSES
+        self.class_to_ind = dict(zip(cls, range(len(cls))))
+
+    def __getitem__(self, index):
+        img_id = self.ids[index]
+        img = Image.open(self._imgpath % img_id).convert("RGB")
+
+        target = self.get_groundtruth(index)
+        target = target.clip_to_image(remove_empty=True)
+
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
+
+        return img, target, index
+
+    def __len__(self):
+        return len(self.ids)
+
+    def get_groundtruth(self, index):
+        img_id = self.ids[index]
+        anno = ET.parse(self._annopath % img_id).getroot()
+        anno = self._preprocess_annotation(anno)
+
+        height, width = anno["im_info"]
+        target = BoxList(anno["boxes"], (width, height), mode="xyxy")
+        target.add_field("labels", anno["labels"])
+        target.add_field("difficult", anno["difficult"])
+        return target
+
+    def _preprocess_annotation(self, target):
+        boxes = []
+        gt_classes = []
+        difficult_boxes = []
+        TO_REMOVE = 1
+        
+        for obj in target.iter("object"):
+            difficult = int(obj.find("difficult").text) == 1
+            if not self.keep_difficult and difficult:
+                continue
+            name = obj.find("name").text.lower().strip()
+            bb = obj.find("bndbox")
+            # Make pixel indexes 0-based
+            # Refer to "https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/pascal_voc.py#L208-L211"
+            box = [
+                bb.find("xmin").text, 
+                bb.find("ymin").text, 
+                bb.find("xmax").text, 
+                bb.find("ymax").text,
+            ]
+            bndbox = tuple(
+                map(lambda x: x - TO_REMOVE, list(map(int, box)))
+            )
+
+            boxes.append(bndbox)
+            gt_classes.append(self.class_to_ind[name])
+            difficult_boxes.append(difficult)
+
+        size = target.find("size")
+        im_info = tuple(map(int, (size.find("height").text, size.find("width").text)))
+
+        res = {
+            "boxes": torch.tensor(boxes, dtype=torch.float32),
+            "labels": torch.tensor(gt_classes),
+            "difficult": torch.tensor(difficult_boxes),
+            "im_info": im_info,
+        }
+        return res
+
+    def get_img_info(self, index):
+        img_id = self.ids[index]
+        anno = ET.parse(self._annopath % img_id).getroot()
+        size = anno.find("size")
+        im_info = tuple(map(int, (size.find("height").text, size.find("width").text)))
+        return {"height": im_info[0], "width": im_info[1]}
+
+    def map_class_id_to_class_name(self, class_id):
+        return PascalVOCDataset.CLASSES[class_id]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/__init__.py
new file mode 100755
index 00000000..27982cbe
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .distributed import DistributedSampler
+from .grouped_batch_sampler import GroupedBatchSampler
+from .iteration_based_batch_sampler import IterationBasedBatchSampler
+
+__all__ = ["DistributedSampler", "GroupedBatchSampler", "IterationBasedBatchSampler"]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/distributed.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/distributed.py
new file mode 100755
index 00000000..5de06e3d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/distributed.py
@@ -0,0 +1,66 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Code is copy-pasted exactly as in torch.utils.data.distributed.
+# FIXME remove this once c10d fixes the bug it has
+import math
+import torch
+import torch.distributed as dist
+from torch.utils.data.sampler import Sampler
+
+
+class DistributedSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+    .. note::
+        Dataset is assumed to be of constant size.
+    Arguments:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+    """
+
+    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = True
+
+    def __iter__(self):
+        if self.shuffle:
+            # deterministically shuffle based on epoch
+            g = torch.Generator()
+            g.manual_seed(self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        offset = self.num_samples * self.rank
+        indices = indices[offset : offset + self.num_samples]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py
new file mode 100755
index 00000000..d72e2f02
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py
@@ -0,0 +1,115 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import itertools
+
+import torch
+from torch.utils.data.sampler import BatchSampler
+from torch.utils.data.sampler import Sampler
+
+
+class GroupedBatchSampler(BatchSampler):
+    """
+    Wraps another sampler to yield a mini-batch of indices.
+    It enforces that elements from the same group should appear in groups of batch_size.
+    It also tries to provide mini-batches which follows an ordering which is
+    as close as possible to the ordering from the original sampler.
+
+    Arguments:
+        sampler (Sampler): Base sampler.
+        batch_size (int): Size of mini-batch.
+        drop_uneven (bool): If ``True``, the sampler will drop the batches whose
+            size is less than ``batch_size``
+
+    """
+
+    def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
+        if not isinstance(sampler, Sampler):
+            raise ValueError(
+                "sampler should be an instance of "
+                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
+            )
+        self.sampler = sampler
+        self.group_ids = torch.as_tensor(group_ids)
+        assert self.group_ids.dim() == 1
+        self.batch_size = batch_size
+        self.drop_uneven = drop_uneven
+
+        self.groups = torch.unique(self.group_ids).sort(0)[0]
+
+        self._can_reuse_batches = False
+
+    def _prepare_batches(self):
+        dataset_size = len(self.group_ids)
+        # get the sampled indices from the sampler
+        sampled_ids = torch.as_tensor(list(self.sampler))
+        # potentially not all elements of the dataset were sampled
+        # by the sampler (e.g., DistributedSampler).
+        # construct a tensor which contains -1 if the element was
+        # not sampled, and a non-negative number indicating the
+        # order where the element was sampled.
+        # for example. if sampled_ids = [3, 1] and dataset_size = 5,
+        # the order is [-1, 1, -1, 0, -1]
+        order = torch.full((dataset_size,), -1, dtype=torch.int64)
+        order[sampled_ids] = torch.arange(len(sampled_ids))
+
+        # get a mask with the elements that were sampled
+        mask = order >= 0
+
+        # find the elements that belong to each individual cluster
+        clusters = [(self.group_ids == i) & mask for i in self.groups]
+        # get relative order of the elements inside each cluster
+        # that follows the order from the sampler
+        relative_order = [order[cluster] for cluster in clusters]
+        # with the relative order, find the absolute order in the
+        # sampled space
+        permutation_ids = [s[s.sort()[1]] for s in relative_order]
+        # permute each cluster so that they follow the order from
+        # the sampler
+        permuted_clusters = [sampled_ids[idx] for idx in permutation_ids]
+
+        # splits each cluster in batch_size, and merge as a list of tensors
+        splits = [c.split(self.batch_size) for c in permuted_clusters]
+        merged = tuple(itertools.chain.from_iterable(splits))
+
+        # now each batch internally has the right order, but
+        # they are grouped by clusters. Find the permutation between
+        # different batches that brings them as close as possible to
+        # the order that we have in the sampler. For that, we will consider the
+        # ordering as coming from the first element of each batch, and sort
+        # correspondingly
+        first_element_of_batch = [t[0].item() for t in merged]
+        # get and inverse mapping from sampled indices and the position where
+        # they occur (as returned by the sampler)
+        inv_sampled_ids_map = {v: k for k, v in enumerate(sampled_ids.tolist())}
+        # from the first element in each batch, get a relative ordering
+        first_index_of_batch = torch.as_tensor(
+            [inv_sampled_ids_map[s] for s in first_element_of_batch]
+        )
+
+        # permute the batches so that they approximately follow the order
+        # from the sampler
+        permutation_order = first_index_of_batch.sort(0)[1].tolist()
+        # finally, permute the batches
+        batches = [merged[i].tolist() for i in permutation_order]
+
+        if self.drop_uneven:
+            kept = []
+            for batch in batches:
+                if len(batch) == self.batch_size:
+                    kept.append(batch)
+            batches = kept
+        return batches
+
+    def __iter__(self):
+        if self._can_reuse_batches:
+            batches = self._batches
+            self._can_reuse_batches = False
+        else:
+            batches = self._prepare_batches()
+        self._batches = batches
+        return iter(batches)
+
+    def __len__(self):
+        if not hasattr(self, "_batches"):
+            self._batches = self._prepare_batches()
+            self._can_reuse_batches = True
+        return len(self._batches)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py
new file mode 100755
index 00000000..93452b64
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py
@@ -0,0 +1,31 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from torch.utils.data.sampler import BatchSampler
+
+
+class IterationBasedBatchSampler(BatchSampler):
+    """
+    Wraps a BatchSampler, resampling from it until
+    a specified number of iterations have been sampled
+    """
+
+    def __init__(self, batch_sampler, num_iterations, start_iter=0):
+        self.batch_sampler = batch_sampler
+        self.num_iterations = num_iterations
+        self.start_iter = start_iter
+
+    def __iter__(self):
+        iteration = self.start_iter
+        while iteration <= self.num_iterations:
+            # if the underlying sampler has a set_epoch method, like
+            # DistributedSampler, used for making each process see
+            # a different split of the dataset, then set it
+            if hasattr(self.batch_sampler.sampler, "set_epoch"):
+                self.batch_sampler.sampler.set_epoch(iteration)
+            for batch in self.batch_sampler:
+                iteration += 1
+                if iteration > self.num_iterations:
+                    break
+                yield batch
+
+    def __len__(self):
+        return self.num_iterations
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/__init__.py
new file mode 100755
index 00000000..00012971
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .transforms import Compose
+from .transforms import Resize
+from .transforms import RandomHorizontalFlip
+from .transforms import ToTensor
+from .transforms import Normalize
+
+from .build import build_transforms
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/build.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/build.py
new file mode 100755
index 00000000..8645d4df
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/build.py
@@ -0,0 +1,28 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from . import transforms as T
+
+
+def build_transforms(cfg, is_train=True):
+    if is_train:
+        min_size = cfg.INPUT.MIN_SIZE_TRAIN
+        max_size = cfg.INPUT.MAX_SIZE_TRAIN
+        flip_prob = 0.5  # cfg.INPUT.FLIP_PROB_TRAIN
+    else:
+        min_size = cfg.INPUT.MIN_SIZE_TEST
+        max_size = cfg.INPUT.MAX_SIZE_TEST
+        flip_prob = 0
+
+    to_bgr255 = cfg.INPUT.TO_BGR255
+    normalize_transform = T.Normalize(
+        mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, to_bgr255=to_bgr255
+    )
+
+    transform = T.Compose(
+        [
+            T.Resize(min_size, max_size),
+            T.RandomHorizontalFlip(flip_prob),
+            T.ToTensor(),
+            normalize_transform,
+        ]
+    )
+    return transform
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/transforms.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/transforms.py
new file mode 100755
index 00000000..71d48d29
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/data/transforms/transforms.py
@@ -0,0 +1,88 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import random
+
+import torch
+import torchvision
+from torchvision.transforms import functional as F
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(t)
+        format_string += "\n)"
+        return format_string
+
+
+class Resize(object):
+    def __init__(self, min_size, max_size):
+        self.min_size = min_size
+        self.max_size = max_size
+
+    # modified from torchvision to add support for max size
+    def get_size(self, image_size):
+        w, h = image_size
+        size = self.min_size
+        max_size = self.max_size
+        if max_size is not None:
+            min_original_size = float(min((w, h)))
+            max_original_size = float(max((w, h)))
+            if max_original_size / min_original_size * size > max_size:
+                size = int(round(max_size * min_original_size / max_original_size))
+
+        if (w <= h and w == size) or (h <= w and h == size):
+            return (h, w)
+
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+        else:
+            oh = size
+            ow = int(size * w / h)
+
+        return (oh, ow)
+
+    def __call__(self, image, target):
+        size = self.get_size(image.size)
+        image = F.resize(image, size)
+        target = target.resize(image.size)
+        return image, target
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, prob=0.5):
+        self.prob = prob
+
+    def __call__(self, image, target):
+        if random.random() < self.prob:
+            image = F.hflip(image)
+            target = target.transpose(0)
+        return image, target
+
+
+class ToTensor(object):
+    def __call__(self, image, target):
+        return F.to_tensor(image), target
+
+
+class Normalize(object):
+    def __init__(self, mean, std, to_bgr255=True):
+        self.mean = mean
+        self.std = std
+        self.to_bgr255 = to_bgr255
+
+    def __call__(self, image, target):
+        if self.to_bgr255:
+            image = image[[2, 1, 0]] * 255
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        return image, target
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/__init__.py
new file mode 100755
index 00000000..5c7f19c6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/inference.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/inference.py
new file mode 100755
index 00000000..a0e30731
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/inference.py
@@ -0,0 +1,111 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+import datetime
+import logging
+import time
+import os
+
+import torch
+from tqdm import tqdm
+
+from maskrcnn_benchmark.data.datasets.evaluation import evaluate
+from ..utils.comm import is_main_process
+from ..utils.comm import all_gather
+from ..utils.comm import synchronize
+
+
+def compute_on_dataset(model, data_loader, device):
+    model.eval()
+    results_dict = {}
+    cpu_device = torch.device("cpu")
+    for i, batch in enumerate(tqdm(data_loader)):
+        images, targets, image_ids = batch
+        images = images.to(device)
+        with torch.no_grad():
+            output = model(images)
+            output = [o.to(cpu_device) for o in output]
+        results_dict.update(
+            {img_id: result for img_id, result in zip(image_ids, output)}
+        )
+    return results_dict
+
+
+def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu):
+    all_predictions = all_gather(predictions_per_gpu)
+    if not is_main_process():
+        return
+    # merge the list of dicts
+    predictions = {}
+    for p in all_predictions:
+        predictions.update(p)
+    # convert a dict where the key is the index in a list
+    image_ids = list(sorted(predictions.keys()))
+    if len(image_ids) != image_ids[-1] + 1:
+        logger = logging.getLogger("maskrcnn_benchmark.inference")
+        logger.warning(
+            "Number of images that were gathered from multiple processes is not "
+            "a contiguous set. Some images might be missing from the evaluation"
+        )
+
+    # convert to a list
+    predictions = [predictions[i] for i in image_ids]
+    return predictions
+
+
+def inference(
+        model,
+        data_loader,
+        dataset_name,
+        iou_types=("bbox",),
+        box_only=False,
+        device="cuda",
+        expected_results=(),
+        expected_results_sigma_tol=4,
+        output_folder=None,
+        skip_eval=False,
+):
+    # convert to a torch.device for efficiency
+    device = torch.device(device)
+    num_devices = (
+        torch.distributed.get_world_size()
+        if torch.distributed.is_initialized()
+        else 1
+    )
+    logger = logging.getLogger("maskrcnn_benchmark.inference")
+    dataset = data_loader.dataset
+    logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
+    start_time = time.time()
+    predictions = compute_on_dataset(model, data_loader, device)
+    # wait for all processes to complete before measuring the time
+    synchronize()
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=total_time))
+    logger.info(
+        "Total inference time: {} ({} s / img per device, on {} devices)".format(
+            total_time_str, total_time * num_devices / len(dataset), num_devices
+        )
+    )
+
+    predictions = _accumulate_predictions_from_multiple_gpus(predictions)
+    if not is_main_process():
+        return
+
+    if output_folder:
+        torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
+
+    if skip_eval:
+        logger.info("Skipping evaluation. Stored predictions to {}".format(os.path.join(output_folder, "predictions.pth")))
+        return
+        
+    extra_args = dict(
+        box_only=box_only,
+        iou_types=iou_types,
+        expected_results=expected_results,
+        expected_results_sigma_tol=expected_results_sigma_tol,
+    )
+
+    return evaluate(dataset=dataset,
+                    predictions=predictions,
+                    output_folder=output_folder,
+                    **extra_args)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/tester.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/tester.py
new file mode 100644
index 00000000..2bb84c0c
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/tester.py
@@ -0,0 +1,42 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+
+import os
+
+from maskrcnn_benchmark.data.build import make_data_loader
+from maskrcnn_benchmark.engine.inference import inference
+from maskrcnn_benchmark.utils.miscellaneous import mkdir
+from maskrcnn_benchmark.utils.comm import synchronize
+
+def test(cfg, model, distributed):
+    if distributed:
+        model = model.module
+    torch.cuda.empty_cache()  # TODO check if it helps
+    iou_types = ("bbox",)
+    if cfg.MODEL.MASK_ON:
+        iou_types = iou_types + ("segm",)
+    output_folders = [None] * len(cfg.DATASETS.TEST)
+    dataset_names = cfg.DATASETS.TEST
+    if cfg.OUTPUT_DIR:
+        for idx, dataset_name in enumerate(dataset_names):
+            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
+            mkdir(output_folder)
+            output_folders[idx] = output_folder
+    data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
+    results = []
+    for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
+        result = inference(
+            model,
+            data_loader_val,
+            dataset_name=dataset_name,
+            iou_types=iou_types,
+            box_only=cfg.MODEL.RPN_ONLY,
+            device=cfg.MODEL.DEVICE,
+            expected_results=cfg.TEST.EXPECTED_RESULTS,
+            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
+            output_folder=output_folder,
+        )
+        synchronize()
+        results.append(result)
+    return results
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/trainer.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/trainer.py
new file mode 100755
index 00000000..6b144e26
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/engine/trainer.py
@@ -0,0 +1,142 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import datetime
+import logging
+import time
+
+import torch
+import torch.distributed as dist
+
+from maskrcnn_benchmark.utils.comm import get_world_size
+from maskrcnn_benchmark.utils.metric_logger import MetricLogger
+
+
+def reduce_loss_dict(loss_dict):
+    """
+    Reduce the loss dictionary from all processes so that process with rank
+    0 has the averaged results. Returns a dict with the same fields as
+    loss_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return loss_dict
+    with torch.no_grad():
+        loss_names = []
+        all_losses = []
+        for k in sorted(loss_dict.keys()):
+            loss_names.append(k)
+            all_losses.append(loss_dict[k])
+        all_losses = torch.stack(all_losses, dim=0)
+        dist.reduce(all_losses, dst=0)
+        if dist.get_rank() == 0:
+            # only main process gets accumulated, so only divide by
+            # world_size in this case
+            all_losses /= world_size
+        reduced_losses = {k: v for k, v in zip(loss_names, all_losses)}
+    return reduced_losses
+
+
+def do_train(
+    model,
+    data_loader,
+    optimizer,
+    scheduler,
+    checkpointer,
+    device,
+    checkpoint_period,
+    arguments,
+    use_amp,
+    cfg,
+    per_iter_end_callback_fn=None,
+):
+    logger = logging.getLogger("maskrcnn_benchmark.trainer")
+    logger.info("Start training")
+    meters = MetricLogger(delimiter="  ")
+    max_iter = len(data_loader)
+    start_iter = arguments["iteration"]
+    model.train()
+    start_training_time = time.time()
+    end = time.time()
+    for iteration, (images, targets, _) in enumerate(data_loader, start_iter):
+        data_time = time.time() - end
+        iteration = iteration + 1
+        arguments["iteration"] = iteration
+
+        scheduler.step()
+
+        images = images.to(device)
+        targets = [target.to(device) for target in targets]
+
+        loss_dict = model(images, targets)
+
+        losses = sum(loss for loss in loss_dict.values())
+
+        # reduce losses over all GPUs for logging purposes
+        loss_dict_reduced = reduce_loss_dict(loss_dict)
+        losses_reduced = sum(loss for loss in loss_dict_reduced.values())
+        meters.update(loss=losses_reduced, **loss_dict_reduced)
+
+
+        # Note: If mixed precision is not used, this ends up doing nothing
+        # Otherwise apply loss scaling for mixed-precision recipe
+        if use_amp:        
+            with optimizer.scale_loss(losses) as scaled_losses:
+                scaled_losses.backward()
+        else:
+            losses.backward()
+
+        if not cfg.SOLVER.ACCUMULATE_GRAD:
+            optimizer.step()
+            optimizer.zero_grad()
+        else:
+            if (iteration + 1) % cfg.SOLVER.ACCUMULATE_STEPS == 0:
+                for param in model.parameters():
+                    if param.grad is not None:
+                        param.grad.data.div_(cfg.SOLVER.ACCUMULATE_STEPS)
+                optimizer.step()
+                optimizer.zero_grad()
+            
+        batch_time = time.time() - end
+        end = time.time()
+        meters.update(time=batch_time, data=data_time)
+
+        eta_seconds = meters.time.global_avg * (max_iter - iteration)
+        eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+
+        if iteration % 20 == 0 or iteration == max_iter:
+            logger.info(
+                meters.delimiter.join(
+                    [
+                        "eta: {eta}",
+                        "iter: {iter}",
+                        "{meters}",
+                        "lr: {lr:.6f}",
+                        "max mem: {memory:.0f}",
+                    ]
+                ).format(
+                    eta=eta_string,
+                    iter=iteration,
+                    meters=str(meters),
+                    lr=optimizer.param_groups[0]["lr"],
+                    memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
+                )
+            )
+        if iteration % checkpoint_period == 0:
+            checkpointer.save("model_{:07d}".format(iteration), **arguments)
+        if iteration == max_iter:
+            checkpointer.save("model_final", **arguments)
+
+        # per-epoch work (testing)
+        if per_iter_end_callback_fn is not None:
+            early_exit = per_iter_end_callback_fn(iteration=iteration)
+            if early_exit:
+                break
+
+
+    total_training_time = time.time() - start_training_time
+    total_time_str = str(datetime.timedelta(seconds=total_training_time))
+    logger.info(
+        "Total training time: {} ({:.4f} s / it)".format(
+            total_time_str, total_training_time / (max_iter)
+        )
+    )
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/__init__.py
new file mode 100755
index 00000000..eb21200f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+from .batch_norm import FrozenBatchNorm2d
+from .misc import Conv2d
+from .misc import ConvTranspose2d
+from .misc import interpolate
+from .nms import nms
+from .roi_align import ROIAlign
+from .roi_align import roi_align
+from .roi_pool import ROIPool
+from .roi_pool import roi_pool
+from .smooth_l1_loss import smooth_l1_loss
+
+__all__ = ["nms", "roi_align", "ROIAlign", "roi_pool", "ROIPool", 
+           "smooth_l1_loss", "Conv2d", "ConvTranspose2d", "interpolate", 
+           "FrozenBatchNorm2d",
+           ]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/_utils.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/_utils.py
new file mode 100755
index 00000000..3dabc127
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/_utils.py
@@ -0,0 +1,39 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import glob
+import os.path
+
+import torch
+
+try:
+    from torch.utils.cpp_extension import load as load_ext
+    from torch.utils.cpp_extension import CUDA_HOME
+except ImportError:
+    raise ImportError("The cpp layer extensions requires PyTorch 0.4 or higher")
+
+
+def _load_C_extensions():
+    this_dir = os.path.dirname(os.path.abspath(__file__))
+    this_dir = os.path.dirname(this_dir)
+    this_dir = os.path.join(this_dir, "csrc")
+
+    main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
+    source_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
+    source_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
+
+    source = main_file + source_cpu
+
+    extra_cflags = []
+    if torch.cuda.is_available() and CUDA_HOME is not None:
+        source.extend(source_cuda)
+        extra_cflags = ["-DWITH_CUDA"]
+    source = [os.path.join(this_dir, s) for s in source]
+    extra_include_paths = [this_dir]
+    return load_ext(
+        "torchvision",
+        source,
+        extra_cflags=extra_cflags,
+        extra_include_paths=extra_include_paths,
+    )
+
+
+_C = _load_C_extensions()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/batch_norm.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/batch_norm.py
new file mode 100755
index 00000000..3762e49e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/batch_norm.py
@@ -0,0 +1,31 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch import nn
+
+
+class FrozenBatchNorm2d(nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters
+    are fixed
+    """
+
+    def __init__(self, n):
+        super(FrozenBatchNorm2d, self).__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def forward(self, x):
+        # Cast all fixed parameters to half() if necessary
+        if x.dtype == torch.float16:
+            self.weight = self.weight.half()
+            self.bias = self.bias.half()
+            self.running_mean = self.running_mean.half()
+            self.running_var = self.running_var.half()
+
+        scale = self.weight * self.running_var.rsqrt()
+        bias = self.bias - self.running_mean * scale
+        scale = scale.reshape(1, -1, 1, 1)
+        bias = bias.reshape(1, -1, 1, 1)
+        return x * scale + bias
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/misc.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/misc.py
new file mode 100755
index 00000000..61f66100
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/misc.py
@@ -0,0 +1,102 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+helper class that supports empty tensors on some nn functions.
+
+Ideally, add support directly in PyTorch to empty tensors in
+those functions.
+
+This can be removed once https://github.com/pytorch/pytorch/issues/12013
+is implemented
+"""
+
+import math
+import torch
+from torch.nn.modules.utils import _ntuple
+
+
+class _NewEmptyTensorOp(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, new_shape):
+        ctx.shape = x.shape
+        return x.new_empty(new_shape)
+
+    @staticmethod
+    def backward(ctx, grad):
+        shape = ctx.shape
+        return _NewEmptyTensorOp.apply(grad, shape), None
+
+
+
+class Conv2d(torch.nn.Conv2d):
+    def forward(self, x):
+        if x.numel() > 0:
+            return super(Conv2d, self).forward(x)
+        # get output shape
+
+        output_shape = [
+            (i + 2 * p - (di * (k - 1) + 1)) // d + 1
+            for i, p, di, k, d in zip(
+                x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride
+            )
+        ]
+        output_shape = [x.shape[0], self.weight.shape[0]] + output_shape
+        return _NewEmptyTensorOp.apply(x, output_shape)
+
+
+class ConvTranspose2d(torch.nn.ConvTranspose2d):
+    def forward(self, x):
+        if x.numel() > 0:
+            return super(ConvTranspose2d, self).forward(x)
+        # get output shape
+
+        output_shape = [
+            (i - 1) * d - 2 * p + (di * (k - 1) + 1) + op
+            for i, p, di, k, d, op in zip(
+                x.shape[-2:],
+                self.padding,
+                self.dilation,
+                self.kernel_size,
+                self.stride,
+                self.output_padding,
+            )
+        ]
+        output_shape = [x.shape[0], self.bias.shape[0]] + output_shape
+        return _NewEmptyTensorOp.apply(x, output_shape)
+
+
+def interpolate(
+    input, size=None, scale_factor=None, mode="nearest", align_corners=None
+):
+    if input.numel() > 0:
+        return torch.nn.functional.interpolate(
+            input, size, scale_factor, mode, align_corners
+        )
+
+    def _check_size_scale_factor(dim):
+        if size is None and scale_factor is None:
+            raise ValueError("either size or scale_factor should be defined")
+        if size is not None and scale_factor is not None:
+            raise ValueError("only one of size or scale_factor should be defined")
+        if (
+            scale_factor is not None
+            and isinstance(scale_factor, tuple)
+            and len(scale_factor) != dim
+        ):
+            raise ValueError(
+                "scale_factor shape must match input shape. "
+                "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
+            )
+
+    def _output_size(dim):
+        _check_size_scale_factor(dim)
+        if size is not None:
+            return size
+        scale_factors = _ntuple(dim)(scale_factor)
+        # math.floor might return float in py2.7
+        return [
+            int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
+        ]
+
+    output_shape = tuple(_output_size(2))
+    output_shape = input.shape[:-2] + output_shape
+    return _NewEmptyTensorOp.apply(input, output_shape)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/nms.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/nms.py
new file mode 100755
index 00000000..8c057a63
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/nms.py
@@ -0,0 +1,12 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+# from ._utils import _C
+from maskrcnn_benchmark import _C
+
+from apex import amp
+
+# Only valid with fp32 inputs - give AMP the hint
+nms = amp.float_function(_C.nms)
+
+# nms.__doc__ = """
+# This function performs Non-maximum suppresion"""
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_align.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_align.py
new file mode 100755
index 00000000..46832aba
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_align.py
@@ -0,0 +1,70 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import torch
+from torch import nn
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.modules.utils import _pair
+
+from maskrcnn_benchmark import _C
+
+from apex import amp
+
+class _ROIAlign(Function):
+    @staticmethod
+    def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
+        ctx.save_for_backward(roi)
+        ctx.output_size = _pair(output_size)
+        ctx.spatial_scale = spatial_scale
+        ctx.sampling_ratio = sampling_ratio
+        ctx.input_shape = input.size()
+        output = _C.roi_align_forward(
+            input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
+        )
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        rois, = ctx.saved_tensors
+        output_size = ctx.output_size
+        spatial_scale = ctx.spatial_scale
+        sampling_ratio = ctx.sampling_ratio
+        bs, ch, h, w = ctx.input_shape
+        grad_input = _C.roi_align_backward(
+            grad_output,
+            rois,
+            spatial_scale,
+            output_size[0],
+            output_size[1],
+            bs,
+            ch,
+            h,
+            w,
+            sampling_ratio,
+        )
+        return grad_input, None, None, None, None
+
+
+roi_align = _ROIAlign.apply
+
+class ROIAlign(nn.Module):
+    def __init__(self, output_size, spatial_scale, sampling_ratio):
+        super(ROIAlign, self).__init__()
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+        self.sampling_ratio = sampling_ratio
+
+    @amp.float_function
+    def forward(self, input, rois):
+        return roi_align(
+            input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
+        )
+
+    def __repr__(self):
+        tmpstr = self.__class__.__name__ + "("
+        tmpstr += "output_size=" + str(self.output_size)
+        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
+        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
+        tmpstr += ")"
+        return tmpstr
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_pool.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_pool.py
new file mode 100755
index 00000000..58633907
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/roi_pool.py
@@ -0,0 +1,65 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch import nn
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.modules.utils import _pair
+
+from maskrcnn_benchmark import _C
+
+from apex import amp
+
+class _ROIPool(Function):
+    @staticmethod
+    def forward(ctx, input, roi, output_size, spatial_scale):
+        ctx.output_size = _pair(output_size)
+        ctx.spatial_scale = spatial_scale
+        ctx.input_shape = input.size()
+        output, argmax = _C.roi_pool_forward(
+            input, roi, spatial_scale, output_size[0], output_size[1]
+        )
+        ctx.save_for_backward(input, roi, argmax)
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        input, rois, argmax = ctx.saved_tensors
+        output_size = ctx.output_size
+        spatial_scale = ctx.spatial_scale
+        bs, ch, h, w = ctx.input_shape
+        grad_input = _C.roi_pool_backward(
+            grad_output,
+            input,
+            rois,
+            argmax,
+            spatial_scale,
+            output_size[0],
+            output_size[1],
+            bs,
+            ch,
+            h,
+            w,
+        )
+        return grad_input, None, None, None
+
+
+roi_pool = _ROIPool.apply
+
+
+class ROIPool(nn.Module):
+    def __init__(self, output_size, spatial_scale):
+        super(ROIPool, self).__init__()
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+
+    @amp.float_function
+    def forward(self, input, rois):
+        return roi_pool(input, rois, self.output_size, self.spatial_scale)
+
+    def __repr__(self):
+        tmpstr = self.__class__.__name__ + "("
+        tmpstr += "output_size=" + str(self.output_size)
+        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
+        tmpstr += ")"
+        return tmpstr
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/smooth_l1_loss.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/smooth_l1_loss.py
new file mode 100755
index 00000000..9c4664bb
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/layers/smooth_l1_loss.py
@@ -0,0 +1,16 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+
+# TODO maybe push this to nn?
+def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
+    """
+    very similar to the smooth_l1_loss from pytorch, but with
+    the extra beta parameter
+    """
+    n = torch.abs(input - target)
+    cond = n < beta
+    loss = torch.where(cond, 0.5 * n ** 2 / beta, n - 0.5 * beta)
+    if size_average:
+        return loss.mean()
+    return loss.sum()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/__init__.py
new file mode 100755
index 00000000..4b3da17b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .backbone import build_backbone
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/backbone.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/backbone.py
new file mode 100755
index 00000000..c9dc392d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/backbone.py
@@ -0,0 +1,50 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from collections import OrderedDict
+
+from torch import nn
+
+from maskrcnn_benchmark.modeling import registry
+from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform
+from . import fpn as fpn_module
+from . import resnet
+
+
+@registry.BACKBONES.register("R-50-C4")
+@registry.BACKBONES.register("R-50-C5")
+@registry.BACKBONES.register("R-101-C4")
+@registry.BACKBONES.register("R-101-C5")
+def build_resnet_backbone(cfg):
+    body = resnet.ResNet(cfg)
+    model = nn.Sequential(OrderedDict([("body", body)]))
+    return model
+
+
+@registry.BACKBONES.register("R-50-FPN")
+@registry.BACKBONES.register("R-101-FPN")
+def build_resnet_fpn_backbone(cfg):
+    body = resnet.ResNet(cfg)
+    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
+    out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
+    fpn = fpn_module.FPN(
+        in_channels_list=[
+            in_channels_stage2,
+            in_channels_stage2 * 2,
+            in_channels_stage2 * 4,
+            in_channels_stage2 * 8,
+        ],
+        out_channels=out_channels,
+        conv_block=conv_with_kaiming_uniform(
+            cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
+        ),
+        top_blocks=fpn_module.LastLevelMaxPool(),
+    )
+    model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
+    return model
+
+
+def build_backbone(cfg):
+    assert cfg.MODEL.BACKBONE.CONV_BODY in registry.BACKBONES, \
+        "cfg.MODEL.BACKBONE.CONV_BODY: {} are not registered in registry".format(
+            cfg.MODEL.BACKBONE.CONV_BODY
+        )
+    return registry.BACKBONES[cfg.MODEL.BACKBONE.CONV_BODY](cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/fpn.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/fpn.py
new file mode 100755
index 00000000..da970ac0
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/fpn.py
@@ -0,0 +1,71 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class FPN(nn.Module):
+    """
+    Module that adds FPN on top of a list of feature maps.
+    The feature maps are currently supposed to be in increasing depth
+    order, and must be consecutive
+    """
+
+    def __init__(
+        self, in_channels_list, out_channels, conv_block, top_blocks=None
+    ):
+        """
+        Arguments:
+            in_channels_list (list[int]): number of channels for each feature map that
+                will be fed
+            out_channels (int): number of channels of the FPN representation
+            top_blocks (nn.Module or None): if provided, an extra operation will
+                be performed on the output of the last (smallest resolution)
+                FPN output, and the result will extend the result list
+        """
+        super(FPN, self).__init__()
+        self.inner_blocks = []
+        self.layer_blocks = []
+        for idx, in_channels in enumerate(in_channels_list, 1):
+            inner_block = "fpn_inner{}".format(idx)
+            layer_block = "fpn_layer{}".format(idx)
+            inner_block_module = conv_block(in_channels, out_channels, 1)
+            layer_block_module = conv_block(out_channels, out_channels, 3, 1)
+            self.add_module(inner_block, inner_block_module)
+            self.add_module(layer_block, layer_block_module)
+            self.inner_blocks.append(inner_block)
+            self.layer_blocks.append(layer_block)
+        self.top_blocks = top_blocks
+
+    def forward(self, x):
+        """
+        Arguments:
+            x (list[Tensor]): feature maps for each feature level.
+        Returns:
+            results (tuple[Tensor]): feature maps after FPN layers.
+                They are ordered from highest resolution first.
+        """
+        last_inner = getattr(self, self.inner_blocks[-1])(x[-1])
+        results = []
+        results.append(getattr(self, self.layer_blocks[-1])(last_inner))
+        for feature, inner_block, layer_block in zip(
+            x[:-1][::-1], self.inner_blocks[:-1][::-1], self.layer_blocks[:-1][::-1]
+        ):
+            inner_top_down = F.interpolate(last_inner, scale_factor=2, mode="nearest")
+            inner_lateral = getattr(self, inner_block)(feature)
+            # TODO use size instead of scale to make it robust to different sizes
+            # inner_top_down = F.upsample(last_inner, size=inner_lateral.shape[-2:],
+            # mode='bilinear', align_corners=False)
+            last_inner = inner_lateral + inner_top_down
+            results.insert(0, getattr(self, layer_block)(last_inner))
+
+        if self.top_blocks is not None:
+            last_results = self.top_blocks(results[-1])
+            results.extend(last_results)
+
+        return tuple(results)
+
+
+class LastLevelMaxPool(nn.Module):
+    def forward(self, x):
+        return [F.max_pool2d(x, 1, 2, 0)]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/resnet.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/resnet.py
new file mode 100755
index 00000000..eed1cdb3
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone/resnet.py
@@ -0,0 +1,410 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+Variant of the resnet module that takes cfg as an argument.
+Example usage. Strings may be specified in the config file.
+    model = ResNet(
+        "StemWithFixedBatchNorm",
+        "BottleneckWithFixedBatchNorm",
+        "ResNet50StagesTo4",
+    )
+OR:
+    model = ResNet(
+        "StemWithGN",
+        "BottleneckWithGN",
+        "ResNet50StagesTo4",
+    )
+Custom implementations may be written in user code and hooked in via the
+`register_*` functions.
+"""
+from collections import namedtuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from maskrcnn_benchmark.layers import FrozenBatchNorm2d
+from maskrcnn_benchmark.layers import Conv2d
+from maskrcnn_benchmark.modeling.make_layers import group_norm
+from maskrcnn_benchmark.utils.registry import Registry
+
+
+# ResNet stage specification
+StageSpec = namedtuple(
+    "StageSpec",
+    [
+        "index",  # Index of the stage, eg 1, 2, ..,. 5
+        "block_count",  # Numer of residual blocks in the stage
+        "return_features",  # True => return the last feature map from this stage
+    ],
+)
+
+# -----------------------------------------------------------------------------
+# Standard ResNet models
+# -----------------------------------------------------------------------------
+# ResNet-50 (including all stages)
+ResNet50StagesTo5 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True))
+)
+# ResNet-50 up to stage 4 (excludes stage 5)
+ResNet50StagesTo4 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True))
+)
+# ResNet-101 (including all stages)
+ResNet101StagesTo5 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True))
+)
+# ResNet-101 up to stage 4 (excludes stage 5)
+ResNet101StagesTo4 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True))
+)
+# ResNet-50-FPN (including all stages)
+ResNet50FPNStagesTo5 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True))
+)
+# ResNet-101-FPN (including all stages)
+ResNet101FPNStagesTo5 = tuple(
+    StageSpec(index=i, block_count=c, return_features=r)
+    for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True))
+)
+
+
+class ResNet(nn.Module):
+    def __init__(self, cfg):
+        super(ResNet, self).__init__()
+
+        # If we want to use the cfg in forward(), then we should make a copy
+        # of it and store it for later use:
+        # self.cfg = cfg.clone()
+
+        # Translate string names to implementations
+        stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC]
+        stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]
+        transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]
+
+        # Construct the stem module
+        self.stem = stem_module(cfg)
+
+        # Constuct the specified ResNet stages
+        num_groups = cfg.MODEL.RESNETS.NUM_GROUPS
+        width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
+        in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
+        stage2_bottleneck_channels = num_groups * width_per_group
+        stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
+        self.stages = []
+        self.return_features = {}
+        for stage_spec in stage_specs:
+            name = "layer" + str(stage_spec.index)
+            stage2_relative_factor = 2 ** (stage_spec.index - 1)
+            bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
+            out_channels = stage2_out_channels * stage2_relative_factor
+            module = _make_stage(
+                transformation_module,
+                in_channels,
+                bottleneck_channels,
+                out_channels,
+                stage_spec.block_count,
+                num_groups,
+                cfg.MODEL.RESNETS.STRIDE_IN_1X1,
+                first_stride=int(stage_spec.index > 1) + 1,
+            )
+            in_channels = out_channels
+            self.add_module(name, module)
+            self.stages.append(name)
+            self.return_features[name] = stage_spec.return_features
+
+        # Optionally freeze (requires_grad=False) parts of the backbone
+        self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
+
+    def _freeze_backbone(self, freeze_at):
+        if freeze_at < 0:
+            return
+        for stage_index in range(freeze_at):
+            if stage_index == 0:
+                m = self.stem  # stage 0 is the stem
+            else:
+                m = getattr(self, "layer" + str(stage_index))
+            for p in m.parameters():
+                p.requires_grad = False
+
+    def forward(self, x):
+        outputs = []
+        x = self.stem(x)
+        for stage_name in self.stages:
+            x = getattr(self, stage_name)(x)
+            if self.return_features[stage_name]:
+                outputs.append(x)
+        return outputs
+
+
+class ResNetHead(nn.Module):
+    def __init__(
+        self,
+        block_module,
+        stages,
+        num_groups=1,
+        width_per_group=64,
+        stride_in_1x1=True,
+        stride_init=None,
+        res2_out_channels=256,
+        dilation=1
+    ):
+        super(ResNetHead, self).__init__()
+
+        stage2_relative_factor = 2 ** (stages[0].index - 1)
+        stage2_bottleneck_channels = num_groups * width_per_group
+        out_channels = res2_out_channels * stage2_relative_factor
+        in_channels = out_channels // 2
+        bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
+
+        block_module = _TRANSFORMATION_MODULES[block_module]
+
+        self.stages = []
+        stride = stride_init
+        for stage in stages:
+            name = "layer" + str(stage.index)
+            if not stride:
+                stride = int(stage.index > 1) + 1
+            module = _make_stage(
+                block_module,
+                in_channels,
+                bottleneck_channels,
+                out_channels,
+                stage.block_count,
+                num_groups,
+                stride_in_1x1,
+                first_stride=stride,
+                dilation=dilation
+            )
+            stride = None
+            self.add_module(name, module)
+            self.stages.append(name)
+
+    def forward(self, x):
+        for stage in self.stages:
+            x = getattr(self, stage)(x)
+        return x
+
+
+def _make_stage(
+    transformation_module,
+    in_channels,
+    bottleneck_channels,
+    out_channels,
+    block_count,
+    num_groups,
+    stride_in_1x1,
+    first_stride,
+    dilation=1
+):
+    blocks = []
+    stride = first_stride
+    for _ in range(block_count):
+        blocks.append(
+            transformation_module(
+                in_channels,
+                bottleneck_channels,
+                out_channels,
+                num_groups,
+                stride_in_1x1,
+                stride,
+                dilation=dilation
+            )
+        )
+        stride = 1
+        in_channels = out_channels
+    return nn.Sequential(*blocks)
+
+
+class Bottleneck(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        bottleneck_channels,
+        out_channels,
+        num_groups,
+        stride_in_1x1,
+        stride,
+        dilation,
+        norm_func
+    ):
+        super(Bottleneck, self).__init__()
+
+        self.downsample = None
+        if in_channels != out_channels:
+            down_stride = stride if dilation == 1 else 1
+            self.downsample = nn.Sequential(
+                Conv2d(
+                    in_channels, out_channels, 
+                    kernel_size=1, stride=down_stride, bias=False
+                ),
+                norm_func(out_channels),
+            )
+            for modules in [self.downsample,]:
+                for l in modules.modules():
+                    if isinstance(l, Conv2d):
+                        nn.init.kaiming_uniform_(l.weight, a=1)
+
+        if dilation > 1:
+            stride = 1 # reset to be 1
+
+        # The original MSRA ResNet models have stride in the first 1x1 conv
+        # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
+        # stride in the 3x3 conv
+        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
+
+        self.conv1 = Conv2d(
+            in_channels,
+            bottleneck_channels,
+            kernel_size=1,
+            stride=stride_1x1,
+            bias=False,
+        )
+        self.bn1 = norm_func(bottleneck_channels)
+        # TODO: specify init for the above
+
+        self.conv2 = Conv2d(
+            bottleneck_channels,
+            bottleneck_channels,
+            kernel_size=3,
+            stride=stride_3x3,
+            padding=dilation,
+            bias=False,
+            groups=num_groups,
+            dilation=dilation
+        )
+        self.bn2 = norm_func(bottleneck_channels)
+
+        self.conv3 = Conv2d(
+            bottleneck_channels, out_channels, kernel_size=1, bias=False
+        )
+        self.bn3 = norm_func(out_channels)
+
+        for l in [self.conv1, self.conv2, self.conv3,]:
+            nn.init.kaiming_uniform_(l.weight, a=1)
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = F.relu_(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = F.relu_(out)
+
+        out0 = self.conv3(out)
+        out = self.bn3(out0)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = F.relu_(out)
+
+        return out
+
+
+class BaseStem(nn.Module):
+    def __init__(self, cfg, norm_func):
+        super(BaseStem, self).__init__()
+
+        out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
+
+        self.conv1 = Conv2d(
+            3, out_channels, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = norm_func(out_channels)
+
+        for l in [self.conv1,]:
+            nn.init.kaiming_uniform_(l.weight, a=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = F.relu_(x)
+        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
+        return x
+
+
+class BottleneckWithFixedBatchNorm(Bottleneck):
+    def __init__(
+        self,
+        in_channels,
+        bottleneck_channels,
+        out_channels,
+        num_groups=1,
+        stride_in_1x1=True,
+        stride=1,
+        dilation=1
+    ):
+        super(BottleneckWithFixedBatchNorm, self).__init__(
+            in_channels=in_channels,
+            bottleneck_channels=bottleneck_channels,
+            out_channels=out_channels,
+            num_groups=num_groups,
+            stride_in_1x1=stride_in_1x1,
+            stride=stride,
+            dilation=dilation,
+            norm_func=FrozenBatchNorm2d
+        )
+
+
+class StemWithFixedBatchNorm(BaseStem):
+    def __init__(self, cfg):
+        super(StemWithFixedBatchNorm, self).__init__(
+            cfg, norm_func=FrozenBatchNorm2d
+        )
+
+
+class BottleneckWithGN(Bottleneck):
+    def __init__(
+        self,
+        in_channels,
+        bottleneck_channels,
+        out_channels,
+        num_groups=1,
+        stride_in_1x1=True,
+        stride=1,
+        dilation=1
+    ):
+        super(BottleneckWithGN, self).__init__(
+            in_channels=in_channels,
+            bottleneck_channels=bottleneck_channels,
+            out_channels=out_channels,
+            num_groups=num_groups,
+            stride_in_1x1=stride_in_1x1,
+            stride=stride,
+            dilation=dilation,
+            norm_func=group_norm
+        )
+
+
+class StemWithGN(BaseStem):
+    def __init__(self, cfg):
+        super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
+
+
+_TRANSFORMATION_MODULES = Registry({
+    "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
+    "BottleneckWithGN": BottleneckWithGN,
+})
+
+_STEM_MODULES = Registry({
+    "StemWithFixedBatchNorm": StemWithFixedBatchNorm,
+    "StemWithGN": StemWithGN,
+})
+
+_STAGE_SPECS = Registry({
+    "R-50-C4": ResNet50StagesTo4,
+    "R-50-C5": ResNet50StagesTo5,
+    "R-101-C4": ResNet101StagesTo4,
+    "R-101-C5": ResNet101StagesTo5,
+    "R-50-FPN": ResNet50FPNStagesTo5,
+    "R-101-FPN": ResNet101FPNStagesTo5,
+})
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py
new file mode 100755
index 00000000..53742167
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py
@@ -0,0 +1,69 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import torch
+
+
+class BalancedPositiveNegativeSampler(object):
+    """
+    This class samples batches, ensuring that they contain a fixed proportion of positives
+    """
+
+    def __init__(self, batch_size_per_image, positive_fraction):
+        """
+        Arguments:
+            batch_size_per_image (int): number of elements to be selected per image
+            positive_fraction (float): percentace of positive elements per batch
+        """
+        self.batch_size_per_image = batch_size_per_image
+        self.positive_fraction = positive_fraction
+
+    def __call__(self, matched_idxs):
+        """
+        Arguments:
+            matched idxs: list of tensors containing -1, 0 or positive values.
+                Each tensor corresponds to a specific image.
+                -1 values are ignored, 0 are considered as negatives and > 0 as
+                positives.
+
+        Returns:
+            pos_idx (list[tensor])
+            neg_idx (list[tensor])
+
+        Returns two lists of binary masks for each image.
+        The first list contains the positive elements that were selected,
+        and the second list the negative example.
+        """
+        pos_idx = []
+        neg_idx = []
+        for matched_idxs_per_image in matched_idxs:
+            positive = torch.nonzero(matched_idxs_per_image >= 1).squeeze(1)
+            negative = torch.nonzero(matched_idxs_per_image == 0).squeeze(1)
+
+            num_pos = int(self.batch_size_per_image * self.positive_fraction)
+            # protect against not enough positive examples
+            num_pos = min(positive.numel(), num_pos)
+            num_neg = self.batch_size_per_image - num_pos
+            # protect against not enough negative examples
+            num_neg = min(negative.numel(), num_neg)
+
+            # randomly select positive and negative examples
+            perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
+            perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
+
+            pos_idx_per_image = positive[perm1]
+            neg_idx_per_image = negative[perm2]
+
+            # create binary mask from indices
+            pos_idx_per_image_mask = torch.zeros_like(
+                matched_idxs_per_image, dtype=torch.uint8
+            )
+            neg_idx_per_image_mask = torch.zeros_like(
+                matched_idxs_per_image, dtype=torch.uint8
+            )
+            pos_idx_per_image_mask[pos_idx_per_image] = 1
+            neg_idx_per_image_mask[neg_idx_per_image] = 1
+
+            pos_idx.append(pos_idx_per_image_mask)
+            neg_idx.append(neg_idx_per_image_mask)
+
+        return pos_idx, neg_idx
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/box_coder.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/box_coder.py
new file mode 100755
index 00000000..f11135e6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/box_coder.py
@@ -0,0 +1,100 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import math
+from maskrcnn_benchmark import _C
+import torch
+
+
+class BoxCoder(object):
+    """
+    This class encodes and decodes a set of bounding boxes into
+    the representation used for training the regressors.
+    """
+
+    def __init__(self, weights, bbox_xform_clip=math.log(1000. / 16)):
+        """
+        Arguments:
+            weights (4-element tuple)
+            bbox_xform_clip (float)
+        """
+        self.weights = weights
+        self.bbox_xform_clip = bbox_xform_clip
+
+    def encode(self, reference_boxes, proposals):
+        """
+        Encode a set of proposals with respect to some
+        reference boxes
+
+        Arguments:
+            reference_boxes (Tensor): reference boxes
+            proposals (Tensor): boxes to be encoded
+        """
+
+        wx, wy, ww, wh = self.weights
+        if reference_boxes.is_cuda and proposals.is_cuda:
+            targets = torch.stack(_C.box_encode(reference_boxes, proposals, wx, wy, ww, wh), dim=1)
+        else:
+            TO_REMOVE = 1  # TODO remove
+            ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE
+            ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE
+            ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths
+            ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights
+
+            gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE
+            gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE
+            gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths
+            gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights
+
+            wx, wy, ww, wh = self.weights
+            targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
+            targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
+            targets_dw = ww * torch.log(gt_widths / ex_widths)
+            targets_dh = wh * torch.log(gt_heights / ex_heights)
+
+            targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
+        return targets
+
+    def decode(self, rel_codes, boxes):
+        """
+        From a set of original boxes and encoded relative box offsets,
+        get the decoded boxes.
+
+        Arguments:
+            rel_codes (Tensor): encoded boxes
+            boxes (Tensor): reference boxes.
+        """
+
+        boxes = boxes.to(rel_codes.dtype)
+
+        TO_REMOVE = 1  # TODO remove
+        widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
+        heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
+        ctr_x = boxes[:, 0] + 0.5 * widths
+        ctr_y = boxes[:, 1] + 0.5 * heights
+
+        wx, wy, ww, wh = self.weights
+        dx = rel_codes[:, 0::4] / wx
+        dy = rel_codes[:, 1::4] / wy
+        dw = rel_codes[:, 2::4] / ww
+        dh = rel_codes[:, 3::4] / wh
+
+        # Prevent sending too large values into torch.exp()
+        dw = torch.clamp(dw, max=self.bbox_xform_clip)
+        dh = torch.clamp(dh, max=self.bbox_xform_clip)
+
+        pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
+        pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
+        pred_w = torch.exp(dw) * widths[:, None]
+        pred_h = torch.exp(dh) * heights[:, None]
+
+        pred_boxes = torch.zeros_like(rel_codes)
+        # x1
+        pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
+        # y1
+        pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
+        # x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
+        pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
+        # y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
+        pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1
+
+        return pred_boxes
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/__init__.py
new file mode 100755
index 00000000..ff421e28
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .detectors import build_detection_model
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/detectors.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/detectors.py
new file mode 100755
index 00000000..af2100ca
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/detectors.py
@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .generalized_rcnn import GeneralizedRCNN
+
+
+_DETECTION_META_ARCHITECTURES = {"GeneralizedRCNN": GeneralizedRCNN}
+
+
+def build_detection_model(cfg):
+    meta_arch = _DETECTION_META_ARCHITECTURES[cfg.MODEL.META_ARCHITECTURE]
+    return meta_arch(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py
new file mode 100755
index 00000000..7a2bf7a6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py
@@ -0,0 +1,66 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+"""
+Implements the Generalized R-CNN framework
+"""
+
+import torch
+from torch import nn
+
+from maskrcnn_benchmark.structures.image_list import to_image_list
+
+from ..backbone import build_backbone
+from ..rpn.rpn import build_rpn
+from ..roi_heads.roi_heads import build_roi_heads
+
+
+class GeneralizedRCNN(nn.Module):
+    """
+    Main class for Generalized R-CNN. Currently supports boxes and masks.
+    It consists of three main parts:
+    - backbone
+    - rpn
+    - heads: takes the features + the proposals from the RPN and computes
+        detections / masks from it.
+    """
+
+    def __init__(self, cfg):
+        super(GeneralizedRCNN, self).__init__()
+
+        self.backbone = build_backbone(cfg)
+        self.rpn = build_rpn(cfg)
+        self.roi_heads = build_roi_heads(cfg)
+
+    def forward(self, images, targets=None):
+        """
+        Arguments:
+            images (list[Tensor] or ImageList): images to be processed
+            targets (list[BoxList]): ground-truth boxes present in the image (optional)
+
+        Returns:
+            result (list[BoxList] or dict[Tensor]): the output from the model.
+                During training, it returns a dict[Tensor] which contains the losses.
+                During testing, it returns list[BoxList] contains additional fields
+                like `scores`, `labels` and `mask` (for Mask R-CNN models).
+
+        """
+        if self.training and targets is None:
+            raise ValueError("In training mode, targets should be passed")
+        images = to_image_list(images)
+        features = self.backbone(images.tensors)
+        proposals, proposal_losses = self.rpn(images, features, targets)
+        if self.roi_heads:
+            x, result, detector_losses = self.roi_heads(features, proposals, targets)
+        else:
+            # RPN-only models don't have roi_heads
+            x = features
+            result = proposals
+            detector_losses = {}
+
+        if self.training:
+            losses = {}
+            losses.update(detector_losses)
+            losses.update(proposal_losses)
+            return losses
+
+        return result
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/make_layers.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/make_layers.py
new file mode 100755
index 00000000..74e56b0e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/make_layers.py
@@ -0,0 +1,122 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+Miscellaneous utility functions
+"""
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from maskrcnn_benchmark.config import cfg
+from maskrcnn_benchmark.layers import Conv2d
+from maskrcnn_benchmark.modeling.poolers import Pooler
+
+
+def get_group_gn(dim, dim_per_gp, num_groups):
+    """get number of groups used by GroupNorm, based on number of channels."""
+    assert dim_per_gp == -1 or num_groups == -1, \
+        "GroupNorm: can only specify G or C/G."
+
+    if dim_per_gp > 0:
+        assert dim % dim_per_gp == 0, \
+            "dim: {}, dim_per_gp: {}".format(dim, dim_per_gp)
+        group_gn = dim // dim_per_gp
+    else:
+        assert dim % num_groups == 0, \
+            "dim: {}, num_groups: {}".format(dim, num_groups)
+        group_gn = num_groups
+
+    return group_gn
+
+
+def group_norm(out_channels, affine=True, divisor=1):
+    out_channels = out_channels // divisor
+    dim_per_gp = cfg.MODEL.GROUP_NORM.DIM_PER_GP // divisor
+    num_groups = cfg.MODEL.GROUP_NORM.NUM_GROUPS // divisor
+    eps = cfg.MODEL.GROUP_NORM.EPSILON # default: 1e-5
+    return torch.nn.GroupNorm(
+        get_group_gn(out_channels, dim_per_gp, num_groups), 
+        out_channels, 
+        eps, 
+        affine
+    )
+
+
+def make_conv3x3(
+    in_channels, 
+    out_channels, 
+    dilation=1, 
+    stride=1, 
+    use_gn=False,
+    use_relu=False,
+    kaiming_init=True
+):
+    conv = Conv2d(
+        in_channels, 
+        out_channels, 
+        kernel_size=3, 
+        stride=stride, 
+        padding=dilation, 
+        dilation=dilation, 
+        bias=False if use_gn else True
+    )
+    if kaiming_init:
+        nn.init.kaiming_normal_(
+            conv.weight, mode="fan_out", nonlinearity="relu"
+        )
+    else:
+        torch.nn.init.normal_(conv.weight, std=0.01)
+    if not use_gn:
+        nn.init.constant_(conv.bias, 0)
+    module = [conv,]
+    if use_gn:
+        module.append(group_norm(out_channels))
+    if use_relu:
+        module.append(nn.ReLU(inplace=True))
+    if len(module) > 1:
+        return nn.Sequential(*module)
+    return conv
+
+
+def make_fc(dim_in, hidden_dim, use_gn=False):
+    '''
+        Caffe2 implementation uses XavierFill, which in fact
+        corresponds to kaiming_uniform_ in PyTorch
+    '''
+    if use_gn:
+        fc = nn.Linear(dim_in, hidden_dim, bias=False)
+        nn.init.kaiming_uniform_(fc.weight, a=1)
+        return nn.Sequential(fc, group_norm(hidden_dim))
+    fc = nn.Linear(dim_in, hidden_dim)
+    nn.init.kaiming_uniform_(fc.weight, a=1)
+    nn.init.constant_(fc.bias, 0)
+    return fc
+
+
+def conv_with_kaiming_uniform(use_gn=False, use_relu=False):
+    def make_conv(
+        in_channels, out_channels, kernel_size, stride=1, dilation=1
+    ):
+        conv = Conv2d(
+            in_channels, 
+            out_channels, 
+            kernel_size=kernel_size, 
+            stride=stride, 
+            padding=dilation * (kernel_size - 1) // 2, 
+            dilation=dilation, 
+            bias=False if use_gn else True
+        )
+        # Caffe2 implementation uses XavierFill, which in fact
+        # corresponds to kaiming_uniform_ in PyTorch
+        nn.init.kaiming_uniform_(conv.weight, a=1)
+        if not use_gn:
+            nn.init.constant_(conv.bias, 0)
+        module = [conv,]
+        if use_gn:
+            module.append(group_norm(out_channels))
+        if use_relu:
+            module.append(nn.ReLU(inplace=True))
+        if len(module) > 1:
+            return nn.Sequential(*module)
+        return conv
+
+    return make_conv
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/matcher.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/matcher.py
new file mode 100755
index 00000000..514d9ea5
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/matcher.py
@@ -0,0 +1,116 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import torch
+from maskrcnn_benchmark import _C
+
+class Matcher(object):
+    """
+    This class assigns to each predicted "element" (e.g., a box) a ground-truth
+    element. Each predicted element will have exactly zero or one matches; each
+    ground-truth element may be assigned to zero or more predicted elements.
+
+    Matching is based on the MxN match_quality_matrix, that characterizes how well
+    each (ground-truth, predicted)-pair match. For example, if the elements are
+    boxes, the matrix may contain box IoU overlap values.
+
+    The matcher returns a tensor of size N containing the index of the ground-truth
+    element m that matches to prediction n. If there is no match, a negative value
+    is returned.
+    """
+
+    BELOW_LOW_THRESHOLD = -1
+    BETWEEN_THRESHOLDS = -2
+
+    def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
+        """
+        Args:
+            high_threshold (float): quality values greater than or equal to
+                this value are candidate matches.
+            low_threshold (float): a lower quality threshold used to stratify
+                matches into three levels:
+                1) matches >= high_threshold
+                2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
+                3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
+            allow_low_quality_matches (bool): if True, produce additional matches
+                for predictions that have only low-quality match candidates. See
+                set_low_quality_matches_ for more details.
+        """
+        assert low_threshold <= high_threshold
+        self.high_threshold = high_threshold
+        self.low_threshold = low_threshold
+        self.allow_low_quality_matches = allow_low_quality_matches
+
+    def __call__(self, match_quality_matrix):
+        """
+        Args:
+            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
+            pairwise quality between M ground-truth elements and N predicted elements.
+
+        Returns:
+            matches (Tensor[int64]): an N tensor where N[i] is a matched gt in
+            [0, M - 1] or a negative value indicating that prediction i could not
+            be matched.
+        """
+        if match_quality_matrix.numel() == 0:
+            # empty targets or proposals not supported during training
+            if match_quality_matrix.shape[0] == 0:
+                raise ValueError(
+                    "No ground-truth boxes available for one of the images "
+                    "during training")
+            else:
+                raise ValueError(
+                    "No proposal boxes available for one of the images "
+                    "during training")
+
+        # match_quality_matrix is M (gt) x N (predicted)
+        # Max over gt elements (dim 0) to find best gt candidate for each prediction
+
+        if match_quality_matrix.is_cuda: 
+            matches = _C.match_proposals(match_quality_matrix,self.allow_low_quality_matches, self.low_threshold, self.high_threshold)
+        else:       
+            matched_vals, matches = match_quality_matrix.max(dim=0)
+            if self.allow_low_quality_matches:
+                all_matches = matches.clone()
+
+            # Assign candidate matches with low quality to negative (unassigned) values
+            below_low_threshold = matched_vals < self.low_threshold
+            between_thresholds = (matched_vals >= self.low_threshold) & (
+                matched_vals < self.high_threshold
+            )
+            matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD
+            matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS
+
+            if self.allow_low_quality_matches:
+                self.set_low_quality_matches_(matches, all_matches, match_quality_matrix)
+        return matches
+
+    def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
+        """
+        Produce additional matches for predictions that have only low-quality matches.
+        Specifically, for each ground-truth find the set of predictions that have
+        maximum overlap with it (including ties); for each prediction in that set, if
+        it is unmatched, then match it to the ground-truth with which it has the highest
+        quality value.
+        """
+        # For each gt, find the prediction with which it has highest quality
+        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
+        # Find highest quality match available, even if it is low, including ties
+        gt_pred_pairs_of_highest_quality = torch.nonzero(
+            match_quality_matrix == highest_quality_foreach_gt[:, None]
+        )
+        # Example gt_pred_pairs_of_highest_quality:
+        #   tensor([[    0, 39796],
+        #           [    1, 32055],
+        #           [    1, 32070],
+        #           [    2, 39190],
+        #           [    2, 40255],
+        #           [    3, 40390],
+        #           [    3, 41455],
+        #           [    4, 45470],
+        #           [    5, 45325],
+        #           [    5, 46390]])
+        # Each row is a (gt index, prediction index)
+        # Note how gt items 1, 2, 3, and 5 each have two ties
+
+        pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
+        matches[pred_inds_to_update] = all_matches[pred_inds_to_update]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/poolers.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/poolers.py
new file mode 100755
index 00000000..f184a888
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/poolers.py
@@ -0,0 +1,121 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from maskrcnn_benchmark.layers import ROIAlign
+
+from .utils import cat
+
+
+class LevelMapper(object):
+    """Determine which FPN level each RoI in a set of RoIs should map to based
+    on the heuristic in the FPN paper.
+    """
+
+    def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
+        """
+        Arguments:
+            k_min (int)
+            k_max (int)
+            canonical_scale (int)
+            canonical_level (int)
+            eps (float)
+        """
+        self.k_min = k_min
+        self.k_max = k_max
+        self.s0 = canonical_scale
+        self.lvl0 = canonical_level
+        self.eps = eps
+
+    def __call__(self, boxlists):
+        """
+        Arguments:
+            boxlists (list[BoxList])
+        """
+        # Compute level ids
+        s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists]))
+
+        # Eqn.(1) in FPN paper
+        target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps))
+        target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
+        return target_lvls.to(torch.int64) - self.k_min
+
+
+class Pooler(nn.Module):
+    """
+    Pooler for Detection with or without FPN.
+    It currently hard-code ROIAlign in the implementation,
+    but that can be made more generic later on.
+    Also, the requirement of passing the scales is not strictly necessary, as they
+    can be inferred from the size of the feature map / size of original image,
+    which is available thanks to the BoxList.
+    """
+
+    def __init__(self, output_size, scales, sampling_ratio):
+        """
+        Arguments:
+            output_size (list[tuple[int]] or list[int]): output size for the pooled region
+            scales (list[float]): scales for each Pooler
+            sampling_ratio (int): sampling ratio for ROIAlign
+        """
+        super(Pooler, self).__init__()
+        poolers = []
+        for scale in scales:
+            poolers.append(
+                ROIAlign(
+                    output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
+                )
+            )
+        self.poolers = nn.ModuleList(poolers)
+        self.output_size = output_size
+        # get the levels in the feature map by leveraging the fact that the network always
+        # downsamples by a factor of 2 at each level.
+        lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
+        lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
+        self.map_levels = LevelMapper(lvl_min, lvl_max)
+
+    def convert_to_roi_format(self, boxes):
+        concat_boxes = cat([b.bbox for b in boxes], dim=0)
+        device, dtype = concat_boxes.device, concat_boxes.dtype
+        ids = cat(
+            [
+                torch.full((len(b), 1), i, dtype=dtype, device=device)
+                for i, b in enumerate(boxes)
+            ],
+            dim=0,
+        )
+        rois = torch.cat([ids, concat_boxes], dim=1)
+        return rois
+
+    def forward(self, x, boxes):
+        """
+        Arguments:
+            x (list[Tensor]): feature maps for each level
+            boxes (list[BoxList]): boxes to be used to perform the pooling operation.
+        Returns:
+            result (Tensor)
+        """
+        num_levels = len(self.poolers)
+        rois = self.convert_to_roi_format(boxes)
+        if num_levels == 1:
+            return self.poolers[0](x[0], rois)
+
+        levels = self.map_levels(boxes)
+
+        num_rois = len(rois)
+        num_channels = x[0].shape[1]
+        output_size = self.output_size[0]
+
+        dtype, device = x[0].dtype, x[0].device
+        result = torch.zeros(
+            (num_rois, num_channels, output_size, output_size),
+            dtype=dtype,
+            device=device,
+        )
+        for level, (per_level_feature, pooler) in enumerate(zip(x, self.poolers)):
+            idx_in_level = torch.nonzero(levels == level).squeeze(1)
+            rois_per_level = rois[idx_in_level]
+            result[idx_in_level] = pooler(per_level_feature, rois_per_level).to(dtype)
+
+        return result
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/registry.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/registry.py
new file mode 100755
index 00000000..7a777da9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/registry.py
@@ -0,0 +1,7 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+from maskrcnn_benchmark.utils.registry import Registry
+
+BACKBONES = Registry()
+ROI_BOX_FEATURE_EXTRACTORS = Registry()
+RPN_HEADS = Registry()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py
new file mode 100755
index 00000000..53ba5315
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py
@@ -0,0 +1,70 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch import nn
+
+from .roi_box_feature_extractors import make_roi_box_feature_extractor
+from .roi_box_predictors import make_roi_box_predictor
+from .inference import make_roi_box_post_processor
+from .loss import make_roi_box_loss_evaluator
+
+
+class ROIBoxHead(torch.nn.Module):
+    """
+    Generic Box Head class.
+    """
+
+    def __init__(self, cfg):
+        super(ROIBoxHead, self).__init__()
+        self.feature_extractor = make_roi_box_feature_extractor(cfg)
+        self.predictor = make_roi_box_predictor(cfg)
+        self.post_processor = make_roi_box_post_processor(cfg)
+        self.loss_evaluator = make_roi_box_loss_evaluator(cfg)
+
+    def forward(self, features, proposals, targets=None):
+        """
+        Arguments:
+            features (list[Tensor]): feature-maps from possibly several levels
+            proposals (list[BoxList]): proposal boxes
+            targets (list[BoxList], optional): the ground-truth targets.
+
+        Returns:
+            x (Tensor): the result of the feature extractor
+            proposals (list[BoxList]): during training, the subsampled proposals
+                are returned. During testing, the predicted boxlists are returned
+            losses (dict[Tensor]): During training, returns the losses for the
+                head. During testing, returns an empty dict.
+        """
+
+        if self.training:
+            # Faster R-CNN subsamples during training the proposals with a fixed
+            # positive / negative ratio
+            with torch.no_grad():
+                proposals = self.loss_evaluator.subsample(proposals, targets)
+
+        # extract features that will be fed to the final classifier. The
+        # feature_extractor generally corresponds to the pooler + heads
+        x = self.feature_extractor(features, proposals)
+        # final classifier that converts the features into predictions
+        class_logits, box_regression = self.predictor(x)
+
+        if not self.training:
+            result = self.post_processor((class_logits, box_regression), proposals)
+            return x, result, {}
+
+        loss_classifier, loss_box_reg = self.loss_evaluator(
+            [class_logits], [box_regression]
+        )
+        return (
+            x,
+            proposals,
+            dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
+        )
+
+
+def build_roi_box_head(cfg):
+    """
+    Constructs a new box head.
+    By default, uses ROIBoxHead, but if it turns out not to be enough, just register a new class
+    and make it a parameter in the config
+    """
+    return ROIBoxHead(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py
new file mode 100755
index 00000000..19689255
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py
@@ -0,0 +1,152 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
+from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
+from maskrcnn_benchmark.modeling.box_coder import BoxCoder
+
+
+class PostProcessor(nn.Module):
+    """
+    From a set of classification scores, box regression and proposals,
+    computes the post-processed boxes, and applies NMS to obtain the
+    final results
+    """
+
+    def __init__(
+        self, score_thresh=0.05, nms=0.5, detections_per_img=100, box_coder=None
+    ):
+        """
+        Arguments:
+            score_thresh (float)
+            nms (float)
+            detections_per_img (int)
+            box_coder (BoxCoder)
+        """
+        super(PostProcessor, self).__init__()
+        self.score_thresh = score_thresh
+        self.nms = nms
+        self.detections_per_img = detections_per_img
+        if box_coder is None:
+            box_coder = BoxCoder(weights=(10., 10., 5., 5.))
+        self.box_coder = box_coder
+
+    def forward(self, x, boxes):
+        """
+        Arguments:
+            x (tuple[tensor, tensor]): x contains the class logits
+                and the box_regression from the model.
+            boxes (list[BoxList]): bounding boxes that are used as
+                reference, one for ech image
+
+        Returns:
+            results (list[BoxList]): one BoxList for each image, containing
+                the extra fields labels and scores
+        """
+        class_logits, box_regression = x
+        class_prob = F.softmax(class_logits, -1)
+
+        # TODO think about a representation of batch of boxes
+        image_shapes = [box.size for box in boxes]
+        boxes_per_image = [len(box) for box in boxes]
+        concat_boxes = torch.cat([a.bbox for a in boxes], dim=0)
+
+        proposals = self.box_coder.decode(
+            box_regression.view(sum(boxes_per_image), -1), concat_boxes
+        )
+
+        num_classes = class_prob.shape[1]
+
+        proposals = proposals.split(boxes_per_image, dim=0)
+        class_prob = class_prob.split(boxes_per_image, dim=0)
+
+        results = []
+        for prob, boxes_per_img, image_shape in zip(
+            class_prob, proposals, image_shapes
+        ):
+            boxlist = self.prepare_boxlist(boxes_per_img, prob, image_shape)
+            boxlist = boxlist.clip_to_image(remove_empty=False)
+            boxlist = self.filter_results(boxlist, num_classes)
+            results.append(boxlist)
+        return results
+
+    def prepare_boxlist(self, boxes, scores, image_shape):
+        """
+        Returns BoxList from `boxes` and adds probability scores information
+        as an extra field
+        `boxes` has shape (#detections, 4 * #classes), where each row represents
+        a list of predicted bounding boxes for each of the object classes in the
+        dataset (including the background class). The detections in each row
+        originate from the same object proposal.
+        `scores` has shape (#detection, #classes), where each row represents a list
+        of object detection confidence scores for each of the object classes in the
+        dataset (including the background class). `scores[i, j]`` corresponds to the
+        box at `boxes[i, j * 4:(j + 1) * 4]`.
+        """
+        boxes = boxes.reshape(-1, 4)
+        scores = scores.reshape(-1)
+        boxlist = BoxList(boxes, image_shape, mode="xyxy")
+        boxlist.add_field("scores", scores)
+        return boxlist
+
+    def filter_results(self, boxlist, num_classes):
+        """Returns bounding-box detection results by thresholding on scores and
+        applying non-maximum suppression (NMS).
+        """
+        # unwrap the boxlist to avoid additional overhead.
+        # if we had multi-class NMS, we could perform this directly on the boxlist
+        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
+        scores = boxlist.get_field("scores").reshape(-1, num_classes)
+
+        device = scores.device
+        result = []
+        # Apply threshold on detection probabilities and apply NMS
+        # Skip j = 0, because it's the background class
+        inds_all = scores > self.score_thresh
+        for j in range(1, num_classes):
+            inds = inds_all[:, j].nonzero().squeeze(1)
+            scores_j = scores[inds, j]
+            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
+            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
+            boxlist_for_class.add_field("scores", scores_j)
+            boxlist_for_class = boxlist_nms(
+                boxlist_for_class, self.nms, score_field="scores"
+            )
+            num_labels = len(boxlist_for_class)
+            boxlist_for_class.add_field(
+                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
+            )
+            result.append(boxlist_for_class)
+
+        result = cat_boxlist(result)
+        number_of_detections = len(result)
+
+        # Limit to max_per_image detections **over all classes**
+        if number_of_detections > self.detections_per_img > 0:
+            cls_scores = result.get_field("scores")
+            image_thresh, _ = torch.kthvalue(
+                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
+            )
+            keep = cls_scores >= image_thresh.item()
+            keep = torch.nonzero(keep).squeeze(1)
+            result = result[keep]
+        return result
+
+
+def make_roi_box_post_processor(cfg):
+    use_fpn = cfg.MODEL.ROI_HEADS.USE_FPN
+
+    bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
+    box_coder = BoxCoder(weights=bbox_reg_weights)
+
+    score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH
+    nms_thresh = cfg.MODEL.ROI_HEADS.NMS
+    detections_per_img = cfg.MODEL.ROI_HEADS.DETECTIONS_PER_IMG
+
+    postprocessor = PostProcessor(
+        score_thresh, nms_thresh, detections_per_img, box_coder
+    )
+    return postprocessor
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py
new file mode 100755
index 00000000..2c21f6cd
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py
@@ -0,0 +1,175 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch.nn import functional as F
+
+from maskrcnn_benchmark.layers import smooth_l1_loss
+from maskrcnn_benchmark.modeling.box_coder import BoxCoder
+from maskrcnn_benchmark.modeling.matcher import Matcher
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
+from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
+    BalancedPositiveNegativeSampler
+)
+from maskrcnn_benchmark.modeling.utils import cat
+
+
+class FastRCNNLossComputation(object):
+    """
+    Computes the loss for Faster R-CNN.
+    Also supports FPN
+    """
+
+    def __init__(self, proposal_matcher, fg_bg_sampler, box_coder):
+        """
+        Arguments:
+            proposal_matcher (Matcher)
+            fg_bg_sampler (BalancedPositiveNegativeSampler)
+            box_coder (BoxCoder)
+        """
+        self.proposal_matcher = proposal_matcher
+        self.fg_bg_sampler = fg_bg_sampler
+        self.box_coder = box_coder
+
+    def match_targets_to_proposals(self, proposal, target):
+        match_quality_matrix = boxlist_iou(target, proposal)
+        matched_idxs = self.proposal_matcher(match_quality_matrix)
+        # Fast RCNN only need "labels" field for selecting the targets
+        target = target.copy_with_fields("labels")
+        # get the targets corresponding GT for each proposal
+        # NB: need to clamp the indices because we can have a single
+        # GT in the image, and matched_idxs can be -2, which goes
+        # out of bounds
+        matched_targets = target[matched_idxs.clamp(min=0)]
+        matched_targets.add_field("matched_idxs", matched_idxs)
+        return matched_targets
+
+    def prepare_targets(self, proposals, targets):
+        labels = []
+        regression_targets = []
+        for proposals_per_image, targets_per_image in zip(proposals, targets):
+            matched_targets = self.match_targets_to_proposals(
+                proposals_per_image, targets_per_image
+            )
+            matched_idxs = matched_targets.get_field("matched_idxs")
+
+            labels_per_image = matched_targets.get_field("labels")
+            labels_per_image = labels_per_image.to(dtype=torch.int64)
+
+            # Label background (below the low threshold)
+            bg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD
+            labels_per_image[bg_inds] = 0
+
+            # Label ignore proposals (between low and high thresholds)
+            ignore_inds = matched_idxs == Matcher.BETWEEN_THRESHOLDS
+            labels_per_image[ignore_inds] = -1  # -1 is ignored by sampler
+
+            # compute regression targets
+            regression_targets_per_image = self.box_coder.encode(
+                matched_targets.bbox, proposals_per_image.bbox
+            )
+
+            labels.append(labels_per_image)
+            regression_targets.append(regression_targets_per_image)
+
+        return labels, regression_targets
+
+    def subsample(self, proposals, targets):
+        """
+        This method performs the positive/negative sampling, and return
+        the sampled proposals.
+        Note: this function keeps a state.
+
+        Arguments:
+            proposals (list[BoxList])
+            targets (list[BoxList])
+        """
+
+        labels, regression_targets = self.prepare_targets(proposals, targets)
+        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
+
+        proposals = list(proposals)
+        # add corresponding label and regression_targets information to the bounding boxes
+        for labels_per_image, regression_targets_per_image, proposals_per_image in zip(
+            labels, regression_targets, proposals
+        ):
+            proposals_per_image.add_field("labels", labels_per_image)
+            proposals_per_image.add_field(
+                "regression_targets", regression_targets_per_image
+            )
+
+        # distributed sampled proposals, that were obtained on all feature maps
+        # concatenated via the fg_bg_sampler, into individual feature map levels
+        for img_idx, (pos_inds_img, neg_inds_img) in enumerate(
+            zip(sampled_pos_inds, sampled_neg_inds)
+        ):
+            img_sampled_inds = torch.nonzero(pos_inds_img | neg_inds_img).squeeze(1)
+            proposals_per_image = proposals[img_idx][img_sampled_inds]
+            proposals[img_idx] = proposals_per_image
+
+        self._proposals = proposals
+        return proposals
+
+    def __call__(self, class_logits, box_regression):
+        """
+        Computes the loss for Faster R-CNN.
+        This requires that the subsample method has been called beforehand.
+
+        Arguments:
+            class_logits (list[Tensor])
+            box_regression (list[Tensor])
+
+        Returns:
+            classification_loss (Tensor)
+            box_loss (Tensor)
+        """
+
+        class_logits = cat(class_logits, dim=0)
+        box_regression = cat(box_regression, dim=0)
+        device = class_logits.device
+
+        if not hasattr(self, "_proposals"):
+            raise RuntimeError("subsample needs to be called before")
+
+        proposals = self._proposals
+
+        labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0)
+        regression_targets = cat(
+            [proposal.get_field("regression_targets") for proposal in proposals], dim=0
+        )
+
+        classification_loss = F.cross_entropy(class_logits, labels)
+
+        # get indices that correspond to the regression targets for
+        # the corresponding ground truth labels, to be used with
+        # advanced indexing
+        sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1)
+        labels_pos = labels[sampled_pos_inds_subset]
+        map_inds = 4 * labels_pos[:, None] + torch.tensor([0, 1, 2, 3], device=device)
+
+        box_loss = smooth_l1_loss(
+            box_regression[sampled_pos_inds_subset[:, None], map_inds],
+            regression_targets[sampled_pos_inds_subset],
+            size_average=False,
+            beta=1,
+        )
+        box_loss = box_loss / labels.numel()
+
+        return classification_loss, box_loss
+
+
+def make_roi_box_loss_evaluator(cfg):
+    matcher = Matcher(
+        cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD,
+        cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD,
+        allow_low_quality_matches=False,
+    )
+
+    bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
+    box_coder = BoxCoder(weights=bbox_reg_weights)
+
+    fg_bg_sampler = BalancedPositiveNegativeSampler(
+        cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION
+    )
+
+    loss_evaluator = FastRCNNLossComputation(matcher, fg_bg_sampler, box_coder)
+
+    return loss_evaluator
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py
new file mode 100755
index 00000000..3dea50c2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py
@@ -0,0 +1,149 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from maskrcnn_benchmark.modeling import registry
+from maskrcnn_benchmark.modeling.backbone import resnet
+from maskrcnn_benchmark.modeling.poolers import Pooler
+from maskrcnn_benchmark.modeling.make_layers import group_norm
+from maskrcnn_benchmark.modeling.make_layers import make_fc
+
+
+@registry.ROI_BOX_FEATURE_EXTRACTORS.register("ResNet50Conv5ROIFeatureExtractor")
+class ResNet50Conv5ROIFeatureExtractor(nn.Module):
+    def __init__(self, config):
+        super(ResNet50Conv5ROIFeatureExtractor, self).__init__()
+
+        resolution = config.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
+        scales = config.MODEL.ROI_BOX_HEAD.POOLER_SCALES
+        sampling_ratio = config.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
+        pooler = Pooler(
+            output_size=(resolution, resolution),
+            scales=scales,
+            sampling_ratio=sampling_ratio,
+        )
+
+        stage = resnet.StageSpec(index=4, block_count=3, return_features=False)
+        head = resnet.ResNetHead(
+            block_module=config.MODEL.RESNETS.TRANS_FUNC,
+            stages=(stage,),
+            num_groups=config.MODEL.RESNETS.NUM_GROUPS,
+            width_per_group=config.MODEL.RESNETS.WIDTH_PER_GROUP,
+            stride_in_1x1=config.MODEL.RESNETS.STRIDE_IN_1X1,
+            stride_init=None,
+            res2_out_channels=config.MODEL.RESNETS.RES2_OUT_CHANNELS,
+            dilation=config.MODEL.RESNETS.RES5_DILATION
+        )
+
+        self.pooler = pooler
+        self.head = head
+
+    def forward(self, x, proposals):
+        x = self.pooler(x, proposals)
+        x = self.head(x)
+        return x
+
+
+@registry.ROI_BOX_FEATURE_EXTRACTORS.register("FPN2MLPFeatureExtractor")
+class FPN2MLPFeatureExtractor(nn.Module):
+    """
+    Heads for FPN for classification
+    """
+
+    def __init__(self, cfg):
+        super(FPN2MLPFeatureExtractor, self).__init__()
+
+        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
+        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
+        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
+        pooler = Pooler(
+            output_size=(resolution, resolution),
+            scales=scales,
+            sampling_ratio=sampling_ratio,
+        )
+        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS * resolution ** 2
+        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
+        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
+        self.pooler = pooler
+        self.fc6 = make_fc(input_size, representation_size, use_gn)
+        self.fc7 = make_fc(representation_size, representation_size, use_gn)
+
+    def forward(self, x, proposals):
+        x = self.pooler(x, proposals)
+        x = x.view(x.size(0), -1)
+
+        x = F.relu(self.fc6(x))
+        x = F.relu(self.fc7(x))
+
+        return x
+
+
+@registry.ROI_BOX_FEATURE_EXTRACTORS.register("FPNXconv1fcFeatureExtractor")
+class FPNXconv1fcFeatureExtractor(nn.Module):
+    """
+    Heads for FPN for classification
+    """
+
+    def __init__(self, cfg):
+        super(FPNXconv1fcFeatureExtractor, self).__init__()
+
+        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
+        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
+        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
+        pooler = Pooler(
+            output_size=(resolution, resolution),
+            scales=scales,
+            sampling_ratio=sampling_ratio,
+        )
+        self.pooler = pooler
+        
+        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
+        in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
+        conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
+        num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
+        dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION
+
+        xconvs = []
+        for ix in range(num_stacked_convs):
+            xconvs.append(
+                nn.Conv2d(
+                    in_channels,
+                    conv_head_dim,
+                    kernel_size=3,
+                    stride=1,
+                    padding=dilation,
+                    dilation=dilation,
+                    bias=False if use_gn else True
+                )
+            )
+            in_channels = conv_head_dim
+            if use_gn:
+                xconvs.append(group_norm(in_channels))
+            xconvs.append(nn.ReLU(inplace=True))
+
+        self.add_module("xconvs", nn.Sequential(*xconvs))
+        for modules in [self.xconvs,]:
+            for l in modules.modules():
+                if isinstance(l, nn.Conv2d):
+                    torch.nn.init.normal_(l.weight, std=0.01)
+                    if not use_gn:
+                        torch.nn.init.constant_(l.bias, 0)
+
+        input_size = conv_head_dim * resolution ** 2
+        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
+        self.fc6 = make_fc(input_size, representation_size, use_gn=False)
+
+    def forward(self, x, proposals):
+        x = self.pooler(x, proposals)
+        x = self.xconvs(x)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc6(x))
+        return x
+
+
+def make_roi_box_feature_extractor(cfg):
+    func = registry.ROI_BOX_FEATURE_EXTRACTORS[
+        cfg.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR
+    ]
+    return func(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py
new file mode 100755
index 00000000..e05fcbb1
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py
@@ -0,0 +1,62 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from torch import nn
+
+
+class FastRCNNPredictor(nn.Module):
+    def __init__(self, config, pretrained=None):
+        super(FastRCNNPredictor, self).__init__()
+
+        stage_index = 4
+        stage2_relative_factor = 2 ** (stage_index - 1)
+        res2_out_channels = config.MODEL.RESNETS.RES2_OUT_CHANNELS
+        num_inputs = res2_out_channels * stage2_relative_factor
+
+        num_classes = config.MODEL.ROI_BOX_HEAD.NUM_CLASSES
+        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=7)
+        self.cls_score = nn.Linear(num_inputs, num_classes)
+        self.bbox_pred = nn.Linear(num_inputs, num_classes * 4)
+
+        nn.init.normal_(self.cls_score.weight, mean=0, std=0.01)
+        nn.init.constant_(self.cls_score.bias, 0)
+
+        nn.init.normal_(self.bbox_pred.weight, mean=0, std=0.001)
+        nn.init.constant_(self.bbox_pred.bias, 0)
+
+    def forward(self, x):
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        cls_logit = self.cls_score(x)
+        bbox_pred = self.bbox_pred(x)
+        return cls_logit, bbox_pred
+
+
+class FPNPredictor(nn.Module):
+    def __init__(self, cfg):
+        super(FPNPredictor, self).__init__()
+        num_classes = cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES
+        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
+
+        self.cls_score = nn.Linear(representation_size, num_classes)
+        self.bbox_pred = nn.Linear(representation_size, num_classes * 4)
+
+        nn.init.normal_(self.cls_score.weight, std=0.01)
+        nn.init.normal_(self.bbox_pred.weight, std=0.001)
+        for l in [self.cls_score, self.bbox_pred]:
+            nn.init.constant_(l.bias, 0)
+
+    def forward(self, x):
+        scores = self.cls_score(x)
+        bbox_deltas = self.bbox_pred(x)
+
+        return scores, bbox_deltas
+
+
+_ROI_BOX_PREDICTOR = {
+    "FastRCNNPredictor": FastRCNNPredictor,
+    "FPNPredictor": FPNPredictor,
+}
+
+
+def make_roi_box_predictor(cfg):
+    func = _ROI_BOX_PREDICTOR[cfg.MODEL.ROI_BOX_HEAD.PREDICTOR]
+    return func(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py
new file mode 100755
index 00000000..3578a79a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py
@@ -0,0 +1,209 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+
+
+# TODO check if want to return a single BoxList or a composite
+# object
+class MaskPostProcessor(nn.Module):
+    """
+    From the results of the CNN, post process the masks
+    by taking the mask corresponding to the class with max
+    probability (which are of fixed size and directly output
+    by the CNN) and return the masks in the mask field of the BoxList.
+
+    If a masker object is passed, it will additionally
+    project the masks in the image according to the locations in boxes,
+    """
+
+    def __init__(self, masker=None):
+        super(MaskPostProcessor, self).__init__()
+        self.masker = masker
+
+    def forward(self, x, boxes):
+        """
+        Arguments:
+            x (Tensor): the mask logits
+            boxes (list[BoxList]): bounding boxes that are used as
+                reference, one for ech image
+
+        Returns:
+            results (list[BoxList]): one BoxList for each image, containing
+                the extra field mask
+        """
+        mask_prob = x.sigmoid()
+
+        # select masks coresponding to the predicted classes
+        num_masks = x.shape[0]
+        labels = [bbox.get_field("labels") for bbox in boxes]
+        labels = torch.cat(labels)
+        index = torch.arange(num_masks, device=labels.device)
+        mask_prob = mask_prob[index, labels][:, None]
+
+        boxes_per_image = [len(box) for box in boxes]
+        mask_prob = mask_prob.split(boxes_per_image, dim=0)
+
+        if self.masker:
+            mask_prob = self.masker(mask_prob, boxes)
+
+        results = []
+        for prob, box in zip(mask_prob, boxes):
+            bbox = BoxList(box.bbox, box.size, mode="xyxy")
+            for field in box.fields():
+                bbox.add_field(field, box.get_field(field))
+            bbox.add_field("mask", prob)
+            results.append(bbox)
+
+        return results
+
+
+class MaskPostProcessorCOCOFormat(MaskPostProcessor):
+    """
+    From the results of the CNN, post process the results
+    so that the masks are pasted in the image, and
+    additionally convert the results to COCO format.
+    """
+
+    def forward(self, x, boxes):
+        import pycocotools.mask as mask_util
+        import numpy as np
+
+        results = super(MaskPostProcessorCOCOFormat, self).forward(x, boxes)
+        for result in results:
+            masks = result.get_field("mask").cpu()
+            rles = [
+                mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
+                for mask in masks
+            ]
+            for rle in rles:
+                rle["counts"] = rle["counts"].decode("utf-8")
+            result.add_field("mask", rles)
+        return results
+
+
+# the next two functions should be merged inside Masker
+# but are kept here for the moment while we need them
+# temporarily gor paste_mask_in_image
+def expand_boxes(boxes, scale):
+    w_half = (boxes[:, 2] - boxes[:, 0]) * .5
+    h_half = (boxes[:, 3] - boxes[:, 1]) * .5
+    x_c = (boxes[:, 2] + boxes[:, 0]) * .5
+    y_c = (boxes[:, 3] + boxes[:, 1]) * .5
+
+    w_half *= scale
+    h_half *= scale
+
+    boxes_exp = torch.zeros_like(boxes)
+    boxes_exp[:, 0] = x_c - w_half
+    boxes_exp[:, 2] = x_c + w_half
+    boxes_exp[:, 1] = y_c - h_half
+    boxes_exp[:, 3] = y_c + h_half
+    return boxes_exp
+
+
+def expand_masks(mask, padding):
+    N = mask.shape[0]
+    M = mask.shape[-1]
+    pad2 = 2 * padding
+    scale = float(M + pad2) / M
+    padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2))
+
+    padded_mask[:, :, padding:-padding, padding:-padding] = mask
+    return padded_mask, scale
+
+
+def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
+    # Need to work on the CPU, where fp16 isn't supported - cast to float to avoid this
+    mask = mask.float()
+    box = box.float()
+
+    padded_mask, scale = expand_masks(mask[None], padding=padding)
+    mask = padded_mask[0, 0]
+    box = expand_boxes(box[None], scale)[0]
+    box = box.to(dtype=torch.int32)
+
+    TO_REMOVE = 1
+    w = int(box[2] - box[0] + TO_REMOVE)
+    h = int(box[3] - box[1] + TO_REMOVE)
+    w = max(w, 1)
+    h = max(h, 1)
+
+    # Set shape to [batchxCxHxW]
+    mask = mask.expand((1, 1, -1, -1))
+
+    # Resize mask
+    mask = mask.to(torch.float32)
+    mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
+    mask = mask[0][0]
+
+    if thresh >= 0:
+        mask = mask > thresh
+    else:
+        # for visualization and debugging, we also
+        # allow it to return an unmodified mask
+        mask = (mask * 255).to(torch.uint8)
+
+    im_mask = torch.zeros((im_h, im_w), dtype=torch.uint8)
+    x_0 = max(box[0], 0)
+    x_1 = min(box[2] + 1, im_w)
+    y_0 = max(box[1], 0)
+    y_1 = min(box[3] + 1, im_h)
+
+    im_mask[y_0:y_1, x_0:x_1] = mask[
+        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
+    ]
+    return im_mask
+
+
+class Masker(object):
+    """
+    Projects a set of masks in an image on the locations
+    specified by the bounding boxes
+    """
+
+    def __init__(self, threshold=0.5, padding=1):
+        self.threshold = threshold
+        self.padding = padding
+
+    def forward_single_image(self, masks, boxes):
+        boxes = boxes.convert("xyxy")
+        im_w, im_h = boxes.size
+        res = [
+            paste_mask_in_image(mask[0], box, im_h, im_w, self.threshold, self.padding)
+            for mask, box in zip(masks, boxes.bbox)
+        ]
+        if len(res) > 0:
+            res = torch.stack(res, dim=0)[:, None]
+        else:
+            res = masks.new_empty((0, 1, masks.shape[-2], masks.shape[-1]))
+        return res
+
+    def __call__(self, masks, boxes):
+        if isinstance(boxes, BoxList):
+            boxes = [boxes]
+
+        # Make some sanity check
+        assert len(boxes) == len(masks), "Masks and boxes should have the same length."
+
+        # TODO:  Is this JIT compatible?
+        # If not we should make it compatible.
+        results = []
+        for mask, box in zip(masks, boxes):
+            assert mask.shape[0] == len(box), "Number of objects should be the same."
+            result = self.forward_single_image(mask, box)
+            results.append(result)
+        return results
+
+
+def make_roi_mask_post_processor(cfg):
+    if cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS:
+        mask_threshold = cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD
+        masker = Masker(threshold=mask_threshold, padding=1)
+    else:
+        masker = None
+    mask_post_processor = MaskPostProcessor(masker)
+    return mask_post_processor
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py
new file mode 100755
index 00000000..dfcdf0d5
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py
@@ -0,0 +1,156 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch.nn import functional as F
+
+from maskrcnn_benchmark.layers import smooth_l1_loss
+from maskrcnn_benchmark.modeling.matcher import Matcher
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
+from maskrcnn_benchmark.modeling.utils import cat
+from maskrcnn_benchmark import _C
+import itertools
+
+def project_masks_on_boxes(segmentation_masks, proposals, discretization_size):
+    """
+    Given segmentation masks and the bounding boxes corresponding
+    to the location of the masks in the image, this function
+    crops and resizes the masks in the position defined by the
+    boxes. This prepares the masks for them to be fed to the
+    loss computation as the targets.
+
+    Arguments:
+        segmentation_masks: an instance of SegmentationMask
+        proposals: an instance of BoxList
+    """
+    masks = []
+    M = discretization_size
+    device = proposals.bbox.device
+    proposals = proposals.convert("xyxy")
+    assert segmentation_masks.size == proposals.size, "{}, {}".format(
+        segmentation_masks, proposals
+    )    
+    # CUDA implementation of RLE encoding needs to be fixed to support larger M
+    if proposals.bbox.is_cuda and M < 32:
+        polygons_list=[]
+        for poly_obj in segmentation_masks.polygons:
+            polygons_per_instance=[]
+            for poly in poly_obj.polygons:
+                polygons_per_instance.append(poly)
+            polygons_list.append(polygons_per_instance)    
+        dense_coordinate_vec=torch.cat(list(itertools.chain(*polygons_list))).double()
+        if len(polygons_list) == 0:
+            return torch.empty(0, dtype=torch.float32, device=device)    
+        if len(polygons_list)>0:
+            masks = _C.generate_mask_targets(dense_coordinate_vec, polygons_list,proposals.bbox,M)     
+        return masks          
+    else: 
+        proposals = proposals.bbox.to(torch.device("cpu"))
+        for segmentation_mask, proposal in zip(segmentation_masks, proposals):
+            # crop the masks, resize them to the desired resolution and
+            # then convert them to the tensor representation,
+            # instead of the list representation that was used
+            cropped_mask = segmentation_mask.crop(proposal)
+            scaled_mask = cropped_mask.resize((M, M))
+            mask = scaled_mask.convert(mode="mask")
+            masks.append(mask)
+        if len(masks) == 0:
+            return torch.empty(0, dtype=torch.float32, device=device)
+        return torch.stack(masks, dim=0).to(device, dtype=torch.float32)
+
+class MaskRCNNLossComputation(object):
+    def __init__(self, proposal_matcher, discretization_size):
+        """
+        Arguments:
+            proposal_matcher (Matcher)
+            discretization_size (int)
+        """
+        self.proposal_matcher = proposal_matcher
+        self.discretization_size = discretization_size
+
+    def match_targets_to_proposals(self, proposal, target):
+        match_quality_matrix = boxlist_iou(target, proposal)
+        matched_idxs = self.proposal_matcher(match_quality_matrix)
+        # Mask RCNN needs "labels" and "masks "fields for creating the targets
+        target = target.copy_with_fields(["labels", "masks"])
+        # get the targets corresponding GT for each proposal
+        # NB: need to clamp the indices because we can have a single
+        # GT in the image, and matched_idxs can be -2, which goes
+        # out of bounds
+        matched_targets = target[matched_idxs.clamp(min=0)]
+        matched_targets.add_field("matched_idxs", matched_idxs)
+        return matched_targets
+
+    def prepare_targets(self, proposals, targets):
+        labels = []
+        masks = []
+        for proposals_per_image, targets_per_image in zip(proposals, targets):
+            matched_targets = self.match_targets_to_proposals(
+                proposals_per_image, targets_per_image
+            )
+            matched_idxs = matched_targets.get_field("matched_idxs")
+
+            labels_per_image = matched_targets.get_field("labels")
+            labels_per_image = labels_per_image.to(dtype=torch.int64)
+
+            # this can probably be removed, but is left here for clarity
+            # and completeness
+            neg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD
+            labels_per_image[neg_inds] = 0
+
+            # mask scores are only computed on positive samples
+            positive_inds = torch.nonzero(labels_per_image > 0).squeeze(1)
+
+            segmentation_masks = matched_targets.get_field("masks")
+            segmentation_masks = segmentation_masks[positive_inds]
+
+            positive_proposals = proposals_per_image[positive_inds]
+
+            masks_per_image = project_masks_on_boxes(
+                segmentation_masks, positive_proposals, self.discretization_size
+            )
+
+            labels.append(labels_per_image)
+            masks.append(masks_per_image)
+
+        return labels, masks
+
+    def __call__(self, proposals, mask_logits, targets):
+        """
+        Arguments:
+            proposals (list[BoxList])
+            mask_logits (Tensor)
+            targets (list[BoxList])
+
+        Return:
+            mask_loss (Tensor): scalar tensor containing the loss
+        """
+        labels, mask_targets = self.prepare_targets(proposals, targets)
+
+        labels = cat(labels, dim=0)
+        mask_targets = cat(mask_targets, dim=0)
+
+        positive_inds = torch.nonzero(labels > 0).squeeze(1)
+        labels_pos = labels[positive_inds]
+
+        # torch.mean (in binary_cross_entropy_with_logits) doesn't
+        # accept empty tensors, so handle it separately
+        if mask_targets.numel() == 0:
+            return mask_logits.sum() * 0
+
+        mask_loss = F.binary_cross_entropy_with_logits(
+            mask_logits[positive_inds, labels_pos], mask_targets
+        )
+        return mask_loss
+
+
+def make_roi_mask_loss_evaluator(cfg):
+    matcher = Matcher(
+        cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD,
+        cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD,
+        allow_low_quality_matches=False,
+    )
+
+    loss_evaluator = MaskRCNNLossComputation(
+        matcher, cfg.MODEL.ROI_MASK_HEAD.RESOLUTION
+    )
+
+    return loss_evaluator
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py
new file mode 100755
index 00000000..e28b1907
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py
@@ -0,0 +1,82 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+from torch import nn
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+
+from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
+from .roi_mask_predictors import make_roi_mask_predictor
+from .inference import make_roi_mask_post_processor
+from .loss import make_roi_mask_loss_evaluator
+
+
+def keep_only_positive_boxes(boxes):
+    """
+    Given a set of BoxList containing the `labels` field,
+    return a set of BoxList for which `labels > 0`.
+
+    Arguments:
+        boxes (list of BoxList)
+    """
+    assert isinstance(boxes, (list, tuple))
+    assert isinstance(boxes[0], BoxList)
+    assert boxes[0].has_field("labels")
+    positive_boxes = []
+    positive_inds = []
+    num_boxes = 0
+    for boxes_per_image in boxes:
+        labels = boxes_per_image.get_field("labels")
+        inds_mask = labels > 0
+        inds = inds_mask.nonzero().squeeze(1)
+        positive_boxes.append(boxes_per_image[inds])
+        positive_inds.append(inds_mask)
+    return positive_boxes, positive_inds
+
+
+class ROIMaskHead(torch.nn.Module):
+    def __init__(self, cfg):
+        super(ROIMaskHead, self).__init__()
+        self.cfg = cfg.clone()
+        self.feature_extractor = make_roi_mask_feature_extractor(cfg)
+        self.predictor = make_roi_mask_predictor(cfg)
+        self.post_processor = make_roi_mask_post_processor(cfg)
+        self.loss_evaluator = make_roi_mask_loss_evaluator(cfg)
+
+    def forward(self, features, proposals, targets=None):
+        """
+        Arguments:
+            features (list[Tensor]): feature-maps from possibly several levels
+            proposals (list[BoxList]): proposal boxes
+            targets (list[BoxList], optional): the ground-truth targets.
+
+        Returns:
+            x (Tensor): the result of the feature extractor
+            proposals (list[BoxList]): during training, the original proposals
+                are returned. During testing, the predicted boxlists are returned
+                with the `mask` field set
+            losses (dict[Tensor]): During training, returns the losses for the
+                head. During testing, returns an empty dict.
+        """
+
+        if self.training:
+            # during training, only focus on positive boxes
+            all_proposals = proposals
+            proposals, positive_inds = keep_only_positive_boxes(proposals)
+        if self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR:
+            x = features
+            x = x[torch.cat(positive_inds, dim=0)]
+        else:
+            x = self.feature_extractor(features, proposals)
+        mask_logits = self.predictor(x)
+
+        if not self.training:
+            result = self.post_processor(mask_logits, proposals)
+            return x, result, {}
+
+        loss_mask = self.loss_evaluator(proposals, mask_logits, targets)
+
+        return x, all_proposals, dict(loss_mask=loss_mask)
+
+
+def build_roi_mask_head(cfg):
+    return ROIMaskHead(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py
new file mode 100755
index 00000000..6a2d7e8b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py
@@ -0,0 +1,69 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from torch import nn
+from torch.nn import functional as F
+
+from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
+from maskrcnn_benchmark.modeling.poolers import Pooler
+from maskrcnn_benchmark.layers import Conv2d
+from maskrcnn_benchmark.modeling.make_layers import make_conv3x3
+
+
+
+class MaskRCNNFPNFeatureExtractor(nn.Module):
+    """
+    Heads for FPN for classification
+    """
+
+    def __init__(self, cfg):
+        """
+        Arguments:
+            num_classes (int): number of output classes
+            input_size (int): number of channels of the input once it's flattened
+            representation_size (int): size of the intermediate representation
+        """
+        super(MaskRCNNFPNFeatureExtractor, self).__init__()
+
+        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
+        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
+        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
+        pooler = Pooler(
+            output_size=(resolution, resolution),
+            scales=scales,
+            sampling_ratio=sampling_ratio,
+        )
+        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS
+        self.pooler = pooler
+
+        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
+        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
+        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION
+
+        next_feature = input_size
+        self.blocks = []
+        for layer_idx, layer_features in enumerate(layers, 1):
+            layer_name = "mask_fcn{}".format(layer_idx)
+            module = make_conv3x3(next_feature, layer_features, 
+                dilation=dilation, stride=1, use_gn=use_gn
+            )
+            self.add_module(layer_name, module)
+            next_feature = layer_features
+            self.blocks.append(layer_name)
+
+    def forward(self, x, proposals):
+        x = self.pooler(x, proposals)
+
+        for layer_name in self.blocks:
+            x = F.relu(getattr(self, layer_name)(x))
+
+        return x
+
+
+_ROI_MASK_FEATURE_EXTRACTORS = {
+    "ResNet50Conv5ROIFeatureExtractor": ResNet50Conv5ROIFeatureExtractor,
+    "MaskRCNNFPNFeatureExtractor": MaskRCNNFPNFeatureExtractor,
+}
+
+
+def make_roi_mask_feature_extractor(cfg):
+    func = _ROI_MASK_FEATURE_EXTRACTORS[cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR]
+    return func(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py
new file mode 100755
index 00000000..c24962f9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py
@@ -0,0 +1,44 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from torch import nn
+from torch.nn import functional as F
+
+from maskrcnn_benchmark.layers import Conv2d
+from maskrcnn_benchmark.layers import ConvTranspose2d
+
+
+class MaskRCNNC4Predictor(nn.Module):
+    def __init__(self, cfg):
+        super(MaskRCNNC4Predictor, self).__init__()
+        num_classes = cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES
+        dim_reduced = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS[-1]
+
+        if cfg.MODEL.ROI_HEADS.USE_FPN:
+            num_inputs = dim_reduced
+        else:
+            stage_index = 4
+            stage2_relative_factor = 2 ** (stage_index - 1)
+            res2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
+            num_inputs = res2_out_channels * stage2_relative_factor
+
+        self.conv5_mask = ConvTranspose2d(num_inputs, dim_reduced, 2, 2, 0)
+        self.mask_fcn_logits = Conv2d(dim_reduced, num_classes, 1, 1, 0)
+
+        for name, param in self.named_parameters():
+            if "bias" in name:
+                nn.init.constant_(param, 0)
+            elif "weight" in name:
+                # Caffe2 implementation uses MSRAFill, which in fact
+                # corresponds to kaiming_normal_ in PyTorch
+                nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
+
+    def forward(self, x):
+        x = F.relu(self.conv5_mask(x))
+        return self.mask_fcn_logits(x)
+
+
+_ROI_MASK_PREDICTOR = {"MaskRCNNC4Predictor": MaskRCNNC4Predictor}
+
+
+def make_roi_mask_predictor(cfg):
+    func = _ROI_MASK_PREDICTOR[cfg.MODEL.ROI_MASK_HEAD.PREDICTOR]
+    return func(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py
new file mode 100755
index 00000000..f09c24d6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py
@@ -0,0 +1,54 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+from .box_head.box_head import build_roi_box_head
+from .mask_head.mask_head import build_roi_mask_head
+
+
+class CombinedROIHeads(torch.nn.ModuleDict):
+    """
+    Combines a set of individual heads (for box prediction or masks) into a single
+    head.
+    """
+
+    def __init__(self, cfg, heads):
+        super(CombinedROIHeads, self).__init__(heads)
+        self.cfg = cfg.clone()
+        if cfg.MODEL.MASK_ON and cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR:
+            self.mask.feature_extractor = self.box.feature_extractor
+
+    def forward(self, features, proposals, targets=None):
+        losses = {}
+        # TODO rename x to roi_box_features, if it doesn't increase memory consumption
+        x, detections, loss_box = self.box(features, proposals, targets)
+        losses.update(loss_box)
+        if self.cfg.MODEL.MASK_ON:
+            mask_features = features
+            # optimization: during training, if we share the feature extractor between
+            # the box and the mask heads, then we can reuse the features already computed
+            if (
+                self.training
+                and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR
+            ):
+                mask_features = x
+            # During training, self.box() will return the unaltered proposals as "detections"
+            # this makes the API consistent during training and testing
+            x, detections, loss_mask = self.mask(mask_features, detections, targets)
+            losses.update(loss_mask)
+        return x, detections, losses
+
+
+def build_roi_heads(cfg):
+    # individually create the heads, that will be combined together
+    # afterwards
+    roi_heads = []
+    if not cfg.MODEL.RPN_ONLY:
+        roi_heads.append(("box", build_roi_box_head(cfg)))
+    if cfg.MODEL.MASK_ON:
+        roi_heads.append(("mask", build_roi_mask_head(cfg)))
+
+    # combine individual heads in a single module
+    if roi_heads:
+        roi_heads = CombinedROIHeads(cfg, roi_heads)
+
+    return roi_heads
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/__init__.py
new file mode 100755
index 00000000..b01f30cf
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# from .rpn import build_rpn
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/anchor_generator.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/anchor_generator.py
new file mode 100755
index 00000000..fd98e16d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/anchor_generator.py
@@ -0,0 +1,263 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import math
+
+import numpy as np
+import torch
+from torch import nn
+
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+
+
+class BufferList(nn.Module):
+    """
+    Similar to nn.ParameterList, but for buffers
+    """
+
+    def __init__(self, buffers=None):
+        super(BufferList, self).__init__()
+        if buffers is not None:
+            self.extend(buffers)
+
+    def extend(self, buffers):
+        offset = len(self)
+        for i, buffer in enumerate(buffers):
+            self.register_buffer(str(offset + i), buffer)
+        return self
+
+    def __len__(self):
+        return len(self._buffers)
+
+    def __iter__(self):
+        return iter(self._buffers.values())
+
+
+class AnchorGenerator(nn.Module):
+    """
+    For a set of image sizes and feature maps, computes a set
+    of anchors
+    """
+
+    def __init__(
+        self,
+        sizes=(128, 256, 512),
+        aspect_ratios=(0.5, 1.0, 2.0),
+        anchor_strides=(8, 16, 32),
+        straddle_thresh=0,
+    ):
+        super(AnchorGenerator, self).__init__()
+
+        if len(anchor_strides) == 1:
+            anchor_stride = anchor_strides[0]
+            cell_anchors = [
+                generate_anchors(anchor_stride, sizes, aspect_ratios).float()
+            ]
+        else:
+            if len(anchor_strides) != len(sizes):
+                raise RuntimeError("FPN should have #anchor_strides == #sizes")
+            cell_anchors = [
+                generate_anchors(anchor_stride, (size,), aspect_ratios).float()
+                for anchor_stride, size in zip(anchor_strides, sizes)
+            ]
+        self.strides = anchor_strides
+        self.cell_anchors = BufferList(cell_anchors)
+        self.straddle_thresh = straddle_thresh
+
+    def num_anchors_per_location(self):
+        return [len(cell_anchors) for cell_anchors in self.cell_anchors]
+
+    def grid_anchors(self, grid_sizes):
+        anchors = []
+        for size, stride, base_anchors in zip(
+            grid_sizes, self.strides, self.cell_anchors
+        ):
+            grid_height, grid_width = size
+            device = base_anchors.device
+            shifts_x = torch.arange(
+                0, grid_width * stride, step=stride, dtype=torch.float32, device=device
+            )
+            shifts_y = torch.arange(
+                0, grid_height * stride, step=stride, dtype=torch.float32, device=device
+            )
+            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
+            shift_x = shift_x.reshape(-1)
+            shift_y = shift_y.reshape(-1)
+            shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
+
+            anchors.append(
+                (shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)
+            )
+
+        return anchors
+
+    def add_visibility_to(self, boxlist):
+        image_width, image_height = boxlist.size
+        anchors = boxlist.bbox
+        if self.straddle_thresh >= 0:
+            inds_inside = (
+                (anchors[..., 0] >= -self.straddle_thresh)
+                & (anchors[..., 1] >= -self.straddle_thresh)
+                & (anchors[..., 2] < image_width + self.straddle_thresh)
+                & (anchors[..., 3] < image_height + self.straddle_thresh)
+            )
+        else:
+            device = anchors.device
+            inds_inside = torch.ones(anchors.shape[0], dtype=torch.uint8, device=device)
+        boxlist.add_field("visibility", inds_inside)
+
+    def forward(self, image_list, feature_maps):
+        grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps]
+        anchors_over_all_feature_maps = self.grid_anchors(grid_sizes)
+        anchors = []
+        for i, (image_height, image_width) in enumerate(image_list.image_sizes):
+            anchors_in_image = []
+            for anchors_per_feature_map in anchors_over_all_feature_maps:
+                boxlist = BoxList(
+                    anchors_per_feature_map, (image_width, image_height), mode="xyxy"
+                )
+                self.add_visibility_to(boxlist)
+                anchors_in_image.append(boxlist)
+            anchors.append(anchors_in_image)
+        return anchors
+
+
+def make_anchor_generator(config):
+    anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES
+    aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS
+    anchor_stride = config.MODEL.RPN.ANCHOR_STRIDE
+    straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH
+
+    if config.MODEL.RPN.USE_FPN:
+        assert len(anchor_stride) == len(
+            anchor_sizes
+        ), "FPN should have len(ANCHOR_STRIDE) == len(ANCHOR_SIZES)"
+    else:
+        assert len(anchor_stride) == 1, "Non-FPN should have a single ANCHOR_STRIDE"
+    anchor_generator = AnchorGenerator(
+        anchor_sizes, aspect_ratios, anchor_stride, straddle_thresh
+    )
+    return anchor_generator
+
+
+# Copyright (c) 2017-present, Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+##############################################################################
+#
+# Based on:
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
+
+
+# Verify that we compute the same anchors as Shaoqing's matlab implementation:
+#
+#    >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
+#    >> anchors
+#
+#    anchors =
+#
+#       -83   -39   100    56
+#      -175   -87   192   104
+#      -359  -183   376   200
+#       -55   -55    72    72
+#      -119  -119   136   136
+#      -247  -247   264   264
+#       -35   -79    52    96
+#       -79  -167    96   184
+#      -167  -343   184   360
+
+# array([[ -83.,  -39.,  100.,   56.],
+#        [-175.,  -87.,  192.,  104.],
+#        [-359., -183.,  376.,  200.],
+#        [ -55.,  -55.,   72.,   72.],
+#        [-119., -119.,  136.,  136.],
+#        [-247., -247.,  264.,  264.],
+#        [ -35.,  -79.,   52.,   96.],
+#        [ -79., -167.,   96.,  184.],
+#        [-167., -343.,  184.,  360.]])
+
+
+def generate_anchors(
+    stride=16, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)
+):
+    """Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors
+    are centered on stride / 2, have (approximate) sqrt areas of the specified
+    sizes, and aspect ratios as given.
+    """
+    return _generate_anchors(
+        stride,
+        np.array(sizes, dtype=np.float) / stride,
+        np.array(aspect_ratios, dtype=np.float),
+    )
+
+
+def _generate_anchors(base_size, scales, aspect_ratios):
+    """Generate anchor (reference) windows by enumerating aspect ratios X
+    scales wrt a reference (0, 0, base_size - 1, base_size - 1) window.
+    """
+    anchor = np.array([1, 1, base_size, base_size], dtype=np.float) - 1
+    anchors = _ratio_enum(anchor, aspect_ratios)
+    anchors = np.vstack(
+        [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]
+    )
+    return torch.from_numpy(anchors)
+
+
+def _whctrs(anchor):
+    """Return width, height, x center, and y center for an anchor (window)."""
+    w = anchor[2] - anchor[0] + 1
+    h = anchor[3] - anchor[1] + 1
+    x_ctr = anchor[0] + 0.5 * (w - 1)
+    y_ctr = anchor[1] + 0.5 * (h - 1)
+    return w, h, x_ctr, y_ctr
+
+
+def _mkanchors(ws, hs, x_ctr, y_ctr):
+    """Given a vector of widths (ws) and heights (hs) around a center
+    (x_ctr, y_ctr), output a set of anchors (windows).
+    """
+    ws = ws[:, np.newaxis]
+    hs = hs[:, np.newaxis]
+    anchors = np.hstack(
+        (
+            x_ctr - 0.5 * (ws - 1),
+            y_ctr - 0.5 * (hs - 1),
+            x_ctr + 0.5 * (ws - 1),
+            y_ctr + 0.5 * (hs - 1),
+        )
+    )
+    return anchors
+
+
+def _ratio_enum(anchor, ratios):
+    """Enumerate a set of anchors for each aspect ratio wrt an anchor."""
+    w, h, x_ctr, y_ctr = _whctrs(anchor)
+    size = w * h
+    size_ratios = size / ratios
+    ws = np.round(np.sqrt(size_ratios))
+    hs = np.round(ws * ratios)
+    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
+    return anchors
+
+
+def _scale_enum(anchor, scales):
+    """Enumerate a set of anchors for each scale wrt an anchor."""
+    w, h, x_ctr, y_ctr = _whctrs(anchor)
+    ws = w * scales
+    hs = h * scales
+    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
+    return anchors
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/inference.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/inference.py
new file mode 100755
index 00000000..b45b553f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/inference.py
@@ -0,0 +1,267 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+from maskrcnn_benchmark.modeling.box_coder import BoxCoder
+from maskrcnn_benchmark.structures.bounding_box import BoxList
+from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
+from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes
+
+from ..utils import cat
+
+from maskrcnn_benchmark import _C as C
+
+class RPNPostProcessor(torch.nn.Module):
+    """
+    Performs post-processing on the outputs of the RPN boxes, before feeding the
+    proposals to the heads
+    """
+
+    def __init__(
+        self,
+        pre_nms_top_n,
+        post_nms_top_n,
+        nms_thresh,
+        min_size,
+        box_coder=None,
+        fpn_post_nms_top_n=None,
+    ):
+        """
+        Arguments:
+            pre_nms_top_n (int)
+            post_nms_top_n (int)
+            nms_thresh (float)
+            min_size (int)
+            box_coder (BoxCoder)
+            fpn_post_nms_top_n (int)
+        """
+        super(RPNPostProcessor, self).__init__()
+        self.pre_nms_top_n = pre_nms_top_n
+        self.post_nms_top_n = post_nms_top_n
+        self.nms_thresh = nms_thresh
+        self.min_size = min_size
+
+        if box_coder is None:
+            box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
+        self.box_coder = box_coder
+
+        if fpn_post_nms_top_n is None:
+            fpn_post_nms_top_n = post_nms_top_n
+        self.fpn_post_nms_top_n = fpn_post_nms_top_n
+
+    def add_gt_proposals(self, proposals, targets):
+        """
+        Arguments:
+            proposals: list[BoxList]
+            targets: list[BoxList]
+        """
+        # Get the device we're operating on
+        device = proposals[0].bbox.device
+
+        gt_boxes = [target.copy_with_fields([]) for target in targets]
+
+        # later cat of bbox requires all fields to be present for all bbox
+        # so we need to add a dummy for objectness that's missing
+        for gt_box in gt_boxes:
+            gt_box.add_field("objectness", torch.ones(len(gt_box), device=device))
+
+        proposals = [
+            cat_boxlist((proposal, gt_box))
+            for proposal, gt_box in zip(proposals, gt_boxes)
+        ]
+
+        return proposals
+
+    def forward_for_single_feature_map(self, anchors, objectness, box_regression):
+        """
+        Arguments:
+            anchors: list[BoxList]
+            objectness: tensor of size N, A, H, W
+            box_regression: tensor of size N, A * 4, H, W
+        """
+        device = objectness.device
+        N, A, H, W = objectness.shape
+
+        num_anchors = A * H * W
+
+        # If inputs are on GPU, use a faster path
+        use_fast_cuda_path = (objectness.is_cuda and box_regression.is_cuda)
+        # Encompasses box decode, clip_to_image and remove_small_boxes calls
+        if use_fast_cuda_path:
+            objectness = objectness.reshape(N, -1) # Now [N, AHW]
+            objectness = objectness.sigmoid()
+
+            pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
+            objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
+
+            # Get all image shapes, and cat them together
+            image_shapes = [box.size for box in anchors]
+            image_shapes_cat = torch.tensor([box.size for box in anchors], device=objectness.device).float()
+
+            # Get a single tensor for all anchors
+            concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
+
+            # Note: Take all anchors, we'll index accordingly inside the kernel
+            # only take the anchors corresponding to the topk boxes
+            concat_anchors = concat_anchors.reshape(N, -1, 4) # [batch_idx, topk_idx]
+
+            # Return pre-nms boxes, associated scores and keep flag
+            # Encompasses:
+            # 1. Box decode
+            # 2. Box clipping
+            # 3. Box filtering
+            # At the end we need to keep only the proposals & scores flagged
+            # Note: topk_idx, objectness are sorted => proposals, objectness, keep are also
+            # sorted -- this is important later
+            proposals, objectness, keep = C.GeneratePreNMSUprightBoxes(
+                                    N,
+                                    A,
+                                    H,
+                                    W,
+                                    topk_idx,
+                                    objectness.float(),    # Need to cast these as kernel doesn't support fp16
+                                    box_regression.float(),
+                                    concat_anchors,
+                                    image_shapes_cat,
+                                    pre_nms_top_n,
+                                    self.min_size,
+                                    self.box_coder.bbox_xform_clip,
+                                    True)
+
+
+            # view as [N, pre_nms_top_n, 4]
+            proposals = proposals.view(N, -1, 4)
+            objectness = objectness.view(N, -1)
+        else:
+            # reverse the reshape from before ready for permutation
+            objectness = objectness.reshape(N, A, H, W)
+            objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
+            objectness = objectness.sigmoid()
+
+            pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
+            objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
+
+            # put in the same format as anchors
+            box_regression = box_regression.view(N, -1, 4, H, W).permute(0, 3, 4, 1, 2)
+            box_regression = box_regression.reshape(N, -1, 4)
+
+
+            batch_idx = torch.arange(N, device=device)[:, None]
+            box_regression = box_regression[batch_idx, topk_idx]
+
+            image_shapes = [box.size for box in anchors]
+            concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
+            concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]
+
+            proposals = self.box_coder.decode(
+                box_regression.view(-1, 4), concat_anchors.view(-1, 4)
+            )
+
+            proposals = proposals.view(N, -1, 4)
+
+        # handle non-fast path without changing the loop
+        if not use_fast_cuda_path:
+            keep = [None for _ in range(N)]
+
+        result = []
+        for proposal, score, im_shape, k in zip(proposals, objectness, image_shapes, keep):
+            if use_fast_cuda_path:
+                # Note: Want k to be applied per-image instead of all-at-once in batched code earlier
+                #       clip_to_image and remove_small_boxes already done in single kernel
+                p = proposal.masked_select(k[:, None]).view(-1, 4)
+                score = score.masked_select(k)
+                boxlist = BoxList(p, im_shape, mode="xyxy")
+            else:
+                boxlist = BoxList(proposal, im_shape, mode="xyxy")
+                boxlist = boxlist.clip_to_image(remove_empty=False)
+                boxlist = remove_small_boxes(boxlist, self.min_size)
+            boxlist.add_field("objectness", score)
+            boxlist = boxlist_nms(
+                boxlist,
+                self.nms_thresh,
+                max_proposals=self.post_nms_top_n,
+                score_field="objectness",
+            )
+            result.append(boxlist)
+        return result
+
+    def forward(self, anchors, objectness, box_regression, targets=None):
+        """
+        Arguments:
+            anchors: list[list[BoxList]]
+            objectness: list[tensor]
+            box_regression: list[tensor]
+
+        Returns:
+            boxlists (list[BoxList]): the post-processed anchors, after
+                applying box decoding and NMS
+        """
+        sampled_boxes = []
+        num_levels = len(objectness)
+        anchors = list(zip(*anchors))
+        for a, o, b in zip(anchors, objectness, box_regression):
+            sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
+
+        boxlists = list(zip(*sampled_boxes))
+        boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
+
+        if num_levels > 1:
+            boxlists = self.select_over_all_levels(boxlists)
+
+        # append ground-truth bboxes to proposals
+        if self.training and targets is not None:
+            boxlists = self.add_gt_proposals(boxlists, targets)
+
+        return boxlists
+
+    def select_over_all_levels(self, boxlists):
+        num_images = len(boxlists)
+        # different behavior during training and during testing:
+        # during training, post_nms_top_n is over *all* the proposals combined, while
+        # during testing, it is over the proposals for each image
+        # TODO resolve this difference and make it consistent. It should be per image,
+        # and not per batch
+        if self.training:
+            objectness = torch.cat(
+                [boxlist.get_field("objectness") for boxlist in boxlists], dim=0
+            )
+            box_sizes = [len(boxlist) for boxlist in boxlists]
+            post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
+            _, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True)
+            inds_mask = torch.zeros_like(objectness, dtype=torch.uint8)
+            inds_mask[inds_sorted] = 1
+            inds_mask = inds_mask.split(box_sizes)
+            for i in range(num_images):
+                boxlists[i] = boxlists[i][inds_mask[i]]
+        else:
+            for i in range(num_images):
+                objectness = boxlists[i].get_field("objectness")
+                post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
+                _, inds_sorted = torch.topk(
+                    objectness, post_nms_top_n, dim=0, sorted=True
+                )
+                boxlists[i] = boxlists[i][inds_sorted]
+        return boxlists
+
+
+def make_rpn_postprocessor(config, rpn_box_coder, is_train):
+    fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN
+    if not is_train:
+        fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST
+
+    pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TRAIN
+    post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TRAIN
+    if not is_train:
+        pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TEST
+        post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TEST
+    nms_thresh = config.MODEL.RPN.NMS_THRESH
+    min_size = config.MODEL.RPN.MIN_SIZE
+    box_selector = RPNPostProcessor(
+        pre_nms_top_n=pre_nms_top_n,
+        post_nms_top_n=post_nms_top_n,
+        nms_thresh=nms_thresh,
+        min_size=min_size,
+        box_coder=rpn_box_coder,
+        fpn_post_nms_top_n=fpn_post_nms_top_n,
+    )
+    return box_selector
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/loss.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/loss.py
new file mode 100755
index 00000000..08472313
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/loss.py
@@ -0,0 +1,151 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+This file contains specific functions for computing losses on the RPN
+file
+"""
+
+import torch
+from torch.nn import functional as F
+
+from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
+from ..utils import cat
+
+from maskrcnn_benchmark.layers import smooth_l1_loss
+from maskrcnn_benchmark.modeling.matcher import Matcher
+from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
+from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
+
+
+class RPNLossComputation(object):
+    """
+    This class computes the RPN loss.
+    """
+
+    def __init__(self, proposal_matcher, fg_bg_sampler, box_coder):
+        """
+        Arguments:
+            proposal_matcher (Matcher)
+            fg_bg_sampler (BalancedPositiveNegativeSampler)
+            box_coder (BoxCoder)
+        """
+        # self.target_preparator = target_preparator
+        self.proposal_matcher = proposal_matcher
+        self.fg_bg_sampler = fg_bg_sampler
+        self.box_coder = box_coder
+
+    def match_targets_to_anchors(self, anchor, target):
+        match_quality_matrix = boxlist_iou(target, anchor)
+        matched_idxs = self.proposal_matcher(match_quality_matrix)
+        # RPN doesn't need any fields from target
+        # for creating the labels, so clear them all
+        target = target.copy_with_fields([])
+        # get the targets corresponding GT for each anchor
+        # NB: need to clamp the indices because we can have a single
+        # GT in the image, and matched_idxs can be -2, which goes
+        # out of bounds
+        matched_targets = target[matched_idxs.clamp(min=0)]
+        matched_targets.add_field("matched_idxs", matched_idxs)
+        return matched_targets
+
+    def prepare_targets(self, anchors, targets):
+        labels = []
+        regression_targets = []
+        for anchors_per_image, targets_per_image in zip(anchors, targets):
+            matched_targets = self.match_targets_to_anchors(
+                anchors_per_image, targets_per_image
+            )
+
+            matched_idxs = matched_targets.get_field("matched_idxs")
+            labels_per_image = matched_idxs >= 0
+            labels_per_image = labels_per_image.to(dtype=torch.float32)
+            # discard anchors that go out of the boundaries of the image
+            labels_per_image[~anchors_per_image.get_field("visibility")] = -1
+
+            # discard indices that are between thresholds
+            inds_to_discard = matched_idxs == Matcher.BETWEEN_THRESHOLDS
+            labels_per_image[inds_to_discard] = -1
+
+            # compute regression targets
+            regression_targets_per_image = self.box_coder.encode(
+                matched_targets.bbox, anchors_per_image.bbox
+            )
+
+            labels.append(labels_per_image)
+            regression_targets.append(regression_targets_per_image)
+
+        return labels, regression_targets
+
+    def __call__(self, anchors, objectness, box_regression, targets):
+        """
+        Arguments:
+            anchors (list[BoxList])
+            objectness (list[Tensor])
+            box_regression (list[Tensor])
+            targets (list[BoxList])
+
+        Returns:
+            objectness_loss (Tensor)
+            box_loss (Tensor
+        """
+        anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors]
+        labels, regression_targets = self.prepare_targets(anchors, targets)
+        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
+        sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1)
+        sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1)
+
+        sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
+
+        objectness_flattened = []
+        box_regression_flattened = []
+        # for each feature level, permute the outputs to make them be in the
+        # same format as the labels. Note that the labels are computed for
+        # all feature levels concatenated, so we keep the same representation
+        # for the objectness and the box_regression
+        for objectness_per_level, box_regression_per_level in zip(
+            objectness, box_regression
+        ):
+            N, A, H, W = objectness_per_level.shape
+            objectness_per_level = objectness_per_level.permute(0, 2, 3, 1).reshape(
+                N, -1
+            )
+            box_regression_per_level = box_regression_per_level.view(N, -1, 4, H, W)
+            box_regression_per_level = box_regression_per_level.permute(0, 3, 4, 1, 2)
+            box_regression_per_level = box_regression_per_level.reshape(N, -1, 4)
+            objectness_flattened.append(objectness_per_level)
+            box_regression_flattened.append(box_regression_per_level)
+        # concatenate on the first dimension (representing the feature levels), to
+        # take into account the way the labels were generated (with all feature maps
+        # being concatenated as well)
+        objectness = cat(objectness_flattened, dim=1).reshape(-1)
+        box_regression = cat(box_regression_flattened, dim=1).reshape(-1, 4)
+
+        labels = torch.cat(labels, dim=0)
+        regression_targets = torch.cat(regression_targets, dim=0)
+
+        box_loss = smooth_l1_loss(
+            box_regression[sampled_pos_inds],
+            regression_targets[sampled_pos_inds],
+            beta=1.0 / 9,
+            size_average=False,
+        ) / (sampled_inds.numel())
+
+        objectness_loss = F.binary_cross_entropy_with_logits(
+            objectness[sampled_inds], labels[sampled_inds]
+        )
+
+        return objectness_loss, box_loss
+
+
+def make_rpn_loss_evaluator(cfg, box_coder):
+    matcher = Matcher(
+        cfg.MODEL.RPN.FG_IOU_THRESHOLD,
+        cfg.MODEL.RPN.BG_IOU_THRESHOLD,
+        allow_low_quality_matches=True,
+    )
+
+    fg_bg_sampler = BalancedPositiveNegativeSampler(
+        cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, cfg.MODEL.RPN.POSITIVE_FRACTION
+    )
+
+    loss_evaluator = RPNLossComputation(matcher, fg_bg_sampler, box_coder)
+    return loss_evaluator
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/rpn.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/rpn.py
new file mode 100755
index 00000000..482c1ed0
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/rpn/rpn.py
@@ -0,0 +1,146 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from maskrcnn_benchmark.modeling import registry
+from maskrcnn_benchmark.modeling.box_coder import BoxCoder
+from .loss import make_rpn_loss_evaluator
+from .anchor_generator import make_anchor_generator
+from .inference import make_rpn_postprocessor
+
+
+@registry.RPN_HEADS.register("SingleConvRPNHead")
+class RPNHead(nn.Module):
+    """
+    Adds a simple RPN Head with classification and regression heads
+    """
+
+    def __init__(self, cfg, in_channels, num_anchors):
+        """
+        Arguments:
+            cfg              : config
+            in_channels (int): number of channels of the input feature
+            num_anchors (int): number of anchors to be predicted
+        """
+        super(RPNHead, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
+        self.bbox_pred = nn.Conv2d(
+            in_channels, num_anchors * 4, kernel_size=1, stride=1
+        )
+
+        for l in [self.conv, self.cls_logits, self.bbox_pred]:
+            torch.nn.init.normal_(l.weight, std=0.01)
+            torch.nn.init.constant_(l.bias, 0)
+
+    def forward(self, x):
+        logits = []
+        bbox_reg = []
+        for feature in x:
+            t = F.relu(self.conv(feature))
+            logits.append(self.cls_logits(t))
+            bbox_reg.append(self.bbox_pred(t))
+        return logits, bbox_reg
+
+
+class RPNModule(torch.nn.Module):
+    """
+    Module for RPN computation. Takes feature maps from the backbone and RPN
+    proposals and losses. Works for both FPN and non-FPN.
+    """
+
+    def __init__(self, cfg):
+        super(RPNModule, self).__init__()
+
+        self.cfg = cfg.clone()
+
+        anchor_generator = make_anchor_generator(cfg)
+
+        in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
+        rpn_head = registry.RPN_HEADS[cfg.MODEL.RPN.RPN_HEAD]
+        head = rpn_head(
+            cfg, in_channels, anchor_generator.num_anchors_per_location()[0]
+        )
+
+        rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
+
+        box_selector_train = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True)
+        box_selector_test = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=False)
+
+        loss_evaluator = make_rpn_loss_evaluator(cfg, rpn_box_coder)
+
+        self.anchor_generator = anchor_generator
+        self.head = head
+        self.box_selector_train = box_selector_train
+        self.box_selector_test = box_selector_test
+        self.loss_evaluator = loss_evaluator
+
+    def forward(self, images, features, targets=None):
+        """
+        Arguments:
+            images (ImageList): images for which we want to compute the predictions
+            features (list[Tensor]): features computed from the images that are
+                used for computing the predictions. Each tensor in the list
+                correspond to different feature levels
+            targets (list[BoxList): ground-truth boxes present in the image (optional)
+
+        Returns:
+            boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per
+                image.
+            losses (dict[Tensor]): the losses for the model during training. During
+                testing, it is an empty dict.
+        """
+        objectness, rpn_box_regression = self.head(features)
+        anchors = self.anchor_generator(images, features)
+
+        if self.training:
+            return self._forward_train(anchors, objectness, rpn_box_regression, targets)
+        else:
+            return self._forward_test(anchors, objectness, rpn_box_regression)
+
+    def _forward_train(self, anchors, objectness, rpn_box_regression, targets):
+        if self.cfg.MODEL.RPN_ONLY:
+            # When training an RPN-only model, the loss is determined by the
+            # predicted objectness and rpn_box_regression values and there is
+            # no need to transform the anchors into predicted boxes; this is an
+            # optimization that avoids the unnecessary transformation.
+            boxes = anchors
+        else:
+            # For end-to-end models, anchors must be transformed into boxes and
+            # sampled into a training batch.
+            with torch.no_grad():
+                boxes = self.box_selector_train(
+                    anchors, objectness, rpn_box_regression, targets
+                )
+        loss_objectness, loss_rpn_box_reg = self.loss_evaluator(
+            anchors, objectness, rpn_box_regression, targets
+        )
+        losses = {
+            "loss_objectness": loss_objectness,
+            "loss_rpn_box_reg": loss_rpn_box_reg,
+        }
+        return boxes, losses
+
+    def _forward_test(self, anchors, objectness, rpn_box_regression):
+        boxes = self.box_selector_test(anchors, objectness, rpn_box_regression)
+        if self.cfg.MODEL.RPN_ONLY:
+            # For end-to-end models, the RPN proposals are an intermediate state
+            # and don't bother to sort them in decreasing score order. For RPN-only
+            # models, the proposals are the final output and we return them in
+            # high-to-low confidence order.
+            inds = [
+                box.get_field("objectness").sort(descending=True)[1] for box in boxes
+            ]
+            boxes = [box[ind] for box, ind in zip(boxes, inds)]
+        return boxes, {}
+
+
+def build_rpn(cfg):
+    """
+    This gives the gist of it. Not super important because it doesn't change as much
+    """
+    return RPNModule(cfg)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/utils.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/utils.py
new file mode 100755
index 00000000..5b1d79a8
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/utils.py
@@ -0,0 +1,16 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+"""
+Miscellaneous utility functions
+"""
+
+import torch
+
+
+def cat(tensors, dim=0):
+    """
+    Efficient version of torch.cat that avoids a copy if there is only a single element in a list
+    """
+    assert isinstance(tensors, (list, tuple))
+    if len(tensors) == 1:
+        return tensors[0]
+    return torch.cat(tensors, dim)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/__init__.py
new file mode 100755
index 00000000..75f40530
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from .build import make_optimizer
+from .build import make_lr_scheduler
+from .lr_scheduler import WarmupMultiStepLR
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/build.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/build.py
new file mode 100755
index 00000000..865a4ec8
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/build.py
@@ -0,0 +1,31 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+from .lr_scheduler import WarmupMultiStepLR
+
+
+def make_optimizer(cfg, model):
+    params = []
+    for key, value in model.named_parameters():
+        if not value.requires_grad:
+            continue
+        lr = cfg.SOLVER.BASE_LR
+        weight_decay = cfg.SOLVER.WEIGHT_DECAY
+        if "bias" in key:
+            lr = cfg.SOLVER.BASE_LR * cfg.SOLVER.BIAS_LR_FACTOR
+            weight_decay = cfg.SOLVER.WEIGHT_DECAY_BIAS
+        params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]
+
+    optimizer = torch.optim.SGD(params, lr, momentum=cfg.SOLVER.MOMENTUM)
+    return optimizer
+
+
+def make_lr_scheduler(cfg, optimizer):
+    return WarmupMultiStepLR(
+        optimizer,
+        cfg.SOLVER.STEPS,
+        cfg.SOLVER.GAMMA,
+        warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
+        warmup_iters=cfg.SOLVER.WARMUP_ITERS,
+        warmup_method=cfg.SOLVER.WARMUP_METHOD,
+    )
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/lr_scheduler.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/lr_scheduler.py
new file mode 100755
index 00000000..7172748e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/solver/lr_scheduler.py
@@ -0,0 +1,67 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+from bisect import bisect_right
+
+import torch
+
+
+# FIXME ideally this would be achieved with a CombinedLRScheduler,
+# separating MultiStepLR with WarmupLR
+# but the current LRScheduler design doesn't allow it
+class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
+    def __init__(
+        self,
+        optimizer,
+        milestones,
+        gamma=0.1,
+        warmup_factor=1.0 / 3,
+        warmup_iters=500,
+        warmup_method="linear",
+        last_epoch=-1,
+    ):
+        if not list(milestones) == sorted(milestones):
+            raise ValueError(
+                "Milestones should be a list of" " increasing integers. Got {}",
+                milestones,
+            )
+
+        #adding mlperf consistent warmpup routine
+        if warmup_method not in ("constant", "linear", "mlperf_linear"):
+            raise ValueError(
+                "Only 'constant' or 'linear' warmup_method accepted"
+                "got {}".format(warmup_method)
+            )
+        self.milestones = milestones
+        self.gamma = gamma
+        self.warmup_factor = warmup_factor
+        self.warmup_iters = warmup_iters
+        self.warmup_method = warmup_method
+        super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        warmup_factor = 1
+        if self.last_epoch < self.warmup_iters:
+            if self.warmup_method == "constant":
+                warmup_factor = self.warmup_factor
+            elif self.warmup_method == "linear":
+                alpha = self.last_epoch / self.warmup_iters
+                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
+
+            #define mlperf warmup routine
+            elif self.warmup_method == "mlperf_linear":
+                # alpha = self.last_epoch / self.warmup_iters
+                # warmup_factor = self.warmup_factor * (1 - alpha) + alpha
+                delta = (self.warmup_iters - self.last_epoch) * self.warmup_factor
+                return [
+                    (base_lr - delta)
+                    * self.gamma ** bisect_right(self.milestones, self.last_epoch)
+                    for base_lr in self.base_lrs
+                ]
+
+        return [
+            base_lr
+            * warmup_factor
+            * self.gamma ** bisect_right(self.milestones, self.last_epoch)
+            for base_lr in self.base_lrs
+        ]
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/bounding_box.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/bounding_box.py
new file mode 100755
index 00000000..5d51a8f2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/bounding_box.py
@@ -0,0 +1,264 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+import torch
+
+# transpose
+FLIP_LEFT_RIGHT = 0
+FLIP_TOP_BOTTOM = 1
+
+
+class BoxList(object):
+    """
+    This class represents a set of bounding boxes.
+    The bounding boxes are represented as a Nx4 Tensor.
+    In order to uniquely determine the bounding boxes with respect
+    to an image, we also store the corresponding image dimensions.
+    They can contain extra information that is specific to each bounding box, such as
+    labels.
+    """
+
+    def __init__(self, bbox, image_size, mode="xyxy"):
+        device = bbox.device if isinstance(bbox, torch.Tensor) else torch.device("cpu")
+        bbox = torch.as_tensor(bbox, dtype=torch.float32, device=device)
+        if bbox.ndimension() != 2:
+            raise ValueError(
+                "bbox should have 2 dimensions, got {}".format(bbox.ndimension())
+            )
+        if bbox.size(-1) != 4:
+            raise ValueError(
+                "last dimenion of bbox should have a "
+                "size of 4, got {}".format(bbox.size(-1))
+            )
+        if mode not in ("xyxy", "xywh"):
+            raise ValueError("mode should be 'xyxy' or 'xywh'")
+
+        self.bbox = bbox
+        self.size = image_size  # (image_width, image_height)
+        self.mode = mode
+        self.extra_fields = {}
+
+    def add_field(self, field, field_data):
+        self.extra_fields[field] = field_data
+
+    def get_field(self, field):
+        return self.extra_fields[field]
+
+    def has_field(self, field):
+        return field in self.extra_fields
+
+    def fields(self):
+        return list(self.extra_fields.keys())
+
+    def _copy_extra_fields(self, bbox):
+        for k, v in bbox.extra_fields.items():
+            self.extra_fields[k] = v
+
+    def convert(self, mode):
+        if mode not in ("xyxy", "xywh"):
+            raise ValueError("mode should be 'xyxy' or 'xywh'")
+        if mode == self.mode:
+            return self
+        # we only have two modes, so don't need to check
+        # self.mode
+        xmin, ymin, xmax, ymax = self._split_into_xyxy()
+        if mode == "xyxy":
+            bbox = torch.cat((xmin, ymin, xmax, ymax), dim=-1)
+            bbox = BoxList(bbox, self.size, mode=mode)
+        else:
+            TO_REMOVE = 1
+            bbox = torch.cat(
+                (xmin, ymin, xmax - xmin + TO_REMOVE, ymax - ymin + TO_REMOVE), dim=-1
+            )
+            bbox = BoxList(bbox, self.size, mode=mode)
+        bbox._copy_extra_fields(self)
+        return bbox
+
+    def _split_into_xyxy(self):
+        if self.mode == "xyxy":
+            xmin, ymin, xmax, ymax = self.bbox.split(1, dim=-1)
+            return xmin, ymin, xmax, ymax
+        elif self.mode == "xywh":
+            TO_REMOVE = 1
+            xmin, ymin, w, h = self.bbox.split(1, dim=-1)
+            return (
+                xmin,
+                ymin,
+                xmin + (w - TO_REMOVE).clamp(min=0),
+                ymin + (h - TO_REMOVE).clamp(min=0),
+            )
+        else:
+            raise RuntimeError("Should not be here")
+
+    def resize(self, size, *args, **kwargs):
+        """
+        Returns a resized copy of this bounding box
+
+        :param size: The requested size in pixels, as a 2-tuple:
+            (width, height).
+        """
+
+        ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
+        if ratios[0] == ratios[1]:
+            ratio = ratios[0]
+            scaled_box = self.bbox * ratio
+            bbox = BoxList(scaled_box, size, mode=self.mode)
+            # bbox._copy_extra_fields(self)
+            for k, v in self.extra_fields.items():
+                if not isinstance(v, torch.Tensor):
+                    v = v.resize(size, *args, **kwargs)
+                bbox.add_field(k, v)
+            return bbox
+
+        ratio_width, ratio_height = ratios
+        xmin, ymin, xmax, ymax = self._split_into_xyxy()
+        scaled_xmin = xmin * ratio_width
+        scaled_xmax = xmax * ratio_width
+        scaled_ymin = ymin * ratio_height
+        scaled_ymax = ymax * ratio_height
+        scaled_box = torch.cat(
+            (scaled_xmin, scaled_ymin, scaled_xmax, scaled_ymax), dim=-1
+        )
+        bbox = BoxList(scaled_box, size, mode="xyxy")
+        # bbox._copy_extra_fields(self)
+        for k, v in self.extra_fields.items():
+            if not isinstance(v, torch.Tensor):
+                v = v.resize(size, *args, **kwargs)
+            bbox.add_field(k, v)
+
+        return bbox.convert(self.mode)
+
+    def transpose(self, method):
+        """
+        Transpose bounding box (flip or rotate in 90 degree steps)
+        :param method: One of :py:attr:`PIL.Image.FLIP_LEFT_RIGHT`,
+          :py:attr:`PIL.Image.FLIP_TOP_BOTTOM`, :py:attr:`PIL.Image.ROTATE_90`,
+          :py:attr:`PIL.Image.ROTATE_180`, :py:attr:`PIL.Image.ROTATE_270`,
+          :py:attr:`PIL.Image.TRANSPOSE` or :py:attr:`PIL.Image.TRANSVERSE`.
+        """
+        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
+            raise NotImplementedError(
+                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
+            )
+
+        image_width, image_height = self.size
+        xmin, ymin, xmax, ymax = self._split_into_xyxy()
+        if method == FLIP_LEFT_RIGHT:
+            TO_REMOVE = 1
+            transposed_xmin = image_width - xmax - TO_REMOVE
+            transposed_xmax = image_width - xmin - TO_REMOVE
+            transposed_ymin = ymin
+            transposed_ymax = ymax
+        elif method == FLIP_TOP_BOTTOM:
+            transposed_xmin = xmin
+            transposed_xmax = xmax
+            transposed_ymin = image_height - ymax
+            transposed_ymax = image_height - ymin
+
+        transposed_boxes = torch.cat(
+            (transposed_xmin, transposed_ymin, transposed_xmax, transposed_ymax), dim=-1
+        )
+        bbox = BoxList(transposed_boxes, self.size, mode="xyxy")
+        # bbox._copy_extra_fields(self)
+        for k, v in self.extra_fields.items():
+            if not isinstance(v, torch.Tensor):
+                v = v.transpose(method)
+            bbox.add_field(k, v)
+        return bbox.convert(self.mode)
+
+    def crop(self, box):
+        """
+        Cropss a rectangular region from this bounding box. The box is a
+        4-tuple defining the left, upper, right, and lower pixel
+        coordinate.
+        """
+        xmin, ymin, xmax, ymax = self._split_into_xyxy()
+        w, h = box[2] - box[0], box[3] - box[1]
+        cropped_xmin = (xmin - box[0]).clamp(min=0, max=w)
+        cropped_ymin = (ymin - box[1]).clamp(min=0, max=h)
+        cropped_xmax = (xmax - box[0]).clamp(min=0, max=w)
+        cropped_ymax = (ymax - box[1]).clamp(min=0, max=h)
+
+        # TODO should I filter empty boxes here?
+        if False:
+            is_empty = (cropped_xmin == cropped_xmax) | (cropped_ymin == cropped_ymax)
+
+        cropped_box = torch.cat(
+            (cropped_xmin, cropped_ymin, cropped_xmax, cropped_ymax), dim=-1
+        )
+        bbox = BoxList(cropped_box, (w, h), mode="xyxy")
+        # bbox._copy_extra_fields(self)
+        for k, v in self.extra_fields.items():
+            if not isinstance(v, torch.Tensor):
+                v = v.crop(box)
+            bbox.add_field(k, v)
+        return bbox.convert(self.mode)
+
+    # Tensor-like methods
+
+    def to(self, device):
+        bbox = BoxList(self.bbox.to(device), self.size, self.mode)
+        for k, v in self.extra_fields.items():
+            if hasattr(v, "to"):
+                v = v.to(device)
+            bbox.add_field(k, v)
+        return bbox
+
+    def __getitem__(self, item):
+        bbox = BoxList(self.bbox[item], self.size, self.mode)
+        for k, v in self.extra_fields.items():
+            bbox.add_field(k, v[item])
+        return bbox
+
+    def __len__(self):
+        return self.bbox.shape[0]
+
+    def clip_to_image(self, remove_empty=True):
+        TO_REMOVE = 1
+        self.bbox[:, 0].clamp_(min=0, max=self.size[0] - TO_REMOVE)
+        self.bbox[:, 1].clamp_(min=0, max=self.size[1] - TO_REMOVE)
+        self.bbox[:, 2].clamp_(min=0, max=self.size[0] - TO_REMOVE)
+        self.bbox[:, 3].clamp_(min=0, max=self.size[1] - TO_REMOVE)
+        if remove_empty:
+            box = self.bbox
+            keep = (box[:, 3] > box[:, 1]) & (box[:, 2] > box[:, 0])
+            return self[keep]
+        return self
+
+    def area(self):
+        box = self.bbox
+        if self.mode == "xyxy":
+            TO_REMOVE = 1
+            area = (box[:, 2] - box[:, 0] + TO_REMOVE) * (box[:, 3] - box[:, 1] + TO_REMOVE)
+        elif self.mode == "xywh":
+            area = box[:, 2] * box[:, 3]
+        else:
+            raise RuntimeError("Should not be here")
+            
+        return area
+
+    def copy_with_fields(self, fields):
+        bbox = BoxList(self.bbox, self.size, self.mode)
+        if not isinstance(fields, (list, tuple)):
+            fields = [fields]
+        for field in fields:
+            bbox.add_field(field, self.get_field(field))
+        return bbox
+
+    def __repr__(self):
+        s = self.__class__.__name__ + "("
+        s += "num_boxes={}, ".format(len(self))
+        s += "image_width={}, ".format(self.size[0])
+        s += "image_height={}, ".format(self.size[1])
+        s += "mode={})".format(self.mode)
+        return s
+
+
+if __name__ == "__main__":
+    bbox = BoxList([[0, 0, 10, 10], [0, 0, 5, 5]], (10, 10))
+    s_bbox = bbox.resize((5, 5))
+    print(s_bbox)
+    print(s_bbox.bbox)
+
+    t_bbox = bbox.transpose(0)
+    print(t_bbox)
+    print(t_bbox.bbox)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py
new file mode 100755
index 00000000..aaf5d8d7
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py
@@ -0,0 +1,130 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+from .bounding_box import BoxList
+
+from maskrcnn_benchmark.layers import nms as _box_nms
+from maskrcnn_benchmark import _C
+
+
+def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="score"):
+    """
+    Performs non-maximum suppression on a boxlist, with scores specified
+    in a boxlist field via score_field.
+
+    Arguments:
+        boxlist(BoxList)
+        nms_thresh (float)
+        max_proposals (int): if > 0, then only the top max_proposals are kept
+            after non-maxium suppression
+        score_field (str)
+    """
+    if nms_thresh <= 0:
+        return boxlist
+    mode = boxlist.mode
+    boxlist = boxlist.convert("xyxy")
+    boxes = boxlist.bbox
+    score = boxlist.get_field(score_field)
+    keep = _box_nms(boxes, score, nms_thresh)
+    if max_proposals > 0:
+        keep = keep[: max_proposals]
+    boxlist = boxlist[keep]
+    return boxlist.convert(mode)
+
+
+def remove_small_boxes(boxlist, min_size):
+    """
+    Only keep boxes with both sides >= min_size
+
+    Arguments:
+        boxlist (Boxlist)
+        min_size (int)
+    """
+    # TODO maybe add an API for querying the ws / hs
+    xywh_boxes = boxlist.convert("xywh").bbox
+    _, _, ws, hs = xywh_boxes.unbind(dim=1)
+    keep = (
+        (ws >= min_size) & (hs >= min_size)
+    ).nonzero().squeeze(1)
+    return boxlist[keep]
+
+
+# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
+# with slight modifications
+def boxlist_iou(boxlist1, boxlist2):
+    """Compute the intersection over union of two set of boxes.
+    The box order must be (xmin, ymin, xmax, ymax).
+
+    Arguments:
+      box1: (BoxList) bounding boxes, sized [N,4].
+      box2: (BoxList) bounding boxes, sized [M,4].
+
+    Returns:
+      (tensor) iou, sized [N,M].
+
+    Reference:
+      https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py
+    """
+    if boxlist1.size != boxlist2.size:
+        raise RuntimeError(
+                "boxlists should have same image size, got {}, {}".format(boxlist1, boxlist2))
+    box1, box2 = boxlist1.bbox, boxlist2.bbox
+    if (box1.is_cuda and box2.is_cuda):
+        iou = _C.box_iou(box1,box2)
+    else:
+        N = len(boxlist1)
+        M = len(boxlist2)
+
+        area1 = boxlist1.area()
+        area2 = boxlist2.area()
+
+        lt = torch.max(box1[:, None, :2], box2[:, :2])  # [N,M,2]
+        rb = torch.min(box1[:, None, 2:], box2[:, 2:])  # [N,M,2]
+
+        TO_REMOVE = 1
+
+        wh = (rb - lt + TO_REMOVE).clamp(min=0)  # [N,M,2]
+        inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+        iou = inter / (area1[:, None] + area2 - inter)
+    return iou
+
+
+# TODO redundant, remove
+def _cat(tensors, dim=0):
+    """
+    Efficient version of torch.cat that avoids a copy if there is only a single element in a list
+    """
+    assert isinstance(tensors, (list, tuple))
+    if len(tensors) == 1:
+        return tensors[0]
+    return torch.cat(tensors, dim)
+
+
+def cat_boxlist(bboxes):
+    """
+    Concatenates a list of BoxList (having the same image size) into a
+    single BoxList
+
+    Arguments:
+        bboxes (list[BoxList])
+    """
+    assert isinstance(bboxes, (list, tuple))
+    assert all(isinstance(bbox, BoxList) for bbox in bboxes)
+
+    size = bboxes[0].size
+    assert all(bbox.size == size for bbox in bboxes)
+
+    mode = bboxes[0].mode
+    assert all(bbox.mode == mode for bbox in bboxes)
+
+    fields = set(bboxes[0].fields())
+    assert all(set(bbox.fields()) == fields for bbox in bboxes)
+
+    cat_boxes = BoxList(_cat([bbox.bbox for bbox in bboxes], dim=0), size, mode)
+
+    for field in fields:
+        data = _cat([bbox.get_field(field) for bbox in bboxes], dim=0)
+        cat_boxes.add_field(field, data)
+
+    return cat_boxes
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/image_list.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/image_list.py
new file mode 100755
index 00000000..9a70418f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/image_list.py
@@ -0,0 +1,70 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from __future__ import division
+
+import torch
+
+
+class ImageList(object):
+    """
+    Structure that holds a list of images (of possibly
+    varying sizes) as a single tensor.
+    This works by padding the images to the same size,
+    and storing in a field the original sizes of each image
+    """
+
+    def __init__(self, tensors, image_sizes):
+        """
+        Arguments:
+            tensors (tensor)
+            image_sizes (list[tuple[int, int]])
+        """
+        self.tensors = tensors
+        self.image_sizes = image_sizes
+
+    def to(self, *args, **kwargs):
+        cast_tensor = self.tensors.to(*args, **kwargs)
+        return ImageList(cast_tensor, self.image_sizes)
+
+
+def to_image_list(tensors, size_divisible=0):
+    """
+    tensors can be an ImageList, a torch.Tensor or
+    an iterable of Tensors. It can't be a numpy array.
+    When tensors is an iterable of Tensors, it pads
+    the Tensors with zeros so that they have the same
+    shape
+    """
+    if isinstance(tensors, torch.Tensor) and size_divisible > 0:
+        tensors = [tensors]
+
+    if isinstance(tensors, ImageList):
+        return tensors
+    elif isinstance(tensors, torch.Tensor):
+        # single tensor shape can be inferred
+        assert tensors.dim() == 4
+        image_sizes = [tensor.shape[-2:] for tensor in tensors]
+        return ImageList(tensors, image_sizes)
+    elif isinstance(tensors, (tuple, list)):
+        max_size = tuple(max(s) for s in zip(*[img.shape for img in tensors]))
+
+        # TODO Ideally, just remove this and let me model handle arbitrary
+        # input sizs
+        if size_divisible > 0:
+            import math
+
+            stride = size_divisible
+            max_size = list(max_size)
+            max_size[1] = int(math.ceil(max_size[1] / stride) * stride)
+            max_size[2] = int(math.ceil(max_size[2] / stride) * stride)
+            max_size = tuple(max_size)
+
+        batch_shape = (len(tensors),) + max_size
+        batched_imgs = tensors[0].new(*batch_shape).zero_()
+        for img, pad_img in zip(tensors, batched_imgs):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+
+        image_sizes = [im.shape[-2:] for im in tensors]
+
+        return ImageList(batched_imgs, image_sizes)
+    else:
+        raise TypeError("Unsupported type for to_image_list: {}".format(type(tensors)))
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/segmentation_mask.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/segmentation_mask.py
new file mode 100755
index 00000000..ba1290b9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/structures/segmentation_mask.py
@@ -0,0 +1,214 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+import pycocotools.mask as mask_utils
+
+# transpose
+FLIP_LEFT_RIGHT = 0
+FLIP_TOP_BOTTOM = 1
+
+
+class Mask(object):
+    """
+    This class is unfinished and not meant for use yet
+    It is supposed to contain the mask for an object as
+    a 2d tensor
+    """
+
+    def __init__(self, masks, size, mode):
+        self.masks = masks
+        self.size = size
+        self.mode = mode
+
+    def transpose(self, method):
+        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
+            raise NotImplementedError(
+                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
+            )
+
+        width, height = self.size
+        if method == FLIP_LEFT_RIGHT:
+            dim = width
+            idx = 2
+        elif method == FLIP_TOP_BOTTOM:
+            dim = height
+            idx = 1
+
+        flip_idx = list(range(dim)[::-1])
+        flipped_masks = self.masks.index_select(dim, flip_idx)
+        return Mask(flipped_masks, self.size, self.mode)
+
+    def crop(self, box):
+        w, h = box[2] - box[0], box[3] - box[1]
+
+        cropped_masks = self.masks[:, box[1] : box[3], box[0] : box[2]]
+        return Mask(cropped_masks, size=(w, h), mode=self.mode)
+
+    def resize(self, size, *args, **kwargs):
+        pass
+
+
+class Polygons(object):
+    """
+    This class holds a set of polygons that represents a single instance
+    of an object mask. The object can be represented as a set of
+    polygons
+    """
+
+    def __init__(self, polygons, size, mode):
+        # assert isinstance(polygons, list), '{}'.format(polygons)
+        if isinstance(polygons, list):
+            polygons = [torch.as_tensor(p, dtype=torch.float32) for p in polygons]
+        elif isinstance(polygons, Polygons):
+            polygons = polygons.polygons
+
+        self.polygons = polygons
+        self.size = size
+        self.mode = mode
+
+    def transpose(self, method):
+        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
+            raise NotImplementedError(
+                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
+            )
+
+        flipped_polygons = []
+        width, height = self.size
+        if method == FLIP_LEFT_RIGHT:
+            dim = width
+            idx = 0
+        elif method == FLIP_TOP_BOTTOM:
+            dim = height
+            idx = 1
+
+        for poly in self.polygons:
+            p = poly.clone()
+            TO_REMOVE = 1
+            p[idx::2] = dim - poly[idx::2] - TO_REMOVE
+            flipped_polygons.append(p)
+
+        return Polygons(flipped_polygons, size=self.size, mode=self.mode)
+
+    def crop(self, box):
+        w, h = box[2] - box[0], box[3] - box[1]
+
+        # TODO chck if necessary
+        w = max(w, 1)
+        h = max(h, 1)
+
+        cropped_polygons = []
+        for poly in self.polygons:
+            p = poly.clone()
+            p[0::2] = p[0::2] - box[0]  # .clamp(min=0, max=w)
+            p[1::2] = p[1::2] - box[1]  # .clamp(min=0, max=h)
+            cropped_polygons.append(p)
+
+        return Polygons(cropped_polygons, size=(w, h), mode=self.mode)
+
+    def resize(self, size, *args, **kwargs):
+        ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
+        if ratios[0] == ratios[1]:
+            ratio = ratios[0]
+            scaled_polys = [p * ratio for p in self.polygons]
+            return Polygons(scaled_polys, size, mode=self.mode)
+
+        ratio_w, ratio_h = ratios
+        scaled_polygons = []
+        for poly in self.polygons:
+            p = poly.clone()
+            p[0::2] *= ratio_w
+            p[1::2] *= ratio_h
+            scaled_polygons.append(p)
+
+        return Polygons(scaled_polygons, size=size, mode=self.mode)
+
+    def convert(self, mode):
+        width, height = self.size
+        if mode == "mask":
+            rles = mask_utils.frPyObjects(
+                [p.numpy() for p in self.polygons], height, width
+            )
+            rle = mask_utils.merge(rles)
+            mask = mask_utils.decode(rle)
+            mask = torch.from_numpy(mask)
+            # TODO add squeeze?
+            return mask
+
+    def __repr__(self):
+        s = self.__class__.__name__ + "("
+        s += "num_polygons={}, ".format(len(self.polygons))
+        s += "image_width={}, ".format(self.size[0])
+        s += "image_height={}, ".format(self.size[1])
+        s += "mode={})".format(self.mode)
+        return s
+
+
+class SegmentationMask(object):
+    """
+    This class stores the segmentations for all objects in the image
+    """
+
+    def __init__(self, polygons, size, mode=None):
+        """
+        Arguments:
+            polygons: a list of list of lists of numbers. The first
+                level of the list correspond to individual instances,
+                the second level to all the polygons that compose the
+                object, and the third level to the polygon coordinates.
+        """
+        assert isinstance(polygons, list)
+
+        self.polygons = [Polygons(p, size, mode) for p in polygons]
+        self.size = size
+        self.mode = mode
+
+    def transpose(self, method):
+        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
+            raise NotImplementedError(
+                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
+            )
+
+        flipped = []
+        for polygon in self.polygons:
+            flipped.append(polygon.transpose(method))
+        return SegmentationMask(flipped, size=self.size, mode=self.mode)
+
+    def crop(self, box):
+        w, h = box[2] - box[0], box[3] - box[1]
+        cropped = []
+        for polygon in self.polygons:
+            cropped.append(polygon.crop(box))
+        return SegmentationMask(cropped, size=(w, h), mode=self.mode)
+
+    def resize(self, size, *args, **kwargs):
+        scaled = []
+        for polygon in self.polygons:
+            scaled.append(polygon.resize(size, *args, **kwargs))
+        return SegmentationMask(scaled, size=size, mode=self.mode)
+
+    def to(self, *args, **kwargs):
+        return self
+
+    def __getitem__(self, item):
+        if isinstance(item, (int, slice)):
+            selected_polygons = [self.polygons[item]]
+        else:
+            # advanced indexing on a single dimension
+            selected_polygons = []
+            if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
+                item = item.nonzero()
+                item = item.squeeze(1) if item.numel() > 0 else item
+                item = item.tolist()
+            for i in item:
+                selected_polygons.append(self.polygons[i])
+        return SegmentationMask(selected_polygons, size=self.size, mode=self.mode)
+
+    def __iter__(self):
+        return iter(self.polygons)
+
+    def __repr__(self):
+        s = self.__class__.__name__ + "("
+        s += "num_instances={}, ".format(len(self.polygons))
+        s += "image_width={}, ".format(self.size[0])
+        s += "image_height={})".format(self.size[1])
+        return s
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/README.md b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/README.md
new file mode 100755
index 00000000..9765b24a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/README.md
@@ -0,0 +1,5 @@
+# Utility functions
+
+This folder contain utility functions that are not used in the
+core library, but are useful for building models or training
+code using the config system.
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/__init__.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/c2_model_loading.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/c2_model_loading.py
new file mode 100755
index 00000000..1781407f
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/c2_model_loading.py
@@ -0,0 +1,170 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import logging
+import pickle
+from collections import OrderedDict
+
+import torch
+
+from maskrcnn_benchmark.utils.model_serialization import load_state_dict
+from maskrcnn_benchmark.utils.registry import Registry
+
+
+def _rename_basic_resnet_weights(layer_keys):
+    layer_keys = [k.replace("_", ".") for k in layer_keys]
+    layer_keys = [k.replace(".w", ".weight") for k in layer_keys]
+    layer_keys = [k.replace(".bn", "_bn") for k in layer_keys]
+    layer_keys = [k.replace(".b", ".bias") for k in layer_keys]
+    layer_keys = [k.replace("_bn.s", "_bn.scale") for k in layer_keys]
+    layer_keys = [k.replace(".biasranch", ".branch") for k in layer_keys]
+    layer_keys = [k.replace("bbox.pred", "bbox_pred") for k in layer_keys]
+    layer_keys = [k.replace("cls.score", "cls_score") for k in layer_keys]
+    layer_keys = [k.replace("res.conv1_", "conv1_") for k in layer_keys]
+
+    # RPN / Faster RCNN
+    layer_keys = [k.replace(".biasbox", ".bbox") for k in layer_keys]
+    layer_keys = [k.replace("conv.rpn", "rpn.conv") for k in layer_keys]
+    layer_keys = [k.replace("rpn.bbox.pred", "rpn.bbox_pred") for k in layer_keys]
+    layer_keys = [k.replace("rpn.cls.logits", "rpn.cls_logits") for k in layer_keys]
+
+    # Affine-Channel -> BatchNorm enaming
+    layer_keys = [k.replace("_bn.scale", "_bn.weight") for k in layer_keys]
+
+    # Make torchvision-compatible
+    layer_keys = [k.replace("conv1_bn.", "bn1.") for k in layer_keys]
+
+    layer_keys = [k.replace("res2.", "layer1.") for k in layer_keys]
+    layer_keys = [k.replace("res3.", "layer2.") for k in layer_keys]
+    layer_keys = [k.replace("res4.", "layer3.") for k in layer_keys]
+    layer_keys = [k.replace("res5.", "layer4.") for k in layer_keys]
+
+    layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2a_bn.", ".bn1.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2b_bn.", ".bn2.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2c_bn.", ".bn3.") for k in layer_keys]
+
+    layer_keys = [k.replace(".branch1.", ".downsample.0.") for k in layer_keys]
+    layer_keys = [k.replace(".branch1_bn.", ".downsample.1.") for k in layer_keys]
+
+    # GroupNorm
+    layer_keys = [k.replace("conv1.gn.s", "bn1.weight") for k in layer_keys]
+    layer_keys = [k.replace("conv1.gn.bias", "bn1.bias") for k in layer_keys]
+    layer_keys = [k.replace("conv2.gn.s", "bn2.weight") for k in layer_keys]
+    layer_keys = [k.replace("conv2.gn.bias", "bn2.bias") for k in layer_keys]
+    layer_keys = [k.replace("conv3.gn.s", "bn3.weight") for k in layer_keys]
+    layer_keys = [k.replace("conv3.gn.bias", "bn3.bias") for k in layer_keys]
+    layer_keys = [k.replace("downsample.0.gn.s", "downsample.1.weight") \
+        for k in layer_keys]
+    layer_keys = [k.replace("downsample.0.gn.bias", "downsample.1.bias") \
+        for k in layer_keys]
+
+    return layer_keys
+
+def _rename_fpn_weights(layer_keys, stage_names):
+    for mapped_idx, stage_name in enumerate(stage_names, 1):
+        suffix = ""
+        if mapped_idx < 4:
+            suffix = ".lateral"
+        layer_keys = [
+            k.replace("fpn.inner.layer{}.sum{}".format(stage_name, suffix), "fpn_inner{}".format(mapped_idx)) for k in layer_keys
+        ]
+        layer_keys = [k.replace("fpn.layer{}.sum".format(stage_name), "fpn_layer{}".format(mapped_idx)) for k in layer_keys]
+
+
+    layer_keys = [k.replace("rpn.conv.fpn2", "rpn.conv") for k in layer_keys]
+    layer_keys = [k.replace("rpn.bbox_pred.fpn2", "rpn.bbox_pred") for k in layer_keys]
+    layer_keys = [
+        k.replace("rpn.cls_logits.fpn2", "rpn.cls_logits") for k in layer_keys
+    ]
+
+    return layer_keys
+
+
+def _rename_weights_for_resnet(weights, stage_names):
+    original_keys = sorted(weights.keys())
+    layer_keys = sorted(weights.keys())
+
+    # for X-101, rename output to fc1000 to avoid conflicts afterwards
+    layer_keys = [k if k != "pred_b" else "fc1000_b" for k in layer_keys]
+    layer_keys = [k if k != "pred_w" else "fc1000_w" for k in layer_keys]
+
+    # performs basic renaming: _ -> . , etc
+    layer_keys = _rename_basic_resnet_weights(layer_keys)
+
+    # FPN
+    layer_keys = _rename_fpn_weights(layer_keys, stage_names)
+
+    # Mask R-CNN
+    layer_keys = [k.replace("mask.fcn.logits", "mask_fcn_logits") for k in layer_keys]
+    layer_keys = [k.replace(".[mask].fcn", "mask_fcn") for k in layer_keys]
+    layer_keys = [k.replace("conv5.mask", "conv5_mask") for k in layer_keys]
+
+    # Keypoint R-CNN
+    layer_keys = [k.replace("kps.score.lowres", "kps_score_lowres") for k in layer_keys]
+    layer_keys = [k.replace("kps.score", "kps_score") for k in layer_keys]
+    layer_keys = [k.replace("conv.fcn", "conv_fcn") for k in layer_keys]
+
+    # Rename for our RPN structure
+    layer_keys = [k.replace("rpn.", "rpn.head.") for k in layer_keys]
+
+    key_map = {k: v for k, v in zip(original_keys, layer_keys)}
+
+    logger = logging.getLogger(__name__)
+    logger.info("Remapping C2 weights")
+    max_c2_key_size = max([len(k) for k in original_keys if "_momentum" not in k])
+
+    new_weights = OrderedDict()
+    for k in original_keys:
+        v = weights[k]
+        if "_momentum" in k:
+            continue
+        # if 'fc1000' in k:
+        #     continue
+        w = torch.from_numpy(v)
+        # if "bn" in k:
+        #     w = w.view(1, -1, 1, 1)
+        logger.info("C2 name: {: <{}} mapped name: {}".format(k, max_c2_key_size, key_map[k]))
+        new_weights[key_map[k]] = w
+
+    return new_weights
+
+
+def _load_c2_pickled_weights(file_path):
+    with open(file_path, "rb") as f:
+        if torch._six.PY3:
+            data = pickle.load(f, encoding="latin1")
+        else:
+            data = pickle.load(f)
+    if "blobs" in data:
+        weights = data["blobs"]
+    else:
+        weights = data
+    return weights
+
+
+_C2_STAGE_NAMES = {
+    "R-50": ["1.2", "2.3", "3.5", "4.2"],
+    "R-101": ["1.2", "2.3", "3.22", "4.2"],
+}
+
+C2_FORMAT_LOADER = Registry()
+
+
+@C2_FORMAT_LOADER.register("R-50-C4")
+@C2_FORMAT_LOADER.register("R-50-C5")
+@C2_FORMAT_LOADER.register("R-101-C4")
+@C2_FORMAT_LOADER.register("R-101-C5")
+@C2_FORMAT_LOADER.register("R-50-FPN")
+@C2_FORMAT_LOADER.register("R-101-FPN")
+def load_resnet_c2_format(cfg, f):
+    state_dict = _load_c2_pickled_weights(f)
+    conv_body = cfg.MODEL.BACKBONE.CONV_BODY
+    arch = conv_body.replace("-C4", "").replace("-C5", "").replace("-FPN", "")
+    stages = _C2_STAGE_NAMES[arch]
+    state_dict = _rename_weights_for_resnet(state_dict, stages)
+    return dict(model=state_dict)
+
+
+def load_c2_format(cfg, f):
+    return C2_FORMAT_LOADER[cfg.MODEL.BACKBONE.CONV_BODY](cfg, f)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/checkpoint.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/checkpoint.py
new file mode 100755
index 00000000..dc403f5d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/checkpoint.py
@@ -0,0 +1,139 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import logging
+import os
+
+import torch
+
+from maskrcnn_benchmark.utils.model_serialization import load_state_dict
+from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format
+from maskrcnn_benchmark.utils.imports import import_file
+from maskrcnn_benchmark.utils.model_zoo import cache_url
+
+
+class Checkpointer(object):
+    def __init__(
+        self,
+        model,
+        optimizer=None,
+        scheduler=None,
+        save_dir="",
+        save_to_disk=None,
+        logger=None,
+    ):
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.save_dir = save_dir
+        self.save_to_disk = save_to_disk
+        if logger is None:
+            logger = logging.getLogger(__name__)
+        self.logger = logger
+
+    def save(self, name, **kwargs):
+        if not self.save_dir:
+            return
+
+        if not self.save_to_disk:
+            return
+
+        data = {}
+        data["model"] = self.model.state_dict()
+        if self.optimizer is not None:
+            data["optimizer"] = self.optimizer.state_dict()
+        if self.scheduler is not None:
+            data["scheduler"] = self.scheduler.state_dict()
+        data.update(kwargs)
+
+        save_file = os.path.join(self.save_dir, "{}.pth".format(name))
+        self.logger.info("Saving checkpoint to {}".format(save_file))
+        torch.save(data, save_file)
+        self.tag_last_checkpoint(save_file)
+
+    def load(self, f=None):
+        if self.has_checkpoint():
+            # override argument with existing checkpoint
+            f = self.get_checkpoint_file()
+        if not f:
+            # no checkpoint could be found
+            self.logger.info("No checkpoint found. Initializing model from scratch")
+            return {}
+        self.logger.info("Loading checkpoint from {}".format(f))
+        checkpoint = self._load_file(f)
+        self._load_model(checkpoint)
+        if "optimizer" in checkpoint and self.optimizer:
+            self.logger.info("Loading optimizer from {}".format(f))
+            self.optimizer.load_state_dict(checkpoint.pop("optimizer"))
+        if "scheduler" in checkpoint and self.scheduler:
+            self.logger.info("Loading scheduler from {}".format(f))
+            self.scheduler.load_state_dict(checkpoint.pop("scheduler"))
+
+        # return any further checkpoint data
+        return checkpoint
+
+    def has_checkpoint(self):
+        save_file = os.path.join(self.save_dir, "last_checkpoint")
+        return os.path.exists(save_file)
+
+    def get_checkpoint_file(self):
+        save_file = os.path.join(self.save_dir, "last_checkpoint")
+        try:
+            with open(save_file, "r") as f:
+                last_saved = f.read()
+                last_saved = last_saved.strip()
+        except IOError:
+            # if file doesn't exist, maybe because it has just been
+            # deleted by a separate process
+            last_saved = ""
+        return last_saved
+
+    def tag_last_checkpoint(self, last_filename):
+        save_file = os.path.join(self.save_dir, "last_checkpoint")
+        with open(save_file, "w") as f:
+            f.write(last_filename)
+
+    def _load_file(self, f):
+        return torch.load(f, map_location=torch.device("cpu"))
+
+    def _load_model(self, checkpoint):
+        load_state_dict(self.model, checkpoint.pop("model"))
+
+
+class DetectronCheckpointer(Checkpointer):
+    def __init__(
+        self,
+        cfg,
+        model,
+        optimizer=None,
+        scheduler=None,
+        save_dir="",
+        save_to_disk=None,
+        logger=None,
+    ):
+        super(DetectronCheckpointer, self).__init__(
+            model, optimizer, scheduler, save_dir, save_to_disk, logger
+        )
+        self.cfg = cfg.clone()
+
+    def _load_file(self, f):
+        # catalog lookup
+        if f.startswith("catalog://"):
+            paths_catalog = import_file(
+                "maskrcnn_benchmark.config.paths_catalog", self.cfg.PATHS_CATALOG, True
+            )
+            catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://") :])
+            self.logger.info("{} points to {}".format(f, catalog_f))
+            f = catalog_f
+        # download url files
+        if f.startswith("http"):
+            # if the file is a url path, download it and cache it
+            cached_f = cache_url(f)
+            self.logger.info("url {} cached in {}".format(f, cached_f))
+            f = cached_f
+        # convert Caffe2 checkpoint from pkl
+        if f.endswith(".pkl"):
+            return load_c2_format(self.cfg, f)
+        # load native detectron.pytorch checkpoint
+        loaded = super(DetectronCheckpointer, self)._load_file(f)
+        if "model" not in loaded:
+            loaded = dict(model=loaded)
+        return loaded
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/collect_env.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/collect_env.py
new file mode 100755
index 00000000..2d0641dd
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/collect_env.py
@@ -0,0 +1,14 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import PIL
+
+from torch.utils.collect_env import get_pretty_env_info
+
+
+def get_pil_version():
+    return "\n        Pillow ({})".format(PIL.__version__)
+
+
+def collect_env_info():
+    env_str = get_pretty_env_info()
+    env_str += get_pil_version()
+    return env_str
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/comm.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/comm.py
new file mode 100755
index 00000000..3967f8ea
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/comm.py
@@ -0,0 +1,118 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+"""
+This file contains primitives for multi-gpu communication.
+This is useful when doing distributed training.
+"""
+
+import pickle
+import time
+
+import torch
+import torch.distributed as dist
+
+
+def get_world_size():
+    if not dist.is_available():
+        return 1
+    if not dist.is_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not dist.is_available():
+        return 0
+    if not dist.is_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def synchronize():
+    """
+    Helper function to synchronize (barrier) among all processes when
+    using distributed training
+    """
+    if not dist.is_available():
+        return
+    if not dist.is_initialized():
+        return
+    world_size = dist.get_world_size()
+    if world_size == 1:
+        return
+    dist.barrier()
+
+
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to("cuda")
+
+    # obtain Tensor size of each rank
+    local_size = torch.IntTensor([tensor.numel()]).to("cuda")
+    size_list = [torch.IntTensor([0]).to("cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
+    if local_size != max_size:
+        padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that process with rank
+    0 has the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.reduce(values, dst=0)
+        if dist.get_rank() == 0 and average:
+            # only main process gets accumulated, so only divide by
+            # world_size in this case
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/cv2_util.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/cv2_util.py
new file mode 100755
index 00000000..16fc0124
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/cv2_util.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+"""
+Module for cv2 utility functions and maintaining version compatibility
+between 3.x and 4.x
+"""
+import cv2
+
+
+def findContours(*args, **kwargs):
+    """
+    Wraps cv2.findContours to maintain compatiblity between versions
+    3 and 4
+
+    Returns:
+        contours, hierarchy
+    """
+    if cv2.__version__.startswith('4'):
+        contours, hierarchy = cv2.findContours(*args, **kwargs)
+    elif cv2.__version__.startswith('3'):
+        _, contours, hierarchy = cv2.findContours(*args, **kwargs)
+    else:
+        raise AssertionError(
+            'cv2 must be either version 3 or 4 to call this method')
+
+    return contours, hierarchy
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/env.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/env.py
new file mode 100755
index 00000000..1c7db32e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/env.py
@@ -0,0 +1,37 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import os
+
+from maskrcnn_benchmark.utils.imports import import_file
+
+
+def setup_environment():
+    """Perform environment setup work. The default setup is a no-op, but this
+    function allows the user to specify a Python source file that performs
+    custom setup work that may be necessary to their computing environment.
+    """
+    custom_module_path = os.environ.get("TORCH_DETECTRON_ENV_MODULE")
+    if custom_module_path:
+        setup_custom_environment(custom_module_path)
+    else:
+        # The default setup is a no-op
+        pass
+
+
+def setup_custom_environment(custom_module_path):
+    """Load custom environment setup from a Python source file and run the setup
+    function.
+    """
+    module = import_file("maskrcnn_benchmark.utils.env.custom_module", custom_module_path)
+    assert hasattr(module, "setup_environment") and callable(
+        module.setup_environment
+    ), (
+        "Custom environment module defined in {} does not have the "
+        "required callable attribute 'setup_environment'."
+    ).format(
+        custom_module_path
+    )
+    module.setup_environment()
+
+
+# Force environment setup when this module is imported
+setup_environment()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/imports.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/imports.py
new file mode 100755
index 00000000..53e27e2b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/imports.py
@@ -0,0 +1,23 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import torch
+
+if torch._six.PY3:
+    import importlib
+    import importlib.util
+    import sys
+
+
+    # from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
+    def import_file(module_name, file_path, make_importable=False):
+        spec = importlib.util.spec_from_file_location(module_name, file_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        if make_importable:
+            sys.modules[module_name] = module
+        return module
+else:
+    import imp
+
+    def import_file(module_name, file_path, make_importable=None):
+        module = imp.load_source(module_name, file_path)
+        return module
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/logger.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/logger.py
new file mode 100755
index 00000000..c2b68f4b
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/logger.py
@@ -0,0 +1,25 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import logging
+import os
+import sys
+
+
+def setup_logger(name, save_dir, distributed_rank):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    # don't log results for the non-master process
+    if distributed_rank > 0:
+        return logger
+    ch = logging.StreamHandler(stream=sys.stdout)
+    ch.setLevel(logging.DEBUG)
+    formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s: %(message)s")
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
+
+    if save_dir:
+        fh = logging.FileHandler(os.path.join(save_dir, "log.txt"))
+        fh.setLevel(logging.DEBUG)
+        fh.setFormatter(formatter)
+        logger.addHandler(fh)
+
+    return logger
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/metric_logger.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/metric_logger.py
new file mode 100755
index 00000000..5e37a72e
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/metric_logger.py
@@ -0,0 +1,66 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from collections import defaultdict
+from collections import deque
+
+import torch
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20):
+        self.deque = deque(maxlen=window_size)
+        self.series = []
+        self.total = 0.0
+        self.count = 0
+
+    def update(self, value):
+        self.deque.append(value)
+        self.series.append(value)
+        self.count += 1
+        self.total += value
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque))
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+                    type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(
+                "{}: {:.4f} ({:.4f})".format(name, meter.median, meter.global_avg)
+            )
+        return self.delimiter.join(loss_str)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/miscellaneous.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/miscellaneous.py
new file mode 100755
index 00000000..db9a8b36
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/miscellaneous.py
@@ -0,0 +1,11 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import errno
+import os
+
+
+def mkdir(path):
+    try:
+        os.makedirs(path)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_serialization.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_serialization.py
new file mode 100755
index 00000000..a95ad8b2
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_serialization.py
@@ -0,0 +1,80 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from collections import OrderedDict
+import logging
+
+import torch
+
+from maskrcnn_benchmark.utils.imports import import_file
+
+
+def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
+    """
+    Strategy: suppose that the models that we will create will have prefixes appended
+    to each of its keys, for example due to an extra level of nesting that the original
+    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
+    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
+    res2.conv1.weight. We thus want to match both parameters together.
+    For that, we look for each model weight, look among all loaded keys if there is one
+    that is a suffix of the current weight name, and use it if that's the case.
+    If multiple matches exist, take the one with longest size
+    of the corresponding name. For example, for the same model as before, the pretrained
+    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
+    we want to match backbone[0].body.conv1.weight to conv1.weight, and
+    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
+    """
+    current_keys = sorted(list(model_state_dict.keys()))
+    loaded_keys = sorted(list(loaded_state_dict.keys()))
+    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
+    # loaded_key string, if it matches
+    match_matrix = [
+        len(j) if i.endswith(j) else 0 for i in current_keys for j in loaded_keys
+    ]
+    match_matrix = torch.as_tensor(match_matrix).view(
+        len(current_keys), len(loaded_keys)
+    )
+    max_match_size, idxs = match_matrix.max(1)
+    # remove indices that correspond to no-match
+    idxs[max_match_size == 0] = -1
+
+    # used for logging
+    max_size = max([len(key) for key in current_keys]) if current_keys else 1
+    max_size_loaded = max([len(key) for key in loaded_keys]) if loaded_keys else 1
+    log_str_template = "{: <{}} loaded from {: <{}} of shape {}"
+    logger = logging.getLogger(__name__)
+    for idx_new, idx_old in enumerate(idxs.tolist()):
+        if idx_old == -1:
+            continue
+        key = current_keys[idx_new]
+        key_old = loaded_keys[idx_old]
+        model_state_dict[key] = loaded_state_dict[key_old]
+        logger.info(
+            log_str_template.format(
+                key,
+                max_size,
+                key_old,
+                max_size_loaded,
+                tuple(loaded_state_dict[key_old].shape),
+            )
+        )
+
+
+def strip_prefix_if_present(state_dict, prefix):
+    keys = sorted(state_dict.keys())
+    if not all(key.startswith(prefix) for key in keys):
+        return state_dict
+    stripped_state_dict = OrderedDict()
+    for key, value in state_dict.items():
+        stripped_state_dict[key.replace(prefix, "")] = value
+    return stripped_state_dict
+
+
+def load_state_dict(model, loaded_state_dict):
+    model_state_dict = model.state_dict()
+    # if the state_dict comes from a model that was wrapped in a
+    # DataParallel or DistributedDataParallel during serialization,
+    # remove the "module" prefix before performing the matching
+    loaded_state_dict = strip_prefix_if_present(loaded_state_dict, prefix="module.")
+    align_and_update_state_dicts(model_state_dict, loaded_state_dict)
+
+    # use strict loading
+    model.load_state_dict(model_state_dict)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_zoo.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_zoo.py
new file mode 100755
index 00000000..7a0ebb34
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/model_zoo.py
@@ -0,0 +1,56 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import os
+import sys
+
+from torch.utils.model_zoo import _download_url_to_file
+from torch.utils.model_zoo import urlparse
+from torch.utils.model_zoo import HASH_REGEX
+
+from maskrcnn_benchmark.utils.comm import is_main_process
+from maskrcnn_benchmark.utils.comm import synchronize
+
+
+# very similar to https://github.com/pytorch/pytorch/blob/master/torch/utils/model_zoo.py
+# but with a few improvements and modifications
+def cache_url(url, model_dir=None, progress=True):
+    r"""Loads the Torch serialized object at the given URL.
+    If the object is already present in `model_dir`, it's deserialized and
+    returned. The filename part of the URL should follow the naming convention
+    ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
+    digits of the SHA256 hash of the contents of the file. The hash is used to
+    ensure unique names and to verify the contents of the file.
+    The default value of `model_dir` is ``$TORCH_HOME/models`` where
+    ``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be
+    overridden with the ``$TORCH_MODEL_ZOO`` environment variable.
+    Args:
+        url (string): URL of the object to download
+        model_dir (string, optional): directory in which to save the object
+        progress (bool, optional): whether or not to display a progress bar to stderr
+    Example:
+        >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
+    """
+    if model_dir is None:
+        torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
+        model_dir = os.getenv('TORCH_MODEL_ZOO', os.path.join(torch_home, 'models'))
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if filename == "model_final.pkl":
+        # workaround as pre-trained Caffe2 models from Detectron have all the same filename
+        # so make the full path the filename by replacing / with _
+        filename = parts.path.replace("/", "_")
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file) and is_main_process():
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        hash_prefix = HASH_REGEX.search(filename)
+        if hash_prefix is not None:
+            hash_prefix = hash_prefix.group(1)
+            # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
+            # which matches the hash PyTorch uses. So we skip the hash matching
+            # if the hash_prefix is less than 6 characters
+            if len(hash_prefix) < 6:
+                hash_prefix = None
+        _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
+    synchronize()
+    return cached_file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/registry.py b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/registry.py
new file mode 100755
index 00000000..c3204e14
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils/registry.py
@@ -0,0 +1,45 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+
+def _register_generic(module_dict, module_name, module):
+    assert module_name not in module_dict
+    module_dict[module_name] = module
+
+
+class Registry(dict):
+    '''
+    A helper class for managing registering modules, it extends a dictionary
+    and provides a register functions.
+
+    Eg. creeting a registry:
+        some_registry = Registry({"default": default_module})
+
+    There're two ways of registering new modules:
+    1): normal way is just calling register function:
+        def foo():
+            ...
+        some_registry.register("foo_module", foo)
+    2): used as decorator when declaring the module:
+        @some_registry.register("foo_module")
+        @some_registry.register("foo_modeul_nickname")
+        def foo():
+            ...
+
+    Access of module is just like using a dictionary, eg:
+        f = some_registry["foo_modeul"]
+    '''
+    def __init__(self, *args, **kwargs):
+        super(Registry, self).__init__(*args, **kwargs)
+
+    def register(self, module_name, module=None):
+        # used as function call
+        if module is not None:
+            _register_generic(self, module_name, module)
+            return
+
+        # used as decorator
+        def register_fn(fn):
+            _register_generic(self, module_name, fn)
+            return fn
+
+        return register_fn
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt b/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt
new file mode 100644
index 00000000..dba313d9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt
@@ -0,0 +1,4 @@
+mlperf-compliance==0.0.10
+opencv-python==3.4.1.15
+yacs
+git+https://github.com/NVIDIA/cocoapi.git@v0.1#egg=cocoapi&subdirectory=PythonAPI
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/build.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/build.sh
new file mode 100755
index 00000000..8bf5c344
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/build.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build --rm -t nvidia_joc_maskrcnn_pt . -f Dockerfile
\ No newline at end of file
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh
new file mode 100755
index 00000000..17c984a6
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+PATH_TO_COCO= $1 #Location of COCO-2014 in local machine or use your own dataset. 
+MOUNT_LOCATION='/datasets/data'
+NAME='maskrcnn_interactive'
+docker run --runtime=nvidia -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host -t -i nvidia_joc_maskrcnn_pt bash
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/eval.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/eval.sh
new file mode 100755
index 00000000..0d2bf153
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/eval.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#Predictions will be stored in `FOLDER`/inference`
+#1x8x4 DGX1V
+
+GPU=8
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+
+#This folder should a file called 'last_checkpoint' which contains the path to the actual checkpoint
+FOLDER='/results'
+
+#Example
+#   /results
+#      ------last_checkpoint
+#      ------model.pth
+
+#   
+#  last_checkpoint
+#-----------------------------
+#|/results/model.pth         |  
+#|                           |
+#|                           |
+#|                           |
+#|                           |
+#|                           |
+#-----------------------------
+
+
+
+LOGFILE="$FOLDER/joblog.log"
+
+if ! [ -d "$FOLDER" ]; then mkdir $FOLDER; fi
+
+python3 -m torch.distributed.launch --nproc_per_node=$GPU tools/test_net.py \
+    --config-file $CONFIG \
+    DATASETS.TEST "(\"coco_2014_minival\",)" \
+    DTYPE "float16" \
+    OUTPUT_DIR $FOLDER \
+    | tee $LOGFILE
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference.sh
new file mode 100644
index 00000000..5eff8b11
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#Predictions will be stored in `FOLDER`/inference`
+#1x8x4 DGX1V
+
+GPU=8
+
+# uncomment below to use default 
+# CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+CONFIG='$1' 
+
+#This folder should a file called 'last_checkpoint' which contains the path to the actual checkpoint
+FOLDER='/results'
+
+#Example
+#   /results
+#      ------last_checkpoint
+#      ------model.pth
+
+#   
+#  last_checkpoint
+#-----------------------------
+#|/results/model.pth         |  
+#|                           |
+#|                           |
+#|                           |
+#|                           |
+#|                           |
+#-----------------------------
+
+
+
+LOGFILE="$FOLDER/joblog.log"
+
+if ! [ -d "$FOLDER" ]; then mkdir $FOLDER; fi
+
+#Use a different argument with DATASET.TEST to use your own
+
+python3 -m torch.distributed.launch --nproc_per_node=$GPU tools/test_net.py \
+    --config-file $CONFIG \
+    --skip-eval \
+    DTYPE "float16" \
+    DATASETS.TEST "(\"coco_2014_minival\",)" \
+    OUTPUT_DIR $FOLDER \
+    | tee $LOGFILE
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference_benchmark.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference_benchmark.sh
new file mode 100644
index 00000000..a63ee364
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/inference_benchmark.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+
+
+#Predictions will be stored in `FOLDER`/inference`
+
+#1x8x4 DGX1V
+
+
+
+GPU=8
+
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+
+DTYPE=$1 
+
+
+
+
+
+#This folder should a file called 'last_checkpoint' which contains the path to the actual checkpoint
+
+FOLDER='/results'
+
+
+
+#Example
+
+#   /results
+
+#      ------last_checkpoint
+
+#      ------model.pth
+
+
+
+#   
+
+#  last_checkpoint
+
+#-----------------------------
+
+#|/results/model.pth         |  
+
+#|                           |
+
+#|                           |
+
+#|                           |
+
+#|                           |
+
+#|                           |
+
+#-----------------------------
+
+
+
+
+
+
+
+LOGFILE="$FOLDER/joblog.log"
+
+
+
+if ! [ -d "$FOLDER" ]; then mkdir $FOLDER; fi
+
+
+
+python3 -m torch.distributed.launch --nproc_per_node=$GPU tools/test_net.py \
+    --config-file $CONFIG \
+    --skip-eval \
+    DATASETS.TEST "(\"coco_2014_minival\",)" \
+    DTYPE "$DTYPE" \
+    OUTPUT_DIR $FOLDER \
+    | tee $LOGFILE
+
+    
+
+#2019-02-22 00:05:39,954 maskrcnn_benchmark.inference INFO: Total inference time: 0:04:55.840343 (0.05916806864738464 s / img per device, on 1 devices)
+
+
+
+time=`cat $LOGFILE | grep -F 'maskrcnn_benchmark.inference INFO: Total inference time' | tail -n 1 | awk -F'(' '{print $2}' | awk -F' s ' '{print $1}' | egrep -o [0-9.]+`
+calc=$(echo $time 1.0 | awk '{ printf "%f", $2 / $1 }')
+
+echo "Inference perf is: "$calc" FPS"
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh
new file mode 100755
index 00000000..938458fd
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#8 V100 x 4 batch_per_gpu DGX1V
+
+GPU=8
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+RESULTS='/results'
+LOGFILE="$RESULTS/joblog.log"
+
+if ! [ -d "$RESULTS" ]; then mkdir $RESULTS; fi
+
+#Use a different argument with DATASET.TRAIN to use your own
+
+python -m torch.distributed.launch --nproc_per_node=$GPU tools/train_net.py \
+        --config-file $CONFIG \
+        DTYPE "float16" \
+        OUTPUT_DIR RESULTS \
+        | tee $LOGFILE
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train_benchmark.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train_benchmark.sh
new file mode 100755
index 00000000..3545c44c
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train_benchmark.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#1x8x4 DGX1V - 1 epoch of training
+
+GPU=8
+GLOBAL_BATCH=32
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+RESULTS='/results'
+LOGFILE="$RESULTS/joblog.log"
+DTYPE="$1"
+
+if ! [ -d "$RESULTS" ]; then mkdir $RESULTS; fi
+
+python -m torch.distributed.launch --nproc_per_node=$GPU tools/train_net.py \
+        --config-file $CONFIG \
+        --skip-test \
+        SOLVER.BASE_LR 0.04 \
+        SOLVER.MAX_ITER 3665 \
+        SOLVER.STEPS "(30000, 40000)" \
+        SOLVER.IMS_PER_BATCH $GLOBAL_BATCH \
+        DTYPE "$DTYPE" \
+        OUTPUT_DIR $RESULTS \
+        | tee $LOGFILE
+        
+time=`cat $LOGFILE | grep -F 'maskrcnn_benchmark.trainer INFO: Total training time' | tail -n 1 | awk -F'(' '{print $2}' | awk -F' s ' '{print $1}' | egrep -o [0-9.]+`
+statement=`cat $LOGFILE | grep -F 'maskrcnn_benchmark.trainer INFO: Total training time' | tail -n 1`
+calc=$(echo $time 1.0 $GLOBAL_BATCH | awk '{ printf "%f", $2 * $3 / $1 }')
+echo "Training perf is: "$calc" FPS"
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/setup.py b/PyTorch/Segmentation/MaskRCNN/pytorch/setup.py
new file mode 100755
index 00000000..81a2fd11
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/setup.py
@@ -0,0 +1,69 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#!/usr/bin/env python
+
+import glob
+import os
+
+import torch
+from setuptools import find_packages
+from setuptools import setup
+from torch.utils.cpp_extension import CUDA_HOME
+from torch.utils.cpp_extension import CppExtension
+from torch.utils.cpp_extension import CUDAExtension
+
+requirements = ["torch", "torchvision"]
+
+
+def get_extensions():
+    this_dir = os.path.dirname(os.path.abspath(__file__))
+    extensions_dir = os.path.join(this_dir, "maskrcnn_benchmark", "csrc")
+
+    main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
+    source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
+    source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
+
+    sources = main_file + source_cpu
+    extension = CppExtension
+
+    extra_compile_args = {"cxx": []}
+    define_macros = []
+
+    if CUDA_HOME is not None:
+        extension = CUDAExtension
+        sources += source_cuda
+        define_macros += [("WITH_CUDA", None)]
+        extra_compile_args["nvcc"] = [
+            "-DCUDA_HAS_FP16=1",
+            "-D__CUDA_NO_HALF_OPERATORS__",
+            "-D__CUDA_NO_HALF_CONVERSIONS__",
+            "-D__CUDA_NO_HALF2_OPERATORS__",
+        ]
+
+    sources = [os.path.join(extensions_dir, s) for s in sources]
+
+    include_dirs = [extensions_dir]
+
+    ext_modules = [
+        extension(
+            "maskrcnn_benchmark._C",
+            sources,
+            include_dirs=include_dirs,
+            define_macros=define_macros,
+            extra_compile_args=extra_compile_args,
+        )
+    ]
+
+    return ext_modules
+
+
+setup(
+    name="maskrcnn_benchmark",
+    version="0.1",
+    author="fmassa",
+    url="https://github.com/facebookresearch/maskrcnn-benchmark",
+    description="object detection in pytorch",
+    packages=find_packages(exclude=("configs", "tests",)),
+    # install_requires=requirements,
+    ext_modules=get_extensions(),
+    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
+)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp16.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp16.sh
new file mode 100755
index 00000000..2c98c011
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp16.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#Script for PyT CI
+#CONFIG: 1x8x4
+
+RESULTS_DIR='maskrcnn_coco2014_test'
+REPO_DIR='/opt/pytorch/examples/Detectron_PyT/pytorch'
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+
+LOGFILE=$REPO_DIR/results/$RESULTS_DIR/log_gpu_0_fp16.log
+mkdir -p $REPO_DIR/results/$RESULTS_DIR
+
+GPU=8
+BBOX_THRESHOLD=0.375
+MASK_THRESHOLD=0.341
+THROUGHPUT=2.57
+THRESHOLD=0.9
+
+cd $REPO_DIR
+
+python -m torch.distributed.launch --nproc_per_node=$GPU tools/train_net.py \
+        --config-file $CONFIG \
+        SOLVER.BASE_LR 0.04 \
+        SOLVER.MAX_ITER 45000 \
+        SOLVER.STEPS "(30000, 40000)" \
+        SOLVER.IMS_PER_BATCH 32 \
+        TEST.IMS_PER_BATCH 8 \
+        DTYPE "float16" \
+        OUTPUT_DIR results/$RESULTS_DIR \
+        PATHS_CATALOG maskrcnn_benchmark/config/paths_catalog_ci.py \
+        2>&1 | tee $LOGFILE
+
+map=`cat $LOGFILE | grep -F 'Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ]' | tail -n 2 | awk -F' = ' '{print $2}' | egrep -o [0-9.]+`
+bbox_map=`echo $map | awk -F' ' '{print $1}' | egrep -o [0-9.]+`
+mask_map=`echo $map | awk -F' ' '{print $2}' | egrep -o [0-9.]+`
+time=`cat $LOGFILE | grep -F 'maskrcnn_benchmark.trainer INFO: Total training time' | tail -n 1 | awk -F'(' '{print $2}' | awk -F' s ' '{print $1}' | egrep -o [0-9.]+`
+throughput=$(echo $time 1.0 | awk '{ printf "%f", $2 / $1 }')
+
+echo 'THRESHOLD:' $BBOX_THRESHOLD $MASK_THRESHOLD $THROUGHPUT
+echo 'RESULT:' $map $throughput
+
+ACCURACY_TEST_RESULT_BBOX=$(awk 'BEGIN {print ('${bbox_map}' >= '${BBOX_THRESHOLD}')}')
+ACCURACY_TEST_RESULT_MASK=$(awk 'BEGIN {print ('${mask_map}' >= '${MASK_THRESHOLD}')}')
+
+if [ $ACCURACY_TEST_RESULT_BBOX == 1 -a $ACCURACY_TEST_RESULT_MASK == 1 ];
+    then
+        echo "&&&& ACCURACY TEST PASSED"
+    else
+        echo "&&&& ACCURACY TEST FAILED"
+    fi
+
+PERFORMANCE_TEST_RESULT=$(awk 'BEGIN {print ('${throughput}' >= \
+      ('${THROUGHPUT}' * '${THRESHOLD}'))}')
+
+if [ $PERFORMANCE_TEST_RESULT == 1 ];
+    then
+        echo "&&&& PERFORMANCE TEST PASSED"
+    else
+        echo "&&&& PERFORMANCE TEST FAILED"
+    fi
+    
+if [ $ACCURACY_TEST_RESULT_BBOX == 1 -a $ACCURACY_TEST_RESULT_MASK == 1 -a $PERFORMANCE_TEST_RESULT == 1 ];
+    then
+        echo "&&&& PASSED"
+        exit 0
+    else
+        echo "&&&& FAILED"
+        exit 1
+    fi
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp32.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp32.sh
new file mode 100755
index 00000000..6ccc2bd3
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/test_fp32.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#Script for PyT CI
+#CONFIG: 1x8x4
+
+RESULTS_DIR='maskrcnn_coco2014_test'
+REPO_DIR='/opt/pytorch/examples/Detectron_PyT/pytorch'
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+
+LOGFILE=$REPO_DIR/results/$RESULTS_DIR/log_gpu_0_fp16.log
+mkdir -p $REPO_DIR/results/$RESULTS_DIR
+
+GPU=8
+BBOX_THRESHOLD=0.375
+MASK_THRESHOLD=0.341
+THROUGHPUT=1.9
+THRESHOLD=0.9
+
+cd $REPO_DIR
+
+python -m torch.distributed.launch --nproc_per_node=$GPU tools/train_net.py \
+        --config-file $CONFIG \
+        SOLVER.BASE_LR 0.04 \
+        SOLVER.MAX_ITER 45000 \
+        SOLVER.STEPS "(30000, 40000)" \
+        SOLVER.IMS_PER_BATCH 32 \
+        TEST.IMS_PER_BATCH 8 \
+        DTYPE "float32" \
+        OUTPUT_DIR results/$RESULTS_DIR \
+        PATHS_CATALOG maskrcnn_benchmark/config/paths_catalog_ci.py \
+        2>&1 | tee $LOGFILE
+
+map=`cat $LOGFILE | grep -F 'Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ]' | tail -n 2 | awk -F' = ' '{print $2}' | egrep -o [0-9.]+`
+bbox_map=`echo $map | awk -F' ' '{print $1}' | egrep -o [0-9.]+`
+mask_map=`echo $map | awk -F' ' '{print $2}' | egrep -o [0-9.]+`
+time=`cat $LOGFILE | grep -F 'maskrcnn_benchmark.trainer INFO: Total training time' | tail -n 1 | awk -F'(' '{print $2}' | awk -F' s ' '{print $1}' | egrep -o [0-9.]+`
+throughput=$(echo $time 1.0 | awk '{ printf "%f", $2 / $1 }')
+
+echo 'THRESHOLD:' $BBOX_THRESHOLD $MASK_THRESHOLD $THROUGHPUT
+echo 'RESULT:' $map $throughput
+
+ACCURACY_TEST_RESULT_BBOX=$(awk 'BEGIN {print ('${bbox_map}' >= '${BBOX_THRESHOLD}')}')
+ACCURACY_TEST_RESULT_MASK=$(awk 'BEGIN {print ('${mask_map}' >= '${MASK_THRESHOLD}')}')
+
+if [ $ACCURACY_TEST_RESULT_BBOX == 1 -a $ACCURACY_TEST_RESULT_MASK == 1 ];
+    then
+        echo "&&&& ACCURACY TEST PASSED"
+    else
+        echo "&&&& ACCURACY TEST FAILED"
+    fi
+
+PERFORMANCE_TEST_RESULT=$(awk 'BEGIN {print ('${throughput}' >= \
+      ('${THROUGHPUT}' * '${THRESHOLD}'))}')
+
+if [ $PERFORMANCE_TEST_RESULT == 1 ];
+    then
+        echo "&&&& PERFORMANCE TEST PASSED"
+    else
+        echo "&&&& PERFORMANCE TEST FAILED"
+    fi
+    
+if [ $ACCURACY_TEST_RESULT_BBOX == 1 -a $ACCURACY_TEST_RESULT_MASK == 1 -a $PERFORMANCE_TEST_RESULT == 1 ];
+    then
+        echo "&&&& PASSED"
+        exit 0
+    else
+        echo "&&&& FAILED"
+        exit 1
+    fi
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tests/checkpoint.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/checkpoint.py
new file mode 100755
index 00000000..82004fb7
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/checkpoint.py
@@ -0,0 +1,118 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from collections import OrderedDict
+import os
+from tempfile import TemporaryDirectory
+import unittest
+
+import torch
+from torch import nn
+
+from maskrcnn_benchmark.utils.model_serialization import load_state_dict
+from maskrcnn_benchmark.utils.checkpoint import Checkpointer
+
+
+class TestCheckpointer(unittest.TestCase):
+    def create_model(self):
+        return nn.Sequential(nn.Linear(2, 3), nn.Linear(3, 1))
+
+    def create_complex_model(self):
+        m = nn.Module()
+        m.block1 = nn.Module()
+        m.block1.layer1 = nn.Linear(2, 3)
+        m.layer2 = nn.Linear(3, 2)
+        m.res = nn.Module()
+        m.res.layer2 = nn.Linear(3, 2)
+
+        state_dict = OrderedDict()
+        state_dict["layer1.weight"] = torch.rand(3, 2)
+        state_dict["layer1.bias"] = torch.rand(3)
+        state_dict["layer2.weight"] = torch.rand(2, 3)
+        state_dict["layer2.bias"] = torch.rand(2)
+        state_dict["res.layer2.weight"] = torch.rand(2, 3)
+        state_dict["res.layer2.bias"] = torch.rand(2)
+
+        return m, state_dict
+
+    def test_from_last_checkpoint_model(self):
+        # test that loading works even if they differ by a prefix
+        for trained_model, fresh_model in [
+            (self.create_model(), self.create_model()),
+            (nn.DataParallel(self.create_model()), self.create_model()),
+            (self.create_model(), nn.DataParallel(self.create_model())),
+            (
+                nn.DataParallel(self.create_model()),
+                nn.DataParallel(self.create_model()),
+            ),
+        ]:
+
+            with TemporaryDirectory() as f:
+                checkpointer = Checkpointer(
+                    trained_model, save_dir=f, save_to_disk=True
+                )
+                checkpointer.save("checkpoint_file")
+
+                # in the same folder
+                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
+                self.assertTrue(fresh_checkpointer.has_checkpoint())
+                self.assertEqual(
+                    fresh_checkpointer.get_checkpoint_file(),
+                    os.path.join(f, "checkpoint_file.pth"),
+                )
+                _ = fresh_checkpointer.load()
+
+            for trained_p, loaded_p in zip(
+                trained_model.parameters(), fresh_model.parameters()
+            ):
+                # different tensor references
+                self.assertFalse(id(trained_p) == id(loaded_p))
+                # same content
+                self.assertTrue(trained_p.equal(loaded_p))
+
+    def test_from_name_file_model(self):
+        # test that loading works even if they differ by a prefix
+        for trained_model, fresh_model in [
+            (self.create_model(), self.create_model()),
+            (nn.DataParallel(self.create_model()), self.create_model()),
+            (self.create_model(), nn.DataParallel(self.create_model())),
+            (
+                nn.DataParallel(self.create_model()),
+                nn.DataParallel(self.create_model()),
+            ),
+        ]:
+            with TemporaryDirectory() as f:
+                checkpointer = Checkpointer(
+                    trained_model, save_dir=f, save_to_disk=True
+                )
+                checkpointer.save("checkpoint_file")
+
+                # on different folders
+                with TemporaryDirectory() as g:
+                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
+                    self.assertFalse(fresh_checkpointer.has_checkpoint())
+                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
+                    _ = fresh_checkpointer.load(os.path.join(f, "checkpoint_file.pth"))
+
+            for trained_p, loaded_p in zip(
+                trained_model.parameters(), fresh_model.parameters()
+            ):
+                # different tensor references
+                self.assertFalse(id(trained_p) == id(loaded_p))
+                # same content
+                self.assertTrue(trained_p.equal(loaded_p))
+
+    def test_complex_model_loaded(self):
+        for add_data_parallel in [False, True]:
+            model, state_dict = self.create_complex_model()
+            if add_data_parallel:
+                model = nn.DataParallel(model)
+
+            load_state_dict(model, state_dict)
+            for loaded, stored in zip(model.state_dict().values(), state_dict.values()):
+                # different tensor references
+                self.assertFalse(id(loaded) == id(stored))
+                # same content
+                self.assertTrue(loaded.equal(stored))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_data_samplers.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_data_samplers.py
new file mode 100755
index 00000000..96338e17
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_data_samplers.py
@@ -0,0 +1,153 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import itertools
+import random
+import unittest
+
+from torch.utils.data.sampler import BatchSampler
+from torch.utils.data.sampler import Sampler
+from torch.utils.data.sampler import SequentialSampler
+from torch.utils.data.sampler import RandomSampler
+
+from maskrcnn_benchmark.data.samplers import GroupedBatchSampler
+from maskrcnn_benchmark.data.samplers import IterationBasedBatchSampler
+
+
+class SubsetSampler(Sampler):
+    def __init__(self, indices):
+        self.indices = indices
+
+    def __iter__(self):
+        return iter(self.indices)
+
+    def __len__(self):
+        return len(self.indices)
+
+
+class TestGroupedBatchSampler(unittest.TestCase):
+    def test_respect_order_simple(self):
+        drop_uneven = False
+        dataset = [i for i in range(40)]
+        group_ids = [i // 10 for i in dataset]
+        sampler = SequentialSampler(dataset)
+        for batch_size in [1, 3, 5, 6]:
+            batch_sampler = GroupedBatchSampler(
+                sampler, group_ids, batch_size, drop_uneven
+            )
+            result = list(batch_sampler)
+            merged_result = list(itertools.chain.from_iterable(result))
+            self.assertEqual(merged_result, dataset)
+
+    def test_respect_order(self):
+        drop_uneven = False
+        dataset = [i for i in range(10)]
+        group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
+        sampler = SequentialSampler(dataset)
+
+        expected = [
+            [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+            [[0, 1, 3], [2, 4, 5], [6, 9], [7, 8]],
+            [[0, 1, 3, 6], [2, 4, 5, 7], [8], [9]],
+        ]
+
+        for idx, batch_size in enumerate([1, 3, 4]):
+            batch_sampler = GroupedBatchSampler(
+                sampler, group_ids, batch_size, drop_uneven
+            )
+            result = list(batch_sampler)
+            self.assertEqual(result, expected[idx])
+
+    def test_respect_order_drop_uneven(self):
+        batch_size = 3
+        drop_uneven = True
+        dataset = [i for i in range(10)]
+        group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
+        sampler = SequentialSampler(dataset)
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+
+        result = list(batch_sampler)
+
+        expected = [[0, 1, 3], [2, 4, 5]]
+        self.assertEqual(result, expected)
+
+    def test_subset_sampler(self):
+        batch_size = 3
+        drop_uneven = False
+        dataset = [i for i in range(10)]
+        group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
+        sampler = SubsetSampler([0, 3, 5, 6, 7, 8])
+
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+        result = list(batch_sampler)
+
+        expected = [[0, 3, 6], [5, 7, 8]]
+        self.assertEqual(result, expected)
+
+    def test_permute_subset_sampler(self):
+        batch_size = 3
+        drop_uneven = False
+        dataset = [i for i in range(10)]
+        group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
+        sampler = SubsetSampler([5, 0, 6, 1, 3, 8])
+
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+        result = list(batch_sampler)
+
+        expected = [[5, 8], [0, 6, 1], [3]]
+        self.assertEqual(result, expected)
+
+    def test_permute_subset_sampler_drop_uneven(self):
+        batch_size = 3
+        drop_uneven = True
+        dataset = [i for i in range(10)]
+        group_ids = [0, 0, 1, 0, 1, 1, 0, 1, 1, 0]
+        sampler = SubsetSampler([5, 0, 6, 1, 3, 8])
+
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+        result = list(batch_sampler)
+
+        expected = [[0, 6, 1]]
+        self.assertEqual(result, expected)
+
+    def test_len(self):
+        batch_size = 3
+        drop_uneven = True
+        dataset = [i for i in range(10)]
+        group_ids = [random.randint(0, 1) for _ in dataset]
+        sampler = RandomSampler(dataset)
+
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+        result = list(batch_sampler)
+        self.assertEqual(len(result), len(batch_sampler))
+        self.assertEqual(len(result), len(batch_sampler))
+
+        batch_sampler = GroupedBatchSampler(sampler, group_ids, batch_size, drop_uneven)
+        batch_sampler_len = len(batch_sampler)
+        result = list(batch_sampler)
+        self.assertEqual(len(result), batch_sampler_len)
+        self.assertEqual(len(result), len(batch_sampler))
+
+
+class TestIterationBasedBatchSampler(unittest.TestCase):
+    def test_number_of_iters_and_elements(self):
+        for batch_size in [2, 3, 4]:
+            for num_iterations in [4, 10, 20]:
+                for drop_last in [False, True]:
+                    dataset = [i for i in range(10)]
+                    sampler = SequentialSampler(dataset)
+                    batch_sampler = BatchSampler(
+                        sampler, batch_size, drop_last=drop_last
+                    )
+
+                    iter_sampler = IterationBasedBatchSampler(
+                        batch_sampler, num_iterations
+                    )
+                    assert len(iter_sampler) == num_iterations
+                    for i, batch in enumerate(iter_sampler):
+                        start = (i % len(batch_sampler)) * batch_size
+                        end = min(start + batch_size, len(dataset))
+                        expected = [x for x in range(start, end)]
+                        self.assertEqual(batch, expected)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_metric_logger.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_metric_logger.py
new file mode 100755
index 00000000..a5b884c9
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tests/test_metric_logger.py
@@ -0,0 +1,28 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+import unittest
+
+from maskrcnn_benchmark.utils.metric_logger import MetricLogger
+
+
+class TestMetricLogger(unittest.TestCase):
+    def test_update(self):
+        meter = MetricLogger()
+        for i in range(10):
+            meter.update(metric=float(i))
+        
+        m = meter.meters["metric"]
+        self.assertEqual(m.count, 10)
+        self.assertEqual(m.total, 45)
+        self.assertEqual(m.median, 4)
+        self.assertEqual(m.avg, 4.5)
+
+    def test_no_attr(self):
+        meter = MetricLogger()
+        _ = meter.meters
+        _ = meter.delimiter
+        def broken():
+            _ = meter.not_existent
+        self.assertRaises(AttributeError, broken)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py
new file mode 100755
index 00000000..b4ddd9ed
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py
@@ -0,0 +1,221 @@
+#!/usr/bin/env python
+
+# Copyright (c) 2017-present, Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+##############################################################################
+
+# This file is copy from https://github.com/facebookresearch/Detectron/tree/master/tools
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import h5py
+import json
+import os
+import scipy.misc
+import sys
+
+import cityscapesscripts.evaluation.instances2dict_with_polygons as cs
+
+import detectron.utils.segms as segms_util
+import detectron.utils.boxes as bboxs_util
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Convert dataset')
+    parser.add_argument(
+        '--dataset', help="cocostuff, cityscapes", default=None, type=str)
+    parser.add_argument(
+        '--outdir', help="output dir for json files", default=None, type=str)
+    parser.add_argument(
+        '--datadir', help="data dir for annotations to be converted",
+        default=None, type=str)
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+    return parser.parse_args()
+
+
+def convert_coco_stuff_mat(data_dir, out_dir):
+    """Convert to png and save json with path. This currently only contains
+    the segmentation labels for objects+stuff in cocostuff - if we need to
+    combine with other labels from original COCO that will be a TODO."""
+    sets = ['train', 'val']
+    categories = []
+    json_name = 'coco_stuff_%s.json'
+    ann_dict = {}
+    for data_set in sets:
+        file_list = os.path.join(data_dir, '%s.txt')
+        images = []
+        with open(file_list % data_set) as f:
+            for img_id, img_name in enumerate(f):
+                img_name = img_name.replace('coco', 'COCO').strip('\n')
+                image = {}
+                mat_file = os.path.join(
+                    data_dir, 'annotations/%s.mat' % img_name)
+                data = h5py.File(mat_file, 'r')
+                labelMap = data.get('S')
+                if len(categories) == 0:
+                    labelNames = data.get('names')
+                    for idx, n in enumerate(labelNames):
+                        categories.append(
+                            {"id": idx, "name": ''.join(chr(i) for i in data[
+                                n[0]])})
+                    ann_dict['categories'] = categories
+                scipy.misc.imsave(
+                    os.path.join(data_dir, img_name + '.png'), labelMap)
+                image['width'] = labelMap.shape[0]
+                image['height'] = labelMap.shape[1]
+                image['file_name'] = img_name
+                image['seg_file_name'] = img_name
+                image['id'] = img_id
+                images.append(image)
+        ann_dict['images'] = images
+        print("Num images: %s" % len(images))
+        with open(os.path.join(out_dir, json_name % data_set), 'wb') as outfile:
+            outfile.write(json.dumps(ann_dict))
+
+
+# for Cityscapes
+def getLabelID(self, instID):
+    if (instID < 1000):
+        return instID
+    else:
+        return int(instID / 1000)
+
+
+def convert_cityscapes_instance_only(
+        data_dir, out_dir):
+    """Convert from cityscapes format to COCO instance seg format - polygons"""
+    sets = [
+        'gtFine_val',
+        'gtFine_train',
+        'gtFine_test',
+
+        # 'gtCoarse_train',
+        # 'gtCoarse_val',
+        # 'gtCoarse_train_extra'
+    ]
+    ann_dirs = [
+        'gtFine_trainvaltest/gtFine/val',
+        'gtFine_trainvaltest/gtFine/train',
+        'gtFine_trainvaltest/gtFine/test',
+
+        # 'gtCoarse/train',
+        # 'gtCoarse/train_extra',
+        # 'gtCoarse/val'
+    ]
+    json_name = 'instancesonly_filtered_%s.json'
+    ends_in = '%s_polygons.json'
+    img_id = 0
+    ann_id = 0
+    cat_id = 1
+    category_dict = {}
+
+    category_instancesonly = [
+        'person',
+        'rider',
+        'car',
+        'truck',
+        'bus',
+        'train',
+        'motorcycle',
+        'bicycle',
+    ]
+
+    for data_set, ann_dir in zip(sets, ann_dirs):
+        print('Starting %s' % data_set)
+        ann_dict = {}
+        images = []
+        annotations = []
+        ann_dir = os.path.join(data_dir, ann_dir)
+        for root, _, files in os.walk(ann_dir):
+            for filename in files:
+                if filename.endswith(ends_in % data_set.split('_')[0]):
+                    if len(images) % 50 == 0:
+                        print("Processed %s images, %s annotations" % (
+                            len(images), len(annotations)))
+                    json_ann = json.load(open(os.path.join(root, filename)))
+                    image = {}
+                    image['id'] = img_id
+                    img_id += 1
+
+                    image['width'] = json_ann['imgWidth']
+                    image['height'] = json_ann['imgHeight']
+                    image['file_name'] = filename[:-len(
+                        ends_in % data_set.split('_')[0])] + 'leftImg8bit.png'
+                    image['seg_file_name'] = filename[:-len(
+                        ends_in % data_set.split('_')[0])] + \
+                        '%s_instanceIds.png' % data_set.split('_')[0]
+                    images.append(image)
+
+                    fullname = os.path.join(root, image['seg_file_name'])
+                    objects = cs.instances2dict_with_polygons(
+                        [fullname], verbose=False)[fullname]
+
+                    for object_cls in objects:
+                        if object_cls not in category_instancesonly:
+                            continue  # skip non-instance categories
+
+                        for obj in objects[object_cls]:
+                            if obj['contours'] == []:
+                                print('Warning: empty contours.')
+                                continue  # skip non-instance categories
+
+                            len_p = [len(p) for p in obj['contours']]
+                            if min(len_p) <= 4:
+                                print('Warning: invalid contours.')
+                                continue  # skip non-instance categories
+
+                            ann = {}
+                            ann['id'] = ann_id
+                            ann_id += 1
+                            ann['image_id'] = image['id']
+                            ann['segmentation'] = obj['contours']
+
+                            if object_cls not in category_dict:
+                                category_dict[object_cls] = cat_id
+                                cat_id += 1
+                            ann['category_id'] = category_dict[object_cls]
+                            ann['iscrowd'] = 0
+                            ann['area'] = obj['pixelCount']
+                            ann['bbox'] = bboxs_util.xyxy_to_xywh(
+                                segms_util.polys_to_boxes(
+                                    [ann['segmentation']])).tolist()[0]
+
+                            annotations.append(ann)
+
+        ann_dict['images'] = images
+        categories = [{"id": category_dict[name], "name": name} for name in
+                      category_dict]
+        ann_dict['categories'] = categories
+        ann_dict['annotations'] = annotations
+        print("Num categories: %s" % len(categories))
+        print("Num images: %s" % len(images))
+        print("Num annotations: %s" % len(annotations))
+        with open(os.path.join(out_dir, json_name % data_set), 'w') as outfile:
+            outfile.write(json.dumps(ann_dict))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    if args.dataset == "cityscapes_instance_only":
+        convert_cityscapes_instance_only(args.datadir, args.outdir)
+    elif args.dataset == "cocostuff":
+        convert_coco_stuff_mat(args.datadir, args.outdir)
+    else:
+        print("Dataset not supported: %s" % args.dataset)
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/instances2dict_with_polygons.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/instances2dict_with_polygons.py
new file mode 100755
index 00000000..0bb56049
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/cityscapes/instances2dict_with_polygons.py
@@ -0,0 +1,81 @@
+#!/usr/bin/python
+#
+# Convert instances from png files to a dictionary
+# This files is created according to https://github.com/facebookresearch/Detectron/issues/111
+
+from __future__ import print_function, absolute_import, division
+import os, sys
+
+sys.path.append( os.path.normpath( os.path.join( os.path.dirname( __file__ ) , '..' , 'helpers' ) ) )
+from csHelpers import *
+
+# Cityscapes imports
+from cityscapesscripts.evaluation.instance import *
+from cityscapesscripts.helpers.csHelpers import *
+import cv2
+from maskrcnn_benchmark.utils import cv2_util
+
+
+def instances2dict_with_polygons(imageFileList, verbose=False):
+    imgCount     = 0
+    instanceDict = {}
+
+    if not isinstance(imageFileList, list):
+        imageFileList = [imageFileList]
+
+    if verbose:
+        print("Processing {} images...".format(len(imageFileList)))
+
+    for imageFileName in imageFileList:
+        # Load image
+        img = Image.open(imageFileName)
+
+        # Image as numpy array
+        imgNp = np.array(img)
+
+        # Initialize label categories
+        instances = {}
+        for label in labels:
+            instances[label.name] = []
+
+        # Loop through all instance ids in instance image
+        for instanceId in np.unique(imgNp):
+            if instanceId < 1000:
+                continue
+            instanceObj = Instance(imgNp, instanceId)
+            instanceObj_dict = instanceObj.toDict()
+
+            #instances[id2label[instanceObj.labelID].name].append(instanceObj.toDict())
+            if id2label[instanceObj.labelID].hasInstances:
+                mask = (imgNp == instanceId).astype(np.uint8)
+                contour, hier = cv2_util.findContours(
+                    mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+
+                polygons = [c.reshape(-1).tolist() for c in contour]
+                instanceObj_dict['contours'] = polygons
+
+            instances[id2label[instanceObj.labelID].name].append(instanceObj_dict)
+
+        imgKey = os.path.abspath(imageFileName)
+        instanceDict[imgKey] = instances
+        imgCount += 1
+
+        if verbose:
+            print("\rImages Processed: {}".format(imgCount), end=' ')
+            sys.stdout.flush()
+
+    if verbose:
+        print("")
+
+    return instanceDict
+
+def main(argv):
+    fileList = []
+    if (len(argv) > 2):
+        for arg in argv:
+            if ("png" in arg):
+                fileList.append(arg)
+    instances2dict_with_polygons(fileList, True)
+
+if __name__ == "__main__":
+    main(sys.argv[1:])
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tools/test_net.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/test_net.py
new file mode 100755
index 00000000..ff18b4fa
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/test_net.py
@@ -0,0 +1,113 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+# Set up custom environment before nearly anything else is imported
+# NOTE: this should be the first import (no not reorder)
+from maskrcnn_benchmark.utils.env import setup_environment  # noqa F401 isort:skip
+
+import argparse
+import os
+
+import torch
+from maskrcnn_benchmark.config import cfg
+from maskrcnn_benchmark.data import make_data_loader
+from maskrcnn_benchmark.engine.inference import inference
+from maskrcnn_benchmark.modeling.detector import build_detection_model
+from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
+from maskrcnn_benchmark.utils.collect_env import collect_env_info
+from maskrcnn_benchmark.utils.comm import synchronize, get_rank
+from maskrcnn_benchmark.utils.logger import setup_logger
+from maskrcnn_benchmark.utils.miscellaneous import mkdir
+
+# Check if we can enable mixed-precision via apex.amp
+try:
+    from apex import amp
+except ImportError:
+    raise ImportError('Use APEX for mixed precision via apex.amp')
+
+
+def main():
+    parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
+    parser.add_argument(
+        "--config-file",
+        default="/workspace/object_detection/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml",
+        metavar="FILE",
+        help="path to config file",
+    )
+    parser.add_argument("--local_rank", type=int, default=0)
+    parser.add_argument(
+        "--skip-eval",
+        dest="skip_eval",
+        help="Do not eval the predictions",
+        action="store_true",
+    )
+    parser.add_argument(
+        "opts",
+        help="Modify config options using the command-line",
+        default=None,
+        nargs=argparse.REMAINDER,
+    )
+
+    args = parser.parse_args()
+
+    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
+    distributed = num_gpus > 1
+
+    if distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(
+            backend="nccl", init_method="env://"
+        )
+        synchronize()
+
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+
+    save_dir = ""
+    logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
+    logger.info("Using {} GPUs".format(num_gpus))
+    logger.info(cfg)
+
+    logger.info("Collecting env info (might take some time)")
+    logger.info("\n" + collect_env_info())
+
+    model = build_detection_model(cfg)
+    model.to(cfg.MODEL.DEVICE)
+
+    # Initialize mixed-precision if necessary
+    use_mixed_precision = cfg.DTYPE == 'float16'
+    amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
+
+    output_dir = cfg.OUTPUT_DIR
+    checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
+    _ = checkpointer.load(cfg.MODEL.WEIGHT)
+
+    iou_types = ("bbox",)
+    if cfg.MODEL.MASK_ON:
+        iou_types = iou_types + ("segm",)
+    output_folders = [None] * len(cfg.DATASETS.TEST)
+    dataset_names = cfg.DATASETS.TEST
+    if cfg.OUTPUT_DIR:
+        for idx, dataset_name in enumerate(dataset_names):
+            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
+            mkdir(output_folder)
+            output_folders[idx] = output_folder
+    data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
+    for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
+        inference(
+            model,
+            data_loader_val,
+            dataset_name=dataset_name,
+            iou_types=iou_types,
+            box_only=cfg.MODEL.RPN_ONLY,
+            device=cfg.MODEL.DEVICE,
+            expected_results=cfg.TEST.EXPECTED_RESULTS,
+            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
+            output_folder=output_folder,
+            skip_eval=args.skip_eval,
+        )
+        synchronize()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/tools/train_net.py b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/train_net.py
new file mode 100755
index 00000000..fef069ff
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/tools/train_net.py
@@ -0,0 +1,265 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+r"""
+Basic training script for PyTorch
+"""
+
+# Set up custom environment before nearly anything else is imported
+# NOTE: this should be the first import (no not reorder)
+from maskrcnn_benchmark.utils.env import setup_environment  # noqa F401 isort:skip
+
+import argparse
+import os
+import logging
+import functools
+
+import torch
+from maskrcnn_benchmark.config import cfg
+from maskrcnn_benchmark.data import make_data_loader
+from maskrcnn_benchmark.solver import make_lr_scheduler
+from maskrcnn_benchmark.solver import make_optimizer
+from maskrcnn_benchmark.engine.inference import inference
+from maskrcnn_benchmark.engine.trainer import do_train
+from maskrcnn_benchmark.modeling.detector import build_detection_model
+from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
+from maskrcnn_benchmark.utils.collect_env import collect_env_info
+from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process
+from maskrcnn_benchmark.utils.imports import import_file
+from maskrcnn_benchmark.utils.logger import setup_logger
+from maskrcnn_benchmark.utils.miscellaneous import mkdir
+from maskrcnn_benchmark.engine.tester import test
+
+# See if we can use apex.DistributedDataParallel instead of the torch default,
+# and enable mixed-precision via apex.amp
+try:
+    from apex import amp
+    use_amp = True
+except ImportError:
+    print('Use APEX for multi-precision via apex.amp')
+    use_amp = False
+try:
+    from apex.parallel import DistributedDataParallel as DDP
+    use_apex_ddp = True
+except ImportError:
+    print('Use APEX for better performance')
+    use_apex_ddp = False
+
+def test_and_exchange_map(tester, model, distributed):
+    results = tester(model=model, distributed=distributed)
+
+    # main process only
+    if is_main_process():
+        # Note: one indirection due to possibility of multiple test datasets, we only care about the first
+        #       tester returns (parsed results, raw results). In our case, don't care about the latter
+        map_results, raw_results = results[0]
+        bbox_map = map_results.results["bbox"]['AP']
+        segm_map = map_results.results["segm"]['AP']
+    else:
+        bbox_map = 0.
+        segm_map = 0.
+
+    if distributed:
+        map_tensor = torch.tensor([bbox_map, segm_map], dtype=torch.float32, device=torch.device("cuda"))
+        torch.distributed.broadcast(map_tensor, 0)
+        bbox_map = map_tensor[0].item()
+        segm_map = map_tensor[1].item()
+
+    return bbox_map, segm_map
+
+def mlperf_test_early_exit(iteration, iters_per_epoch, tester, model, distributed, min_bbox_map, min_segm_map):
+    if iteration > 0 and iteration % iters_per_epoch == 0:
+        epoch = iteration // iters_per_epoch
+
+        logger = logging.getLogger('maskrcnn_benchmark.trainer')
+        logger.info("Starting evaluation...")
+
+        bbox_map, segm_map = test_and_exchange_map(tester, model, distributed)
+
+        # necessary for correctness
+        model.train()
+        logger.info('bbox mAP: {}, segm mAP: {}'.format(bbox_map, segm_map))
+
+        # terminating condition
+        if bbox_map >= min_bbox_map and segm_map >= min_segm_map:
+            logger.info("Target mAP reached, exiting...")
+            return True
+
+    return False
+
+
+def train(cfg, local_rank, distributed):
+    model = build_detection_model(cfg)
+    device = torch.device(cfg.MODEL.DEVICE)
+    model.to(device)
+
+    optimizer = make_optimizer(cfg, model)
+    scheduler = make_lr_scheduler(cfg, optimizer)
+
+    if use_amp:
+        # Initialize mixed-precision training
+        use_mixed_precision = cfg.DTYPE == "float16"
+        amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
+
+        # wrap the optimizer for mixed precision
+        if cfg.SOLVER.ACCUMULATE_GRAD:
+            # also specify number of steps to accumulate over
+            optimizer = amp_handle.wrap_optimizer(optimizer, num_loss=cfg.SOLVER.ACCUMULATE_STEPS)
+        else:
+            optimizer = amp_handle.wrap_optimizer(optimizer)
+
+    if distributed:
+        if use_apex_ddp:
+            model = DDP(model, delay_allreduce=True)
+        else:
+            model = torch.nn.parallel.DistributedDataParallel(
+                model, device_ids=[local_rank], output_device=local_rank,
+                # this should be removed if we update BatchNorm stats
+                broadcast_buffers=False,
+            )
+
+    arguments = {}
+    arguments["iteration"] = 0
+
+    output_dir = cfg.OUTPUT_DIR
+
+    save_to_disk = get_rank() == 0
+    checkpointer = DetectronCheckpointer(
+        cfg, model, optimizer, scheduler, output_dir, save_to_disk
+    )
+    extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
+    arguments.update(extra_checkpoint_data)
+
+    data_loader, iters_per_epoch = make_data_loader(
+        cfg,
+        is_train=True,
+        is_distributed=distributed,
+        start_iter=arguments["iteration"],
+    )
+
+    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
+
+    # set the callback function to evaluate and potentially
+    # early exit each epoch
+    if cfg.PER_EPOCH_EVAL:
+        per_iter_callback_fn = functools.partial(
+                mlperf_test_early_exit,
+                iters_per_epoch=iters_per_epoch,
+                tester=functools.partial(test, cfg=cfg),
+                model=model,
+                distributed=distributed,
+                min_bbox_map=cfg.MIN_BBOX_MAP,
+                min_segm_map=cfg.MIN_MASK_MAP)
+    else:
+        per_iter_callback_fn = None
+
+    do_train(
+        model,
+        data_loader,
+        optimizer,
+        scheduler,
+        checkpointer,
+        device,
+        checkpoint_period,
+        arguments,
+        use_amp,
+        cfg,
+        per_iter_end_callback_fn=per_iter_callback_fn,
+    )
+
+    return model
+
+
+def test_model(cfg, model, distributed):
+    if distributed:
+        model = model.module
+    torch.cuda.empty_cache()  # TODO check if it helps
+    iou_types = ("bbox",)
+    if cfg.MODEL.MASK_ON:
+        iou_types = iou_types + ("segm",)
+    output_folders = [None] * len(cfg.DATASETS.TEST)
+    dataset_names = cfg.DATASETS.TEST
+    if cfg.OUTPUT_DIR:
+        for idx, dataset_name in enumerate(dataset_names):
+            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
+            mkdir(output_folder)
+            output_folders[idx] = output_folder
+    data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
+    for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
+        inference(
+            model,
+            data_loader_val,
+            dataset_name=dataset_name,
+            iou_types=iou_types,
+            box_only=cfg.MODEL.RPN_ONLY,
+            device=cfg.MODEL.DEVICE,
+            expected_results=cfg.TEST.EXPECTED_RESULTS,
+            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
+            output_folder=output_folder,
+        )
+        synchronize()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
+    parser.add_argument(
+        "--config-file",
+        default="",
+        metavar="FILE",
+        help="path to config file",
+        type=str,
+    )
+    parser.add_argument("--local_rank", type=int, default=0)
+    parser.add_argument(
+        "--skip-test",
+        dest="skip_test",
+        help="Do not test the final model",
+        action="store_true",
+    )
+    parser.add_argument(
+        "opts",
+        help="Modify config options using the command-line",
+        default=None,
+        nargs=argparse.REMAINDER,
+    )
+
+    args = parser.parse_args()
+
+    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
+    args.distributed = num_gpus > 1
+
+    if args.distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(
+            backend="nccl", init_method="env://"
+        )
+        synchronize()
+
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+
+    output_dir = cfg.OUTPUT_DIR
+    if output_dir:
+        mkdir(output_dir)
+
+    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
+    logger.info("Using {} GPUs".format(num_gpus))
+    logger.info(args)
+
+    logger.info("Collecting env info (might take some time)")
+    logger.info("\n" + collect_env_info())
+
+    logger.info("Loaded configuration file {}".format(args.config_file))
+    with open(args.config_file, "r") as cf:
+        config_str = "\n" + cf.read()
+        logger.info(config_str)
+    logger.info("Running with config:\n{}".format(cfg))
+
+    model = train(cfg, args.local_rank, args.distributed)
+
+    if not args.skip_test:
+        if not cfg.PER_EPOCH_EVAL:
+            test_model(cfg, model, args.distributed)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/train.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/train.sh
new file mode 100755
index 00000000..dd67c52a
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/pytorch/train.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#8 GPUS x 4 batch/GPU
+
+IMAGE=`docker build . --pull | tail -n 1 | awk '{print $3}'`
+GPU=2
+NAME='MRCNN_TRAIN'
+CONFIG='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml'
+PATH_TO_COCO='/home/sharath/Downloads/11419' #Location on COCO-2014 on local machine
+
+
+#PATH_TO_RN50 - SCRIPT assumes R-50.pth exists in PATH_TO_COCO/models/R-50.pth
+
+#Specify datasets of your choice with parameter DATASETS.TRAIN and DATASETS.TEST
+MOUNT_LOCATION='/datasets/coco'
+DOCKER_RESULTS='/results'
+LOGFILE='joblog.log'
+COMMAND="python -m torch.distributed.launch --nproc_per_node=$GPU tools/train_net.py \
+        --config-file $CONFIG \
+        DATASETS.TRAIN "(\"coco_2014_train\", \"coco_2014_valminusminival\")" \
+        DATASETS.TEST "(\"coco_2014_minival\",)" \
+        SOLVER.BASE_LR 0.04 \
+        SOLVER.MAX_ITER 45000 \
+        SOLVER.STEPS \"(30000, 40000)\" \
+        SOLVER.IMS_PER_BATCH 4 \
+        DTYPE \"float16\" \
+        OUTPUT_DIR $DOCKER_RESULTS \
+        | tee $LOGFILE"
+
+echo $COMMAND
+docker run --runtime=nvidia -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host -t -i $IMAGE bash -c "$COMMAND"
diff --git a/PyTorch/Segmentation/MaskRCNN/weights.md5 b/PyTorch/Segmentation/MaskRCNN/weights.md5
new file mode 100755
index 00000000..35c7a78d
--- /dev/null
+++ b/PyTorch/Segmentation/MaskRCNN/weights.md5
@@ -0,0 +1 @@
+b19105fdd7baafb40480251377de18f2  R-50.pkl
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/.gitmodules b/PyTorch/SpeechSynthesis/Tacotron2/.gitmodules
new file mode 100644
index 00000000..e69de29b
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile b/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile
new file mode 100644
index 00000000..c0972a3d
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile
@@ -0,0 +1,5 @@
+FROM nvcr.io/nvidia/pytorch:18.12.1-py3
+
+ADD . /workspace/tacotron2
+WORKDIR /workspace/tacotron2
+RUN pip install -r requirements.txt
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/LICENCE b/PyTorch/SpeechSynthesis/Tacotron2/LICENCE
new file mode 100644
index 00000000..fc9f02cf
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/LICENCE
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2018, NVIDIA Corporation
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/README.md b/PyTorch/SpeechSynthesis/Tacotron2/README.md
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+# Tacotron 2 And WaveGlow v1.0 For PyTorch
+
+This repository provides a script and recipe to train Tacotron 2 and WaveGlow v1.0 to achieve state of the art accuracy, and is tested and maintained by NVIDIA.
+
+## Table Of Contents
+* [The model](#the-model)
+	* [Default configuration](#default-configuration)
+* [Setup](#setup)
+	* [Requirements](#requirements)
+* [Quick Start Guide](#quick-start-guide)
+* [Details](#details)
+	* [Training process](#training-process)
+		* [Hyperparameters and command line arguments](#hyperparameters-and-command-line-arguments)
+		    * [Shared parameters](#shared-parameters)
+            * [Shared audio/STFT parameters](#shared-audiostft-parameters)
+            * [Tacotron 2 parameters](#tacotron-2-parameters)
+            * [WaveGlow parameters](#waveglow-parameters)
+	* [Enabling mixed precision](#enabling-mixed-precision)
+	* [Inference process](#inference-process)
+* [Benchmarking](#benchmarking)
+	* [Inference performance benchmark](#inference-performance-benchmark)
+	* [Training performance benchmark](#training-performance-benchmark)
+* [Results](#results)
+	* [Training accuracy results](#training-accuracy-results)
+	* [Training performance results](#training-performance-results)
+		* [Expected training time](#expected-training-time)
+	* [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+
+
+# The model
+This text-to-speech (TTS) system is a combination of two neural network models:
+* a modified Tacotron 2 model from the [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884) paper and
+* a flow-based neural network model from the [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://arxiv.org/abs/1811.00002) paper.
+
+The Tacotron 2 and WaveGlow model form a text-to-speech system that enables
+user to synthesise a natural sounding speech from raw transcripts without
+any additional prosody information.
+
+Our implementation of Tacotron 2 model differs from the model described in the
+paper. Our implementation uses Dropout instead of Zoneout to regularize the LSTM layers.
+Also, the original text-to-speech system proposed in the paper used the [WaveNet](https://arxiv.org/abs/1609.03499)
+model to synthesize waveforms.
+In our implementation, we use the WaveGlow model for this purpose.
+
+Both models are based on implementations of NVIDIA GitHub repositories
+[Tacotron 2](https://github.com/NVIDIA/tacotron2) and
+[WaveGlow](https://github.com/NVIDIA/waveglow), and are trained on a publicly
+available [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/).
+
+This model trains with mixed precision tensor cores on Volta, therefore researchers
+can get results much faster than training without tensor cores.  This model is
+tested against each NGC monthly container release to ensure consistent accuracy
+and performance over time.
+
+## Default configuration
+The Tacotron 2 model produces mel spectrograms from input text using
+encoder-decoder architecture. WaveGlow is a flow-based model that consumes the
+mel spectrograms to generate speech. Both models support multi-gpu and mixed
+precision training with dynamic loss scaling (see Apex code [here](https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py)),
+as well as mixed precision inference.
+
+# Setup
+The following sections list the requirements in order to
+start training the Tacotron 2 and WaveGlow models.
+
+## Requirements
+This repository contains `Dockerfile` which extends the PyTorch NGC container
+and encapsulates some dependencies. Aside from these dependencies, ensure you
+have the following components:
+
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [PyTorch 18.12.1-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer
+* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
+Documentation:
+
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)
+
+# Quick Start Guide
+To train your model using mixed precision with tensor cores or using FP32,
+perform the following steps using the default parameters of the Tacrotron 2
+and WaveGlow model on the [LJ Speech](https://keithito.com/LJ-Speech-Dataset/) dataset.
+
+## 1. Clone the repository.
+```bash
+git clone https://github.com/NVIDIA/DeepLearningExamples.git
+cd DeepLearningExamples/PyTorch/SpeechSynthesis/Tacotron2
+```
+
+## 2. Download and preprocess the dataset.
+Use the `./scripts/prepare-dataset.sh` download script to automatically
+download and preprocess the training, validation and test datasets. To run this script, issue:
+```bash
+bash scripts/prepare-dataset.sh
+```
+Data is downloaded to the `./LJSpeech-1.1` directory (on the host).  The
+`./LJSpeech-1.1` directory is mounted to the `/workspace/tacotron2/LJSpeech-1.1`
+location in the NGC container.  The script will also generate the necessary
+filelists for training and validation in `./filelists` if they are not already present.
+
+## 3. Build the Tacotron 2 and WaveGlow PyTorch NGC container.
+```bash
+bash scripts/docker/build.sh
+```
+
+## 4. Start an interactive session in the NGC container to run training/inference.
+After you build the container image, you can start an interactive CLI session with
+
+```bash
+bash scripts/docker/interactive.sh
+```
+
+The `interactive.sh` script requires that the location on the dataset is specified.
+For example, `LJSpeech-1.1`.
+
+## 5. Start training.
+To run Tacotron 2 training, run:
+```bash
+bash scripts/train_tacotron2.sh
+```
+
+To run WaveGlow training, run:
+```bash
+bash scripts/train_waveglow.sh
+```
+
+## 6. Start validation/evaluation.
+Ensure your loss values are comparable to those listed in the table in the 
+Results section. For both models, the loss values are stored in the 
+`./output/nvlog.json` log file. 
+
+After you have trained the Tacotron 2 model for 1500 epochs and the 
+WaveGlow model for 800 epochs, you should get audio results similar to the 
+samples in the `./audio` folder. For details about generating audio, see the 
+[Inference process](#inference-process) section below.
+
+The training scripts automatically run the validation after each training 
+epoch. The results from the validation are printed to the standard output 
+(`stdout`) and saved to the log files.
+
+## 7. Start inference.
+After you have trained the Tacotron 2 and WaveGlow models, you can perform 
+inference using the respective checkpoints that are passed as `--tacotron2` 
+and `--waveglow` arguments. 
+
+To run inference issue:
+```bash
+python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> -o output/ -i text.txt --fp16-run
+```
+The speech is generated from text file passed with `-i` argument. To run 
+inference in mixed precision, use `--fp16-run` flag. The output audio will 
+be stored in the path specified by `-o` argument.
+
+# Details
+The following sections provide greater details of the dataset, running training 
+and inference, and the training results.
+
+## Training process
+The Tacotron2 and WaveGlow models are trained separately and independently. 
+Both models obtain mel spectrograms from short time Fourier transform (STFT) 
+during training. These mel spectrograms are used for loss computation in case 
+of Tacotron 2 and as conditioning input to the network in case of WaveGlow.
+
+The training loss is averaged over an entire training epoch, whereas the 
+validation loss is averaged over the validation dataset. Performance is 
+reported in total input tokens per second for the Tacotron 2 model, and 
+in total output samples per second for the WaveGlow model. Both measures are 
+recorded as `train_iter_items/sec` (after each iteration) and `train_epoch_items/sec` 
+(averaged over epoch) in the output log. The result is averaged over an 
+entire training epoch and summed over all GPUs that were included in the training.
+
+Even though the training script uses all available GPUs, you can change 
+this behavior by setting the `CUDA_VISIBLE_DEVICES` variable in your 
+environment or by setting the `NV_GPU` variable at the Docker container launch 
+([see section "GPU isolation"](https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker#gpu-isolation)). 
+
+### Hyperparameters and command line arguments
+Here, we list the most important hyperparameters and command line arguments, 
+together with their default values that are used to train Tacotron 2 and 
+WaveGlow models.
+
+#### Shared parameters
+`--epochs` - number of epochs (Tacotron 2: 1500, WaveGlow: 1000)
+
+`--learning-rate` - learning rate (Tacotron 2: 1e-3, WaveGlow: 1e-4)
+
+`--batch-size` - batch size (Tacotron 2 FP16/FP32: 80/48, WaveGlow FP16/FP32: 8/4)
+
+`--fp16-run` - use mixed precision training
+
+#### Shared audio/STFT parameters
+`--sampling-rate` - Sampling rate in Hz of input and output audio (22050)
+
+`--filter-length` - (1024)
+
+`--hop-length` - Hop length for FFT, i.e., sample stride between consecutive FFTs (256)
+
+`--win-length` - Window size for FFT (1024)
+
+`--mel-fmin` - Lowest frequency in Hz (0.0)
+
+`--mel-fmax` - Highest frequency in Hz (8.000)
+
+#### Tacotron 2 parameters
+`--anneal-steps` - epochs at which to anneal the learning rate (500 1000 1500)
+
+`--anneal-factor` - factor by which to anneal the learning rate (FP16/FP32: 0.3/0.1)
+
+#### WaveGlow parameters
+`--segment-length` - segment length of input audio processed by the neural network (8000)
+
+
+## Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant 
+computational speedup by performing operations in half-precision format, while 
+storing minimal information in single-precision to retain as much information as 
+possible in critical parts of the network. Since the introduction of 
+[tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing 
+architectures, significant training speedups are experienced by switching to 
+mixed precision -- up to 3x overall speedup on the most arithmetically intense 
+model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) 
+previously required two steps:
+
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+ 
+
+Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision 
+(AMP),  library from [APEX](https://github.com/NVIDIA/apex) that casts variables 
+to half-precision upon retrieval, while storing variables in single-precision 
+format. Furthermore, to preserve small gradient magnitudes in backpropagation, 
+a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) 
+step must be included when applying gradients. In PyTorch, loss scaling can be 
+easily applied by using `scale_loss()` method provided by AMP. The scaling value 
+to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.
+
+By default, the `train_tacotron2.sh` and `train_waveglow.sh` scripts will launch 
+mixed precision training with tensor cores. You can change this behaviour by 
+removing the `--fp16-run` flag from the `train.py` script.
+
+For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). 
+[APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains 
+utility libraries, such as AMP, which require minimal network code changes to 
+leverage tensor cores performance.
+
+To enable mixed precision, you can:
+* Import AMP from APEX, for example: 
+    ```bash
+    from apex import amp
+    ```
+
+* Initialize an AMP handle, for example: 
+    ```bash
+    amp_handle = amp.init(enabled=True, verbose=True)
+    ```
+
+* Wrap your optimizer with the AMP handle, for example:
+    ```bash
+    optimizer = amp_handle.wrap_optimizer(optimizer)    
+    ```
+
+* Scale loss before backpropagation (assuming loss is stored in a variable called losses)
+    * Default backpropagate for FP32:
+        ```bash
+        losses.backward()
+        ```
+    
+    * Scale loss and backpropagate with AMP:
+        ```bash
+        with optimizer.scale_loss(losses) as scaled_losses:
+            scaled_losses.backward()
+        ````
+
+For information about:
+* How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) 
+paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+* Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+* APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).
+
+## Inference process
+You can run inference using the `./inference.py` script. This script takes text 
+as input, and runs Tacotron 2 and then WaveGlow inference to produce an audio 
+file. It requires  pre-trained checkpoints from Tacotron 2 and WaveGlow models 
+and input text as a text file, with one phrase per line.
+
+To run inference, issue:
+```bash
+python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> -o output/ -i text.txt --fp16-run
+```
+Here, `Tacotron2_checkpoint` and `WaveGlow_checkpoint` are pre-trained 
+checkpoints for the respective models, and `text.txt` contains input phrases. 
+Audio will be saved in the output folder.
+
+You can find all available options by calling `python inference.py --help`.
+
+# Benchmarking
+The following section shows how to run benchmarks measuring the model 
+performance in training mode.
+
+## Inference performance benchmark
+To benchmark the inference performance on a batch size=1, run:
+
+* For FP32
+    ```bash
+    python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> -o output/ -i text.txt --log-file=output/nvlog_fp32.json
+    ```
+* For FP16
+    ```bash
+    python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> -o output/ -i text.txt --fp16-run--log-file=output/nvlog_fp16.json
+    ```
+
+The log files will contain performance numbers for Tacotron 2 model 
+(number of input tokens per second, reported as `tacotron2_items_per_sec`) 
+and for WaveGlow (number of output samples per second, reported as 
+`waveglow_items_per_sec`).
+
+## Training performance benchmark
+To benchmark the training performance on a specific batch size, run:
+
+**Tacotron 2**
+
+* For 1 GPU
+	* FP32
+        ```bash
+        python train.py -m Tacotron2 -o <output_dir> -lr 1e-3 --epochs 10 -bs <batch_size> --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt --dataset-path <dataset-path>
+        ```
+	* FP16
+        ```bash
+        python train.py -m Tacotron2 -o <output_dir> -lr 1e-3 --epochs 10 -bs <batch_size> --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt --dataset-path <dataset-path> --fp16-run
+        ```
+
+* For multiple GPUs
+	* FP32
+        ```bash
+        python -m multiproc train.py -m Tacotron2 -o <output_dir> -lr 1e-3 --epochs 10 -bs <batch_size> --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt --dataset-path <dataset-path>
+        ```
+	* FP16
+        ```bash
+        python -m multiproc train.py -m Tacotron2 -o <output_dir> -lr 1e-3 --epochs 10 -bs <batch_size> --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt --dataset-path <dataset-path> --fp16-run
+        ```
+
+**WaveGlow**
+
+* For 1 GPU
+	* FP32
+        ```bash
+        python train.py -m WaveGlow -o <output_dir> -lr 1e-4 --epochs 10 -bs <batch_size> --segment-length  8000 --weight-decay 0 --grad-clip-thresh 3.4028234663852886e+38 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt --dataset-path <dataset-path>
+        ```
+	* FP16
+        ```bash
+        python train.py -m WaveGlow -o <output_dir> -lr 1e-4 --epochs 10 -bs <batch_size> --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt --dataset-path <dataset-path> --fp16-run
+        ```
+
+* For multiple GPUs
+	* FP32
+        ```bash
+        python -m multiproc train.py -m WaveGlow -o <output_dir> -lr 1e-4 --epochs 10 -bs <batch_size> --segment-length 8000 --weight-decay 0 --grad-clip-thresh 3.4028234663852886e+38 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt --dataset-path <dataset-path>
+        ```
+	* FP16
+        ```bash
+        python -m multiproc train.py -m WaveGlow -o <output_dir> -lr 1e-4 --epochs 10 -bs <batch_size> --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --cudnn-benchmark=True --log-file nvlog.json --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt --dataset-path <dataset-path> --fp16-run
+        ```
+
+Each of these scripts runs for 10 epochs and for each epoch measures the averaged number of items per second. The performance results can be read from the nvlog.json files produced by the commands.
+
+# Results
+The following sections provide details on how we achieved our performance 
+and accuracy in training and inference.
+
+## Training accuracy results
+Our results were obtained by running the `./platform/train_{tacotron2,waveglow}_{FP16,FP32}_DGX1_16GB_8GPU.sh` 
+training script in the PyTorch-18.12.1-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+
+All of the results were produced using the `train.py` as described in the 
+[Training process](#training-process) section of this document.
+
+| Loss (Model/Epoch) |       1 |     250 |     500 |     750 |    1000 |
+| :----------------: | ------: | ------: | ------: | ------: | ------: |
+| **Tacotron 2 FP16** | 26.7176 |   0.473 |  0.3985 |  0.3725 |  0.3645 |
+| **Tacotron 2 FP32** |  5.3406 |  0.4317 |  0.3699 |  0.3635 |  0.3629 |
+| **WaveGlow FP16**  | -2.2054 | -5.7602 |  -5.901 | -5.9706 | -6.0258 |
+| **WaveGlow FP32**  | -3.0327 |  -5.858 | -6.0056 | -6.0613 | -6.1087 |
+
+Tacotron 2 FP16 loss - batch size 80 (mean and std over 16 runs)
+![](./img/tacotron2_fp16_loss.png "Tacotron 2 FP16 loss")
+Tacotron 2 FP32 loss - batch size 48 (mean and std over 16 runs)
+![](./img/tacotron2_fp32_loss.png "Tacotron 2 FP16 loss")
+WaveGlow FP16 loss - batch size 8 (mean and std over 16 runs)
+![](./img/waveglow_fp16_loss.png "WaveGlow FP16 loss")
+WaveGlow FP32 loss - batch size 4 (mean and std over 16 runs)
+![](./img/waveglow_fp32_loss.png "WaveGlow FP32 loss")
+
+## Training performance results
+Our results were obtained by running the `./platform/train_{tacotron2,waveglow}_{FP16,FP32}_DGX1_16GB_8GPU.sh` 
+training script in the PyTorch-18.12.1-py3 NGC container on NVIDIA DGX-1 with 
+8x V100 16G GPUs. Performance numbers (in input tokens per second for 
+Tacotron 2 and output samples per second for WaveGlow) were averaged over 
+an entire training epoch.
+
+This table shows the results for Tacotron 2, with batch size equal 48 and 80 
+for mixed precision and FP32 training, respectively.
+
+|Number of GPUs|Mixed precision tokens/sec|FP32 tokens/sec|Speed-up with mixed precision|Multi-gpu weak scaling with mixed precision|Multi-gpu weak scaling with FP32|
+|---:|---:|---:|---:|---:|---:|
+|**1**|2,424|1,826|1.33|1.00|1.00|
+|**4**|7,280|5,944|1.22|3.00|3.26|
+|**8**|12,742|10,843|1.18|5.26|5.94|
+
+The following table shows the results for WaveGlow, with batch size equal 4 and 8 for mixed precision and FP32 training, respectively.
+
+|Number of GPUs|Mixed precision samples/sec|FP32 samples/sec|Speed-up with mixed precision|Multi-gpu weak scaling with mixed precision|Multi-gpu weak scaling with FP32|
+|---:|---:|---:|---:|---:|---:|
+|**1**| 70,362 | 35,180 | 2.00 | 1.00 | 1.00 |
+|**4**| 215,380 | 118,961 | 1.81 | 3.06 | 3.38 |
+|**8**| 500,375 | 257,687 | 1.94 | 7.11 | 7.32 |
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+### Expected training time
+
+This table shows the expected training time for convergence for Tacotron 2 (1500 epochs).
+
+|Number of GPUs|Expected training time with mixed precision|Expected training time with FP32|Speed-up with mixed precision|
+|---:|---:|---:|---:|
+|**1**| 208.00 | 288.03 | 1.38 | 
+|**4**| 67.53 | 84.20 | 1.25 |
+|**8**| 33.14 | 44.00 | 1.33 |
+
+This table shows the expected training time for convergence for WaveGlow (1000 epochs).
+
+|Number of GPUs|Expected training time with mixed precision|Expected training time with FP32|Speed-up with mixed precision|
+|---:|---:|---:|---:|
+|**1**| 437.03 | 814.30 | 1.86 |
+|**4**|	108.26 | 223.04 | 2.06 |
+|**8**|	54.83 | 109.96 | 2.01 |
+
+## Inference performance results
+Our results were obtained by running the `./inference.py` inference script in the 
+PyTorch-18.12.1-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+Performance numbers (in input tokens per second for Tacotron 2 and output 
+samples per second for WaveGlow) were averaged over 16 runs.
+
+
+This table shows the inference performance results for Tacotron 2.
+Results are measured in the number of input tokens per second.
+
+|Number of GPUs|Mixed precision tokens/sec|FP32 tokens/sec|Speed-up with mixed precision|
+|---:|---:|---:|---:|
+|**1**|170|178|0.96|
+
+
+This table shows the inference performance results for WaveGlow. 
+Results are measured in the number of output audio samples per second.<sup>1</sup>
+
+|Number of GPUs|Mixed precision samples/sec|FP32 samples/sec|Speed-up with mixed precision|
+|---:|---:|---:|---:|
+|**1**|537525|404206|1.33|
+
+<sup>1</sup>With sampling rate equal to 22050, one second of audio is generated from 22050 samples.
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+# Changelog
+March 2019
+
+* Initial release
+
+# Known issues
+For mixed precision training of Tacotron 2, dropouts on LSTMCells 
+cause overflow leading to dynamic loss scaling going to 1, see [here](https://github.com/NVIDIA/tacotron2/issues/112). 
+The current workaround, which is already applied in our model implementation, 
+is to convert `attention_rnn` and `decoder_rnn` back to FP32 precision.
\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/audio/audio_fp16.wav b/PyTorch/SpeechSynthesis/Tacotron2/audio/audio_fp16.wav
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/common/audio_processing.py b/PyTorch/SpeechSynthesis/Tacotron2/common/audio_processing.py
new file mode 100644
index 00000000..84bd1224
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/common/audio_processing.py
@@ -0,0 +1,120 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import torch
+import numpy as np
+from scipy.signal import get_window
+import librosa.util as librosa_util
+
+
+def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
+                     n_fft=800, dtype=np.float32, norm=None):
+    """
+    # from librosa 0.6
+    Compute the sum-square envelope of a window function at a given hop length.
+
+    This is used to estimate modulation effects induced by windowing
+    observations in short-time fourier transforms.
+
+    Parameters
+    ----------
+    window : string, tuple, number, callable, or list-like
+        Window specification, as in `get_window`
+
+    n_frames : int > 0
+        The number of analysis frames
+
+    hop_length : int > 0
+        The number of samples to advance between frames
+
+    win_length : [optional]
+        The length of the window function.  By default, this matches `n_fft`.
+
+    n_fft : int > 0
+        The length of each analysis frame.
+
+    dtype : np.dtype
+        The data type of the output
+
+    Returns
+    -------
+    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
+        The sum-squared envelope of the window function
+    """
+    if win_length is None:
+        win_length = n_fft
+
+    n = n_fft + hop_length * (n_frames - 1)
+    x = np.zeros(n, dtype=dtype)
+
+    # Compute the squared window at the desired length
+    win_sq = get_window(window, win_length, fftbins=True)
+    win_sq = librosa_util.normalize(win_sq, norm=norm)**2
+    win_sq = librosa_util.pad_center(win_sq, n_fft)
+
+    # Fill the envelope
+    for i in range(n_frames):
+        sample = i * hop_length
+        x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
+    return x
+
+
+def griffin_lim(magnitudes, stft_fn, n_iters=30):
+    """
+    PARAMS
+    ------
+    magnitudes: spectrogram magnitudes
+    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
+    """
+
+    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
+    angles = angles.astype(np.float32)
+    angles = torch.autograd.Variable(torch.from_numpy(angles))
+    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+
+    for i in range(n_iters):
+        _, angles = stft_fn.transform(signal)
+        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+    return signal
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/common/layers.py b/PyTorch/SpeechSynthesis/Tacotron2/common/layers.py
new file mode 100644
index 00000000..b3c7ecf4
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/common/layers.py
@@ -0,0 +1,106 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from common.audio_processing import dynamic_range_compression, dynamic_range_decompression
+from common.stft import STFT
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert(kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        return self.conv(signal)
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
+                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
+                 mel_fmax=8000.0):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        mel_basis = librosa_mel_fn(
+            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer('mel_basis', mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert(torch.min(y.data) >= -1)
+        assert(torch.max(y.data) <= 1)
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        return mel_output
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py b/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py
new file mode 100644
index 00000000..9655190d
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py
@@ -0,0 +1,140 @@
+"""
+BSD 3-Clause License
+
+Copyright (c) 2017, Prem Seetharaman
+All rights reserved.
+
+* Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import torch
+import numpy as np
+import torch.nn.functional as F
+from torch.autograd import Variable
+from scipy.signal import get_window
+from librosa.util import pad_center, tiny
+from common.audio_processing import window_sumsquare
+
+
+class STFT(torch.nn.Module):
+    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
+    def __init__(self, filter_length=800, hop_length=200, win_length=800,
+                 window='hann'):
+        super(STFT, self).__init__()
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = window
+        self.forward_transform = None
+        scale = self.filter_length / self.hop_length
+        fourier_basis = np.fft.fft(np.eye(self.filter_length))
+
+        cutoff = int((self.filter_length / 2 + 1))
+        fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
+                                   np.imag(fourier_basis[:cutoff, :])])
+
+        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
+        inverse_basis = torch.FloatTensor(
+            np.linalg.pinv(scale * fourier_basis).T[:, None, :])
+
+        if window is not None:
+            assert(filter_length >= win_length)
+            # get window and zero center pad it to filter_length
+            fft_window = get_window(window, win_length, fftbins=True)
+            fft_window = pad_center(fft_window, filter_length)
+            fft_window = torch.from_numpy(fft_window).float()
+
+            # window the bases
+            forward_basis *= fft_window
+            inverse_basis *= fft_window
+
+        self.register_buffer('forward_basis', forward_basis.float())
+        self.register_buffer('inverse_basis', inverse_basis.float())
+
+    def transform(self, input_data):
+        num_batches = input_data.size(0)
+        num_samples = input_data.size(1)
+
+        self.num_samples = num_samples
+
+        # similar to librosa, reflect-pad the input
+        input_data = input_data.view(num_batches, 1, num_samples)
+        input_data = F.pad(
+            input_data.unsqueeze(1),
+            (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
+            mode='reflect')
+        input_data = input_data.squeeze(1)
+
+        forward_transform = F.conv1d(
+            input_data,
+            Variable(self.forward_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        cutoff = int((self.filter_length / 2) + 1)
+        real_part = forward_transform[:, :cutoff, :]
+        imag_part = forward_transform[:, cutoff:, :]
+
+        magnitude = torch.sqrt(real_part**2 + imag_part**2)
+        phase = torch.autograd.Variable(
+            torch.atan2(imag_part.data, real_part.data))
+
+        return magnitude, phase
+
+    def inverse(self, magnitude, phase):
+        recombine_magnitude_phase = torch.cat(
+            [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
+
+        inverse_transform = F.conv_transpose1d(
+            recombine_magnitude_phase,
+            Variable(self.inverse_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        if self.window is not None:
+            window_sum = window_sumsquare(
+                self.window, magnitude.size(-1), hop_length=self.hop_length,
+                win_length=self.win_length, n_fft=self.filter_length,
+                dtype=np.float32)
+            # remove modulation effects
+            approx_nonzero_indices = torch.from_numpy(
+                np.where(window_sum > tiny(window_sum))[0])
+            window_sum = torch.autograd.Variable(
+                torch.from_numpy(window_sum), requires_grad=False)
+            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
+
+            # scale by hop ratio
+            inverse_transform *= float(self.filter_length) / self.hop_length
+
+        inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
+        inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
+
+        return inverse_transform
+
+    def forward(self, input_data):
+        self.magnitude, self.phase = self.transform(input_data)
+        reconstruction = self.inverse(self.magnitude, self.phase)
+        return reconstruction
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/common/utils.py b/PyTorch/SpeechSynthesis/Tacotron2/common/utils.py
new file mode 100644
index 00000000..ac1fdadc
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/common/utils.py
@@ -0,0 +1,65 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+import os
+
+
+def get_mask_from_lengths(lengths):
+    max_len = torch.max(lengths).item()
+    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
+    mask = (ids < lengths.unsqueeze(1)).byte()
+    return mask
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(dataset_path, filename, split="|"):
+    with open(filename, encoding='utf-8') as f:
+        def split_line(root, line):
+            parts = line.strip().split(split)
+            if len(parts) > 2:
+                raise Exception(
+                    "incorrect line format for file: {}".format(filename))
+            path = os.path.join(root, parts[0])
+            text = parts[1]
+            return path,text
+        filepaths_and_text = [split_line(dataset_path, line) for line in f]
+    return filepaths_and_text
+
+
+def to_gpu(x):
+    x = x.contiguous()
+
+    if torch.cuda.is_available():
+        x = x.cuda(non_blocking=True)
+    return torch.autograd.Variable(x)
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/data_functions.py b/PyTorch/SpeechSynthesis/Tacotron2/data_functions.py
new file mode 100644
index 00000000..8d00d1da
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/data_functions.py
@@ -0,0 +1,68 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import torch
+from tacotron2.data_function import TextMelCollate
+from tacotron2.data_function import TextMelLoader
+from waveglow.data_function import MelAudioLoader
+from tacotron2.data_function import batch_to_gpu as batch_to_gpu_tacotron2
+from waveglow.data_function import batch_to_gpu as batch_to_gpu_waveglow
+
+
+def get_collate_function(model_name, n_frames_per_step):
+    if model_name == 'Tacotron2':
+        collate_fn = TextMelCollate(n_frames_per_step)
+    elif model_name == 'WaveGlow':
+        collate_fn = torch.utils.data.dataloader.default_collate
+    else:
+        raise NotImplementedError(
+            "unknown collate function requested: {}".format(model_name))
+
+    return collate_fn
+
+
+def get_data_loader(model_name, dataset_path, audiopaths_and_text, args):
+    if model_name == 'Tacotron2':
+        data_loader = TextMelLoader(dataset_path, audiopaths_and_text, args)
+    elif model_name == 'WaveGlow':
+        data_loader = MelAudioLoader(dataset_path, audiopaths_and_text, args)
+    else:
+        raise NotImplementedError(
+            "unknown data loader requested: {}".format(model_name))
+
+    return data_loader
+
+
+def get_batch_to_gpu(model_name):
+    if model_name == 'Tacotron2':
+        batch_to_gpu = batch_to_gpu_tacotron2
+    elif model_name == 'WaveGlow':
+        batch_to_gpu = batch_to_gpu_waveglow
+    else:
+        raise NotImplementedError(
+            "unknown batch_to_gpu requested: {}".format(model_name))
+    return batch_to_gpu
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/dllogger/LICENSE b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/LICENSE
new file mode 100644
index 00000000..93144ec8
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/LICENSE
@@ -0,0 +1,12 @@
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/dllogger/__init__.py b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/dllogger/autologging.py b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/autologging.py
new file mode 100644
index 00000000..7dddad82
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/autologging.py
@@ -0,0 +1,61 @@
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import subprocess
+import xml.etree.ElementTree as ET
+
+from dllogger.logger import LOGGER
+
+#TODO: print CUDA version, container version etc
+
+def log_hardware():
+    # TODO: asserts - what if you cannot launch those commands?
+    # number of CPU threads
+    cpu_info_command = 'cat /proc/cpuinfo'
+    cpu_info = subprocess.run(cpu_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    cpu_num_index = len(cpu_info) - cpu_info[::-1].index(b'processor') + 1
+    cpu_num = int(cpu_info[cpu_num_index]) + 1
+
+    # CPU name
+    cpu_name_begin_index = cpu_info.index(b'name')
+    cpu_name_end_index = cpu_info.index(b'stepping')
+    cpu_name = b' '.join(cpu_info[cpu_name_begin_index + 2:cpu_name_end_index]).decode('utf-8')
+
+    LOGGER.log(key='cpu_info', value={"num": cpu_num, "name": cpu_name})
+
+    # RAM memory
+    ram_info_command = 'free -m -h'
+    ram_info = subprocess.run(ram_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    ram_index = ram_info.index(b'Mem:') + 1
+    ram = ram_info[ram_index].decode('utf-8')
+
+    LOGGER.log(key='mem_info', value={"ram": ram})
+
+    # GPU
+    nvidia_smi_command = 'nvidia-smi -q -x'
+    nvidia_smi_output = subprocess.run(nvidia_smi_command.split(), stdout=subprocess.PIPE).stdout
+    nvidia_smi = ET.fromstring(nvidia_smi_output)
+    gpus = nvidia_smi.findall('gpu')
+    ver = nvidia_smi.findall('driver_version')
+
+    LOGGER.log(key="gpu_info",
+                 value={
+                      "driver_version": ver[0].text,
+                      "num": len(gpus),
+                      "name": [g.find('product_name').text for g in gpus],
+                      "mem": [g.find('fb_memory_usage').find('total').text for g in gpus]})
+
+def log_args(args):
+    LOGGER.log(key='args', value=vars(args))
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/dllogger/logger.py b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/logger.py
new file mode 100644
index 00000000..47c41bbe
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/logger.py
@@ -0,0 +1,429 @@
+# Copyright 2018 MLBenchmark Group. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import json
+import logging
+import os
+import inspect
+import sys
+import re
+from contextlib import contextmanager
+import functools
+from collections import OrderedDict
+
+NVLOGGER_NAME = 'nv_logger'
+NVLOGGER_VERSION = '0.2.2'
+NVLOGGER_TOKEN = ':::NVLOG'
+
+MLPERF_NAME = 'mlperf_logger'
+MLPERF_VERSION = '0.5.0'
+MLPERF_TOKEN = ':::MLP'
+
+DEFAULT_JSON_FILENAME = 'nvlog.json'
+
+RUN_SCOPE = 0
+EPOCH_SCOPE = 1
+TRAIN_ITER_SCOPE = 2
+
+_data = OrderedDict([
+    ('model', None),
+    ('epoch', -1),
+    ('iteration', -1),
+    ('total_iteration', -1),
+    ('metrics', OrderedDict()),
+    ('timed_blocks', OrderedDict()),
+    ('current_scope', RUN_SCOPE)
+    ])
+
+def get_caller(stack_index=2, root_dir=None):
+    caller = inspect.getframeinfo(inspect.stack()[stack_index][0])
+
+    # Trim the file names for readability.
+    filename = caller.filename
+    if root_dir is not None:
+        filename = re.sub("^" + root_dir + "/", "", filename)
+    return "%s:%d" % (filename, caller.lineno)
+
+class StandardMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        pass
+
+    def record(self, value):
+        self.value = value
+
+    def get_value(self):
+        return self.value
+
+    def get_last(self):
+        return self.value
+
+class AverageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.n = 0
+        self.value = 0
+        self.last = 0
+
+    def record(self, value, n = 1):
+        self.last = value
+        self.n += n
+        self.value += value * n
+
+    def get_value(self):
+        return self.value / self.n
+
+    def get_last(self):
+        return self.last
+
+class JsonBackend(object):
+
+    def __init__(self, log_file=DEFAULT_JSON_FILENAME, logging_scope=TRAIN_ITER_SCOPE,
+            iteration_interval=1):
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.json_log = OrderedDict([
+            ('run', OrderedDict()),
+            ('epoch', OrderedDict()),
+            ('iter', OrderedDict()),
+            ('event', OrderedDict()),
+            ])
+        
+        self.json_log['epoch']['x'] = []
+        if self.logging_scope == TRAIN_ITER_SCOPE:
+            self.json_log['iter']['x'] = [[]]
+
+    def register_metric(self, key, metric_scope):
+        if (metric_scope == TRAIN_ITER_SCOPE and 
+                self.logging_scope == TRAIN_ITER_SCOPE):
+            if not key in self.json_log['iter'].keys():
+                self.json_log['iter'][key] = [[]]
+        if metric_scope == EPOCH_SCOPE:
+            if not key in self.json_log['epoch'].keys():
+                self.json_log['epoch'][key] = []
+
+    def log(self, key, value):
+        if _data['current_scope'] == RUN_SCOPE:
+            self.json_log['run'][key] = value
+        elif _data['current_scope'] == EPOCH_SCOPE: 
+            pass
+        elif _data['current_scope'] == TRAIN_ITER_SCOPE:
+            pass
+        else:
+            raise ValueError('log function for scope "', _data['current_scope'], 
+                    '" not implemented')
+    
+    def log_event(self, key, value):
+        if not key in self.json_log['event'].keys():
+            self.json_log['event'][key] = []
+        entry = OrderedDict()
+        entry['epoch'] = _data['epoch']
+        entry['iter'] = _data['iteration']
+        entry['timestamp'] = time.time()
+        if value:
+            entry['value'] = value
+        self.json_log['event'][key].append(entry)
+
+    def log_iteration_summary(self):
+        if (self.logging_scope == TRAIN_ITER_SCOPE and 
+                _data['total_iteration'] % self.iteration_interval == 0):
+            for key, m in _data['metrics'].items():
+                if m.metric_scope == TRAIN_ITER_SCOPE:
+                    self.json_log['iter'][key][-1].append(m.get_last())
+
+            # log x for iteration number
+            self.json_log['iter']['x'][-1].append(_data['iteration'])
+
+
+    def dump_json(self):
+        if self.log_file is None:
+            print(json.dumps(self.json_log, indent=4))
+        else:
+            with open(self.log_file, 'w') as f:
+                json.dump(self.json_log, fp=f, indent=4)
+
+    def log_epoch_summary(self):
+        for key, m in _data['metrics'].items():
+            if m.metric_scope == EPOCH_SCOPE:
+                self.json_log['epoch'][key].append(m.get_value())
+            elif (m.metric_scope == TRAIN_ITER_SCOPE and 
+                    self.logging_scope == TRAIN_ITER_SCOPE):
+                # create new sublists for each iter metric in the next epoch
+                self.json_log['iter'][key].append([])
+        
+        # log x for epoch number
+        self.json_log['epoch']['x'].append(_data['epoch'])
+
+        # create new sublist for iter's x in the next epoch
+        if self.logging_scope == TRAIN_ITER_SCOPE:
+            self.json_log['iter']['x'].append([])
+
+        self.dump_json()
+
+    def timed_block_start(self, name):
+        pass
+
+    def timed_block_stop(self, name):
+        pass
+
+    def finish(self):
+        self.dump_json()
+
+class _ParentStdOutBackend(object):
+
+    def __init__(self, name, token, version, log_file, logging_scope, iteration_interval):
+
+        self.root_dir = None
+        self.worker = [0]
+        self.prefix = ''
+
+        self.name = name
+        self.token = token
+        self.version = version
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.logger = logging.getLogger(self.name)
+        self.logger.setLevel(logging.DEBUG)
+        self.logger.handlers = []
+
+        if (self.log_file == None):
+            self.stream_handler = logging.StreamHandler(stream=sys.stdout)
+            self.stream_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.stream_handler)
+        else:
+            self.file_handler = logging.FileHandler(self.log_file, mode='w')
+            self.file_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.file_handler)
+
+    def register_metric(self, key, meter=None, metric_scope=EPOCH_SCOPE):
+        pass
+
+    def log_epoch_summary(self):
+        pass
+
+    def log_iteration_summary(self):
+        pass
+
+    def log(self, key, value):
+        if _data['current_scope'] > self.logging_scope:
+            pass
+        elif (_data['current_scope'] == TRAIN_ITER_SCOPE and 
+                _data['total_iteration'] % self.iteration_interval != 0):
+            pass
+        else:
+            self.log_stdout(key, value)
+
+    def log_event(self, key, value):
+        self.log_stdout(key, value)
+        
+    def log_stdout(self, key, value=None, forced=False):
+        # TODO: worker 0 
+        # only the 0-worker will log
+        #if not forced and self.worker != 0:
+        #    pass
+
+        if value is None:
+            msg = key
+        else:
+            str_json = json.dumps(value)
+            msg = '{key}: {value}'.format(key=key, value=str_json)
+
+        call_site = get_caller(root_dir=self.root_dir)
+        now = time.time()
+
+        message = '{prefix}{token}v{ver} {model} {secs:.9f} ({call_site}) {msg}'.format(
+            prefix=self.prefix, token=self.token, ver=self.version, secs=now, 
+            model=_data['model'],
+            call_site=call_site, msg=msg)
+
+        self.logger.debug(message)
+
+    def timed_block_start(self, name):
+        self.log_stdout(key=name + "_start")
+
+    def timed_block_stop(self, name):
+        self.log_stdout(key=name + "_stop")
+
+    def finish(self):
+        pass
+
+class StdOutBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=EPOCH_SCOPE, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=NVLOGGER_NAME, token=NVLOGGER_TOKEN, 
+                version=NVLOGGER_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+        
+class MLPerfBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=TRAIN_ITER_SCOPE, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=MLPERF_NAME, token=MLPERF_TOKEN, 
+                version=MLPERF_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+
+class _Logger(object):
+    def __init__(self):
+
+        self.backends = [
+                StdOutBackend(),
+                JsonBackend()
+                ]
+   
+    def set_model_name(self, name):
+        _data['model'] = name
+
+
+    def set_backends(self, backends):
+        self.backends = backends
+
+        
+    def register_metric(self, key, meter=None, metric_scope=EPOCH_SCOPE):
+        if meter == None:
+            meter = StandardMeter()
+        #TODO: move to argument of Meter?
+        meter.metric_scope = metric_scope
+        _data['metrics'][key] = meter
+        for b in self.backends:
+            b.register_metric(key, metric_scope)
+
+    def log(self, key, value=None, forced=False):
+        if _data['current_scope'] == TRAIN_ITER_SCOPE or _data['current_scope'] == EPOCH_SCOPE:
+            if key in _data['metrics'].keys():
+                if _data['metrics'][key].metric_scope == _data['current_scope']:
+                    _data['metrics'][key].record(value)
+        for b in self.backends:
+            b.log(key, value)
+
+    def log_event(self, key, value=None):
+        for b in self.backends:
+            b.log_event(key, value)
+    
+    def timed_block_start(self, name):
+        if not name in _data['timed_blocks']:
+            _data['timed_blocks'][name] = OrderedDict()
+        _data['timed_blocks'][name]['start'] = time.time()
+        for b in self.backends:
+            b.timed_block_start(name)
+    
+    def timed_block_stop(self, name):
+        if not name in _data['timed_blocks']:
+            raise ValueError('timed_block_stop called before timed_block_start for ' + name)
+        _data['timed_blocks'][name]['stop'] = time.time()
+        delta = _data['timed_blocks'][name]['stop'] - _data['timed_blocks'][name]['start']
+        self.log(name + '_time', delta)
+        for b in self.backends:
+            b.timed_block_stop(name)
+
+    def iteration_start(self):
+        _data['current_scope'] = TRAIN_ITER_SCOPE
+        _data['iteration'] += 1
+        _data['total_iteration'] += 1
+
+
+    def iteration_stop(self):
+        for b in self.backends:
+            b.log_iteration_summary()
+        _data['current_scope'] = EPOCH_SCOPE
+
+    def epoch_start(self):
+        _data['current_scope'] = EPOCH_SCOPE 
+        _data['epoch'] += 1
+        _data['iteration'] = -1
+
+        for n, m in _data['metrics'].items():
+            if m.metric_scope == TRAIN_ITER_SCOPE:
+                m.reset()
+
+    def epoch_stop(self):
+        for b in self.backends:
+            b.log_epoch_summary()
+        _data['current_scope'] = RUN_SCOPE
+
+    def finish(self):
+        for b in self.backends:
+            b.finish()
+
+    def iteration_generator_wrapper(self, gen):
+        for g in gen:
+            self.iteration_start()
+            yield g
+            self.iteration_stop()
+
+    def epoch_generator_wrapper(self, gen):
+        for g in gen:
+            self.epoch_start()
+            yield g
+            self.epoch_stop()
+
+LOGGER = _Logger()
+
+@contextmanager
+def timed_block(prefix, value=None, logger=LOGGER, forced=False):
+    """ This function helps with timed blocks
+        ----
+        Parameters:
+        prefix - one of items from TIMED_BLOCKS; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    if logger is None:
+        pass
+    logger.timed_block_start(prefix)
+    yield logger
+    logger.timed_block_stop(prefix)
+
+def timed_function(prefix, variable=None, forced=False):
+    """ This decorator helps with timed functions
+        ----
+        Parameters:
+        prefix - one of items from TIME_BLOCK; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    def timed_function_decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            logger = kwargs.get('logger', LOGGER)
+            value = kwargs.get(variable, next(iter(args), None))
+            with timed_block(prefix=prefix, logger=logger, value=value, forced=forced):
+                    func(*args, **kwargs)
+        return wrapper
+    return timed_function_decorator
+
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/dllogger/tags.py b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/tags.py
new file mode 100644
index 00000000..4a9a1dce
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/dllogger/tags.py
@@ -0,0 +1,257 @@
+# Copyright 2018 MLBenchmark Group. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Common values reported
+
+VALUE_EPOCH = "epoch"
+VALUE_ITERATION = "iteration"
+VALUE_ACCURACY = "accuracy"
+VALUE_BLEU = "bleu"
+VALUE_TOP1 = "top1"
+VALUE_TOP5 = "top5"
+VALUE_BBOX_MAP = "bbox_map"
+VALUE_MASK_MAP = "mask_map"
+VALUE_BCE = "binary_cross_entropy"
+
+
+# Timed blocks (used with timed_function & timed_block
+# For each there should be *_start and *_stop tags defined
+
+RUN_BLOCK = "run"
+SETUP_BLOCK = "setup"
+PREPROC_BLOCK = "preproc"
+
+TRAIN_BLOCK = "train"
+TRAIN_PREPROC_BLOCK = "train_preproc"
+TRAIN_EPOCH_BLOCK = "train_epoch"
+TRAIN_EPOCH_PREPROC_BLOCK = "train_epoch_preproc"
+TRAIN_CHECKPOINT_BLOCK = "train_checkpoint"
+TRAIN_ITER_BLOCK = "train_iteration"
+
+EVAL_BLOCK = "eval"
+EVAL_ITER_BLOCK = "eval_iteration"
+
+#TODO: to remove?
+TIMED_BLOCKS = {
+    RUN_BLOCK,
+    SETUP_BLOCK,
+    PREPROC_BLOCK,
+    TRAIN_BLOCK,
+    TRAIN_PREPROC_BLOCK,
+    TRAIN_EPOCH_BLOCK,
+    TRAIN_EPOCH_PREPROC_BLOCK,
+    TRAIN_CHECKPOINT_BLOCK,
+    TRAIN_ITER_BLOCK,
+    EVAL_BLOCK,
+    EVAL_ITER_BLOCK,
+}
+
+
+# Events
+
+RUN_INIT = "run_init"
+
+SETUP_START = "setup_start"
+SETUP_STOP = "setup_stop"
+
+PREPROC_START = "preproc_start"
+PREPROC_STOP = "preproc_stop"
+
+RUN_START = "run_start"
+RUN_STOP = "run_stop"
+RUN_FINAL = "run_final"
+
+TRAIN_CHECKPOINT_START = "train_checkpoint_start"
+TRAIN_CHECKPOINT_STOP = "train_checkpoint_stop"
+
+TRAIN_PREPROC_START = "train_preproc_start"
+TRAIN_PREPROC_STOP = "train_preproc_stop"
+
+TRAIN_EPOCH_PREPROC_START = "train_epoch_preproc_start"
+TRAIN_EPOCH_PREPROC_STOP = "train_epoch_preproc_stop"
+
+TRAIN_ITER_START = "train_iter_start"
+TRAIN_ITER_STOP = "train_iter_stop"
+
+TRAIN_EPOCH_START = "train_epoch_start"
+TRAIN_EPOCH_STOP = "train_epoch_stop"
+
+
+# MLPerf specific tags
+
+RUN_CLEAR_CACHES = "run_clear_caches"
+
+PREPROC_NUM_TRAIN_EXAMPLES = "preproc_num_train_examples"
+PREPROC_NUM_EVAL_EXAMPLES = "preproc_num_eval_examples"
+PREPROC_TOKENIZE_TRAINING = "preproc_tokenize_training"
+PREPROC_TOKENIZE_EVAL = "preproc_tokenize_eval"
+PREPROC_VOCAB_SIZE = "preproc_vocab_size"
+
+RUN_SET_RANDOM_SEED = "run_set_random_seed"
+
+INPUT_SIZE = "input_size"
+INPUT_BATCH_SIZE = "input_batch_size"
+INPUT_ORDER = "input_order"
+INPUT_SHARD = "input_shard"
+INPUT_BN_SPAN = "input_bn_span"
+
+INPUT_CENTRAL_CROP = "input_central_crop"
+INPUT_CROP_USES_BBOXES = "input_crop_uses_bboxes"
+INPUT_DISTORTED_CROP_MIN_OBJ_COV = "input_distorted_crop_min_object_covered"
+INPUT_DISTORTED_CROP_RATIO_RANGE = "input_distorted_crop_aspect_ratio_range"
+INPUT_DISTORTED_CROP_AREA_RANGE = "input_distorted_crop_area_range"
+INPUT_DISTORTED_CROP_MAX_ATTEMPTS = "input_distorted_crop_max_attempts"
+INPUT_MEAN_SUBTRACTION = "input_mean_subtraction"
+INPUT_RANDOM_FLIP = "input_random_flip"
+
+INPUT_RESIZE = "input_resize"
+INPUT_RESIZE_ASPECT_PRESERVING = "input_resize_aspect_preserving"
+
+
+# Opt
+
+OPT_NAME = "opt_name"
+
+OPT_LR = "opt_learning_rate"
+OPT_MOMENTUM = "opt_momentum"
+
+OPT_WEIGHT_DECAY = "opt_weight_decay"
+
+OPT_HP_ADAM_BETA1 = "opt_hp_Adam_beta1"
+OPT_HP_ADAM_BETA2 = "opt_hp_Adam_beta2"
+OPT_HP_ADAM_EPSILON = "opt_hp_Adam_epsilon"
+
+OPT_LR_WARMUP_STEPS = "opt_learning_rate_warmup_steps"
+
+
+#  Train
+
+TRAIN_LOOP = "train_loop"
+TRAIN_EPOCH = "train_epoch"
+TRAIN_CHECKPOINT = "train_checkpoint"
+TRAIN_LOSS = "train_loss"
+TRAIN_ITERATION_LOSS = "train_iteration_loss"
+
+
+# Eval
+
+EVAL_START = "eval_start"
+EVAL_SIZE = "eval_size"
+EVAL_TARGET = "eval_target"
+EVAL_ACCURACY = "eval_accuracy"
+EVAL_STOP = "eval_stop"
+
+
+# Perf
+
+PERF_IT_PER_SEC = "perf_it_per_sec"
+PERF_TIME_TO_TRAIN = "time_to_train"
+
+EVAL_ITERATION_ACCURACY = "eval_iteration_accuracy"
+
+
+# Model
+
+MODEL_HP_LOSS_FN = "model_hp_loss_fn"
+
+MODEL_HP_INITIAL_SHAPE = "model_hp_initial_shape"
+MODEL_HP_FINAL_SHAPE = "model_hp_final_shape"
+
+MODEL_L2_REGULARIZATION = "model_l2_regularization"
+MODEL_EXCLUDE_BN_FROM_L2 = "model_exclude_bn_from_l2"
+
+MODEL_HP_RELU = "model_hp_relu"
+MODEL_HP_CONV2D_FIXED_PADDING = "model_hp_conv2d_fixed_padding"
+MODEL_HP_BATCH_NORM = "model_hp_batch_norm"
+MODEL_HP_DENSE = "model_hp_dense"
+
+
+# GNMT specific
+
+MODEL_HP_LOSS_SMOOTHING = "model_hp_loss_smoothing"
+MODEL_HP_NUM_LAYERS = "model_hp_num_layers"
+MODEL_HP_HIDDEN_SIZE = "model_hp_hidden_size"
+MODEL_HP_DROPOUT = "model_hp_dropout"
+
+EVAL_HP_BEAM_SIZE = "eval_hp_beam_size"
+TRAIN_HP_MAX_SEQ_LEN = "train_hp_max_sequence_length"
+EVAL_HP_MAX_SEQ_LEN = "eval_hp_max_sequence_length"
+EVAL_HP_LEN_NORM_CONST = "eval_hp_length_normalization_constant"
+EVAL_HP_LEN_NORM_FACTOR = "eval_hp_length_normalization_factor"
+EVAL_HP_COV_PENALTY_FACTOR = "eval_hp_coverage_penalty_factor"
+
+
+# NCF specific
+
+PREPROC_HP_MIN_RATINGS = "preproc_hp_min_ratings"
+PREPROC_HP_NUM_EVAL = "preproc_hp_num_eval"
+PREPROC_HP_SAMPLE_EVAL_REPLACEMENT = "preproc_hp_sample_eval_replacement"
+
+INPUT_HP_NUM_NEG = "input_hp_num_neg"
+INPUT_HP_SAMPLE_TRAIN_REPLACEMENT = "input_hp_sample_train_replacement"
+INPUT_STEP_TRAIN_NEG_GEN = "input_step_train_neg_gen"
+INPUT_STEP_EVAL_NEG_GEN = "input_step_eval_neg_gen"
+
+EVAL_HP_NUM_USERS = "eval_hp_num_users"
+EVAL_HP_NUM_NEG = "eval_hp_num_neg"
+
+MODEL_HP_MF_DIM = "model_hp_mf_dim"
+MODEL_HP_MLP_LAYER_SIZES = "model_hp_mlp_layer_sizes"
+
+
+# RESNET specific
+
+EVAL_EPOCH_OFFSET = "eval_offset"
+
+MODEL_HP_INITIAL_MAX_POOL = "model_hp_initial_max_pool"
+MODEL_HP_BEGIN_BLOCK = "model_hp_begin_block"
+MODEL_HP_END_BLOCK = "model_hp_end_block"
+MODEL_HP_BLOCK_TYPE = "model_hp_block_type"
+MODEL_HP_PROJECTION_SHORTCUT = "model_hp_projection_shortcut"
+MODEL_HP_SHORTCUT_ADD = "model_hp_shorcut_add"
+MODEL_HP_RESNET_TOPOLOGY = "model_hp_resnet_topology"
+
+
+# Transformer specific
+
+INPUT_MAX_LENGTH = "input_max_length"
+
+MODEL_HP_INITIALIZER_GAIN = "model_hp_initializer_gain"
+MODEL_HP_VOCAB_SIZE = "model_hp_vocab_size"
+MODEL_HP_NUM_HIDDEN_LAYERS = "model_hp_hidden_layers"
+MODEL_HP_EMBEDDING_SHARED_WEIGHTS = "model_hp_embedding_shared_weights"
+MODEL_HP_ATTENTION_DENSE = "model_hp_attention_dense"
+MODEL_HP_ATTENTION_DROPOUT = "model_hp_attention_dropout"
+MODEL_HP_FFN_OUTPUT_DENSE = "model_hp_ffn_output_dense"
+MODEL_HP_FFN_FILTER_DENSE = "model_hp_ffn_filter_dense"
+MODEL_HP_RELU_DROPOUT = "model_hp_relu_dropout"
+MODEL_HP_LAYER_POSTPROCESS_DROPOUT = "model_hp_layer_postprocess_dropout"
+MODEL_HP_NORM = "model_hp_norm"
+MODEL_HP_SEQ_BEAM_SEARCH = "model_hp_sequence_beam_search"
+
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_test_filelist.txt b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_test_filelist.txt
new file mode 100755
index 00000000..e496e954
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_test_filelist.txt
@@ -0,0 +1,500 @@
+LJSpeech-1.1/wavs/LJ045-0096.wav|Mrs. De Mohrenschildt thought that Oswald,
+LJSpeech-1.1/wavs/LJ049-0022.wav|The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.
+LJSpeech-1.1/wavs/LJ033-0042.wav|Between the hours of eight and nine p.m. they were occupied with the children in the bedrooms located at the extreme east end of the house.
+LJSpeech-1.1/wavs/LJ016-0117.wav|The prisoner had nothing to deal with but wooden panels, and by dint of cutting and chopping he got both the lower panels out.
+LJSpeech-1.1/wavs/LJ025-0157.wav|Under these circumstances, unnatural as they are, with proper management, the bean will thrust forth its radicle and its plumule;
+LJSpeech-1.1/wavs/LJ042-0219.wav|Oswald demonstrated his thinking in connection with his return to the United States by preparing two sets of identical questions of the type which he might have thought
+LJSpeech-1.1/wavs/LJ032-0164.wav|it is not possible to state with scientific certainty that a particular small group of fibers come from a certain piece of clothing
+LJSpeech-1.1/wavs/LJ046-0092.wav|has confidence in the dedicated Secret Service men who are ready to lay down their lives for him
+LJSpeech-1.1/wavs/LJ050-0118.wav|Since these agencies are already obliged constantly to evaluate the activities of such groups,
+LJSpeech-1.1/wavs/LJ043-0016.wav|Jeanne De Mohrenschildt said, quote,
+LJSpeech-1.1/wavs/LJ021-0078.wav|no economic panacea, which could simply revive over-night the heavy industries and the trades dependent upon them.
+LJSpeech-1.1/wavs/LJ039-0148.wav|Examination of the cartridge cases found on the sixth floor of the Depository Building
+LJSpeech-1.1/wavs/LJ047-0202.wav|testified that the information available to the Federal Government about Oswald before the assassination would, if known to PRS,
+LJSpeech-1.1/wavs/LJ023-0056.wav|It is an easy document to understand when you remember that it was called into being
+LJSpeech-1.1/wavs/LJ021-0025.wav|And in many directions, the intervention of that organized control which we call government
+LJSpeech-1.1/wavs/LJ030-0105.wav|Communications in the motorcade.
+LJSpeech-1.1/wavs/LJ021-0012.wav|with respect to industry and business, but nearly all are agreed that private enterprise in times such as these
+LJSpeech-1.1/wavs/LJ019-0169.wav|and one or two men were allowed to mend clothes and make shoes. The rules made by the Secretary of State were hung up in conspicuous parts of the prison;
+LJSpeech-1.1/wavs/LJ039-0088.wav|It just is an aid in seeing in the fact that you only have the one element, the crosshair,
+LJSpeech-1.1/wavs/LJ016-0192.wav|"I think I could do that sort of job," said Calcraft, on the spur of the moment.
+LJSpeech-1.1/wavs/LJ014-0142.wav|was strewn in front of the dock, and sprinkled it towards the bench with a contemptuous gesture.
+LJSpeech-1.1/wavs/LJ012-0015.wav|Weedon and Lecasser to twelve and six months respectively in Coldbath Fields.
+LJSpeech-1.1/wavs/LJ048-0033.wav|Prior to November twenty-two, nineteen sixty-three
+LJSpeech-1.1/wavs/LJ028-0349.wav|who were each required to send so large a number to Babylon, that in all there were collected no fewer than fifty thousand.
+LJSpeech-1.1/wavs/LJ030-0197.wav|At first Mrs. Connally thought that her husband had been killed,
+LJSpeech-1.1/wavs/LJ017-0133.wav|Palmer speedily found imitators.
+LJSpeech-1.1/wavs/LJ034-0123.wav|Although Brennan testified that the man in the window was standing when he fired the shots, most probably he was either sitting or kneeling.
+LJSpeech-1.1/wavs/LJ003-0282.wav|Many years were to elapse before these objections should be fairly met and universally overcome.
+LJSpeech-1.1/wavs/LJ032-0204.wav|Special Agent Lyndal L. Shaneyfelt, a photography expert with the FBI,
+LJSpeech-1.1/wavs/LJ016-0241.wav|Calcraft served the city of London till eighteen seventy-four, when he was pensioned at the rate of twenty-five shillings per week.
+LJSpeech-1.1/wavs/LJ023-0033.wav|we will not allow ourselves to run around in new circles of futile discussion and debate, always postponing the day of decision.
+LJSpeech-1.1/wavs/LJ009-0286.wav|There has never been much science in the system of carrying out the extreme penalty in this country; the "finisher of the law"
+LJSpeech-1.1/wavs/LJ008-0181.wav|he had his pockets filled with bread and cheese, and it was generally supposed that he had come a long distance to see the fatal show.
+LJSpeech-1.1/wavs/LJ015-0052.wav|to the value of twenty thousand pounds.
+LJSpeech-1.1/wavs/LJ016-0314.wav|Sir George Grey thought there was a growing feeling in favor of executions within the prison precincts.
+LJSpeech-1.1/wavs/LJ047-0056.wav|From August nineteen sixty-two
+LJSpeech-1.1/wavs/LJ010-0027.wav|Nor did the methods by which they were perpetrated greatly vary from those in times past.
+LJSpeech-1.1/wavs/LJ010-0065.wav|At the former the "Provisional Government" was to be established,
+LJSpeech-1.1/wavs/LJ046-0113.wav|The Commission has concluded that at the time of the assassination
+LJSpeech-1.1/wavs/LJ028-0410.wav|There among the ruins they still live in the same kind of houses,
+LJSpeech-1.1/wavs/LJ044-0137.wav|More seriously, the facts of his defection had become known, leaving him open to almost unanswerable attack by those who opposed his views.
+LJSpeech-1.1/wavs/LJ008-0215.wav|One by one the huge uprights of black timber were fitted together,
+LJSpeech-1.1/wavs/LJ030-0084.wav|or when the press of the crowd made it impossible for the escort motorcycles to stay in position on the car's rear flanks.
+LJSpeech-1.1/wavs/LJ020-0092.wav|Have yourself called on biscuit mornings an hour earlier than usual.
+LJSpeech-1.1/wavs/LJ029-0096.wav|On November fourteen, Lawson and Sorrels attended a meeting at Love Field
+LJSpeech-1.1/wavs/LJ015-0308.wav|and others who swore to the meetings of the conspirators and their movements. Saward was found guilty,
+LJSpeech-1.1/wavs/LJ012-0067.wav|But Mrs. Solomons could not resist the temptation to dabble in stolen goods, and she was found shipping watches of the wrong category to New York.
+LJSpeech-1.1/wavs/LJ018-0231.wav|namely, to suppress it and substitute another.
+LJSpeech-1.1/wavs/LJ014-0265.wav|and later he became manager of the newly rebuilt Olympic at Wych Street.
+LJSpeech-1.1/wavs/LJ024-0102.wav|would be the first to exclaim as soon as an amendment was proposed
+LJSpeech-1.1/wavs/LJ007-0233.wav|it consists of several circular perforations, about two inches in diameter,
+LJSpeech-1.1/wavs/LJ013-0213.wav|This seems to have decided Courvoisier,
+LJSpeech-1.1/wavs/LJ032-0045.wav|This price included nineteen dollars, ninety-five cents for the rifle and the scope, and one dollar, fifty cents for postage and handling.
+LJSpeech-1.1/wavs/LJ011-0048.wav|Wherefore let him that thinketh he standeth take heed lest he fall," and was full of the most pointed allusions to the culprit.
+LJSpeech-1.1/wavs/LJ005-0294.wav|It was frequently stated in evidence that the jail of the borough was in so unfit a state for the reception of prisoners,
+LJSpeech-1.1/wavs/LJ016-0007.wav|There were others less successful.
+LJSpeech-1.1/wavs/LJ028-0138.wav|perhaps the tales that travelers told him were exaggerated as travelers' tales are likely to be,
+LJSpeech-1.1/wavs/LJ050-0029.wav|that is reflected in definite and comprehensive operating procedures.
+LJSpeech-1.1/wavs/LJ014-0121.wav|The prisoners were in due course transferred to Newgate, to be put upon their trial at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ014-0146.wav|They had to handcuff her by force against the most violent resistance, and still she raged and stormed,
+LJSpeech-1.1/wavs/LJ046-0111.wav|The Secret Service has attempted to perform this function through the activities of its Protective Research Section
+LJSpeech-1.1/wavs/LJ012-0257.wav|But the affair still remained a profound mystery. No light was thrown upon it till, towards the end of March,
+LJSpeech-1.1/wavs/LJ002-0260.wav|Yet the public opinion of the whole body seems to have checked dissipation.
+LJSpeech-1.1/wavs/LJ031-0014.wav|the Presidential limousine arrived at the emergency entrance of the Parkland Hospital at about twelve:thirty-five p.m.
+LJSpeech-1.1/wavs/LJ047-0093.wav|Oswald was arrested and jailed by the New Orleans Police Department for disturbing the peace, in connection with a street fight which broke out when he was accosted
+LJSpeech-1.1/wavs/LJ003-0324.wav|gaming of all sorts should be peremptorily forbidden under heavy pains and penalties.
+LJSpeech-1.1/wavs/LJ021-0115.wav|we have reached into the heart of the problem which is to provide such annual earnings for the lowest paid worker as will meet his minimum needs.
+LJSpeech-1.1/wavs/LJ046-0191.wav|it had established periodic regular review of the status of four hundred individuals;
+LJSpeech-1.1/wavs/LJ034-0197.wav|who was one of the first witnesses to alert the police to the Depository as the source of the shots, as has been discussed in chapter three.
+LJSpeech-1.1/wavs/LJ002-0253.wav|were governed by rules which they themselves had framed, and under which subscriptions were levied
+LJSpeech-1.1/wavs/LJ048-0288.wav|might have been more alert in the Dallas motorcade if they had retired promptly in Fort Worth.
+LJSpeech-1.1/wavs/LJ007-0112.wav|Many of the old customs once prevalent in the State Side, so properly condemned and abolished,
+LJSpeech-1.1/wavs/LJ017-0189.wav|who was presently attacked in the same way as the others, but, but, thanks to the prompt administration of remedies, he recovered.
+LJSpeech-1.1/wavs/LJ042-0230.wav|basically, although I hate the USSR and socialist system I still think marxism can work under different circumstances, end quote.
+LJSpeech-1.1/wavs/LJ050-0161.wav|The Secret Service should not and does not plan to develop its own intelligence gathering facilities to duplicate the existing facilities of other Federal agencies.
+LJSpeech-1.1/wavs/LJ003-0011.wav|that not more than one bottle of wine or one quart of beer could be issued at one time. No account was taken of the amount of liquors admitted in one day,
+LJSpeech-1.1/wavs/LJ008-0206.wav|and caused a number of stout additional barriers to be erected in front of the scaffold,
+LJSpeech-1.1/wavs/LJ002-0261.wav|The poorer prisoners were not in abject want, as in other prisons,
+LJSpeech-1.1/wavs/LJ012-0189.wav|Hunt, in consideration of the information he had given, escaped death, and was sentenced to transportation for life.
+LJSpeech-1.1/wavs/LJ019-0317.wav|The former, which consisted principally of the tread-wheel, cranks, capstans, shot-drill,
+LJSpeech-1.1/wavs/LJ011-0041.wav|Visited Mr. Fauntleroy. My application for books for him not having been attended, I had no prayer-book to give him.
+LJSpeech-1.1/wavs/LJ023-0089.wav|That is not only my accusation.
+LJSpeech-1.1/wavs/LJ044-0224.wav|would not agree with that particular wording, end quote.
+LJSpeech-1.1/wavs/LJ013-0104.wav|He found them at length residing at the latter place, one as a landed proprietor, the other as a publican.
+LJSpeech-1.1/wavs/LJ013-0055.wav|The jury did not believe him, and the verdict was for the defendants.
+LJSpeech-1.1/wavs/LJ014-0306.wav|These had been attributed to political action; some thought that the large purchases in foreign grains, effected at losing prices,
+LJSpeech-1.1/wavs/LJ029-0052.wav|To supplement the PRS files, the Secret Service depends largely on local police departments and local offices of other Federal agencies
+LJSpeech-1.1/wavs/LJ028-0459.wav|Its bricks, measuring about thirteen inches square and three inches in thickness, were burned and stamped with the usual short inscription:
+LJSpeech-1.1/wavs/LJ017-0183.wav|Soon afterwards Dixon died, showing all the symptoms already described.
+LJSpeech-1.1/wavs/LJ009-0084.wav|At length the ordinary pauses, and then, in a deep tone, which, though hardly above a whisper, is audible to all, says,
+LJSpeech-1.1/wavs/LJ007-0170.wav|That in this vast metropolis, the center of wealth, civilization, and information;
+LJSpeech-1.1/wavs/LJ016-0277.wav|This is proved by contemporary accounts, especially one graphic and realistic article which appeared in the 'Times,'
+LJSpeech-1.1/wavs/LJ009-0061.wav|He staggers towards the pew, reels into it, stumbles forward, flings himself on the ground, and, by a curious twist of the spine,
+LJSpeech-1.1/wavs/LJ019-0201.wav|to select a sufficiently spacious piece of ground, and erect a prison which from foundations to roofs should be in conformity with the newest ideas.
+LJSpeech-1.1/wavs/LJ030-0063.wav|He had repeated this wish only a few days before, during his visit to Tampa, Florida.
+LJSpeech-1.1/wavs/LJ010-0257.wav|a third miscreant made a similar but far less serious attempt in the month of July following.
+LJSpeech-1.1/wavs/LJ009-0106.wav|The keeper tries to appear unmoved, but his eye wanders anxiously over the combustible assembly.
+LJSpeech-1.1/wavs/LJ008-0121.wav|After the construction and action of the machine had been explained, the doctor asked the governor what kind of men he had commanded at Goree,
+LJSpeech-1.1/wavs/LJ050-0069.wav|the Secret Service had received from the FBI some nine thousand reports on members of the Communist Party.
+LJSpeech-1.1/wavs/LJ006-0202.wav|The news-vendor was also a tobacconist,
+LJSpeech-1.1/wavs/LJ012-0230.wav|Shortly before the day fixed for execution, Bishop made a full confession, the bulk of which bore the impress of truth,
+LJSpeech-1.1/wavs/LJ005-0248.wav|and stated that in his opinion Newgate, as the common jail of Middlesex, was wholly inadequate to the proper confinement of its prisoners.
+LJSpeech-1.1/wavs/LJ037-0053.wav|who had been greatly upset by her experience, was able to view a lineup of four men handcuffed together at the police station.
+LJSpeech-1.1/wavs/LJ045-0177.wav|For the first time
+LJSpeech-1.1/wavs/LJ004-0036.wav|it was hoped that their rulers would hire accommodation in the county prisons, and that the inferior establishments would in course of time disappear.
+LJSpeech-1.1/wavs/LJ026-0054.wav|carbohydrates (starch, cellulose) and fats.
+LJSpeech-1.1/wavs/LJ020-0085.wav|Break apart from one another and pile on a plate, throwing a clean doily or a small napkin over them. Break open at table.
+LJSpeech-1.1/wavs/LJ046-0226.wav|The several military intelligence agencies reported crank mail and similar threats involving the President.
+LJSpeech-1.1/wavs/LJ014-0233.wav|he shot an old soldier who had attempted to detain him. He was convicted and executed.
+LJSpeech-1.1/wavs/LJ033-0152.wav|The portion of the palm which was identified was the heel of the right palm, i.e., the area near the wrist, on the little finger side.
+LJSpeech-1.1/wavs/LJ004-0009.wav|as indefatigable and self-sacrificing, found by personal visitation that the condition of jails throughout the kingdom was,
+LJSpeech-1.1/wavs/LJ017-0134.wav|Within a few weeks occurred the Leeds poisoning case, in which the murderer undoubtedly was inspired by the facts made public at Palmer's trial.
+LJSpeech-1.1/wavs/LJ019-0318.wav|was to be the rule for all convicted prisoners throughout the early stages of their detention;
+LJSpeech-1.1/wavs/LJ020-0093.wav|Rise, wash face and hands, rinse the mouth out and brush back the hair.
+LJSpeech-1.1/wavs/LJ012-0188.wav|Probert was then admitted as a witness, and the case was fully proved against Thurtell, who was hanged in front of Hertford Jail.
+LJSpeech-1.1/wavs/LJ019-0202.wav|The preference given to the Pentonville system destroyed all hopes of a complete reformation of Newgate.
+LJSpeech-1.1/wavs/LJ039-0027.wav|Oswald's revolver
+LJSpeech-1.1/wavs/LJ040-0176.wav|He admitted to fantasies about being powerful and sometimes hurting and killing people, but refused to elaborate on them.
+LJSpeech-1.1/wavs/LJ018-0354.wav|Doubts were long entertained whether Thomas Wainwright,
+LJSpeech-1.1/wavs/LJ031-0185.wav|From the Presidential airplane, the Vice President telephoned Attorney General Robert F. Kennedy,
+LJSpeech-1.1/wavs/LJ006-0137.wav|They were not obliged to attend chapel, and seldom if ever went; "prisoners," said one of them under examination, "did not like the trouble of going to chapel."
+LJSpeech-1.1/wavs/LJ032-0085.wav|The Hidell signature on the notice of classification was in the handwriting of Oswald.
+LJSpeech-1.1/wavs/LJ009-0037.wav|the schoolmaster and the juvenile prisoners being seated round the communion-table, opposite the pulpit.
+LJSpeech-1.1/wavs/LJ006-0021.wav|Later on he had devoted himself to the personal investigation of the prisons of the United States.
+LJSpeech-1.1/wavs/LJ006-0082.wav|and this particular official took excellent care to select as residents for his own ward those most suitable from his own point of view.
+LJSpeech-1.1/wavs/LJ016-0380.wav|with hope to the last. There is always the chance of a flaw in the indictment, of a missing witness, or extenuating circumstances.
+LJSpeech-1.1/wavs/LJ019-0344.wav|monitor, or schoolmaster, nor to be engaged in the service of any officer of the prison.
+LJSpeech-1.1/wavs/LJ019-0161.wav|These disciplinary improvements were, however, only slowly and gradually introduced.
+LJSpeech-1.1/wavs/LJ028-0145.wav|And here I may not omit to tell the use to which the mould dug out of the great moat was turned, nor the manner wherein the wall was wrought.
+LJSpeech-1.1/wavs/LJ018-0349.wav|His disclaimer, distinct and detailed on every point, was intended simply for effect.
+LJSpeech-1.1/wavs/LJ043-0010.wav|Some of the members of that group saw a good deal of the Oswalds through the fall of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ027-0178.wav|These were undoubtedly perennibranchs. In the Permian and Triassic higher forms appeared, which were certainly caducibranch.
+LJSpeech-1.1/wavs/LJ041-0070.wav|He did not rise above the rank of private first class, even though he had passed a qualifying examination for the rank of corporal.
+LJSpeech-1.1/wavs/LJ008-0266.wav|Thus in the years between May first, eighteen twenty-seven, and thirtieth April, eighteen thirty-one,
+LJSpeech-1.1/wavs/LJ021-0091.wav|In this recent reorganization we have recognized three distinct functions:
+LJSpeech-1.1/wavs/LJ019-0129.wav|which marked the growth of public interest in prison affairs, and which was the germ of the new system
+LJSpeech-1.1/wavs/LJ018-0215.wav|William Roupell was the eldest but illegitimate son of a wealthy man who subsequently married Roupell's mother, and had further legitimate issue.
+LJSpeech-1.1/wavs/LJ015-0194.wav|and behaved so as to justify a belief that he had been a jail-bird all his life.
+LJSpeech-1.1/wavs/LJ016-0137.wav|that numbers of men, "lifers," and others with ten, fourteen, or twenty years to do, can be trusted to work out of doors without bolts and bars
+LJSpeech-1.1/wavs/LJ002-0289.wav|the latter raised eighteen pence among them to pay for a truss of straw for the poor woman to lie on.
+LJSpeech-1.1/wavs/LJ023-0016.wav|In nineteen thirty-three you and I knew that we must never let our economic system get completely out of joint again
+LJSpeech-1.1/wavs/LJ011-0141.wav|There were at the moment in Newgate six convicts sentenced to death for forging wills.
+LJSpeech-1.1/wavs/LJ016-0283.wav|to do them mere justice, there was at least till then a half-drunken ribald gaiety among the crowd that made them all akin."
+LJSpeech-1.1/wavs/LJ035-0082.wav|The only interval was the time necessary to ride in the elevator from the second to the sixth floor and walk back to the southeast corner.
+LJSpeech-1.1/wavs/LJ045-0194.wav|Anyone who was familiar with that area of Dallas would have known that the motorcade would probably pass the Texas School Book Depository to get from Main Street
+LJSpeech-1.1/wavs/LJ009-0124.wav|occupied when they saw it last, but a few hours ago, by their comrades who are now dead;
+LJSpeech-1.1/wavs/LJ030-0162.wav|In the Presidential Limousine
+LJSpeech-1.1/wavs/LJ050-0223.wav|The plan provides for an additional two hundred five agents for the Secret Service. Seventeen of this number are proposed for the Protective Research Section;
+LJSpeech-1.1/wavs/LJ008-0228.wav|their harsh and half-cracked voices full of maudlin, besotted sympathy for those about to die.
+LJSpeech-1.1/wavs/LJ002-0096.wav|The eight courts above enumerated were well supplied with water;
+LJSpeech-1.1/wavs/LJ018-0288.wav|After this the other conspirators traveled to obtain genuine bills and master the system of the leading houses at home and abroad.
+LJSpeech-1.1/wavs/LJ002-0106.wav|in which latterly a copper had been fixed for the cooking of provisions sent in by charitable persons.
+LJSpeech-1.1/wavs/LJ025-0129.wav|On each lobe of the bi-lobed leaf of Venus flytrap are three delicate filaments which stand out at right angles from the surface of the leaf.
+LJSpeech-1.1/wavs/LJ044-0013.wav|Hands Off Cuba, end quote, an application form for, and a membership card in,
+LJSpeech-1.1/wavs/LJ049-0115.wav|of the person who is actually in the exercise of the executive power, or
+LJSpeech-1.1/wavs/LJ019-0145.wav|But reformation was only skin deep. Below the surface many of the old evils still rankled.
+LJSpeech-1.1/wavs/LJ019-0355.wav|came up in all respects to modern requirements.
+LJSpeech-1.1/wavs/LJ019-0289.wav|There was unrestrained association of untried and convicted, juvenile with adult prisoners, vagrants, misdemeanants, felons.
+LJSpeech-1.1/wavs/LJ048-0222.wav|in Fort Worth, there occurred a breach of discipline by some members of the Secret Service who were officially traveling with the President.
+LJSpeech-1.1/wavs/LJ016-0367.wav|Under the new system the whole of the arrangements from first to last fell upon the officers.
+LJSpeech-1.1/wavs/LJ047-0097.wav|Agent Quigley did not know of Oswald's prior FBI record when he interviewed him,
+LJSpeech-1.1/wavs/LJ007-0075.wav|as effectually to rebuke and abash the profane spirit of the more insolent and daring of the criminals.
+LJSpeech-1.1/wavs/LJ047-0022.wav|provided by other agencies.
+LJSpeech-1.1/wavs/LJ007-0085.wav|at Newgate and York Castle as long as five years; "at Ilchester and Morpeth for seven years; at Warwick for eight years,
+LJSpeech-1.1/wavs/LJ047-0075.wav|Hosty had inquired earlier and found no evidence that it was functioning in the Dallas area.
+LJSpeech-1.1/wavs/LJ008-0098.wav|One was the "yeoman of the halter," a Newgate official, the executioner's assistant, whom Mr. J. T. Smith, who was present at the execution,
+LJSpeech-1.1/wavs/LJ017-0102.wav|The second attack was fatal, and ended in Cook's death from tetanus.
+LJSpeech-1.1/wavs/LJ046-0105.wav|Second, the adequacy of other advance preparations for the security of the President, during his visit to Dallas,
+LJSpeech-1.1/wavs/LJ018-0206.wav|He was a tall, slender man, with a long face and iron-gray hair.
+LJSpeech-1.1/wavs/LJ012-0271.wav|Whether it was greed or a quarrel that drove Greenacre to the desperate deed remains obscure.
+LJSpeech-1.1/wavs/LJ005-0086.wav|with such further separation as the justices should deem conducive to good order and discipline.
+LJSpeech-1.1/wavs/LJ042-0097.wav|and considerably better living quarters than those accorded to Soviet citizens of equal age and station.
+LJSpeech-1.1/wavs/LJ047-0126.wav|we would handle it in due course, in accord with the whole context of the investigation. End quote.
+LJSpeech-1.1/wavs/LJ041-0022.wav|Oswald first wrote, quote, Edward Vogel, end quote, an obvious misspelling of Voebel's name,
+LJSpeech-1.1/wavs/LJ015-0025.wav|The bank enjoyed an excellent reputation, it had a good connection, and was supposed to be perfectly sound.
+LJSpeech-1.1/wavs/LJ012-0194.wav|But Burke and Hare had their imitators further south,
+LJSpeech-1.1/wavs/LJ028-0416.wav|(if man may speak so confidently of His great impenetrable counsels), for an eternal Testimony of His great work in the confusion of Man's pride,
+LJSpeech-1.1/wavs/LJ007-0130.wav|are all huddled together without discrimination, oversight, or control."
+LJSpeech-1.1/wavs/LJ015-0005.wav|About this time Davidson and Gordon, the people above-mentioned,
+LJSpeech-1.1/wavs/LJ016-0125.wav|with this, placed against the wall near the chevaux-de-frise, he made an escalade.
+LJSpeech-1.1/wavs/LJ014-0224.wav|As Dwyer survived, Cannon escaped the death sentence, which was commuted to penal servitude for life.
+LJSpeech-1.1/wavs/LJ005-0019.wav|refuted by abundant evidence, and having no foundation whatever in truth.
+LJSpeech-1.1/wavs/LJ042-0221.wav|With either great ambivalence, or cold calculation he prepared completely different answers to the same questions.
+LJSpeech-1.1/wavs/LJ001-0063.wav|which was generally more formally Gothic than the printing of the German workmen,
+LJSpeech-1.1/wavs/LJ030-0006.wav|They took off in the Presidential plane, Air Force One, at eleven a.m., arriving at San Antonio at one:thirty p.m., Eastern Standard Time.
+LJSpeech-1.1/wavs/LJ024-0054.wav|democracy will have failed far beyond the importance to it of any king of precedent concerning the judiciary.
+LJSpeech-1.1/wavs/LJ006-0044.wav|the same callous indifference to the moral well-being of the prisoners, the same want of employment and of all disciplinary control.
+LJSpeech-1.1/wavs/LJ039-0154.wav|four point eight to five point six seconds if the second shot missed,
+LJSpeech-1.1/wavs/LJ050-0090.wav|they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.
+LJSpeech-1.1/wavs/LJ028-0421.wav|it was the beginning of the great collections of Babylonian antiquities in the museums of the Western world.
+LJSpeech-1.1/wavs/LJ033-0205.wav|then I would say the possibility exists, these fibers could have come from this blanket, end quote.
+LJSpeech-1.1/wavs/LJ019-0335.wav|The books and journals he was to keep were minutely specified, and his constant presence in or near the jail was insisted upon.
+LJSpeech-1.1/wavs/LJ013-0045.wav|Wallace's relations warned him against his Liverpool friend,
+LJSpeech-1.1/wavs/LJ037-0002.wav|Chapter four. The Assassin: Part six.
+LJSpeech-1.1/wavs/LJ018-0159.wav|This was all the police wanted to know.
+LJSpeech-1.1/wavs/LJ026-0140.wav|In the plant as in the animal metabolism must consist of anabolic and catabolic processes.
+LJSpeech-1.1/wavs/LJ014-0171.wav|I will briefly describe one or two of the more remarkable murders in the years immediately following, then pass on to another branch of crime.
+LJSpeech-1.1/wavs/LJ037-0007.wav|Three others subsequently identified Oswald from a photograph.
+LJSpeech-1.1/wavs/LJ033-0174.wav|microscopic and UV (ultra violet) characteristics, end quote.
+LJSpeech-1.1/wavs/LJ040-0110.wav|he apparently adjusted well enough there to have had an average, although gradually deteriorating, school record
+LJSpeech-1.1/wavs/LJ039-0192.wav|he had a total of between four point eight and five point six seconds between the two shots which hit
+LJSpeech-1.1/wavs/LJ032-0261.wav|When he appeared before the Commission, Michael Paine lifted the blanket
+LJSpeech-1.1/wavs/LJ040-0097.wav|Lee was brought up in this atmosphere of constant money problems, and I am sure it had quite an effect on him, and also Robert, end quote.
+LJSpeech-1.1/wavs/LJ037-0249.wav|Mrs. Earlene Roberts, the housekeeper at Oswald's roominghouse and the last person known to have seen him before he reached tenth Street and Patton Avenue,
+LJSpeech-1.1/wavs/LJ016-0248.wav|Marwood was proud of his calling, and when questioned as to whether his process was satisfactory, replied that he heard "no complaints."
+LJSpeech-1.1/wavs/LJ004-0083.wav|As Mr. Buxton pointed out, many old acts of parliament designed to protect the prisoner were still in full force.
+LJSpeech-1.1/wavs/LJ014-0029.wav|This was Delarue's watch, fully identified as such, which Hocker told his brother Delarue had given him the morning of the murder.
+LJSpeech-1.1/wavs/LJ021-0110.wav|have been best calculated to promote industrial recovery and a permanent improvement of business and labor conditions.
+LJSpeech-1.1/wavs/LJ003-0107.wav|he slept in the same bed with a highwayman on one side, and a man charged with murder on the other.
+LJSpeech-1.1/wavs/LJ039-0076.wav|Ronald Simmons, chief of the U.S. Army Infantry Weapons Evaluation Branch of the Ballistics Research Laboratory, said, quote,
+LJSpeech-1.1/wavs/LJ016-0347.wav|had undoubtedly a solemn, impressive effect upon those outside.
+LJSpeech-1.1/wavs/LJ001-0072.wav|After the end of the fifteenth century the degradation of printing, especially in Germany and Italy,
+LJSpeech-1.1/wavs/LJ024-0018.wav|Consequently, although there never can be more than fifteen, there may be only fourteen, or thirteen, or twelve.
+LJSpeech-1.1/wavs/LJ032-0180.wav|that the fibers were caught in the crevice of the rifle's butt plate, quote, in the recent past, end quote,
+LJSpeech-1.1/wavs/LJ010-0083.wav|and measures taken to arrest them when their plans were so far developed that no doubt could remain as to their guilt.
+LJSpeech-1.1/wavs/LJ002-0299.wav|and gave the garnish for the common side at that sum, which is five shillings more than Mr. Neild says was extorted on the common side.
+LJSpeech-1.1/wavs/LJ048-0143.wav|the Secret Service did not at the time of the assassination have any established procedure governing its relationships with them.
+LJSpeech-1.1/wavs/LJ012-0054.wav|Solomons, while waiting to appear in court, persuaded the turnkeys to take him to a public-house, where all might "refresh."
+LJSpeech-1.1/wavs/LJ019-0270.wav|Vegetables, especially the potato, that most valuable anti-scorbutic, was too often omitted.
+LJSpeech-1.1/wavs/LJ035-0164.wav|three minutes after the shooting.
+LJSpeech-1.1/wavs/LJ014-0326.wav|Maltby and Co. would issue warrants on them deliverable to the importer, and the goods were then passed to be stored in neighboring warehouses.
+LJSpeech-1.1/wavs/LJ001-0173.wav|The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page,
+LJSpeech-1.1/wavs/LJ050-0056.wav|On December twenty-six, nineteen sixty-three, the FBI circulated additional instructions to all its agents,
+LJSpeech-1.1/wavs/LJ003-0319.wav|provided only that their security was not jeopardized, and dependent upon the enforcement of another new rule,
+LJSpeech-1.1/wavs/LJ006-0040.wav|The fact was that the years as they passed, nearly twenty in all, had worked but little permanent improvement in this detestable prison.
+LJSpeech-1.1/wavs/LJ017-0231.wav|His body was found lying in a pool of blood in a night-dress, stabbed over and over again in the left side.
+LJSpeech-1.1/wavs/LJ017-0226.wav|One half of the mutineers fell upon him unawares with handspikes and capstan-bars.
+LJSpeech-1.1/wavs/LJ004-0239.wav|He had been committed for an offense for which he was acquitted.
+LJSpeech-1.1/wavs/LJ048-0112.wav|The Commission also regards the security arrangements worked out by Lawson and Sorrels at Love Field as entirely adequate.
+LJSpeech-1.1/wavs/LJ039-0125.wav|that Oswald was a good shot, somewhat better than or equal to -- better than the average let us say.
+LJSpeech-1.1/wavs/LJ030-0196.wav|He cried out, quote, Oh, no, no, no. My God, they are going to kill us all, end quote,
+LJSpeech-1.1/wavs/LJ010-0228.wav|He was released from Broadmoor in eighteen seventy-eight, and went abroad.
+LJSpeech-1.1/wavs/LJ045-0228.wav|On the other hand, he could have traveled some distance with the money he did have and he did return to his room where he obtained his revolver.
+LJSpeech-1.1/wavs/LJ028-0168.wav|in the other was the sacred precinct of Jupiter Belus,
+LJSpeech-1.1/wavs/LJ021-0140.wav|and in such an effort we should be able to secure for employers and employees and consumers
+LJSpeech-1.1/wavs/LJ009-0280.wav|Again the wretched creature succeeded in obtaining foothold, but this time on the left side of the drop.
+LJSpeech-1.1/wavs/LJ003-0159.wav|To constitute this the aristocratic quarter, unwarrantable demands were made upon the space properly allotted to the female felons,
+LJSpeech-1.1/wavs/LJ016-0274.wav|and the windows of the opposite houses, which commanded a good view, as usual fetched high prices.
+LJSpeech-1.1/wavs/LJ035-0014.wav|it sounded high and I immediately kind of looked up,
+LJSpeech-1.1/wavs/LJ033-0120.wav|which he believed was where the bag reached when it was laid on the seat with one edge against the door.
+LJSpeech-1.1/wavs/LJ045-0015.wav|which Johnson said he did not receive until after the assassination. The letter said in part, quote,
+LJSpeech-1.1/wavs/LJ003-0299.wav|the latter end of the nineteenth century, several of which still fall far short of our English ideal,
+LJSpeech-1.1/wavs/LJ032-0206.wav|After comparing the rifle in the simulated photograph with the rifle in Exhibit Number one thirty-three A, Shaneyfelt testified, quote,
+LJSpeech-1.1/wavs/LJ028-0494.wav|Between the several sections were wide spaces where foot soldiers and charioteers might fight.
+LJSpeech-1.1/wavs/LJ005-0099.wav|and report at length upon the condition of the prisons of the country.
+LJSpeech-1.1/wavs/LJ015-0144.wav|developed to a colossal extent the frauds he had already practiced as a subordinate.
+LJSpeech-1.1/wavs/LJ019-0221.wav|It was intended as far as possible that, except awaiting trial, no prisoner should find himself relegated to Newgate.
+LJSpeech-1.1/wavs/LJ003-0088.wav|in one, for seven years -- that of a man sentenced to death, for whom great interest had been made, but whom it was not thought right to pardon.
+LJSpeech-1.1/wavs/LJ045-0216.wav|nineteen sixty-three, merely to disarm her and to provide a justification of sorts,
+LJSpeech-1.1/wavs/LJ042-0135.wav|that he was not yet twenty years old when he went to the Soviet Union with such high hopes and not quite twenty-three when he returned bitterly disappointed.
+LJSpeech-1.1/wavs/LJ049-0196.wav|On the other hand, it is urged that all features of the protection of the President and his family should be committed to an elite and independent corps.
+LJSpeech-1.1/wavs/LJ018-0278.wav|This was the well and astutely devised plot of the brothers Bidwell,
+LJSpeech-1.1/wavs/LJ030-0238.wav|and then looked around again and saw more of this movement, and so I proceeded to go to the back seat and get on top of him.
+LJSpeech-1.1/wavs/LJ018-0309.wav|where probably the money still remains.
+LJSpeech-1.1/wavs/LJ041-0199.wav|is shown most clearly by his employment relations after his return from the Soviet Union. Of course, he made his real problems worse to the extent
+LJSpeech-1.1/wavs/LJ007-0076.wav|The lax discipline maintained in Newgate was still further deteriorated by the presence of two other classes of prisoners who ought never to have been inmates of such a jail.
+LJSpeech-1.1/wavs/LJ039-0118.wav|He had high motivation. He had presumably a good to excellent rifle and good ammunition.
+LJSpeech-1.1/wavs/LJ024-0019.wav|And there may be only nine.
+LJSpeech-1.1/wavs/LJ008-0085.wav|The fire had not quite burnt out at twelve, in nearly four hours, that is to say.
+LJSpeech-1.1/wavs/LJ018-0031.wav|This fixed the crime pretty certainly upon Müller, who had already left the country, thus increasing suspicion under which he lay.
+LJSpeech-1.1/wavs/LJ030-0032.wav|Dallas police stood at intervals along the fence and Dallas plain clothes men mixed in the crowd.
+LJSpeech-1.1/wavs/LJ050-0004.wav|General Supervision of the Secret Service
+LJSpeech-1.1/wavs/LJ039-0096.wav|This is a definite advantage to the shooter, the vehicle moving directly away from him and the downgrade of the street, and he being in an elevated position
+LJSpeech-1.1/wavs/LJ041-0195.wav|Oswald's interest in Marxism led some people to avoid him,
+LJSpeech-1.1/wavs/LJ047-0158.wav|After a moment's hesitation, she told me that he worked at the Texas School Book Depository near the downtown area of Dallas.
+LJSpeech-1.1/wavs/LJ050-0162.wav|In planning its data processing techniques,
+LJSpeech-1.1/wavs/LJ001-0051.wav|and paying great attention to the "press work" or actual process of printing,
+LJSpeech-1.1/wavs/LJ028-0136.wav|Of all the ancient descriptions of the famous walls and the city they protected, that of Herodotus is the fullest.
+LJSpeech-1.1/wavs/LJ034-0134.wav|Shortly after the assassination Brennan noticed
+LJSpeech-1.1/wavs/LJ019-0348.wav|Every facility was promised. The sanction of the Secretary of State would not be withheld if plans and estimates were duly submitted,
+LJSpeech-1.1/wavs/LJ010-0219.wav|While one stood over the fire with the papers, another stood with lighted torch to fire the house.
+LJSpeech-1.1/wavs/LJ011-0245.wav|Mr. Mullay called again, taking with him five hundred pounds in cash. Howard discovered this, and his manner was very suspicious;
+LJSpeech-1.1/wavs/LJ030-0035.wav|Organization of the Motorcade
+LJSpeech-1.1/wavs/LJ044-0135.wav|While he had drawn some attention to himself and had actually appeared on two radio programs, he had been attacked by Cuban exiles and arrested,
+LJSpeech-1.1/wavs/LJ045-0090.wav|He was very much interested in autobiographical works of outstanding statesmen of the United States, to whom his wife thought he compared himself.
+LJSpeech-1.1/wavs/LJ026-0034.wav|When any given "protist" has to be classified the case must be decided on its individual merits;
+LJSpeech-1.1/wavs/LJ045-0092.wav|as to the fact that he was an outstanding man, end quote.
+LJSpeech-1.1/wavs/LJ017-0050.wav|Palmer, who was only thirty-one at the time of his trial, was in appearance short and stout, with a round head
+LJSpeech-1.1/wavs/LJ036-0104.wav|Whaley picked Oswald.
+LJSpeech-1.1/wavs/LJ019-0055.wav|High authorities were in favor of continuous separation.
+LJSpeech-1.1/wavs/LJ010-0030.wav|The brutal ferocity of the wild beast once aroused, the same means, the same weapons were employed to do the dreadful deed,
+LJSpeech-1.1/wavs/LJ038-0047.wav|Some of the officers saw Oswald strike McDonald with his fist. Most of them heard a click which they assumed to be a click of the hammer of the revolver.
+LJSpeech-1.1/wavs/LJ009-0074.wav|Let us pass on.
+LJSpeech-1.1/wavs/LJ048-0069.wav|Efforts made by the Bureau since the assassination, on the other hand,
+LJSpeech-1.1/wavs/LJ003-0211.wav|They were never left quite alone for fear of suicide, and for the same reason they were searched for weapons or poisons.
+LJSpeech-1.1/wavs/LJ048-0053.wav|It is the conclusion of the Commission that, even in the absence of Secret Service criteria
+LJSpeech-1.1/wavs/LJ033-0093.wav|Frazier estimated that the bag was two feet long, quote, give and take a few inches, end quote, and about five or six inches wide.
+LJSpeech-1.1/wavs/LJ006-0149.wav|The turnkeys left the prisoners very much to themselves, never entering the wards after locking-up time, at dusk, till unlocking next morning,
+LJSpeech-1.1/wavs/LJ018-0211.wav|The false coin was bought by an agent from an agent, and dealings were carried on secretly at the "Clock House" in Seven Dials.
+LJSpeech-1.1/wavs/LJ008-0054.wav|This contrivance appears to have been copied with improvements from that which had been used in Dublin at a still earlier date,
+LJSpeech-1.1/wavs/LJ040-0052.wav|that his commitment to Marxism was an important factor influencing his conduct during his adult years.
+LJSpeech-1.1/wavs/LJ028-0023.wav|Two weeks pass, and at last you stand on the eastern edge of the plateau
+LJSpeech-1.1/wavs/LJ009-0184.wav|Lord Ferrers' body was brought to Surgeons' Hall after execution in his own carriage and six;
+LJSpeech-1.1/wavs/LJ005-0252.wav|A committee was appointed, under the presidency of the Duke of Richmond
+LJSpeech-1.1/wavs/LJ015-0266.wav|has probably no parallel in the annals of crime. Saward himself is a striking and in some respects an unique figure in criminal history.
+LJSpeech-1.1/wavs/LJ017-0059.wav|even after sentence, and until within a few hours of execution, he was buoyed up with the hope of reprieve.
+LJSpeech-1.1/wavs/LJ024-0034.wav|What do they mean by the words "packing the Court"?
+LJSpeech-1.1/wavs/LJ016-0089.wav|He was engaged in whitewashing and cleaning; the officer who had him in charge left him on the stairs leading to the gallery.
+LJSpeech-1.1/wavs/LJ039-0227.wav|with two hits, within four point eight and five point six seconds.
+LJSpeech-1.1/wavs/LJ001-0096.wav|have now come into general use and are obviously a great improvement on the ordinary "modern style" in use in England, which is in fact the Bodoni type
+LJSpeech-1.1/wavs/LJ018-0129.wav|who threatened to betray the theft. But Brewer, either before or after this, succumbed to temptation,
+LJSpeech-1.1/wavs/LJ010-0157.wav|and that, as he was starving, he had resolved on this desperate deed,
+LJSpeech-1.1/wavs/LJ038-0264.wav|He concluded that, quote, the general rifling characteristics of the rifle are of the same type as those found on the bullet
+LJSpeech-1.1/wavs/LJ031-0165.wav|When security arrangements at the airport were complete, the Secret Service made the necessary arrangements for the Vice President to leave the hospital.
+LJSpeech-1.1/wavs/LJ018-0244.wav|The effect of establishing the forgeries would be to restore to the Roupell family lands for which a price had already been paid
+LJSpeech-1.1/wavs/LJ007-0071.wav|in the face of impediments confessedly discouraging
+LJSpeech-1.1/wavs/LJ028-0340.wav|Such of the Babylonians as witnessed the treachery took refuge in the temple of Jupiter Belus;
+LJSpeech-1.1/wavs/LJ017-0164.wav|with the idea of subjecting her to the irritant poison slowly but surely until the desired effect, death, was achieved.
+LJSpeech-1.1/wavs/LJ048-0197.wav|I then told the officers that their primary duty was traffic and crowd control and that they should be alert for any persons who might attempt to throw anything
+LJSpeech-1.1/wavs/LJ013-0098.wav|Mr. Oxenford having denied that he had made any transfer of stock, the matter was at once put into the hands of the police.
+LJSpeech-1.1/wavs/LJ012-0049.wav|led him to think seriously of trying his fortunes in another land.
+LJSpeech-1.1/wavs/LJ030-0014.wav|quote, that the crowd was about the same as the one which came to see him before but there were one hundred thousand extra people on hand who came to see Mrs. Kennedy.
+LJSpeech-1.1/wavs/LJ014-0186.wav|A milliner's porter,
+LJSpeech-1.1/wavs/LJ015-0027.wav|Yet even so early as the death of the first Sir John Paul,
+LJSpeech-1.1/wavs/LJ047-0049.wav|Marina Oswald, however, recalled that her husband was upset by this interview.
+LJSpeech-1.1/wavs/LJ012-0021.wav|at fourteen he was a pickpocket and a "duffer," or a seller of sham goods.
+LJSpeech-1.1/wavs/LJ003-0140.wav|otherwise he would have been stripped of his clothes. End quote.
+LJSpeech-1.1/wavs/LJ042-0130.wav|Shortly thereafter, less than eighteen months after his defection, about six weeks before he met Marina Prusakova,
+LJSpeech-1.1/wavs/LJ019-0180.wav|His letter to the Corporation, under date fourth June,
+LJSpeech-1.1/wavs/LJ017-0108.wav|He was struck with the appearance of the corpse, which was not emaciated, as after a long disease ending in death;
+LJSpeech-1.1/wavs/LJ006-0268.wav|Women saw men if they merely pretended to be wives; even boys were visited by their sweethearts.
+LJSpeech-1.1/wavs/LJ044-0125.wav|of residence in the U.S.S.R. against any cause which I join, by association,
+LJSpeech-1.1/wavs/LJ015-0231.wav|It was Tester's business, who had access to the railway company's books, to watch for this.
+LJSpeech-1.1/wavs/LJ002-0225.wav|The rentals of rooms and fees went to the warden, whose income was two thousand three hundred seventy-two pounds.
+LJSpeech-1.1/wavs/LJ034-0072.wav|The employees raced the elevators to the first floor. Givens saw Oswald standing at the gate on the fifth floor as the elevator went by.
+LJSpeech-1.1/wavs/LJ045-0033.wav|He began to treat me better. He helped me more -- although he always did help. But he was more attentive, end quote.
+LJSpeech-1.1/wavs/LJ031-0058.wav|to infuse blood and fluids into the circulatory system.
+LJSpeech-1.1/wavs/LJ029-0197.wav|During November the Dallas papers reported frequently on the plans for protecting the President, stressing the thoroughness of the preparations.
+LJSpeech-1.1/wavs/LJ043-0047.wav|Oswald and his family lived for a brief period with his mother at her urging, but Oswald soon decided to move out.
+LJSpeech-1.1/wavs/LJ021-0026.wav|seems necessary to produce the same result of justice and right conduct
+LJSpeech-1.1/wavs/LJ003-0230.wav|The prison allowances were eked out by the broken victuals generously given by several eating-house keepers in the city,
+LJSpeech-1.1/wavs/LJ037-0252.wav|Ted Callaway, who saw the gunman moments after the shooting, testified that Commission Exhibit Number one sixty-two
+LJSpeech-1.1/wavs/LJ031-0008.wav|Meanwhile, Chief Curry ordered the police base station to notify Parkland Hospital that the wounded President was en route.
+LJSpeech-1.1/wavs/LJ030-0021.wav|all one had to do was get a high building someday with a telescopic rifle, and there was nothing anybody could do to defend against such an attempt.
+LJSpeech-1.1/wavs/LJ046-0179.wav|being reviewed regularly.
+LJSpeech-1.1/wavs/LJ025-0118.wav|and that, however diverse may be the fabrics or tissues of which their bodies are composed, all these varied structures result
+LJSpeech-1.1/wavs/LJ028-0278.wav|Zopyrus, when they told him, not thinking that it could be true, went and saw the colt with his own eyes;
+LJSpeech-1.1/wavs/LJ007-0090.wav|Not only did their presence tend greatly to interfere with the discipline of the prison, but their condition was deplorable in the extreme.
+LJSpeech-1.1/wavs/LJ045-0045.wav|that she would be able to leave the Soviet Union. Marina Oswald has denied this.
+LJSpeech-1.1/wavs/LJ028-0289.wav|For he cut off his own nose and ears, and then, clipping his hair close and flogging himself with a scourge,
+LJSpeech-1.1/wavs/LJ009-0276.wav|Calcraft, the moment he had adjusted the cap and rope, ran down the steps, drew the bolt, and disappeared.
+LJSpeech-1.1/wavs/LJ031-0122.wav|treated the gunshot wound in the left thigh.
+LJSpeech-1.1/wavs/LJ016-0205.wav|he received a retaining fee of five pounds, five shillings, with the usual guinea for each job;
+LJSpeech-1.1/wavs/LJ019-0248.wav|leading to an inequality, uncertainty, and inefficiency of punishment productive of the most prejudicial results.
+LJSpeech-1.1/wavs/LJ033-0183.wav|it was not surprising that the replica sack made on December one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ037-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ018-0218.wav|In eighteen fifty-five
+LJSpeech-1.1/wavs/LJ001-0102.wav|Here and there a book is printed in France or Germany with some pretension to good taste,
+LJSpeech-1.1/wavs/LJ007-0125.wav|It was diverted from its proper uses, and, as the "place of the greatest comfort," was allotted to persons who should not have been sent to Newgate at all.
+LJSpeech-1.1/wavs/LJ050-0022.wav|A formal and thorough description of the responsibilities of the advance agent is now in preparation by the Service.
+LJSpeech-1.1/wavs/LJ028-0212.wav|On the night of the eleventh day Gobrias killed the son of the King.
+LJSpeech-1.1/wavs/LJ028-0357.wav|yet we may be sure that Babylon was taken by Darius only by use of stratagem. Its walls were impregnable.
+LJSpeech-1.1/wavs/LJ014-0199.wav|there was no case to make out; why waste money on lawyers for the defense? His demeanor was cool and collected throughout;
+LJSpeech-1.1/wavs/LJ016-0077.wav|A man named Lears, under sentence of transportation for an attempt at murder on board ship, got up part of the way,
+LJSpeech-1.1/wavs/LJ009-0194.wav|and that executors or persons having lawful possession of the bodies
+LJSpeech-1.1/wavs/LJ014-0094.wav|Discovery of the murder came in this wise. O'Connor, a punctual and well-conducted official, was at once missed at the London Docks.
+LJSpeech-1.1/wavs/LJ001-0079.wav|Caslon's type is clear and neat, and fairly well designed;
+LJSpeech-1.1/wavs/LJ026-0052.wav|In the nutrition of the animal the most essential and characteristic part of the food supply is derived from vegetable
+LJSpeech-1.1/wavs/LJ013-0005.wav|One of the earliest of the big operators in fraudulent finance was Edward Beaumont Smith,
+LJSpeech-1.1/wavs/LJ033-0072.wav|I then stepped off of it and the officer picked it up in the middle and it bent so.
+LJSpeech-1.1/wavs/LJ036-0067.wav|According to McWatters, the Beckley bus was behind the Marsalis bus, but he did not actually see it.
+LJSpeech-1.1/wavs/LJ025-0098.wav|and it is probable that amyloid substances are universally present in the animal organism, though not in the precise form of starch.
+LJSpeech-1.1/wavs/LJ005-0257.wav|during which time a host of witnesses were examined, and the committee presented three separate reports,
+LJSpeech-1.1/wavs/LJ004-0024.wav|Thus in eighteen thirteen the exaction of jail fees had been forbidden by law,
+LJSpeech-1.1/wavs/LJ049-0154.wav|In eighteen ninety-four,
+LJSpeech-1.1/wavs/LJ039-0059.wav|(three) his experience and practice after leaving the Marine Corps, and (four) the accuracy of the weapon and the quality of the ammunition.
+LJSpeech-1.1/wavs/LJ007-0150.wav|He is allowed intercourse with prostitutes who, in nine cases out of ten, have originally conduced to his ruin;
+LJSpeech-1.1/wavs/LJ015-0001.wav|Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eighteen: Newgate notorieties continued, part three.
+LJSpeech-1.1/wavs/LJ010-0158.wav|feeling, as he said, that he might as well be shot or hanged as remain in such a state.
+LJSpeech-1.1/wavs/LJ010-0281.wav|who had borne the Queen's commission, first as cornet, and then lieutenant, in the tenth Hussars.
+LJSpeech-1.1/wavs/LJ033-0055.wav|and he could disassemble it more rapidly.
+LJSpeech-1.1/wavs/LJ015-0218.wav|A new accomplice was now needed within the company's establishment, and Pierce looked about long before he found the right person.
+LJSpeech-1.1/wavs/LJ027-0006.wav|In all these lines the facts are drawn together by a strong thread of unity.
+LJSpeech-1.1/wavs/LJ016-0049.wav|He had here completed his ascent.
+LJSpeech-1.1/wavs/LJ006-0088.wav|It was not likely that a system which left innocent men -- for the great bulk of new arrivals were still untried
+LJSpeech-1.1/wavs/LJ042-0133.wav|a great change must have occurred in Oswald's thinking to induce him to return to the United States.
+LJSpeech-1.1/wavs/LJ045-0234.wav|While he did become enraged at at least one point in his interrogation,
+LJSpeech-1.1/wavs/LJ046-0033.wav|The adequacy of existing procedures can fairly be assessed only after full consideration of the difficulty of the protective assignment,
+LJSpeech-1.1/wavs/LJ037-0061.wav|and having, quote, somewhat bushy, end quote, hair.
+LJSpeech-1.1/wavs/LJ032-0025.wav|the officers of Klein's discovered that a rifle bearing serial number C two seven six six had been shipped to one A. Hidell,
+LJSpeech-1.1/wavs/LJ047-0197.wav|in view of all the information concerning Oswald in its files, should have alerted the Secret Service to Oswald's presence in Dallas
+LJSpeech-1.1/wavs/LJ018-0130.wav|and stole paper on a much larger scale than Brown.
+LJSpeech-1.1/wavs/LJ005-0265.wav|It was recommended that the dietaries should be submitted and approved like the rules; that convicted prisoners should not receive any food but the jail allowance;
+LJSpeech-1.1/wavs/LJ044-0105.wav|He presented Arnold Johnson, Gus Hall,
+LJSpeech-1.1/wavs/LJ015-0043.wav|This went on for some time, and might never have been discovered had some good stroke of luck provided any of the partners
+LJSpeech-1.1/wavs/LJ030-0125.wav|On several occasions when the Vice President's car was slowed down by the throng, Special Agent Youngblood stepped out to hold the crowd back.
+LJSpeech-1.1/wavs/LJ043-0140.wav|He also studied Dallas bus schedules to prepare for his later use of buses to travel to and from General Walker's house.
+LJSpeech-1.1/wavs/LJ002-0220.wav|In consequence of these disclosures, both Bambridge and Huggin, his predecessor in the office, were committed to Newgate,
+LJSpeech-1.1/wavs/LJ034-0117.wav|At one:twenty-nine p.m. the police radio reported
+LJSpeech-1.1/wavs/LJ018-0276.wav|The first plot was against Mr. Harry Emmanuel, but he escaped, and the attempt was made upon Loudon and Ryder.
+LJSpeech-1.1/wavs/LJ004-0077.wav|nor has he a right to poison or starve his fellow-creatures."
+LJSpeech-1.1/wavs/LJ042-0194.wav|they should not be confused with slowness, indecision or fear. Only the intellectually fearless could even be remotely attracted to our doctrine,
+LJSpeech-1.1/wavs/LJ029-0114.wav|The route chosen from the airport to Main Street was the normal one, except where Harwood Street was selected as the means of access to Main Street
+LJSpeech-1.1/wavs/LJ014-0194.wav|The policemen were now in possession;
+LJSpeech-1.1/wavs/LJ032-0027.wav|According to its microfilm records, Klein's received an order for a rifle on March thirteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ048-0289.wav|However, there is no evidence that these men failed to take any action in Dallas within their power that would have averted the tragedy.
+LJSpeech-1.1/wavs/LJ043-0188.wav|that he was the leader of a fascist organization, and when I said that even though all of that might be true, just the same he had no right to take his life,
+LJSpeech-1.1/wavs/LJ011-0118.wav|In eighteen twenty-nine the gallows claimed two more victims for this offense.
+LJSpeech-1.1/wavs/LJ040-0201.wav|After her interview with Mrs. Oswald,
+LJSpeech-1.1/wavs/LJ033-0056.wav|While the rifle may have already been disassembled when Oswald arrived home on Thursday, he had ample time that evening to disassemble the rifle
+LJSpeech-1.1/wavs/LJ047-0073.wav|Hosty considered the information to be, quote, stale, unquote, by that time, and did not attempt to verify Oswald's reported statement.
+LJSpeech-1.1/wavs/LJ001-0153.wav|only nominally so, however, in many cases, since when he uses a headline he counts that in,
+LJSpeech-1.1/wavs/LJ007-0158.wav|or any kind of moral improvement was impossible; the prisoner's career was inevitably downward, till he struck the lowest depths.
+LJSpeech-1.1/wavs/LJ028-0502.wav|The Ishtar gateway leading to the palace was encased with beautiful blue glazed bricks,
+LJSpeech-1.1/wavs/LJ028-0226.wav|Though Herodotus wrote nearly a hundred years after Babylon fell, his story seems to bear the stamp of truth.
+LJSpeech-1.1/wavs/LJ010-0038.wav|as there had been before; as in the year eighteen forty-nine, a year memorable for the Rush murders at Norwich,
+LJSpeech-1.1/wavs/LJ019-0241.wav|But in the interval very comprehensive and, I think it must be admitted, salutary changes were successively introduced into the management of prisons.
+LJSpeech-1.1/wavs/LJ001-0094.wav|were induced to cut punches for a series of "old style" letters.
+LJSpeech-1.1/wavs/LJ001-0015.wav|the forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves.
+LJSpeech-1.1/wavs/LJ047-0015.wav|From defection to return to Fort Worth.
+LJSpeech-1.1/wavs/LJ044-0139.wav|since there was no background to the New Orleans FPCC, quote, organization, end quote, which consisted solely of Oswald.
+LJSpeech-1.1/wavs/LJ050-0031.wav|that the Secret Service consciously set about the task of inculcating and maintaining the highest standard of excellence and esprit, for all of its personnel.
+LJSpeech-1.1/wavs/LJ050-0235.wav|It has also used other Federal law enforcement agents during Presidential visits to cities in which such agents are stationed.
+LJSpeech-1.1/wavs/LJ050-0137.wav|FBI, and the Secret Service.
+LJSpeech-1.1/wavs/LJ031-0109.wav|At one:thirty-five p.m., after Governor Connally had been moved to the operating room, Dr. Shaw started the first operation
+LJSpeech-1.1/wavs/LJ031-0041.wav|He noted that the President was blue-white or ashen in color; had slow, spasmodic, agonal respiration without any coordination;
+LJSpeech-1.1/wavs/LJ021-0139.wav|There should be at least a full and fair trial given to these means of ending industrial warfare;
+LJSpeech-1.1/wavs/LJ029-0004.wav|The narrative of these events is based largely on the recollections of the participants,
+LJSpeech-1.1/wavs/LJ023-0122.wav|It was said in last year's Democratic platform,
+LJSpeech-1.1/wavs/LJ005-0264.wav|inspectors of prisons should be appointed, who should visit all the prisons from time to time and report to the Secretary of State.
+LJSpeech-1.1/wavs/LJ002-0105.wav|and beyond it was a room called the "wine room," because formerly used for the sale of wine, but
+LJSpeech-1.1/wavs/LJ017-0035.wav|in the interests and for the due protection of the public, that the fullest and fairest inquiry should be made,
+LJSpeech-1.1/wavs/LJ048-0252.wav|Three of these agents occupied positions on the running boards of the car, and the fourth was seated in the car.
+LJSpeech-1.1/wavs/LJ013-0109.wav|The proceeds of the robbery were lodged in a Boston bank,
+LJSpeech-1.1/wavs/LJ039-0139.wav|Oswald obtained a hunting license, joined a hunting club and went hunting about six times, as discussed more fully in chapter six.
+LJSpeech-1.1/wavs/LJ044-0047.wav|that anyone ever attacked any street demonstration in which Oswald was involved, except for the Bringuier incident mentioned above,
+LJSpeech-1.1/wavs/LJ016-0417.wav|Catherine Wilson, the poisoner, was reserved and reticent to the last, expressing no contrition, but also no fear --
+LJSpeech-1.1/wavs/LJ045-0178.wav|he left his wedding ring in a cup on the dresser in his room. He also left one hundred seventy dollars in a wallet in one of the dresser drawers.
+LJSpeech-1.1/wavs/LJ009-0172.wav|While in London, for instance, in eighteen twenty-nine, twenty-four persons had been executed for crimes other than murder,
+LJSpeech-1.1/wavs/LJ049-0202.wav|incident to its responsibilities.
+LJSpeech-1.1/wavs/LJ032-0103.wav|The name "Hidell" was stamped on some of the "Chapter's" printed literature and on the membership application blanks.
+LJSpeech-1.1/wavs/LJ013-0091.wav|and Elder had to be assisted by two bank porters, who carried it for him to a carriage waiting near the Mansion House.
+LJSpeech-1.1/wavs/LJ037-0208.wav|nineteen dollars, ninety-five cents, plus one dollar, twenty-seven cents shipping charge, had been collected from the consignee, Hidell.
+LJSpeech-1.1/wavs/LJ014-0128.wav|her hair was dressed in long crepe bands. She had lace ruffles at her wrist, and wore primrose-colored kid gloves.
+LJSpeech-1.1/wavs/LJ015-0007.wav|This affected Cole's credit, and ugly reports were in circulation charging him with the issue of simulated warrants.
+LJSpeech-1.1/wavs/LJ036-0169.wav|he would have reached his destination at approximately twelve:fifty-four p.m.
+LJSpeech-1.1/wavs/LJ021-0040.wav|The second step we have taken in the restoration of normal business enterprise
+LJSpeech-1.1/wavs/LJ015-0036.wav|The bank was already insolvent,
+LJSpeech-1.1/wavs/LJ034-0041.wav|Although Bureau experiments had shown that twenty-four hours was a likely maximum time, Latona stated
+LJSpeech-1.1/wavs/LJ009-0192.wav|The dissection of executed criminals was abolished soon after the discovery of the crime of burking,
+LJSpeech-1.1/wavs/LJ037-0248.wav|The eyewitnesses vary in their identification of the jacket.
+LJSpeech-1.1/wavs/LJ015-0289.wav|As each transaction was carried out from a different address, and a different messenger always employed,
+LJSpeech-1.1/wavs/LJ005-0072.wav|After a few years of active exertion the Society was rewarded by fresh legislation.
+LJSpeech-1.1/wavs/LJ023-0047.wav|The three horses are, of course, the three branches of government -- the Congress, the Executive and the courts.
+LJSpeech-1.1/wavs/LJ009-0126.wav|Hardly any one.
+LJSpeech-1.1/wavs/LJ034-0097.wav|The window was approximately one hundred twenty feet away.
+LJSpeech-1.1/wavs/LJ028-0462.wav|They were laid in bitumen.
+LJSpeech-1.1/wavs/LJ046-0055.wav|It is now possible for Presidents to travel the length and breadth of a land far larger than the United States
+LJSpeech-1.1/wavs/LJ019-0371.wav|Yet the law was seldom if ever enforced.
+LJSpeech-1.1/wavs/LJ039-0207.wav|Although all of the shots were a few inches high and to the right of the target,
+LJSpeech-1.1/wavs/LJ002-0174.wav|Mr. Buxton's friends at once paid the forty shillings, and the boy was released.
+LJSpeech-1.1/wavs/LJ016-0233.wav|In his own profession
+LJSpeech-1.1/wavs/LJ026-0108.wav|It is clear that there are upward and downward currents of water containing food (comparable to blood of an animal),
+LJSpeech-1.1/wavs/LJ038-0035.wav|Oswald rose from his seat, bringing up both hands.
+LJSpeech-1.1/wavs/LJ026-0148.wav|water which is lost by evaporation, especially from the leaf surface through the stomata;
+LJSpeech-1.1/wavs/LJ001-0186.wav|the position of our Society that a work of utility might be also a work of art, if we cared to make it so.
+LJSpeech-1.1/wavs/LJ016-0264.wav|The upturned faces of the eager spectators resembled those of the 'gods' at Drury Lane on Boxing Night;
+LJSpeech-1.1/wavs/LJ009-0041.wav|The occupants of this terrible black pew were the last always to enter the chapel.
+LJSpeech-1.1/wavs/LJ010-0297.wav|But there were other notorious cases of forgery.
+LJSpeech-1.1/wavs/LJ040-0018.wav|the Commission is not able to reach any definite conclusions as to whether or not he was, quote, sane, unquote, under prevailing legal standards.
+LJSpeech-1.1/wavs/LJ005-0253.wav|"to inquire into and report upon the several jails and houses of correction in the counties, cities, and corporate towns within England and Wales
+LJSpeech-1.1/wavs/LJ027-0176.wav|Fishes first appeared in the Devonian and Upper Silurian in very reptilian or rather amphibian forms.
+LJSpeech-1.1/wavs/LJ034-0035.wav|The position of this palmprint on the carton was parallel with the long axis of the box, and at right angles with the short axis;
+LJSpeech-1.1/wavs/LJ016-0054.wav|But he did not like the risk of entering a room by the fireplace, and the chances of detection it offered.
+LJSpeech-1.1/wavs/LJ018-0262.wav|Roupell received the announcement with a cheerful countenance,
+LJSpeech-1.1/wavs/LJ044-0237.wav|with thirteen dollars, eighty-seven cents when considerably greater resources were available to him.
+LJSpeech-1.1/wavs/LJ034-0166.wav|Two other witnesses were able to offer partial descriptions of a man they saw in the southeast corner window
+LJSpeech-1.1/wavs/LJ016-0238.wav|"just to steady their legs a little;" in other words, to add his weight to that of the hanging bodies.
+LJSpeech-1.1/wavs/LJ042-0198.wav|The discussion above has already set forth examples of his expression of hatred for the United States.
+LJSpeech-1.1/wavs/LJ031-0189.wav|At two:thirty-eight p.m., Eastern Standard Time, Lyndon Baines Johnson took the oath of office as the thirty-sixth President of the United States.
+LJSpeech-1.1/wavs/LJ050-0084.wav|or, quote, other high government officials in the nature of a complaint coupled with an expressed or implied determination to use a means,
+LJSpeech-1.1/wavs/LJ044-0158.wav|As for my return entrance visa please consider it separately. End quote.
+LJSpeech-1.1/wavs/LJ045-0082.wav|it appears that Marina Oswald also complained that her husband was not able to provide more material things for her.
+LJSpeech-1.1/wavs/LJ045-0190.wav|appeared in The Dallas Times Herald on November fifteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ035-0155.wav|The only exit from the office in the direction Oswald was moving was through the door to the front stairway.
+LJSpeech-1.1/wavs/LJ044-0004.wav|Political Activities
+LJSpeech-1.1/wavs/LJ046-0016.wav|The Commission has not undertaken a comprehensive examination of all facets of this subject;
+LJSpeech-1.1/wavs/LJ019-0368.wav|The latter too was to be laid before the House of Commons.
+LJSpeech-1.1/wavs/LJ010-0062.wav|But they proceeded in all seriousness, and would have shrunk from no outrage or atrocity in furtherance of their foolhardy enterprise.
+LJSpeech-1.1/wavs/LJ033-0159.wav|It was from Oswald's right hand, in which he carried the long package as he walked from Frazier's car to the building.
+LJSpeech-1.1/wavs/LJ002-0171.wav|The boy declared he saw no one, and accordingly passed through without paying the toll of a penny.
+LJSpeech-1.1/wavs/LJ002-0298.wav|in his evidence in eighteen fourteen, said it was more,
+LJSpeech-1.1/wavs/LJ012-0219.wav|and in one corner, at some depth, a bundle of clothes were unearthed, which, with a hairy cap,
+LJSpeech-1.1/wavs/LJ017-0190.wav|After this came the charge of administering oil of vitriol, which failed, as has been described.
+LJSpeech-1.1/wavs/LJ019-0179.wav|This, with a scheme for limiting the jail to untried prisoners, had been urgently recommended by Lord John Russell in eighteen thirty.
+LJSpeech-1.1/wavs/LJ050-0188.wav|each patrolman might be given a prepared booklet of instructions explaining what is expected of him. The Secret Service has expressed concern
+LJSpeech-1.1/wavs/LJ006-0043.wav|The disgraceful overcrowding had been partially ended, but the same evils of indiscriminate association were still present; there was the old neglect of decency,
+LJSpeech-1.1/wavs/LJ029-0060.wav|A number of people who resembled some of those in the photographs were placed under surveillance at the Trade Mart.
+LJSpeech-1.1/wavs/LJ019-0052.wav|Both systems came to us from the United States. The difference was really more in degree than in principle,
+LJSpeech-1.1/wavs/LJ037-0081.wav|Later in the day each woman found an empty shell on the ground near the house. These two shells were delivered to the police.
+LJSpeech-1.1/wavs/LJ048-0200.wav|paying particular attention to the crowd for any unusual activity.
+LJSpeech-1.1/wavs/LJ016-0426.wav|come along, gallows.
+LJSpeech-1.1/wavs/LJ008-0182.wav|A tremendous crowd assembled when Bellingham was executed in eighteen twelve for the murder of Spencer Percival, at that time prime minister;
+LJSpeech-1.1/wavs/LJ043-0107.wav|Upon moving to New Orleans on April twenty-four, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ006-0084.wav|and so numerous were his opportunities of showing favoritism, that all the prisoners may be said to be in his power.
+LJSpeech-1.1/wavs/LJ025-0081.wav|has no permanent digestive cavity or mouth, but takes in its food anywhere and digests, so to speak, all over its body.
+LJSpeech-1.1/wavs/LJ019-0042.wav|These were either satisfied with a makeshift, and modified existing buildings, without close regard to their suitability, or for a long time did nothing at all.
+LJSpeech-1.1/wavs/LJ047-0240.wav|They agree that Hosty told Revill
+LJSpeech-1.1/wavs/LJ032-0012.wav|the resistance to arrest and the attempted shooting of another police officer by the man (Lee Harvey Oswald) subsequently accused of assassinating President Kennedy
+LJSpeech-1.1/wavs/LJ050-0209.wav|The assistant to the Director of the FBI testified that
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_filelist.txt b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_filelist.txt
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+LJSpeech-1.1/wavs/LJ050-0234.wav|It has used other Treasury law enforcement agents on special experiments in building and route surveys in places to which the President frequently travels.
+LJSpeech-1.1/wavs/LJ019-0373.wav|to avail himself of his powers, as it was difficult to bring home the derelictions of duties and evasion of the acts. Too much was left to the inspectors.
+LJSpeech-1.1/wavs/LJ050-0207.wav|Although Chief Rowley does not complain about the pay scale for Secret Service agents,
+LJSpeech-1.1/wavs/LJ048-0203.wav|The three officers confirm that their primary concern was crowd and traffic control,
+LJSpeech-1.1/wavs/LJ003-0182.wav|The tried and the untried, young and old, were herded together
+LJSpeech-1.1/wavs/LJ044-0166.wav|According to Marina Oswald, he thought that would help him when he got to Cuba.
+LJSpeech-1.1/wavs/LJ019-0208.wav|The proposal made was to purchase some fifty thousand square feet between Newgate, Warwick Lane, and the Sessions House,
+LJSpeech-1.1/wavs/LJ021-0146.wav|I shall seek assurances of the making and maintenance of agreements, which can be mutually relied upon,
+LJSpeech-1.1/wavs/LJ013-0214.wav|who took a carving-knife from the sideboard in the dining-room, went upstairs to Lord William's bedroom, and drew the knife across his throat.
+LJSpeech-1.1/wavs/LJ011-0256.wav|By this time the neighbors were aroused, and several people came to the scene of the affray.
+LJSpeech-1.1/wavs/LJ014-0083.wav|which, having possessed herself of the murdered man's keys, she rifled from end to end.
+LJSpeech-1.1/wavs/LJ035-0121.wav|This is the period during which Oswald would have descended the stairs. In all likelihood
+LJSpeech-1.1/wavs/LJ049-0118.wav|Enactment of this statute would mean that the investigation of any of the acts covered and of the possibility of a further attempt
+LJSpeech-1.1/wavs/LJ006-0132.wav|All the wardsmen alike were more or less irresponsible.
+LJSpeech-1.1/wavs/LJ049-0084.wav|Murder of the President has never been covered by Federal law, however, so that once it became reasonably clear that the killing was the act of a single person,
+LJSpeech-1.1/wavs/LJ012-0052.wav|He claimed to be admitted to bail, and was taken from Newgate on a writ of habeas before one of the judges sitting at Westminster.
+LJSpeech-1.1/wavs/LJ011-0203.wav|Monsieur le Maire was appealed to, and decided to leave it to the young lady, who at once abandoned Wakefield.
+LJSpeech-1.1/wavs/LJ019-0141.wav|The old wards, day rooms and sleeping rooms combined, of which the reader has already heard so much,
+LJSpeech-1.1/wavs/LJ003-0322.wav|except for the use of the debtors, or as medical comforts for the infirmary.
+LJSpeech-1.1/wavs/LJ027-0028.wav|Such structures or organs are most often found internally.
+LJSpeech-1.1/wavs/LJ047-0068.wav|and his recommendation was accepted. He decided, however, not to interview Marina Oswald at that time, and merely determined
+LJSpeech-1.1/wavs/LJ036-0053.wav|End quote. As Mrs. Bledsoe said these words, she pointed to her right elbow.
+LJSpeech-1.1/wavs/LJ007-0046.wav|laughing and uproarious, utterly unmindful of the companionship of men upon whom lay the shadow of an impending shameful death.
+LJSpeech-1.1/wavs/LJ033-0149.wav|the FBI Laboratory developed a latent palmprint and latent fingerprint on the bag.
+LJSpeech-1.1/wavs/LJ018-0334.wav|Webster, it may be mentioned here, was one of the worst prisoners ever remembered in Newgate
+LJSpeech-1.1/wavs/LJ043-0073.wav|On October nine, nineteen sixty-two he went to the Dallas office of the Texas Employment Commission
+LJSpeech-1.1/wavs/LJ028-0169.wav|a square enclosure two furlongs each way, with gates of solid brass; which was also remaining in my time.
+LJSpeech-1.1/wavs/LJ032-0052.wav|According to Klein's shipping order form, one Italian carbine six point five x four x scope,
+LJSpeech-1.1/wavs/LJ015-0030.wav|The bank had been conducted on false principles;
+LJSpeech-1.1/wavs/LJ006-0115.wav|Their original capital had been a few shillings, and for this they purchased the right to tax their fellows to the extent of pounds per week.
+LJSpeech-1.1/wavs/LJ039-0237.wav|The Commission agrees with the testimony of Marine marksmanship expert Zahm
+LJSpeech-1.1/wavs/LJ027-0081.wav|exhibit a remarkable series of homologies pointing to a five-toed ancestor,
+LJSpeech-1.1/wavs/LJ031-0045.wav|Dr. Carrico noted two wounds: a small bullet wound in the front lower neck,
+LJSpeech-1.1/wavs/LJ019-0393.wav|Three years after the advent of the prison commissioners, it was decided that Newgate was an excessively costly and redundant establishment.
+LJSpeech-1.1/wavs/LJ040-0209.wav|well I've got to live with her. I guess I love her, end quote.
+LJSpeech-1.1/wavs/LJ004-0222.wav|Meat was no longer issued raw, to be imperfectly cooked before a ward fire and bolted gluttonously, the whole two pounds at one sitting.
+LJSpeech-1.1/wavs/LJ009-0139.wav|In spite of the throng, owing to the excellent arrangements made by the sheriffs,
+LJSpeech-1.1/wavs/LJ020-0087.wav|One word with regard to getting up early in order to give dough a chance for the second rising.
+LJSpeech-1.1/wavs/LJ008-0175.wav|This went on for more than an hour, and until the malefactors were cut down and the gallows removed;
+LJSpeech-1.1/wavs/LJ025-0058.wav|but the fact, important as it was, fell into oblivion and had to be rediscovered by Treviranus in eighteen oh seven.
+LJSpeech-1.1/wavs/LJ022-0011.wav|When one of these ships is under construction and the steel frames have been set in the keel,
+LJSpeech-1.1/wavs/LJ037-0219.wav|Oswald's Jacket
+LJSpeech-1.1/wavs/LJ008-0190.wav|were blocked by persons who had overflowed from the area in front of the jail.
+LJSpeech-1.1/wavs/LJ019-0357.wav|The Secretary of State was empowered to deal rather summarily with "inadequate" prisons, in other words,
+LJSpeech-1.1/wavs/LJ043-0122.wav|who had previously interviewed Oswald, saw him on television and heard a radio debate in which he engaged on August twenty-one, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ013-0161.wav|the furniture turned upside down, the drawers of the escritoire open and rifled,
+LJSpeech-1.1/wavs/LJ002-0319.wav|issued once a week, and divided as far as it would go -- a very precarious and uncertain ration.
+LJSpeech-1.1/wavs/LJ001-0178.wav|The due relation of letter to pictures and other ornament was thoroughly understood by the old printers; so that
+LJSpeech-1.1/wavs/LJ015-0170.wav|Redpath was called in and informed of the intended investigation. He tried to stave off the evil hour by declaring that everything was perfectly right;
+LJSpeech-1.1/wavs/LJ012-0224.wav|who examined Bishop and his fellows, and further incriminating evidence adduced, to the effect that the prisoners had bartered for a coach to carry "a stiff 'un";
+LJSpeech-1.1/wavs/LJ047-0047.wav|According to Fain's contemporaneous memorandum and his present recollection,
+LJSpeech-1.1/wavs/LJ020-0052.wav|Cut this into round cakes with a biscuit-cutter or a sharp-edged tumbler and fold, not quite in the middle,
+LJSpeech-1.1/wavs/LJ013-0077.wav|Barber and Fletcher were both transported for life, although Fletcher declared that Barber was innocent, and had no guilty knowledge of what was being done.
+LJSpeech-1.1/wavs/LJ048-0153.wav|while on the overpass overlooking the assassination scene railroad and yard terminal workmen were permitted to remain under police supervision,
+LJSpeech-1.1/wavs/LJ003-0028.wav|and no doubt made the meat also pay toll.
+LJSpeech-1.1/wavs/LJ002-0277.wav|There were fifteen rooms of various sizes, and as the numbers imprisoned rarely exceeded five-and-twenty, the place was never overcrowded,
+LJSpeech-1.1/wavs/LJ009-0050.wav|He has, however, doubly earned his sentence, and is actually condemned for burglary committed since his arrival in England.
+LJSpeech-1.1/wavs/LJ003-0084.wav|Quote, a common-sized man, says the keeper, Mr. Newman, can turn in nineteen inches, end quote.
+LJSpeech-1.1/wavs/LJ018-0305.wav|The bills were sent as a matter of form to the drawer to have the date added, and the forgery was at once detected.
+LJSpeech-1.1/wavs/LJ015-0118.wav|paid him the compliment of describing him as "a young man of great intelligence, considerable powers of mind,
+LJSpeech-1.1/wavs/LJ034-0128.wav|But the testimony of these employees, together with photographs subsequently taken of them at the scene of the assassination,
+LJSpeech-1.1/wavs/LJ035-0074.wav|The second test run required one minute and fifteen seconds.
+LJSpeech-1.1/wavs/LJ028-0466.wav|This inner part was about twenty-four feet wide, and at intervals of about one hundred and forty feet it was surmounted with towers.
+LJSpeech-1.1/wavs/LJ048-0241.wav|These agents were each relieved for a half hour break during the night.
+LJSpeech-1.1/wavs/LJ028-0184.wav|he extended his empire to the Mediterranean and even to Egypt.
+LJSpeech-1.1/wavs/LJ030-0034.wav|Approximately ten minutes after the arrival at Love Field, the President and Mrs. Kennedy went to the Presidential automobile to begin the motorcade.
+LJSpeech-1.1/wavs/LJ005-0222.wav|and as the window-frames would not shut tight, the prisoners complained much of the cold, especially at night.
+LJSpeech-1.1/wavs/LJ004-0067.wav|or to make him sleep in close contact with the victims of contagious and loathsome disease,
+LJSpeech-1.1/wavs/LJ010-0215.wav|This letter went on to say that a new member had been introduced by Lt. Mars, "a fine, tall, gentlemanly young man,
+LJSpeech-1.1/wavs/LJ013-0096.wav|Suspicions were aroused when it was found that he had been employed in selling stock for Mr. Oxenford, which developed into certainty
+LJSpeech-1.1/wavs/LJ028-0397.wav|When Julian saw this, he caused a large part of the wall to be overthrown, and as the beasts escaped they were shot down by his soldiers.
+LJSpeech-1.1/wavs/LJ046-0069.wav|I think he felt very strongly that the President ought to get out of Washington, and go meet the people on a regular basis. End quote.
+LJSpeech-1.1/wavs/LJ022-0096.wav|and of making certain that people who are employed will be taken from the relief rolls.
+LJSpeech-1.1/wavs/LJ041-0151.wav|An old lady handed me a pamphlet about saving the Rosenbergs. I looked at that paper and I still remember it for some reason, I don't know why. End quote.
+LJSpeech-1.1/wavs/LJ030-0109.wav|The Vice-Presidential car
+LJSpeech-1.1/wavs/LJ014-0064.wav|He lived at Mile End, whence he walked often to call at three, Minver Place, Bermondsey, the residence of his old love.
+LJSpeech-1.1/wavs/LJ015-0273.wav|But Saward's operations were mainly directed to the fabrication and uttering of forged cheques.
+LJSpeech-1.1/wavs/LJ046-0012.wav|there have been attempts on the lives of one out of every three.
+LJSpeech-1.1/wavs/LJ009-0100.wav|struggles violently; his feet, legs, hands, and arms, even the muscles of his back,
+LJSpeech-1.1/wavs/LJ050-0181.wav|The Commission regards this as a most useful innovation and urges that the practice be continued.
+LJSpeech-1.1/wavs/LJ002-0264.wav|Legacies had also been left to free a certain number of debtors, notably that of one hundred pounds per annum
+LJSpeech-1.1/wavs/LJ009-0118.wav|could hardly witness this ceremony without being affected. The men, who were so lately snatched from the jaws of death,
+LJSpeech-1.1/wavs/LJ025-0154.wav|and which certainly hold good for the vast majority of animals and plants, are of universal application.
+LJSpeech-1.1/wavs/LJ006-0077.wav|The fact was, he did not keep the classification of prisoners on first arrival in his own hands, nor even in that of his officers.
+LJSpeech-1.1/wavs/LJ049-0167.wav|in the field of preventive investigation in regard to the President's security.
+LJSpeech-1.1/wavs/LJ009-0012.wav|This was noticeable with Fauntleroy, who, on account of his birth and antecedents, was allowed to occupy a turnkey's room,
+LJSpeech-1.1/wavs/LJ035-0200.wav|Fritz believed that he learned of Oswald's absence after the rifle was found.
+LJSpeech-1.1/wavs/LJ027-0059.wav|These, therefore, may well be called backboned animals.
+LJSpeech-1.1/wavs/LJ016-0022.wav|A harrowing scene followed;
+LJSpeech-1.1/wavs/LJ040-0055.wav|The discussion below will describe the events known to the Commission which most clearly reveals the formation and nature of Oswald's character.
+LJSpeech-1.1/wavs/LJ019-0178.wav|and abandoned because of the expense. As to the entire reconstruction of Newgate, nothing had been done as yet.
+LJSpeech-1.1/wavs/LJ018-0286.wav|and Austin Bidwell opened a bona fide credit in the Burlington or West End branch of the Bank of England,
+LJSpeech-1.1/wavs/LJ044-0145.wav|By August of nineteen sixty-three, Oswald had for some time been considering the possibility of leaving the United States again.
+LJSpeech-1.1/wavs/LJ003-0222.wav|The jail was neither suitable nor sufficiently large. It was not even kept weather-tight.
+LJSpeech-1.1/wavs/LJ034-0086.wav|This clipboard had been made by Kaiser and had his name on it.
+LJSpeech-1.1/wavs/LJ044-0050.wav|Police reports also fail to reflect any activity on Oswald's part prior to August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ003-0198.wav|Few realizing that the dreadful fate would overtake them, dismissed the prospect of death,
+LJSpeech-1.1/wavs/LJ028-0352.wav|whether of former or of later times, except only Cyrus with whom no person ever yet thought himself worthy to compare.
+LJSpeech-1.1/wavs/LJ038-0022.wav|I don't know if this is the man they want. But he is running from them for some reason, end quote, She then called the police.
+LJSpeech-1.1/wavs/LJ017-0278.wav|No soldiers either.
+LJSpeech-1.1/wavs/LJ014-0096.wav|and at their request two police officers were sent to Bermondsey to inquire for him at the Mannings, with whom it was well known that he was very intimate.
+LJSpeech-1.1/wavs/LJ011-0105.wav|His defense was that the forged acceptances would have been met on coming to maturity, and that he had no real desire to defraud.
+LJSpeech-1.1/wavs/LJ002-0038.wav|Of these wards, three were appropriated to the "cabin side," so called because
+LJSpeech-1.1/wavs/LJ028-0123.wav|I dug its moat to the water level.
+LJSpeech-1.1/wavs/LJ003-0097.wav|with convicts of all ages and characters, to render it next to impossible but that, with the obliteration of all sense of self-respect,
+LJSpeech-1.1/wavs/LJ019-0107.wav|District prisons were to be established for this purpose, each of which would be in the heart of a farm of a thousand acres.
+LJSpeech-1.1/wavs/LJ039-0187.wav|In fact, one of the firers in the rapid fire test in firing his two series of three shots,
+LJSpeech-1.1/wavs/LJ011-0227.wav|After their release from Lancaster and Newgate respectively, both Wakefields went abroad.
+LJSpeech-1.1/wavs/LJ032-0270.wav|Having reviewed the evidence that (one) Lee Harvey Oswald purchased the rifle used in the assassination,
+LJSpeech-1.1/wavs/LJ015-0019.wav|The Strahan of the firm which came to grief was a Snow who changed his name for a fortune of two hundred thousand pounds;
+LJSpeech-1.1/wavs/LJ015-0073.wav|Within a year he was advanced to the post of chief clerk in the transfer department, at a salary of one hundred fifty pounds a year.
+LJSpeech-1.1/wavs/LJ043-0165.wav|Leaving the note and picture as he did would seem to indicate that he had considered the possibility of capture.
+LJSpeech-1.1/wavs/LJ023-0064.wav|Having in mind that in succeeding generations many other problems then undreamed of would become national problems
+LJSpeech-1.1/wavs/LJ008-0223.wav|The approaching ceremony had evidently no terrors for these "idolaters of the gallows."
+LJSpeech-1.1/wavs/LJ034-0114.wav|Brennan's description should also be compared with the eyewitness description broadcast over the Dallas police radio at one:twenty-two p.m.
+LJSpeech-1.1/wavs/LJ045-0105.wav|She testified that she told him, quote,
+LJSpeech-1.1/wavs/LJ012-0281.wav|The most ghastly part of the story is that which deals with his getting rid of the head.
+LJSpeech-1.1/wavs/LJ019-0090.wav|The alternative between labor or idleness, or the selection of the form of labor,
+LJSpeech-1.1/wavs/LJ045-0020.wav|In any event, the Commission has been unable to find any credible evidence that Oswald had direct contact or association with
+LJSpeech-1.1/wavs/LJ025-0096.wav|Amylaceous and saccharine substances are largely manufactured, even by the highest animals.
+LJSpeech-1.1/wavs/LJ029-0212.wav|"Welcome Mr. Kennedy to Dallas," sponsored by the American Fact-finding Committee, which the sponsor later testified was an ad hoc committee
+LJSpeech-1.1/wavs/LJ047-0192.wav|On November eighteen the FBI learned that Oswald recently had been in communication with the Soviet Embassy in Washington
+LJSpeech-1.1/wavs/LJ021-0195.wav|the great Chief Justice White said:
+LJSpeech-1.1/wavs/LJ004-0125.wav|in the balance some work was done, but with the most meager results.
+LJSpeech-1.1/wavs/LJ003-0201.wav|unlimited drink was not denied them provided it was obtained in regulated quantities at one time.
+LJSpeech-1.1/wavs/LJ023-0078.wav|to presume in favor of its validity until its violation of the Constitution is proved beyond all reasonable doubt.
+LJSpeech-1.1/wavs/LJ035-0040.wav|As Baker reached the second floor, he was about twenty feet from the vestibule door. He intended to continue around to his left toward the stairway going up
+LJSpeech-1.1/wavs/LJ010-0188.wav|Oxford expressed little anxiety or concern.
+LJSpeech-1.1/wavs/LJ045-0051.wav|On the other hand, Oswald objected to the invitation that his wife had received to live with Mrs. Ruth Paine,
+LJSpeech-1.1/wavs/LJ019-0174.wav|It was less prompt apparently in dealing with structural alterations and improvements.
+LJSpeech-1.1/wavs/LJ042-0004.wav|Defection to the Soviet Union
+LJSpeech-1.1/wavs/LJ010-0002.wav|In chapter two of the present volume I brought down the record of crime to the second decade of the present century.
+LJSpeech-1.1/wavs/LJ017-0177.wav|Wilson was acquitted on this charge, but other suspicious facts cropped up while she was in Newgate.
+LJSpeech-1.1/wavs/LJ037-0139.wav|he subsequently testified before a Commission staff member and, when shown two photographs of Oswald, stated that they were photographs of the man he saw.
+LJSpeech-1.1/wavs/LJ015-0119.wav|and possessed of an education very much beyond the rank of life to which he originally belonged."
+LJSpeech-1.1/wavs/LJ004-0032.wav|The onus of improvement lay upon the magistracy, the local authorities administering local funds,
+LJSpeech-1.1/wavs/LJ028-0413.wav|In his quaint way he says:
+LJSpeech-1.1/wavs/LJ012-0203.wav|Eventually the law overtook them, but almost by accident.
+LJSpeech-1.1/wavs/LJ034-0155.wav|When specifically asked before the Commission
+LJSpeech-1.1/wavs/LJ021-0118.wav|During the last twelve months our industrial recovery has been to some extent retarded by strikes,
+LJSpeech-1.1/wavs/LJ013-0052.wav|The executors to the estate disputed the validity of this document.
+LJSpeech-1.1/wavs/LJ012-0120.wav|one of them of considerable size.
+LJSpeech-1.1/wavs/LJ034-0213.wav|Just before snapping the picture Altgens heard a noise which sounded like the popping of a firecracker.
+LJSpeech-1.1/wavs/LJ005-0284.wav|of more depraved and systematic criminals.
+LJSpeech-1.1/wavs/LJ004-0246.wav|They used their own tools, and this without any dangerous consequences as regards facilitating the escape of others,
+LJSpeech-1.1/wavs/LJ049-0040.wav|The assassination suggests that it would have been of prime importance
+LJSpeech-1.1/wavs/LJ020-0056.wav|Arrange the rolls in regular rows in the pan about a quarter of an inch apart.
+LJSpeech-1.1/wavs/LJ015-0281.wav|Saward's method of negotiating the cheques was equally well planned.
+LJSpeech-1.1/wavs/LJ032-0071.wav|Oswald's use of the name "Hidell" to purchase the assassination weapon was one of several instances in which he used this name as an alias.
+LJSpeech-1.1/wavs/LJ022-0089.wav|charged with carrying on work relief projects.
+LJSpeech-1.1/wavs/LJ035-0139.wav|They reentered the building by the rear door several minutes after Baker and Truly rushed through the front entrance.
+LJSpeech-1.1/wavs/LJ016-0439.wav|Often the convicts give way to despair. They are too closely watched to be allowed to do themselves much mischief, or suicides would probably be more frequent.
+LJSpeech-1.1/wavs/LJ046-0011.wav|One out of every five Presidents since eighteen sixty-five has been assassinated;
+LJSpeech-1.1/wavs/LJ022-0099.wav|to move as rapidly as possible back into private employment when such employment is available.
+LJSpeech-1.1/wavs/LJ005-0170.wav|that many of them will build new jails, and that when in a year or two we come to examine their situation,
+LJSpeech-1.1/wavs/LJ029-0021.wav|The three agreed that the President would come to Texas in late November nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ012-0036.wav|On one occasion the whole of the proceeds of a robbery from a boot shop was traced to Solomons';
+LJSpeech-1.1/wavs/LJ027-0082.wav|and any other series of organs of vertebrates would give the same evidence of fundamental resemblances (homologies).
+LJSpeech-1.1/wavs/LJ046-0129.wav|The total volume of information received by PRS has risen steadily.
+LJSpeech-1.1/wavs/LJ001-0143.wav|For where these are boldly and carefully designed, and each letter is thoroughly individual in form,
+LJSpeech-1.1/wavs/LJ018-0293.wav|The ground thus cleared, the forgers poured in from Birmingham numbers of forged acceptances,
+LJSpeech-1.1/wavs/LJ018-0017.wav|The friends of Mr. Briggs were communicated with, and it was ascertained that when he left home the morning of the murderous attack,
+LJSpeech-1.1/wavs/LJ024-0125.wav|This proposal of mine will not infringe in the slightest upon the civil or religious liberties so dear to every American.
+LJSpeech-1.1/wavs/LJ030-0127.wav|to the intersection of Houston Street, which marks the beginning of Dealey Plaza.
+LJSpeech-1.1/wavs/LJ008-0211.wav|Gin-shops and coffee-houses were the first to open doors, and touts began to bid for tenants for the various rooms upstairs.
+LJSpeech-1.1/wavs/LJ036-0215.wav|He approached the car and apparently exchanged words with Tippit through the right front or vent window.
+LJSpeech-1.1/wavs/LJ030-0250.wav|Other Secret Service agents assigned to the motorcade remained at their posts during the race to the hospital.
+LJSpeech-1.1/wavs/LJ020-0072.wav|Yolk of one egg beaten light.
+LJSpeech-1.1/wavs/LJ029-0198.wav|They conveyed the pleas of Dallas leaders that citizens not demonstrate or create disturbances during the President's visit.
+LJSpeech-1.1/wavs/LJ003-0206.wav|and their gibes and jollity counteracted the ordinary's counsels or the independent preacher's earnest prayers.
+LJSpeech-1.1/wavs/LJ022-0083.wav|six. Projects will be allocated to localities or relief areas in relation to the number of workers on relief rolls in those areas.
+LJSpeech-1.1/wavs/LJ008-0125.wav|The sheriff arrived, attended by his officers, to receive the prisoner from the keeper.
+LJSpeech-1.1/wavs/LJ048-0137.wav|there have been references to the numerous discussions between Secret Service representatives and the Dallas Police Department.
+LJSpeech-1.1/wavs/LJ016-0061.wav|and then following her down a step ladder into the house, told his story, appealed to and won her compassion.
+LJSpeech-1.1/wavs/LJ036-0212.wav|About one hundred feet past the intersection Tippit stopped a man walking east along the south side of Patton.
+LJSpeech-1.1/wavs/LJ038-0268.wav|the FBI refuses to draw any conclusion as to probability.
+LJSpeech-1.1/wavs/LJ005-0202.wav|An examination of this report shows how even the most insignificant township had its jail.
+LJSpeech-1.1/wavs/LJ025-0087.wav|On the other hand, the definition thus amended will exclude all ordinary vegetable organisms.
+LJSpeech-1.1/wavs/LJ018-0311.wav|had undoubtedly the command of large funds while there, and would have readily disbursed it to effect their enlargement.
+LJSpeech-1.1/wavs/LJ002-0185.wav|The King's Bench was a national prison, in which were confined all debtors arrested for debt or for contempt of the court of the King's Bench.
+LJSpeech-1.1/wavs/LJ050-0257.wav|Recognition that the responsibility must be shared increases the likelihood that it will be met.
+LJSpeech-1.1/wavs/LJ004-0132.wav|the old evils of indiscriminate association still continued unchecked.
+LJSpeech-1.1/wavs/LJ019-0382.wav|As the years passed, great want of uniformity continued to prevail throughout the prisons of the United Kingdom.
+LJSpeech-1.1/wavs/LJ028-0428.wav|has been removed, and the surrounding city walls have been traced.
+LJSpeech-1.1/wavs/LJ017-0244.wav|The murders were perpetrated on the tenth September, and the ship continued her voyage for nearly three weeks, meeting and speaking one ship only.
+LJSpeech-1.1/wavs/LJ009-0047.wav|The next convict is clearly and unmistakably a villain.
+LJSpeech-1.1/wavs/LJ010-0225.wav|The whole of the evidence pointed so strongly towards insanity, that the jury brought in a verdict of acquittal on that ground,
+LJSpeech-1.1/wavs/LJ011-0164.wav|Miss Turner, greatly agitated, accompanied the messenger who had brought this news, a disguised servant of Wakefield's,
+LJSpeech-1.1/wavs/LJ006-0069.wav|consign many of the more petty and unpracticed offenders to this place,
+LJSpeech-1.1/wavs/LJ047-0131.wav|In early September nineteen sixty-three
+LJSpeech-1.1/wavs/LJ044-0055.wav|While the legend, quote, FPCC, five four four Camp Street New Orleans, Louisiana, end quote,
+LJSpeech-1.1/wavs/LJ016-0214.wav|a silver coin bearing the same relation to the Scottish pound that an English shilling does to an English pound, was made to pass current in England.
+LJSpeech-1.1/wavs/LJ038-0192.wav|six. You can either throw out or give my clothing, etc. away. Do not keep these.
+LJSpeech-1.1/wavs/LJ011-0046.wav|As I have already said, they took the chaplain seriously to task for the bad taste shown in the condemned sermon preached before Fauntleroy.
+LJSpeech-1.1/wavs/LJ020-0067.wav|Five tablespoonfuls of yeast.
+LJSpeech-1.1/wavs/LJ027-0017.wav|All organisms have the same need of reproduction.
+LJSpeech-1.1/wavs/LJ028-0181.wav|He was followed by three kings whose reigns were short,
+LJSpeech-1.1/wavs/LJ015-0100.wav|They drove there, and Robson on arrival did the honors of his house, rang for lunch to gain time,
+LJSpeech-1.1/wavs/LJ024-0005.wav|has reached the age of seventy and does not avail himself of the opportunity to retire on a pension,
+LJSpeech-1.1/wavs/LJ006-0133.wav|The turnkeys complained bitterly that these old prisoners had more power than they themselves.
+LJSpeech-1.1/wavs/LJ003-0302.wav|the work being constant and suitable, with certain hours of relaxation and for food and exercise.
+LJSpeech-1.1/wavs/LJ017-0280.wav|The convicts were pinioned one by one and sent singly out to the gallows.
+LJSpeech-1.1/wavs/LJ035-0149.wav|She ran inside and up the front stairs into the large open office reserved for clerical employees.
+LJSpeech-1.1/wavs/LJ030-0208.wav|Hill heard a noise, which seemed to be a firecracker, coming from his right rear.
+LJSpeech-1.1/wavs/LJ001-0099.wav|It must be remembered, however, that most modern printing is done by machinery on soft paper, and not by the hand press,
+LJSpeech-1.1/wavs/LJ004-0013.wav|some prisons that had been ameliorated under the persuasive influence of his kind advice were relapsing into their former horrid state of privation,
+LJSpeech-1.1/wavs/LJ041-0109.wav|The hearing officer agreed with the latter claim but found Oswald guilty of wrongfully using provoking words and sentenced him to twenty-eight days,
+LJSpeech-1.1/wavs/LJ014-0045.wav|that Delarue had suffered by the hands of imaginary outraged brothers acting as the avengers of females deeply injured by him.
+LJSpeech-1.1/wavs/LJ006-0123.wav|The man intrusted with this privilege was often the inner gatesman,
+LJSpeech-1.1/wavs/LJ016-0250.wav|It may, however, be remarked that the wisdom of appointing any regular hangman is very open to question,
+LJSpeech-1.1/wavs/LJ038-0128.wav|He also denied that he had received the rifle through this box.
+LJSpeech-1.1/wavs/LJ039-0174.wav|The marksmen took as much time as they wanted for the first target and all hit the target.
+LJSpeech-1.1/wavs/LJ023-0141.wav|can, if they choose, hold office for life, no matter how old they may get to be.
+LJSpeech-1.1/wavs/LJ032-0049.wav|The specific rifle shipped against the order had been received by Klein's from Crescent on February twenty-one, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ042-0103.wav|Oswald apparently resented the exercise of authority over him and the better treatment afforded to Communist Party officials.
+LJSpeech-1.1/wavs/LJ029-0115.wav|in preference to a short stretch of the Central Expressway, which presented a minor safety hazard
+LJSpeech-1.1/wavs/LJ008-0064.wav|When the criminals are tied up and prepared for their fate, this floor suddenly falls down, upon withdrawing the supporters inwards.
+LJSpeech-1.1/wavs/LJ019-0238.wav|The first "glass house," or room in which prisoners could talk in private with their attorneys, but yet be seen by the warder on the watch, had been constructed
+LJSpeech-1.1/wavs/LJ037-0185.wav|Records of Seaport Traders, Incorporated, a mail-order division of George Rose and Co.,
+LJSpeech-1.1/wavs/LJ044-0036.wav|then national director of FPCC. In one of those letters, dated August one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ045-0131.wav|Neither Hosty nor any other agent of the FBI spoke to Oswald on any subject from August ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ039-0228.wav|Concerning the shots which struck the President in the back of the neck,
+LJSpeech-1.1/wavs/LJ041-0058.wav|which might appear to be inconsistent with his desire to join the Marines, could have been another manifestation of Oswald's rejection of his environment.
+LJSpeech-1.1/wavs/LJ007-0060.wav|In his evidence before the inspectors he declared that "for years he gave his whole time to his duties, from an early hour in the morning till late in the afternoon.
+LJSpeech-1.1/wavs/LJ001-0076.wav|and things got worse and worse through the whole of the seventeenth century, so that in the eighteenth printing was very miserably performed.
+LJSpeech-1.1/wavs/LJ045-0091.wav|She said he was different from other people in, quote, At, least his imagination, his fantasy, which was quite unfounded,
+LJSpeech-1.1/wavs/LJ010-0308.wav|by the death of one of the partners, a large sum in cash had to be withdrawn from the bank to pay his heirs.
+LJSpeech-1.1/wavs/LJ008-0222.wav|of those who had shown the white, and those who had died game.
+LJSpeech-1.1/wavs/LJ047-0019.wav|Oswald's defection was also the occasion for the opening of files by the Department of State, CIA, and the Office of Naval Intelligence.
+LJSpeech-1.1/wavs/LJ008-0059.wav|Under this bar at a proper distance is a piece of flooring or platform,
+LJSpeech-1.1/wavs/LJ035-0029.wav|Neither elevator was there.
+LJSpeech-1.1/wavs/LJ004-0076.wav|"Disease, cold, famine, nakedness, and contagious and polluted air are not lawful punishments in the hands of the civil magistrates;
+LJSpeech-1.1/wavs/LJ003-0119.wav|This was carried out by putting the criminal's head through the legs of a chair, and stretching out his arms and tying them to the legs.
+LJSpeech-1.1/wavs/LJ003-0228.wav|and the general insufficiency was such
+LJSpeech-1.1/wavs/LJ003-0326.wav|so that the various classes might be seated separately, and so as not to see each other.
+LJSpeech-1.1/wavs/LJ031-0019.wav|These rooms were for the emergency treatment of acutely ill or injured patients. Although the first message mentioned an injury only to President Kennedy,
+LJSpeech-1.1/wavs/LJ010-0275.wav|but it was proved that the pistol was not loaded with ball,
+LJSpeech-1.1/wavs/LJ040-0127.wav|or for detention pending court appearance or commitment to a child-caring or custodial institution such as a training school.
+LJSpeech-1.1/wavs/LJ048-0164.wav|For the inauguration and certain other parades in Washington where the traditional route is known to the public long in advance of the event,
+LJSpeech-1.1/wavs/LJ032-0082.wav|and the photograph of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ033-0110.wav|Frazier and Mrs. Randle testified
+LJSpeech-1.1/wavs/LJ004-0207.wav|and all the material for prisoners' apparel was manufactured in the jail.
+LJSpeech-1.1/wavs/LJ041-0189.wav|He was concerned with his image in history and I do think that is why he chose the particular method of defecting he chose and did it in the way he did.
+LJSpeech-1.1/wavs/LJ048-0264.wav|the special agent is officially employed and should not use liquor, until the completion of all of his official duties for the day,
+LJSpeech-1.1/wavs/LJ010-0171.wav|On that day Oxford was on the watch at Buckingham Palace.
+LJSpeech-1.1/wavs/LJ021-0077.wav|There is no magic formula,
+LJSpeech-1.1/wavs/LJ021-0015.wav|The underlying necessity for such activity
+LJSpeech-1.1/wavs/LJ021-0177.wav|Did England hold to the gold standard when her reserves were threatened?
+LJSpeech-1.1/wavs/LJ019-0303.wav|of these, sixty-three gave admittance during the entire year to less than twenty-five prisoners;
+LJSpeech-1.1/wavs/LJ016-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section nineteen: Later Records
+LJSpeech-1.1/wavs/LJ049-0009.wav|When President and Mrs. Kennedy shook hands with members of the public along the fences surrounding the reception area, they were closely guarded by Secret Service agents
+LJSpeech-1.1/wavs/LJ037-0216.wav|in the picture she took in late March or early April nineteen sixty-three when the family was living on Neely Street in Dallas.
+LJSpeech-1.1/wavs/LJ008-0292.wav|but it was seldom less than six weeks. It all depended upon the sovereign's disposition to do business.
+LJSpeech-1.1/wavs/LJ002-0066.wav|The master felons' side consisted of a yard the same size as the preceding, appropriated nominally to the most decent and better-behaved prisoners,
+LJSpeech-1.1/wavs/LJ039-0044.wav|Mr. Johnson was the then Vice President and his visit took place on April twenty-third.
+LJSpeech-1.1/wavs/LJ039-0220.wav|and that one would not have to be an expert marksman to have accomplished the assassination with the weapon which was used.
+LJSpeech-1.1/wavs/LJ006-0274.wav|Perhaps the worst feature of the visiting system was the permission accorded to male prisoners "under the name of husbands, brothers, and sons"
+LJSpeech-1.1/wavs/LJ005-0250.wav|The prisoners were crowded together in the jail, contrary to the requirements of the four George the fourth
+LJSpeech-1.1/wavs/LJ038-0050.wav|they might have heard a snapping noise resulting from the police officer grabbing the cylinder of the revolver and pulling it away from Oswald while he was attempting to pull the trigger.
+LJSpeech-1.1/wavs/LJ012-0144.wav|and passed it on to Solomons by his daughter, a widow named Abrahams.
+LJSpeech-1.1/wavs/LJ004-0139.wav|"In the morning the stench and heat were so oppressive that he and every one else on waking rushed unclothed into the yard;"
+LJSpeech-1.1/wavs/LJ020-0100.wav|Unless it is too hot, they will get no harm while you finish dressing in ten minutes, just in season to turn the pan.
+LJSpeech-1.1/wavs/LJ017-0247.wav|The rest of the crew were also permitted to embark, except the Chinamen, one of whom was thrown into the water and drowned,
+LJSpeech-1.1/wavs/LJ009-0108.wav|The two masses of prisoners for trial undulate and slightly murmur,
+LJSpeech-1.1/wavs/LJ044-0066.wav|Oswald wrote that he had appeared on Mr. William Stuckey's fifteen-minute television program over WDSU-TV called, quote,
+LJSpeech-1.1/wavs/LJ032-0105.wav|quote, I knew there was no such organization. And I know Hidell is merely an altered Fidel, and I laughed at such foolishness.
+LJSpeech-1.1/wavs/LJ035-0186.wav|After he returned to the street he directed Sergeant Harkness to station two patrolmen at the front door and not let anyone in or out;
+LJSpeech-1.1/wavs/LJ036-0125.wav|and that sometimes he made his entry right after a trip while at other times he waited to record three or four trips.
+LJSpeech-1.1/wavs/LJ045-0241.wav|that it can ascribe to him any one motive or group of motives.
+LJSpeech-1.1/wavs/LJ006-0153.wav|It was not strange that subordinates should neglect their duty when superiors set the example.
+LJSpeech-1.1/wavs/LJ024-0010.wav|to make the administration of all federal justice speedier and, therefore, less costly;
+LJSpeech-1.1/wavs/LJ033-0139.wav|Oswald's Mannlicher-Carcano rifle, serial Number C two seven six six, which was also found on the sixth floor.
+LJSpeech-1.1/wavs/LJ032-0265.wav|Each man testified that he thought he could detect the outline of a rifle in the blanket, even though the blanket was empty.
+LJSpeech-1.1/wavs/LJ022-0155.wav|Power production in this country is virtually back to the nineteen twenty-nine peak.
+LJSpeech-1.1/wavs/LJ049-0183.wav|regarding such threats and that its Protective Research Section is not adequately staffed or equipped
+LJSpeech-1.1/wavs/LJ035-0085.wav|from the southeast corner of the sixth floor down the stairway to the lunchroom.
+LJSpeech-1.1/wavs/LJ005-0178.wav|Many of them consisted of one or two rooms at most.
+LJSpeech-1.1/wavs/LJ011-0130.wav|This was on the last day of eighteen twenty-nine. In the following session Sir Robert Peel brought in a bill to consolidate the acts relating to forgery.
+LJSpeech-1.1/wavs/LJ045-0005.wav|especially an atmosphere of extreme opposition to President Kennedy that was present in some parts of the Dallas community
+LJSpeech-1.1/wavs/LJ028-0333.wav|he put them also to the sword.
+LJSpeech-1.1/wavs/LJ024-0105.wav|to block the amendment, although I would be awfully glad to help get some other kind of amendment ratified.
+LJSpeech-1.1/wavs/LJ018-0330.wav|Violent passions too easily aroused prompted the Frenchwoman Marguerite Dixblanc
+LJSpeech-1.1/wavs/LJ042-0062.wav|It shows how willing he was to act dramatically and decisively when he faced an emotional crisis
+LJSpeech-1.1/wavs/LJ043-0164.wav|and his role would have been made clear by the evidence which he had left behind.
+LJSpeech-1.1/wavs/LJ012-0277.wav|It is scarcely probable that he would have gone to this extremity if he had had no previous evil intention,
+LJSpeech-1.1/wavs/LJ045-0128.wav|that the FBI was, quote, not now, end quote, interested in his Fair Play for Cuba Committee activities, but noted that the FBI, quote,
+LJSpeech-1.1/wavs/LJ042-0170.wav|There are two great representatives of power in the world,
+LJSpeech-1.1/wavs/LJ023-0139.wav|In the case of Supreme Court justices, that pension is twenty thousand dollars a year.
+LJSpeech-1.1/wavs/LJ015-0179.wav|The Central Criminal Court, when the trial came on,
+LJSpeech-1.1/wavs/LJ031-0063.wav|When Dr. Perry noted free air and blood in the President's chest cavity, he asked that chest tubes be inserted to allow for drainage of blood and air.
+LJSpeech-1.1/wavs/LJ034-0208.wav|In evaluating the evidence that Oswald was at the southeast corner window of the sixth floor at the time of the shooting,
+LJSpeech-1.1/wavs/LJ003-0292.wav|This committee was to compare allowances, examine rules, and certify as to the condition of prisoners;
+LJSpeech-1.1/wavs/LJ005-0035.wav|the Society pursued its laudable undertaking with remarkable energy and great singleness of purpose.
+LJSpeech-1.1/wavs/LJ034-0074.wav|I discovered I left my cigarettes in my jacket pocket upstairs, and I took the elevator back upstairs to get my jacket with my cigarettes in it, end quote.
+LJSpeech-1.1/wavs/LJ006-0143.wav|There was no penalty attached to drunkenness.
+LJSpeech-1.1/wavs/LJ016-0337.wav|In the houses opposite the prison numbers of detectives mixed with the spectators;
+LJSpeech-1.1/wavs/LJ029-0145.wav|in order to reach the Dallas-Fort Worth Turnpike, which has the same access road from Elm Street as does the Stemmons Freeway.
+LJSpeech-1.1/wavs/LJ025-0104.wav|even in the case of the green plant, which in the dark absorbs oxygen and gives out carbonic acid like any animal.
+LJSpeech-1.1/wavs/LJ005-0063.wav|It was thought that this would guarantee constant supervision and inspection, but it did nothing of the kind, and only the presence of warders on duty
+LJSpeech-1.1/wavs/LJ019-0216.wav|Eventually Holloway was decided upon as a site for the new city prison.
+LJSpeech-1.1/wavs/LJ042-0142.wav|He was very irritable, sometimes for a trifle, end quote, and that, quote, Lee was very unrestrained and very explosive, end quote.
+LJSpeech-1.1/wavs/LJ018-0078.wav|The next time Thain entered his cabin he fired at him point-blank, and lodged three bullets in his breast.
+LJSpeech-1.1/wavs/LJ001-0169.wav|The paper used for printing the small highly ornamented French service-books about the beginning of the sixteenth century is a model in this respect,
+LJSpeech-1.1/wavs/LJ011-0021.wav|Further investigations brought other similar frauds to light,
+LJSpeech-1.1/wavs/LJ049-0145.wav|As improvements are recommended for the advance detection of potential threats to the President, it could act as a final review board.
+LJSpeech-1.1/wavs/LJ021-0056.wav|Under its guidance, trades and industries covering over ninety percent of all industrial employees
+LJSpeech-1.1/wavs/LJ039-0163.wav|the Infantry Weapons Evaluation Branch of the Ballistics Research Laboratory had expert riflemen fire the assassination weapon
+LJSpeech-1.1/wavs/LJ003-0290.wav|the court of aldermen appointed a committee of its own body, assisted by the town clerk, Mr. Dance, city surveyor, son to the architect of Newgate,
+LJSpeech-1.1/wavs/LJ016-0122.wav|He made a strong rope with several of the sheets; then, returning to the male yard,
+LJSpeech-1.1/wavs/LJ022-0179.wav|speaking through their government.
+LJSpeech-1.1/wavs/LJ022-0101.wav|I have felt it to be essentially wise and prudent to avoid, so far as possible,
+LJSpeech-1.1/wavs/LJ002-0147.wav|that thirteen hundred and twelve debtors were committed by them to Newgate between seventeen ninety-seven and eighteen oh eight,
+LJSpeech-1.1/wavs/LJ045-0010.wav|The Commission has found no evidence that the extreme views expressed toward President Kennedy
+LJSpeech-1.1/wavs/LJ015-0076.wav|Some time elapsed before Robson succumbed to temptation.
+LJSpeech-1.1/wavs/LJ014-0226.wav|Only a few have vied with Cannon in fiendish cruelty and brutality.
+LJSpeech-1.1/wavs/LJ006-0031.wav|that punishment must be reformatory as well as deterrent.
+LJSpeech-1.1/wavs/LJ047-0210.wav|I think all of those, if we had them all together,
+LJSpeech-1.1/wavs/LJ022-0072.wav|through the most comprehensive work plan in the history of the nation.
+LJSpeech-1.1/wavs/LJ038-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ008-0281.wav|They found at Newgate, under disgraceful conditions as already described,
+LJSpeech-1.1/wavs/LJ042-0064.wav|The entry in his self-styled, quote, Historic Diary, end quote, for October twenty-one, nineteen fifty-nine, reports, quote,
+LJSpeech-1.1/wavs/LJ002-0307.wav|only out of a satisfaction of all his former debts, end quote. The fees were not always extorted, it is true;
+LJSpeech-1.1/wavs/LJ021-0100.wav|Let me call your attention to the fact that the national Industrial Recovery Act
+LJSpeech-1.1/wavs/LJ030-0241.wav|President Johnson emphasized Youngblood's instantaneous reaction after the first shot:
+LJSpeech-1.1/wavs/LJ005-0135.wav|and the average daily number committed that year amounted to three thousand, nine hundred eighty-five.
+LJSpeech-1.1/wavs/LJ022-0144.wav|As we have proceeded with the administration of this Act,
+LJSpeech-1.1/wavs/LJ012-0081.wav|Being inexperienced, they decided to call in the services of a couple of professional housebreakers,
+LJSpeech-1.1/wavs/LJ048-0204.wav|and that they had no opportunity to scan the windows of the Depository or any other building in the vicinity of Elm and Houston when the motorcade was passing.
+LJSpeech-1.1/wavs/LJ009-0141.wav|Lord Paget, Lord Bruce, several members of the House of Commons, and a few ladies.
+LJSpeech-1.1/wavs/LJ012-0065.wav|There he devoted himself to the circulation of forged notes. He was also anxious to do business in watches,
+LJSpeech-1.1/wavs/LJ015-0020.wav|he was a man esteemed and respected in society and the world of finance, incapable as it was thought of a dishonest deed.
+LJSpeech-1.1/wavs/LJ017-0209.wav|more particularly as regards locomotion.
+LJSpeech-1.1/wavs/LJ043-0112.wav|Mrs. Paine brought Oswald's family to New Orleans.
+LJSpeech-1.1/wavs/LJ003-0193.wav|Once, during the long illness of George the third, as many as one hundred were there waiting the "Report," as it was called.
+LJSpeech-1.1/wavs/LJ049-0158.wav|These informal and part-time arrangements led to more systematic protection in nineteen oh two, after the assassination of President McKinley;
+LJSpeech-1.1/wavs/LJ039-0055.wav|In deciding whether Lee Harvey Oswald fired the shots which killed President Kennedy and wounded Governor Connally,
+LJSpeech-1.1/wavs/LJ017-0233.wav|The second mate, who had heard the hammering of the capstan-bars and the handspikes, with the first mate's and captain's agonized cries, had come out,
+LJSpeech-1.1/wavs/LJ044-0032.wav|in which one man single handedly created publicity for his cause or for himself.
+LJSpeech-1.1/wavs/LJ002-0310.wav|that debtors who could afford the cabin and master's side were not permitted to share in the prison charities.
+LJSpeech-1.1/wavs/LJ004-0134.wav|The debtors were crowded almost inconceivably. In a space twenty feet long by six wide,
+LJSpeech-1.1/wavs/LJ006-0130.wav|had a key of both the master's side and middle side yards, was the only person present at the distribution of beer, and was trusted to examine,
+LJSpeech-1.1/wavs/LJ037-0201.wav|Heinz W. Michaelis, office manager of both George Rose and Co., Incorporated and Seaport Traders, Incorporated.
+LJSpeech-1.1/wavs/LJ050-0222.wav|would take approximately twenty months to implement and require expenditures of approximately three million dollars during that period.
+LJSpeech-1.1/wavs/LJ023-0084.wav|When the Congress has sought to stabilize national agriculture, to improve the conditions of labor,
+LJSpeech-1.1/wavs/LJ036-0210.wav|At approximately one:fifteen p.m., Tippit, who was cruising east on tenth Street,
+LJSpeech-1.1/wavs/LJ009-0206.wav|parish officers, peace officers, the high constable of the county of Middlesex on horseback, and then the body of Williams,
+LJSpeech-1.1/wavs/LJ005-0128.wav|The interior of one prison was illuminated throughout with gas, -- still a novelty, which had been generally adopted in London only four years previously,
+LJSpeech-1.1/wavs/LJ001-0062.wav|Even in Italy most of the theological and law books were printed in Gothic letter,
+LJSpeech-1.1/wavs/LJ037-0005.wav|By the evening of November twenty-two, five of them had identified Lee Harvey Oswald in police lineups as the man they saw.
+LJSpeech-1.1/wavs/LJ016-0052.wav|he passed to the top of the ordinary's residence, hoping to find an open sky-light by which he might enter and so work downstairs.
+LJSpeech-1.1/wavs/LJ022-0157.wav|under holding company domination the utility industry has long been hopelessly at war within itself and with public sentiment.
+LJSpeech-1.1/wavs/LJ009-0133.wav|the outside public continued to be excluded from the Newgate chapel on the day the condemned sermon was preached.
+LJSpeech-1.1/wavs/LJ029-0173.wav|beginning on September thirteen, when the Times-Herald announced in a front page article that President Kennedy was planning a brief one-day tour of four Texas cities
+LJSpeech-1.1/wavs/LJ026-0091.wav|This must, in the first instance, be provided for them by green plants, and hence without chlorophyll-bearing plants,
+LJSpeech-1.1/wavs/LJ018-0153.wav|the officers ensconced themselves in the latter, and waited for Buncher's expected visit.
+LJSpeech-1.1/wavs/LJ049-0114.wav|what this bill means to punish is the crime of interruption of the Government of the United States and the destruction of its security by striking down the life
+LJSpeech-1.1/wavs/LJ005-0182.wav|that for long no idea of interfering with them was entertained.
+LJSpeech-1.1/wavs/LJ028-0399.wav|The spread of Mohammedanism caused new cities to be built, and Babylon was the quarry for their building material.
+LJSpeech-1.1/wavs/LJ032-0243.wav|Rifle Among Oswald's Possessions
+LJSpeech-1.1/wavs/LJ001-0008.wav|has never been surpassed.
+LJSpeech-1.1/wavs/LJ010-0173.wav|where he waited till four p.m., the time at which the Queen and Prince Consort usually took an afternoon drive.
+LJSpeech-1.1/wavs/LJ028-0193.wav|and to them came hope of returning to Jerusalem.
+LJSpeech-1.1/wavs/LJ033-0059.wav|A short time later Mrs. Paine told her that someone had shot the President, quote, from the building in which Lee is working, end quote.
+LJSpeech-1.1/wavs/LJ012-0222.wav|plainly pointing to the perpetration of other crimes.
+LJSpeech-1.1/wavs/LJ014-0157.wav|Nevertheless, she attempted to commit suicide by driving her nails, purposely left long, into her throat.
+LJSpeech-1.1/wavs/LJ035-0034.wav|The stairs from one floor to the next are "L-shaped," with both legs of the "L" approximately the same length.
+LJSpeech-1.1/wavs/LJ029-0051.wav|in the territory of the Secret Service regional office which includes Dallas and Fort Worth.
+LJSpeech-1.1/wavs/LJ042-0104.wav|After he returned to the United States
+LJSpeech-1.1/wavs/LJ048-0244.wav|Chief Rowley testified that agents on duty in such a situation usually stay within the building during their relief,
+LJSpeech-1.1/wavs/LJ032-0260.wav|He thought it contained tent poles, or possibly other camping equipment such as a folding shovel.
+LJSpeech-1.1/wavs/LJ016-0441.wav|The man, Bousfield, however, whose execution was so sadly bungled,
+LJSpeech-1.1/wavs/LJ006-0155.wav|He may have erred in some points through ignorance, but in others he was clearly guilty of culpable neglect.
+LJSpeech-1.1/wavs/LJ048-0223.wav|After the President had retired at his hotel,
+LJSpeech-1.1/wavs/LJ047-0027.wav|In this way, it learned that when Oswald had arrived in the Soviet Union
+LJSpeech-1.1/wavs/LJ040-0140.wav|Mrs. Evelyn D Siegel, a social worker who interviewed both Lee and his mother while Lee was confined in Youth House,
+LJSpeech-1.1/wavs/LJ011-0189.wav|the other brother was accordingly sent on a pretended mission to Shrigley to bring Mr. Turner on to London, whither Wakefield and Miss Turner also proceeded.
+LJSpeech-1.1/wavs/LJ033-0019.wav|According to the testimony of Frazier, Marina Oswald, and Ruth Paine, it appears that Oswald never returned to Irving in midweek
+LJSpeech-1.1/wavs/LJ008-0255.wav|the iron knell that rang upon the beating heart of the living man who was about to die;
+LJSpeech-1.1/wavs/LJ036-0189.wav|at the southeast corner of tenth Street and Patton Avenue, moments before the Tippit shooting.
+LJSpeech-1.1/wavs/LJ047-0122.wav|Our interest in this man at this point was to determine whether his activities constituted a threat to the internal security of the country.
+LJSpeech-1.1/wavs/LJ048-0263.wav|During entire periods of travel status,
+LJSpeech-1.1/wavs/LJ007-0141.wav|and "to dispose him, by meditation and seclusion, to return to an honest life."
+LJSpeech-1.1/wavs/LJ019-0327.wav|No less close was the care as regards preservation of health.
+LJSpeech-1.1/wavs/LJ026-0117.wav|are directly oxidized to excretions and, lacking nitrogen, cannot serve for making new animal protoplasm.
+LJSpeech-1.1/wavs/LJ046-0205.wav|with the headquarters of the Federal intelligence agencies, particularly the FBI,
+LJSpeech-1.1/wavs/LJ028-0173.wav|When one is about halfway up, one finds a resting place and seats, where persons are wont to sit sometimes on their way to the summit.
+LJSpeech-1.1/wavs/LJ018-0122.wav|but not before the forged paper had been put in circulation for more than a couple of years. In eighteen sixty-one,
+LJSpeech-1.1/wavs/LJ028-0492.wav|and surmounted with towers, and then finally a sixth wall
+LJSpeech-1.1/wavs/LJ043-0125.wav|because the president of the photographic firm for which he had previously worked
+LJSpeech-1.1/wavs/LJ025-0004.wav|are often designated Organic Functions.
+LJSpeech-1.1/wavs/LJ026-0090.wav|This power is possessed by green plants alone; all other organisms being dependent for energy upon the potential energy of ready-made food.
+LJSpeech-1.1/wavs/LJ019-0018.wav|moreover, the gross expenditure was increased "partly from its being considered necessary, as it was a national prison,
+LJSpeech-1.1/wavs/LJ019-0148.wav|The prisoners were still left to themselves till next morning's unlocking,
+LJSpeech-1.1/wavs/LJ002-0287.wav|There were no beds or bedding, no straw even.
+LJSpeech-1.1/wavs/LJ046-0004.wav|four Presidents of the United States have been assassinated
+LJSpeech-1.1/wavs/LJ044-0182.wav|When faced with that situation Oswald became greatly agitated, and although he later unsuccessfully attempted
+LJSpeech-1.1/wavs/LJ044-0091.wav|with a fellow who had lived in Russia for three years and who was an admitted Marxist.
+LJSpeech-1.1/wavs/LJ019-0389.wav|passed under the more direct control of the State.
+LJSpeech-1.1/wavs/LJ011-0069.wav|and that after hanging for the regulated time he was taken down and easily restored to consciousness.
+LJSpeech-1.1/wavs/LJ046-0003.wav|In the one hundred years since eighteen sixty-five
+LJSpeech-1.1/wavs/LJ007-0096.wav|It was high time that the unsatisfactory state of the law as regards the treatment of criminal lunatics should be remedied
+LJSpeech-1.1/wavs/LJ044-0113.wav|After he had begun his Cuban activity in New Orleans, quote,
+LJSpeech-1.1/wavs/LJ034-0081.wav|Givens thought this was about eleven:fifty-five a.m.
+LJSpeech-1.1/wavs/LJ017-0103.wav|This tetanus, according to the prosecution, was produced by strychnia, and followed the administration of pills by Palmer
+LJSpeech-1.1/wavs/LJ040-0185.wav|He exhibits some difficulty in relationship to the maternal figure suggesting more anxiety in this area than in any other.
+LJSpeech-1.1/wavs/LJ012-0157.wav|It may be added that prison life did not agree with "Money Moses"; a striking change came over his appearance while in Newgate.
+LJSpeech-1.1/wavs/LJ050-0152.wav|The Secret Service and the Department of the Treasury now recognize this critical need.
+LJSpeech-1.1/wavs/LJ021-0190.wav|We do not get greatly excited nor is our peace of mind disturbed, whether we be businessmen or workers or farmers,
+LJSpeech-1.1/wavs/LJ003-0151.wav|All who could scrape together the cash seem to have gladly availed themselves of the privilege of entering the master's side.
+LJSpeech-1.1/wavs/LJ014-0330.wav|He had several narrow escapes.
+LJSpeech-1.1/wavs/LJ018-0367.wav|but she eventually passed into Broadmoor Lunatic Asylum, where she busies herself with watercolor drawing.
+LJSpeech-1.1/wavs/LJ001-0028.wav|but by printers in Strasburg, Basle, Paris, Lubeck, and other cities.
+LJSpeech-1.1/wavs/LJ003-0012.wav|and debtors might practically have as much as they liked, if they could only pay for it.
+LJSpeech-1.1/wavs/LJ003-0314.wav|the surgeon should see all prisoners, whether ill or well, once a week, and take general charge of the infirmaries.
+LJSpeech-1.1/wavs/LJ022-0169.wav|It will not destroy values.
+LJSpeech-1.1/wavs/LJ048-0240.wav|by members of the midnight to eight a.m. shift of the White House detail.
+LJSpeech-1.1/wavs/LJ031-0195.wav|Air Force One landed at Andrews Air Force Base, where President Kennedy had begun his last trip only thirty-one hours before.
+LJSpeech-1.1/wavs/LJ046-0048.wav|Desired by both the President and the public, it is an indispensable means of communication between the two.
+LJSpeech-1.1/wavs/LJ025-0050.wav|Thus wrote Cuvier in eighteen twenty-eight.
+LJSpeech-1.1/wavs/LJ032-0096.wav|listing the names of those, other than the owner of the box, entitled to receive mail through the box.
+LJSpeech-1.1/wavs/LJ003-0174.wav|Mr. Davison, sent to Newgate for embezzlement, and whose case is given in the preceding chapter,
+LJSpeech-1.1/wavs/LJ012-0201.wav|Bishop confessed that he was moved to this by the example of Burke and Hare.
+LJSpeech-1.1/wavs/LJ032-0160.wav|He compared them with the fibers found in the shirt which Oswald was wearing when arrested in the Texas Theatre.
+LJSpeech-1.1/wavs/LJ003-0291.wav|and Mr. Addison, keeper of Newgate, to make a visitation of the jails supposed to be the best managed, including those of Petworth and Gloucester.
+LJSpeech-1.1/wavs/LJ048-0088.wav|Examination of these procedures shows that in most respects they were well conceived and ably executed by the personnel of the Service.
+LJSpeech-1.1/wavs/LJ033-0079.wav|sister of the man with whom Oswald drove to work -- Buell Wesley Frazier.
+LJSpeech-1.1/wavs/LJ042-0028.wav|Oswald had managed to save enough money to cover the expenses of his forthcoming trip.
+LJSpeech-1.1/wavs/LJ031-0085.wav|Since the Dallas doctors directed all their efforts to controlling the massive bleeding caused by the head wound, and to reconstructing an airway to his lungs,
+LJSpeech-1.1/wavs/LJ024-0024.wav|Why was the age fixed at seventy?
+LJSpeech-1.1/wavs/LJ009-0196.wav|Another brutal practice had nearly disappeared about the time of the abolition of dissection. This was the public exhibition of the body,
+LJSpeech-1.1/wavs/LJ010-0025.wav|those against the person became more and more limited to the most heinous, or those which menaced or destroyed life.
+LJSpeech-1.1/wavs/LJ028-0486.wav|yet the palace of Nebuchadnezzar was protected by a third defense far stronger.
+LJSpeech-1.1/wavs/LJ010-0194.wav|but gazed with complacency around the crowded court, and eagerly inquired what persons of distinction were present.
+LJSpeech-1.1/wavs/LJ012-0246.wav|The murder in both cases was long a profound mystery. In this which I am now describing,
+LJSpeech-1.1/wavs/LJ038-0265.wav|and, further, on this basis the bullet could have been fired from the rifle on the basis of its land and groove impressions, end quote.
+LJSpeech-1.1/wavs/LJ019-0361.wav|who was bound to report any deficiencies and abuses he might find at his periodical visits. The Secretary of State might go further.
+LJSpeech-1.1/wavs/LJ044-0024.wav|While Oswald publicly engaged in the activities described above, his, quote, organization, end quote, was a product of his imagination.
+LJSpeech-1.1/wavs/LJ050-0010.wav|Daily supervision of the operations of the Secret Service within the Department of the Treasury should be improved.
+LJSpeech-1.1/wavs/LJ017-0266.wav|They none of them spoke much English except Leon, commonly called Lyons.
+LJSpeech-1.1/wavs/LJ043-0104.wav|His supervisor admitted, however,
+LJSpeech-1.1/wavs/LJ002-0262.wav|owing to many charitable gifts and bequests, which included annual donations from the Archbishop of Canterbury,
+LJSpeech-1.1/wavs/LJ012-0128.wav|A young man named Caspar, clerk to a steam-ship company,
+LJSpeech-1.1/wavs/LJ022-0135.wav|but I am jealous of the right of every citizen to call to the attention of his or her government
+LJSpeech-1.1/wavs/LJ001-0123.wav|this is awkward if you have to read Bradshaw's Guide in a hurry.
+LJSpeech-1.1/wavs/LJ015-0035.wav|produced immediate embarrassment and financial distress.
+LJSpeech-1.1/wavs/LJ049-0188.wav|in one or another Government agency, to clarify the division of authority between the agencies involved, and to retain the existing system
+LJSpeech-1.1/wavs/LJ050-0191.wav|More importantly, the lack of carefully prepared and carefully transmitted instructions for typical visits to cities
+LJSpeech-1.1/wavs/LJ021-0188.wav|that much of our New Deal program is only an attempt to catch up with English reforms that go back ten years or more.
+LJSpeech-1.1/wavs/LJ006-0085.wav|If a man is poor and ragged, however inexperienced in crime, or however trifling may be the offense for which he has been committed
+LJSpeech-1.1/wavs/LJ024-0026.wav|and the rules of many of our universities and of almost every great private business enterprise,
+LJSpeech-1.1/wavs/LJ024-0067.wav|have now given us a Court in which five justices will be over seventy-five years of age before next June
+LJSpeech-1.1/wavs/LJ050-0179.wav|Based on its experience during this period, the Secret Service now recommends that additional personnel be made available to PRS
+LJSpeech-1.1/wavs/LJ016-0098.wav|or any of the new establishments at home, at Portland, Dartmoor, or elsewhere.
+LJSpeech-1.1/wavs/LJ011-0218.wav|spoke severely of the gross deception practiced upon an innocent girl, and sentenced the brothers each to three years' imprisonment,
+LJSpeech-1.1/wavs/LJ042-0037.wav|and under which, quote, art, culture and the sprit of man are subjected to commercial enterprising,
+LJSpeech-1.1/wavs/LJ032-0197.wav|Photograph of Oswald With Rifle
+LJSpeech-1.1/wavs/LJ037-0220.wav|Approximately fifteen minutes before the shooting of Tippit, Oswald was seen leaving his roominghouse.
+LJSpeech-1.1/wavs/LJ006-0055.wav|and when the population fell they shut up one half the jail and crowded up the other.
+LJSpeech-1.1/wavs/LJ031-0121.wav|Dr. George T. Shires, assisted by Drs. Robert McClelland, Charles Baxter, and Ralph Don Patman,
+LJSpeech-1.1/wavs/LJ019-0144.wav|the indiscriminate visitation of friends, and the almost unlimited admission of extra food, these more glaring defects had disappeared.
+LJSpeech-1.1/wavs/LJ003-0118.wav|Various punishments were inflicted, the heaviest of which was standing in the pillory.
+LJSpeech-1.1/wavs/LJ028-0013.wav|The Damascus mosques and their minarets sink beneath the western sky.
+LJSpeech-1.1/wavs/LJ038-0289.wav|As shown above, the note and the photographs of Walker's house and of the nearby railroad tracks
+LJSpeech-1.1/wavs/LJ015-0190.wav|that of dishonest rogues who assume piety and philanthropy as a cloak for their misdeeds.
+LJSpeech-1.1/wavs/LJ028-0504.wav|designed in colors of white and blue and yellow and black.
+LJSpeech-1.1/wavs/LJ035-0007.wav|The encounter in the lunchroom.
+LJSpeech-1.1/wavs/LJ010-0092.wav|Lord Harrowby's dinner-party was postponed, but the conspirators knew nothing of it,
+LJSpeech-1.1/wavs/LJ002-0250.wav|Their conduct too was orderly on the whole.
+LJSpeech-1.1/wavs/LJ006-0033.wav|The ink was barely dry upon their letters of appointment before they appeared at Newgate, and commenced a searching investigation.
+LJSpeech-1.1/wavs/LJ009-0262.wav|The crowd vociferously yelled their disapproval, and at length
+LJSpeech-1.1/wavs/LJ032-0029.wav|The order coupon was signed, in handprinting, "A. Hidell, P.O. Box two nine one five, Dallas, Texas."
+LJSpeech-1.1/wavs/LJ038-0106.wav|These photographs were shown to Oswald on the evening of November twenty-three and again on the morning of the twenty-fourth.
+LJSpeech-1.1/wavs/LJ047-0193.wav|and so advised the Dallas office in the ordinary course of business.
+LJSpeech-1.1/wavs/LJ014-0237.wav|I will now pass to grave but less atrocious crimes.
+LJSpeech-1.1/wavs/LJ011-0191.wav|The strangely-married couple thereupon pressed on to Dover, and crossed over to Calais.
+LJSpeech-1.1/wavs/LJ039-0208.wav|this was because of a defect in the scope which was recognized by the FBI agents and which they could have compensated for if they were aiming to hit a bull's-eye.
+LJSpeech-1.1/wavs/LJ021-0183.wav|Is it not a fact that ever since the year nineteen oh nine,
+LJSpeech-1.1/wavs/LJ002-0057.wav|These wards were all fitted with barrack-beds, but no bedding was supplied.
+LJSpeech-1.1/wavs/LJ009-0044.wav|His features have no felonious cast;
+LJSpeech-1.1/wavs/LJ050-0244.wav|the assistance of trained Federal law enforcement officers.
+LJSpeech-1.1/wavs/LJ007-0128.wav|"what must be the state of discipline in a place filled with characters so various as were assembled there, where the tried and the untried, the sick and the healthy,
+LJSpeech-1.1/wavs/LJ037-0213.wav|Marina Oswald gave as her opinion that the mail-order coupon was in Oswald's handwriting.
+LJSpeech-1.1/wavs/LJ002-0232.wav|Disturbances were frequent, owing to the riotous conduct of intoxicated women.
+LJSpeech-1.1/wavs/LJ023-0126.wav|In other words, we said we would seek an amendment only if every other possible means by legislation were to fail.
+LJSpeech-1.1/wavs/LJ042-0178.wav|He thought the new alternative would have its best chance to be accepted after, quote, conflict between the two world systems leaves the world country
+LJSpeech-1.1/wavs/LJ017-0185.wav|She was in good health on leaving home, and had with her a large sum of money.
+LJSpeech-1.1/wavs/LJ045-0078.wav|Oswald read the letter, but refused to believe that it was sincere, even though his wife insisted to him that it was.
+LJSpeech-1.1/wavs/LJ002-0335.wav|so were candles, salt, pepper, mops and brooms. But the latter could have been of little service. Dirt prevailed everywhere;
+LJSpeech-1.1/wavs/LJ022-0132.wav|I, therefore, hope you will watch the work in every corner of this Nation.
+LJSpeech-1.1/wavs/LJ050-0183.wav|Advice by the Secret Service to local police in metropolitan areas relating to the assistance expected in connection with a Presidential visit
+LJSpeech-1.1/wavs/LJ017-0274.wav|When the warder went in to call him he asked for his clothes. He was told he would have to wear his own.
+LJSpeech-1.1/wavs/LJ018-0351.wav|and that, in spite of the verdict of the jury, "he left the dock with a calm and quiet conscience;"
+LJSpeech-1.1/wavs/LJ042-0175.wav|could possibly make a choice between them, there is no choice,
+LJSpeech-1.1/wavs/LJ002-0273.wav|Ludgate to the last was the debtors' prison for freemen of the city of London,
+LJSpeech-1.1/wavs/LJ030-0131.wav|which curves in a southwesterly direction as it proceeds downgrade toward the Triple Underpass and the Stemmons Freeway.
+LJSpeech-1.1/wavs/LJ017-0255.wav|repaired there secretly, and so gave information to the Brazilian authorities.
+LJSpeech-1.1/wavs/LJ009-0129.wav|that many of the kneeling men or boys laughed while they knelt, pinched each other, and, when they could do so without fear of being seen by any officer of the prison,
+LJSpeech-1.1/wavs/LJ007-0213.wav|Again the following year the inspectors repeat their charge.
+LJSpeech-1.1/wavs/LJ027-0063.wav|enlarged and consolidated to form the pelvis, to serve as a firm support to the hind-limbs.
+LJSpeech-1.1/wavs/LJ007-0240.wav|that a strict compliance with their duties gave them no choice "but to report matters as we found them,
+LJSpeech-1.1/wavs/LJ011-0144.wav|but Mr. Joseph Hume pressed the Government hard, and obtained an assurance that the men should not be executed.
+LJSpeech-1.1/wavs/LJ017-0204.wav|who said that he feared it was only too true that secret poisoning was at that time very rife in the metropolis.
+LJSpeech-1.1/wavs/LJ028-0071.wav|Late in his life, in five sixty-seven, he invaded Egypt.
+LJSpeech-1.1/wavs/LJ013-0071.wav|Here again the money, with ten years' interest, was handed over to Barber and another calling himself Thomas Hunt, an executor of the will.
+LJSpeech-1.1/wavs/LJ019-0072.wav|It is scarcely necessary to follow the controversy further. I have only introduced the subject as showing how little as yet the State
+LJSpeech-1.1/wavs/LJ043-0055.wav|He never saw or communicated with her in any way again until she came to see him after the assassination.
+LJSpeech-1.1/wavs/LJ050-0166.wav|The Commission was struck by the apparent lack of effort, on an interagency basis,
+LJSpeech-1.1/wavs/LJ017-0138.wav|Three years later came the case of Dr. Smethurst,
+LJSpeech-1.1/wavs/LJ002-0129.wav|would have amounted to sixty-eight thousand, seven hundred twenty-eight pounds, and if defended,
+LJSpeech-1.1/wavs/LJ007-0019.wav|Even in the daytime, when supervision was withdrawn, "the language used to be dreadful," says one of the women when under examination;
+LJSpeech-1.1/wavs/LJ015-0172.wav|The inquiry soon revealed the colossal character of the frauds.
+LJSpeech-1.1/wavs/LJ009-0256.wav|Still he resisted.
+LJSpeech-1.1/wavs/LJ019-0341.wav|The old custom, so fruitful of the worst evils, of keeping a tap inside the prison was made illegal.
+LJSpeech-1.1/wavs/LJ018-0386.wav|the whole manufactured while in the condemned cell of the crumb of bread made into paste.
+LJSpeech-1.1/wavs/LJ006-0275.wav|to have access to the female side on Sundays and Wednesdays, in order to visit their supposed relations there.
+LJSpeech-1.1/wavs/LJ050-0267.wav|between Secretary Dillon and Donald F. Hornig, Special Assistant to the President for Science and Technology, is a useful effort in the right direction.
+LJSpeech-1.1/wavs/LJ032-0189.wav|since there was limited, if any, opportunity for Oswald to handle the weapon during the two months prior to November twenty-two.
+LJSpeech-1.1/wavs/LJ041-0162.wav|Thornley also testified about an incident which grew
+LJSpeech-1.1/wavs/LJ017-0008.wav|who had poisoned his master and many of his dependents.
+LJSpeech-1.1/wavs/LJ013-0138.wav|Ashley was the son of an army agent and banker,
+LJSpeech-1.1/wavs/LJ048-0020.wav|The interview with him in jail is not significant from the standpoint of whether he had a propensity for violence.
+LJSpeech-1.1/wavs/LJ010-0087.wav|and it was arranged that one of their number should knock at Lord Harrowby's door on the pretense of leaving a parcel,
+LJSpeech-1.1/wavs/LJ019-0182.wav|Lord John Russell, commenting upon the offer of the Corporation to improve Newgate,
+LJSpeech-1.1/wavs/LJ020-0053.wav|in the form of turnovers, pinching the corners of the fold pretty hard to hinder the flap of dough from flying up as the rising proceeds.
+LJSpeech-1.1/wavs/LJ050-0036.wav|is not consistent with the standards which the responsibilities of the Secret Service require it to meet.
+LJSpeech-1.1/wavs/LJ042-0212.wav|I have often wondered why it is that the communist, anarchist capitalist, and even the fascist and anarchist elements in American,
+LJSpeech-1.1/wavs/LJ003-0021.wav|The head of the debtors' prison was a prisoner called the steward, who was chosen by the whole body from six whom the keeper nominated.
+LJSpeech-1.1/wavs/LJ011-0157.wav|He thereupon conceived an idea of carrying her off and marrying her willy nilly at Gretna Green.
+LJSpeech-1.1/wavs/LJ046-0161.wav|Many of these cases were not investigated by PRS.
+LJSpeech-1.1/wavs/LJ025-0075.wav|with the advance of microscopic anatomy, the universality of the fact itself among animals has ceased to be predicable.
+LJSpeech-1.1/wavs/LJ002-0314.wav|was after eighteen oh seven, through the exertions of the keeper of the jail, spent in the purchase of necessaries.
+LJSpeech-1.1/wavs/LJ044-0230.wav|His unhappy experience with the Cuban consul seems thus to have reduced his enthusiasm for the Castro regime and his desire to go to Cuba.
+LJSpeech-1.1/wavs/LJ038-0250.wav|Another photograph of railroad tracks found among Oswald's possessions was not identified by his wife,
+LJSpeech-1.1/wavs/LJ013-0227.wav|The order was, however, at length obeyed, and the whole of the prisoner's clothes were minutely searched.
+LJSpeech-1.1/wavs/LJ038-0117.wav|He falsely alleged that he bought the revolver in Fort Worth, when in fact he purchased it from a mail-order house in Los Angeles.
+LJSpeech-1.1/wavs/LJ031-0131.wav|An agent was stationed at the entrance to stop anyone who was not a member of the Presidential party.
+LJSpeech-1.1/wavs/LJ026-0011.wav|Because of these uncertain forms of life,
+LJSpeech-1.1/wavs/LJ015-0307.wav|The evidence was corroborated by that of many of the victims who had acted as messengers,
+LJSpeech-1.1/wavs/LJ009-0080.wav|The text, without another word, is enough to raise the wildest passions of the audience
+LJSpeech-1.1/wavs/LJ049-0146.wav|The expert assistance and resources which it could draw upon would be particularly desirable in this complex and sensitive area.
+LJSpeech-1.1/wavs/LJ040-0083.wav|After the divorce Mrs. Oswald complained considerably about how unfairly she was treated,
+LJSpeech-1.1/wavs/LJ017-0097.wav|He had swallowed his pills as usual, at least Palmer had administered them
+LJSpeech-1.1/wavs/LJ028-0165.wav|There is, however, a second inner wall, of less thickness than the first, but very little inferior to it in strength.
+LJSpeech-1.1/wavs/LJ047-0004.wav|No information concerning Lee Harvey Oswald appeared in PRS files before the President's trip to Dallas.
+LJSpeech-1.1/wavs/LJ046-0218.wav|Advise the Bureau at the same time by teletype of the information so furnished to the Secret Service and the fact that it has been so disseminated.
+LJSpeech-1.1/wavs/LJ048-0058.wav|Agent Hosty testified
+LJSpeech-1.1/wavs/LJ002-0009.wav|On the twenty-seventh April, in the following year,
+LJSpeech-1.1/wavs/LJ010-0078.wav|On his second release, goaded by his fancied wrongs, he began to plot a dark and dreadful revenge,
+LJSpeech-1.1/wavs/LJ011-0192.wav|The fact of the abduction did not transpire for some days.
+LJSpeech-1.1/wavs/LJ029-0181.wav|called on the people of Dallas to be "congenial hosts" even though "Dallas didn't vote for Mr. Kennedy in nineteen sixty,
+LJSpeech-1.1/wavs/LJ025-0031.wav|there were no means by which the motion of its fluids could be produced by internal causes.
+LJSpeech-1.1/wavs/LJ005-0043.wav|and obtained full details, from places where they had been adopted, of the nature of these new machines
+LJSpeech-1.1/wavs/LJ003-0197.wav|This procrastination bred certain callousness.
+LJSpeech-1.1/wavs/LJ011-0139.wav|This time they sent back the bill, re-enacting only the two penalties for will forging and the forging of powers of attorney;
+LJSpeech-1.1/wavs/LJ028-0122.wav|To the west of Babylon, at a greater distance from the outer wall, I constructed an enclosing wall four thousand cubits in length about the city.
+LJSpeech-1.1/wavs/LJ011-0052.wav|The sermon ended with an appeal to the dying man, exhorting him to penitence.
+LJSpeech-1.1/wavs/LJ029-0011.wav|President Kennedy's visit to Texas in November nineteen sixty-three had been under consideration for almost a year before it occurred.
+LJSpeech-1.1/wavs/LJ016-0202.wav|Calcraft's emoluments were a guinea per week, and an extra guinea for every execution.
+LJSpeech-1.1/wavs/LJ024-0039.wav|and no Senate of honorable men fit for their office would confirm, that kind of appointees to the Supreme Court.
+LJSpeech-1.1/wavs/LJ030-0122.wav|to the right side of the President's car.
+LJSpeech-1.1/wavs/LJ028-0098.wav|the wise, the pious, the maintainer of Esagil and Ezida,
+LJSpeech-1.1/wavs/LJ007-0102.wav|Pilgrim, Bond, and Clamp, had refused to give evidence before the House of Commons' Committee; a Speaker's warrant was issued for their arrest when they absconded.
+LJSpeech-1.1/wavs/LJ008-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eleven: Executions, part one.
+LJSpeech-1.1/wavs/LJ026-0042.wav|The lower plants and animals referred to above are so far from everyday observation
+LJSpeech-1.1/wavs/LJ047-0074.wav|Under a general Bureau request to be on the alert for activities of the Fair Play for Cuba Committee
+LJSpeech-1.1/wavs/LJ034-0064.wav|This finding, in addition to the freshness of one of the prints and the presence of Oswald's prints on two of the four cartons and the paper bag
+LJSpeech-1.1/wavs/LJ008-0114.wav|His hands were clasped, and he was truly penitent.
+LJSpeech-1.1/wavs/LJ003-0298.wav|for giving every prisoner a sleeping cell to himself, an amount of enlightenment which is hardly general among European nations at this
+LJSpeech-1.1/wavs/LJ011-0195.wav|The terror and dismay of her parents were soon intensified by the receipt of a letter from Wakefield, at Carlisle, announcing the marriage.
+LJSpeech-1.1/wavs/LJ045-0129.wav|has visited us here in Dallas, Texas, on November one. Agent James P. Hasty
+LJSpeech-1.1/wavs/LJ014-0040.wav|that he rushed to a slaughterhouse in Hampstead and purposely stained his clothes with blood.
+LJSpeech-1.1/wavs/LJ014-0209.wav|was perpetrated in eighteen fifty-two. The case was accompanied with the most shocking brutality.
+LJSpeech-1.1/wavs/LJ042-0007.wav|At the age of nineteen, Oswald thus committed an act which was the most striking indication he had yet given
+LJSpeech-1.1/wavs/LJ021-0080.wav|In these gains and in the policies of the administration there are assurances that hearten all forward- looking men and women
+LJSpeech-1.1/wavs/LJ039-0221.wav|The various tests showed that the Mannlicher-Carcano was an accurate rifle and that the use of a four-power scope
+LJSpeech-1.1/wavs/LJ010-0029.wav|the cold-blooded, calculating atrocity born of self-interest, were still the irresistible incentives to kill.
+LJSpeech-1.1/wavs/LJ025-0028.wav|before it can be converted into substances fitted for absorption,
+LJSpeech-1.1/wavs/LJ016-0148.wav|This was not strong enough to carry him, and he fell through.
+LJSpeech-1.1/wavs/LJ019-0041.wav|Other jurisdictions were less prompt to recognize their responsibilities, the city of London among the number, as I shall presently show at length.
+LJSpeech-1.1/wavs/LJ004-0140.wav|and the turnkey told Mr. Buxton that the "smell on first opening the door was enough to knock down a horse.
+LJSpeech-1.1/wavs/LJ009-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section twelve: Executions, part two.
+LJSpeech-1.1/wavs/LJ039-0069.wav|Maj. Eugene D. Anderson, assistant head of the Marksmanship Branch of U.S. Marine Corps
+LJSpeech-1.1/wavs/LJ007-0034.wav|At times the numbers congregated together were very great; as many as fifty and sixty, even more, were crowded indiscriminately into the press-yard.
+LJSpeech-1.1/wavs/LJ046-0131.wav|in nineteen fifty-three this had increased to more than seventeen thousand items;
+LJSpeech-1.1/wavs/LJ009-0002.wav|It is satisfactory to be able to record that some consideration was shown the capital convict actually awaiting execution.
+LJSpeech-1.1/wavs/LJ027-0139.wav|and developmental changes which may be observed during the life history of now existing individuals belonging to the same group of animals.
+LJSpeech-1.1/wavs/LJ028-0366.wav|Therefore, to destroy the power of the old capital, he planned to build Seleucia on the Tigris about fifty miles to the east.
+LJSpeech-1.1/wavs/LJ019-0386.wav|that no complete reform could be accomplished so long as the prisons were left under the jurisdiction of the local authorities.
+LJSpeech-1.1/wavs/LJ039-0232.wav|the assassin was then required to hit the target one more time within a space of from four point eight to five point six seconds.
+LJSpeech-1.1/wavs/LJ047-0175.wav|or telephone number in Dallas. Mrs. Paine testified that she learned Oswald's telephone number at the Beckley Street roominghouse in the middle of October
+LJSpeech-1.1/wavs/LJ005-0031.wav|no visiting of friends, no education but religious education, no freedom of diet,
+LJSpeech-1.1/wavs/LJ047-0233.wav|his lies to Agent Quigley, his recent visit to Mexico City -- indicated that Oswald was capable of violence.
+LJSpeech-1.1/wavs/LJ011-0005.wav|A lady in the country, who had thirteen thousand pounds in the stocks, desired her London agent to sell them out.
+LJSpeech-1.1/wavs/LJ048-0231.wav|According to their statements, the agents remained at the Press Club for periods varying from thirty minutes to an hour and a half,
+LJSpeech-1.1/wavs/LJ020-0010.wav|One heaping cup of sifted flour.
+LJSpeech-1.1/wavs/LJ012-0207.wav|The porter asked what he had got, and the answer was, a male subject.
+LJSpeech-1.1/wavs/LJ009-0070.wav|The youth's hands tremble as they hold the book upside down.
+LJSpeech-1.1/wavs/LJ008-0006.wav|as the exposure was brutal and cruel towards the principal actors.
+LJSpeech-1.1/wavs/LJ046-0121.wav|At the time of the assassination PRS was a very small group, comprised of twelve specialists and three clerks.
+LJSpeech-1.1/wavs/LJ019-0138.wav|at the most crowded periods, or just before the sessional jail delivery; and at its lowest it fell sometimes to fifty or sixty.
+LJSpeech-1.1/wavs/LJ041-0037.wav|and subsequently, after this conversation, my father came in and we were kind of arguing back and forth about the situation, and my father came in the room,
+LJSpeech-1.1/wavs/LJ019-0105.wav|and that a new system of prison discipline should be introduced.
+LJSpeech-1.1/wavs/LJ036-0056.wav|Mrs. Bledsoe recalled that Oswald sat halfway to the rear of the bus which moved slowly and intermittently as traffic became heavy.
+LJSpeech-1.1/wavs/LJ032-0111.wav|Letters received by Marina Oswald from her husband signed "Alek" were given to the Commission.
+LJSpeech-1.1/wavs/LJ017-0045.wav|The Central Criminal Court was crowded to suffocation.
+LJSpeech-1.1/wavs/LJ005-0092.wav|and every prisoner was to be provided with a hammock or cot to himself, suitable bedding, and, if possible, a separate cell.
+LJSpeech-1.1/wavs/LJ003-0165.wav|The better class of inmate complained bitterly of this enforced companionship with the vile,
+LJSpeech-1.1/wavs/LJ040-0016.wav|and in the history of his life which does give some insight into his character and, possibly, into the motives for his act.
+LJSpeech-1.1/wavs/LJ003-0237.wav|The fees on reception and discharge must be deemed exorbitant, when it is remembered the impoverished class who usually crowded the jail;
+LJSpeech-1.1/wavs/LJ026-0037.wav|It will no doubt occur to the reader that, on the theory of evolution,
+LJSpeech-1.1/wavs/LJ026-0007.wav|Many of the lowest forms of life have long been puzzles
+LJSpeech-1.1/wavs/LJ004-0173.wav|and sometimes slept in "the pit," loaded with heavy irons for a whole year, waiting the jail delivery.
+LJSpeech-1.1/wavs/LJ003-0069.wav|In this heterogeneous society were also thrown the unfortunate journalists to whom I have already referred, and on whom imprisonment in Newgate
+LJSpeech-1.1/wavs/LJ032-0255.wav|About one week after the return from New Orleans,
+LJSpeech-1.1/wavs/LJ015-0160.wav|he patronized the best tailors, and had a fashionable coiffeur from Hanover Square daily to curl his hair.
+LJSpeech-1.1/wavs/LJ032-0002.wav|Chapter four. The Assassin: Part one.
+LJSpeech-1.1/wavs/LJ016-0290.wav|There were but few women, except of the most degraded sort; the men were mostly young men --
+LJSpeech-1.1/wavs/LJ044-0018.wav|and was arrested because of a dispute with three anti-Castro Cuban exiles, and again on August sixteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ016-0271.wav|The reply evinced equal satisfaction, and the speaker, with a profane oath, declared that he would like to act as Jack Ketch to the whole lot.
+LJSpeech-1.1/wavs/LJ001-0016.wav|The Middle Ages brought calligraphy to perfection, and it was natural therefore
+LJSpeech-1.1/wavs/LJ006-0151.wav|Many of them were otherwise and improperly occupied for hours every day in menial services for the governor, cleaning his windows or grooming his horse.
+LJSpeech-1.1/wavs/LJ020-0002.wav|Bread raised with what is known to bakers as a “sponge,” requires more time and a trifle more work than the simpler form for which I have just already given directions.
+LJSpeech-1.1/wavs/LJ001-0070.wav|and was used there with very little variation all through the sixteenth and seventeenth centuries, and indeed into the eighteenth.
+LJSpeech-1.1/wavs/LJ007-0097.wav|and not the least of the good services rendered by the new inspectors was their inquiry into the status of these unfortunate people, and their recommendation to improve it.
+LJSpeech-1.1/wavs/LJ022-0024.wav|They know that the process of the constructive rebuilding of America cannot be done in a day or a year,
+LJSpeech-1.1/wavs/LJ022-0139.wav|with which we have been concerned for two years.
+LJSpeech-1.1/wavs/LJ021-0050.wav|I sincerely hope that as a result
+LJSpeech-1.1/wavs/LJ028-0392.wav|The statement of St. Jerome is confirmed by the following passage from Zosimus, a Greek writer of the fifth century A.D.:
+LJSpeech-1.1/wavs/LJ049-0215.wav|the protection of the President is in a real sense a Government-wide responsibility which must necessarily assumed by the Department of State,
+LJSpeech-1.1/wavs/LJ029-0130.wav|the President's motorcade would pass the Texas School Book Depository Building on the northwest corner of Houston and Elm Streets.
+LJSpeech-1.1/wavs/LJ028-0194.wav|But how did the "mighty city" fall? How could Cyrus take Babylon whose walls were strong enough to resist any army?
+LJSpeech-1.1/wavs/LJ028-0452.wav|The outer wall bore the name of Nimitti-Bel. Its direction was northeast and southwest, forming a triangle with the river.
+LJSpeech-1.1/wavs/LJ028-0192.wav|When the news came to the Hebrews, who were held there in exile, they excitedly rushed about the streets, crying: "Babylon is fallen,"
+LJSpeech-1.1/wavs/LJ048-0259.wav|However, Chief Rowley did not condone the action of the off-duty agents, particularly since it violated a regulation of the Secret Service,
+LJSpeech-1.1/wavs/LJ006-0144.wav|A wardsman did not necessarily lose his situation for it. Nor was drink the only creature comfort he might enjoy.
+LJSpeech-1.1/wavs/LJ005-0157.wav|Here and there food was not issued in kind, but a money allowance which the prisoner might expend himself.
+LJSpeech-1.1/wavs/LJ016-0133.wav|There is, however, no explanation of the motives which prompt prisoners to attempt escapes.
+LJSpeech-1.1/wavs/LJ018-0226.wav|leaving everything to the widow, and constituting William sole executor.
+LJSpeech-1.1/wavs/LJ045-0184.wav|Then on Thursday morning, November twenty-one,
+LJSpeech-1.1/wavs/LJ010-0072.wav|Returning to England,
+LJSpeech-1.1/wavs/LJ002-0118.wav|a table showing the figures for the year ending Michaelmas eighteen oh two.
+LJSpeech-1.1/wavs/LJ011-0291.wav|Two Bow Street runners were dispatched to the house in York Street, which had evidently been taken on purpose for the outrage.
+LJSpeech-1.1/wavs/LJ036-0023.wav|On the basis of the date and time on the transfer, McWatters was able to testify that the transfer had been issued by him
+LJSpeech-1.1/wavs/LJ016-0276.wav|Never, indeed, had an execution been more generally patronized.
+LJSpeech-1.1/wavs/LJ008-0073.wav|One case is preserved by Catnach,
+LJSpeech-1.1/wavs/LJ010-0066.wav|which under Thistlewood as dictator was to rule the nation, by first handing over its capital to fire and pillage.
+LJSpeech-1.1/wavs/LJ003-0226.wav|The rations of food were notoriously inadequate, and so carelessly distributed, that many were left to starve.
+LJSpeech-1.1/wavs/LJ046-0240.wav|Bouck explained the failure to try to identify the individuals involved in the Stevenson incident after it occurred on the ground that
+LJSpeech-1.1/wavs/LJ002-0251.wav|Drunkenness was not common, chiefly because liquor was not to be had freely, although the tapster paid a rent of two guineas a week for permission to sell it.
+LJSpeech-1.1/wavs/LJ013-0028.wav|and at last, when within fifteen miles of port, with wind and weather perfectly fair, he ran her on to the rocks.
+LJSpeech-1.1/wavs/LJ017-0092.wav|He decided to use strychnia, or the vegetable poison otherwise known as nux vomica;
+LJSpeech-1.1/wavs/LJ031-0112.wav|in its greatest diameter, was treated by cutting away the damaged skin and suturing the back muscle and skin.
+LJSpeech-1.1/wavs/LJ038-0144.wav|that must have been some other time he picked me up, end quote,
+LJSpeech-1.1/wavs/LJ034-0184.wav|Approximately one week after the assassination, according to Fisher, policemen showed him a picture of Oswald.
+LJSpeech-1.1/wavs/LJ042-0067.wav|Soak fist in cold water to numb the pain. Then slash my left wrist. Than plunge wrist into bathtub of hot water.
+LJSpeech-1.1/wavs/LJ015-0271.wav|He dealt too in the precious metals, when they had been improperly acquired,
+LJSpeech-1.1/wavs/LJ049-0222.wav|Pending any such determination, however, this Commission is convinced of the necessity of better coordination
+LJSpeech-1.1/wavs/LJ042-0233.wav|His first set of questions and answers indicated his belief that there were no outstanding differences between the Soviet Union and the United States,
+LJSpeech-1.1/wavs/LJ017-0213.wav|the first and second mates, Karswell and Taffir; there were two other Englishmen on board, and the rest of the crew were a polyglot lot,
+LJSpeech-1.1/wavs/LJ008-0117.wav|"You have tied me very tight," upon which Dr. Forde ordered him to slacken the cord, which he did, but not without muttering.
+LJSpeech-1.1/wavs/LJ008-0074.wav|that of Phoebe Harris, who in seventeen eighty-eight was "barbariously" executed and burnt before Newgate for coining.
+LJSpeech-1.1/wavs/LJ021-0108.wav|from the standpoint of the good of the industries themselves, as well as the general public interest,
+LJSpeech-1.1/wavs/LJ029-0045.wav|to select the luncheon site and motorcade route, and to plan security measures for the luncheon and the motorcade.
+LJSpeech-1.1/wavs/LJ013-0179.wav|The latter was so placed that it could throw no light on the former, which was a 'Life of Sir Samuel Romilly.'
+LJSpeech-1.1/wavs/LJ028-0336.wav|Darius now, still keeping to the plan agreed upon,
+LJSpeech-1.1/wavs/LJ028-0514.wav|The great outer wall seems to have disappeared completely beneath the desert surface.
+LJSpeech-1.1/wavs/LJ015-0023.wav|Mr. Bates had been confidential managing clerk, and was taken into the firm not alone as a reward for long and faithful service
+LJSpeech-1.1/wavs/LJ040-0062.wav|The Early Years
+LJSpeech-1.1/wavs/LJ038-0225.wav|which would indicate that it was probably written before the birth of Oswald's second child on October twenty, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ027-0161.wav|It is essentially a fish, and would be so classed if it remained in this condition.
+LJSpeech-1.1/wavs/LJ006-0023.wav|had made such prisons as Auburn a model for imitation.
+LJSpeech-1.1/wavs/LJ046-0044.wav|in accordance with George Washington's maxim that Americans have a government, quote, of accommodation as well as a government of laws, end quote,
+LJSpeech-1.1/wavs/LJ008-0020.wav|Here John Sutherland, commander of the British armed transport 'The Friends,' suffered on the twenty-ninth June, eighteen oh nine,
+LJSpeech-1.1/wavs/LJ017-0056.wav|Although the strain lasted fourteen days, he showed no signs of exhaustion, either physical or mental.
+LJSpeech-1.1/wavs/LJ038-0172.wav|Although the bullet was recovered from Walker's house, in the absence of a weapon it was of little investigatory value.
+LJSpeech-1.1/wavs/LJ030-0110.wav|and Vice-Presidential follow-up car used portable sets with a separate frequency for their own car-to-car communication.
+LJSpeech-1.1/wavs/LJ025-0122.wav|The contractility which is the fundamental condition of locomotion," continues Huxley,
+LJSpeech-1.1/wavs/LJ039-0178.wav|This angle was used in the test because the majority of the eyewitnesses to the assassination
+LJSpeech-1.1/wavs/LJ031-0169.wav|Motorcade policemen who escorted the automobiles were requested by the Vice President and Agent Youngblood not to use sirens.
+LJSpeech-1.1/wavs/LJ018-0144.wav|The amount of Brewer's abstractions (who was eventually acquitted) was never exactly estimated.
+LJSpeech-1.1/wavs/LJ012-0251.wav|the lockman of "Ben Jonson lock," in Stepney Fields, found a human head jammed into the lock gates.
+LJSpeech-1.1/wavs/LJ044-0101.wav|He had previously sent, apparently unsolicited, to the Party newspaper, the Worker,
+LJSpeech-1.1/wavs/LJ040-0205.wav|and of the, quote, protective shell he has drawn around himself, end quote. Dr. Hartogs reported that Mrs. Oswald did not understand
+LJSpeech-1.1/wavs/LJ027-0073.wav|may be shown to be modifications one of another.
+LJSpeech-1.1/wavs/LJ020-0031.wav|Into the “crater” dug out in the middle, pour the sponge, warm water, the molasses, and soda dissolved in hot water.
+LJSpeech-1.1/wavs/LJ004-0130.wav|incommodious, as has been stated, insecure, unhealthy, and unprovided with the printed or written regulations required by law.
+LJSpeech-1.1/wavs/LJ029-0062.wav|It also advised the Secret Service of the circulation on November twenty-one of a handbill sharply critical of President Kennedy,
+LJSpeech-1.1/wavs/LJ015-0217.wav|The next step was to get impressions and fabricate false keys.
+LJSpeech-1.1/wavs/LJ006-0292.wav|male prisoners carried coal into the female prison, when they saw and could speak or pass letters to the female prisoners;
+LJSpeech-1.1/wavs/LJ037-0040.wav|She saw the man come to the right window of the police car. As he talked, he leaned on the ledge of the right window with his arms.
+LJSpeech-1.1/wavs/LJ043-0183.wav|Oswald did not lack the determination and other traits required
+LJSpeech-1.1/wavs/LJ050-0009.wav|disclosed by the Commission's investigation. Other measures should be taken as well to improve the overall operation of the Secret Service.
+LJSpeech-1.1/wavs/LJ005-0246.wav|where "prisoners before and after trial are under no efficient superintendence," and where "there was no restraint, or attempt at restraint."
+LJSpeech-1.1/wavs/LJ011-0073.wav|several persons were sentenced to or suffered death for this crime.
+LJSpeech-1.1/wavs/LJ025-0025.wav|he naturally sees the primary and the most important distinction between animals and plants,
+LJSpeech-1.1/wavs/LJ025-0017.wav|He affirms that living beings have been subdivided from the earliest times into animated beings, which possess sense and motion,
+LJSpeech-1.1/wavs/LJ004-0050.wav|Mr. Buxton had already been associated with Mrs. Fry in the Newgate visitation, and his attention had thus been drawn to the neglected state of English prisons.
+LJSpeech-1.1/wavs/LJ050-0058.wav|which was the possibility of an attempt against the person or safety of the President.
+LJSpeech-1.1/wavs/LJ026-0146.wav|They are absorbed by the circulating liquids and carried to the eliminating organs, lungs and kidneys chiefly, for elimination.
+LJSpeech-1.1/wavs/LJ031-0150.wav|Other Secret Service agents posted themselves in the corridors and other areas near the emergency room.
+LJSpeech-1.1/wavs/LJ017-0217.wav|Navigation and discipline could not be easy with such a nondescript crew.
+LJSpeech-1.1/wavs/LJ017-0175.wav|The circumstances were strange. Mrs. Wilson had gone to the chemist's for medicine,
+LJSpeech-1.1/wavs/LJ036-0045.wav|Mrs. Bledsoe told him, quote, I am not going to rent to you any more, end quote,
+LJSpeech-1.1/wavs/LJ002-0284.wav|The Borough Compter was in a disgraceful state to the last. The men's ward had an earth, or rather a mud, floor,
+LJSpeech-1.1/wavs/LJ018-0212.wav|The annals of fraudulent crime probably contain nothing
+LJSpeech-1.1/wavs/LJ014-0253.wav|there was no independent check upon him if he chose to tamper with it.
+LJSpeech-1.1/wavs/LJ017-0222.wav|hatched amongst them a plot to murder their officers and seize the ship.
+LJSpeech-1.1/wavs/LJ040-0051.wav|While there is doubt about how fully Oswald understood the doctrine which he so often espoused, it seems clear
+LJSpeech-1.1/wavs/LJ036-0008.wav|(one) positive identification of the killer by two eyewitnesses who saw the shooting
+LJSpeech-1.1/wavs/LJ008-0166.wav|The pieman stooped down to pick up his scattered stock, and some of the mob, not seeing what had happened, stumbled over him.
+LJSpeech-1.1/wavs/LJ019-0360.wav|This he might do on the representation of the inspector of prisons,
+LJSpeech-1.1/wavs/LJ048-0102.wav|In particular, the Commission believes that the motorcade route selected by Agent Lawson, upon the advice of Agent in Charge Sorrels
+LJSpeech-1.1/wavs/LJ047-0219.wav|Quote, Mr. Shanklin advised us, among other things,
+LJSpeech-1.1/wavs/LJ033-0135.wav|Location of Bag
+LJSpeech-1.1/wavs/LJ006-0262.wav|all of them instruments calculated to facilitate attempts at breaking out of prison,
+LJSpeech-1.1/wavs/LJ007-0149.wav|the jail, and the scaffold.
+LJSpeech-1.1/wavs/LJ044-0118.wav|He anticipated that the full disclosure of his defection would hinder him in, quote, the struggle for progress and freedom in the United States, end quote.
+LJSpeech-1.1/wavs/LJ014-0072.wav|and that during the whole time he was never in his right senses.
+LJSpeech-1.1/wavs/LJ036-0073.wav|A bus moving through heavy traffic on Elm from Murphy to Lamar was timed at four minutes.
+LJSpeech-1.1/wavs/LJ046-0149.wav|There are many actions, situations, and incidents that may indicate such potential danger. Some are specific, such as threats;
+LJSpeech-1.1/wavs/LJ011-0061.wav|Let this monster give his name; I am ready to fight him. I am still determined to put myself in the place of Mr. Fauntleroy.
+LJSpeech-1.1/wavs/LJ040-0003.wav|The evidence reviewed above identifies Lee Harvey Oswald as the assassin of President Kennedy and indicates that he acted alone in that event.
+LJSpeech-1.1/wavs/LJ047-0063.wav|Agent Hosty was told by Mrs. M. F. Tobias, a former landlady of the Oswalds at six oh two Elsbeth Street in Dallas,
+LJSpeech-1.1/wavs/LJ015-0268.wav|Instead of following an honorable profession, he preferred to turn his great natural talents and ready wits to the most nefarious practices.
+LJSpeech-1.1/wavs/LJ039-0016.wav|he finished reading a morning newspaper, quote, and put on a good suit. I saw that he took a pistol.
+LJSpeech-1.1/wavs/LJ037-0195.wav|The order was signed in ink by, quote,
+LJSpeech-1.1/wavs/LJ018-0338.wav|Greed in another form led the Stauntons to make away with Mrs. Patrick Staunton, murdering her with devilish cruelty by slow degrees.
+LJSpeech-1.1/wavs/LJ015-0184.wav|and he looked about him with wayward, furtive glances.
+LJSpeech-1.1/wavs/LJ040-0132.wav|with Puerto Ricans and Negroes and everything, end quote.
+LJSpeech-1.1/wavs/LJ002-0242.wav|to be held before the steward of the royal household, the knight marshal, and the steward of the court,
+LJSpeech-1.1/wavs/LJ042-0150.wav|Marina Oswald's judgment of her husband's state of mind may be substantiated by comparing material which he wrote in the Soviet Union
+LJSpeech-1.1/wavs/LJ044-0056.wav|was stamped on some literature that Oswald had in his possession at the time of his arrest in New Orleans,
+LJSpeech-1.1/wavs/LJ019-0014.wav|had embarked on a considerable expenditure in order to provide a model prison for general imitation.
+LJSpeech-1.1/wavs/LJ018-0059.wav|He saw Mr. Briggs' watch-chain, and followed him instantly into the carriage, determined to have it at all costs.
+LJSpeech-1.1/wavs/LJ036-0200.wav|the radio dispatcher on channel one ordered all downtown patrol squads to report to Elm and Houston, code three (emergency).
+LJSpeech-1.1/wavs/LJ007-0027.wav|Bad as were the various parts of the jail already dealt with,
+LJSpeech-1.1/wavs/LJ003-0030.wav|These subordinate chiefs were also rewarded out of the scanty prison rations.
+LJSpeech-1.1/wavs/LJ014-0268.wav|whither he was taken every morning in a smart brougham, they would have seen him alight from it in Cornhill,
+LJSpeech-1.1/wavs/LJ006-0119.wav|When the wardsman was a man of some education, with some knowledge of legal chicanery gained by personal experience, he might add considerably to his emoluments
+LJSpeech-1.1/wavs/LJ022-0075.wav|We are losing no time getting the government's vast work relief program underway,
+LJSpeech-1.1/wavs/LJ025-0066.wav|and his skepticism was the more justified since Ehrenberg in his elaborate and comprehensive work on the infusoria,
+LJSpeech-1.1/wavs/LJ004-0137.wav|and that it was accomplished by "sleeping edgewise."
+LJSpeech-1.1/wavs/LJ023-0079.wav|But since the rise of the modern movement for social and economic progress through legislation,
+LJSpeech-1.1/wavs/LJ007-0100.wav|These were the gentlemen concerned in the bribery case at Ipswich in eighteen thirty-five,
+LJSpeech-1.1/wavs/LJ048-0032.wav|so far as the FBI knew he had not shown any potential for violence.
+LJSpeech-1.1/wavs/LJ045-0049.wav|Lee would say that it would be better for me if I went to Russia. I did not know why. I did not know what he had in mind.
+LJSpeech-1.1/wavs/LJ035-0018.wav|end quote, After the third shot, he, quote, revved that motorcycle up, end quote,
+LJSpeech-1.1/wavs/LJ041-0174.wav|and had not intended any criticism of Oswald's political views which is the way in which, Thornley thought, Oswald took his remarks.
+LJSpeech-1.1/wavs/LJ016-0087.wav|Among the escapes still remembered was one in eighteen forty-nine, accomplished by a man who had been employed
+LJSpeech-1.1/wavs/LJ018-0322.wav|Nor were these two abortive efforts all that were planned.
+LJSpeech-1.1/wavs/LJ012-0186.wav|The crime was brought home to Thurtell by the confession of Hunt,
+LJSpeech-1.1/wavs/LJ004-0162.wav|It was "a scene of infernal passions and distresses," says Buxton, "which few have imagination sufficient to picture,
+LJSpeech-1.1/wavs/LJ036-0016.wav|Lee Harvey Oswald left the building approximately three minutes after the assassination.
+LJSpeech-1.1/wavs/LJ038-0227.wav|From Neely Street the Oswalds would have traveled downtown on the Beckley bus, across the Commerce Street viaduct
+LJSpeech-1.1/wavs/LJ006-0263.wav|and capable of becoming most dangerous weapons in the hands of desperate and determined men.
+LJSpeech-1.1/wavs/LJ030-0054.wav|Because the skies had cleared in Dallas, Lawson directed that the top not be used for the day's activities.
+LJSpeech-1.1/wavs/LJ018-0074.wav|Sattler probably misunderstood, and he declared that the police officer had broken faith with him, having, moreover, stated that
+LJSpeech-1.1/wavs/LJ048-0221.wav|In the early morning hours on November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ011-0211.wav|when indictments were preferred against both brothers "for having carried away Ellen Turner, spinster,
+LJSpeech-1.1/wavs/LJ044-0153.wav|While that first request, made according to Marina Oswald at her husband's insistence, specifically stated that Oswald was to remain in the United States,
+LJSpeech-1.1/wavs/LJ030-0015.wav|Late in the evening, the Presidential party flew to Fort Worth where they spent the night at the Texas Hotel.
+LJSpeech-1.1/wavs/LJ007-0165.wav|any awakening truth, salutary exhortation, or imperfect resolutions of amendment can take root or grow.
+LJSpeech-1.1/wavs/LJ043-0184.wav|to carry out a carefully planned killing of another human being and was willing to consummate such a purpose if he thought there was sufficient reason to do so.
+LJSpeech-1.1/wavs/LJ035-0159.wav|Mrs. Reid believes that she returned to her desk from the street about two minutes after the shooting.
+LJSpeech-1.1/wavs/LJ014-0215.wav|threw the constable on his back, and violently assaulted him by jumping on his chest and stomach,
+LJSpeech-1.1/wavs/LJ018-0220.wav|The old gentleman's signature to this deed of gift was a forgery,
+LJSpeech-1.1/wavs/LJ017-0054.wav|His features were not careworn, but rather set, and he looked older than his age.
+LJSpeech-1.1/wavs/LJ012-0030.wav|Thus, a watch was paid for as a watch, whether it was of gold or silver; a piece of linen as such, whether the stuff was coarse or fine.
+LJSpeech-1.1/wavs/LJ010-0286.wav|nine shillings, all in shillings, and no other coin.
+LJSpeech-1.1/wavs/LJ022-0137.wav|I now come, my friends, to a part of the remaining business before the Congress.
+LJSpeech-1.1/wavs/LJ007-0009.wav|They had tried, moreover,
+LJSpeech-1.1/wavs/LJ014-0197.wav|the police magistrate, that Marley was the man who had beaten him to death.
+LJSpeech-1.1/wavs/LJ039-0113.wav|as a, quote, rather poor shot, end quote.
+LJSpeech-1.1/wavs/LJ003-0082.wav|The middle yard, as far as its limits would permit, was appropriated to felons and transports. The wards here were generally very crowded.
+LJSpeech-1.1/wavs/LJ011-0038.wav|Fauntleroy meanwhile lay in Newgate, not herded with other condemned prisoners, as the custom was,
+LJSpeech-1.1/wavs/LJ002-0258.wav|Bad language, quarreling, throwing water over one another was forbidden on pain of fine and being sent to Coventry;
+LJSpeech-1.1/wavs/LJ036-0033.wav|asked for a transfer and got off at the same place in the middle of the block where the lady did.
+LJSpeech-1.1/wavs/LJ011-0116.wav|A society which had already been started against capital punishment
+LJSpeech-1.1/wavs/LJ044-0087.wav|Oswald was confronted with those facts at the beginning of the debate and was so thrown on the defensive by this that he was forced to state that
+LJSpeech-1.1/wavs/LJ009-0246.wav|Good people, do not be hurried; I am not, I can wait.
+LJSpeech-1.1/wavs/LJ021-0099.wav|in order that the functions of N.R.A. which have proved their worth may be made a part of the permanent machinery of government.
+LJSpeech-1.1/wavs/LJ035-0114.wav|Neither elevator could have been used by Oswald as a means of descent.
+LJSpeech-1.1/wavs/LJ030-0246.wav|Almost in the same moment in which he hit or pushed me, he vaulted over the back seat and sat on me.
+LJSpeech-1.1/wavs/LJ046-0173.wav|to advise the field office if the subject displays signs of increased danger or plans to leave his home area.
+LJSpeech-1.1/wavs/LJ022-0154.wav|I consider this legislation a positive recovery measure.
+LJSpeech-1.1/wavs/LJ045-0053.wav|Many times Oswald has recalled this matter to me and said that I am just waiting for an opportunity to hurt him.
+LJSpeech-1.1/wavs/LJ022-0082.wav|In all cases projects must be of a character to give employment to those on the relief rolls.
+LJSpeech-1.1/wavs/LJ028-0099.wav|the first-born son of Nabopolassar, King of Babylon, am I.
+LJSpeech-1.1/wavs/LJ045-0116.wav|While the facts of his defection had become known in New Orleans as a result of his radio debate with Bringuier,
+LJSpeech-1.1/wavs/LJ048-0071.wav|Most important, notwithstanding that both agencies have professed to the Commission that the liaison between them was close and fully sufficient,
+LJSpeech-1.1/wavs/LJ028-0033.wav|There is a Hebrew tradition that it was the oldest of all cities, but now we know that great empires flourished and passed away before Babylon was built.
+LJSpeech-1.1/wavs/LJ025-0016.wav|and justify biologists in regarding them as representative expressions of the most extensive, if not the profoundest, knowledge of his time.
+LJSpeech-1.1/wavs/LJ011-0123.wav|He forged the names of people who held stock on the Bank books, and got the value of the stock;
+LJSpeech-1.1/wavs/LJ019-0255.wav|Great numbers were not lighted at night, and were without means by which their inmates could communicate, in case of urgent necessity, with their keepers.
+LJSpeech-1.1/wavs/LJ042-0241.wav|Soon after his arrival he wrote to the Soviet Embassy in Washington
+LJSpeech-1.1/wavs/LJ041-0013.wav|Another time, he fought with two brothers who claimed that he had picked on the younger of them, three years Oswald's junior.
+LJSpeech-1.1/wavs/LJ040-0169.wav|She observed that since Lee's mother worked all day, he made his own meals and spent all his time alone
+LJSpeech-1.1/wavs/LJ047-0007.wav|It had interviewed him twice shortly after his return to the United States, again a year later at his request
+LJSpeech-1.1/wavs/LJ003-0059.wav|The sleeping accommodation in the chapel-yard wards, and indeed throughout the prison, consisted of a barrack bed,
+LJSpeech-1.1/wavs/LJ017-0167.wav|Smethurst was found guilty by the jury, and sentenced to death.
+LJSpeech-1.1/wavs/LJ040-0211.wav|This would make him approximately ten, well, almost eleven years old. End quote.
+LJSpeech-1.1/wavs/LJ011-0009.wav|Nothing more was heard of the affair, although the lady declared that she had never instructed Fauntleroy to sell.
+LJSpeech-1.1/wavs/LJ006-0064.wav|In the master's side yard, which had only one washing place, as many as seventy-eight prisoners, frequently more,
+LJSpeech-1.1/wavs/LJ030-0142.wav|Governor Connally sustained bullet wounds in his back, the right side of his chest, right wrist, and left thigh.
+LJSpeech-1.1/wavs/LJ049-0102.wav|the President, Vice President, or other officer next in the order of succession to the Office of President, the President-elect and the Vice-President-elect,
+LJSpeech-1.1/wavs/LJ028-0224.wav|I quieted their sighings and soothed their sorrows.
+LJSpeech-1.1/wavs/LJ005-0088.wav|Every prison containing female prisoners was to have a matron who was to reside constantly in the prison.
+LJSpeech-1.1/wavs/LJ048-0002.wav|Chapter eight. The Protection of the President. Part three.
+LJSpeech-1.1/wavs/LJ036-0130.wav|Whaley described the ensuing events as follows, quote,
+LJSpeech-1.1/wavs/LJ036-0038.wav|and who, during the ride south on Marsalis, had an argument with a woman passenger.
+LJSpeech-1.1/wavs/LJ012-0034.wav|diamonds refaced; all marks by which other articles might be identified, the selvages of linen, the stamps on shoes,
+LJSpeech-1.1/wavs/LJ004-0158.wav|All were in ill health; almost all were in rags; almost all were filthy in the extreme.
+LJSpeech-1.1/wavs/LJ017-0194.wav|that immediately afterwards Mrs. Soames was taken ill with vomiting and purging,
+LJSpeech-1.1/wavs/LJ018-0302.wav|For these cash was obtained in notes; the notes were exchanged by one of the conspirators for gold at the Bank of England, and again the same day
+LJSpeech-1.1/wavs/LJ042-0201.wav|He wrote, quote, The Communist Party of the United States has betrayed itself!
+LJSpeech-1.1/wavs/LJ003-0089.wav|Occasionally the transports made themselves so useful in the jail that they were passed over.
+LJSpeech-1.1/wavs/LJ015-0114.wav|Thence he was transferred to Copenhagen and surrendered in due course to a London police officer.
+LJSpeech-1.1/wavs/LJ035-0056.wav|Meanwhile, Truly had run up several steps toward the third floor.
+LJSpeech-1.1/wavs/LJ026-0076.wav|The green plant owes its power of absorbing the energy of sunlight
+LJSpeech-1.1/wavs/LJ037-0241.wav|The suspect last seen running west on Jefferson from four hundred East Jefferson.
+LJSpeech-1.1/wavs/LJ002-0241.wav|The court of the Marshalsea was instituted by Charles the first in the sixth year of his reign,
+LJSpeech-1.1/wavs/LJ021-0076.wav|could not be brought about in one year.
+LJSpeech-1.1/wavs/LJ014-0281.wav|which he had paid into his own bankers, and on this he was committed to Newgate for trial.
+LJSpeech-1.1/wavs/LJ033-0010.wav|the presence of a long handmade brown paper bag near the point from which the shots were fired, and (five) the palmprint,
+LJSpeech-1.1/wavs/LJ050-0208.wav|salaries are below those of the FBI and leading municipal police forces.
+LJSpeech-1.1/wavs/LJ022-0055.wav|the second is to establish the practical means to help those who are unemployed in this present emergency.
+LJSpeech-1.1/wavs/LJ004-0047.wav|One of the moving spirits was the Honorable H. G. Bennet, M.P., whose vigorous protests against the lamentable condition of Newgate have already been recorded.
+LJSpeech-1.1/wavs/LJ018-0393.wav|Messrs. Swindlehurst, Saffery, and Langley cleared a large profit
+LJSpeech-1.1/wavs/LJ049-0218.wav|between the Secret Service and the President and his family is contemplated.
+LJSpeech-1.1/wavs/LJ050-0218.wav|Chief Rowley explained that this would not provide enough additional manpower to take all the measures which he considers required.
+LJSpeech-1.1/wavs/LJ030-0023.wav|Arrival at Love Field
+LJSpeech-1.1/wavs/LJ003-0276.wav|Although attempted partially at Bridewell, and more systematically at the new Millbank penitentiary,
+LJSpeech-1.1/wavs/LJ007-0039.wav|and when the inspectors visited Newgate they found the three certain to die in a day-room by themselves;
+LJSpeech-1.1/wavs/LJ016-0190.wav|"I have no idea who the sheriffs will get to do the work after me," said Foxen, adding that his assistant, Tom Cheshire,
+LJSpeech-1.1/wavs/LJ033-0089.wav|After entering the car, Frazier glanced over his shoulder and noticed a brown paper package on the back seat.
+LJSpeech-1.1/wavs/LJ037-0217.wav|Police found an empty revolver holster when they searched Oswald's room on Beckley Avenue after his arrest.
+LJSpeech-1.1/wavs/LJ018-0372.wav|mutinied, murdered the captain and mates, sparing the steward only on condition that he would navigate the ship to the Mediterranean.
+LJSpeech-1.1/wavs/LJ002-0132.wav|An elaborate machinery planned for the protection of the trader, and altogether on his side, had long existed for the recovery of debts.
+LJSpeech-1.1/wavs/LJ033-0050.wav|Marina Oswald testified that it was between nine and ten p.m.
+LJSpeech-1.1/wavs/LJ016-0355.wav|Mackay's fortitude, which had been great,
+LJSpeech-1.1/wavs/LJ007-0022.wav|After locking-up time, which varied, as on the male side, according to the daylight, the scenes were often riotous and disgraceful.
+LJSpeech-1.1/wavs/LJ014-0144.wav|She uttered loud imprecations, cursing judge, jury, barristers, witnesses, and all who stood around.
+LJSpeech-1.1/wavs/LJ044-0039.wav|they said something about remodeling, etc. I'm sure you understand, end quote.
+LJSpeech-1.1/wavs/LJ038-0259.wav|The oral report was negative because of the battered condition of the bullet.
+LJSpeech-1.1/wavs/LJ032-0202.wav|Two pictures were taken. The Commission has concluded that the rifle shown in these pictures is the same rifle which was found on the sixth floor of the Depository Building
+LJSpeech-1.1/wavs/LJ028-0246.wav|But, as it was, the Persians came upon them by surprise and so took the city.
+LJSpeech-1.1/wavs/LJ041-0106.wav|At the court-martial hearing which followed, Oswald admitted that he had been rather drunk when the incident occurred.
+LJSpeech-1.1/wavs/LJ044-0008.wav|and into his possible motivation for the assassination.
+LJSpeech-1.1/wavs/LJ018-0073.wav|This reply of his contained no promise of immediate release.
+LJSpeech-1.1/wavs/LJ045-0050.wav|He said he loved me but that it would be better for me if I went to Russia, and what he had in mind I don't know. End quote.
+LJSpeech-1.1/wavs/LJ027-0024.wav|is the theory of descent -- i.e., that similarities are due to blood relationship and that differences come from adaptive modifications.
+LJSpeech-1.1/wavs/LJ024-0074.wav|instead of leaving the composition of our federal courts, including the highest,
+LJSpeech-1.1/wavs/LJ018-0125.wav|Burnett had only just come out of jail after completing a sentence of penal servitude.
+LJSpeech-1.1/wavs/LJ007-0091.wav|The lunatic became the sport of the idle and the depraved. His cure was out of the question;
+LJSpeech-1.1/wavs/LJ026-0116.wav|The foods containing only the elements carbon, hydrogen and oxygen (fats and carbohydrates)
+LJSpeech-1.1/wavs/LJ014-0325.wav|Goods would be landed at this wharf;
+LJSpeech-1.1/wavs/LJ013-0025.wav|But another ship, concluding that he was acting in ignorance, rendered him assistance.
+LJSpeech-1.1/wavs/LJ020-0099.wav|When habited for the day in all except the outer gown, collar, etc., slip on the wrapper again and run down to put the biscuits in the oven.
+LJSpeech-1.1/wavs/LJ005-0216.wav|But it was pointed out in eighteen twenty-three that defective construction must always bar the way to any radical improvement in Newgate.
+LJSpeech-1.1/wavs/LJ022-0134.wav|Neither you nor I want criticism conceived in a purely fault-finding or partisan spirit,
+LJSpeech-1.1/wavs/LJ038-0141.wav|therefore, he made his weekly visit on Thursday night.
+LJSpeech-1.1/wavs/LJ009-0188.wav|One was attached to Hicks' Hall, the Clerkenwell Sessions House, built out of monies provided by Sir Baptist Hicks, a wealthy alderman of the reign of James the first
+LJSpeech-1.1/wavs/LJ013-0187.wav|near it was his Waterloo medal, and the above-mentioned ten-pound note.
+LJSpeech-1.1/wavs/LJ036-0217.wav|As Tippit reached the left front wheel the man pulled out a revolver and fired several shots.
+LJSpeech-1.1/wavs/LJ009-0288.wav|Calcraft, of whom mention has just been made, was by trade a lady's shoemaker,
+LJSpeech-1.1/wavs/LJ013-0254.wav|and evidence was given of close relation between Good and a third woman, a girl named Butcher, residing at Woolwich.
+LJSpeech-1.1/wavs/LJ008-0170.wav|the man, needing all his care for his own life, threw the child from him,
+LJSpeech-1.1/wavs/LJ009-0132.wav|Although this misapplication of religious services still went on,
+LJSpeech-1.1/wavs/LJ016-0123.wav|got hold of the step ladder used in lighting the gas, and which under our more careful supervision would have been, as now-a-days, chained up.
+LJSpeech-1.1/wavs/LJ019-0211.wav|It was urged that the area selected for purchase must be excessively costly to acquire, and still quite inadequate for the city needs.
+LJSpeech-1.1/wavs/LJ003-0092.wav|There were frequently in the middle yard seven or eight children, the youngest barely nine,
+LJSpeech-1.1/wavs/LJ011-0057.wav|urging that Fauntleroy was a father, a citizen: "his life is useful, mine a burthen, to the State."
+LJSpeech-1.1/wavs/LJ019-0158.wav|Later on a more efficacious but still imperfect method of supervision was introduced. Iron cages, which are still to be seen in Newgate,
+LJSpeech-1.1/wavs/LJ017-0004.wav|I have attempted to draw of crime in connection with Newgate, from first to last, I must make some mention, in this my penultimate chapter,
+LJSpeech-1.1/wavs/LJ044-0034.wav|and of his need to present himself to others as well as to himself in a light more favorable than was justified by reality.
+LJSpeech-1.1/wavs/LJ034-0073.wav|Givens testified that after reaching the first floor, quote,
+LJSpeech-1.1/wavs/LJ050-0241.wav|The FBI has indicated that it is willing to continue to make such assistance available,
+LJSpeech-1.1/wavs/LJ002-0144.wav|and when once appointed continued to serve sine die; they were generally near in rank to the parties whose causes they decided.
+LJSpeech-1.1/wavs/LJ018-0375.wav|The 'Lennie's' men were all Greeks, except one known as French Peter,
+LJSpeech-1.1/wavs/LJ011-0263.wav|It was perpetrated upon a respectable country solicitor,
+LJSpeech-1.1/wavs/LJ014-0190.wav|Marley, disturbed, picked up a cigar and parcel from the counter, then ran out, pursued by Lerigo only.
+LJSpeech-1.1/wavs/LJ014-0133.wav|She had not been treated like a Christian, but like a wild beast of the forest. She declared that the money found in her possession had been sent her from abroad;
+LJSpeech-1.1/wavs/LJ018-0247.wav|William Roupell himself was brought as a principal witness to clench the case by a confession altogether against himself.
+LJSpeech-1.1/wavs/LJ039-0216.wav|It should be noted, however, that the President's car was curving slightly to the right when the third shot was fired.
+LJSpeech-1.1/wavs/LJ005-0156.wav|in others meat, soup, gruel, beer were given.
+LJSpeech-1.1/wavs/LJ048-0026.wav|He gave evidence of settling down.
+LJSpeech-1.1/wavs/LJ048-0189.wav|the Dallas police to scan windows of buildings lining the motorcade route, although it was his usual practice to do so.
+LJSpeech-1.1/wavs/LJ018-0100.wav|took to appropriating the bills intrusted to him, and so lost his business, after which he became a clerk to Messrs. Wagner and Bateman.
+LJSpeech-1.1/wavs/LJ030-0137.wav|The Assassination
+LJSpeech-1.1/wavs/LJ019-0164.wav|the personal superintendence of night officers, as already described, became possible.
+LJSpeech-1.1/wavs/LJ046-0160.wav|A case file was established if the information available suggested that the subject might be a danger to the President.
+LJSpeech-1.1/wavs/LJ009-0186.wav|Pennant speaks of Surgeons' Hall as a handsome building, ornamented with Ionic pilasters, and with a double flight of steps to the first floor.
+LJSpeech-1.1/wavs/LJ038-0119.wav|The arresting officers found a forged selective service card with a picture of Oswald and the name "Alek J. Hidell"
+LJSpeech-1.1/wavs/LJ012-0112.wav|notwithstanding a minute search through the room they had occupied.
+LJSpeech-1.1/wavs/LJ047-0054.wav|This is an administrative classification indicating that no further work has been scheduled.
+LJSpeech-1.1/wavs/LJ002-0010.wav|these numbers had risen to two hundred seventy-five and three hundred seventy-five respectively, or six hundred fifty in all.
+LJSpeech-1.1/wavs/LJ027-0032.wav|Internal organs may persist unchanged and hence they offer good guides to classification.
+LJSpeech-1.1/wavs/LJ003-0190.wav|Except in murder cases, where the execution was generally very promptly performed,
+LJSpeech-1.1/wavs/LJ032-0148.wav|fingerprint expert with the New York City Police Department, conducted an independent examination and also determined that this was the right palmprint of Oswald.
+LJSpeech-1.1/wavs/LJ043-0024.wav|Even though it appears that they may have left Oswald a few days before, it seems that he resisted the move as best he could.
+LJSpeech-1.1/wavs/LJ031-0091.wav|You have to determine which things, which are immediately life threatening and cope with them, before attempting to evaluate the full extent of the injuries.
+LJSpeech-1.1/wavs/LJ044-0074.wav|an agent of Bringuier's attempting to learn more about the true nature
+LJSpeech-1.1/wavs/LJ020-0008.wav|One tablespoonful of butter.
+LJSpeech-1.1/wavs/LJ028-0201.wav|"Thou art weighed in the balances and art found wanting."
+LJSpeech-1.1/wavs/LJ016-0140.wav|No report of it was made public, as the man was almost immediately recaptured.
+LJSpeech-1.1/wavs/LJ030-0016.wav|On the morning of November twenty-two, President Kennedy attended a breakfast at the hotel and afterward addressed a crowd at an open parking lot.
+LJSpeech-1.1/wavs/LJ014-0118.wav|Mrs. Manning was transferred to London and lodged in the Horsemonger Lane Jail, where her husband soon afterwards joined her.
+LJSpeech-1.1/wavs/LJ006-0098.wav|There was some provision of clothing, but it was quite insufficient, and nothing at all was given if prisoners had enough of their own to cover their nakedness.
+LJSpeech-1.1/wavs/LJ007-0195.wav|to give better light and ventilation; the construction of bed-places, three tiers high alongside the walls for males, two tiers for females;
+LJSpeech-1.1/wavs/LJ046-0112.wav|and requests to other agencies, Federal and local, for useful information.
+LJSpeech-1.1/wavs/LJ018-0021.wav|The facts of the murder and its object, robbery, were thus conclusively proved.
+LJSpeech-1.1/wavs/LJ023-0004.wav|For the many messages which have come to me after that speech, and which it is physically impossible to answer individually,
+LJSpeech-1.1/wavs/LJ016-0073.wav|Mr. Cope, the governor of Newgate, having been communicated with, proceeded to Winchester, where he at once identified Williams.
+LJSpeech-1.1/wavs/LJ028-0388.wav|The old walls were restored to a height sufficient to prevent the escape of the animals, and among the ruins the kings enjoyed their favorite sport.
+LJSpeech-1.1/wavs/LJ041-0184.wav|who might, quote, lead an expedition to some of these other islands and free them too, end quote.
+LJSpeech-1.1/wavs/LJ029-0093.wav|he was told that forty-five minutes had been allotted for a motorcade procession from Love Field to the luncheon site.
+LJSpeech-1.1/wavs/LJ014-0088.wav|This was on a Thursday evening.
+LJSpeech-1.1/wavs/LJ049-0082.wav|Had there been reason to believe that the assassination was the result of a conspiracy, Federal jurisdiction could have been asserted;
+LJSpeech-1.1/wavs/LJ023-0113.wav|reached the point as a nation where we must take action to save the Constitution from the Court and the Court from itself.
+LJSpeech-1.1/wavs/LJ021-0202.wav|the equally untenable theory that it is an interference with liberty to offer reasonable help when private enterprise is in need of help.
+LJSpeech-1.1/wavs/LJ019-0108.wav|The prisoners were to cultivate the land and raise sufficient produce for their own support.
+LJSpeech-1.1/wavs/LJ003-0014.wav|Quarreling among the debtors was not unfrequent. Blows were struck, and fights often ensued.
+LJSpeech-1.1/wavs/LJ012-0040.wav|at the price Solomons had paid for it, and it cost him about a hundred pounds to re-stock his shop with his own goods.
+LJSpeech-1.1/wavs/LJ040-0045.wav|In the Marines, he evidenced a strong conviction as to the correctness of Marxist doctrine,
+LJSpeech-1.1/wavs/LJ002-0235.wav|were places set apart for skittles, fives, and tennis, which strangers frequented as any other place of public amusement.
+LJSpeech-1.1/wavs/LJ012-0135.wav|The police were at once employed, and after infinite pains they discovered the person, one Moss, who had acted as the messenger.
+LJSpeech-1.1/wavs/LJ010-0209.wav|Louis and Amadeus among the captains; and Hercules, Neptune, and Mars among the lieutenants of the association.
+LJSpeech-1.1/wavs/LJ008-0176.wav|then the mob began to thin, and the streets were cleared by the city marshals and a number of constables.
+LJSpeech-1.1/wavs/LJ017-0032.wav|The first intimation conveyed to society of the new terror which threatened it was in the arrest and arraignment of William Palmer,
+LJSpeech-1.1/wavs/LJ044-0085.wav|The basic facts of the event were uncovered independently by William Stuckey, who arranged the debate, and Edward Butler,
+LJSpeech-1.1/wavs/LJ037-0059.wav|as the man she saw kill Officer Tippit. In evaluating Mrs. Markham's identification of Oswald, the Commission considered certain allegations
+LJSpeech-1.1/wavs/LJ041-0153.wav|at least in the unit to which he was assigned after his return from the Far East.
+LJSpeech-1.1/wavs/LJ039-0205.wav|five point six, and six point five seconds.
+LJSpeech-1.1/wavs/LJ023-0051.wav|It is the American people themselves who are in the driver's seat.
+LJSpeech-1.1/wavs/LJ008-0256.wav|and with blanched cheek, and sinking, we turned away from the scene.
+LJSpeech-1.1/wavs/LJ014-0005.wav|but too late to give substantial aid.
+LJSpeech-1.1/wavs/LJ001-0182.wav|the books so ornamented are amongst the most delightful works of art that have ever been produced.
+LJSpeech-1.1/wavs/LJ041-0032.wav|Oswald also became interested in the New Orleans Amateur Astronomy Association, an organization of high school students.
+LJSpeech-1.1/wavs/LJ022-0085.wav|I have set up a Division of Applications and Information
+LJSpeech-1.1/wavs/LJ044-0212.wav|It also dealt with the fear of the Castro regime that such a policy might result in its abandonment by the Soviet Union.
+LJSpeech-1.1/wavs/LJ014-0245.wav|It was the custom in this office to make the banker's passbook the basis of the entries in the company's ledgers.
+LJSpeech-1.1/wavs/LJ027-0007.wav|There are numberless similarities and correlations and surprising uniformities.
+LJSpeech-1.1/wavs/LJ009-0152.wav|The crowd outside Newgate on the day of execution has already been described; but there was also a select gathering of distinguished visitors within the jail.
+LJSpeech-1.1/wavs/LJ014-0037.wav|Soon afterwards Hocker heard cries of "murder,"
+LJSpeech-1.1/wavs/LJ035-0204.wav|Oswald was seen in the vicinity of the southeast corner of the sixth floor approximately thirty-five minutes before the assassination
+LJSpeech-1.1/wavs/LJ015-0302.wav|who was at length taken in a coffee-shop near Oxford Street, under the name of Hopkins. He resisted at first, and denied his identity,
+LJSpeech-1.1/wavs/LJ035-0028.wav|to the northwest corner of the floor where Truly hoped to find one of the two freight elevators.
+LJSpeech-1.1/wavs/LJ003-0133.wav|prevented me from going inside but seldom, and I used to communicate with him through the bars from the passage. End quote.
+LJSpeech-1.1/wavs/LJ025-0080.wav|Finally amid the lowest forms of animal life the speck of gelatinous protoplasm, which constitutes the whole body,
+LJSpeech-1.1/wavs/LJ050-0109.wav|Since the assassination, the Service has recognized that these relationships must be far more formal
+LJSpeech-1.1/wavs/LJ033-0177.wav|On December one, nineteen sixty-three, a replica bag was made from materials found on that date in the shipping room.
+LJSpeech-1.1/wavs/LJ036-0022.wav|the transfer was conclusively identified as having been issued by Cecil J. McWatters, a busdriver for the Dallas Transit Co.
+LJSpeech-1.1/wavs/LJ006-0159.wav|The prisoners declared that they did not see him oftener than twice a week;
+LJSpeech-1.1/wavs/LJ030-0037.wav|are designed to provide protection while permitting large numbers of people to see the President.
+LJSpeech-1.1/wavs/LJ050-0275.wav|materially improve upon the procedures in effect at the time of President Kennedy's assassination and result in a substantial lessening of the danger.
+LJSpeech-1.1/wavs/LJ035-0158.wav|A blue jacket, later identified by Marina Oswald as her husband's, was subsequently found in the building, apparently left behind by Oswald.
+LJSpeech-1.1/wavs/LJ046-0068.wav|It helped him in his job here, he was able to come back here with a fresh view of many things.
+LJSpeech-1.1/wavs/LJ048-0208.wav|Agent Sorrels, riding in the lead car, did observe the Texas School Book Depository Building as he passed by,
+LJSpeech-1.1/wavs/LJ038-0016.wav|heard police sirens and then saw a man as he, quote, ducked into, end quote, the outer lobby space of the theatre near the ticket office.
+LJSpeech-1.1/wavs/LJ015-0026.wav|Moreover, the partners were sober, steady men, who paid unremitting attention to business.
+LJSpeech-1.1/wavs/LJ013-0170.wav|the fresh marks of violence upon the door had evidently been made inside, and not from outside;
+LJSpeech-1.1/wavs/LJ047-0104.wav|reluctant and actually as far as I was concerned, was completely evasive on them. End quote.
+LJSpeech-1.1/wavs/LJ021-0034.wav|Closely following this great constructive effort we have, through various federal agencies,
+LJSpeech-1.1/wavs/LJ015-0109.wav|and he discussed the latter with appetite, but begged that they would never give him curry again, as he did not like it.
+LJSpeech-1.1/wavs/LJ014-0337.wav|wishing to verify the value of warrants they held, and sending to Maltby and Co.'s wharf, found out half the truth.
+LJSpeech-1.1/wavs/LJ021-0122.wav|has been far less than in any previous, comparable period.
+LJSpeech-1.1/wavs/LJ036-0196.wav|Tippit patroled district Number seventy-eight in the Oak Cliff area of Dallas during daylight hours.
+LJSpeech-1.1/wavs/LJ042-0036.wav|that that government supported an economic system, quote, which exploits all its workers, end quote,
+LJSpeech-1.1/wavs/LJ050-0239.wav|From February eleven through June thirty, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ005-0100.wav|For this purpose it kept up an extensive correspondence with all parts of the kingdom, and circulated queries to be answered in detail,
+LJSpeech-1.1/wavs/LJ002-0332.wav|Sometimes rugs were urgently required and not forthcoming;
+LJSpeech-1.1/wavs/LJ016-0002.wav|The old notion always prevailed that Newgate was impregnable, so to speak, from within,
+LJSpeech-1.1/wavs/LJ021-0136.wav|We have passed through more than a year of education.
+LJSpeech-1.1/wavs/LJ035-0090.wav|possible delayed reaction to the shot, jostling with the crowd of people on the steps and scanning the area along Elm Street and the parkway.
+LJSpeech-1.1/wavs/LJ030-0065.wav|Governor Connally occupied the right jump seat, Mrs. Connally the left.
+LJSpeech-1.1/wavs/LJ032-0173.wav|The testimony of those who saw him after the assassination was inconclusive about the color of Oswald's shirt,
+LJSpeech-1.1/wavs/LJ028-0196.wav|Every child knows the story of "the writing of the hand on the wall." It was the night that Babylon fell.
+LJSpeech-1.1/wavs/LJ048-0183.wav|Admittedly, protective measures cannot ordinarily be taken with regard to all buildings along a motorcade route.
+LJSpeech-1.1/wavs/LJ032-0171.wav|and that this was the same shirt which Oswald wore on the morning of the assassination.
+LJSpeech-1.1/wavs/LJ015-0078.wav|who preferred to risk his future reputation and liberty to the present discomfort of living upon narrow means.
+LJSpeech-1.1/wavs/LJ015-0138.wav|His fault was generosity,
+LJSpeech-1.1/wavs/LJ037-0204.wav|was shipped on March twenty, nineteen sixty-three, to A. J. Hidell, Post Office Box two nine one five, Dallas, Texas.
+LJSpeech-1.1/wavs/LJ044-0093.wav|and was extremely believable before this. We thought the fellow could probably get quite a few members if he was really indeed serious about getting members.
+LJSpeech-1.1/wavs/LJ002-0257.wav|and a master of the ale-room, who kept this the scene of their revels clean, and saw that boiling water was provided for grog.
+LJSpeech-1.1/wavs/LJ046-0221.wav|where it will be made available to Secret Service headquarters in Washington.
+LJSpeech-1.1/wavs/LJ045-0069.wav|Katherine Ford, with whom Marina Oswald stayed during her separation from her husband in November of nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ003-0070.wav|was frequently adjudged for so-called libels, or too out-spoken comments in print.
+LJSpeech-1.1/wavs/LJ045-0074.wav|In the letter Marina Oswald stated that her husband had changed a great deal and that she was very lonely in the United States.
+LJSpeech-1.1/wavs/LJ045-0149.wav|She said, quote,
+LJSpeech-1.1/wavs/LJ012-0009.wav|It turned out that he was really married to Mrs. Canning,
+LJSpeech-1.1/wavs/LJ001-0024.wav|But the first Bible actually dated (which also was printed at Maintz by Peter Schoeffer in the year fourteen sixty-two)
+LJSpeech-1.1/wavs/LJ008-0068.wav|But the fall apart and inwards of two leaves is considered superior.
+LJSpeech-1.1/wavs/LJ002-0143.wav|The commissioners who presided were, quote, little otherwise than self-elected
+LJSpeech-1.1/wavs/LJ028-0489.wav|Had the enemy of Babylon succeeded in breaking through the outer and inner defenses of the city the royal palace would have still been far from his reach.
+LJSpeech-1.1/wavs/LJ018-0102.wav|Besides his employers, a jeweler named Humphreys was in the "swim," at whose shop in Red Lion Square was discovered a quantity of base gold
+LJSpeech-1.1/wavs/LJ018-0038.wav|and Müller at the time of his capture was actually wearing Mr. Briggs' hat, cut down and somewhat altered.
+LJSpeech-1.1/wavs/LJ002-0064.wav|directly under the chapel, in which there were three cells, used either for the confinement of disorderly and refractory prisoners,
+LJSpeech-1.1/wavs/LJ040-0037.wav|His wife testified that he compared himself with great readers of history.
+LJSpeech-1.1/wavs/LJ032-0139.wav|Day, on the other hand, believed that sufficient traces of the print had been left on the rifle barrel,
+LJSpeech-1.1/wavs/LJ012-0176.wav|To obtain possession of this, Thurtell with his two associates resolved to kill him.
+LJSpeech-1.1/wavs/LJ016-0300.wav|Such too the scene remained with little change or respite till the old hangman (Calcraft) slunk again along the drop,
+LJSpeech-1.1/wavs/LJ014-0227.wav|One of these was Mobbs, who lived in the Minories,
+LJSpeech-1.1/wavs/LJ018-0270.wav|The assistant called with the jewels on approbation at a house specially hired for the purpose in the West End,
+LJSpeech-1.1/wavs/LJ025-0151.wav|And it must be allowed to be possible that further research may reveal the existence of something comparable to a nervous system in plants.
+LJSpeech-1.1/wavs/LJ015-0013.wav|He was found guilty, and sentenced to the seemingly inadequate punishment of four years' transportation.
+LJSpeech-1.1/wavs/LJ035-0171.wav|Barnett ran to the building, noted its name, and then returned to the corner.
+LJSpeech-1.1/wavs/LJ008-0178.wav|the bodies for identification, the wounded to hospitals, a cart-load of shoes, hats, petticoats, and fragments of wearing apparel were picked up.
+LJSpeech-1.1/wavs/LJ006-0234.wav|More cruel injuries were common enough, which did not result from honest hand-to-hand fights.
+LJSpeech-1.1/wavs/LJ023-0137.wav|Congress has provided financial security
+LJSpeech-1.1/wavs/LJ003-0018.wav|and discipline maintained by a system open to grave abuses, and which had the prescription of long usage,
+LJSpeech-1.1/wavs/LJ002-0102.wav|near it a grating through which the debtors receive their beer from the neighboring public-houses.
+LJSpeech-1.1/wavs/LJ043-0022.wav|it appears that De Mohrenschildt and his wife actually went to Oswald's apartment early in November of nineteen sixty-two
+LJSpeech-1.1/wavs/LJ008-0269.wav|Already the severity of our criminal code, and the number of capital felonies upon the statute book, had brought a reaction;
+LJSpeech-1.1/wavs/LJ019-0258.wav|Both, however, varied greatly in mechanism and in the amount of energy they called forth,
+LJSpeech-1.1/wavs/LJ048-0261.wav|a. Employees are strictly enjoined to refrain from the use of intoxicating liquor
+LJSpeech-1.1/wavs/LJ007-0029.wav|The condition of the capitally-convicted prisoners after sentence was still very disgraceful. The side they occupied, still known as the press-yard,
+LJSpeech-1.1/wavs/LJ028-0211.wav|He appointed Gobrias governor of Babylon.
+LJSpeech-1.1/wavs/LJ045-0041.wav|Oswald's diary reports that he married his wife shortly after his proposal of marriage to another girl had been rejected.
+LJSpeech-1.1/wavs/LJ006-0171.wav|He was satisfied to let matters run on as in the old times, he said in his own justification; with him what was, was right,
+LJSpeech-1.1/wavs/LJ008-0230.wav|fell with a strange unnatural sound upon the ear
+LJSpeech-1.1/wavs/LJ016-0194.wav|Calcraft thought no more of what had occurred till the next sessions at the Old Bailey, when the sheriffs sent for him,
+LJSpeech-1.1/wavs/LJ045-0019.wav|Could you advise me as to the general view we have on the American Civil Liberties Union? End quote.
+LJSpeech-1.1/wavs/LJ007-0030.wav|consisted of two dozen rooms and fifteen cells. In these various chambers, until just before the inspectors made their report,
+LJSpeech-1.1/wavs/LJ034-0095.wav|When the shots were fired, Brennan was in an excellent position to observe anyone in the window. He was sitting on a concrete wall
+LJSpeech-1.1/wavs/LJ027-0074.wav|Sometimes a piece is enlarged, sometimes diminished, or even becomes obsolete; sometimes several pieces are consolidated into one;
+LJSpeech-1.1/wavs/LJ020-0107.wav|The grand secret of keeping house well and without worry, lies in the art of packing and fitting different kinds of work and in picking up the minutes.
+LJSpeech-1.1/wavs/LJ049-0004.wav|The motorcade in Dallas.
+LJSpeech-1.1/wavs/LJ007-0164.wav|forbid the faintest shadow of a hope that in a soil so unfavorable for moral culture
+LJSpeech-1.1/wavs/LJ033-0111.wav|that the bag which Oswald was carrying was approximately twenty-seven or twenty-eight inches long, whereas the wooden stock of the rifle, which is its largest component,
+LJSpeech-1.1/wavs/LJ044-0213.wav|The October seven, nineteen sixty-three, issue of the Militant
+LJSpeech-1.1/wavs/LJ037-0074.wav|and her positive identification of Oswald at a police lineup, the Commission considers her testimony reliable.
+LJSpeech-1.1/wavs/LJ028-0316.wav|Then the doorkeepers, when they heard this, carried him at once before the Magistrates.
+LJSpeech-1.1/wavs/LJ021-0192.wav|We are not frightened by reactionary lawyers or political editors.
+LJSpeech-1.1/wavs/LJ016-0132.wav|he was still untried, but would almost certainly have escaped with a minor penalty, say of three or four months' imprisonment.
+LJSpeech-1.1/wavs/LJ016-0328.wav|Nevertheless, in the very next session
+LJSpeech-1.1/wavs/LJ032-0271.wav|(two) Oswald's palmprint was on the rifle in a position which shows that he had handled it while it was disassembled,
+LJSpeech-1.1/wavs/LJ009-0226.wav|gaming-tables were set up, cards were played under the gibbet, to the disturbance of the public peace and the annoyance of all decent people.
+LJSpeech-1.1/wavs/LJ003-0238.wav|and they were exacted to relieve a rich corporation from paying for the maintenance of their own prison.
+LJSpeech-1.1/wavs/LJ044-0176.wav|He went almost directly to the Cuban Embassy and applied for a visa to Cuba in transit to Russia.
+LJSpeech-1.1/wavs/LJ012-0141.wav|Moss presently turned approver,
+LJSpeech-1.1/wavs/LJ030-0130.wav|The spectators were still thickly congregated in front of the buildings which lined the east side of Houston Street, but the crowd thinned abruptly along Elm Street,
+LJSpeech-1.1/wavs/LJ015-0162.wav|Just after Robson's frauds had agitated the minds of all directors of companies, the chairman of the Great Northern (Mr. Denison)
+LJSpeech-1.1/wavs/LJ030-0088.wav|proceeded approximately two to three car lengths behind the President's follow-up car.
+LJSpeech-1.1/wavs/LJ013-0139.wav|and many witnesses were brought to attest to his previous good character, but he was found guilty and sentenced to seven years' transportation.
+LJSpeech-1.1/wavs/LJ047-0146.wav|that he had sought and obtained a passport on June twenty-five, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ011-0060.wav|He wrote afterwards to the effect that the moment he had offered himself, an unknown assassin came to aim a blow at him.
+LJSpeech-1.1/wavs/LJ047-0177.wav|As discussed in chapter six, she failed to report this to Agent Hosty
+LJSpeech-1.1/wavs/LJ028-0476.wav|The entire width of this inner defense was about fifty-five feet; its height is uncertain.
+LJSpeech-1.1/wavs/LJ002-0315.wav|But this weekly pittance did not go far when the debtors' side was crowded, as it often was;
+LJSpeech-1.1/wavs/LJ021-0204.wav|of regulating only to meet concrete needs -- a practice of courageous recognition of change.
+LJSpeech-1.1/wavs/LJ004-0154.wav|One hundred and fifty were lodged in a prison just capable of holding fifty-two.
+LJSpeech-1.1/wavs/LJ014-0097.wav|The Mannings had seen or heard nothing of him, of course. As O'Connor still did not turn up, the police after a couple of days returned to Minver Place.
+LJSpeech-1.1/wavs/LJ050-0141.wav|As a beginning step to improve liaison with local law enforcement officials, the Secret Service on August twenty-six, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ050-0240.wav|the Service had the advantage of nine thousand, five hundred hours of work by other enforcement agencies.
+LJSpeech-1.1/wavs/LJ036-0214.wav|Tippit stopped the man and called him to his car.
+LJSpeech-1.1/wavs/LJ013-0105.wav|Elder, the former, was soon apprehended at his house, but he evaded the law by hanging himself with his pocket-handkerchief.
+LJSpeech-1.1/wavs/LJ027-0013.wav|the mysterious semi-fluid network of protoplasm, which is, so far as is known, the physical basis of all life;
+LJSpeech-1.1/wavs/LJ026-0129.wav|Sooner or later the starch grains are changed into a kind of sugar (glucose), which, unlike starch, dissolves in the sap
+LJSpeech-1.1/wavs/LJ050-0200.wav|The Secret Service should utilize the personnel of other Federal law enforcement offices
+LJSpeech-1.1/wavs/LJ040-0117.wav|The short-lived stay with the Pics was terminated after an incident in which Lee allegedly pulled out a pocket knife during an argument
+LJSpeech-1.1/wavs/LJ036-0165.wav|The Greyhound Bus Station at Lamar and Jackson Streets, where Oswald entered Whaley's cab,
+LJSpeech-1.1/wavs/LJ016-0281.wav|Rain fell heavily at intervals, but did not thin the concourse.
+LJSpeech-1.1/wavs/LJ007-0206.wav|In endeavoring to arrest contamination, prisoners were more closely confined, and associated in smaller numbers;
+LJSpeech-1.1/wavs/LJ004-0150.wav|Matters were on much the same footing at St. Albans.
+LJSpeech-1.1/wavs/LJ047-0089.wav|inasmuch as the facts relating to Oswald's activities at that time did not warrant such action.
+LJSpeech-1.1/wavs/LJ014-0184.wav|The noise of the struggle was heard outside in the street,
+LJSpeech-1.1/wavs/LJ007-0223.wav|The prison officials appear to be on the side of the inspectors, to the great dissatisfaction of the corporation, who claimed the full allegiance and support of its servants.
+LJSpeech-1.1/wavs/LJ019-0127.wav|or the still more costly process of walling in the whole farm, would have greatly added to the charges of these establishments.
+LJSpeech-1.1/wavs/LJ010-0245.wav|one of the equerries. The Queen was untouched, and at first, it is said, hardly realized the danger she had escaped.
+LJSpeech-1.1/wavs/LJ049-0051.wav|Planning for motorcade contingencies.
+LJSpeech-1.1/wavs/LJ027-0004.wav|are grouped by Romanes and other writers on organic evolution under the heads of morphology, embryology, classification,
+LJSpeech-1.1/wavs/LJ028-0472.wav|Its outer part, about twelve feet in width, was protected with towers at intervals of sixty-five feet.
+LJSpeech-1.1/wavs/LJ020-0009.wav|Three cups of lukewarm water in which the potatoes were boiled, strained through a coarse cloth.
+LJSpeech-1.1/wavs/LJ031-0034.wav|The Governor was lifted onto a stretcher and taken into trauma room two.
+LJSpeech-1.1/wavs/LJ011-0117.wav|devoted its efforts first to a mitigation of the forgery statute, but could not immediately accomplish much.
+LJSpeech-1.1/wavs/LJ024-0119.wav|When before have you found them really at your side in your fights for progress?
+LJSpeech-1.1/wavs/LJ037-0157.wav|taken from Oswald.
+LJSpeech-1.1/wavs/LJ046-0159.wav|accumulated over a twenty-year period, some of which included more than one individual.
+LJSpeech-1.1/wavs/LJ042-0040.wav|He complained in his letter about segregation,
+LJSpeech-1.1/wavs/LJ012-0195.wav|and of these Bishop and Williams, who were guilty of many peculiar atrocities, ended their murderous careers in front of the debtors' door at Newgate.
+LJSpeech-1.1/wavs/LJ044-0003.wav|Lee Harvey Oswald: Background and Possible Motives, Part five.
+LJSpeech-1.1/wavs/LJ028-0408.wav|Some are Arabs of the same tribes which used to roam the desert in Nebuchadnezzar's days. Some are the children of the Hebrew exiles of old.
+LJSpeech-1.1/wavs/LJ016-0261.wav|simultaneously, of the five pirates, part of the mutinous crew of the 'Flowery Land,' was greater than on any previous occasion.
+LJSpeech-1.1/wavs/LJ029-0081.wav|The third possibility, the Trade Mart, a handsome new building with all the necessary facilities, presented security problems. It had numerous entrances,
+LJSpeech-1.1/wavs/LJ015-0304.wav|He then confessed that he was the redoubtable Jem Saward, or Jem the Penman, and was conveyed to a police-court, and thence to Newgate.
+LJSpeech-1.1/wavs/LJ013-0095.wav|Burgess' continued absence was soon noticed at the bank.
+LJSpeech-1.1/wavs/LJ001-0164.wav|which should be indications of a delicacy of material and manufacture which would of necessity increase its cost.
+LJSpeech-1.1/wavs/LJ045-0171.wav|What did he say to that?
+LJSpeech-1.1/wavs/LJ021-0088.wav|We have passed through the formative period of code making in the National Recovery Administration
+LJSpeech-1.1/wavs/LJ006-0092.wav|and under the scanty covering of a couple of dirty stable-rugs apiece.
+LJSpeech-1.1/wavs/LJ030-0072.wav|On previous occasions, the President had requested that, to the extent possible, these flanking motorcycles keep back from the sides of his car.
+LJSpeech-1.1/wavs/LJ018-0181.wav|and was perpetually in trouble. Commencing life as a resurrection man, when that trade failed through the change in the law,
+LJSpeech-1.1/wavs/LJ028-0024.wav|gazing down upon the great Euphrates winding along the valley beneath.
+LJSpeech-1.1/wavs/LJ025-0021.wav|yet none of these movements justify the ascription to plants of perception of will.
+LJSpeech-1.1/wavs/LJ014-0143.wav|On being removed to Newgate from the court Mrs. Manning became perfectly furious.
+LJSpeech-1.1/wavs/LJ012-0212.wav|The snatchers were detained, the police sent for, and arrest followed as a matter of course.
+LJSpeech-1.1/wavs/LJ014-0124.wav|Manning, who was dressed in black, appeared to be a heavy, bull-necked, repulsive-looking man, with a very fair complexion and light hair.
+LJSpeech-1.1/wavs/LJ027-0088.wav|Extensive comparison, on the contrary, shows them to be the same, although the essential identity is obscured by adaptive modifications.
+LJSpeech-1.1/wavs/LJ032-0185.wav|Moreover, the Commission found no reliable evidence that Oswald used the rifle at any time between September twenty-three,
+LJSpeech-1.1/wavs/LJ031-0006.wav|On receipt of the radio message from Kellerman to the lead car that the President had been hit,
+LJSpeech-1.1/wavs/LJ044-0048.wav|which occurred eight days after Oswald wrote the above letter to V. T. Lee.
+LJSpeech-1.1/wavs/LJ010-0317.wav|The bank began first to refuse our acceptances, and to destroy the credit of our house; the bank shall smart for it.
+LJSpeech-1.1/wavs/LJ015-0166.wav|"What do you know of our clerk?" asked Mr. Denison of his lordship. "Only that he is a capital fellow, who gives the best dinners and balls in town."
+LJSpeech-1.1/wavs/LJ007-0181.wav|A committee of aldermen was forthwith appointed to report upon the inspectors' report,
+LJSpeech-1.1/wavs/LJ031-0219.wav|but it was difficult to measure accurately because multiple crisscross fractures radiated from the large defect.
+LJSpeech-1.1/wavs/LJ045-0246.wav|He sought for himself a place in history -- a role as the "great man" who would be recognized as having been in advance of his times.
+LJSpeech-1.1/wavs/LJ017-0232.wav|The captain's brother, a passenger on board the 'Flowery Land,' was also stabbed to death and his body thrown overboard.
+LJSpeech-1.1/wavs/LJ003-0144.wav|the wards being furnished with barrack bedsteads, upon which each prisoner had the regulation allowance of sleeping room
+LJSpeech-1.1/wavs/LJ002-0252.wav|The inmates, who euphemistically styled themselves "collegians,"
+LJSpeech-1.1/wavs/LJ030-0245.wav|I was pushed down by Agent Youngblood.
+LJSpeech-1.1/wavs/LJ010-0316.wav|I kept up the payments of the dividends, but made no entries of such payments in my books.
+LJSpeech-1.1/wavs/LJ028-0106.wav|His palace in Babylon was one of the world's largest buildings, but the walls with which he protected his palace and city were the wonder of the whole world.
+LJSpeech-1.1/wavs/LJ046-0020.wav|prepared by the Secret Service for the Secretary of the Treasury following the assassination. As a result of this study,
+LJSpeech-1.1/wavs/LJ008-0067.wav|The machinery in use in Dublin is much the same as that employed at many jails now-a-days.
+LJSpeech-1.1/wavs/LJ024-0122.wav|its meaning would depend upon the kind of justices who would be sitting on the Supreme Court bench.
+LJSpeech-1.1/wavs/LJ038-0160.wav|he asked me what were the people gathering around on the corner for and I told him that the President was supposed to pass that morning,
+LJSpeech-1.1/wavs/LJ035-0039.wav|This vestibule door is solid except for a small glass window in the upper part of the door.
+LJSpeech-1.1/wavs/LJ040-0156.wav|No finding of neurological impairment or psychotic mental changes could be made.
+LJSpeech-1.1/wavs/LJ038-0088.wav|The results were positive for the hands and negative for the right cheek.
+LJSpeech-1.1/wavs/LJ014-0183.wav|meaning to kill him and rifle the shop.
+LJSpeech-1.1/wavs/LJ009-0302.wav|Another man was hired, himself a convict, whose fees for self and wife were twelve guineas.
+LJSpeech-1.1/wavs/LJ018-0357.wav|Poisoning has still its victims.
+LJSpeech-1.1/wavs/LJ002-0109.wav|called the clerk of the papers, attends daily. End quote.
+LJSpeech-1.1/wavs/LJ040-0175.wav|through which they could not reach him, but that he preferred the veil to remain intact.
+LJSpeech-1.1/wavs/LJ049-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ031-0089.wav|considerable time which at this juncture was not available.
+LJSpeech-1.1/wavs/LJ033-0032.wav|In the Paines' garage, along with many other objects of a household character,
+LJSpeech-1.1/wavs/LJ016-0208.wav|The onus and responsibility of carrying out the sentence is personal to the sheriff. A good story is told illustrating this.
+LJSpeech-1.1/wavs/LJ046-0047.wav|Exposure of the President to public view through travel among the people of this country is a great and historic tradition of American life.
+LJSpeech-1.1/wavs/LJ033-0083.wav|Oswald gripped the bag in his right hand near the top, quote, It tapered like this as he hugged it in his hand.
+LJSpeech-1.1/wavs/LJ005-0146.wav|and was indeed destined to continue for another half century, in other words, until the introduction of the Prison Act of eighteen seventy-seven.
+LJSpeech-1.1/wavs/LJ046-0249.wav|about persons other than those who were obvious threats to the President.
+LJSpeech-1.1/wavs/LJ036-0205.wav|Tippit must have heard the description of the suspect wanted for the President's shooting; it was broadcast over channel one at twelve:forty-five p.m.,
+LJSpeech-1.1/wavs/LJ019-0124.wav|thinking perhaps that prisoners so well disposed would cheerfully remain in jail of their own accord.
+LJSpeech-1.1/wavs/LJ049-0037.wav|In contrast, the Vice Presidential vehicle, although not specially designed for that purpose,
+LJSpeech-1.1/wavs/LJ039-0224.wav|show that he possessed ample capability to commit the assassination.
+LJSpeech-1.1/wavs/LJ048-0104.wav|There were far safer routes via freeways directly to the Trade Mart,
+LJSpeech-1.1/wavs/LJ028-0239.wav|where he did exactly what she had done formerly: he turned the Euphrates by a canal into the basin, which was then a marsh,
+LJSpeech-1.1/wavs/LJ036-0192.wav|Tippit's murder was recorded on the police radio tape at about one:sixteen p.m.
+LJSpeech-1.1/wavs/LJ047-0187.wav|It was then my plan to interview Marina Oswald in detail concerning both herself and her husband's background. Question:
+LJSpeech-1.1/wavs/LJ041-0026.wav|from which Oswald appeared to suffer.
+LJSpeech-1.1/wavs/LJ045-0021.wav|any of the personalities or groups epitomizing or representing the so-called right wing,
+LJSpeech-1.1/wavs/LJ047-0008.wav|and was investigating him at the time of the assassination. The Commission has taken the testimony of Bureau agents
+LJSpeech-1.1/wavs/LJ014-0056.wav|while in Ireland a wife dashed out her husband's brains with a hammer.
+LJSpeech-1.1/wavs/LJ033-0094.wav|As they sat in the car, Frazier asked Oswald where his lunch was, and Oswald replied that he was going to buy his lunch that day.
+LJSpeech-1.1/wavs/LJ045-0119.wav|which had led to the disclosure of his defection in New Orleans.
+LJSpeech-1.1/wavs/LJ050-0175.wav|Since the assassination, Secret Service procedures have been changed to require that a member of PRS accompany each advance survey team
+LJSpeech-1.1/wavs/LJ004-0061.wav|by want of bed-clothing by night or firing by day
+LJSpeech-1.1/wavs/LJ032-0227.wav|Shaneyfelt testified that the published photographs appeared to be based on a copy of the original which the publications had each retouched differently.
+LJSpeech-1.1/wavs/LJ047-0087.wav|According to the Bureau, quote,
+LJSpeech-1.1/wavs/LJ028-0225.wav|A much longer account of the capture of Babylon by Cyrus appears in the writings of Herodotus.
+LJSpeech-1.1/wavs/LJ028-0457.wav|The moat, ten feet deep, and of a width no longer known, ran close to its base. The wall was double.
+LJSpeech-1.1/wavs/LJ028-0140.wav|The city stands on a broad plain, and is an exact square, a hundred and twenty furlongs in length each way,
+LJSpeech-1.1/wavs/LJ024-0127.wav|You who know me can have no fear that I would tolerate the destruction by any branch of government of any part of our heritage of freedom.
+LJSpeech-1.1/wavs/LJ014-0078.wav|that she stood behind him as he stood near the open grave she herself had dug for him,
+LJSpeech-1.1/wavs/LJ007-0065.wav|on the one hand, the Ladies' Association, supported and encouraged by public approval, trenched upon his peculiar province;
+LJSpeech-1.1/wavs/LJ005-0233.wav|There was no instruction of adult prisoners, in accordance with the law. The sleeping accommodation was still altogether contrary to the latest ideas.
+LJSpeech-1.1/wavs/LJ010-0053.wav|and when the people were beginning to agitate for a larger share of political power, was among the earliest, and to some extent the most desperate, of these.
+LJSpeech-1.1/wavs/LJ022-0100.wav|Moreover, this Division will be charged with keeping projects moving on schedule.
+LJSpeech-1.1/wavs/LJ037-0143.wav|Two of the arresting officers placed their initials on the weapon and a third inscribed his name.
+LJSpeech-1.1/wavs/LJ039-0105.wav|Following that training, Oswald was tested in December of nineteen fifty-six, and obtained a score of two hundred twelve,
+LJSpeech-1.1/wavs/LJ028-0009.wav|At first, paying little heed to you, she hesitates and glances anxiously about the desert as if in search of an enemy.
+LJSpeech-1.1/wavs/LJ009-0295.wav|who had been a convicted prisoner at York, but who consented to act as hangman when Calcraft was engaged, and no other functionary could be obtained.
+LJSpeech-1.1/wavs/LJ028-0048.wav|In six twenty-six Assurbanipal died,
+LJSpeech-1.1/wavs/LJ006-0293.wav|and the men could also at any time go for tea, coffee, and sugar to Mrs. Brown's shop, which was inside the female gate.
+LJSpeech-1.1/wavs/LJ012-0174.wav|Thurtell was a gambler, and Weare had won a good deal of money from him.
+LJSpeech-1.1/wavs/LJ016-0365.wav|Moreover, after the execution, under the old system, the latter had only to receive the body for burial after it had been cut down by the hangman,
+LJSpeech-1.1/wavs/LJ008-0282.wav|seventeen capital convicts, upon all of whom the sentence of death had been passed. Eventually two only of the whole number suffered;
+LJSpeech-1.1/wavs/LJ028-0484.wav|Grant me as a gift a life of distant days.
+LJSpeech-1.1/wavs/LJ014-0280.wav|But it was proved that Watts had appropriated one cheque for fourteen hundred pounds,
+LJSpeech-1.1/wavs/LJ045-0130.wav|warned me that if I engaged in F.P.C.C. activities in Texas the F.B.I. will again take an interest in me, end quote.
+LJSpeech-1.1/wavs/LJ036-0157.wav|Oswald could not possibly have been wearing the blue jacket during the trip with Whaley, since it was found in the "domino" room of the Depository late in November.
+LJSpeech-1.1/wavs/LJ009-0038.wav|In the center of the chapel was the condemned pew, a large dock-like erection painted black.
+LJSpeech-1.1/wavs/LJ049-0013.wav|and on one occasion Agent Kellerman left the front seat of the President's car to take a similar position.
+LJSpeech-1.1/wavs/LJ028-0301.wav|Now, therefore, if there be no failure on thy part, we shall take Babylon.
+LJSpeech-1.1/wavs/LJ029-0117.wav|According to Lawson, the chosen route seemed to be the best.
+LJSpeech-1.1/wavs/LJ030-0027.wav|Vice President Johnson's airplane, Air Force Two, had arrived at Love Field at approximately eleven:thirty-five a.m.,
+LJSpeech-1.1/wavs/LJ028-0325.wav|They were ready, therefore, to grant him anything he asked;
+LJSpeech-1.1/wavs/LJ028-0303.wav|I think they will believe my words and entrust me with a command of troops. Thou, on thy part, must wait
+LJSpeech-1.1/wavs/LJ011-0243.wav|Mr. Mullay went, and a second interview was agreed upon, when a third person, Mr. Owen,
+LJSpeech-1.1/wavs/LJ035-0112.wav|The west elevator was not on the fifth floor when Baker and Truly reached that floor,
+LJSpeech-1.1/wavs/LJ016-0121.wav|Krapps walked at once into the yard and across to the female side, where he found some of the washing still hanging out to dry.
+LJSpeech-1.1/wavs/LJ039-0141.wav|he again went hunting with his brother, Robert, and used a borrowed twenty-two caliber bolt-action rifle.
+LJSpeech-1.1/wavs/LJ021-0105.wav|and the vital necessity of improving labor conditions,
+LJSpeech-1.1/wavs/LJ020-0083.wav|prick each with a fork and let them rise near the fire for half an hour, covered with a light cloth.
+LJSpeech-1.1/wavs/LJ036-0020.wav|The transfer was dated "Friday November twenty-two, 'sixty-three" and was punched in two places by the bus driver.
+LJSpeech-1.1/wavs/LJ022-0124.wav|There are chiselers in every walk of life; there are those in every industry who are guilty of unfair practices;
+LJSpeech-1.1/wavs/LJ015-0202.wav|The precious metal was sent from place to place carefully locked up and guarded, no doubt;
+LJSpeech-1.1/wavs/LJ003-0106.wav|The first night in Newgate, and for the subsequent fortnight,
+LJSpeech-1.1/wavs/LJ011-0033.wav|deeming him the last person capable of a dishonorable action.
+LJSpeech-1.1/wavs/LJ007-0115.wav|slept there, and had their meals provided for them from the Sessions House or London Coffee-House.
+LJSpeech-1.1/wavs/LJ018-0285.wav|With this as capital they came back to England via Buenos Aires,
+LJSpeech-1.1/wavs/LJ037-0188.wav|Among these guns was a thirty-eight Special caliber Smith and Wesson revolver, serial Number V five one zero two one zero,
+LJSpeech-1.1/wavs/LJ009-0169.wav|Within a couple of years the number of persons sentenced to death in England had fallen from four hundred and thirty-eight in eighteen thirty-seven
+LJSpeech-1.1/wavs/LJ013-0097.wav|as soon as that gentleman was referred to.
+LJSpeech-1.1/wavs/LJ007-0089.wav|It was manifestly wrong that such persons, "visited by the most awful of calamities," should be detained in a common prison.
+LJSpeech-1.1/wavs/LJ018-0160.wav|They next watched Buncher, and found that he paid frequent visits to Birmingham.
+LJSpeech-1.1/wavs/LJ049-0023.wav|Since the assassination, the Secret Service, with the assistance of other Federal agencies and of private industry,
+LJSpeech-1.1/wavs/LJ016-0099.wav|These men managed to cut a hole in the ceiling of the ward near the iron cage on the landing, and so got access to the roof.
+LJSpeech-1.1/wavs/LJ008-0250.wav|and lay disregarded beside the body of the poor dog which, while searching for its master in the crowd,
+LJSpeech-1.1/wavs/LJ035-0169.wav|testified that immediately after the shots he went to the rear of the building to check the fire escape.
+LJSpeech-1.1/wavs/LJ012-0122.wav|The condign punishment meted out to these Custom House robbers had no deterrent effect seemingly.
+LJSpeech-1.1/wavs/LJ025-0010.wav|One of the most important summaries of this similarity
+LJSpeech-1.1/wavs/LJ013-0167.wav|The discovery of the murdered man immediately followed. The neighborhood was alarmed, the police sent for, and a close inquiry forthwith commenced.
+LJSpeech-1.1/wavs/LJ049-0129.wav|while the CIA has the primary responsibility for collecting intelligence overseas to supplement information acquired by the Department of State.
+LJSpeech-1.1/wavs/LJ013-0250.wav|In the same room a large axe and saw were found covered with blood.
+LJSpeech-1.1/wavs/LJ010-0069.wav|She died early, and Thistlewood, left to his own resources,
+LJSpeech-1.1/wavs/LJ008-0313.wav|who in solemn tones communicated to each in turn the fate in store for him.
+LJSpeech-1.1/wavs/LJ013-0134.wav|and it was contended that these with the files were used to alter the marks on the plate.
+LJSpeech-1.1/wavs/LJ038-0105.wav|They discovered two photographs, each showing Oswald with a rifle and a pistol.
+LJSpeech-1.1/wavs/LJ013-0169.wav|It was also clearly proved that no forcible entry had been made into the house;
+LJSpeech-1.1/wavs/LJ018-0397.wav|out of large sums in sham sporting speculations. The conviction of the principals in this nefarious transaction,
+LJSpeech-1.1/wavs/LJ030-0067.wav|on his right sat Kellerman.
+LJSpeech-1.1/wavs/LJ042-0144.wav|After the assassination she wrote that, quote,
+LJSpeech-1.1/wavs/LJ019-0195.wav|A proviso was also made that the magistrates should continue to exercise full control over the new jail,
+LJSpeech-1.1/wavs/LJ044-0027.wav|to her husband's membership card in the New Orleans chapter.
+LJSpeech-1.1/wavs/LJ003-0268.wav|This delectable society enticed into their set a clerk who had been imprisoned for fraud,
+LJSpeech-1.1/wavs/LJ031-0096.wav|Question: Was any effort made to inspect the President's back after he had expired? Answer: No, sir.
+LJSpeech-1.1/wavs/LJ023-0048.wav|Two of the horses are pulling in unison today; the third is not.
+LJSpeech-1.1/wavs/LJ002-0077.wav|from twenty-one by eighteen feet to fifteen feet square, which were furnished with bedsteads and bedding.
+LJSpeech-1.1/wavs/LJ038-0077.wav|Statements of Oswald during Detention.
+LJSpeech-1.1/wavs/LJ009-0165.wav|House-breaking, as distinguished from burglary, was similarly exempted in the following year;
+LJSpeech-1.1/wavs/LJ026-0043.wav|and hence so unfamiliar that to most people the comparison made will mean little in terms of ordinary green flowering plants and common vertebrate animals.
+LJSpeech-1.1/wavs/LJ035-0044.wav|If the man had passed from the vestibule into the lunchroom, Baker could not have seen him.
+LJSpeech-1.1/wavs/LJ048-0226.wav|All of the agents stayed for a drink of beer, or in several cases, a mixed drink.
+LJSpeech-1.1/wavs/LJ008-0316.wav|"A. B., your case has been taken into consideration by the king in council, and His Majesty has been mercifully pleased to spare your life."
+LJSpeech-1.1/wavs/LJ009-0198.wav|and her body publicly exhibited in a place built for the purpose in the Old Bailey.
+LJSpeech-1.1/wavs/LJ033-0078.wav|Neither she nor Mrs. Paine saw him leave the house. About half-a-block away from the Paine house was the residence of Mrs. Linnie Mae Randle,
+LJSpeech-1.1/wavs/LJ035-0087.wav|Baker required fifteen seconds after the simulated shot to ride his motorcycle one hundred eighty to two hundred feet,
+LJSpeech-1.1/wavs/LJ029-0068.wav|The adequacy of the intelligence system maintained by the Secret Service at the time of the assassination,
+LJSpeech-1.1/wavs/LJ022-0153.wav|legislation to provide for the elimination of unnecessary holding companies in the public utility field.
+LJSpeech-1.1/wavs/LJ017-0058.wav|He was confident to the very last that it would be impossible to find him guilty;
+LJSpeech-1.1/wavs/LJ021-0054.wav|that the way to wealth is through work.
+LJSpeech-1.1/wavs/LJ032-0123.wav|the rifle was released to the FBI and forwarded to Washington where it was examined on the morning of November twenty-three
+LJSpeech-1.1/wavs/LJ028-0391.wav|that there is a royal hunting ground at Babylon, and that wild game of every kind is contained within the circuit of its walls.
+LJSpeech-1.1/wavs/LJ028-0482.wav|but I, the devout petitioner, the worshipper of the gods, built the moat, and made its wall of burned brick and bitumen mountain high.
+LJSpeech-1.1/wavs/LJ011-0208.wav|On this Wakefield gave in.
+LJSpeech-1.1/wavs/LJ032-0192.wav|The rifle used in the assassination probably had been wrapped in a blanket for about eight weeks prior to November twenty-two.
+LJSpeech-1.1/wavs/LJ009-0292.wav|He was well known in the neighborhood where he resided, and the street gamins cried "Jack Ketch" as he went along the street.
+LJSpeech-1.1/wavs/LJ027-0057.wav|In all vertebrates, and in none other, the axis of this skeleton is a jointed backbone (vertebral column)
+LJSpeech-1.1/wavs/LJ011-0254.wav|A life and death struggle ensued. Mullay cried "Murder!"
+LJSpeech-1.1/wavs/LJ018-0245.wav|in all good faith to another, but a criminal member of the family.
+LJSpeech-1.1/wavs/LJ044-0234.wav|one hundred seventy dollars in a wallet in his wife's room in Irving.
+LJSpeech-1.1/wavs/LJ032-0258.wav|Ruth and Michael Paine both noticed the rolled-up blanket in the garage during the time that Marina Oswald was living in their home.
+LJSpeech-1.1/wavs/LJ010-0136.wav|He told the executioner to "do it tidy," to pull it tight, and was in a state of hysterical exaltation up to the very last.
+LJSpeech-1.1/wavs/LJ017-0029.wav|The crime of poisoning is essentially one which will be most prevalent in a high state of civilization,
+LJSpeech-1.1/wavs/LJ046-0139.wav|to refer all communications on identified existing cases and, in addition,
+LJSpeech-1.1/wavs/LJ048-0079.wav|mutual understanding of FBI and agency jurisdictions, and an indicated willingness by the agency representative
+LJSpeech-1.1/wavs/LJ027-0124.wav|For instance, although in nearly all the numerous species of snakes there are no vestiges of limbs,
+LJSpeech-1.1/wavs/LJ011-0133.wav|This amendment was strongly supported outside the House, and a petition in favor of its passing was presented,
+LJSpeech-1.1/wavs/LJ050-0060.wav|Subversives, ultrarightists, racists and fascists (a) possessing emotional instability or irrational behavior,
+LJSpeech-1.1/wavs/LJ009-0243.wav|Other cases might be quoted,
+LJSpeech-1.1/wavs/LJ049-0128.wav|The FBI is the major domestic investigating agency of the United States,
+LJSpeech-1.1/wavs/LJ011-0173.wav|Miss Turner consented to go on, and they traveled night and day towards the north.
+LJSpeech-1.1/wavs/LJ034-0211.wav|who was standing on the south side of Elm Street between the Triple Underpass and the Depository Building.
+LJSpeech-1.1/wavs/LJ041-0035.wav|and then came out with a statement that he was looking for a Communist cell in town to join but he couldn't find any.
+LJSpeech-1.1/wavs/LJ033-0071.wav|And I then translated this to the officers that she knew that her husband had a gun that he had stored in here.
+LJSpeech-1.1/wavs/LJ010-0161.wav|two years after she ascended the throne.
+LJSpeech-1.1/wavs/LJ008-0246.wav|Never once from that pale face passed away its benign and patient expression.
+LJSpeech-1.1/wavs/LJ015-0141.wav|from which he rose to be assistant registrar, with the special duties of transferring shares.
+LJSpeech-1.1/wavs/LJ032-0127.wav|the formations, the ridge formations and characteristics, were insufficient for purposes of either effecting identification
+LJSpeech-1.1/wavs/LJ013-0123.wav|The club spoons and other articles of plate were long a source of profitable income to a gentleman named Ashley,
+LJSpeech-1.1/wavs/LJ018-0304.wav|They had omitted to insert the dates in certain bills.
+LJSpeech-1.1/wavs/LJ030-0057.wav|If the weather is clear and it is not raining, have that bubbletop off, end quote,
+LJSpeech-1.1/wavs/LJ018-0180.wav|But he was not long at large; he was too active an evil-doer
+LJSpeech-1.1/wavs/LJ035-0099.wav|Baker, not realizing that there were two elevators, thought that only one elevator was in the shaft and that it was two or three floors above the second floor.
+LJSpeech-1.1/wavs/LJ021-0082.wav|lines which as I have so often made clear, are in complete accord with the underlying principles of orderly popular government
+LJSpeech-1.1/wavs/LJ031-0156.wav|The Secret Service group awaiting the President in Austin were instructed to return to Washington.
+LJSpeech-1.1/wavs/LJ032-0014.wav|(six) the lies told to the police by Oswald, (seven)
+LJSpeech-1.1/wavs/LJ006-0254.wav|Other regulations laid down by the Jail Acts were still defied. One of these was that prisoners should be restricted to the jail allowance of food;
+LJSpeech-1.1/wavs/LJ035-0184.wav|He then entered the building and took the front passenger elevator as far as it would go -- the fourth floor.
+LJSpeech-1.1/wavs/LJ048-0170.wav|While certain streets thought to be too narrow could be avoided and other choices made,
+LJSpeech-1.1/wavs/LJ028-0204.wav|It refers to the death of the King's son, possibly to Belshazzar of the Bible story.
+LJSpeech-1.1/wavs/LJ026-0022.wav|if chlorophyll is absent, carbon is obtained from sugar or some similar compound,
+LJSpeech-1.1/wavs/LJ049-0074.wav|Their actions demonstrate that the President and the Nation can expect courage and devotion to duty from the agents of the Secret Service.
+LJSpeech-1.1/wavs/LJ009-0054.wav|He is quite content to die;
+LJSpeech-1.1/wavs/LJ049-0096.wav|There have been a number of efforts to make assassination a Federal crime, particularly after the assassination of President McKinley
+LJSpeech-1.1/wavs/LJ012-0265.wav|A woman named Gale, who lived with him, was arrested at the same time. The prisoners were examined at the Marylebone police court.
+LJSpeech-1.1/wavs/LJ048-0081.wav|Each field office should determine those Federal agencies which are represented locally and with which liaison should be conducted. End quote.
+LJSpeech-1.1/wavs/LJ009-0163.wav|In the same year capital punishment was further restricted, and ceased to be the legal sentence for coining,
+LJSpeech-1.1/wavs/LJ034-0077.wav|Oswald said, quote, No, sir. When you get downstairs, close the gate to the elevator, end quote.
+LJSpeech-1.1/wavs/LJ003-0332.wav|A certain number of bedsteads were provided, and there was a slight increase in the ration of bread.
+LJSpeech-1.1/wavs/LJ045-0231.wav|two officers have testified that at the time of his arrest Oswald said something to the effect that, quote, it's all over now, end quote.
+LJSpeech-1.1/wavs/LJ040-0080.wav|That situation, however, was short-lived,
+LJSpeech-1.1/wavs/LJ049-0067.wav|the radio net in use in motorcades is elaborate and permits a number of different means of communication with various local points.
+LJSpeech-1.1/wavs/LJ027-0016.wav|The same laws of heredity, variability and of response to outside stimulus hold in all parts of the organic world.
+LJSpeech-1.1/wavs/LJ049-0113.wav|This point was ably made in the nineteen oh two debate by Senator George F. Hoar, the sponsor of the Senate bill, quote,
+LJSpeech-1.1/wavs/LJ041-0196.wav|even though as his wife suggested, that interest may have been motivated by a desire to gain attention. He used his Marxist and associated activities
+LJSpeech-1.1/wavs/LJ019-0239.wav|and others were subsequently added.
+LJSpeech-1.1/wavs/LJ016-0256.wav|It was thought that capital punishment would lose its deterrent effect if it ceased to be public,
+LJSpeech-1.1/wavs/LJ018-0147.wav|The next step was to take the principals, and under such circumstances as would insure their conviction.
+LJSpeech-1.1/wavs/LJ014-0003.wav|The cries of his victim, a Mr. Delarue,
+LJSpeech-1.1/wavs/LJ004-0141.wav|The hospital was filled with infectious cases, and in one room, seven feet by nine, with closed windows,
+LJSpeech-1.1/wavs/LJ005-0136.wav|The want of sleeping cells long continued a crying need.
+LJSpeech-1.1/wavs/LJ002-0280.wav|It was generally crowded, as debtors who would have gone to the Poultry Compter were sent to Giltspur Street when the former was condemned as unfit to receive prisoners.
+LJSpeech-1.1/wavs/LJ030-0176.wav|In his testimony before the Commission, Governor Connally was certain that he was hit by the second shot, which he stated he did not hear.
+LJSpeech-1.1/wavs/LJ042-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter seven. Lee Harvey Oswald:
+LJSpeech-1.1/wavs/LJ047-0072.wav|stating that he had distributed its pamphlets on the streets of Dallas. This information did not reach Agent Hosty in Dallas until June.
+LJSpeech-1.1/wavs/LJ004-0092.wav|A much later act, the fourteen George the third c. fifty-nine (seventeen seventy-four),
+LJSpeech-1.1/wavs/LJ032-0149.wav|Latona's findings were also confirmed by Ronald G. Wittmus, another FBI fingerprint expert.
+LJSpeech-1.1/wavs/LJ041-0011.wav|Several witnesses testified that Lee Oswald was not aggressive. He was, however, involved in some fights.
+LJSpeech-1.1/wavs/LJ007-0148.wav|prefers that description which are expressly prepared for his own class, and which abound in vulgar adventure in criminal enterprise, and in the histories of the police,
+LJSpeech-1.1/wavs/LJ005-0016.wav|The Society for the Improvement of Prison Discipline was taxed with a desire to introduce a system
+LJSpeech-1.1/wavs/LJ001-0056.wav|It was these great Venetian printers, together with their brethren of Rome, Milan,
+LJSpeech-1.1/wavs/LJ014-0187.wav|Lerigo, was also attracted by the noise of the row, but after walking a few paces he felt dissatisfied, and returned to the spot.
+LJSpeech-1.1/wavs/LJ027-0092.wav|A man having made a steam-engine, and desiring to use it for a different purpose from that for which it was first designed and used,
+LJSpeech-1.1/wavs/LJ004-0108.wav|which was the first legislative attempt to compel the classification of prisoners, or their separation into classes
+LJSpeech-1.1/wavs/LJ024-0046.wav|then I say that I and with me the vast majority of the American people favor doing just that thing -- now.
+LJSpeech-1.1/wavs/LJ038-0276.wav|In explaining the difference between his policy and that of the FBI on the matter of probable identification, Nicol said, quote,
+LJSpeech-1.1/wavs/LJ044-0222.wav|Oswald was asked during the New Orleans radio debate in which he engaged on August twenty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ028-0177.wav|However, they teach us that in those early days when most cities were surrounded by enormous walls,
+LJSpeech-1.1/wavs/LJ011-0178.wav|But another bank had since failed, and nothing could save Mr. Turner but the transfer of some property to Miss Turner, and its settlement on her,
+LJSpeech-1.1/wavs/LJ002-0036.wav|one. The male debtors' side consisted of a yard forty-nine feet by thirty-one,
+LJSpeech-1.1/wavs/LJ017-0256.wav|The mutineers were arrested, the case inquired into by a naval court-martial,
+LJSpeech-1.1/wavs/LJ017-0263.wav|the remaining five were executed in one batch.
+LJSpeech-1.1/wavs/LJ030-0248.wav|Clifton C. Carter, riding in the Vice President's follow-up car a short distance behind,
+LJSpeech-1.1/wavs/LJ002-0224.wav|but a number of rooms, fifteen more or less, were reserved for poor debtors under the name of Bartholomew Fair.
+LJSpeech-1.1/wavs/LJ042-0182.wav|and they would never accept a new order complete beyond their understanding, end quote.
+LJSpeech-1.1/wavs/LJ043-0139.wav|He outlined his plans in a notebook and studied them at considerable length before his attack.
+LJSpeech-1.1/wavs/LJ039-0004.wav|Chapter four. The Assassin: Part eight.
+LJSpeech-1.1/wavs/LJ038-0212.wav|which appeared to be the work of a man expecting to be killed, or imprisoned, or to disappear.
+LJSpeech-1.1/wavs/LJ008-0273.wav|At the Old Bailey almost every one capitally convicted by a jury was sentenced to be hanged.
+LJSpeech-1.1/wavs/LJ037-0054.wav|She identified Lee Harvey Oswald as the man who shot the policeman.
+LJSpeech-1.1/wavs/LJ002-0108.wav|watch all night. Adjoining the felons' side lodge is the keeper's office, where the prison books are kept, and his clerk,
+LJSpeech-1.1/wavs/LJ037-0221.wav|He was wearing a zipper jacket which he had not been wearing moments before when he had arrived home.
+LJSpeech-1.1/wavs/LJ041-0168.wav|even though Oswald seemed to be lost in his own thoughts. After a brief period of silence Oswald remarked on the stupidity of the parade
+LJSpeech-1.1/wavs/LJ009-0242.wav|and the sentence was properly and completely carried out.
+LJSpeech-1.1/wavs/LJ019-0250.wav|In many prisons the prisoners were still associated, and, from the want of a sufficient number of cells, the principle of separation was still greatly neglected.
+LJSpeech-1.1/wavs/LJ022-0129.wav|in making this the most efficient and the cleanest example of public enterprise the world has ever seen.
+LJSpeech-1.1/wavs/LJ019-0308.wav|the penultimate of such enactments, many of the provisions of which still remain in force.
+LJSpeech-1.1/wavs/LJ038-0166.wav|an active and controversial figure on the American political scene since his resignation from the U.S. Army in nineteen sixty-one
+LJSpeech-1.1/wavs/LJ043-0130.wav|and had been seen reading a foreign newspaper, end quote.
+LJSpeech-1.1/wavs/LJ031-0103.wav|While Dr. Carrico went on to attend the President, Dr. Dulany stayed with the Governor and was soon joined by several other doctors.
+LJSpeech-1.1/wavs/LJ015-0227.wav|Still watching and waiting for the first chance, they seized it when the clerks left the office empty for a moment.
+LJSpeech-1.1/wavs/LJ009-0278.wav|Bousfield slowly drew himself up, and rested with his feet on the right side of the drop.
+LJSpeech-1.1/wavs/LJ038-0080.wav|Throughout this interrogation he denied that he had anything to do either with the assassination of President Kennedy or the murder of Patrolman Tippit.
+LJSpeech-1.1/wavs/LJ024-0080.wav|Our difficulty with the Court today rises not from the Court as an institution but from human beings within it.
+LJSpeech-1.1/wavs/LJ050-0018.wav|and to keep the Secretary fully informed regarding all significant developments relating to Presidential protection.
+LJSpeech-1.1/wavs/LJ019-0081.wav|The use of gas or some other means of lighting might be adopted, but more often was dispensed with.
+LJSpeech-1.1/wavs/LJ038-0174.wav|Their results were negative.
+LJSpeech-1.1/wavs/LJ040-0220.wav|could have led anyone to predict the outburst of violence which finally occurred.
+LJSpeech-1.1/wavs/LJ025-0061.wav|Agardh and other of the botanists of Cuvier's generation who occupied themselves with the lower plants had observed that,
+LJSpeech-1.1/wavs/LJ041-0113.wav|Oswald was transferred from active duty to the Marine Corps Reserve under honorable conditions in September of nineteen fifty-nine,
+LJSpeech-1.1/wavs/LJ004-0022.wav|The executive, although harassed by internal commotion and foreign war, was not entirely callous to the crying need for amelioration in jails.
+LJSpeech-1.1/wavs/LJ017-0260.wav|Vartos the Turk, and Carlos the Greek.
+LJSpeech-1.1/wavs/LJ008-0087.wav|The change from Tyburn to the Old Bailey had worked no improvement as regards the gathering together of the crowd or its demeanor.
+LJSpeech-1.1/wavs/LJ015-0300.wav|Saward's letter to Hardwicke fell into the hands of the police and compromised him.
+LJSpeech-1.1/wavs/LJ029-0067.wav|nor did PRS develop any additional information between November twelve, when Lawson left Washington, and November twenty-two.
+LJSpeech-1.1/wavs/LJ045-0174.wav|Thank you. That it would be better if he bought something for himself -- that I would manage. End quote. That night Oswald went to bed before his wife retired.
+LJSpeech-1.1/wavs/LJ047-0011.wav|and the Assistant to the Director in charge of all investigative activities under the Director and Associate Director. In addition,
+LJSpeech-1.1/wavs/LJ041-0131.wav|and also because he had not received any notice of the original discharge proceedings, since his whereabouts were not known.
+LJSpeech-1.1/wavs/LJ016-0096.wav|They were penal servitude men, their names Bell, Brown, and Barry, and they were awaiting transfer to Leicester,
+LJSpeech-1.1/wavs/LJ007-0184.wav|The inspectors retorted without loss of time, reiterating their charges, and pointing out that the committee of aldermen by its own admission
+LJSpeech-1.1/wavs/LJ030-0120.wav|had to leave the left front running board of the President's follow-up car four times to ride on the rear of the President's limousine.
+LJSpeech-1.1/wavs/LJ044-0123.wav|Stating that he had used his "position" with what he claimed to be the local branch of the Fair Play for Cuba Committee to, quote, foster communist ideals, end quote,
+LJSpeech-1.1/wavs/LJ031-0228.wav|concluded that the bullet had emerged from the front portion of the President's neck that had been cut away by the tracheotomy at Parkland.
+LJSpeech-1.1/wavs/LJ031-0010.wav|Chief Curry radioed, quote, Go to the hospital -- Parkland Hospital. Have them stand by, end quote,
+LJSpeech-1.1/wavs/LJ004-0161.wav|"the indescribable stench," presented together a concentration of the utmost misery and the utmost guilt.
+LJSpeech-1.1/wavs/LJ028-0479.wav|Nebuchadnezzar, King of Babylon,
+LJSpeech-1.1/wavs/LJ001-0136.wav|modern printers understand this, but it is only practiced in the very best establishments.
+LJSpeech-1.1/wavs/LJ042-0147.wav|Lee became very irritable, and sometimes some completely trivial thing would drive him into a rage.
+LJSpeech-1.1/wavs/LJ034-0187.wav|Fischer described the man's hair as some shade of brown, quote, it wasn't dark, and it wasn't light, end quote.
+LJSpeech-1.1/wavs/LJ037-0064.wav|that Mrs. Markham strongly reaffirmed her positive identification of Oswald and denied having described the killer
+LJSpeech-1.1/wavs/LJ008-0304.wav|to utter imprecations against the Recorder, the Secretary of State, the Council, and the King;
+LJSpeech-1.1/wavs/LJ035-0083.wav|Howlett was not short winded at the end of either test run.
+LJSpeech-1.1/wavs/LJ043-0007.wav|Somewhat of a nonconformist,
+LJSpeech-1.1/wavs/LJ039-0101.wav|He went through a series of exercises called dry firing where he assumed all positions which would later be used in the qualification course.
+LJSpeech-1.1/wavs/LJ045-0097.wav|whom she compared to, quote, a puppy dog that everybody kicked, end quote, had a lot of good qualities, in spite of the fact that, quote,
+LJSpeech-1.1/wavs/LJ038-0217.wav|indicated that the note was written when they were living in a rented apartment; therefore it could not have been written while Marina Oswald was living with the Paines.
+LJSpeech-1.1/wavs/LJ044-0102.wav|samples of his photographic work, offering to contribute that sort of service without charge.
+LJSpeech-1.1/wavs/LJ004-0114.wav|King's evidences were also to be lodged apart.
+LJSpeech-1.1/wavs/LJ016-0257.wav|and the raison d'être of the penalty, which in principle so many opposed, would be gone.
+LJSpeech-1.1/wavs/LJ028-0130.wav|And after he had walled the city, and adorned its gates, he built another palace before his father's palace; but so that they joined to it:
+LJSpeech-1.1/wavs/LJ019-0017.wav|This building was a costly affair. The site was uneven, and had to be leveled;
+LJSpeech-1.1/wavs/LJ043-0021.wav|While the exact sequence of events is not clear because of conflicting testimony,
+LJSpeech-1.1/wavs/LJ028-0188.wav|In five thirty-eight the city fell, and for a time it became the home of the Persian King.
+LJSpeech-1.1/wavs/LJ005-0004.wav|unless they could afford to pay for a vehicle out of their own funds.
+LJSpeech-1.1/wavs/LJ019-0172.wav|prisoners who had been acquitted were not permitted to return to the prison to show their joy and receive the congratulations of their unfortunate fellows.
+LJSpeech-1.1/wavs/LJ004-0204.wav|Masons, bricklayers, carpenters, painters had been employed upon the buildings, and the work was pronounced excellent by competent judges.
+LJSpeech-1.1/wavs/LJ023-0052.wav|It is the American people themselves who want the furrow plowed.
+LJSpeech-1.1/wavs/LJ003-0019.wav|and which was never wholly rooted out for many years to come.
+LJSpeech-1.1/wavs/LJ015-0206.wav|He laid the suggestion before Agar, a professional thief, who was of opinion it contained elements of success.
+LJSpeech-1.1/wavs/LJ014-0011.wav|Even then he followed it as far as Belsize Lane.
+LJSpeech-1.1/wavs/LJ039-0239.wav|would include the President's head.
+LJSpeech-1.1/wavs/LJ045-0147.wav|She asked Oswald, quote,
+LJSpeech-1.1/wavs/LJ036-0177.wav|knew Lee Harvey Oswald under the alias of O. H. Lee.
+LJSpeech-1.1/wavs/LJ018-0087.wav|Several cases of gigantic fraud, rivaling any already recorded, were brought to light between eighteen fifty-six and eighteen seventy-three.
+LJSpeech-1.1/wavs/LJ045-0179.wav|He took with him thirteen dollars, eighty-seven cents and the long brown package that Frazier and Mrs. Randle saw him carry
+LJSpeech-1.1/wavs/LJ001-0074.wav|the best, mostly French or Low-Country, was neat and clear, but without any distinction;
+LJSpeech-1.1/wavs/LJ022-0043.wav|I shall not confuse my discussion by a multitude of figures. So many figures are quoted to prove so many things.
+LJSpeech-1.1/wavs/LJ038-0043.wav|The other officers who helped subdue Oswald corroborated McDonald in his testimony
+LJSpeech-1.1/wavs/LJ034-0037.wav|Someone sitting on the box facing the window would have his palm in this position if he placed his hand alongside his right hip.
+LJSpeech-1.1/wavs/LJ001-0111.wav|the ends of many of the letters such as the t and e are hooked up in a vulgar and meaningless way,
+LJSpeech-1.1/wavs/LJ017-0022.wav|was that of Eliza Fenning, who was convicted of an attempt to poison a whole family
+LJSpeech-1.1/wavs/LJ026-0094.wav|This, however, is probably not a source of vital energy, but only contributes to the maintenance of the body temperature.
+LJSpeech-1.1/wavs/LJ045-0095.wav|She said that if she ever spoke to her husband the way Marina Oswald spoke to her husband, quote, we would not last long, end quote.
+LJSpeech-1.1/wavs/LJ012-0293.wav|But the medical evidence was clear that the decapitation had been effected during life, and the jury, after a short deliberation,
+LJSpeech-1.1/wavs/LJ050-0272.wav|and the traditions of the office in a democracy such as ours are so deep-seated as to preclude absolute security.
+LJSpeech-1.1/wavs/LJ042-0222.wav|Judged by his other statements and writings, however, he appears to have indicated his true feelings in the set of answers first presented
+LJSpeech-1.1/wavs/LJ016-0078.wav|but had to come down again covered with soot and filth just as the officers entered the ward.
+LJSpeech-1.1/wavs/LJ045-0094.wav|Jeanne De Mohrenschildt, however, thought that Marina Oswald, quote, said things that will hurt men's pride, end quote.
+LJSpeech-1.1/wavs/LJ007-0168.wav|"We cannot close these remarks," say the inspectors, "without an expression of the painful feelings with which we submit to your Lordship
+LJSpeech-1.1/wavs/LJ005-0212.wav|and the prison allowance was still limited to bread and water.
+LJSpeech-1.1/wavs/LJ012-0116.wav|A fire was lit in it, and the maid in doing so threw a lot of rubbish, as it seemed, which had accumulated under the grate, on top of the burning coals.
+LJSpeech-1.1/wavs/LJ049-0151.wav|Responsibilities for Presidential Protection
+LJSpeech-1.1/wavs/LJ043-0123.wav|He consulted with his supervisor and, quote, it was determined that we should not undertake to furnish employment references for him, end quote.
+LJSpeech-1.1/wavs/LJ005-0289.wav|In these the commissioners found "additional proof of the evils of continuing the present constitution of the local tribunals.
+LJSpeech-1.1/wavs/LJ038-0008.wav|Brewer knew from radio broadcasts that the President had been shot and that a patrolman had also been shot in Oak Cliff.
+LJSpeech-1.1/wavs/LJ046-0177.wav|which is maintained on a geographical field office basis.
+LJSpeech-1.1/wavs/LJ042-0132.wav|Return to the United States. In view of the intensity of his earlier commitment to the Soviet Union,
+LJSpeech-1.1/wavs/LJ006-0148.wav|Probably authority would not have been so recklessly usurped by the wardsmen had not the proper officials too readily surrendered it.
+LJSpeech-1.1/wavs/LJ041-0088.wav|which he strove to maintain and, quote, felt the Marine Corps kept a pretty close watch on him because of his subversive activities, end quote.
+LJSpeech-1.1/wavs/LJ045-0024.wav|to shoot General Walker.
+LJSpeech-1.1/wavs/LJ033-0150.wav|Sebastian F. Latona, supervisor of the FBI's Latent Fingerprint Section,
+LJSpeech-1.1/wavs/LJ050-0070.wav|The FBI now transmits information on all defectors, a category which would, of course, have included Oswald.
+LJSpeech-1.1/wavs/LJ006-0170.wav|and was surprised when the inspectors proved to him that practices of which he was ignorant, and which he admitted that he reprehended, went on without hindrance.
+LJSpeech-1.1/wavs/LJ030-0045.wav|The lead car. -- Described as a "rolling command car," this was an unmarked Dallas police car, driven by Chief of Police Curry
+LJSpeech-1.1/wavs/LJ011-0205.wav|he carried me away by fraud and stratagem, and forced me to accompany him to Gretna Green
+LJSpeech-1.1/wavs/LJ001-0167.wav|a device which deceives nobody, and makes a book very unpleasant to read.
+LJSpeech-1.1/wavs/LJ013-0259.wav|The actual method of the murder was never exactly ascertained.
+LJSpeech-1.1/wavs/LJ048-0283.wav|so that nothing can interfere with their bringing to their task the finest qualities and maximum resources of mind and body.
+LJSpeech-1.1/wavs/LJ011-0272.wav|The coach immediately drove off; Mr. Gee entered the house, asked for Mr. Heath, was told he would find him in the back kitchen at breakfast.
+LJSpeech-1.1/wavs/LJ034-0168.wav|Ronald Fischer and Robert Edwards were standing on the curb at the southwest corner of Elm and Houston Streets,
+LJSpeech-1.1/wavs/LJ039-0209.wav|They were instead firing to determine how rapidly the weapon could be fired and the area within which three shots could be placed.
+LJSpeech-1.1/wavs/LJ025-0077.wav|Their food is provided for them,
+LJSpeech-1.1/wavs/LJ033-0122.wav|Mrs. Randle said, when shown the paper bag,
+LJSpeech-1.1/wavs/LJ016-0109.wav|but not till they had had an exciting chase down the street.
+LJSpeech-1.1/wavs/LJ048-0138.wav|The wholehearted support of these local authorities was indispensable to the Service in carrying out its duties.
+LJSpeech-1.1/wavs/LJ004-0040.wav|They felt that private enterprise might
+LJSpeech-1.1/wavs/LJ011-0027.wav|The veil was lifted from his private life, and he was accused of persistent immorality.
+LJSpeech-1.1/wavs/LJ028-0446.wav|There lie the ruins of the famous temple of Esagil, sacred to Marduk.
+LJSpeech-1.1/wavs/LJ041-0173.wav|Thornley said that he had made his remark only in the context of "nineteen eighty-four"
+LJSpeech-1.1/wavs/LJ028-0282.wav|for it seemed to him that there was a divine providence in the man having used the phrase, and then his mule having foaled.
+LJSpeech-1.1/wavs/LJ027-0091.wav|See, then, the difference between man's mode of working and Nature's.
+LJSpeech-1.1/wavs/LJ018-0043.wav|It was really evidence of facts which could not be controverted or explained away.
+LJSpeech-1.1/wavs/LJ016-0129.wav|Down below were market-carts waiting for daylight, and among them Krapps found a refuge and friends.
+LJSpeech-1.1/wavs/LJ041-0138.wav|and that she could not think of any reason why Oswald would want to kill President Kennedy.
+LJSpeech-1.1/wavs/LJ025-0074.wav|has broken down, so far, at least, as his mode of stating it goes. And
+LJSpeech-1.1/wavs/LJ018-0224.wav|It had been supposed up to this date that he had willed his property, amounting in all to upwards of two hundred thousand pounds,
+LJSpeech-1.1/wavs/LJ017-0166.wav|but was careful to prime them with his facts and lead them if possible to accept his diagnosis of the case.
+LJSpeech-1.1/wavs/LJ003-0331.wav|The privileges of the master's side also disappeared; fees were nominally abolished, and garnish was scotched, although not yet killed outright.
+LJSpeech-1.1/wavs/LJ003-0056.wav|This was the chapel yard, with its five wards, which were calculated to hold seventy prisoners, but often held many more.
+LJSpeech-1.1/wavs/LJ046-0076.wav|He cannot and will not take the precautions of a dictator or a sovereign.
+LJSpeech-1.1/wavs/LJ039-0085.wav|it just takes a minor move in aiming to bring the crosshairs to bear, and then it is a quick squeeze.
+LJSpeech-1.1/wavs/LJ040-0223.wav|his lack of friends, the apparent unavailability of any agency assistance and the ineffectualness of his mother
+LJSpeech-1.1/wavs/LJ008-0075.wav|She is described as a well-made little woman, something more than thirty years of age, of a pale complexion and not disagreeable features.
+LJSpeech-1.1/wavs/LJ010-0207.wav|Thus, among the presidents were those of Gowrie, Justinian, Aloman, Colsman, Kenneth, and Godfrey;
+LJSpeech-1.1/wavs/LJ023-0039.wav|by meeting squarely our modern social and economic conditions.
+LJSpeech-1.1/wavs/LJ029-0105.wav|The route was further reviewed by Lawson and Sorrels with Assistant Chief Batchelor and members of the local host committee on November fifteen.
+LJSpeech-1.1/wavs/LJ034-0120.wav|the police radio reported that, quote, an eyeball witness, end quote, described the suspect in the Tippit shooting as, quote, a white male,
+LJSpeech-1.1/wavs/LJ032-0145.wav|also bore impressions of the same irregularities that appeared on the barrel of the rifle.
+LJSpeech-1.1/wavs/LJ002-0240.wav|At one time the Marshalsea was the receptacle of pirates, but none were committed to it after seventeen eighty-nine.
+LJSpeech-1.1/wavs/LJ046-0189.wav|PRS had received, over a twenty-year period, basic information on some fifty thousand cases;
+LJSpeech-1.1/wavs/LJ035-0162.wav|approximately thirty to forty-five seconds after Oswald's lunchroom encounter with Baker and Truly.
+LJSpeech-1.1/wavs/LJ040-0063.wav|Significant in shaping the character of Lee Harvey Oswald was the death of his father, a collector of insurance premiums.
+LJSpeech-1.1/wavs/LJ023-0093.wav|Chief Justice Hughes said in a dissenting opinion that the majority opinion was "a departure from sound principles,"
+LJSpeech-1.1/wavs/LJ024-0098.wav|And thirteen states which contain only five percent of the voting population can block ratification
+LJSpeech-1.1/wavs/LJ028-0036.wav|But in those very early days Babylon was little more than a shrine, surrounded with mud huts and date palms.
+LJSpeech-1.1/wavs/LJ002-0155.wav|for which the costs were six shillings ten pence.
+LJSpeech-1.1/wavs/LJ017-0034.wav|The case contained elements of much uncertainty, and yet it was so essential,
+LJSpeech-1.1/wavs/LJ048-0129.wav|In view of the constant change in the nature of threats to the President and the diversity of the dangers which may arise in the various cities within the United States,
+LJSpeech-1.1/wavs/LJ006-0176.wav|the punishments he inflicted were unequal,
+LJSpeech-1.1/wavs/LJ028-0082.wav|it was recognized that the cameo was genuine, and that it bore the likeness of the great King.
+LJSpeech-1.1/wavs/LJ009-0166.wav|next, the offenses of returning from transportation, stealing post-office letters, and sacrilege were no longer punishable with death.
+LJSpeech-1.1/wavs/LJ019-0337.wav|both in controlling his subordinates and in protecting the prisoners from petty tyranny and oppression, every one of whom he was to see once every twenty-four hours.
+LJSpeech-1.1/wavs/LJ050-0231.wav|Before the assassination the Secret Service infrequently requested other Federal law enforcement agencies to provide personnel
+LJSpeech-1.1/wavs/LJ024-0112.wav|To them I say: I do not think you will be able long to fool the American people as to your purposes.
+LJSpeech-1.1/wavs/LJ031-0075.wav|In the absence of any neurological, muscular, or heart response, the doctors concluded that efforts to revive the President were hopeless.
+LJSpeech-1.1/wavs/LJ033-0021.wav|after the birth of their second child.
+LJSpeech-1.1/wavs/LJ015-0101.wav|but at Mr. Fasson's pressing demands went upstairs to fetch the certificates.
+LJSpeech-1.1/wavs/LJ038-0228.wav|and into downtown Dallas through the Triple Underpass.
+LJSpeech-1.1/wavs/LJ041-0006.wav|His mother exercised little control over him and thought he could decide for himself whether to go on in school.
+LJSpeech-1.1/wavs/LJ003-0177.wav|if they happened to be in funds -- among whom was the Marquis of Sligo in eighteen eleven.
+LJSpeech-1.1/wavs/LJ048-0173.wav|arrangements were made for building and roof security by posting police officers where appropriate.
+LJSpeech-1.1/wavs/LJ031-0230.wav|After the autopsy was concluded at approximately eleven p.m., the President's body was prepared for burial.
+LJSpeech-1.1/wavs/LJ026-0063.wav|The food supply of the higher plant, like that of the animal, is the source of the required matter and energy,
+LJSpeech-1.1/wavs/LJ030-0112.wav|The motorcade left Love Field shortly after eleven:fifty a.m. and drove at speeds up to twenty-five to thirty miles an hour
+LJSpeech-1.1/wavs/LJ005-0013.wav|One smug alderman, a member of the House of Commons, sneered at the ultra philanthropy of the champions of prison improvement.
+LJSpeech-1.1/wavs/LJ003-0307.wav|but as yet no suggestion was made to provide prison uniform.
+LJSpeech-1.1/wavs/LJ005-0174.wav|in the deplorable situation in which many of them now are."
+LJSpeech-1.1/wavs/LJ042-0215.wav|but our, the people, culture, traditions, heritage, and very people itself, and yet
+LJSpeech-1.1/wavs/LJ005-0204.wav|Clun, in Shropshire, had a lock-up under the town hall.
+LJSpeech-1.1/wavs/LJ006-0086.wav|his place is assigned among the most depraved, the most experienced, and the most incorrigible offenders in the middle yard.
+LJSpeech-1.1/wavs/LJ037-0111.wav|Guinyard and Callaway ran to tenth and Patton and found Tippit lying in the street beside his car.
+LJSpeech-1.1/wavs/LJ039-0177.wav|was greater than from the second to the third shot and required a movement in the basic firing position of the marksmen.
+LJSpeech-1.1/wavs/LJ006-0038.wav|"a subject of magnitude and importance sufficient to exclude other jails," they soon narrowed their inquiry still further,
+LJSpeech-1.1/wavs/LJ010-0070.wav|followed the profession of arms, first in the British service, and then in that of the French revolutionary Government.
+LJSpeech-1.1/wavs/LJ027-0022.wav|An examination of the facts in each of the lines of evidence makes it clear
+LJSpeech-1.1/wavs/LJ018-0053.wav|he whispered in the ear of the German pastor who attended him on the scaffold,
+LJSpeech-1.1/wavs/LJ018-0027.wav|In little more than a week a cabman came forward and voluntarily made a statement which at once drew suspicion to a German, Franz Müller,
+LJSpeech-1.1/wavs/LJ023-0067.wav|That, my friends, is what I honestly believe
+LJSpeech-1.1/wavs/LJ010-0011.wav|This led to a rapid and marked increase in all kinds of fraud;
+LJSpeech-1.1/wavs/LJ023-0031.wav|It was almost too late.
+LJSpeech-1.1/wavs/LJ022-0022.wav|That is a tremendous gain for the principles of democracy.
+LJSpeech-1.1/wavs/LJ016-0220.wav|On another occasion an importunate person begged Calcraft eagerly to claim his right to the clothes, and give them to him.
+LJSpeech-1.1/wavs/LJ013-0166.wav|While Courvoisier opened the shutters, the housemaid, approaching the bed, saw that the pillow was saturated with blood.
+LJSpeech-1.1/wavs/LJ050-0165.wav|the Commission had occasion to become informed, to a limited extent, about the data processing techniques of other Federal intelligence and law enforcement agencies.
+LJSpeech-1.1/wavs/LJ024-0055.wav|We think it so much in the public interest to maintain a vigorous judiciary
+LJSpeech-1.1/wavs/LJ014-0214.wav|They walked together quietly for some little distance, then Cannon, without the slightest warning,
+LJSpeech-1.1/wavs/LJ036-0028.wav|McWatters' recollection is that he issued this transfer to a man who entered his bus just beyond Field Street,
+LJSpeech-1.1/wavs/LJ029-0135.wav|Elm Street, the northernmost of the three, after intersecting Houston curves in a southwesterly arc
+LJSpeech-1.1/wavs/LJ012-0027.wav|He had such great aptitude for business, and such a thorough knowledge of the real value of goods,
+LJSpeech-1.1/wavs/LJ032-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ046-0148.wav|that the safety of the President is or might be in danger, either at the present or in the future.
+LJSpeech-1.1/wavs/LJ030-0134.wav|because it convinced him that the average Dallas resident was like other American citizens in respecting and admiring the President.
+LJSpeech-1.1/wavs/LJ006-0042.wav|Relapse was rapid and inevitable, so that the latter state of the prison was worse than the first.
+LJSpeech-1.1/wavs/LJ017-0252.wav|The place at which they landed was not far from the entrance to the River Plate.
+LJSpeech-1.1/wavs/LJ047-0194.wav|Hosty received this information on the afternoon of November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ002-0190.wav|The best, or at least the most influential prisoners, got lodging in the State House, which contained "eight large handsome rooms."
+LJSpeech-1.1/wavs/LJ010-0032.wav|Pegsworth, and Greenacre, and Daniel Good merely reproduced types that had gone before, and that have since reappeared.
+LJSpeech-1.1/wavs/LJ045-0148.wav|After all, when will all your foolishness come to an end? All of these comedies. First one thing and then another. And now this fictitious name, end quote.
+LJSpeech-1.1/wavs/LJ018-0279.wav|assisted by Macdonell and Noyes, all of them citizens of the United States, by which the bank lost upwards of one hundred thousand pounds.
+LJSpeech-1.1/wavs/LJ032-0102.wav|According to her testimony, he compelled her to write the name "Hidell" on membership cards in the space designated for the signature of the "Chapter President."
+LJSpeech-1.1/wavs/LJ031-0191.wav|Nine minutes later, the Presidential airplane departed for Washington, D.C.
+LJSpeech-1.1/wavs/LJ028-0491.wav|then a second wall still higher, a third and fourth and a fifth, each stronger and higher than the others,
+LJSpeech-1.1/wavs/LJ029-0169.wav|so that crowds can get a good view of President Kennedy and his wife.
+LJSpeech-1.1/wavs/LJ008-0032.wav|a scaffold was erected in front of that prison for the execution of several convicts named by the Recorder.
+LJSpeech-1.1/wavs/LJ016-0391.wav|He ate constantly and voraciously after sentence, as though eager to cram as many meals as possible into the few hours still left him to live.
+LJSpeech-1.1/wavs/LJ006-0007.wav|as the inspectors of prisons found them in eighteen thirty-five to six.
+LJSpeech-1.1/wavs/LJ005-0082.wav|unless such ability (to work) should cease by reason of sickness, infirmity, the want of sufficient work, or from any other cause.
+LJSpeech-1.1/wavs/LJ044-0231.wav|While some of Castro's more severe criticisms of President Kennedy might have led Oswald to believe that he would be well received in Cuba
+LJSpeech-1.1/wavs/LJ011-0159.wav|At Manchester, en route, a traveling carriage was purchased, which was driven up to Mrs. Daulby's door at eight in the morning,
+LJSpeech-1.1/wavs/LJ030-0004.wav|The trip to Texas began with the departure of President and Mrs. Kennedy from the White House
+LJSpeech-1.1/wavs/LJ035-0023.wav|Baker testified that he entered the lobby of the building and, quote, spoke out and asked where the stairs or elevator was
+LJSpeech-1.1/wavs/LJ006-0012.wav|but he would not arm them with any authority lest their cooperation might be offensive.
+LJSpeech-1.1/wavs/LJ028-0216.wav|commanded him (Cyrus) to go forth to his city Babylon, and he went by his side as a friend and companion.
+LJSpeech-1.1/wavs/LJ042-0049.wav|I never believed I would find more material advantages at this stage of development in the Soviet Union than I might of had in the U.S.
+LJSpeech-1.1/wavs/LJ046-0053.wav|In recent years, Presidential journeys have been frequent and extensive,
+LJSpeech-1.1/wavs/LJ005-0153.wav|Sometimes this money might be expended in the purchase of extra articles of food.
+LJSpeech-1.1/wavs/LJ005-0152.wav|Earnings were very differently appropriated. Here the prisoners were given the whole amount, there a half or a third.
+LJSpeech-1.1/wavs/LJ034-0085.wav|found a clipboard hidden by book cartons in the northwest corner of the sixth floor at the west wall a few feet from where the rifle had been found.
+LJSpeech-1.1/wavs/LJ048-0154.wav|as discussed in chapter three.
+LJSpeech-1.1/wavs/LJ030-0107.wav|Presidential car, Presidential follow-up car,
+LJSpeech-1.1/wavs/LJ034-0177.wav|Fischer placed the man in the easternmost window on the south side of the Depository Building on either the fifth or the sixth floor.
+LJSpeech-1.1/wavs/LJ025-0140.wav|are not such, but are simply the visible aggregations of vastly more attenuated filaments,
+LJSpeech-1.1/wavs/LJ035-0024.wav|and this man, Mr. Truly, spoke up and says, it seems to me like he says,
+LJSpeech-1.1/wavs/LJ018-0050.wav|His arguments were specious and evasive when pressed to confess. "Why should man confess to man?" he replied;
+LJSpeech-1.1/wavs/LJ007-0179.wav|defeats the ends of justice, and disgraces the profession of a Christian country.
+LJSpeech-1.1/wavs/LJ044-0104.wav|He later wrote to another official of the Worker, seeking employment, and mentioning the praise he had received for submitting his photographic work.
+LJSpeech-1.1/wavs/LJ050-0178.wav|to make certain that all protective intelligence activities are coordinated.
+LJSpeech-1.1/wavs/LJ034-0162.wav|Brennan described to the police the man he saw in the window and then identified Oswald as the person who most nearly resembled the man he saw.
+LJSpeech-1.1/wavs/LJ016-0306.wav|and the question, as part of the whole subject of capital punishment, had been referred to a royal commission in January eighteen sixty-four.
+LJSpeech-1.1/wavs/LJ030-0128.wav|From Main Street the motorcade turned right and went north on Houston Street, passing tall buildings on the right,
+LJSpeech-1.1/wavs/LJ024-0101.wav|Bar Association, Manufacturers' Associations, who are trying to give the impression that they really do want a constitutional amendment
+LJSpeech-1.1/wavs/LJ006-0036.wav|They visited the wards after locking-up time, and saw with their own eyes what went on.
+LJSpeech-1.1/wavs/LJ003-0131.wav|He was an inmate of the same ward with others of the most dreadful sort, quote,
+LJSpeech-1.1/wavs/LJ048-0075.wav|each SAC should specifically designate an Agent (or Agents) to be responsible for developing
+LJSpeech-1.1/wavs/LJ011-0190.wav|On arrival, Wakefield pretended that they had missed Mr. Turner, and must follow him over to France.
+LJSpeech-1.1/wavs/LJ006-0117.wav|that none of its inmates were suffered to make tea or coffee for themselves lest it should interfere with his sales.
+LJSpeech-1.1/wavs/LJ010-0048.wav|These crimes had their origin too often in the disordered brains of lunatics at large, like Captain Goode.
+LJSpeech-1.1/wavs/LJ024-0096.wav|No amendment which any powerful economic interests or the leaders of any powerful political party have had reason to oppose
+LJSpeech-1.1/wavs/LJ027-0076.wav|These remarkable similarities in the common general plan alone are convincing evidences of descent,
+LJSpeech-1.1/wavs/LJ009-0167.wav|In eighteen thirty-seven Lord John Russell's acts swept away a number of capital offenses, including cutting and maiming, rick-burning,
+LJSpeech-1.1/wavs/LJ016-0030.wav|they preferred to credit it to carelessness or collusion from officers of the jail.
+LJSpeech-1.1/wavs/LJ039-0142.wav|After Oswald purchased the Mannlicher-Carcano rifle, he told his wife that he practiced with it.
+LJSpeech-1.1/wavs/LJ010-0190.wav|A craze for notoriety, to be achieved at any cost, was the one absorbing idea in young Oxford's disordered brain.
+LJSpeech-1.1/wavs/LJ035-0195.wav|Satisfying himself that Oswald was missing, Truly obtained Oswald's address, phone number, and description from his employment application card.
+LJSpeech-1.1/wavs/LJ034-0121.wav|twenty-seven, five foot eleven, one hundred sixty-five pounds, black wavy hair, end quote,
+LJSpeech-1.1/wavs/LJ003-0153.wav|Idleness was not so universally the rule in this part of the jail.
+LJSpeech-1.1/wavs/LJ015-0061.wav|They both reproduced many of the features of the case of Watts, already described,
+LJSpeech-1.1/wavs/LJ012-0139.wav|Solomons, had sold bar gold to the value of one thousand two hundred pounds to certain bullion dealers.
+LJSpeech-1.1/wavs/LJ022-0118.wav|No sectional, no political distinctions can be permitted.
+LJSpeech-1.1/wavs/LJ004-0006.wav|Howard's energy and devotion were rewarded by lively sympathy, but the desire to improve which followed his exposures was but short-lived.
+LJSpeech-1.1/wavs/LJ029-0049.wav|after undertaking the responsibility for advance preparations for the visit to Dallas, Agent Lawson went to the PRS offices in Washington.
+LJSpeech-1.1/wavs/LJ003-0127.wav|Quote, by insensible degrees he began to lose his repugnance to their society,
+LJSpeech-1.1/wavs/LJ015-0034.wav|The engagements of the bank on these two heads amounting to nearly half a million of money,
+LJSpeech-1.1/wavs/LJ044-0038.wav|promptly closed three days later for some obscure reasons by the renters,
+LJSpeech-1.1/wavs/LJ033-0053.wav|Only if disassembled could the rifle fit into the paper bag found near the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ019-0224.wav|With the reduction of numbers to be accommodated, there was ample space in Newgate for its reconstruction on the most approved modern lines.
+LJSpeech-1.1/wavs/LJ005-0266.wav|that food and fuel should be issued in kind, and never provided by the prisoners themselves out of monies granted them.
+LJSpeech-1.1/wavs/LJ009-0059.wav|Friends, even utter strangers, have interceded for him, and to the last he has buoyed himself up by hope of reprieve.
+LJSpeech-1.1/wavs/LJ035-0124.wav|Neither Jarman, Norman, Williams, or Dougherty saw Oswald.
+LJSpeech-1.1/wavs/LJ042-0238.wav|freedom of speech, travel outspoken opposition to unpopular policies, freedom to believe in god, end quote, while the Soviet Union did not.
+LJSpeech-1.1/wavs/LJ048-0229.wav|The statements of the agents involved are supported by statements of members of the Fort Worth press who accompanied or observed them
+LJSpeech-1.1/wavs/LJ016-0379.wav|All as a rule, unless poignant remorse has brought a desire to court their richly-merited retribution, are buoyed up
+LJSpeech-1.1/wavs/LJ004-0010.wav|with a few bright exceptions, still deplorable and disgraceful.
+LJSpeech-1.1/wavs/LJ049-0063.wav|Beyond these two principles the Secret Service believes a detailed contingency or emergency plan is not feasible
+LJSpeech-1.1/wavs/LJ042-0013.wav|that while he had begun to read Marx and Engels at the age of fifteen, the conclusive thing that made him decide that Marxism was the answer
+LJSpeech-1.1/wavs/LJ043-0173.wav|and the trail of evidence he left behind him on that occasion.
+LJSpeech-1.1/wavs/LJ005-0094.wav|to call for information as to the observance of its provisions.
+LJSpeech-1.1/wavs/LJ030-0166.wav|On turning she saw a quizzical look on her husband's face as he raised his left hand to his throat.
+LJSpeech-1.1/wavs/LJ016-0343.wav|The sufferer was a porter on the London, Chatham, and Dover railway, sentenced to death for shooting the station-master at Dover.
+LJSpeech-1.1/wavs/LJ030-0136.wav|end quote, the President replied, "That is very obvious."
+LJSpeech-1.1/wavs/LJ002-0133.wav|Alfred the Great established the Court Baron, the Hundred Court, and the County Court, which among other matters entertained pleas for debt.
+LJSpeech-1.1/wavs/LJ014-0339.wav|asked Davidson and Gordon, a firm with which Cole was closely allied, whether the warrants meant goods or nothing.
+LJSpeech-1.1/wavs/LJ003-0248.wav|The irons weighed from three to four pounds, but heavier irons, seven or eight pounds' weight,
+LJSpeech-1.1/wavs/LJ045-0219.wav|that he wanted to make peace with her and bring his family to Dallas where they could live with him again.
+LJSpeech-1.1/wavs/LJ049-0192.wav|It was also suggested that it would take a substantial period of time for the Secret Service to build up the experience and skills necessary to meet the problem.
+LJSpeech-1.1/wavs/LJ049-0006.wav|These precautions included reserving a ceremonial area for the Presidential party,
+LJSpeech-1.1/wavs/LJ043-0177.wav|and his Communist and Socialist Worker's Party newspapers would probably have appeared on the front pages of newspapers or magazines all over the country,
+LJSpeech-1.1/wavs/LJ003-0041.wav|There was no lack of air and light for the new jail, and several exercising yards.
+LJSpeech-1.1/wavs/LJ006-0026.wav|England was represented by Mr. Crawford, and the result of his inquiry was given to the public as an appendix to the House of Commons' Report on Secondary Punishments.
+LJSpeech-1.1/wavs/LJ047-0046.wav|Agent Fain was not satisfied by this interview and arranged to see Oswald again on August sixteen, nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ044-0192.wav|and as far as the performance of the Cubans themselves was concerned, he was, quote, disappointed at not being able to get to Cuba
+LJSpeech-1.1/wavs/LJ006-0157.wav|This was only one of many grave omissions on his part. He did not feel it incumbent on himself to visit his prison often or see his prisoners.
+LJSpeech-1.1/wavs/LJ039-0233.wav|On the basis of Oswald's training and the accuracy of the weapon as established by the tests,
+LJSpeech-1.1/wavs/LJ018-0119.wav|His offer was not, however, accepted.
+LJSpeech-1.1/wavs/LJ044-0220.wav|The general conflict of views between the United States and Cuba was, of course, reflected in other media to such an extent
+LJSpeech-1.1/wavs/LJ038-0237.wav|The Commission confirmed, by comparison with other photographs, that these were, indeed, photographs of the rear of Walker's house.
+LJSpeech-1.1/wavs/LJ043-0065.wav|Oswald experienced some difficulty finding employment. Perhaps this was partially because of his lack of any specific skill or training.
+LJSpeech-1.1/wavs/LJ019-0333.wav|were elected to inspect the prisons frequently, to examine the prisoners, hear complaints, and check abuses.
+LJSpeech-1.1/wavs/LJ014-0251.wav|In this way Watts had complete control over the whole of the monetary transactions of the company.
+LJSpeech-1.1/wavs/LJ007-0067.wav|stigmatized his often most just strictures on abuses as "a bundle of nonsense," and the aldermen, when he appealed to them for protection and countenance,
+LJSpeech-1.1/wavs/LJ008-0171.wav|and it passed along the heads of the crowd, to be caught at last by a person who struggled with it to a cart and deposited it there in safety.
+LJSpeech-1.1/wavs/LJ014-0275.wav|but it was long before the public realized that the fraudulent clerk and the great theatrical manager were one and the same person.
+LJSpeech-1.1/wavs/LJ040-0238.wav|Such a placement was postponed, however, perhaps in part at least because Lee's behavior suddenly improved.
+LJSpeech-1.1/wavs/LJ038-0194.wav|seven. Certain of my documents are in the small blue valise.
+LJSpeech-1.1/wavs/LJ003-0218.wav|The chapel was filled with a curious but callous congregation, who came to stare at the miserable people thus publicly exposed.
+LJSpeech-1.1/wavs/LJ002-0039.wav|they each contained four small rooms or "cabins" seven feet square,
+LJSpeech-1.1/wavs/LJ041-0063.wav|He said that Oswald, quote, seemed to guard against developing real close friendships, end quote,
+LJSpeech-1.1/wavs/LJ002-0219.wav|and to have been guilty of extortion to others. One Sir William Rich, Bart., he had loaded with heavy irons
+LJSpeech-1.1/wavs/LJ038-0030.wav|The man was Oswald. He was sitting alone in the rear of the main floor of the theatre near the right center aisle.
+LJSpeech-1.1/wavs/LJ028-0363.wav|and there on June thirteen, three twenty-three B.C., he met his death.
+LJSpeech-1.1/wavs/LJ024-0066.wav|But chance and the disinclination of individuals to leave the Supreme bench
+LJSpeech-1.1/wavs/LJ026-0121.wav|The destruction of protoplasm, excess proteids or the fat and carbohydrate foods is catabolism, destructive metabolism.
+LJSpeech-1.1/wavs/LJ027-0062.wav|viz., the brain; and also usually, but not always, a number of posterior joints,
+LJSpeech-1.1/wavs/LJ048-0022.wav|Answer: Yes. It was a self-serving interview. The visits with the Soviet Embassy were evidently for the purpose of securing a visa,
+LJSpeech-1.1/wavs/LJ032-0144.wav|was confirmed by FBI Laboratory tests which established that the adhesive material bearing the print
+LJSpeech-1.1/wavs/LJ013-0041.wav|He was an honest sea-captain, he said, trading from Liverpool, where once he had the misfortune to be introduced to a man of wealth,
+LJSpeech-1.1/wavs/LJ023-0029.wav|we cannot delay one moment in making certain that our national government has power to carry through.
+LJSpeech-1.1/wavs/LJ012-0019.wav|The name of Ikey Solomons was long remembered by thief and thief-taker.
+LJSpeech-1.1/wavs/LJ022-0044.wav|Sometimes it depends upon what paper you read and what broadcast you hear.
+LJSpeech-1.1/wavs/LJ017-0085.wav|Palmer's plan was to administer poison in quantities insufficient to cause death, but enough to produce illness which would account for death.
+LJSpeech-1.1/wavs/LJ050-0121.wav|Detailed formal agreements embodying these arrangements should be worked out between the Secret Service and both of these agencies.
+LJSpeech-1.1/wavs/LJ027-0102.wav|which in other and allied kinds of animals and plants are of large size and functional utility.
+LJSpeech-1.1/wavs/LJ046-0032.wav|or their desire to have frequent and easy access to the people.
+LJSpeech-1.1/wavs/LJ016-0134.wav|Cases well authenticated have been known of men who had all but completed their sentences, and for whom the prison gates would open within a few days,
+LJSpeech-1.1/wavs/LJ002-0170.wav|There was in the City Road a temporary bar, with a collector of tolls who was sometimes on the spot and sometimes not.
+LJSpeech-1.1/wavs/LJ024-0058.wav|or make independent on upon the desire or prejudice of any individual justice?
+LJSpeech-1.1/wavs/LJ004-0028.wav|Various acts were also passed to consolidate and amend previous jail acts.
+LJSpeech-1.1/wavs/LJ005-0190.wav|the knowledge and practice of every species of criminality.
+LJSpeech-1.1/wavs/LJ048-0160.wav|either by police or by custodians of the buildings, since it was not the usual practice of the Secret Service to do so.
+LJSpeech-1.1/wavs/LJ004-0104.wav|By the thirty-one George the third c. forty-six, s. five,
+LJSpeech-1.1/wavs/LJ039-0007.wav|In January nineteen sixty-four, Marina Oswald and her business manager, James Martin,
+LJSpeech-1.1/wavs/LJ029-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ046-0074.wav|If the sole goal were to protect the life of the President, it could be accomplished with reasonable assurance despite the multiple roles he must play.
+LJSpeech-1.1/wavs/LJ027-0180.wav|The general similarity of the three series is complete.
+LJSpeech-1.1/wavs/LJ028-0051.wav|He enlarged the old city, erected temples, and began the construction of its walls.
+LJSpeech-1.1/wavs/LJ005-0067.wav|The Prison Society reproves the misdirected efforts of ambitious architects, who by a lavish and improvident expenditure of public money
+LJSpeech-1.1/wavs/LJ028-0237.wav|with orders to march into the town by the bed of the stream, as soon as the water became shallow enough:
+LJSpeech-1.1/wavs/LJ027-0083.wav|For such a series of facts the reader must be referred to special books like Wiedersheim's "Comparative Anatomy of the Vertebrates,"
+LJSpeech-1.1/wavs/LJ029-0029.wav|When the trip was planned for only one day, Governor Connally had opposed the motorcade because there was not enough time. The Governor stated, however, that, quote,
+LJSpeech-1.1/wavs/LJ037-0028.wav|He saw him empty the gun and throw the shells into some bushes on the southeast corner lot.
+LJSpeech-1.1/wavs/LJ013-0141.wav|whose ship brought home a mixed cargo from Bahia and other ports.
+LJSpeech-1.1/wavs/LJ050-0230.wav|Manpower and Technical Assistance From Other Agencies
+LJSpeech-1.1/wavs/LJ032-0065.wav|In accordance with postal regulations, the portion of the application which lists names of persons, other than the applicant, entitled to receive mail
+LJSpeech-1.1/wavs/LJ033-0073.wav|Mrs. Paine had the actual blanket before her as she testified and she indicated that the blanket hung limp in the officer's hand.
+LJSpeech-1.1/wavs/LJ013-0013.wav|with other underwriters for a second sum of two thousand pounds.
+LJSpeech-1.1/wavs/LJ013-0062.wav|to draw the dividends on more than one sum.
+LJSpeech-1.1/wavs/LJ035-0116.wav|Three employees -- James Jarman, Jr.,
+LJSpeech-1.1/wavs/LJ026-0153.wav|the other part of respiration, elimination of carbon dioxide, has been treated under excretions.
+LJSpeech-1.1/wavs/LJ039-0116.wav|he had just completed a very intensive preliminary training period.
+LJSpeech-1.1/wavs/LJ045-0229.wav|He then killed Patrolman Tippit when that police officer apparently tried to question him after he had left his roominghouse and he vigorously resisted arrest
+LJSpeech-1.1/wavs/LJ028-0157.wav|This river is the Euphrates, a broad, deep, swift stream, which rises in Armenia, and empties itself into the Erythraean Sea.
+LJSpeech-1.1/wavs/LJ031-0181.wav|onto the rear of the Presidential plane where seats had been removed to make room.
+LJSpeech-1.1/wavs/LJ016-0072.wav|where he presently committed a burglary at Lymington, was caught, and lodged in Winchester Jail.
+LJSpeech-1.1/wavs/LJ043-0154.wav|after he thought that what he had written in his book might be proof against him, and he destroyed it (the book), end quote.
+LJSpeech-1.1/wavs/LJ036-0112.wav|Whaley said that Oswald was the man under Number two.
+LJSpeech-1.1/wavs/LJ006-0161.wav|Again, a turnkey deposed that his chief did not enter the wards more than once a fortnight.
+LJSpeech-1.1/wavs/LJ013-0038.wav|The merchant Wallace said he had been led into the crime by the advice and example of a city friend who had gone largely into this nefarious business;
+LJSpeech-1.1/wavs/LJ009-0033.wav|while behind their pew stood a couple of tall footmen in state liveries.
+LJSpeech-1.1/wavs/LJ044-0070.wav|WDSU has no program of any kind called, quote, Latin American Focus, end quote.
+LJSpeech-1.1/wavs/LJ039-0218.wav|Simmons described it as, quote, quite accurate, end quote, in fact, as accurate as current military rifles.
+LJSpeech-1.1/wavs/LJ022-0068.wav|Another helpful feature of unemployment insurance is the incentive it will give to employers to plan more carefully
+LJSpeech-1.1/wavs/LJ042-0106.wav|He is reported to have expressed the conclusion that they had, quote, fat stinking politicians over there just like we have over here, end quote.
+LJSpeech-1.1/wavs/LJ002-0228.wav|The Committee on Jails reported that, quote, although the house of the warden looked into the court,
+LJSpeech-1.1/wavs/LJ007-0215.wav|are the association of prisoners, and the unusual contamination to which such association gives rise.
+LJSpeech-1.1/wavs/LJ014-0048.wav|But at the fatal hour his hardihood forsook him, and he was almost insensible when taken out of his cell for execution.
+LJSpeech-1.1/wavs/LJ025-0053.wav|to the investigation of organic structure,
+LJSpeech-1.1/wavs/LJ034-0090.wav|Eyewitness Identification of Assassin
+LJSpeech-1.1/wavs/LJ035-0183.wav|At twelve:thirty-four p.m. Sawyer heard a call over the police radio that the shots had come from the Depository Building.
+LJSpeech-1.1/wavs/LJ014-0060.wav|Marie de Roux up to the time of her marriage had been in service as lady's maid to Lady Blantyre, daughter of the Duchess of Sutherland,
+LJSpeech-1.1/wavs/LJ011-0265.wav|This Mr. Canning had left his widow a life interest in two thousand pounds so long as she remained unmarried.
+LJSpeech-1.1/wavs/LJ022-0070.wav|Provisions for social security, however, are protections for the future.
+LJSpeech-1.1/wavs/LJ032-0175.wav|and identified the shirt as being the one worn by Oswald primarily because of a distinctive hole in the shirt's right elbow.
+LJSpeech-1.1/wavs/LJ003-0336.wav|but it at once deprecated the idea that the city could follow the laudable example thus set in the provinces. Quote,
+LJSpeech-1.1/wavs/LJ033-0155.wav|Both concluded that the prints were the right palm and left index finger of Lee Oswald. No other identifiable prints were found on the bag.
+LJSpeech-1.1/wavs/LJ026-0045.wav|e.g., a bean plant with a higher animal, e.g., frog or even man.
+LJSpeech-1.1/wavs/LJ011-0136.wav|Still no sentence of death was carried out for the offense, and in eighteen thirty-two
+LJSpeech-1.1/wavs/LJ034-0061.wav|The results of this investigation are fully discussed in chapter six, page two forty-nine.
+LJSpeech-1.1/wavs/LJ016-0339.wav|troops were held in readiness to act if required. Everything passed off quite quietly, however,
+LJSpeech-1.1/wavs/LJ023-0135.wav|In forty-five out of the forty-eight states of the Union, judges are chosen not for life but for a period of years.
+LJSpeech-1.1/wavs/LJ042-0032.wav|and of his initial commitment to that country can best be understood, however, in the context
+LJSpeech-1.1/wavs/LJ049-0186.wav|the Commission received a number of proposals designed to improve current arrangements for protecting the President.
+LJSpeech-1.1/wavs/LJ041-0030.wav|One of his fellow employees, Palmer McBride, stated that Oswald said he would like to kill President Eisenhower because he was exploiting the working class.
+LJSpeech-1.1/wavs/LJ019-0093.wav|and because the machine was often so imperfect that the amount of effort could not be exactly regulated.
+LJSpeech-1.1/wavs/LJ018-0107.wav|eighty years of age, who had spent his whole life in imitating other people's signatures, and had acquired the most consummate skill in the practice.
+LJSpeech-1.1/wavs/LJ010-0142.wav|Be this as it may, the weapon used was only an ordinary axe, which rather indicates that force, not skill, was employed.
+LJSpeech-1.1/wavs/LJ027-0033.wav|On the other hand, external structures are likely to undergo adaptation when habits or conditions of life change.
+LJSpeech-1.1/wavs/LJ025-0134.wav|A molecular change takes place in the nerve of the tentacle,
+LJSpeech-1.1/wavs/LJ022-0125.wav|every profession has its black sheep, but long experience in government has taught me
+LJSpeech-1.1/wavs/LJ041-0041.wav|This was one year before his actual enlistment and just a little over two point five years after he left New York.
+LJSpeech-1.1/wavs/LJ049-0156.wav|Secret Service men accompanied the President and his family to their vacation home in Massachusetts
+LJSpeech-1.1/wavs/LJ039-0042.wav|I was absolutely convinced it was Nixon and now after all these questions I wonder if I am right in my mind? End quote.
+LJSpeech-1.1/wavs/LJ029-0092.wav|On November eight, when Lawson was briefed on the itinerary for the trip to Dallas,
+LJSpeech-1.1/wavs/LJ026-0030.wav|The distinction between the higher plants and animals is perfectly sharp and obvious,
+LJSpeech-1.1/wavs/LJ048-0017.wav|We satisfied ourselves that we had met our requirement, namely to find out whether he had been recruited by Soviet intelligence. The case was closed.
+LJSpeech-1.1/wavs/LJ014-0070.wav|a heavy brutish fellow, was yet aghast at his wife's resolve, and tried hard to dissuade her from bad purpose.
+LJSpeech-1.1/wavs/LJ013-0119.wav|Howse, the steward, accused the other servants, but they retorted, declaring that he had been visited by the thief the day previous,
+LJSpeech-1.1/wavs/LJ036-0092.wav|and said, quote, Everybody will know who I am now, end quote.
+LJSpeech-1.1/wavs/LJ029-0025.wav|When Governor Connally called at the White House on October four to discuss the details of the visit,
+LJSpeech-1.1/wavs/LJ042-0112.wav|said that Oswald told her that he had returned because, quote, I didn't find what I was looking for, end quote.
+LJSpeech-1.1/wavs/LJ045-0156.wav|Answer: He tried to talk to me but I would not answer him, and he was very upset. Question: Were you upset with him?
+LJSpeech-1.1/wavs/LJ005-0044.wav|the method by which they were worked, and the dietaries of the prisoners employed upon them.
+LJSpeech-1.1/wavs/LJ037-0125.wav|As Oswald ran south on Patton Avenue toward Jefferson Boulevard he was moving in the direction of a used-car lot
+LJSpeech-1.1/wavs/LJ024-0129.wav|brings again to mind that crude and cruel strategy tried by the same opposition
+LJSpeech-1.1/wavs/LJ006-0017.wav|with those who made the selection of the first inspectors, and the two gentlemen appointed were probably the most fitted in England to be so employed.
+LJSpeech-1.1/wavs/LJ025-0047.wav|The plant withdraws water and carbonic acid from the atmosphere, the animal contributes both to it.
+LJSpeech-1.1/wavs/LJ035-0057.wav|Missing Baker, he came back to find the officer in the doorway to the lunchroom facing Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ005-0046.wav|The good it tried to do took active shape in the establishment of temporary refuges -- at Hoxton for males, and in the Hackney Road for females
+LJSpeech-1.1/wavs/LJ002-0211.wav|The sale of spirits was forbidden, but gin could always be had at the whistling shops, where it was known as Moonshine, Sky Blue,
+LJSpeech-1.1/wavs/LJ013-0189.wav|next, and in the same place, a chased gold key;
+LJSpeech-1.1/wavs/LJ008-0238.wav|as it lay, white and ghastly, upon the black boards, and then again was as suddenly withdrawn, as if ashamed of the deed it had done.
+LJSpeech-1.1/wavs/LJ010-0292.wav|which was authorized by a recent act, on account of Mr. Pate's family and position in life.
+LJSpeech-1.1/wavs/LJ006-0270.wav|But no search was made to intercept prohibited articles at the gate, and there was no permanent gate-keeper,
+LJSpeech-1.1/wavs/LJ048-0242.wav|Three members of this shift separately took this opportunity to visit the Cellar Coffee House.
+LJSpeech-1.1/wavs/LJ040-0134.wav|that anybody entering this home had to be searched in case the parents were bringing cigarettes or narcotics or anything, end quote.
+LJSpeech-1.1/wavs/LJ001-0057.wav|Parma, and one or two other cities, who produced the splendid editions of the Classics, which are one of the great glories of the printer's art,
+LJSpeech-1.1/wavs/LJ027-0050.wav|Homology thus means identity of structure which is the result of identity of parentage. It is the stamp of heredity.
+LJSpeech-1.1/wavs/LJ014-0300.wav|Sentence of death had been recorded against Donovan, who, like Watts, had seemingly been overcome with sudden despair.
+LJSpeech-1.1/wavs/LJ018-0163.wav|Griffiths was an unusually clever and skilful workman,
+LJSpeech-1.1/wavs/LJ025-0107.wav|They absorb oxygen and give out carbonic acid.
+LJSpeech-1.1/wavs/LJ003-0208.wav|a number of amateurs were ever ready to give their gratuitous ministrations to the condemned.
+LJSpeech-1.1/wavs/LJ037-0048.wav|Helen Markham was screaming as she leaned over the body.
+LJSpeech-1.1/wavs/LJ003-0167.wav|The fee for admission to the state side, as fixed by the table of fees, was three guineas, but Mr. Newman declared that he never took more than two.
+LJSpeech-1.1/wavs/LJ032-0066.wav|was thrown away after the box was closed in May nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ007-0174.wav|should be for a number of years in undisturbed operation, not only in contempt of religion and humanity,
+LJSpeech-1.1/wavs/LJ039-0147.wav|sighting with the telescopic lens and operating the bolt.
+LJSpeech-1.1/wavs/LJ049-0205.wav|this Commission has necessarily examined into the functioning of the various Federal agencies concerned with the tragic trip of President Kennedy to Dallas
+LJSpeech-1.1/wavs/LJ036-0129.wav|The man asked, quote, May I have the cab?, end quote, and got into the front seat.
+LJSpeech-1.1/wavs/LJ005-0048.wav|which he could bestow on prisoners on release, and so save the better-disposed or the completely destitute from lapsing at once into crime.
+LJSpeech-1.1/wavs/LJ022-0170.wav|Not only business recovery, but the general economic recovery of the nation will be greatly stimulated by the enactment of legislation
+LJSpeech-1.1/wavs/LJ007-0053.wav|"Joseph Coleman put in irons for three days for striking one of the prisoners," in the same place.
+LJSpeech-1.1/wavs/LJ036-0168.wav|If the cab ride was approximately six minutes, as was the reconstructed ride,
+LJSpeech-1.1/wavs/LJ028-0351.wav|As for Zopyrus he was considered by Darius to have surpassed, in the greatness of his achievements, all other Persians,
+LJSpeech-1.1/wavs/LJ037-0227.wav|Mrs. Markham told Poe
+LJSpeech-1.1/wavs/LJ011-0220.wav|The marriage was annulled by an Act of Parliament, although Wakefield petitioned against it,
+LJSpeech-1.1/wavs/LJ044-0112.wav|Marina Oswald said that such correspondence from people he considered important meant much to Oswald.
+LJSpeech-1.1/wavs/LJ037-0038.wav|at the southeast corner of the intersection, approximately fifty feet away.
+LJSpeech-1.1/wavs/LJ017-0205.wav|Wilson was duly sentenced to death, and suffered impenitent, hardened, and without any confession of her guilt.
+LJSpeech-1.1/wavs/LJ025-0152.wav|So that I know not where we can hope to find any absolute distinction between animals and plants, unless we return to their mode of nutrition
+LJSpeech-1.1/wavs/LJ030-0227.wav|Special Agent George W. Hickey, Jr., in the rear seat of the Presidential follow-up car,
+LJSpeech-1.1/wavs/LJ025-0123.wav|has not only been discovered to exist far more widely among plants than was formerly imagined,
+LJSpeech-1.1/wavs/LJ006-0193.wav|Idle hands and unoccupied brains found in mischief the only means of whiling away the long hours of incarceration.
+LJSpeech-1.1/wavs/LJ021-0065.wav|but also to the owners and managers of industry because,
+LJSpeech-1.1/wavs/LJ015-0290.wav|the forgers always escaped detection. But fate overtook two of the gang,
+LJSpeech-1.1/wavs/LJ035-0042.wav|Since the vestibule door is only a few feet from the lunchroom door, the man must have entered the vestibule only a second or two before Baker arrived at the top of the stairwell.
+LJSpeech-1.1/wavs/LJ044-0221.wav|that there can be no doubt that Oswald was aware generally of the critical attitude that Castro expressed about President Kennedy.
+LJSpeech-1.1/wavs/LJ018-0103.wav|and silver coins, with all the latest appliances for coining, including those of electroplating;
+LJSpeech-1.1/wavs/LJ014-0002.wav|Hocker's murder is in its way interesting, as affording another proof of the extraordinary way in which the culprit returned to the scene of his guilt.
+LJSpeech-1.1/wavs/LJ027-0043.wav|and mean nothing more than similarity of environment.
+LJSpeech-1.1/wavs/LJ006-0294.wav|In the bail-dock, where most improper general association was permitted, the female prisoners were often altogether in the charge of male turnkeys.
+LJSpeech-1.1/wavs/LJ002-0032.wav|These were: one. The male debtors' side.
+LJSpeech-1.1/wavs/LJ043-0082.wav|and noted that Oswald's verbal and clerical potential was, quote, outstanding, end quote.
+LJSpeech-1.1/wavs/LJ006-0296.wav|and was in the habit of drawing frequently upon the female prison for prisoners to act as domestic servants in his own private dwelling.
+LJSpeech-1.1/wavs/LJ004-0138.wav|One poor wretch, who had slept next the wall, said he had been literally unable to move for the pressure.
+LJSpeech-1.1/wavs/LJ028-0283.wav|As soon therefore as he felt within himself that Babylon was fated to be taken, he went to Darius and asked him if he set a very high value on its conquest.
+LJSpeech-1.1/wavs/LJ028-0012.wav|she realizes that she is bound on the long journey across the desert. Hour after hour she bears you over the hard monotonous plain.
+LJSpeech-1.1/wavs/LJ001-0075.wav|the worst, which perhaps was the English, was a terrible falling-off from the work of the earlier presses;
+LJSpeech-1.1/wavs/LJ007-0159.wav|Forced and constant intercourse with the most depraved individuals of his own class;
+LJSpeech-1.1/wavs/LJ021-0174.wav|England has her peculiarities and we have ours
+LJSpeech-1.1/wavs/LJ029-0200.wav|Two days before the President's arrival Chief Curry warned that the Dallas police would not permit improper conduct during the President's visit.
+LJSpeech-1.1/wavs/LJ018-0094.wav|Forged cheques and bills were soon uttered in great numbers, as well as base coin.
+LJSpeech-1.1/wavs/LJ014-0023.wav|and many of the same sort were found in the possession of the accused. This was enough to obtain a committal,
+LJSpeech-1.1/wavs/LJ005-0243.wav|Nevertheless a committee of the House of Commons was appointed in eighteen thirty-one to report upon the whole system of secondary punishments,
+LJSpeech-1.1/wavs/LJ018-0248.wav|He told his story with perfect coolness and self-possession, but in a grave and serious tone.
+LJSpeech-1.1/wavs/LJ033-0209.wav|The preponderance of the evidence supports the conclusion that Lee Harvey Oswald (one)
+LJSpeech-1.1/wavs/LJ049-0117.wav|that the interruption shall not take place for an hour, end quote.
+LJSpeech-1.1/wavs/LJ007-0140.wav|The primary object of committing a prisoner to jail, as the inspectors pointed out, was to deter not only the criminal himself, but others from crime,
+LJSpeech-1.1/wavs/LJ030-0119.wav|The crowds were so dense that Special Agent Clinton J. Hill
+LJSpeech-1.1/wavs/LJ019-0187.wav|and he was therefore prepared to submit to Parliament a proposal that half the cost of reconstruction should be borne by public funds.
+LJSpeech-1.1/wavs/LJ007-0114.wav|A certain number, who could afford the high rate of a guinea per diem, fixed by the under sheriff, were lodged in the governor's house,
+LJSpeech-1.1/wavs/LJ014-0316.wav|Inquiries were instituted when the cheque was protested, which led to the discovery of the forgeries.
+LJSpeech-1.1/wavs/LJ032-0081.wav|On the Hidell Selective Service card there appeared a signature, "Alek J. Hidell,"
+LJSpeech-1.1/wavs/LJ044-0090.wav|I think that we finished him on that program because we had publicly linked the Fair Play for Cuba Committee
+LJSpeech-1.1/wavs/LJ022-0166.wav|This legislation will not only in the long run result in providing lower electric and gas rates to the consumer,
+LJSpeech-1.1/wavs/LJ028-0270.wav|This was said by a Babylonian who thought that a mule would never foal.
+LJSpeech-1.1/wavs/LJ009-0182.wav|This dissection was carried out for Newgate prisoners in Surgeons' Hall, adjoining Newgate,
+LJSpeech-1.1/wavs/LJ003-0168.wav|Ten and sixpence a week more was charged as rent for a single bed; where two or more slept in a bed the rent was seven shillings a week each.
+LJSpeech-1.1/wavs/LJ035-0105.wav|They took the east elevator to the seventh floor and ran up a stairway to the roof where they searched for several minutes.
+LJSpeech-1.1/wavs/LJ028-0394.wav|he passed other unimportant fortresses, and came at last to a walled enclosure, which the natives pointed out as a royal hunting ground.
+LJSpeech-1.1/wavs/LJ016-0182.wav|he was a shoemaker by trade, and settled in London after his marriage in eighteen twenty-five.
+LJSpeech-1.1/wavs/LJ018-0281.wav|Their operations were no less worldwide.
+LJSpeech-1.1/wavs/LJ022-0060.wav|We must begin now to make provision for the future.
+LJSpeech-1.1/wavs/LJ042-0073.wav|Oswald went on October thirty-one, to the American Embassy to renounce his U.S. citizenship.
+LJSpeech-1.1/wavs/LJ022-0071.wav|Our responsibility for the immediate necessities of the unemployed has been met by the Congress
+LJSpeech-1.1/wavs/LJ003-0036.wav|Authority was given to raise money on the Orphans' Fund to the extent of ninety thousand pounds.
+LJSpeech-1.1/wavs/LJ004-0119.wav|which had continued through that long period, are forcibly pointed out.
+LJSpeech-1.1/wavs/LJ031-0149.wav|A nurse was asked to identify hospital personnel and to tell everyone, except necessary medical staff members, to leave the emergency room.
+LJSpeech-1.1/wavs/LJ004-0131.wav|To specify more particularly one or two of the worst, it may be mentioned that in the Borough Compter
+LJSpeech-1.1/wavs/LJ016-0265.wav|the crowd had come to witness a popular and gratuitous public performance -- better than a prize-fight or a play.
+LJSpeech-1.1/wavs/LJ010-0090.wav|Hand-grenades were to be thrown into the dining-room, and during the noise and confusion the assassination of the ministers was to be completed,
+LJSpeech-1.1/wavs/LJ026-0141.wav|The construction in the cells of new proteid from the absorbed carbohydrate and the materials from the soil is true anabolism.
+LJSpeech-1.1/wavs/LJ034-0194.wav|Their testimony is of probative value, however, because their limited description is consistent with that of the man who has been found by the Commission,
+LJSpeech-1.1/wavs/LJ039-0015.wav|and testified that a few days before her husband's departure from Dallas to New Orleans on April twenty-four, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ007-0037.wav|In the midst of the noisy and blasphemous talk no one could pursue his meditations; any who tried to pray became the sport and ridicule of his brutal fellows.
+LJSpeech-1.1/wavs/LJ033-0091.wav|Frazier told the Commission, quote,
+LJSpeech-1.1/wavs/LJ029-0056.wav|Although there was no mention in PRS files of the demonstration in Dallas against Ambassador Adlai Stevenson on October twenty-fourth,
+LJSpeech-1.1/wavs/LJ026-0009.wav|The fact is that they are on the border line, are neither plants nor animals but simply organisms.
+LJSpeech-1.1/wavs/LJ030-0090.wav|Vice President Johnson sat on the right-hand side of the rear seat, Mrs. Johnson in the center, and Senator Yarborough on the left.
+LJSpeech-1.1/wavs/LJ035-0143.wav|and she actually came down the stairs several minutes after Oswald and after Truly and Baker as well.
+LJSpeech-1.1/wavs/LJ021-0071.wav|although millions of hitherto underprivileged workers are today far better paid than ever before.
+LJSpeech-1.1/wavs/LJ001-0122.wav|that he has a five, an eight, or a three before him, unless the press work is of the best:
+LJSpeech-1.1/wavs/LJ034-0108.wav|man in his early thirties, fair complexion, slender, but neat, neat slender, possible five foot ten
+LJSpeech-1.1/wavs/LJ011-0278.wav|addressed him, insisting that he should forthwith sign a cheque for the eight hundred pounds of the Canning inheritance still uninvested,
+LJSpeech-1.1/wavs/LJ015-0280.wav|was the return from transportation of an old "pal" and confederate, who brought with him some bills of exchange.
+LJSpeech-1.1/wavs/LJ030-0254.wav|Forrest V. Sorrels, special agent in charge of the Dallas office, was the first Secret Service agent to return to the scene of the assassination,
+LJSpeech-1.1/wavs/LJ048-0218.wav|However, the Commission does not believe that agents stationed in a car behind the Presidential car,
+LJSpeech-1.1/wavs/LJ016-0062.wav|She suffered him to pass downstairs.
+LJSpeech-1.1/wavs/LJ001-0086.wav|are dazzling and unpleasant to the eye owing to the clumsy thickening and vulgar thinning of the lines:
+LJSpeech-1.1/wavs/LJ030-0100.wav|three cars for press photographers, an official party bus for White House staff members and others, and two press buses.
+LJSpeech-1.1/wavs/LJ002-0027.wav|and this number did not include the debtors, a numerous class, who were still committed to Newgate pending the completion of the White Cross Street prison.
+LJSpeech-1.1/wavs/LJ002-0034.wav|five. The master felons' side. six. The female felons' side. seven. The state side.
+LJSpeech-1.1/wavs/LJ005-0008.wav|they were followed by a crowd of reckless boys, who jeered at and insulted them.
+LJSpeech-1.1/wavs/LJ032-0063.wav|Since the rifle was shipped from Chicago on March twenty, nineteen sixty-three, it was received in Dallas during the period when Oswald rented and used the box.
+LJSpeech-1.1/wavs/LJ001-0176.wav|because the modern practice is to disregard the relation between the printing and the ornament altogether,
+LJSpeech-1.1/wavs/LJ040-0172.wav|Mrs. Siegel concluded that Lee, quote, just felt that his mother never gave a damn for him.
+LJSpeech-1.1/wavs/LJ018-0271.wav|and was rendered insensible by chloroform, after which he was bound and the precious stones stolen.
+LJSpeech-1.1/wavs/LJ010-0204.wav|to wear a black crape mask, to obey punctually the orders of their commander-in-chief,
+LJSpeech-1.1/wavs/LJ022-0032.wav|to go fishing or back home to Hyde Park, so that I can have a chance to think quietly about the country as a whole.
+LJSpeech-1.1/wavs/LJ015-0225.wav|This was at length effected by Agar and Pierce.
+LJSpeech-1.1/wavs/LJ004-0002.wav|While Mrs. Fry was diligently engaged upon her self-imposed task in Newgate,
+LJSpeech-1.1/wavs/LJ015-0062.wav|but in neither did the sums misappropriated reach quite the same high figure.
+LJSpeech-1.1/wavs/LJ041-0203.wav|to engage in activities on behalf of the Fair Play for Cuba Committee in the summer of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ011-0156.wav|While on a visit to Macclesfield he heard by chance of Miss Turner, and that she would inherit all her father's possessions.
+LJSpeech-1.1/wavs/LJ022-0108.wav|For many months preparations have been under way.
+LJSpeech-1.1/wavs/LJ007-0007.wav|had provided work for those who had hitherto passed their time in total idleness, and had made the treatment of female transports on the way to New South Wales
+LJSpeech-1.1/wavs/LJ048-0107.wav|Love Field and the Trade Mart.
+LJSpeech-1.1/wavs/LJ024-0103.wav|Oh! I was for an amendment all right, but this amendment you proposed is not the kind of amendment that I was thinking about.
+LJSpeech-1.1/wavs/LJ017-0077.wav|The brother too died conveniently, but the life office took some exception to the manner of the death, and hesitated to disburse the funds claimed by Palmer.
+LJSpeech-1.1/wavs/LJ044-0136.wav|an event which his wife thought upset him and as a result of which, quote, he became less active, he cooled off a little, end quote.
+LJSpeech-1.1/wavs/LJ017-0200.wav|but a more careful investigation attributed it beyond doubt to over-doses of colchicum.
+LJSpeech-1.1/wavs/LJ004-0071.wav|Nor even when found guilty and his liberty forfeited did his privileges cease. The law appointed a suitable punishment for the offense;
+LJSpeech-1.1/wavs/LJ011-0080.wav|His first forgery was the marvelous imitation of the signature of the Hon. Mr. Neville, M.P., who wrote an extremely cramped and curious hand.
+LJSpeech-1.1/wavs/LJ002-0168.wav|The lad in question was found in Coldbath Fields prison, to which he had been sent for a month in default of paying a fine of forty shillings.
+LJSpeech-1.1/wavs/LJ010-0270.wav|exactly tallied with that of the deformed person "wanted" for the assault on the Queen.
+LJSpeech-1.1/wavs/LJ025-0043.wav|The essential character of vegetable life is the exhalation of oxygen, which is effected through the agency of light.
+LJSpeech-1.1/wavs/LJ016-0315.wav|Colonel (now Sir Edmund) Henderson was strongly in favor of them,
+LJSpeech-1.1/wavs/LJ019-0044.wav|The south and west of England were also very laggard, and many years were still to elapse before the prisons in these parts were properly reconstituted.
+LJSpeech-1.1/wavs/LJ026-0118.wav|Fat and carbohydrate foods, then, never become living matter.
+LJSpeech-1.1/wavs/LJ015-0081.wav|He conceived the idea of transferring shares, bogus shares from a person who held none, to any one who would buy them in the open market.
+LJSpeech-1.1/wavs/LJ035-0199.wav|but it was probably no earlier than one:twenty-two p.m., the time when the rifle was found.
+LJSpeech-1.1/wavs/LJ023-0043.wav|I want to talk with you very simply about the need for present action in this crisis
+LJSpeech-1.1/wavs/LJ023-0026.wav|Individual or local or state effort alone cannot protect us in nineteen thirty-seven any better than ten years ago.
+LJSpeech-1.1/wavs/LJ019-0220.wav|the uses of Newgate were narrowed almost entirely to those of a prison of detention.
+LJSpeech-1.1/wavs/LJ048-0167.wav|buildings are not checked either by Secret Service agents or by any other law enforcement officers at the request of the Secret Service.
+LJSpeech-1.1/wavs/LJ012-0126.wav|The next year twelve thousand sovereigns were cleverly stolen in the Mile End Road.
+LJSpeech-1.1/wavs/LJ025-0111.wav|But has the advance of biology simply tended to break down old distinctions without establishing new ones?
+LJSpeech-1.1/wavs/LJ035-0202.wav|Fingerprint and palmprint evidence establishes that Oswald handled two of the four cartons next to the window
+LJSpeech-1.1/wavs/LJ033-0068.wav|Mrs. Paine testified, quote,
+LJSpeech-1.1/wavs/LJ009-0071.wav|The burglar is heard to mutter an angry oath.
+LJSpeech-1.1/wavs/LJ013-0102.wav|and thence to Halifax, whither he followed them, accompanied by a confidential clerk from the bank.
+LJSpeech-1.1/wavs/LJ004-0034.wav|Moreover, the laws applied more particularly to county jurisdictions.
+LJSpeech-1.1/wavs/LJ013-0206.wav|Next morning he made a full confession in presence of his attorney, and the governor, Mr. Cope.
+LJSpeech-1.1/wavs/LJ037-0008.wav|Two witnesses testified that Oswald resembled the man they had seen.
+LJSpeech-1.1/wavs/LJ031-0007.wav|Chief of Police Curry and police motorcyclists at the head of the motorcade led the way to the hospital.
+LJSpeech-1.1/wavs/LJ029-0063.wav|discussed in chapter six of this report.
+LJSpeech-1.1/wavs/LJ004-0153.wav|In eighteen eighteen the old jail was so densely packed that it was nearly impossible to pass through the yards for the throng.
+LJSpeech-1.1/wavs/LJ013-0027.wav|He crept along the coast close in shore, looking for a quiet spot to cast away the ship,
+LJSpeech-1.1/wavs/LJ008-0169.wav|She was killed, but in the act of falling she forced her child into the arms of a man near her, and implored him in God's name to save it;
+LJSpeech-1.1/wavs/LJ006-0238.wav|"There was a disturbance in the transport yard yesterday evening, and the police were called in.
+LJSpeech-1.1/wavs/LJ028-0259.wav|That same year, when Darius Hystaspes came to the Persian throne, the Babylonians openly rebelled.
+LJSpeech-1.1/wavs/LJ014-0155.wav|Now she abused the jury, now called Manning a vagabond,
+LJSpeech-1.1/wavs/LJ017-0161.wav|But probably the lump sum was the bait, or perhaps Smethurst wished to return to his temporarily deserted first wife.
+LJSpeech-1.1/wavs/LJ012-0296.wav|The execution was, as usual, attended by an immense concourse, and Greenacre died amidst the loudest execrations.
+LJSpeech-1.1/wavs/LJ020-0064.wav|Breakfast Biscuit.
+LJSpeech-1.1/wavs/LJ050-0035.wav|Nevertheless, such a breach, in which so many agents participated,
+LJSpeech-1.1/wavs/LJ002-0182.wav|the Fleet, and the Marshalsea prisons especially devoted to them,
+LJSpeech-1.1/wavs/LJ030-0224.wav|heard noises that sounded like firecrackers and ran toward the President's limousine.
+LJSpeech-1.1/wavs/LJ036-0093.wav|If Oswald had made such a statement, Captain Fritz and others present would probably have remembered it.
+LJSpeech-1.1/wavs/LJ029-0020.wav|and had stopped in El Paso to discuss the proposed visit and other matters with the Vice President and the Governor.
+LJSpeech-1.1/wavs/LJ007-0078.wav|As the law stood since the passing of the ninth George the fourth c. forty, any two justices might remove a prisoner found to be insane, either on commitment
+LJSpeech-1.1/wavs/LJ010-0279.wav|I shall mention briefly one more case, in which, however, there was no murderous intent, before I pass on to other crimes.
+LJSpeech-1.1/wavs/LJ003-0215.wav|from Friday to Monday, so as to include one Sunday, on which day there was a special service for the condemned in the prison chapel.
+LJSpeech-1.1/wavs/LJ007-0183.wav|traversing where it was possible the statements of the inspectors, and offering explanation and palliation of such evils as could not be denied.
+LJSpeech-1.1/wavs/LJ014-0274.wav|Rumors were abroad that serious defalcations had been discovered in one of the insurance offices,
+LJSpeech-1.1/wavs/LJ042-0181.wav|Oswald realized that, quote, their thinking and education will be steeped in the traditions of those systems
+LJSpeech-1.1/wavs/LJ010-0073.wav|he found himself rich in a small landed property, which he presently sold to a man who became bankrupt before he had paid over the purchase money.
+LJSpeech-1.1/wavs/LJ028-0354.wav|And he honored Zopyrus greatly; year by year he presented him with all the gifts which are held in most esteem among the Persians;
+LJSpeech-1.1/wavs/LJ040-0239.wav|Before the court took any action, the Oswalds left New York in January of nineteen fifty-four,
+LJSpeech-1.1/wavs/LJ030-0046.wav|and occupied by Secret Service Agents Sorrels and Lawson and by Dallas County Sheriff J. E. Decker.
+LJSpeech-1.1/wavs/LJ018-0365.wav|Kate Webster followed the same course; but these pleas of pregnancy are not common now-a-days.
+LJSpeech-1.1/wavs/LJ021-0011.wav|Men may differ as to the particular form of governmental activity
+LJSpeech-1.1/wavs/LJ023-0095.wav|And three other justices agreed with him.
+LJSpeech-1.1/wavs/LJ001-0036.wav|But about the same year Mentelin at Strasburg began to print in a type which is distinctly Roman;
+LJSpeech-1.1/wavs/LJ022-0045.wav|Therefore, let us keep our minds on two or three simple, essential facts in connection with this problem of unemployment.
+LJSpeech-1.1/wavs/LJ043-0043.wav|When they arrived from the Soviet Union, Oswald and his family lived at first with his brother Robert.
+LJSpeech-1.1/wavs/LJ010-0238.wav|but draw it back again, exclaiming presently, "I wish I had done it."
+LJSpeech-1.1/wavs/LJ015-0156.wav|Down at Weybridge, where he had a country place, his name was long remembered with gratitude by the poor.
+LJSpeech-1.1/wavs/LJ032-0067.wav|Postal Inspector Harry D. Holmes of the Dallas Post Office testified, however, that when a package is received for a certain box,
+LJSpeech-1.1/wavs/LJ012-0175.wav|Weare was supposed to carry a "private bank" about with him in a pocket in his under waistcoat.
+LJSpeech-1.1/wavs/LJ029-0208.wav|It was fashioned after the "wanted" circulars issued by law enforcement agencies.
+LJSpeech-1.1/wavs/LJ015-0094.wav|All this time his position was one of extreme insecurity. He was standing over a mine which at any moment might explode.
+LJSpeech-1.1/wavs/LJ015-0178.wav|The Stock Market was greatly affected, and society, more especially that which frequents Exeter Hall, was convulsed.
+LJSpeech-1.1/wavs/LJ013-0084.wav|Burgess fraudulently transferred consols to the above amount, standing in the name of Mr. Oxenford, to another party.
+LJSpeech-1.1/wavs/LJ034-0171.wav|Edwards said, quote, Look at that guy there in that window, end quote,
+LJSpeech-1.1/wavs/LJ040-0198.wav|On the other hand he also told her that he wished his mother had been more firm with him in her attempts to get him to return to school.
+LJSpeech-1.1/wavs/LJ025-0088.wav|Cuvier himself practically gives up his second distinctive mark when he admits that it is wanting in the simpler animals.
+LJSpeech-1.1/wavs/LJ005-0215.wav|The regular daily visitation of the chaplain was also insisted upon.
+LJSpeech-1.1/wavs/LJ024-0009.wav|By bringing into the judicial system a steady and continuing stream of new and younger blood, I hope, first,
+LJSpeech-1.1/wavs/LJ035-0077.wav|He placed the rifle on the floor near the site where Oswald's rifle was actually found after the shooting.
+LJSpeech-1.1/wavs/LJ001-0046.wav|their type is on the lines of the German and French rather than of the Roman printers.
+LJSpeech-1.1/wavs/LJ002-0266.wav|His bequest, which was charged upon his manor at Goring, Oxon, and hence called the Oxford Charity,
+LJSpeech-1.1/wavs/LJ050-0108.wav|The Secret Service's liaison with the agencies that supply information to it has been too casual.
+LJSpeech-1.1/wavs/LJ015-0108.wav|where a fish curry and a brace of partridges were set before him,
+LJSpeech-1.1/wavs/LJ006-0230.wav|Often the victim, goaded to madness, attacked his tormentors; a fight was then certain to follow.
+LJSpeech-1.1/wavs/LJ024-0073.wav|In this way I propose to enforce a sound public policy by law
+LJSpeech-1.1/wavs/LJ019-0038.wav|The cost in each varied considerably, the general average being from one hundred twenty pounds to one hundred thirty pounds per cell.
+LJSpeech-1.1/wavs/LJ025-0106.wav|which contain no chlorophyll and are not green, are always, so far as respiration is concerned, in the exact position of animals.
+LJSpeech-1.1/wavs/LJ040-0114.wav|Relations soon became strained, however, so in late September Lee and his mother moved to their own apartment in the Bronx.
+LJSpeech-1.1/wavs/LJ028-0262.wav|having first set apart their mothers, each man chose besides out of his whole household one woman whomsoever he pleased;
+LJSpeech-1.1/wavs/LJ010-0040.wav|Men like Mobbs, the miscreant known as "General Haynau" on account of his blood-thirstiness, still murdered their wives;
+LJSpeech-1.1/wavs/LJ019-0133.wav|The condition of that prison in eighteen fifty may be gathered from the pages of the report. Not much had been done to remedy the old defects;
+LJSpeech-1.1/wavs/LJ028-0405.wav|Many of the ten thousand people living in Hillah still gain their livelihood by digging the bricks from the ruins to sell to the modern builders.
+LJSpeech-1.1/wavs/LJ003-0342.wav|and checked in its efforts towards reform by the prohibitory costliness of the land about Newgate.
+LJSpeech-1.1/wavs/LJ042-0080.wav|My request for the revoking of my American citizenship
+LJSpeech-1.1/wavs/LJ028-0339.wav|he threw open the Cissian and Belian gates, and admitted the enemy.
+LJSpeech-1.1/wavs/LJ047-0009.wav|who interviewed Oswald after his return from the Soviet Union and prior to November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ017-0240.wav|Immediately after the murder cases of champagne, which formed part of the cargo, were brought on deck and broached;
+LJSpeech-1.1/wavs/LJ045-0073.wav|Oswald once struck his wife because of a letter which she wrote to a former boyfriend in Russia.
+LJSpeech-1.1/wavs/LJ041-0144.wav|Oswald would have had other and more favorable opportunities to strike at the Governor than on this occasion when, as a member of the President's party,
+LJSpeech-1.1/wavs/LJ017-0141.wav|could not divest his mind of serious doubt, and of which the murderer got the benefit.
+LJSpeech-1.1/wavs/LJ013-0185.wav|Three days later a close search of the butler's pantry produced fresh circumstantial evidence.
+LJSpeech-1.1/wavs/LJ018-0023.wav|Marylebone; while within a few days Mr. Briggs' gold chain was traced to a jeweler's in Cheapside,
+LJSpeech-1.1/wavs/LJ041-0054.wav|It probably contributed greatly to the general dissatisfaction which he exhibited with his environment,
+LJSpeech-1.1/wavs/LJ033-0211.wav|and the obvious bulk of the package which he intended to bring to work the next day;
+LJSpeech-1.1/wavs/LJ016-0413.wav|No less than sixty-seven persons were present, admitted by special permission of the sheriff.
+LJSpeech-1.1/wavs/LJ002-0099.wav|to which access was granted rarely and as a great favor. But they were allowed the luxury of drink -- if they could pay for it.
+LJSpeech-1.1/wavs/LJ035-0016.wav|He saw pigeons flutter upward. He was not certain, quote, but I am pretty sure they came from the building right on the northwest corner, end quote.
+LJSpeech-1.1/wavs/LJ019-0111.wav|healthful, easily learnt, and well adapted to the circumstances of unskilled laborers.
+LJSpeech-1.1/wavs/LJ044-0081.wav|to make himself and his activities appear far more important than they really were.
+LJSpeech-1.1/wavs/LJ002-0005.wav|The prison population fluctuated a great deal,
+LJSpeech-1.1/wavs/LJ013-0253.wav|with a son of Good's by a former wife. Another wife, real or fictitious, existed in Spitalfields,
+LJSpeech-1.1/wavs/LJ042-0038.wav|and religion and education are used as a tool to suppress what would otherwise be a population questioning their government's unfair
+LJSpeech-1.1/wavs/LJ041-0119.wav|stating that he would, quote, employ all means to right this gross mistake or injustice, end quote.
+LJSpeech-1.1/wavs/LJ043-0072.wav|Even though he told his wife that he had been fired, he voluntarily left on October eight, nineteen sixty-two, and moved to Dallas.
+LJSpeech-1.1/wavs/LJ037-0242.wav|Police Capt. W. R. Westbrook and several other officers concentrated their search along Jefferson Boulevard.
+LJSpeech-1.1/wavs/LJ002-0180.wav|Before dealing with the debtors in Newgate, I may refer incidentally
+LJSpeech-1.1/wavs/LJ037-0224.wav|At one:twenty-two p.m. the Dallas police radio described the man wanted for the murder of Tippit as, quote, a white male about thirty,
+LJSpeech-1.1/wavs/LJ027-0044.wav|Turning to more fundamental resemblances, such as the wing of a bat and the wing of a bird,
+LJSpeech-1.1/wavs/LJ012-0022.wav|He early saw the profits to be made out of purchasing stolen goods, but could not embark in it at first for want of capital.
+LJSpeech-1.1/wavs/LJ047-0227.wav|He testified that he did not read the newspaper story describing the motorcade route in detail, since he was interested only in the fact
+LJSpeech-1.1/wavs/LJ050-0077.wav|and in taking preventive steps.
+LJSpeech-1.1/wavs/LJ015-0104.wav|Robson again left him, but only to gather together hastily all the money and valuables on which he could lay his hands, with which he left the house.
+LJSpeech-1.1/wavs/LJ028-0373.wav|The Hebrew exiles, whose ancestors Nebuchadnezzar had brought from Jerusalem,
+LJSpeech-1.1/wavs/LJ047-0106.wav|Oswald, quote, was probably making a self-serving statement in attempting to explain to me why he was distributing this literature, and for no other reason,
+LJSpeech-1.1/wavs/LJ009-0075.wav|All have sung 'the Lamentation of a Sinner,' and have seemed to pray 'especially for those now awaiting the awful execution of the law.'
+LJSpeech-1.1/wavs/LJ028-0102.wav|There is scarcely a royal record from his reign which is not chiefly occupied with descriptions of his building operations.
+LJSpeech-1.1/wavs/LJ005-0224.wav|but nothing had been done to improve the prison buildings.
+LJSpeech-1.1/wavs/LJ013-0215.wav|"He appeared to die instantly," said the murderer, in conclusion.
+LJSpeech-1.1/wavs/LJ042-0180.wav|after which the survivors would, quote, seek an alternative opposed to those systems which have brought them misery, end quote.
+LJSpeech-1.1/wavs/LJ034-0016.wav|Behind these boxes was another carton placed on the floor on which a man sitting
+LJSpeech-1.1/wavs/LJ003-0271.wav|Some of the worst and most extensive burglaries were planned there.
+LJSpeech-1.1/wavs/LJ010-0240.wav|The Queen was apprised of the danger, and begged not to go abroad;
+LJSpeech-1.1/wavs/LJ015-0286.wav|A confederate followed the emissary closely,
+LJSpeech-1.1/wavs/LJ002-0117.wav|Neild gives, on the authority of Mr. Burchell, the under sheriff of Middlesex,
+LJSpeech-1.1/wavs/LJ042-0119.wav|Under the entry for August to September of that year he wrote, quote,
+LJSpeech-1.1/wavs/LJ006-0096.wav|The only convenience for personal ablutions were the pumps in the yards, and the far-off baths in the condemned or press-yard.
+LJSpeech-1.1/wavs/LJ019-0234.wav|The old buildings were entirely disused, and the whole of the inmates of Newgate were kept constantly in separate confinement.
+LJSpeech-1.1/wavs/LJ030-0135.wav|Mrs. Connally, elated by the reception, turned to President Kennedy and said, quote, Mr. President, you can't say Dallas doesn't love you.
+LJSpeech-1.1/wavs/LJ009-0204.wav|A long procession was formed, headed by constables, who cleared the way with their staves.
+LJSpeech-1.1/wavs/LJ046-0132.wav|in nineteen sixty-three the total exceeded thirty-two thousand items.
+LJSpeech-1.1/wavs/LJ050-0274.wav|made certain recommendations which it believes would, if adopted,
+LJSpeech-1.1/wavs/LJ023-0116.wav|In our courts we want a government of laws and not of men.
+LJSpeech-1.1/wavs/LJ029-0037.wav|Advance preparations for President Kennedy's visit to Dallas were primarily the responsibility of two Secret Service agents:
+LJSpeech-1.1/wavs/LJ026-0079.wav|After passing through manifold but imperfectly known processes, the elements of these substances finally reappear as starch,
+LJSpeech-1.1/wavs/LJ043-0089.wav|to a commercial advertising photography firm in Dallas, where he was employed as a trainee starting October twelve, nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ014-0266.wav|No one cared too closely to inquire into the sources of wealth. Some said he was a fortunate speculator in stocks,
+LJSpeech-1.1/wavs/LJ037-0189.wav|the only revolver made by Smith and Wesson with this serial number. When first manufactured, it had a five-inch barrel.
+LJSpeech-1.1/wavs/LJ002-0100.wav|A recent reform had closed the tap kept by the jailer within the precincts, but
+LJSpeech-1.1/wavs/LJ036-0036.wav|At about six:thirty p.m. on the day of the assassination, McWatters viewed four men in a police lineup.
+LJSpeech-1.1/wavs/LJ016-0325.wav|The judge of the Admiralty Court, the Right Hon. Stephen Lushington, the Right Hon. James Moncrieff,
+LJSpeech-1.1/wavs/LJ012-0051.wav|While thus incarcerated he managed to escape from custody, but not actually from jail, by an ingenious contrivance which is worth mentioning.
+LJSpeech-1.1/wavs/LJ028-0503.wav|and decorated here and there with large reliefs representing bulls and lions and dragons,
+LJSpeech-1.1/wavs/LJ029-0091.wav|The Motorcade Route.
+LJSpeech-1.1/wavs/LJ010-0162.wav|The cowardly crime was probably encouraged by the fearless and confiding manner in which the Queen,
+LJSpeech-1.1/wavs/LJ044-0144.wav|Interest in Cuba
+LJSpeech-1.1/wavs/LJ021-0207.wav|I am not for a return to that definition of liberty under which for many years a free people were being gradually regimented
+LJSpeech-1.1/wavs/LJ034-0062.wav|Although a person could handle a carton and not leave identifiable prints,
+LJSpeech-1.1/wavs/LJ044-0058.wav|that an anti-Castro organization had maintained offices there for a period ending early in nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ047-0167.wav|On November four, Hosty telephoned the Texas School Book Depository and learned that Oswald was working there
+LJSpeech-1.1/wavs/LJ046-0082.wav|must depend upon the utmost cooperation and understanding from the public and the President.
+LJSpeech-1.1/wavs/LJ002-0270.wav|The latter's salary, with fees, the rent of the tap and of the chandler's shop, amounted to about six hundred pounds a year.
+LJSpeech-1.1/wavs/LJ046-0099.wav|thoroughly professional personnel, using the best technical equipment that can be devised.
+LJSpeech-1.1/wavs/LJ030-0223.wav|Special Agent Ready, on the right front running board of the Presidential follow-up car,
+LJSpeech-1.1/wavs/LJ009-0270.wav|A worse case still was that of William Bousfield, who, when awaiting execution for murder, about the same date,
+LJSpeech-1.1/wavs/LJ032-0056.wav|bearing serial number C two seven six six.
+LJSpeech-1.1/wavs/LJ006-0256.wav|Visitors were still permitted to come with supplies on given days of the week, about the only limitation being that the food should be cooked, and cold;
+LJSpeech-1.1/wavs/LJ006-0104.wav|but its issue was precarious, and dependent on the good will of the wardsmen, who measured out the portions to each according to his eye,
+LJSpeech-1.1/wavs/LJ005-0138.wav|that in four prisons, which at one time of the year contained one thousand three hundred eight prisoners, there were only sixty-eight sleeping rooms or cells,
+LJSpeech-1.1/wavs/LJ010-0291.wav|Pate was found guilty, and sentenced to seven years' transportation, the judge, Baron Alderson, abstaining from inflicting the penalty of whipping,
+LJSpeech-1.1/wavs/LJ032-0028.wav|on a coupon clipped from the February nineteen sixty-three issue of the American Rifleman magazine.
+LJSpeech-1.1/wavs/LJ002-0164.wav|for the paltry sum of four pence, with costs of seven shillings, six pence.
+LJSpeech-1.1/wavs/LJ010-0202.wav|Its sworn members, known only to Oxford, and all of them mere shadows,
+LJSpeech-1.1/wavs/LJ025-0072.wav|Hence the teleological argument for Cuvier's first diagnostic character
+LJSpeech-1.1/wavs/LJ047-0012.wav|the Director and Deputy Director for Plans of the CIA testified concerning that Agency's limited knowledge of Oswald before the assassination.
+LJSpeech-1.1/wavs/LJ019-0095.wav|which was yet under full control, and might be made to work corn-mills or prove otherwise productive;
+LJSpeech-1.1/wavs/LJ015-0054.wav|Police officers went down at night to Nutfield, near Reigate, and arrested Sir John Paul, but allowed the prisoner to sleep there.
+LJSpeech-1.1/wavs/LJ003-0008.wav|Friends arrived about nine a.m., and went out at nine p.m., when as many as two hundred visitors have been observed leaving the debtors' yards at one time.
+LJSpeech-1.1/wavs/LJ043-0057.wav|he attempted for the first time to learn something about his family background when he went to New Orleans in April of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ042-0246.wav|In July of nineteen sixty-three, Oswald also requested the Soviet Union to provide a visa for his return to that country.
+LJSpeech-1.1/wavs/LJ008-0203.wav|Numbers of ladies were present, although the public feeling was much against their attendance.
+LJSpeech-1.1/wavs/LJ008-0231.wav|Hush! the unceasing murmur of the mob now breaks into a loud deep roar,
+LJSpeech-1.1/wavs/LJ010-0071.wav|It was during this period that he was said to have imbibed his revolutionary ideas.
+LJSpeech-1.1/wavs/LJ035-0203.wav|and also handled a paper bag which was found near the cartons.
+LJSpeech-1.1/wavs/LJ025-0137.wav|but it suggests a suspicion of their identity which needs careful testing.
+LJSpeech-1.1/wavs/LJ036-0147.wav|The five hundred block of North Beckley is five blocks south of the roominghouse.
+LJSpeech-1.1/wavs/LJ018-0041.wav|The case was one of circumstantial evidence, but, as Sir Robert Collyer the Solicitor-General pointed out,
+LJSpeech-1.1/wavs/LJ026-0021.wav|nitrogen is obtained from simple salts and the nutritive processes result in deoxidation;
+LJSpeech-1.1/wavs/LJ034-0054.wav|In addition, Mandella was of the opinion that the print taken from the carton on the floor
+LJSpeech-1.1/wavs/LJ050-0263.wav|it will continue to rely in many respects upon the greater resources of the Office of Science and Technology and other agencies.
+LJSpeech-1.1/wavs/LJ013-0125.wav|the Junior United Service, the Union, Reform, Colonial, and Erechtheum clubs.
+LJSpeech-1.1/wavs/LJ001-0023.wav|and was in fact the kind of letter used in the many splendid missals, psalters, etc., produced by printing in the fifteenth century.
+LJSpeech-1.1/wavs/LJ009-0216.wav|Hanging in chains upon the gibbet which had served for the execution,
+LJSpeech-1.1/wavs/LJ013-0068.wav|and a person calling herself Miss Slack duly attended at the bank, where the money was handed over to her in proper form.
+LJSpeech-1.1/wavs/LJ002-0200.wav|The marshal was supposed to be resident either within the prison or the rules.
+LJSpeech-1.1/wavs/LJ047-0035.wav|Oswald's file at the Department of State Passport Office was reviewed in June nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ025-0045.wav|They get rid of the superfluous hydrogen and carbon and accumulate nitrogen.
+LJSpeech-1.1/wavs/LJ033-0132.wav|I didn't pay too much attention the way he was walking because I was walking along there looking at the railroad cars and watching the men on the diesel switch them cars
+LJSpeech-1.1/wavs/LJ032-0172.wav|Marina Oswald testified that she thought her husband wore this shirt to work on that day.
+LJSpeech-1.1/wavs/LJ025-0011.wav|is Huxley's famous essay, "The Border Territory Between the Animal and Vegetable Kingdoms," written in eighteen seventy-six,
+LJSpeech-1.1/wavs/LJ007-0038.wav|Owing to the repeated entreaties of the criminals who could hardly hope to escape the gallows, some show of classification was carried out,
+LJSpeech-1.1/wavs/LJ006-0188.wav|The days were passed in idleness, debauchery, riotous quarreling, immoral conversation,
+LJSpeech-1.1/wavs/LJ030-0073.wav|Presidential follow-up car.
+LJSpeech-1.1/wavs/LJ041-0139.wav|It should be noted, however, that at the time Oswald fired the shots at the Presidential limousine the Governor occupied the seat in front of the President,
+LJSpeech-1.1/wavs/LJ039-0199.wav|The three FBI experts each fired three shots from the weapon at fifteen yards in six, seven, and nine seconds,
+LJSpeech-1.1/wavs/LJ013-0049.wav|A notorious miser, Robert Smith, had recently died in Seven Dials, where he had amassed a considerable fortune.
+LJSpeech-1.1/wavs/LJ048-0250.wav|In Dallas, one of the nine agents was assigned to assist in security measures at Love Field, and four had protective assignments at the Trade Mart.
+LJSpeech-1.1/wavs/LJ017-0068.wav|and public opinion at the termination of the trial coincided with the verdict of the jury.
+LJSpeech-1.1/wavs/LJ014-0150.wav|and expressed the utmost contempt for her husband, whom she never intended to acknowledge or speak to again.
+LJSpeech-1.1/wavs/LJ043-0167.wav|Even after his wife told him to destroy the notebook
+LJSpeech-1.1/wavs/LJ006-0142.wav|The matron deposed to having seen the gates-woman "exceedingly drunk," and having been insulted by her.
+LJSpeech-1.1/wavs/LJ021-0047.wav|The country now enjoys the safety of bank savings under the new banking laws,
+LJSpeech-1.1/wavs/LJ019-0040.wav|the difficulty of access, and the admitted necessity of giving architectural importance to this the national model prison.
+LJSpeech-1.1/wavs/LJ015-0314.wav|He was in person a short, square-built man of gentlemanly address, sharp and shrewd in conversation and manner.
+LJSpeech-1.1/wavs/LJ037-0230.wav|Mrs. Davis gave Poe the same general description: a, quote, white male in his early twenties, around five foot seven inches
+LJSpeech-1.1/wavs/LJ042-0029.wav|While there is no proof that he saved fifteen hundred dollars, as he claimed,
+LJSpeech-1.1/wavs/LJ013-0180.wav|The intention of the real murderer to shift the crime to burglars was evident although futile,
+LJSpeech-1.1/wavs/LJ013-0228.wav|In the pocket of the coat Mr. Cope, the governor, found a neatly-folded cloth, and asked what it was for.
+LJSpeech-1.1/wavs/LJ039-0248.wav|possessed the capability with a rifle which would have enabled him to commit the assassination.
+LJSpeech-1.1/wavs/LJ046-0209.wav|In the absence of more specific instructions, other Federal agencies interpreted the Secret Service's informal requests
+LJSpeech-1.1/wavs/LJ016-0124.wav|Cutting the cord which fastened the two legs of the step ladder, he opened them out and made one long length;
+LJSpeech-1.1/wavs/LJ030-0203.wav|From the left front running board of the President's follow-up car,
+LJSpeech-1.1/wavs/LJ031-0033.wav|Then he experienced his first sensation of pain, which became excruciating.
+LJSpeech-1.1/wavs/LJ013-0067.wav|a note purporting to be from Miss Slack was addressed to the governor of the Bank of England, begging that the said stock might be handed over to her,
+LJSpeech-1.1/wavs/LJ005-0221.wav|and the panes were filled in with oiled paper, an insufficient protection against the weather;
+LJSpeech-1.1/wavs/LJ016-0142.wav|who permitted him to go up on to the roof of the old wards, in order to throw water for flushing purposes down a shoot.
+LJSpeech-1.1/wavs/LJ029-0057.wav|nineteen sixty-three, Lawson inquired about the incident and obtained through the local police photographs of some of the persons involved.
+LJSpeech-1.1/wavs/LJ015-0187.wav|Redpath passed away into the outer darkness of a penal colony, where he was still living a year or two back
+LJSpeech-1.1/wavs/LJ007-0191.wav|has been productive of at least some advantage,
+LJSpeech-1.1/wavs/LJ023-0080.wav|the Court has more and more often and more and more boldly
+LJSpeech-1.1/wavs/LJ015-0134.wav|had assets in the shape of land, house, furniture, pictures, and objets d'art to the value of fifty thousand pounds.
+LJSpeech-1.1/wavs/LJ012-0105.wav|The police were of opinion that these robberies were both the work of the same hand.
+LJSpeech-1.1/wavs/LJ016-0366.wav|and placed decently in a shell by the workmen who removed the gallows.
+LJSpeech-1.1/wavs/LJ026-0024.wav|There is no special excretory organ, and, except in the case of certain reproductive bodies, there is usually no locomotion.
+LJSpeech-1.1/wavs/LJ036-0111.wav|Whaley's memory of the lineup is inaccurate. There were four men altogether, not six men, in the lineup with Oswald.
+LJSpeech-1.1/wavs/LJ030-0050.wav|The Presidential limousine.
+LJSpeech-1.1/wavs/LJ049-0089.wav|The murder of Federal judges, U.S. attorneys and marshals, and a number of other specifically designated
+LJSpeech-1.1/wavs/LJ018-0299.wav|Long before they came to maturity the forgers hoped to be well beyond arrest. They had, moreover, sought to destroy all clue.
+LJSpeech-1.1/wavs/LJ024-0143.wav|You and I will do our part.
+LJSpeech-1.1/wavs/LJ028-0515.wav|Such were the walls of Babylon,
+LJSpeech-1.1/wavs/LJ037-0009.wav|One witness felt he was too distant from the gunman to make a positive identification.
+LJSpeech-1.1/wavs/LJ029-0048.wav|On November eight, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ034-0045.wav|Since cartons absorb perspiration, powder can successfully develop a print on such material only within a limited time.
+LJSpeech-1.1/wavs/LJ040-0006.wav|The Commission has considered many possible motives for the assassination, including those which might flow from Oswald's commitment to Marxism or communism,
+LJSpeech-1.1/wavs/LJ029-0213.wav|quote, formed strictly for the purpose of having a name to put in the paper, end quote,
+LJSpeech-1.1/wavs/LJ047-0053.wav|Fain determined that nothing further remained to be done at that time and recommended that the case be placed in a closed status.
+LJSpeech-1.1/wavs/LJ037-0238.wav|she identified a picture of Oswald as being the same person she saw on November twenty-two.
+LJSpeech-1.1/wavs/LJ001-0014.wav|And it was a matter of course that in the Middle Ages, when the craftsmen took care that beautiful form should always be a part of their productions whatever they were,
+LJSpeech-1.1/wavs/LJ008-0253.wav|Then came the cry of 'Hats off!' and 'Down in front!' as at a theatre.
+LJSpeech-1.1/wavs/LJ014-0035.wav|in a lonely part of Hampstead.
+LJSpeech-1.1/wavs/LJ011-0049.wav|Fauntleroy constantly groaned aloud while the sermon proceeded, and contemporary reports declared
+LJSpeech-1.1/wavs/LJ001-0183.wav|Therefore, granted well-designed type, due spacing of the lines and words, and proper position of the page on the paper,
+LJSpeech-1.1/wavs/LJ013-0149.wav|The circumstances of this purchase of brilliants from a stranger at such an inadequate price was strongly commented upon at Ker's trial.
+LJSpeech-1.1/wavs/LJ041-0025.wav|It has been suggested that this misspelling of names, apparently on a phonetic basis, was caused by a reading-spelling disability
+LJSpeech-1.1/wavs/LJ030-0132.wav|As the motorcade approached the intersection of Houston and Elm Streets, there was general gratification in the Presidential party about the enthusiastic reception.
+LJSpeech-1.1/wavs/LJ035-0208.wav|Oswald's known actions in the building immediately after the assassination are consistent with his having been at the southeast corner window of the sixth floor
+LJSpeech-1.1/wavs/LJ022-0168.wav|who have little protection under the old laws against what used to be called frenzied finance.
+LJSpeech-1.1/wavs/LJ009-0173.wav|from eighteen thirty-two to eighteen forty-four not a single person had been executed in the metropolis except for this the gravest crime.
+LJSpeech-1.1/wavs/LJ027-0164.wav|At this stage it breathes equally by lungs and by gills -- i.e., both air and water.
+LJSpeech-1.1/wavs/LJ042-0138.wav|His return to the United States publicly testified to the utter failure of what had been the most important act of his life.
+LJSpeech-1.1/wavs/LJ020-0098.wav|Having made out your rolls and tucked them up snugly for the final rise, return to your chamber for a comfortable bath and toilet.
+LJSpeech-1.1/wavs/LJ032-0088.wav|Post Office Box three zero zero one six, New Orleans
+LJSpeech-1.1/wavs/LJ043-0096.wav|became involved in conflicts, some of which were fairly heated, with his fellow employees.
+LJSpeech-1.1/wavs/LJ003-0003.wav|Prisoners were committed to it quite without reference to its capacity.
+LJSpeech-1.1/wavs/LJ050-0277.wav|with the active cooperation of the responsible agencies and with the understanding of the people of the United States in their demands upon their President,
+LJSpeech-1.1/wavs/LJ003-0348.wav|Again, there was the new Millbank penitentiary now ready for occupation.
+LJSpeech-1.1/wavs/LJ043-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ016-0254.wav|Executions long continued to be in public, in spite of remonstrance and reprobation.
+LJSpeech-1.1/wavs/LJ011-0162.wav|and it stated that Mrs. Turner had been stricken with paralysis.
+LJSpeech-1.1/wavs/LJ012-0028.wav|that he was soon admitted to be one of the best judges known of all kinds of property, from a glass bottle to a five hundred guinea chronometer.
+LJSpeech-1.1/wavs/LJ008-0233.wav|the wide dark sea of heads is all at once in motion;
+LJSpeech-1.1/wavs/LJ018-0016.wav|T. Briggs, Esq., Robarts and Co., Lombard Street.
+LJSpeech-1.1/wavs/LJ029-0047.wav|maintains records of people who have threatened the President or so conducted themselves as to be deemed a potential danger to him.
+LJSpeech-1.1/wavs/LJ015-0057.wav|All three were tried at the Central Criminal Court, and sentenced to fourteen years' transportation, passing some time in Newgate en route.
+LJSpeech-1.1/wavs/LJ034-0181.wav|and he had brown hair, a slender face and neck with light complexion, and looked to be twenty-two or twenty-four years old.
+LJSpeech-1.1/wavs/LJ027-0071.wav|But much more remains which is not only suggestive, but demonstrative of such origin.
+LJSpeech-1.1/wavs/LJ034-0023.wav|The carton on the windowsill and the large carton below the window contained no prints which could be identified as being those of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ048-0130.wav|the Commission believes that standard procedures in use for many years and applied in all parts of the country may not be sufficient.
+LJSpeech-1.1/wavs/LJ010-0107.wav|and a military sash.
+LJSpeech-1.1/wavs/LJ036-0095.wav|but the Commission concluded that this man was not Lee Harvey Oswald,
+LJSpeech-1.1/wavs/LJ015-0248.wav|with Ostend tickets previously procured, returned to London without mishap, and by degrees disposed of much of the stolen gold.
+LJSpeech-1.1/wavs/LJ018-0284.wav|Dresden, Bordeaux, Marseilles, and Lyons.
+LJSpeech-1.1/wavs/LJ018-0128.wav|Brown took several sheets, and then was detected by Brewer, a fellow-workman of superior grade,
+LJSpeech-1.1/wavs/LJ038-0221.wav|and the second month's rent was paid on either April two or April three.
+LJSpeech-1.1/wavs/LJ017-0084.wav|It was there that Cook was first taken ill with violent retchings and vomitings, all dating from visits of Palmer, who brought him medicines and food.
+LJSpeech-1.1/wavs/LJ014-0015.wav|picked up a coat-button, which afterwards played an important part in the identification of the murderer.
+LJSpeech-1.1/wavs/LJ043-0031.wav|I am surprised that he didn't do something worse, end quote.
+LJSpeech-1.1/wavs/LJ011-0262.wav|A more complicated and altogether most extraordinary case of assault, with intent to extort money, occurred a few years later.
+LJSpeech-1.1/wavs/LJ005-0115.wav|or an imaginary boundary line, and nothing prevented parties from passing to either side
+LJSpeech-1.1/wavs/LJ026-0013.wav|Parker's definition of animals and plants,
+LJSpeech-1.1/wavs/LJ004-0160.wav|the uproar of oaths, complaints, and obscenity,
+LJSpeech-1.1/wavs/LJ002-0187.wav|Every new-comer was entitled to a "chummage" ticket, but did not always get it,
+LJSpeech-1.1/wavs/LJ047-0168.wav|and that he had given as his address Mrs. Paine's residence in Irving.
+LJSpeech-1.1/wavs/LJ017-0110.wav|He said nothing, but began to feel uneasy when he found that Cook's betting-book was missing, and that Palmer put it forward
+LJSpeech-1.1/wavs/LJ013-0070.wav|Mrs. Hunt had left money in the funds which remained unclaimed, and had been transferred, as in Miss Slack's case.
+LJSpeech-1.1/wavs/LJ017-0157.wav|For some months Smethurst and Miss Bankes lived together as man and wife, first in London, and then at Richmond.
+LJSpeech-1.1/wavs/LJ025-0112.wav|With a qualification, to be considered presently, the answer to this question is undoubtedly in the affirmative.
+LJSpeech-1.1/wavs/LJ013-0230.wav|The next inquiry was how he hoped to open a vein. "With a bit of sharpened stick picked out of the ordinary firewood."
+LJSpeech-1.1/wavs/LJ046-0107.wav|Finally, the performance of those charged with the immediate responsibility of protecting the President on November twenty-two is reviewed.
+LJSpeech-1.1/wavs/LJ042-0009.wav|While his defection resulted in part from Oswald's commitment to Marxism,
+LJSpeech-1.1/wavs/LJ026-0061.wav|These substances, which are dissolved in a watery liquid to form the digestive fluid, are secreted by the walls of the alimentary tube.
+LJSpeech-1.1/wavs/LJ043-0150.wav|She indicated that she had no advance knowledge of Oswald's plans,
+LJSpeech-1.1/wavs/LJ004-0019.wav|were as yet but imperfectly understood, and such portions of the "improved" jails of that period as were still extant a few years back,
+LJSpeech-1.1/wavs/LJ035-0097.wav|When Truly, accompanied by Baker, ran to the rear of the first floor,
+LJSpeech-1.1/wavs/LJ025-0167.wav|and in the seeds which it produces are exactly equivalent to the weights of the same elements which have disappeared from the materials supplied to the bean during its growth.
+LJSpeech-1.1/wavs/LJ032-0126.wav|He examined these prints, as well as photographs of them which the Dallas police had made, and concluded that:
+LJSpeech-1.1/wavs/LJ027-0054.wav|Since backboned animals are best known to most readers, they may be taken as an illustration.
+LJSpeech-1.1/wavs/LJ012-0274.wav|and that in a moment of anger he knocked her down.
+LJSpeech-1.1/wavs/LJ041-0066.wav|his general personality would alienate the group against him, end quote.
+LJSpeech-1.1/wavs/LJ027-0045.wav|careful study shows detailed internal as well as external similarities of structure. Such cases are "homologies".
+LJSpeech-1.1/wavs/LJ007-0084.wav|The inspectors in the following year, on examining the facts, found that some of these poor creatures had been in confinement for long periods:
+LJSpeech-1.1/wavs/LJ043-0006.wav|and he did not establish any close friendships or associations, although it appears that he came to respect George De Mohrenschildt.
+LJSpeech-1.1/wavs/LJ008-0241.wav|were vended under the names of notorious murderers, highwaymen, and criminals,
+LJSpeech-1.1/wavs/LJ018-0272.wav|Mrs. Tarpey was almost immediately captured and put on her trial, but she was acquitted on the plea that she had acted under the coercion of her husband.
+LJSpeech-1.1/wavs/LJ049-0068.wav|A doctor is in the motorcade.
+LJSpeech-1.1/wavs/LJ026-0138.wav|Apparently when this reserve supply is finally needed at any point in the plant, it is again changed to glucose and transported thither.
+LJSpeech-1.1/wavs/LJ026-0002.wav|Life Processes. Part One.
+LJSpeech-1.1/wavs/LJ016-0321.wav|Duly impressed with the weight of evidence in favor of abolition,
+LJSpeech-1.1/wavs/LJ009-0205.wav|Then came the newly-formed horse patrol, with drawn cutlasses,
+LJSpeech-1.1/wavs/LJ041-0132.wav|He continued his efforts to reverse the discharge by petitioning the Navy Discharge Review Board,
+LJSpeech-1.1/wavs/LJ033-0166.wav|Among other tests, the paper and tape were submitted to fiber analysis and spectrographic examination.
+LJSpeech-1.1/wavs/LJ027-0120.wav|This reasonable expectation, however, is far from being realized.
+LJSpeech-1.1/wavs/LJ019-0369.wav|In the same way, six months' notice was required in cases where the closing of a prison was contemplated;
+LJSpeech-1.1/wavs/LJ032-0068.wav|a notice is placed in that box regardless of whether the name on the package is listed on the application as a person entitled to receive mail through that box.
+LJSpeech-1.1/wavs/LJ038-0208.wav|I don't remember the exact time, but it was very late. And he told me not to ask him any questions. He only told me he had shot at General Walker.
+LJSpeech-1.1/wavs/LJ050-0080.wav|The suggestions of Federal agencies for revision of these guidelines were solicited.
+LJSpeech-1.1/wavs/LJ004-0218.wav|a week's allowance at a time, was abolished, and there was a regular scale of daily rations adopted.
+LJSpeech-1.1/wavs/LJ009-0191.wav|as we have heard some relate, and with pale and terrified features, to get a breath of air.
+LJSpeech-1.1/wavs/LJ035-0189.wav|Special Agent Forrest V. Sorrels of the Secret Service, who had been in the motorcade,
+LJSpeech-1.1/wavs/LJ031-0167.wav|Chief Curry drove one automobile occupied by Vice President Johnson, U.S. Representatives Thomas and Thornberry, and Special Agent Youngblood.
+LJSpeech-1.1/wavs/LJ013-0159.wav|The fact of the murder was first discovered by the housemaid,
+LJSpeech-1.1/wavs/LJ023-0070.wav|for ourselves and our posterity.
+LJSpeech-1.1/wavs/LJ009-0015.wav|I will quote an extract from the reverend gentleman's own journal.
+LJSpeech-1.1/wavs/LJ011-0179.wav|so that it might become the exclusive property of her husband, "whoever he might be."
+LJSpeech-1.1/wavs/LJ007-0178.wav|and when the first municipal authority of our land will be no longer subjected to the reproach of fostering an institution which outrages the rights and feelings of humanity,
+LJSpeech-1.1/wavs/LJ014-0180.wav|secure the stock of watches and jewelry, then lock up the place and take on the keys to Mr. Berry's private house in Pimlico.
+LJSpeech-1.1/wavs/LJ017-0083.wav|whom he persuaded to go and stay at Rugeley in an hotel just opposite his own house.
+LJSpeech-1.1/wavs/LJ001-0019.wav|and which developed more completely and satisfactorily on the side of the "lower-case" than the capital letters;
+LJSpeech-1.1/wavs/LJ009-0117.wav|The firmest disbeliever in religion, if he had not lately been irritated by taking part in such a scene as the condemned service in Newgate,
+LJSpeech-1.1/wavs/LJ016-0212.wav|Calcraft's salary was more than the proverbial "thirteenpence halfpenny -- hangman's wages."
+LJSpeech-1.1/wavs/LJ045-0191.wav|The next day that paper announced the final approval of the Trade Mart as the luncheon site and stated that the motorcade, quote,
+LJSpeech-1.1/wavs/LJ026-0078.wav|Entering the chlorophyll bodies, the kinetic energy of sunlight is applied to the decomposition of carbon dioxide and water.
+LJSpeech-1.1/wavs/LJ012-0150.wav|Mrs. Abrahams imposed upon her father by abstracting a portion of the dust and selling it on her own account;
+LJSpeech-1.1/wavs/LJ016-0249.wav|The strange competition amongst hundreds to succeed Marwood is a strange fact too recently before the public to need mention here.
+LJSpeech-1.1/wavs/LJ006-0304.wav|committed by the House of Commons, who had been lodged in the governor's own house.
+LJSpeech-1.1/wavs/LJ014-0286.wav|The sentence of the court, one of ten years' transportation, struck the prisoner with dismay.
+LJSpeech-1.1/wavs/LJ035-0160.wav|Reconstructing her movements, Mrs. Reid ran the distance three times and was timed in two minutes by stopwatch.
+LJSpeech-1.1/wavs/LJ023-0104.wav|And two other justices agreed with him.
+LJSpeech-1.1/wavs/LJ031-0126.wav|As President Kennedy and Governor Connally were being removed from the limousine onto stretchers,
+LJSpeech-1.1/wavs/LJ006-0030.wav|the great architectural experiment which grew out of the strong representations of Jeremy Bentham and others, and was the first national recognition of the principle
+LJSpeech-1.1/wavs/LJ029-0126.wav|to reach the northbound lanes of the Stemmons Freeway is via Elm Street, which Route Number seventy-seven traffic is instructed to follow in this part of the city.
+LJSpeech-1.1/wavs/LJ003-0234.wav|The bedding was scanty; fuel and light had to be purchased out of prisoners' private means; clothing was issued but rarely,
+LJSpeech-1.1/wavs/LJ016-0153.wav|would make death a certainty, so limited and imperfect are the means generally available.
+LJSpeech-1.1/wavs/LJ022-0092.wav|and such projects as they approve will be next submitted to the President who under the Act is required to make final allocations.
+LJSpeech-1.1/wavs/LJ048-0005.wav|There was nothing up to the time of the assassination that gave any indication that this man was a dangerous character who might do harm to the President
+LJSpeech-1.1/wavs/LJ019-0245.wav|The inquiry was most searching and complete, and the committee spoke plainly in its report.
+LJSpeech-1.1/wavs/LJ021-0006.wav|have had to do with industry and labor and with respect to these, certain developments have taken place which I consider of importance.
+LJSpeech-1.1/wavs/LJ033-0189.wav|enabled the Commission to conclude that the bag was made from these materials.
+LJSpeech-1.1/wavs/LJ036-0043.wav|Riding on the bus was an elderly woman, Mary Bledsoe, who confirmed the mute evidence of the transfer.
+LJSpeech-1.1/wavs/LJ030-0189.wav|he realized that something was wrong, and he pressed down on the accelerator as Kellerman said, quote, Get out of here fast, end quote.
+LJSpeech-1.1/wavs/LJ016-0297.wav|The crowd was for a moment awed and stilled by the quiet rapid passage from life to death!
+LJSpeech-1.1/wavs/LJ046-0187.wav|In June nineteen sixty-four PRS had arrangements to be notified about the release or escape of approximately one thousand persons.
+LJSpeech-1.1/wavs/LJ028-0263.wav|these alone were allowed to live, while all the rest were brought to one place and strangled.
+LJSpeech-1.1/wavs/LJ048-0042.wav|There were no Secret Service criteria which specifically required the referral of Oswald's case to the Secret Service;
+LJSpeech-1.1/wavs/LJ031-0092.wav|Did you ever have occasion to look at the President's back?
+LJSpeech-1.1/wavs/LJ008-0141.wav|the doubtfulness of the issue, or the superior position of the perpetrator,
+LJSpeech-1.1/wavs/LJ046-0225.wav|or crank visitors and furnished reports concerning any assassination or attempted assassination of a ruler or other major official anywhere in the world.
+LJSpeech-1.1/wavs/LJ016-0136.wav|On the other hand, at the great convict establishments, such is the moral restraint of a systematic discipline,
+LJSpeech-1.1/wavs/LJ027-0053.wav|The most striking fact of similar structure among plants and among animals is the existence of a common general plan in any group.
+LJSpeech-1.1/wavs/LJ013-0145.wav|Ker came at once to London, and, by the help of the landlord of a public-house in Smithfield and others, disposed of the whole of the diamonds.
+LJSpeech-1.1/wavs/LJ019-0002.wav|The time at length approached when a radical and complete change was to come over the old city jail.
+LJSpeech-1.1/wavs/LJ018-0227.wav|This will was a deliberate forgery.
+LJSpeech-1.1/wavs/LJ043-0135.wav|Oswald shot at Maj. Gen. Edwin A. Walker (Resigned, U.S. Army),
+LJSpeech-1.1/wavs/LJ028-0203.wav|Less picturesque than this Hebrew legend is the royal record of Babylon, which fortunately was inscribed upon a clay cylinder from the ruins of the city.
+LJSpeech-1.1/wavs/LJ027-0079.wav|a series of either fore or hind limbs of a mammal with one toe (horse),
+LJSpeech-1.1/wavs/LJ024-0069.wav|Thus a sound public policy has been defeated.
+LJSpeech-1.1/wavs/LJ012-0047.wav|But it was eventually discovered by Mrs. Solomons, a very jealous wife,
+LJSpeech-1.1/wavs/LJ030-0218.wav|the right rear tail, when she noticed that I was trying to climb on the car.
+LJSpeech-1.1/wavs/LJ013-0260.wav|Good himself remained at large for some weeks. He had tramped as far as Tunbridge, where he obtained work as a bricklayer's laborer;
+LJSpeech-1.1/wavs/LJ017-0043.wav|The direful suspicions which surrounded the case filled the whole country with uneasiness and misgiving,
+LJSpeech-1.1/wavs/LJ018-0117.wav|Wagner, after conviction, offered to reveal, for a reward of three thousand pounds
+LJSpeech-1.1/wavs/LJ011-0270.wav|but on arrival he was met by a young sailor with a letter which begged Mr. Gee to go to Heath's house, as the latter was not well.
+LJSpeech-1.1/wavs/LJ010-0024.wav|While the varying conditions of social life thus brought about many changes in the character of offenses against property,
+LJSpeech-1.1/wavs/LJ037-0159.wav|Cunningham compared four lead bullets recovered from the body of Patrolman Tippit with test bullets fired from Oswald's revolver.
+LJSpeech-1.1/wavs/LJ028-0155.wav|Lumps of bitumen are found in great abundance in this river.
+LJSpeech-1.1/wavs/LJ028-0133.wav|He also erected elevated places for walking, of stone; and made it resemble mountains: and built it so that it might be planted with all sorts of trees.
+LJSpeech-1.1/wavs/LJ018-0176.wav|Cummings, who had introduced Buncher to Griffiths, was also tried for being in possession of stolen bank paper for improper purposes.
+LJSpeech-1.1/wavs/LJ041-0198.wav|His use of those excuses to present himself to the world as a person who was being unfairly treated
+LJSpeech-1.1/wavs/LJ016-0159.wav|who contrived to hang himself from a hammock hook only eighteen inches from the ground.
+LJSpeech-1.1/wavs/LJ030-0187.wav|When he heard the same noise again, Greer glanced over his shoulder and saw Governor Connally fall.
+LJSpeech-1.1/wavs/LJ049-0120.wav|At present, Federal agencies participate only upon the sufferance of the local authorities.
+LJSpeech-1.1/wavs/LJ010-0003.wav|I propose now to continue the subject, and to devote a couple of chapters to criminal occurrences of a more recent date,
+LJSpeech-1.1/wavs/LJ011-0215.wav|and found guilty, remaining in Lancaster Jail for a couple of months, when they were brought up to the court of King's Bench for judgment.
+LJSpeech-1.1/wavs/LJ021-0061.wav|The emergency purpose of the N.R.A. was to put men to work and since its creation more than four million persons have been reemployed,
+LJSpeech-1.1/wavs/LJ014-0305.wav|So enormous were his transactions, that they often affected the markets, and caused great fluctuations in prices.
+LJSpeech-1.1/wavs/LJ046-0096.wav|Nevertheless the procedures and advice should be accepted if the President wishes to have any security.
+LJSpeech-1.1/wavs/LJ015-0068.wav|He began life as a law-writer, earning thereby some fifteen or eighteen shillings a week;
+LJSpeech-1.1/wavs/LJ028-0027.wav|For ten days you follow down the river, through little villages and black tent encampments, among scenes of strange Arab life which never lose their charm.
+LJSpeech-1.1/wavs/LJ047-0002.wav|Chapter eight. The Protection of the President. Part two.
+LJSpeech-1.1/wavs/LJ034-0080.wav|When he reached the first floor, the west elevator -- the one with the gate was not there.
+LJSpeech-1.1/wavs/LJ047-0206.wav|I would think his continued association with the Russian Embassy after his return,
+LJSpeech-1.1/wavs/LJ009-0154.wav|Mr. Charles Kean the tragedian was also present,
+LJSpeech-1.1/wavs/LJ001-0034.wav|They printed very few books in this type, three only; but in their very first books in Rome, beginning with the year fourteen sixty-eight,
+LJSpeech-1.1/wavs/LJ009-0284.wav|It was stated in evidence before the Commission on Capital Punishment in eighteen sixty-four,
+LJSpeech-1.1/wavs/LJ006-0124.wav|the prisoner official already mentioned, who held the fate of new arrivals as regards location in his hands.
+LJSpeech-1.1/wavs/LJ014-0036.wav|Hocker and the brother went to the spot, where the latter left him to meet his sister's seducer alone.
+LJSpeech-1.1/wavs/LJ018-0131.wav|All that was taken was handed over to Burnett, or a "woman in black" whom Brown met by appointment at Waterloo station.
+LJSpeech-1.1/wavs/LJ005-0161.wav|too much discretion was still left to the magistracy to fill in the details. The legislature only recommended,
+LJSpeech-1.1/wavs/LJ018-0060.wav|His crime under this aspect of it was less premeditated, and less atrocious therefore, than that of Lefroy.
+LJSpeech-1.1/wavs/LJ025-0128.wav|can be distinguished from those acts of contraction following upon stimuli, which are called "reflex" in animals.
+LJSpeech-1.1/wavs/LJ011-0146.wav|and no person ever after suffered death for any variety of this crime.
+LJSpeech-1.1/wavs/LJ026-0025.wav|The important point to recognize is that these boundaries are artificial and that there are no scientific frontiers in Nature.
+LJSpeech-1.1/wavs/LJ045-0138.wav|In fact, his wife testified that she only said that she would prefer not to receive any more visits from the Bureau
+LJSpeech-1.1/wavs/LJ015-0235.wav|and last, but not least, Agar frequently traveled up and down the line to test the false keys he had manufactured with Pierce's assistance.
+LJSpeech-1.1/wavs/LJ030-0249.wav|reported that Youngblood was in the rear seat using his body to shield the Vice President before the second and third shots were fired.
+LJSpeech-1.1/wavs/LJ048-0061.wav|Under proper procedures knowledge of the pending Presidential visit might have prompted Hosty
+LJSpeech-1.1/wavs/LJ003-0249.wav|were imposed in case of misconduct; and when there had been an attempt at escape,
+LJSpeech-1.1/wavs/LJ014-0299.wav|that of a prize-fighter named Donovan, tried the same day, and convicted of manslaughter.
+LJSpeech-1.1/wavs/LJ045-0220.wav|The Commission does not believe that the relations between Oswald and his wife caused him to assassinate the President.
+LJSpeech-1.1/wavs/LJ033-0148.wav|Using a standard chemical method involving silver nitrates
+LJSpeech-1.1/wavs/LJ040-0191.wav|Lee told Carro, his probation officer, that he liked to be by himself because he had too much difficulty in making friends.
+LJSpeech-1.1/wavs/LJ008-0047.wav|This movable platform is raised six inches higher than the rest of the scaffold, and on it the convicts stand;
+LJSpeech-1.1/wavs/LJ028-0276.wav|a marvelous thing happened to Zopyrus, son of the Megabyzus who was among the seven men that overthrew the Magus.
+LJSpeech-1.1/wavs/LJ016-0371.wav|The interment in a shell filled with quicklime in the passage-way leading to the Old Bailey is also a part of the duty of the prison officials.
+LJSpeech-1.1/wavs/LJ050-0259.wav|some of which must be specially designed to fit unique requirements. Even before the assassination, and to a far greater extent thereafter,
+LJSpeech-1.1/wavs/LJ006-0201.wav|went at once to his customers, who bought their paper and paid for it themselves.
+LJSpeech-1.1/wavs/LJ016-0184.wav|his attention was drawn early one morning to a man who leant against a lamp-post in Finsbury Square, coughing violently.
+LJSpeech-1.1/wavs/LJ040-0098.wav|Marguerite Oswald worked in miscellaneous jobs after her divorce from Ekdahl.
+LJSpeech-1.1/wavs/LJ040-0212.wav|The factors in Lee Oswald's personality which were noted by those who had contact with him in New York indicate
+LJSpeech-1.1/wavs/LJ039-0087.wav|It allows you to see your target clearly, and it is still of a minimum amount of power that it doesn't exaggerate your own body movements.
+LJSpeech-1.1/wavs/LJ030-0157.wav|The car maintained this average speed over a distance of approximately one hundred eighty-six feet immediately preceding the shot which struck the President in the head.
+LJSpeech-1.1/wavs/LJ049-0080.wav|Assassination a Federal Crime
+LJSpeech-1.1/wavs/LJ011-0124.wav|he also forged dividend receipts and got the dividends. He was only six-and-twenty when he was hanged.
+LJSpeech-1.1/wavs/LJ006-0056.wav|Some rooms remained quite empty and unoccupied, while others were full to overflowing.
+LJSpeech-1.1/wavs/LJ039-0190.wav|Simmons testified that familiarity with the bolt could be achieved in dry practice and, as has been indicated above,
+LJSpeech-1.1/wavs/LJ010-0100.wav|The first constable who entered Thistlewood ran through the body with a sword, but others quickly followed,
+LJSpeech-1.1/wavs/LJ042-0185.wav|economic, political, or military crisis, internal or external,
+LJSpeech-1.1/wavs/LJ007-0201.wav|each was provided with an iron bedstead, and a small desk at which the condemned man might read or write.
+LJSpeech-1.1/wavs/LJ041-0052.wav|Oswald's inability or lack of desire to enter into meaningful relationships with other people
+LJSpeech-1.1/wavs/LJ032-0009.wav|(two) the means by which the weapon was brought into the Depository Building,
+LJSpeech-1.1/wavs/LJ028-0249.wav|continued dancing and reveling until they learned the capture but too certainly.
+LJSpeech-1.1/wavs/LJ015-0047.wav|Its debts amounted to three-quarters of a million, and the dividend it eventually paid was three and twopence in the pound.
+LJSpeech-1.1/wavs/LJ050-0158.wav|the Department hopes to design a practical system which will fully meet the needs of the Protective Research Section of the Secret Service.
+LJSpeech-1.1/wavs/LJ040-0149.wav|intense anxiety, shyness, feelings of awkwardness and insecurity, end quote.
+LJSpeech-1.1/wavs/LJ048-0168.wav|With the number of men available to the Secret Service and the time available, surveys of hundreds of buildings and thousands of windows is not practical.
+LJSpeech-1.1/wavs/LJ005-0006.wav|who handed a shilling to the escort warder to provide her with a hackney coach; but this functionary pocketed the cash, and obliged the woman to walk
+LJSpeech-1.1/wavs/LJ021-0073.wav|This is because of the establishment of fair, competitive standards and because of relief from unfair competition
+LJSpeech-1.1/wavs/LJ019-0242.wav|Newgate naturally shared in any advantages due to these reforms. I propose, therefore, to refer to them in the concluding pages of this work,
+LJSpeech-1.1/wavs/LJ047-0045.wav|and promised to advise the FBI if he heard from them.
+LJSpeech-1.1/wavs/LJ038-0239.wav|indicated that the bullet was fired from a position near the point where one of the photographs was taken.
+LJSpeech-1.1/wavs/LJ038-0046.wav|until Oswald had been knocked to the floor by McDonald and the others.
+LJSpeech-1.1/wavs/LJ046-0170.wav|which requires a checkup at least, every six months.
+LJSpeech-1.1/wavs/LJ005-0281.wav|that the greatest mischief followed from the intercourse which was still permitted in so many prisons; to use its words,
+LJSpeech-1.1/wavs/LJ049-0172.wav|questions inevitably arise as to the scope of each agency's authority and responsibility.
+LJSpeech-1.1/wavs/LJ010-0153.wav|William the fourth was also the victim of a murderous outrage on Ascot race-course in eighteen thirty-two,
+LJSpeech-1.1/wavs/LJ050-0064.wav|whose prior acts or statements depict propensity for violence and hatred against organized government, end quote.
+LJSpeech-1.1/wavs/LJ007-0054.wav|There were disputes and quarrels constantly among these doomed men; it was a word and blow, an argument clenched always with a fight.
+LJSpeech-1.1/wavs/LJ027-0109.wav|Wiedersheim has recorded more than one hundred and eighty such structural reminiscences in man.
+LJSpeech-1.1/wavs/LJ003-0147.wav|Besides the jail fees, there was garnish of half-a-guinea, collected by the steward,
+LJSpeech-1.1/wavs/LJ044-0045.wav|events which, quote, robbed me of what support I had leaving me alone, end quote.
+LJSpeech-1.1/wavs/LJ037-0049.wav|A few minutes later she described the gunman to a policeman.
+LJSpeech-1.1/wavs/LJ010-0105.wav|Thistlewood was discovered next morning in a mean house in White Street, Moorfields.
+LJSpeech-1.1/wavs/LJ006-0222.wav|and enlivened by flash songs and thrilling long-winded descriptions of robberies and other "plants."
+LJSpeech-1.1/wavs/LJ042-0084.wav|referring to them as experiences that he did not intend to have himself, even though he stated that he had never held a civilian job.
+LJSpeech-1.1/wavs/LJ031-0224.wav|with a sizable metal fragment lying just above the right eye.
+LJSpeech-1.1/wavs/LJ022-0152.wav|There is likewise pending before the Congress
+LJSpeech-1.1/wavs/LJ003-0317.wav|No prisoner should be allowed to obtain superior accommodation on the payment of any fees. Fees indeed should be generally abolished, garnish also.
+LJSpeech-1.1/wavs/LJ002-0186.wav|The population generally amounted to from five hundred to seven hundred, the accommodation being calculated for two hundred.
+LJSpeech-1.1/wavs/LJ028-0042.wav|The place where the old city had stood for three thousand years again became a desert.
+LJSpeech-1.1/wavs/LJ020-0034.wav|When light, knead well and long;
+LJSpeech-1.1/wavs/LJ050-0043.wav|The Secret Service has embarked upon a complete overhaul of its research activities. The staff of the Protective Research Section (PRS)
+LJSpeech-1.1/wavs/LJ038-0086.wav|During the evening of November twenty-two, the Dallas Police Department performed paraffin tests on Oswald's hands and right cheek
+LJSpeech-1.1/wavs/LJ030-0019.wav|According to O'Donnell, the President commented that, quote,
+LJSpeech-1.1/wavs/LJ028-0055.wav|and surrounded it with walls the like of which no other city has ever seen.
+LJSpeech-1.1/wavs/LJ009-0236.wav|To the great surprise of all who witnessed this dreadful scene,
+LJSpeech-1.1/wavs/LJ004-0120.wav|In eighteen eighteen, out of five hundred and eighteen prisons in the United Kingdom,
+LJSpeech-1.1/wavs/LJ011-0209.wav|He surrendered the bride who had never been a wife, and she returned to England with her friends, while Wakefield went on alone to Paris.
+LJSpeech-1.1/wavs/LJ002-0175.wav|The costs in heavier debts always doubled the sum; if the arrest was made in the country it trebled it.
+LJSpeech-1.1/wavs/LJ021-0038.wav|and with the full expectation that the money used to assist these enterprises will eventually be repaid.
+LJSpeech-1.1/wavs/LJ015-0037.wav|and the partners had to decide between suspending payment or continuing to hold its head above water by flagitious processes.
+LJSpeech-1.1/wavs/LJ028-0020.wav|Late at night the weary dromedary kneels, and on the ground, close beside her, you lie down to sleep.
+LJSpeech-1.1/wavs/LJ023-0065.wav|they gave to the Congress the ample broad powers "to levy taxes
+LJSpeech-1.1/wavs/LJ025-0103.wav|of attempting to draw any general distinction between animals and vegetables on this ground. In fact, the difference vanishes with the sunshine,
+LJSpeech-1.1/wavs/LJ028-0451.wav|In places even the bases have disappeared, and their moats have long been filled with the drifting sand.
+LJSpeech-1.1/wavs/LJ002-0033.wav|two. The female debtors' side. three. The chapel yard. four. The middle yard.
+LJSpeech-1.1/wavs/LJ005-0062.wav|Experience has since shown that in some respects these plans are defective, especially in the placing of the governor's residence in the center of the prison.
+LJSpeech-1.1/wavs/LJ035-0063.wav|But I cannot recall any change in expression of any kind on his face, end quote.
+LJSpeech-1.1/wavs/LJ029-0074.wav|and that following the luncheon the President would return to the airport by the most direct route.
+LJSpeech-1.1/wavs/LJ046-0063.wav|stated the President's views of his responsibilities with simplicity and clarity, quote,
+LJSpeech-1.1/wavs/LJ013-0060.wav|In the first a maiden lady, Miss Slack,
+LJSpeech-1.1/wavs/LJ047-0118.wav|that Oswald had told him that he had worked and been married in the Soviet Union.
+LJSpeech-1.1/wavs/LJ018-0308.wav|Large sums, as they were realized, were transmitted to the United States, and invested in various American securities,
+LJSpeech-1.1/wavs/LJ018-0179.wav|On his return to Newgate to be finally discharged, Cummings jumped up the stairs and fairly danced for joy.
+LJSpeech-1.1/wavs/LJ011-0106.wav|Hunton accepted his sentence with great resignation, although he protested against the inhumanity of the laws which condemned him to death.
+LJSpeech-1.1/wavs/LJ006-0250.wav|The officers were obliged to go in among the prisoners to restore order with drawn cutlasses,
+LJSpeech-1.1/wavs/LJ018-0183.wav|he devoted his energies to coining and forgery, and in the latter line was a friend and associate of Saward's.
+LJSpeech-1.1/wavs/LJ043-0077.wav|While that counselor found that he was qualified to handle many different types of jobs,
+LJSpeech-1.1/wavs/LJ024-0051.wav|I suggest only the addition of justices to the bench
+LJSpeech-1.1/wavs/LJ050-0174.wav|Protective Research participation in advance arrangements.
+LJSpeech-1.1/wavs/LJ023-0124.wav|we shall seek such clarifying amendment as will assure the power to enact those laws,
+LJSpeech-1.1/wavs/LJ002-0236.wav|Matters were rather better at the Marshalsea.
+LJSpeech-1.1/wavs/LJ028-0343.wav|Darius having become master of the place, destroyed the wall, and tore down all the gates;
+LJSpeech-1.1/wavs/LJ014-0311.wav|His extensive business had been carried on by fraud.
+LJSpeech-1.1/wavs/LJ028-0298.wav|Surely thou hadst gone out of thy mind when thou didst so misuse thyself.
+LJSpeech-1.1/wavs/LJ005-0041.wav|a much-needed and, according to our ideas, indispensable reform, already initiated by the Ladies' Committee at Newgate.
+LJSpeech-1.1/wavs/LJ010-0021.wav|The credulity of investors, the unscrupulous dishonesty of bankers,
+LJSpeech-1.1/wavs/LJ045-0204.wav|he had never found anything to which he felt he could really belong.
+LJSpeech-1.1/wavs/LJ012-0233.wav|According to the confession, death had been inflicted by drowning in a well, whereas the medical evidence all pointed to violence.
+LJSpeech-1.1/wavs/LJ016-0440.wav|But it is neither easy to obtain the instruments of self-destruction nor to elude the vigilance of their guard.
+LJSpeech-1.1/wavs/LJ037-0131.wav|When he reached a gasoline service station one block away he turned north and walked toward a parking area in the rear of the station.
+LJSpeech-1.1/wavs/LJ022-0138.wav|It has under consideration many measures which provide for the rounding out of the program of economic and social reconstruction
+LJSpeech-1.1/wavs/LJ002-0160.wav|and John Lancaster for one shilling, eight pence, with costs of seven shillings, six pence. Quote.
+LJSpeech-1.1/wavs/LJ003-0315.wav|All three, governor, chaplain, and surgeon, should keep journals, which should be inspected periodically by the visiting magistrates.
+LJSpeech-1.1/wavs/LJ012-0215.wav|and the jury returned a verdict of willful murder against persons unknown, expressing a strong opinion that Bishop,
+LJSpeech-1.1/wavs/LJ039-0099.wav|In accordance with standard Marine procedures, Oswald received extensive training in marksmanship.
+LJSpeech-1.1/wavs/LJ003-0284.wav|that when they were not carousing, plotting, or scheming,
+LJSpeech-1.1/wavs/LJ019-0340.wav|The latter was rendered nearly impossible by the penalties imposed on persons bringing spirituous liquors into the jail.
+LJSpeech-1.1/wavs/LJ031-0211.wav|weighed one hundred seventy pounds, had blue eyes and reddish-brown hair.
+LJSpeech-1.1/wavs/LJ012-0044.wav|When his trade was busiest he set up a second establishment, at the head of which, although he was married,
+LJSpeech-1.1/wavs/LJ026-0005.wav|that is, the forms may possess a mouth for the ingestion of solid food and green coloring matter, chlorophyll,
+LJSpeech-1.1/wavs/LJ026-0056.wav|It is, however, impossible for the animal to build these materials directly into the substance of its own body.
+LJSpeech-1.1/wavs/LJ011-0242.wav|Howard replied, desiring Mr. Mullay to call upon him in a house in Red Lion Square.
+LJSpeech-1.1/wavs/LJ011-0125.wav|The other and the last criminal executed for forgery in this country was one Maynard, who was convicted of a fraud upon the Custom House.
+LJSpeech-1.1/wavs/LJ048-0094.wav|Lawson was responsible for working out a great many arrangements for the President's trip.
+LJSpeech-1.1/wavs/LJ035-0140.wav|On entering, Lovelady saw a girl on the first floor who he believes was Victoria Adams.
+LJSpeech-1.1/wavs/LJ028-0300.wav|as it is, I kept my own counsel, and so accomplished my plans.
+LJSpeech-1.1/wavs/LJ003-0293.wav|also to make such proposals as might appear salutary, and calculated to improve Newgate and the rest of the city jails.
+LJSpeech-1.1/wavs/LJ045-0061.wav|While Marina Oswald said that her husband wanted her to learn English,
+LJSpeech-1.1/wavs/LJ040-0214.wav|His usual reaction to the problems which he encountered there was simply withdrawal.
+LJSpeech-1.1/wavs/LJ048-0028.wav|Consequently, there was no basis for Hosty to go to Secret Service and advise them of Oswald's presence. End quote
+LJSpeech-1.1/wavs/LJ030-0059.wav|was a metallic frame with four handholds that riders in the car could grip while standing in the rear seat during parades.
+LJSpeech-1.1/wavs/LJ008-0289.wav|Capitally convicted prisoners did, as a matter of fact, "treat with habitual and inexpressible levity the sentence of death."
+LJSpeech-1.1/wavs/LJ035-0079.wav|The first test, run at normal walking pace, required one minute, eighteen seconds;
+LJSpeech-1.1/wavs/LJ025-0054.wav|by the introduction of exact and easily manageable methods of conducting the chemical analysis of organic compounds and finally
+LJSpeech-1.1/wavs/LJ006-0210.wav|Drink, in more or less unlimited quantities, was still to be had.
+LJSpeech-1.1/wavs/LJ031-0203.wav|Mrs. Kennedy and the Attorney General,
+LJSpeech-1.1/wavs/LJ013-0234.wav|The cloth above referred to belonged to the inner seam of his trousers, which he had managed to tear out.
+LJSpeech-1.1/wavs/LJ048-0015.wav|We talked to him twice.
+LJSpeech-1.1/wavs/LJ045-0087.wav|Mrs. Oswald told another of her friends that Oswald was very cold to her, that they very seldom had sexual relations
+LJSpeech-1.1/wavs/LJ014-0041.wav|Such an extravagant defense did not weigh with judge or jury;
+LJSpeech-1.1/wavs/LJ028-0159.wav|thence from the corners of the wall there is carried along each bank of the river a fence of burned bricks.
+LJSpeech-1.1/wavs/LJ016-0282.wav|"Till three o'clock it was one long revelry of songs and laughter, shouting, and often quarreling, though,
+LJSpeech-1.1/wavs/LJ002-0070.wav|The state side was the part stolen from the female felons' side.
+LJSpeech-1.1/wavs/LJ006-0182.wav|Under the reckless contempt for regulations,
+LJSpeech-1.1/wavs/LJ001-0029.wav|But though on the whole, except in Italy, Gothic letter was most often used
+LJSpeech-1.1/wavs/LJ049-0079.wav|Many changes have already been made and others are contemplated, some of them in response to the Commission's questions and informal suggestions.
+LJSpeech-1.1/wavs/LJ016-0047.wav|But the worst part of the business was to jump from this irksome foothold of the iron grating on to the top of the building just mentioned,
+LJSpeech-1.1/wavs/LJ048-0127.wav|as those followed in nineteen thirty-six during a trip to Dallas by President Roosevelt,
+LJSpeech-1.1/wavs/LJ014-0173.wav|It was understood also that he had served in the army as a private, and had, moreover, undergone a sentence of transportation.
+LJSpeech-1.1/wavs/LJ030-0118.wav|In the downtown area, large crowds of spectators gave the President a tremendous reception.
+LJSpeech-1.1/wavs/LJ017-0090.wav|and so got the money for which he was prepared to barter his soul.
+LJSpeech-1.1/wavs/LJ010-0086.wav|The occasion was considered excellent by the conspirators for disposal of the whole Cabinet at one blow,
+LJSpeech-1.1/wavs/LJ045-0112.wav|When Oswald called the next day his wife became very angry about his use of the alias. He said that he used it because, quote,
+LJSpeech-1.1/wavs/LJ002-0021.wav|the grand jury made a special presentment to the Court of Common Council, pointing out that on the debtors' side, which was intended for only one hundred,
+LJSpeech-1.1/wavs/LJ006-0245.wav|After severe suffering prolonged for many months, the inflammation was subdued, but the cartilage of the knee-joint was destroyed, and he was crippled for life.
+LJSpeech-1.1/wavs/LJ015-0133.wav|Even when the bubble burst Redpath, who had lived at the rate of twenty thousand a year,
+LJSpeech-1.1/wavs/LJ032-0135.wav|"Lifting" a print involves the use of adhesive material to remove the fingerprint powder which adheres to the original print.
+LJSpeech-1.1/wavs/LJ016-0336.wav|But Newgate and its neighborhood was carefully held by the police, both city and metropolitan.
+LJSpeech-1.1/wavs/LJ018-0134.wav|In this part of the mills a large quantity of bank-note paper was kept at the period of the robbery,
+LJSpeech-1.1/wavs/LJ045-0137.wav|I and my wife strongly protested these tactics by the notorious F.B.I., end quote.
+LJSpeech-1.1/wavs/LJ011-0022.wav|and fixed the whole sum misappropriated at one hundred seventy thousand pounds, the first forgery dating back to eighteen fourteen.
+LJSpeech-1.1/wavs/LJ007-0079.wav|or arraignment, to an asylum, and the Secretary of State had the same power as regards any who became insane while undergoing sentence.
+LJSpeech-1.1/wavs/LJ041-0014.wav|Two days later, quote, some big guy, probably from a high school -- he looked like a tremendous football player, end quote,
+LJSpeech-1.1/wavs/LJ006-0217.wav|There were more varied, and at times, especially when beer had circulated freely, more uproarious diversions.
+LJSpeech-1.1/wavs/LJ014-0174.wav|But it was supposed that he had been once in a good position, well born, and well educated.
+LJSpeech-1.1/wavs/LJ028-0210.wav|Nabonidus was taken prisoner in Babylon. On the third of Marchesvan Cyrus entered Babylon and proclaimed peace to all the city.
+LJSpeech-1.1/wavs/LJ047-0242.wav|Revill testified that Hosty said also that the FBI had information that Oswald was, quote, capable of committing this assassination, end quote.
+LJSpeech-1.1/wavs/LJ034-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ044-0044.wav|to an attack by Cuban exiles in a street demonstration and being, quote, officialy cautioned, end quote, by the police.
+LJSpeech-1.1/wavs/LJ038-0157.wav|The only employee at the Depository Building named "Junior" was James Jarman, Jr.
+LJSpeech-1.1/wavs/LJ041-0081.wav|Oswald probably engaged his superiors in arguments on a subject that he had studied
+LJSpeech-1.1/wavs/LJ013-0120.wav|whom he had shown over the plate closet.
+LJSpeech-1.1/wavs/LJ040-0174.wav|Lee confirmed some of those observations by saying that he felt almost as if there were a veil between him and other people
+LJSpeech-1.1/wavs/LJ035-0098.wav|he was certain that both elevators, which occupy the same shaft, were on the fifth floor.
+LJSpeech-1.1/wavs/LJ023-0062.wav|and which could not be met by merely local action.
+LJSpeech-1.1/wavs/LJ048-0284.wav|This is the salutary goal to which the Secret Service regulation is directed,
+LJSpeech-1.1/wavs/LJ050-0206.wav|suggest that the Secret Service is trying to accomplish its job with too few people and without adequate modern equipment.
+LJSpeech-1.1/wavs/LJ012-0080.wav|This was known to some clerks in the office, who began to consider how they might lay hands on a lot of cash.
+LJSpeech-1.1/wavs/LJ041-0009.wav|Mrs. Murret believes that he talked at length with a girl on the telephone, but no one remembers that he had any dates.
+LJSpeech-1.1/wavs/LJ036-0137.wav|And he never said anything. So I figured he was one of these people that don't like to talk so I never said any more to him.
+LJSpeech-1.1/wavs/LJ021-0064.wav|not only to labor in the form of new jobs, in relief from overwork and in relief from underpay,
+LJSpeech-1.1/wavs/LJ018-0190.wav|"By all means," replies Bob, and a close investigation follows, without any detection of the corpse concealed.
+LJSpeech-1.1/wavs/LJ009-0138.wav|all the avenues to the prison gates were blocked by ticket-holders.
+LJSpeech-1.1/wavs/LJ015-0124.wav|but it was based upon more extended and audacious forgeries.
+LJSpeech-1.1/wavs/LJ020-0059.wav|When the time is up and the rolls are puffy and promising, set them in a pretty quick oven and bake half an hour,
+LJSpeech-1.1/wavs/LJ016-0284.wav|There were preachers among the crowd, but they could not get a patient hearing.
+LJSpeech-1.1/wavs/LJ020-0036.wav|Take care that it does not burn in baking. The molasses renders it liable to scorching.
+LJSpeech-1.1/wavs/LJ040-0113.wav|They moved shortly after Robert joined the Marines; they lived for a time with John Pic who was stationed there with the Coast Guard.
+LJSpeech-1.1/wavs/LJ028-0146.wav|As fast as they dug the moat, the soil which they got from the cutting was made into bricks,
+LJSpeech-1.1/wavs/LJ036-0211.wav|passed the intersection of tenth and Patton, about eight blocks from where he had reported at twelve:fifty-four p.m.
+LJSpeech-1.1/wavs/LJ045-0071.wav|Mrs. Ford said that Marina Oswald admitted that she provoked Oswald on occasion.
+LJSpeech-1.1/wavs/LJ003-0308.wav|A laundry should be established, and a matron appointed on the female side, where all the prisoners' washing could be performed.
+LJSpeech-1.1/wavs/LJ032-0129.wav|Accordingly, my opinion simply was that the latent prints which were there were of no value, end quote.
+LJSpeech-1.1/wavs/LJ050-0246.wav|which would permit it to provide adequate protective manpower for all situations.
+LJSpeech-1.1/wavs/LJ008-0213.wav|By this time the workmen might be heard busily erecting the gallows;
+LJSpeech-1.1/wavs/LJ041-0065.wav|testified that Oswald seemed, quote, always to be striving for a relationship, but whenever he did
+LJSpeech-1.1/wavs/LJ015-0090.wav|To account for his revenues he pretended to have been very lucky on the Stock Exchange, which was at one time true to a limited extent,
+LJSpeech-1.1/wavs/LJ012-0152.wav|which he refused to redeem on account of the row about the robbery.
+LJSpeech-1.1/wavs/LJ041-0182.wav|and believed that our Government did not have, quote, too much to offer, end quote, but was not in favor of, quote, the Communist way of life, end quote.
+LJSpeech-1.1/wavs/LJ038-0179.wav|(three) firearm identification of the bullet found in Walker's home, and (four)
+LJSpeech-1.1/wavs/LJ043-0020.wav|The relations between Oswald and his wife became such that Bouhe wanted to "liberate" her from Oswald.
+LJSpeech-1.1/wavs/LJ002-0058.wav|The chapel yard led to the chapel, and on the staircase were two rooms frequently set apart for the king's witnesses,
+LJSpeech-1.1/wavs/LJ008-0221.wav|They talked of the days when the convicts were hung up a dozen or more in a row;
+LJSpeech-1.1/wavs/LJ017-0196.wav|The last time Mrs. Soames showed great reluctance to take it, but Wilson said it would certainly do her good.
+LJSpeech-1.1/wavs/LJ033-0210.wav|told the curtain rod story to Frazier to explain both the return to Irving on a Thursday
+LJSpeech-1.1/wavs/LJ032-0055.wav|Information received from the Italian Armed Forces Intelligence Service has established that this particular rifle was the only rifle of its type
+LJSpeech-1.1/wavs/LJ042-0227.wav|His second answer was that he, quote, went as a citizen of the U.S. (as a tourist) residing in a foreign country which I have a perfect right to do.
+LJSpeech-1.1/wavs/LJ015-0083.wav|This ingenious method of raising funds he adopted and carried on without detection,
+LJSpeech-1.1/wavs/LJ010-0223.wav|You must attend; and if your master will not give you leave, you must come in defiance of him.
+LJSpeech-1.1/wavs/LJ049-0069.wav|This basic approach to the problem of planning for emergencies is sound.
+LJSpeech-1.1/wavs/LJ050-0027.wav|While manuals and memoranda are no guarantee of effective operations,
+LJSpeech-1.1/wavs/LJ009-0036.wav|Down below between the galleries was the mass of the prison population;
+LJSpeech-1.1/wavs/LJ041-0089.wav|Thornley added, quote, I think it was kind of necessary to him to believe that he was being picked on.
+LJSpeech-1.1/wavs/LJ017-0036.wav|that the trial was transferred to the Central Criminal Court,
+LJSpeech-1.1/wavs/LJ040-0029.wav|He seemed to prefer the Soviet Union and he spoke highly of Cuba.
+LJSpeech-1.1/wavs/LJ043-0084.wav|business suit, alert replies -- Expresses self extremely well, end quote.
+LJSpeech-1.1/wavs/LJ050-0245.wav|In view of the ever-increasing mobility of American Presidents, it seems unlikely that the Service could or should increase its own staff to a size
+LJSpeech-1.1/wavs/LJ033-0144.wav|At the time the bag was found, Lieutenant Day of the Dallas police wrote on it, quote, Found next to the sixth floor window gun fired from.
+LJSpeech-1.1/wavs/LJ050-0015.wav|The Commission recommends that the Secretary of the Treasury appoint a special assistant with the responsibility of supervising the Service.
+LJSpeech-1.1/wavs/LJ011-0006.wav|He went to the bank, and found that no stocks stood in her name. He called at once upon Fauntleroy, his client's bankers, for an explanation,
+LJSpeech-1.1/wavs/LJ009-0009.wav|This rule has some, but very few, exceptions; such as where a hardened offender behaves with great levity and brutality, as if he cared nought for his life,
+LJSpeech-1.1/wavs/LJ006-0308.wav|The governor sent down wine on festive occasions, of which no doubt the prisoner housemaid had her share.
+LJSpeech-1.1/wavs/LJ050-0021.wav|Actions by the Service since the assassination indicate its awareness of the necessity for substantial improvement in its administration.
+LJSpeech-1.1/wavs/LJ002-0282.wav|Those who could not pay were thrown into the wards with the night charges,
+LJSpeech-1.1/wavs/LJ036-0048.wav|She boarded the Marsalis bus at St. Paul and Elm Streets to return home. She testified further, quote,
+LJSpeech-1.1/wavs/LJ024-0023.wav|ever since a similar proposal passed the House of Representatives in eighteen sixty-nine.
+LJSpeech-1.1/wavs/LJ038-0234.wav|Although Oswald destroyed the notebook, three photographs found among Oswald's possessions after the assassination
+LJSpeech-1.1/wavs/LJ038-0089.wav|Expert testimony before the Commission
+LJSpeech-1.1/wavs/LJ025-0105.wav|On the other hand, those plants, such as the fungi,
+LJSpeech-1.1/wavs/LJ019-0347.wav|chairman of quarter sessions, or even by a couple of justices.
+LJSpeech-1.1/wavs/LJ029-0065.wav|Neither the Dallas police nor the FBI had yet learned the source of the handbill.
+LJSpeech-1.1/wavs/LJ041-0142.wav|Once it had started down Elm Street toward the Triple Underpass, however,
+LJSpeech-1.1/wavs/LJ031-0143.wav|and decided that there would be no release of the news of the President's death until the Vice President had left the hospital.
+LJSpeech-1.1/wavs/LJ021-0096.wav|under the able and energetic leadership of General Johnson.
+LJSpeech-1.1/wavs/LJ020-0084.wav|Bake from twenty to twenty-five minutes in a quick oven, turning the pan around once, quickly and lightly.
+LJSpeech-1.1/wavs/LJ028-0470.wav|Time has dealt even less kindly with it, for it may be traced only for the distance of about a mile along its eastern side.
+LJSpeech-1.1/wavs/LJ015-0288.wav|so that Saward and the rest might make themselves scarce.
+LJSpeech-1.1/wavs/LJ019-0045.wav|Not less remarkable than this diverse interpretation of a manifest duty
+LJSpeech-1.1/wavs/LJ027-0005.wav|paleontology, distribution and domestication.
+LJSpeech-1.1/wavs/LJ044-0098.wav|He did not think Oswald looked like the, quote, type, end quote, that he would have expected to find associating with a group such as the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ003-0105.wav|he was sent on to Newgate in a coach, handcuffed to a noted house-breaker, who was afterwards cast for death.
+LJSpeech-1.1/wavs/LJ028-0252.wav|Only the King's son, Belshazzar, was killed.
+LJSpeech-1.1/wavs/LJ030-0113.wav|through thinly populated areas on the outskirts of Dallas.
+LJSpeech-1.1/wavs/LJ018-0289.wav|When all was ready, Bidwell first "refreshed his credit" at the Bank of England, as well as disarmed suspicion,
+LJSpeech-1.1/wavs/LJ016-0008.wav|Charles Thomas White, awaiting execution for arson, made a desperate effort to escape from Newgate in eighteen twenty-seven.
+LJSpeech-1.1/wavs/LJ029-0119.wav|It afforded us a chance to have alternative routes if something happened on the motorcade route. It was the type of suburban area a good part of the way
+LJSpeech-1.1/wavs/LJ041-0094.wav|He had the nickname, quote, Ozzie Rabbit, end quote.
+LJSpeech-1.1/wavs/LJ044-0089.wav|Stuckey testified that uncovering Oswald's defection was very important, quote,
+LJSpeech-1.1/wavs/LJ049-0049.wav|About three point seven seconds after the President received this wound,
+LJSpeech-1.1/wavs/LJ028-0513.wav|Should you cross the river to search for the western inner wall, you would find but a small fragment of it.
+LJSpeech-1.1/wavs/LJ047-0092.wav|On August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ038-0019.wav|She said, quote, No; by golly, he didn't, end quote, and turned around, but the man was nowhere in sight.
+LJSpeech-1.1/wavs/LJ011-0237.wav|The jewelers were always a favorite prey of the London thieves.
+LJSpeech-1.1/wavs/LJ020-0003.wav|But it keeps fresh longer, is softer and more nutritious, and a second-rate brand of flour thus treated produces a better loaf
+LJSpeech-1.1/wavs/LJ036-0187.wav|Mrs. Roberts saw him a few seconds later standing near the bus stop in front of the house on the east side of Beckley.
+LJSpeech-1.1/wavs/LJ047-0107.wav|and when I got to questioning him further then he felt that his purpose had been served and he wouldn't say anything further, end quote.
+LJSpeech-1.1/wavs/LJ041-0146.wav|it was more in terms of a general hostility against the government and its representatives rather than a grudge against any particular person.
+LJSpeech-1.1/wavs/LJ003-0162.wav|the convicted felon, the transport awaiting removal, the lunatic whose case was still undecided,
+LJSpeech-1.1/wavs/LJ047-0117.wav|William Stuckey, who had appeared on the radio program with Oswald, told the Bureau on August thirty
+LJSpeech-1.1/wavs/LJ047-0101.wav|Quigley testified, quote,
+LJSpeech-1.1/wavs/LJ046-0116.wav|The main job of the Protective Research Section (PRS)
+LJSpeech-1.1/wavs/LJ032-0229.wav|This information enabled the Commission to conclude
+LJSpeech-1.1/wavs/LJ044-0211.wav|The Militant also reflected a critical attitude toward President Kennedy's attempts to reduce tensions between the United States and the Soviet Union.
+LJSpeech-1.1/wavs/LJ033-0013.wav|During October and November of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ002-0191.wav|Besides those actually resident within the walls,
+LJSpeech-1.1/wavs/LJ039-0247.wav|(seven) attempted, in April nineteen sixty-three, to kill Maj. Gen. Edwin A. Walker, and (eight)
+LJSpeech-1.1/wavs/LJ024-0114.wav|and who would be willing to support a reasonable amendment if they could agree on one.
+LJSpeech-1.1/wavs/LJ031-0183.wav|He was informed that takeoff would be delayed until Vice President Johnson was sworn in.
+LJSpeech-1.1/wavs/LJ033-0049.wav|According to Mrs. Paine, Oswald had gone to bed by nine p.m.;
+LJSpeech-1.1/wavs/LJ027-0106.wav|Other well-known examples among vertebrates are: Vestiges of hind limbs in certain snakes, reduced wings in the Apteryx and ostriches,
+LJSpeech-1.1/wavs/LJ038-0293.wav|until that Wednesday because he had heard that there was to be a gathering at the church next door to Walker's house on that evening.
+LJSpeech-1.1/wavs/LJ012-0268.wav|Suspicion grew almost to certainty as the evidence was unfolded.
+LJSpeech-1.1/wavs/LJ049-0058.wav|Secondly, agents are instructed to remove the President as quickly as possible from known or impending danger.
+LJSpeech-1.1/wavs/LJ010-0313.wav|the sum total amounting to some one hundred seventy thousand pounds, with a declaration in his own handwriting to the following effect.
+LJSpeech-1.1/wavs/LJ007-0202.wav|But the one great and most crying evil remained unremedied.
+LJSpeech-1.1/wavs/LJ011-0058.wav|He was summoned to the Mansion House, where he repeated his request, crying, "Accordez moi cette grâce," with much urgency.
+LJSpeech-1.1/wavs/LJ010-0250.wav|of the criminal intent to kill.
+LJSpeech-1.1/wavs/LJ047-0051.wav|particularly since his employment did not involve any sensitive information.
+LJSpeech-1.1/wavs/LJ021-0106.wav|the representatives of trade and industry were permitted to write their ideas into the codes.
+LJSpeech-1.1/wavs/LJ048-0159.wav|Agent Lawson did not arrange for a prior inspection of buildings along the motorcade route,
+LJSpeech-1.1/wavs/LJ032-0191.wav|quote, put aside, end quote, after catching the fibers.
+LJSpeech-1.1/wavs/LJ028-0186.wav|but when Nabonidus joined with the King of Egypt and with the wealthy Croesus of Lydia in an alliance against him,
+LJSpeech-1.1/wavs/LJ013-0018.wav|beef, pork, butter, and earthenwares, on all of which they effected insurances.
+LJSpeech-1.1/wavs/LJ003-0337.wav|Were a metropolitan prison erected on the same lines, with all the space not only for air and exercise, but for day rooms and sleeping cells
+LJSpeech-1.1/wavs/LJ016-0065.wav|To walk out into the street was an easy affair, and he was now free, with one and fourpence in his pocket and a shirt and trousers for all his clothing.
+LJSpeech-1.1/wavs/LJ007-0243.wav|It was not till the erection of the new prison at Holloway in eighteen fifty, and the entire internal reconstruction of Newgate according to new ideas,
+LJSpeech-1.1/wavs/LJ037-0056.wav|testified that she was, quote, quite hysterical, end quote, and was, quote, crying and upset, end quote.
+LJSpeech-1.1/wavs/LJ006-0229.wav|which they dropped smoldering into their fellow-prisoners' pockets.
+LJSpeech-1.1/wavs/LJ022-0051.wav|In spite of the fact that unemployment remains a serious problem
+LJSpeech-1.1/wavs/LJ011-0034.wav|These arguments availed little with the jury, who after a short deliberation found Fauntleroy guilty, and he was sentenced to death.
+LJSpeech-1.1/wavs/LJ040-0072.wav|he was cared for principally by his mother's sister, by babysitters and by his mother, when she had time for him.
+LJSpeech-1.1/wavs/LJ026-0162.wav|In non-green plants like the Indian pipe and mushrooms the breathing of oxygen and the excretion of carbon dioxide are as in the animal.
+LJSpeech-1.1/wavs/LJ022-0017.wav|The general good was at a discount.
+LJSpeech-1.1/wavs/LJ037-0190.wav|George Rose and Co. had the barrel shortened by a gunsmith to two and one quarter inches.
+LJSpeech-1.1/wavs/LJ048-0272.wav|Chief Rowley testified
+LJSpeech-1.1/wavs/LJ003-0025.wav|In the latter duties he was, however, supervised by three auditors, freely chosen by the prisoners among themselves.
+LJSpeech-1.1/wavs/LJ013-0240.wav|Good was followed to his stables by a policeman, but obstinately denied the theft.
+LJSpeech-1.1/wavs/LJ018-0256.wav|Roupell made a long statement more in exculpation than in his defense.
+LJSpeech-1.1/wavs/LJ011-0128.wav|Maynard was convicted of uttering the forged document, Jones of being an accessory; the third prisoner was acquitted.
+LJSpeech-1.1/wavs/LJ008-0122.wav|where the murder for which he was condemned had been committed.
+LJSpeech-1.1/wavs/LJ033-0081.wav|she looked out the breakfast-room window and saw Oswald cross the street and walk toward the driveway where her brother parked his car near the carport.
+LJSpeech-1.1/wavs/LJ028-0014.wav|The desert about you shows no signs of life;
+LJSpeech-1.1/wavs/LJ017-0171.wav|Smethurst was therefore given a free pardon for the offense of murder,
+LJSpeech-1.1/wavs/LJ038-0038.wav|McDonald struck back with his right hand and grabbed the gun with his left hand. They both fell into the seats.
+LJSpeech-1.1/wavs/LJ037-0228.wav|that the man was a, quote, white male, about twenty-five, about five feet eight, brown hair, medium, end quote, and wearing a, quote,
+LJSpeech-1.1/wavs/LJ032-0184.wav|For ten days prior to the eve of the assassination Oswald had not been present at Ruth Paine's house in Irving, Texas, where the rifle was kept.
+LJSpeech-1.1/wavs/LJ047-0160.wav|found it to be four one one Elm  Street. End quote.
+LJSpeech-1.1/wavs/LJ032-0153.wav|A palmprint could not be placed on this portion of the rifle, when assembled, because the wooden foregrip covers the barrel at this point.
+LJSpeech-1.1/wavs/LJ045-0143.wav|and use it against him as had been done in New Orleans.
+LJSpeech-1.1/wavs/LJ049-0064.wav|because the variations possible preclude effective planning.
+LJSpeech-1.1/wavs/LJ029-0127.wav|Elm Street was to be reached from Main by turning right at Houston,
+LJSpeech-1.1/wavs/LJ038-0176.wav|The Commission evaluated the following evidence in considering whether Lee Harvey Oswald fired the shot which almost killed General Walker:
+LJSpeech-1.1/wavs/LJ013-0235.wav|There is nothing to show that Courvoisier really contemplated self-destruction.
+LJSpeech-1.1/wavs/LJ016-0048.wav|a distance of eight or nine feet.
+LJSpeech-1.1/wavs/LJ001-0064.wav|many of whose types, indeed, like that of the Subiaco works, are of a transitional character.
+LJSpeech-1.1/wavs/LJ003-0020.wav|This was the pernicious plan of governing by prisoners, or of setting a favored few in authority over the many.
+LJSpeech-1.1/wavs/LJ002-0259.wav|but the prevailing moral tone may be guessed from the penalty inflicted upon persons singing obscene songs before nine p.m.
+LJSpeech-1.1/wavs/LJ045-0009.wav|and in the extreme anti-Kennedy newspaper advertisement and handbills that appeared in Dallas at the time of the President's visit there.
+LJSpeech-1.1/wavs/LJ029-0177.wav|Articles, editorials, and letters to the editor in the Dallas Morning News and the Dallas Times-Herald after September thirteen
+LJSpeech-1.1/wavs/LJ004-0029.wav|The erection of new prison buildings was made imperative under certain conditions and following certain rules
+LJSpeech-1.1/wavs/LJ042-0091.wav|The Soviet authorities finally permitted Oswald to remain in their country.
+LJSpeech-1.1/wavs/LJ042-0008.wav|of his willingness to act on his beliefs in quite extraordinary ways.
+LJSpeech-1.1/wavs/LJ046-0220.wav|When the threat is in the form of a written communication, give a copy to local Secret Service and forward the original to the Bureau
+LJSpeech-1.1/wavs/LJ017-0073.wav|In eighteen fifty-four he owed a very large sum of money, but he was temporarily relieved by the death of his wife,
+LJSpeech-1.1/wavs/LJ039-0244.wav|killed Dallas Police Officer J. D. Tippit in an apparent attempt to escape,
+LJSpeech-1.1/wavs/LJ016-0352.wav|the officials spoke in whispers; there was but little moving to and fro.
+LJSpeech-1.1/wavs/LJ027-0160.wav|that the embryo or larva of a frog or toad, when first hatched, is a legless, tail-swimming, water-breathing, gill-breathing animal.
+LJSpeech-1.1/wavs/LJ016-0188.wav|He told Calcraft that he was Foxen the executioner, and that he was that moment on his way to Newgate to hang a man,
+LJSpeech-1.1/wavs/LJ022-0069.wav|in order that unemployment may be prevented by the stabilizing of employment itself.
+LJSpeech-1.1/wavs/LJ013-0221.wav|that he could not resist the opportunity for robbery offered him by his situation at Lord William Russell's; that when vexed with his master
+LJSpeech-1.1/wavs/LJ019-0188.wav|He forwarded plans prepared by the inspectors of prisons, not for blind adoption, but as a guide.
+LJSpeech-1.1/wavs/LJ027-0067.wav|The exceptions are of two kinds, viz.: (a) some lowest fishes, amphioxus and lampreys,
+LJSpeech-1.1/wavs/LJ047-0140.wav|advising that an individual tentatively identified as Oswald had been in touch with the Soviet Embassy in Mexico City
+LJSpeech-1.1/wavs/LJ045-0122.wav|While Oswald's concern about the FBI had some basis in fact, in that FBI agents had interviewed him in the past and had renewed their interest
+LJSpeech-1.1/wavs/LJ038-0098.wav|Denial of Rifle Ownership
+LJSpeech-1.1/wavs/LJ030-0097.wav|Remainder of motorcade.
+LJSpeech-1.1/wavs/LJ038-0110.wav|Fritz told him that the two small photographs were found in the Paine garage.
+LJSpeech-1.1/wavs/LJ018-0370.wav|unhappily show that society is more than ever at the mercy of the insidious and unscrupulous administration of poisonous drugs.
+LJSpeech-1.1/wavs/LJ045-0100.wav|She thought that he might not have become involved in the assassination if people had been kinder to him.
+LJSpeech-1.1/wavs/LJ013-0196.wav|This placed them in a position of much embarrassment. To have thrown up their brief would have been to have secured Courvoisier's conviction.
+LJSpeech-1.1/wavs/LJ006-0136.wav|These wardsmen, besides thus ruling the roast, had numerous special privileges, if such they can be called.
+LJSpeech-1.1/wavs/LJ042-0044.wav|I will not say your grandchildren will live under communism, look for yourself at history, look at a world map! America is a dicing country,
+LJSpeech-1.1/wavs/LJ044-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ006-0099.wav|The inspectors paraded the prisoners, and found them generally ragged and ill-clad, squalid and filthy in the extreme;
+LJSpeech-1.1/wavs/LJ037-0266.wav|(two) the cartridge cases found near the scene of the shooting were fired from the revolver in the possession of Oswald at the time of his arrest,
+LJSpeech-1.1/wavs/LJ022-0053.wav|These measures are of two kinds.
+LJSpeech-1.1/wavs/LJ011-0011.wav|and took the instrument out to a clerk with the ink not dry.
+LJSpeech-1.1/wavs/LJ013-0029.wav|Even then she might have been saved, but the captain would not suffer the crew to act. Nearly the whole of the cargo was lost as well as the ship.
+LJSpeech-1.1/wavs/LJ008-0299.wav|On one occasion, Mr. Wakefield tells us, the Recorder, who had attended the council at Windsor, did not deliver the report till the following day.
+LJSpeech-1.1/wavs/LJ019-0117.wav|That industry might thus be stimulated and encouraged was probable enough,
+LJSpeech-1.1/wavs/LJ036-0094.wav|Craig may have seen a person enter a white Rambler station wagon fifteen or twenty minutes after the shooting and travel west on Elm Street
+LJSpeech-1.1/wavs/LJ014-0117.wav|An examination of her boxes disclosed a quantity of O'Connor's property.
+LJSpeech-1.1/wavs/LJ049-0039.wav|to take a protective position in the passenger compartment before the third shot was fired.
+LJSpeech-1.1/wavs/LJ043-0158.wav|Question: And then he burned the book?
+LJSpeech-1.1/wavs/LJ002-0134.wav|The County Court was the sheriff's, who sat there surrounded by the bishop and the magnates of the county;
+LJSpeech-1.1/wavs/LJ048-0237.wav|Most of the agents were there from about one:thirty or one:forty-five a.m. to about two:forty-five or three a.m.;
+LJSpeech-1.1/wavs/LJ040-0154.wav|Quote, this thirteen year old well built boy has superior mental resources and functions only slightly below his capacity level
+LJSpeech-1.1/wavs/LJ004-0220.wav|for breakfast a pint of gruel; for dinner half a pound of boiled meat, or a quart of soup with vegetables, on alternate days.
+LJSpeech-1.1/wavs/LJ028-0207.wav|On the fourteenth day Sippar was taken without a battle.
+LJSpeech-1.1/wavs/LJ021-0007.wav|I am happy to report that after years of uncertainty, culminating in the collapse of the spring of nineteen thirty-three,
+LJSpeech-1.1/wavs/LJ034-0051.wav|and a Dallas police officer after the cartons had been processed with powder by the Dallas Police.
+LJSpeech-1.1/wavs/LJ003-0135.wav|She went up to the ward and found him lying down, quote,
+LJSpeech-1.1/wavs/LJ016-0171.wav|were as far as possible mitigated and toned down. But there was little improvement in the ceremony itself.
+LJSpeech-1.1/wavs/LJ035-0017.wav|He heard two more shots spaced, quote, pretty well even to me.
+LJSpeech-1.1/wavs/LJ021-0069.wav|Now it should not be expected that even employed labor and capital would be completely satisfied with present conditions.
+LJSpeech-1.1/wavs/LJ049-0130.wav|The Secret Service must rely in large part
+LJSpeech-1.1/wavs/LJ013-0197.wav|Mr. Phillips, who led in the case, went to the other extreme,
+LJSpeech-1.1/wavs/LJ019-0232.wav|During this reconstruction the female prisoners were lodged in Holloway,
+LJSpeech-1.1/wavs/LJ003-0100.wav|yet the society they were driven to live in, the language they daily heard, and the lessons they were taught in this academy,
+LJSpeech-1.1/wavs/LJ004-0087.wav|and the poor there living idle and unemployed become debauched, and come forth instructed in the practice of thievery and lewdness."
+LJSpeech-1.1/wavs/LJ040-0115.wav|Pic and his wife would have been happy to have kept Lee, however,
+LJSpeech-1.1/wavs/LJ004-0186.wav|It is consoling to know that there were a few brilliant exceptions to this cruel, callous neglect.
+LJSpeech-1.1/wavs/LJ046-0056.wav|in seventeen eighty-nine in less time than it took George Washington to travel from New York to Mount Vernon
+LJSpeech-1.1/wavs/LJ003-0138.wav|Though they could not force him to gamble, he was compelled to drink,
+LJSpeech-1.1/wavs/LJ050-0133.wav|In his testimony Secretary of the Treasury C. Douglas Dillon informed the Commission
+LJSpeech-1.1/wavs/LJ049-0194.wav|should be transferred to the FBI,
+LJSpeech-1.1/wavs/LJ016-0368.wav|It was they who formed the chief part of the small select group of spectators;
+LJSpeech-1.1/wavs/LJ005-0198.wav|In another borough, with a population of ten thousand, the prison was of the same dimensions.
+LJSpeech-1.1/wavs/LJ031-0141.wav|It was decided that the Vice President should return on the Presidential plane rather than on the Vice-Presidential plane because it had better communication equipment.
+LJSpeech-1.1/wavs/LJ044-0073.wav|It appears that Oswald had only one caller in response to all of his FPCC activities,
+LJSpeech-1.1/wavs/LJ028-0171.wav|upon which was raised a second tower, and on that a third, and so on up to eight.
+LJSpeech-1.1/wavs/LJ031-0161.wav|Secret Service agents working with police cleared the areas adjacent to the aircraft, including warehouses,
+LJSpeech-1.1/wavs/LJ038-0202.wav|Prior to the Walker shooting on April ten, Oswald had been attending typing classes on Monday, Tuesday, and Thursday evenings.
+LJSpeech-1.1/wavs/LJ037-0176.wav|And though only one bullet of Remington-Peters manufacture was recovered, two empty cartridge cases of that make were retrieved.
+LJSpeech-1.1/wavs/LJ011-0287.wav|but at length managed to wriggle out of the chain which confined his body, and soon loosened the ropes round his feet.
+LJSpeech-1.1/wavs/LJ041-0147.wav|Interest in Marxism
+LJSpeech-1.1/wavs/LJ031-0077.wav|my direct services to him at that moment would have interfered with the action of the team which was in progress, end quote,
+LJSpeech-1.1/wavs/LJ038-0300.wav|Based on (one) the contents of the note which Oswald left for his wife on April ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ020-0016.wav|it is well to stir into the yeast a bit of soda no larger than a grain of corn already wet up in a teaspoonful of boiling water.
+LJSpeech-1.1/wavs/LJ016-0039.wav|Williams surveyed these formidable obstacles to evasion, and calmly proceeded to surmount them.
+LJSpeech-1.1/wavs/LJ014-0304.wav|He was a corn merchant who operated largely in grain.
+LJSpeech-1.1/wavs/LJ002-0095.wav|and the rest being provided with barrack beds, and in dimensions varying from thirty feet by fifteen to fifteen feet by ten.
+LJSpeech-1.1/wavs/LJ020-0023.wav|If too stiff, warm water, a spoonful at a time until you can handle the paste easily. The danger is in getting it too stiff. Now.
+LJSpeech-1.1/wavs/LJ047-0171.wav|Mrs. Paine had nothing to add to what she had already told him, except that during a visit that past weekend,
+LJSpeech-1.1/wavs/LJ012-0143.wav|"Money Moses" had received the stolen gold-dust from Moss' father-in-law, Davis, or Isaacs, who was never arrested,
+LJSpeech-1.1/wavs/LJ021-0041.wav|has been to clean up thoroughly unwholesome conditions in the field of investment.
+LJSpeech-1.1/wavs/LJ049-0065.wav|A number of steps are taken, however, to permit appropriate steps to be taken in an emergency.
+LJSpeech-1.1/wavs/LJ005-0005.wav|Even then they were not certain of the favor, for I find a reference to a decent and respectable woman sent to Newgate
+LJSpeech-1.1/wavs/LJ009-0055.wav|indeed the chaplain and others have brought him firmly to believe that his situation is enviable, and that the gates of heaven are open to receive him.
+LJSpeech-1.1/wavs/LJ013-0186.wav|Behind the skirting board several of his lordship's rings were discovered;
+LJSpeech-1.1/wavs/LJ047-0064.wav|that other tenants had complained because Oswald was drinking to excess and beating his wife.
+LJSpeech-1.1/wavs/LJ015-0305.wav|At his trial Atwell and Hardwicke, two of his chief allies and accomplices, turned approvers,
+LJSpeech-1.1/wavs/LJ002-0274.wav|clergymen, proctors, attorneys, and persons specially selected by the Corporation.
+LJSpeech-1.1/wavs/LJ035-0181.wav|At that time, Inspector Herbert V. Sawyer's car was parked in front of the building.
+LJSpeech-1.1/wavs/LJ020-0096.wav|Before beginning operations downstairs eat a half-slice of dry bread or a biscuit.
+LJSpeech-1.1/wavs/LJ046-0134.wav|In the period from November nineteen sixty-one to November nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ050-0074.wav|They emphasized the necessity that the information now being furnished be handled with judgment and care.
+LJSpeech-1.1/wavs/LJ016-0287.wav|This was presently superseded by a fresh catch --
+LJSpeech-1.1/wavs/LJ046-0104.wav|is set out and the reasons why this information was not furnished to the Secret Service appraised.
+LJSpeech-1.1/wavs/LJ019-0125.wav|But an open farm of a thousand acres would have offered abundant chances of escape, which some at least would have attempted, probably with success.
+LJSpeech-1.1/wavs/LJ022-0090.wav|The group will also include representatives of cities, and of labor, farming, banking and industry.
+LJSpeech-1.1/wavs/LJ046-0125.wav|Robert I. Bouck, special agent in charge of PRS,
+LJSpeech-1.1/wavs/LJ040-0057.wav|It will examine his defection to the Soviet Union in nineteen fifty-nine, his subsequent return to the United States and his life here
+LJSpeech-1.1/wavs/LJ027-0171.wav|the highest order (anoura) of this class. Thus, then, in ontogeny the fish goes no further than the fish stages.
+LJSpeech-1.1/wavs/LJ011-0194.wav|Friends went in pursuit and traced her to Huddersfield and further north.
+LJSpeech-1.1/wavs/LJ002-0165.wav|Bird was, however, discharged within three days by a subscription raised among his fellow-prisoners.
+LJSpeech-1.1/wavs/LJ004-0184.wav|There reigned through the whole edifice a chilly, damp, unwholesome atmosphere, and the effluvia from the prisoners was so nauseous
+LJSpeech-1.1/wavs/LJ013-0252.wav|One called his sister, but supposed to be his wife, had occupied a room in South Street, Manchester Square,
+LJSpeech-1.1/wavs/LJ043-0013.wav|Part of the problem resulted from the fact that, as Jeanne De Mohrenschildt testified,
+LJSpeech-1.1/wavs/LJ037-0210.wav|testified before the Commission that the writing on the coupon was Oswald's.
+LJSpeech-1.1/wavs/LJ027-0038.wav|between the heart of an insect and the heart of a vertebrate animal
+LJSpeech-1.1/wavs/LJ015-0226.wav|After hanging about the Folkestone office for some time, they saw at last that the key was kept in a certain cupboard.
+LJSpeech-1.1/wavs/LJ019-0010.wav|As regards the first, however, Sir James Graham, when Home Secretary in eighteen forty-three,
+LJSpeech-1.1/wavs/LJ046-0202.wav|The head of PRS testified that the Secret Service requested other agencies to provide, quote,
+LJSpeech-1.1/wavs/LJ039-0030.wav|mentioned any proposed visit by Mr. Nixon to Dallas.
+LJSpeech-1.1/wavs/LJ022-0098.wav|of making full use of existing employment services and to assist people engaged in relief work
+LJSpeech-1.1/wavs/LJ002-0104.wav|can be permitted to enter the interior of this prison. End quote. The tap-room and bar were just behind the felons' entrance lodge,
+LJSpeech-1.1/wavs/LJ049-0127.wav|agencies other than the Secret Service have become involved in phases of the overall problem of protecting our national leaders.
+LJSpeech-1.1/wavs/LJ047-0144.wav|The FBI representative in Mexico City arranged to follow up this information with the CIA and to verify Oswald's entry into Mexico.
+LJSpeech-1.1/wavs/LJ039-0119.wav|We have nothing here to show under what conditions the B course was fired. It might well have been a bad day for firing the rifle
+LJSpeech-1.1/wavs/LJ042-0188.wav|which would achieve the goal, which was, quote, the emplacement of a separate, democratic, pure communist society
+LJSpeech-1.1/wavs/LJ018-0209.wav|It was not easy to detect coiners, or bring home their guilt to them.
+LJSpeech-1.1/wavs/LJ045-0227.wav|and suggests the possibility, as did his note to his wife just prior to the attempt on General Walker, that he did not expect to escape at all.
+LJSpeech-1.1/wavs/LJ045-0022.wav|even though he did, as he told Johnson, attend a meeting at which General Walker spoke to approximately thirteen hundred persons.
+LJSpeech-1.1/wavs/LJ004-0237.wav|The first still produced deplorable results -- results to be observable for many years to come.
+LJSpeech-1.1/wavs/LJ044-0052.wav|Furthermore, the general tenor of Oswald's next letter to V. T. Lee,
+LJSpeech-1.1/wavs/LJ028-0061.wav|at the head of the Babylonian army, he defeated the Egyptians in the famous battle of Carchemish, the old Hittite capital,
+LJSpeech-1.1/wavs/LJ003-0325.wav|The feelings of the condemned prisoners should no longer be outraged by their exposure in the chapel, and the chapel should be rearranged,
+LJSpeech-1.1/wavs/LJ014-0049.wav|Restoratives were applied, but he was in a fainting condition when tied, and had to be supported by the assistant executioner
+LJSpeech-1.1/wavs/LJ048-0132.wav|which would bring to bear the judgment and experience of members of the White House detail other than the advance agent.
+LJSpeech-1.1/wavs/LJ005-0133.wav|The provision of separate sleeping cells was still quite inadequate. For instance,
+LJSpeech-1.1/wavs/LJ020-0022.wav|Wash out the bowl with a little lukewarm water and add this to the dough. If it should prove too soft, work in, cautiously, a little more flour.
+LJSpeech-1.1/wavs/LJ022-0202.wav|That faith is receiving its just reward.
+LJSpeech-1.1/wavs/LJ012-0003.wav|The two men on guard had gone off immediately after Mr. Gee had escaped, but they returned later in the day, and were apprehended.
+LJSpeech-1.1/wavs/LJ015-0247.wav|At that station the safes were given out, heavy with shot, not gold; the thieves went on to Dover, and by-and-by,
+LJSpeech-1.1/wavs/LJ018-0169.wav|In one corner was a printing-press actually in use, and on it were twenty-one forged Bank of England notes, without date or signature.
+LJSpeech-1.1/wavs/LJ038-0003.wav|Oswald's Arrest
+LJSpeech-1.1/wavs/LJ031-0015.wav|Arriving almost simultaneously were the President's follow-up car, the Vice President's automobile, and the Vice President's follow-up car.
+LJSpeech-1.1/wavs/LJ025-0008.wav|This is true only as a matter of detail,
+LJSpeech-1.1/wavs/LJ024-0016.wav|If, for instance, any one of the six justices of the Supreme Court now over the age of seventy should retire as provided under the plan,
+LJSpeech-1.1/wavs/LJ027-0034.wav|Hence, as Jordan has said, "the inside of an animal tells the real history of its ancestry; the outside tells us only where its ancestors have been."
+LJSpeech-1.1/wavs/LJ039-0134.wav|During one of his leaves from the Marines, Oswald hunted with his brother Robert,
+LJSpeech-1.1/wavs/LJ031-0214.wav|The autopsy examination revealed two wounds in the President's head.
+LJSpeech-1.1/wavs/LJ018-0331.wav|to murder her mistress, Madame Riel, in Park Lane, as Courvoisier, the Swiss, had been tempted to murder Lord William Russell.
+LJSpeech-1.1/wavs/LJ037-0077.wav|Barbara Jeanette Davis and Virginia Davis, were in an apartment of a multiple-unit house on the southeast corner of tenth and Patton
+LJSpeech-1.1/wavs/LJ018-0012.wav|It was that of an aged man, whose head had been battered in by a life-preserver.
+LJSpeech-1.1/wavs/LJ003-0285.wav|they should gamble with dice or cards, and play at bumble puppy or some other disreputable game of chance.
+LJSpeech-1.1/wavs/LJ010-0018.wav|jewel-boxes cleverly stolen under the very noses of owners or care-takers.
+LJSpeech-1.1/wavs/LJ028-0430.wav|The Euphrates flowed through it, but the greater part of the city was on the eastern shore.
+LJSpeech-1.1/wavs/LJ035-0190.wav|testified that after driving to Parkland Hospital, he returned to the Depository Building about twenty minutes after the shooting,
+LJSpeech-1.1/wavs/LJ018-0126.wav|His object in visiting Whitchurch was to undermine the honesty of some workman in the mills;
+LJSpeech-1.1/wavs/LJ012-0227.wav|one that of the murder of the Italian boy, the other that of a boy unknown.
+LJSpeech-1.1/wavs/LJ043-0128.wav|The plant superintendent of the new firm testified that, one of the employees of the old firm, quote,
+LJSpeech-1.1/wavs/LJ019-0395.wav|at others an expensive staff was maintained with little or nothing to do.
+LJSpeech-1.1/wavs/LJ021-0126.wav|to organize themselves for collective bargaining as embodied in Section seven (a) of the national Industrial Recovery Act.
+LJSpeech-1.1/wavs/LJ014-0091.wav|This cold-blooded indifference after the event was only outdone by the premeditation of this horrible murder.
+LJSpeech-1.1/wavs/LJ028-0286.wav|He therefore reviewed all ways of bringing the city under,
+LJSpeech-1.1/wavs/LJ036-0002.wav|Chapter four. The Assassin: Part five.
+LJSpeech-1.1/wavs/LJ014-0244.wav|which offered peculiar chances of profit to an ingenious and unscrupulous man.
+LJSpeech-1.1/wavs/LJ037-0214.wav|When shown the revolver, she stated that she recognized it as the one owned by her husband.
+LJSpeech-1.1/wavs/LJ041-0023.wav|and, quote, Arthor Abear, end quote, most likely Arthur Hebert, a classmate who has said that he did not know Oswald well.
+LJSpeech-1.1/wavs/LJ017-0030.wav|when the spread of scientific knowledge places nefarious means at the disposal of many,
+LJSpeech-1.1/wavs/LJ023-0092.wav|But in the case holding the Railroad Retirement Act unconstitutional, for instance,
+LJSpeech-1.1/wavs/LJ006-0046.wav|There was no longer the faintest possible excuse for overcrowding. The numbers now committed to Newgate had sensibly diminished.
+LJSpeech-1.1/wavs/LJ008-0174.wav|One cart-load of spectators having broken down, some of its occupants fell off the vehicle, and were instantly trampled to death.
+LJSpeech-1.1/wavs/LJ044-0121.wav|handicapped as it were, by my past record and compete with anti-progressive forces, above-ground or whether in your opinion
+LJSpeech-1.1/wavs/LJ049-0033.wav|to move into the passenger section without hindrance or delay. Had the vehicle been so designed it is possible that an agent riding in the front seat
+LJSpeech-1.1/wavs/LJ005-0089.wav|The religious and moral welfare of the prisoners were to be attended to,
+LJSpeech-1.1/wavs/LJ045-0060.wav|He apparently attempted to be "the Commander" by dictating many of the details of their married life.
+LJSpeech-1.1/wavs/LJ043-0120.wav|His Fair Play for Cuba Committee activities, however, made it more difficult for him to obtain other employment.
+LJSpeech-1.1/wavs/LJ037-0095.wav|Smith failed to make himself known to the police on November twenty-two.
+LJSpeech-1.1/wavs/LJ016-0436.wav|pointing to an aperture for ventilating the cell. On the morning of execution he asked how far it was to the gallows, and was told it was quite close.
+LJSpeech-1.1/wavs/LJ041-0167.wav|While waiting for the parade to start they talked briefly about "nineteen eighty-four"
+LJSpeech-1.1/wavs/LJ046-0027.wav|However, all information considered by the Commission which pertains to the protective function as it was carried out in Dallas has been published as part of this report.
+LJSpeech-1.1/wavs/LJ011-0247.wav|Mr. Mullay became alarmed, and as Mr. Owen did not appear, withdrew;
+LJSpeech-1.1/wavs/LJ027-0131.wav|of descent discovered in the development of plant and animal embryos.
+LJSpeech-1.1/wavs/LJ027-0173.wav|The caducibranch takes first the fish form, then the perennibranch form, and finally the caducibranch form, but goes no further.
+LJSpeech-1.1/wavs/LJ032-0094.wav|listing Marina Oswald and A. J. Hidell
+LJSpeech-1.1/wavs/LJ038-0131.wav|Oswald replied, quote, I don't know anything about that, end quote.
+LJSpeech-1.1/wavs/LJ011-0119.wav|One was Richard Gifford, a well-educated youth who had been at Christ's Hospital, and afterwards in the National Debt Office.
+LJSpeech-1.1/wavs/LJ024-0142.wav|I seek to make American democracy succeed.
+LJSpeech-1.1/wavs/LJ010-0255.wav|This was in May eighteen forty-two.
+LJSpeech-1.1/wavs/LJ010-0221.wav|Another letter directed Oxford to attend an extraordinary meeting of "Young England"
+LJSpeech-1.1/wavs/LJ050-0192.wav|can lead to lapses in protection, such as the confusion in Dallas about whether members of the public were permitted on overpasses.
+LJSpeech-1.1/wavs/LJ016-0292.wav|bricklayers' laborers, dock workmen, German artisans and sugar-bakers
+LJSpeech-1.1/wavs/LJ037-0211.wav|The signature of the witness, D. F. Drittal,
+LJSpeech-1.1/wavs/LJ041-0016.wav|Voebel took Oswald back to the school to attend to his wounds, and their, quote, mild friendship, end quote, stemmed from that incident.
+LJSpeech-1.1/wavs/LJ008-0026.wav|Palm, as soon as he was seated in the cart,
+LJSpeech-1.1/wavs/LJ013-0176.wav|also the jewel-box and note-case, from the latter of which was abstracted a ten-pound note known to have been in the possession of the deceased.
+LJSpeech-1.1/wavs/LJ013-0238.wav|He was coachman to a gentleman at Roehampton. One day he went into a pawnbroker's at Wandsworth, and bought a pair of breeches on credit.
+LJSpeech-1.1/wavs/LJ005-0060.wav|The Society did not limit its remarks to the description of what had already been done
+LJSpeech-1.1/wavs/LJ009-0237.wav|the poor criminal rose straight upon his feet, with the hangman on his shoulders, and immediately loosened the rope with his fingers.
+LJSpeech-1.1/wavs/LJ001-0059.wav|the greater part of these Italian printers, it should be mentioned, were Germans or Frenchmen, working under the influence of Italian opinion and aims.
+LJSpeech-1.1/wavs/LJ042-0204.wav|but in servile conformity to the wishes of the Soviet Union and in anticipation of Soviet Russia's complete domination of the American continent.
+LJSpeech-1.1/wavs/LJ042-0184.wav|Expanding on his ideas on how his alternative to communism and capitalism might be introduced, he wrote of a, quote, readily foreseeable
+LJSpeech-1.1/wavs/LJ028-0066.wav|transported the Jews to Babylon, and brought the Hebrew kingdom to an end.
+LJSpeech-1.1/wavs/LJ012-0221.wav|In another portion of the garden more clothing, partly male and partly female, was discovered,
+LJSpeech-1.1/wavs/LJ040-0058.wav|after June of nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ033-0188.wav|and the paper and tape found in the shipping room of the Depository on November twenty-two
+LJSpeech-1.1/wavs/LJ028-0053.wav|The next year, in six oh five, Nabopolassar died, and Nebuchadnezzar succeeded him to the throne.
+LJSpeech-1.1/wavs/LJ018-0229.wav|The day his father died he got the keys of his private bureau, opened it, and took out the authentic will.
+LJSpeech-1.1/wavs/LJ030-0048.wav|Their main function was to spot trouble in advance and to direct any necessary steps to meet the trouble.
+LJSpeech-1.1/wavs/LJ038-0136.wav|In concluding that Oswald was carrying a rifle in the paper bag on the morning of November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ028-0320.wav|will prove the greatest gain that you could possibly receive, while to Darius and the Persians it will be the severest loss.
+LJSpeech-1.1/wavs/LJ039-0056.wav|the Commission considered whether Oswald, using his own rifle,
+LJSpeech-1.1/wavs/LJ012-0162.wav|Having brought down the records of great frauds, forgeries, and thefts from about eighteen twenty-five to eighteen forty,
+LJSpeech-1.1/wavs/LJ040-0203.wav|but essentially a, quote, defensive, rigid, self-involved person
+LJSpeech-1.1/wavs/LJ046-0216.wav|members of the immediate family of the President, the President-Elect or the Vice-President
+LJSpeech-1.1/wavs/LJ003-0309.wav|Proper hours for locking and unlocking prisoners should be insisted upon;
+LJSpeech-1.1/wavs/LJ022-0127.wav|The most effective means of preventing such evils in this Works Relief program will be the eternal vigilance of the American people themselves.
+LJSpeech-1.1/wavs/LJ028-0271.wav|Now when a year and seven months had passed, Darius and his army were quite wearied out, finding that they could not anyhow take the city.
+LJSpeech-1.1/wavs/LJ047-0178.wav|because she thought the FBI was in possession of a great deal of information and certainly would find it very easy to learn where Oswald was living.
+LJSpeech-1.1/wavs/LJ048-0141.wav|Fire Department, County Sheriff's Department, and the Texas Department of Public Safety.
+LJSpeech-1.1/wavs/LJ003-0245.wav|preparatory to their appearance in the Old Bailey. Irons were seldom removed from the convicted until discharge;
+LJSpeech-1.1/wavs/LJ013-0218.wav|His last statement contains the words, "The public now think I am a liar, and they will not believe me when I say the truth."
+LJSpeech-1.1/wavs/LJ044-0029.wav|It appears to have been a solitary operation on Oswald's part in spite of his misstatements to the New Orleans police that it had thirty-five members,
+LJSpeech-1.1/wavs/LJ035-0165.wav|At that time the building had not yet been sealed off by the police.
+LJSpeech-1.1/wavs/LJ009-0179.wav|the most recent enactment in force was the ninth George the fourth cap. thirty-one, which directed the dissection of all bodies of executed murderers,
+LJSpeech-1.1/wavs/LJ006-0127.wav|in view of speaking to it at the trial, and he could easily do them a good turn -- or a very bad one.
+LJSpeech-1.1/wavs/LJ034-0144.wav|Prior to the lineup,
+LJSpeech-1.1/wavs/LJ044-0015.wav|He first distributed his handbills and other material uneventfully in the vicinity of the U.S.S. Wasp,
+LJSpeech-1.1/wavs/LJ050-0046.wav|it has obtained the services of outside consultants, such as the Rand Corporation,
+LJSpeech-1.1/wavs/LJ042-0115.wav|The best description of Oswald's state of mind, however, is set forth in his own "Historic Diary."
+LJSpeech-1.1/wavs/LJ013-0009.wav|This was the willful shipwreck and casting away of a vessel which, with her supposed cargo, had been heavily insured.
+LJSpeech-1.1/wavs/LJ007-0238.wav|At the same time the inspectors animadvert strongly upon the misconduct of prisoners and the frequency of prison punishments,
+LJSpeech-1.1/wavs/LJ035-0088.wav|park it, and run forty-five feet to the building.
+LJSpeech-1.1/wavs/LJ050-0232.wav|to assist in its protection functions.
+LJSpeech-1.1/wavs/LJ027-0042.wav|All such cases of resemblance in function but not in detailed plan of structure are called "analogies,"
+LJSpeech-1.1/wavs/LJ002-0248.wav|The population, as reported by the committee in eighteen fourteen, averaged about sixty,
+LJSpeech-1.1/wavs/LJ029-0034.wav|In his experience, quote, it would be automatic, end quote, for the Secret Service to arrange a route which would, within the time allotted,
+LJSpeech-1.1/wavs/LJ022-0109.wav|The allotment of funds for desirable projects has already begun.
+LJSpeech-1.1/wavs/LJ011-0100.wav|to which the signature of one Wilkins of Abingdon was found to be forged.
+LJSpeech-1.1/wavs/LJ037-0236.wav|She last saw him in the parking lot directly behind the service station.
+LJSpeech-1.1/wavs/LJ047-0179.wav|Hosty did nothing further in connection with the Oswald case until after the assassination. On November one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ033-0043.wav|On the west end of the house is the attached garage, which can be reached from the kitchen or from the outside.
+LJSpeech-1.1/wavs/LJ010-0218.wav|in an instant our faces were covered, we cocked our pistols, and with drawn swords stood waiting to receive the enemy.
+LJSpeech-1.1/wavs/LJ021-0124.wav|when the farmers were demanding legislation which would give them opportunities and incentives to organize themselves for a common advance,
+LJSpeech-1.1/wavs/LJ010-0168.wav|after which he devoted himself to pistol practice in shooting-galleries, sometimes in Leicester Square,
+LJSpeech-1.1/wavs/LJ042-0250.wav|He sought an immediate grant of visa on his trip to Mexico City in late September of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ008-0131.wav|the other he kept between his hands.
+LJSpeech-1.1/wavs/LJ003-0339.wav|could possibly afford.
+LJSpeech-1.1/wavs/LJ050-0176.wav|to establish liaison with local intelligence gathering agencies and to provide for the immediate evaluation of information received from them.
+LJSpeech-1.1/wavs/LJ047-0069.wav|that the Oswalds were living at two one four Neely Street in Dallas.
+LJSpeech-1.1/wavs/LJ001-0125.wav|this is the narrowing of the modern letters.
+LJSpeech-1.1/wavs/LJ021-0032.wav|have been restored to complete public confidence.
+LJSpeech-1.1/wavs/LJ019-0168.wav|Renewed recommendations to provide employment resulted in the provision of a certain amount of oakum for picking,
+LJSpeech-1.1/wavs/LJ028-0116.wav|I raised its summit mountain high. I constructed a moat-wall of burned bricks about the west wall of Babylon.
+LJSpeech-1.1/wavs/LJ042-0055.wav|In the event of war I would kill any American who put a uniform on in defense of the American government -- any American.
+LJSpeech-1.1/wavs/LJ034-0201.wav|but a radio reporter testified that Euins described the man to him as, quote, colored, end quote.
+LJSpeech-1.1/wavs/LJ019-0367.wav|and with the warning a copy of the particular defects and allegations was to be sent to the local authority.
+LJSpeech-1.1/wavs/LJ039-0189.wav|The others would have been able to reduce their times if they had been given the opportunity to become familiar with the movement of the bolt and the trigger pull.
+LJSpeech-1.1/wavs/LJ025-0090.wav|and resemblances between the constituents of animal and vegetable organisms, for which Cuvier is not responsible,
+LJSpeech-1.1/wavs/LJ002-0044.wav|the whole about fifteen feet wide.
+LJSpeech-1.1/wavs/LJ031-0035.wav|For a moment, Mrs. Kennedy refused to release the President, whom she held in her lap,
+LJSpeech-1.1/wavs/LJ015-0294.wav|He forgot to add that it was to be placed to Ralph's credit, and when he called as Ralph,
+LJSpeech-1.1/wavs/LJ016-0308.wav|yet the witnesses were not unanimous.
+LJSpeech-1.1/wavs/LJ003-0085.wav|These twenty-four could just sleep on the barrack bed; when the number was higher, and it often rose to forty, the surplus had to sleep on the floor.
+LJSpeech-1.1/wavs/LJ039-0012.wav|The Commission first learned of this incident when Robert Oswald related it to FBI agents on February nineteen, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ028-0235.wav|In this distress either someone made this suggestion to him, or he bethought himself of a plan which he proceeded to put in execution.
+LJSpeech-1.1/wavs/LJ043-0074.wav|where he expressed a reluctance to work in the industrial field.
+LJSpeech-1.1/wavs/LJ015-0284.wav|In order to obtain messengers of this sort, Saward answered advertisements of persons seeking employment,
+LJSpeech-1.1/wavs/LJ011-0202.wav|The uncle claimed her. The husband resisted.
+LJSpeech-1.1/wavs/LJ027-0036.wav|For example, there is a superficial resemblance between the wing of an insect and the wing of a bird
+LJSpeech-1.1/wavs/LJ027-0030.wav|is associated with an adaptive modification of locomotor structures, legs and wings,
+LJSpeech-1.1/wavs/LJ019-0228.wav|it also contained eleven reception cells, six punishment cells, and a couple of cells for condemned criminals.
+LJSpeech-1.1/wavs/LJ036-0040.wav|In a later interview, Jones confirmed that he had exchanged words with a woman passenger on the bus during the ride south on Marsalis.
+LJSpeech-1.1/wavs/LJ010-0263.wav|The policemen treated the matter as a hoax, and allowed the culprit to make off.
+LJSpeech-1.1/wavs/LJ034-0142.wav|During the evening of November twenty-two, Brennan identified Oswald as the person in the lineup who bore the closest resemblance to the man in the window
+LJSpeech-1.1/wavs/LJ027-0014.wav|and the equally mysterious nuclear substance or chromatin
+LJSpeech-1.1/wavs/LJ019-0275.wav|that the committee of eighteen sixty-three fully endorsed Sir Joshua's recommendations as regards a "hard bed," and recommended that
+LJSpeech-1.1/wavs/LJ029-0009.wav|An evaluation of the procedures employed to safeguard the President, with recommendations for improving these procedures, appears in Chapter eight of the report.
+LJSpeech-1.1/wavs/LJ030-0153.wav|William Greer, operator of the Presidential limousine, estimated the car's speed at the time of the first shot as twelve to fifteen miles per hour.
+LJSpeech-1.1/wavs/LJ021-0022.wav|between the small producer, the small trader, the consumer,
+LJSpeech-1.1/wavs/LJ047-0095.wav|On the next day, he asked the New Orleans police to arrange for him to be interviewed by the FBI.
+LJSpeech-1.1/wavs/LJ003-0204.wav|I saw Cashman a few hours before his execution, smoking and drinking with the utmost unconcern and indifference, end quote.
+LJSpeech-1.1/wavs/LJ045-0081.wav|Although she denied it in some of her testimony before the Commission,
+LJSpeech-1.1/wavs/LJ018-0175.wav|This was penal servitude for life, Buncher's sentence being twenty-five, and Burnett's twenty years.
+LJSpeech-1.1/wavs/LJ002-0169.wav|He had been in the employ of a corn-chandler at Islington, and went into London with his master's cart and horse.
+LJSpeech-1.1/wavs/LJ035-0104.wav|The east elevator was on the fifth floor when they arrived; the west elevator was not.
+LJSpeech-1.1/wavs/LJ037-0037.wav|As she waited on the northwest corner of the intersection for traffic to pass, she noticed a young man as he was, quote, almost ready to get up on the curb, end quote.
+LJSpeech-1.1/wavs/LJ013-0110.wav|but four hundred sovereigns were found on Elder, while two hundred more were found in Burgess' effects.
+LJSpeech-1.1/wavs/LJ011-0276.wav|"A chain fixed to staples at his back passed round his chest under his arms, and was padlocked on the left side;"
+LJSpeech-1.1/wavs/LJ048-0038.wav|The Commission believes, however,
+LJSpeech-1.1/wavs/LJ049-0119.wav|would be conducted by Federal law enforcement officials, in particular, the FBI with the assistance of the Secret Service.
+LJSpeech-1.1/wavs/LJ048-0063.wav|The formal FBI instructions to its agents outlining the information to be referred to the Secret Service were too narrow at the time of the assassination.
+LJSpeech-1.1/wavs/LJ036-0158.wav|Moreover, Mrs. Bledsoe saw Oswald in the bus without a jacket and wearing a shirt with a hole at the elbow.
+LJSpeech-1.1/wavs/LJ049-0189.wav|but expand both the scope and the operations of the existing agencies, particularly those of the Secret Service and the FBI.
+LJSpeech-1.1/wavs/LJ049-0201.wav|and becoming merely the recipient of information gathered by others would become limited solely to acts of physical alertness and personal courage
+LJSpeech-1.1/wavs/LJ038-0279.wav|I think is going overboard in the other direction.
+LJSpeech-1.1/wavs/LJ008-0024.wav|of the trading vessel 'Adventure,' upon the high seas.
+LJSpeech-1.1/wavs/LJ003-0120.wav|The culprit was then compelled to carry the chair about with him.
+LJSpeech-1.1/wavs/LJ011-0277.wav|his feet were bound with cords and made fast to rings in the floor. Thus manacled, one of the party, who pretended to be Mrs. Canning's brother,
+LJSpeech-1.1/wavs/LJ025-0165.wav|Neither protein, nor fat, nor starch, nor sugar, nor any substance in the slightest degree resembling them has formed part of the food of the bean.
+LJSpeech-1.1/wavs/LJ021-0176.wav|Did England let nature take her course? No.
+LJSpeech-1.1/wavs/LJ037-0094.wav|He looked west on tenth and saw a man running to the west and a policeman falling to the ground.
+LJSpeech-1.1/wavs/LJ018-0314.wav|Certain friends of the prisoners were watched, and found to be in communication with these warders,
+LJSpeech-1.1/wavs/LJ007-0036.wav|one man declared that the language of the condemned rooms was disgusting, that he was dying a death every day in being compelled to associate with such characters.
+LJSpeech-1.1/wavs/LJ019-0390.wav|Since then a strong central authority has labored steadfastly to compass concentration,
+LJSpeech-1.1/wavs/LJ034-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter four. The Assassin: Part three.
+LJSpeech-1.1/wavs/LJ010-0315.wav|and sold out to the amount here stated, and without the knowledge of my partners.
+LJSpeech-1.1/wavs/LJ038-0219.wav|would be consistent with the period when the Oswalds were living on Neely Street since the apartment was rented on March three, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ013-0236.wav|A murder which reproduced many of the features of that committed by Greenacre soon followed, and excited the public mind even more than that of Courvoisier's.
+LJSpeech-1.1/wavs/LJ008-0088.wav|As many spectators as ever thronged to see the dreadful show,
+LJSpeech-1.1/wavs/LJ049-0059.wav|Agents are instructed that it is not their responsibility to investigate or evaluate a present danger,
+LJSpeech-1.1/wavs/LJ025-0166.wav|But the weights of the carbon, hydrogen, oxygen, nitrogen, phosphorus, sulfur and other elementary bodies contained in the bean-plant
+LJSpeech-1.1/wavs/LJ021-0180.wav|to issue new bonds therefore bearing only three and one half percent interest,
+LJSpeech-1.1/wavs/LJ007-0203.wav|"The mischief of jail associations," say the inspectors,
+LJSpeech-1.1/wavs/LJ014-0205.wav|On the dread morning he came out to execution quite gaily, and tripped up the stairs to the scaffold.
+LJSpeech-1.1/wavs/LJ006-0307.wav|There was reveling and roistering, as usual, with "high life below-stairs.
+LJSpeech-1.1/wavs/LJ005-0123.wav|with a few of the more important city or borough prisons, these acts more especially applied.
+LJSpeech-1.1/wavs/LJ008-0025.wav|They were taken in a cart to the place of execution, amidst a vast concourse of people.
+LJSpeech-1.1/wavs/LJ014-0219.wav|Once more Dwyer got to his feet, and managed, by drawing his staff, to keep Cannon at bay until a second constable came to his aid.
+LJSpeech-1.1/wavs/LJ004-0241.wav|Other cases of moral deterioration have already been recorded.
+LJSpeech-1.1/wavs/LJ032-0233.wav|reinforce the belief that the rifle in the photograph is the rifle which Oswald bought from Klein's.
+LJSpeech-1.1/wavs/LJ009-0257.wav|and having got from the falling drop to the firm part of the platform, he nearly succeeded in tearing the handkerchief from his eyes.
+LJSpeech-1.1/wavs/LJ005-0126.wav|Religious worship became more generally the rule; chaplains were appointed, and chapels provided for them; surgeons and hospitals also.
+LJSpeech-1.1/wavs/LJ040-0034.wav|Marina Oswald thought that he would not be happy anywhere, quote, Only on the moon, perhaps, end quote.
+LJSpeech-1.1/wavs/LJ009-0028.wav|Probably the reader will side with the aldermen against the ordinary.
+LJSpeech-1.1/wavs/LJ015-0095.wav|The blow fell suddenly, and when least expected. One morning Mr. Fasson asked casually for certain certificates,
+LJSpeech-1.1/wavs/LJ006-0282.wav|the pump was the only provision, and this in a place within sight of visitors, of the windows of the male turnkeys, and unprotected from the weather.
+LJSpeech-1.1/wavs/LJ017-0024.wav|and as Fenning, although convicted and executed, protested her innocence in the most solemn manner to the last,
+LJSpeech-1.1/wavs/LJ044-0031.wav|Oswald's Fair Play for Cuba activities may be viewed as a very shrewd political operation
+LJSpeech-1.1/wavs/LJ039-0064.wav|An aerial photograph of Dealey Plaza shows that Elm Street runs at an angle
+LJSpeech-1.1/wavs/LJ030-0005.wav|by helicopter at ten:forty-five A.M., Eastern Standard Time, on November twenty-one, nineteen sixty-three, for Andrews Air Force Base.
+LJSpeech-1.1/wavs/LJ002-0124.wav|Quite half of the foregoing writs and arrests applied to sums under thirty pounds.
+LJSpeech-1.1/wavs/LJ046-0228.wav|the Secret Service had no standard procedure for the systematic review of its requests for and receipt of information from other Federal agencies.
+LJSpeech-1.1/wavs/LJ028-0415.wav|but only part, and that a small part, of the great tower, which God hath suffered to stand
+LJSpeech-1.1/wavs/LJ029-0152.wav|to inspect buildings along the motorcade route since the Secret Service did not normally request or make such a check.
+LJSpeech-1.1/wavs/LJ012-0123.wav|Within three months, three new and most mysterious burglaries were committed at the West End, all in houses adjoining each other.
+LJSpeech-1.1/wavs/LJ008-0052.wav|it is supported by strong posts fixed into grooves made in the street;
+LJSpeech-1.1/wavs/LJ002-0210.wav|the keeper of the large tap-room called the Brace, because it was once kept by two brothers named Partridge, also paid him toll.
+LJSpeech-1.1/wavs/LJ045-0209.wav|but it was also, at least in part, because his wife did not want to live there with him.
+LJSpeech-1.1/wavs/LJ038-0013.wav|The man wore a T-shirt beneath his outer shirt and he had no jacket.
+LJSpeech-1.1/wavs/LJ049-0027.wav|Presidents have made it clear, however, that they did not favor this or any other arrangement which interferes with the privacy of the President and his guests.
+LJSpeech-1.1/wavs/LJ020-0051.wav|Flour a rolling-pin and roll the dough into a sheet not more than half an inch thick.
+LJSpeech-1.1/wavs/LJ013-0092.wav|The thieves, for Elder was soon joined by Burgess, drove together to Ben Caunt's, the pugilist's, public-house in St. Martin's Lane,
+LJSpeech-1.1/wavs/LJ005-0175.wav|At this time there were in England one hundred and seventy boroughs, cities, towns, and liberties
+LJSpeech-1.1/wavs/LJ030-0068.wav|Kellerman's responsibilities included maintaining radio communications with the lead and follow-up cars,
+LJSpeech-1.1/wavs/LJ039-0112.wav|evaluated the sharpshooter qualification as a, quote, fairly good shot, end quote, and a low marksman rating
+LJSpeech-1.1/wavs/LJ039-0013.wav|and to the Commission on February twenty-one.
+LJSpeech-1.1/wavs/LJ033-0107.wav|No other employee has been found who saw Oswald enter that morning.
+LJSpeech-1.1/wavs/LJ014-0230.wav|For this he was executed in front of Newgate in eighteen thirty-three.
+LJSpeech-1.1/wavs/LJ038-0059.wav|that there was no one near Oswald who had a shotgun and he saw no one strike Oswald in the back with a rifle butt or the butt of a gun.
+LJSpeech-1.1/wavs/LJ044-0099.wav|Stuckey thought that Oswald acted very much as would a young attorney.
+LJSpeech-1.1/wavs/LJ009-0215.wav|All the shops in the neighborhood were shut, and the windows and tops of the houses were crowded with spectators.
+LJSpeech-1.1/wavs/LJ050-0260.wav|the Secret Service has been receiving full cooperation in scientific research and technological development
+LJSpeech-1.1/wavs/LJ014-0058.wav|These great criminals suffered at Horsemonger Lane Jail, but they were tried at the Central Criminal Court, and were for some time inmates of Newgate.
+LJSpeech-1.1/wavs/LJ005-0007.wav|chained to the rest. As the miserable crew filed through the public streets, exposed to the scornful gaze of every passenger,
+LJSpeech-1.1/wavs/LJ029-0066.wav|No one else was identified to the Secret Service through local inquiry as potentially dangerous,
+LJSpeech-1.1/wavs/LJ016-0204.wav|For acting as executioner of Horsemonger Lane Jail
+LJSpeech-1.1/wavs/LJ019-0295.wav|A single officer was the only custodian and disciplinary authority in the jail.
+LJSpeech-1.1/wavs/LJ025-0145.wav|Hence it is conceivable that even the simplest living being may possess a nervous system.
+LJSpeech-1.1/wavs/LJ033-0098.wav|Oswald left the car first, picked up the brown paper bag, and proceeded toward the building ahead of Frazier.
+LJSpeech-1.1/wavs/LJ006-0060.wav|The classification prescribed by the Jail Act, which laid down that certain prisoners should not intermix, was openly neglected,
+LJSpeech-1.1/wavs/LJ006-0298.wav|while older women in infirm health were sent across the seas.
+LJSpeech-1.1/wavs/LJ030-0155.wav|A more precise determination has been made from motion pictures taken on the scene by an amateur photographer, Abraham Zapruder.
+LJSpeech-1.1/wavs/LJ016-0245.wav|Until the time of his death he kept a small shop close to the church in Horncastle.
+LJSpeech-1.1/wavs/LJ038-0242.wav|An examination of certain construction work appearing in the background of this photograph revealed that the picture was taken between March eight
+LJSpeech-1.1/wavs/LJ021-0172.wav|They point to England.
+LJSpeech-1.1/wavs/LJ038-0042.wav|Detective Bob K. Carroll, who was standing beside McDonald, seized the gun from him.
+LJSpeech-1.1/wavs/LJ010-0079.wav|and thus the conspiracy in which he was the prime mover took shape, and came to a head.
+LJSpeech-1.1/wavs/LJ018-0236.wav|One large item of his expenditure was a contested election at Lambeth, which he gained at a cost of ten thousand pounds.
+LJSpeech-1.1/wavs/LJ013-0177.wav|His lordship's watch was gone.
+LJSpeech-1.1/wavs/LJ045-0235.wav|the testimony of the officers present indicates that he handled himself with considerable composure during his questioning.
+LJSpeech-1.1/wavs/LJ011-0037.wav|Appeals were made to the Home Secretary, and all possible political interest brought to bear, but without success.
+LJSpeech-1.1/wavs/LJ005-0229.wav|By another clause of the Jail Act, two justices were to be appointed to visit the prison at least thrice in every quarter, and "oftener if occasion required."
+LJSpeech-1.1/wavs/LJ047-0021.wav|information regarding his relations with the U.S. Embassy in Moscow and background data relating largely to his prior military service,
+LJSpeech-1.1/wavs/LJ019-0204.wav|a serious effort was made to reconstruct Newgate.
+LJSpeech-1.1/wavs/LJ005-0242.wav|In the year immediately preceding this, Parliament was too busy with the great question of its own reform to spare much time for domestic legislation.
+LJSpeech-1.1/wavs/LJ018-0112.wav|It was one of these, Glendinning, who had allowed himself to be utilized for some time in this way, whose capture led to the breaking up of the gang.
+LJSpeech-1.1/wavs/LJ049-0123.wav|over the assassination of President Kennedy led to embarrassment and confusion in the subsequent investigation by Federal and local authorities.
+LJSpeech-1.1/wavs/LJ022-0031.wav|That is why I occasionally leave this scene of action for a few days
+LJSpeech-1.1/wavs/LJ005-0054.wav|traced the progress of prison architecture from the days when the jail was the mere annexe of the baronial or episcopal castle
+LJSpeech-1.1/wavs/LJ014-0038.wav|and proceeding to where they came from, found Delarue dead, slain by the furious brother.
+LJSpeech-1.1/wavs/LJ032-0093.wav|but Oswald had rented post office box three zero zero six one in New Orleans on June three, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ002-0333.wav|a severe winter set in, the new stock had not been supplied by the contractors, and the poor debtors perished of cold.
+LJSpeech-1.1/wavs/LJ048-0279.wav|They work long, hard hours, under very great strain, and must travel frequently.
+LJSpeech-1.1/wavs/LJ048-0012.wav|According to Belmont, when Oswald returned from the Soviet Union, quote,
+LJSpeech-1.1/wavs/LJ017-0262.wav|Two of the seven, Santos and Marsolino, were reprieved, and their sentences commuted to penal servitude for life;
+LJSpeech-1.1/wavs/LJ050-0172.wav|The Commission, therefore, recommends that the President consider ordering an inquiry into the possibility
+LJSpeech-1.1/wavs/LJ019-0134.wav|radical improvement was generally considered impossible. The great evil, however, had been sensibly diminished.
+LJSpeech-1.1/wavs/LJ006-0185.wav|Edward Gibbon Wakefield, who spent three years in Newgate a little before the time of the inspectors' first report,
+LJSpeech-1.1/wavs/LJ011-0083.wav|For a long time justice did not overtake him for any criminal offense, but he was frequently in Newgate and in the King's Bench for debt.
+LJSpeech-1.1/wavs/LJ006-0169.wav|He really did not know what passed in his jail
+LJSpeech-1.1/wavs/LJ044-0202.wav|by a desire to aid the Castro regime, which President Kennedy so outspokenly criticized.
+LJSpeech-1.1/wavs/LJ050-0083.wav|Information is requested also concerning individuals or groups who have demonstrated an interest in the President
+LJSpeech-1.1/wavs/LJ021-0137.wav|Step by step we have created all the government agencies necessary to insure, as a general rule, industrial peace,
+LJSpeech-1.1/wavs/LJ024-0087.wav|I have thus explained to you the reasons that lie behind our efforts to secure results by legislation within the Constitution.
+LJSpeech-1.1/wavs/LJ046-0230.wav|prior to November twenty-two, nineteen sixty-three, were inadequate.
+LJSpeech-1.1/wavs/LJ006-0156.wav|We have seen that he took no pains to classify and separate prisoners on reception.
+LJSpeech-1.1/wavs/LJ046-0062.wav|His friend and Special Assistant Kenneth O'Donnell, who accompanied him on his last visit to Dallas,
+LJSpeech-1.1/wavs/LJ032-0165.wav|to the exclusion of all others because there are not enough microscopic characteristics present in fibers.
+LJSpeech-1.1/wavs/LJ018-0194.wav|Cummings was repeatedly "run in" for the offense of coining and uttering bad money, whether coin or notes.
+LJSpeech-1.1/wavs/LJ003-0311.wav|The committee took upon itself to lay down stringent rules for the discipline of the prison.
+LJSpeech-1.1/wavs/LJ041-0092.wav|an, quote, individual that you would brainwash, and quite easy, but I think once he believed in something, he stood in his beliefs, end quote.
+LJSpeech-1.1/wavs/LJ020-0043.wav|After mixing your bread in the morning either with sponge or with yeast, divide the kneaded dough into two portions.
+LJSpeech-1.1/wavs/LJ017-0104.wav|prescribed nominally by the fresh doctor, for which Palmer had substituted his own.
+LJSpeech-1.1/wavs/LJ011-0028.wav|In his defense
+LJSpeech-1.1/wavs/LJ035-0080.wav|the second test, at a "fast walk" took one minute, fourteen seconds.
+LJSpeech-1.1/wavs/LJ030-0244.wav|hitting me on the shoulder, and shouted to all of us in the back seat to get down.
+LJSpeech-1.1/wavs/LJ041-0158.wav|Thornley, who thought Oswald had an "irrevocable conviction" that his Marxist beliefs were correct, testified, quote,
+LJSpeech-1.1/wavs/LJ003-0010.wav|wine and beer might be had in any quantity, the only limitation being
+LJSpeech-1.1/wavs/LJ034-0170.wav|Fischer testified that about ten or fifteen seconds before the motorcade turned onto Houston Street from Main Street,
+LJSpeech-1.1/wavs/LJ021-0121.wav|But I would point out that the extent and severity of labor disputes during this period
+LJSpeech-1.1/wavs/LJ006-0269.wav|Decency was, however, insured by a line of demarcation, and visitors were kept upon each side of a separated double iron railing.
+LJSpeech-1.1/wavs/LJ029-0187.wav|On the evening of October twenty-four, nineteen sixty-three, after addressing a meeting in Dallas,
+LJSpeech-1.1/wavs/LJ004-0030.wav|the principle of classification was freshly enunciated; prison regulations were framed for general observance.
+LJSpeech-1.1/wavs/LJ014-0340.wav|They could not deny that the latter was the truth, and were forthwith stigmatized by Mr. Chapman, Overend and Gurney's representative, as rogues.
+LJSpeech-1.1/wavs/LJ048-0136.wav|In the description of the important aspects of the advance preparations,
+LJSpeech-1.1/wavs/LJ006-0139.wav|Nobody interfered with them or regulated their conduct. They might get drunk when so disposed, and did so frequently, alone or in company.
+LJSpeech-1.1/wavs/LJ018-0290.wav|by paying in a genuine bill of Messrs. Rothschilds' for forty-five hundred pounds, which was duly discounted.
+LJSpeech-1.1/wavs/LJ041-0201.wav|Of greater importance,
+LJSpeech-1.1/wavs/LJ043-0144.wav|and the March eleven, nineteen sixty-three, issue of the Militant.
+LJSpeech-1.1/wavs/LJ043-0088.wav|On October eleven, nineteen sixty-two, the Employment Commission referred Oswald
+LJSpeech-1.1/wavs/LJ036-0039.wav|In his Commission testimony, McWatters said he had been in error and that a teenager named Milton Jones was the passenger he had in mind.
+LJSpeech-1.1/wavs/LJ030-0017.wav|The President liked outdoor appearances because more people could see and hear him.
+LJSpeech-1.1/wavs/LJ017-0144.wav|There was no convincing proof that the accused had administered the poison, although beyond question that poison had occasioned the death.
+LJSpeech-1.1/wavs/LJ040-0158.wav|Lee has to be seen as an emotionally, quite disturbed youngster
+LJSpeech-1.1/wavs/LJ016-0086.wav|were fixed over the fireplaces, and the prisoners had no longer access to the chimneys.
+LJSpeech-1.1/wavs/LJ039-0008.wav|told Robert Oswald, Lee Harvey Oswald's brother, that Oswald had once threatened to shoot former Vice President Richard M. Nixon.
+LJSpeech-1.1/wavs/LJ008-0265.wav|although in the long run mercy generally prevailed, and very few capitally convicted for crimes less than murder actually suffered.
+LJSpeech-1.1/wavs/LJ047-0236.wav|was capable or potentially an assassin of the President of the United States, end quote.
+LJSpeech-1.1/wavs/LJ003-0275.wav|These malpractices were fostered by the absence of all supervision and the generally unbroken idleness.
+LJSpeech-1.1/wavs/LJ033-0116.wav|Frazier placed the upper part of the package under his armpit and attempted to cup his right hand beneath the bottom of the bag.
+LJSpeech-1.1/wavs/LJ010-0274.wav|Much the same motives of seeking notoriety seem to have impelled Bean, who was perfectly sane, to his rash act;
+LJSpeech-1.1/wavs/LJ009-0225.wav|but not before the greatest scandal had been caused by the unseemly proceedings of the crowd that flocked to see the sight. A sort of fair was held,
+LJSpeech-1.1/wavs/LJ003-0172.wav|The keeper went still further in his efforts to make money.
+LJSpeech-1.1/wavs/LJ028-0077.wav|offering for sale a small cameo engraved with a helmeted head of a Greek type.
+LJSpeech-1.1/wavs/LJ021-0109.wav|whether the methods and policies adopted in the emergency
+LJSpeech-1.1/wavs/LJ034-0099.wav|After hearing the first shot, which he thought was a motorcycle backfire, Brennan glanced up at the window. He testified that, quote,
+LJSpeech-1.1/wavs/LJ022-0150.wav|to eliminate so far as humanly possible, the kinds of unfair practices by selfish minorities which unfortunately
+LJSpeech-1.1/wavs/LJ021-0107.wav|It is now time to review these actions as a whole to determine through deliberative means in the light of experience,
+LJSpeech-1.1/wavs/LJ016-0278.wav|and which contributed in no small degree to the introduction of private executions. A great crowd was expected, and a great crowd came.
+LJSpeech-1.1/wavs/LJ043-0168.wav|he removed at least some of the pictures which had been pasted in it and saved them among his effects, where they were found after the assassination.
+LJSpeech-1.1/wavs/LJ013-0024.wav|At a place called the Silver Keys he ran the ship on the reef.
+LJSpeech-1.1/wavs/LJ037-0187.wav|the company received from Empire Wholesale Sporting Goods, Ltd., Montreal, a shipment of ninety-nine guns in one case.
+LJSpeech-1.1/wavs/LJ010-0063.wav|The massacre of the whole of the Cabinet Ministers at one stroke was to be followed by an attack
+LJSpeech-1.1/wavs/LJ009-0252.wav|On the scaffold he made a violent attempt to loosen his bonds, and succeeded in getting his hands free.
+LJSpeech-1.1/wavs/LJ024-0002.wav|Section ten. March nine, nineteen thirty-seven. Part two.
+LJSpeech-1.1/wavs/LJ002-0295.wav|This was the reprehensible claim for "garnish," which had already been abolished in all well-conducted prisons, but which still was demanded in Newgate.
+LJSpeech-1.1/wavs/LJ024-0091.wav|Each one is radically different from the other.
+LJSpeech-1.1/wavs/LJ037-0152.wav|Identification was effected through breech face marks and firing pin marks.
+LJSpeech-1.1/wavs/LJ028-0208.wav|Nabonidus fled.
+LJSpeech-1.1/wavs/LJ016-0309.wav|Some of the judges would have retained the public spectacle; the ordinary of Newgate was not certain that public executions were not the best.
+LJSpeech-1.1/wavs/LJ003-0158.wav|from whom still higher fees were exacted, with the same discreditable idea of swelling the revenues of the prison.
+LJSpeech-1.1/wavs/LJ021-0170.wav|complain that all we have done is unnecessary and subject to great risks.
+LJSpeech-1.1/wavs/LJ045-0063.wav|Oswald apparently wished to continue practicing his own Russian with her. Lieutenant Martello of the New Orleans police testified
+LJSpeech-1.1/wavs/LJ005-0087.wav|Female prisoners were in all cases to be under the charge of female officers.
+LJSpeech-1.1/wavs/LJ045-0200.wav|that over the weekend he did think about his wife's request that he not come to Irving, which was prompted by the birthday party being held at the Paine home.
+LJSpeech-1.1/wavs/LJ037-0089.wav|She identified Oswald, who was the Number two man in the lineup, as the man she saw running with the gun:
+LJSpeech-1.1/wavs/LJ011-0091.wav|In one corner after much search a phial was found labeled "Prussic acid,"
+LJSpeech-1.1/wavs/LJ050-0131.wav|unless a system is established for the frequent formal review of activities thereunder. In this regard
+LJSpeech-1.1/wavs/LJ050-0204.wav|Secret Service Personnel and Facilities
+LJSpeech-1.1/wavs/LJ038-0159.wav|Jarman did talk to Oswald that morning: Quote,
+LJSpeech-1.1/wavs/LJ015-0016.wav|the failure and subsequent sentence of the bankers Messrs. Strahan, Paul, and Bates,
+LJSpeech-1.1/wavs/LJ006-0266.wav|On these occasions precautions were supposed to be taken to exclude bad characters,
+LJSpeech-1.1/wavs/LJ002-0138.wav|and four common councilmen to hold Courts of Requests, or Courts of Conscience, to hear and determine all causes of debt
+LJSpeech-1.1/wavs/LJ035-0064.wav|Truly thought that the officer's gun at that time appeared to be almost touching the middle portion of Oswald's body.
+LJSpeech-1.1/wavs/LJ009-0040.wav|whose desk and pulpit were just in front of the condemned pew, and within a couple of yards of it.
+LJSpeech-1.1/wavs/LJ041-0122.wav|and he must have known that President Kennedy had had nothing to do with it.
+LJSpeech-1.1/wavs/LJ003-0220.wav|quote, so discreditable to the metropolis, end quote.
+LJSpeech-1.1/wavs/LJ038-0147.wav|Frazier testified before the Commission that Oswald carried no lunch sack that day.
+LJSpeech-1.1/wavs/LJ004-0027.wav|it was subsequently amplified, and the rates of salaries fixed.
+LJSpeech-1.1/wavs/LJ028-0172.wav|The ascent to the top is on the outside, by a path which winds round all the towers.
+LJSpeech-1.1/wavs/LJ050-0104.wav|Each of them was a failure in his work and in his relations with others, a victim of delusions and fancies which led to the conviction
+LJSpeech-1.1/wavs/LJ050-0102.wav|John Schrank, and Guiseppe Zangara -- four assassins or would-be assassins
+LJSpeech-1.1/wavs/LJ046-0024.wav|to the Bureau of the Budget for review and approval.
+LJSpeech-1.1/wavs/LJ028-0269.wav|One even shouted to them and said, "Why sit ye there, Persians? Why do ye not go back to your homes? Till mules foal ye will not take our city!"
+LJSpeech-1.1/wavs/LJ045-0233.wav|He consistently refused to admit involvement in the assassination or in the killing of Patrolman Tippit.
+LJSpeech-1.1/wavs/LJ011-0036.wav|but the result in both cases was unfavorable.
+LJSpeech-1.1/wavs/LJ008-0104.wav|Here's the identical rope at sixpence an inch.
+LJSpeech-1.1/wavs/LJ033-0076.wav|On the morning of November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ041-0155.wav|He always wanted to play the red pieces in chess because, as he said in an apparently humorous context,
+LJSpeech-1.1/wavs/LJ017-0269.wav|let down. A full view of them was thus at all times obtainable by the officers who, without intermission, day and night patrolled the ward.
+LJSpeech-1.1/wavs/LJ025-0037.wav|while in plants nitrogen is only accidentally joined with the three other fundamental constituents of organic beings
+LJSpeech-1.1/wavs/LJ024-0120.wav|And remember one thing more.
+LJSpeech-1.1/wavs/LJ005-0261.wav|As a means of securing this uniformity,
+LJSpeech-1.1/wavs/LJ038-0251.wav|but investigation revealed that it was taken from a point slightly less than half a mile from General Walker's house.
+LJSpeech-1.1/wavs/LJ002-0217.wav|the warden of the Fleet at the commencement of the eighteenth century, are too well known to need more than a passing reference.
+LJSpeech-1.1/wavs/LJ044-0235.wav|If there was no conspiracy which would help him escape, the possibility of which has been considered in chapter six,
+LJSpeech-1.1/wavs/LJ002-0061.wav|or they lodged with the gatesmen, the prisoner officers who had charge of the inner gates.
+LJSpeech-1.1/wavs/LJ035-0075.wav|A test was also conducted to determine the time required to walk from the southeast corner of the sixth floor to the second-floor lunchroom by stairway.
+LJSpeech-1.1/wavs/LJ050-0199.wav|The Commission strongly encourages these efforts to improve protection along a motorcade route.
+LJSpeech-1.1/wavs/LJ036-0173.wav|but in either event he would have been in the roominghouse at about one p.m.
+LJSpeech-1.1/wavs/LJ012-0234.wav|It was, however, pretty clear that this victim, like preceding ones, had been lured to Nova Scotia Gardens, and there drugged with a large dose of laudanum.
+LJSpeech-1.1/wavs/LJ013-0156.wav|a scion of the ducal house of Bedford, by his confidential valet and personal attendant.
+LJSpeech-1.1/wavs/LJ022-0091.wav|This Allotment Division will consider all of the recommendations submitted to it
+LJSpeech-1.1/wavs/LJ009-0218.wav|But there was an attempt to revive it at that date, when the act for dispensing with the dissection of criminals was passed.
+LJSpeech-1.1/wavs/LJ041-0017.wav|Voebel also recalled that Oswald once outlined a plan to cut the glass in the window of a store on Rampart Street and steal a pistol,
+LJSpeech-1.1/wavs/LJ046-0023.wav|The Secretary of the Treasury submitted this planning document on August thirty-one, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ040-0189.wav|On the Monroe Silent Reading Test, Lee's score indicated no retardation in reading speed and comprehension;
+LJSpeech-1.1/wavs/LJ036-0121.wav|a twelve:fifteen p.m. pickup at Continental to Greyhound, unloaded at twelve:thirty p.m.,
+LJSpeech-1.1/wavs/LJ046-0049.wav|More often than not, Presidential journeys have served more than one purpose at the same time: ceremonial,
+LJSpeech-1.1/wavs/LJ003-0192.wav|Hence there was a terrible accumulation of prisoners in the condemned cells.
+LJSpeech-1.1/wavs/LJ035-0069.wav|Baker placed himself on a motorcycle about two hundred feet from the corner of Elm and Houston Streets where he said he heard the shots.
+LJSpeech-1.1/wavs/LJ017-0005.wav|of some of the most heinous offenses of modern times.
+LJSpeech-1.1/wavs/LJ038-0103.wav|On the afternoon of November twenty-three, Officers H. M. Moore,
+LJSpeech-1.1/wavs/LJ006-0129.wav|He was on the most intimate and improperly familiar terms with the turnkeys,
+LJSpeech-1.1/wavs/LJ009-0140.wav|no inconvenience was suffered by the congregation, among whom were Lord Adolphus Fitz Clarence, Lord Coventry,
+LJSpeech-1.1/wavs/LJ023-0072.wav|Congress passed a statute which, in eighteen oh three, the Court said violated an express provision of the Constitution.
+LJSpeech-1.1/wavs/LJ021-0173.wav|They would have you believe that England has made progress out of her depression by a do-nothing policy, by letting nature take her course.
+LJSpeech-1.1/wavs/LJ039-0021.wav|I called him into the bathroom and I closed the door and I wanted to prevent him and then I started to cry.
+LJSpeech-1.1/wavs/LJ016-0444.wav|They were still much swollen when he was led out to execution.
+LJSpeech-1.1/wavs/LJ045-0120.wav|Furthermore, even though it appears that at times Oswald was really upset by visits of the FBI,
+LJSpeech-1.1/wavs/LJ033-0198.wav|Despite these matches, however, Stombaugh was unable to render on opinion that the fibers which he found in the bag had probably come from the blanket,
+LJSpeech-1.1/wavs/LJ035-0066.wav|In an effort to determine whether Oswald could have descended to the lunchroom
+LJSpeech-1.1/wavs/LJ048-0181.wav|The danger from a concealed sniper on the Dallas trip was of concern to those who had considered the problem.
+LJSpeech-1.1/wavs/LJ041-0079.wav|Nelson Delgado, one of Oswald's fellow Marines, testified that Oswald tried to, quote, cut up anybody that was high ranking, end quote,
+LJSpeech-1.1/wavs/LJ019-0287.wav|They were very numerous, very imperfect in construction and management, and they were very little required.
+LJSpeech-1.1/wavs/LJ038-0107.wav|According to Fritz, Oswald sneered, saying that they were fake photographs, that he had been photographed a number of times the day before by the police,
+LJSpeech-1.1/wavs/LJ017-0270.wav|On the morning of execution the noise of fixing the gallows in the street outside awoke one or two of them.
+LJSpeech-1.1/wavs/LJ012-0007.wav|Edwards, whom Mr. Gee easily identified with the others, at once admitted that he was the prime mover of the conspiracy.
+LJSpeech-1.1/wavs/LJ024-0004.wav|It is simply this: whenever a judge or justice of any federal court
+LJSpeech-1.1/wavs/LJ019-0054.wav|But about eighteen fifty the two sides were distinctly hostile, and the controversy ran high.
+LJSpeech-1.1/wavs/LJ049-0230.wav|can and should be recommended by this Commission in the interest of the more efficient protection of the President.
+LJSpeech-1.1/wavs/LJ047-0229.wav|Even if he had recalled that Oswald's place of employment was on the President's route,
+LJSpeech-1.1/wavs/LJ008-0291.wav|The time thus spent varied considerably,
+LJSpeech-1.1/wavs/LJ029-0026.wav|it was agreed that the planning of events in Texas would be left largely to the Governor.
+LJSpeech-1.1/wavs/LJ008-0157.wav|By eight o'clock not an inch of ground in front of the platform was unoccupied.
+LJSpeech-1.1/wavs/LJ030-0228.wav|picked up and cocked an automatic rifle as he heard the last shot.
+LJSpeech-1.1/wavs/LJ040-0138.wav|On the other hand, Lee told his probation officer, John Carro, that, quote,
+LJSpeech-1.1/wavs/LJ029-0164.wav|Also on November nineteen, the Morning News reported that the President's motorcade would travel from Love Field along specified streets, then
+LJSpeech-1.1/wavs/LJ015-0219.wav|At last he decided to enlist one Tester, a clerk in the traffic department, whom he thought would prove a likely tool.
+LJSpeech-1.1/wavs/LJ044-0150.wav|and that it would be better to have less and not have to be concerned about tomorrow.
+LJSpeech-1.1/wavs/LJ001-0045.wav|fourteen sixty-nine, fourteen seventy;
+LJSpeech-1.1/wavs/LJ019-0033.wav|Some jurisdictions, greatly to their credit, strove at once to follow the lead of the central authority.
+LJSpeech-1.1/wavs/LJ016-0041.wav|and working with his hands behind him, while he used his bare feet like claws upon the other side of the wall angle.
+LJSpeech-1.1/wavs/LJ012-0082.wav|Jordan and Sullivan, who at once set to work in a business-like way to obtain impressions of the keys of the strong room and chest.
+LJSpeech-1.1/wavs/LJ045-0164.wav|He said that he was tired of living alone and perhaps the reason for my being so angry was the fact that we were not living together.
+LJSpeech-1.1/wavs/LJ017-0079.wav|Meanwhile the bill discounters who held the forged acceptances, with other promissory notes, began to clamor for payment, and talk of issuing writs.
+LJSpeech-1.1/wavs/LJ028-0321.wav|Verily he by whom I have been so mutilated shall not escape unpunished. And truly all the paths of his counsels are known to me.
+LJSpeech-1.1/wavs/LJ045-0008.wav|in the treatment of Ambassador Adlai Stevenson late in October of nineteen sixty-three
+LJSpeech-1.1/wavs/LJ034-0046.wav|When the FBI in Washington received the cartons, the remaining prints, including Oswald's on the Rolling Readers carton,
+LJSpeech-1.1/wavs/LJ002-0037.wav|leading to thirteen wards on various floors, and a day room.
+LJSpeech-1.1/wavs/LJ005-0139.wav|making an average of nineteen persons occupying each room.
+LJSpeech-1.1/wavs/LJ036-0026.wav|and he estimated that it took him three to four minutes to drive three blocks west from the checkpoint to Field Street,
+LJSpeech-1.1/wavs/LJ010-0116.wav|on the one inveigling silly enthusiasts to join in the plot, and denouncing them on the other.
+LJSpeech-1.1/wavs/LJ020-0086.wav|Hot rolls and muffins should never be cut.
+LJSpeech-1.1/wavs/LJ028-0078.wav|About the head was an inscription in Greek characters saying that the face was that of Nebuchadnezzar.
+LJSpeech-1.1/wavs/LJ013-0074.wav|A third similar fraud to the amount of two thousand pounds was also brought to light.
+LJSpeech-1.1/wavs/LJ050-0040.wav|and reports from other agencies which independently evaluate their information for potential sources of danger.
+LJSpeech-1.1/wavs/LJ048-0059.wav|that several matters brought to his attention in late October and early November, including the visit to the Soviet Embassy in Mexico City,
+LJSpeech-1.1/wavs/LJ038-0167.wav|narrowly escaped death when a rifle bullet fired from outside his home passed near his head as he was seated at his desk.
+LJSpeech-1.1/wavs/LJ018-0202.wav|But the police nearly paid the penalty of capture with their lives.
+LJSpeech-1.1/wavs/LJ016-0213.wav|The origin of this expression dates, it is said, from the time when the Scottish mark,
+LJSpeech-1.1/wavs/LJ016-0201.wav|When he had taken the oath he was dismissed with the words, "Get thee hence, wretch!"
+LJSpeech-1.1/wavs/LJ001-0117.wav|which title can only be claimed by artistic practice, whether the art in it be conscious or unconscious.
+LJSpeech-1.1/wavs/LJ046-0190.wav|it had arrangements to be notified about release from confinement in roughly one thousand cases;
+LJSpeech-1.1/wavs/LJ022-0199.wav|Fear is vanishing and confidence is growing on every side,
+LJSpeech-1.1/wavs/LJ013-0203.wav|On his removal to Newgate after sentence,
+LJSpeech-1.1/wavs/LJ009-0083.wav|while the eyes of the latter are wandering round the chapel, and one of them is occasionally winked impudently at some acquaintance amongst the prisoners for trial.
+LJSpeech-1.1/wavs/LJ028-0404.wav|The courtyards and streets are paved with them, and as you walk about the city the name of Nebuchadnezzar everywhere meets your eye.
+LJSpeech-1.1/wavs/LJ030-0181.wav|Turning to his right in the direction of the noise, Kellerman heard the President say
+LJSpeech-1.1/wavs/LJ032-0072.wav|When arrested on the day of the assassination, he had in his possession a Smith and Wesson thirty-eight caliber revolver
+LJSpeech-1.1/wavs/LJ017-0120.wav|He had already been arrested on a writ issued at the instance of the money-lenders, and an action had been commenced against Mrs. Palmer on her acceptances.
+LJSpeech-1.1/wavs/LJ022-0046.wav|It is true that while business and industry are definitely better our relief rolls are still too large.
+LJSpeech-1.1/wavs/LJ042-0070.wav|Oswald was discovered in time to thwart his attempt at suicide.
+LJSpeech-1.1/wavs/LJ006-0204.wav|In the same way the wardsman laid in his stock to be retailed. Other light literature besides the daily journals were in circulation:
+LJSpeech-1.1/wavs/LJ042-0039.wav|economic system and plans for war, end quote.
+LJSpeech-1.1/wavs/LJ028-0152.wav|In the circuit of the wall are a hundred gates, all of brass, with brazen lintels and sideposts.
+LJSpeech-1.1/wavs/LJ021-0092.wav|first, the legislative or policy making function;
+LJSpeech-1.1/wavs/LJ042-0108.wav|Although Marina Oswald told the Commission that her husband had good personal relationships in the Soviet Union, Katherine Ford,
+LJSpeech-1.1/wavs/LJ038-0203.wav|He had quit these classes at least a week before the shooting, which occurred on a Wednesday night.
+LJSpeech-1.1/wavs/LJ008-0015.wav|was executed on a gibbet in front of his victim's house.
+LJSpeech-1.1/wavs/LJ018-0362.wav|The trial of Miss Edmunds was transferred to the Central Criminal Court under Lord Campbell's Act, already referred to.
+LJSpeech-1.1/wavs/LJ024-0110.wav|And the strategy of that last stand is to suggest the time-consuming process of amendment in order to kill off by delay
+LJSpeech-1.1/wavs/LJ044-0042.wav|In his letter to V. T. Lee,
+LJSpeech-1.1/wavs/LJ018-0195.wav|His regular trade, followed before he took to the life of resurrectionist, was that of an engraver.
+LJSpeech-1.1/wavs/LJ040-0111.wav|with no behavior or truancy problems.
+LJSpeech-1.1/wavs/LJ042-0207.wav|with the mass extermination so typical of Stalin, and the individual suppression and regimentation under Khrushchev.
+LJSpeech-1.1/wavs/LJ036-0047.wav|On November twenty-two, Mrs. Bledsoe came downtown to watch the Presidential motorcade.
+LJSpeech-1.1/wavs/LJ008-0065.wav|They are both drawn at once by a windlass, and the unhappy culprits remain suspended.
+LJSpeech-1.1/wavs/LJ030-0007.wav|They were greeted by Vice President Johnson and Governor Connally, who joined the Presidential party in a motorcade through San Antonio.
+LJSpeech-1.1/wavs/LJ033-0213.wav|(three) removed the rifle from the blanket in the Paines' garage on Thursday evening;
+LJSpeech-1.1/wavs/LJ019-0091.wav|were mere matters of chance, and decided according to the views of the local magistracy.
+LJSpeech-1.1/wavs/LJ022-0079.wav|Projects shall be of a nature that a considerable proportion of the money spent will go into wages for labor.
+LJSpeech-1.1/wavs/LJ049-0170.wav|indicating the possibility of an attempt against the person or safety of the President, end quote, or other protected persons.
+LJSpeech-1.1/wavs/LJ027-0029.wav|For illustration: a change in the locomotive habit of a bird from that of flying to that of an ostrich
+LJSpeech-1.1/wavs/LJ043-0156.wav|After he brought the rifle home, then, he showed you the book?
+LJSpeech-1.1/wavs/LJ014-0250.wav|after they had been cashed by the bank.
+LJSpeech-1.1/wavs/LJ043-0099.wav|It appears that his discharge was hastened by the fact that he brought a Russian language newspaper to work.
+LJSpeech-1.1/wavs/LJ040-0023.wav|may be obtained from a study of the events, relationships and influences which appear to have been significant in shaping his character and in guiding him.
+LJSpeech-1.1/wavs/LJ010-0111.wav|and there seems to have been good reason for supposing that he was a greater villain than any of those arraigned.
+LJSpeech-1.1/wavs/LJ025-0070.wav|in nowise distinguishable from that of one of the simpler animals, and
+LJSpeech-1.1/wavs/LJ033-0109.wav|the Commission has carefully considered the testimony of these two witnesses with regard to the length of the bag.
+LJSpeech-1.1/wavs/LJ042-0149.wav|but I had to change my character a great deal in order to maintain a more or less peaceful family life. End quote.
+LJSpeech-1.1/wavs/LJ016-0172.wav|There were still untoward accidents occasionally at executions, and even the chief practitioner of recent times, Calcraft,
+LJSpeech-1.1/wavs/LJ049-0139.wav|and the Attorney General at any meetings which are concerned with Presidential protection.
+LJSpeech-1.1/wavs/LJ042-0200.wav|even though he later referred to the latter as, quote, trusted long time fighters for progress, end quote.
+LJSpeech-1.1/wavs/LJ037-0093.wav|William Arthur Smith was about a block east of tenth and Patton when he heard shots.
+LJSpeech-1.1/wavs/LJ029-0050.wav|A check of the geographic indexes there revealed no listing for any individual deemed to be a potential danger to the President
+LJSpeech-1.1/wavs/LJ023-0027.wav|It will take time -- and plenty of time -- to work out our remedies administratively even after legislation is passed.
+LJSpeech-1.1/wavs/LJ006-0068.wav|We have reason to fear that poverty, ragged clothes, and an inability to pay the ward dues, elsewhere exacted for better accommodation,
+LJSpeech-1.1/wavs/LJ029-0144.wav|In conformity with these arrangements, traffic proceeding west on Main is directed to turn right at Houston
+LJSpeech-1.1/wavs/LJ035-0011.wav|a strong wind blowing from the north almost unseated him.
+LJSpeech-1.1/wavs/LJ007-0122.wav|Even Mr. Cope admitted that the committal of this class of prisoners to Newgate was most inconvenient,
+LJSpeech-1.1/wavs/LJ019-0334.wav|Under them the governor or jailer was held strictly responsible.
+LJSpeech-1.1/wavs/LJ019-0312.wav|with the local jurisdictions, although still very leniently disposed.
+LJSpeech-1.1/wavs/LJ028-0465.wav|served as an elevated road where several chariots might have been driven abreast.
+LJSpeech-1.1/wavs/LJ027-0002.wav|Biology. Chapter ten. Morphology and Embryology, Part One.
+LJSpeech-1.1/wavs/LJ019-0163.wav|Thus the wards, which, as I have said, were left in complete darkness, were now to be lighted with gas; and after this most salutary addition,
+LJSpeech-1.1/wavs/LJ027-0168.wav|Now this is the permanent, mature condition of many amphibians,
+LJSpeech-1.1/wavs/LJ012-0089.wav|he broke open the chest and stole four thousand, seven hundred pounds in notes, with a quantity of gold and some silver.
+LJSpeech-1.1/wavs/LJ045-0163.wav|On that day, he suggested that we rent an apartment in Dallas.
+LJSpeech-1.1/wavs/LJ020-0015.wav|Beat hard one minute before pouring in the yeast.
+LJSpeech-1.1/wavs/LJ031-0204.wav|with three Secret Service agents, accompanied President Kennedy's body on the forty-five-minute automobile trip from Andrews Air Force Base to the hospital.
+LJSpeech-1.1/wavs/LJ037-0240.wav|At one:twenty-four p.m., the police radio reported, quote,
+LJSpeech-1.1/wavs/LJ050-0003.wav|By The President's Commission on the Assassination of President Kennedy. Chapter eight. The Protection of the President. Part five.
+LJSpeech-1.1/wavs/LJ003-0343.wav|With the seeming impossibility of extending the limits of the prison as it then stood,
+LJSpeech-1.1/wavs/LJ042-0202.wav|It has turned itself into the traditional lever of a foreign power to overthrow the government of the United States;
+LJSpeech-1.1/wavs/LJ001-0040.wav|and is very simple and legible, and unaffectedly designed for use; but it is by no means without beauty.
+LJSpeech-1.1/wavs/LJ004-0094.wav|This act set forth that "whereas the malignant fever commonly called the jail distemper
+LJSpeech-1.1/wavs/LJ018-0057.wav|He was short in stature, his legs were light for the upper part of his body, which was powerful, almost herculean.
+LJSpeech-1.1/wavs/LJ018-0049.wav|Müller knew that any confession would ruin his chances of escape.
+LJSpeech-1.1/wavs/LJ006-0224.wav|New arrivals, especially the innocent and still guileless debutant, were tormented with rude horse-play, and assailed by the most insulting "chaff."
+LJSpeech-1.1/wavs/LJ041-0141.wav|Oswald could have shot the Governor as the car approached the Depository or as it was making the turn onto Elm Street.
+LJSpeech-1.1/wavs/LJ040-0152.wav|turning around the topics of omnipotence and power, through which he tries to compensate for his present shortcomings and frustrations, end quote.
+LJSpeech-1.1/wavs/LJ044-0080.wav|was apparently not enough to satisfy him. He exaggerated in his letters to V. T. Lee in an apparent attempt
+LJSpeech-1.1/wavs/LJ048-0184.wav|Levels of risk can be determined, however, as has been confirmed by building surveys made since the assassination for the Department of the Treasury.
+LJSpeech-1.1/wavs/LJ008-0056.wav|The Dublin "engine of death," as the gallows are styled in the account from which the following description is taken, consisted of an iron bar
+LJSpeech-1.1/wavs/LJ017-0063.wav|He made no distinct admissions even on the scaffold; but when the chaplain at the last moment exhorted him to confess,
+LJSpeech-1.1/wavs/LJ041-0049.wav|According to Marguerite Oswald, quote, Lee lived for the time that he would become seventeen years old to join the Marines -- that whole year, end quote.
+LJSpeech-1.1/wavs/LJ043-0081.wav|The counselor testified that there was some indication that Oswald was capable of doing college work
+LJSpeech-1.1/wavs/LJ015-0188.wav|But his name lingers still in this country as that of the first swindler of his time,
+LJSpeech-1.1/wavs/LJ007-0123.wav|and the inspectors expressed themselves still more strongly in reprehension of the practice.
+LJSpeech-1.1/wavs/LJ010-0133.wav|and catching sight of the coffins ranged around the gallows, he smiled at the show with contemptuous indifference.
+LJSpeech-1.1/wavs/LJ035-0103.wav|There was no elevator on the third or fourth floor.
+LJSpeech-1.1/wavs/LJ018-0241.wav|forthwith brought an ejectment on the possessor of lands purchased on the authority of the forged conveyance and will.
+LJSpeech-1.1/wavs/LJ033-0096.wav|quote, when he rode with me, I say he always brought lunch except that one day on November twenty-two he didn't bring his lunch that day, end quote,
+LJSpeech-1.1/wavs/LJ045-0027.wav|During the period from Oswald's return from Mexico to the assassination,
+LJSpeech-1.1/wavs/LJ040-0216.wav|In view of his experiences when he visited his relatives in New Orleans in the spring of nineteen fifty, and his other solitary habits,
+LJSpeech-1.1/wavs/LJ040-0125.wav|Truancy charges were brought against him alleging that he was, quote, beyond the control of his mother insofar as school attendance is concerned, end quote.
+LJSpeech-1.1/wavs/LJ014-0324.wav|Cole's plan was to have a wharf of his own, nominally occupied by a creature trading as Maltby and Co.
+LJSpeech-1.1/wavs/LJ049-0185.wav|During the period the Commission was giving thought to this situation,
+LJSpeech-1.1/wavs/LJ003-0121.wav|But all punishments might readily be commuted into a fine to be spent in gin for judge and jury.
+LJSpeech-1.1/wavs/LJ037-0043.wav|She raised her hands to her eyes as the man started to walk back toward Patton.
+LJSpeech-1.1/wavs/LJ008-0186.wav|The concourse was very great, notwithstanding these warnings.
+LJSpeech-1.1/wavs/LJ043-0078.wav|because of his need for immediate employment she attempted to obtain for him any job that was available at the time. Oswald made qualifying marks
+LJSpeech-1.1/wavs/LJ037-0019.wav|The next day Scoggins viewed a lineup of four persons and identified Oswald as the man whom he had seen the day before at tenth and Patton.
+LJSpeech-1.1/wavs/LJ013-0229.wav|Courvoisier admitted that he had intended to bind it tightly round his arm and bleed himself to death in the night.
+LJSpeech-1.1/wavs/LJ018-0341.wav|But it was scarcely worse than that of Mrs. Brownrigg, or that of the Meteyards, both of whom did their helpless apprentices to death.
+LJSpeech-1.1/wavs/LJ004-0171.wav|was by a kind of chimney, which the prisoners kept hermetically sealed, and which had never been opened in the memory of the turnkey.
+LJSpeech-1.1/wavs/LJ009-0282.wav|who for the fourth time regained his foothold. Again he was repelled,
+LJSpeech-1.1/wavs/LJ039-0203.wav|The shots fired by Frazier at the range of twenty-five yards landed within an area of two inches and five inches respectively.
+LJSpeech-1.1/wavs/LJ008-0199.wav|At the George public-house to the south of the drop, Sir W. Watkin Wynn, Baronet,
+LJSpeech-1.1/wavs/LJ020-0088.wav|It is not a wholesome practice for any woman -- least of all a young girl to be out of bed two hours before she eats her breakfast.
+LJSpeech-1.1/wavs/LJ035-0176.wav|Barnett estimated that approximately three minutes elapsed between the time he heard the last of the shots and the time he started guarding the front door.
+LJSpeech-1.1/wavs/LJ006-0020.wav|William Crawford had been one of the promoters and managers of the Philanthropic Society's farm school.
+LJSpeech-1.1/wavs/LJ016-0390.wav|and was arrested on board ship just as he was about to leave the country, had a little spare cash, which he devoted entirely to the purchase of extra food.
+LJSpeech-1.1/wavs/LJ037-0135.wav|Russell confirmed this statement in a sworn affidavit for the Commission.
+LJSpeech-1.1/wavs/LJ017-0132.wav|He suffered at Stafford in front of the jail.
+LJSpeech-1.1/wavs/LJ019-0167.wav|wore no distinguishing costume, although its use was admitted, not only for cleanliness, but as a badge of condition, and a security against escape.
+LJSpeech-1.1/wavs/LJ032-0007.wav|In this chapter the Commission evaluates the evidence upon which it has based its conclusion concerning the identity of the assassin.
+LJSpeech-1.1/wavs/LJ025-0046.wav|The relations of plants and animals to the atmosphere are therefore inverse.
+LJSpeech-1.1/wavs/LJ044-0075.wav|of the alleged FPCC, quote, organization, end quote, in New Orleans.
+LJSpeech-1.1/wavs/LJ028-0132.wav|Yet as large and lofty as they were, they were completed in fifteen days.
+LJSpeech-1.1/wavs/LJ034-0137.wav|The two men, Harold Norman and James Jarman, Jr., each confirmed that when they came out of the building,
+LJSpeech-1.1/wavs/LJ048-0227.wav|According to their affidavits, the drinking in no case amounted to more than three glasses of beer or one and a half mixed drinks,
+LJSpeech-1.1/wavs/LJ028-0062.wav|and drove them from Asia.
+LJSpeech-1.1/wavs/LJ008-0275.wav|but there was a further appeal then, as now, to the king himself, or practically to the Home Secretary.
+LJSpeech-1.1/wavs/LJ050-0129.wav|and this source should not be neglected by undue concentration on relationships with other Federal agencies.
+LJSpeech-1.1/wavs/LJ012-0055.wav|While there he was joined by his wife and other friends.
+LJSpeech-1.1/wavs/LJ045-0054.wav|It has been the cause of many of our arguments. End quote. Oswald claimed that his wife preferred others to him.
+LJSpeech-1.1/wavs/LJ045-0083.wav|On that issue George De Mohrenschildt, who was probably as close to the Oswalds as anyone else during their first stay in Dallas,
+LJSpeech-1.1/wavs/LJ033-0031.wav|had curtains and curtain rods, and that Oswald had never discussed the subject with her.
+LJSpeech-1.1/wavs/LJ033-0136.wav|A handmade bag of wrapping paper and tape was found in the southeast corner of the sixth floor alongside the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ022-0067.wav|but it will, by sustaining purchasing power, cushion the shock of economic distress.
+LJSpeech-1.1/wavs/LJ042-0093.wav|They sent him to Minsk to work in a radio and television factory as a metal worker.
+LJSpeech-1.1/wavs/LJ001-0060.wav|It must be understood that through the whole of the fifteenth and the first quarter of the sixteenth centuries
+LJSpeech-1.1/wavs/LJ023-0086.wav|to protect our national resources, and in many other ways, to serve our clearly national needs,
+LJSpeech-1.1/wavs/LJ017-0091.wav|The last act now approached, and in order to avoid the detection of this last fraud, Palmer laid his plans for disposing of Cook.
+LJSpeech-1.1/wavs/LJ019-0350.wav|The funds necessary would be advanced by the Public Works Loan Commissioners, and the interest might be charged against the county or borough rates.
+LJSpeech-1.1/wavs/LJ006-0118.wav|He made collections when it suited him for ward purposes, to be spent as he chose, in candles and so forth.
+LJSpeech-1.1/wavs/LJ001-0128.wav|it is not a real gain, for the modern printer throws the gain away by putting inordinately wide spaces between his lines, which, probably,
+LJSpeech-1.1/wavs/LJ028-0139.wav|yet he at least tried to be accurate. He says:
+LJSpeech-1.1/wavs/LJ046-0219.wav|The above action should be taken without delay in order to attempt to verify the information and no evaluation of the information should be attempted.
+LJSpeech-1.1/wavs/LJ006-0053.wav|and the whole by proper management might have been so accommodated as to prevent overcrowding.
+LJSpeech-1.1/wavs/LJ013-0184.wav|He had hung over the body in a state of dreadful agitation, answering no questions, and taking no part in the proceedings.
+LJSpeech-1.1/wavs/LJ018-0033.wav|Directly the foregoing facts were established, a couple of detective officers, armed with a warrant to arrest Müller,
+LJSpeech-1.1/wavs/LJ037-0078.wav|when they heard the sound of gunfire and the screams of Helen Markham.
+LJSpeech-1.1/wavs/LJ032-0211.wav|The authenticity of these pictures has been established by expert testimony which links the second picture,
+LJSpeech-1.1/wavs/LJ029-0058.wav|On November twenty-two a Secret Service agent stood at the entrance to the Trade Mart, where the President was scheduled to speak, with copies of these photographs.
+LJSpeech-1.1/wavs/LJ026-0028.wav|it is futile to argue about the exact period when, for instance, the egg becomes a tadpole or the tadpole a frog,
+LJSpeech-1.1/wavs/LJ007-0242.wav|No complete and permanent improvement was indeed possible while Newgate remained unchanged.
+LJSpeech-1.1/wavs/LJ018-0394.wav|by swindling the Artisans' Dwellings Company; and Madame Rachel passed through Newgate on her way to Millbank
+LJSpeech-1.1/wavs/LJ013-0129.wav|and one day a police constable tracked him to a silversmith's in Holborn Hill,
+LJSpeech-1.1/wavs/LJ037-0091.wav|Barbara Jeanette Davis and Virginia Davis were sitting alongside each other when they made their positive identifications of Oswald.
+LJSpeech-1.1/wavs/LJ005-0154.wav|The rations varied considerably everywhere.
+LJSpeech-1.1/wavs/LJ028-0072.wav|During all his reign there was little peace in his great mixed turbulent empire.
+LJSpeech-1.1/wavs/LJ030-0154.wav|Other witnesses in the motorcade estimated the speed of the President's limousine from seven to twenty-two miles per hour.
+LJSpeech-1.1/wavs/LJ030-0031.wav|Secret Service agents formed a cordon to keep the press and photographers from impeding their passage and scanned the crowd for threatening movements.
+LJSpeech-1.1/wavs/LJ028-0223.wav|I gave heed to the needs of Babylon and its cities, and the servitude of the Babylonians, whatever was oppressive, I removed from them.
+LJSpeech-1.1/wavs/LJ013-0246.wav|Some time elapsed before the imprisoned party could force open the doors, and by then the fugitive had escaped.
+LJSpeech-1.1/wavs/LJ044-0053.wav|in which he supported his report on the Bringuier incident with a copy of the charges made against him
+LJSpeech-1.1/wavs/LJ006-0072.wav|I shall have occasion to speak again, and more at length, of Mr. Cope's careless and perfunctory discharge
+LJSpeech-1.1/wavs/LJ028-0358.wav|Cyrus had permitted them to stand, and as long as he made Babylon his home, the city was as strongly protected as ever.
+LJSpeech-1.1/wavs/LJ050-0136.wav|the Committee will include representatives of the President's Office of Science and Technology, Department of Defense, CIA,
+LJSpeech-1.1/wavs/LJ014-0331.wav|Once a warrant-holder sent down a clerk to view certain goods, and the clerk found that these goods had already a "stop" upon them, or were pledged.
+LJSpeech-1.1/wavs/LJ011-0019.wav|when it was found that a sum of ten thousand pounds, standing in the name of three trustees, of whom Fauntleroy was one,
+LJSpeech-1.1/wavs/LJ045-0188.wav|While there is no way to tell when he first began to think specifically of assassinating the President
+LJSpeech-1.1/wavs/LJ019-0274.wav|It is interesting to note here
+LJSpeech-1.1/wavs/LJ031-0043.wav|evidenced no palpable pulse; and had a few chest sounds which were thought to be heartbeats.
+LJSpeech-1.1/wavs/LJ042-0021.wav|shortly after he arrived in the Soviet Union, and probably before Soviet authorities had given him permission to stay indefinitely,
+LJSpeech-1.1/wavs/LJ006-0259.wav|Many other articles were introduced by visitors, including money, tobacco, pipes, and snuff.
+LJSpeech-1.1/wavs/LJ049-0216.wav|the FBI, the CIA, and the military intelligence agencies as well as the Secret Service.
+LJSpeech-1.1/wavs/LJ027-0023.wav|that the only reasonable explanation for the existence of a fundamental unity in organic life
+LJSpeech-1.1/wavs/LJ029-0027.wav|At the White House, Kenneth O'Donnell, special assistant to the President, acted as coordinator for the trip.
+LJSpeech-1.1/wavs/LJ028-0442.wav|At a distance of about two miles to the south of Babel is the larger and lower mound called the Kasr, or the Fortress,
+LJSpeech-1.1/wavs/LJ022-0181.wav|but that its resources be most fully utilized in the economic life of the country
+LJSpeech-1.1/wavs/LJ035-0060.wav|Baker stated later that the man did not seem to be out of breath; he seemed calm. Quote, He never did say a word or nothing.
+LJSpeech-1.1/wavs/LJ037-0186.wav|disclosed that on January three, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ039-0195.wav|were able to fire the rounds within the time period which would have been available to the assassin under those conditions.
+LJSpeech-1.1/wavs/LJ034-0031.wav|The box on the floor, behind the three near the window,
+LJSpeech-1.1/wavs/LJ017-0273.wav|Lopez was more talkative.
+LJSpeech-1.1/wavs/LJ039-0222.wav|was a substantial aid to rapid, accurate firing.
+LJSpeech-1.1/wavs/LJ001-0091.wav|so far as fine printing is concerned, though paper did not get to its worst till about eighteen forty.
+LJSpeech-1.1/wavs/LJ014-0210.wav|Cannon, by trade a chimney-sweep, had long been characterized by the bitterest hatred of the police force,
+LJSpeech-1.1/wavs/LJ015-0038.wav|They chose, unhappily for themselves, the latter alternative.
+LJSpeech-1.1/wavs/LJ011-0204.wav|As he still urged his rights over his wife, Miss Turner cried out in protest, "No, no, I am not his wife;
+LJSpeech-1.1/wavs/LJ032-0039.wav|and opposite the printed word "From"
+LJSpeech-1.1/wavs/LJ012-0092.wav|This occurred in November eighteen thirty-four. The Custom House officials were in a state of consternation,
+LJSpeech-1.1/wavs/LJ019-0285.wav|In one jail fifteen hours were spent in bed out of the twenty-four. This was in keeping with other grave defects and omissions.
+LJSpeech-1.1/wavs/LJ005-0147.wav|At the time of which I am writing there was great diversity of practice as regards the hours of labor.
+LJSpeech-1.1/wavs/LJ030-0094.wav|Driven by an officer of the Dallas Police Department,
+LJSpeech-1.1/wavs/LJ017-0080.wav|Palmer, alive to the danger he ran of a prosecution for forgery, should the fraud he had committed be brought to light,
+LJSpeech-1.1/wavs/LJ020-0024.wav|knead and set for risings first and second, as you have already been instructed. This sponge will be found especially useful in making Graham Bread.
+LJSpeech-1.1/wavs/LJ043-0179.wav|The circumstances of the attack on Walker coupled with other indications that Oswald was concerned about his place in history
+LJSpeech-1.1/wavs/LJ038-0065.wav|It is unlikely that any of the police officers referred to Oswald as a suspect in the assassination.
+LJSpeech-1.1/wavs/LJ022-0025.wav|but that it is being done in spite of the few who seek to confuse them and to profit by their confusion.
+LJSpeech-1.1/wavs/LJ021-0027.wav|which obtained through the attrition of individuals before the new conditions arose.
+LJSpeech-1.1/wavs/LJ046-0243.wav|if the presence in Dallas of the Stevenson pickets might have created a danger for the President on a visit to that city,
+LJSpeech-1.1/wavs/LJ007-0005.wav|The inspectors paid tribute to the excellence of the motives of these philanthropic ladies, and recognized the good they did.
+LJSpeech-1.1/wavs/LJ017-0148.wav|Smethurst's victim was a Miss Bankes, with whom he had contracted a bigamous marriage.
+LJSpeech-1.1/wavs/LJ002-0093.wav|There were nine wards in all on the female side, one of them in the attic,
+LJSpeech-1.1/wavs/LJ019-0183.wav|provided it was henceforth used only for untried prisoners, suggested that Newgate should be entirely reconstructed, and the new building adopted as a model.
+LJSpeech-1.1/wavs/LJ018-0164.wav|who had devoted all his talent and all his energies for some seventeen years to the fabrication of false bank-notes.
+LJSpeech-1.1/wavs/LJ049-0126.wav|Committee of Cabinet Officers. As our Government has become more complex,
+LJSpeech-1.1/wavs/LJ029-0162.wav|The motorcade will then pass through downtown on Harwood
+LJSpeech-1.1/wavs/LJ016-0266.wav|No notion that they were assisting at a vindication of the law filled the minds of those present with dread.
+LJSpeech-1.1/wavs/LJ045-0150.wav|On Monday, November eighteen, nineteen sixty-three, he called several times, but after I hung up on him and didn't want to talk to him he did not call again.
+LJSpeech-1.1/wavs/LJ002-0052.wav|three. The chapel yard was about forty-three feet by twenty-five.
+LJSpeech-1.1/wavs/LJ010-0224.wav|No serious importance could be attached to these, the manifest inventions of a disordered intellect.
+LJSpeech-1.1/wavs/LJ025-0135.wav|is propagated to the muscles by which the body is retracted, and causing them to contract, the act of retraction is brought about.
+LJSpeech-1.1/wavs/LJ012-0011.wav|which Mrs. Canning had lost by remarriage.
+LJSpeech-1.1/wavs/LJ024-0042.wav|who understand those modern conditions,
+LJSpeech-1.1/wavs/LJ021-0196.wav|There is great danger it seems to me to arise from the constant habit which prevails where anything is opposed or objected to,
+LJSpeech-1.1/wavs/LJ014-0093.wav|and during that time he must frequently have stood or sat over his own grave.
+LJSpeech-1.1/wavs/LJ007-0018.wav|and that when the restraining influences of the ladies were absent, the female prisoners relapsed into immoral and uncleanly discourse.
+LJSpeech-1.1/wavs/LJ019-0007.wav|By other acts local authorities were empowered to construct new jails or hire accommodation in the district;
+LJSpeech-1.1/wavs/LJ034-0087.wav|Kaiser identified it as the clipboard which Oswald had appropriated from him when Oswald came to work at the Depository.
+LJSpeech-1.1/wavs/LJ009-0063.wav|The sheriffs shudder, their inquisitive friends crane forward, the keeper frowns on the excited congregation,
+LJSpeech-1.1/wavs/LJ040-0182.wav|He appears to be a somewhat insecure youngster exhibiting much inclination for warm and satisfying relationships to others.
+LJSpeech-1.1/wavs/LJ029-0210.wav|end quote, followed by a scurrilous bill of particulars that constituted a vilification of the President.
+LJSpeech-1.1/wavs/LJ047-0191.wav|The official Bureau files confirm Hosty's statement that from November five until the assassination, no active investigation was conducted.
+LJSpeech-1.1/wavs/LJ020-0035.wav|make into two loaves, then put into well-greased pans and leave for an hour, or until it becomes more than twice the original size of the dough.
+LJSpeech-1.1/wavs/LJ039-0213.wav|So that if you aimed with this weapon as it actually was received at the laboratory, it would not be necessary to take any lead whatsoever
+LJSpeech-1.1/wavs/LJ003-0171.wav|But the keeper protested that no single prisoner could thus monopolize space if the state side was crowded.
+LJSpeech-1.1/wavs/LJ008-0268.wav|but of these three hundred and ninety-six were reversed by the king in council, and only fifty-two were really executed.
+LJSpeech-1.1/wavs/LJ034-0165.wav|Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ003-0186.wav|Only when the order of execution came down were those about to suffer placed apart in one of the rooms in the arcade of the middle ward.
+LJSpeech-1.1/wavs/LJ028-0242.wav|entered the stream, which had now sunk so as to reach about midway up a man's thigh, and thus got into the town.
+LJSpeech-1.1/wavs/LJ012-0006.wav|A watch was set on him at her house, where he was soon afterwards arrested.
+LJSpeech-1.1/wavs/LJ028-0329.wav|Then the Babylonians, seeing that his deeds were as brave as his words, were beyond measure pleased, and set no bounds to their trust.
+LJSpeech-1.1/wavs/LJ002-0223.wav|The principle of "chummage" prevailed as in the King's Bench,
+LJSpeech-1.1/wavs/LJ028-0160.wav|The houses are mostly three and four stories high;
+LJSpeech-1.1/wavs/LJ038-0169.wav|he saw two men, in separate cars, drive out of a church parking lot adjacent to Walker's home. A friend of Walker's testified that
+LJSpeech-1.1/wavs/LJ002-0050.wav|and the other eighteen by fifteen; and they nominally held twenty-two persons.
+LJSpeech-1.1/wavs/LJ028-0083.wav|Unfortunately, the little stone seal, perhaps the only one to preserve for us his features, appears to have been lost for ever.
+LJSpeech-1.1/wavs/LJ037-0003.wav|Eyewitnesses
+LJSpeech-1.1/wavs/LJ037-0261.wav|that the jacket belonged to Lee Harvey Oswald, and that when he was arrested at approximately one:fifty p.m., he was in shirt sleeves.
+LJSpeech-1.1/wavs/LJ033-0214.wav|(four) carried the rifle into the Depository Building, concealed in the bag;
+LJSpeech-1.1/wavs/LJ011-0145.wav|The new Forgery Act with the Lords' amendment passed into law, but the latter proved perfectly harmless,
+LJSpeech-1.1/wavs/LJ028-0044.wav|He rebuilt Babylon that in accordance with the ancient custom he might be crowned in the sacred city.
+LJSpeech-1.1/wavs/LJ031-0023.wav|four anesthesiologists, Drs. Marion T. Jenkins, Adolph H. Giesecke, Jr., Jackie H. Hunt,
+LJSpeech-1.1/wavs/LJ018-0238.wav|and in eighteen sixty-two he was obliged to fly the country, hopelessly and irretrievably ruined.
+LJSpeech-1.1/wavs/LJ016-0410.wav|nodded pleasantly to the governor, who stood just opposite, and then walked briskly towards the execution shed, smiling as he went along.
+LJSpeech-1.1/wavs/LJ033-0158.wav|The palmprint was found on the closed end of the bag.
+LJSpeech-1.1/wavs/LJ018-0315.wav|to whom it was said one hundred pounds apiece had been given down as the price of their infidelity.
+LJSpeech-1.1/wavs/LJ041-0053.wav|continued during this period in New Orleans (nineteen fifty-four to nineteen fifty-six).
+LJSpeech-1.1/wavs/LJ014-0087.wav|A quantity of quicklime was thrown in with the body to destroy all identification.
+LJSpeech-1.1/wavs/LJ011-0095.wav|He had prospered in early life, was a slop-seller on a large scale at Bury St. Edmunds, and a sugar-baker in the metropolis.
+LJSpeech-1.1/wavs/LJ050-0057.wav|specifying criteria for information to be furnished to the Secret Service in addition to that covered by the former standard,
+LJSpeech-1.1/wavs/LJ029-0036.wav|Advance Preparations for the Dallas Trip
+LJSpeech-1.1/wavs/LJ037-0226.wav|According to Patrolman Poe this description came from Mrs. Markham and Mrs. Barbara Jeanette Davis.
+LJSpeech-1.1/wavs/LJ045-0042.wav|He stated that the other girl rejected him partly because he was an American, a fact that he said she had exploited. He stated that, quote,
+LJSpeech-1.1/wavs/LJ034-0065.wav|led the Commission to attach some probative value to the fingerprint and palmprint identifications in reaching the conclusion
+LJSpeech-1.1/wavs/LJ039-0025.wav|After further questioning
+LJSpeech-1.1/wavs/LJ039-0246.wav|(six) lied to the police after his arrest concerning important substantive matters,
+LJSpeech-1.1/wavs/LJ025-0109.wav|has been as completely invalidated as the third and second, and even the first can be retained only in a modified form
+LJSpeech-1.1/wavs/LJ028-0434.wav|the large high mound, which resembles a mountain from a distance, still bears the ancient name Babel.
+LJSpeech-1.1/wavs/LJ014-0105.wav|He was lying on his face, his legs tied up to his hips so as to allow of the body fitting into the hole.
+LJSpeech-1.1/wavs/LJ028-0073.wav|The walls of the palaces of many of the Assyrian kings were lined with great stone slabs engraved with reliefs and sometimes with the portrait of a king.
+LJSpeech-1.1/wavs/LJ036-0071.wav|In a reconstruction of this bus trip, agents of the Secret Service and the FBI walked the seven blocks from the front entrance of the Depository Building
+LJSpeech-1.1/wavs/LJ009-0056.wav|The last of the four is said to have been a clergyman of the Church of England, condemned for forgery, "a miserable old man in a tattered suit of black.
+LJSpeech-1.1/wavs/LJ001-0112.wav|instead of ending in the sharp and clear stroke of Jenson's letters;
+LJSpeech-1.1/wavs/LJ022-0165.wav|both in its public relations and in its internal relations.
+LJSpeech-1.1/wavs/LJ008-0272.wav|Judges on assize were satisfied with simply recording a sentence of death against offenders whom they did not think deserved the extreme penalty.
+LJSpeech-1.1/wavs/LJ013-0048.wav|The Rev. W. Bailey, LL.D., was convicted at the Central Criminal Court, in February eighteen forty-three, of forgery.
+LJSpeech-1.1/wavs/LJ008-0303.wav|The crowd of men and women who passed the night in front of Newgate, began, as soon as the hour was passed when they had expected the report,
+LJSpeech-1.1/wavs/LJ049-0066.wav|For instance, the lead car always is manned by Secret Service agents familiar with the area and with local law enforcement officials;
+LJSpeech-1.1/wavs/LJ004-0062.wav|and the reason is plain: you have taken him from his home, and have deprived him of the means of providing himself with the necessaries or comforts of life,
+LJSpeech-1.1/wavs/LJ036-0180.wav|Mrs. Roberts testified that on Thursday, November twenty-one, Oswald did not come home.
+LJSpeech-1.1/wavs/LJ046-0124.wav|or with undue persistence.
+LJSpeech-1.1/wavs/LJ016-0361.wav|the moment too that the condemned man had passed through the debtors' door on to the scaffold the prison had done with him,
+LJSpeech-1.1/wavs/LJ016-0348.wav|The same was realized in the first private execution within Newgate,
+LJSpeech-1.1/wavs/LJ031-0003.wav|Parkland Memorial Hospital. The Race to the Hospital
+LJSpeech-1.1/wavs/LJ002-0113.wav|in an age when insolvent acts and bankruptcy courts do so much to relieve the impecunious,
+LJSpeech-1.1/wavs/LJ048-0256.wav|as a result of the investigation he ordered, he was satisfied that each of the agents performed his duties in an entirely satisfactory manner
+LJSpeech-1.1/wavs/LJ010-0259.wav|a deformed lad among the crowd was seen to present a pistol at Her Majesty's carriage,
+LJSpeech-1.1/wavs/LJ005-0134.wav|in twenty-two county jails there were one thousand sixty-three sleeping cells in all (in eighteen twenty-three)
+LJSpeech-1.1/wavs/LJ012-0279.wav|His measures for the disposal of the corpus delicti remind us of those taken by Mrs. Hayes and her associates,
+LJSpeech-1.1/wavs/LJ005-0002.wav|Newgate prisoners were the victims to another most objectionable practice which obtained all over London.
+LJSpeech-1.1/wavs/LJ034-0017.wav|could look southwesterly down Elm Street over the top of the "Rolling Readers" cartons.
+LJSpeech-1.1/wavs/LJ049-0045.wav|It is clear that at the time of the shots in Dallas, Agent Clinton J. Hill leaped to the President's rescue as quickly as humanly possible.
+LJSpeech-1.1/wavs/LJ036-0166.wav|is three to four short blocks south of Lamar and Elm. If Oswald left the bus at twelve:forty-four p.m.
+LJSpeech-1.1/wavs/LJ031-0054.wav|At that point, direction of the President's treatment was undertaken by Dr. Malcolm O. Perry, who arrived at trauma room one a few moments after the President.
+LJSpeech-1.1/wavs/LJ022-0173.wav|for the regulation of transportation by water, for the strengthening of our Merchant Marine and Air Transport,
+LJSpeech-1.1/wavs/LJ030-0195.wav|Observing his blood-covered chest as he was pulled into his wife's lap, Governor Connally believed himself mortally wounded.
+LJSpeech-1.1/wavs/LJ021-0003.wav|Three months have passed since I talked with you shortly after the adjournment of the Congress.
+LJSpeech-1.1/wavs/LJ013-0194.wav|but on the second day the discovery of fresh evidence, more particularly the recovery of some of Lord William's stolen plate,
+LJSpeech-1.1/wavs/LJ044-0187.wav|a person like him (Oswald) in place of aiding the Cuban Revolution, was doing it harm, end quote.
+LJSpeech-1.1/wavs/LJ002-0159.wav|detained for one shilling nine pence, with costs of five shillings.
+LJSpeech-1.1/wavs/LJ043-0069.wav|the evidence indicates that Oswald usually told his prospective employers and employment counselors that he had recently been discharged from the Marine Corps.
+LJSpeech-1.1/wavs/LJ004-0178.wav|The bedclothes consisted only of a single "very slight" rug.
+LJSpeech-1.1/wavs/LJ007-0138.wav|who constantly breathed an air heavy, and, when the wards were first opened in the morning, particularly offensive.
+LJSpeech-1.1/wavs/LJ047-0023.wav|In April nineteen sixty, Mrs. Marguerite Oswald and Robert Oswald were interviewed
+LJSpeech-1.1/wavs/LJ016-0400.wav|Wainwright's demeanor was one of reckless effrontery steadily maintained to the last.
+LJSpeech-1.1/wavs/LJ040-0008.wav|a desire to effect changes in the structure of society or simply to go down in history as a well publicized assassin.
+LJSpeech-1.1/wavs/LJ007-0105.wav|counsel for the two M.P.'s, was also concerned.
+LJSpeech-1.1/wavs/LJ047-0220.wav|that in view of the President's visit to Dallas, that if anyone had any indication of any possibility of any acts of violence or any demonstrations against the President,
+LJSpeech-1.1/wavs/LJ042-0247.wav|In August of nineteen sixty-three, he gave the New Orleans police as a reason for refusing to permit his family to learn English,
+LJSpeech-1.1/wavs/LJ029-0132.wav|From Houston Street, which forms the base of the triangle, three streets -- Commerce, Main, and Elm --
+LJSpeech-1.1/wavs/LJ002-0137.wav|So much inconvenience ensued, that in fifteen eighteen the Corporation obtained from Parliament an act empowering two aldermen
+LJSpeech-1.1/wavs/LJ046-0207.wav|in the training programs of other law enforcement agencies, and agents from other agencies attended the regular Secret Service training schools.
+LJSpeech-1.1/wavs/LJ031-0206.wav|Mrs. Kennedy was guarded by Secret Service agents in quarters assigned to her in the naval hospital.
+LJSpeech-1.1/wavs/LJ047-0151.wav|On the same day Hosty interviewed neighbors on Fifth Street and learned that the address was that of Mrs. Ruth Paine.
+LJSpeech-1.1/wavs/LJ011-0030.wav|was substantially true
+LJSpeech-1.1/wavs/LJ003-0134.wav|One day he was too ill to come down and meet her.
+LJSpeech-1.1/wavs/LJ016-0032.wav|Williams as a capital convict was lodged in the press-yard or condemned ward.
+LJSpeech-1.1/wavs/LJ015-0237.wav|One night Tester whispered to Agar and Pierce, "All right," as they cautiously lounged about London Bridge.
+LJSpeech-1.1/wavs/LJ034-0136.wav|When the three employees appeared before the Commission, Brennan identified the two whom he saw leave the building.
+LJSpeech-1.1/wavs/LJ011-0153.wav|Edward Gibbon Wakefield, the prime mover in the abduction, was a barrister, not exactly briefless, but without a large practice.
+LJSpeech-1.1/wavs/LJ023-0007.wav|I am reminded of that evening in March, four years ago, when I made my first radio report to you.
+LJSpeech-1.1/wavs/LJ014-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section seventeen: Newgate notorieties continued, part two.
+LJSpeech-1.1/wavs/LJ027-0137.wav|"It is an observable fact," says Romanes,
+LJSpeech-1.1/wavs/LJ027-0167.wav|Then the gills gradually dry up, as the lungs develop, and they now breathe wholly by lungs, but still retain the tail.
+LJSpeech-1.1/wavs/LJ009-0190.wav|whom the horrid scene, with the additional effect of some noted criminals hanging on the walls, drove out again sick and faint,
+LJSpeech-1.1/wavs/LJ005-0183.wav|All that was urged was that the borough magistracy had no right to govern their jails
+LJSpeech-1.1/wavs/LJ028-0368.wav|learned of his purpose, and when they were consulted as to the most favorable time for beginning the work upon the new city,
+LJSpeech-1.1/wavs/LJ028-0043.wav|Esarhaddon, the son of Sinacherib, was the next King of Nineveh.
+LJSpeech-1.1/wavs/LJ028-0150.wav|On the top, along the edges of the wall, they constructed buildings of a single chamber facing one another,
+LJSpeech-1.1/wavs/LJ002-0234.wav|In the yard behind the prison
+LJSpeech-1.1/wavs/LJ041-0099.wav|Powers believed that when Oswald arrived in Japan he acquired a girlfriend, quote,
+LJSpeech-1.1/wavs/LJ016-0015.wav|White, when taxed with his attempt, admitted that the accusation was true, and spoke "with pride and satisfaction of the practicability of his scheme."
+LJSpeech-1.1/wavs/LJ045-0085.wav|Why don't you make some money? Poor guy was going out of his mind. We told her she should not annoy him -- poor guy, he is doing his best, "Don't annoy him so much."
+LJSpeech-1.1/wavs/LJ019-0346.wav|They might raise funds for this purpose, provided a certificate for the necessity for the new works was given, either by the recorder
+LJSpeech-1.1/wavs/LJ037-0181.wav|but not with the other eyewitnesses who claim to have heard from two to four shots.
+LJSpeech-1.1/wavs/LJ016-0079.wav|Lears was rewarded by being obliged to wear cross irons on his legs, a punishment rarely inflicted in Newgate,
+LJSpeech-1.1/wavs/LJ049-0053.wav|of emergency procedures to be taken in a contingency such as that which actually occurred, the Secret Service responded, quote,
+LJSpeech-1.1/wavs/LJ020-0030.wav|Sift into it white flour, meal and salt, and stir up well while dry.
+LJSpeech-1.1/wavs/LJ019-0063.wav|who maintained that under this system prisoners were more industrious and more healthy
+LJSpeech-1.1/wavs/LJ014-0115.wav|and they had given information to the police.
+LJSpeech-1.1/wavs/LJ009-0279.wav|One of the turnkeys rushed forward and pushed him off.
+LJSpeech-1.1/wavs/LJ038-0036.wav|As McDonald started to search Oswald's waist for a gun, he heard him say, quote, Well, it's all over now, end quote.
+LJSpeech-1.1/wavs/LJ044-0209.wav|for the three-month period prior to the assassination reflects an extremely critical attitude toward President Kennedy and his administration
+LJSpeech-1.1/wavs/LJ028-0464.wav|The intervening space, which was filled with dirt probably to the upper inner edge of the outer part,
+LJSpeech-1.1/wavs/LJ013-0057.wav|A gigantic conspiracy to defraud was discovered in the following year, when a solicitor named William Henry Barber,
+LJSpeech-1.1/wavs/LJ028-0076.wav|However, several decades ago, an Oriental appeared at the Berlin Museum,
+LJSpeech-1.1/wavs/LJ034-0012.wav|Atop this carton was a small carton marked "Rolling Readers," measuring approximately thirteen by nine by eight inches.
+LJSpeech-1.1/wavs/LJ033-0142.wav|because the cartons stacked around the southeast corner would shield him.
+LJSpeech-1.1/wavs/LJ016-0095.wav|In eighteen fifty-three three men escaped in company from one of the wards in the middle yard.
+LJSpeech-1.1/wavs/LJ013-0162.wav|a bundle lying on the floor, as though thieves had been interrupted in the act.
+LJSpeech-1.1/wavs/LJ017-0264.wav|They were an abject, miserable crew, cowards at heart; but some, especially Lopez, continued bloodthirsty to the last.
+LJSpeech-1.1/wavs/LJ003-0164.wav|or the daring reporter of parliamentary debates.
+LJSpeech-1.1/wavs/LJ033-0088.wav|Frazier met Oswald at the kitchen door and together they walked to the car.
+LJSpeech-1.1/wavs/LJ030-0013.wav|David F. Powers of the President's staff later stated that when the President asked for his assessment of the day's activities, Powers replied
+LJSpeech-1.1/wavs/LJ036-0072.wav|to Murphy and Elm three times, averaging six point five minutes for the three trips.
+LJSpeech-1.1/wavs/LJ039-0074.wav|you should not have any difficulty in hitting your target. I mean it requires no training at all to shoot a weapon with a telescopic sight
+LJSpeech-1.1/wavs/LJ047-0174.wav|During neither interview did Hosty learn Oswald's address
+LJSpeech-1.1/wavs/LJ018-0061.wav|One other curious murder may be added to the two foregoing.
+LJSpeech-1.1/wavs/LJ050-0203.wav|in this crucial area of Presidential protection.
+LJSpeech-1.1/wavs/LJ014-0169.wav|It will be in the memory of many that Mrs. Manning appeared on the scaffold in a black satin dress, which was bound tightly round her waist.
+LJSpeech-1.1/wavs/LJ028-0468.wav|its height was probably more than double its width, but that may never be determined.
+LJSpeech-1.1/wavs/LJ036-0160.wav|He also stated he saw a silver identification bracelet on his passenger's left wrist.
+LJSpeech-1.1/wavs/LJ048-0162.wav|Except for inauguration or other parades involving foreign dignitaries accompanied by the President in Washington,
+LJSpeech-1.1/wavs/LJ028-0323.wav|The Babylonians, seeing a Persian of such exalted rank in so grievous a plight, his nose and ears cut off,
+LJSpeech-1.1/wavs/LJ015-0008.wav|These indeed were out to the value of three hundred sixty-seven thousand, eight hundred pounds.
+LJSpeech-1.1/wavs/LJ029-0194.wav|Any thought that the President might cancel his visit to Dallas was ended
+LJSpeech-1.1/wavs/LJ028-0170.wav|In the middle of the precinct there was a tower of solid masonry, a furlong in length and breadth,
+LJSpeech-1.1/wavs/LJ028-0478.wav|there were drains of large burned bricks, some of which bore the following long inscription:
+LJSpeech-1.1/wavs/LJ013-0003.wav|They not only became more numerous, but they were on a wider scale.
+LJSpeech-1.1/wavs/LJ012-0266.wav|Greenacre, a stout, middle-aged man, wrapped in a brown greatcoat, assumed an air of insolent bravado;
+LJSpeech-1.1/wavs/LJ017-0109.wav|while the muscles of the fingers were tightly clenched, not open, as usual in a corpse.
+LJSpeech-1.1/wavs/LJ012-0249.wav|returned a verdict of willful murder against some person unknown.
+LJSpeech-1.1/wavs/LJ007-0236.wav|Sometimes the stench in hot weather is said to be very bad.
+LJSpeech-1.1/wavs/LJ034-0056.wav|Another expert with the FBI, Ronald G. Wittmus, conducted a separate examination and also agreed with Latona that the prints were Oswald's.
+LJSpeech-1.1/wavs/LJ030-0121.wav|Several times Special Agent John D. Ready came forward from the right front running board of the Presidential follow-up car
+LJSpeech-1.1/wavs/LJ050-0128.wav|Much useful information will come to the attention of local law enforcement agencies in the regular course of their activities,
+LJSpeech-1.1/wavs/LJ048-0249.wav|and then at a breakfast, after which the entourage would proceed to Dallas.
+LJSpeech-1.1/wavs/LJ043-0004.wav|Personal Relations
+LJSpeech-1.1/wavs/LJ001-0103.wav|but the general revival of the old forms has made no way in those countries.
+LJSpeech-1.1/wavs/LJ024-0076.wav|If such a law as I propose is regarded as establishing a new precedent,
+LJSpeech-1.1/wavs/LJ021-0057.wav|have adopted codes of fair competition, which have been approved by the President.
+LJSpeech-1.1/wavs/LJ009-0301.wav|who deserted, and was pursued, but without success.
+LJSpeech-1.1/wavs/LJ019-0071.wav|moreover, it was nearly impossible to prevent communication and mutual contamination.
+LJSpeech-1.1/wavs/LJ020-0027.wav|Half the quantity of sponge given in preceding receipt.
+LJSpeech-1.1/wavs/LJ034-0186.wav|That that could have been the man that I saw in the window in the School Book Depository Building, but that I was not sure, end quote.
+LJSpeech-1.1/wavs/LJ028-0317.wav|Introduced into their assembly, he began to bewail his misfortunes, telling them that
+LJSpeech-1.1/wavs/LJ022-0077.wav|In directing it, I shall recognize six fundamental principles:
+LJSpeech-1.1/wavs/LJ009-0195.wav|of people who had died friendless, would voluntarily surrender them for the advancement of medical science.
+LJSpeech-1.1/wavs/LJ015-0055.wav|Next morning they only just saved the train to town, and left Sir John behind on the platform, but he subsequently surrendered himself.
+LJSpeech-1.1/wavs/LJ028-0444.wav|Deep down in the mound the Germans discovered the palace of Nebuchadnezzar with its hundreds of small chambers and its huge surrounding walls.
+LJSpeech-1.1/wavs/LJ031-0093.wav|Answer: No, sir. Before -- well, in trying to treat an acutely injured patient, you have to establish an airway, adequate ventilation
+LJSpeech-1.1/wavs/LJ006-0277.wav|But there were evils akin to those on the male side, prominent amongst which was the undue influence accorded to prisoners.
+LJSpeech-1.1/wavs/LJ008-0315.wav|"So-and-so, I am sorry to tell you that it is all against you;" or,
+LJSpeech-1.1/wavs/LJ033-0005.wav|was brought into the Depository Building, where it was found on the sixth floor shortly after the assassination.
+LJSpeech-1.1/wavs/LJ041-0202.wav|Oswald's commitment to Marxism contributed to the decisions which led him to defect to the Soviet Union in nineteen fifty-nine, and later
+LJSpeech-1.1/wavs/LJ046-0186.wav|the prosecution of persons who have committed an offense such as threatening the President.
+LJSpeech-1.1/wavs/LJ045-0173.wav|Question: What did you say to that? Answer:
+LJSpeech-1.1/wavs/LJ002-0305.wav|and thus when the debtor's debt had been actually paid, or when he had abandoned his property to the creditors, and, almost destitute,
+LJSpeech-1.1/wavs/LJ042-0063.wav|with few readily available alternatives at hand. He was shocked to find that the Soviet Union did not accept him with open arms.
+LJSpeech-1.1/wavs/LJ031-0129.wav|The agents moved a nurse and patient out of a nearby room, lowered the shades, and took emergency security measures to protect the Vice President.
+LJSpeech-1.1/wavs/LJ042-0016.wav|He said it was in Japan that he made up his mind to go to Russia
+LJSpeech-1.1/wavs/LJ048-0172.wav|At the two places in Dallas where the President would remain for a period of time, Love Field and the Trade Mart,
+LJSpeech-1.1/wavs/LJ010-0056.wav|Later on, the Chartists agitated persistently for the concession embraced in the so-called People's Charter, many of which
+LJSpeech-1.1/wavs/LJ033-0173.wav|The papers I also found were similar in fiber composition, therefore, in addition to the visual characteristics,
+LJSpeech-1.1/wavs/LJ030-0232.wav|Roberts noticed that the Vice President's car was approximately one-half block
+LJSpeech-1.1/wavs/LJ009-0069.wav|The ordinary observes him, looks to the sheriffs, and says aloud, 'The service for the dead!'
+LJSpeech-1.1/wavs/LJ025-0121.wav|but present a close resemblance when those of animals and those of plants are compared together.
+LJSpeech-1.1/wavs/LJ016-0215.wav|The mark was valued at thirteenpence halfpenny, or rather more than the shilling, which from time immemorial had been the hangman's wages.
+LJSpeech-1.1/wavs/LJ015-0292.wav|One named Hardwicke assumed the name of Ralph, and, to obtain commercial credit in Yarmouth, paid in two hundred fifty pounds to a Yarmouth bank
+LJSpeech-1.1/wavs/LJ026-0145.wav|In the animal carbon dioxide, water and nitrogen compounds are the chief excretions.
+LJSpeech-1.1/wavs/LJ022-0015.wav|It is that way with the making of a national policy. The objective of the nation has greatly changed in three years
+LJSpeech-1.1/wavs/LJ016-0059.wav|Here he came upon a woman on the leads hanging out clothes to dry.
+LJSpeech-1.1/wavs/LJ014-0044.wav|He drew up several long statements, containing narratives purely fictitious, imputing crimes to his victim, and repeating his line of defense
+LJSpeech-1.1/wavs/LJ016-0312.wav|Foreign witnesses, too, were in favor of publicity.
+LJSpeech-1.1/wavs/LJ037-0179.wav|was discarded along with the others as Oswald left the scene.
+LJSpeech-1.1/wavs/LJ044-0210.wav|concerning Cuban policy in general as well as on the issues of automation and civil rights, issues which appeared to concern Oswald a great deal.
+LJSpeech-1.1/wavs/LJ014-0231.wav|Emmanuel Barthelemy again,
+LJSpeech-1.1/wavs/LJ012-0155.wav|and kept the Black Lion in Vinegar Yard, Drury Lane, where secretly he did business as one of the most daring and successful fencers ever known in the metropolis.
+LJSpeech-1.1/wavs/LJ040-0196.wav|into which he appeared to be retreating.
+LJSpeech-1.1/wavs/LJ032-0106.wav|end quote. Hidell was a fictitious president of an organization of which Oswald was the only member.
+LJSpeech-1.1/wavs/LJ022-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section seven.
+LJSpeech-1.1/wavs/LJ011-0002.wav|Many stories were in circulation at the time of Fauntleroy's trial with regard to his forgeries. It was said that he had by means of them
+LJSpeech-1.1/wavs/LJ021-0102.wav|If the codes which have been written have been too complicated,
+LJSpeech-1.1/wavs/LJ036-0087.wav|Captain Fritz stated that a deputy sheriff whom he could not identify did ask to see him that afternoon
+LJSpeech-1.1/wavs/LJ047-0048.wav|while Oswald remained somewhat evasive at this interview, he was not antagonistic and seemed generally to be settling down.
+LJSpeech-1.1/wavs/LJ004-0216.wav|The most noticeable of the improvements introduced was a better regulation of dietaries within the prison.
+LJSpeech-1.1/wavs/LJ035-0047.wav|and as I got to this door here I caught a glimpse of this man, just, you know, a sudden glimpse. And it looked to me like he was going away from me.
+LJSpeech-1.1/wavs/LJ014-0127.wav|She was smartly dressed in a plaid shawl, a white lace cap;
+LJSpeech-1.1/wavs/LJ034-0156.wav|whether or not he could positively identify the man he saw in the sixth-floor window as the same man he saw in the police station,
+LJSpeech-1.1/wavs/LJ020-0049.wav|When it is light for the second time flour your board, rubbing in the flour and blowing lightly away what does not adhere to the surface.
+LJSpeech-1.1/wavs/LJ008-0254.wav|It was followed by the deep and solemn booming of the death-bell from the church of St. Sepulchre
+LJSpeech-1.1/wavs/LJ041-0140.wav|and it would have been almost impossible for Oswald to have hit the Governor without hitting the President first.
+LJSpeech-1.1/wavs/LJ012-0062.wav|He passed through and out at the back, and was soon beyond pursuit.
+LJSpeech-1.1/wavs/LJ038-0122.wav|On Sunday morning, November twenty-four, Oswald denied that he knew A. J. Hidell.
+LJSpeech-1.1/wavs/LJ048-0029.wav|As reflected in this testimony,
+LJSpeech-1.1/wavs/LJ030-0033.wav|Vice President and Mrs. Johnson followed along the fence, guarded by four members of the Vice-Presidential detail.
+LJSpeech-1.1/wavs/LJ011-0184.wav|Nothing could release him from his trouble but the arrival of the marriage certificate from Gretna Green.
+LJSpeech-1.1/wavs/LJ046-0178.wav|At the time of the assassination the names of about one hundred persons were in this index, all of whom were included in the group of four hundred
+LJSpeech-1.1/wavs/LJ038-0082.wav|Representatives of other law enforcement agencies were also present, including the FBI and the U.S. Secret Service.
+LJSpeech-1.1/wavs/LJ010-0156.wav|Collins, when charged, pleaded that he had lost his leg in action, that he had petitioned without success for a pension,
+LJSpeech-1.1/wavs/LJ010-0140.wav|Contemporary reports state that from the skillful manner in which he performed the decapitation,
+LJSpeech-1.1/wavs/LJ029-0015.wav|The party itself saw an opportunity to raise funds by having the President speak at a political dinner eventually planned for Austin.
+LJSpeech-1.1/wavs/LJ021-0208.wav|into the service of the privileged few.
+LJSpeech-1.1/wavs/LJ024-0088.wav|I hope that thereby the difficult process of constitutional amendment may be rendered unnecessary. But,
+LJSpeech-1.1/wavs/LJ004-0118.wav|published some six-and-thirty years after the promulgation of this act, the flagrant and persistent violations of it and others
+LJSpeech-1.1/wavs/LJ007-0052.wav|Benjamin Vines and Daniel Ward put in irons for two days for breaking the windows of the day room in the condemned cells.
+LJSpeech-1.1/wavs/LJ006-0281.wav|in some yards
+LJSpeech-1.1/wavs/LJ037-0062.wav|The Commission reviewed the transcript of a phone conversation in which Mrs. Markham is alleged to have provided such a description.
+LJSpeech-1.1/wavs/LJ026-0031.wav|but when the two groups are traced downward they are found gradually to merge,
+LJSpeech-1.1/wavs/LJ039-0063.wav|with the alinement of the assassin's rifle, at a range of one hundred seventy-seven to two hundred sixty-six feet.
+LJSpeech-1.1/wavs/LJ002-0042.wav|and supposed to accommodate twenty persons. The eight remaining wards were for the common side debtors,
+LJSpeech-1.1/wavs/LJ048-0023.wav|and he had told us during one of the interviews that he would probably take his wife back to Soviet Russia some time in the future.
+LJSpeech-1.1/wavs/LJ014-0071.wav|In his confession after sentence he declared that she plied him well with brandy at this period,
+LJSpeech-1.1/wavs/LJ016-0017.wav|John Williams, a young fellow only twenty-three years of age, awaited execution in eighteen twenty-seven for stealing in a dwelling-house.
+LJSpeech-1.1/wavs/LJ006-0113.wav|The authority of these wardsmen so improperly exalted, and so entirely unchecked, degenerated into a baneful despotism.
+LJSpeech-1.1/wavs/LJ021-0149.wav|through the mediation or arbitration of state or federal agencies.
+LJSpeech-1.1/wavs/LJ029-0202.wav|the president of the Dallas Chamber of Commerce referred to the city's reputation for being the friendliest town in America and asserted that citizens would, quote,
+LJSpeech-1.1/wavs/LJ050-0019.wav|This report has already pointed out several respects
+LJSpeech-1.1/wavs/LJ047-0248.wav|that he ever said that Oswald was capable of violence, or that he had any information suggesting this.
+LJSpeech-1.1/wavs/LJ028-0166.wav|The center of each division of the town is occupied by a fortress.
+LJSpeech-1.1/wavs/LJ035-0145.wav|Within a minute after Baker and Truly left Oswald in the lunchroom, Mrs. R. A. Reid, clerical supervisor for the Texas School Book Depository,
+LJSpeech-1.1/wavs/LJ047-0232.wav|In his opinion, none of the information in the FBI files -- Oswald's defection, his Fair Play for Cuba activities in New Orleans,
+LJSpeech-1.1/wavs/LJ031-0147.wav|Secret Service Emergency Security Arrangements
+LJSpeech-1.1/wavs/LJ009-0023.wav|and that from the time of his conviction nothing but what is consolatory should be addressed to a criminal.
+LJSpeech-1.1/wavs/LJ044-0190.wav|by the officials of both Cuba and the Soviet Union in Mexico City.
+LJSpeech-1.1/wavs/LJ007-0048.wav|so subversive of meditation, so disturbing to the thoughts;
+LJSpeech-1.1/wavs/LJ023-0130.wav|was to infuse new blood into all our courts.
+LJSpeech-1.1/wavs/LJ028-0137.wav|Perhaps Herodotus had never been in Babylon;
+LJSpeech-1.1/wavs/LJ021-0184.wav|Great Britain in many ways has advanced further along lines of social security than the United States?
+LJSpeech-1.1/wavs/LJ001-0025.wav|imitates a much freer hand, simpler, rounder, and less spiky, and therefore far pleasanter and easier to read.
+LJSpeech-1.1/wavs/LJ005-0226.wav|and that consequently the observance of their most important provisions was habitually neglected.
+LJSpeech-1.1/wavs/LJ028-0109.wav|In one of them he says:
+LJSpeech-1.1/wavs/LJ040-0130.wav|and interviewed and observed by other members of the Youth House staff.
+LJSpeech-1.1/wavs/LJ035-0131.wav|she would have seen Baker or Truly on the first floor or on the stairs, unless they were already in the second-floor lunchroom talking to Oswald.
+LJSpeech-1.1/wavs/LJ046-0009.wav|when visiting Miami on February fifteen, nineteen thirty-three; and on President Harry S. Truman on November one, nineteen fifty,
+LJSpeech-1.1/wavs/LJ011-0032.wav|declared that they had hitherto formed a high opinion of his honor, integrity, and goodness of disposition,
+LJSpeech-1.1/wavs/LJ016-0045.wav|and at length reached a point opposite the top of the building containing the condemned wards. This had been a perilous and painful task;
+LJSpeech-1.1/wavs/LJ003-0094.wav|Mr. Bennet mentions also the case of young men of better stamp, clerks in city offices, and youths of good parentage,
+LJSpeech-1.1/wavs/LJ039-0083.wav|Using the scope, rapidly working a bolt and using the scope to relocate your target quickly and at the same time when you locate that target
+LJSpeech-1.1/wavs/LJ016-0141.wav|He was at work under the supervision of the artisan warder of the prison,
+LJSpeech-1.1/wavs/LJ041-0145.wav|he had more protection than usual. It would appear, therefore, that to the extent Oswald's undesirable discharge affected his motivation,
+LJSpeech-1.1/wavs/LJ018-0085.wav|He would shoot any man or any policeman like a dog, or any number of them, who had treated him in that way.
+LJSpeech-1.1/wavs/LJ042-0143.wav|during the period from November nineteen, nineteen sixty-two to March of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ039-0065.wav|so that the President would have been moving in an almost straight line away from the assassin's rifle.
+LJSpeech-1.1/wavs/LJ001-0147.wav|The position of the page on the paper should be considered if the book is to have a satisfactory look.
+LJSpeech-1.1/wavs/LJ024-0095.wav|Then would come the long course of ratification by three-fourths of all the states.
+LJSpeech-1.1/wavs/LJ017-0218.wav|The captain was kindly but somewhat intemperate, the first mate a man of some determination,
+LJSpeech-1.1/wavs/LJ043-0114.wav|Oswald told his wife and Mrs. Paine that he was working as a commercial photographer.
+LJSpeech-1.1/wavs/LJ037-0117.wav|Both men picked Oswald as the man who had run south on Patton with a gun in his hand.
+LJSpeech-1.1/wavs/LJ036-0161.wav|Oswald was wearing such a bracelet when he was arrested.
+LJSpeech-1.1/wavs/LJ034-0075.wav|He saw Oswald, a clipboard in hand, walking from the southeast corner of the sixth floor toward the elevator.
+LJSpeech-1.1/wavs/LJ007-0026.wav|and with all this "the most dreadful oaths, the worst language, too bad to be repeated," were made use of every night.
+LJSpeech-1.1/wavs/LJ017-0192.wav|Here, however, the evidence was strong and sufficient.
+LJSpeech-1.1/wavs/LJ003-0007.wav|hundreds of women and children came in every morning to spend the day there, and there was no limitation whatever to the numbers of visitors admitted to the debtors' side.
+LJSpeech-1.1/wavs/LJ033-0201.wav|All I would say here is that it is possible that these fibers could have come from this blanket.
+LJSpeech-1.1/wavs/LJ013-0233.wav|The bed was searched, but no piece of sharpened wood was found. It was thought that it might have been lost in changing the mattresses.
+LJSpeech-1.1/wavs/LJ042-0031.wav|The extent of Oswald's desire to go to the Soviet Union
+LJSpeech-1.1/wavs/LJ018-0110.wav|Much the same plan was adopted by these forgers as by Saward to get their cheques cashed.
+LJSpeech-1.1/wavs/LJ042-0078.wav|I have entered the Soviet Union for the express purpose of applying for citizenship in the Soviet Union, through the means of naturalization.
+LJSpeech-1.1/wavs/LJ003-0068.wav|the man guilty of a common assault found himself side by side with the fraudulent, or others who had attempted abominable crimes.
+LJSpeech-1.1/wavs/LJ037-0088.wav|Similarly, Virginia Davis had not been shown pictures of anyone prior to the lineup and had not seen either television or the newspapers during the afternoon.
+LJSpeech-1.1/wavs/LJ020-0004.wav|than when mixed up with yeast and water only.
+LJSpeech-1.1/wavs/LJ030-0204.wav|Special Agent Hill was scanning the few people standing on the south side of Elm Street after the motorcade had turned off Houston Street.
+LJSpeech-1.1/wavs/LJ004-0145.wav|At Guildford prison, which Mr. Buxton also visited in eighteen eighteen,
+LJSpeech-1.1/wavs/LJ020-0058.wav|Peep under the cloth two or three times to see whether they rise evenly, and turn the pan around once that all may be equally exposed to the heat.
+LJSpeech-1.1/wavs/LJ020-0007.wav|Five tablespoonfuls of yeast. One tablespoonful of white sugar.
+LJSpeech-1.1/wavs/LJ024-0022.wav|It has been discussed and approved by many persons of high authority
+LJSpeech-1.1/wavs/LJ008-0035.wav|were told that the plan had been well considered, and would be persevered in.
+LJSpeech-1.1/wavs/LJ046-0079.wav|The rights of private individuals must not be infringed.
+LJSpeech-1.1/wavs/LJ005-0051.wav|At the time of its greatest prosperity, its annual income from donations and subscriptions was about one thousand six hundred pounds.
+LJSpeech-1.1/wavs/LJ015-0181.wav|He is described by a contemporary account as a fresh-looking man of forty years of age, slightly bald, inclined to embonpoint,
+LJSpeech-1.1/wavs/LJ009-0213.wav|The procession traversed Ratcliffe twice, halting for a quarter of an hour in front of the victims' dwelling,
+LJSpeech-1.1/wavs/LJ032-0158.wav|On November twenty-three, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0002.wav|On the arrival of the police the house was empty.
+LJSpeech-1.1/wavs/LJ002-0322.wav|Share in the weekly allowance of meat might also be denied to debtors who had not paid "garnish," as well as in the weekly grant from the charitable fund.
+LJSpeech-1.1/wavs/LJ015-0147.wav|Leading social and artistic personages were to be met with at his house, and his hospitality was far famed.
+LJSpeech-1.1/wavs/LJ036-0149.wav|The route of the taxicab was retraced under the direction of Whaley.
+LJSpeech-1.1/wavs/LJ008-0288.wav|The consequences were plainly proved to the inspectors.
+LJSpeech-1.1/wavs/LJ002-0007.wav|Neild gives some figures which well illustrate this.
+LJSpeech-1.1/wavs/LJ038-0111.wav|At that point, Oswald refused to answer any further questions.
+LJSpeech-1.1/wavs/LJ015-0046.wav|then came a run on the bank, and it was compelled to stop payment.
+LJSpeech-1.1/wavs/LJ044-0189.wav|In spite of his former residence in the Soviet Union and his Fair Play for Cuba Committee activities he had been rebuffed
+LJSpeech-1.1/wavs/LJ039-0184.wav|Considering the various probabilities which may have prevailed during the actual assassination,
+LJSpeech-1.1/wavs/LJ038-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter four. The Assassin: Part seven.
+LJSpeech-1.1/wavs/LJ042-0083.wav|Oswald stated, quote, I am a Marxist, end quote. He also alluded to hardships endured by his mother as a worker,
+LJSpeech-1.1/wavs/LJ033-0207.wav|In light of the other evidence linking Lee Harvey Oswald, the blanket, and the rifle to the paper bag found on the sixth floor,
+LJSpeech-1.1/wavs/LJ032-0131.wav|He stated that the poor quality of the wood and the metal would cause the rifle to absorb moisture from the skin, thereby making a clear print unlikely.
+LJSpeech-1.1/wavs/LJ036-0027.wav|which he reached at about twelve:forty p.m.
+LJSpeech-1.1/wavs/LJ034-0175.wav|He looked like he was looking down toward the Trinity River and the Triple Underpass down at the end -- toward the end of Elm Street. And
+LJSpeech-1.1/wavs/LJ028-0086.wav|the lips are thin, the chin prominent; the neck is that of a strong vigorous man.
+LJSpeech-1.1/wavs/LJ048-0151.wav|asked the police to station guards at each overpass to keep, quote, unauthorized personnel, end quote, off, this term was not defined.
+LJSpeech-1.1/wavs/LJ042-0127.wav|Finally, the entry of January four to thirty-one of nineteen sixty-one, quote,
+LJSpeech-1.1/wavs/LJ009-0134.wav|This very proper rule was, however, set aside on the Sunday preceding Courvoisier's execution.
+LJSpeech-1.1/wavs/LJ010-0132.wav|As the executioner fastened the noose, he nodded to a friend he saw in the crowd;
+LJSpeech-1.1/wavs/LJ040-0005.wav|There remains the question of what impelled Oswald to conceive and to carry out the assassination of the President of the United States.
+LJSpeech-1.1/wavs/LJ017-0259.wav|Eight were arraigned at the same time: six Spaniards, Leon, Blanco, Duranno, Santos, and Marsolino;
+LJSpeech-1.1/wavs/LJ035-0154.wav|Oswald mumbled something and walked by. She paid no more attention to him.
+LJSpeech-1.1/wavs/LJ014-0042.wav|the first summed up dead against the prisoner, and the latter, after retiring for ten minutes, found him guilty.
+LJSpeech-1.1/wavs/LJ005-0287.wav|The same language was used by the commissioners appointed to inquire into the municipal corporations in eighteen thirty-five,
+LJSpeech-1.1/wavs/LJ041-0120.wav|Governor Connally had just resigned to run for Governor of Texas, so he advised Oswald that he had forwarded the letter to his successor.
+LJSpeech-1.1/wavs/LJ028-0299.wav|"Had I told thee," rejoined the other, "what I was bent on doing, thou wouldst not have suffered it;
+LJSpeech-1.1/wavs/LJ037-0018.wav|Scoggins saw him and heard him mutter either "Poor damn cop" or "Poor dumb cop."
+LJSpeech-1.1/wavs/LJ030-0129.wav|and headed toward the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ026-0039.wav|the earliest organisms were protists and that from them animals and plants were evolved along divergent lines of descent.
+LJSpeech-1.1/wavs/LJ033-0154.wav|and by Arthur Mandella, a fingerprint expert with the New York City Police Department.
+LJSpeech-1.1/wavs/LJ032-0100.wav|Marina Oswald
+LJSpeech-1.1/wavs/LJ016-0189.wav|but that his cough was getting so much the master of him that he feared he would not be able to carry on his duties much longer.
+LJSpeech-1.1/wavs/LJ043-0136.wav|demonstrating once again his propensity to act dramatically and, in this instance violently, in furtherance of his beliefs.
+LJSpeech-1.1/wavs/LJ044-0206.wav|The Castro regime severely attacked President Kennedy in connection with the Bay of Pigs affair, the Cuban missile crisis, the ban on travel to Cuba,
+LJSpeech-1.1/wavs/LJ008-0040.wav|A full description of this new gallows, which was erected in front of the debtors' door, is to be found in contemporary records.
+LJSpeech-1.1/wavs/LJ019-0338.wav|But discipline was to be maintained if necessary by punishment,
+LJSpeech-1.1/wavs/LJ022-0143.wav|After careful consideration, I have asked the Congress to extend the life of this useful agency of government.
+LJSpeech-1.1/wavs/LJ001-0152.wav|The modern printer, in the teeth of the evidence given by his own eyes, considers the single page as the unit, and prints the page in the middle of his paper
+LJSpeech-1.1/wavs/LJ014-0212.wav|a fine young officer who had been summoned to take Cannon into custody when the latter was drunk and riotous in front of a public-house.
+LJSpeech-1.1/wavs/LJ015-0014.wav|Davidson and Gordon were also sentenced to imprisonment.
+LJSpeech-1.1/wavs/LJ044-0122.wav|I should always remain in the background, i.e. underground, end quote.
+LJSpeech-1.1/wavs/LJ009-0300.wav|Traveling expenses of these agents cost fifteen pounds, and another ten pounds were spent in the hire of a Shropshire man,
+LJSpeech-1.1/wavs/LJ037-0070.wav|within minutes of the shooting and before the lineup, Mrs. Markham described the man to the police as five foot eight inches tall.
+LJSpeech-1.1/wavs/LJ008-0149.wav|and after a long interval his dead body was discovered, shockingly disfigured, in a ditch. This was in eighteen oh two.
+LJSpeech-1.1/wavs/LJ046-0181.wav|and who do not have a fixed place of residence. Members of the White House detail of the Secret Service have copies of this album.
+LJSpeech-1.1/wavs/LJ017-0188.wav|Mrs. Atkinson's symptoms had been the same as Dixon's. Then Mrs. Wilson went to live with a man named Taylor,
+LJSpeech-1.1/wavs/LJ026-0109.wav|but no system of complete circulation as in the blood vessels of a higher animal.
+LJSpeech-1.1/wavs/LJ018-0048.wav|and powerful influence was exerted both here and abroad to obtain a reprieve.
+LJSpeech-1.1/wavs/LJ018-0165.wav|On a certain day, the twenty-seventh October, eighteen sixty-two, the two were arrested simultaneously;
+LJSpeech-1.1/wavs/LJ018-0257.wav|He complained that he had at first been the dupe of others, and admitted that he had too readily fallen astray.
+LJSpeech-1.1/wavs/LJ013-0058.wav|Joshua Fletcher a surgeon, and three others were charged with forging wills for the purpose of obtaining unclaimed stock in the funds.
+LJSpeech-1.1/wavs/LJ028-0107.wav|The ancients never tired of describing them.
+LJSpeech-1.1/wavs/LJ037-0047.wav|Mrs. Markham then ran to Officer Tippit's side and saw him lying in a pool of blood.
+LJSpeech-1.1/wavs/LJ030-0117.wav|On the other occasion, the President halted the motorcade to speak to a Catholic nun and a group of small children.
+LJSpeech-1.1/wavs/LJ030-0074.wav|This vehicle, a nineteen fifty-five Cadillac eight-passenger convertible especially outfitted for the Secret Service,
+LJSpeech-1.1/wavs/LJ013-0190.wav|and at last his lordship's watch was found secreted under the leads of the sink.
+LJSpeech-1.1/wavs/LJ034-0131.wav|a person squatting or kneeling exposes more of his body than would normally be the case.
+LJSpeech-1.1/wavs/LJ034-0167.wav|of the sixth floor approximately one minute before the assassination, although neither witness saw the shots being fired.
+LJSpeech-1.1/wavs/LJ002-0152.wav|fourteen men and women who had lain there ten, eleven, and thirteen years for debts of a few shillings,
+LJSpeech-1.1/wavs/LJ025-0092.wav|It is now established that nitrogen is as essential a constituent of vegetable as of animal living matter
+LJSpeech-1.1/wavs/LJ045-0197.wav|On November fifteen, nineteen sixty-three, the same day that his wife told him not to come to Irving, Oswald could have assumed
+LJSpeech-1.1/wavs/LJ005-0247.wav|Mr. Samuel Hoare was examined by this committee
+LJSpeech-1.1/wavs/LJ038-0091.wav|The Commission has, therefore, placed no reliance on the paraffin tests administered by the Dallas police.
+LJSpeech-1.1/wavs/LJ016-0362.wav|and the great outside world shared in the completion of the sacrifice.
+LJSpeech-1.1/wavs/LJ048-0266.wav|and special agents cooperating with them on Presidential and similar protective assignments are considered to be subject to call for official duty
+LJSpeech-1.1/wavs/LJ011-0251.wav|Mullay protested, and then Howard, under the influence of ungovernable rage, as it seemed, jumped up,
+LJSpeech-1.1/wavs/LJ005-0148.wav|In some prisons the prisoners worked seven hours a day, in others ten and ten and a half.
+LJSpeech-1.1/wavs/LJ040-0009.wav|None of these possibilities satisfactorily explains Oswald's act if it is judged by the standards of reasonable men.
+LJSpeech-1.1/wavs/LJ009-0223.wav|but it was actually tried in two provincial towns, Leicester and Durham.
+LJSpeech-1.1/wavs/LJ032-0203.wav|on November twenty-two, nineteen sixty-three. One of these pictures, Exhibit Number one thirty-three A, shows most of the rifle's configuration.
+LJSpeech-1.1/wavs/LJ047-0037.wav|in which he protested his discharge and declared that he would use, quote, all means, end quote, to correct it.
+LJSpeech-1.1/wavs/LJ039-0206.wav|Each series of three shots landed within areas ranging in diameter from three to five inches.
+LJSpeech-1.1/wavs/LJ018-0298.wav|Time was clearly an important factor in the fraud, hence the bills were sent forward in quick succession.
+LJSpeech-1.1/wavs/LJ008-0011.wav|This was partly due to the survival of the old notion that the scene of the crime ought also to witness the retribution;
+LJSpeech-1.1/wavs/LJ039-0201.wav|fired two series of three shots at twenty-five yards in four point six and four point eight seconds.
+LJSpeech-1.1/wavs/LJ018-0250.wav|but at the same time with an air of the most entire truthfulness and candor.
+LJSpeech-1.1/wavs/LJ044-0035.wav|This is suggested by his misleading and sometime untruthful statements in his letters to Mr. V. T. Lee,
+LJSpeech-1.1/wavs/LJ047-0013.wav|Finally, the Commission has reviewed the complete files on Oswald, as they existed at the time of the assassination, of the Department of State,
+LJSpeech-1.1/wavs/LJ048-0254.wav|advised, in the course of the Secret Service investigation of these events, that each agent reported for duty on time,
+LJSpeech-1.1/wavs/LJ017-0221.wav|eight truculent and reckless scoundrels, who, brooding over their fancied wrongs, and burning for revenge,
+LJSpeech-1.1/wavs/LJ046-0025.wav|This planning document has been made a part of the Commission's published record; the underlying staff and consultants' reports reviewed by the Commission have not,
+LJSpeech-1.1/wavs/LJ031-0221.wav|When put together, these fragments accounted for approximately three-quarters of the missing portion of the skull.
+LJSpeech-1.1/wavs/LJ019-0342.wav|So was the employment of prisoners in any position of trust or authority;
+LJSpeech-1.1/wavs/LJ024-0081.wav|But we cannot yield our constitutional destiny to the personal judgment of a few men who, being fearful of the future,
+LJSpeech-1.1/wavs/LJ012-0284.wav|It was not until he left the bus, and walked up by the Regent's Canal, that he conceived the idea of throwing the head into the water.
+LJSpeech-1.1/wavs/LJ008-0191.wav|At Courvoisier's execution in eighteen forty it was the same, or worse.
+LJSpeech-1.1/wavs/LJ031-0174.wav|After the President was pronounced dead,
+LJSpeech-1.1/wavs/LJ048-0110.wav|Since the President was to be at the Trade Mart longer than at any other location in Dallas and in view of the security hazards presented by the building,
+LJSpeech-1.1/wavs/LJ034-0020.wav|The cartons were forwarded to the FBI in Washington.
+LJSpeech-1.1/wavs/LJ001-0157.wav|of this it may be said that though there is some good paper made now,
+LJSpeech-1.1/wavs/LJ025-0041.wav|and carbonic acid containing carbon and oxygen.
+LJSpeech-1.1/wavs/LJ032-0010.wav|(three) the identity of the person present at the window from which the shots were fired,
+LJSpeech-1.1/wavs/LJ011-0175.wav|Wakefield found it necessary to become more explicit regarding her father's affairs.
+LJSpeech-1.1/wavs/LJ003-0013.wav|No attempt was made to check drunkenness, beyond the penalty of shutting out friends from any ward in which a prisoner exceeded.
+LJSpeech-1.1/wavs/LJ050-0202.wav|Lack of adequate resources is an unacceptable excuse for failing to improve advance precautions
+LJSpeech-1.1/wavs/LJ032-0030.wav|It was sent in an envelope bearing the same name and return address in handwriting.
+LJSpeech-1.1/wavs/LJ019-0379.wav|matters went on after the eighteen sixty-five Act much the same as they had done before. Districts differed greatly in the attention they paid to prison affairs.
+LJSpeech-1.1/wavs/LJ049-0048.wav|and therefore approximately one point six seconds after the President was shot in the head.
+LJSpeech-1.1/wavs/LJ008-0158.wav|The pressure soon became so frightful that many would have willingly escaped from the crowd; but their attempts only increased the general confusion.
+LJSpeech-1.1/wavs/LJ002-0074.wav|and tells us that in the state side, quote, such prisoners were safely associated whose manners and conduct evince a more liberal style of education,
+LJSpeech-1.1/wavs/LJ041-0137.wav|In support of her conclusion Mrs. Oswald noted her husband's undesirable discharge
+LJSpeech-1.1/wavs/LJ048-0234.wav|described by some as a beatnik place and by its manager as, quote, a unique showplace with continuous light entertainment all night
+LJSpeech-1.1/wavs/LJ007-0099.wav|were the ten persons committed to Newgate by the House of Commons in eighteen thirty-five.
+LJSpeech-1.1/wavs/LJ018-0174.wav|and the prominent part he played secured for him on conviction the heaviest sentence of the law.
+LJSpeech-1.1/wavs/LJ010-0091.wav|the heads of Lord Castlereagh and Lord Sidmouth being carried away in a bag.
+LJSpeech-1.1/wavs/LJ034-0018.wav|Next to these cartons was the handmade paper bag, previously discussed,
+LJSpeech-1.1/wavs/LJ002-0123.wav|Barely half of these gave bail bonds on arrests, and the remainder went to prison.
+LJSpeech-1.1/wavs/LJ002-0316.wav|notably as when numbers filled Newgate in anticipation of Lord Redesdale's bill for insolvent debtors,
+LJSpeech-1.1/wavs/LJ022-0073.wav|Our problem is to put to work three and one-half million employable persons now on the relief rolls.
+LJSpeech-1.1/wavs/LJ049-0180.wav|The Commission has the impression
+LJSpeech-1.1/wavs/LJ008-0031.wav|when, in pursuance of an order issued by the Recorder to the sheriffs of Middlesex and the keeper of His Majesty's jail, Newgate,
+LJSpeech-1.1/wavs/LJ008-0159.wav|Very soon women began to scream with terror;
+LJSpeech-1.1/wavs/LJ038-0090.wav|was to the effect that the paraffin test was unreliable in determining whether or not a person has fired a rifle or revolver.
+LJSpeech-1.1/wavs/LJ014-0130.wav|Manning, when sentence of death was passed on him, said nothing;
+LJSpeech-1.1/wavs/LJ050-0054.wav|Since the assassination, both the Secret Service and the FBI have recognized
+LJSpeech-1.1/wavs/LJ016-0443.wav|He was promptly rescued from his perilous condition, but not before his face and hands were badly scorched.
+LJSpeech-1.1/wavs/LJ011-0107.wav|On entering Newgate he said,
+LJSpeech-1.1/wavs/LJ011-0126.wav|In conjunction with two others, one of whom was a clerk in the Custom House,
+LJSpeech-1.1/wavs/LJ002-0313.wav|It generally ran to about six pounds per week. The money, which at one time had been distributed quarterly, and all went in drink,
+LJSpeech-1.1/wavs/LJ028-0015.wav|only a tall column of whirling sand, rearing its head until it is lost in the blue above, moves majestically along.
+LJSpeech-1.1/wavs/LJ034-0190.wav|Robert Edwards said that, while looking at the south side of the Depository Building shortly before the motorcade,
+LJSpeech-1.1/wavs/LJ015-0022.wav|he was himself a prominent member of the Low Church, of austere piety, active in all good works.
+LJSpeech-1.1/wavs/LJ012-0260.wav|The murdered individual was thus identified. The next step was to ascertain where and with whom she had last been seen.
+LJSpeech-1.1/wavs/LJ008-0055.wav|for that city claims the priority in establishing the custom of hanging criminals at the jail itself.
+LJSpeech-1.1/wavs/LJ016-0260.wav|has already been quoted. Again, the concourse of people collected in front of Newgate to witness the execution,
+LJSpeech-1.1/wavs/LJ037-0174.wav|but only two of the four discarded cartridge cases found on the lawn at tenth Street and Patton Avenue were of Winchester-Western manufacture.
+LJSpeech-1.1/wavs/LJ018-0097.wav|At length a man was caught in the act of passing a forged cheque at the Union Bank,
+LJSpeech-1.1/wavs/LJ025-0044.wav|Animals, on the contrary, derive their nourishment either directly or indirectly from plants.
+LJSpeech-1.1/wavs/LJ039-0197.wav|The purpose of this experiment was not to test the rifle under conditions which prevailed at the time of the assassination
+LJSpeech-1.1/wavs/LJ025-0139.wav|converge toward the conclusion that the nerve fibers, which have been regarded as ultimate elements of nervous tissue,
+LJSpeech-1.1/wavs/LJ008-0083.wav|Two cart-loads of faggots were piled about her, and after she had hung for half-an-hour the fire was kindled.
+LJSpeech-1.1/wavs/LJ046-0114.wav|the arrangements relied upon by the Secret Service to perform this function were seriously deficient.
+LJSpeech-1.1/wavs/LJ046-0194.wav|Members of the White House detail were expected to familiarize themselves with the descriptions and photographs of the highest risk cases.
+LJSpeech-1.1/wavs/LJ043-0080.wav|with fifty percent of the people taking it who score less than one hundred.
+LJSpeech-1.1/wavs/LJ036-0025.wav|McWatters was sure that he left the checkpoint on time
+LJSpeech-1.1/wavs/LJ028-0174.wav|Other ancient descriptions of the walls have been left us by Ctesias of the fifth century B.C., and by Strabo of the beginning of the Christian era,
+LJSpeech-1.1/wavs/LJ029-0134.wav|almost five hundred feet from Houston Street.
+LJSpeech-1.1/wavs/LJ043-0067.wav|that his employment difficulties were caused by his telling prospective employers that he had last been employed in Minsk.
+LJSpeech-1.1/wavs/LJ007-0127.wav|"It may easily be imagined," say the inspectors, in speaking of the prison generally,
+LJSpeech-1.1/wavs/LJ028-0032.wav|Babylon, even in the days of Nebuchadnezzar, was an old, old city.
+LJSpeech-1.1/wavs/LJ018-0384.wav|and to show his gratitude to the warder who procured this privilege for him, he made him a present.
+LJSpeech-1.1/wavs/LJ016-0404.wav|The man who had made a pious life a cloak for his misdeeds, the once exemplary young man and indefatigable Sunday School teacher,
+LJSpeech-1.1/wavs/LJ004-0042.wav|The association was organized under the most promising auspices.
+LJSpeech-1.1/wavs/LJ010-0166.wav|From this he was promoted to barman, and as such had charge of the business in various public-houses.
+LJSpeech-1.1/wavs/LJ017-0137.wav|In the latter hope he was happily disappointed. But as this case is beyond my subject, I merely mention it as one of the group already referred to.
+LJSpeech-1.1/wavs/LJ017-0150.wav|and whom he left without scruple to join Miss Bankes.
+LJSpeech-1.1/wavs/LJ009-0145.wav|and he seemed to suffer great inward agitation when the ordinary particularly alluded to the crime for the perpetration of which he stood condemned.
+LJSpeech-1.1/wavs/LJ031-0110.wav|by cutting away the edges of the wound on the front of the Governor's chest and suturing the damaged lung and lacerated muscles.
+LJSpeech-1.1/wavs/LJ028-0420.wav|That was the first object taken from Babylon to Europe;
+LJSpeech-1.1/wavs/LJ021-0023.wav|and the great transporting and manufacturing and distributing agencies,
+LJSpeech-1.1/wavs/LJ003-0304.wav|with the penalty of forfeiting the day's allowance of food, an increase of which the committee had recommended.
+LJSpeech-1.1/wavs/LJ017-0121.wav|It came out at once that these had been forged, and the whole affair at once took the ugliest complexion.
+LJSpeech-1.1/wavs/LJ005-0254.wav|upon the rules and discipline therein established with regard to the treatment of unconvicted as well as convicted persons."
+LJSpeech-1.1/wavs/LJ025-0057.wav|such as the Charae, are in constant and regular motion was made out by Bonaventura Corti a century ago;
+LJSpeech-1.1/wavs/LJ050-0115.wav|This is especially necessary with regard to the FBI and CIA,
+LJSpeech-1.1/wavs/LJ007-0126.wav|All the evils of indiscriminate association were strongly accentuated by the crowd collected within its narrow limits.
+LJSpeech-1.1/wavs/LJ003-0258.wav|Hence the frequent cases of drunkenness, of which no notice was taken, unless people grew riotous in their cups
+LJSpeech-1.1/wavs/LJ019-0022.wav|On the other hand, it must be admitted
+LJSpeech-1.1/wavs/LJ028-0396.wav|they were supplied with food by keepers, and gave the king the opportunity of hunting whenever he felt inclined.
+LJSpeech-1.1/wavs/LJ032-0221.wav|it is reasonably certain that Exhibit Number one thirty-three A was taken by the same camera at the same time, as Marina Oswald testified.
+LJSpeech-1.1/wavs/LJ032-0210.wav|to the exclusion of all other rifles of the same general configuration, end quote.
+LJSpeech-1.1/wavs/LJ004-0159.wav|The state of the prison, the desperation of the prisoners, broadly hinted in their conversation and plainly expressed in their conduct,
+LJSpeech-1.1/wavs/LJ028-0091.wav|and most of the records which have come from his time speak chiefly of his deeds of piety.
+LJSpeech-1.1/wavs/LJ002-0173.wav|The lad was proved to be of good character and the son of respectable parents.
+LJSpeech-1.1/wavs/LJ035-0167.wav|the earliest estimates would still have permitted Oswald to leave the building by twelve:thirty-three.
+LJSpeech-1.1/wavs/LJ013-0209.wav|he was annoyed with his man for various small omissions and acts of forgetfulness, and on the night of the murder had taken Courvoisier to task rather sharply.
+LJSpeech-1.1/wavs/LJ012-0090.wav|He went out next morning with the booty when the doors were re-opened, and attracted no attention.
+LJSpeech-1.1/wavs/LJ004-0068.wav|or amidst the noxious effluvia of dirt and corruption.
+LJSpeech-1.1/wavs/LJ010-0276.wav|and he was only convicted of an attempt "to harass, vex, and grieve the sovereign.
+LJSpeech-1.1/wavs/LJ042-0172.wav|must have as its nucleus the traditional ideological best of both systems, and yet be utterly opposed to both systems.
+LJSpeech-1.1/wavs/LJ033-0167.wav|In addition the tape was compared to determine whether the sample tape and the tape on the bag had been taken from the tape dispensing machine at the Depository.
+LJSpeech-1.1/wavs/LJ027-0156.wav|According to the evolutionary interpretation of such facts, we can see a very good reason why the life-history of the individual
+LJSpeech-1.1/wavs/LJ038-0294.wav|He indicated that he wanted more people in the vicinity at the time of the attempt so that his arrival and departure would not attract great attention.
+LJSpeech-1.1/wavs/LJ017-0096.wav|The night he bought it, Cook, who had been taking certain pills under medical advice, not Palmer's, was seized with violent convulsions.
+LJSpeech-1.1/wavs/LJ031-0047.wav|He observed shredded brain tissue and, quote, considerable slow oozing, end quote, from the latter wound,
+LJSpeech-1.1/wavs/LJ044-0154.wav|she wrote in her letter of July nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ038-0170.wav|two nights before the shooting he saw, quote, two men around the house peeking in windows, end quote.
+LJSpeech-1.1/wavs/LJ013-0047.wav|A clergyman nearly a century later followed in the steps of Dr. Dodd, but did not under more humane laws lose his life.
+LJSpeech-1.1/wavs/LJ036-0218.wav|Four bullets hit Tippit and killed him instantly.
+LJSpeech-1.1/wavs/LJ005-0273.wav|or prisoners employed in positions of trust, who should not be permitted to traffic with their fellow-prisoners in any way.
+LJSpeech-1.1/wavs/LJ016-0046.wav|the spikes of the railing penetrated his flesh and made progression slow and difficult.
+LJSpeech-1.1/wavs/LJ027-0108.wav|as well as useless muscles to move the ears and the skin, and also a very much reduced hairy covering over the surface of the body.
+LJSpeech-1.1/wavs/LJ015-0116.wav|When brought to trial he took matters very coolly, and at first pleaded "Not Guilty," but subsequently withdrew the plea.
+LJSpeech-1.1/wavs/LJ045-0109.wav|On Sunday, November seventeen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ002-0336.wav|indeed the place, with its oak floors caulked with pitch, and smoked ceilings,
+LJSpeech-1.1/wavs/LJ008-0108.wav|The ordinary "gravely uttered, 'Come this way, Mr. Smith.'
+LJSpeech-1.1/wavs/LJ024-0107.wav|The first includes those who fundamentally object to social and economic legislation along modern lines.
+LJSpeech-1.1/wavs/LJ037-0132.wav|Neither Reynolds nor Patterson saw the man after he turned off Jefferson at the service station.
+LJSpeech-1.1/wavs/LJ044-0022.wav|one of the Cuban exiles who had been arrested with him on August nine. Bringuier claimed that on August five, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ017-0211.wav|which left London for Singapore on the twenty-eighth July, eighteen sixty-three, with a cargo of wine and other goods.
+LJSpeech-1.1/wavs/LJ042-0118.wav|I begin to feel uneasy inside, its true! End quote.
+LJSpeech-1.1/wavs/LJ001-0119.wav|and the elegance and legibility of the ancient more striking than in the Arabic numerals.
+LJSpeech-1.1/wavs/LJ044-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter seven.
+LJSpeech-1.1/wavs/LJ047-0198.wav|prior to President Kennedy's visit.
+LJSpeech-1.1/wavs/LJ029-0106.wav|The police officials agreed that the route recommended by Sorrels was the proper one and did not express a belief that any other route might be better.
+LJSpeech-1.1/wavs/LJ005-0171.wav|we shall find but few which have not in one or other of these ways removed the grievance of which such just complaint is made.
+LJSpeech-1.1/wavs/LJ046-0029.wav|It is unlikely that measures can be devised to eliminate entirely the multitude of diverse dangers that may arise,
+LJSpeech-1.1/wavs/LJ027-0011.wav|It is, moreover, true that all living forms are but series of modifications and extensions of one single plan of structure.
+LJSpeech-1.1/wavs/LJ022-0066.wav|will not only help to guard the individual in future periods of lay-off against dependence upon relief,
+LJSpeech-1.1/wavs/LJ001-0092.wav|The Chiswick press in eighteen forty-four revived Caslon's founts, printing for Messrs. Longman the Diary of Lady Willoughby.
+LJSpeech-1.1/wavs/LJ011-0068.wav|It was said that strangulation had been prevented by the insertion of a silver tube in his wind-pipe,
+LJSpeech-1.1/wavs/LJ038-0287.wav|Additional corroborative evidence.
+LJSpeech-1.1/wavs/LJ038-0127.wav|but denied that he had received a package in this box addressed to Hidell.
+LJSpeech-1.1/wavs/LJ038-0057.wav|Deputy Sheriff Walthers brought a shotgun into the theatre but laid it on some seats before helping subdue Oswald.
+LJSpeech-1.1/wavs/LJ003-0027.wav|He had a double allowance of bread, deducted, of course, from the already too limited portion of the rest,
+LJSpeech-1.1/wavs/LJ049-0073.wav|who were immediately responsible for the President's safety reacted promptly at the time the shots were fired.
+LJSpeech-1.1/wavs/LJ021-0116.wav|We also question the wisdom of extending code requirements suited to the great industrial centers and to large employers,
+LJSpeech-1.1/wavs/LJ046-0059.wav|Since nineteen forty-five, Roosevelt's successors have ranged the world,
+LJSpeech-1.1/wavs/LJ036-0110.wav|Whaley believes that Oswald's conduct did not aid him in his identification, quote, because I knew he was the right one as soon as I saw him.
+LJSpeech-1.1/wavs/LJ035-0126.wav|claimed that within about one minute following the shots she ran from a window on the south side of the fourth floor,
+LJSpeech-1.1/wavs/LJ024-0021.wav|It seeks to maintain the federal bench in full vigor.
+LJSpeech-1.1/wavs/LJ015-0232.wav|Meanwhile the others completed their preparations with the utmost care.
+LJSpeech-1.1/wavs/LJ001-0080.wav|he seems to have taken the letter of the Elzevirs of the seventeenth century for his model:
+LJSpeech-1.1/wavs/LJ010-0081.wav|One of the conspirators, by name Edwards,
+LJSpeech-1.1/wavs/LJ015-0048.wav|But worse than the bankruptcy was the confession made by the partners in the court.
+LJSpeech-1.1/wavs/LJ005-0176.wav|which possessed the right of trying criminals for various offenses.
+LJSpeech-1.1/wavs/LJ003-0250.wav|the culprit was chained down to the floor by running a chain through his irons which prevented him from climbing to the window of his cell.
+LJSpeech-1.1/wavs/LJ046-0045.wav|it is the President's right and duty to be the active leader of his party, as when he seeks to be reelected or to maintain his party in power.
+LJSpeech-1.1/wavs/LJ032-0044.wav|shows an imprint made by the cash register which recorded the receipt of twenty-one dollars, forty-five cents on March thirteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ038-0205.wav|She thought he was attending a class or was on his own business.
+LJSpeech-1.1/wavs/LJ034-0161.wav|Immediately after the assassination, however,
+LJSpeech-1.1/wavs/LJ038-0177.wav|A note which Oswald left for his wife on the evening of the shooting,
+LJSpeech-1.1/wavs/LJ018-0010.wav|Inside the carriage was a hat, a walking-stick, and a small black leather bag.
+LJSpeech-1.1/wavs/LJ002-0244.wav|but its business was much reduced by the extension of the Courts of Conscience.
+LJSpeech-1.1/wavs/LJ034-0047.wav|were developed by chemical processes.
+LJSpeech-1.1/wavs/LJ022-0149.wav|to safeguard, define and enforce collective bargaining, and, while retaining fair competition,
+LJSpeech-1.1/wavs/LJ016-0158.wav|It was that of a "Long Firm" swindler, by name Johnson,
+LJSpeech-1.1/wavs/LJ042-0237.wav|He apparently concluded that the United States offered, quote,
+LJSpeech-1.1/wavs/LJ015-0129.wav|by inserting say one before five hundred, and thus making it
+LJSpeech-1.1/wavs/LJ045-0199.wav|Whether he thought about assassinating the President over the weekend can never be known, but it is reasonably certain
+LJSpeech-1.1/wavs/LJ001-0156.wav|The paper on which the printing is to be done is a necessary part of our subject:
+LJSpeech-1.1/wavs/LJ049-0207.wav|that it was not within the Commission's responsibility to make specific recommendations as to the long-range organization of the President's protection,
+LJSpeech-1.1/wavs/LJ018-0047.wav|He adhered to this almost to the very last. His case had been warmly espoused by the Society for the Protection of Germans in this country,
+LJSpeech-1.1/wavs/LJ045-0175.wav|She did not speak to him when she joined him there, although she thought that he was still awake.
+LJSpeech-1.1/wavs/LJ048-0207.wav|there were several Secret Service agents in it who shared the responsibility of scanning the windows of nearby buildings.
+LJSpeech-1.1/wavs/LJ004-0112.wav|three. Prisoners guilty of misdemeanors. four. Prisoners charged with misdemeanors. five. Debtors.
+LJSpeech-1.1/wavs/LJ047-0081.wav|On June twenty-four, Oswald applied in New Orleans for a passport, stating that he planned to depart by ship
+LJSpeech-1.1/wavs/LJ010-0010.wav|Street robberies offered fewer inducements to depredators, and evil-doers were compelled to adopt other methods of preying upon their fellows.
+LJSpeech-1.1/wavs/LJ019-0399.wav|and it is now, except during sessions or when the gallows is in requisition, practically and for ever closed.
+LJSpeech-1.1/wavs/LJ009-0265.wav|Even now his sufferings were not at an end,
+LJSpeech-1.1/wavs/LJ011-0224.wav|That Mr. Wakefield was a shrewd critic and close observer of all that went on in the Newgate of those days,
+LJSpeech-1.1/wavs/LJ042-0240.wav|he continued to be interested in that country after he returned to the United States.
+LJSpeech-1.1/wavs/LJ006-0051.wav|and for offenses of the most varying description, by the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ033-0103.wav|It was the first time that Oswald had not walked with Frazier from the parking lot to the building entrance.
+LJSpeech-1.1/wavs/LJ040-0119.wav|When Pic returned home, Mrs. Oswald tried to play down the event but Mrs. Pic took a different view and asked the Oswalds to leave.
+LJSpeech-1.1/wavs/LJ039-0045.wav|This was one day before Oswald left for New Orleans and Marina appeared certain that the Nixon incident, quote,
+LJSpeech-1.1/wavs/LJ002-0013.wav|till, eighteen oh eight, the lowest point was touched of one hundred ninety-seven debtors and one hundred eighty-two felons, or three hundred seventy-nine in all.
+LJSpeech-1.1/wavs/LJ008-0113.wav|and his soul shot out so piercingly through the port-holes of his head, that the first glance of him nearly petrified me
+LJSpeech-1.1/wavs/LJ026-0047.wav|circulation, metabolism, excretion, oxygenation (part of respiration),
+LJSpeech-1.1/wavs/LJ037-0130.wav|Reynolds and Patterson decided to follow him.
+LJSpeech-1.1/wavs/LJ041-0135.wav|In that connection, it should be noted that Marina Oswald testified on September six, nineteen sixty-four, that she thought her husband, quote,
+LJSpeech-1.1/wavs/LJ038-0164.wav|The Attempt on the Life of Maj. Gen. Edwin A. Walker
+LJSpeech-1.1/wavs/LJ027-0121.wav|In numberless cases, such as that of the fore-limbs of serpents, no vestige of a rudiment is present.
+LJSpeech-1.1/wavs/LJ013-0249.wav|half consumed in the ashes of a wood fire.
+LJSpeech-1.1/wavs/LJ045-0195.wav|onto the Stemmons Freeway.
+LJSpeech-1.1/wavs/LJ019-0329.wav|every prison was ordered to keep up an infirmary, and the medical supervision was to be strict and continuous.
+LJSpeech-1.1/wavs/LJ026-0081.wav|Thus the leaf of a green plant in the light is continually absorbing carbon dioxide and giving forth free oxygen.
+LJSpeech-1.1/wavs/LJ005-0249.wav|From the moment of a person's committal he was certain to be plunged deeper and deeper in guilt.
+LJSpeech-1.1/wavs/LJ004-0176.wav|Sick women and their children remained in the ordinary wards, and propagated disease.
+LJSpeech-1.1/wavs/LJ049-0124.wav|In addition, the proposed legislation will insure
+LJSpeech-1.1/wavs/LJ046-0093.wav|and accepts the necessary security precautions which they recommend.
+LJSpeech-1.1/wavs/LJ019-0149.wav|and they spent some fourteen or fifteen hours in total darkness, and almost without check or control.
+LJSpeech-1.1/wavs/LJ046-0140.wav|any communication, quote, that in any way indicates anyone may have possible intention of harming the President, end quote.
+LJSpeech-1.1/wavs/LJ035-0138.wav|Shelley and Lovelady, at a fast walk or trot, turned west into the railroad yards and then to the west side of the Depository Building.
+LJSpeech-1.1/wavs/LJ004-0097.wav|to provide such accommodations in jails as may be necessary to answer this salutary purpose,
+LJSpeech-1.1/wavs/LJ047-0165.wav|At the end of the interview, Marina Oswald came into the room. When he observed that she seemed, quote, quite alarmed, end quote, about the visit,
+LJSpeech-1.1/wavs/LJ050-0071.wav|Both Director Hoover and Belmont expressed to the Commission the great concern of the FBI, which is shared by the Secret Service,
+LJSpeech-1.1/wavs/LJ040-0026.wav|He had very few, if any, close relationships with other people and he appeared to have great difficulty in finding a meaningful place in the world.
+LJSpeech-1.1/wavs/LJ028-0406.wav|The great irrigating dams across the Euphrates are constructed entirely of them.
+LJSpeech-1.1/wavs/LJ002-0081.wav|and beyond it was now a day room for the capital convicts or those awaiting execution.
+LJSpeech-1.1/wavs/LJ019-0304.wav|twenty-two others received between eleven and twenty-five;
+LJSpeech-1.1/wavs/LJ010-0290.wav|The blow crushed the bonnet and bruised the forehead of the Queen, who was happily not otherwise injured.
+LJSpeech-1.1/wavs/LJ009-0024.wav|One of the aldermen, moreover, informed me that the whole court of aldermen were unanimous in their opinion on this subject.
+LJSpeech-1.1/wavs/LJ016-0393.wav|called for a roast duck directly he entered the condemned cell.
+LJSpeech-1.1/wavs/LJ026-0072.wav|The sap travels throughout the whole plant, the main though not the only cause of movement
+LJSpeech-1.1/wavs/LJ045-0023.wav|Oswald's writings and his reading habits indicate that he had an extreme dislike of the right wing, an attitude most clearly reflected by his attempt
+LJSpeech-1.1/wavs/LJ028-0148.wav|Then they set to building, and began by bricking the borders of the moat, after which they proceeded to construct the wall itself,
+LJSpeech-1.1/wavs/LJ005-0164.wav|When the new acts were introduced, numerous prisons under local jurisdiction were exempted from the operation of the law.
+LJSpeech-1.1/wavs/LJ030-0190.wav|As he issued his instructions to Greer and to the lead car, Kellerman heard a flurry of shots within five seconds of the first noise.
+LJSpeech-1.1/wavs/LJ045-0244.wav|Long before the assassination he expressed his hatred for American society and acted in protest against it.
+LJSpeech-1.1/wavs/LJ005-0027.wav|He was altogether against too liberal a diet; he disapproved of industrial occupations in jails, as not calculated to render prisons terrible.
+LJSpeech-1.1/wavs/LJ018-0022.wav|It was also easily established that the hat found in the carriage had been bought at Walker's, a hatter's in Crawford Street,
+LJSpeech-1.1/wavs/LJ003-0253.wav|Clothes or prison uniform would not have served the purpose, for a disguise can be rapidly and secretly put on,
+LJSpeech-1.1/wavs/LJ004-0058.wav|The untried, and in the eyes of the law still innocent, could claim pure air, wholesome and sufficient food, and opportunities for exercise.
+LJSpeech-1.1/wavs/LJ044-0111.wav|for anybody who is concerned about developments in Cuba, end quote,
+LJSpeech-1.1/wavs/LJ015-0205.wav|who had been concerned in various "jobs" of a dishonest character, and who for the moment was a clerk in a betting office.
+LJSpeech-1.1/wavs/LJ030-0095.wav|this vehicle was occupied by three Secret Service agents and Clifton C. Garter, assistant to the Vice President.
+LJSpeech-1.1/wavs/LJ016-0157.wav|One curious instance of a suicide carried out under the most adverse and extraordinary circumstances may be quoted.
+LJSpeech-1.1/wavs/LJ042-0192.wav|are preferred rather than loud and useless manifestations of protest, end quote, Oswald went on to note, quote,
+LJSpeech-1.1/wavs/LJ031-0148.wav|Immediately after President Kennedy's stretcher was wheeled into trauma room one, Secret Service agents took positions at the door of the small emergency room.
+LJSpeech-1.1/wavs/LJ007-0232.wav|The plan adopted for ventilating the dining-room on the 'master's side' and that of the middle yard is very inefficient;
+LJSpeech-1.1/wavs/LJ026-0020.wav|If chlorophyll is present, the carbon dioxide of the air serves as a source of carbon,
+LJSpeech-1.1/wavs/LJ043-0040.wav|by so-called exploitation of his mother by the capitalist system.
+LJSpeech-1.1/wavs/LJ042-0126.wav|He is a no-nonsense party regular.
+LJSpeech-1.1/wavs/LJ045-0196.wav|That fact was made precisely clear in subsequent news stories on November nineteen, twenty, and twenty-two.
+LJSpeech-1.1/wavs/LJ049-0017.wav|The limousine used by President Kennedy in Dallas was a convertible with a detachable, rigid plastic "bubble" top
+LJSpeech-1.1/wavs/LJ043-0033.wav|Bouhe thoroughly disapproved of this and as a result almost all communication between the Oswalds and members of the Russian community ceased.
+LJSpeech-1.1/wavs/LJ009-0220.wav|the bodies of all prisoners convicted of murder should either be hung in chains, or buried under the gallows on which they had been executed,
+LJSpeech-1.1/wavs/LJ016-0397.wav|The craving for tobacco which so dominates the habitual smoker often leads the convicted to plead hard for a last smoke.
+LJSpeech-1.1/wavs/LJ046-0090.wav|Any travel, any contact with the general public, involves a calculated risk on the part of the President and the men responsible for his protection.
+LJSpeech-1.1/wavs/LJ002-0111.wav|Of these the debtors, male and female, formed a large proportion.
+LJSpeech-1.1/wavs/LJ033-0168.wav|When asked to explain the similarity of characteristics, Cadigan stated, quote, well briefly
+LJSpeech-1.1/wavs/LJ005-0034.wav|Undeterred by these sarcasms and misrepresentations,
+LJSpeech-1.1/wavs/LJ012-0078.wav|One of the largest robberies of its class was that effected upon the Custom House in the winter of eighteen thirty-four.
+LJSpeech-1.1/wavs/LJ048-0018.wav|We again exhibited interest on the basis of these contacts with The Worker, Fair Play for Cuba Committee, which are relatively inconsequential.
+LJSpeech-1.1/wavs/LJ010-0183.wav|Meanwhile the pistol-shots had attracted the attention of the bystanders, of whom there was a fair collection, as usual, waiting to see the Queen pass.
+LJSpeech-1.1/wavs/LJ016-0305.wav|Already the urgent necessity for abolishing public executions had been brought before the House of Commons by Mr. Hibbert,
+LJSpeech-1.1/wavs/LJ013-0237.wav|Daniel Good's crime might have remained long undiscovered but for his own careless stupidity.
+LJSpeech-1.1/wavs/LJ020-0075.wav|Into a second hollow pour the yeast and knead thoroughly for fifteen minutes.
+LJSpeech-1.1/wavs/LJ007-0218.wav|Newgate is only less extensively injurious than formerly because it is less crowded.
+LJSpeech-1.1/wavs/LJ034-0096.wav|on the southwest corner of Elm and Houston Streets, looking north at the Depository Building which was directly in front of him.
+LJSpeech-1.1/wavs/LJ009-0229.wav|Later on, after dark, some friends of the deceased stole the body and buried it in the sand, and this was the end of hanging in chains.
+LJSpeech-1.1/wavs/LJ015-0262.wav|came out at the Old Bailey, and was acknowledged to be one of the most extraordinary on record.
+LJSpeech-1.1/wavs/LJ041-0101.wav|aggressive and even somewhat pugnacious, although Powers, quote, wouldn't say that this guy is a troublemaker, end quote.
+LJSpeech-1.1/wavs/LJ030-0114.wav|At the President's direction, his automobile stopped twice, the first time to permit him to respond to a sign asking him to shake hands.
+LJSpeech-1.1/wavs/LJ037-0133.wav|These four witnesses were interviewed by FBI agents two months after the shooting.
+LJSpeech-1.1/wavs/LJ020-0108.wav|Other things besides rising dough get on quite as well without your standing by to watch them.
+LJSpeech-1.1/wavs/LJ043-0097.wav|In February or March of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ029-0073.wav|that the President would arrive and depart from Dallas' Love Field; that a motorcade through the downtown area of Dallas to the luncheon site should be arranged;
+LJSpeech-1.1/wavs/LJ049-0204.wav|While in accordance with its mandate
+LJSpeech-1.1/wavs/LJ039-0171.wav|On the second series they required five point one five, six point four five, and seven seconds.
+LJSpeech-1.1/wavs/LJ045-0214.wav|ended when Marina Oswald hung up and refused to talk to him. Although he may long before have decided on the course he was to follow
+LJSpeech-1.1/wavs/LJ043-0042.wav|He first told his wife that he did not have a mother, but later admitted that he did but that, quote, he didn't love her very much, end quote.
+LJSpeech-1.1/wavs/LJ001-0144.wav|the words may be set much closer together, without loss of clearness.
+LJSpeech-1.1/wavs/LJ007-0221.wav|The reports as the years flow on reiterate the same complaints.
+LJSpeech-1.1/wavs/LJ049-0010.wav|who responded to the unplanned event with dispatch.
+LJSpeech-1.1/wavs/LJ040-0194.wav|He had refused to salute the flag and was doing very little, if any, work.
+LJSpeech-1.1/wavs/LJ004-0012.wav|was in several places merely temporary:
+LJSpeech-1.1/wavs/LJ016-0021.wav|Although his execution was imminent, a surgeon attended to his wounds, and he was carried more dead than alive to the scaffold.
+LJSpeech-1.1/wavs/LJ036-0116.wav|Lee Oswald was Number three;
+LJSpeech-1.1/wavs/LJ005-0056.wav|It is interesting to observe that the plan of "radiation," by which the prison blocks radiated from a central hall, like spokes in a wheel
+LJSpeech-1.1/wavs/LJ018-0277.wav|The last great case of fraud upon the Bank of England will fitly close this branch of the criminal records of Newgate.
+LJSpeech-1.1/wavs/LJ043-0127.wav|and that he, quote, may be a damn Communist. I can't tell you. If I was you, I wouldn't hire him, end quote.
+LJSpeech-1.1/wavs/LJ008-0103.wav|who had just arrived from the purlieus of Black Boy Alley, woebegone as Romeo's apothecary, exclaiming,
+LJSpeech-1.1/wavs/LJ011-0271.wav|Mr. Gee went in the coach sent for him, and alighted at twenty-seven, York Street, West, Commercial Road.
+LJSpeech-1.1/wavs/LJ035-0055.wav|Along a side wall of the lunchroom was a soft drink rending machine, but at that time the man had nothing in his hands.
+LJSpeech-1.1/wavs/LJ023-0009.wav|Soon after, with the authority of the Congress,
+LJSpeech-1.1/wavs/LJ009-0230.wav|After this a law was passed which prescribed that the bodies of all executed murderers should be buried within the walls of the jail.
+LJSpeech-1.1/wavs/LJ028-0030.wav|Your journey is at an end. Before you is Babylon, the "Gate of God," as the old name means.
+LJSpeech-1.1/wavs/LJ050-0072.wav|that referrals to the Secret Service under the new criteria might, if not properly handled,
+LJSpeech-1.1/wavs/LJ017-0272.wav|"Ah!" he remarked, "they will have to wait for us then till eight."
+LJSpeech-1.1/wavs/LJ005-0025.wav|"A poor man who is lucky enough," he said, "to have his son committed for a felony
+LJSpeech-1.1/wavs/LJ032-0259.wav|On several occasions, Michael Paine moved the blanket in the garage.
+LJSpeech-1.1/wavs/LJ011-0025.wav|and it was openly stated in the press and in conversation that the proceeds of these frauds had been squandered in chambering, gambling, and debauchery.
+LJSpeech-1.1/wavs/LJ031-0217.wav|(external occipital protuberance) which juts out at the center of the lower part of the back of the skull.
+LJSpeech-1.1/wavs/LJ037-0031.wav|He found two empty shells in the bushes and gave them to Patrolman J. M. Poe who arrived on the scene shortly after the shooting.
+LJSpeech-1.1/wavs/LJ022-0059.wav|it is obvious that we cannot continue to create governmental deficits for that purpose year after year.
+LJSpeech-1.1/wavs/LJ042-0035.wav|and communist's would like to see the present capitalist government of the U.S. overthrown, end quote, Oswald stated
+LJSpeech-1.1/wavs/LJ023-0066.wav|and provide for the common defense and general welfare of the United States.
+LJSpeech-1.1/wavs/LJ044-0064.wav|In addition, there is no evidence that he received any substantial amount of materials from the national headquarters.
+LJSpeech-1.1/wavs/LJ029-0150.wav|Police were assigned to each overpass on the route and instructed to keep them clear of unauthorized persons.
+LJSpeech-1.1/wavs/LJ015-0169.wav|It was at once decided at the board to make a thorough examination of all his books.
+LJSpeech-1.1/wavs/LJ013-0032.wav|Mate, carpenter, and crew were eager to disavow complicity, and voluntarily gave information.
+LJSpeech-1.1/wavs/LJ019-0223.wav|misdemeanants have found their way into Newgate, and within the last few years one offender against the privileges of the House of Commons.
+LJSpeech-1.1/wavs/LJ006-0194.wav|Gaming of all kinds, although forbidden by the Jail Acts, was habitually practiced.
+LJSpeech-1.1/wavs/LJ003-0338.wav|End quote. it would cover some thirty acres, and cost a great deal more than the city, with the example of Whitecross Street prison before it,
+LJSpeech-1.1/wavs/LJ032-0107.wav|When seeking employment in New Orleans,
+LJSpeech-1.1/wavs/LJ028-0022.wav|Day after day you travel on, scorched by the heat of noon-day, shivering in the chill winds of the night.
+LJSpeech-1.1/wavs/LJ031-0042.wav|made no voluntary movements; had his eyes open with the pupils dilated without any reaction to light,
+LJSpeech-1.1/wavs/LJ006-0059.wav|the felon and the misdemeanant, the sane and the insane, the old and young offender.
+LJSpeech-1.1/wavs/LJ037-0191.wav|Sometime after January twenty-seven, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0237.wav|A very painful scene occurred in Newgate when the news of his escape from death was imparted to May.
+LJSpeech-1.1/wavs/LJ002-0161.wav|These surely, I thought," says Mr. Neild, "were bad enough! But it was not so. End quote.
+LJSpeech-1.1/wavs/LJ002-0030.wav|Full details of the arrangements are to be found in Mr. Neild's "State of Prisons in England, Scotland, and Wales," published in eighteen twelve.
+LJSpeech-1.1/wavs/LJ023-0100.wav|that the majority were actually reading into the Constitution their own "personal economic predilections," and that
+LJSpeech-1.1/wavs/LJ047-0127.wav|On August twenty-one, nineteen sixty-three, Bureau headquarters instructed the New Orleans and Dallas field offices
+LJSpeech-1.1/wavs/LJ008-0219.wav|their conversation was of companions and associates of former years, long ago imprisoned, transported, hanged, while they,
+LJSpeech-1.1/wavs/LJ014-0336.wav|Last of all, the well-known bankers Overend and Gurney, whose own affairs created much excitement some years later,
+LJSpeech-1.1/wavs/LJ028-0025.wav|You have crossed the Arabian Desert, the first stage of the long journey to the walls of Babylon.
+LJSpeech-1.1/wavs/LJ009-0189.wav|Persons were still living in eighteen fifty-five who had witnessed dissections at Hicks' Hall, and
+LJSpeech-1.1/wavs/LJ005-0091.wav|Last, but not least, the use of irons was strictly forbidden, "except in cases of urgent and absolute necessity,"
+LJSpeech-1.1/wavs/LJ001-0058.wav|and are worthy representatives of the eager enthusiasm for the revived learning of that epoch. By far,
+LJSpeech-1.1/wavs/LJ001-0172.wav|The ornamentation of printed books is too wide a subject to be dealt with fully here; but one thing must be said on it.
+LJSpeech-1.1/wavs/LJ043-0093.wav|He also had difficulty in working with the other employees.
+LJSpeech-1.1/wavs/LJ014-0135.wav|It was against common sense to charge her with murdering the only friend she had in the world;
+LJSpeech-1.1/wavs/LJ044-0108.wav|Arnold Johnson, director of the information and lecture bureau of the Communist Party, U.S.A., replied stating, quote,
+LJSpeech-1.1/wavs/LJ030-0214.wav|At the instant that Hill stepped onto the left rear step of the President's automobile and grasped the handhold,
+LJSpeech-1.1/wavs/LJ039-0024.wav|She stated that it was not physical force which kept him from leaving the house. Quote, I couldn't keep him from going out if he really wanted to, end quote.
+LJSpeech-1.1/wavs/LJ007-0199.wav|baths, fumigating places for clothing, wash-house, and the removal of dust-bins, completed the new arrangements in the main prison.
+LJSpeech-1.1/wavs/LJ017-0282.wav|the seemingly most courageous was selected to lead the way.
+LJSpeech-1.1/wavs/LJ042-0140.wav|She said that, quote, immediately after coming to the United States, Lee changed. I did not know him as such a man in Russia, end quote.
+LJSpeech-1.1/wavs/LJ008-0070.wav|The sentences inflicted in front of Newgate were not limited to hanging.
+LJSpeech-1.1/wavs/LJ040-0116.wav|who was becoming quite a disciplinary problem for his mother, having struck her on at least one occasion.
+LJSpeech-1.1/wavs/LJ026-0026.wav|As in the liquefaction of gases, there is a "critical point" at which the substance under experiment is neither gaseous nor liquid.
+LJSpeech-1.1/wavs/LJ036-0052.wav|he looked so bad in his face, and his face was so distorted. Hole in his sleeve right here.
+LJSpeech-1.1/wavs/LJ016-0227.wav|whose business it was to provide the rope and do the pinioning, and who was paid a fee of five shillings.
+LJSpeech-1.1/wavs/LJ047-0082.wav|for an extended tour of Western European countries, the Soviet Union, Finland, and Poland.
+LJSpeech-1.1/wavs/LJ015-0067.wav|one at least of which, 'Love and Loyalty,' with Wallack in a leading part, achieved a certain success.
+LJSpeech-1.1/wavs/LJ010-0023.wav|and lodged men like Cole, Robson, and Redpath in Newgate.
+LJSpeech-1.1/wavs/LJ015-0015.wav|A more distressing case stands next on the criminal records --
+LJSpeech-1.1/wavs/LJ007-0228.wav|In their tenth report
+LJSpeech-1.1/wavs/LJ016-0275.wav|As much as twenty-five pounds was paid for a first-floor front on this occasion.
+LJSpeech-1.1/wavs/LJ004-0127.wav|In one hundred jails,
+LJSpeech-1.1/wavs/LJ003-0032.wav|So valuable were they deemed, that as much as fifty guineas was offered to the keeper for the post.
+LJSpeech-1.1/wavs/LJ013-0007.wav|A not entirely novel kind of fraud, but one carried out on a larger scale than heretofore,
+LJSpeech-1.1/wavs/LJ046-0017.wav|rather, it has devoted its time and resources to those broader aspects of Presidential protection
+LJSpeech-1.1/wavs/LJ009-0082.wav|except the clergyman and the burglar, the former of whom is still rolled up at the bottom of the condemned pew,
+LJSpeech-1.1/wavs/LJ047-0088.wav|We did not request the State Department to include Oswald on a list which would have resulted in advising us of any application for a passport
+LJSpeech-1.1/wavs/LJ018-0225.wav|but after the funeral William Roupell produced another and a later will,
+LJSpeech-1.1/wavs/LJ027-0100.wav|are the thousands of cases of rudimentary or vestigial structures to be found.
+LJSpeech-1.1/wavs/LJ012-0223.wav|These facts were represented before the police magistrate
+LJSpeech-1.1/wavs/LJ010-0283.wav|and walking for choice through prickly gorse bushes.
+LJSpeech-1.1/wavs/LJ035-0051.wav|He saw a man walking away from him in the lunchroom.
+LJSpeech-1.1/wavs/LJ046-0212.wav|Threats against the President of the U.S.,
+LJSpeech-1.1/wavs/LJ017-0020.wav|would probably have had the benefit in these days of the doubts raised at his trial.
+LJSpeech-1.1/wavs/LJ019-0305.wav|fourteen received less than eleven and more than six;
+LJSpeech-1.1/wavs/LJ006-0178.wav|and for years, felons who should have been sent beyond the seas.
+LJSpeech-1.1/wavs/LJ023-0123.wav|If these problems cannot be effectively solved within the Constitution,
+LJSpeech-1.1/wavs/LJ028-0219.wav|All the people of Babylon prostrated themselves before him, and, kissing his feet, rejoiced in his sovereignty, while happiness shone on their faces.
+LJSpeech-1.1/wavs/LJ017-0093.wav|and one of the many links in the long chain of evidence was an entry in a book of Palmer's to the effect that Quote.
+LJSpeech-1.1/wavs/LJ043-0157.wav|Answer: Yes. Question: And you said it was not a good idea to keep this book? Answer: Yes.
+LJSpeech-1.1/wavs/LJ016-0075.wav|It was for some time after this a constant practice to go up the chimneys in the hopes of escaping by the flue.
+LJSpeech-1.1/wavs/LJ042-0122.wav|Compulsory attendance at lectures and the sending of the entire shop collective (except me) to pick potatoes on a Sunday, at a state collective farm
+LJSpeech-1.1/wavs/LJ032-0155.wav|Fibers on Rifle
+LJSpeech-1.1/wavs/LJ004-0149.wav|The diet was limited to dry bread, which was of the best certainly, and a pound and a half in weight.
+LJSpeech-1.1/wavs/LJ028-0400.wav|The walls of Babylon were transformed into the sacred cities of Kerbela and Nejef.
+LJSpeech-1.1/wavs/LJ010-0034.wav|Thurtell and Hunt followed in the footsteps of Billings, Wood, and Catherine Hayes.
+LJSpeech-1.1/wavs/LJ037-0013.wav|About one hundred feet from the corner the police car pulled up alongside a man on the sidewalk. This man, dressed in a light-colored jacket, approached the car.
+LJSpeech-1.1/wavs/LJ049-0190.wav|It has been pointed out that the FBI, as our chief investigative agency,
+LJSpeech-1.1/wavs/LJ016-0064.wav|when he explained that he was running away from the gallows they left him the road clear.
+LJSpeech-1.1/wavs/LJ047-0199.wav|The Secret Service and the FBI differ
+LJSpeech-1.1/wavs/LJ021-0200.wav|we have avoided on the one hand the theory that business should and must be taken over into an all-embracing government.
+LJSpeech-1.1/wavs/LJ025-0023.wav|reduces the necessity of the existence in them of an alimentary cavity, or reservoir of food,
+LJSpeech-1.1/wavs/LJ017-0127.wav|But the chain of circumstantial evidence was strong enough to satisfy the jury, who agreed to their verdict in an hour.
+LJSpeech-1.1/wavs/LJ006-0265.wav|The untried might see their friends three times a week, the convicted only once.
+LJSpeech-1.1/wavs/LJ013-0063.wav|The other, remaining unclaimed for ten years, was transferred at the end of that time to the commissioners for the reduction of the National Debt.
+LJSpeech-1.1/wavs/LJ032-0236.wav|By checking the actual mailing dates of these issues and the time it usually takes to effect delivery to Dallas,
+LJSpeech-1.1/wavs/LJ014-0100.wav|In the back kitchen one of the detectives remarked that the cement between certain stones looked lighter than the rest, and on trying it with a knife,
+LJSpeech-1.1/wavs/LJ001-0161.wav|There seems to be no reason why ordinary paper should not be better made,
+LJSpeech-1.1/wavs/LJ006-0103.wav|The allowance of food was not illiberal,
+LJSpeech-1.1/wavs/LJ012-0023.wav|He was taken up when still in his teens for stealing a pocketbook, and was sentenced to transportation, but did not get beyond the hulks at Chatham.
+LJSpeech-1.1/wavs/LJ044-0030.wav|five of which were usually present at meetings which were held once a month.
+LJSpeech-1.1/wavs/LJ008-0038.wav|After hanging the usual time they were taken down, and the machine cleared away in half-an-hour.
+LJSpeech-1.1/wavs/LJ021-0043.wav|most of whom recognize the past evils in the banking system, in the sale of securities, in the deliberate encouragement of stock gambling,
+LJSpeech-1.1/wavs/LJ035-0037.wav|This door leads into a small vestibule, and another door leads from the vestibule into the second-floor lunchroom.
+LJSpeech-1.1/wavs/LJ009-0123.wav|Could any one, knowing the late situation of the kneeling men, looking as they do at the empty pew,
+LJSpeech-1.1/wavs/LJ019-0019.wav|to make a great archway, and to make the character of it more imposing than if it had been situated in the country, and had been an ordinary prison.
+LJSpeech-1.1/wavs/LJ049-0203.wav|So circumscribed, it could not maintain the esprit de corps or the necessary alertness for this unique and challenging responsibility.
+LJSpeech-1.1/wavs/LJ032-0092.wav|There is no post office box three zero zero one six in the New Orleans Post Office
+LJSpeech-1.1/wavs/LJ038-0063.wav|but that once he was subdued, no officer struck him.
+LJSpeech-1.1/wavs/LJ023-0129.wav|the only method which was clearly constitutional, and would at the same time carry out other much needed reforms,
+LJSpeech-1.1/wavs/LJ033-0011.wav|fiber, and paper analyses linking Oswald and the assassination weapon to this bag.
+LJSpeech-1.1/wavs/LJ038-0188.wav|two. Send the information as to what has happened to me to the Embassy.
+LJSpeech-1.1/wavs/LJ027-0128.wav|and even in that case should have maintained his ideal very inefficiently, inasmuch as only two limbs, instead of four, are represented?
+LJSpeech-1.1/wavs/LJ010-0244.wav|leveled a pistol at the Queen, and fired. He was so close to the carriage that the smoke of his pistol enveloped the face of Colonel Wylde,
+LJSpeech-1.1/wavs/LJ029-0172.wav|The two Dallas newspapers provided their readers with a steady stream of information and speculation about the trip,
+LJSpeech-1.1/wavs/LJ017-0088.wav|Quantities of antimony were found in the body after death.
+LJSpeech-1.1/wavs/LJ037-0172.wav|even though only four bullets were recovered.
+LJSpeech-1.1/wavs/LJ047-0196.wav|The Commission has considered carefully the question whether the FBI,
+LJSpeech-1.1/wavs/LJ047-0195.wav|Nonreferral of Oswald to the Secret Service.
+LJSpeech-1.1/wavs/LJ009-0151.wav|The atrocity of the murder no doubt attracted extraordinary attention to it.
+LJSpeech-1.1/wavs/LJ018-0168.wav|but proofs of Griffiths' guilt were at once apparent on entering his work-room.
+LJSpeech-1.1/wavs/LJ043-0015.wav|He also expressed considerable resentment at the help given to his wife by her Russian-American friends.
+LJSpeech-1.1/wavs/LJ049-0104.wav|Such a statute would cover the President and Vice President or, in the absence of a Vice President, the person next in order of succession.
+LJSpeech-1.1/wavs/LJ017-0147.wav|the protracted dissimulation, as with unshrinking, relentless wickedness the deadly work is carried on to the end.
+LJSpeech-1.1/wavs/LJ019-0024.wav|Nor must it be overlooked that this, the first model prison, although obtained at a considerable cost, became actually what its name implied.
+LJSpeech-1.1/wavs/LJ007-0093.wav|The matter was still further complicated at Newgate by the presence within the walls of sham lunatics. Some of those included in the category
+LJSpeech-1.1/wavs/LJ010-0080.wav|The Government obtained early and full information of the nefarious scheme.
+LJSpeech-1.1/wavs/LJ026-0119.wav|They may be stored, especially as fat, until needed for oxidation to supply energy.
+LJSpeech-1.1/wavs/LJ035-0188.wav|This was no earlier than twelve:thirty-seven p.m. and may have been later.
+LJSpeech-1.1/wavs/LJ037-0129.wav|When the man reached Jefferson, he turned right and headed west.
+LJSpeech-1.1/wavs/LJ006-0141.wav|The female wards-women were also given to intemperance.
+LJSpeech-1.1/wavs/LJ027-0012.wav|All have the same ultimate substance
+LJSpeech-1.1/wavs/LJ009-0264.wav|His face was visible to the whole crowd, and was fearful to behold.
+LJSpeech-1.1/wavs/LJ018-0091.wav|Wagner and Bateman, who had already been convicted of systematic forgery, and sentenced to transportation, but they had been released on ticket-of-leave
+LJSpeech-1.1/wavs/LJ016-0071.wav|Dissatisfied with this remuneration, he again took to the road, and tramped into Hampshire,
+LJSpeech-1.1/wavs/LJ012-0258.wav|a Mr. Gay of Goodge Street came to view the head, and immediately recognized it as that of a widowed sister, Hannah Brown,
+LJSpeech-1.1/wavs/LJ011-0016.wav|His countenance had an expression of most subdued resignation.
+LJSpeech-1.1/wavs/LJ001-0118.wav|In no characters is the contrast between the ugly and vulgar illegibility of the modern type
+LJSpeech-1.1/wavs/LJ046-0188.wav|In summary, at the time of the assassination
+LJSpeech-1.1/wavs/LJ017-0258.wav|Their trial followed at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ002-0263.wav|the Lord Steward of the Household, the steward and officers of the Marshalsea Court, and others.
+LJSpeech-1.1/wavs/LJ048-0120.wav|has been that provided by a requirement that he file interim and final reports on each advance assignment.
+LJSpeech-1.1/wavs/LJ048-0080.wav|to coordinate activities and to discuss problems of mutual interest.
+LJSpeech-1.1/wavs/LJ045-0067.wav|did not want her to drink, smoke or wear cosmetics and generally treated her with lack of respect in the presence of others.
+LJSpeech-1.1/wavs/LJ024-0121.wav|Even if an amendment were passed, and even if in the years to come it were to be ratified,
+LJSpeech-1.1/wavs/LJ035-0166.wav|While it was difficult to determine exactly when the police sealed off the building,
+LJSpeech-1.1/wavs/LJ007-0234.wav|slanting downwards from the top of the walls to the outside adjoining the slaughterhouses of Newgate market; and occasionally, in hot weather,
+LJSpeech-1.1/wavs/LJ016-0376.wav|literally walked over what, in case of conviction, would be their own graves.
+LJSpeech-1.1/wavs/LJ036-0069.wav|Instead of waiting there, Oswald apparently went as far away as he could and boarded the first Oak Cliff bus which came along
+LJSpeech-1.1/wavs/LJ033-0125.wav|Frazier doubted whether the bag that Oswald carried was as wide as the bag found on the sixth floor, although Mrs. Randle testified
+LJSpeech-1.1/wavs/LJ005-0244.wav|which dealt with jails of all classes, as well as transportation.
+LJSpeech-1.1/wavs/LJ041-0110.wav|canceling the suspension of a twenty-day sentence that Oswald had received in an earlier court-martial
+LJSpeech-1.1/wavs/LJ037-0055.wav|Detective L. C. Graves, who had been with Mrs. Markham before the lineup
+LJSpeech-1.1/wavs/LJ035-0209.wav|at twelve:thirty p.m
+LJSpeech-1.1/wavs/LJ016-0060.wav|Williams concealed himself behind a chimney till she had re-entered her garret,
+LJSpeech-1.1/wavs/LJ045-0075.wav|She was, quote, sorry that I had not married him (the Russian boyfriend) instead, that it would have been much easier for me, end quote.
+LJSpeech-1.1/wavs/LJ044-0151.wav|As a result of that conversation, Marina Oswald wrote the Soviet Embassy in Washington concerning a request she had first made
+LJSpeech-1.1/wavs/LJ001-0089.wav|but his letters, though uninteresting and poor, are not nearly so gross and vulgar as those of either the Italian or the Frenchman.
+LJSpeech-1.1/wavs/LJ012-0045.wav|he put another lady, with whom he was on intimate terms.
+LJSpeech-1.1/wavs/LJ036-0078.wav|claimed that about fifteen minutes after the assassination he saw a man, whom he later identified as Oswald,
+LJSpeech-1.1/wavs/LJ008-0280.wav|The absurdity of the system, its irregularity and cruelty, were fully touched upon by the inspectors of prisons in their first report.
+LJSpeech-1.1/wavs/LJ016-0392.wav|Jeffrey, who murdered his own child, an infant of six, by hanging him in a cellar in Seven Dials,
+LJSpeech-1.1/wavs/LJ033-0187.wav|However, the complete identity of characteristics between the paper and tape in the bag found on the sixth floor
+LJSpeech-1.1/wavs/LJ009-0241.wav|They were taken back to jail, and were again brought out in the afternoon, by which time fresh and stronger ropes had been procured,
+LJSpeech-1.1/wavs/LJ013-0153.wav|The gravest crimes continued at intervals to inspire the town with horror, and concentrate public attention upon the jail of Newgate,
+LJSpeech-1.1/wavs/LJ016-0116.wav|The doors, through incomplete knowledge of prison needs, were not, as now, sheeted with iron.
+LJSpeech-1.1/wavs/LJ050-0145.wav|and the Commission recommends that these and the other measures suggested by the Commission be pursued vigorously by Secret Service.
+LJSpeech-1.1/wavs/LJ006-0286.wav|Thus, when the inspectors visited there were twenty-three strangers, and only twenty-eight prisoners.
+LJSpeech-1.1/wavs/LJ046-0100.wav|The assassination of President Kennedy demands an examination of the protective measures employed to safe guard him
+LJSpeech-1.1/wavs/LJ049-0220.wav|that the determination of whether or not there should be a relocation of responsibilities and functions should be left to the Executive and the Congress,
+LJSpeech-1.1/wavs/LJ032-0134.wav|quote, near the firing end of the barrel about three inches under the woodstock when I took the woodstock loose, end quote,
+LJSpeech-1.1/wavs/LJ047-0038.wav|The file reflected the Department's determination that Oswald had not expatriated himself.
+LJSpeech-1.1/wavs/LJ032-0143.wav|The print's positive identity as having been lifted from the rifle
+LJSpeech-1.1/wavs/LJ039-0200.wav|and one of these agents, Robert A. Frazier,
+LJSpeech-1.1/wavs/LJ046-0086.wav|is dependent to a considerable extent upon the degree of contact with the general public desired by the President.
+LJSpeech-1.1/wavs/LJ015-0072.wav|He had a pleasant address, showed good business aptitudes, and quickly acquired the approval of his superiors.
+LJSpeech-1.1/wavs/LJ029-0120.wav|where the crowds would be able to be controlled for a great distance, and we figured that the largest crowds would be downtown, which they were,
+LJSpeech-1.1/wavs/LJ043-0161.wav|Answer: It never came to me, myself, to ask him that question.
+LJSpeech-1.1/wavs/LJ008-0160.wav|some, especially of low stature, found it difficult to remain standing, and several, although held up for some time by the men nearest them,
+LJSpeech-1.1/wavs/LJ037-0229.wav|white jacket, end quote,
+LJSpeech-1.1/wavs/LJ001-0148.wav|Here once more the almost invariable modern practice is in opposition to a natural sense of proportion.
+LJSpeech-1.1/wavs/LJ014-0338.wav|These bankers, wishing for more specific information,
+LJSpeech-1.1/wavs/LJ041-0159.wav|I think you could sit down and argue with him for a number of years, and I don't think you could have changed his mind on that unless you knew why he believed it in the first place.
+LJSpeech-1.1/wavs/LJ034-0084.wav|became apparent on December two, nineteen sixty-three, when an employee, Frankie Kaiser,
+LJSpeech-1.1/wavs/LJ016-0403.wav|His effrontery was only outdone by his cool contempt for the consolations of religion.
+LJSpeech-1.1/wavs/LJ030-0051.wav|The President's automobile was specially designed nineteen sixty-one Lincoln convertible
+LJSpeech-1.1/wavs/LJ017-0173.wav|Catherine Wilson was a female poisoner who did business wholesale.
+LJSpeech-1.1/wavs/LJ018-0377.wav|Conviction was obtained through the evidence of the steward and two of the least culpable of the crew.
+LJSpeech-1.1/wavs/LJ034-0109.wav|one-sixty to one-seventy pounds.
+LJSpeech-1.1/wavs/LJ044-0049.wav|Bringuier, who seemed to be familiar with many anti-Castro activities in New Orleans, was not aware of any such incident.
+LJSpeech-1.1/wavs/LJ032-0216.wav|He concluded that the negative of Exhibit Number one thirty-three B was exposed in Oswald's Imperial Reflex camera to the exclusion of all other cameras.
+LJSpeech-1.1/wavs/LJ040-0231.wav|Lee Oswald never received that help.
+LJSpeech-1.1/wavs/LJ002-0131.wav|three times the amount would be expended.
+LJSpeech-1.1/wavs/LJ009-0177.wav|With the amelioration of the criminal code, other cruel concomitants of execution also disappeared.
+LJSpeech-1.1/wavs/LJ047-0129.wav|FBI informants in the New Orleans area, familiar with pro-Castro or Communist Party activity there,
+LJSpeech-1.1/wavs/LJ014-0236.wav|Barthelemy was generally supposed to have been a secret agent of the French police.
+LJSpeech-1.1/wavs/LJ019-0030.wav|Major, afterwards Sir Joshua Jebb,
+LJSpeech-1.1/wavs/LJ046-0095.wav|because these precautions reduce the President's privacy and the access to him of the people of the country.
+LJSpeech-1.1/wavs/LJ028-0255.wav|In five twenty-nine Cyrus died.
+LJSpeech-1.1/wavs/LJ008-0154.wav|Conviction followed mainly on his testimony; but the two men, especially Holloway, stoutly maintained their innocence to the last.
+LJSpeech-1.1/wavs/LJ016-0120.wav|This was considered safer than intrusting him with keys.
+LJSpeech-1.1/wavs/LJ009-0303.wav|Then came the cost of the gallows,
+LJSpeech-1.1/wavs/LJ009-0014.wav|It cannot be denied, however, that the ordinary's treatment was somewhat unfeeling, and in proof thereof
+LJSpeech-1.1/wavs/LJ002-0306.wav|looked forward to his liberty, he was still delayed until he had paid a new debt arising, quote,
+LJSpeech-1.1/wavs/LJ009-0296.wav|It was not always easy to hire a hangman.
+LJSpeech-1.1/wavs/LJ043-0003.wav|Background and Possible Motives, Part four.
+LJSpeech-1.1/wavs/LJ029-0097.wav|and on their return to Dallas drove over the route which Sorrels believed best suited for the proposed motorcade.
+LJSpeech-1.1/wavs/LJ010-0103.wav|Nine of the latter were captured, with all the war material, cutlasses, pistols, hand-grenades, and ammunition.
+LJSpeech-1.1/wavs/LJ019-0349.wav|and they met with the approval of his professional adviser, the surveyor-general of prisons.
+LJSpeech-1.1/wavs/LJ032-0174.wav|but Mary Bledsoe, a former landlady of Oswald, saw him on a bus approximately ten minutes after the assassination
+LJSpeech-1.1/wavs/LJ029-0141.wav|from Main across Elm to the access road to Stemmons Freeway and the Dallas-Fort Worth Turnpike.
+LJSpeech-1.1/wavs/LJ046-0145.wav|The Secret Service responded, quote,
+LJSpeech-1.1/wavs/LJ015-0228.wav|Pierce boldly stepped in, found the cupboard unlocked; he removed the key, handed it to Agar outside,
+LJSpeech-1.1/wavs/LJ045-0107.wav|She testified that he responded, quote, As you wish. If you don't want me to come, I won't, end quote.
+LJSpeech-1.1/wavs/LJ012-0241.wav|the memory of which is still fresh in the minds of Londoners, although half a century has passed since it was committed.
+LJSpeech-1.1/wavs/LJ021-0052.wav|The average person almost always loses.
+LJSpeech-1.1/wavs/LJ023-0106.wav|that something in the Constitution has compelled them regretfully to thwart the will of the people.
+LJSpeech-1.1/wavs/LJ002-0055.wav|This arrangement was, however, modified after eighteen eleven, and the chapel yard was allotted to misdemeanants and prisoners awaiting trial.
+LJSpeech-1.1/wavs/LJ016-0031.wav|Yet from the circumstantial account given by Williams after recapture, there can be little doubt that he got away as will be described.
+LJSpeech-1.1/wavs/LJ016-0084.wav|the fugitive, uncomfortably ensconced in the flue, came down of his own accord, like Colonel Colt's raccoon.
+LJSpeech-1.1/wavs/LJ050-0068.wav|According to Chief Rowley, by mid-June nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ006-0225.wav|If any man presumed to turn in too early
+LJSpeech-1.1/wavs/LJ006-0301.wav|This woman was really his servant, employed to help in cleaning, and taken on whenever there was extra work to be done.
+LJSpeech-1.1/wavs/LJ006-0154.wav|Nothing was more prominently brought out by the inspectors than the inefficiency of the governor at that time, Mr. Cope.
+LJSpeech-1.1/wavs/LJ010-0049.wav|Their perpetrators were charged with high treason, but met with merciful clemency as irresponsible beings.
+LJSpeech-1.1/wavs/LJ045-0202.wav|He had great difficulty in establishing meaningful relations with other people. Except for his family he was completely alone.
+LJSpeech-1.1/wavs/LJ049-0142.wav|by defining responsibilities clearly and overseeing their execution.
+LJSpeech-1.1/wavs/LJ029-0028.wav|Everyone agreed that, if there was sufficient time, a motorcade through downtown Dallas would be the best way for the people to see their President.
+LJSpeech-1.1/wavs/LJ016-0118.wav|Through the aperture he crept out on to the landing at the dead of night, and so down into the central space of the building.
+LJSpeech-1.1/wavs/LJ038-0094.wav|While Oswald's untrue statements during interrogation were not considered items of positive proof
+LJSpeech-1.1/wavs/LJ036-0120.wav|unloaded at twelve:fifteen p.m.,
+LJSpeech-1.1/wavs/LJ013-0193.wav|Courvoisier, when put on his trial, pleaded not guilty;
+LJSpeech-1.1/wavs/LJ031-0160.wav|The police were cautioned to prevent picture taking.
+LJSpeech-1.1/wavs/LJ028-0234.wav|Cyrus was now reduced to great perplexity, as time went on and he made no progress against the place.
+LJSpeech-1.1/wavs/LJ023-0117.wav|I want -- as all Americans want -- an independent judiciary as proposed by the framers of the Constitution.
+LJSpeech-1.1/wavs/LJ042-0074.wav|Mr. Richard E. Snyder, then Second Secretary and senior consular official at the Embassy,
+LJSpeech-1.1/wavs/LJ012-0063.wav|By-and-by the turnkeys, sobered by their loss, returned to Newgate alone, and pleaded in excuse that they had been drugged.
+LJSpeech-1.1/wavs/LJ028-0256.wav|During the reigns of the two following Persian kings Babylon was slowly regaining its independence,
+LJSpeech-1.1/wavs/LJ019-0306.wav|while twenty-seven received less than six prisoners, and were in some instances absolutely tenantless.
+LJSpeech-1.1/wavs/LJ009-0089.wav|childless parents, widows and helpless orphans, broken and contrite hearts, and death tomorrow morning for the benefit of society.
+LJSpeech-1.1/wavs/LJ035-0170.wav|He then returned to the corner of Elm and Houston where he met a sergeant who instructed him to find out the name of the building.
+LJSpeech-1.1/wavs/LJ008-0276.wav|Neither in town or country were cases entirely taken on their own merits.
+LJSpeech-1.1/wavs/LJ016-0051.wav|Clambering along the roof,
+LJSpeech-1.1/wavs/LJ037-0162.wav|Consecutive bullets fired from the revolver by the FBI experts could not be identified as having been fired from that revolver.
+LJSpeech-1.1/wavs/LJ010-0179.wav|The Queen saw this second movement, and stooped to avoid the shot;
+LJSpeech-1.1/wavs/LJ001-0129.wav|the lateral compression of his letters renders necessary.
+LJSpeech-1.1/wavs/LJ012-0013.wav|At the trial the indictment charging them with felony could not be sustained, but they were found guilty of conspiracy and assault.
+LJSpeech-1.1/wavs/LJ028-0233.wav|for when they saw Cyrus conquering nation after nation, they were convinced that he would never stop, and their turn would come at last.
+LJSpeech-1.1/wavs/LJ031-0229.wav|The nature and characteristics of this neck wound are discussed fully in the next chapter.
+LJSpeech-1.1/wavs/LJ030-0165.wav|which caused her to look to her right.
+LJSpeech-1.1/wavs/LJ010-0230.wav|who was arrested in the very act, just as he had fired one shot.
+LJSpeech-1.1/wavs/LJ027-0064.wav|All vertebrates, and none other, have two cavities,
+LJSpeech-1.1/wavs/LJ001-0039.wav|The Roman type of all these printers is similar in character,
+LJSpeech-1.1/wavs/LJ026-0166.wav|back to starch usable as food and the comparison of the green plant and the animal would be complete.
+LJSpeech-1.1/wavs/LJ050-0213.wav|field agents supplement those on the detail, particularly when the President is traveling.
+LJSpeech-1.1/wavs/LJ005-0290.wav|Instances rarely occur in which the borough jails admit of any proper classification of the prisoners.
+LJSpeech-1.1/wavs/LJ032-0200.wav|One Sunday, while his wife was hanging diapers, Oswald asked her to take a picture of him holding a rifle, a pistol
+LJSpeech-1.1/wavs/LJ004-0014.wav|filthiness, severity, or neglect; many new dungeons had aggravated the evils against which his sagacity could not but remonstrate;
+LJSpeech-1.1/wavs/LJ045-0176.wav|The next morning he left for work before anyone else arose.
+LJSpeech-1.1/wavs/LJ045-0134.wav|Hosty had come to the Paine residence on November one and five, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ006-0081.wav|The wardsman still exacted dues, of which more directly,
+LJSpeech-1.1/wavs/LJ026-0055.wav|These materials are used by the animal in the manufacture of new protoplasm to take the place of that which has been used up.
+LJSpeech-1.1/wavs/LJ019-0177.wav|A scheme for heating the whole prison with hot-water pipes, after the system now generally adopted elsewhere, was considered,
+LJSpeech-1.1/wavs/LJ038-0180.wav|admissions and other statements made to Marina Oswald by Oswald concerning the shooting. Note left by Oswald.
+LJSpeech-1.1/wavs/LJ008-0060.wav|projecting somewhat beyond the range of the iron bar, and swinging upon hinges affixed to the wall.
+LJSpeech-1.1/wavs/LJ027-0152.wav|for instance, were separately created, additional prongs were successively added to their antlers; and yet that,
+LJSpeech-1.1/wavs/LJ001-0150.wav|the page so lay on the paper that there was more space allowed to the bottom and fore margin than to the top and back of the paper,
+LJSpeech-1.1/wavs/LJ044-0128.wav|there are a number of organizations, including possibly Fair Play, which are of a very broad character,
+LJSpeech-1.1/wavs/LJ043-0038.wav|nor his mother were objects of his affection, quote, but only examples of workers in the U.S., end quote.
+LJSpeech-1.1/wavs/LJ024-0029.wav|There is general approval so far as the lower federal courts are concerned.
+LJSpeech-1.1/wavs/LJ017-0040.wav|The trial of Catherine Winsor, the baby farmer,
+LJSpeech-1.1/wavs/LJ017-0146.wav|He had all the characteristics of the poisoner -- the calm deliberation,
+LJSpeech-1.1/wavs/LJ028-0369.wav|they intentionally mentioned a most unfavorable hour.
+LJSpeech-1.1/wavs/LJ003-0243.wav|Ironing was still the rule, not only for the convicted, but for those charged with felonies; only the misdemeanants escaped.
+LJSpeech-1.1/wavs/LJ022-0050.wav|and every day that passes offers more chances to work for those who want to work.
+LJSpeech-1.1/wavs/LJ048-0085.wav|the Commission believes that the liaison between all Federal agencies responsible for Presidential protection should be improved.
+LJSpeech-1.1/wavs/LJ046-0171.wav|This might involve a personal interview or interviews with members of the person's household. Wherever possible,
+LJSpeech-1.1/wavs/LJ008-0243.wav|Threading his way among these itinerant vendors was seen the meek-faced deliverer of tracts, the man of good intentions, now bonneted,
+LJSpeech-1.1/wavs/LJ019-0213.wav|Now for the first time the Tuffnell estate in Holloway was mentioned.
+LJSpeech-1.1/wavs/LJ029-0030.wav|once we got San Antonio moved from Friday to Thursday afternoon, where that was his initial stop in Texas,
+LJSpeech-1.1/wavs/LJ003-0117.wav|A bribe to the judge was certain to secure acquittal, and the neglect of the formality was as certainly followed by condemnation.
+LJSpeech-1.1/wavs/LJ002-0127.wav|and the aggregate amount of debts sued for was eighty-one thousand, seven hundred ninety-one pounds.
+LJSpeech-1.1/wavs/LJ029-0054.wav|Upon his arrival in Dallas on November twelve
+LJSpeech-1.1/wavs/LJ014-0178.wav|His offense was the murder of Richard Cope,
+LJSpeech-1.1/wavs/LJ021-0005.wav|Recently the most notable public questions that have concerned us all
+LJSpeech-1.1/wavs/LJ021-0004.wav|Tonight I continue that report, though, because of the shortness of time, I must defer a number of subjects to a later date.
+LJSpeech-1.1/wavs/LJ003-0187.wav|I have kept till the last that part of the prison which was usually the last resting-place of so many.
+LJSpeech-1.1/wavs/LJ009-0045.wav|he steps boldly with head upright, looks to the women's gallery, and smiles. His intention is to pass for a brave fellow,
+LJSpeech-1.1/wavs/LJ024-0061.wav|Until my first term practically every President of the United States has appointed at least one member of the Supreme Court.
+LJSpeech-1.1/wavs/LJ046-0251.wav|to the other agencies. No specific guidance was provided.
+LJSpeech-1.1/wavs/LJ027-0162.wav|The fish retains permanently this form, but the frog passes on.
+LJSpeech-1.1/wavs/LJ016-0226.wav|To this curious etiquette was due the appointment of an official whose office has long since disappeared, "the yeoman of the halter,"
+LJSpeech-1.1/wavs/LJ036-0090.wav|If Craig saw Oswald that afternoon, he saw him through the glass windows of the office.
+LJSpeech-1.1/wavs/LJ047-0211.wav|would have added up to pointing out a pretty bad individual, and I think that, together, had we known that he had a vantage point
+LJSpeech-1.1/wavs/LJ019-0217.wav|The necessary preliminaries took some time, but the contracts for the new building were completed in eighteen forty-nine, when the works were commenced.
+LJSpeech-1.1/wavs/LJ017-0107.wav|After Cook's death his stepfather, who was much attached to him, came to Rugeley.
+LJSpeech-1.1/wavs/LJ036-0185.wav|He hurried to his room and stayed no longer than three or four minutes.
+LJSpeech-1.1/wavs/LJ045-0115.wav|and he thought that he loses jobs because the FBI visits the place of his employment, end quote.
+LJSpeech-1.1/wavs/LJ009-0150.wav|So far as I can find, Courvoisier was the last condemned criminal who was thus exhibited to a crowd of morbidly curious spectators.
+LJSpeech-1.1/wavs/LJ042-0134.wav|The psychological effects of that change must have been highly unsettling. It should be remembered
+LJSpeech-1.1/wavs/LJ005-0111.wav|In a county prison adjoining the metropolis, the separation between the male and female quarters was supposed to be accomplished by the erection of an iron railing;
+LJSpeech-1.1/wavs/LJ045-0032.wav|She testified that after his return from Mexico Oswald, quote, changed for the better.
+LJSpeech-1.1/wavs/LJ001-0163.wav|e.g. the cheap paper should not sacrifice toughness and durability to a smooth and white surface,
+LJSpeech-1.1/wavs/LJ007-0080.wav|These powers were not invariably put in force, and there were in consequence many unhappy lunatics in Newgate and other jails,
+LJSpeech-1.1/wavs/LJ030-0215.wav|the car lurched forward, causing him to lose his footing. He ran three or four steps, regained his position and mounted the car.
+LJSpeech-1.1/wavs/LJ014-0161.wav|The actual execution, which took place at another prison than Newgate, is rather beyond the scope of this work.
+LJSpeech-1.1/wavs/LJ012-0039.wav|In the end the injured bootmaker agreed to buy back his stolen stock
+LJSpeech-1.1/wavs/LJ025-0078.wav|not only ready cooked but ready digested, and the alimentary canal, become superfluous, has disappeared, and, again,
+LJSpeech-1.1/wavs/LJ048-0260.wav|which provides, quote, Liquor, use of
+LJSpeech-1.1/wavs/LJ025-0076.wav|Many animals of even complex structure which live parasitically within others are wholly devoid of an alimentary cavity.
+LJSpeech-1.1/wavs/LJ009-0136.wav|that they reluctantly agreed to open the gallery which had formerly been occupied by strangers on these occasions.
+LJSpeech-1.1/wavs/LJ037-0257.wav|Scoggins thought it was lighter.
+LJSpeech-1.1/wavs/LJ047-0055.wav|It does not preclude the agent in charge of the case from reopening it if he feels that further work should be done.
+LJSpeech-1.1/wavs/LJ050-0229.wav|as soon as it can be justified. The Congress has often stressed that it will support any reasonable request for funds for the protection of the President.
+LJSpeech-1.1/wavs/LJ016-0156.wav|at no elevation affords the only drop, strangulation would seldom supervene but for the resolution of the miserable felo de se.
+LJSpeech-1.1/wavs/LJ030-0230.wav|but Hickey kept the automatic weapon ready as the car raced to the hospital.
+LJSpeech-1.1/wavs/LJ044-0204.wav|prominently reported President Kennedy as having, quote, all but invited the Cuban people today to overthrow Fidel Castro's Communist regime
+LJSpeech-1.1/wavs/LJ032-0213.wav|The negative of that picture, Commission Exhibit Number one thirty-three B, was found among Oswald's possessions.
+LJSpeech-1.1/wavs/LJ018-0137.wav|None of the stolen paper was glazed, and this was an important clue to the subsequent discovery of the crime.
+LJSpeech-1.1/wavs/LJ045-0151.wav|He then arrived on Thursday, November twenty-one, nineteen sixty-three, end quote.
+LJSpeech-1.1/wavs/LJ006-0302.wav|The governor had a great dislike, he said, to seeing strangers in his house.
+LJSpeech-1.1/wavs/LJ005-0213.wav|Newgate through all these years continued a bye-word with the Society.
+LJSpeech-1.1/wavs/LJ005-0144.wav|Yet to Clerkenwell were now committed the juveniles, and all who were inexperienced in crime.
+LJSpeech-1.1/wavs/LJ026-0036.wav|and then a balance must be struck and the doubtful form placed in the kingdom with which it has, on the whole, most points in common.
+LJSpeech-1.1/wavs/LJ048-0180.wav|This justification of the Secret Service's standing policy is not persuasive.
+LJSpeech-1.1/wavs/LJ008-0196.wav|High prices were asked and paid for front seats or good standing room. As much as five pounds was given for the attic story
+LJSpeech-1.1/wavs/LJ045-0240.wav|Many factors were undoubtedly involved in Oswald's motivation for the assassination, and the Commission does not believe
+LJSpeech-1.1/wavs/LJ018-0347.wav|and the circumstances which led to detection were scarcely novel proofs of the old adage that murder will out.
+LJSpeech-1.1/wavs/LJ028-0355.wav|he gave him likewise the government of Babylon for his life, free from tribute, and he also granted him many other favors.
+LJSpeech-1.1/wavs/LJ025-0120.wav|which are not only similar in animals and in plants respectively,
+LJSpeech-1.1/wavs/LJ017-0206.wav|Although murder by insidious methods had become more common, cases where violence of the most deadly and determined kind was offered
+LJSpeech-1.1/wavs/LJ016-0181.wav|Calcraft was born at Baddow, in Essex, in eighteen hundred;
+LJSpeech-1.1/wavs/LJ036-0156.wav|He was in error, however.
+LJSpeech-1.1/wavs/LJ014-0138.wav|Mrs. Manning became still more violent, shouting, "No, no, I will not stand it! You ought to be ashamed of yourselves!"
+LJSpeech-1.1/wavs/LJ045-0002.wav|Chapter seven. Lee Harvey Oswald: Background and Possible Motives, Part six.
+LJSpeech-1.1/wavs/LJ037-0250.wav|said that she may have seen the gray zipper jacket but she was not certain.
+LJSpeech-1.1/wavs/LJ030-0243.wav|but I had no time to speculate as to its origin because Agent Youngblood turned in a flash, immediately after the first explosion,
+LJSpeech-1.1/wavs/LJ001-0137.wav|But another point which they should attend to they almost always disregard;
+LJSpeech-1.1/wavs/LJ001-0005.wav|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.
+LJSpeech-1.1/wavs/LJ032-0231.wav|and other details in the picture.
+LJSpeech-1.1/wavs/LJ010-0296.wav|At one session of the Old Bailey, in eighteen twenty-one, no less than thirty-five true bills were found for passing forged notes.
+LJSpeech-1.1/wavs/LJ016-0092.wav|He stepped in at a garret window, coolly walked downstairs, and entered the bar.
+LJSpeech-1.1/wavs/LJ036-0011.wav|(three) evidence establishing the ownership of the murder weapon,
+LJSpeech-1.1/wavs/LJ006-0251.wav|but the presence and authority of the governor himself became indispensable.
+LJSpeech-1.1/wavs/LJ005-0239.wav|The Prison Society did not relax its efforts as time passed, but its leading members had other and more pressing claims upon their energies.
+LJSpeech-1.1/wavs/LJ031-0115.wav|From approximately four p.m. to four:fifty p.m. on November twenty-two,
+LJSpeech-1.1/wavs/LJ032-0013.wav|and killing Patrolman Tippit,
+LJSpeech-1.1/wavs/LJ001-0018.wav|The first books were printed in black letter, i.e. the letter which was a Gothic development of the ancient Roman character,
+LJSpeech-1.1/wavs/LJ050-0258.wav|Much of the Secret Service work requires the development and use of highly sophisticated equipment,
+LJSpeech-1.1/wavs/LJ044-0207.wav|the economic embargo against that country, and the general policy of the United States with regard to Cuba.
+LJSpeech-1.1/wavs/LJ035-0107.wav|testified that he took the west elevator to the first floor after hearing a noise which sounded like a backfire.
+LJSpeech-1.1/wavs/LJ038-0068.wav|cursing a little bit and hollering police brutality, end quote.
+LJSpeech-1.1/wavs/LJ022-0102.wav|the creation of new governmental machinery for supervising this work.
+LJSpeech-1.1/wavs/LJ021-0081.wav|with the confidence that we are definitely rebuilding our political and economic system on the lines laid down by the New Deal
+LJSpeech-1.1/wavs/LJ047-0060.wav|Fain had been assigned to see Marina Oswald at an appropriate time.
+LJSpeech-1.1/wavs/LJ011-0087.wav|Montgomery was duly sentenced to death, but he preferred suicide to the gallows. After sentence his demeanor was serious yet firm.
+LJSpeech-1.1/wavs/LJ011-0212.wav|then a maid and heir apparent unto her father, for the sake of the lucre of her substance; and for having afterwards unlawfully and against her will
+LJSpeech-1.1/wavs/LJ017-0038.wav|That the administration of justice should never be interfered with by local prejudice or local feeling
+LJSpeech-1.1/wavs/LJ039-0092.wav|to one hundred ninety point eight feet was, quote, very easy, end quote, and the shot which struck the President in the head
+LJSpeech-1.1/wavs/LJ005-0142.wav|occupying barely three hundred ninety feet lineal
+LJSpeech-1.1/wavs/LJ015-0096.wav|whether representing real or fictitious shares does not appear; but they were certificates connected in some way with Robson's long practiced frauds
+LJSpeech-1.1/wavs/LJ023-0050.wav|overlook the simple fact that the President, as Chief Executive, is himself one of the three horses.
+LJSpeech-1.1/wavs/LJ007-0024.wav|who supped royally on the supplies provided from outside, and kept it up till ten or eleven o'clock.
+LJSpeech-1.1/wavs/LJ050-0255.wav|This no longer appears to be the case.
+LJSpeech-1.1/wavs/LJ022-0049.wav|The simple fact is that many million more people have private work today than two years ago today or one year ago today,
+LJSpeech-1.1/wavs/LJ007-0227.wav|The inspectors were not to be deterred, however, by any opposition from the earnest discharge of their functions, and continued to report against Newgate.
+LJSpeech-1.1/wavs/LJ007-0095.wav|Prisoners indeed were known to boast that they had saved their necks by feigning insanity.
+LJSpeech-1.1/wavs/LJ042-0217.wav|I wonder what would happen it somebody was to stand up and say he was utterly opposed not only to the governments, but to the people,
+LJSpeech-1.1/wavs/LJ010-0064.wav|upon "the old man and the old woman," as they styled the Mansion House and the Bank of England.
+LJSpeech-1.1/wavs/LJ013-0043.wav|This person made much of Wallace, encouraged his attentions to his daughter,
+LJSpeech-1.1/wavs/LJ046-0193.wav|and twelve to fifteen of these cases as highly dangerous risks.
+LJSpeech-1.1/wavs/LJ031-0220.wav|During the autopsy examination, Federal agents brought the surgeons three pieces of bone recovered from Elm Street and the Presidential automobile.
+LJSpeech-1.1/wavs/LJ018-0162.wav|he had been introduced to a man named Griffiths, an engraver and copper-plate printer.
+LJSpeech-1.1/wavs/LJ040-0011.wav|For a motive that appears incomprehensible to other men may be the moving force of a man whose view of the world has been twisted,
+LJSpeech-1.1/wavs/LJ046-0061.wav|John F. Kennedy's journey to Texas in November nineteen sixty-three was in this tradition.
+LJSpeech-1.1/wavs/LJ005-0026.wav|educates him under such a system for nothing, while the virtuous simpleton who is on the other side of the wall is paying by the quarter for these attainments."
+LJSpeech-1.1/wavs/LJ008-0229.wav|Above the murmur and tumult of that noisy assembly, the lowing and bleating of cattle as they were driven into the stalls and pens of Smithfield
+LJSpeech-1.1/wavs/LJ009-0293.wav|While Calcraft was in office other aspirants to fame appeared in the field.
+LJSpeech-1.1/wavs/LJ042-0026.wav|Whatever Oswald's reasons for going to the Soviet Union might have been, however, there can be little doubt that his desire to go was quite strong.
+LJSpeech-1.1/wavs/LJ044-0007.wav|that he was involved in any conspiracy, his political activities do provide insight into certain aspects of Oswald's character
+LJSpeech-1.1/wavs/LJ023-0082.wav|In the last four years the sound rule of giving statutes the benefit of all reasonable doubt has been cast aside.
+LJSpeech-1.1/wavs/LJ046-0097.wav|Evaluation Of Presidential Protection At The Time Of The Assassination Of President Kennedy
+LJSpeech-1.1/wavs/LJ029-0207.wav|On November twenty-one there appeared on the streets of Dallas the anonymous handbill mentioned above.
+LJSpeech-1.1/wavs/LJ014-0298.wav|Strange to say, a second suicide occurred in Newgate the same night,
+LJSpeech-1.1/wavs/LJ046-0040.wav|In this role he appears to the American people, in the words of William Howard Taft, as, quote,
+LJSpeech-1.1/wavs/LJ040-0163.wav|He also recommended that Mrs. Oswald seek, quote, psychotherapeutic guidance through contact with a family agency, end quote.
+LJSpeech-1.1/wavs/LJ050-0171.wav|but it seems to warrant further study before each agency becomes irrevocably committed to separate action.
+LJSpeech-1.1/wavs/LJ003-0283.wav|It was not strange, therefore, that the inmates of Newgate should turn their unoccupied brains and idle hands to all manner of mischief;
+LJSpeech-1.1/wavs/LJ004-0227.wav|Mr. Brown also, much to his own credit, brought about the abandonment of the practice of ironing all prisoners as a matter of course.
+LJSpeech-1.1/wavs/LJ008-0096.wav|Pieman and ballad-monger did their usual roaring trade amidst the dense throng.
+LJSpeech-1.1/wavs/LJ028-0488.wav|They may best be described by means of the accompanying diagram representing a cross section.
+LJSpeech-1.1/wavs/LJ003-0270.wav|Owing to the facility of intercourse between inside and outside, many crimes were doubtless hatched in Newgate.
+LJSpeech-1.1/wavs/LJ022-0146.wav|No reasonable person wants to abandon our present gains
+LJSpeech-1.1/wavs/LJ025-0170.wav|for it is only the green parts of the plant which, under the influence of sunlight, have the marvelous power of decomposing carbonic acid,
+LJSpeech-1.1/wavs/LJ027-0061.wav|in order to enclose and protect a similar enlargement of the nervous center,
+LJSpeech-1.1/wavs/LJ030-0206.wav|on the turn at the intersection of Houston and Elm Streets and then proceeded at a rate of twelve to fifteen miles per hour
+LJSpeech-1.1/wavs/LJ032-0128.wav|or a determination that the print was not identical with the prints of people.
+LJSpeech-1.1/wavs/LJ005-0103.wav|often highly damnatory, which were embodied in its annual reports.
+LJSpeech-1.1/wavs/LJ016-0303.wav|It was preposterous to claim for such a scene as this that it conveyed any great moral lesson, or had any deterring influence.
+LJSpeech-1.1/wavs/LJ014-0290.wav|In Giltspur Street Compter, where he was first lodged
+LJSpeech-1.1/wavs/LJ029-0109.wav|Representatives of the local host committee and the White House staff were advised by the Secret Service of the actual route on the afternoon of November eighteen.
+LJSpeech-1.1/wavs/LJ014-0327.wav|The owners of the latter would then issue a second set of warrants on these goods, in total ignorance of the fact that they were already pledged.
+LJSpeech-1.1/wavs/LJ028-0037.wav|It was about twenty-two fifty B.C., when the great Hammurabi made it his capital, that it became the chief city of Babylonia.
+LJSpeech-1.1/wavs/LJ012-0071.wav|which presently followed, and again found himself an inmate of Newgate, waiting trial as a receiver and a prison-breaker.
+LJSpeech-1.1/wavs/LJ008-0240.wav|Then followed sounds more distinct and audible, in which ginger-beer, pies, fried fish, sandwiches, and fruit
+LJSpeech-1.1/wavs/LJ033-0034.wav|Oswald never asked Mrs. Paine about the use of curtain rods,
+LJSpeech-1.1/wavs/LJ011-0255.wav|Howard swore he would finish him, but proved the weaker of the two, and Mullay got him down on the floor.
+LJSpeech-1.1/wavs/LJ002-0087.wav|a circular aperture had been let in to give ventilation and secure a free current of air.
+LJSpeech-1.1/wavs/LJ004-0018.wav|generally through the personal activity of influential and benevolent local magnates, but the true principles of prison construction
+LJSpeech-1.1/wavs/LJ028-0310.wav|one at the Belian, the other at the Cissian gates; for I expect that, on account of my successes,
+LJSpeech-1.1/wavs/LJ031-0152.wav|Agents Kellerman and Hill telephoned the head of the White House detail, Gerald A. Behn, to advise him of the assassination.
+LJSpeech-1.1/wavs/LJ040-0107.wav|but apparently was not able to spend as much time with them as he would have liked, because of the age gaps of five and seven years,
+LJSpeech-1.1/wavs/LJ007-0187.wav|the neglect of the condemned convicts, the filthy condition of the wards, the insufficiency of bedding and clothing,
+LJSpeech-1.1/wavs/LJ015-0120.wav|Robson was found guilty, and sentenced to two terms of transportation, one for twenty and one for fourteen years.
+LJSpeech-1.1/wavs/LJ028-0490.wav|He would have had to cross a deep moat, to scale a wall of burned bricks about twenty feet in thickness and perhaps three times as high,
+LJSpeech-1.1/wavs/LJ016-0302.wav|He, after failing once to cut the rope, made a second attempt more successfully, and the body of Müller disappeared from view.
+LJSpeech-1.1/wavs/LJ047-0190.wav|Question: Did you take any action on this case between November five and November twenty-two? Answer: No, sir.
+LJSpeech-1.1/wavs/LJ018-0026.wav|showed how greatly the publicity and wide dissemination of the news regarding murder facilitate the detection of crime.
+LJSpeech-1.1/wavs/LJ016-0151.wav|prison suicides seldom do, except as regards ingenuity and determination.
+LJSpeech-1.1/wavs/LJ008-0132.wav|Over the handkerchief around his brows he placed a white cap, the new hat being on top of all.
+LJSpeech-1.1/wavs/LJ019-0162.wav|Other changes affecting the condition and proper treatment of prisoners were not made until the inspector had urged and recommended them.
+LJSpeech-1.1/wavs/LJ012-0012.wav|All three men were committed for trial, although Edwards wished to exculpate the others as having only acted under his order.
+LJSpeech-1.1/wavs/LJ045-0144.wav|On the other hand, the concern he expressed about the FBI may have been just another story to support the objective he sought in his letter.
+LJSpeech-1.1/wavs/LJ003-0260.wav|Another frightful consequence of this indiscriminate admission was the influx of numbers of abandoned women,
+LJSpeech-1.1/wavs/LJ038-0178.wav|(two) photographs found among Oswald's possessions after the assassination of President Kennedy,
+LJSpeech-1.1/wavs/LJ006-0120.wav|by drawing briefs and petitions for his fellows. There was a recognized charge of five shillings per brief,
+LJSpeech-1.1/wavs/LJ023-0011.wav|Today's recovery proves how right that policy was.
+LJSpeech-1.1/wavs/LJ035-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ019-0121.wav|The hope of reward might do much, but no system of penal discipline is complete unless it can also count upon the fear of punishment.
+LJSpeech-1.1/wavs/LJ015-0060.wav|will long be remembered both within and without the commercial world.
+LJSpeech-1.1/wavs/LJ006-0054.wav|But incredible as it may appear, the authorities of Newgate declined to avail themselves of the advantages offered them,
+LJSpeech-1.1/wavs/LJ036-0148.wav|After a review of these inconsistencies in his testimony before the Commission, Whaley was interviewed again in Dallas.
+LJSpeech-1.1/wavs/LJ005-0291.wav|In some large towns, as at Berwick on Tweed, Southampton, and Southwark, they (the prisons) are in a very discreditable condition.
+LJSpeech-1.1/wavs/LJ023-0017.wav|that we could not afford to take the risk of another great depression.
+LJSpeech-1.1/wavs/LJ043-0151.wav|that she became quite angry when Oswald told her what he had done, and that she made him promise never to repeat such a performance.
+LJSpeech-1.1/wavs/LJ039-0133.wav|Oswald's Rifle Practice Outside the Marines
+LJSpeech-1.1/wavs/LJ025-0034.wav|Animals further needed muscles for locomotion and nerves for sensibility.
+LJSpeech-1.1/wavs/LJ003-0054.wav|An imperfect attempt at classification was, however, made in eighteen twelve, and a yard was as far as possible set apart for the untried,
+LJSpeech-1.1/wavs/LJ002-0337.wav|could not be made even to look clean while there was no obligation of personal cleanliness on individuals, who often came into the prison in filthy rags.
+LJSpeech-1.1/wavs/LJ016-0091.wav|This was a public-house.
+LJSpeech-1.1/wavs/LJ028-0423.wav|In eighteen twelve, James Claudius Rich, the British Resident at Baghdad, made the first complete examination of the ruins.
+LJSpeech-1.1/wavs/LJ030-0070.wav|Motorcycles. -- Four motorcycles, two on each side, flanked the rear of the Presidential car.
+LJSpeech-1.1/wavs/LJ050-0150.wav|Its present manual filing system is obsolete;
+LJSpeech-1.1/wavs/LJ030-0024.wav|In Dallas the rain had stopped, and by midmorning a gloomy overcast sky had given way to the bright sunshine that greeted the Presidential party
+LJSpeech-1.1/wavs/LJ009-0185.wav|after the post mortem had been carried out, the corpse was exposed to view in a first-floor room.
+LJSpeech-1.1/wavs/LJ019-0113.wav|The scheme was no doubt fascinating, and in many respects feasible;
+LJSpeech-1.1/wavs/LJ042-0210.wav|A suggestion that Oswald hated more than just capitalism and communism
+LJSpeech-1.1/wavs/LJ016-0359.wav|Private executions at their first introduction were not popular with the Newgate officials, and for intelligible reasons.
+LJSpeech-1.1/wavs/LJ049-0032.wav|had no special design or equipment which would have permitted the Secret Service agent riding in the driver's compartment
+LJSpeech-1.1/wavs/LJ018-0321.wav|An increase of policemen on duty sufficed to prevent any attempt of this kind.
+LJSpeech-1.1/wavs/LJ038-0298.wav|Oswald could have taken any one of several different buses to Walker's house or to a point near the railroad tracks where he may have concealed the rifle.
+LJSpeech-1.1/wavs/LJ010-0137.wav|Davidson, who was the only one who seemed to realize his awful situation, listened patiently and with thankfulness to the chaplain,
+LJSpeech-1.1/wavs/LJ010-0131.wav|Come, old cock-of-wax, it will soon be over.
+LJSpeech-1.1/wavs/LJ037-0170.wav|But he agreed that because the other three bullets were mutilated, he could not determine if they had been fired from the same weapon as the test bullets.
+LJSpeech-1.1/wavs/LJ014-0313.wav|In this way he obtained vast sums from several firms, and one to which he was indebted upwards of fifty thousand pounds subsequently stopped payment.
+LJSpeech-1.1/wavs/LJ004-0052.wav|which Howard had eulogized some forty years before.
+LJSpeech-1.1/wavs/LJ045-0215.wav|and may have told his wife the things he did on the evening of November twenty-one,
+LJSpeech-1.1/wavs/LJ028-0450.wav|but the walls of Babylon have so long been used to supply bricks to the builders of the neighboring cities that only their bases remain.
+LJSpeech-1.1/wavs/LJ029-0077.wav|One building, Market Hall, was unavailable for November twenty-two.
+LJSpeech-1.1/wavs/LJ033-0178.wav|This was done as an investigatory aid
+LJSpeech-1.1/wavs/LJ013-0244.wav|Just at this moment, however, the searchers found concealed under two trusses of hay a woman's headless and dismembered trunk.
+LJSpeech-1.1/wavs/LJ031-0196.wav|Detailed security arrangements had been made by radio from the President's plane on the return flight.
+LJSpeech-1.1/wavs/LJ024-0041.wav|that I would appoint and the Senate would confirm justices worthy to sit beside present members of the Court
+LJSpeech-1.1/wavs/LJ001-0030.wav|a very few years saw the birth of Roman character not only in Italy, but in Germany and France.
+LJSpeech-1.1/wavs/LJ034-0145.wav|Brennan had seen Oswald's picture on television, and he told the Commission that whether this affected his identification, quote, is something I do not know.
+LJSpeech-1.1/wavs/LJ019-0009.wav|or to insist upon the construction of prisons on the most approved plan.
+LJSpeech-1.1/wavs/LJ015-0085.wav|With the proceeds of these flagitious frauds Robson feasted and made merry.
+LJSpeech-1.1/wavs/LJ019-0119.wav|but Mr. Pearson hardly considered the converse sufficiently, and
+LJSpeech-1.1/wavs/LJ047-0128.wav|to conduct an additional investigation of Oswald in view of the activities which had led to his arrest.
+LJSpeech-1.1/wavs/LJ040-0187.wav|According to Sokolow, this indicated a, quote, present intellectual functioning in the upper range of bright normal intelligence, end quote.
+LJSpeech-1.1/wavs/LJ046-0150.wav|danger may be implied from others, such as membership or activity in an organization which believes in assassination as a political weapon.
+LJSpeech-1.1/wavs/LJ044-0140.wav|Furthermore, he had apparently not received any letters from the national headquarters of FPCC since May twenty-nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0248.wav|The inquest on these remains, which medical examination showed to be those of a female,
+LJSpeech-1.1/wavs/LJ028-0441.wav|trees and overhanging vines were planted, and thus the travelers' tales arose.
+LJSpeech-1.1/wavs/LJ013-0154.wav|and the murderers immured within its walls.
+LJSpeech-1.1/wavs/LJ005-0268.wav|should be strictly prohibited, "as a stimulating luxury inconsistent with any notion of strict discipline and the due pressure of just punishment."
+LJSpeech-1.1/wavs/LJ035-0157.wav|When he left home that morning, Marina Oswald, who was still in bed, suggested that he wear a jacket.
+LJSpeech-1.1/wavs/LJ050-0170.wav|This matter is obviously beyond the jurisdiction of the Commission,
+LJSpeech-1.1/wavs/LJ027-0165.wav|Now, the lower forms of amphibians, such as siredon, menobranchus, siren, etc.,
+LJSpeech-1.1/wavs/LJ015-0234.wav|Courier bags were bought to carry the "stuff" slung over the shoulders;
+LJSpeech-1.1/wavs/LJ029-0156.wav|the two Dallas newspapers carried several reports of his motorcade route.
+LJSpeech-1.1/wavs/LJ018-0356.wav|But a conversation overheard between the two brothers in Newgate satisfactorily exonerated Thomas Wainwright.
+LJSpeech-1.1/wavs/LJ011-0282.wav|he gave in, and signed the documents thus illegally extorted.
+LJSpeech-1.1/wavs/LJ030-0104.wav|A Dallas police car and several motorcycles at the rear kept the motorcade together and prevented unauthorized vehicles from joining the motorcade.
+LJSpeech-1.1/wavs/LJ044-0068.wav|as a result of which he was, quote, flooded with callers and invitations to debates, etc. as well as people interested in joining the F.P.C.C.
+LJSpeech-1.1/wavs/LJ023-0035.wav|For in the last three national elections
+LJSpeech-1.1/wavs/LJ045-0146.wav|to whom, quote, it was nothing terrible if people were to find out that he had been in Russia, end quote.
+LJSpeech-1.1/wavs/LJ036-0206.wav|again at twelve:forty-eight p.m., and again at twelve:fifty-five p.m.
+LJSpeech-1.1/wavs/LJ014-0204.wav|with his head resting on his hands, looked up and observed calmly, "Go along, clock; come along, gallows."
+LJSpeech-1.1/wavs/LJ017-0072.wav|whose signature he counterfeited.
+LJSpeech-1.1/wavs/LJ035-0135.wav|ran up and said that the President had been shot.
+LJSpeech-1.1/wavs/LJ041-0044.wav|While he apparently was able to induce his mother to make a false statement about his age
+LJSpeech-1.1/wavs/LJ013-0126.wav|When one of these clubs was taken in at the Army and Navy, that establishment also suffered.
+LJSpeech-1.1/wavs/LJ004-0074.wav|No judge ever condemned a man to be half-perished with cold by day, or half-suffocated with heat by night.
+LJSpeech-1.1/wavs/LJ002-0231.wav|There was no distinct place for the female debtors, who lived in the same galleries as the men.
+LJSpeech-1.1/wavs/LJ028-0518.wav|It is not strange, then, that they were included among the Seven Wonders of the World,
+LJSpeech-1.1/wavs/LJ007-0182.wav|and the result was another lengthy blue book, printed in the parliamentary papers, eighteen thirty-six,
+LJSpeech-1.1/wavs/LJ019-0013.wav|As regards the second, the Government had set a good example, and in deciding upon the erection of Pentonville prison
+LJSpeech-1.1/wavs/LJ044-0172.wav|and left for Irving with Marina Oswald and June and most of the Oswalds' effects three days later.
+LJSpeech-1.1/wavs/LJ017-0193.wav|It was proved that Mrs. Wilson had given Mrs. Soames something peculiar to drink,
+LJSpeech-1.1/wavs/LJ011-0249.wav|On this renewed visit Mr. Owen was still absent, and Mr. Mullay agreed to write him a note from a copy Howard gave him.
+LJSpeech-1.1/wavs/LJ027-0095.wav|It is as if Nature were not free to use any and every device to accomplish her end, but were conditioned by her own plans of structure;
+LJSpeech-1.1/wavs/LJ002-0218.wav|A committee of the House of Commons investigated the charges against Bambridge, who was proved to have connived at the escape of some debtors,
+LJSpeech-1.1/wavs/LJ018-0186.wav|It was well known that they were resurrectionists, and a strict watch was kept at all the turnpike gates on the southern roads leading into London.
+LJSpeech-1.1/wavs/LJ046-0242.wav|Such an approach seriously undermines the precautionary nature of PRS work;
+LJSpeech-1.1/wavs/LJ018-0187.wav|An officer was placed for this purpose at New Cross, Camberwell, and Kennington gates.
+LJSpeech-1.1/wavs/LJ040-0235.wav|When Lee became a disciplinary problem upon his return to school in the fall of nineteen fifty-three,
+LJSpeech-1.1/wavs/LJ018-0177.wav|But as there was no independent corroboration of the informer's evidence, according to the custom of the British law,
+LJSpeech-1.1/wavs/LJ016-0221.wav|Calcraft consented, got and bestowed the clothes, only to find that the person he had obliged exhibited them publicly.
+LJSpeech-1.1/wavs/LJ009-0077.wav|The ordinary of Newgate is an orthodox, unaffected, Church of England divine,
+LJSpeech-1.1/wavs/LJ036-0098.wav|William Whaley, a taxicab driver, told his employer on Saturday morning, November twenty-three
+LJSpeech-1.1/wavs/LJ014-0260.wav|In the West End he was a man of fashion, with a town house, a house at Brighton, and a cellar full of good wine at both.
+LJSpeech-1.1/wavs/LJ028-0156.wav|The city is divided into two portions by the river which runs through the midst of it.
+LJSpeech-1.1/wavs/LJ025-0029.wav|while the atmosphere and the earth supply plants with juices ready prepared and which can be absorbed immediately.
+LJSpeech-1.1/wavs/LJ005-0047.wav|for the reception of deserving cases discharged from prison. The governor of Newgate and other metropolitan prisons had orders of admission to this refuge
+LJSpeech-1.1/wavs/LJ007-0083.wav|gives a total of thirty-nine lunatics confined in various jails, many of them guilty of murder and other serious crimes.
+LJSpeech-1.1/wavs/LJ010-0008.wav|the streets of London were well lighted, the suburbs were more populous and regularly patrolled.
+LJSpeech-1.1/wavs/LJ005-0282.wav|"the comparatively innocent are seduced, the unwary are entrapped,
+LJSpeech-1.1/wavs/LJ018-0161.wav|They also discovered that through the intermediacy of one Robert Cummings, well known as a reputed coiner,
+LJSpeech-1.1/wavs/LJ006-0280.wav|The clothing was still meager and ragged: the washing places insufficient, and wanting in decency;
+LJSpeech-1.1/wavs/LJ016-0013.wav|It was noticed that he was most anxious to receive a pair of shoes for which he had asked, and when they arrived they were closely examined.
+LJSpeech-1.1/wavs/LJ050-0082.wav|the interest of the Secret Service goes beyond information on individuals or groups threatening to cause harm or embarrassment to the President.
+LJSpeech-1.1/wavs/LJ003-0039.wav|Giltspur Street, and the Poultry, or about four hundred and seventy-six in all.
+LJSpeech-1.1/wavs/LJ024-0136.wav|Third, because it will provide a series of federal courts willing to enforce the Constitution as written,
+LJSpeech-1.1/wavs/LJ035-0106.wav|Jack Dougherty, an employee working on the fifth floor,
+LJSpeech-1.1/wavs/LJ019-0077.wav|There was an absolute want of uniformity in dietaries; in some prisons it was too liberal, in others too low.
+LJSpeech-1.1/wavs/LJ017-0210.wav|The first case was that of the 'Flowery Land,'
+LJSpeech-1.1/wavs/LJ037-0137.wav|He was then shown two photographs of Oswald and he advised that Oswald was, quote, unquestionably, end quote, the man he saw.
+LJSpeech-1.1/wavs/LJ004-0221.wav|The food was properly prepared in the prison kitchen.
+LJSpeech-1.1/wavs/LJ019-0110.wav|Field labor, he urged, and with reason, was a very suitable employment;
+LJSpeech-1.1/wavs/LJ006-0164.wav|But when he did visit, his inspections were of the most superficial character
+LJSpeech-1.1/wavs/LJ017-0229.wav|Meanwhile the captain, roused from his berth,
+LJSpeech-1.1/wavs/LJ022-0172.wav|There is need for legislation providing for the regulation of interstate transportation by buses and trucks,
+LJSpeech-1.1/wavs/LJ022-0147.wav|we must continue to protect children,
+LJSpeech-1.1/wavs/LJ028-0501.wav|Nor were the walls about the palace a great mass of dull brick masonry.
+LJSpeech-1.1/wavs/LJ014-0256.wav|It seems incredible that this passbook, which when produced in court
+LJSpeech-1.1/wavs/LJ016-0298.wav|But before "the slight slow vibrations of the body had well ended,
+LJSpeech-1.1/wavs/LJ014-0247.wav|and without reference to or comparison with the documents in which the payment was claimed.
+LJSpeech-1.1/wavs/LJ004-0129.wav|Many of the jails were in the most deplorable condition:
+LJSpeech-1.1/wavs/LJ040-0136.wav|There are children in here who have killed people, and smoke. I want to get out, end quote,
+LJSpeech-1.1/wavs/LJ016-0424.wav|During the trial and after sentence he remained perfectly cool and collected.
+LJSpeech-1.1/wavs/LJ023-0134.wav|and reject the legislative powers which the courts have today assumed.
+LJSpeech-1.1/wavs/LJ016-0196.wav|He accepted, having at first Tom Cheshire as his assistant, then for a time, when Cheshire was dismissed for drunkenness, a man named Osborne.
+LJSpeech-1.1/wavs/LJ046-0204.wav|These requests were communicated in writing by the Secret Service; rather, the Service depended on the personal liaison maintained by PRS
+LJSpeech-1.1/wavs/LJ025-0172.wav|In fact, the bean obtains two of the absolutely indispensable elements of its substance from two distinct sources.
+LJSpeech-1.1/wavs/LJ038-0010.wav|a recessed area extending about fifteen feet between the sidewalk and the front door of his store.
+LJSpeech-1.1/wavs/LJ030-0052.wav|with two collapsible jump seats between the front and rear seats.
+LJSpeech-1.1/wavs/LJ019-0151.wav|stated in evidence that he was visiting Newgate one night, when he heard a great disturbance in one of the day and sleeping rooms,
+LJSpeech-1.1/wavs/LJ036-0024.wav|on a trip which passed a check point at St. Paul and Elm Streets at twelve:thirty-six p.m., November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ006-0221.wav|The weekly introduction of food, to which I shall presently refer, formed the basis of luxurious banquets, washed down by liquor
+LJSpeech-1.1/wavs/LJ016-0230.wav|His nature must have been kindly.
+LJSpeech-1.1/wavs/LJ005-0097.wav|and could expose all the local authorities that still lagged behind, or neglected to comply with the provisions of the new laws.
+LJSpeech-1.1/wavs/LJ035-0173.wav|This worker told Barnett that the shots had been fired from a window in the Depository Building,
+LJSpeech-1.1/wavs/LJ009-0200.wav|were publicly exposed in a stable in Little Bridge Street, near Apothecaries' Hall,
+LJSpeech-1.1/wavs/LJ049-0176.wav|On the other hand, the Secret Service had no knowledge whatever of Oswald, his background, or his employment at the Book Depository,
+LJSpeech-1.1/wavs/LJ011-0206.wav|By the same forcible means I was compelled to quit England, and to trust myself to the protection of this person,
+LJSpeech-1.1/wavs/LJ037-0225.wav|five foot eight inches, black hair, slender, wearing a white jacket, white shirt and dark slacks, end quote,
+LJSpeech-1.1/wavs/LJ013-0034.wav|The case was clearly proved against them, and both were sentenced to transportation for life.
+LJSpeech-1.1/wavs/LJ011-0182.wav|Miss Turner, thus pressed, consented to go on to Gretna Green.
+LJSpeech-1.1/wavs/LJ048-0097.wav|and had also scheduled visits to other parts of the country immediately before the Texas trip,
+LJSpeech-1.1/wavs/LJ004-0023.wav|Measures remedial, although at best partial and incomplete, were introduced from time to time.
+LJSpeech-1.1/wavs/LJ048-0014.wav|was disenchanted with Russia, and had a renewed concept -- I am paraphrasing, a renewed concept -- of the American free society.
+LJSpeech-1.1/wavs/LJ049-0132.wav|The Commission believes that it is necessary to improve the cooperation among these agencies
+LJSpeech-1.1/wavs/LJ010-0051.wav|The well-known Cato Street conspiracy,
+LJSpeech-1.1/wavs/LJ011-0279.wav|and write an order sufficient to secure the surrender of the other one thousand two hundred pounds.
+LJSpeech-1.1/wavs/LJ040-0053.wav|It was an obvious element in his decision to go to Russia and later to Cuba and it probably influenced his decision to shoot at General Walker.
+LJSpeech-1.1/wavs/LJ050-0094.wav|I shall employ all means to right this gross mistake or injustice to a bona fide U.S. citizen and ex-service man.
+LJSpeech-1.1/wavs/LJ040-0021.wav|ascertained the facts surrounding the assassination but did not draw conclusions concerning Oswald's legal guilt.
+LJSpeech-1.1/wavs/LJ038-0006.wav|police sirens sounded along Jefferson Boulevard.
+LJSpeech-1.1/wavs/LJ005-0132.wav|Only a few glaring evils still demanded a remedy.
+LJSpeech-1.1/wavs/LJ044-0077.wav|This would have made him a real center of attention as he must have been when he first arrived in the Soviet Union
+LJSpeech-1.1/wavs/LJ012-0066.wav|and begged his wife to send him over a consignment of cheap "righteous" watches, or such as had been honestly obtained, and not "on the cross."
+LJSpeech-1.1/wavs/LJ010-0009.wav|People, too, were getting into the habit of carrying but little cash about them, and no valuables but their watches or personal jewelery.
+LJSpeech-1.1/wavs/LJ034-0101.wav|Brennan saw the man fire the last shot and disappear from the window.
+LJSpeech-1.1/wavs/LJ028-0253.wav|The city was spared; the great walls were left standing; the daily sacrifices were continued in the temples, and Cyrus made his home in the royal palace.
+LJSpeech-1.1/wavs/LJ017-0130.wav|To the last, therefore, he played the part of a man wrongfully convicted, and did not abandon hope
+LJSpeech-1.1/wavs/LJ003-0136.wav|pale as death, very ill, and in a dreadfully dirty state, the wretches making game of him, and enjoying my distress;
+LJSpeech-1.1/wavs/LJ009-0095.wav|which he has bitten unconsciously, or from rage, or to rouse his fainting courage.
+LJSpeech-1.1/wavs/LJ043-0061.wav|at the time of his defection, when he evidenced no interest in his father and hardly mentioned him, even when questioned.
+LJSpeech-1.1/wavs/LJ048-0161.wav|The Chief of the Service has provided the Commission a detailed explanation of this policy, quote,
+LJSpeech-1.1/wavs/LJ010-0165.wav|but he came as a lad to London, and took service as a pot-boy to a publican.
+LJSpeech-1.1/wavs/LJ038-0060.wav|John Gibson, another patron in the theatre, saw an officer grab Oswald, and he claims that he heard the click of a gun misfiring.
+LJSpeech-1.1/wavs/LJ018-0326.wav|I propose to end at this point the detailed account of the more prominent criminal cases which lodged their perpetrators in Newgate.
+LJSpeech-1.1/wavs/LJ049-0099.wav|A number of bills were introduced immediately following the assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ035-0032.wav|When the elevator failed to come, Baker said, quote, let's take the stairs, end quote, and he followed Truly up the stairway, which is to the west of the elevator.
+LJSpeech-1.1/wavs/LJ011-0079.wav|he soon left the service and betook himself to dishonest ways.
+LJSpeech-1.1/wavs/LJ039-0070.wav|testified that the shots which struck the President in the neck and in the head were, quote, not particularly difficult, end quote.
+LJSpeech-1.1/wavs/LJ016-0420.wav|is remembered at the former prison as a defiant,
+LJSpeech-1.1/wavs/LJ032-0016.wav|and (eight) Oswald's capability with a rifle.
+LJSpeech-1.1/wavs/LJ013-0168.wav|That Lord William Russell had committed suicide was at once declared impossible.
+LJSpeech-1.1/wavs/LJ008-0279.wav|In this latter it might fairly be said that a man was put to death less for his own sins than for the crimes of others.
+LJSpeech-1.1/wavs/LJ022-0029.wav|So many people come to Washington who know things that are not so,
+LJSpeech-1.1/wavs/LJ013-0094.wav|The same evening both started for Liverpool, and embarking on board the mail steamer 'Britannia,' escaped to the United States.
+LJSpeech-1.1/wavs/LJ049-0021.wav|to permit its removal on those occasions when the President wished to ride in an open car.
+LJSpeech-1.1/wavs/LJ016-0068.wav|and continued his march to Kingston, where he slept soundly under a hedge till next morning.
+LJSpeech-1.1/wavs/LJ045-0039.wav|His past relationships with his wife had been stormy, however, and it did not seem that she respected him very much.
+LJSpeech-1.1/wavs/LJ008-0202.wav|Those who had hired apartments spent the night in them, keeping up their courage with liquids and cigars.
+LJSpeech-1.1/wavs/LJ021-0158.wav|Demoralization caused by vast unemployment is our greatest extravagance.
+LJSpeech-1.1/wavs/LJ030-0231.wav|Most of the other Secret Service agents in the motorcade had drawn their sidearms.
+LJSpeech-1.1/wavs/LJ025-0117.wav|that a fundamental unity of structure obtains in animals and plants,
+LJSpeech-1.1/wavs/LJ037-0136.wav|Patterson, when asked later to confirm his identification by affidavit said he did not recall having been shown the photograph.
+LJSpeech-1.1/wavs/LJ011-0110.wav|He was, however, visited by and received his wife, and several members of the Society of Friends.
+LJSpeech-1.1/wavs/LJ028-0215.wav|Marduk, the great lord, looking with joy on his pious works and upright heart,
+LJSpeech-1.1/wavs/LJ037-0039.wav|The man continued along tenth Street. Mrs. Markham saw a police car slowly approach the man from the rear and stop alongside of him.
+LJSpeech-1.1/wavs/LJ039-0078.wav|The effect of a four-power telescopic sight on the difficulty of these shots was considered in detail
+LJSpeech-1.1/wavs/LJ013-0258.wav|The poor creature was never seen again alive.
+LJSpeech-1.1/wavs/LJ042-0058.wav|That my mother and you are (in spite of what the newspaper said) not objects of affection, but only examples of workers in the U.S.
+LJSpeech-1.1/wavs/LJ011-0176.wav|He now pretended that Mr. Turner was also on his way to the border, pursued by sheriffs' officers.
+LJSpeech-1.1/wavs/LJ032-0006.wav|six point five-millimeter Italian rifle bearing the serial number C two seven six six.
+LJSpeech-1.1/wavs/LJ030-0111.wav|The Drive through Dallas
+LJSpeech-1.1/wavs/LJ038-0116.wav|When Captain Fritz asked him why he carried the revolver, he answered, quote, Well, you know about a pistol. I just carried it, end quote.
+LJSpeech-1.1/wavs/LJ019-0120.wav|omitted the fact that he might have to deal with that persistent idleness which is not an unknown characteristic of the criminal class.
+LJSpeech-1.1/wavs/LJ002-0206.wav|Dissolute habits prevailed on all sides; drunkenness was universal, gambling perpetual.
+LJSpeech-1.1/wavs/LJ035-0177.wav|According to Barnett, quote, there were people going in and out, end quote, during this period.
+LJSpeech-1.1/wavs/LJ032-0133.wav|Lieutenant Day of the Dallas Police Department had "lifted" a palmprint from the underside of the gun barrel
+LJSpeech-1.1/wavs/LJ017-0113.wav|After this the step-father insisted upon a post-mortem, which was conducted somewhat carelessly.
+LJSpeech-1.1/wavs/LJ027-0025.wav|The facts adduced from morphology, being the result of researches into the structure of adult animals and plants,
+LJSpeech-1.1/wavs/LJ016-0295.wav|curse, or shout, as in this heaving and struggling forward they gained or lost in their strong efforts to get nearer where Müller was to die.
+LJSpeech-1.1/wavs/LJ022-0167.wav|but it will protect the actual value and earning power of properties now owned by thousands of investors
+LJSpeech-1.1/wavs/LJ019-0376.wav|The task imposed upon them, tending as it did to the imposition of a fine upon the local authorities, was not a pleasant one,
+LJSpeech-1.1/wavs/LJ002-0148.wav|and that no more than one hundred ninety-seven creditors recovered debts and costs.
+LJSpeech-1.1/wavs/LJ034-0044.wav|The freshness of this print could be estimated only because the Dallas police developed it through the use of powder.
+LJSpeech-1.1/wavs/LJ010-0212.wav|With Oxford's other papers were found letters from the secretary, written as it seemed by Oxford to himself, after the manner of Mr. Toots,
+LJSpeech-1.1/wavs/LJ017-0149.wav|He had met her at a boarding-house, where he lived with his own wife, a person of "shady" antecedents,
+LJSpeech-1.1/wavs/LJ028-0505.wav|It seems that the bricks of the reliefs were molded and glazed separately and so accurately that when built into the wall they fitted perfectly.
+LJSpeech-1.1/wavs/LJ012-0038.wav|but could not positively identify it, and Ikey defied them to remove a single shoe.
+LJSpeech-1.1/wavs/LJ046-0206.wav|and at the working level with personnel of the field offices of the various agencies. The Service frequently participated
+LJSpeech-1.1/wavs/LJ014-0259.wav|Watts led two lives.
+LJSpeech-1.1/wavs/LJ018-0358.wav|Christina Edmunds had resort to strychnia, the same lethal drug that Palmer used;
+LJSpeech-1.1/wavs/LJ038-0049.wav|Although the witnesses did not hear the sound of a misfire,
+LJSpeech-1.1/wavs/LJ008-0245.wav|though pulled right and left, or sent headlong into the crowd by the swing of some brutal and muscular arm,
+LJSpeech-1.1/wavs/LJ011-0234.wav|Bigger "jobs" than ever were planned and attempted,
+LJSpeech-1.1/wavs/LJ050-0261.wav|from many Government agencies including the Department of Defense and the President's Office of Science and Technology.
+LJSpeech-1.1/wavs/LJ026-0101.wav|The causes of this ascent are not clear,
+LJSpeech-1.1/wavs/LJ024-0040.wav|But if by that phrase the charge is made
+LJSpeech-1.1/wavs/LJ028-0291.wav|Wrath stirred within the King at the sight of a man of his lofty rank in such a condition;
+LJSpeech-1.1/wavs/LJ026-0019.wav|they cannot ingest solid food, but are nourished by a watery solution of nutrient materials.
+LJSpeech-1.1/wavs/LJ004-0168.wav|The smell at one o'clock of the day "was something more than can be expressed by the term disgusting."
+LJSpeech-1.1/wavs/LJ004-0069.wav|In short, attention to his feelings, mental and bodily, a supply of every necessary, abstraction from evil society,
+LJSpeech-1.1/wavs/LJ038-0245.wav|A photography expert with the FBI
+LJSpeech-1.1/wavs/LJ050-0205.wav|Testimony and other evidence before the Commission
+LJSpeech-1.1/wavs/LJ042-0235.wav|Freedoms are about the same. Medical aid and the educational system in the USSR is better than in the USA, end quote.
+LJSpeech-1.1/wavs/LJ047-0040.wav|Oswald was first interviewed by FBI Agents John W. Fain and B. Tom Carter on June twenty-six, nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ011-0174.wav|But at Kendal there was no Mr. Turner, and, to allay Miss Turner's growing anxiety,
+LJSpeech-1.1/wavs/LJ019-0276.wav|during short sentences, or the earlier stages of a long confinement,
+LJSpeech-1.1/wavs/LJ003-0078.wav|Nor were the misdemeanants and bail prisoners any longer separated from those whose crimes were of a more serious character.
+LJSpeech-1.1/wavs/LJ027-0133.wav|to present the mass of embryological evidence, but a few salient facts will illustrate the kind of evidence to be deduced from embryology.
+LJSpeech-1.1/wavs/LJ028-0474.wav|It too was surmounted with towers.
+LJSpeech-1.1/wavs/LJ030-0171.wav|and the thought immediately crossed his mind that it was an assassination attempt.
+LJSpeech-1.1/wavs/LJ014-0074.wav|One fine afternoon in August, O'Connor was met walking in the direction of Bermondsey.
+LJSpeech-1.1/wavs/LJ013-0182.wav|took Courvoisier into custody, and placed the two female servants under surveillance.
+LJSpeech-1.1/wavs/LJ031-0106.wav|whose major wound fell within Dr. Shaw's area of specialization.
+LJSpeech-1.1/wavs/LJ022-0161.wav|Even more significantly it has given the country as a whole an uneasy apprehension of overconcentrated economic power.
+LJSpeech-1.1/wavs/LJ028-0308.wav|Let neither these nor the former troops be armed with any weapons but their swords those thou mayest leave them.
+LJSpeech-1.1/wavs/LJ028-0311.wav|the Babylonians will entrust everything, even the keys of their gates, to me. Then it will be for me and my Persians to do the rest.
+LJSpeech-1.1/wavs/LJ005-0235.wav|Last, and worst of all, the arrangements for keeping the condemned prisoners between sentence and execution were more than unsatisfactory.
+LJSpeech-1.1/wavs/LJ039-0120.wav|windy, rainy, dark. There is little probability that he had good, expert coach,
+LJSpeech-1.1/wavs/LJ040-0200.wav|Furthermore she did not appear to understand her own relationship to Lee's psychological problems.
+LJSpeech-1.1/wavs/LJ016-0389.wav|Giovanni Lanni, the Italian boy who murdered a Frenchwoman in the Haymarket,
+LJSpeech-1.1/wavs/LJ006-0199.wav|the most popular being the "Times," "Morning Herald," and "Morning Chronicle"; on Sunday the "Weekly Dispatch," "Bell's Life," and the "Weekly Messenger."
+LJSpeech-1.1/wavs/LJ014-0262.wav|He played high, and spent his nights at the club, or in joyous and dissolute company.
+LJSpeech-1.1/wavs/LJ018-0273.wav|Tarpey was caught through his wife,
+LJSpeech-1.1/wavs/LJ028-0007.wav|With mingling fear and wonder at the mystery always lying beyond the desert horizon,
+LJSpeech-1.1/wavs/LJ037-0155.wav|At the request of the Commission, Joseph D. Nicol, superintendent of the Illinois Bureau of Criminal Identification Investigation,
+LJSpeech-1.1/wavs/LJ026-0085.wav|And this is due to the fact that in the manufacture of starch in the chlorophyll bodies
+LJSpeech-1.1/wavs/LJ027-0019.wav|And at last each organism or each alliance of organisms must come to the greatest concession of all, which is called death.
+LJSpeech-1.1/wavs/LJ005-0269.wav|Prison officers should not have any share in prisoners' earnings,
+LJSpeech-1.1/wavs/LJ007-0017.wav|It is to be feared too that although the surface was thus whitewashed and decorous, much that was vicious still festered and rankled beneath,
+LJSpeech-1.1/wavs/LJ003-0123.wav|The most trifling acts were magnified into offenses.
+LJSpeech-1.1/wavs/LJ028-0390.wav|I was informed by a certain Elamite brother, who came from those regions, and now leads the life of a monk at Jerusalem,
+LJSpeech-1.1/wavs/LJ003-0286.wav|The report of the Committee of the House of Commons painted so black a picture of Newgate as then conducted, that the Corporation were roused in very shame
+LJSpeech-1.1/wavs/LJ042-0199.wav|He also expressed hatred of the Soviet Union and of the Communist Party, U.S.A.,
+LJSpeech-1.1/wavs/LJ014-0318.wav|Another set of frauds, which resembled those of Pries in principle, although not in practice, were soon afterwards discovered.
+LJSpeech-1.1/wavs/LJ038-0296.wav|Marina Oswald also testified that her husband had used a bus to return home.
+LJSpeech-1.1/wavs/LJ039-0068.wav|Four marksmanship experts testified before the Commission.
+LJSpeech-1.1/wavs/LJ011-0168.wav|Miss Turner was shown into a private room, where Mr. Wakefield soon presented himself.
+LJSpeech-1.1/wavs/LJ014-0257.wav|was a mass of blots and erasures, should not have created suspicion of foul play either at the bank or at the company's board.
+LJSpeech-1.1/wavs/LJ041-0027.wav|Other evidence of the existence of such a disability is provided by the many other misspellings that appear in Oswald's writings, portions of which are quoted below.
+LJSpeech-1.1/wavs/LJ008-0192.wav|As early as six a.m. the number assembled already exceeded that seen on ordinary occasions;
+LJSpeech-1.1/wavs/LJ002-0073.wav|Neild takes it for granted that the former rather than the latter prevailed in the selection,
+LJSpeech-1.1/wavs/LJ024-0075.wav|to be determined by chance or the personal indecision of individuals.
+LJSpeech-1.1/wavs/LJ036-0064.wav|Marsalis Street is seven blocks from Beckley.
+LJSpeech-1.1/wavs/LJ038-0132.wav|When asked why he lived at his roominghouse under the name O. H. Lee,
+LJSpeech-1.1/wavs/LJ050-0044.wav|has been augmented, and a Secret Service inspector has been put in charge of this operation. With the assistance of the President's Office of Science and Technology,
+LJSpeech-1.1/wavs/LJ050-0001.wav|For more information, or to volunteer, please visit librivox dot org. Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ039-0037.wav|Vice President Lyndon B. Johnson was in Dallas for a visit which had been publicized in the Dallas newspapers
+LJSpeech-1.1/wavs/LJ028-0345.wav|He then chose out near three thousand of the leading citizens and caused them to be crucified, while he allowed the remainder still to inhabit the city.
+LJSpeech-1.1/wavs/LJ003-0154.wav|Artisans and others were at liberty to work at their trades, provided they were not dangerous.
+LJSpeech-1.1/wavs/LJ041-0067.wav|Other marines also testified that Oswald had few friends and kept very much to himself.
+LJSpeech-1.1/wavs/LJ032-0109.wav|Both names were found to be fictitious.
+LJSpeech-1.1/wavs/LJ035-0070.wav|Truly stood in front of the building.
+LJSpeech-1.1/wavs/LJ023-0111.wav|reading into the Constitution words and implications which are not there, and which were never intended to be there.
+LJSpeech-1.1/wavs/LJ033-0023.wav|Frazier, surprised, asked him why he was going to Irving on Thursday night rather than Friday.
+LJSpeech-1.1/wavs/LJ050-0140.wav|Secretary Dillon testified that the use of such liaison officers is the only effective way to insure that adequate liaison is maintained.
+LJSpeech-1.1/wavs/LJ029-0071.wav|An important purpose of the President's visit to Dallas was to speak at a luncheon given by business and civic leaders.
+LJSpeech-1.1/wavs/LJ048-0095.wav|The Service prefers to have two agents perform advance preparations.
+LJSpeech-1.1/wavs/LJ019-0006.wav|laid it down that individuals might be confined separately and apart in single cells.
+LJSpeech-1.1/wavs/LJ041-0095.wav|Oswald read a good deal, said Powers, but, quote, he would never be reading any of the shoot-em-up westerns or anything like that.
+LJSpeech-1.1/wavs/LJ034-0130.wav|Since the window ledges in the Depository Building are lower than in most buildings,
+LJSpeech-1.1/wavs/LJ014-0163.wav|who wrote to the 'Times,' saying that he believed "a sight so inconceivably awful
+LJSpeech-1.1/wavs/LJ048-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ017-0139.wav|presenting still greater features of resemblance with Palmer's, for both were medical men, and both raised difficult questions of medical jurisprudence.
+LJSpeech-1.1/wavs/LJ040-0126.wav|Oswald was remanded for psychiatric observation to Youth House, an institution in which children are kept for psychiatric observation
+LJSpeech-1.1/wavs/LJ032-0246.wav|It had been purchased in March nineteen sixty-three, and taken to New Orleans where Marina Oswald saw it in their rented apartment
+LJSpeech-1.1/wavs/LJ010-0208.wav|Hannibal and Ethelred were on the council; Anthony, Augustus, and Frederic were among the generals;
+LJSpeech-1.1/wavs/LJ013-0243.wav|Good now offered to go to Wandsworth and satisfy the pawnbroker.
+LJSpeech-1.1/wavs/LJ016-0408.wav|During the singing of these hymns Wainwright fainted, but whether from real emotion or the desire to make a sensation was never exactly known.
+LJSpeech-1.1/wavs/LJ026-0016.wav|They ingest solid proteinaceous food,
+LJSpeech-1.1/wavs/LJ016-0320.wav|But these, it was thought, might be provided by the admission of the press and the holding of a coroner's inquest.
+LJSpeech-1.1/wavs/LJ039-0034.wav|The Commission has concluded, therefore, that regardless of what Oswald may have said to his wife
+LJSpeech-1.1/wavs/LJ045-0018.wav|As you can see, political friction between "left" and "right" is very great here.
+LJSpeech-1.1/wavs/LJ008-0081.wav|and after praying fervently for a few minutes, the steps on which she stood were drawn away, and she was left suspended.
+LJSpeech-1.1/wavs/LJ002-0157.wav|and Susannah Evans, in October the same year, for two shillings, with costs of six shillings, eight pence.
+LJSpeech-1.1/wavs/LJ048-0245.wav|but that their visits to the Cellar were, quote, neither consistent nor inconsistent, end quote, with their duty.
+LJSpeech-1.1/wavs/LJ043-0068.wav|While he might have expected difficulty from such an approach, in fact
+LJSpeech-1.1/wavs/LJ018-0232.wav|He himself prepared it on a blank form which he had brought with him on purpose.
+LJSpeech-1.1/wavs/LJ006-0189.wav|gambling, indirect contravention of parliamentary rules, instruction in all nefarious processes,
+LJSpeech-1.1/wavs/LJ019-0191.wav|feeling that it approached too nearly to solitary confinement.
+LJSpeech-1.1/wavs/LJ035-0093.wav|On the basis of this time test, therefore,
+LJSpeech-1.1/wavs/LJ045-0208.wav|His family lived with Mrs. Paine, ostensibly because Oswald could not afford to keep an apartment in Dallas,
+LJSpeech-1.1/wavs/LJ031-0116.wav|Dr. Charles F. Gregory, chief of orthopedic surgery, operated on the wounds of Governor Connally's right wrist,
+LJSpeech-1.1/wavs/LJ049-0153.wav|The Secret Service was organized as a division of the Department of the Treasury in eighteen sixty-five, to deal with counterfeiting.
+LJSpeech-1.1/wavs/LJ047-0170.wav|On November five, Hosty was traveling near Mrs. Paine's home and took the occasion to stop by to ask whether she had any further information.
+LJSpeech-1.1/wavs/LJ032-0032.wav|that the bold printing on the face of the mail-order coupon was in the handprinting of Lee Harvey Oswald and that the writing on the envelope was also his.
+LJSpeech-1.1/wavs/LJ031-0094.wav|and you have to establish adequate circulation.
+LJSpeech-1.1/wavs/LJ016-0415.wav|but the story was never substantiated, and we may hope that it rested only on the idle gossip of the day.
+LJSpeech-1.1/wavs/LJ018-0328.wav|Few of the Newgate notorieties of late years show any marked peculiarities;
+LJSpeech-1.1/wavs/LJ037-0207.wav|Michaelis furnished the shipping copy of the invoice, and the Railway Express Agency shipping documents, showing that
+LJSpeech-1.1/wavs/LJ040-0121.wav|On September thirty, nineteen fifty-two, Lee enrolled in P.S. one seventeen
+LJSpeech-1.1/wavs/LJ022-0009.wav|The job of creating a program for the nation's welfare is, in some respects, like the building of a ship.
+LJSpeech-1.1/wavs/LJ046-0154.wav|as indicating some potential danger to the President -- no matter how small -- it was indexed in the general PRS files
+LJSpeech-1.1/wavs/LJ032-0070.wav|Ordinarily, Inspector Holmes testified, identification is not requested because it is assumed that the person with the notice is entitled to the package.
+LJSpeech-1.1/wavs/LJ014-0099.wav|The circumstance was suspicious, and a search was at once made of the whole premises.
+LJSpeech-1.1/wavs/LJ030-0008.wav|During the afternoon, President Kennedy dedicated the U.S. Air Force School of Aerospace Medicine at Brooks Air Force Base.
+LJSpeech-1.1/wavs/LJ030-0040.wav|The order of vehicles in the Dallas motorcade was as follows:
+LJSpeech-1.1/wavs/LJ028-0510.wav|Probably their metal was far too valuable for the enemy to leave behind.
+LJSpeech-1.1/wavs/LJ001-0120.wav|In the old print each figure has its definite individuality, and one cannot be mistaken for the other;
+LJSpeech-1.1/wavs/LJ013-0075.wav|Fletcher was the moving spirit of the whole business. It was he who had introduced Barber to Miss Slack,
+LJSpeech-1.1/wavs/LJ032-0234.wav|The rifle was shipped from Klein's in Chicago on March twenty, nineteen sixty-three, at a time when the Oswalds were living on Neely Street.
+LJSpeech-1.1/wavs/LJ026-0077.wav|to the chlorophyll bodies or chromatophores, for plants which, like fungi, etc., are devoid of chlorophyll, are unable thus to acquire energy.
+LJSpeech-1.1/wavs/LJ022-0159.wav|The absentee management of unnecessary holding company control
+LJSpeech-1.1/wavs/LJ037-0180.wav|If a bullet is missing, five were fired. This corresponds with the observation and memory of Ted Callaway, and possibly Warren Reynolds,
+LJSpeech-1.1/wavs/LJ007-0157.wav|The evils of such association were aggravated by the unbroken idleness; one "evil inflamed the other;" reformation
+LJSpeech-1.1/wavs/LJ030-0163.wav|Mrs. John F. Kennedy, on the left of the rear seat of the limousine, looked toward her left and waved to the crowds along the route.
+LJSpeech-1.1/wavs/LJ035-0100.wav|In the few seconds which elapsed while Baker and Truly ran from the first to the second floor,
+LJSpeech-1.1/wavs/LJ050-0038.wav|In attempting to identify those individuals who might prove a danger to the President,
+LJSpeech-1.1/wavs/LJ027-0077.wav|but attention may be called to a like similarity extending to the details of structure.
+LJSpeech-1.1/wavs/LJ011-0113.wav|He met his death with unshaken firmness, only entreating that a certain blue handkerchief,
+LJSpeech-1.1/wavs/LJ003-0006.wav|Besides this, although the families of debtors were no longer permitted to live with them inside the jail,
+LJSpeech-1.1/wavs/LJ028-0143.wav|It is surrounded, in the first place, by a broad and deep moat, full of water, behind which rises a wall
+LJSpeech-1.1/wavs/LJ002-0110.wav|Having thus briefly described the plan and appropriation of the prison, I propose to deal now with the general condition of the inmates, and the manner of their life.
+LJSpeech-1.1/wavs/LJ038-0280.wav|And for purposes of probative value, for whatever it might be worth,
+LJSpeech-1.1/wavs/LJ013-0072.wav|It was shown that the will must be a forgery,
+LJSpeech-1.1/wavs/LJ018-0332.wav|Greed in the latter case was a secondary motive;
+LJSpeech-1.1/wavs/LJ047-0138.wav|He checked in Oswald's old neighborhood and throughout the Dallas-Fort Worth area but was unable to locate Oswald.
+LJSpeech-1.1/wavs/LJ043-0048.wav|Marguerite Oswald visited her son and his family at the first apartment which he rented after his return, and tried to help them get settled there.
+LJSpeech-1.1/wavs/LJ049-0155.wav|while investigating a plot to assassinate President Cleveland, the Service assigned a small protective detail of agents to the White House.
+LJSpeech-1.1/wavs/LJ018-0251.wav|He confessed himself a perjurer in having sworn to the false will, and a wholesale forger, having manufactured no less than ten false signatures
+LJSpeech-1.1/wavs/LJ028-0248.wav|long after the outer portions of the town were taken, knew nothing of what had chanced, but as they were engaged in a festival,
+LJSpeech-1.1/wavs/LJ016-0058.wav|he worked along to the roofs in Warwick Lane.
+LJSpeech-1.1/wavs/LJ016-0396.wav|but to which additions are sometimes made, chiefly of stimulants, if deemed necessary, by the medical officer of the jail.
+LJSpeech-1.1/wavs/LJ010-0280.wav|On June eighteen fifty the Queen was once more subjected to cowardly outrage, the offender being a Mr. Pate, a gentleman by birth,
+LJSpeech-1.1/wavs/LJ014-0017.wav|In the mean time the police were not idle.
+LJSpeech-1.1/wavs/LJ044-0163.wav|Marina Oswald testified that her husband engaged in Fair Play for Cuba Committee activities, quote,
+LJSpeech-1.1/wavs/LJ024-0090.wav|There are many types of amendment proposed.
+LJSpeech-1.1/wavs/LJ007-0225.wav|I find it ordered "that the ordinary of Newgate be restricted from making any communications to the Home Office
+LJSpeech-1.1/wavs/LJ023-0076.wav|It is but a decent respect due to the wisdom, the integrity and the patriotism of the legislative body,
+LJSpeech-1.1/wavs/LJ019-0069.wav|and it was unsafe to fix this limit above twelve months, although some rash advocates were in favor of eighteen months, some indeed of two years.
+LJSpeech-1.1/wavs/LJ006-0276.wav|On this female side, where the Ladies' Association still reigned supreme, more system and a greater semblance of decorum was maintained.
+LJSpeech-1.1/wavs/LJ010-0298.wav|That of Fauntleroy the banker, in eighteen twenty-four,
+LJSpeech-1.1/wavs/LJ037-0168.wav|Nicol differed with the FBI experts on one bullet taken from Tippit's body.
+LJSpeech-1.1/wavs/LJ018-0345.wav|As in that case, the grave had been dug long in anticipation, and the chloride of lime purchased to destroy the corpse.
+LJSpeech-1.1/wavs/LJ028-0435.wav|Arabs, searching for bricks, have burrowed their way down deep into it, revealing massive walls and arches.
+LJSpeech-1.1/wavs/LJ048-0210.wav|He was handicapped, however, by the fact that he was riding in a closed car whose roof at times obscured his view.
+LJSpeech-1.1/wavs/LJ010-0047.wav|while in eighteen fifty Her Majesty was the victim of another outrage at the hands of one Pate.
+LJSpeech-1.1/wavs/LJ024-0134.wav|Second, because it will provide a reinvigorated
+LJSpeech-1.1/wavs/LJ027-0080.wav|two toes (sheep), four toes (hog) and five toes (dog)
+LJSpeech-1.1/wavs/LJ010-0206.wav|The officers of the society were to be known only by "factitious names."
+LJSpeech-1.1/wavs/LJ014-0329.wav|and so developed his business that in one year his transactions amounted to a couple of millions of pounds.
+LJSpeech-1.1/wavs/LJ039-0067.wav|which is ordinarily required when a marksman must raise his rifle as a target moves farther away.
+LJSpeech-1.1/wavs/LJ013-0127.wav|Suspicion fell at length upon Ashley, who was seen to handle the forks and spoons at table in a strange manner.
+LJSpeech-1.1/wavs/LJ021-0160.wav|Some people try to tell me that we must make up our minds that for the future we shall permanently have millions of unemployed
+LJSpeech-1.1/wavs/LJ046-0037.wav|The nature of the protective assignment.
+LJSpeech-1.1/wavs/LJ028-0485.wav|The outer and inner defenses of Babylon were so strong and so high that no enemy could hope to take them,
+LJSpeech-1.1/wavs/LJ045-0249.wav|Out of these and the many other factors which may have molded the character of Lee Harvey Oswald
+LJSpeech-1.1/wavs/LJ028-0218.wav|Without a skirmish or a battle, he permitted them to enter Babylon, and, sparing the city, he delivered the King Nabonidus to him.
+LJSpeech-1.1/wavs/LJ010-0128.wav|they with Brunt and Tidd scorned the ordinary's ministrations,
+LJSpeech-1.1/wavs/LJ023-0090.wav|It is the accusation of most distinguished justices of the present Supreme Court.
+LJSpeech-1.1/wavs/LJ018-0066.wav|He at length stole a carpet-bag containing valuables, and fled to Hamburgh.
+LJSpeech-1.1/wavs/LJ047-0029.wav|had described himself as a Marxist, had said he would give the Soviet Union any useful information he had acquired
+LJSpeech-1.1/wavs/LJ035-0109.wav|but in his testimony Piper did not mention either seeing or talking with Dougherty during these moments of excitement.
+LJSpeech-1.1/wavs/LJ046-0019.wav|In this part of its inquiry the Commission has had full access to a major study of all phases of protective activities
+LJSpeech-1.1/wavs/LJ012-0168.wav|was tried at the Old Bailey the following year for horse-stealing, and hanged in front of Newgate.
+LJSpeech-1.1/wavs/LJ039-0003.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ011-0111.wav|Two elders of the meeting sat up with him in the press yard the whole of the night previous to execution, and a third,
+LJSpeech-1.1/wavs/LJ036-0017.wav|He probably walked east on Elm Street for seven blocks to the corner of Elm and Murphy
+LJSpeech-1.1/wavs/LJ021-0150.wav|I shall not ask either employers or employees permanently to lay aside the weapons common to industrial war.
+LJSpeech-1.1/wavs/LJ048-0054.wav|which specifically required the referral of such a case as Oswald's to the Secret Service,
+LJSpeech-1.1/wavs/LJ030-0103.wav|Police car and motorcycles.
+LJSpeech-1.1/wavs/LJ012-0196.wav|Bishop, whose real name was Head, married a half-sister of Williams'. Williams was a professional resurrectionist, or body-snatcher,
+LJSpeech-1.1/wavs/LJ032-0062.wav|by which Oswald requested that mail addressed to that box be forwarded to him in New Orleans, where he had moved on April twenty-four.
+LJSpeech-1.1/wavs/LJ030-0061.wav|with a metallic handle for the rider to grasp.
+LJSpeech-1.1/wavs/LJ040-0181.wav|They reflect a considerable amount of impoverishment in the social and emotional areas.
+LJSpeech-1.1/wavs/LJ004-0167.wav|The floor was on the level of the river, and very damp.
+LJSpeech-1.1/wavs/LJ005-0073.wav|To its efforts, and their effect upon Parliament and the public mind, we must attribute the new Jail Acts of four George the fourth
+LJSpeech-1.1/wavs/LJ022-0203.wav|For that we can be thankful to the God who watches over America.
+LJSpeech-1.1/wavs/LJ028-0315.wav|He replied that he was Zopyrus, and deserted to them from the Persians.
+LJSpeech-1.1/wavs/LJ008-0302.wav|I have heard the protracted agony of both classes described by those who witnessed it in terms so strong, that I am unwilling to repeat them.
+LJSpeech-1.1/wavs/LJ048-0193.wav|Three officers from the Dallas Police Department were assigned to the intersection of Elm and Houston
+LJSpeech-1.1/wavs/LJ030-0200.wav|At that point, both Governor and Mrs. Connally observed brain tissue splattered over the interior of the car.
+LJSpeech-1.1/wavs/LJ012-0070.wav|He was, however, recognized, and ere long an order came out from home for his arrest and transfer to England,
+LJSpeech-1.1/wavs/LJ006-0101.wav|some, who had the semblance of covering on the upper part of their feet, had no soles to the shoes, and their bare feet were on the ground.
+LJSpeech-1.1/wavs/LJ044-0110.wav|We do not have any organizational ties with the Committee, and yet there is much material that we issue from time to time that is important
+LJSpeech-1.1/wavs/LJ012-0270.wav|She had realized all her effects, and brought them with her furniture to Greenacre's lodgings. The two when married were to emigrate to Hudson's Bay.
+LJSpeech-1.1/wavs/LJ047-0115.wav|Several days later, the Bureau received additional evidence that Oswald had lied to Agent Quigley.
+LJSpeech-1.1/wavs/LJ022-0004.wav|In the many weeks since that time the Congress has devoted itself to the arduous task of formulating legislation necessary to the country's welfare.
+LJSpeech-1.1/wavs/LJ016-0239.wav|Marwood till latterly seemed to have done his work more effectually, and has been known to give as much as six feet fall.
+LJSpeech-1.1/wavs/LJ010-0226.wav|and Oxford was ordered to be detained during Her Majesty's pleasure.
+LJSpeech-1.1/wavs/LJ016-0259.wav|The graphic and terrible account given by Charles Dickens of the awful scene before Horsemonger Lane Jail, at the execution of the Mannings,
+LJSpeech-1.1/wavs/LJ008-0136.wav|These evidently deprived him of the small portion of fortitude he had summoned up.
+LJSpeech-1.1/wavs/LJ030-0141.wav|a subsequent bullet, which was lethal, shattered the right side of his skull.
+LJSpeech-1.1/wavs/LJ026-0014.wav|based on the foregoing considerations, is convenient for distinguishing between animals and plants in all cases except the doubtful unicellar forms.
+LJSpeech-1.1/wavs/LJ035-0002.wav|Chapter four. The Assassin: Part four. Oswald's Actions in Building After Assassination.
+LJSpeech-1.1/wavs/LJ045-0113.wav|he did not want his landlady to know his real name because she might read in the paper of the fact that he had been in Russia and that he had been questioned, end quote.
+LJSpeech-1.1/wavs/LJ008-0146.wav|who kept a lavender warehouse in the city, and who had gardens at Feltham,
+LJSpeech-1.1/wavs/LJ004-0039.wav|They were hopeless of any general reform by the action of the executive alone.
+LJSpeech-1.1/wavs/LJ046-0108.wav|Intelligence Functions Relating to Presidential Protection at the Time of the Dallas Trip
+LJSpeech-1.1/wavs/LJ027-0001.wav|The Science: History of the Universe, Volume five. Edited by Francis Rolt-Wheeler.
+LJSpeech-1.1/wavs/LJ019-0088.wav|There was no general rule of employment. Hard labor was often not insisted upon in separate confinement;
+LJSpeech-1.1/wavs/LJ041-0018.wav|but he was not sure then that Oswald meant to carry out the plan, and in fact they never did.
+LJSpeech-1.1/wavs/LJ047-0176.wav|shortly after Oswald rented the room on October fourteen.
+LJSpeech-1.1/wavs/LJ002-0212.wav|Mexico, and was consumed at the rate of a hogshead per week.
+LJSpeech-1.1/wavs/LJ041-0048.wav|who studied it during the year following his unsuccessful attempt to enlist until, quote, He knew it by heart, end quote.
+LJSpeech-1.1/wavs/LJ028-0384.wav|So Babylon was buried and forgotten.
+LJSpeech-1.1/wavs/LJ048-0232.wav|and the last agent left the Press Club by two a.m.
+LJSpeech-1.1/wavs/LJ004-0080.wav|because they are not specified in his sentence; he is therefore entitled to a wholesome atmosphere,
+LJSpeech-1.1/wavs/LJ032-0091.wav|There is no "Dr. Hideel" licensed to practice medicine in Louisiana.
+LJSpeech-1.1/wavs/LJ043-0039.wav|He also indicated to officials at the American Embassy in Moscow that his defection was motivated at least in part
+LJSpeech-1.1/wavs/LJ006-0287.wav|The presence of so many strangers, many of them gentlemen, distracted the prisoners' attention, and could not be productive of much good.
+LJSpeech-1.1/wavs/LJ031-0176.wav|A casket was obtained and the President's body was prepared for removal.
+LJSpeech-1.1/wavs/LJ012-0147.wav|Moss peached because he declared he had been done out of the proper price of the gold-dust; but it was clear that he had tried to appropriate the whole of the stuff,
+LJSpeech-1.1/wavs/LJ004-0217.wav|The old haphazard system, by which meat was issued in bulk,
+LJSpeech-1.1/wavs/LJ034-0043.wav|The print, therefore, could have been placed on the carton at any time within this period.
+LJSpeech-1.1/wavs/LJ027-0122.wav|But the vacillating policy in the matter of rudiments does not end here; for it is shown in a still more aggravated form
+LJSpeech-1.1/wavs/LJ022-0058.wav|While our present and projected expenditures for work relief are wholly within the reasonable limits of our national credit resources,
+LJSpeech-1.1/wavs/LJ019-0244.wav|The last inquiry into the condition and management of our jails and houses of correction was that made by the Lords' Committee in eighteen sixty-three.
+LJSpeech-1.1/wavs/LJ021-0165.wav|On the contrary,
+LJSpeech-1.1/wavs/LJ004-0065.wav|his morals by compelling him to mix with a promiscuous assemblage of hardened and convicted criminals,
+LJSpeech-1.1/wavs/LJ014-0235.wav|I want forgiveness of man; I want those doors (of the prison) opened.
+LJSpeech-1.1/wavs/LJ048-0117.wav|all instructions from higher authority were communicated to him orally.
+LJSpeech-1.1/wavs/LJ045-0155.wav|Question: Did you say anything to him then?
+LJSpeech-1.1/wavs/LJ033-0151.wav|identified these prints as the left index fingerprint and right palmprint of Lee Harvey Oswald
+LJSpeech-1.1/wavs/LJ044-0021.wav|he debated over radio station WDSU, New Orleans, with Carlos Bringuier,
+LJSpeech-1.1/wavs/LJ040-0155.wav|in spite of chronic truancy from school which brought him into Youth House.
+LJSpeech-1.1/wavs/LJ049-0168.wav|As discussed above, the Bureau has attempted to meet its responsibilities in this field by spelling out in its Handbook
+LJSpeech-1.1/wavs/LJ038-0189.wav|and include newspaper clippings (should there be anything about me in the newspapers). I believe that the Embassy will come quickly to your assistance on learning everything.
+LJSpeech-1.1/wavs/LJ047-0186.wav|I can now afford to wait until New Orleans forwarded the necessary papers to me to show me I now had all the information.
+LJSpeech-1.1/wavs/LJ034-0027.wav|Depository employees were laying a new floor at the west end and transferring books from the west to the east end of the building.
+LJSpeech-1.1/wavs/LJ040-0086.wav|In the fall of nineteen forty-eight she told John Pic and Robert Oswald
+LJSpeech-1.1/wavs/LJ047-0213.wav|would meet our specific criteria, none of them alone. But, it is when you begin adding them up to some degree that you begin to get criteria that, are meaningful.
+LJSpeech-1.1/wavs/LJ012-0164.wav|No murder has created greater sensation and horror throughout England than that of Mr. Weare by Thurtell, Hunt, and Probert.
+LJSpeech-1.1/wavs/LJ010-0258.wav|As the Queen was driving from Buckingham Palace to the Chapel Royal,
+LJSpeech-1.1/wavs/LJ005-0275.wav|religious worship, and instruction, as "absolutely necessary for preventing contamination,
+LJSpeech-1.1/wavs/LJ029-0102.wav|After the selection of the Trade Mart as the luncheon site,
+LJSpeech-1.1/wavs/LJ031-0154.wav|Secret Service agents stationed at later stops on the President's itinerary of November twenty-two were redeployed.
+LJSpeech-1.1/wavs/LJ033-0186.wav|one cannot estimate when, prior to November twenty-two, Oswald made the paper bag.
+LJSpeech-1.1/wavs/LJ003-0048.wav|I pass now to the criminal side of Newgate, which consisted of the six quarters or yards already enumerated and described.
+LJSpeech-1.1/wavs/LJ007-0020.wav|"swearing and talking of what crimes they had committed, and how they had done it." Another witness declared she had heard the most shocking language in the yard; she said
+LJSpeech-1.1/wavs/LJ042-0005.wav|After Oswald left the Marine Corps in September of nineteen fifty-nine, ostensibly to care for his mother,
+LJSpeech-1.1/wavs/LJ037-0154.wav|independently examined the four cartridge cases and arrived at the same conclusion as Cunningham.
+LJSpeech-1.1/wavs/LJ013-0152.wav|at Montreuil. He was arraigned at the Old Bailey, and the case fully proved. His sentence was seven years' transportation.
+LJSpeech-1.1/wavs/LJ012-0069.wav|Ikey joined her at Hobart Town, where they set up a general shop, and soon began to prosper.
+LJSpeech-1.1/wavs/LJ038-0229.wav|Either the viaduct or the underpass might have been the "bridge" mentioned in the last paragraph of the note.
+LJSpeech-1.1/wavs/LJ007-0012.wav|who not strangely resented the orders issued by the aldermen
+LJSpeech-1.1/wavs/LJ011-0240.wav|No more curious instance of this ever occurred than the assault made by one Howard upon a Mr. Mullay, with intent to rob him.
+LJSpeech-1.1/wavs/LJ040-0089.wav|In order to supplement their income further she falsely swore that Pic was seventeen years old so that he could join the Marine Corps Reserves.
+LJSpeech-1.1/wavs/LJ018-0166.wav|Buncher in London, and Griffiths in Birmingham.
+LJSpeech-1.1/wavs/LJ018-0300.wav|The sums obtained by Bidwell in the name of "Warren" at the Bank of England
+LJSpeech-1.1/wavs/LJ011-0261.wav|He was found guilty of an assault with intent, and sentenced to transportation for fourteen years.
+LJSpeech-1.1/wavs/LJ042-0046.wav|This should answer your question, and also give you a glimpse of my way of thinking. So you speak of advantages. Do you think that is why I am here?
+LJSpeech-1.1/wavs/LJ004-0057.wav|For the first time the doctrine was enunciated that prisoners had rights of their own.
+LJSpeech-1.1/wavs/LJ031-0119.wav|The fracture was set, and a cast was applied with some traction utilized.
+LJSpeech-1.1/wavs/LJ003-0277.wav|but just open (eighteen sixteen), the regular employment of prisoners had never yet been accepted as a principle in the metropolitan prisons.
+LJSpeech-1.1/wavs/LJ010-0141.wav|he was generally supposed to be a surgeon.
+LJSpeech-1.1/wavs/LJ008-0112.wav|He was death's counterfeit, tall, shriveled, and pale;
+LJSpeech-1.1/wavs/LJ001-0133.wav|One very important matter in "setting up" for fine printing is the "spacing," that is, the lateral distance of words from one another.
+LJSpeech-1.1/wavs/LJ004-0192.wav|The prison stood on a dry, airy situation outside the town.
+LJSpeech-1.1/wavs/LJ016-0349.wav|that of Alexander Mackay, who murdered his mistress at Norton Folgate by beating her with a rolling-pin and furnace-rake,
+LJSpeech-1.1/wavs/LJ038-0222.wav|The main post office, quote, on Ervay Street, end quote, refers to the post office where Oswald rented box two nine one five
+LJSpeech-1.1/wavs/LJ008-0217.wav|a throng of people whom neither rain, snow, storm, nor darkness ever hindered from attending the show.
+LJSpeech-1.1/wavs/LJ009-0153.wav|First came the sheriffs, the under-sheriffs, and several aldermen and city officials, then Lord Powerscourt and several other peers of the realm.
+LJSpeech-1.1/wavs/LJ007-0041.wav|The whole of these seventeen had, however, a common airing-yard, and took their exercise there at the same time,
+LJSpeech-1.1/wavs/LJ006-0219.wav|leap-frog, puss in the corner, and "fly the garter," for which purpose the rugs were spread out to prevent feet slipping on the floor.
+LJSpeech-1.1/wavs/LJ026-0015.wav|He says: Animals are organisms of fixed and definite form, in which the cell-body is not covered with a cellulose wall.
+LJSpeech-1.1/wavs/LJ047-0249.wav|The only witness to the conversation was Dallas Police Detective V. J. Brian, who was accompanying Revill.
+LJSpeech-1.1/wavs/LJ031-0192.wav|Return to Washington, D.C.
+LJSpeech-1.1/wavs/LJ049-0014.wav|The Commission regards such impromptu stops as presenting an unnecessary danger,
+LJSpeech-1.1/wavs/LJ042-0087.wav|He stated that he had volunteered to give Soviet officials any information that he had concerning Marine Corps operations, and intimated
+LJSpeech-1.1/wavs/LJ019-0132.wav|in the inquiry which followed, attention was again attracted to Newgate.
+LJSpeech-1.1/wavs/LJ015-0252.wav|When he knew that he could not escape his fate,
+LJSpeech-1.1/wavs/LJ028-0460.wav|Nebuchadnezzar, King of Babylon, the restorer of the temples Esagil and Ezida,
+LJSpeech-1.1/wavs/LJ014-0086.wav|This job was not completed till the following day, as the hole had to be enlarged, and the only tool they had was a dust-shovel.
+LJSpeech-1.1/wavs/LJ003-0224.wav|Supplies of common necessaries, such as have now been part of the furniture of every British jail for many years,
+LJSpeech-1.1/wavs/LJ046-0119.wav|as obtaining clearance of some categories of White House employees and all tradesmen who service the White House,
+LJSpeech-1.1/wavs/LJ050-0005.wav|The intimacy of the Secret Service's relationship to the White House
+LJSpeech-1.1/wavs/LJ039-0161.wav|and the Infantry Weapons Evaluation Branch of the U.S. Army. There were no misfires.
+LJSpeech-1.1/wavs/LJ031-0087.wav|When asked why he did not turn the President over, Dr. Carrico testified as follows:
+LJSpeech-1.1/wavs/LJ046-0065.wav|and allow them to see him, and discuss, if possible, the views of the world as he sees it, the problems of the country as he sees them.
+LJSpeech-1.1/wavs/LJ033-0137.wav|It was not a standard type bag which could be obtained in a store and it was presumably made for a particular purpose.
+LJSpeech-1.1/wavs/LJ016-0425.wav|When visited one day in the condemned cell, just as St. Sepulchre's clock was striking, he looked up and said laughingly, "Go along, clock;
+LJSpeech-1.1/wavs/LJ042-0156.wav|Oswald prefaced his manuscript with a short autobiographical sketch which reads in part as follows, quote,
+LJSpeech-1.1/wavs/LJ008-0216.wav|until presently the huge stage loomed dark above the crowd which was now ranged round the barriers;
+LJSpeech-1.1/wavs/LJ034-0118.wav|that the description of the suspect in the Tippit shooting was similar to the description which had been given by Brennan in connection with the assassination.
+LJSpeech-1.1/wavs/LJ003-0300.wav|that all prisoners should always be in separate cells by night, and those of short sentences by day.
+LJSpeech-1.1/wavs/LJ033-0009.wav|Oswald's arrival at the Depository Building on November twenty-two, carrying a long and bulky brown paper package,
+LJSpeech-1.1/wavs/LJ021-0189.wav|Nearly all Americans are sensible and calm people.
+LJSpeech-1.1/wavs/LJ045-0013.wav|There is, of course, no way to judge what the effect of the general political ferment present in that city might have been, even though Oswald was aware of it.
+LJSpeech-1.1/wavs/LJ004-0200.wav|Another jail, that of Ilchester, was also worthy of all commendation. It exhibited all the good points of that at Bury.
+LJSpeech-1.1/wavs/LJ012-0132.wav|and he with his father arranged that a messenger should call for the stuff with forged credentials, and anticipating the rightful owner.
+LJSpeech-1.1/wavs/LJ029-0161.wav|From the airport, the President's party will proceed to Mockingbird Lane to Lemmon and then to Turtle Creek, turning south to Cedar Springs.
+LJSpeech-1.1/wavs/LJ041-0188.wav|The eyes of the future became the eyes of God.
+LJSpeech-1.1/wavs/LJ030-0144.wav|Special Agent Rufus W. Youngblood observed that the large electric sign clock atop the Texas School Book Depository Building
+LJSpeech-1.1/wavs/LJ004-0107.wav|The most important jail act of that early period, however, was the twenty-four George the third c. fifty-four, s. four (seventeen eighty-four)
+LJSpeech-1.1/wavs/LJ014-0067.wav|His wealth was well known to "Maria," as he called Mrs. Manning, who made several ineffectual attempts to get money out of him.
+LJSpeech-1.1/wavs/LJ049-0111.wav|The activities of the victim at the time an assassination occurs and the motive for the assassination
+LJSpeech-1.1/wavs/LJ047-0085.wav|The FBI had not asked to be informed of any effort by Oswald to obtain a passport,
+LJSpeech-1.1/wavs/LJ015-0197.wav|which at one time spread terror throughout London. Thieves preferred now to use ingenuity rather than brute force.
+LJSpeech-1.1/wavs/LJ032-0034.wav|with that appearing on documents known to have been written by Oswald, such as his letters, passport application, and endorsements of checks.
+LJSpeech-1.1/wavs/LJ024-0065.wav|Such a succession of appointments should have provided a Court well-balanced as to age.
+LJSpeech-1.1/wavs/LJ002-0080.wav|The long narrow yard still remained as we saw it in Jacobite times,
+LJSpeech-1.1/wavs/LJ004-0151.wav|They were far worse at Bristol,
+LJSpeech-1.1/wavs/LJ017-0198.wav|but its nature was betrayed when this last victim also died.
+LJSpeech-1.1/wavs/LJ047-0034.wav|and instructed the Dallas office to interview him when he got back to determine whether he had been recruited by a Soviet intelligence service.
+LJSpeech-1.1/wavs/LJ021-0024.wav|all present new questions for the solution of which the old reliance upon the free action of individual wills appears quite inadequate.
+LJSpeech-1.1/wavs/LJ025-0164.wav|iron and the like in combination with phosphoric, sulphuric and other acids.
+LJSpeech-1.1/wavs/LJ004-0188.wav|and which even fulfilled many of the exacting requirements of modern days.
+LJSpeech-1.1/wavs/LJ028-0295.wav|I maimed myself because I could not endure that the Assyrians should laugh at the Persians. "Wretched man," said Darius,
+LJSpeech-1.1/wavs/LJ044-0181.wav|The Cubans would not, however, give him a visa until he had received one from the Soviets, which involved a delay of several months.
+LJSpeech-1.1/wavs/LJ003-0227.wav|So unjust and unequal was the system, that the allowance to convicted criminals was better than that of the innocent debtor,
+LJSpeech-1.1/wavs/LJ044-0143.wav|Those events no doubt had their effects on Oswald.
+LJSpeech-1.1/wavs/LJ042-0076.wav|He took charge, in a sense, of the conversation right from the beginning, end quote. He presented the following signed note:
+LJSpeech-1.1/wavs/LJ039-0188.wav|hit the target twice within a span of four point six and five point one five seconds.
+LJSpeech-1.1/wavs/LJ021-0169.wav|Those, fortunately few in number, who are frightened by boldness and cowed by the necessity for making decisions,
+LJSpeech-1.1/wavs/LJ010-0198.wav|His father, it was proved in evidence, had been at times quite mad; and Oxford's mental state might be inferred from his own proceedings.
+LJSpeech-1.1/wavs/LJ015-0167.wav|Redpath had industriously circulated reports that he had prospered greatly in speculation;
+LJSpeech-1.1/wavs/LJ010-0124.wav|Five of the conspirators, Thistlewood, Ings, Brunt, Davidson, and Tidd, were sentenced to death,
+LJSpeech-1.1/wavs/LJ035-0132.wav|When she reached the first floor, she actually saw Shelley and Lovelady slightly east of the east elevator.
+LJSpeech-1.1/wavs/LJ028-0326.wav|and on his suing for a command, they entrusted to him a body of troops with the help of which he proceeded to do as he had arranged with Darius.
+LJSpeech-1.1/wavs/LJ025-0052.wav|the greatest and most rapid revolution which biological science has ever undergone was effected by the application of the modern microscope
+LJSpeech-1.1/wavs/LJ006-0102.wav|This, too, was in the depth of the winter, and during a most inclement season.
+LJSpeech-1.1/wavs/LJ039-0072.wav|From my own experience in shooting over the years,
+LJSpeech-1.1/wavs/LJ042-0095.wav|to attend a university in Moscow, but they gave him a monthly allowance of seven hundred rubles a month
+LJSpeech-1.1/wavs/LJ046-0039.wav|As the ceremonial head of the Government the President must discharge a wide range of public duties, not only in Washington but throughout the land.
+LJSpeech-1.1/wavs/LJ019-0157.wav|sharp tests which generally broke down the fidelity of the inmates of the ward to one another.
+LJSpeech-1.1/wavs/LJ002-0201.wav|He seems to have felt no responsibility as to the welfare or comfort of those in charge, and out of whom he made all his money.
+LJSpeech-1.1/wavs/LJ019-0152.wav|and on entering it found the prisoners engaged in kicking bundles of wood from one end of the ward to the other.
+LJSpeech-1.1/wavs/LJ039-0210.wav|Frazier testified that while he could not tell when the defect occurred, but that a person familiar with the weapon could compensate for it.
+LJSpeech-1.1/wavs/LJ020-0080.wav|Pinch the dough hard to stop the mouths of these cavities,
+LJSpeech-1.1/wavs/LJ003-0185.wav|There was no separation even for the women under sentence of death, who lived in a common and perpetually crowded ward.
+LJSpeech-1.1/wavs/LJ003-0026.wav|The auditors were paid a shilling each for their services each time the poor-box was opened. The steward was also remunerated for his trouble.
+LJSpeech-1.1/wavs/LJ046-0038.wav|The President is Head of State, Chief Executive, Commander in Chief, and leader of a political party.
+LJSpeech-1.1/wavs/LJ039-0149.wav|established that they had been previously loaded and ejected from the assassination rifle,
+LJSpeech-1.1/wavs/LJ007-0023.wav|The poor, who could afford no luxuries, went to bed early, but were kept awake by the revelries of the rich,
+LJSpeech-1.1/wavs/LJ042-0161.wav|Immediately after serving out his three years in the U.S. Marine Corps, he abandoned his American life to seek a new life in the USSR.
+LJSpeech-1.1/wavs/LJ028-0424.wav|Porter, Layard, and Rawlinson followed him, but the real scientific exploration of Babylon and its walls
+LJSpeech-1.1/wavs/LJ028-0367.wav|The priests of the temple of Bel, so a story tells us,
+LJSpeech-1.1/wavs/LJ002-0075.wav|and who are therefore lodged apart from all other districts of the jail. End quote.
+LJSpeech-1.1/wavs/LJ048-0220.wav|Conduct of Secret Service agents in Fort Worth on November twenty-two.
+LJSpeech-1.1/wavs/LJ033-0196.wav|The green cotton fibers found in the paper bag matched some of the green cotton fibers in the blanket
+LJSpeech-1.1/wavs/LJ008-0251.wav|was an hour before kicked to death by the savage and brutal mob.
+LJSpeech-1.1/wavs/LJ039-0054.wav|Oswald's Rifle Capability
+LJSpeech-1.1/wavs/LJ037-0166.wav|He concluded, however, that he could not say whether the four bullets were fired from the revolver in Oswald's possession.
+LJSpeech-1.1/wavs/LJ029-0053.wav|which advise it of potential threats immediately before the visit of the President to their community.
+LJSpeech-1.1/wavs/LJ002-0089.wav|eight. The female felons were deprived of part of the space which the architect had intended for them.
+LJSpeech-1.1/wavs/LJ017-0181.wav|who had been in the habit of taking colchicum for rheumatism. Mrs. Wilson heard then casually from a medical man
+LJSpeech-1.1/wavs/LJ048-0101.wav|The Commission concludes that the most significant advance arrangements for the President's trip were soundly planned.
+LJSpeech-1.1/wavs/LJ027-0070.wav|So much concerns the general plan of skeletal structures and is strongly suggestive of -- in fact it is inexplicable without -- common origin.
+LJSpeech-1.1/wavs/LJ007-0143.wav|"is permitted to purchase whatever his own means or the means of his friends in or out of prison can afford,
+LJSpeech-1.1/wavs/LJ040-0061.wav|Various possible motives will be treated in the appropriate context of the discussion outlined above.
+LJSpeech-1.1/wavs/LJ042-0249.wav|Even though his primary purpose probably was to get to Cuba,
+LJSpeech-1.1/wavs/LJ010-0143.wav|This axe is still in existence, and is preserved at Newgate with various other unpleasant curiosities,
+LJSpeech-1.1/wavs/LJ013-0099.wav|A smart detective, Forrester, after a little inquiry,
+LJSpeech-1.1/wavs/LJ032-0219.wav|They are photographs of the same scene.
+LJSpeech-1.1/wavs/LJ029-0008.wav|the activities at Parkland Memorial Hospital, and the return of the Presidential party to Washington.
+LJSpeech-1.1/wavs/LJ037-0060.wav|That Mrs. Markham described the man who killed Patrolman Tippit as, quote, short, a little on the heavy side, end quote.
+LJSpeech-1.1/wavs/LJ016-0363.wav|This feeling was the stronger because
+LJSpeech-1.1/wavs/LJ008-0116.wav|and tied the cord with so little feeling that the governor (Wall), who had not given the wretch his accustomed fee, observed,
+LJSpeech-1.1/wavs/LJ040-0227.wav|Mrs. Siegel concluded her report with the statement that, quote,
+LJSpeech-1.1/wavs/LJ015-0032.wav|and on the other by backing up
+LJSpeech-1.1/wavs/LJ018-0018.wav|he wore gold-rimmed eye-glasses and a gold watch and chain.
+LJSpeech-1.1/wavs/LJ035-0076.wav|Special Agent John Howlett of the Secret Service carried a rifle from the southeast corner of the sixth floor along the east aisle to the northeast corner.
+LJSpeech-1.1/wavs/LJ017-0165.wav|As she became worse and worse, Smethurst called in the best medical advice in Richmond,
+LJSpeech-1.1/wavs/LJ022-0084.wav|I next want to make it clear exactly how we shall direct the work.
+LJSpeech-1.1/wavs/LJ035-0141.wav|If Miss Adams accurately recalled meeting Shelley and Lovelady when she reached the bottom of the stairs,
+LJSpeech-1.1/wavs/LJ010-0236.wav|The deed was long premeditated, and would have been executed a day earlier had not his courage failed him at the last moment.
+LJSpeech-1.1/wavs/LJ031-0066.wav|the doctors were able to maintain peripheral circulation as monitored at the neck (carotid) artery and at the wrist (radial) pulse.
+LJSpeech-1.1/wavs/LJ014-0012.wav|It was here that the others engaged in their dismal office in removing the dead first got a good look at the stranger's face.
+LJSpeech-1.1/wavs/LJ009-0098.wav|There! there! I see the Lamb of God! Oh! how happy! Oh! this is happy!
+LJSpeech-1.1/wavs/LJ018-0196.wav|He was a notorious criminal,
+LJSpeech-1.1/wavs/LJ038-0161.wav|and he asked me did I know which way he was coming, and I told him, yes, he probably come down Main and turn on Houston and then back again on Elm.
+LJSpeech-1.1/wavs/LJ039-0046.wav|wasn't the day before. Perhaps three days before, end quote.
+LJSpeech-1.1/wavs/LJ033-0108.wav|In deciding whether Oswald carried the assassination weapon in the bag which Frazier and Mrs. Randle saw,
+LJSpeech-1.1/wavs/LJ018-0219.wav|he instructed certain solicitors to prepare a deed of gift as from his father, conveying to him estates near Kingston.
+LJSpeech-1.1/wavs/LJ040-0096.wav|Pic said, quote,
+LJSpeech-1.1/wavs/LJ034-0032.wav|had been one of these moved by the floor layers from the west wall to near the east side of the building in preparation for the laying of the floor.
+LJSpeech-1.1/wavs/LJ030-0085.wav|The two agents on the rear of the running boards were to advance toward the front of the President's car whenever it stopped or slowed down sufficiently for them to do so.
+LJSpeech-1.1/wavs/LJ023-0030.wav|Four years ago action did not come until the eleventh hour.
+LJSpeech-1.1/wavs/LJ044-0103.wav|The Worker replied, quote, Your kind offer is most welcomed and from time to time we shall call on you, end quote.
+LJSpeech-1.1/wavs/LJ050-0264.wav|The Commission recommends that the present arrangements
+LJSpeech-1.1/wavs/LJ011-0071.wav|but there is good evidence to show that the body after execution was handed over to his friends and interred privately.
+LJSpeech-1.1/wavs/LJ044-0195.wav|was another act which expressed his hostility toward the United States and its institutions as well as a concomitant attachment
+LJSpeech-1.1/wavs/LJ050-0088.wav|to further the intention to satisfy a grievance by unlawful means.
+LJSpeech-1.1/wavs/LJ042-0190.wav|democratic socializing of production, and without regard to the twisting apart of Marxism Marxist Communism by other powers.
+LJSpeech-1.1/wavs/LJ007-0049.wav|they suffered sickening anxiety, and wished to be locked up alone. This indiscriminate association lasted for months,
+LJSpeech-1.1/wavs/LJ044-0152.wav|on February seventeen, nineteen sixty-three, for permission for herself and June to return to the Soviet Union.
+LJSpeech-1.1/wavs/LJ044-0227.wav|Although Oswald could possibly have been motivated in part by his sympathy for the Castro government,
+LJSpeech-1.1/wavs/LJ038-0005.wav|from where several witnesses last saw Oswald running west on Jefferson Boulevard. Shortly after the Tippit murder,
+LJSpeech-1.1/wavs/LJ034-0176.wav|all the time I watched him, he never moved his head, he never -- he never moved anything. Just was there transfixed.
+LJSpeech-1.1/wavs/LJ016-0405.wav|went impenitent to the gallows. The only sign of feeling he showed was in asking to be allowed to choose the hymns on the Sunday
+LJSpeech-1.1/wavs/LJ023-0119.wav|that will refuse to amend the Constitution by the arbitrary exercise of judicial power -- amended by judicial say-so.
+LJSpeech-1.1/wavs/LJ045-0121.wav|it does not appear that he ever lost his job because of its activities, although he may well not have been aware of that fact.
+LJSpeech-1.1/wavs/LJ031-0020.wav|two rooms were prepared.
+LJSpeech-1.1/wavs/LJ035-0201.wav|The fact that Truly found Fritz in the northwest corner of the floor, near the point where the rifle was found, supports Fritz' recollection.
+LJSpeech-1.1/wavs/LJ003-0015.wav|For this and other acts of misconduct there was the discipline of the refractory ward, or "strong room" on the debtors' side.
+LJSpeech-1.1/wavs/LJ044-0043.wav|he stated that he was then alone in his efforts on behalf of FPCC, but he attributed his lack of support
+LJSpeech-1.1/wavs/LJ003-0210.wav|at night they were placed in the fifteen cells, two, three, or more together, according to the total number to be accommodated.
+LJSpeech-1.1/wavs/LJ046-0235.wav|Except for its special "trip index" file of four hundred names,
+LJSpeech-1.1/wavs/LJ022-0028.wav|I am reminded sometimes of what President Wilson once said:
+LJSpeech-1.1/wavs/LJ019-0251.wav|Yet this principle, as the committee pointed out, "must now be accepted as the foundation of prison discipline,"
+LJSpeech-1.1/wavs/LJ044-0114.wav|he received a letter from somebody in New York, some Communist -- probably from New York -- I am not sure from where -- from some Communist leader and he was very happy,
+LJSpeech-1.1/wavs/LJ014-0084.wav|Returning to her own home, where Manning meantime had been calmly smoking and talking to the neighbors over the basement wall,
+LJSpeech-1.1/wavs/LJ043-0155.wav|She later gave the following testimony. Question:
+LJSpeech-1.1/wavs/LJ023-0010.wav|we asked the nation to turn over all of its privately held gold, dollar for dollar, to the government of the United States.
+LJSpeech-1.1/wavs/LJ046-0180.wav|PRS also maintains an album of photographs and descriptions of about twelve to fifteen individuals who are regarded as clear risks to the President
+LJSpeech-1.1/wavs/LJ029-0205.wav|for President Kennedy, stating that "in many respects Dallas County has isolated itself from the main stream of life in the world in this decade.
+LJSpeech-1.1/wavs/LJ018-0025.wav|More precise clues to the murderer were not long wanting; indeed the readiness with which they were produced and followed up
+LJSpeech-1.1/wavs/LJ020-0054.wav|Rub the bottom and sides of a baking-pan with sweet lard or butter. Do this with a bit of clean soft rag or tissue-paper,
+LJSpeech-1.1/wavs/LJ044-0167.wav|He asked his wife to help him to hijack an airplane to get there, but gave up that scheme when she refused.
+LJSpeech-1.1/wavs/LJ016-0395.wav|The diet of the condemned is the ordinary diet of the prison,
+LJSpeech-1.1/wavs/LJ041-0100.wav|finally attaining a male status or image in his own eyes, end quote. That apparently caused Oswald to become more self-confident,
+LJSpeech-1.1/wavs/LJ032-0257.wav|When she started to open the blanket, she saw the stock of the rifle.
+LJSpeech-1.1/wavs/LJ040-0118.wav|and threatened to use it on Mrs. Pic.
+LJSpeech-1.1/wavs/LJ028-0431.wav|The city walls, of which the ancients were so proud, appear here and there like low ridges far out on the plain;
+LJSpeech-1.1/wavs/LJ004-0003.wav|other earnest people, inspired doubtless by her noble example, were stirred up to activity in the same great work.
+LJSpeech-1.1/wavs/LJ013-0065.wav|and that he gained access to Miss Slack on pretense of conveying to her some funded property left her by an aunt.
+LJSpeech-1.1/wavs/LJ043-0108.wav|Oswald's employment problems became more difficult. He left his wife and child at the home of a friend, Mrs. Ruth Paine, of Irving, Texas.
+LJSpeech-1.1/wavs/LJ003-0023.wav|All the allowances of food passed through his hands; he had the control of the poor-box for chance charities,
+LJSpeech-1.1/wavs/LJ050-0273.wav|The Commission has, however, from its examination of the facts of President Kennedy's assassination
+LJSpeech-1.1/wavs/LJ014-0283.wav|The jury found him guilty of the latter only, with a point of law reserved. This was fully argued before three judges,
+LJSpeech-1.1/wavs/LJ030-0193.wav|Kellerman saw Governor Connally in his wife's lap and Special Agent Clinton J. Hill lying across the trunk of the car.
+LJSpeech-1.1/wavs/LJ002-0068.wav|Here were also lodged the gatesmen, the prisoners who had charge of the inner gates, and who were entrusted with the duty of escorting visitors from the gates
+LJSpeech-1.1/wavs/LJ023-0069.wav|with national power, intended as they said, "to form a more perfect union
+LJSpeech-1.1/wavs/LJ037-0067.wav|the transcript establishes that she was referring to the uncombed state of his hair, a description fully supported by a photograph of Oswald
+LJSpeech-1.1/wavs/LJ018-0359.wav|her object being first to dispose of the wife of a man for whom she had conceived a guilty passion,
+LJSpeech-1.1/wavs/LJ013-0202.wav|Coolness amounting almost to effrontery gave way to hopeless dejection.
+LJSpeech-1.1/wavs/LJ022-0171.wav|designed to improve the status of our transportation agencies.
+LJSpeech-1.1/wavs/LJ019-0126.wav|The creation of an expensive staff for supervision,
+LJSpeech-1.1/wavs/LJ034-0014.wav|These two small cartons had been moved from a stack about three aisles away.
+LJSpeech-1.1/wavs/LJ022-0188.wav|Certain proposals made to amend the Federal Reserve Act deserve prompt and favorable action by the Congress.
+LJSpeech-1.1/wavs/LJ014-0052.wav|In November of the first-named year occurred the wholesale slaughter of the Jermys in their house, Stanfield Hall, by the miscreant Rush.
+LJSpeech-1.1/wavs/LJ009-0135.wav|So many applications for admission were made to the sheriffs,
+LJSpeech-1.1/wavs/LJ036-0055.wav|Mrs. Bledsoe identified the shirt as the one Oswald was wearing and she stated she was certain that it was Oswald who boarded the bus.
+LJSpeech-1.1/wavs/LJ018-0115.wav|It was stated in evidence that the monies obtained by these forgeries amounted to eight thousand pounds or ten thousand pounds,
+LJSpeech-1.1/wavs/LJ011-0140.wav|in other words, they had advanced in eighteen thirty-two to the point at which the Lower House had arrived in eighteen thirty.
+LJSpeech-1.1/wavs/LJ046-0155.wav|under the name of the individual or group of individuals to whom that material related. End quote.
+LJSpeech-1.1/wavs/LJ045-0180.wav|and which he was to take to the School Book Depository. The Unanswered Questions.
+LJSpeech-1.1/wavs/LJ018-0139.wav|Soon afterwards the inspectors of bank-notes at the Bank of England detected the presentation at the bank of spurious notes on genuine paper.
+LJSpeech-1.1/wavs/LJ020-0014.wav|beating the batter smooth as you go on until all of the liquid and flour has gone in.
+LJSpeech-1.1/wavs/LJ018-0142.wav|By this time Brown alone had stolen three or four hundred sheets,
+LJSpeech-1.1/wavs/LJ042-0069.wav|I think to myself "How easy to Die" and "A Sweet Death, (to violins), end quote.
+LJSpeech-1.1/wavs/LJ005-0039.wav|constant inspection, regular employment, and humane treatment generally, with religious and moral instruction.
+LJSpeech-1.1/wavs/LJ014-0062.wav|He had failed in this as well as in the business of a publican, which he had at one time adopted.
+LJSpeech-1.1/wavs/LJ001-0179.wav|even when the woodcuts are very rude indeed,
+LJSpeech-1.1/wavs/LJ003-0163.wav|the misdemeanant tried or untried, the debtor who wished to avoid the discomfort of the crowded debtors' side, the outspoken newspaper editor,
+LJSpeech-1.1/wavs/LJ034-0028.wav|The "Rolling Readers" cartons, however, had not been moved by the floor layers and had apparently been taken to the window from their regular position for some particular purpose.
+LJSpeech-1.1/wavs/LJ039-0098.wav|Oswald's Marine Training
+LJSpeech-1.1/wavs/LJ009-0031.wav|The extract is from Mr. E. Gibbon Wakefield's brochure, the date eighteen twenty-eight, just three years after Fauntleroy's death.
+LJSpeech-1.1/wavs/LJ034-0022.wav|testified that twenty identifiable fingerprints and eight palmprints were developed on these cartons.
+LJSpeech-1.1/wavs/LJ027-0169.wav|such as the triton, the salamander, etc., which are therefore called "caducibranchs," but the frog still passes on.
+LJSpeech-1.1/wavs/LJ016-0326.wav|Lord Advocate, Mr. Charles Neate, Mr. William Ewart, and last, but not least, Mr. John Bright
+LJSpeech-1.1/wavs/LJ019-0294.wav|The prison buildings were in many places out of repair; other houses often overlooked them.
+LJSpeech-1.1/wavs/LJ046-0031.wav|The protective task is further complicated by the reluctance of Presidents to take security precautions which might interfere with the performance of their duties,
+LJSpeech-1.1/wavs/LJ018-0171.wav|"Mother plates" for engraving the body of the notes lay about, and other plates for various processes.
+LJSpeech-1.1/wavs/LJ049-0071.wav|If the advance preparation is thorough, and the protective devices and techniques employed are sound,
+LJSpeech-1.1/wavs/LJ019-0343.wav|they were not to be turnkeys or assistant turnkeys, neither wardsman nor yardsman, overseer,
+LJSpeech-1.1/wavs/LJ045-0182.wav|Instead of returning to Irving on November fifteen for his customary weekend visit, he remained in Dallas at his wife's suggestion because of the birthday party.
+LJSpeech-1.1/wavs/LJ017-0195.wav|and that Mrs. Wilson administered the same medicine again and again.
+LJSpeech-1.1/wavs/LJ047-0123.wav|It was apparent that he had made a self-serving statement to Agent Quigley. It became a matter of record in our files as a part of the case,
+LJSpeech-1.1/wavs/LJ049-0020.wav|It was not then replaced because the state of the art did not permit the development of a bulletproof top of sufficiently light weight
+LJSpeech-1.1/wavs/LJ048-0187.wav|In addition, Secret Service agents riding in the motorcade were trained to scan buildings as part of their general observation of the crowd of spectators.
+LJSpeech-1.1/wavs/LJ033-0044.wav|In the garage were the personal belongings of the Oswald family including, as the evidence has shown, the rifle wrapped in the old brown and green blanket.
+LJSpeech-1.1/wavs/LJ050-0130.wav|Finally, these agreements with Federal and local authorities will be of little value
+LJSpeech-1.1/wavs/LJ024-0126.wav|My record as Governor and President proves my devotion to those liberties.
+LJSpeech-1.1/wavs/LJ008-0016.wav|Lawrence Jones, a burglar, was in seventeen ninety-three ordered for execution in Hatton Garden, near the house he had robbed;
+LJSpeech-1.1/wavs/LJ043-0101.wav|or whether he brought the Russian language newspaper with him one day after his other difficulties became clear.
+LJSpeech-1.1/wavs/LJ013-0044.wav|and tempted him to take to fraudulent insurance as a certain method of achieving fortune.
+LJSpeech-1.1/wavs/LJ044-0229.wav|and had lost his desire to do so because of the bureaucracy and red tape which he had encountered.
+LJSpeech-1.1/wavs/LJ022-0197.wav|We have survived all of the arduous burdens and the threatening dangers of a great economic calamity.
+LJSpeech-1.1/wavs/LJ010-0120.wav|to commit outrage, to set fire to houses, throw hand-grenades into the carriages of ministers;
+LJSpeech-1.1/wavs/LJ048-0034.wav|no law enforcement agency had any information to connect Oswald with the attempted shooting of General Walker.
+LJSpeech-1.1/wavs/LJ020-0050.wav|Toss the lump dough upon it and knead thoroughly for five minutes.
+LJSpeech-1.1/wavs/LJ049-0200.wav|It is suggested that an organization shorn of its power to investigate all the possibilities of danger to the President
+LJSpeech-1.1/wavs/LJ024-0135.wav|liberal-minded judiciary necessary to furnish quicker and cheaper justice from bottom to top.
+LJSpeech-1.1/wavs/LJ005-0071.wav|differed little from that kept in view in the construction of the latest and best modern jails.
+LJSpeech-1.1/wavs/LJ048-0083.wav|were discussed twice officially by the special agent in charge of the FBI office in Dallas. As discussed in chapter two,
+LJSpeech-1.1/wavs/LJ004-0232.wav|Ironing it will be remembered, was a distinguishing badge, so that when the jail was cleared the free might be readily known from the captive, and escapes prevented.
+LJSpeech-1.1/wavs/LJ004-0008.wav|Mr. Neild, a second Howard,
+LJSpeech-1.1/wavs/LJ028-0213.wav|Nor does the royal record of Babylon contain the only contemporary account of the fall of the city,
+LJSpeech-1.1/wavs/LJ008-0184.wav|Precautions had been taken by the erection of barriers, and the posting of placards at all the avenues to the Old Bailey, on which was printed,
+LJSpeech-1.1/wavs/LJ005-0085.wav|Classification was insisted upon, in the manner laid down by the twenty-four George the third cap. fifty-four,
+LJSpeech-1.1/wavs/LJ012-0182.wav|After the murder the villains divided the spoil, and went on to Probert's cottage, and supped off pork-chops brought down on purpose.
+LJSpeech-1.1/wavs/LJ042-0099.wav|gave Oswald an income which he said approximated that of the director of the factory in which he worked.
+LJSpeech-1.1/wavs/LJ042-0214.wav|to the bitter destruction of all and everything. I am quite sure these people must hate not only the government
+LJSpeech-1.1/wavs/LJ016-0026.wav|managed to get away in the very same spot where his namesake had nine years before so miserably failed.
+LJSpeech-1.1/wavs/LJ044-0146.wav|On June twenty-four, nineteen sixty-three, he applied for a new passport
+LJSpeech-1.1/wavs/LJ045-0207.wav|He could not keep them with him in Dallas, where at least he could see his children whom, several witnesses testified, he seemed to love.
+LJSpeech-1.1/wavs/LJ018-0149.wav|Buncher was then tracked to North Kent Terrace, New Cross, where a Mr. and Mrs. Campbell resided,
+LJSpeech-1.1/wavs/LJ018-0082.wav|he had yielded to an irresistible impulse to kill him.
+LJSpeech-1.1/wavs/LJ016-0253.wav|that the ceremony may be made more mechanical, thus rendering the personal intervention of a skilled functionary unnecessary.
+LJSpeech-1.1/wavs/LJ025-0130.wav|Touch one of them with the end of a fine human hair
+LJSpeech-1.1/wavs/LJ003-0004.wav|No remonstrance was attended to,
+LJSpeech-1.1/wavs/LJ003-0232.wav|These were fetched away in a large tub on a truck by a turnkey.
+LJSpeech-1.1/wavs/LJ023-0041.wav|It is a quiet crisis. There are no lines of depositors outside closed banks.
+LJSpeech-1.1/wavs/LJ032-0132.wav|On November twenty-two, however, before surrendering possession of the rifle to the FBI Laboratory,
+LJSpeech-1.1/wavs/LJ003-0075.wav|thirty-five tried and thirty-seven untried. Of the former, three were transports for life,
+LJSpeech-1.1/wavs/LJ008-0226.wav|the latter unsexed and brutalized by dissipation,
+LJSpeech-1.1/wavs/LJ017-0261.wav|Seven were found guilty of murder on the high seas, and one, Carlos, acquitted.
+LJSpeech-1.1/wavs/LJ005-0234.wav|The visits of friends was once more unreservedly allowed, and these incomers freely brought in extra provisions and beer.
+LJSpeech-1.1/wavs/LJ038-0028.wav|Brewer met McDonald and the other policemen at the alley exit door,
+LJSpeech-1.1/wavs/LJ014-0026.wav|running at a dog-trot into London, and others swore that they plainly recognized him as the man seen soon afterwards in the lane.
+LJSpeech-1.1/wavs/LJ024-0036.wav|If by that phrase "packing the Court" it is charged that I wish to place on the bench spineless puppets
+LJSpeech-1.1/wavs/LJ014-0234.wav|He died impenitent, declaring that he had no belief, and that it was idle to ask forgiveness of God.
+LJSpeech-1.1/wavs/LJ009-0035.wav|in the other opposite were the convicts capitally convicted who had been respited.
+LJSpeech-1.1/wavs/LJ012-0178.wav|Thurtell drove him down in a gig, "to be killed as he traveled," in Thurtell's own words.
+LJSpeech-1.1/wavs/LJ009-0157.wav|But there is little doubt that as executions became more rare they made more impression on the public mind.
+LJSpeech-1.1/wavs/LJ016-0446.wav|but ineffectively, by running his head against his cell wall.
+LJSpeech-1.1/wavs/LJ018-0324.wav|much uneasiness was caused at one of the convict prisons by information that bribery on a large scale was again at work amongst the officials.
+LJSpeech-1.1/wavs/LJ032-0209.wav|He stated, however, that while he, quote, found no differences, end quote, between the rifles in the two photographs, he could not make a, quote, positive identification
+LJSpeech-1.1/wavs/LJ030-0003.wav|Visits to Other Texas Cities
+LJSpeech-1.1/wavs/LJ002-0091.wav|and what remained was divided into a master's and a common side for female felons.
+LJSpeech-1.1/wavs/LJ006-0048.wav|To these were still added an average of about fifty expecting the last penalty of the law; a certain number of transports awaiting removal to the colonies;
+LJSpeech-1.1/wavs/LJ005-0300.wav|and the establishment of municipal institutions upon a proper footing,
+LJSpeech-1.1/wavs/LJ009-0104.wav|the women set up a yell, which is mixed with a rustling noise, occasioned by the removal of those whose hysterics have ended in fainting.
+LJSpeech-1.1/wavs/LJ016-0042.wav|The condition of the stone surface just mentioned assisted him in this, and he managed to get beyond the cistern to the railing below the chevaux-de-frise.
+LJSpeech-1.1/wavs/LJ028-0021.wav|Again, long before the stars have been scattered by the morning sun, you are on your way.
+LJSpeech-1.1/wavs/LJ016-0375.wav|As this burial-ground leads to the adjacent Central Criminal Court, accused murderers, on going to and returning from trial,
+LJSpeech-1.1/wavs/LJ019-0363.wav|he could close the "inadequate" prison, by declaring it unfit for the reception of prisoners.
+LJSpeech-1.1/wavs/LJ013-0111.wav|Burgess was eventually brought back to England, tried at the Central Criminal Court, and sentenced to transportation for life.
+LJSpeech-1.1/wavs/LJ044-0010.wav|April six to twenty-four, nineteen sixty-three Oswald's first public identification with that cause was in New Orleans.
+LJSpeech-1.1/wavs/LJ028-0154.wav|at the point where the city of the same name stands, eight days' journey from Babylon.
+LJSpeech-1.1/wavs/LJ029-0179.wav|Although there were critical editorials and letters to the editors, the news stories reflected the desire of Dallas officials to welcome the President with dignity and courtesy.
+LJSpeech-1.1/wavs/LJ024-0113.wav|The other groups is composed of those who honestly believe the amendment process is the best
+LJSpeech-1.1/wavs/LJ005-0158.wav|Bedding and clothing was still denied, but only in a few jails;
+LJSpeech-1.1/wavs/LJ043-0181.wav|that such concern is an important factor to consider in assessing possible motivation for the assassination.
+LJSpeech-1.1/wavs/LJ028-0322.wav|Thus did Zopyrus speak.
+LJSpeech-1.1/wavs/LJ024-0137.wav|and unwilling to assert legislative powers by writing into it their own political and economic policies.
+LJSpeech-1.1/wavs/LJ016-0191.wav|was given to drink, and not to be trusted.
+LJSpeech-1.1/wavs/LJ004-0020.wav|contrast ludicrously with the prison architecture based upon a century's experience of our own age.
+LJSpeech-1.1/wavs/LJ034-0188.wav|On November twenty-two, Fischer had apparently described the man as, quote, light-headed, end quote.
+LJSpeech-1.1/wavs/LJ046-0147.wav|were broad and flexible. All material is and was desired, accepted, and filed if it indicated or tended to indicate
+LJSpeech-1.1/wavs/LJ006-0215.wav|The beer-man brought in as much as he pleased; he sold it without the controlling presence of an officer.
+LJSpeech-1.1/wavs/LJ050-0014.wav|The incumbent has no technical qualifications in the area of Presidential protection.
+LJSpeech-1.1/wavs/LJ035-0013.wav|Having recently heard the sounds of rifles while on a hunting trip, Baker recognized the shots as that of a high-powered rifle; Quote,
+LJSpeech-1.1/wavs/LJ034-0143.wav|but he said he was unable to make a positive identification.
+LJSpeech-1.1/wavs/LJ047-0065.wav|This information led Hosty to review Oswald's file, from which he learned that Oswald had become a subscriber to the Worker,
+LJSpeech-1.1/wavs/LJ041-0084.wav|fitted into a general personality pattern of his: to do whatever was not wanted of him, a recalcitrant trend in his personality, end quote,
+LJSpeech-1.1/wavs/LJ025-0097.wav|Cellulose is widespread as a constituent of the skeletons of the lower animals
+LJSpeech-1.1/wavs/LJ030-0255.wav|approximately twenty or twenty-five minutes after the shots were fired.
+LJSpeech-1.1/wavs/LJ036-0063.wav|across Houston in front of the Depository Building, past the Triple Underpass into west Dallas, and south on Beckley.
+LJSpeech-1.1/wavs/LJ028-0375.wav|Slowly the few remaining walls fell, and were buried in their own ruins.
+LJSpeech-1.1/wavs/LJ040-0081.wav|for the relations between Marguerite Oswald and Ekdahl were stormy and they were finally divorced, after several separations and reunions,
+LJSpeech-1.1/wavs/LJ047-0052.wav|Having concluded that Oswald was not a security risk or potentially dangerous or violent,
+LJSpeech-1.1/wavs/LJ045-0098.wav|Nobody said anything good about him. End quote.
+LJSpeech-1.1/wavs/LJ046-0022.wav|which recommends additional personnel and facilities to enable the Secret Service to expand its protection capabilities.
+LJSpeech-1.1/wavs/LJ025-0115.wav|and from that day to this the rapid improvement of methods of investigation and the energy of a host of accurate observers
+LJSpeech-1.1/wavs/LJ048-0064.wav|While the Secret Service bears the principal responsibility for this failure,
+LJSpeech-1.1/wavs/LJ015-0222.wav|in such a way that Tester had possession of this key for a time.
+LJSpeech-1.1/wavs/LJ037-0203.wav|S and W Special two-inch Commando, serial number V five one zero two one zero, end quote,
+LJSpeech-1.1/wavs/LJ047-0031.wav|it learned also that Oswald had been discharged from the Marine Corps Reserve as undesirable in August nineteen sixty.
+LJSpeech-1.1/wavs/LJ006-0131.wav|and, if he chose, pass in, all provisions, money, clothes, and letters brought for prisoners by their friends.
+LJSpeech-1.1/wavs/LJ004-0177.wav|No prison dress was allowed; no reception-room was provided, no soap, towels, or baths.
+LJSpeech-1.1/wavs/LJ028-0179.wav|It is only from their ruins that we may hope to obtain accurate information of the strongest fortifications in the ancient world.
+LJSpeech-1.1/wavs/LJ014-0312.wav|His method was to obtain advances twice over on the same bills of lading or corn warrants. The duplicates were forged.
+LJSpeech-1.1/wavs/LJ006-0039.wav|and limited it to Newgate alone. Newgate indeed became the sole theme of their first report.
+LJSpeech-1.1/wavs/LJ045-0101.wav|In spite of these difficulties, however, and in the face of the economic problems that were always with them,
+LJSpeech-1.1/wavs/LJ044-0083.wav|was greatly impeded by the fact that the radio debate over WDSU on August twenty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ033-0052.wav|The period between eight and nine p.m., however, provided ample opportunity for Oswald to prepare the rifle for his departure the next morning.
+LJSpeech-1.1/wavs/LJ031-0188.wav|Members of the Presidential and Vice-Presidential parties filled the central compartment of the plane to witness the swearing in.
+LJSpeech-1.1/wavs/LJ047-0014.wav|the Office of Naval Intelligence, the FBI and the CIA. The information known to the FBI is summarized below.
+LJSpeech-1.1/wavs/LJ015-0087.wav|entertained literary, artistic, and dramatic celebrities; had a smart "turn out," attended all the race-meetings, and dressed in the latest fashion.
+LJSpeech-1.1/wavs/LJ038-0058.wav|Officer Ray Hawkins said
+LJSpeech-1.1/wavs/LJ008-0019.wav|Again, as late as eighteen oh nine and eighteen twelve, Execution Dock, on the banks of the Thames, was still retained.
+LJSpeech-1.1/wavs/LJ007-0068.wav|generally sided with his opponents. Nevertheless the inspectors summed up against him.
+LJSpeech-1.1/wavs/LJ032-0054.wav|"A. Hidell, P.O. Box two nine one five, Dallas, Texas," on March twenty, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ028-0126.wav|Berossus, a priest of the temple of Bel at Babylon, writing about two fifty B.C.,
+LJSpeech-1.1/wavs/LJ004-0223.wav|Mr. Brown confidently asserted that no jail in England now fed its inmates so well as did Newgate.
+LJSpeech-1.1/wavs/LJ049-0225.wav|The Commission feels the Secret Service and the FBI, as well as the State Department and the CIA when the President travels abroad,
+LJSpeech-1.1/wavs/LJ019-0301.wav|Some idea of the comparative uselessness of these small borough prisons was conveyed by some figures quoted by the committee.
+LJSpeech-1.1/wavs/LJ032-0256.wav|Marina Oswald was looking in the garage for parts to the baby's crib and thought that the parts might be in the blanket.
+LJSpeech-1.1/wavs/LJ026-0032.wav|as it were, into an assemblage of organisms which partake of the characters of both kingdoms and cannot without a certain violence
+LJSpeech-1.1/wavs/LJ046-0014.wav|at the time of President Kennedy's assassination.
+LJSpeech-1.1/wavs/LJ037-0156.wav|also examined the four cartridge cases found near the site of the homicide and compared them with the test cartridge cases fired from the Smith and Wesson revolver
+LJSpeech-1.1/wavs/LJ040-0144.wav|potentially dangerous, that, quote, his outlook on life had strongly paranoid overtones, end quote, or that he should be institutionalized.
+LJSpeech-1.1/wavs/LJ039-0057.wav|possessed the capability to hit his target with two out of three shots under the conditions described in chapter three.
+LJSpeech-1.1/wavs/LJ046-0115.wav|Adequacy of preventive intelligence operations of the Secret Service.
+LJSpeech-1.1/wavs/LJ012-0134.wav|Presently the proper person arrived from the consignees, but found the gold-dust gone.
+LJSpeech-1.1/wavs/LJ016-0027.wav|Escape seemed absolutely hopeless,
+LJSpeech-1.1/wavs/LJ029-0033.wav|particularly in large cities where the purpose was to let the President be seen by as many people as possible.
+LJSpeech-1.1/wavs/LJ007-0073.wav|to that line of conduct which his duty imposed on him
+LJSpeech-1.1/wavs/LJ030-0219.wav|She turned toward me and I grabbed her and put her back in the back seat, crawled up on top of the back seat and lay there.
+LJSpeech-1.1/wavs/LJ019-0288.wav|In them, according to the committee, the old objectionable practices were still in full force.
+LJSpeech-1.1/wavs/LJ042-0168.wav|He must be opposed to their basic foundations and representatives, end quote.
+LJSpeech-1.1/wavs/LJ025-0158.wav|the former will grow down into roots, the latter grow up into the stem and leaves of a vigorous bean-plant,
+LJSpeech-1.1/wavs/LJ005-0283.wav|and the tendency to crime in offenders not entirely hardened is confirmed by the language, the suggestions, and the example
+LJSpeech-1.1/wavs/LJ048-0213.wav|watching his car, watching the sides, watching the crowds, giving advice or asking advice from the Chief
+LJSpeech-1.1/wavs/LJ002-0291.wav|I come now to the debtors in Newgate. The quarters they occupied were divided, as I have said, into three principal divisions
+LJSpeech-1.1/wavs/LJ047-0043.wav|Oswald, quote, denied that he had ever denounced his U.S. citizenship, and that he had ever applied for Soviet citizenship specifically, end quote.
+LJSpeech-1.1/wavs/LJ023-0120.wav|It does not mean a judiciary so independent that it can deny the existence of facts which are universally recognized.
+LJSpeech-1.1/wavs/LJ023-0083.wav|The Court has been acting not as a judicial body, but as a policy-making body.
+LJSpeech-1.1/wavs/LJ021-0035.wav|saved debtors and creditors alike in many other fields of enterprise, such as loans on farm mortgages and home mortgages;
+LJSpeech-1.1/wavs/LJ008-0110.wav|The prisoners had not risen.
+LJSpeech-1.1/wavs/LJ034-0094.wav|whom he viewed in a police lineup on the night of the assassination, was the man he saw fire the shots from the sixth-floor window of the Depository Building.
+LJSpeech-1.1/wavs/LJ034-0058.wav|the Commission considered the possibility that Oswald handled these cartons as part of his normal duties.
+LJSpeech-1.1/wavs/LJ014-0335.wav|which were soon developed, much to that firm's subsequent anger and regret.
+LJSpeech-1.1/wavs/LJ013-0030.wav|The captain and crew, however, got safely to Jamaica, and so to England; the captain dying on the voyage home.
+LJSpeech-1.1/wavs/LJ020-0061.wav|Break the rolls apart from one another and eat warm. They are also good cold, and if the directions be followed implicitly, very good always.
+LJSpeech-1.1/wavs/LJ012-0117.wav|By-and-by the occupant of the room noticed something glittering in the center of the fire, which, to inspect more closely, he took out with the tongs.
+LJSpeech-1.1/wavs/LJ045-0058.wav|nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ018-0352.wav|but there was no doubt of his guilt, as the Lord Chief Justice told him, while expressing great regret at his rash assertion.
+LJSpeech-1.1/wavs/LJ005-0295.wav|that plaintiffs were unwilling to consign the defendants against whom they had obtained execution to confinement within its walls."
+LJSpeech-1.1/wavs/LJ046-0141.wav|Slightly more specific criteria were established for PRS personnel processing White House mail referred by the White House mailroom,
+LJSpeech-1.1/wavs/LJ030-0229.wav|At this point the cars were speeding through the underpass and had left the scene of the shooting,
+LJSpeech-1.1/wavs/LJ017-0143.wav|which followed close on its heels, although in that the verdict of "Not Guilty" was excusable, as the evidence was entirely circumstantial.
+LJSpeech-1.1/wavs/LJ045-0217.wav|both she and Mrs. Paine thought he had come home to make up after the fight on Monday.
+LJSpeech-1.1/wavs/LJ050-0195.wav|Inspection of Buildings
+LJSpeech-1.1/wavs/LJ046-0084.wav|for the President soon after the assassination, quote,
+LJSpeech-1.1/wavs/LJ014-0080.wav|She ran upstairs, told her husband, made him go down to look at her handiwork, and as O'Connor was not quite dead,
+LJSpeech-1.1/wavs/LJ044-0061.wav|there also seems to be no basis for his claim that he had distributed, quote,
+LJSpeech-1.1/wavs/LJ015-0009.wav|Cole's difficulties increased more and more; warrant-holders came down upon him demanding to realize their goods.
+LJSpeech-1.1/wavs/LJ006-0080.wav|by the inner gatesman, himself a convicted prisoner, and a "wardsman" or responsible head of a room.
+LJSpeech-1.1/wavs/LJ015-0287.wav|not only to ensure fair play and the surrender of the proceeds if the cheque was cashed, but to give timely notice if it was not,
+LJSpeech-1.1/wavs/LJ018-0004.wav|and as some time elapsed before the discovery and apprehension of the supposed murderer,
+LJSpeech-1.1/wavs/LJ046-0042.wav|As Chief Executive, the President controls the exercise of the vast, almost incalculable powers of the executive branch of the Federal Government.
+LJSpeech-1.1/wavs/LJ006-0177.wav|and it was not the least part of the blame imputed to him that he made special favorites of particular prisoners, retaining of his own accord in Newgate,
+LJSpeech-1.1/wavs/LJ046-0174.wav|At the time of the assassination there were approximately four hundred persons throughout the country who were subject to periodic review.
+LJSpeech-1.1/wavs/LJ037-0255.wav|testified that Commission Exhibit Number one sixty-two was the jacket worn by the man they saw on November twenty-two.
+LJSpeech-1.1/wavs/LJ029-0083.wav|On November four, Sorrels told Behn he believed security difficulties at the Trade Mart could be overcome by special precautions.
+LJSpeech-1.1/wavs/LJ021-0074.wav|in wage cutting which depresses markets and destroys purchasing power.
+LJSpeech-1.1/wavs/LJ015-0106.wav|A reward was forthwith offered for Robson's apprehension.
+LJSpeech-1.1/wavs/LJ014-0200.wav|he seemed while in Newgate to realize thoroughly that there was no hope for him, and was determined to face his fate bravely.
+LJSpeech-1.1/wavs/LJ022-0062.wav|It proposes, by means of old age pensions,
+LJSpeech-1.1/wavs/LJ033-0069.wav|As she [Marina] told me about it I stepped onto the blanket roll
+LJSpeech-1.1/wavs/LJ050-0215.wav|these figures suggest that the agents of the Secret Service are substantially overworked.
+LJSpeech-1.1/wavs/LJ041-0005.wav|He concluded that school had nothing to offer him.
+LJSpeech-1.1/wavs/LJ018-0141.wav|led to the employment of the police, and the offer of a reward of fifteen hundred pounds for the detection of the offenders.
+LJSpeech-1.1/wavs/LJ048-0070.wav|reflect keen awareness of the necessity of communicating a much wider range of intelligence information to the Service.
+LJSpeech-1.1/wavs/LJ006-0090.wav|within this the greatest nursery of crime in London, should exercise even the commonest care for the personal decency or comfort of the prisoners.
+LJSpeech-1.1/wavs/LJ003-0287.wav|to undertake some kind of reform.
+LJSpeech-1.1/wavs/LJ025-0161.wav|of the oily, starchy, saccharine and woody substances contained in the full-grown plant and its seeds
+LJSpeech-1.1/wavs/LJ010-0075.wav|He married again and came to London, where he soon became notorious as a reckless gambler and a politician holding the most extreme views.
+LJSpeech-1.1/wavs/LJ017-0064.wav|he made use of the remarkable words, quote, If it is necessary for my soul's sake to confess this murder,
+LJSpeech-1.1/wavs/LJ034-0126.wav|shows three employees looking out of the fifth-floor window directly below the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ015-0151.wav|The philanthropy he had displayed in a small way when less prosperous became now a passion.
+LJSpeech-1.1/wavs/LJ018-0088.wav|I propose next to describe the leading features of the most important of these.
+LJSpeech-1.1/wavs/LJ015-0224.wav|the difficulty was to get a copy of the second key.
+LJSpeech-1.1/wavs/LJ023-0044.wav|the need to meet the unanswered challenge of one-third of a Nation ill-nourished, ill-clad, ill-housed.
+LJSpeech-1.1/wavs/LJ041-0012.wav|Once a group of white boys beat him up for sitting in the Negro section of a bus, which he apparently did simply out of ignorance.
+LJSpeech-1.1/wavs/LJ040-0019.wav|Under our system of justice no forum could properly make that determination unless Oswald were before it.
+LJSpeech-1.1/wavs/LJ006-0145.wav|He could indulge in snuff if a snuff-taker,
+LJSpeech-1.1/wavs/LJ040-0075.wav|Apparently that action was taken in anticipation of her marriage to Edwin A. Ekdahl, which took place in May of nineteen forty-five.
+LJSpeech-1.1/wavs/LJ044-0026.wav|Marina Oswald said she signed that name, apparently chosen because it rhymed with "Fidel,"
+LJSpeech-1.1/wavs/LJ050-0106.wav|to devise a practical system which has any reasonable possibility of revealing such malcontents.
+LJSpeech-1.1/wavs/LJ029-0112.wav|in nineteen thirty-six, as testified that the traditional parade route in Dallas was along Main Street, since the tall buildings along the street
+LJSpeech-1.1/wavs/LJ035-0073.wav|was one minute and thirty seconds.
+LJSpeech-1.1/wavs/LJ010-0175.wav|Oxford, who had been pacing backwards and forwards with his hands under the lapels of his coat, saw the carriage approach.
+LJSpeech-1.1/wavs/LJ025-0082.wav|But although Cuvier's leading diagnosis of the animal from the plant will not stand a strict test,
+LJSpeech-1.1/wavs/LJ048-0268.wav|Therefore, the use of intoxicating liquor of any kind, including beer and wine, by members of the White House Detail
+LJSpeech-1.1/wavs/LJ026-0139.wav|It is probable that new leaves and new tissues generally are always formed in part from this reserve starch.
+LJSpeech-1.1/wavs/LJ038-0112.wav|As previously indicated, Marina Oswald testified that she took the two pictures with her husband's Imperial Reflex camera
+LJSpeech-1.1/wavs/LJ007-0186.wav|the undue authority given to prisoners, the levying of garnish under another name
+LJSpeech-1.1/wavs/LJ012-0173.wav|The event which he styles calamitous we may well characterize as one of the most deliberately atrocious murders on record.
+LJSpeech-1.1/wavs/LJ048-0035.wav|It was against this background and consistent with the criteria followed by the FBI prior to November twenty-two
+LJSpeech-1.1/wavs/LJ046-0073.wav|and the greater diversity of the audiences he must face in a world torn by conflicting ideologies.
+LJSpeech-1.1/wavs/LJ005-0012.wav|yet there were those who, wedded to ancient ideas, were intolerant of change; they would not admit the existence of any evils.
+LJSpeech-1.1/wavs/LJ026-0103.wav|Just as the solid food of animals must be digested in preparation for absorption,
+LJSpeech-1.1/wavs/LJ002-0122.wav|The number of arrests actually made was one hundred fourteen thousand, three hundred for the kingdom, and seven thousand twenty for Middlesex.
+LJSpeech-1.1/wavs/LJ021-0062.wav|in great part through the cooperation of American business brought about under the codes.
+LJSpeech-1.1/wavs/LJ018-0297.wav|It was the discovery of this flaw in the banking system which had encouraged the Americans to attempt this crime.
+LJSpeech-1.1/wavs/LJ010-0262.wav|charged him with the offense.
+LJSpeech-1.1/wavs/LJ003-0130.wav|His wife visited him in Newgate, and wrote a pitiable account of the state in which she found her husband.
+LJSpeech-1.1/wavs/LJ040-0139.wav|while he liked Youth House, he missed the freedom of doing what he wanted. He indicated that he did not miss his mother, end quote.
+LJSpeech-1.1/wavs/LJ040-0229.wav|in a sustained relationship with one therapist might bring results.
+LJSpeech-1.1/wavs/LJ006-0111.wav|He could trump up a false complaint against his fellow-prisoner, and so get him punished;
+LJSpeech-1.1/wavs/LJ028-0500.wav|The ruins reach the height of about forty feet.
+LJSpeech-1.1/wavs/LJ040-0090.wav|Pic did turn over part of his income to his mother,
+LJSpeech-1.1/wavs/LJ015-0201.wav|There were still big prizes, however, to tempt the daring, and none appealed more to the thievish instinct than the custom of transmitting gold by rail.
+LJSpeech-1.1/wavs/LJ008-0283.wav|two others were sentenced to three months' imprisonment, and the balance to varying terms.
+LJSpeech-1.1/wavs/LJ007-0224.wav|In a resolution passed by the Court of Aldermen on eighteenth March, eighteen forty-two,
+LJSpeech-1.1/wavs/LJ045-0035.wav|She testified that Oswald, quote, was very happy, end quote, about the birth of the child.
+LJSpeech-1.1/wavs/LJ016-0025.wav|who, while detained in the press-yard as a capital convict, under sentence of death for burglary,
+LJSpeech-1.1/wavs/LJ012-0295.wav|The woman Gale was also found guilty, but sentence of death was only passed on Greenacre.
+LJSpeech-1.1/wavs/LJ007-0082.wav|At the time the Lords' Committee sat there were eight thus retained in Newgate, and a return in the appendix of the Lords' report
+LJSpeech-1.1/wavs/LJ048-0239.wav|The lobby of the hotel and the areas adjacent to the quarters of the President were guarded during the night
+LJSpeech-1.1/wavs/LJ018-0213.wav|which in dramatic interest can compare with the conviction of William Roupell for forgery.
+LJSpeech-1.1/wavs/LJ011-0055.wav|Edmund Angelini, to take Fauntleroy's place.
+LJSpeech-1.1/wavs/LJ021-0129.wav|The employer who turns away from impartial agencies of peace,
+LJSpeech-1.1/wavs/LJ034-0157.wav|Brennan stated, quote, I could at that time -- I could, with all sincerity, identify him as being the same man, end quote.
+LJSpeech-1.1/wavs/LJ046-0122.wav|Many persons call themselves to the attention of PRS by attempting to visit, the President
+LJSpeech-1.1/wavs/LJ048-0041.wav|six hundred thirty-six thousand, three hundred seventy-one investigative matters during fiscal year nineteen sixty-three
+LJSpeech-1.1/wavs/LJ039-0202.wav|At fifteen yards each man's shots landed within the size of a dime.
+LJSpeech-1.1/wavs/LJ046-0030.wav|particularly when the President is traveling in this country or abroad.
+LJSpeech-1.1/wavs/LJ039-0097.wav|made an almost stationary target while he was aiming in, very little movement if any, end quote.
+LJSpeech-1.1/wavs/LJ031-0016.wav|Admiral Burkley, the President's physician, arrived at the hospital, quote, between three and five minutes following the arrival of the President, end quote.
+LJSpeech-1.1/wavs/LJ030-0075.wav|followed closely behind the President's automobile.
+LJSpeech-1.1/wavs/LJ013-0226.wav|The governor insisted, but Courvoisier showed great reluctance to strip.
+LJSpeech-1.1/wavs/LJ050-0146.wav|Automatic data processing
+LJSpeech-1.1/wavs/LJ046-0043.wav|As Commander in Chief of the Armed Forces, he must maintain ultimate authority over the development and disposition of our military power.
+LJSpeech-1.1/wavs/LJ039-0111.wav|head, Records Branch, Personnel Department, Headquarters U.S. Marine Corps,
+LJSpeech-1.1/wavs/LJ039-0036.wav|On April twenty-three, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ021-0148.wav|may be determined and any later adjustments shall be made either by agreement or, in case of disagreement,
+LJSpeech-1.1/wavs/LJ038-0246.wav|was able to determine that, this picture was taken with the Imperial Reflex camera owned by Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ047-0018.wav|for the purpose of correlating information inasmuch as he was considered a possible security risk in the event he returned to this country, end quote.
+LJSpeech-1.1/wavs/LJ003-0080.wav|two committed by the Bankruptcy Commissioner, one for perjury, and two transports.
+LJSpeech-1.1/wavs/LJ026-0051.wav|These comparisons will, however, be translated into terms applicable to any species of higher plants or animals.
+LJSpeech-1.1/wavs/LJ045-0158.wav|He spent quite a bit of time putting away diapers and played with the children on the street.
+LJSpeech-1.1/wavs/LJ018-0034.wav|and accompanied by Mr. Death the jeweler and the cabman, went down to Liverpool and took the first steamer across the Atlantic.
+LJSpeech-1.1/wavs/LJ019-0067.wav|The opposing champions were not slow to find faults and flaws in the system they condemned.
+LJSpeech-1.1/wavs/LJ028-0332.wav|Once more, however, he waited till the interval appointed had gone by, and then leading the troops to the place where the four thousand were,
+LJSpeech-1.1/wavs/LJ013-0037.wav|The crime of fraudulent insurance they declared was very common, and the underwriters must have lost great sums in this way.
+LJSpeech-1.1/wavs/LJ048-0192.wav|show the foot patrolmen facing the passing motorcade, and not the adjacent crowds and buildings, as the procession passed.
+LJSpeech-1.1/wavs/LJ037-0202.wav|identified records of Seaport Traders, Incorporated, which showed that a, quote, point three eight
+LJSpeech-1.1/wavs/LJ047-0124.wav|and if we determined that the course of the investigation required us to clarify or face him down with this information, we would do it at the appropriate time.
+LJSpeech-1.1/wavs/LJ016-0034.wav|In one corner of the airing yard stood a cistern at some height from the ground;
+LJSpeech-1.1/wavs/LJ011-0151.wav|only child of a gentleman of large property in Cheshire, of which county he was actually high sheriff at the time of his daughter's abduction.
+LJSpeech-1.1/wavs/LJ012-0088.wav|May was smuggled in through a window on the esplanade behind an opened umbrella. When the place was quite deserted
+LJSpeech-1.1/wavs/LJ030-0167.wav|Mrs. Kennedy then heard a second shot and saw the President's skull torn open under the impact of the bullet.
+LJSpeech-1.1/wavs/LJ019-0383.wav|The whole question assumed sufficient importance to become a part of the Government program when Lord Beaconsfield took office in eighteen seventy-four.
+LJSpeech-1.1/wavs/LJ013-0256.wav|Good had told her, much to her perturbation, that she was to move from South Street to Roehampton, and one day he fetched her.
+LJSpeech-1.1/wavs/LJ050-0041.wav|This was the consequence of the Service's lack of an adequate investigative staff,
+LJSpeech-1.1/wavs/LJ002-0196.wav|The foregoing rentals and payments for privileges, together with fees exacted on commitment and discharge, went to the marshal or keeper of the prison,
+LJSpeech-1.1/wavs/LJ028-0427.wav|The enormous amount of debris which buried the palaces and temples and walls of Nebuchadnezzar's city, in places to the depth of a hundred feet,
+LJSpeech-1.1/wavs/LJ035-0178.wav|Sgt. D. V. Harkness of the Dallas police
+LJSpeech-1.1/wavs/LJ034-0038.wav|This print which had been cut out of the box was also forwarded to the FBI and Latona identified it as Oswald's right palmprint.
+LJSpeech-1.1/wavs/LJ019-0049.wav|the principles of which were debated by disputants of widely opposite opinions with an earnestness that sometimes bordered upon acrimony.
+LJSpeech-1.1/wavs/LJ029-0072.wav|The White House staff informed the Secret Service
+LJSpeech-1.1/wavs/LJ049-0165.wav|which first appeared in the appropriation of the Department of Justice in nineteen ten under the heading, quote, Miscellaneous Objects, end quote.
+LJSpeech-1.1/wavs/LJ009-0116.wav|On the following day the capital convicts, whose companions have been hanged, are required to return thanks for their narrow escape.
+LJSpeech-1.1/wavs/LJ034-0148.wav|In another interview with FBI agents on January seven, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ007-0153.wav|is generally known to all the worst men in the country; not only without the inclination, but almost without the ability of returning to an honest life."
+LJSpeech-1.1/wavs/LJ006-0289.wav|We have seen that male prisoners visited their female relations and friends on the female side. Besides this,
+LJSpeech-1.1/wavs/LJ005-0206.wav|At Godmanchester there was no jail, but a cage to secure prisoners till they could be taken before a magistrate.
+LJSpeech-1.1/wavs/LJ004-0225.wav|the practice was falling into abeyance, and the prisoners seldom required private assistance to eke out their meals.
+LJSpeech-1.1/wavs/LJ006-0013.wav|Sir Frederick Roe was of the same opinion as regards the appointment, but he would give the inspectors the power of acting as well as reporting.
+LJSpeech-1.1/wavs/LJ045-0141.wav|in an increasingly complex conspiracy against him.
+LJSpeech-1.1/wavs/LJ030-0251.wav|None stayed at the scene of the shooting, and none entered the Texas School Book Depository Building at or immediately after the shooting.
+LJSpeech-1.1/wavs/LJ049-0036.wav|and by the passengers in the jump seats.
+LJSpeech-1.1/wavs/LJ021-0030.wav|Our first problem was, of course, the banking situation because, as you know, the banks had collapsed.
+LJSpeech-1.1/wavs/LJ047-0244.wav|Revill promptly made a memorandum of this conversation in which the quoted statement appears.
+LJSpeech-1.1/wavs/LJ017-0115.wav|but it was proved that Palmer tried hard to get possession of the jar containing them,
+LJSpeech-1.1/wavs/LJ048-0066.wav|The handbook referred thus to, quote, the possibility of an attempt against the person or safety of the President, end quote.
+LJSpeech-1.1/wavs/LJ034-0029.wav|The "Rolling Readers" boxes contained, instead of books, light blocks used as reading aids.
+LJSpeech-1.1/wavs/LJ032-0275.wav|the Commission concluded that the rifle used to assassinate President Kennedy and wound Governor Connally was owned and possessed by Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ030-0002.wav|Chapter two. The Assassination: Part two.
+LJSpeech-1.1/wavs/LJ007-0086.wav|at Buckingham and Hereford for eleven years
+LJSpeech-1.1/wavs/LJ016-0427.wav|He tripped up the chapel-stairs to hear the condemned sermon, and came out with cheerful alacrity on the morning he was to die.
+LJSpeech-1.1/wavs/LJ021-0019.wav|working through vast agencies of commerce and employing great masses of men in movements of production
+LJSpeech-1.1/wavs/LJ031-0123.wav|This punctuate missile wound, about two-fifths inch in diameter (one centimeter) and located approximately five inches above the left knee,
+LJSpeech-1.1/wavs/LJ007-0015.wav|it had the bad effect of distracting attention,
+LJSpeech-1.1/wavs/LJ046-0144.wav|When requested to provide a specific statement of the standards employed by PRS in deciding what information to seek and retain,
+LJSpeech-1.1/wavs/LJ039-0091.wav|Sergeant Zahm expressed the opinion that the shot which struck President Kennedy in the neck at one hundred seventy-six point nine
+LJSpeech-1.1/wavs/LJ007-0219.wav|The effects of the imprisonment are to vitiate its inmates, to extend their acquaintanceship with each other,
+LJSpeech-1.1/wavs/LJ013-0183.wav|The valet's strange demeanor had attracted attention from the first.
+LJSpeech-1.1/wavs/LJ028-0407.wav|The people of Hillah, too, are a survival of Babylonian times.
+LJSpeech-1.1/wavs/LJ018-0275.wav|On Tarpey's defense it was stated that the idea of the theft had been suggested to him by a novel, at a time he had lost largely on the turf.
+LJSpeech-1.1/wavs/LJ050-0073.wav|result in some degree of interference with the personal liberty of those involved.
+LJSpeech-1.1/wavs/LJ024-0133.wav|First, because I believe that it can be passed at this session of the Congress.
+LJSpeech-1.1/wavs/LJ011-0044.wav|will no longer be herded indiscriminately together.
+LJSpeech-1.1/wavs/LJ044-0084.wav|brought out the history of his defection to the Soviet Union.
+LJSpeech-1.1/wavs/LJ029-0163.wav|and then west on Main, turning back to Elm at Houston and then out Stemmons Freeway to the Trade Mart.
+LJSpeech-1.1/wavs/LJ028-0017.wav|but soon the phantom lake vanishes, while others, still farther beyond, appear and vanish in rapid succession.
+LJSpeech-1.1/wavs/LJ028-0327.wav|On the tenth day after his flight he led out his detachment, and surrounding the thousand men,
+LJSpeech-1.1/wavs/LJ021-0119.wav|including a few of major importance.
+LJSpeech-1.1/wavs/LJ028-0445.wav|The mound still farther south is called Amran, because upon its summit stands the tomb of a Mohammedan saint of that name.
+LJSpeech-1.1/wavs/LJ017-0060.wav|The conviction that he would escape had taken so firm a hold of him,
+LJSpeech-1.1/wavs/LJ037-0235.wav|five feet, ten inches, wearing light clothing, a light-colored jacket" walk past her at a fast pace with his hands in his pocket.
+LJSpeech-1.1/wavs/LJ028-0499.wav|Ctesias mentions three hundred feet; probably they were not far from the truth.
+LJSpeech-1.1/wavs/LJ048-0016.wav|He likewise indicated he was disenchanted with Russia.
+LJSpeech-1.1/wavs/LJ010-0036.wav|Hocker was found upon the scene of his crime, irresistibly attracted thither, as was Theodore Gardelle.
+LJSpeech-1.1/wavs/LJ032-0015.wav|the evidence linking Oswald to the attempted killing of Maj. Gen. Edwin A. Walker (Resigned, U.S. Army) on April ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ023-0127.wav|When I commenced to review the situation with the problem squarely before me,
+LJSpeech-1.1/wavs/LJ019-0028.wav|Mr. Shaw-Lefevre, the Speaker of the House of Commons, Sir Benjamin Brodie,
+LJSpeech-1.1/wavs/LJ020-0073.wav|Sift the flour, salt and sugar into a bowl,
+LJSpeech-1.1/wavs/LJ016-0387.wav|All too have a fairly good appetite, and eat with relish, up to the last moment.
+LJSpeech-1.1/wavs/LJ048-0273.wav|that under ordinary circumstances he would have taken disciplinary action against those agents who had been drinking in clear violation of the regulation.
+LJSpeech-1.1/wavs/LJ003-0330.wav|The state side ceased to exist, and the female prisoners thus regained the space of which their quadrangle had been robbed.
+LJSpeech-1.1/wavs/LJ046-0165.wav|Most of these cases involved persons who used threatening language in communications to or about the President.
+LJSpeech-1.1/wavs/LJ007-0121.wav|There were no restraints, cards and backgammon were played, and the time passed in feasting and revelry.
+LJSpeech-1.1/wavs/LJ016-0418.wav|a tall, gaunt, repulsive-looking woman, who no more shrank from cowardly, secret crimes than from the penalty they entailed.
+LJSpeech-1.1/wavs/LJ015-0165.wav|But the peer rushed forward and shook Redpath warmly by the hand.
+LJSpeech-1.1/wavs/LJ040-0168.wav|She thought that he had detached himself from the world around him because, quote, no one in it ever met any of his needs for love, end quote.
+LJSpeech-1.1/wavs/LJ047-0134.wav|The FBI was advised by the rental agent for the Oswalds' apartment in New Orleans that they had moved again.
+LJSpeech-1.1/wavs/LJ047-0159.wav|She didn't have the exact address, and it is my recollection that we went to the phone book and looked it up,
+LJSpeech-1.1/wavs/LJ031-0099.wav|Moreover, the Parkland doctors took no further action after the President had expired because they concluded that it was beyond the scope of their permissible duties.
+LJSpeech-1.1/wavs/LJ009-0178.wav|In eighteen thirty-two the dissection of bodies cut down from the gallows, which had been decreed centuries previously, was abolished;
+LJSpeech-1.1/wavs/LJ016-0170.wav|greater attention was paid to the capital convicts, and the horrors of their situation while awaiting sentence
+LJSpeech-1.1/wavs/LJ045-0079.wav|As a result Oswald struck her, as to which she testified, quote,
+LJSpeech-1.1/wavs/LJ005-0276.wav|and for securing a proper system of prison discipline.
+LJSpeech-1.1/wavs/LJ042-0152.wav|While in the Soviet Union he wrote his longest and clearest piece of work, "The Collective."
+LJSpeech-1.1/wavs/LJ046-0006.wav|During this same period there were three other attacks on the life of a President,
+LJSpeech-1.1/wavs/LJ044-0218.wav|In this connection it should be noted that in speaking of the Worker, Oswald told Michael Paine, apparently in all seriousness, that, quote,
+LJSpeech-1.1/wavs/LJ008-0306.wav|I myself heard more than one of those whose lives were spared by that decision of the council,
+LJSpeech-1.1/wavs/LJ004-0179.wav|The allowance of food daily to felons was a fourpenny loaf,
+LJSpeech-1.1/wavs/LJ026-0112.wav|In an animal the foods in the circulating liquids, blood and lymph, are selected and absorbed by the cells.
+LJSpeech-1.1/wavs/LJ030-0082.wav|The agents on the front of the running boards had directions to move immediately to positions just to the rear of the President and Mrs. Kennedy when the President's car slowed
+LJSpeech-1.1/wavs/LJ007-0163.wav|all the tumultuous and diversified passions and emotions which circumstances like these must necessarily generate
+LJSpeech-1.1/wavs/LJ045-0159.wav|Question: How did you indicate to him that you were angry with him? Answer: By not talking to him.
+LJSpeech-1.1/wavs/LJ034-0196.wav|Another person who saw the assassin as the shots were fired was Amos L. Euins, age fifteen,
+LJSpeech-1.1/wavs/LJ019-0020.wav|Up to the twenty-first December, eighteen forty-two,
+LJSpeech-1.1/wavs/LJ004-0164.wav|There was still worse to come. Having explored the yards and adjacent day rooms, and sleeping cells, a door was unlocked,
+LJSpeech-1.1/wavs/LJ039-0165.wav|The target at two hundred sixty-five feet was placed to the right of the two hundred forty-foot target
+LJSpeech-1.1/wavs/LJ001-0131.wav|the size known as "Long primer" ought to be the smallest size used in a book meant to be read.
+LJSpeech-1.1/wavs/LJ003-0252.wav|of distinguishing a prisoner from a stranger and temporary visitor.
+LJSpeech-1.1/wavs/LJ045-0014.wav|His awareness is shown by a letter that he wrote to Arnold Johnson of the Communist Party U.S.A.,
+LJSpeech-1.1/wavs/LJ003-0241.wav|A further and a more iniquitous method of extorting money
+LJSpeech-1.1/wavs/LJ006-0095.wav|The rugs used were never washed; an order existed that the bedding should be taken into the yards to be aired, but it was not very punctually obeyed.
+LJSpeech-1.1/wavs/LJ028-0158.wav|The city wall is brought down on both sides to the edge of the stream,
+LJSpeech-1.1/wavs/LJ028-0040.wav|In the year six eighty-nine B.C., Sinacherib, King of Nineveh, captured Babylon,
+LJSpeech-1.1/wavs/LJ031-0132.wav|U.S. Representatives Henry B. Gonzalez,
+LJSpeech-1.1/wavs/LJ007-0241.wav|and again and again to protest against Newgate as it at present exists.
+LJSpeech-1.1/wavs/LJ048-0165.wav|buildings along the route can be checked by teams of law enforcement officers, and armed guards are posted along the route as appropriate.
+LJSpeech-1.1/wavs/LJ040-0230.wav|There are indications that he has suffered serious personality damage but if he can receive help quickly this might be repaired to some extent, end quote.
+LJSpeech-1.1/wavs/LJ031-0114.wav|Two additional operations were performed on Governor Connally for wounds which he had not realized he had sustained until he regained consciousness the following day.
+LJSpeech-1.1/wavs/LJ047-0061.wav|This assignment was given to Agent James P. Hosty, Jr. of the Dallas office upon Fain's retirement.
+LJSpeech-1.1/wavs/LJ002-0209.wav|How to make most profit out of the wretched denizens of the jail was the marshal's only care. He got a rent for the coffee-house and the bake-house;
+LJSpeech-1.1/wavs/LJ044-0060.wav|In view of the limited amount of public activity on Oswald's part before August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ050-0100.wav|The Commission recognizes that no set of meaningful criteria will yield the names of all potential assassins. Charles J. Guiteau,
+LJSpeech-1.1/wavs/LJ018-0127.wav|and he eventually succeeded, his wife making the first overtures, in persuading a lad named Brown to steal some of the bank paper.
+LJSpeech-1.1/wavs/LJ035-0033.wav|The stairway is located in the northwest corner of the Depository Building.
+LJSpeech-1.1/wavs/LJ022-0136.wav|examples of how the public money can be more effectively spent for the benefit of the American people.
+LJSpeech-1.1/wavs/LJ010-0242.wav|Nothing happened till Her Majesty returned to Buckingham Palace about six p.m., when, on descending Constitution Hill,
+LJSpeech-1.1/wavs/LJ034-0172.wav|Fischer looked up and watched the man in the window for ten or fifteen seconds and then started watching the motorcade, which came into view on Houston Street.
+LJSpeech-1.1/wavs/LJ042-0211.wav|is provided by the following, which was apparently written either on the ship coming back, or after his return from the Soviet Union, quote,
+LJSpeech-1.1/wavs/LJ039-0128.wav|Based on that training, his basic knowledge in sight manipulation and trigger squeeze and what not, I would say that he would be capable of sighting that rifle in well,
+LJSpeech-1.1/wavs/LJ032-0115.wav|A few minutes after the rifle was discovered on the sixth floor of the Depository Building
+LJSpeech-1.1/wavs/LJ018-0366.wav|Although sentence of death was passed on Edmunds, it was commuted to penal servitude for life;
+LJSpeech-1.1/wavs/LJ049-0223.wav|and direction of the activities of all existing agencies of Government which are in a position to and do, furnish information
+LJSpeech-1.1/wavs/LJ037-0212.wav|who attested that the fictitious Hidell was an American citizen and had not been convicted of a felony, was also in Oswald's handwriting.
+LJSpeech-1.1/wavs/LJ038-0270.wav|which would indicate that the bullet was not fired from the Mannlicher-Carcano rifle owned by Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ022-0112.wav|and I assure my fellow citizens that no energy will be spared in using these funds effectively
+LJSpeech-1.1/wavs/LJ031-0170.wav|During the drive Vice President Johnson, at Youngblood's instruction, kept below window level.
+LJSpeech-1.1/wavs/LJ019-0215.wav|Why not move the city prison bodily into this more rural spot, with its purer air and greater breathing space?
+LJSpeech-1.1/wavs/LJ022-0182.wav|To this end it was decided more than twenty years ago
+LJSpeech-1.1/wavs/LJ011-0289.wav|and creeping out into the garden at the back, climbed the wall, and got into the street.
+LJSpeech-1.1/wavs/LJ050-0047.wav|International Business Machines Corporation, and a panel of psychiatric and psychological experts.
+LJSpeech-1.1/wavs/LJ015-0261.wav|The whole strange story, the long incubation and the elaborate accomplishment of the plot,
+LJSpeech-1.1/wavs/LJ016-0162.wav|he fastened one end of the strap above mentioned to the hook, and then fell down.
+LJSpeech-1.1/wavs/LJ002-0090.wav|More than half their quadrangle had been partitioned off for another purpose,
+LJSpeech-1.1/wavs/LJ012-0024.wav|On his release an uncle, a slop-seller in Chatham, gave him a situation as "barker," or salesman,
+LJSpeech-1.1/wavs/LJ042-0061.wav|It seems that Oswald immediately attempted suicide -- a striking indication of how much he desired to remain in the Soviet Union.
+LJSpeech-1.1/wavs/LJ036-0100.wav|Notified of Whaley's statement, the police brought him to the police station that afternoon.
+LJSpeech-1.1/wavs/LJ016-0294.wav|But all, whether young or old, men or women, seemed to know nothing, feel nothing, to have no object but the gallows, and to laugh,
+LJSpeech-1.1/wavs/LJ031-0017.wav|since the riders in his car, quote, were not exactly aware what had happened, end quote, and the car went on to the Trade Mart first.
+LJSpeech-1.1/wavs/LJ040-0133.wav|She said that her pocketbook was searched, quote, because the children in this home were such criminals, dope fiends, and had been in criminal offenses,
+LJSpeech-1.1/wavs/LJ025-0071.wav|while in this condition their movements are, to all appearances, as spontaneous -- as much the product of volition -- as those of such animals.
+LJSpeech-1.1/wavs/LJ046-0015.wav|This study has led the Commission to conclude that the public interest might be served by any contribution it can make to the improvement of protective arrangements.
+LJSpeech-1.1/wavs/LJ018-0123.wav|a man named Burnett came with his wife and took up his residence at Whitchurch, Hampshire, at no great distance from Laverstock,
+LJSpeech-1.1/wavs/LJ016-0004.wav|Yet the jail, in spite of its fortress-like aspect, was by no means really safe.
+LJSpeech-1.1/wavs/LJ036-0155.wav|Whaley testified that Oswald was wearing either the gray zippered jacket or the heavy blue jacket.
+LJSpeech-1.1/wavs/LJ038-0070.wav|Captain Fritz returned to police headquarters from the Texas School Book Depository at two:fifteen after a brief stop at the sheriff's office.
+LJSpeech-1.1/wavs/LJ038-0056.wav|Nor did Oswald ever complain that he was hit with a gun, or injured in the back.
+LJSpeech-1.1/wavs/LJ036-0182.wav|She recalled that it was subsequent to the time the President had been shot.
+LJSpeech-1.1/wavs/LJ048-0282.wav|that it is reasonable to expect them to meet very high standards of personal conduct,
+LJSpeech-1.1/wavs/LJ044-0096.wav|Mr. Oswald handled himself very well, as usual, end quote.
+LJSpeech-1.1/wavs/LJ047-0112.wav|Knack was aware of the facts known to the FBI and recognized Oswald's false statements.
+LJSpeech-1.1/wavs/LJ020-0074.wav|hollow the heap in the center and pour in the milk, working down the flour into the liquid with a spoon or your hands until it is thoroughly melted.
+LJSpeech-1.1/wavs/LJ041-0165.wav|Shortly after Thornley finished reading that book the Marine unit to which both men were assigned was required to take part in a Saturday morning parade
+LJSpeech-1.1/wavs/LJ009-0109.wav|while the capital convicts who were lately in that black pew appear faint with emotion.
+LJSpeech-1.1/wavs/LJ050-0169.wav|and that many strongly held views are advanced in resistance to any suggestion that an effort be made to impose any degree of coordination.
+LJSpeech-1.1/wavs/LJ023-0008.wav|We were then in the midst of the great banking crisis.
+LJSpeech-1.1/wavs/LJ014-0208.wav|A murderous assault on a police constable, which so nearly ended fatally that the culprit was sentenced to death, although not executed,
+LJSpeech-1.1/wavs/LJ011-0177.wav|The fact was, Wakefield went on to say, an uncle of his had advanced Mr. Turner sixty thousand pounds, which had temporarily staved off ruin.
+LJSpeech-1.1/wavs/LJ006-0087.wav|It must be admitted that so far but little effort had been made to counteract the evils of indiscriminate association.
+LJSpeech-1.1/wavs/LJ044-0160.wav|he may not have intended to go to the Soviet Union directly, if at all. It appears that he really wanted to go to Cuba.
+LJSpeech-1.1/wavs/LJ012-0225.wav|they had also been seen to leave their cottage, carrying out a sack with something heavy inside. On this they were fully committed to Newgate for trial.
+LJSpeech-1.1/wavs/LJ048-0092.wav|The advance preparations in Dallas by Agent Winston G. Lawson of the White House detail have been described in chapter two.
+LJSpeech-1.1/wavs/LJ006-0255.wav|but all could still obtain as much extra, and of a luxurious kind, as their friends chose to bring them in.
+LJSpeech-1.1/wavs/LJ003-0170.wav|A Mr. Lundy, charged with forgery, was thus accommodated on the state side for upwards of five years.
+LJSpeech-1.1/wavs/LJ017-0220.wav|The six Spaniards, the Greek, and the Turk were in the same watch,
+LJSpeech-1.1/wavs/LJ009-0021.wav|in which it was said I had enlarged upon the heinous nature of his crime, and warned the public to avoid such conduct.
+LJSpeech-1.1/wavs/LJ016-0093.wav|They asked him how he had cut his hand, which was bleeding, and he said he had done it while working up on the roof.
+LJSpeech-1.1/wavs/LJ038-0156.wav|He stated that at the time the President was shot he was having lunch with "Junior" but he did not give Junior's last name.
+LJSpeech-1.1/wavs/LJ018-0182.wav|and no more bodies were to be bought
+LJSpeech-1.1/wavs/LJ008-0089.wav|and they were packed into a more limited space, disporting themselves as heretofore by brutal horse-play, coarse jests, and frantic yells.
+LJSpeech-1.1/wavs/LJ026-0040.wav|And in this connection the fact that some bacteria -- the simplest organisms known and devoid of chlorophyll
+LJSpeech-1.1/wavs/LJ041-0050.wav|In John Pic's view,
+LJSpeech-1.1/wavs/LJ032-0124.wav|by Sebastian F. Latona, supervisor of the Latent Fingerprint Section of the FBI's Identification Division.
+LJSpeech-1.1/wavs/LJ002-0112.wav|The frequency and extent of processes against debtors seventy or eighty years ago will appear almost incredible
+LJSpeech-1.1/wavs/LJ050-0251.wav|the amount of assistance that it can expect from other agencies.
+LJSpeech-1.1/wavs/LJ032-0021.wav|was a distributor of surplus Italian six point five-millimeter military rifles. During the evening of November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ023-0099.wav|In the case holding the New York Minimum Wage Law unconstitutional, Justice Stone said
+LJSpeech-1.1/wavs/LJ003-0295.wav|The committee seems to have fully realized, even at this early date (eighteen fifteen),
+LJSpeech-1.1/wavs/LJ018-0065.wav|At the disbandment of the force, as he was without resources, he turned his attention to hotel robberies, by which he lived for some years.
+LJSpeech-1.1/wavs/LJ013-0232.wav|The prisoner replied that he had left it in the mattress of which he had just been deprived.
+LJSpeech-1.1/wavs/LJ010-0146.wav|Attacks upon the sovereign, as I have said, became more common after the accession of the young Queen Victoria in eighteen thirty-eight.
+LJSpeech-1.1/wavs/LJ007-0188.wav|the misemployment of officers and prisoners by the governor.
+LJSpeech-1.1/wavs/LJ035-0005.wav|The Commission has found that Oswald's movements, as described by these witnesses,
+LJSpeech-1.1/wavs/LJ018-0009.wav|When the train arrived at Hackney station, a passenger who was about to enter one of the carriages found the cushions soaked with blood.
+LJSpeech-1.1/wavs/LJ048-0093.wav|With the assistance of Agent in Charge Sorrels of the Dallas field office of the Secret Service,
+LJSpeech-1.1/wavs/LJ021-0125.wav|it was natural that the workers should seek and obtain a statutory declaration of their constitutional right
+LJSpeech-1.1/wavs/LJ001-0159.wav|The paper that is used for ordinary books is exceedingly bad even in this country, but is beaten in the race for vileness
+LJSpeech-1.1/wavs/LJ045-0203.wav|Even though he had searched -- in the Marine Corps, in his ideal of communism, in the Soviet Union and in his attempt to get to Cuba
+LJSpeech-1.1/wavs/LJ008-0277.wav|Convicted offenders might have good or bad luck; they might be arraigned when their particular crime was uncommon, and were then nearly certain to escape;
+LJSpeech-1.1/wavs/LJ031-0073.wav|According to Dr. Clark, the small bullet hole on the right rear of the President's head discovered during the subsequent autopsy
+LJSpeech-1.1/wavs/LJ011-0235.wav|as in the burglary at Lambeth Palace, when the thieves were fortunately disappointed, the archbishop having, before he left town,
+LJSpeech-1.1/wavs/LJ016-0211.wav|But, fortunately for him, just at the last moment Calcraft was set free.
+LJSpeech-1.1/wavs/LJ025-0116.wav|have given greater and greater breadth and firmness to Schwann's great generalization
+LJSpeech-1.1/wavs/LJ010-0289.wav|That offense was a brutal assault upon Her Majesty, whom he struck in the face with a small stick just as she was leaving Cambridge House.
+LJSpeech-1.1/wavs/LJ031-0146.wav|and wait for Mrs. Kennedy and the President's body.
+LJSpeech-1.1/wavs/LJ026-0135.wav|If a larger quantity of starch is formed in the chlorophyll bodies than is immediately needed by the protoplasm for purposes of repair or growth,
+LJSpeech-1.1/wavs/LJ035-0059.wav|Truly replied, "Yes."
+LJSpeech-1.1/wavs/LJ048-0019.wav|His activities for the Fair Play for Cuba Committee in New Orleans, we knew, were not of real consequence as he was not connected with any organized activity there.
+LJSpeech-1.1/wavs/LJ025-0014.wav|Cuvier devotes a special section to the Division of Organized Beings into Animals and Vegetables,
+LJSpeech-1.1/wavs/LJ014-0272.wav|and eighteen fifty to embezzle and apply to his own purposes some seventy-one thousand pounds.
+LJSpeech-1.1/wavs/LJ017-0082.wav|He fixed upon a sporting friend, Mr. John Parsons Cook, who had been in luck at Shrewsbury races, both as a winner and a backer,
+LJSpeech-1.1/wavs/LJ005-0159.wav|in others both were supplied in ample quantities, the cost varying per prisoner from twenty shillings to five pounds.
+LJSpeech-1.1/wavs/LJ001-0052.wav|yet their type is artistically on a much lower level than Jenson's, and in fact
+LJSpeech-1.1/wavs/LJ037-0084.wav|Barbara Jeanette Davis testified that no one had shown her a picture of Oswald before the identification and that she had not seen him on television.
+LJSpeech-1.1/wavs/LJ024-0138.wav|During the past half century the balance of power between the three great branches of the federal government,
+LJSpeech-1.1/wavs/LJ011-0210.wav|Mr. William Wakefield was arrested at Dover, conveyed to Chester, and committed to Lancaster Jail for trial at the next assizes,
+LJSpeech-1.1/wavs/LJ018-0028.wav|who had been a lodger of his. Müller had given the cabman's little daughter a jeweler's cardboard box bearing the name of Mr. Death.
+LJSpeech-1.1/wavs/LJ034-0093.wav|Brennan also testified that Lee Harvey Oswald,
+LJSpeech-1.1/wavs/LJ039-0131.wav|Sergeant Zahm concluded, quote, I would say in the Marine Corps he is a good shot, slightly above average,
+LJSpeech-1.1/wavs/LJ022-0189.wav|They are a minimum of wise readjustments of our Federal Reserve System in the light of past experience and present needs.
+LJSpeech-1.1/wavs/LJ012-0136.wav|Moss was known to be intimate with the elder Caspar,
+LJSpeech-1.1/wavs/LJ004-0116.wav|"Care also was to be taken that the prisoners shall not be kept in any apartment underground."
+LJSpeech-1.1/wavs/LJ008-0094.wav|His appearance on the scaffold was the signal for three prolonged shouts from an innumerable populace, "the brutal effusion of one common sentiment."
+LJSpeech-1.1/wavs/LJ016-0358.wav|all who could were glad to escape from the last act of the ghastly drama at which they had assisted.
+LJSpeech-1.1/wavs/LJ009-0162.wav|Nevertheless, after that date no person whatever was executed for this offense.
+LJSpeech-1.1/wavs/LJ050-0067.wav|more than five thousand names were referred to the Secret Service in the first four months of nineteen sixty-four.
+LJSpeech-1.1/wavs/LJ029-0176.wav|with Dallas scheduled as one of the stops.
+LJSpeech-1.1/wavs/LJ044-0208.wav|An examination of the Militant, to which Oswald subscribed,
+LJSpeech-1.1/wavs/LJ017-0186.wav|While with Mrs. Wilson she became suddenly and alarmingly ill, and died in great agony.
+LJSpeech-1.1/wavs/LJ040-0147.wav|so that he was not bothered and did not have to make the effort of communicating.
+LJSpeech-1.1/wavs/LJ032-0253.wav|From September twenty-four, nineteen sixty-three, when Marina Oswald arrived in Irving from New Orleans, until the morning of the assassination,
+LJSpeech-1.1/wavs/LJ040-0025.wav|His life was characterized by isolation, frustration, and failure.
+LJSpeech-1.1/wavs/LJ018-0201.wav|where the coiners, unexpectedly disturbed, fell an easy prey.
+LJSpeech-1.1/wavs/LJ014-0069.wav|Her husband, to whom she coolly confided her intention,
+LJSpeech-1.1/wavs/LJ041-0071.wav|His Marine career was not helped by his attitude
+LJSpeech-1.1/wavs/LJ043-0052.wav|Oswald objected to his mother visiting the apartment
+LJSpeech-1.1/wavs/LJ026-0165.wav|Imagine a higher animal with an attachment for turning the carbon dioxide and water excreted
+LJSpeech-1.1/wavs/LJ025-0013.wav|In the second edition of the "Regne Animal," published in eighteen twenty-eight,
+LJSpeech-1.1/wavs/LJ012-0106.wav|But it was not until the autumn that they traced some of the notes stolen from the Custom House to Jordan and Sullivan.
+LJSpeech-1.1/wavs/LJ005-0045.wav|Nor did it confine itself to mere verbal recommendations.
+LJSpeech-1.1/wavs/LJ014-0288.wav|That same evening he committed suicide in Newgate.
+LJSpeech-1.1/wavs/LJ006-0027.wav|It is an able and exhaustive state paper, testifying to the keenness of the writer's perception, and his unremitting labor in pursuing his researches.
+LJSpeech-1.1/wavs/LJ045-0242.wav|It is apparent, however, that Oswald was moved by an overriding hostility to his environment.
+LJSpeech-1.1/wavs/LJ003-0063.wav|but when the ward was obliged to accommodate double the ordinary number, as was frequently the case,
+LJSpeech-1.1/wavs/LJ011-0127.wav|and had access to the official records, he forged a warrant for one thousand nine-hundred seventy-three pounds and was paid the money by the comptroller general.
+LJSpeech-1.1/wavs/LJ012-0079.wav|A large amount of specie was nearly always retained here in the department of the Receiver of Fines.
+LJSpeech-1.1/wavs/LJ006-0271.wav|which would have greatly helped to keep out bad characters. Some idea of the difficulty and inconvenience of these lax regulations as regards visiting,
+LJSpeech-1.1/wavs/LJ014-0240.wav|without capital, and at railroad speed.
+LJSpeech-1.1/wavs/LJ033-0129.wav|Mrs. Randle saw the bag fleetingly
+LJSpeech-1.1/wavs/LJ022-0064.wav|and to give to all a feeling of security as they look toward old age.
+LJSpeech-1.1/wavs/LJ021-0037.wav|In all of these efforts the government has come to the assistance of business
+LJSpeech-1.1/wavs/LJ012-0255.wav|These were the missing members of the same mutilated trunk,
+LJSpeech-1.1/wavs/LJ016-0198.wav|I cannot find that Calcraft was sworn in when appointed, or any exact information when the old forbidding ceremony ceased to be practiced.
+LJSpeech-1.1/wavs/LJ018-0086.wav|His demeanor immediately preceding his execution I have referred to in the last chapter.
+LJSpeech-1.1/wavs/LJ034-0067.wav|Oswald's Presence on Sixth Floor Approximately thirty-five Minutes Before the Assassination
+LJSpeech-1.1/wavs/LJ037-0260.wav|that he was seen running along Jefferson Boulevard, that a jacket was found under a car in a lot adjoining Jefferson Boulevard
+LJSpeech-1.1/wavs/LJ040-0112.wav|That was not the case, however, after he and his mother moved to New York in August of nineteen fifty-two, shortly before Lee's thirteenth birthday.
+LJSpeech-1.1/wavs/LJ038-0121.wav|or to answer any questions concerning the card.
+LJSpeech-1.1/wavs/LJ016-0113.wav|but only to be taken soon afterwards for a fresh offense, and again pass through Newgate with sentences of penal servitude.
+LJSpeech-1.1/wavs/LJ038-0297.wav|A study of the bus routes indicates that
+LJSpeech-1.1/wavs/LJ031-0086.wav|the President remained on his back throughout his medical treatment at Parkland.
+LJSpeech-1.1/wavs/LJ014-0068.wav|At last this fiendish woman made up her mind to murder O'Connor and appropriate all his possessions.
+LJSpeech-1.1/wavs/LJ009-0248.wav|Sometimes the condemned man made a hard fight for life.
+LJSpeech-1.1/wavs/LJ047-0150.wav|West Fifth Street, Irving, Texas. After receiving this information on October twenty-nine, Agent Hosty attempted to locate Oswald.
+LJSpeech-1.1/wavs/LJ048-0251.wav|The remaining four had key responsibilities as members of the complement of the follow-up car in the motorcade.
+LJSpeech-1.1/wavs/LJ042-0206.wav|The Soviets have committed crimes unsurpassed even by their early day capitalist counterparts, the imprisonment of their own peoples,
+LJSpeech-1.1/wavs/LJ006-0063.wav|minor offenders charged with small thefts or non-payment of small sums were cheek by jowl with convicts sentenced to long terms of transportation.
+LJSpeech-1.1/wavs/LJ028-0198.wav|In the great banquet hall of the palace, when the guests were drinking from the golden cups, and the revelry was at its highest,
+LJSpeech-1.1/wavs/LJ006-0191.wav|No provision whatever was made for the employment of prisoners, no materials were purchased, no trade instructors appointed.
+LJSpeech-1.1/wavs/LJ032-0074.wav|The mail-order coupon listed the purchaser as "A. J. Hidell Age twenty-eight"
+LJSpeech-1.1/wavs/LJ039-0170.wav|and eight point two five seconds respectively.
+LJSpeech-1.1/wavs/LJ029-0016.wav|As Chief of State, the President always welcomed the opportunity to learn, firsthand, about the problems which concerned the American people.
+LJSpeech-1.1/wavs/LJ018-0233.wav|To this fraudulent instrument he appended forged signatures, and in due course obtained probate.
+LJSpeech-1.1/wavs/LJ007-0173.wav|that in the metropolis of this highly-favored country, to which the eyes of other lands turn for example, a system of prison discipline such as that enforced in Newgate
+LJSpeech-1.1/wavs/LJ046-0152.wav|and a determination made as to whether the information might indicate possible harm to the President.
+LJSpeech-1.1/wavs/LJ022-0115.wav|This is a great national crusade to destroy enforced idleness which is an enemy of the human spirit
+LJSpeech-1.1/wavs/LJ003-0246.wav|sometimes the wearer was declared medically unfit, or he obtained release by long good conduct,
+LJSpeech-1.1/wavs/LJ028-0350.wav|It is from these women that the Babylonians of our times are sprung.
+LJSpeech-1.1/wavs/LJ035-0038.wav|The lunchroom door is usually open, but the first door is kept shut by a closing mechanism on the door.
+LJSpeech-1.1/wavs/LJ011-0239.wav|Where robbery with violence was intended, the perpetrators had now to adopt various shifts and contrivances to secure their victim.
+LJSpeech-1.1/wavs/LJ009-0099.wav|Meanwhile the clergyman, still bent into the form of a sleeping dog,
+LJSpeech-1.1/wavs/LJ021-0103.wav|if they have gone too far in such matters as price fixing and limitation of production,
+LJSpeech-1.1/wavs/LJ046-0234.wav|or an organized group, other than specific threats, which suggested a source of danger against which timely precautions could be taken.
+LJSpeech-1.1/wavs/LJ046-0203.wav|any and all information that they may come in contact with that would indicate danger to the President, end quote.
+LJSpeech-1.1/wavs/LJ038-0214.wav|and the other paragraphs instructed her on the disposal of Oswald's personal effects and the management of her affairs if he should not return.
+LJSpeech-1.1/wavs/LJ045-0007.wav|Some of that feeling was expressed in the incident involving then vice-presidential candidate Johnson during the nineteen sixty campaign,
+LJSpeech-1.1/wavs/LJ010-0114.wav|His sudden access to means unlimited was no doubt due to the profitable role he soon adopted of Government informer and spy,
+LJSpeech-1.1/wavs/LJ008-0027.wav|put a quid of tobacco into his mouth, and offered another to his companion, who refused it with indignation
+LJSpeech-1.1/wavs/LJ016-0442.wav|made a determined effort to burn himself to death by throwing himself bodily on to the fire in the condemned ward.
+LJSpeech-1.1/wavs/LJ016-0304.wav|Numbers of humane and thoughtful persons had long been convinced of this.
+LJSpeech-1.1/wavs/LJ050-0006.wav|and the dissimilarity of its protective functions to most activities of the Department of the Treasury
+LJSpeech-1.1/wavs/LJ037-0042.wav|The man pulled a gun. Mrs. Markham heard three shots and saw the policeman fall to the ground near the left front wheel.
+LJSpeech-1.1/wavs/LJ023-0022.wav|Today we are only part-way through that program
+LJSpeech-1.1/wavs/LJ001-0050.wav|and though the famous family of Aldus restored its technical excellence, rejecting battered letters,
+LJSpeech-1.1/wavs/LJ018-0216.wav|William was brought up as an attorney, and became in due course his father's man of business.
+LJSpeech-1.1/wavs/LJ008-0138.wav|and at his request the ordinary drew the cap further down over his face, when in an instant,
+LJSpeech-1.1/wavs/LJ017-0202.wav|fairly electrified the court by declaring it his opinion that many deaths, supposed to be from cholera, were really due to poison.
+LJSpeech-1.1/wavs/LJ025-0091.wav|as it was current among contemporary chemists.
+LJSpeech-1.1/wavs/LJ013-0265.wav|was a piece of a woman's calico apron stained with blood, which he had used to save the pressure on his shoulder by the hod.
+LJSpeech-1.1/wavs/LJ016-0165.wav|three fingers might have been inserted between it and the flesh; the pressure was all on the arteries behind the ears,
+LJSpeech-1.1/wavs/LJ008-0195.wav|Every window had its party of occupants; the adjoining roofs were equally crowded.
+LJSpeech-1.1/wavs/LJ016-0236.wav|Marwood being, on the other hand, in favor of giving a man as much rope as possible.
+LJSpeech-1.1/wavs/LJ043-0116.wav|and because he spent too much time loitering in the garage next door, where he read rifle and hunting magazines.
+LJSpeech-1.1/wavs/LJ030-0147.wav|that it was twelve:thirty p.m., the time they were due at the Trade Mart.
+LJSpeech-1.1/wavs/LJ007-0108.wav|Dasent and Pilgrim were released in ten days, on making due submission.
+LJSpeech-1.1/wavs/LJ021-0033.wav|This has given safety to millions of depositors in these banks.
+LJSpeech-1.1/wavs/LJ042-0094.wav|The Soviet authorities denied Oswald permission
+LJSpeech-1.1/wavs/LJ003-0157.wav|The best accommodation the jail could offer was reserved for the prisoners on the state side,
+LJSpeech-1.1/wavs/LJ004-0182.wav|As for the debtors, they had no allowance whatever, and were often in imminent danger of starvation.
+LJSpeech-1.1/wavs/LJ002-0012.wav|After that they fell as steadily,
+LJSpeech-1.1/wavs/LJ025-0036.wav|and it is so, inasmuch as an additional substance -- nitrogen -- enters into it as an essential element;
+LJSpeech-1.1/wavs/LJ007-0214.wav|"The prominent evils of this prison (Newgate) -- evils which the alterations made within the last four years have failed to remove
+LJSpeech-1.1/wavs/LJ050-0030.wav|The Commission also recommends
+LJSpeech-1.1/wavs/LJ020-0005.wav|Sponge-making is, therefore, an important if not an essential accomplishment in a cook, be she novice or veteran.
+LJSpeech-1.1/wavs/LJ019-0062.wav|The system of associated labor in silence had also its warm supporters,
+LJSpeech-1.1/wavs/LJ014-0065.wav|O'Connor was a man of substance. He had long followed the profitable trade of a money-lender,
+LJSpeech-1.1/wavs/LJ015-0269.wav|He was known to the whole criminal fraternity as a high-class receiver of stolen goods, a negotiator more especially of stolen paper,
+LJSpeech-1.1/wavs/LJ029-0087.wav|In addition to the preventive measures already mentioned, they provided for controlling access to the building, closing off and policing areas around it,
+LJSpeech-1.1/wavs/LJ016-0088.wav|working at the roof of the chapel on the female side.
+LJSpeech-1.1/wavs/LJ018-0037.wav|In searching the prisoner's box, Mr. Briggs' watch was found wrapped up in a piece of leather,
+LJSpeech-1.1/wavs/LJ032-0004.wav|were fired from the southeast corner window of the sixth floor of the Texas School Book Depository Building
+LJSpeech-1.1/wavs/LJ017-0094.wav|strychnia kills by causing tetanic fixing of the respiratory muscles. End quote.
+LJSpeech-1.1/wavs/LJ014-0320.wav|proposed to gain the capital he needed for business purposes by raising money on dock warrants for imported goods which had no real existence.
+LJSpeech-1.1/wavs/LJ013-0086.wav|Burgess, who was purposely on leave from the bank, effected the sale, which was paid for with a cheque for nearly the whole amount on Lubbock's Bank.
+LJSpeech-1.1/wavs/LJ016-0195.wav|and offered him the post of executioner for the city of London and Middlesex.
+LJSpeech-1.1/wavs/LJ046-0233.wav|it made little effort to identify factors in the activities of an individual
+LJSpeech-1.1/wavs/LJ008-0072.wav|and the abolition of the practice of burning females for petty treason, more than one woman suffered this penalty at the Old Bailey.
+LJSpeech-1.1/wavs/LJ013-0131.wav|Ashley was arrested as he left the shop; the spoons were impounded, and it was found that the club monogram had been erased from them.
+LJSpeech-1.1/wavs/LJ030-0056.wav|Kellerman had discussed the matter with O'Donnell, whose instructions were, quote,
+LJSpeech-1.1/wavs/LJ005-0018.wav|an imputation which the Society indignantly and very justly repudiated, the statement being, as they said,
+LJSpeech-1.1/wavs/LJ006-0006.wav|I shall now return to the great jail of the city of London, and give a more detailed account of its condition and inner life
+LJSpeech-1.1/wavs/LJ045-0026.wav|The relations between Lee and Marina Oswald are of great importance in any attempt to understand Oswald's possible motivation.
+LJSpeech-1.1/wavs/LJ044-0201.wav|Oswald's activities with regard to Cuba raise serious questions as to how much he might have been motivated in the assassination
+LJSpeech-1.1/wavs/LJ006-0146.wav|and might always smoke his pipe undisturbed; for although the use of tobacco had been prohibited since the report of the Lords Committee,
+LJSpeech-1.1/wavs/LJ011-0167.wav|At Manchester, however, the carriage stopped at the Albion Hotel.
+LJSpeech-1.1/wavs/LJ002-0140.wav|These courts were extended two centuries later to several large provincial towns, and all were in full activity when Neild wrote,
+LJSpeech-1.1/wavs/LJ032-0084.wav|made by photographing the Oswald cards, retouching the resulting negatives, and producing prints from the retouched negatives.
+LJSpeech-1.1/wavs/LJ039-0061.wav|For a rifleman situated on the sixth floor of the Texas School Book Depository Building
+LJSpeech-1.1/wavs/LJ025-0049.wav|is the specially animal function of animals and constitutes their fourth distinctive character.
+LJSpeech-1.1/wavs/LJ012-0291.wav|as she leant back he put his foot against the chair, and so tilted it over.
+LJSpeech-1.1/wavs/LJ047-0067.wav|Hosty decided that the Lee Harvey Oswald case should be reopened because of the alleged personal difficulties and the contact with the Worker,
+LJSpeech-1.1/wavs/LJ049-0055.wav|All agents are so instructed.
+LJSpeech-1.1/wavs/LJ028-0361.wav|Yet Babylon continued to live,
+LJSpeech-1.1/wavs/LJ017-0014.wav|was never introduced into this country. Its admission was probably checked by the increased vigilance at the custom houses,
+LJSpeech-1.1/wavs/LJ029-0042.wav|As advance agent working closely with Sorrels, Lawson had responsibility for arranging the timetable for the President's visit to Dallas
+LJSpeech-1.1/wavs/LJ018-0173.wav|Griffiths afterwards admitted that he had been employed in defrauding the bank since eighteen forty-six,
+LJSpeech-1.1/wavs/LJ041-0080.wav|in those arguments, quote, and make himself come out top dog, end quote.
+LJSpeech-1.1/wavs/LJ007-0208.wav|In the inspectors' fourth report, dated eighteen thirty-nine,
+LJSpeech-1.1/wavs/LJ032-0140.wav|because he did not release the lifted print until November twenty-six, when he received instructions to send "everything that we had" to the FBI.
+LJSpeech-1.1/wavs/LJ003-0329.wav|In common justice to them, it must be admitted that immediate effect was given to all that could be easily carried out.
+LJSpeech-1.1/wavs/LJ037-0233.wav|two witnesses, Warren Reynolds and B. M. Patterson, saw the gunman run toward the rear of a gasoline service station on Jefferson Boulevard.
+LJSpeech-1.1/wavs/LJ036-0141.wav|[east side of the street]. Of course, the traffic was moving through there and I put it in gear and moved on, that is the last I saw of him.
+LJSpeech-1.1/wavs/LJ027-0147.wav|For as it is of the essence of this theory that new forms arise from older forms by way of hereditary descent,
+LJSpeech-1.1/wavs/LJ018-0136.wav|One more remains, that of "glazing," without which no paper is issued for engraving.
+LJSpeech-1.1/wavs/LJ030-0025.wav|when Air Force One touched down at Love Field at eleven:forty a.m., Eastern Standard Time.
+LJSpeech-1.1/wavs/LJ001-0184.wav|all books might be at least comely and well-looking: and if to these good qualities were added really beautiful ornament and pictures,
+LJSpeech-1.1/wavs/LJ014-0176.wav|His determined addiction to evil courses had led to his being cast off by his family,
+LJSpeech-1.1/wavs/LJ019-0214.wav|The Corporation owned lands there covering from nineteen to twenty acres.
+LJSpeech-1.1/wavs/LJ027-0072.wav|By extensive comparison in the taxonomic and ontogenic series, the whole vertebrate structure in all its details in different animals
+LJSpeech-1.1/wavs/LJ009-0103.wav|The silence is short. As the ordinary proceeds 'to conclude,'
+LJSpeech-1.1/wavs/LJ015-0045.wav|the firm's paper went down further and further in value; an application to the Committee of Bankers for assistance was peremptorily refused,
+LJSpeech-1.1/wavs/LJ020-0038.wav|Turn the pans once while baking, moving them as gently as possible.
+LJSpeech-1.1/wavs/LJ024-0063.wav|President Wilson, three; President Harding, four, including a Chief Justice; President Coolidge, one;
+LJSpeech-1.1/wavs/LJ036-0198.wav|Tippit rode alone, as only one man was normally assigned to a patrol car in residential areas during daylight shifts.
+LJSpeech-1.1/wavs/LJ028-0412.wav|Among the first of the modern travelers to describe the ruins of Babylon was Anthony Shirley, an Englishman who visited Mesopotamia in fifteen ninety-nine.
+LJSpeech-1.1/wavs/LJ007-0172.wav|where is to be found in operation every expedient by which Ignorance may be superseded by Knowledge, Idleness by Industry, and Suffering by Benevolence;
+LJSpeech-1.1/wavs/LJ018-0099.wav|Among them was a man named Chandler, formerly a bill discounter by profession, who by degrees, to meet his extravagant expenditure,
+LJSpeech-1.1/wavs/LJ013-0066.wav|By this means her signature was obtained; a forged will was prepared bequeathing the unclaimed stock to Miss Slack;
+LJSpeech-1.1/wavs/LJ025-0127.wav|I know of no test by which the reaction of the leaves of the Sundew and of other plants to stimuli, so fully and carefully studied by Mr. Darwin,
+LJSpeech-1.1/wavs/LJ037-0118.wav|Callaway told the Commission, quote, So they brought four men in.
+LJSpeech-1.1/wavs/LJ010-0129.wav|but Ings said he hoped God would be more merciful to him than men had been.
+LJSpeech-1.1/wavs/LJ012-0181.wav|Then he attacked the other's throat with a penknife, and last of all drove the pistol barrel into his forehead.
+LJSpeech-1.1/wavs/LJ003-0002.wav|The squalor and uncleanness of the debtors' side was intensified by constant overcrowding.
+LJSpeech-1.1/wavs/LJ027-0138.wav|that there is often a close correspondence between developmental changes as revealed by any chronological series of fossils which may happen to have been preserved,
+LJSpeech-1.1/wavs/LJ037-0015.wav|but he saw the policeman leave the car, heard three or four shots, and then saw the policeman fall.
+LJSpeech-1.1/wavs/LJ038-0029.wav|stepped out onto the stage with them and pointed out the man who had come into the theatre without paying.
+LJSpeech-1.1/wavs/LJ004-0126.wav|The want of room was still a crying evil.
+LJSpeech-1.1/wavs/LJ046-0013.wav|Prompted by these dismaying statistics, the Commission has inquired into the problems and methods of Presidential protection in effect
+LJSpeech-1.1/wavs/LJ014-0276.wav|Watts's crime was discovered by the secretary of the Globe Company, who came suddenly upon the extensive falsification of the passbook.
+LJSpeech-1.1/wavs/LJ038-0118.wav|The Aliases "Hidell" and "O. H. Lee"
+LJSpeech-1.1/wavs/LJ016-0411.wav|There was a smile on his face when it was last seen, and just as the terrible white cap was drawn over it.
+LJSpeech-1.1/wavs/LJ018-0150.wav|with whom he did business in exchanging the false notes.
+LJSpeech-1.1/wavs/LJ005-0200.wav|Most of these small jails were still in existence and in much the same state eight years later,
+LJSpeech-1.1/wavs/LJ026-0082.wav|Carbon dioxide and water contain no potential energy, since the affinities of their constituent elements are completely satisfied.
+LJSpeech-1.1/wavs/LJ040-0124.wav|This continued despite the efforts of the school authorities and, to a lesser extent, of his mother to have him return to school.
+LJSpeech-1.1/wavs/LJ015-0258.wav|went to the police in great fury and distress, and disclosed all she knew of the affair.
+LJSpeech-1.1/wavs/LJ007-0070.wav|and that he had again and again protested against the obstacles thrown in his way, the inspectors "cannot forbear expressing their opinion that he might have shown greater perseverance,
+LJSpeech-1.1/wavs/LJ009-0250.wav|in such a way that the executioner found a difficulty in pinioning his hands.
+LJSpeech-1.1/wavs/LJ048-0224.wav|nine agents who were off duty went to the nearby Fort Worth Press Club at midnight or slightly thereafter, expecting to obtain food;
+LJSpeech-1.1/wavs/LJ023-0096.wav|In the case of holding the A.A.A. unconstitutional,
+LJSpeech-1.1/wavs/LJ011-0097.wav|that of Dickson and Co.
+LJSpeech-1.1/wavs/LJ038-0104.wav|R. S. Stovall, and G. F. Rose obtained a search warrant and examined Oswald's effects in the Paine garage.
+LJSpeech-1.1/wavs/LJ028-0209.wav|On the sixteenth day the troops of Cyrus entered Babylon without a battle.
+LJSpeech-1.1/wavs/LJ027-0010.wav|and all the rest are modifications from these few types.
+LJSpeech-1.1/wavs/LJ046-0237.wav|As reported in chapter two, when the special file was reviewed on November eight,
+LJSpeech-1.1/wavs/LJ015-0249.wav|The theft was discovered at Boulogne, when the boxes were found not to weigh exactly what they ought. But no clue was obtained to the thieves,
+LJSpeech-1.1/wavs/LJ044-0133.wav|He had not found another job. His wife was expecting their second child in October and there was concern about the cost which would be involved.
+LJSpeech-1.1/wavs/LJ039-0109.wav|forty-five caliber pistol, and twelve-gauge riot gun.
+LJSpeech-1.1/wavs/LJ029-0101.wav|For the President's return to Love Field following the luncheon, the agents selected the most direct route, which was approximately four miles.
+LJSpeech-1.1/wavs/LJ014-0297.wav|The prison officers were called, but Watts was quite cold and stiff when he was cut down.
+LJSpeech-1.1/wavs/LJ047-0228.wav|that the motorcade was coming up Main Street, quote, where maybe I could watch it if I had a chance, end quote.
+LJSpeech-1.1/wavs/LJ003-0191.wav|strange and inconceivable delay occurred in carrying out the extreme sentence.
+LJSpeech-1.1/wavs/LJ049-0147.wav|This arrangement would provide a continuing high-level contact for agencies that may wish to consult respecting particular protective measures.
+LJSpeech-1.1/wavs/LJ002-0024.wav|Quote. the apartments set apart for them, being built to accommodate sixty persons, now contain about one hundred twenty. End quote.
+LJSpeech-1.1/wavs/LJ041-0157.wav|He studied the Russian language, read a Russian language newspaper and seemed interested in what was going on in the Soviet Union.
+LJSpeech-1.1/wavs/LJ043-0100.wav|It is not possible to tell whether Oswald did this to provide an excuse for his eventual discharge,
+LJSpeech-1.1/wavs/LJ016-0143.wav|He was out of sight while so employed, and remained so long absent that the warder, becoming uneasy, went in search of him.
+LJSpeech-1.1/wavs/LJ010-0303.wav|According to Fauntleroy's own case, he found at once that the firm was heavily involved,
+LJSpeech-1.1/wavs/LJ049-0198.wav|in all its ramifications and alert to every danger that might befall it,
+LJSpeech-1.1/wavs/LJ001-0090.wav|With this change the art of printing touched bottom,
+LJSpeech-1.1/wavs/LJ038-0249.wav|Investigation determined that this photograph was taken approximately seven-tenths of a mile from Walker's house.
+LJSpeech-1.1/wavs/LJ025-0009.wav|for in the past fifty years it has been made evident that in general principles all living things are fundamentally similar.
+LJSpeech-1.1/wavs/LJ038-0137.wav|the Commission found that Oswald lied when he told Frazier that he was returning to Irving to obtain curtain rods.
+LJSpeech-1.1/wavs/LJ050-0159.wav|The Commission recommends that prompt and favorable consideration be given to this request.
+LJSpeech-1.1/wavs/LJ009-0214.wav|and was accompanied throughout by "an immense concourse of persons, eager to get a sight of the murderer's remains.
+LJSpeech-1.1/wavs/LJ008-0039.wav|By practice the art is much improved, and there is no part of the world in which villains are hanged in so neat a manner, and with so little ceremony.
+LJSpeech-1.1/wavs/LJ032-0217.wav|He could not test Exhibit Number one thirty-three A in the same way because the negative was never recovered.
+LJSpeech-1.1/wavs/LJ014-0223.wav|The judge, in passing sentence of death, told him he richly deserved the punishment.
+LJSpeech-1.1/wavs/LJ009-0086.wav|But why should I repeat the whole?
+LJSpeech-1.1/wavs/LJ045-0225.wav|Oswald's behavior after the assassination throws little light on his motives.
+LJSpeech-1.1/wavs/LJ011-0180.wav|Wakefield added that it had been suggested he should marry Miss Turner, but that he had laughed at the idea.
+LJSpeech-1.1/wavs/LJ008-0042.wav|is enclosed by a temporary roof, under which are placed two seats for the reception of the sheriffs, one on each side of the stairs leading to the scaffold.
+LJSpeech-1.1/wavs/LJ011-0246.wav|there were weapons in the room -- a long knife, a heavy trap-ball bat, and a poker.
+LJSpeech-1.1/wavs/LJ020-0045.wav|Make a hole in the middle of the other batch and pour into it a tablespoonful of butter, just melted, but not hot.
+LJSpeech-1.1/wavs/LJ013-0023.wav|that he got a chance of accomplishing his crime.
+LJSpeech-1.1/wavs/LJ009-0010.wav|and thought every one anxious to promote his death.
+LJSpeech-1.1/wavs/LJ009-0011.wav|Mr. Wakefield goes on to remark that persons convicted of forgery "excited an extraordinary degree of interest in all who approached them."
+LJSpeech-1.1/wavs/LJ003-0334.wav|It made this too the excuse for begging the most important issue of the whole question.
+LJSpeech-1.1/wavs/LJ016-0317.wav|but believed that a public ceremony destroyed the whole value of an execution.
+LJSpeech-1.1/wavs/LJ003-0046.wav|could not be carried out till then. It is to be feared that long after the opening of White Cross Street prison,
+LJSpeech-1.1/wavs/LJ047-0181.wav|and realized immediately that Oswald had given false biographic information. Hosty knew that he would eventually have to investigate this, and, quote,
+LJSpeech-1.1/wavs/LJ030-0242.wav|I was startled by the sharp report or explosion,
+LJSpeech-1.1/wavs/LJ012-0145.wav|Solomons was now also admitted as a witness, and his evidence, with that of Moss, secured the transportation of the principal actors in the theft.
+LJSpeech-1.1/wavs/LJ020-0044.wav|Mould one into a round ball, and set aside for a loaf as already directed.
+LJSpeech-1.1/wavs/LJ044-0100.wav|Following the disclosure of his defection, Oswald sought advice from the Communist Party, U.S.A., concerning his Fair Play for Cuba activity.
+LJSpeech-1.1/wavs/LJ028-0247.wav|Owing to the vast size of the place, the inhabitants of the central parts (as the residents of Babylon declare),
+LJSpeech-1.1/wavs/LJ023-0012.wav|But when, almost two years later, it came before the Supreme Court its constitutionality was upheld only by a five-to-four vote.
+LJSpeech-1.1/wavs/LJ004-0041.wav|with advantage step in, and by the collection and diffusion of information, and the reiteration of sound advice, greatly assist the good work.
+LJSpeech-1.1/wavs/LJ035-0205.wav|and no one could be found who saw Oswald anywhere else in the building until after the shooting.
+LJSpeech-1.1/wavs/LJ029-0069.wav|including a detailed description of the available data on Lee Harvey Oswald and the reasons why his name had not been furnished to the Secret Service,
+LJSpeech-1.1/wavs/LJ049-0054.wav|The Secret Service has consistently followed two general principles in emergencies involving the President.
+LJSpeech-1.1/wavs/LJ005-0011.wav|were first made use of about eighteen twenty-seven. That the need for prison reform was imperative may be gathered from the few out of many instances I have adduced,
+LJSpeech-1.1/wavs/LJ025-0042.wav|They retain the hydrogen and the carbon, exhale the superfluous oxygen and absorb little or no nitrogen.
+LJSpeech-1.1/wavs/LJ005-0059.wav|With some important modifications this principle of radiation is still the rule.
+LJSpeech-1.1/wavs/LJ050-0186.wav|in formats that can be communicated to each level of local authorities.
+LJSpeech-1.1/wavs/LJ006-0035.wav|took voluminous evidence from all classes of individuals, from the governor down to the convict in the condemned cells.
+LJSpeech-1.1/wavs/LJ009-0120.wav|Almighty God, Father of all mercies, we thine unworthy servants do give thee most humble and hearty thanks
+LJSpeech-1.1/wavs/LJ010-0176.wav|He was on the right or north side of the road. Prince Albert occupied the same side of the carriage, the Queen the left.
+LJSpeech-1.1/wavs/LJ009-0143.wav|He sat on a bench before the pulpit, -- the hideous condemned pew had been swept away, -- and never once raised his eyes during the service.
+LJSpeech-1.1/wavs/LJ033-0082.wav|He carried a, quote, heavy brown bag, end quote.
+LJSpeech-1.1/wavs/LJ044-0129.wav|and often it is advisable for some people to remain in the background, not underground, end quote.
+LJSpeech-1.1/wavs/LJ008-0028.wav|Some indications of pity were offered for the fate of Tilling; Palm, execration alone.
+LJSpeech-1.1/wavs/LJ007-0025.wav|There were frequent quarrels and fights; shoes and other missiles were freely bandied about;
+LJSpeech-1.1/wavs/LJ006-0216.wav|Not only did prisoners come again and again for a "pint," but large quantities were carried off to the wards to be drunk later in the day.
+LJSpeech-1.1/wavs/LJ030-0133.wav|Evaluating the political overtones, Kenneth O'Donnell was especially pleased
+LJSpeech-1.1/wavs/LJ015-0098.wav|Robson said they were at Kilburn Priory.
+LJSpeech-1.1/wavs/LJ049-0171.wav|With two Federal agencies operating in the same general field of preventive investigation,
+LJSpeech-1.1/wavs/LJ028-0057.wav|but about his name so many legends have grown that it is sometimes difficult to learn the facts of his life.
+LJSpeech-1.1/wavs/LJ010-0220.wav|We then sent the old woman to open the door, and it proved to be some little boys who had knocked and ran away.
+LJSpeech-1.1/wavs/LJ019-0256.wav|Still greater were the differences with regard to employment. The various authorities held widely different opinions as to what constituted hard labor.
+LJSpeech-1.1/wavs/LJ015-0250.wav|and the theft might have remained a mystery but for the subsequent bad faith of Pierce to his accomplice Agar.
+LJSpeech-1.1/wavs/LJ050-0025.wav|or from which it derives assistance and support.
+LJSpeech-1.1/wavs/LJ031-0136.wav|The Vice President decided to wait until he received definitive word of the President's condition.
+LJSpeech-1.1/wavs/LJ024-0048.wav|The number of justices has been changed several times before,
+LJSpeech-1.1/wavs/LJ036-0108.wav|and they asked me which one and I told them. It was him all right, the same man. He showed no respect for the policemen,
+LJSpeech-1.1/wavs/LJ032-0050.wav|It bore the manufacturer's serial number C two seven six six.
+LJSpeech-1.1/wavs/LJ008-0236.wav|coiled up on the floor of the scaffold like a serpent, the hangman's rope!
+LJSpeech-1.1/wavs/LJ009-0235.wav|and having first pulled him sideways, then got upon his shoulders, so that the rope broke.
+LJSpeech-1.1/wavs/LJ028-0214.wav|for upon a barrel-shaped cylinder of clay bearing a long inscription we have Cyrus's account of his capture of Babylon. Extracts from it are as follows:
+LJSpeech-1.1/wavs/LJ043-0056.wav|Even though Oswald cut off relations with his mother,
+LJSpeech-1.1/wavs/LJ034-0055.wav|was probably made within a day or a day and a half of the examination on November twenty-two.
+LJSpeech-1.1/wavs/LJ018-0252.wav|to deeds involving on the whole some three hundred fifty thousand pounds.
+LJSpeech-1.1/wavs/LJ018-0380.wav|They paid close attention to the counsels of the archimandrite, and died quite penitent. A story is told of one of them, "Big Harry,"
+LJSpeech-1.1/wavs/LJ031-0175.wav|O'Donnell tried to persuade Mrs. Kennedy to leave the area, but she refused.
+LJSpeech-1.1/wavs/LJ031-0056.wav|Observing that an effective airway had to be established if treatment was to be effective, Dr. Perry performed a tracheotomy, which required three to five minutes.
+LJSpeech-1.1/wavs/LJ045-0224.wav|and such a favorable opportunity to strike at a figure as great as the President would probably never have come to him again.
+LJSpeech-1.1/wavs/LJ016-0160.wav|The noose was one of his hammock straps, which he buckled round his throat.
+LJSpeech-1.1/wavs/LJ029-0012.wav|He had made only a few brief visits to the State since the nineteen sixty Presidential campaign and in nineteen sixty-two he began to consider a formal visit.
+LJSpeech-1.1/wavs/LJ049-0169.wav|the procedures which its agents are to follow in connection with information received, quote,
+LJSpeech-1.1/wavs/LJ011-0094.wav|The second conviction for forgery in eighteen twenty-eight was that of the Quaker Joseph Hunton, a man of previously the highest repute in the city of London.
+LJSpeech-1.1/wavs/LJ031-0098.wav|I suppose nobody really had the heart to do it.
+LJSpeech-1.1/wavs/LJ015-0123.wav|In many respects the embezzlement of which Leopold Redpath was guilty closely resembled that of Robson,
+LJSpeech-1.1/wavs/LJ001-0035.wav|they discarded this for a more completely Roman and far less beautiful letter.
+LJSpeech-1.1/wavs/LJ012-0172.wav|Since the calamitous event," he went on, "that happened at Hertford, I have been a lost man."
+LJSpeech-1.1/wavs/LJ007-0002.wav|Eighteen years had elapsed since the formation of the "Ladies' Association,"
+LJSpeech-1.1/wavs/LJ035-0134.wav|that they were watching the parade from the top step of the building entrance when Gloria Calverly, who works in the Depository Building,
+LJSpeech-1.1/wavs/LJ026-0083.wav|Starch, however, contains potential energy, since the molecule is relatively unstable
+LJSpeech-1.1/wavs/LJ009-0025.wav|As to the exclusion of strangers on these occasions,
+LJSpeech-1.1/wavs/LJ039-0014.wav|Marina Oswald appeared before the Commission again on June eleven, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ046-0215.wav|Any information indicating the possibility of an attempt against the person or safety of the President,
+LJSpeech-1.1/wavs/LJ016-0401.wav|His conversation turned always upon his influence over the weaker sex, and the extraordinary success he had achieved.
+LJSpeech-1.1/wavs/LJ019-0345.wav|The Act of eighteen sixty-five also encouraged and empowered the local authorities to "alter, enlarge, or rebuild" their prisons.
+LJSpeech-1.1/wavs/LJ004-0157.wav|All charged with felony were in heavy irons, without distinction of age.
+LJSpeech-1.1/wavs/LJ037-0022.wav|He had not seen Oswald on television and had not been shown any photographs of Oswald by the police.
+LJSpeech-1.1/wavs/LJ027-0086.wav|To the superficial observer the bodies of animals of different classes seem to differ fundamentally in plan,
+LJSpeech-1.1/wavs/LJ037-0138.wav|Reynolds did not make a positive identification when interviewed by the FBI, but
+LJSpeech-1.1/wavs/LJ019-0226.wav|the erection of a wing or large block of cells was commenced within the original walls of the prison, and upon the north or male side.
+LJSpeech-1.1/wavs/LJ030-0164.wav|Soon after the motorcade turned onto Elm Street, she heard a sound similar to a motorcycle noise and a cry from Governor Connally,
+LJSpeech-1.1/wavs/LJ018-0344.wav|In cold-blooded premeditation it rivaled that of the Mannings.
+LJSpeech-1.1/wavs/LJ043-0175.wav|The items which Oswald left at home when he made his attack on Walker suggest a strong concern for his place in history.
+LJSpeech-1.1/wavs/LJ001-0033.wav|but which must certainly have come from the study of the twelfth or even the eleventh century MSS.
+LJSpeech-1.1/wavs/LJ015-0021.wav|Sir John Dean Paul had inherited a baronetcy from his father, together with an honored name;
+LJSpeech-1.1/wavs/LJ030-0138.wav|At twelve:thirty p.m., Eastern Standard Time, as the President's open limousine proceeded at approximately eleven miles per hour along Elm Street
+LJSpeech-1.1/wavs/LJ014-0307.wav|were intended by the protectionists to depress the wheat market, and secure the support of the farmers at the forthcoming election;
+LJSpeech-1.1/wavs/LJ010-0104.wav|Thistlewood and fourteen more succeeded for the moment in making their escape, but most of them were subsequently taken.
+LJSpeech-1.1/wavs/LJ045-0012.wav|or rightwing extremism which may have existed in the city of Dallas had any connection with Oswald's actions on November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ006-0032.wav|Messrs. Crawford and Russell proceeded to carry out their new functions with commendable energy, and without a moment's loss of time.
+LJSpeech-1.1/wavs/LJ015-0306.wav|and the whole scheme of systematic forgery was laid bare.
+LJSpeech-1.1/wavs/LJ032-0186.wav|when it was transported from New Orleans, and November twenty-two, the day of the assassination.
+LJSpeech-1.1/wavs/LJ049-0019.wav|The last Presidential vehicle with any protection against small-arms fire left the White House in nineteen fifty-three.
+LJSpeech-1.1/wavs/LJ042-0197.wav|seemed to place him in a situation in which he could not live with satisfaction either in the United States or in the Soviet Union.
+LJSpeech-1.1/wavs/LJ049-0150.wav|A Cabinet-level committee which is actively concerned with these problems would be able to discuss these matters more effectively with the President.
+LJSpeech-1.1/wavs/LJ028-0016.wav|In the distance your eyes detect a beautiful lake with shores fringed with trees,
+LJSpeech-1.1/wavs/LJ019-0237.wav|The Act for private executions led to the erection of the gallows shed in the exercising yard, and at the flank of the passage from the condemned cells.
+LJSpeech-1.1/wavs/LJ037-0134.wav|Russell and Patterson were shown a picture of Oswald and they stated that Oswald was the man they saw on November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ032-0193.wav|Because the relative freshness of these fibers might be explained by the continuous storage of the rifle in the blanket,
+LJSpeech-1.1/wavs/LJ047-0098.wav|inasmuch as the police had not given Oswald's name to the Bureau when they called the office.
+LJSpeech-1.1/wavs/LJ048-0271.wav|will be cause for removal from the Service, end quote.
+LJSpeech-1.1/wavs/LJ019-0061.wav|Another argument was, that it afforded more hope of the reformation of criminals.
+LJSpeech-1.1/wavs/LJ038-0288.wav|The admissions made to Marina Oswald by her husband are an important element in the evidence that Lee Harvey Oswald fired the shot at General Walker.
+LJSpeech-1.1/wavs/LJ042-0208.wav|The deportations, the purposeful curtailment of diet in the consumer slighted population of Russia,
+LJSpeech-1.1/wavs/LJ005-0230.wav|These justices were to inspect every part of the prison, and examine into the state and condition of prisoners.
+LJSpeech-1.1/wavs/LJ009-0224.wav|At the first-named the exhibition nearly created a tumult, and the body was taken down and buried,
+LJSpeech-1.1/wavs/LJ005-0076.wav|By the preamble of the first-named act it was declared
+LJSpeech-1.1/wavs/LJ030-0213.wav|Hill heard a second shot, proximately five seconds after the first, which removed a portion of the President's head.
+LJSpeech-1.1/wavs/LJ011-0085.wav|a distinguished officer, and would have married an heiress had not the imposture been discovered in time.
+LJSpeech-1.1/wavs/LJ027-0097.wav|Thus, in the fin of a fish, the fore-paw of a reptile or a mammal, the wing of a bird, and the arm and hand of a man
+LJSpeech-1.1/wavs/LJ049-0100.wav|The Commission recommends to the Congress that it adopt legislation which would:
+LJSpeech-1.1/wavs/LJ043-0018.wav|He was offensive with the people. And I can understand why, because that hurt him.
+LJSpeech-1.1/wavs/LJ019-0359.wav|He could in the first place withhold the government grant in aid of prison funds by refusing the certificate to the Treasury upon which the allowance was paid.
+LJSpeech-1.1/wavs/LJ003-0240.wav|with ruined character, bad habits, and often bad health contracted in the jail.
+LJSpeech-1.1/wavs/LJ012-0077.wav|and the thieves seem to have had no difficulty in disposing of their spoil.
+LJSpeech-1.1/wavs/LJ028-0240.wav|on which the river sank to such an extent that the natural bed of the stream became fordable.
+LJSpeech-1.1/wavs/LJ048-0082.wav|The testimony reveals that liaison responsibilities in connection with the President's visit
+LJSpeech-1.1/wavs/LJ017-0178.wav|It appeared that several persons with whom she was intimate had succumbed suddenly.
+LJSpeech-1.1/wavs/LJ014-0018.wav|It came out by degrees that the person who had been seen in Belsize Lane on the night the body was found was a friend of the deceased.
+LJSpeech-1.1/wavs/LJ042-0158.wav|left a far mean streak of independence brought on by neglect.
+LJSpeech-1.1/wavs/LJ004-0106.wav|They were to report in writing to quarter sessions as to the state of the jail, and as to all abuses which they might observe therein.
+LJSpeech-1.1/wavs/LJ031-0013.wav|Traveling at speeds estimated at times to be up to seventy or eighty miles per hour down the Stemmons Freeway and Harry Hines Boulevard
+LJSpeech-1.1/wavs/LJ011-0023.wav|A run upon the bank immediately followed, which was only met by a suspension of payment and the closing of its doors.
+LJSpeech-1.1/wavs/LJ018-0348.wav|Henry Wainwright's impassioned denial of his crime, even after it had been brought fully home to him, has many parallels in the criminal records.
+LJSpeech-1.1/wavs/LJ028-0493.wav|whose summit reached into the sky as far, perhaps, as the tallest of the modern buildings.
+LJSpeech-1.1/wavs/LJ007-0136.wav|But there were others remediable under a better system of management.
+LJSpeech-1.1/wavs/LJ004-0037.wav|Yet the borough jails were destined to survive many years, and to exhibit for a long time to come all the worst features of jail mismanagement.
+LJSpeech-1.1/wavs/LJ045-0038.wav|might also have been important to him. It would not have been the first time that Oswald sought closer ties with his family in time of adversity.
+LJSpeech-1.1/wavs/LJ023-0105.wav|In the face of these dissenting opinions, there is no basis for the claim made by some members of the Court
+LJSpeech-1.1/wavs/LJ028-0281.wav|Till mules foal ye shall not take our city, he thought, as he reflected on this speech, that Babylon might now be taken,
+LJSpeech-1.1/wavs/LJ047-0003.wav|Information known about Lee Harvey Oswald prior to the assassination.
+LJSpeech-1.1/wavs/LJ037-0263.wav|The foregoing evidence establishes that (one) two eyewitnesses who heard the shots and saw the shooting of Dallas Police Patrolman J. D. Tippit
+LJSpeech-1.1/wavs/LJ014-0195.wav|two of them supported Cope, who was still alive, although insensible, and Marley was apprehended. The evidence against him was completed
+LJSpeech-1.1/wavs/LJ021-0029.wav|that we approached our task of reviving private enterprise in March, nineteen thirty-three.
+LJSpeech-1.1/wavs/LJ046-0035.wav|After reviewing this aspect of the matter this chapter will set forth the Commission's conclusions
+LJSpeech-1.1/wavs/LJ037-0254.wav|Two other witnesses, Sam Guinyard and William Arthur Smith,
+LJSpeech-1.1/wavs/LJ009-0209.wav|which was dressed in blue trousers and a blue-and-white striped waistcoat, but without a coat, as when found in the cell.
+LJSpeech-1.1/wavs/LJ009-0020.wav|previous to the execution of Henry Fauntleroy for uttering a forged security
+LJSpeech-1.1/wavs/LJ001-0174.wav|should be a part of the whole scheme of the book.
+LJSpeech-1.1/wavs/LJ035-0006.wav|are consistent with his having been at the window at twelve:thirty p.m.
+LJSpeech-1.1/wavs/LJ020-0082.wav|Roll out into a sheet half an inch thick with a floured rolling-pin; cut into round cakes, set these closely together in a well-greased pan;
+LJSpeech-1.1/wavs/LJ014-0175.wav|When lying under sentence of death in Newgate, he was visited by a lady, a gentlewoman in every sense of the word, who was said to be his sister.
+LJSpeech-1.1/wavs/LJ019-0325.wav|the ministrations of ministers of their own form of belief.
+LJSpeech-1.1/wavs/LJ032-0008.wav|This evidence includes (one) the ownership and possession of the weapon used to commit the assassination,
+LJSpeech-1.1/wavs/LJ050-0225.wav|and be available to protect the President or Vice President when they travel;
+LJSpeech-1.1/wavs/LJ012-0193.wav|The crimes of these inhuman purveyors to medical science do not fall within the limits of this work.
+LJSpeech-1.1/wavs/LJ047-0163.wav|According to Hosty, Mrs. Paine indicated that she thought she could find out where Oswald was living and would let him know.
+LJSpeech-1.1/wavs/LJ012-0148.wav|instead of handing it or the price of it back to the Caspars.
+LJSpeech-1.1/wavs/LJ008-0100.wav|The yeoman was, however, under-sold by his wife, "Rosy Emma,"
+LJSpeech-1.1/wavs/LJ048-0217.wav|The agents in the follow-up car also were expected to scan adjacent buildings.
+LJSpeech-1.1/wavs/LJ036-0097.wav|The taxicab ride.
+LJSpeech-1.1/wavs/LJ021-0059.wav|The work day and the work week have been shortened.
+LJSpeech-1.1/wavs/LJ043-0090.wav|Even though Oswald indicated that he liked photographic work, his employer found that he was not an efficient worker.
+LJSpeech-1.1/wavs/LJ037-0160.wav|He explained that the bullets were slightly smaller than the barrel of the pistol which had fired them. This caused the bullets to have an erratic passage through the barrel
+LJSpeech-1.1/wavs/LJ016-0203.wav|He got besides half-a-crown for every man he flogged, and an allowance to provide cats or birch rods.
+LJSpeech-1.1/wavs/LJ038-0041.wav|McDonald felt the pistol scratch his cheek as he wrenched it away from Oswald.
+LJSpeech-1.1/wavs/LJ008-0018.wav|extended upon a plank on the top of an open cart, in his clothes, and fettered.
+LJSpeech-1.1/wavs/LJ006-0008.wav|These gentlemen were appointed in October eighteen thirty-five, owing to the strong representations of the Lords' Committee
+LJSpeech-1.1/wavs/LJ015-0303.wav|but on being searched, two blank cheques of the London and Westminster Bank were found in his pocket.
+LJSpeech-1.1/wavs/LJ006-0297.wav|Some member of the Ladies' Association observed and commented upon the fact that a "young rosy-cheeked girl" had been kept by the governor from transportation,
+LJSpeech-1.1/wavs/LJ040-0095.wav|Referring to the period after the divorce from Ekdahl, which was apparently caused in part by Marguerite's desire to get more money from him,
+LJSpeech-1.1/wavs/LJ001-0065.wav|This was notably the case with the early works printed at Ulm, and in a somewhat lesser degree at Augsburg.
+LJSpeech-1.1/wavs/LJ011-0187.wav|Returning to Carlisle, she now heard that her father had been set free, and had gone home to Shrigley, whither they were to follow him.
+LJSpeech-1.1/wavs/LJ002-0062.wav|The middle yard was at first given up to the least heinous offenders. After eighteen twelve it changed functions with the chapel yard.
+LJSpeech-1.1/wavs/LJ040-0065.wav|That death strained the financial fortunes of the remainder of the Oswald family.
+LJSpeech-1.1/wavs/LJ017-0053.wav|there was certainly nothing in it which indicated cruel cunning or deliberate truculence.
+LJSpeech-1.1/wavs/LJ020-0047.wav|Put it into a floured bowl, cover with a cloth and set away out of draught and undue heat, for three hours.
+LJSpeech-1.1/wavs/LJ014-0061.wav|and Manning hoped to get some small Government appointment through his wife's interest.
+LJSpeech-1.1/wavs/LJ002-0076.wav|The state side contained twelve good-sized rooms,
+LJSpeech-1.1/wavs/LJ008-0076.wav|When she came out of prison she appeared languid and terrified, and trembled greatly as she advanced to the stake,
+LJSpeech-1.1/wavs/LJ046-0167.wav|When PRS learns of an individual whose conduct warrants scrutiny, it requests an investigation by the closest Secret Service field office,
+LJSpeech-1.1/wavs/LJ005-0028.wav|"There should be no tea and sugar, no assemblage of female felons around the washing-tub,
+LJSpeech-1.1/wavs/LJ045-0065.wav|Marina Oswald's inability to speak English also made it more difficult for her to have an independent existence in this country.
+LJSpeech-1.1/wavs/LJ036-0140.wav|He gave me a dollar bill and didn't say anything, just got out and closed the door and walked around the front of the cab over to the other side of the street
+LJSpeech-1.1/wavs/LJ015-0259.wav|Agar too, in Newgate, heard how Pierce had treated him, and at once readily turned approver.
+LJSpeech-1.1/wavs/LJ039-0234.wav|the Commission concluded that Oswald was capable of accomplishing this second hit even if there was an intervening shot which missed.
+LJSpeech-1.1/wavs/LJ042-0220.wav|he would be asked at a press conference when he returned.
+LJSpeech-1.1/wavs/LJ008-0152.wav|made a full confession, and implicated Holloway and Haggerty.
+LJSpeech-1.1/wavs/LJ025-0089.wav|The third distinction is based on a completely erroneous conception of the chemical differences
+LJSpeech-1.1/wavs/LJ048-0009.wav|Both Director Hoover and his assistant, Alan H. Belmont,
+LJSpeech-1.1/wavs/LJ003-0188.wav|The old press yard has been fully described in a previous chapter.
+LJSpeech-1.1/wavs/LJ023-0054.wav|I hope that you have re-read the Constitution of the United States in these past few weeks.
+LJSpeech-1.1/wavs/LJ004-0105.wav|two or more justices were appointed visitors of prisons, and directed to visit and inspect three times every quarter.
+LJSpeech-1.1/wavs/LJ044-0078.wav|and as he was to some extent when he returned to the United States.
+LJSpeech-1.1/wavs/LJ028-0454.wav|but both sections originally reached the river.
+LJSpeech-1.1/wavs/LJ021-0128.wav|Both employers and employees must share the blame of not using them as fully as they should.
+LJSpeech-1.1/wavs/LJ012-0158.wav|Before his confinement he had been a sleek round person, addicted obviously to the pleasures of the table.
+LJSpeech-1.1/wavs/LJ008-0017.wav|and when he evaded the sentence by suicide, his body was exhibited in the same neighborhood,
+LJSpeech-1.1/wavs/LJ026-0069.wav|as the potential energy of foodstuffs, but comes in principally as the kinetic energy of sunlight absorbed in the leaves.
+LJSpeech-1.1/wavs/LJ029-0139.wav|in order to avoid the traffic hazards which would otherwise exist if right turns were permitted from both Main and Elm into the freeway.
+LJSpeech-1.1/wavs/LJ030-0030.wav|President and Mrs. Kennedy walked along a chain-link fence at the reception area greeting a large crowd of spectators that had gathered behind it.
+LJSpeech-1.1/wavs/LJ010-0231.wav|His motives for thus imitating the dastardly crime of Oxford are shrouded in obscurity.
+LJSpeech-1.1/wavs/LJ006-0105.wav|and not with weights and measures, no turnkey being present.
+LJSpeech-1.1/wavs/LJ049-0081.wav|There was no Federal criminal jurisdiction over the assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ033-0146.wav|Scientific Evidence Linking Rifle and Oswald to Paper Bag
+LJSpeech-1.1/wavs/LJ026-0041.wav|may flourish in solutions wholly devoid of organic matter is very significant.
+LJSpeech-1.1/wavs/LJ012-0170.wav|According to his statement, when sentenced to death, he had been driven to horse-stealing by the execration which had pursued him after the murder.
+LJSpeech-1.1/wavs/LJ029-0160.wav|on its way to the Trade Mart. On November nineteen, the Times-Herald afternoon paper detailed the precise route:
+LJSpeech-1.1/wavs/LJ026-0136.wav|it may be reconverted into starch after journeying as glucose through the plant
+LJSpeech-1.1/wavs/LJ047-0111.wav|Quigley advised Knack of his interview and gave him a detailed memorandum.
+LJSpeech-1.1/wavs/LJ008-0147.wav|whither he often went to distill the lavender, returning to London the same evening.
+LJSpeech-1.1/wavs/LJ017-0245.wav|On the second October they sighted land, ten miles distant;
+LJSpeech-1.1/wavs/LJ040-0049.wav|He stated several times that he was a Communist but apparently never joined any Communist Party.
+LJSpeech-1.1/wavs/LJ044-0016.wav|which was berthed at the Dumaine Street wharf in New Orleans, on June sixteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ040-0078.wav|Lee Oswald remained with his mother and Ekdahl, to whom he became quite attached.
+LJSpeech-1.1/wavs/LJ034-0217.wav|One of these employees was alleged to resemble Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ029-0142.wav|This concrete barrier extends far enough beyond the access road to make it impracticable for vehicles to turn right from Main directly to the access road.
+LJSpeech-1.1/wavs/LJ022-0119.wav|It must, however, be recognized that when an enterprise of this character is extended over more than three thousand counties throughout the nation,
+LJSpeech-1.1/wavs/LJ036-0201.wav|At twelve:forty-five p.m. the dispatcher ordered Number seventy-eight (Tippit) to, quote, move into central Oak Cliff area, end quote.
+LJSpeech-1.1/wavs/LJ029-0121.wav|and that the wide streets that we would use downtown would be of sufficient width to keep the public out of our way.
+LJSpeech-1.1/wavs/LJ037-0069.wav|Although in the phone conversation she described the man as, quote, short, end quote, on November twenty-second,
+LJSpeech-1.1/wavs/LJ019-0252.wav|while its rigid maintenance was in its opinion vital to the efficiency of the jails.
+LJSpeech-1.1/wavs/LJ041-0074.wav|John E. Donovan, one of his former officers, testified that Oswald thought, quote,
+LJSpeech-1.1/wavs/LJ048-0247.wav|had duty assignments beginning no later than eight a.m. that morning.
+LJSpeech-1.1/wavs/LJ016-0100.wav|At that time rope mats were still used as beds.
+LJSpeech-1.1/wavs/LJ045-0006.wav|and which received publicity there prior to the assassination.
+LJSpeech-1.1/wavs/LJ012-0154.wav|He was ostensibly a publican,
+LJSpeech-1.1/wavs/LJ048-0109.wav|by these Secret Service agents with the cooperation of the Dallas police and other local law enforcement agents, were carefully executed.
+LJSpeech-1.1/wavs/LJ011-0092.wav|which it was asserted he had been in the habit of carrying about his person ever since he had taken to passing forged notes, as an antidote against disgrace.
+LJSpeech-1.1/wavs/LJ013-0026.wav|The 'Dryad' was got off, repaired, and her voyage renewed to Santa Cruz.
+LJSpeech-1.1/wavs/LJ047-0094.wav|by anti-Castro Cubans while distributing leaflets on behalf of the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ009-0258.wav|However, the ceremony went forward, and when the signal was given the drop sank.
+LJSpeech-1.1/wavs/LJ024-0038.wav|that no President fit for his office would appoint,
+LJSpeech-1.1/wavs/LJ039-0146.wav|Marina Oswald testified that in New Orleans in May of nineteen sixty-three, she observed Oswald sitting with the rifle on their screened porch at night,
+LJSpeech-1.1/wavs/LJ005-0209.wav|through the bars of which quills or reeds were inserted, and drink conveyed to the prisoners.
+LJSpeech-1.1/wavs/LJ041-0108.wav|He said that he had simply wanted to discuss the question with the sergeant and the drink had been spilled accidentally.
+LJSpeech-1.1/wavs/LJ017-0154.wav|that Mrs. Smethurst had no right to the name, but had a husband of her own, one Johnson, alive -- a story subsequently disproved.
+LJSpeech-1.1/wavs/LJ024-0123.wav|An amendment, like the rest of the Constitution,
+LJSpeech-1.1/wavs/LJ019-0207.wav|The site of which was to be sold to Christ's Hospital, but the intention was of course to embody and absorb old Newgate in the new construction.
+LJSpeech-1.1/wavs/LJ011-0018.wav|The crime, long carried on without detection, was first discovered in eighteen twenty,
+LJSpeech-1.1/wavs/LJ002-0151.wav|Neild found at his visit to Newgate in eighteen ten,
+LJSpeech-1.1/wavs/LJ023-0098.wav|And two other justices agreed with him.
+LJSpeech-1.1/wavs/LJ018-0267.wav|His uniform good conduct gained him release from Portland on ticket-of-leave in eighteen eighty-two, just twenty years after his conviction.
+LJSpeech-1.1/wavs/LJ033-0006.wav|In this connection the Commission considered (one)
+LJSpeech-1.1/wavs/LJ015-0240.wav|Agar at once got to work on the first safe.
+LJSpeech-1.1/wavs/LJ010-0243.wav|with an equerry riding close on each side of her carriage, a man who had been leaning against the palace garden wall suddenly advanced,
+LJSpeech-1.1/wavs/LJ012-0253.wav|A further discovery was made in an osier bed near Cold Harbor Lane, Camberwell,
+LJSpeech-1.1/wavs/LJ037-0076.wav|Two young women,
+LJSpeech-1.1/wavs/LJ018-0184.wav|One narrow escape he had, however, before he abandoned his old business.
+LJSpeech-1.1/wavs/LJ041-0062.wav|testified that, quote, definitely the Marine Corps was not what he had expected it to be when he joined, end quote,
+LJSpeech-1.1/wavs/LJ038-0286.wav|when combined with the other testimony linking Oswald to the shooting.
+LJSpeech-1.1/wavs/LJ002-0135.wav|but as time passed, difficulties and delays in obtaining judgment led to the removal of causes to the great Court of King's Bench,
+LJSpeech-1.1/wavs/LJ020-0066.wav|One quart and a cup of flour sifted.
+LJSpeech-1.1/wavs/LJ032-0097.wav|Expert testimony confirmed that the writing on this application was that of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ039-0212.wav|Frazier said, quote, The fact that the crosshairs are set high would actually compensate for any lead which had to be taken
+LJSpeech-1.1/wavs/LJ006-0128.wav|The brief-drawing gatesman and wardsman at the time of the inspectors' first visit must have been a particularly powerful personage.
+LJSpeech-1.1/wavs/LJ034-0173.wav|He said that the man held his attention until the motorcade came because the man, quote,
+LJSpeech-1.1/wavs/LJ030-0234.wav|Directing the security detail for the Vice President from the right front seat of the Vice-Presidential car, Special Agent Youngblood recalled, quote,
+LJSpeech-1.1/wavs/LJ016-0126.wav|The top of the wall was gained without difficulty.
+LJSpeech-1.1/wavs/LJ033-0101.wav|so that it was carried straight and parallel to his body.
+LJSpeech-1.1/wavs/LJ049-0108.wav|In addition, the restriction would probably eliminate a need for the requirement which has been urged as necessary for the exercise of Federal power,
+LJSpeech-1.1/wavs/LJ042-0173.wav|Such an alternative was to be opposed both to capitalism and communism because, quote,
+LJSpeech-1.1/wavs/LJ017-0028.wav|that she had had a quarrel with her mistress, and that the latter with all others who tasted the dumplings were similarly attacked, although no one died.
+LJSpeech-1.1/wavs/LJ025-0101.wav|decomposes carbonic acid and exhales oxygen while the animal absorbs oxygen and exhales carbonic acid,
+LJSpeech-1.1/wavs/LJ015-0031.wav|it had assumed enormous responsibilities -- on one side by the ownership of the Mostyn collieries, a valueless property,
+LJSpeech-1.1/wavs/LJ038-0155.wav|The next day, Oswald added to his story.
+LJSpeech-1.1/wavs/LJ005-0069.wav|Absence of embellishment is in perfect unison with the character of the establishment.
+LJSpeech-1.1/wavs/LJ035-0091.wav|Baker said, quote, We simulated the shots and by the time we got there, we did everything that I did that day,
+LJSpeech-1.1/wavs/LJ031-0044.wav|On the basis of these findings, Dr. Carrico concluded that President Kennedy was still alive.
+LJSpeech-1.1/wavs/LJ038-0143.wav|When told that Frazier and Mrs. Randle had seen him carrying a long heavy package, Oswald replied, quote, Well, they was mistaken.
+LJSpeech-1.1/wavs/LJ004-0095.wav|is found to be owing to want of cleanliness and fresh air in the several jails,
+LJSpeech-1.1/wavs/LJ038-0064.wav|He further stated that while fists were flying he heard one of the officers say, quote, Kill the President, will you, end quote.
+LJSpeech-1.1/wavs/LJ048-0243.wav|Only one stayed as long as a half hour, and none had any beverage there.
+LJSpeech-1.1/wavs/LJ041-0010.wav|A friend, Edward Voebel, testified that, quote, he was more bashful about girls than anything else, end quote.
+LJSpeech-1.1/wavs/LJ007-0207.wav|but this had the effect of throwing them into closer contact, and of making them more intimately acquainted with, more directly influential upon, one another.
+LJSpeech-1.1/wavs/LJ039-0140.wav|Soon after Oswald returned from the Soviet Union
+LJSpeech-1.1/wavs/LJ042-0059.wav|Despite this commitment to the Soviet Union
+LJSpeech-1.1/wavs/LJ012-0111.wav|Both the thieves were now apprehended, but only a small portion of the lost property was recovered,
+LJSpeech-1.1/wavs/LJ021-0021.wav|The relations between the employer and the employed, between the owners of aggregated capital and the units of organized labor,
+LJSpeech-1.1/wavs/LJ043-0070.wav|Oswald obtained a job in July of nineteen sixty-two as a sheet metal worker with a company in Fort Worth.
+LJSpeech-1.1/wavs/LJ012-0094.wav|While the excitement was still fresh, a new robbery of diamonds was committed at a bonded warehouse in the immediate neighborhood, on Custom House Quay.
+LJSpeech-1.1/wavs/LJ003-0166.wav|association at one time forbidden by custom, but which greed and rapacity long made the rule.
+LJSpeech-1.1/wavs/LJ005-0113.wav|In another jail not far off male and female felons still occupied the same room -- underground, and reached by a ladder of ten steps.
+LJSpeech-1.1/wavs/LJ029-0035.wav|bring the President, quote, through an area which exposes him to the greatest number of people, end quote.
+LJSpeech-1.1/wavs/LJ007-0111.wav|or witnesses who did not voluntarily come forward when the chance was given them.
+LJSpeech-1.1/wavs/LJ037-0153.wav|Robert A. Frazier and Charles Killion, other FBI firearms experts,
+LJSpeech-1.1/wavs/LJ002-0327.wav|or from persons who made a trade of it, or they might bring their beds with them into the prison.
+LJSpeech-1.1/wavs/LJ008-0198.wav|two pounds was a common price for a window.
+LJSpeech-1.1/wavs/LJ034-0010.wav|Below the southeast corner window on the sixth floor was a large carton of books
+LJSpeech-1.1/wavs/LJ028-0111.wav|At the entrance of the great gates I erected strong bulls of bronze, and terrible serpents standing upright.
+LJSpeech-1.1/wavs/LJ037-0231.wav|or eight inches, about one hundred forty-five pounds, end quote, and wearing a white jacket.
+LJSpeech-1.1/wavs/LJ015-0128.wav|but he bought bona fide amounts, and increased their value by altering the figures,
+LJSpeech-1.1/wavs/LJ015-0002.wav|The course of the swindlers was by no means smooth, but it was not till eighteen fifty-four that suspicion arose that anything was wrong.
+LJSpeech-1.1/wavs/LJ002-0245.wav|The prison was a nest of abuses, like its neighbor the King's Bench
+LJSpeech-1.1/wavs/LJ028-0475.wav|No traces of its moat have appeared.
+LJSpeech-1.1/wavs/LJ010-0135.wav|yelled out three cheers to the populace whom he faced.
+LJSpeech-1.1/wavs/LJ049-0016.wav|The Presidential limousine.
+LJSpeech-1.1/wavs/LJ046-0106.wav|largely measures taken by the Secret Service, is considered.
+LJSpeech-1.1/wavs/LJ032-0042.wav|From Klein's records it was possible to trace the processing of the order after its receipt.
+LJSpeech-1.1/wavs/LJ014-0239.wav|which followed each other at no long intervals, which had a strong family likeness, and originated all of them to make money easily,
+LJSpeech-1.1/wavs/LJ022-0121.wav|When cases of this kind occur,
+LJSpeech-1.1/wavs/LJ013-0115.wav|Of this class was the ingenious yet peculiarly simple robbery effected at the house of Lord Fitzgerald in Belgrave Square.
+LJSpeech-1.1/wavs/LJ030-0066.wav|Driving the Presidential limousine was Special Agent William R. Greer of the Secret Service;
+LJSpeech-1.1/wavs/LJ004-0088.wav|As a remedy, justices of the peace were empowered to provide materials for the setting of poor prisoners to work,
+LJSpeech-1.1/wavs/LJ034-0102.wav|Within minutes of the assassination, Brennan described the man to the police.
+LJSpeech-1.1/wavs/LJ028-0302.wav|I will desert to the enemy as I am, and when I get into their city I will tell them that it is by thee that I have been thus treated.
+LJSpeech-1.1/wavs/LJ036-0150.wav|He directed the driver of the car to a point twenty feet north of the northwest corner of the intersection of Beckley and Neely
+LJSpeech-1.1/wavs/LJ017-0197.wav|This mysterious medicine Wilson kept carefully locked up, and allowed no one to see it,
+LJSpeech-1.1/wavs/LJ038-0223.wav|from October nine, nineteen sixty-two, to May fourteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ001-0077.wav|In England about this time, an attempt was made (notably by Caslon, who started business in London as a type-founder in seventeen twenty)
+LJSpeech-1.1/wavs/LJ018-0015.wav|His identity was established by a bundle of letters in his pocket, which bore his full address:
+LJSpeech-1.1/wavs/LJ018-0320.wav|in which the plan of action was changed, and the freedom of the prisoners was to be obtained by means of a rescue from the dock during the trial.
+LJSpeech-1.1/wavs/LJ019-0314.wav|The separation of prisoners in cells duly certified by the inspectors was insisted upon,
+LJSpeech-1.1/wavs/LJ028-0438.wav|However, it is possible that the hanging gardens existed only in the imagination of the Greek writers,
+LJSpeech-1.1/wavs/LJ013-0255.wav|The victim was the first of these three.
+LJSpeech-1.1/wavs/LJ020-0055.wav|visiting every corner of the pan, but not leaving thick layers and streaks of grease after it.
+LJSpeech-1.1/wavs/LJ019-0046.wav|was the variety of views as regards the discipline to be introduced in these new prisons. The time was one
+LJSpeech-1.1/wavs/LJ017-0019.wav|Captain Donellan, who was convicted of poisoning his brother-in-law, Sir Theodosius Broughton, and executed for it,
+LJSpeech-1.1/wavs/LJ015-0012.wav|Cole too was apprehended, and in due course tried at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ007-0040.wav|in a second room were fourteen more who had every hope of a reprieve.
+LJSpeech-1.1/wavs/LJ019-0099.wav|moved for a committee to report upon the best means of securing some uniform system which should be "punitive, reformatory, and self-supporting;"
+LJSpeech-1.1/wavs/LJ013-0059.wav|There were two separate affairs.
+LJSpeech-1.1/wavs/LJ016-0197.wav|After that he worked alone.
+LJSpeech-1.1/wavs/LJ033-0065.wav|With Ruth Paine acting as an interpreter, Detective Rose asked Marina whether her husband had a rifle.
+LJSpeech-1.1/wavs/LJ041-0085.wav|Oswald, quote, seemed to be a person who would go out of his way to get into trouble, end quote,
+LJSpeech-1.1/wavs/LJ044-0223.wav|whether or not he agreed with Castro that President Kennedy was a, quote, ruffian and a thief, end quote. He replied that he, quote,
+LJSpeech-1.1/wavs/LJ032-0031.wav|Document examiners for the Treasury Department and the FBI testified unequivocally
+LJSpeech-1.1/wavs/LJ031-0155.wav|Men at the Trade Mart were driven to Parkland Hospital in Dallas police cars.
+LJSpeech-1.1/wavs/LJ013-0208.wav|Lord William, according to the valet, was of a peevish, difficult temper;
+LJSpeech-1.1/wavs/LJ005-0033.wav|hard, incessant, irksome, eternal labor, a planned and regulated and unrelenting exclusion of happiness and comfort.
+LJSpeech-1.1/wavs/LJ005-0245.wav|This committee animadverted strongly upon the system in force at the metropolitan jails, and more especially upon the condition of Newgate
+LJSpeech-1.1/wavs/LJ047-0090.wav|Our investigation of Oswald had disclosed no evidence that Oswald was acting under the instructions or on behalf of
+LJSpeech-1.1/wavs/LJ015-0238.wav|The thieves took first-class tickets, handed their bags full of shot to the porters, who placed them in the guard's van.
+LJSpeech-1.1/wavs/LJ018-0398.wav|Benson, the two Kurrs, Bale, and Murray, led to strange revelations of dishonest practices amongst the detective police,
+LJSpeech-1.1/wavs/LJ022-0094.wav|in whose field the project falls, and also to notify another agency which I am creating -- a Progress Division.
+LJSpeech-1.1/wavs/LJ003-0259.wav|and attracted attention by their disorderly behavior.
+LJSpeech-1.1/wavs/LJ039-0071.wav|Robert A. Frazier, FBI expert in firearms identification and training, said, quote,
+LJSpeech-1.1/wavs/LJ004-0185.wav|that the chaplain found it necessary to take his place before they entered chapel, as he could not otherwise have faced the smell.
+LJSpeech-1.1/wavs/LJ024-0071.wav|I propose that hereafter, when a judge reaches the age of seventy,
+LJSpeech-1.1/wavs/LJ047-0130.wav|advised the Bureau that Oswald was unknown in such circles.
+LJSpeech-1.1/wavs/LJ014-0034.wav|Hocker, whose skill in counterfeiting handwriting was known, was asked to fabricate a letter making an assignation with Delarue
+LJSpeech-1.1/wavs/LJ024-0108.wav|This is the same group who during the campaign last Fall tried to block the mandate of the people.
+LJSpeech-1.1/wavs/LJ031-0021.wav|As the President's limousine sped toward the hospital, twelve doctors to the emergency area: surgeons, Drs. Malcolm O. Perry,
+LJSpeech-1.1/wavs/LJ049-0152.wav|The assignment of the responsibility of protecting the President to an agency of the Department of the Treasury was largely an historical accident.
+LJSpeech-1.1/wavs/LJ010-0234.wav|backed possibly with the hope that, as in a much more recent case,
+LJSpeech-1.1/wavs/LJ011-0171.wav|was the state of her father's affairs.
+LJSpeech-1.1/wavs/LJ013-0016.wav|It was fully proved in evidence that when the Dryad sailed she carried nothing but the cargo belonging to Zulueta and Co.
+LJSpeech-1.1/wavs/LJ049-0087.wav|without making an attack on the President a crime. Threatening harm to the President is a Federal offense,
+LJSpeech-1.1/wavs/LJ018-0045.wav|An alibi was set up for the defense, but not well substantiated, and the jury without hesitation returned a verdict of guilty.
+LJSpeech-1.1/wavs/LJ021-0166.wav|we must make it a national principle that we will not tolerate a large army of unemployed and that we will arrange our national economy
+LJSpeech-1.1/wavs/LJ043-0180.wav|and with the circumstances surrounding the assassination, have led the Commission to believe
+LJSpeech-1.1/wavs/LJ049-0094.wav|The Commission agrees with the Secret Service that it should be authorized to make arrests without warrant
+LJSpeech-1.1/wavs/LJ034-0030.wav|They could be easily adjusted and were still solid enough to serve as a gun rest.
+LJSpeech-1.1/wavs/LJ030-0089.wav|This distance was maintained so that spectators would normally turn their gaze from the President's automobile by the time the Vice President came into view.
+LJSpeech-1.1/wavs/LJ019-0074.wav|The legislature was content to let local jurisdictions experimentalize for themselves; with the strange, anomalous result,
+LJSpeech-1.1/wavs/LJ045-0125.wav|For example, in his letter of November nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ011-0101.wav|Hunton tried to fly the country on the detection of the fraud, but was arrested at Plymouth just as he was on the point of leaving England in the New York packet.
+LJSpeech-1.1/wavs/LJ033-0097.wav|Frazier parked the car in the company parking lot about two blocks north of the Depository Building.
+LJSpeech-1.1/wavs/LJ019-0311.wav|The legislature was beginning to overcome its disinclination to interfere actively or authoritatively
+LJSpeech-1.1/wavs/LJ007-0117.wav|where the upper ward was exclusively appropriated to their use. They also had their meals sent in, and, with the food, wine almost ad libitum.
+LJSpeech-1.1/wavs/LJ029-0099.wav|From Love Field the route passed through a portion of suburban Dallas,
+LJSpeech-1.1/wavs/LJ016-0006.wav|The inspectors' reports mention many cases of evasion accomplished.
+LJSpeech-1.1/wavs/LJ026-0006.wav|for the manufacture of starchy food from gaseous matter.
+LJSpeech-1.1/wavs/LJ037-0023.wav|Another witness, Domingo Benavides, was driving a pickup truck west on tenth Street.
+LJSpeech-1.1/wavs/LJ012-0119.wav|The fire was raked out, and in the ashes were found seven large and four dozen small brilliants, also seven emeralds,
+LJSpeech-1.1/wavs/LJ043-0036.wav|he did not see his brother Robert from Thanksgiving of nineteen sixty-two until November twenty-three, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ050-0002.wav|The Warren Commission Report.
+LJSpeech-1.1/wavs/LJ044-0006.wav|Although, as indicated above, the Commission has been unable to find any credible evidence
+LJSpeech-1.1/wavs/LJ012-0100.wav|Some ham sandwiches, several cigar ends, and two empty champagne bottles were found on the premises next day, showing how they had passed their time.
+LJSpeech-1.1/wavs/LJ012-0177.wav|The victim was invited to visit Probert's cottage in the country near Elstree.
+LJSpeech-1.1/wavs/LJ019-0353.wav|they were at liberty to hire suitable cell accommodation from any neighbors who might have it to spare; the only proviso,
+LJSpeech-1.1/wavs/LJ016-0333.wav|from Clerkenwell prison, by which many persons lost their lives.
+LJSpeech-1.1/wavs/LJ046-0072.wav|have been confronted in recent years with increasingly difficult problems, created by the greater exposure of the President during his travels
+LJSpeech-1.1/wavs/LJ046-0143.wav|These instructions to PRS personnel appear to be the only instance where an effort was made to reduce the criteria to writing.
+LJSpeech-1.1/wavs/LJ009-0121.wav|for all thy goodness and loving-kindness to us, and to all men;
+LJSpeech-1.1/wavs/LJ002-0254.wav|and fines imposed for conduct disapproved of by the "college."
+LJSpeech-1.1/wavs/LJ046-0080.wav|If the protective job is well done, its performance will be evident only in the unexceptional fact of its success.
+LJSpeech-1.1/wavs/LJ034-0140.wav|Jarman heard Brennan, quote, talking to this officer about that he had heard these shots and he had seen the barrel of the gun sticking out the window,
+LJSpeech-1.1/wavs/LJ001-0127.wav|the modern letters are narrowed by a third or thereabout; but while this gain of space very much hampers the possibility of beauty of design,
+LJSpeech-1.1/wavs/LJ045-0118.wav|would see anything in the newspaper about his defection, unless he engaged in activities similar to those
+LJSpeech-1.1/wavs/LJ043-0086.wav|through a work-study program at a local college.
+LJSpeech-1.1/wavs/LJ013-0090.wav|But when the latter was filled with gold it was too heavy to lift,
+LJSpeech-1.1/wavs/LJ001-0149.wav|From the time when books first took their present shape till the end of the sixteenth century, or indeed later,
+LJSpeech-1.1/wavs/LJ016-0232.wav|and he owned a pet pony which would follow him about like a dog.
+LJSpeech-1.1/wavs/LJ019-0372.wav|It was practically inoperative as regards the penalties for neglect. It was no doubt as irksome and inconvenient to the Secretary of State
+LJSpeech-1.1/wavs/LJ026-0049.wav|In turn these will be compared for the animal and the plant,
+LJSpeech-1.1/wavs/LJ032-0269.wav|It could have been caused by the telescopic sight of the rifle which was approximately eleven inches long.
+LJSpeech-1.1/wavs/LJ018-0325.wav|But extra precautions and close supervision have so far proved effectual, and the prisoners are still in custody after a lapse of ten years.
+LJSpeech-1.1/wavs/LJ006-0267.wav|yet many persons of notoriously loose life continually obtained egress.
+LJSpeech-1.1/wavs/LJ039-0084.wav|you identify it and the crosshairs are in close relationship to the point you want to shoot at,
+LJSpeech-1.1/wavs/LJ027-0047.wav|totally different structures are modified to perform the same functions.
+LJSpeech-1.1/wavs/LJ033-0160.wav|Materials used to make bag.
+LJSpeech-1.1/wavs/LJ032-0273.wav|(four) a photograph taken in the yard of Oswald's apartment showed him holding this rifle, and (five)
+LJSpeech-1.1/wavs/LJ006-0005.wav|just before the public mind was first awakened to the need for thorough reform.
+LJSpeech-1.1/wavs/LJ016-0398.wav|As a special favor
+LJSpeech-1.1/wavs/LJ035-0043.wav|Yet he must have entered the vestibule door before Truly reached the top of the stairwell, since Truly did not see him.
+LJSpeech-1.1/wavs/LJ018-0221.wav|but upon this forged and false conveyance William Roupell, who had already embarked upon a career of wild extravagance,
+LJSpeech-1.1/wavs/LJ001-0061.wav|the Roman letter was used side by side with the Gothic.
+LJSpeech-1.1/wavs/LJ013-0128.wav|A watch was set on his house, in Allington Street, Pimlico,
+LJSpeech-1.1/wavs/LJ037-0182.wav|Ownership of Revolver
+LJSpeech-1.1/wavs/LJ028-0309.wav|After the twenty days are over, bid thy whole army attack the city on every side, and put me two bodies of Persians,
+LJSpeech-1.1/wavs/LJ007-0056.wav|leaden hearts, and "grinding the impressions off penny-pieces, then pricking figures or words on them to give to their friends as memorials.
+LJSpeech-1.1/wavs/LJ042-0209.wav|the murder of history, the prostitution of art and culture, end quote.
+LJSpeech-1.1/wavs/LJ037-0262.wav|These facts warrant the finding that Lee Harvey Oswald disposed of his jacket as he fled from the scene of the Tippit killing.
+LJSpeech-1.1/wavs/LJ006-0071.wav|No doubt the governor for the time being, Mr. Cope, was in a great measure to blame for all this, and for the want of proper classification.
+LJSpeech-1.1/wavs/LJ048-0114.wav|that the Secret Service has inadequately defined the responsibilities of its advance agents, who have been given broad discretion
+LJSpeech-1.1/wavs/LJ044-0178.wav|he stated his desire that he should be accepted as a friend of the Cuban Revolution.
+LJSpeech-1.1/wavs/LJ030-0236.wav|I quickly observed unnatural movement of crowds, like ducking or scattering, and quick movements in the Presidential follow-up car.
+LJSpeech-1.1/wavs/LJ035-0136.wav|Lovelady and Shelley moved out into the street.
+LJSpeech-1.1/wavs/LJ012-0202.wav|They pursued their terrible trade for five years without scruple and without detection.
+LJSpeech-1.1/wavs/LJ011-0280.wav|Mr. Gee at first stoutly refused.
+LJSpeech-1.1/wavs/LJ012-0239.wav|The execution took place a week or two later, in the presence of such a crowd as had not been seen near Newgate for years.
+LJSpeech-1.1/wavs/LJ006-0070.wav|where they inevitably meet with further contamination from the society of the most abandoned and incorrigible inmates of the jail."
+LJSpeech-1.1/wavs/LJ039-0156.wav|to seven point nine seconds.
+LJSpeech-1.1/wavs/LJ005-0140.wav|At the New Prison, Clerkenwell, which had become the principal reception jail of Middlesex, and so took all the untried,
+LJSpeech-1.1/wavs/LJ001-0031.wav|In fourteen sixty-five Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome,
+LJSpeech-1.1/wavs/LJ026-0062.wav|Through their action the solid portions are liquefied and the food is rendered capable of absorption into the body proper.
+LJSpeech-1.1/wavs/LJ037-0107.wav|They saw a man coming south on Patton with a revolver held high in his right hand. According to Callaway, the man crossed to the west side of Patton.
+LJSpeech-1.1/wavs/LJ044-0168.wav|During this period Oswald may have practiced opening and closing the bolt on his rifle in a screened porch in his apartment.
+LJSpeech-1.1/wavs/LJ022-0198.wav|We have in the darkest moments of our national trials retained our faith in our own ability to master our destiny.
+LJSpeech-1.1/wavs/LJ003-0113.wav|A prisoner, generally the oldest and most dexterous thief,
+LJSpeech-1.1/wavs/LJ035-0004.wav|the Commission has reviewed the testimony of witnesses who saw Oswald in the building within minutes after the assassination.
+LJSpeech-1.1/wavs/LJ047-0235.wav|complete surprise, end quote, because he had no reason to believe that Oswald, quote,
+LJSpeech-1.1/wavs/LJ006-0198.wav|But this was not the only amusement. Most of the wards took in the daily papers,
+LJSpeech-1.1/wavs/LJ047-0121.wav|stated the Bureau's reasoning in this way, quote,
+LJSpeech-1.1/wavs/LJ038-0230.wav|The county jail is at the corner of Houston and Main Streets, quote, right in the beginning of the city, end quote, after one travels through the underpass.
+LJSpeech-1.1/wavs/LJ015-0070.wav|whence he passed to a better position under the Crystal Palace Company.
+LJSpeech-1.1/wavs/LJ023-0112.wav|We have, therefore,
+LJSpeech-1.1/wavs/LJ013-0019.wav|Loose had his instructions to cast away the ship on the first possible opportunity, and from the time of his leaving Liverpool
+LJSpeech-1.1/wavs/LJ039-0117.wav|He had the services of an experienced highly trained coach.
+LJSpeech-1.1/wavs/LJ022-0178.wav|We all know that private banking actually exists by virtue of the permission of and regulation by the people as a whole,
+LJSpeech-1.1/wavs/LJ038-0024.wav|Patrol cars bearing at least fifteen officers converged on the Texas Theatre.
+LJSpeech-1.1/wavs/LJ020-0006.wav|Three potatoes of fair size, peeled and boiled mealy.
+LJSpeech-1.1/wavs/LJ017-0277.wav|The soldiers then?
+LJSpeech-1.1/wavs/LJ047-0109.wav|When Quigley returned to his office, he learned
+LJSpeech-1.1/wavs/LJ019-0264.wav|and it was decidedly of opinion that in all short sentences the hard labor of the tread-wheel, crank, and so forth should be the invariable rule.
+LJSpeech-1.1/wavs/LJ003-0247.wav|or the faithful discharge of some petty office, such as gatesman or captain of a ward.
+LJSpeech-1.1/wavs/LJ010-0265.wav|and on his information handbills were circulated, giving the exact description of the deformed youth, who had a hump-back,
+LJSpeech-1.1/wavs/LJ031-0078.wav|At approximately one p.m., after last rites were administered to the President by Father Oscar L. Huber, Dr. Clark pronounced the President dead.
+LJSpeech-1.1/wavs/LJ004-0078.wav|"The convicted delinquent has his rights," said Mr. Buxton authoritatively.
+LJSpeech-1.1/wavs/LJ003-0202.wav|These capital convicts, says Mr. Bennet, quote, lessened the ennui and despair of their situation by unbecoming merriment
+LJSpeech-1.1/wavs/LJ026-0075.wav|are dissolved by the sap in the leaves and elsewhere and thus may pass to every portion of the plant.
+LJSpeech-1.1/wavs/LJ033-0192.wav|When Paul M. Stombaugh of the FBI Laboratory examined the paper bag,
+LJSpeech-1.1/wavs/LJ045-0072.wav|There can be little doubt that some provocation existed.
+LJSpeech-1.1/wavs/LJ016-0167.wav|Probably dissolution came as easily and almost without pain.
+LJSpeech-1.1/wavs/LJ003-0179.wav|which will deal with Mrs. Fry's philanthropic exertions at this period in this particular part of the prison.
+LJSpeech-1.1/wavs/LJ022-0056.wav|Our social security legislation is an attempt to answer the first of these questions; our Works Relief program, the second.
+LJSpeech-1.1/wavs/LJ038-0018.wav|Shortly thereafter, Johnny Brewer, who had come from the nearby shoestore, asked Mrs. Postal whether the fellow that had ducked in had bought a ticket.
+LJSpeech-1.1/wavs/LJ022-0097.wav|It will also have the responsibility of determining work payments in various localities,
+LJSpeech-1.1/wavs/LJ047-0050.wav|Oswald again agreed to advise the FBI if he were approached under suspicious circumstances; however, he deprecated the possibility of this happening,
+LJSpeech-1.1/wavs/LJ047-0156.wav|She said that Lee Oswald was living somewhere in Dallas. She didn't know where. She said it was in the Oak Cliff area but she didn't have his address.
+LJSpeech-1.1/wavs/LJ040-0219.wav|It would be incorrect, however, to believe that those aspects of Lee's personality which were observed in New York
+LJSpeech-1.1/wavs/LJ028-0280.wav|Calling to mind then the words of the Babylonian at the beginning of the siege:
+LJSpeech-1.1/wavs/LJ043-0160.wav|Question: Did you ask him why he had not destroyed the book before he actually went to shoot General Walker?
+LJSpeech-1.1/wavs/LJ008-0274.wav|The result in the latter case was left in the first place to the king in council,
+LJSpeech-1.1/wavs/LJ038-0027.wav|Detective Paul L. Bentley rushed to the balcony and told the projectionist to turn up the house lights.
+LJSpeech-1.1/wavs/LJ048-0007.wav|Director Hoover emphasized that the first indication of Oswald's capacity for violence
+LJSpeech-1.1/wavs/LJ014-0164.wav|as the wickedness and levity of the immense crowd collected at the execution this morning could be imagined by no man,
+LJSpeech-1.1/wavs/LJ028-0429.wav|The excavations have shown that Babylon, as the ancients told us, was nearly square.
+LJSpeech-1.1/wavs/LJ044-0127.wav|In reply Arnold Johnson advised Oswald that, while as an American citizen he had a right to participate in such organizations as he wished, quote,
+LJSpeech-1.1/wavs/LJ050-0196.wav|Since the assassination of President Kennedy, the Secret Service has been experimenting with new techniques in the inspection of buildings along a motorcade route.
+LJSpeech-1.1/wavs/LJ012-0101.wav|They had had serious work to get at the diamonds. It was necessary to force one heavy door from its hinges, and to cut through the thick panels of another.
+LJSpeech-1.1/wavs/LJ012-0073.wav|At his own request he was reconveyed to Hobart Town, where his son had been carrying on the business.
+LJSpeech-1.1/wavs/LJ043-0071.wav|His performance for that company was satisfactory.
+LJSpeech-1.1/wavs/LJ050-0182.wav|Liaison With Local Law Enforcement Agencies
+LJSpeech-1.1/wavs/LJ006-0024.wav|Several European states had dispatched emissaries to examine and report upon them.
+LJSpeech-1.1/wavs/LJ010-0016.wav|thefts of bullion, bank-notes, specie, and gold-dust, planned with consummate ingenuity,
+LJSpeech-1.1/wavs/LJ045-0161.wav|He was upset over the fact that I would not answer him.
+LJSpeech-1.1/wavs/LJ014-0027.wav|A woman whom he called on the same evening declared he had worn a mackintosh, his coat was much torn, there was a stain of blood on his shirt-cuff,
+LJSpeech-1.1/wavs/LJ040-0160.wav|and deprivation, lack of affection, absence of family life and rejection by a self involved and conflicted mother.
+LJSpeech-1.1/wavs/LJ035-0123.wav|None of these three men saw Dougherty, probably because of the anxiety of the moment and because of the books which may have blocked the view.
+LJSpeech-1.1/wavs/LJ009-0164.wav|sheep or horse stealing, and stealing in a dwelling-house.
+LJSpeech-1.1/wavs/LJ023-0101.wav|if the legislative power is not left free to choose the methods of solving the problems of poverty,
+LJSpeech-1.1/wavs/LJ018-0339.wav|The judge, Sir Henry Hawkins, in passing sentence
+LJSpeech-1.1/wavs/LJ018-0230.wav|After reading it, and finding this unfavorable to himself, he resolved to carry out his deliberate plan,
+LJSpeech-1.1/wavs/LJ008-0022.wav|On the eighteenth December, eighteen twelve,
+LJSpeech-1.1/wavs/LJ032-0198.wav|During the period from March two, nineteen sixty-three, to April twenty-four, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ029-0006.wav|Beginning with the advance plans and Secret Service preparations for the trip,
+LJSpeech-1.1/wavs/LJ036-0213.wav|The man's general description was similar to the one broadcast over the police radio.
+LJSpeech-1.1/wavs/LJ027-0144.wav|Now, it must be obvious
+LJSpeech-1.1/wavs/LJ002-0047.wav|The day room was fitted with benches and settles after the manner of the tap in a public-house.
+LJSpeech-1.1/wavs/LJ008-0084.wav|The flames presently burned the halter, the body fell a few inches, and hung then by the iron chain.
+LJSpeech-1.1/wavs/LJ048-0126.wav|Special Agent in Charge Sorrels testified that the advance preparations followed on this occasion were, quote, pretty much the same, end quote,
+LJSpeech-1.1/wavs/LJ046-0060.wav|and their foreign journeys have come to be accepted as normal rather than extraordinary.
+LJSpeech-1.1/wavs/LJ018-0058.wav|It is generally supposed that he committed the murder under a sudden access of covetousness and greed.
+LJSpeech-1.1/wavs/LJ008-0034.wav|the scaffold hung with black; and the inhabitants of the neighborhood, having petitioned the sheriffs to remove the scene of execution to the old place,
+LJSpeech-1.1/wavs/LJ019-0277.wav|the prisoners should be made to dispense with the use of a mattress, and should sleep on planks.
+LJSpeech-1.1/wavs/LJ047-0086.wav|as it might have under existing procedures, and did not know of his application.
+LJSpeech-1.1/wavs/LJ018-0114.wav|the others to twenty and ten years.
+LJSpeech-1.1/wavs/LJ006-0248.wav|Matters were at times still worse, and the rioting went on to such dangerous lengths as to endanger the safety of the building.
+LJSpeech-1.1/wavs/LJ010-0151.wav|Again, a soldier, by name Hatfield, who had been wounded in the head, and discharged from the army for unsoundness of mind,
+LJSpeech-1.1/wavs/LJ031-0081.wav|President Kennedy could have survived the neck injury, but the head wound was fatal.
+LJSpeech-1.1/wavs/LJ042-0186.wav|which will bring about the final destruction of the capitalist system, and indicated that
+LJSpeech-1.1/wavs/LJ021-0045.wav|They saw that without changes in the policies and methods of investment
+LJSpeech-1.1/wavs/LJ008-0086.wav|A great concourse of people attended on this melancholy occasion.
+LJSpeech-1.1/wavs/LJ022-0176.wav|Finally, the reestablishment of public confidence in the banks of the nation
+LJSpeech-1.1/wavs/LJ049-0015.wav|but finds that the Secret Service agents did all that could have been done to take protective measures.
+LJSpeech-1.1/wavs/LJ045-0135.wav|but did not issue any such warning or suggest that Marina Oswald defect from the Soviet Union and remain in the United States under FBI protection,
+LJSpeech-1.1/wavs/LJ002-0213.wav|The Fleet, which stood in Farringdon Street,
+LJSpeech-1.1/wavs/LJ015-0066.wav|His proclivities were theatrical, and he was the author of several plays,
+LJSpeech-1.1/wavs/LJ017-0142.wav|Smethurst's escape may have influenced the jury in the Poplar poisoning case,
+LJSpeech-1.1/wavs/LJ009-0219.wav|A clause was inserted to the effect that
+LJSpeech-1.1/wavs/LJ009-0210.wav|On the left side of the head the fatal mall,
+LJSpeech-1.1/wavs/LJ012-0098.wav|These were deposited in an iron chest of great strength on the second floor.
+LJSpeech-1.1/wavs/LJ024-0131.wav|The workers were not fooled by that propaganda then. The people of America will not be fooled by such propaganda now.
+LJSpeech-1.1/wavs/LJ010-0196.wav|These antics may have been assumed to bear out the plea of insanity set up in his defense,
+LJSpeech-1.1/wavs/LJ039-0145.wav|According to George De Mohrenschildt, Oswald said that he went target shooting with that rifle.
+LJSpeech-1.1/wavs/LJ009-0290.wav|He was at first engaged as assistant to the executioner Tom Cheshire, but in due course rose to be chief.
+LJSpeech-1.1/wavs/LJ016-0081.wav|On another occasion Mr. Cope the governor came in and missed a man.
+LJSpeech-1.1/wavs/LJ028-0260.wav|The following story from Herodotus tells the results:
+LJSpeech-1.1/wavs/LJ026-0008.wav|and the beginner in biological study is surprised to find them described in textbooks of both botany and zoology.
+LJSpeech-1.1/wavs/LJ002-0184.wav|the latter two being also a prison for felons and vagrants arrested within certain limits.
+LJSpeech-1.1/wavs/LJ019-0284.wav|According to this committee of eighteen sixty-three, beds in the smaller and most carelessly conducted prisons formed a large element in the life of a prisoner.
+LJSpeech-1.1/wavs/LJ046-0239.wav|notwithstanding the fact that Ambassador Stevenson had been abused by pickets in Dallas less than a month before.
+LJSpeech-1.1/wavs/LJ044-0086.wav|executive director of the Information Council of the Americas, who also appeared on the program.
+LJSpeech-1.1/wavs/LJ007-0152.wav|Having thus passed his time, he returns a greater adept in crime, with a wider acquaintance among criminals, and, what perhaps is even more injurious to him,
+LJSpeech-1.1/wavs/LJ027-0078.wav|For example: the wings of a bat (a mammal), a bird and a fossil flying reptile all show the same bones adaptively modified;
+LJSpeech-1.1/wavs/LJ014-0232.wav|the French refugee, was a murderer of the same description, who dispatched his victim with a loaded cane, after which, to secure his escape,
+LJSpeech-1.1/wavs/LJ019-0101.wav|In eighteen fifty Sir George Grey brought forward a new motion to the same effect,
+LJSpeech-1.1/wavs/LJ014-0136.wav|the culprit was really her husband, who killed O'Connor out of jealousy and revengeful feelings.
+LJSpeech-1.1/wavs/LJ044-0194.wav|Oswald's attempt to go to Cuba
+LJSpeech-1.1/wavs/LJ033-0002.wav|Chapter four. The Assassin: Part two.
+LJSpeech-1.1/wavs/LJ032-0079.wav|Also in his wallet at that time
+LJSpeech-1.1/wavs/LJ044-0023.wav|Oswald had attempted to infiltrate an anti-Castro organization with which he was associated.
+LJSpeech-1.1/wavs/LJ021-0199.wav|In our efforts for recovery
+LJSpeech-1.1/wavs/LJ006-0232.wav|More often they occurred at night, and were continued to the bitter end.
+LJSpeech-1.1/wavs/LJ035-0050.wav|With his revolver drawn, Baker opened the vestibule door and ran into the vestibule.
+LJSpeech-1.1/wavs/LJ016-0234.wav|he was not unskilful, but he proceeded entirely by rule of thumb, leaving the result very much to chance and the strength of the rope.
+LJSpeech-1.1/wavs/LJ042-0100.wav|Even though he received more money and better living quarters than other Russians doing similar work,
+LJSpeech-1.1/wavs/LJ019-0200.wav|It would be far better to begin at the beginning,
+LJSpeech-1.1/wavs/LJ010-0294.wav|frauds indicating great boldness, extensive design, and ingenuity became more prevalent.
+LJSpeech-1.1/wavs/LJ016-0242.wav|The last execution at which he acted was that of Godwin, on the twenty-fifth May, eighteen seventy-four.
+LJSpeech-1.1/wavs/LJ027-0105.wav|a number of organs which never could have been of use to any kind of creature save a terrestrial quadruped.
+LJSpeech-1.1/wavs/LJ047-0141.wav|in early October of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ050-0103.wav|were all men who acted alone in their criminal acts against our leaders. None had a serious record of prior violence.
+LJSpeech-1.1/wavs/LJ002-0189.wav|No fixed rates or rules governed the hiring out of rooms or parts of a room, and all sorts of imposition was practiced.
+LJSpeech-1.1/wavs/LJ024-0117.wav|you will find that many of those who pretend to support you will sabotage any constructive amendment which is proposed.
+LJSpeech-1.1/wavs/LJ016-0070.wav|from a widow woman, who gave him "bub and grub," or food and one-and-sixpence, for every nine days' work.
+LJSpeech-1.1/wavs/LJ005-0231.wav|The city justices had not fulfilled this obligation.
+LJSpeech-1.1/wavs/LJ035-0072.wav|On the first test, the elapsed time between the simulated first shot and Baker's arrival on the second-floor stair landing
+LJSpeech-1.1/wavs/LJ012-0091.wav|The spoil was fairly divided; part of the notes were disposed of to a traveling "receiver," who passed over to the Continent and there cashed them easily.
+LJSpeech-1.1/wavs/LJ028-0257.wav|and in five twenty-one Nebuchadnezzar the third, a native Babylonian, was placed on the throne.
+LJSpeech-1.1/wavs/LJ044-0012.wav|he had printed a handbill headed in large letters, quote,
+LJSpeech-1.1/wavs/LJ043-0142.wav|Oswald posed for two pictures
+LJSpeech-1.1/wavs/LJ028-0038.wav|Its history for the next fifteen hundred years or more is obscure.
+LJSpeech-1.1/wavs/LJ018-0145.wav|Suspicion appears to have rested on Brown, who had left Laverstock,
+LJSpeech-1.1/wavs/LJ018-0254.wav|He declined to plead, but a plea of "Not Guilty" was recorded.
+LJSpeech-1.1/wavs/LJ034-0013.wav|In front of this small carton and resting partially on the windowsill was another small "Rolling Readers" carton.
+LJSpeech-1.1/wavs/LJ034-0193.wav|Fischer and Edwards did not see the man clearly enough or long enough to identify him.
+LJSpeech-1.1/wavs/LJ019-0115.wav|In his proposal he dwelt much upon the humanizing effects of healthful open-air toil,
+LJSpeech-1.1/wavs/LJ048-0008.wav|was his attempt on General Walker's life, which did not become known to the FBI until after the assassination.
+LJSpeech-1.1/wavs/LJ031-0218.wav|The second head wound measured approximately five inches (thirteen centimeters) in its greatest diameter,
+LJSpeech-1.1/wavs/LJ015-0298.wav|But while Hardwicke was in communication with Saward, the bank was in communication with London
+LJSpeech-1.1/wavs/LJ005-0299.wav|The whole question was again dealt with in Lord John Russell's bill for the reform of the municipal corporations, and with a more liberal election of town councilors,
+LJSpeech-1.1/wavs/LJ003-0281.wav|and the danger of losing material, raw or manufactured.
+LJSpeech-1.1/wavs/LJ002-0272.wav|on the opening of Whitecross prison for debtors in eighteen fifteen.
+LJSpeech-1.1/wavs/LJ033-0102.wav|When Oswald entered the rear door of the Depository Building, he was about fifty feet ahead of Frazier.
+LJSpeech-1.1/wavs/LJ031-0125.wav|Vice President Johnson at Parkland
+LJSpeech-1.1/wavs/LJ045-0084.wav|said that, quote, She was annoying him all the time
+LJSpeech-1.1/wavs/LJ019-0263.wav|and sought to make profitable returns from prisoners' labor the test of prison efficiency. In this view the committee could not coincide,
+LJSpeech-1.1/wavs/LJ028-0380.wav|The prophecy of the Hebrew Isaiah was fulfilled:
+LJSpeech-1.1/wavs/LJ031-0018.wav|When Parkland Hospital received the notification, the staff in the emergency area was alerted and trauma rooms one and two were prepared.
+LJSpeech-1.1/wavs/LJ035-0015.wav|and I had a feeling that it came from the building, either right in front of me [the Depository Building] or of the one across to the right of it, end quote,
+LJSpeech-1.1/wavs/LJ001-0105.wav|America has produced a good many showy books, the typography, paper, and illustrations of which are, however, all wrong,
+LJSpeech-1.1/wavs/LJ014-0261.wav|He rode a priceless hack in Rotten Row, or drove down to Richmond in a mail phaeton and pair.
+LJSpeech-1.1/wavs/LJ008-0051.wav|says, "The scaffold on which these miserable people suffered is a temporary machine which was drawn out of the yard of the sessions house by horses;
+LJSpeech-1.1/wavs/LJ005-0149.wav|The nature of the employment varied greatly in severity, especially the tread-wheel labor.
+LJSpeech-1.1/wavs/LJ011-0196.wav|Mr. Turner at once set off for London, where he sought the assistance of the police,
+LJSpeech-1.1/wavs/LJ003-0037.wav|A site was purchased between Red Lion and White Cross streets, and a new prison planned,
+LJSpeech-1.1/wavs/LJ002-0172.wav|For this he was summoned before a magistrate, and sentenced as already stated.
+LJSpeech-1.1/wavs/LJ032-0040.wav|were written the words "A. Hidell, P.O. Box two nine one five Dallas, Texas."
+LJSpeech-1.1/wavs/LJ002-0203.wav|The half or wholly starved prisoners fished for alms or food at the gratings.
+LJSpeech-1.1/wavs/LJ016-0313.wav|On the other hand, Lords Cranworth and Wensleydale recommended private executions; so did Mr. Spencer Walpole, M.P.
+LJSpeech-1.1/wavs/LJ033-0062.wav|She did not unroll the blanket. She saw that it was in its usual position and it appeared to her to have something inside.
+LJSpeech-1.1/wavs/LJ043-0032.wav|After about a two-week separation, Marina Oswald returned to her husband.
+LJSpeech-1.1/wavs/LJ016-0106.wav|with a civil note to the governor, saying he had no further use for them. All three fugitives were recaptured,
+LJSpeech-1.1/wavs/LJ013-0051.wav|was a promissory note for two thousand eight hundred seventy-five pounds, produced by Dr. Bailey, and purporting to be signed by Smith.
+LJSpeech-1.1/wavs/LJ010-0160.wav|None of the foregoing attempts were, however, so dastardly or determined as that made by Oxford upon our present gracious Queen
+LJSpeech-1.1/wavs/LJ002-0294.wav|a further fee was, however, demanded from the new-comer before he was made free of either the master's or the cabin side.
+LJSpeech-1.1/wavs/LJ028-0389.wav|St. Jerome said:
+LJSpeech-1.1/wavs/LJ010-0201.wav|"Young England" was a secret society, with no aim or object.
+LJSpeech-1.1/wavs/LJ004-0051.wav|When in Belgium he had examined with great satisfaction the admirable management of the great "Maison de Force" at Ghent,
+LJSpeech-1.1/wavs/LJ014-0007.wav|He entered into conversation with the policemen, and learnt, as it seemed for the first time, what had happened.
+LJSpeech-1.1/wavs/LJ008-0107.wav|in his canonicals, and with his head as stiffly erect as a sheriff's coachman.
+LJSpeech-1.1/wavs/LJ038-0201.wav|James C. Cadigan, FBI handwriting expert, testified that this note was written by Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ012-0137.wav|father of the clerk to the steam-ship company, and these facts were deemed sufficient to justify the arrest of all three.
+LJSpeech-1.1/wavs/LJ030-0081.wav|They were instructed to watch particularly for thrown objects, sudden actions in the crowd, and any movements toward the Presidential car.
+LJSpeech-1.1/wavs/LJ019-0231.wav|and by the following year forty-seven new cells had been built on the most approved plan.
+LJSpeech-1.1/wavs/LJ005-0208.wav|the only aperture through which air could be admitted being an iron grating level with the street,
+LJSpeech-1.1/wavs/LJ037-0124.wav|The Commission is satisfied that the lineups were conducted fairly.
+LJSpeech-1.1/wavs/LJ002-0290.wav|Neild found the prisoners in the Borough Compter ragged, starving, and dirty.
+LJSpeech-1.1/wavs/LJ021-0127.wav|Machinery set up by the federal government has provided some new methods of adjustment.
+LJSpeech-1.1/wavs/LJ019-0323.wav|The law made it imperative that every prison should have a prison chapel, and that daily and Sunday services should be held.
+LJSpeech-1.1/wavs/LJ007-0142.wav|But at Newgate the convicted prisoner, instead of privation and hard fare,
+LJSpeech-1.1/wavs/LJ047-0238.wav|Hosty parked his car in the basement of police headquarters and there met an acquaintance, Lt. Jack Revill of the Dallas police force.
+LJSpeech-1.1/wavs/LJ007-0055.wav|The more peaceably disposed found some occupation in making Newgate tokens,
+LJSpeech-1.1/wavs/LJ015-0168.wav|but the chairman of the Great Northern could not realize that a clerk of the company could honestly be in the possession of unlimited wealth.
+LJSpeech-1.1/wavs/LJ003-0318.wav|No prisoners should in future be ironed, except in cases of misconduct,
+LJSpeech-1.1/wavs/LJ036-0037.wav|He picked Oswald from the lineup as the man who had boarded the bus at the, quote, lower end of town on Elm around Houston, end quote,
+LJSpeech-1.1/wavs/LJ019-0249.wav|The varieties in construction were still very marked.
+LJSpeech-1.1/wavs/LJ019-0114.wav|but Mr. Pearson overlooked some points in which a more practical mind would have foreseen difficulty, and perhaps forecasted failure.
+LJSpeech-1.1/wavs/LJ043-0045.wav|He also said that he and his family got along well with Marina Oswald and enjoyed showing her American things.
+LJSpeech-1.1/wavs/LJ012-0263.wav|The police wished to refer to Greenacre, but as he was not forthcoming,
+LJSpeech-1.1/wavs/LJ034-0104.wav|slender, weighing about one sixty-five pounds, about five foot ten inches tall, and in his early thirties.
+LJSpeech-1.1/wavs/LJ033-0114.wav|When Frazier appeared before the Commission and was asked to demonstrate how Oswald carried the package, he said, quote, Like I said,
+LJSpeech-1.1/wavs/LJ002-0178.wav|affidavit of service, and many more, amounting in all to twenty-seven,
+LJSpeech-1.1/wavs/LJ039-0018.wav|He also said that he would use the pistol if the opportunity arose.
+LJSpeech-1.1/wavs/LJ029-0196.wav|and that he would visit San Antonio, Houston, Fort Worth, Dallas, and Austin.
+LJSpeech-1.1/wavs/LJ031-0069.wav|Dr. Clark noted some electrical activity on the cardiotachyscope attached to monitor the President's heart responses.
+LJSpeech-1.1/wavs/LJ040-0197.wav|He told Mrs. Siegel that he would run away if sent to a boarding school.
+LJSpeech-1.1/wavs/LJ035-0137.wav|About this time Shelley saw Truly and Patrolman Baker go into the building
+LJSpeech-1.1/wavs/LJ005-0023.wav|He took exception to various of the proposals of the Society. He thought they leant too much to a system of indulgence and education in jails.
+LJSpeech-1.1/wavs/LJ001-0116.wav|still clings to a foolish, because misunderstood conventionality, deduced from what was once ornament, and is by no means useful;
+LJSpeech-1.1/wavs/LJ006-0295.wav|The governor was also personally responsible for gross contravention of this rule of separation,
+LJSpeech-1.1/wavs/LJ043-0029.wav|De Mohrenschildt admitted that, quote,
+LJSpeech-1.1/wavs/LJ043-0147.wav|He had left a note for his wife telling her what to do in case he were apprehended, as well as his notebook and the pictures of himself holding the rifle.
+LJSpeech-1.1/wavs/LJ048-0134.wav|would be a desirable innovation.
+LJSpeech-1.1/wavs/LJ038-0283.wav|without going so far as to say to the exclusion of all other guns. This I could not do, end quote.
+LJSpeech-1.1/wavs/LJ044-0191.wav|Now there appeared to be no chance to get to Cuba, where he had thought he might find his communist ideal. The U.S. Government would not permit travel there
+LJSpeech-1.1/wavs/LJ004-0203.wav|The new jail had been in a great measure constructed by the prisoners themselves.
+LJSpeech-1.1/wavs/LJ031-0157.wav|Meanwhile, the Secret Service agents in charge of security at Love Field started to make arrangements for departure.
+LJSpeech-1.1/wavs/LJ009-0147.wav|and to have dwelt with more emphasis than good taste upon the nature of the crime, and the necessity for repentance.
+LJSpeech-1.1/wavs/LJ028-0149.wav|using throughout for their cement hot bitumen, and interposing a layer of wattled reeds at every thirtieth course of the bricks.
+LJSpeech-1.1/wavs/LJ032-0245.wav|Moreover, it was the only rifle owned by her husband following his return from the Soviet Union in June nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ003-0254.wav|whereas irons cannot well be exchanged without loss of time and attracting much attention.
+LJSpeech-1.1/wavs/LJ016-0338.wav|inside the jail was Colonel Frazer, the chief commissioner of the city police, and at no great distance, although in the background,
+LJSpeech-1.1/wavs/LJ006-0212.wav|brought in beer for sale from a neighboring public-house, and visited all the wards with no other escort than the prisoner gatesman.
+LJSpeech-1.1/wavs/LJ038-0146.wav|was a lunch sack which he kept on his lap during the ride from Irving to Dallas.
+LJSpeech-1.1/wavs/LJ048-0176.wav|Even if it were possible with a vastly larger force of security officers to do so, many observers have felt
+LJSpeech-1.1/wavs/LJ019-0159.wav|were constructed on the landings, ensconced in which warders spent the night, on duty, and alert to watch the sleepers below,
+LJSpeech-1.1/wavs/LJ031-0012.wav|The base station replied, quote, They have been notified, end quote,
+LJSpeech-1.1/wavs/LJ034-0091.wav|Howard L. Brennan was an eyewitness to the shooting. As indicated previously the Commission considered his testimony as probative
+LJSpeech-1.1/wavs/LJ010-0123.wav|an ample pension, so long as he did not return to England.
+LJSpeech-1.1/wavs/LJ004-0211.wav|The moral welfare of the inmates was as closely looked after as the physical.
+LJSpeech-1.1/wavs/LJ040-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ004-0101.wav|Warm and cold baths, or "commodious bathing tubs,"
+LJSpeech-1.1/wavs/LJ008-0189.wav|All the avenues and approaches, places even whence nothing whatever could be seen of the scaffold,
+LJSpeech-1.1/wavs/LJ019-0192.wav|The court was, however, prepared to consider Lord John Russell's proposal with regard to the cost of rebuilding;
+LJSpeech-1.1/wavs/LJ012-0209.wav|and after some haggling they agreed on a price, and in the afternoon the snatchers brought a hamper which contained a body in a sack.
+LJSpeech-1.1/wavs/LJ034-0071.wav|At about eleven:forty-five a.m. the floor-laying crew used both elevators to come down from the sixth floor.
+LJSpeech-1.1/wavs/LJ002-0107.wav|Quote, On the top of the jail, continues Neild, are a watch-house and a sentry-box, where two or more guards, with dogs and firearms,
+LJSpeech-1.1/wavs/LJ018-0140.wav|The two facts taken in conjunction
+LJSpeech-1.1/wavs/LJ016-0111.wav|The officers dropped on to him while he was still in bed, but as they came upstairs he jumped up and hid in a cupboard.
+LJSpeech-1.1/wavs/LJ050-0256.wav|Protecting the President is a difficult and complex task which requires full use of the best resources of many parts of our Government.
+LJSpeech-1.1/wavs/LJ031-0144.wav|When told that Mrs. Kennedy refused to leave without the President's body, the Vice President said that he would not leave Dallas without her.
+LJSpeech-1.1/wavs/LJ048-0128.wav|which was Sorrels' first important assignment in connection with Presidential work.
+LJSpeech-1.1/wavs/LJ027-0039.wav|both adaptations for pumping blood;
+LJSpeech-1.1/wavs/LJ029-0154.wav|and Secret Service agents riding in the motorcade.
+LJSpeech-1.1/wavs/LJ002-0275.wav|At one time the Ludgate debtors, accompanied by the keeper,
+LJSpeech-1.1/wavs/LJ025-0175.wav|but its nitrogen is in the state of a free gas, in which condition the bean can make no use of it,
+LJSpeech-1.1/wavs/LJ007-0074.wav|would, it is probable, have eventually overcome the reluctance of some of the prisoners at least, and would have possessed so much moral dignity
+LJSpeech-1.1/wavs/LJ004-0166.wav|At the bottom was a circular space, through which ran a narrow passage, and the sides of which were fitted with barrack bedsteads.
+LJSpeech-1.1/wavs/LJ007-0131.wav|Enough has probably been extracted from this most damnatory report to give a complete picture of the disgraceful state in which Newgate still remained in eighteen thirty-five.
+LJSpeech-1.1/wavs/LJ025-0163.wav|But nothing has been supplied to the bean save water, carbonic acid, ammonia, potash, lime,
+LJSpeech-1.1/wavs/LJ045-0142.wav|He may have felt he could never tell when the FBI was going to appear on the scene or who else was going to find out about his defection
+LJSpeech-1.1/wavs/LJ028-0477.wav|To protect the sun-dried bricks of the inner wall from the winter rains
+LJSpeech-1.1/wavs/LJ042-0169.wav|And yet, it is immature to take the sort of attitude which says, quote, a curse on both your houses! End quote.
+LJSpeech-1.1/wavs/LJ014-0102.wav|The stones were at once taken up;
+LJSpeech-1.1/wavs/LJ043-0132.wav|He subsequently found a job with the Texas School Book Depository for which he performed his duties satisfactorily.
+LJSpeech-1.1/wavs/LJ032-0073.wav|purchased by mail-order coupon from Seaport-Traders, Inc., a mail-order division of George Rose and Co., Los Angeles.
+LJSpeech-1.1/wavs/LJ038-0183.wav|In translation, the note read as follows: one.
+LJSpeech-1.1/wavs/LJ012-0208.wav|Reference was made to Mr. Partridge, the demonstrator in anatomy,
+LJSpeech-1.1/wavs/LJ022-0162.wav|A business that loses the confidence of its customers and the goodwill of the public cannot long continue to be a good risk for the investor.
+LJSpeech-1.1/wavs/LJ041-0076.wav|and put them in positions of prominence, end quote. Oswald manifested this feeling about authority by baiting his officers.
+LJSpeech-1.1/wavs/LJ015-0211.wav|Each safe had three sets of double keys, all held by confidential servants of the company.
+LJSpeech-1.1/wavs/LJ032-0179.wav|In light of these findings the Commission evaluated the additional testimony of Stombaugh
+LJSpeech-1.1/wavs/LJ045-0003.wav|Possible Influence of Anti-Kennedy Sentiment in Dallas
+LJSpeech-1.1/wavs/LJ045-0086.wav|The De Mohrenschildts also testified that, quote, right in front, end quote, of Oswald Marina Oswald complained about Oswald's inadequacy as a husband.
+LJSpeech-1.1/wavs/LJ018-0258.wav|But while repudiating the charges made against him of systematic extravagance and immorality,
+LJSpeech-1.1/wavs/LJ039-0093.wav|at a distance of two hundred sixty-five point three feet was, quote, an easy shot, end quote.
+LJSpeech-1.1/wavs/LJ049-0214.wav|duplication, and confusion of authority where more than one agency is operating in the same field; but on the other hand
+LJSpeech-1.1/wavs/LJ033-0202.wav|because this blanket is composed of brown and green woolen fibers, brown and green delustered viscose fibers, and brown and green cotton fibers.
+LJSpeech-1.1/wavs/LJ018-0079.wav|The unfortunate man survived till he landed, but died in Guy's Hospital.
+LJSpeech-1.1/wavs/LJ010-0278.wav|but the place of durance was changed, to meet the existing law, to Millbank Penitentiary.
+LJSpeech-1.1/wavs/LJ005-0030.wav|"In prisons, which are really meant to keep the multitude in order, and to be a terror to evil-doers, there must be no sharings of profits,
+LJSpeech-1.1/wavs/LJ042-0051.wav|and what I observed plus my Marxist learning brought me here to the Soviet Union.
+LJSpeech-1.1/wavs/LJ019-0212.wav|The Home Secretary had laid it down that at least five acres would be indispensable, and such an area it was impossible to obtain within the limits of the city.
+LJSpeech-1.1/wavs/LJ002-0054.wav|those who were the oldest offenders, sentenced to transportation, and who had narrowly escaped the penalty of death.
+LJSpeech-1.1/wavs/LJ018-0185.wav|A Bow Street officer saw him leaving London in the evening by Camberwell Green, accompanied by two other men.
+LJSpeech-1.1/wavs/LJ018-0101.wav|Chandler while in Newgate turned informer, and betrayed the whole conspiracy.
+LJSpeech-1.1/wavs/LJ019-0254.wav|they were frequently below the standard size, and were therefore not certified for occupation as was required by law.
+LJSpeech-1.1/wavs/LJ002-0092.wav|The two yards were adjoining, that for the common side much the largest.
+LJSpeech-1.1/wavs/LJ010-0252.wav|the old traitor's doom, but was spared, and subsequently transported for life.
+LJSpeech-1.1/wavs/LJ048-0025.wav|and had told Mrs. Paine that when he got the money he was going to take an apartment, when the baby was old enough, he was going to take an apartment, and the family would live together.
+LJSpeech-1.1/wavs/LJ021-0068.wav|to a level of sustained profits within one year from the inauguration of N.R.A.
+LJSpeech-1.1/wavs/LJ048-0111.wav|the Secret Service correctly gave particular attention in the advance preparations to those arrangements.
+LJSpeech-1.1/wavs/LJ043-0115.wav|He lost his job on July nineteen, nineteen sixty-three, because his work was not satisfactory
+LJSpeech-1.1/wavs/LJ015-0033.wav|an impecunious and rotten firm of contractors with vast liabilities and pledged to impossible works abroad.
+LJSpeech-1.1/wavs/LJ043-0149.wav|where she had gone, contrary to his instructions, after she became, worried about his absence.
+LJSpeech-1.1/wavs/LJ021-0017.wav|Instead of the give and take of free individual contract,
+LJSpeech-1.1/wavs/LJ034-0179.wav|and, quote, seemed to be sitting a little forward, end quote,
+LJSpeech-1.1/wavs/LJ041-0143.wav|the President almost completely blocked Oswald's view of the Governor prior to the time the first shot struck the President.
+LJSpeech-1.1/wavs/LJ015-0089.wav|in receiving guests at home, or could be spared from two rival establishments in other parts of the town.
+LJSpeech-1.1/wavs/LJ038-0257.wav|which had come to rest on a stack of paper.
+LJSpeech-1.1/wavs/LJ031-0009.wav|The radio log of the Dallas Police Department shows that at twelve:thirty p.m. on November twenty-two
+LJSpeech-1.1/wavs/LJ008-0003.wav|which we left at the time of the discontinuance of the long-practiced procession to Tyburn.
+LJSpeech-1.1/wavs/LJ035-0110.wav|Both Dougherty and Piper were confused witnesses. They had no exact memory of the events of that afternoon.
+LJSpeech-1.1/wavs/LJ004-0195.wav|Personal cleanliness was insisted upon, and all parts of the prison were kept scrupulously clean.
+LJSpeech-1.1/wavs/LJ014-0116.wav|Her arrest was planned, and, when the telegram arrived from London, completed.
+LJSpeech-1.1/wavs/LJ003-0024.wav|he collected the garnish money, and distributed the weekly grant from the prison charitable fund.
+LJSpeech-1.1/wavs/LJ004-0165.wav|the visitors were furnished with candles, and they descended eighteen long steps into a vault.
+LJSpeech-1.1/wavs/LJ031-0101.wav|While one medical team tried to revive President Kennedy, a second performed a series of operations on the bullet wounds sustained by Governor Connally.
+LJSpeech-1.1/wavs/LJ002-0325.wav|or the sixpenny allowance was claimed for the creditors, which seldom happened, owing to the expense the process entailed.
+LJSpeech-1.1/wavs/LJ028-0092.wav|Read the introduction to any of his inscriptions, of which the following is one, and you will call him vain and proud,
+LJSpeech-1.1/wavs/LJ003-0109.wav|he found he must adopt the manners of his companions, or that his life would be in danger.
+LJSpeech-1.1/wavs/LJ045-0133.wav|The claimed warning was one more of Oswald's fabrications.
+LJSpeech-1.1/wavs/LJ038-0148.wav|Actions During and After Shooting
+LJSpeech-1.1/wavs/LJ017-0216.wav|there were also a Frenchman, a Norwegian (the carpenter), three Chinamen, a "Sclavonian," and a black on board.
+LJSpeech-1.1/wavs/LJ040-0042.wav|He defected to the Soviet Union, shot at General Walker, tried to go to Cuba and even contemplated hijacking an airplane to get there.
+LJSpeech-1.1/wavs/LJ009-0091.wav|The young stealer in a dwelling-house no longer has the least pretense to bravery. He grasps the back of the pew,
+LJSpeech-1.1/wavs/LJ024-0037.wav|who would disregard the law and would decide specific cases as I wished them to be decided, I make this answer:
+LJSpeech-1.1/wavs/LJ033-0143.wav|The presence of the bag in this corner is cogent evidence that it was used as the container for the rifle.
+LJSpeech-1.1/wavs/LJ036-0057.wav|She heard a passing motorist tell the driver that the President had been shot.
+LJSpeech-1.1/wavs/LJ034-0158.wav|Although the record indicates that Brennan was an accurate observer, he declined to make a positive identification of Oswald when he first saw him in the police lineup.
+LJSpeech-1.1/wavs/LJ002-0072.wav|The inmates were privileged, either by antecedents or the fortunate possession of sufficient funds to pay the charges of the place.
+LJSpeech-1.1/wavs/LJ037-0106.wav|Both ran to the sidewalk on the east side of Patton at a point about a half a block south of tenth.
+LJSpeech-1.1/wavs/LJ003-0061.wav|No beds were issued, only two rugs per prisoner.
+LJSpeech-1.1/wavs/LJ047-0200.wav|as to whether Oswald fell within the category of, quote, threats against the President, end quote, which should be referred to the Service.
+LJSpeech-1.1/wavs/LJ005-0260.wav|a theory which did not become a practical fact for forty more years.
+LJSpeech-1.1/wavs/LJ007-0058.wav|The chaplain, who might have been expected to make these men his peculiar care, and who at one time had visited them frequently, often several times a week,
+LJSpeech-1.1/wavs/LJ014-0282.wav|There were two counts in the indictment: one for stealing a cheque value fourteen hundred pounds, the second for stealing a bit of paper value one penny.
+LJSpeech-1.1/wavs/LJ046-0018.wav|to which the events of last November called attention.
+LJSpeech-1.1/wavs/LJ009-0051.wav|His look at the sheriffs and the ordinary is full of scorn and defiance.
+LJSpeech-1.1/wavs/LJ046-0211.wav|For example, at the time of the assassination, the FBI Handbook, which is in the possession of every Bureau special agent, provided, quote,
+LJSpeech-1.1/wavs/LJ048-0280.wav|It might seem harsh to circumscribe their opportunities for relaxation.
+LJSpeech-1.1/wavs/LJ022-0183.wav|that the government should assume the responsibility of providing a means by which the credit of the nation might be controlled,
+LJSpeech-1.1/wavs/LJ010-0213.wav|all of which declared their approval of the commander-in-chief.
+LJSpeech-1.1/wavs/LJ015-0283.wav|The bearer of the cheque was always innocent and ignorant of the fraudulent nature of the document he presented.
+LJSpeech-1.1/wavs/LJ002-0011.wav|For two more years these high figures were steadily maintained, and in eighteen oh three the total rose to seven hundred ten.
+LJSpeech-1.1/wavs/LJ006-0014.wav|They should be persons, he thought, selected from the highest class; the duty was most important,
+LJSpeech-1.1/wavs/LJ040-0073.wav|Marguerite Oswald withdrew Lee from the orphans' home and took him with her to Dallas when he was a little over four years old.
+LJSpeech-1.1/wavs/LJ046-0089.wav|His travel would be in secret; his public appearances would be behind bulletproof glass. A more practical approach necessitates compromise.
+LJSpeech-1.1/wavs/LJ045-0213.wav|The conversation on Monday, November eighteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ015-0296.wav|Hardwicke, or "Ralph," appealed to Saward in his difficulty
+LJSpeech-1.1/wavs/LJ050-0262.wav|Even if the manpower and technological resources of the Secret Service are adequately augmented,
+LJSpeech-1.1/wavs/LJ023-0061.wav|that they were all the powers needed to meet each and every problem which then had a national character
+LJSpeech-1.1/wavs/LJ012-0149.wav|"Money Moses" and Mrs. Abrahams imposed upon Moss as to the price paid by Solomons;
+LJSpeech-1.1/wavs/LJ027-0115.wav|it will be suffered to dwindle away in successive generations, under the influence of certain natural causes.
+LJSpeech-1.1/wavs/LJ038-0269.wav|Frazier testified, however, that he found no microscopic characteristics or other evidence
+LJSpeech-1.1/wavs/LJ019-0100.wav|but the session was far advanced, and the matter was relegated to the following year.
+LJSpeech-1.1/wavs/LJ045-0238.wav|and in the face of the overwhelming evidence against him
+LJSpeech-1.1/wavs/LJ034-0133.wav|Brennan could have seen enough of the body of a kneeling or squatting person to estimate his height.
+LJSpeech-1.1/wavs/LJ047-0241.wav|that the FBI had known about Oswald and, in particular, of his presence in Dallas and his employment at the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ022-0190.wav|These measures I have mentioned are, in large part, the program which under my constitutional duty I have recommended to the Congress.
+LJSpeech-1.1/wavs/LJ028-0141.wav|so that the entire circuit is four hundred and eighty furlongs.
+LJSpeech-1.1/wavs/LJ047-0246.wav|and District Attorney Henry M. Wade both testified that they saw it later that day.
+LJSpeech-1.1/wavs/LJ039-0135.wav|using a twenty-two caliber bolt-action rifle belonging either to Robert or Robert's in-laws.
+LJSpeech-1.1/wavs/LJ040-0020.wav|It certainly could not be made by this Commission which, as has been pointed out above,
+LJSpeech-1.1/wavs/LJ012-0032.wav|Solomons also established a system of provincial agency, by which stolen goods were passed on from London to the seaports, and so abroad.
+LJSpeech-1.1/wavs/LJ036-0034.wav|It was the intersection near Lamar Street, it was near Poydras and Lamar Street.
+LJSpeech-1.1/wavs/LJ008-0214.wav|the sounds of hammer and saw intermingled with the broad jeers and coarse jests of the rapidly increasing mob.
+LJSpeech-1.1/wavs/LJ015-0246.wav|The train was now approaching Folkestone, and Agar and Pierce hid themselves in a dark part of the van.
+LJSpeech-1.1/wavs/LJ011-0199.wav|Meanwhile, a second letter turned up from Wakefield at Calais, in which he assured Mrs. Turner that Miss Turner was fondly attached to him,
+LJSpeech-1.1/wavs/LJ009-0110.wav|This exhibition lasts for some minutes, and then the congregation disperses,
+LJSpeech-1.1/wavs/LJ020-0097.wav|You will not relish it, but take it all the same to appease the empty, discontented stomach.
+LJSpeech-1.1/wavs/LJ048-0286.wav|Nor is this goal served when agents remain out until early morning hours, and lose the opportunity to get a reasonable amount of sleep.
+LJSpeech-1.1/wavs/LJ044-0126.wav|they could say the organization of which I am a member, is Russian controlled, etc, end quote.
+LJSpeech-1.1/wavs/LJ032-0114.wav|Additional evidence of ownership was provided in the form of palmprint identification which indicated that Oswald had possession of the rifle he had purchased.
+LJSpeech-1.1/wavs/LJ013-0080.wav|the evidence to establish his (Barber's) connivance in the frauds was too doubtful for us to continue his exclusion any longer.
+LJSpeech-1.1/wavs/LJ013-0093.wav|where the cash was transferred from the carpet-bag to a portmanteau.
+LJSpeech-1.1/wavs/LJ012-0287.wav|On the other hand, it was adduced in evidence that Mrs. Gale had been at his lodgings the very day after the murder,
+LJSpeech-1.1/wavs/LJ033-0124.wav|And she folded the bag to length of about twenty-eight and a half inches.
+LJSpeech-1.1/wavs/LJ012-0254.wav|where a workman found a bundle containing two human legs, in a drain.
+LJSpeech-1.1/wavs/LJ041-0105.wav|led to an incident in which he spilled a drink on one of his sergeants and abusively challenged him to fight.
+LJSpeech-1.1/wavs/LJ008-0185.wav|Beware of entering the crowd! Remember thirty poor persons were pressed to death by the crowd when Haggerty and Holloway were executed!
+LJSpeech-1.1/wavs/LJ027-0145.wav|that such a recapitulation in the life history of an existing animal of developmental changes successively distinctive of sundry allied,
+LJSpeech-1.1/wavs/LJ040-0236.wav|and when his mother failed to cooperate in any way with school authorities,
+LJSpeech-1.1/wavs/LJ049-0193.wav|Consequently the suggestion has been made, on the one hand, that all preventive investigative functions relating to the security of the President
+LJSpeech-1.1/wavs/LJ037-0268.wav|(three) the revolver in Oswald's possession at the time of his arrest was purchased by and belonged to Oswald, and (four)
+LJSpeech-1.1/wavs/LJ047-0212.wav|would have seemed somewhat serious to us, even though I must admit, that none of these in themselves would be
+LJSpeech-1.1/wavs/LJ035-0025.wav|I am a building manager. Follow me, officer, and I will show you, end quote.
+LJSpeech-1.1/wavs/LJ003-0176.wav|Mr. Cobbett was also a lodger of Mr. Newman's; and so were any members of the aristocracy,
+LJSpeech-1.1/wavs/LJ046-0127.wav|or from the occasional investigations initiated by the Secret Service,
+LJSpeech-1.1/wavs/LJ012-0151.wav|Solomons cheated the whole lot by retaining half the gold in his possession, and only giving an I. O. U. for it,
+LJSpeech-1.1/wavs/LJ035-0147.wav|Mrs. Reid had watched the parade from the sidewalk in front of the building with Truly and Mr. O. V. Campbell, vice president of the Depository.
+LJSpeech-1.1/wavs/LJ017-0248.wav|while the other two were left to go down in the ship, and were seen clinging to the tops until the waters closed over them.
+LJSpeech-1.1/wavs/LJ036-0061.wav|A Beckley bus which also served the Oak Cliff area,
+LJSpeech-1.1/wavs/LJ048-0004.wav|After summarizing the Bureau's investigative interest in Oswald prior to the assassination, J. Edgar Hoover concluded that, quote,
+LJSpeech-1.1/wavs/LJ010-0170.wav|His acquaintances often asked his object in this, but he kept his own counsel till the tenth June.
+LJSpeech-1.1/wavs/LJ010-0019.wav|As time passed,
+LJSpeech-1.1/wavs/LJ023-0006.wav|Tonight, sitting at my desk in the White House, I make my first radio report to the people in my second term of office.
+LJSpeech-1.1/wavs/LJ004-0123.wav|sixty-eight had only two divisions, and so on.
+LJSpeech-1.1/wavs/LJ025-0073.wav|the presence in animals of an alimentary cavity, or internal pocket, in which they can carry about their nutriment
+LJSpeech-1.1/wavs/LJ040-0225.wav|Carro testified that, quote, There was nothing that would lead me to believe when I saw him at the age of twelve that them would be seeds of destruction for somebody.
+LJSpeech-1.1/wavs/LJ003-0267.wav|kept open house, so to speak, and entertained daily within the walls a select party of the most noted thieves in London.
+LJSpeech-1.1/wavs/LJ019-0378.wav|As the almost inevitable consequence, while the more glaring defects in prison management disappeared,
+LJSpeech-1.1/wavs/LJ038-0078.wav|Oswald was questioned intermittently for approximately twelve hours between two:thirty p.m., on November twenty-two, and eleven a.m.,
+LJSpeech-1.1/wavs/LJ047-0203.wav|have made Oswald a subject of concern to the Secret Service.
+LJSpeech-1.1/wavs/LJ005-0120.wav|Half the jails had no code of rules properly prepared and sanctioned by the judges, according to law.
+LJSpeech-1.1/wavs/LJ050-0268.wav|The Service should negotiate a memorandum of understanding with each agency that has been assisting it and from which it can expect to need help in the future.
+LJSpeech-1.1/wavs/LJ014-0082.wav|After this Mrs. Manning changed her dress and went off in a cab to O'Connor's lodgings,
+LJSpeech-1.1/wavs/LJ048-0158.wav|Check of buildings along route of motorcade.
+LJSpeech-1.1/wavs/LJ040-0046.wav|which one associate described as, quote, irrevocable, end quote, but also as, quote, theoretical, end quote,
+LJSpeech-1.1/wavs/LJ034-0202.wav|In his Commission testimony
+LJSpeech-1.1/wavs/LJ008-0237.wav|The human hand that placed it there was only seen for a moment,
+LJSpeech-1.1/wavs/LJ005-0172.wav|When that time arrives
+LJSpeech-1.1/wavs/LJ034-0066.wav|that Oswald was at the window from which the shots were fired, although the prints do not establish the exact time he was there.
+LJSpeech-1.1/wavs/LJ022-0114.wav|Our responsibility is to all of the people in this country.
+LJSpeech-1.1/wavs/LJ023-0094.wav|and placed "an unwarranted limitation upon the commerce clause."
+LJSpeech-1.1/wavs/LJ025-0093.wav|and that the latter is, chemically speaking, just as complicated as the former.
+LJSpeech-1.1/wavs/LJ003-0173.wav|He continued the ancient practice of letting out a portion of his own house, and by a poetical fiction treated it as an annex of the state side.
+LJSpeech-1.1/wavs/LJ012-0060.wav|to let the coach change and pass Petticoat Lane en route to the jail, where the suffering woman might be handed over to her friends.
+LJSpeech-1.1/wavs/LJ011-0014.wav|A newspaper report of the time describes Fauntleroy as a well-made man of middle stature.
+LJSpeech-1.1/wavs/LJ021-0014.wav|but also our processes of civilization.
+LJSpeech-1.1/wavs/LJ028-0274.wav|The Babylonians were ever upon the watch, and he found no way of conquering them.
+LJSpeech-1.1/wavs/LJ038-0181.wav|On December two, nineteen sixty-three, Mrs. Ruth Paine turned over to the police some of the Oswalds' belongings,
+LJSpeech-1.1/wavs/LJ016-0291.wav|sharpers, thieves, gamblers, betting men, the outsiders of the boxing ring,
+LJSpeech-1.1/wavs/LJ031-0084.wav|But his condition was hopeless, and the extraordinary efforts of the doctors to save him could not help but to have been unavailing.
+LJSpeech-1.1/wavs/LJ019-0230.wav|In eighteen sixty-one a similar work was undertaken to provide separate cellular accommodation for the female inmates of Newgate,
+LJSpeech-1.1/wavs/LJ041-0038.wav|heard what we were arguing on communism, and that this boy was loud-mouthed, boisterous, and my father asked him to leave the house and politely put him out of the house,
+LJSpeech-1.1/wavs/LJ046-0217.wav|must be referred immediately by the most expeditious means of communication to the nearest office of the U.S. Secret Service.
+LJSpeech-1.1/wavs/LJ031-0173.wav|Mrs. Kennedy alternated between watching them and waiting outside.
+LJSpeech-1.1/wavs/LJ016-0406.wav|the condemned sermon was preached in the prison chapel, and this was probably only that he might hear the singing of a lady with a magnificent voice
+LJSpeech-1.1/wavs/LJ019-0365.wav|and with the concurrence of the other authority, and on payment. A few provisos governed these rather extensive powers.
+LJSpeech-1.1/wavs/LJ021-0185.wav|Is it not a fact that relations between capital and labor on the basis of collective bargaining are much further advanced in Great Britain
+LJSpeech-1.1/wavs/LJ021-0113.wav|or whether their effect may have been to prevent that volume of production which would make possible lower prices and increased employment.
+LJSpeech-1.1/wavs/LJ003-0044.wav|The reforms which were to be attempted in that prison
+LJSpeech-1.1/wavs/LJ019-0015.wav|The first stone of Pentonville prison was laid on the tenth April, eighteen forty, by the Marquis of Normanby,
+LJSpeech-1.1/wavs/LJ048-0198.wav|and although it was not a violation of the law to carry a placard, that they were not to tolerate any actions such as the Stevenson incident
+LJSpeech-1.1/wavs/LJ028-0279.wav|after which he commanded his servants to tell no one what had come to pass, while he himself pondered the matter.
+LJSpeech-1.1/wavs/LJ008-0144.wav|in eighteen oh seven, an event long remembered from the fatal and disastrous consequences which followed it.
+LJSpeech-1.1/wavs/LJ014-0165.wav|and presented by no heathen land under the sun.
+LJSpeech-1.1/wavs/LJ008-0142.wav|the attendance at the execution was certain to be tumultuous, and the conduct of the mob disorderly.
+LJSpeech-1.1/wavs/LJ048-0265.wav|after which time a very moderate use of liquor will not be considered a violation. However, all members of the White House Detail
+LJSpeech-1.1/wavs/LJ003-0065.wav|All the misdemeanants, whatever their offense, were lodged in this chapel ward.
+LJSpeech-1.1/wavs/LJ047-0102.wav|When I began asking him specific details with respect to his activities in the Fair Play for Cuba Committee in New Orleans as to where meetings were held,
+LJSpeech-1.1/wavs/LJ043-0023.wav|and helped to move the personal effects of Marina Oswald and the baby.
+LJSpeech-1.1/wavs/LJ032-0138.wav|Nor was there any indication that the lift had been performed.
+LJSpeech-1.1/wavs/LJ016-0255.wav|The old prejudices, such as that which enlisted Dr. Johnson on the side of the Tyburn procession, still lingered and prevented any change.
+LJSpeech-1.1/wavs/LJ047-0036.wav|It revealed his letter of January thirty, nineteen sixty-two, to Secretary of the Navy Connally,
+LJSpeech-1.1/wavs/LJ031-0055.wav|Dr. Perry noted the President's back brace as he felt for a femoral pulse, which he did not find.
+LJSpeech-1.1/wavs/LJ049-0199.wav|and ready at any instant to hazard great danger to themselves in the performance of their tremendous responsibility.
+LJSpeech-1.1/wavs/LJ025-0102.wav|yet the exact researches of the modern chemical investigators of the physiological processes of plants have clearly demonstrated the fallacy
+LJSpeech-1.1/wavs/LJ029-0188.wav|Stevenson was jeered, jostled, and spat upon by hostile demonstrators outside the Dallas Memorial Auditorium Theater.
+LJSpeech-1.1/wavs/LJ029-0131.wav|The building overlooks Dealey Plaza, an attractively landscaped triangle of three acres.
+LJSpeech-1.1/wavs/LJ036-0050.wav|Oswald got on. He looks like a maniac. His sleeve was out here. His shirt was undone.
+LJSpeech-1.1/wavs/LJ005-0256.wav|in order to insure uniformity of discipline. It met on the thirty-first March, eighteen thirty-five, and continued its sittings well into July
+LJSpeech-1.1/wavs/LJ035-0210.wav|On the basis of these findings the Commission has concluded that Oswald, at the time of the assassination,
+LJSpeech-1.1/wavs/LJ016-0206.wav|he was also at liberty to engage himself in the country, where he demanded and was paid ten pounds on each occasion.
+LJSpeech-1.1/wavs/LJ012-0056.wav|After a short carouse the prisoner went into Westminster, his case was heard, bail refused, and he was ordered back to Newgate.
+LJSpeech-1.1/wavs/LJ042-0213.wav|always profess patriotism toward the land and the people, if not the government; although their ideals movements must surely lead
+LJSpeech-1.1/wavs/LJ038-0095.wav|by the Commission, they had probative value in deciding the weight to be given to his denials that he assassinated President Kennedy
+LJSpeech-1.1/wavs/LJ045-0250.wav|there emerged a man capable of assassinating President Kennedy.
+LJSpeech-1.1/wavs/LJ036-0204.wav|According to Chief Curry, Tippit was free to patrol the central Oak Cliff area.
+LJSpeech-1.1/wavs/LJ029-0108.wav|Sorrels and Lawson drove over the selected route with Batchelor and other police officers, verifying that it could be traversed within forty-five minutes.
+LJSpeech-1.1/wavs/LJ003-0239.wav|This imposition of fees left prisoners destitute on their discharge, without funds to support them in their first struggle to recommence life,
+LJSpeech-1.1/wavs/LJ045-0066.wav|Oswald struck his wife on occasion,
+LJSpeech-1.1/wavs/LJ014-0014.wav|It was noticed that he wore a mackintosh. Next day the police, in making a careful search of the scene of the murder,
+LJSpeech-1.1/wavs/LJ005-0114.wav|In others the separation between the sexes consisted in a hanging curtain
+LJSpeech-1.1/wavs/LJ043-0110.wav|beginning May ten, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ031-0137.wav|At approximately one:twenty p.m., Vice President Johnson was notified by O'Donnell that President Kennedy was dead.
+LJSpeech-1.1/wavs/LJ005-0258.wav|embodying recommendations which may be said to have formed the basis of modern prison management.
+LJSpeech-1.1/wavs/LJ004-0235.wav|Indeed the best consequences followed from the removal of irons. The prisoners were much better disposed; there were no riots, and fewer disturbances.
+LJSpeech-1.1/wavs/LJ018-0381.wav|the wildest and most cut-throat looking of the lot, which proves that he could be grateful for kindness, and was not all bad.
+LJSpeech-1.1/wavs/LJ019-0165.wav|The rule became general as regards the prison dress; hitherto clothing had been issued only to such as were destitute or in rags, and all classes of prisoners,
+LJSpeech-1.1/wavs/LJ007-0116.wav|A few others, who could not afford a payment of more than half a guinea, were permitted to monopolize a part of the prison infirmary,
+LJSpeech-1.1/wavs/LJ050-0253.wav|that the job of protecting the President has not been and cannot be exclusively the responsibility of the Secret Service.
+LJSpeech-1.1/wavs/LJ009-0105.wav|The sheriffs cover their faces, and one of their inquisitive friends blows his nose with his glove.
+LJSpeech-1.1/wavs/LJ016-0139.wav|The last escape from Newgate was only three years ago, and occurred just before the final closing of the prison.
+LJSpeech-1.1/wavs/LJ003-0095.wav|Quote, in this dreadful situation, end quote. who had been rescued from the hulks through the kindness and attention of the Secretary of State.
+LJSpeech-1.1/wavs/LJ041-0093.wav|Powers also testified that Oswald was reserved and seemed to be, quote, somewhat the frail, little puppy in the litter, end quote.
+LJSpeech-1.1/wavs/LJ005-0125.wav|or to reappropriate, reconstruct, and patch up the existing prisons till they were more in accordance with the growing requirements of the times.
+LJSpeech-1.1/wavs/LJ028-0029.wav|At last you see before you a mound rising like a mountain from the level plain.
+LJSpeech-1.1/wavs/LJ035-0021.wav|A woman screamed, quote, Oh, they have shot that man, they have shot that man, end quote.
+LJSpeech-1.1/wavs/LJ039-0211.wav|Moreover, the defect was one which would have assisted the assassin aiming at a target which was moving away.
+LJSpeech-1.1/wavs/LJ005-0137.wav|Four years later the Prison Society reported
+LJSpeech-1.1/wavs/LJ020-0065.wav|Two cups of fresh milk slightly warmed.
+LJSpeech-1.1/wavs/LJ021-0123.wav|When the businessmen of the country were demanding the right to organize themselves adequately to promote their legitimate interests;
+LJSpeech-1.1/wavs/LJ006-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section nine: The first report of the inspector of prisons.
+LJSpeech-1.1/wavs/LJ003-0341.wav|by a new jail built on the outskirts, as Holloway eventually was, and permitted itself to be altogether countered
+LJSpeech-1.1/wavs/LJ037-0075.wav|However, even in the absence of Mrs. Markham's testimony, there is ample evidence to identify Oswald as the killer of Tippit.
+LJSpeech-1.1/wavs/LJ025-0169.wav|The bean has been able to perform this great chemical feat by the help of its green coloring matter, or chlorophyll,
+LJSpeech-1.1/wavs/LJ032-0263.wav|About three hours after the assassination, a detective and deputy sheriff saw the blanket-roll,
+LJSpeech-1.1/wavs/LJ013-0116.wav|The butler, on the occasion of a death in the family, when the house was in some confusion, arranged with a burglar to come in,
+LJSpeech-1.1/wavs/LJ039-0124.wav|End quote. Major Anderson concluded, quote, I would say that as compared to other Marines receiving the same type of training,
+LJSpeech-1.1/wavs/LJ043-0118.wav|The correctness of that conclusion is supported by the fact that he does not seem to have been publicly identified with that organization until August nine,
+LJSpeech-1.1/wavs/LJ048-0068.wav|as requiring evidence of a plan or conspiracy to injure the President.
+LJSpeech-1.1/wavs/LJ032-0087.wav|was a purported international certificate of vaccination signed by "Dr. A. J. Hideel,
+LJSpeech-1.1/wavs/LJ016-0344.wav|The ceremony, which was witnessed by only a few officials and representatives of the press, was performed with the utmost decency and decorum.
+LJSpeech-1.1/wavs/LJ037-0071.wav|During her testimony Mrs. Markham initially denied that she ever had the above phone conversation.
+LJSpeech-1.1/wavs/LJ003-0081.wav|Persons convicted of publishing libels were still immured in the same rooms with transports and felons.
+LJSpeech-1.1/wavs/LJ028-0119.wav|but I, his first-born, the beloved of his heart,
+LJSpeech-1.1/wavs/LJ012-0259.wav|who had been missing since the previous Christmas Day.
+LJSpeech-1.1/wavs/LJ025-0149.wav|that, while locomotive, their movements may have as much appearance of spontaneity as those of the lowest animals,
+LJSpeech-1.1/wavs/LJ027-0118.wav|"for, without waiting to dispute the wisdom of making dwarfed and useless structures merely for the whimsical motive assigned,
+LJSpeech-1.1/wavs/LJ022-0180.wav|Wise public policy, however, requires not only that banking be safe
+LJSpeech-1.1/wavs/LJ005-0015.wav|our prisoners have all that prisoners ought to have, without gentlemen think they ought to be indulged with Turkey carpets.
+LJSpeech-1.1/wavs/LJ011-0047.wav|This was on the text,
+LJSpeech-1.1/wavs/LJ002-0020.wav|In eighteen thirteen
+LJSpeech-1.1/wavs/LJ023-0045.wav|Last Thursday I described the American form of government as a three horse team provided by the Constitution to the American people
+LJSpeech-1.1/wavs/LJ006-0209.wav|the donation of a philanthropic gentleman, Captain Brown, but these, particularly the Bibles, bore little appearance of having been used.
+LJSpeech-1.1/wavs/LJ030-0247.wav|I was bent over under the weight of Agent Youngblood's body, toward Mrs. Johnson and Senator Yarborough, end quote,
+LJSpeech-1.1/wavs/LJ046-0223.wav|to Secret Service locally, end quote.
+LJSpeech-1.1/wavs/LJ034-0021.wav|Sebastian F. Latona, supervisor of the Latent Fingerprint Section,
+LJSpeech-1.1/wavs/LJ042-0020.wav|indicating that his motivation was at least in part a personal one. On November twenty-six, nineteen fifty-nine,
+LJSpeech-1.1/wavs/LJ006-0285.wav|which numbers of well-meaning but inquisitive people were anxious to witness.
+LJSpeech-1.1/wavs/LJ001-0134.wav|In good printing the spaces between the words should be as near as possible equal
+LJSpeech-1.1/wavs/LJ028-0113.wav|With mortar and bricks he built two moat-walls about the city,
+LJSpeech-1.1/wavs/LJ022-0140.wav|I can mention only a few of them tonight, but I do not want my mention of specific measures
+LJSpeech-1.1/wavs/LJ030-0077.wav|Each agent carried a thirty-eight-caliber pistol, and a shotgun and automatic rifle were also available.
+LJSpeech-1.1/wavs/LJ031-0053.wav|Dr. Carrico inserted a cuffed endotracheal tube past the injury, inflated the cuff, and connected it to a Bennett machine to assist in respiration.
+LJSpeech-1.1/wavs/LJ033-0130.wav|and her first remembrance is that it was held in Oswald's right hand, quote, and it almost touched the ground as he carried it, end quote.
+LJSpeech-1.1/wavs/LJ018-0388.wav|and the bird's legs were a couple of teeth broken off the prisoner's comb.
+LJSpeech-1.1/wavs/LJ038-0054.wav|He added that one officer grabbed the muzzle of a shotgun, drew back, and hit Oswald with the butt end of the gun in the back.
+LJSpeech-1.1/wavs/LJ042-0216.wav|they stand up and piously pronounce themselves patriots, displaying their war medals that they gained in conflicts long past between themselves.
+LJSpeech-1.1/wavs/LJ038-0175.wav|Until December three, nineteen sixty-three, the Walker shooting remained unsolved.
+LJSpeech-1.1/wavs/LJ038-0012.wav|the man in the lobby, quote, looked over his shoulder and turned around and walked up West Jefferson towards the theatre, end quote.
+LJSpeech-1.1/wavs/LJ006-0122.wav|and by these payments a wardsman had been known to amass as much as forty pounds.
+LJSpeech-1.1/wavs/LJ015-0213.wav|a third with the captain of the Folkestone and Boulogne boat.
+LJSpeech-1.1/wavs/LJ031-0138.wav|Special Agent Youngblood learned from Mrs. Johnson the location of her two daughters
+LJSpeech-1.1/wavs/LJ022-0201.wav|through the instrumentality of the democratic form of government.
+LJSpeech-1.1/wavs/LJ015-0111.wav|then, accompanied by a lady, not Mrs. Robson, he took steamer and started for Copenhagen.
+LJSpeech-1.1/wavs/LJ042-0060.wav|Oswald met disappointments there just as he had in the past. At the outset the Soviets told him that he could not remain.
+LJSpeech-1.1/wavs/LJ001-0108.wav|it is obvious that legibility is the first thing to be aimed at in the forms of the letters;
+LJSpeech-1.1/wavs/LJ019-0016.wav|then Home Secretary, and the prison, which contained five hundred and twenty cells, was occupied on the twenty-first December, eighteen forty-two.
+LJSpeech-1.1/wavs/LJ007-0147.wav|communicating his own skill and aptitude in crime, or acquiring the lessons of greater adepts. He has access to newspapers, and of course
+LJSpeech-1.1/wavs/LJ002-0324.wav|or the keeper made them a special grant of six pence per diem out of the common stock;
+LJSpeech-1.1/wavs/LJ008-0091.wav|The highwayman, whose exploits many in the crowd admired or emulated, was cheered and bidden to die game;
+LJSpeech-1.1/wavs/LJ014-0159.wav|Manning's demeanor was more in harmony with his situation, and the full confession he made
+LJSpeech-1.1/wavs/LJ037-0100.wav|Later, the FBI showed Smith a picture of Oswald. In the picture the hair was brown.
+LJSpeech-1.1/wavs/LJ011-0142.wav|The question was whether the Government would dare to take their lives at the bidding of the House of Lords,
+LJSpeech-1.1/wavs/LJ006-0041.wav|Changes introduced under pressure had been only skin deep.
+LJSpeech-1.1/wavs/LJ009-0005.wav|which persons under sentence of death obtain from all the officers of the prison.
+LJSpeech-1.1/wavs/LJ037-0223.wav|Shortly after Tippit was slain, policemen found a light-colored zipper jacket along the route taken by the killer as he attempted to escape.
+LJSpeech-1.1/wavs/LJ006-0239.wav|During the tumult a prisoner, who was one of the worst of the rioters, was bruised about the head and body.
+LJSpeech-1.1/wavs/LJ010-0205.wav|and to assume any disguise, if required to go into the country on the business of the association.
+LJSpeech-1.1/wavs/LJ037-0167.wav|Quote: The only thing I can testify is they could have on the basis of the rifling characteristics -- they could have been, end quote,
+LJSpeech-1.1/wavs/LJ011-0269.wav|An appointment was made and kept by Mr. Gee,
+LJSpeech-1.1/wavs/LJ019-0057.wav|was the true basis of a sound system of prison discipline.
+LJSpeech-1.1/wavs/LJ011-0267.wav|Mr. Gee had invested one thousand two hundred pounds of this, and was seeking how best to place the remaining eight hundred pounds,
+LJSpeech-1.1/wavs/LJ050-0212.wav|While these statistics relate to the activities of Secret Service agents stationed in field offices and not the White House detail,
+LJSpeech-1.1/wavs/LJ010-0147.wav|It was a form of high treason not unknown in earlier reigns. In seventeen eighty-six a mad woman, Margaret Nicholson,
+LJSpeech-1.1/wavs/LJ028-0356.wav|How much truth there may be in this interesting tale of Herodotus, we may never know,
+LJSpeech-1.1/wavs/LJ014-0264.wav|Under his auspices several "stars" appeared on the boards of the Marylebone theatre,
+LJSpeech-1.1/wavs/LJ008-0188.wav|Every window and roof which could command a view of the horrible performance was occupied.
+LJSpeech-1.1/wavs/LJ003-0005.wav|no steps taken to reduce the number of committals, and the governor was obliged to utilize the chapel as a day and night room.
+LJSpeech-1.1/wavs/LJ011-0216.wav|The prosecution pressed for a severe penalty. Edward Wakefield pleaded that his trial had already cost him three thousand pounds.
+LJSpeech-1.1/wavs/LJ047-0161.wav|Mrs. Paine told Hosty also
+LJSpeech-1.1/wavs/LJ014-0294.wav|and in the middle of the night one of the fellow-prisoners who slept in the same ward noticed that he was not in his bed.
+LJSpeech-1.1/wavs/LJ002-0256.wav|A committee of collegians was elected to act as the executive, also a secretary or accountant to receive monies and keep books,
+LJSpeech-1.1/wavs/LJ031-0104.wav|At approximately twelve:forty-five p.m., Dr. Robert Shaw,
+LJSpeech-1.1/wavs/LJ022-0175.wav|in which the benefits of private ownership are retained while the public stake in these important services is protected by the public's government.
+LJSpeech-1.1/wavs/LJ048-0147.wav|The Commission believes
+LJSpeech-1.1/wavs/LJ048-0275.wav|to an inference that the violation of the regulation had contributed to the tragic events of November twenty-two.
+LJSpeech-1.1/wavs/LJ013-0158.wav|One morning in May his lordship was found dead in his bed with his throat cut.
+LJSpeech-1.1/wavs/LJ031-0199.wav|President and Mrs. Johnson were flown by helicopter to the White House, from where Mrs. Johnson was driven to her residence under Secret Service escort.
+LJSpeech-1.1/wavs/LJ017-0099.wav|Cook recovered this time; it was probably Palmer's intention that he should recover, wishing to encourage the supposition that Cook was in a bad way.
+LJSpeech-1.1/wavs/LJ040-0054.wav|It was a factor which contributed to his character and thereby might have influenced his decision to assassinate President Kennedy.
+LJSpeech-1.1/wavs/LJ042-0248.wav|that, quote, he hated America and he did not want them to become Americanized, and that his plans were to go back to Russia, end quote.
+LJSpeech-1.1/wavs/LJ047-0080.wav|A confidential informant advised the FBI that Oswald was not known to be engaged in Communist Party activities in New Orleans.
+LJSpeech-1.1/wavs/LJ021-0153.wav|Closely allied to the N.R.A.
+LJSpeech-1.1/wavs/LJ013-0035.wav|While lying in Newgate, awaiting removal to the convict ship, both prisoners made full confessions.
+LJSpeech-1.1/wavs/LJ036-0178.wav|She first saw him the day he rented a room at that address on October fourteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ014-0243.wav|he with unusual quickness of apprehension discovered and promptly turned to account an inexcusably lax system of management,
+LJSpeech-1.1/wavs/LJ043-0062.wav|Oswald's defection, his interest in the Soviet Union, and his activities on behalf of the Fair Play for Cuba Committee
+LJSpeech-1.1/wavs/LJ021-0157.wav|I answer that no country, however rich, can afford the waste of its human resources.
+LJSpeech-1.1/wavs/LJ018-0346.wav|Henry Wainwright's attempt to get rid of the body was ingenious, but not original,
+LJSpeech-1.1/wavs/LJ019-0060.wav|that in districts where it was the rule, evil-doers especially dreaded coming under its irksome conditions.
+LJSpeech-1.1/wavs/LJ033-0033.wav|there were two flat lightweight curtain rods belonging to Ruth Paine but they were still there on Friday afternoon after Oswald's arrest.
+LJSpeech-1.1/wavs/LJ032-0254.wav|the rifle was, according to the evidence, stored in a green and brown blanket in the Paines' garage among the Oswalds' other possessions.
+LJSpeech-1.1/wavs/LJ028-0011.wav|As the taps upon her shoulder are repeated, she stretches out her long neck, and with long strides makes for the eastern horizon;
+LJSpeech-1.1/wavs/LJ010-0197.wav|but that there was madness in his family, and that he himself was of unsound mind, could not be well denied.
+LJSpeech-1.1/wavs/LJ014-0220.wav|All this time not one of a numerous body of bystanders offered to assist the policeman in his extremity.
+LJSpeech-1.1/wavs/LJ020-0105.wav|Weave the little duties in and under and among what seem to be the greater.
+LJSpeech-1.1/wavs/LJ049-0137.wav|and the other Federal agencies that assist in safeguarding the President. The Committee should include the Secretary of the Treasury and the Attorney General,
+LJSpeech-1.1/wavs/LJ001-0098.wav|and the whole effect is a little too gray, owing to the thinness of the letters.
+LJSpeech-1.1/wavs/LJ018-0152.wav|With her assistance on a certain day a couple of bricks were taken out of the wall dividing her front and back parlors;
+LJSpeech-1.1/wavs/LJ047-0033.wav|The Bureau made arrangements to be advised by immigration authorities of his return,
+LJSpeech-1.1/wavs/LJ028-0455.wav|It seems that the circuit of the outer wall was about eleven miles.
+LJSpeech-1.1/wavs/LJ008-0005.wav|The terrible spectacle was as demoralizing to the public, for whose admonition it was intended,
+LJSpeech-1.1/wavs/LJ004-0148.wav|No prison dress was allowed, and half the inmates were without shirts or shoes or stockings.
+LJSpeech-1.1/wavs/LJ017-0098.wav|whether the ordinary or his own pills will never be known, except as may be inferred from the results, which indicate that he had taken the latter.
+LJSpeech-1.1/wavs/LJ050-0016.wav|This special assistant should be required to have sufficient stature and experience in law enforcement, intelligence, or allied fields
+LJSpeech-1.1/wavs/LJ048-0090.wav|certain shortcomings and lapses from the high standards which the Commission believes should prevail in the field of Presidential protection are evident.
+LJSpeech-1.1/wavs/LJ018-0146.wav|and he was soon approached by the police. Almost directly he was questioned he made a clean breast of the whole affair.
+LJSpeech-1.1/wavs/LJ014-0202.wav|One of them went into his cell just at dinner-time;
+LJSpeech-1.1/wavs/LJ032-0108.wav|Oswald listed a "Sgt. Robert Hidell" as a reference on one job application and "George Hidell" as a reference on another.
+LJSpeech-1.1/wavs/LJ026-0060.wav|through which it slowly passes, being subjected meanwhile to the action of certain chemical substances or reagents, known as digestive ferments.
+LJSpeech-1.1/wavs/LJ002-0279.wav|The Giltspur Street Compter received sheriffs' debtors, also felons, vagrants, and night charges.
+LJSpeech-1.1/wavs/LJ003-0067.wav|in the then existing state of the law,
+LJSpeech-1.1/wavs/LJ005-0083.wav|It was distinctly laid down that male and female prisoners should be confined in separate buildings or parts of the prison,
+LJSpeech-1.1/wavs/LJ047-0166.wav|Hosty assured her, through Mrs. Paine as interpreter, that the FBI would not harm or harass her.
+LJSpeech-1.1/wavs/LJ038-0023.wav|At one:forty-five p.m., the police radio stated, quote, Have information a suspect just went in the Texas Theatre on West Jefferson, end quote.
+LJSpeech-1.1/wavs/LJ042-0155.wav|it appears to be the work of a fairly well organized person.
+LJSpeech-1.1/wavs/LJ017-0284.wav|was too much for his nerves, and he so nearly fainted that he had to be seated in a chair.
+LJSpeech-1.1/wavs/LJ030-0140.wav|One bullet passed through the President's neck;
+LJSpeech-1.1/wavs/LJ015-0041.wav|and the various discount houses would not advance sufficient sums to relieve the necessities of the bank.
+LJSpeech-1.1/wavs/LJ010-0253.wav|The enthusiasm of the people at the Queen's escape was uproarious, and her drive next day was one long triumphal progress.
+LJSpeech-1.1/wavs/LJ015-0272.wav|and it was to him that Agar, Pierce, and the rest applied when seeking to dispose of their stolen bullion.
+LJSpeech-1.1/wavs/LJ038-0292.wav|She testified that her husband had postponed his attempt to kill Walker
+LJSpeech-1.1/wavs/LJ010-0122.wav|The Government were probably not proud of their agent, for Edwards, after the conviction had been assured, went abroad to enjoy, it was said,
+LJSpeech-1.1/wavs/LJ028-0419.wav|and digging from the wall an inscribed square brick bearing the name of Nebuchadnezzar, he took it to Rome where it may still be seen.
+LJSpeech-1.1/wavs/LJ025-0126.wav|comparable to that which was found by Du Bois Reymond to be a concomitant of the activity of ordinary muscle in animals. Again,
+LJSpeech-1.1/wavs/LJ010-0182.wav|the Duchess of Kent.
+LJSpeech-1.1/wavs/LJ032-0101.wav|testified that she first learned of Oswald's use of the fictitious name "Hidell" in connection with his pro-Castro activities in New Orleans.
+LJSpeech-1.1/wavs/LJ046-0067.wav|that he expose himself to the actual basic problems that were disturbing the American people.
+LJSpeech-1.1/wavs/LJ009-0263.wav|the executioner forced the struggling criminal from the platform, so that the rope sustained his whole weight.
+LJSpeech-1.1/wavs/LJ008-0210.wav|the crowd began to congregate in and about the Old Bailey.
+LJSpeech-1.1/wavs/LJ038-0093.wav|Frequently, however, he was confronted with evidence which he could not explain, and he resorted to statements which are known to be lies.
+LJSpeech-1.1/wavs/LJ010-0200.wav|"Young England," which Oxford had evolved out of his own inflated self-conceit, and which had never any real corporeal existence.
+LJSpeech-1.1/wavs/LJ013-0124.wav|who belonged to five good London clubs --
+LJSpeech-1.1/wavs/LJ048-0216.wav|stated that he scanned the Depository Building, but not sufficiently to be alerted by anything in the windows or on the roof.
+LJSpeech-1.1/wavs/LJ032-0076.wav|Handwriting experts from the FBI and the Treasury Department testified that the writing on the mail-order form was that of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ013-0056.wav|Subsequently Bailey was arrested on a charge of forgery, and after a long trial found guilty. His sentence was transportation for life.
+LJSpeech-1.1/wavs/LJ024-0032.wav|Those opposing this plan have sought to arouse prejudice and fear by crying that I am seeking to "pack" the Supreme Court
+LJSpeech-1.1/wavs/LJ042-0077.wav|Quote, I Lee Harvey Oswald do hereby request that my present citizenship in the United States of America, be revoked.
+LJSpeech-1.1/wavs/LJ006-0140.wav|Evidence was given before the inspectors of eight or ten prisoners seen "giddy drunk, not able to sit upon forms."
+LJSpeech-1.1/wavs/LJ016-0107.wav|Brown almost at once; then Barry, who was taken at the East End in a public-house where he had arranged to meet a pal.
+LJSpeech-1.1/wavs/LJ043-0079.wav|in nineteen of twenty-three categories included on the general aptitude examination and scored one hundred twenty-seven on the verbal test, as compared
+LJSpeech-1.1/wavs/LJ026-0133.wav|combined with nitrogen and sulphur (probably also with salts, water, etc.) to form proteid matter.
+LJSpeech-1.1/wavs/LJ019-0271.wav|In a word, the value of diet as a part of penal discipline was still insufficiently recognized.
+LJSpeech-1.1/wavs/LJ035-0127.wav|down the rear stairs to the first floor, where she encountered two Depository employees -- William Shelley and Billy Lovelady.
+LJSpeech-1.1/wavs/LJ034-0105.wav|In his sworn statement to the police later that day,
+LJSpeech-1.1/wavs/LJ003-0306.wav|The clothes of prisoners arriving dirty, or in rags, should be fumigated before worn in the jail,
+LJSpeech-1.1/wavs/LJ015-0112.wav|But the continental police had been warned to look out for him, and two Danish inspectors got upon his track,
+LJSpeech-1.1/wavs/LJ008-0044.wav|and at the distance of five feet from the same is fixed a strong railing all round the scaffold to enclose a place for the constables.
+LJSpeech-1.1/wavs/LJ048-0105.wav|but these routes would not have been in accordance with the White House staff instructions given the Secret Service for a desirable motorcade route.
+LJSpeech-1.1/wavs/LJ048-0278.wav|The Commission recognizes that the responsibilities of members of the White House detail of the Secret Service are arduous.
+LJSpeech-1.1/wavs/LJ044-0198.wav|as he experienced successive failures in his jobs, in his political activity, and in his personal relationships.
+LJSpeech-1.1/wavs/LJ002-0249.wav|and the prison, although wives and children resided within the walls, was not overcrowded.
+LJSpeech-1.1/wavs/LJ001-0168.wav|On the whole, a small book should be printed on paper which is as thin as may be without being transparent.
+LJSpeech-1.1/wavs/LJ009-0029.wav|This episode throws some doubt upon the tenderness and proper feeling exhibited by the chaplain towards the most deserving members of his criminal flock;
+LJSpeech-1.1/wavs/LJ019-0131.wav|Mr. Pearson and the committee of eighteen fifty have the more claim on our consideration, because,
+LJSpeech-1.1/wavs/LJ027-0127.wav|while neglecting to maintain his unity of ideal in the case of nearly all the numerous species of snakes, he should have added a tiny rudiment in the case of the Python
+LJSpeech-1.1/wavs/LJ005-0057.wav|was introduced as early as seventeen ninety by Mr. Blackburn
+LJSpeech-1.1/wavs/LJ032-0142.wav|quote, off underside gun barrel near end of grip C two seven six six, end quote,
+LJSpeech-1.1/wavs/LJ001-0048.wav|his letter is admirably clear and regular, but at least as beautiful as any other Roman type.
+LJSpeech-1.1/wavs/LJ027-0123.wav|where within the limits of the same natural group of organisms a rudiment is sometimes present and sometimes absent.
+LJSpeech-1.1/wavs/LJ029-0175.wav|Both Dallas papers cited White House sources on September twenty-six as confirming the President's intention to visit Texas on November twenty-one and twenty-two,
+LJSpeech-1.1/wavs/LJ044-0174.wav|and possibly to Philadelphia to look for work.
+LJSpeech-1.1/wavs/LJ039-0023.wav|I remember that I held him. We actually struggled for several minutes and then he quieted down. End quote.
+LJSpeech-1.1/wavs/LJ050-0048.wav|It has received assistance also from data processing experts at the CIA
+LJSpeech-1.1/wavs/LJ010-0149.wav|She was seized before she could do any mischief,
+LJSpeech-1.1/wavs/LJ032-0187.wav|The fact that on the morning of the assassination Oswald was wearing the shirt from which these relatively fresh fibers most probably originated,
+LJSpeech-1.1/wavs/LJ047-0226.wav|and never realized that the motorcade would pass the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ047-0189.wav|Answer: No. I would have to wait until I had talked to Marina to see what I could determine, and from there I could make my plans.
+LJSpeech-1.1/wavs/LJ049-0028.wav|The Secret Service has therefore suggested this practice only on extraordinary occasions.
+LJSpeech-1.1/wavs/LJ012-0187.wav|one of his accomplices, who took the police to the pond, where the remains of the unfortunate Mr. Weare were discovered, sunk in a sack weighted by stones.
+LJSpeech-1.1/wavs/LJ021-0104.wav|let it be remembered that so far as possible, consistent with the immediate public interest of this past year
+LJSpeech-1.1/wavs/LJ003-0115.wav|The judge sat in proper form; he was punctiliously styled "my lord."
+LJSpeech-1.1/wavs/LJ039-0127.wav|When Sergeant Zahm was asked whether Oswald's Marine Corps training would have made it easier to operate a rifle with a four-power scope, he replied, quote,
+LJSpeech-1.1/wavs/LJ001-0101.wav|It is discouraging to note that the improvement of the last fifty years is almost wholly confined to Great Britain.
+LJSpeech-1.1/wavs/LJ029-0209.wav|Beneath two photographs of President Kennedy, one full- face and one profile, appeared the caption, quote, Wanted for Treason,
+LJSpeech-1.1/wavs/LJ050-0065.wav|Alan H. Belmont, Assistant to the Director of the FBI, testified that this revision was initiated by the FBI itself.
+LJSpeech-1.1/wavs/LJ046-0213.wav|members of his immediate family, the President-elect, and the Vice-President. Investigation of threats against the President of the United States,
+LJSpeech-1.1/wavs/LJ038-0253.wav|he replied that he had buried it in the ground or hidden it in some bushes and that he also mentioned a railroad track in this connection.
+LJSpeech-1.1/wavs/LJ029-0075.wav|Accordingly, it was important to determine the luncheon site as quickly as possible, so that security could be established at the site and the motorcade route selected.
+LJSpeech-1.1/wavs/LJ020-0063.wav|They are even better when cold than hot.
+LJSpeech-1.1/wavs/LJ014-0021.wav|a coat among them much torn and stained, with three buttons missing, one of which corresponded with that picked up at Hampstead.
+LJSpeech-1.1/wavs/LJ003-0076.wav|four for fourteen years, and three of them persons sentenced to fines or short imprisonment -- one for little more than a month.
+LJSpeech-1.1/wavs/LJ045-0232.wav|Oswald was overbearing and arrogant throughout much of the time between his arrest and his own death.
+LJSpeech-1.1/wavs/LJ016-0388.wav|A few go further, and are almost gluttonous.
+LJSpeech-1.1/wavs/LJ026-0128.wav|for it is here that oxidation occurs and the need for a renewal of matter and energy consequently arises.
+LJSpeech-1.1/wavs/LJ010-0082.wav|made a voluntary confession to Sir Herbert Taylor one morning at Windsor; after which Thistlewood and his accomplices were closely watched,
+LJSpeech-1.1/wavs/LJ008-0049.wav|and this, being much more sudden and regular than that of a cart being drawn away, has the effect of immediate death.
+LJSpeech-1.1/wavs/LJ007-0151.wav|and his connection with them is confirmed by that devotion and generosity towards their paramours in adversity for which these otherwise degraded women are remarkable.
+LJSpeech-1.1/wavs/LJ038-0207.wav|When he came back I asked him what had happened. He was very pale.
+LJSpeech-1.1/wavs/LJ012-0057.wav|But he once more persuaded the turnkeys to pause at the public, where more liquor was consumed.
+LJSpeech-1.1/wavs/LJ003-0183.wav|sometimes girls of thirteen, twelve, even ten or nine years of age, were exposed to, quote,
+LJSpeech-1.1/wavs/LJ005-0179.wav|he total number of prisoners they received during the year varied from two persons to many hundreds.
+LJSpeech-1.1/wavs/LJ041-0024.wav|Oswald erased those names, however, and indicated that he had no close friends.
+LJSpeech-1.1/wavs/LJ029-0090.wav|were deployed in and around the Trade Mart.
+LJSpeech-1.1/wavs/LJ019-0356.wav|But the act was not limited to permissive legislation. Its provisions and enactments were backed up by certain penalties.
+LJSpeech-1.1/wavs/LJ002-0023.wav|On the female side matters were much worse;
+LJSpeech-1.1/wavs/LJ010-0233.wav|Here again probably it was partly the love of notoriety which was the incentive,
+LJSpeech-1.1/wavs/LJ022-0061.wav|That is why our social security program is an important part of the complete picture.
+LJSpeech-1.1/wavs/LJ003-0043.wav|and it was not ready to relieve Newgate till late in eighteen fifteen.
+LJSpeech-1.1/wavs/LJ033-0170.wav|the width of the tape, the knurled markings on the surface of the fiber, the texture of the fiber, the letting pattern
+LJSpeech-1.1/wavs/LJ037-0025.wav|Benavides saw a man standing at the right side of the parked police car. He then heard three shots and saw the policeman fall to the ground.
+LJSpeech-1.1/wavs/LJ005-0236.wav|They were not confined apart from each other, but were crowded thirty or forty together in the press yard,
+LJSpeech-1.1/wavs/LJ043-0111.wav|After securing this job and an apartment, Oswald asked his wife to join him.
+LJSpeech-1.1/wavs/LJ033-0185.wav|On the other hand, since two rolls could be made from the same batch of paper,
+LJSpeech-1.1/wavs/LJ039-0229.wav|Sergeant Zahm testified, quote,
+LJSpeech-1.1/wavs/LJ028-0304.wav|till the tenth day after I am entered within the town, and then place near to the gates of Semiramis a detachment of thy army,
+LJSpeech-1.1/wavs/LJ040-0082.wav|in the summer of nineteen forty-eight.
+LJSpeech-1.1/wavs/LJ013-0117.wav|and with another carry off the plate-chest in broad daylight, and as a matter of business. No one interfered or asked any questions.
+LJSpeech-1.1/wavs/LJ018-0032.wav|There was no mystery about his departure; he had gone to Canada, by the 'Victoria' sailing ship, starting from the London docks, and bound to New York.
+LJSpeech-1.1/wavs/LJ030-0058.wav|Elevated approximately fifteen inches above the back of the front seat
+LJSpeech-1.1/wavs/LJ038-0244.wav|Oswald purchased the money order for the rifle on March twelve, the rifle was shipped on March twenty, and the shooting occurred on April ten.
+LJSpeech-1.1/wavs/LJ003-0194.wav|At another time there were fifty, one of whom had been under sentence a couple of years.
+LJSpeech-1.1/wavs/LJ022-0186.wav|Twenty years of experience with this system have justified the efforts made to create it,
+LJSpeech-1.1/wavs/LJ017-0016.wav|The cases of poisoning in the British calendars are rare, nor indeed was the guilt of the accused always clearly established.
+LJSpeech-1.1/wavs/LJ025-0100.wav|in so far as under these circumstances the green plant
+LJSpeech-1.1/wavs/LJ049-0107.wav|or who hold important governmental posts.
+LJSpeech-1.1/wavs/LJ037-0021.wav|Scoggins stated that he thought he had seen a picture of Oswald in the newspapers prior to the lineup identification on Saturday.
+LJSpeech-1.1/wavs/LJ018-0378.wav|In Newgate the 'Lennie' mutineers were extremely well behaved.
+LJSpeech-1.1/wavs/LJ041-0051.wav|Oswald was motivated to join the Marines in large part by a desire, quote, to get from out and under the yoke of oppression from my mother, end quote.
+LJSpeech-1.1/wavs/LJ013-0103.wav|At Halifax Forrester learnt that the men he wanted had gone on to Boston, thence to Buffalo and Canada, and back to Boston.
+LJSpeech-1.1/wavs/LJ019-0073.wav|was impressed with the necessity for authoritative interference.
+LJSpeech-1.1/wavs/LJ004-0174.wav|Confinement for twelve months in the Bristol jail was counted a punishment equivalent to seven years' transportation.
+LJSpeech-1.1/wavs/LJ032-0020.wav|learned from retail outlets in Dallas that Crescent Firearms, Inc., of New York City,
+LJSpeech-1.1/wavs/LJ031-0039.wav|Dr. Carrico was in the emergency area, examining another patient, when he was notified that President Kennedy was en route to the hospital.
+LJSpeech-1.1/wavs/LJ022-0184.wav|not by a few private banking institutions, but by a body with public prestige and authority.
+LJSpeech-1.1/wavs/LJ032-0235.wav|From an examination of one of the photographs, the Commission determined the dates of the issues of the Militant and the Worker which Oswald was holding in his hand.
+LJSpeech-1.1/wavs/LJ042-0228.wav|I went there to see the land, the people and how their system works, end quote.
+LJSpeech-1.1/wavs/LJ005-0207.wav|Kidderminster had a prison, one damp chill room,
+LJSpeech-1.1/wavs/LJ036-0190.wav|If Oswald left his roominghouse shortly after one p.m. and walked at a brisk pace,
+LJSpeech-1.1/wavs/LJ030-0028.wav|and the Vice President and Mrs. Johnson were in the receiving line to greet President and Mrs. Kennedy.
+LJSpeech-1.1/wavs/LJ015-0301.wav|While Hardwicke and Atwell were in Newgate awaiting trial, active search was made for Saward,
+LJSpeech-1.1/wavs/LJ035-0133.wav|Shelley and Lovelady, however, have testified
+LJSpeech-1.1/wavs/LJ011-0285.wav|The door of his place of durance stood open, and Mr. Gee began to consider whether he might not escape.
+LJSpeech-1.1/wavs/LJ022-0122.wav|there will be those, of course, who will try to tell you that the exceptional failure is characteristic of the entire endeavor.
+LJSpeech-1.1/wavs/LJ028-0314.wav|observing him, hastened down, and setting one of the gates slightly ajar, questioned him who he was, and on what errand he had come.
+LJSpeech-1.1/wavs/LJ007-0035.wav|The better-disposed complained bitterly of what they had to endure;
+LJSpeech-1.1/wavs/LJ011-0053.wav|This "personality," and it can be called by no other name, is carefully excluded from prison pulpit utterances on the eve of an execution.
+LJSpeech-1.1/wavs/LJ034-0152.wav|Brennan told the Commission that he could have made a positive identification in the lineup on November twenty-two
+LJSpeech-1.1/wavs/LJ046-0077.wav|Under our system, measures must be sought to afford security without impeding the President's performance of his many functions.
+LJSpeech-1.1/wavs/LJ025-0143.wav|nothing but a linear tract of specially modified protoplasm between two points of an organism
+LJSpeech-1.1/wavs/LJ028-0046.wav|Another son, Assurbanipal, or the great Sardanapalus of the Greeks, became the King of Nineveh.
+LJSpeech-1.1/wavs/LJ040-0012.wav|possibly by factors of which those around him were only dimly aware.
+LJSpeech-1.1/wavs/LJ033-0015.wav|approximately fifteen miles from Oswald's place of work at the Texas School Book Depository.
+LJSpeech-1.1/wavs/LJ033-0197.wav|quote, in all observable microscopic characteristics, end quote.
+LJSpeech-1.1/wavs/LJ048-0163.wav|it has not been the practice of the Secret Service to make surveys or checks of buildings along the route of a Presidential motorcade.
+LJSpeech-1.1/wavs/LJ007-0211.wav|"In our late visits," they say, "we have seen manifest indications of a retrograde movement in this respect,
+LJSpeech-1.1/wavs/LJ036-0197.wav|He drove a police car painted distinctive colors with Number ten prominently displayed on each side.
+LJSpeech-1.1/wavs/LJ032-0220.wav|Since Exhibit Number one thirty-three B was taken with Oswald's camera,
+LJSpeech-1.1/wavs/LJ006-0162.wav|But it is only fair to Mr. Cope to state that he himself said he went whenever he could find time
+LJSpeech-1.1/wavs/LJ003-0149.wav|supported themselves; they had the ration of prison bread only,
+LJSpeech-1.1/wavs/LJ014-0046.wav|Hocker made several pretended confessions and revelations, all of which were proved to be absolutely false by the police on inquiry.
+LJSpeech-1.1/wavs/LJ010-0115.wav|and it is pretty certain that for some time he served both sides;
+LJSpeech-1.1/wavs/LJ012-0278.wav|and the most probable inference is that he inveigled Mrs. Brown to his lodgings with the set purpose of taking her life.
+LJSpeech-1.1/wavs/LJ006-0093.wav|So closely did they lie together, that the inspectors at their night visits found it difficult in stepping across the room to avoid treading on them.
+LJSpeech-1.1/wavs/LJ038-0040.wav|As McDonald fell into the seat with his left hand on the gun, he felt something graze across his hand and heard what sounded like the snap of the hammer.
+LJSpeech-1.1/wavs/LJ045-0064.wav|that Oswald stated that he did not speak English in his family because he did not want them to become Americanized.
+LJSpeech-1.1/wavs/LJ023-0021.wav|to make it bomb-proof against the causes of nineteen twenty-nine.
+LJSpeech-1.1/wavs/LJ008-0029.wav|But the Old Bailey gradually, and in spite of all objections urged, monopolized the dread business of execution.
+LJSpeech-1.1/wavs/LJ037-0103.wav|Two other important eyewitnesses to Oswald's flight were Ted Callaway,
+LJSpeech-1.1/wavs/LJ003-0053.wav|At one time the whole of these different categories were thrown together pell-mell, young and old, the untried with the convicted.
+LJSpeech-1.1/wavs/LJ023-0138.wav|by offering life pensions at full pay for federal judges on all courts who are willing to retire at seventy.
+LJSpeech-1.1/wavs/LJ042-0162.wav|Full of optimism and hope, he stood in Red Square in the Fall of nineteen fifty-nine, vowing to see his chosen course through,
+LJSpeech-1.1/wavs/LJ032-0038.wav|Opposite the printed words "Pay To" were written the words "Kleins Sporting Goods,"
+LJSpeech-1.1/wavs/LJ016-0033.wav|He had access to the airing yard, and there was for hours no kind of supervision.
+LJSpeech-1.1/wavs/LJ041-0200.wav|that his use of those excuses prevented him from discovering the real reasons for and attempting to overcome his difficulties.
+LJSpeech-1.1/wavs/LJ033-0007.wav|the circumstances surrounding Oswald's return to Irving, Texas, on Thursday, November twenty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ044-0147.wav|and in late June or early July he told his wife that he wanted to return to the Soviet Union with her. She said that he was extremely upset,
+LJSpeech-1.1/wavs/LJ033-0162.wav|from the shipping room of the Depository and forwarded it to the FBI Laboratory in Washington.
+LJSpeech-1.1/wavs/LJ047-0020.wav|Until April nineteen sixty, FBI activity consisted of placing in Oswald's file
+LJSpeech-1.1/wavs/LJ004-0100.wav|An infirmary, consisting of two distinct rooms, one for males and one for females, should be provided for the separate accommodation of the sick.
+LJSpeech-1.1/wavs/LJ015-0088.wav|To his wife, poor soul, he made no pretense of fidelity, and she enjoyed only so much of his company as was necessarily spent
+LJSpeech-1.1/wavs/LJ013-0198.wav|and in an impassioned address implored the jury not to send an innocent man to the gallows.
+LJSpeech-1.1/wavs/LJ004-0102.wav|were to be kept in every jail, and the prisoners directed to wash in them before release. These provisions were almost a dead letter.
+LJSpeech-1.1/wavs/LJ049-0031.wav|The Presidential vehicle in use in Dallas, described in chapter two,
+LJSpeech-1.1/wavs/LJ016-0080.wav|and probably one of the few cases of a recurrence, but under proper safeguards and limitations, to the old system of chains.
+LJSpeech-1.1/wavs/LJ036-0010.wav|(two) testimony of firearms identification experts establishing the identity of the murder weapon,
+LJSpeech-1.1/wavs/LJ050-0026.wav|The Commission urges that the Service continue this effort to overhaul and define its procedures.
+LJSpeech-1.1/wavs/LJ017-0122.wav|A government prosecution was instituted, and Palmer was brought to Newgate for trial at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ041-0083.wav|Thornley also testified that he thought that Oswald's extreme personal sloppiness in the Marine Corps, quote,
+LJSpeech-1.1/wavs/LJ014-0109.wav|Part of this she had deposited to be left till called for at one station, while she had gone herself to another, that at Euston Square.
+LJSpeech-1.1/wavs/LJ022-0048.wav|They are still declining.
+LJSpeech-1.1/wavs/LJ048-0048.wav|All this does seem to amount to enough to have induced an alert agency, such as the FBI,
+LJSpeech-1.1/wavs/LJ036-0035.wav|The man was on the bus approximately four minutes.
+LJSpeech-1.1/wavs/LJ028-0382.wav|and their houses shall be full of doleful creatures, and owls shall dwell there, and satyrs shall dance there,
+LJSpeech-1.1/wavs/LJ004-0070.wav|the conservation of his health and industrious habits, are the clear, evident, undeniable rights of an unconvicted prisoner."
+LJSpeech-1.1/wavs/LJ024-0003.wav|What is my proposal?
+LJSpeech-1.1/wavs/LJ050-0153.wav|In the planning document currently under review by the Bureau of the Budget, the Department recommends that it be permitted to hire five qualified persons, quote,
+LJSpeech-1.1/wavs/LJ002-0098.wav|The prisoners had few comforts, beyond the occasional use of a bath at some distance, situated in the press yard,
+LJSpeech-1.1/wavs/LJ042-0111.wav|Jeanne De Mohrenschildt, another member of that group,
+LJSpeech-1.1/wavs/LJ020-0077.wav|Study the temperature in different parts of the kitchen and kitchen closets to the end of finding the best places for raising dough and sponge.
+LJSpeech-1.1/wavs/LJ017-0101.wav|That day Palmer had bought more strychnia, and had called in a fresh doctor.
+LJSpeech-1.1/wavs/LJ042-0019.wav|The validity of the latter observation is borne out by some of the things Oswald wrote in connection with his defection
+LJSpeech-1.1/wavs/LJ004-0016.wav|I have shown in a previous chapter what Newgate was at this period, despite a vast expenditure and boasted efforts to introduce reforms.
+LJSpeech-1.1/wavs/LJ010-0109.wav|Thistlewood made a long and rambling defense, the chief features of which were abuse of Lord Sidmouth, and the vilification of the informer Edwards.
+LJSpeech-1.1/wavs/LJ017-0168.wav|But a long public discussion followed, and in consequence he was reprieved.
+LJSpeech-1.1/wavs/LJ038-0236.wav|Two of these photographs were taken from the rear of Walker's house.
+LJSpeech-1.1/wavs/LJ013-0108.wav|Next day a person betrayed him for the reward, and he was soon captured.
+LJSpeech-1.1/wavs/LJ006-0168.wav|to taking the descriptions of newly-arrived prisoners.
+LJSpeech-1.1/wavs/LJ033-0030.wav|Mrs. A. C. Johnson, his landlady, testified that Oswald's room at ten twenty-six North Beckley Avenue
+LJSpeech-1.1/wavs/LJ045-0117.wav|it would appear to be unlikely that his landlady in Dallas
+LJSpeech-1.1/wavs/LJ047-0096.wav|The police called the local FBI office and an agent, John L. Quigley, was sent to the police station.
+LJSpeech-1.1/wavs/LJ033-0212.wav|(two) took paper and tape from the wrapping bench of the Depository and fashioned a bag large enough to carry the disassembled rifle;
+LJSpeech-1.1/wavs/LJ034-0138.wav|they saw and heard Brennan describing what he had seen.
+LJSpeech-1.1/wavs/LJ017-0250.wav|from Peru bound to Bordeaux, which had foundered at sea;
+LJSpeech-1.1/wavs/LJ027-0049.wav|the structural elements remain, but are profoundly modified to perform totally different functions.
+LJSpeech-1.1/wavs/LJ014-0185.wav|and bystanders peeped in through the shutters, but no one entered or sought to interfere in what seemed only a domestic quarrel.
+LJSpeech-1.1/wavs/LJ009-0053.wav|but who is left to die on the supposition that this is not his first conviction, and still more because a good many sheep have of late been stolen by other people.
+LJSpeech-1.1/wavs/LJ003-0051.wav|(two) persons under sentence of imprisonment for a fixed period, or until they shall have paid certain fines;
+LJSpeech-1.1/wavs/LJ050-0085.wav|other than legal or peaceful, to satisfy any grievance, real or imagined.
+LJSpeech-1.1/wavs/LJ017-0276.wav|Then he wanted to know when the policemen would arrive, and was told none would come.
+LJSpeech-1.1/wavs/LJ002-0006.wav|but it was almost always in excess of the accommodation available, and there were times when the place was full to overflowing.
+LJSpeech-1.1/wavs/LJ028-0305.wav|troops for whose loss thou wilt care little, a thousand men.
+LJSpeech-1.1/wavs/LJ009-0294.wav|One was Askern,
+LJSpeech-1.1/wavs/LJ049-0159.wav|the Secret Service, then the only Federal investigative agency, assumed full-time responsibility for the safety of the President.
+LJSpeech-1.1/wavs/LJ002-0330.wav|female visitors carried them out of the prisons, or the debtors destroyed them when the weather was warm,
+LJSpeech-1.1/wavs/LJ013-0064.wav|Barber, it was said, became aware of this,
+LJSpeech-1.1/wavs/LJ017-0055.wav|Throughout his trial he preserved an impassive countenance, but he clearly took a deep interest in all that passed.
+LJSpeech-1.1/wavs/LJ037-0171.wav|The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired,
+LJSpeech-1.1/wavs/LJ009-0239.wav|The whole case was referred to the king, and the poor wretch, whose crime had been a military one, was eventually pardoned.
+LJSpeech-1.1/wavs/LJ007-0129.wav|the sane and the insane, the young and the old, the trivial offender and the man about to suffer the extreme penalty of the law,
+LJSpeech-1.1/wavs/LJ002-0317.wav|and there were as many as three hundred and fifty prisoners in at one time.
+LJSpeech-1.1/wavs/LJ018-0054.wav|While in the condemned cell he conversed freely with the warders in broken English or through an interpreter.
+LJSpeech-1.1/wavs/LJ036-0126.wav|As he unloaded his Continental bus station passenger in front of Greyhound, he started to get out to buy a package of cigarettes.
+LJSpeech-1.1/wavs/LJ014-0152.wav|On reaching the condemned cell she threw herself upon the floor and shrieked in an hysterical agony of tears.
+LJSpeech-1.1/wavs/LJ003-0349.wav|Why not relieve Newgate by drawing more largely upon the superior accommodation which Millbank offered?
+LJSpeech-1.1/wavs/LJ032-0147.wav|At the request of the Commission, Arthur Mandella,
+LJSpeech-1.1/wavs/LJ025-0138.wav|The results of inquiries into the structure of the nervous system of animals
+LJSpeech-1.1/wavs/LJ032-0207.wav|I found it to be the same general configuration. All appearances were the same, end quote,
+LJSpeech-1.1/wavs/LJ037-0264.wav|and seven eyewitnesses who saw the flight of the gunman with revolver in hand
+LJSpeech-1.1/wavs/LJ046-0120.wav|the security processing of gifts sent to the President, and technical inspections against covert listening devices.
+LJSpeech-1.1/wavs/LJ034-0204.wav|and not to his race.
+LJSpeech-1.1/wavs/LJ033-0048.wav|Mrs. Paine was certain that she had not left the light on in the garage after dinner.
+LJSpeech-1.1/wavs/LJ041-0178.wav|not unpopular, end quote, at that time. Donovan testified
+LJSpeech-1.1/wavs/LJ015-0214.wav|At the other side of the Channel the French railway authorities took charge.
+LJSpeech-1.1/wavs/LJ019-0056.wav|Colonel Jebb preferred it; Messrs. Crawford and Whitworth Russell were convinced that the complete isolation of criminals from one another
+LJSpeech-1.1/wavs/LJ021-0144.wav|in order to seek their cooperation in establishing what I may describe as a specific trial period of industrial peace.
+LJSpeech-1.1/wavs/LJ012-0025.wav|at which he realized one hundred fifty pounds within a couple of years.
+LJSpeech-1.1/wavs/LJ031-0080.wav|The time was fixed at one p.m., as an approximation, since it was impossible to determine the precise moment when life left the President.
+LJSpeech-1.1/wavs/LJ048-0078.wav|In each instance, liaison contacts should be developed to include a close friendly relationship,
+LJSpeech-1.1/wavs/LJ032-0249.wav|that Oswald may have sat on the screened-in porch at night practicing with the rifle by looking through the telescopic sight and operating the bolt.
+LJSpeech-1.1/wavs/LJ049-0144.wav|The Committee would be able to provide guidance in defining the general nature of domestic and foreign dangers to Presidential security.
+LJSpeech-1.1/wavs/LJ018-0135.wav|and in the states known as "water-leaf" and "sized," which are the penultimate processes of manufacture.
+LJSpeech-1.1/wavs/LJ012-0110.wav|The police lost all trace of them for some days, but at length Sullivan's brother was followed from the house in Kennington to the above-mentioned tavern.
+LJSpeech-1.1/wavs/LJ014-0166.wav|faded in my mind before the atrocious bearing, looks, and language of the assembled spectators.
+LJSpeech-1.1/wavs/LJ028-0101.wav|There is scarcely one of the thousands of ruin mounds in Babylonia which does not contain bricks bearing his name.
+LJSpeech-1.1/wavs/LJ012-0231.wav|although it included statements that were improbable and unsubstantiated.
+LJSpeech-1.1/wavs/LJ013-0046.wav|but he would not take their advice, and developing his transactions, ended as we have seen.
+LJSpeech-1.1/wavs/LJ013-0267.wav|He made a rambling defense, ending by saying,
+LJSpeech-1.1/wavs/LJ033-0012.wav|The Curtain Rod Story
+LJSpeech-1.1/wavs/LJ041-0156.wav|he preferred the, quote, Red Army, end quote.
+LJSpeech-1.1/wavs/LJ002-0276.wav|went outside and beyond the prison to call on their creditors, and try to arrange their debts, but this practice was discontinued.
+LJSpeech-1.1/wavs/LJ019-0397.wav|with spare accommodation sufficient to receive all prisoners who were then committed to Newgate. These arguments were unanswerable.
+LJSpeech-1.1/wavs/LJ028-0268.wav|Mounting upon the battlements that crowned their walls, they insulted and jeered at Darius and his mighty host.
+LJSpeech-1.1/wavs/LJ039-0182.wav|Five of the six shots hit the third target where the angle of movement of the weapon was small.
+LJSpeech-1.1/wavs/LJ049-0135.wav|(which is responsible for advising the President respecting the coordination
+LJSpeech-1.1/wavs/LJ005-0195.wav|There was no decency whatever in the internal arrangements;
+LJSpeech-1.1/wavs/LJ004-0209.wav|The looms were constantly busy. Tailors were always at work, and every article of clothing and bedding was made up within the walls.
+LJSpeech-1.1/wavs/LJ015-0220.wav|The four waited patiently for their opportunity,
+LJSpeech-1.1/wavs/LJ010-0222.wav|in consequence of having received some information of an important nature from Hanover.
+LJSpeech-1.1/wavs/LJ028-0330.wav|and when the next period agreed on had elapsed, again with a band of picked men he sallied forth, and slaughtered the two thousand.
+LJSpeech-1.1/wavs/LJ026-0120.wav|The building up of the protoplasm from proteids is anabolism, constructive metabolism.
+LJSpeech-1.1/wavs/LJ038-0193.wav|However, I prefer that you hold on to my personal papers (military, civil, etc.).
+LJSpeech-1.1/wavs/LJ027-0058.wav|enclosing and protecting the nervous centers (cerebrospinal axis).
+LJSpeech-1.1/wavs/LJ046-0126.wav|estimated that most of the material received by his office originated in this fashion
+LJSpeech-1.1/wavs/LJ027-0003.wav|The facts of biology which admit of adequate explanation only in connection with the theory of descent
+LJSpeech-1.1/wavs/LJ008-0267.wav|no less than four hundred and fifty-one sentences of death for capital crimes were passed at the Old Bailey;
+LJSpeech-1.1/wavs/LJ040-0048.wav|Oswald did not always distinguish between Marxism and communism.
+LJSpeech-1.1/wavs/LJ003-0175.wav|was accommodated with a room in Mr. Newman's house at the extravagant rental of thirty guineas per week;
+LJSpeech-1.1/wavs/LJ025-0032.wav|Hence arose the second great distinctive character of animals, or the circulatory system, which is less important than the digestive,
+LJSpeech-1.1/wavs/LJ013-0004.wav|The most extensive and systematic robberies were planned and carried out so as long to escape detection.
+LJSpeech-1.1/wavs/LJ011-0219.wav|William Wakefield in Lancaster Jail, and Edward Gibbon Wakefield in Newgate, which sentences were duly enforced.
+LJSpeech-1.1/wavs/LJ044-0185.wav|He engaged in an angry argument with the consul who finally told him that, quote, as far as he was concerned
+LJSpeech-1.1/wavs/LJ048-0106.wav|Much of Lawson's time was taken with establishing adequate security over the motorcade route and at the two places where the President would stop,
+LJSpeech-1.1/wavs/LJ030-0087.wav|The Vice-Presidential automobile, a four-door Lincoln convertible obtained locally for use in the motorcade,
+LJSpeech-1.1/wavs/LJ049-0149.wav|that it has been unable, as a practical matter, to exercise sufficient influence over the security precautions which surround Presidential activities.
+LJSpeech-1.1/wavs/LJ016-0016.wav|There is an attempt at escape mentioned in Mr. Wakefield's book, which might have been an intended suicide.
+LJSpeech-1.1/wavs/LJ041-0103.wav|Oswald once told Powers that he didn't care if he returned to the United States at all.
+LJSpeech-1.1/wavs/LJ022-0106.wav|This will make certain that the largest possible portion of the funds allotted
+LJSpeech-1.1/wavs/LJ007-0135.wav|The yards were narrow and confined, mainly because the ground plan was radically vicious. These were evils inseparable from the place.
+LJSpeech-1.1/wavs/LJ021-0036.wav|loans to the railroads and insurance companies and, finally, help for home owners and industry itself.
+LJSpeech-1.1/wavs/LJ008-0061.wav|The entrance upon this floor or leaf is from the middle window over the gate of the prison;
+LJSpeech-1.1/wavs/LJ037-0029.wav|It was Benavides, using Tippit's car radio, who first reported the killing of Patrolman Tippit at about one:sixteen p.m.:
+LJSpeech-1.1/wavs/LJ034-0203.wav|Euins stated that he could not ascertain the man's race and that the statement in the affidavit was intended to refer only to the white spot on the man's head
+LJSpeech-1.1/wavs/LJ040-0135.wav|She recalled that Lee cried and said, quote, Mother, I want to get out of here.
+LJSpeech-1.1/wavs/LJ012-0087.wav|should be introduced into the building, and secreted there during the night to accomplish the robbery.
+LJSpeech-1.1/wavs/LJ013-0079.wav|when Lord Campbell delivered judgment on Barber's petition, to the effect that
+LJSpeech-1.1/wavs/LJ040-0064.wav|This occurred two months before Lee was born in New Orleans on October eighteen, nineteen thirty-nine.
+LJSpeech-1.1/wavs/LJ036-0188.wav|Oswald was next seen about nine-tenths of a mile away
+LJSpeech-1.1/wavs/LJ029-0082.wav|several tiers of balconies surrounding the central court where the luncheon would be held, and several catwalks crossing the court at each level.
+LJSpeech-1.1/wavs/LJ029-0024.wav|through the evening of Friday, November twenty-two.
+LJSpeech-1.1/wavs/LJ017-0031.wav|instead of limiting them, as in the days of the Borgias and Brinvilliers, to the specially informed and unscrupulously powerful few.
+LJSpeech-1.1/wavs/LJ010-0174.wav|About six p.m. the royal carriage, a low open vehicle drawn by four horses, ridden by postilions, left the palace.
+LJSpeech-1.1/wavs/LJ020-0081.wav|and knead for ten minutes, carefully at first, lest the liquids should be wasted, and more boldly when they are absorbed by the paste.
+LJSpeech-1.1/wavs/LJ016-0421.wav|brutal creature who showed no remorse, but was subject to fits of ungovernable passion, when she broke out into language the most appalling.
+LJSpeech-1.1/wavs/LJ002-0286.wav|The rooms had no fireplaces, so it mattered little that no coals were allowed.
+LJSpeech-1.1/wavs/LJ016-0166.wav|and surgical opinion stated that the stoppage of circulation was the cause of death.
+LJSpeech-1.1/wavs/LJ032-0212.wav|Commission Exhibit Number one thirty-three B, to Oswald's Imperial Reflex camera, with which Marina Oswald testified she took the pictures.
+LJSpeech-1.1/wavs/LJ046-0169.wav|If the field office determines that the case should be subject to continuing review, PRS establishes a file
+LJSpeech-1.1/wavs/LJ040-0106.wav|It also appears that Lee tried to tag along with his older brothers
+LJSpeech-1.1/wavs/LJ036-0058.wav|People on the bus began talking about it. As the bus neared Lamar Street, Oswald left the bus and disappeared into the crowd.
+LJSpeech-1.1/wavs/LJ033-0039.wav|and one which provided an excuse for the carrying of a bulky package the following morning.
+LJSpeech-1.1/wavs/LJ031-0040.wav|Approximately two minutes later, Dr. Carrico saw the President on his back, being wheeled into the emergency area.
+LJSpeech-1.1/wavs/LJ020-0071.wav|One tablespoonful of butter, just melted, not hot.
+LJSpeech-1.1/wavs/LJ050-0185.wav|The Service should consider preparing formal explanations of the cooperation anticipated during a Presidential visit to a city,
+LJSpeech-1.1/wavs/LJ049-0056.wav|The first duty of the agents in the motorcade is to attempt to cover the President as closely as possible and practicable
+LJSpeech-1.1/wavs/LJ007-0118.wav|A prisoner, one of the wardsmen, waited on those in the infirmary; the occupants of the governor's house had their own servants, or the governor's.
+LJSpeech-1.1/wavs/LJ017-0021.wav|A third case, more especially interesting to us as having passed through Newgate,
+LJSpeech-1.1/wavs/LJ005-0194.wav|The keeper and his officers resided at a distance from the jail, and left its inmates to their own devices.
+LJSpeech-1.1/wavs/LJ047-0024.wav|in the course of a routine FBI investigation of transfers of small sums of money from Mrs. Oswald to her son in Russia.
+LJSpeech-1.1/wavs/LJ010-0187.wav|He was taken into custody, and removed first to a police cell, thence committed to Newgate, after he had been examined before the Privy Council.
+LJSpeech-1.1/wavs/LJ013-0113.wav|Robberies as daring in conception as they were boldly executed were common enough.
+LJSpeech-1.1/wavs/LJ048-0040.wav|The Commission appreciates the large volume of cases handled by the FBI
+LJSpeech-1.1/wavs/LJ032-0113.wav|Based on the above evidence, the Commission concluded that Oswald purchased the rifle found on the sixth floor of the Depository Building.
+LJSpeech-1.1/wavs/LJ022-0005.wav|It has made and is making distinct progress.
+LJSpeech-1.1/wavs/LJ033-0199.wav|because other types of fibers present in the blanket were not found in the bag.
+LJSpeech-1.1/wavs/LJ020-0029.wav|Put the brown or Graham flour unsifted into the bread-bowl.
+LJSpeech-1.1/wavs/LJ045-0206.wav|it must have appeared to him that he was unable to command even the attention of his family.
+LJSpeech-1.1/wavs/LJ006-0061.wav|and "the greatest contempt shown for the law."
+LJSpeech-1.1/wavs/LJ014-0207.wav|The former was given a reward of twenty and the latter of ten pounds.
+LJSpeech-1.1/wavs/LJ029-0122.wav|Elm Street, parallel to Main Street and one block north,
+LJSpeech-1.1/wavs/LJ022-0120.wav|there may be occasional instances of inefficiency, bad management, or misuse of funds.
+LJSpeech-1.1/wavs/LJ034-0207.wav|Accordingly, Euins' testimony is considered probative as to the source of the shots but is inconclusive as to the identity of the man in the window.
+LJSpeech-1.1/wavs/LJ002-0003.wav|with criminals and misdemeanants of all shades crowding perpetually into its narrow limits, the latter state of Newgate was worse than the first.
+LJSpeech-1.1/wavs/LJ003-0035.wav|and about eighteen twelve application was made to Parliament for funds to build a new debtors' prison.
+LJSpeech-1.1/wavs/LJ011-0292.wav|There was no furniture in the place, and the den in the kitchen had been recently and specially constructed of boards of immense strength and thickness.
+LJSpeech-1.1/wavs/LJ036-0081.wav|The station wagon stopped to pick up the man and then drove off.
+LJSpeech-1.1/wavs/LJ008-0135.wav|and stepped out on to the platform, to be received, as has been said, by prolonged yells.
+LJSpeech-1.1/wavs/LJ002-0281.wav|The demands for fees were excessive in Giltspur Street.
+LJSpeech-1.1/wavs/LJ028-0039.wav|We know the names of its kings, and the records speak of long wars with the Assyrians.
+LJSpeech-1.1/wavs/LJ028-0312.wav|Having left these instructions, Zopyrus fled towards the gates of the town, often looking back, to give himself the air of a deserter.
+LJSpeech-1.1/wavs/LJ011-0115.wav|Hunton's execution no doubt aroused public attention to the cruelty and futility of the capital law against forgery.
+LJSpeech-1.1/wavs/LJ026-0097.wav|furnish the materials and energy required for the life and growth of the plant.
+LJSpeech-1.1/wavs/LJ008-0205.wav|The city authorities had endeavored to take all precautions against panic and excitement among the crowd,
+LJSpeech-1.1/wavs/LJ031-0193.wav|On the return flight, Mrs. Kennedy sat with David Powers, Kenneth O'Donnell, and Lawrence O'Brien.
+LJSpeech-1.1/wavs/LJ028-0182.wav|and in five fifty-five Nabonidus, the father of the Biblical Belshazzar, came to the throne.
+LJSpeech-1.1/wavs/LJ004-0226.wav|It was also claimed for the more ample and more orderly distribution of victuals, that the general health of the prisoners had greatly improved.
+LJSpeech-1.1/wavs/LJ050-0132.wav|the Commission notes with approval several recent measures taken and proposed by the Secret Service to improve its liaison arrangements.
+LJSpeech-1.1/wavs/LJ028-0254.wav|The people, enjoying the greater freedom which Cyrus permitted them, were contented, and life in Babylon went on about as before.
+LJSpeech-1.1/wavs/LJ004-0142.wav|where a lad lay ill with fever, three other prisoners, at first perfectly healthy, were lodged. Of course they were seized with the fever;
+LJSpeech-1.1/wavs/LJ010-0126.wav|A crowd as great as any known collected in the Old Bailey to see the ceremony, about which there were some peculiar features worth recording.
+LJSpeech-1.1/wavs/LJ005-0186.wav|They neither built new jails nor contracted with the counties, as had been expected, for the transfer of their prisoners.
+LJSpeech-1.1/wavs/LJ041-0002.wav|Chapter seven. Lee Harvey Oswald: Background and Possible Motives, Part two.
+LJSpeech-1.1/wavs/LJ032-0118.wav|Capt. J. W. Fritz then ejected a cartridge by operating the bolt,
+LJSpeech-1.1/wavs/LJ002-0125.wav|Neild also says that in seventeen ninety-three,
+LJSpeech-1.1/wavs/LJ037-0092.wav|Each woman whispered Oswald's number to the detective. Each testified that she was the first to make the identification.
+LJSpeech-1.1/wavs/LJ004-0015.wav|the motives for a transient amendment were becoming paralyzed, and the effect had ceased with the cause."
+LJSpeech-1.1/wavs/LJ048-0118.wav|He did not have a checklist of the tasks he was expected to accomplish, either by his own efforts or with the cooperation of local authorities.
+LJSpeech-1.1/wavs/LJ038-0034.wav|When he reached the row where the suspect was sitting, McDonald stopped abruptly and told the man to get on his feet.
+LJSpeech-1.1/wavs/LJ010-0067.wav|This Thistlewood had seen many vicissitudes throughout his strange, adventurous career. The son of a respectable Lincolnshire farmer,
+LJSpeech-1.1/wavs/LJ040-0186.wav|Lee scored an I.Q. of one eighteen on the Wechsler Intelligence Scale for Children.
+LJSpeech-1.1/wavs/LJ040-0146.wav|He noted that Lee liked to give the impression that he did not care for other people but preferred to keep to himself,
+LJSpeech-1.1/wavs/LJ008-0009.wav|But the Old Bailey was not exclusively used;
+LJSpeech-1.1/wavs/LJ016-0105.wav|These men were all in prison dress at the time of their escape, but one of their number, Bell, sent back his clothes a few days later by parcel's delivery,
+LJSpeech-1.1/wavs/LJ006-0206.wav|and publications which in these days would have been made the subject of a criminal prosecution.
+LJSpeech-1.1/wavs/LJ017-0123.wav|There was not much reserve about him when there.
+LJSpeech-1.1/wavs/LJ007-0045.wav|They amused themselves after their own fashion; played all day long at blind-man's-buff and leap-frog, or beat each other with a knotted handkerchief,
+LJSpeech-1.1/wavs/LJ007-0033.wav|All privacy was impossible under the circumstances.
+LJSpeech-1.1/wavs/LJ018-0395.wav|convicted of obtaining jewelery under the false pretense of making silly women "beautiful for ever."
+LJSpeech-1.1/wavs/LJ013-0012.wav|Her owners insured her for a full sum of two thousand pounds, after which the Wallaces insured her privily
+LJSpeech-1.1/wavs/LJ008-0311.wav|The whole of the convicts were assembled together in one ward, and made to kneel down.
+LJSpeech-1.1/wavs/LJ024-0047.wav|Is it a dangerous precedent for the Congress to change the number of the justices? The Congress has always had, and will have, that power.
+LJSpeech-1.1/wavs/LJ041-0124.wav|Marina Oswald testified that she, quote, had never heard anything bad about Kennedy from Lee. And he never had anything against him, end quote.
+LJSpeech-1.1/wavs/LJ016-0243.wav|Marwood, who succeeded him, and who died while these sheets were in the press, was a Lincolnshire man, a native of Horncastle,
+LJSpeech-1.1/wavs/LJ047-0153.wav|Having determined that Mrs. Paine was a responsible and reliable citizen, Hosty interviewed her on November one.
+LJSpeech-1.1/wavs/LJ016-0101.wav|One of the three, shamming ill, remained all day in his ward, where he employed himself unraveling the rope from the sleeping-mats.
+LJSpeech-1.1/wavs/LJ010-0098.wav|The police were the first to arrive on the spot, the Guards having entered the street at the wrong end.
+LJSpeech-1.1/wavs/LJ023-0102.wav|subsistence, and health of large numbers in the community, then
+LJSpeech-1.1/wavs/LJ023-0110.wav|a super-legislature, as one of the justices has called it
+LJSpeech-1.1/wavs/LJ016-0432.wav|The only subject another showed any interest in was the theatres and the new pieces that were being produced. A third, Christian Satler,
+LJSpeech-1.1/wavs/LJ010-0057.wav|are by this time actually, and by more legitimate efforts, engrafted upon our Constitution.
+LJSpeech-1.1/wavs/LJ029-0078.wav|The second, the Women's Building at the State Fair Grounds,
+LJSpeech-1.1/wavs/LJ033-0070.wav|And she indicated to me that she had peered into this roll and saw a portion of what she took to be a gun she knew her husband to have, a rifle.
+LJSpeech-1.1/wavs/LJ012-0014.wav|Edwards was sentenced to two years' imprisonment in Newgate,
+LJSpeech-1.1/wavs/LJ001-0114.wav|an ugly pear-shaped swelling defacing the form of the letter:
+LJSpeech-1.1/wavs/LJ038-0048.wav|Testimony of a firearms expert before the Commission established that the hammer of the revolver never touched the shell in the chamber.
+LJSpeech-1.1/wavs/LJ028-0519.wav|or that the Babylonian soldier stood confidently upon their summit, and jeering at the Persian army encamped below, shouted:
+LJSpeech-1.1/wavs/LJ028-0487.wav|Fortunately its walls have suffered less from the hands of the brick hunters, and the German excavators have been able to reconstruct their plan.
+LJSpeech-1.1/wavs/LJ050-0079.wav|guidelines for an experimental program to develop more detailed criteria.
+LJSpeech-1.1/wavs/LJ050-0116.wav|which carry the major responsibility for supplying information about potential threats,
+LJSpeech-1.1/wavs/LJ050-0147.wav|Unless the Secret Service is able to deal rapidly and accurately with a growing body of data,
+LJSpeech-1.1/wavs/LJ048-0125.wav|the precautions taken for the President's trip were the usual safeguards employed on trips of this kind in the United States during the previous year, end quote.
+LJSpeech-1.1/wavs/LJ031-0005.wav|approximately four miles from the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ009-0269.wav|his chest heaved, and it was evident that his struggle was a fearful one.
+LJSpeech-1.1/wavs/LJ004-0043.wav|A king's son, the Duke of Gloucester, was the patron; among the vice-presidents were many great peers of the realm
+LJSpeech-1.1/wavs/LJ017-0006.wav|The crime of poisoning has always been viewed with peculiar loathing and terror in this country.
+LJSpeech-1.1/wavs/LJ024-0086.wav|The Court itself can best undo what the Court has done.
+LJSpeech-1.1/wavs/LJ024-0056.wav|that we encourage the retirement of elderly judges by offering them a life pension at full salary.
+LJSpeech-1.1/wavs/LJ018-0371.wav|A case reproducing many of the features of the 'Flowery Land' occurred twelve years later, when the crew of the 'Lennie'
+LJSpeech-1.1/wavs/LJ049-0034.wav|could have reached the President in time to protect him from the second and fatal shot to hit the President.
+LJSpeech-1.1/wavs/LJ004-0075.wav|"Who ever heard of a criminal being sentenced to catch the rheumatism or the typhus fever?"
+LJSpeech-1.1/wavs/LJ032-0130.wav|Latona then processed the complete weapon but developed no identifiable prints.
+LJSpeech-1.1/wavs/LJ035-0113.wav|probably because Jack Dougherty took it to the first floor while Baker and Truly were running up the stairs or in the lunchroom with Oswald.
+LJSpeech-1.1/wavs/LJ030-0116.wav|looking out toward the crowd, and Special Agent Kellerman assumed his position next to the car.
+LJSpeech-1.1/wavs/LJ027-0015.wav|which in some fashion presides over all the movements of the protoplasm and is the physical basis of the phenomena of heredity.
+LJSpeech-1.1/wavs/LJ018-0090.wav|This conspiracy was cleverly planned, but had scarcely so many ramifications as that of Saward. Its originators were a couple of men,
+LJSpeech-1.1/wavs/LJ043-0064.wav|which were largely of his own making.
+LJSpeech-1.1/wavs/LJ029-0129.wav|On this last portion of the journey, only five minutes from the Trade Mart,
+LJSpeech-1.1/wavs/LJ045-0029.wav|The sole exception was the weekend of November sixteen to seventeen, nineteen sixty-three, the weekend before the assassination,
+LJSpeech-1.1/wavs/LJ042-0042.wav|Asking his brother why he supported the American Government and what ideals he put forward, Oswald wrote, quote,
+LJSpeech-1.1/wavs/LJ019-0070.wav|The other side retorted that the system of associated labor was most costly, so many officers being required to maintain the discipline of silence;
+LJSpeech-1.1/wavs/LJ024-0115.wav|To them I say: we cannot rely on an amendment as the immediate or only answer to our present difficulties.
+LJSpeech-1.1/wavs/LJ042-0225.wav|as a mark of discuss and protest against American political policies in foreign countries, my personal sign of discontent
+LJSpeech-1.1/wavs/LJ012-0289.wav|Greenacre, when tried at the Old Bailey, admitted that he had been guilty of manslaughter.
+LJSpeech-1.1/wavs/LJ021-0049.wav|and the curtailment of rank stock speculation through the Securities Exchange Act.
+LJSpeech-1.1/wavs/LJ046-0236.wav|none of the cases in the PRS general files was available for systematic review on a geographic basis when the President planned a particular trip.
+LJSpeech-1.1/wavs/LJ036-0059.wav|The Marsalis bus which Oswald boarded traveled a route west on Elm,
+LJSpeech-1.1/wavs/LJ039-0241.wav|owned and possessed the rifle used to kill President Kennedy and wound Governor Connally,
+LJSpeech-1.1/wavs/LJ002-0053.wav|It had been for some time devoted principally to felons of the worst types,
+LJSpeech-1.1/wavs/LJ039-0102.wav|After familiarization with live ammunition in the twenty-two rifle and the twenty-two pistol,
+LJSpeech-1.1/wavs/LJ019-0227.wav|This block contained one hundred and thirty cells, embracing every modern improvement;
+LJSpeech-1.1/wavs/LJ003-0102.wav|Mr. Buxton, in the work already quoted, instances another grievous case of the horrors of indiscriminate association in Newgate.
+LJSpeech-1.1/wavs/LJ046-0005.wav|Abraham Lincoln, James A. Garfield, William McKinley, and John F. Kennedy.
+LJSpeech-1.1/wavs/LJ029-0017.wav|Moreover, he looked forward to the public appearances which he personally enjoyed. The basic decision on the November trip to Texas was made at a meeting of President Kennedy,
+LJSpeech-1.1/wavs/LJ039-0009.wav|When Marina Oswald testified before the Commission on February three to six, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ032-0077.wav|Among other identification cards in Oswald's wallet at the time of his arrest were a Selective Service notice of classification,
+LJSpeech-1.1/wavs/LJ013-0200.wav|The jury without hesitation found Courvoisier guilty, and he was sentenced to death.
+LJSpeech-1.1/wavs/LJ028-0264.wav|The women chosen were kept to make bread for the men; while the others were strangled that they might not consume the stores.
+LJSpeech-1.1/wavs/LJ027-0175.wav|Now, this is undoubtedly the order of succession of forms in geological times -- i.e., in the phylogenic series.
+LJSpeech-1.1/wavs/LJ037-0098.wav|that he saw Oswald on television the night of the murder and thought that Oswald was the man he had seen running away from the shooting.
+LJSpeech-1.1/wavs/LJ049-0098.wav|In nineteen oh two bills passed both Houses of Congress but failed of enactment when the Senate refused to accept the conference report.
+LJSpeech-1.1/wavs/LJ019-0358.wav|with those in which there was no separation, no proper enforcement of hard labor, no chapel, infirmary, and so forth.
+LJSpeech-1.1/wavs/LJ010-0154.wav|when John Collins, "a person in the garb of a sailor, of wretched appearance, and having a wooden leg,"
+LJSpeech-1.1/wavs/LJ010-0014.wav|to cover enormous defalcations; the fabrication of deeds, wills, and false securities
+LJSpeech-1.1/wavs/LJ004-0055.wav|he personally visited several English jails, and pointed his observations by drawing forcible contrasts between the good and bad.
+LJSpeech-1.1/wavs/LJ045-0077.wav|because of insufficient postage, which apparently resulted from an increase in postal rates of which his wife had been unaware.
+LJSpeech-1.1/wavs/LJ001-0027.wav|especially as regards the lower-case letters; and type very similar was used during the next fifteen or twenty years not only by Schoeffer,
+LJSpeech-1.1/wavs/LJ039-0060.wav|The Nature of the Shots
+LJSpeech-1.1/wavs/LJ007-0226.wav|or the Inspectors of Prisons, and that he be required wholly to confine himself to the performance of his duty as prescribed by Act of Parliament.
+LJSpeech-1.1/wavs/LJ033-0099.wav|Frazier walked behind and as they crossed the railroad tracks he watched the switching of the cars.
+LJSpeech-1.1/wavs/LJ012-0124.wav|One was occupied by the Portuguese ambassador, who lost a quantity of jewelery from an escritoire, and his neighbors lost plate and cash.
+LJSpeech-1.1/wavs/LJ019-0286.wav|The minor borough prisons were the worst blot on the still dark and imperfect system.
+LJSpeech-1.1/wavs/LJ015-0099.wav|"Let us go to Kilburn for them together," said Mr. Fasson, growing suspicious.
+LJSpeech-1.1/wavs/LJ030-0020.wav|if anybody really wanted to shoot the President of the United States, it was not a very difficult job
+LJSpeech-1.1/wavs/LJ044-0200.wav|his last gambit to extricate himself from the mediocrity and defeat which plagued him throughout most of his life.
+LJSpeech-1.1/wavs/LJ004-0004.wav|It began to be understood that prison reform could only be compassed by continuous and combined effort.
+LJSpeech-1.1/wavs/LJ047-0066.wav|a Communist Party publication.
+LJSpeech-1.1/wavs/LJ003-0049.wav|The inmates of this part, as distinguished from the debtors, were comprised in four classes:
+LJSpeech-1.1/wavs/LJ040-0173.wav|He always felt like a burden that she simply just had to tolerate, end quote.
+LJSpeech-1.1/wavs/LJ006-0067.wav|"Here," say the inspectors, "are herded together the very worst class of prisoners; certainly a more wretched combination of human beings can hardly be imagined.
+LJSpeech-1.1/wavs/LJ048-0195.wav|All received their instructions early in the morning from Capt. P. W. Lawrence of the traffic division.
+LJSpeech-1.1/wavs/LJ028-0068.wav|They tell how he forced the exiles to carry heavy bags of sand across the desert to increase their burdens;
+LJSpeech-1.1/wavs/LJ020-0037.wav|The oven must be steady, but not so hot as for white bread, nor will the Graham bread be done quite so soon as that made of bolted flour.
+LJSpeech-1.1/wavs/LJ014-0188.wav|Pushing the shop-door open, he saw Marley finishing his murderous assault.
+LJSpeech-1.1/wavs/LJ017-0174.wav|She was tried in April eighteen sixty-two on suspicion of having attempted to poison a neighbor with oil of vitriol.
+LJSpeech-1.1/wavs/LJ042-0145.wav|In general, our family life began to deteriorate after we arrived in America.
+LJSpeech-1.1/wavs/LJ035-0067.wav|from the sixth floor by the time Baker and Truly arrived, Commission counsel asked Baker and Truly to repeat their movements from the time of the shot
+LJSpeech-1.1/wavs/LJ036-0179.wav|He signed his name as O. H. Lee on the roominghouse register.
+LJSpeech-1.1/wavs/LJ017-0187.wav|Her husband, who came up to town, would not allow a post-mortem, and again Mrs. Wilson escaped.
+LJSpeech-1.1/wavs/LJ041-0171.wav|And I remember his voice cracked as he said this. He was definitely disturbed at what I had said and I didn't really think I had said that much.
+LJSpeech-1.1/wavs/LJ019-0392.wav|in the administration of prisons.
+LJSpeech-1.1/wavs/LJ033-0200.wav|He concluded, quote,
+LJSpeech-1.1/wavs/LJ014-0172.wav|Robert Marley at the time of his arrest called himself a surgical instrument maker.
+LJSpeech-1.1/wavs/LJ006-0016.wav|These considerations no doubt had weight
+LJSpeech-1.1/wavs/LJ020-0091.wav|Yet our bread and rolls must be looked after at the proper time.
+LJSpeech-1.1/wavs/LJ015-0130.wav|fifteen hundred pounds, which larger amount was duly carried to his credit on the register, and entered upon the certificates of transfer.
+LJSpeech-1.1/wavs/LJ019-0058.wav|Prison chaplains of experience and high repute, such as Messrs. Field, Clay, Kingsmill, Burt, and Osborne, also advocated it.
+LJSpeech-1.1/wavs/LJ008-0257.wav|In thus describing the saturnalia before the gallows I have been drawn on somewhat beyond the period with which I am at present dealing.
+LJSpeech-1.1/wavs/LJ035-0026.wav|Baker and building superintendent Roy Truly went through a second set of doors and stopped at a swinging door where Baker bumped into Truly's back.
+LJSpeech-1.1/wavs/LJ005-0109.wav|or contriving some means of escape by climbing the fluted columns which supported the Gothic arches of the aisles,
+LJSpeech-1.1/wavs/LJ021-0039.wav|I believe it will be.
+LJSpeech-1.1/wavs/LJ001-0160.wav|by that made in America, which is the worst conceivable.
+LJSpeech-1.1/wavs/LJ019-0380.wav|In one part the most praiseworthy activity prevailed,
+LJSpeech-1.1/wavs/LJ031-0037.wav|Treatment of President Kennedy
+LJSpeech-1.1/wavs/LJ008-0105.wav|Mr. Smith's account of the condemned convict, whose cell he was permitted to enter, may be inserted here.
+LJSpeech-1.1/wavs/LJ037-0096.wav|Several days later he reported what he had seen and was questioned by FBI agents.
+LJSpeech-1.1/wavs/LJ008-0014.wav|John Hogan, the murderer of a Mr. Odell, an attorney who resided in Charlotte Street, Rathbone Place,
+LJSpeech-1.1/wavs/LJ041-0133.wav|which finally declined to modify the discharge and so advised him in a letter dated July nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ049-0184.wav|to conduct the wider investigative work that is required today for the security of the President.
+LJSpeech-1.1/wavs/LJ003-0321.wav|No wine or beer should be in future admitted into or sold in the jail,
+LJSpeech-1.1/wavs/LJ047-0120.wav|was considered sufficiently unusual to necessitate another interview. Alan H. Belmont, Assistant to the Director of the FBI,
+LJSpeech-1.1/wavs/LJ017-0025.wav|the justice of the sentence was doubted at the time.
+LJSpeech-1.1/wavs/LJ012-0096.wav|At her death the diamonds were divided between her four daughters, but only half had been claimed,
+LJSpeech-1.1/wavs/LJ033-0134.wav|Frazier could easily have been mistaken when he stated that Oswald held the bottom of the bag cupped in his hand with the upper end tucked into his armpit.
+LJSpeech-1.1/wavs/LJ018-0193.wav|With the police officers it had passed muster as a living member of the party.
+LJSpeech-1.1/wavs/LJ016-0176.wav|After Calcraft's resignation no successor was really appointed.
+LJSpeech-1.1/wavs/LJ017-0172.wav|but he was subsequently again tried for bigamy, and sentenced to twelve months' imprisonment.
+LJSpeech-1.1/wavs/LJ017-0051.wav|covered rather scantily with light sandy hair.
+LJSpeech-1.1/wavs/LJ022-0030.wav|and so few people who know anything about what the people of the United States are thinking about.
+LJSpeech-1.1/wavs/LJ001-0022.wav|of the more formal ecclesiastical writing which obtained at that time; this has since been called "missal type,"
+LJSpeech-1.1/wavs/LJ045-0102.wav|things apparently went quite smoothly from the time Oswald returned from Mexico until the weekend of November sixteen to seventeen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ008-0137.wav|He bowed his head under extreme pressure of ignominy,
+LJSpeech-1.1/wavs/LJ027-0155.wav|Now I do not deny that this view is a possible view; but I do deny that it is a probable one.
+LJSpeech-1.1/wavs/LJ044-0025.wav|The imaginary president of the nonexistent chapter was named A. J. Hidell, the name that Oswald used when he purchased the assassination weapon.
+LJSpeech-1.1/wavs/LJ041-0191.wav|Thornley thought that Oswald not only wanted a place in history but also wanted to live comfortably in the present.
+LJSpeech-1.1/wavs/LJ046-0091.wav|Such risks can be lessened when the President recognizes the security problem,
+LJSpeech-1.1/wavs/LJ003-0323.wav|Drunkenness, if it ever occurred, should be visited with severe punishment;
+LJSpeech-1.1/wavs/LJ004-0072.wav|it was for those charged with the administration of the law to guard carefully against any aggravation of that punishment,
+LJSpeech-1.1/wavs/LJ043-0178.wav|as, in fact, one of them did appear after the assassination.
+LJSpeech-1.1/wavs/LJ043-0092.wav|which adhered with sufficient precision to the job specifications and as a result too much of his work had to be redone.
+LJSpeech-1.1/wavs/LJ014-0229.wav|Mobbs systematically ill-used his wife for a long space of time, and at last cut her throat.
+LJSpeech-1.1/wavs/LJ038-0168.wav|There were no eyewitnesses, although a fourteen-year-old boy in a neighboring house claimed that immediately after the shooting
+LJSpeech-1.1/wavs/LJ043-0041.wav|Consistent with this attitude
+LJSpeech-1.1/wavs/LJ010-0093.wav|and those who watched his house were further encouraged in their mistake by the arrival of many carriages,
+LJSpeech-1.1/wavs/LJ043-0083.wav|Employment Commission records concerning Oswald stated, quote, Well-groomed and spoken,
+LJSpeech-1.1/wavs/LJ015-0086.wav|He kept open house at Kilburn Priory;
+LJSpeech-1.1/wavs/LJ016-0428.wav|Some condemned convicts converse but little with the warders who have them unceasingly in charge.
+LJSpeech-1.1/wavs/LJ028-0088.wav|Religion and cruelty frequently go hand in hand, and Nebuchadnezzar was exceedingly religious.
+LJSpeech-1.1/wavs/LJ037-0151.wav|Cunningham declared that this weapon fired the four cartridges to the exclusion of all other weapons.
+LJSpeech-1.1/wavs/LJ023-0023.wav|and recovery is speeding up to a point where the dangers of nineteen twenty-nine are again becoming possible,
+LJSpeech-1.1/wavs/LJ043-0105.wav|that while he did not fire Oswald because of the newspaper incident or even weigh it heavily in his decision, quote,
+LJSpeech-1.1/wavs/LJ026-0057.wav|They must first undergo certain preparatory chemical changes known collectively as digestion
+LJSpeech-1.1/wavs/LJ030-0161.wav|This represents a speed of eleven point two miles per hour.
+LJSpeech-1.1/wavs/LJ035-0206.wav|An eyewitness to the shooting immediately provided a description of the man in the window which was similar to Oswald's actual appearance.
+LJSpeech-1.1/wavs/LJ013-0223.wav|Courvoisier wished to commit suicide in Newgate, but was prevented by the vigilant supervision to which he was subjected while in jail.
+LJSpeech-1.1/wavs/LJ034-0009.wav|Palmprints and Fingerprints on Cartons and Paper Bag
+LJSpeech-1.1/wavs/LJ040-0237.wav|authorities were finally forced to consider placement in a home for boys.
+LJSpeech-1.1/wavs/LJ031-0118.wav|The wound on the back of the wrist was left partially open for draining, and the wound on the palm side was enlarged, cleansed, and closed.
+LJSpeech-1.1/wavs/LJ025-0063.wav|and with all the appearances of spontaneity as locomotive bodies,
+LJSpeech-1.1/wavs/LJ005-0181.wav|The right and privilege of the borough to maintain its own place of confinement was so "ancient and indisputable,"
+LJSpeech-1.1/wavs/LJ003-0344.wav|all chances of classification and separation vanished, and the greatest evils remained untouched.
+LJSpeech-1.1/wavs/LJ009-0275.wav|who bad been further upset by a letter threatening to shoot him when he appeared to perform his task.
+LJSpeech-1.1/wavs/LJ033-0025.wav|The two men left work at four:forty p.m. and drove to Irving.
+LJSpeech-1.1/wavs/LJ008-0235.wav|Every link in that great human chain is shaken, along the whole lengthened line has the motion jarred, and each in turn sees,
+LJSpeech-1.1/wavs/LJ003-0057.wav|A further sub-classification was attempted by separating at night those charged with misdemeanors from those charged with felony,
+LJSpeech-1.1/wavs/LJ014-0168.wav|denoting that they came from a concourse of boys and girls already assembled in the best places, made my blood run cold.
+LJSpeech-1.1/wavs/LJ040-0102.wav|return to it for lunch and then again at night, his mother having trained him to do that rather than to play with other children.
+LJSpeech-1.1/wavs/LJ029-0089.wav|Ultimately more than two hundred law enforcement officers, mainly Dallas police but including eight Secret Service agents,
+LJSpeech-1.1/wavs/LJ038-0206.wav|When he failed to return by ten or ten:thirty p.m., Marina Oswald went to his room and discovered the note. She testified: quote,
+LJSpeech-1.1/wavs/LJ031-0082.wav|From a medical viewpoint, President Kennedy was alive when he arrived at Parkland Hospital;
+LJSpeech-1.1/wavs/LJ033-0026.wav|There was little conversation between them on the way home.
+LJSpeech-1.1/wavs/LJ032-0059.wav|Experts on handwriting identification from the Treasury Department and the FBI
+LJSpeech-1.1/wavs/LJ018-0373.wav|The mutineers were of the same stamp as the crew of the 'Flowery Land'
+LJSpeech-1.1/wavs/LJ044-0115.wav|he felt that this was a great man that he had received the letter from, end quote.
+LJSpeech-1.1/wavs/LJ028-0265.wav|When tidings reached Darius of what had happened,
+LJSpeech-1.1/wavs/LJ006-0174.wav|The aldermen never called upon him to report, and left him nearly unsupervised and uncontrolled.
+LJSpeech-1.1/wavs/LJ029-0185.wav|by Vice President Johnson during the nineteen sixty campaign.
+LJSpeech-1.1/wavs/LJ028-0272.wav|All stratagems and all arts had been used, and yet the King could not prevail
+LJSpeech-1.1/wavs/LJ028-0096.wav|who reverences their lordship; the untiring governor, who ponders daily concerning the maintenance of Esagil and Ezida,
+LJSpeech-1.1/wavs/LJ033-0003.wav|The rifle in the building.
+LJSpeech-1.1/wavs/LJ014-0193.wav|Escorted by his two captors, Marley was taken back into Parliament Street to the jeweler's shop.
+LJSpeech-1.1/wavs/LJ016-0019.wav|and climbed up the pipe of a cistern in the corner of the press yard; some thought with the idea of drowning himself.
+LJSpeech-1.1/wavs/LJ044-0170.wav|He approved arrangements for his family to return to Irving, Texas, to live with Mrs. Ruth Paine.
+LJSpeech-1.1/wavs/LJ017-0048.wav|People came to stare at the supposed cold-blooded prisoner;
+LJSpeech-1.1/wavs/LJ008-0102.wav|A little further off, says Mr. Smith, was "a lath of a fellow past three-score years and ten,
+LJSpeech-1.1/wavs/LJ030-0159.wav|Since the camera operates at a speed of eighteen point three frames per second,
+LJSpeech-1.1/wavs/LJ019-0075.wav|that a thief or other criminal might be quite differently treated according as he was incarcerated on one side or another of a border line.
+LJSpeech-1.1/wavs/LJ001-0067.wav|In the Low Countries and Cologne, which were very fertile of printed books, Gothic was the favorite.
+LJSpeech-1.1/wavs/LJ014-0292.wav|His health improved, but was still indifferent when he was brought up for sentence, and he was an occupant of the Newgate infirmary.
+LJSpeech-1.1/wavs/LJ032-0157.wav|was a tuft of several cotton fibers of dark blue, gray-black, and orange-yellow shades.
+LJSpeech-1.1/wavs/LJ024-0006.wav|a new member shall be appointed by the President then in office,
+LJSpeech-1.1/wavs/LJ040-0162.wav|There, he suggested, Lee should be treated by a male psychiatrist who could substitute for the lack of a father figure.
+LJSpeech-1.1/wavs/LJ026-0149.wav|excretions which are lost by osmosis through the roots and the accumulated but useless mineral substances which are eliminated by leaf fall.
+LJSpeech-1.1/wavs/LJ048-0030.wav|the officials of the FBI believed that there was no data in its files which gave warning that Oswald was a source of danger to President Kennedy.
+LJSpeech-1.1/wavs/LJ034-0132.wav|From the street, this creates the impression that the person is standing.
+LJSpeech-1.1/wavs/LJ041-0177.wav|and expressed opposition to the Batista regime and sympathy for Castro, an attitude which, Donovan said, was, quote,
+LJSpeech-1.1/wavs/LJ003-0146.wav|without them the keeper declared that he could not pay the salaries of turnkeys and servants, nor keep the prison going at all.
+LJSpeech-1.1/wavs/LJ015-0210.wav|The gold, packed in an iron-bound box, was securely lodged in safes locked with patent Chubbs.
+LJSpeech-1.1/wavs/LJ018-0042.wav|it was the strongest circumstantial evidence which had ever been brought forward in a murder case.
+LJSpeech-1.1/wavs/LJ003-0203.wav|or sought relief in the constant application of intoxicating stimulants.
+LJSpeech-1.1/wavs/LJ036-0089.wav|Fritz did not bring him into his office to identify Oswald but turned him over to Lieutenant Baker for questioning.
+LJSpeech-1.1/wavs/LJ003-0091.wav|Constantly associated with these convicted felons were numbers of juveniles, infants of tender years.
+LJSpeech-1.1/wavs/LJ019-0222.wav|This principle became more and more generally the rule, although it has never been punctiliously observed. Now and again
+LJSpeech-1.1/wavs/LJ034-0206.wav|confirmed that Euins could neither describe the man in the window nor indicate his race.
+LJSpeech-1.1/wavs/LJ015-0142.wav|He soon proved his ability, and by unremitting attention mastered the whole work of the office.
+LJSpeech-1.1/wavs/LJ032-0116.wav|it was examined by Lt. J. C. Day of the identification bureau of the Dallas police.
+LJSpeech-1.1/wavs/LJ040-0157.wav|Lee has to be diagnosed as, quote, personality pattern disturbance with schizoid features and passive-aggressive tendencies, end quote.
+LJSpeech-1.1/wavs/LJ016-0130.wav|The first intimation of his escape was afforded by the police, who informed the prison authorities next day that a rope was hanging down from the cook-house roof.
+LJSpeech-1.1/wavs/LJ011-0109.wav|my heart has undergone that horrible penalty.
+LJSpeech-1.1/wavs/LJ036-0175.wav|Arrival and departure from roominghouse.
+LJSpeech-1.1/wavs/LJ028-0277.wav|One of his sumpter-mules gave birth to a foal.
+LJSpeech-1.1/wavs/LJ041-0192.wav|He testified that if Oswald could not have that, quote, degree of physical comfort that he expected or sought,
+LJSpeech-1.1/wavs/LJ047-0119.wav|Neither these discrepancies nor the fact that Oswald had initiated the FBI interview
+LJSpeech-1.1/wavs/LJ050-0164.wav|In evaluating data processing techniques of the Secret Service,
+LJSpeech-1.1/wavs/LJ026-0156.wav|In the plant also oxygen is absorbed through the epidermis and stomata from the air.
+LJSpeech-1.1/wavs/LJ027-0153.wav|in order to be so added to successive species, every individual deer belonging to later species was required to repeat in his own lifetime
+LJSpeech-1.1/wavs/LJ028-0498.wav|Herodotus says that it was three hundred and thirty-five feet;
+LJSpeech-1.1/wavs/LJ043-0113.wav|Refusing to admit that he could only get work as a greaser,
+LJSpeech-1.1/wavs/LJ035-0081.wav|The second test followed immediately after the first.
+LJSpeech-1.1/wavs/LJ002-0177.wav|affidavit of debt, drawing praecipe (one pound, five shillings), capias, fee to officer on arrest,
+LJSpeech-1.1/wavs/LJ044-0062.wav|thousands, end quote, of circulars, especially since he had claimed to have printed only two thousand
+LJSpeech-1.1/wavs/LJ023-0073.wav|The Court claimed the power to declare it unconstitutional and did so declare it.
+LJSpeech-1.1/wavs/LJ039-0179.wav|stated that there was a shorter interval between shots two and three than between shots one and two.
+LJSpeech-1.1/wavs/LJ022-0014.wav|the creation of a useful instrument for man ultimately comes.
+LJSpeech-1.1/wavs/LJ017-0126.wav|He relied on the absence of the strychnia.
+LJSpeech-1.1/wavs/LJ001-0084.wav|But for the beauty of the earlier work they might have seemed tolerable.
+LJSpeech-1.1/wavs/LJ029-0080.wav|and had certain unattractive features, including a low ceiling with exposed conduits and beams.
+LJSpeech-1.1/wavs/LJ004-0113.wav|It was further ordered that male prisoners should be kept perfectly distinct from the females.
+LJSpeech-1.1/wavs/LJ016-0299.wav|robbery and violence, loud laughing, oaths, fighting, obscene conduct, and still more filthy language reigned round the gallows far and near.
+LJSpeech-1.1/wavs/LJ039-0160.wav|In tests with the Mannlicher-Carano C twenty-seven sixty-six rifle, over one hundred rounds of this ammunition were fired by the FBI
+LJSpeech-1.1/wavs/LJ025-0173.wav|The watery solution, in which its roots are plunged, contains nitrogen but no carbon;
+LJSpeech-1.1/wavs/LJ004-0201.wav|At Ilchester the rule of employment had been carried further.
+LJSpeech-1.1/wavs/LJ010-0287.wav|But this was not sufficient to constitute lunacy, nor was his plea of "momentary uncontrollable impulse"
+LJSpeech-1.1/wavs/LJ003-0274.wav|"that there is no place in the metropolis where more crimes are projected or where stolen property is more secreted than in Newgate."
+LJSpeech-1.1/wavs/LJ048-0236.wav|There is no indication that any of the agents who visited the Cellar Coffee House had any intoxicating drink at that establishment.
+LJSpeech-1.1/wavs/LJ009-0065.wav|and the old clerk lifts up his cracked voice, saying, 'Let us sing to the praise and glory of God.'
+LJSpeech-1.1/wavs/LJ023-0077.wav|by which any law is passed,
+LJSpeech-1.1/wavs/LJ016-0296.wav|The actual execution made some impression.
+LJSpeech-1.1/wavs/LJ049-0206.wav|and while it has arrived at certain conclusions in respect thereto, it seems clear
+LJSpeech-1.1/wavs/LJ023-0074.wav|But a little later the Court itself admitted
+LJSpeech-1.1/wavs/LJ030-0177.wav|Mrs. Connally, too, heard a frightening noise from her right.
+LJSpeech-1.1/wavs/LJ017-0111.wav|that his friend had died greatly embarrassed, with bills to the amount of four thousand pounds out in his name.
+LJSpeech-1.1/wavs/LJ016-0356.wav|broke down at the supreme moment before the horror of the stillness, the awful impressiveness of the scene in which he was the principal actor.
+LJSpeech-1.1/wavs/LJ006-0233.wav|The prisoners in this way administered serious punishment on one another. Black eyes and broken noses were always to be seen.
+LJSpeech-1.1/wavs/LJ047-0071.wav|was advised that Oswald was in contact with the Fair Play for Cuba Committee in New York, and that he had written to the committee
+LJSpeech-1.1/wavs/LJ017-0236.wav|Leon, or Lyons, who spoke English,
+LJSpeech-1.1/wavs/LJ012-0191.wav|of atrocious murders perpetrated in Edinburgh by the miscreants Burke and Hare,
+LJSpeech-1.1/wavs/LJ018-0208.wav|were a low lot, the lowest among criminals except, perhaps, the 'smashers,' or those who passed the counterfeit money.
+LJSpeech-1.1/wavs/LJ044-0037.wav|Oswald wrote that an office which he had previously claimed to have rented for FPCC activities had been, quote,
+LJSpeech-1.1/wavs/LJ001-0037.wav|and the next year Gunther Zeiner at Augsburg followed suit;
+LJSpeech-1.1/wavs/LJ010-0239.wav|Pearson weakly allowed Francis to go off without securing his apprehension, but later he gave full information.
+LJSpeech-1.1/wavs/LJ010-0260.wav|in the Mall, about half-way between Buckingham and St. James's Palaces.
+LJSpeech-1.1/wavs/LJ032-0137.wav|The lifting had been so complete in this case that there was no trace of the print on the rifle itself when it was examined by Latona.
+LJSpeech-1.1/wavs/LJ022-0033.wav|"To get away from the trees", as they say, "and to look at the whole forest."
+LJSpeech-1.1/wavs/LJ035-0180.wav|that a witness (Amos Euins) had seen shots fired from a window of the building.
+LJSpeech-1.1/wavs/LJ043-0060.wav|from one of the elderly relatives with whom he spoke. Oswald's interest in such things presents a sharp contrast with his attitude
+LJSpeech-1.1/wavs/LJ023-0063.wav|But the framers went further.
+LJSpeech-1.1/wavs/LJ003-0231.wav|such as Messrs. Birch of Cornhill and Messrs. Leach and Dollimore of Ludgate Hill.
+LJSpeech-1.1/wavs/LJ023-0085.wav|to safeguard business against unfair competition,
+LJSpeech-1.1/wavs/LJ014-0198.wav|Marley at his trial was undefended, and the sheriffs offered him counsel; but he declined. The witnesses against him all spoke the truth, he said;
+LJSpeech-1.1/wavs/LJ035-0192.wav|Although Oswald probably left the building at about twelve:thirty-three p.m., his absence was not noticed until at least one-half hour later.
+LJSpeech-1.1/wavs/LJ031-0231.wav|This was finished at approximately four a.m.
+LJSpeech-1.1/wavs/LJ041-0186.wav|as some kind of tribunal,
+LJSpeech-1.1/wavs/LJ013-0087.wav|Burgess and Elder proceeded in company to cash this, but as they wanted all gold,
+LJSpeech-1.1/wavs/LJ021-0044.wav|in the sale of unsound mortgages and in many other ways in which the public lost billions of dollars.
+LJSpeech-1.1/wavs/LJ032-0272.wav|(three) fibers found on the rifle most probably came from the shirt Oswald was wearing on the day of the assassination,
+LJSpeech-1.1/wavs/LJ039-0121.wav|and he probably didn't have as high a motivation because he was no longer in recruit training and under the care of the drill instructor.
+LJSpeech-1.1/wavs/LJ023-0133.wav|judges who will retain in the courts the judicial functions of a court,
+LJSpeech-1.1/wavs/LJ010-0189.wav|He asked more than once whether the Queen was hurt, and acknowledged that the pistols were loaded with ball.
+LJSpeech-1.1/wavs/LJ016-0316.wav|based on his experience of them in Western Australia. He not only thought them likely to be more deterrent,
+LJSpeech-1.1/wavs/LJ043-0027.wav|De Mohrenschildt said that the whole affair made him nervous since he was, quote, interfering in other people's affairs, after all, end quote.
+LJSpeech-1.1/wavs/LJ002-0215.wav|It was so used for the date of the abolition of the Star Chamber in the sixteenth Charles the first
+LJSpeech-1.1/wavs/LJ042-0195.wav|and yet this doctrine requires the utmost, utmost restraint, a state of being in itself majestic in power, end quote.
+LJSpeech-1.1/wavs/LJ016-0414.wav|Rumour even went so far as to assert that among the spectators were several women, disguised in male habiliments;
+LJSpeech-1.1/wavs/LJ050-0037.wav|Preventive Intelligence
+LJSpeech-1.1/wavs/LJ040-0056.wav|It will attempt to summarize the events of his early life, his experience in New York City and in the Marine Corps, and his interest in Marxism.
+LJSpeech-1.1/wavs/LJ031-0076.wav|This was verified by Admiral Burkley, the President's physician, who arrived at the hospital after emergency treatment was underway and concluded that, quote,
+LJSpeech-1.1/wavs/LJ004-0240.wav|He left Newgate utterly corrupted, and after lapsing into crime, soon returned with a very different character.
+LJSpeech-1.1/wavs/LJ046-0197.wav|to a particular part of the country. These were the files reviewed by PRS on November eight, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ004-0190.wav|There were eighty-four separate sleeping-cells, and unless the jail was overcrowded, every inmate passed the night alone,
+LJSpeech-1.1/wavs/LJ019-0155.wav|The enforcement of discipline depended upon the want of honor among thieves.
+LJSpeech-1.1/wavs/LJ008-0218.wav|They were mainly members of the criminal classes.
+LJSpeech-1.1/wavs/LJ008-0079.wav|She walked from the debtors' door to a stake fixed in the ground about half-way between the scaffold and Newgate Street.
+LJSpeech-1.1/wavs/LJ047-0230.wav|Hosty testified that he would not have cited him to the Secret Service as a potential threat to the President. Hosty interpreted his instructions as requiring, quote,
+LJSpeech-1.1/wavs/LJ038-0055.wav|No other theatre patron or officer has testified that Oswald was hit by a gun.
+LJSpeech-1.1/wavs/LJ001-0044.wav|John of Spires and his brother Vindelin, followed by Nicholas Jenson, began to print in that city,
+LJSpeech-1.1/wavs/LJ005-0061.wav|but it offered suggestions for future buildings, with numerous carefully-executed drawings and designs of the model it recommended for imitation.
+LJSpeech-1.1/wavs/LJ031-0059.wav|Dr. Carrico treated the President's known ad-renal insufficiency by administering hydrocortisone.
+LJSpeech-1.1/wavs/LJ032-0266.wav|Paul M. Stombaugh, of the FBI Laboratory,
+LJSpeech-1.1/wavs/LJ019-0225.wav|In eighteen fifty-seven
+LJSpeech-1.1/wavs/LJ031-0180.wav|At approximately two:fifteen p.m. the casket was loaded, with some difficulty because of the narrow airplane door,
+LJSpeech-1.1/wavs/LJ026-0143.wav|Probably foods containing carbon, hydrogen and oxygen are the sources of energy in the higher plants as in animals.
+LJSpeech-1.1/wavs/LJ027-0084.wav|Romanes's "Darwin and After Darwin", and Le Conte's "Evolution."
+LJSpeech-1.1/wavs/LJ014-0333.wav|Again, some of the duplicate and fictitious warrants were held by a firm which suspended payment, and there was no knowing into whose hands they might fall.
+LJSpeech-1.1/wavs/LJ045-0068.wav|The difficulties which Oswald's problems would have caused him in any relationship were probably not reduced by his wife's conduct.
+LJSpeech-1.1/wavs/LJ016-0350.wav|and who expiated his crime on the eighth September, eighteen sixty-eight.
+LJSpeech-1.1/wavs/LJ014-0156.wav|and through all ate heartily at every meal, slept soundly at nights, and talked with cheerfulness on almost any subject.
+LJSpeech-1.1/wavs/LJ018-0269.wav|from an assistant of Loudon and Ryder's, the jewelers in Bond Street. The trick was an old one.
+LJSpeech-1.1/wavs/LJ009-0058.wav|Great efforts have been made to save his life.
+LJSpeech-1.1/wavs/LJ008-0012.wav|partly perhaps because residents in and about the Old Bailey raised a loud protest against the constant erection of the scaffold in their neighborhood.
+LJSpeech-1.1/wavs/LJ013-0112.wav|Within a month or two the bank of Messrs. Rogers and Co., Clement's Lane, was broken into.
+LJSpeech-1.1/wavs/LJ008-0004.wav|The reasons for this change were fully set forth in a previous chapter.
+LJSpeech-1.1/wavs/LJ021-0210.wav|to greater security for the average man than he has ever known before in the history of America.
+LJSpeech-1.1/wavs/LJ009-0171.wav|Gradually the application of capital punishment became more and more restricted, and was soon the penalty for murder alone.
+LJSpeech-1.1/wavs/LJ017-0037.wav|under the authority of an Act passed on purpose, known as the Trial of offenses Act, and sometimes as Lord Campbell's Act.
+LJSpeech-1.1/wavs/LJ040-0105.wav|Despite their urgings, he refused to play with the other children his own age.
+LJSpeech-1.1/wavs/LJ019-0260.wav|At other prisons "shot-drill," the lifting and carrying of heavy round shot, was the favorite method of inflicting penal labor.
+LJSpeech-1.1/wavs/LJ011-0078.wav|Montgomery was an adept at forgery. He had gone wrong early. Although born of respectable parents, and gazetted to a commission in the army,
+LJSpeech-1.1/wavs/LJ009-0115.wav|He says, "On the day of execution there is no service in the chapel of Newgate.
+LJSpeech-1.1/wavs/LJ034-0216.wav|In the background of this picture were several employees watching the parade from the steps of the Depository Building.
+LJSpeech-1.1/wavs/LJ038-0275.wav|and concluded, quote, that there is a fair probability, end quote, that the bullet was fired from the rifle used in the assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ019-0106.wav|His plan was to devote the whole labor of prisoners sentenced to any term between three months and four years to agriculture.
+LJSpeech-1.1/wavs/LJ036-0079.wav|coming from the direction of the Depository Building and running down the hill north of Elm Street
+LJSpeech-1.1/wavs/LJ040-0206.wav|that Lee's withdrawal was a form of, quote, violent but silent protest against his neglect by her
+LJSpeech-1.1/wavs/LJ030-0183.wav|As he told the driver, quote, Let's get out of here; we are hit, end quote,
+LJSpeech-1.1/wavs/LJ006-0208.wav|There was also a good supply of Bibles and prayers,
+LJSpeech-1.1/wavs/LJ028-0187.wav|Cyrus decided that Babylon must be taken.
+LJSpeech-1.1/wavs/LJ002-0128.wav|He also makes the curious calculation that the costs of these actions if undefended
+LJSpeech-1.1/wavs/LJ016-0244.wav|who first took to the work from predilection, and the idea of being useful in his generation, as he himself assured the writer of these pages.
+LJSpeech-1.1/wavs/LJ006-0028.wav|Mr. Crawford was thoroughly versed in the still imperfectly understood science of prison management, and fully qualified for his new duties.
+LJSpeech-1.1/wavs/LJ020-0042.wav|and these also tend to nourish and strengthen the brain.
+LJSpeech-1.1/wavs/LJ007-0066.wav|on the other, the governor of the jail sneered at his zeal
+LJSpeech-1.1/wavs/LJ040-0228.wav|Despite his withdrawal, he gives the impression that he is not so difficult to reach as he appears and patient, prolonged effort
+LJSpeech-1.1/wavs/LJ016-0040.wav|His first task was to gain the top of the cistern; this he effected by keeping his back to one side of the angle,
+LJSpeech-1.1/wavs/LJ047-0137.wav|On October three, Hosty reopened the case in Dallas to assist the New Orleans office.
+LJSpeech-1.1/wavs/LJ004-0146.wav|there was no infirmary, no chapel, no work, no classification.
+LJSpeech-1.1/wavs/LJ039-0041.wav|I am getting a little confused with so many questions.
+LJSpeech-1.1/wavs/LJ044-0107.wav|and advised them of some of his activities on behalf of the organization.
+LJSpeech-1.1/wavs/LJ019-0011.wav|had appointed a committee of prison inspectors, presided over by the Under Secretary of State, to draw up rules and dietaries,
+LJSpeech-1.1/wavs/LJ027-0111.wav|usually, indeed, a considerable number.
+LJSpeech-1.1/wavs/LJ023-0132.wav|But, at the same time, we must have judges who will bring to the courts a present-day sense of the Constitution
+LJSpeech-1.1/wavs/LJ016-0324.wav|But it is curious to note that there were several dissentients among the commissioners to this paragraph of the report.
+LJSpeech-1.1/wavs/LJ031-0145.wav|On the recommendation of the Secret Service agents, Vice President Johnson decided to board the Presidential airplane, Air Force One,
+LJSpeech-1.1/wavs/LJ014-0075.wav|He was dressed with particular care, as he was to dine at the Mannings and meet friends, one a young lady.
+LJSpeech-1.1/wavs/LJ004-0086.wav|of "poor and needy prisoners committed to the common jail for felony and other misdemeanors, who many times perish before their trial;
+LJSpeech-1.1/wavs/LJ029-0190.wav|Mayor Earle Cabell called on the city to redeem itself during President Kennedy's visit.
+LJSpeech-1.1/wavs/LJ044-0161.wav|In his wife's words, quote,
+LJSpeech-1.1/wavs/LJ003-0181.wav|There was a master's side for females who could pay the usual fees, but they associated with the rest in the one narrow yard common to all.
+LJSpeech-1.1/wavs/LJ009-0039.wav|Those who sat in it were visible to the whole congregation, and still more to the ordinary,
+LJSpeech-1.1/wavs/LJ013-0245.wav|At the constable's cry of alarm Good rushed from the stable and locked the door behind him.
+LJSpeech-1.1/wavs/LJ038-0099.wav|From the outset, Oswald denied owning a rifle.
+LJSpeech-1.1/wavs/LJ036-0143.wav|He marked what, he thought was the intersection of Neches and Beckley on a map of Dallas with a large "X."
+LJSpeech-1.1/wavs/LJ031-0071.wav|described a large, gaping wound in the right rear part of the head, with substantial damage and exposure of brain tissue, and a considerable loss of blood.
+LJSpeech-1.1/wavs/LJ013-0061.wav|who was the possessor of two separate sums in consols, neglected through strange carelessness on her own part and that of her friends
+LJSpeech-1.1/wavs/LJ046-0192.wav|it regarded approximately one hundred of these four hundred cases as serious risks
+LJSpeech-1.1/wavs/LJ021-0090.wav|suited to the needs of the next phase, which is, in turn, a period of preparation for legislation which will determine its permanent form.
+LJSpeech-1.1/wavs/LJ014-0113.wav|advised the Edinburgh police of the whole affair, and within an hour an answer was telegraphed, stating that Mrs. Manning was in custody.
+LJSpeech-1.1/wavs/LJ038-0135.wav|The Curtain Rod Story
+LJSpeech-1.1/wavs/LJ038-0231.wav|In her testimony before the Commission in February nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ042-0089.wav|I leave Embassy, elated at this showdown, returning to my hotel I feel now my energies are not spent in vain.
+LJSpeech-1.1/wavs/LJ007-0180.wav|The publication of this report raised a storm in the city, and the corporation was roused to make an immediate protest.
+LJSpeech-1.1/wavs/LJ030-0205.wav|He estimated that the motorcade had slowed down to approximately nine or ten miles per hour
+LJSpeech-1.1/wavs/LJ018-0266.wav|was at length advanced to the comparative ease of a post much coveted by convicts, that of hospital nurse.
+LJSpeech-1.1/wavs/LJ018-0280.wav|The commercial experience of these clever rogues was cosmopolitan.
+LJSpeech-1.1/wavs/LJ006-0173.wav|He kept no daily journal of occurrences, and nothing, however important, was recorded at the time.
+LJSpeech-1.1/wavs/LJ040-0184.wav|He appears slightly withdrawn and in view of the lack of detail within the drawings this may assume a more significant characteristic.
+LJSpeech-1.1/wavs/LJ019-0173.wav|The Corporation seems to have introduced these salutary changes without hesitation.
+LJSpeech-1.1/wavs/LJ032-0048.wav|the scope was mounted on the rifle by a gunsmith employed by Klein's, and the rifle was shipped fully assembled in accordance with customary company procedures.
+LJSpeech-1.1/wavs/LJ019-0043.wav|Among the latter were notably the counties of Cheshire, Lincolnshire, Norfolk, Suffolk, Nottinghamshire, the East and North Ridings of Yorkshire.
+LJSpeech-1.1/wavs/LJ015-0279.wav|One stroke of luck which he turned to great account
+LJSpeech-1.1/wavs/LJ001-0124.wav|One of the differences between the fine type and the utilitarian must probably be put down to a misapprehension of a commercial necessity:
+LJSpeech-1.1/wavs/LJ028-0153.wav|The bitumen used in the work was brought to Babylon from Is, a small stream which flows into the Euphrates
+LJSpeech-1.1/wavs/LJ024-0062.wav|President Taft appointed five members and named a Chief Justice;
+LJSpeech-1.1/wavs/LJ034-0113.wav|The autopsy report indicated an estimated weight of one hundred fifty pounds.
+LJSpeech-1.1/wavs/LJ050-0154.wav|to plan and develop a workable and efficient automated file and retrieval system, end quote.
+LJSpeech-1.1/wavs/LJ045-0212.wav|this time concerning his use of an alias.
+LJSpeech-1.1/wavs/LJ038-0185.wav|This is the same street where the drugstore, in which you always waited is located.
+LJSpeech-1.1/wavs/LJ015-0207.wav|But the collusion and active assistance of employees of the railway carriers were indispensable, and together
+LJSpeech-1.1/wavs/LJ048-0060.wav|required further attention.
+LJSpeech-1.1/wavs/LJ040-0010.wav|The motives of any man, however, must be analyzed in terms of the character and state of mind of the particular individual involved.
+LJSpeech-1.1/wavs/LJ046-0128.wav|while the balance was furnished to PRS by other Federal agencies, with primary source being the FBI.
+LJSpeech-1.1/wavs/LJ037-0122.wav|The Dallas Police Department furnished the Commission with pictures of the men who appeared in the lineups with Oswald,
+LJSpeech-1.1/wavs/LJ009-0013.wav|and kept altogether separate from the other prisoners until the day of his death.
+LJSpeech-1.1/wavs/LJ019-0362.wav|Where the local authority had neglected to comply with the provisions of the eighteen sixty-five Act for four consecutive years,
+LJSpeech-1.1/wavs/LJ033-0171.wav|I found that the paper sack found on the sixth floor and the sample
+LJSpeech-1.1/wavs/LJ003-0200.wav|Visitors were permitted access to them without stint;
+LJSpeech-1.1/wavs/LJ005-0188.wav|that the jails attached to corporate jurisdictions continue to be the fruitful sources
+LJSpeech-1.1/wavs/LJ040-0151.wav|He was also described as having a, quote, Vivid fantasy life,
+LJSpeech-1.1/wavs/LJ005-0151.wav|in others women were very properly exempted from it, and also from all severe labor.
+LJSpeech-1.1/wavs/LJ042-0023.wav|and that he went there, quote, only to find freedom. I could never have been personally happy in the U.S., end quote.
+LJSpeech-1.1/wavs/LJ010-0068.wav|he became a militia officer, and married a woman with ten thousand pounds, in which, however, she had only a life interest.
+LJSpeech-1.1/wavs/LJ032-0208.wav|He found, quote, one notch in the stock at this point that appears very faintly in the photograph, end quote.
+LJSpeech-1.1/wavs/LJ011-0207.wav|whom I never saw until I was taken from Liverpool, and never want to see again.
+LJSpeech-1.1/wavs/LJ006-0045.wav|All these evils were set forth at length in the inspectors' first report.
+LJSpeech-1.1/wavs/LJ039-0126.wav|As compared to a civilian who had not received this intensive training, he would be considered as a good to excellent shot. End quote.
+LJSpeech-1.1/wavs/LJ041-0129.wav|he continued to be concerned about his undesirable discharge. It is clear that he thought he had been unjustly treated.
+LJSpeech-1.1/wavs/LJ043-0050.wav|As Marguerite Oswald testified, quote,
+LJSpeech-1.1/wavs/LJ039-0175.wav|For the first four attempts, the firers missed the second shot by several inches.
+LJSpeech-1.1/wavs/LJ032-0183.wav|The relative freshness of the fibers is strong evidence that they were caught on the rifle on the morning of the assassination or during the preceding evening.
+LJSpeech-1.1/wavs/LJ029-0040.wav|Lawson received a tentative schedule of the Texas trip on November eight from Roy H. Kellerman, assistant special agent in charge of the White House detail,
+LJSpeech-1.1/wavs/LJ001-0010.wav|Now, as all books not primarily intended as picture-books consist principally of types composed to form letterpress,
+LJSpeech-1.1/wavs/LJ012-0256.wav|and there was now evidence sufficient to establish conclusively that the woman thus collected piecemeal had been barbarously done to death.
+LJSpeech-1.1/wavs/LJ035-0049.wav|All I did was catch a glance at him, and evidently he was -- this door might have been, you know, closing and almost shut at that time, end quote.
+LJSpeech-1.1/wavs/LJ038-0220.wav|Oswald had paid the first month's rent in advance on March two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ001-0041.wav|It must be said that it is in no way like the transition type of Subiaco,
+LJSpeech-1.1/wavs/LJ050-0228.wav|The Commission urges that the Bureau of the Budget review these recommendations with the Secret Service and authorize a request for the necessary supplemental appropriation,
+LJSpeech-1.1/wavs/LJ020-0101.wav|From the beginning of your apprenticeship in housewifery, learn how to "dovetail" your duties neatly into one another.
+LJSpeech-1.1/wavs/LJ003-0207.wav|For while Roman Catholics and Dissenters were encouraged to see ministers of their own persuasion,
+LJSpeech-1.1/wavs/LJ010-0096.wav|Edgeware Road, completing their dispositions for assuming supreme power after the blow had been struck.
+LJSpeech-1.1/wavs/LJ050-0193.wav|Such instructions will not fit all circumstances, of course,
+LJSpeech-1.1/wavs/LJ014-0222.wav|To Cannon's infinite surprise, he was indicted for attempt to murder, and not for a simple assault, and found guilty.
+LJSpeech-1.1/wavs/LJ016-0146.wav|from the building he was on to a distant wall, with a drop of sixty feet between.
+LJSpeech-1.1/wavs/LJ017-0087.wav|antimony, which caused Cook's constant sickness.
+LJSpeech-1.1/wavs/LJ030-0049.wav|Following normal practice, the lead automobile stayed approximately four to five car lengths ahead of the President's limousine.
+LJSpeech-1.1/wavs/LJ019-0066.wav|with the means of earning an honest livelihood if so disposed.
+LJSpeech-1.1/wavs/LJ029-0007.wav|this chapter reviews the motorcade through Dallas, the fleeting moments of the assassination,
+LJSpeech-1.1/wavs/LJ001-0162.wav|even allowing the necessity for a very low price; but any improvement must be based on showing openly that the cheap article is cheap,
+LJSpeech-1.1/wavs/LJ012-0099.wav|The thieves it was supposed had secreted themselves in the warehouse during business hours, and waited till night to carry out their plans.
+LJSpeech-1.1/wavs/LJ011-0066.wav|The concourse in front of Newgate was enormous, but much sympathy was evinced for this unfortunate victim to human weakness and ruthless laws.
+LJSpeech-1.1/wavs/LJ026-0053.wav|or animal matter in the form of various organic compounds, of which the most important are proteids (protoplasm, albumen, etc.),
+LJSpeech-1.1/wavs/LJ039-0238.wav|that it was an easy shot to hit some part of the President's body, and that the range where the rifleman would be expected to hit
+LJSpeech-1.1/wavs/LJ026-0087.wav|In this way some of the radiant and kinetic energy of the sun comes to be stored up as potential energy in the starch.
+LJSpeech-1.1/wavs/LJ009-0066.wav|The morning hymn is sung first, as if to remind the condemned that next morning at eight a.m. they are to die.
+LJSpeech-1.1/wavs/LJ017-0046.wav|Great personages occupied seats upon the bench;
+LJSpeech-1.1/wavs/LJ026-0093.wav|The plant absorbs also a small amount of kinetic energy, independently of the sunlight, in the form of heat.
+LJSpeech-1.1/wavs/LJ002-0019.wav|who seldom let a session go by without visiting Newgate.
+LJSpeech-1.1/wavs/LJ031-0090.wav|A thorough inspection would have involved washing and cleansing the back, and this is not practical in treating an acutely injured patient.
+LJSpeech-1.1/wavs/LJ030-0216.wav|Between the time he originally seized the handhold and the time he mounted the car, Hill recalled:
+LJSpeech-1.1/wavs/LJ036-0207.wav|The suspect was described as a, quote,
+LJSpeech-1.1/wavs/LJ003-0346.wav|It pointed out that the Government was to blame for the overcrowding, and might diminish it if it chose.
+LJSpeech-1.1/wavs/LJ039-0053.wav|was of no probative value in the Commission's decision concerning the identity of the assassin of President Kennedy.
+LJSpeech-1.1/wavs/LJ018-0312.wav|A plot was soon discovered,
+LJSpeech-1.1/wavs/LJ022-0035.wav|is one which, in a very special manner, attaches to this office to which you have chosen me.
+LJSpeech-1.1/wavs/LJ048-0175.wav|Nor is it practical to prevent people from entering such buildings, or to limit access in every building to those employed or having business there.
+LJSpeech-1.1/wavs/LJ035-0036.wav|On the second-floor landing there is a small open area with a door at the east end.
+LJSpeech-1.1/wavs/LJ002-0304.wav|The sheriff demanded four shillings, six pence for his liberate, the jailer six shillings, ten pence more, and the turnkey two shillings;
+LJSpeech-1.1/wavs/LJ034-0083.wav|The significance of Givens' observation that Oswald was carrying his clipboard
+LJSpeech-1.1/wavs/LJ039-0005.wav|Richard M. Nixon Incident
+LJSpeech-1.1/wavs/LJ004-0233.wav|Under the new rule visitors were not allowed to pass into the interior of the prison, but were detained between the grating.
+LJSpeech-1.1/wavs/LJ022-0104.wav|and the experience and the competence necessary to carry on the two hundred and fifty or three hundred kinds of work that will be undertaken.
+LJSpeech-1.1/wavs/LJ028-0074.wav|But in Babylonia stone was difficult to obtain, and sculptures were very rare.
+LJSpeech-1.1/wavs/LJ035-0094.wav|the Commission concluded that Oswald could have fired the shots and still have been present in the second-floor lunchroom when seen by Baker and Truly.
+LJSpeech-1.1/wavs/LJ008-0177.wav|The catastrophe exceeded the worst anticipations. Nearly one hundred dead and dying lay about; and after all had been removed,
+LJSpeech-1.1/wavs/LJ033-0172.wav|had the same observable characteristics both under the microscope and all the visual tests that I could conduct.
+LJSpeech-1.1/wavs/LJ019-0352.wav|or hesitated to embark upon any considerable expenditure to alter or rebuild,
+LJSpeech-1.1/wavs/LJ048-0087.wav|The President's trip to Dallas called into play many standard operating procedures of the Secret Service in addition to its preventive intelligence operations.
+LJSpeech-1.1/wavs/LJ043-0143.wav|with his recently acquired rifle and pistol, a copy of the March twenty-four, nineteen sixty-three, issue of the Worker,
+LJSpeech-1.1/wavs/LJ026-0132.wav|after absorption into the cells the elements of the starch (or glucose) are, by the living protoplasm, in some unknown way
+LJSpeech-1.1/wavs/LJ047-0247.wav|Hosty has unequivocally denied, first by affidavit and then in his testimony before the Commission,
+LJSpeech-1.1/wavs/LJ011-0186.wav|by the blacksmith in the usual way.
+LJSpeech-1.1/wavs/LJ048-0027.wav|Nowhere during the course of this investigation or the information that came to us from other agencies was there any indication of a potential for violence on his part.
+LJSpeech-1.1/wavs/LJ012-0058.wav|When the journey was resumed, Mrs. Solomons accompanied her husband in the coach. Half-way to Newgate she was taken with a fit.
+LJSpeech-1.1/wavs/LJ001-0071.wav|Most of Caxton's own types are of an earlier character, though they also much resemble Flemish or Cologne letter.
+LJSpeech-1.1/wavs/LJ048-0142.wav|Despite this dependence on local authorities, which would be substantially the same on a visit by the President to any large city,
+LJSpeech-1.1/wavs/LJ014-0151.wav|Later her mood changed to abject despair.
+LJSpeech-1.1/wavs/LJ050-0107.wav|Liaison with other agencies regarding intelligence.
+LJSpeech-1.1/wavs/LJ043-0017.wav|Marina had a hundred dresses given to her, and he objected to that lavish help, because Marina was throwing it into his face.
+LJSpeech-1.1/wavs/LJ005-0166.wav|Mr. Peel, who as Home Secretary had charge of the bill,
+LJSpeech-1.1/wavs/LJ030-0212.wav|At about the time he reached the President's automobile,
+LJSpeech-1.1/wavs/LJ005-0297.wav|They therefore recommended that the prisoners should be removed
+LJSpeech-1.1/wavs/LJ005-0210.wav|At Walsall, in Staffordshire,
+LJSpeech-1.1/wavs/LJ050-0173.wav|that coordination might be achieved to a greater extent than seems now to be contemplated, without interference with the primary mission of each agency involved.
+LJSpeech-1.1/wavs/LJ018-0084.wav|While in Newgate awaiting trial he frequently tried to justify his murder by declaring that the police officer had broken faith with him.
+LJSpeech-1.1/wavs/LJ012-0245.wav|in the hope that the features might some day be recognized, were alike in both.
+LJSpeech-1.1/wavs/LJ028-0045.wav|When Esarhaddon died, one of his sons, Samas-sum-yukin, was made King of Babylon.
+LJSpeech-1.1/wavs/LJ038-0097.wav|the Commission gave little weight to his denials of guilt.
+LJSpeech-1.1/wavs/LJ042-0176.wav|One offers oppression, the other poverty. Both offer imperialistic injustice, tinted with two brands of slavery, end quote.
+LJSpeech-1.1/wavs/LJ042-0041.wav|unemployment, automation, and the use of military forces to suppress other populations.
+LJSpeech-1.1/wavs/LJ041-0187.wav|and he wanted to be on the winning side so that ten thousand years from-now people would look in the history books and say, "Well, this man was ahead of his time."
+LJSpeech-1.1/wavs/LJ030-0139.wav|toward the Triple Underpass, shots fired from a rifle mortally wounded President Kennedy and seriously injured Governor Connally.
+LJSpeech-1.1/wavs/LJ033-0175.wav|Mr. Cadigan concluded that the paper and tape from the bag were identical in all respects to the sample paper and tape
+LJSpeech-1.1/wavs/LJ002-0094.wav|with four casements and two fireplaces, being allotted for a female infirmary
+LJSpeech-1.1/wavs/LJ018-0189.wav|The officers rushed out to detain him. "What have you got here? We must search the cart," they cry.
+LJSpeech-1.1/wavs/LJ031-0050.wav|Observing the serious problems presented by the head wound and inadequate respiration, Dr. Carrico directed his attention to improving the President's breathing.
+LJSpeech-1.1/wavs/LJ042-0166.wav|The latter material expresses great hostility to both communism and capitalism. He wrote, that to a person knowing both of those systems, quote,
+LJSpeech-1.1/wavs/LJ044-0131.wav|Oswald had fallen on difficult times.
+LJSpeech-1.1/wavs/LJ002-0227.wav|absence or neglect of divine service, were present as in the King's Bench, but in an exaggerated form.
+LJSpeech-1.1/wavs/LJ032-0237.wav|it was established that the photographs must have been taken sometime after March twenty-seven.
+LJSpeech-1.1/wavs/LJ039-0082.wav|this is the ideal type of weapon for moving targets
+LJSpeech-1.1/wavs/LJ023-0003.wav|Last Thursday I described in detail certain economic problems which everyone admits now face the nation.
+LJSpeech-1.1/wavs/LJ004-0244.wav|A few fortunate criminals, many of them kept back from transportation on purpose, who were skilled in trades, were employed at them.
+LJSpeech-1.1/wavs/LJ010-0314.wav|In order to keep up the credit of our house, I have forged powers of attorney for the above sums and parties,
+LJSpeech-1.1/wavs/LJ048-0086.wav|Other Protective Measures and Aspects of Secret Service Performance
+LJSpeech-1.1/wavs/LJ038-0072.wav|who had driven from the theatre with Oswald.
+LJSpeech-1.1/wavs/LJ028-0371.wav|Then the world metropolis, stripped of most of its population, became a mere village.
+LJSpeech-1.1/wavs/LJ046-0224.wav|The State Department advised the Secret Service of all crank and threat letter mail
+LJSpeech-1.1/wavs/LJ014-0024.wav|after several remands; but the case contained elements of doubt, and the evidence at the trial was entirely circumstantial.
+LJSpeech-1.1/wavs/LJ022-0076.wav|and we have every reason to believe that it should be in full swing by autumn.
+LJSpeech-1.1/wavs/LJ017-0078.wav|Palmer tried to get a new insurance on the life of a hanger-on, one Bates, but no office would accept it, no doubt greatly to Bates's longevity.
+LJSpeech-1.1/wavs/LJ049-0025.wav|Access to passenger compartment of Presidential car.
+LJSpeech-1.1/wavs/LJ008-0298.wav|Sometimes the report was delayed.
+LJSpeech-1.1/wavs/LJ002-0208.wav|into holes marked with numbers.
+LJSpeech-1.1/wavs/LJ037-0175.wav|Therefore, one cartridge case of this type was not recovered.
+LJSpeech-1.1/wavs/LJ015-0243.wav|At Redhill Tester met the train and relieved the thieves of a portion of the stolen gold.
+LJSpeech-1.1/wavs/LJ010-0272.wav|This son, John William Bean, was fully identified by Dasset, and presently examined by the Privy Council.
+LJSpeech-1.1/wavs/LJ004-0111.wav|one. Prisoners convicted of felony. two. Prisoners committed on a charge or suspicion of felony.
+LJSpeech-1.1/wavs/LJ028-0110.wav|I completed Imgur-Bel and Nimitti-Bel, the great walls of Babylon, the mighty city, the city of his exalted power.
+LJSpeech-1.1/wavs/LJ017-0169.wav|The Home Secretary, in a letter to the Lord Chief Baron, stated that, quote, although the facts are full of suspicion against Smethurst,
+LJSpeech-1.1/wavs/LJ005-0102.wav|Upon these and the private visitations made by various members the Society obtained the facts,
+LJSpeech-1.1/wavs/LJ015-0186.wav|and received his sentence of transportation for life without much surprise.
+LJSpeech-1.1/wavs/LJ026-0058.wav|and only after the completion of this process can all the food be absorbed into the circulation.
+LJSpeech-1.1/wavs/LJ038-0051.wav|Two patrons of the theatre and John Brewer
+LJSpeech-1.1/wavs/LJ019-0189.wav|This plan was on the principle of cellular separation, a system, according to Lord John Russell, desirable in all prisons,
+LJSpeech-1.1/wavs/LJ030-0102.wav|was in a car following those, quote, containing the local and national representatives, end quote,
+LJSpeech-1.1/wavs/LJ049-0091.wav|Equally anomalous are statutory provisions which specifically authorize the Secret Service to protect the President,
+LJSpeech-1.1/wavs/LJ029-0055.wav|Lawson conferred with the local police and the local office of the Federal Bureau of Investigation about potential dangers to the President.
+LJSpeech-1.1/wavs/LJ019-0039.wav|At Pentonville the rate was higher, but there the expense had been increased by the site,
+LJSpeech-1.1/wavs/LJ038-0145.wav|In one interview, he told Fritz that the only sack he carried to work that day
+LJSpeech-1.1/wavs/LJ024-0104.wav|I am therefore, going to spend my time, my efforts and my money
+LJSpeech-1.1/wavs/LJ013-0076.wav|and held all the threads of these intricate and nefarious transactions.
+LJSpeech-1.1/wavs/LJ048-0178.wav|In accordance with its regular procedures, no survey or other check was made by the Secret Service, or by any other law enforcement agency at its request,
+LJSpeech-1.1/wavs/LJ014-0051.wav|There was an epidemic of murder in the United Kingdom about eighteen forty-eight to nine.
+LJSpeech-1.1/wavs/LJ046-0078.wav|The protection of the President must be thorough but inconspicuous to avoid even the suggestion of a garrison state.
+LJSpeech-1.1/wavs/LJ026-0110.wav|However, the result in distributed food is the same in the plant and in the animal.
+LJSpeech-1.1/wavs/LJ001-0083.wav|The seventeenth century founts were bad rather negatively than positively.
+LJSpeech-1.1/wavs/LJ008-0118.wav|"Thank you, sir," said the governor to the doctor, "it is of little moment."
+LJSpeech-1.1/wavs/LJ030-0071.wav|They provided some cover for the President, but their main purpose was to keep back the crowd.
+LJSpeech-1.1/wavs/LJ005-0010.wav|the germs, more or less developed, of contagious disease. "Caravans," the forerunners of the prison vans,
+LJSpeech-1.1/wavs/LJ031-0024.wav|Gene C. Akin;
+LJSpeech-1.1/wavs/LJ022-0078.wav|one. The projects should be useful.
+LJSpeech-1.1/wavs/LJ042-0165.wav|which appears to be more an expression of his own psychological condition than of a reasoned analysis.
+LJSpeech-1.1/wavs/LJ042-0048.wav|Happiness is taking part in the struggle, where there is no borderline between one's own personal world, and the world in general.
+LJSpeech-1.1/wavs/LJ042-0006.wav|he almost immediately left for the Soviet Union where he attempted to renounce his citizenship.
+LJSpeech-1.1/wavs/LJ005-0124.wav|Most of the local authorities embarked into considerable expenditure, determined to rebuild their jails de novo on the most approved pattern,
+LJSpeech-1.1/wavs/LJ030-0064.wav|President Kennedy rode on the right-hand side of the rear seat with Mrs. Kennedy on his left.
+LJSpeech-1.1/wavs/LJ033-0058.wav|On the day of the assassination, Marina Oswald was watching television when she learned of the shooting.
+LJSpeech-1.1/wavs/LJ031-0117.wav|assisted by Drs. William Osborne and John Parker.
+LJSpeech-1.1/wavs/LJ022-0192.wav|They contemplate the enrichment of our national life
+LJSpeech-1.1/wavs/LJ031-0210.wav|The autopsy report noted that President Kennedy was forty-six years of age, seventy-two and one half inches tall,
+LJSpeech-1.1/wavs/LJ013-0054.wav|then brought an action against the administrators, and at the trial Dr. Bailey swore that the note had been given him by Smith.
+LJSpeech-1.1/wavs/LJ003-0160.wav|and no lodger was rejected, whatever his status, who offered himself and could bring grist to the mill.
+LJSpeech-1.1/wavs/LJ008-0194.wav|Some persons were kept for more than five hours standing against the barriers, and many nearly fainted from exhaustion.
+LJSpeech-1.1/wavs/LJ034-0218.wav|The Commission has determined that the employee was in fact Billy Lovelady, who identified himself in the picture.
+LJSpeech-1.1/wavs/LJ016-0409.wav|On the fatal morning he came gaily out of his cell,
+LJSpeech-1.1/wavs/LJ019-0035.wav|and the total number of separate cells provided amounted to eleven thousand odd.
+LJSpeech-1.1/wavs/LJ012-0053.wav|He was conveyed in a coach driven by a confederate, and under the escort of a couple of turnkeys.
+LJSpeech-1.1/wavs/LJ016-0053.wav|If the worst came to the worst, he intended to have gone down some chimney, as he had often done before in the way of business.
+LJSpeech-1.1/wavs/LJ028-0414.wav|All the ground on which Babylon was spread is left now desolate; nothing standing in that Peninsula between the Euphrates and the Tigris,
+LJSpeech-1.1/wavs/LJ008-0082.wav|A chain fastened by nails to the stake was then put round her body by the executioner with his assistants.
+LJSpeech-1.1/wavs/LJ046-0245.wav|Other agencies occasionally provided information to the Secret Service concerning potentially dangerous political groups.
+LJSpeech-1.1/wavs/LJ006-0220.wav|Feasting alternated with fighting.
+LJSpeech-1.1/wavs/LJ006-0252.wav|The worst fights occurred on Sunday afternoons; but nearly every night the act of locking up became, from the consequent removal of all supervision,
+LJSpeech-1.1/wavs/LJ028-0056.wav|Nebuchadnezzar, or Nebuchadrezzar, as his name should be spelled, was the greatest character in Babylonian history,
+LJSpeech-1.1/wavs/LJ016-0216.wav|That very ancient perquisite the convict's clothes was never claimed by Calcraft, and it may be doubted whether he was entitled to it.
+LJSpeech-1.1/wavs/LJ030-0198.wav|but then she noticed an almost imperceptible movement and knew that he was still alive. She said, quote, It's all right. Be still, end quote,
+LJSpeech-1.1/wavs/LJ034-0100.wav|this man I saw previously was aiming for his last shot. As it appeared to me he was standing up and resting against the left window sill, end quote.
+LJSpeech-1.1/wavs/LJ049-0227.wav|Without, therefore, coming to final conclusions respecting the long-range organization of the President's security,
+LJSpeech-1.1/wavs/LJ050-0270.wav|This Commission can recommend no procedures for the future protection of our Presidents which will guarantee security.
+LJSpeech-1.1/wavs/LJ011-0160.wav|and a servant hurriedly alighted from it, bearing a letter for Miss Turner.
+LJSpeech-1.1/wavs/LJ019-0243.wav|and thus bring the history of prison discipline down to our own times.
+LJSpeech-1.1/wavs/LJ004-0238.wav|Mr. Buxton mentions the case of a boy whose apparent innocence and artlessness had attracted his attention.
+LJSpeech-1.1/wavs/LJ049-0191.wav|is properly manned and equipped to carry on extensive information gathering functions within the United States.
+LJSpeech-1.1/wavs/LJ048-0205.wav|They had, however, occasionally observed the windows of buildings in the area before the motorcade arrived, in accordance with their own understanding of their function.
+LJSpeech-1.1/wavs/LJ031-0068.wav|While these medical efforts were in progress,
+LJSpeech-1.1/wavs/LJ005-0116.wav|but an empty regulation which all so disposed could defy.
+LJSpeech-1.1/wavs/LJ019-0298.wav|The total want of administration was very marked,
+LJSpeech-1.1/wavs/LJ040-0190.wav|he had better than average ability in arithmetical reasoning for his age group.
+LJSpeech-1.1/wavs/LJ005-0189.wav|of vice and misery, debasing all who are confined within their walls, and disseminating through their respective communities
+LJSpeech-1.1/wavs/LJ011-0222.wav|He also wrote and published a pamphlet from the jail to show that Miss Turner had been a consenting party to the marriage, and was really his wife.
+LJSpeech-1.1/wavs/LJ032-0151.wav|Experts testifying before the Commission agreed that palmprints are as unique as fingerprints for purposes of establishing identification.
+LJSpeech-1.1/wavs/LJ019-0085.wav|the prisoners might be supervised and watched at every step, and made liable to punishment for a trifling breach of an irksome code of regulations,
+LJSpeech-1.1/wavs/LJ047-0234.wav|Hosty's initial reaction on hearing that Oswald was a suspect in the assassination, was, quote, shock
+LJSpeech-1.1/wavs/LJ028-0293.wav|Zopyrus answered, "There is not a man in the world, but thou, O King, that could reduce me to such a plight
+LJSpeech-1.1/wavs/LJ010-0266.wav|and a long, sickly, pale face, with light hair;
+LJSpeech-1.1/wavs/LJ036-0134.wav|I asked him where he wanted to go. And he said, "five hundred North Beckley. Well, I started up,
+LJSpeech-1.1/wavs/LJ009-0273.wav|two supporting his body and two his legs.
+LJSpeech-1.1/wavs/LJ011-0266.wav|The money went after her to her children.
+LJSpeech-1.1/wavs/LJ014-0158.wav|She was discovered just as she was getting black in the face.
+LJSpeech-1.1/wavs/LJ004-0170.wav|The only ventilation of this pit, this "dark, cheerless, damp, unwholesome cavern -- a dungeon in its worst sense"
+LJSpeech-1.1/wavs/LJ031-0233.wav|The President's body was taken to the East Room of the White House where it was placed under ceremonial military guard.
+LJSpeech-1.1/wavs/LJ029-0039.wav|special agent in charge of the Dallas office. Both agents were advised of the trip on November four.
+LJSpeech-1.1/wavs/LJ017-0235.wav|He was soon summoned on deck, but as he would not move, the mutineers came down and stood in a circle round his berth.
+LJSpeech-1.1/wavs/LJ002-0130.wav|two hundred eighty-five thousand, nine hundred fifty pounds; in other words, that to recover eighty odd thousand pounds,
+LJSpeech-1.1/wavs/LJ028-0463.wav|The inner part of the wall was constructed of unburned bricks, and at a distance of about thirty-six feet from the outer part.
+LJSpeech-1.1/wavs/LJ019-0236.wav|the architectural records of the prison end. Nothing much was done at Newgate in the way of building, outside or in, after eighteen sixty-two.
+LJSpeech-1.1/wavs/LJ043-0106.wav|it didn't do his case any good, end quote.
+LJSpeech-1.1/wavs/LJ041-0136.wav|was shooting at Connally rather than President Kennedy, end quote.
+LJSpeech-1.1/wavs/LJ028-0001.wav|The Seven Wonders of the Ancient World. By Edgar J. Banks. Chapter two. The Walls of Babylon.
+LJSpeech-1.1/wavs/LJ015-0241.wav|opened it, took out and broke into the bullion box, removed the gold, substituted the shot from a carpet bag,
+LJSpeech-1.1/wavs/LJ043-0037.wav|At the time of his defection, Oswald had said that neither his brother, Robert,
+LJSpeech-1.1/wavs/LJ032-0205.wav|photographed the rifle used in the assassination, attempting to duplicate the position of the rifle and the lighting in Exhibit Number one thirty-three A.
+LJSpeech-1.1/wavs/LJ039-0129.wav|firing it, with ten rounds.
+LJSpeech-1.1/wavs/LJ013-0101.wav|Forrester next traced the fugitives to Liverpool,
+LJSpeech-1.1/wavs/LJ030-0222.wav|Mrs. Kennedy had no recollection of climbing onto the back of the car.
+LJSpeech-1.1/wavs/LJ021-0075.wav|But it is an undeniable fact that the restoration of other billions of sound investments to a reasonable earning power
+LJSpeech-1.1/wavs/LJ005-0096.wav|It had now a legal and authoritative standard of efficiency to apply,
+LJSpeech-1.1/wavs/LJ036-0151.wav|the point at which he said his passenger alighted.
+LJSpeech-1.1/wavs/LJ035-0191.wav|found no police officers at the rear door and was able to enter through this door without identifying himself.
+LJSpeech-1.1/wavs/LJ018-0243.wav|the hardship to the holders of these lands being plain, should the allegations of invalidity be made good.
+LJSpeech-1.1/wavs/LJ039-0153.wav|that the assassin in all probability hit two out of the three shots during the maximum time span of
+LJSpeech-1.1/wavs/LJ036-0068.wav|Both buses stopped within one block of the Depository Building.
+LJSpeech-1.1/wavs/LJ033-0077.wav|Lee Harvey Oswald left the Paine house in Irving at approximately seven:fifteen a.m., while Marina Oswald was still in bed.
+LJSpeech-1.1/wavs/LJ018-0323.wav|A year or two after, when the prisoners were undergoing their life sentences of penal servitude,
+LJSpeech-1.1/wavs/LJ011-0075.wav|One was that of Captain Montgomery, who assumed the aliases of Colonel Wallace and Colonel Morgan.
+LJSpeech-1.1/wavs/LJ012-0118.wav|It was a large gold brooch set in pearls, but a portion of the mounting had melted with the heat.
+LJSpeech-1.1/wavs/LJ046-0198.wav|at the request of Special Agent Lawson, advance agent for President Kennedy's trip to Dallas.
+LJSpeech-1.1/wavs/LJ033-0147.wav|Oswald's fingerprint and palmprint found on bag.
+LJSpeech-1.1/wavs/LJ027-0094.wav|Nature rarely makes new parts -- never, if she can avoid it -- but, on the contrary, adapts an old part to the new function.
+LJSpeech-1.1/wavs/LJ032-0121.wav|He applied fingerprint powder to the side of the metal housing near the trigger, and noticed traces of two prints.
+LJSpeech-1.1/wavs/LJ014-0271.wav|Nevertheless, in this position, through the culpable carelessness which left him unfettered, he managed between eighteen forty-four
+LJSpeech-1.1/wavs/LJ046-0168.wav|of which there are sixty-five throughout the country.
+LJSpeech-1.1/wavs/LJ028-0403.wav|The walls of the houses are built of them.
+LJSpeech-1.1/wavs/LJ039-0006.wav|Another alleged threat by Oswald against a public figure involved former Vice President Richard M. Nixon.
+LJSpeech-1.1/wavs/LJ045-0192.wav|apparently will loop through the downtown area, probably on Main Street,
+LJSpeech-1.1/wavs/LJ037-0035.wav|He testified that the picture of Oswald which he saw later on television bore a resemblance to the man who shot Officer Tippit.
+LJSpeech-1.1/wavs/LJ008-0261.wav|In the case of convicted murderers only was prompt punishment inflicted,
+LJSpeech-1.1/wavs/LJ036-0144.wav|He said, quote, Yes, sir; that is right, because that is the five hundred block of North Beckley, end quote,
+LJSpeech-1.1/wavs/LJ024-0020.wav|There is nothing novel or radical about this idea.
+LJSpeech-1.1/wavs/LJ040-0215.wav|Those factors indicated a severe inability to enter into relationships with other people.
+LJSpeech-1.1/wavs/LJ028-0002.wav|In the old city of Damascus you climb to the hump of a tall fleet dromedary.
+LJSpeech-1.1/wavs/LJ050-0238.wav|which represents a departure from its prior practice.
+LJSpeech-1.1/wavs/LJ012-0199.wav|After a little
+LJSpeech-1.1/wavs/LJ018-0056.wav|blue eyes which were generally half closed, and very fair hair.
+LJSpeech-1.1/wavs/LJ017-0158.wav|She had a little fortune of her own, some one thousand seven hundred pounds or one thousand eight hundred pounds,
+LJSpeech-1.1/wavs/LJ011-0233.wav|that thieves or depredators were idle or entirely unsuccessful.
+LJSpeech-1.1/wavs/LJ011-0062.wav|If the law of this country can receive such a sacrifice, my death will render to heaven an innocent man, and to earth a repentant sinner.
+LJSpeech-1.1/wavs/LJ012-0217.wav|Meanwhile, a search had been made at Nova Scotia Gardens, Bethnal Green, where Bishop and Williams lived.
+LJSpeech-1.1/wavs/LJ015-0299.wav|and the circumstances were deemed sufficiently suspicious to warrant the arrest of the gentlemen at Yarmouth on a charge of forgery and conspiracy.
+LJSpeech-1.1/wavs/LJ048-0003.wav|Hosty's interpretation of the prevailing FBI instructions on referrals to the Secret Service was defended before the Commission by his superiors.
+LJSpeech-1.1/wavs/LJ045-0165.wav|That if I want to he would rent an apartment in Dallas tomorrow -- that he didn't want me to remain with Ruth any longer, but wanted me to live with him in Dallas.
+LJSpeech-1.1/wavs/LJ034-0024.wav|The other "Rolling Readers" carton, however, contained a palmprint and a fingerprint which were identified by Latona
+LJSpeech-1.1/wavs/LJ044-0079.wav|The limited notoriety that Oswald received as a result of the street fracas and in the subsequent radio debate
+LJSpeech-1.1/wavs/LJ002-0022.wav|no less than three hundred forty were crowded, to the great inconvenience and danger of the inmates.
+LJSpeech-1.1/wavs/LJ009-0211.wav|and on the right the ripping chisel, with which the murders had been committed, were exposed to view.
+LJSpeech-1.1/wavs/LJ012-0229.wav|but the jury, after a short deliberation, did not hesitate to bring in a verdict of guilty, and all three were condemned to death.
+LJSpeech-1.1/wavs/LJ038-0021.wav|She sent Brewer into the theatre to find the man and check the exits, told him about the assassination, and said, quote,
+LJSpeech-1.1/wavs/LJ016-0193.wav|Foxen asked him his name and address, and went away.
+LJSpeech-1.1/wavs/LJ041-0116.wav|after it was learned that he had defected to the Soviet Union.
+LJSpeech-1.1/wavs/LJ047-0076.wav|In New Orleans. In the middle of May of nineteen sixty-three, Agent Hosty checked Oswald's last known residence and found that he had moved.
+LJSpeech-1.1/wavs/LJ048-0177.wav|that such a procedure would not be consistent with the nature and purpose of the motorcade to let the people see their President and to welcome him to their city.
+LJSpeech-1.1/wavs/LJ025-0150.wav|and that many exhibit actions comparable to those which are brought about by the agency of a nervous system in animals.
+LJSpeech-1.1/wavs/LJ035-0068.wav|until Baker came upon Oswald in the lunchroom.
+LJSpeech-1.1/wavs/LJ013-0212.wav|"You can have no good intentions; you must quit my service tomorrow morning."
+LJSpeech-1.1/wavs/LJ024-0025.wav|Because the laws of many states, the practice of the Civil Service, the regulations of the Army and Navy,
+LJSpeech-1.1/wavs/LJ040-0165.wav|Lee's withdrawal was also noted by Mrs. Siegel, who described him as a, quote, seriously detached, withdrawn youngster, end quote.
+LJSpeech-1.1/wavs/LJ037-0169.wav|He declared that this bullet was fired from the same weapon that fired the test bullets to the exclusion of all other weapons.
+LJSpeech-1.1/wavs/LJ001-0155.wav|and that laterally the page looks as if it were being driven off the paper.
+LJSpeech-1.1/wavs/LJ042-0125.wav|I am increasingly aware of the presence, in all thing, of Lebizen, shop party secretary, fat, fortyish, and jovial on the outside.
+LJSpeech-1.1/wavs/LJ041-0029.wav|Oswald started to read Communist literature, which he obtained from the public library.
+LJSpeech-1.1/wavs/LJ015-0282.wav|Like his great predecessor Old Patch, he never went to a bank himself, nor did any of his accomplices.
+LJSpeech-1.1/wavs/LJ010-0043.wav|although happily never passing beyond dastardly attempts, cropped up in these times, and was often frequently repeated within a short interval.
+LJSpeech-1.1/wavs/LJ041-0057.wav|His study of Communist literature,
+LJSpeech-1.1/wavs/LJ047-0237.wav|Shortly after Oswald was apprehended and identified, Hosty's superior sent him to observe the interrogation of Oswald.
+LJSpeech-1.1/wavs/LJ009-0030.wav|and the idea will be strengthened by the following account of the Sunday service in the prison chapel on the occasion when the condemned sermon was preached.
+LJSpeech-1.1/wavs/LJ038-0204.wav|According to Marina Oswald's testimony, on the night of the Walker shooting, her husband left their apartment on Neely Street shortly after dinner.
+LJSpeech-1.1/wavs/LJ040-0234.wav|for some reason the record does not show, advantage was never taken of the chance afforded to Oswald.
+LJSpeech-1.1/wavs/LJ039-0020.wav|Marina Oswald related the events which followed, quote,
+LJSpeech-1.1/wavs/LJ047-0221.wav|or Vice President, to immediately notify the Secret Service and confirm it in writing.
+LJSpeech-1.1/wavs/LJ045-0124.wav|Marina Oswald thought he did so in order to emphasize his importance.
+LJSpeech-1.1/wavs/LJ014-0107.wav|The corpse settled all doubts, and the next point was to lay hands upon the Mannings.
+LJSpeech-1.1/wavs/LJ046-0046.wav|In all of these roles the President must go to the people.
+LJSpeech-1.1/wavs/LJ039-0169.wav|In the first series the firers required time spans of four point six, six point seven five,
+LJSpeech-1.1/wavs/LJ042-0065.wav|I am shocked! My dreams! I have waited for two years to be accepted.
+LJSpeech-1.1/wavs/LJ042-0052.wav|I have always considered this country to be my own. End quote.
+LJSpeech-1.1/wavs/LJ048-0285.wav|when it absolutely forbids drinking by any agent accompanying the President on a trip.
+LJSpeech-1.1/wavs/LJ033-0085.wav|She then opened the kitchen door and saw Oswald open the right rear door of her brother's car and place the package in the back of the car.
+LJSpeech-1.1/wavs/LJ014-0225.wav|A handsome sum was subscribed for the injured constable, who was disabled for life.
+LJSpeech-1.1/wavs/LJ040-0109.wav|Whatever problems may have been created by Lee's home life in Louisiana and Texas,
+LJSpeech-1.1/wavs/LJ042-0124.wav|they don't seem to be especially enthusiastic about any of the "collective" duties, a natural feeling.
+LJSpeech-1.1/wavs/LJ015-0053.wav|Bates was at once captured in Norfolk Street, Strand.
+LJSpeech-1.1/wavs/LJ011-0026.wav|Fauntleroy was scouted as a licentious libertine, a deep and determined gamester, a spendthrift whose extravagance knew no bounds.
+LJSpeech-1.1/wavs/LJ020-0089.wav|Studying upon an empty stomach provokes dyspepsia and injures the eyes.
+LJSpeech-1.1/wavs/LJ033-0208.wav|the Commission considered Stombaugh's testimony of probative value in deciding whether Oswald carried the rifle into the building in the paper bag.
+LJSpeech-1.1/wavs/LJ025-0039.wav|he afterward affirms that nitrogen is peculiar to animals, and herein he places the third distinction between the animal and the plant.
+LJSpeech-1.1/wavs/LJ037-0051.wav|describing the slayer as, quote, about thirty, five foot eight inches, black hair, slender, end quote.
+LJSpeech-1.1/wavs/LJ013-0268.wav|Good ladies and gentlemen all, I have a great deal more to say, but I am so bad I cannot say it.
+LJSpeech-1.1/wavs/LJ016-0384.wav|when the first shock of the verdict and the solemn notification of the impending blow keeps nearly all awake, or at least disturbs their night's rest.
+LJSpeech-1.1/wavs/LJ016-0422.wav|The man Marley displayed fortitude of a less repulsive kind. He acknowledged his guilt from the first.
+LJSpeech-1.1/wavs/LJ050-0114.wav|and the respective responsibilities for any further investigation that may be required.
+LJSpeech-1.1/wavs/LJ006-0083.wav|"So great is the authority exercised by him,
+LJSpeech-1.1/wavs/LJ048-0084.wav|some limited information was made available to the Secret Service. But there was no fully adequate liaison between the two agencies. Indeed,
+LJSpeech-1.1/wavs/LJ016-0090.wav|Taking advantage of being unobserved, he got out through the roof on to the leads, and traveled along them towards Number one, Newgate Street.
+LJSpeech-1.1/wavs/LJ010-0097.wav|Here they were surprised by the police, headed by a magistrate, and supported by a strong detachment of Her Majesty's Guards.
+LJSpeech-1.1/wavs/LJ043-0008.wav|De Mohrenschildt was a peripheral member of the so-called Russian community, with which Oswald made contact through Mr. Peter Gregory,
+LJSpeech-1.1/wavs/LJ018-0063.wav|He had led a wild life; had left his native land and enlisted first in the French army in Algeria,
+LJSpeech-1.1/wavs/LJ008-0162.wav|Cries of Murder! murder! were now raised, and added greatly to the horrors of the scene.
+LJSpeech-1.1/wavs/LJ001-0181.wav|When, as is most often the case, there is actual beauty in the cuts,
+LJSpeech-1.1/wavs/LJ019-0064.wav|that their condition was more natural, and approximated more nearly to that of daily life.
+LJSpeech-1.1/wavs/LJ006-0211.wav|Spirits certainly were now excluded; but a potman, with full permission of the sheriffs,
+LJSpeech-1.1/wavs/LJ040-0035.wav|While Oswald appeared to most of those who knew him as a meek and harmless person, he sometimes imagined himself as, quote, the Commander, end quote,
+LJSpeech-1.1/wavs/LJ034-0034.wav|Lieutenant Day of the Dallas police dusted this carton with powder and developed a palmprint on the top edge of the carton on the side nearest the window.
+LJSpeech-1.1/wavs/LJ038-0210.wav|according to Marina Oswald when he learned on the radio and in the newspapers the next day that he had missed,
+LJSpeech-1.1/wavs/LJ040-0128.wav|He was in Youth House from April sixteen to May seven, nineteen fifty-three,
+LJSpeech-1.1/wavs/LJ024-0017.wav|no additional place would be created.
+LJSpeech-1.1/wavs/LJ018-0093.wav|As a blind for their new frauds, they set up as law-stationers in York Buildings, Adelphi, and at once commenced their nefarious traffic.
+LJSpeech-1.1/wavs/LJ009-0032.wav|Strangers were now excluded, but the sheriffs attended in state, wearing their gold chains,
+LJSpeech-1.1/wavs/LJ018-0108.wav|His copies were generally pronounced indistinguishable from and as good as the originals.
+LJSpeech-1.1/wavs/LJ005-0177.wav|Nearly every one of these jurisdictions had its own prison, and there were one hundred and sixty such jails in all.
+LJSpeech-1.1/wavs/LJ007-0200.wav|In the press-yard, the press-room and ward above it were parceled out into nine separate sleeping cells;
+LJSpeech-1.1/wavs/LJ006-0184.wav|the most hardened and the oldest in vice had the best of it, while the inexperienced beginner went to the wall.
+LJSpeech-1.1/wavs/LJ018-0295.wav|The fraud was rendered possible by the absence of a check usual in the United States.
+LJSpeech-1.1/wavs/LJ018-0342.wav|It was to effect the rupture of an irksome tie that led Henry Wainwright to murder Harriet Lane deliberately and in cold blood.
+LJSpeech-1.1/wavs/LJ010-0309.wav|"During these numerous and trying difficulties" -- it is Mr. Fauntleroy who speaks --
+LJSpeech-1.1/wavs/LJ048-0131.wav|There is, for example, no Secret Service arrangement for evaluating before a trip particular difficulties that might be anticipated,
+LJSpeech-1.1/wavs/LJ003-0148.wav|and spent in providing coals, candles, plates, knives, and forks; while all the occupants of this part of the prison
+LJSpeech-1.1/wavs/LJ020-0090.wav|Active exercise in like circumstances tempts debility and disease.
+LJSpeech-1.1/wavs/LJ041-0169.wav|and on how angry it made him, to which Thornley replied, quote, Well, comes the revolution you will change all that, end quote.
+LJSpeech-1.1/wavs/LJ002-0029.wav|as it was occupied and appropriated in eighteen ten.
+LJSpeech-1.1/wavs/LJ017-0033.wav|a medical practitioner, charged with doing to death persons who relied upon his professional skill.
+LJSpeech-1.1/wavs/LJ016-0335.wav|There was no interference with the crowd, which collected as usual, although not to the customary extent.
+LJSpeech-1.1/wavs/LJ007-0204.wav|"which has been demonstrably proved to be the fruitful source of all the abuses and irregularities which have so long disgraced Newgate,
+LJSpeech-1.1/wavs/LJ006-0244.wav|Violent inflammation and extensive suppuration ensued, and for a considerable time amputation seemed inevitable.
+LJSpeech-1.1/wavs/LJ013-0266.wav|Good was committed to Newgate, and tried at the Central Criminal Court before a crowded court.
+LJSpeech-1.1/wavs/LJ015-0039.wav|Money they must have, and money they raised to meet their urgent necessities upon the balances and securities deposited with them by their customers.
+LJSpeech-1.1/wavs/LJ030-0038.wav|Every effort is made to prevent unscheduled stops, although the President may, and in Dallas did, order stops in order to greet the public.
+LJSpeech-1.1/wavs/LJ014-0032.wav|as the seducer of an innocent girl to whom he (Hocker) had been fondly attached.
+LJSpeech-1.1/wavs/LJ009-0217.wav|or on another specially erected on some commanding spot, had fallen into disuse by eighteen thirty-two.
+LJSpeech-1.1/wavs/LJ045-0037.wav|after his recent rebuffs in Mexico City
+LJSpeech-1.1/wavs/LJ024-0044.wav|that I will appoint justices who will act as justices and not as legislators
+LJSpeech-1.1/wavs/LJ009-0222.wav|The revival of this barbarous practice caused much indignation in certain quarters,
+LJSpeech-1.1/wavs/LJ020-0001.wav|Marion Harland's Cookery for Beginners. Bread Sponge and Breakfast Breads.
+LJSpeech-1.1/wavs/LJ028-0236.wav|He placed a portion of his army at the point where the river enters the city, and another body at the back of the place where it issues forth,
+LJSpeech-1.1/wavs/LJ009-0094.wav|but his face is of an ashy paleness; and if you look carefully you may see the blood trickling from his lip,
+LJSpeech-1.1/wavs/LJ015-0223.wav|He lent it to Agar for a brief space, who promptly took an impression on wax. But the safes had a double lock;
+LJSpeech-1.1/wavs/LJ013-0263.wav|He was recognized by an ex-policeman who had known him at Roehampton, and immediately arrested.
+LJSpeech-1.1/wavs/LJ043-0063.wav|not only caused him difficulties in his employment relations, but they also provided him with excuses for employment failures
+LJSpeech-1.1/wavs/LJ006-0100.wav|many without stockings, and with hardly shoes to their feet;
+LJSpeech-1.1/wavs/LJ019-0310.wav|which had long been admitted as indispensable, and had never as yet been properly obtained.
+LJSpeech-1.1/wavs/LJ046-0117.wav|is to collect, process, and evaluate information about persons or groups who may be a danger to the President.
+LJSpeech-1.1/wavs/LJ010-0148.wav|tried to stab George the third as he was alighting from his carriage at the gate of St. James's Palace.
+LJSpeech-1.1/wavs/LJ019-0375.wav|There were only two of them, and they could not visit each prison more than once in each year, sometimes not oftener than once in eighteen months.
+LJSpeech-1.1/wavs/LJ011-0257.wav|Howard was secured, given into custody, and committed to Newgate.
+LJSpeech-1.1/wavs/LJ047-0078.wav|and Hosty asked the New Orleans FBI office to determine Oswald's address and what he was doing.
+LJSpeech-1.1/wavs/LJ028-0005.wav|Leaving the city by the eastern gate, and passing a small village or two,
+LJSpeech-1.1/wavs/LJ004-0124.wav|In four hundred and forty-five prisons no work of any description had been introduced for the employment of prisoners;
+LJSpeech-1.1/wavs/LJ041-0179.wav|that he never heard Oswald express a desire personally to take part in the elimination of injustices anywhere in the world and that he, quote,
+LJSpeech-1.1/wavs/LJ009-0233.wav|The rope still broke sometimes, although it was not often that the horrid scene seen at Jersey at the beginning of the century was repeated.
+LJSpeech-1.1/wavs/LJ004-0093.wav|which was contemporaneous with Howard's first journeys, laid down precise rules as regards cleanliness, and the proper supply of space and air.
+LJSpeech-1.1/wavs/LJ014-0218.wav|after which he kicked his prostrate foe in the most brutal and cowardly manner, and until he was almost senseless, and bruised from head to foot.
+LJSpeech-1.1/wavs/LJ021-0060.wav|Minimum wages have been established and other wages adjusted toward a rising standard of living.
+LJSpeech-1.1/wavs/LJ015-0265.wav|was the prime mover,
+LJSpeech-1.1/wavs/LJ049-0161.wav|and also for the preventive investigation of potential threats against the President.
+LJSpeech-1.1/wavs/LJ014-0008.wav|His remark was, "It is a nasty job;" he took hold of the dead hand, and confessed that he felt "queer" at the shocking sight.
+LJSpeech-1.1/wavs/LJ049-0003.wav|Chapter eight. The Protection of the President. Part four.
+LJSpeech-1.1/wavs/LJ034-0069.wav|was provided by the testimony of Charles Givens, who was the last known employee to see Oswald inside the building prior to the assassination.
+LJSpeech-1.1/wavs/LJ003-0128.wav|caught their flash terms and sung their songs, was admitted to their revels, and acquired, in place of habits of perfect sobriety,
+LJSpeech-1.1/wavs/LJ047-0149.wav|the New Orleans office of the FBI learned that in September Oswald had given a forwarding address of two five one five
+LJSpeech-1.1/wavs/LJ028-0047.wav|War broke out between the two brothers, and again Babylon was captured.
+LJSpeech-1.1/wavs/LJ050-0028.wav|no sizable organization can achieve efficiency without the careful analysis and demarcation of responsibility
+LJSpeech-1.1/wavs/LJ017-0116.wav|and even sought to upset the vehicle by which they were being conveyed a part of the way to London.
+LJSpeech-1.1/wavs/LJ038-0026.wav|Other policemen entered the front door and searched the balcony.
+LJSpeech-1.1/wavs/LJ017-0125.wav|He never actually said that he was not guilty, but he was confident he would not be convicted.
+LJSpeech-1.1/wavs/LJ028-0026.wav|Here in the valley the water is sweet and the food abundant.
+LJSpeech-1.1/wavs/LJ034-0026.wav|The Commission has considered the possibility that the cartons might have been moved in connection with the work that was being performed on the sixth floor on November twenty-two.
+LJSpeech-1.1/wavs/LJ009-0285.wav|that Calcraft's method of hanging was very rough, much the same as if he had been hanging a dog.
+LJSpeech-1.1/wavs/LJ018-0030.wav|Last of all, the cabman swore that he had bought the very hat found in the carriage for Müller at the hatter's, Walker's of Crawford Street.
+LJSpeech-1.1/wavs/LJ028-0258.wav|Then the Babylonians secretly plotted to throw off the Persian yoke.
+LJSpeech-1.1/wavs/LJ037-0121.wav|Both Callaway and Guinyard testified that they had not been shown any pictures by the police before the lineup.
+LJSpeech-1.1/wavs/LJ022-0195.wav|But it is more than the recovery of the material basis of our individual lives.
+LJSpeech-1.1/wavs/LJ014-0308.wav|others, that Napoleon the third, but recently proclaimed Emperor of the French, wished to gain the popularity necessary to secure the people.
+LJSpeech-1.1/wavs/LJ042-0109.wav|one of the members of the Russian community in Dallas with which the Oswalds became acquainted upon their arrival in the United States,
+LJSpeech-1.1/wavs/LJ011-0169.wav|Miss Turner, not knowing him, would have left the room, but he said he came from her father, and she remained.
+LJSpeech-1.1/wavs/LJ003-0310.wav|a bell should give notice thereof, and of meal-hours, working-hours, or of escapes.
+LJSpeech-1.1/wavs/LJ004-0109.wav|according to their categories or crimes.
+LJSpeech-1.1/wavs/LJ017-0124.wav|He frequently declared before and during the trial that it would be impossible to find him guilty.
+LJSpeech-1.1/wavs/LJ039-0049.wav|He was the kind of person who could try and wound somebody in that way. Possibly he didn't want to go out at all but was just doing this all as a sort of joke,
+LJSpeech-1.1/wavs/LJ050-0034.wav|and the breach of regulations which occurred on the night of November twenty-one at Fort Worth.
+LJSpeech-1.1/wavs/LJ025-0067.wav|had declared the greater number of what are now recognized as locomotive plants to be animals.
+LJSpeech-1.1/wavs/LJ008-0053.wav|the whole is temporary, being all calculated to take to pieces, which are preserved within the prison."
+LJSpeech-1.1/wavs/LJ037-0104.wav|manager of a used-car lot on the northeast corner of Patton Avenue and Jefferson Boulevard, and Sam Guinyard, a porter at the lot.
+LJSpeech-1.1/wavs/LJ002-0071.wav|It was large and comparatively commodious, being maintained on a better footing than any other part of the prison.
+LJSpeech-1.1/wavs/LJ010-0232.wav|He could not plead insanity like his predecessor, and no attempt was made at his trial to prove him of unsound mind.
+LJSpeech-1.1/wavs/LJ016-0185.wav|Calcraft, who, in spite of the dreadful calling he subsequently followed, was always reputed a kindly man,
+LJSpeech-1.1/wavs/LJ032-0110.wav|Moreover, the use of "Alek" as a first name for Hidell is a further link to Oswald because "Alek" was Oswald's nickname in Russia.
+LJSpeech-1.1/wavs/LJ045-0248.wav|He also had demonstrated a capacity to act decisively and without regard to the consequences when such action would further his aims of the moment.
+LJSpeech-1.1/wavs/LJ015-0103.wav|Mr. Fasson, more and more ill at ease, would not accept this subterfuge, and declared they must be found.
+LJSpeech-1.1/wavs/LJ028-0288.wav|To do this seeming to him a light matter, he mutilated himself in a way that was utterly without remedy.
+LJSpeech-1.1/wavs/LJ028-0142.wav|While such is its size, in magnificence there is no other city that approaches to it.
+LJSpeech-1.1/wavs/LJ029-0199.wav|On November eighteen the Dallas City Council adopted a new city ordinance prohibiting interference with attendance at lawful assemblies.
+LJSpeech-1.1/wavs/LJ044-0109.wav|It is good to know that movements in support of fair play for Cuba has developed in New Orleans as well as in other cities.
+LJSpeech-1.1/wavs/LJ038-0125.wav|Now, I've told you all I'm going to tell you about that card in my billfolds -- you have the card yourself and you know as much about it as I do, end quote.
+LJSpeech-1.1/wavs/LJ014-0322.wav|The dock warrant was issued by the wharfinger as certificate that he held the goods.
+LJSpeech-1.1/wavs/LJ029-0211.wav|And on the morning of the President's arrival, there appeared in the Morning News a full, black-bordered advertisement headed:
+LJSpeech-1.1/wavs/LJ047-0105.wav|In Quigley's judgment,
+LJSpeech-1.1/wavs/LJ009-0149.wav|was more appropriate for serious reflection and profitable ministration than this exciting occasion before a large and public assembly.
+LJSpeech-1.1/wavs/LJ041-0043.wav|In fact, he had quit school in an attempt to obtain his mother's assistance to join the Marines.
+LJSpeech-1.1/wavs/LJ026-0038.wav|the fact of the animal and vegetable kingdoms being related to one another like two trees united at the roots may be accounted for by the hypothesis that
+LJSpeech-1.1/wavs/LJ017-0074.wav|whose life he had insured for thirteen thousand pounds.
+LJSpeech-1.1/wavs/LJ037-0193.wav|S T. W. two inch BBL, unquote, cost twenty-nine dollars, ninety-five cents.
+LJSpeech-1.1/wavs/LJ044-0155.wav|that things are improving due to the fact that my husband expresses a sincere wish to return together with me to the USSR. Unknown to his wife, however,
+LJSpeech-1.1/wavs/LJ009-0283.wav|this time Calcraft adding his weight to the body, and the strangulation was completed.
+LJSpeech-1.1/wavs/LJ009-0161.wav|capital punishment was abolished for forgery, except in cases of forging or altering wills or powers of attorney to transfer stock.
+LJSpeech-1.1/wavs/LJ010-0177.wav|As the carriage came up to him Oxford turned, put his hand into his breast, drew a pistol, and fired at the Queen.
+LJSpeech-1.1/wavs/LJ007-0171.wav|distinguished as the seat of religion, worth, and philanthropy,
+LJSpeech-1.1/wavs/LJ047-0016.wav|The FBI opened a file on Oswald in October nineteen fifty-nine, when news reports appeared of his defection to the Soviet Union.
+LJSpeech-1.1/wavs/LJ003-0145.wav|or about a foot and a half laterally. These fees were in reality a substantial contribution towards the expenses of the jail;
+LJSpeech-1.1/wavs/LJ036-0003.wav|The Killing of Patrolman J. D. Tippit
+LJSpeech-1.1/wavs/LJ047-0017.wav|The file was opened, quote,
+LJSpeech-1.1/wavs/LJ014-0079.wav|and which he mistook for a drain, and that while he was speaking to her she put the muzzle of a pistol close to the back of his head and shot him down.
+LJSpeech-1.1/wavs/LJ005-0032.wav|no weavers' looms or carpenters' benches. There must be a great deal of solitude, coarse food, a dress of shame,
+LJSpeech-1.1/wavs/LJ048-0052.wav|and less narrow interpretation of their responsibilities.
+LJSpeech-1.1/wavs/LJ007-0061.wav|He left off because he was so much interfered with and laughed at, and from seeing that no success attended his efforts, owing to the evils arising from association.
+LJSpeech-1.1/wavs/LJ006-0299.wav|His excuse was that he had given the girl his promise that she should not go, an assumption of prerogative which by no means rested with him;
+LJSpeech-1.1/wavs/LJ036-0006.wav|At about one:sixteen p.m., a Dallas police officer, J. D. Tippit, was shot less than one mile from Oswald's roominghouse.
+LJSpeech-1.1/wavs/LJ036-0164.wav|And when interrogated about a cab ride, Oswald also admitted that he left the slow-moving bus and took a cab to his roominghouse.
+LJSpeech-1.1/wavs/LJ015-0257.wav|Kay, who had been living with Agar at the time of the bullion robbery,
+LJSpeech-1.1/wavs/LJ003-0142.wav|to purchase the greater ease and comfort of the master's side.
+LJSpeech-1.1/wavs/LJ030-0080.wav|scanning not only the crowds but the windows and roofs of buildings, overpasses, and crossings.
+LJSpeech-1.1/wavs/LJ003-0242.wav|was still practiced, that of loading newly-arrived prisoners until they paid certain fees.
+LJSpeech-1.1/wavs/LJ034-0182.wav|The person in the window was a white man and, quote, looked to me like he was looking straight at the Triple Underpass, end quote, down Elm Street.
+LJSpeech-1.1/wavs/LJ045-0025.wav|Relationship With Wife
+LJSpeech-1.1/wavs/LJ006-0261.wav|together with four bradawls, several large iron spikes, screws, nails, and knives;
+LJSpeech-1.1/wavs/LJ046-0050.wav|administrative, political.
+LJSpeech-1.1/wavs/LJ003-0038.wav|which would accommodate the inmates of Newgate and of the three compters, Ludgate,
+LJSpeech-1.1/wavs/LJ009-0102.wav|Suddenly he utters a short sharp scream, and all is still.
+LJSpeech-1.1/wavs/LJ003-0347.wav|It was very desirable that there should be a more speedy removal of transports from Newgate to the ships.
+LJSpeech-1.1/wavs/LJ041-0148.wav|As indicated above, Oswald started to read Communist literature after he and his mother left New York and moved to New Orleans.
+LJSpeech-1.1/wavs/LJ016-0280.wav|"There were well-dressed and ill-dressed, old men and lads, women and girls."
+LJSpeech-1.1/wavs/LJ004-0082.wav|These somewhat novel but undoubtedly indisputable propositions were backed up, not by sound arguments only, but by the letter of the law.
+LJSpeech-1.1/wavs/LJ046-0157.wav|The files are manually maintained, without use of any automatic data-processing techniques.
+LJSpeech-1.1/wavs/LJ050-0123.wav|and investigative capabilities of the agencies now operating in this field but will continue
+LJSpeech-1.1/wavs/LJ038-0247.wav|A fourth photograph, showing a stretch of railroad tracks,
+LJSpeech-1.1/wavs/LJ032-0178.wav|the evidence indicates that he continued wearing the same shirt which he was wearing all morning and which he was still wearing when arrested.
+LJSpeech-1.1/wavs/LJ042-0072.wav|Still intent, however, on staying in the Soviet Union,
+LJSpeech-1.1/wavs/LJ040-0094.wav|Pic thought that his mother overstated her financial problems and was unduly concerned about money.
+LJSpeech-1.1/wavs/LJ006-0172.wav|and evils that should have been speedily rooted out remained because they had the prescription of long usage.
+LJSpeech-1.1/wavs/LJ029-0128.wav|going one block north and then turning left onto Elm.
+LJSpeech-1.1/wavs/LJ012-0232.wav|He asserted that the victim was a Lincolnshire lad, and not an Italian boy, although the latter was fully proved.
+LJSpeech-1.1/wavs/LJ048-0037.wav|overlooking the motorcade route as a source of danger to the President and did not inform the Secret Service of his employment in the Depository Building.
+LJSpeech-1.1/wavs/LJ040-0137.wav|Marguerite Oswald said that she had not realized until then in what kind of place her son had been confined.
+LJSpeech-1.1/wavs/LJ041-0059.wav|His difficulty in relating to other people and his general dissatisfaction with the world around him continued while he was in the Marine Corps.
+LJSpeech-1.1/wavs/LJ030-0174.wav|Unable to see the President as he turned to the right,
+LJSpeech-1.1/wavs/LJ012-0280.wav|or of Gardelle's frantic efforts to conceal his crime.
+LJSpeech-1.1/wavs/LJ005-0169.wav|but I indulge a hope that many of them will contract with the counties,
+LJSpeech-1.1/wavs/LJ006-0196.wav|Prisoners confessed that it was a favorite occupation, the chief games being "shoving halfpence" on the table,
+LJSpeech-1.1/wavs/LJ015-0229.wav|who quickly took the wax impression, handed it back to Pierce; Pierce replaced it, left the office, and the thing was done.
+LJSpeech-1.1/wavs/LJ007-0064.wav|and the day of whose execution was fixed. He had no doubt a difficult mission to discharge;
+LJSpeech-1.1/wavs/LJ018-0092.wav|in eighteen fifty-six.
+LJSpeech-1.1/wavs/LJ022-0086.wav|to which all proposals for the expenditure of money must go for preliminary study and consideration.
+LJSpeech-1.1/wavs/LJ042-0121.wav|Mass gymnastics, compulsory afterwork meeting, usually political information meeting.
+LJSpeech-1.1/wavs/LJ013-0136.wav|Several pawnbrokers were subpoenaed and obliged to surrender plate, to the extent in some cases of a couple of dozen of spoons or forks,
+LJSpeech-1.1/wavs/LJ021-0070.wav|Employed workers have not by any means all enjoyed a return to the earnings of prosperous times,
+LJSpeech-1.1/wavs/LJ018-0067.wav|Thither, he was pursued by a detective officer,
+LJSpeech-1.1/wavs/LJ040-0221.wav|Carro was the only one of Oswald's three principal observers who recommended that he be placed in a boy's home or similar institution.
+LJSpeech-1.1/wavs/LJ023-0018.wav|We also became convinced that the only way to avoid a repetition of those dark days was to have a government with power to prevent
+LJSpeech-1.1/wavs/LJ044-0149.wav|He said that nothing kept him in the United States, that he would not lose anything if he returned to the Soviet Union, that he wanted to be with her
+LJSpeech-1.1/wavs/LJ003-0090.wav|Mr. Newman admitted that he had petitioned that certain "trusty men" might be left in the jail.
+LJSpeech-1.1/wavs/LJ004-0090.wav|Again, the twenty-two Charles the second c twenty ordered the jailer to keep felons and debtors "separate and apart from one another,
+LJSpeech-1.1/wavs/LJ050-0276.wav|As has been pointed out, the Commission has not resolved all the proposals which could be made. The Commission nevertheless is confident that,
+LJSpeech-1.1/wavs/LJ031-0194.wav|At five:fifty-eight p.m. Eastern Standard Time,
+LJSpeech-1.1/wavs/LJ042-0075.wav|testified that Oswald was extremely sure of himself and seemed, quote, to know what his mission was.
+LJSpeech-1.1/wavs/LJ021-0101.wav|gave businessmen the opportunity they had sought for years to improve business conditions through what has been called self-government in industry.
+LJSpeech-1.1/wavs/LJ022-0151.wav|did more than anything else to bring about the recent collapse of industries.
+LJSpeech-1.1/wavs/LJ009-0168.wav|robbery, burglary, and arson.
+LJSpeech-1.1/wavs/LJ011-0274.wav|fell upon him, and in spite of his resistance carried him into a sort of den partitioned off at the end of the back kitchen.
+LJSpeech-1.1/wavs/LJ014-0073.wav|Meanwhile this woman, unflinching in her cold, bloody determination, carefully laid all her plans for the consummation of the deed.
+LJSpeech-1.1/wavs/LJ013-0078.wav|Barber was subsequently pardoned, but was not replaced on the rolls as an attorney till eighteen fifty-five,
+LJSpeech-1.1/wavs/LJ027-0166.wav|retain permanently this form, and are therefore called "perennibranchs," but the frog still passes on.
+LJSpeech-1.1/wavs/LJ009-0254.wav|The hangman summoned assistance, and with help tied the cap over White's face with a handkerchief.
+LJSpeech-1.1/wavs/LJ012-0109.wav|But they at once made tracks, and took up their residence under assumed names in a tavern in Bloomsbury.
+LJSpeech-1.1/wavs/LJ045-0226.wav|The fact that he took so little money with him when he left Irving in the morning indicates that he did not expect to get very far from Dallas on his own
+LJSpeech-1.1/wavs/LJ015-0251.wav|The latter was ere long arrested on a charge of uttering forged cheques, convicted, and sentenced to transportation for life.
+LJSpeech-1.1/wavs/LJ025-0133.wav|The reflex action of the snail is the result of the presence of a nervous system in the animal.
+LJSpeech-1.1/wavs/LJ034-0129.wav|establishes that they were either squatting or kneeling.
+LJSpeech-1.1/wavs/LJ008-0030.wav|The first affair of the kind on this spot was on the third December, seventeen eighty-three,
+LJSpeech-1.1/wavs/LJ049-0211.wav|It would have been necessary for the Commission to take considerable testimony, much of it extraneous to the facts of the assassination of President Kennedy,
+LJSpeech-1.1/wavs/LJ012-0285.wav|Another day elapsed before he got rid of the rest of the body,
+LJSpeech-1.1/wavs/LJ003-0180.wav|These wards were always full to overflowing; sometimes double the number the rooms could accommodate were crowded into them.
+LJSpeech-1.1/wavs/LJ037-0044.wav|She peered through her fingers, lowered her hands, and saw the man doing something with his gun. Quote,
+LJSpeech-1.1/wavs/LJ033-0105.wav|One employee, Jack Dougherty,
+LJSpeech-1.1/wavs/LJ007-0124.wav|The infirmary at this particular period epitomized the condition of the jail at large.
+LJSpeech-1.1/wavs/LJ037-0184.wav|agents of the FBI determined that George Rose and Co. of Los Angeles was a major distributor of this type of revolver.
+LJSpeech-1.1/wavs/LJ044-0071.wav|Stuckey had a radio program called, quote, Latin Listening Post, end quote,
+LJSpeech-1.1/wavs/LJ029-0124.wav|To reach the Trade Mart from Main Street the agents decided to use the Stemmons Freeway (Route Number seventy-seven), the most direct route.
+LJSpeech-1.1/wavs/LJ003-0212.wav|But they nevertheless frequently managed to secrete the means of making away with themselves, and accomplished their purpose.
+LJSpeech-1.1/wavs/LJ017-0153.wav|Probably the doctor had told her the story he brought forward when tried for bigamy, namely,
+LJSpeech-1.1/wavs/LJ009-0085.wav|'Now for you, my poor fellow mortals, who are about to suffer the last penalty of the law.'
+LJSpeech-1.1/wavs/LJ036-0154.wav|The walk from Beckley and Neely to ten twenty-six North Beckley was timed by Commission counsel at five minutes and forty-five seconds.
+LJSpeech-1.1/wavs/LJ002-0193.wav|In these cases security was given for the amount of the debt,
+LJSpeech-1.1/wavs/LJ039-0050.wav|not really as a joke but rather to simply wound me, to make me feel bad, end quote.
+LJSpeech-1.1/wavs/LJ006-0243.wav|He was in the transport side, when one of his fellows, in endeavoring to strike another prisoner with a large poker, missed his aim, and struck Watkins' knee
+LJSpeech-1.1/wavs/LJ047-0110.wav|that another Bureau agent, Milton R. Knack, had been conducting a background investigation of Oswald at the request of Agent Hosty in Dallas.
+LJSpeech-1.1/wavs/LJ010-0178.wav|The shot missed, and as the carriage passed on, Oxford drew a second pistol and fired again.
+LJSpeech-1.1/wavs/LJ006-0002.wav|In the preceding chapter I have been tempted by the importance of the general question to give it prominence and precedence over the particular branch of which I am treating.
+LJSpeech-1.1/wavs/LJ022-0177.wav|is one of the most hopeful results of our efforts as a Nation to reestablish public confidence in private banking.
+LJSpeech-1.1/wavs/LJ015-0006.wav|who had fraudulent warrants out of their own to the extent of one hundred fifty thousand pounds, suspended payment and absconded.
+LJSpeech-1.1/wavs/LJ026-0124.wav|In the plant, starch, as has been seen, is first formed in the chlorophyll-bodies.
+LJSpeech-1.1/wavs/LJ024-0013.wav|This plan will save our national Constitution from hardening of the judicial arteries.
+LJSpeech-1.1/wavs/LJ002-0246.wav|and came under the strong animadversion of the Jail Committee of seventeen twenty-nine.
+LJSpeech-1.1/wavs/LJ004-0091.wav|in distinct rooms, on pain of forfeiting his office and treble damages to the party aggrieved."
+LJSpeech-1.1/wavs/LJ013-0081.wav|Banks and bankers continued to be victimized.
+LJSpeech-1.1/wavs/LJ024-0111.wav|the legislation demanded by the mandate.
+LJSpeech-1.1/wavs/LJ006-0240.wav|Watkins' knee-joint is very severely injured.
+LJSpeech-1.1/wavs/LJ015-0135.wav|He began in a very small way.
+LJSpeech-1.1/wavs/LJ017-0066.wav|Yet he was silent when specifically pressed to confess that he had killed his wife and his brother.
+LJSpeech-1.1/wavs/LJ050-0148.wav|the increased information supplied by other agencies will be wasted.
+LJSpeech-1.1/wavs/LJ029-0123.wav|was not used for the main portion of the downtown part of the motorcade because Main Street offered better vantage points for spectators.
+LJSpeech-1.1/wavs/LJ041-0008.wav|He took walks and visited museums, and sometimes rode a rented bicycle in the park on Saturday mornings.
+LJSpeech-1.1/wavs/LJ028-0483.wav|O Marduk, great god, look joyfully upon the precious work of my hands. Be thou my protector.
+LJSpeech-1.1/wavs/LJ008-0126.wav|A new hat was partly flattened on his head, for, owing to its being too small in the crown, it stood many inches too high behind.
+LJSpeech-1.1/wavs/LJ034-0178.wav|He said that he could see the man from the middle of his chest to the top of his head, and that as he was facing the window the man was in the lower right-hand portion of the window
+LJSpeech-1.1/wavs/LJ015-0155.wav|that so good a man had really been for years a swindler and a rogue.
+LJSpeech-1.1/wavs/LJ019-0008.wav|but no steps had been taken in Parliament to enforce a better system of discipline,
+LJSpeech-1.1/wavs/LJ028-0251.wav|When Cyrus took Babylon, little or no force was employed.
+LJSpeech-1.1/wavs/LJ047-0215.wav|including the significant fact that Oswald was employed in a building which overlooked the motorcade route.
+LJSpeech-1.1/wavs/LJ011-0045.wav|The kindliness of the city authorities to Fauntleroy was not limited to the assignment of a separate place of durance.
+LJSpeech-1.1/wavs/LJ041-0161.wav|It was just -- never getting back to looking at things from any other way once he had become a Marxist, whenever that was, end quote.
+LJSpeech-1.1/wavs/LJ001-0100.wav|and these somewhat wiry letters are suitable for the machine process, which would not do justice to letters of more generous design.
+LJSpeech-1.1/wavs/LJ034-0005.wav|He worked principally on the first and sixth floors of the building, gathering books listed on orders and delivering them to the shipping room on the first floor.
+LJSpeech-1.1/wavs/LJ017-0199.wav|The first post-mortem indicated death from natural causes,
+LJSpeech-1.1/wavs/LJ004-0085.wav|As far back as the reign of Charles the second, a law was passed declaring that sufficient provision should be made for the relief and setting on work
+LJSpeech-1.1/wavs/LJ034-0153.wav|but did not do so because he felt that the assassination was, quote, a Communist activity,
+LJSpeech-1.1/wavs/LJ010-0277.wav|Lord Abinger sentenced him to eighteen months' imprisonment in Newgate,
+LJSpeech-1.1/wavs/LJ012-0061.wav|On stopping at a door in this low street, Ikey jumped out, ran into the house, slamming the door behind him.
+LJSpeech-1.1/wavs/LJ028-0331.wav|After this second exploit, his praise was in all mouths.
+LJSpeech-1.1/wavs/LJ002-0060.wav|But these king's witnesses were also put at times into the press yard among the capital convicts, seemingly a very dangerous proceeding,
+LJSpeech-1.1/wavs/LJ038-0302.wav|(three) the testimony of firearms identification experts, and (four) the testimony of Marina Oswald,
+LJSpeech-1.1/wavs/LJ039-0103.wav|Oswald, like all Marine recruits, received training on the rifle range at distances up to five hundred yards,
+LJSpeech-1.1/wavs/LJ028-0346.wav|Further, wishing to prevent the race of the Babylonians from becoming extinct,
+LJSpeech-1.1/wavs/LJ003-0265.wav|the thieves and burglars who carried on the active business of their profession, from which their confederates were temporarily debarred.
+LJSpeech-1.1/wavs/LJ001-0132.wav|Here, again, if the practice of "leading" were retrenched larger type could be used without enhancing the price of a book.
+LJSpeech-1.1/wavs/LJ014-0131.wav|but Mrs. Manning, speaking in a foreign accent, addressed the court with great fluency and vehemence.
+LJSpeech-1.1/wavs/LJ038-0079.wav|on November twenty-four.
+LJSpeech-1.1/wavs/LJ032-0047.wav|According to the vice president of Klein's, William Waldman,
+LJSpeech-1.1/wavs/LJ018-0192.wav|But they had the body, all the same; it had been dressed up in decent clothes and made to stand upright in the cart.
+LJSpeech-1.1/wavs/LJ014-0120.wav|Each tried to throw the blame on the other; Manning declared his wife had committed the murder, Mrs. Manning indignantly denied the charge.
+LJSpeech-1.1/wavs/LJ003-0125.wav|The evidence was invariably sufficient to convict, and the judge never hesitated to inflict the heaviest penalties.
+LJSpeech-1.1/wavs/LJ006-0186.wav|said with justice that "incredible scenes of horror occur in Newgate."
+LJSpeech-1.1/wavs/LJ028-0147.wav|and when a sufficient number were completed they baked the bricks in kilns.
+LJSpeech-1.1/wavs/LJ038-0304.wav|on April ten, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ008-0036.wav|The following twenty-third April, it is stated that the malefactors ordered for execution on the eighteenth instead
+LJSpeech-1.1/wavs/LJ018-0296.wav|There such bills would be sent to the drawer to be initialed, and the forgery would have been at once detected.
+LJSpeech-1.1/wavs/LJ044-0069.wav|New Orleans branch, end quote.
+LJSpeech-1.1/wavs/LJ002-0166.wav|Mr. Buxton, in his "Inquiry into the System of Prison Discipline,"
+LJSpeech-1.1/wavs/LJ010-0299.wav|caused much excitement at the time on account of the magnitude of the fraud, and the seeming probity of the culprit.
+LJSpeech-1.1/wavs/LJ034-0007.wav|The Commission evaluated the physical evidence found near the window after the assassination and the testimony of eyewitnesses
+LJSpeech-1.1/wavs/LJ041-0033.wav|The association's then president, William E. Wulf, testified that he remembered an occasion when Oswald, quote,
+LJSpeech-1.1/wavs/LJ012-0085.wav|The chest was opened to give change, and a heavy tray lifted out which plainly held some four thousand pounds in cash.
+LJSpeech-1.1/wavs/LJ016-0385.wav|But the uneasiness soon wears off. The second night sleep comes readily, and is sound;
+LJSpeech-1.1/wavs/LJ018-0313.wav|deep laid, and with many ramifications, by which some of the Newgate warders were to be bribed to allow the prisoners to escape from their cells at night.
+LJSpeech-1.1/wavs/LJ030-0210.wav|and I saw President Kennedy grab at himself and lurch forward and to the left, end quote.
+LJSpeech-1.1/wavs/LJ006-0197.wav|pitch in the hole, cribbage, dominoes, and common tossing, at which as much as four or five shillings would change hands in an hour.
+LJSpeech-1.1/wavs/LJ048-0156.wav|and suggested the desirability of such a statement.
+LJSpeech-1.1/wavs/LJ003-0199.wav|and until the day was actually fixed, spent the time in roistering, swearing, gambling, or playing at ball.
+LJSpeech-1.1/wavs/LJ005-0049.wav|The refuge, which had for its object the training of its inmates in habits of industry,
+LJSpeech-1.1/wavs/LJ001-0066.wav|In fact Gunther Zeiner's first type (afterwards used by Schussler) is remarkably like the type of the before-mentioned Subiaco books.
+LJSpeech-1.1/wavs/LJ031-0216.wav|was located about an inch (two point five centimeters) to the right and slightly above the large bony protrusion
+LJSpeech-1.1/wavs/LJ019-0118.wav|and later experience has fully proved the advantage of a judicious system of gratuities for labor;
+LJSpeech-1.1/wavs/LJ029-0013.wav|During nineteen sixty-three, the reasons for making the trip became more persuasive.
+LJSpeech-1.1/wavs/LJ044-0226.wav|Oswald had expressed approval of President Kennedy's active role in the area of civil rights.
+LJSpeech-1.1/wavs/LJ046-0162.wav|The case file served merely as a repository for information until enough had accumulated to warrant an investigation.
+LJSpeech-1.1/wavs/LJ036-0152.wav|This was the seven hundred block of North Beckley
+LJSpeech-1.1/wavs/LJ021-0094.wav|consumer complaints and the settlement of disputes between employers and employees and between one employer and another.
+LJSpeech-1.1/wavs/LJ028-0221.wav|When I made my gracious entry into Babylon, with exceeding joy I took up my abode in the royal palace.
+LJSpeech-1.1/wavs/LJ022-0093.wav|The next step will be to notify the proper government agency
+LJSpeech-1.1/wavs/LJ049-0085.wav|the State of Texas had exclusive jurisdiction.
+LJSpeech-1.1/wavs/LJ048-0179.wav|of the Texas School Book Depository Building or those employed there prior to the time the President was shot. End quote.
+LJSpeech-1.1/wavs/LJ011-0166.wav|The road taken was via Manchester, where the servant said a Dr. Hull was to be picked up to go on with them to Shrigley.
+LJSpeech-1.1/wavs/LJ024-0014.wav|The number of judges to be appointed would depend wholly on the decision of present judges now over seventy,
+LJSpeech-1.1/wavs/LJ033-0027.wav|Mrs. Linnie Mae Randle, Frazier's sister, commented to her brother about Oswald's unusual midweek return to Irving.
+LJSpeech-1.1/wavs/LJ021-0008.wav|we are bringing order out of the old chaos
+LJSpeech-1.1/wavs/LJ018-0191.wav|Bob was therefore allowed to pass on.
+LJSpeech-1.1/wavs/LJ022-0160.wav|has lost touch with, and has lost the sympathy of, the communities it pretends to serve.
+LJSpeech-1.1/wavs/LJ031-0184.wav|Swearing in of the New President
+LJSpeech-1.1/wavs/LJ045-0028.wav|he and his wife spent every weekend but one together at the Irving, Texas home of Mrs. Ruth Paine, who was then separated from her husband.
+LJSpeech-1.1/wavs/LJ030-0185.wav|quote, we are hit. Get us to the hospital immediately, end quote.
+LJSpeech-1.1/wavs/LJ041-0175.wav|Lieutenant Donovan testified that Oswald thought that, quote, there were many grave injustices concerning the affairs in the international situation, end quote.
+LJSpeech-1.1/wavs/LJ011-0031.wav|He called many witnesses, seventeen in all, to speak of him as they had found him; and these, all respectable city merchants and business men,
+LJSpeech-1.1/wavs/LJ023-0118.wav|That means a Supreme Court that will enforce the Constitution as written
+LJSpeech-1.1/wavs/LJ047-0172.wav|Oswald had said that he was a, quote, Trotskyite Communist, end quote.
+LJSpeech-1.1/wavs/LJ010-0192.wav|or of the fatal consequences which might have ensued.
+LJSpeech-1.1/wavs/LJ019-0068.wav|Separation was injurious to health, mental or physical, said one side; men broke down when subjected to it for more than a certain period,
+LJSpeech-1.1/wavs/LJ037-0119.wav|I stepped to the back of the room, so I could kind of see him from the same distance which I had seen him before. And when he came out I knew him, end quote.
+LJSpeech-1.1/wavs/LJ006-0247.wav|The prisoner who used the knife was secured, but it was the wardsman, and not the officers, to whom the report was made, and no official inquiry or punishment followed.
+LJSpeech-1.1/wavs/LJ040-0076.wav|In the fall of that year John Pic and Robert Oswald went to a military academy
+LJSpeech-1.1/wavs/LJ035-0071.wav|At a given signal, they reenacted the event. Baker's movements were timed with a stopwatch.
+LJSpeech-1.1/wavs/LJ010-0285.wav|invariably went out in a cab, for which he always paid the same fare,
+LJSpeech-1.1/wavs/LJ008-0007.wav|The decision to remove the scene of action to the immediate front of the jail itself
+LJSpeech-1.1/wavs/LJ034-0116.wav|The suspect was described as, quote, a white male about thirty, five foot eight, black hair, slender, end quote.
+LJSpeech-1.1/wavs/LJ014-0053.wav|Soon afterwards, in Gloucestershire,
+LJSpeech-1.1/wavs/LJ015-0152.wav|His name headed every subscription list; his purse was always open.
+LJSpeech-1.1/wavs/LJ040-0066.wav|It had its effect on Lee's mother, Marguerite, his brother Robert, who had been born in nineteen thirty-four,
+LJSpeech-1.1/wavs/LJ044-0165.wav|He wanted to be arrested. I think he wanted to get into the newspapers, so that he would be known. End quote.
+LJSpeech-1.1/wavs/LJ005-0084.wav|"so as to prevent them from seeing, conversing, or holding any intercourse with each other."
+LJSpeech-1.1/wavs/LJ010-0295.wav|The increase of bank forgeries, and its cause, I referred to in a previous chapter.
+LJSpeech-1.1/wavs/LJ003-0266.wav|One notorious character, while a prisoner awaiting transfer to the hulks,
+LJSpeech-1.1/wavs/LJ032-0244.wav|Marina Oswald testified that the rifle found on the sixth floor of the Depository Building was the "fateful rifle of Lee Oswald."
+LJSpeech-1.1/wavs/LJ025-0160.wav|The weight of the nitrogenous protein compounds,
+LJSpeech-1.1/wavs/LJ015-0077.wav|He was not the first man of loose morality and expensive tastes
+LJSpeech-1.1/wavs/LJ004-0084.wav|Some might be in abeyance, but they had never been repealed, and some were quite freshly imported upon the Statute Book.
+LJSpeech-1.1/wavs/LJ030-0042.wav|Manned by officers of the Dallas Police Department, this automobile preceded the main party by approximately quarter of a mile.
+LJSpeech-1.1/wavs/LJ038-0014.wav|Brewer said, quote, He just looked funny to me. His hair was sort of messed up and looked like he had been running, and he looked scared, and he looked funny, end quote.
+LJSpeech-1.1/wavs/LJ037-0259.wav|that the man who killed Tippit was wearing a light-colored jacket,
+LJSpeech-1.1/wavs/LJ041-0075.wav|that authority, particularly the Marine Corps, ought to be able to recognize talent such as his own, without a given magic college degree,
+LJSpeech-1.1/wavs/LJ028-0244.wav|but would have destroyed them utterly; for they would have made fast all the street gates which gave upon the river,
+LJSpeech-1.1/wavs/LJ009-0158.wav|Already a strong dislike to the reckless and almost indiscriminate application of the extreme penalty was apparent in all classes,
+LJSpeech-1.1/wavs/LJ007-0098.wav|The other inmates of the prison of an exceptional character, and exempted from the regular discipline, such as it was,
+LJSpeech-1.1/wavs/LJ042-0085.wav|He said that his Marine service in Okinawa and elsewhere had given him, quote, a chance to observe American imperialism, end quote.
+LJSpeech-1.1/wavs/LJ018-0132.wav|To facilitate his operations, Brewer obtained a false master key from Burnett,
+LJSpeech-1.1/wavs/LJ009-0087.wav|It is enough to say that in the same solemn tone he talks about the minutest of crimes, punishments, bonds, shame,
+LJSpeech-1.1/wavs/LJ023-0114.wav|We must find a way to take an appeal from the Supreme Court to the Constitution itself.
+LJSpeech-1.1/wavs/LJ028-0064.wav|In five ninety-seven, when he sent his army to Jerusalem, he won the hatred of the Jews by taking Jehoiakin, the King, captive.
+LJSpeech-1.1/wavs/LJ027-0134.wav|Most remarkable of all the principles which have been discovered by embryologists is the "Recapitulation Doctrine"
+LJSpeech-1.1/wavs/LJ038-0115.wav|At the first interrogation, Oswald claimed that his only crime was carrying a gun and resisting arrest.
+LJSpeech-1.1/wavs/LJ011-0258.wav|The defense he set up was, that Mullay had used epithets towards him while they were negotiating a business matter,
+LJSpeech-1.1/wavs/LJ006-0283.wav|There was the same crowding in the sleeping arrangements as on the male side; the same scarcity of bedding.
+LJSpeech-1.1/wavs/LJ045-0172.wav|Answer: He said he would buy me a washing machine.
+LJSpeech-1.1/wavs/LJ005-0274.wav|The committee most of all insisted upon the entire individual separation of prisoners, except during the hours of labor,
+LJSpeech-1.1/wavs/LJ010-0044.wav|The present Queen very soon after her accession
+LJSpeech-1.1/wavs/LJ015-0236.wav|Burgess admitted him into the guard's van, where he fitted and filed the keys till they worked easily and satisfactorily in the locks of the safe.
+LJSpeech-1.1/wavs/LJ040-0038.wav|Such ideas of grandeur were apparently accompanied by notions of oppression.
+LJSpeech-1.1/wavs/LJ019-0004.wav|Elsewhere the spirit had been more or less active, although not uniformly or always to the same extent.
+LJSpeech-1.1/wavs/LJ036-0163.wav|When queried the following morning concerning a bus transfer found in his possession at the time of his arrest, he admitted receiving it.
+LJSpeech-1.1/wavs/LJ031-0163.wav|The agents decided not to shift the Presidential aircraft to the far side of the airport because the original landing area was secure
+LJSpeech-1.1/wavs/LJ024-0049.wav|in the administration of John Adams and Thomas Jefferson -- both signers of the Declaration of Independence
+LJSpeech-1.1/wavs/LJ032-0232.wav|The dates surrounding the taking of this picture and the purchase of the rifle
+LJSpeech-1.1/wavs/LJ040-0103.wav|An indication of the nature of Lee's character at this time was provided in the spring of nineteen fifty,
+LJSpeech-1.1/wavs/LJ008-0062.wav|and this floor is supported below,
+LJSpeech-1.1/wavs/LJ049-0070.wav|Any effort to prepare detailed contingency plans might well have the undesirable effect of inhibiting quick and imaginative responses.
+LJSpeech-1.1/wavs/LJ011-0081.wav|He was not prosecuted for this fraud on account of the respectability of his family, and soon after this escape
+LJSpeech-1.1/wavs/LJ003-0235.wav|even to prisoners almost in nakedness, and as a special charitable gift. Extortion was practiced right and left.
+LJSpeech-1.1/wavs/LJ003-0029.wav|Under the steward there were captains of wards, chosen in the same way, and performing analogous duties.
+LJSpeech-1.1/wavs/LJ019-0229.wav|This block was completed in eighteen fifty-nine, after which the hitherto unavoidable and long-continued promiscuous association of prisoners
+LJSpeech-1.1/wavs/LJ033-0195.wav|The single brown viscose fiber found in the bag matched some of the brown viscose fibers from the blanket in all observable characteristics.
+LJSpeech-1.1/wavs/LJ025-0094.wav|Starchy substances, cellulose and sugar,
+LJSpeech-1.1/wavs/LJ005-0050.wav|and in moral and religious duty, and which after a time sought to provide them with suitable situations, was supported entirely out of the funds of the Society.
+LJSpeech-1.1/wavs/LJ008-0262.wav|and with them indeed this dispatch amounted to undue precipitancy.
+LJSpeech-1.1/wavs/LJ045-0157.wav|Answer: I was angry, of course. He was not angry -- he was upset. I was angry. He tried very hard to please me.
+LJSpeech-1.1/wavs/LJ024-0141.wav|You who know me will accept my solemn assurance that in a world in which democracy is under attack,
+LJSpeech-1.1/wavs/LJ032-0274.wav|the rifle was kept among Oswald's possessions from the time of its purchase until the day of the assassination,
+LJSpeech-1.1/wavs/LJ029-0104.wav|Deputy Chief N. T. Fisher, and several other command officers to discuss details of the motorcade and possible routes.
+LJSpeech-1.1/wavs/LJ036-0076.wav|he would have boarded the bus at approximately twelve:forty p.m. and left it at approximately twelve:forty-four p.m.
+LJSpeech-1.1/wavs/LJ039-0095.wav|Zahm stated further, quote,
+LJSpeech-1.1/wavs/LJ021-0093.wav|second, the administrative function of code making and revision; and, third, the judicial function, which includes enforcement,
+LJSpeech-1.1/wavs/LJ018-0319.wav|A second was nevertheless set on foot,
+LJSpeech-1.1/wavs/LJ019-0171.wav|Another custom which had led to disorder was abolished;
+LJSpeech-1.1/wavs/LJ028-0085.wav|The eyes are suggestive of the Semitic; the nose is of the Greek type;
+LJSpeech-1.1/wavs/LJ045-0052.wav|which Mrs. Paine had made in part to give her an alternative to returning to the Soviet Union. Marina Oswald wrote to Mrs. Paine that, quote,
+LJSpeech-1.1/wavs/LJ035-0010.wav|As he turned the corner from Main onto Houston at a speed of about five to ten miles per hour,
+LJSpeech-1.1/wavs/LJ015-0216.wav|This was an important step, and they might easily be robbed some day when Burgess was the guard, provided only that they could be opened.
+LJSpeech-1.1/wavs/LJ044-0156.wav|Oswald apparently enclosed a note with her letter of July in which
+LJSpeech-1.1/wavs/LJ014-0047.wav|His demeanor was a strange compound of wickedness, falsehood, and deceit.
+LJSpeech-1.1/wavs/LJ002-0329.wav|the supply of which was, however, limited, and there were not always enough to give bedding to all. The stock was diminished by theft;
+LJSpeech-1.1/wavs/LJ025-0162.wav|will be vastly greater than the weight of the same substances contained in the bean from which it sprang.
+LJSpeech-1.1/wavs/LJ036-0075.wav|walked seven blocks directly to Murphy and Elm, and boarded a bus almost immediately,
+LJSpeech-1.1/wavs/LJ005-0197.wav|One borough prison consisted of nothing more than a couple of cells, about ten yards square, and absolutely nothing more.
+LJSpeech-1.1/wavs/LJ008-0187.wav|It was still greater at Fauntleroy's execution in eighteen twenty-four, when no less than one hundred thousand persons assembled, it was said.
+LJSpeech-1.1/wavs/LJ014-0066.wav|and by dint of usurious interest on small sums advanced to needy neighbors, had amassed as much as eight thousand pounds or ten thousand pounds.
+LJSpeech-1.1/wavs/LJ032-0215.wav|Shaneyfelt compared this negative with a negative which he made by taking a new picture with Oswald's camera.
+LJSpeech-1.1/wavs/LJ005-0241.wav|till the whole body of the slaves were manumitted in eighteen thirty-three.
+LJSpeech-1.1/wavs/LJ050-0214.wav|Although the Commission does not know whether the cases involved are entirely comparable,
+LJSpeech-1.1/wavs/LJ013-0140.wav|A robbery of a somewhat novel kind was executed in rather a bungling fashion by Ker, a sea-captain,
+LJSpeech-1.1/wavs/LJ013-0033.wav|The Wallaces were arrested, committed to Newgate, and tried at the Old Bailey.
+LJSpeech-1.1/wavs/LJ040-0145.wav|Dr. Hartogs did find Oswald to be a tense, withdrawn, and evasive boy who intensely disliked talking about himself and his feelings.
+LJSpeech-1.1/wavs/LJ025-0159.wav|and this plant will, in due time, flower and produce its crop of beans just as if it were grown in the garden or in the field.
+LJSpeech-1.1/wavs/LJ028-0191.wav|The old enemies of Babylon rejoiced.
+LJSpeech-1.1/wavs/LJ004-0143.wav|so that the culprit, in addition to his sentence,
+LJSpeech-1.1/wavs/LJ034-0122.wav|As will be discussed fully below, the Commission has concluded that this suspect was Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ042-0056.wav|two. That in my own mind I have no attachments of any kind in the U.S.
+LJSpeech-1.1/wavs/LJ028-0052.wav|In six oh six, Nineveh, the old enemy of Babylon, fell, never to rise again.
+LJSpeech-1.1/wavs/LJ041-0115.wav|He was undesirably discharged from the Marine Corps Reserve, to which he had been assigned on inactive status following his transfer from active duty,
+LJSpeech-1.1/wavs/LJ032-0086.wav|In Oswald's personal effects found in his room at ten twenty-six North Beckley Avenue in Dallas
+LJSpeech-1.1/wavs/LJ016-0240.wav|This generally produces instantaneous death, although cases where complete fracture of the spinal cord occurred are said to be rare.
+LJSpeech-1.1/wavs/LJ028-0284.wav|When he found that Darius did indeed value it highly, he considered further with himself how he might make the deed his own, and be the man to take Babylon.
+LJSpeech-1.1/wavs/LJ042-0120.wav|As my Russian improves I become increasingly conscious of just what sort of a society I live in.
+LJSpeech-1.1/wavs/LJ037-0068.wav|taken at the time of his arrest.
+LJSpeech-1.1/wavs/LJ003-0263.wav|which might have done something towards increasing the prison receipts, had it not been appropriated by the turnkey who winked at this evasion of the rules.
+LJSpeech-1.1/wavs/LJ008-0101.wav|exuberant in talk and hissing hot from Pie Corner, where she had taken her morning dose of gin-and-bitters.
+LJSpeech-1.1/wavs/LJ030-0202.wav|Reaction by Secret Service Agents
+LJSpeech-1.1/wavs/LJ021-0161.wav|just as other countries have had them for over a decade.
+LJSpeech-1.1/wavs/LJ023-0115.wav|We want a Supreme Court which will do justice under the Constitution -- not over it.
+LJSpeech-1.1/wavs/LJ014-0081.wav|Manning gave the coup de grace with a crowbar.
+LJSpeech-1.1/wavs/LJ008-0248.wav|Another fight followed the score which had already taken place; this time two women were the combatants.
+LJSpeech-1.1/wavs/LJ028-0261.wav|At last when the time came for rebelling openly they did as follows:
+LJSpeech-1.1/wavs/LJ037-0065.wav|as short, stocky and having bushy hair. She stated that the man weighed about one hundred fifty pounds.
+LJSpeech-1.1/wavs/LJ037-0164.wav|All four bullets were fired from a weapon
+LJSpeech-1.1/wavs/LJ044-0020.wav|On August seventeen, nineteen sixty-three, he appeared briefly on a radio program and on August twenty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ010-0015.wav|for the purpose of misappropriating funds or feloniously obtaining cash;
+LJSpeech-1.1/wavs/LJ033-0095.wav|Frazier testified that Oswald carried no lunch bag that day.
+LJSpeech-1.1/wavs/LJ008-0092.wav|the man of better birth could hope for no sympathy, whatever his crime.
+LJSpeech-1.1/wavs/LJ032-0043.wav|A bank deposit made on March thirteen, nineteen sixty-three, included an item of twenty-one dollars, forty-five cents. Klein's shipping order form
+LJSpeech-1.1/wavs/LJ011-0200.wav|and went on to say, "I do assure you, madam, that it shall be the anxious endeavor of my life to promote her happiness by every means in my power."
+LJSpeech-1.1/wavs/LJ029-0159.wav|quote, apparently will loop through the downtown area, probably on Main Street, en route from Dallas Love Field, end quote.
+LJSpeech-1.1/wavs/LJ042-0146.wav|Lee was always hot-tempered, and now this trait of character more and more prevented us from living together in harmony.
+LJSpeech-1.1/wavs/LJ015-0028.wav|the bank was insolvent, and instead of starting on a fresh life with a new name, it should then and there have closed its doors.
+LJSpeech-1.1/wavs/LJ049-0103.wav|whether or not the act is committed while the victim is in the performance of his official duties or on account of such performance.
+LJSpeech-1.1/wavs/LJ013-0006.wav|who was convicted in eighteen forty-one of uttering false exchequer bills to an almost fabulous amount.
+LJSpeech-1.1/wavs/LJ031-0186.wav|who advised that Mr. Johnson take the Presidential oath of office before the plane left Dallas.
+LJSpeech-1.1/wavs/LJ049-0043.wav|However, these agents could not reach the President's car when it was traveling at an appreciable rate of speed.
+LJSpeech-1.1/wavs/LJ007-0192.wav|inasmuch as it has aroused the attention of those upon whom parliamentary reports and grand jury presentments had hitherto failed to make the slightest impression.
+LJSpeech-1.1/wavs/LJ004-0175.wav|In this prison there was no female infirmary.
+LJSpeech-1.1/wavs/LJ034-0070.wav|During the morning of November twenty-two, Givens was working with the floor-laying crew in the southwest section of the sixth floor.
+LJSpeech-1.1/wavs/LJ037-0052.wav|At about four:thirty p.m., Mrs. Markham,
+LJSpeech-1.1/wavs/LJ019-0299.wav|but in one prison it was such that the prisoners' food was supplied daily from the neighboring inn, and the innkeeper's bill constituted the only accounts kept.
+LJSpeech-1.1/wavs/LJ028-0511.wav|Should you walk along the shore of the Euphrates at Babylon, you would still see the embankments which Nebuchadnezzar constructed of bricks bearing his name,
+LJSpeech-1.1/wavs/LJ002-0292.wav|the master's side, the cabin side, and the common side. Payment of a fee of three shillings gained the debtor admission to the two first named;
+LJSpeech-1.1/wavs/LJ016-0175.wav|I may be permitted to digress for a moment to give a few particulars concerning the last officially appointed hangman of the city of London.
+LJSpeech-1.1/wavs/LJ018-0157.wav|In the course of his remarks, however, he said,
+LJSpeech-1.1/wavs/LJ005-0036.wav|The objects it had in view were set forth in one of its earliest meetings.
+LJSpeech-1.1/wavs/LJ027-0021.wav|Whatever emphasis is laid upon the diversity of life, the essential unity of all organisms must not be forgotten.
+LJSpeech-1.1/wavs/LJ027-0117.wav|"Now, here again the former theory appears to be triumphant over the latter," says Romanes,
+LJSpeech-1.1/wavs/LJ049-0005.wav|Rigorous security precautions had been arranged at Love Field with the local law enforcement authorities by Agents Sorrels and Lawson.
+LJSpeech-1.1/wavs/LJ011-0121.wav|Suddenly, after being at the lowest depths, he emerged, and was found by his friends living in comfort in the Waterloo Road.
+LJSpeech-1.1/wavs/LJ009-0006.wav|Before sentence a prisoner has only to observe the regulations of the jail in order to remain neglected and unnoticed.
+LJSpeech-1.1/wavs/LJ017-0039.wav|is obviously of paramount importance, and the powers granted by this Act have been frequently put in practice since.
+LJSpeech-1.1/wavs/LJ018-0020.wav|A desperate and deadly struggle must have taken place in the carriage, and the stain of a bloody hand marked the door.
+LJSpeech-1.1/wavs/LJ033-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ031-0127.wav|a protective circle of Secret Service agents surrounded Vice President and Mrs. Johnson
+LJSpeech-1.1/wavs/LJ013-0148.wav|four thousand pounds
+LJSpeech-1.1/wavs/LJ006-0195.wav|This was admitted in evidence by the turnkeys, and was proved by the appearance of the prison tables, which bore the marks of gaming-boards deeply cut into them.
+LJSpeech-1.1/wavs/LJ045-0036.wav|While those considerations no doubt had an effect on Oswald's attitude toward his family it would seem that the need for support and sympathy
+LJSpeech-1.1/wavs/LJ001-0003.wav|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process
+LJSpeech-1.1/wavs/LJ001-0043.wav|A further development of the Roman letter took place at Venice.
+LJSpeech-1.1/wavs/LJ029-0189.wav|The local, national, and international reaction to this incident evoked from Dallas officials and newspapers strong condemnations of the demonstrators.
+LJSpeech-1.1/wavs/LJ031-0213.wav|Under "Pathological Diagnosis" the cause of death was set forth as "Gunshot wound, head."
+LJSpeech-1.1/wavs/LJ017-0114.wav|The intestines were, however, preserved and sent for analysis,
+LJSpeech-1.1/wavs/LJ028-0379.wav|fearing the wild beasts and evil spirits which lurk among all old ruins, refused to pitch their tents there.
+LJSpeech-1.1/wavs/LJ018-0234.wav|As he possessed nearly unbounded influence over his mother, her accession to the property meant that William could dispose of it as he pleased.
+LJSpeech-1.1/wavs/LJ003-0340.wav|The committee does not seem to have yet understood that Newgate could be only and properly replaced
+LJSpeech-1.1/wavs/LJ037-0165.wav|with five lands and grooves and a right twist which were the rifling characteristics of the revolver taken from Oswald.
+LJSpeech-1.1/wavs/LJ046-0101.wav|and an inquiry whether improvements can be made which will reduce the risk of another such tragedy.
+LJSpeech-1.1/wavs/LJ029-0203.wav|greet the President of the United States with the warmth and pride that keep the Dallas spirit famous the world over, end quote,
+LJSpeech-1.1/wavs/LJ034-0082.wav|None of the Depository employees is known to have seen Oswald again until after the shooting.
+LJSpeech-1.1/wavs/LJ031-0036.wav|but then Kellerman, Greer, and Lawson lifted the President onto a stretcher and pushed it into trauma room one.
+LJSpeech-1.1/wavs/LJ005-0288.wav|when speaking more particularly of the borough jails.
+LJSpeech-1.1/wavs/LJ004-0208.wav|There were work-rooms for wool-washing, dyeing, carding, and spinning.
+LJSpeech-1.1/wavs/LJ002-0181.wav|to those in other London prisons, for Newgate was not the only place of durance for these unfortunate people. There were also the King's Bench,
+LJSpeech-1.1/wavs/LJ033-0017.wav|who also worked at the Depository.
+LJSpeech-1.1/wavs/LJ021-0020.wav|and transportation and trade, so great in the mass that each individual concerned in them is quite helpless by himself.
+LJSpeech-1.1/wavs/LJ008-0127.wav|As we were crossing the press yard,
+LJSpeech-1.1/wavs/LJ016-0223.wav|Capital convicts go to the gallows in their own clothing, and not in prison dress, unless the former is quite unfit to be worn.
+LJSpeech-1.1/wavs/LJ015-0075.wav|that ere long the whole management of the transfer department was entrusted to him.
+LJSpeech-1.1/wavs/LJ032-0112.wav|Oswald's Palmprint on Rifle Barrel
+LJSpeech-1.1/wavs/LJ028-0197.wav|Belshazzar, the King, he was really the King's son, gave a feast to a thousand of his nobles.
+LJSpeech-1.1/wavs/LJ003-0261.wav|only a few of whom had the commendable prudery to pass themselves off as the wives of prisoners.
+LJSpeech-1.1/wavs/LJ015-0150.wav|But his chief extravagance, his favorite folly, was the exercise of an ostentatious benevolence.
+LJSpeech-1.1/wavs/LJ016-0082.wav|The ward was one short of its number. What had become of the fellow?
+LJSpeech-1.1/wavs/LJ002-0016.wav|Previous to that date there had been seven hundred or eight hundred frequently, and once, in Mr. Akerman's time, one thousand.
+LJSpeech-1.1/wavs/LJ012-0286.wav|all of which, according to his own confession, made no doubt with the idea of exonerating Mrs. Gale, he accomplished without her assistance.
+LJSpeech-1.1/wavs/LJ027-0027.wav|First, it must be noted that some structures are not non-adaptive, that is, do not change to fit changed habits or conditions of life.
+LJSpeech-1.1/wavs/LJ006-0203.wav|and he had thus ample means of introducing to the prisoners the prohibited but always much-coveted and generally procurable weed.
+LJSpeech-1.1/wavs/LJ045-0185.wav|he asked Frazier for a ride to Irving that night, stating falsely that he wanted to pick up some curtain rods to put in an apartment.
+LJSpeech-1.1/wavs/LJ004-0194.wav|No irons were worn except as a punishment.
+LJSpeech-1.1/wavs/LJ037-0006.wav|A sixth did so the next day.
+LJSpeech-1.1/wavs/LJ007-0104.wav|the parliamentary agents of the members whose election was impugned, were implicated in aiding and abetting the others to abscond, and a Mr. O'Mally,
+LJSpeech-1.1/wavs/LJ006-0107.wav|he sold tea, coffee, sugar, tobacco, although prohibited, and extra beer.
+LJSpeech-1.1/wavs/LJ016-0383.wav|All almost without exception sleep soundly at night, except the first after sentence,
+LJSpeech-1.1/wavs/LJ047-0250.wav|Brian did not hear Hosty make any statement concerning Oswald's capacity to be an assassin
+LJSpeech-1.1/wavs/LJ003-0126.wav|The unfortunate man was compelled at length to adopt the habits of his associates;
+LJSpeech-1.1/wavs/LJ002-0085.wav|Security was provided for in these condemned cells by lining the substantial stone walls with planks studded with broad-headed nails;
+LJSpeech-1.1/wavs/LJ002-0026.wav|in the three years between eighteen thirteen and eighteen sixteen,
+LJSpeech-1.1/wavs/LJ027-0158.wav|But according to the opposite view no reason can be assigned why such should be the case.
+LJSpeech-1.1/wavs/LJ008-0247.wav|but ever the same form moved along in the fulfillment of his mission, in spite of all persecution.
+LJSpeech-1.1/wavs/LJ036-0044.wav|Oswald had rented a room from Mrs. Bledsoe about six weeks before, on October seven, but she had asked him to leave at the end of a week.
+LJSpeech-1.1/wavs/LJ014-0278.wav|The latter, when first taxed with his offense, protested his innocence boldly, and positively denied all knowledge of the affair;
+LJSpeech-1.1/wavs/LJ037-0199.wav|The mail-order form had a line for the name of a witness to attest that the person ordering the gun was a U.S. citizen and had not been convicted of a felony.
+LJSpeech-1.1/wavs/LJ015-0183.wav|His manner was generally self-possessed, but his face was marked with "uneasy earnestness,"
+LJSpeech-1.1/wavs/LJ030-0151.wav|and issued his initial orders at twelve:thirty p.m.
+LJSpeech-1.1/wavs/LJ019-0092.wav|They were approved of and employed at some prisons, at others objected to because they were unproductive,
+LJSpeech-1.1/wavs/LJ006-0025.wav|France had sent Misseurs Beaumont and De Tocqueville, who subsequently published several interesting works on the subject.
+LJSpeech-1.1/wavs/LJ007-0057.wav|Turnkeys occasionally visited the press-yard, but its occupants were under little or no control.
+LJSpeech-1.1/wavs/LJ025-0006.wav|that the functions or workings of the organs of plants, animals or man are quite distinct,
+LJSpeech-1.1/wavs/LJ041-0176.wav|He recalled that Oswald had a specific interest in Latin America, particularly Cuba,
+LJSpeech-1.1/wavs/LJ010-0138.wav|and died in a manner strongly contrasting with that of his fellows.
+LJSpeech-1.1/wavs/LJ021-0198.wav|that the Constitution is but a barrier to progress instead of being the broad highway through which alone true progress may be enjoyed.
+LJSpeech-1.1/wavs/LJ009-0187.wav|Beneath is a door for the admission of the bodies of murderers and other felons. There were other public dissecting rooms for criminals.
+LJSpeech-1.1/wavs/LJ024-0116.wav|When the time comes for action,
+LJSpeech-1.1/wavs/LJ042-0102.wav|Reminiscent of his attitude toward his superiors in the Marine Corps,
+LJSpeech-1.1/wavs/LJ028-0006.wav|you ascend the hill to the plateau, and before you, as far as the eye can reach, stretches the great Arabian Desert.
+LJSpeech-1.1/wavs/LJ040-0161.wav|Dr. Hartogs recommended that Oswald be placed on probation on condition that he seek help and guidance through a child guidance clinic.
+LJSpeech-1.1/wavs/LJ026-0001.wav|The Science: History of the Universe, Volume five. Edited by Francis Rolt-Wheeler. Biology. Chapter eight.
+LJSpeech-1.1/wavs/LJ031-0205.wav|On the seventeenth floor of the Hospital, Mrs. Kennedy and the Attorney General joined other members of the Kennedy family to await the conclusion of the autopsy.
+LJSpeech-1.1/wavs/LJ002-0283.wav|and denied admission to the "charity wards," which partook of all the benefits of bequests and donations to poor debtors.
+LJSpeech-1.1/wavs/LJ004-0214.wav|Its evils were inherent and irremediable, but some ameliorating measures had been introduced,
+LJSpeech-1.1/wavs/LJ050-0075.wav|The Commission shares this concern.
+LJSpeech-1.1/wavs/LJ017-0145.wav|Dr. Smethurst was long an inmate of Newgate, and was tried at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ029-0168.wav|the Morning News noted that the motorcade would travel through downtown Dallas onto the Stemmons Freeway, and reported that, quote, the motorcade will move slowly
+LJSpeech-1.1/wavs/LJ007-0155.wav|to be subjected to the same baneful influences, and to suffer the same moral deterioration, whether ultimately convicted or set free.
+LJSpeech-1.1/wavs/LJ011-0284.wav|His captors having thus succeeded in their designs, left him, no doubt to realize the money.
+LJSpeech-1.1/wavs/LJ047-0136.wav|they had vacated their apartment, and Marina Oswald had departed with their child in a station wagon with Texas registration.
+LJSpeech-1.1/wavs/LJ018-0029.wav|A photograph of Müller shown the jeweler was identified as the likeness of the man who had exchanged Mr. Briggs' chain.
+LJSpeech-1.1/wavs/LJ014-0315.wav|through a dishonored cheque for three thousand pounds, paid over as an installment of eighteen thousand pounds owing for an advance on warrants.
+LJSpeech-1.1/wavs/LJ038-0158.wav|Jarman testified that he ate his lunch on the first floor around five minutes to twelve, and that he neither ate lunch with nor saw Oswald.
+LJSpeech-1.1/wavs/LJ050-0089.wav|While these tentative criteria are a step in the right direction,
+LJSpeech-1.1/wavs/LJ028-0516.wav|the strongest, the thickest, the loftiest, the most intricate, perhaps the most beautiful that ever protected a city,
+LJSpeech-1.1/wavs/LJ037-0215.wav|She also testified that this appeared to be the revolver seen in Oswald's belt
+LJSpeech-1.1/wavs/LJ006-0052.wav|The sum total thus produced was inconsiderable compared with the hundreds that had formerly filled the jail,
+LJSpeech-1.1/wavs/LJ030-0239.wav|Youngblood was not positive that he was in the rear seat before the second shot, but thought it probable because of President Johnson's statement
+LJSpeech-1.1/wavs/LJ011-0143.wav|and in defiance of the vote of the assembly which more accurately represented public opinion. It was indeed announced that their fate was sealed;
+LJSpeech-1.1/wavs/LJ007-0081.wav|whose proper place was the asylum.
+LJSpeech-1.1/wavs/LJ018-0172.wav|More than this, Griffiths took the police to a field where, in a bank, a number of other plates were secreted.
+LJSpeech-1.1/wavs/LJ016-0114.wav|A later case was still more remarkable, as it was effected after the alteration of the prison and its reconstruction on the newest lines.
+LJSpeech-1.1/wavs/LJ050-0210.wav|the caseload of each FBI agent averaged twenty to twenty-five, and he felt that this was high.
+LJSpeech-1.1/wavs/LJ008-0300.wav|The prisoners and their friends, therefore, were kept in a state of the most violent suspense for many hours,
+LJSpeech-1.1/wavs/LJ047-0116.wav|On August twenty-two, it learned that Oswald had appeared on a radio discussion program on August twenty-one.
+LJSpeech-1.1/wavs/LJ003-0209.wav|The prisoners in the press yard had free access during the day to the yard and large day room;
+LJSpeech-1.1/wavs/LJ005-0214.wav|Some reforms had certainly been introduced, such as the abolition of irons, already referred to, and the establishment of male and female infirmaries.
+LJSpeech-1.1/wavs/LJ033-0206.wav|Stombaugh confirmed that the rifle could have picked up fibers from the blanket and transferred them to the paper bag.
+LJSpeech-1.1/wavs/LJ042-0203.wav|not in the name of freedom or high ideals,
+LJSpeech-1.1/wavs/LJ009-0176.wav|Seven other crimes, however, were still capital by law, and so continued till the passing of the Criminal Consolidation Acts of eighteen sixty-one.
+LJSpeech-1.1/wavs/LJ037-0232.wav|As has been discussed previously,
+LJSpeech-1.1/wavs/LJ039-0038.wav|throughout April. The Commission asked Marina Oswald whether she might have misunderstood the object of her husband's threat. She stated, quote,
+LJSpeech-1.1/wavs/LJ015-0105.wav|Mr. Fasson waited and waited for his subordinate to re-appear, and at last discovered his flight.
+LJSpeech-1.1/wavs/LJ021-0143.wav|with small groups of those truly representative of large employers of labor and of large groups of organized labor,
+LJSpeech-1.1/wavs/LJ039-0029.wav|No edition of either Dallas newspaper during the period January one, nineteen sixty-three, to May fifteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ040-0041.wav|There was some quality about him that led him to act with an apparent disregard for possible consequences.
+LJSpeech-1.1/wavs/LJ009-0081.wav|For a while the preacher addresses himself to the congregation at large, who listen attentively
+LJSpeech-1.1/wavs/LJ039-0231.wav|Having fired this slot
+LJSpeech-1.1/wavs/LJ015-0082.wav|He took it for granted that the certificates representing these bogus shares, and which practically did not exist, would never be called for.
+LJSpeech-1.1/wavs/LJ039-0017.wav|I asked him where he was going, and why he was getting dressed. He answered Nixon is coming. I want to go and have a look, end quote.
+LJSpeech-1.1/wavs/LJ002-0048.wav|two. The female debtors' side consisted of a court-yard forty-nine by sixteen feet,
+LJSpeech-1.1/wavs/LJ017-0212.wav|Her captain was John Smith;
+LJSpeech-1.1/wavs/LJ028-0376.wav|As the centuries passed the mounds into which the city had turned grew higher and higher with the ruins of the huts later built upon them,
+LJSpeech-1.1/wavs/LJ029-0155.wav|As the date for the President's visit approached,
+LJSpeech-1.1/wavs/LJ025-0113.wav|The famous researches of Schwann and Schleiden in eighteen thirty-seven and the following years
+LJSpeech-1.1/wavs/LJ041-0190.wav|It got him in the newspapers. It did broadcast his name out, end quote.
+LJSpeech-1.1/wavs/LJ031-0168.wav|In another car Mrs. Johnson was driven to the airport accompanied by Secret Service agents and Representative Brooks.
+LJSpeech-1.1/wavs/LJ050-0233.wav|Since the assassination, the Service has experimented with the use of agents borrowed for short periods from such agencies.
+LJSpeech-1.1/wavs/LJ009-0155.wav|drawn to this terrible exhibition by the example of his father, the more celebrated Edmund Kean, who had witnessed the execution of Thistlewood
+LJSpeech-1.1/wavs/LJ027-0163.wav|Next, it forms first one pair and then another pair of legs; and meanwhile it begins to breathe also by lungs.
+LJSpeech-1.1/wavs/LJ007-0239.wav|both offenses and punishments affording a sufficient index to the practices going forward; and they wind up by declaring
+LJSpeech-1.1/wavs/LJ003-0016.wav|Bad cases were removed to a cell on the felons' side, and here they were locked in solitary confinement for three days at a time.
+LJSpeech-1.1/wavs/LJ027-0125.wav|in the Python we find very tiny rudiments of the hindlimbs. Now,
+LJSpeech-1.1/wavs/LJ009-0004.wav|would be astonished to observe the peculiar tenderness, I was going to add respect,
+LJSpeech-1.1/wavs/LJ018-0329.wav|their crimes follow in the lines of others already found, and often more than once, in the calendars.
+LJSpeech-1.1/wavs/LJ008-0290.wav|Of this I have treated at length in the last chapter.
+LJSpeech-1.1/wavs/LJ039-0144.wav|Oswald told her he was going to practice with it.
+LJSpeech-1.1/wavs/LJ026-0071.wav|Water and dissolved salts continually pass by diffusion from the soil into the roots, where together they constitute the sap.
+LJSpeech-1.1/wavs/LJ048-0021.wav|Question: This is the Quigley interview you are talking about?
+LJSpeech-1.1/wavs/LJ011-0064.wav|He was also attended by the Rev. Mr. Springett and the indefatigable Mr. Baker, whose name has already been mentioned.
+LJSpeech-1.1/wavs/LJ017-0227.wav|He was struck down, imploring mercy, but they beat him about the head and face
+LJSpeech-1.1/wavs/LJ008-0209.wav|Soon after midnight on the Sunday night, for by this time the present practice of executing on Monday morning had been pretty generally introduced,
+LJSpeech-1.1/wavs/LJ045-0057.wav|He specifically made that claim when his wife refused to come to live with him in Dallas as he asked her to do on the evening of November twenty-one,
+LJSpeech-1.1/wavs/LJ050-0098.wav|determination to use a means, other than legal or peaceful, to satisfy his grievance, end quote, within the meaning of the new criteria.
+LJSpeech-1.1/wavs/LJ050-0226.wav|eighteen agents are proposed for a rotating pool which will go through an intensive training cycle and also be available to supplement the White House detail
+LJSpeech-1.1/wavs/LJ002-0116.wav|The number of processes against debtors annually was extraordinary.
+LJSpeech-1.1/wavs/LJ050-0151.wav|it makes no use of the recent developments in automatic data processing which are widely used in the business world and in other Government offices.
+LJSpeech-1.1/wavs/LJ009-0016.wav|He seems to have improved the occasion when preaching the condemned sermon before Fauntleroy, by pointing a moral from that unhappy man's own case.
+LJSpeech-1.1/wavs/LJ027-0151.wav|The only alternative view is that as species of deer,
+LJSpeech-1.1/wavs/LJ019-0324.wav|The chaplain's duties were enlarged, and the principle of toleration accepted to the extent of securing to all prisoners
+LJSpeech-1.1/wavs/LJ019-0109.wav|Mr. Pearson backed up his recommendations by many sound arguments.
+LJSpeech-1.1/wavs/LJ033-0127.wav|The Commission has weighed the visual recollection of Frazier and Mrs. Randle against the evidence here presented
+LJSpeech-1.1/wavs/LJ023-0032.wav|If we learned anything from the depression
+LJSpeech-1.1/wavs/LJ028-0387.wav|The Sassanian kings of Persia were fond of hunting, and Babylon, then overgrown with trees, was their game preserve.
+LJSpeech-1.1/wavs/LJ026-0161.wav|with the part played by the same substances in starch formation (photosynthesis).
+LJSpeech-1.1/wavs/LJ007-0094.wav|had actually been returned as sane from the asylum to which they had been sent, and there was always some uncertainty as to who was mad and who not.
+LJSpeech-1.1/wavs/LJ010-0172.wav|He saw Prince Albert return there from a visit to Woolwich, and then passed on to Constitution Hill,
+LJSpeech-1.1/wavs/LJ026-0106.wav|The change is from starch to a sugar capable of diffusion.
+LJSpeech-1.1/wavs/LJ028-0497.wav|We do not know their height, for the statements of the ancient writers disagree.
+LJSpeech-1.1/wavs/LJ038-0092.wav|Oswald provided little information during his questioning.
+LJSpeech-1.1/wavs/LJ047-0006.wav|The FBI had been interested in him, to some degree at least, since the time of his defection in October nineteen fifty-nine.
+LJSpeech-1.1/wavs/LJ008-0293.wav|Sometimes the Privy Council did not meet for months, and during all that time the convicts languished with hope
+LJSpeech-1.1/wavs/LJ036-0191.wav|he would have reached tenth and Patton shortly after one:fifteen p.m.
+LJSpeech-1.1/wavs/LJ019-0206.wav|This jail was nominally to replace the Giltspur Street Compter,
+LJSpeech-1.1/wavs/LJ022-0145.wav|we have found from time to time more and more useful ways of promoting its purposes.
+LJSpeech-1.1/wavs/LJ017-0140.wav|In both the jury had no doubt as to the guilt of the accused, only in Smethurst's case the then Home Secretary, Sir George Cornewall Lewis,
+LJSpeech-1.1/wavs/LJ048-0258.wav|or in the slightest way prevent them from taking any action that might have averted the tragedy.
+LJSpeech-1.1/wavs/LJ047-0039.wav|From return to Fort Worth to move to New Orleans.
+LJSpeech-1.1/wavs/LJ014-0112.wav|A telegraphic message, then newly adapted to the purposes of criminal detection,
+LJSpeech-1.1/wavs/LJ022-0187.wav|but these twenty years have shown by experience definite possibilities for improvement.
+LJSpeech-1.1/wavs/LJ015-0311.wav|had not been guided by a sense of virtue, and directed to more honorable and useful pursuits.
+LJSpeech-1.1/wavs/LJ040-0153.wav|Dr. Hartogs summarized his report by stating:
+LJSpeech-1.1/wavs/LJ010-0246.wav|Francis had already been seized by a policeman named Trounce, who saw his movement with the pistol, but too late to prevent its discharge.
+LJSpeech-1.1/wavs/LJ003-0294.wav|This committee made its report in September the following year, and an excellent report it is, so far as its recommendations are concerned.
+LJSpeech-1.1/wavs/LJ031-0151.wav|Special Agent Lawson made certain that the Dallas police kept the public and press away from the immediate area of the hospital.
+LJSpeech-1.1/wavs/LJ028-0115.wav|I laid its foundation deep to the water level;
+LJSpeech-1.1/wavs/LJ009-0203.wav|it was determined that a public exhibition should be made of the body through the neighborhood which had been the scene of the monster's crimes.
+LJSpeech-1.1/wavs/LJ032-0242.wav|the revolver shipped from Los Angeles on the same date, and the two newspapers which he was holding in the picture.
+LJSpeech-1.1/wavs/LJ002-0199.wav|but in the twenty-seventh George the second the right of presentation was bought by the Crown for ten thousand, five hundred pounds.
+LJSpeech-1.1/wavs/LJ050-0201.wav|in the locality to assure adequate manpower for this task, as it is now doing.
+LJSpeech-1.1/wavs/LJ045-0222.wav|The feelings of hostility and aggression which seem to have played such an important, part in Oswald's life
+LJSpeech-1.1/wavs/LJ032-0156.wav|In a crevice between the butt plate of the rifle and the wooden stock
+LJSpeech-1.1/wavs/LJ028-0334.wav|This last victory gave him the finishing stroke to his power and made him all in all with the Babylonians:
+LJSpeech-1.1/wavs/LJ032-0023.wav|to Klein's Sporting Goods Co., of Chicago, Illinois.
+LJSpeech-1.1/wavs/LJ018-0207.wav|The community of coiners of which he was so notorious a member
+LJSpeech-1.1/wavs/LJ012-0236.wav|Bishop's confession was endorsed by Williams, and the immediate result was the respite of May.
+LJSpeech-1.1/wavs/LJ009-0026.wav|the experience I have had convinces me that one, and perhaps the only, good of an execution, i. e. the solemn admonition to the public,
+LJSpeech-1.1/wavs/LJ008-0259.wav|and of their demeanor after sentence until the last penalty was paid.
+LJSpeech-1.1/wavs/LJ028-0359.wav|Darius, who besieged the rebellious city twice, weakened it by destroying some of its walls.
+LJSpeech-1.1/wavs/LJ026-0114.wav|and even of proteids only a limited amount, seventy-five to one hundred grams a day for a man, is built into new protoplasm.
+LJSpeech-1.1/wavs/LJ020-0033.wav|It will not swell so fast as the white, so give yourself more time for making it.
+LJSpeech-1.1/wavs/LJ020-0106.wav|While your bread is taking a three hours’ rise, you are free in body and mind for other things.
+LJSpeech-1.1/wavs/LJ048-0157.wav|Agent Lawson agreed that such a procedure would assist him and other agents in fulfilling their responsibilities as advance agents.
+LJSpeech-1.1/wavs/LJ006-0272.wav|may be gathered from the statement that as many as three hundred were often admitted on the same day
+LJSpeech-1.1/wavs/LJ014-0206.wav|His captors, it may be added (Lerigo and Allen), were warmly commended by the judge for their courage and activity.
+LJSpeech-1.1/wavs/LJ002-0297.wav|on the master's side it was thirteen and fourpence, and a gallon of beer on entrance, although Mr. Newman,
+LJSpeech-1.1/wavs/LJ023-0059.wav|In its Preamble, the Constitution states that it was intended to form a more perfect Union and promote the general welfare;
+LJSpeech-1.1/wavs/LJ011-0065.wav|When led out on the morning of his execution, these two last-named gentlemen each took hold of one of his arms, and so accompanied him to the scaffold.
+LJSpeech-1.1/wavs/LJ008-0220.wav|hoary-headed and hardened in guilt, were still at large.
+LJSpeech-1.1/wavs/LJ012-0179.wav|The others followed, and on overtaking Thurtell, found he had done the job alone in a retired part of the road known as Gill's Hill Lane.
+LJSpeech-1.1/wavs/LJ006-0112.wav|he might keep him from the fire, or give him his soup or gruel in a pail instead of a basin.
+LJSpeech-1.1/wavs/LJ037-0149.wav|Cortlandt Cunningham, of the Firearms Identification Unit of the FBI Laboratory, testified
+LJSpeech-1.1/wavs/LJ012-0159.wav|He did not thrive on prison fare,
+LJSpeech-1.1/wavs/LJ046-0102.wav|This section considers first the means used to locate potential sources of danger to the President in time to take appropriate precautions.
+LJSpeech-1.1/wavs/LJ029-0184.wav|that President Kennedy would receive a "good welcome" and would not face demonstrations like those encountered
+LJSpeech-1.1/wavs/LJ031-0088.wav|This man was in obvious extreme distress and any more thorough inspection would have involved several minutes -- well, several
+LJSpeech-1.1/wavs/LJ036-0136.wav|What the hell. I wonder what the hell is the uproar?
+LJSpeech-1.1/wavs/LJ018-0376.wav|who was the ringleader, and who had long been an habitual criminal, a reputed murderer, and certainly an inmate more than once of a French bagne.
+LJSpeech-1.1/wavs/LJ001-0109.wav|this is best furthered by the avoidance of irrational swellings and spiky projections, and by the using of careful purity of line.
+LJSpeech-1.1/wavs/LJ030-0235.wav|As we were beginning to go down this incline, all of a sudden there was an explosive noise.
+LJSpeech-1.1/wavs/LJ050-0138.wav|In addition, the Department of the Treasury has requested five additional agents for its Protective Research Section
+LJSpeech-1.1/wavs/LJ022-0027.wav|The most difficult place in the world to get a clear open perspective of the country as a whole is Washington.
+LJSpeech-1.1/wavs/LJ038-0108.wav|that they had superimposed upon the photographs a rifle and a revolver. He told Fritz a number of times that the smaller photograph
+LJSpeech-1.1/wavs/LJ016-0186.wav|invited the man with the cough to enter a neighboring house and try a little peppermint for it.
+LJSpeech-1.1/wavs/LJ036-0086.wav|The Commission could not accept important elements of Craig's testimony.
+LJSpeech-1.1/wavs/LJ013-0122.wav|Stealing plate was about this period the crime of a more aristocratic thief.
+LJSpeech-1.1/wavs/LJ017-0214.wav|most of them, as was proved by their subsequent acts, blackguards of the deepest dye.
+LJSpeech-1.1/wavs/LJ018-0283.wav|and by means of forged letters of credit and introduction from London, obtained large sums from continental banks, in Berlin,
+LJSpeech-1.1/wavs/LJ044-0232.wav|after he had assassinated the American President, it does not appear that he had any plans to go there.
+LJSpeech-1.1/wavs/LJ017-0047.wav|the rest of the available space was allotted by ticket, to secure which the greatest influence was necessary.
+LJSpeech-1.1/wavs/LJ035-0020.wav|As he was parking he noted that people were, quote, falling, and they were rolling around down there grabbing their children, end quote, and rushing about.
+LJSpeech-1.1/wavs/LJ013-0050.wav|But among the charges on the estate he left
+LJSpeech-1.1/wavs/LJ002-0221.wav|and many reforms instituted. But the condition of the prison and its inmates remained unsatisfactory to the last.
+LJSpeech-1.1/wavs/LJ046-0246.wav|This was done in the case of the Nationalist Party of Puerto Rico, for example, but only after members of the group had resorted to political violence.
+LJSpeech-1.1/wavs/LJ043-0051.wav|he strongly put me in my place about buying things for his wife that he himself could not buy, end quote.
+LJSpeech-1.1/wavs/LJ032-0181.wav|Although Stombaugh was unable to estimate the period of time the fibers were on the rifle he said that the fibers, quote,
+LJSpeech-1.1/wavs/LJ038-0152.wav|Oswald told Fritz that after lunch he went outside, talked with Foreman Bill Shelley for five or ten minutes and then left for home.
+LJSpeech-1.1/wavs/LJ019-0247.wav|which existed in the various prisons,
+LJSpeech-1.1/wavs/LJ016-0225.wav|which his predecessors were not, and who were paid their wages over the gate to obviate the necessity for letting them enter.
+LJSpeech-1.1/wavs/LJ010-0005.wav|taking in preference those which are typical and invested with peculiar interest. It is somewhat remarkable that a marked change soon comes over the Calendar.
+LJSpeech-1.1/wavs/LJ031-0079.wav|He made the official determination because the ultimate cause of death, the severe head injury, was within his sphere of specialization.
+LJSpeech-1.1/wavs/LJ009-0127.wav|But what are the feelings of those who take part in it?
+LJSpeech-1.1/wavs/LJ003-0217.wav|The condemned occupied an open pew in the center of the chapel, hung with black; in front of them, upon a table, was a black coffin in full view.
+LJSpeech-1.1/wavs/LJ042-0187.wav|preparation in a special party could safeguard an independent course of action after the debacle, end quote.
+LJSpeech-1.1/wavs/LJ047-0042.wav|and unwilling to answer questions regarding his motive for going to the Soviet Union.
+LJSpeech-1.1/wavs/LJ018-0242.wav|The case was tried at Guildford Assizes, and caused intense excitement,
+LJSpeech-1.1/wavs/LJ014-0216.wav|and by getting his hand inside Dwyer's stock, with the idea of strangling him.
+LJSpeech-1.1/wavs/LJ009-0090.wav|The dying men are dreadfully agitated.
+LJSpeech-1.1/wavs/LJ003-0034.wav|This gradually was forced upon the consciousness of the Corporation,
+LJSpeech-1.1/wavs/LJ042-0116.wav|Under the entry for May one, nineteen sixty,
+LJSpeech-1.1/wavs/LJ045-0153.wav|Question: Did your husband give any reason for coming home on Thursday?
+LJSpeech-1.1/wavs/LJ040-0024.wav|Perhaps the most outstanding conclusion of such a study is that Oswald was profoundly alienated from the world in which he lived.
+LJSpeech-1.1/wavs/LJ008-0002.wav|I propose to return now to the subject of Newgate executions,
+LJSpeech-1.1/wavs/LJ027-0119.wav|surely if such a method were adopted in so many cases, we should expect that in consistency it would be adopted in all cases.
+LJSpeech-1.1/wavs/LJ027-0143.wav|whereby the primitive horn was gradually superseded by horns presenting a greater and greater number of prongs in successive species of extinct deer.
+LJSpeech-1.1/wavs/LJ048-0155.wav|Assistant Chief Batchelor of the Dallas police noted the absence of any formal statement by the Secret Service of specific work assigned to the police
+LJSpeech-1.1/wavs/LJ039-0166.wav|which was in turn placed to the right of the closest silhouette.
+LJSpeech-1.1/wavs/LJ009-0199.wav|About this time I find that the bodies of two murderers, Clench and Mackay,
+LJSpeech-1.1/wavs/LJ023-0057.wav|because the Articles of Confederation under which the original thirteen States tried to operate after the Revolution
+LJSpeech-1.1/wavs/LJ021-0053.wav|Only a very small minority of the people of this country believe in gambling as a substitute for the old philosophy of Benjamin Franklin
+LJSpeech-1.1/wavs/LJ040-0195.wav|It appears that he did not want to do any of the things which the authorities suggested in their efforts to bring him out of the shell
+LJSpeech-1.1/wavs/LJ032-0251.wav|Oswald loaded their possessions into a station wagon owned by Ruth Paine, who had invited Marina Oswald and the baby to live at her home in Irving, Texas.
+LJSpeech-1.1/wavs/LJ005-0278.wav|which was eventually to replace the attempted arrangement of prisoners by classes according to antecedents and crimes,
+LJSpeech-1.1/wavs/LJ004-0066.wav|or his health by forcing him at night into a damp, unventilated cell, with such crowds of companions as very speedily render the air foul and putrid;
+LJSpeech-1.1/wavs/LJ040-0104.wav|when he was sent to New Orleans to visit the family of his mother's sister, Mrs. Lillian Murret, for two or three weeks.
+LJSpeech-1.1/wavs/LJ014-0196.wav|by his identification by Cope in Westminster Hospital, who survived long enough to make a formal deposition before Mr. Jardine,
+LJSpeech-1.1/wavs/LJ037-0148.wav|in Oswald's possession to the exclusion of all other weapons.
+LJSpeech-1.1/wavs/LJ006-0264.wav|The nearly indiscriminate admission of visitors, although restricted to certain days, continued to be an unmixed evil.
+LJSpeech-1.1/wavs/LJ043-0053.wav|and became quite incensed with his wife when she would open the door for her in spite of his instructions to the contrary.
+LJSpeech-1.1/wavs/LJ012-0113.wav|After their arrest, Jordan's wife and Sullivan's brother came to the inn, and begged to be allowed to visit this room;
+LJSpeech-1.1/wavs/LJ038-0154.wav|Shelley denied seeing Oswald after twelve noon or at any time after the shooting.
+LJSpeech-1.1/wavs/LJ040-0233.wav|and when one of the city's clinics did find room to handle him,
+LJSpeech-1.1/wavs/LJ046-0185.wav|PRS attempts to eliminate serious risks by hospitalization or, where necessary,
+LJSpeech-1.1/wavs/LJ031-0232.wav|Shortly thereafter, the President's wife, family and aides left Bethesda Naval Hospital.
+LJSpeech-1.1/wavs/LJ028-0124.wav|I walled up its side with mortar and burned bricks, and I united it securely with the moat-walls of my father.
+LJSpeech-1.1/wavs/LJ030-0011.wav|At Rice Stadium a very large, enthusiastic crowd greeted the President.
+LJSpeech-1.1/wavs/LJ023-0125.wav|adequately to regulate commerce, protect public health and safety, and safeguard economic security.
+LJSpeech-1.1/wavs/LJ028-0418.wav|About that same time Pietro della Valle, an Italian, visited Babylon,
+LJSpeech-1.1/wavs/LJ008-0010.wav|at first, and for some few years after seventeen eighty-four, executions took place occasionally at a distance from Newgate.
+LJSpeech-1.1/wavs/LJ029-0151.wav|No arrangements were made for police or building custodians
+LJSpeech-1.1/wavs/LJ028-0075.wav|Therefore it was useless to hope that Nebuchadnezzar's portrait would be found on his palace walls.
+LJSpeech-1.1/wavs/LJ034-0191.wav|he saw nothing of importance, quote, except maybe one individual who was up there in the corner room of the sixth floor which was crowded in among boxes, end quote.
+LJSpeech-1.1/wavs/LJ047-0010.wav|the agent who was assigned his case at the time of the assassination, the Director of the FBI,
+LJSpeech-1.1/wavs/LJ015-0264.wav|The series of boldly-conceived and cleverly-executed forgeries in which James Townshend Saward, commonly called "Jem the Penman,"
+LJSpeech-1.1/wavs/LJ011-0223.wav|Neither his address nor his pamphlet availed much, for the bill for the divorce passed both Houses.
+LJSpeech-1.1/wavs/LJ047-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ005-0003.wav|Persons committed to a metropolitan jail at that time were taken in gangs, men and women handcuffed together, or linked on to a long chain,
+LJSpeech-1.1/wavs/LJ016-0374.wav|Underfoot and upon the walls roughly cut into the stones, are single initial letters, the brief epitaphs of those who lie below.
+LJSpeech-1.1/wavs/LJ018-0327.wav|The most recent affairs are still too fresh in the public mind to need more than a passing reference.
+LJSpeech-1.1/wavs/LJ038-0015.wav|Mrs. Julia Postal, selling tickets at the box office of the Texas Theatre,
+LJSpeech-1.1/wavs/LJ004-0169.wav|On the dirty bedstead lay a wretched being in the throes of severe illness.
+LJSpeech-1.1/wavs/LJ017-0242.wav|Leon wished to make every one on board share and share alike, so as to implicate the innocent with the guilty;
+LJSpeech-1.1/wavs/LJ001-0171.wav|However, the fact must not be blinked that machine-made paper cannot in the nature of things be made of so good a texture as that made by hand.
+LJSpeech-1.1/wavs/LJ045-0048.wav|if he wanted me to go then that meant that he didn't love me, and that in that case what was the idea of coming to the United States in the first place.
+LJSpeech-1.1/wavs/LJ015-0267.wav|A man of birth and education, a member of the bar, and of acknowledged legal attainments, his proclivities were all downward.
+LJSpeech-1.1/wavs/LJ026-0017.wav|their nutritive processes result in oxidation, they have a definite organ of excretion and are capable of automatic movement
+LJSpeech-1.1/wavs/LJ038-0140.wav|He explained that a party for the Paine children had been planned for the weekend and he preferred not to be in the Paine house at that time;
+LJSpeech-1.1/wavs/LJ015-0174.wav|Thither police officers followed, only to find that he had returned to London.
+LJSpeech-1.1/wavs/LJ048-0196.wav|According to Captain Lawrence, quote,
+LJSpeech-1.1/wavs/LJ046-0164.wav|PRS investigated thirty-four newly established or reactivated cases concerning residents of Texas.
+LJSpeech-1.1/wavs/LJ012-0156.wav|His arrest and conviction cast dismay over the whole gang of receivers, and for a time seriously checked the nefarious traffic.
+LJSpeech-1.1/wavs/LJ026-0150.wav|In both animals and plants oxygen is essential to the catabolic part of metabolism.
+LJSpeech-1.1/wavs/LJ023-0140.wav|But all federal judges, once appointed,
+LJSpeech-1.1/wavs/LJ020-0104.wav|Master these, and do not let them master you.
+LJSpeech-1.1/wavs/LJ009-0101.wav|move with a quick, jerking motion, not naturally, but, as it were, like the affected parts of a galvanized corpse.
+LJSpeech-1.1/wavs/LJ039-0136.wav|After he left the Marines and before departing for Russia, Oswald, his brother, and a third companion went hunting for squirrels and rabbits.
+LJSpeech-1.1/wavs/LJ040-0093.wav|he accepted the responsibility for that decision himself and signed his mother's name to all his own excuses and report cards.
+LJSpeech-1.1/wavs/LJ024-0140.wav|It is my purpose to restore that balance.
+LJSpeech-1.1/wavs/LJ016-0108.wav|The Newgate officers obtained information of this, and went to the spot, where they effected the capture,
+LJSpeech-1.1/wavs/LJ033-0060.wav|Marina Oswald testified that at that time, quote, My heart dropped.
+LJSpeech-1.1/wavs/LJ005-0205.wav|At Eye, in Suffolk, the jail was part of the poor-house; so it was at Richmond, in Yorkshire, where the master of the workhouse was also keeper of the jail.
+LJSpeech-1.1/wavs/LJ014-0221.wav|On the contrary, many of them encouraged the brutal assailant in his savage attack.
+LJSpeech-1.1/wavs/LJ018-0071.wav|a proceeding which he vehemently resented, and begged that they might be removed, as the handcuffs hurt his wrists.
+LJSpeech-1.1/wavs/LJ025-0027.wav|and its appurtenances must needs vary according to the nature of the aliment and the operations which it has to undergo
+LJSpeech-1.1/wavs/LJ044-0196.wav|to a country in which he must have thought were embodied the political principles to which he had been committed for so long.
+LJSpeech-1.1/wavs/LJ019-0293.wav|In one prison the bedsteads had been removed lest the prisoners should break them up and convert them into weapons of offense.
+LJSpeech-1.1/wavs/LJ039-0130.wav|After reviewing Oswald's marksmanship scores,
+LJSpeech-1.1/wavs/LJ050-0032.wav|This involves tight and unswerving discipline as well as the promotion of an outstanding degree of dedication and loyalty to duty.
+LJSpeech-1.1/wavs/LJ017-0207.wav|had not quite disappeared. I will mention two cases of this class, one accompanied with piracy on the high seas,
+LJSpeech-1.1/wavs/LJ044-0011.wav|There, in late May and early June of nineteen sixty-three, under the name Lee Osborne,
+LJSpeech-1.1/wavs/LJ029-0183.wav|On October three the Dallas Morning News quoted U.S. Representative Joe Pool's hope
+LJSpeech-1.1/wavs/LJ008-0244.wav|now laughed at, the skirt of his seedy black coat torn across; yet,
+LJSpeech-1.1/wavs/LJ049-0187.wav|These proposals included suggestions to locate exclusive responsibility for all phases of the work
+LJSpeech-1.1/wavs/LJ039-0031.wav|Mr. Nixon advised the Commission that the only time he was in Dallas in nineteen sixty-three
+LJSpeech-1.1/wavs/LJ002-0008.wav|On the fourteenth June, eighteen hundred, there were one hundred ninety-nine debtors and two hundred eighty-nine felons in the prison.
+LJSpeech-1.1/wavs/LJ030-0126.wav|According to plan, the President's motorcade proceeded west through downtown Dallas on Main Street
+LJSpeech-1.1/wavs/LJ043-0186.wav|Marina Oswald indicated that her husband had compared General Walker to Adolph Hitler.
+LJSpeech-1.1/wavs/LJ044-0175.wav|Oswald left for Mexico City on September twenty-five, nineteen sixty-three, and arrived on September twenty-seven, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ034-0025.wav|as being the left palmprint and right index fingerprint of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ031-0162.wav|other terminal buildings and the neighboring parking lots, of all people.
+LJSpeech-1.1/wavs/LJ003-0216.wav|This latter was an ordeal which all dreaded, and many avoided by denying their faith.
+LJSpeech-1.1/wavs/LJ006-0237.wav|"A lad named Matthew White has had a wound in his eye by a bone thrown at him, which very nearly destroyed vision."
+LJSpeech-1.1/wavs/LJ047-0113.wav|For example, Oswald claimed that his wife's maiden name was Prossa
+LJSpeech-1.1/wavs/LJ016-0252.wav|Possibly such changes may be introduced into the method of execution
+LJSpeech-1.1/wavs/LJ017-0176.wav|and on her return had administered a dose of something which burnt the mouth badly, but did not prove fatal.
+LJSpeech-1.1/wavs/LJ019-0278.wav|This suggestion was adopted in the Act of eighteen sixty-five, which followed the committee's report, and of which more directly.
+LJSpeech-1.1/wavs/LJ042-0043.wav|Ask me and I will tell you I fight for communism.
+LJSpeech-1.1/wavs/LJ026-0157.wav|This process is, however, obscured during the day because of the oxygen freed in the manufacture of starch which goes on at that time.
+LJSpeech-1.1/wavs/LJ038-0266.wav|Frazier testified further that the FBI avoids the category of "probable" identification.
+LJSpeech-1.1/wavs/LJ016-0029.wav|Even after Williams had got out, persons were disposed to disbelieve that the escape had been accomplished in the manner indicated;
+LJSpeech-1.1/wavs/LJ036-0128.wav|The man was dressed in faded blue color khaki work clothes, a brown shirt, and some kind of work jacket that almost matched his pants.
+LJSpeech-1.1/wavs/LJ042-0105.wav|he took the position that the Communist Party officials in the Soviet Union were opportunists who were betraying their positions for personal gain.
+LJSpeech-1.1/wavs/LJ034-0150.wav|but, in his testimony before the Commission, Brennan stated that his remarks of January seven were intended by him merely as an accurate report
+LJSpeech-1.1/wavs/LJ008-0204.wav|One well-dressed woman fell out of a first-floor window on to the shoulders of the crowd below, but neither she nor any one else was greatly hurt.
+LJSpeech-1.1/wavs/LJ048-0185.wav|An attempt to cover only the most obvious points of possible ambush along the route in Dallas might well have included the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ037-0158.wav|He concluded that all of these cartridges were fired from the same weapon.
+LJSpeech-1.1/wavs/LJ042-0114.wav|of the presence of an American wore off and he began to be less the center of attention.
+LJSpeech-1.1/wavs/LJ005-0185.wav|As time passed, however, these magistrates made no effort at reform.
+LJSpeech-1.1/wavs/LJ050-0242.wav|even though it agrees with the Secret Service that it is preferable for the Service to have enough agents to handle all protective demands.
+LJSpeech-1.1/wavs/LJ045-0031.wav|During the week, Oswald lived in a roominghouse in Dallas, but he usually called his wife on the telephone twice a day.
+LJSpeech-1.1/wavs/LJ034-0063.wav|none of these employees except Oswald left identifiable prints on the cartons.
+LJSpeech-1.1/wavs/LJ043-0148.wav|She testified that she was agitated because she had found the note in Oswald's room,
+LJSpeech-1.1/wavs/LJ028-0069.wav|how he cast Hebrew lads into a fiery furnace and into the lions' den,
+LJSpeech-1.1/wavs/LJ010-0125.wav|and suffered in the usual way in front of Newgate, with the additional penalty of decapitation, as traitors, after they had been hanged.
+LJSpeech-1.1/wavs/LJ015-0242.wav|re-fastened and re-sealed the bullion box, and replaced it in the safe.
+LJSpeech-1.1/wavs/LJ001-0032.wav|and used an exceedingly beautiful type, which is indeed to look at a transition between Gothic and Roman,
+LJSpeech-1.1/wavs/LJ001-0177.wav|so that if the two are helpful to one another it is a mere matter of accident.
+LJSpeech-1.1/wavs/LJ023-0046.wav|so that their field might be plowed.
+LJSpeech-1.1/wavs/LJ028-0206.wav|he subdued the people, and wherever they collected, he slew them.
+LJSpeech-1.1/wavs/LJ017-0075.wav|There is every reason to suppose that he poisoned his wife to obtain possession of this sum upon her death.
+LJSpeech-1.1/wavs/LJ015-0040.wav|This borrowing continued, and on such a scale that their paper was soon at a discount,
+LJSpeech-1.1/wavs/LJ010-0155.wav|threw a stone at the king, which hit him on the forehead, but did no serious injury.
+LJSpeech-1.1/wavs/LJ002-0230.wav|The state of morals was disgraceful. Any woman obtained admission if sober, and if she got drunk she was not turned out.
+LJSpeech-1.1/wavs/LJ032-0262.wav|with the rifle wrapped inside and testified that it appeared to be the same approximate weight and shape as the package in his garage.
+LJSpeech-1.1/wavs/LJ013-0251.wav|Inquiry into the character of Good exposed him as a loose liver, who "kept company" with several women.
+LJSpeech-1.1/wavs/LJ049-0221.wav|perhaps upon recommendations based on further studies by the Cabinet-level committee recommended above or the National Security Council.
+LJSpeech-1.1/wavs/LJ003-0066.wav|As many various and, according to our ideas, heinous crimes came under this head,
+LJSpeech-1.1/wavs/LJ044-0225.wav|It should also be noted, however, that one witness testified that shortly before the assassination
+LJSpeech-1.1/wavs/LJ035-0095.wav|That Oswald descended by stairway from the sixth floor to the second-floor lunchroom
+LJSpeech-1.1/wavs/LJ034-0147.wav|Brennan stated that he was sure that the person firing the rifle was Oswald.
+LJSpeech-1.1/wavs/LJ003-0244.wav|At the commencement of every sessions, such of the untried as had purchased "easement" of irons were called up and re-fettered,
+LJSpeech-1.1/wavs/LJ018-0040.wav|His trial followed at the next sessions of the Central Criminal Court, and ended in his conviction.
+LJSpeech-1.1/wavs/LJ001-0115.wav|in short, it happens to this craft, as to others, that the utilitarian practice, though it professes to avoid ornament,
+LJSpeech-1.1/wavs/LJ046-0166.wav|An additional one hundred fifteen cases concerning Texas residents were established but not investigated.
+LJSpeech-1.1/wavs/LJ028-0338.wav|While the Babylonians, crowding to the walls, did their best to resist the Persian assault,
+LJSpeech-1.1/wavs/LJ049-0136.wav|of departmental policies relating to the national security) the responsibility to review and oversee the protective activities of the Secret Service
+LJSpeech-1.1/wavs/LJ009-0174.wav|In eighteen thirty-seven the death penalty was practically limited to murder or attempts to murder,
+LJSpeech-1.1/wavs/LJ029-0043.wav|and coordinating local activities with the White House staff, the organizations directly concerned with the visit, and local law enforcement officials.
+LJSpeech-1.1/wavs/LJ011-0072.wav|Some years were still to elapse before capital punishment ceased to be the penalty for forgery, and in the interval
+LJSpeech-1.1/wavs/LJ049-0181.wav|that too much emphasis is placed by both on the investigation of specific threats by individuals and not enough on dangers from other sources.
+LJSpeech-1.1/wavs/LJ045-0243.wav|He does not appear to have been able to establish meaningful relationships with other people. He was perpetually discontented with the world around him.
+LJSpeech-1.1/wavs/LJ041-0003.wav|Return to New Orleans and Joining the Marine Corps
+LJSpeech-1.1/wavs/LJ018-0374.wav|foreigners, vindictive, reckless, and truculent ruffians, easily moved to murderous rage.
+LJSpeech-1.1/wavs/LJ014-0189.wav|Lerigo turned for assistance to take the man into custody.
+LJSpeech-1.1/wavs/LJ029-0041.wav|who was the Secret Service official responsible for the entire Texas journey.
+LJSpeech-1.1/wavs/LJ022-0013.wav|It may seem confused to some, but out of the multitude of detailed parts that go into the making of the structure
+LJSpeech-1.1/wavs/LJ014-0050.wav|while Calcraft adjusted the noose.
+LJSpeech-1.1/wavs/LJ010-0288.wav|deemed valid as any palliation of his offense.
+LJSpeech-1.1/wavs/LJ047-0079.wav|The New Orleans office investigated and located Oswald, learning his address and former place of employment on August five, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ019-0047.wav|when thoughtful people who concerned themselves closely with social questions were greatly exercised as to the best system of treating the inmates of a jail.
+LJSpeech-1.1/wavs/LJ017-0018.wav|was ignorant of the destructive character of the powders, probably arsenic, which she administered.
+LJSpeech-1.1/wavs/LJ003-0009.wav|The day passed in revelry and drunkenness. Although spirituous liquors were forbidden,
+LJSpeech-1.1/wavs/LJ014-0085.wav|the corpse lying just inside the kitchen all the while, the two set to work to strip the body and hide it under the stones of the floor.
+LJSpeech-1.1/wavs/LJ038-0233.wav|He showed her a notebook three days later containing photographs of General Walker's home and a map of the area where the house was located.
+LJSpeech-1.1/wavs/LJ037-0113.wav|Callaway picked up the gun.
+LJSpeech-1.1/wavs/LJ029-0084.wav|Lawson also evaluated the security hazards at the Trade Mart on November thirteen.
+LJSpeech-1.1/wavs/LJ039-0158.wav|were capable of firing the shots which were fired by the assassin on November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ024-0052.wav|in accordance with a clearly defined principle relating to a clearly defined age limit.
+LJSpeech-1.1/wavs/LJ037-0265.wav|positively identified Lee Harvey Oswald as the man they saw fire the shots or flee from the scene,
+LJSpeech-1.1/wavs/LJ008-0164.wav|The most affecting and distressing scene was at Green Arbor Lane, just opposite the debtors' door of the prison.
+LJSpeech-1.1/wavs/LJ005-0285.wav|This committee, as well as the one preceding it, also reported in terms of strong reprobation on the small prisons and jails
+LJSpeech-1.1/wavs/LJ050-0101.wav|Leon F. Czolgosz,
+LJSpeech-1.1/wavs/LJ038-0067.wav|As Oswald, handcuffed, was led from the theatre, he was, according to McDonald, quote,
+LJSpeech-1.1/wavs/LJ009-0277.wav|For a second or two the body hung motionless, then, with a strength that astonished the attendant officials,
+LJSpeech-1.1/wavs/LJ030-0180.wav|Roy Kellerman, in the right front seat of the limousine, heard a report like a firecracker pop.
+LJSpeech-1.1/wavs/LJ018-0154.wav|He came to complete a sale of forged notes, and he wanted a couple of hundred pounds for what he had.
+LJSpeech-1.1/wavs/LJ046-0123.wav|for bizarre reasons or by writing or in some other way attempting to communicate with him in a threatening or abusive manner
+LJSpeech-1.1/wavs/LJ033-0104.wav|When Frazier entered the building, he did not see Oswald.
+LJSpeech-1.1/wavs/LJ027-0110.wav|Now, rudimentary organs of this kind are of such frequent occurrence, that almost every species of organism presents one or more of them
+LJSpeech-1.1/wavs/LJ040-0028.wav|When he was in the United States he resented the capitalist system which he thought was exploiting him and others like him.
+LJSpeech-1.1/wavs/LJ031-0095.wav|Before this was accomplished the President's cardiac activity had ceased and closed cardiac massage was instituted, which made it impossible to inspect his back.
+LJSpeech-1.1/wavs/LJ017-0257.wav|and the prisoners eventually surrendered to the British authorities, brought to England, and lodged in Newgate.
+LJSpeech-1.1/wavs/LJ048-0119.wav|The only systematic supervision of the activities of the advance agent
+LJSpeech-1.1/wavs/LJ011-0149.wav|The abduction of Miss Turner by the brothers Wakefield bore a strong resemblance to the carrying off and forcible marrying of heiresses as already described.
+LJSpeech-1.1/wavs/LJ018-0246.wav|At first the case was contested hotly, but, to the profound astonishment of every one inside and outside the court,
+LJSpeech-1.1/wavs/LJ039-0040.wav|When asked later whether it might have been Mr. Johnson, she said, quote, Yes, no.
+LJSpeech-1.1/wavs/LJ040-0022.wav|Indications of Oswald's motivation
+LJSpeech-1.1/wavs/LJ028-0411.wav|dressing the same, eating the same food as did their ancestors when Nebuchadnezzar built the walls of Babylon.
+LJSpeech-1.1/wavs/LJ008-0145.wav|They were accused by a confederate, who, goaded by conscience, had turned approver, of the murder of a Mr. Steele,
+LJSpeech-1.1/wavs/LJ015-0198.wav|It was no longer possible to stop a coach or carriage, or rob the postman who carried the mail.
+LJSpeech-1.1/wavs/LJ046-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By
+LJSpeech-1.1/wavs/LJ028-0117.wav|My father built the moat-wall of the Arachtu canal securely with mortar and bricks.
+LJSpeech-1.1/wavs/LJ002-0179.wav|and costing eleven pounds, fifteen shillings, eight pence, within ten days.
+LJSpeech-1.1/wavs/LJ010-0284.wav|He always kept the blinds down at his chambers in Jermyn Street; and as the St. James's clock chimed quarter-past three,
+LJSpeech-1.1/wavs/LJ035-0053.wav|The man turned and walked back toward Baker.
+LJSpeech-1.1/wavs/LJ028-0447.wav|Upon the little mound Jumjuma farther on, an Arab village has long stood.
+LJSpeech-1.1/wavs/LJ003-0236.wav|Garnish continued to be demanded long after it had disappeared in other and better-regulated prisons.
+LJSpeech-1.1/wavs/LJ028-0134.wav|He also erected what is called a pensile paradise:
+LJSpeech-1.1/wavs/LJ025-0125.wav|as Dr. Burdon Sanderson's interesting investigations have shown, by a disturbance of the electrical state of the contractile substance
+LJSpeech-1.1/wavs/LJ006-0062.wav|In another part there were men charged with and convicted of unnatural offenses shut up with lads of tender years;
+LJSpeech-1.1/wavs/LJ017-0203.wav|This fact was referred to by the judge in his summing up,
+LJSpeech-1.1/wavs/LJ011-0132.wav|except the forgery of wills and powers of attorney.
+LJSpeech-1.1/wavs/LJ041-0082.wav|in an attempt to attract attention to himself and to support his exaggerated idea of his own abilities.
+LJSpeech-1.1/wavs/LJ015-0159.wav|In his person he was neat and fastidious;
+LJSpeech-1.1/wavs/LJ034-0192.wav|He said that this was a white man about average in size, quote, possibly thin, end quote, and that he thought the man had light-brown hair.
+LJSpeech-1.1/wavs/LJ035-0117.wav|Harold Norman, and Bonnie Ray Williams -- were watching the parade from the fifth floor, directly below the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ013-0042.wav|the foundations of which had been laid by buying old ships on purpose to cast them away.
+LJSpeech-1.1/wavs/LJ028-0028.wav|Everywhere the valley is dotted with the mounds of buried cities carefully guarding the secrets of the centuries of long ago.
+LJSpeech-1.1/wavs/LJ012-0244.wav|The decapitation and dismemberment, the bestowal of the remains in various parts of the town, the preservation of the head in spirits of wine,
+LJSpeech-1.1/wavs/LJ038-0052.wav|testified regarding the arrest of Oswald, as did the various police officers who participated in the fight.
+LJSpeech-1.1/wavs/LJ013-0031.wav|The crime soon became public.
+LJSpeech-1.1/wavs/LJ021-0182.wav|And let it be recorded that the British bankers helped.
+LJSpeech-1.1/wavs/LJ028-0370.wav|The priests' deception was unavailing, and in two seventy-five B.C., the inhabitants of Babylon were transported to Seleucia.
+LJSpeech-1.1/wavs/LJ037-0032.wav|Benavides never saw Oswald after the arrest.
+LJSpeech-1.1/wavs/LJ031-0029.wav|Special Agent Hill removed his suit jacket and covered the President's head and upper chest to prevent the taking of photographs.
+LJSpeech-1.1/wavs/LJ028-0112.wav|My father did that which no previous king had done.
+LJSpeech-1.1/wavs/LJ003-0279.wav|There was fear as to the unrestricted use of tools,
+LJSpeech-1.1/wavs/LJ010-0130.wav|Ings was especially defiant. He sought to cheer Davidson, who seemed affected, crying out,
+LJSpeech-1.1/wavs/LJ007-0047.wav|"Men whose cases were dangerous, and those most seriously inclined, complained of these annoyances,"
+LJSpeech-1.1/wavs/LJ005-0255.wav|The committee was also to report upon the manner in which sentences were carried out, and to recommend any alterations necessary in the rules
+LJSpeech-1.1/wavs/LJ038-0062.wav|Johnny Brewer testified he saw Oswald pull the revolver and the officers struggle with him to take it away
+LJSpeech-1.1/wavs/LJ011-0250.wav|While thus engaged, Howard thrust the poker into the fire.
+LJSpeech-1.1/wavs/LJ012-0016.wav|At no period could thieves in London or elsewhere have prospered had they been unable to dispose of their ill-gotten goods.
+LJSpeech-1.1/wavs/LJ048-0013.wav|he indicated that he had learned his lesson,
+LJSpeech-1.1/wavs/LJ005-0101.wav|whence it deduced the practice and condition of every prison that replied.
+LJSpeech-1.1/wavs/LJ008-0078.wav|seemed to strike her mind with horror and consternation, to the exclusion of all power of recollectedness in preparation for the approaching awful moment.
+LJSpeech-1.1/wavs/LJ026-0131.wav|Wherever there is need for new protoplasm, whether to repair previous waste or to supply materials for growth,
+LJSpeech-1.1/wavs/LJ022-0111.wav|I well realize that the country is expecting before this year is out to see the "dirt fly", as they say, in carrying on the work,
+LJSpeech-1.1/wavs/LJ029-0103.wav|Lawson and Sorrels met with Dallas Chief of Police Jesse E. Curry, Assistant Chief Charles Batchelor,
+LJSpeech-1.1/wavs/LJ010-0256.wav|Undeterred by the well-merited punishment which had overtaken Francis,
+LJSpeech-1.1/wavs/LJ045-0186.wav|He must have planned his attack at the very latest prior to Thursday morning when he spoke to Frazier.
+LJSpeech-1.1/wavs/LJ018-0121.wav|was the case of the forgeries upon the Bank of England discovered in eighteen sixty-three,
+LJSpeech-1.1/wavs/LJ028-0285.wav|Noble exploits in Persia are ever highly honored and bring their authors to greatness.
+LJSpeech-1.1/wavs/LJ037-0150.wav|that he compared the four empty cartridge cases found near the scene of the shooting with a test cartridge fired from the weapon in Oswald's possession when he was arrested.
+LJSpeech-1.1/wavs/LJ018-0070.wav|Sattler was ironed for safe custody,
+LJSpeech-1.1/wavs/LJ032-0046.wav|The rifle without the scope cost only twelve dollars, seventy-eight cents.
+LJSpeech-1.1/wavs/LJ003-0225.wav|were meager or altogether absent.
+LJSpeech-1.1/wavs/LJ027-0066.wav|All vertebrates, with few exceptions, and no other animals, have two and only two pair of limbs.
+LJSpeech-1.1/wavs/LJ045-0160.wav|Question: And how did he show that he was upset?
+LJSpeech-1.1/wavs/LJ014-0267.wav|others that he had had extraordinary luck as a gold-digger. Had his West End and little-informed associates followed him into the city,
+LJSpeech-1.1/wavs/LJ028-0041.wav|tore down its palaces and temples and walls, and scraped even the foundations of the city into the river.
+LJSpeech-1.1/wavs/LJ045-0189.wav|it should be noted that mention of the Trade Mart as the expected site of the Presidential luncheon
+LJSpeech-1.1/wavs/LJ017-0027.wav|that arsenic was within her reach in the house,
+LJSpeech-1.1/wavs/LJ012-0166.wav|where the principal actor suffered death, the case hardly comes within the limits of my subject.
+LJSpeech-1.1/wavs/LJ018-0310.wav|The prisoners, who were committed to Newgate for trial,
+LJSpeech-1.1/wavs/LJ036-0083.wav|and told Captain Fritz that Oswald was the man he saw.
+LJSpeech-1.1/wavs/LJ024-0079.wav|But the welfare of the United States, and indeed of the Constitution itself, is what we all must think about first.
+LJSpeech-1.1/wavs/LJ019-0316.wav|Hard labor of the first and second class was carefully defined.
+LJSpeech-1.1/wavs/LJ041-0055.wav|a dissatisfaction which seemed to find expression at this particular point
+LJSpeech-1.1/wavs/LJ007-0209.wav|they return to the charge, and again call the corporation to task for their mismanagement of Newgate.
+LJSpeech-1.1/wavs/LJ017-0117.wav|The examination of the stomach betrayed the presence of antimony in large quantities, but no strychnia,
+LJSpeech-1.1/wavs/LJ039-0191.wav|Oswald engaged in such practice. If the assassin missed either the first or third shot,
+LJSpeech-1.1/wavs/LJ036-0070.wav|rather than wait for one which stopped across the street from his roominghouse.
+LJSpeech-1.1/wavs/LJ005-0263.wav|and not, as heretofore, to the judges of assize; that, both to check abuses and watch the progress of improvement,
+LJSpeech-1.1/wavs/LJ029-0031.wav|then we had the time, and I withdrew my objections to a motorcade. End quote.
+LJSpeech-1.1/wavs/LJ003-0313.wav|instruct the prisoners, quote, give spiritual advice and administer religious consolation, end quote. to all who might need them;
+LJSpeech-1.1/wavs/LJ010-0061.wav|That some thirty or more needy men should hope to revolutionize England is a sufficient proof of the absurdity of their attempt.
+LJSpeech-1.1/wavs/LJ003-0083.wav|Each ward was calculated to hold twenty-four, allowing each individual one foot and a half;
+LJSpeech-1.1/wavs/LJ018-0217.wav|As such he had pretty general control over his father's estates and affairs.
+LJSpeech-1.1/wavs/LJ045-0059.wav|The instability of their relations was probably a function of the personalities of both people. Oswald was overbearing in relations with his wife.
+LJSpeech-1.1/wavs/LJ050-0126.wav|the Secret Service, through its field offices,
+LJSpeech-1.1/wavs/LJ049-0229.wav|while assuming no radical relocation of responsibilities,
+LJSpeech-1.1/wavs/LJ019-0209.wav|the situation having been proved by long experience to be salubrious.
+LJSpeech-1.1/wavs/LJ030-0098.wav|The remainder of the motorcade consisted of five cars for other dignitaries, including the mayor of Dallas and Texas Congressmen,
+LJSpeech-1.1/wavs/LJ028-0059.wav|His military career began while he was still the crown prince, and his father was on the throne.
+LJSpeech-1.1/wavs/LJ025-0146.wav|And the question whether plants are provided with a nervous system or not thus acquires a new aspect and presents the histologist and physiologist
+LJSpeech-1.1/wavs/LJ046-0137.wav|PRS expressed its interest in receiving information on suspects in very general terms. For example,
+LJSpeech-1.1/wavs/LJ027-0170.wav|Finally, it loses the tail, or rather its tail is absorbed and its material used in further development, and it becomes a perfect frog,
+LJSpeech-1.1/wavs/LJ018-0255.wav|The case was easily and rapidly disposed of.
+LJSpeech-1.1/wavs/LJ042-0071.wav|He was taken to a hospital in Moscow where he was kept until October twenty-eight, nineteen fifty-nine.
+LJSpeech-1.1/wavs/LJ033-0040.wav|Before dinner on November twenty-one, Oswald played on the lawn of the Paines' home with his daughter June.
+LJSpeech-1.1/wavs/LJ034-0151.wav|of what he said on November twenty-two.
+LJSpeech-1.1/wavs/LJ008-0043.wav|Round the north, west, and south sides are erected galleries for the reception of officers, attendants, etc.
+LJSpeech-1.1/wavs/LJ050-0177.wav|This PRS agent will also be responsible for establishing an informal local liaison committee
+LJSpeech-1.1/wavs/LJ048-0096.wav|In the case of Dallas, because President Kennedy had scheduled visits to five Texas cities
+LJSpeech-1.1/wavs/LJ026-0134.wav|The particles of this newly formed compound are incorporated into the protoplasm.
+LJSpeech-1.1/wavs/LJ047-0091.wav|any foreign government or instrumentality thereof. End quote.
+LJSpeech-1.1/wavs/LJ015-0313.wav|Saward spent all his share at low gaming houses, and in all manner of debaucheries.
+LJSpeech-1.1/wavs/LJ039-0033.wav|An investigation failed to reveal any invitation extended to Mr. Nixon during the period when Oswald's threat reportedly occurred.
+LJSpeech-1.1/wavs/LJ016-0419.wav|Kate Webster, who was tried at the Central Criminal Court, and passed through Newgate, although she suffered at Wandsworth,
+LJSpeech-1.1/wavs/LJ003-0060.wav|which was a wooden flooring on a slightly inclined plane, with a beam running across the top to serve as a pillow.
+LJSpeech-1.1/wavs/LJ020-0076.wav|Wrap bowl and biscuit in a thick cloth and set to rise where it will neither become chilled nor sour over night.
+LJSpeech-1.1/wavs/LJ050-0122.wav|It should be made clear that the Secret Service will in no way seek to duplicate the intelligence
+LJSpeech-1.1/wavs/LJ034-0141.wav|and he said that the shots came from inside the building, end quote.
+LJSpeech-1.1/wavs/LJ016-0399.wav|Wainwright was allowed a cigar the night before execution, which he smoked in the prison yard, walking up and down with the governor, Mr. Sydney Smith.
+LJSpeech-1.1/wavs/LJ021-0051.wav|people will be discouraged in unhappy efforts to get rich quick by speculating in securities.
+LJSpeech-1.1/wavs/LJ037-0251.wav|It seemed to her that the jacket Oswald wore was darker than Commission Exhibit Number one sixty-two.
+LJSpeech-1.1/wavs/LJ009-0008.wav|the keepers, and the sheriffs all seem interested in his fate, and he can make no reasonable request that is not at once granted by whomsoever he may address.
+LJSpeech-1.1/wavs/LJ040-0047.wav|That associate did not think that Oswald was a Communist.
+LJSpeech-1.1/wavs/LJ043-0133.wav|Attack on General Walker
+LJSpeech-1.1/wavs/LJ016-0036.wav|About fifty feet from the ground level, and above the cistern, a revolving chevaux-de-frise of iron was fixed,
+LJSpeech-1.1/wavs/LJ042-0242.wav|requesting information on how to subscribe to Russian newspapers and magazines and asked for, quote,
+LJSpeech-1.1/wavs/LJ039-0172.wav|None of the marksmen had any practice with the assassination weapon except for exercising the bolt for two or three minutes on a dry run.
+LJSpeech-1.1/wavs/LJ005-0098.wav|The Society did not shrink from its self-imposed duty, but continued year after year, with unflagging energy and unflinching spirit, to watch closely
+LJSpeech-1.1/wavs/LJ042-0033.wav|of his concomitant hatred of the United States, which was most clearly expressed in his November twenty-six,
+LJSpeech-1.1/wavs/LJ048-0230.wav|and by a Secret Service investigation.
+LJSpeech-1.1/wavs/LJ013-0002.wav|As the century advanced crimes of fraud increased.
+LJSpeech-1.1/wavs/LJ011-0122.wav|His funds, which he pretended came to him with a rich wife, were really the proceeds of frauds upon the Bank of England.
+LJSpeech-1.1/wavs/LJ046-0156.wav|The general files of PRS consist of folders on individuals, card indexed by name.
+LJSpeech-1.1/wavs/LJ019-0330.wav|Dietaries were drawn up for adoption on the recommendation of a committee of experts.
+LJSpeech-1.1/wavs/LJ008-0314.wav|The form of imparting the intelligence was generally the same.
+LJSpeech-1.1/wavs/LJ048-0146.wav|Discussions with the Dallas authorities and requests made of them were entirely informal.
+LJSpeech-1.1/wavs/LJ007-0103.wav|Mr. J. E. Sparrow and Mr. Clipperton
+LJSpeech-1.1/wavs/LJ014-0104.wav|Six inches lower the body of O'Connor was uncovered.
+LJSpeech-1.1/wavs/LJ005-0075.wav|which having gone through several sessions, at last became law in eighteen twenty-three to four
+LJSpeech-1.1/wavs/LJ006-0187.wav|It was, moreover, in his opinion undoubtedly the greatest nursery of crime in London.
+LJSpeech-1.1/wavs/LJ038-0007.wav|One of the persons who heard the sirens was Johnny Calvin Brewer, manager of Hardy's Shoestore, a few doors east of the Texas Theatre.
+LJSpeech-1.1/wavs/LJ009-0231.wav|Although these objectionable practices had disappeared,
+LJSpeech-1.1/wavs/LJ016-0351.wav|A more marked change from the old scene can hardly be conceived. Instead of the roar of the brutalized crowd,
+LJSpeech-1.1/wavs/LJ014-0246.wav|Thus, when a payment was made by the company, the amount disbursed was carried to account in the general books from its entry in the passbook,
+LJSpeech-1.1/wavs/LJ047-0041.wav|Agent Fain reported to headquarters that Oswald was impatient and arrogant,
+LJSpeech-1.1/wavs/LJ019-0283.wav|Beds might well be made hard and their use strictly limited.
+LJSpeech-1.1/wavs/LJ030-0182.wav|"My God, I am hit," and saw both of the President's hands move up toward his neck.
+LJSpeech-1.1/wavs/LJ026-0067.wav|We have here the direct absorption into the body proper of food-stuffs precisely as the animal takes in water and oxygen.
+LJSpeech-1.1/wavs/LJ031-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter two. The Assassination: Part three.
+LJSpeech-1.1/wavs/LJ019-0370.wav|but if these conditions were observed, the Secretary of State could deal sharply enough with the defaulting jurisdictions.
+LJSpeech-1.1/wavs/LJ005-0219.wav|female prisoners were still exposed to the full view of the males, the netting in front of the gallery being perfectly useless as a screen.
+LJSpeech-1.1/wavs/LJ050-0156.wav|this money would be used to compensate consultants, to lease standard equipment or to purchase specially designed pilot equipment.
+LJSpeech-1.1/wavs/LJ011-0024.wav|Meanwhile public gossip was busy with Fauntleroy's name,
+LJSpeech-1.1/wavs/LJ025-0084.wav|And if we substitute for the possession of an alimentary cavity the power of taking solid nutriment into the body and there digesting it,
+LJSpeech-1.1/wavs/LJ045-0154.wav|Answer: He said that he was lonely because he hadn't come the preceding weekend, and he wanted to make his peace with me.
+LJSpeech-1.1/wavs/LJ045-0167.wav|Question: What did you say to that?
+LJSpeech-1.1/wavs/LJ016-0038.wav|in the wall above the chevaux-de-frise projected a series of iron spikes sharp enough to forbid further ascent.
+LJSpeech-1.1/wavs/LJ048-0150.wav|while the Secret Service representatives in Dallas
+LJSpeech-1.1/wavs/LJ008-0080.wav|She was immediately tied by the neck to an iron bolt fixed near the top of the stake,
+LJSpeech-1.1/wavs/LJ004-0038.wav|It was in eighteen seventeen that a small band of philanthropists resolved to form themselves into an association for the improvement of prison discipline.
+LJSpeech-1.1/wavs/LJ010-0282.wav|Pate was said to be an eccentric person, given to strange acts and antics, such as mixing whiskey and camphor with his morning bath-water,
+LJSpeech-1.1/wavs/LJ005-0105.wav|It was long before the many jurisdictions imitated the few.
+LJSpeech-1.1/wavs/LJ002-0025.wav|Returns laid before the House of Commons showed that six thousand, four hundred thirty-nine persons had been committed to Newgate
+LJSpeech-1.1/wavs/LJ033-0117.wav|The disassembled rifle was too long to be carried in this manner.
+LJSpeech-1.1/wavs/LJ039-0081.wav|Referring to a rifle with a four-power telescope, Sergeant Zahm said, quote,
+LJSpeech-1.1/wavs/LJ014-0028.wav|and he was in possession, the first time to her knowledge, of a watch.
+LJSpeech-1.1/wavs/LJ019-0296.wav|Complete idleness was tolerated; there was neither penal labor nor light employment.
+LJSpeech-1.1/wavs/LJ038-0271.wav|It was a six point five-millimeter bullet
+LJSpeech-1.1/wavs/LJ023-0121.wav|How then could we proceed to perform the mandate given us?
+LJSpeech-1.1/wavs/LJ024-0007.wav|with the approval, as required by the Constitution, of the Senate of the United States.
+LJSpeech-1.1/wavs/LJ001-0106.wav|oddity rather than rational beauty and meaning being apparently the thing sought for both in the letters and the illustrations.
+LJSpeech-1.1/wavs/LJ015-0115.wav|Little more remains to be said about Robson. He appears to have accepted his position, and to have at once resigned himself to his fate.
+LJSpeech-1.1/wavs/LJ019-0332.wav|The administration of good government was to be watched over by the local magistracy, certain of whom, styled visiting justices,
+LJSpeech-1.1/wavs/LJ016-0066.wav|Denied admission everywhere as a ragged, half-naked beggar,
+LJSpeech-1.1/wavs/LJ013-0191.wav|All this was evidence sufficient to warrant Courvoisier's committal for trial;
+LJSpeech-1.1/wavs/LJ022-0113.wav|to make a major attack upon the problem of unemployment.
+LJSpeech-1.1/wavs/LJ027-0085.wav|The existence of great similarities in vertebrate structure is not always fully recognized.
+LJSpeech-1.1/wavs/LJ030-0220.wav|David Powers, who witnessed the scene from the President's follow-up car,
+LJSpeech-1.1/wavs/LJ011-0165.wav|who had plausibly explained that he had only recently been engaged at Shrigley.
+LJSpeech-1.1/wavs/LJ027-0087.wav|to be entirely different machines, made each for its own purposes, at once, out of hand.
+LJSpeech-1.1/wavs/LJ009-0274.wav|His wretched, abject condition, seated in a chair under the drop, was such as almost to unnerve the executioner Calcraft,
+LJSpeech-1.1/wavs/LJ027-0126.wav|is it a worthy conception of Deity that,
+LJSpeech-1.1/wavs/LJ015-0110.wav|After dinner he went into hiding for a day or two,
+LJSpeech-1.1/wavs/LJ047-0223.wav|I don't recall the exact date. It was about a week prior. End quote.
+LJSpeech-1.1/wavs/LJ005-0251.wav|Again in eighteen thirty-five prisons and their inmates became once more the care of the senate, and the subject was taken up this time by the House of Lords.
+LJSpeech-1.1/wavs/LJ002-0046.wav|and were occupied as a rule by ten to fifteen people when the prison was not crowded, but double the number was occasionally placed in them.
+LJSpeech-1.1/wavs/LJ023-0103.wav|government is to be rendered impotent.
+LJSpeech-1.1/wavs/LJ038-0130.wav|as one entitled to receive mail.
+LJSpeech-1.1/wavs/LJ007-0010.wav|by their presence and their pious, disinterested efforts, to restrain the dissolute manners and vicious language of the unhappy and depraved inmates.
+LJSpeech-1.1/wavs/LJ036-0080.wav|toward a light-colored Rambler station wagon, which was moving slowly along Elm toward the underpass:
+LJSpeech-1.1/wavs/LJ011-0003.wav|sold out so large an amount of stock, that he paid sixteen thousand pounds a year in dividends to escape detection.
+LJSpeech-1.1/wavs/LJ031-0178.wav|two Dallas officials informed members of the President's start that the body could not be removed from the city until an autopsy was performed.
+LJSpeech-1.1/wavs/LJ047-0224.wav|In fact, Hosty participated in transmitting to the Secret Service two pieces of information pertaining to the visit.
+LJSpeech-1.1/wavs/LJ007-0087.wav|at Appleby for thirteen years, at Anglesea for fifteen years, at Exeter for sixteen years, and at Pembroke
+LJSpeech-1.1/wavs/LJ018-0249.wav|Every word he uttered was said with consideration, and sometimes with a long pause,
+LJSpeech-1.1/wavs/LJ037-0144.wav|All three identified Exhibit Number one forty-three as the revolver taken from Oswald when he was arrested.
+LJSpeech-1.1/wavs/LJ005-0117.wav|Numbers of the jails were still unprovided with chaplains, and the prisoners never heard Divine service.
+LJSpeech-1.1/wavs/LJ028-0449.wav|Parts of the walls of Nineveh are still standing to the height of one hundred and twenty-five feet,
+LJSpeech-1.1/wavs/LJ017-0234.wav|verified the murderers, and then shut himself up in his cabin.
+LJSpeech-1.1/wavs/LJ015-0312.wav|The proceeds of these forgeries amounted, it was said, to some thousands per annum.
+LJSpeech-1.1/wavs/LJ028-0344.wav|for Cyrus had done neither the one nor the other when he took Babylon.
+LJSpeech-1.1/wavs/LJ018-0052.wav|But on the gallows, when the cap was over his eyes and the rope had been adjusted round his neck, and within a second of the moment when he would be launched into eternity,
+LJSpeech-1.1/wavs/LJ033-0022.wav|During the morning of November twenty-one, Oswald asked Frazier whether he could ride home with him that afternoon.
+LJSpeech-1.1/wavs/LJ018-0109.wav|The aged but wary Kerp, the moment the plot was discovered, vanished, and was never more heard of.
+LJSpeech-1.1/wavs/LJ021-0147.wav|under which wages, hours and working conditions
+LJSpeech-1.1/wavs/LJ033-0131.wav|Frazier's view of the bag was from the rear. He continually advised that he was not paying close attention. For example, he said, quote,
+LJSpeech-1.1/wavs/LJ002-0041.wav|Two other wards were appropriated to the master's side debtors; they were each twenty-three feet by fourteen and a half,
+LJSpeech-1.1/wavs/LJ042-0066.wav|My fondest dreams are shattered because of a petty official. I decide to end it.
+LJSpeech-1.1/wavs/LJ018-0240.wav|were fictitious documents. His next brother, who should have inherited under the authentic will,
+LJSpeech-1.1/wavs/LJ039-0173.wav|They had not even pulled the trigger because of concern about breaking the firing pin.
+LJSpeech-1.1/wavs/LJ038-0305.wav|The finding that Lee Harvey Oswald attempted to murder a public figure in April nineteen sixty-three
+LJSpeech-1.1/wavs/LJ029-0136.wav|through the underpass and leads into an access road,
+LJSpeech-1.1/wavs/LJ030-0093.wav|Vice-Presidential follow-up car.
+LJSpeech-1.1/wavs/LJ038-0037.wav|Oswald then struck McDonald between the eyes with his left fist; with his right hand he drew a gun from his waist.
+LJSpeech-1.1/wavs/LJ048-0139.wav|The Service had twenty-eight agents participating in the Dallas visit.
+LJSpeech-1.1/wavs/LJ007-0145.wav|Instead of seclusion and meditation, his time is passed in the midst of a body of criminals of every class and degree, in riot, debauchery, and gaming,
+LJSpeech-1.1/wavs/LJ012-0292.wav|Mrs. Brown fell with it, and Greenacre, to his horror, found that she was dead.
+LJSpeech-1.1/wavs/LJ008-0123.wav|"Sir," he answered, "they sent me the very riff-raff."
+LJSpeech-1.1/wavs/LJ046-0010.wav|when his temporary residence, Blair House, was attacked by Puerto Rican Nationalists.
+LJSpeech-1.1/wavs/LJ002-0121.wav|Fifteen thousand of these were issued in Middlesex alone, which at that time was reckoned as only a fifteenth of Great Britain.
+LJSpeech-1.1/wavs/LJ046-0070.wav|Whatever their purposes Presidential journeys have greatly enlarged and complicated the task of protecting the President.
+LJSpeech-1.1/wavs/LJ046-0182.wav|Individuals who are regarded as dangerous to the President
+LJSpeech-1.1/wavs/LJ038-0165.wav|At approximately nine p.m., on April ten, nineteen sixty-three, in Dallas, Texas, Maj. Gen. Edwin A. Walker,
+LJSpeech-1.1/wavs/LJ018-0014.wav|Strange to say, the unfortunate man was not yet dead, and he actually survived more than four-and-twenty hours.
+LJSpeech-1.1/wavs/LJ027-0089.wav|The simplest, in fact the only scientific, explanation of the phenomena of vertebrate structure is the idea of a primal vertebrate,
+LJSpeech-1.1/wavs/LJ021-0155.wav|both directly on the public works themselves, and indirectly in the industries supplying the materials for these public works.
+LJSpeech-1.1/wavs/LJ017-0119.wav|All the circumstances were so suspicious that he could not escape the criminal charge.
+LJSpeech-1.1/wavs/LJ003-0110.wav|They viewed him with some suspicion, as one of whom they knew nothing.
+LJSpeech-1.1/wavs/LJ011-0161.wav|This purported to be from the medical attendant of Mr. Turner, written at Shrigley, Mr. Turner's place of residence;
+LJSpeech-1.1/wavs/LJ028-0035.wav|seems to have been the first to mention Babylon, and one of his inscriptions seems to say that he built the city and gave it its name.
+LJSpeech-1.1/wavs/LJ037-0140.wav|L.J. Lewis said in an interview that because of the distance from which he observed the gunman he would hesitate to state whether the man was identical with Oswald.
+LJSpeech-1.1/wavs/LJ026-0100.wav|In the plant the inorganic matter in water from the soil are absorbed by the roots and carried up definite tubes in the woody part of the stem.
+LJSpeech-1.1/wavs/LJ042-0117.wav|he noted that one of his acquaintances, quote, relates many things I do not know about the U.S.S.R.
+LJSpeech-1.1/wavs/LJ034-0036.wav|the bottom of the palm rested on the box.
+LJSpeech-1.1/wavs/LJ008-0143.wav|This was notably the case at the execution of Holloway and Haggerty
+LJSpeech-1.1/wavs/LJ007-0230.wav|Newgate, Giltspur St. Compter, and the City Bridewell,
+LJSpeech-1.1/wavs/LJ032-0119.wav|but only after Day viewed the knob on the bolt through a magnifying glass and found no prints.
+LJSpeech-1.1/wavs/LJ016-0416.wav|Many, like Wainwright, were calm and imperturbable throughout their trying ordeal.
+LJSpeech-1.1/wavs/LJ049-0160.wav|Since that time, the Secret Service has had and exercised responsibility for the physical protection of the President
+LJSpeech-1.1/wavs/LJ049-0143.wav|Major needs of personnel or other resources might be met more easily on its recommendation than they have been in the past.
+LJSpeech-1.1/wavs/LJ018-0104.wav|also a furniture dealer and one or two more commonplace rogues. The arch villain was never taken into custody.
+LJSpeech-1.1/wavs/LJ030-0173.wav|he instinctively turned to his right because the shot appeared to come from over his right shoulder.
+LJSpeech-1.1/wavs/LJ015-0277.wav|When he could get nothing but the blank cheque, he set in motion all sorts of schemes for obtaining signatures, such as
+LJSpeech-1.1/wavs/LJ009-0212.wav|The countenance of Williams was ghastly in the extreme, and the whole had an appearance too horrible for description.
+LJSpeech-1.1/wavs/LJ025-0148.wav|"Thus it must be admitted," he says again, "that plants may be contractile and locomotive;
+LJSpeech-1.1/wavs/LJ008-0033.wav|Ten were executed
+LJSpeech-1.1/wavs/LJ038-0020.wav|Brewer told Mrs. Postal that he had seen the man ducking into his place of business and that he had followed him to the theatre.
+LJSpeech-1.1/wavs/LJ001-0165.wav|One fruitful source of badness in paper
+LJSpeech-1.1/wavs/LJ020-0103.wav|are better than mere personal strength in the accomplishment of such tasks as fall to women to perform.
+LJSpeech-1.1/wavs/LJ046-0176.wav|that an individual presents a significant danger to the life of the President, his name is placed in a "trip index file"
+LJSpeech-1.1/wavs/LJ004-0046.wav|An active committee was appointed, comprising many names already well known, some of them destined to become famous in the annals of philanthropy.
+LJSpeech-1.1/wavs/LJ035-0144.wav|Oswald's departure from building.
+LJSpeech-1.1/wavs/LJ019-0381.wav|in another there was half-heartedness, even apathy and an almost complete contempt for the provisions of the act.
+LJSpeech-1.1/wavs/LJ045-0011.wav|by some rightwing groups centered in Dallas or any other general atmosphere of hate
+LJSpeech-1.1/wavs/LJ008-0008.wav|was in the right direction, as making the performance shorter and diminishing the area of display.
+LJSpeech-1.1/wavs/LJ002-0014.wav|The numbers soon increased, however, and by eighteen eleven had again risen to six hundred twenty-nine; and Mr. Neild was told that there had been at one time
+LJSpeech-1.1/wavs/LJ037-0237.wav|When interviewed by FBI agents on January twenty-one, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ016-0251.wav|and must be strongly deprecated on moral grounds, as tending to the utter degradation of one individual.
+LJSpeech-1.1/wavs/LJ031-0207.wav|The Secret Service established a communication system with the White House and screened all telephone calls and visitors.
+LJSpeech-1.1/wavs/LJ026-0023.wav|nitrogen either from simple salts or from proteids, and the process of nutrition is one of oxidation.
+LJSpeech-1.1/wavs/LJ013-0217.wav|varied so considerably in the several documents he left behind, that too much reliance cannot be placed upon his confession.
+LJSpeech-1.1/wavs/LJ031-0032.wav|Although he was reclining in his wife's arms, he lurched forward in an effort to stand upright and get out of the car, but he collapsed again.
+LJSpeech-1.1/wavs/LJ025-0020.wav|although the parts of some plants exhibit oscillating movements without any perceptible cause and the leaves of others retract when touched,
+LJSpeech-1.1/wavs/LJ038-0262.wav|Robert A. Frazier, an FBI ballistics identification expert, testified that he was, quote, unable to reach a conclusion, end quote,
+LJSpeech-1.1/wavs/LJ050-0033.wav|The Commission emphasizes that it finds no causal connection between the assassination
+LJSpeech-1.1/wavs/LJ014-0111.wav|At the second exact information was obtained of Mrs. Manning's movements. She had gone to Edinburgh.
+LJSpeech-1.1/wavs/LJ041-0072.wav|that he was a man of great ability and intelligence and that many of his superiors in the Marine Corps were not sufficiently competent to give him orders.
+LJSpeech-1.1/wavs/LJ050-0093.wav|because of Oswald's demonstrated hostility toward the Secretary of the Navy in his letter of January thirty, nineteen sixty-two. Quote,
+LJSpeech-1.1/wavs/LJ010-0184.wav|Oxford was seized by a person named Lowe, who was at first mistaken for the assailant.
+LJSpeech-1.1/wavs/LJ010-0235.wav|he would be in some way provided for, he having been for some time previously in abject circumstances.
+LJSpeech-1.1/wavs/LJ032-0053.wav|control number V C eight three six, serial number C two seven six six, was shipped parcel post to
+LJSpeech-1.1/wavs/LJ034-0057.wav|In evaluating the significance of these fingerprint and palmprint identifications,
+LJSpeech-1.1/wavs/LJ049-0109.wav|that the hostile act occur while the victim is engaged in or because of the performance of official duties.
+LJSpeech-1.1/wavs/LJ026-0115.wav|The excess undergoes oxidation and forms nitrogen excretions.
+LJSpeech-1.1/wavs/LJ011-0093.wav|This phial he had managed to retain in his possession in spite of the frequent searches to which he was subjected in Newgate.
+LJSpeech-1.1/wavs/LJ027-0040.wav|between the fin of a fish and the paddle of a whale
+LJSpeech-1.1/wavs/LJ020-0025.wav|One quart of Graham flour, one cup of white flour.
+LJSpeech-1.1/wavs/LJ003-0296.wav|many of the indispensable conditions of a model prison according to modern ideas.
+LJSpeech-1.1/wavs/LJ039-0215.wav|Frazier added that the scope would cause a slight miss to the right.
+LJSpeech-1.1/wavs/LJ032-0214.wav|Using a recognized technique of determining whether a picture was taken with a particular camera,
+LJSpeech-1.1/wavs/LJ016-0035.wav|the wall beneath and above it was "rusticated," in other words, the granite surface had become roughened, and offered a sort of foothold.
+LJSpeech-1.1/wavs/LJ041-0036.wav|He was a little dismayed at this, and he said that he couldn't find any that would show any interest in him as a Communist,
+LJSpeech-1.1/wavs/LJ033-0123.wav|that the bag she saw Oswald carrying, quote, wasn't that long, I mean it was folded down at the top as I told you. It definitely wasn't that long, end quote,
+LJSpeech-1.1/wavs/LJ007-0235.wav|instead of ventilating the apartments, they only serve to convey the offensive effluvia arising from the decaying animal matter into the dining-rooms.
+LJSpeech-1.1/wavs/LJ017-0095.wav|The purchase by Palmer of strychnia was proved.
+LJSpeech-1.1/wavs/LJ041-0160.wav|I certainly don't. I don't think with any kind of formal argument you could have shaken that conviction. And that is why I say irrevocable.
+LJSpeech-1.1/wavs/LJ050-0254.wav|The Secret Service in the past has sometimes guarded its right to be acknowledged as the sole protector of the Chief Executive.
+LJSpeech-1.1/wavs/LJ007-0175.wav|but in opposition to the recorded denunciations of authority, and in defiance of the express enactments of the law,
+LJSpeech-1.1/wavs/LJ003-0137.wav|and I learned he had been up with the others the whole night.
+LJSpeech-1.1/wavs/LJ008-0271.wav|This was more particularly the practice in London.
+LJSpeech-1.1/wavs/LJ014-0177.wav|and he must have been at the end of his resources when he committed the crime for which he suffered.
+LJSpeech-1.1/wavs/LJ008-0090.wav|It was still the custom to offer warm encouragement or bitter disapproval, according to the character and antecedents of the sufferer.
+LJSpeech-1.1/wavs/LJ038-0186.wav|You will find the mailbox in the post office which is located four blocks from the drugstore on that street.
+LJSpeech-1.1/wavs/LJ050-0099.wav|It is apparent that a good deal of further consideration and experimentation will be required before adequate criteria can be framed.
+LJSpeech-1.1/wavs/LJ029-0192.wav|On October twenty-six the press reported Chief of Police Curry's plans
+LJSpeech-1.1/wavs/LJ033-0067.wav|She pointed to the blanket which was on the floor very close to where Ruth Paine was standing.
+LJSpeech-1.1/wavs/LJ030-0221.wav|stated that Mrs. Kennedy would probably have fallen off the rear end of the car and been killed if Hill had not pushed her back into the Presidential automobile.
+LJSpeech-1.1/wavs/LJ031-0108.wav|Rubber tubes were inserted between the second and third ribs to reexpand the right lung, which had collapsed because of the opening in the chest wall.
+LJSpeech-1.1/wavs/LJ012-0114.wav|but their request, in spite of their earnest entreaties,
+LJSpeech-1.1/wavs/LJ019-0076.wav|This variety was often extended to all branches of prison economy.
+LJSpeech-1.1/wavs/LJ034-0060.wav|who, like Oswald, might have handled the cartons. They were also compared with the prints of those law enforcement officials who might have handled the cartons.
+LJSpeech-1.1/wavs/LJ017-0281.wav|As the first to appear would have some time to wait for his fellows, a difficult and painful ordeal,
+LJSpeech-1.1/wavs/LJ037-0036.wav|Just prior to the shooting, Mrs. Helen Markham, a waitress in downtown Dallas, was about to cross tenth Street at Patton.
+LJSpeech-1.1/wavs/LJ044-0082.wav|His attempt to express himself through his Fair Play for Cuba activities, however,
+LJSpeech-1.1/wavs/LJ050-0059.wav|he new instructions require FBI agents to report immediately information concerning, quote,
+LJSpeech-1.1/wavs/LJ041-0004.wav|After his return to New Orleans Oswald was teased at school because of the northern accent which he had acquired.
+LJSpeech-1.1/wavs/LJ026-0084.wav|i.e., capable of decomposition into simpler, stabler molecules in which stronger affinities are satisfied.
+LJSpeech-1.1/wavs/LJ037-0011.wav|was eating lunch in his cab which was parked on Patton facing the southeast corner of tenth Street and Patton Avenue a few feet to the north.
+LJSpeech-1.1/wavs/LJ048-0212.wav|Lawson stated that he, quote, was looking back a good deal of the time,
+LJSpeech-1.1/wavs/LJ010-0013.wav|forgeries committed systematically, and for long periods, as in the case of Fauntleroy,
+LJSpeech-1.1/wavs/LJ027-0130.wav|these evidences have been in the past thirty years somewhat overshadowed by the far more surprising evidences
+LJSpeech-1.1/wavs/LJ016-0112.wav|All three after recapture passed on, as originally intended, to Leicester, where they did their "bit" and were released;
+LJSpeech-1.1/wavs/LJ016-0115.wav|A sailor, Krapps by name, occupied one of the upper cells in the new block.
+LJSpeech-1.1/wavs/LJ027-0068.wav|which probably represent the vertebrate condition before limbs were acquired;
+LJSpeech-1.1/wavs/LJ028-0163.wav|which are, like the great gates in the outer wall, of brass, and open on the water.
+LJSpeech-1.1/wavs/LJ010-0269.wav|whose description, as given by his father to the police,
+LJSpeech-1.1/wavs/LJ035-0054.wav|He had been proceeding toward the rear of the lunchroom.
+LJSpeech-1.1/wavs/LJ028-0313.wav|The men upon the towers, whose business it was to keep a lookout,
+LJSpeech-1.1/wavs/LJ049-0101.wav|Punish the murder or manslaughter of, attempt or conspiracy to murder, kidnaping of and assault upon
+LJSpeech-1.1/wavs/LJ032-0117.wav|He lifted the rifle by the wooden stock after his examination convinced him that the wood was too rough to take fingerprints.
+LJSpeech-1.1/wavs/LJ037-0141.wav|When Oswald was arrested, he had in his possession a Smith and Wesson thirty-eight Special caliber revolver,
+LJSpeech-1.1/wavs/LJ036-0170.wav|If he was discharged at Neely and Beckley and walked directly to his roominghouse,
+LJSpeech-1.1/wavs/LJ003-0184.wav|all the contagion and profligacy which prevailed in this part of the prison, end quote.
+LJSpeech-1.1/wavs/LJ010-0191.wav|After his arrest he thought only of the excitement his attempt had raised, nothing of its atrocity,
+LJSpeech-1.1/wavs/LJ004-0135.wav|twenty men slept on eight straw beds, with sixteen rugs amongst them, and a piece of timber for a bolster.
+LJSpeech-1.1/wavs/LJ030-0022.wav|Upon concluding the conversation, the President prepared to depart for Dallas.
+LJSpeech-1.1/wavs/LJ015-0107.wav|Meanwhile the absconding clerk had coolly driven to a favorite dining-place in the West End,
+LJSpeech-1.1/wavs/LJ039-0026.wav|she stated that she might have been confused about shutting him in the bathroom, but that, quote, there is no doubt that he got dressed and got a gun, end quote,
+LJSpeech-1.1/wavs/LJ016-0386.wav|many of the most abandoned murderers snore peacefully their eight hours, even on the night immediately preceding execution.
+LJSpeech-1.1/wavs/LJ014-0129.wav|The case rested upon the facts which have been already set forth, and was proved to the satisfaction of the jury, who brought in a verdict of guilty.
+LJSpeech-1.1/wavs/LJ012-0130.wav|brought in a man-of-war from Brazil had been transhipped at Falmouth for conveyance to London.
+LJSpeech-1.1/wavs/LJ012-0282.wav|This, wrapped up in a silk handkerchief,
+LJSpeech-1.1/wavs/LJ039-0062.wav|the shots were at a slow-moving target proceeding on a downgrade in virtually a straight line
+LJSpeech-1.1/wavs/LJ014-0077.wav|It came out in the husband's confession that Mrs. Manning induced O'Connor to go down to the kitchen to wash his hands, that she followed him to the basement,
+LJSpeech-1.1/wavs/LJ011-0201.wav|The game, however, was nearly up. Miss Turner was met by her uncle on Calais pier as she was walking with Wakefield.
+LJSpeech-1.1/wavs/LJ005-0150.wav|In some county jails, as I have already said, female prisoners were placed upon the tread-wheel;
+LJSpeech-1.1/wavs/LJ016-0402.wav|No woman could resist him, he calmly assured Mr. Smith that night as they walked together, and he recounted his villanies one by one.
+LJSpeech-1.1/wavs/LJ042-0183.wav|As a result he thought it would be, quote, necessary to oppose the old systems but at the same time support their cherished traditions, end quote.
+LJSpeech-1.1/wavs/LJ028-0178.wav|the walls of Babylon were so long and wide and high that all who saw them were amazed.
+LJSpeech-1.1/wavs/LJ032-0089.wav|It certified that Lee Harvey Oswald had been vaccinated for smallpox on June eight, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ049-0209.wav|The Commission was not asked to apply itself as did the Hoover Commission in nineteen forty-nine,
+LJSpeech-1.1/wavs/LJ002-0069.wav|to the various wards their friends occupied.
+LJSpeech-1.1/wavs/LJ018-0396.wav|The greatest causes célèbre, however, of recent times were the turf frauds by which the Comtesse de Goncourt was swindled
+LJSpeech-1.1/wavs/LJ022-0158.wav|By far the greater part of the general decline in utility securities had occurred before I was inaugurated.
+LJSpeech-1.1/wavs/LJ027-0065.wav|enclosed and protected by the skeleton, viz., the neural cavity above, and the visceral or body cavity below, the vertebral column.
+LJSpeech-1.1/wavs/LJ032-0238.wav|Marina Oswald testified that the photographs were taken on a Sunday about two weeks before the attempted shooting of Maj. Gen. Edwin A. Walker
+LJSpeech-1.1/wavs/LJ013-0132.wav|On a search of the prisoner's lodgings in Allington Street, a silver fork was found,
+LJSpeech-1.1/wavs/LJ031-0060.wav|Dr. Robert N. McClelland entered at that point and assisted Dr. Perry with the tracheotomy.
+LJSpeech-1.1/wavs/LJ025-0022.wav|From the mobility of animals Cuvier, with his characteristic partiality for teleological reasoning,
+LJSpeech-1.1/wavs/LJ018-0111.wav|They advertised for clerks, and employed the most likely of the applicants by sending them to the bank.
+LJSpeech-1.1/wavs/LJ039-0086.wav|I consider it a real advantage, particularly at the range of one hundred yards, in identifying your target.
+LJSpeech-1.1/wavs/LJ002-0265.wav|left by a Mr. Henry Allnutt, who was long a prisoner in the Marshalsea, and came into a fortune while there.
+LJSpeech-1.1/wavs/LJ018-0044.wav|There was the prisoner's poverty, his inability to account for himself on the night of the murder, and his possession of the property of the murdered man.
+LJSpeech-1.1/wavs/LJ038-0069.wav|At one:fifty-one p.m., police car two reported by radio that it was on the way to headquarters with the suspect.
+LJSpeech-1.1/wavs/LJ033-0084.wav|It was more bulky toward the bottom, end quote, than toward the top.
+LJSpeech-1.1/wavs/LJ019-0354.wav|That no such contract was valid between one jurisdiction and another unless the Secretary of State was satisfied that the prison it was intended to use
+LJSpeech-1.1/wavs/LJ045-0047.wav|and I told him that
+LJSpeech-1.1/wavs/LJ003-0178.wav|The female felons' wards I shall describe at length in the next chapter,
+LJSpeech-1.1/wavs/LJ002-0285.wav|and was so unfit to sleep on that it had not been used for many years, so that the men and women associated together indiscriminately.
+LJSpeech-1.1/wavs/LJ011-0076.wav|His offense was uttering forged notes, and there was strong suspicion that he had long subsisted entirely by this fraud.
+LJSpeech-1.1/wavs/LJ033-0133.wav|and I didn't pay too much attention on how he carried the package at all, end quote.
+LJSpeech-1.1/wavs/LJ048-0098.wav|there were not enough men available to permit two agents to be assigned to all the advance work.
+LJSpeech-1.1/wavs/LJ011-0129.wav|Maynard was the only one who suffered death.
+LJSpeech-1.1/wavs/LJ013-0022.wav|The ship, however, met with exasperatingly fine weather, and it was not until the captain reached the West India Islands
+LJSpeech-1.1/wavs/LJ039-0194.wav|All three of the firers in these tests
+LJSpeech-1.1/wavs/LJ043-0094.wav|This was at least in part because of the close physical confines in which some of the work had to be done.
+LJSpeech-1.1/wavs/LJ049-0228.wav|the Commission believes that the facts of the assassination of President Kennedy point to certain measures which,
+LJSpeech-1.1/wavs/LJ038-0073.wav|Hill testified that Fritz told the detective to get a search warrant, go to an address on Fifth Street in Irving,
+LJSpeech-1.1/wavs/LJ018-0383.wav|He was glad, therefore, to get an extra allowance of bread,
+LJSpeech-1.1/wavs/LJ002-0214.wav|was a prison for debtors and persons committed for contempt by the courts of Chancery, Exchequer, and Common Pleas.
+LJSpeech-1.1/wavs/LJ007-0110.wav|The rest were detained for more than a month, it being considered that they were the most guilty, as being either professional agents, who advised the others to abscond,
+LJSpeech-1.1/wavs/LJ011-0244.wav|through whose interest an appointment under Government was to be obtained for Mullay, would be present.
+LJSpeech-1.1/wavs/LJ050-0096.wav|I ask you to look into this case and take the necessary steps to repair the damage done to me and my family. End quote.
+LJSpeech-1.1/wavs/LJ008-0021.wav|for the murder of his cabin-boy, whom he stabbed after much ill-usage on board the ship as it lay in the Tagus.
+LJSpeech-1.1/wavs/LJ043-0028.wav|Oswald attempted to get his wife to come back and, over Bouhe's protest, De Mohrenschildt finally told him where she was.
+LJSpeech-1.1/wavs/LJ033-0106.wav|believed that he saw Oswald coming to work, but he does not remember that Oswald had anything in his hands as he entered the door.
+LJSpeech-1.1/wavs/LJ048-0269.wav|and special agents cooperating with them, or by special agents on similar assignments, while they are in a travel status, is prohibited. End quote.
+LJSpeech-1.1/wavs/LJ013-0107.wav|but he escaped through a back door on to the river, and rowed off in a boat to a hiding-place in the woods.
+LJSpeech-1.1/wavs/LJ011-0102.wav|He had gone on board in his Quaker dress, but when captured was found in a light-green frock,
+LJSpeech-1.1/wavs/LJ021-0111.wav|There may be a serious question as to the wisdom of many of those devices to control production,
+LJSpeech-1.1/wavs/LJ018-0069.wav|where the arrest was made.
+LJSpeech-1.1/wavs/LJ050-0143.wav|county, and State law enforcement agencies in their districts.
+LJSpeech-1.1/wavs/LJ015-0127.wav|All the signatures in the transfer were forged. Not only did he thus transfer and realize "bogus" stock
+LJSpeech-1.1/wavs/LJ006-0138.wav|They had a standing bedstead to sleep on, and a good flock mattress; double allowance of provisions, filched from the common stock.
+LJSpeech-1.1/wavs/LJ046-0051.wav|From George Washington to John F. Kennedy, such journeys have been a normal part of the President's activities.
+LJSpeech-1.1/wavs/LJ014-0019.wav|His name was Hocker; he was by trade a ladies' shoemaker; and it was also ascertained that after the day of the murder he was flush of money.
+LJSpeech-1.1/wavs/LJ023-0042.wav|But to the far-sighted it is far-reaching in its possibilities of injury to America.
+LJSpeech-1.1/wavs/LJ045-0062.wav|he made no attempt to help her and there are other indications that he did not want her to learn that language.
+LJSpeech-1.1/wavs/LJ011-0017.wav|The impression which his appearance altogether was calculated to make was that of the profoundest commiseration.
+LJSpeech-1.1/wavs/LJ048-0006.wav|or to the Vice President, end quote.
+LJSpeech-1.1/wavs/LJ046-0142.wav|but again the standards were very general.
+LJSpeech-1.1/wavs/LJ035-0012.wav|At about this time he heard the first shot.
+LJSpeech-1.1/wavs/LJ005-0020.wav|Among those whom the Society found arrayed against it was Sydney Smith,
+LJSpeech-1.1/wavs/LJ033-0193.wav|he found, on the inside, a single brown delustered viscose fiber and several light green cotton fibers.
+LJSpeech-1.1/wavs/LJ034-0115.wav|of the man who shot Patrolman J. D. Tippit.
+LJSpeech-1.1/wavs/LJ007-0120.wav|some to the infirmary, many more to the governor's house.
+LJSpeech-1.1/wavs/LJ048-0091.wav|Advance preparations.
+LJSpeech-1.1/wavs/LJ013-0178.wav|Further suspicion was caused by the position of a book and a wax candle by the bedside.
+LJSpeech-1.1/wavs/LJ028-0105.wav|Tradition says that to please his foreign wife from the mountainous country he built the famous hanging gardens, but that may be only a tradition.
+LJSpeech-1.1/wavs/LJ011-0231.wav|Miss Turner subsequently married Mr. Legh of Lym Hall, Cheshire.
+LJSpeech-1.1/wavs/LJ048-0246.wav|Each of the agents who visited the Press Club or the Cellar Coffee House (apart from the three members of the midnight shift)
+LJSpeech-1.1/wavs/LJ015-0185.wav|When the jury found a verdict of guilty he remained unmoved. He listened without emotion to the judge's well-merited censures,
+LJSpeech-1.1/wavs/LJ025-0124.wav|but in the plants the act of contraction has been found to be accompanied,
+LJSpeech-1.1/wavs/LJ049-0092.wav|without authorizing it to arrest anyone who harms him. The same provisions authorize the Service to arrest without warrant
+LJSpeech-1.1/wavs/LJ043-0187.wav|She testified that Oswald said that General Walker, quote, was a very bad man, that he was a fascist,
+LJSpeech-1.1/wavs/LJ010-0094.wav|bound, as it happened, to the Archbishop of York's.
+LJSpeech-1.1/wavs/LJ004-0243.wav|The bulk of the prisoners were still left in idleness.
+LJSpeech-1.1/wavs/LJ047-0157.wav|I asked her if she knew where he worked.
+LJSpeech-1.1/wavs/LJ002-0176.wav|Neild gives a list of the various items charged upon a debt of ten pounds, which included instructions to sue,
+LJSpeech-1.1/wavs/LJ049-0086.wav|It is anomalous that Congress has legislated in other ways touching upon the safety of the Chief Executive or other Federal officers,
+LJSpeech-1.1/wavs/LJ005-0141.wav|the sleeping space per head was only sixteen inches, and often as many as two hundred ninety-three men had to be accommodated on barrack beds
+LJSpeech-1.1/wavs/LJ032-0159.wav|these fibers were examined by Paul M. Stombaugh, a special agent assigned to the Hair and Fiber Unit of the FBI Laboratory.
+LJSpeech-1.1/wavs/LJ028-0512.wav|but the river walls have disappeared, and the buttresses of the bridges have been torn or washed away.
+LJSpeech-1.1/wavs/LJ008-0095.wav|It was said that so large a crowd had never collected since the execution of Mrs. Brownrigg, nor had the public indignation risen so high.
+LJSpeech-1.1/wavs/LJ016-0341.wav|The sufferer was stolid and reticent to the last.
+LJSpeech-1.1/wavs/LJ028-0294.wav|no stranger's hands have wrought this work on me, but my own only.
+LJSpeech-1.1/wavs/LJ047-0152.wav|He conducted a limited background investigation of the Paines, intending to interview Mrs. Paine and ask her particularly about Oswald's whereabouts.
+LJSpeech-1.1/wavs/LJ020-0013.wav|Add a few spoonfuls at a time, the potato-water alternately with the flour by the handful,
+LJSpeech-1.1/wavs/LJ005-0093.wav|The second act, passed in the following year, enlarged and amended the first, and at the same time gave powers to the House
+LJSpeech-1.1/wavs/LJ006-0257.wav|hot meat, poultry, and fish were also forbidden.
+LJSpeech-1.1/wavs/LJ003-0223.wav|The roof of the female prison, says the grand jury in their presentment in eighteen thirteen, let in the rain.
+LJSpeech-1.1/wavs/LJ048-0281.wav|Yet their role of protecting the President is so important to the well-being of the country
+LJSpeech-1.1/wavs/LJ009-0049.wav|For this offense the punishment is death.
+LJSpeech-1.1/wavs/LJ032-0060.wav|testified that the signature and other writing on the application for that box were in the handwriting of Lee Harvey Oswald
+LJSpeech-1.1/wavs/LJ050-0112.wav|and the leading State and local agencies that might be a source of such information.
+LJSpeech-1.1/wavs/LJ036-0049.wav|And, after we got past Akard, at Murphy -- I figured it out. Let's see. I don't know for sure.
+LJSpeech-1.1/wavs/LJ010-0305.wav|Its embarrassments were greatly increased by the bankruptcy of two of its clients in the building trade,
+LJSpeech-1.1/wavs/LJ040-0084.wav|dwelling on the fact that she was a widow with three children.
+LJSpeech-1.1/wavs/LJ018-0158.wav|I am the man that has got all the bank paper; I have thirty thousand pounds now, and the Bank of England cannot stop it.
+LJSpeech-1.1/wavs/LJ043-0085.wav|Oswald said that he hoped eventually to develop qualifications for employment as a junior executive
+LJSpeech-1.1/wavs/LJ032-0005.wav|and that the weapon which fired these bullets was a Mannlicher-Carcano
+LJSpeech-1.1/wavs/LJ030-0252.wav|Secret Service procedure requires that each agent stay with the person being protected
+LJSpeech-1.1/wavs/LJ045-0099.wav|She had, quote, the impression that he was just pushed, pushed, pushed, and she Marina Oswald was probably nagging, nagging, nagging, end quote.
+LJSpeech-1.1/wavs/LJ005-0270.wav|which should be paid into general prison funds, and no part of them handed over to the prisoners themselves.
+LJSpeech-1.1/wavs/LJ045-0127.wav|He absolved the Soviet Embassy in Mexico City of any blame for his difficulties there. He advised the Washington Embassy
+LJSpeech-1.1/wavs/LJ026-0130.wav|and may thus be easily transported to all parts of the plant.
+LJSpeech-1.1/wavs/LJ019-0240.wav|But no structural alterations were made from the date first quoted until the time of closing the prison in eighteen eighty-one.
+LJSpeech-1.1/wavs/LJ028-0517.wav|walls which no ancient army was ever able to take by storm.
+LJSpeech-1.1/wavs/LJ004-0231.wav|This was effected by stopping the nearly indiscriminate admission of visitors, which had hitherto prevailed all over the jail.
+LJSpeech-1.1/wavs/LJ023-0014.wav|In effect, four Justices ruled that the right under a private contract
+LJSpeech-1.1/wavs/LJ029-0182.wav|may not endorse him in 'sixty-four.
+LJSpeech-1.1/wavs/LJ044-0205.wav|and promised prompt U.S. aid if they do, end quote.
+LJSpeech-1.1/wavs/LJ042-0226.wav|and horror at the misguided line of reasoning of the United States Government, end quote.
+LJSpeech-1.1/wavs/LJ038-0100.wav|On November twenty-three, Fritz confronted Oswald with the evidence that he had purchased a rifle under the fictitious name of "Hidell."
+LJSpeech-1.1/wavs/LJ026-0160.wav|In comparing a higher animal and a green plant confusion must be avoided regarding the part played by oxygen and carbon dioxide in true respiration
+LJSpeech-1.1/wavs/LJ028-0195.wav|It is a long story. Poets have sung it. Historians have written it. Prophets have preached it. Legends have gathered about it.
+LJSpeech-1.1/wavs/LJ043-0153.wav|When asked if Oswald requested the note back she testified that, quote, He forgot about it. But apparently
+LJSpeech-1.1/wavs/LJ006-0179.wav|But, indeed, his whole rule was far too mild, and under this mistaken leniency
+LJSpeech-1.1/wavs/LJ043-0025.wav|He even threatened to tear up his wife's dresses and break all the baby things. According to De Mohrenschildt,
+LJSpeech-1.1/wavs/LJ029-0079.wav|was a one-story building with few entrances and easy to make secure, but it lacked necessary food-handling facilities
+LJSpeech-1.1/wavs/LJ050-0049.wav|and from a specialist in psychiatric prognostication at Walter Reed Hospital.
+LJSpeech-1.1/wavs/LJ005-0193.wav|All descriptions of offenders congregated together in the felons' wards.
+LJSpeech-1.1/wavs/LJ034-0212.wav|As the motorcade started its descent down Elm Street., Altgens snapped a picture of the Presidential limousine with the entrance to the Depository Building in the background.
+LJSpeech-1.1/wavs/LJ043-0124.wav|Ironically, he failed to get a job in another photographic firm after his return to Dallas in October of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ049-0030.wav|the Commission does believe that there are aspects of the protective measures employed in the motorcade at Dallas which deserve special comment.
+LJSpeech-1.1/wavs/LJ049-0097.wav|and the attempt on the life of President-elect Franklin D. Roosevelt.
+LJSpeech-1.1/wavs/LJ014-0291.wav|he showed symptoms of delirium tremens, and admitted that he had been addicted to the excessive use of stimulants.
+LJSpeech-1.1/wavs/LJ008-0120.wav|"Do tell me, sir; I am informed I shall go down with great force; is it so?"
+LJSpeech-1.1/wavs/LJ014-0310.wav|Pries, although enjoying a high reputation in the city, had long been in a bad way.
+LJSpeech-1.1/wavs/LJ050-0157.wav|On the basis of such a feasibility study,
+LJSpeech-1.1/wavs/LJ018-0198.wav|It is told of "Bob" Brennan, an official specially employed for years by the Mint
+LJSpeech-1.1/wavs/LJ026-0155.wav|and later is absorbed from the blood and lymph by all the living cells.
+LJSpeech-1.1/wavs/LJ043-0141.wav|Sometime after March twenty-seven, but according to Marina Oswald, prior to April ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ006-0279.wav|Wards-women were allowed much the same authority, with the same temptations to excess, and intoxication was not unknown among them and others.
+LJSpeech-1.1/wavs/LJ047-0025.wav|During the next two years the FBI continued to accumulate information,
+LJSpeech-1.1/wavs/LJ046-0232.wav|Although the Service recognized that its advance preventive measures must encompass more than these most obvious dangers,
+LJSpeech-1.1/wavs/LJ039-0168.wav|each fired two series of three shots.
+LJSpeech-1.1/wavs/LJ029-0166.wav|On November twenty a front page story reported that the streets on which the Presidential motorcade would travel included "Main and Stemmons Freeway."
+LJSpeech-1.1/wavs/LJ029-0204.wav|Two days later, a local Republican leader called for a "civilized nonpartisan" welcome
+LJSpeech-1.1/wavs/LJ050-0020.wav|in which the Commission believes that the Secret Service has operated with insufficient planning or control.
+LJSpeech-1.1/wavs/LJ007-0006.wav|They had introduced "much order and cleanliness,"
+LJSpeech-1.1/wavs/LJ031-0100.wav|Treatment of Governor Connally
+LJSpeech-1.1/wavs/LJ038-0306.wav|was considered of probative value in this investigation, although the Commission's conclusion concerning the identity of the assassin
+LJSpeech-1.1/wavs/LJ016-0152.wav|Only great resolution indeed, persisted in to the bitter end,
+LJSpeech-1.1/wavs/LJ021-0079.wav|Nevertheless the gains of trade and industry, as a whole, have been substantial.
+LJSpeech-1.1/wavs/LJ017-0041.wav|was thus brought to the Central Criminal Court from Exeter assizes, and that of the Stauntons from Maidstone.
+LJSpeech-1.1/wavs/LJ011-0077.wav|The act for which he was taken into custody was the payment of a forged ten-pound note for half-a-dozen silver spoons.
+LJSpeech-1.1/wavs/LJ049-0061.wav|No responsibility rests upon those agents near the President for the identification or arrest of any assassin or an attacker.
+LJSpeech-1.1/wavs/LJ039-0240.wav|On the basis of the evidence reviewed in this chapter, the Commission has found that Lee Harvey Oswald (one)
+LJSpeech-1.1/wavs/LJ012-0184.wav|Meanwhile the discovery of pistol and knife spattered with human blood and brains
+LJSpeech-1.1/wavs/LJ032-0026.wav|Post Office Box two nine one five, Dallas, Texas, on March twenty, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ050-0155.wav|Also the Department requests the sum of one hundred thousand dollars to conduct a detailed feasibility study;
+LJSpeech-1.1/wavs/LJ041-0091.wav|Powers considered Oswald to be meek and easily led
+LJSpeech-1.1/wavs/LJ025-0171.wav|setting free the oxygen and laying hold of the carbon which it contains.
+LJSpeech-1.1/wavs/LJ012-0018.wav|One of the most notorious, and for a time most successful practitioners in this illicit trade, passed through Newgate in eighteen thirty-one.
+LJSpeech-1.1/wavs/LJ019-0026.wav|of course with the added improvements following longer experience, of the pattern originated by the architectural genius of Sir Joshua Jebb.
+LJSpeech-1.1/wavs/LJ039-0138.wav|Lee Oswald exhibited an average amount of proficiency with that weapon.
+LJSpeech-1.1/wavs/LJ028-0127.wav|was living in the city while the walls were still standing, though in a ruinous condition.
+LJSpeech-1.1/wavs/LJ002-0031.wav|The jail at that date was divided into eight separate and more or less distinct departments, each of which had its own wards and yard.
+LJSpeech-1.1/wavs/LJ009-0119.wav|kneel, whilst the rest of the congregation sit, and the ordinary, in a tone of peculiar solemnity, says,
+LJSpeech-1.1/wavs/LJ005-0090.wav|the first by daily services, the latter by the appointment of schoolmasters and instruction in reading and writing.
+LJSpeech-1.1/wavs/LJ042-0068.wav|Somewhere, a violin plays, as I watch my life whirl away.
+LJSpeech-1.1/wavs/LJ039-0052.wav|the Commission concluded that the incident, as described by Marina Oswald,
+LJSpeech-1.1/wavs/LJ037-0173.wav|Three of the bullets recovered from Tippit's body were manufactured by Winchester-Western, and the fourth bullet by Remington-Peters,
+LJSpeech-1.1/wavs/LJ044-0076.wav|Oswald's statements suggest that he hoped to be flooded with callers and invitations to debate.
+LJSpeech-1.1/wavs/LJ011-0283.wav|One was a cheque for eight hundred pounds on his bankers, the other an order to Mr. Bell of Newport, Essex, requesting the surrender of a deed.
+LJSpeech-1.1/wavs/LJ046-0248.wav|to Federal intelligence and law enforcement agencies were not well designed to elicit information from them
+LJSpeech-1.1/wavs/LJ031-0197.wav|The public had been excluded from the base, and only Government officials and the press were permitted near the landing area.
+LJSpeech-1.1/wavs/LJ049-0138.wav|and, if the Council is used, arrangements should be made for the attendance of the Secretary of the Treasury
+LJSpeech-1.1/wavs/LJ025-0001.wav|The Science: History of the Universe, Volume five.
+LJSpeech-1.1/wavs/LJ014-0103.wav|beneath them was a layer of fresh mortar, beneath that a lot of loose earth, amongst which a stocking was turned up, and presently a human toe.
+LJSpeech-1.1/wavs/LJ015-0121.wav|Newgate officers who remember Robson still describe him as a fine young man, who behaved well as a prisoner,
+LJSpeech-1.1/wavs/LJ011-0226.wav|which was based on his jail experiences, and of which I have availed myself in the last chapter.
+LJSpeech-1.1/wavs/LJ021-0085.wav|and the incentive of fair private profit,
+LJSpeech-1.1/wavs/LJ006-0079.wav|The inspectors found that prisoners had their places assigned to them
+LJSpeech-1.1/wavs/LJ004-0236.wav|But nothing short of radical reform and complete reconstruction could touch the deep-seated evils of association, overcrowding, and idleness.
+LJSpeech-1.1/wavs/LJ047-0204.wav|Bouck pointed to a number of characteristics besides Oswald's defection the cumulative effect of which would have been to alert the Secret Service
+LJSpeech-1.1/wavs/LJ009-0027.wav|will thereby be lost.
+LJSpeech-1.1/wavs/LJ041-0118.wav|Oswald wrote to then Secretary of the Navy Connally on January thirty, nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ018-0350.wav|He might swear he was not the murderer, that he never fired a pistol in his life,
+LJSpeech-1.1/wavs/LJ018-0013.wav|There was a deep wound just over the ear, the skull was fractured, and there were several other blows and wounds on the head.
+LJSpeech-1.1/wavs/LJ042-0223.wav|and to have stated in the second what he thought would be least harmful to him as he resumed life in the United States.
+LJSpeech-1.1/wavs/LJ010-0169.wav|sometimes in the Strand, or the West End.
+LJSpeech-1.1/wavs/LJ015-0024.wav|but that he might strengthen it by his long experience and known business capacity.
+LJSpeech-1.1/wavs/LJ030-0018.wav|Before leaving the hotel, the President, Mrs. Kennedy, and Kenneth O'Donnell talked about the risks inherent in Presidential public appearances.
+LJSpeech-1.1/wavs/LJ019-0315.wav|also their constant employment in labor appropriate to their condition.
+LJSpeech-1.1/wavs/LJ035-0027.wav|They went through the swinging door and continued at, quote, a good trot, end quote,
+LJSpeech-1.1/wavs/LJ038-0278.wav|However, to say that because one does not find sufficient marks for identification that it is a negative,
+LJSpeech-1.1/wavs/LJ022-0080.wav|Projects will be sought which promise ultimate return to the federal treasury of a considerable proportion of the costs.
+LJSpeech-1.1/wavs/LJ022-0088.wav|they will be sent to an Allotment Division composed of representatives of the more important governmental agencies
+LJSpeech-1.1/wavs/LJ024-0139.wav|has been tipped out of balance by the courts in direct contradiction of the high purposes of the framers of the Constitution.
+LJSpeech-1.1/wavs/LJ017-0184.wav|Soon afterwards a friend, Mrs. Atkinson, came to London from Westmoreland, and stayed in Mrs. Wilson's house.
+LJSpeech-1.1/wavs/LJ010-0301.wav|He had entered the house as clerk in eighteen hundred;
+LJSpeech-1.1/wavs/LJ035-0196.wav|The address listed was for the Paine home in Irving.
+LJSpeech-1.1/wavs/LJ037-0085.wav|She was not sure whether she had seen his picture in a newspaper on the afternoon or evening of November twenty-two prior to the lineup.
+LJSpeech-1.1/wavs/LJ036-0176.wav|Earlene Roberts, housekeeper for Mrs. A. C. Johnson at ten twenty-six North Beckley
+LJSpeech-1.1/wavs/LJ011-0004.wav|Once he ran a narrow risk of being found out.
+LJSpeech-1.1/wavs/LJ019-0012.wav|which were then recommended to and generally adopted by the visiting justices all over the kingdom.
+LJSpeech-1.1/wavs/LJ010-0004.wav|only premising that as accounts become more voluminous I shall be compelled to deal with fewer cases,
+LJSpeech-1.1/wavs/LJ038-0282.wav|there is enough on it to say that it could have come, and even perhaps a little stronger, to say that it probably came from this,
+LJSpeech-1.1/wavs/LJ037-0014.wav|Scoggins lost sight of him behind some shrubbery on the southeast corner lot,
+LJSpeech-1.1/wavs/LJ002-0338.wav|Only now and again, in extreme cases, an unusually nasty companion was stripped, haled to the pump,
+LJSpeech-1.1/wavs/LJ048-0201.wav|I stressed the fact that this was our President and he should be shown every respect due his position and that it was our duty to see that this was done.
+LJSpeech-1.1/wavs/LJ018-0089.wav|Another case of long-continued successful forgery was brought to light two years after the convictions of Saward and his accomplices.
+LJSpeech-1.1/wavs/LJ002-0097.wav|they had dust-bins, sewers, and so forth, "properly disposed," and the city scavenger paid periodical visits to the prison.
+LJSpeech-1.1/wavs/LJ013-0015.wav|being a party to the intended fraud, they obtained further insurances on goods never shipped.
+LJSpeech-1.1/wavs/LJ021-0164.wav|a permanent army of unemployed.
+LJSpeech-1.1/wavs/LJ002-0119.wav|In that period upwards of two hundred thousand writs
+LJSpeech-1.1/wavs/LJ006-0109.wav|a perpetuation under another form of the old detestable custom of garnish.
+LJSpeech-1.1/wavs/LJ003-0017.wav|Order throughout the debtors' side was preserved
+LJSpeech-1.1/wavs/LJ013-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section sixteen: Newgate notorieties continued, part one.
+LJSpeech-1.1/wavs/LJ037-0073.wav|Addressing itself solely to the probative value of Mrs. Markham's contemporaneous description of the gunman
+LJSpeech-1.1/wavs/LJ037-0087.wav|I was pretty sure it was the same man I saw. When they made him turn sideways, I was positive that was the one I seen, end quote.
+LJSpeech-1.1/wavs/LJ011-0225.wav|will be admitted by those who have read his book on "the punishment of death,
+LJSpeech-1.1/wavs/LJ006-0181.wav|It was the same old story -- evil constantly in the ascendant, the least criminal at the mercy of the most depraved.
+LJSpeech-1.1/wavs/LJ021-0018.wav|the tremendous power of organization has combined great aggregations of capital in enormous industrial establishments
+LJSpeech-1.1/wavs/LJ025-0038.wav|carbon, hydrogen and oxygen.
+LJSpeech-1.1/wavs/LJ050-0224.wav|one hundred forty-five are proposed for the field offices to handle the increased volume of security investigations
+LJSpeech-1.1/wavs/LJ009-0122.wav|particularly to those who desire now to offer up their praises and thanksgivings for thy late mercies vouchsafed unto them.
+LJSpeech-1.1/wavs/LJ012-0210.wav|The porter received it, but from its freshness became suspicious of foul play.
+LJSpeech-1.1/wavs/LJ037-0066.wav|Although she used the words, quote, a little bit bushy, end quote, to describe the gunman's hair,
+LJSpeech-1.1/wavs/LJ010-0119.wav|It was asserted, not without foundation, at these trials, that Edwards repeatedly incited the associates he was betraying
+LJSpeech-1.1/wavs/LJ009-0092.wav|his legs give way, he utters a faint groan, and sinks on the floor.
+LJSpeech-1.1/wavs/LJ048-0045.wav|his pro-Castro tendencies, his lies when interrogated by the FBI,
+LJSpeech-1.1/wavs/LJ005-0068.wav|sought to "rank the prisons they built among the most splendid buildings of the city or town."
+LJSpeech-1.1/wavs/LJ029-0059.wav|Dallas detectives in the lobby of the Trade Mart and in the luncheon area also had copies of these photographs.
+LJSpeech-1.1/wavs/LJ006-0049.wav|an occasional prisoner or two committed by the Houses of Parliament, the Courts of King's Bench, Common Pleas,
+LJSpeech-1.1/wavs/LJ039-0137.wav|On that occasion Oswald again used a bolt-action twenty-two caliber rifle; and according to Robert,
+LJSpeech-1.1/wavs/LJ044-0014.wav|the New Orleans branch of the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ038-0102.wav|Oswald also denied owning a rifle and said that since leaving the Marine Corps he had fired only a small bore twenty-two rifle.
+LJSpeech-1.1/wavs/LJ021-0133.wav|are likewise not fully cooperating with their government.
+LJSpeech-1.1/wavs/LJ023-0025.wav|National laws are needed to complete that program.
+LJSpeech-1.1/wavs/LJ002-0035.wav|eight. The press yard.
+LJSpeech-1.1/wavs/LJ019-0051.wav|the other supported the theory of labor in association, but under a stringent rule of silence, with isolation only at night.
+LJSpeech-1.1/wavs/LJ035-0125.wav|Victoria Adams, who worked on the fourth floor of the Depository Building,
+LJSpeech-1.1/wavs/LJ036-0085.wav|Oswald rose from his chair, looked directly at Fritz, and said, quote, Everybody will know who I am now, end quote,
+LJSpeech-1.1/wavs/LJ039-0214.wav|in order to hit the intended object. The scope would accomplish the lead for you, end quote,
+LJSpeech-1.1/wavs/LJ039-0243.wav|(three) was present, at the time of the assassination, at the window from which the shots were fired
+LJSpeech-1.1/wavs/LJ019-0176.wav|The wards had open fires, but the separate cells were not warmed at all.
+LJSpeech-1.1/wavs/LJ019-0193.wav|but as the plan was "confessedly experimental, for the benefit of the country generally, the amount for which the city should be responsible should be distinctly limited
+LJSpeech-1.1/wavs/LJ019-0036.wav|This list included Wakefield, Leeds, Kirkdale, Manchester, Birmingham, and Dublin.
+LJSpeech-1.1/wavs/LJ050-0236.wav|Thus, in the four months following the assassination,
+LJSpeech-1.1/wavs/LJ028-0267.wav|The Babylonians, however, cared not a whit for his siege.
+LJSpeech-1.1/wavs/LJ048-0055.wav|a more alert and carefully considered treatment of the Oswald case by the Bureau might have brought about such a referral.
+LJSpeech-1.1/wavs/LJ037-0115.wav|Early in the evening of November twenty-two,
+LJSpeech-1.1/wavs/LJ050-0051.wav|nineteen sixty-four, makes several significant recommendations in this field.
+LJSpeech-1.1/wavs/LJ011-0063.wav|Fauntleroy was not entirely dependent upon the ordinary for ghostly counsel in his extremity.
+LJSpeech-1.1/wavs/LJ005-0184.wav|so as to corrupt those committed, "to the injury of the peace and morals of the public."
+LJSpeech-1.1/wavs/LJ009-0107.wav|The children round the communion-table stare and gape with childish wonder.
+LJSpeech-1.1/wavs/LJ017-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section twenty: Newgate Notorieties, part one.
+LJSpeech-1.1/wavs/LJ001-0001.wav|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition
+LJSpeech-1.1/wavs/LJ040-0177.wav|He took the position that such matters were his own business.
+LJSpeech-1.1/wavs/LJ037-0045.wav|He was just fooling with it. I didn't know what he was doing. I was afraid he was fixing to kill me. End quote.
+LJSpeech-1.1/wavs/LJ041-0117.wav|In an attempt to have this discharge reversed,
+LJSpeech-1.1/wavs/LJ027-0018.wav|All are forced to make concession after concession to their surroundings, and in these concessions all progress in life consists.
+LJSpeech-1.1/wavs/LJ034-0185.wav|In his testimony he said, quote, I told them that that could have been the man.
+LJSpeech-1.1/wavs/LJ025-0069.wav|innumerable plants and free plant cells are known to pass the whole or part of their lives in an actively locomotive condition,
+LJSpeech-1.1/wavs/LJ016-0224.wav|Calcraft shared the odium which his office, not strangely, has always inspired. But he was admitted into the jail,
+LJSpeech-1.1/wavs/LJ036-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ009-0048.wav|He is a hardened offender, previously cast for life, reprieved, transported to Australia, and since returned without pardon.
+LJSpeech-1.1/wavs/LJ041-0125.wav|Mrs. Oswald said that her husband did not say anything about Governor Connally after his return to the United States. She testified, quote,
+LJSpeech-1.1/wavs/LJ005-0279.wav|an incomplete and fallacious method of preventing contamination.
+LJSpeech-1.1/wavs/LJ006-0163.wav|and that he was constantly engaged attending sessions and going with drafts to the hulks.
+LJSpeech-1.1/wavs/LJ031-0130.wav|Two men from the President's follow-up car were detailed to help protect the Vice President.
+LJSpeech-1.1/wavs/LJ011-0236.wav|sent his plate-chests, eight in number, to the silversmith's for greater security.
+LJSpeech-1.1/wavs/LJ045-0126.wav|to the Soviet Embassy in Washington, he asked about the entrance visas for which he and his wife had previously applied.
+LJSpeech-1.1/wavs/LJ032-0146.wav|Latona testified that this palmprint was the right palmprint of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ030-0211.wav|Hill jumped from the follow-up car and ran to the President's automobile.
+LJSpeech-1.1/wavs/LJ012-0037.wav|the owner came with the police, and was morally convinced that it was his property,
+LJSpeech-1.1/wavs/LJ027-0114.wav|Namely that when from changed conditions of life, an organ which was previously useful becomes useless,
+LJSpeech-1.1/wavs/LJ046-0021.wav|the Secretary of the Treasury has prepared a planning document dated August twenty-seven, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ009-0097.wav|He throws his hands far from him, and shouts aloud, 'Mercy, good Lord! mercy is all I ask. The Lord in His mercy come!
+LJSpeech-1.1/wavs/LJ028-0243.wav|Had the Babylonians been apprised of what Cyrus was about, or had they noticed their danger, they would never have allowed the Persians to enter the city,
+LJSpeech-1.1/wavs/LJ018-0155.wav|Mrs. Campbell offered him less, and there was an altercation, in the course of which Buncher became very violent, and at length,
+LJSpeech-1.1/wavs/LJ035-0182.wav|Harkness did not know whether or not two officers with Sawyer were guarding the doors.
+LJSpeech-1.1/wavs/LJ010-0037.wav|Now and again there seemed to be a recurrence of a murder epidemic,
+LJSpeech-1.1/wavs/LJ030-0172.wav|From his position in the right jump seat immediately in front of the President,
+LJSpeech-1.1/wavs/LJ037-0079.wav|They ran to the door in time to see a man with a revolver cut across their lawn and disappear around a corner of the house onto Patton.
+LJSpeech-1.1/wavs/LJ008-0156.wav|An enormous crowd assembled to witness the execution, amounting, it was said, to the hitherto unparalleled number of forty thousand.
+LJSpeech-1.1/wavs/LJ021-0209.wav|I prefer and I am sure you prefer that broader definition of liberty under which we are moving forward to greater freedom,
+LJSpeech-1.1/wavs/LJ007-0156.wav|The whole system, or more correctly the want of system, was baneful and pernicious to the last degree.
+LJSpeech-1.1/wavs/LJ032-0099.wav|to serve as president of the so-called New Orleans Chapter of the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ047-0059.wav|However, pursuant to a regular Bureau practice of interviewing certain immigrants from Iron Curtain countries,
+LJSpeech-1.1/wavs/LJ005-0119.wav|No attempt was made to maintain discipline.
+LJSpeech-1.1/wavs/LJ030-0201.wav|According to Governor and Mrs. Connally, it was after this shot that Kellerman issued his emergency instructions and the car accelerated.
+LJSpeech-1.1/wavs/LJ025-0141.wav|the diameter of which dwindles down to the limits of our present microscopic vision,
+LJSpeech-1.1/wavs/LJ040-0192.wav|The reports of Carro and Mrs. Siegel also indicate an ambivalent attitude toward authority on Oswald's part.
+LJSpeech-1.1/wavs/LJ007-0016.wav|it tended to "dissipate reflection, diminish the gloom of the prison, and mitigate the punishment which the law has sentenced the prisoner to undergo."
+LJSpeech-1.1/wavs/LJ012-0131.wav|The letter informed him of the marks and sizes of the cases containing the precious metal,
+LJSpeech-1.1/wavs/LJ009-0287.wav|has come more by chance than fitness or special education to exercise his loathsome office.
+LJSpeech-1.1/wavs/LJ048-0144.wav|It had no prepared checklist of matters to be covered with local police on such visits to metropolitan areas
+LJSpeech-1.1/wavs/LJ028-0448.wav|All of the ancient writers agree in saying that Babylon was surrounded with both inner and outer walls, and the ruins confirm their statements.
+LJSpeech-1.1/wavs/LJ045-0193.wav|en route from Dallas Love Field, end quote, on its way to the Trade Mart on Stemmons Freeway.
+LJSpeech-1.1/wavs/LJ037-0058.wav|In testimony before the Commission, Mrs. Markham confirmed her positive identification of Lee Harvey Oswald
+LJSpeech-1.1/wavs/LJ004-0197.wav|No idleness was permitted among the inmates. Trades were taught, or prisoners were allowed to follow their own if suitable.
+LJSpeech-1.1/wavs/LJ017-0224.wav|A simultaneous attack was made upon the captain and the first mate.
+LJSpeech-1.1/wavs/LJ041-0061.wav|who, shortly after Oswald's defection, wrote an as yet unpublished novel based in considerable part on Oswald's life,
+LJSpeech-1.1/wavs/LJ035-0119.wav|until after they saw Patrolman Baker's white helmet on the fifth floor moving toward the elevator.
+LJSpeech-1.1/wavs/LJ041-0069.wav|he did not adjust well to conditions which he found in that service.
+LJSpeech-1.1/wavs/LJ035-0030.wav|Truly pushed the button for the west elevator which operates automatically if the gate is closed.
+LJSpeech-1.1/wavs/LJ032-0239.wav|on April ten, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ019-0391.wav|to close useless prisons, and to insure that uniformity of system which all thoughtful persons had long admitted to be of paramount importance
+LJSpeech-1.1/wavs/LJ040-0070.wav|she placed John Pic and Robert Oswald in an orphans' home.
+LJSpeech-1.1/wavs/LJ036-0117.wav|and Daniel Lujan, aged twenty-six, was Number four.
+LJSpeech-1.1/wavs/LJ024-0043.wav|that I will appoint justices who will not undertake to override the judgment of the Congress on legislative policy,
+LJSpeech-1.1/wavs/LJ031-0030.wav|Governor Connally, who had lost consciousness on the ride to the hospital, regained consciousness when the limousine stopped abruptly at the emergency entrance.
+LJSpeech-1.1/wavs/LJ028-0348.wav|These he levied from the nations bordering on Babylonia,
+LJSpeech-1.1/wavs/LJ050-0247.wav|The Commission recommends that the agencies involved determine how much periodic assistance they can provide, and that each such agency
+LJSpeech-1.1/wavs/LJ048-0215.wav|Agent Roy H. Kellerman, riding in the front seat of the Presidential car,
+LJSpeech-1.1/wavs/LJ049-0208.wav|except as conclusions flowing directly from its examination of the President's assassination can be drawn.
+LJSpeech-1.1/wavs/LJ021-0178.wav|Has England gone back to the gold standard today?
+LJSpeech-1.1/wavs/LJ008-0099.wav|describes as "a most diabolical-looking little wretch -- Jack Ketch's head man.
+LJSpeech-1.1/wavs/LJ026-0144.wav|In both plants and animals simple waste substances result from the catabolic processes in the cells.
+LJSpeech-1.1/wavs/LJ016-0237.wav|With Calcraft's method there were undoubtedly many failures, and it was a common custom for him to go below the gallows
+LJSpeech-1.1/wavs/LJ018-0039.wav|The prisoner was forthwith extradited and sent back to England, which he reached with his escort on the seventeenth September the same year.
+LJSpeech-1.1/wavs/LJ043-0117.wav|Oswald apparently concluded that his Fair Play for Cuba Committee activities were not related to his discharge.
+LJSpeech-1.1/wavs/LJ018-0019.wav|The stick and bag were his, but not the hat.
+LJSpeech-1.1/wavs/LJ026-0010.wav|Since they cannot be classified, it is necessary that they be listed both under botany and zoology, in order to make sure that they will not be omitted entirely.
+LJSpeech-1.1/wavs/LJ019-0300.wav|The committee might well suggest the abolition of these jails, or their amalgamation with the larger county establishments in their immediate neighborhood.
+LJSpeech-1.1/wavs/LJ019-0089.wav|sometimes it embraced the tread-wheel or the newly-invented instruments known as cranks, which ground air.
+LJSpeech-1.1/wavs/LJ029-0088.wav|securing the roof and insuring the presence of numerous police officers inside and around the building.
+LJSpeech-1.1/wavs/LJ001-0158.wav|it is never used except for very expensive books, although it would not materially increase the cost in all but the very cheapest.
+LJSpeech-1.1/wavs/LJ002-0114.wav|and imprisonment for debt has almost entirely disappeared.
+LJSpeech-1.1/wavs/LJ011-0054.wav|A very curious and, in its way, amusing circumstance in connection with this case was the offer of a certain Italian,
+LJSpeech-1.1/wavs/LJ039-0110.wav|Based on the general Marine Corps ratings, Lt. Col. A. G. Folsom, Jr.
+LJSpeech-1.1/wavs/LJ012-0129.wav|learnt through the firm's correspondence that a quantity of gold-dust
+LJSpeech-1.1/wavs/LJ008-0148.wav|One night he was missing
+LJSpeech-1.1/wavs/LJ032-0122.wav|At eleven:forty-five p.m. on November twenty-two,
+LJSpeech-1.1/wavs/LJ041-0068.wav|While there is nothing in Oswald's military records to indicate that he was mentally unstable or otherwise psychologically unfit for duty in the Marine Corps,
+LJSpeech-1.1/wavs/LJ041-0152.wav|Oswald studied Marxism after he joined the Marines and his sympathies in that direction and for the Soviet Union appear to have been widely known,
+LJSpeech-1.1/wavs/LJ030-0186.wav|The driver, William Greer, heard a noise which he took to be a backfire from one of the motorcycles flanking the Presidential car.
+LJSpeech-1.1/wavs/LJ008-0071.wav|In the few years which elapsed between the establishment of the gallows at Newgate
+LJSpeech-1.1/wavs/LJ011-0260.wav|It was, however, proved that Howard was in needy circumstances, and that his proposals to Mr. Mullay could only have originated in a desire to rob him.
+LJSpeech-1.1/wavs/LJ044-0179.wav|He apparently based his claim for a visa in transit to Russia
+LJSpeech-1.1/wavs/LJ003-0062.wav|When each sleeper had the full lateral space allotted to him, it amounted to one foot and a half on the barrack bed;
+LJSpeech-1.1/wavs/LJ033-0194.wav|The blanket in which the rifle was stored was composed of brown and green cotton, viscose and woolen fibers.
+LJSpeech-1.1/wavs/LJ031-0113.wav|This operation was concluded at three:twenty p.m.
+LJSpeech-1.1/wavs/LJ020-0011.wav|Put the potatoes into a large bowl or tray and mash them to powder with a potato beetle, or a wooden spoon.
+LJSpeech-1.1/wavs/LJ031-0027.wav|Upon arriving at Parkland Hospital,
+LJSpeech-1.1/wavs/LJ013-0188.wav|Further investigation was rewarded by the discovery in the pantry of a split gold ring, used by Lord William to carry his keys on;
+LJSpeech-1.1/wavs/LJ006-0011.wav|that inspectors should watch over the observance of the law. He saw no objection on the score of their probable interference with the local jurisdiction,
+LJSpeech-1.1/wavs/LJ003-0229.wav|that it multiplied beyond all reason the number of visitors, many of whom came merely as the purveyors of food to their friends.
+LJSpeech-1.1/wavs/LJ030-0168.wav|As she cradled her mortally wounded husband, Mrs. Kennedy cried, quote,
+LJSpeech-1.1/wavs/LJ005-0106.wav|Jails, of which the old prison at Reading was a specimen, were still left intact.
+LJSpeech-1.1/wavs/LJ010-0074.wav|After this he tried farming, but failed.
+LJSpeech-1.1/wavs/LJ041-0019.wav|Voebel said that Oswald, quote, wouldn't start any fights, but if you wanted to start one with him, he was going to make sure that he ended it,
+LJSpeech-1.1/wavs/LJ009-0181.wav|that authentic cases were known previous to the first cited act of criminals selling their own bodies to surgeons for dissection.
+LJSpeech-1.1/wavs/LJ006-0242.wav|Watkins' case, referred to above, is made the subject of another and a special report from the surgeon.
+LJSpeech-1.1/wavs/LJ016-0272.wav|To the disgrace of the better-educated and better-bred public, executions could still command the attendance of curious aristocrats from the West End.
+LJSpeech-1.1/wavs/LJ036-0172.wav|From the five hundred block of North Beckley, the walk would be a few minutes longer,
+LJSpeech-1.1/wavs/LJ011-0213.wav|married the said Ellen Turner.
+LJSpeech-1.1/wavs/LJ012-0247.wav|a bricklayer found a human trunk near some new buildings in the Edgeware Road, one morning in the last week of eighteen thirty-six.
+LJSpeech-1.1/wavs/LJ046-0007.wav|a President-elect, and a candidate for the Presidency, which narrowly failed:
+LJSpeech-1.1/wavs/LJ038-0232.wav|Marina Oswald stated that when Oswald returned home on the night of the Walker shooting, he told her that he had been planning the attempt for two months.
+LJSpeech-1.1/wavs/LJ004-0245.wav|Painters, plasterers, and carpenters were allowed to follow their handicrafts, with the reward of sixpence per diem and a double allowance of food.
+LJSpeech-1.1/wavs/LJ017-0003.wav|Nevertheless, in order to give completeness to the picture
+LJSpeech-1.1/wavs/LJ049-0093.wav|persons committing certain offenses, including counterfeiting and certain frauds involving Federal checks or securities.
+LJSpeech-1.1/wavs/LJ037-0046.wav|The man, quote, in kind of a little trot, end quote, headed down Patton toward Jefferson Boulevard, a block away.
+LJSpeech-1.1/wavs/LJ012-0033.wav|Jewels were re-set,
+LJSpeech-1.1/wavs/LJ032-0224.wav|One of the photographs taken by Marina Oswald was widely published in newspapers and magazines,
+LJSpeech-1.1/wavs/LJ041-0015.wav|accosted Oswald on the way home from school and punched him in the mouth, making his lip bleed and loosening a tooth.
+LJSpeech-1.1/wavs/LJ028-0473.wav|A space of about twenty-three feet separated it from its inner part, which was about twenty feet in width.
+LJSpeech-1.1/wavs/LJ016-0076.wav|Even then, however, irons across barred the ascent after a certain distance, and in no one case did a fugitive get clear away.
+LJSpeech-1.1/wavs/LJ013-0114.wav|One night a quantity of plate was stolen from Windsor Castle; another time Buckingham Palace was robbed.
+LJSpeech-1.1/wavs/LJ050-0113.wav|Such agreements should describe in detail the information which is sought, the manner in which it will be provided to the Secret Service,
+LJSpeech-1.1/wavs/LJ006-0015.wav|one which required discretion, judgment, and knowledge of law, with sufficient insight and experience to discover defects in prison discipline.
+LJSpeech-1.1/wavs/LJ049-0174.wav|to notify the Secret Service of the substantial information about Lee Harvey Oswald which the FBI had accumulated
+LJSpeech-1.1/wavs/LJ018-0068.wav|Inspector Thain, who, being unable to obtain his extradition legally, had him inveigled on board an English steamer,
+LJSpeech-1.1/wavs/LJ050-0243.wav|The Commission endorses these efforts to supplement the Service's own personnel by obtaining, for short periods of time,
+LJSpeech-1.1/wavs/LJ034-0015.wav|The boxes in the window appeared to have been arranged as a convenient gun rest.
+LJSpeech-1.1/wavs/LJ030-0079.wav|The agents in this car, under established procedure, had instructions to watch the route for signs of trouble,
+LJSpeech-1.1/wavs/LJ031-0052.wav|and also ragged tissue which indicated a tracheal injury.
+LJSpeech-1.1/wavs/LJ040-0170.wav|because he didn't make friends with the boys in the neighborhood. She thought that he, quote, withdrew into a completely solitary and detached existence
+LJSpeech-1.1/wavs/LJ050-0086.wav|Under these criteria, whether the case should be referred to the Secret Service depends on the existence of a previous history of mental instability,
+LJSpeech-1.1/wavs/LJ048-0171.wav|it was not practical to select a route where the President could not be seen from roofs or windows of buildings.
+LJSpeech-1.1/wavs/LJ048-0067.wav|It is clear from Hosty's testimony that this was construed, at least by him,
+LJSpeech-1.1/wavs/LJ004-0249.wav|As the Prison Discipline Society pertinently observed in a report dated eighteen twenty,
+LJSpeech-1.1/wavs/LJ019-0003.wav|It was impossible for Newgate to escape for ever the influences pressing so strongly towards prison reform.
+LJSpeech-1.1/wavs/LJ009-0221.wav|according to the discretion of the court before whom the prisoners might be tried.
+LJSpeech-1.1/wavs/LJ015-0193.wav|By-and-by, when escape seemed hopeless, and after sentence, he suddenly degenerated into the lowest stamp of criminal,
+LJSpeech-1.1/wavs/LJ050-0062.wav|(c) who express or have expressed strong or violent anti-U.S. sentiments
+LJSpeech-1.1/wavs/LJ013-0135.wav|At most of the clubs the servants had been mulcted to make good lost plate, which had no doubt been stolen by the prisoner.
+LJSpeech-1.1/wavs/LJ029-0010.wav|Planning the Texas Trip
+LJSpeech-1.1/wavs/LJ047-0026.wav|and kept itself informed on Oswald's status by periodic reviews of State Department and Office of Naval Intelligence files.
+LJSpeech-1.1/wavs/LJ026-0099.wav|are absorbed by the circulating liquids (blood and lymph) and transported to all cells of the animal body.
+LJSpeech-1.1/wavs/LJ004-0044.wav|several bishops, and a number of members of the House of Commons, including Mr. Manners Sutton,
+LJSpeech-1.1/wavs/LJ012-0276.wav|While she was senseless, but really still alive, he cut off her head, and dismembered the body in the manner already described.
+LJSpeech-1.1/wavs/LJ023-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section nine.
+LJSpeech-1.1/wavs/LJ040-0199.wav|The reports of the New York authorities indicate that Lee's mother gave him very little affection and did not serve as any sort of substitute for a father.
+LJSpeech-1.1/wavs/LJ011-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section fourteen: Newgate notorieties, part one
+LJSpeech-1.1/wavs/LJ049-0116.wav|of such persons as have been constitutionally and lawfully provided to succeed thereto in case of a vacancy. It is important to this country
+LJSpeech-1.1/wavs/LJ001-0130.wav|Commercialism again compels the use of type too small in size to be comfortable reading:
+LJSpeech-1.1/wavs/LJ016-0056.wav|running a great risk of discovery as he passed by a lot of workmen at Tyler's manufactory in Warwick Square, which had formerly been the College of Physicians.
+LJSpeech-1.1/wavs/LJ010-0249.wav|There was some doubt as to whether the pistol when fired was actually loaded with ball, but the jury brought in a verdict of guilty
+LJSpeech-1.1/wavs/LJ038-0235.wav|were identified by Marina Oswald as photographs of General Walker's house.
+LJSpeech-1.1/wavs/LJ019-0023.wav|that this was an experimental construction, and that too strict a limitation of outlay would have militated seriously against the usefulness of the building.
+LJSpeech-1.1/wavs/LJ016-0311.wav|that any secrecy in the treatment of the condemned would invest them with a new and greater interest, which was much to be deprecated.
+LJSpeech-1.1/wavs/LJ048-0219.wav|who must concentrate primarily on the possibility of threats from crowds along the route, provide a significant safeguard against dangers in nearby buildings.
+LJSpeech-1.1/wavs/LJ002-0015.wav|three hundred debtors and nine hundred criminals in Newgate, or twelve hundred prisoners in all.
+LJSpeech-1.1/wavs/LJ014-0279.wav|and he had so cleverly destroyed all traces that it was not easy to bring home the charge.
+LJSpeech-1.1/wavs/LJ050-0227.wav|in case of unexpected need; and twenty-five additional agents are recommended to provide the Vice President full protection.
+LJSpeech-1.1/wavs/LJ042-0012.wav|who had arranged a radio debate on Oswald's activities on behalf of the Fair Play for Cuba Committee,
+LJSpeech-1.1/wavs/LJ014-0228.wav|generally known by the soubriquet of "General Haynau," a name execrated in England about this time.
+LJSpeech-1.1/wavs/LJ033-0118.wav|Similarly, when the butt of the rifle was placed in Frazier's hand, it extended above his shoulder to ear level.
+LJSpeech-1.1/wavs/LJ022-0131.wav|If you will help, this can be done.
+LJSpeech-1.1/wavs/LJ041-0130.wav|Probably his complaint was due to the fact that his discharge was not related to anything he had done while on active duty
+LJSpeech-1.1/wavs/LJ026-0164.wav|These constitute a sort of extra mechanism, enabling green plants to make their own carbohydrate food.
+LJSpeech-1.1/wavs/LJ040-0014.wav|He cannot, of course, be questioned or observed by those charged with the responsibility for this report or by experts on their behalf.
+LJSpeech-1.1/wavs/LJ016-0319.wav|The only doubts expressed were as to the sufficiency of the safeguards, as to the certainty of death and its subsequent publication.
+LJSpeech-1.1/wavs/LJ038-0011.wav|A police car made a U-turn, and as the sirens grew fainter,
+LJSpeech-1.1/wavs/LJ012-0197.wav|a trade almost openly countenanced when "subjects" for the anatomy schools were only to be got by rifling graves, or worse.
+LJSpeech-1.1/wavs/LJ014-0332.wav|Cole escaped by throwing the blame on a careless partner, and at once removed the "stop."
+LJSpeech-1.1/wavs/LJ015-0161.wav|There was something dramatic in Redpath's detection
+LJSpeech-1.1/wavs/LJ038-0258.wav|The Dallas City-County Investigation Laboratory tried to determine the type of weapon which fired the bullet.
+LJSpeech-1.1/wavs/LJ016-0147.wav|Then he ran along the coping of the wall towards its angle with Tyler's manufactory, and dropped down on to the gridiron below.
+LJSpeech-1.1/wavs/LJ010-0134.wav|He roared out snatches of a song about Death or Liberty, and just before he was turned off,
+LJSpeech-1.1/wavs/LJ032-0182.wav|were clean, they had good color to them, there was no grease on them and they were not fragmented. They looked as if they had just been picked up, end quote.
+LJSpeech-1.1/wavs/LJ044-0017.wav|He distributed literature in downtown New Orleans on August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ040-0004.wav|There is no evidence that he had accomplices or that he was involved in any conspiracy directed to the assassination of the President.
+LJSpeech-1.1/wavs/LJ028-0131.wav|to describe whose vast height and immense riches it would perhaps be too much for me to attempt.
+LJSpeech-1.1/wavs/LJ030-0191.wav|According to Kellerman, Mrs. Kennedy then cried out, quote,
+LJSpeech-1.1/wavs/LJ011-0008.wav|whereupon he produced exchequer bills to the amount.
+LJSpeech-1.1/wavs/LJ023-0107.wav|In the face of such dissenting opinions, it is perfectly clear that, as Chief Justice Hughes has said,
+LJSpeech-1.1/wavs/LJ015-0149.wav|peas at ten shillings a quart, five-guinea pines, and early asparagus were to be found on his table.
+LJSpeech-1.1/wavs/LJ006-0165.wav|sometimes he looked at his bolts and bars, but he never examined the cupboards, coal-boxes, or other possible hiding-places for cards
+LJSpeech-1.1/wavs/LJ012-0163.wav|I will now retrace my steps and give some account of the more remarkable murders during that period.
+LJSpeech-1.1/wavs/LJ030-0226.wav|who did not believe that he could reach, the President's car at the speed it was then traveling.
+LJSpeech-1.1/wavs/LJ016-0037.wav|with only a short interval between it and the wall, supported by a horizontal iron railing with upright points;
+LJSpeech-1.1/wavs/LJ031-0166.wav|Unmarked police cars took the Vice President and Mrs. Johnson from Parkland Hospital to Love Field.
+LJSpeech-1.1/wavs/LJ028-0440.wav|Possibly along the terraces of the walls, or upon the stages of some lofty temple tower,
+LJSpeech-1.1/wavs/LJ014-0269.wav|and walk forward on foot to enter as a humble and unpretending employee the doors of the Globe Assurance office.
+LJSpeech-1.1/wavs/LJ003-0273.wav|"I believe," says Mr. Bennet in the letter already largely quoted,
+LJSpeech-1.1/wavs/LJ028-0347.wav|he provided wives for them in the room of those whom (as I explained before) they strangled to save their stores.
+LJSpeech-1.1/wavs/LJ008-0297.wav|and its contents were anxiously awaited by both convicts in the press-yard and their friends collected in a crowd outside the gates.
+LJSpeech-1.1/wavs/LJ028-0467.wav|The entire width of the outer defense, not including the moat, was therefore about eighty-two feet;
+LJSpeech-1.1/wavs/LJ026-0066.wav|both from the soil through the roots (liquids) and from the atmosphere through the leaves (gases).
+LJSpeech-1.1/wavs/LJ017-0015.wav|the necessity for which was urged by Mr. Addison, when Secretary of State, in seventeen seventeen.
+LJSpeech-1.1/wavs/LJ032-0035.wav|In addition to the order coupon the envelope contained a. U.S. postal money order for twenty-one dollars, forty-five cents,
+LJSpeech-1.1/wavs/LJ010-0304.wav|through advances made to various builders, and that it could only maintain its credit by wholesale discounting.
+LJSpeech-1.1/wavs/LJ035-0008.wav|The first person to see Oswald after the assassination was Patrolman M. L. Baker of the Dallas Police Department.
+LJSpeech-1.1/wavs/LJ030-0053.wav|It was outfitted with a clear plastic bubbletop which was neither bulletproof nor bullet resistant.
+LJSpeech-1.1/wavs/LJ035-0086.wav|The time actually required for Baker and Truly to reach the second floor on November twenty-two was probably longer than in the test runs. For example,
+LJSpeech-1.1/wavs/LJ040-0040.wav|which he expressed in striking and sometimes violent acts long before the assassination.
+LJSpeech-1.1/wavs/LJ038-0171.wav|General Walker gave this information to the police before the shooting, but it did not help solve the crime.
+LJSpeech-1.1/wavs/LJ041-0104.wav|While in Japan, Oswald's new found apparent self confidence and pugnaciousness
+LJSpeech-1.1/wavs/LJ027-0098.wav|is found the same part, variously modified for many purposes.
+LJSpeech-1.1/wavs/LJ017-0152.wav|in spite of the existence of the other Mrs. Smethurst.
+LJSpeech-1.1/wavs/LJ021-0117.wav|to the great number of small employers in the smaller communities.
+LJSpeech-1.1/wavs/LJ050-0187.wav|Thus, the local chief of police could be given a master plan, prepared for the occasion, of all protective measures to be taken during the visit;
+LJSpeech-1.1/wavs/LJ047-0239.wav|The two men disagree about the conversation which took place between them.
+LJSpeech-1.1/wavs/LJ021-0067.wav|a rise from a deficit figure in the first quarter of nineteen thirty-three
+LJSpeech-1.1/wavs/LJ019-0059.wav|It was claimed for it that it was more deterrent;
+LJSpeech-1.1/wavs/LJ014-0054.wav|a maidservant, Sarah Thomas, murdered her mistress, an aged woman, by beating out her brains with a stone.
+LJSpeech-1.1/wavs/LJ016-0412.wav|Wainwright's execution was within the jail, but only nominally private.
+LJSpeech-1.1/wavs/LJ010-0033.wav|Esther Hibner was as inhuman in her ill-usage of the parish apprentice she killed as Martha Brownrigg had been.
+LJSpeech-1.1/wavs/LJ003-0047.wav|Newgate continued to be a reproach to those responsible for its management.
+LJSpeech-1.1/wavs/LJ026-0086.wav|the kinetic energy of sunlight was expended in lifting the atoms into position of vantage, thus endowing them with energy of position.
+LJSpeech-1.1/wavs/LJ004-0210.wav|There was a prison laundry too, where all the prisoners' linen was regularly washed.
+LJSpeech-1.1/wavs/LJ043-0172.wav|The attempt on General Walker's life deserves close attention in any consideration of Oswald's possible motive for the assassination
+LJSpeech-1.1/wavs/LJ029-0180.wav|An editorial in the Times-Herald of September seventeen
+LJSpeech-1.1/wavs/LJ016-0104.wav|whence they let themselves down into the street by the rope.
+LJSpeech-1.1/wavs/LJ026-0098.wav|The circulatory system distributes these foods. In animals foods prepared for absorption in the stomach and intestine (by digestion)
+LJSpeech-1.1/wavs/LJ028-0120.wav|built the moat-walls of Arachtu with mortar and bricks, and, joining them together with those of my father, made them very solid.
+LJSpeech-1.1/wavs/LJ022-0012.wav|it is difficult for a person who does not know ships to tell how it will finally look when it is sailing the high seas.
+LJSpeech-1.1/wavs/LJ039-0090.wav|Characterizing the four-power scope as, quote, a real aid, an extreme aid, end quote, in rapid fire shooting,
+LJSpeech-1.1/wavs/LJ042-0174.wav|No man, having known, having lived, under the Russian Communist and American capitalist system,
+LJSpeech-1.1/wavs/LJ037-0127.wav|Four men -- Warren Reynolds, Harold Russell, Pat Patterson, and L. J. Lewis
+LJSpeech-1.1/wavs/LJ050-0163.wav|the Secret Service should attempt to develop a system compatible with those of the agencies from which most of its data will come. Note:
+LJSpeech-1.1/wavs/LJ012-0243.wav|There were many features of resemblance in these crimes.
+LJSpeech-1.1/wavs/LJ030-0047.wav|The occupants scanned the crowd and the buildings along the route.
+LJSpeech-1.1/wavs/LJ023-0068.wav|to have been the clear and underlying purpose of the patriots who wrote a federal constitution to create a national government
+LJSpeech-1.1/wavs/LJ011-0050.wav|that he appeared to feel deeply the force of the reverend gentleman's observations, especially when the chaplain spoke of
+LJSpeech-1.1/wavs/LJ019-0307.wav|The result of the recommendation of the committee of eighteen sixty-two was the Prison Act of eighteen sixty-five,
+LJSpeech-1.1/wavs/LJ019-0094.wav|Opinions differed greatly with regard to the tread-wheel; some authorities advocated it as a very severe and irksome punishment,
+LJSpeech-1.1/wavs/LJ012-0206.wav|This man, May, asked the porter at King's College if "he wanted anything?" the euphemism for offering a body.
+LJSpeech-1.1/wavs/LJ002-0183.wav|whilst Ludgate, the Giltspur Street, and Borough Compters also received them
+LJSpeech-1.1/wavs/LJ007-0107.wav|A resolution at once passed the House without division to commit the whole to Newgate, where they remained for various terms.
+LJSpeech-1.1/wavs/LJ040-0188.wav|Sokolow said that although Lee was, quote, presumably disinterested in school subjects he operates on a much higher than average level, end quote.
+LJSpeech-1.1/wavs/LJ019-0210.wav|But when this suggestion was brought before the court of aldermen, various amendments were proposed.
+LJSpeech-1.1/wavs/LJ025-0024.wav|whence their nutrition may be drawn by vessels, which are a sort of internal roots; and, in the presence of this alimentary cavity
+LJSpeech-1.1/wavs/LJ006-0180.wav|the interior of the jail was more like a bear-garden or the noisy purlieus of a public-house than a prison.
+LJSpeech-1.1/wavs/LJ019-0246.wav|It animadverted strongly on "the many and wide differences as regards construction, labor, diet, and general discipline"
+LJSpeech-1.1/wavs/LJ048-0253.wav|The supervisor of each of the off-duty agents who visited the Press Club or the Cellar Coffee House
+LJSpeech-1.1/wavs/LJ020-0028.wav|Warm water for rinsing bowl -- about half a cup.
+LJSpeech-1.1/wavs/LJ035-0172.wav|There he was met by a construction worker -- in all likelihood Howard Brennan, who was wearing his work helmet.
+LJSpeech-1.1/wavs/LJ042-0245.wav|While Marina Oswald tried to obtain permission to return to the Soviet Union, she testified that she did so at her husband's insistence.
+LJSpeech-1.1/wavs/LJ050-0248.wav|and the Secret Service enter into a formal agreement defining such arrangements.
+LJSpeech-1.1/wavs/LJ047-0207.wav|his association with the Castro groups would have been of concern to us, a knowledge that he had, I believe,
+LJSpeech-1.1/wavs/LJ011-0154.wav|He had, it was said, a good private income, and was already a widower with two children at the time of his committing the offense for which he was subsequently tried.
+LJSpeech-1.1/wavs/LJ041-0107.wav|He testified that he had felt the sergeant had a grudge against him and that he had unsuccessfully sought a transfer from the sergeant's unit.
+LJSpeech-1.1/wavs/LJ023-0108.wav|We are under a Constitution, but the Constitution is what the judges say it is.
+LJSpeech-1.1/wavs/LJ046-0071.wav|The Secret Service and the Federal, State, and local law enforcement agencies which cooperate with it,
+LJSpeech-1.1/wavs/LJ026-0035.wav|the organism must be compared in detail with all those which resemble it closely in structure, physiology and life history,
+LJSpeech-1.1/wavs/LJ014-0090.wav|On the Sunday Mrs. Manning roasted a goose at this same kitchen fire, and ate it with relish in the afternoon.
+LJSpeech-1.1/wavs/LJ003-0335.wav|The committee did not deny the superior advantages offered by such prisons as Gloucester and Petworth,
+LJSpeech-1.1/wavs/LJ010-0028.wav|The causes also continued much the same. Passion, revenge, cupidity, sudden ebullitions of homicidal rage,
+LJSpeech-1.1/wavs/LJ024-0077.wav|is it not a most desirable precedent?
+LJSpeech-1.1/wavs/LJ010-0020.wav|the extraordinary extension of all commercial operations led to many entirely novel and often gigantic financial frauds.
+LJSpeech-1.1/wavs/LJ028-0067.wav|Centuries afterward, even to this day, Jewish mothers teach their children to hate his name.
+LJSpeech-1.1/wavs/LJ015-0092.wav|When his friends asked why he, a wealthy man of independent means, continued to slave on as a clerk on a pittance,
+LJSpeech-1.1/wavs/LJ042-0011.wav|On August seventeen, nineteen sixty-three, Oswald told Mr. William Stuckey,
+LJSpeech-1.1/wavs/LJ012-0198.wav|Bishop was a carpenter, but having been suddenly thrown out of work, he joined his brother-in-law in his line of business.
+LJSpeech-1.1/wavs/LJ046-0229.wav|The Commission believes that the facilities and procedures of the Protective Research Section of the Secret Service
+LJSpeech-1.1/wavs/LJ032-0075.wav|with the address of post office box two nine one five in Dallas.
+LJSpeech-1.1/wavs/LJ031-0164.wav|and a move would require new measures.
+LJSpeech-1.1/wavs/LJ039-0047.wav|Marina Oswald speculated that the incident may have been unrelated to an actual threat.
+LJSpeech-1.1/wavs/LJ023-0024.wav|not this week or month perhaps, but within a year or two.
+LJSpeech-1.1/wavs/LJ039-0226.wav|the Marine marksmanship experts, Major Anderson and Sergeant Zahm, concurred in the opinion that Oswald had the capability to fire three shots,
+LJSpeech-1.1/wavs/LJ047-0057.wav|until March nineteen sixty-three, the FBI continued to accumulate information regarding Oswald but engaged in no active investigation.
+LJSpeech-1.1/wavs/LJ030-0209.wav|He immediately looked to his right, quote, and, in so doing, my eyes had to cross the Presidential limousine
+LJSpeech-1.1/wavs/LJ049-0213.wav|There are always dangers of divided responsibility,
+LJSpeech-1.1/wavs/LJ010-0117.wav|The employment of Edwards, and the manner in which the conspirators were allowed to commit themselves further and further before the law was set in motion against them,
+LJSpeech-1.1/wavs/LJ047-0147.wav|The Department of State did not advise either the CIA or the FBI of these facts.
+LJSpeech-1.1/wavs/LJ027-0101.wav|Throughout both the animal and vegetable kingdoms dwarfed and useless representatives of organs are constantly met with,
+LJSpeech-1.1/wavs/LJ008-0161.wav|presently fell, and were at once trampled to death.
+LJSpeech-1.1/wavs/LJ049-0112.wav|bear no relationship to the injury to the United States which follows from the act.
+LJSpeech-1.1/wavs/LJ019-0319.wav|while the latter, which included various forms of industrial employment,
+LJSpeech-1.1/wavs/LJ016-0097.wav|which with Wakefield was utilized as a receptacle for convicts not going to Western Australia,
+LJSpeech-1.1/wavs/LJ014-0039.wav|Hocker was so overcome, feeling himself the principal cause of the tragedy,
+LJSpeech-1.1/wavs/LJ011-0163.wav|She was not in immediate danger, but she wished to see her daughter, "as it was possible she might soon become incapable of recognizing any one."
+LJSpeech-1.1/wavs/LJ046-0252.wav|Although the CIA had on file requests from the Treasury Department for information on the counterfeiting of U.S. currency and certain smuggling matters,
+LJSpeech-1.1/wavs/LJ016-0085.wav|After this great iron guards, just as are to be seen in lunatic asylums,
+LJSpeech-1.1/wavs/LJ010-0050.wav|But at various dates treason more distinct and tangible came to the front: attempts to levy war against the State.
+LJSpeech-1.1/wavs/LJ025-0086.wav|and the few and exceptional cases of non-parasitic animals which do not feed at all.
+LJSpeech-1.1/wavs/LJ047-0155.wav|readily admitted that Mrs. Marina Oswald and Lee Oswald's two children were staying with her.
+LJSpeech-1.1/wavs/LJ030-0178.wav|Looking over her right shoulder, she saw that the President had both hands at his neck but she observed no blood and heard nothing.
+LJSpeech-1.1/wavs/LJ034-0098.wav|In the six to eight minute period before the motorcade arrived, Brennan saw a man leave and return to the window, quote, a couple of times, end quote,
+LJSpeech-1.1/wavs/LJ037-0197.wav|and the return address was Post Office Box two nine one five, Dallas, Texas.
+LJSpeech-1.1/wavs/LJ031-0140.wav|When consulted by the Vice President, O'Donnell advised him to go to the airfield immediately and return to Washington.
+LJSpeech-1.1/wavs/LJ042-0027.wav|In addition to studying the Russian language while he was in the Marines,
+LJSpeech-1.1/wavs/LJ048-0057.wav|there might conceivably have been additional investigation of the Oswald case between November five and November twenty-two.
+LJSpeech-1.1/wavs/LJ038-0290.wav|provide important corroboration for her account of the incident.
+LJSpeech-1.1/wavs/LJ038-0273.wav|Joseph D. Nicol,
+LJSpeech-1.1/wavs/LJ036-0162.wav|On November twenty-two, Oswald told Captain Fritz that he rode a bus to a stop near his home and then walked to his roominghouse.
+LJSpeech-1.1/wavs/LJ047-0132.wav|the FBI transferred the principal responsibility for the Oswald case from the Dallas office to the New Orleans office.
+LJSpeech-1.1/wavs/LJ001-0007.wav|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about fourteen fifty-five,
+LJSpeech-1.1/wavs/LJ044-0130.wav|By August of nineteen sixty-three, after a short three months in New Orleans, the city in which he had been born and had lived most of his early life,
+LJSpeech-1.1/wavs/LJ032-0041.wav|These words were also in the handwriting of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ010-0312.wav|Among the prisoner's private papers, one was found giving full details of the stock he had feloniously sold out,
+LJSpeech-1.1/wavs/LJ018-0138.wav|Some time in eighteen sixty-two, a large deficiency in stock of bank paper unglazed was discovered at the mills.
+LJSpeech-1.1/wavs/LJ015-0208.wav|they sounded one Burgess, a guard on the South-Eastern Railway, a line by which large quantities of bullion were sent to the Continent.
+LJSpeech-1.1/wavs/LJ041-0031.wav|Oswald praised Khrushchev and suggested that he and McBride join the Communist Party, quote, to take advantage of their social functions, end quote.
+LJSpeech-1.1/wavs/LJ008-0093.wav|At the execution of Governor Wall, in eighteen oh two, the furious hatred of the mob was plainly apparent in their appalling cries.
+LJSpeech-1.1/wavs/LJ032-0136.wav|In this way the powdered impression is actually removed from the object.
+LJSpeech-1.1/wavs/LJ038-0009.wav|When he heard police sirens, he, quote, looked up and saw the man enter the lobby, end quote.
+LJSpeech-1.1/wavs/LJ012-0272.wav|They were apparently good friends when last seen together at a neighbor's, where they seemed "perfectly happy and sociable, and eager for the wedding day."
+LJSpeech-1.1/wavs/LJ050-0078.wav|In June nineteen sixty-four, the Secret Service sent to a number of Federal law enforcement and intelligence agencies
+LJSpeech-1.1/wavs/LJ012-0004.wav|Inquiries set on foot also elicited the suspicion that the person who had represented Mrs. Canning's brother
+LJSpeech-1.1/wavs/LJ005-0196.wav|still no separation of the sexes, no means of ablution or other necessary services.
+LJSpeech-1.1/wavs/LJ016-0023.wav|the wounds broke open and bled profusely while the last dread penalty was being performed, to the manifest excitement and indignation of the crowd.
+LJSpeech-1.1/wavs/LJ004-0136.wav|Mr. Buxton, who found this, declared that it seemed physically impossible, but he was assured that it was true,
+LJSpeech-1.1/wavs/LJ018-0072.wav|The inspector said that they could not be removed till he reached England.
+LJSpeech-1.1/wavs/LJ050-0066.wav|The volume of references to the Secret Service has increased substantially since the new instructions went into effect;
+LJSpeech-1.1/wavs/LJ038-0211.wav|he said that he, quote, was very sorry that he had not hit him, end quote. Marina Oswald's testimony was fully supported by the note itself
+LJSpeech-1.1/wavs/LJ035-0193.wav|Truly, who had returned with Patrolman Baker from the roof, saw the police questioning the warehouse employees.
+LJSpeech-1.1/wavs/LJ039-0010.wav|she had failed to mention the incident when she was asked whether Oswald had ever expressed any hostility
+LJSpeech-1.1/wavs/LJ019-0266.wav|The efforts made by Sir James Graham years before to introduce uniformity in this particular had failed of effect.
+LJSpeech-1.1/wavs/LJ044-0188.wav|Oswald must have been thoroughly disillusioned when he left Mexico City on October two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ028-0180.wav|In the year five sixty-two, after a long reign of forty-three years, Nebuchadnezzar died.
+LJSpeech-1.1/wavs/LJ012-0238.wav|He fainted, and the warrant of mercy nearly proved his death-blow. The other two looked on at his agitation with an indifference amounting to apathy.
+LJSpeech-1.1/wavs/LJ042-0024.wav|He wrote in another letter that he would, quote, never return to the United States which is a country I hate, end quote.
+LJSpeech-1.1/wavs/LJ001-0093.wav|This experiment was so far successful that about eighteen fifty Messrs. Miller and Richard of Edinburgh
+LJSpeech-1.1/wavs/LJ024-0078.wav|Like all lawyers, like all Americans, I regret the necessity of this controversy.
+LJSpeech-1.1/wavs/LJ041-0078.wav|When the officers were unable to discuss foreign affairs satisfactorily with him, Oswald regarded them as unfit to exercise command over him.
+LJSpeech-1.1/wavs/LJ046-0133.wav|Since many items may pertain to a single case, these figures do not show the caseload.
+LJSpeech-1.1/wavs/LJ006-0166.wav|dice, dangerous implements, or other prohibited articles.
+LJSpeech-1.1/wavs/LJ019-0190.wav|"but in a metropolitan prison absolutely essential." The Corporation in reply demurred rather to accepting strict separation as a rule,
+LJSpeech-1.1/wavs/LJ002-0002.wav|Under the conditions referred to in the previous chapter,
+LJSpeech-1.1/wavs/LJ003-0132.wav|whose language and manners, whose female associates of the most abandoned description, and the scenes consequent with such lost wretches
+LJSpeech-1.1/wavs/LJ044-0054.wav|and a newspaper clipping reporting the event, suggests that his previous story of an attack by Cuban exiles was at least greatly exaggerated.
+LJSpeech-1.1/wavs/LJ045-0093.wav|She said that she, quote, always tried to point out to him that he was a man like any others who were around us. But he simply could not understand that? End quote.
+LJSpeech-1.1/wavs/LJ019-0374.wav|It was nearly impossible for them to exercise a very close supervision over the whole of the prisons of the country.
+LJSpeech-1.1/wavs/LJ036-0133.wav|and he opened the door a little bit like he was going to get out and he said, "I will let you have this one," and she says, "No, the driver can call me one."
+LJSpeech-1.1/wavs/LJ001-0006.wav|And it is worth mention in passing that, as an example of fine typography,
+LJSpeech-1.1/wavs/LJ010-0307.wav|New liabilities were incurred to the extent of one hundred thousand pounds by more failures, and in eighteen nineteen,
+LJSpeech-1.1/wavs/LJ005-0065.wav|and none are more commendable than that which deprecates the excessive ornamentation of the external parts of the edifice.
+LJSpeech-1.1/wavs/LJ045-0043.wav|In spite of fact I married Marina to hurt Ella (the girl that had rejected him) I found myself in love with Marina, end quote.
+LJSpeech-1.1/wavs/LJ029-0061.wav|The FBI office in Dallas gave the local Secret Service representatives the name of a possibly dangerous individual in the Dallas area who was investigated.
+LJSpeech-1.1/wavs/LJ007-0177.wav|We trust, however, that the day is at hand when this stain will be removed from the character of the city of London,
+LJSpeech-1.1/wavs/LJ009-0112.wav|the burglar muttering curses and savage expressions of defiance; whilst the poor sheep-stealer shakes hands with the turnkeys,
+LJSpeech-1.1/wavs/LJ019-0203.wav|But the condition of the great city jail was evidently considered a reproach by the city authorities, and a year after the opening of the new "model" at Pentonville,
+LJSpeech-1.1/wavs/LJ032-0057.wav|The post office box to which the rifle was shipped was rented to "Lee H. Oswald" from October nine, nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ012-0167.wav|But Probert, who turned king's evidence, and materially assisted conviction,
+LJSpeech-1.1/wavs/LJ045-0080.wav|Generally, I think that was right, for such things that is the right thing to do. There was some grounds for it. End quote.
+LJSpeech-1.1/wavs/LJ010-0088.wav|and that when it was opened the whole band should rush in.
+LJSpeech-1.1/wavs/LJ038-0153.wav|He said that he left work because Bill Shelley said that there would be no more work done that day in the building.
+LJSpeech-1.1/wavs/LJ030-0152.wav|Speed of the Limousine
+LJSpeech-1.1/wavs/LJ004-0026.wav|In eighteen fourteen a bill was brought in to insist upon the appointment of chaplains in jails, and when this had passed into law,
+LJSpeech-1.1/wavs/LJ027-0129.wav|Convincing as are the evidences of descent recorded in the structure of plants and animals,
+LJSpeech-1.1/wavs/LJ031-0139.wav|and made arrangements through Secret Service headquarters in Washington to provide them with protection immediately.
+LJSpeech-1.1/wavs/LJ014-0296.wav|He had made use of a piece of rope cut out from the sacking of his bedstead, and had tied his feet together with a silk pocket-handkerchief.
+LJSpeech-1.1/wavs/LJ014-0254.wav|This he did to an enormous extent,
+LJSpeech-1.1/wavs/LJ017-0268.wav|and they were each kept in an ordinary separate cell of the newly-constructed block, with the "traps," or square openings in the cell door,
+LJSpeech-1.1/wavs/LJ029-0113.wav|gave more people an opportunity to participate.
+LJSpeech-1.1/wavs/LJ004-0060.wav|"You have no right," he says, addressing the authorities, "to subject a prisoner to suffering from cold,
+LJSpeech-1.1/wavs/LJ038-0045.wav|Deputy Sheriff Eddy R. Walthers recalled such a remark but he did not reach the scene of the struggle
+LJSpeech-1.1/wavs/LJ018-0264.wav|Roupell was quiet and submissive while in Newgate, unassuming in manner, and ready to make the best of his position.
+LJSpeech-1.1/wavs/LJ010-0145.wav|These were the last executions for high treason, but not the last prisoners by many who passed through Newgate charged with sedition.
+LJSpeech-1.1/wavs/LJ034-0019.wav|on which appeared the print of the left index finger and right palm of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ046-0110.wav|is the identification and elimination of possible sources of danger to the President before the danger becomes actual.
+LJSpeech-1.1/wavs/LJ001-0068.wav|The characteristic Dutch type, as represented by the excellent printer Gerard Leew, is very pronounced and uncompromising Gothic.
+LJSpeech-1.1/wavs/LJ033-0038.wav|the Commission gave weight to the fact that Oswald gave a false reason for returning home on November twenty-one,
+LJSpeech-1.1/wavs/LJ008-0295.wav|When the council had decided, the news was conveyed to Newgate by the Recorder, who made his "report," as it was called.
+LJSpeech-1.1/wavs/LJ015-0176.wav|He was arrested, examined before a police magistrate, and committed to Newgate.
+LJSpeech-1.1/wavs/LJ008-0163.wav|Panic became general. More women, children, and many men were borne down, to perish beneath the feet of the rest.
+LJSpeech-1.1/wavs/LJ010-0059.wav|Some five hundred in all were arrested, but as only three of these were lodged in Newgate, I shall not recur to them in my narrative.
+LJSpeech-1.1/wavs/LJ003-0262.wav|Any reputed, and indeed any real, wife might spend the night in Newgate if she would pay the shilling fee, commonly known as the "bad money,"
+LJSpeech-1.1/wavs/LJ016-0301.wav|amid hisses and sneering inquiries of what he had had to drink that morning.
+LJSpeech-1.1/wavs/LJ008-0193.wav|by seven a.m. the whole space was so thronged that it was impossible to move one way or the other.
+LJSpeech-1.1/wavs/LJ038-0134.wav|An examination of the roominghouse register revealed that Oswald actually signed the name O. H. Lee.
+LJSpeech-1.1/wavs/LJ016-0209.wav|Some wags in Scotland seized Calcraft and kept him in durance the night before the execution.
+LJSpeech-1.1/wavs/LJ008-0270.wav|and while the courts adhered to the letter of the law, appeals were constantly made to the royal prerogative of mercy.
+LJSpeech-1.1/wavs/LJ036-0066.wav|He could not reach his roominghouse on the Marsalis bus, but the Beckley bus stopped across the street.
+LJSpeech-1.1/wavs/LJ007-0161.wav|drink, gaming, obscene and blasphemous language; utter idleness, the almost unrestricted admission of money and luxuries;
+LJSpeech-1.1/wavs/LJ050-0217.wav|the Secret Service sought funds for twenty-five new positions, primarily in field offices. This increase has been approved by the Congress.
+LJSpeech-1.1/wavs/LJ028-0383.wav|and the wild beasts of the islands shall cry in their desolate houses, and dragons in their pleasant palaces.
+LJSpeech-1.1/wavs/LJ014-0182.wav|the shutters were up, and he was preparing to close, when Marley entered and fell upon him with a life-preserver,
+LJSpeech-1.1/wavs/LJ033-0204.wav|So if I found all of these then I would have been able to say these fibers probably had come from this blanket. But since I found so few,
+LJSpeech-1.1/wavs/LJ048-0099.wav|Agent Lawson did the advance work alone from November thirteen to November eighteen, when he was joined by Agent David B. Grant,
+LJSpeech-1.1/wavs/LJ047-0133.wav|Soon after, on October one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ032-0036.wav|purchased as Number two two zero two one three zero four six two in Dallas, Texas, on March twelve, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ001-0012.wav|especially as no more time is occupied, or cost incurred, in casting, setting, or printing beautiful letters
+LJSpeech-1.1/wavs/LJ038-0241.wav|Also seen in the picture is the fence on which Walker's assailant apparently rested the rifle.
+LJSpeech-1.1/wavs/LJ019-0027.wav|The internal arrangements of the new model were carefully supervised by a body of distinguished men, among which were many peers, Lord John Russell,
+LJSpeech-1.1/wavs/LJ040-0202.wav|Mrs. Siegel described her as a smartly dressed, gray haired woman, very self-possessed and alert and superficially affable,
+LJSpeech-1.1/wavs/LJ018-0368.wav|The still more recent cases of poisoning which have occurred were not connected with Newgate.
+LJSpeech-1.1/wavs/LJ013-0036.wav|According to their own statements the loss of the 'Dryad' was only one of six intentional shipwrecks with which they had been concerned.
+LJSpeech-1.1/wavs/LJ035-0045.wav|Baker said, quote, He [Truly] had already started around the bend to come to the next elevator going up,
+LJSpeech-1.1/wavs/LJ043-0163.wav|If he had been successful and had been apprehended even for routine questioning, his apartment would undoubtedly have been searched,
+LJSpeech-1.1/wavs/LJ045-0111.wav|They asked for Lee Oswald who was not called to the telephone because he was known by the other name.
+LJSpeech-1.1/wavs/LJ016-0331.wav|The last public execution in front of Newgate was that of the Fenian Michael Barrett,
+LJSpeech-1.1/wavs/LJ002-0293.wav|those who could pay nothing went, as a matter of course, to the common side;
+LJSpeech-1.1/wavs/LJ008-0048.wav|it is supported by two beams, which are held in their place by bolts. The movement of the lever withdraws the bolts, the platform falls in;
+LJSpeech-1.1/wavs/LJ033-0128.wav|that the bag Oswald carried contained the assassination weapon and has concluded that Frazier and Randle are mistaken as to the length of the bag.
+LJSpeech-1.1/wavs/LJ011-0040.wav|I find in the chaplain's journal, under date eighteen twenty-four, various entries relative to this prisoner.
+LJSpeech-1.1/wavs/LJ016-0323.wav|to prevent abuse, and satisfy the public that the law had been complied with.
+LJSpeech-1.1/wavs/LJ031-0142.wav|The Vice President conferred with White House Assistant Press Secretary Malcolm Kilduff
+LJSpeech-1.1/wavs/LJ026-0074.wav|The gaseous matters (carbon dioxide, oxygen, nitrogen) enter the plant mainly by diffusion from the atmosphere,
+LJSpeech-1.1/wavs/LJ024-0068.wav|and one over seventy.
+LJSpeech-1.1/wavs/LJ050-0081.wav|The new tentative criteria are useful in making clear that
+LJSpeech-1.1/wavs/LJ007-0014.wav|The admission of a crowd of visitors to assist in these lay services has already been remarked upon; as the inspectors pointed out,
+LJSpeech-1.1/wavs/LJ015-0010.wav|Cole now suspended payment.
+LJSpeech-1.1/wavs/LJ046-0241.wav|PRS required a more direct indication of a threat to the President, and that there was no such indication until the President's scheduled visit to that area became known.
+LJSpeech-1.1/wavs/LJ008-0173.wav|In this convulsive struggle for bare existence people fought fiercely with one another, and the weakest, of course the women, went under.
+LJSpeech-1.1/wavs/LJ018-0260.wav|Mr. Justice Byles, in passing sentence, commented severely upon the commission of such crimes by a man in Roupell's position in life,
+LJSpeech-1.1/wavs/LJ003-0156.wav|All the money earned by prisoners was at their own disposal, and was spent almost habitually in drink, chambering, and wantonness.
+LJSpeech-1.1/wavs/LJ022-0081.wav|Funds allotted for each project should be actually and promptly spent and not held over until later years.
+LJSpeech-1.1/wavs/LJ043-0103.wav|In any event, Oswald was discharged on April six, nineteen sixty-three, ostensibly because of his inefficiency and difficult personality.
+LJSpeech-1.1/wavs/LJ026-0137.wav|and be laid down as "reserve starch" in the cells of root or stem or elsewhere.
+LJSpeech-1.1/wavs/LJ012-0064.wav|Ikey left no traces, and the police could hear nothing of him. He had in fact gone out of the country, to Copenhagen, whence he passed on to New York.
+LJSpeech-1.1/wavs/LJ018-0391.wav|Mr. Bamell Oakley made a rich harvest for a time, and was said at the time of his trial
+LJSpeech-1.1/wavs/LJ043-0134.wav|The Commission has concluded that on April ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ036-0202.wav|At twelve:fifty-four p.m., Tippit reported that he was in the central Oak Cliff area at Lancaster and Eighth.
+LJSpeech-1.1/wavs/LJ038-0162.wav|Then he said, "Oh, I see," and that was all.
+LJSpeech-1.1/wavs/LJ037-0177.wav|Therefore, either one bullet of Remington-Peters manufacture is missing
+LJSpeech-1.1/wavs/LJ037-0041.wav|The man appeared to step back as the policeman, quote, calmly opened the car door, end quote, and very slowly got out and walked toward the front of the car.
+LJSpeech-1.1/wavs/LJ045-0046.wav|Marina Oswald expressed one aspect of her husband's attitude toward her when she testified that, quote, Lee wanted me to go to Russia.
+LJSpeech-1.1/wavs/LJ047-0154.wav|The interview lasted about twenty to twenty-five minutes. In response to Hosty's inquiries, Mrs. Paine, quote,
+LJSpeech-1.1/wavs/LJ026-0096.wav|water, nitrites or nitrates and various mineral substances in solution in water
+LJSpeech-1.1/wavs/LJ003-0116.wav|A jury having been selected and duly sworn, the culprit was then arraigned. Justice, however, was not administered with absolute integrity.
+LJSpeech-1.1/wavs/LJ010-0180.wav|the Prince too rose to shield her with his person. Again, providentially, the bullet went wide of the mark,
+LJSpeech-1.1/wavs/LJ041-0056.wav|in his intense desire to join the Marines and get away from his surroundings and his mother.
+LJSpeech-1.1/wavs/LJ019-0309.wav|The main object of this act was to compass that uniformity in discipline and treatment generally
+LJSpeech-1.1/wavs/LJ004-0199.wav|This made exertion compulsory, and imposed hard labor as a proper punishment.
+LJSpeech-1.1/wavs/LJ024-0118.wav|Look at these strange bed-fellows of yours.
+LJSpeech-1.1/wavs/LJ029-0023.wav|In September, the White House decided to permit further visits by the President and extended the trip to run from the afternoon of November twenty-one
+LJSpeech-1.1/wavs/LJ047-0142.wav|The Bureau had no earlier information suggesting that Oswald had left the United States.
+LJSpeech-1.1/wavs/LJ010-0007.wav|Police protection was better and more effective;
+LJSpeech-1.1/wavs/LJ037-0192.wav|Seaport Traders, Incorporated, received through the mail a mail-order coupon for one, quote, point three-eight
+LJSpeech-1.1/wavs/LJ012-0183.wav|During the night they sought to dispose of the body by throwing it into a pond, but two days later had to throw it into another pond.
+LJSpeech-1.1/wavs/LJ048-0276.wav|Since he was convinced that this was not the case, he believed that it would be unfair to the agents and their families to take explicit disciplinary measures.
+LJSpeech-1.1/wavs/LJ003-0278.wav|Insuperable difficulties were still supposed to stand in the way of any general employment of prisoners at their trades.
+LJSpeech-1.1/wavs/LJ025-0040.wav|The soil and the atmosphere supply plants with water composed of hydrogen and oxygen
+LJSpeech-1.1/wavs/LJ011-0020.wav|had been sold out under a forged power of attorney.
+LJSpeech-1.1/wavs/LJ047-0143.wav|The possible contact with the Soviet Embassy in Mexico intensified the FBI's interest in learning Oswald's whereabouts.
+LJSpeech-1.1/wavs/LJ010-0041.wav|or like Cannon the chimney-sweeper, who savagely killed the policeman.
+LJSpeech-1.1/wavs/LJ030-0055.wav|He acted on instructions he had received earlier from Assistant Special Agent in Charge Roy H. Kellerman, who was in Fort Worth with the President.
+LJSpeech-1.1/wavs/LJ048-0124.wav|The Secret Service has advised the Commission that no unusual precautions were taken for the Dallas trip, and that, quote,
+LJSpeech-1.1/wavs/LJ021-0114.wav|Another question arises as to whether in fixing minimum wages on the basis of an hourly or weekly wage
+LJSpeech-1.1/wavs/LJ029-0005.wav|although in many instances documentary or other evidence has also been used by the Commission.
+LJSpeech-1.1/wavs/LJ020-0041.wav|The phosphates which the process of “bolting” removes to a large extent from white flour, go directly to the manufacture of bone,
+LJSpeech-1.1/wavs/LJ021-0072.wav|Also, billions of dollars of invested capital have today a greater security of present and future earning power than before.
+LJSpeech-1.1/wavs/LJ015-0204.wav|This was the question which presented itself to the fertile brain of one Pierce,
+LJSpeech-1.1/wavs/LJ038-0215.wav|It is clear that the note was written while the Oswalds were living in Dallas before they moved to New Orleans in the spring of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ009-0207.wav|extended at full length on an inclined platform
+LJSpeech-1.1/wavs/LJ010-0229.wav|Within a couple of years a second attempt to assassinate the Queen was perpetrated in nearly the same spot, by a man named John Francis,
+LJSpeech-1.1/wavs/LJ016-0258.wav|This line of argument prevailed over the manifest horrors of the spectacle. These increased as time passed.
+LJSpeech-1.1/wavs/LJ033-0051.wav|Neither Marina Oswald nor Ruth Paine saw Oswald in the garage.
+LJSpeech-1.1/wavs/LJ014-0141.wav|and stretching out her hand, she gathered up a quantity of the rue which, following ancient custom dating from the days of the jail fever,
+LJSpeech-1.1/wavs/LJ048-0145.wav|and no written description of the role the local police were expected to perform.
+LJSpeech-1.1/wavs/LJ006-0288.wav|The separation of the sexes was not indeed rigidly carried out in Newgate as yet.
+LJSpeech-1.1/wavs/LJ048-0248.wav|President Kennedy was scheduled to speak across the street from his hotel in Fort Worth at eight:thirty a.m.
+LJSpeech-1.1/wavs/LJ025-0136.wav|Of course the similarity of the acts does not necessarily involve the conclusion that the mechanism by which they are effected is the same
+LJSpeech-1.1/wavs/LJ035-0108.wav|Eddie Piper, the janitor, told Dougherty that the President had been shot,
+LJSpeech-1.1/wavs/LJ025-0147.wav|with a problem of extreme difficulty, which must be attacked from a new point of view and by the aid of methods which have yet to be invented.
+LJSpeech-1.1/wavs/LJ008-0155.wav|Very great excitement prevailed in the town throughout the trial, and this greatly increased when the verdict was known.
+LJSpeech-1.1/wavs/LJ036-0184.wav|When Oswald came in she said, quote, Oh, you are in a hurry, end quote, but Oswald did not respond.
+LJSpeech-1.1/wavs/LJ049-0134.wav|The Commission suggests that consideration might be given to assigning to a Cabinet-level committee or the National Security Council
+LJSpeech-1.1/wavs/LJ006-0192.wav|There was no school for adults; only the boys were taught anything, and their instructor, with his assistant, were convicted prisoners.
+LJSpeech-1.1/wavs/LJ015-0125.wav|Redpath's crime arose from his peculiar and independent position as registrar of stock of the Great Northern Railway Company.
+LJSpeech-1.1/wavs/LJ002-0197.wav|whose net annual income thus entirely derived from the impecunious amounted to between three and four thousand pounds.
+LJSpeech-1.1/wavs/LJ018-0228.wav|Five or six years later, William Roupell minutely described how he had effected the fraud.
+LJSpeech-1.1/wavs/LJ022-0065.wav|The unemployment insurance part of the legislation
+LJSpeech-1.1/wavs/LJ046-0036.wav|regarding certain protective measures in force at the time of the Dallas trip and propose recommendations for improvements.
+LJSpeech-1.1/wavs/LJ013-0106.wav|The inn belonging to Burgess was surrounded
+LJSpeech-1.1/wavs/LJ011-0043.wav|that this may prove the beginning of a better system of confinement, and that every description of persons who may be unfortunately under sentence of death
+LJSpeech-1.1/wavs/LJ048-0077.wav|FBI-agency community of interests, location of agency head quarters, and the responsiveness of agency representatives.
+LJSpeech-1.1/wavs/LJ004-0031.wav|But the effect of this legislation was rather weakened by the remoteness of the pressure exercised.
+LJSpeech-1.1/wavs/LJ006-0213.wav|The quantity to be issued per head was limited by the prison regulations to one pint
+LJSpeech-1.1/wavs/LJ015-0260.wav|As the evidence he gave incriminated Pierce, Burgess, and Tester, all three were arrested and committed to Newgate for trial.
+LJSpeech-1.1/wavs/LJ016-0177.wav|Marwood, whose name is so familiar with the present generation,
+LJSpeech-1.1/wavs/LJ030-0188.wav|At the sound of the second shot
+LJSpeech-1.1/wavs/LJ016-0310.wav|Another distinguished witness feared
+LJSpeech-1.1/wavs/LJ014-0119.wav|He had fled to Jersey, where he was recognized and arrested.
+LJSpeech-1.1/wavs/LJ028-0003.wav|With guides and guards about you, you ride through the covered bazaars crowded with dark-faced Arabs in strange costumes,
+LJSpeech-1.1/wavs/LJ036-0042.wav|However, McWatters' recollection alone was too vague to be a basis for placing Oswald on the bus.
+LJSpeech-1.1/wavs/LJ033-0046.wav|went out to the garage to paint some children's blocks, and worked in the garage for half an hour or so.
+LJSpeech-1.1/wavs/LJ028-0287.wav|but found none by which he could hope to prevail, unless he maimed himself and then went over to the enemy.
+LJSpeech-1.1/wavs/LJ038-0291.wav|Other details described by Marina Oswald coincide with facts developed independently of her statements.
+LJSpeech-1.1/wavs/LJ007-0185.wav|justified the original allegations. It was impossible to deny the indiscriminate association; the gambling, drinking, smoking, quarreling in the jail;
+LJSpeech-1.1/wavs/LJ009-0160.wav|In eighteen thirty-two
+LJSpeech-1.1/wavs/LJ010-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section thirteen: Newgate notorieties, part one.
+LJSpeech-1.1/wavs/LJ016-0069.wav|Entering a town, he obtained employment at once as a chimney-sweep
+LJSpeech-1.1/wavs/LJ013-0121.wav|Howse and his accomplice were arrested; the former was found guilty and sentenced to fifteen years, but the latter was acquitted.
+LJSpeech-1.1/wavs/LJ015-0189.wav|and the prototype of a class not uncommon in our later days
+LJSpeech-1.1/wavs/LJ017-0017.wav|It is quite possible that Catherine Blandy, who poisoned her father at the instigation of her lover,
+LJSpeech-1.1/wavs/LJ020-0017.wav|Now whip up the batter with a wooden spoon for another minute, and the sponge is made.
+LJSpeech-1.1/wavs/LJ011-0108.wav|I wish after this day to have communication with nobody; let me take leave of my wife, and family, and friends. I have already suffered an execution;
+LJSpeech-1.1/wavs/LJ039-0183.wav|On the basis of these results, Simmons testified that in his opinion the probability of hitting the targets at the relatively short range at which they were hit
+LJSpeech-1.1/wavs/LJ029-0107.wav|On November eighteen,
+LJSpeech-1.1/wavs/LJ019-0321.wav|The infliction of punishment more or less uniform was thus aimed at.
+LJSpeech-1.1/wavs/LJ027-0051.wav|It means blood relationship.
+LJSpeech-1.1/wavs/LJ003-0195.wav|Mr. Bennet speaks of thirty-eight capital convicts he found in the press yard in February eighteen seventeen,
+LJSpeech-1.1/wavs/LJ015-0215.wav|The safes while on the line en route between London and Folkestone were in the guard's van.
+LJSpeech-1.1/wavs/LJ022-0164.wav|It will put the public utility operating industry on a sound basis for the future,
+LJSpeech-1.1/wavs/LJ050-0095.wav|The U.S. government has no charges or complaints against me.
+LJSpeech-1.1/wavs/LJ038-0066.wav|While the police radio had noted the similarity in description of the two suspects, the arresting officers were pursuing Oswald for the murder of Tippit.
+LJSpeech-1.1/wavs/LJ001-0139.wav|a blemish which can be nearly, though not wholly, avoided by care and forethought
+LJSpeech-1.1/wavs/LJ016-0307.wav|Full evidence was taken on all points, and on that regarding public executions there was a great preponderance of opinion towards their abolition,
+LJSpeech-1.1/wavs/LJ040-0213.wav|that he had great difficulty in adapting himself to conditions in that city.
+LJSpeech-1.1/wavs/LJ034-0200.wav|Shortly after the assassination, Euins signed an affidavit describing the man as "white,"
+LJSpeech-1.1/wavs/LJ015-0293.wav|as coming from a Mr. Whitney.
+LJSpeech-1.1/wavs/LJ034-0076.wav|Givens said to Oswald, quote, Boy are you going downstairs? It's near lunch time, end quote.
+LJSpeech-1.1/wavs/LJ012-0041.wav|As a general rule Ikey Solomons confined his purchases to small articles, mostly of jewelery and plate,
+LJSpeech-1.1/wavs/LJ049-0075.wav|Recommendations.
+LJSpeech-1.1/wavs/LJ010-0035.wav|Courvoisier might have lived a century earlier.
+LJSpeech-1.1/wavs/LJ049-0212.wav|to put it in a position to reach final conclusions in this respect.
+LJSpeech-1.1/wavs/LJ018-0036.wav|The officers went on board the 'Victoria' at once, Müller was identified by Mr. Death, and the arrest was made.
+LJSpeech-1.1/wavs/LJ035-0046.wav|I was coming out this one on the second floor, and I don't know, I was kind of sweeping this area as I come up, I was looking from right to left
+LJSpeech-1.1/wavs/LJ002-0101.wav|there was still a "convenient room" which served, and, quote,
+LJSpeech-1.1/wavs/LJ043-0145.wav|He told his wife that he wanted to send the pictures to the Militant and he also asked her to keep one of the pictures for his daughter, June.
+LJSpeech-1.1/wavs/LJ011-0221.wav|and was brought from Newgate, at his own request, to oppose the second reading of the bill.
+LJSpeech-1.1/wavs/LJ003-0303.wav|The personal cleanliness of all prisoners was to be insisted upon; they should be made to wash at least once a day,
+LJSpeech-1.1/wavs/LJ022-0141.wav|to be interpreted as lack of interest in or disapproval of many other important proposals that are pending.
+LJSpeech-1.1/wavs/LJ040-0179.wav|Irving Sokolow, a Youth House psychologist reported that, quote,
+LJSpeech-1.1/wavs/LJ002-0079.wav|Its name and its situation were the same as those of the old place of carrying out the terrible sentence inflicted on accused persons who stood mute.
+LJSpeech-1.1/wavs/LJ002-0247.wav|As the business of the Marshalsea Court declined, the numbers in its prison diminished.
+LJSpeech-1.1/wavs/LJ033-0161.wav|On the day of the assassination, the Dallas police obtained a sample of wrapping paper and tape
+LJSpeech-1.1/wavs/LJ004-0144.wav|had to endure by "the regulations of the city a disease very dangerous in its nature," and ran the risk of a lingering and painful death.
+LJSpeech-1.1/wavs/LJ004-0017.wav|Some of the county jails, and one or two borough jails, had been rebuilt,
+LJSpeech-1.1/wavs/LJ019-0005.wav|There had been a pause in legislation, except of a permissive kind. The second and third Victoria, cap. fifty-six
+LJSpeech-1.1/wavs/LJ016-0050.wav|His next job was to descend outside Newgate.
+LJSpeech-1.1/wavs/LJ004-0115.wav|Infirmaries separating the sexes were also to be provided, a chapel too, and warm and cold baths.
+LJSpeech-1.1/wavs/LJ015-0196.wav|Highway robberies, for instance, had disappeared, if we except the spasmodic and severely repressed outbreak of "garotting,"
+LJSpeech-1.1/wavs/LJ028-0480.wav|the exalted prince, the protector of Esagil and Ezida, son of Nabopolassar, King of Babylon, am I.
+LJSpeech-1.1/wavs/LJ044-0236.wav|it is unlikely that a reasoning person would plan to attempt to travel from Dallas, Texas to Cuba
+LJSpeech-1.1/wavs/LJ025-0060.wav|and now such movements of the living substance of plants are well known to be some of the most widely prevalent phenomena of vegetable life.
+LJSpeech-1.1/wavs/LJ026-0158.wav|Probably this freed oxygen is used for the purpose of oxygenation, but more is freed in the photosynthetic process than is needed for oxygenation
+LJSpeech-1.1/wavs/LJ013-0231.wav|"Where is it?" asked the governor.
+LJSpeech-1.1/wavs/LJ004-0228.wav|In eighteen eighteen prisoners awaiting trial in Newgate, were at length relieved from this illegal infliction.
+LJSpeech-1.1/wavs/LJ016-0231.wav|When he came to the prison for his wages his grandchildren often accompanied him, affectionately clinging to his hands;
+LJSpeech-1.1/wavs/LJ031-0200.wav|The President then walked to the Executive Office Building, where he worked until nine p.m.
+LJSpeech-1.1/wavs/LJ016-0340.wav|and Calcraft, who had been threatened with summary retribution if he executed Barrett, carried out the sentence without mishap.
+LJSpeech-1.1/wavs/LJ002-0222.wav|It contained generally from six to seven hundred inmates, while another hundred more or less resided in the rules outside.
+LJSpeech-1.1/wavs/LJ016-0329.wav|a bill was introduced by Mr. Hibbert, M.P., and accepted by the Government, providing for the future carrying out of executions within prisons.
+LJSpeech-1.1/wavs/LJ025-0099.wav|Moreover, although it remains true that there is an inverse relation between the green plant in sunshine and the animal,
+LJSpeech-1.1/wavs/LJ004-0064.wav|"You have for the same reason," he goes on, "no right to ruin his habits by compelling him to be idle,
+LJSpeech-1.1/wavs/LJ012-0192.wav|the first of whom has added to the British language a synonym for illegal suppression.
+LJSpeech-1.1/wavs/LJ040-0183.wav|There is some indication that he may relate to men more easily than to women in view of the more mature conceptualisation.
+LJSpeech-1.1/wavs/LJ026-0070.wav|Of the substances the solids (salts, etc.) must be dissolved in water before they can be taken in.
+LJSpeech-1.1/wavs/LJ015-0146.wav|He had a nice taste in bric-à-brac, and was considered a good judge of pictures.
+LJSpeech-1.1/wavs/LJ032-0170.wav|the Commission has concluded that the fibers in the tuft on the rifle most probably came from the shirt worn by Oswald when he was arrested,
+LJSpeech-1.1/wavs/LJ030-0148.wav|Seconds after the shooting, Roy Kellerman, riding in the front seat of the Presidential limousine,
+LJSpeech-1.1/wavs/LJ031-0133.wav|Jack Brooks, Homer Thornberry, and Albert Thomas joined Clifton C. Carter and the group of special agents protecting the Vice President.
+LJSpeech-1.1/wavs/LJ018-0363.wav|She was found guilty.
+LJSpeech-1.1/wavs/LJ042-0128.wav|I am stating to reconsider my desire about staying. The work is drab. The money I get has nowhere to be spent.
+LJSpeech-1.1/wavs/LJ010-0195.wav|He smiled continually, and when the indictment was read, burst into loud and discordant fits of laughter.
+LJSpeech-1.1/wavs/LJ019-0137.wav|The prison population of Newgate was still subject to great fluctuations, but it seldom rose above two hundred and fifty or three hundred
+LJSpeech-1.1/wavs/LJ031-0025.wav|urological surgeon, Dr Paul C. Peters; an oral surgeon, Dr. Don T. Curtis; and a heart specialist,
+LJSpeech-1.1/wavs/LJ040-0226.wav|I couldn't in all honesty sincerely say such a thing.
+LJSpeech-1.1/wavs/LJ027-0055.wav|"All vertebrate animals, and none other," says Le Conte, "have an internal jointed skeleton worked by muscles on the outside.
+LJSpeech-1.1/wavs/LJ005-0228.wav|As yet no rules or regulations had been printed or prepared.
+LJSpeech-1.1/wavs/LJ033-0100.wav|Frazier recalled that one end of the package was under Oswald's armpit and the lower part was held with his right hand
+LJSpeech-1.1/wavs/LJ016-0011.wav|The ladder was actually made, of black sewing-thread firmly and closely interwoven. But White could not remove the bars;
+LJSpeech-1.1/wavs/LJ050-0127.wav|should negotiate similar arrangements with such other State and local law enforcement agencies as may provide meaningful assistance.
+LJSpeech-1.1/wavs/LJ049-0012.wav|At these stops, agents from the Presidential follow-up car stood between the President and the public,
+LJSpeech-1.1/wavs/LJ026-0142.wav|It is also clear that catabolism or oxidation for the liberation of energy occurs as in animals, but this process is slower.
+LJSpeech-1.1/wavs/LJ014-0059.wav|Their victim was a man named Patrick O'Connor, a Custom-House gauger, who had been a suitor of Marie de Roux before she became Mrs. Manning.
+LJSpeech-1.1/wavs/LJ037-0267.wav|to the exclusion of all other weapons,
+LJSpeech-1.1/wavs/LJ018-0379.wav|Resolute, determined-looking men, their courage broke down in confinement.
+LJSpeech-1.1/wavs/LJ046-0066.wav|And he felt that leaving Washington for the President of the United States was a most necessary -- not only for the people, but for the President himself,
+LJSpeech-1.1/wavs/LJ014-0321.wav|When such goods arrived they were frequently left at a wharf, paying rent until it suited the importer to remove them.
+LJSpeech-1.1/wavs/LJ021-0179.wav|Did England hesitate to call in ten billion dollars of her war bonds bearing five percent interest,
+LJSpeech-1.1/wavs/LJ002-0142.wav|These courts were open to many and grave objections.
+LJSpeech-1.1/wavs/LJ005-0121.wav|By degrees, however,
+LJSpeech-1.1/wavs/LJ007-0059.wav|had relaxed his efforts, because, according to his own account, he was so frequently stopped in the performance of his duties.
+LJSpeech-1.1/wavs/LJ030-0225.wav|But he was immediately called back by Special Agent Emory P. Roberts, in charge of the follow-up car,
+LJSpeech-1.1/wavs/LJ044-0217.wav|was also reported in the October one, nineteen sixty-three, issue of the Worker, to which Oswald also subscribed.
+LJSpeech-1.1/wavs/LJ043-0054.wav|Oswald moved to Dallas on about October eight, nineteen sixty-two, without telling his mother where he was going.
+LJSpeech-1.1/wavs/LJ044-0138.wav|It would not have been possible to have followed Arnold Johnson's advice to remain in the background,
+LJSpeech-1.1/wavs/LJ018-0167.wav|Nothing was found in Buncher's premises in Strutton Ground, which were thoroughly searched,
+LJSpeech-1.1/wavs/LJ014-0031.wav|The prisoner made an elaborate defense, in which he sought to vilify the character of deceased
+LJSpeech-1.1/wavs/LJ008-0301.wav|during which they counted the moments -- the prisoners in their cells as usual, and their friends in the street in front of Newgate, where they passed the night.
+LJSpeech-1.1/wavs/LJ011-0217.wav|Mr. Justice Bayley, in summing up,
+LJSpeech-1.1/wavs/LJ047-0183.wav|When asked what his next step would have been, Hosty replied, quote,
+LJSpeech-1.1/wavs/LJ011-0193.wav|Then Mrs. Daulby learnt that Miss Turner had not arrived at Shrigley, but that she had gone to Manchester.
+LJSpeech-1.1/wavs/LJ019-0184.wav|The Corporation had agreed to spend twenty thousand pounds on alterations, but sixty thousand pounds would suffice to reconstruct.
+LJSpeech-1.1/wavs/LJ017-0062.wav|In the condemned cell he frequently repeated, quote, I go to my death a murdered man, end quote.
+LJSpeech-1.1/wavs/LJ002-0145.wav|Often a commissioner had to leave the bench because he was himself a party to the suit that was sub judice.
+LJSpeech-1.1/wavs/LJ019-0112.wav|Such excellent returns might be counted upon, that a margin of profit would be left after the cost of the prisons had been defrayed.
+LJSpeech-1.1/wavs/LJ009-0113.wav|whistles merrily, and points upwards with madness in his look.
+LJSpeech-1.1/wavs/LJ015-0276.wav|Cheques blank and canceled were his stock-in-trade. The former he filled up by exact imitation of the latter, signature and all.
+LJSpeech-1.1/wavs/LJ011-0275.wav|There he was seated on some sort of wooden bench and securely fastened.
+LJSpeech-1.1/wavs/LJ044-0199.wav|In retrospect his attempt to go to Cuba or return to the Soviet Union may well have been Oswald's last escape hatch,
+LJSpeech-1.1/wavs/LJ015-0153.wav|Not content with giving where assistance was solicited, he himself sought out deserving cases and personally afforded relief.
+LJSpeech-1.1/wavs/LJ012-0228.wav|The evidence from first to last was circumstantial,
+LJSpeech-1.1/wavs/LJ040-0171.wav|where he did as he wanted and he didn't have to live by any rules or come into contact with people, end quote.
+LJSpeech-1.1/wavs/LJ015-0245.wav|The two remaining safes were attacked and nearly entirely despoiled in the same way as the first, and the contents transferred to the courier bags.
+LJSpeech-1.1/wavs/LJ021-0171.wav|Now that these people are coming out of their storm cellars, they forget that there ever was a storm.
+LJSpeech-1.1/wavs/LJ024-0128.wav|The present attempt by those opposed to progress to play upon the fears of danger to personal liberty
+LJSpeech-1.1/wavs/LJ045-0076.wav|The letter fell into Oswald's hands when it was returned to his post office box
+LJSpeech-1.1/wavs/LJ041-0028.wav|Sometime during this period, and under circumstances to be discussed more fully below,
+LJSpeech-1.1/wavs/LJ018-0055.wav|He is described as not a bad-looking man, with a square German type of face,
+LJSpeech-1.1/wavs/LJ028-0065.wav|Eleven years later, in five eighty-six, he destroyed the sacred Hebrew city,
+LJSpeech-1.1/wavs/LJ015-0049.wav|They admitted that they had made away with many of the securities entrusted to their keeping.
+LJSpeech-1.1/wavs/LJ002-0141.wav|and indeed supplied the bulk of the poor debtors committed to prison.
+LJSpeech-1.1/wavs/LJ002-0302.wav|and so were unable either to warm themselves or to cook their food.
+LJSpeech-1.1/wavs/LJ033-0075.wav|The Long and Bulky Package
+LJSpeech-1.1/wavs/LJ036-0012.wav|(four) evidence establishing the ownership of a zipper jacket
+LJSpeech-1.1/wavs/LJ019-0032.wav|The movement thus laudably initiated by the Government soon spread to the provinces.
+LJSpeech-1.1/wavs/LJ035-0058.wav|Baker turned to Truly and said, quote, Do you know this man, does he work here? end quote.
+LJSpeech-1.1/wavs/LJ028-0200.wav|High up, where all might see it, the armless hand wrote the King's fate.
+LJSpeech-1.1/wavs/LJ041-0150.wav|I'm a Marxist. I became interested about the age of fifteen. From an ideological viewpoint.
+LJSpeech-1.1/wavs/LJ008-0050.wav|A broadsheet dated April twenty-fourth, seventeen eighty-seven, describing an execution on the newly-invented scaffold before the debtors' door,
+LJSpeech-1.1/wavs/LJ045-0136.wav|as Oswald went on to say. In Oswald's imagination, quote,
+LJSpeech-1.1/wavs/LJ030-0060.wav|At the rear on each side of the automobile were small running boards, each designed to hold a Secret Service agent,
+LJSpeech-1.1/wavs/LJ042-0054.wav|Oswald told his brother, quote, on what terms I want this arrangement, end quote. He advised Robert that: one.
+LJSpeech-1.1/wavs/LJ046-0196.wav|were filed on a geographic basis, and could conveniently be reviewed by a Secret Service agent preparing for a Presidential trip
+LJSpeech-1.1/wavs/LJ021-0168.wav|I do not want to think that it is the destiny of any American to remain permanently on relief rolls.
+LJSpeech-1.1/wavs/LJ015-0148.wav|The choicest wines, the finest fruits,
+LJSpeech-1.1/wavs/LJ010-0095.wav|Meanwhile the main body remained at their headquarters, a ruined stable in Cato Street,
+LJSpeech-1.1/wavs/LJ040-0178.wav|A psychological human figure-drawing test corroborated the interviewer's findings that Lee was insecure and had limited social contacts.
+LJSpeech-1.1/wavs/LJ019-0235.wav|With the last re-edification of Newgate, a work executed some seven centuries after the first stone of the old jail was laid,
+LJSpeech-1.1/wavs/LJ047-0245.wav|His secretary testified that she prepared such a report for him that afternoon and Chief of Police Jesse E. Curry
+LJSpeech-1.1/wavs/LJ010-0006.wav|Certain crimes, those against the person especially, diminished gradually. They became less easy or remunerative.
+LJSpeech-1.1/wavs/LJ012-0125.wav|Not the slightest clue to these large affairs was ever obtained, but it is probable that they were "put up" jobs, or managed with the complicity of servants.
+LJSpeech-1.1/wavs/LJ028-0353.wav|Darius, as the story goes, would often say that "he had rather Zopyrus were unmaimed, than be master of twenty more Babylons."
+LJSpeech-1.1/wavs/LJ004-0247.wav|thus disposing of the objection so long raised against the industrial employment of prisoners in Newgate.
+LJSpeech-1.1/wavs/LJ021-0206.wav|whatever they need to have done but cannot do at all or cannot do so well for themselves in their separate and individual capacities."
+LJSpeech-1.1/wavs/LJ012-0107.wav|About this time also suspicion fell upon Huey, one of the clerks, who was arrested soon afterwards, and made a clean breast of the whole affair.
+LJSpeech-1.1/wavs/LJ050-0266.wav|The exchange of letters dated August thirty-one, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ005-0038.wav|to suggest beneficial regulations, and circulate tracts demonstrating the advantages of classification,
+LJSpeech-1.1/wavs/LJ031-0172.wav|While the team of doctors at Parkland Hospital tried desperately to save the life of President Kennedy,
+LJSpeech-1.1/wavs/LJ037-0205.wav|The invoice was prepared on March thirteen, nineteen sixty-three; the revolver was actually shipped on March twenty by Railway Express.
+LJSpeech-1.1/wavs/LJ002-0088.wav|In each cell there was a barrack bedstead on the floor without bedding.
+LJSpeech-1.1/wavs/LJ047-0114.wav|and that they had been married in Fort Worth and lived there until coming to New Orleans. He had told the New Orleans arresting officers that he had been born in Cuba.
+LJSpeech-1.1/wavs/LJ035-0142.wav|then her estimate of the time when she descended from the fourth floor is incorrect,
+LJSpeech-1.1/wavs/LJ013-0192.wav|but still he found friends, and a liberal subscription was raised among the foreign servants in London to provide funds for his defense.
+LJSpeech-1.1/wavs/LJ017-0182.wav|that it was a very dangerous medicine, and she profited by what she had heard.
+LJSpeech-1.1/wavs/LJ025-0048.wav|Respiration -- that is, the absorption of oxygen and the exhalation of carbonic acid
+LJSpeech-1.1/wavs/LJ015-0285.wav|and when these presented themselves, entrusted them as a beginning with the duty of cashing cheques.
+LJSpeech-1.1/wavs/LJ016-0364.wav|all the ghastly paraphernalia, the gallows itself and the process of erecting and removing it, rested with the city architect, and not with the prison officials.
+LJSpeech-1.1/wavs/LJ013-0157.wav|Lord William Russell lived alone in Norfolk Street, Park Lane. He was a widower, and seventy-three years of age.
+LJSpeech-1.1/wavs/LJ039-0001.wav|For more information, or to volunteer, please visit librivox dot org.
+LJSpeech-1.1/wavs/LJ015-0017.wav|for the fraudulent disposal of securities lodged in their hands. This firm was one of the oldest banking establishments in the kingdom,
+LJSpeech-1.1/wavs/LJ031-0225.wav|From this head wound two small irregularly shaped fragments of metal were recovered and turned over to the FBI.
+LJSpeech-1.1/wavs/LJ021-0186.wav|than in the United States?
+LJSpeech-1.1/wavs/LJ016-0074.wav|The success, although very short-lived, which attended him, no doubt inspired other inmates of Newgate to follow his example.
+LJSpeech-1.1/wavs/LJ025-0064.wav|which, from their similarity to animals of simple organization, were called "zoospores."
+LJSpeech-1.1/wavs/LJ049-0047.wav|reveals that Hill first placed his hand on the Presidential car at frame three forty-three, thirty frames
+LJSpeech-1.1/wavs/LJ023-0109.wav|The Court in addition to the proper use of its judicial functions has improperly set itself up as a third house of the Congress
+LJSpeech-1.1/wavs/LJ035-0078.wav|Then Howlett walked down the stairway to the second-floor landing and entered the lunchroom.
+LJSpeech-1.1/wavs/LJ046-0210.wav|to relate principally to overt threats to harm the President or other specific manifestations of hostility.
+LJSpeech-1.1/wavs/LJ032-0230.wav|that the published pictures were the same as the original except for retouching done by these publications, apparently for the purpose of clarifying the lines of the rifle
+LJSpeech-1.1/wavs/LJ026-0122.wav|Catabolism is probably due to enzyme action, but the final result is chiefly carbon dioxide and water,
+LJSpeech-1.1/wavs/LJ003-0205.wav|Those who were thus reckless reacted upon the penitent who knew their days were numbered,
+LJSpeech-1.1/wavs/LJ035-0035.wav|Because the stairway itself is enclosed, neither Baker nor Truly could see anything on the second-floor hallway until they reached the landing at the top of the stairs.
+LJSpeech-1.1/wavs/LJ025-0003.wav|The facts dealing with the physiology of organisms, the activities associated with that which we call life
+LJSpeech-1.1/wavs/LJ006-0273.wav|enough to altogether upset what small show of decorum and discipline was still preserved in the prison.
+LJSpeech-1.1/wavs/LJ037-0120.wav|Guinyard said, quote, I told them that was him right there. I pointed him out right there, end quote.
+LJSpeech-1.1/wavs/LJ019-0136.wav|This was obviated by the frequent sessions of the Central Criminal Court, and the utilization of the two subsidiary prisons in Giltspur Street and Southwark.
+LJSpeech-1.1/wavs/LJ003-0161.wav|The luxury of the state side was for a long time open to all who could pay
+LJSpeech-1.1/wavs/LJ020-0057.wav|Cover with a cloth and set nearer the fire than you dared trust the dough, and let them rise for an hour.
+LJSpeech-1.1/wavs/LJ038-0256.wav|In the room beyond the one in which General Walker was sitting on the night of the shooting the Dallas police recovered a badly mutilated bullet
+LJSpeech-1.1/wavs/LJ049-0179.wav|The Commission believes that both the FBI and the Secret Service have too narrowly construed their respective responsibilities.
+LJSpeech-1.1/wavs/LJ018-0116.wav|and that the forged cheques which had been presented, but refused, amounted to double the sum.
+LJSpeech-1.1/wavs/LJ011-0131.wav|Upon the third reading of this bill Sir James Macintosh moved as an amendment that capital punishment should be abolished for all crimes of forgery,
+LJSpeech-1.1/wavs/LJ045-0205.wav|After he returned from his trip to Mexico where his application to go to Cuba had been sharply rejected,
+LJSpeech-1.1/wavs/LJ035-0185.wav|After inspecting this floor, Sawyer returned to the street about three minutes after he entered the building.
+LJSpeech-1.1/wavs/LJ037-0097.wav|Smith subsequently told a Commission staff member
+LJSpeech-1.1/wavs/LJ050-0265.wav|with the Office of Science and Technology and the other Federal agencies that have been so helpful to the Secret Service be placed on a permanent and formal basis.
+LJSpeech-1.1/wavs/LJ008-0317.wav|The fatal news was not always received in the same way.
+LJSpeech-1.1/wavs/LJ005-0267.wav|The use of tobacco, hitherto pretty generally indulged in both by men and women,
+LJSpeech-1.1/wavs/LJ016-0327.wav|declared that they were not prepared to agree to the resolution respecting private executions.
+LJSpeech-1.1/wavs/LJ028-0381.wav|Wild beasts of the desert shall lie there;
+LJSpeech-1.1/wavs/LJ036-0171.wav|he would have arrived there about twelve:fifty-nine to one p.m.
+LJSpeech-1.1/wavs/LJ004-0213.wav|Compared with those highly meritorious institutions Newgate still showed but badly.
+LJSpeech-1.1/wavs/LJ005-0129.wav|"a measure which must greatly tend to discourage attempts to escape."
+LJSpeech-1.1/wavs/LJ003-0055.wav|or class (one), with whom, under the imperious demand for accommodation, were also associated the misdemeanants, or class (two).
+LJSpeech-1.1/wavs/LJ018-0148.wav|A watch was set on Burnett, who was followed to the shop of one Buncher, a butcher in Strutton Ground.
+LJSpeech-1.1/wavs/LJ037-0161.wav|and impressed upon the lead of the bullets inconsistent individual characteristics which made identification impossible.
+LJSpeech-1.1/wavs/LJ028-0034.wav|Old King Sargon I., who may have lived as early as three thousand eight hundred B.C.,
+LJSpeech-1.1/wavs/LJ038-0150.wav|Oswald told him that he ate lunch in the first-floor lunchroom and then went to the second floor for a Coke which he brought downstairs.
+LJSpeech-1.1/wavs/LJ050-0142.wav|directed its field representatives to send a form request for intelligence information to all local,
+LJSpeech-1.1/wavs/LJ031-0065.wav|As a result of the infusion of liquids through the cutdowns, the cardiac massage, and the airway,
+LJSpeech-1.1/wavs/LJ027-0041.wav|both adaptive swimming organs, yet the resemblance in these cases does not go deeper than the surface -- it is one of function only.
+LJSpeech-1.1/wavs/LJ011-0084.wav|After three years' confinement in the latter prison he passed himself off as his brother, Colonel Montgomery,
+LJSpeech-1.1/wavs/LJ035-0065.wav|Truly also noted at this time that Oswald's hands were empty.
+LJSpeech-1.1/wavs/LJ042-0160.wav|the Philippines, and the scores of odd islands in the Pacific.
+LJSpeech-1.1/wavs/LJ022-0142.wav|The National Industrial Recovery Act expires on the sixteenth of June.
+LJSpeech-1.1/wavs/LJ041-0123.wav|In that connection, it does not appear that Oswald ever expressed any dissatisfaction of any kind with either the President or Governor Connally.
+LJSpeech-1.1/wavs/LJ042-0030.wav|it would have taken considerable discipline to save whatever amount was required to finance his defection out of the salary of a low ranking enlisted man.
+LJSpeech-1.1/wavs/LJ032-0037.wav|The canceled money order was obtained from the Post Office Department.
+LJSpeech-1.1/wavs/LJ030-0156.wav|Based on these films, the speed of the President's automobile is computed at an average speed of eleven point two miles per hour.
+LJSpeech-1.1/wavs/LJ012-0074.wav|Whether Ikey was "assigned" to his own family is not recorded, but no doubt he succeeded to his own property when the term of servitude had expired.
+LJSpeech-1.1/wavs/LJ008-0023.wav|two sailors, Charles Palm and Sam Tilling, were hanged at the same place for the murder of their captain, James Keith
+LJSpeech-1.1/wavs/LJ003-0280.wav|limits of space, the interference of the ill-disposed, who would neither work nor let others do so,
+LJSpeech-1.1/wavs/LJ033-0035.wav|and Marina Oswald testified that Oswald did not say anything about curtain rods on the day before the assassination.
+LJSpeech-1.1/wavs/LJ025-0155.wav|A bean may be supplied with water in which salts of ammonia and certain other mineral salts are dissolved in due proportion,
+LJSpeech-1.1/wavs/LJ015-0274.wav|His method was comprehensive and deeply laid.
+LJSpeech-1.1/wavs/LJ011-0090.wav|But next morning he was found dead in his cell.
+LJSpeech-1.1/wavs/LJ031-0226.wav|The autopsy also disclosed a wound near the base of the back of President Kennedy's neck slightly to the right of his spine.
+LJSpeech-1.1/wavs/LJ032-0161.wav|This shirt was also composed of dark blue, gray- black and orange-yellow cotton fibers.
+LJSpeech-1.1/wavs/LJ023-0075.wav|that it was an extraordinary power to exercise and through Mr. Justice Washington laid down this limitation upon it:
+LJSpeech-1.1/wavs/LJ038-0263.wav|as to whether or not the bullet recovered from Walker's house had been fired from the rifle found on the sixth floor of the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ049-0076.wav|The Commission's review of the provisions for Presidential protection at the time of President Kennedy's trip to Dallas demonstrates the need for substantial improvements.
+LJSpeech-1.1/wavs/LJ005-0296.wav|The Lords' Committee on Jails were of the same opinion, and considered the prisons under corporate or peculiar jurisdiction in a very unsatisfactory condition.
+LJSpeech-1.1/wavs/LJ015-0117.wav|Sergeant Ballantine, who prosecuted,
+LJSpeech-1.1/wavs/LJ047-0201.wav|Robert I Bouck, special agent in charge of the Protective Research Section,
+LJSpeech-1.1/wavs/LJ046-0183.wav|and who are in penal or hospital custody are listed only in the general files of PRS,
+LJSpeech-1.1/wavs/LJ038-0240.wav|The third photograph identified by Marina Oswald depicts the entrance to General Walker's driveway from a back alley.
+LJSpeech-1.1/wavs/LJ008-0133.wav|He was dressed in a mixed-colored loose coat with a black collar, swandown waistcoat, blue pantaloons, and white silk stockings.
+LJSpeech-1.1/wavs/LJ008-0057.wav|parallel to the prison wall, and about four feet from it, but strongly affixed thereto with iron scroll clamps.
+LJSpeech-1.1/wavs/LJ019-0366.wav|It was necessary, for instance, to give due notice when the government grant was to be withdrawn,
+LJSpeech-1.1/wavs/LJ005-0223.wav|There was a diminution in the numbers in custody, due to the adoption of the practice of not committing at once to Newgate every offender for trial at the Old Bailey
+LJSpeech-1.1/wavs/LJ006-0246.wav|On another occasion a young man, who was being violently teased, seized a knife and stabbed his tormentor in the back.
+LJSpeech-1.1/wavs/LJ048-0024.wav|He had come back to Dallas. Hosty had established that he had a job, he was working,
+LJSpeech-1.1/wavs/LJ008-0139.wav|without waiting for any signal, the platform dropped, and he was launched into eternity.
+LJSpeech-1.1/wavs/LJ011-0013.wav|replaced the stock in the names of the original holders, who might otherwise have been completely ruined.
+LJSpeech-1.1/wavs/LJ011-0155.wav|He had eloped with his first wife from school.
+LJSpeech-1.1/wavs/LJ001-0142.wav|modern printers generally overdo the "whites" in the spacing, a defect probably forced on them by the characterless quality of the letters.
+LJSpeech-1.1/wavs/LJ026-0102.wav|but root pressure due to osmosis, capillary action and evaporation from the leaves are factors.
+LJSpeech-1.1/wavs/LJ008-0212.wav|Cries of "Comfortable room!" "Excellent situation!" "Beautiful prospect!" "Splendid view!" resounded on every side.
+LJSpeech-1.1/wavs/LJ033-0036.wav|No curtain rods were known to have been discovered in the Depository Building after the assassination.
+LJSpeech-1.1/wavs/LJ045-0166.wav|He repeated this not once but several times, but I refused. And he said that once again I was preferring my friends to him, and that I didn't need him.
+LJSpeech-1.1/wavs/LJ026-0111.wav|In the cells the foods undergo metabolic changes.
+LJSpeech-1.1/wavs/LJ001-0140.wav|the desirable thing being "the breaking of the line" as in bonding masonry or brickwork
+LJSpeech-1.1/wavs/LJ019-0290.wav|There were dormitories without light, control, or regulation at night,
+LJSpeech-1.1/wavs/LJ007-0162.wav|uncontrolled conversation with visitors of the very worst description -- prostitutes, thieves, receivers of stolen goods
+LJSpeech-1.1/wavs/LJ040-0085.wav|John Pic, however, did not think her position was worse than that of many other people.
+LJSpeech-1.1/wavs/LJ029-0195.wav|when Governor Connally confirmed on November eight that the President would come to Texas on November twenty-one and twenty-two,
+LJSpeech-1.1/wavs/LJ006-0226.wav|he was "toed," that is to say, a string was fastened to his big toe while he was asleep, and he was dragged from off his mat,
+LJSpeech-1.1/wavs/LJ035-0118.wav|They rushed to the west windows after the shots were fired and remained there
+LJSpeech-1.1/wavs/LJ029-0118.wav|It afforded us wide streets most of the way, because of the buses that were in the motorcade.
+LJSpeech-1.1/wavs/LJ021-0120.wav|I would not minimize the inevitable losses to employers and employees and to the general public through such conflicts.
+LJSpeech-1.1/wavs/LJ019-0384.wav|The Home Secretary in that administration,
+LJSpeech-1.1/wavs/LJ014-0154.wav|mocking at the chaplain, and turning a deaf ear to the counsels of a benevolent lady who came to visit.
+LJSpeech-1.1/wavs/LJ036-0118.wav|When he first testified before the Commission,
+LJSpeech-1.1/wavs/LJ010-0150.wav|and eventually lodged in Bethlehem Hospital, where she died after forty years' detention, at the advanced age of one hundred.
+LJSpeech-1.1/wavs/LJ036-0018.wav|where he boarded a bus which was heading back in the direction of the Depository Building, on its way to the Oak Cliff section of Dallas.
+LJSpeech-1.1/wavs/LJ037-0027.wav|Benavides stopped and waited in the truck until the gunman ran to the corner.
+LJSpeech-1.1/wavs/LJ045-0187.wav|There is, of course, no way to determine the degree to which he was committed to his plan at that time.
+LJSpeech-1.1/wavs/LJ040-0088.wav|which he did for four months in the fall of nineteen forty-eight.
+LJSpeech-1.1/wavs/LJ030-0039.wav|Men the motorcade slows or stops, agents take positions between the President and the crowd.
+LJSpeech-1.1/wavs/LJ012-0214.wav|Carlo Ferrari, who made a living by exhibiting white mice about the streets,
+LJSpeech-1.1/wavs/LJ022-0148.wav|to enforce minimum wages, to prevent excessive hours,
+LJSpeech-1.1/wavs/LJ010-0203.wav|were bound to provide themselves with sword, rifle, dagger, and a pair of pistols;
+LJSpeech-1.1/wavs/LJ032-0152.wav|Oswald's palmprint on the underside of the barrel demonstrates that he handled the rifle when it was disassembled.
+LJSpeech-1.1/wavs/LJ039-0080.wav|of the Marine Corps School at Quantico, Virginia.
+LJSpeech-1.1/wavs/LJ033-0176.wav|taken from the Texas School Book Depository shipping room on November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ038-0124.wav|Oswald became angry and said, quote,
+LJSpeech-1.1/wavs/LJ008-0318.wav|The men who were doomed often fell down in convulsions upon the floor.
+LJSpeech-1.1/wavs/LJ036-0004.wav|After leaving the Depository Building at approximately twelve:thirty-three p.m., Lee Harvey Oswald proceeded to his roominghouse by bus and taxi.
+LJSpeech-1.1/wavs/LJ042-0244.wav|Oswald subsequently did subscribe to several Soviet journals.
+LJSpeech-1.1/wavs/LJ044-0106.wav|and Benjamin J. Davis honorary membership cards in his nonexistent New Orleans chapter of the Fair Play for Cuba Committee,
+LJSpeech-1.1/wavs/LJ010-0163.wav|secure as it seemed in the affections of her loyal people, freely appeared in public.
+LJSpeech-1.1/wavs/LJ013-0264.wav|In his effects were found the clothes he had on at the time of his escape from the stables, and under the jacket he was wearing
+LJSpeech-1.1/wavs/LJ010-0045.wav|became the victim of the most cowardly and unmanly outrages, and the attempted murder of the sovereign by Oxford in eighteen forty
+LJSpeech-1.1/wavs/LJ040-0036.wav|and, apparently seriously, as a political prophet -- a man who said that after twenty years he would be prime minister.
+LJSpeech-1.1/wavs/LJ048-0214.wav|and also looking ahead to the known hazards like overpasses, under-passes, railroads, et cetera, end quote.
+LJSpeech-1.1/wavs/LJ041-0045.wav|he was nevertheless unable to convince the proper authorities that he was really seventeen years old.
+LJSpeech-1.1/wavs/LJ025-0085.wav|the definition so changed will cover all animals, except certain parasites,
+LJSpeech-1.1/wavs/LJ028-0508.wav|others have been taken to the Berlin Museum.
+LJSpeech-1.1/wavs/LJ047-0135.wav|According to the information received by the Bureau
+LJSpeech-1.1/wavs/LJ042-0189.wav|but one with union communes,
+LJSpeech-1.1/wavs/LJ039-0204.wav|Frazier later fired four groups of three shots at a distance of one hundred yards in five point nine, six point two,
+LJSpeech-1.1/wavs/LJ042-0113.wav|George De Mohrenschildt thought that Oswald must have become disgusted with life in the Soviet Union as the novelty
+LJSpeech-1.1/wavs/LJ040-0167.wav|which grows as one speaks to him, end quote.
+LJSpeech-1.1/wavs/LJ003-0108.wav|Spirits were freely introduced, and although he at first abstained,
+LJSpeech-1.1/wavs/LJ036-0062.wav|followed the same route as the Marsalis bus through downtown Dallas, except that it continued west on Elm,
+LJSpeech-1.1/wavs/LJ024-0094.wav|It would take months and years thereafter to get a two-thirds majority in favor of that amendment in both Houses of the Congress.
+LJSpeech-1.1/wavs/LJ008-0097.wav|No sooner was the "job" finished than half-a-dozen competitors appeared, each offering the identical rope for sale at a shilling an inch.
+LJSpeech-1.1/wavs/LJ045-0221.wav|It is unlikely that the motivation was that simple.
+LJSpeech-1.1/wavs/LJ013-0151.wav|Forrester, the detective who had pursued and captured Burgess at Boston, tracked Ker to France, and following him there, eventually captured him
+LJSpeech-1.1/wavs/LJ035-0022.wav|Baker, quote, had it in mind that the shots came from the top of this building here, end quote, so he ran straight to the entrance of the Depository Building.
+LJSpeech-1.1/wavs/LJ007-0167.wav|framing a terrible indictment against those responsible for the condition of Newgate. Their words deserve to be quoted in full.
+LJSpeech-1.1/wavs/LJ029-0201.wav|Meanwhile, on November seventeen
+LJSpeech-1.1/wavs/LJ034-0068.wav|Additional testimony linking Oswald with the point from which the shots were fired
+LJSpeech-1.1/wavs/LJ015-0126.wav|This offered him great facilities for the creation of artificial stock, its sale from a fictitious holder, and transfer to himself.
+LJSpeech-1.1/wavs/LJ012-0103.wav|which if introduced into a keyhole, and worked like a bit and brace, will soon destroy the strongest lock.
+LJSpeech-1.1/wavs/LJ001-0078.wav|to improve the letter in form.
+LJSpeech-1.1/wavs/LJ037-0178.wav|or one used Remington-Peters cartridge case, which may have been in the revolver before the shooting,
+LJSpeech-1.1/wavs/LJ028-0385.wav|It had become, as Dio Cassius said, "Mounds and legends and ruins."
+LJSpeech-1.1/wavs/LJ010-0251.wav|Francis was sentenced to be hanged, decapitated, and quartered,
+LJSpeech-1.1/wavs/LJ049-0164.wav|In the appropriations of the FBI there has recurred annually an item for the, quote, protection of the person of the President of the United States, end quote,
+LJSpeech-1.1/wavs/LJ038-0299.wav|It would have been possible for him to take different routes in approaching and leaving the scene of the shooting.
+LJSpeech-1.1/wavs/LJ013-0014.wav|After this, on the faith of forced bills of lading, the captain, Loose by name,
+LJSpeech-1.1/wavs/LJ020-0020.wav|When the sponge is light sift a quart and a cup of flour into a bowl or tray with two teaspoonfuls of salt.
+LJSpeech-1.1/wavs/LJ046-0026.wav|since a disclosure of such detailed information relating to protective measures might undermine present methods of protecting the President.
+LJSpeech-1.1/wavs/LJ006-0058.wav|but the occupants of each were huddled together indiscriminately. The inspectors found in the same wards in the chapel yard the convicted and the untried,
+LJSpeech-1.1/wavs/LJ006-0135.wav|the victim of this cruel ill-usage having "more fear of the power of the wardsman to injure him, than confidence in the governor's power to protect him."
+LJSpeech-1.1/wavs/LJ046-0163.wav|During the period November nineteen sixty-one to November nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ044-0005.wav|Oswald's political activities after his return to the United States center around his interest in Cuba and in the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ017-0061.wav|that he steadily refused to confess his guilt, lest it should militate against his chances.
+LJSpeech-1.1/wavs/LJ048-0039.wav|that the FBI took an unduly restrictive view of its responsibilities in preventive intelligence work, prior to the assassination.
+LJSpeech-1.1/wavs/LJ043-0162.wav|Marina Oswald's testimony indicates that her husband was not particularly concerned about his continued possession of the most incriminating sort of evidence.
+LJSpeech-1.1/wavs/LJ050-0237.wav|the FBI, on sixteen separate occasions, supplied a total of one hundred thirty-nine agents to assist in protection work during a Presidential visit,
+LJSpeech-1.1/wavs/LJ040-0217.wav|Lee had apparently been experiencing similar problems before going to New York,
+LJSpeech-1.1/wavs/LJ025-0156.wav|with atmospheric air containing its ordinary minute dose of carbonic acid and with nothing else but sunlight and heat.
+LJSpeech-1.1/wavs/LJ026-0050.wav|following in part the comparisons of certain animals and plants by Sedgwick and Wilson and others.
+LJSpeech-1.1/wavs/LJ041-0154.wav|His interest in Russia led some of his associates to call him "comrade" or "Oswaldskovitch."
+LJSpeech-1.1/wavs/LJ046-0085.wav|The degree of security that can be afforded the President of the United States
+LJSpeech-1.1/wavs/LJ001-0049.wav|After his death in the "fourteen eighties," or at least by fourteen ninety, printing in Venice had declined very much;
+LJSpeech-1.1/wavs/LJ032-0083.wav|Experts on questioned documents from the Treasury Department and the FBI testified that the Hidell cards were counterfeit photographic reproductions
+LJSpeech-1.1/wavs/LJ036-0105.wav|He said, quote,
+LJSpeech-1.1/wavs/LJ013-0053.wav|Miss Bailey, the doctor's sister, in whose favor the note was said to have been given,
+LJSpeech-1.1/wavs/LJ034-0219.wav|Standing alongside him were Buell Wesley Frazier and William Shelley, who also identified Lovelady.
+LJSpeech-1.1/wavs/LJ045-0004.wav|It has been suggested that one of the motivating influences operating on Lee Oswald was the atmosphere in the city of Dallas,
+LJSpeech-1.1/wavs/LJ015-0192.wav|From the moment of his reception he gave himself great airs, as a martyr and a man heavily wronged.
+LJSpeech-1.1/wavs/LJ037-0090.wav|she testified, quote, I would say that was him for sure, end quote.
+LJSpeech-1.1/wavs/LJ047-0222.wav|He had made the same statement about a week prior at another special conference which we had held.
+LJSpeech-1.1/wavs/LJ039-0150.wav|which would indicate that Oswald practiced operating the bolt.
+LJSpeech-1.1/wavs/LJ036-0013.wav|found along the path of flight taken by the gunman from the scene of the shooting to the place of arrest.
+LJSpeech-1.1/wavs/LJ045-0239.wav|which has been set forth above, only served to prolong the period during which he was the center of the attention of the entire world.
+LJSpeech-1.1/wavs/LJ014-0241.wav|Walter Watts was an inventor, a creator, who struck an entirely new and original line of crime.
+LJSpeech-1.1/wavs/LJ045-0108.wav|Ruth Paine testified that she heard Marina Oswald tell Oswald about the birthday party.
+LJSpeech-1.1/wavs/LJ004-0215.wav|mainly through the exertions of a new governor, Mr. Brown, who succeeded Mr. Newman at Newgate in eighteen seventeen.
+LJSpeech-1.1/wavs/LJ012-0146.wav|In the course of the trial it came out that almost every one concerned except the Caspars had endeavored to defraud his accomplices.
+LJSpeech-1.1/wavs/LJ016-0262.wav|It was a callous, careless crowd of coarse-minded, semi-brutalized folk, who came to enjoy themselves.
+LJSpeech-1.1/wavs/LJ015-0004.wav|The goods having no existence, Cole of course could not deliver them.
+LJSpeech-1.1/wavs/LJ049-0125.wav|that any suspects who are arrested will be Federal prisoners, subject to Federal protection from vigilante justice and other threats.
+LJSpeech-1.1/wavs/LJ004-0196.wav|There was an infirmary, properly found and duly looked after.
+LJSpeech-1.1/wavs/LJ008-0151.wav|but in the beginning of eighteen oh seven one of them, at that time just sentenced to transportation,
+LJSpeech-1.1/wavs/LJ003-0093.wav|the oldest only twelve or thirteen, exposed to all the contaminating influences of the place.
+LJSpeech-1.1/wavs/LJ012-0153.wav|Moses, it may be added, was a direct descendant of Ikey Solomons.
+LJSpeech-1.1/wavs/LJ046-0222.wav|The referral of the copy to local Secret Service should not delay the immediate referral of the information by the fastest available means of communication
+LJSpeech-1.1/wavs/LJ034-0174.wav|appeared uncomfortable for one, and secondly, he wasn't watching. he didn't look like he was watching for the parade.
+LJSpeech-1.1/wavs/LJ018-0294.wav|all of which were discounted to the value of one hundred two thousand, two hundred seventeen pounds.
+LJSpeech-1.1/wavs/LJ011-0281.wav|Then, as they warned him that he would be kept a prisoner in total darkness in this horrible den until he agreed to their demands,
+LJSpeech-1.1/wavs/LJ031-0171.wav|Removal of the President's Body
+LJSpeech-1.1/wavs/LJ015-0139.wav|an open-handed, unthinking charity which gave freely to the poor and needy the money which belonged to his creditors.
+LJSpeech-1.1/wavs/LJ008-0077.wav|where the apparatus for the punishment she was about to experience
+LJSpeech-1.1/wavs/LJ011-0229.wav|after which he went to New Zealand, and held an important post in that colony.
+LJSpeech-1.1/wavs/LJ038-0260.wav|On November thirty, nineteen sixty-three, the FBI requested the bullet for ballistics examination;
+LJSpeech-1.1/wavs/LJ015-0164.wav|Redpath passed and lifted his hat to his chairman; the latter acknowledged the salute.
+LJSpeech-1.1/wavs/LJ016-0018.wav|On the very morning on which he was to suffer he eluded the vigilance, such as it was, of his officers
+LJSpeech-1.1/wavs/LJ044-0095.wav|In spite of the fact that Oswald had been surprised and was on the defensive throughout the debate, according to Stuckey, quote,
+LJSpeech-1.1/wavs/LJ017-0049.wav|with morbid curiosity to scan his features and watch his demeanor through the shifting, nicely-balanced phases of his protracted trial.
+LJSpeech-1.1/wavs/LJ002-0139.wav|under forty shillings arising within the city.
+LJSpeech-1.1/wavs/LJ005-0173.wav|I shall not hesitate to ask Parliament for powers to compel them to make the necessary alterations, for it is not to be endured that these local jurisdictions should remain
+LJSpeech-1.1/wavs/LJ014-0022.wav|The letter found in the pocket of the deceased was sealed with a wafer marked F,
+LJSpeech-1.1/wavs/LJ021-0197.wav|of referring without rhyme or reason to the Constitution as a means of preventing its accomplishment, thus creating the general impression
+LJSpeech-1.1/wavs/LJ024-0064.wav|President Hoover, three, including a Chief Justice.
+LJSpeech-1.1/wavs/LJ008-0285.wav|on the one hand the gallows, on the other a short imprisonment.
+LJSpeech-1.1/wavs/LJ003-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section five: Newgate down to eighteen eighteen, part two.
+LJSpeech-1.1/wavs/LJ049-0157.wav|and special details protected him in Washington, on trips, and at special functions.
+LJSpeech-1.1/wavs/LJ013-0143.wav|These packages were consigned to Messrs. Shroeder of London; and as it was known that they were to arrive in Ker's ship,
+LJSpeech-1.1/wavs/LJ048-0238.wav|one agent was there from two until five a.m.
+LJSpeech-1.1/wavs/LJ018-0046.wav|Müller protested after sentence of death had been passed upon him that he had been convicted on a false statement of facts.
+LJSpeech-1.1/wavs/LJ017-0136.wav|with exquisite torture, and with a poison that would leave, as he thought, no trace.
+LJSpeech-1.1/wavs/LJ019-0034.wav|Within half-a-dozen years no less than fifty-four new prisons were built on the Pentonville plan, others were in progress,
+LJSpeech-1.1/wavs/LJ017-0160.wav|as indicating a motive for keeping her alive rather than killing her.
+LJSpeech-1.1/wavs/LJ039-0185.wav|the highest level of firing performance which would have been required of the assassin and the C two seven six six rifle
+LJSpeech-1.1/wavs/LJ004-0230.wav|It was found that shackles might be safely dispensed with, even in the case of the most desperate characters.
+LJSpeech-1.1/wavs/LJ035-0156.wav|Mrs. Reid testified that when she saw Oswald, he was wearing a T-shirt and no jacket.
+LJSpeech-1.1/wavs/LJ027-0093.wav|will nearly always be compelled to add new parts not contemplated in the original machine.
+LJSpeech-1.1/wavs/LJ013-0069.wav|A second will, also forged, was propounded at Doctors Commons as that of a Mrs. Hunt of Bristol.
+LJSpeech-1.1/wavs/LJ003-0072.wav|that some other and less mixed prison should be used for the confinement of persons convicted of libels. But this suggestion was ignored.
+LJSpeech-1.1/wavs/LJ030-0158.wav|While the car traveled this distance, the Zapruder camera ran one hundred fifty-two frames.
+LJSpeech-1.1/wavs/LJ019-0037.wav|Liverpool was building a new prison with a thousand cells, the county of Surrey one with seven hundred.
+LJSpeech-1.1/wavs/LJ031-0198.wav|Upon arrival, President Johnson made a brief statement over television and radio.
+LJSpeech-1.1/wavs/LJ004-0098.wav|it is enacted that the justices shall order the walls of every room to be scraped and white-washed once every year."
+LJSpeech-1.1/wavs/LJ040-0050.wav|His attachment to Marxist and Communist doctrine was probably, in some measure, an expression of his hostility to his environment.
+LJSpeech-1.1/wavs/LJ002-0188.wav|being often obliged to pay a high rent for a bed at the coffee-house or in some room which was vacated by its regular occupant.
+LJSpeech-1.1/wavs/LJ008-0263.wav|Forty-eight hours was the limit of time allowed to the unhappy man to make his peace, and during that time he was still kept on a bare allowance of bread and water.
+LJSpeech-1.1/wavs/LJ034-0164.wav|Brennan saw a man in the window who closely resembled Lee Harvey Oswald, and that Brennan believes the man he saw was in fact
+LJSpeech-1.1/wavs/LJ001-0020.wav|the "lower-case" being in fact invented in the early Middle Ages.
+LJSpeech-1.1/wavs/LJ006-0106.wav|Too much was left to the wardsman. It was he who could issue small luxuries;
+LJSpeech-1.1/wavs/LJ016-0445.wav|Miller, the Chelsea murderer, who packed his victim's body in a box, and tried to send it by parcels delivery, tried to kill himself,
+LJSpeech-1.1/wavs/LJ031-0107.wav|Governor Connally had a large sucking wound in the front of the right chest which caused extreme pain and difficulty in breathing.
+LJSpeech-1.1/wavs/LJ023-0020.wav|We then began a program of remedying those abuses and inequalities -- to give balance and stability to our economic system
+LJSpeech-1.1/wavs/LJ049-0217.wav|Moreover, a number of imponderable questions have to be weighed if any change in the intimate association now established
+LJSpeech-1.1/wavs/LJ050-0050.wav|As a result of these studies, the planning document submitted by the Secretary of the Treasury to the Bureau of the Budget on August thirty-one,
+LJSpeech-1.1/wavs/LJ044-0186.wav|he would not give him a visa, end quote, and that, quote,
+LJSpeech-1.1/wavs/LJ032-0226.wav|The Commission sought to determine whether these photographs were touched prior to publication.
+LJSpeech-1.1/wavs/LJ011-0248.wav|Howard, strange to say, making no attempt to detain him; probably because Mullay promised to return a few days later, and to bring more money.
+LJSpeech-1.1/wavs/LJ006-0305.wav|The house at this time was full of men and visitors; waiters came in from the taverns with meals.
+LJSpeech-1.1/wavs/LJ028-0019.wav|The stars come out one by one and shine brighter than elsewhere as if to light you on your way.
+LJSpeech-1.1/wavs/LJ003-0269.wav|and offered him half the booty if he would give full information as to the transactions and correspondence of his late employers.
+LJSpeech-1.1/wavs/LJ006-0110.wav|He had power where his exactions were resisted of making the ward most uncomfortable for the defaulter.
+LJSpeech-1.1/wavs/LJ016-0199.wav|It was customary to make the executioner take the Bible in his hand, and swear solemnly that he would dispatch every criminal condemned to die,
+LJSpeech-1.1/wavs/LJ030-0240.wav|to that effect immediately after the assassination.
+LJSpeech-1.1/wavs/LJ013-0118.wav|The thief walked into the house in Belgrave Square, and openly carried off the plate-chest, deposited it in a light cart at the door, and drove away.
+LJSpeech-1.1/wavs/LJ013-0173.wav|The researches of the police soon laid bare other suspicious facts.
+LJSpeech-1.1/wavs/LJ038-0113.wav|when they lived on Neely Street.
+LJSpeech-1.1/wavs/LJ002-0269.wav|he did nothing beyond visiting the prison occasionally, and left the administration to the deputy marshal.
+LJSpeech-1.1/wavs/LJ006-0278.wav|A female prisoner kept the registers.
+LJSpeech-1.1/wavs/LJ026-0064.wav|but unlike that of the animal, it is not chiefly an income of foods, but only of the raw materials of food.
+LJSpeech-1.1/wavs/LJ008-0066.wav|This mode of execution, it is alleged, gave rise to the old vulgar "chaff," "Take care, or you'll die at the fall of the leaf."
+LJSpeech-1.1/wavs/LJ003-0058.wav|but all mingled freely during the day in the yard.
+LJSpeech-1.1/wavs/LJ042-0047.wav|For personal, material advantages? Happiness is not based on oneself, it does not consist of a small home, of taking and getting,
+LJSpeech-1.1/wavs/LJ039-0236.wav|thereby leaving a time span of four point eight to five point six seconds between the two shots which struck their mark.
+LJSpeech-1.1/wavs/LJ022-0126.wav|that the exceptional instances of wrong-doing in government are probably less numerous than in almost every other line of endeavor.
+LJSpeech-1.1/wavs/LJ024-0027.wav|commonly fix the retirement age at seventy years or less.
+LJSpeech-1.1/wavs/LJ003-0073.wav|Indeed the partial classification attempted seems to have been abandoned within a year or two.
+LJSpeech-1.1/wavs/LJ032-0019.wav|Shortly after the Mannlicher-Carcano rifle was found on the sixth floor of the Texas School Book Depository Building, agents of the FBI
+LJSpeech-1.1/wavs/LJ011-0183.wav|Passing through Carlisle, she was told that Mr. Turner was in the town, but could not show himself.
+LJSpeech-1.1/wavs/LJ032-0078.wav|a Selective Service registration certificate, and a certificate of service in the U.S. Marine Corps, all three cards being in his own name.
+LJSpeech-1.1/wavs/LJ022-0196.wav|It is the recovery of confidence in our democratic processes and institutions.
+LJSpeech-1.1/wavs/LJ031-0011.wav|A moment later Curry added, quote, Looks like the President has been hit. Have Parkland stand by, end quote,
+LJSpeech-1.1/wavs/LJ007-0217.wav|They go on to say
+LJSpeech-1.1/wavs/LJ030-0184.wav|Kellerman grabbed his microphone and radioed ahead to the lead car,
+LJSpeech-1.1/wavs/LJ016-0187.wav|The other accepted, and they got into conversation.
+LJSpeech-1.1/wavs/LJ028-0318.wav|Darius had maltreated him in the way they could see, only because he had given advice that the siege should be raised, since there seemed no hope of taking the city.
+LJSpeech-1.1/wavs/LJ014-0249.wav|The cheques drawn by the directors also passed through his hands; to him too they came back to be verified and put by,
+LJSpeech-1.1/wavs/LJ006-0284.wav|It was a special evil of this part of the prison, that the devotional exercises, originally so profitable, had grown into a kind of edifying spectacle,
+LJSpeech-1.1/wavs/LJ007-0077.wav|One of these were the criminal lunatics, who were at this time and for long previous continuously imprisoned there.
+LJSpeech-1.1/wavs/LJ008-0296.wav|The time of the arrival of this report was generally known at Newgate,
+LJSpeech-1.1/wavs/LJ014-0255.wav|continually altering, erasing, and adding figures to correspond with and cover the abstractions he made of various cheques as they were drawn.
+LJSpeech-1.1/wavs/LJ026-0029.wav|so in the case under discussion.
+LJSpeech-1.1/wavs/LJ009-0128.wav|I have been present at the scene not less than twenty times, and have invariably observed
+LJSpeech-1.1/wavs/LJ017-0100.wav|Next night Cook had a second and a more violent attack.
+LJSpeech-1.1/wavs/LJ038-0303.wav|the Commission has concluded that Lee Harvey Oswald attempted to take the life of Maj. Gen. Edwin A. Walker (Resigned, U.S. Army)
+LJSpeech-1.1/wavs/LJ026-0012.wav|Haeckel proposed once to include all one-celled animals and plants in a third kingdom to be called Protista (meaning the first of all life).
+LJSpeech-1.1/wavs/LJ042-0050.wav|I have been a pro-communist for years and yet I have never met a communist, instead I kept silent and observed,
+LJSpeech-1.1/wavs/LJ009-0072.wav|The sheep-stealer smiles, and, extending his arms upwards, looks with a glad expression to the roof of the chapel.
+LJSpeech-1.1/wavs/LJ049-0195.wav|leaving with the Secret Service only the responsibility for the physical protection of the President, that is, the guarding function alone.
+LJSpeech-1.1/wavs/LJ019-0116.wav|anticipating the best results from a system which made earnings, and indeed release, dependent upon the amount of work done.
+LJSpeech-1.1/wavs/LJ048-0050.wav|This conclusion may be tinged with hindsight, but
+LJSpeech-1.1/wavs/LJ027-0008.wav|The great variety of life as exhibited in the countless species of plants and animals has been referred to, and yet, great as this variety is,
+LJSpeech-1.1/wavs/LJ046-0195.wav|The cases subject to periodic review and the one hundred or so cases in the higher risk category
+LJSpeech-1.1/wavs/LJ011-0112.wav|Mr. Sparks Moline, came to attend him to the scaffold.
+LJSpeech-1.1/wavs/LJ032-0176.wav|Moreover, the bus transfer which he obtained as he left the bus was still in the pocket when he was arrested.
+LJSpeech-1.1/wavs/LJ004-0035.wav|The borough jails, those in fact under corporate management, were not included in the new measures;
+LJSpeech-1.1/wavs/LJ010-0113.wav|he had neither a bed to lie upon nor a coat to his back.
+LJSpeech-1.1/wavs/LJ048-0206.wav|As the motorcade approached Elm Street
+LJSpeech-1.1/wavs/LJ025-0030.wav|As the animal body required to be independent of heat and of the atmosphere,
+LJSpeech-1.1/wavs/LJ044-0193.wav|and he didn't have any great desire to do so any more because he had run into, as he himself said -- into bureaucracy and red tape, end quote.
+LJSpeech-1.1/wavs/LJ050-0092.wav|Chief Rowley believed that they would,
+LJSpeech-1.1/wavs/LJ011-0197.wav|and presently ascertained that Wakefield had gone to the Continent with his involuntary bride.
+LJSpeech-1.1/wavs/LJ002-0028.wav|In order to realize the evils entailed by incarceration in Newgate in these days, it is necessary to give some account of its interior
+LJSpeech-1.1/wavs/LJ021-0130.wav|who denies freedom of organization to his employees, or fails to make every reasonable effort at a peaceful solution of their differences,
+LJSpeech-1.1/wavs/LJ043-0005.wav|Apart from his relatives, Oswald had no friends or close associates in Texas when he returned there in June of nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ009-0297.wav|There is still extant a curious petition presented to the Treasury by Ralph Griffith, Esq., high sheriff of Flintshire,
+LJSpeech-1.1/wavs/LJ046-0088.wav|to operate in a sort of vacuum, isolated from the general public and behind impregnable barriers.
+LJSpeech-1.1/wavs/LJ013-0073.wav|as its signature was dated eighteen twenty-nine, whereas Mrs. Hunt actually died in eighteen oh six.
+LJSpeech-1.1/wavs/LJ026-0059.wav|For this purpose the food is taken not into the body proper, but into a kind of tubular chemical laboratory; called the alimentary canal,
+LJSpeech-1.1/wavs/LJ036-0084.wav|Craig also claimed that when Fritz pointed out to Oswald that Craig had identified him,
+LJSpeech-1.1/wavs/LJ008-0308.wav|The Recorder's report generally reached Newgate late at night.
+LJSpeech-1.1/wavs/LJ036-0082.wav|Craig testified that later in the afternoon he saw Oswald in the police interrogation room
+LJSpeech-1.1/wavs/LJ031-0190.wav|Mrs. Kennedy and Mrs. Johnson stood at the side of the new President as he took the oath of office.
+LJSpeech-1.1/wavs/LJ041-0046.wav|There is evidence that Oswald was greatly influenced in his decision to join the Marines by the fact that his brother Robert had done so
+LJSpeech-1.1/wavs/LJ036-0019.wav|When Oswald was apprehended, a bus transfer marked for the Lakewood-Marsalis route was found in his shirt pocket.
+LJSpeech-1.1/wavs/LJ039-0032.wav|was on November twenty to twenty-one, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ029-0070.wav|is discussed in chapter eight.
+LJSpeech-1.1/wavs/LJ045-0103.wav|Mrs. Paine was planning a birthday party for one of her children on that weekend and her husband, Michael, was to be at the house.
+LJSpeech-1.1/wavs/LJ027-0104.wav|which are never destined to cut the gums; and throughout its life this animal retains, in a similarly rudimentary condition,
+LJSpeech-1.1/wavs/LJ038-0114.wav|Her testimony was fully supported by a photography expert who testified that in his opinion the pictures were not composites.
+LJSpeech-1.1/wavs/LJ036-0015.wav|According to the reconstruction of time and events which the Commission found most credible,
+LJSpeech-1.1/wavs/LJ014-0160.wav|elucidated all dark and uncertain points in connection with the crime.
+LJSpeech-1.1/wavs/LJ002-0067.wav|but really kept for the few who had funds sufficient to gain them admission to these more comfortable quarters.
+LJSpeech-1.1/wavs/LJ048-0149.wav|is essential. This would help to eliminate varying interpretations of Secret Service instructions by different local law enforcement representatives. For example,
+LJSpeech-1.1/wavs/LJ024-0093.wav|It would take months or years to get substantial agreement upon the type and language of the amendment.
+LJSpeech-1.1/wavs/LJ011-0138.wav|It passed the Commons, but opposition was again encountered in the Lords.
+LJSpeech-1.1/wavs/LJ013-0210.wav|Finally, on coming downstairs after bed-time, Lord William had found Courvoisier in the dining-room.
+LJSpeech-1.1/wavs/LJ009-0007.wav|Once ordered to the cells, friends of all classes suddenly rise up; his fellow-prisoners, the turnkeys, the chaplain,
+LJSpeech-1.1/wavs/LJ016-0434.wav|When they brought in the two watchers to relieve guard one night, Sattler said,
+LJSpeech-1.1/wavs/LJ001-0017.wav|that the forms of printed letters should follow more or less closely those of the written character, and they followed them very closely.
+LJSpeech-1.1/wavs/LJ037-0114.wav|He and Scoggins attempted to chase down the gunman in Scoggin's taxicab, but he had disappeared.
+LJSpeech-1.1/wavs/LJ045-0034.wav|Marina Oswald attributed that to their living apart and to the imminent birth of their second child.
+LJSpeech-1.1/wavs/LJ015-0122.wav|but who had all the appearance of a careless, thoughtless, happy-go-lucky fellow.
+LJSpeech-1.1/wavs/LJ032-0228.wav|Several of the publications furnished the Commission with the prints they had used, or described by correspondence the retouching they had done.
+LJSpeech-1.1/wavs/LJ050-0135.wav|According to Secretary Dillon,
+LJSpeech-1.1/wavs/LJ041-0134.wav|Governor Connally's connection with the discharge, although indirect, caused the Commission to consider whether he might have been Oswald's real target.
+LJSpeech-1.1/wavs/LJ027-0056.wav|The relation of skeleton and muscle in arthropods is exactly the reverse.
+LJSpeech-1.1/wavs/LJ023-0088.wav|and to approve or disapprove the public policy written into these laws.
+LJSpeech-1.1/wavs/LJ035-0048.wav|I can't say whether he had gone on through that door [the lunchroom door] or not.
+LJSpeech-1.1/wavs/LJ015-0079.wav|The temptation was all the greater because the chances of successful fraud lay ready to hand.
+LJSpeech-1.1/wavs/LJ005-0227.wav|It was enacted that the court of aldermen should make rules for the government of the prison, and that these should be posted publicly within the walls.
+LJSpeech-1.1/wavs/LJ048-0277.wav|He felt that each agent recognized the seriousness of the infraction and that there was no danger of a repetition.
+LJSpeech-1.1/wavs/LJ025-0051.wav|But in the fourth and fifth decades of this century
+LJSpeech-1.1/wavs/LJ027-0179.wav|Finally, only in the Tertiary, so far as we yet know, do the highest form (anoura) appear.
+LJSpeech-1.1/wavs/LJ034-0006.wav|He had ready access to the sixth floor, from the southeast corner window of which the shots were fired.
+LJSpeech-1.1/wavs/LJ039-0039.wav|there is no question that in this incident it was a question of Mr. Nixon, end quote.
+LJSpeech-1.1/wavs/LJ014-0334.wav|Cole found out where they were, and redeemed them at a heavy outlay, thus obtaining business relations with the firm that held them,
+LJSpeech-1.1/wavs/LJ025-0005.wav|The terms animal physiology, plant physiology and human physiology are in common use and often suggest to the lay reader
+LJSpeech-1.1/wavs/LJ013-0165.wav|The housemaid suggested that they should see if his lordship was all right, and the three went to his bedroom.
+LJSpeech-1.1/wavs/LJ015-0102.wav|He came back to explain that he had mislaid them.
+LJSpeech-1.1/wavs/LJ042-0101.wav|he envied his wife's uncle, a colonel in the MVD, because of the larger apartment in which he lived.
+LJSpeech-1.1/wavs/LJ012-0068.wav|For this she received a sentence of fourteen years' transportation, and was sent to Van Diemen's Land.
+LJSpeech-1.1/wavs/LJ042-0218.wav|to the entire land and complete foundations of his society, end quote.
+LJSpeech-1.1/wavs/LJ021-0141.wav|the benefits that all derive from the continuous, peaceful operation of our essential enterprises.
+LJSpeech-1.1/wavs/LJ015-0051.wav|the specific charge being the unlawful negotiation of Danish bonds and other shares belonging to the Rev. Dr. Griffiths of Rochester
+LJSpeech-1.1/wavs/LJ024-0100.wav|A very large percentage of newspaper publishers, Chambers of Commerce,
+LJSpeech-1.1/wavs/LJ001-0088.wav|were the leaders in this luckless change, though our own Baskerville, who was at work some years before them, went much on the same lines;
+LJSpeech-1.1/wavs/LJ048-0043.wav|nor was there any requirement to report the names of defectors. However, there was much material in the hands of the FBI about Oswald:
+LJSpeech-1.1/wavs/LJ013-0181.wav|and the police, feeling convinced that the crime had been committed by some inmate of the house,
+LJSpeech-1.1/wavs/LJ024-0082.wav|would deny us the necessary means of dealing with the present.
+LJSpeech-1.1/wavs/LJ002-0083.wav|The ground floor cells were nine feet by six;
+LJSpeech-1.1/wavs/LJ047-0216.wav|Agent Hosty testified that he was fully aware of the pending Presidential visit to Dallas.
+LJSpeech-1.1/wavs/LJ032-0168.wav|There is no way, however, to eliminate the possibility of the fibers having come from another identical shirt, end quote,
+LJSpeech-1.1/wavs/LJ039-0104.wav|firing fifty rounds each day for five days.
+LJSpeech-1.1/wavs/LJ038-0274.wav|superintendent of the Illinois Bureau of Criminal Identification and Investigation, conducted an independent examination of this bullet
+LJSpeech-1.1/wavs/LJ011-0181.wav|Wakefield's uncle took the matter more seriously, and declared that unless the marriage came off Mr. Turner must be sold up.
+LJSpeech-1.1/wavs/LJ031-0158.wav|As soon as one of the agents learned of the shooting, he asked the officer in charge of the police detail at the airport
+LJSpeech-1.1/wavs/LJ038-0218.wav|Moreover, the reference in paragraph three to paying, quote, the house rent on the second, end quote,
+LJSpeech-1.1/wavs/LJ014-0277.wav|An inquiry was at once set on foot, and the frauds were traced to Watts.
+LJSpeech-1.1/wavs/LJ046-0008.wav|on Theodore Roosevelt while campaigning in October of nineteen twelve; on President-elect Franklin Delano Roosevelt,
+LJSpeech-1.1/wavs/LJ044-0051.wav|except for the uneventful distribution of literature at the Dumaine Street wharf in June.
+LJSpeech-1.1/wavs/LJ017-0162.wav|Whatever the exact cause which impelled him to crime, it seems certain that he began to give her some poison,
+LJSpeech-1.1/wavs/LJ049-0095.wav|for all offenses within its jurisdiction, as are FBI agents and Federal marshals.
+LJSpeech-1.1/wavs/LJ022-0018.wav|Three years of hard thinking have changed the picture.
+LJSpeech-1.1/wavs/LJ041-0098.wav|All the Marine Corps did was to teach you to kill and after you got out of the Marines you might be good gangsters, end quote.
+LJSpeech-1.1/wavs/LJ015-0309.wav|and the judge, in passing sentence on him of transportation for life, expressed deep regret that "the ingenuity, skill, and talent,
+LJSpeech-1.1/wavs/LJ029-0167.wav|On the morning of the President's arrival,
+LJSpeech-1.1/wavs/LJ017-0191.wav|Last of all Mrs. Wilson poisoned her landlady, Mrs. Soames, under precisely the same conditions as the foregoing.
+LJSpeech-1.1/wavs/LJ006-0094.wav|Sometimes two mats were allotted to three sleepers. Sometimes four slept under the same bedding, and left their mats unoccupied.
+LJSpeech-1.1/wavs/LJ017-0241.wav|the captain's cabin ransacked, his money and clothes divided amongst the mutineers, as well as much of the merchandise on board.
+LJSpeech-1.1/wavs/LJ017-0010.wav|and it is hinted that James himself nearly fell a victim to a nefarious attempt of the Duke of Buckingham.
+LJSpeech-1.1/wavs/LJ045-0123.wav|to some extent after his Fair Play for Cuba Committee activities had become known, he exaggerated their concern for him.
+LJSpeech-1.1/wavs/LJ048-0211.wav|Lawson, also in the lead car, did not scan any buildings since an important part of his job was to look backward at the President's car.
+LJSpeech-1.1/wavs/LJ022-0107.wav|will be spent for actually creating new work and not for building up expensive overhead organizations here in Washington.
+LJSpeech-1.1/wavs/LJ050-0271.wav|The demands on the President in the execution of His responsibilities in today's world are so varied and complex
+LJSpeech-1.1/wavs/LJ024-0045.wav|if the appointment of such justices can be called "packing the Courts,"
+LJSpeech-1.1/wavs/LJ044-0157.wav|he requested the Embassy to rush his wife's entrance visa because of the impending birth of the second child but stated that, quote,
+LJSpeech-1.1/wavs/LJ028-0100.wav|However cruel and religiously intolerant Nebuchadnezzar may have been, he was undoubtedly the greatest builder the world has ever seen.
+LJSpeech-1.1/wavs/LJ010-0127.wav|The reckless demeanor of all the convicts except Davidson was most marked. Thistlewood and Ings sucked oranges on the scaffold;
+LJSpeech-1.1/wavs/LJ049-0105.wav|During the period between election and inauguration, the President-elect and Vice-President-elect would also be covered.
+LJSpeech-1.1/wavs/LJ046-0254.wav|in advance of Presidential trips outside the United States.
+LJSpeech-1.1/wavs/LJ036-0199.wav|At about twelve:forty-four p.m. on November twenty-two,
+LJSpeech-1.1/wavs/LJ018-0336.wav|Webster's devices for disposing of the body of her victim will call to mind those of Theodore Gardelle,
+LJSpeech-1.1/wavs/LJ006-0076.wav|He blamed the construction of Newgate for the neglect of classification, and was yet compelled to confess that he had made no attempt whatever to carry it out.
+LJSpeech-1.1/wavs/LJ049-0106.wav|Restricting the coverage in this way would avoid unnecessary controversy over the inclusion or exclusion of other officials who are in the order of succession
+LJSpeech-1.1/wavs/LJ015-0263.wav|Scarcely had the conviction of these daring and astute thieves been assured, than another gigantic fraud was brought to light.
+LJSpeech-1.1/wavs/LJ044-0180.wav|on his previous residence, his work permit for that country, and several unidentified letters in the Russian language.
+LJSpeech-1.1/wavs/LJ016-0322.wav|the commission recommended that death sentences should be carried out within the jail, under such regulations as might be considered necessary
+LJSpeech-1.1/wavs/LJ035-0150.wav|As she approached her desk, she saw Oswald.
+LJSpeech-1.1/wavs/LJ028-0125.wav|Along its edge I built a great wall of mortar and burned bricks mountain high.
+LJSpeech-1.1/wavs/LJ003-0233.wav|Amongst the heap was often the meat that had made turtle soup, which, when heated and stirred together in a saucepan, was said to be very good eating.
+LJSpeech-1.1/wavs/LJ022-0185.wav|The answer to this demand was the Federal Reserve System.
+LJSpeech-1.1/wavs/LJ038-0213.wav|The last paragraph directed her to the jail
+LJSpeech-1.1/wavs/LJ015-0059.wav|Two cases of extensive embezzlement which were discovered almost simultaneously, those of Robson and Redpath,
+LJSpeech-1.1/wavs/LJ030-0149.wav|looked at his watch and said "twelve:thirty" to the driver, Special Agent Greer.
+LJSpeech-1.1/wavs/LJ015-0137.wav|afterwards he set up as an insurance broker on his own account, but presently failed.
+LJSpeech-1.1/wavs/LJ018-0214.wav|As the case must still be well remembered by the present generation, it will be necessary to give here only the briefest summary.
+LJSpeech-1.1/wavs/LJ029-0165.wav|Harwood to Main, Main to Houston, Houston to Elm, Elm under the Triple Underpass to Stemmons Freeway, and on to the Trade Mart.
+LJSpeech-1.1/wavs/LJ014-0192.wav|then into Palace Yard, where the waterman of the cab-tank, in obedience to Lerigo's shouts, collared the fugitive.
+LJSpeech-1.1/wavs/LJ021-0135.wav|which is one of the high purposes of the Recovery Act.
+LJSpeech-1.1/wavs/LJ042-0236.wav|In the second simulated transcript which ended with the statement, quote, Newspapers thank you, sir. You are a real patriot! End quote.
+LJSpeech-1.1/wavs/LJ015-0297.wav|and that clever schemer sent an elaborate letter of instructions how to ask for the money.
+LJSpeech-1.1/wavs/LJ036-0208.wav|white male, approximately thirty, slender build, height five foot ten inches, weight one hundred sixty-five pounds, end quote.
+LJSpeech-1.1/wavs/LJ048-0174.wav|Similar arrangements for a motorcade of ten miles, including many blocks of tall commercial buildings is not practical.
+LJSpeech-1.1/wavs/LJ007-0216.wav|For nearly twenty-two hours out of the twenty-four the prisoners are locked up, during which time no officer is stationed in the ward with them.
+LJSpeech-1.1/wavs/LJ046-0227.wav|According to Special Agent in Charge Bouck,
+LJSpeech-1.1/wavs/LJ028-0469.wav|The inner wall of Babylon was called Imgur-Bel, and like the outer wall, it was double.
+LJSpeech-1.1/wavs/LJ038-0031.wav|About six or seven people were seated on the theatre's main floor and an equal number in the balcony.
+LJSpeech-1.1/wavs/LJ004-0103.wav|Yet another act passed in seventeen ninety-one, if properly observed, should have insured proper attention to them.
+LJSpeech-1.1/wavs/LJ035-0031.wav|He shouted twice, quote, Turn loose the elevator, end quote,
+LJSpeech-1.1/wavs/LJ005-0203.wav|Thus Dinas Mwddy, in Merionethshire, had, "besides the pinfold and the stocks or crib, a little prison."
+LJSpeech-1.1/wavs/LJ030-0115.wav|During this brief stop, agents in the front positions on the running boards of the Presidential follow-up car came forward and stood beside the President's car,
+LJSpeech-1.1/wavs/LJ028-0205.wav|In the month Tammuz, when Cyrus fought the troops of Akkad (Babylonia) at Opis on the river Salsallat,
+LJSpeech-1.1/wavs/LJ011-0088.wav|The night previous to that fixed for his execution he wrote several letters, one of them being to Edward Gibbon Wakefield, a fellow-prisoner,
+LJSpeech-1.1/wavs/LJ036-0107.wav|because he was bawling out the policeman, telling them it wasn't right to put him in line with these teenagers and all of that
+LJSpeech-1.1/wavs/LJ039-0022.wav|And I told him that he shouldn't do this, and that he had promised me.
+LJSpeech-1.1/wavs/LJ049-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ043-0009.wav|a Russian-speaking petroleum engineer whom Oswald met as a result of his contact with the Texas Employment Commission office in Fort Worth.
+LJSpeech-1.1/wavs/LJ038-0267.wav|Unless the missile or cartridge case can be identified as coming from a particular weapon to the exclusion of all others,
+LJSpeech-1.1/wavs/LJ015-0175.wav|A further search discovered him at breakfast at a small house in the New Road.
+LJSpeech-1.1/wavs/LJ036-0051.wav|Was a hole in it, hole, and he was dirty, and I didn't look at him. I didn't want to know I even seen him.
+LJSpeech-1.1/wavs/LJ011-0228.wav|Mr. W. Wakefield served in a continental army, and rose to the rank of colonel,
+LJSpeech-1.1/wavs/LJ019-0104.wav|That gentleman had come to the conclusion that the ordinary and hackneyed methods of treatment were practically inefficacious,
+LJSpeech-1.1/wavs/LJ035-0052.wav|Baker stopped at the door of the lunchroom and commanded, quote, Come here, end quote.
+LJSpeech-1.1/wavs/LJ049-0044.wav|Even if the car is traveling more slowly, the delay involved in reaching the President may be crucial.
+LJSpeech-1.1/wavs/LJ038-0053.wav|George Jefferson Applin, Jr., confirmed that Oswald fought with four or five officers before he was handcuffed.
+LJSpeech-1.1/wavs/LJ006-0009.wav|backed up by the evidence of several influential witnesses.
+LJSpeech-1.1/wavs/LJ044-0162.wav|I only know that his basic desire was to get to Cuba by any means, and that all the rest of it was window dressing for that purpose. End quote.
+LJSpeech-1.1/wavs/LJ049-0088.wav|as is advocacy of the overthrow of the Government by the assassination of any of its officers.
+LJSpeech-1.1/wavs/LJ048-0056.wav|Had such a review been undertaken by the FBI,
+LJSpeech-1.1/wavs/LJ044-0132.wav|He had not liked his job as a greaser of coffee processing machinery and he held it for only a little over two months.
+LJSpeech-1.1/wavs/LJ019-0197.wav|No doubt wiser counsels prevailed with Lord John Russell,
+LJSpeech-1.1/wavs/LJ010-0210.wav|The various grades were distinguished by cockades and bows of different colors.
+LJSpeech-1.1/wavs/LJ006-0029.wav|The second inspector, the Rev. Whitworth Russell, was the chaplain of Millbank penitentiary,
+LJSpeech-1.1/wavs/LJ002-0049.wav|leading to two wards, one of which was thirty-six feet by fifteen,
+LJSpeech-1.1/wavs/LJ003-0098.wav|the inevitable consequence of such a situation, their morals must have been destroyed;
+LJSpeech-1.1/wavs/LJ023-0028.wav|To complete our program of protection in time, therefore,
+LJSpeech-1.1/wavs/LJ012-0097.wav|and at the time of the robbery there were still six thousand pounds worth in the warehouse.
+LJSpeech-1.1/wavs/LJ050-0269.wav|The essential terms of such memoranda might well be embodied in an Executive order.
+LJSpeech-1.1/wavs/LJ017-0265.wav|Lopez took a violent dislike to the officer of the ward in charge of them, and often expressed a keen desire to do for him.
+LJSpeech-1.1/wavs/LJ050-0012.wav|through an Assistant Secretary whose duties also include the direct supervision of the Bureau of the Mint
+LJSpeech-1.1/wavs/LJ032-0177.wav|Although Oswald returned to his roominghouse after the assassination and when questioned by the police, claimed to have changed his shirt,
+LJSpeech-1.1/wavs/LJ022-0039.wav|I speak, therefore, tonight, to and of the American people as a whole.
+LJSpeech-1.1/wavs/LJ004-0081.wav|decent clothing and bedding, and a diet sufficient to support him."
+LJSpeech-1.1/wavs/LJ014-0025.wav|A witness deposed to meeting Hocker, soon after the cries of murder were heard,
+LJSpeech-1.1/wavs/LJ017-0223.wav|The mutiny was organized with great secrecy, and broke out most unexpectedly in the middle of the night.
+LJSpeech-1.1/wavs/LJ005-0112.wav|in this same prison capital convicts were chained to the floor until execution.
+LJSpeech-1.1/wavs/LJ011-0170.wav|Wakefield, in reply to her inquiries, satisfied her that her mother was well, and that the real reason for summoning her from school
+LJSpeech-1.1/wavs/LJ022-0008.wav|Each of our steps has a definite relationship to every other step.
+LJSpeech-1.1/wavs/LJ033-0014.wav|Lee Harvey Oswald lived in a roominghouse in Dallas while his wife and children lived in Irving, at the home of Ruth Paine,
+LJSpeech-1.1/wavs/LJ017-0105.wav|Cook's death was horrible
+LJSpeech-1.1/wavs/LJ050-0211.wav|Chief Rowley testified that the present workload of each Secret Service agent averages one hundred ten point one cases.
+LJSpeech-1.1/wavs/LJ010-0060.wav|The Cato Street conspiracy would have been simply ridiculous but for the recklessness of the desperadoes who planned it.
+LJSpeech-1.1/wavs/LJ009-0247.wav|Similar cases were not wanting as regards the executions before Newgate. Others were not less horrible, although there was no failure of apparatus.
+LJSpeech-1.1/wavs/LJ050-0097.wav|Even with the advantage of hindsight, this letter does not appear to express or imply Oswald's, quote,
+LJSpeech-1.1/wavs/LJ017-0225.wav|The latter had the watch on deck.
+LJSpeech-1.1/wavs/LJ011-0029.wav|he sought to rebut these charges, which indeed were never clearly made out, and it is pretty certain that his own account of the causes which led him into dishonesty
+LJSpeech-1.1/wavs/LJ013-0216.wav|His account of his acts and movements after the deed
+LJSpeech-1.1/wavs/LJ028-0401.wav|In the eleventh century, on the site of the southern part of Babylon, the city of Hillah was built.
+LJSpeech-1.1/wavs/LJ015-0154.wav|When the crash came there were pensioners and other recipients of his bounty who could not believe
+LJSpeech-1.1/wavs/LJ012-0242.wav|The horror with which Greenacre's crime struck the town was unparalleled since the time when Catherine Hayes slew her husband.
+LJSpeech-1.1/wavs/LJ006-0253.wav|the signal for the commencement of obscene talk, revelry, and violence.
+LJSpeech-1.1/wavs/LJ009-0093.wav|Why does no one stir to help him? Where would be the use? The hardened burglar moves not, nor does he speak;
+LJSpeech-1.1/wavs/LJ033-0004.wav|The Commission has evaluated the evidence tending to show how Lee Harvey Oswald's Mannlicher-Carcano rifle, serial number C two seven six six,
+LJSpeech-1.1/wavs/LJ014-0147.wav|shaking her clenched and manacled hands in the officers' faces.
+LJSpeech-1.1/wavs/LJ042-0022.wav|he wrote to his brother Robert that the Soviet Union was a country which, quote, I have always considered to be my own, end quote,
+LJSpeech-1.1/wavs/LJ043-0035.wav|Shortly after his return from the Soviet Union, Oswald severed all relations with his mother;
+LJSpeech-1.1/wavs/LJ031-0072.wav|Dr. Clark did not see any other hole or wound on the President's head.
+LJSpeech-1.1/wavs/LJ034-0125.wav|It is understandable, however, for Brennan to have believed that the man with the rifle was standing. A photograph of the building taken seconds after the assassination
+LJSpeech-1.1/wavs/LJ018-0287.wav|to which he was introduced by a well-known tailor in Saville Row.
+LJSpeech-1.1/wavs/LJ036-0142.wav|Whaley was somewhat imprecise as to where he unloaded his passenger.
+LJSpeech-1.1/wavs/LJ036-0153.wav|The elapsed time of the reconstructed run from the Greyhound Bus Station to Neely and Beckley was five minutes and thirty seconds by stopwatch.
+LJSpeech-1.1/wavs/LJ005-0055.wav|or a dungeon above or below the gate of a town, to the first attempts at systematic reconstruction carried out under the advice and supervision of Howard.
+LJSpeech-1.1/wavs/LJ022-0026.wav|Americans as a whole are feeling a lot better -- a lot more cheerful than for many, many years.
+LJSpeech-1.1/wavs/LJ036-0145.wav|However, Neches and Beckley do not intersect.
+LJSpeech-1.1/wavs/LJ005-0095.wav|The promulgation of these two Jail Acts strengthened the hands of the Prison Discipline Society enormously.
+LJSpeech-1.1/wavs/LJ039-0028.wav|was shipped from Los Angeles on March twenty, nineteen sixty-three, and he left for New Orleans on April twenty-four, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ023-0019.wav|and to cure the abuses and the inequalities which had thrown that system out of joint.
+LJSpeech-1.1/wavs/LJ040-0224.wav|and not on any particular mental disturbance, in the boy himself.
+LJSpeech-1.1/wavs/LJ023-0040.wav|We are at a crisis in our ability to proceed with that protection.
+LJSpeech-1.1/wavs/LJ032-0169.wav|Having considered the probabilities as explained in Stombaugh's testimony,
+LJSpeech-1.1/wavs/LJ015-0244.wav|At the same station Pierce joined Agar in the guard's van, and there were now three to carry on the robbery.
+LJSpeech-1.1/wavs/LJ004-0198.wav|There was, besides, a mill for grinding corn, somewhat similar to a turn-spit, which prisoners turned by walking in rows.
+LJSpeech-1.1/wavs/LJ038-0123.wav|Captain Fritz produced the selective service card bearing the name "Alek J. Hidell."
+LJSpeech-1.1/wavs/LJ018-0301.wav|were lodged at once by drafts to "Horton," another alias, in the Continental Bank.
+LJSpeech-1.1/wavs/LJ034-0112.wav|The New Orleans police records of his arrest in August of nineteen sixty-three show a weight of one hundred thirty-six pounds.
+LJSpeech-1.1/wavs/LJ006-0019.wav|The first named had long been an active philanthropist, devoting himself more particularly to the reformation of juvenile criminals.
+LJSpeech-1.1/wavs/LJ046-0098.wav|The history of Presidential protection shows growing recognition over the years that the job must be done by able, dedicated,
+LJSpeech-1.1/wavs/LJ017-0163.wav|either arsenic or antimony, or both, in small quantities,
+LJSpeech-1.1/wavs/LJ009-0183.wav|the site of the present Sessions House of the Old Bailey, and the operation was witnessed by students and a number of curious spectators.
+LJSpeech-1.1/wavs/LJ034-0004.wav|Lee Harvey Oswald was hired on October fifteen, nineteen sixty-three, by the Texas School Book Depository as an "order filler."
+LJSpeech-1.1/wavs/LJ005-0199.wav|One cell was a dungeon, and the other an "improper and unhealthy abode for any human being," with a watercourse running through it.
+LJSpeech-1.1/wavs/LJ045-0211.wav|where he had spent every previous weekend since his return from Mexico and his wife was once again calling into question his judgment,
+LJSpeech-1.1/wavs/LJ023-0013.wav|The change of one vote would have thrown all the affairs of this great Nation back into hopeless chaos.
+LJSpeech-1.1/wavs/LJ032-0022.wav|a review of the records of Crescent Firearms revealed that the firm had shipped an Italian carbine, serial number C two seven six six,
+LJSpeech-1.1/wavs/LJ048-0152.wav|At some overpasses all persons were excluded
+LJSpeech-1.1/wavs/LJ046-0200.wav|Secret Service requests to other agencies for intelligence information
+LJSpeech-1.1/wavs/LJ021-0016.wav|is indeed as strong now as it was years ago when Elihu Root said the following very significant words:
+LJSpeech-1.1/wavs/LJ028-0507.wav|Some of these decorations, the most valuable objects found in the ruins of the great city, still remain in their places on the walls;
+LJSpeech-1.1/wavs/LJ008-0183.wav|but there were no serious accidents, beyond those caused by the goring of a maddened, over-driven ox which forced its way through the crowd.
+LJSpeech-1.1/wavs/LJ013-0082.wav|In eighteen forty-four
+LJSpeech-1.1/wavs/LJ049-0197.wav|It is also contended that the agents should be intimately associated with the life of the Presidential family
+LJSpeech-1.1/wavs/LJ019-0170.wav|more officers were appointed, as the time of so many of those already on the staff was monopolized by attendance at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ038-0044.wav|except that they did not hear Oswald say, quote, It's all over now, end quote.
+LJSpeech-1.1/wavs/LJ009-0272.wav|He was in consequence so weak when brought out for execution, that he had to be carried by four men,
+LJSpeech-1.1/wavs/LJ019-0098.wav|and the question was once more taken up in the House of Commons. In eighteen forty-nine Mr. Charles Pearson, M.P.,
+LJSpeech-1.1/wavs/LJ024-0085.wav|and to have it resume its high task of building anew on the Constitution "a system of living law."
+LJSpeech-1.1/wavs/LJ016-0289.wav|As day broke the character of the crowd was betrayed.
+LJSpeech-1.1/wavs/LJ018-0204.wav|Where it had lain was a yawning gulf or trap sufficient to do for the whole body of police engaged in the capture.
+LJSpeech-1.1/wavs/LJ016-0334.wav|Unusual precautions were taken upon this occasion, as some fresh outrage was apprehended.
+LJSpeech-1.1/wavs/LJ003-0256.wav|By this means spirits, otherwise unattainable and strictly prohibited, were smuggled into the jail.
+LJSpeech-1.1/wavs/LJ006-0227.wav|or his bedclothes were drawn away across the room.
+LJSpeech-1.1/wavs/LJ008-0119.wav|He then made some observations to the attendant about the fire, and turning to the doctor, questioned him.
+LJSpeech-1.1/wavs/LJ005-0080.wav|Accordingly due provision was made for the enforcement of hard labor on all prisoners sentenced to it, and for the employment of all others.
+LJSpeech-1.1/wavs/LJ030-0091.wav|Rufus W. Youngblood, special agent in charge of the Vice President's detail,
+LJSpeech-1.1/wavs/LJ044-0171.wav|On September twenty, nineteen sixty-three, Mrs. Paine and her two children arrived in New Orleans from a trip to the East Coast
+LJSpeech-1.1/wavs/LJ050-0111.wav|Once the Secret Service has formulated its new standards for collection of information, it should enter into written agreements with each Federal agency
+LJSpeech-1.1/wavs/LJ033-0164.wav|He testified, quote,
+LJSpeech-1.1/wavs/LJ021-0201.wav|We have avoided on the other hand
+LJSpeech-1.1/wavs/LJ029-0038.wav|Special Agent Winston G. Lawson, a member of the White House detail who acted as the advance agent, and Forrest V. Sorrels,
+LJSpeech-1.1/wavs/LJ013-0163.wav|The housemaid summoned the cook, and both went to call the valet, Courvoisier,
+LJSpeech-1.1/wavs/LJ013-0219.wav|This was no doubt the case, but this much truth his confession may be taken to contain:
+LJSpeech-1.1/wavs/LJ009-0131.wav|boast to their companions after the service that they had wiped their eyes during the thanksgiving, to make the ordinary believe they had been crying.
+LJSpeech-1.1/wavs/LJ022-0074.wav|It is a problem quite as much for private industry as for the government.
+LJSpeech-1.1/wavs/LJ008-0124.wav|The poor soul then joined the doctor in prayer, and never did I witness more contrition at any condemned sermon than he then evinced.
+LJSpeech-1.1/wavs/LJ037-0146.wav|Barbara Jeanette Davis, and Virginia Davis.
+LJSpeech-1.1/wavs/LJ043-0095.wav|He did not seem to be able to make the accommodations necessary when people work under such conditions and as a result
+LJSpeech-1.1/wavs/LJ006-0073.wav|of his many manifest duties, but I shall here confine myself to animadverting on his neglect as regards the appropriation of his prison.
+LJSpeech-1.1/wavs/LJ012-0043.wav|He lived in Rosemary Lane, and sometimes he had as much as twenty thousand pounds worth of goods secreted on the premises.
+LJSpeech-1.1/wavs/LJ021-0152.wav|and to experiment for a reasonable time with measures suitable to civilize our industrial civilization.
+LJSpeech-1.1/wavs/LJ003-0214.wav|in couples, from the time of sentence to that of execution, which was about three or four days generally,
+LJSpeech-1.1/wavs/LJ006-0003.wav|Newgate has remained rather in the background while the whole of the jails as a body were under discussion.
+LJSpeech-1.1/wavs/LJ015-0093.wav|he replied gaily that his regular work at the Crystal Palace office was useful as a sort of discipline, and kept him steady.
+LJSpeech-1.1/wavs/LJ015-0256.wav|Pierce made Kay only a few small payments, then appropriated the rest of the money.
+LJSpeech-1.1/wavs/LJ018-0237.wav|No fortune could stand the inroads he made into his mother's money,
+LJSpeech-1.1/wavs/LJ037-0200.wav|The name written in this space was D. F. Drittal.
+LJSpeech-1.1/wavs/LJ033-0156.wav|Oswald's palmprint on the bottom of the paper bag indicated, of course, that he had handled the bag.
+LJSpeech-1.1/wavs/LJ020-0062.wav|Graham Rolls are made by treating the dough mixed for Graham bread as above and following the foregoing receipt in every section, but allowing more time for rising and baking.
+LJSpeech-1.1/wavs/LJ038-0243.wav|and twelve, nineteen sixty-three, and most probably on either March nine or March ten.
+LJSpeech-1.1/wavs/LJ042-0179.wav|without defense or foundation of government, end quote,
+LJSpeech-1.1/wavs/LJ012-0252.wav|Closer investigation proved that it belonged to the trunk already discovered on the second February.
+LJSpeech-1.1/wavs/LJ016-0433.wav|laughed and jested with the officers about "Jack Ketch," who, through the postponement of the execution, would lose his Christmas dinner.
+LJSpeech-1.1/wavs/LJ038-0295.wav|An official of this church told FBI agents that services are held every Wednesday at the church except during the month of August.
+LJSpeech-1.1/wavs/LJ033-0153.wav|These prints were examined independently by Ronald G. Wittmus of the FBI,
+LJSpeech-1.1/wavs/LJ010-0293.wav|I have already remarked that as violence was more and more eliminated from crimes against the person,
+LJSpeech-1.1/wavs/LJ008-0129.wav|he soon arrived at the room where the sheriff was to give a receipt for his body, according to the usual custom.
+LJSpeech-1.1/wavs/LJ025-0083.wav|it remains one of the most constant of the distinctive characters of animals.
+LJSpeech-1.1/wavs/LJ047-0225.wav|Hosty testified that he did not know until the evening of Thursday, November twenty-one, that there was to be a motorcade, however,
+LJSpeech-1.1/wavs/LJ018-0006.wav|Mr. Briggs left the bank one afternoon as usual, dined with his daughter at Peckham,
+LJSpeech-1.1/wavs/LJ028-0437.wav|Here, it has been suggested, were the famous hanging gardens which some ancient authors included among the Seven Wonders of the World.
+LJSpeech-1.1/wavs/LJ030-0076.wav|It carried eight Secret Service agents -- two in the front seat, two in the rear, and two on each of the right and left running boards.
+LJSpeech-1.1/wavs/LJ015-0069.wav|but the firm he served got him a situation as clerk in the office of the Great Northern Railway,
+LJSpeech-1.1/wavs/LJ016-0217.wav|On one particular occasion, however, he got them. A gentleman whose sins brought him to the gallows at Maidstone
+LJSpeech-1.1/wavs/LJ018-0178.wav|the case was considered not proved, and he was acquitted.
+LJSpeech-1.1/wavs/LJ004-0133.wav|All prisoners passed their time in absolute idleness, or killed it by gambling and loose conversation.
+LJSpeech-1.1/wavs/LJ040-0039.wav|He had a great hostility toward his environment, whatever it happened to be,
+LJSpeech-1.1/wavs/LJ042-0224.wav|For example, in response to his questions about his decision to go to the Soviet Union, his first draft answered, quote,
+LJSpeech-1.1/wavs/LJ028-0432.wav|other parts of them have disappeared entirely.
+LJSpeech-1.1/wavs/LJ042-0015.wav|He said living conditions over there convinced him something was wrong with the system, and that possibly Marxism was the answer.
+LJSpeech-1.1/wavs/LJ049-0072.wav|those in command should be able to direct the response appropriate to the emergency. The Commission finds that the Secret Service agents in the motorcade
+LJSpeech-1.1/wavs/LJ036-0132.wav|Driver, will you call me a cab down here? She had seen him get this cab and she wanted one, too,
+LJSpeech-1.1/wavs/LJ028-0324.wav|his body red with marks of scourging and with blood, had no suspicion but that he spoke the truth, and was really come to be their friend and helper.
+LJSpeech-1.1/wavs/LJ006-0114.wav|They bought their offices from one another, and were thus considered to have a vested interest in them.
+LJSpeech-1.1/wavs/LJ011-0148.wav|had invariably been visited with the death penalty, and which was of a distinctly fraudulent nature.
+LJSpeech-1.1/wavs/LJ042-0159.wav|entering the US Marine corp at seventeen, this streak of independence was strengthened by exotic journeys to Japan,
+LJSpeech-1.1/wavs/LJ007-0229.wav|they state that they are compelled by an imperative sense of duty to advert in terms of decided condemnation to the lamentable condition of the prisons of the city of London,
+LJSpeech-1.1/wavs/LJ029-0095.wav|Even before the Trade Mart had been definitely selected, Lawson and Sorrels began to consider the best motorcade route from Love Field to the Trade Mart.
+LJSpeech-1.1/wavs/LJ007-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section ten: The first report of the inspector of prisons.
+LJSpeech-1.1/wavs/LJ011-0241.wav|The latter had advertised, offering a sum of one thousand pounds to anyone who would introduce him to some mercantile equipment.
+LJSpeech-1.1/wavs/LJ037-0194.wav|Ten dollars in cash was enclosed.
+LJSpeech-1.1/wavs/LJ009-0245.wav|He soon rose to his feet, and hearing the sorrowful exclamations of the populace, coolly said,
+LJSpeech-1.1/wavs/LJ035-0197.wav|Truly gave this information to Captain Fritz who was on the sixth floor at the time.
+LJSpeech-1.1/wavs/LJ014-0242.wav|Employed as a clerk in the Globe Assurance,
+LJSpeech-1.1/wavs/LJ002-0051.wav|A high wall fifteen feet in height divided the females' court-yard from the men's.
+LJSpeech-1.1/wavs/LJ004-0122.wav|fifty-nine had no division whatever to separate males and females; one hundred and thirty-six had only one division for the purpose;
+LJSpeech-1.1/wavs/LJ028-0190.wav|A great empire which had existed for more than three thousand years was brought to an end.
+LJSpeech-1.1/wavs/LJ037-0206.wav|The balance due on the purchase was nineteen dollars, ninety-five cents.
+LJSpeech-1.1/wavs/LJ029-0147.wav|Sorrels and Lawson reviewed the route in cooperation with Assistant Chief Bachelor and other Dallas police officials who took notes on the requirements
+LJSpeech-1.1/wavs/LJ037-0004.wav|At least twelve persons saw the man with the revolver in the vicinity of the Tippit crime scene at or immediately after the shooting.
+LJSpeech-1.1/wavs/LJ018-0005.wav|the public mind was greatly agitated by the affair for several months. The story of the murder must be pretty familiar to most of my readers.
+LJSpeech-1.1/wavs/LJ039-0225.wav|Based on the known facts of the assassination,
+LJSpeech-1.1/wavs/LJ043-0137.wav|The shooting occurred two weeks before Oswald moved to New Orleans and a few days after he had been discharged by the photographic firm.
+LJSpeech-1.1/wavs/LJ018-0096.wav|and thus obtained personal knowledge of all who passed in and out, but without obtaining any direct evidence.
+LJSpeech-1.1/wavs/LJ050-0134.wav|that an interagency committee has been established to develop more effective criteria.
+LJSpeech-1.1/wavs/LJ019-0084.wav|The greatest pains might be taken to secure isolation,
+LJSpeech-1.1/wavs/LJ028-0220.wav|The inscription continues: I am Cyrus, king of the world.
+LJSpeech-1.1/wavs/LJ016-0228.wav|They did not dislike Calcraft, however, at Newgate. He was an illiterate, simple-minded man, who scarcely remembered what executions he had performed.
+LJSpeech-1.1/wavs/LJ003-0150.wav|but they had no share in the prison meat or other charities, and they or their friends found them in food.
+LJSpeech-1.1/wavs/LJ016-0381.wav|Even when in the condemned cell, with a shameful death within measurable distance,
+LJSpeech-1.1/wavs/LJ010-0310.wav|the house was nearly without resources, and the whole burthen of management falling on me, I sought resources where I could;
+LJSpeech-1.1/wavs/LJ022-0133.wav|Feel free to criticize. Tell me of instances where work can be done better, or where improper practices prevail.
+LJSpeech-1.1/wavs/LJ035-0146.wav|saw him walk through the clerical office on the second floor toward the door leading to the front stairway.
+LJSpeech-1.1/wavs/LJ048-0036.wav|that agents of the FBI in Dallas did not consider Oswald's presence in the Texas School Book Depository Building
+LJSpeech-1.1/wavs/LJ015-0270.wav|cheques and bills, of which he made a particular use.
+LJSpeech-1.1/wavs/LJ013-0261.wav|he there gave satisfaction for industry, but he was taciturn, and would hold no converse with his fellows.
+LJSpeech-1.1/wavs/LJ028-0084.wav|Its impression shows the face of a beardless young man, intelligent and refined.
+LJSpeech-1.1/wavs/LJ014-0132.wav|She complained that she had no justice; there was no law for her, she had found no protection either from judges, the prosecutor, or her husband.
+LJSpeech-1.1/wavs/LJ037-0116.wav|Guinyard and Callaway viewed the same lineup of four men from which Mrs. Markham had earlier made her identification of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ004-0202.wav|A system not adopted generally till nearly half a century later had already prevailed at Ilchester.
+LJSpeech-1.1/wavs/LJ010-0185.wav|But Oxford at once assumed the responsibility for his crime, saying, "It was I. I did it. I'll give myself up.
+LJSpeech-1.1/wavs/LJ040-0077.wav|where they stayed, except for vacations, until the spring of nineteen forty-eight.
+LJSpeech-1.1/wavs/LJ031-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ016-0430.wav|When vanity is strongly developed there is the keen anxiety to hear what is being said about them outside.
+LJSpeech-1.1/wavs/LJ048-0089.wav|Against the background of the critical events of November twenty-two, however,
+LJSpeech-1.1/wavs/LJ028-0097.wav|and is continually anxious for the shrines of Babylon and Borsippa;
+LJSpeech-1.1/wavs/LJ004-0155.wav|In the crowd, all of them persons who had "no other avocation or mode of livelihood but thieving," Mr. Buxton counted eleven children
+LJSpeech-1.1/wavs/LJ030-0237.wav|So I turned around and hit the Vice President on the shoulder and hollered, get down,
+LJSpeech-1.1/wavs/LJ011-0099.wav|and losing heavily, to meet the claims upon him he put out a number of forged bills of exchange or acceptances,
+LJSpeech-1.1/wavs/LJ010-0254.wav|At the Italian Opera in the evening the audience, on the Queen's appearance, greeted her with loud cheers, and called for the national anthem.
+LJSpeech-1.1/wavs/LJ048-0148.wav|that a more formal statement of assigned responsibilities, supplemented in each case to reflect the peculiar conditions of each Presidential trip
+LJSpeech-1.1/wavs/LJ039-0196.wav|Three FBI firearms experts tested the rifle in order to determine the speed with which it could be fired.
+LJSpeech-1.1/wavs/LJ032-0250.wav|In September nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ036-0009.wav|and seven eyewitnesses who heard the shots and saw the gunman flee the scene with the revolver in his hand,
+LJSpeech-1.1/wavs/LJ011-0059.wav|There were doubts of his sanity.
+LJSpeech-1.1/wavs/LJ025-0019.wav|Although the roots of plants direct themselves toward moisture and their leaves toward air and light,
+LJSpeech-1.1/wavs/LJ029-0137.wav|which branches off to the right and is used by traffic going to the Stemmons Freeway and the Dallas-Fort Worth Turnpike.
+LJSpeech-1.1/wavs/LJ042-0193.wav|But these preferred tactics now may prove to be too limited in the near future,
+LJSpeech-1.1/wavs/LJ019-0218.wav|The prison was to contain four hundred and four prisoners, and the estimated expenditure was seventy-nine thousand pounds.
+LJSpeech-1.1/wavs/LJ033-0169.wav|it would be the thickness of both the paper and the tape, the color under various lighting conditions of both the paper and the tape,
+LJSpeech-1.1/wavs/LJ002-0065.wav|or female convicts ordered for execution.
+LJSpeech-1.1/wavs/LJ049-0166.wav|Although the FBI is not charged with the physical protection of the President, it does have an assignment, as do other Government agencies,
+LJSpeech-1.1/wavs/LJ041-0194.wav|I think he wanted both if he could have them. If he didn't, he wanted to die with the knowledge that, or with the idea that he was somebody, end quote.
+LJSpeech-1.1/wavs/LJ033-0074.wav|Marina Oswald testified that this was her first knowledge that the rifle was not in its accustomed place.
+LJSpeech-1.1/wavs/LJ016-0246.wav|Over the door, in gilt letters, were the words "Crown Office"; in the window was a pile of official envelopes, ostentatiously displayed,
+LJSpeech-1.1/wavs/LJ041-0126.wav|But while we were in Russia he spoke well of him. Lee said that when he would return to the United States he would vote for him for Governor, end quote.
+LJSpeech-1.1/wavs/LJ029-0085.wav|Kenneth O'Donnell made the final decision to hold the luncheon at the Trade Mart; Behn so notified Lawson on November fourteen.
+LJSpeech-1.1/wavs/LJ003-0316.wav|It should be peremptorily forbidden to the keeper or any officer to make a pecuniary profit out of the supplies of food, fuel, or other necessaries.
+LJSpeech-1.1/wavs/LJ005-0009.wav|Many thus led in procession were in a shocking condition of dirt and misery, frequently nearly naked, and often bearing upon them
+LJSpeech-1.1/wavs/LJ028-0443.wav|because great masses of masonry used to project from its surface.
+LJSpeech-1.1/wavs/LJ024-0124.wav|is what the justices say it is rather than what its framers or you might hope it is.
+LJSpeech-1.1/wavs/LJ019-0291.wav|which warders, dreading assault, were afraid to enter after dark, even to check rioting and disturbance.
+LJSpeech-1.1/wavs/LJ016-0423.wav|When the sheriff offered him counsel for his defense, he declined, saying he wished to make none -- "the witnesses for the prosecution spoke the truth."
+LJSpeech-1.1/wavs/LJ041-0042.wav|He wrote a note in his mother's name to school authorities in New Orleans saying that he was leaving school because he and his mother were moving to San Diego.
+LJSpeech-1.1/wavs/LJ010-0054.wav|Its ringleaders, Thistlewood and the rest, were after capture honored by committal as State prisoners to the Tower,
+LJSpeech-1.1/wavs/LJ021-0203.wav|The course we have followed fits the American practice of government -- a practice of taking action step by step,
+LJSpeech-1.1/wavs/LJ035-0198.wav|Truly estimated that he gave this information to Fritz about fifteen or twenty minutes after the shots,
+LJSpeech-1.1/wavs/LJ026-0152.wav|Oxygenation is the term used to denote the oxygen-supplying part of respiration;
+LJSpeech-1.1/wavs/LJ011-0259.wav|and that, being an irritable temper, he had struck Mullay, after which a violent scuffle took place.
+LJSpeech-1.1/wavs/LJ025-0131.wav|and the lobes of the leaf instantly close together in virtue of an act of contraction of part of their substance,
+LJSpeech-1.1/wavs/LJ011-0134.wav|signed by more than a thousand members of banking firms.
+LJSpeech-1.1/wavs/LJ022-0041.wav|Its first objective is to put men and women now on the relief rolls to work and, incidentally,
+LJSpeech-1.1/wavs/LJ004-0156.wav|children hardly old enough to be released from the nursery.
+LJSpeech-1.1/wavs/LJ047-0162.wav|that Oswald was living alone in Dallas because she did not want him staying at her house, although she was willing to let Oswald visit his wife and children.
+LJSpeech-1.1/wavs/LJ005-0078.wav|but shall also tend more effectually to preserve the health
+LJSpeech-1.1/wavs/LJ005-0262.wav|it was suggested that the rules framed for prison government should be subjected to the Secretary of State for approval,
+LJSpeech-1.1/wavs/LJ007-0205.wav|is not only permitted still to exist in the prison, but is rendered more powerful than before.
+LJSpeech-1.1/wavs/LJ039-0186.wav|would have been to fire three times and hit the target twice within a span of four point eight to five point six seconds.
+LJSpeech-1.1/wavs/LJ028-0425.wav|was begun by the Deutsche Orientgesellschaft, in eighteen eighty-nine, and continued till the summer of nineteen fifteen.
+LJSpeech-1.1/wavs/LJ043-0174.wav|While there are differences between the two events as far as Oswald's actions and planning are concerned, there are also similarities that should be considered.
+LJSpeech-1.1/wavs/LJ050-0008.wav|The Commission believes that the recommended Cabinet-level committee will help to correct many of the major deficiencies of supervision
+LJSpeech-1.1/wavs/LJ018-0389.wav|Of the lesser criminals, forgers, thieves, swindlers, Newgate continued to receive its full share up to the last.
+LJSpeech-1.1/wavs/LJ002-0154.wav|Thus, amongst others, Thomas Blackburn had been committed on October fifteenth for a debt of one shilling five pence.
+LJSpeech-1.1/wavs/LJ003-0052.wav|(three) transports awaiting removal to the colonies, and (four) capital convicts, condemned to death and awaiting execution.
+LJSpeech-1.1/wavs/LJ018-0303.wav|a second conspirator exchanged the gold for notes. But just as all promised well, the frauds were detected through the carelessness of the forgers.
+LJSpeech-1.1/wavs/LJ036-0096.wav|because of the overwhelming evidence that Oswald was far away from the building by that time.
+LJSpeech-1.1/wavs/LJ039-0048.wav|She said, quote, It might have been that he was just trying to test me.
+LJSpeech-1.1/wavs/LJ050-0013.wav|and the Department's Employment Policy Program, and who also represents the Secretary of the Treasury on various committees and groups.
+LJSpeech-1.1/wavs/LJ016-0164.wav|When discovered next morning, quite dead, it was found that the strap actually did not touch his throat;
+LJSpeech-1.1/wavs/LJ050-0011.wav|The Chief of the Service now reports to the Secretary of the Treasury
+LJSpeech-1.1/wavs/LJ006-0121.wav|for a petition of from one shilling, half pence to eight shillings, according to its length,
+LJSpeech-1.1/wavs/LJ032-0017.wav|Ownership And Possession Of Assassination Weapon
+LJSpeech-1.1/wavs/LJ040-0180.wav|The Human Figure Drawings are empty, poor characterizations of persons approximately the same age as the subject.
+LJSpeech-1.1/wavs/LJ050-0042.wav|its inability to process large amounts of data, and its failure to provide specific descriptions of the kind of information it sought.
+LJSpeech-1.1/wavs/LJ016-0373.wav|and for the greater security of prisoners it is roofed in with iron bars which gives it, at least overhead, the aspect of a huge cage.
+LJSpeech-1.1/wavs/LJ003-0086.wav|The crowding was in consequence of the delay in removing transports.
+LJSpeech-1.1/wavs/LJ046-0231.wav|Its efforts appear to have been too largely directed at the "crank" threat.
+LJSpeech-1.1/wavs/LJ001-0185.wav|printed books might once again illustrate to the full
+LJSpeech-1.1/wavs/LJ019-0320.wav|was the boon to which willing industry extending over a long period established a certain claim.
+LJSpeech-1.1/wavs/LJ001-0135.wav|it is impossible that they should be quite equal except in lines of poetry
+LJSpeech-1.1/wavs/LJ045-0139.wav|because of the, quote, very exciting and disturbing effect, end quote, they had upon her husband, who was not even present at that time.
+LJSpeech-1.1/wavs/LJ033-0041.wav|After dinner Ruth Paine and Marina Oswald were busy cleaning house and preparing their children for bed.
+LJSpeech-1.1/wavs/LJ004-0172.wav|Untried persons were often lodged in this nauseous underground den,
+LJSpeech-1.1/wavs/LJ018-0317.wav|and one warder admitted that he was to have one thousand pounds more paid to him, and to be provided with a passage to Australia.
+LJSpeech-1.1/wavs/LJ005-0052.wav|Another point to which the Society devoted infinite pains was the preparation of plans for the guidance of architects in the construction of prisons.
+LJSpeech-1.1/wavs/LJ050-0091.wav|It is questionable whether such criteria would have resulted in the referral of Oswald to the Secret Service.
+LJSpeech-1.1/wavs/LJ050-0144.wav|Each of these efforts appears sound,
+LJSpeech-1.1/wavs/LJ037-0198.wav|Also on the order form was an order, written in ink, for one box of ammunition and one holster, but a line was drawn through these items.
+LJSpeech-1.1/wavs/LJ021-0156.wav|To those who say that our expenditures for public works and other means for recovery are a waste that we cannot afford,
+LJSpeech-1.1/wavs/LJ035-0102.wav|Truly and Baker continued up the stairs after the encounter with Oswald in the lunchroom.
+LJSpeech-1.1/wavs/LJ001-0180.wav|the proportions of the page still give pleasure by the sense of richness that the cuts and letter together convey.
+LJSpeech-1.1/wavs/LJ015-0136.wav|First a lawyer's clerk, he then got an appointment in the Peninsular and Oriental Company's office;
+LJSpeech-1.1/wavs/LJ041-0087.wav|as a means of getting or attempting to get sympathy, end quote. In Thornley's view, Oswald labored under a persecution complex
+LJSpeech-1.1/wavs/LJ013-0171.wav|moreover, the instruments, poker and chisel, by which they had no doubt been effected,
+LJSpeech-1.1/wavs/LJ029-0110.wav|The route impressed the agents as a natural and desirable one.
+LJSpeech-1.1/wavs/LJ015-0163.wav|was standing at a railway station talking to a certain well-known peer of the realm.
+LJSpeech-1.1/wavs/LJ015-0158.wav|of the St. Ann's Society, and one of the supporters and managers of the Patriotic Fund.
+LJSpeech-1.1/wavs/LJ017-0052.wav|His skin was extraordinarily fair, his cheeks fresh and ruddy; altogether his face, though commonplace, was not exactly ugly;
+LJSpeech-1.1/wavs/LJ002-0040.wav|intended to accommodate a couple of prisoners apiece, but often much more crowded.
+LJSpeech-1.1/wavs/LJ008-0310.wav|which was done with a sort of ceremony intended to be impressive.
+LJSpeech-1.1/wavs/LJ044-0033.wav|It is also evidence of Oswald's reluctance to describe events accurately
+LJSpeech-1.1/wavs/LJ027-0113.wav|Of course the theory of descent with adaptive modification has a simple answer to supply
+LJSpeech-1.1/wavs/LJ046-0094.wav|Many Presidents have been understandably impatient with the security precautions which many years of experience dictate
+LJSpeech-1.1/wavs/LJ030-0029.wav|After a welcome from the Dallas reception committee,
+LJSpeech-1.1/wavs/LJ037-0016.wav|Scoggins hurriedly left his seat and hid behind the cab as the man came back toward the corner with gun in hand.
+LJSpeech-1.1/wavs/LJ030-0096.wav|These agents performed for the Vice President the same functions that the agents in the Presidential follow-up car performed for the President.
+LJSpeech-1.1/wavs/LJ008-0140.wav|Whenever the public attention had been specially called to a particular crime, either on account of its atrocity,
+LJSpeech-1.1/wavs/LJ001-0082.wav|In spite, however, of his praiseworthy efforts, printing had still one last degradation to undergo.
+LJSpeech-1.1/wavs/LJ004-0025.wav|and two other acts more peremptory and precise followed on the same subject in succeeding years.
+LJSpeech-1.1/wavs/LJ005-0131.wav|In many places too where the prisoners earned money by their work, they were granted a portion of it for their own use after proper deduction for maintenance.
+LJSpeech-1.1/wavs/LJ022-0036.wav|Did you ever stop to think that there are, after all, only two positions in the nation
+LJSpeech-1.1/wavs/LJ009-0003.wav|Even so severe a critic as Mr. Wakefield states that "a stranger to the scene
+LJSpeech-1.1/wavs/LJ049-0121.wav|While the police work of the Dallas authorities in the early identification and apprehension of Oswald was both efficient and prompt,
+LJSpeech-1.1/wavs/LJ025-0035.wav|Hence, says Cuvier, it was necessary that the chemical composition of the animal body should be more complicated than that of the plant;
+LJSpeech-1.1/wavs/LJ033-0057.wav|and insert it into the paper bag.
+LJSpeech-1.1/wavs/LJ046-0002.wav|The President's Commission on the Assassination of President Kennedy. Chapter eight. The Protection of the President. Part one.
+LJSpeech-1.1/wavs/LJ036-0091.wav|And neither Captain Fritz nor any other officer can remember that Oswald dramatically arose from his chair
+LJSpeech-1.1/wavs/LJ003-0289.wav|Upon the twenty-ninth July the same year,
+LJSpeech-1.1/wavs/LJ049-0224.wav|and services related to the security of the President.
+LJSpeech-1.1/wavs/LJ020-0069.wav|One even teaspoonful of salt.
+LJSpeech-1.1/wavs/LJ039-0073.wav|when you shoot at one hundred seventy-five feet or two hundred sixty feet, which is less than a hundred yards, with a telescopic sight,
+LJSpeech-1.1/wavs/LJ010-0181.wav|and the royal party drove back to Clarence House, the Queen being anxious to give the first news of the outrage and of her safety to her mother,
+LJSpeech-1.1/wavs/LJ041-0102.wav|Powers said, quote, now he was Oswald the man rather than Oswald the rabbit, end quote.
+LJSpeech-1.1/wavs/LJ033-0020.wav|prior to November twenty-one, nineteen sixty-three, except on Monday, October twenty-one, when he visited his wife in the hospital
+LJSpeech-1.1/wavs/LJ034-0146.wav|In an interview with FBI agents on December seventeen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ003-0320.wav|which recommended restrictions upon the number of visitors admitted.
+LJSpeech-1.1/wavs/LJ007-0032.wav|were jumbled up together, higgledy-piggledy, the old and the young, the murderer and the child who had broken into a dwelling.
+LJSpeech-1.1/wavs/LJ006-0205.wav|novels, flash songs, play-books, such as "Jane Shore," "Grimm's German Tales," with Cruikshank's illustrations,
+LJSpeech-1.1/wavs/LJ025-0095.wav|once supposed to be exclusively confined to plants, are now known to be regular and normal products of animals.
+LJSpeech-1.1/wavs/LJ018-0261.wav|and passed the heaviest sentence of the law, transportation for life.
+LJSpeech-1.1/wavs/LJ002-0017.wav|Trustworthy evidence is forthcoming to the effect that these high figures were constantly maintained for many months at a time.
+LJSpeech-1.1/wavs/LJ033-0115.wav|I remember that I didn't look at the package very much but when I did look at it he did have his hands on the package like that, end quote, and at this point
+LJSpeech-1.1/wavs/LJ009-0304.wav|four pounds, twelve shillings; and finally the funeral, cart, coffin, and other petty expenses, amounting to seven pounds ten
+LJSpeech-1.1/wavs/LJ021-0205.wav|I believe with Abraham Lincoln, that "The legitimate object of government is to do for a community of people
+LJSpeech-1.1/wavs/LJ001-0069.wav|This type was introduced into England by Wynkyn de Worde, Caxton's successor,
+LJSpeech-1.1/wavs/LJ033-0037.wav|In deciding whether Oswald carried a rifle to work in a long paper bag on November twenty-two,
+LJSpeech-1.1/wavs/LJ014-0089.wav|For the remainder of that week and part of the next the murderers stayed in the house, and occupied the kitchen, close to the remains of their victim.
+LJSpeech-1.1/wavs/LJ036-0183.wav|After a friend had called and told her, "President Kennedy has been shot," she turned on the television.
+LJSpeech-1.1/wavs/LJ019-0385.wav|Mr. (now Sir Richard) Cross, having applied himself vigorously to the task of reorganizing the whole system, became convinced
+LJSpeech-1.1/wavs/LJ048-0166.wav|But on out-of-town trips where the route is decided on and made public only a few days in advance,
+LJSpeech-1.1/wavs/LJ006-0228.wav|The ragged part of the prisoners were very anxious to destroy the clothes of the better dressed, and often lighted small pieces of cloth,
+LJSpeech-1.1/wavs/LJ035-0122.wav|Dougherty took the elevator down from the fifth floor after Jarman, Norman, and Williams ran to the west windows and were deciding what to do.
+LJSpeech-1.1/wavs/LJ009-0073.wav|The forger has never moved.
+LJSpeech-1.1/wavs/LJ033-0182.wav|Since the Depository normally used approximately one roll of paper every three working days,
+LJSpeech-1.1/wavs/LJ022-0040.wav|My most immediate concern is in carrying out the purposes of the great work program just enacted by the Congress.
+LJSpeech-1.1/wavs/LJ009-0022.wav|I was informed that this unnecessarily harassed his feelings, and that the object of such sermons was solely to console the prisoner,
+LJSpeech-1.1/wavs/LJ009-0289.wav|and before he took to hanging he was employed as a watchman at Reid's brewery in Liquorpond Street.
+LJSpeech-1.1/wavs/LJ019-0267.wav|The Secretary of State's suggested scale of diet had seldom been closely followed.
+LJSpeech-1.1/wavs/LJ033-0163.wav|James C. Cadigan, a questioned-documents expert with the Bureau, compared the samples with the paper and tape in the actual bag.
+LJSpeech-1.1/wavs/LJ011-0158.wav|The two brothers started at once for Liverpool, where Miss Turner was at school with a Mrs. Daulby.
+LJSpeech-1.1/wavs/LJ015-0080.wav|Shares in the company were represented by certificates, which often enough never left the company's, or more exactly Robson's, hands.
+LJSpeech-1.1/wavs/LJ005-0077.wav|"expedient to introduce such measures and arrangements as shall not only provide for the safe custody,
+LJSpeech-1.1/wavs/LJ008-0252.wav|Another deep roar, louder than any which had preceded it, broke from the multitude.
+LJSpeech-1.1/wavs/LJ006-0074.wav|He was unable to give any reason whatever for not utilizing the whole of the wards.
+LJSpeech-1.1/wavs/LJ048-0100.wav|who had just completed advance work on the President's trip to Tampa.
+LJSpeech-1.1/wavs/LJ016-0346.wav|a fact duly advertised as completed by the hoisting of the black flag over the jail,
+LJSpeech-1.1/wavs/LJ014-0314.wav|Pries at length was discovered
+LJSpeech-1.1/wavs/LJ026-0154.wav|In the animal oxygen is absorbed by the blood, in excess by the hemoglobin of the red cells of the blood
+LJSpeech-1.1/wavs/LJ050-0197.wav|According to Secretary Dillon,
+LJSpeech-1.1/wavs/LJ013-0205.wav|Later in the day he tried to commit suicide by cramming a towel down his throat, but was prevented.
+LJSpeech-1.1/wavs/LJ005-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eight: The beginnings of prison reform.
+LJSpeech-1.1/wavs/LJ022-0087.wav|After the Division of Applications and Information has sifted those projects,
+LJSpeech-1.1/wavs/LJ042-0205.wav|There can be no sympathy for those who have turned the idea of communism into a vile curse to Western man.
+LJSpeech-1.1/wavs/LJ044-0197.wav|It should be noted that his interest in Cuba seems to have increased along with the sense of frustration which must have developed
+LJSpeech-1.1/wavs/LJ030-0043.wav|Its function was to alert police along the route that the motorcade was approaching and to check for signs of trouble.
+LJSpeech-1.1/wavs/LJ016-0145.wav|Encouraged by the shouts and signals of some workmen employed on a building outside, the prisoner made one of the most marvellous jumps on record,
+LJSpeech-1.1/wavs/LJ030-0175.wav|the Governor started to look back over his left shoulder, but he never completed the turn because he felt something strike him in the back.
+LJSpeech-1.1/wavs/LJ036-0194.wav|Patrolman J. D. Tippit joined the Dallas Police Department in July nineteen fifty-two.
+LJSpeech-1.1/wavs/LJ022-0020.wav|cause of clearer thinking and a better understanding, are considering the whole rather than a mere part relating to one section or to one crop,
+LJSpeech-1.1/wavs/LJ001-0166.wav|is the habit that publishers have of eking out a thin volume by printing it on thick paper almost of the substance of cardboard,
+LJSpeech-1.1/wavs/LJ010-0300.wav|Mr. Fauntleroy was a member of a banking firm, which his father had established in conjunction with a gentleman of the name of Marsh, and others.
+LJSpeech-1.1/wavs/LJ013-0011.wav|She was freighted by the firm of Zulueta and Co. for a voyage to Santa Cruz.
+LJSpeech-1.1/wavs/LJ028-0341.wav|the rest who did not see it kept at their posts, till at last they too learned that they were betrayed.
+LJSpeech-1.1/wavs/LJ012-0275.wav|He thought he had killed her, and in his terror began at once to consider how he might dispose of the body and escape arrest.
+LJSpeech-1.1/wavs/LJ047-0139.wav|The next word about Oswald's location was a communication from the CIA to the FBI on October ten,
+LJSpeech-1.1/wavs/LJ013-0242.wav|This increased when the officer, accompanied by two others, a neighbor and a bailiff, entered one of the stables.
+LJSpeech-1.1/wavs/LJ013-0262.wav|The woman where he lodged noticed that he was very restless at night, moaning and sighing much. Detection came unexpectedly.
+LJSpeech-1.1/wavs/LJ031-0048.wav|followed by, quote, more profuse bleeding, end quote, after some circulation was established.
+LJSpeech-1.1/wavs/LJ041-0185.wav|Thornley also believed that Oswald's Marxist beliefs led to an extraordinary view of history under which, quote, He looked upon the eyes of future people
+LJSpeech-1.1/wavs/LJ037-0142.wav|serial number V five one zero two one zero.
+LJSpeech-1.1/wavs/LJ007-0028.wav|there still remained one where the general callous indifference and mismanagement culminated in cruel culpable neglect.
+LJSpeech-1.1/wavs/LJ010-0055.wav|but they came one and all to Newgate for trial at the Old Bailey, and remained there after conviction till they were hanged.
+LJSpeech-1.1/wavs/LJ019-0160.wav|and check by remonstrance or threat of punishment all who broke the peace of the prison.
+LJSpeech-1.1/wavs/LJ007-0196.wav|the provision of dining-rooms and dining-tables.
+LJSpeech-1.1/wavs/LJ032-0194.wav|the Commission was unable to reach any firm conclusion as to when the fibers were caught in the rifle.
+LJSpeech-1.1/wavs/LJ002-0158.wav|Other cases are recorded elsewhere, as at the Giltspur Street Compter, where in eighteen oh five Mr. Neild found a man named William Grant
+LJSpeech-1.1/wavs/LJ043-0126.wav|told the prospective employer that Oswald was, quote, kinda peculiar sometimes and that he had some knowledge of the Russian language, end quote.
+LJSpeech-1.1/wavs/LJ001-0055.wav|some of which -- as, e.g., that of Jacobus Rubeus or Jacques le Rouge -- is scarcely distinguishable from his.
+LJSpeech-1.1/wavs/LJ028-0185.wav|Perhaps Babylon was so strongly fortified that at first he made no attempt to add it to his empire,
+LJSpeech-1.1/wavs/LJ031-0067.wav|A femoral pulse was also detected in the President's leg.
+LJSpeech-1.1/wavs/LJ005-0298.wav|to the county jails from such prisons as were past improvement, and that the borough funds should be charged for the accommodation.
+LJSpeech-1.1/wavs/LJ020-0012.wav|While still hot, mix in the sugar and butter, beating all to a lumpless cream.
+LJSpeech-1.1/wavs/LJ029-0138.wav|The Elm Street approach to the Stemmons Freeway is necessary
+LJSpeech-1.1/wavs/LJ006-0167.wav|He only attended chapel once on Sunday, never on the week-day, and generally devoted the time service was in progress
+LJSpeech-1.1/wavs/LJ038-0061.wav|He saw no shotgun in the possession of any policeman near Oswald.
+LJSpeech-1.1/wavs/LJ018-0392.wav|to have obtained as much as forty thousand pounds by false and fraudulent pretenses.
+LJSpeech-1.1/wavs/LJ037-0269.wav|Oswald's jacket was found along the path of flight taken by the gunman as he fled from the scene of the killing.
+LJSpeech-1.1/wavs/LJ009-0062.wav|buries his head under his body.
+LJSpeech-1.1/wavs/LJ002-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section four: Newgate down to eighteen eighteen.
+LJSpeech-1.1/wavs/LJ022-0130.wav|It is time to provide a smashing answer for those cynical men who say that a democracy cannot be honest and efficient.
+LJSpeech-1.1/wavs/LJ029-0003.wav|This chapter describes President Kennedy's trip to Dallas, from its origin through its tragic conclusion.
+LJSpeech-1.1/wavs/LJ002-0303.wav|Besides these fees, legitimate and illegitimate, there were others which must be paid before release.
+LJSpeech-1.1/wavs/LJ049-0029.wav|Without attempting to prescribe or recommend specific measures which should be employed for the future protection of Presidents,
+LJSpeech-1.1/wavs/LJ034-0110.wav|Oswald was five foot nine inches, slender and twenty-four years old.
+LJSpeech-1.1/wavs/LJ033-0029.wav|It would appear, however, that obtaining curtain rods was not the purpose of Oswald's trip to Irving on November twenty-one.
+LJSpeech-1.1/wavs/LJ050-0119.wav|they should be responsible for advising the Secret Service if information develops indicating the existence of an assassination plot
+LJSpeech-1.1/wavs/LJ034-0169.wav|the same corner where Brennan was sitting on a concrete wall.
+LJSpeech-1.1/wavs/LJ042-0045.wav|I do not wish to be a part of it, nor do I ever again wish to be used as a tool in its military aggressions.
+LJSpeech-1.1/wavs/LJ022-0194.wav|Never since my inauguration in March, nineteen thirty-three, have I felt so unmistakably the atmosphere of recovery.
+LJSpeech-1.1/wavs/LJ039-0162.wav|In an effort to test the rifle under conditions which simulated those which prevailed during the assassination,
+LJSpeech-1.1/wavs/LJ043-0146.wav|Following his unsuccessful attack on Walker, Oswald returned home.
+LJSpeech-1.1/wavs/LJ050-0167.wav|to develop coordinated and mutually compatible systems, even where such coordination would not seem inconsistent
+LJSpeech-1.1/wavs/LJ017-0011.wav|But secret poisoning on a wholesale scale such as was practiced in Italy and France was happily never popularized in England.
+LJSpeech-1.1/wavs/LJ016-0382.wav|many cling still to life, expecting much from the intercession of friends or the humanitarianism of the age.
+LJSpeech-1.1/wavs/LJ006-0214.wav|but no steps were taken to prevent any prisoner from obtaining more if he could pay for it.
+LJSpeech-1.1/wavs/LJ041-0112.wav|At his own request,
+LJSpeech-1.1/wavs/LJ008-0319.wav|Sometimes any who had had a narrow escape fainted, but the bulk of those respited looked on with unfeeling indifference.
+LJSpeech-1.1/wavs/LJ028-0386.wav|But the walls of the old city had not yet served their full purpose.
+LJSpeech-1.1/wavs/LJ031-0159.wav|to institute strict security measures for the Presidential aircraft, the airport terminal, and the surrounding area.
+LJSpeech-1.1/wavs/LJ017-0230.wav|came out of the cabin, was caught near the 'companion' by the rest of the mutineers, and promptly dispatched with daggers.
+LJSpeech-1.1/wavs/LJ015-0132.wav|The total amount was never exactly made out, but the false stock created and issued by him was estimated at two hundred twenty thousand pounds.
+LJSpeech-1.1/wavs/LJ001-0009.wav|Printing, then, for our purpose, may be considered as the art of making books by means of movable types.
+LJSpeech-1.1/wavs/LJ018-0035.wav|This was the 'City of Manchester,' which was expected to arrive some days before the 'Victoria,' and did so.
+LJSpeech-1.1/wavs/LJ028-0393.wav|As the Emperor Julian was marching forward through Babylonia,
+LJSpeech-1.1/wavs/LJ007-0008.wav|their especial care.
+LJSpeech-1.1/wavs/LJ034-0003.wav|Oswald at Window
+LJSpeech-1.1/wavs/LJ029-0064.wav|Shortly before, the Dallas police had reported to the Secret Service that the handbill had appeared on the streets of Dallas.
+LJSpeech-1.1/wavs/LJ032-0247.wav|during the summer of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ011-0214.wav|They were tried in March of the following year, Edward Wakefield having apparently given himself up,
+LJSpeech-1.1/wavs/LJ049-0226.wav|could improve their existing capacities and procedures so as to lessen the chances of assassination.
+LJSpeech-1.1/wavs/LJ042-0057.wav|three. That I want to, and I shall, live a normal happy and peaceful life here in the Soviet Union for the rest of my life.
+LJSpeech-1.1/wavs/LJ032-0051.wav|On that date, Klein's placed an internal control number V C eight three six on this rifle.
+LJSpeech-1.1/wavs/LJ033-0112.wav|measured thirty-four point eight inches.
+LJSpeech-1.1/wavs/LJ026-0027.wav|as in a mountainous country, it is impossible to say where mountain ends and valley begins; as in the development of an animal,
+LJSpeech-1.1/wavs/LJ005-0292.wav|In many of the smaller boroughs they are totally unfit for the confinement of human beings.
+LJSpeech-1.1/wavs/LJ048-0287.wav|It is conceivable that those men who had little sleep, and who had consumed alcoholic beverages, even in limited quantities,
+LJSpeech-1.1/wavs/LJ028-0031.wav|About you is all that remains of the second of the Seven Wonders of the World.
+LJSpeech-1.1/wavs/LJ028-0372.wav|The poor of the surrounding country occupied its dismantled palaces.
+LJSpeech-1.1/wavs/LJ035-0179.wav|said that to his knowledge the building was not sealed off at twelve:thirty-six p.m. when he called in on police radio
+LJSpeech-1.1/wavs/LJ026-0080.wav|often in the form of granules embedded in the chlorophyll bodies and free oxygen, most of which is returned to the atmosphere.
+LJSpeech-1.1/wavs/LJ010-0077.wav|On his release he sent a challenge to Lord Sidmouth, the Home Secretary, and was again arrested and imprisoned.
+LJSpeech-1.1/wavs/LJ024-0008.wav|That plan has two chief purposes.
+LJSpeech-1.1/wavs/LJ023-0087.wav|the majority of the Court has been assuming the power to pass on the wisdom of these acts of the Congress
+LJSpeech-1.1/wavs/LJ012-0261.wav|This brought suspicion on to a certain James Greenacre,
+LJSpeech-1.1/wavs/LJ016-0180.wav|But Calcraft regularly succeeded Foxen, who followed Botting, and Dennis, the actor in the seventeen eighty riots.
+LJSpeech-1.1/wavs/LJ004-0183.wav|With all this, the inmates were crowded together at night to such a degree as to excite surprise that they should escape suffocation.
+LJSpeech-1.1/wavs/LJ041-0039.wav|and that is the last I have seen or spoken with Oswald. End quote.
+LJSpeech-1.1/wavs/LJ017-0007.wav|It will be remembered that as far back as the reign of Henry the eighth a new and most cruel penalty was devised for the punishment of the Bishop of Rochester's cook,
+LJSpeech-1.1/wavs/LJ019-0396.wav|At a short distance stood another prison of detention, that of Clerkenwell,
+LJSpeech-1.1/wavs/LJ031-0064.wav|Drs. Paul C. Peters and Charles R. Baxter initiated these procedures.
+LJSpeech-1.1/wavs/LJ013-0039.wav|this Wallace added that his friend had made several voyages with the distinct intention of superintending the predetermined shipwrecks.
+LJSpeech-1.1/wavs/LJ035-0152.wav|carrying a full bottle of Coca-Cola in his hand, presumably purchased after the encounter with Baker and Truly.
+LJSpeech-1.1/wavs/LJ042-0053.wav|Responding to Robert's statement that he had not "renounced" him,
+LJSpeech-1.1/wavs/LJ005-0191.wav|The Society proceeded to support this indictment by facts. It is much the old story.
+LJSpeech-1.1/wavs/LJ049-0018.wav|which was neither bulletproof nor bullet resistant.
+LJSpeech-1.1/wavs/LJ030-0099.wav|telephone and Western Union vehicles, a White House communications car,
+LJSpeech-1.1/wavs/LJ033-0008.wav|(two) the disappearance of the rifle from its normal place of storage, (three)
+LJSpeech-1.1/wavs/LJ002-0278.wav|while the funds of several bequests and charities were applied in adding to the material comfort of the prisoners.
+LJSpeech-1.1/wavs/LJ005-0259.wav|It was laid down as a first and indispensable principle that uniformity of discipline should prevail everywhere,
+LJSpeech-1.1/wavs/LJ007-0063.wav|It is only fair to Mr. Cotton to add that, according to his own journal, he was unremitting in his attentions to convicts who were actually cast for death,
+LJSpeech-1.1/wavs/LJ040-0143.wav|Contrary to reports that appeared after the assassination, the psychiatric examination did not indicate that Lee Oswald was a potential assassin,
+LJSpeech-1.1/wavs/LJ042-0003.wav|Background and Possible Motives, Part three.
+LJSpeech-1.1/wavs/LJ028-0395.wav|It was a low rampart, enclosing a wide space planted with trees of every sort, in which all kinds of beasts were shut up;
+LJSpeech-1.1/wavs/LJ022-0002.wav|April twenty-eight, nineteen thirty-five.
+LJSpeech-1.1/wavs/LJ041-0181.wav|Delgado testified that Oswald was, quote, a complete believer that our way of government was not quite right, end quote.
+LJSpeech-1.1/wavs/LJ019-0292.wav|Prisoners still slept two in a bed.
+LJSpeech-1.1/wavs/LJ004-0147.wav|The irons, which nearly every one wore, were remarkably heavy; those double ironed could not take off their small clothes.
+LJSpeech-1.1/wavs/LJ009-0259.wav|The wretched man did not fall with it, but jumped on to the platform, and seizing the rope with his hands, tried to avoid strangulation.
+LJSpeech-1.1/wavs/LJ016-0268.wav|The remarks heard amongst the crowd were of coarse approval.
+LJSpeech-1.1/wavs/LJ015-0018.wav|and dated back to the Commonwealth, when, under the title of Snow and Walton, it carried on business as pawnbrokers.
+LJSpeech-1.1/wavs/LJ039-0035.wav|he was not actually planning to shoot Mr. Nixon at that time in Dallas.
+LJSpeech-1.1/wavs/LJ029-0046.wav|Preventive Intelligence Activities. The Protective Research Section (PRS) of the Secret Service
+LJSpeech-1.1/wavs/LJ007-0088.wav|for not less a period than twenty-four years.
+LJSpeech-1.1/wavs/LJ043-0098.wav|it began to appear that Oswald was having considerable difficulty doing accurate work and in getting along with the other employees.
+LJSpeech-1.1/wavs/LJ024-0050.wav|Andrew Jackson, Abraham Lincoln and Ulysses S. Grant.
+LJSpeech-1.1/wavs/LJ009-0240.wav|A somewhat similar event happened at Chester not long afterwards; the ropes by which two offenders were turned off broke a few inches from their necks.
+LJSpeech-1.1/wavs/LJ040-0141.wav|reported that Lee, quote, confided that the worse thing about Youth House was the fact that he had to be with other boys all the time,
+LJSpeech-1.1/wavs/LJ009-0156.wav|"with a view," as he himself said, "to his professional studies."
+LJSpeech-1.1/wavs/LJ044-0094.wav|We figured after this broadcast of August twenty-one, why, that was no longer possible, end quote.
+LJSpeech-1.1/wavs/LJ019-0087.wav|or make signs; or the chances of recognizing or being recognized were reduced to a minimum by the use of a mask.
+LJSpeech-1.1/wavs/LJ050-0061.wav|(b) who have made threats of bodily harm against officials or employees of Federal, state or local government or officials of a foreign government,
+LJSpeech-1.1/wavs/LJ009-0299.wav|The man to be hanged belonged to Wales, and no Welshman would do the job.
+LJSpeech-1.1/wavs/LJ038-0032.wav|McDonald first searched two men in the center of the main floor, about ten rows from the front.
+LJSpeech-1.1/wavs/LJ004-0079.wav|"All measures and practices in prison which may injure him in any way are illegal,
+LJSpeech-1.1/wavs/LJ039-0157.wav|A series of tests were performed to determine whether the weapon and ammunition used in the assassination
+LJSpeech-1.1/wavs/LJ008-0260.wav|One of the worst evils was the terrible and long-protracted uncertainty as to the result.
+LJSpeech-1.1/wavs/LJ029-0170.wav|Dallas Before the Visit
+LJSpeech-1.1/wavs/LJ028-0398.wav|The walls of Babylon were destined to serve still another purpose.
+LJSpeech-1.1/wavs/LJ010-0267.wav|his nose was marked with a scar or black patch, and he was altogether of a dirty appearance.
+LJSpeech-1.1/wavs/LJ050-0007.wav|have made it difficult for the Treasury to maintain close and continuing supervision.
+LJSpeech-1.1/wavs/LJ013-0220.wav|that Courvoisier was idle, discontented, ready to take offense, greedy of gain;
+LJSpeech-1.1/wavs/LJ030-0233.wav|behind the Presidential follow-up car at the time of the shooting and signaled for it to move in closer.
+LJSpeech-1.1/wavs/LJ005-0280.wav|The Lords' Committee fully recognized the painful fact
+LJSpeech-1.1/wavs/LJ007-0106.wav|Pilgrim and Dasent were caught and given into the custody of the sergeant-at-arms, and the rest were either arrested or they surrendered.
+LJSpeech-1.1/wavs/LJ038-0133.wav|Oswald responded that the landlady simply made a mistake, because he told her that his name was Lee, meaning his first name.
+LJSpeech-1.1/wavs/LJ045-0201.wav|Oswald had a highly exaggerated sense of his own importance, but he had failed at almost everything he had ever tried to do.
+LJSpeech-1.1/wavs/LJ028-0199.wav|there suddenly appeared upon the wall an armless hand.
+LJSpeech-1.1/wavs/LJ045-0110.wav|Ruth Paine and Marina Oswald decided to call Oswald at the place where he was living, unbeknownst to them, under the name of O. H. Lee.
+LJSpeech-1.1/wavs/LJ036-0186.wav|Oswald had entered the house in his shirt sleeves, but when he left, he was zipping up a jacket.
+LJSpeech-1.1/wavs/LJ031-0120.wav|While the second operation was in progress,
+LJSpeech-1.1/wavs/LJ009-0234.wav|There the hangman added his weight to that of the suspended culprit,
+LJSpeech-1.1/wavs/LJ046-0153.wav|If the material was evaluated
+LJSpeech-1.1/wavs/LJ028-0495.wav|It must have been an imposing sight to one standing without to have seen the walls, one after another,
+LJSpeech-1.1/wavs/LJ036-0138.wav|But when I got pretty close to five hundred block at Neches and North Beckley which is the five hundred block, he said, "This will do fine," and I pulled over to the curb right there.
+LJSpeech-1.1/wavs/LJ048-0011.wav|Neither believed that the Bureau investigation of him up to November twenty-two revealed any information which would have justified referral to the Secret Service.
+LJSpeech-1.1/wavs/LJ011-0172.wav|Mr. Turner was on the verge of bankruptcy. He was at that moment at Kendal, and wished her to join him there at once.
+LJSpeech-1.1/wavs/LJ028-0063.wav|Then Syria and Palestine were added to his future empire.
+LJSpeech-1.1/wavs/LJ019-0078.wav|The amount of exercise varied from one or two hours daily to half the working day.
+LJSpeech-1.1/wavs/LJ019-0123.wav|On that account he made no provision to insure safe custody,
+LJSpeech-1.1/wavs/LJ008-0264.wav|But the murderers formed only a small proportion of the total number sentenced to death, and for the rest there was a long period of anxious suspense,
+LJSpeech-1.1/wavs/LJ050-0249.wav|It may eventually be desirable to codify the practice in an Executive order.
+LJSpeech-1.1/wavs/LJ035-0161.wav|The reconstruction was the minimum time. Accordingly, she probably met Oswald at about twelve:thirty-two,
+LJSpeech-1.1/wavs/LJ014-0303.wav|Nothing to equal the excitement caused by the forgeries of Robert Ferdinand Pries had been known before in the city of London.
+LJSpeech-1.1/wavs/LJ038-0301.wav|(two) the photographs found among Oswald's possessions,
+LJSpeech-1.1/wavs/LJ019-0377.wav|and it is not strange if they did not very frequently hand up the offenders to the reproof and correction of the Secretary of State.
+LJSpeech-1.1/wavs/LJ009-0291.wav|He was always known as a mild-mannered man of simple tastes, much given to angling in the New River, and a devoted rabbit fancier.
+LJSpeech-1.1/wavs/LJ011-0042.wav|Visited Mr. Fauntleroy. The sheriffs have very kindly permitted him to remain in the turnkey's room where he was originally placed; nor can I omit expressing a hope
+LJSpeech-1.1/wavs/LJ015-0145.wav|Now he launched out into great expenditure, took a house in Chester Terrace, and became known as a Maecenas and patron of the arts.
+LJSpeech-1.1/wavs/LJ049-0175.wav|before the President reached Dallas.
+LJSpeech-1.1/wavs/LJ028-0342.wav|Thus was Babylon taken for the second time.
+LJSpeech-1.1/wavs/LJ007-0004.wav|although their ministrations were occasionally extended to the male side.
+LJSpeech-1.1/wavs/LJ034-0214.wav|Investigation has established that Altgens' picture was taken approximately two seconds after the firing of the shot
+LJSpeech-1.1/wavs/LJ044-0040.wav|He wrote that, quote, thousands of circulars were distributed, end quote.
+LJSpeech-1.1/wavs/LJ017-0201.wav|Dr. Alfred Taylor, the great authority and writer on medical jurisprudence, corroborated this, and in his evidence on the trial
+LJSpeech-1.1/wavs/LJ043-0131.wav|As a result Oswald was not hired.
+LJSpeech-1.1/wavs/LJ001-0042.wav|and though more Roman than that, yet scarcely more like the complete Roman type of the earliest printers of Rome.
+LJSpeech-1.1/wavs/LJ017-0271.wav|Lyons asked the time, and was told it was only five.
+LJSpeech-1.1/wavs/LJ049-0148.wav|For various reasons the Secret Service has functioned largely as an informal part of the White House staff, with the result
+LJSpeech-1.1/wavs/LJ002-0239.wav|also for prisoners committed by the Admiralty Court.
+LJSpeech-1.1/wavs/LJ014-0238.wav|In eighteen fifty occurred the first of a series of gigantic frauds,
+LJSpeech-1.1/wavs/LJ034-0215.wav|which entered the back of the President's neck.
+LJSpeech-1.1/wavs/LJ016-0014.wav|Sewn in between the upper and lower leathers several spring saws were found, which would have easily cut through any bars.
+LJSpeech-1.1/wavs/LJ032-0267.wav|examined the blanket and discovered a bulge approximately ten inches long midway in the blanket.
+LJSpeech-1.1/wavs/LJ034-0159.wav|The Commission, therefore, does not base its conclusion concerning the identity of the assassin
+LJSpeech-1.1/wavs/LJ001-0053.wav|they must be considered to have ended the age of fine printing in Italy.
+LJSpeech-1.1/wavs/LJ027-0031.wav|but not in any striking way is there change in the internal organs.
+LJSpeech-1.1/wavs/LJ013-0008.wav|came to light in this same year, eighteen forty-one.
+LJSpeech-1.1/wavs/LJ036-0041.wav|McWatters also remembered that a man received a transfer at Lamar and Elm Streets and that a man in the lineup was about the size of this man.
+LJSpeech-1.1/wavs/LJ031-0124.wav|was cleansed and closed with sutures; but a small metallic fragment remained in the Governor's leg.
+LJSpeech-1.1/wavs/LJ006-0241.wav|A prisoner Baxter is in the infirmary in consequence of a severe injury to his wrist-joint.
+LJSpeech-1.1/wavs/LJ016-0377.wav|The older officers, with several of whom I have conversed, have thus had unusual opportunities of watching the demeanor of murderers both before trial
+LJSpeech-1.1/wavs/LJ009-0144.wav|In fact his looks denoted extreme sorrow and contrition,
+LJSpeech-1.1/wavs/LJ004-0234.wav|This change led to some discontent, until it was found that the much greater boon of relief from irons accompanied it, and the reform was quietly accepted.
+LJSpeech-1.1/wavs/LJ021-0163.wav|But as for this country, I stand or fall by my refusal to accept as a necessary condition of our future
+LJSpeech-1.1/wavs/LJ045-0089.wav|Marina Oswald also ridiculed her husband's political views, thereby tearing down his view of his own importance.
+LJSpeech-1.1/wavs/LJ001-0138.wav|this is the tendency to the formation of ugly meandering white lines or "rivers" in the page
+LJSpeech-1.1/wavs/LJ032-0003.wav|The preceding chapter has established that the bullets which killed President Kennedy and wounded Governor Connally
+LJSpeech-1.1/wavs/LJ041-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ040-0067.wav|and his half-brother John Pic, who had been born in nineteen thirty-two during Marguerite's previous marriage.
+LJSpeech-1.1/wavs/LJ046-0041.wav|the personal embodiment and representative of their dignity and majesty, end quote.
+LJSpeech-1.1/wavs/LJ037-0012.wav|A police car moving east on tenth at about ten or twelve miles an hour passed in front of his cab.
+LJSpeech-1.1/wavs/LJ038-0101.wav|Oswald said that this was not true. Oswald denied that he had a rifle wrapped up in a blanket in the Paine garage.
+LJSpeech-1.1/wavs/LJ036-0101.wav|He was taken to the lineup room where, according to Whaley, five young teenagers, all handcuffed together, were displayed with Oswald.
+LJSpeech-1.1/wavs/LJ034-0008.wav|in deciding whether Lee Harvey Oswald was present at this window at the time of the assassination.
+LJSpeech-1.1/wavs/LJ004-0089.wav|and to pay overseers or instructors out of the county rates.
+LJSpeech-1.1/wavs/LJ016-0370.wav|All that the hangman, whoever he may be, does under the new regime is to unhook the halter and remove the pinioning straps.
+LJSpeech-1.1/wavs/LJ040-0074.wav|About six months later she also withdrew John Pic and Robert Oswald.
+LJSpeech-1.1/wavs/LJ005-0237.wav|so that "corrupt conversation obliterated from the mind of him who is doomed to suffer every serious feeling and valuable impression."
+LJSpeech-1.1/wavs/LJ030-0170.wav|Governor Connally testified that he recognized the first noise as a rifle shot
+LJSpeech-1.1/wavs/LJ027-0020.wav|The unity in life, then, is not less a fact than is life's great diversity.
+LJSpeech-1.1/wavs/LJ050-0023.wav|Work is going forward
+LJSpeech-1.1/wavs/LJ033-0018.wav|Oswald generally would go to Irving on Friday afternoon and return to Dallas Monday morning.
+LJSpeech-1.1/wavs/LJ038-0033.wav|He walked out of the row up the right center aisle.
+LJSpeech-1.1/wavs/LJ038-0191.wav|five. The money from work will possibly be coming. The money will be sent to our post office box. Go to the bank and cash the check.
+LJSpeech-1.1/wavs/LJ047-0058.wav|Agent Fain retired from the FBI in October nineteen sixty-two, and the closed Oswald case was not reassigned.
+LJSpeech-1.1/wavs/LJ010-0106.wav|He was in bed with his breeches on (in the pockets of which were found a number of cartridges), the black belt he had worn at Cato Street,
+LJSpeech-1.1/wavs/LJ008-0284.wav|Nothing could be more strongly marked than the contrast between the ultimate destiny of different individuals all abiding the same awful doom:
+LJSpeech-1.1/wavs/LJ027-0009.wav|there are, after all, only a few types of structure among all animals and plants, some three or four or eight or ten general modes of development,
+LJSpeech-1.1/wavs/LJ019-0096.wav|other authorities as strongly condemned it as brutalizing, unequal in its operation, and altogether a "deplorable invention."
+LJSpeech-1.1/wavs/LJ040-0033.wav|At the same time he professed his love for her and said that he could not get along without her.
+LJSpeech-1.1/wavs/LJ049-0178.wav|should have constituted a sufficient basis to warn the Secret Service of the Oswald risk.
+LJSpeech-1.1/wavs/LJ018-0353.wav|Wainwright's demeanor after sentence has been described in the last chapter.
+LJSpeech-1.1/wavs/LJ010-0159.wav|He was eventually sentenced to death, but the plea of lunacy was allowed, and he was confined for life.
+LJSpeech-1.1/wavs/LJ020-0048.wav|Knead it again, then, and wait upon its rising for another three hours. The dough should be as soft as can be handled.
+LJSpeech-1.1/wavs/LJ026-0107.wav|Dissolved in water, the sugar is transported down delicate tubes, chiefly in the growing bark region of the stem.
+LJSpeech-1.1/wavs/LJ046-0138.wav|PRS instructed the White House mailroom, a source of much PRS data,
+LJSpeech-1.1/wavs/LJ002-0063.wav|It was fifty feet by twenty-five, and had five wards each thirty-eight by fifteen. At one end of the yard was an arcade,
+LJSpeech-1.1/wavs/LJ042-0154.wav|While it was apparently intended for publication in the United States, and is in many respects critical of certain aspects of life in the Soviet Union,
+LJSpeech-1.1/wavs/LJ015-0295.wav|he was told it was only at Mr. Whitney's disposal, and that it could be paid to no one else.
+LJSpeech-1.1/wavs/LJ033-0138.wav|It was the appropriate size to contain, in disassembled form,
+LJSpeech-1.1/wavs/LJ044-0041.wav|and that he continued to receive inquiries through his post office box which he endeavored, quote, to keep answering to the best of my ability, end quote.
+LJSpeech-1.1/wavs/LJ010-0261.wav|Only one person saw the movement, a lad named Dasset, who at once collared the cripple, and taking him up to two policemen,
+LJSpeech-1.1/wavs/LJ016-0144.wav|He had disappeared.
+LJSpeech-1.1/wavs/LJ009-0159.wav|and the mitigation of the criminal code, for which Romilly had so strenuously labored, was daily more and more of an accomplished fact.
+LJSpeech-1.1/wavs/LJ019-0339.wav|while decency and good order were to be insured by the strict prohibition of gambling and drunkenness.
+LJSpeech-1.1/wavs/LJ001-0141.wav|The general solidity of a page is much to be sought for
+LJSpeech-1.1/wavs/LJ022-0057.wav|The program for social security now pending before the Congress is a necessary part of the future unemployment policy of the government.
+LJSpeech-1.1/wavs/LJ003-0031.wav|The same system was extended to the criminal side, and cases were on record of the place of wardsman being sold for considerable sums.
+LJSpeech-1.1/wavs/LJ036-0114.wav|Only two of the men in the lineup with Oswald were teenagers: John T. Horn, aged eighteen, was Number one;
+LJSpeech-1.1/wavs/LJ042-0229.wav|To the question of, quote, Are you a Communist? End quote, he first answered "Yes,"
+LJSpeech-1.1/wavs/LJ039-0164.wav|from a tower at three silhouette targets at distances of one hundred seventy-five, two hundred forty, and two hundred sixty-five feet.
+LJSpeech-1.1/wavs/LJ040-0013.wav|Oswald's complete state of mind and character are now outside of the power of man to know.
+LJSpeech-1.1/wavs/LJ032-0201.wav|and issues of two newspapers later identified as the Worker and the Militant.
+LJSpeech-1.1/wavs/LJ007-0231.wav|in which the master evil of jail association and consequent contamination still continues to operate directly to the encouragement of crime.
+LJSpeech-1.1/wavs/LJ031-0135.wav|Concern that the Vice President might also be a target for assassination prompted the Secret Service agents to urge him to leave the hospital and return to Washington immediately.
+LJSpeech-1.1/wavs/LJ030-0217.wav|quote, Mrs. Kennedy had jumped up from the seat and was, it appeared to me, reaching for something coming off the fight rear bumper of the car,
+LJSpeech-1.1/wavs/LJ050-0278.wav|the recommendations we have here suggested would greatly advance the security of the office without any impairment of our fundamental liberties.
+LJSpeech-1.1/wavs/LJ028-0232.wav|Here they shut themselves up and made light of his siege, having laid in a store of provision for many years in preparation against this attack;
+LJSpeech-1.1/wavs/LJ039-0132.wav|and as compared to the average male of his age throughout the civilian, throughout the United States, that he is an excellent shot, end quote.
+LJSpeech-1.1/wavs/LJ013-0137.wav|which the various club secretaries identified as the property of their respective clubs.
+LJSpeech-1.1/wavs/LJ018-0008.wav|He lived at Hackney, but he never reached it alive.
+LJSpeech-1.1/wavs/LJ042-0171.wav|simply expressed, the left and right, and their offspring factions and concerns. Any practical attempt at one alternative
+LJSpeech-1.1/wavs/LJ012-0010.wav|both having been recognized by the clergyman who had performed the ceremony, and the assault had been committed to secure the money
+LJSpeech-1.1/wavs/LJ002-0195.wav|Besides these, a number had the privilege of a "run on the key," which allowed a prisoner to go into the rules for the day.
+LJSpeech-1.1/wavs/LJ002-0086.wav|they were lighted by a double-grated window two feet nine inches by fourteen inches; and in the doors, which were four inches thick,
+LJSpeech-1.1/wavs/LJ044-0215.wav|it also was increasing its efforts to tighten the noose around Cuba.
+LJSpeech-1.1/wavs/LJ028-0365.wav|one of Alexander's generals, who for a time made it his home, but he was a Greek and cared little for things Babylonian.
+LJSpeech-1.1/wavs/LJ048-0140.wav|Agent Lawson's advance planning called for the deployment of almost six hundred members of the Dallas Police Department,
+LJSpeech-1.1/wavs/LJ011-0007.wav|and was told by Mr. Fauntleroy that the lady had desired him to sell out, "which I have done," added the fraudulent banker, "and here are the proceeds,"
+LJSpeech-1.1/wavs/LJ029-0191.wav|He asserted that Dallas had shed its reputation of the twenties as the, quote, Southwest hate capital of Dixie, end quote
+LJSpeech-1.1/wavs/LJ004-0187.wav|Already, as early as eighteen eighteen, a prison existed at Bury St. Edmunds which was a model for imitation to others at that time,
+LJSpeech-1.1/wavs/LJ046-0054.wav|partly because of the greater speed and comfort of travel and partly because of the greater demands made on the President.
+LJSpeech-1.1/wavs/LJ050-0045.wav|and of the Advanced Research Projects Agency of the Department of Defense,
+LJSpeech-1.1/wavs/LJ038-0074.wav|and pick up a man named Lee Oswald. When Hill asked why Oswald was wanted,
+LJSpeech-1.1/wavs/LJ015-0199.wav|The improved methods of locomotion had put a stop to these depredations. People traveled in company, as a rule;
+LJSpeech-1.1/wavs/LJ021-0083.wav|which Americans have demanded since the white man first came to these shores.
+LJSpeech-1.1/wavs/LJ031-0227.wav|The doctors traced the course of the bullet through the body and, as information was received from Parkland Hospital,
+LJSpeech-1.1/wavs/LJ039-0235.wav|The probability of hitting the President a second time would have been markedly increased if, in fact, he had missed either the first or third shots
+LJSpeech-1.1/wavs/LJ010-0214.wav|One expressed pleasure that Oxford improved so much in speaking, and declared that his (Oxford's) speech the last time "was beautiful."
+LJSpeech-1.1/wavs/LJ041-0021.wav|In a space for the names of, quote, close friends, end quote, on the ninth grade personal history record,
+LJSpeech-1.1/wavs/LJ002-0146.wav|The activity as well as the futility of these courts may be estimated from the statement given by Neild
+LJSpeech-1.1/wavs/LJ017-0246.wav|the mutineers took command of the ship, put her about till night-fall, by which time they had scuttled her, got out the boats, and all left the ship.
+LJSpeech-1.1/wavs/LJ041-0097.wav|According to Powers, Oswald said, quote,
+LJSpeech-1.1/wavs/LJ027-0149.wav|that the phases of development presented by the individual organism would follow, in their main outlines, those phases of development
+LJSpeech-1.1/wavs/LJ018-0259.wav|he confessed that his whole life had been a gigantic mistake, and he was ready to make what atonement he could.
+LJSpeech-1.1/wavs/LJ002-0078.wav|seven. The press yard was that part set aside for the condemned.
+LJSpeech-1.1/wavs/LJ016-0119.wav|Under superior orders all the doors and gates of this block were left open at night, to allow the night watchman to pass freely to all parts.
+LJSpeech-1.1/wavs/LJ010-0248.wav|On searching him the pistol was found in his pocket, the barrel still warm; also some loose powder and a bullet.
+LJSpeech-1.1/wavs/LJ010-0302.wav|in eighteen oh seven, and when only twenty-two, he succeeded to his father's share in the business.
+LJSpeech-1.1/wavs/LJ019-0079.wav|The cells inhabited by prisoners were of very varying dimensions;
+LJSpeech-1.1/wavs/LJ016-0131.wav|Nothing more was heard of Krapps. The curious thing in his case was that his offense was a trifling one;
+LJSpeech-1.1/wavs/LJ001-0026.wav|On the whole the type of this book may be considered the ne-plus-ultra of Gothic type,
+LJSpeech-1.1/wavs/LJ034-0033.wav|During the afternoon of November twenty-two,
+LJSpeech-1.1/wavs/LJ033-0157.wav|Furthermore, it was consistent with the bag having contained a heavy or bulky object when he handled it since a light object is usually held by the fingers.
+LJSpeech-1.1/wavs/LJ006-0152.wav|One turnkey had been so employed several hours daily for nearly eleven years.
+LJSpeech-1.1/wavs/LJ033-0061.wav|I then went to the garage to see whether the rifle was there and I saw that the blanket was still there and I said Thank God, end quote.
+LJSpeech-1.1/wavs/LJ028-0202.wav|"In that night," so the story ends, "Belshazzar, the Chaldean King, was slain."
+LJSpeech-1.1/wavs/LJ042-0110.wav|stated that Mrs. Oswald told her everybody in Russia, quote, hated him, end quote.
+LJSpeech-1.1/wavs/LJ017-0228.wav|till every feature was obliterated, and then, still living, flung him into the sea.
+LJSpeech-1.1/wavs/LJ043-0166.wav|Possibly he might have wanted to be caught, and wanted his involvement made clear if he was in fact apprehended.
+LJSpeech-1.1/wavs/LJ008-0232.wav|a sound as if the ocean had suddenly broken through some ancient boundary, against which its ever restless billows had for ages battered;
+LJSpeech-1.1/wavs/LJ028-0439.wav|for none of the many building inscriptions from Nebuchadnezzar mentions them.
+LJSpeech-1.1/wavs/LJ011-0070.wav|Afterwards, according to the common rumor, he went abroad and lived there for many years; but the story is not only wholly unsubstantiated,
+LJSpeech-1.1/wavs/LJ022-0010.wav|At different points on the coast where I often visit they build great seagoing ships.
+LJSpeech-1.1/wavs/LJ008-0224.wav|With them were younger men and women:
+LJSpeech-1.1/wavs/LJ042-0153.wav|This was a fairly coherent description of life in that country, basically centered around the radio and television factory in which he worked.
+LJSpeech-1.1/wavs/LJ019-0265.wav|In dietaries, again, the same wide diversity of practice obtained.
+LJSpeech-1.1/wavs/LJ006-0066.wav|In the middle yard it was still worse.
+LJSpeech-1.1/wavs/LJ026-0126.wav|as a preliminary to the real processes of nutrition.
+LJSpeech-1.1/wavs/LJ007-0176.wav|is indeed a subject which cannot but impress every considerate mind with humiliation and sorrow.
+LJSpeech-1.1/wavs/LJ049-0024.wav|has developed a vehicle for the better protection of the President.
+LJSpeech-1.1/wavs/LJ011-0293.wav|It was a cell five feet by three, within another, the intervening being filled with rammed earth to deaden the sound.
+LJSpeech-1.1/wavs/LJ036-0014.wav|Oswald's Movements After Leaving Depository Building
+LJSpeech-1.1/wavs/LJ019-0253.wav|Even where cells had been built
+LJSpeech-1.1/wavs/LJ019-0364.wav|His order would at the same time specify some neighboring and more satisfactory prison which the local authority would be compelled to utilize instead,
+LJSpeech-1.1/wavs/LJ003-0022.wav|This steward was practically supreme.
+LJSpeech-1.1/wavs/LJ050-0063.wav|and who have been involved in bombing or bomb-making or whose past conduct indicates tendencies toward violence, and (d)
+LJSpeech-1.1/wavs/LJ016-0435.wav|"Two fresh men! May I speak to them? Yes! I must caution you," he went on to the warders, "not to go to sleep, or I shall be off through that little hole,"
+LJSpeech-1.1/wavs/LJ004-0206.wav|Blanket weaving and cloth spinning was carried on prosperously,
+LJSpeech-1.1/wavs/LJ014-0122.wav|A great number of distinguished people assembled as usual at the Old Bailey on the day of trial.
+LJSpeech-1.1/wavs/LJ017-0180.wav|vomiting, violent retching, purging, such as are visible in cholera, and all dated from the time when she knew a young man named Dixon,
+LJSpeech-1.1/wavs/LJ046-0136.wav|Before the assassination of President Kennedy,
+LJSpeech-1.1/wavs/LJ028-0453.wav|The northeastern section may now be traced for a distance of less than three miles, and the southwestern "for more than a mile,
+LJSpeech-1.1/wavs/LJ026-0163.wav|This is true also of green plants in darkness and even in the light of all parts of green plants except the chlorophyll-bodies.
+LJSpeech-1.1/wavs/LJ019-0196.wav|free from any other interference than that of the inspectors on the part of Government.
+LJSpeech-1.1/wavs/LJ019-0128.wav|I have lingered too long perhaps over Mr. Pearson's proposal, but some reference was indispensable to a scheme
+LJSpeech-1.1/wavs/LJ047-0099.wav|Quigley recalled that Oswald was receptive when questioned about his general background
+LJSpeech-1.1/wavs/LJ004-0181.wav|but as no similar variation occurred in the prisoner's appetite, his ration was somewhat precarious.
+LJSpeech-1.1/wavs/LJ037-0256.wav|Mrs. Markham and Barbara Davis thought that the jacket worn by the slayer of Tippit was darker than the jacket found by Westbrook.
+LJSpeech-1.1/wavs/LJ027-0035.wav|In the second place, it must be noted that adaptations to similar conditions may result in superficial resemblances.
+LJSpeech-1.1/wavs/LJ018-0360.wav|then to divert suspicion from herself by throwing it on a confectioner, whose sweetmeats she bought,
+LJSpeech-1.1/wavs/LJ022-0128.wav|I call upon my fellow citizens everywhere to cooperate with me
+LJSpeech-1.1/wavs/LJ008-0168.wav|Among the rest was a woman with an infant at the breast.
+LJSpeech-1.1/wavs/LJ028-0222.wav|My many troops marched peacefully into Babylon.
+LJSpeech-1.1/wavs/LJ022-0156.wav|The operating companies in the gas and electric utility field are by and large in good condition, but
+LJSpeech-1.1/wavs/LJ015-0209.wav|Burgess detailed the whole system of transmission.
+LJSpeech-1.1/wavs/LJ016-0178.wav|had no official status, and was merely an operator selected by the Corporation, and who, on the strength of it,
+LJSpeech-1.1/wavs/LJ050-0184.wav|has hitherto been handled on an informal basis.
+LJSpeech-1.1/wavs/LJ004-0049.wav|Mr. Gurney, Mr. Fry, Messrs. Forster, and Mr. T. F. Buxton, the coadjutor of Wilberforce in the great anti-slavery struggle.
+LJSpeech-1.1/wavs/LJ018-0222.wav|obtained a mortgage of seven thousand pounds.
+LJSpeech-1.1/wavs/LJ013-0224.wav|The attempt was to have been made by opening a vein and allowing himself to bleed to death.
+LJSpeech-1.1/wavs/LJ033-0016.wav|Oswald traveled between Dallas and Irving on weekends in a car driven by a neighbor of the Paines, Buell Wesley Frazier,
+LJSpeech-1.1/wavs/LJ043-0159.wav|Answer: Yes.
+LJSpeech-1.1/wavs/LJ036-0119.wav|Whaley displayed a trip manifest which showed a twelve o'clock trip from Travis Hotel to the Continental bus station,
+LJSpeech-1.1/wavs/LJ032-0162.wav|Stombaugh testified that the colors, shades, and twist of the fibers found in the tuft on the rifle matched those in Oswald's shirt.
+LJSpeech-1.1/wavs/LJ011-0098.wav|He soon, however, became deeply involved in Stock Exchange speculations,
+LJSpeech-1.1/wavs/LJ041-0111.wav|for possessing an unauthorized pistol with which he had accidentally shot himself.
+LJSpeech-1.1/wavs/LJ050-0139.wav|to serve as liaison officers with law enforcement and intelligence agencies. On the basis of the Department's review during the past several months,
+LJSpeech-1.1/wavs/LJ032-0033.wav|Oswald's writing on these and other documents was identified by comparing the writing and printing on the documents in question
+LJSpeech-1.1/wavs/LJ012-0072.wav|He was indicted on eight charges, two only of which were substantiated, but on each of them he received a sentence of seven years' transportation.
+LJSpeech-1.1/wavs/LJ013-0160.wav|who, on going down early, was surprised to find the dining-room in a state of utter confusion;
+LJSpeech-1.1/wavs/LJ035-0163.wav|After leaving Mrs. Reid in the front office, Oswald could have gone down the stairs and out the front door by twelve:thirty-three p.m.
+LJSpeech-1.1/wavs/LJ044-0169.wav|In September he began to review Spanish.
+LJSpeech-1.1/wavs/LJ032-0120.wav|Day continued to examine the rifle with the magnifying glass, looking for possible fingerprints.
+LJSpeech-1.1/wavs/LJ041-0047.wav|approximately three years before. Robert Oswald had given his Marine Corps manual to his brother Lee,
+LJSpeech-1.1/wavs/LJ019-0048.wav|A new and still imperfectly understood science had arisen,
+LJSpeech-1.1/wavs/LJ001-0146.wav|which requires the constant exercise of judgment and taste on the part of the printer.
+LJSpeech-1.1/wavs/LJ028-0481.wav|Nabopolassar, the father, my begetter, built Imgur-Bel, the great wall of Babylon,
+LJSpeech-1.1/wavs/LJ004-0219.wav|The diet was now ample. It consisted of a pound and a half of bread per diem;
+LJSpeech-1.1/wavs/LJ005-0163.wav|One great impediment to wide amelioration was that a vast number of small jails lay out of reach of the law.
+LJSpeech-1.1/wavs/LJ006-0200.wav|The newsman had free access to the prison; he passed in unsearched and unexamined, and, unaccompanied by an officer,
+LJSpeech-1.1/wavs/LJ027-0107.wav|rudiments of eyes in cave fishes, hind limbs beneath the skin of whales, the vermiform appendix in man,
+LJSpeech-1.1/wavs/LJ017-0238.wav|Taffir, the second mate, agreed, but constantly went in fear of his life for the remainder of the voyage;
+LJSpeech-1.1/wavs/LJ023-0136.wav|In many states judges must retire at the age of seventy.
+LJSpeech-1.1/wavs/LJ039-0094.wav|After viewing photographs depicting the alignment of Elm Street in relation to the Texas School Book Depository Building,
+LJSpeech-1.1/wavs/LJ048-0257.wav|and that their conduct the night before did not impede their actions on duty
+LJSpeech-1.1/wavs/LJ034-0059.wav|Since other identifiable prints were developed on the cartons, the Commission requested that they be compared with the prints of the twelve warehouse employees
+LJSpeech-1.1/wavs/LJ044-0173.wav|While Marina Oswald knew of her husband's plan to go to Mexico and thence to Cuba if possible, Mrs. Paine was told that Oswald was going to Houston
+LJSpeech-1.1/wavs/LJ018-0118.wav|a system which had long been in practice of defrauding the Exchequer of vast sums by means of forged stamps.
+LJSpeech-1.1/wavs/LJ002-0082.wav|Beyond the press yard were three stories, condemned cells, fifteen in all, with vaulted ceilings nine feet high to the crown of the arch.
+LJSpeech-1.1/wavs/LJ025-0114.wav|founded the modern science of histology or that branch of anatomy which deals with the ultimate visible structure of organisms as revealed by the microscope,
+LJSpeech-1.1/wavs/LJ037-0239.wav|She confirmed this interview by a sworn affidavit.
+LJSpeech-1.1/wavs/LJ021-0138.wav|with justice for all those willing to use these agencies whenever their voluntary bargaining fails to produce a necessary agreement.
+LJSpeech-1.1/wavs/LJ048-0113.wav|The Commission believes, however,
+LJSpeech-1.1/wavs/LJ014-0153.wav|After this, until the day of execution, she recovered her spirits, and displayed reckless effrontery,
+LJSpeech-1.1/wavs/LJ027-0172.wav|The perennibranch passes through the fish stage to the perennibranch amphibian.
+LJSpeech-1.1/wavs/LJ021-0131.wav|is not fully supporting the recovery effort of his government.
+LJSpeech-1.1/wavs/LJ037-0243.wav|Westbrook walked through the parking lot behind the service station and found a light-colored jacket lying under the rear of one of the cars.
+LJSpeech-1.1/wavs/LJ028-0250.wav|Such, then, were the circumstances of the first taking of Babylon.
+LJSpeech-1.1/wavs/LJ036-0203.wav|The dispatcher ordered Tippit to be, quote, at large for any emergency that comes in, end quote.
+LJSpeech-1.1/wavs/LJ022-0110.wav|The key men for the major responsibilities of this great task already have been selected.
+LJSpeech-1.1/wavs/LJ038-0039.wav|Three other officers, moving toward the scuffle, grabbed Oswald from the front, rear and side.
+LJSpeech-1.1/wavs/LJ013-0144.wav|one of the owners had met her at Deal, but the captain had already absconded with the packages of precious stones in his pocket.
+LJSpeech-1.1/wavs/LJ035-0151.wav|He was walking into the office from the back hallway,
+LJSpeech-1.1/wavs/LJ042-0196.wav|Oswald's decided rejection of both capitalism and communism
+LJSpeech-1.1/wavs/LJ050-0250.wav|The Secret Service will be better able to plan its own long-range personnel requirements if it knows with reasonable certainty
+LJSpeech-1.1/wavs/LJ012-0048.wav|and this, with the danger arising from an extensive robbery of watches in Cheapside, in which Ikey was implicated as a receiver,
+LJSpeech-1.1/wavs/LJ014-0114.wav|She had been to brokers to negotiate the sale of certain foreign railway stock, with which they had been warned from London not to deal,
+LJSpeech-1.1/wavs/LJ018-0223.wav|In eighteen fifty-six the father died.
+LJSpeech-1.1/wavs/LJ048-0062.wav|to have made more vigorous efforts to locate Oswald's roominghouse address in Dallas and to interview him regarding these unresolved matters.
+LJSpeech-1.1/wavs/LJ010-0102.wav|The Guards, headed by Lord Adolphus Fitz Clarence, now reinforced the police, and the conspirators gave way.
+LJSpeech-1.1/wavs/LJ016-0353.wav|Almost absolute silence prevailed until the great bell began to toll its deep note, and broke the stillness with its regular and monotonous clangour,
+LJSpeech-1.1/wavs/LJ015-0143.wav|Later on he became registrar, and in this more independent position
+LJSpeech-1.1/wavs/LJ002-0163.wav|a market porter, was arrested and committed at the suit of a publican
+LJSpeech-1.1/wavs/LJ044-0057.wav|extensive investigation was not able to connect Oswald with that address, although it did develop the fact
+LJSpeech-1.1/wavs/LJ040-0101.wav|When she worked during the school year, Lee had to leave an empty house in the morning,
+LJSpeech-1.1/wavs/LJ050-0189.wav|that written instructions might come into the hands of local newspapers, to the prejudice of the precautions described.
+LJSpeech-1.1/wavs/LJ003-0328.wav|Had they been accepted in their entirety, little fault could in future have been found with the managers of Newgate.
+LJSpeech-1.1/wavs/LJ030-0143.wav|The exact time of the assassination was fixed by the testimony of four witnesses.
+LJSpeech-1.1/wavs/LJ043-0102.wav|It is possible that his immediate supervisor noticed the newspaper at that time because his attention had otherwise been drawn more directly to Oswald.
+LJSpeech-1.1/wavs/LJ035-0061.wav|In fact, he didn't change his expression one bit, end quote.
+LJSpeech-1.1/wavs/LJ042-0025.wav|His idea that he was to find, quote, freedom, end quote, in the Soviet Union was to be rudely shattered.
+LJSpeech-1.1/wavs/LJ036-0124.wav|Whaley testified that he did not keep an accurate time record of his trips but recorded them by the quarter hour,
+LJSpeech-1.1/wavs/LJ038-0252.wav|Marina Oswald stated that- when she asked her husband what be had done with the rifle,
+LJSpeech-1.1/wavs/LJ031-0074.wav|quote, could have easily been hidden in the blood and hair, end quote,
+LJSpeech-1.1/wavs/LJ030-0062.wav|The President had frequently stated that he did not want agents to ride on these steps during a motorcade except when necessary.
+LJSpeech-1.1/wavs/LJ012-0127.wav|The gold-dust robbery of eighteen thirty-nine, the first of its kind, was cleverly and carefully planned with the assistance of a dishonest employee.
+LJSpeech-1.1/wavs/LJ036-0113.wav|Actually Oswald was under Number three.
+LJSpeech-1.1/wavs/LJ029-0158.wav|The following day, the newspaper reported that the Presidential party
+LJSpeech-1.1/wavs/LJ036-0216.wav|Tippit got out and started to walk around the front of the car
+LJSpeech-1.1/wavs/LJ049-0052.wav|In response to inquiry by the Commission regarding the instructions to agents in a motorcade
+LJSpeech-1.1/wavs/LJ028-0360.wav|During the reign of Xerxes again the city rebelled, and in four eighty-four B.C. he captured it, and completely demolished its defenses.
+LJSpeech-1.1/wavs/LJ005-0145.wav|Great want of uniformity in treatment in the various prisons was still noticeable
+LJSpeech-1.1/wavs/LJ014-0106.wav|The lime had done its work so rapidly that the features would have been indistinguishable but for the prominent chin and a set of false teeth.
+LJSpeech-1.1/wavs/LJ013-0175.wav|Upstairs in the bedroom a rouleaux box for sovereigns had been broken open,
+LJSpeech-1.1/wavs/LJ040-0142.wav|was disturbed about disrobing in front of them, taking showers with them etc., end quote.
+LJSpeech-1.1/wavs/LJ027-0135.wav|which, briefly stated, is that individual development (ontogeny) recapitulates ancestral history (phylogeny).
+LJSpeech-1.1/wavs/LJ016-0354.wav|and the ordinary, in a voice trembling with emotion, read the burial service aloud.
+LJSpeech-1.1/wavs/LJ018-0291.wav|Then he explained to the bank manager
+LJSpeech-1.1/wavs/LJ033-0140.wav|Three cartons had been placed at the window apparently to act as a gun rest and a fourth carton was placed behind those at the window.
+LJSpeech-1.1/wavs/LJ015-0171.wav|but finding he could not escape, he said he would resign his post, and leaving the boardroom, disappeared.
+LJSpeech-1.1/wavs/LJ019-0142.wav|now seldom contained more than ten or a dozen each. Some sort of decorum was maintained among the occupants in the day-time.
+LJSpeech-1.1/wavs/LJ012-0104.wav|The thieves were satisfied with the diamonds; they broke open other cases containing gold watches and plate, but abstracted nothing.
+LJSpeech-1.1/wavs/LJ009-0111.wav|the condemned returning to the cells: the forger carried by turnkeys; the youth sobbing aloud convulsively, as a passionate child;
+LJSpeech-1.1/wavs/LJ045-0030.wav|when his wife asked Oswald not to come to Irving.
+LJSpeech-1.1/wavs/LJ001-0011.wav|it is of the first importance that the letter used should be fine in form;
+LJSpeech-1.1/wavs/LJ034-0195.wav|based on other evidence, to have fired the shots from the window.
+LJSpeech-1.1/wavs/LJ034-0183.wav|Boxes and cases were stacked behind him.
+LJSpeech-1.1/wavs/LJ021-0087.wav|We have the right to expect that this driving power will be given patriotically and whole-heartedly to our nation.
+LJSpeech-1.1/wavs/LJ040-0091.wav|but he returned to high school in January of nineteen forty-nine, where he stayed until three days before he was scheduled to graduate,
+LJSpeech-1.1/wavs/LJ047-0005.wav|Oswald was known to other Federal agencies with which the Secret Service maintained intelligence liaison.
+LJSpeech-1.1/wavs/LJ046-0109.wav|A basic element of Presidential protection
+LJSpeech-1.1/wavs/LJ048-0072.wav|the Commission does not believe that the liaison between the FBI and the Secret Service prior to the assassination was as effective as it should have been.
+LJSpeech-1.1/wavs/LJ049-0035.wav|However, such access to the President was interfered with both by the metal bar some fifteen inches above the back of the front seat
+LJSpeech-1.1/wavs/LJ037-0050.wav|Her description and that of other eyewitnesses led to the police broadcast at one:twenty-two p.m.
+LJSpeech-1.1/wavs/LJ030-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ014-0191.wav|Marley ran along the street, down into Cannon Row
+LJSpeech-1.1/wavs/LJ037-0017.wav|The man cut across the yard through some bushes, passed within twelve feet of Scoggins, and ran south on Patton.
+LJSpeech-1.1/wavs/LJ028-0229.wav|The Babylonians, encamped without their walls, awaited his coming.
+LJSpeech-1.1/wavs/LJ018-0307.wav|Through Noyes the rest of the conspirators were eventually apprehended. Very little if any of the ill-gotten proceeds, however, was ever recovered.
+LJSpeech-1.1/wavs/LJ021-0167.wav|to end our present unemployment as soon as we can and then to take wise measures against its return.
+LJSpeech-1.1/wavs/LJ003-0139.wav|and I was obliged afterwards to let him have five shillings to pay his share,
+LJSpeech-1.1/wavs/LJ015-0074.wav|His immediate chief was a Mr. Fasson, upon whose confidence he gained so rapidly, through his activity, industry, and engaging manners,
+LJSpeech-1.1/wavs/LJ028-0436.wav|The Germans maintain that it is the ruin of the Tower of Babel.
+LJSpeech-1.1/wavs/LJ046-0135.wav|PRS received items in eight thousand, seven hundred nine cases.
+LJSpeech-1.1/wavs/LJ033-0184.wav|had different characteristics from both the actual bag and the sample taken on November twenty-two.
+LJSpeech-1.1/wavs/LJ015-0071.wav|He now married, although his salary was only a pound a week; but he soon got on.
+LJSpeech-1.1/wavs/LJ025-0056.wav|That the semi-fluid contents (which we now term protoplasm) of the cells of certain plants,
+LJSpeech-1.1/wavs/LJ019-0103.wav|The latter had reference more especially to a proposal emanating from Mr. Charles Pearson himself.
+LJSpeech-1.1/wavs/LJ013-0130.wav|where Ashley produced four silver spoons, and begged that his initials might be engraved upon them.
+LJSpeech-1.1/wavs/LJ035-0115.wav|Oswald's use of the stairway is consistent with the testimony of other employees in the building.
+LJSpeech-1.1/wavs/LJ036-0099.wav|that he recognized Oswald from a newspaper photograph as a man whom he had driven to the Oak Cliff area the day before.
+LJSpeech-1.1/wavs/LJ043-0182.wav|In any event, the Walker incident indicates that in spite of the belief among those who knew him that he was apparently not dangerous,
+LJSpeech-1.1/wavs/LJ038-0255.wav|Firearms identification.
+LJSpeech-1.1/wavs/LJ007-0062.wav|Latterly his ministrations to the condemned had been restricted to a visit on Sunday afternoons, and occasionally about once a fortnight on a week-day.
+LJSpeech-1.1/wavs/LJ031-0153.wav|The telephone line to Washington was kept open throughout the remainder of the stay at the hospital.
+LJSpeech-1.1/wavs/LJ045-0152.wav|The events of that evening can best be appreciated through Marina Oswald's testimony
+LJSpeech-1.1/wavs/LJ040-0159.wav|who suffers under the impact of really existing emotional isolation
+LJSpeech-1.1/wavs/LJ033-0086.wav|Mrs. Randle estimated that the package was approximately twenty-eight inches long and about eight inches wide.
+LJSpeech-1.1/wavs/LJ011-0230.wav|Mr. E. G. Wakefield took part in the scheme for the colonization of North Australia, and for some years resided in that colony.
+LJSpeech-1.1/wavs/LJ028-0509.wav|Nebuchadnezzar speaks of great bronze gates and of images of bronze, but none have been discovered.
+LJSpeech-1.1/wavs/LJ001-0145.wav|No definite rules, however, except the avoidance of "rivers" and excess of white, can be given for the spacing,
+LJSpeech-1.1/wavs/LJ037-0244.wav|Westbrook identified Commission Exhibit Number one sixty-two as the light-colored jacket which he discovered underneath the automobile.
+LJSpeech-1.1/wavs/LJ022-0037.wav|that are filled by the vote of all of the voters -- the President and the Vice-President?
+LJSpeech-1.1/wavs/LJ006-0126.wav|had been known to employ threats, declaring that he was often consulted by the governor as to a prisoner's character,
+LJSpeech-1.1/wavs/LJ028-0121.wav|A thing which no king before had ever done:
+LJSpeech-1.1/wavs/LJ002-0288.wav|In one room Mr. Neild found a woman ill of a flux shut up with three men;
+LJSpeech-1.1/wavs/LJ034-0189.wav|Fischer explained that he did not mean by the earlier statement that the man was blond, but rather that his hair was not black.
+LJSpeech-1.1/wavs/LJ019-0143.wav|Drinking and gaming,
+LJSpeech-1.1/wavs/LJ006-0089.wav|to be pitchforked by chance anywhere, into any sort of company,
+LJSpeech-1.1/wavs/LJ028-0010.wav|Now and then she reaches down to graze the thorny argool along the way.
+LJSpeech-1.1/wavs/LJ041-0073.wav|While Oswald did not seem to object to authority in the abstract, he did think that he should be the one to exercise it.
+LJSpeech-1.1/wavs/LJ031-0187.wav|Federal Judge Sarah T. Hughes hastened to the plane to administer the oath.
+LJSpeech-1.1/wavs/LJ028-0456.wav|The small portions of it which have been excavated suffice to show its construction.
+LJSpeech-1.1/wavs/LJ042-0098.wav|The subsidy, apparently similar to those sometimes given to foreigners allowed to remain in the Soviet Union, together with his salary,
+LJSpeech-1.1/wavs/LJ016-0102.wav|By evening he manufactured a good long length,
+LJSpeech-1.1/wavs/LJ048-0074.wav|To insure adequate and effective liaison arrangements,
+LJSpeech-1.1/wavs/LJ016-0229.wav|He kept no record of them, and when asked questions, referred to the officers of the jail.
+LJSpeech-1.1/wavs/LJ021-0028.wav|It was in this spirit thus described by Secretary Root
+LJSpeech-1.1/wavs/LJ016-0168.wav|A laudable desire to invest executions with more and more solemnity and decorum gained ground as they became more rare.
+LJSpeech-1.1/wavs/LJ009-0125.wav|could any one, not disgusted with the religious ceremonials of Newgate, witness this scene without emotion?
+LJSpeech-1.1/wavs/LJ030-0124.wav|When a teenage boy ran toward the rear of the President's car, Ready left the running board to chase the boy back into the crowd.
+LJSpeech-1.1/wavs/LJ039-0011.wav|toward any official of the United States.
+LJSpeech-1.1/wavs/LJ032-0150.wav|In the opinion of these experts, it was not possible to estimate the time which elapsed between the placing of the print on the rifle and the date of the lift.
+LJSpeech-1.1/wavs/LJ033-0064.wav|Mrs. Paine pointed out that most of the Oswalds' possessions were in the garage.
+LJSpeech-1.1/wavs/LJ002-0301.wav|They were made to wash and swab the ward, or they were shut out from the ward fireplace, and forbidden to pass a chalked line drawn on the floor,
+LJSpeech-1.1/wavs/LJ030-0012.wav|In Houston, as elsewhere during the trip, the crowds showed much interest in Mrs. Kennedy.
+LJSpeech-1.1/wavs/LJ038-0120.wav|in Oswald's billfold. On November twenty-two and twenty-three, Oswald refused to tell Fritz why this card was in his possession,
+LJSpeech-1.1/wavs/LJ031-0026.wav|Dr. Fouad A. Bashour.
+LJSpeech-1.1/wavs/LJ019-0102.wav|which was promptly carried, with the additional instruction to the committee to suggest any improvements.
+LJSpeech-1.1/wavs/LJ014-0162.wav|But it may be mentioned that the concourse was so enormous that it drew down the well-merited and trenchant disapproval of Charles Dickens,
+LJSpeech-1.1/wavs/LJ048-0270.wav|The regulations provide further that, quote, violation or slight disregard, end quote, of these provisions, quote,
+LJSpeech-1.1/wavs/LJ016-0210.wav|Meanwhile the convener or sheriff was in despair, expecting that, failing the executioner, he would have to do the job himself.
+LJSpeech-1.1/wavs/LJ042-0141.wav|She added that while he helped her as he had done before, he became more of a recluse, that, quote,
+LJSpeech-1.1/wavs/LJ040-0032.wav|He accused his wife of preferring others to himself and told her to return to the Soviet Union without him but without a divorce.
+LJSpeech-1.1/wavs/LJ028-0426.wav|For fifteen years Dr. Koldewey and his assistants, with a force of two hundred native workmen, have labored there winter and summer.
+LJSpeech-1.1/wavs/LJ036-0159.wav|On the other hand, Whaley identified Commission Exhibit Number one fifty (the shirt taken from Oswald upon arrest) as the shirt his passenger was wearing.
+LJSpeech-1.1/wavs/LJ032-0080.wav|were a Selective Service notice of classification and a Marine certificate of service in the name of Alek James Hidell.
+LJSpeech-1.1/wavs/LJ029-0157.wav|The selection of the Trade Mart as the possible site for the luncheon first appeared in the Dallas Times-Herald on November fifteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ014-0137.wav|When the judge assumed the black cap
+LJSpeech-1.1/wavs/LJ046-0199.wav|The general files of PRS were not indexed by geographic location and were of little use in preparing for a Presidential visit to a specific locality.
+LJSpeech-1.1/wavs/LJ004-0021.wav|The neglect of prison reform in those days was not to be visited upon the legislature.
+LJSpeech-1.1/wavs/LJ024-0030.wav|The plan has met opposition only so far as the Supreme Court of the United States itself is concerned.
+LJSpeech-1.1/wavs/LJ033-0113.wav|The bag found on the sixth floor was eighty-eight inches long.
+LJSpeech-1.1/wavs/LJ022-0095.wav|This Division will have the duty of coordinating the purchases of materials and supplies
+LJSpeech-1.1/wavs/LJ001-0126.wav|Most of Jenson's letters are designed within a square,
+LJSpeech-1.1/wavs/LJ030-0083.wav|to a walking pace or stopped,
+LJSpeech-1.1/wavs/LJ006-0207.wav|One of these, published by Stockdale, the inspectors styled "a book of the most disgusting nature."
+LJSpeech-1.1/wavs/LJ045-0145.wav|Those arguments, however, were not persuasive to Marina Oswald,
+LJSpeech-1.1/wavs/LJ031-0208.wav|The hospital received the President's body for autopsy at approximately seven:thirty-five p.m.
+LJSpeech-1.1/wavs/LJ027-0090.wav|modified more and more through successive generations by the necessities of different modes of life.
+LJSpeech-1.1/wavs/LJ012-0200.wav|Bishop got weary of the dangers and fatigues of exhumation, and proposed to Williams that instead of disinterring they should murder their subjects.
+LJSpeech-1.1/wavs/LJ017-0012.wav|The well-known and lethal aqua Toffania,
+LJSpeech-1.1/wavs/LJ040-0232.wav|Few social agencies even in New York were equipped to provide the kind of intensive treatment that he needed,
+LJSpeech-1.1/wavs/LJ018-0203.wav|Proceeding cautiously down the stairs, they found that the flooring at the bottom had been taken up.
+LJSpeech-1.1/wavs/LJ016-0263.wav|Few, if any, showed any feeling of terror, none were impressed with the solemnity, or realized the warning which the sight conveyed.
+LJSpeech-1.1/wavs/LJ001-0104.wav|Italy is contentedly stagnant.
+LJSpeech-1.1/wavs/LJ027-0048.wav|On the other hand are found structures which are perfectly homologous and yet in no way analogous:
+LJSpeech-1.1/wavs/LJ011-0074.wav|There were two notable capital convictions for forgery in eighteen twenty-eight.
+LJSpeech-1.1/wavs/LJ035-0187.wav|he also directed that the back door be sealed off.
+LJSpeech-1.1/wavs/LJ009-0180.wav|the idea being to intensify the dread of capital punishment. That such dread was not universal or deep-seated may be gathered from the fact
+LJSpeech-1.1/wavs/LJ036-0074.wav|If Oswald left the Depository Building at twelve:thirty-three p.m.,
+LJSpeech-1.1/wavs/LJ038-0087.wav|in an apparent effort to determine, by means of a scientific test, whether Oswald had recently fired a weapon.
+LJSpeech-1.1/wavs/LJ014-0108.wav|It was soon ascertained that the wife had gone off in a cab with a quantity of luggage.
+LJSpeech-1.1/wavs/LJ028-0090.wav|He was fond of restoring the ruined temples of the old Babylonian cities,
+LJSpeech-1.1/wavs/LJ017-0179.wav|In all cases the symptoms were much the same,
+LJSpeech-1.1/wavs/LJ027-0154.wav|the process of successive additions which had previously taken place in a remote series of extinct species.
+LJSpeech-1.1/wavs/LJ024-0109.wav|Now they are making a last stand.
+LJSpeech-1.1/wavs/LJ028-0402.wav|Hillah might be called a child of Babylon, for it is almost entirely constructed with Nebuchadnezzar's bricks.
+LJSpeech-1.1/wavs/LJ036-0193.wav|Description of Shooting
+LJSpeech-1.1/wavs/LJ015-0044.wav|with money enough to retrieve the position of the bank. But that passed from bad to worse;
+LJSpeech-1.1/wavs/LJ048-0133.wav|Constant reevaluation of procedures, with attention to special problems and the development of instructions specific to particular trips
+LJSpeech-1.1/wavs/LJ011-0067.wav|A report was, moreover, widely circulated, and the impression long prevailed, that he actually escaped death.
+LJSpeech-1.1/wavs/LJ031-0028.wav|Lawson jumped from the lead car and rushed into the emergency entrance, where he was met by hospital staff members wheeling stretchers out to the automobile.
+LJSpeech-1.1/wavs/LJ050-0198.wav|the studies indicate that there is some utility in attempting to designate certain buildings as involving a higher risk than others.
+LJSpeech-1.1/wavs/LJ021-0193.wav|All of these cries have been heard before.
+LJSpeech-1.1/wavs/LJ029-0148.wav|for controlling the crowds and traffic, watching the overpasses, and providing motorcycle escort.
+LJSpeech-1.1/wavs/LJ021-0031.wav|Some banks could not be saved but the great majority of them, either through their own resources or with government aid,
+LJSpeech-1.1/wavs/LJ039-0180.wav|As has been shown in chapter three, if the three shots were fired within a period of from four point eight to five point six seconds,
+LJSpeech-1.1/wavs/LJ025-0153.wav|and inquire whether certain differences of a more occult character than those imagined to exist by Cuvier,
+LJSpeech-1.1/wavs/LJ009-0067.wav|The service proceeds. At last the burial service is reached.
+LJSpeech-1.1/wavs/LJ038-0126.wav|At the last interrogation in November Oswald admitted to Postal Inspector Holmes that he had rented post office box two nine one five, Dallas,
+LJSpeech-1.1/wavs/LJ012-0115.wav|was refused, at the instigation of the police. A few days later a frequent guest at the tavern arrived, and had this same room allotted to him.
+LJSpeech-1.1/wavs/LJ012-0171.wav|Every door had been closed against him, every hope of future support blasted.
+LJSpeech-1.1/wavs/LJ004-0248.wav|But this boon of toil was denied to all but a very limited number.
+LJSpeech-1.1/wavs/LJ033-0045.wav|At approximately nine p.m., after the children had been put to bed, Mrs. Paine, according to her testimony before the Commission, quote,
+LJSpeech-1.1/wavs/LJ042-0232.wav|I have never even know a communist, outside of the ones in the USSR but you can't help that, end quote.
+LJSpeech-1.1/wavs/LJ013-0085.wav|A person, Elder of course, who personated Oxenford, attended at the bank to complete the transfer and sell the stock.
+LJSpeech-1.1/wavs/LJ024-0097.wav|has ever been ratified within anything like a reasonable time.
+LJSpeech-1.1/wavs/LJ014-0057.wav|London did not escape the contagion, and prominent among the detestable crimes of the period stands that of the Mannings at Bermondsey.
+LJSpeech-1.1/wavs/LJ031-0057.wav|While Dr. Perry was performing the tracheotomy, Drs. Carrico and Ronald Jones made cutdowns on the President's right leg and left arm, respectively,
+LJSpeech-1.1/wavs/LJ006-0134.wav|The governor himself admitted that a prisoner of weak intellect who had been severely beaten and much injured by a wardsman did not dare complain
+LJSpeech-1.1/wavs/LJ019-0351.wav|Nor were these the only inducements offered. Where local authorities were indisposed to set their prisons in order,
+LJSpeech-1.1/wavs/LJ038-0184.wav|This is the key to the mailbox which is located in the main post office in the city on Ervay Street.
+LJSpeech-1.1/wavs/LJ049-0078.wav|have properly taken the initiative in reexamining major aspects of Presidential protection.
+LJSpeech-1.1/wavs/LJ025-0002.wav|Edited by Francis Rolt-Wheeler. Biology. Chapter seven. Organic Functions. Part One.
+LJSpeech-1.1/wavs/LJ018-0156.wav|after using much intemperate language, he left the place in a huff.
+LJSpeech-1.1/wavs/LJ005-0107.wav|In that prison, with its cells and yards arranged within the shell of an ancient abbey chapel,
+LJSpeech-1.1/wavs/LJ019-0050.wav|One school were strongly in favor of the continuous separation of prisoners,
+LJSpeech-1.1/wavs/LJ002-0328.wav|Failing any of these methods, seeing that straw was forbidden for fear of fire, they had to be satisfied with a couple of the rugs provided by the city
+LJSpeech-1.1/wavs/LJ010-0216.wav|and it is said that he is a military officer, but his name has not yet transpired.
+LJSpeech-1.1/wavs/LJ043-0026.wav|Oswald submitted to the inevitable, presumably because he was, quote, small, you know, and he was rather a puny individual, end quote,
+LJSpeech-1.1/wavs/LJ016-0067.wav|he tramped across London Bridge to Wandsworth, where he refreshed himself with a pint of strong ale, the first sustenance he had taken since his escape,
+LJSpeech-1.1/wavs/LJ033-0024.wav|Oswald replied, quote, I'm going home to get some curtain rods to put in an apartment, end quote.
+LJSpeech-1.1/wavs/LJ007-0011.wav|But it was already plain that they constituted an independent authority within the jails; they were frequently in conflict with the chaplain,
+LJSpeech-1.1/wavs/LJ007-0003.wav|and Mrs. Fry with her colleagues still labored assiduously in Newgate, devoting themselves mainly to the female prison,
+LJSpeech-1.1/wavs/LJ022-0034.wav|This duty of seeing the country in a long-range perspective
+LJSpeech-1.1/wavs/LJ037-0063.wav|A review of the complete transcript has satisfied the Commission
+LJSpeech-1.1/wavs/LJ028-0114.wav|and I, with mortar and bricks, built a third great moat-wall, and joined it and united it closely with the moat-walls of my father.
+LJSpeech-1.1/wavs/LJ018-0106.wav|He was a German named Kerp,
+LJSpeech-1.1/wavs/LJ050-0220.wav|and requests for additional personnel were not made because of the studies then being conducted.
+LJSpeech-1.1/wavs/LJ042-0107.wav|Oswald apparently continued to have personal difficulties while he was in Minsk.
+LJSpeech-1.1/wavs/LJ045-0169.wav|That this was better because while he was living alone and I stayed with Ruth, we were spending less money. And I told him to buy me a washing machine, because two children
+LJSpeech-1.1/wavs/LJ038-0187.wav|I paid for the box last month so don't worry about it.
+LJSpeech-1.1/wavs/LJ012-0190.wav|Widespread horror and indignation was evoked throughout the kingdom by the discovery of the series
+LJSpeech-1.1/wavs/LJ035-0194.wav|Approximately fifteen men worked in the warehouse and Truly noticed that Oswald was not among those being questioned.
+LJSpeech-1.1/wavs/LJ002-0334.wav|Again, there was no regular allowance of fuel. Coals were purchased out of the garnish money and the charitable fund;
+LJSpeech-1.1/wavs/LJ037-0145.wav|Four cartridge cases were found in the shrubbery on the corner of tenth and Patton by three of the eyewitnesses -- Domingo Benavides,
+LJSpeech-1.1/wavs/LJ014-0258.wav|Implicit confidence appears to have been placed in Watts, who was the son of an old and trusted employee, and, moreover, a young man of plausible address.
+LJSpeech-1.1/wavs/LJ016-0342.wav|The first private execution under the new law took place within the precincts of Maidstone Jail.
+LJSpeech-1.1/wavs/LJ024-0060.wav|Normally every President appoints a large number of district and circuit court judges and a few members of the Supreme Court.
+LJSpeech-1.1/wavs/LJ028-0422.wav|Among the later visitors to Babylon was the great Niebuhr.
+LJSpeech-1.1/wavs/LJ031-0102.wav|Governor Connally was originally seen by Dr. Carrico and Dr. Richard Dulany.
+LJSpeech-1.1/wavs/LJ026-0048.wav|movement, irritability (nervous activity) and reproduction.
+LJSpeech-1.1/wavs/LJ011-0188.wav|They set out, but at Leeds Wakefield found himself called suddenly to Paris;
+LJSpeech-1.1/wavs/LJ021-0010.wav|These governmental and industrial developments hold promise of new achievements for the nation.
+LJSpeech-1.1/wavs/LJ007-0133.wav|Valuable space was cumbered with many long and winding passages, numerous staircases, and unnecessarily thick and cumbrous inner walls.
+LJSpeech-1.1/wavs/LJ014-0287.wav|He had been led to suppose that twelve months' imprisonment was the utmost the law could inflict, and he broke down utterly under the unexpected blow.
+LJSpeech-1.1/wavs/LJ009-0068.wav|The youth alone is able to read, but from long want of practice he is at a loss to find the place in his prayer-book.
+LJSpeech-1.1/wavs/LJ005-0218.wav|The chapel still continued incommodious and insufficient
+LJSpeech-1.1/wavs/LJ047-0125.wav|In other words, he committed no violation of the law by telling us something that wasn't true, and unless this required further investigation at that time,
+LJSpeech-1.1/wavs/LJ043-0152.wav|She said that she kept the note to use against him, quote, if something like that should be repeated again, end quote.
+LJSpeech-1.1/wavs/LJ021-0048.wav|the careful checking of new securities under the Securities Act
+LJSpeech-1.1/wavs/LJ014-0125.wav|Mrs. Manning was not without personal charms;
+LJSpeech-1.1/wavs/LJ018-0343.wav|In this case the tie was unsanctified, but it was not more inconvenient than that which urged Greenacre to a similar crime.
+LJSpeech-1.1/wavs/LJ044-0233.wav|Oswald was carrying only thirteen dollars, eighty-seven cents at the time of his arrest, although he had left, apparently by design,
+LJSpeech-1.1/wavs/LJ028-0060.wav|In six oh five,
+LJSpeech-1.1/wavs/LJ016-0024.wav|A more daring and skilful escape was effected in eighteen thirty-six by the chimney-sweep Henry Williams,
+LJSpeech-1.1/wavs/LJ030-0086.wav|Vice-Presidential car.
+LJSpeech-1.1/wavs/LJ016-0173.wav|was not always to be trusted to do his fell work efficiently.
+LJSpeech-1.1/wavs/LJ014-0179.wav|a working jeweler, shopman to a Mr. Berry of Parliament Street. It was Cope's duty to stay in the shop till the last, close the shutters,
+LJSpeech-1.1/wavs/LJ005-0211.wav|the jail consisted of six cells, frequently so damp that the moisture trickled down the walls; there was not space for air or exercise,
+LJSpeech-1.1/wavs/LJ014-0123.wav|The Mannings were arraigned together; the husband standing at one of the front corners of the dock, his wife at the other end.
+LJSpeech-1.1/wavs/LJ009-0281.wav|Calcraft was forced to return, and he once more pushed Bousfield off,
+LJSpeech-1.1/wavs/LJ026-0088.wav|In short, the green plant is able by cooperation with sunlight to use simple raw materials (carbon dioxide, water, oxygen, etc.)
+LJSpeech-1.1/wavs/LJ021-0187.wav|It is perhaps not strange that the conservative British press has told us with pardonable irony
+LJSpeech-1.1/wavs/LJ021-0002.wav|September thirty, nineteen thirty-four.
+LJSpeech-1.1/wavs/LJ041-0114.wav|three months prior to his regularly scheduled separation date, ostensibly to care for his mother who had been injured in an accident at her work.
+LJSpeech-1.1/wavs/LJ036-0109.wav|he told them what he thought about them. They knew what they were doing and they were trying to railroad him and he wanted his lawyer.
+LJSpeech-1.1/wavs/LJ047-0214.wav|End quote. Mr. Bouck pointed out, however, that he had no reason to believe that any one Federal agency had access to all this information,
+LJSpeech-1.1/wavs/LJ016-0222.wav|It may be added that of late years the clothes in which a convict has suffered are invariably burnt.
+LJSpeech-1.1/wavs/LJ013-0257.wav|They were seen together on Barnes Common, and again in Putney Park Lane, where they were talking loud and angrily.
+LJSpeech-1.1/wavs/LJ008-0115.wav|After the yeoman had requested him to stand up, 'he pinioned him,' as the Newgate phrase is
+LJSpeech-1.1/wavs/LJ037-0128.wav|were on the lot at the time, and they saw a white male with a revolver in his hands running south on Patton.
+LJSpeech-1.1/wavs/LJ002-0153.wav|weighted by treble the amount of costs.
+LJSpeech-1.1/wavs/LJ005-0232.wav|Idleness was still the general rule for all prisoners in Newgate, in defiance of the law.
+LJSpeech-1.1/wavs/LJ016-0163.wav|He might have saved himself at any moment by merely extending an arm; but he lay there patiently till death supervened.
+LJSpeech-1.1/wavs/LJ006-0075.wav|He saw certain rooms fill up, and yet took no steps to open others that were locked up and empty.
+LJSpeech-1.1/wavs/LJ029-0178.wav|reflected the feeling in the community toward the forthcoming Presidential visit.
+LJSpeech-1.1/wavs/LJ007-0144.wav|and he can almost invariably procure the luxuries of his class of life, beer and tobacco, in abundance.
+LJSpeech-1.1/wavs/LJ030-0010.wav|spoke at the Rice University Stadium, and attended a dinner in honor of U.S. Representative Albert Thomas.
+LJSpeech-1.1/wavs/LJ029-0146.wav|The planning for the motorcade also included advance preparations for security arrangements along the route.
+LJSpeech-1.1/wavs/LJ044-0088.wav|Fair Play for Cuba was, quote, not at all Communist controlled regardless of the fact that I had the experience of living in Russia, end quote.
+LJSpeech-1.1/wavs/LJ003-0040.wav|The evils of association for these debtors were perpetuated, although the plan provided for the separation of the various contingents committed to it.
+LJSpeech-1.1/wavs/LJ048-0209.wav|at least for a sufficient number of seconds to gain a, quote, general impression, end quote, of the lack of any unusual activity.
+LJSpeech-1.1/wavs/LJ016-0009.wav|He had friends and auxiliaries inside the jail and out. The cell he occupied was near the outer wall,
+LJSpeech-1.1/wavs/LJ008-0037.wav|were brought out of Newgate about eight in the morning, and suspended on a gallows of a new construction.
+LJSpeech-1.1/wavs/LJ035-0062.wav|Truly said of Oswald: quote, He didn't seem to be excited or overly afraid or anything. He might have been a bit startled, like I might have been if somebody confronted me.
+LJSpeech-1.1/wavs/LJ019-0282.wav|Their origin it will be seen dates back much further than that.
+LJSpeech-1.1/wavs/LJ019-0268.wav|In some places the dietary was too full, in others too meager. Its constituents were not of the most suitable character.
+LJSpeech-1.1/wavs/LJ006-0303.wav|This girl had been first engaged on account of the extra work entailed by certain prisoners
+LJSpeech-1.1/wavs/LJ041-0086.wav|and then used the, quote, special treatment, end quote, he received as an example of the way in which he was being picked on and, quote,
+LJSpeech-1.1/wavs/LJ017-0151.wav|The latter seems to have succumbed only too willingly to his fascinations, and to have as readily agreed to marry him,
+LJSpeech-1.1/wavs/LJ018-0340.wav|characterized this as a crime more black and hideous than any in the criminal annals of the country.
+LJSpeech-1.1/wavs/LJ019-0194.wav|not to exceed a certain sum to be agreed upon.
+LJSpeech-1.1/wavs/LJ018-0170.wav|On the bed were twenty forged ten-pound notes complete and ready for use, and twenty-five five-pound notes.
+LJSpeech-1.1/wavs/LJ039-0043.wav|She stated further that Oswald had only mentioned Nixon's name once during the incident. Marina Oswald might have misunderstood her husband.
+LJSpeech-1.1/wavs/LJ001-0081.wav|type cast from his matrices is still in everyday use.
+LJSpeech-1.1/wavs/LJ016-0043.wav|The least slip now would have been fatal to him. But he could not thrust his body in through the narrow space left by the chevaux-de-frise,
+LJSpeech-1.1/wavs/LJ005-0168.wav|"It is not," he said, "that I am insensible of the lamentable and disgraceful situation in which many of them are,
+LJSpeech-1.1/wavs/LJ040-0060.wav|and his unsuccessful attempt to go to Cuba in late September of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ044-0141.wav|even though he had written four detailed letters since that time to Mr. V. T. Lee and had also kept the national headquarters informed
+LJSpeech-1.1/wavs/LJ038-0238.wav|An examination of the window at the rear of the house, the wall through which the bullet passed, and the fence behind the house
+LJSpeech-1.1/wavs/LJ032-0104.wav|Marina Oswald testified,
+LJSpeech-1.1/wavs/LJ015-0291.wav|partly through their own carelessness, when transferring their operations to Yarmouth.
+LJSpeech-1.1/wavs/LJ002-0300.wav|Numerous tyrannies were practiced on all who would not and could not pay the garnish.
+LJSpeech-1.1/wavs/LJ048-0169.wav|In Dallas the route selected necessarily involved passing through the principal downtown section between tall buildings.
+LJSpeech-1.1/wavs/LJ047-0103.wav|who was involved, what occurred, he was reticent to furnish information,
+LJSpeech-1.1/wavs/LJ028-0049.wav|and in that same year Nabopolassar, the father of the great Nebuchadnezzar, became the King of Babylon.
+LJSpeech-1.1/wavs/LJ018-0205.wav|Cummings was caught shortly afterwards.
+LJSpeech-1.1/wavs/LJ028-0471.wav|Nebuchadnezzar says that he built it of burned bricks, but only sun-dried bricks laid in mud now appear.
+LJSpeech-1.1/wavs/LJ013-0010.wav|The 'Dryad' was a brig owned principally by two persons named Wallace, one a seaman, the other merchant.
+LJSpeech-1.1/wavs/LJ049-0046.wav|Even so, analysis of the motion picture films taken by amateur photographer Zapruder
+LJSpeech-1.1/wavs/LJ006-0010.wav|Mr. Samuel Hoare, when examined, considered it indispensably necessary, to carry out whatever system might be established,
+LJSpeech-1.1/wavs/LJ014-0201.wav|After sentence, the Newgate officers who had special charge of him noticed that he slept well and ate well, enjoying all his meals.
+LJSpeech-1.1/wavs/LJ028-0296.wav|thou coverest the foulest deeds with the fairest possible name, when thou sayest thy maiming is to help our siege forward.
+LJSpeech-1.1/wavs/LJ003-0033.wav|Enough has been said, probably, to prove that there was room for improvement in the condition and treatment of debtors in the prisons of the city of London.
+LJSpeech-1.1/wavs/LJ049-0060.wav|but to consider any untoward circumstances as serious and to afford the President maximum protection at all times.
+LJSpeech-1.1/wavs/LJ018-0076.wav|As Sattler brooded over his wrongs, his rage got the upper hand, and he resolved to wreak it upon Thain.
+LJSpeech-1.1/wavs/LJ002-0167.wav|quotes a case which came within his own knowledge of a boy sent to prison for non-payment of one penny.
+LJSpeech-1.1/wavs/LJ017-0215.wav|Six were Spaniards, or rather natives of Manilla, and men of color; one was a Greek, another a Turk;
+LJSpeech-1.1/wavs/LJ007-0109.wav|O'Mally sent in a medical certificate, declaring that the imprisonment was endangering his life, and after some question he was also released.
+LJSpeech-1.1/wavs/LJ006-0235.wav|The surgeon's journal produced to the inspectors contained numerous entries of terrible wounds inflicted in a cowardly way.
+LJSpeech-1.1/wavs/LJ036-0122.wav|and a pickup from Greyhound (bus station) at twelve:thirty p.m.,
+LJSpeech-1.1/wavs/LJ018-0382.wav|He had steadfastly refused to eat meat on some religions scruples, and for the same reason would not touch soup.
+LJSpeech-1.1/wavs/LJ048-0188.wav|These substitute measures were of limited value. Agent Lawson was unable to state whether he had actually instructed
+LJSpeech-1.1/wavs/LJ040-0108.wav|which became more significant as the children grew older.
+LJSpeech-1.1/wavs/LJ011-0056.wav|Angelini wrote to the Lord Mayor to this effect,
+LJSpeech-1.1/wavs/LJ005-0238.wav|I shall have more to say on this subject, and upon the state of Newgate generally, in the following chapter.
+LJSpeech-1.1/wavs/LJ010-0152.wav|fired a pistol at George the third from the pit of Drury Lane theatre in eighteen hundred.
+LJSpeech-1.1/wavs/LJ030-0160.wav|it was calculated that the car required eight point three seconds to cover the one hundred thirty-six feet.
+LJSpeech-1.1/wavs/LJ030-0150.wav|The Dallas police radio log reflects that Chief of Police Curry reported the shooting of the President
+LJSpeech-1.1/wavs/LJ028-0337.wav|attacked the walls on every side, whereupon Zopyrus played out the remainder of his stratagem.
+LJSpeech-1.1/wavs/LJ016-0028.wav|and would certainly have been impossible to any one less nimble than a chimney-sweep, trained under the old system to ascend the most intricate flues.
+LJSpeech-1.1/wavs/LJ018-0333.wav|it was the principal incentive with Kate Webster, that fierce and brutal female savage who took the life of her mistress at Richmond.
+LJSpeech-1.1/wavs/LJ008-0234.wav|each wave seems trying to overleap the other as they are drawn onwards towards this outlet.
+LJSpeech-1.1/wavs/LJ027-0146.wav|though now extinct species, speaks strongly in favor of evolution.
+LJSpeech-1.1/wavs/LJ050-0221.wav|The Secret Service has now presented its recommendations to the Bureau of the Budget. The plan proposed by the Service
+LJSpeech-1.1/wavs/LJ045-0162.wav|He tried to start a conversation with me several times, but I would not answer. And he said that he didn't want me to be angry at him because this upsets him.
+LJSpeech-1.1/wavs/LJ017-0071.wav|To meet his liabilities, he raised large sums on forged bills of acceptance drawn upon his mother, a woman of some means,
+LJSpeech-1.1/wavs/LJ004-0250.wav|"It is obvious that reformation must be materially impeded, and in some cases utterly defeated, when the prisoners are defectively classed,
+LJSpeech-1.1/wavs/LJ017-0249.wav|The boats reached the shore on the fourth October. Leon had prepared a plausible tale to the effect that they belonged to an American ship
+LJSpeech-1.1/wavs/LJ010-0099.wav|The conspirators were in a loft, approached by a ladder and a trap-door, access through which could only be obtained one by one.
+LJSpeech-1.1/wavs/LJ037-0163.wav|Cunningham testified that all of the bullets were mutilated, one being useless for comparison purposes.
+LJSpeech-1.1/wavs/LJ028-0175.wav|but they add little to our knowledge.
+LJSpeech-1.1/wavs/LJ029-0206.wav|Another reaction to the impending visit -- hostile to the President -- came to a head shortly before his arrival.
+LJSpeech-1.1/wavs/LJ033-0066.wav|Mrs. Paine, who had no knowledge of the rifle, first said "No," but when the question was translated, Marina Oswald replied "Yes."
+LJSpeech-1.1/wavs/LJ013-0147.wav|who declared that the rough diamond market was in such a depressed condition that they could only afford to give one thousand seven fifty pounds for stones worth
+LJSpeech-1.1/wavs/LJ029-0100.wav|through the downtown area along Main Street and then to the Trade Mart via Stemmons Freeway.
+LJSpeech-1.1/wavs/LJ006-0004.wav|But this digression was necessary in order to present a more complete picture of the state of jails in the early part of the present century,
+LJSpeech-1.1/wavs/LJ006-0218.wav|Wrestling, in which legs were occasionally broken, was freely indulged in; also such low games as "cobham,"
+LJSpeech-1.1/wavs/LJ013-0017.wav|Yet the Wallaces pretended to have put on board quantities of flannels, cloths, cotton prints,
+LJSpeech-1.1/wavs/LJ012-0160.wav|now more strictly meager, thanks to the inspectors and the more stringent discipline, and before he embarked for Australia to undergo his fourteen years,
+LJSpeech-1.1/wavs/LJ038-0198.wav|sixty dollars on the second of the month. You and the baby can live for another two months using ten dollars per week.
+LJSpeech-1.1/wavs/LJ046-0253.wav|it had no written specification of intelligence information collected by CIA abroad which was desired by the Secret Service
+LJSpeech-1.1/wavs/LJ050-0180.wav|so that these arrangements can be made permanent without adversely affecting the operations of the Service's field offices.
+LJSpeech-1.1/wavs/LJ004-0180.wav|a price which in those days fluctuated enormously -- as much as a hundred percent in a couple of years;
+LJSpeech-1.1/wavs/LJ039-0176.wav|The angle from the first to the second shot
+LJSpeech-1.1/wavs/LJ013-0100.wav|established the fact that the man who had personated Mr. Oxenford was a horse-dealer named Joseph Elder, an intimate acquaintance of Burgess'.
+LJSpeech-1.1/wavs/LJ002-0216.wav|The shameful malpractices of Bambridge,
+LJSpeech-1.1/wavs/LJ030-0101.wav|Admiral George G. Burkley, physician to the President,
+LJSpeech-1.1/wavs/LJ003-0096.wav|Quote, yet they had been long enough, he goes on to say, in the prison associated with the lowest and vilest criminals,
+LJSpeech-1.1/wavs/LJ009-0202.wav|In eighteen eleven Williams, who murdered the Marrs in Ratcliffe Highway, having committed suicide in jail to escape hanging,
+LJSpeech-1.1/wavs/LJ007-0119.wav|As a rule, visitors, many of them persons of good position, came and went all day long, and as late as nine at night;
+LJSpeech-1.1/wavs/LJ017-0219.wav|and punishment such as rope's-ending and tying to the bulwarks had to be applied to get the work properly done.
+LJSpeech-1.1/wavs/LJ031-0031.wav|Despite his serious wounds, Governor Connally tried to get out of the way so that medical help could reach the President.
+LJSpeech-1.1/wavs/LJ047-0028.wav|he had attempted to renounce his U.S. citizenship and applied for Soviet citizenship,
+LJSpeech-1.1/wavs/LJ016-0218.wav|wished to do Calcraft a good turn, and sent to his London tailor for a complete new suit, in which he appeared at his execution.
+LJSpeech-1.1/wavs/LJ031-0209.wav|X-rays and photographs were taken preliminarily and the pathological examination began at about eight p.m.
+LJSpeech-1.1/wavs/LJ001-0047.wav|Of Jenson it must be said that he carried the development of Roman type as far as it can go:
+LJSpeech-1.1/wavs/LJ042-0131.wav|Oswald opened negotiations with the U.S. Embassy in Moscow looking toward his return to the United States.
+LJSpeech-1.1/wavs/LJ018-0210.wav|Those who manufactured and those who passed had no direct dealings with each other.
+LJSpeech-1.1/wavs/LJ018-0318.wav|The vigilance of the Newgate officials, assisted by the city police, completely frustrated this plot.
+LJSpeech-1.1/wavs/LJ046-0247.wav|However, the vague requests for information which the Secret Service made
+LJSpeech-1.1/wavs/LJ014-0302.wav|It outshone the defalcations of Watts.
+LJSpeech-1.1/wavs/LJ012-0059.wav|One turnkey was stupidly drunk, and Ikey persuaded the other, who was not much better,
+LJSpeech-1.1/wavs/LJ037-0030.wav|quote, We've had a shooting out here, end quote,
+LJSpeech-1.1/wavs/LJ033-0090.wav|He asked, quote, What's the package, Lee? End quote. Oswald replied, quote, curtain rods, end quote.
+LJSpeech-1.1/wavs/LJ016-0174.wav|Having mentioned Calcraft's name,
+LJSpeech-1.1/wavs/LJ019-0065.wav|Better industrial results were obtained from it, and instruction in trades was easier, and prisoners were more likely to leave jail
+LJSpeech-1.1/wavs/LJ028-0128.wav|His brief description of them should not be omitted. He says that Nebuchadnezzar
+LJSpeech-1.1/wavs/LJ013-0222.wav|he did not shrink from murder, both for revenge and to conceal his other crimes.
+LJSpeech-1.1/wavs/LJ006-0300.wav|but he afterwards admitted that the girl had been recommended to him by the principal turnkey, who knew something of her friends.
+LJSpeech-1.1/wavs/LJ009-0052.wav|The third convict is a sheep-stealer, a poor ignorant fellow in whose crime are mitigating circumstances,
+LJSpeech-1.1/wavs/LJ029-0193.wav|to call in one hundred extra off-duty officers to help protect President Kennedy.
+LJSpeech-1.1/wavs/LJ001-0151.wav|the unit of the book being looked on as the two pages forming an opening.
+LJSpeech-1.1/wavs/LJ019-0181.wav|is an interesting document, and shows that even at that date the Government contemplated the erection of a model prison.
+LJSpeech-1.1/wavs/LJ002-0323.wav|Hence starvation stared many in the face, unless friends from outside came to their assistance,
+LJSpeech-1.1/wavs/LJ048-0121.wav|The interim report must be in the hands of the agent supervising the protective group traveling with the President
+LJSpeech-1.1/wavs/LJ024-0011.wav|secondly, to bring to the decision of social and economic problems younger men who have had personal experience and contact
+LJSpeech-1.1/wavs/LJ025-0119.wav|from the metamorphosis of morphological units (termed cells in a more general sense than that in which the word "cells" was at first employed),
+LJSpeech-1.1/wavs/LJ038-0163.wav|Prior attempt to kill.
+LJSpeech-1.1/wavs/LJ012-0083.wav|But before committing themselves to an attempt on the latter, it was of importance to ascertain how much it usually contained.
+LJSpeech-1.1/wavs/LJ010-0237.wav|A youth named Pearson had seen him present a pistol at the Queen's carriage,
+LJSpeech-1.1/wavs/LJ021-0055.wav|In meeting the problems of industrial recovery the chief agency of the government has been the National Recovery Administration.
+LJSpeech-1.1/wavs/LJ015-0254.wav|together with the unrealized part of the bullion, amounting in all to some fifteen thousand pounds,
+LJSpeech-1.1/wavs/LJ009-0244.wav|especially that of William Snow, alias Sketch, who slipped from the gallows at Exeter and fell to the ground.
+LJSpeech-1.1/wavs/LJ013-0083.wav|the Bank of England was defrauded of a sum of eight thousand pounds by one of its clerks, Burgess, in conjunction with an accomplice named Elder.
+LJSpeech-1.1/wavs/LJ047-0169.wav|Hosty took the necessary steps to have the Dallas office of the FBI, rather than the New Orleans office, reestablished as the office with principal responsibility.
+LJSpeech-1.1/wavs/LJ033-0080.wav|Mrs. Randle stated that on the morning of November twenty-two, while her brother was eating breakfast,
+LJSpeech-1.1/wavs/LJ035-0041.wav|but through the window in the door he caught a fleeting glimpse of a man walking in the vestibule toward the lunchroom.
+LJSpeech-1.1/wavs/LJ023-0128.wav|I came by a process of elimination to the conclusion that, short of amendments,
+LJSpeech-1.1/wavs/LJ023-0037.wav|not after long years of debate, but now.
+LJSpeech-1.1/wavs/LJ018-0083.wav|Sattler was a very excitable although not an ill-tempered man.
+LJSpeech-1.1/wavs/LJ049-0007.wav|stationing police on the rooftops of all buildings overlooking the reception area,
+LJSpeech-1.1/wavs/LJ005-0040.wav|It earnestly advocated the appointment of female officers to take exclusive charge of female prisoners,
+LJSpeech-1.1/wavs/LJ022-0007.wav|The administration and the Congress are not proceeding in any haphazard fashion in this task of government.
+LJSpeech-1.1/wavs/LJ018-0024.wav|Mr. Death, who had given another in exchange for it to a man supposed to be a foreigner.
+LJSpeech-1.1/wavs/LJ044-0063.wav|and actually had only one thousand printed.
+LJSpeech-1.1/wavs/LJ007-0069.wav|While admitting that he had had many difficulties to contend with,
+LJSpeech-1.1/wavs/LJ019-0328.wav|Stringent rules were prescribed for the prison surgeons;
+LJSpeech-1.1/wavs/LJ015-0221.wav|which came when the safes were sent to Chubbs' to be repaired; and Chubbs sent them back, but only with one key,
+LJSpeech-1.1/wavs/LJ017-0135.wav|Dove, a fiendish brute, found from the evidence in that case that he could kill his wife, whom he hated,
+LJSpeech-1.1/wavs/LJ011-0152.wav|The elder brother,
+LJSpeech-1.1/wavs/LJ031-0004.wav|In the final instant of the assassination, the Presidential motorcade began a race to Parkland Memorial Hospital,
+LJSpeech-1.1/wavs/LJ049-0057.wav|and to shield him by attempting to place themselves between the President and any source of danger.
+LJSpeech-1.1/wavs/LJ040-0059.wav|The review of the latter period will evaluate his personal and employment relations, his attempt to kill General Walker, his political activities,
+LJSpeech-1.1/wavs/LJ029-0171.wav|The President's intention to pay a visit to Texas in the fall of nineteen sixty-three aroused interest throughout the State.
+LJSpeech-1.1/wavs/LJ001-0107.wav|To say a few words on the principles of design in typography:
+LJSpeech-1.1/wavs/LJ019-0272.wav|The prisons were still far from inflicting the three punishments, hard labor, hard fare, and a hard bed
+LJSpeech-1.1/wavs/LJ034-0079.wav|and rode down in the east elevator.
+LJSpeech-1.1/wavs/LJ009-0137.wav|Cards were issued, and to such an extent, that although the service was not to commence till half-past ten, by nine a.m.
+LJSpeech-1.1/wavs/LJ050-0124.wav|to use the data developed by these agencies to carry out its special duties.
+LJSpeech-1.1/wavs/LJ044-0059.wav|The Commission has not been able to find any other indication that Oswald had rented an office in New Orleans.
+LJSpeech-1.1/wavs/LJ005-0220.wav|In eighteen twenty-four Newgate had no glass in its windows, except in the infirmary and one ward of the chapel yard;
+LJSpeech-1.1/wavs/LJ044-0203.wav|For example, the Dallas Times Herald of November nineteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ038-0182.wav|including a Russian volume entitled, quote, Book of Useful Advice, end quote. In this book was an undated note written in Russian.
+LJSpeech-1.1/wavs/LJ045-0114.wav|Oswald also said that he did not want the FBI to know where he lived, quote, Because their visits were not very pleasant for him
+LJSpeech-1.1/wavs/LJ014-0181.wav|Cope, a small man, crippled, and of weakly constitution, was alone in the shop about nine:thirty.
+LJSpeech-1.1/wavs/LJ014-0285.wav|and not for the slight offense as it appeared on the record.
+LJSpeech-1.1/wavs/LJ023-0036.wav|an overwhelming majority of them voted a mandate that the Congress and the President begin the task of providing that protection
+LJSpeech-1.1/wavs/LJ003-0189.wav|The name still survived in the new press yard, which was the receptacle of the male condemned prisoners. It was generally crowded, like the rest of the prison.
+LJSpeech-1.1/wavs/LJ022-0042.wav|to assist materially in our already unmistakable march toward recovery.
+LJSpeech-1.1/wavs/LJ016-0005.wav|Year after year prisoners determined to get free, and occasionally succeeded in their efforts.
+LJSpeech-1.1/wavs/LJ036-0088.wav|and told him a similar story to Craig's.
+LJSpeech-1.1/wavs/LJ043-0049.wav|After she had bought some clothes for Marina Oswald and a highchair for the baby, Oswald emphatically told her to stop.
+LJSpeech-1.1/wavs/LJ009-0088.wav|ignominy, sorrow, sufferings, wretchedness, pangs,
+LJSpeech-1.1/wavs/LJ045-0055.wav|He said this about members of the Russian-speaking group in the Dallas-Ft. Worth area, whom she said he tried to forbid her from seeing,
+LJSpeech-1.1/wavs/LJ042-0014.wav|was his service in Japan.
+LJSpeech-1.1/wavs/LJ030-0036.wav|Secret Service arrangements for Presidential trips, which were followed in the Dallas motorcade,
+LJSpeech-1.1/wavs/LJ039-0193.wav|and a total minimum time period of from seven point one to seven point nine seconds for all three shots.
+LJSpeech-1.1/wavs/LJ027-0096.wav|as, indeed, she must be according to the derivation theory.
+LJSpeech-1.1/wavs/LJ028-0496.wav|rising higher and higher, like a great terraced, turreted mountain.
+LJSpeech-1.1/wavs/LJ032-0018.wav|Purchase of Rifle by Oswald
+LJSpeech-1.1/wavs/LJ039-0114.wav|When asked to explain the different scores achieved by Oswald on the two occasions when he fired for record,
+LJSpeech-1.1/wavs/LJ003-0042.wav|The completion of this very necessary building was, however, much delayed for want of funds,
+LJSpeech-1.1/wavs/LJ014-0203.wav|the great clock of St. Sepulchre's close by was striking the hour, and Marley, who had his elbows on the table,
+LJSpeech-1.1/wavs/LJ036-0115.wav|David Knapp, aged eighteen, was Number two;
+LJSpeech-1.1/wavs/LJ025-0026.wav|Following out his teleological argument, Cuvier remarks that the organization of this cavity
+LJSpeech-1.1/wavs/LJ019-0326.wav|Steps were taken to provide the illiterate with secular instruction.
+LJSpeech-1.1/wavs/LJ038-0272.wav|and, according to Frazier, "relatively few" types of rifles could produce the characteristics found on the bullet.
+LJSpeech-1.1/wavs/LJ034-0119.wav|Approximately seven or eight minutes later
+LJSpeech-1.1/wavs/LJ008-0208.wav|Some years later an eye-witness published a graphic account of one of these scenes.
+LJSpeech-1.1/wavs/LJ010-0089.wav|While a few secured the servants, the rest were to fall upon Lord Harrowby and his guests.
+LJSpeech-1.1/wavs/LJ010-0193.wav|When brought to trial he hardly realized his position,
+LJSpeech-1.1/wavs/LJ042-0163.wav|After, however, two years and a lot of growing up, I decided to return to the USA.
+LJSpeech-1.1/wavs/LJ008-0128.wav|the dreadful execrations of some of the felons so shook his frame that he observed "the clock had struck;" and quickening his pace,
+LJSpeech-1.1/wavs/LJ005-0160.wav|It was plain that although the law had defined general principles of prison government,
+LJSpeech-1.1/wavs/LJ028-0374.wav|settled there, and finally the place was abandoned to the Arabs of the desert.
+LJSpeech-1.1/wavs/LJ048-0233.wav|Two of the nine agents returned to their rooms. The seven others proceeded to an establishment called the Cellar Coffee House,
+LJSpeech-1.1/wavs/LJ038-0109.wav|was either made from the larger, or the larger photograph was made from the smaller and that at the proper time he would show that the pictures were fakes.
+LJSpeech-1.1/wavs/LJ038-0004.wav|The Texas Theatre is on the north side of Jefferson Boulevard, approximately eight blocks from the scene of the Tippit shooting and six blocks
+LJSpeech-1.1/wavs/LJ043-0138.wav|As indicated in chapter four, Oswald had been planning his attack on General Walker for at least one and perhaps as much as two months.
+LJSpeech-1.1/wavs/LJ005-0021.wav|who, in a caustic article contributed to the 'Edinburgh Review,' protested against the pampering of criminals
+LJSpeech-1.1/wavs/LJ009-0078.wav|who preaches plain, homely discourses, as fit as any religious discourse can be fit for the irritated audience.
+LJSpeech-1.1/wavs/LJ020-0026.wav|One half cup of Indian meal. One half cup of molasses. Two teaspoonfuls of salt. Soda, the size of a pea.
+LJSpeech-1.1/wavs/LJ039-0245.wav|(five) resisted arrest by drawing a fully loaded pistol and attempting to shoot another police officer,
+LJSpeech-1.1/wavs/LJ048-0065.wav|the FBI instructions did not reflect fully the Secret Service's need for information regarding potential threats.
+LJSpeech-1.1/wavs/LJ049-0140.wav|The Council already includes, in addition to the President and Vice President, the Secretaries of State and Defense and has a competent staff.
+LJSpeech-1.1/wavs/LJ018-0077.wav|Although manacled, he managed to get a pistol from his chest and load it.
+LJSpeech-1.1/wavs/LJ013-0199.wav|It will be remembered that the question whether Mr. Phillips had not exceeded the limits usually allowed to counsel was much debated at the time.
+LJSpeech-1.1/wavs/LJ004-0056.wav|Mr. Buxton's small work on prison discipline gave a new aspect to the question he had so much at heart.
+LJSpeech-1.1/wavs/LJ001-0054.wav|Jenson, however, had many contemporaries who used beautiful type,
+LJSpeech-1.1/wavs/LJ016-0369.wav|upon them devolved the painful duty of cutting down the body and preparing for the inquest.
+LJSpeech-1.1/wavs/LJ019-0080.wav|some were not sufficiently ventilated, others were warmed artificially, and were unwholesomely close.
+LJSpeech-1.1/wavs/LJ024-0057.wav|Why then should we leave the fulfillment of this public policy to chance
+LJSpeech-1.1/wavs/LJ050-0120.wav|and for reporting such events as a change in leadership or dogma which indicate that the group may present a danger to the President.
+LJSpeech-1.1/wavs/LJ009-0079.wav|The sermon of this day, whether eloquent or plain, useful or useless, must produce a striking effect at the moment of its delivery.
+LJSpeech-1.1/wavs/LJ027-0046.wav|On the one hand, then, are found structures which are perfectly analogous and yet in no way homologous:
+LJSpeech-1.1/wavs/LJ013-0172.wav|were found in the butler's pantry, used by Courvoisier.
+LJSpeech-1.1/wavs/LJ014-0140.wav|After judgment was passed she repeatedly cried out Shame!
+LJSpeech-1.1/wavs/LJ045-0070.wav|thought that Marina Oswald was immature in her thinking and partly responsible for the difficulties that the Oswalds were having at that time.
+LJSpeech-1.1/wavs/LJ014-0289.wav|The details of the suicide were given at the inquest. Watts had been in ill-health from the time of his first arrest.
+LJSpeech-1.1/wavs/LJ026-0089.wav|poor in energy or devoid of it, and out of them to manufacture food -- i.e. complex compounds rich in available potential energy.
+LJSpeech-1.1/wavs/LJ021-0112.wav|or to prevent destructive price cutting which many business organizations have insisted were necessary,
+LJSpeech-1.1/wavs/LJ023-0060.wav|and the powers given to the Congress to carry out those purposes can be best described by saying
+LJSpeech-1.1/wavs/LJ030-0069.wav|scanning the route, and getting out and standing near the President when the cars stopped.
+LJSpeech-1.1/wavs/LJ039-0077.wav|Well, in order to achieve three hits, it would not be required that a man be an exceptional shot. A proficient man with this weapon, yes, end quote.
+LJSpeech-1.1/wavs/LJ002-0296.wav|Garnish on the cabin side was a guinea at entrance for coals, candles, brooms, etc., and a gallon of beer on discharge;
+LJSpeech-1.1/wavs/LJ036-0030.wav|About two blocks later, a woman asked to get off to make a one o'clock train at Union Station
+LJSpeech-1.1/wavs/LJ033-0028.wav|Frazier told her that Oswald had come home to get curtain rods.
+LJSpeech-1.1/wavs/LJ024-0015.wav|or those who would subsequently reach the age of seventy.
+LJSpeech-1.1/wavs/LJ036-0135.wav|I started to that address, and the police cars, the sirens was going, running crisscrossing everywhere, just a big uproar in that end of town and I said,
+LJSpeech-1.1/wavs/LJ045-0237.wav|His denials under questioning, which have no probative value in view of the many readily demonstrable lies he told at that time
+LJSpeech-1.1/wavs/LJ037-0222.wav|When Oswald was arrested, he did not have a jacket.
+LJSpeech-1.1/wavs/LJ002-0267.wav|was applied only to the release of poor debtors whom four pounds each could free.
+LJSpeech-1.1/wavs/LJ016-0010.wav|and had he but been able to remove its iron bars, he might have descended into Newgate Street by means of a rope ladder.
+LJSpeech-1.1/wavs/LJ031-0128.wav|and escorted them into Parkland Hospital through the emergency entrance.
+LJSpeech-1.1/wavs/LJ007-0134.wav|The wards were in some cases spacious, but they were entirely unsuited for separation or the inspection of prisoners.
+LJSpeech-1.1/wavs/LJ012-0084.wav|For this purpose Jordan waited on the receiver to make a small payment, for which he tendered a fifty-pound note.
+LJSpeech-1.1/wavs/LJ045-0181.wav|No one will ever know what passed through Oswald's mind during the week before November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ036-0123.wav|unloaded at five hundred North Beckley at twelve:forty-five p.m.
+LJSpeech-1.1/wavs/LJ003-0050.wav|(one) those awaiting trial;
+LJSpeech-1.1/wavs/LJ019-0139.wav|These numbers would have still further decreased, and the jail would have been almost empty, but for the misdemeanants who were still sent to Newgate
+LJSpeech-1.1/wavs/LJ011-0015.wav|His hair, though gray, was thick, and lay smooth over his forehead.
+LJSpeech-1.1/wavs/LJ009-0232.wav|there were still many shocking incidents at executions, owing to the bungling and unskilful way in which the operation was performed.
+LJSpeech-1.1/wavs/LJ018-0292.wav|that his transactions at Birmingham would shortly be very large, owing to the development of his business there in the alleged manufacture of Pullman cars.
+LJSpeech-1.1/wavs/LJ005-0108.wav|the prisoners, without firing, bedding, or sufficient food, spent their days "in surveying their grotesque prison,
+LJSpeech-1.1/wavs/LJ043-0091.wav|He was not able to produce photographic work
+LJSpeech-1.1/wavs/LJ005-0066.wav|"The new jails," as Howard says, "having pompous fronts, appear like palaces to the lower class of people, and many persons are against them on this account."
+LJSpeech-1.1/wavs/LJ024-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt
+LJSpeech-1.1/wavs/LJ043-0075.wav|He indicated an interest in writing. An employment counselor testified, on the basis of a general aptitude test Oswald had taken,
+LJSpeech-1.1/wavs/LJ018-0080.wav|Sattler was tried for murder and convicted;
+LJSpeech-1.1/wavs/LJ032-0196.wav|This adds to the conviction of the Commission that Oswald owned and handled the weapon used in the assassination.
+LJSpeech-1.1/wavs/LJ046-0057.wav|or Thomas Jefferson from Washington to Monticello.
+LJSpeech-1.1/wavs/LJ012-0269.wav|Mrs. Brown was a washer-woman, supposed to be worth some money; hence Greenacre's offer of marriage.
+LJSpeech-1.1/wavs/LJ014-0009.wav|This sight was his own handiwork,
+LJSpeech-1.1/wavs/LJ022-0019.wav|More and more people,
+LJSpeech-1.1/wavs/LJ045-0170.wav|it became too difficult to wash by hand.
+LJSpeech-1.1/wavs/LJ032-0188.wav|provides some evidence that they were placed on the rifle that day
+LJSpeech-1.1/wavs/LJ014-0317.wav|Pries was lodged in Newgate, tried at the Old Bailey, and transported for life.
+LJSpeech-1.1/wavs/LJ050-0087.wav|propensity toward violent action, or some similar characteristic, coupled with some evaluation of the capability of the individual or group
+LJSpeech-1.1/wavs/LJ041-0163.wav|out of a combination of Oswald's known Marxist sympathies and George Orwell's book "nineteen eighty-four," one of Oswald's favorite books
+LJSpeech-1.1/wavs/LJ022-0006.wav|Before I come to any of the specific measures, however, I want to leave in your minds one clear fact.
+LJSpeech-1.1/wavs/LJ012-0290.wav|While conversing with Mrs. Brown, he declared the unfortunate woman was rocking herself to and fro in a chair;
+LJSpeech-1.1/wavs/LJ043-0119.wav|nineteen sixty-three, almost a month after he lost his job.
+LJSpeech-1.1/wavs/LJ018-0200.wav|He went there with a posse of officers and forced his way upstairs to the first floor,
+LJSpeech-1.1/wavs/LJ016-0438.wav|that the convict's clothes were still the executioner's perquisite.
+LJSpeech-1.1/wavs/LJ015-0212.wav|One pair was with the traffic superintendent in London, another with an official in Folkestone,
+LJSpeech-1.1/wavs/LJ019-0280.wav|They are now the universal rule,
+LJSpeech-1.1/wavs/LJ018-0235.wav|He embarked forthwith in a career of the wildest extravagance, and ere long he had parted in his mother's name with most of the landed estates.
+LJSpeech-1.1/wavs/LJ003-0264.wav|Among the daily visitors were members of the criminal classes still at large,
+LJSpeech-1.1/wavs/LJ034-0078.wav|Oswald was referring to the west elevator which operates by pushbutton and only with the gate closed. Givens said, "Okay,"
+LJSpeech-1.1/wavs/LJ005-0240.wav|Mr. Buxton had succeeded to the great work which William Wilberforce had commenced, and led the repeated attacks upon slavery in British colonies
+LJSpeech-1.1/wavs/LJ021-0084.wav|We count, in the future as in the past, on the driving power of individual initiative
+LJSpeech-1.1/wavs/LJ026-0105.wav|This is done by diastase, an enzyme of plant cells.
+LJSpeech-1.1/wavs/LJ044-0214.wav|reported Castro as saying Cuba could not accept a situation where at the same time the United States was trying to ease world tensions
+LJSpeech-1.1/wavs/LJ039-0219.wav|Frazier testified that the rifle was accurate, that it had less recoil than the average military rifle
+LJSpeech-1.1/wavs/LJ040-0030.wav|When he was in the Soviet Union, he apparently resented the Communist Party members,
+LJSpeech-1.1/wavs/LJ006-0236.wav|"A serious accident: one of the prisoners had a hot poker run into his eye."
+LJSpeech-1.1/wavs/LJ034-0205.wav|A Secret Service agent who spoke to Euins approximately twenty to thirty minutes after the assassination
+LJSpeech-1.1/wavs/LJ036-0139.wav|He gave me a dollar bill, the trip was ninety-five cents.
+LJSpeech-1.1/wavs/LJ002-0156.wav|Thomas Dobson, on twenty-second August, seventeen ninety-nine, for one shilling, with costs of eight shillings, ten pence.
+LJSpeech-1.1/wavs/LJ047-0062.wav|In March nineteen sixty-three, while attempting to locate Marina Oswald,
+LJSpeech-1.1/wavs/LJ015-0140.wav|After his bankruptcy he obtained a place as clerk in the Great Northern Railway office,
+LJSpeech-1.1/wavs/LJ030-0194.wav|Mrs. Connally heard a second shot fired and pulled her husband down into her lap.
+LJSpeech-1.1/wavs/LJ038-0138.wav|When asked about the curtain rod story, Oswald lied again.
+LJSpeech-1.1/wavs/LJ003-0169.wav|Prisoners who could afford it sometimes paid for four beds, at the rate of twenty-eight shillings, and so secured the luxury of a private room.
+LJSpeech-1.1/wavs/LJ002-0318.wav|The city also allowed the poor debtors fourteen ounces of bread daily, and their share of eight stone of meat, an allowance which never varied,
+LJSpeech-1.1/wavs/LJ043-0046.wav|After about a month with his brother,
+LJSpeech-1.1/wavs/LJ002-0207.wav|The yards were taken up with rackets and five courts, and here and there were "bumble puppy grounds," a game in which the players rolled iron balls
+LJSpeech-1.1/wavs/LJ019-0219.wav|It was to accommodate only the convicted prisoners sentenced to terms short of penal servitude, and after its completion
+LJSpeech-1.1/wavs/LJ021-0162.wav|What may be necessary for those countries is not my responsibility to determine.
+LJSpeech-1.1/wavs/LJ034-0050.wav|Most of the prints were found to have been placed on the cartons by an FBI clerk
+LJSpeech-1.1/wavs/LJ005-0122.wav|the changes necessary to bring the prisons into conformity with the recent acts were attempted, if not actually introduced into the county prisons, to which,
+LJSpeech-1.1/wavs/LJ034-0209.wav|the Commission has considered the allegation that Oswald was photographed standing in front of the building when the shots were fired.
+LJSpeech-1.1/wavs/LJ017-0267.wav|After condemnation, as the rules now kept capital convicts strictly apart, they could not be lodged in the two condemned cells,
+LJSpeech-1.1/wavs/LJ028-0433.wav|In the northern part of the enclosure to the east of the river,
+LJSpeech-1.1/wavs/LJ003-0122.wav|The prisoner mentioned above was continually persecuted by trials of this kind.
+LJSpeech-1.1/wavs/LJ046-0201.wav|were no more specific than the broad and general instructions its own agents and the White House mailroom.
+LJSpeech-1.1/wavs/LJ048-0182.wav|President Kennedy himself had mentioned it that morning, as had Agent Sorrels when he and Agent Lawson were fixing the motorcade route.
+LJSpeech-1.1/wavs/LJ002-0150.wav|Costs were the gallons of sack to the pennyworth of debt.
+LJSpeech-1.1/wavs/LJ021-0098.wav|modifying it where it needs modification and finally making recommendations to the Congress,
+LJSpeech-1.1/wavs/LJ002-0311.wav|These were lumped together into a general fund,
+LJSpeech-1.1/wavs/LJ021-0159.wav|Morally, it is the greatest menace to our social order.
+LJSpeech-1.1/wavs/LJ003-0099.wav|and though distress or the seduction of others might have led to the commission of this their first offense,
+LJSpeech-1.1/wavs/LJ043-0185.wav|Some idea of what he thought was sufficient reason for such an act may be found in the nature of the motive that he stated for his attack on General Walker.
+LJSpeech-1.1/wavs/LJ005-0187.wav|As the Society put it in eighteen twenty-seven, "the friends to the improvement of prison discipline will regret to learn
+LJSpeech-1.1/wavs/LJ043-0019.wav|He could never give her what the people were showering on her no matter how hard he worked -- and he worked very hard, end quote.
+LJSpeech-1.1/wavs/LJ042-0191.wav|While, quote, resourcefulness and patient working towards the aforesaid goals
+LJSpeech-1.1/wavs/LJ006-0047.wav|The prison had become more or less a place of detention only, harboring mainly those awaiting trial.
+LJSpeech-1.1/wavs/LJ031-0097.wav|Question: And why was no effort made at that time to inspect his back? Answer:
+LJSpeech-1.1/wavs/LJ014-0006.wav|While the body lay there still warm, battered and bleeding from the cruel blows inflicted upon him by his cowardly assailant, a man came by singing.
+LJSpeech-1.1/wavs/LJ012-0283.wav|he carried under his coat-flaps through the streets, and afterwards on his cap in a crowded city omnibus.
+LJSpeech-1.1/wavs/LJ018-0105.wav|He, like Saward, was an artist in penmanship.
+LJSpeech-1.1/wavs/LJ050-0105.wav|that society and its leaders had combined to thwart him. It will require every available resource of our Government
+LJSpeech-1.1/wavs/LJ032-0252.wav|Marina Oswald has stated that the rifle was among these possessions, although Ruth Paine testified that she was not aware of it.
+LJSpeech-1.1/wavs/LJ033-0190.wav|The Depository shipping department was on the first floor to which Oswald had access in the normal performance of his duties filling orders.
+LJSpeech-1.1/wavs/LJ017-0159.wav|and a life-interest in five thousand pounds, a fact on which Smethurst's counsel dwelt with much weight,
+LJSpeech-1.1/wavs/LJ041-0170.wav|Thornley testified, quote, At which time he looked at me like a betrayed Caesar and screamed, screamed definitely, "Not you, too, Thornley!"
+LJSpeech-1.1/wavs/LJ021-0191.wav|by awesome pronouncements concerning the unconstitutionality of some of our measures of recovery and relief and reform.
+LJSpeech-1.1/wavs/LJ007-0237.wav|Many rats also come through these so-called ventilators, as they open close to the ground at the back of the prison.
+LJSpeech-1.1/wavs/LJ031-0046.wav|and an extensive wound in the President's head where a sizable portion of the skull was missing.
+LJSpeech-1.1/wavs/LJ044-0148.wav|very unhappy, and that he actually wept when he told her that.
+LJSpeech-1.1/wavs/LJ026-0046.wav|In each the life is the sum total of a series of definite processes -- nutrition or food supply,
+LJSpeech-1.1/wavs/LJ033-0121.wav|The distance between the point on the seat and the door was twenty-seven inches.
+LJSpeech-1.1/wavs/LJ026-0033.wav|be either included in or excluded from either.
+LJSpeech-1.1/wavs/LJ037-0099.wav|On television Oswald's hair looked blond, whereas Smith remembered that the man who ran away had hair that was brown or brownish black.
+LJSpeech-1.1/wavs/LJ050-0017.wav|to be able to provide effective continuing supervision
+LJSpeech-1.1/wavs/LJ011-0273.wav|He was about to descend the stairs when three persons, one of them the young sailor,
+LJSpeech-1.1/wavs/LJ017-0118.wav|and it was on the entire absence of the latter that the defense was principally based when Palmer was brought to trial.
+LJSpeech-1.1/wavs/LJ039-0115.wav|Major Anderson said, quote, when he fired that two twelve
+LJSpeech-1.1/wavs/LJ050-0110.wav|and each agency given clear understanding of the assistance which the Secret Service expects.
+LJSpeech-1.1/wavs/LJ016-0044.wav|and was compelled to work along the railing round three-quarters of the square of the yard,
+LJSpeech-1.1/wavs/LJ014-0134.wav|that O'Connor had been more to her than her husband, that she ought to have married him.
+LJSpeech-1.1/wavs/LJ011-0135.wav|Macintosh's amendment was carried in the Commons, but the new law did not pass the Lords, who re-enacted the capital penalty.
+LJSpeech-1.1/wavs/LJ010-0012.wav|and prominent in the criminal annals of Newgate in these later years will be found numerous remarkable instances of this class of offense --
+LJSpeech-1.1/wavs/LJ006-0175.wav|In his administration of discipline he was quite uncertain;
+LJSpeech-1.1/wavs/LJ034-0039.wav|In Latona's opinion, quote,
+LJSpeech-1.1/wavs/LJ029-0019.wav|The President had spoken earlier that day at the Air Force Academy in Colorado Springs, Colorado.
+LJSpeech-1.1/wavs/LJ002-0268.wav|The supreme control of the Marshalsea was vested in the marshal of the royal household; but although he drew a salary of five hundred pounds a year,
+LJSpeech-1.1/wavs/LJ015-0058.wav|Bates, the least guilty, was pardoned in eighteen fifty-eight.
+LJSpeech-1.1/wavs/LJ013-0239.wav|At the same time he was seen to steal and secrete a pair of trousers. The shop-boy gave information.
+LJSpeech-1.1/wavs/LJ024-0035.wav|Let me answer this question with a bluntness that will end all honest misunderstanding of my purposes.
+LJSpeech-1.1/wavs/LJ028-0183.wav|Cyrus, the King of Persia, was rising to power, and after he had defeated the Medes
+LJSpeech-1.1/wavs/LJ028-0095.wav|the beloved of Nabu, the arbiter, the possessor of wisdom, who seeks out the path of their divinity,
+LJSpeech-1.1/wavs/LJ027-0136.wav|Illustrations quoted from the works of Romanes and Le Conte will make this principle clear.
+LJSpeech-1.1/wavs/LJ049-0038.wav|had no passenger in a jump seat between Agent Youngblood and Vice President Johnson to interfere with Agent Youngblood's ability
+LJSpeech-1.1/wavs/LJ048-0010.wav|stressed also the decision by the Department of State that Oswald should be permitted to return to the United States.
+LJSpeech-1.1/wavs/LJ019-0205.wav|In eighteen forty-five the Jail Committee brought forward a definite proposal to purchase ground in the immediate vicinity for the erection of a new jail.
+LJSpeech-1.1/wavs/LJ042-0251.wav|He also inquired about visas for himself and his wife
+LJSpeech-1.1/wavs/LJ043-0012.wav|In fact, his relations with some of them, particularly George Bouhe, became quite hostile.
+LJSpeech-1.1/wavs/LJ026-0095.wav|Food (starch) thus produced in the green leaves of higher plants and the inorganic foods
+LJSpeech-1.1/wavs/LJ040-0166.wav|She also noted that there was, quote, a rather pleasant, appealing quality about this emotionally starved, affectionless youngster
+LJSpeech-1.1/wavs/LJ047-0032.wav|In June nineteen sixty-two, the Bureau was advised by the Department of State of Oswald's plan to return to the United States.
+LJSpeech-1.1/wavs/LJ049-0162.wav|Although the Secret Service has had the primary responsibility for the protection of the President,
+LJSpeech-1.1/wavs/LJ050-0125.wav|Once experience has been gained in implementing such agreements with the Federal and leading State and local agencies,
+LJSpeech-1.1/wavs/LJ033-0165.wav|In all of the observations and physical tests that I made I found the bag and the paper sample were the same, end quote.
+LJSpeech-1.1/wavs/LJ004-0163.wav|and of which fewer still would believe that the original is to be found in this enlightened and happy country."
+LJSpeech-1.1/wavs/LJ014-0328.wav|Cole quickly raised money on both sets of warrants. He carried on this game for some time with great success,
+LJSpeech-1.1/wavs/LJ032-0090.wav|This, too, was a forgery. The signature of "A. J. Hideel" was in the handwriting of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ010-0121.wav|that he was, to use Thistlewood's words, "a contriver, instigator, and entrapper."
+LJSpeech-1.1/wavs/LJ038-0149.wav|During the first interrogation on November twenty-two, Fritz asked Oswald to account for himself at the time the President was shot.
+LJSpeech-1.1/wavs/LJ004-0189.wav|The great principles of classification, cleanliness, and employment were closely observed.
+LJSpeech-1.1/wavs/LJ039-0107.wav|In May of nineteen fifty-nine, on another range, Oswald scored one hundred ninety-one, which was one point over the minimum for ranking as a "marksman."
+LJSpeech-1.1/wavs/LJ018-0274.wav|who was followed, disguised, and with her hair dyed black, to a house in the Marylebone Road, where she met her husband.
+LJSpeech-1.1/wavs/LJ010-0101.wav|the lights were extinguished, and a desperate conflict ensued.
+LJSpeech-1.1/wavs/LJ039-0155.wav|or, if either the first or third shots missed, the assassin fired the three shots during a minimum time span of seven point one
+LJSpeech-1.1/wavs/LJ001-0121.wav|in reading the modern figures the eyes must be strained before the reader can have any reasonable assurance
+LJSpeech-1.1/wavs/LJ014-0149.wav|On this journey her manner changed completely. She became flippant, joked with the officers, asked how they liked her "resolution" in the dock,
+LJSpeech-1.1/wavs/LJ008-0180.wav|Among the dead was a sailor lad whom no one knew;
+LJSpeech-1.1/wavs/LJ025-0108.wav|Thus, by the progress of knowledge, Cuvier's fourth distinction between the animal and the plant
+LJSpeech-1.1/wavs/LJ026-0065.wav|Matter enters the plant in the liquid or gaseous form by diffusion,
+LJSpeech-1.1/wavs/LJ014-0252.wav|He could do what he liked with the passbook, and by its adoption, as described as the basis of all entries,
+LJSpeech-1.1/wavs/LJ014-0126.wav|her face was comely, she had dark hair and good eyes, and was above the middle height, yet inclined to be stout.
+LJSpeech-1.1/wavs/LJ011-0120.wav|Unfortunately he took to drink, lost his appointment, and fell from bad to worse.
+LJSpeech-1.1/wavs/LJ018-0007.wav|then returned to the city to take the train from Fenchurch Street home, traveling by the North London Railway.
+LJSpeech-1.1/wavs/LJ019-0082.wav|In a great number of prisons no provision was made for the education of prisoners, in some others there was a sufficient staff of schoolmasters and instructors.
+LJSpeech-1.1/wavs/LJ008-0013.wav|As regards the first, I find that in seventeen eighty-six
+LJSpeech-1.1/wavs/LJ012-0211.wav|Mr. Partridge was sent for, and he with some of the students soon decided that the corpse had not died a natural death.
+LJSpeech-1.1/wavs/LJ012-0142.wav|and implicated "Money Moses," another Jew, for the whole affair had been planned and executed by members of the Hebrew persuasion.
+LJSpeech-1.1/wavs/LJ006-0183.wav|the apathy of the authorities, and the undue ascendancy of those who, as convicted felons, should have been most sternly repressed,
+LJSpeech-1.1/wavs/LJ034-0092.wav|in reaching the conclusion that the shots came from the sixth floor, southeast corner window of the Depository Building.
+LJSpeech-1.1/wavs/LJ016-0332.wav|who was convicted of complicity in the Clerkenwell explosion, intended to effect the release of Burke and Casey
+LJSpeech-1.1/wavs/LJ015-0131.wav|By these means Redpath misappropriated vast sums during a period extending over ten years.
+LJSpeech-1.1/wavs/LJ019-0021.wav|with the additions made to that date, the total expenditure amounted to nearly ninety thousand pounds, or about one hundred eighty pounds per cell.
+LJSpeech-1.1/wavs/LJ036-0031.wav|and requested a transfer which she might use if she got through the traffic.
+LJSpeech-1.1/wavs/LJ012-0220.wav|were known to be what Ferrari had worn when last seen.
+LJSpeech-1.1/wavs/LJ021-0132.wav|The workers who turn away from these same impartial agencies and decline to use their good offices to gain their ends
+LJSpeech-1.1/wavs/LJ042-0231.wav|His second answer to this question was, quote, No of course not,
+LJSpeech-1.1/wavs/LJ031-0177.wav|Before the body could be taken from the hospital,
+LJSpeech-1.1/wavs/LJ005-0074.wav|cap. sixty-four, and five George the fourth cap. eighty-five
+LJSpeech-1.1/wavs/LJ028-0070.wav|and how, in punishment for all his wickedness, he became a calf, and for seven years grazed the grass in the fields about the city.
+LJSpeech-1.1/wavs/LJ003-0077.wav|Two of the untried were for murder, and several for house-breaking and highway robbery.
+LJSpeech-1.1/wavs/LJ003-0213.wav|Convicted murderers were kept continuously in the cells on bread and water,
+LJSpeech-1.1/wavs/LJ019-0140.wav|at times on long terms of imprisonment, and for the transports, whom the Home Office were often, as of old, slow to remove.
+LJSpeech-1.1/wavs/LJ004-0033.wav|and they were not threatened with any particular penalties if they evaded or ignored the new acts.
+LJSpeech-1.1/wavs/LJ032-0125.wav|In his testimony before the Commission, Latona stated that when he received the rifle, the area where prints were visible was protected by cellophane.
+LJSpeech-1.1/wavs/LJ018-0335.wav|most violent in temper, and addicted to the most frightful language.
+LJSpeech-1.1/wavs/LJ005-0272.wav|Various other recommendations were made as regards the appointment of chaplain and schoolmasters; the limitation of the powers of wardsmen,
+LJSpeech-1.1/wavs/LJ015-0050.wav|Following this, warrants were issued for their arrest,
+LJSpeech-1.1/wavs/LJ040-0017.wav|Since Oswald is dead,
+LJSpeech-1.1/wavs/LJ016-0286.wav|Oh, my! Think I've got to die.
+LJSpeech-1.1/wavs/LJ013-0195.wav|induced the prisoner to make a full confession of his crime to the lawyers who defended him.
+LJSpeech-1.1/wavs/LJ009-0208.wav|erected on the cart, about four feet high at the head, and gradually sloping towards the horse, giving a full view of the body,
+LJSpeech-1.1/wavs/LJ043-0059.wav|While it does not appear that he established any new relationships as a result of his investigation, he did obtain a large picture of his father
+LJSpeech-1.1/wavs/LJ046-0087.wav|Absolute security is neither practical nor possible. An approach to complete security would require the President
+LJSpeech-1.1/wavs/LJ016-0149.wav|Suicides and executions were, however, always the most effectual methods of making exit from durance.
+LJSpeech-1.1/wavs/LJ029-0174.wav|Dallas, Fort Worth, San Antonio, and Houston
+LJSpeech-1.1/wavs/LJ015-0191.wav|In Newgate Redpath is remembered by the prison officer as a difficult man to deal with.
+LJSpeech-1.1/wavs/LJ018-0385.wav|It was his own handiwork -- a bird pecking at a flower;
+LJSpeech-1.1/wavs/LJ028-0364.wav|The city then fell to Seleucus,
+LJSpeech-1.1/wavs/LJ036-0167.wav|and walked directly to the terminal, he would have entered the cab at twelve:forty-seven or twelve:forty-eight p.m.
+LJSpeech-1.1/wavs/LJ009-0249.wav|When Charles White was executed in eighteen twenty-three for arson, he arranged a handkerchief
+LJSpeech-1.1/wavs/LJ019-0185.wav|Lord John, with great fairness, admitted that the whole of this burthen could not be imposed upon the city
+LJSpeech-1.1/wavs/LJ029-0143.wav|A sign located on this barrier instructs Main Street traffic not to make any turns.
+LJSpeech-1.1/wavs/LJ010-0046.wav|was followed in the very next year by those of Francis and of Bean in two consecutive months,
+LJSpeech-1.1/wavs/LJ007-0210.wav|Abuses and irregularities, which had been partially remedied by the reform introduced in eighteen thirty-seven, were once more in the ascendant.
+LJSpeech-1.1/wavs/LJ017-0106.wav|fearful paroxysms and cramps, ending in suffocation by the tetanic rigor which caught the muscles of the chest.
+LJSpeech-1.1/wavs/LJ011-0104.wav|Hunton was put upon his trial at the Old Bailey, and in due course sentenced to death.
+LJSpeech-1.1/wavs/LJ020-0019.wav|If you intend to bake in the forenoon, make the sponge at bedtime. If in the afternoon, early in the morning.
+LJSpeech-1.1/wavs/LJ014-0055.wav|Next year John Gleeson Wilson, at Liverpool, murdered a woman, Ann Henrichson, also a maidservant and two children;
+LJSpeech-1.1/wavs/LJ022-0116.wav|generated by this depression
+LJSpeech-1.1/wavs/LJ038-0139.wav|He denied that he had ever told Frazier that he wanted a ride to Irving to get curtain rods for an apartment.
+LJSpeech-1.1/wavs/LJ004-0212.wav|There was an attentive chaplain, a schoolmaster, and regular religious and other instruction.
+LJSpeech-1.1/wavs/LJ007-0013.wav|that women should be frequently kept from chapel in order that they might attend the ladies' lectures and exhortations.
+LJSpeech-1.1/wavs/LJ025-0068.wav|"At the present day," writes Huxley,
+LJSpeech-1.1/wavs/LJ045-0132.wav|to the time of the assassination.
+LJSpeech-1.1/wavs/LJ012-0026.wav|With this capital he returned to London and set up as a fence.
+LJSpeech-1.1/wavs/LJ004-0099.wav|Ventilators, hand and others, were to be supplied.
+LJSpeech-1.1/wavs/LJ016-0135.wav|who yet faced extraordinary risks to advance their enlargement by only a few hours.
+LJSpeech-1.1/wavs/LJ002-0320.wav|The bread was issued every alternate day; and while some prisoners often ate their whole allowance at once,
+LJSpeech-1.1/wavs/LJ028-0417.wav|and that Arke of Nebuchadnezzar for as perpetual a memory of his great idolatry and condigne punishment.
+LJSpeech-1.1/wavs/LJ021-0175.wav|but I do not believe any intelligent observer can accuse England of undue orthodoxy in the present emergency.
+LJSpeech-1.1/wavs/LJ039-0198.wav|but to determine the maximum speed at which it could be fired.
+LJSpeech-1.1/wavs/LJ008-0200.wav|hired a room for the night and morning, which he and a large party of friends occupied before and during the execution;
+LJSpeech-1.1/wavs/LJ019-0154.wav|If any disturbance reached his ears, he reported the case to the governor, who next morning visited the ward in fault, and asked for the culprit.
+LJSpeech-1.1/wavs/LJ009-0261.wav|the convict was half on the platform, half hanging, and the convulsions of his body were appalling.
+LJSpeech-1.1/wavs/LJ042-0018.wav|On the other hand, at least one person who knew Oswald after his return thought that his defection had a more personal and psychological basis.
+LJSpeech-1.1/wavs/LJ014-0248.wav|This passbook, when not at the bank, was in the exclusive custody of Watts.
+LJSpeech-1.1/wavs/LJ022-0016.wav|Before that time individual self- interest and group selfishness were paramount in public thinking.
+LJSpeech-1.1/wavs/LJ046-0028.wav|The protection of the President of the United States is an immensely difficult and complex task.
+LJSpeech-1.1/wavs/LJ006-0022.wav|At that time the mild and intelligent prison discipline in force in Pennsylvania, the legacy of the old Quaker immigrants,
+LJSpeech-1.1/wavs/LJ043-0058.wav|He visited some of his father's elderly relatives and the cemetery where his father was buried in an effort to develop the facts of his genealogy.
+LJSpeech-1.1/wavs/LJ019-0025.wav|Pentonville has really been the model on which all subsequent prison construction has been based. All prisons at home and abroad are but variations,
+LJSpeech-1.1/wavs/LJ018-0113.wav|The principals in this conspiracy, Wagner and Bateman, were sentenced to penal servitude for life,
+LJSpeech-1.1/wavs/LJ012-0095.wav|The jewels had belonged to a Spanish countess recently deceased, who had sent them to England for greater security on the outbreak of the first Carlist war.
+LJSpeech-1.1/wavs/LJ009-0175.wav|and in eighteen forty-one this was accepted as the almost universally established rule.
+LJSpeech-1.1/wavs/LJ038-0248.wav|was also identified by Marina Oswald as having been taken by her husband, presumably in connection with the Walker shooting.
+LJSpeech-1.1/wavs/LJ035-0207.wav|This witness identified Oswald in a lineup as the man most nearly resembling the man he saw and later identified Oswald as the man he observed.
+LJSpeech-1.1/wavs/LJ019-0261.wav|With these differences were others as opposed concerning industrial occupation.
+LJSpeech-1.1/wavs/LJ021-0142.wav|Accordingly, I propose to confer within the coming month
+LJSpeech-1.1/wavs/LJ048-0051.wav|it stated primarily to direct the thought of those responsible for the future safety of our Presidents to the need for a more imaginative
+LJSpeech-1.1/wavs/LJ045-0198.wav|that the Presidential motorcade would pass in front of his place of work.
+LJSpeech-1.1/wavs/LJ044-0028.wav|She testified that he threatened to beat her if she did not do so. The chapter had never been chartered by the national FPCC organization.
+LJSpeech-1.1/wavs/LJ015-0063.wav|But neither Robson nor Redpath would have been able to pursue their fraudulent designs with success had they not, like Watts,
+LJSpeech-1.1/wavs/LJ046-0158.wav|At the time of the assassination, the active PRS general files contained approximately fifty thousand cases
+LJSpeech-1.1/wavs/LJ027-0037.wav|both adaptations to an aerial environment;
+LJSpeech-1.1/wavs/LJ049-0110.wav|The governmental consequences of assassination of one of the specified officials give the United States ample power to act for its own protection.
+LJSpeech-1.1/wavs/LJ012-0180.wav|The murderer explained that he had first fired a pistol at Weare's head, but the shot glanced off his cheek.
+LJSpeech-1.1/wavs/LJ042-0234.wav|quote, except in the US, the living standard is a little higher.
+LJSpeech-1.1/wavs/LJ008-0134.wav|Thus appareled he ascended the stairs at the debtors' door,
+LJSpeech-1.1/wavs/LJ047-0070.wav|On April twenty-one, nineteen sixty-three, the FBI field office in New York
+LJSpeech-1.1/wavs/LJ001-0004.wav|produced the block books, which were the immediate predecessors of the true printed book,
+LJSpeech-1.1/wavs/LJ019-0053.wav|and our modern practice has prudently tried to steer between the two extremes, accepting as the best system a judicious combination of both.
+LJSpeech-1.1/wavs/LJ003-0071.wav|It was particularly recommended by the Committee on Jails in eighteen fourteen
+LJSpeech-1.1/wavs/LJ047-0188.wav|Had you planned any steps beyond that point?
+LJSpeech-1.1/wavs/LJ023-0131.wav|We must have men worthy and equipped to carry out impartial justice.
+LJSpeech-1.1/wavs/LJ034-0149.wav|Brennan appeared to revert to his earlier inability to make a positive identification,
+LJSpeech-1.1/wavs/LJ040-0015.wav|There is, however, a large amount of material available in his writings
+LJSpeech-1.1/wavs/LJ029-0014.wav|As a political leader, the President wished to resolve the factional controversy within the Democratic Party in Texas before the election of nineteen sixty-four.
+LJSpeech-1.1/wavs/LJ021-0063.wav|Benefits of the Industrial Recovery Program have come,
+LJSpeech-1.1/wavs/LJ005-0192.wav|The prisoners were lodged in rooms whence they could converse with passengers in the streets, and freely obtain spirits and other prohibited articles.
+LJSpeech-1.1/wavs/LJ046-0052.wav|To promote nationwide acceptance of his administration Washington made grand tours that served also to excite interest in the Presidency.
+LJSpeech-1.1/wavs/LJ025-0168.wav|Whence it follows that the bean has taken in only the raw materials of its fabric and has manufactured them into bean-stuffs.
+LJSpeech-1.1/wavs/LJ022-0191.wav|They are essential factors in a rounded program for national recovery.
+LJSpeech-1.1/wavs/LJ041-0007.wav|Neighbors and others who knew him at that time recall an introverted boy who read a great deal.
+LJSpeech-1.1/wavs/LJ045-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ028-0241.wav|Hereupon the Persians who had been left for the purpose at Babylon by the river side
+LJSpeech-1.1/wavs/LJ024-0099.wav|even though the thirty- five states with ninety-five percent of the population are in favor of it.
+LJSpeech-1.1/wavs/LJ017-0009.wav|Sir Thomas Overbury was undoubtedly poisoned by Lord Rochester in the reign of James the first,
+LJSpeech-1.1/wavs/LJ049-0008.wav|and detailing police in civilian clothes to be scattered throughout the sizable crowd.
+LJSpeech-1.1/wavs/LJ006-0223.wav|There was much swearing and bad language, the very worst that could be used, from the first thing in the morning to the last thing at night.
+LJSpeech-1.1/wavs/LJ012-0008.wav|He had sought by all legal means to obtain possession of the two thousand pounds, but had failed, and had had recourse to more violent means.
+LJSpeech-1.1/wavs/LJ042-0239.wav|Despite the hatred that Oswald expressed toward the Soviet Union after his residence there,
+LJSpeech-1.1/wavs/LJ023-0071.wav|For nearly twenty years there was no conflict between the Congress and the Court.
+LJSpeech-1.1/wavs/LJ027-0148.wav|we should antecedently expect, if the theory is true,
+LJSpeech-1.1/wavs/LJ007-0044.wav|The latter, light-hearted and reckless, conducted themselves in the most unseemly fashion, and "with as much indifference as the inmates of the other parts of the prison."
+LJSpeech-1.1/wavs/LJ013-0150.wav|The moment it was discovered that the diamonds had disappeared, the affair was taken up by the police.
+LJSpeech-1.1/wavs/LJ008-0106.wav|He was introduced by the ordinary, Dr. Forde, a name familiar to the reader, who met him at the felons' door
+LJSpeech-1.1/wavs/LJ005-0217.wav|Without enlargement no material change in discipline or interior economy could possibly be introduced.
+LJSpeech-1.1/wavs/LJ039-0143.wav|Marina Oswald testified that on one occasion she saw him take the rifle, concealed in a raincoat, from the house on Neely Street.
+LJSpeech-1.1/wavs/LJ009-0260.wav|The spectacle was horrible;
+LJSpeech-1.1/wavs/LJ014-0301.wav|In eighteen fifty-three a second case of gigantic fraud alarmed and scandalized the financial world.
+LJSpeech-1.1/wavs/LJ037-0105.wav|They heard the sound of shots to the north of their lot. Callaway heard five shots, and Guinyard three.
+LJSpeech-1.1/wavs/LJ024-0084.wav|it seeks to restore the Court to its rightful and historic place in our constitutional government
+LJSpeech-1.1/wavs/LJ012-0031.wav|This rule in dealing with stolen goods continues to this day, and has made the fortune of many since Ikey.
+LJSpeech-1.1/wavs/LJ050-0117.wav|particularly those arising from organized groups, within their special jurisdiction.
+LJSpeech-1.1/wavs/LJ042-0148.wav|I myself do not have a particularly quiet disposition,
+LJSpeech-1.1/wavs/LJ008-0130.wav|Before the colonel had been pinioned he had pulled out two white handkerchiefs, one of which he bound over his temples so as nearly to conceal his eyes,
+LJSpeech-1.1/wavs/LJ003-0152.wav|It was the only way to escape the horrors, the distress, penury, and rags of the common yards.
+LJSpeech-1.1/wavs/LJ034-0127.wav|Brennan testified that they were standing, which is their apparent position in the photograph.
+LJSpeech-1.1/wavs/LJ023-0049.wav|Those who have intimated that the President of the United States is trying to drive that team
+LJSpeech-1.1/wavs/LJ029-0111.wav|Sorrels, who had participated in Presidential protection assignments in Dallas since a visit by President Franklin D. Roosevelt
+LJSpeech-1.1/wavs/LJ049-0163.wav|the FBI, which was established within the Department of Justice in nineteen oh eight, has had in recent years an increasingly important role to play.
+LJSpeech-1.1/wavs/LJ017-0089.wav|While Cook lay ill, Palmer in his name pocketed the proceeds of the Shrewsbury settling,
+LJSpeech-1.1/wavs/LJ028-0144.wav|fifty royal cubits in width, and two hundred in height.
+LJSpeech-1.1/wavs/LJ007-0043.wav|were associated continually with a number of those who could look with certainty on a mitigation of punishment.
+LJSpeech-1.1/wavs/LJ005-0225.wav|In eighteen twenty-seven the Society was compelled to report that "no material change had taken place in Newgate since the passing of the prison laws,
+LJSpeech-1.1/wavs/LJ040-0204.wav|who had real difficulty in accepting and relating to people, end quote, and who had, quote, little understanding, end quote, of Lee's behavior
+LJSpeech-1.1/wavs/LJ011-0089.wav|and listened attentively to the ordinary, who read him the well-known address written and delivered by Dr. Dodd previous to his own execution for forgery.
+LJSpeech-1.1/wavs/LJ031-0222.wav|The surgeons observed, through X-ray analysis, thirty or forty tiny dustlike fragments of metal
+LJSpeech-1.1/wavs/LJ026-0125.wav|But the formation of starch, all important as it is, is after all only the manufacture of food
+LJSpeech-1.1/wavs/LJ011-0288.wav|Thus free, he eluded the vigilance of two of the party, who were at dinner in the front kitchen,
+LJSpeech-1.1/wavs/LJ008-0058.wav|From this bar hang several iron loops, in which the halters are tied.
+LJSpeech-1.1/wavs/LJ014-0033.wav|When her ruin was discovered her brother panted for revenge.
+LJSpeech-1.1/wavs/LJ030-0169.wav|Oh, my God, they have shot my husband. I love you, Jack.
+LJSpeech-1.1/wavs/LJ047-0164.wav|At this point in the interview, Hosty gave Mrs. Paine his name and office telephone number on a piece of paper.
+LJSpeech-1.1/wavs/LJ026-0147.wav|In the higher plants the excretions are carbon dioxide, which escapes through the epidermis of root, stem and leaf and through the stomata;
+LJSpeech-1.1/wavs/LJ048-0135.wav|Liaison with local law enforcement authorities.
+LJSpeech-1.1/wavs/LJ012-0093.wav|and the police were unable at first to get on the track of the thieves.
+LJSpeech-1.1/wavs/LJ046-0081.wav|The men in charge of protecting the President, confronted by complex problems and limited as they are in the measures they may employ,
+LJSpeech-1.1/wavs/LJ016-0094.wav|No further notice was taken of him; no one seemingly suspected that he was a prisoner, and he was suffered to walk off without let or hindrance.
+LJSpeech-1.1/wavs/LJ005-0155.wav|It was still limited to bread in some places, the allowance of which varied from one to three pounds;
+LJSpeech-1.1/wavs/LJ040-0210.wav|It may also be significant that, as reported by John Pic, quote, Lee slept with my mother until I joined the service in nineteen fifty.
+LJSpeech-1.1/wavs/LJ028-0161.wav|the streets all run in straight lines, not only those parallel to the river, but also the cross streets which lead down to the waterside.
+LJSpeech-1.1/wavs/LJ032-0095.wav|as additional persons entitled to receive mail in the box. The New Orleans postal authorities had not discarded the portion of the application
+LJSpeech-1.1/wavs/LJ016-0360.wav|The change added greatly to the responsibilities of the governor and his subordinates. Hitherto the public had seemed to assist at the ceremony;
+LJSpeech-1.1/wavs/LJ046-0083.wav|The problem and the reasonable approach to its solution were ably stated in a memorandum prepared by FBI Director J. Edgar Hoover
+LJSpeech-1.1/wavs/LJ034-0103.wav|This description most probably led to the radio alert sent to police cars at approximately twelve:forty-five p.m., which described the suspect as white,
+LJSpeech-1.1/wavs/LJ012-0267.wav|but his despair must have been great, as was evident from his attempt to strangle himself in the station-house.
+LJSpeech-1.1/wavs/LJ014-0309.wav|Few realized that these mysterious operations were the "convulsive attempt" of a ruined and dishonest speculator to sustain his credit.
+LJSpeech-1.1/wavs/LJ002-0226.wav|The same evils of overcrowding, uncleanliness, want of medical attendance,
+LJSpeech-1.1/wavs/LJ007-0072.wav|as regards the private teaching of prisoners; and they went on to say that "a resolved adherence, in spite of discouragements the most disheartening,
+LJSpeech-1.1/wavs/LJ034-0011.wav|measuring approximately eighteen by twelve by fourteen inches which had been moved from a stack along the south wall.
+LJSpeech-1.1/wavs/LJ019-0031.wav|was the moving spirit among these commissioners, and he is now generally recognized as the originator of modern prison architecture.
+LJSpeech-1.1/wavs/LJ016-0357.wav|No time was lost in carrying out the dread ceremony; but it was not completed without some of the officials turning sick, and the moment it was over,
+LJSpeech-1.1/wavs/LJ038-0151.wav|He acknowledged the encounter with the police officer on the second floor.
+LJSpeech-1.1/wavs/LJ028-0378.wav|Even the shepherds ceased to graze their sheep there, and the wandering Arabs,
+LJSpeech-1.1/wavs/LJ016-0055.wav|He traversed vainly all the roofs in Newgate Street,
+LJSpeech-1.1/wavs/LJ019-0198.wav|and on a more mature consideration he realized that the limited area of the existing Newgate site,
+LJSpeech-1.1/wavs/LJ017-0023.wav|by putting arsenic in the dumplings she had prepared for them. The charge rested entirely on circumstantial evidence,
+LJSpeech-1.1/wavs/LJ048-0235.wav|serving only coffee, fruit juices and no hard liquors or beer, end quote.
+LJSpeech-1.1/wavs/LJ050-0160.wav|The Commission further recommends that the Secret Service coordinate its planning as closely as possible with all of the Federal agencies from which it receives information.
+LJSpeech-1.1/wavs/LJ004-0191.wav|and in comparative comfort, with a bed and proper bedding.
+LJSpeech-1.1/wavs/LJ044-0184.wav|partisanship, and personal activities on behalf of the Cuban government.
+LJSpeech-1.1/wavs/LJ034-0088.wav|Three invoices on this clipboard, each dated November twenty-two, were for Scott-Foresman books, located on the first and sixth floors.
+LJSpeech-1.1/wavs/LJ008-0225.wav|the former already vowed to the same criminal career, and looking up to their elders with the respect due to successful practitioners;
+LJSpeech-1.1/wavs/LJ014-0295.wav|This man got up to look for him, and found him hanging from the bars of a neighboring room.
+LJSpeech-1.1/wavs/LJ036-0029.wav|where a man beat on the front door of the bus, boarded it and paid his fare.
+LJSpeech-1.1/wavs/LJ006-0258.wav|But the inspectors found in the ward cupboards mince-pies and other pasties, cold joints, hams, and so forth.
+LJSpeech-1.1/wavs/LJ013-0020.wav|he acted in a manner which excited the suspicions of the crew.
+LJSpeech-1.1/wavs/LJ029-0133.wav|trisect the plaza, converging at the apex of the triangle to form a triple underpass beneath a multiple railroad bridge
+LJSpeech-1.1/wavs/LJ039-0108.wav|The Marine Corps records maintained on Oswald further show that he had fired and was familiar with the Browning Automatic rifle,
+LJSpeech-1.1/wavs/LJ023-0002.wav|March nine, nineteen thirty-seven. Part one.
+LJSpeech-1.1/wavs/LJ040-0193.wav|Carro reported that Lee was disruptive in class after he returned to school on a regular basis in the fall of nineteen fifty-three.
+LJSpeech-1.1/wavs/LJ003-0074.wav|The Hon. H. G. Bennet, who visited Newgate in eighteen seventeen, saw in one yard, in a total of seventy-two prisoners,
+LJSpeech-1.1/wavs/LJ038-0284.wav|Although the Commission recognizes that neither expert was able to state
+LJSpeech-1.1/wavs/LJ010-0039.wav|the Gleeson Wilson murder at Liverpool, that of the Mannings in London, and of many more.
+LJSpeech-1.1/wavs/LJ038-0085.wav|A full discussion of Oswald's detention and interrogation is presented in chapter five of this report.
+LJSpeech-1.1/wavs/LJ028-0227.wav|He certainly mentions details which neither Nabonidus nor Cyrus would care to have appear in their royal records. His story is as follows:
+LJSpeech-1.1/wavs/LJ028-0362.wav|for history mentions the names of two of its later rulers. The palace of Nebuchadnezzar was occupied by Alexander the Great,
+LJSpeech-1.1/wavs/LJ015-0230.wav|After this nothing remained but to wait for some occasion when the amount transmitted would be sufficient to justify the risks of robbery.
+LJSpeech-1.1/wavs/LJ003-0112.wav|When any prisoner committed an offense against the community or against an individual, he was tried by a court in the jail.
+LJSpeech-1.1/wavs/LJ004-0121.wav|to which a total of upwards of one hundred thousand prisoners had been committed in the year, only twenty-three prisons were divided according to law;
+LJSpeech-1.1/wavs/LJ004-0011.wav|Mr. Neild was compelled to admit in eighteen twelve that "the great reformation produced by Howard
+LJSpeech-1.1/wavs/LJ033-0092.wav|the main reason he was going over there that Thursday afternoon when he was to bring back some curtain rods, so I didn't think any more about it when he told me that, end quote,
+LJSpeech-1.1/wavs/LJ028-0228.wav|Cyrus, with the first approach of the ensuing spring, marched forward against Babylon.
+LJSpeech-1.1/wavs/LJ028-0306.wav|Wait, after that, seven days, and post me another detachment, two thousand strong, at the Nineveh gates;
+LJSpeech-1.1/wavs/LJ027-0060.wav|All vertebrates, and none other, have a number of their anterior vertebral joints enlarged and consolidated into a box to form the skull,
+LJSpeech-1.1/wavs/LJ045-0106.wav|that he shouldn't come every week, that perhaps it is not convenient for Ruth that the whole family be there, live there, end quote.
+LJSpeech-1.1/wavs/LJ021-0042.wav|In this we have had assistance from many bankers and businessmen,
+LJSpeech-1.1/wavs/LJ032-0225.wav|and in many instances the details of these pictures differed from the original, and even from each other, particularly as to the configuration of the rifle.
+LJSpeech-1.1/wavs/LJ003-0255.wav|The unchecked admission of crowds of visitors to the felons' as well as the debtors' side was another unmixed evil.
+LJSpeech-1.1/wavs/LJ007-0137.wav|More attention to ventilation, which was altogether neglected and inadequate, would have secured a better atmosphere for the unhappy inmates
+LJSpeech-1.1/wavs/LJ040-0240.wav|and returned to New Orleans where Lee finished the ninth grade before he left school to work for a year.
+LJSpeech-1.1/wavs/LJ017-0069.wav|Originally a doctor in practice at Rugeley, in Staffordshire, he had gradually withdrawn from medicine, and devoted himself to the turf;
+LJSpeech-1.1/wavs/LJ002-0312.wav|and a calculation made as to the amount that might be expended per week from the whole sum, so that the latter might last out the year.
+LJSpeech-1.1/wavs/LJ034-0111.wav|When arrested, he gave his weight as one hundred forty pounds. On other occasions he gave weights of both one hundred forty and one hundred fifty pounds.
+LJSpeech-1.1/wavs/LJ037-0183.wav|By checking certain importers and dealers after the assassination of President Kennedy and slaying of Officer Tippit,
+LJSpeech-1.1/wavs/LJ044-0159.wav|Thus while Oswald's real intentions, assuming that they were known to himself, are not clear,
+LJSpeech-1.1/wavs/LJ028-0189.wav|The fall of Babylon with its lofty walls was a most important event in the history of the ancient world.
+LJSpeech-1.1/wavs/LJ024-0028.wav|The statute would apply to all the courts in the federal system.
+LJSpeech-1.1/wavs/LJ045-0088.wav|and that Oswald, quote, was not a man, end quote. She also told Mrs. Paine that she was not satisfied with her sexual relations with Oswald.
+LJSpeech-1.1/wavs/LJ030-0207.wav|with the follow-up car trailing the President's automobile by approximately five feet.
+LJSpeech-1.1/wavs/LJ032-0098.wav|Hidell's name on the post office box application was part of Oswald's use of a nonexistent Hidell
+LJSpeech-1.1/wavs/LJ038-0076.wav|Hill said, quote, Captain, we will save you a trip. There he sits, end quote.
+LJSpeech-1.1/wavs/LJ028-0328.wav|whom Darius according to agreement had sent first, he fell upon them and slew them all.
+LJSpeech-1.1/wavs/LJ019-0387.wav|The Prisons Bill of eighteen seventy-six contemplated the transfer of the prisons to Government.
+LJSpeech-1.1/wavs/LJ026-0104.wav|so starch manufactured in the leaves must be digested (dissolved) before it can be transported.
+LJSpeech-1.1/wavs/LJ037-0010.wav|A taxi driver, William Scoggins,
+LJSpeech-1.1/wavs/LJ038-0096.wav|and killed Patrolman Tippit. Since independent evidence revealed that Oswald repeatedly and blatantly lied to the police,
+LJSpeech-1.1/wavs/LJ016-0110.wav|The third, Bell, remained longest at large. He too was run into at a lodging in the Kingsland Road.
+LJSpeech-1.1/wavs/LJ009-0096.wav|The poor sheep-stealer is in a frenzy.
+LJSpeech-1.1/wavs/LJ013-0204.wav|he admitted that he had been justly convicted, and expressed great anxiety that his fellow-servants should be relieved from all suspicion.
+LJSpeech-1.1/wavs/LJ038-0081.wav|Captain Fritz of the homicide and robbery bureau did most of the questioning, but he kept no notes and there were no stenographic or tape recordings.
+LJSpeech-1.1/wavs/LJ049-0133.wav|and to emphasize that the task of Presidential protection is one of broad national concern.
+LJSpeech-1.1/wavs/LJ044-0072.wav|on which Oswald was heard for less than five minutes on August seventeen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ014-0043.wav|Hocker's conduct in Newgate while under sentence of death was most extraordinary.
+LJSpeech-1.1/wavs/LJ005-0180.wav|It was in these jails, withdrawn from the pressure of authority, that the new rules were invariably ignored.
+LJSpeech-1.1/wavs/LJ008-0312.wav|To them entered the chaplain or ordinary of Newgate in full canonicals,
+LJSpeech-1.1/wavs/LJ048-0267.wav|at any time while in travel status.
+LJSpeech-1.1/wavs/LJ037-0246.wav|The blue jacket was found in the Texas School Book Depository and was identified by Marina Oswald as her husband's.
+LJSpeech-1.1/wavs/LJ048-0047.wav|his presence in the School Book Depository job and its location along the route of the motorcade.
+LJSpeech-1.1/wavs/LJ006-0231.wav|These fights sometimes took place in the daytime, when a ring was regularly formed, and two or three stood by the door to watch for the officer's approach.
+LJSpeech-1.1/wavs/LJ034-0180.wav|The man was dressed in a light-colored, open-neck shirt which could have been either a sports shirt or a T-shirt,
+LJSpeech-1.1/wavs/LJ038-0190.wav|three. I paid the house rent on the second so don't worry about it. four. Recently I also paid for water and gas.
+LJSpeech-1.1/wavs/LJ048-0123.wav|Agent Lawson's interim report was received by Agent Kellerman on November twenty, the day before departure on the Texas trip.
+LJSpeech-1.1/wavs/LJ008-0153.wav|They were accordingly apprehended and brought to trial, the informer, Hanfield by name, being accepted as king's evidence.
+LJSpeech-1.1/wavs/LJ006-0018.wav|One was Mr. William Crawford, the other the Rev. Whitworth Russell.
+LJSpeech-1.1/wavs/LJ047-0217.wav|He recalled that the special agent in charge of the Dallas office of the FBI, J. Gordon Shanklin,
+LJSpeech-1.1/wavs/LJ035-0148.wav|She testified that she heard three shots which she thought came from the building.
+LJSpeech-1.1/wavs/LJ008-0309.wav|Its receipt was immediately followed by the promulgation of its contents to the persons most closely concerned,
+LJSpeech-1.1/wavs/LJ017-0208.wav|the other perpetrated in a railway-carriage, and showing the promptitude with which criminals accept and utilize altered conditions of life,
+LJSpeech-1.1/wavs/LJ003-0327.wav|It will hardly be denied that these proposals went to the root of the matter.
+LJSpeech-1.1/wavs/LJ018-0051.wav|man cannot forgive man, only God can do so. Man is therefore only accountable to God.
+LJSpeech-1.1/wavs/LJ018-0197.wav|an habitual offender in his own particular line, one who would stick at no trifles to evade detection or escape capture.
+LJSpeech-1.1/wavs/LJ016-0273.wav|At Müller's execution there was great competition for front seats,
+LJSpeech-1.1/wavs/LJ028-0103.wav|He rebuilt scores of the ancient temples, surrounded many cities with walls,
+LJSpeech-1.1/wavs/LJ002-0103.wav|The felons' side has a similar accommodation, and this mode of introducing the beverage is adopted because no publican as such
+LJSpeech-1.1/wavs/LJ009-0064.wav|the lately smirking footmen close their eyes and forget their liveries, the ordinary clasps his hands, the turnkeys cry 'Hush!'
+LJSpeech-1.1/wavs/LJ010-0264.wav|Later on, however, Dasset was himself seized and interrogated,
+LJSpeech-1.1/wavs/LJ025-0142.wav|greatly as these have been extended by modern improvements of the microscope, and that a nerve is, in its essence,
+LJSpeech-1.1/wavs/LJ048-0274.wav|However, he felt that any disciplinary action might have given rise
+LJSpeech-1.1/wavs/LJ010-0042.wav|A not altogether new crime, however, akin to murder,
+LJSpeech-1.1/wavs/LJ042-0167.wav|There can be no mediation between those systems as they exist today, and that person
+LJSpeech-1.1/wavs/LJ028-0094.wav|Nebuchadnezzar, King of Babylon, the exalted prince, the favorite of Marduk, the lofty patesi,
+LJSpeech-1.1/wavs/LJ028-0058.wav|Early he married Amuhia, a daughter of the Medean king.
+LJSpeech-1.1/wavs/LJ013-0021.wav|The larboard pump was suffered to remain choked up, and the long-boat was fitted with tackles and held ready for use at a moment's notice.
+LJSpeech-1.1/wavs/LJ039-0079.wav|by Master Sgt. James A. Zahm, noncommissioned officer in charge of the Marksmanship Training Unit in the Weapons Training Battalion
+LJSpeech-1.1/wavs/LJ003-0196.wav|five of whom had been condemned the previous July, four in September, and twenty-nine in October.
+LJSpeech-1.1/wavs/LJ038-0025.wav|Patrolman M. N. McDonald, with Patrolmen R. Hawkins, T. A. Hutson, and C. T. Walker, entered the theatre from the rear.
+LJSpeech-1.1/wavs/LJ015-0275.wav|Burglars brought him the cheques they stole from houses, thieves what they got in pocketbooks.
+LJSpeech-1.1/wavs/LJ040-0150.wav|He was reported to have said, quote, I don't want a friend and I don't like to talk to people, end quote, and, quote, I dislike everybody, end quote.
+LJSpeech-1.1/wavs/LJ033-0145.wav|May have been used to carry gun. Lt. J. C. Day, end quote.
+LJSpeech-1.1/wavs/LJ048-0255.wav|with full possession of his mental and physical capabilities and entirely ready for the performance of his assigned duties. Chief Rowley testified that,
+LJSpeech-1.1/wavs/LJ006-0260.wav|From the same source came the two or three strong files which the inspectors found in one ward,
+LJSpeech-1.1/wavs/LJ045-0040.wav|They had been married after a courtship of only about six weeks, a part of which Oswald spent in the hospital.
+LJSpeech-1.1/wavs/LJ012-0205.wav|an avowed "snatcher" and habitué of the Fortune of War, a public-house in Smithfield frequented openly by men of this awful profession.
+LJSpeech-1.1/wavs/LJ018-0337.wav|of Good, and Greenacre, and Catherine Hayes.
+LJSpeech-1.1/wavs/LJ037-0033.wav|When questioned by police officers on the evening of November twenty-two, Benavides told them that he did not think that he could identify the man who fired the shots.
+LJSpeech-1.1/wavs/LJ029-0153.wav|Under standard procedures, the responsibility for watching the windows of buildings was shared by local police stationed along the route
+LJSpeech-1.1/wavs/LJ029-0116.wav|and could not accommodate spectators as conveniently as Harwood Street.
+LJSpeech-1.1/wavs/LJ033-0179.wav|since the original bag had been discolored during various laboratory examinations and could not be used for valid identification by witnesses.
+LJSpeech-1.1/wavs/LJ028-0238.wav|he then himself drew off with the unwarlike portion of his host, and made for the place where Nitocris dug the basin for the river,
+LJSpeech-1.1/wavs/LJ002-0326.wav|The poor debtors were not supplied with beds. Those who could pay the price might hire them from each other,
+LJSpeech-1.1/wavs/LJ003-0064.wav|the sleepers covered the entire floor, with the exception of a passage in the middle.
+LJSpeech-1.1/wavs/LJ019-0175.wav|Well-founded complaints had been made of the want of heating appliances in the jail.
+LJSpeech-1.1/wavs/LJ017-0129.wav|Even after the death sentence had been passed upon him he clung to the hope that the Government would grant him a reprieve.
+LJSpeech-1.1/wavs/LJ024-0031.wav|If such a plan is good for the lower courts it certainly ought to be equally good for the highest court from which there is no appeal.
+LJSpeech-1.1/wavs/LJ035-0089.wav|No allowance was made for the special conditions which existed on the day of the assassination
+LJSpeech-1.1/wavs/LJ028-0104.wav|lined the shores of the rivers with embankments, and spanned the rivers with bridges.
+LJSpeech-1.1/wavs/LJ022-0174.wav|for the strengthening of the Interstate Commerce Commission to enable it to carry out a rounded conception of the national transportation system
+LJSpeech-1.1/wavs/LJ034-0124.wav|The half-open window, the arrangement of the boxes, and the angle of the shots virtually preclude a standing position.
+LJSpeech-1.1/wavs/LJ006-0290.wav|the gatesman who prepared the briefs had interviews with female prisoners alone while taking their instructions; a female came alone and unaccompanied by a matron
+LJSpeech-1.1/wavs/LJ014-0063.wav|After the marriage a close intimacy was still maintained between O'Connor and the Mannings.
+LJSpeech-1.1/wavs/LJ048-0049.wav|possessed of this information to list Oswald as a potential threat to the safety of the President.
+LJSpeech-1.1/wavs/LJ011-0264.wav|Mr. Gee, of Bishop Stortford, who administered the estate of a certain Mr. Canning, deceased.
+LJSpeech-1.1/wavs/LJ043-0169.wav|His behavior was entirely consistent with his wife's testimony that, quote,
+LJSpeech-1.1/wavs/LJ030-0108.wav|White House communications car, Trade Mart, Love Field, and the Presidential and Vice-Presidential airplanes.
+LJSpeech-1.1/wavs/LJ028-0319.wav|"And now," he went on to say, "my coming to you, Babylonians,
+LJSpeech-1.1/wavs/LJ002-0192.wav|another two hundred more or less took advantage of "the rules," and lived outside within a circumference of two miles and a half.
+LJSpeech-1.1/wavs/LJ011-0086.wav|He then took to forging bank-notes, and was arrested as I have described above.
+LJSpeech-1.1/wavs/LJ011-0114.wav|to which he seemed fondly attached, should be used to bandage his eyes, which request was readily granted.
+LJSpeech-1.1/wavs/LJ004-0059.wav|They had a right, Mr. Buxton affirmed, to be employed in their own crafts, provided it could be safely followed in prison.
+LJSpeech-1.1/wavs/LJ043-0170.wav|I asked him what for he was making all these entries in the book and he answered that he wanted to leave a complete record so that all the details would be in it.
+LJSpeech-1.1/wavs/LJ036-0046.wav|She testified, quote, I didn't like his attitude. There was just something about him I didn't like or want him. Just didn't want him around me, end quote,
+LJSpeech-1.1/wavs/LJ021-0086.wav|strengthened with the acceptance of those obligations to the public interest which rest upon us all.
+LJSpeech-1.1/wavs/LJ009-0197.wav|as was done in the case of Mrs. Phipoe, the murderess, who was executed in front of Newgate in seventeen ninety-eight,
+LJSpeech-1.1/wavs/LJ002-0233.wav|Twice a week there was a wine and beer club held at night, which lasted till two or three in the morning.
+LJSpeech-1.1/wavs/LJ001-0073.wav|went on apace; and by the end of the sixteenth century there was no really beautiful printing done:
+LJSpeech-1.1/wavs/LJ011-0103.wav|a pair of light gray pantaloons, a black stock and a foraging cap.
+LJSpeech-1.1/wavs/LJ027-0116.wav|On the other hand, the theory of special creation can only maintain that these rudiments are formed for the sake of adhering to an ideal type.
+LJSpeech-1.1/wavs/LJ024-0072.wav|a new and younger judge shall be added to the court automatically.
+LJSpeech-1.1/wavs/LJ028-0292.wav|leaping down from his throne he exclaimed aloud and asked Zopyrus who it was that had disfigured him, and what he had done to be so treated.
+LJSpeech-1.1/wavs/LJ028-0164.wav|The outer wall is the main defense of the city.
+LJSpeech-1.1/wavs/LJ017-0283.wav|This was Duranno; but the sight of the heaving mass of uplifted, impassioned faces
+LJSpeech-1.1/wavs/LJ025-0012.wav|extracts from which follow.
+LJSpeech-1.1/wavs/LJ008-0242.wav|famous in the annals of Newgate for the hardihood they had displayed in the hour of execution, when they terminated their career of crime at the gallows.
+LJSpeech-1.1/wavs/LJ016-0394.wav|The request was not granted, as the old custom of allowing capital convicts whatever they asked for in the way of food has not been the rule in Newgate.
+LJSpeech-1.1/wavs/LJ038-0281.wav|in the absence of very definite negative evidence, I think it is permissible to say that in an exhibit such as five seven three
+LJSpeech-1.1/wavs/LJ033-0126.wav|that the width was approximately the same.
+LJSpeech-1.1/wavs/LJ016-0293.wav|with the rakings of cheap singing-halls and billiard-rooms, the fast young men of London.
+LJSpeech-1.1/wavs/LJ001-0113.wav|there is a grossness in the upper finishings of letters like the c, the a, and so on,
+LJSpeech-1.1/wavs/LJ010-0085.wav|It was the day on which Lord Harrowby was to entertain his colleagues at dinner in Grosvenor Square.
+LJSpeech-1.1/wavs/LJ008-0305.wav|they never ceased cursing until the passion of anger so excited was exchanged for joy in some and grief in others.
+LJSpeech-1.1/wavs/LJ034-0210.wav|The picture which gave rise to these allegations was taken by Associated Press Photographer James W. Altgens,
+LJSpeech-1.1/wavs/LJ031-0182.wav|Concerned that the local officials might try to prevent the plane's departure, O'Donnell asked that the pilot take off immediately.
+LJSpeech-1.1/wavs/LJ047-0218.wav|had discussed the President's visit on several occasions, including the regular biweekly conference on the morning of November twenty-two
+LJSpeech-1.1/wavs/LJ009-0042.wav|Upon the occasion which I am describing they were four in number; and here I will continue the narrative in Mr. Wakefield's own words:
+LJSpeech-1.1/wavs/LJ031-0179.wav|Despite the protests of these officials, the casket was wheeled out of the hospital, placed in an ambulance, and transported to the airport shortly after two p.m.
+LJSpeech-1.1/wavs/LJ044-0065.wav|In another letter to V. T. Lee, dated August seventeen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0216.wav|Williams, and May had been concerned in the transaction.
+LJSpeech-1.1/wavs/LJ038-0200.wav|the city jail is located at the end of the bridge through which we always passed on going to the city (right in the beginning of the city after crossing the bridge).
+LJSpeech-1.1/wavs/LJ005-0127.wav|Workshops were built at many prisons, various kinds of manufactures and trades were set on foot, including weaving, matting, shoe-making, and tailoring.
+LJSpeech-1.1/wavs/LJ002-0120.wav|had been issued for the arrests of debtors in the kingdom, for sums varying from fourpence to five hundred pounds and upwards.
+LJSpeech-1.1/wavs/LJ046-0208.wav|Presidential protection was an important topic in these training programs.
+LJSpeech-1.1/wavs/LJ046-0250.wav|The requests shifted the responsibility for evaluating difficult cases from the Service, the agency most responsible for performing that task,
+LJSpeech-1.1/wavs/LJ014-0148.wav|From Newgate the Mannings were taken in separate cabs to Horsemonger Lane Jail.
+LJSpeech-1.1/wavs/LJ002-0237.wav|This very ancient prison, which stood in the High Street, Southwark,
+LJSpeech-1.1/wavs/LJ025-0174.wav|the air, to which the leaves are exposed, contains carbon,
+LJSpeech-1.1/wavs/LJ006-0078.wav|A new prisoner's fate, as to location, rested really with a powerful fellow-prisoner.
+LJSpeech-1.1/wavs/LJ010-0144.wav|but is only an ordinary commonplace tool.
+LJSpeech-1.1/wavs/LJ037-0057.wav|He said that Mrs. Markham started crying when Oswald walked into the lineup room.
+LJSpeech-1.1/wavs/LJ006-0190.wav|lively discourse upon past criminal exploits, elaborate discussion of others to be perpetrated after release.
+LJSpeech-1.1/wavs/LJ005-0162.wav|it did not peremptorily insist. Too often the letter of the law was observed, but not its spirit.
+LJSpeech-1.1/wavs/LJ012-0086.wav|Some difficulty then arose as to gaining admission to the strong room, and it was arranged that a man, May, another Custom House clerk,
+LJSpeech-1.1/wavs/LJ010-0306.wav|and the bank became liable for a sum of one hundred seventy thousand pounds.
+LJSpeech-1.1/wavs/LJ012-0005.wav|was a blind man named Edwards, who had taken this house in York Street, and who was known to be a frequent visitor at Mrs. Canning's.
+LJSpeech-1.1/wavs/LJ042-0137.wav|The dramatic break with society in America now had to be undone.
+LJSpeech-1.1/wavs/LJ011-0147.wav|I will include in this part of the present chapter almost one of the last instances of a crime which in time past
+LJSpeech-1.1/wavs/LJ001-0002.wav|in being comparatively modern.
+LJSpeech-1.1/wavs/LJ018-0265.wav|He carried this character with him into penal servitude, and after enduring the full severity of his punishment for several years,
+LJSpeech-1.1/wavs/LJ002-0136.wav|and the disuse of the inferior courts.
+LJSpeech-1.1/wavs/LJ004-0054.wav|In order to give greater value to the pamphlet,
+LJSpeech-1.1/wavs/LJ050-0076.wav|The problem is aggravated by the necessity that the Service obtain the assistance of local law enforcement officials in evaluating the information which it receives
+LJSpeech-1.1/wavs/LJ033-0063.wav|Soon afterward, at about three p.m., police officers arrived and searched the house.
+LJSpeech-1.1/wavs/LJ027-0174.wav|Last, the anoura takes first the fish-form, then that of the perennibranch, then that of the caducibranch, and finally becomes anoura.
+LJSpeech-1.1/wavs/LJ016-0267.wav|On the contrary, the prevailing sentiment was one of satisfaction at the success of the spectacle.
+LJSpeech-1.1/wavs/LJ013-0142.wav|Part of the freight were four hundred rough diamonds valued at four thousand pounds.
+LJSpeech-1.1/wavs/LJ002-0321.wav|others who arrived just after the time of distribution were often forty-eight hours without food. The latter might also be six days without meat.
+LJSpeech-1.1/wavs/LJ037-0109.wav|He slowed down, halted, said something, and then kept on going to the corner, turned right, and continued west on Jefferson.
+LJSpeech-1.1/wavs/LJ007-0198.wav|The windows were to be glazed and painted to prevent prisoners from looking out;
+LJSpeech-1.1/wavs/LJ045-0218.wav|Thoughts of his personal difficulties must have been at least partly on his mind when he went to Irving on Thursday night and told his wife that he was lonely,
+LJSpeech-1.1/wavs/LJ038-0224.wav|Another statement which limits the time when it could have been written is the reference, quote, you and the baby, end quote,
+LJSpeech-1.1/wavs/LJ005-0110.wav|and so passing by the roof down into the garden and on to freedom.
+LJSpeech-1.1/wavs/LJ008-0201.wav|in an adjoining house, that of an undertaker, was Lord Alfred Paget, also with several friends.
+LJSpeech-1.1/wavs/LJ020-0070.wav|Bit of soda as large as a pea, dissolved in hot water.
+LJSpeech-1.1/wavs/LJ029-0098.wav|This route, eventually selected for the motorcade from the airport to the Trade Mart, measured ten miles and could be driven easily within the allotted forty-five minutes.
+LJSpeech-1.1/wavs/LJ029-0018.wav|Vice President Johnson, and Governor Connally on June fifth, nineteen sixty-three, at the Cortez Hotel in El Paso, Texas.
+LJSpeech-1.1/wavs/LJ004-0242.wav|Some attempt was made to reduce the overcrowding, on the recommendation of the House of Commons Committee of eighteen eighteen, but this applied only a partial remedy.
+LJSpeech-1.1/wavs/LJ033-0203.wav|We found no brown cotton fibers, no green viscose fibers, and no woolen fibers.
+LJSpeech-1.1/wavs/LJ008-0286.wav|The inspectors very properly desired to call attention to the inevitable tendency in this mode of dealing with "the most awful sanctions of the law,"
+LJSpeech-1.1/wavs/LJ012-0294.wav|without hesitation brought in a verdict of willful murder.
+LJSpeech-1.1/wavs/LJ016-0161.wav|Having carefully spread out a blanket on the floor just below the hammock as it lay suspended,
+LJSpeech-1.1/wavs/LJ009-0298.wav|which sets forth that the petitioner had been at great expense by sending clerks and agents to Liverpool and Shrewsbury to hire an executioner.
+LJSpeech-1.1/wavs/LJ035-0153.wav|As Oswald passed Mrs. Reid she said, quote, Oh, the President has been shot, but maybe they didn't hit him, end quote.
+LJSpeech-1.1/wavs/LJ007-0160.wav|the employment of those means and agents by which the lowest passions and the most vulgar propensities of man are perpetually kept in the highest state of excitement
+LJSpeech-1.1/wavs/LJ002-0271.wav|The compters of Ludgate, Giltspur Street, and the Borough were discontinued as debtors' prisons (as was Newgate also)
+LJSpeech-1.1/wavs/LJ031-0022.wav|Charles R. Baxter, Robert N. McClelland, Ronald C. Jones; the chief neurologist, Dr. William Kemp Clark;
+LJSpeech-1.1/wavs/LJ018-0199.wav|to watch and prosecute coiners, that he received information that coining was carried on by Cummings and others at a place in Westminster.
+LJSpeech-1.1/wavs/LJ008-0165.wav|Here a couple of piemen had been selling their wares; the basket of one of them, which was raised upon a four-legged stool, was upset.
+LJSpeech-1.1/wavs/LJ044-0019.wav|Following his arrest, he was interviewed by the police, and at his own request, by an agent of the FBI.
+LJSpeech-1.1/wavs/LJ042-0136.wav|His attempt to renounce his citizenship had been an open expression of hostility against the United States and a profound rejection of his early life.
+LJSpeech-1.1/wavs/LJ028-0266.wav|he drew together all his power and began the war by marching straight upon Babylon and laying siege to the place.
+LJSpeech-1.1/wavs/LJ018-0361.wav|tampered with, and returned to the shop.
+LJSpeech-1.1/wavs/LJ032-0222.wav|Moreover, Shaneyfelt testified that in his opinion the photographs were not composites of two different photographs
+LJSpeech-1.1/wavs/LJ037-0126.wav|located on the southeast corner of this intersection.
+LJSpeech-1.1/wavs/LJ042-0139.wav|Marina Oswald confirmed the fact that her husband was experiencing psychological difficulties at the time of his return.
+LJSpeech-1.1/wavs/LJ017-0155.wav|Miss Bankes seems to have counted upon some species of whitewashing, no less than the repudiation of the other marriage,
+LJSpeech-1.1/wavs/LJ002-0238.wav|was used for debtors arrested for the lowest sums within twelve miles of the palace of Whitehall;
+LJSpeech-1.1/wavs/LJ001-0095.wav|These and similar founts, cast by the above firm and others,
+LJSpeech-1.1/wavs/LJ004-0005.wav|The pleadings, however eloquent, of a single individual were unable to more than partially remedy the widespread and colossal evils of British prisons.
+LJSpeech-1.1/wavs/LJ047-0145.wav|The CIA message was sent also to the Department of State where it was reviewed by personnel of the Passport Office, who knew from Oswald's file
+LJSpeech-1.1/wavs/LJ004-0117.wav|In an early report of the Prison Discipline Improvement Society,
+LJSpeech-1.1/wavs/LJ019-0269.wav|More animal food was given than was necessary.
+LJSpeech-1.1/wavs/LJ016-0219.wav|He expressly bequeathed them to Calcraft, who was graciously pleased to accept them.
+LJSpeech-1.1/wavs/LJ047-0030.wav|as a marine radar technician and had displayed an arrogant and aggressive attitude at the U.S. Embassy;
+LJSpeech-1.1/wavs/LJ003-0301.wav|It recommended day cells or rooms for regular labor, which should be compulsory upon all transports and prisoners sentenced to hard labor,
+LJSpeech-1.1/wavs/LJ033-0181.wav|The science of paper analysis enabled him to distinguish between different rolls of paper even though they were produced by the same manufacturer.
+LJSpeech-1.1/wavs/LJ009-0018.wav|and informed that the public would not in future be admitted to hear the condemned sermon. "I was also informed," writes Mr. Cotton,
+LJSpeech-1.1/wavs/LJ023-0015.wav|to exact a pound of flesh was more sacred than the main objectives of the Constitution to establish an enduring Nation.
+LJSpeech-1.1/wavs/LJ032-0064.wav|It is not known whether the application for post office box two nine one five listed "A. Hidell" as a person entitled to receive mail at this box.
+LJSpeech-1.1/wavs/LJ009-0148.wav|But the chaplain admitted that the solitude of the convict's cell
+LJSpeech-1.1/wavs/LJ009-0271.wav|had attempted to throw himself upon the fire in his condemned cell.
+LJSpeech-1.1/wavs/LJ012-0050.wav|He was about to emigrate to New South Wales, when he was arrested at Islington and committed to Newgate on a charge of receiving stolen goods.
+LJSpeech-1.1/wavs/LJ041-0020.wav|or you were going to really have one, because he wasn't going to take anything from anybody, end quote.
+LJSpeech-1.1/wavs/LJ049-0141.wav|The foremost assignment of the Committee would be to insure that the maximum resources of the Federal Government are fully engaged in the job of protecting the President,
+LJSpeech-1.1/wavs/LJ003-0143.wav|The entrance fee was at least thirteen shillings, six pence a head, with half-a-crown a week more for bed and bedding,
+LJSpeech-1.1/wavs/LJ021-0151.wav|But I shall ask both groups to give a fair trial to peaceful methods of adjusting their conflicts of opinion and interest,
+LJSpeech-1.1/wavs/LJ012-0240.wav|I will close this chapter with a brief account of another murder,
+LJSpeech-1.1/wavs/LJ003-0297.wav|It admitted the paramount necessity
+LJSpeech-1.1/wavs/LJ032-0240.wav|By Sunday, March thirty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ019-0336.wav|His disciplinary powers were defined by the act, and his duties,
+LJSpeech-1.1/wavs/LJ002-0084.wav|those on the first floor were rather larger on account of a set-off in the wall; and the uppermost were the largest, for the same reason.
+LJSpeech-1.1/wavs/LJ008-0249.wav|Blinded with their long hair, they tore at each other like two furies; their bonnets and caps were trodden underfoot in the kennel,
+LJSpeech-1.1/wavs/LJ026-0151.wav|Hence oxygen must be supplied to the cells.
+LJSpeech-1.1/wavs/LJ042-0017.wav|and see for himself how a revolutionary society operates, a Marxist society.
+LJSpeech-1.1/wavs/LJ035-0168.wav|One of the police officers assigned to the corner of Elm and Houston Streets for the Presidential motorcade, W. E. Barnett,
+LJSpeech-1.1/wavs/LJ005-0042.wav|It made the subject of the newly-invented tread-wheels, or stepping-wheels, as they were at first called, its peculiar affair,
+LJSpeech-1.1/wavs/LJ009-0017.wav|For this the chaplain was a few days later summoned before the jail committee of aldermen,
+LJSpeech-1.1/wavs/LJ047-0205.wav|to potential danger, quote,
+LJSpeech-1.1/wavs/LJ009-0019.wav|that this resolution was in consequence of their (the aldermen's) disapproving of the last discourse delivered by me,
+LJSpeech-1.1/wavs/LJ005-0165.wav|They were so radically bad that reform seemed hopeless, and it was thought wiser not to bring them under provisions which clearly could not be enforced.
+LJSpeech-1.1/wavs/LJ007-0146.wav|vaunting his own adventures, or listening to those of others;
+LJSpeech-1.1/wavs/LJ002-0162.wav|He recites another most outrageous and extraordinary case, in which one John Bird,
+LJSpeech-1.1/wavs/LJ018-0064.wav|afterwards in the British German Legion raised for the Crimean War.
+LJSpeech-1.1/wavs/LJ017-0013.wav|so called after its inventress, a Roman woman named Toffana, and which was so widely adopted by ladies anxious to get rid of their husbands,
+LJSpeech-1.1/wavs/LJ032-0241.wav|ten days prior to the Walker attempt, Oswald had undoubtedly received the rifle shipped from Chicago on March twenty,
+LJSpeech-1.1/wavs/LJ012-0075.wav|No doubt, on the removal of Ikey Solomons from the scene, his mantle fell upon worthy successors.
+LJSpeech-1.1/wavs/LJ003-0219.wav|Well might Mr. Bennet write that the condition of the condemned side was the most prominent of the manifold evils in the present system of Newgate,
+LJSpeech-1.1/wavs/LJ001-0097.wav|a little reduced in ugliness. The design of the letters of this modern "old style" leaves a good deal to be desired,
+LJSpeech-1.1/wavs/LJ018-0133.wav|which gave him access to all parts of the mills, the packing-room included.
+LJSpeech-1.1/wavs/LJ015-0233.wav|A weight of shot was bought and stowed in carpet bags ready to replace exactly the abstracted gold.
+LJSpeech-1.1/wavs/LJ040-0208.wav|Carro reported that when questioned about his mother Lee said, quote,
+LJSpeech-1.1/wavs/LJ021-0134.wav|It is time that we made a clean-cut effort to bring about that united action of management and labor,
+LJSpeech-1.1/wavs/LJ002-0255.wav|A court of the collegians was held every Monday to manage its affairs, at which all prisoners were required to attend.
+LJSpeech-1.1/wavs/LJ040-0122.wav|a junior high school in the Bronx, where the other children apparently teased him because of his, quote, western clothes and Texas accent.
+LJSpeech-1.1/wavs/LJ014-0217.wav|Dwyer managed to overpower his assailant, and got to his feet; but Cannon butted at him with his head, and again threw him to the ground,
+LJSpeech-1.1/wavs/LJ008-0167.wav|No one who fell ever rose again.
+LJSpeech-1.1/wavs/LJ009-0060.wav|Now his doom is sealed irrevocably, and he has given himself up to despair.
+LJSpeech-1.1/wavs/LJ028-0018.wav|Like a great ball of fire the sun sinks in the west.
+LJSpeech-1.1/wavs/LJ035-0174.wav|where upon Barnett posted himself at the front door to make certain that no one left the building.
+LJSpeech-1.1/wavs/LJ007-0139.wav|Again, the discipline commonly deemed inseparable from every place of durance was entirely wanting.
+LJSpeech-1.1/wavs/LJ018-0253.wav|For these crimes William Roupell was tried at the Central Criminal Court on the twenty-fourth September, eighteen sixty-two.
+LJSpeech-1.1/wavs/LJ007-0021.wav|"she had never witnessed such scenes before, and hopes she never shall again -- it was dreadful!"
+LJSpeech-1.1/wavs/LJ004-0048.wav|Mrs. Fry's brother, Mr. Samuel Hoare, Junior, was chairman of the committee, on which also served many noted members of the Society of Friends
+LJSpeech-1.1/wavs/LJ039-0159.wav|The ammunition used by the assassin was manufactured by Western Cartridge Co. of East Alton, Illinois.
+LJSpeech-1.1/wavs/LJ021-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section six.
+LJSpeech-1.1/wavs/LJ030-0044.wav|Motorcycles. -- Next came four to six motorcycle policemen whose main purpose was to keep the crowd back.
+LJSpeech-1.1/wavs/LJ017-0026.wav|Yet it was clearly proved that the dumplings contained arsenic, that she, and she alone, had made the dough,
+LJSpeech-1.1/wavs/LJ021-0095.wav|We are now prepared to move into this second phase, on the basis of our experience in the first phase
+LJSpeech-1.1/wavs/LJ007-0031.wav|all classes of the condemned, those certain to suffer, and the larger number who were nearly certain of a reprieve,
+LJSpeech-1.1/wavs/LJ020-0102.wav|A wise accommodation of parts and angles, and compactness in the adjustment of "must-be-dones"
+LJSpeech-1.1/wavs/LJ032-0061.wav|as was a change-of-address card dated May twelve, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ047-0173.wav|and that she found this and similar statements illogical and somewhat amusing. On this occasion Hosty was at the Paine residence for only a few minutes.
+LJSpeech-1.1/wavs/LJ042-0092.wav|No evidence has been found that they used him for any particular propaganda or other political or informational purposes.
+LJSpeech-1.1/wavs/LJ009-0142.wav|Contemporary accounts give a minute description of the demeanor of the convict upon this solemn occasion.
+LJSpeech-1.1/wavs/LJ009-0193.wav|with the idea that ignominy would no longer attach to an operation which ceased to be compulsory for the most degraded beings;
+LJSpeech-1.1/wavs/LJ029-0002.wav|Chapter two. The Assassination: Part one.
+LJSpeech-1.1/wavs/LJ015-0173.wav|Warrants were issued for Redpath's arrest, but he had flown to Paris.
+LJSpeech-1.1/wavs/LJ007-0092.wav|he was placed in a situation "beyond all others calculated to confirm his malady and prolong his sufferings."
+LJSpeech-1.1/wavs/LJ036-0032.wav|So I gave her a transfer and opened the door and she was going out the gentleman I had picked up about two blocks [back]
+LJSpeech-1.1/wavs/LJ030-0009.wav|Late in the afternoon he flew to Houston where he rode through the city in a motorcade,
+LJSpeech-1.1/wavs/LJ035-0084.wav|The minimum time required by Baker to park his motorcycle and reach the second-floor lunchroom was within three seconds of the time needed to walk
+LJSpeech-1.1/wavs/LJ020-0078.wav|Do all this at bedtime. Early in the morning turn out the dough upon a floured board, work it for a minute into manageable shape;
+LJSpeech-1.1/wavs/LJ013-0211.wav|"What are you doing here?" asked his lordship.
+LJSpeech-1.1/wavs/LJ031-0049.wav|Dr. Carrico felt the President's back and determined that there was no large wound there which would be an immediate threat to life.
+LJSpeech-1.1/wavs/LJ010-0084.wav|The day appointed for the murder and rising actually arrived before the authorities interfered.
+LJSpeech-1.1/wavs/LJ037-0083.wav|Barbara Jeanette and Virginia Davis viewed a group of four men in a lineup and each one picked Oswald as the man who crossed their lawn while emptying his pistol.
+LJSpeech-1.1/wavs/LJ037-0196.wav|A. J. Hidell, aged twenty-eight, end quote. The date of the order was January twenty-seven. No year shown.
+LJSpeech-1.1/wavs/LJ011-0286.wav|For three hours he struggled without success with his bonds,
+LJSpeech-1.1/wavs/LJ039-0122.wav|There is some possibility that the rifle he was firing might not have been as good a rifle as the rifle that he was firing in his A course firing,
+LJSpeech-1.1/wavs/LJ038-0083.wav|They occasionally participated in the questioning.
+LJSpeech-1.1/wavs/LJ024-0106.wav|Two groups oppose my plan on the ground that they favor a constitutional amendment.
+LJSpeech-1.1/wavs/LJ019-0199.wav|and the costliness of enlarging it, forbade all idea of entirely reconstructing the jail so as to constitute it a model prison.
+LJSpeech-1.1/wavs/LJ027-0132.wav|A dozen volumes would be necessary
+LJSpeech-1.1/wavs/LJ018-0124.wav|where are Messrs. Portal's mills for the manufacture of bank-note paper.
+LJSpeech-1.1/wavs/LJ011-0253.wav|Mullay defended himself, and managed to break the knife, but not before he had cut himself severely.
+LJSpeech-1.1/wavs/LJ036-0181.wav|On Friday, November twenty-two, about one p.m., he entered the house in unusual haste.
+LJSpeech-1.1/wavs/LJ018-0003.wav|As the circumstances under which it was perpetrated were somewhat novel,
+LJSpeech-1.1/wavs/LJ022-0038.wav|That makes it particularly necessary for the Vice- President and for me to conceive of our duty toward the entire country.
+LJSpeech-1.1/wavs/LJ019-0262.wav|The jail authorities often gave the highest, possibly undue, importance to the value of remunerative employment,
+LJSpeech-1.1/wavs/LJ037-0253.wav|looked like the jacket he was wearing but, quote, I thought it had a little more tan to it, end quote.
+LJSpeech-1.1/wavs/LJ003-0288.wav|The above-mentioned report was ordered to be printed upon the ninth May.
+LJSpeech-1.1/wavs/LJ025-0110.wav|and subject to exceptions.
+LJSpeech-1.1/wavs/LJ022-0047.wav|However, for the first time in five years the relief rolls have declined instead of increased during the winter months.
+LJSpeech-1.1/wavs/LJ048-0190.wav|If such instructions were in fact given, they were not effectively carried out.
+LJSpeech-1.1/wavs/LJ046-0034.wav|with particular attention to the diverse roles which the President is expected to fill.
+LJSpeech-1.1/wavs/LJ008-0041.wav|The criminals are not exposed to view till they mount the fatal stage. The last part of the stage, or that next to the jail,
+LJSpeech-1.1/wavs/LJ034-0135.wav|two of these employees leaving the building and immediately identified them as having been in the fifth-floor windows.
+LJSpeech-1.1/wavs/LJ024-0132.wav|I am in favor of action through legislation:
+LJSpeech-1.1/wavs/LJ050-0039.wav|the Secret Service has largely been the passive recipient of threatening communications to the President
+LJSpeech-1.1/wavs/LJ042-0082.wav|End quote. As his, quote, principal reason, end quote, for renouncing his citizenship
+LJSpeech-1.1/wavs/LJ019-0083.wav|The discipline also varied greatly, from the severely penal to the culpably lax.
+LJSpeech-1.1/wavs/LJ046-0214.wav|members of his immediate family, the President-Elect, and the Vice-President is within the exclusive jurisdiction of the U.S. Secret Service.
+LJSpeech-1.1/wavs/LJ046-0238.wav|it contained the names of no persons from the entire Dallas-Fort Worth area,
+LJSpeech-1.1/wavs/LJ044-0124.wav|Oswald wrote that he felt that he might have compromised the FPCC and expressed concern lest, quote, Our opponents could use my background
+LJSpeech-1.1/wavs/LJ044-0097.wav|Stuckey thought Oswald, quote, appeared to be a very logical, intelligent fellow, end quote, and, quote, was arrested by his cleancutness, end quote.
+LJSpeech-1.1/wavs/LJ005-0293.wav|In these places the prisoners are often without a proper supply of air and light; frequently the jails are mere dungeons under the town hall.
+LJSpeech-1.1/wavs/LJ020-0039.wav|If rudely shaken or jarred, there will be heavy streaks in loaves.
+LJSpeech-1.1/wavs/LJ014-0098.wav|The house was empty, bare and stripped of all its furniture, and its former occupants had decamped.
+LJSpeech-1.1/wavs/LJ021-0089.wav|and have effected a reorganization of the N.R.A.
+LJSpeech-1.1/wavs/LJ044-0067.wav|Latin American Focus, end quote,
+LJSpeech-1.1/wavs/LJ002-0198.wav|The office of marshal had been hereditary,
+LJSpeech-1.1/wavs/LJ007-0132.wav|The inspectors, however, honestly admitted that although the site of the prison was convenient, its construction was as bad as bad could be.
+LJSpeech-1.1/wavs/LJ019-0122.wav|Mr. Pearson seems to have taken for granted that all prisoners would behave well in his district prisons.
+LJSpeech-1.1/wavs/LJ030-0026.wav|Governor and Mrs. Connally and Senator Ralph W. Yarborough had come with the President from Fort Worth.
+LJSpeech-1.1/wavs/LJ011-0252.wav|locked the door, and attacked Mullay violently with the trap-ball bat and knife.
+LJSpeech-1.1/wavs/LJ047-0243.wav|According to Revill, Hosty indicated that he was going to tell this to Lieutenant Wells of the homicide and robbery bureau.
+LJSpeech-1.1/wavs/LJ024-0053.wav|Fundamentally, if in the future, America cannot trust the Congress it elects to refrain from abuse of our Constitutional usages
+LJSpeech-1.1/wavs/LJ034-0040.wav|not too long, end quote, a time had elapsed between the time that the print was placed on the carton and the time that it had been developed by the Dallas police.
+LJSpeech-1.1/wavs/LJ030-0078.wav|Presidential Assistants David F. Powers and Kenneth O'Donnell sat in the right and left jump seats, respectively.
+LJSpeech-1.1/wavs/LJ024-0092.wav|There is no substantial groups within the Congress or outside it who are agreed on any single amendment.
+LJSpeech-1.1/wavs/LJ010-0110.wav|Several of the other prisoners took the same line as regards Edwards,
+LJSpeech-1.1/wavs/LJ009-0170.wav|to fifty-six in eighteen thirty-nine.
+LJSpeech-1.1/wavs/LJ037-0108.wav|From across the street Callaway yelled, quote, Hey, man, what the hell is going on? End quote.
+LJSpeech-1.1/wavs/LJ016-0200.wav|without favoring father or mother or any other relation or friend.
+LJSpeech-1.1/wavs/LJ040-0100.wav|she would sometimes take Lee with her, apparently leaving him alone in the car while she transacted her business.
+LJSpeech-1.1/wavs/LJ015-0065.wav|Robson was of humble origin, but he was well educated, and he had some literary abilities.
+LJSpeech-1.1/wavs/LJ019-0394.wav|It was only filled at the periods when the sessions of the Central Criminal Court were in progress;
+LJSpeech-1.1/wavs/LJ014-0095.wav|On the third day his friends began to inquire for him,
+LJSpeech-1.1/wavs/LJ016-0431.wav|One was vexed to think that his victims had a finer funeral than he would have.
+LJSpeech-1.1/wavs/LJ038-0071.wav|When he entered the homicide and robbery bureau office, he saw two detectives standing there with Sgt. Gerald L. Hill,
+LJSpeech-1.1/wavs/LJ010-0052.wav|which grew out of disturbed social conditions after the last French war, amidst general distress,
+LJSpeech-1.1/wavs/LJ040-0164.wav|The possibility of commitment was to be considered only if the probation plan was not successful.
+LJSpeech-1.1/wavs/LJ018-0151.wav|The police officers now taxed Mrs. Campbell with complicity, and frightened her into collusion.
+LJSpeech-1.1/wavs/LJ048-0076.wav|and maintaining liaison with other Federal Agencies. This liaison should take into consideration
+LJSpeech-1.1/wavs/LJ036-0007.wav|In deciding whether Oswald killed Patrolman Tippit the Commission considered the following:
+LJSpeech-1.1/wavs/LJ013-0174.wav|The bundle found in the dining-room contained, with clothes, various small articles of plate and jewelery which a thief would probably have put into his pocket.
+LJSpeech-1.1/wavs/LJ019-0322.wav|On the other hand, new and careful regulations were framed to secure the moral and material well-being of the inmates of the jails.
+LJSpeech-1.1/wavs/LJ010-0026.wav|There was no increase in murder or manslaughter; the number of such crimes remained pretty constant proportionately to population.
+LJSpeech-1.1/wavs/LJ008-0239.wav|The loud shout of the multitude once more subsided, or only fell upon the abstracted ear like the dreamy murmur of an ocean shell.
+LJSpeech-1.1/wavs/LJ039-0100.wav|During the first week of an intensive eight-week training period he received instruction in sighting, aiming, and manipulation of the trigger.
+LJSpeech-1.1/wavs/LJ029-0125.wav|The only practical way for westbound traffic on Main Street
+LJSpeech-1.1/wavs/LJ010-0022.wav|the slackness of supervision over wholly irresponsible agents, produced many terrible monetary catastrophes,
+LJSpeech-1.1/wavs/LJ006-0158.wav|The act prescribed that he should do both every twenty-four hours, but days passed without his entering the wards.
+LJSpeech-1.1/wavs/LJ038-0261.wav|the Dallas Police Department forwarded it on December two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ013-0155.wav|Courvoisier's case made a great stir. There was unusual atrocity in this murder of an aged, infirm gentleman,
+LJSpeech-1.1/wavs/LJ014-0273.wav|The detection of these frauds came while he was still prominently before the world as the lessee of the Olympic.
+LJSpeech-1.1/wavs/LJ043-0014.wav|Oswald was, quote, very, very disagreeable and disappointed, end quote,
+LJSpeech-1.1/wavs/LJ022-0105.wav|These agencies, therefore, will simply be doing on a somewhat enlarged scale the same sort of things that they have been doing.
+LJSpeech-1.1/wavs/LJ015-0064.wav|been afforded peculiar facilities by the slackness of system and the want of methodical administration in the concerns by which they were employed.
+LJSpeech-1.1/wavs/LJ037-0110.wav|Guinyard claimed that the man ran down the east side of Patton and passed within ten feet of him before crossing to the other side.
+LJSpeech-1.1/wavs/LJ041-0077.wav|He led them into discussions of foreign affairs about which they often knew less than he did, since he had apparently devoted considerable time to a study of such matters.
+LJSpeech-1.1/wavs/LJ006-0306.wav|Some of the prisoners had their valets, and all these were constantly in and out of the kitchen where this female prisoner was employed.
+LJSpeech-1.1/wavs/LJ027-0157.wav|is thus a condensed resume of the life history of its ancestral species.
+LJSpeech-1.1/wavs/LJ010-0186.wav|There is no occasion to use violence. I will go with you.
+LJSpeech-1.1/wavs/LJ002-0331.wav|and they were not in great demand, in order to convert them into mop-heads or cleaning-rags.
+LJSpeech-1.1/wavs/LJ016-0063.wav|Below he met another woman and a girl, both of whom were terrified at his appearance, but
+LJSpeech-1.1/wavs/LJ028-0290.wav|he came in this plight before Darius.
+LJSpeech-1.1/wavs/LJ016-0183.wav|The story goes, that about eighteen twenty-eight
+LJSpeech-1.1/wavs/LJ043-0171.wav|I am guessing that perhaps he did it to appear to be a brave man in case he were arrested, but that is my supposition, end quote.
+LJSpeech-1.1/wavs/LJ010-0017.wav|eluding the keenest vigilance, and carried out with reckless daring;
+LJSpeech-1.1/wavs/LJ010-0273.wav|He was eventually charged with a misdemeanor, the capital charge having been abandoned, and committed for trial.
+LJSpeech-1.1/wavs/LJ007-0190.wav|Next year we find the inspectors expressing their satisfaction that "the full and faithful exposure which we felt it our duty to make of Newgate
+LJSpeech-1.1/wavs/LJ014-0101.wav|he found that it was soft and new, while elsewhere it was set and hard.
+LJSpeech-1.1/wavs/LJ005-0022.wav|While fully admitting the good intentions of the Society, he condemned their ultra humanitarianism as misplaced.
+LJSpeech-1.1/wavs/LJ050-0216.wav|In its budget request for the fiscal year beginning July one, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ044-0119.wav|into which Oswald, in his own words, had, quote, thrown himself. He sought advice from the central committee of the Communist Party, U.S.A.,
+LJSpeech-1.1/wavs/LJ028-0118.wav|He built well the quays along the opposite shore of the Euphrates, but he did not finish all his work,
+LJSpeech-1.1/wavs/LJ045-0016.wav|On October twenty-third, I had attended a ultra-right meeting headed by General Edwin A. Walker, who lives in Dallas.
+LJSpeech-1.1/wavs/LJ009-0043.wav|First is a youth of eighteen, condemned for stealing in a dwelling-house goods valued above five pounds.
+LJSpeech-1.1/wavs/LJ019-0302.wav|In eighteen sixty-two there were in all one hundred and ninety-three jails in England and Wales;
+LJSpeech-1.1/wavs/LJ016-0235.wav|He was so much in favor of short drops that his immediate successor, Marwood, stigmatized him as "short-drop" man.
+LJSpeech-1.1/wavs/LJ013-0089.wav|Thither Elder went alone, provided with a number of canvas and one large carpet-bag.
+LJSpeech-1.1/wavs/LJ039-0242.wav|(two) brought this rifle into the Depository Building on the morning of the assassination,
+LJSpeech-1.1/wavs/LJ048-0031.wav|While he had expressed hostility at times toward the State Department, the Marine Corps, and the FBI as agents of the Government,
+LJSpeech-1.1/wavs/LJ046-0244.wav|PRS should have investigated and been prepared to guard against it.
+LJSpeech-1.1/wavs/LJ026-0018.wav|Plants are organisms of constantly varying form in which the cell body is surrounded by a cellulose wall;
+LJSpeech-1.1/wavs/LJ018-0263.wav|and left the dock with evident satisfaction and relief at the termination of a most painful ordeal.
+LJSpeech-1.1/wavs/LJ017-0243.wav|but Vartos, or Watto, the Turk, would not allow any but the eight mutineers to have anything.
+LJSpeech-1.1/wavs/LJ032-0024.wav|After searching their records from ten p.m. to four a.m.
+LJSpeech-1.1/wavs/LJ012-0218.wav|At first nothing peculiar was found; but at a second search the back-garden ground was dug up,
+LJSpeech-1.1/wavs/LJ012-0046.wav|The second house was in Lower Queen Street, Islington, and he used it for some time as a depot for valuables.
+LJSpeech-1.1/wavs/LJ018-0120.wav|A more elaborate plot in many ways, more secretly, more patiently prepared than the preceding, or indeed than any in the calendar,
+LJSpeech-1.1/wavs/LJ001-0038.wav|while in fourteen seventy at Paris Udalric Gering and his associates turned out the first books printed in France, also in Roman character.
+LJSpeech-1.1/wavs/LJ044-0117.wav|that there were people who understood his activity, end quote.
+LJSpeech-1.1/wavs/LJ010-0217.wav|Soon after he was introduced we were alarmed by a violent knocking at the door;
+LJSpeech-1.1/wavs/LJ048-0046.wav|his trip to Mexico where he was in contact with Soviet authorities,
+LJSpeech-1.1/wavs/LJ007-0222.wav|Much bitterness of feeling is evidently engendered, and the corporation grows more and more angry with the inspectors.
+LJSpeech-1.1/wavs/LJ012-0121.wav|A part of the "swag" stolen from the bonded warehouse was thus recovered, but it was supposed that a number of the stolen notes had perished in the fire.
+LJSpeech-1.1/wavs/LJ011-0150.wav|Miss Turner was a school-girl of barely fifteen,
+LJSpeech-1.1/wavs/LJ043-0087.wav|He indicated, however, that he would have to delay that program because of his immediate financial needs and responsibilities.
+LJSpeech-1.1/wavs/LJ019-0147.wav|this occurred in summer at eight, but in the winter months it took place at dusk, and was often as early as four or five.
+LJSpeech-1.1/wavs/LJ047-0180.wav|he had received a copy of the report of the New Orleans office which contained Agent Quigley's memorandum of the interview in the New Orleans jail on August ten,
+LJSpeech-1.1/wavs/LJ027-0099.wav|Another striking class of the facts of morphology which admit of scientific explanation only along the line of homology
+LJSpeech-1.1/wavs/LJ005-0286.wav|still under the borough corporations. The Commons' Committee gave it as their opinion that they were in a deplorable state.
+LJSpeech-1.1/wavs/LJ037-0247.wav|Marina Oswald also identified Commission Exhibit Number one sixty-two, the jacket found by Captain Westbrook, as her husband's second jacket.
+LJSpeech-1.1/wavs/LJ038-0017.wav|Attracted by the sound of the sirens, Mrs. Postal stepped out of the box office and walked to the curb.
+LJSpeech-1.1/wavs/LJ046-0130.wav|In nineteen forty-three PRS received approximately nine thousand items of information;
+LJSpeech-1.1/wavs/LJ002-0149.wav|The latter indeed hung like millstones round the neck of the unhappy insolvent wretches who found themselves in limbo.
+LJSpeech-1.1/wavs/LJ025-0018.wav|and inanimated beings, which are devoid of these functions and simply vegetable.
+LJSpeech-1.1/wavs/LJ037-0080.wav|Barbara Jeanette Davis assumed that he was emptying his gun as, quote, he had it open and was shaking it, end quote. She immediately called the police.
+LJSpeech-1.1/wavs/LJ042-0086.wav|but he also displayed some sensitivity at not having reached a higher rank in the Marine Corps.
+LJSpeech-1.1/wavs/LJ016-0279.wav|They collected over night in the bright light of a November moon.
+LJSpeech-1.1/wavs/LJ015-0177.wav|Great excitement prevailed in the city and the West End when Redpath's defalcations were made public.
+LJSpeech-1.1/wavs/LJ041-0166.wav|in honor of some retiring noncommissioned officers, an event which they both approached with little enthusiasm.
+LJSpeech-1.1/wavs/LJ028-0080.wav|yet they took an impression of it, and returned it to its owner.
+LJSpeech-1.1/wavs/LJ030-0192.wav|What are they doing to you! end quote. Looking back from the front seat,
+LJSpeech-1.1/wavs/LJ040-0120.wav|Lee refused to discuss the matter with Pic, whom he had previously idolized, and their relations were strained thereafter.
+LJSpeech-1.1/wavs/LJ019-0388.wav|This bill, reintroduced in eighteen seventy-seven, became law that year, after which the whole of the prisons, including Newgate,
+LJSpeech-1.1/wavs/LJ038-0197.wav|ten. I left you as much money as I could,
+LJSpeech-1.1/wavs/LJ035-0092.wav|and this would be the minimum, because I am sure that I, you know, it took me a little longer, end quote,
+LJSpeech-1.1/wavs/LJ006-0125.wav|It was not strange that he should sometimes misuse his power, and when prisoners were not to be cajoled into securing his legal services,
+LJSpeech-1.1/wavs/LJ048-0044.wav|the knowledge of his defection, his arrogance and hostility to the United States,
+LJSpeech-1.1/wavs/LJ007-0212.wav|and a tendency to return to much of that laxity and remissness which formerly marked the management of this prison."
+LJSpeech-1.1/wavs/LJ014-0013.wav|He wanted a light for a cigar, and got it from a lantern which was lifted up and fully betrayed his features.
+LJSpeech-1.1/wavs/LJ023-0038.wav|The courts, however, have cast doubts on the ability of the elected Congress to protect us against catastrophe
+LJSpeech-1.1/wavs/LJ041-0197.wav|as excuses for his difficulties in getting along in the world, which were usually caused by entirely different factors.
+LJSpeech-1.1/wavs/LJ014-0213.wav|Dwyer found Cannon bleeding profusely from a wound in the head, and persuaded him to go to a doctor's.
+LJSpeech-1.1/wavs/LJ049-0062.wav|Their primary responsibility is to stay with and protect the President.
+LJSpeech-1.1/wavs/LJ034-0049.wav|so no conclusions can be drawn as to whether these remaining prints preceded or followed the print developed in Dallas by powder.
+LJSpeech-1.1/wavs/LJ040-0099.wav|When she worked for a time as an insurance saleslady,
+LJSpeech-1.1/wavs/LJ036-0131.wav|And about that time an old lady, I think she was an old lady, I don't remember nothing but her sticking her head down past him in the door and said,
+LJSpeech-1.1/wavs/LJ012-0133.wav|The fraudulent messenger, by the help of young Caspar, established his claim to the boxes, paid the wharfage dues, and carried off the gold-dust.
+LJSpeech-1.1/wavs/LJ039-0058.wav|The Commission evaluated (one) the nature of the shots, (two) Oswald's Marine training in marksmanship,
+LJSpeech-1.1/wavs/LJ023-0091.wav|I have not the time to quote to you all the language used by dissenting justices in many of these cases.
+LJSpeech-1.1/wavs/LJ029-0094.wav|Lawson was not specifically instructed to select the parade route, but he understood that this was one of his functions.
+LJSpeech-1.1/wavs/LJ020-0040.wav|Graham bread is wholesome and sweet, and ought to be eaten frequently in every family, particularly by young people whose bones and teeth are in forming.
+LJSpeech-1.1/wavs/LJ009-0268.wav|through the improper adjustment of the rope his sufferings were prolonged for five minutes;
+LJSpeech-1.1/wavs/LJ018-0369.wav|The mysterious Bravo case, that of Dr. Lamson, and that of Kate Dover
+LJSpeech-1.1/wavs/LJ037-0034.wav|As a result, they did not take him to the police station.
+LJSpeech-1.1/wavs/LJ040-0079.wav|John Pic testified that he thought Lee found in Ekdahl the father that he never had.
+LJSpeech-1.1/wavs/LJ004-0128.wav|capable of accommodating only eight thousand five hundred and forty-five persons, as many as thirteen thousand and fifty-seven were crowded.
+LJSpeech-1.1/wavs/LJ044-0009.wav|While it appears that he may have distributed Fair Play for Cuba Committee materials on one uneventful occasion in Dallas sometime during the period
+LJSpeech-1.1/wavs/LJ047-0084.wav|and his application was approved on the following day.
+LJSpeech-1.1/wavs/LJ040-0123.wav|He began to stay away from school, preferring to read magazines and watch television at home by himself.
+LJSpeech-1.1/wavs/LJ016-0127.wav|Along this Krapps crawled, and then dropped down on to the cook-house.
+LJSpeech-1.1/wavs/LJ032-0199.wav|the Oswalds lived on Neely Street in Dallas in a rented house which had a small back yard.
+LJSpeech-1.1/wavs/LJ050-0024.wav|toward the preparation of formal understandings of the respective roles of the Secret Service and other agencies with which it collaborates
+LJSpeech-1.1/wavs/LJ044-0177.wav|Representing himself as the head of the New Orleans branch of the, quote, organization called Fair Play for Cuba, end quote,
+LJSpeech-1.1/wavs/LJ037-0072.wav|She has subsequently admitted the existence of the conversation and offered an explanation for her denial.
+LJSpeech-1.1/wavs/LJ038-0084.wav|The reports prepared by those present at these interviews are set forth in appendix eleven.
+LJSpeech-1.1/wavs/LJ007-0220.wav|to corrupt the prisoner charged with an offense of which he may be innocent, and to confirm in guilt the young and inexperienced offender.
+LJSpeech-1.1/wavs/LJ008-0197.wav|of the Lamb's Coffee House;
+LJSpeech-1.1/wavs/LJ044-0120.wav|in a letter dated August twenty-eight, nineteen sixty-three, about whether he could, quote, continue to fight,
+LJSpeech-1.1/wavs/LJ026-0003.wav|Nutrition thus, as has been pointed out, makes it possible to classify most organisms as animals or plants.
+LJSpeech-1.1/wavs/LJ039-0151.wav|Accuracy of Weapon
+LJSpeech-1.1/wavs/LJ033-0180.wav|Cadigan found that the paper used to make this replica sack had different characteristics from the paper in the original bag.
+LJSpeech-1.1/wavs/LJ039-0123.wav|because he may well have carried this rifle for quite some time, and it got banged around in normal usage.
+LJSpeech-1.1/wavs/LJ044-0142.wav|of each of his changes of mailing address.
+LJSpeech-1.1/wavs/LJ028-0245.wav|and mounting upon the walls along both sides of the stream, would so have caught the enemy as it were in a trap.
+LJSpeech-1.1/wavs/LJ049-0083.wav|it has long been a Federal crime to conspire to injure any Federal officer, on account of, or while he is engaged in, the lawful discharge of the duties of his office.
+LJSpeech-1.1/wavs/LJ010-0271.wav|A visit to the father's residence was followed by the arrest of the son, who had by this time returned.
+LJSpeech-1.1/wavs/LJ016-0247.wav|while round about were shoe-strings, boot-laces, and lasts. Marwood, strange to say, followed the same trade as Calcraft.
+LJSpeech-1.1/wavs/LJ022-0163.wav|This legislation will serve the investor by ending the conditions which have caused that lack of confidence and goodwill.
+LJSpeech-1.1/wavs/LJ034-0048.wav|The freshness of prints developed in this manner cannot be estimated,
+LJSpeech-1.1/wavs/LJ019-0097.wav|This want of uniformity in prison discipline became ere long an acknowledged evil pressing for some remedy,
+LJSpeech-1.1/wavs/LJ040-0068.wav|It forced Marguerite Oswald to go to work to provide for her family.
+LJSpeech-1.1/wavs/LJ011-0238.wav|Shops were broken into, as when that of Grimaldi and Johnson, in the Strand, was robbed of watches to the value of six thousand pounds.
+LJSpeech-1.1/wavs/LJ011-0082.wav|he came to London, where he practiced as a professional swindler and cheat.
+LJSpeech-1.1/wavs/LJ016-0150.wav|Suicides at Newgate were numerous enough, but they seldom possessed any novel or unusual features;
+LJSpeech-1.1/wavs/LJ005-0167.wav|which became the four George the fourth cap. sixty-four, said that he had abstained from legislating for these small jurisdictions "on mature deliberation."
+LJSpeech-1.1/wavs/LJ008-0069.wav|The latter is the method still followed at Newgate.
+LJSpeech-1.1/wavs/LJ040-0129.wav|during which time he was examined by its Chief Psychiatrist, Dr. Renatus Hartogs,
+LJSpeech-1.1/wavs/LJ025-0132.wav|just as the body of a snail contracts into its shell when one of its "horns" is irritated.
+LJSpeech-1.1/wavs/LJ032-0141.wav|The print arrived in the FBI Laboratory in Washington on November twenty-nine, mounted on a card on which Lieutenant Day had written the words
+LJSpeech-1.1/wavs/LJ016-0207.wav|It was not always easy to get a hangman so cheap, as I have already indicated on a previous page.
+LJSpeech-1.1/wavs/LJ044-0183.wav|to obtain a Soviet visa at the Soviet Embassy in Mexico City, he insisted that he was entitled to the Cuban visa because of his background,
+LJSpeech-1.1/wavs/LJ037-0218.wav|Marina Oswald testified that this was the holster which contained the revolver in the photographs taken on Neely Street.
+LJSpeech-1.1/wavs/LJ018-0387.wav|The flower had berries also of bread fixed on stems made from the fiber drawn from the stuffing of his mattress,
+LJSpeech-1.1/wavs/LJ016-0154.wav|When a bit of rope carefully secreted,
+LJSpeech-1.1/wavs/LJ007-0194.wav|the fixing of "inspection holes" in the doors and walls, so as to insure more supervision; of windows opening into the well-holes,
+LJSpeech-1.1/wavs/LJ050-0055.wav|that the PRS files can no longer be limited largely to persons communicating actual threats to the President.
+LJSpeech-1.1/wavs/LJ009-0267.wav|When Luigi Buranelli was executed in eighteen fifty-five,
+LJSpeech-1.1/wavs/LJ036-0021.wav|On the basis of this punchmark, which was distinctive to each Dallas driver,
+LJSpeech-1.1/wavs/LJ048-0073.wav|The FBI Manual of Instructions provided, quote, Liaison With Other Government Agencies
+LJSpeech-1.1/wavs/LJ049-0177.wav|and Robert I. Bouck, who was in charge of the Protective Research Section of the Secret Service, believed that the accumulation of the facts known to the FBI
+LJSpeech-1.1/wavs/LJ004-0205.wav|Industrial labor had also been introduced with satisfactory results.
+LJSpeech-1.1/wavs/LJ041-0096.wav|Normally, it would be a good type of literature; and the one that I recall was "Leaves of Grass," by Walt Whitman, end quote.
+LJSpeech-1.1/wavs/LJ039-0167.wav|Using the assassination rifle mounted with the telescopic sight, three marksmen, rated as master by the National Rifle Association,
+LJSpeech-1.1/wavs/LJ045-0056.wav|and also about Mrs. Paine.
+LJSpeech-1.1/wavs/LJ048-0202.wav|End quote. Captain Lawrence was not instructed to have his men watch buildings along the motorcade route and did not mention the observation of buildings to them.
+LJSpeech-1.1/wavs/LJ045-0247.wav|His commitment to Marxism and communism appears to have been another important factor in his motivation.
+LJSpeech-1.1/wavs/LJ008-0063.wav|while the criminals stand upon it, by two pieces of timber, which are made to slide in and out of the prison wall through apertures made for that purpose.
+LJSpeech-1.1/wavs/LJ025-0033.wav|since it was unnecessary, and therefore is absent, in the more simple animals.
+LJSpeech-1.1/wavs/LJ015-0310.wav|which had received so perverted and mistaken direction,
+LJSpeech-1.1/wavs/LJ016-0429.wav|Others talk freely enough on various topics, but principally upon their own cases.
+LJSpeech-1.1/wavs/LJ002-0309.wav|There was this much honest forbearance in Newgate in these days,
+LJSpeech-1.1/wavs/LJ015-0003.wav|A firm which held a lot of warrants suddenly demanded the delivery of the goods they covered.
+LJSpeech-1.1/wavs/LJ042-0090.wav|I'm sure Russians will accept me after this sign of my faith in them, end quote.
+LJSpeech-1.1/wavs/LJ012-0204.wav|They presented themselves about noon one day at the dissecting room of King's College Hospital, accompanied by a third man,
+LJSpeech-1.1/wavs/LJ017-0254.wav|Taffir, the mate, finding there was a man who could speak English at another place twenty miles off,
+LJSpeech-1.1/wavs/LJ046-0118.wav|In addition to this function, PRS is responsible for such tasks
+LJSpeech-1.1/wavs/LJ028-0230.wav|A battle was fought at a short distance from the city, in which the Babylonians were defeated by the Persian King,
+LJSpeech-1.1/wavs/LJ028-0079.wav|The museum authorities believed that the cameo was one of the many spurious objects which the Eastern forgers were constantly sending to Europe,
+LJSpeech-1.1/wavs/LJ004-0110.wav|It was made incumbent upon the justices to provide distinct places of confinement for five classes of prisoners, viz.
+LJSpeech-1.1/wavs/LJ002-0004.wav|The new jail fell as far short of the demands made on it as did the old.
+LJSpeech-1.1/wavs/LJ018-0268.wav|A daring and cleverly-planned robbery of diamonds was that of the Tarpeys, man and wife,
+LJSpeech-1.1/wavs/LJ020-0060.wav|turning the pan once in this time, and covering with clean -- never printed -- paper, should they brown too fast.
+LJSpeech-1.1/wavs/LJ047-0209.wav|that he had owned a weapon and did a good deal of hunting or use of it, perhaps in Russia, plus a number of items about his disposition and unreliability of character,
+LJSpeech-1.1/wavs/LJ041-0128.wav|Even though Oswald apparently did not express any hostility against the President or Governor Connally,
+LJSpeech-1.1/wavs/LJ003-0312.wav|The jailer should be required to visit every part and see every prisoner daily; the chaplain should perform service, visit the sick,
+LJSpeech-1.1/wavs/LJ028-0129.wav|built three walls round about the inner city, and three others about that which was the outer; and this he did with burnt brick.
+LJSpeech-1.1/wavs/LJ043-0129.wav|implied that Oswald's fellow employees did not like him because he was propagandizing
+LJSpeech-1.1/wavs/LJ012-0108.wav|There was a hunt for the two well-known house-breakers, who were eventually heard of at a lodging in Kennington.
+LJSpeech-1.1/wavs/LJ012-0169.wav|The murder was still fresh in the memory of the populace, and Probert was all but lynched on his way to jail.
+LJSpeech-1.1/wavs/LJ018-0364.wav|It will be remembered that she made a statement which led to the empaneling of a jury of matrons, who decided that there was no cause for an arrest of judgment.
+LJSpeech-1.1/wavs/LJ039-0066.wav|In addition, the three degree downward slope of Elm Street was of assistance in eliminating at least some of the adjustment
+LJSpeech-1.1/wavs/LJ001-0013.wav|than in the same operations with ugly ones.
+LJSpeech-1.1/wavs/LJ022-0054.wav|The first is to make provisions intended to relieve, to minimize, and to prevent future unemployment;
+LJSpeech-1.1/wavs/LJ037-0147.wav|It was the unanimous and unequivocal testimony of expert witnesses before the Commission that these used cartridge cases were fired from the revolver
+LJSpeech-1.1/wavs/LJ032-0167.wav|He concluded, quote, There is no doubt in my mind that these fibers could have come from this shirt.
+LJSpeech-1.1/wavs/LJ005-0017.wav|tending to divest punishment of its just and salutary terrors;
+LJSpeech-1.1/wavs/LJ036-0127.wav|He saw a man walking south on Lamar from Commerce.
+LJSpeech-1.1/wavs/LJ003-0087.wav|These often remained in Newgate for six months, sometimes a year, in some cases longer;
+LJSpeech-1.1/wavs/LJ018-0282.wav|In eighteen seventy-one they crossed the Atlantic,
+LJSpeech-1.1/wavs/LJ030-0146.wav|Just prior to the shooting, David F. Powers, riding in the Secret Service follow-up car, remarked to Kenneth O'Donnell
+LJSpeech-1.1/wavs/LJ035-0111.wav|Truly was probably correct in stating that the west elevator was on the fifth floor when he looked up the elevator shaft from the first floor.
+LJSpeech-1.1/wavs/LJ023-0005.wav|I take this means of saying "thank you."
+LJSpeech-1.1/wavs/LJ004-0063.wav|and therefore you are bound to furnish him with moderate indeed but suitable accommodation."
+LJSpeech-1.1/wavs/LJ030-0123.wav|Special Agent Glen A. Bennett once left his place inside the follow-up car to help keep the crowd away from the President's car.
+LJSpeech-1.1/wavs/LJ006-0097.wav|Water might not be taken into the ward for washing purposes.
+LJSpeech-1.1/wavs/LJ045-0104.wav|Marina Oswald said that she knew her husband did not like Michael Paine and so she asked him not to come out that weekend, even though he wanted to do so.
+LJSpeech-1.1/wavs/LJ033-0119.wav|Moreover, in an interview on December one, nineteen sixty-three, with agents of the FBI, Frazier had marked the point on the back seat of his car
+LJSpeech-1.1/wavs/LJ041-0090.wav|It wasn't anything extreme. I wouldn't go as far as to call it, call him a paranoid, but a definite tendency there was in that direction, I think, end quote.
+LJSpeech-1.1/wavs/LJ002-0243.wav|with jurisdiction to hold pleas in all actions within the prescribed limits. The court was chiefly used for the recovery of small debts under ten pounds
+LJSpeech-1.1/wavs/LJ010-0164.wav|Oxford, who was only nineteen at the time his offense was committed, had been born at Birmingham,
+LJSpeech-1.1/wavs/LJ042-0088.wav|that he might know something of special interest. Oswald's "Historic Diary" describes the event in part as follows, quote,
+LJSpeech-1.1/wavs/LJ043-0109.wav|In New Orleans he obtained work as a greaser and oiler of coffee processing machines for the William B. Reily Co.,
+LJSpeech-1.1/wavs/LJ030-0253.wav|and not be diverted unless it is necessary to accomplish the protective assignment.
+LJSpeech-1.1/wavs/LJ040-0031.wav|who were accorded special privileges and who he thought were betraying communism, and he spoke well of the United States.
+LJSpeech-1.1/wavs/LJ020-0018.wav|Throw a cloth over the bowl and set by for five or six hours to rise.
+LJSpeech-1.1/wavs/LJ012-0020.wav|He began as an itinerant street vendor at eight, at ten he passed bad money,
+LJSpeech-1.1/wavs/LJ004-0224.wav|So plentiful was this dietary, that although the old permission remained in force of allowing the friends of prisoners to bring them supplies from outside,
+LJSpeech-1.1/wavs/LJ027-0142.wav|the antlers of an existing deer furnish in their development a kind of "resume," or recapitulation, of the successive phases
+LJSpeech-1.1/wavs/LJ037-0245.wav|This jacket belonged to Lee Harvey Oswald. Marina Oswald stated that her husband owned only two jackets, one blue and the other gray.
+LJSpeech-1.1/wavs/LJ035-0003.wav|In considering whether Oswald was at the southeast corner window at the time the shots were fired,
+LJSpeech-1.1/wavs/LJ031-0201.wav|Given a choice between the National Naval Medical Center at Bethesda, Maryland, and the Army's Walter Reed Hospital,
+LJSpeech-1.1/wavs/LJ035-0175.wav|The sergeant did the same thing at the rear of the building.
+LJSpeech-1.1/wavs/LJ004-0193.wav|Prisoners on reception were treated as they are now-a-days -- bathed, dressed in prison clothes, and inspected by the surgeon.
+LJSpeech-1.1/wavs/LJ006-0116.wav|The wardsman had a monopoly in supplying provisions, gave dinner and breakfast at his own price, and was such complete master of the ward
+LJSpeech-1.1/wavs/LJ015-0180.wav|was densely crowded, and many curious eyes were turned upon the somewhat remarkable man who occupied the dock.
+LJSpeech-1.1/wavs/LJ015-0084.wav|till the defalcations from fraudulent transfers and fraudulent issues combined amounted to twenty-seven thousand pounds.
+LJSpeech-1.1/wavs/LJ009-0130.wav|winked at other prisoners in derision of what was taking place; and I have frequently heard men and lads who had been of the kneeling party
+LJSpeech-1.1/wavs/LJ043-0044.wav|The latter testified that they, quote, were just together again, end quote, as if his brother, quote, had not been to Russia, end quote.
+LJSpeech-1.1/wavs/LJ043-0011.wav|and attempted to help Mrs. Oswald particularly, in various ways. In general, Oswald did not like the members of the Russian community.
+LJSpeech-1.1/wavs/LJ012-0262.wav|whom she was to have married, and in whose company she had left her own lodgings to visit his in Camberwell.
+LJSpeech-1.1/wavs/LJ039-0152.wav|It will be recalled from the discussion in chapter three
+LJSpeech-1.1/wavs/LJ030-0145.wav|showed the numerals twelve:thirty as the Vice-Presidential automobile proceeded north on Houston Street, a few seconds before the shots were fired.
+LJSpeech-1.1/wavs/LJ018-0062.wav|Christian Sattler was by birth a German.
+LJSpeech-1.1/wavs/LJ010-0268.wav|It happened that a lad named Bean had absconded from his father's home some weeks before,
+LJSpeech-1.1/wavs/LJ025-0065.wav|Even as late as eighteen forty-five, however, a botanist of Schleiden's eminence dealt very skeptically with these statements,
+LJSpeech-1.1/wavs/LJ025-0007.wav|so much so as to require discussion in different treatises.
+LJSpeech-1.1/wavs/LJ044-0134.wav|His brief foray on behalf of the Fair Play for Cuba Committee had failed to win any support.
+LJSpeech-1.1/wavs/LJ006-0034.wav|They attended early and late; they mustered the prisoners, examined into their condition,
+LJSpeech-1.1/wavs/LJ040-0007.wav|the existence of some personal grievance,
+LJSpeech-1.1/wavs/LJ048-0115.wav|to determine what matters require attention in making advance preparations and to decide what action to take.
+LJSpeech-1.1/wavs/LJ030-0199.wav|The Governor was lying with his head on his wife's lap when he heard a shot hit the President.
+LJSpeech-1.1/wavs/LJ012-0165.wav|As this was accomplished beyond the limits of the metropolis, and its perpetrators arraigned at Hertford,
+LJSpeech-1.1/wavs/LJ038-0226.wav|Oswald had apparently mistaken the county jail for the city jail.
+LJSpeech-1.1/wavs/LJ008-0227.wav|slipshod and slovenly, in crushed bonnet and dirty shawl, the gown fastened by a single hook,
+LJSpeech-1.1/wavs/LJ045-0183.wav|He had argued with her over the use of an alias and had not called her after that argument, although he usually telephoned once or twice a day.
+LJSpeech-1.1/wavs/LJ002-0059.wav|those who had turned king's evidence, whose safety might have been imperiled had they been lodged with the men against whom they had informed.
+LJSpeech-1.1/wavs/LJ028-0167.wav|In the one stood the palace of the kings, surrounded by a wall of great strength and size;
+LJSpeech-1.1/wavs/LJ012-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section fifteen: Newgate notorieties, part three
+LJSpeech-1.1/wavs/LJ019-0233.wav|and when it was completed, both sides of the prison were brought into harmony with modern ideas.
+LJSpeech-1.1/wavs/LJ003-0333.wav|But here the recommendations touched at once upon the delicate subject of expense, and it is clear that the committee hesitated on this score.
+LJSpeech-1.1/wavs/LJ037-0082.wav|On the evening of November twenty-two,
+LJSpeech-1.1/wavs/LJ050-0149.wav|PRS must develop the capacity to classify its subjects on a more sophisticated basis than the present geographic breakdown.
+LJSpeech-1.1/wavs/LJ042-0079.wav|My request for citizenship is now pending before the Supreme Soviet of the U.S.S.R. I take these steps for political reasons.
+LJSpeech-1.1/wavs/LJ002-0194.wav|and a heavy fee at the rate of eight pounds per one hundred pounds, with four pounds for every additional hundred.
+LJSpeech-1.1/wavs/LJ005-0064.wav|is found now-a-days to be really efficacious. The main recommendations, however, are based upon common sense
+LJSpeech-1.1/wavs/LJ005-0118.wav|In many others there were no infirmaries, no places set apart for the confinement of prisoners afflicted with dangerous and infectious disorders.
+LJSpeech-1.1/wavs/LJ024-0089.wav|let us examine the process.
+LJSpeech-1.1/wavs/LJ002-0126.wav|five thousand, seven hundred nineteen writs and executions for debts between ten pounds and twenty pounds were issued in Middlesex,
+LJSpeech-1.1/wavs/LJ014-0170.wav|This preference brought the costly stuff into disrepute, and its unpopularity lasted for nearly thirty years.
+LJSpeech-1.1/wavs/LJ028-0087.wav|Such was the appearance of the builder of the walls of Babylon.
+LJSpeech-1.1/wavs/LJ028-0273.wav|not even when he tried the means by which Cyrus had made himself master of the place.
+LJSpeech-1.1/wavs/LJ028-0176.wav|Should we compare these ancient descriptions of the walls, we should find them hopelessly conflicting.
+LJSpeech-1.1/wavs/LJ038-0254.wav|She testified that several days later Oswald recovered his rifle and brought it back to their apartment.
+LJSpeech-1.1/wavs/LJ006-0147.wav|it was still freely introduced into the prison.
+LJSpeech-1.1/wavs/LJ015-0200.wav|only when single and unprotected were they in any danger of attack, and that but rarely.
+LJSpeech-1.1/wavs/LJ026-0004.wav|Yet there are many unicellular forms in which both kinds of nutrition go on at the same time;
+LJSpeech-1.1/wavs/LJ031-0051.wav|He noted contusions, hematoma to the right of the larynx, which was deviated slightly to the left,
+LJSpeech-1.1/wavs/LJ010-0167.wav|He left his last situation in April eighteen forty, and established himself in lodgings in Lambeth,
+LJSpeech-1.1/wavs/LJ017-0002.wav|As these records draw to a close, the crimes I chronicle become so much more recent in date that they will be fresh in the memory of most of my readers.
+LJSpeech-1.1/wavs/LJ025-0079.wav|the males of most Rotifers have no digestive apparatus.
+LJSpeech-1.1/wavs/LJ010-0139.wav|After the five bodies had hung for half-an-hour, a man in a mask came forward to complete the sentence.
+LJSpeech-1.1/wavs/LJ046-0175.wav|If PRS concludes after investigation
+LJSpeech-1.1/wavs/LJ034-0052.wav|In his independent investigation, Arthur Mandella of the New York City Police Department
+LJSpeech-1.1/wavs/LJ017-0065.wav|I ought also to confess the others: I mean my wife and my brother's. End quote.
+LJSpeech-1.1/wavs/LJ027-0159.wav|"It is well known," likewise comments Le Conte,
+LJSpeech-1.1/wavs/LJ006-0091.wav|Their treatment was also a matter of chance. They still slept on rope mats on the floor, herded together in companies of four or more to keep one another warm
+LJSpeech-1.1/wavs/LJ005-0143.wav|The "scenes of tumult and obscenity" in these night rooms are said to have been beyond description; a prisoner in one nocturnal riot lost an eye.
+LJSpeech-1.1/wavs/LJ017-0081.wav|sought about for a fresh victim to supply him with funds.
+LJSpeech-1.1/wavs/LJ012-0102.wav|The lock and fastenings of the chest were forced by means of a "jack," an instrument known to housebreakers,
+LJSpeech-1.1/wavs/LJ017-0112.wav|Palmer too showed an indecent haste in preparing the body for interment, and in obtaining the usual certificate.
+LJSpeech-1.1/wavs/LJ040-0148.wav|Oswald's withdrawn tendencies and solitary habits were thought to be the result of, quote,
+LJSpeech-1.1/wavs/LJ015-0056.wav|Mr. Strahan was arrested at a friend's house in Bryanston Square.
+LJSpeech-1.1/wavs/LJ035-0009.wav|Baker was riding a two-wheeled motorcycle behind the last press car of the motorcade.
+LJSpeech-1.1/wavs/LJ038-0173.wav|General Walker hired two investigators to determine whether a former employee might have been involved in the shooting.
+LJSpeech-1.1/wavs/LJ050-0190.wav|However, the instructions must be communicated to the local police in any event and can be leaked to the press whether or not they are in writing.
+LJSpeech-1.1/wavs/LJ037-0112.wav|Apparently he had reached for his gun; it lay beneath him outside of the holster.
+LJSpeech-1.1/wavs/LJ037-0086.wav|Her reaction when she saw Oswald in the lineup was that, quote,
+LJSpeech-1.1/wavs/LJ002-0229.wav|and the turnkeys slept in the prison, yet scenes of riot, drunkenness, and disorder were most prevalent, end quote.
+LJSpeech-1.1/wavs/LJ045-0245.wav|Oswald's search for what he conceived to be the perfect society was doomed from the start.
+LJSpeech-1.1/wavs/LJ002-0205.wav|A man dying of liver complaint lay on the cold stones without a bed or food to eat.
+LJSpeech-1.1/wavs/LJ009-0255.wav|The miserable wretch during the whole of this time was struggling with the most determined violence, to the great horror of the spectators.
+LJSpeech-1.1/wavs/LJ016-0378.wav|and after sentence.
+LJSpeech-1.1/wavs/LJ012-0273.wav|But Greenacre in his confession pretended that he and his intended had quarreled over her property or the want of it,
+LJSpeech-1.1/wavs/LJ028-0377.wav|until at last the foundations of the temples and palaces were buried fully a hundred feet beneath the surface.
+LJSpeech-1.1/wavs/LJ004-0229.wav|Convicts were not even compelled to wear irons, providing they behaved well.
+LJSpeech-1.1/wavs/LJ032-0069.wav|The person having access to the box then takes the notice to the window and is given the package.
+LJSpeech-1.1/wavs/LJ040-0071.wav|From the time Marguerite Oswald returned to work until December twenty-six, nineteen forty-two, when Lee too was sent to the orphans' home,
+LJSpeech-1.1/wavs/LJ005-0053.wav|A very valuable volume published by the Society
+LJSpeech-1.1/wavs/LJ007-0050.wav|during the whole of which time the unhappy convicts who had but little hope of commutation were exposed to the mockery of their reckless associates.
+LJSpeech-1.1/wavs/LJ029-0076.wav|On November four, Gerald A. Behn, agent in charge of the White House detail, asked Sorrels to examine three potential sites for the luncheon.
+LJSpeech-1.1/wavs/LJ041-0040.wav|Despite this apparent interest in communism, Oswald tried to join the Marines when he was sixteen years old.
+LJSpeech-1.1/wavs/LJ014-0139.wav|and would have left the dock had not Mr. Cope, the governor of Newgate, restrained her.
+LJSpeech-1.1/wavs/LJ046-0075.wav|But his very position as representative of the people prevents him from effectively shielding himself from the people.
+LJSpeech-1.1/wavs/LJ032-0011.wav|(four) the killing of Dallas Patrolman J. D. Tippit within forty-five minutes after the assassination, (five)
+LJSpeech-1.1/wavs/LJ026-0123.wav|which could be derived by the ordinary chemical evolution of protoplasm, proteid, sugar, starch or fats.
+LJSpeech-1.1/wavs/LJ024-0033.wav|and that a baneful precedent will be established.
+LJSpeech-1.1/wavs/LJ021-0097.wav|we shall watch carefully the working of this new machinery for the second phase of N.R.A.,
+LJSpeech-1.1/wavs/LJ035-0130.wav|Actually she noticed no one on the back stairs. If she descended from the fourth to the first floor as fast as she claimed in her testimony,
+LJSpeech-1.1/wavs/LJ039-0089.wav|in relation to the target as opposed to iron sights with aligning the sights and then aligning them on the target, end quote.
+LJSpeech-1.1/wavs/LJ039-0217.wav|Based on these tests the experts agreed that the assassination rifle was an accurate weapon.
+LJSpeech-1.1/wavs/LJ007-0169.wav|this picture of the existing state of Newgate
+LJSpeech-1.1/wavs/LJ015-0255.wav|and begged his accomplice to invest it as a settlement on a woman named Kay, by whom he had had a child.
+LJSpeech-1.1/wavs/LJ019-0086.wav|or they might herd together or communicate freely as in the old worst days. They might see each other when they liked, and converse sotto voce,
+LJSpeech-1.1/wavs/LJ004-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section seven: The beginnings of prison reform.
+LJSpeech-1.1/wavs/LJ041-0060.wav|Kerry Thornley, a marine associate,
+LJSpeech-1.1/wavs/LJ005-0130.wav|There were tread-wheels at most of the prisons, and regular employment thereon or at some other kind of hard labor.
+LJSpeech-1.1/wavs/LJ020-0095.wav|Cover your hair entirely with a handkerchief or sweeping cap.
+LJSpeech-1.1/wavs/LJ047-0184.wav|Well, as I had previously stated, I have between twenty-five and forty cases assigned to me at any one time. I had other matters to take care of.
+LJSpeech-1.1/wavs/LJ013-0241.wav|The policeman insisted on searching the premises, at which Good displayed some uneasiness.
+LJSpeech-1.1/wavs/LJ047-0208.wav|been courtmartialed for illegal possession of a gun, of a handgun in the Marines,
+LJSpeech-1.1/wavs/LJ050-0252.wav|The occasional use of personnel from other Federal agencies to assist in protecting the President has a further advantage. It symbolizes the reality
+LJSpeech-1.1/wavs/LJ034-0163.wav|The Commission is satisfied that, at the least,
+LJSpeech-1.1/wavs/LJ002-0308.wav|nor was non-payment made a pretext for further imprisonment, thanks to the humanity of the jailer, or the funds provided by various charities.
+LJSpeech-1.1/wavs/LJ013-0088.wav|the cashier gave them eight Bank of England notes for one thousand pounds each, saying that they could get so much specie nowhere else.
+LJSpeech-1.1/wavs/LJ027-0177.wav|Then in the Carboniferous, fishes still continuing, there appeared the lowest -- i.e., most fish-like forms of amphibians.
+LJSpeech-1.1/wavs/LJ019-0130.wav|since admirably developed in the convict prisons of this country.
+LJSpeech-1.1/wavs/LJ017-0251.wav|that they had been in the boats five days and nights, but that the captain and others had been lost.
+LJSpeech-1.1/wavs/LJ049-0042.wav|At that time the agents on the framing boards of the follow-up car were expected to perform such a function.
+LJSpeech-1.1/wavs/LJ017-0076.wav|His brother was supposed to have been his next victim, upon whose life he had also effected an insurance for another thirteen thousand pounds.
+LJSpeech-1.1/wavs/LJ019-0135.wav|There was no longer, or at worst but rarely, and for short periods, the same overcrowding.
+LJSpeech-1.1/wavs/LJ047-0108.wav|During the interview Quigley obtained background information from Oswald which was inconsistent with information already in the Bureau's possession.
+LJSpeech-1.1/wavs/LJ006-0108.wav|He charged a weekly sum as ward dues for the use of knives, forks, and plates
+LJSpeech-1.1/wavs/LJ045-0044.wav|Many of the people with whom the Oswalds became acquainted after their arrival in the United States thought that Marina Oswald had married her husband primarily in the hope
+LJSpeech-1.1/wavs/LJ041-0127.wav|Oswald must have already learned that the Governor could not help him with his discharge because he was no longer Secretary of the Navy, at the time he made that remark.
+LJSpeech-1.1/wavs/LJ011-0268.wav|when he was asked to meet a Mr. Heath in London with regard to the sale of certain lands at Bishop Stortford.
+LJSpeech-1.1/wavs/LJ042-0123.wav|A "patriotic duty" to bring in the harvest. The opinions of the workers (unvoiced) are that it's a great pain in the neck:
+LJSpeech-1.1/wavs/LJ027-0103.wav|Thus, for instance, the unborn whale has rudimentary teeth,
+LJSpeech-1.1/wavs/LJ012-0029.wav|But he never paid more than a fixed price for all articles of the same class, whatever their intrinsic value.
+LJSpeech-1.1/wavs/LJ019-0331.wav|Baths were provided, ablutions ordered, and all appliances to insure personal cleanliness.
+LJSpeech-1.1/wavs/LJ010-0112.wav|He had been in a state of abject misery, and when he first joined "the reformers," as the Cato Street conspirators called themselves,
+LJSpeech-1.1/wavs/LJ003-0221.wav|Yet it must have been abundantly plain to the reader that the other evils existing were great and glaring. A brief summary of them will best prove this.
+LJSpeech-1.1/wavs/LJ006-0065.wav|were associated together, "of every variety of age, habit, and delinquency, without employment, oversight, or control."
+LJSpeech-1.1/wavs/LJ003-0079.wav|Mr. Bennet refers to a gentleman confined for want of bail, who occupied a room with five others
+LJSpeech-1.1/wavs/LJ038-0216.wav|The references to house rent and payments for water and gas
+LJSpeech-1.1/wavs/LJ018-0075.wav|while at sea the captain of the ship was responsible for the security of the prisoner.
+LJSpeech-1.1/wavs/LJ022-0117.wav|Our attack upon these enemies must be without stint and without discrimination.
+LJSpeech-1.1/wavs/LJ012-0185.wav|raised the alarm, and suspicion fell upon the three murderers, who were arrested.
+LJSpeech-1.1/wavs/LJ016-0155.wav|braces, shoe-strings, shirt torn into strips are the only instruments, and a bar or small hook
+LJSpeech-1.1/wavs/LJ016-0407.wav|who generally attended the prison services.
+LJSpeech-1.1/wavs/LJ039-0106.wav|which was two points above the minimum for qualifications as a "sharpshooter" in a scale of marksman, sharpshooter, expert.
+LJSpeech-1.1/wavs/LJ037-0101.wav|According to his testimony, Smith told the FBI, quote, It looked more like him than it did on television, end quote,
+LJSpeech-1.1/wavs/LJ013-0040.wav|The other Wallace, the sailor, also traced his lapse into crime to evil counsel.
+LJSpeech-1.1/wavs/LJ024-0059.wav|It is the clear intention of our public policy to provide for a constant flow of new and younger blood into the judiciary.
+LJSpeech-1.1/wavs/LJ046-0172.wav|the Secret Service arranges for the family and friends of the individual, and local law enforcement officials,
+LJSpeech-1.1/wavs/LJ009-0227.wav|At Jarrow Stake, where the Durham murderer's body was exposed, there were similar scenes, mingled with compassion for the culprit's family,
+LJSpeech-1.1/wavs/LJ020-0046.wav|Close the dough over it, dust your hands and kneading-board with flour and work in the shortening until the dough is elastic and ceases to be sticky.
+LJSpeech-1.1/wavs/LJ025-0055.wav|by the employment of instruments of precision for the measurement of the physical forces which are at work in the living economy.
+LJSpeech-1.1/wavs/LJ041-0121.wav|It is thus clear that Oswald knew that Governor Connally was never directly concerned with his discharge
+LJSpeech-1.1/wavs/LJ048-0122.wav|long enough before his departure to apprise him of any particular problems encountered and the responsive action taken.
+LJSpeech-1.1/wavs/LJ037-0258.wav|There is no doubt, however, that Oswald was seen leaving his roominghouse at about one p.m. wearing a zipper jacket,
+LJSpeech-1.1/wavs/LJ014-0004.wav|brought passers-by and policemen to the spot, a lonely place near a dead wall beyond Belsize Hall, Hampstead,
+LJSpeech-1.1/wavs/LJ050-0052.wav|Based on the Commission's investigation, the following minimum goals for improvements are indicated:
+LJSpeech-1.1/wavs/LJ005-0037.wav|It sought to obtain and diffuse useful information
+LJSpeech-1.1/wavs/LJ022-0103.wav|The national government now has at least sixty different agencies with the staff
+LJSpeech-1.1/wavs/LJ003-0251.wav|Among other excuses offered for thus manacling all almost without exception, was that it was the best and safest method
+LJSpeech-1.1/wavs/LJ032-0223.wav|and that Oswald's face had not been superimposed on another body.
+LJSpeech-1.1/wavs/LJ043-0176.wav|If the attack had succeeded and Oswald had been caught, the pictures showing him with his rifle
+LJSpeech-1.1/wavs/LJ002-0202.wav|The prison was always in "the most filthy state imaginable."
+LJSpeech-1.1/wavs/LJ005-0058.wav|an architect of eminence who was very largely employed in the erection of prison buildings at the close of the last century.
+LJSpeech-1.1/wavs/LJ040-0087.wav|that she could not afford to send them back to the military school and she asked Pic to quit school entirely to help support the family.
+LJSpeech-1.1/wavs/LJ041-0193.wav|I think he would then throw himself entirely on the other thing he also wanted, which was the image in history.
+LJSpeech-1.1/wavs/LJ035-0128.wav|If her estimate of time is correct, she reached the bottom of the stairs before Truly and Baker started up,
+LJSpeech-1.1/wavs/LJ007-0101.wav|when a petition was presented against the return of Messrs. Adam Dundas and Fitzroy Kelly. Various witnesses, including Messrs. J. B. Dasent,
+LJSpeech-1.1/wavs/LJ042-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ003-0104.wav|Having been committed to Clerkenwell,
+LJSpeech-1.1/wavs/LJ041-0183.wav|Delgado and Oswald talked more about Cuba than Russia, and sometimes imagined themselves as leaders in the Cuban Army or Government,
+LJSpeech-1.1/wavs/LJ001-0175.wav|Simple as this proposition is, it is necessary to be stated,
+LJSpeech-1.1/wavs/LJ008-0287.wav|to make those sanctions an object of contemptuous mockery.
+LJSpeech-1.1/wavs/LJ029-0044.wav|Lawson's most important responsibilities were to take preventive action against anyone in Dallas considered a threat to the President,
+LJSpeech-1.1/wavs/LJ045-0168.wav|Answer: I said it would be better if I remained with Ruth until the holidays, he would come, and we would all meet together.
+LJSpeech-1.1/wavs/LJ019-0398.wav|Accordingly, it was ordered by Sir William Harcourt, the present Secretary of State, that Newgate should cease to be used as a regular prison,
+LJSpeech-1.1/wavs/LJ011-0039.wav|but in a separate chamber, that belonging to one of the warders of the jail.
+LJSpeech-1.1/wavs/LJ007-0051.wav|The brutal callousness of the bulk of the inmates of the press-yard may be gathered from the prison punishment-book, which frequently recorded such entries as the following:
+LJSpeech-1.1/wavs/LJ010-0108.wav|The trial of the conspirators came on some six weeks later, at the Old Bailey.
+LJSpeech-1.1/wavs/LJ016-0128.wav|He now put in requisition the rope made of the sheets, and with its help lowered himself into the street.
+LJSpeech-1.1/wavs/LJ005-0070.wav|These are principles fully recognized now-a-days, and it may fairly be conceded that the Prison Discipline Society's ideal
+LJSpeech-1.1/wavs/LJ027-0112.wav|How, then, are they to be accounted for?
+LJSpeech-1.1/wavs/LJ024-0070.wav|I now propose that we establish by law an assurance against any such ill-balanced court in the future.
+LJSpeech-1.1/wavs/LJ003-0305.wav|The provision of more baths was also suggested, and the daily sweeping out of the prison.
+LJSpeech-1.1/wavs/LJ021-0046.wav|there could be no recovery of public confidence in the security of savings.
+LJSpeech-1.1/wavs/LJ003-0101.wav|must have had a tendency to turn them into the world hardened and accomplished in the ways of vice and crime. End quote.
+LJSpeech-1.1/wavs/LJ007-0042.wav|so that men in the most awful situation, daily expecting to be hanged,
+LJSpeech-1.1/wavs/LJ023-0081.wav|asserted a power to veto laws passed by the Congress and state legislatures in complete disregard of this original limitation.
+LJSpeech-1.1/wavs/LJ036-0195.wav|He was described by Chief Curry as having the reputation of being "a very fine, dedicated officer."
+LJSpeech-1.1/wavs/LJ047-0231.wav|some indication that the person planned to take some action against the safety of the President of the United States or the Vice President. End quote.
+LJSpeech-1.1/wavs/LJ006-0150.wav|and then only went round to count the number.
+LJSpeech-1.1/wavs/LJ018-0390.wav|But there were few cases so remarkable as the great ones already recorded.
+LJSpeech-1.1/wavs/LJ019-0153.wav|Some attempt at supervision was exercised by the night watchman stationed on the leads, who might hear what went on inside.
+LJSpeech-1.1/wavs/LJ037-0026.wav|By this time the pickup truck was across the street and about twenty-five feet from the police car.
+LJSpeech-1.1/wavs/LJ016-0330.wav|It was read for the first time in March eighteen sixty-six, but did not become law till eighteen sixty-eight.
+LJSpeech-1.1/wavs/LJ015-0029.wav|In December eighteen fifty-one the balance sheet showed a deficiency of upwards of seventy thousand pounds.
+LJSpeech-1.1/wavs/LJ034-0199.wav|According to Euins, however, as the man lowered his head in order to aim the rifle down Elm Street, he appeared to have a white bald spot, on his head.
+LJSpeech-1.1/wavs/LJ016-0012.wav|the instruments needed for the purpose never reached him.
+LJSpeech-1.1/wavs/LJ036-0065.wav|Oswald lived at ten twenty-six North Beckley.
+LJSpeech-1.1/wavs/LJ018-0002.wav|In July eighteen sixty-four occurred the murder of Mr. Briggs, a gentleman advanced in years and chief clerk in Robarts' bank.
+LJSpeech-1.1/wavs/LJ010-0076.wav|In this way he formed the acquaintance of Watson and others, with whom he was arraigned for treasonable practices, and imprisoned.
+LJSpeech-1.1/wavs/LJ005-0104.wav|The progress of improvement was certainly extremely slow.
+LJSpeech-1.1/wavs/LJ020-0068.wav|One even tablespoonful of white sugar.
+LJSpeech-1.1/wavs/LJ001-0154.wav|the result as measured by the eye being that the lower margin is less than the top one, and that the whole opening has an upside-down look vertically
+LJSpeech-1.1/wavs/LJ025-0015.wav|in which the question is treated with that comprehensiveness of knowledge and clear critical judgment which characterize his writings
+LJSpeech-1.1/wavs/LJ010-0211.wav|The society was supposed to meet regularly, and its proceedings, together with the speeches made, were duly recorded.
+LJSpeech-1.1/wavs/LJ022-0123.wav|It should be remembered that in every big job there are some imperfections.
+LJSpeech-1.1/wavs/LJ007-0166.wav|Strong as were the foregoing remarks, the inspectors wound up their report in still more trenchant language
+LJSpeech-1.1/wavs/LJ013-0201.wav|The prisoner's demeanor had greatly changed during the trial.
+LJSpeech-1.1/wavs/LJ009-0251.wav|White managed to keep his wrists asunder, and continued to struggle with the officials for some time. Eventually he was pinioned with a cord in the usual manner.
+LJSpeech-1.1/wavs/LJ013-0133.wav|a number of pawnbrokers' duplicates, and three small files. It was proved at the trial that Ashley had asked his landlady for brick-dust and leather,
+LJSpeech-1.1/wavs/LJ037-0123.wav|and the Commission has inquired into general lineup procedures used by the Dallas police as well as the specific procedures in the lineups involving Oswald.
+LJSpeech-1.1/wavs/LJ039-0002.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ009-0034.wav|The sheriffs were in one gallery;
+LJSpeech-1.1/wavs/LJ048-0116.wav|Agent Lawson was not given written instructions concerning the Dallas trip or advice about any peculiar problems which it might involve;
+LJSpeech-1.1/wavs/LJ011-0137.wav|the Attorney-General introduced a bill to abolish capital punishment entirely for forgery.
+LJSpeech-1.1/wavs/LJ012-0288.wav|and was seen to be busily engaged in washing down the house with bucket and mop.
+LJSpeech-1.1/wavs/LJ017-0042.wav|Palmer's trial caused the most intense excitement.
+LJSpeech-1.1/wavs/LJ008-0150.wav|Four years passed without the detection of the murderers,
+LJSpeech-1.1/wavs/LJ026-0073.wav|being the constant transpiration (evaporation) of watery vapor from the leaves, especially through the stomata.
+LJSpeech-1.1/wavs/LJ040-0044.wav|Oswald apparently started reading about communism when he was about fifteen.
+LJSpeech-1.1/wavs/LJ009-0253.wav|Then with a strong effort he pushed off the white cap, and tried to liberate his neck from the halter, which by this time had been adjusted.
+LJSpeech-1.1/wavs/LJ044-0164.wav|primarily for purposes of self-advertising.
+LJSpeech-1.1/wavs/LJ022-0193.wav|by a sound and rational ordering of its various elements and wise provisions for the protection of the weak against the strong.
+LJSpeech-1.1/wavs/LJ044-0219.wav|you could tell what they wanted you to do by reading between the lines, reading the thing and doing a little reading between the lines, end quote.
+LJSpeech-1.1/wavs/LJ036-0005.wav|He arrived at approximately one p.m. and left a few minutes later.
+LJSpeech-1.1/wavs/LJ003-0045.wav|more particularly as regarded the classification of prisoners, and which were dependent on the space to be gained by the removal of the debtors,
+LJSpeech-1.1/wavs/LJ027-0150.wav|through which their long line of ancestors had passed.
+LJSpeech-1.1/wavs/LJ042-0081.wav|is made only after the longest and most serious considerations. I affirm that my allegiance is to the Union of Soviet Socialist Republics.
+LJSpeech-1.1/wavs/LJ043-0066.wav|Some of his acquaintances, feeling that Oswald tried to impress people with the fact that he had lived and worked in Russia, were led to the belief
+LJSpeech-1.1/wavs/LJ013-0146.wav|A Jew named Benjamin effected the sale to certain merchants named Blogg and Martin,
+LJSpeech-1.1/wavs/LJ028-0054.wav|He continued the building operations of his father, until Babylon became the greatest city of its age,
+LJSpeech-1.1/wavs/LJ001-0087.wav|for the seventeenth-century letters are at least pure and simple in line. The Italian, Bodoni, and the Frenchman, Didot,
+LJSpeech-1.1/wavs/LJ026-0127.wav|These processes must take place everywhere in ordinary protoplasm,
+LJSpeech-1.1/wavs/LJ049-0182.wav|In addition, the Commission has concluded that the Secret Service particularly tends to be the passive recipient of information
+LJSpeech-1.1/wavs/LJ032-0248.wav|It appears from his wife's testimony
+LJSpeech-1.1/wavs/LJ042-0010.wav|it appears that personal and psychological factors were also involved.
+LJSpeech-1.1/wavs/LJ012-0264.wav|a warrant was issued for his apprehension, which was effected at Kennington on the twenty-fourth March.
+LJSpeech-1.1/wavs/LJ026-0092.wav|animals (and colorless plants as well) apparently could not long exist.
+LJSpeech-1.1/wavs/LJ047-0182.wav|was quite interested in determining the nature of his contact with the Soviet Embassy in Mexico City, end quote.
+LJSpeech-1.1/wavs/LJ009-0201.wav|Surgeons' Hall being let to the lieutenancy of the county for the accommodation of the militia.
+LJSpeech-1.1/wavs/LJ007-0113.wav|were revived for the convenience of these gentlemen, whose incarceration was thus rendered as little like imprisonment as possible.
+LJSpeech-1.1/wavs/LJ008-0109.wav|As we crossed the press yard a cock crew, and the solitary clanking of a restless chain was dreadfully horrible.
+LJSpeech-1.1/wavs/LJ003-0114.wav|was appointed judge, and a towel tied in knots was hung on each side in imitation of a wig.
+LJSpeech-1.1/wavs/LJ033-0141.wav|A person seated on the fourth carton could assemble the rifle without being seen from the rest of the sixth floor
+LJSpeech-1.1/wavs/LJ006-0249.wav|On one occasion a disturbance was raised which was not quelled until windows had been broken and forms and tables burnt.
+LJSpeech-1.1/wavs/LJ014-0323.wav|The warrant thus represented money, and was often used as such, being endorsed and passed from hand to hand as other negotiable bills.
+LJSpeech-1.1/wavs/LJ018-0095.wav|The police suspecting the house in York Buildings, put a watch on the premises, which they kept up for more than a year,
+LJSpeech-1.1/wavs/LJ030-0179.wav|She watched as he slumped down with an empty expression on his face.
+LJSpeech-1.1/wavs/LJ010-0311.wav|in other words, he forged powers of attorney, and proceeded to realize securities lodged in his bank under various names.
+LJSpeech-1.1/wavs/LJ049-0210.wav|for examples to a determination of the optimum organization of the President's protection.
+LJSpeech-1.1/wavs/LJ003-0257.wav|Searches were made certainly, but they were too often superficial, or they might be evaded by a trifling bribe.
+LJSpeech-1.1/wavs/LJ002-0204.wav|When they were sick no more notice was taken of them than of a dog.
+LJSpeech-1.1/wavs/LJ028-0151.wav|leaving between them room for a four-horse chariot to turn.
+LJSpeech-1.1/wavs/LJ036-0146.wav|Neches is within one-half block of the roominghouse at ten twenty-six North Beckley where Oswald was living.
+LJSpeech-1.1/wavs/LJ030-0106.wav|A base station at a fixed location in Dallas operated a radio network which linked together the lead car,
+LJSpeech-1.1/wavs/LJ030-0092.wav|occupied the right-hand side of the front seat, and Hurchel Jacks of the Texas State Highway patrol was the driver.
+LJSpeech-1.1/wavs/LJ019-0259.wav|while the former was intended for the congregate labor of a number, and the latter, as its name implies, imposed continuous solitary toil.
+LJSpeech-1.1/wavs/LJ038-0075.wav|Fritz replied, quote, Well, he was employed down at the Book Depository and he had not been present for a roll call of the employees, end quote.
+LJSpeech-1.1/wavs/LJ014-0167.wav|When I came upon the scene at midnight, the shrillness of the cries and howls that were raised from time to time,
+LJSpeech-1.1/wavs/LJ040-0043.wav|He assassinated the President, shot Officer Tippit, resisted arrest and tried to kill another policeman in the process.
+LJSpeech-1.1/wavs/LJ003-0103.wav|It was that of a person, quote, who practiced in the law, and who was connected by marriage with some very respectable families.
+LJSpeech-1.1/wavs/LJ023-0058.wav|showed the need of a national government with power enough to handle national problems.
+LJSpeech-1.1/wavs/LJ046-0064.wav|The President's views of his responsibilities as President of the United States were that he meet the people, that he go out to their homes and see them,
+LJSpeech-1.1/wavs/LJ013-0247.wav|Medical assistance having been summoned, it was ascertained how the dismemberment had been effected.
+LJSpeech-1.1/wavs/LJ034-0089.wav|Oswald had not filled any of the three orders.
+LJSpeech-1.1/wavs/LJ017-0239.wav|and although the mutineers spared him, they ill-treated the Chinamen, and cut one badly with knives.
+LJSpeech-1.1/wavs/LJ014-0016.wav|A letter, which afforded an additional clue, was also found in the pocket of the deceased. Still it was many weeks before any arrest was made.
+LJSpeech-1.1/wavs/LJ008-0046.wav|on the middle of which is placed the gibbet, extending from the jail across the Old Bailey.
+LJSpeech-1.1/wavs/LJ003-0124.wav|He was charged with moving something which should not be touched, with leaving a door open, or coughing maliciously to the disturbance of his companions.
+LJSpeech-1.1/wavs/LJ028-0458.wav|Its outer part was about twenty-four feet in thickness, and its foundations, as Nebuchadnezzar said, were carried down to the water level.
+LJSpeech-1.1/wavs/LJ019-0279.wav|Clause ninety-two, Schedule one of that act authorized the use of plank beds, which were adopted in many prisons.
+LJSpeech-1.1/wavs/LJ035-0120.wav|While they were at the west windows their view of the stairwell was completely blocked by shelves and boxes.
+LJSpeech-1.1/wavs/LJ036-0102.wav|He testified that Oswald looked older than the other boys.
+LJSpeech-1.1/wavs/LJ025-0062.wav|under particular circumstances, the contents of the cells of certain water-weeds were set free and moved about with considerable velocity
+LJSpeech-1.1/wavs/LJ019-0156.wav|Unless the guilty prisoner was given up, the whole ward was punished, either by the exclusion of visitors or the deprivation of fire,
+LJSpeech-1.1/wavs/LJ015-0091.wav|and to have succeeded in other speculations.
+LJSpeech-1.1/wavs/LJ026-0159.wav|and hence the excess oxygen is eliminated while starch manufacture is in process.
+LJSpeech-1.1/wavs/LJ041-0172.wav|I never said anything to him again and he never said anything to me again, end quote.
+LJSpeech-1.1/wavs/LJ018-0306.wav|Noyes was seized without difficulty, as it was a part of the scheme that he should act as the dupe, and remain on the spot in London till all the money was obtained.
+LJSpeech-1.1/wavs/LJ015-0097.wav|and he could not produce them. His chief asked sternly where they were.
+LJSpeech-1.1/wavs/LJ015-0011.wav|Maltby, who had bolted, was pursued and arrested, to end his life miserably by committing suicide in a Newgate cell.
+LJSpeech-1.1/wavs/LJ040-0069.wav|Reminding her sons that they were orphans and that the family's financial condition was poor,
+LJSpeech-1.1/wavs/LJ028-0231.wav|whereupon they withdrew within their defenses.
+LJSpeech-1.1/wavs/LJ047-0100.wav|but less than completely truthful or cooperative when interrogated about the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ032-0264.wav|tied with a string, lying on the floor of the Paines' garage.
+LJSpeech-1.1/wavs/LJ028-0461.wav|the first-born son of Nabopolassar, King of Babylon.
+LJSpeech-1.1/wavs/LJ018-0011.wav|About the same time a body was discovered on the line near the railway-bridge by Victoria Park.
+LJSpeech-1.1/wavs/LJ011-0290.wav|His first act was to send a messenger to stop the cheque and the order to Mr. Bell, his next to seek the help of the police.
+LJSpeech-1.1/wavs/LJ017-0067.wav|Palmer was ably defended, but the weight of evidence was clearly with the prosecution, led by Sir Alexander Cockburn,
+LJSpeech-1.1/wavs/LJ013-0225.wav|The Sunday night before his execution he would not go to bed when ordered.
+LJSpeech-1.1/wavs/LJ049-0219.wav|These considerations have induced the Commission to believe
+LJSpeech-1.1/wavs/LJ023-0055.wav|Like the Bible, it ought to be read again and again.
+LJSpeech-1.1/wavs/LJ015-0182.wav|and thoroughly embodying the idea of English respectability.
+LJSpeech-1.1/wavs/LJ020-0079.wav|drill several finger-holes in it and fill them with the melted butter, the dissolved soda and the beaten yolk of egg.
+LJSpeech-1.1/wavs/LJ018-0143.wav|each containing two notes, many of the sheets suitable for engraving any kind of note from one thousand pounds downwards.
+LJSpeech-1.1/wavs/LJ047-0083.wav|The Passport Office of the Department of State in Washington had no listing for Oswald requiring special treatment,
+LJSpeech-1.1/wavs/LJ008-0207.wav|and although one of these gave way owing to the extraordinary pressure, no serious accident occurred.
+LJSpeech-1.1/wavs/LJ009-0046.wav|but the attempt fails; he trembles, his knees knock together, and his head droops as he enters the condemned pew.
+LJSpeech-1.1/wavs/LJ031-0212.wav|The body was muscular and well developed with no gross skeletal abnormalities except for those caused by the gunshot wounds.
+LJSpeech-1.1/wavs/LJ021-0154.wav|is the program of Public Works provided for in the same Act and designed to put more men back to work,
+LJSpeech-1.1/wavs/LJ038-0129.wav|Holmes reminded Oswald that A. J. Hidell was listed on post office box three zero zero six one, New Orleans,
+LJSpeech-1.1/wavs/LJ020-0094.wav|Put on stockings and slippers, such underclothing as may be needed to prevent cold, a wrapper and the kitchen apron.
+LJSpeech-1.1/wavs/LJ050-0194.wav|and should not be relied upon to the detriment of the imaginative application of judgment in special cases.
+LJSpeech-1.1/wavs/LJ044-0238.wav|The fact that Oswald left behind the funds which might have enabled him to reach Cuba suggests the absence of any plan to try to flee there
+LJSpeech-1.1/wavs/LJ032-0154.wav|The print is additional proof that the rifle was in Oswald's possession.
+LJSpeech-1.1/wavs/LJ025-0144.wav|one of which is able to affect the other by means of the communication so established.
+LJSpeech-1.1/wavs/LJ020-0021.wav|Into a hollow, like a crater in the middle of the flour, empty your sponge-bowl, and work the flour down into it.
+LJSpeech-1.1/wavs/LJ015-0195.wav|It has been already remarked in these pages that with changed social conditions came a great change in the character of crimes.
+LJSpeech-1.1/wavs/LJ042-0151.wav|with what he wrote while on the way back to the United States and after his return.
+LJSpeech-1.1/wavs/LJ004-0007.wav|It was so powerless against the persistent neglect of those intrusted with prison management, that, five-and-twenty years later,
+LJSpeech-1.1/wavs/LJ006-0050.wav|the Exchequer, the Commissioners of bankruptcy and of taxes; smugglers, and a larger number sentenced for very short terms,
+LJSpeech-1.1/wavs/LJ048-0199.wav|and arrest any person who might attempt to throw anything or try to get at the President and his party;
+LJSpeech-1.1/wavs/LJ021-0009.wav|with a greater certainty of the employment of labor at a reasonable wage and of more business at a fair profit.
+LJSpeech-1.1/wavs/LJ031-0061.wav|Dr. Fouad Bashour, chief of cardiology, Dr. M. T. Jenkins, chief of anesthesiology, and Dr. A. H. Giesecke, Jr.,
+LJSpeech-1.1/wavs/LJ047-0185.wav|I had now established that Lee Oswald was not employed in a sensitive industry.
+LJSpeech-1.1/wavs/LJ038-0277.wav|I am aware of their position. This is not, I am sure, arrived at without careful consideration.
+LJSpeech-1.1/wavs/LJ015-0278.wav|commencing sham actions, and addressing formal applications, merely for the reply.
+LJSpeech-1.1/wavs/LJ048-0262.wav|during the hours they are officially employed at their post of duty, or when they may reasonably expect that they may be called upon to perform an official duty.
+LJSpeech-1.1/wavs/LJ017-0156.wav|and told her sister as much when they last met.
+LJSpeech-1.1/wavs/LJ028-0089.wav|Though a great warrior, it was not for his military deeds that he was best known.
+LJSpeech-1.1/wavs/LJ007-0193.wav|The measures of improvement introduced were mainly as follows:
+LJSpeech-1.1/wavs/LJ015-0113.wav|followed him over to Sweden, and arrested him at Helsingfors.
+LJSpeech-1.1/wavs/LJ034-0106.wav|Brennan described the man in similar terms, except that he gave the weight as between one hundred sixty-five and one hundred seventy-five pounds and the height was omitted.
+LJSpeech-1.1/wavs/LJ041-0064.wav|Daniel Powers, another marine who was stationed with Oswald for part of his marine career,
+LJSpeech-1.1/wavs/LJ018-0188.wav|Presently "Old Bob" drove up to Camberwell Gate in the same cart in which he had been seen to start.
+LJSpeech-1.1/wavs/LJ041-0034.wav|started expounding the Communist doctrine and saying that he was highly interested in communism, that communism was the only way of life for the worker, et cetera,
+LJSpeech-1.1/wavs/LJ009-0266.wav|and his death was not compassed until the executioner terminated his sufferings by hanging on to his legs.
+LJSpeech-1.1/wavs/LJ014-0211.wav|and had been repeatedly sentenced to imprisonment for most desperate and ferocious attacks upon various constables. His last victim was Dwyer,
+LJSpeech-1.1/wavs/LJ041-0164.wav|which Thornley read at Oswald's suggestion.
+LJSpeech-1.1/wavs/LJ016-0103.wav|and after all was quiet the three got on to the roof through the hole, and so on to Tyler's manufactory close by,
+LJSpeech-1.1/wavs/LJ022-0003.wav|Since my annual message to the Congress on January fourth, last, I have not addressed the general public over the air.
+LJSpeech-1.1/wavs/LJ005-0271.wav|As a means of increasing the severity of imprisonment, letters and visits from outside should not be permitted during the first six months of an imprisonment.
+LJSpeech-1.1/wavs/LJ019-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section twenty-two. Newgate Reformed.
+LJSpeech-1.1/wavs/LJ028-0162.wav|At the river end of these cross streets are low gates in the fence that skirts the stream,
+LJSpeech-1.1/wavs/LJ007-0189.wav|The corporation evidently had the worst of it, and began to feel the necessity for undertaking the great work of reform.
+LJSpeech-1.1/wavs/LJ012-0017.wav|The trade of fence, or receiver, therefore, is very nearly as old as the crimes which it so obviously fostered.
+LJSpeech-1.1/wavs/LJ009-0146.wav|Mr. Carver, the ordinary, appears to have addressed himself directly to Courvoisier,
+LJSpeech-1.1/wavs/LJ040-0207.wav|and represents his reaction to a complete absence of any real family life, end quote.
+LJSpeech-1.1/wavs/LJ028-0297.wav|How will thy disfigurement, thou simpleton, induce the enemy to yield one day sooner?
+LJSpeech-1.1/wavs/LJ022-0063.wav|to help those who have reached the age of retirement to give up their jobs and thus give to the younger generation greater opportunities for work
+LJSpeech-1.1/wavs/LJ027-0026.wav|lead to a preview of certain principles of adaptation, necessary for their interpretation.
+LJSpeech-1.1/wavs/LJ029-0140.wav|To create this traffic pattern, a concrete barrier between Main and Elm Streets presents an obstacle to a right turn
+LJSpeech-1.1/wavs/LJ045-0223.wav|were part of his character long before he met his wife
+LJSpeech-1.1/wavs/LJ048-0225.wav|they had little opportunity to eat during the day. No food was available at the Press Club.
+LJSpeech-1.1/wavs/LJ005-0024.wav|He objected to the instruction of prisoners in reading and writing.
+LJSpeech-1.1/wavs/LJ037-0024.wav|As he crossed the intersection a block east of tenth and Patton, he saw a policeman standing by the left door of the police car parked along the south side of tenth.
+LJSpeech-1.1/wavs/LJ039-0230.wav|With the equipment he [Oswald] had and with his ability I consider it a very easy shot, end quote.
+LJSpeech-1.1/wavs/LJ011-0010.wav|On another occasion the banker forged a gentleman's name while the latter was sitting with him in his private room,
+LJSpeech-1.1/wavs/LJ033-0054.wav|A firearms expert with the FBI assembled the rifle in six minutes using a ten-cent coin as a tool,
+LJSpeech-1.1/wavs/LJ032-0218.wav|Both pictures, however, have identical backgrounds and lighting and, judging from the shadows, were taken at the same angle.
+LJSpeech-1.1/wavs/LJ022-0021.wav|or to one industry, or to an individual private occupation.
+LJSpeech-1.1/wavs/LJ017-0128.wav|At the last moment Palmer tossed a bit of paper over to his counsel, on which he had written, quote, I think there will be a verdict of Not Guilty, end quote.
+LJSpeech-1.1/wavs/LJ012-0076.wav|There was an increase rather than an abatement in jewel and bullion robberies in the years immediately following,
+LJSpeech-1.1/wavs/LJ016-0057.wav|As his coat was an encumbrance, he left it on the top of the third house in Newgate Street, and thus in shirt-sleeves, barefoot and bareheaded,
+LJSpeech-1.1/wavs/LJ045-0236.wav|He admitted nothing that would damage him but discussed other matters quite freely.
+LJSpeech-1.1/wavs/LJ015-0157.wav|During the days of his prosperity he was a governor of Christ's Hospital,
+LJSpeech-1.1/wavs/LJ044-0228.wav|it should be remembered that his wife testified that he was disappointed with his failure to get to Cuba
+LJSpeech-1.1/wavs/LJ011-0012.wav|It must be added that the Bank of England, on discovering the forgeries,
+LJSpeech-1.1/wavs/LJ033-0087.wav|She thought that its color was similar to that of the bag found on the sixth floor of the School Book Depository after the assassination.
+LJSpeech-1.1/wavs/LJ040-0092.wav|when he left school in order to get into the Coast Guard. Since his mother did not approve of his decision to continue school
+LJSpeech-1.1/wavs/LJ036-0054.wav|When Oswald was arrested in the Texas Theatre, he was wearing a brown sport shirt with a hole in the right sleeve at the elbow.
+LJSpeech-1.1/wavs/LJ019-0166.wav|those for trial, and those sentenced for short terms or long
+LJSpeech-1.1/wavs/LJ003-0141.wav|Felons who could pay the price were permitted, irrespective of their character or offenses,
+LJSpeech-1.1/wavs/LJ014-0092.wav|The hole must have been excavated and the quicklime purchased quite three weeks before O'Connor met his death,
+LJSpeech-1.1/wavs/LJ028-0409.wav|Some, calling themselves Christians, are the descendants of Babylonians, perhaps of Nebuchadnezzar himself.
+LJSpeech-1.1/wavs/LJ037-0102.wav|He stated further that from, quote, What I saw of him, end quote, the man looked like the man in the picture.
+LJSpeech-1.1/wavs/LJ014-0293.wav|He returned from court in a state of gloomy dejection,
+LJSpeech-1.1/wavs/LJ043-0076.wav|that he had some aptitude in that area, quote, because the verbal score is high and the clerical score is high, end quote,
+LJSpeech-1.1/wavs/LJ031-0083.wav|the doctors observed that he had a heartbeat and was making some respiratory efforts.
+LJSpeech-1.1/wavs/LJ010-0031.wav|the same and happily often futile precautions taken to conceal the crime.
+LJSpeech-1.1/wavs/LJ049-0122.wav|FBI Director J. Edgar Hoover, who strongly supports such legislation, testified that the absence of clear Federal jurisdiction
+LJSpeech-1.1/wavs/LJ017-0057.wav|On returning to jail each day he talked freely and without reserve to the warders in charge of him, chiefly on incidents in the day's proceedings.
+LJSpeech-1.1/wavs/LJ031-0105.wav|chief of thoracic surgery, arrived at trauma room two, to take charge of the care of Governor Connally,
+LJSpeech-1.1/wavs/LJ041-0180.wav|never heard him in any way, shape or form confess that he was a Communist, or that he ever thought about being a Communist, end quote.
+LJSpeech-1.1/wavs/LJ049-0011.wav|As described in chapter two, the President directed that his car stop on two occasions during the motorcade so that he could greet members of the public.
+LJSpeech-1.1/wavs/LJ038-0142.wav|Actually, the party for one of the Paine's children was the preceding weekend, when Marina Oswald suggested that Oswald remain in Dallas.
+LJSpeech-1.1/wavs/LJ016-0169.wav|As more humane principles were introduced into prison management,
+LJSpeech-1.1/wavs/LJ029-0186.wav|Increased concern about the President's visit was aroused by the incident involving the U.S. Ambassador to the United Nations, Adlai E. Stevenson.
+LJSpeech-1.1/wavs/LJ032-0268.wav|This bulge was apparently caused by a hard protruding object which had stretched the blanket's fibers.
+LJSpeech-1.1/wavs/LJ042-0177.wav|Oswald actually did attempt to formulate such an alternative which he planned to, quote, put forward, end quote, himself.
+LJSpeech-1.1/wavs/LJ047-0077.wav|Oswald was tentatively located in New Orleans in June,
+LJSpeech-1.1/wavs/LJ022-0052.wav|here as in every other nation, we have come to recognize the possibility and the necessity of certain helpful remedial measures.
+LJSpeech-1.1/wavs/LJ048-0103.wav|and with the concurrence of the Dallas police, was entirely appropriate, in view of the known desires of the President.
+LJSpeech-1.1/wavs/LJ011-0185.wav|Filial affection rose superior to all scruples, and Miss Turner, having crossed the border, was married to Wakefield
+LJSpeech-1.1/wavs/LJ028-0004.wav|and along the narrow winding lane which was once called the "Street Called Straight."
+LJSpeech-1.1/wavs/LJ038-0209.wav|Oswald told his wife that he did not know whether he had hit Walker;
+LJSpeech-1.1/wavs/LJ017-0237.wav|when asked said they would spare his life if he would navigate the ship for them to the River Plate or Buenos Aires.
+LJSpeech-1.1/wavs/LJ016-0003.wav|and that none of its inmates could hope to escape from its secure precincts.
+LJSpeech-1.1/wavs/LJ049-0090.wav|Federal law enforcement officers is a Federal crime.
+LJSpeech-1.1/wavs/LJ028-0050.wav|The building of the Babylon so famous in history began with Nabopolassar.
+LJSpeech-1.1/wavs/LJ026-0044.wav|In order to emphasize the fundamental similarity of organic function in higher and lower animals and plants, let us compare any higher plant
+LJSpeech-1.1/wavs/LJ023-0034.wav|The American people have learned from the depression.
+LJSpeech-1.1/wavs/LJ015-0042.wav|Then it was that instead of merely pledging securities, the bank sold them outright, and thus passed the Rubicon of fraud.
+LJSpeech-1.1/wavs/LJ049-0077.wav|Since the assassination, the Secret Service and the Department of the Treasury
+LJSpeech-1.1/wavs/LJ042-0157.wav|Lee Harvey Oswald was born in October nineteen thirty-nine in New Orleans, Louisiana, the son of a Insurance Salesmen whose early death
+LJSpeech-1.1/wavs/LJ021-0181.wav|thereby saving the British treasury one hundred and fifty million dollars a year in interest alone?
+LJSpeech-1.1/wavs/LJ003-0155.wav|Tailoring and shoemaking was permitted, but it was deemed unsafe to allow a carpenter or blacksmith to have his tools.
+LJSpeech-1.1/wavs/LJ036-0060.wav|south on Houston, and southwest across the Houston viaduct to service the Oak Cliff area along Marsalis.
+LJSpeech-1.1/wavs/LJ032-0190.wav|On the other hand Stombaugh pointed out that fibers might retain their freshness if the rifle had been
+LJSpeech-1.1/wavs/LJ035-0096.wav|is consistent with the movements of the two elevators, which would have provided the other possible means of descent.
+LJSpeech-1.1/wavs/LJ038-0285.wav|that the bullet which missed General Walker was fired from Oswald's rifle to the exclusion of all others, this testimony was considered probative
+LJSpeech-1.1/wavs/LJ039-0019.wav|She reminded him that after the Walker shooting he had promised never to repeat such an act.
+LJSpeech-1.1/wavs/LJ024-0012.wav|with modern facts and circumstances under which average men have to live and work.
+LJSpeech-1.1/wavs/LJ046-0103.wav|In this connection the information available to Federal agencies about Lee Harvey Oswald
+LJSpeech-1.1/wavs/LJ007-0197.wav|The infirmary was enlarged, the admission of visitors limited, and the passing of articles prevented by a wire screen.
+LJSpeech-1.1/wavs/LJ027-0075.wav|but, in spite of all these obscurations, corresponding parts usually may be made out.
+LJSpeech-1.1/wavs/LJ029-0149.wav|To control traffic, arrangements were made for the deployment of foot patrolmen and motorcycle police at various positions along the route.
+LJSpeech-1.1/wavs/LJ011-0232.wav|It must not be imagined that although highway robbery was now nearly extinct, and felonious outrages in the streets were rare,
+LJSpeech-1.1/wavs/LJ046-0151.wav|All material received by PRS was separately screened
+LJSpeech-1.1/wavs/LJ018-0355.wav|who was convicted as an accessory after the fact, had not really taken an active part in the murder.
+LJSpeech-1.1/wavs/LJ010-0199.wav|Among his papers was found a curious document, purporting to be the rules of an association called
+LJSpeech-1.1/wavs/LJ048-0191.wav|Television films taken of parts of the motorcade by a Dallas television station
+LJSpeech-1.1/wavs/LJ016-0285.wav|Then one struck up the hymn of the Promised Land, and the refrain was at once taken up with a mighty chorus --
+LJSpeech-1.1/wavs/LJ048-0186.wav|Instead of such advance precautions, the Secret Service depended in part on the efforts of local law enforcement personnel stationed along the route.
+LJSpeech-1.1/wavs/LJ031-0062.wav|then joined in the effort to revive the President.
+LJSpeech-1.1/wavs/LJ015-0239.wav|Just as the train was starting Agar slipped into the van with Burgess, and Pierce got into a first-class carriage.
+LJSpeech-1.1/wavs/LJ042-0164.wav|"The Collective" contrasts sharply with material which Oswald seems to have written after he left the Soviet Union.
+LJSpeech-1.1/wavs/LJ040-0222.wav|But Carro was quite specific that his recommendation was based primarily on the adverse factors in Lee's environment
+LJSpeech-1.1/wavs/LJ005-0029.wav|nothing but beating hemp and pulling oakum and pounding bricks -- no work but what was tedious, unusual."
+LJSpeech-1.1/wavs/LJ021-0194.wav|More than twenty years ago, when Theodore Roosevelt and Woodrow Wilson were attempting to correct abuses in our national life,
+LJSpeech-1.1/wavs/LJ020-0032.wav|Knead as you would white bread, and set aside for the rising.
+LJSpeech-1.1/wavs/LJ031-0111.wav|The elliptical wound in the Governor's back, located slightly to the left of the Governor's right armpit approximately five-eighths inch (a centimeter and a half)
+LJSpeech-1.1/wavs/LJ022-0200.wav|renewed faith in the vast possibilities of human beings to improve their material and spiritual status
+LJSpeech-1.1/wavs/LJ043-0034.wav|Contacts with De Mohrenschildt and his wife did continue and they saw the Oswalds occasionally until the spring of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ011-0198.wav|An uncle of Miss Wakefield's, accompanied by his solicitor and a Bow Street runner, at once went in pursuit.
+LJSpeech-1.1/wavs/LJ032-0195.wav|The Commission was able to conclude, however, that the fibers most probably came from Oswald's shirt.
+LJSpeech-1.1/wavs/LJ019-0297.wav|The prisoners inter-communicated freely, and exercised the most injurious, corrupting influences upon one another.
+LJSpeech-1.1/wavs/LJ015-0253.wav|he handed over to Pierce a sum of three thousand pounds, his own, whether rightly or wrongly acquired never came out,
+LJSpeech-1.1/wavs/LJ011-0035.wav|Every endeavor was used, however, to obtain a commutation of sentence. His case was twice argued before the judges on points of law,
+LJSpeech-1.1/wavs/LJ045-0017.wav|This meeting preceded by one day the attack on A. E. Stevenson at the United Nations Day meeting at which he spoke
+LJSpeech-1.1/wavs/LJ044-0239.wav|and raises serious questions as to whether or not he ever expected to escape.
+LJSpeech-1.1/wavs/LJ013-0207.wav|In this he gave as the motives of his crime a quarrel he had with his master, who threatened to discharge him without a character.
+LJSpeech-1.1/wavs/LJ009-0057.wav|Already he is half dead.
+LJSpeech-1.1/wavs/LJ003-0129.wav|a taste for spirits. End quote.
+LJSpeech-1.1/wavs/LJ019-0146.wav|There was as yet no control over the prisoners after locking-up time;
+LJSpeech-1.1/wavs/LJ025-0059.wav|Robert Brown noted the more complex motions of the protoplasm in the cells of Tradescantia in eighteen thirty-one,
+LJSpeech-1.1/wavs/LJ012-0213.wav|An inquest was held on the body, which was identified as that of an Italian boy,
+LJSpeech-1.1/wavs/LJ024-0130.wav|to frighten the workers of America in a pay-envelope propaganda against the Social Security Law.
+LJSpeech-1.1/wavs/LJ018-0316.wav|Further sums were to have been paid after the escape;
+LJSpeech-1.1/wavs/LJ012-0138.wav|They also ascertained that a gold-refiner,
+LJSpeech-1.1/wavs/LJ006-0057.wav|Not only were the wards thus needlessly crammed, and for no reason but the niggardliness of the corporation which refused a proper supply of bedding
+LJSpeech-1.1/wavs/LJ032-0166.wav|Judgments as to probability will depend on the number and types of matches.
+LJSpeech-1.1/wavs/LJ034-0107.wav|In his testimony before the Commission, Brennan described the person he saw as, quote,
+LJSpeech-1.1/wavs/LJ039-0051.wav|In the absence of other evidence that Oswald actually intended to shoot someone at this time,
+LJSpeech-1.1/wavs/LJ011-0051.wav|the great magnitude of our erring brother's offense, one of the most dangerous description in a trading community.
+LJSpeech-1.1/wavs/LJ045-0210.wav|Now it appeared that he was not welcome at the Paine home,
+LJSpeech-1.1/wavs/LJ032-0058.wav|to May fourteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ018-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section twenty-one: Newgate Notorieties, part two.
+LJSpeech-1.1/wavs/LJ036-0209.wav|A similar description was given on channel two at twelve:forty-five p.m.
+LJSpeech-1.1/wavs/LJ010-0118.wav|were not altogether creditable to the Government.
+LJSpeech-1.1/wavs/LJ009-0228.wav|and a subscription was set on foot for them then and there at the foot of the gibbet.
+LJSpeech-1.1/wavs/LJ002-0045.wav|The various wards were all about eleven feet in height,
+LJSpeech-1.1/wavs/LJ029-0086.wav|Once the Trade Mart had been selected, Sorrels and Lawson worked out detailed arrangements for security at the building.
+LJSpeech-1.1/wavs/LJ023-0097.wav|Justice Stone said of the majority opinion that it was a "tortured construction of the Constitution."
+LJSpeech-1.1/wavs/LJ028-0108.wav|Fortunately in several of his long inscriptions, recently discovered in the Babylonian mounds, Nebuchadnezzar speaks of the building of the walls.
+LJSpeech-1.1/wavs/LJ041-0149.wav|He told Aline Mosby, a reporter who interviewed him after he arrived in Moscow, quote,
+LJSpeech-1.1/wavs/LJ028-0217.wav|His many troops, whose number, like the waters of the river, could not be counted, marched in full armor at his side,
+LJSpeech-1.1/wavs/LJ008-0045.wav|In the middle of this machinery is placed a movable platform, in form of a trap-door, ten feet long by eight wide,
+LJSpeech-1.1/wavs/LJ010-0241.wav|but she declared she would not remain a prisoner in her own palace, and next day drove out as usual in an open barouche.
+LJSpeech-1.1/wavs/LJ010-0058.wav|But the Chartists sought their ends by riot and rebellion, and gained only imprisonment for their pains.
+LJSpeech-1.1/wavs/LJ017-0253.wav|A farmer took them in for the night, and drove them next day to Rocha, a place north of Maldonado.
+LJSpeech-1.1/wavs/LJ043-0121.wav|A placement interviewer of the Louisiana Department of Labor
+LJSpeech-1.1/wavs/LJ001-0021.wav|The earliest book printed with movable type, the aforesaid Gutenberg Bible, is printed in letters which are an exact imitation
+LJSpeech-1.1/wavs/LJ012-0226.wav|This trial came off in due course at the Central Criminal Court, where the prisoners were charged on two counts,
+LJSpeech-1.1/wavs/LJ039-0181.wav|the shots would have been evenly spaced and the assassin would not have incurred so sharp an angular movement.
+LJSpeech-1.1/wavs/LJ049-0173.wav|As the testimony of J. Edgar Hoover and other Bureau officials revealed, the FBI did not believe that its directive required the Bureau
+LJSpeech-1.1/wavs/LJ035-0101.wav|neither of these slow elevators could have descended from the fifth to the second floor. Furthermore, no elevator was at the second floor when they arrived there.
+LJSpeech-1.1/wavs/LJ019-0313.wav|However, it now laid down in plain language and with precise details the requirements of a good jail system.
+LJSpeech-1.1/wavs/LJ004-0073.wav|to see that "no circumstances of severity are found in his treatment which are not found in his sentence."
+LJSpeech-1.1/wavs/LJ019-0029.wav|Major Jebb, R.E., and the two prison inspectors, Messrs. Crawford and Russell, with whose names the reader is already familiar.
+LJSpeech-1.1/wavs/LJ030-0041.wav|Motorcycles. -- Dallas police motorcycles preceded the pilot car.
+LJSpeech-1.1/wavs/LJ044-0092.wav|The interesting thing, or rather the danger involved, was the fact that Oswald seemed like such a nice, bright boy
+LJSpeech-1.1/wavs/LJ031-0223.wav|running in a line from the wound in the rear of the President's head toward the front part of the skull,
+LJSpeech-1.1/wavs/LJ050-0053.wav|Broader and more selective criteria
+LJSpeech-1.1/wavs/LJ002-0056.wav|The wards in this part were five in number, all in dimensions twenty feet by fifteen, with a sixth ward fifteen feet square.
+LJSpeech-1.1/wavs/LJ034-0154.wav|and I felt like there hadn't been more than one eyewitness, and if it got to be a known fact that I was an eyewitness, my family or I, either one, might not be safe.
+LJSpeech-1.1/wavs/LJ033-0191.wav|Fibers in paper bag matched fibers in blanket
+LJSpeech-1.1/wavs/LJ027-0069.wav|and (b) degenerate forms like snakes and some lizards, which have lost their limbs by disuse.
+LJSpeech-1.1/wavs/LJ017-0170.wav|there is not absolute and complete evidence of his guilt. End quote.
+LJSpeech-1.1/wavs/LJ006-0160.wav|one man who had been in the condemned ward for two months, said the governor only came there four times.
+LJSpeech-1.1/wavs/LJ031-0215.wav|One wound, approximately one-fourth of an inch by five-eighths of an inch (six by fifteen millimeters),
+LJSpeech-1.1/wavs/LJ036-0106.wav|You could have picked him out without identifying him by just listening to him.
+LJSpeech-1.1/wavs/LJ040-0131.wav|Marguerite Oswald visited her son at Youth House, where she recalled that she waited in line, quote,
+LJSpeech-1.1/wavs/LJ044-0116.wav|Since he seemed to feel that no one else understood his political views, the letter was of great value to him for it, quote, was proof
+LJSpeech-1.1/wavs/LJ021-0058.wav|Under these codes, in the industries covered, child labor has been eliminated.
+LJSpeech-1.1/wavs/LJ001-0170.wav|being thin, tough, and opaque.
+LJSpeech-1.1/wavs/LJ006-0291.wav|to clean the governor's office in the male prison;
+LJSpeech-1.1/wavs/LJ032-0163.wav|Stombaugh explained in his testimony that in fiber analysis, as distinct from fingerprint or firearms identification,
+LJSpeech-1.1/wavs/LJ010-0227.wav|He went from Newgate first to Bethlehem, from which he was removed to Broadmoor on the opening of the great criminal lunatic asylum at that place.
+LJSpeech-1.1/wavs/LJ013-0248.wav|At the same time an overpowering odor attracted them to the adjoining harness-room, where the missing remains were raked out
+LJSpeech-1.1/wavs/LJ049-0041.wav|in the protection of the President if the Presidential car permitted immediate access to the President by a Secret Service agent at the first sign of danger.
+LJSpeech-1.1/wavs/LJ040-0218.wav|and as will be shown below, this failure to adapt to his environment was a dominant trait in his later life.
+LJSpeech-1.1/wavs/LJ019-0281.wav|introduced, as was erroneously supposed, by the prison commissioners appointed under the Prison Act of eighteen seventy-seven.
+LJSpeech-1.1/wavs/LJ012-0140.wav|Solomons was not straightforward in his replies as to where he got the gold, and he was soon placed in the dock with the Caspars and Moss.
+LJSpeech-1.1/wavs/LJ001-0085.wav|It was reserved for the founders of the later eighteenth century to produce letters which are positively ugly, and which, it may be added,
+LJSpeech-1.1/wavs/LJ017-0086.wav|For this purpose he gave, or there was the strongest presumption that he gave,
+LJSpeech-1.1/wavs/LJ026-0113.wav|Only proteid foods form new protoplasm
+LJSpeech-1.1/wavs/LJ025-0176.wav|and the chlorophyll is the apparatus by which the carbon is extracted from the atmospheric carbonic acid, the leaves being the chief laboratories
+LJSpeech-1.1/wavs/LJ037-0020.wav|In his testimony before the Commission,
+LJSpeech-1.1/wavs/LJ005-0277.wav|This was the first enunciation of the system of separate confinement,
+LJSpeech-1.1/wavs/LJ019-0150.wav|Captain Williams, who was the inspector of prisons for the home district in succession to Messrs. Crawford and Russell,
+LJSpeech-1.1/wavs/LJ008-0172.wav|In another part seven persons met their death by suffocation.
+LJSpeech-1.1/wavs/LJ050-0219.wav|However, the nineteen sixty-four to sixty-five budget request was submitted in November nineteen sixty-three
+LJSpeech-1.1/wavs/LJ023-0053.wav|It is the American people themselves who expect the third horse to pull in unison with the other two.
+LJSpeech-1.1/wavs/LJ037-0209.wav|Handwriting experts, Alwyn Cole of the Treasury Department and James C. Cadigan of the FBI,
+LJSpeech-1.1/wavs/LJ010-0247.wav|The prisoner was conveyed without delay to the Home Office, and there examined by the Privy Council, which had been hastily summoned for the purpose.
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_1250_filelist.txt b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_1250_filelist.txt
new file mode 100644
index 00000000..2000a426
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_1250_filelist.txt
@@ -0,0 +1,1250 @@
+LJSpeech-1.1/wavs/LJ040-0100.wav|she would sometimes take Lee with her, apparently leaving him alone in the car while she transacted her business.
+LJSpeech-1.1/wavs/LJ011-0248.wav|Howard, strange to say, making no attempt to detain him; probably because Mullay promised to return a few days later, and to bring more money.
+LJSpeech-1.1/wavs/LJ016-0442.wav|made a determined effort to burn himself to death by throwing himself bodily on to the fire in the condemned ward.
+LJSpeech-1.1/wavs/LJ026-0036.wav|and then a balance must be struck and the doubtful form placed in the kingdom with which it has, on the whole, most points in common.
+LJSpeech-1.1/wavs/LJ042-0176.wav|One offers oppression, the other poverty. Both offer imperialistic injustice, tinted with two brands of slavery, end quote.
+LJSpeech-1.1/wavs/LJ003-0323.wav|Drunkenness, if it ever occurred, should be visited with severe punishment;
+LJSpeech-1.1/wavs/LJ045-0161.wav|He was upset over the fact that I would not answer him.
+LJSpeech-1.1/wavs/LJ028-0187.wav|Cyrus decided that Babylon must be taken.
+LJSpeech-1.1/wavs/LJ037-0178.wav|or one used Remington-Peters cartridge case, which may have been in the revolver before the shooting,
+LJSpeech-1.1/wavs/LJ010-0164.wav|Oxford, who was only nineteen at the time his offense was committed, had been born at Birmingham,
+LJSpeech-1.1/wavs/LJ019-0178.wav|and abandoned because of the expense. As to the entire reconstruction of Newgate, nothing had been done as yet.
+LJSpeech-1.1/wavs/LJ050-0117.wav|particularly those arising from organized groups, within their special jurisdiction.
+LJSpeech-1.1/wavs/LJ033-0128.wav|that the bag Oswald carried contained the assassination weapon and has concluded that Frazier and Randle are mistaken as to the length of the bag.
+LJSpeech-1.1/wavs/LJ007-0179.wav|defeats the ends of justice, and disgraces the profession of a Christian country.
+LJSpeech-1.1/wavs/LJ033-0067.wav|She pointed to the blanket which was on the floor very close to where Ruth Paine was standing.
+LJSpeech-1.1/wavs/LJ004-0139.wav|"In the morning the stench and heat were so oppressive that he and every one else on waking rushed unclothed into the yard;"
+LJSpeech-1.1/wavs/LJ009-0208.wav|erected on the cart, about four feet high at the head, and gradually sloping towards the horse, giving a full view of the body,
+LJSpeech-1.1/wavs/LJ012-0144.wav|and passed it on to Solomons by his daughter, a widow named Abrahams.
+LJSpeech-1.1/wavs/LJ001-0020.wav|the "lower-case" being in fact invented in the early Middle Ages.
+LJSpeech-1.1/wavs/LJ014-0227.wav|One of these was Mobbs, who lived in the Minories,
+LJSpeech-1.1/wavs/LJ040-0146.wav|He noted that Lee liked to give the impression that he did not care for other people but preferred to keep to himself,
+LJSpeech-1.1/wavs/LJ001-0149.wav|From the time when books first took their present shape till the end of the sixteenth century, or indeed later,
+LJSpeech-1.1/wavs/LJ002-0143.wav|The commissioners who presided were, quote, little otherwise than self-elected
+LJSpeech-1.1/wavs/LJ014-0217.wav|Dwyer managed to overpower his assailant, and got to his feet; but Cannon butted at him with his head, and again threw him to the ground,
+LJSpeech-1.1/wavs/LJ005-0250.wav|The prisoners were crowded together in the jail, contrary to the requirements of the four George the fourth
+LJSpeech-1.1/wavs/LJ042-0049.wav|I never believed I would find more material advantages at this stage of development in the Soviet Union than I might of had in the U.S.
+LJSpeech-1.1/wavs/LJ014-0198.wav|Marley at his trial was undefended, and the sheriffs offered him counsel; but he declined. The witnesses against him all spoke the truth, he said;
+LJSpeech-1.1/wavs/LJ034-0093.wav|Brennan also testified that Lee Harvey Oswald,
+LJSpeech-1.1/wavs/LJ016-0237.wav|With Calcraft's method there were undoubtedly many failures, and it was a common custom for him to go below the gallows
+LJSpeech-1.1/wavs/LJ015-0156.wav|Down at Weybridge, where he had a country place, his name was long remembered with gratitude by the poor.
+LJSpeech-1.1/wavs/LJ018-0047.wav|He adhered to this almost to the very last. His case had been warmly espoused by the Society for the Protection of Germans in this country,
+LJSpeech-1.1/wavs/LJ013-0020.wav|he acted in a manner which excited the suspicions of the crew.
+LJSpeech-1.1/wavs/LJ002-0041.wav|Two other wards were appropriated to the master's side debtors; they were each twenty-three feet by fourteen and a half,
+LJSpeech-1.1/wavs/LJ008-0227.wav|slipshod and slovenly, in crushed bonnet and dirty shawl, the gown fastened by a single hook,
+LJSpeech-1.1/wavs/LJ007-0029.wav|The condition of the capitally-convicted prisoners after sentence was still very disgraceful. The side they occupied, still known as the press-yard,
+LJSpeech-1.1/wavs/LJ018-0358.wav|Christina Edmunds had resort to strychnia, the same lethal drug that Palmer used;
+LJSpeech-1.1/wavs/LJ007-0198.wav|The windows were to be glazed and painted to prevent prisoners from looking out;
+LJSpeech-1.1/wavs/LJ043-0032.wav|After about a two-week separation, Marina Oswald returned to her husband.
+LJSpeech-1.1/wavs/LJ035-0071.wav|At a given signal, they reenacted the event. Baker's movements were timed with a stopwatch.
+LJSpeech-1.1/wavs/LJ009-0092.wav|his legs give way, he utters a faint groan, and sinks on the floor.
+LJSpeech-1.1/wavs/LJ019-0310.wav|which had long been admitted as indispensable, and had never as yet been properly obtained.
+LJSpeech-1.1/wavs/LJ038-0071.wav|When he entered the homicide and robbery bureau office, he saw two detectives standing there with Sgt. Gerald L. Hill,
+LJSpeech-1.1/wavs/LJ014-0291.wav|he showed symptoms of delirium tremens, and admitted that he had been addicted to the excessive use of stimulants.
+LJSpeech-1.1/wavs/LJ014-0283.wav|The jury found him guilty of the latter only, with a point of law reserved. This was fully argued before three judges,
+LJSpeech-1.1/wavs/LJ021-0096.wav|under the able and energetic leadership of General Johnson.
+LJSpeech-1.1/wavs/LJ045-0075.wav|She was, quote, sorry that I had not married him (the Russian boyfriend) instead, that it would have been much easier for me, end quote.
+LJSpeech-1.1/wavs/LJ022-0203.wav|For that we can be thankful to the God who watches over America.
+LJSpeech-1.1/wavs/LJ029-0073.wav|that the President would arrive and depart from Dallas' Love Field; that a motorcade through the downtown area of Dallas to the luncheon site should be arranged;
+LJSpeech-1.1/wavs/LJ040-0187.wav|According to Sokolow, this indicated a, quote, present intellectual functioning in the upper range of bright normal intelligence, end quote.
+LJSpeech-1.1/wavs/LJ016-0101.wav|One of the three, shamming ill, remained all day in his ward, where he employed himself unraveling the rope from the sleeping-mats.
+LJSpeech-1.1/wavs/LJ015-0086.wav|He kept open house at Kilburn Priory;
+LJSpeech-1.1/wavs/LJ028-0427.wav|The enormous amount of debris which buried the palaces and temples and walls of Nebuchadnezzar's city, in places to the depth of a hundred feet,
+LJSpeech-1.1/wavs/LJ048-0248.wav|President Kennedy was scheduled to speak across the street from his hotel in Fort Worth at eight:thirty a.m.
+LJSpeech-1.1/wavs/LJ021-0095.wav|We are now prepared to move into this second phase, on the basis of our experience in the first phase
+LJSpeech-1.1/wavs/LJ030-0081.wav|They were instructed to watch particularly for thrown objects, sudden actions in the crowd, and any movements toward the Presidential car.
+LJSpeech-1.1/wavs/LJ032-0176.wav|Moreover, the bus transfer which he obtained as he left the bus was still in the pocket when he was arrested.
+LJSpeech-1.1/wavs/LJ044-0129.wav|and often it is advisable for some people to remain in the background, not underground, end quote.
+LJSpeech-1.1/wavs/LJ018-0177.wav|But as there was no independent corroboration of the informer's evidence, according to the custom of the British law,
+LJSpeech-1.1/wavs/LJ049-0113.wav|This point was ably made in the nineteen oh two debate by Senator George F. Hoar, the sponsor of the Senate bill, quote,
+LJSpeech-1.1/wavs/LJ050-0141.wav|As a beginning step to improve liaison with local law enforcement officials, the Secret Service on August twenty-six, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ013-0156.wav|a scion of the ducal house of Bedford, by his confidential valet and personal attendant.
+LJSpeech-1.1/wavs/LJ032-0222.wav|Moreover, Shaneyfelt testified that in his opinion the photographs were not composites of two different photographs
+LJSpeech-1.1/wavs/LJ004-0052.wav|which Howard had eulogized some forty years before.
+LJSpeech-1.1/wavs/LJ006-0017.wav|with those who made the selection of the first inspectors, and the two gentlemen appointed were probably the most fitted in England to be so employed.
+LJSpeech-1.1/wavs/LJ049-0046.wav|Even so, analysis of the motion picture films taken by amateur photographer Zapruder
+LJSpeech-1.1/wavs/LJ017-0124.wav|He frequently declared before and during the trial that it would be impossible to find him guilty.
+LJSpeech-1.1/wavs/LJ048-0150.wav|while the Secret Service representatives in Dallas
+LJSpeech-1.1/wavs/LJ017-0082.wav|He fixed upon a sporting friend, Mr. John Parsons Cook, who had been in luck at Shrewsbury races, both as a winner and a backer,
+LJSpeech-1.1/wavs/LJ041-0095.wav|Oswald read a good deal, said Powers, but, quote, he would never be reading any of the shoot-em-up westerns or anything like that.
+LJSpeech-1.1/wavs/LJ002-0089.wav|eight. The female felons were deprived of part of the space which the architect had intended for them.
+LJSpeech-1.1/wavs/LJ050-0264.wav|The Commission recommends that the present arrangements
+LJSpeech-1.1/wavs/LJ039-0177.wav|was greater than from the second to the third shot and required a movement in the basic firing position of the marksmen.
+LJSpeech-1.1/wavs/LJ047-0016.wav|The FBI opened a file on Oswald in October nineteen fifty-nine, when news reports appeared of his defection to the Soviet Union.
+LJSpeech-1.1/wavs/LJ028-0036.wav|But in those very early days Babylon was little more than a shrine, surrounded with mud huts and date palms.
+LJSpeech-1.1/wavs/LJ013-0173.wav|The researches of the police soon laid bare other suspicious facts.
+LJSpeech-1.1/wavs/LJ014-0138.wav|Mrs. Manning became still more violent, shouting, "No, no, I will not stand it! You ought to be ashamed of yourselves!"
+LJSpeech-1.1/wavs/LJ028-0165.wav|There is, however, a second inner wall, of less thickness than the first, but very little inferior to it in strength.
+LJSpeech-1.1/wavs/LJ006-0048.wav|To these were still added an average of about fifty expecting the last penalty of the law; a certain number of transports awaiting removal to the colonies;
+LJSpeech-1.1/wavs/LJ032-0133.wav|Lieutenant Day of the Dallas Police Department had "lifted" a palmprint from the underside of the gun barrel
+LJSpeech-1.1/wavs/LJ038-0093.wav|Frequently, however, he was confronted with evidence which he could not explain, and he resorted to statements which are known to be lies.
+LJSpeech-1.1/wavs/LJ018-0228.wav|Five or six years later, William Roupell minutely described how he had effected the fraud.
+LJSpeech-1.1/wavs/LJ046-0084.wav|for the President soon after the assassination, quote,
+LJSpeech-1.1/wavs/LJ033-0109.wav|the Commission has carefully considered the testimony of these two witnesses with regard to the length of the bag.
+LJSpeech-1.1/wavs/LJ013-0158.wav|One morning in May his lordship was found dead in his bed with his throat cut.
+LJSpeech-1.1/wavs/LJ036-0111.wav|Whaley's memory of the lineup is inaccurate. There were four men altogether, not six men, in the lineup with Oswald.
+LJSpeech-1.1/wavs/LJ044-0082.wav|His attempt to express himself through his Fair Play for Cuba activities, however,
+LJSpeech-1.1/wavs/LJ036-0208.wav|white male, approximately thirty, slender build, height five foot ten inches, weight one hundred sixty-five pounds, end quote.
+LJSpeech-1.1/wavs/LJ038-0255.wav|Firearms identification.
+LJSpeech-1.1/wavs/LJ031-0111.wav|The elliptical wound in the Governor's back, located slightly to the left of the Governor's right armpit approximately five-eighths inch (a centimeter and a half)
+LJSpeech-1.1/wavs/LJ006-0246.wav|On another occasion a young man, who was being violently teased, seized a knife and stabbed his tormentor in the back.
+LJSpeech-1.1/wavs/LJ027-0167.wav|Then the gills gradually dry up, as the lungs develop, and they now breathe wholly by lungs, but still retain the tail.
+LJSpeech-1.1/wavs/LJ033-0187.wav|However, the complete identity of characteristics between the paper and tape in the bag found on the sixth floor
+LJSpeech-1.1/wavs/LJ009-0284.wav|It was stated in evidence before the Commission on Capital Punishment in eighteen sixty-four,
+LJSpeech-1.1/wavs/LJ009-0249.wav|When Charles White was executed in eighteen twenty-three for arson, he arranged a handkerchief
+LJSpeech-1.1/wavs/LJ015-0149.wav|peas at ten shillings a quart, five-guinea pines, and early asparagus were to be found on his table.
+LJSpeech-1.1/wavs/LJ019-0330.wav|Dietaries were drawn up for adoption on the recommendation of a committee of experts.
+LJSpeech-1.1/wavs/LJ012-0118.wav|It was a large gold brooch set in pearls, but a portion of the mounting had melted with the heat.
+LJSpeech-1.1/wavs/LJ008-0071.wav|In the few years which elapsed between the establishment of the gallows at Newgate
+LJSpeech-1.1/wavs/LJ015-0253.wav|he handed over to Pierce a sum of three thousand pounds, his own, whether rightly or wrongly acquired never came out,
+LJSpeech-1.1/wavs/LJ045-0102.wav|things apparently went quite smoothly from the time Oswald returned from Mexico until the weekend of November sixteen to seventeen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ009-0256.wav|Still he resisted.
+LJSpeech-1.1/wavs/LJ050-0055.wav|that the PRS files can no longer be limited largely to persons communicating actual threats to the President.
+LJSpeech-1.1/wavs/LJ034-0037.wav|Someone sitting on the box facing the window would have his palm in this position if he placed his hand alongside his right hip.
+LJSpeech-1.1/wavs/LJ020-0081.wav|and knead for ten minutes, carefully at first, lest the liquids should be wasted, and more boldly when they are absorbed by the paste.
+LJSpeech-1.1/wavs/LJ009-0077.wav|The ordinary of Newgate is an orthodox, unaffected, Church of England divine,
+LJSpeech-1.1/wavs/LJ008-0107.wav|in his canonicals, and with his head as stiffly erect as a sheriff's coachman.
+LJSpeech-1.1/wavs/LJ043-0013.wav|Part of the problem resulted from the fact that, as Jeanne De Mohrenschildt testified,
+LJSpeech-1.1/wavs/LJ037-0225.wav|five foot eight inches, black hair, slender, wearing a white jacket, white shirt and dark slacks, end quote,
+LJSpeech-1.1/wavs/LJ012-0294.wav|without hesitation brought in a verdict of willful murder.
+LJSpeech-1.1/wavs/LJ042-0192.wav|are preferred rather than loud and useless manifestations of protest, end quote, Oswald went on to note, quote,
+LJSpeech-1.1/wavs/LJ016-0078.wav|but had to come down again covered with soot and filth just as the officers entered the ward.
+LJSpeech-1.1/wavs/LJ028-0174.wav|Other ancient descriptions of the walls have been left us by Ctesias of the fifth century B.C., and by Strabo of the beginning of the Christian era,
+LJSpeech-1.1/wavs/LJ019-0002.wav|The time at length approached when a radical and complete change was to come over the old city jail.
+LJSpeech-1.1/wavs/LJ032-0271.wav|(two) Oswald's palmprint was on the rifle in a position which shows that he had handled it while it was disassembled,
+LJSpeech-1.1/wavs/LJ018-0325.wav|But extra precautions and close supervision have so far proved effectual, and the prisoners are still in custody after a lapse of ten years.
+LJSpeech-1.1/wavs/LJ048-0259.wav|However, Chief Rowley did not condone the action of the off-duty agents, particularly since it violated a regulation of the Secret Service,
+LJSpeech-1.1/wavs/LJ009-0099.wav|Meanwhile the clergyman, still bent into the form of a sleeping dog,
+LJSpeech-1.1/wavs/LJ034-0180.wav|The man was dressed in a light-colored, open-neck shirt which could have been either a sports shirt or a T-shirt,
+LJSpeech-1.1/wavs/LJ024-0057.wav|Why then should we leave the fulfillment of this public policy to chance
+LJSpeech-1.1/wavs/LJ018-0260.wav|Mr. Justice Byles, in passing sentence, commented severely upon the commission of such crimes by a man in Roupell's position in life,
+LJSpeech-1.1/wavs/LJ007-0095.wav|Prisoners indeed were known to boast that they had saved their necks by feigning insanity.
+LJSpeech-1.1/wavs/LJ005-0117.wav|Numbers of the jails were still unprovided with chaplains, and the prisoners never heard Divine service.
+LJSpeech-1.1/wavs/LJ006-0168.wav|to taking the descriptions of newly-arrived prisoners.
+LJSpeech-1.1/wavs/LJ011-0117.wav|devoted its efforts first to a mitigation of the forgery statute, but could not immediately accomplish much.
+LJSpeech-1.1/wavs/LJ007-0223.wav|The prison officials appear to be on the side of the inspectors, to the great dissatisfaction of the corporation, who claimed the full allegiance and support of its servants.
+LJSpeech-1.1/wavs/LJ009-0176.wav|Seven other crimes, however, were still capital by law, and so continued till the passing of the Criminal Consolidation Acts of eighteen sixty-one.
+LJSpeech-1.1/wavs/LJ034-0119.wav|Approximately seven or eight minutes later
+LJSpeech-1.1/wavs/LJ014-0226.wav|Only a few have vied with Cannon in fiendish cruelty and brutality.
+LJSpeech-1.1/wavs/LJ045-0074.wav|In the letter Marina Oswald stated that her husband had changed a great deal and that she was very lonely in the United States.
+LJSpeech-1.1/wavs/LJ012-0044.wav|When his trade was busiest he set up a second establishment, at the head of which, although he was married,
+LJSpeech-1.1/wavs/LJ027-0012.wav|All have the same ultimate substance
+LJSpeech-1.1/wavs/LJ028-0254.wav|The people, enjoying the greater freedom which Cyrus permitted them, were contented, and life in Babylon went on about as before.
+LJSpeech-1.1/wavs/LJ002-0326.wav|The poor debtors were not supplied with beds. Those who could pay the price might hire them from each other,
+LJSpeech-1.1/wavs/LJ014-0259.wav|Watts led two lives.
+LJSpeech-1.1/wavs/LJ035-0067.wav|from the sixth floor by the time Baker and Truly arrived, Commission counsel asked Baker and Truly to repeat their movements from the time of the shot
+LJSpeech-1.1/wavs/LJ010-0146.wav|Attacks upon the sovereign, as I have said, became more common after the accession of the young Queen Victoria in eighteen thirty-eight.
+LJSpeech-1.1/wavs/LJ007-0084.wav|The inspectors in the following year, on examining the facts, found that some of these poor creatures had been in confinement for long periods:
+LJSpeech-1.1/wavs/LJ049-0204.wav|While in accordance with its mandate
+LJSpeech-1.1/wavs/LJ011-0035.wav|Every endeavor was used, however, to obtain a commutation of sentence. His case was twice argued before the judges on points of law,
+LJSpeech-1.1/wavs/LJ021-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section six.
+LJSpeech-1.1/wavs/LJ008-0148.wav|One night he was missing
+LJSpeech-1.1/wavs/LJ011-0237.wav|The jewelers were always a favorite prey of the London thieves.
+LJSpeech-1.1/wavs/LJ017-0272.wav|"Ah!" he remarked, "they will have to wait for us then till eight."
+LJSpeech-1.1/wavs/LJ049-0067.wav|the radio net in use in motorcades is elaborate and permits a number of different means of communication with various local points.
+LJSpeech-1.1/wavs/LJ032-0171.wav|and that this was the same shirt which Oswald wore on the morning of the assassination.
+LJSpeech-1.1/wavs/LJ048-0132.wav|which would bring to bear the judgment and experience of members of the White House detail other than the advance agent.
+LJSpeech-1.1/wavs/LJ006-0025.wav|France had sent Misseurs Beaumont and De Tocqueville, who subsequently published several interesting works on the subject.
+LJSpeech-1.1/wavs/LJ043-0176.wav|If the attack had succeeded and Oswald had been caught, the pictures showing him with his rifle
+LJSpeech-1.1/wavs/LJ044-0191.wav|Now there appeared to be no chance to get to Cuba, where he had thought he might find his communist ideal. The U.S. Government would not permit travel there
+LJSpeech-1.1/wavs/LJ038-0011.wav|A police car made a U-turn, and as the sirens grew fainter,
+LJSpeech-1.1/wavs/LJ002-0244.wav|but its business was much reduced by the extension of the Courts of Conscience.
+LJSpeech-1.1/wavs/LJ031-0209.wav|X-rays and photographs were taken preliminarily and the pathological examination began at about eight p.m.
+LJSpeech-1.1/wavs/LJ042-0032.wav|and of his initial commitment to that country can best be understood, however, in the context
+LJSpeech-1.1/wavs/LJ009-0132.wav|Although this misapplication of religious services still went on,
+LJSpeech-1.1/wavs/LJ034-0048.wav|The freshness of prints developed in this manner cannot be estimated,
+LJSpeech-1.1/wavs/LJ043-0023.wav|and helped to move the personal effects of Marina Oswald and the baby.
+LJSpeech-1.1/wavs/LJ015-0216.wav|This was an important step, and they might easily be robbed some day when Burgess was the guard, provided only that they could be opened.
+LJSpeech-1.1/wavs/LJ006-0180.wav|the interior of the jail was more like a bear-garden or the noisy purlieus of a public-house than a prison.
+LJSpeech-1.1/wavs/LJ016-0342.wav|The first private execution under the new law took place within the precincts of Maidstone Jail.
+LJSpeech-1.1/wavs/LJ025-0170.wav|for it is only the green parts of the plant which, under the influence of sunlight, have the marvelous power of decomposing carbonic acid,
+LJSpeech-1.1/wavs/LJ047-0076.wav|In New Orleans. In the middle of May of nineteen sixty-three, Agent Hosty checked Oswald's last known residence and found that he had moved.
+LJSpeech-1.1/wavs/LJ005-0011.wav|were first made use of about eighteen twenty-seven. That the need for prison reform was imperative may be gathered from the few out of many instances I have adduced,
+LJSpeech-1.1/wavs/LJ033-0142.wav|because the cartons stacked around the southeast corner would shield him.
+LJSpeech-1.1/wavs/LJ018-0005.wav|the public mind was greatly agitated by the affair for several months. The story of the murder must be pretty familiar to most of my readers.
+LJSpeech-1.1/wavs/LJ049-0183.wav|regarding such threats and that its Protective Research Section is not adequately staffed or equipped
+LJSpeech-1.1/wavs/LJ036-0031.wav|and requested a transfer which she might use if she got through the traffic.
+LJSpeech-1.1/wavs/LJ011-0285.wav|The door of his place of durance stood open, and Mr. Gee began to consider whether he might not escape.
+LJSpeech-1.1/wavs/LJ041-0114.wav|three months prior to his regularly scheduled separation date, ostensibly to care for his mother who had been injured in an accident at her work.
+LJSpeech-1.1/wavs/LJ012-0134.wav|Presently the proper person arrived from the consignees, but found the gold-dust gone.
+LJSpeech-1.1/wavs/LJ011-0005.wav|A lady in the country, who had thirteen thousand pounds in the stocks, desired her London agent to sell them out.
+LJSpeech-1.1/wavs/LJ028-0087.wav|Such was the appearance of the builder of the walls of Babylon.
+LJSpeech-1.1/wavs/LJ016-0329.wav|a bill was introduced by Mr. Hibbert, M.P., and accepted by the Government, providing for the future carrying out of executions within prisons.
+LJSpeech-1.1/wavs/LJ034-0017.wav|could look southwesterly down Elm Street over the top of the "Rolling Readers" cartons.
+LJSpeech-1.1/wavs/LJ044-0086.wav|executive director of the Information Council of the Americas, who also appeared on the program.
+LJSpeech-1.1/wavs/LJ038-0100.wav|On November twenty-three, Fritz confronted Oswald with the evidence that he had purchased a rifle under the fictitious name of "Hidell."
+LJSpeech-1.1/wavs/LJ049-0019.wav|The last Presidential vehicle with any protection against small-arms fire left the White House in nineteen fifty-three.
+LJSpeech-1.1/wavs/LJ021-0125.wav|it was natural that the workers should seek and obtain a statutory declaration of their constitutional right
+LJSpeech-1.1/wavs/LJ019-0294.wav|The prison buildings were in many places out of repair; other houses often overlooked them.
+LJSpeech-1.1/wavs/LJ009-0211.wav|and on the right the ripping chisel, with which the murders had been committed, were exposed to view.
+LJSpeech-1.1/wavs/LJ044-0172.wav|and left for Irving with Marina Oswald and June and most of the Oswalds' effects three days later.
+LJSpeech-1.1/wavs/LJ047-0129.wav|FBI informants in the New Orleans area, familiar with pro-Castro or Communist Party activity there,
+LJSpeech-1.1/wavs/LJ024-0139.wav|has been tipped out of balance by the courts in direct contradiction of the high purposes of the framers of the Constitution.
+LJSpeech-1.1/wavs/LJ005-0106.wav|Jails, of which the old prison at Reading was a specimen, were still left intact.
+LJSpeech-1.1/wavs/LJ042-0247.wav|In August of nineteen sixty-three, he gave the New Orleans police as a reason for refusing to permit his family to learn English,
+LJSpeech-1.1/wavs/LJ047-0092.wav|On August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ026-0166.wav|back to starch usable as food and the comparison of the green plant and the animal would be complete.
+LJSpeech-1.1/wavs/LJ033-0019.wav|According to the testimony of Frazier, Marina Oswald, and Ruth Paine, it appears that Oswald never returned to Irving in midweek
+LJSpeech-1.1/wavs/LJ042-0172.wav|must have as its nucleus the traditional ideological best of both systems, and yet be utterly opposed to both systems.
+LJSpeech-1.1/wavs/LJ027-0018.wav|All are forced to make concession after concession to their surroundings, and in these concessions all progress in life consists.
+LJSpeech-1.1/wavs/LJ041-0187.wav|and he wanted to be on the winning side so that ten thousand years from-now people would look in the history books and say, "Well, this man was ahead of his time."
+LJSpeech-1.1/wavs/LJ048-0286.wav|Nor is this goal served when agents remain out until early morning hours, and lose the opportunity to get a reasonable amount of sleep.
+LJSpeech-1.1/wavs/LJ018-0037.wav|In searching the prisoner's box, Mr. Briggs' watch was found wrapped up in a piece of leather,
+LJSpeech-1.1/wavs/LJ009-0044.wav|His features have no felonious cast;
+LJSpeech-1.1/wavs/LJ045-0100.wav|She thought that he might not have become involved in the assassination if people had been kinder to him.
+LJSpeech-1.1/wavs/LJ035-0149.wav|She ran inside and up the front stairs into the large open office reserved for clerical employees.
+LJSpeech-1.1/wavs/LJ028-0188.wav|In five thirty-eight the city fell, and for a time it became the home of the Persian King.
+LJSpeech-1.1/wavs/LJ003-0320.wav|which recommended restrictions upon the number of visitors admitted.
+LJSpeech-1.1/wavs/LJ013-0241.wav|The policeman insisted on searching the premises, at which Good displayed some uneasiness.
+LJSpeech-1.1/wavs/LJ018-0194.wav|Cummings was repeatedly "run in" for the offense of coining and uttering bad money, whether coin or notes.
+LJSpeech-1.1/wavs/LJ046-0135.wav|PRS received items in eight thousand, seven hundred nine cases.
+LJSpeech-1.1/wavs/LJ046-0143.wav|These instructions to PRS personnel appear to be the only instance where an effort was made to reduce the criteria to writing.
+LJSpeech-1.1/wavs/LJ048-0103.wav|and with the concurrence of the Dallas police, was entirely appropriate, in view of the known desires of the President.
+LJSpeech-1.1/wavs/LJ038-0279.wav|I think is going overboard in the other direction.
+LJSpeech-1.1/wavs/LJ044-0117.wav|that there were people who understood his activity, end quote.
+LJSpeech-1.1/wavs/LJ028-0485.wav|The outer and inner defenses of Babylon were so strong and so high that no enemy could hope to take them,
+LJSpeech-1.1/wavs/LJ031-0174.wav|After the President was pronounced dead,
+LJSpeech-1.1/wavs/LJ026-0020.wav|If chlorophyll is present, the carbon dioxide of the air serves as a source of carbon,
+LJSpeech-1.1/wavs/LJ027-0136.wav|Illustrations quoted from the works of Romanes and Le Conte will make this principle clear.
+LJSpeech-1.1/wavs/LJ002-0113.wav|in an age when insolvent acts and bankruptcy courts do so much to relieve the impecunious,
+LJSpeech-1.1/wavs/LJ004-0113.wav|It was further ordered that male prisoners should be kept perfectly distinct from the females.
+LJSpeech-1.1/wavs/LJ044-0115.wav|he felt that this was a great man that he had received the letter from, end quote.
+LJSpeech-1.1/wavs/LJ039-0012.wav|The Commission first learned of this incident when Robert Oswald related it to FBI agents on February nineteen, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ014-0164.wav|as the wickedness and levity of the immense crowd collected at the execution this morning could be imagined by no man,
+LJSpeech-1.1/wavs/LJ050-0018.wav|and to keep the Secretary fully informed regarding all significant developments relating to Presidential protection.
+LJSpeech-1.1/wavs/LJ012-0131.wav|The letter informed him of the marks and sizes of the cases containing the precious metal,
+LJSpeech-1.1/wavs/LJ016-0308.wav|yet the witnesses were not unanimous.
+LJSpeech-1.1/wavs/LJ028-0332.wav|Once more, however, he waited till the interval appointed had gone by, and then leading the troops to the place where the four thousand were,
+LJSpeech-1.1/wavs/LJ006-0251.wav|but the presence and authority of the governor himself became indispensable.
+LJSpeech-1.1/wavs/LJ006-0016.wav|These considerations no doubt had weight
+LJSpeech-1.1/wavs/LJ031-0093.wav|Answer: No, sir. Before -- well, in trying to treat an acutely injured patient, you have to establish an airway, adequate ventilation
+LJSpeech-1.1/wavs/LJ042-0163.wav|After, however, two years and a lot of growing up, I decided to return to the USA.
+LJSpeech-1.1/wavs/LJ031-0220.wav|During the autopsy examination, Federal agents brought the surgeons three pieces of bone recovered from Elm Street and the Presidential automobile.
+LJSpeech-1.1/wavs/LJ030-0050.wav|The Presidential limousine.
+LJSpeech-1.1/wavs/LJ012-0010.wav|both having been recognized by the clergyman who had performed the ceremony, and the assault had been committed to secure the money
+LJSpeech-1.1/wavs/LJ004-0213.wav|Compared with those highly meritorious institutions Newgate still showed but badly.
+LJSpeech-1.1/wavs/LJ010-0061.wav|That some thirty or more needy men should hope to revolutionize England is a sufficient proof of the absurdity of their attempt.
+LJSpeech-1.1/wavs/LJ022-0195.wav|But it is more than the recovery of the material basis of our individual lives.
+LJSpeech-1.1/wavs/LJ039-0102.wav|After familiarization with live ammunition in the twenty-two rifle and the twenty-two pistol,
+LJSpeech-1.1/wavs/LJ020-0073.wav|Sift the flour, salt and sugar into a bowl,
+LJSpeech-1.1/wavs/LJ040-0038.wav|Such ideas of grandeur were apparently accompanied by notions of oppression.
+LJSpeech-1.1/wavs/LJ019-0049.wav|the principles of which were debated by disputants of widely opposite opinions with an earnestness that sometimes bordered upon acrimony.
+LJSpeech-1.1/wavs/LJ050-0012.wav|through an Assistant Secretary whose duties also include the direct supervision of the Bureau of the Mint
+LJSpeech-1.1/wavs/LJ007-0117.wav|where the upper ward was exclusively appropriated to their use. They also had their meals sent in, and, with the food, wine almost ad libitum.
+LJSpeech-1.1/wavs/LJ004-0169.wav|On the dirty bedstead lay a wretched being in the throes of severe illness.
+LJSpeech-1.1/wavs/LJ019-0127.wav|or the still more costly process of walling in the whole farm, would have greatly added to the charges of these establishments.
+LJSpeech-1.1/wavs/LJ014-0141.wav|and stretching out her hand, she gathered up a quantity of the rue which, following ancient custom dating from the days of the jail fever,
+LJSpeech-1.1/wavs/LJ037-0041.wav|The man appeared to step back as the policeman, quote, calmly opened the car door, end quote, and very slowly got out and walked toward the front of the car.
+LJSpeech-1.1/wavs/LJ012-0023.wav|He was taken up when still in his teens for stealing a pocketbook, and was sentenced to transportation, but did not get beyond the hulks at Chatham.
+LJSpeech-1.1/wavs/LJ032-0115.wav|A few minutes after the rifle was discovered on the sixth floor of the Depository Building
+LJSpeech-1.1/wavs/LJ047-0007.wav|It had interviewed him twice shortly after his return to the United States, again a year later at his request
+LJSpeech-1.1/wavs/LJ006-0049.wav|an occasional prisoner or two committed by the Houses of Parliament, the Courts of King's Bench, Common Pleas,
+LJSpeech-1.1/wavs/LJ028-0065.wav|Eleven years later, in five eighty-six, he destroyed the sacred Hebrew city,
+LJSpeech-1.1/wavs/LJ049-0076.wav|The Commission's review of the provisions for Presidential protection at the time of President Kennedy's trip to Dallas demonstrates the need for substantial improvements.
+LJSpeech-1.1/wavs/LJ003-0091.wav|Constantly associated with these convicted felons were numbers of juveniles, infants of tender years.
+LJSpeech-1.1/wavs/LJ050-0030.wav|The Commission also recommends
+LJSpeech-1.1/wavs/LJ013-0122.wav|Stealing plate was about this period the crime of a more aristocratic thief.
+LJSpeech-1.1/wavs/LJ046-0013.wav|Prompted by these dismaying statistics, the Commission has inquired into the problems and methods of Presidential protection in effect
+LJSpeech-1.1/wavs/LJ035-0134.wav|that they were watching the parade from the top step of the building entrance when Gloria Calverly, who works in the Depository Building,
+LJSpeech-1.1/wavs/LJ016-0232.wav|and he owned a pet pony which would follow him about like a dog.
+LJSpeech-1.1/wavs/LJ020-0023.wav|If too stiff, warm water, a spoonful at a time until you can handle the paste easily. The danger is in getting it too stiff. Now.
+LJSpeech-1.1/wavs/LJ005-0046.wav|The good it tried to do took active shape in the establishment of temporary refuges -- at Hoxton for males, and in the Hackney Road for females
+LJSpeech-1.1/wavs/LJ010-0019.wav|As time passed,
+LJSpeech-1.1/wavs/LJ049-0130.wav|The Secret Service must rely in large part
+LJSpeech-1.1/wavs/LJ024-0023.wav|ever since a similar proposal passed the House of Representatives in eighteen sixty-nine.
+LJSpeech-1.1/wavs/LJ018-0315.wav|to whom it was said one hundred pounds apiece had been given down as the price of their infidelity.
+LJSpeech-1.1/wavs/LJ029-0037.wav|Advance preparations for President Kennedy's visit to Dallas were primarily the responsibility of two Secret Service agents:
+LJSpeech-1.1/wavs/LJ049-0218.wav|between the Secret Service and the President and his family is contemplated.
+LJSpeech-1.1/wavs/LJ003-0155.wav|Tailoring and shoemaking was permitted, but it was deemed unsafe to allow a carpenter or blacksmith to have his tools.
+LJSpeech-1.1/wavs/LJ013-0113.wav|Robberies as daring in conception as they were boldly executed were common enough.
+LJSpeech-1.1/wavs/LJ045-0047.wav|and I told him that
+LJSpeech-1.1/wavs/LJ006-0065.wav|were associated together, "of every variety of age, habit, and delinquency, without employment, oversight, or control."
+LJSpeech-1.1/wavs/LJ003-0316.wav|It should be peremptorily forbidden to the keeper or any officer to make a pecuniary profit out of the supplies of food, fuel, or other necessaries.
+LJSpeech-1.1/wavs/LJ021-0004.wav|Tonight I continue that report, though, because of the shortness of time, I must defer a number of subjects to a later date.
+LJSpeech-1.1/wavs/LJ031-0022.wav|Charles R. Baxter, Robert N. McClelland, Ronald C. Jones; the chief neurologist, Dr. William Kemp Clark;
+LJSpeech-1.1/wavs/LJ007-0030.wav|consisted of two dozen rooms and fifteen cells. In these various chambers, until just before the inspectors made their report,
+LJSpeech-1.1/wavs/LJ021-0137.wav|Step by step we have created all the government agencies necessary to insure, as a general rule, industrial peace,
+LJSpeech-1.1/wavs/LJ033-0081.wav|she looked out the breakfast-room window and saw Oswald cross the street and walk toward the driveway where her brother parked his car near the carport.
+LJSpeech-1.1/wavs/LJ003-0218.wav|The chapel was filled with a curious but callous congregation, who came to stare at the miserable people thus publicly exposed.
+LJSpeech-1.1/wavs/LJ028-0317.wav|Introduced into their assembly, he began to bewail his misfortunes, telling them that
+LJSpeech-1.1/wavs/LJ047-0014.wav|the Office of Naval Intelligence, the FBI and the CIA. The information known to the FBI is summarized below.
+LJSpeech-1.1/wavs/LJ002-0067.wav|but really kept for the few who had funds sufficient to gain them admission to these more comfortable quarters.
+LJSpeech-1.1/wavs/LJ003-0101.wav|must have had a tendency to turn them into the world hardened and accomplished in the ways of vice and crime. End quote.
+LJSpeech-1.1/wavs/LJ036-0048.wav|She boarded the Marsalis bus at St. Paul and Elm Streets to return home. She testified further, quote,
+LJSpeech-1.1/wavs/LJ022-0129.wav|in making this the most efficient and the cleanest example of public enterprise the world has ever seen.
+LJSpeech-1.1/wavs/LJ038-0121.wav|or to answer any questions concerning the card.
+LJSpeech-1.1/wavs/LJ031-0095.wav|Before this was accomplished the President's cardiac activity had ceased and closed cardiac massage was instituted, which made it impossible to inspect his back.
+LJSpeech-1.1/wavs/LJ007-0131.wav|Enough has probably been extracted from this most damnatory report to give a complete picture of the disgraceful state in which Newgate still remained in eighteen thirty-five.
+LJSpeech-1.1/wavs/LJ001-0067.wav|In the Low Countries and Cologne, which were very fertile of printed books, Gothic was the favorite.
+LJSpeech-1.1/wavs/LJ011-0061.wav|Let this monster give his name; I am ready to fight him. I am still determined to put myself in the place of Mr. Fauntleroy.
+LJSpeech-1.1/wavs/LJ019-0381.wav|in another there was half-heartedness, even apathy and an almost complete contempt for the provisions of the act.
+LJSpeech-1.1/wavs/LJ012-0170.wav|According to his statement, when sentenced to death, he had been driven to horse-stealing by the execration which had pursued him after the murder.
+LJSpeech-1.1/wavs/LJ005-0090.wav|the first by daily services, the latter by the appointment of schoolmasters and instruction in reading and writing.
+LJSpeech-1.1/wavs/LJ049-0127.wav|agencies other than the Secret Service have become involved in phases of the overall problem of protecting our national leaders.
+LJSpeech-1.1/wavs/LJ004-0100.wav|An infirmary, consisting of two distinct rooms, one for males and one for females, should be provided for the separate accommodation of the sick.
+LJSpeech-1.1/wavs/LJ003-0148.wav|and spent in providing coals, candles, plates, knives, and forks; while all the occupants of this part of the prison
+LJSpeech-1.1/wavs/LJ005-0073.wav|To its efforts, and their effect upon Parliament and the public mind, we must attribute the new Jail Acts of four George the fourth
+LJSpeech-1.1/wavs/LJ003-0166.wav|association at one time forbidden by custom, but which greed and rapacity long made the rule.
+LJSpeech-1.1/wavs/LJ028-0076.wav|However, several decades ago, an Oriental appeared at the Berlin Museum,
+LJSpeech-1.1/wavs/LJ012-0253.wav|A further discovery was made in an osier bed near Cold Harbor Lane, Camberwell,
+LJSpeech-1.1/wavs/LJ024-0053.wav|Fundamentally, if in the future, America cannot trust the Congress it elects to refrain from abuse of our Constitutional usages
+LJSpeech-1.1/wavs/LJ032-0069.wav|The person having access to the box then takes the notice to the window and is given the package.
+LJSpeech-1.1/wavs/LJ037-0082.wav|On the evening of November twenty-two,
+LJSpeech-1.1/wavs/LJ040-0085.wav|John Pic, however, did not think her position was worse than that of many other people.
+LJSpeech-1.1/wavs/LJ028-0099.wav|the first-born son of Nabopolassar, King of Babylon, am I.
+LJSpeech-1.1/wavs/LJ004-0170.wav|The only ventilation of this pit, this "dark, cheerless, damp, unwholesome cavern -- a dungeon in its worst sense"
+LJSpeech-1.1/wavs/LJ022-0110.wav|The key men for the major responsibilities of this great task already have been selected.
+LJSpeech-1.1/wavs/LJ024-0116.wav|When the time comes for action,
+LJSpeech-1.1/wavs/LJ040-0161.wav|Dr. Hartogs recommended that Oswald be placed on probation on condition that he seek help and guidance through a child guidance clinic.
+LJSpeech-1.1/wavs/LJ032-0266.wav|Paul M. Stombaugh, of the FBI Laboratory,
+LJSpeech-1.1/wavs/LJ006-0086.wav|his place is assigned among the most depraved, the most experienced, and the most incorrigible offenders in the middle yard.
+LJSpeech-1.1/wavs/LJ038-0228.wav|and into downtown Dallas through the Triple Underpass.
+LJSpeech-1.1/wavs/LJ028-0319.wav|"And now," he went on to say, "my coming to you, Babylonians,
+LJSpeech-1.1/wavs/LJ023-0054.wav|I hope that you have re-read the Constitution of the United States in these past few weeks.
+LJSpeech-1.1/wavs/LJ028-0108.wav|Fortunately in several of his long inscriptions, recently discovered in the Babylonian mounds, Nebuchadnezzar speaks of the building of the walls.
+LJSpeech-1.1/wavs/LJ042-0134.wav|The psychological effects of that change must have been highly unsettling. It should be remembered
+LJSpeech-1.1/wavs/LJ032-0083.wav|Experts on questioned documents from the Treasury Department and the FBI testified that the Hidell cards were counterfeit photographic reproductions
+LJSpeech-1.1/wavs/LJ036-0216.wav|Tippit got out and started to walk around the front of the car
+LJSpeech-1.1/wavs/LJ002-0281.wav|The demands for fees were excessive in Giltspur Street.
+LJSpeech-1.1/wavs/LJ034-0169.wav|the same corner where Brennan was sitting on a concrete wall.
+LJSpeech-1.1/wavs/LJ009-0004.wav|would be astonished to observe the peculiar tenderness, I was going to add respect,
+LJSpeech-1.1/wavs/LJ004-0094.wav|This act set forth that "whereas the malignant fever commonly called the jail distemper
+LJSpeech-1.1/wavs/LJ034-0122.wav|As will be discussed fully below, the Commission has concluded that this suspect was Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ033-0179.wav|since the original bag had been discolored during various laboratory examinations and could not be used for valid identification by witnesses.
+LJSpeech-1.1/wavs/LJ022-0094.wav|in whose field the project falls, and also to notify another agency which I am creating -- a Progress Division.
+LJSpeech-1.1/wavs/LJ003-0334.wav|It made this too the excuse for begging the most important issue of the whole question.
+LJSpeech-1.1/wavs/LJ004-0034.wav|Moreover, the laws applied more particularly to county jurisdictions.
+LJSpeech-1.1/wavs/LJ048-0254.wav|advised, in the course of the Secret Service investigation of these events, that each agent reported for duty on time,
+LJSpeech-1.1/wavs/LJ025-0038.wav|carbon, hydrogen and oxygen.
+LJSpeech-1.1/wavs/LJ036-0217.wav|As Tippit reached the left front wheel the man pulled out a revolver and fired several shots.
+LJSpeech-1.1/wavs/LJ043-0100.wav|It is not possible to tell whether Oswald did this to provide an excuse for his eventual discharge,
+LJSpeech-1.1/wavs/LJ005-0222.wav|and as the window-frames would not shut tight, the prisoners complained much of the cold, especially at night.
+LJSpeech-1.1/wavs/LJ032-0040.wav|were written the words "A. Hidell, P.O. Box two nine one five Dallas, Texas."
+LJSpeech-1.1/wavs/LJ015-0011.wav|Maltby, who had bolted, was pursued and arrested, to end his life miserably by committing suicide in a Newgate cell.
+LJSpeech-1.1/wavs/LJ032-0153.wav|A palmprint could not be placed on this portion of the rifle, when assembled, because the wooden foregrip covers the barrel at this point.
+LJSpeech-1.1/wavs/LJ029-0092.wav|On November eight, when Lawson was briefed on the itinerary for the trip to Dallas,
+LJSpeech-1.1/wavs/LJ004-0132.wav|the old evils of indiscriminate association still continued unchecked.
+LJSpeech-1.1/wavs/LJ039-0067.wav|which is ordinarily required when a marksman must raise his rifle as a target moves farther away.
+LJSpeech-1.1/wavs/LJ044-0235.wav|If there was no conspiracy which would help him escape, the possibility of which has been considered in chapter six,
+LJSpeech-1.1/wavs/LJ028-0144.wav|fifty royal cubits in width, and two hundred in height.
+LJSpeech-1.1/wavs/LJ029-0102.wav|After the selection of the Trade Mart as the luncheon site,
+LJSpeech-1.1/wavs/LJ009-0116.wav|On the following day the capital convicts, whose companions have been hanged, are required to return thanks for their narrow escape.
+LJSpeech-1.1/wavs/LJ040-0228.wav|Despite his withdrawal, he gives the impression that he is not so difficult to reach as he appears and patient, prolonged effort
+LJSpeech-1.1/wavs/LJ022-0020.wav|cause of clearer thinking and a better understanding, are considering the whole rather than a mere part relating to one section or to one crop,
+LJSpeech-1.1/wavs/LJ047-0104.wav|reluctant and actually as far as I was concerned, was completely evasive on them. End quote.
+LJSpeech-1.1/wavs/LJ031-0127.wav|a protective circle of Secret Service agents surrounded Vice President and Mrs. Johnson
+LJSpeech-1.1/wavs/LJ043-0021.wav|While the exact sequence of events is not clear because of conflicting testimony,
+LJSpeech-1.1/wavs/LJ007-0005.wav|The inspectors paid tribute to the excellence of the motives of these philanthropic ladies, and recognized the good they did.
+LJSpeech-1.1/wavs/LJ018-0307.wav|Through Noyes the rest of the conspirators were eventually apprehended. Very little if any of the ill-gotten proceeds, however, was ever recovered.
+LJSpeech-1.1/wavs/LJ029-0191.wav|He asserted that Dallas had shed its reputation of the twenties as the, quote, Southwest hate capital of Dixie, end quote
+LJSpeech-1.1/wavs/LJ009-0088.wav|ignominy, sorrow, sufferings, wretchedness, pangs,
+LJSpeech-1.1/wavs/LJ021-0192.wav|We are not frightened by reactionary lawyers or political editors.
+LJSpeech-1.1/wavs/LJ038-0179.wav|(three) firearm identification of the bullet found in Walker's home, and (four)
+LJSpeech-1.1/wavs/LJ028-0518.wav|It is not strange, then, that they were included among the Seven Wonders of the World,
+LJSpeech-1.1/wavs/LJ026-0026.wav|As in the liquefaction of gases, there is a "critical point" at which the substance under experiment is neither gaseous nor liquid.
+LJSpeech-1.1/wavs/LJ031-0090.wav|A thorough inspection would have involved washing and cleansing the back, and this is not practical in treating an acutely injured patient.
+LJSpeech-1.1/wavs/LJ016-0110.wav|The third, Bell, remained longest at large. He too was run into at a lodging in the Kingsland Road.
+LJSpeech-1.1/wavs/LJ032-0019.wav|Shortly after the Mannlicher-Carcano rifle was found on the sixth floor of the Texas School Book Depository Building, agents of the FBI
+LJSpeech-1.1/wavs/LJ044-0146.wav|On June twenty-four, nineteen sixty-three, he applied for a new passport
+LJSpeech-1.1/wavs/LJ048-0003.wav|Hosty's interpretation of the prevailing FBI instructions on referrals to the Secret Service was defended before the Commission by his superiors.
+LJSpeech-1.1/wavs/LJ013-0194.wav|but on the second day the discovery of fresh evidence, more particularly the recovery of some of Lord William's stolen plate,
+LJSpeech-1.1/wavs/LJ038-0224.wav|Another statement which limits the time when it could have been written is the reference, quote, you and the baby, end quote,
+LJSpeech-1.1/wavs/LJ014-0147.wav|shaking her clenched and manacled hands in the officers' faces.
+LJSpeech-1.1/wavs/LJ019-0168.wav|Renewed recommendations to provide employment resulted in the provision of a certain amount of oakum for picking,
+LJSpeech-1.1/wavs/LJ029-0175.wav|Both Dallas papers cited White House sources on September twenty-six as confirming the President's intention to visit Texas on November twenty-one and twenty-two,
+LJSpeech-1.1/wavs/LJ033-0078.wav|Neither she nor Mrs. Paine saw him leave the house. About half-a-block away from the Paine house was the residence of Mrs. Linnie Mae Randle,
+LJSpeech-1.1/wavs/LJ040-0235.wav|When Lee became a disciplinary problem upon his return to school in the fall of nineteen fifty-three,
+LJSpeech-1.1/wavs/LJ003-0322.wav|except for the use of the debtors, or as medical comforts for the infirmary.
+LJSpeech-1.1/wavs/LJ018-0359.wav|her object being first to dispose of the wife of a man for whom she had conceived a guilty passion,
+LJSpeech-1.1/wavs/LJ030-0128.wav|From Main Street the motorcade turned right and went north on Houston Street, passing tall buildings on the right,
+LJSpeech-1.1/wavs/LJ033-0204.wav|So if I found all of these then I would have been able to say these fibers probably had come from this blanket. But since I found so few,
+LJSpeech-1.1/wavs/LJ013-0042.wav|the foundations of which had been laid by buying old ships on purpose to cast them away.
+LJSpeech-1.1/wavs/LJ041-0174.wav|and had not intended any criticism of Oswald's political views which is the way in which, Thornley thought, Oswald took his remarks.
+LJSpeech-1.1/wavs/LJ030-0245.wav|I was pushed down by Agent Youngblood.
+LJSpeech-1.1/wavs/LJ031-0103.wav|While Dr. Carrico went on to attend the President, Dr. Dulany stayed with the Governor and was soon joined by several other doctors.
+LJSpeech-1.1/wavs/LJ048-0152.wav|At some overpasses all persons were excluded
+LJSpeech-1.1/wavs/LJ018-0232.wav|He himself prepared it on a blank form which he had brought with him on purpose.
+LJSpeech-1.1/wavs/LJ050-0200.wav|The Secret Service should utilize the personnel of other Federal law enforcement offices
+LJSpeech-1.1/wavs/LJ012-0167.wav|But Probert, who turned king's evidence, and materially assisted conviction,
+LJSpeech-1.1/wavs/LJ006-0225.wav|If any man presumed to turn in too early
+LJSpeech-1.1/wavs/LJ014-0127.wav|She was smartly dressed in a plaid shawl, a white lace cap;
+LJSpeech-1.1/wavs/LJ033-0021.wav|after the birth of their second child.
+LJSpeech-1.1/wavs/LJ036-0080.wav|toward a light-colored Rambler station wagon, which was moving slowly along Elm toward the underpass:
+LJSpeech-1.1/wavs/LJ008-0083.wav|Two cart-loads of faggots were piled about her, and after she had hung for half-an-hour the fire was kindled.
+LJSpeech-1.1/wavs/LJ010-0282.wav|Pate was said to be an eccentric person, given to strange acts and antics, such as mixing whiskey and camphor with his morning bath-water,
+LJSpeech-1.1/wavs/LJ013-0088.wav|the cashier gave them eight Bank of England notes for one thousand pounds each, saying that they could get so much specie nowhere else.
+LJSpeech-1.1/wavs/LJ028-0279.wav|after which he commanded his servants to tell no one what had come to pass, while he himself pondered the matter.
+LJSpeech-1.1/wavs/LJ002-0057.wav|These wards were all fitted with barrack-beds, but no bedding was supplied.
+LJSpeech-1.1/wavs/LJ032-0253.wav|From September twenty-four, nineteen sixty-three, when Marina Oswald arrived in Irving from New Orleans, until the morning of the assassination,
+LJSpeech-1.1/wavs/LJ043-0135.wav|Oswald shot at Maj. Gen. Edwin A. Walker (Resigned, U.S. Army),
+LJSpeech-1.1/wavs/LJ025-0115.wav|and from that day to this the rapid improvement of methods of investigation and the energy of a host of accurate observers
+LJSpeech-1.1/wavs/LJ050-0166.wav|The Commission was struck by the apparent lack of effort, on an interagency basis,
+LJSpeech-1.1/wavs/LJ038-0026.wav|Other policemen entered the front door and searched the balcony.
+LJSpeech-1.1/wavs/LJ028-0470.wav|Time has dealt even less kindly with it, for it may be traced only for the distance of about a mile along its eastern side.
+LJSpeech-1.1/wavs/LJ018-0253.wav|For these crimes William Roupell was tried at the Central Criminal Court on the twenty-fourth September, eighteen sixty-two.
+LJSpeech-1.1/wavs/LJ019-0147.wav|this occurred in summer at eight, but in the winter months it took place at dusk, and was often as early as four or five.
+LJSpeech-1.1/wavs/LJ045-0148.wav|After all, when will all your foolishness come to an end? All of these comedies. First one thing and then another. And now this fictitious name, end quote.
+LJSpeech-1.1/wavs/LJ043-0031.wav|I am surprised that he didn't do something worse, end quote.
+LJSpeech-1.1/wavs/LJ033-0039.wav|and one which provided an excuse for the carrying of a bulky package the following morning.
+LJSpeech-1.1/wavs/LJ010-0006.wav|Certain crimes, those against the person especially, diminished gradually. They became less easy or remunerative.
+LJSpeech-1.1/wavs/LJ049-0005.wav|Rigorous security precautions had been arranged at Love Field with the local law enforcement authorities by Agents Sorrels and Lawson.
+LJSpeech-1.1/wavs/LJ004-0142.wav|where a lad lay ill with fever, three other prisoners, at first perfectly healthy, were lodged. Of course they were seized with the fever;
+LJSpeech-1.1/wavs/LJ042-0038.wav|and religion and education are used as a tool to suppress what would otherwise be a population questioning their government's unfair
+LJSpeech-1.1/wavs/LJ046-0079.wav|The rights of private individuals must not be infringed.
+LJSpeech-1.1/wavs/LJ026-0123.wav|which could be derived by the ordinary chemical evolution of protoplasm, proteid, sugar, starch or fats.
+LJSpeech-1.1/wavs/LJ037-0255.wav|testified that Commission Exhibit Number one sixty-two was the jacket worn by the man they saw on November twenty-two.
+LJSpeech-1.1/wavs/LJ028-0345.wav|He then chose out near three thousand of the leading citizens and caused them to be crucified, while he allowed the remainder still to inhabit the city.
+LJSpeech-1.1/wavs/LJ045-0076.wav|The letter fell into Oswald's hands when it was returned to his post office box
+LJSpeech-1.1/wavs/LJ027-0103.wav|Thus, for instance, the unborn whale has rudimentary teeth,
+LJSpeech-1.1/wavs/LJ011-0076.wav|His offense was uttering forged notes, and there was strong suspicion that he had long subsisted entirely by this fraud.
+LJSpeech-1.1/wavs/LJ047-0223.wav|I don't recall the exact date. It was about a week prior. End quote.
+LJSpeech-1.1/wavs/LJ016-0369.wav|upon them devolved the painful duty of cutting down the body and preparing for the inquest.
+LJSpeech-1.1/wavs/LJ050-0189.wav|that written instructions might come into the hands of local newspapers, to the prejudice of the precautions described.
+LJSpeech-1.1/wavs/LJ019-0095.wav|which was yet under full control, and might be made to work corn-mills or prove otherwise productive;
+LJSpeech-1.1/wavs/LJ029-0205.wav|for President Kennedy, stating that "in many respects Dallas County has isolated itself from the main stream of life in the world in this decade.
+LJSpeech-1.1/wavs/LJ047-0045.wav|and promised to advise the FBI if he heard from them.
+LJSpeech-1.1/wavs/LJ036-0069.wav|Instead of waiting there, Oswald apparently went as far away as he could and boarded the first Oak Cliff bus which came along
+LJSpeech-1.1/wavs/LJ014-0180.wav|secure the stock of watches and jewelry, then lock up the place and take on the keys to Mr. Berry's private house in Pimlico.
+LJSpeech-1.1/wavs/LJ021-0060.wav|Minimum wages have been established and other wages adjusted toward a rising standard of living.
+LJSpeech-1.1/wavs/LJ002-0128.wav|He also makes the curious calculation that the costs of these actions if undefended
+LJSpeech-1.1/wavs/LJ028-0437.wav|Here, it has been suggested, were the famous hanging gardens which some ancient authors included among the Seven Wonders of the World.
+LJSpeech-1.1/wavs/LJ028-0234.wav|Cyrus was now reduced to great perplexity, as time went on and he made no progress against the place.
+LJSpeech-1.1/wavs/LJ001-0050.wav|and though the famous family of Aldus restored its technical excellence, rejecting battered letters,
+LJSpeech-1.1/wavs/LJ006-0154.wav|Nothing was more prominently brought out by the inspectors than the inefficiency of the governor at that time, Mr. Cope.
+LJSpeech-1.1/wavs/LJ022-0148.wav|to enforce minimum wages, to prevent excessive hours,
+LJSpeech-1.1/wavs/LJ035-0070.wav|Truly stood in front of the building.
+LJSpeech-1.1/wavs/LJ028-0250.wav|Such, then, were the circumstances of the first taking of Babylon.
+LJSpeech-1.1/wavs/LJ043-0001.wav|Report of the President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ004-0171.wav|was by a kind of chimney, which the prisoners kept hermetically sealed, and which had never been opened in the memory of the turnkey.
+LJSpeech-1.1/wavs/LJ025-0009.wav|for in the past fifty years it has been made evident that in general principles all living things are fundamentally similar.
+LJSpeech-1.1/wavs/LJ010-0066.wav|which under Thistlewood as dictator was to rule the nation, by first handing over its capital to fire and pillage.
+LJSpeech-1.1/wavs/LJ022-0139.wav|with which we have been concerned for two years.
+LJSpeech-1.1/wavs/LJ014-0056.wav|while in Ireland a wife dashed out her husband's brains with a hammer.
+LJSpeech-1.1/wavs/LJ037-0079.wav|They ran to the door in time to see a man with a revolver cut across their lawn and disappear around a corner of the house onto Patton.
+LJSpeech-1.1/wavs/LJ032-0044.wav|shows an imprint made by the cash register which recorded the receipt of twenty-one dollars, forty-five cents on March thirteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ036-0116.wav|Lee Oswald was Number three;
+LJSpeech-1.1/wavs/LJ028-0476.wav|The entire width of this inner defense was about fifty-five feet; its height is uncertain.
+LJSpeech-1.1/wavs/LJ004-0137.wav|and that it was accomplished by "sleeping edgewise."
+LJSpeech-1.1/wavs/LJ003-0113.wav|A prisoner, generally the oldest and most dexterous thief,
+LJSpeech-1.1/wavs/LJ037-0128.wav|were on the lot at the time, and they saw a white male with a revolver in his hands running south on Patton.
+LJSpeech-1.1/wavs/LJ031-0137.wav|At approximately one:twenty p.m., Vice President Johnson was notified by O'Donnell that President Kennedy was dead.
+LJSpeech-1.1/wavs/LJ005-0008.wav|they were followed by a crowd of reckless boys, who jeered at and insulted them.
+LJSpeech-1.1/wavs/LJ001-0083.wav|The seventeenth century founts were bad rather negatively than positively.
+LJSpeech-1.1/wavs/LJ006-0224.wav|New arrivals, especially the innocent and still guileless debutant, were tormented with rude horse-play, and assailed by the most insulting "chaff."
+LJSpeech-1.1/wavs/LJ015-0298.wav|But while Hardwicke was in communication with Saward, the bank was in communication with London
+LJSpeech-1.1/wavs/LJ017-0212.wav|Her captain was John Smith;
+LJSpeech-1.1/wavs/LJ049-0096.wav|There have been a number of efforts to make assassination a Federal crime, particularly after the assassination of President McKinley
+LJSpeech-1.1/wavs/LJ015-0069.wav|but the firm he served got him a situation as clerk in the office of the Great Northern Railway,
+LJSpeech-1.1/wavs/LJ013-0243.wav|Good now offered to go to Wandsworth and satisfy the pawnbroker.
+LJSpeech-1.1/wavs/LJ015-0235.wav|and last, but not least, Agar frequently traveled up and down the line to test the false keys he had manufactured with Pierce's assistance.
+LJSpeech-1.1/wavs/LJ016-0096.wav|They were penal servitude men, their names Bell, Brown, and Barry, and they were awaiting transfer to Leicester,
+LJSpeech-1.1/wavs/LJ029-0110.wav|The route impressed the agents as a natural and desirable one.
+LJSpeech-1.1/wavs/LJ011-0098.wav|He soon, however, became deeply involved in Stock Exchange speculations,
+LJSpeech-1.1/wavs/LJ001-0016.wav|The Middle Ages brought calligraphy to perfection, and it was natural therefore
+LJSpeech-1.1/wavs/LJ005-0130.wav|There were tread-wheels at most of the prisons, and regular employment thereon or at some other kind of hard labor.
+LJSpeech-1.1/wavs/LJ018-0091.wav|Wagner and Bateman, who had already been convicted of systematic forgery, and sentenced to transportation, but they had been released on ticket-of-leave
+LJSpeech-1.1/wavs/LJ019-0053.wav|and our modern practice has prudently tried to steer between the two extremes, accepting as the best system a judicious combination of both.
+LJSpeech-1.1/wavs/LJ023-0071.wav|For nearly twenty years there was no conflict between the Congress and the Court.
+LJSpeech-1.1/wavs/LJ019-0390.wav|Since then a strong central authority has labored steadfastly to compass concentration,
+LJSpeech-1.1/wavs/LJ047-0163.wav|According to Hosty, Mrs. Paine indicated that she thought she could find out where Oswald was living and would let him know.
+LJSpeech-1.1/wavs/LJ016-0035.wav|the wall beneath and above it was "rusticated," in other words, the granite surface had become roughened, and offered a sort of foothold.
+LJSpeech-1.1/wavs/LJ015-0211.wav|Each safe had three sets of double keys, all held by confidential servants of the company.
+LJSpeech-1.1/wavs/LJ043-0148.wav|She testified that she was agitated because she had found the note in Oswald's room,
+LJSpeech-1.1/wavs/LJ028-0207.wav|On the fourteenth day Sippar was taken without a battle.
+LJSpeech-1.1/wavs/LJ007-0062.wav|Latterly his ministrations to the condemned had been restricted to a visit on Sunday afternoons, and occasionally about once a fortnight on a week-day.
+LJSpeech-1.1/wavs/LJ049-0186.wav|the Commission received a number of proposals designed to improve current arrangements for protecting the President.
+LJSpeech-1.1/wavs/LJ011-0196.wav|Mr. Turner at once set off for London, where he sought the assistance of the police,
+LJSpeech-1.1/wavs/LJ003-0227.wav|So unjust and unequal was the system, that the allowance to convicted criminals was better than that of the innocent debtor,
+LJSpeech-1.1/wavs/LJ047-0243.wav|According to Revill, Hosty indicated that he was going to tell this to Lieutenant Wells of the homicide and robbery bureau.
+LJSpeech-1.1/wavs/LJ007-0116.wav|A few others, who could not afford a payment of more than half a guinea, were permitted to monopolize a part of the prison infirmary,
+LJSpeech-1.1/wavs/LJ018-0243.wav|the hardship to the holders of these lands being plain, should the allegations of invalidity be made good.
+LJSpeech-1.1/wavs/LJ007-0080.wav|These powers were not invariably put in force, and there were in consequence many unhappy lunatics in Newgate and other jails,
+LJSpeech-1.1/wavs/LJ038-0037.wav|Oswald then struck McDonald between the eyes with his left fist; with his right hand he drew a gun from his waist.
+LJSpeech-1.1/wavs/LJ043-0175.wav|The items which Oswald left at home when he made his attack on Walker suggest a strong concern for his place in history.
+LJSpeech-1.1/wavs/LJ040-0114.wav|Relations soon became strained, however, so in late September Lee and his mother moved to their own apartment in the Bronx.
+LJSpeech-1.1/wavs/LJ010-0241.wav|but she declared she would not remain a prisoner in her own palace, and next day drove out as usual in an open barouche.
+LJSpeech-1.1/wavs/LJ037-0202.wav|identified records of Seaport Traders, Incorporated, which showed that a, quote, point three eight
+LJSpeech-1.1/wavs/LJ019-0230.wav|In eighteen sixty-one a similar work was undertaken to provide separate cellular accommodation for the female inmates of Newgate,
+LJSpeech-1.1/wavs/LJ010-0134.wav|He roared out snatches of a song about Death or Liberty, and just before he was turned off,
+LJSpeech-1.1/wavs/LJ014-0005.wav|but too late to give substantial aid.
+LJSpeech-1.1/wavs/LJ005-0186.wav|They neither built new jails nor contracted with the counties, as had been expected, for the transfer of their prisoners.
+LJSpeech-1.1/wavs/LJ017-0003.wav|Nevertheless, in order to give completeness to the picture
+LJSpeech-1.1/wavs/LJ020-0014.wav|beating the batter smooth as you go on until all of the liquid and flour has gone in.
+LJSpeech-1.1/wavs/LJ014-0245.wav|It was the custom in this office to make the banker's passbook the basis of the entries in the company's ledgers.
+LJSpeech-1.1/wavs/LJ008-0180.wav|Among the dead was a sailor lad whom no one knew;
+LJSpeech-1.1/wavs/LJ019-0022.wav|On the other hand, it must be admitted
+LJSpeech-1.1/wavs/LJ027-0034.wav|Hence, as Jordan has said, "the inside of an animal tells the real history of its ancestry; the outside tells us only where its ancestors have been."
+LJSpeech-1.1/wavs/LJ040-0124.wav|This continued despite the efforts of the school authorities and, to a lesser extent, of his mother to have him return to school.
+LJSpeech-1.1/wavs/LJ006-0192.wav|There was no school for adults; only the boys were taught anything, and their instructor, with his assistant, were convicted prisoners.
+LJSpeech-1.1/wavs/LJ014-0229.wav|Mobbs systematically ill-used his wife for a long space of time, and at last cut her throat.
+LJSpeech-1.1/wavs/LJ031-0162.wav|other terminal buildings and the neighboring parking lots, of all people.
+LJSpeech-1.1/wavs/LJ032-0094.wav|listing Marina Oswald and A. J. Hidell
+LJSpeech-1.1/wavs/LJ022-0155.wav|Power production in this country is virtually back to the nineteen twenty-nine peak.
+LJSpeech-1.1/wavs/LJ009-0291.wav|He was always known as a mild-mannered man of simple tastes, much given to angling in the New River, and a devoted rabbit fancier.
+LJSpeech-1.1/wavs/LJ006-0130.wav|had a key of both the master's side and middle side yards, was the only person present at the distribution of beer, and was trusted to examine,
+LJSpeech-1.1/wavs/LJ040-0131.wav|Marguerite Oswald visited her son at Youth House, where she recalled that she waited in line, quote,
+LJSpeech-1.1/wavs/LJ009-0113.wav|whistles merrily, and points upwards with madness in his look.
+LJSpeech-1.1/wavs/LJ037-0078.wav|when they heard the sound of gunfire and the screams of Helen Markham.
+LJSpeech-1.1/wavs/LJ006-0093.wav|So closely did they lie together, that the inspectors at their night visits found it difficult in stepping across the room to avoid treading on them.
+LJSpeech-1.1/wavs/LJ008-0061.wav|The entrance upon this floor or leaf is from the middle window over the gate of the prison;
+LJSpeech-1.1/wavs/LJ001-0156.wav|The paper on which the printing is to be done is a necessary part of our subject:
+LJSpeech-1.1/wavs/LJ029-0195.wav|when Governor Connally confirmed on November eight that the President would come to Texas on November twenty-one and twenty-two,
+LJSpeech-1.1/wavs/LJ040-0080.wav|That situation, however, was short-lived,
+LJSpeech-1.1/wavs/LJ010-0165.wav|but he came as a lad to London, and took service as a pot-boy to a publican.
+LJSpeech-1.1/wavs/LJ018-0334.wav|Webster, it may be mentioned here, was one of the worst prisoners ever remembered in Newgate
+LJSpeech-1.1/wavs/LJ046-0227.wav|According to Special Agent in Charge Bouck,
+LJSpeech-1.1/wavs/LJ019-0089.wav|sometimes it embraced the tread-wheel or the newly-invented instruments known as cranks, which ground air.
+LJSpeech-1.1/wavs/LJ034-0005.wav|He worked principally on the first and sixth floors of the building, gathering books listed on orders and delivering them to the shipping room on the first floor.
+LJSpeech-1.1/wavs/LJ043-0089.wav|to a commercial advertising photography firm in Dallas, where he was employed as a trainee starting October twelve, nineteen sixty-two.
+LJSpeech-1.1/wavs/LJ016-0247.wav|while round about were shoe-strings, boot-laces, and lasts. Marwood, strange to say, followed the same trade as Calcraft.
+LJSpeech-1.1/wavs/LJ045-0105.wav|She testified that she told him, quote,
+LJSpeech-1.1/wavs/LJ020-0027.wav|Half the quantity of sponge given in preceding receipt.
+LJSpeech-1.1/wavs/LJ028-0211.wav|He appointed Gobrias governor of Babylon.
+LJSpeech-1.1/wavs/LJ019-0314.wav|The separation of prisoners in cells duly certified by the inspectors was insisted upon,
+LJSpeech-1.1/wavs/LJ005-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eight: The beginnings of prison reform.
+LJSpeech-1.1/wavs/LJ009-0178.wav|In eighteen thirty-two the dissection of bodies cut down from the gallows, which had been decreed centuries previously, was abolished;
+LJSpeech-1.1/wavs/LJ034-0062.wav|Although a person could handle a carton and not leave identifiable prints,
+LJSpeech-1.1/wavs/LJ027-0088.wav|Extensive comparison, on the contrary, shows them to be the same, although the essential identity is obscured by adaptive modifications.
+LJSpeech-1.1/wavs/LJ032-0240.wav|By Sunday, March thirty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ036-0024.wav|on a trip which passed a check point at St. Paul and Elm Streets at twelve:thirty-six p.m., November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ039-0201.wav|fired two series of three shots at twenty-five yards in four point six and four point eight seconds.
+LJSpeech-1.1/wavs/LJ006-0113.wav|The authority of these wardsmen so improperly exalted, and so entirely unchecked, degenerated into a baneful despotism.
+LJSpeech-1.1/wavs/LJ048-0137.wav|there have been references to the numerous discussions between Secret Service representatives and the Dallas Police Department.
+LJSpeech-1.1/wavs/LJ007-0014.wav|The admission of a crowd of visitors to assist in these lay services has already been remarked upon; as the inspectors pointed out,
+LJSpeech-1.1/wavs/LJ007-0057.wav|Turnkeys occasionally visited the press-yard, but its occupants were under little or no control.
+LJSpeech-1.1/wavs/LJ010-0121.wav|that he was, to use Thistlewood's words, "a contriver, instigator, and entrapper."
+LJSpeech-1.1/wavs/LJ011-0176.wav|He now pretended that Mr. Turner was also on his way to the border, pursued by sheriffs' officers.
+LJSpeech-1.1/wavs/LJ036-0189.wav|at the southeast corner of tenth Street and Patton Avenue, moments before the Tippit shooting.
+LJSpeech-1.1/wavs/LJ006-0068.wav|We have reason to fear that poverty, ragged clothes, and an inability to pay the ward dues, elsewhere exacted for better accommodation,
+LJSpeech-1.1/wavs/LJ006-0097.wav|Water might not be taken into the ward for washing purposes.
+LJSpeech-1.1/wavs/LJ048-0085.wav|the Commission believes that the liaison between all Federal agencies responsible for Presidential protection should be improved.
+LJSpeech-1.1/wavs/LJ039-0160.wav|In tests with the Mannlicher-Carano C twenty-seven sixty-six rifle, over one hundred rounds of this ammunition were fired by the FBI
+LJSpeech-1.1/wavs/LJ038-0052.wav|testified regarding the arrest of Oswald, as did the various police officers who participated in the fight.
+LJSpeech-1.1/wavs/LJ010-0063.wav|The massacre of the whole of the Cabinet Ministers at one stroke was to be followed by an attack
+LJSpeech-1.1/wavs/LJ009-0295.wav|who had been a convicted prisoner at York, but who consented to act as hangman when Calcraft was engaged, and no other functionary could be obtained.
+LJSpeech-1.1/wavs/LJ011-0250.wav|While thus engaged, Howard thrust the poker into the fire.
+LJSpeech-1.1/wavs/LJ018-0273.wav|Tarpey was caught through his wife,
+LJSpeech-1.1/wavs/LJ047-0131.wav|In early September nineteen sixty-three
+LJSpeech-1.1/wavs/LJ040-0232.wav|Few social agencies even in New York were equipped to provide the kind of intensive treatment that he needed,
+LJSpeech-1.1/wavs/LJ010-0051.wav|The well-known Cato Street conspiracy,
+LJSpeech-1.1/wavs/LJ008-0077.wav|where the apparatus for the punishment she was about to experience
+LJSpeech-1.1/wavs/LJ006-0115.wav|Their original capital had been a few shillings, and for this they purchased the right to tax their fellows to the extent of pounds per week.
+LJSpeech-1.1/wavs/LJ048-0262.wav|during the hours they are officially employed at their post of duty, or when they may reasonably expect that they may be called upon to perform an official duty.
+LJSpeech-1.1/wavs/LJ020-0101.wav|From the beginning of your apprenticeship in housewifery, learn how to "dovetail" your duties neatly into one another.
+LJSpeech-1.1/wavs/LJ045-0207.wav|He could not keep them with him in Dallas, where at least he could see his children whom, several witnesses testified, he seemed to love.
+LJSpeech-1.1/wavs/LJ021-0009.wav|with a greater certainty of the employment of labor at a reasonable wage and of more business at a fair profit.
+LJSpeech-1.1/wavs/LJ038-0137.wav|the Commission found that Oswald lied when he told Frazier that he was returning to Irving to obtain curtain rods.
+LJSpeech-1.1/wavs/LJ041-0164.wav|which Thornley read at Oswald's suggestion.
+LJSpeech-1.1/wavs/LJ001-0006.wav|And it is worth mention in passing that, as an example of fine typography,
+LJSpeech-1.1/wavs/LJ003-0131.wav|He was an inmate of the same ward with others of the most dreadful sort, quote,
+LJSpeech-1.1/wavs/LJ003-0208.wav|a number of amateurs were ever ready to give their gratuitous ministrations to the condemned.
+LJSpeech-1.1/wavs/LJ010-0172.wav|He saw Prince Albert return there from a visit to Woolwich, and then passed on to Constitution Hill,
+LJSpeech-1.1/wavs/LJ028-0203.wav|Less picturesque than this Hebrew legend is the royal record of Babylon, which fortunately was inscribed upon a clay cylinder from the ruins of the city.
+LJSpeech-1.1/wavs/LJ007-0146.wav|vaunting his own adventures, or listening to those of others;
+LJSpeech-1.1/wavs/LJ021-0087.wav|We have the right to expect that this driving power will be given patriotically and whole-heartedly to our nation.
+LJSpeech-1.1/wavs/LJ025-0077.wav|Their food is provided for them,
+LJSpeech-1.1/wavs/LJ028-0185.wav|Perhaps Babylon was so strongly fortified that at first he made no attempt to add it to his empire,
+LJSpeech-1.1/wavs/LJ030-0207.wav|with the follow-up car trailing the President's automobile by approximately five feet.
+LJSpeech-1.1/wavs/LJ012-0109.wav|But they at once made tracks, and took up their residence under assumed names in a tavern in Bloomsbury.
+LJSpeech-1.1/wavs/LJ032-0230.wav|that the published pictures were the same as the original except for retouching done by these publications, apparently for the purpose of clarifying the lines of the rifle
+LJSpeech-1.1/wavs/LJ049-0095.wav|for all offenses within its jurisdiction, as are FBI agents and Federal marshals.
+LJSpeech-1.1/wavs/LJ024-0142.wav|I seek to make American democracy succeed.
+LJSpeech-1.1/wavs/LJ050-0177.wav|This PRS agent will also be responsible for establishing an informal local liaison committee
+LJSpeech-1.1/wavs/LJ011-0006.wav|He went to the bank, and found that no stocks stood in her name. He called at once upon Fauntleroy, his client's bankers, for an explanation,
+LJSpeech-1.1/wavs/LJ029-0012.wav|He had made only a few brief visits to the State since the nineteen sixty Presidential campaign and in nineteen sixty-two he began to consider a formal visit.
+LJSpeech-1.1/wavs/LJ026-0022.wav|if chlorophyll is absent, carbon is obtained from sugar or some similar compound,
+LJSpeech-1.1/wavs/LJ019-0233.wav|and when it was completed, both sides of the prison were brought into harmony with modern ideas.
+LJSpeech-1.1/wavs/LJ010-0096.wav|Edgeware Road, completing their dispositions for assuming supreme power after the blow had been struck.
+LJSpeech-1.1/wavs/LJ045-0111.wav|They asked for Lee Oswald who was not called to the telephone because he was known by the other name.
+LJSpeech-1.1/wavs/LJ005-0298.wav|to the county jails from such prisons as were past improvement, and that the borough funds should be charged for the accommodation.
+LJSpeech-1.1/wavs/LJ009-0224.wav|At the first-named the exhibition nearly created a tumult, and the body was taken down and buried,
+LJSpeech-1.1/wavs/LJ014-0179.wav|a working jeweler, shopman to a Mr. Berry of Parliament Street. It was Cope's duty to stay in the shop till the last, close the shutters,
+LJSpeech-1.1/wavs/LJ035-0044.wav|If the man had passed from the vestibule into the lunchroom, Baker could not have seen him.
+LJSpeech-1.1/wavs/LJ008-0113.wav|and his soul shot out so piercingly through the port-holes of his head, that the first glance of him nearly petrified me
+LJSpeech-1.1/wavs/LJ050-0087.wav|propensity toward violent action, or some similar characteristic, coupled with some evaluation of the capability of the individual or group
+LJSpeech-1.1/wavs/LJ047-0135.wav|According to the information received by the Bureau
+LJSpeech-1.1/wavs/LJ049-0066.wav|For instance, the lead car always is manned by Secret Service agents familiar with the area and with local law enforcement officials;
+LJSpeech-1.1/wavs/LJ030-0005.wav|by helicopter at ten:forty-five A.M., Eastern Standard Time, on November twenty-one, nineteen sixty-three, for Andrews Air Force Base.
+LJSpeech-1.1/wavs/LJ027-0158.wav|But according to the opposite view no reason can be assigned why such should be the case.
+LJSpeech-1.1/wavs/LJ048-0225.wav|they had little opportunity to eat during the day. No food was available at the Press Club.
+LJSpeech-1.1/wavs/LJ033-0149.wav|the FBI Laboratory developed a latent palmprint and latent fingerprint on the bag.
+LJSpeech-1.1/wavs/LJ018-0255.wav|The case was easily and rapidly disposed of.
+LJSpeech-1.1/wavs/LJ014-0276.wav|Watts's crime was discovered by the secretary of the Globe Company, who came suddenly upon the extensive falsification of the passbook.
+LJSpeech-1.1/wavs/LJ039-0219.wav|Frazier testified that the rifle was accurate, that it had less recoil than the average military rifle
+LJSpeech-1.1/wavs/LJ036-0213.wav|The man's general description was similar to the one broadcast over the police radio.
+LJSpeech-1.1/wavs/LJ037-0179.wav|was discarded along with the others as Oswald left the scene.
+LJSpeech-1.1/wavs/LJ037-0009.wav|One witness felt he was too distant from the gunman to make a positive identification.
+LJSpeech-1.1/wavs/LJ038-0163.wav|Prior attempt to kill.
+LJSpeech-1.1/wavs/LJ006-0139.wav|Nobody interfered with them or regulated their conduct. They might get drunk when so disposed, and did so frequently, alone or in company.
+LJSpeech-1.1/wavs/LJ039-0091.wav|Sergeant Zahm expressed the opinion that the shot which struck President Kennedy in the neck at one hundred seventy-six point nine
+LJSpeech-1.1/wavs/LJ036-0016.wav|Lee Harvey Oswald left the building approximately three minutes after the assassination.
+LJSpeech-1.1/wavs/LJ030-0109.wav|The Vice-Presidential car
+LJSpeech-1.1/wavs/LJ019-0030.wav|Major, afterwards Sir Joshua Jebb,
+LJSpeech-1.1/wavs/LJ015-0154.wav|When the crash came there were pensioners and other recipients of his bounty who could not believe
+LJSpeech-1.1/wavs/LJ038-0039.wav|Three other officers, moving toward the scuffle, grabbed Oswald from the front, rear and side.
+LJSpeech-1.1/wavs/LJ017-0146.wav|He had all the characteristics of the poisoner -- the calm deliberation,
+LJSpeech-1.1/wavs/LJ036-0171.wav|he would have arrived there about twelve:fifty-nine to one p.m.
+LJSpeech-1.1/wavs/LJ039-0099.wav|In accordance with standard Marine procedures, Oswald received extensive training in marksmanship.
+LJSpeech-1.1/wavs/LJ004-0216.wav|The most noticeable of the improvements introduced was a better regulation of dietaries within the prison.
+LJSpeech-1.1/wavs/LJ045-0136.wav|as Oswald went on to say. In Oswald's imagination, quote,
+LJSpeech-1.1/wavs/LJ004-0135.wav|twenty men slept on eight straw beds, with sixteen rugs amongst them, and a piece of timber for a bolster.
+LJSpeech-1.1/wavs/LJ045-0173.wav|Question: What did you say to that? Answer:
+LJSpeech-1.1/wavs/LJ040-0030.wav|When he was in the Soviet Union, he apparently resented the Communist Party members,
+LJSpeech-1.1/wavs/LJ024-0096.wav|No amendment which any powerful economic interests or the leaders of any powerful political party have had reason to oppose
+LJSpeech-1.1/wavs/LJ018-0208.wav|were a low lot, the lowest among criminals except, perhaps, the 'smashers,' or those who passed the counterfeit money.
+LJSpeech-1.1/wavs/LJ030-0215.wav|the car lurched forward, causing him to lose his footing. He ran three or four steps, regained his position and mounted the car.
+LJSpeech-1.1/wavs/LJ012-0156.wav|His arrest and conviction cast dismay over the whole gang of receivers, and for a time seriously checked the nefarious traffic.
+LJSpeech-1.1/wavs/LJ019-0028.wav|Mr. Shaw-Lefevre, the Speaker of the House of Commons, Sir Benjamin Brodie,
+LJSpeech-1.1/wavs/LJ019-0079.wav|The cells inhabited by prisoners were of very varying dimensions;
+LJSpeech-1.1/wavs/LJ046-0046.wav|In all of these roles the President must go to the people.
+LJSpeech-1.1/wavs/LJ018-0054.wav|While in the condemned cell he conversed freely with the warders in broken English or through an interpreter.
+LJSpeech-1.1/wavs/LJ014-0338.wav|These bankers, wishing for more specific information,
+LJSpeech-1.1/wavs/LJ026-0113.wav|Only proteid foods form new protoplasm
+LJSpeech-1.1/wavs/LJ015-0310.wav|which had received so perverted and mistaken direction,
+LJSpeech-1.1/wavs/LJ049-0040.wav|The assassination suggests that it would have been of prime importance
+LJSpeech-1.1/wavs/LJ022-0052.wav|here as in every other nation, we have come to recognize the possibility and the necessity of certain helpful remedial measures.
+LJSpeech-1.1/wavs/LJ032-0054.wav|"A. Hidell, P.O. Box two nine one five, Dallas, Texas," on March twenty, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ005-0290.wav|Instances rarely occur in which the borough jails admit of any proper classification of the prisoners.
+LJSpeech-1.1/wavs/LJ028-0314.wav|observing him, hastened down, and setting one of the gates slightly ajar, questioned him who he was, and on what errand he had come.
+LJSpeech-1.1/wavs/LJ028-0324.wav|his body red with marks of scourging and with blood, had no suspicion but that he spoke the truth, and was really come to be their friend and helper.
+LJSpeech-1.1/wavs/LJ033-0035.wav|and Marina Oswald testified that Oswald did not say anything about curtain rods on the day before the assassination.
+LJSpeech-1.1/wavs/LJ050-0082.wav|the interest of the Secret Service goes beyond information on individuals or groups threatening to cause harm or embarrassment to the President.
+LJSpeech-1.1/wavs/LJ046-0190.wav|it had arrangements to be notified about release from confinement in roughly one thousand cases;
+LJSpeech-1.1/wavs/LJ015-0096.wav|whether representing real or fictitious shares does not appear; but they were certificates connected in some way with Robson's long practiced frauds
+LJSpeech-1.1/wavs/LJ019-0146.wav|There was as yet no control over the prisoners after locking-up time;
+LJSpeech-1.1/wavs/LJ007-0091.wav|The lunatic became the sport of the idle and the depraved. His cure was out of the question;
+LJSpeech-1.1/wavs/LJ033-0087.wav|She thought that its color was similar to that of the bag found on the sixth floor of the School Book Depository after the assassination.
+LJSpeech-1.1/wavs/LJ050-0086.wav|Under these criteria, whether the case should be referred to the Secret Service depends on the existence of a previous history of mental instability,
+LJSpeech-1.1/wavs/LJ025-0011.wav|is Huxley's famous essay, "The Border Territory Between the Animal and Vegetable Kingdoms," written in eighteen seventy-six,
+LJSpeech-1.1/wavs/LJ003-0338.wav|End quote. it would cover some thirty acres, and cost a great deal more than the city, with the example of Whitecross Street prison before it,
+LJSpeech-1.1/wavs/LJ038-0282.wav|there is enough on it to say that it could have come, and even perhaps a little stronger, to say that it probably came from this,
+LJSpeech-1.1/wavs/LJ037-0075.wav|However, even in the absence of Mrs. Markham's testimony, there is ample evidence to identify Oswald as the killer of Tippit.
+LJSpeech-1.1/wavs/LJ003-0033.wav|Enough has been said, probably, to prove that there was room for improvement in the condition and treatment of debtors in the prisons of the city of London.
+LJSpeech-1.1/wavs/LJ041-0011.wav|Several witnesses testified that Lee Oswald was not aggressive. He was, however, involved in some fights.
+LJSpeech-1.1/wavs/LJ026-0102.wav|but root pressure due to osmosis, capillary action and evaporation from the leaves are factors.
+LJSpeech-1.1/wavs/LJ048-0078.wav|In each instance, liaison contacts should be developed to include a close friendly relationship,
+LJSpeech-1.1/wavs/LJ028-0489.wav|Had the enemy of Babylon succeeded in breaking through the outer and inner defenses of the city the royal palace would have still been far from his reach.
+LJSpeech-1.1/wavs/LJ007-0192.wav|inasmuch as it has aroused the attention of those upon whom parliamentary reports and grand jury presentments had hitherto failed to make the slightest impression.
+LJSpeech-1.1/wavs/LJ006-0210.wav|Drink, in more or less unlimited quantities, was still to be had.
+LJSpeech-1.1/wavs/LJ018-0361.wav|tampered with, and returned to the shop.
+LJSpeech-1.1/wavs/LJ033-0132.wav|I didn't pay too much attention the way he was walking because I was walking along there looking at the railroad cars and watching the men on the diesel switch them cars
+LJSpeech-1.1/wavs/LJ029-0131.wav|The building overlooks Dealey Plaza, an attractively landscaped triangle of three acres.
+LJSpeech-1.1/wavs/LJ030-0221.wav|stated that Mrs. Kennedy would probably have fallen off the rear end of the car and been killed if Hill had not pushed her back into the Presidential automobile.
+LJSpeech-1.1/wavs/LJ002-0257.wav|and a master of the ale-room, who kept this the scene of their revels clean, and saw that boiling water was provided for grog.
+LJSpeech-1.1/wavs/LJ025-0066.wav|and his skepticism was the more justified since Ehrenberg in his elaborate and comprehensive work on the infusoria,
+LJSpeech-1.1/wavs/LJ030-0110.wav|and Vice-Presidential follow-up car used portable sets with a separate frequency for their own car-to-car communication.
+LJSpeech-1.1/wavs/LJ019-0119.wav|but Mr. Pearson hardly considered the converse sufficiently, and
+LJSpeech-1.1/wavs/LJ048-0070.wav|reflect keen awareness of the necessity of communicating a much wider range of intelligence information to the Service.
+LJSpeech-1.1/wavs/LJ033-0060.wav|Marina Oswald testified that at that time, quote, My heart dropped.
+LJSpeech-1.1/wavs/LJ037-0077.wav|Barbara Jeanette Davis and Virginia Davis, were in an apartment of a multiple-unit house on the southeast corner of tenth and Patton
+LJSpeech-1.1/wavs/LJ018-0115.wav|It was stated in evidence that the monies obtained by these forgeries amounted to eight thousand pounds or ten thousand pounds,
+LJSpeech-1.1/wavs/LJ025-0032.wav|Hence arose the second great distinctive character of animals, or the circulatory system, which is less important than the digestive,
+LJSpeech-1.1/wavs/LJ014-0248.wav|This passbook, when not at the bank, was in the exclusive custody of Watts.
+LJSpeech-1.1/wavs/LJ003-0256.wav|By this means spirits, otherwise unattainable and strictly prohibited, were smuggled into the jail.
+LJSpeech-1.1/wavs/LJ009-0216.wav|Hanging in chains upon the gibbet which had served for the execution,
+LJSpeech-1.1/wavs/LJ034-0168.wav|Ronald Fischer and Robert Edwards were standing on the curb at the southwest corner of Elm and Houston Streets,
+LJSpeech-1.1/wavs/LJ028-0481.wav|Nabopolassar, the father, my begetter, built Imgur-Bel, the great wall of Babylon,
+LJSpeech-1.1/wavs/LJ028-0086.wav|the lips are thin, the chin prominent; the neck is that of a strong vigorous man.
+LJSpeech-1.1/wavs/LJ003-0278.wav|Insuperable difficulties were still supposed to stand in the way of any general employment of prisoners at their trades.
+LJSpeech-1.1/wavs/LJ047-0067.wav|Hosty decided that the Lee Harvey Oswald case should be reopened because of the alleged personal difficulties and the contact with the Worker,
+LJSpeech-1.1/wavs/LJ028-0472.wav|Its outer part, about twelve feet in width, was protected with towers at intervals of sixty-five feet.
+LJSpeech-1.1/wavs/LJ008-0024.wav|of the trading vessel 'Adventure,' upon the high seas.
+LJSpeech-1.1/wavs/LJ036-0022.wav|the transfer was conclusively identified as having been issued by Cecil J. McWatters, a busdriver for the Dallas Transit Co.
+LJSpeech-1.1/wavs/LJ023-0136.wav|In many states judges must retire at the age of seventy.
+LJSpeech-1.1/wavs/LJ028-0428.wav|has been removed, and the surrounding city walls have been traced.
+LJSpeech-1.1/wavs/LJ037-0258.wav|There is no doubt, however, that Oswald was seen leaving his roominghouse at about one p.m. wearing a zipper jacket,
+LJSpeech-1.1/wavs/LJ042-0087.wav|He stated that he had volunteered to give Soviet officials any information that he had concerning Marine Corps operations, and intimated
+LJSpeech-1.1/wavs/LJ016-0400.wav|Wainwright's demeanor was one of reckless effrontery steadily maintained to the last.
+LJSpeech-1.1/wavs/LJ040-0021.wav|ascertained the facts surrounding the assassination but did not draw conclusions concerning Oswald's legal guilt.
+LJSpeech-1.1/wavs/LJ036-0196.wav|Tippit patroled district Number seventy-eight in the Oak Cliff area of Dallas during daylight hours.
+LJSpeech-1.1/wavs/LJ038-0254.wav|She testified that several days later Oswald recovered his rifle and brought it back to their apartment.
+LJSpeech-1.1/wavs/LJ039-0117.wav|He had the services of an experienced highly trained coach.
+LJSpeech-1.1/wavs/LJ028-0371.wav|Then the world metropolis, stripped of most of its population, became a mere village.
+LJSpeech-1.1/wavs/LJ017-0080.wav|Palmer, alive to the danger he ran of a prosecution for forgery, should the fraud he had committed be brought to light,
+LJSpeech-1.1/wavs/LJ022-0198.wav|We have in the darkest moments of our national trials retained our faith in our own ability to master our destiny.
+LJSpeech-1.1/wavs/LJ047-0048.wav|while Oswald remained somewhat evasive at this interview, he was not antagonistic and seemed generally to be settling down.
+LJSpeech-1.1/wavs/LJ029-0126.wav|to reach the northbound lanes of the Stemmons Freeway is via Elm Street, which Route Number seventy-seven traffic is instructed to follow in this part of the city.
+LJSpeech-1.1/wavs/LJ015-0133.wav|Even when the bubble burst Redpath, who had lived at the rate of twenty thousand a year,
+LJSpeech-1.1/wavs/LJ028-0440.wav|Possibly along the terraces of the walls, or upon the stages of some lofty temple tower,
+LJSpeech-1.1/wavs/LJ018-0388.wav|and the bird's legs were a couple of teeth broken off the prisoner's comb.
+LJSpeech-1.1/wavs/LJ028-0011.wav|As the taps upon her shoulder are repeated, she stretches out her long neck, and with long strides makes for the eastern horizon;
+LJSpeech-1.1/wavs/LJ047-0074.wav|Under a general Bureau request to be on the alert for activities of the Fair Play for Cuba Committee
+LJSpeech-1.1/wavs/LJ035-0046.wav|I was coming out this one on the second floor, and I don't know, I was kind of sweeping this area as I come up, I was looking from right to left
+LJSpeech-1.1/wavs/LJ021-0136.wav|We have passed through more than a year of education.
+LJSpeech-1.1/wavs/LJ002-0262.wav|owing to many charitable gifts and bequests, which included annual donations from the Archbishop of Canterbury,
+LJSpeech-1.1/wavs/LJ044-0070.wav|WDSU has no program of any kind called, quote, Latin American Focus, end quote.
+LJSpeech-1.1/wavs/LJ017-0216.wav|there were also a Frenchman, a Norwegian (the carpenter), three Chinamen, a "Sclavonian," and a black on board.
+LJSpeech-1.1/wavs/LJ013-0070.wav|Mrs. Hunt had left money in the funds which remained unclaimed, and had been transferred, as in Miss Slack's case.
+LJSpeech-1.1/wavs/LJ005-0006.wav|who handed a shilling to the escort warder to provide her with a hackney coach; but this functionary pocketed the cash, and obliged the woman to walk
+LJSpeech-1.1/wavs/LJ008-0267.wav|no less than four hundred and fifty-one sentences of death for capital crimes were passed at the Old Bailey;
+LJSpeech-1.1/wavs/LJ030-0069.wav|scanning the route, and getting out and standing near the President when the cars stopped.
+LJSpeech-1.1/wavs/LJ027-0035.wav|In the second place, it must be noted that adaptations to similar conditions may result in superficial resemblances.
+LJSpeech-1.1/wavs/LJ004-0188.wav|and which even fulfilled many of the exacting requirements of modern days.
+LJSpeech-1.1/wavs/LJ049-0037.wav|In contrast, the Vice Presidential vehicle, although not specially designed for that purpose,
+LJSpeech-1.1/wavs/LJ015-0004.wav|The goods having no existence, Cole of course could not deliver them.
+LJSpeech-1.1/wavs/LJ018-0220.wav|The old gentleman's signature to this deed of gift was a forgery,
+LJSpeech-1.1/wavs/LJ050-0196.wav|Since the assassination of President Kennedy, the Secret Service has been experimenting with new techniques in the inspection of buildings along a motorcade route.
+LJSpeech-1.1/wavs/LJ002-0279.wav|The Giltspur Street Compter received sheriffs' debtors, also felons, vagrants, and night charges.
+LJSpeech-1.1/wavs/LJ004-0122.wav|fifty-nine had no division whatever to separate males and females; one hundred and thirty-six had only one division for the purpose;
+LJSpeech-1.1/wavs/LJ039-0090.wav|Characterizing the four-power scope as, quote, a real aid, an extreme aid, end quote, in rapid fire shooting,
+LJSpeech-1.1/wavs/LJ027-0065.wav|enclosed and protected by the skeleton, viz., the neural cavity above, and the visceral or body cavity below, the vertebral column.
+LJSpeech-1.1/wavs/LJ023-0083.wav|The Court has been acting not as a judicial body, but as a policy-making body.
+LJSpeech-1.1/wavs/LJ015-0229.wav|who quickly took the wax impression, handed it back to Pierce; Pierce replaced it, left the office, and the thing was done.
+LJSpeech-1.1/wavs/LJ005-0085.wav|Classification was insisted upon, in the manner laid down by the twenty-four George the third cap. fifty-four,
+LJSpeech-1.1/wavs/LJ003-0300.wav|that all prisoners should always be in separate cells by night, and those of short sentences by day.
+LJSpeech-1.1/wavs/LJ048-0276.wav|Since he was convinced that this was not the case, he believed that it would be unfair to the agents and their families to take explicit disciplinary measures.
+LJSpeech-1.1/wavs/LJ011-0228.wav|Mr. W. Wakefield served in a continental army, and rose to the rank of colonel,
+LJSpeech-1.1/wavs/LJ045-0117.wav|it would appear to be unlikely that his landlady in Dallas
+LJSpeech-1.1/wavs/LJ033-0010.wav|the presence of a long handmade brown paper bag near the point from which the shots were fired, and (five) the palmprint,
+LJSpeech-1.1/wavs/LJ050-0110.wav|and each agency given clear understanding of the assistance which the Secret Service expects.
+LJSpeech-1.1/wavs/LJ003-0315.wav|All three, governor, chaplain, and surgeon, should keep journals, which should be inspected periodically by the visiting magistrates.
+LJSpeech-1.1/wavs/LJ048-0196.wav|According to Captain Lawrence, quote,
+LJSpeech-1.1/wavs/LJ042-0055.wav|In the event of war I would kill any American who put a uniform on in defense of the American government -- any American.
+LJSpeech-1.1/wavs/LJ029-0029.wav|When the trip was planned for only one day, Governor Connally had opposed the motorcade because there was not enough time. The Governor stated, however, that, quote,
+LJSpeech-1.1/wavs/LJ047-0138.wav|He checked in Oswald's old neighborhood and throughout the Dallas-Fort Worth area but was unable to locate Oswald.
+LJSpeech-1.1/wavs/LJ003-0047.wav|Newgate continued to be a reproach to those responsible for its management.
+LJSpeech-1.1/wavs/LJ018-0242.wav|The case was tried at Guildford Assizes, and caused intense excitement,
+LJSpeech-1.1/wavs/LJ005-0067.wav|The Prison Society reproves the misdirected efforts of ambitious architects, who by a lavish and improvident expenditure of public money
+LJSpeech-1.1/wavs/LJ009-0281.wav|Calcraft was forced to return, and he once more pushed Bousfield off,
+LJSpeech-1.1/wavs/LJ018-0246.wav|At first the case was contested hotly, but, to the profound astonishment of every one inside and outside the court,
+LJSpeech-1.1/wavs/LJ007-0082.wav|At the time the Lords' Committee sat there were eight thus retained in Newgate, and a return in the appendix of the Lords' report
+LJSpeech-1.1/wavs/LJ037-0114.wav|He and Scoggins attempted to chase down the gunman in Scoggin's taxicab, but he had disappeared.
+LJSpeech-1.1/wavs/LJ045-0115.wav|and he thought that he loses jobs because the FBI visits the place of his employment, end quote.
+LJSpeech-1.1/wavs/LJ049-0030.wav|the Commission does believe that there are aspects of the protective measures employed in the motorcade at Dallas which deserve special comment.
+LJSpeech-1.1/wavs/LJ011-0155.wav|He had eloped with his first wife from school.
+LJSpeech-1.1/wavs/LJ034-0016.wav|Behind these boxes was another carton placed on the floor on which a man sitting
+LJSpeech-1.1/wavs/LJ046-0047.wav|Exposure of the President to public view through travel among the people of this country is a great and historic tradition of American life.
+LJSpeech-1.1/wavs/LJ022-0048.wav|They are still declining.
+LJSpeech-1.1/wavs/LJ016-0427.wav|He tripped up the chapel-stairs to hear the condemned sermon, and came out with cheerful alacrity on the morning he was to die.
+LJSpeech-1.1/wavs/LJ049-0134.wav|The Commission suggests that consideration might be given to assigning to a Cabinet-level committee or the National Security Council
+LJSpeech-1.1/wavs/LJ006-0237.wav|"A lad named Matthew White has had a wound in his eye by a bone thrown at him, which very nearly destroyed vision."
+LJSpeech-1.1/wavs/LJ046-0136.wav|Before the assassination of President Kennedy,
+LJSpeech-1.1/wavs/LJ009-0246.wav|Good people, do not be hurried; I am not, I can wait.
+LJSpeech-1.1/wavs/LJ050-0016.wav|This special assistant should be required to have sufficient stature and experience in law enforcement, intelligence, or allied fields
+LJSpeech-1.1/wavs/LJ004-0033.wav|and they were not threatened with any particular penalties if they evaded or ignored the new acts.
+LJSpeech-1.1/wavs/LJ009-0020.wav|previous to the execution of Henry Fauntleroy for uttering a forged security
+LJSpeech-1.1/wavs/LJ041-0161.wav|It was just -- never getting back to looking at things from any other way once he had become a Marxist, whenever that was, end quote.
+LJSpeech-1.1/wavs/LJ002-0108.wav|watch all night. Adjoining the felons' side lodge is the keeper's office, where the prison books are kept, and his clerk,
+LJSpeech-1.1/wavs/LJ005-0198.wav|In another borough, with a population of ten thousand, the prison was of the same dimensions.
+LJSpeech-1.1/wavs/LJ038-0226.wav|Oswald had apparently mistaken the county jail for the city jail.
+LJSpeech-1.1/wavs/LJ041-0183.wav|Delgado and Oswald talked more about Cuba than Russia, and sometimes imagined themselves as leaders in the Cuban Army or Government,
+LJSpeech-1.1/wavs/LJ031-0012.wav|The base station replied, quote, They have been notified, end quote,
+LJSpeech-1.1/wavs/LJ022-0060.wav|We must begin now to make provision for the future.
+LJSpeech-1.1/wavs/LJ047-0166.wav|Hosty assured her, through Mrs. Paine as interpreter, that the FBI would not harm or harass her.
+LJSpeech-1.1/wavs/LJ029-0147.wav|Sorrels and Lawson reviewed the route in cooperation with Assistant Chief Bachelor and other Dallas police officials who took notes on the requirements
+LJSpeech-1.1/wavs/LJ008-0274.wav|The result in the latter case was left in the first place to the king in council,
+LJSpeech-1.1/wavs/LJ031-0098.wav|I suppose nobody really had the heart to do it.
+LJSpeech-1.1/wavs/LJ044-0219.wav|you could tell what they wanted you to do by reading between the lines, reading the thing and doing a little reading between the lines, end quote.
+LJSpeech-1.1/wavs/LJ002-0271.wav|The compters of Ludgate, Giltspur Street, and the Borough were discontinued as debtors' prisons (as was Newgate also)
+LJSpeech-1.1/wavs/LJ025-0095.wav|once supposed to be exclusively confined to plants, are now known to be regular and normal products of animals.
+LJSpeech-1.1/wavs/LJ018-0350.wav|He might swear he was not the murderer, that he never fired a pistol in his life,
+LJSpeech-1.1/wavs/LJ022-0189.wav|They are a minimum of wise readjustments of our Federal Reserve System in the light of past experience and present needs.
+LJSpeech-1.1/wavs/LJ016-0056.wav|running a great risk of discovery as he passed by a lot of workmen at Tyler's manufactory in Warwick Square, which had formerly been the College of Physicians.
+LJSpeech-1.1/wavs/LJ049-0198.wav|in all its ramifications and alert to every danger that might befall it,
+LJSpeech-1.1/wavs/LJ010-0211.wav|The society was supposed to meet regularly, and its proceedings, together with the speeches made, were duly recorded.
+LJSpeech-1.1/wavs/LJ037-0170.wav|But he agreed that because the other three bullets were mutilated, he could not determine if they had been fired from the same weapon as the test bullets.
+LJSpeech-1.1/wavs/LJ039-0072.wav|From my own experience in shooting over the years,
+LJSpeech-1.1/wavs/LJ013-0162.wav|a bundle lying on the floor, as though thieves had been interrupted in the act.
+LJSpeech-1.1/wavs/LJ042-0085.wav|He said that his Marine service in Okinawa and elsewhere had given him, quote, a chance to observe American imperialism, end quote.
+LJSpeech-1.1/wavs/LJ044-0102.wav|samples of his photographic work, offering to contribute that sort of service without charge.
+LJSpeech-1.1/wavs/LJ017-0223.wav|The mutiny was organized with great secrecy, and broke out most unexpectedly in the middle of the night.
+LJSpeech-1.1/wavs/LJ032-0052.wav|According to Klein's shipping order form, one Italian carbine six point five x four x scope,
+LJSpeech-1.1/wavs/LJ025-0046.wav|The relations of plants and animals to the atmosphere are therefore inverse.
+LJSpeech-1.1/wavs/LJ030-0003.wav|Visits to Other Texas Cities
+LJSpeech-1.1/wavs/LJ028-0389.wav|St. Jerome said:
+LJSpeech-1.1/wavs/LJ007-0035.wav|The better-disposed complained bitterly of what they had to endure;
+LJSpeech-1.1/wavs/LJ034-0178.wav|He said that he could see the man from the middle of his chest to the top of his head, and that as he was facing the window the man was in the lower right-hand portion of the window
+LJSpeech-1.1/wavs/LJ047-0032.wav|In June nineteen sixty-two, the Bureau was advised by the Department of State of Oswald's plan to return to the United States.
+LJSpeech-1.1/wavs/LJ047-0010.wav|the agent who was assigned his case at the time of the assassination, the Director of the FBI,
+LJSpeech-1.1/wavs/LJ032-0200.wav|One Sunday, while his wife was hanging diapers, Oswald asked her to take a picture of him holding a rifle, a pistol
+LJSpeech-1.1/wavs/LJ006-0163.wav|and that he was constantly engaged attending sessions and going with drafts to the hulks.
+LJSpeech-1.1/wavs/LJ002-0127.wav|and the aggregate amount of debts sued for was eighty-one thousand, seven hundred ninety-one pounds.
+LJSpeech-1.1/wavs/LJ003-0207.wav|For while Roman Catholics and Dissenters were encouraged to see ministers of their own persuasion,
+LJSpeech-1.1/wavs/LJ050-0185.wav|The Service should consider preparing formal explanations of the cooperation anticipated during a Presidential visit to a city,
+LJSpeech-1.1/wavs/LJ040-0130.wav|and interviewed and observed by other members of the Youth House staff.
+LJSpeech-1.1/wavs/LJ014-0135.wav|It was against common sense to charge her with murdering the only friend she had in the world;
+LJSpeech-1.1/wavs/LJ022-0038.wav|That makes it particularly necessary for the Vice- President and for me to conceive of our duty toward the entire country.
+LJSpeech-1.1/wavs/LJ030-0087.wav|The Vice-Presidential automobile, a four-door Lincoln convertible obtained locally for use in the motorcade,
+LJSpeech-1.1/wavs/LJ049-0068.wav|A doctor is in the motorcade.
+LJSpeech-1.1/wavs/LJ022-0165.wav|both in its public relations and in its internal relations.
+LJSpeech-1.1/wavs/LJ037-0120.wav|Guinyard said, quote, I told them that was him right there. I pointed him out right there, end quote.
+LJSpeech-1.1/wavs/LJ035-0206.wav|An eyewitness to the shooting immediately provided a description of the man in the window which was similar to Oswald's actual appearance.
+LJSpeech-1.1/wavs/LJ050-0157.wav|On the basis of such a feasibility study,
+LJSpeech-1.1/wavs/LJ014-0295.wav|This man got up to look for him, and found him hanging from the bars of a neighboring room.
+LJSpeech-1.1/wavs/LJ019-0102.wav|which was promptly carried, with the additional instruction to the committee to suggest any improvements.
+LJSpeech-1.1/wavs/LJ030-0136.wav|end quote, the President replied, "That is very obvious."
+LJSpeech-1.1/wavs/LJ010-0044.wav|The present Queen very soon after her accession
+LJSpeech-1.1/wavs/LJ050-0065.wav|Alan H. Belmont, Assistant to the Director of the FBI, testified that this revision was initiated by the FBI itself.
+LJSpeech-1.1/wavs/LJ009-0275.wav|who bad been further upset by a letter threatening to shoot him when he appeared to perform his task.
+LJSpeech-1.1/wavs/LJ014-0064.wav|He lived at Mile End, whence he walked often to call at three, Minver Place, Bermondsey, the residence of his old love.
+LJSpeech-1.1/wavs/LJ044-0061.wav|there also seems to be no basis for his claim that he had distributed, quote,
+LJSpeech-1.1/wavs/LJ039-0138.wav|Lee Oswald exhibited an average amount of proficiency with that weapon.
+LJSpeech-1.1/wavs/LJ018-0191.wav|Bob was therefore allowed to pass on.
+LJSpeech-1.1/wavs/LJ050-0219.wav|However, the nineteen sixty-four to sixty-five budget request was submitted in November nineteen sixty-three
+LJSpeech-1.1/wavs/LJ030-0075.wav|followed closely behind the President's automobile.
+LJSpeech-1.1/wavs/LJ035-0167.wav|the earliest estimates would still have permitted Oswald to leave the building by twelve:thirty-three.
+LJSpeech-1.1/wavs/LJ017-0168.wav|But a long public discussion followed, and in consequence he was reprieved.
+LJSpeech-1.1/wavs/LJ016-0363.wav|This feeling was the stronger because
+LJSpeech-1.1/wavs/LJ040-0042.wav|He defected to the Soviet Union, shot at General Walker, tried to go to Cuba and even contemplated hijacking an airplane to get there.
+LJSpeech-1.1/wavs/LJ028-0046.wav|Another son, Assurbanipal, or the great Sardanapalus of the Greeks, became the King of Nineveh.
+LJSpeech-1.1/wavs/LJ001-0098.wav|and the whole effect is a little too gray, owing to the thinness of the letters.
+LJSpeech-1.1/wavs/LJ008-0004.wav|The reasons for this change were fully set forth in a previous chapter.
+LJSpeech-1.1/wavs/LJ010-0109.wav|Thistlewood made a long and rambling defense, the chief features of which were abuse of Lord Sidmouth, and the vilification of the informer Edwards.
+LJSpeech-1.1/wavs/LJ050-0038.wav|In attempting to identify those individuals who might prove a danger to the President,
+LJSpeech-1.1/wavs/LJ005-0070.wav|These are principles fully recognized now-a-days, and it may fairly be conceded that the Prison Discipline Society's ideal
+LJSpeech-1.1/wavs/LJ027-0022.wav|An examination of the facts in each of the lines of evidence makes it clear
+LJSpeech-1.1/wavs/LJ018-0351.wav|and that, in spite of the verdict of the jury, "he left the dock with a calm and quiet conscience;"
+LJSpeech-1.1/wavs/LJ021-0181.wav|thereby saving the British treasury one hundred and fifty million dollars a year in interest alone?
+LJSpeech-1.1/wavs/LJ005-0163.wav|One great impediment to wide amelioration was that a vast number of small jails lay out of reach of the law.
+LJSpeech-1.1/wavs/LJ019-0237.wav|The Act for private executions led to the erection of the gallows shed in the exercising yard, and at the flank of the passage from the condemned cells.
+LJSpeech-1.1/wavs/LJ010-0149.wav|She was seized before she could do any mischief,
+LJSpeech-1.1/wavs/LJ028-0499.wav|Ctesias mentions three hundred feet; probably they were not far from the truth.
+LJSpeech-1.1/wavs/LJ038-0294.wav|He indicated that he wanted more people in the vicinity at the time of the attempt so that his arrival and departure would not attract great attention.
+LJSpeech-1.1/wavs/LJ035-0017.wav|He heard two more shots spaced, quote, pretty well even to me.
+LJSpeech-1.1/wavs/LJ016-0436.wav|pointing to an aperture for ventilating the cell. On the morning of execution he asked how far it was to the gallows, and was told it was quite close.
+LJSpeech-1.1/wavs/LJ012-0228.wav|The evidence from first to last was circumstantial,
+LJSpeech-1.1/wavs/LJ028-0179.wav|It is only from their ruins that we may hope to obtain accurate information of the strongest fortifications in the ancient world.
+LJSpeech-1.1/wavs/LJ015-0165.wav|But the peer rushed forward and shook Redpath warmly by the hand.
+LJSpeech-1.1/wavs/LJ036-0159.wav|On the other hand, Whaley identified Commission Exhibit Number one fifty (the shirt taken from Oswald upon arrest) as the shirt his passenger was wearing.
+LJSpeech-1.1/wavs/LJ037-0127.wav|Four men -- Warren Reynolds, Harold Russell, Pat Patterson, and L. J. Lewis
+LJSpeech-1.1/wavs/LJ009-0218.wav|But there was an attempt to revive it at that date, when the act for dispensing with the dissection of criminals was passed.
+LJSpeech-1.1/wavs/LJ027-0142.wav|the antlers of an existing deer furnish in their development a kind of "resume," or recapitulation, of the successive phases
+LJSpeech-1.1/wavs/LJ001-0122.wav|that he has a five, an eight, or a three before him, unless the press work is of the best:
+LJSpeech-1.1/wavs/LJ032-0270.wav|Having reviewed the evidence that (one) Lee Harvey Oswald purchased the rifle used in the assassination,
+LJSpeech-1.1/wavs/LJ016-0438.wav|that the convict's clothes were still the executioner's perquisite.
+LJSpeech-1.1/wavs/LJ006-0061.wav|and "the greatest contempt shown for the law."
+LJSpeech-1.1/wavs/LJ038-0275.wav|and concluded, quote, that there is a fair probability, end quote, that the bullet was fired from the rifle used in the assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ022-0168.wav|who have little protection under the old laws against what used to be called frenzied finance.
+LJSpeech-1.1/wavs/LJ037-0074.wav|and her positive identification of Oswald at a police lineup, the Commission considers her testimony reliable.
+LJSpeech-1.1/wavs/LJ012-0276.wav|While she was senseless, but really still alive, he cut off her head, and dismembered the body in the manner already described.
+LJSpeech-1.1/wavs/LJ047-0221.wav|or Vice President, to immediately notify the Secret Service and confirm it in writing.
+LJSpeech-1.1/wavs/LJ021-0061.wav|The emergency purpose of the N.R.A. was to put men to work and since its creation more than four million persons have been reemployed,
+LJSpeech-1.1/wavs/LJ023-0094.wav|and placed "an unwarranted limitation upon the commerce clause."
+LJSpeech-1.1/wavs/LJ044-0236.wav|it is unlikely that a reasoning person would plan to attempt to travel from Dallas, Texas to Cuba
+LJSpeech-1.1/wavs/LJ029-0066.wav|No one else was identified to the Secret Service through local inquiry as potentially dangerous,
+LJSpeech-1.1/wavs/LJ039-0101.wav|He went through a series of exercises called dry firing where he assumed all positions which would later be used in the qualification course.
+LJSpeech-1.1/wavs/LJ013-0008.wav|came to light in this same year, eighteen forty-one.
+LJSpeech-1.1/wavs/LJ010-0181.wav|and the royal party drove back to Clarence House, the Queen being anxious to give the first news of the outrage and of her safety to her mother,
+LJSpeech-1.1/wavs/LJ030-0042.wav|Manned by officers of the Dallas Police Department, this automobile preceded the main party by approximately quarter of a mile.
+LJSpeech-1.1/wavs/LJ024-0141.wav|You who know me will accept my solemn assurance that in a world in which democracy is under attack,
+LJSpeech-1.1/wavs/LJ012-0095.wav|The jewels had belonged to a Spanish countess recently deceased, who had sent them to England for greater security on the outbreak of the first Carlist war.
+LJSpeech-1.1/wavs/LJ041-0181.wav|Delgado testified that Oswald was, quote, a complete believer that our way of government was not quite right, end quote.
+LJSpeech-1.1/wavs/LJ023-0075.wav|that it was an extraordinary power to exercise and through Mr. Justice Washington laid down this limitation upon it:
+LJSpeech-1.1/wavs/LJ018-0223.wav|In eighteen fifty-six the father died.
+LJSpeech-1.1/wavs/LJ008-0292.wav|but it was seldom less than six weeks. It all depended upon the sovereign's disposition to do business.
+LJSpeech-1.1/wavs/LJ015-0053.wav|Bates was at once captured in Norfolk Street, Strand.
+LJSpeech-1.1/wavs/LJ030-0067.wav|on his right sat Kellerman.
+LJSpeech-1.1/wavs/LJ027-0083.wav|For such a series of facts the reader must be referred to special books like Wiedersheim's "Comparative Anatomy of the Vertebrates,"
+LJSpeech-1.1/wavs/LJ007-0165.wav|any awakening truth, salutary exhortation, or imperfect resolutions of amendment can take root or grow.
+LJSpeech-1.1/wavs/LJ050-0255.wav|This no longer appears to be the case.
+LJSpeech-1.1/wavs/LJ037-0004.wav|At least twelve persons saw the man with the revolver in the vicinity of the Tippit crime scene at or immediately after the shooting.
+LJSpeech-1.1/wavs/LJ041-0170.wav|Thornley testified, quote, At which time he looked at me like a betrayed Caesar and screamed, screamed definitely, "Not you, too, Thornley!"
+LJSpeech-1.1/wavs/LJ050-0050.wav|As a result of these studies, the planning document submitted by the Secretary of the Treasury to the Bureau of the Budget on August thirty-one,
+LJSpeech-1.1/wavs/LJ030-0165.wav|which caused her to look to her right.
+LJSpeech-1.1/wavs/LJ028-0224.wav|I quieted their sighings and soothed their sorrows.
+LJSpeech-1.1/wavs/LJ001-0180.wav|the proportions of the page still give pleasure by the sense of richness that the cuts and letter together convey.
+LJSpeech-1.1/wavs/LJ010-0277.wav|Lord Abinger sentenced him to eighteen months' imprisonment in Newgate,
+LJSpeech-1.1/wavs/LJ027-0070.wav|So much concerns the general plan of skeletal structures and is strongly suggestive of -- in fact it is inexplicable without -- common origin.
+LJSpeech-1.1/wavs/LJ046-0214.wav|members of his immediate family, the President-Elect, and the Vice-President is within the exclusive jurisdiction of the U.S. Secret Service.
+LJSpeech-1.1/wavs/LJ003-0189.wav|The name still survived in the new press yard, which was the receptacle of the male condemned prisoners. It was generally crowded, like the rest of the prison.
+LJSpeech-1.1/wavs/LJ015-0204.wav|This was the question which presented itself to the fertile brain of one Pierce,
+LJSpeech-1.1/wavs/LJ008-0010.wav|at first, and for some few years after seventeen eighty-four, executions took place occasionally at a distance from Newgate.
+LJSpeech-1.1/wavs/LJ011-0039.wav|but in a separate chamber, that belonging to one of the warders of the jail.
+LJSpeech-1.1/wavs/LJ003-0128.wav|caught their flash terms and sung their songs, was admitted to their revels, and acquired, in place of habits of perfect sobriety,
+LJSpeech-1.1/wavs/LJ026-0153.wav|the other part of respiration, elimination of carbon dioxide, has been treated under excretions.
+LJSpeech-1.1/wavs/LJ032-0227.wav|Shaneyfelt testified that the published photographs appeared to be based on a copy of the original which the publications had each retouched differently.
+LJSpeech-1.1/wavs/LJ007-0149.wav|the jail, and the scaffold.
+LJSpeech-1.1/wavs/LJ006-0119.wav|When the wardsman was a man of some education, with some knowledge of legal chicanery gained by personal experience, he might add considerably to his emoluments
+LJSpeech-1.1/wavs/LJ037-0264.wav|and seven eyewitnesses who saw the flight of the gunman with revolver in hand
+LJSpeech-1.1/wavs/LJ024-0070.wav|I now propose that we establish by law an assurance against any such ill-balanced court in the future.
+LJSpeech-1.1/wavs/LJ017-0243.wav|but Vartos, or Watto, the Turk, would not allow any but the eight mutineers to have anything.
+LJSpeech-1.1/wavs/LJ038-0081.wav|Captain Fritz of the homicide and robbery bureau did most of the questioning, but he kept no notes and there were no stenographic or tape recordings.
+LJSpeech-1.1/wavs/LJ002-0090.wav|More than half their quadrangle had been partitioned off for another purpose,
+LJSpeech-1.1/wavs/LJ050-0221.wav|The Secret Service has now presented its recommendations to the Bureau of the Budget. The plan proposed by the Service
+LJSpeech-1.1/wavs/LJ012-0265.wav|A woman named Gale, who lived with him, was arrested at the same time. The prisoners were examined at the Marylebone police court.
+LJSpeech-1.1/wavs/LJ045-0147.wav|She asked Oswald, quote,
+LJSpeech-1.1/wavs/LJ005-0153.wav|Sometimes this money might be expended in the purchase of extra articles of food.
+LJSpeech-1.1/wavs/LJ021-0062.wav|in great part through the cooperation of American business brought about under the codes.
+LJSpeech-1.1/wavs/LJ023-0077.wav|by which any law is passed,
+LJSpeech-1.1/wavs/LJ030-0025.wav|when Air Force One touched down at Love Field at eleven:forty a.m., Eastern Standard Time.
+LJSpeech-1.1/wavs/LJ032-0209.wav|He stated, however, that while he, quote, found no differences, end quote, between the rifles in the two photographs, he could not make a, quote, positive identification
+LJSpeech-1.1/wavs/LJ004-0099.wav|Ventilators, hand and others, were to be supplied.
+LJSpeech-1.1/wavs/LJ007-0034.wav|At times the numbers congregated together were very great; as many as fifty and sixty, even more, were crowded indiscriminately into the press-yard.
+LJSpeech-1.1/wavs/LJ039-0157.wav|A series of tests were performed to determine whether the weapon and ammunition used in the assassination
+LJSpeech-1.1/wavs/LJ042-0191.wav|While, quote, resourcefulness and patient working towards the aforesaid goals
+LJSpeech-1.1/wavs/LJ016-0441.wav|The man, Bousfield, however, whose execution was so sadly bungled,
+LJSpeech-1.1/wavs/LJ003-0306.wav|The clothes of prisoners arriving dirty, or in rags, should be fumigated before worn in the jail,
+LJSpeech-1.1/wavs/LJ028-0351.wav|As for Zopyrus he was considered by Darius to have surpassed, in the greatness of his achievements, all other Persians,
+LJSpeech-1.1/wavs/LJ022-0002.wav|April twenty-eight, nineteen thirty-five.
+LJSpeech-1.1/wavs/LJ008-0149.wav|and after a long interval his dead body was discovered, shockingly disfigured, in a ditch. This was in eighteen oh two.
+LJSpeech-1.1/wavs/LJ010-0212.wav|With Oxford's other papers were found letters from the secretary, written as it seemed by Oxford to himself, after the manner of Mr. Toots,
+LJSpeech-1.1/wavs/LJ044-0074.wav|an agent of Bringuier's attempting to learn more about the true nature
+LJSpeech-1.1/wavs/LJ016-0435.wav|"Two fresh men! May I speak to them? Yes! I must caution you," he went on to the warders, "not to go to sleep, or I shall be off through that little hole,"
+LJSpeech-1.1/wavs/LJ003-0172.wav|The keeper went still further in his efforts to make money.
+LJSpeech-1.1/wavs/LJ038-0202.wav|Prior to the Walker shooting on April ten, Oswald had been attending typing classes on Monday, Tuesday, and Thursday evenings.
+LJSpeech-1.1/wavs/LJ007-0123.wav|and the inspectors expressed themselves still more strongly in reprehension of the practice.
+LJSpeech-1.1/wavs/LJ029-0163.wav|and then west on Main, turning back to Elm at Houston and then out Stemmons Freeway to the Trade Mart.
+LJSpeech-1.1/wavs/LJ045-0073.wav|Oswald once struck his wife because of a letter which she wrote to a former boyfriend in Russia.
+LJSpeech-1.1/wavs/LJ009-0293.wav|While Calcraft was in office other aspirants to fame appeared in the field.
+LJSpeech-1.1/wavs/LJ030-0247.wav|I was bent over under the weight of Agent Youngblood's body, toward Mrs. Johnson and Senator Yarborough, end quote,
+LJSpeech-1.1/wavs/LJ012-0143.wav|"Money Moses" had received the stolen gold-dust from Moss' father-in-law, Davis, or Isaacs, who was never arrested,
+LJSpeech-1.1/wavs/LJ029-0011.wav|President Kennedy's visit to Texas in November nineteen sixty-three had been under consideration for almost a year before it occurred.
+LJSpeech-1.1/wavs/LJ036-0195.wav|He was described by Chief Curry as having the reputation of being "a very fine, dedicated officer."
+LJSpeech-1.1/wavs/LJ011-0212.wav|then a maid and heir apparent unto her father, for the sake of the lucre of her substance; and for having afterwards unlawfully and against her will
+LJSpeech-1.1/wavs/LJ044-0098.wav|He did not think Oswald looked like the, quote, type, end quote, that he would have expected to find associating with a group such as the Fair Play for Cuba Committee.
+LJSpeech-1.1/wavs/LJ028-0159.wav|thence from the corners of the wall there is carried along each bank of the river a fence of burned bricks.
+LJSpeech-1.1/wavs/LJ019-0312.wav|with the local jurisdictions, although still very leniently disposed.
+LJSpeech-1.1/wavs/LJ015-0257.wav|Kay, who had been living with Agar at the time of the bullion robbery,
+LJSpeech-1.1/wavs/LJ035-0023.wav|Baker testified that he entered the lobby of the building and, quote, spoke out and asked where the stairs or elevator was
+LJSpeech-1.1/wavs/LJ028-0249.wav|continued dancing and reveling until they learned the capture but too certainly.
+LJSpeech-1.1/wavs/LJ029-0094.wav|Lawson was not specifically instructed to select the parade route, but he understood that this was one of his functions.
+LJSpeech-1.1/wavs/LJ025-0065.wav|Even as late as eighteen forty-five, however, a botanist of Schleiden's eminence dealt very skeptically with these statements,
+LJSpeech-1.1/wavs/LJ002-0142.wav|These courts were open to many and grave objections.
+LJSpeech-1.1/wavs/LJ011-0244.wav|through whose interest an appointment under Government was to be obtained for Mullay, would be present.
+LJSpeech-1.1/wavs/LJ007-0043.wav|were associated continually with a number of those who could look with certainty on a mitigation of punishment.
+LJSpeech-1.1/wavs/LJ009-0056.wav|The last of the four is said to have been a clergyman of the Church of England, condemned for forgery, "a miserable old man in a tattered suit of black.
+LJSpeech-1.1/wavs/LJ039-0166.wav|which was in turn placed to the right of the closest silhouette.
+LJSpeech-1.1/wavs/LJ014-0155.wav|Now she abused the jury, now called Manning a vagabond,
+LJSpeech-1.1/wavs/LJ047-0186.wav|I can now afford to wait until New Orleans forwarded the necessary papers to me to show me I now had all the information.
+LJSpeech-1.1/wavs/LJ043-0068.wav|While he might have expected difficulty from such an approach, in fact
+LJSpeech-1.1/wavs/LJ033-0049.wav|According to Mrs. Paine, Oswald had gone to bed by nine p.m.;
+LJSpeech-1.1/wavs/LJ009-0270.wav|A worse case still was that of William Bousfield, who, when awaiting execution for murder, about the same date,
+LJSpeech-1.1/wavs/LJ022-0009.wav|The job of creating a program for the nation's welfare is, in some respects, like the building of a ship.
+LJSpeech-1.1/wavs/LJ046-0102.wav|This section considers first the means used to locate potential sources of danger to the President in time to take appropriate precautions.
+LJSpeech-1.1/wavs/LJ008-0302.wav|I have heard the protracted agony of both classes described by those who witnessed it in terms so strong, that I am unwilling to repeat them.
+LJSpeech-1.1/wavs/LJ029-0021.wav|The three agreed that the President would come to Texas in late November nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ012-0178.wav|Thurtell drove him down in a gig, "to be killed as he traveled," in Thurtell's own words.
+LJSpeech-1.1/wavs/LJ017-0122.wav|A government prosecution was instituted, and Palmer was brought to Newgate for trial at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ016-0295.wav|curse, or shout, as in this heaving and struggling forward they gained or lost in their strong efforts to get nearer where Müller was to die.
+LJSpeech-1.1/wavs/LJ034-0183.wav|Boxes and cases were stacked behind him.
+LJSpeech-1.1/wavs/LJ043-0066.wav|Some of his acquaintances, feeling that Oswald tried to impress people with the fact that he had lived and worked in Russia, were led to the belief
+LJSpeech-1.1/wavs/LJ009-0062.wav|buries his head under his body.
+LJSpeech-1.1/wavs/LJ012-0177.wav|The victim was invited to visit Probert's cottage in the country near Elstree.
+LJSpeech-1.1/wavs/LJ015-0275.wav|Burglars brought him the cheques they stole from houses, thieves what they got in pocketbooks.
+LJSpeech-1.1/wavs/LJ045-0005.wav|especially an atmosphere of extreme opposition to President Kennedy that was present in some parts of the Dallas community
+LJSpeech-1.1/wavs/LJ009-0289.wav|and before he took to hanging he was employed as a watchman at Reid's brewery in Liquorpond Street.
+LJSpeech-1.1/wavs/LJ050-0071.wav|Both Director Hoover and Belmont expressed to the Commission the great concern of the FBI, which is shared by the Secret Service,
+LJSpeech-1.1/wavs/LJ017-0264.wav|They were an abject, miserable crew, cowards at heart; but some, especially Lopez, continued bloodthirsty to the last.
+LJSpeech-1.1/wavs/LJ047-0170.wav|On November five, Hosty was traveling near Mrs. Paine's home and took the occasion to stop by to ask whether she had any further information.
+LJSpeech-1.1/wavs/LJ015-0132.wav|The total amount was never exactly made out, but the false stock created and issued by him was estimated at two hundred twenty thousand pounds.
+LJSpeech-1.1/wavs/LJ025-0111.wav|But has the advance of biology simply tended to break down old distinctions without establishing new ones?
+LJSpeech-1.1/wavs/LJ004-0061.wav|by want of bed-clothing by night or firing by day
+LJSpeech-1.1/wavs/LJ026-0139.wav|It is probable that new leaves and new tissues generally are always formed in part from this reserve starch.
+LJSpeech-1.1/wavs/LJ012-0128.wav|A young man named Caspar, clerk to a steam-ship company,
+LJSpeech-1.1/wavs/LJ014-0054.wav|a maidservant, Sarah Thomas, murdered her mistress, an aged woman, by beating out her brains with a stone.
+LJSpeech-1.1/wavs/LJ004-0199.wav|This made exertion compulsory, and imposed hard labor as a proper punishment.
+LJSpeech-1.1/wavs/LJ012-0136.wav|Moss was known to be intimate with the elder Caspar,
+LJSpeech-1.1/wavs/LJ049-0180.wav|The Commission has the impression
+LJSpeech-1.1/wavs/LJ025-0093.wav|and that the latter is, chemically speaking, just as complicated as the former.
+LJSpeech-1.1/wavs/LJ046-0076.wav|He cannot and will not take the precautions of a dictator or a sovereign.
+LJSpeech-1.1/wavs/LJ049-0173.wav|As the testimony of J. Edgar Hoover and other Bureau officials revealed, the FBI did not believe that its directive required the Bureau
+LJSpeech-1.1/wavs/LJ034-0177.wav|Fischer placed the man in the easternmost window on the south side of the Depository Building on either the fifth or the sixth floor.
+LJSpeech-1.1/wavs/LJ050-0096.wav|I ask you to look into this case and take the necessary steps to repair the damage done to me and my family. End quote.
+LJSpeech-1.1/wavs/LJ018-0322.wav|Nor were these two abortive efforts all that were planned.
+LJSpeech-1.1/wavs/LJ040-0240.wav|and returned to New Orleans where Lee finished the ninth grade before he left school to work for a year.
+LJSpeech-1.1/wavs/LJ018-0070.wav|Sattler was ironed for safe custody,
+LJSpeech-1.1/wavs/LJ024-0113.wav|The other groups is composed of those who honestly believe the amendment process is the best
+LJSpeech-1.1/wavs/LJ042-0105.wav|he took the position that the Communist Party officials in the Soviet Union were opportunists who were betraying their positions for personal gain.
+LJSpeech-1.1/wavs/LJ032-0265.wav|Each man testified that he thought he could detect the outline of a rifle in the blanket, even though the blanket was empty.
+LJSpeech-1.1/wavs/LJ003-0015.wav|For this and other acts of misconduct there was the discipline of the refractory ward, or "strong room" on the debtors' side.
+LJSpeech-1.1/wavs/LJ005-0274.wav|The committee most of all insisted upon the entire individual separation of prisoners, except during the hours of labor,
+LJSpeech-1.1/wavs/LJ041-0037.wav|and subsequently, after this conversation, my father came in and we were kind of arguing back and forth about the situation, and my father came in the room,
+LJSpeech-1.1/wavs/LJ016-0421.wav|brutal creature who showed no remorse, but was subject to fits of ungovernable passion, when she broke out into language the most appalling.
+LJSpeech-1.1/wavs/LJ009-0098.wav|There! there! I see the Lamb of God! Oh! how happy! Oh! this is happy!
+LJSpeech-1.1/wavs/LJ015-0089.wav|in receiving guests at home, or could be spared from two rival establishments in other parts of the town.
+LJSpeech-1.1/wavs/LJ008-0201.wav|in an adjoining house, that of an undertaker, was Lord Alfred Paget, also with several friends.
+LJSpeech-1.1/wavs/LJ013-0255.wav|The victim was the first of these three.
+LJSpeech-1.1/wavs/LJ015-0124.wav|but it was based upon more extended and audacious forgeries.
+LJSpeech-1.1/wavs/LJ006-0235.wav|The surgeon's journal produced to the inspectors contained numerous entries of terrible wounds inflicted in a cowardly way.
+LJSpeech-1.1/wavs/LJ031-0115.wav|From approximately four p.m. to four:fifty p.m. on November twenty-two,
+LJSpeech-1.1/wavs/LJ050-0261.wav|from many Government agencies including the Department of Defense and the President's Office of Science and Technology.
+LJSpeech-1.1/wavs/LJ006-0277.wav|But there were evils akin to those on the male side, prominent amongst which was the undue influence accorded to prisoners.
+LJSpeech-1.1/wavs/LJ047-0090.wav|Our investigation of Oswald had disclosed no evidence that Oswald was acting under the instructions or on behalf of
+LJSpeech-1.1/wavs/LJ005-0134.wav|in twenty-two county jails there were one thousand sixty-three sleeping cells in all (in eighteen twenty-three)
+LJSpeech-1.1/wavs/LJ023-0140.wav|But all federal judges, once appointed,
+LJSpeech-1.1/wavs/LJ019-0322.wav|On the other hand, new and careful regulations were framed to secure the moral and material well-being of the inmates of the jails.
+LJSpeech-1.1/wavs/LJ046-0165.wav|Most of these cases involved persons who used threatening language in communications to or about the President.
+LJSpeech-1.1/wavs/LJ015-0138.wav|His fault was generosity,
+LJSpeech-1.1/wavs/LJ028-0411.wav|dressing the same, eating the same food as did their ancestors when Nebuchadnezzar built the walls of Babylon.
+LJSpeech-1.1/wavs/LJ008-0187.wav|It was still greater at Fauntleroy's execution in eighteen twenty-four, when no less than one hundred thousand persons assembled, it was said.
+LJSpeech-1.1/wavs/LJ045-0055.wav|He said this about members of the Russian-speaking group in the Dallas-Ft. Worth area, whom she said he tried to forbid her from seeing,
+LJSpeech-1.1/wavs/LJ040-0071.wav|From the time Marguerite Oswald returned to work until December twenty-six, nineteen forty-two, when Lee too was sent to the orphans' home,
+LJSpeech-1.1/wavs/LJ044-0040.wav|He wrote that, quote, thousands of circulars were distributed, end quote.
+LJSpeech-1.1/wavs/LJ023-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section nine.
+LJSpeech-1.1/wavs/LJ004-0021.wav|The neglect of prison reform in those days was not to be visited upon the legislature.
+LJSpeech-1.1/wavs/LJ003-0169.wav|Prisoners who could afford it sometimes paid for four beds, at the rate of twenty-eight shillings, and so secured the luxury of a private room.
+LJSpeech-1.1/wavs/LJ010-0139.wav|After the five bodies had hung for half-an-hour, a man in a mask came forward to complete the sentence.
+LJSpeech-1.1/wavs/LJ039-0074.wav|you should not have any difficulty in hitting your target. I mean it requires no training at all to shoot a weapon with a telescopic sight
+LJSpeech-1.1/wavs/LJ008-0239.wav|The loud shout of the multitude once more subsided, or only fell upon the abstracted ear like the dreamy murmur of an ocean shell.
+LJSpeech-1.1/wavs/LJ033-0026.wav|There was little conversation between them on the way home.
+LJSpeech-1.1/wavs/LJ008-0305.wav|they never ceased cursing until the passion of anger so excited was exchanged for joy in some and grief in others.
+LJSpeech-1.1/wavs/LJ012-0114.wav|but their request, in spite of their earnest entreaties,
+LJSpeech-1.1/wavs/LJ025-0092.wav|It is now established that nitrogen is as essential a constituent of vegetable as of animal living matter
+LJSpeech-1.1/wavs/LJ016-0257.wav|and the raison d'être of the penalty, which in principle so many opposed, would be gone.
+LJSpeech-1.1/wavs/LJ044-0133.wav|He had not found another job. His wife was expecting their second child in October and there was concern about the cost which would be involved.
+LJSpeech-1.1/wavs/LJ025-0039.wav|he afterward affirms that nitrogen is peculiar to animals, and herein he places the third distinction between the animal and the plant.
+LJSpeech-1.1/wavs/LJ043-0060.wav|from one of the elderly relatives with whom he spoke. Oswald's interest in such things presents a sharp contrast with his attitude
+LJSpeech-1.1/wavs/LJ015-0040.wav|This borrowing continued, and on such a scale that their paper was soon at a discount,
+LJSpeech-1.1/wavs/LJ003-0163.wav|the misdemeanant tried or untried, the debtor who wished to avoid the discomfort of the crowded debtors' side, the outspoken newspaper editor,
+LJSpeech-1.1/wavs/LJ033-0091.wav|Frazier told the Commission, quote,
+LJSpeech-1.1/wavs/LJ049-0187.wav|These proposals included suggestions to locate exclusive responsibility for all phases of the work
+LJSpeech-1.1/wavs/LJ028-0442.wav|At a distance of about two miles to the south of Babel is the larger and lower mound called the Kasr, or the Fortress,
+LJSpeech-1.1/wavs/LJ006-0134.wav|The governor himself admitted that a prisoner of weak intellect who had been severely beaten and much injured by a wardsman did not dare complain
+LJSpeech-1.1/wavs/LJ023-0127.wav|When I commenced to review the situation with the problem squarely before me,
+LJSpeech-1.1/wavs/LJ037-0117.wav|Both men picked Oswald as the man who had run south on Patton with a gun in his hand.
+LJSpeech-1.1/wavs/LJ039-0180.wav|As has been shown in chapter three, if the three shots were fired within a period of from four point eight to five point six seconds,
+LJSpeech-1.1/wavs/LJ043-0038.wav|nor his mother were objects of his affection, quote, but only examples of workers in the U.S., end quote.
+LJSpeech-1.1/wavs/LJ024-0059.wav|It is the clear intention of our public policy to provide for a constant flow of new and younger blood into the judiciary.
+LJSpeech-1.1/wavs/LJ009-0163.wav|In the same year capital punishment was further restricted, and ceased to be the legal sentence for coining,
+LJSpeech-1.1/wavs/LJ019-0007.wav|By other acts local authorities were empowered to construct new jails or hire accommodation in the district;
+LJSpeech-1.1/wavs/LJ018-0317.wav|and one warder admitted that he was to have one thousand pounds more paid to him, and to be provided with a passage to Australia.
+LJSpeech-1.1/wavs/LJ040-0169.wav|She observed that since Lee's mother worked all day, he made his own meals and spent all his time alone
+LJSpeech-1.1/wavs/LJ037-0259.wav|that the man who killed Tippit was wearing a light-colored jacket,
+LJSpeech-1.1/wavs/LJ048-0101.wav|The Commission concludes that the most significant advance arrangements for the President's trip were soundly planned.
+LJSpeech-1.1/wavs/LJ010-0312.wav|Among the prisoner's private papers, one was found giving full details of the stock he had feloniously sold out,
+LJSpeech-1.1/wavs/LJ009-0215.wav|All the shops in the neighborhood were shut, and the windows and tops of the houses were crowded with spectators.
+LJSpeech-1.1/wavs/LJ028-0130.wav|And after he had walled the city, and adorned its gates, he built another palace before his father's palace; but so that they joined to it:
+LJSpeech-1.1/wavs/LJ026-0040.wav|And in this connection the fact that some bacteria -- the simplest organisms known and devoid of chlorophyll
+LJSpeech-1.1/wavs/LJ016-0328.wav|Nevertheless, in the very next session
+LJSpeech-1.1/wavs/LJ004-0069.wav|In short, attention to his feelings, mental and bodily, a supply of every necessary, abstraction from evil society,
+LJSpeech-1.1/wavs/LJ016-0235.wav|He was so much in favor of short drops that his immediate successor, Marwood, stigmatized him as "short-drop" man.
+LJSpeech-1.1/wavs/LJ042-0229.wav|To the question of, quote, Are you a Communist? End quote, he first answered "Yes,"
+LJSpeech-1.1/wavs/LJ008-0217.wav|a throng of people whom neither rain, snow, storm, nor darkness ever hindered from attending the show.
+LJSpeech-1.1/wavs/LJ032-0194.wav|the Commission was unable to reach any firm conclusion as to when the fibers were caught in the rifle.
+LJSpeech-1.1/wavs/LJ021-0187.wav|It is perhaps not strange that the conservative British press has told us with pardonable irony
+LJSpeech-1.1/wavs/LJ047-0236.wav|was capable or potentially an assassin of the President of the United States, end quote.
+LJSpeech-1.1/wavs/LJ039-0161.wav|and the Infantry Weapons Evaluation Branch of the U.S. Army. There were no misfires.
+LJSpeech-1.1/wavs/LJ006-0282.wav|the pump was the only provision, and this in a place within sight of visitors, of the windows of the male turnkeys, and unprotected from the weather.
+LJSpeech-1.1/wavs/LJ010-0036.wav|Hocker was found upon the scene of his crime, irresistibly attracted thither, as was Theodore Gardelle.
+LJSpeech-1.1/wavs/LJ008-0273.wav|At the Old Bailey almost every one capitally convicted by a jury was sentenced to be hanged.
+LJSpeech-1.1/wavs/LJ006-0181.wav|It was the same old story -- evil constantly in the ascendant, the least criminal at the mercy of the most depraved.
+LJSpeech-1.1/wavs/LJ018-0289.wav|When all was ready, Bidwell first "refreshed his credit" at the Bank of England, as well as disarmed suspicion,
+LJSpeech-1.1/wavs/LJ018-0087.wav|Several cases of gigantic fraud, rivaling any already recorded, were brought to light between eighteen fifty-six and eighteen seventy-three.
+LJSpeech-1.1/wavs/LJ016-0098.wav|or any of the new establishments at home, at Portland, Dartmoor, or elsewhere.
+LJSpeech-1.1/wavs/LJ015-0240.wav|Agar at once got to work on the first safe.
+LJSpeech-1.1/wavs/LJ041-0182.wav|and believed that our Government did not have, quote, too much to offer, end quote, but was not in favor of, quote, the Communist way of life, end quote.
+LJSpeech-1.1/wavs/LJ012-0288.wav|and was seen to be busily engaged in washing down the house with bucket and mop.
+LJSpeech-1.1/wavs/LJ025-0015.wav|in which the question is treated with that comprehensiveness of knowledge and clear critical judgment which characterize his writings
+LJSpeech-1.1/wavs/LJ038-0060.wav|John Gibson, another patron in the theatre, saw an officer grab Oswald, and he claims that he heard the click of a gun misfiring.
+LJSpeech-1.1/wavs/LJ007-0207.wav|but this had the effect of throwing them into closer contact, and of making them more intimately acquainted with, more directly influential upon, one another.
+LJSpeech-1.1/wavs/LJ003-0025.wav|In the latter duties he was, however, supervised by three auditors, freely chosen by the prisoners among themselves.
+LJSpeech-1.1/wavs/LJ017-0213.wav|the first and second mates, Karswell and Taffir; there were two other Englishmen on board, and the rest of the crew were a polyglot lot,
+LJSpeech-1.1/wavs/LJ009-0064.wav|the lately smirking footmen close their eyes and forget their liveries, the ordinary clasps his hands, the turnkeys cry 'Hush!'
+LJSpeech-1.1/wavs/LJ019-0238.wav|The first "glass house," or room in which prisoners could talk in private with their attorneys, but yet be seen by the warder on the watch, had been constructed
+LJSpeech-1.1/wavs/LJ009-0165.wav|House-breaking, as distinguished from burglary, was similarly exempted in the following year;
+LJSpeech-1.1/wavs/LJ032-0145.wav|also bore impressions of the same irregularities that appeared on the barrel of the rifle.
+LJSpeech-1.1/wavs/LJ006-0136.wav|These wardsmen, besides thus ruling the roast, had numerous special privileges, if such they can be called.
+LJSpeech-1.1/wavs/LJ015-0129.wav|by inserting say one before five hundred, and thus making it
+LJSpeech-1.1/wavs/LJ031-0191.wav|Nine minutes later, the Presidential airplane departed for Washington, D.C.
+LJSpeech-1.1/wavs/LJ031-0117.wav|assisted by Drs. William Osborne and John Parker.
+LJSpeech-1.1/wavs/LJ010-0250.wav|of the criminal intent to kill.
+LJSpeech-1.1/wavs/LJ026-0103.wav|Just as the solid food of animals must be digested in preparation for absorption,
+LJSpeech-1.1/wavs/LJ002-0234.wav|In the yard behind the prison
+LJSpeech-1.1/wavs/LJ030-0086.wav|Vice-Presidential car.
+LJSpeech-1.1/wavs/LJ006-0179.wav|But, indeed, his whole rule was far too mild, and under this mistaken leniency
+LJSpeech-1.1/wavs/LJ042-0184.wav|Expanding on his ideas on how his alternative to communism and capitalism might be introduced, he wrote of a, quote, readily foreseeable
+LJSpeech-1.1/wavs/LJ034-0217.wav|One of these employees was alleged to resemble Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ032-0220.wav|Since Exhibit Number one thirty-three B was taken with Oswald's camera,
+LJSpeech-1.1/wavs/LJ039-0151.wav|Accuracy of Weapon
+LJSpeech-1.1/wavs/LJ028-0079.wav|The museum authorities believed that the cameo was one of the many spurious objects which the Eastern forgers were constantly sending to Europe,
+LJSpeech-1.1/wavs/LJ013-0108.wav|Next day a person betrayed him for the reward, and he was soon captured.
+LJSpeech-1.1/wavs/LJ038-0102.wav|Oswald also denied owning a rifle and said that since leaving the Marine Corps he had fired only a small bore twenty-two rifle.
+LJSpeech-1.1/wavs/LJ044-0007.wav|that he was involved in any conspiracy, his political activities do provide insight into certain aspects of Oswald's character
+LJSpeech-1.1/wavs/LJ008-0316.wav|"A. B., your case has been taken into consideration by the king in council, and His Majesty has been mercifully pleased to spare your life."
+LJSpeech-1.1/wavs/LJ019-0259.wav|while the former was intended for the congregate labor of a number, and the latter, as its name implies, imposed continuous solitary toil.
+LJSpeech-1.1/wavs/LJ014-0090.wav|On the Sunday Mrs. Manning roasted a goose at this same kitchen fire, and ate it with relish in the afternoon.
+LJSpeech-1.1/wavs/LJ029-0123.wav|was not used for the main portion of the downtown part of the motorcade because Main Street offered better vantage points for spectators.
+LJSpeech-1.1/wavs/LJ016-0204.wav|For acting as executioner of Horsemonger Lane Jail
+LJSpeech-1.1/wavs/LJ042-0224.wav|For example, in response to his questions about his decision to go to the Soviet Union, his first draft answered, quote,
+LJSpeech-1.1/wavs/LJ001-0085.wav|It was reserved for the founders of the later eighteenth century to produce letters which are positively ugly, and which, it may be added,
+LJSpeech-1.1/wavs/LJ040-0127.wav|or for detention pending court appearance or commitment to a child-caring or custodial institution such as a training school.
+LJSpeech-1.1/wavs/LJ013-0190.wav|and at last his lordship's watch was found secreted under the leads of the sink.
+LJSpeech-1.1/wavs/LJ012-0120.wav|one of them of considerable size.
+LJSpeech-1.1/wavs/LJ021-0168.wav|I do not want to think that it is the destiny of any American to remain permanently on relief rolls.
+LJSpeech-1.1/wavs/LJ023-0082.wav|In the last four years the sound rule of giving statutes the benefit of all reasonable doubt has been cast aside.
+LJSpeech-1.1/wavs/LJ030-0116.wav|looking out toward the crowd, and Special Agent Kellerman assumed his position next to the car.
+LJSpeech-1.1/wavs/LJ038-0176.wav|The Commission evaluated the following evidence in considering whether Lee Harvey Oswald fired the shot which almost killed General Walker:
+LJSpeech-1.1/wavs/LJ042-0193.wav|But these preferred tactics now may prove to be too limited in the near future,
+LJSpeech-1.1/wavs/LJ048-0044.wav|the knowledge of his defection, his arrogance and hostility to the United States,
+LJSpeech-1.1/wavs/LJ043-0177.wav|and his Communist and Socialist Worker's Party newspapers would probably have appeared on the front pages of newspapers or magazines all over the country,
+LJSpeech-1.1/wavs/LJ028-0503.wav|and decorated here and there with large reliefs representing bulls and lions and dragons,
+LJSpeech-1.1/wavs/LJ031-0017.wav|since the riders in his car, quote, were not exactly aware what had happened, end quote, and the car went on to the Trade Mart first.
+LJSpeech-1.1/wavs/LJ015-0062.wav|but in neither did the sums misappropriated reach quite the same high figure.
+LJSpeech-1.1/wavs/LJ016-0087.wav|Among the escapes still remembered was one in eighteen forty-nine, accomplished by a man who had been employed
+LJSpeech-1.1/wavs/LJ041-0101.wav|aggressive and even somewhat pugnacious, although Powers, quote, wouldn't say that this guy is a troublemaker, end quote.
+LJSpeech-1.1/wavs/LJ014-0340.wav|They could not deny that the latter was the truth, and were forthwith stigmatized by Mr. Chapman, Overend and Gurney's representative, as rogues.
+LJSpeech-1.1/wavs/LJ048-0002.wav|Chapter eight. The Protection of the President. Part three.
+LJSpeech-1.1/wavs/LJ004-0211.wav|The moral welfare of the inmates was as closely looked after as the physical.
+LJSpeech-1.1/wavs/LJ050-0230.wav|Manpower and Technical Assistance From Other Agencies
+LJSpeech-1.1/wavs/LJ040-0209.wav|well I've got to live with her. I guess I love her, end quote.
+LJSpeech-1.1/wavs/LJ012-0272.wav|They were apparently good friends when last seen together at a neighbor's, where they seemed "perfectly happy and sociable, and eager for the wedding day."
+LJSpeech-1.1/wavs/LJ014-0167.wav|When I came upon the scene at midnight, the shrillness of the cries and howls that were raised from time to time,
+LJSpeech-1.1/wavs/LJ038-0027.wav|Detective Paul L. Bentley rushed to the balcony and told the projectionist to turn up the house lights.
+LJSpeech-1.1/wavs/LJ043-0049.wav|After she had bought some clothes for Marina Oswald and a highchair for the baby, Oswald emphatically told her to stop.
+LJSpeech-1.1/wavs/LJ040-0171.wav|where he did as he wanted and he didn't have to live by any rules or come into contact with people, end quote.
+LJSpeech-1.1/wavs/LJ019-0363.wav|he could close the "inadequate" prison, by declaring it unfit for the reception of prisoners.
+LJSpeech-1.1/wavs/LJ016-0080.wav|and probably one of the few cases of a recurrence, but under proper safeguards and limitations, to the old system of chains.
+LJSpeech-1.1/wavs/LJ036-0060.wav|south on Houston, and southwest across the Houston viaduct to service the Oak Cliff area along Marsalis.
+LJSpeech-1.1/wavs/LJ010-0170.wav|His acquaintances often asked his object in this, but he kept his own counsel till the tenth June.
+LJSpeech-1.1/wavs/LJ026-0093.wav|The plant absorbs also a small amount of kinetic energy, independently of the sunlight, in the form of heat.
+LJSpeech-1.1/wavs/LJ046-0074.wav|If the sole goal were to protect the life of the President, it could be accomplished with reasonable assurance despite the multiple roles he must play.
+LJSpeech-1.1/wavs/LJ020-0086.wav|Hot rolls and muffins should never be cut.
+LJSpeech-1.1/wavs/LJ021-0017.wav|Instead of the give and take of free individual contract,
+LJSpeech-1.1/wavs/LJ029-0185.wav|by Vice President Johnson during the nineteen sixty campaign.
+LJSpeech-1.1/wavs/LJ023-0066.wav|and provide for the common defense and general welfare of the United States.
+LJSpeech-1.1/wavs/LJ040-0236.wav|and when his mother failed to cooperate in any way with school authorities,
+LJSpeech-1.1/wavs/LJ002-0028.wav|In order to realize the evils entailed by incarceration in Newgate in these days, it is necessary to give some account of its interior
+LJSpeech-1.1/wavs/LJ048-0278.wav|The Commission recognizes that the responsibilities of members of the White House detail of the Secret Service are arduous.
+LJSpeech-1.1/wavs/LJ008-0125.wav|The sheriff arrived, attended by his officers, to receive the prisoner from the keeper.
+LJSpeech-1.1/wavs/LJ005-0207.wav|Kidderminster had a prison, one damp chill room,
+LJSpeech-1.1/wavs/LJ016-0432.wav|The only subject another showed any interest in was the theatres and the new pieces that were being produced. A third, Christian Satler,
+LJSpeech-1.1/wavs/LJ044-0118.wav|He anticipated that the full disclosure of his defection would hinder him in, quote, the struggle for progress and freedom in the United States, end quote.
+LJSpeech-1.1/wavs/LJ015-0183.wav|His manner was generally self-possessed, but his face was marked with "uneasy earnestness,"
+LJSpeech-1.1/wavs/LJ017-0026.wav|Yet it was clearly proved that the dumplings contained arsenic, that she, and she alone, had made the dough,
+LJSpeech-1.1/wavs/LJ048-0245.wav|but that their visits to the Cellar were, quote, neither consistent nor inconsistent, end quote, with their duty.
+LJSpeech-1.1/wavs/LJ006-0109.wav|a perpetuation under another form of the old detestable custom of garnish.
+LJSpeech-1.1/wavs/LJ045-0125.wav|For example, in his letter of November nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ047-0087.wav|According to the Bureau, quote,
+LJSpeech-1.1/wavs/LJ018-0377.wav|Conviction was obtained through the evidence of the steward and two of the least culpable of the crew.
+LJSpeech-1.1/wavs/LJ013-0027.wav|He crept along the coast close in shore, looking for a quiet spot to cast away the ship,
+LJSpeech-1.1/wavs/LJ035-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ031-0182.wav|Concerned that the local officials might try to prevent the plane's departure, O'Donnell asked that the pilot take off immediately.
+LJSpeech-1.1/wavs/LJ033-0184.wav|had different characteristics from both the actual bag and the sample taken on November twenty-two.
+LJSpeech-1.1/wavs/LJ018-0235.wav|He embarked forthwith in a career of the wildest extravagance, and ere long he had parted in his mother's name with most of the landed estates.
+LJSpeech-1.1/wavs/LJ012-0204.wav|They presented themselves about noon one day at the dissecting room of King's College Hospital, accompanied by a third man,
+LJSpeech-1.1/wavs/LJ032-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ048-0147.wav|The Commission believes
+LJSpeech-1.1/wavs/LJ013-0189.wav|next, and in the same place, a chased gold key;
+LJSpeech-1.1/wavs/LJ020-0036.wav|Take care that it does not burn in baking. The molasses renders it liable to scorching.
+LJSpeech-1.1/wavs/LJ010-0029.wav|the cold-blooded, calculating atrocity born of self-interest, were still the irresistible incentives to kill.
+LJSpeech-1.1/wavs/LJ002-0156.wav|Thomas Dobson, on twenty-second August, seventeen ninety-nine, for one shilling, with costs of eight shillings, ten pence.
+LJSpeech-1.1/wavs/LJ021-0064.wav|not only to labor in the form of new jobs, in relief from overwork and in relief from underpay,
+LJSpeech-1.1/wavs/LJ050-0151.wav|it makes no use of the recent developments in automatic data processing which are widely used in the business world and in other Government offices.
+LJSpeech-1.1/wavs/LJ046-0099.wav|thoroughly professional personnel, using the best technical equipment that can be devised.
+LJSpeech-1.1/wavs/LJ030-0201.wav|According to Governor and Mrs. Connally, it was after this shot that Kellerman issued his emergency instructions and the car accelerated.
+LJSpeech-1.1/wavs/LJ016-0126.wav|The top of the wall was gained without difficulty.
+LJSpeech-1.1/wavs/LJ032-0078.wav|a Selective Service registration certificate, and a certificate of service in the U.S. Marine Corps, all three cards being in his own name.
+LJSpeech-1.1/wavs/LJ005-0179.wav|he total number of prisoners they received during the year varied from two persons to many hundreds.
+LJSpeech-1.1/wavs/LJ006-0053.wav|and the whole by proper management might have been so accommodated as to prevent overcrowding.
+LJSpeech-1.1/wavs/LJ019-0385.wav|Mr. (now Sir Richard) Cross, having applied himself vigorously to the task of reorganizing the whole system, became convinced
+LJSpeech-1.1/wavs/LJ037-0062.wav|The Commission reviewed the transcript of a phone conversation in which Mrs. Markham is alleged to have provided such a description.
+LJSpeech-1.1/wavs/LJ016-0429.wav|Others talk freely enough on various topics, but principally upon their own cases.
+LJSpeech-1.1/wavs/LJ047-0187.wav|It was then my plan to interview Marina Oswald in detail concerning both herself and her husband's background. Question:
+LJSpeech-1.1/wavs/LJ033-0163.wav|James C. Cadigan, a questioned-documents expert with the Bureau, compared the samples with the paper and tape in the actual bag.
+LJSpeech-1.1/wavs/LJ026-0009.wav|The fact is that they are on the border line, are neither plants nor animals but simply organisms.
+LJSpeech-1.1/wavs/LJ033-0092.wav|the main reason he was going over there that Thursday afternoon when he was to bring back some curtain rods, so I didn't think any more about it when he told me that, end quote,
+LJSpeech-1.1/wavs/LJ001-0059.wav|the greater part of these Italian printers, it should be mentioned, were Germans or Frenchmen, working under the influence of Italian opinion and aims.
+LJSpeech-1.1/wavs/LJ025-0171.wav|setting free the oxygen and laying hold of the carbon which it contains.
+LJSpeech-1.1/wavs/LJ044-0068.wav|as a result of which he was, quote, flooded with callers and invitations to debates, etc. as well as people interested in joining the F.P.C.C.
+LJSpeech-1.1/wavs/LJ040-0054.wav|It was a factor which contributed to his character and thereby might have influenced his decision to assassinate President Kennedy.
+LJSpeech-1.1/wavs/LJ025-0030.wav|As the animal body required to be independent of heat and of the atmosphere,
+LJSpeech-1.1/wavs/LJ019-0071.wav|moreover, it was nearly impossible to prevent communication and mutual contamination.
+LJSpeech-1.1/wavs/LJ023-0084.wav|When the Congress has sought to stabilize national agriculture, to improve the conditions of labor,
+LJSpeech-1.1/wavs/LJ040-0115.wav|Pic and his wife would have been happy to have kept Lee, however,
+LJSpeech-1.1/wavs/LJ011-0162.wav|and it stated that Mrs. Turner had been stricken with paralysis.
+LJSpeech-1.1/wavs/LJ004-0106.wav|They were to report in writing to quarter sessions as to the state of the jail, and as to all abuses which they might observe therein.
+LJSpeech-1.1/wavs/LJ015-0095.wav|The blow fell suddenly, and when least expected. One morning Mr. Fasson asked casually for certain certificates,
+LJSpeech-1.1/wavs/LJ005-0251.wav|Again in eighteen thirty-five prisons and their inmates became once more the care of the senate, and the subject was taken up this time by the House of Lords.
+LJSpeech-1.1/wavs/LJ006-0005.wav|just before the public mind was first awakened to the need for thorough reform.
+LJSpeech-1.1/wavs/LJ005-0034.wav|Undeterred by these sarcasms and misrepresentations,
+LJSpeech-1.1/wavs/LJ004-0040.wav|They felt that private enterprise might
+LJSpeech-1.1/wavs/LJ034-0018.wav|Next to these cartons was the handmade paper bag, previously discussed,
+LJSpeech-1.1/wavs/LJ043-0024.wav|Even though it appears that they may have left Oswald a few days before, it seems that he resisted the move as best he could.
+LJSpeech-1.1/wavs/LJ007-0048.wav|so subversive of meditation, so disturbing to the thoughts;
+LJSpeech-1.1/wavs/LJ001-0143.wav|For where these are boldly and carefully designed, and each letter is thoroughly individual in form,
+LJSpeech-1.1/wavs/LJ003-0132.wav|whose language and manners, whose female associates of the most abandoned description, and the scenes consequent with such lost wretches
+LJSpeech-1.1/wavs/LJ021-0055.wav|In meeting the problems of industrial recovery the chief agency of the government has been the National Recovery Administration.
+LJSpeech-1.1/wavs/LJ012-0162.wav|Having brought down the records of great frauds, forgeries, and thefts from about eighteen twenty-five to eighteen forty,
+LJSpeech-1.1/wavs/LJ005-0043.wav|and obtained full details, from places where they had been adopted, of the nature of these new machines
+LJSpeech-1.1/wavs/LJ050-0136.wav|the Committee will include representatives of the President's Office of Science and Technology, Department of Defense, CIA,
+LJSpeech-1.1/wavs/LJ037-0107.wav|They saw a man coming south on Patton with a revolver held high in his right hand. According to Callaway, the man crossed to the west side of Patton.
+LJSpeech-1.1/wavs/LJ012-0187.wav|one of his accomplices, who took the police to the pond, where the remains of the unfortunate Mr. Weare were discovered, sunk in a sack weighted by stones.
+LJSpeech-1.1/wavs/LJ046-0018.wav|to which the events of last November called attention.
+LJSpeech-1.1/wavs/LJ024-0104.wav|I am therefore, going to spend my time, my efforts and my money
+LJSpeech-1.1/wavs/LJ014-0195.wav|two of them supported Cope, who was still alive, although insensible, and Marley was apprehended. The evidence against him was completed
+LJSpeech-1.1/wavs/LJ042-0081.wav|is made only after the longest and most serious considerations. I affirm that my allegiance is to the Union of Soviet Socialist Republics.
+LJSpeech-1.1/wavs/LJ028-0280.wav|Calling to mind then the words of the Babylonian at the beginning of the siege:
+LJSpeech-1.1/wavs/LJ016-0164.wav|When discovered next morning, quite dead, it was found that the strap actually did not touch his throat;
+LJSpeech-1.1/wavs/LJ016-0095.wav|In eighteen fifty-three three men escaped in company from one of the wards in the middle yard.
+LJSpeech-1.1/wavs/LJ050-0175.wav|Since the assassination, Secret Service procedures have been changed to require that a member of PRS accompany each advance survey team
+LJSpeech-1.1/wavs/LJ041-0180.wav|never heard him in any way, shape or form confess that he was a Communist, or that he ever thought about being a Communist, end quote.
+LJSpeech-1.1/wavs/LJ002-0213.wav|The Fleet, which stood in Farringdon Street,
+LJSpeech-1.1/wavs/LJ012-0060.wav|to let the coach change and pass Petticoat Lane en route to the jail, where the suffering woman might be handed over to her friends.
+LJSpeech-1.1/wavs/LJ034-0126.wav|shows three employees looking out of the fifth-floor window directly below the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ015-0002.wav|The course of the swindlers was by no means smooth, but it was not till eighteen fifty-four that suspicion arose that anything was wrong.
+LJSpeech-1.1/wavs/LJ017-0162.wav|Whatever the exact cause which impelled him to crime, it seems certain that he began to give her some poison,
+LJSpeech-1.1/wavs/LJ010-0216.wav|and it is said that he is a military officer, but his name has not yet transpired.
+LJSpeech-1.1/wavs/LJ039-0069.wav|Maj. Eugene D. Anderson, assistant head of the Marksmanship Branch of U.S. Marine Corps
+LJSpeech-1.1/wavs/LJ019-0359.wav|He could in the first place withhold the government grant in aid of prison funds by refusing the certificate to the Treasury upon which the allowance was paid.
+LJSpeech-1.1/wavs/LJ005-0292.wav|In many of the smaller boroughs they are totally unfit for the confinement of human beings.
+LJSpeech-1.1/wavs/LJ035-0139.wav|They reentered the building by the rear door several minutes after Baker and Truly rushed through the front entrance.
+LJSpeech-1.1/wavs/LJ013-0220.wav|that Courvoisier was idle, discontented, ready to take offense, greedy of gain;
+LJSpeech-1.1/wavs/LJ048-0213.wav|watching his car, watching the sides, watching the crowds, giving advice or asking advice from the Chief
+LJSpeech-1.1/wavs/LJ030-0193.wav|Kellerman saw Governor Connally in his wife's lap and Special Agent Clinton J. Hill lying across the trunk of the car.
+LJSpeech-1.1/wavs/LJ018-0018.wav|he wore gold-rimmed eye-glasses and a gold watch and chain.
+LJSpeech-1.1/wavs/LJ047-0182.wav|was quite interested in determining the nature of his contact with the Soviet Embassy in Mexico City, end quote.
+LJSpeech-1.1/wavs/LJ041-0008.wav|He took walks and visited museums, and sometimes rode a rented bicycle in the park on Saturday mornings.
+LJSpeech-1.1/wavs/LJ028-0169.wav|a square enclosure two furlongs each way, with gates of solid brass; which was also remaining in my time.
+LJSpeech-1.1/wavs/LJ038-0216.wav|The references to house rent and payments for water and gas
+LJSpeech-1.1/wavs/LJ006-0306.wav|Some of the prisoners had their valets, and all these were constantly in and out of the kitchen where this female prisoner was employed.
+LJSpeech-1.1/wavs/LJ050-0028.wav|no sizable organization can achieve efficiency without the careful analysis and demarcation of responsibility
+LJSpeech-1.1/wavs/LJ038-0095.wav|by the Commission, they had probative value in deciding the weight to be given to his denials that he assassinated President Kennedy
+LJSpeech-1.1/wavs/LJ039-0213.wav|So that if you aimed with this weapon as it actually was received at the laboratory, it would not be necessary to take any lead whatsoever
+LJSpeech-1.1/wavs/LJ004-0079.wav|"All measures and practices in prison which may injure him in any way are illegal,
+LJSpeech-1.1/wavs/LJ015-0312.wav|The proceeds of these forgeries amounted, it was said, to some thousands per annum.
+LJSpeech-1.1/wavs/LJ028-0287.wav|but found none by which he could hope to prevail, unless he maimed himself and then went over to the enemy.
+LJSpeech-1.1/wavs/LJ034-0147.wav|Brennan stated that he was sure that the person firing the rifle was Oswald.
+LJSpeech-1.1/wavs/LJ023-0128.wav|I came by a process of elimination to the conclusion that, short of amendments,
+LJSpeech-1.1/wavs/LJ011-0007.wav|and was told by Mr. Fauntleroy that the lady had desired him to sell out, "which I have done," added the fraudulent banker, "and here are the proceeds,"
+LJSpeech-1.1/wavs/LJ025-0045.wav|They get rid of the superfluous hydrogen and carbon and accumulate nitrogen.
+LJSpeech-1.1/wavs/LJ005-0093.wav|The second act, passed in the following year, enlarged and amended the first, and at the same time gave powers to the House
+LJSpeech-1.1/wavs/LJ018-0217.wav|As such he had pretty general control over his father's estates and affairs.
+LJSpeech-1.1/wavs/LJ019-0258.wav|Both, however, varied greatly in mechanism and in the amount of energy they called forth,
+LJSpeech-1.1/wavs/LJ025-0006.wav|that the functions or workings of the organs of plants, animals or man are quite distinct,
+LJSpeech-1.1/wavs/LJ003-0123.wav|The most trifling acts were magnified into offenses.
+LJSpeech-1.1/wavs/LJ026-0059.wav|For this purpose the food is taken not into the body proper, but into a kind of tubular chemical laboratory; called the alimentary canal,
+LJSpeech-1.1/wavs/LJ032-0250.wav|In September nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ007-0163.wav|all the tumultuous and diversified passions and emotions which circumstances like these must necessarily generate
+LJSpeech-1.1/wavs/LJ043-0110.wav|beginning May ten, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ007-0215.wav|are the association of prisoners, and the unusual contamination to which such association gives rise.
+LJSpeech-1.1/wavs/LJ021-0178.wav|Has England gone back to the gold standard today?
+LJSpeech-1.1/wavs/LJ049-0160.wav|Since that time, the Secret Service has had and exercised responsibility for the physical protection of the President
+LJSpeech-1.1/wavs/LJ001-0084.wav|But for the beauty of the earlier work they might have seemed tolerable.
+LJSpeech-1.1/wavs/LJ014-0040.wav|that he rushed to a slaughterhouse in Hampstead and purposely stained his clothes with blood.
+LJSpeech-1.1/wavs/LJ047-0114.wav|and that they had been married in Fort Worth and lived there until coming to New Orleans. He had told the New Orleans arresting officers that he had been born in Cuba.
+LJSpeech-1.1/wavs/LJ038-0024.wav|Patrol cars bearing at least fifteen officers converged on the Texas Theatre.
+LJSpeech-1.1/wavs/LJ048-0277.wav|He felt that each agent recognized the seriousness of the infraction and that there was no danger of a repetition.
+LJSpeech-1.1/wavs/LJ001-0170.wav|being thin, tough, and opaque.
+LJSpeech-1.1/wavs/LJ030-0152.wav|Speed of the Limousine
+LJSpeech-1.1/wavs/LJ010-0213.wav|all of which declared their approval of the commander-in-chief.
+LJSpeech-1.1/wavs/LJ038-0132.wav|When asked why he lived at his roominghouse under the name O. H. Lee,
+LJSpeech-1.1/wavs/LJ028-0439.wav|for none of the many building inscriptions from Nebuchadnezzar mentions them.
+LJSpeech-1.1/wavs/LJ028-0152.wav|In the circuit of the wall are a hundred gates, all of brass, with brazen lintels and sideposts.
+LJSpeech-1.1/wavs/LJ038-0190.wav|three. I paid the house rent on the second so don't worry about it. four. Recently I also paid for water and gas.
+LJSpeech-1.1/wavs/LJ014-0336.wav|Last of all, the well-known bankers Overend and Gurney, whose own affairs created much excitement some years later,
+LJSpeech-1.1/wavs/LJ019-0226.wav|the erection of a wing or large block of cells was commenced within the original walls of the prison, and upon the north or male side.
+LJSpeech-1.1/wavs/LJ002-0040.wav|intended to accommodate a couple of prisoners apiece, but often much more crowded.
+LJSpeech-1.1/wavs/LJ028-0305.wav|troops for whose loss thou wilt care little, a thousand men.
+LJSpeech-1.1/wavs/LJ047-0136.wav|they had vacated their apartment, and Marina Oswald had departed with their child in a station wagon with Texas registration.
+LJSpeech-1.1/wavs/LJ016-0198.wav|I cannot find that Calcraft was sworn in when appointed, or any exact information when the old forbidding ceremony ceased to be practiced.
+LJSpeech-1.1/wavs/LJ042-0171.wav|simply expressed, the left and right, and their offspring factions and concerns. Any practical attempt at one alternative
+LJSpeech-1.1/wavs/LJ014-0092.wav|The hole must have been excavated and the quicklime purchased quite three weeks before O'Connor met his death,
+LJSpeech-1.1/wavs/LJ019-0176.wav|The wards had open fires, but the separate cells were not warmed at all.
+LJSpeech-1.1/wavs/LJ005-0214.wav|Some reforms had certainly been introduced, such as the abolition of irons, already referred to, and the establishment of male and female infirmaries.
+LJSpeech-1.1/wavs/LJ008-0310.wav|which was done with a sort of ceremony intended to be impressive.
+LJSpeech-1.1/wavs/LJ016-0022.wav|A harrowing scene followed;
+LJSpeech-1.1/wavs/LJ013-0137.wav|which the various club secretaries identified as the property of their respective clubs.
+LJSpeech-1.1/wavs/LJ020-0060.wav|turning the pan once in this time, and covering with clean -- never printed -- paper, should they brown too fast.
+LJSpeech-1.1/wavs/LJ042-0155.wav|it appears to be the work of a fairly well organized person.
+LJSpeech-1.1/wavs/LJ025-0159.wav|and this plant will, in due time, flower and produce its crop of beans just as if it were grown in the garden or in the field.
+LJSpeech-1.1/wavs/LJ022-0080.wav|Projects will be sought which promise ultimate return to the federal treasury of a considerable proportion of the costs.
+LJSpeech-1.1/wavs/LJ044-0168.wav|During this period Oswald may have practiced opening and closing the bolt on his rifle in a screened porch in his apartment.
+LJSpeech-1.1/wavs/LJ040-0219.wav|It would be incorrect, however, to believe that those aspects of Lee's personality which were observed in New York
+LJSpeech-1.1/wavs/LJ040-0067.wav|and his half-brother John Pic, who had been born in nineteen thirty-two during Marguerite's previous marriage.
+LJSpeech-1.1/wavs/LJ007-0121.wav|There were no restraints, cards and backgammon were played, and the time passed in feasting and revelry.
+LJSpeech-1.1/wavs/LJ011-0019.wav|when it was found that a sum of ten thousand pounds, standing in the name of three trustees, of whom Fauntleroy was one,
+LJSpeech-1.1/wavs/LJ003-0314.wav|the surgeon should see all prisoners, whether ill or well, once a week, and take general charge of the infirmaries.
+LJSpeech-1.1/wavs/LJ014-0196.wav|by his identification by Cope in Westminster Hospital, who survived long enough to make a formal deposition before Mr. Jardine,
+LJSpeech-1.1/wavs/LJ036-0003.wav|The Killing of Patrolman J. D. Tippit
+LJSpeech-1.1/wavs/LJ011-0063.wav|Fauntleroy was not entirely dependent upon the ordinary for ghostly counsel in his extremity.
+LJSpeech-1.1/wavs/LJ014-0208.wav|A murderous assault on a police constable, which so nearly ended fatally that the culprit was sentenced to death, although not executed,
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_2500_filelist.txt b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_2500_filelist.txt
new file mode 100644
index 00000000..95dae9ab
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_train_subset_2500_filelist.txt
@@ -0,0 +1,2500 @@
+LJSpeech-1.1/wavs/LJ026-0104.wav|so starch manufactured in the leaves must be digested (dissolved) before it can be transported.
+LJSpeech-1.1/wavs/LJ012-0101.wav|They had had serious work to get at the diamonds. It was necessary to force one heavy door from its hinges, and to cut through the thick panels of another.
+LJSpeech-1.1/wavs/LJ012-0130.wav|brought in a man-of-war from Brazil had been transhipped at Falmouth for conveyance to London.
+LJSpeech-1.1/wavs/LJ034-0049.wav|so no conclusions can be drawn as to whether these remaining prints preceded or followed the print developed in Dallas by powder.
+LJSpeech-1.1/wavs/LJ011-0271.wav|Mr. Gee went in the coach sent for him, and alighted at twenty-seven, York Street, West, Commercial Road.
+LJSpeech-1.1/wavs/LJ016-0393.wav|called for a roast duck directly he entered the condemned cell.
+LJSpeech-1.1/wavs/LJ009-0256.wav|Still he resisted.
+LJSpeech-1.1/wavs/LJ032-0195.wav|The Commission was able to conclude, however, that the fibers most probably came from Oswald's shirt.
+LJSpeech-1.1/wavs/LJ032-0016.wav|and (eight) Oswald's capability with a rifle.
+LJSpeech-1.1/wavs/LJ043-0053.wav|and became quite incensed with his wife when she would open the door for her in spite of his instructions to the contrary.
+LJSpeech-1.1/wavs/LJ008-0162.wav|Cries of Murder! murder! were now raised, and added greatly to the horrors of the scene.
+LJSpeech-1.1/wavs/LJ027-0026.wav|lead to a preview of certain principles of adaptation, necessary for their interpretation.
+LJSpeech-1.1/wavs/LJ037-0073.wav|Addressing itself solely to the probative value of Mrs. Markham's contemporaneous description of the gunman
+LJSpeech-1.1/wavs/LJ048-0182.wav|President Kennedy himself had mentioned it that morning, as had Agent Sorrels when he and Agent Lawson were fixing the motorcade route.
+LJSpeech-1.1/wavs/LJ048-0199.wav|and arrest any person who might attempt to throw anything or try to get at the President and his party;
+LJSpeech-1.1/wavs/LJ040-0104.wav|when he was sent to New Orleans to visit the family of his mother's sister, Mrs. Lillian Murret, for two or three weeks.
+LJSpeech-1.1/wavs/LJ018-0312.wav|A plot was soon discovered,
+LJSpeech-1.1/wavs/LJ016-0193.wav|Foxen asked him his name and address, and went away.
+LJSpeech-1.1/wavs/LJ014-0330.wav|He had several narrow escapes.
+LJSpeech-1.1/wavs/LJ028-0309.wav|After the twenty days are over, bid thy whole army attack the city on every side, and put me two bodies of Persians,
+LJSpeech-1.1/wavs/LJ018-0321.wav|An increase of policemen on duty sufficed to prevent any attempt of this kind.
+LJSpeech-1.1/wavs/LJ030-0039.wav|Men the motorcade slows or stops, agents take positions between the President and the crowd.
+LJSpeech-1.1/wavs/LJ009-0290.wav|He was at first engaged as assistant to the executioner Tom Cheshire, but in due course rose to be chief.
+LJSpeech-1.1/wavs/LJ038-0301.wav|(two) the photographs found among Oswald's possessions,
+LJSpeech-1.1/wavs/LJ022-0028.wav|I am reminded sometimes of what President Wilson once said:
+LJSpeech-1.1/wavs/LJ006-0214.wav|but no steps were taken to prevent any prisoner from obtaining more if he could pay for it.
+LJSpeech-1.1/wavs/LJ047-0209.wav|that he had owned a weapon and did a good deal of hunting or use of it, perhaps in Russia, plus a number of items about his disposition and unreliability of character,
+LJSpeech-1.1/wavs/LJ046-0140.wav|any communication, quote, that in any way indicates anyone may have possible intention of harming the President, end quote.
+LJSpeech-1.1/wavs/LJ016-0415.wav|but the story was never substantiated, and we may hope that it rested only on the idle gossip of the day.
+LJSpeech-1.1/wavs/LJ048-0106.wav|Much of Lawson's time was taken with establishing adequate security over the motorcade route and at the two places where the President would stop,
+LJSpeech-1.1/wavs/LJ045-0030.wav|when his wife asked Oswald not to come to Irving.
+LJSpeech-1.1/wavs/LJ001-0067.wav|In the Low Countries and Cologne, which were very fertile of printed books, Gothic was the favorite.
+LJSpeech-1.1/wavs/LJ003-0320.wav|which recommended restrictions upon the number of visitors admitted.
+LJSpeech-1.1/wavs/LJ018-0340.wav|characterized this as a crime more black and hideous than any in the criminal annals of the country.
+LJSpeech-1.1/wavs/LJ045-0114.wav|Oswald also said that he did not want the FBI to know where he lived, quote, Because their visits were not very pleasant for him
+LJSpeech-1.1/wavs/LJ038-0092.wav|Oswald provided little information during his questioning.
+LJSpeech-1.1/wavs/LJ030-0047.wav|The occupants scanned the crowd and the buildings along the route.
+LJSpeech-1.1/wavs/LJ033-0024.wav|Oswald replied, quote, I'm going home to get some curtain rods to put in an apartment, end quote.
+LJSpeech-1.1/wavs/LJ010-0081.wav|One of the conspirators, by name Edwards,
+LJSpeech-1.1/wavs/LJ028-0406.wav|The great irrigating dams across the Euphrates are constructed entirely of them.
+LJSpeech-1.1/wavs/LJ048-0230.wav|and by a Secret Service investigation.
+LJSpeech-1.1/wavs/LJ039-0053.wav|was of no probative value in the Commission's decision concerning the identity of the assassin of President Kennedy.
+LJSpeech-1.1/wavs/LJ034-0039.wav|In Latona's opinion, quote,
+LJSpeech-1.1/wavs/LJ039-0013.wav|and to the Commission on February twenty-one.
+LJSpeech-1.1/wavs/LJ027-0160.wav|that the embryo or larva of a frog or toad, when first hatched, is a legless, tail-swimming, water-breathing, gill-breathing animal.
+LJSpeech-1.1/wavs/LJ014-0283.wav|The jury found him guilty of the latter only, with a point of law reserved. This was fully argued before three judges,
+LJSpeech-1.1/wavs/LJ014-0166.wav|faded in my mind before the atrocious bearing, looks, and language of the assembled spectators.
+LJSpeech-1.1/wavs/LJ013-0093.wav|where the cash was transferred from the carpet-bag to a portmanteau.
+LJSpeech-1.1/wavs/LJ005-0208.wav|the only aperture through which air could be admitted being an iron grating level with the street,
+LJSpeech-1.1/wavs/LJ028-0303.wav|I think they will believe my words and entrust me with a command of troops. Thou, on thy part, must wait
+LJSpeech-1.1/wavs/LJ030-0146.wav|Just prior to the shooting, David F. Powers, riding in the Secret Service follow-up car, remarked to Kenneth O'Donnell
+LJSpeech-1.1/wavs/LJ003-0305.wav|The provision of more baths was also suggested, and the daily sweeping out of the prison.
+LJSpeech-1.1/wavs/LJ017-0068.wav|and public opinion at the termination of the trial coincided with the verdict of the jury.
+LJSpeech-1.1/wavs/LJ014-0136.wav|the culprit was really her husband, who killed O'Connor out of jealousy and revengeful feelings.
+LJSpeech-1.1/wavs/LJ041-0042.wav|He wrote a note in his mother's name to school authorities in New Orleans saying that he was leaving school because he and his mother were moving to San Diego.
+LJSpeech-1.1/wavs/LJ012-0053.wav|He was conveyed in a coach driven by a confederate, and under the escort of a couple of turnkeys.
+LJSpeech-1.1/wavs/LJ048-0094.wav|Lawson was responsible for working out a great many arrangements for the President's trip.
+LJSpeech-1.1/wavs/LJ033-0197.wav|quote, in all observable microscopic characteristics, end quote.
+LJSpeech-1.1/wavs/LJ015-0142.wav|He soon proved his ability, and by unremitting attention mastered the whole work of the office.
+LJSpeech-1.1/wavs/LJ012-0152.wav|which he refused to redeem on account of the row about the robbery.
+LJSpeech-1.1/wavs/LJ018-0208.wav|were a low lot, the lowest among criminals except, perhaps, the 'smashers,' or those who passed the counterfeit money.
+LJSpeech-1.1/wavs/LJ020-0017.wav|Now whip up the batter with a wooden spoon for another minute, and the sponge is made.
+LJSpeech-1.1/wavs/LJ021-0081.wav|with the confidence that we are definitely rebuilding our political and economic system on the lines laid down by the New Deal
+LJSpeech-1.1/wavs/LJ005-0288.wav|when speaking more particularly of the borough jails.
+LJSpeech-1.1/wavs/LJ045-0172.wav|Answer: He said he would buy me a washing machine.
+LJSpeech-1.1/wavs/LJ032-0009.wav|(two) the means by which the weapon was brought into the Depository Building,
+LJSpeech-1.1/wavs/LJ044-0148.wav|very unhappy, and that he actually wept when he told her that.
+LJSpeech-1.1/wavs/LJ015-0279.wav|One stroke of luck which he turned to great account
+LJSpeech-1.1/wavs/LJ034-0080.wav|When he reached the first floor, the west elevator -- the one with the gate was not there.
+LJSpeech-1.1/wavs/LJ004-0089.wav|and to pay overseers or instructors out of the county rates.
+LJSpeech-1.1/wavs/LJ044-0040.wav|He wrote that, quote, thousands of circulars were distributed, end quote.
+LJSpeech-1.1/wavs/LJ045-0213.wav|The conversation on Monday, November eighteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ029-0183.wav|On October three the Dallas Morning News quoted U.S. Representative Joe Pool's hope
+LJSpeech-1.1/wavs/LJ014-0023.wav|and many of the same sort were found in the possession of the accused. This was enough to obtain a committal,
+LJSpeech-1.1/wavs/LJ011-0291.wav|Two Bow Street runners were dispatched to the house in York Street, which had evidently been taken on purpose for the outrage.
+LJSpeech-1.1/wavs/LJ045-0151.wav|He then arrived on Thursday, November twenty-one, nineteen sixty-three, end quote.
+LJSpeech-1.1/wavs/LJ027-0088.wav|Extensive comparison, on the contrary, shows them to be the same, although the essential identity is obscured by adaptive modifications.
+LJSpeech-1.1/wavs/LJ028-0352.wav|whether of former or of later times, except only Cyrus with whom no person ever yet thought himself worthy to compare.
+LJSpeech-1.1/wavs/LJ031-0152.wav|Agents Kellerman and Hill telephoned the head of the White House detail, Gerald A. Behn, to advise him of the assassination.
+LJSpeech-1.1/wavs/LJ025-0084.wav|And if we substitute for the possession of an alimentary cavity the power of taking solid nutriment into the body and there digesting it,
+LJSpeech-1.1/wavs/LJ025-0149.wav|that, while locomotive, their movements may have as much appearance of spontaneity as those of the lowest animals,
+LJSpeech-1.1/wavs/LJ050-0158.wav|the Department hopes to design a practical system which will fully meet the needs of the Protective Research Section of the Secret Service.
+LJSpeech-1.1/wavs/LJ030-0123.wav|Special Agent Glen A. Bennett once left his place inside the follow-up car to help keep the crowd away from the President's car.
+LJSpeech-1.1/wavs/LJ022-0089.wav|charged with carrying on work relief projects.
+LJSpeech-1.1/wavs/LJ030-0008.wav|During the afternoon, President Kennedy dedicated the U.S. Air Force School of Aerospace Medicine at Brooks Air Force Base.
+LJSpeech-1.1/wavs/LJ007-0059.wav|had relaxed his efforts, because, according to his own account, he was so frequently stopped in the performance of his duties.
+LJSpeech-1.1/wavs/LJ037-0258.wav|There is no doubt, however, that Oswald was seen leaving his roominghouse at about one p.m. wearing a zipper jacket,
+LJSpeech-1.1/wavs/LJ039-0062.wav|the shots were at a slow-moving target proceeding on a downgrade in virtually a straight line
+LJSpeech-1.1/wavs/LJ032-0199.wav|the Oswalds lived on Neely Street in Dallas in a rented house which had a small back yard.
+LJSpeech-1.1/wavs/LJ014-0244.wav|which offered peculiar chances of profit to an ingenious and unscrupulous man.
+LJSpeech-1.1/wavs/LJ040-0128.wav|He was in Youth House from April sixteen to May seven, nineteen fifty-three,
+LJSpeech-1.1/wavs/LJ042-0062.wav|It shows how willing he was to act dramatically and decisively when he faced an emotional crisis
+LJSpeech-1.1/wavs/LJ050-0007.wav|have made it difficult for the Treasury to maintain close and continuing supervision.
+LJSpeech-1.1/wavs/LJ028-0021.wav|Again, long before the stars have been scattered by the morning sun, you are on your way.
+LJSpeech-1.1/wavs/LJ030-0033.wav|Vice President and Mrs. Johnson followed along the fence, guarded by four members of the Vice-Presidential detail.
+LJSpeech-1.1/wavs/LJ033-0123.wav|that the bag she saw Oswald carrying, quote, wasn't that long, I mean it was folded down at the top as I told you. It definitely wasn't that long, end quote,
+LJSpeech-1.1/wavs/LJ009-0130.wav|winked at other prisoners in derision of what was taking place; and I have frequently heard men and lads who had been of the kneeling party
+LJSpeech-1.1/wavs/LJ035-0201.wav|The fact that Truly found Fritz in the northwest corner of the floor, near the point where the rifle was found, supports Fritz' recollection.
+LJSpeech-1.1/wavs/LJ031-0113.wav|This operation was concluded at three:twenty p.m.
+LJSpeech-1.1/wavs/LJ014-0025.wav|A witness deposed to meeting Hocker, soon after the cries of murder were heard,
+LJSpeech-1.1/wavs/LJ014-0007.wav|He entered into conversation with the policemen, and learnt, as it seemed for the first time, what had happened.
+LJSpeech-1.1/wavs/LJ009-0110.wav|This exhibition lasts for some minutes, and then the congregation disperses,
+LJSpeech-1.1/wavs/LJ035-0090.wav|possible delayed reaction to the shot, jostling with the crowd of people on the steps and scanning the area along Elm Street and the parkway.
+LJSpeech-1.1/wavs/LJ028-0449.wav|Parts of the walls of Nineveh are still standing to the height of one hundred and twenty-five feet,
+LJSpeech-1.1/wavs/LJ050-0210.wav|the caseload of each FBI agent averaged twenty to twenty-five, and he felt that this was high.
+LJSpeech-1.1/wavs/LJ021-0119.wav|including a few of major importance.
+LJSpeech-1.1/wavs/LJ003-0279.wav|There was fear as to the unrestricted use of tools,
+LJSpeech-1.1/wavs/LJ031-0184.wav|Swearing in of the New President
+LJSpeech-1.1/wavs/LJ027-0030.wav|is associated with an adaptive modification of locomotor structures, legs and wings,
+LJSpeech-1.1/wavs/LJ006-0155.wav|He may have erred in some points through ignorance, but in others he was clearly guilty of culpable neglect.
+LJSpeech-1.1/wavs/LJ025-0053.wav|to the investigation of organic structure,
+LJSpeech-1.1/wavs/LJ047-0027.wav|In this way, it learned that when Oswald had arrived in the Soviet Union
+LJSpeech-1.1/wavs/LJ033-0046.wav|went out to the garage to paint some children's blocks, and worked in the garage for half an hour or so.
+LJSpeech-1.1/wavs/LJ037-0038.wav|at the southeast corner of the intersection, approximately fifty feet away.
+LJSpeech-1.1/wavs/LJ003-0117.wav|A bribe to the judge was certain to secure acquittal, and the neglect of the formality was as certainly followed by condemnation.
+LJSpeech-1.1/wavs/LJ030-0208.wav|Hill heard a noise, which seemed to be a firecracker, coming from his right rear.
+LJSpeech-1.1/wavs/LJ010-0041.wav|or like Cannon the chimney-sweeper, who savagely killed the policeman.
+LJSpeech-1.1/wavs/LJ002-0122.wav|The number of arrests actually made was one hundred fourteen thousand, three hundred for the kingdom, and seven thousand twenty for Middlesex.
+LJSpeech-1.1/wavs/LJ032-0080.wav|were a Selective Service notice of classification and a Marine certificate of service in the name of Alek James Hidell.
+LJSpeech-1.1/wavs/LJ036-0101.wav|He was taken to the lineup room where, according to Whaley, five young teenagers, all handcuffed together, were displayed with Oswald.
+LJSpeech-1.1/wavs/LJ044-0233.wav|Oswald was carrying only thirteen dollars, eighty-seven cents at the time of his arrest, although he had left, apparently by design,
+LJSpeech-1.1/wavs/LJ015-0134.wav|had assets in the shape of land, house, furniture, pictures, and objets d'art to the value of fifty thousand pounds.
+LJSpeech-1.1/wavs/LJ021-0067.wav|a rise from a deficit figure in the first quarter of nineteen thirty-three
+LJSpeech-1.1/wavs/LJ001-0121.wav|in reading the modern figures the eyes must be strained before the reader can have any reasonable assurance
+LJSpeech-1.1/wavs/LJ038-0304.wav|on April ten, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ014-0089.wav|For the remainder of that week and part of the next the murderers stayed in the house, and occupied the kitchen, close to the remains of their victim.
+LJSpeech-1.1/wavs/LJ008-0209.wav|Soon after midnight on the Sunday night, for by this time the present practice of executing on Monday morning had been pretty generally introduced,
+LJSpeech-1.1/wavs/LJ028-0336.wav|Darius now, still keeping to the plan agreed upon,
+LJSpeech-1.1/wavs/LJ031-0102.wav|Governor Connally was originally seen by Dr. Carrico and Dr. Richard Dulany.
+LJSpeech-1.1/wavs/LJ009-0133.wav|the outside public continued to be excluded from the Newgate chapel on the day the condemned sermon was preached.
+LJSpeech-1.1/wavs/LJ006-0177.wav|and it was not the least part of the blame imputed to him that he made special favorites of particular prisoners, retaining of his own accord in Newgate,
+LJSpeech-1.1/wavs/LJ049-0098.wav|In nineteen oh two bills passed both Houses of Congress but failed of enactment when the Senate refused to accept the conference report.
+LJSpeech-1.1/wavs/LJ014-0176.wav|His determined addiction to evil courses had led to his being cast off by his family,
+LJSpeech-1.1/wavs/LJ009-0301.wav|who deserted, and was pursued, but without success.
+LJSpeech-1.1/wavs/LJ047-0013.wav|Finally, the Commission has reviewed the complete files on Oswald, as they existed at the time of the assassination, of the Department of State,
+LJSpeech-1.1/wavs/LJ037-0157.wav|taken from Oswald.
+LJSpeech-1.1/wavs/LJ041-0087.wav|as a means of getting or attempting to get sympathy, end quote. In Thornley's view, Oswald labored under a persecution complex
+LJSpeech-1.1/wavs/LJ003-0090.wav|Mr. Newman admitted that he had petitioned that certain "trusty men" might be left in the jail.
+LJSpeech-1.1/wavs/LJ040-0015.wav|There is, however, a large amount of material available in his writings
+LJSpeech-1.1/wavs/LJ007-0068.wav|generally sided with his opponents. Nevertheless the inspectors summed up against him.
+LJSpeech-1.1/wavs/LJ029-0148.wav|for controlling the crowds and traffic, watching the overpasses, and providing motorcycle escort.
+LJSpeech-1.1/wavs/LJ035-0205.wav|and no one could be found who saw Oswald anywhere else in the building until after the shooting.
+LJSpeech-1.1/wavs/LJ017-0269.wav|let down. A full view of them was thus at all times obtainable by the officers who, without intermission, day and night patrolled the ward.
+LJSpeech-1.1/wavs/LJ010-0304.wav|through advances made to various builders, and that it could only maintain its credit by wholesale discounting.
+LJSpeech-1.1/wavs/LJ025-0138.wav|The results of inquiries into the structure of the nervous system of animals
+LJSpeech-1.1/wavs/LJ005-0262.wav|it was suggested that the rules framed for prison government should be subjected to the Secretary of State for approval,
+LJSpeech-1.1/wavs/LJ020-0105.wav|Weave the little duties in and under and among what seem to be the greater.
+LJSpeech-1.1/wavs/LJ010-0313.wav|the sum total amounting to some one hundred seventy thousand pounds, with a declaration in his own handwriting to the following effect.
+LJSpeech-1.1/wavs/LJ014-0169.wav|It will be in the memory of many that Mrs. Manning appeared on the scaffold in a black satin dress, which was bound tightly round her waist.
+LJSpeech-1.1/wavs/LJ020-0001.wav|Marion Harland's Cookery for Beginners. Bread Sponge and Breakfast Breads.
+LJSpeech-1.1/wavs/LJ048-0047.wav|his presence in the School Book Depository job and its location along the route of the motorcade.
+LJSpeech-1.1/wavs/LJ050-0145.wav|and the Commission recommends that these and the other measures suggested by the Commission be pursued vigorously by Secret Service.
+LJSpeech-1.1/wavs/LJ010-0151.wav|Again, a soldier, by name Hatfield, who had been wounded in the head, and discharged from the army for unsoundness of mind,
+LJSpeech-1.1/wavs/LJ016-0253.wav|that the ceremony may be made more mechanical, thus rendering the personal intervention of a skilled functionary unnecessary.
+LJSpeech-1.1/wavs/LJ036-0078.wav|claimed that about fifteen minutes after the assassination he saw a man, whom he later identified as Oswald,
+LJSpeech-1.1/wavs/LJ014-0066.wav|and by dint of usurious interest on small sums advanced to needy neighbors, had amassed as much as eight thousand pounds or ten thousand pounds.
+LJSpeech-1.1/wavs/LJ040-0126.wav|Oswald was remanded for psychiatric observation to Youth House, an institution in which children are kept for psychiatric observation
+LJSpeech-1.1/wavs/LJ009-0152.wav|The crowd outside Newgate on the day of execution has already been described; but there was also a select gathering of distinguished visitors within the jail.
+LJSpeech-1.1/wavs/LJ007-0179.wav|defeats the ends of justice, and disgraces the profession of a Christian country.
+LJSpeech-1.1/wavs/LJ028-0140.wav|The city stands on a broad plain, and is an exact square, a hundred and twenty furlongs in length each way,
+LJSpeech-1.1/wavs/LJ033-0044.wav|In the garage were the personal belongings of the Oswald family including, as the evidence has shown, the rifle wrapped in the old brown and green blanket.
+LJSpeech-1.1/wavs/LJ028-0472.wav|Its outer part, about twelve feet in width, was protected with towers at intervals of sixty-five feet.
+LJSpeech-1.1/wavs/LJ044-0016.wav|which was berthed at the Dumaine Street wharf in New Orleans, on June sixteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ031-0030.wav|Governor Connally, who had lost consciousness on the ride to the hospital, regained consciousness when the limousine stopped abruptly at the emergency entrance.
+LJSpeech-1.1/wavs/LJ029-0011.wav|President Kennedy's visit to Texas in November nineteen sixty-three had been under consideration for almost a year before it occurred.
+LJSpeech-1.1/wavs/LJ034-0157.wav|Brennan stated, quote, I could at that time -- I could, with all sincerity, identify him as being the same man, end quote.
+LJSpeech-1.1/wavs/LJ023-0110.wav|a super-legislature, as one of the justices has called it
+LJSpeech-1.1/wavs/LJ028-0231.wav|whereupon they withdrew within their defenses.
+LJSpeech-1.1/wavs/LJ016-0163.wav|He might have saved himself at any moment by merely extending an arm; but he lay there patiently till death supervened.
+LJSpeech-1.1/wavs/LJ003-0336.wav|but it at once deprecated the idea that the city could follow the laudable example thus set in the provinces. Quote,
+LJSpeech-1.1/wavs/LJ016-0147.wav|Then he ran along the coping of the wall towards its angle with Tyler's manufactory, and dropped down on to the gridiron below.
+LJSpeech-1.1/wavs/LJ036-0086.wav|The Commission could not accept important elements of Craig's testimony.
+LJSpeech-1.1/wavs/LJ030-0241.wav|President Johnson emphasized Youngblood's instantaneous reaction after the first shot:
+LJSpeech-1.1/wavs/LJ004-0058.wav|The untried, and in the eyes of the law still innocent, could claim pure air, wholesome and sufficient food, and opportunities for exercise.
+LJSpeech-1.1/wavs/LJ003-0041.wav|There was no lack of air and light for the new jail, and several exercising yards.
+LJSpeech-1.1/wavs/LJ048-0027.wav|Nowhere during the course of this investigation or the information that came to us from other agencies was there any indication of a potential for violence on his part.
+LJSpeech-1.1/wavs/LJ048-0045.wav|his pro-Castro tendencies, his lies when interrogated by the FBI,
+LJSpeech-1.1/wavs/LJ003-0217.wav|The condemned occupied an open pew in the center of the chapel, hung with black; in front of them, upon a table, was a black coffin in full view.
+LJSpeech-1.1/wavs/LJ011-0061.wav|Let this monster give his name; I am ready to fight him. I am still determined to put myself in the place of Mr. Fauntleroy.
+LJSpeech-1.1/wavs/LJ037-0127.wav|Four men -- Warren Reynolds, Harold Russell, Pat Patterson, and L. J. Lewis
+LJSpeech-1.1/wavs/LJ029-0208.wav|It was fashioned after the "wanted" circulars issued by law enforcement agencies.
+LJSpeech-1.1/wavs/LJ003-0236.wav|Garnish continued to be demanded long after it had disappeared in other and better-regulated prisons.
+LJSpeech-1.1/wavs/LJ041-0036.wav|He was a little dismayed at this, and he said that he couldn't find any that would show any interest in him as a Communist,
+LJSpeech-1.1/wavs/LJ007-0155.wav|to be subjected to the same baneful influences, and to suffer the same moral deterioration, whether ultimately convicted or set free.
+LJSpeech-1.1/wavs/LJ028-0070.wav|and how, in punishment for all his wickedness, he became a calf, and for seven years grazed the grass in the fields about the city.
+LJSpeech-1.1/wavs/LJ021-0136.wav|We have passed through more than a year of education.
+LJSpeech-1.1/wavs/LJ013-0250.wav|In the same room a large axe and saw were found covered with blood.
+LJSpeech-1.1/wavs/LJ002-0263.wav|the Lord Steward of the Household, the steward and officers of the Marshalsea Court, and others.
+LJSpeech-1.1/wavs/LJ032-0162.wav|Stombaugh testified that the colors, shades, and twist of the fibers found in the tuft on the rifle matched those in Oswald's shirt.
+LJSpeech-1.1/wavs/LJ038-0279.wav|I think is going overboard in the other direction.
+LJSpeech-1.1/wavs/LJ048-0023.wav|and he had told us during one of the interviews that he would probably take his wife back to Soviet Russia some time in the future.
+LJSpeech-1.1/wavs/LJ038-0118.wav|The Aliases "Hidell" and "O. H. Lee"
+LJSpeech-1.1/wavs/LJ015-0139.wav|an open-handed, unthinking charity which gave freely to the poor and needy the money which belonged to his creditors.
+LJSpeech-1.1/wavs/LJ015-0037.wav|and the partners had to decide between suspending payment or continuing to hold its head above water by flagitious processes.
+LJSpeech-1.1/wavs/LJ025-0090.wav|and resemblances between the constituents of animal and vegetable organisms, for which Cuvier is not responsible,
+LJSpeech-1.1/wavs/LJ012-0078.wav|One of the largest robberies of its class was that effected upon the Custom House in the winter of eighteen thirty-four.
+LJSpeech-1.1/wavs/LJ021-0208.wav|into the service of the privileged few.
+LJSpeech-1.1/wavs/LJ028-0245.wav|and mounting upon the walls along both sides of the stream, would so have caught the enemy as it were in a trap.
+LJSpeech-1.1/wavs/LJ022-0010.wav|At different points on the coast where I often visit they build great seagoing ships.
+LJSpeech-1.1/wavs/LJ017-0217.wav|Navigation and discipline could not be easy with such a nondescript crew.
+LJSpeech-1.1/wavs/LJ010-0126.wav|A crowd as great as any known collected in the Old Bailey to see the ceremony, about which there were some peculiar features worth recording.
+LJSpeech-1.1/wavs/LJ035-0046.wav|I was coming out this one on the second floor, and I don't know, I was kind of sweeping this area as I come up, I was looking from right to left
+LJSpeech-1.1/wavs/LJ037-0166.wav|He concluded, however, that he could not say whether the four bullets were fired from the revolver in Oswald's possession.
+LJSpeech-1.1/wavs/LJ045-0176.wav|The next morning he left for work before anyone else arose.
+LJSpeech-1.1/wavs/LJ012-0264.wav|a warrant was issued for his apprehension, which was effected at Kennington on the twenty-fourth March.
+LJSpeech-1.1/wavs/LJ014-0088.wav|This was on a Thursday evening.
+LJSpeech-1.1/wavs/LJ032-0017.wav|Ownership And Possession Of Assassination Weapon
+LJSpeech-1.1/wavs/LJ034-0093.wav|Brennan also testified that Lee Harvey Oswald,
+LJSpeech-1.1/wavs/LJ045-0137.wav|I and my wife strongly protested these tactics by the notorious F.B.I., end quote.
+LJSpeech-1.1/wavs/LJ004-0119.wav|which had continued through that long period, are forcibly pointed out.
+LJSpeech-1.1/wavs/LJ005-0067.wav|The Prison Society reproves the misdirected efforts of ambitious architects, who by a lavish and improvident expenditure of public money
+LJSpeech-1.1/wavs/LJ016-0080.wav|and probably one of the few cases of a recurrence, but under proper safeguards and limitations, to the old system of chains.
+LJSpeech-1.1/wavs/LJ002-0078.wav|seven. The press yard was that part set aside for the condemned.
+LJSpeech-1.1/wavs/LJ030-0043.wav|Its function was to alert police along the route that the motorcade was approaching and to check for signs of trouble.
+LJSpeech-1.1/wavs/LJ003-0148.wav|and spent in providing coals, candles, plates, knives, and forks; while all the occupants of this part of the prison
+LJSpeech-1.1/wavs/LJ033-0051.wav|Neither Marina Oswald nor Ruth Paine saw Oswald in the garage.
+LJSpeech-1.1/wavs/LJ010-0176.wav|He was on the right or north side of the road. Prince Albert occupied the same side of the carriage, the Queen the left.
+LJSpeech-1.1/wavs/LJ043-0153.wav|When asked if Oswald requested the note back she testified that, quote, He forgot about it. But apparently
+LJSpeech-1.1/wavs/LJ018-0160.wav|They next watched Buncher, and found that he paid frequent visits to Birmingham.
+LJSpeech-1.1/wavs/LJ018-0229.wav|The day his father died he got the keys of his private bureau, opened it, and took out the authentic will.
+LJSpeech-1.1/wavs/LJ031-0204.wav|with three Secret Service agents, accompanied President Kennedy's body on the forty-five-minute automobile trip from Andrews Air Force Base to the hospital.
+LJSpeech-1.1/wavs/LJ029-0184.wav|that President Kennedy would receive a "good welcome" and would not face demonstrations like those encountered
+LJSpeech-1.1/wavs/LJ049-0229.wav|while assuming no radical relocation of responsibilities,
+LJSpeech-1.1/wavs/LJ029-0136.wav|through the underpass and leads into an access road,
+LJSpeech-1.1/wavs/LJ003-0228.wav|and the general insufficiency was such
+LJSpeech-1.1/wavs/LJ033-0166.wav|Among other tests, the paper and tape were submitted to fiber analysis and spectrographic examination.
+LJSpeech-1.1/wavs/LJ049-0033.wav|to move into the passenger section without hindrance or delay. Had the vehicle been so designed it is possible that an agent riding in the front seat
+LJSpeech-1.1/wavs/LJ042-0121.wav|Mass gymnastics, compulsory afterwork meeting, usually political information meeting.
+LJSpeech-1.1/wavs/LJ018-0344.wav|In cold-blooded premeditation it rivaled that of the Mannings.
+LJSpeech-1.1/wavs/LJ003-0029.wav|Under the steward there were captains of wards, chosen in the same way, and performing analogous duties.
+LJSpeech-1.1/wavs/LJ020-0099.wav|When habited for the day in all except the outer gown, collar, etc., slip on the wrapper again and run down to put the biscuits in the oven.
+LJSpeech-1.1/wavs/LJ018-0214.wav|As the case must still be well remembered by the present generation, it will be necessary to give here only the briefest summary.
+LJSpeech-1.1/wavs/LJ015-0130.wav|fifteen hundred pounds, which larger amount was duly carried to his credit on the register, and entered upon the certificates of transfer.
+LJSpeech-1.1/wavs/LJ010-0105.wav|Thistlewood was discovered next morning in a mean house in White Street, Moorfields.
+LJSpeech-1.1/wavs/LJ041-0163.wav|out of a combination of Oswald's known Marxist sympathies and George Orwell's book "nineteen eighty-four," one of Oswald's favorite books
+LJSpeech-1.1/wavs/LJ006-0305.wav|The house at this time was full of men and visitors; waiters came in from the taverns with meals.
+LJSpeech-1.1/wavs/LJ006-0010.wav|Mr. Samuel Hoare, when examined, considered it indispensably necessary, to carry out whatever system might be established,
+LJSpeech-1.1/wavs/LJ009-0053.wav|but who is left to die on the supposition that this is not his first conviction, and still more because a good many sheep have of late been stolen by other people.
+LJSpeech-1.1/wavs/LJ017-0145.wav|Dr. Smethurst was long an inmate of Newgate, and was tried at the Central Criminal Court.
+LJSpeech-1.1/wavs/LJ035-0119.wav|until after they saw Patrolman Baker's white helmet on the fifth floor moving toward the elevator.
+LJSpeech-1.1/wavs/LJ017-0177.wav|Wilson was acquitted on this charge, but other suspicious facts cropped up while she was in Newgate.
+LJSpeech-1.1/wavs/LJ045-0044.wav|Many of the people with whom the Oswalds became acquainted after their arrival in the United States thought that Marina Oswald had married her husband primarily in the hope
+LJSpeech-1.1/wavs/LJ008-0108.wav|The ordinary "gravely uttered, 'Come this way, Mr. Smith.'
+LJSpeech-1.1/wavs/LJ010-0241.wav|but she declared she would not remain a prisoner in her own palace, and next day drove out as usual in an open barouche.
+LJSpeech-1.1/wavs/LJ050-0237.wav|the FBI, on sixteen separate occasions, supplied a total of one hundred thirty-nine agents to assist in protection work during a Presidential visit,
+LJSpeech-1.1/wavs/LJ032-0203.wav|on November twenty-two, nineteen sixty-three. One of these pictures, Exhibit Number one thirty-three A, shows most of the rifle's configuration.
+LJSpeech-1.1/wavs/LJ005-0116.wav|but an empty regulation which all so disposed could defy.
+LJSpeech-1.1/wavs/LJ047-0041.wav|Agent Fain reported to headquarters that Oswald was impatient and arrogant,
+LJSpeech-1.1/wavs/LJ012-0178.wav|Thurtell drove him down in a gig, "to be killed as he traveled," in Thurtell's own words.
+LJSpeech-1.1/wavs/LJ047-0193.wav|and so advised the Dallas office in the ordinary course of business.
+LJSpeech-1.1/wavs/LJ006-0130.wav|had a key of both the master's side and middle side yards, was the only person present at the distribution of beer, and was trusted to examine,
+LJSpeech-1.1/wavs/LJ009-0179.wav|the most recent enactment in force was the ninth George the fourth cap. thirty-one, which directed the dissection of all bodies of executed murderers,
+LJSpeech-1.1/wavs/LJ012-0046.wav|The second house was in Lower Queen Street, Islington, and he used it for some time as a depot for valuables.
+LJSpeech-1.1/wavs/LJ028-0483.wav|O Marduk, great god, look joyfully upon the precious work of my hands. Be thou my protector.
+LJSpeech-1.1/wavs/LJ041-0127.wav|Oswald must have already learned that the Governor could not help him with his discharge because he was no longer Secretary of the Navy, at the time he made that remark.
+LJSpeech-1.1/wavs/LJ012-0074.wav|Whether Ikey was "assigned" to his own family is not recorded, but no doubt he succeeded to his own property when the term of servitude had expired.
+LJSpeech-1.1/wavs/LJ009-0029.wav|This episode throws some doubt upon the tenderness and proper feeling exhibited by the chaplain towards the most deserving members of his criminal flock;
+LJSpeech-1.1/wavs/LJ032-0176.wav|Moreover, the bus transfer which he obtained as he left the bus was still in the pocket when he was arrested.
+LJSpeech-1.1/wavs/LJ037-0107.wav|They saw a man coming south on Patton with a revolver held high in his right hand. According to Callaway, the man crossed to the west side of Patton.
+LJSpeech-1.1/wavs/LJ004-0127.wav|In one hundred jails,
+LJSpeech-1.1/wavs/LJ006-0077.wav|The fact was, he did not keep the classification of prisoners on first arrival in his own hands, nor even in that of his officers.
+LJSpeech-1.1/wavs/LJ009-0259.wav|The wretched man did not fall with it, but jumped on to the platform, and seizing the rope with his hands, tried to avoid strangulation.
+LJSpeech-1.1/wavs/LJ035-0128.wav|If her estimate of time is correct, she reached the bottom of the stairs before Truly and Baker started up,
+LJSpeech-1.1/wavs/LJ005-0138.wav|that in four prisons, which at one time of the year contained one thousand three hundred eight prisoners, there were only sixty-eight sleeping rooms or cells,
+LJSpeech-1.1/wavs/LJ040-0108.wav|which became more significant as the children grew older.
+LJSpeech-1.1/wavs/LJ010-0167.wav|He left his last situation in April eighteen forty, and established himself in lodgings in Lambeth,
+LJSpeech-1.1/wavs/LJ036-0107.wav|because he was bawling out the policeman, telling them it wasn't right to put him in line with these teenagers and all of that
+LJSpeech-1.1/wavs/LJ044-0128.wav|there are a number of organizations, including possibly Fair Play, which are of a very broad character,
+LJSpeech-1.1/wavs/LJ017-0125.wav|He never actually said that he was not guilty, but he was confident he would not be convicted.
+LJSpeech-1.1/wavs/LJ039-0041.wav|I am getting a little confused with so many questions.
+LJSpeech-1.1/wavs/LJ003-0339.wav|could possibly afford.
+LJSpeech-1.1/wavs/LJ003-0156.wav|All the money earned by prisoners was at their own disposal, and was spent almost habitually in drink, chambering, and wantonness.
+LJSpeech-1.1/wavs/LJ035-0210.wav|On the basis of these findings the Commission has concluded that Oswald, at the time of the assassination,
+LJSpeech-1.1/wavs/LJ010-0256.wav|Undeterred by the well-merited punishment which had overtaken Francis,
+LJSpeech-1.1/wavs/LJ011-0013.wav|replaced the stock in the names of the original holders, who might otherwise have been completely ruined.
+LJSpeech-1.1/wavs/LJ036-0148.wav|After a review of these inconsistencies in his testimony before the Commission, Whaley was interviewed again in Dallas.
+LJSpeech-1.1/wavs/LJ019-0130.wav|since admirably developed in the convict prisons of this country.
+LJSpeech-1.1/wavs/LJ028-0389.wav|St. Jerome said:
+LJSpeech-1.1/wavs/LJ006-0195.wav|This was admitted in evidence by the turnkeys, and was proved by the appearance of the prison tables, which bore the marks of gaming-boards deeply cut into them.
+LJSpeech-1.1/wavs/LJ048-0034.wav|no law enforcement agency had any information to connect Oswald with the attempted shooting of General Walker.
+LJSpeech-1.1/wavs/LJ045-0243.wav|He does not appear to have been able to establish meaningful relationships with other people. He was perpetually discontented with the world around him.
+LJSpeech-1.1/wavs/LJ016-0057.wav|As his coat was an encumbrance, he left it on the top of the third house in Newgate Street, and thus in shirt-sleeves, barefoot and bareheaded,
+LJSpeech-1.1/wavs/LJ014-0061.wav|and Manning hoped to get some small Government appointment through his wife's interest.
+LJSpeech-1.1/wavs/LJ013-0084.wav|Burgess fraudulently transferred consols to the above amount, standing in the name of Mr. Oxenford, to another party.
+LJSpeech-1.1/wavs/LJ018-0126.wav|His object in visiting Whitchurch was to undermine the honesty of some workman in the mills;
+LJSpeech-1.1/wavs/LJ038-0201.wav|James C. Cadigan, FBI handwriting expert, testified that this note was written by Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ019-0310.wav|which had long been admitted as indispensable, and had never as yet been properly obtained.
+LJSpeech-1.1/wavs/LJ029-0182.wav|may not endorse him in 'sixty-four.
+LJSpeech-1.1/wavs/LJ043-0066.wav|Some of his acquaintances, feeling that Oswald tried to impress people with the fact that he had lived and worked in Russia, were led to the belief
+LJSpeech-1.1/wavs/LJ004-0132.wav|the old evils of indiscriminate association still continued unchecked.
+LJSpeech-1.1/wavs/LJ042-0197.wav|seemed to place him in a situation in which he could not live with satisfaction either in the United States or in the Soviet Union.
+LJSpeech-1.1/wavs/LJ018-0366.wav|Although sentence of death was passed on Edmunds, it was commuted to penal servitude for life;
+LJSpeech-1.1/wavs/LJ010-0237.wav|A youth named Pearson had seen him present a pistol at the Queen's carriage,
+LJSpeech-1.1/wavs/LJ049-0039.wav|to take a protective position in the passenger compartment before the third shot was fired.
+LJSpeech-1.1/wavs/LJ049-0155.wav|while investigating a plot to assassinate President Cleveland, the Service assigned a small protective detail of agents to the White House.
+LJSpeech-1.1/wavs/LJ008-0138.wav|and at his request the ordinary drew the cap further down over his face, when in an instant,
+LJSpeech-1.1/wavs/LJ027-0139.wav|and developmental changes which may be observed during the life history of now existing individuals belonging to the same group of animals.
+LJSpeech-1.1/wavs/LJ049-0010.wav|who responded to the unplanned event with dispatch.
+LJSpeech-1.1/wavs/LJ005-0149.wav|The nature of the employment varied greatly in severity, especially the tread-wheel labor.
+LJSpeech-1.1/wavs/LJ021-0041.wav|has been to clean up thoroughly unwholesome conditions in the field of investment.
+LJSpeech-1.1/wavs/LJ032-0065.wav|In accordance with postal regulations, the portion of the application which lists names of persons, other than the applicant, entitled to receive mail
+LJSpeech-1.1/wavs/LJ022-0020.wav|cause of clearer thinking and a better understanding, are considering the whole rather than a mere part relating to one section or to one crop,
+LJSpeech-1.1/wavs/LJ035-0146.wav|saw him walk through the clerical office on the second floor toward the door leading to the front stairway.
+LJSpeech-1.1/wavs/LJ018-0259.wav|he confessed that his whole life had been a gigantic mistake, and he was ready to make what atonement he could.
+LJSpeech-1.1/wavs/LJ040-0113.wav|They moved shortly after Robert joined the Marines; they lived for a time with John Pic who was stationed there with the Coast Guard.
+LJSpeech-1.1/wavs/LJ003-0239.wav|This imposition of fees left prisoners destitute on their discharge, without funds to support them in their first struggle to recommence life,
+LJSpeech-1.1/wavs/LJ028-0011.wav|As the taps upon her shoulder are repeated, she stretches out her long neck, and with long strides makes for the eastern horizon;
+LJSpeech-1.1/wavs/LJ041-0157.wav|He studied the Russian language, read a Russian language newspaper and seemed interested in what was going on in the Soviet Union.
+LJSpeech-1.1/wavs/LJ015-0015.wav|A more distressing case stands next on the criminal records --
+LJSpeech-1.1/wavs/LJ001-0048.wav|his letter is admirably clear and regular, but at least as beautiful as any other Roman type.
+LJSpeech-1.1/wavs/LJ006-0034.wav|They attended early and late; they mustered the prisoners, examined into their condition,
+LJSpeech-1.1/wavs/LJ004-0124.wav|In four hundred and forty-five prisons no work of any description had been introduced for the employment of prisoners;
+LJSpeech-1.1/wavs/LJ032-0106.wav|end quote. Hidell was a fictitious president of an organization of which Oswald was the only member.
+LJSpeech-1.1/wavs/LJ002-0026.wav|in the three years between eighteen thirteen and eighteen sixteen,
+LJSpeech-1.1/wavs/LJ043-0126.wav|told the prospective employer that Oswald was, quote, kinda peculiar sometimes and that he had some knowledge of the Russian language, end quote.
+LJSpeech-1.1/wavs/LJ019-0338.wav|But discipline was to be maintained if necessary by punishment,
+LJSpeech-1.1/wavs/LJ037-0077.wav|Barbara Jeanette Davis and Virginia Davis, were in an apartment of a multiple-unit house on the southeast corner of tenth and Patton
+LJSpeech-1.1/wavs/LJ034-0206.wav|confirmed that Euins could neither describe the man in the window nor indicate his race.
+LJSpeech-1.1/wavs/LJ006-0178.wav|and for years, felons who should have been sent beyond the seas.
+LJSpeech-1.1/wavs/LJ037-0112.wav|Apparently he had reached for his gun; it lay beneath him outside of the holster.
+LJSpeech-1.1/wavs/LJ028-0396.wav|they were supplied with food by keepers, and gave the king the opportunity of hunting whenever he felt inclined.
+LJSpeech-1.1/wavs/LJ047-0187.wav|It was then my plan to interview Marina Oswald in detail concerning both herself and her husband's background. Question:
+LJSpeech-1.1/wavs/LJ037-0220.wav|Approximately fifteen minutes before the shooting of Tippit, Oswald was seen leaving his roominghouse.
+LJSpeech-1.1/wavs/LJ040-0014.wav|He cannot, of course, be questioned or observed by those charged with the responsibility for this report or by experts on their behalf.
+LJSpeech-1.1/wavs/LJ045-0185.wav|he asked Frazier for a ride to Irving that night, stating falsely that he wanted to pick up some curtain rods to put in an apartment.
+LJSpeech-1.1/wavs/LJ011-0261.wav|He was found guilty of an assault with intent, and sentenced to transportation for fourteen years.
+LJSpeech-1.1/wavs/LJ046-0107.wav|Finally, the performance of those charged with the immediate responsibility of protecting the President on November twenty-two is reviewed.
+LJSpeech-1.1/wavs/LJ023-0073.wav|The Court claimed the power to declare it unconstitutional and did so declare it.
+LJSpeech-1.1/wavs/LJ048-0277.wav|He felt that each agent recognized the seriousness of the infraction and that there was no danger of a repetition.
+LJSpeech-1.1/wavs/LJ018-0050.wav|His arguments were specious and evasive when pressed to confess. "Why should man confess to man?" he replied;
+LJSpeech-1.1/wavs/LJ018-0170.wav|On the bed were twenty forged ten-pound notes complete and ready for use, and twenty-five five-pound notes.
+LJSpeech-1.1/wavs/LJ017-0163.wav|either arsenic or antimony, or both, in small quantities,
+LJSpeech-1.1/wavs/LJ003-0149.wav|supported themselves; they had the ration of prison bread only,
+LJSpeech-1.1/wavs/LJ016-0268.wav|The remarks heard amongst the crowd were of coarse approval.
+LJSpeech-1.1/wavs/LJ010-0180.wav|the Prince too rose to shield her with his person. Again, providentially, the bullet went wide of the mark,
+LJSpeech-1.1/wavs/LJ030-0183.wav|As he told the driver, quote, Let's get out of here; we are hit, end quote,
+LJSpeech-1.1/wavs/LJ014-0027.wav|A woman whom he called on the same evening declared he had worn a mackintosh, his coat was much torn, there was a stain of blood on his shirt-cuff,
+LJSpeech-1.1/wavs/LJ039-0011.wav|toward any official of the United States.
+LJSpeech-1.1/wavs/LJ016-0158.wav|It was that of a "Long Firm" swindler, by name Johnson,
+LJSpeech-1.1/wavs/LJ048-0213.wav|watching his car, watching the sides, watching the crowds, giving advice or asking advice from the Chief
+LJSpeech-1.1/wavs/LJ046-0050.wav|administrative, political.
+LJSpeech-1.1/wavs/LJ019-0306.wav|while twenty-seven received less than six prisoners, and were in some instances absolutely tenantless.
+LJSpeech-1.1/wavs/LJ030-0076.wav|It carried eight Secret Service agents -- two in the front seat, two in the rear, and two on each of the right and left running boards.
+LJSpeech-1.1/wavs/LJ024-0005.wav|has reached the age of seventy and does not avail himself of the opportunity to retire on a pension,
+LJSpeech-1.1/wavs/LJ004-0099.wav|Ventilators, hand and others, were to be supplied.
+LJSpeech-1.1/wavs/LJ030-0113.wav|through thinly populated areas on the outskirts of Dallas.
+LJSpeech-1.1/wavs/LJ019-0009.wav|or to insist upon the construction of prisons on the most approved plan.
+LJSpeech-1.1/wavs/LJ048-0287.wav|It is conceivable that those men who had little sleep, and who had consumed alcoholic beverages, even in limited quantities,
+LJSpeech-1.1/wavs/LJ023-0090.wav|It is the accusation of most distinguished justices of the present Supreme Court.
+LJSpeech-1.1/wavs/LJ039-0198.wav|but to determine the maximum speed at which it could be fired.
+LJSpeech-1.1/wavs/LJ016-0042.wav|The condition of the stone surface just mentioned assisted him in this, and he managed to get beyond the cistern to the railing below the chevaux-de-frise.
+LJSpeech-1.1/wavs/LJ049-0116.wav|of such persons as have been constitutionally and lawfully provided to succeed thereto in case of a vacancy. It is important to this country
+LJSpeech-1.1/wavs/LJ016-0400.wav|Wainwright's demeanor was one of reckless effrontery steadily maintained to the last.
+LJSpeech-1.1/wavs/LJ048-0236.wav|There is no indication that any of the agents who visited the Cellar Coffee House had any intoxicating drink at that establishment.
+LJSpeech-1.1/wavs/LJ008-0264.wav|But the murderers formed only a small proportion of the total number sentenced to death, and for the rest there was a long period of anxious suspense,
+LJSpeech-1.1/wavs/LJ039-0165.wav|The target at two hundred sixty-five feet was placed to the right of the two hundred forty-foot target
+LJSpeech-1.1/wavs/LJ035-0150.wav|As she approached her desk, she saw Oswald.
+LJSpeech-1.1/wavs/LJ009-0199.wav|About this time I find that the bodies of two murderers, Clench and Mackay,
+LJSpeech-1.1/wavs/LJ028-0516.wav|the strongest, the thickest, the loftiest, the most intricate, perhaps the most beautiful that ever protected a city,
+LJSpeech-1.1/wavs/LJ042-0150.wav|Marina Oswald's judgment of her husband's state of mind may be substantiated by comparing material which he wrote in the Soviet Union
+LJSpeech-1.1/wavs/LJ035-0081.wav|The second test followed immediately after the first.
+LJSpeech-1.1/wavs/LJ013-0009.wav|This was the willful shipwreck and casting away of a vessel which, with her supposed cargo, had been heavily insured.
+LJSpeech-1.1/wavs/LJ047-0232.wav|In his opinion, none of the information in the FBI files -- Oswald's defection, his Fair Play for Cuba activities in New Orleans,
+LJSpeech-1.1/wavs/LJ006-0028.wav|Mr. Crawford was thoroughly versed in the still imperfectly understood science of prison management, and fully qualified for his new duties.
+LJSpeech-1.1/wavs/LJ019-0168.wav|Renewed recommendations to provide employment resulted in the provision of a certain amount of oakum for picking,
+LJSpeech-1.1/wavs/LJ002-0030.wav|Full details of the arrangements are to be found in Mr. Neild's "State of Prisons in England, Scotland, and Wales," published in eighteen twelve.
+LJSpeech-1.1/wavs/LJ031-0208.wav|The hospital received the President's body for autopsy at approximately seven:thirty-five p.m.
+LJSpeech-1.1/wavs/LJ023-0108.wav|We are under a Constitution, but the Constitution is what the judges say it is.
+LJSpeech-1.1/wavs/LJ032-0021.wav|was a distributor of surplus Italian six point five-millimeter military rifles. During the evening of November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ017-0171.wav|Smethurst was therefore given a free pardon for the offense of murder,
+LJSpeech-1.1/wavs/LJ030-0096.wav|These agents performed for the Vice President the same functions that the agents in the Presidential follow-up car performed for the President.
+LJSpeech-1.1/wavs/LJ015-0065.wav|Robson was of humble origin, but he was well educated, and he had some literary abilities.
+LJSpeech-1.1/wavs/LJ032-0167.wav|He concluded, quote, There is no doubt in my mind that these fibers could have come from this shirt.
+LJSpeech-1.1/wavs/LJ003-0265.wav|the thieves and burglars who carried on the active business of their profession, from which their confederates were temporarily debarred.
+LJSpeech-1.1/wavs/LJ034-0218.wav|The Commission has determined that the employee was in fact Billy Lovelady, who identified himself in the picture.
+LJSpeech-1.1/wavs/LJ028-0141.wav|so that the entire circuit is four hundred and eighty furlongs.
+LJSpeech-1.1/wavs/LJ050-0140.wav|Secretary Dillon testified that the use of such liaison officers is the only effective way to insure that adequate liaison is maintained.
+LJSpeech-1.1/wavs/LJ027-0115.wav|it will be suffered to dwindle away in successive generations, under the influence of certain natural causes.
+LJSpeech-1.1/wavs/LJ009-0298.wav|which sets forth that the petitioner had been at great expense by sending clerks and agents to Liverpool and Shrewsbury to hire an executioner.
+LJSpeech-1.1/wavs/LJ036-0008.wav|(one) positive identification of the killer by two eyewitnesses who saw the shooting
+LJSpeech-1.1/wavs/LJ046-0019.wav|In this part of its inquiry the Commission has had full access to a major study of all phases of protective activities
+LJSpeech-1.1/wavs/LJ001-0086.wav|are dazzling and unpleasant to the eye owing to the clumsy thickening and vulgar thinning of the lines:
+LJSpeech-1.1/wavs/LJ010-0265.wav|and on his information handbills were circulated, giving the exact description of the deformed youth, who had a hump-back,
+LJSpeech-1.1/wavs/LJ028-0154.wav|at the point where the city of the same name stands, eight days' journey from Babylon.
+LJSpeech-1.1/wavs/LJ011-0285.wav|The door of his place of durance stood open, and Mr. Gee began to consider whether he might not escape.
+LJSpeech-1.1/wavs/LJ047-0082.wav|for an extended tour of Western European countries, the Soviet Union, Finland, and Poland.
+LJSpeech-1.1/wavs/LJ014-0100.wav|In the back kitchen one of the detectives remarked that the cement between certain stones looked lighter than the rest, and on trying it with a knife,
+LJSpeech-1.1/wavs/LJ048-0272.wav|Chief Rowley testified
+LJSpeech-1.1/wavs/LJ027-0057.wav|In all vertebrates, and in none other, the axis of this skeleton is a jointed backbone (vertebral column)
+LJSpeech-1.1/wavs/LJ018-0062.wav|Christian Sattler was by birth a German.
+LJSpeech-1.1/wavs/LJ015-0193.wav|By-and-by, when escape seemed hopeless, and after sentence, he suddenly degenerated into the lowest stamp of criminal,
+LJSpeech-1.1/wavs/LJ004-0066.wav|or his health by forcing him at night into a damp, unventilated cell, with such crowds of companions as very speedily render the air foul and putrid;
+LJSpeech-1.1/wavs/LJ018-0314.wav|Certain friends of the prisoners were watched, and found to be in communication with these warders,
+LJSpeech-1.1/wavs/LJ034-0112.wav|The New Orleans police records of his arrest in August of nineteen sixty-three show a weight of one hundred thirty-six pounds.
+LJSpeech-1.1/wavs/LJ031-0051.wav|He noted contusions, hematoma to the right of the larynx, which was deviated slightly to the left,
+LJSpeech-1.1/wavs/LJ007-0025.wav|There were frequent quarrels and fights; shoes and other missiles were freely bandied about;
+LJSpeech-1.1/wavs/LJ015-0148.wav|The choicest wines, the finest fruits,
+LJSpeech-1.1/wavs/LJ045-0027.wav|During the period from Oswald's return from Mexico to the assassination,
+LJSpeech-1.1/wavs/LJ047-0137.wav|On October three, Hosty reopened the case in Dallas to assist the New Orleans office.
+LJSpeech-1.1/wavs/LJ002-0325.wav|or the sixpenny allowance was claimed for the creditors, which seldom happened, owing to the expense the process entailed.
+LJSpeech-1.1/wavs/LJ045-0162.wav|He tried to start a conversation with me several times, but I would not answer. And he said that he didn't want me to be angry at him because this upsets him.
+LJSpeech-1.1/wavs/LJ013-0096.wav|Suspicions were aroused when it was found that he had been employed in selling stock for Mr. Oxenford, which developed into certainty
+LJSpeech-1.1/wavs/LJ031-0187.wav|Federal Judge Sarah T. Hughes hastened to the plane to administer the oath.
+LJSpeech-1.1/wavs/LJ047-0009.wav|who interviewed Oswald after his return from the Soviet Union and prior to November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ013-0035.wav|While lying in Newgate, awaiting removal to the convict ship, both prisoners made full confessions.
+LJSpeech-1.1/wavs/LJ021-0003.wav|Three months have passed since I talked with you shortly after the adjournment of the Congress.
+LJSpeech-1.1/wavs/LJ023-0131.wav|We must have men worthy and equipped to carry out impartial justice.
+LJSpeech-1.1/wavs/LJ016-0311.wav|that any secrecy in the treatment of the condemned would invest them with a new and greater interest, which was much to be deprecated.
+LJSpeech-1.1/wavs/LJ045-0174.wav|Thank you. That it would be better if he bought something for himself -- that I would manage. End quote. That night Oswald went to bed before his wife retired.
+LJSpeech-1.1/wavs/LJ030-0037.wav|are designed to provide protection while permitting large numbers of people to see the President.
+LJSpeech-1.1/wavs/LJ034-0082.wav|None of the Depository employees is known to have seen Oswald again until after the shooting.
+LJSpeech-1.1/wavs/LJ004-0194.wav|No irons were worn except as a punishment.
+LJSpeech-1.1/wavs/LJ043-0108.wav|Oswald's employment problems became more difficult. He left his wife and child at the home of a friend, Mrs. Ruth Paine, of Irving, Texas.
+LJSpeech-1.1/wavs/LJ039-0081.wav|Referring to a rifle with a four-power telescope, Sergeant Zahm said, quote,
+LJSpeech-1.1/wavs/LJ040-0200.wav|Furthermore she did not appear to understand her own relationship to Lee's psychological problems.
+LJSpeech-1.1/wavs/LJ025-0069.wav|innumerable plants and free plant cells are known to pass the whole or part of their lives in an actively locomotive condition,
+LJSpeech-1.1/wavs/LJ008-0184.wav|Precautions had been taken by the erection of barriers, and the posting of placards at all the avenues to the Old Bailey, on which was printed,
+LJSpeech-1.1/wavs/LJ003-0121.wav|But all punishments might readily be commuted into a fine to be spent in gin for judge and jury.
+LJSpeech-1.1/wavs/LJ047-0163.wav|According to Hosty, Mrs. Paine indicated that she thought she could find out where Oswald was living and would let him know.
+LJSpeech-1.1/wavs/LJ021-0065.wav|but also to the owners and managers of industry because,
+LJSpeech-1.1/wavs/LJ005-0089.wav|The religious and moral welfare of the prisoners were to be attended to,
+LJSpeech-1.1/wavs/LJ041-0130.wav|Probably his complaint was due to the fact that his discharge was not related to anything he had done while on active duty
+LJSpeech-1.1/wavs/LJ036-0095.wav|but the Commission concluded that this man was not Lee Harvey Oswald,
+LJSpeech-1.1/wavs/LJ030-0244.wav|hitting me on the shoulder, and shouted to all of us in the back seat to get down.
+LJSpeech-1.1/wavs/LJ021-0184.wav|Great Britain in many ways has advanced further along lines of social security than the United States?
+LJSpeech-1.1/wavs/LJ032-0247.wav|during the summer of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ017-0022.wav|was that of Eliza Fenning, who was convicted of an attempt to poison a whole family
+LJSpeech-1.1/wavs/LJ010-0141.wav|he was generally supposed to be a surgeon.
+LJSpeech-1.1/wavs/LJ036-0042.wav|However, McWatters' recollection alone was too vague to be a basis for placing Oswald on the bus.
+LJSpeech-1.1/wavs/LJ018-0281.wav|Their operations were no less worldwide.
+LJSpeech-1.1/wavs/LJ044-0057.wav|extensive investigation was not able to connect Oswald with that address, although it did develop the fact
+LJSpeech-1.1/wavs/LJ044-0033.wav|It is also evidence of Oswald's reluctance to describe events accurately
+LJSpeech-1.1/wavs/LJ046-0210.wav|to relate principally to overt threats to harm the President or other specific manifestations of hostility.
+LJSpeech-1.1/wavs/LJ024-0072.wav|a new and younger judge shall be added to the court automatically.
+LJSpeech-1.1/wavs/LJ007-0107.wav|A resolution at once passed the House without division to commit the whole to Newgate, where they remained for various terms.
+LJSpeech-1.1/wavs/LJ050-0191.wav|More importantly, the lack of carefully prepared and carefully transmitted instructions for typical visits to cities
+LJSpeech-1.1/wavs/LJ020-0051.wav|Flour a rolling-pin and roll the dough into a sheet not more than half an inch thick.
+LJSpeech-1.1/wavs/LJ016-0074.wav|The success, although very short-lived, which attended him, no doubt inspired other inmates of Newgate to follow his example.
+LJSpeech-1.1/wavs/LJ031-0221.wav|When put together, these fragments accounted for approximately three-quarters of the missing portion of the skull.
+LJSpeech-1.1/wavs/LJ015-0205.wav|who had been concerned in various "jobs" of a dishonest character, and who for the moment was a clerk in a betting office.
+LJSpeech-1.1/wavs/LJ001-0104.wav|Italy is contentedly stagnant.
+LJSpeech-1.1/wavs/LJ001-0157.wav|of this it may be said that though there is some good paper made now,
+LJSpeech-1.1/wavs/LJ047-0192.wav|On November eighteen the FBI learned that Oswald recently had been in communication with the Soviet Embassy in Washington
+LJSpeech-1.1/wavs/LJ001-0006.wav|And it is worth mention in passing that, as an example of fine typography,
+LJSpeech-1.1/wavs/LJ048-0100.wav|who had just completed advance work on the President's trip to Tampa.
+LJSpeech-1.1/wavs/LJ012-0004.wav|Inquiries set on foot also elicited the suspicion that the person who had represented Mrs. Canning's brother
+LJSpeech-1.1/wavs/LJ008-0086.wav|A great concourse of people attended on this melancholy occasion.
+LJSpeech-1.1/wavs/LJ023-0061.wav|that they were all the powers needed to meet each and every problem which then had a national character
+LJSpeech-1.1/wavs/LJ012-0255.wav|These were the missing members of the same mutilated trunk,
+LJSpeech-1.1/wavs/LJ018-0112.wav|It was one of these, Glendinning, who had allowed himself to be utilized for some time in this way, whose capture led to the breaking up of the gang.
+LJSpeech-1.1/wavs/LJ049-0052.wav|In response to inquiry by the Commission regarding the instructions to agents in a motorcade
+LJSpeech-1.1/wavs/LJ030-0026.wav|Governor and Mrs. Connally and Senator Ralph W. Yarborough had come with the President from Fort Worth.
+LJSpeech-1.1/wavs/LJ037-0085.wav|She was not sure whether she had seen his picture in a newspaper on the afternoon or evening of November twenty-two prior to the lineup.
+LJSpeech-1.1/wavs/LJ014-0092.wav|The hole must have been excavated and the quicklime purchased quite three weeks before O'Connor met his death,
+LJSpeech-1.1/wavs/LJ050-0014.wav|The incumbent has no technical qualifications in the area of Presidential protection.
+LJSpeech-1.1/wavs/LJ040-0203.wav|but essentially a, quote, defensive, rigid, self-involved person
+LJSpeech-1.1/wavs/LJ033-0026.wav|There was little conversation between them on the way home.
+LJSpeech-1.1/wavs/LJ043-0084.wav|business suit, alert replies -- Expresses self extremely well, end quote.
+LJSpeech-1.1/wavs/LJ022-0200.wav|renewed faith in the vast possibilities of human beings to improve their material and spiritual status
+LJSpeech-1.1/wavs/LJ008-0030.wav|The first affair of the kind on this spot was on the third December, seventeen eighty-three,
+LJSpeech-1.1/wavs/LJ044-0199.wav|In retrospect his attempt to go to Cuba or return to the Soviet Union may well have been Oswald's last escape hatch,
+LJSpeech-1.1/wavs/LJ029-0046.wav|Preventive Intelligence Activities. The Protective Research Section (PRS) of the Secret Service
+LJSpeech-1.1/wavs/LJ002-0048.wav|two. The female debtors' side consisted of a court-yard forty-nine by sixteen feet,
+LJSpeech-1.1/wavs/LJ029-0026.wav|it was agreed that the planning of events in Texas would be left largely to the Governor.
+LJSpeech-1.1/wavs/LJ050-0068.wav|According to Chief Rowley, by mid-June nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ012-0171.wav|Every door had been closed against him, every hope of future support blasted.
+LJSpeech-1.1/wavs/LJ042-0245.wav|While Marina Oswald tried to obtain permission to return to the Soviet Union, she testified that she did so at her husband's insistence.
+LJSpeech-1.1/wavs/LJ028-0353.wav|Darius, as the story goes, would often say that "he had rather Zopyrus were unmaimed, than be master of twenty more Babylons."
+LJSpeech-1.1/wavs/LJ006-0199.wav|the most popular being the "Times," "Morning Herald," and "Morning Chronicle"; on Sunday the "Weekly Dispatch," "Bell's Life," and the "Weekly Messenger."
+LJSpeech-1.1/wavs/LJ005-0209.wav|through the bars of which quills or reeds were inserted, and drink conveyed to the prisoners.
+LJSpeech-1.1/wavs/LJ005-0136.wav|The want of sleeping cells long continued a crying need.
+LJSpeech-1.1/wavs/LJ003-0179.wav|which will deal with Mrs. Fry's philanthropic exertions at this period in this particular part of the prison.
+LJSpeech-1.1/wavs/LJ034-0004.wav|Lee Harvey Oswald was hired on October fifteen, nineteen sixty-three, by the Texas School Book Depository as an "order filler."
+LJSpeech-1.1/wavs/LJ028-0182.wav|and in five fifty-five Nabonidus, the father of the Biblical Belshazzar, came to the throne.
+LJSpeech-1.1/wavs/LJ035-0007.wav|The encounter in the lunchroom.
+LJSpeech-1.1/wavs/LJ027-0131.wav|of descent discovered in the development of plant and animal embryos.
+LJSpeech-1.1/wavs/LJ013-0227.wav|The order was, however, at length obeyed, and the whole of the prisoner's clothes were minutely searched.
+LJSpeech-1.1/wavs/LJ039-0179.wav|stated that there was a shorter interval between shots two and three than between shots one and two.
+LJSpeech-1.1/wavs/LJ010-0057.wav|are by this time actually, and by more legitimate efforts, engrafted upon our Constitution.
+LJSpeech-1.1/wavs/LJ040-0115.wav|Pic and his wife would have been happy to have kept Lee, however,
+LJSpeech-1.1/wavs/LJ023-0006.wav|Tonight, sitting at my desk in the White House, I make my first radio report to the people in my second term of office.
+LJSpeech-1.1/wavs/LJ033-0148.wav|Using a standard chemical method involving silver nitrates
+LJSpeech-1.1/wavs/LJ016-0262.wav|It was a callous, careless crowd of coarse-minded, semi-brutalized folk, who came to enjoy themselves.
+LJSpeech-1.1/wavs/LJ011-0258.wav|The defense he set up was, that Mullay had used epithets towards him while they were negotiating a business matter,
+LJSpeech-1.1/wavs/LJ013-0050.wav|But among the charges on the estate he left
+LJSpeech-1.1/wavs/LJ002-0149.wav|The latter indeed hung like millstones round the neck of the unhappy insolvent wretches who found themselves in limbo.
+LJSpeech-1.1/wavs/LJ016-0416.wav|Many, like Wainwright, were calm and imperturbable throughout their trying ordeal.
+LJSpeech-1.1/wavs/LJ040-0111.wav|with no behavior or truancy problems.
+LJSpeech-1.1/wavs/LJ012-0045.wav|he put another lady, with whom he was on intimate terms.
+LJSpeech-1.1/wavs/LJ038-0234.wav|Although Oswald destroyed the notebook, three photographs found among Oswald's possessions after the assassination
+LJSpeech-1.1/wavs/LJ002-0134.wav|The County Court was the sheriff's, who sat there surrounded by the bishop and the magnates of the county;
+LJSpeech-1.1/wavs/LJ038-0247.wav|A fourth photograph, showing a stretch of railroad tracks,
+LJSpeech-1.1/wavs/LJ004-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section seven: The beginnings of prison reform.
+LJSpeech-1.1/wavs/LJ038-0132.wav|When asked why he lived at his roominghouse under the name O. H. Lee,
+LJSpeech-1.1/wavs/LJ042-0240.wav|he continued to be interested in that country after he returned to the United States.
+LJSpeech-1.1/wavs/LJ034-0100.wav|this man I saw previously was aiming for his last shot. As it appeared to me he was standing up and resting against the left window sill, end quote.
+LJSpeech-1.1/wavs/LJ014-0228.wav|generally known by the soubriquet of "General Haynau," a name execrated in England about this time.
+LJSpeech-1.1/wavs/LJ018-0103.wav|and silver coins, with all the latest appliances for coining, including those of electroplating;
+LJSpeech-1.1/wavs/LJ038-0217.wav|indicated that the note was written when they were living in a rented apartment; therefore it could not have been written while Marina Oswald was living with the Paines.
+LJSpeech-1.1/wavs/LJ050-0222.wav|would take approximately twenty months to implement and require expenditures of approximately three million dollars during that period.
+LJSpeech-1.1/wavs/LJ001-0050.wav|and though the famous family of Aldus restored its technical excellence, rejecting battered letters,
+LJSpeech-1.1/wavs/LJ010-0170.wav|His acquaintances often asked his object in this, but he kept his own counsel till the tenth June.
+LJSpeech-1.1/wavs/LJ028-0284.wav|When he found that Darius did indeed value it highly, he considered further with himself how he might make the deed his own, and be the man to take Babylon.
+LJSpeech-1.1/wavs/LJ019-0351.wav|Nor were these the only inducements offered. Where local authorities were indisposed to set their prisons in order,
+LJSpeech-1.1/wavs/LJ030-0218.wav|the right rear tail, when she noticed that I was trying to climb on the car.
+LJSpeech-1.1/wavs/LJ012-0030.wav|Thus, a watch was paid for as a watch, whether it was of gold or silver; a piece of linen as such, whether the stuff was coarse or fine.
+LJSpeech-1.1/wavs/LJ042-0244.wav|Oswald subsequently did subscribe to several Soviet journals.
+LJSpeech-1.1/wavs/LJ035-0043.wav|Yet he must have entered the vestibule door before Truly reached the top of the stairwell, since Truly did not see him.
+LJSpeech-1.1/wavs/LJ018-0068.wav|Inspector Thain, who, being unable to obtain his extradition legally, had him inveigled on board an English steamer,
+LJSpeech-1.1/wavs/LJ023-0091.wav|I have not the time to quote to you all the language used by dissenting justices in many of these cases.
+LJSpeech-1.1/wavs/LJ022-0005.wav|It has made and is making distinct progress.
+LJSpeech-1.1/wavs/LJ028-0178.wav|the walls of Babylon were so long and wide and high that all who saw them were amazed.
+LJSpeech-1.1/wavs/LJ050-0193.wav|Such instructions will not fit all circumstances, of course,
+LJSpeech-1.1/wavs/LJ029-0212.wav|"Welcome Mr. Kennedy to Dallas," sponsored by the American Fact-finding Committee, which the sponsor later testified was an ad hoc committee
+LJSpeech-1.1/wavs/LJ019-0063.wav|who maintained that under this system prisoners were more industrious and more healthy
+LJSpeech-1.1/wavs/LJ019-0126.wav|The creation of an expensive staff for supervision,
+LJSpeech-1.1/wavs/LJ045-0237.wav|His denials under questioning, which have no probative value in view of the many readily demonstrable lies he told at that time
+LJSpeech-1.1/wavs/LJ042-0007.wav|At the age of nineteen, Oswald thus committed an act which was the most striking indication he had yet given
+LJSpeech-1.1/wavs/LJ047-0143.wav|The possible contact with the Soviet Embassy in Mexico intensified the FBI's interest in learning Oswald's whereabouts.
+LJSpeech-1.1/wavs/LJ049-0176.wav|On the other hand, the Secret Service had no knowledge whatever of Oswald, his background, or his employment at the Book Depository,
+LJSpeech-1.1/wavs/LJ050-0232.wav|to assist in its protection functions.
+LJSpeech-1.1/wavs/LJ008-0017.wav|and when he evaded the sentence by suicide, his body was exhibited in the same neighborhood,
+LJSpeech-1.1/wavs/LJ042-0072.wav|Still intent, however, on staying in the Soviet Union,
+LJSpeech-1.1/wavs/LJ010-0245.wav|one of the equerries. The Queen was untouched, and at first, it is said, hardly realized the danger she had escaped.
+LJSpeech-1.1/wavs/LJ006-0260.wav|From the same source came the two or three strong files which the inspectors found in one ward,
+LJSpeech-1.1/wavs/LJ027-0165.wav|Now, the lower forms of amphibians, such as siredon, menobranchus, siren, etc.,
+LJSpeech-1.1/wavs/LJ033-0192.wav|When Paul M. Stombaugh of the FBI Laboratory examined the paper bag,
+LJSpeech-1.1/wavs/LJ018-0117.wav|Wagner, after conviction, offered to reveal, for a reward of three thousand pounds
+LJSpeech-1.1/wavs/LJ003-0271.wav|Some of the worst and most extensive burglaries were planned there.
+LJSpeech-1.1/wavs/LJ008-0068.wav|But the fall apart and inwards of two leaves is considered superior.
+LJSpeech-1.1/wavs/LJ018-0152.wav|With her assistance on a certain day a couple of bricks were taken out of the wall dividing her front and back parlors;
+LJSpeech-1.1/wavs/LJ019-0396.wav|At a short distance stood another prison of detention, that of Clerkenwell,
+LJSpeech-1.1/wavs/LJ016-0338.wav|inside the jail was Colonel Frazer, the chief commissioner of the city police, and at no great distance, although in the background,
+LJSpeech-1.1/wavs/LJ021-0089.wav|and have effected a reorganization of the N.R.A.
+LJSpeech-1.1/wavs/LJ044-0230.wav|His unhappy experience with the Cuban consul seems thus to have reduced his enthusiasm for the Castro regime and his desire to go to Cuba.
+LJSpeech-1.1/wavs/LJ018-0133.wav|which gave him access to all parts of the mills, the packing-room included.
+LJSpeech-1.1/wavs/LJ039-0190.wav|Simmons testified that familiarity with the bolt could be achieved in dry practice and, as has been indicated above,
+LJSpeech-1.1/wavs/LJ050-0250.wav|The Secret Service will be better able to plan its own long-range personnel requirements if it knows with reasonable certainty
+LJSpeech-1.1/wavs/LJ040-0168.wav|She thought that he had detached himself from the world around him because, quote, no one in it ever met any of his needs for love, end quote.
+LJSpeech-1.1/wavs/LJ010-0097.wav|Here they were surprised by the police, headed by a magistrate, and supported by a strong detachment of Her Majesty's Guards.
+LJSpeech-1.1/wavs/LJ040-0064.wav|This occurred two months before Lee was born in New Orleans on October eighteen, nineteen thirty-nine.
+LJSpeech-1.1/wavs/LJ050-0199.wav|The Commission strongly encourages these efforts to improve protection along a motorcade route.
+LJSpeech-1.1/wavs/LJ028-0322.wav|Thus did Zopyrus speak.
+LJSpeech-1.1/wavs/LJ008-0208.wav|Some years later an eye-witness published a graphic account of one of these scenes.
+LJSpeech-1.1/wavs/LJ027-0023.wav|that the only reasonable explanation for the existence of a fundamental unity in organic life
+LJSpeech-1.1/wavs/LJ012-0170.wav|According to his statement, when sentenced to death, he had been driven to horse-stealing by the execration which had pursued him after the murder.
+LJSpeech-1.1/wavs/LJ003-0242.wav|was still practiced, that of loading newly-arrived prisoners until they paid certain fees.
+LJSpeech-1.1/wavs/LJ007-0213.wav|Again the following year the inspectors repeat their charge.
+LJSpeech-1.1/wavs/LJ014-0108.wav|It was soon ascertained that the wife had gone off in a cab with a quantity of luggage.
+LJSpeech-1.1/wavs/LJ035-0151.wav|He was walking into the office from the back hallway,
+LJSpeech-1.1/wavs/LJ010-0253.wav|The enthusiasm of the people at the Queen's escape was uproarious, and her drive next day was one long triumphal progress.
+LJSpeech-1.1/wavs/LJ001-0116.wav|still clings to a foolish, because misunderstood conventionality, deduced from what was once ornament, and is by no means useful;
+LJSpeech-1.1/wavs/LJ041-0203.wav|to engage in activities on behalf of the Fair Play for Cuba Committee in the summer of nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ030-0195.wav|Observing his blood-covered chest as he was pulled into his wife's lap, Governor Connally believed himself mortally wounded.
+LJSpeech-1.1/wavs/LJ045-0119.wav|which had led to the disclosure of his defection in New Orleans.
+LJSpeech-1.1/wavs/LJ019-0359.wav|He could in the first place withhold the government grant in aid of prison funds by refusing the certificate to the Treasury upon which the allowance was paid.
+LJSpeech-1.1/wavs/LJ001-0174.wav|should be a part of the whole scheme of the book.
+LJSpeech-1.1/wavs/LJ037-0148.wav|in Oswald's possession to the exclusion of all other weapons.
+LJSpeech-1.1/wavs/LJ009-0260.wav|The spectacle was horrible;
+LJSpeech-1.1/wavs/LJ028-0152.wav|In the circuit of the wall are a hundred gates, all of brass, with brazen lintels and sideposts.
+LJSpeech-1.1/wavs/LJ031-0108.wav|Rubber tubes were inserted between the second and third ribs to reexpand the right lung, which had collapsed because of the opening in the chest wall.
+LJSpeech-1.1/wavs/LJ005-0298.wav|to the county jails from such prisons as were past improvement, and that the borough funds should be charged for the accommodation.
+LJSpeech-1.1/wavs/LJ020-0087.wav|One word with regard to getting up early in order to give dough a chance for the second rising.
+LJSpeech-1.1/wavs/LJ005-0069.wav|Absence of embellishment is in perfect unison with the character of the establishment.
+LJSpeech-1.1/wavs/LJ003-0328.wav|Had they been accepted in their entirety, little fault could in future have been found with the managers of Newgate.
+LJSpeech-1.1/wavs/LJ007-0008.wav|their especial care.
+LJSpeech-1.1/wavs/LJ048-0134.wav|would be a desirable innovation.
+LJSpeech-1.1/wavs/LJ046-0134.wav|In the period from November nineteen sixty-one to November nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ008-0016.wav|Lawrence Jones, a burglar, was in seventeen ninety-three ordered for execution in Hatton Garden, near the house he had robbed;
+LJSpeech-1.1/wavs/LJ019-0277.wav|the prisoners should be made to dispense with the use of a mattress, and should sleep on planks.
+LJSpeech-1.1/wavs/LJ012-0133.wav|The fraudulent messenger, by the help of young Caspar, established his claim to the boxes, paid the wharfage dues, and carried off the gold-dust.
+LJSpeech-1.1/wavs/LJ028-0086.wav|the lips are thin, the chin prominent; the neck is that of a strong vigorous man.
+LJSpeech-1.1/wavs/LJ047-0204.wav|Bouck pointed to a number of characteristics besides Oswald's defection the cumulative effect of which would have been to alert the Secret Service
+LJSpeech-1.1/wavs/LJ019-0106.wav|His plan was to devote the whole labor of prisoners sentenced to any term between three months and four years to agriculture.
+LJSpeech-1.1/wavs/LJ040-0120.wav|Lee refused to discuss the matter with Pic, whom he had previously idolized, and their relations were strained thereafter.
+LJSpeech-1.1/wavs/LJ025-0058.wav|but the fact, important as it was, fell into oblivion and had to be rediscovered by Treviranus in eighteen oh seven.
+LJSpeech-1.1/wavs/LJ035-0118.wav|They rushed to the west windows after the shots were fired and remained there
+LJSpeech-1.1/wavs/LJ009-0129.wav|that many of the kneeling men or boys laughed while they knelt, pinched each other, and, when they could do so without fear of being seen by any officer of the prison,
+LJSpeech-1.1/wavs/LJ019-0340.wav|The latter was rendered nearly impossible by the penalties imposed on persons bringing spirituous liquors into the jail.
+LJSpeech-1.1/wavs/LJ048-0223.wav|After the President had retired at his hotel,
+LJSpeech-1.1/wavs/LJ019-0325.wav|the ministrations of ministers of their own form of belief.
+LJSpeech-1.1/wavs/LJ035-0098.wav|he was certain that both elevators, which occupy the same shaft, were on the fifth floor.
+LJSpeech-1.1/wavs/LJ016-0325.wav|The judge of the Admiralty Court, the Right Hon. Stephen Lushington, the Right Hon. James Moncrieff,
+LJSpeech-1.1/wavs/LJ034-0216.wav|In the background of this picture were several employees watching the parade from the steps of the Depository Building.
+LJSpeech-1.1/wavs/LJ017-0101.wav|That day Palmer had bought more strychnia, and had called in a fresh doctor.
+LJSpeech-1.1/wavs/LJ022-0099.wav|to move as rapidly as possible back into private employment when such employment is available.
+LJSpeech-1.1/wavs/LJ033-0115.wav|I remember that I didn't look at the package very much but when I did look at it he did have his hands on the package like that, end quote, and at this point
+LJSpeech-1.1/wavs/LJ049-0072.wav|those in command should be able to direct the response appropriate to the emergency. The Commission finds that the Secret Service agents in the motorcade
+LJSpeech-1.1/wavs/LJ021-0100.wav|Let me call your attention to the fact that the national Industrial Recovery Act
+LJSpeech-1.1/wavs/LJ016-0156.wav|at no elevation affords the only drop, strangulation would seldom supervene but for the resolution of the miserable felo de se.
+LJSpeech-1.1/wavs/LJ010-0291.wav|Pate was found guilty, and sentenced to seven years' transportation, the judge, Baron Alderson, abstaining from inflicting the penalty of whipping,
+LJSpeech-1.1/wavs/LJ037-0172.wav|even though only four bullets were recovered.
+LJSpeech-1.1/wavs/LJ044-0229.wav|and had lost his desire to do so because of the bureaucracy and red tape which he had encountered.
+LJSpeech-1.1/wavs/LJ049-0217.wav|Moreover, a number of imponderable questions have to be weighed if any change in the intimate association now established
+LJSpeech-1.1/wavs/LJ039-0194.wav|All three of the firers in these tests
+LJSpeech-1.1/wavs/LJ003-0256.wav|By this means spirits, otherwise unattainable and strictly prohibited, were smuggled into the jail.
+LJSpeech-1.1/wavs/LJ050-0171.wav|but it seems to warrant further study before each agency becomes irrevocably committed to separate action.
+LJSpeech-1.1/wavs/LJ050-0151.wav|it makes no use of the recent developments in automatic data processing which are widely used in the business world and in other Government offices.
+LJSpeech-1.1/wavs/LJ017-0211.wav|which left London for Singapore on the twenty-eighth July, eighteen sixty-three, with a cargo of wine and other goods.
+LJSpeech-1.1/wavs/LJ003-0181.wav|There was a master's side for females who could pay the usual fees, but they associated with the rest in the one narrow yard common to all.
+LJSpeech-1.1/wavs/LJ035-0051.wav|He saw a man walking away from him in the lunchroom.
+LJSpeech-1.1/wavs/LJ022-0187.wav|but these twenty years have shown by experience definite possibilities for improvement.
+LJSpeech-1.1/wavs/LJ013-0204.wav|he admitted that he had been justly convicted, and expressed great anxiety that his fellow-servants should be relieved from all suspicion.
+LJSpeech-1.1/wavs/LJ039-0113.wav|as a, quote, rather poor shot, end quote.
+LJSpeech-1.1/wavs/LJ014-0032.wav|as the seducer of an innocent girl to whom he (Hocker) had been fondly attached.
+LJSpeech-1.1/wavs/LJ045-0154.wav|Answer: He said that he was lonely because he hadn't come the preceding weekend, and he wanted to make his peace with me.
+LJSpeech-1.1/wavs/LJ013-0206.wav|Next morning he made a full confession in presence of his attorney, and the governor, Mr. Cope.
+LJSpeech-1.1/wavs/LJ021-0205.wav|I believe with Abraham Lincoln, that "The legitimate object of government is to do for a community of people
+LJSpeech-1.1/wavs/LJ003-0260.wav|Another frightful consequence of this indiscriminate admission was the influx of numbers of abandoned women,
+LJSpeech-1.1/wavs/LJ030-0044.wav|Motorcycles. -- Next came four to six motorcycle policemen whose main purpose was to keep the crowd back.
+LJSpeech-1.1/wavs/LJ034-0143.wav|but he said he was unable to make a positive identification.
+LJSpeech-1.1/wavs/LJ033-0150.wav|Sebastian F. Latona, supervisor of the FBI's Latent Fingerprint Section,
+LJSpeech-1.1/wavs/LJ012-0144.wav|and passed it on to Solomons by his daughter, a widow named Abrahams.
+LJSpeech-1.1/wavs/LJ004-0094.wav|This act set forth that "whereas the malignant fever commonly called the jail distemper
+LJSpeech-1.1/wavs/LJ015-0113.wav|followed him over to Sweden, and arrested him at Helsingfors.
+LJSpeech-1.1/wavs/LJ047-0179.wav|Hosty did nothing further in connection with the Oswald case until after the assassination. On November one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ010-0292.wav|which was authorized by a recent act, on account of Mr. Pate's family and position in life.
+LJSpeech-1.1/wavs/LJ013-0187.wav|near it was his Waterloo medal, and the above-mentioned ten-pound note.
+LJSpeech-1.1/wavs/LJ049-0216.wav|the FBI, the CIA, and the military intelligence agencies as well as the Secret Service.
+LJSpeech-1.1/wavs/LJ006-0248.wav|Matters were at times still worse, and the rioting went on to such dangerous lengths as to endanger the safety of the building.
+LJSpeech-1.1/wavs/LJ031-0087.wav|When asked why he did not turn the President over, Dr. Carrico testified as follows:
+LJSpeech-1.1/wavs/LJ004-0201.wav|At Ilchester the rule of employment had been carried further.
+LJSpeech-1.1/wavs/LJ025-0092.wav|It is now established that nitrogen is as essential a constituent of vegetable as of animal living matter
+LJSpeech-1.1/wavs/LJ016-0170.wav|greater attention was paid to the capital convicts, and the horrors of their situation while awaiting sentence
+LJSpeech-1.1/wavs/LJ007-0243.wav|It was not till the erection of the new prison at Holloway in eighteen fifty, and the entire internal reconstruction of Newgate according to new ideas,
+LJSpeech-1.1/wavs/LJ050-0166.wav|The Commission was struck by the apparent lack of effort, on an interagency basis,
+LJSpeech-1.1/wavs/LJ040-0209.wav|well I've got to live with her. I guess I love her, end quote.
+LJSpeech-1.1/wavs/LJ002-0075.wav|and who are therefore lodged apart from all other districts of the jail. End quote.
+LJSpeech-1.1/wavs/LJ016-0213.wav|The origin of this expression dates, it is said, from the time when the Scottish mark,
+LJSpeech-1.1/wavs/LJ001-0146.wav|which requires the constant exercise of judgment and taste on the part of the printer.
+LJSpeech-1.1/wavs/LJ027-0040.wav|between the fin of a fish and the paddle of a whale
+LJSpeech-1.1/wavs/LJ029-0097.wav|and on their return to Dallas drove over the route which Sorrels believed best suited for the proposed motorcade.
+LJSpeech-1.1/wavs/LJ022-0104.wav|and the experience and the competence necessary to carry on the two hundred and fifty or three hundred kinds of work that will be undertaken.
+LJSpeech-1.1/wavs/LJ005-0031.wav|no visiting of friends, no education but religious education, no freedom of diet,
+LJSpeech-1.1/wavs/LJ032-0233.wav|reinforce the belief that the rifle in the photograph is the rifle which Oswald bought from Klein's.
+LJSpeech-1.1/wavs/LJ028-0429.wav|The excavations have shown that Babylon, as the ancients told us, was nearly square.
+LJSpeech-1.1/wavs/LJ049-0032.wav|had no special design or equipment which would have permitted the Secret Service agent riding in the driver's compartment
+LJSpeech-1.1/wavs/LJ025-0139.wav|converge toward the conclusion that the nerve fibers, which have been regarded as ultimate elements of nervous tissue,
+LJSpeech-1.1/wavs/LJ011-0037.wav|Appeals were made to the Home Secretary, and all possible political interest brought to bear, but without success.
+LJSpeech-1.1/wavs/LJ002-0151.wav|Neild found at his visit to Newgate in eighteen ten,
+LJSpeech-1.1/wavs/LJ044-0181.wav|The Cubans would not, however, give him a visa until he had received one from the Soviets, which involved a delay of several months.
+LJSpeech-1.1/wavs/LJ033-0132.wav|I didn't pay too much attention the way he was walking because I was walking along there looking at the railroad cars and watching the men on the diesel switch them cars
+LJSpeech-1.1/wavs/LJ036-0040.wav|In a later interview, Jones confirmed that he had exchanged words with a woman passenger on the bus during the ride south on Marsalis.
+LJSpeech-1.1/wavs/LJ039-0195.wav|were able to fire the rounds within the time period which would have been available to the assassin under those conditions.
+LJSpeech-1.1/wavs/LJ003-0298.wav|for giving every prisoner a sleeping cell to himself, an amount of enlightenment which is hardly general among European nations at this
+LJSpeech-1.1/wavs/LJ047-0149.wav|the New Orleans office of the FBI learned that in September Oswald had given a forwarding address of two five one five
+LJSpeech-1.1/wavs/LJ033-0083.wav|Oswald gripped the bag in his right hand near the top, quote, It tapered like this as he hugged it in his hand.
+LJSpeech-1.1/wavs/LJ049-0138.wav|and, if the Council is used, arrangements should be made for the attendance of the Secretary of the Treasury
+LJSpeech-1.1/wavs/LJ023-0141.wav|can, if they choose, hold office for life, no matter how old they may get to be.
+LJSpeech-1.1/wavs/LJ015-0044.wav|with money enough to retrieve the position of the bank. But that passed from bad to worse;
+LJSpeech-1.1/wavs/LJ021-0111.wav|There may be a serious question as to the wisdom of many of those devices to control production,
+LJSpeech-1.1/wavs/LJ002-0097.wav|they had dust-bins, sewers, and so forth, "properly disposed," and the city scavenger paid periodical visits to the prison.
+LJSpeech-1.1/wavs/LJ012-0085.wav|The chest was opened to give change, and a heavy tray lifted out which plainly held some four thousand pounds in cash.
+LJSpeech-1.1/wavs/LJ018-0179.wav|On his return to Newgate to be finally discharged, Cummings jumped up the stairs and fairly danced for joy.
+LJSpeech-1.1/wavs/LJ016-0402.wav|No woman could resist him, he calmly assured Mr. Smith that night as they walked together, and he recounted his villanies one by one.
+LJSpeech-1.1/wavs/LJ037-0082.wav|On the evening of November twenty-two,
+LJSpeech-1.1/wavs/LJ010-0114.wav|His sudden access to means unlimited was no doubt due to the profitable role he soon adopted of Government informer and spy,
+LJSpeech-1.1/wavs/LJ019-0068.wav|Separation was injurious to health, mental or physical, said one side; men broke down when subjected to it for more than a certain period,
+LJSpeech-1.1/wavs/LJ045-0037.wav|after his recent rebuffs in Mexico City
+LJSpeech-1.1/wavs/LJ004-0205.wav|Industrial labor had also been introduced with satisfactory results.
+LJSpeech-1.1/wavs/LJ035-0027.wav|They went through the swinging door and continued at, quote, a good trot, end quote,
+LJSpeech-1.1/wavs/LJ034-0121.wav|twenty-seven, five foot eleven, one hundred sixty-five pounds, black wavy hair, end quote,
+LJSpeech-1.1/wavs/LJ001-0047.wav|Of Jenson it must be said that he carried the development of Roman type as far as it can go:
+LJSpeech-1.1/wavs/LJ019-0176.wav|The wards had open fires, but the separate cells were not warmed at all.
+LJSpeech-1.1/wavs/LJ020-0061.wav|Break the rolls apart from one another and eat warm. They are also good cold, and if the directions be followed implicitly, very good always.
+LJSpeech-1.1/wavs/LJ033-0095.wav|Frazier testified that Oswald carried no lunch bag that day.
+LJSpeech-1.1/wavs/LJ028-0195.wav|It is a long story. Poets have sung it. Historians have written it. Prophets have preached it. Legends have gathered about it.
+LJSpeech-1.1/wavs/LJ011-0288.wav|Thus free, he eluded the vigilance of two of the party, who were at dinner in the front kitchen,
+LJSpeech-1.1/wavs/LJ009-0045.wav|he steps boldly with head upright, looks to the women's gallery, and smiles. His intention is to pass for a brave fellow,
+LJSpeech-1.1/wavs/LJ027-0064.wav|All vertebrates, and none other, have two cavities,
+LJSpeech-1.1/wavs/LJ037-0218.wav|Marina Oswald testified that this was the holster which contained the revolver in the photographs taken on Neely Street.
+LJSpeech-1.1/wavs/LJ049-0048.wav|and therefore approximately one point six seconds after the President was shot in the head.
+LJSpeech-1.1/wavs/LJ050-0156.wav|this money would be used to compensate consultants, to lease standard equipment or to purchase specially designed pilot equipment.
+LJSpeech-1.1/wavs/LJ006-0222.wav|and enlivened by flash songs and thrilling long-winded descriptions of robberies and other "plants."
+LJSpeech-1.1/wavs/LJ018-0086.wav|His demeanor immediately preceding his execution I have referred to in the last chapter.
+LJSpeech-1.1/wavs/LJ044-0236.wav|it is unlikely that a reasoning person would plan to attempt to travel from Dallas, Texas to Cuba
+LJSpeech-1.1/wavs/LJ028-0355.wav|he gave him likewise the government of Babylon for his life, free from tribute, and he also granted him many other favors.
+LJSpeech-1.1/wavs/LJ031-0198.wav|Upon arrival, President Johnson made a brief statement over television and radio.
+LJSpeech-1.1/wavs/LJ027-0105.wav|a number of organs which never could have been of use to any kind of creature save a terrestrial quadruped.
+LJSpeech-1.1/wavs/LJ038-0238.wav|An examination of the window at the rear of the house, the wall through which the bullet passed, and the fence behind the house
+LJSpeech-1.1/wavs/LJ014-0058.wav|These great criminals suffered at Horsemonger Lane Jail, but they were tried at the Central Criminal Court, and were for some time inmates of Newgate.
+LJSpeech-1.1/wavs/LJ018-0303.wav|a second conspirator exchanged the gold for notes. But just as all promised well, the frauds were detected through the carelessness of the forgers.
+LJSpeech-1.1/wavs/LJ030-0240.wav|to that effect immediately after the assassination.
+LJSpeech-1.1/wavs/LJ003-0066.wav|As many various and, according to our ideas, heinous crimes came under this head,
+LJSpeech-1.1/wavs/LJ015-0035.wav|produced immediate embarrassment and financial distress.
+LJSpeech-1.1/wavs/LJ032-0230.wav|that the published pictures were the same as the original except for retouching done by these publications, apparently for the purpose of clarifying the lines of the rifle
+LJSpeech-1.1/wavs/LJ007-0239.wav|both offenses and punishments affording a sufficient index to the practices going forward; and they wind up by declaring
+LJSpeech-1.1/wavs/LJ002-0310.wav|that debtors who could afford the cabin and master's side were not permitted to share in the prison charities.
+LJSpeech-1.1/wavs/LJ033-0036.wav|No curtain rods were known to have been discovered in the Depository Building after the assassination.
+LJSpeech-1.1/wavs/LJ002-0235.wav|were places set apart for skittles, fives, and tennis, which strangers frequented as any other place of public amusement.
+LJSpeech-1.1/wavs/LJ038-0295.wav|An official of this church told FBI agents that services are held every Wednesday at the church except during the month of August.
+LJSpeech-1.1/wavs/LJ014-0045.wav|that Delarue had suffered by the hands of imaginary outraged brothers acting as the avengers of females deeply injured by him.
+LJSpeech-1.1/wavs/LJ021-0036.wav|loans to the railroads and insurance companies and, finally, help for home owners and industry itself.
+LJSpeech-1.1/wavs/LJ004-0039.wav|They were hopeless of any general reform by the action of the executive alone.
+LJSpeech-1.1/wavs/LJ017-0011.wav|But secret poisoning on a wholesale scale such as was practiced in Italy and France was happily never popularized in England.
+LJSpeech-1.1/wavs/LJ008-0037.wav|were brought out of Newgate about eight in the morning, and suspended on a gallows of a new construction.
+LJSpeech-1.1/wavs/LJ027-0034.wav|Hence, as Jordan has said, "the inside of an animal tells the real history of its ancestry; the outside tells us only where its ancestors have been."
+LJSpeech-1.1/wavs/LJ011-0066.wav|The concourse in front of Newgate was enormous, but much sympathy was evinced for this unfortunate victim to human weakness and ruthless laws.
+LJSpeech-1.1/wavs/LJ046-0159.wav|accumulated over a twenty-year period, some of which included more than one individual.
+LJSpeech-1.1/wavs/LJ027-0079.wav|a series of either fore or hind limbs of a mammal with one toe (horse),
+LJSpeech-1.1/wavs/LJ019-0247.wav|which existed in the various prisons,
+LJSpeech-1.1/wavs/LJ034-0059.wav|Since other identifiable prints were developed on the cartons, the Commission requested that they be compared with the prints of the twelve warehouse employees
+LJSpeech-1.1/wavs/LJ001-0106.wav|oddity rather than rational beauty and meaning being apparently the thing sought for both in the letters and the illustrations.
+LJSpeech-1.1/wavs/LJ019-0158.wav|Later on a more efficacious but still imperfect method of supervision was introduced. Iron cages, which are still to be seen in Newgate,
+LJSpeech-1.1/wavs/LJ040-0079.wav|John Pic testified that he thought Lee found in Ekdahl the father that he never had.
+LJSpeech-1.1/wavs/LJ019-0309.wav|The main object of this act was to compass that uniformity in discipline and treatment generally
+LJSpeech-1.1/wavs/LJ017-0271.wav|Lyons asked the time, and was told it was only five.
+LJSpeech-1.1/wavs/LJ037-0015.wav|but he saw the policeman leave the car, heard three or four shots, and then saw the policeman fall.
+LJSpeech-1.1/wavs/LJ029-0109.wav|Representatives of the local host committee and the White House staff were advised by the Secret Service of the actual route on the afternoon of November eighteen.
+LJSpeech-1.1/wavs/LJ021-0006.wav|have had to do with industry and labor and with respect to these, certain developments have taken place which I consider of importance.
+LJSpeech-1.1/wavs/LJ039-0065.wav|so that the President would have been moving in an almost straight line away from the assassin's rifle.
+LJSpeech-1.1/wavs/LJ011-0144.wav|but Mr. Joseph Hume pressed the Government hard, and obtained an assurance that the men should not be executed.
+LJSpeech-1.1/wavs/LJ014-0246.wav|Thus, when a payment was made by the company, the amount disbursed was carried to account in the general books from its entry in the passbook,
+LJSpeech-1.1/wavs/LJ007-0124.wav|The infirmary at this particular period epitomized the condition of the jail at large.
+LJSpeech-1.1/wavs/LJ030-0013.wav|David F. Powers of the President's staff later stated that when the President asked for his assessment of the day's activities, Powers replied
+LJSpeech-1.1/wavs/LJ038-0224.wav|Another statement which limits the time when it could have been written is the reference, quote, you and the baby, end quote,
+LJSpeech-1.1/wavs/LJ033-0213.wav|(three) removed the rifle from the blanket in the Paines' garage on Thursday evening;
+LJSpeech-1.1/wavs/LJ017-0051.wav|covered rather scantily with light sandy hair.
+LJSpeech-1.1/wavs/LJ039-0202.wav|At fifteen yards each man's shots landed within the size of a dime.
+LJSpeech-1.1/wavs/LJ016-0245.wav|Until the time of his death he kept a small shop close to the church in Horncastle.
+LJSpeech-1.1/wavs/LJ026-0145.wav|In the animal carbon dioxide, water and nitrogen compounds are the chief excretions.
+LJSpeech-1.1/wavs/LJ016-0087.wav|Among the escapes still remembered was one in eighteen forty-nine, accomplished by a man who had been employed
+LJSpeech-1.1/wavs/LJ001-0049.wav|After his death in the "fourteen eighties," or at least by fourteen ninety, printing in Venice had declined very much;
+LJSpeech-1.1/wavs/LJ016-0349.wav|that of Alexander Mackay, who murdered his mistress at Norton Folgate by beating her with a rolling-pin and furnace-rake,
+LJSpeech-1.1/wavs/LJ009-0070.wav|The youth's hands tremble as they hold the book upside down.
+LJSpeech-1.1/wavs/LJ047-0079.wav|The New Orleans office investigated and located Oswald, learning his address and former place of employment on August five, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ012-0185.wav|raised the alarm, and suspicion fell upon the three murderers, who were arrested.
+LJSpeech-1.1/wavs/LJ015-0102.wav|He came back to explain that he had mislaid them.
+LJSpeech-1.1/wavs/LJ046-0110.wav|is the identification and elimination of possible sources of danger to the President before the danger becomes actual.
+LJSpeech-1.1/wavs/LJ012-0205.wav|an avowed "snatcher" and habitué of the Fortune of War, a public-house in Smithfield frequented openly by men of this awful profession.
+LJSpeech-1.1/wavs/LJ048-0035.wav|It was against this background and consistent with the criteria followed by the FBI prior to November twenty-two
+LJSpeech-1.1/wavs/LJ017-0260.wav|Vartos the Turk, and Carlos the Greek.
+LJSpeech-1.1/wavs/LJ029-0204.wav|Two days later, a local Republican leader called for a "civilized nonpartisan" welcome
+LJSpeech-1.1/wavs/LJ010-0264.wav|Later on, however, Dasset was himself seized and interrogated,
+LJSpeech-1.1/wavs/LJ038-0037.wav|Oswald then struck McDonald between the eyes with his left fist; with his right hand he drew a gun from his waist.
+LJSpeech-1.1/wavs/LJ035-0137.wav|About this time Shelley saw Truly and Patrolman Baker go into the building
+LJSpeech-1.1/wavs/LJ016-0344.wav|The ceremony, which was witnessed by only a few officials and representatives of the press, was performed with the utmost decency and decorum.
+LJSpeech-1.1/wavs/LJ014-0229.wav|Mobbs systematically ill-used his wife for a long space of time, and at last cut her throat.
+LJSpeech-1.1/wavs/LJ022-0087.wav|After the Division of Applications and Information has sifted those projects,
+LJSpeech-1.1/wavs/LJ004-0047.wav|One of the moving spirits was the Honorable H. G. Bennet, M.P., whose vigorous protests against the lamentable condition of Newgate have already been recorded.
+LJSpeech-1.1/wavs/LJ048-0147.wav|The Commission believes
+LJSpeech-1.1/wavs/LJ027-0073.wav|may be shown to be modifications one of another.
+LJSpeech-1.1/wavs/LJ030-0082.wav|The agents on the front of the running boards had directions to move immediately to positions just to the rear of the President and Mrs. Kennedy when the President's car slowed
+LJSpeech-1.1/wavs/LJ036-0134.wav|I asked him where he wanted to go. And he said, "five hundred North Beckley. Well, I started up,
+LJSpeech-1.1/wavs/LJ008-0295.wav|When the council had decided, the news was conveyed to Newgate by the Recorder, who made his "report," as it was called.
+LJSpeech-1.1/wavs/LJ038-0153.wav|He said that he left work because Bill Shelley said that there would be no more work done that day in the building.
+LJSpeech-1.1/wavs/LJ012-0126.wav|The next year twelve thousand sovereigns were cleverly stolen in the Mile End Road.
+LJSpeech-1.1/wavs/LJ012-0087.wav|should be introduced into the building, and secreted there during the night to accomplish the robbery.
+LJSpeech-1.1/wavs/LJ008-0141.wav|the doubtfulness of the issue, or the superior position of the perpetrator,
+LJSpeech-1.1/wavs/LJ018-0100.wav|took to appropriating the bills intrusted to him, and so lost his business, after which he became a clerk to Messrs. Wagner and Bateman.
+LJSpeech-1.1/wavs/LJ012-0237.wav|A very painful scene occurred in Newgate when the news of his escape from death was imparted to May.
+LJSpeech-1.1/wavs/LJ031-0168.wav|In another car Mrs. Johnson was driven to the airport accompanied by Secret Service agents and Representative Brooks.
+LJSpeech-1.1/wavs/LJ048-0087.wav|The President's trip to Dallas called into play many standard operating procedures of the Secret Service in addition to its preventive intelligence operations.
+LJSpeech-1.1/wavs/LJ040-0174.wav|Lee confirmed some of those observations by saying that he felt almost as if there were a veil between him and other people
+LJSpeech-1.1/wavs/LJ031-0166.wav|Unmarked police cars took the Vice President and Mrs. Johnson from Parkland Hospital to Love Field.
+LJSpeech-1.1/wavs/LJ014-0301.wav|In eighteen fifty-three a second case of gigantic fraud alarmed and scandalized the financial world.
+LJSpeech-1.1/wavs/LJ019-0086.wav|or they might herd together or communicate freely as in the old worst days. They might see each other when they liked, and converse sotto voce,
+LJSpeech-1.1/wavs/LJ039-0074.wav|you should not have any difficulty in hitting your target. I mean it requires no training at all to shoot a weapon with a telescopic sight
+LJSpeech-1.1/wavs/LJ001-0087.wav|for the seventeenth-century letters are at least pure and simple in line. The Italian, Bodoni, and the Frenchman, Didot,
+LJSpeech-1.1/wavs/LJ004-0054.wav|In order to give greater value to the pamphlet,
+LJSpeech-1.1/wavs/LJ010-0186.wav|There is no occasion to use violence. I will go with you.
+LJSpeech-1.1/wavs/LJ011-0135.wav|Macintosh's amendment was carried in the Commons, but the new law did not pass the Lords, who re-enacted the capital penalty.
+LJSpeech-1.1/wavs/LJ040-0092.wav|when he left school in order to get into the Coast Guard. Since his mother did not approve of his decision to continue school
+LJSpeech-1.1/wavs/LJ010-0285.wav|invariably went out in a cab, for which he always paid the same fare,
+LJSpeech-1.1/wavs/LJ012-0209.wav|and after some haggling they agreed on a price, and in the afternoon the snatchers brought a hamper which contained a body in a sack.
+LJSpeech-1.1/wavs/LJ016-0327.wav|declared that they were not prepared to agree to the resolution respecting private executions.
+LJSpeech-1.1/wavs/LJ028-0477.wav|To protect the sun-dried bricks of the inner wall from the winter rains
+LJSpeech-1.1/wavs/LJ025-0173.wav|The watery solution, in which its roots are plunged, contains nitrogen but no carbon;
+LJSpeech-1.1/wavs/LJ048-0279.wav|They work long, hard hours, under very great strain, and must travel frequently.
+LJSpeech-1.1/wavs/LJ019-0331.wav|Baths were provided, ablutions ordered, and all appliances to insure personal cleanliness.
+LJSpeech-1.1/wavs/LJ019-0300.wav|The committee might well suggest the abolition of these jails, or their amalgamation with the larger county establishments in their immediate neighborhood.
+LJSpeech-1.1/wavs/LJ031-0092.wav|Did you ever have occasion to look at the President's back?
+LJSpeech-1.1/wavs/LJ014-0131.wav|but Mrs. Manning, speaking in a foreign accent, addressed the court with great fluency and vehemence.
+LJSpeech-1.1/wavs/LJ016-0009.wav|He had friends and auxiliaries inside the jail and out. The cell he occupied was near the outer wall,
+LJSpeech-1.1/wavs/LJ039-0181.wav|the shots would have been evenly spaced and the assassin would not have incurred so sharp an angular movement.
+LJSpeech-1.1/wavs/LJ013-0202.wav|Coolness amounting almost to effrontery gave way to hopeless dejection.
+LJSpeech-1.1/wavs/LJ003-0234.wav|The bedding was scanty; fuel and light had to be purchased out of prisoners' private means; clothing was issued but rarely,
+LJSpeech-1.1/wavs/LJ004-0014.wav|filthiness, severity, or neglect; many new dungeons had aggravated the evils against which his sagacity could not but remonstrate;
+LJSpeech-1.1/wavs/LJ018-0188.wav|Presently "Old Bob" drove up to Camberwell Gate in the same cart in which he had been seen to start.
+LJSpeech-1.1/wavs/LJ002-0028.wav|In order to realize the evils entailed by incarceration in Newgate in these days, it is necessary to give some account of its interior
+LJSpeech-1.1/wavs/LJ033-0029.wav|It would appear, however, that obtaining curtain rods was not the purpose of Oswald's trip to Irving on November twenty-one.
+LJSpeech-1.1/wavs/LJ039-0067.wav|which is ordinarily required when a marksman must raise his rifle as a target moves farther away.
+LJSpeech-1.1/wavs/LJ038-0105.wav|They discovered two photographs, each showing Oswald with a rifle and a pistol.
+LJSpeech-1.1/wavs/LJ040-0049.wav|He stated several times that he was a Communist but apparently never joined any Communist Party.
+LJSpeech-1.1/wavs/LJ015-0030.wav|The bank had been conducted on false principles;
+LJSpeech-1.1/wavs/LJ039-0172.wav|None of the marksmen had any practice with the assassination weapon except for exercising the bolt for two or three minutes on a dry run.
+LJSpeech-1.1/wavs/LJ029-0088.wav|securing the roof and insuring the presence of numerous police officers inside and around the building.
+LJSpeech-1.1/wavs/LJ009-0235.wav|and having first pulled him sideways, then got upon his shoulders, so that the rope broke.
+LJSpeech-1.1/wavs/LJ013-0180.wav|The intention of the real murderer to shift the crime to burglars was evident although futile,
+LJSpeech-1.1/wavs/LJ019-0215.wav|Why not move the city prison bodily into this more rural spot, with its purer air and greater breathing space?
+LJSpeech-1.1/wavs/LJ009-0136.wav|that they reluctantly agreed to open the gallery which had formerly been occupied by strangers on these occasions.
+LJSpeech-1.1/wavs/LJ034-0052.wav|In his independent investigation, Arthur Mandella of the New York City Police Department
+LJSpeech-1.1/wavs/LJ007-0055.wav|The more peaceably disposed found some occupation in making Newgate tokens,
+LJSpeech-1.1/wavs/LJ003-0018.wav|and discipline maintained by a system open to grave abuses, and which had the prescription of long usage,
+LJSpeech-1.1/wavs/LJ028-0482.wav|but I, the devout petitioner, the worshipper of the gods, built the moat, and made its wall of burned brick and bitumen mountain high.
+LJSpeech-1.1/wavs/LJ032-0123.wav|the rifle was released to the FBI and forwarded to Washington where it was examined on the morning of November twenty-three
+LJSpeech-1.1/wavs/LJ043-0102.wav|It is possible that his immediate supervisor noticed the newspaper at that time because his attention had otherwise been drawn more directly to Oswald.
+LJSpeech-1.1/wavs/LJ002-0212.wav|Mexico, and was consumed at the rate of a hogshead per week.
+LJSpeech-1.1/wavs/LJ003-0039.wav|Giltspur Street, and the Poultry, or about four hundred and seventy-six in all.
+LJSpeech-1.1/wavs/LJ047-0065.wav|This information led Hosty to review Oswald's file, from which he learned that Oswald had become a subscriber to the Worker,
+LJSpeech-1.1/wavs/LJ032-0264.wav|tied with a string, lying on the floor of the Paines' garage.
+LJSpeech-1.1/wavs/LJ005-0291.wav|In some large towns, as at Berwick on Tweed, Southampton, and Southwark, they (the prisons) are in a very discreditable condition.
+LJSpeech-1.1/wavs/LJ007-0132.wav|The inspectors, however, honestly admitted that although the site of the prison was convenient, its construction was as bad as bad could be.
+LJSpeech-1.1/wavs/LJ010-0067.wav|This Thistlewood had seen many vicissitudes throughout his strange, adventurous career. The son of a respectable Lincolnshire farmer,
+LJSpeech-1.1/wavs/LJ004-0147.wav|The irons, which nearly every one wore, were remarkably heavy; those double ironed could not take off their small clothes.
+LJSpeech-1.1/wavs/LJ028-0128.wav|His brief description of them should not be omitted. He says that Nebuchadnezzar
+LJSpeech-1.1/wavs/LJ048-0019.wav|His activities for the Fair Play for Cuba Committee in New Orleans, we knew, were not of real consequence as he was not connected with any organized activity there.
+LJSpeech-1.1/wavs/LJ025-0047.wav|The plant withdraws water and carbonic acid from the atmosphere, the animal contributes both to it.
+LJSpeech-1.1/wavs/LJ031-0027.wav|Upon arriving at Parkland Hospital,
+LJSpeech-1.1/wavs/LJ039-0167.wav|Using the assassination rifle mounted with the telescopic sight, three marksmen, rated as master by the National Rifle Association,
+LJSpeech-1.1/wavs/LJ035-0117.wav|Harold Norman, and Bonnie Ray Williams -- were watching the parade from the fifth floor, directly below the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ037-0201.wav|Heinz W. Michaelis, office manager of both George Rose and Co., Incorporated and Seaport Traders, Incorporated.
+LJSpeech-1.1/wavs/LJ006-0203.wav|and he had thus ample means of introducing to the prisoners the prohibited but always much-coveted and generally procurable weed.
+LJSpeech-1.1/wavs/LJ007-0209.wav|they return to the charge, and again call the corporation to task for their mismanagement of Newgate.
+LJSpeech-1.1/wavs/LJ019-0084.wav|The greatest pains might be taken to secure isolation,
+LJSpeech-1.1/wavs/LJ009-0138.wav|all the avenues to the prison gates were blocked by ticket-holders.
+LJSpeech-1.1/wavs/LJ039-0215.wav|Frazier added that the scope would cause a slight miss to the right.
+LJSpeech-1.1/wavs/LJ018-0172.wav|More than this, Griffiths took the police to a field where, in a bank, a number of other plates were secreted.
+LJSpeech-1.1/wavs/LJ030-0091.wav|Rufus W. Youngblood, special agent in charge of the Vice President's detail,
+LJSpeech-1.1/wavs/LJ037-0046.wav|The man, quote, in kind of a little trot, end quote, headed down Patton toward Jefferson Boulevard, a block away.
+LJSpeech-1.1/wavs/LJ046-0223.wav|to Secret Service locally, end quote.
+LJSpeech-1.1/wavs/LJ049-0175.wav|before the President reached Dallas.
+LJSpeech-1.1/wavs/LJ021-0106.wav|the representatives of trade and industry were permitted to write their ideas into the codes.
+LJSpeech-1.1/wavs/LJ009-0021.wav|in which it was said I had enlarged upon the heinous nature of his crime, and warned the public to avoid such conduct.
+LJSpeech-1.1/wavs/LJ016-0343.wav|The sufferer was a porter on the London, Chatham, and Dover railway, sentenced to death for shooting the station-master at Dover.
+LJSpeech-1.1/wavs/LJ030-0128.wav|From Main Street the motorcade turned right and went north on Houston Street, passing tall buildings on the right,
+LJSpeech-1.1/wavs/LJ022-0003.wav|Since my annual message to the Congress on January fourth, last, I have not addressed the general public over the air.
+LJSpeech-1.1/wavs/LJ016-0275.wav|As much as twenty-five pounds was paid for a first-floor front on this occasion.
+LJSpeech-1.1/wavs/LJ002-0136.wav|and the disuse of the inferior courts.
+LJSpeech-1.1/wavs/LJ018-0253.wav|For these crimes William Roupell was tried at the Central Criminal Court on the twenty-fourth September, eighteen sixty-two.
+LJSpeech-1.1/wavs/LJ012-0200.wav|Bishop got weary of the dangers and fatigues of exhumation, and proposed to Williams that instead of disinterring they should murder their subjects.
+LJSpeech-1.1/wavs/LJ008-0005.wav|The terrible spectacle was as demoralizing to the public, for whose admonition it was intended,
+LJSpeech-1.1/wavs/LJ049-0096.wav|There have been a number of efforts to make assassination a Federal crime, particularly after the assassination of President McKinley
+LJSpeech-1.1/wavs/LJ028-0148.wav|Then they set to building, and began by bricking the borders of the moat, after which they proceeded to construct the wall itself,
+LJSpeech-1.1/wavs/LJ047-0218.wav|had discussed the President's visit on several occasions, including the regular biweekly conference on the morning of November twenty-two
+LJSpeech-1.1/wavs/LJ005-0299.wav|The whole question was again dealt with in Lord John Russell's bill for the reform of the municipal corporations, and with a more liberal election of town councilors,
+LJSpeech-1.1/wavs/LJ017-0250.wav|from Peru bound to Bordeaux, which had foundered at sea;
+LJSpeech-1.1/wavs/LJ004-0062.wav|and the reason is plain: you have taken him from his home, and have deprived him of the means of providing himself with the necessaries or comforts of life,
+LJSpeech-1.1/wavs/LJ013-0112.wav|Within a month or two the bank of Messrs. Rogers and Co., Clement's Lane, was broken into.
+LJSpeech-1.1/wavs/LJ042-0110.wav|stated that Mrs. Oswald told her everybody in Russia, quote, hated him, end quote.
+LJSpeech-1.1/wavs/LJ047-0210.wav|I think all of those, if we had them all together,
+LJSpeech-1.1/wavs/LJ003-0210.wav|at night they were placed in the fifteen cells, two, three, or more together, according to the total number to be accommodated.
+LJSpeech-1.1/wavs/LJ048-0083.wav|were discussed twice officially by the special agent in charge of the FBI office in Dallas. As discussed in chapter two,
+LJSpeech-1.1/wavs/LJ002-0156.wav|Thomas Dobson, on twenty-second August, seventeen ninety-nine, for one shilling, with costs of eight shillings, ten pence.
+LJSpeech-1.1/wavs/LJ028-0179.wav|It is only from their ruins that we may hope to obtain accurate information of the strongest fortifications in the ancient world.
+LJSpeech-1.1/wavs/LJ037-0122.wav|The Dallas Police Department furnished the Commission with pictures of the men who appeared in the lineups with Oswald,
+LJSpeech-1.1/wavs/LJ014-0055.wav|Next year John Gleeson Wilson, at Liverpool, murdered a woman, Ann Henrichson, also a maidservant and two children;
+LJSpeech-1.1/wavs/LJ049-0028.wav|The Secret Service has therefore suggested this practice only on extraordinary occasions.
+LJSpeech-1.1/wavs/LJ001-0020.wav|the "lower-case" being in fact invented in the early Middle Ages.
+LJSpeech-1.1/wavs/LJ003-0333.wav|But here the recommendations touched at once upon the delicate subject of expense, and it is clear that the committee hesitated on this score.
+LJSpeech-1.1/wavs/LJ038-0216.wav|The references to house rent and payments for water and gas
+LJSpeech-1.1/wavs/LJ008-0287.wav|to make those sanctions an object of contemptuous mockery.
+LJSpeech-1.1/wavs/LJ035-0003.wav|In considering whether Oswald was at the southeast corner window at the time the shots were fired,
+LJSpeech-1.1/wavs/LJ028-0069.wav|how he cast Hebrew lads into a fiery furnace and into the lions' den,
+LJSpeech-1.1/wavs/LJ043-0005.wav|Apart from his relatives, Oswald had no friends or close associates in Texas when he returned there in June of nineteen sixty-two,
+LJSpeech-1.1/wavs/LJ016-0315.wav|Colonel (now Sir Edmund) Henderson was strongly in favor of them,
+LJSpeech-1.1/wavs/LJ013-0152.wav|at Montreuil. He was arraigned at the Old Bailey, and the case fully proved. His sentence was seven years' transportation.
+LJSpeech-1.1/wavs/LJ015-0090.wav|To account for his revenues he pretended to have been very lucky on the Stock Exchange, which was at one time true to a limited extent,
+LJSpeech-1.1/wavs/LJ028-0185.wav|Perhaps Babylon was so strongly fortified that at first he made no attempt to add it to his empire,
+LJSpeech-1.1/wavs/LJ014-0086.wav|This job was not completed till the following day, as the hole had to be enlarged, and the only tool they had was a dust-shovel.
+LJSpeech-1.1/wavs/LJ018-0272.wav|Mrs. Tarpey was almost immediately captured and put on her trial, but she was acquitted on the plea that she had acted under the coercion of her husband.
+LJSpeech-1.1/wavs/LJ004-0177.wav|No prison dress was allowed; no reception-room was provided, no soap, towels, or baths.
+LJSpeech-1.1/wavs/LJ002-0079.wav|Its name and its situation were the same as those of the old place of carrying out the terrible sentence inflicted on accused persons who stood mute.
+LJSpeech-1.1/wavs/LJ039-0231.wav|Having fired this slot
+LJSpeech-1.1/wavs/LJ041-0106.wav|At the court-martial hearing which followed, Oswald admitted that he had been rather drunk when the incident occurred.
+LJSpeech-1.1/wavs/LJ008-0305.wav|they never ceased cursing until the passion of anger so excited was exchanged for joy in some and grief in others.
+LJSpeech-1.1/wavs/LJ046-0081.wav|The men in charge of protecting the President, confronted by complex problems and limited as they are in the measures they may employ,
+LJSpeech-1.1/wavs/LJ003-0330.wav|The state side ceased to exist, and the female prisoners thus regained the space of which their quadrangle had been robbed.
+LJSpeech-1.1/wavs/LJ022-0004.wav|In the many weeks since that time the Congress has devoted itself to the arduous task of formulating legislation necessary to the country's welfare.
+LJSpeech-1.1/wavs/LJ017-0110.wav|He said nothing, but began to feel uneasy when he found that Cook's betting-book was missing, and that Palmer put it forward
+LJSpeech-1.1/wavs/LJ014-0288.wav|That same evening he committed suicide in Newgate.
+LJSpeech-1.1/wavs/LJ047-0245.wav|His secretary testified that she prepared such a report for him that afternoon and Chief of Police Jesse E. Curry
+LJSpeech-1.1/wavs/LJ045-0200.wav|that over the weekend he did think about his wife's request that he not come to Irving, which was prompted by the birthday party being held at the Paine home.
+LJSpeech-1.1/wavs/LJ025-0055.wav|by the employment of instruments of precision for the measurement of the physical forces which are at work in the living economy.
+LJSpeech-1.1/wavs/LJ013-0222.wav|he did not shrink from murder, both for revenge and to conceal his other crimes.
+LJSpeech-1.1/wavs/LJ050-0242.wav|even though it agrees with the Secret Service that it is preferable for the Service to have enough agents to handle all protective demands.
+LJSpeech-1.1/wavs/LJ036-0046.wav|She testified, quote, I didn't like his attitude. There was just something about him I didn't like or want him. Just didn't want him around me, end quote,
+LJSpeech-1.1/wavs/LJ014-0221.wav|On the contrary, many of them encouraged the brutal assailant in his savage attack.
+LJSpeech-1.1/wavs/LJ045-0244.wav|Long before the assassination he expressed his hatred for American society and acted in protest against it.
+LJSpeech-1.1/wavs/LJ010-0090.wav|Hand-grenades were to be thrown into the dining-room, and during the noise and confusion the assassination of the ministers was to be completed,
+LJSpeech-1.1/wavs/LJ033-0199.wav|because other types of fibers present in the blanket were not found in the bag.
+LJSpeech-1.1/wavs/LJ009-0141.wav|Lord Paget, Lord Bruce, several members of the House of Commons, and a few ladies.
+LJSpeech-1.1/wavs/LJ010-0071.wav|It was during this period that he was said to have imbibed his revolutionary ideas.
+LJSpeech-1.1/wavs/LJ027-0170.wav|Finally, it loses the tail, or rather its tail is absorbed and its material used in further development, and it becomes a perfect frog,
+LJSpeech-1.1/wavs/LJ046-0049.wav|More often than not, Presidential journeys have served more than one purpose at the same time: ceremonial,
+LJSpeech-1.1/wavs/LJ021-0045.wav|They saw that without changes in the policies and methods of investment
+LJSpeech-1.1/wavs/LJ039-0145.wav|According to George De Mohrenschildt, Oswald said that he went target shooting with that rifle.
+LJSpeech-1.1/wavs/LJ019-0392.wav|in the administration of prisons.
+LJSpeech-1.1/wavs/LJ032-0067.wav|Postal Inspector Harry D. Holmes of the Dallas Post Office testified, however, that when a package is received for a certain box,
+LJSpeech-1.1/wavs/LJ008-0249.wav|Blinded with their long hair, they tore at each other like two furies; their bonnets and caps were trodden underfoot in the kennel,
+LJSpeech-1.1/wavs/LJ026-0019.wav|they cannot ingest solid food, but are nourished by a watery solution of nutrient materials.
+LJSpeech-1.1/wavs/LJ011-0218.wav|spoke severely of the gross deception practiced upon an innocent girl, and sentenced the brothers each to three years' imprisonment,
+LJSpeech-1.1/wavs/LJ021-0072.wav|Also, billions of dollars of invested capital have today a greater security of present and future earning power than before.
+LJSpeech-1.1/wavs/LJ006-0174.wav|The aldermen never called upon him to report, and left him nearly unsupervised and uncontrolled.
+LJSpeech-1.1/wavs/LJ044-0223.wav|whether or not he agreed with Castro that President Kennedy was a, quote, ruffian and a thief, end quote. He replied that he, quote,
+LJSpeech-1.1/wavs/LJ045-0010.wav|The Commission has found no evidence that the extreme views expressed toward President Kennedy
+LJSpeech-1.1/wavs/LJ040-0041.wav|There was some quality about him that led him to act with an apparent disregard for possible consequences.
+LJSpeech-1.1/wavs/LJ032-0174.wav|but Mary Bledsoe, a former landlady of Oswald, saw him on a bus approximately ten minutes after the assassination
+LJSpeech-1.1/wavs/LJ013-0223.wav|Courvoisier wished to commit suicide in Newgate, but was prevented by the vigilant supervision to which he was subjected while in jail.
+LJSpeech-1.1/wavs/LJ023-0014.wav|In effect, four Justices ruled that the right under a private contract
+LJSpeech-1.1/wavs/LJ050-0127.wav|should negotiate similar arrangements with such other State and local law enforcement agencies as may provide meaningful assistance.
+LJSpeech-1.1/wavs/LJ019-0139.wav|These numbers would have still further decreased, and the jail would have been almost empty, but for the misdemeanants who were still sent to Newgate
+LJSpeech-1.1/wavs/LJ022-0049.wav|The simple fact is that many million more people have private work today than two years ago today or one year ago today,
+LJSpeech-1.1/wavs/LJ013-0246.wav|Some time elapsed before the imprisoned party could force open the doors, and by then the fugitive had escaped.
+LJSpeech-1.1/wavs/LJ025-0095.wav|once supposed to be exclusively confined to plants, are now known to be regular and normal products of animals.
+LJSpeech-1.1/wavs/LJ022-0033.wav|"To get away from the trees", as they say, "and to look at the whole forest."
+LJSpeech-1.1/wavs/LJ038-0305.wav|The finding that Lee Harvey Oswald attempted to murder a public figure in April nineteen sixty-three
+LJSpeech-1.1/wavs/LJ027-0101.wav|Throughout both the animal and vegetable kingdoms dwarfed and useless representatives of organs are constantly met with,
+LJSpeech-1.1/wavs/LJ022-0054.wav|The first is to make provisions intended to relieve, to minimize, and to prevent future unemployment;
+LJSpeech-1.1/wavs/LJ012-0110.wav|The police lost all trace of them for some days, but at length Sullivan's brother was followed from the house in Kennington to the above-mentioned tavern.
+LJSpeech-1.1/wavs/LJ038-0158.wav|Jarman testified that he ate his lunch on the first floor around five minutes to twelve, and that he neither ate lunch with nor saw Oswald.
+LJSpeech-1.1/wavs/LJ027-0061.wav|in order to enclose and protect a similar enlargement of the nervous center,
+LJSpeech-1.1/wavs/LJ042-0092.wav|No evidence has been found that they used him for any particular propaganda or other political or informational purposes.
+LJSpeech-1.1/wavs/LJ017-0141.wav|could not divest his mind of serious doubt, and of which the murderer got the benefit.
+LJSpeech-1.1/wavs/LJ032-0181.wav|Although Stombaugh was unable to estimate the period of time the fibers were on the rifle he said that the fibers, quote,
+LJSpeech-1.1/wavs/LJ008-0076.wav|When she came out of prison she appeared languid and terrified, and trembled greatly as she advanced to the stake,
+LJSpeech-1.1/wavs/LJ029-0205.wav|for President Kennedy, stating that "in many respects Dallas County has isolated itself from the main stream of life in the world in this decade.
+LJSpeech-1.1/wavs/LJ014-0126.wav|her face was comely, she had dark hair and good eyes, and was above the middle height, yet inclined to be stout.
+LJSpeech-1.1/wavs/LJ025-0087.wav|On the other hand, the definition thus amended will exclude all ordinary vegetable organisms.
+LJSpeech-1.1/wavs/LJ028-0158.wav|The city wall is brought down on both sides to the edge of the stream,
+LJSpeech-1.1/wavs/LJ019-0064.wav|that their condition was more natural, and approximated more nearly to that of daily life.
+LJSpeech-1.1/wavs/LJ001-0036.wav|But about the same year Mentelin at Strasburg began to print in a type which is distinctly Roman;
+LJSpeech-1.1/wavs/LJ018-0153.wav|the officers ensconced themselves in the latter, and waited for Buncher's expected visit.
+LJSpeech-1.1/wavs/LJ049-0150.wav|A Cabinet-level committee which is actively concerned with these problems would be able to discuss these matters more effectively with the President.
+LJSpeech-1.1/wavs/LJ018-0389.wav|Of the lesser criminals, forgers, thieves, swindlers, Newgate continued to receive its full share up to the last.
+LJSpeech-1.1/wavs/LJ021-0121.wav|But I would point out that the extent and severity of labor disputes during this period
+LJSpeech-1.1/wavs/LJ046-0241.wav|PRS required a more direct indication of a threat to the President, and that there was no such indication until the President's scheduled visit to that area became known.
+LJSpeech-1.1/wavs/LJ009-0009.wav|This rule has some, but very few, exceptions; such as where a hardened offender behaves with great levity and brutality, as if he cared nought for his life,
+LJSpeech-1.1/wavs/LJ011-0256.wav|By this time the neighbors were aroused, and several people came to the scene of the affray.
+LJSpeech-1.1/wavs/LJ020-0098.wav|Having made out your rolls and tucked them up snugly for the final rise, return to your chamber for a comfortable bath and toilet.
+LJSpeech-1.1/wavs/LJ018-0223.wav|In eighteen fifty-six the father died.
+LJSpeech-1.1/wavs/LJ028-0045.wav|When Esarhaddon died, one of his sons, Samas-sum-yukin, was made King of Babylon.
+LJSpeech-1.1/wavs/LJ019-0242.wav|Newgate naturally shared in any advantages due to these reforms. I propose, therefore, to refer to them in the concluding pages of this work,
+LJSpeech-1.1/wavs/LJ022-0185.wav|The answer to this demand was the Federal Reserve System.
+LJSpeech-1.1/wavs/LJ024-0104.wav|I am therefore, going to spend my time, my efforts and my money
+LJSpeech-1.1/wavs/LJ028-0276.wav|a marvelous thing happened to Zopyrus, son of the Megabyzus who was among the seven men that overthrew the Magus.
+LJSpeech-1.1/wavs/LJ013-0140.wav|A robbery of a somewhat novel kind was executed in rather a bungling fashion by Ker, a sea-captain,
+LJSpeech-1.1/wavs/LJ028-0372.wav|The poor of the surrounding country occupied its dismantled palaces.
+LJSpeech-1.1/wavs/LJ025-0076.wav|Many animals of even complex structure which live parasitically within others are wholly devoid of an alimentary cavity.
+LJSpeech-1.1/wavs/LJ016-0120.wav|This was considered safer than intrusting him with keys.
+LJSpeech-1.1/wavs/LJ015-0285.wav|and when these presented themselves, entrusted them as a beginning with the duty of cashing cheques.
+LJSpeech-1.1/wavs/LJ046-0201.wav|were no more specific than the broad and general instructions its own agents and the White House mailroom.
+LJSpeech-1.1/wavs/LJ030-0109.wav|The Vice-Presidential car
+LJSpeech-1.1/wavs/LJ009-0093.wav|Why does no one stir to help him? Where would be the use? The hardened burglar moves not, nor does he speak;
+LJSpeech-1.1/wavs/LJ013-0194.wav|but on the second day the discovery of fresh evidence, more particularly the recovery of some of Lord William's stolen plate,
+LJSpeech-1.1/wavs/LJ001-0065.wav|This was notably the case with the early works printed at Ulm, and in a somewhat lesser degree at Augsburg.
+LJSpeech-1.1/wavs/LJ034-0171.wav|Edwards said, quote, Look at that guy there in that window, end quote,
+LJSpeech-1.1/wavs/LJ040-0107.wav|but apparently was not able to spend as much time with them as he would have liked, because of the age gaps of five and seven years,
+LJSpeech-1.1/wavs/LJ017-0280.wav|The convicts were pinioned one by one and sent singly out to the gallows.
+LJSpeech-1.1/wavs/LJ028-0079.wav|The museum authorities believed that the cameo was one of the many spurious objects which the Eastern forgers were constantly sending to Europe,
+LJSpeech-1.1/wavs/LJ022-0066.wav|will not only help to guard the individual in future periods of lay-off against dependence upon relief,
+LJSpeech-1.1/wavs/LJ012-0095.wav|The jewels had belonged to a Spanish countess recently deceased, who had sent them to England for greater security on the outbreak of the first Carlist war.
+LJSpeech-1.1/wavs/LJ024-0035.wav|Let me answer this question with a bluntness that will end all honest misunderstanding of my purposes.
+LJSpeech-1.1/wavs/LJ007-0163.wav|all the tumultuous and diversified passions and emotions which circumstances like these must necessarily generate
+LJSpeech-1.1/wavs/LJ034-0021.wav|Sebastian F. Latona, supervisor of the Latent Fingerprint Section,
+LJSpeech-1.1/wavs/LJ008-0284.wav|Nothing could be more strongly marked than the contrast between the ultimate destiny of different individuals all abiding the same awful doom:
+LJSpeech-1.1/wavs/LJ041-0138.wav|and that she could not think of any reason why Oswald would want to kill President Kennedy.
+LJSpeech-1.1/wavs/LJ017-0043.wav|The direful suspicions which surrounded the case filled the whole country with uneasiness and misgiving,
+LJSpeech-1.1/wavs/LJ008-0219.wav|their conversation was of companions and associates of former years, long ago imprisoned, transported, hanged, while they,
+LJSpeech-1.1/wavs/LJ030-0120.wav|had to leave the left front running board of the President's follow-up car four times to ride on the rear of the President's limousine.
+LJSpeech-1.1/wavs/LJ037-0165.wav|with five lands and grooves and a right twist which were the rifling characteristics of the revolver taken from Oswald.
+LJSpeech-1.1/wavs/LJ012-0020.wav|He began as an itinerant street vendor at eight, at ten he passed bad money,
+LJSpeech-1.1/wavs/LJ016-0337.wav|In the houses opposite the prison numbers of detectives mixed with the spectators;
+LJSpeech-1.1/wavs/LJ050-0011.wav|The Chief of the Service now reports to the Secretary of the Treasury
+LJSpeech-1.1/wavs/LJ035-0016.wav|He saw pigeons flutter upward. He was not certain, quote, but I am pretty sure they came from the building right on the northwest corner, end quote.
+LJSpeech-1.1/wavs/LJ018-0364.wav|It will be remembered that she made a statement which led to the empaneling of a jury of matrons, who decided that there was no cause for an arrest of judgment.
+LJSpeech-1.1/wavs/LJ006-0073.wav|of his many manifest duties, but I shall here confine myself to animadverting on his neglect as regards the appropriation of his prison.
+LJSpeech-1.1/wavs/LJ026-0130.wav|and may thus be easily transported to all parts of the plant.
+LJSpeech-1.1/wavs/LJ017-0228.wav|till every feature was obliterated, and then, still living, flung him into the sea.
+LJSpeech-1.1/wavs/LJ003-0098.wav|the inevitable consequence of such a situation, their morals must have been destroyed;
+LJSpeech-1.1/wavs/LJ006-0151.wav|Many of them were otherwise and improperly occupied for hours every day in menial services for the governor, cleaning his windows or grooming his horse.
+LJSpeech-1.1/wavs/LJ007-0172.wav|where is to be found in operation every expedient by which Ignorance may be superseded by Knowledge, Idleness by Industry, and Suffering by Benevolence;
+LJSpeech-1.1/wavs/LJ004-0034.wav|Moreover, the laws applied more particularly to county jurisdictions.
+LJSpeech-1.1/wavs/LJ024-0099.wav|even though the thirty- five states with ninety-five percent of the population are in favor of it.
+LJSpeech-1.1/wavs/LJ048-0263.wav|During entire periods of travel status,
+LJSpeech-1.1/wavs/LJ011-0226.wav|which was based on his jail experiences, and of which I have availed myself in the last chapter.
+LJSpeech-1.1/wavs/LJ041-0006.wav|His mother exercised little control over him and thought he could decide for himself whether to go on in school.
+LJSpeech-1.1/wavs/LJ011-0267.wav|Mr. Gee had invested one thousand two hundred pounds of this, and was seeking how best to place the remaining eight hundred pounds,
+LJSpeech-1.1/wavs/LJ002-0120.wav|had been issued for the arrests of debtors in the kingdom, for sums varying from fourpence to five hundred pounds and upwards.
+LJSpeech-1.1/wavs/LJ014-0064.wav|He lived at Mile End, whence he walked often to call at three, Minver Place, Bermondsey, the residence of his old love.
+LJSpeech-1.1/wavs/LJ027-0123.wav|where within the limits of the same natural group of organisms a rudiment is sometimes present and sometimes absent.
+LJSpeech-1.1/wavs/LJ019-0224.wav|With the reduction of numbers to be accommodated, there was ample space in Newgate for its reconstruction on the most approved modern lines.
+LJSpeech-1.1/wavs/LJ036-0031.wav|and requested a transfer which she might use if she got through the traffic.
+LJSpeech-1.1/wavs/LJ025-0034.wav|Animals further needed muscles for locomotion and nerves for sensibility.
+LJSpeech-1.1/wavs/LJ050-0106.wav|to devise a practical system which has any reasonable possibility of revealing such malcontents.
+LJSpeech-1.1/wavs/LJ019-0066.wav|with the means of earning an honest livelihood if so disposed.
+LJSpeech-1.1/wavs/LJ026-0011.wav|Because of these uncertain forms of life,
+LJSpeech-1.1/wavs/LJ028-0515.wav|Such were the walls of Babylon,
+LJSpeech-1.1/wavs/LJ016-0332.wav|who was convicted of complicity in the Clerkenwell explosion, intended to effect the release of Burke and Casey
+LJSpeech-1.1/wavs/LJ028-0344.wav|for Cyrus had done neither the one nor the other when he took Babylon.
+LJSpeech-1.1/wavs/LJ006-0032.wav|Messrs. Crawford and Russell proceeded to carry out their new functions with commendable energy, and without a moment's loss of time.
+LJSpeech-1.1/wavs/LJ049-0187.wav|These proposals included suggestions to locate exclusive responsibility for all phases of the work
+LJSpeech-1.1/wavs/LJ010-0063.wav|The massacre of the whole of the Cabinet Ministers at one stroke was to be followed by an attack
+LJSpeech-1.1/wavs/LJ001-0139.wav|a blemish which can be nearly, though not wholly, avoided by care and forethought
+LJSpeech-1.1/wavs/LJ045-0083.wav|On that issue George De Mohrenschildt, who was probably as close to the Oswalds as anyone else during their first stay in Dallas,
+LJSpeech-1.1/wavs/LJ019-0264.wav|and it was decidedly of opinion that in all short sentences the hard labor of the tread-wheel, crank, and so forth should be the invariable rule.
+LJSpeech-1.1/wavs/LJ015-0050.wav|Following this, warrants were issued for their arrest,
+LJSpeech-1.1/wavs/LJ019-0040.wav|the difficulty of access, and the admitted necessity of giving architectural importance to this the national model prison.
+LJSpeech-1.1/wavs/LJ012-0199.wav|After a little
+LJSpeech-1.1/wavs/LJ020-0014.wav|beating the batter smooth as you go on until all of the liquid and flour has gone in.
+LJSpeech-1.1/wavs/LJ009-0022.wav|I was informed that this unnecessarily harassed his feelings, and that the object of such sermons was solely to console the prisoner,
+LJSpeech-1.1/wavs/LJ030-0024.wav|In Dallas the rain had stopped, and by midmorning a gloomy overcast sky had given way to the bright sunshine that greeted the Presidential party
+LJSpeech-1.1/wavs/LJ047-0176.wav|shortly after Oswald rented the room on October fourteen.
+LJSpeech-1.1/wavs/LJ013-0107.wav|but he escaped through a back door on to the river, and rowed off in a boat to a hiding-place in the woods.
+LJSpeech-1.1/wavs/LJ008-0109.wav|As we crossed the press yard a cock crew, and the solitary clanking of a restless chain was dreadfully horrible.
+LJSpeech-1.1/wavs/LJ018-0381.wav|the wildest and most cut-throat looking of the lot, which proves that he could be grateful for kindness, and was not all bad.
+LJSpeech-1.1/wavs/LJ050-0135.wav|According to Secretary Dillon,
+LJSpeech-1.1/wavs/LJ030-0247.wav|I was bent over under the weight of Agent Youngblood's body, toward Mrs. Johnson and Senator Yarborough, end quote,
+LJSpeech-1.1/wavs/LJ012-0184.wav|Meanwhile the discovery of pistol and knife spattered with human blood and brains
+LJSpeech-1.1/wavs/LJ021-0046.wav|there could be no recovery of public confidence in the security of savings.
+LJSpeech-1.1/wavs/LJ042-0236.wav|In the second simulated transcript which ended with the statement, quote, Newspapers thank you, sir. You are a real patriot! End quote.
+LJSpeech-1.1/wavs/LJ048-0002.wav|Chapter eight. The Protection of the President. Part three.
+LJSpeech-1.1/wavs/LJ047-0199.wav|The Secret Service and the FBI differ
+LJSpeech-1.1/wavs/LJ002-0238.wav|was used for debtors arrested for the lowest sums within twelve miles of the palace of Whitehall;
+LJSpeech-1.1/wavs/LJ036-0072.wav|to Murphy and Elm three times, averaging six point five minutes for the three trips.
+LJSpeech-1.1/wavs/LJ048-0220.wav|Conduct of Secret Service agents in Fort Worth on November twenty-two.
+LJSpeech-1.1/wavs/LJ015-0208.wav|they sounded one Burgess, a guard on the South-Eastern Railway, a line by which large quantities of bullion were sent to the Continent.
+LJSpeech-1.1/wavs/LJ043-0031.wav|I am surprised that he didn't do something worse, end quote.
+LJSpeech-1.1/wavs/LJ002-0287.wav|There were no beds or bedding, no straw even.
+LJSpeech-1.1/wavs/LJ003-0294.wav|This committee made its report in September the following year, and an excellent report it is, so far as its recommendations are concerned.
+LJSpeech-1.1/wavs/LJ002-0256.wav|A committee of collegians was elected to act as the executive, also a secretary or accountant to receive monies and keep books,
+LJSpeech-1.1/wavs/LJ034-0096.wav|on the southwest corner of Elm and Houston Streets, looking north at the Depository Building which was directly in front of him.
+LJSpeech-1.1/wavs/LJ028-0308.wav|Let neither these nor the former troops be armed with any weapons but their swords those thou mayest leave them.
+LJSpeech-1.1/wavs/LJ008-0004.wav|The reasons for this change were fully set forth in a previous chapter.
+LJSpeech-1.1/wavs/LJ044-0080.wav|was apparently not enough to satisfy him. He exaggerated in his letters to V. T. Lee in an apparent attempt
+LJSpeech-1.1/wavs/LJ006-0169.wav|He really did not know what passed in his jail
+LJSpeech-1.1/wavs/LJ040-0098.wav|Marguerite Oswald worked in miscellaneous jobs after her divorce from Ekdahl.
+LJSpeech-1.1/wavs/LJ038-0306.wav|was considered of probative value in this investigation, although the Commission's conclusion concerning the identity of the assassin
+LJSpeech-1.1/wavs/LJ038-0187.wav|I paid for the box last month so don't worry about it.
+LJSpeech-1.1/wavs/LJ015-0243.wav|At Redhill Tester met the train and relieved the thieves of a portion of the stolen gold.
+LJSpeech-1.1/wavs/LJ005-0191.wav|The Society proceeded to support this indictment by facts. It is much the old story.
+LJSpeech-1.1/wavs/LJ037-0028.wav|He saw him empty the gun and throw the shells into some bushes on the southeast corner lot.
+LJSpeech-1.1/wavs/LJ010-0011.wav|This led to a rapid and marked increase in all kinds of fraud;
+LJSpeech-1.1/wavs/LJ043-0020.wav|The relations between Oswald and his wife became such that Bouhe wanted to "liberate" her from Oswald.
+LJSpeech-1.1/wavs/LJ011-0177.wav|The fact was, Wakefield went on to say, an uncle of his had advanced Mr. Turner sixty thousand pounds, which had temporarily staved off ruin.
+LJSpeech-1.1/wavs/LJ028-0434.wav|the large high mound, which resembles a mountain from a distance, still bears the ancient name Babel.
+LJSpeech-1.1/wavs/LJ042-0202.wav|It has turned itself into the traditional lever of a foreign power to overthrow the government of the United States;
+LJSpeech-1.1/wavs/LJ027-0074.wav|Sometimes a piece is enlarged, sometimes diminished, or even becomes obsolete; sometimes several pieces are consolidated into one;
+LJSpeech-1.1/wavs/LJ006-0145.wav|He could indulge in snuff if a snuff-taker,
+LJSpeech-1.1/wavs/LJ035-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ012-0162.wav|Having brought down the records of great frauds, forgeries, and thefts from about eighteen twenty-five to eighteen forty,
+LJSpeech-1.1/wavs/LJ039-0021.wav|I called him into the bathroom and I closed the door and I wanted to prevent him and then I started to cry.
+LJSpeech-1.1/wavs/LJ017-0128.wav|At the last moment Palmer tossed a bit of paper over to his counsel, on which he had written, quote, I think there will be a verdict of Not Guilty, end quote.
+LJSpeech-1.1/wavs/LJ043-0143.wav|with his recently acquired rifle and pistol, a copy of the March twenty-four, nineteen sixty-three, issue of the Worker,
+LJSpeech-1.1/wavs/LJ035-0147.wav|Mrs. Reid had watched the parade from the sidewalk in front of the building with Truly and Mr. O. V. Campbell, vice president of the Depository.
+LJSpeech-1.1/wavs/LJ048-0092.wav|The advance preparations in Dallas by Agent Winston G. Lawson of the White House detail have been described in chapter two.
+LJSpeech-1.1/wavs/LJ006-0179.wav|But, indeed, his whole rule was far too mild, and under this mistaken leniency
+LJSpeech-1.1/wavs/LJ017-0187.wav|Her husband, who came up to town, would not allow a post-mortem, and again Mrs. Wilson escaped.
+LJSpeech-1.1/wavs/LJ028-0132.wav|Yet as large and lofty as they were, they were completed in fifteen days.
+LJSpeech-1.1/wavs/LJ042-0053.wav|Responding to Robert's statement that he had not "renounced" him,
+LJSpeech-1.1/wavs/LJ009-0112.wav|the burglar muttering curses and savage expressions of defiance; whilst the poor sheep-stealer shakes hands with the turnkeys,
+LJSpeech-1.1/wavs/LJ019-0150.wav|Captain Williams, who was the inspector of prisons for the home district in succession to Messrs. Crawford and Russell,
+LJSpeech-1.1/wavs/LJ030-0020.wav|if anybody really wanted to shoot the President of the United States, it was not a very difficult job
+LJSpeech-1.1/wavs/LJ029-0033.wav|particularly in large cities where the purpose was to let the President be seen by as many people as possible.
+LJSpeech-1.1/wavs/LJ028-0098.wav|the wise, the pious, the maintainer of Esagil and Ezida,
+LJSpeech-1.1/wavs/LJ028-0014.wav|The desert about you shows no signs of life;
+LJSpeech-1.1/wavs/LJ003-0346.wav|It pointed out that the Government was to blame for the overcrowding, and might diminish it if it chose.
+LJSpeech-1.1/wavs/LJ038-0214.wav|and the other paragraphs instructed her on the disposal of Oswald's personal effects and the management of her affairs if he should not return.
+LJSpeech-1.1/wavs/LJ050-0122.wav|It should be made clear that the Secret Service will in no way seek to duplicate the intelligence
+LJSpeech-1.1/wavs/LJ018-0143.wav|each containing two notes, many of the sheets suitable for engraving any kind of note from one thousand pounds downwards.
+LJSpeech-1.1/wavs/LJ023-0126.wav|In other words, we said we would seek an amendment only if every other possible means by legislation were to fail.
+LJSpeech-1.1/wavs/LJ048-0015.wav|We talked to him twice.
+LJSpeech-1.1/wavs/LJ049-0179.wav|The Commission believes that both the FBI and the Secret Service have too narrowly construed their respective responsibilities.
+LJSpeech-1.1/wavs/LJ050-0269.wav|The essential terms of such memoranda might well be embodied in an Executive order.
+LJSpeech-1.1/wavs/LJ006-0265.wav|The untried might see their friends three times a week, the convicted only once.
+LJSpeech-1.1/wavs/LJ013-0160.wav|who, on going down early, was surprised to find the dining-room in a state of utter confusion;
+LJSpeech-1.1/wavs/LJ032-0040.wav|were written the words "A. Hidell, P.O. Box two nine one five Dallas, Texas."
+LJSpeech-1.1/wavs/LJ006-0261.wav|together with four bradawls, several large iron spikes, screws, nails, and knives;
+LJSpeech-1.1/wavs/LJ033-0114.wav|When Frazier appeared before the Commission and was asked to demonstrate how Oswald carried the package, he said, quote, Like I said,
+LJSpeech-1.1/wavs/LJ034-0163.wav|The Commission is satisfied that, at the least,
+LJSpeech-1.1/wavs/LJ032-0125.wav|In his testimony before the Commission, Latona stated that when he received the rifle, the area where prints were visible was protected by cellophane.
+LJSpeech-1.1/wavs/LJ008-0189.wav|All the avenues and approaches, places even whence nothing whatever could be seen of the scaffold,
+LJSpeech-1.1/wavs/LJ016-0334.wav|Unusual precautions were taken upon this occasion, as some fresh outrage was apprehended.
+LJSpeech-1.1/wavs/LJ006-0114.wav|They bought their offices from one another, and were thus considered to have a vested interest in them.
+LJSpeech-1.1/wavs/LJ007-0214.wav|"The prominent evils of this prison (Newgate) -- evils which the alterations made within the last four years have failed to remove
+LJSpeech-1.1/wavs/LJ005-0213.wav|Newgate through all these years continued a bye-word with the Society.
+LJSpeech-1.1/wavs/LJ013-0011.wav|She was freighted by the firm of Zulueta and Co. for a voyage to Santa Cruz.
+LJSpeech-1.1/wavs/LJ009-0249.wav|When Charles White was executed in eighteen twenty-three for arson, he arranged a handkerchief
+LJSpeech-1.1/wavs/LJ039-0005.wav|Richard M. Nixon Incident
+LJSpeech-1.1/wavs/LJ019-0083.wav|The discipline also varied greatly, from the severely penal to the culpably lax.
+LJSpeech-1.1/wavs/LJ037-0217.wav|Police found an empty revolver holster when they searched Oswald's room on Beckley Avenue after his arrest.
+LJSpeech-1.1/wavs/LJ042-0117.wav|he noted that one of his acquaintances, quote, relates many things I do not know about the U.S.S.R.
+LJSpeech-1.1/wavs/LJ008-0035.wav|were told that the plan had been well considered, and would be persevered in.
+LJSpeech-1.1/wavs/LJ048-0016.wav|He likewise indicated he was disenchanted with Russia.
+LJSpeech-1.1/wavs/LJ014-0130.wav|Manning, when sentence of death was passed on him, said nothing;
+LJSpeech-1.1/wavs/LJ026-0050.wav|following in part the comparisons of certain animals and plants by Sedgwick and Wilson and others.
+LJSpeech-1.1/wavs/LJ009-0044.wav|His features have no felonious cast;
+LJSpeech-1.1/wavs/LJ010-0024.wav|While the varying conditions of social life thus brought about many changes in the character of offenses against property,
+LJSpeech-1.1/wavs/LJ047-0021.wav|information regarding his relations with the U.S. Embassy in Moscow and background data relating largely to his prior military service,
+LJSpeech-1.1/wavs/LJ012-0224.wav|who examined Bishop and his fellows, and further incriminating evidence adduced, to the effect that the prisoners had bartered for a coach to carry "a stiff 'un";
+LJSpeech-1.1/wavs/LJ019-0096.wav|other authorities as strongly condemned it as brutalizing, unequal in its operation, and altogether a "deplorable invention."
+LJSpeech-1.1/wavs/LJ006-0048.wav|To these were still added an average of about fifty expecting the last penalty of the law; a certain number of transports awaiting removal to the colonies;
+LJSpeech-1.1/wavs/LJ038-0277.wav|I am aware of their position. This is not, I am sure, arrived at without careful consideration.
+LJSpeech-1.1/wavs/LJ041-0170.wav|Thornley testified, quote, At which time he looked at me like a betrayed Caesar and screamed, screamed definitely, "Not you, too, Thornley!"
+LJSpeech-1.1/wavs/LJ046-0136.wav|Before the assassination of President Kennedy,
+LJSpeech-1.1/wavs/LJ029-0056.wav|Although there was no mention in PRS files of the demonstration in Dallas against Ambassador Adlai Stevenson on October twenty-fourth,
+LJSpeech-1.1/wavs/LJ007-0217.wav|They go on to say
+LJSpeech-1.1/wavs/LJ046-0194.wav|Members of the White House detail were expected to familiarize themselves with the descriptions and photographs of the highest risk cases.
+LJSpeech-1.1/wavs/LJ038-0239.wav|indicated that the bullet was fired from a position near the point where one of the photographs was taken.
+LJSpeech-1.1/wavs/LJ008-0263.wav|Forty-eight hours was the limit of time allowed to the unhappy man to make his peace, and during that time he was still kept on a bare allowance of bread and water.
+LJSpeech-1.1/wavs/LJ023-0084.wav|When the Congress has sought to stabilize national agriculture, to improve the conditions of labor,
+LJSpeech-1.1/wavs/LJ039-0146.wav|Marina Oswald testified that in New Orleans in May of nineteen sixty-three, she observed Oswald sitting with the rifle on their screened porch at night,
+LJSpeech-1.1/wavs/LJ039-0226.wav|the Marine marksmanship experts, Major Anderson and Sergeant Zahm, concurred in the opinion that Oswald had the capability to fire three shots,
+LJSpeech-1.1/wavs/LJ049-0076.wav|The Commission's review of the provisions for Presidential protection at the time of President Kennedy's trip to Dallas demonstrates the need for substantial improvements.
+LJSpeech-1.1/wavs/LJ014-0293.wav|He returned from court in a state of gloomy dejection,
+LJSpeech-1.1/wavs/LJ043-0033.wav|Bouhe thoroughly disapproved of this and as a result almost all communication between the Oswalds and members of the Russian community ceased.
+LJSpeech-1.1/wavs/LJ023-0035.wav|For in the last three national elections
+LJSpeech-1.1/wavs/LJ028-0001.wav|The Seven Wonders of the Ancient World. By Edgar J. Banks. Chapter two. The Walls of Babylon.
+LJSpeech-1.1/wavs/LJ007-0095.wav|Prisoners indeed were known to boast that they had saved their necks by feigning insanity.
+LJSpeech-1.1/wavs/LJ002-0237.wav|This very ancient prison, which stood in the High Street, Southwark,
+LJSpeech-1.1/wavs/LJ036-0182.wav|She recalled that it was subsequent to the time the President had been shot.
+LJSpeech-1.1/wavs/LJ032-0089.wav|It certified that Lee Harvey Oswald had been vaccinated for smallpox on June eight, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ043-0018.wav|He was offensive with the people. And I can understand why, because that hurt him.
+LJSpeech-1.1/wavs/LJ048-0210.wav|He was handicapped, however, by the fact that he was riding in a closed car whose roof at times obscured his view.
+LJSpeech-1.1/wavs/LJ033-0074.wav|Marina Oswald testified that this was her first knowledge that the rifle was not in its accustomed place.
+LJSpeech-1.1/wavs/LJ040-0195.wav|It appears that he did not want to do any of the things which the authorities suggested in their efforts to bring him out of the shell
+LJSpeech-1.1/wavs/LJ007-0183.wav|traversing where it was possible the statements of the inspectors, and offering explanation and palliation of such evils as could not be denied.
+LJSpeech-1.1/wavs/LJ038-0232.wav|Marina Oswald stated that when Oswald returned home on the night of the Walker shooting, he told her that he had been planning the attempt for two months.
+LJSpeech-1.1/wavs/LJ031-0055.wav|Dr. Perry noted the President's back brace as he felt for a femoral pulse, which he did not find.
+LJSpeech-1.1/wavs/LJ018-0332.wav|Greed in the latter case was a secondary motive;
+LJSpeech-1.1/wavs/LJ022-0001.wav|The Fireside Chats of Franklin Delano Roosevelt, by Franklin D Roosevelt, Section seven.
+LJSpeech-1.1/wavs/LJ024-0107.wav|The first includes those who fundamentally object to social and economic legislation along modern lines.
+LJSpeech-1.1/wavs/LJ030-0070.wav|Motorcycles. -- Four motorcycles, two on each side, flanked the rear of the Presidential car.
+LJSpeech-1.1/wavs/LJ025-0048.wav|Respiration -- that is, the absorption of oxygen and the exhalation of carbonic acid
+LJSpeech-1.1/wavs/LJ038-0237.wav|The Commission confirmed, by comparison with other photographs, that these were, indeed, photographs of the rear of Walker's house.
+LJSpeech-1.1/wavs/LJ043-0032.wav|After about a two-week separation, Marina Oswald returned to her husband.
+LJSpeech-1.1/wavs/LJ021-0177.wav|Did England hold to the gold standard when her reserves were threatened?
+LJSpeech-1.1/wavs/LJ001-0089.wav|but his letters, though uninteresting and poor, are not nearly so gross and vulgar as those of either the Italian or the Frenchman.
+LJSpeech-1.1/wavs/LJ034-0210.wav|The picture which gave rise to these allegations was taken by Associated Press Photographer James W. Altgens,
+LJSpeech-1.1/wavs/LJ010-0272.wav|This son, John William Bean, was fully identified by Dasset, and presently examined by the Privy Council.
+LJSpeech-1.1/wavs/LJ012-0179.wav|The others followed, and on overtaking Thurtell, found he had done the job alone in a retired part of the road known as Gill's Hill Lane.
+LJSpeech-1.1/wavs/LJ032-0126.wav|He examined these prints, as well as photographs of them which the Dallas police had made, and concluded that:
+LJSpeech-1.1/wavs/LJ016-0413.wav|No less than sixty-seven persons were present, admitted by special permission of the sheriff.
+LJSpeech-1.1/wavs/LJ010-0032.wav|Pegsworth, and Greenacre, and Daniel Good merely reproduced types that had gone before, and that have since reappeared.
+LJSpeech-1.1/wavs/LJ029-0015.wav|The party itself saw an opportunity to raise funds by having the President speak at a political dinner eventually planned for Austin.
+LJSpeech-1.1/wavs/LJ033-0169.wav|it would be the thickness of both the paper and the tape, the color under various lighting conditions of both the paper and the tape,
+LJSpeech-1.1/wavs/LJ047-0121.wav|stated the Bureau's reasoning in this way, quote,
+LJSpeech-1.1/wavs/LJ021-0197.wav|of referring without rhyme or reason to the Constitution as a means of preventing its accomplishment, thus creating the general impression
+LJSpeech-1.1/wavs/LJ004-0216.wav|The most noticeable of the improvements introduced was a better regulation of dietaries within the prison.
+LJSpeech-1.1/wavs/LJ042-0162.wav|Full of optimism and hope, he stood in Red Square in the Fall of nineteen fifty-nine, vowing to see his chosen course through,
+LJSpeech-1.1/wavs/LJ012-0008.wav|He had sought by all legal means to obtain possession of the two thousand pounds, but had failed, and had had recourse to more violent means.
+LJSpeech-1.1/wavs/LJ036-0191.wav|he would have reached tenth and Patton shortly after one:fifteen p.m.
+LJSpeech-1.1/wavs/LJ010-0137.wav|Davidson, who was the only one who seemed to realize his awful situation, listened patiently and with thankfulness to the chaplain,
+LJSpeech-1.1/wavs/LJ006-0063.wav|minor offenders charged with small thefts or non-payment of small sums were cheek by jowl with convicts sentenced to long terms of transportation.
+LJSpeech-1.1/wavs/LJ022-0188.wav|Certain proposals made to amend the Federal Reserve Act deserve prompt and favorable action by the Congress.
+LJSpeech-1.1/wavs/LJ039-0026.wav|she stated that she might have been confused about shutting him in the bathroom, but that, quote, there is no doubt that he got dressed and got a gun, end quote,
+LJSpeech-1.1/wavs/LJ038-0036.wav|As McDonald started to search Oswald's waist for a gun, he heard him say, quote, Well, it's all over now, end quote.
+LJSpeech-1.1/wavs/LJ041-0146.wav|it was more in terms of a general hostility against the government and its representatives rather than a grudge against any particular person.
+LJSpeech-1.1/wavs/LJ034-0128.wav|But the testimony of these employees, together with photographs subsequently taken of them at the scene of the assassination,
+LJSpeech-1.1/wavs/LJ039-0121.wav|and he probably didn't have as high a motivation because he was no longer in recruit training and under the care of the drill instructor.
+LJSpeech-1.1/wavs/LJ011-0182.wav|Miss Turner, thus pressed, consented to go on to Gretna Green.
+LJSpeech-1.1/wavs/LJ008-0042.wav|is enclosed by a temporary roof, under which are placed two seats for the reception of the sheriffs, one on each side of the stairs leading to the scaffold.
+LJSpeech-1.1/wavs/LJ026-0135.wav|If a larger quantity of starch is formed in the chlorophyll bodies than is immediately needed by the protoplasm for purposes of repair or growth,
+LJSpeech-1.1/wavs/LJ003-0226.wav|The rations of food were notoriously inadequate, and so carelessly distributed, that many were left to starve.
+LJSpeech-1.1/wavs/LJ044-0115.wav|he felt that this was a great man that he had received the letter from, end quote.
+LJSpeech-1.1/wavs/LJ011-0270.wav|but on arrival he was met by a young sailor with a letter which begged Mr. Gee to go to Heath's house, as the latter was not well.
+LJSpeech-1.1/wavs/LJ016-0105.wav|These men were all in prison dress at the time of their escape, but one of their number, Bell, sent back his clothes a few days later by parcel's delivery,
+LJSpeech-1.1/wavs/LJ003-0033.wav|Enough has been said, probably, to prove that there was room for improvement in the condition and treatment of debtors in the prisons of the city of London.
+LJSpeech-1.1/wavs/LJ036-0156.wav|He was in error, however.
+LJSpeech-1.1/wavs/LJ028-0039.wav|We know the names of its kings, and the records speak of long wars with the Assyrians.
+LJSpeech-1.1/wavs/LJ046-0089.wav|His travel would be in secret; his public appearances would be behind bulletproof glass. A more practical approach necessitates compromise.
+LJSpeech-1.1/wavs/LJ004-0110.wav|It was made incumbent upon the justices to provide distinct places of confinement for five classes of prisoners, viz.
+LJSpeech-1.1/wavs/LJ032-0091.wav|There is no "Dr. Hideel" licensed to practice medicine in Louisiana.
+LJSpeech-1.1/wavs/LJ038-0171.wav|General Walker gave this information to the police before the shooting, but it did not help solve the crime.
+LJSpeech-1.1/wavs/LJ034-0159.wav|The Commission, therefore, does not base its conclusion concerning the identity of the assassin
+LJSpeech-1.1/wavs/LJ018-0375.wav|The 'Lennie's' men were all Greeks, except one known as French Peter,
+LJSpeech-1.1/wavs/LJ004-0113.wav|It was further ordered that male prisoners should be kept perfectly distinct from the females.
+LJSpeech-1.1/wavs/LJ030-0250.wav|Other Secret Service agents assigned to the motorcade remained at their posts during the race to the hospital.
+LJSpeech-1.1/wavs/LJ035-0011.wav|a strong wind blowing from the north almost unseated him.
+LJSpeech-1.1/wavs/LJ037-0161.wav|and impressed upon the lead of the bullets inconsistent individual characteristics which made identification impossible.
+LJSpeech-1.1/wavs/LJ034-0156.wav|whether or not he could positively identify the man he saw in the sixth-floor window as the same man he saw in the police station,
+LJSpeech-1.1/wavs/LJ003-0108.wav|Spirits were freely introduced, and although he at first abstained,
+LJSpeech-1.1/wavs/LJ048-0074.wav|To insure adequate and effective liaison arrangements,
+LJSpeech-1.1/wavs/LJ031-0188.wav|Members of the Presidential and Vice-Presidential parties filled the central compartment of the plane to witness the swearing in.
+LJSpeech-1.1/wavs/LJ018-0232.wav|He himself prepared it on a blank form which he had brought with him on purpose.
+LJSpeech-1.1/wavs/LJ029-0125.wav|The only practical way for westbound traffic on Main Street
+LJSpeech-1.1/wavs/LJ017-0196.wav|The last time Mrs. Soames showed great reluctance to take it, but Wilson said it would certainly do her good.
+LJSpeech-1.1/wavs/LJ030-0152.wav|Speed of the Limousine
+LJSpeech-1.1/wavs/LJ004-0238.wav|Mr. Buxton mentions the case of a boy whose apparent innocence and artlessness had attracted his attention.
+LJSpeech-1.1/wavs/LJ032-0237.wav|it was established that the photographs must have been taken sometime after March twenty-seven.
+LJSpeech-1.1/wavs/LJ024-0130.wav|to frighten the workers of America in a pay-envelope propaganda against the Social Security Law.
+LJSpeech-1.1/wavs/LJ044-0129.wav|and often it is advisable for some people to remain in the background, not underground, end quote.
+LJSpeech-1.1/wavs/LJ039-0124.wav|End quote. Major Anderson concluded, quote, I would say that as compared to other Marines receiving the same type of training,
+LJSpeech-1.1/wavs/LJ025-0167.wav|and in the seeds which it produces are exactly equivalent to the weights of the same elements which have disappeared from the materials supplied to the bean during its growth.
+LJSpeech-1.1/wavs/LJ042-0033.wav|of his concomitant hatred of the United States, which was most clearly expressed in his November twenty-six,
+LJSpeech-1.1/wavs/LJ046-0228.wav|the Secret Service had no standard procedure for the systematic review of its requests for and receipt of information from other Federal agencies.
+LJSpeech-1.1/wavs/LJ039-0159.wav|The ammunition used by the assassin was manufactured by Western Cartridge Co. of East Alton, Illinois.
+LJSpeech-1.1/wavs/LJ028-0104.wav|lined the shores of the rivers with embankments, and spanned the rivers with bridges.
+LJSpeech-1.1/wavs/LJ023-0102.wav|subsistence, and health of large numbers in the community, then
+LJSpeech-1.1/wavs/LJ041-0136.wav|was shooting at Connally rather than President Kennedy, end quote.
+LJSpeech-1.1/wavs/LJ045-0129.wav|has visited us here in Dallas, Texas, on November one. Agent James P. Hasty
+LJSpeech-1.1/wavs/LJ022-0115.wav|This is a great national crusade to destroy enforced idleness which is an enemy of the human spirit
+LJSpeech-1.1/wavs/LJ047-0087.wav|According to the Bureau, quote,
+LJSpeech-1.1/wavs/LJ005-0242.wav|In the year immediately preceding this, Parliament was too busy with the great question of its own reform to spare much time for domestic legislation.
+LJSpeech-1.1/wavs/LJ032-0252.wav|Marina Oswald has stated that the rifle was among these possessions, although Ruth Paine testified that she was not aware of it.
+LJSpeech-1.1/wavs/LJ044-0225.wav|It should also be noted, however, that one witness testified that shortly before the assassination
+LJSpeech-1.1/wavs/LJ018-0085.wav|He would shoot any man or any policeman like a dog, or any number of them, who had treated him in that way.
+LJSpeech-1.1/wavs/LJ014-0105.wav|He was lying on his face, his legs tied up to his hips so as to allow of the body fitting into the hole.
+LJSpeech-1.1/wavs/LJ012-0290.wav|While conversing with Mrs. Brown, he declared the unfortunate woman was rocking herself to and fro in a chair;
+LJSpeech-1.1/wavs/LJ028-0505.wav|It seems that the bricks of the reliefs were molded and glazed separately and so accurately that when built into the wall they fitted perfectly.
+LJSpeech-1.1/wavs/LJ036-0068.wav|Both buses stopped within one block of the Depository Building.
+LJSpeech-1.1/wavs/LJ018-0011.wav|About the same time a body was discovered on the line near the railway-bridge by Victoria Park.
+LJSpeech-1.1/wavs/LJ050-0267.wav|between Secretary Dillon and Donald F. Hornig, Special Assistant to the President for Science and Technology, is a useful effort in the right direction.
+LJSpeech-1.1/wavs/LJ043-0041.wav|Consistent with this attitude
+LJSpeech-1.1/wavs/LJ039-0171.wav|On the second series they required five point one five, six point four five, and seven seconds.
+LJSpeech-1.1/wavs/LJ047-0084.wav|and his application was approved on the following day.
+LJSpeech-1.1/wavs/LJ013-0088.wav|the cashier gave them eight Bank of England notes for one thousand pounds each, saying that they could get so much specie nowhere else.
+LJSpeech-1.1/wavs/LJ032-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ018-0125.wav|Burnett had only just come out of jail after completing a sentence of penal servitude.
+LJSpeech-1.1/wavs/LJ005-0218.wav|The chapel still continued incommodious and insufficient
+LJSpeech-1.1/wavs/LJ022-0082.wav|In all cases projects must be of a character to give employment to those on the relief rolls.
+LJSpeech-1.1/wavs/LJ042-0201.wav|He wrote, quote, The Communist Party of the United States has betrayed itself!
+LJSpeech-1.1/wavs/LJ002-0069.wav|to the various wards their friends occupied.
+LJSpeech-1.1/wavs/LJ023-0133.wav|judges who will retain in the courts the judicial functions of a court,
+LJSpeech-1.1/wavs/LJ044-0239.wav|and raises serious questions as to whether or not he ever expected to escape.
+LJSpeech-1.1/wavs/LJ007-0186.wav|the undue authority given to prisoners, the levying of garnish under another name
+LJSpeech-1.1/wavs/LJ009-0078.wav|who preaches plain, homely discourses, as fit as any religious discourse can be fit for the irritated audience.
+LJSpeech-1.1/wavs/LJ010-0311.wav|in other words, he forged powers of attorney, and proceeded to realize securities lodged in his bank under various names.
+LJSpeech-1.1/wavs/LJ015-0159.wav|In his person he was neat and fastidious;
+LJSpeech-1.1/wavs/LJ016-0322.wav|the commission recommended that death sentences should be carried out within the jail, under such regulations as might be considered necessary
+LJSpeech-1.1/wavs/LJ038-0223.wav|from October nine, nineteen sixty-two, to May fourteen, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ016-0016.wav|There is an attempt at escape mentioned in Mr. Wakefield's book, which might have been an intended suicide.
+LJSpeech-1.1/wavs/LJ024-0091.wav|Each one is radically different from the other.
+LJSpeech-1.1/wavs/LJ021-0116.wav|We also question the wisdom of extending code requirements suited to the great industrial centers and to large employers,
+LJSpeech-1.1/wavs/LJ031-0033.wav|Then he experienced his first sensation of pain, which became excruciating.
+LJSpeech-1.1/wavs/LJ018-0377.wav|Conviction was obtained through the evidence of the steward and two of the least culpable of the crew.
+LJSpeech-1.1/wavs/LJ012-0082.wav|Jordan and Sullivan, who at once set to work in a business-like way to obtain impressions of the keys of the strong room and chest.
+LJSpeech-1.1/wavs/LJ015-0026.wav|Moreover, the partners were sober, steady men, who paid unremitting attention to business.
+LJSpeech-1.1/wavs/LJ045-0039.wav|His past relationships with his wife had been stormy, however, and it did not seem that she respected him very much.
+LJSpeech-1.1/wavs/LJ030-0073.wav|Presidential follow-up car.
+LJSpeech-1.1/wavs/LJ025-0045.wav|They get rid of the superfluous hydrogen and carbon and accumulate nitrogen.
+LJSpeech-1.1/wavs/LJ005-0204.wav|Clun, in Shropshire, had a lock-up under the town hall.
+LJSpeech-1.1/wavs/LJ001-0024.wav|But the first Bible actually dated (which also was printed at Maintz by Peter Schoeffer in the year fourteen sixty-two)
+LJSpeech-1.1/wavs/LJ026-0111.wav|In the cells the foods undergo metabolic changes.
+LJSpeech-1.1/wavs/LJ010-0109.wav|Thistlewood made a long and rambling defense, the chief features of which were abuse of Lord Sidmouth, and the vilification of the informer Edwards.
+LJSpeech-1.1/wavs/LJ038-0188.wav|two. Send the information as to what has happened to me to the Embassy.
+LJSpeech-1.1/wavs/LJ012-0139.wav|Solomons, had sold bar gold to the value of one thousand two hundred pounds to certain bullion dealers.
+LJSpeech-1.1/wavs/LJ019-0112.wav|Such excellent returns might be counted upon, that a margin of profit would be left after the cost of the prisons had been defrayed.
+LJSpeech-1.1/wavs/LJ033-0107.wav|No other employee has been found who saw Oswald enter that morning.
+LJSpeech-1.1/wavs/LJ042-0189.wav|but one with union communes,
+LJSpeech-1.1/wavs/LJ018-0079.wav|The unfortunate man survived till he landed, but died in Guy's Hospital.
+LJSpeech-1.1/wavs/LJ035-0040.wav|As Baker reached the second floor, he was about twenty feet from the vestibule door. He intended to continue around to his left toward the stairway going up
+LJSpeech-1.1/wavs/LJ046-0163.wav|During the period November nineteen sixty-one to November nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ038-0103.wav|On the afternoon of November twenty-three, Officers H. M. Moore,
+LJSpeech-1.1/wavs/LJ014-0247.wav|and without reference to or comparison with the documents in which the payment was claimed.
+LJSpeech-1.1/wavs/LJ037-0225.wav|five foot eight inches, black hair, slender, wearing a white jacket, white shirt and dark slacks, end quote,
+LJSpeech-1.1/wavs/LJ010-0119.wav|It was asserted, not without foundation, at these trials, that Edwards repeatedly incited the associates he was betraying
+LJSpeech-1.1/wavs/LJ018-0037.wav|In searching the prisoner's box, Mr. Briggs' watch was found wrapped up in a piece of leather,
+LJSpeech-1.1/wavs/LJ033-0173.wav|The papers I also found were similar in fiber composition, therefore, in addition to the visual characteristics,
+LJSpeech-1.1/wavs/LJ012-0113.wav|After their arrest, Jordan's wife and Sullivan's brother came to the inn, and begged to be allowed to visit this room;
+LJSpeech-1.1/wavs/LJ030-0004.wav|The trip to Texas began with the departure of President and Mrs. Kennedy from the White House
+LJSpeech-1.1/wavs/LJ042-0090.wav|I'm sure Russians will accept me after this sign of my faith in them, end quote.
+LJSpeech-1.1/wavs/LJ044-0203.wav|For example, the Dallas Times Herald of November nineteen, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0243.wav|There were many features of resemblance in these crimes.
+LJSpeech-1.1/wavs/LJ008-0247.wav|but ever the same form moved along in the fulfillment of his mission, in spite of all persecution.
+LJSpeech-1.1/wavs/LJ005-0258.wav|embodying recommendations which may be said to have formed the basis of modern prison management.
+LJSpeech-1.1/wavs/LJ017-0064.wav|he made use of the remarkable words, quote, If it is necessary for my soul's sake to confess this murder,
+LJSpeech-1.1/wavs/LJ016-0188.wav|He told Calcraft that he was Foxen the executioner, and that he was that moment on his way to Newgate to hang a man,
+LJSpeech-1.1/wavs/LJ048-0190.wav|If such instructions were in fact given, they were not effectively carried out.
+LJSpeech-1.1/wavs/LJ037-0197.wav|and the return address was Post Office Box two nine one five, Dallas, Texas.
+LJSpeech-1.1/wavs/LJ025-0077.wav|Their food is provided for them,
+LJSpeech-1.1/wavs/LJ030-0217.wav|quote, Mrs. Kennedy had jumped up from the seat and was, it appeared to me, reaching for something coming off the fight rear bumper of the car,
+LJSpeech-1.1/wavs/LJ005-0175.wav|At this time there were in England one hundred and seventy boroughs, cities, towns, and liberties
+LJSpeech-1.1/wavs/LJ006-0182.wav|Under the reckless contempt for regulations,
+LJSpeech-1.1/wavs/LJ019-0302.wav|In eighteen sixty-two there were in all one hundred and ninety-three jails in England and Wales;
+LJSpeech-1.1/wavs/LJ036-0202.wav|At twelve:fifty-four p.m., Tippit reported that he was in the central Oak Cliff area at Lancaster and Eighth.
+LJSpeech-1.1/wavs/LJ018-0083.wav|Sattler was a very excitable although not an ill-tempered man.
+LJSpeech-1.1/wavs/LJ032-0207.wav|I found it to be the same general configuration. All appearances were the same, end quote,
+LJSpeech-1.1/wavs/LJ015-0018.wav|and dated back to the Commonwealth, when, under the title of Snow and Walton, it carried on business as pawnbrokers.
+LJSpeech-1.1/wavs/LJ035-0089.wav|No allowance was made for the special conditions which existed on the day of the assassination
+LJSpeech-1.1/wavs/LJ012-0094.wav|While the excitement was still fresh, a new robbery of diamonds was committed at a bonded warehouse in the immediate neighborhood, on Custom House Quay.
+LJSpeech-1.1/wavs/LJ045-0004.wav|It has been suggested that one of the motivating influences operating on Lee Oswald was the atmosphere in the city of Dallas,
+LJSpeech-1.1/wavs/LJ030-0036.wav|Secret Service arrangements for Presidential trips, which were followed in the Dallas motorcade,
+LJSpeech-1.1/wavs/LJ010-0307.wav|New liabilities were incurred to the extent of one hundred thousand pounds by more failures, and in eighteen nineteen,
+LJSpeech-1.1/wavs/LJ024-0038.wav|that no President fit for his office would appoint,
+LJSpeech-1.1/wavs/LJ008-0301.wav|during which they counted the moments -- the prisoners in their cells as usual, and their friends in the street in front of Newgate, where they passed the night.
+LJSpeech-1.1/wavs/LJ028-0073.wav|The walls of the palaces of many of the Assyrian kings were lined with great stone slabs engraved with reliefs and sometimes with the portrait of a king.
+LJSpeech-1.1/wavs/LJ019-0022.wav|On the other hand, it must be admitted
+LJSpeech-1.1/wavs/LJ001-0109.wav|this is best furthered by the avoidance of irrational swellings and spiky projections, and by the using of careful purity of line.
+LJSpeech-1.1/wavs/LJ041-0149.wav|He told Aline Mosby, a reporter who interviewed him after he arrived in Moscow, quote,
+LJSpeech-1.1/wavs/LJ028-0092.wav|Read the introduction to any of his inscriptions, of which the following is one, and you will call him vain and proud,
+LJSpeech-1.1/wavs/LJ002-0073.wav|Neild takes it for granted that the former rather than the latter prevailed in the selection,
+LJSpeech-1.1/wavs/LJ042-0178.wav|He thought the new alternative would have its best chance to be accepted after, quote, conflict between the two world systems leaves the world country
+LJSpeech-1.1/wavs/LJ045-0224.wav|and such a favorable opportunity to strike at a figure as great as the President would probably never have come to him again.
+LJSpeech-1.1/wavs/LJ021-0105.wav|and the vital necessity of improving labor conditions,
+LJSpeech-1.1/wavs/LJ017-0142.wav|Smethurst's escape may have influenced the jury in the Poplar poisoning case,
+LJSpeech-1.1/wavs/LJ005-0115.wav|or an imaginary boundary line, and nothing prevented parties from passing to either side
+LJSpeech-1.1/wavs/LJ039-0038.wav|throughout April. The Commission asked Marina Oswald whether she might have misunderstood the object of her husband's threat. She stated, quote,
+LJSpeech-1.1/wavs/LJ005-0005.wav|Even then they were not certain of the favor, for I find a reference to a decent and respectable woman sent to Newgate
+LJSpeech-1.1/wavs/LJ029-0057.wav|nineteen sixty-three, Lawson inquired about the incident and obtained through the local police photographs of some of the persons involved.
+LJSpeech-1.1/wavs/LJ003-0013.wav|No attempt was made to check drunkenness, beyond the penalty of shutting out friends from any ward in which a prisoner exceeded.
+LJSpeech-1.1/wavs/LJ024-0081.wav|But we cannot yield our constitutional destiny to the personal judgment of a few men who, being fearful of the future,
+LJSpeech-1.1/wavs/LJ028-0068.wav|They tell how he forced the exiles to carry heavy bags of sand across the desert to increase their burdens;
+LJSpeech-1.1/wavs/LJ005-0200.wav|Most of these small jails were still in existence and in much the same state eight years later,
+LJSpeech-1.1/wavs/LJ031-0082.wav|From a medical viewpoint, President Kennedy was alive when he arrived at Parkland Hospital;
+LJSpeech-1.1/wavs/LJ032-0270.wav|Having reviewed the evidence that (one) Lee Harvey Oswald purchased the rifle used in the assassination,
+LJSpeech-1.1/wavs/LJ024-0109.wav|Now they are making a last stand.
+LJSpeech-1.1/wavs/LJ023-0087.wav|the majority of the Court has been assuming the power to pass on the wisdom of these acts of the Congress
+LJSpeech-1.1/wavs/LJ043-0115.wav|He lost his job on July nineteen, nineteen sixty-three, because his work was not satisfactory
+LJSpeech-1.1/wavs/LJ010-0037.wav|Now and again there seemed to be a recurrence of a murder epidemic,
+LJSpeech-1.1/wavs/LJ035-0045.wav|Baker said, quote, He [Truly] had already started around the bend to come to the next elevator going up,
+LJSpeech-1.1/wavs/LJ013-0099.wav|A smart detective, Forrester, after a little inquiry,
+LJSpeech-1.1/wavs/LJ050-0046.wav|it has obtained the services of outside consultants, such as the Rand Corporation,
+LJSpeech-1.1/wavs/LJ032-0035.wav|In addition to the order coupon the envelope contained a. U.S. postal money order for twenty-one dollars, forty-five cents,
+LJSpeech-1.1/wavs/LJ033-0162.wav|from the shipping room of the Depository and forwarded it to the FBI Laboratory in Washington.
+LJSpeech-1.1/wavs/LJ029-0141.wav|from Main across Elm to the access road to Stemmons Freeway and the Dallas-Fort Worth Turnpike.
+LJSpeech-1.1/wavs/LJ019-0127.wav|or the still more costly process of walling in the whole farm, would have greatly added to the charges of these establishments.
+LJSpeech-1.1/wavs/LJ032-0013.wav|and killing Patrolman Tippit,
+LJSpeech-1.1/wavs/LJ007-0069.wav|While admitting that he had had many difficulties to contend with,
+LJSpeech-1.1/wavs/LJ049-0006.wav|These precautions included reserving a ceremonial area for the Presidential party,
+LJSpeech-1.1/wavs/LJ029-0169.wav|so that crowds can get a good view of President Kennedy and his wife.
+LJSpeech-1.1/wavs/LJ011-0205.wav|he carried me away by fraud and stratagem, and forced me to accompany him to Gretna Green
+LJSpeech-1.1/wavs/LJ039-0170.wav|and eight point two five seconds respectively.
+LJSpeech-1.1/wavs/LJ015-0048.wav|But worse than the bankruptcy was the confession made by the partners in the court.
+LJSpeech-1.1/wavs/LJ046-0052.wav|To promote nationwide acceptance of his administration Washington made grand tours that served also to excite interest in the Presidency.
+LJSpeech-1.1/wavs/LJ033-0058.wav|On the day of the assassination, Marina Oswald was watching television when she learned of the shooting.
+LJSpeech-1.1/wavs/LJ046-0240.wav|Bouck explained the failure to try to identify the individuals involved in the Stevenson incident after it occurred on the ground that
+LJSpeech-1.1/wavs/LJ008-0003.wav|which we left at the time of the discontinuance of the long-practiced procession to Tyburn.
+LJSpeech-1.1/wavs/LJ015-0020.wav|he was a man esteemed and respected in society and the world of finance, incapable as it was thought of a dishonest deed.
+LJSpeech-1.1/wavs/LJ049-0058.wav|Secondly, agents are instructed to remove the President as quickly as possible from known or impending danger.
+LJSpeech-1.1/wavs/LJ050-0207.wav|Although Chief Rowley does not complain about the pay scale for Secret Service agents,
+LJSpeech-1.1/wavs/LJ011-0222.wav|He also wrote and published a pamphlet from the jail to show that Miss Turner had been a consenting party to the marriage, and was really his wife.
+LJSpeech-1.1/wavs/LJ045-0085.wav|Why don't you make some money? Poor guy was going out of his mind. We told her she should not annoy him -- poor guy, he is doing his best, "Don't annoy him so much."
+LJSpeech-1.1/wavs/LJ009-0154.wav|Mr. Charles Kean the tragedian was also present,
+LJSpeech-1.1/wavs/LJ002-0016.wav|Previous to that date there had been seven hundred or eight hundred frequently, and once, in Mr. Akerman's time, one thousand.
+LJSpeech-1.1/wavs/LJ008-0118.wav|"Thank you, sir," said the governor to the doctor, "it is of little moment."
+LJSpeech-1.1/wavs/LJ006-0142.wav|The matron deposed to having seen the gates-woman "exceedingly drunk," and having been insulted by her.
+LJSpeech-1.1/wavs/LJ008-0155.wav|Very great excitement prevailed in the town throughout the trial, and this greatly increased when the verdict was known.
+LJSpeech-1.1/wavs/LJ003-0153.wav|Idleness was not so universally the rule in this part of the jail.
+LJSpeech-1.1/wavs/LJ027-0062.wav|viz., the brain; and also usually, but not always, a number of posterior joints,
+LJSpeech-1.1/wavs/LJ010-0028.wav|The causes also continued much the same. Passion, revenge, cupidity, sudden ebullitions of homicidal rage,
+LJSpeech-1.1/wavs/LJ019-0183.wav|provided it was henceforth used only for untried prisoners, suggested that Newgate should be entirely reconstructed, and the new building adopted as a model.
+LJSpeech-1.1/wavs/LJ015-0160.wav|he patronized the best tailors, and had a fashionable coiffeur from Hanover Square daily to curl his hair.
+LJSpeech-1.1/wavs/LJ008-0313.wav|who in solemn tones communicated to each in turn the fate in store for him.
+LJSpeech-1.1/wavs/LJ020-0053.wav|in the form of turnovers, pinching the corners of the fold pretty hard to hinder the flap of dough from flying up as the rising proceeds.
+LJSpeech-1.1/wavs/LJ024-0026.wav|and the rules of many of our universities and of almost every great private business enterprise,
+LJSpeech-1.1/wavs/LJ026-0046.wav|In each the life is the sum total of a series of definite processes -- nutrition or food supply,
+LJSpeech-1.1/wavs/LJ049-0165.wav|which first appeared in the appropriation of the Department of Justice in nineteen ten under the heading, quote, Miscellaneous Objects, end quote.
+LJSpeech-1.1/wavs/LJ039-0077.wav|Well, in order to achieve three hits, it would not be required that a man be an exceptional shot. A proficient man with this weapon, yes, end quote.
+LJSpeech-1.1/wavs/LJ048-0219.wav|who must concentrate primarily on the possibility of threats from crowds along the route, provide a significant safeguard against dangers in nearby buildings.
+LJSpeech-1.1/wavs/LJ018-0135.wav|and in the states known as "water-leaf" and "sized," which are the penultimate processes of manufacture.
+LJSpeech-1.1/wavs/LJ014-0057.wav|London did not escape the contagion, and prominent among the detestable crimes of the period stands that of the Mannings at Bermondsey.
+LJSpeech-1.1/wavs/LJ040-0220.wav|could have led anyone to predict the outburst of violence which finally occurred.
+LJSpeech-1.1/wavs/LJ013-0037.wav|The crime of fraudulent insurance they declared was very common, and the underwriters must have lost great sums in this way.
+LJSpeech-1.1/wavs/LJ050-0231.wav|Before the assassination the Secret Service infrequently requested other Federal law enforcement agencies to provide personnel
+LJSpeech-1.1/wavs/LJ011-0170.wav|Wakefield, in reply to her inquiries, satisfied her that her mother was well, and that the real reason for summoning her from school
+LJSpeech-1.1/wavs/LJ007-0161.wav|drink, gaming, obscene and blasphemous language; utter idleness, the almost unrestricted admission of money and luxuries;
+LJSpeech-1.1/wavs/LJ010-0149.wav|She was seized before she could do any mischief,
+LJSpeech-1.1/wavs/LJ002-0100.wav|A recent reform had closed the tap kept by the jailer within the precincts, but
+LJSpeech-1.1/wavs/LJ048-0020.wav|The interview with him in jail is not significant from the standpoint of whether he had a propensity for violence.
+LJSpeech-1.1/wavs/LJ004-0165.wav|the visitors were furnished with candles, and they descended eighteen long steps into a vault.
+LJSpeech-1.1/wavs/LJ021-0070.wav|Employed workers have not by any means all enjoyed a return to the earnings of prosperous times,
+LJSpeech-1.1/wavs/LJ011-0202.wav|The uncle claimed her. The husband resisted.
+LJSpeech-1.1/wavs/LJ019-0268.wav|In some places the dietary was too full, in others too meager. Its constituents were not of the most suitable character.
+LJSpeech-1.1/wavs/LJ048-0043.wav|nor was there any requirement to report the names of defectors. However, there was much material in the hands of the FBI about Oswald:
+LJSpeech-1.1/wavs/LJ012-0254.wav|where a workman found a bundle containing two human legs, in a drain.
+LJSpeech-1.1/wavs/LJ011-0084.wav|After three years' confinement in the latter prison he passed himself off as his brother, Colonel Montgomery,
+LJSpeech-1.1/wavs/LJ001-0154.wav|the result as measured by the eye being that the lower margin is less than the top one, and that the whole opening has an upside-down look vertically
+LJSpeech-1.1/wavs/LJ036-0058.wav|People on the bus began talking about it. As the bus neared Lamar Street, Oswald left the bus and disappeared into the crowd.
+LJSpeech-1.1/wavs/LJ007-0028.wav|there still remained one where the general callous indifference and mismanagement culminated in cruel culpable neglect.
+LJSpeech-1.1/wavs/LJ048-0221.wav|In the early morning hours on November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ004-0139.wav|"In the morning the stench and heat were so oppressive that he and every one else on waking rushed unclothed into the yard;"
+LJSpeech-1.1/wavs/LJ016-0360.wav|The change added greatly to the responsibilities of the governor and his subordinates. Hitherto the public had seemed to assist at the ceremony;
+LJSpeech-1.1/wavs/LJ027-0143.wav|whereby the primitive horn was gradually superseded by horns presenting a greater and greater number of prongs in successive species of extinct deer.
+LJSpeech-1.1/wavs/LJ043-0159.wav|Answer: Yes.
+LJSpeech-1.1/wavs/LJ005-0027.wav|He was altogether against too liberal a diet; he disapproved of industrial occupations in jails, as not calculated to render prisons terrible.
+LJSpeech-1.1/wavs/LJ020-0033.wav|It will not swell so fast as the white, so give yourself more time for making it.
+LJSpeech-1.1/wavs/LJ028-0194.wav|But how did the "mighty city" fall? How could Cyrus take Babylon whose walls were strong enough to resist any army?
+LJSpeech-1.1/wavs/LJ017-0081.wav|sought about for a fresh victim to supply him with funds.
+LJSpeech-1.1/wavs/LJ028-0443.wav|because great masses of masonry used to project from its surface.
+LJSpeech-1.1/wavs/LJ019-0297.wav|The prisoners inter-communicated freely, and exercised the most injurious, corrupting influences upon one another.
+LJSpeech-1.1/wavs/LJ035-0189.wav|Special Agent Forrest V. Sorrels of the Secret Service, who had been in the motorcade,
+LJSpeech-1.1/wavs/LJ012-0265.wav|A woman named Gale, who lived with him, was arrested at the same time. The prisoners were examined at the Marylebone police court.
+LJSpeech-1.1/wavs/LJ041-0072.wav|that he was a man of great ability and intelligence and that many of his superiors in the Marine Corps were not sufficiently competent to give him orders.
+LJSpeech-1.1/wavs/LJ030-0034.wav|Approximately ten minutes after the arrival at Love Field, the President and Mrs. Kennedy went to the Presidential automobile to begin the motorcade.
+LJSpeech-1.1/wavs/LJ003-0349.wav|Why not relieve Newgate by drawing more largely upon the superior accommodation which Millbank offered?
+LJSpeech-1.1/wavs/LJ040-0173.wav|He always felt like a burden that she simply just had to tolerate, end quote.
+LJSpeech-1.1/wavs/LJ036-0145.wav|However, Neches and Beckley do not intersect.
+LJSpeech-1.1/wavs/LJ014-0152.wav|On reaching the condemned cell she threw herself upon the floor and shrieked in an hysterical agony of tears.
+LJSpeech-1.1/wavs/LJ015-0263.wav|Scarcely had the conviction of these daring and astute thieves been assured, than another gigantic fraud was brought to light.
+LJSpeech-1.1/wavs/LJ019-0254.wav|they were frequently below the standard size, and were therefore not certified for occupation as was required by law.
+LJSpeech-1.1/wavs/LJ030-0192.wav|What are they doing to you! end quote. Looking back from the front seat,
+LJSpeech-1.1/wavs/LJ007-0003.wav|and Mrs. Fry with her colleagues still labored assiduously in Newgate, devoting themselves mainly to the female prison,
+LJSpeech-1.1/wavs/LJ046-0248.wav|to Federal intelligence and law enforcement agencies were not well designed to elicit information from them
+LJSpeech-1.1/wavs/LJ048-0025.wav|and had told Mrs. Paine that when he got the money he was going to take an apartment, when the baby was old enough, he was going to take an apartment, and the family would live together.
+LJSpeech-1.1/wavs/LJ044-0235.wav|If there was no conspiracy which would help him escape, the possibility of which has been considered in chapter six,
+LJSpeech-1.1/wavs/LJ026-0049.wav|In turn these will be compared for the animal and the plant,
+LJSpeech-1.1/wavs/LJ040-0051.wav|While there is doubt about how fully Oswald understood the doctrine which he so often espoused, it seems clear
+LJSpeech-1.1/wavs/LJ016-0109.wav|but not till they had had an exciting chase down the street.
+LJSpeech-1.1/wavs/LJ003-0092.wav|There were frequently in the middle yard seven or eight children, the youngest barely nine,
+LJSpeech-1.1/wavs/LJ028-0074.wav|But in Babylonia stone was difficult to obtain, and sculptures were very rare.
+LJSpeech-1.1/wavs/LJ033-0108.wav|In deciding whether Oswald carried the assassination weapon in the bag which Frazier and Mrs. Randle saw,
+LJSpeech-1.1/wavs/LJ047-0083.wav|The Passport Office of the Department of State in Washington had no listing for Oswald requiring special treatment,
+LJSpeech-1.1/wavs/LJ022-0108.wav|For many months preparations have been under way.
+LJSpeech-1.1/wavs/LJ008-0064.wav|When the criminals are tied up and prepared for their fate, this floor suddenly falls down, upon withdrawing the supporters inwards.
+LJSpeech-1.1/wavs/LJ034-0141.wav|and he said that the shots came from inside the building, end quote.
+LJSpeech-1.1/wavs/LJ015-0216.wav|This was an important step, and they might easily be robbed some day when Burgess was the guard, provided only that they could be opened.
+LJSpeech-1.1/wavs/LJ047-0007.wav|It had interviewed him twice shortly after his return to the United States, again a year later at his request
+LJSpeech-1.1/wavs/LJ002-0190.wav|The best, or at least the most influential prisoners, got lodging in the State House, which contained "eight large handsome rooms."
+LJSpeech-1.1/wavs/LJ031-0083.wav|the doctors observed that he had a heartbeat and was making some respiratory efforts.
+LJSpeech-1.1/wavs/LJ044-0196.wav|to a country in which he must have thought were embodied the political principles to which he had been committed for so long.
+LJSpeech-1.1/wavs/LJ009-0017.wav|For this the chaplain was a few days later summoned before the jail committee of aldermen,
+LJSpeech-1.1/wavs/LJ004-0057.wav|For the first time the doctrine was enunciated that prisoners had rights of their own.
+LJSpeech-1.1/wavs/LJ005-0126.wav|Religious worship became more generally the rule; chaplains were appointed, and chapels provided for them; surgeons and hospitals also.
+LJSpeech-1.1/wavs/LJ036-0025.wav|McWatters was sure that he left the checkpoint on time
+LJSpeech-1.1/wavs/LJ022-0152.wav|There is likewise pending before the Congress
+LJSpeech-1.1/wavs/LJ002-0142.wav|These courts were open to many and grave objections.
+LJSpeech-1.1/wavs/LJ034-0165.wav|Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ040-0130.wav|and interviewed and observed by other members of the Youth House staff.
+LJSpeech-1.1/wavs/LJ044-0114.wav|he received a letter from somebody in New York, some Communist -- probably from New York -- I am not sure from where -- from some Communist leader and he was very happy,
+LJSpeech-1.1/wavs/LJ031-0212.wav|The body was muscular and well developed with no gross skeletal abnormalities except for those caused by the gunshot wounds.
+LJSpeech-1.1/wavs/LJ016-0041.wav|and working with his hands behind him, while he used his bare feet like claws upon the other side of the wall angle.
+LJSpeech-1.1/wavs/LJ005-0173.wav|I shall not hesitate to ask Parliament for powers to compel them to make the necessary alterations, for it is not to be endured that these local jurisdictions should remain
+LJSpeech-1.1/wavs/LJ014-0332.wav|Cole escaped by throwing the blame on a careless partner, and at once removed the "stop."
+LJSpeech-1.1/wavs/LJ010-0143.wav|This axe is still in existence, and is preserved at Newgate with various other unpleasant curiosities,
+LJSpeech-1.1/wavs/LJ035-0066.wav|In an effort to determine whether Oswald could have descended to the lunchroom
+LJSpeech-1.1/wavs/LJ016-0335.wav|There was no interference with the crowd, which collected as usual, although not to the customary extent.
+LJSpeech-1.1/wavs/LJ029-0067.wav|nor did PRS develop any additional information between November twelve, when Lawson left Washington, and November twenty-two.
+LJSpeech-1.1/wavs/LJ048-0115.wav|to determine what matters require attention in making advance preparations and to decide what action to take.
+LJSpeech-1.1/wavs/LJ005-0183.wav|All that was urged was that the borough magistracy had no right to govern their jails
+LJSpeech-1.1/wavs/LJ014-0134.wav|that O'Connor had been more to her than her husband, that she ought to have married him.
+LJSpeech-1.1/wavs/LJ018-0093.wav|As a blind for their new frauds, they set up as law-stationers in York Buildings, Adelphi, and at once commenced their nefarious traffic.
+LJSpeech-1.1/wavs/LJ006-0052.wav|The sum total thus produced was inconsiderable compared with the hundreds that had formerly filled the jail,
+LJSpeech-1.1/wavs/LJ033-0081.wav|she looked out the breakfast-room window and saw Oswald cross the street and walk toward the driveway where her brother parked his car near the carport.
+LJSpeech-1.1/wavs/LJ012-0010.wav|both having been recognized by the clergyman who had performed the ceremony, and the assault had been committed to secure the money
+LJSpeech-1.1/wavs/LJ008-0267.wav|no less than four hundred and fifty-one sentences of death for capital crimes were passed at the Old Bailey;
+LJSpeech-1.1/wavs/LJ025-0086.wav|and the few and exceptional cases of non-parasitic animals which do not feed at all.
+LJSpeech-1.1/wavs/LJ005-0132.wav|Only a few glaring evils still demanded a remedy.
+LJSpeech-1.1/wavs/LJ004-0213.wav|Compared with those highly meritorious institutions Newgate still showed but badly.
+LJSpeech-1.1/wavs/LJ003-0206.wav|and their gibes and jollity counteracted the ordinary's counsels or the independent preacher's earnest prayers.
+LJSpeech-1.1/wavs/LJ030-0243.wav|but I had no time to speculate as to its origin because Agent Youngblood turned in a flash, immediately after the first explosion,
+LJSpeech-1.1/wavs/LJ015-0228.wav|Pierce boldly stepped in, found the cupboard unlocked; he removed the key, handed it to Agar outside,
+LJSpeech-1.1/wavs/LJ032-0171.wav|and that this was the same shirt which Oswald wore on the morning of the assassination.
+LJSpeech-1.1/wavs/LJ028-0418.wav|About that same time Pietro della Valle, an Italian, visited Babylon,
+LJSpeech-1.1/wavs/LJ032-0074.wav|The mail-order coupon listed the purchaser as "A. J. Hidell Age twenty-eight"
+LJSpeech-1.1/wavs/LJ036-0209.wav|A similar description was given on channel two at twelve:forty-five p.m.
+LJSpeech-1.1/wavs/LJ009-0300.wav|Traveling expenses of these agents cost fifteen pounds, and another ten pounds were spent in the hire of a Shropshire man,
+LJSpeech-1.1/wavs/LJ004-0112.wav|three. Prisoners guilty of misdemeanors. four. Prisoners charged with misdemeanors. five. Debtors.
+LJSpeech-1.1/wavs/LJ036-0098.wav|William Whaley, a taxicab driver, told his employer on Saturday morning, November twenty-three
+LJSpeech-1.1/wavs/LJ032-0200.wav|One Sunday, while his wife was hanging diapers, Oswald asked her to take a picture of him holding a rifle, a pistol
+LJSpeech-1.1/wavs/LJ008-0174.wav|One cart-load of spectators having broken down, some of its occupants fell off the vehicle, and were instantly trampled to death.
+LJSpeech-1.1/wavs/LJ006-0120.wav|by drawing briefs and petitions for his fellows. There was a recognized charge of five shillings per brief,
+LJSpeech-1.1/wavs/LJ018-0264.wav|Roupell was quiet and submissive while in Newgate, unassuming in manner, and ready to make the best of his position.
+LJSpeech-1.1/wavs/LJ035-0086.wav|The time actually required for Baker and Truly to reach the second floor on November twenty-two was probably longer than in the test runs. For example,
+LJSpeech-1.1/wavs/LJ038-0262.wav|Robert A. Frazier, an FBI ballistics identification expert, testified that he was, quote, unable to reach a conclusion, end quote,
+LJSpeech-1.1/wavs/LJ019-0116.wav|anticipating the best results from a system which made earnings, and indeed release, dependent upon the amount of work done.
+LJSpeech-1.1/wavs/LJ046-0036.wav|regarding certain protective measures in force at the time of the Dallas trip and propose recommendations for improvements.
+LJSpeech-1.1/wavs/LJ033-0094.wav|As they sat in the car, Frazier asked Oswald where his lunch was, and Oswald replied that he was going to buy his lunch that day.
+LJSpeech-1.1/wavs/LJ042-0027.wav|In addition to studying the Russian language while he was in the Marines,
+LJSpeech-1.1/wavs/LJ006-0140.wav|Evidence was given before the inspectors of eight or ten prisoners seen "giddy drunk, not able to sit upon forms."
+LJSpeech-1.1/wavs/LJ012-0138.wav|They also ascertained that a gold-refiner,
+LJSpeech-1.1/wavs/LJ013-0207.wav|In this he gave as the motives of his crime a quarrel he had with his master, who threatened to discharge him without a character.
+LJSpeech-1.1/wavs/LJ011-0032.wav|declared that they had hitherto formed a high opinion of his honor, integrity, and goodness of disposition,
+LJSpeech-1.1/wavs/LJ019-0369.wav|In the same way, six months' notice was required in cases where the closing of a prison was contemplated;
+LJSpeech-1.1/wavs/LJ016-0384.wav|when the first shock of the verdict and the solemn notification of the impending blow keeps nearly all awake, or at least disturbs their night's rest.
+LJSpeech-1.1/wavs/LJ014-0034.wav|Hocker, whose skill in counterfeiting handwriting was known, was asked to fabricate a letter making an assignation with Delarue
+LJSpeech-1.1/wavs/LJ003-0006.wav|Besides this, although the families of debtors were no longer permitted to live with them inside the jail,
+LJSpeech-1.1/wavs/LJ043-0149.wav|where she had gone, contrary to his instructions, after she became, worried about his absence.
+LJSpeech-1.1/wavs/LJ012-0006.wav|A watch was set on him at her house, where he was soon afterwards arrested.
+LJSpeech-1.1/wavs/LJ042-0105.wav|he took the position that the Communist Party officials in the Soviet Union were opportunists who were betraying their positions for personal gain.
+LJSpeech-1.1/wavs/LJ005-0219.wav|female prisoners were still exposed to the full view of the males, the netting in front of the gallery being perfectly useless as a screen.
+LJSpeech-1.1/wavs/LJ040-0212.wav|The factors in Lee Oswald's personality which were noted by those who had contact with him in New York indicate
+LJSpeech-1.1/wavs/LJ042-0205.wav|There can be no sympathy for those who have turned the idea of communism into a vile curse to Western man.
+LJSpeech-1.1/wavs/LJ006-0066.wav|In the middle yard it was still worse.
+LJSpeech-1.1/wavs/LJ003-0124.wav|He was charged with moving something which should not be touched, with leaving a door open, or coughing maliciously to the disturbance of his companions.
+LJSpeech-1.1/wavs/LJ038-0009.wav|When he heard police sirens, he, quote, looked up and saw the man enter the lobby, end quote.
+LJSpeech-1.1/wavs/LJ015-0303.wav|but on being searched, two blank cheques of the London and Westminster Bank were found in his pocket.
+LJSpeech-1.1/wavs/LJ028-0373.wav|The Hebrew exiles, whose ancestors Nebuchadnezzar had brought from Jerusalem,
+LJSpeech-1.1/wavs/LJ009-0183.wav|the site of the present Sessions House of the Old Bailey, and the operation was witnessed by students and a number of curious spectators.
+LJSpeech-1.1/wavs/LJ044-0111.wav|for anybody who is concerned about developments in Cuba, end quote,
+LJSpeech-1.1/wavs/LJ031-0065.wav|As a result of the infusion of liquids through the cutdowns, the cardiac massage, and the airway,
+LJSpeech-1.1/wavs/LJ035-0139.wav|They reentered the building by the rear door several minutes after Baker and Truly rushed through the front entrance.
+LJSpeech-1.1/wavs/LJ041-0105.wav|led to an incident in which he spilled a drink on one of his sergeants and abusively challenged him to fight.
+LJSpeech-1.1/wavs/LJ050-0182.wav|Liaison With Local Law Enforcement Agencies
+LJSpeech-1.1/wavs/LJ009-0198.wav|and her body publicly exhibited in a place built for the purpose in the Old Bailey.
+LJSpeech-1.1/wavs/LJ004-0085.wav|As far back as the reign of Charles the second, a law was passed declaring that sufficient provision should be made for the relief and setting on work
+LJSpeech-1.1/wavs/LJ027-0032.wav|Internal organs may persist unchanged and hence they offer good guides to classification.
+LJSpeech-1.1/wavs/LJ026-0010.wav|Since they cannot be classified, it is necessary that they be listed both under botany and zoology, in order to make sure that they will not be omitted entirely.
+LJSpeech-1.1/wavs/LJ009-0117.wav|The firmest disbeliever in religion, if he had not lately been irritated by taking part in such a scene as the condemned service in Newgate,
+LJSpeech-1.1/wavs/LJ012-0272.wav|They were apparently good friends when last seen together at a neighbor's, where they seemed "perfectly happy and sociable, and eager for the wedding day."
+LJSpeech-1.1/wavs/LJ005-0110.wav|and so passing by the roof down into the garden and on to freedom.
+LJSpeech-1.1/wavs/LJ027-0096.wav|as, indeed, she must be according to the derivation theory.
+LJSpeech-1.1/wavs/LJ038-0025.wav|Patrolman M. N. McDonald, with Patrolmen R. Hawkins, T. A. Hutson, and C. T. Walker, entered the theatre from the rear.
+LJSpeech-1.1/wavs/LJ029-0202.wav|the president of the Dallas Chamber of Commerce referred to the city's reputation for being the friendliest town in America and asserted that citizens would, quote,
+LJSpeech-1.1/wavs/LJ003-0044.wav|The reforms which were to be attempted in that prison
+LJSpeech-1.1/wavs/LJ032-0146.wav|Latona testified that this palmprint was the right palmprint of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ046-0219.wav|The above action should be taken without delay in order to attempt to verify the information and no evaluation of the information should be attempted.
+LJSpeech-1.1/wavs/LJ015-0155.wav|that so good a man had really been for years a swindler and a rogue.
+LJSpeech-1.1/wavs/LJ015-0307.wav|The evidence was corroborated by that of many of the victims who had acted as messengers,
+LJSpeech-1.1/wavs/LJ023-0093.wav|Chief Justice Hughes said in a dissenting opinion that the majority opinion was "a departure from sound principles,"
+LJSpeech-1.1/wavs/LJ009-0111.wav|the condemned returning to the cells: the forger carried by turnkeys; the youth sobbing aloud convulsively, as a passionate child;
+LJSpeech-1.1/wavs/LJ039-0100.wav|During the first week of an intensive eight-week training period he received instruction in sighting, aiming, and manipulation of the trigger.
+LJSpeech-1.1/wavs/LJ047-0150.wav|West Fifth Street, Irving, Texas. After receiving this information on October twenty-nine, Agent Hosty attempted to locate Oswald.
+LJSpeech-1.1/wavs/LJ034-0037.wav|Someone sitting on the box facing the window would have his palm in this position if he placed his hand alongside his right hip.
+LJSpeech-1.1/wavs/LJ048-0008.wav|was his attempt on General Walker's life, which did not become known to the FBI until after the assassination.
+LJSpeech-1.1/wavs/LJ043-0061.wav|at the time of his defection, when he evidenced no interest in his father and hardly mentioned him, even when questioned.
+LJSpeech-1.1/wavs/LJ026-0107.wav|Dissolved in water, the sugar is transported down delicate tubes, chiefly in the growing bark region of the stem.
+LJSpeech-1.1/wavs/LJ048-0242.wav|Three members of this shift separately took this opportunity to visit the Cellar Coffee House.
+LJSpeech-1.1/wavs/LJ012-0104.wav|The thieves were satisfied with the diamonds; they broke open other cases containing gold watches and plate, but abstracted nothing.
+LJSpeech-1.1/wavs/LJ003-0173.wav|He continued the ancient practice of letting out a portion of his own house, and by a poetical fiction treated it as an annex of the state side.
+LJSpeech-1.1/wavs/LJ037-0221.wav|He was wearing a zipper jacket which he had not been wearing moments before when he had arrived home.
+LJSpeech-1.1/wavs/LJ048-0201.wav|I stressed the fact that this was our President and he should be shown every respect due his position and that it was our duty to see that this was done.
+LJSpeech-1.1/wavs/LJ003-0237.wav|The fees on reception and discharge must be deemed exorbitant, when it is remembered the impoverished class who usually crowded the jail;
+LJSpeech-1.1/wavs/LJ017-0092.wav|He decided to use strychnia, or the vegetable poison otherwise known as nux vomica;
+LJSpeech-1.1/wavs/LJ042-0190.wav|democratic socializing of production, and without regard to the twisting apart of Marxism Marxist Communism by other powers.
+LJSpeech-1.1/wavs/LJ018-0027.wav|In little more than a week a cabman came forward and voluntarily made a statement which at once drew suspicion to a German, Franz Müller,
+LJSpeech-1.1/wavs/LJ001-0169.wav|The paper used for printing the small highly ornamented French service-books about the beginning of the sixteenth century is a model in this respect,
+LJSpeech-1.1/wavs/LJ016-0202.wav|Calcraft's emoluments were a guinea per week, and an extra guinea for every execution.
+LJSpeech-1.1/wavs/LJ047-0186.wav|I can now afford to wait until New Orleans forwarded the necessary papers to me to show me I now had all the information.
+LJSpeech-1.1/wavs/LJ026-0070.wav|Of the substances the solids (salts, etc.) must be dissolved in water before they can be taken in.
+LJSpeech-1.1/wavs/LJ008-0140.wav|Whenever the public attention had been specially called to a particular crime, either on account of its atrocity,
+LJSpeech-1.1/wavs/LJ038-0115.wav|At the first interrogation, Oswald claimed that his only crime was carrying a gun and resisting arrest.
+LJSpeech-1.1/wavs/LJ028-0511.wav|Should you walk along the shore of the Euphrates at Babylon, you would still see the embankments which Nebuchadnezzar constructed of bricks bearing his name,
+LJSpeech-1.1/wavs/LJ011-0228.wav|Mr. W. Wakefield served in a continental army, and rose to the rank of colonel,
+LJSpeech-1.1/wavs/LJ028-0005.wav|Leaving the city by the eastern gate, and passing a small village or two,
+LJSpeech-1.1/wavs/LJ025-0007.wav|so much so as to require discussion in different treatises.
+LJSpeech-1.1/wavs/LJ001-0033.wav|but which must certainly have come from the study of the twelfth or even the eleventh century MSS.
+LJSpeech-1.1/wavs/LJ026-0166.wav|back to starch usable as food and the comparison of the green plant and the animal would be complete.
+LJSpeech-1.1/wavs/LJ007-0113.wav|were revived for the convenience of these gentlemen, whose incarceration was thus rendered as little like imprisonment as possible.
+LJSpeech-1.1/wavs/LJ006-0284.wav|It was a special evil of this part of the prison, that the devotional exercises, originally so profitable, had grown into a kind of edifying spectacle,
+LJSpeech-1.1/wavs/LJ018-0329.wav|their crimes follow in the lines of others already found, and often more than once, in the calendars.
+LJSpeech-1.1/wavs/LJ039-0187.wav|In fact, one of the firers in the rapid fire test in firing his two series of three shots,
+LJSpeech-1.1/wavs/LJ050-0170.wav|This matter is obviously beyond the jurisdiction of the Commission,
+LJSpeech-1.1/wavs/LJ003-0081.wav|Persons convicted of publishing libels were still immured in the same rooms with transports and felons.
+LJSpeech-1.1/wavs/LJ046-0082.wav|must depend upon the utmost cooperation and understanding from the public and the President.
+LJSpeech-1.1/wavs/LJ010-0174.wav|About six p.m. the royal carriage, a low open vehicle drawn by four horses, ridden by postilions, left the palace.
+LJSpeech-1.1/wavs/LJ014-0041.wav|Such an extravagant defense did not weigh with judge or jury;
+LJSpeech-1.1/wavs/LJ050-0266.wav|The exchange of letters dated August thirty-one, nineteen sixty-four,
+LJSpeech-1.1/wavs/LJ045-0187.wav|There is, of course, no way to determine the degree to which he was committed to his plan at that time.
+LJSpeech-1.1/wavs/LJ014-0106.wav|The lime had done its work so rapidly that the features would have been indistinguishable but for the prominent chin and a set of false teeth.
+LJSpeech-1.1/wavs/LJ038-0117.wav|He falsely alleged that he bought the revolver in Fort Worth, when in fact he purchased it from a mail-order house in Los Angeles.
+LJSpeech-1.1/wavs/LJ034-0127.wav|Brennan testified that they were standing, which is their apparent position in the photograph.
+LJSpeech-1.1/wavs/LJ030-0163.wav|Mrs. John F. Kennedy, on the left of the rear seat of the limousine, looked toward her left and waved to the crowds along the route.
+LJSpeech-1.1/wavs/LJ005-0245.wav|This committee animadverted strongly upon the system in force at the metropolitan jails, and more especially upon the condition of Newgate
+LJSpeech-1.1/wavs/LJ038-0202.wav|Prior to the Walker shooting on April ten, Oswald had been attending typing classes on Monday, Tuesday, and Thursday evenings.
+LJSpeech-1.1/wavs/LJ015-0302.wav|who was at length taken in a coffee-shop near Oxford Street, under the name of Hopkins. He resisted at first, and denied his identity,
+LJSpeech-1.1/wavs/LJ036-0013.wav|found along the path of flight taken by the gunman from the scene of the shooting to the place of arrest.
+LJSpeech-1.1/wavs/LJ026-0004.wav|Yet there are many unicellular forms in which both kinds of nutrition go on at the same time;
+LJSpeech-1.1/wavs/LJ014-0282.wav|There were two counts in the indictment: one for stealing a cheque value fourteen hundred pounds, the second for stealing a bit of paper value one penny.
+LJSpeech-1.1/wavs/LJ016-0160.wav|The noose was one of his hammock straps, which he buckled round his throat.
+LJSpeech-1.1/wavs/LJ007-0164.wav|forbid the faintest shadow of a hope that in a soil so unfavorable for moral culture
+LJSpeech-1.1/wavs/LJ041-0119.wav|stating that he would, quote, employ all means to right this gross mistake or injustice, end quote.
+LJSpeech-1.1/wavs/LJ044-0090.wav|I think that we finished him on that program because we had publicly linked the Fair Play for Cuba Committee
+LJSpeech-1.1/wavs/LJ007-0191.wav|has been productive of at least some advantage,
+LJSpeech-1.1/wavs/LJ010-0270.wav|exactly tallied with that of the deformed person "wanted" for the assault on the Queen.
+LJSpeech-1.1/wavs/LJ037-0029.wav|It was Benavides, using Tippit's car radio, who first reported the killing of Patrolman Tippit at about one:sixteen p.m.:
+LJSpeech-1.1/wavs/LJ009-0101.wav|move with a quick, jerking motion, not naturally, but, as it were, like the affected parts of a galvanized corpse.
+LJSpeech-1.1/wavs/LJ016-0162.wav|he fastened one end of the strap above mentioned to the hook, and then fell down.
+LJSpeech-1.1/wavs/LJ032-0169.wav|Having considered the probabilities as explained in Stombaugh's testimony,
+LJSpeech-1.1/wavs/LJ031-0200.wav|The President then walked to the Executive Office Building, where he worked until nine p.m.
+LJSpeech-1.1/wavs/LJ044-0132.wav|He had not liked his job as a greaser of coffee processing machinery and he held it for only a little over two months.
+LJSpeech-1.1/wavs/LJ042-0179.wav|without defense or foundation of government, end quote,
+LJSpeech-1.1/wavs/LJ018-0251.wav|He confessed himself a perjurer in having sworn to the false will, and a wholesale forger, having manufactured no less than ten false signatures
+LJSpeech-1.1/wavs/LJ044-0171.wav|On September twenty, nineteen sixty-three, Mrs. Paine and her two children arrived in New Orleans from a trip to the East Coast
+LJSpeech-1.1/wavs/LJ018-0001.wav|The Chronicles of Newgate, Volume two. By Arthur Griffiths. Section twenty-one: Newgate Notorieties, part two.
+LJSpeech-1.1/wavs/LJ031-0013.wav|Traveling at speeds estimated at times to be up to seventy or eighty miles per hour down the Stemmons Freeway and Harry Hines Boulevard
+LJSpeech-1.1/wavs/LJ006-0188.wav|The days were passed in idleness, debauchery, riotous quarreling, immoral conversation,
+LJSpeech-1.1/wavs/LJ032-0105.wav|quote, I knew there was no such organization. And I know Hidell is merely an altered Fidel, and I laughed at such foolishness.
+LJSpeech-1.1/wavs/LJ010-0259.wav|a deformed lad among the crowd was seen to present a pistol at Her Majesty's carriage,
+LJSpeech-1.1/wavs/LJ014-0084.wav|Returning to her own home, where Manning meantime had been calmly smoking and talking to the neighbors over the basement wall,
+LJSpeech-1.1/wavs/LJ037-0102.wav|He stated further that from, quote, What I saw of him, end quote, the man looked like the man in the picture.
+LJSpeech-1.1/wavs/LJ001-0184.wav|all books might be at least comely and well-looking: and if to these good qualities were added really beautiful ornament and pictures,
+LJSpeech-1.1/wavs/LJ012-0096.wav|At her death the diamonds were divided between her four daughters, but only half had been claimed,
+LJSpeech-1.1/wavs/LJ001-0059.wav|the greater part of these Italian printers, it should be mentioned, were Germans or Frenchmen, working under the influence of Italian opinion and aims.
+LJSpeech-1.1/wavs/LJ026-0083.wav|Starch, however, contains potential energy, since the molecule is relatively unstable
+LJSpeech-1.1/wavs/LJ002-0321.wav|others who arrived just after the time of distribution were often forty-eight hours without food. The latter might also be six days without meat.
+LJSpeech-1.1/wavs/LJ035-0073.wav|was one minute and thirty seconds.
+LJSpeech-1.1/wavs/LJ028-0166.wav|The center of each division of the town is occupied by a fortress.
+LJSpeech-1.1/wavs/LJ028-0209.wav|On the sixteenth day the troops of Cyrus entered Babylon without a battle.
+LJSpeech-1.1/wavs/LJ011-0132.wav|except the forgery of wills and powers of attorney.
+LJSpeech-1.1/wavs/LJ022-0117.wav|Our attack upon these enemies must be without stint and without discrimination.
+LJSpeech-1.1/wavs/LJ019-0118.wav|and later experience has fully proved the advantage of a judicious system of gratuities for labor;
+LJSpeech-1.1/wavs/LJ034-0089.wav|Oswald had not filled any of the three orders.
+LJSpeech-1.1/wavs/LJ003-0114.wav|was appointed judge, and a towel tied in knots was hung on each side in imitation of a wig.
+LJSpeech-1.1/wavs/LJ011-0130.wav|This was on the last day of eighteen twenty-nine. In the following session Sir Robert Peel brought in a bill to consolidate the acts relating to forgery.
+LJSpeech-1.1/wavs/LJ042-0083.wav|Oswald stated, quote, I am a Marxist, end quote. He also alluded to hardships endured by his mother as a worker,
+LJSpeech-1.1/wavs/LJ044-0068.wav|as a result of which he was, quote, flooded with callers and invitations to debates, etc. as well as people interested in joining the F.P.C.C.
+LJSpeech-1.1/wavs/LJ026-0129.wav|Sooner or later the starch grains are changed into a kind of sugar (glucose), which, unlike starch, dissolves in the sap
+LJSpeech-1.1/wavs/LJ014-0309.wav|Few realized that these mysterious operations were the "convulsive attempt" of a ruined and dishonest speculator to sustain his credit.
+LJSpeech-1.1/wavs/LJ032-0224.wav|One of the photographs taken by Marina Oswald was widely published in newspapers and magazines,
+LJSpeech-1.1/wavs/LJ026-0126.wav|as a preliminary to the real processes of nutrition.
+LJSpeech-1.1/wavs/LJ035-0140.wav|On entering, Lovelady saw a girl on the first floor who he believes was Victoria Adams.
+LJSpeech-1.1/wavs/LJ038-0054.wav|He added that one officer grabbed the muzzle of a shotgun, drew back, and hit Oswald with the butt end of the gun in the back.
+LJSpeech-1.1/wavs/LJ012-0117.wav|By-and-by the occupant of the room noticed something glittering in the center of the fire, which, to inspect more closely, he took out with the tongs.
+LJSpeech-1.1/wavs/LJ006-0308.wav|The governor sent down wine on festive occasions, of which no doubt the prisoner housemaid had her share.
+LJSpeech-1.1/wavs/LJ037-0021.wav|Scoggins stated that he thought he had seen a picture of Oswald in the newspapers prior to the lineup identification on Saturday.
+LJSpeech-1.1/wavs/LJ031-0194.wav|At five:fifty-eight p.m. Eastern Standard Time,
+LJSpeech-1.1/wavs/LJ016-0154.wav|When a bit of rope carefully secreted,
+LJSpeech-1.1/wavs/LJ003-0151.wav|All who could scrape together the cash seem to have gladly availed themselves of the privilege of entering the master's side.
+LJSpeech-1.1/wavs/LJ038-0175.wav|Until December three, nineteen sixty-three, the Walker shooting remained unsolved.
+LJSpeech-1.1/wavs/LJ045-0134.wav|Hosty had come to the Paine residence on November one and five, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ013-0066.wav|By this means her signature was obtained; a forged will was prepared bequeathing the unclaimed stock to Miss Slack;
+LJSpeech-1.1/wavs/LJ013-0185.wav|Three days later a close search of the butler's pantry produced fresh circumstantial evidence.
+LJSpeech-1.1/wavs/LJ047-0109.wav|When Quigley returned to his office, he learned
+LJSpeech-1.1/wavs/LJ021-0204.wav|of regulating only to meet concrete needs -- a practice of courageous recognition of change.
+LJSpeech-1.1/wavs/LJ049-0191.wav|is properly manned and equipped to carry on extensive information gathering functions within the United States.
+LJSpeech-1.1/wavs/LJ003-0135.wav|She went up to the ward and found him lying down, quote,
+LJSpeech-1.1/wavs/LJ023-0106.wav|that something in the Constitution has compelled them regretfully to thwart the will of the people.
+LJSpeech-1.1/wavs/LJ006-0282.wav|the pump was the only provision, and this in a place within sight of visitors, of the windows of the male turnkeys, and unprotected from the weather.
+LJSpeech-1.1/wavs/LJ026-0143.wav|Probably foods containing carbon, hydrogen and oxygen are the sources of energy in the higher plants as in animals.
+LJSpeech-1.1/wavs/LJ011-0178.wav|But another bank had since failed, and nothing could save Mr. Turner but the transfer of some property to Miss Turner, and its settlement on her,
+LJSpeech-1.1/wavs/LJ016-0310.wav|Another distinguished witness feared
+LJSpeech-1.1/wavs/LJ049-0203.wav|So circumscribed, it could not maintain the esprit de corps or the necessary alertness for this unique and challenging responsibility.
+LJSpeech-1.1/wavs/LJ032-0272.wav|(three) fibers found on the rifle most probably came from the shirt Oswald was wearing on the day of the assassination,
+LJSpeech-1.1/wavs/LJ048-0171.wav|it was not practical to select a route where the President could not be seen from roofs or windows of buildings.
+LJSpeech-1.1/wavs/LJ044-0076.wav|Oswald's statements suggest that he hoped to be flooded with callers and invitations to debate.
+LJSpeech-1.1/wavs/LJ032-0147.wav|At the request of the Commission, Arthur Mandella,
+LJSpeech-1.1/wavs/LJ004-0222.wav|Meat was no longer issued raw, to be imperfectly cooked before a ward fire and bolted gluttonously, the whole two pounds at one sitting.
+LJSpeech-1.1/wavs/LJ035-0072.wav|On the first test, the elapsed time between the simulated first shot and Baker's arrival on the second-floor stair landing
+LJSpeech-1.1/wavs/LJ012-0173.wav|The event which he styles calamitous we may well characterize as one of the most deliberately atrocious murders on record.
+LJSpeech-1.1/wavs/LJ007-0087.wav|at Appleby for thirteen years, at Anglesea for fifteen years, at Exeter for sixteen years, and at Pembroke
+LJSpeech-1.1/wavs/LJ040-0238.wav|Such a placement was postponed, however, perhaps in part at least because Lee's behavior suddenly improved.
+LJSpeech-1.1/wavs/LJ019-0220.wav|the uses of Newgate were narrowed almost entirely to those of a prison of detention.
+LJSpeech-1.1/wavs/LJ005-0284.wav|of more depraved and systematic criminals.
+LJSpeech-1.1/wavs/LJ015-0261.wav|The whole strange story, the long incubation and the elaborate accomplishment of the plot,
+LJSpeech-1.1/wavs/LJ013-0113.wav|Robberies as daring in conception as they were boldly executed were common enough.
+LJSpeech-1.1/wavs/LJ028-0383.wav|and the wild beasts of the islands shall cry in their desolate houses, and dragons in their pleasant palaces.
+LJSpeech-1.1/wavs/LJ015-0227.wav|Still watching and waiting for the first chance, they seized it when the clerks left the office empty for a moment.
+LJSpeech-1.1/wavs/LJ036-0106.wav|You could have picked him out without identifying him by just listening to him.
+LJSpeech-1.1/wavs/LJ023-0078.wav|to presume in favor of its validity until its violation of the Constitution is proved beyond all reasonable doubt.
+LJSpeech-1.1/wavs/LJ023-0077.wav|by which any law is passed,
+LJSpeech-1.1/wavs/LJ031-0074.wav|quote, could have easily been hidden in the blood and hair, end quote,
+LJSpeech-1.1/wavs/LJ017-0063.wav|He made no distinct admissions even on the scaffold; but when the chaplain at the last moment exhorted him to confess,
+LJSpeech-1.1/wavs/LJ039-0168.wav|each fired two series of three shots.
+LJSpeech-1.1/wavs/LJ026-0075.wav|are dissolved by the sap in the leaves and elsewhere and thus may pass to every portion of the plant.
+LJSpeech-1.1/wavs/LJ042-0229.wav|To the question of, quote, Are you a Communist? End quote, he first answered "Yes,"
+LJSpeech-1.1/wavs/LJ011-0125.wav|The other and the last criminal executed for forgery in this country was one Maynard, who was convicted of a fraud upon the Custom House.
+LJSpeech-1.1/wavs/LJ028-0024.wav|gazing down upon the great Euphrates winding along the valley beneath.
+LJSpeech-1.1/wavs/LJ017-0076.wav|His brother was supposed to have been his next victim, upon whose life he had also effected an insurance for another thirteen thousand pounds.
+LJSpeech-1.1/wavs/LJ028-0374.wav|settled there, and finally the place was abandoned to the Arabs of the desert.
+LJSpeech-1.1/wavs/LJ029-0014.wav|As a political leader, the President wished to resolve the factional controversy within the Democratic Party in Texas before the election of nineteen sixty-four.
+LJSpeech-1.1/wavs/LJ005-0188.wav|that the jails attached to corporate jurisdictions continue to be the fruitful sources
+LJSpeech-1.1/wavs/LJ010-0045.wav|became the victim of the most cowardly and unmanly outrages, and the attempted murder of the sovereign by Oxford in eighteen forty
+LJSpeech-1.1/wavs/LJ033-0088.wav|Frazier met Oswald at the kitchen door and together they walked to the car.
+LJSpeech-1.1/wavs/LJ045-0034.wav|Marina Oswald attributed that to their living apart and to the imminent birth of their second child.
+LJSpeech-1.1/wavs/LJ018-0315.wav|to whom it was said one hundred pounds apiece had been given down as the price of their infidelity.
+LJSpeech-1.1/wavs/LJ046-0127.wav|or from the occasional investigations initiated by the Secret Service,
+LJSpeech-1.1/wavs/LJ038-0211.wav|he said that he, quote, was very sorry that he had not hit him, end quote. Marina Oswald's testimony was fully supported by the note itself
+LJSpeech-1.1/wavs/LJ037-0154.wav|independently examined the four cartridge cases and arrived at the same conclusion as Cunningham.
+LJSpeech-1.1/wavs/LJ024-0116.wav|When the time comes for action,
+LJSpeech-1.1/wavs/LJ008-0230.wav|fell with a strange unnatural sound upon the ear
+LJSpeech-1.1/wavs/LJ038-0089.wav|Expert testimony before the Commission
+LJSpeech-1.1/wavs/LJ008-0273.wav|At the Old Bailey almost every one capitally convicted by a jury was sentenced to be hanged.
+LJSpeech-1.1/wavs/LJ028-0036.wav|But in those very early days Babylon was little more than a shrine, surrounded with mud huts and date palms.
+LJSpeech-1.1/wavs/LJ005-0235.wav|Last, and worst of all, the arrangements for keeping the condemned prisoners between sentence and execution were more than unsatisfactory.
+LJSpeech-1.1/wavs/LJ046-0202.wav|The head of PRS testified that the Secret Service requested other agencies to provide, quote,
+LJSpeech-1.1/wavs/LJ047-0147.wav|The Department of State did not advise either the CIA or the FBI of these facts.
+LJSpeech-1.1/wavs/LJ018-0088.wav|I propose next to describe the leading features of the most important of these.
+LJSpeech-1.1/wavs/LJ027-0011.wav|It is, moreover, true that all living forms are but series of modifications and extensions of one single plan of structure.
+LJSpeech-1.1/wavs/LJ036-0059.wav|The Marsalis bus which Oswald boarded traveled a route west on Elm,
+LJSpeech-1.1/wavs/LJ006-0131.wav|and, if he chose, pass in, all provisions, money, clothes, and letters brought for prisoners by their friends.
+LJSpeech-1.1/wavs/LJ019-0111.wav|healthful, easily learnt, and well adapted to the circumstances of unskilled laborers.
+LJSpeech-1.1/wavs/LJ015-0222.wav|in such a way that Tester had possession of this key for a time.
+LJSpeech-1.1/wavs/LJ024-0007.wav|with the approval, as required by the Constitution, of the Senate of the United States.
+LJSpeech-1.1/wavs/LJ013-0228.wav|In the pocket of the coat Mr. Cope, the governor, found a neatly-folded cloth, and asked what it was for.
+LJSpeech-1.1/wavs/LJ002-0300.wav|Numerous tyrannies were practiced on all who would not and could not pay the garnish.
+LJSpeech-1.1/wavs/LJ029-0151.wav|No arrangements were made for police or building custodians
+LJSpeech-1.1/wavs/LJ016-0296.wav|The actual execution made some impression.
+LJSpeech-1.1/wavs/LJ033-0158.wav|The palmprint was found on the closed end of the bag.
+LJSpeech-1.1/wavs/LJ041-0030.wav|One of his fellow employees, Palmer McBride, stated that Oswald said he would like to kill President Eisenhower because he was exploiting the working class.
+LJSpeech-1.1/wavs/LJ020-0075.wav|Into a second hollow pour the yeast and knead thoroughly for fifteen minutes.
+LJSpeech-1.1/wavs/LJ017-0216.wav|there were also a Frenchman, a Norwegian (the carpenter), three Chinamen, a "Sclavonian," and a black on board.
+LJSpeech-1.1/wavs/LJ044-0174.wav|and possibly to Philadelphia to look for work.
+LJSpeech-1.1/wavs/LJ050-0252.wav|The occasional use of personnel from other Federal agencies to assist in protecting the President has a further advantage. It symbolizes the reality
+LJSpeech-1.1/wavs/LJ013-0242.wav|This increased when the officer, accompanied by two others, a neighbor and a bailiff, entered one of the stables.
+LJSpeech-1.1/wavs/LJ031-0103.wav|While Dr. Carrico went on to attend the President, Dr. Dulany stayed with the Governor and was soon joined by several other doctors.
+LJSpeech-1.1/wavs/LJ029-0154.wav|and Secret Service agents riding in the motorcade.
+LJSpeech-1.1/wavs/LJ006-0053.wav|and the whole by proper management might have been so accommodated as to prevent overcrowding.
+LJSpeech-1.1/wavs/LJ011-0034.wav|These arguments availed little with the jury, who after a short deliberation found Fauntleroy guilty, and he was sentenced to death.
+LJSpeech-1.1/wavs/LJ028-0300.wav|as it is, I kept my own counsel, and so accomplished my plans.
+LJSpeech-1.1/wavs/LJ019-0379.wav|matters went on after the eighteen sixty-five Act much the same as they had done before. Districts differed greatly in the attention they paid to prison affairs.
+LJSpeech-1.1/wavs/LJ004-0180.wav|a price which in those days fluctuated enormously -- as much as a hundred percent in a couple of years;
+LJSpeech-1.1/wavs/LJ008-0256.wav|and with blanched cheek, and sinking, we turned away from the scene.
+LJSpeech-1.1/wavs/LJ050-0115.wav|This is especially necessary with regard to the FBI and CIA,
+LJSpeech-1.1/wavs/LJ009-0200.wav|were publicly exposed in a stable in Little Bridge Street, near Apothecaries' Hall,
+LJSpeech-1.1/wavs/LJ019-0231.wav|and by the following year forty-seven new cells had been built on the most approved plan.
+LJSpeech-1.1/wavs/LJ046-0010.wav|when his temporary residence, Blair House, was attacked by Puerto Rican Nationalists.
+LJSpeech-1.1/wavs/LJ018-0157.wav|In the course of his remarks, however, he said,
+LJSpeech-1.1/wavs/LJ028-0208.wav|Nabonidus fled.
+LJSpeech-1.1/wavs/LJ020-0046.wav|Close the dough over it, dust your hands and kneading-board with flour and work in the shortening until the dough is elastic and ceases to be sticky.
+LJSpeech-1.1/wavs/LJ044-0026.wav|Marina Oswald said she signed that name, apparently chosen because it rhymed with "Fidel,"
+LJSpeech-1.1/wavs/LJ013-0002.wav|As the century advanced crimes of fraud increased.
+LJSpeech-1.1/wavs/LJ043-0150.wav|She indicated that she had no advance knowledge of Oswald's plans,
+LJSpeech-1.1/wavs/LJ025-0156.wav|with atmospheric air containing its ordinary minute dose of carbonic acid and with nothing else but sunlight and heat.
+LJSpeech-1.1/wavs/LJ018-0286.wav|and Austin Bidwell opened a bona fide credit in the Burlington or West End branch of the Bank of England,
+LJSpeech-1.1/wavs/LJ040-0180.wav|The Human Figure Drawings are empty, poor characterizations of persons approximately the same age as the subject.
+LJSpeech-1.1/wavs/LJ047-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ038-0072.wav|who had driven from the theatre with Oswald.
+LJSpeech-1.1/wavs/LJ029-0012.wav|He had made only a few brief visits to the State since the nineteen sixty Presidential campaign and in nineteen sixty-two he began to consider a formal visit.
+LJSpeech-1.1/wavs/LJ012-0259.wav|who had been missing since the previous Christmas Day.
+LJSpeech-1.1/wavs/LJ034-0084.wav|became apparent on December two, nineteen sixty-three, when an employee, Frankie Kaiser,
+LJSpeech-1.1/wavs/LJ048-0184.wav|Levels of risk can be determined, however, as has been confirmed by building surveys made since the assassination for the Department of the Treasury.
+LJSpeech-1.1/wavs/LJ027-0087.wav|to be entirely different machines, made each for its own purposes, at once, out of hand.
+LJSpeech-1.1/wavs/LJ026-0100.wav|In the plant the inorganic matter in water from the soil are absorbed by the roots and carried up definite tubes in the woody part of the stem.
+LJSpeech-1.1/wavs/LJ008-0032.wav|a scaffold was erected in front of that prison for the execution of several convicts named by the Recorder.
+LJSpeech-1.1/wavs/LJ002-0124.wav|Quite half of the foregoing writs and arrests applied to sums under thirty pounds.
+LJSpeech-1.1/wavs/LJ019-0159.wav|were constructed on the landings, ensconced in which warders spent the night, on duty, and alert to watch the sleepers below,
+LJSpeech-1.1/wavs/LJ034-0108.wav|man in his early thirties, fair complexion, slender, but neat, neat slender, possible five foot ten
+LJSpeech-1.1/wavs/LJ017-0179.wav|In all cases the symptoms were much the same,
+LJSpeech-1.1/wavs/LJ028-0428.wav|has been removed, and the surrounding city walls have been traced.
+LJSpeech-1.1/wavs/LJ048-0070.wav|reflect keen awareness of the necessity of communicating a much wider range of intelligence information to the Service.
+LJSpeech-1.1/wavs/LJ035-0094.wav|the Commission concluded that Oswald could have fired the shots and still have been present in the second-floor lunchroom when seen by Baker and Truly.
+LJSpeech-1.1/wavs/LJ013-0248.wav|At the same time an overpowering odor attracted them to the adjoining harness-room, where the missing remains were raked out
+LJSpeech-1.1/wavs/LJ008-0269.wav|Already the severity of our criminal code, and the number of capital felonies upon the statute book, had brought a reaction;
+LJSpeech-1.1/wavs/LJ027-0117.wav|"Now, here again the former theory appears to be triumphant over the latter," says Romanes,
+LJSpeech-1.1/wavs/LJ024-0043.wav|that I will appoint justices who will not undertake to override the judgment of the Congress on legislative policy,
+LJSpeech-1.1/wavs/LJ020-0059.wav|When the time is up and the rolls are puffy and promising, set them in a pretty quick oven and bake half an hour,
+LJSpeech-1.1/wavs/LJ048-0049.wav|possessed of this information to list Oswald as a potential threat to the safety of the President.
+LJSpeech-1.1/wavs/LJ048-0177.wav|that such a procedure would not be consistent with the nature and purpose of the motorcade to let the people see their President and to welcome him to their city.
+LJSpeech-1.1/wavs/LJ007-0056.wav|leaden hearts, and "grinding the impressions off penny-pieces, then pricking figures or words on them to give to their friends as memorials.
+LJSpeech-1.1/wavs/LJ032-0059.wav|Experts on handwriting identification from the Treasury Department and the FBI
+LJSpeech-1.1/wavs/LJ015-0097.wav|and he could not produce them. His chief asked sternly where they were.
+LJSpeech-1.1/wavs/LJ036-0130.wav|Whaley described the ensuing events as follows, quote,
+LJSpeech-1.1/wavs/LJ012-0060.wav|to let the coach change and pass Petticoat Lane en route to the jail, where the suffering woman might be handed over to her friends.
+LJSpeech-1.1/wavs/LJ027-0033.wav|On the other hand, external structures are likely to undergo adaptation when habits or conditions of life change.
+LJSpeech-1.1/wavs/LJ047-0141.wav|in early October of nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ047-0219.wav|Quote, Mr. Shanklin advised us, among other things,
+LJSpeech-1.1/wavs/LJ024-0004.wav|It is simply this: whenever a judge or justice of any federal court
+LJSpeech-1.1/wavs/LJ003-0103.wav|It was that of a person, quote, who practiced in the law, and who was connected by marriage with some very respectable families.
+LJSpeech-1.1/wavs/LJ016-0256.wav|It was thought that capital punishment would lose its deterrent effect if it ceased to be public,
+LJSpeech-1.1/wavs/LJ047-0164.wav|At this point in the interview, Hosty gave Mrs. Paine his name and office telephone number on a piece of paper.
+LJSpeech-1.1/wavs/LJ033-0179.wav|since the original bag had been discolored during various laboratory examinations and could not be used for valid identification by witnesses.
+LJSpeech-1.1/wavs/LJ019-0071.wav|moreover, it was nearly impossible to prevent communication and mutual contamination.
+LJSpeech-1.1/wavs/LJ041-0125.wav|Mrs. Oswald said that her husband did not say anything about Governor Connally after his return to the United States. She testified, quote,
+LJSpeech-1.1/wavs/LJ001-0141.wav|The general solidity of a page is much to be sought for
+LJSpeech-1.1/wavs/LJ004-0242.wav|Some attempt was made to reduce the overcrowding, on the recommendation of the House of Commons Committee of eighteen eighteen, but this applied only a partial remedy.
+LJSpeech-1.1/wavs/LJ022-0114.wav|Our responsibility is to all of the people in this country.
+LJSpeech-1.1/wavs/LJ048-0152.wav|At some overpasses all persons were excluded
+LJSpeech-1.1/wavs/LJ044-0173.wav|While Marina Oswald knew of her husband's plan to go to Mexico and thence to Cuba if possible, Mrs. Paine was told that Oswald was going to Houston
+LJSpeech-1.1/wavs/LJ018-0342.wav|It was to effect the rupture of an irksome tie that led Henry Wainwright to murder Harriet Lane deliberately and in cold blood.
+LJSpeech-1.1/wavs/LJ004-0101.wav|Warm and cold baths, or "commodious bathing tubs,"
+LJSpeech-1.1/wavs/LJ031-0090.wav|A thorough inspection would have involved washing and cleansing the back, and this is not practical in treating an acutely injured patient.
+LJSpeech-1.1/wavs/LJ047-0018.wav|for the purpose of correlating information inasmuch as he was considered a possible security risk in the event he returned to this country, end quote.
+LJSpeech-1.1/wavs/LJ010-0070.wav|followed the profession of arms, first in the British service, and then in that of the French revolutionary Government.
+LJSpeech-1.1/wavs/LJ008-0090.wav|It was still the custom to offer warm encouragement or bitter disapproval, according to the character and antecedents of the sufferer.
+LJSpeech-1.1/wavs/LJ025-0153.wav|and inquire whether certain differences of a more occult character than those imagined to exist by Cuvier,
+LJSpeech-1.1/wavs/LJ048-0036.wav|that agents of the FBI in Dallas did not consider Oswald's presence in the Texas School Book Depository Building
+LJSpeech-1.1/wavs/LJ008-0145.wav|They were accused by a confederate, who, goaded by conscience, had turned approver, of the murder of a Mr. Steele,
+LJSpeech-1.1/wavs/LJ048-0150.wav|while the Secret Service representatives in Dallas
+LJSpeech-1.1/wavs/LJ021-0117.wav|to the great number of small employers in the smaller communities.
+LJSpeech-1.1/wavs/LJ006-0277.wav|But there were evils akin to those on the male side, prominent amongst which was the undue influence accorded to prisoners.
+LJSpeech-1.1/wavs/LJ049-0075.wav|Recommendations.
+LJSpeech-1.1/wavs/LJ010-0209.wav|Louis and Amadeus among the captains; and Hercules, Neptune, and Mars among the lieutenants of the association.
+LJSpeech-1.1/wavs/LJ048-0080.wav|to coordinate activities and to discuss problems of mutual interest.
+LJSpeech-1.1/wavs/LJ036-0160.wav|He also stated he saw a silver identification bracelet on his passenger's left wrist.
+LJSpeech-1.1/wavs/LJ040-0090.wav|Pic did turn over part of his income to his mother,
+LJSpeech-1.1/wavs/LJ015-0277.wav|When he could get nothing but the blank cheque, he set in motion all sorts of schemes for obtaining signatures, such as
+LJSpeech-1.1/wavs/LJ046-0120.wav|the security processing of gifts sent to the President, and technical inspections against covert listening devices.
+LJSpeech-1.1/wavs/LJ006-0029.wav|The second inspector, the Rev. Whitworth Russell, was the chaplain of Millbank penitentiary,
+LJSpeech-1.1/wavs/LJ049-0153.wav|The Secret Service was organized as a division of the Department of the Treasury in eighteen sixty-five, to deal with counterfeiting.
+LJSpeech-1.1/wavs/LJ041-0019.wav|Voebel said that Oswald, quote, wouldn't start any fights, but if you wanted to start one with him, he was going to make sure that he ended it,
+LJSpeech-1.1/wavs/LJ018-0115.wav|It was stated in evidence that the monies obtained by these forgeries amounted to eight thousand pounds or ten thousand pounds,
+LJSpeech-1.1/wavs/LJ049-0079.wav|Many changes have already been made and others are contemplated, some of them in response to the Commission's questions and informal suggestions.
+LJSpeech-1.1/wavs/LJ036-0096.wav|because of the overwhelming evidence that Oswald was far away from the building by that time.
+LJSpeech-1.1/wavs/LJ009-0103.wav|The silence is short. As the ordinary proceeds 'to conclude,'
+LJSpeech-1.1/wavs/LJ018-0106.wav|He was a German named Kerp,
+LJSpeech-1.1/wavs/LJ039-0116.wav|he had just completed a very intensive preliminary training period.
+LJSpeech-1.1/wavs/LJ045-0113.wav|he did not want his landlady to know his real name because she might read in the paper of the fact that he had been in Russia and that he had been questioned, end quote.
+LJSpeech-1.1/wavs/LJ014-0196.wav|by his identification by Cope in Westminster Hospital, who survived long enough to make a formal deposition before Mr. Jardine,
+LJSpeech-1.1/wavs/LJ046-0124.wav|or with undue persistence.
+LJSpeech-1.1/wavs/LJ012-0292.wav|Mrs. Brown fell with it, and Greenacre, to his horror, found that she was dead.
+LJSpeech-1.1/wavs/LJ002-0282.wav|Those who could not pay were thrown into the wards with the night charges,
+LJSpeech-1.1/wavs/LJ016-0104.wav|whence they let themselves down into the street by the rope.
+LJSpeech-1.1/wavs/LJ040-0149.wav|intense anxiety, shyness, feelings of awkwardness and insecurity, end quote.
+LJSpeech-1.1/wavs/LJ049-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+LJSpeech-1.1/wavs/LJ014-0242.wav|Employed as a clerk in the Globe Assurance,
+LJSpeech-1.1/wavs/LJ045-0164.wav|He said that he was tired of living alone and perhaps the reason for my being so angry was the fact that we were not living together.
+LJSpeech-1.1/wavs/LJ042-0155.wav|it appears to be the work of a fairly well organized person.
+LJSpeech-1.1/wavs/LJ012-0011.wav|which Mrs. Canning had lost by remarriage.
+LJSpeech-1.1/wavs/LJ011-0120.wav|Unfortunately he took to drink, lost his appointment, and fell from bad to worse.
+LJSpeech-1.1/wavs/LJ006-0074.wav|He was unable to give any reason whatever for not utilizing the whole of the wards.
+LJSpeech-1.1/wavs/LJ030-0231.wav|Most of the other Secret Service agents in the motorcade had drawn their sidearms.
+LJSpeech-1.1/wavs/LJ017-0107.wav|After Cook's death his stepfather, who was much attached to him, came to Rugeley.
+LJSpeech-1.1/wavs/LJ004-0219.wav|The diet was now ample. It consisted of a pound and a half of bread per diem;
+LJSpeech-1.1/wavs/LJ021-0075.wav|But it is an undeniable fact that the restoration of other billions of sound investments to a reasonable earning power
+LJSpeech-1.1/wavs/LJ030-0184.wav|Kellerman grabbed his microphone and radioed ahead to the lead car,
+LJSpeech-1.1/wavs/LJ011-0102.wav|He had gone on board in his Quaker dress, but when captured was found in a light-green frock,
+LJSpeech-1.1/wavs/LJ047-0153.wav|Having determined that Mrs. Paine was a responsible and reliable citizen, Hosty interviewed her on November one.
+LJSpeech-1.1/wavs/LJ039-0241.wav|owned and possessed the rifle used to kill President Kennedy and wound Governor Connally,
+LJSpeech-1.1/wavs/LJ039-0232.wav|the assassin was then required to hit the target one more time within a space of from four point eight to five point six seconds.
+LJSpeech-1.1/wavs/LJ050-0149.wav|PRS must develop the capacity to classify its subjects on a more sophisticated basis than the present geographic breakdown.
+LJSpeech-1.1/wavs/LJ025-0062.wav|under particular circumstances, the contents of the cells of certain water-weeds were set free and moved about with considerable velocity
+LJSpeech-1.1/wavs/LJ018-0301.wav|were lodged at once by drafts to "Horton," another alias, in the Continental Bank.
+LJSpeech-1.1/wavs/LJ036-0218.wav|Four bullets hit Tippit and killed him instantly.
+LJSpeech-1.1/wavs/LJ024-0033.wav|and that a baneful precedent will be established.
+LJSpeech-1.1/wavs/LJ040-0061.wav|Various possible motives will be treated in the appropriate context of the discussion outlined above.
+LJSpeech-1.1/wavs/LJ030-0012.wav|In Houston, as elsewhere during the trip, the crowds showed much interest in Mrs. Kennedy.
+LJSpeech-1.1/wavs/LJ048-0257.wav|and that their conduct the night before did not impede their actions on duty
+LJSpeech-1.1/wavs/LJ011-0173.wav|Miss Turner consented to go on, and they traveled night and day towards the north.
+LJSpeech-1.1/wavs/LJ011-0023.wav|A run upon the bank immediately followed, which was only met by a suspension of payment and the closing of its doors.
+LJSpeech-1.1/wavs/LJ017-0235.wav|He was soon summoned on deck, but as he would not move, the mutineers came down and stood in a circle round his berth.
+LJSpeech-1.1/wavs/LJ045-0086.wav|The De Mohrenschildts also testified that, quote, right in front, end quote, of Oswald Marina Oswald complained about Oswald's inadequacy as a husband.
+LJSpeech-1.1/wavs/LJ036-0004.wav|After leaving the Depository Building at approximately twelve:thirty-three p.m., Lee Harvey Oswald proceeded to his roominghouse by bus and taxi.
+LJSpeech-1.1/wavs/LJ036-0020.wav|The transfer was dated "Friday November twenty-two, 'sixty-three" and was punched in two places by the bus driver.
+LJSpeech-1.1/wavs/LJ016-0425.wav|When visited one day in the condemned cell, just as St. Sepulchre's clock was striking, he looked up and said laughingly, "Go along, clock;
+LJSpeech-1.1/wavs/LJ031-0133.wav|Jack Brooks, Homer Thornberry, and Albert Thomas joined Clifton C. Carter and the group of special agents protecting the Vice President.
+LJSpeech-1.1/wavs/LJ009-0144.wav|In fact his looks denoted extreme sorrow and contrition,
+LJSpeech-1.1/wavs/LJ036-0029.wav|where a man beat on the front door of the bus, boarded it and paid his fare.
+LJSpeech-1.1/wavs/LJ011-0234.wav|Bigger "jobs" than ever were planned and attempted,
+LJSpeech-1.1/wavs/LJ027-0145.wav|that such a recapitulation in the life history of an existing animal of developmental changes successively distinctive of sundry allied,
+LJSpeech-1.1/wavs/LJ006-0291.wav|to clean the governor's office in the male prison;
+LJSpeech-1.1/wavs/LJ006-0225.wav|If any man presumed to turn in too early
+LJSpeech-1.1/wavs/LJ026-0037.wav|It will no doubt occur to the reader that, on the theory of evolution,
+LJSpeech-1.1/wavs/LJ012-0195.wav|and of these Bishop and Williams, who were guilty of many peculiar atrocities, ended their murderous careers in front of the debtors' door at Newgate.
+LJSpeech-1.1/wavs/LJ007-0135.wav|The yards were narrow and confined, mainly because the ground plan was radically vicious. These were evils inseparable from the place.
+LJSpeech-1.1/wavs/LJ012-0284.wav|It was not until he left the bus, and walked up by the Regent's Canal, that he conceived the idea of throwing the head into the water.
+LJSpeech-1.1/wavs/LJ025-0112.wav|With a qualification, to be considered presently, the answer to this question is undoubtedly in the affirmative.
+LJSpeech-1.1/wavs/LJ029-0090.wav|were deployed in and around the Trade Mart.
+LJSpeech-1.1/wavs/LJ012-0270.wav|She had realized all her effects, and brought them with her furniture to Greenacre's lodgings. The two when married were to emigrate to Hudson's Bay.
+LJSpeech-1.1/wavs/LJ008-0260.wav|One of the worst evils was the terrible and long-protracted uncertainty as to the result.
+LJSpeech-1.1/wavs/LJ034-0023.wav|The carton on the windowsill and the large carton below the window contained no prints which could be identified as being those of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ007-0094.wav|had actually been returned as sane from the asylum to which they had been sent, and there was always some uncertainty as to who was mad and who not.
+LJSpeech-1.1/wavs/LJ037-0205.wav|The invoice was prepared on March thirteen, nineteen sixty-three; the revolver was actually shipped on March twenty by Railway Express.
+LJSpeech-1.1/wavs/LJ042-0009.wav|While his defection resulted in part from Oswald's commitment to Marxism,
+LJSpeech-1.1/wavs/LJ019-0164.wav|the personal superintendence of night officers, as already described, became possible.
+LJSpeech-1.1/wavs/LJ046-0011.wav|One out of every five Presidents since eighteen sixty-five has been assassinated;
+LJSpeech-1.1/wavs/LJ019-0339.wav|while decency and good order were to be insured by the strict prohibition of gambling and drunkenness.
+LJSpeech-1.1/wavs/LJ043-0144.wav|and the March eleven, nineteen sixty-three, issue of the Militant.
+LJSpeech-1.1/wavs/LJ035-0209.wav|at twelve:thirty p.m
+LJSpeech-1.1/wavs/LJ044-0048.wav|which occurred eight days after Oswald wrote the above letter to V. T. Lee.
+LJSpeech-1.1/wavs/LJ022-0065.wav|The unemployment insurance part of the legislation
+LJSpeech-1.1/wavs/LJ007-0193.wav|The measures of improvement introduced were mainly as follows:
+LJSpeech-1.1/wavs/LJ011-0062.wav|If the law of this country can receive such a sacrifice, my death will render to heaven an innocent man, and to earth a repentant sinner.
+LJSpeech-1.1/wavs/LJ008-0216.wav|until presently the huge stage loomed dark above the crowd which was now ranged round the barriers;
+LJSpeech-1.1/wavs/LJ005-0190.wav|the knowledge and practice of every species of criminality.
+LJSpeech-1.1/wavs/LJ030-0224.wav|heard noises that sounded like firecrackers and ran toward the President's limousine.
+LJSpeech-1.1/wavs/LJ034-0043.wav|The print, therefore, could have been placed on the carton at any time within this period.
+LJSpeech-1.1/wavs/LJ005-0263.wav|and not, as heretofore, to the judges of assize; that, both to check abuses and watch the progress of improvement,
+LJSpeech-1.1/wavs/LJ028-0481.wav|Nabopolassar, the father, my begetter, built Imgur-Bel, the great wall of Babylon,
+LJSpeech-1.1/wavs/LJ041-0144.wav|Oswald would have had other and more favorable opportunities to strike at the Governor than on this occasion when, as a member of the President's party,
+LJSpeech-1.1/wavs/LJ031-0089.wav|considerable time which at this juncture was not available.
+LJSpeech-1.1/wavs/LJ008-0106.wav|He was introduced by the ordinary, Dr. Forde, a name familiar to the reader, who met him at the felons' door
+LJSpeech-1.1/wavs/LJ012-0066.wav|and begged his wife to send him over a consignment of cheap "righteous" watches, or such as had been honestly obtained, and not "on the cross."
+LJSpeech-1.1/wavs/LJ036-0129.wav|The man asked, quote, May I have the cab?, end quote, and got into the front seat.
+LJSpeech-1.1/wavs/LJ030-0025.wav|when Air Force One touched down at Love Field at eleven:forty a.m., Eastern Standard Time.
+LJSpeech-1.1/wavs/LJ011-0207.wav|whom I never saw until I was taken from Liverpool, and never want to see again.
+LJSpeech-1.1/wavs/LJ010-0254.wav|At the Italian Opera in the evening the audience, on the Queen's appearance, greeted her with loud cheers, and called for the national anthem.
+LJSpeech-1.1/wavs/LJ001-0107.wav|To say a few words on the principles of design in typography:
+LJSpeech-1.1/wavs/LJ021-0063.wav|Benefits of the Industrial Recovery Program have come,
+LJSpeech-1.1/wavs/LJ029-0070.wav|is discussed in chapter eight.
+LJSpeech-1.1/wavs/LJ047-0243.wav|According to Revill, Hosty indicated that he was going to tell this to Lieutenant Wells of the homicide and robbery bureau.
+LJSpeech-1.1/wavs/LJ017-0146.wav|He had all the characteristics of the poisoner -- the calm deliberation,
+LJSpeech-1.1/wavs/LJ021-0148.wav|may be determined and any later adjustments shall be made either by agreement or, in case of disagreement,
+LJSpeech-1.1/wavs/LJ040-0131.wav|Marguerite Oswald visited her son at Youth House, where she recalled that she waited in line, quote,
+LJSpeech-1.1/wavs/LJ022-0047.wav|However, for the first time in five years the relief rolls have declined instead of increased during the winter months.
+LJSpeech-1.1/wavs/LJ029-0050.wav|A check of the geographic indexes there revealed no listing for any individual deemed to be a potential danger to the President
+LJSpeech-1.1/wavs/LJ033-0076.wav|On the morning of November twenty-two, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ047-0233.wav|his lies to Agent Quigley, his recent visit to Mexico City -- indicated that Oswald was capable of violence.
+LJSpeech-1.1/wavs/LJ050-0190.wav|However, the instructions must be communicated to the local police in any event and can be leaked to the press whether or not they are in writing.
+LJSpeech-1.1/wavs/LJ031-0135.wav|Concern that the Vice President might also be a target for assassination prompted the Secret Service agents to urge him to leave the hospital and return to Washington immediately.
+LJSpeech-1.1/wavs/LJ002-0009.wav|On the twenty-seventh April, in the following year,
+LJSpeech-1.1/wavs/LJ015-0265.wav|was the prime mover,
+LJSpeech-1.1/wavs/LJ026-0132.wav|after absorption into the cells the elements of the starch (or glucose) are, by the living protoplasm, in some unknown way
+LJSpeech-1.1/wavs/LJ032-0258.wav|Ruth and Michael Paine both noticed the rolled-up blanket in the garage during the time that Marina Oswald was living in their home.
+LJSpeech-1.1/wavs/LJ048-0005.wav|There was nothing up to the time of the assassination that gave any indication that this man was a dangerous character who might do harm to the President
+LJSpeech-1.1/wavs/LJ006-0234.wav|More cruel injuries were common enough, which did not result from honest hand-to-hand fights.
+LJSpeech-1.1/wavs/LJ019-0252.wav|while its rigid maintenance was in its opinion vital to the efficiency of the jails.
+LJSpeech-1.1/wavs/LJ015-0251.wav|The latter was ere long arrested on a charge of uttering forged cheques, convicted, and sentenced to transportation for life.
+LJSpeech-1.1/wavs/LJ010-0110.wav|Several of the other prisoners took the same line as regards Edwards,
+LJSpeech-1.1/wavs/LJ032-0227.wav|Shaneyfelt testified that the published photographs appeared to be based on a copy of the original which the publications had each retouched differently.
+LJSpeech-1.1/wavs/LJ033-0121.wav|The distance between the point on the seat and the door was twenty-seven inches.
+LJSpeech-1.1/wavs/LJ041-0168.wav|even though Oswald seemed to be lost in his own thoughts. After a brief period of silence Oswald remarked on the stupidity of the parade
+LJSpeech-1.1/wavs/LJ004-0086.wav|of "poor and needy prisoners committed to the common jail for felony and other misdemeanors, who many times perish before their trial;
+LJSpeech-1.1/wavs/LJ022-0148.wav|to enforce minimum wages, to prevent excessive hours,
+LJSpeech-1.1/wavs/LJ034-0137.wav|The two men, Harold Norman and James Jarman, Jr., each confirmed that when they came out of the building,
+LJSpeech-1.1/wavs/LJ007-0221.wav|The reports as the years flow on reiterate the same complaints.
+LJSpeech-1.1/wavs/LJ018-0033.wav|Directly the foregoing facts were established, a couple of detective officers, armed with a warrant to arrest Müller,
+LJSpeech-1.1/wavs/LJ016-0119.wav|Under superior orders all the doors and gates of this block were left open at night, to allow the night watchman to pass freely to all parts.
+LJSpeech-1.1/wavs/LJ019-0104.wav|That gentleman had come to the conclusion that the ordinary and hackneyed methods of treatment were practically inefficacious,
+LJSpeech-1.1/wavs/LJ011-0145.wav|The new Forgery Act with the Lords' amendment passed into law, but the latter proved perfectly harmless,
+LJSpeech-1.1/wavs/LJ003-0069.wav|In this heterogeneous society were also thrown the unfortunate journalists to whom I have already referred, and on whom imprisonment in Newgate
+LJSpeech-1.1/wavs/LJ038-0091.wav|The Commission has, therefore, placed no reliance on the paraffin tests administered by the Dallas police.
+LJSpeech-1.1/wavs/LJ018-0034.wav|and accompanied by Mr. Death the jeweler and the cabman, went down to Liverpool and took the first steamer across the Atlantic.
+LJSpeech-1.1/wavs/LJ013-0167.wav|The discovery of the murdered man immediately followed. The neighborhood was alarmed, the police sent for, and a close inquiry forthwith commenced.
+LJSpeech-1.1/wavs/LJ025-0021.wav|yet none of these movements justify the ascription to plants of perception of will.
+LJSpeech-1.1/wavs/LJ033-0214.wav|(four) carried the rifle into the Depository Building, concealed in the bag;
+LJSpeech-1.1/wavs/LJ017-0159.wav|and a life-interest in five thousand pounds, a fact on which Smethurst's counsel dwelt with much weight,
+LJSpeech-1.1/wavs/LJ042-0063.wav|with few readily available alternatives at hand. He was shocked to find that the Soviet Union did not accept him with open arms.
+LJSpeech-1.1/wavs/LJ028-0159.wav|thence from the corners of the wall there is carried along each bank of the river a fence of burned bricks.
+LJSpeech-1.1/wavs/LJ036-0193.wav|Description of Shooting
+LJSpeech-1.1/wavs/LJ027-0099.wav|Another striking class of the facts of morphology which admit of scientific explanation only along the line of homology
+LJSpeech-1.1/wavs/LJ050-0003.wav|By The President's Commission on the Assassination of President Kennedy. Chapter eight. The Protection of the President. Part five.
+LJSpeech-1.1/wavs/LJ017-0166.wav|but was careful to prime them with his facts and lead them if possible to accept his diagnosis of the case.
+LJSpeech-1.1/wavs/LJ005-0282.wav|"the comparatively innocent are seduced, the unwary are entrapped,
+LJSpeech-1.1/wavs/LJ005-0150.wav|In some county jails, as I have already said, female prisoners were placed upon the tread-wheel;
+LJSpeech-1.1/wavs/LJ016-0032.wav|Williams as a capital convict was lodged in the press-yard or condemned ward.
+LJSpeech-1.1/wavs/LJ028-0221.wav|When I made my gracious entry into Babylon, with exceeding joy I took up my abode in the royal palace.
+LJSpeech-1.1/wavs/LJ050-0134.wav|that an interagency committee has been established to develop more effective criteria.
+LJSpeech-1.1/wavs/LJ010-0078.wav|On his second release, goaded by his fancied wrongs, he began to plot a dark and dreadful revenge,
+LJSpeech-1.1/wavs/LJ011-0223.wav|Neither his address nor his pamphlet availed much, for the bill for the divorce passed both Houses.
+LJSpeech-1.1/wavs/LJ002-0186.wav|The population generally amounted to from five hundred to seven hundred, the accommodation being calculated for two hundred.
+LJSpeech-1.1/wavs/LJ022-0164.wav|It will put the public utility operating industry on a sound basis for the future,
+LJSpeech-1.1/wavs/LJ034-0183.wav|Boxes and cases were stacked behind him.
+LJSpeech-1.1/wavs/LJ013-0079.wav|when Lord Campbell delivered judgment on Barber's petition, to the effect that
+LJSpeech-1.1/wavs/LJ033-0101.wav|so that it was carried straight and parallel to his body.
+LJSpeech-1.1/wavs/LJ038-0289.wav|As shown above, the note and the photographs of Walker's house and of the nearby railroad tracks
+LJSpeech-1.1/wavs/LJ047-0136.wav|they had vacated their apartment, and Marina Oswald had departed with their child in a station wagon with Texas registration.
+LJSpeech-1.1/wavs/LJ033-0097.wav|Frazier parked the car in the company parking lot about two blocks north of the Depository Building.
+LJSpeech-1.1/wavs/LJ045-0250.wav|there emerged a man capable of assassinating President Kennedy.
+LJSpeech-1.1/wavs/LJ016-0353.wav|Almost absolute silence prevailed until the great bell began to toll its deep note, and broke the stillness with its regular and monotonous clangour,
+LJSpeech-1.1/wavs/LJ019-0053.wav|and our modern practice has prudently tried to steer between the two extremes, accepting as the best system a judicious combination of both.
+LJSpeech-1.1/wavs/LJ013-0012.wav|Her owners insured her for a full sum of two thousand pounds, after which the Wallaces insured her privily
+LJSpeech-1.1/wavs/LJ016-0157.wav|One curious instance of a suicide carried out under the most adverse and extraordinary circumstances may be quoted.
+LJSpeech-1.1/wavs/LJ038-0169.wav|he saw two men, in separate cars, drive out of a church parking lot adjacent to Walker's home. A friend of Walker's testified that
+LJSpeech-1.1/wavs/LJ029-0071.wav|An important purpose of the President's visit to Dallas was to speak at a luncheon given by business and civic leaders.
+LJSpeech-1.1/wavs/LJ007-0117.wav|where the upper ward was exclusively appropriated to their use. They also had their meals sent in, and, with the food, wine almost ad libitum.
+LJSpeech-1.1/wavs/LJ033-0092.wav|the main reason he was going over there that Thursday afternoon when he was to bring back some curtain rods, so I didn't think any more about it when he told me that, end quote,
+LJSpeech-1.1/wavs/LJ045-0076.wav|The letter fell into Oswald's hands when it was returned to his post office box
+LJSpeech-1.1/wavs/LJ049-0152.wav|The assignment of the responsibility of protecting the President to an agency of the Department of the Treasury was largely an historical accident.
+LJSpeech-1.1/wavs/LJ013-0029.wav|Even then she might have been saved, but the captain would not suffer the crew to act. Nearly the whole of the cargo was lost as well as the ship.
+LJSpeech-1.1/wavs/LJ030-0189.wav|he realized that something was wrong, and he pressed down on the accelerator as Kellerman said, quote, Get out of here fast, end quote.
+LJSpeech-1.1/wavs/LJ004-0166.wav|At the bottom was a circular space, through which ran a narrow passage, and the sides of which were fitted with barrack bedsteads.
+LJSpeech-1.1/wavs/LJ028-0191.wav|The old enemies of Babylon rejoiced.
+LJSpeech-1.1/wavs/LJ036-0166.wav|is three to four short blocks south of Lamar and Elm. If Oswald left the bus at twelve:forty-four p.m.
+LJSpeech-1.1/wavs/LJ033-0057.wav|and insert it into the paper bag.
+LJSpeech-1.1/wavs/LJ050-0066.wav|The volume of references to the Secret Service has increased substantially since the new instructions went into effect;
+LJSpeech-1.1/wavs/LJ045-0166.wav|He repeated this not once but several times, but I refused. And he said that once again I was preferring my friends to him, and that I didn't need him.
+LJSpeech-1.1/wavs/LJ007-0024.wav|who supped royally on the supplies provided from outside, and kept it up till ten or eleven o'clock.
+LJSpeech-1.1/wavs/LJ036-0142.wav|Whaley was somewhat imprecise as to where he unloaded his passenger.
+LJSpeech-1.1/wavs/LJ008-0044.wav|and at the distance of five feet from the same is fixed a strong railing all round the scaffold to enclose a place for the constables.
+LJSpeech-1.1/wavs/LJ015-0309.wav|and the judge, in passing sentence on him of transportation for life, expressed deep regret that "the ingenuity, skill, and talent,
+LJSpeech-1.1/wavs/LJ050-0272.wav|and the traditions of the office in a democracy such as ours are so deep-seated as to preclude absolute security.
+LJSpeech-1.1/wavs/LJ014-0238.wav|In eighteen fifty occurred the first of a series of gigantic frauds,
+LJSpeech-1.1/wavs/LJ027-0084.wav|Romanes's "Darwin and After Darwin", and Le Conte's "Evolution."
+LJSpeech-1.1/wavs/LJ041-0067.wav|Other marines also testified that Oswald had few friends and kept very much to himself.
+LJSpeech-1.1/wavs/LJ037-0261.wav|that the jacket belonged to Lee Harvey Oswald, and that when he was arrested at approximately one:fifty p.m., he was in shirt sleeves.
+LJSpeech-1.1/wavs/LJ021-0182.wav|And let it be recorded that the British bankers helped.
+LJSpeech-1.1/wavs/LJ004-0197.wav|No idleness was permitted among the inmates. Trades were taught, or prisoners were allowed to follow their own if suitable.
+LJSpeech-1.1/wavs/LJ002-0065.wav|or female convicts ordered for execution.
+LJSpeech-1.1/wavs/LJ050-0274.wav|made certain recommendations which it believes would, if adopted,
+LJSpeech-1.1/wavs/LJ003-0146.wav|without them the keeper declared that he could not pay the salaries of turnkeys and servants, nor keep the prison going at all.
+LJSpeech-1.1/wavs/LJ039-0225.wav|Based on the known facts of the assassination,
+LJSpeech-1.1/wavs/LJ029-0198.wav|They conveyed the pleas of Dallas leaders that citizens not demonstrate or create disturbances during the President's visit.
+LJSpeech-1.1/wavs/LJ016-0408.wav|During the singing of these hymns Wainwright fainted, but whether from real emotion or the desire to make a sensation was never exactly known.
+LJSpeech-1.1/wavs/LJ038-0063.wav|but that once he was subdued, no officer struck him.
+LJSpeech-1.1/wavs/LJ011-0180.wav|Wakefield added that it had been suggested he should marry Miss Turner, but that he had laughed at the idea.
+LJSpeech-1.1/wavs/LJ003-0285.wav|they should gamble with dice or cards, and play at bumble puppy or some other disreputable game of chance.
+LJSpeech-1.1/wavs/LJ045-0184.wav|Then on Thursday morning, November twenty-one,
+LJSpeech-1.1/wavs/LJ008-0213.wav|By this time the workmen might be heard busily erecting the gallows;
+LJSpeech-1.1/wavs/LJ040-0140.wav|Mrs. Evelyn D Siegel, a social worker who interviewed both Lee and his mother while Lee was confined in Youth House,
+LJSpeech-1.1/wavs/LJ028-0153.wav|The bitumen used in the work was brought to Babylon from Is, a small stream which flows into the Euphrates
+LJSpeech-1.1/wavs/LJ038-0168.wav|There were no eyewitnesses, although a fourteen-year-old boy in a neighboring house claimed that immediately after the shooting
+LJSpeech-1.1/wavs/LJ008-0285.wav|on the one hand the gallows, on the other a short imprisonment.
+LJSpeech-1.1/wavs/LJ019-0298.wav|The total want of administration was very marked,
+LJSpeech-1.1/wavs/LJ028-0059.wav|His military career began while he was still the crown prince, and his father was on the throne.
+LJSpeech-1.1/wavs/LJ005-0121.wav|By degrees, however,
+LJSpeech-1.1/wavs/LJ015-0200.wav|only when single and unprotected were they in any danger of attack, and that but rarely.
+LJSpeech-1.1/wavs/LJ014-0295.wav|This man got up to look for him, and found him hanging from the bars of a neighboring room.
+LJSpeech-1.1/wavs/LJ027-0151.wav|The only alternative view is that as species of deer,
+LJSpeech-1.1/wavs/LJ039-0130.wav|After reviewing Oswald's marksmanship scores,
+LJSpeech-1.1/wavs/LJ022-0070.wav|Provisions for social security, however, are protections for the future.
+LJSpeech-1.1/wavs/LJ044-0039.wav|they said something about remodeling, etc. I'm sure you understand, end quote.
+LJSpeech-1.1/wavs/LJ049-0078.wav|have properly taken the initiative in reexamining major aspects of Presidential protection.
+LJSpeech-1.1/wavs/LJ037-0004.wav|At least twelve persons saw the man with the revolver in the vicinity of the Tippit crime scene at or immediately after the shooting.
+LJSpeech-1.1/wavs/LJ001-0145.wav|No definite rules, however, except the avoidance of "rivers" and excess of white, can be given for the spacing,
+LJSpeech-1.1/wavs/LJ039-0230.wav|With the equipment he [Oswald] had and with his ability I consider it a very easy shot, end quote.
+LJSpeech-1.1/wavs/LJ023-0112.wav|We have, therefore,
+LJSpeech-1.1/wavs/LJ026-0092.wav|animals (and colorless plants as well) apparently could not long exist.
+LJSpeech-1.1/wavs/LJ040-0139.wav|while he liked Youth House, he missed the freedom of doing what he wanted. He indicated that he did not miss his mother, end quote.
+LJSpeech-1.1/wavs/LJ030-0191.wav|According to Kellerman, Mrs. Kennedy then cried out, quote,
+LJSpeech-1.1/wavs/LJ028-0084.wav|Its impression shows the face of a beardless young man, intelligent and refined.
+LJSpeech-1.1/wavs/LJ016-0394.wav|The request was not granted, as the old custom of allowing capital convicts whatever they asked for in the way of food has not been the rule in Newgate.
+LJSpeech-1.1/wavs/LJ016-0376.wav|literally walked over what, in case of conviction, would be their own graves.
+LJSpeech-1.1/wavs/LJ001-0062.wav|Even in Italy most of the theological and law books were printed in Gothic letter,
+LJSpeech-1.1/wavs/LJ007-0234.wav|slanting downwards from the top of the walls to the outside adjoining the slaughterhouses of Newgate market; and occasionally, in hot weather,
+LJSpeech-1.1/wavs/LJ028-0414.wav|All the ground on which Babylon was spread is left now desolate; nothing standing in that Peninsula between the Euphrates and the Tigris,
+LJSpeech-1.1/wavs/LJ044-0044.wav|to an attack by Cuban exiles in a street demonstration and being, quote, officialy cautioned, end quote, by the police.
+LJSpeech-1.1/wavs/LJ032-0222.wav|Moreover, Shaneyfelt testified that in his opinion the photographs were not composites of two different photographs
+LJSpeech-1.1/wavs/LJ007-0073.wav|to that line of conduct which his duty imposed on him
+LJSpeech-1.1/wavs/LJ017-0204.wav|who said that he feared it was only too true that secret poisoning was at that time very rife in the metropolis.
+LJSpeech-1.1/wavs/LJ002-0313.wav|It generally ran to about six pounds per week. The money, which at one time had been distributed quarterly, and all went in drink,
+LJSpeech-1.1/wavs/LJ045-0209.wav|but it was also, at least in part, because his wife did not want to live there with him.
+LJSpeech-1.1/wavs/LJ003-0303.wav|The personal cleanliness of all prisoners was to be insisted upon; they should be made to wash at least once a day,
+LJSpeech-1.1/wavs/LJ010-0280.wav|On June eighteen fifty the Queen was once more subjected to cowardly outrage, the offender being a Mr. Pate, a gentleman by birth,
+LJSpeech-1.1/wavs/LJ011-0087.wav|Montgomery was duly sentenced to death, but he preferred suicide to the gallows. After sentence his demeanor was serious yet firm.
+LJSpeech-1.1/wavs/LJ040-0071.wav|From the time Marguerite Oswald returned to work until December twenty-six, nineteen forty-two, when Lee too was sent to the orphans' home,
+LJSpeech-1.1/wavs/LJ037-0104.wav|manager of a used-car lot on the northeast corner of Patton Avenue and Jefferson Boulevard, and Sam Guinyard, a porter at the lot.
+LJSpeech-1.1/wavs/LJ018-0245.wav|in all good faith to another, but a criminal member of the family.
+LJSpeech-1.1/wavs/LJ039-0082.wav|this is the ideal type of weapon for moving targets
+LJSpeech-1.1/wavs/LJ001-0182.wav|the books so ornamented are amongst the most delightful works of art that have ever been produced.
+LJSpeech-1.1/wavs/LJ007-0196.wav|the provision of dining-rooms and dining-tables.
+LJSpeech-1.1/wavs/LJ002-0148.wav|and that no more than one hundred ninety-seven creditors recovered debts and costs.
+LJSpeech-1.1/wavs/LJ013-0197.wav|Mr. Phillips, who led in the case, went to the other extreme,
+LJSpeech-1.1/wavs/LJ040-0134.wav|that anybody entering this home had to be searched in case the parents were bringing cigarettes or narcotics or anything, end quote.
+LJSpeech-1.1/wavs/LJ015-0255.wav|and begged his accomplice to invest it as a settlement on a woman named Kay, by whom he had had a child.
+LJSpeech-1.1/wavs/LJ015-0141.wav|from which he rose to be assistant registrar, with the special duties of transferring shares.
+LJSpeech-1.1/wavs/LJ049-0110.wav|The governmental consequences of assassination of one of the specified officials give the United States ample power to act for its own protection.
+LJSpeech-1.1/wavs/LJ040-0152.wav|turning around the topics of omnipotence and power, through which he tries to compensate for his present shortcomings and frustrations, end quote.
+LJSpeech-1.1/wavs/LJ009-0173.wav|from eighteen thirty-two to eighteen forty-four not a single person had been executed in the metropolis except for this the gravest crime.
+LJSpeech-1.1/wavs/LJ003-0079.wav|Mr. Bennet refers to a gentleman confined for want of bail, who occupied a room with five others
+LJSpeech-1.1/wavs/LJ019-0027.wav|The internal arrangements of the new model were carefully supervised by a body of distinguished men, among which were many peers, Lord John Russell,
+LJSpeech-1.1/wavs/LJ048-0125.wav|the precautions taken for the President's trip were the usual safeguards employed on trips of this kind in the United States during the previous year, end quote.
+LJSpeech-1.1/wavs/LJ022-0073.wav|Our problem is to put to work three and one-half million employable persons now on the relief rolls.
+LJSpeech-1.1/wavs/LJ013-0105.wav|Elder, the former, was soon apprehended at his house, but he evaded the law by hanging himself with his pocket-handkerchief.
+LJSpeech-1.1/wavs/LJ011-0083.wav|For a long time justice did not overtake him for any criminal offense, but he was frequently in Newgate and in the King's Bench for debt.
+LJSpeech-1.1/wavs/LJ002-0041.wav|Two other wards were appropriated to the master's side debtors; they were each twenty-three feet by fourteen and a half,
+LJSpeech-1.1/wavs/LJ046-0213.wav|members of his immediate family, the President-elect, and the Vice-President. Investigation of threats against the President of the United States,
+LJSpeech-1.1/wavs/LJ005-0195.wav|There was no decency whatever in the internal arrangements;
+LJSpeech-1.1/wavs/LJ019-0020.wav|Up to the twenty-first December, eighteen forty-two,
+LJSpeech-1.1/wavs/LJ003-0161.wav|The luxury of the state side was for a long time open to all who could pay
+LJSpeech-1.1/wavs/LJ050-0172.wav|The Commission, therefore, recommends that the President consider ordering an inquiry into the possibility
+LJSpeech-1.1/wavs/LJ050-0089.wav|While these tentative criteria are a step in the right direction,
+LJSpeech-1.1/wavs/LJ046-0098.wav|The history of Presidential protection shows growing recognition over the years that the job must be done by able, dedicated,
+LJSpeech-1.1/wavs/LJ037-0074.wav|and her positive identification of Oswald at a police lineup, the Commission considers her testimony reliable.
+LJSpeech-1.1/wavs/LJ033-0020.wav|prior to November twenty-one, nineteen sixty-three, except on Monday, October twenty-one, when he visited his wife in the hospital
+LJSpeech-1.1/wavs/LJ019-0362.wav|Where the local authority had neglected to comply with the provisions of the eighteen sixty-five Act for four consecutive years,
+LJSpeech-1.1/wavs/LJ019-0036.wav|This list included Wakefield, Leeds, Kirkdale, Manchester, Birmingham, and Dublin.
+LJSpeech-1.1/wavs/LJ005-0212.wav|and the prison allowance was still limited to bread and water.
+LJSpeech-1.1/wavs/LJ048-0206.wav|As the motorcade approached Elm Street
+LJSpeech-1.1/wavs/LJ024-0037.wav|who would disregard the law and would decide specific cases as I wished them to be decided, I make this answer:
+LJSpeech-1.1/wavs/LJ015-0063.wav|But neither Robson nor Redpath would have been able to pursue their fraudulent designs with success had they not, like Watts,
+LJSpeech-1.1/wavs/LJ033-0102.wav|When Oswald entered the rear door of the Depository Building, he was about fifty feet ahead of Frazier.
+LJSpeech-1.1/wavs/LJ022-0151.wav|did more than anything else to bring about the recent collapse of industries.
+LJSpeech-1.1/wavs/LJ003-0054.wav|An imperfect attempt at classification was, however, made in eighteen twelve, and a yard was as far as possible set apart for the untried,
+LJSpeech-1.1/wavs/LJ015-0192.wav|From the moment of his reception he gave himself great airs, as a martyr and a man heavily wronged.
+LJSpeech-1.1/wavs/LJ025-0123.wav|has not only been discovered to exist far more widely among plants than was formerly imagined,
+LJSpeech-1.1/wavs/LJ035-0134.wav|that they were watching the parade from the top step of the building entrance when Gloria Calverly, who works in the Depository Building,
+LJSpeech-1.1/wavs/LJ014-0310.wav|Pries, although enjoying a high reputation in the city, had long been in a bad way.
+LJSpeech-1.1/wavs/LJ030-0179.wav|She watched as he slumped down with an empty expression on his face.
+LJSpeech-1.1/wavs/LJ008-0150.wav|Four years passed without the detection of the murderers,
+LJSpeech-1.1/wavs/LJ039-0174.wav|The marksmen took as much time as they wanted for the first target and all hit the target.
+LJSpeech-1.1/wavs/LJ041-0089.wav|Thornley added, quote, I think it was kind of necessary to him to believe that he was being picked on.
+LJSpeech-1.1/wavs/LJ029-0207.wav|On November twenty-one there appeared on the streets of Dallas the anonymous handbill mentioned above.
+LJSpeech-1.1/wavs/LJ010-0092.wav|Lord Harrowby's dinner-party was postponed, but the conspirators knew nothing of it,
+LJSpeech-1.1/wavs/LJ022-0113.wav|to make a major attack upon the problem of unemployment.
+LJSpeech-1.1/wavs/LJ028-0060.wav|In six oh five,
+LJSpeech-1.1/wavs/LJ019-0044.wav|The south and west of England were also very laggard, and many years were still to elapse before the prisons in these parts were properly reconstituted.
+LJSpeech-1.1/wavs/LJ029-0079.wav|was a one-story building with few entrances and easy to make secure, but it lacked necessary food-handling facilities
+LJSpeech-1.1/wavs/LJ020-0050.wav|Toss the lump dough upon it and knead thoroughly for five minutes.
+LJSpeech-1.1/wavs/LJ002-0176.wav|Neild gives a list of the various items charged upon a debt of ten pounds, which included instructions to sue,
+LJSpeech-1.1/wavs/LJ005-0202.wav|An examination of this report shows how even the most insignificant township had its jail.
+LJSpeech-1.1/wavs/LJ049-0019.wav|The last Presidential vehicle with any protection against small-arms fire left the White House in nineteen fifty-three.
+LJSpeech-1.1/wavs/LJ011-0165.wav|who had plausibly explained that he had only recently been engaged at Shrigley.
+LJSpeech-1.1/wavs/LJ039-0031.wav|Mr. Nixon advised the Commission that the only time he was in Dallas in nineteen sixty-three
+LJSpeech-1.1/wavs/LJ028-0218.wav|Without a skirmish or a battle, he permitted them to enter Babylon, and, sparing the city, he delivered the King Nabonidus to him.
+LJSpeech-1.1/wavs/LJ004-0163.wav|and of which fewer still would believe that the original is to be found in this enlightened and happy country."
+LJSpeech-1.1/wavs/LJ037-0255.wav|testified that Commission Exhibit Number one sixty-two was the jacket worn by the man they saw on November twenty-two.
+LJSpeech-1.1/wavs/LJ044-0106.wav|and Benjamin J. Davis honorary membership cards in his nonexistent New Orleans chapter of the Fair Play for Cuba Committee,
+LJSpeech-1.1/wavs/LJ019-0214.wav|The Corporation owned lands there covering from nineteen to twenty acres.
+LJSpeech-1.1/wavs/LJ030-0213.wav|Hill heard a second shot, proximately five seconds after the first, which removed a portion of the President's head.
+LJSpeech-1.1/wavs/LJ013-0043.wav|This person made much of Wallace, encouraged his attentions to his daughter,
+LJSpeech-1.1/wavs/LJ008-0013.wav|As regards the first, I find that in seventeen eighty-six
+LJSpeech-1.1/wavs/LJ004-0076.wav|"Disease, cold, famine, nakedness, and contagious and polluted air are not lawful punishments in the hands of the civil magistrates;
+LJSpeech-1.1/wavs/LJ040-0133.wav|She said that her pocketbook was searched, quote, because the children in this home were such criminals, dope fiends, and had been in criminal offenses,
+LJSpeech-1.1/wavs/LJ048-0031.wav|While he had expressed hostility at times toward the State Department, the Marine Corps, and the FBI as agents of the Government,
+LJSpeech-1.1/wavs/LJ001-0073.wav|went on apace; and by the end of the sixteenth century there was no really beautiful printing done:
+LJSpeech-1.1/wavs/LJ020-0076.wav|Wrap bowl and biscuit in a thick cloth and set to rise where it will neither become chilled nor sour over night.
+LJSpeech-1.1/wavs/LJ019-0166.wav|those for trial, and those sentenced for short terms or long
+LJSpeech-1.1/wavs/LJ038-0097.wav|the Commission gave little weight to his denials of guilt.
+LJSpeech-1.1/wavs/LJ042-0116.wav|Under the entry for May one, nineteen sixty,
+LJSpeech-1.1/wavs/LJ035-0012.wav|At about this time he heard the first shot.
+LJSpeech-1.1/wavs/LJ019-0102.wav|which was promptly carried, with the additional instruction to the committee to suggest any improvements.
+LJSpeech-1.1/wavs/LJ003-0115.wav|The judge sat in proper form; he was punctiliously styled "my lord."
+LJSpeech-1.1/wavs/LJ002-0154.wav|Thus, amongst others, Thomas Blackburn had been committed on October fifteenth for a debt of one shilling five pence.
+LJSpeech-1.1/wavs/LJ048-0010.wav|stressed also the decision by the Department of State that Oswald should be permitted to return to the United States.
+LJSpeech-1.1/wavs/LJ003-0309.wav|Proper hours for locking and unlocking prisoners should be insisted upon;
+LJSpeech-1.1/wavs/LJ036-0032.wav|So I gave her a transfer and opened the door and she was going out the gentleman I had picked up about two blocks [back]
+LJSpeech-1.1/wavs/LJ001-0177.wav|so that if the two are helpful to one another it is a mere matter of accident.
+LJSpeech-1.1/wavs/LJ033-0069.wav|As she [Marina] told me about it I stepped onto the blanket roll
+LJSpeech-1.1/wavs/LJ035-0172.wav|There he was met by a construction worker -- in all likelihood Howard Brennan, who was wearing his work helmet.
+LJSpeech-1.1/wavs/LJ041-0057.wav|His study of Communist literature,
+LJSpeech-1.1/wavs/LJ044-0208.wav|An examination of the Militant, to which Oswald subscribed,
+LJSpeech-1.1/wavs/LJ036-0207.wav|The suspect was described as a, quote,
+LJSpeech-1.1/wavs/LJ011-0081.wav|He was not prosecuted for this fraud on account of the respectability of his family, and soon after this escape
+LJSpeech-1.1/wavs/LJ028-0229.wav|The Babylonians, encamped without their walls, awaited his coming.
+LJSpeech-1.1/wavs/LJ016-0368.wav|It was they who formed the chief part of the small select group of spectators;
+LJSpeech-1.1/wavs/LJ007-0011.wav|But it was already plain that they constituted an independent authority within the jails; they were frequently in conflict with the chaplain,
+LJSpeech-1.1/wavs/LJ011-0250.wav|While thus engaged, Howard thrust the poker into the fire.
+LJSpeech-1.1/wavs/LJ021-0180.wav|to issue new bonds therefore bearing only three and one half percent interest,
+LJSpeech-1.1/wavs/LJ011-0128.wav|Maynard was convicted of uttering the forged document, Jones of being an accessory; the third prisoner was acquitted.
+LJSpeech-1.1/wavs/LJ028-0469.wav|The inner wall of Babylon was called Imgur-Bel, and like the outer wall, it was double.
+LJSpeech-1.1/wavs/LJ040-0030.wav|When he was in the Soviet Union, he apparently resented the Communist Party members,
+LJSpeech-1.1/wavs/LJ015-0291.wav|partly through their own carelessness, when transferring their operations to Yarmouth.
+LJSpeech-1.1/wavs/LJ045-0222.wav|The feelings of hostility and aggression which seem to have played such an important, part in Oswald's life
+LJSpeech-1.1/wavs/LJ001-0026.wav|On the whole the type of this book may be considered the ne-plus-ultra of Gothic type,
+LJSpeech-1.1/wavs/LJ024-0006.wav|a new member shall be appointed by the President then in office,
+LJSpeech-1.1/wavs/LJ024-0114.wav|and who would be willing to support a reasonable amendment if they could agree on one.
+LJSpeech-1.1/wavs/LJ042-0059.wav|Despite this commitment to the Soviet Union
+LJSpeech-1.1/wavs/LJ013-0119.wav|Howse, the steward, accused the other servants, but they retorted, declaring that he had been visited by the thief the day previous,
+LJSpeech-1.1/wavs/LJ029-0102.wav|After the selection of the Trade Mart as the luncheon site,
+LJSpeech-1.1/wavs/LJ046-0002.wav|The President's Commission on the Assassination of President Kennedy. Chapter eight. The Protection of the President. Part one.
+LJSpeech-1.1/wavs/LJ019-0372.wav|It was practically inoperative as regards the penalties for neglect. It was no doubt as irksome and inconvenient to the Secretary of State
+LJSpeech-1.1/wavs/LJ047-0248.wav|that he ever said that Oswald was capable of violence, or that he had any information suggesting this.
+LJSpeech-1.1/wavs/LJ003-0134.wav|One day he was too ill to come down and meet her.
+LJSpeech-1.1/wavs/LJ005-0094.wav|to call for information as to the observance of its provisions.
+LJSpeech-1.1/wavs/LJ021-0171.wav|Now that these people are coming out of their storm cellars, they forget that there ever was a storm.
+LJSpeech-1.1/wavs/LJ004-0007.wav|It was so powerless against the persistent neglect of those intrusted with prison management, that, five-and-twenty years later,
+LJSpeech-1.1/wavs/LJ016-0361.wav|the moment too that the condemned man had passed through the debtors' door on to the scaffold the prison had done with him,
+LJSpeech-1.1/wavs/LJ019-0357.wav|The Secretary of State was empowered to deal rather summarily with "inadequate" prisons, in other words,
+LJSpeech-1.1/wavs/LJ002-0163.wav|a market porter, was arrested and committed at the suit of a publican
+LJSpeech-1.1/wavs/LJ049-0204.wav|While in accordance with its mandate
+LJSpeech-1.1/wavs/LJ031-0121.wav|Dr. George T. Shires, assisted by Drs. Robert McClelland, Charles Baxter, and Ralph Don Patman,
+LJSpeech-1.1/wavs/LJ019-0198.wav|and on a more mature consideration he realized that the limited area of the existing Newgate site,
+LJSpeech-1.1/wavs/LJ011-0155.wav|He had eloped with his first wife from school.
+LJSpeech-1.1/wavs/LJ015-0293.wav|as coming from a Mr. Whitney.
+LJSpeech-1.1/wavs/LJ036-0206.wav|again at twelve:forty-eight p.m., and again at twelve:fifty-five p.m.
+LJSpeech-1.1/wavs/LJ016-0003.wav|and that none of its inmates could hope to escape from its secure precincts.
+LJSpeech-1.1/wavs/LJ020-0037.wav|The oven must be steady, but not so hot as for white bread, nor will the Graham bread be done quite so soon as that made of bolted flour.
+LJSpeech-1.1/wavs/LJ028-0387.wav|The Sassanian kings of Persia were fond of hunting, and Babylon, then overgrown with trees, was their game preserve.
+LJSpeech-1.1/wavs/LJ017-0168.wav|But a long public discussion followed, and in consequence he was reprieved.
+LJSpeech-1.1/wavs/LJ015-0173.wav|Warrants were issued for Redpath's arrest, but he had flown to Paris.
+LJSpeech-1.1/wavs/LJ006-0033.wav|The ink was barely dry upon their letters of appointment before they appeared at Newgate, and commenced a searching investigation.
+LJSpeech-1.1/wavs/LJ010-0283.wav|and walking for choice through prickly gorse bushes.
+LJSpeech-1.1/wavs/LJ009-0085.wav|'Now for you, my poor fellow mortals, who are about to suffer the last penalty of the law.'
+LJSpeech-1.1/wavs/LJ006-0297.wav|Some member of the Ladies' Association observed and commented upon the fact that a "young rosy-cheeked girl" had been kept by the governor from transportation,
+LJSpeech-1.1/wavs/LJ024-0094.wav|It would take months and years thereafter to get a two-thirds majority in favor of that amendment in both Houses of the Congress.
+LJSpeech-1.1/wavs/LJ002-0092.wav|The two yards were adjoining, that for the common side much the largest.
+LJSpeech-1.1/wavs/LJ008-0186.wav|The concourse was very great, notwithstanding these warnings.
+LJSpeech-1.1/wavs/LJ009-0202.wav|In eighteen eleven Williams, who murdered the Marrs in Ratcliffe Highway, having committed suicide in jail to escape hanging,
+LJSpeech-1.1/wavs/LJ009-0277.wav|For a second or two the body hung motionless, then, with a strength that astonished the attendant officials,
+LJSpeech-1.1/wavs/LJ047-0060.wav|Fain had been assigned to see Marina Oswald at an appropriate time.
+LJSpeech-1.1/wavs/LJ020-0042.wav|and these also tend to nourish and strengthen the brain.
+LJSpeech-1.1/wavs/LJ034-0005.wav|He worked principally on the first and sixth floors of the building, gathering books listed on orders and delivering them to the shipping room on the first floor.
+LJSpeech-1.1/wavs/LJ050-0063.wav|and who have been involved in bombing or bomb-making or whose past conduct indicates tendencies toward violence, and (d)
+LJSpeech-1.1/wavs/LJ045-0081.wav|Although she denied it in some of her testimony before the Commission,
+LJSpeech-1.1/wavs/LJ006-0266.wav|On these occasions precautions were supposed to be taken to exclude bad characters,
+LJSpeech-1.1/wavs/LJ047-0045.wav|and promised to advise the FBI if he heard from them.
+LJSpeech-1.1/wavs/LJ017-0267.wav|After condemnation, as the rules now kept capital convicts strictly apart, they could not be lodged in the two condemned cells,
+LJSpeech-1.1/wavs/LJ008-0243.wav|Threading his way among these itinerant vendors was seen the meek-faced deliverer of tracts, the man of good intentions, now bonneted,
+LJSpeech-1.1/wavs/LJ034-0054.wav|In addition, Mandella was of the opinion that the print taken from the carton on the floor
+LJSpeech-1.1/wavs/LJ013-0216.wav|His account of his acts and movements after the deed
+LJSpeech-1.1/wavs/LJ011-0233.wav|that thieves or depredators were idle or entirely unsuccessful.
+LJSpeech-1.1/wavs/LJ033-0176.wav|taken from the Texas School Book Depository shipping room on November twenty-two, nineteen sixty-three.
+LJSpeech-1.1/wavs/LJ028-0487.wav|Fortunately its walls have suffered less from the hands of the brick hunters, and the German excavators have been able to reconstruct their plan.
+LJSpeech-1.1/wavs/LJ047-0205.wav|to potential danger, quote,
+LJSpeech-1.1/wavs/LJ031-0085.wav|Since the Dallas doctors directed all their efforts to controlling the massive bleeding caused by the head wound, and to reconstructing an airway to his lungs,
+LJSpeech-1.1/wavs/LJ006-0252.wav|The worst fights occurred on Sunday afternoons; but nearly every night the act of locking up became, from the consequent removal of all supervision,
+LJSpeech-1.1/wavs/LJ015-0295.wav|he was told it was only at Mr. Whitney's disposal, and that it could be paid to no one else.
+LJSpeech-1.1/wavs/LJ004-0169.wav|On the dirty bedstead lay a wretched being in the throes of severe illness.
+LJSpeech-1.1/wavs/LJ009-0168.wav|robbery, burglary, and arson.
+LJSpeech-1.1/wavs/LJ009-0148.wav|But the chaplain admitted that the solitude of the convict's cell
+LJSpeech-1.1/wavs/LJ048-0187.wav|In addition, Secret Service agents riding in the motorcade were trained to scan buildings as part of their general observation of the crowd of spectators.
+LJSpeech-1.1/wavs/LJ004-0141.wav|The hospital was filled with infectious cases, and in one room, seven feet by nine, with closed windows,
+LJSpeech-1.1/wavs/LJ032-0153.wav|A palmprint could not be placed on this portion of the rifle, when assembled, because the wooden foregrip covers the barrel at this point.
+LJSpeech-1.1/wavs/LJ019-0243.wav|and thus bring the history of prison discipline down to our own times.
+LJSpeech-1.1/wavs/LJ037-0174.wav|but only two of the four discarded cartridge cases found on the lawn at tenth Street and Patton Avenue were of Winchester-Western manufacture.
+LJSpeech-1.1/wavs/LJ010-0146.wav|Attacks upon the sovereign, as I have said, became more common after the accession of the young Queen Victoria in eighteen thirty-eight.
+LJSpeech-1.1/wavs/LJ014-0019.wav|His name was Hocker; he was by trade a ladies' shoemaker; and it was also ascertained that after the day of the murder he was flush of money.
+LJSpeech-1.1/wavs/LJ008-0105.wav|Mr. Smith's account of the condemned convict, whose cell he was permitted to enter, may be inserted here.
+LJSpeech-1.1/wavs/LJ028-0317.wav|Introduced into their assembly, he began to bewail his misfortunes, telling them that
+LJSpeech-1.1/wavs/LJ033-0186.wav|one cannot estimate when, prior to November twenty-two, Oswald made the paper bag.
+LJSpeech-1.1/wavs/LJ035-0110.wav|Both Dougherty and Piper were confused witnesses. They had no exact memory of the events of that afternoon.
+LJSpeech-1.1/wavs/LJ045-0131.wav|Neither Hosty nor any other agent of the FBI spoke to Oswald on any subject from August ten, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ012-0156.wav|His arrest and conviction cast dismay over the whole gang of receivers, and for a time seriously checked the nefarious traffic.
+LJSpeech-1.1/wavs/LJ004-0130.wav|incommodious, as has been stated, insecure, unhealthy, and unprovided with the printed or written regulations required by law.
+LJSpeech-1.1/wavs/LJ013-0220.wav|that Courvoisier was idle, discontented, ready to take offense, greedy of gain;
+LJSpeech-1.1/wavs/LJ033-0085.wav|She then opened the kitchen door and saw Oswald open the right rear door of her brother's car and place the package in the back of the car.
+LJSpeech-1.1/wavs/LJ017-0143.wav|which followed close on its heels, although in that the verdict of "Not Guilty" was excusable, as the evidence was entirely circumstantial.
+LJSpeech-1.1/wavs/LJ021-0068.wav|to a level of sustained profits within one year from the inauguration of N.R.A.
+LJSpeech-1.1/wavs/LJ030-0135.wav|Mrs. Connally, elated by the reception, turned to President Kennedy and said, quote, Mr. President, you can't say Dallas doesn't love you.
+LJSpeech-1.1/wavs/LJ015-0045.wav|the firm's paper went down further and further in value; an application to the Committee of Bankers for assistance was peremptorily refused,
+LJSpeech-1.1/wavs/LJ008-0009.wav|But the Old Bailey was not exclusively used;
+LJSpeech-1.1/wavs/LJ004-0019.wav|were as yet but imperfectly understood, and such portions of the "improved" jails of that period as were still extant a few years back,
+LJSpeech-1.1/wavs/LJ002-0303.wav|Besides these fees, legitimate and illegitimate, there were others which must be paid before release.
+LJSpeech-1.1/wavs/LJ014-0178.wav|His offense was the murder of Richard Cope,
+LJSpeech-1.1/wavs/LJ045-0089.wav|Marina Oswald also ridiculed her husband's political views, thereby tearing down his view of his own importance.
+LJSpeech-1.1/wavs/LJ037-0111.wav|Guinyard and Callaway ran to tenth and Patton and found Tippit lying in the street beside his car.
+LJSpeech-1.1/wavs/LJ020-0018.wav|Throw a cloth over the bowl and set by for five or six hours to rise.
+LJSpeech-1.1/wavs/LJ046-0242.wav|Such an approach seriously undermines the precautionary nature of PRS work;
+LJSpeech-1.1/wavs/LJ029-0161.wav|From the airport, the President's party will proceed to Mockingbird Lane to Lemmon and then to Turtle Creek, turning south to Cedar Springs.
+LJSpeech-1.1/wavs/LJ050-0024.wav|toward the preparation of formal understandings of the respective roles of the Secret Service and other agencies with which it collaborates
+LJSpeech-1.1/wavs/LJ013-0218.wav|His last statement contains the words, "The public now think I am a liar, and they will not believe me when I say the truth."
+LJSpeech-1.1/wavs/LJ015-0029.wav|In December eighteen fifty-one the balance sheet showed a deficiency of upwards of seventy thousand pounds.
+LJSpeech-1.1/wavs/LJ008-0236.wav|coiled up on the floor of the scaffold like a serpent, the hangman's rope!
+LJSpeech-1.1/wavs/LJ014-0070.wav|a heavy brutish fellow, was yet aghast at his wife's resolve, and tried hard to dissuade her from bad purpose.
+LJSpeech-1.1/wavs/LJ014-0307.wav|were intended by the protectionists to depress the wheat market, and secure the support of the farmers at the forthcoming election;
+LJSpeech-1.1/wavs/LJ017-0144.wav|There was no convincing proof that the accused had administered the poison, although beyond question that poison had occasioned the death.
+LJSpeech-1.1/wavs/LJ011-0163.wav|She was not in immediate danger, but she wished to see her daughter, "as it was possible she might soon become incapable of recognizing any one."
+LJSpeech-1.1/wavs/LJ025-0061.wav|Agardh and other of the botanists of Cuvier's generation who occupied themselves with the lower plants had observed that,
+LJSpeech-1.1/wavs/LJ004-0078.wav|"The convicted delinquent has his rights," said Mr. Buxton authoritatively.
+LJSpeech-1.1/wavs/LJ031-0005.wav|approximately four miles from the Texas School Book Depository Building.
+LJSpeech-1.1/wavs/LJ046-0045.wav|it is the President's right and duty to be the active leader of his party, as when he seeks to be reelected or to maintain his party in power.
+LJSpeech-1.1/wavs/LJ014-0323.wav|The warrant thus represented money, and was often used as such, being endorsed and passed from hand to hand as other negotiable bills.
+LJSpeech-1.1/wavs/LJ029-0047.wav|maintains records of people who have threatened the President or so conducted themselves as to be deemed a potential danger to him.
+LJSpeech-1.1/wavs/LJ042-0123.wav|A "patriotic duty" to bring in the harvest. The opinions of the workers (unvoiced) are that it's a great pain in the neck:
+LJSpeech-1.1/wavs/LJ019-0389.wav|passed under the more direct control of the State.
+LJSpeech-1.1/wavs/LJ007-0199.wav|baths, fumigating places for clothing, wash-house, and the removal of dust-bins, completed the new arrangements in the main prison.
+LJSpeech-1.1/wavs/LJ014-0117.wav|An examination of her boxes disclosed a quantity of O'Connor's property.
+LJSpeech-1.1/wavs/LJ004-0013.wav|some prisons that had been ameliorated under the persuasive influence of his kind advice were relapsing into their former horrid state of privation,
+LJSpeech-1.1/wavs/LJ017-0086.wav|For this purpose he gave, or there was the strongest presumption that he gave,
+LJSpeech-1.1/wavs/LJ008-0002.wav|I propose to return now to the subject of Newgate executions,
+LJSpeech-1.1/wavs/LJ028-0471.wav|Nebuchadnezzar says that he built it of burned bricks, but only sun-dried bricks laid in mud now appear.
+LJSpeech-1.1/wavs/LJ044-0185.wav|He engaged in an angry argument with the consul who finally told him that, quote, as far as he was concerned
+LJSpeech-1.1/wavs/LJ026-0094.wav|This, however, is probably not a source of vital energy, but only contributes to the maintenance of the body temperature.
+LJSpeech-1.1/wavs/LJ004-0196.wav|There was an infirmary, properly found and duly looked after.
+LJSpeech-1.1/wavs/LJ032-0158.wav|On November twenty-three, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ037-0219.wav|Oswald's Jacket
+LJSpeech-1.1/wavs/LJ008-0167.wav|No one who fell ever rose again.
+LJSpeech-1.1/wavs/LJ008-0073.wav|One case is preserved by Catnach,
+LJSpeech-1.1/wavs/LJ047-0134.wav|The FBI was advised by the rental agent for the Oswalds' apartment in New Orleans that they had moved again.
+LJSpeech-1.1/wavs/LJ031-0213.wav|Under "Pathological Diagnosis" the cause of death was set forth as "Gunshot wound, head."
+LJSpeech-1.1/wavs/LJ035-0124.wav|Neither Jarman, Norman, Williams, or Dougherty saw Oswald.
+LJSpeech-1.1/wavs/LJ014-0190.wav|Marley, disturbed, picked up a cigar and parcel from the counter, then ran out, pursued by Lerigo only.
+LJSpeech-1.1/wavs/LJ040-0145.wav|Dr. Hartogs did find Oswald to be a tense, withdrawn, and evasive boy who intensely disliked talking about himself and his feelings.
+LJSpeech-1.1/wavs/LJ017-0124.wav|He frequently declared before and during the trial that it would be impossible to find him guilty.
+LJSpeech-1.1/wavs/LJ001-0127.wav|the modern letters are narrowed by a third or thereabout; but while this gain of space very much hampers the possibility of beauty of design,
+LJSpeech-1.1/wavs/LJ009-0122.wav|particularly to those who desire now to offer up their praises and thanksgivings for thy late mercies vouchsafed unto them.
+LJSpeech-1.1/wavs/LJ006-0204.wav|In the same way the wardsman laid in his stock to be retailed. Other light literature besides the daily journals were in circulation:
+LJSpeech-1.1/wavs/LJ042-0241.wav|Soon after his arrival he wrote to the Soviet Embassy in Washington
+LJSpeech-1.1/wavs/LJ009-0229.wav|Later on, after dark, some friends of the deceased stole the body and buried it in the sand, and this was the end of hanging in chains.
+LJSpeech-1.1/wavs/LJ014-0062.wav|He had failed in this as well as in the business of a publican, which he had at one time adopted.
+LJSpeech-1.1/wavs/LJ026-0047.wav|circulation, metabolism, excretion, oxygenation (part of respiration),
+LJSpeech-1.1/wavs/LJ038-0051.wav|Two patrons of the theatre and John Brewer
+LJSpeech-1.1/wavs/LJ036-0014.wav|Oswald's Movements After Leaving Depository Building
+LJSpeech-1.1/wavs/LJ023-0050.wav|overlook the simple fact that the President, as Chief Executive, is himself one of the three horses.
+LJSpeech-1.1/wavs/LJ019-0125.wav|But an open farm of a thousand acres would have offered abundant chances of escape, which some at least would have attempted, probably with success.
+LJSpeech-1.1/wavs/LJ004-0155.wav|In the crowd, all of them persons who had "no other avocation or mode of livelihood but thieving," Mr. Buxton counted eleven children
+LJSpeech-1.1/wavs/LJ048-0173.wav|arrangements were made for building and roof security by posting police officers where appropriate.
+LJSpeech-1.1/wavs/LJ038-0028.wav|Brewer met McDonald and the other policemen at the alley exit door,
+LJSpeech-1.1/wavs/LJ048-0088.wav|Examination of these procedures shows that in most respects they were well conceived and ably executed by the personnel of the Service.
+LJSpeech-1.1/wavs/LJ028-0448.wav|All of the ancient writers agree in saying that Babylon was surrounded with both inner and outer walls, and the ruins confirm their statements.
+LJSpeech-1.1/wavs/LJ015-0165.wav|But the peer rushed forward and shook Redpath warmly by the hand.
+LJSpeech-1.1/wavs/LJ033-0136.wav|A handmade bag of wrapping paper and tape was found in the southeast corner of the sixth floor alongside the window from which the shots were fired.
+LJSpeech-1.1/wavs/LJ015-0226.wav|After hanging about the Folkestone office for some time, they saw at last that the key was kept in a certain cupboard.
+LJSpeech-1.1/wavs/LJ015-0054.wav|Police officers went down at night to Nutfield, near Reigate, and arrested Sir John Paul, but allowed the prisoner to sleep there.
+LJSpeech-1.1/wavs/LJ048-0017.wav|We satisfied ourselves that we had met our requirement, namely to find out whether he had been recruited by Soviet intelligence. The case was closed.
+LJSpeech-1.1/wavs/LJ050-0211.wav|Chief Rowley testified that the present workload of each Secret Service agent averages one hundred ten point one cases.
+LJSpeech-1.1/wavs/LJ012-0141.wav|Moss presently turned approver,
+LJSpeech-1.1/wavs/LJ028-0298.wav|Surely thou hadst gone out of thy mind when thou didst so misuse thyself.
+LJSpeech-1.1/wavs/LJ035-0112.wav|The west elevator was not on the fifth floor when Baker and Truly reached that floor,
+LJSpeech-1.1/wavs/LJ036-0137.wav|And he never said anything. So I figured he was one of these people that don't like to talk so I never said any more to him.
+LJSpeech-1.1/wavs/LJ038-0144.wav|that must have been some other time he picked me up, end quote,
+LJSpeech-1.1/wavs/LJ017-0138.wav|Three years later came the case of Dr. Smethurst,
+LJSpeech-1.1/wavs/LJ049-0221.wav|perhaps upon recommendations based on further studies by the Cabinet-level committee recommended above or the National Security Council.
+LJSpeech-1.1/wavs/LJ031-0160.wav|The police were cautioned to prevent picture taking.
+LJSpeech-1.1/wavs/LJ018-0138.wav|Some time in eighteen sixty-two, a large deficiency in stock of bank paper unglazed was discovered at the mills.
+LJSpeech-1.1/wavs/LJ016-0324.wav|But it is curious to note that there were several dissentients among the commissioners to this paragraph of the report.
+LJSpeech-1.1/wavs/LJ007-0216.wav|For nearly twenty-two hours out of the twenty-four the prisoners are locked up, during which time no officer is stationed in the ward with them.
+LJSpeech-1.1/wavs/LJ044-0214.wav|reported Castro as saying Cuba could not accept a situation where at the same time the United States was trying to ease world tensions
+LJSpeech-1.1/wavs/LJ045-0111.wav|They asked for Lee Oswald who was not called to the telephone because he was known by the other name.
+LJSpeech-1.1/wavs/LJ031-0211.wav|weighed one hundred seventy pounds, had blue eyes and reddish-brown hair.
+LJSpeech-1.1/wavs/LJ005-0046.wav|The good it tried to do took active shape in the establishment of temporary refuges -- at Hoxton for males, and in the Hackney Road for females
+LJSpeech-1.1/wavs/LJ050-0245.wav|In view of the ever-increasing mobility of American Presidents, it seems unlikely that the Service could or should increase its own staff to a size
+LJSpeech-1.1/wavs/LJ040-0028.wav|When he was in the United States he resented the capitalist system which he thought was exploiting him and others like him.
+LJSpeech-1.1/wavs/LJ003-0084.wav|Quote, a common-sized man, says the keeper, Mr. Newman, can turn in nineteen inches, end quote.
+LJSpeech-1.1/wavs/LJ007-0175.wav|but in opposition to the recorded denunciations of authority, and in defiance of the express enactments of the law,
+LJSpeech-1.1/wavs/LJ050-0278.wav|the recommendations we have here suggested would greatly advance the security of the office without any impairment of our fundamental liberties.
+LJSpeech-1.1/wavs/LJ014-0275.wav|but it was long before the public realized that the fraudulent clerk and the great theatrical manager were one and the same person.
+LJSpeech-1.1/wavs/LJ005-0119.wav|No attempt was made to maintain discipline.
+LJSpeech-1.1/wavs/LJ040-0124.wav|This continued despite the efforts of the school authorities and, to a lesser extent, of his mother to have him return to school.
+LJSpeech-1.1/wavs/LJ006-0065.wav|were associated together, "of every variety of age, habit, and delinquency, without employment, oversight, or control."
+LJSpeech-1.1/wavs/LJ005-0077.wav|"expedient to introduce such measures and arrangements as shall not only provide for the safe custody,
+LJSpeech-1.1/wavs/LJ015-0039.wav|Money they must have, and money they raised to meet their urgent necessities upon the balances and securities deposited with them by their customers.
+LJSpeech-1.1/wavs/LJ003-0258.wav|Hence the frequent cases of drunkenness, of which no notice was taken, unless people grew riotous in their cups
+LJSpeech-1.1/wavs/LJ031-0222.wav|The surgeons observed, through X-ray analysis, thirty or forty tiny dustlike fragments of metal
+LJSpeech-1.1/wavs/LJ048-0119.wav|The only systematic supervision of the activities of the advance agent
+LJSpeech-1.1/wavs/LJ026-0102.wav|but root pressure due to osmosis, capillary action and evaporation from the leaves are factors.
+LJSpeech-1.1/wavs/LJ007-0116.wav|A few others, who could not afford a payment of more than half a guinea, were permitted to monopolize a part of the prison infirmary,
+LJSpeech-1.1/wavs/LJ023-0038.wav|The courts, however, have cast doubts on the ability of the elected Congress to protect us against catastrophe
+LJSpeech-1.1/wavs/LJ005-0172.wav|When that time arrives
+LJSpeech-1.1/wavs/LJ036-0114.wav|Only two of the men in the lineup with Oswald were teenagers: John T. Horn, aged eighteen, was Number one;
+LJSpeech-1.1/wavs/LJ009-0079.wav|The sermon of this day, whether eloquent or plain, useful or useless, must produce a striking effect at the moment of its delivery.
+LJSpeech-1.1/wavs/LJ016-0445.wav|Miller, the Chelsea murderer, who packed his victim's body in a box, and tried to send it by parcels delivery, tried to kill himself,
+LJSpeech-1.1/wavs/LJ003-0003.wav|Prisoners were committed to it quite without reference to its capacity.
+LJSpeech-1.1/wavs/LJ048-0278.wav|The Commission recognizes that the responsibilities of members of the White House detail of the Secret Service are arduous.
+LJSpeech-1.1/wavs/LJ025-0032.wav|Hence arose the second great distinctive character of animals, or the circulatory system, which is less important than the digestive,
+LJSpeech-1.1/wavs/LJ014-0280.wav|But it was proved that Watts had appropriated one cheque for fourteen hundred pounds,
+LJSpeech-1.1/wavs/LJ025-0041.wav|and carbonic acid containing carbon and oxygen.
+LJSpeech-1.1/wavs/LJ026-0003.wav|Nutrition thus, as has been pointed out, makes it possible to classify most organisms as animals or plants.
+LJSpeech-1.1/wavs/LJ039-0004.wav|Chapter four. The Assassin: Part eight.
+LJSpeech-1.1/wavs/LJ022-0060.wav|We must begin now to make provision for the future.
+LJSpeech-1.1/wavs/LJ046-0064.wav|The President's views of his responsibilities as President of the United States were that he meet the people, that he go out to their homes and see them,
+LJSpeech-1.1/wavs/LJ005-0114.wav|In others the separation between the sexes consisted in a hanging curtain
+LJSpeech-1.1/wavs/LJ029-0130.wav|the President's motorcade would pass the Texas School Book Depository Building on the northwest corner of Houston and Elm Streets.
+LJSpeech-1.1/wavs/LJ033-0106.wav|believed that he saw Oswald coming to work, but he does not remember that Oswald had anything in his hands as he entered the door.
+LJSpeech-1.1/wavs/LJ019-0160.wav|and check by remonstrance or threat of punishment all who broke the peace of the prison.
+LJSpeech-1.1/wavs/LJ046-0053.wav|In recent years, Presidential journeys have been frequent and extensive,
+LJSpeech-1.1/wavs/LJ028-0238.wav|he then himself drew off with the unwarlike portion of his host, and made for the place where Nitocris dug the basin for the river,
+LJSpeech-1.1/wavs/LJ038-0023.wav|At one:forty-five p.m., the police radio stated, quote, Have information a suspect just went in the Texas Theatre on West Jefferson, end quote.
+LJSpeech-1.1/wavs/LJ003-0344.wav|all chances of classification and separation vanished, and the greatest evils remained untouched.
+LJSpeech-1.1/wavs/LJ046-0156.wav|The general files of PRS consist of folders on individuals, card indexed by name.
+LJSpeech-1.1/wavs/LJ031-0154.wav|Secret Service agents stationed at later stops on the President's itinerary of November twenty-two were redeployed.
+LJSpeech-1.1/wavs/LJ033-0211.wav|and the obvious bulk of the package which he intended to bring to work the next day;
+LJSpeech-1.1/wavs/LJ004-0209.wav|The looms were constantly busy. Tailors were always at work, and every article of clothing and bedding was made up within the walls.
+LJSpeech-1.1/wavs/LJ042-0079.wav|My request for citizenship is now pending before the Supreme Soviet of the U.S.S.R. I take these steps for political reasons.
+LJSpeech-1.1/wavs/LJ031-0142.wav|The Vice President conferred with White House Assistant Press Secretary Malcolm Kilduff
+LJSpeech-1.1/wavs/LJ028-0107.wav|The ancients never tired of describing them.
+LJSpeech-1.1/wavs/LJ018-0010.wav|Inside the carriage was a hat, a walking-stick, and a small black leather bag.
+LJSpeech-1.1/wavs/LJ028-0510.wav|Probably their metal was far too valuable for the enemy to leave behind.
+LJSpeech-1.1/wavs/LJ043-0008.wav|De Mohrenschildt was a peripheral member of the so-called Russian community, with which Oswald made contact through Mr. Peter Gregory,
+LJSpeech-1.1/wavs/LJ034-0131.wav|a person squatting or kneeling exposes more of his body than would normally be the case.
+LJSpeech-1.1/wavs/LJ019-0382.wav|As the years passed, great want of uniformity continued to prevail throughout the prisons of the United Kingdom.
+LJSpeech-1.1/wavs/LJ019-0320.wav|was the boon to which willing industry extending over a long period established a certain claim.
+LJSpeech-1.1/wavs/LJ039-0186.wav|would have been to fire three times and hit the target twice within a span of four point eight to five point six seconds.
+LJSpeech-1.1/wavs/LJ049-0193.wav|Consequently the suggestion has been made, on the one hand, that all preventive investigative functions relating to the security of the President
+LJSpeech-1.1/wavs/LJ021-0123.wav|When the businessmen of the country were demanding the right to organize themselves adequately to promote their legitimate interests;
+LJSpeech-1.1/wavs/LJ001-0150.wav|the page so lay on the paper that there was more space allowed to the bottom and fore margin than to the top and back of the paper,
+LJSpeech-1.1/wavs/LJ028-0299.wav|"Had I told thee," rejoined the other, "what I was bent on doing, thou wouldst not have suffered it;
+LJSpeech-1.1/wavs/LJ022-0134.wav|Neither you nor I want criticism conceived in a purely fault-finding or partisan spirit,
+LJSpeech-1.1/wavs/LJ022-0041.wav|Its first objective is to put men and women now on the relief rolls to work and, incidentally,
+LJSpeech-1.1/wavs/LJ008-0229.wav|Above the murmur and tumult of that noisy assembly, the lowing and bleating of cattle as they were driven into the stalls and pens of Smithfield
+LJSpeech-1.1/wavs/LJ033-0003.wav|The rifle in the building.
+LJSpeech-1.1/wavs/LJ022-0021.wav|or to one industry, or to an individual private occupation.
+LJSpeech-1.1/wavs/LJ050-0128.wav|Much useful information will come to the attention of local law enforcement agencies in the regular course of their activities,
+LJSpeech-1.1/wavs/LJ047-0095.wav|On the next day, he asked the New Orleans police to arrange for him to be interviewed by the FBI.
+LJSpeech-1.1/wavs/LJ043-0037.wav|At the time of his defection, Oswald had said that neither his brother, Robert,
+LJSpeech-1.1/wavs/LJ012-0150.wav|Mrs. Abrahams imposed upon her father by abstracting a portion of the dust and selling it on her own account;
+LJSpeech-1.1/wavs/LJ042-0028.wav|Oswald had managed to save enough money to cover the expenses of his forthcoming trip.
+LJSpeech-1.1/wavs/LJ041-0080.wav|in those arguments, quote, and make himself come out top dog, end quote.
+LJSpeech-1.1/wavs/LJ002-0068.wav|Here were also lodged the gatesmen, the prisoners who had charge of the inner gates, and who were entrusted with the duty of escorting visitors from the gates
+LJSpeech-1.1/wavs/LJ016-0255.wav|The old prejudices, such as that which enlisted Dr. Johnson on the side of the Tyburn procession, still lingered and prevented any change.
+LJSpeech-1.1/wavs/LJ019-0292.wav|Prisoners still slept two in a bed.
+LJSpeech-1.1/wavs/LJ009-0156.wav|"with a view," as he himself said, "to his professional studies."
+LJSpeech-1.1/wavs/LJ022-0199.wav|Fear is vanishing and confidence is growing on every side,
+LJSpeech-1.1/wavs/LJ043-0071.wav|His performance for that company was satisfactory.
+LJSpeech-1.1/wavs/LJ011-0038.wav|Fauntleroy meanwhile lay in Newgate, not herded with other condemned prisoners, as the custom was,
+LJSpeech-1.1/wavs/LJ006-0108.wav|He charged a weekly sum as ward dues for the use of knives, forks, and plates
+LJSpeech-1.1/wavs/LJ026-0125.wav|But the formation of starch, all important as it is, is after all only the manufacture of food
+LJSpeech-1.1/wavs/LJ029-0041.wav|who was the Secret Service official responsible for the entire Texas journey.
+LJSpeech-1.1/wavs/LJ039-0089.wav|in relation to the target as opposed to iron sights with aligning the sights and then aligning them on the target, end quote.
+LJSpeech-1.1/wavs/LJ035-0120.wav|While they were at the west windows their view of the stairwell was completely blocked by shelves and boxes.
+LJSpeech-1.1/wavs/LJ019-0360.wav|This he might do on the representation of the inspector of prisons,
+LJSpeech-1.1/wavs/LJ032-0183.wav|The relative freshness of the fibers is strong evidence that they were caught on the rifle on the morning of the assassination or during the preceding evening.
+LJSpeech-1.1/wavs/LJ031-0046.wav|and an extensive wound in the President's head where a sizable portion of the skull was missing.
+LJSpeech-1.1/wavs/LJ030-0180.wav|Roy Kellerman, in the right front seat of the limousine, heard a report like a firecracker pop.
+LJSpeech-1.1/wavs/LJ028-0051.wav|He enlarged the old city, erected temples, and began the construction of its walls.
+LJSpeech-1.1/wavs/LJ015-0127.wav|All the signatures in the transfer were forged. Not only did he thus transfer and realize "bogus" stock
+LJSpeech-1.1/wavs/LJ025-0082.wav|But although Cuvier's leading diagnosis of the animal from the plant will not stand a strict test,
+LJSpeech-1.1/wavs/LJ013-0209.wav|he was annoyed with his man for various small omissions and acts of forgetfulness, and on the night of the murder had taken Courvoisier to task rather sharply.
+LJSpeech-1.1/wavs/LJ011-0011.wav|and took the instrument out to a clerk with the ink not dry.
+LJSpeech-1.1/wavs/LJ034-0151.wav|of what he said on November twenty-two.
+LJSpeech-1.1/wavs/LJ039-0064.wav|An aerial photograph of Dealey Plaza shows that Elm Street runs at an angle
+LJSpeech-1.1/wavs/LJ011-0211.wav|when indictments were preferred against both brothers "for having carried away Ellen Turner, spinster,
+LJSpeech-1.1/wavs/LJ026-0064.wav|but unlike that of the animal, it is not chiefly an income of foods, but only of the raw materials of food.
+LJSpeech-1.1/wavs/LJ019-0143.wav|Drinking and gaming,
+LJSpeech-1.1/wavs/LJ010-0268.wav|It happened that a lad named Bean had absconded from his father's home some weeks before,
+LJSpeech-1.1/wavs/LJ049-0119.wav|would be conducted by Federal law enforcement officials, in particular, the FBI with the assistance of the Secret Service.
+LJSpeech-1.1/wavs/LJ005-0215.wav|The regular daily visitation of the chaplain was also insisted upon.
+LJSpeech-1.1/wavs/LJ042-0204.wav|but in servile conformity to the wishes of the Soviet Union and in anticipation of Soviet Russia's complete domination of the American continent.
+LJSpeech-1.1/wavs/LJ018-0168.wav|but proofs of Griffiths' guilt were at once apparent on entering his work-room.
+LJSpeech-1.1/wavs/LJ002-0200.wav|The marshal was supposed to be resident either within the prison or the rules.
+LJSpeech-1.1/wavs/LJ018-0019.wav|The stick and bag were his, but not the hat.
+LJSpeech-1.1/wavs/LJ036-0143.wav|He marked what, he thought was the intersection of Neches and Beckley on a map of Dallas with a large "X."
+LJSpeech-1.1/wavs/LJ028-0490.wav|He would have had to cross a deep moat, to scale a wall of burned bricks about twenty feet in thickness and perhaps three times as high,
+LJSpeech-1.1/wavs/LJ033-0007.wav|the circumstances surrounding Oswald's return to Irving, Texas, on Thursday, November twenty-one, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ008-0101.wav|exuberant in talk and hissing hot from Pie Corner, where she had taken her morning dose of gin-and-bitters.
+LJSpeech-1.1/wavs/LJ014-0054.wav|a maidservant, Sarah Thomas, murdered her mistress, an aged woman, by beating out her brains with a stone.
+LJSpeech-1.1/wavs/LJ047-0088.wav|We did not request the State Department to include Oswald on a list which would have resulted in advising us of any application for a passport
+LJSpeech-1.1/wavs/LJ045-0158.wav|He spent quite a bit of time putting away diapers and played with the children on the street.
+LJSpeech-1.1/wavs/LJ044-0074.wav|an agent of Bringuier's attempting to learn more about the true nature
+LJSpeech-1.1/wavs/LJ027-0180.wav|The general similarity of the three series is complete.
+LJSpeech-1.1/wavs/LJ046-0148.wav|that the safety of the President is or might be in danger, either at the present or in the future.
+LJSpeech-1.1/wavs/LJ027-0012.wav|All have the same ultimate substance
+LJSpeech-1.1/wavs/LJ042-0132.wav|Return to the United States. In view of the intensity of his earlier commitment to the Soviet Union,
+LJSpeech-1.1/wavs/LJ016-0123.wav|got hold of the step ladder used in lighting the gas, and which under our more careful supervision would have been, as now-a-days, chained up.
+LJSpeech-1.1/wavs/LJ045-0161.wav|He was upset over the fact that I would not answer him.
+LJSpeech-1.1/wavs/LJ001-0179.wav|even when the woodcuts are very rude indeed,
+LJSpeech-1.1/wavs/LJ021-0154.wav|is the program of Public Works provided for in the same Act and designed to put more men back to work,
+LJSpeech-1.1/wavs/LJ016-0136.wav|On the other hand, at the great convict establishments, such is the moral restraint of a systematic discipline,
+LJSpeech-1.1/wavs/LJ044-0006.wav|Although, as indicated above, the Commission has been unable to find any credible evidence
+LJSpeech-1.1/wavs/LJ012-0092.wav|This occurred in November eighteen thirty-four. The Custom House officials were in a state of consternation,
+LJSpeech-1.1/wavs/LJ024-0125.wav|This proposal of mine will not infringe in the slightest upon the civil or religious liberties so dear to every American.
+LJSpeech-1.1/wavs/LJ037-0156.wav|also examined the four cartridge cases found near the site of the homicide and compared them with the test cartridge cases fired from the Smith and Wesson revolver
+LJSpeech-1.1/wavs/LJ042-0046.wav|This should answer your question, and also give you a glimpse of my way of thinking. So you speak of advantages. Do you think that is why I am here?
+LJSpeech-1.1/wavs/LJ040-0230.wav|There are indications that he has suffered serious personality damage but if he can receive help quickly this might be repaired to some extent, end quote.
+LJSpeech-1.1/wavs/LJ040-0232.wav|Few social agencies even in New York were equipped to provide the kind of intensive treatment that he needed,
+LJSpeech-1.1/wavs/LJ025-0089.wav|The third distinction is based on a completely erroneous conception of the chemical differences
+LJSpeech-1.1/wavs/LJ023-0060.wav|and the powers given to the Congress to carry out those purposes can be best described by saying
+LJSpeech-1.1/wavs/LJ046-0062.wav|His friend and Special Assistant Kenneth O'Donnell, who accompanied him on his last visit to Dallas,
+LJSpeech-1.1/wavs/LJ016-0097.wav|which with Wakefield was utilized as a receptacle for convicts not going to Western Australia,
+LJSpeech-1.1/wavs/LJ001-0054.wav|Jenson, however, had many contemporaries who used beautiful type,
+LJSpeech-1.1/wavs/LJ046-0249.wav|about persons other than those who were obvious threats to the President.
+LJSpeech-1.1/wavs/LJ042-0037.wav|and under which, quote, art, culture and the sprit of man are subjected to commercial enterprising,
+LJSpeech-1.1/wavs/LJ005-0090.wav|the first by daily services, the latter by the appointment of schoolmasters and instruction in reading and writing.
+LJSpeech-1.1/wavs/LJ029-0166.wav|On November twenty a front page story reported that the streets on which the Presidential motorcade would travel included "Main and Stemmons Freeway."
+LJSpeech-1.1/wavs/LJ043-0035.wav|Shortly after his return from the Soviet Union, Oswald severed all relations with his mother;
+LJSpeech-1.1/wavs/LJ041-0142.wav|Once it had started down Elm Street toward the Triple Underpass, however,
+LJSpeech-1.1/wavs/LJ007-0036.wav|one man declared that the language of the condemned rooms was disgusting, that he was dying a death every day in being compelled to associate with such characters.
+LJSpeech-1.1/wavs/LJ015-0278.wav|commencing sham actions, and addressing formal applications, merely for the reply.
+LJSpeech-1.1/wavs/LJ016-0271.wav|The reply evinced equal satisfaction, and the speaker, with a profane oath, declared that he would like to act as Jack Ketch to the whole lot.
+LJSpeech-1.1/wavs/LJ007-0093.wav|The matter was still further complicated at Newgate by the presence within the walls of sham lunatics. Some of those included in the category
+LJSpeech-1.1/wavs/LJ009-0056.wav|The last of the four is said to have been a clergyman of the Church of England, condemned for forgery, "a miserable old man in a tattered suit of black.
+LJSpeech-1.1/wavs/LJ050-0098.wav|determination to use a means, other than legal or peaceful, to satisfy his grievance, end quote, within the meaning of the new criteria.
+LJSpeech-1.1/wavs/LJ018-0030.wav|Last of all, the cabman swore that he had bought the very hat found in the carriage for Müller at the hatter's, Walker's of Crawford Street.
+LJSpeech-1.1/wavs/LJ006-0104.wav|but its issue was precarious, and dependent on the good will of the wardsmen, who measured out the portions to each according to his eye,
+LJSpeech-1.1/wavs/LJ005-0229.wav|By another clause of the Jail Act, two justices were to be appointed to visit the prison at least thrice in every quarter, and "oftener if occasion required."
+LJSpeech-1.1/wavs/LJ034-0196.wav|Another person who saw the assassin as the shots were fired was Amos L. Euins, age fifteen,
+LJSpeech-1.1/wavs/LJ012-0105.wav|The police were of opinion that these robberies were both the work of the same hand.
+LJSpeech-1.1/wavs/LJ039-0008.wav|told Robert Oswald, Lee Harvey Oswald's brother, that Oswald had once threatened to shoot former Vice President Richard M. Nixon.
+LJSpeech-1.1/wavs/LJ011-0189.wav|the other brother was accordingly sent on a pretended mission to Shrigley to bring Mr. Turner on to London, whither Wakefield and Miss Turner also proceeded.
+LJSpeech-1.1/wavs/LJ029-0007.wav|this chapter reviews the motorcade through Dallas, the fleeting moments of the assassination,
+LJSpeech-1.1/wavs/LJ014-0329.wav|and so developed his business that in one year his transactions amounted to a couple of millions of pounds.
+LJSpeech-1.1/wavs/LJ014-0225.wav|A handsome sum was subscribed for the injured constable, who was disabled for life.
+LJSpeech-1.1/wavs/LJ016-0088.wav|working at the roof of the chapel on the female side.
+LJSpeech-1.1/wavs/LJ008-0127.wav|As we were crossing the press yard,
+LJSpeech-1.1/wavs/LJ001-0125.wav|this is the narrowing of the modern letters.
+LJSpeech-1.1/wavs/LJ006-0022.wav|At that time the mild and intelligent prison discipline in force in Pennsylvania, the legacy of the old Quaker immigrants,
+LJSpeech-1.1/wavs/LJ038-0245.wav|A photography expert with the FBI
+LJSpeech-1.1/wavs/LJ044-0017.wav|He distributed literature in downtown New Orleans on August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ032-0273.wav|(four) a photograph taken in the yard of Oswald's apartment showed him holding this rifle, and (five)
+LJSpeech-1.1/wavs/LJ001-0143.wav|For where these are boldly and carefully designed, and each letter is thoroughly individual in form,
+LJSpeech-1.1/wavs/LJ003-0005.wav|no steps taken to reduce the number of committals, and the governor was obliged to utilize the chapel as a day and night room.
+LJSpeech-1.1/wavs/LJ046-0169.wav|If the field office determines that the case should be subject to continuing review, PRS establishes a file
+LJSpeech-1.1/wavs/LJ033-0017.wav|who also worked at the Depository.
+LJSpeech-1.1/wavs/LJ038-0287.wav|Additional corroborative evidence.
+LJSpeech-1.1/wavs/LJ048-0079.wav|mutual understanding of FBI and agency jurisdictions, and an indicated willingness by the agency representative
+LJSpeech-1.1/wavs/LJ002-0017.wav|Trustworthy evidence is forthcoming to the effect that these high figures were constantly maintained for many months at a time.
+LJSpeech-1.1/wavs/LJ047-0231.wav|some indication that the person planned to take some action against the safety of the President of the United States or the Vice President. End quote.
+LJSpeech-1.1/wavs/LJ007-0018.wav|and that when the restraining influences of the ladies were absent, the female prisoners relapsed into immoral and uncleanly discourse.
+LJSpeech-1.1/wavs/LJ017-0047.wav|the rest of the available space was allotted by ticket, to secure which the greatest influence was necessary.
+LJSpeech-1.1/wavs/LJ014-0339.wav|asked Davidson and Gordon, a firm with which Cole was closely allied, whether the warrants meant goods or nothing.
+LJSpeech-1.1/wavs/LJ022-0046.wav|It is true that while business and industry are definitely better our relief rolls are still too large.
+LJSpeech-1.1/wavs/LJ010-0189.wav|He asked more than once whether the Queen was hurt, and acknowledged that the pistols were loaded with ball.
+LJSpeech-1.1/wavs/LJ028-0019.wav|The stars come out one by one and shine brighter than elsewhere as if to light you on your way.
+LJSpeech-1.1/wavs/LJ026-0021.wav|nitrogen is obtained from simple salts and the nutritive processes result in deoxidation;
+LJSpeech-1.1/wavs/LJ002-0283.wav|and denied admission to the "charity wards," which partook of all the benefits of bequests and donations to poor debtors.
+LJSpeech-1.1/wavs/LJ036-0047.wav|On November twenty-two, Mrs. Bledsoe came downtown to watch the Presidential motorcade.
+LJSpeech-1.1/wavs/LJ028-0112.wav|My father did that which no previous king had done.
+LJSpeech-1.1/wavs/LJ009-0176.wav|Seven other crimes, however, were still capital by law, and so continued till the passing of the Criminal Consolidation Acts of eighteen sixty-one.
+LJSpeech-1.1/wavs/LJ028-0423.wav|In eighteen twelve, James Claudius Rich, the British Resident at Baghdad, made the first complete examination of the ruins.
+LJSpeech-1.1/wavs/LJ003-0202.wav|These capital convicts, says Mr. Bennet, quote, lessened the ennui and despair of their situation by unbecoming merriment
+LJSpeech-1.1/wavs/LJ020-0108.wav|Other things besides rising dough get on quite as well without your standing by to watch them.
+LJSpeech-1.1/wavs/LJ014-0223.wav|The judge, in passing sentence of death, told him he richly deserved the punishment.
+LJSpeech-1.1/wavs/LJ028-0351.wav|As for Zopyrus he was considered by Darius to have surpassed, in the greatness of his achievements, all other Persians,
+LJSpeech-1.1/wavs/LJ028-0496.wav|rising higher and higher, like a great terraced, turreted mountain.
+LJSpeech-1.1/wavs/LJ032-0190.wav|On the other hand Stombaugh pointed out that fibers might retain their freshness if the rifle had been
+LJSpeech-1.1/wavs/LJ009-0181.wav|that authentic cases were known previous to the first cited act of criminals selling their own bodies to surgeons for dissection.
+LJSpeech-1.1/wavs/LJ050-0148.wav|the increased information supplied by other agencies will be wasted.
+LJSpeech-1.1/wavs/LJ010-0247.wav|The prisoner was conveyed without delay to the Home Office, and there examined by the Privy Council, which had been hastily summoned for the purpose.
+LJSpeech-1.1/wavs/LJ039-0224.wav|show that he possessed ample capability to commit the assassination.
+LJSpeech-1.1/wavs/LJ047-0108.wav|During the interview Quigley obtained background information from Oswald which was inconsistent with information already in the Bureau's possession.
+LJSpeech-1.1/wavs/LJ017-0158.wav|She had a little fortune of her own, some one thousand seven hundred pounds or one thousand eight hundred pounds,
+LJSpeech-1.1/wavs/LJ049-0183.wav|regarding such threats and that its Protective Research Section is not adequately staffed or equipped
+LJSpeech-1.1/wavs/LJ031-0057.wav|While Dr. Perry was performing the tracheotomy, Drs. Carrico and Ronald Jones made cutdowns on the President's right leg and left arm, respectively,
+LJSpeech-1.1/wavs/LJ004-0158.wav|All were in ill health; almost all were in rags; almost all were filthy in the extreme.
+LJSpeech-1.1/wavs/LJ043-0050.wav|As Marguerite Oswald testified, quote,
+LJSpeech-1.1/wavs/LJ023-0119.wav|that will refuse to amend the Constitution by the arbitrary exercise of judicial power -- amended by judicial say-so.
+LJSpeech-1.1/wavs/LJ021-0015.wav|The underlying necessity for such activity
+LJSpeech-1.1/wavs/LJ027-0125.wav|in the Python we find very tiny rudiments of the hindlimbs. Now,
+LJSpeech-1.1/wavs/LJ016-0153.wav|would make death a certainty, so limited and imperfect are the means generally available.
+LJSpeech-1.1/wavs/LJ040-0172.wav|Mrs. Siegel concluded that Lee, quote, just felt that his mother never gave a damn for him.
+LJSpeech-1.1/wavs/LJ040-0159.wav|who suffers under the impact of really existing emotional isolation
+LJSpeech-1.1/wavs/LJ028-0087.wav|Such was the appearance of the builder of the walls of Babylon.
+LJSpeech-1.1/wavs/LJ048-0233.wav|Two of the nine agents returned to their rooms. The seven others proceeded to an establishment called the Cellar Coffee House,
+LJSpeech-1.1/wavs/LJ038-0288.wav|The admissions made to Marina Oswald by her husband are an important element in the evidence that Lee Harvey Oswald fired the shot at General Walker.
+LJSpeech-1.1/wavs/LJ008-0199.wav|At the George public-house to the south of the drop, Sir W. Watkin Wynn, Baronet,
+LJSpeech-1.1/wavs/LJ029-0188.wav|Stevenson was jeered, jostled, and spat upon by hostile demonstrators outside the Dallas Memorial Auditorium Theater.
+LJSpeech-1.1/wavs/LJ012-0132.wav|and he with his father arranged that a messenger should call for the stuff with forged credentials, and anticipating the rightful owner.
+LJSpeech-1.1/wavs/LJ033-0139.wav|Oswald's Mannlicher-Carcano rifle, serial Number C two seven six six, which was also found on the sixth floor.
+LJSpeech-1.1/wavs/LJ048-0085.wav|the Commission believes that the liaison between all Federal agencies responsible for Presidential protection should be improved.
+LJSpeech-1.1/wavs/LJ006-0209.wav|the donation of a philanthropic gentleman, Captain Brown, but these, particularly the Bibles, bore little appearance of having been used.
+LJSpeech-1.1/wavs/LJ028-0283.wav|As soon therefore as he felt within himself that Babylon was fated to be taken, he went to Darius and asked him if he set a very high value on its conquest.
+LJSpeech-1.1/wavs/LJ016-0071.wav|Dissatisfied with this remuneration, he again took to the road, and tramped into Hampshire,
+LJSpeech-1.1/wavs/LJ043-0064.wav|which were largely of his own making.
+LJSpeech-1.1/wavs/LJ043-0039.wav|He also indicated to officials at the American Embassy in Moscow that his defection was motivated at least in part
+LJSpeech-1.1/wavs/LJ041-0182.wav|and believed that our Government did not have, quote, too much to offer, end quote, but was not in favor of, quote, the Communist way of life, end quote.
+LJSpeech-1.1/wavs/LJ005-0060.wav|The Society did not limit its remarks to the description of what had already been done
+LJSpeech-1.1/wavs/LJ036-0131.wav|And about that time an old lady, I think she was an old lady, I don't remember nothing but her sticking her head down past him in the door and said,
+LJSpeech-1.1/wavs/LJ026-0067.wav|We have here the direct absorption into the body proper of food-stuffs precisely as the animal takes in water and oxygen.
+LJSpeech-1.1/wavs/LJ012-0180.wav|The murderer explained that he had first fired a pistol at Weare's head, but the shot glanced off his cheek.
+LJSpeech-1.1/wavs/LJ047-0161.wav|Mrs. Paine told Hosty also
+LJSpeech-1.1/wavs/LJ008-0131.wav|the other he kept between his hands.
+LJSpeech-1.1/wavs/LJ034-0190.wav|Robert Edwards said that, while looking at the south side of the Depository Building shortly before the motorcade,
+LJSpeech-1.1/wavs/LJ041-0074.wav|John E. Donovan, one of his former officers, testified that Oswald thought, quote,
+LJSpeech-1.1/wavs/LJ022-0051.wav|In spite of the fact that unemployment remains a serious problem
+LJSpeech-1.1/wavs/LJ027-0166.wav|retain permanently this form, and are therefore called "perennibranchs," but the frog still passes on.
+LJSpeech-1.1/wavs/LJ038-0249.wav|Investigation determined that this photograph was taken approximately seven-tenths of a mile from Walker's house.
+LJSpeech-1.1/wavs/LJ034-0214.wav|Investigation has established that Altgens' picture was taken approximately two seconds after the firing of the shot
+LJSpeech-1.1/wavs/LJ046-0077.wav|Under our system, measures must be sought to afford security without impeding the President's performance of his many functions.
+LJSpeech-1.1/wavs/LJ006-0121.wav|for a petition of from one shilling, half pence to eight shillings, according to its length,
+LJSpeech-1.1/wavs/LJ037-0060.wav|That Mrs. Markham described the man who killed Patrolman Tippit as, quote, short, a little on the heavy side, end quote.
+LJSpeech-1.1/wavs/LJ023-0064.wav|Having in mind that in succeeding generations many other problems then undreamed of would become national problems
+LJSpeech-1.1/wavs/LJ005-0211.wav|the jail consisted of six cells, frequently so damp that the moisture trickled down the walls; there was not space for air or exercise,
+LJSpeech-1.1/wavs/LJ036-0121.wav|a twelve:fifteen p.m. pickup at Continental to Greyhound, unloaded at twelve:thirty p.m.,
+LJSpeech-1.1/wavs/LJ029-0002.wav|Chapter two. The Assassination: Part one.
+LJSpeech-1.1/wavs/LJ038-0010.wav|a recessed area extending about fifteen feet between the sidewalk and the front door of his store.
+LJSpeech-1.1/wavs/LJ023-0018.wav|We also became convinced that the only way to avoid a repetition of those dark days was to have a government with power to prevent
+LJSpeech-1.1/wavs/LJ018-0119.wav|His offer was not, however, accepted.
+LJSpeech-1.1/wavs/LJ002-0304.wav|The sheriff demanded four shillings, six pence for his liberate, the jailer six shillings, ten pence more, and the turnkey two shillings;
+LJSpeech-1.1/wavs/LJ019-0342.wav|So was the employment of prisoners in any position of trust or authority;
+LJSpeech-1.1/wavs/LJ043-0131.wav|As a result Oswald was not hired.
+LJSpeech-1.1/wavs/LJ035-0048.wav|I can't say whether he had gone on through that door [the lunchroom door] or not.
+LJSpeech-1.1/wavs/LJ047-0050.wav|Oswald again agreed to advise the FBI if he were approached under suspicious circumstances; however, he deprecated the possibility of this happening,
+LJSpeech-1.1/wavs/LJ027-0045.wav|careful study shows detailed internal as well as external similarities of structure. Such cases are "homologies".
+LJSpeech-1.1/wavs/LJ005-0139.wav|making an average of nineteen persons occupying each room.
+LJSpeech-1.1/wavs/LJ044-0060.wav|In view of the limited amount of public activity on Oswald's part before August nine, nineteen sixty-three,
+LJSpeech-1.1/wavs/LJ050-0070.wav|The FBI now transmits information on all defectors, a category which would, of course, have included Oswald.
+LJSpeech-1.1/wavs/LJ009-0275.wav|who bad been further upset by a letter threatening to shoot him when he appeared to perform his task.
+LJSpeech-1.1/wavs/LJ025-0066.wav|and his skepticism was the more justified since Ehrenberg in his elaborate and comprehensive work on the infusoria,
+LJSpeech-1.1/wavs/LJ039-0240.wav|On the basis of the evidence reviewed in this chapter, the Commission has found that Lee Harvey Oswald (one)
+LJSpeech-1.1/wavs/LJ029-0144.wav|In conformity with these arrangements, traffic proceeding west on Main is directed to turn right at Houston
+LJSpeech-1.1/wavs/LJ015-0117.wav|Sergeant Ballantine, who prosecuted,
+LJSpeech-1.1/wavs/LJ019-0322.wav|On the other hand, new and careful regulations were framed to secure the moral and material well-being of the inmates of the jails.
+LJSpeech-1.1/wavs/LJ028-0155.wav|Lumps of bitumen are found in great abundance in this river.
+LJSpeech-1.1/wavs/LJ011-0073.wav|several persons were sentenced to or suffered death for this crime.
+LJSpeech-1.1/wavs/LJ009-0210.wav|On the left side of the head the fatal mall,
+LJSpeech-1.1/wavs/LJ023-0134.wav|and reject the legislative powers which the courts have today assumed.
+LJSpeech-1.1/wavs/LJ026-0117.wav|are directly oxidized to excretions and, lacking nitrogen, cannot serve for making new animal protoplasm.
+LJSpeech-1.1/wavs/LJ014-0296.wav|He had made use of a piece of rope cut out from the sacking of his bedstead, and had tied his feet together with a silk pocket-handkerchief.
+LJSpeech-1.1/wavs/LJ017-0126.wav|He relied on the absence of the strychnia.
+LJSpeech-1.1/wavs/LJ034-0103.wav|This description most probably led to the radio alert sent to police cars at approximately twelve:forty-five p.m., which described the suspect as white,
+LJSpeech-1.1/wavs/LJ007-0140.wav|The primary object of committing a prisoner to jail, as the inspectors pointed out, was to deter not only the criminal himself, but others from crime,
+LJSpeech-1.1/wavs/LJ040-0231.wav|Lee Oswald never received that help.
+LJSpeech-1.1/wavs/LJ033-0039.wav|and one which provided an excuse for the carrying of a bulky package the following morning.
+LJSpeech-1.1/wavs/LJ050-0143.wav|county, and State law enforcement agencies in their districts.
+LJSpeech-1.1/wavs/LJ001-0093.wav|This experiment was so far successful that about eighteen fifty Messrs. Miller and Richard of Edinburgh
+LJSpeech-1.1/wavs/LJ048-0118.wav|He did not have a checklist of the tasks he was expected to accomplish, either by his own efforts or with the cooperation of local authorities.
+LJSpeech-1.1/wavs/LJ005-0176.wav|which possessed the right of trying criminals for various offenses.
+LJSpeech-1.1/wavs/LJ003-0158.wav|from whom still higher fees were exacted, with the same discreditable idea of swelling the revenues of the prison.
+LJSpeech-1.1/wavs/LJ047-0115.wav|Several days later, the Bureau received additional evidence that Oswald had lied to Agent Quigley.
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_val_filelist.txt b/PyTorch/SpeechSynthesis/Tacotron2/filelists/ljs_audio_text_val_filelist.txt
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index 00000000..6f1c3fad
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@@ -0,0 +1,100 @@
+LJSpeech-1.1/wavs/LJ022-0023.wav|The overwhelming majority of people in this country know how to sift the wheat from the chaff in what they hear and what they read.
+LJSpeech-1.1/wavs/LJ043-0030.wav|If somebody did that to me, a lousy trick like that, to take my wife away, and all the furniture, I would be mad as hell, too.
+LJSpeech-1.1/wavs/LJ005-0201.wav|as is shown by the report of the Commissioners to inquire into the state of the municipal corporations in eighteen thirty-five.
+LJSpeech-1.1/wavs/LJ001-0110.wav|Even the Caslon type when enlarged shows great shortcomings in this respect:
+LJSpeech-1.1/wavs/LJ003-0345.wav|All the committee could do in this respect was to throw the responsibility on others.
+LJSpeech-1.1/wavs/LJ007-0154.wav|These pungent and well-grounded strictures applied with still greater force to the unconvicted prisoner, the man who came to the prison innocent, and still uncontaminated,
+LJSpeech-1.1/wavs/LJ018-0098.wav|and recognized as one of the frequenters of the bogus law-stationers. His arrest led to that of others.
+LJSpeech-1.1/wavs/LJ047-0044.wav|Oswald was, however, willing to discuss his contacts with Soviet authorities. He denied having any involvement with Soviet intelligence agencies
+LJSpeech-1.1/wavs/LJ031-0038.wav|The first physician to see the President at Parkland Hospital was Dr. Charles J. Carrico, a resident in general surgery.
+LJSpeech-1.1/wavs/LJ048-0194.wav|during the morning of November twenty-two prior to the motorcade.
+LJSpeech-1.1/wavs/LJ049-0026.wav|On occasion the Secret Service has been permitted to have an agent riding in the passenger compartment with the President.
+LJSpeech-1.1/wavs/LJ004-0152.wav|although at Mr. Buxton's visit a new jail was in process of erection, the first step towards reform since Howard's visitation in seventeen seventy-four.
+LJSpeech-1.1/wavs/LJ008-0278.wav|or theirs might be one of many, and it might be considered necessary to "make an example."
+LJSpeech-1.1/wavs/LJ043-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter seven. Lee Harvey Oswald:
+LJSpeech-1.1/wavs/LJ009-0114.wav|Mr. Wakefield winds up his graphic but somewhat sensational account by describing another religious service, which may appropriately be inserted here.
+LJSpeech-1.1/wavs/LJ028-0506.wav|A modern artist would have difficulty in doing such accurate work.
+LJSpeech-1.1/wavs/LJ050-0168.wav|with the particular purposes of the agency involved. The Commission recognizes that this is a controversial area
+LJSpeech-1.1/wavs/LJ039-0223.wav|Oswald's Marine training in marksmanship, his other rifle experience and his established familiarity with this particular weapon
+LJSpeech-1.1/wavs/LJ029-0032.wav|According to O'Donnell, quote, we had a motorcade wherever we went, end quote.
+LJSpeech-1.1/wavs/LJ031-0070.wav|Dr. Clark, who most closely observed the head wound,
+LJSpeech-1.1/wavs/LJ034-0198.wav|Euins, who was on the southwest corner of Elm and Houston Streets testified that he could not describe the man he saw in the window.
+LJSpeech-1.1/wavs/LJ026-0068.wav|Energy enters the plant, to a small extent,
+LJSpeech-1.1/wavs/LJ039-0075.wav|once you know that you must put the crosshairs on the target and that is all that is necessary.
+LJSpeech-1.1/wavs/LJ004-0096.wav|the fatal consequences whereof might be prevented if the justices of the peace were duly authorized
+LJSpeech-1.1/wavs/LJ005-0014.wav|Speaking on a debate on prison matters, he declared that
+LJSpeech-1.1/wavs/LJ012-0161.wav|he was reported to have fallen away to a shadow.
+LJSpeech-1.1/wavs/LJ018-0239.wav|His disappearance gave color and substance to evil reports already in circulation that the will and conveyance above referred to
+LJSpeech-1.1/wavs/LJ019-0257.wav|Here the tread-wheel was in use, there cellular cranks, or hard-labor machines.
+LJSpeech-1.1/wavs/LJ028-0008.wav|you tap gently with your heel upon the shoulder of the dromedary to urge her on.
+LJSpeech-1.1/wavs/LJ024-0083.wav|This plan of mine is no attack on the Court;
+LJSpeech-1.1/wavs/LJ042-0129.wav|No night clubs or bowling alleys, no places of recreation except the trade union dances. I have had enough.
+LJSpeech-1.1/wavs/LJ036-0103.wav|The police asked him whether he could pick out his passenger from the lineup.
+LJSpeech-1.1/wavs/LJ046-0058.wav|During his Presidency, Franklin D. Roosevelt made almost four hundred journeys and traveled more than three hundred fifty thousand miles.
+LJSpeech-1.1/wavs/LJ014-0076.wav|He was seen afterwards smoking and talking with his hosts in their back parlor, and never seen again alive.
+LJSpeech-1.1/wavs/LJ002-0043.wav|long narrow rooms -- one thirty-six feet, six twenty-three feet, and the eighth eighteen,
+LJSpeech-1.1/wavs/LJ009-0076.wav|We come to the sermon.
+LJSpeech-1.1/wavs/LJ017-0131.wav|even when the high sheriff had told him there was no possibility of a reprieve, and within a few hours of execution.
+LJSpeech-1.1/wavs/LJ046-0184.wav|but there is a system for the immediate notification of the Secret Service by the confining institution when a subject is released or escapes.
+LJSpeech-1.1/wavs/LJ014-0263.wav|When other pleasures palled he took a theatre, and posed as a munificent patron of the dramatic art.
+LJSpeech-1.1/wavs/LJ042-0096.wav|(old exchange rate) in addition to his factory salary of approximately equal amount
+LJSpeech-1.1/wavs/LJ049-0050.wav|Hill had both feet on the car and was climbing aboard to assist President and Mrs. Kennedy.
+LJSpeech-1.1/wavs/LJ019-0186.wav|seeing that since the establishment of the Central Criminal Court, Newgate received prisoners for trial from several counties,
+LJSpeech-1.1/wavs/LJ028-0307.wav|then let twenty days pass, and at the end of that time station near the Chaldasan gates a body of four thousand.
+LJSpeech-1.1/wavs/LJ012-0235.wav|While they were in a state of insensibility the murder was committed.
+LJSpeech-1.1/wavs/LJ034-0053.wav|reached the same conclusion as Latona that the prints found on the cartons were those of Lee Harvey Oswald.
+LJSpeech-1.1/wavs/LJ014-0030.wav|These were damnatory facts which well supported the prosecution.
+LJSpeech-1.1/wavs/LJ015-0203.wav|but were the precautions too minute, the vigilance too close to be eluded or overcome?
+LJSpeech-1.1/wavs/LJ028-0093.wav|but his scribe wrote it in the manner customary for the scribes of those days to write of their royal masters.
+LJSpeech-1.1/wavs/LJ002-0018.wav|The inadequacy of the jail was noticed and reported upon again and again by the grand juries of the city of London,
+LJSpeech-1.1/wavs/LJ028-0275.wav|At last, in the twentieth month,
+LJSpeech-1.1/wavs/LJ012-0042.wav|which he kept concealed in a hiding-place with a trap-door just under his bed.
+LJSpeech-1.1/wavs/LJ011-0096.wav|He married a lady also belonging to the Society of Friends, who brought him a large fortune, which, and his own money, he put into a city firm,
+LJSpeech-1.1/wavs/LJ036-0077.wav|Roger D. Craig, a deputy sheriff of Dallas County,
+LJSpeech-1.1/wavs/LJ016-0318.wav|Other officials, great lawyers, governors of prisons, and chaplains supported this view.
+LJSpeech-1.1/wavs/LJ013-0164.wav|who came from his room ready dressed, a suspicious circumstance, as he was always late in the morning.
+LJSpeech-1.1/wavs/LJ027-0141.wav|is closely reproduced in the life-history of existing deer. Or, in other words,
+LJSpeech-1.1/wavs/LJ028-0335.wav|accordingly they committed to him the command of their whole army, and put the keys of their city into his hands.
+LJSpeech-1.1/wavs/LJ031-0202.wav|Mrs. Kennedy chose the hospital in Bethesda for the autopsy because the President had served in the Navy.
+LJSpeech-1.1/wavs/LJ021-0145.wav|From those willing to join in establishing this hoped-for period of peace,
+LJSpeech-1.1/wavs/LJ016-0288.wav|"Müller, Müller, He's the man," till a diversion was created by the appearance of the gallows, which was received with continuous yells.
+LJSpeech-1.1/wavs/LJ028-0081.wav|Years later, when the archaeologists could readily distinguish the false from the true,
+LJSpeech-1.1/wavs/LJ018-0081.wav|his defense being that he had intended to commit suicide, but that, on the appearance of this officer who had wronged him,
+LJSpeech-1.1/wavs/LJ021-0066.wav|together with a great increase in the payrolls, there has come a substantial rise in the total of industrial profits
+LJSpeech-1.1/wavs/LJ009-0238.wav|After this the sheriffs sent for another rope, but the spectators interfered, and the man was carried back to jail.
+LJSpeech-1.1/wavs/LJ005-0079.wav|and improve the morals of the prisoners, and shall insure the proper measure of punishment to convicted offenders.
+LJSpeech-1.1/wavs/LJ035-0019.wav|drove to the northwest corner of Elm and Houston, and parked approximately ten feet from the traffic signal.
+LJSpeech-1.1/wavs/LJ036-0174.wav|This is the approximate time he entered the roominghouse, according to Earlene Roberts, the housekeeper there.
+LJSpeech-1.1/wavs/LJ046-0146.wav|The criteria in effect prior to November twenty-two, nineteen sixty-three, for determining whether to accept material for the PRS general files
+LJSpeech-1.1/wavs/LJ017-0044.wav|and the deepest anxiety was felt that the crime, if crime there had been, should be brought home to its perpetrator.
+LJSpeech-1.1/wavs/LJ017-0070.wav|but his sporting operations did not prosper, and he became a needy man, always driven to desperate straits for cash.
+LJSpeech-1.1/wavs/LJ014-0020.wav|He was soon afterwards arrested on suspicion, and a search of his lodgings brought to light several garments saturated with blood;
+LJSpeech-1.1/wavs/LJ016-0020.wav|He never reached the cistern, but fell back into the yard, injuring his legs severely.
+LJSpeech-1.1/wavs/LJ045-0230.wav|when he was finally apprehended in the Texas Theatre. Although it is not fully corroborated by others who were present,
+LJSpeech-1.1/wavs/LJ035-0129.wav|and she must have run down the stairs ahead of Oswald and would probably have seen or heard him.
+LJSpeech-1.1/wavs/LJ008-0307.wav|afterwards express a wish to murder the Recorder for having kept them so long in suspense.
+LJSpeech-1.1/wavs/LJ008-0294.wav|nearly indefinitely deferred.
+LJSpeech-1.1/wavs/LJ047-0148.wav|On October twenty-five,
+LJSpeech-1.1/wavs/LJ008-0111.wav|They entered a "stone cold room," and were presently joined by the prisoner.
+LJSpeech-1.1/wavs/LJ034-0042.wav|that he could only testify with certainty that the print was less than three days old.
+LJSpeech-1.1/wavs/LJ037-0234.wav|Mrs. Mary Brock, the wife of a mechanic who worked at the station, was there at the time and she saw a white male,
+LJSpeech-1.1/wavs/LJ040-0002.wav|Chapter seven. Lee Harvey Oswald: Background and Possible Motives, Part one.
+LJSpeech-1.1/wavs/LJ045-0140.wav|The arguments he used to justify his use of the alias suggest that Oswald may have come to think that the whole world was becoming involved
+LJSpeech-1.1/wavs/LJ012-0035.wav|the number and names on watches, were carefully removed or obliterated after the goods passed out of his hands.
+LJSpeech-1.1/wavs/LJ012-0250.wav|On the seventh July, eighteen thirty-seven,
+LJSpeech-1.1/wavs/LJ016-0179.wav|contracted with sheriffs and conveners to work by the job.
+LJSpeech-1.1/wavs/LJ016-0138.wav|at a distance from the prison.
+LJSpeech-1.1/wavs/LJ027-0052.wav|These principles of homology are essential to a correct interpretation of the facts of morphology.
+LJSpeech-1.1/wavs/LJ031-0134.wav|On one occasion Mrs. Johnson, accompanied by two Secret Service agents, left the room to see Mrs. Kennedy and Mrs. Connally.
+LJSpeech-1.1/wavs/LJ019-0273.wav|which Sir Joshua Jebb told the committee he considered the proper elements of penal discipline.
+LJSpeech-1.1/wavs/LJ014-0110.wav|At the first the boxes were impounded, opened, and found to contain many of O'Connor's effects.
+LJSpeech-1.1/wavs/LJ034-0160.wav|on Brennan's subsequent certain identification of Lee Harvey Oswald as the man he saw fire the rifle.
+LJSpeech-1.1/wavs/LJ038-0199.wav|eleven. If I am alive and taken prisoner,
+LJSpeech-1.1/wavs/LJ014-0010.wav|yet he could not overcome the strange fascination it had for him, and remained by the side of the corpse till the stretcher came.
+LJSpeech-1.1/wavs/LJ033-0047.wav|I noticed when I went out that the light was on, end quote,
+LJSpeech-1.1/wavs/LJ040-0027.wav|He was never satisfied with anything.
+LJSpeech-1.1/wavs/LJ048-0228.wav|and others who were present say that no agent was inebriated or acted improperly.
+LJSpeech-1.1/wavs/LJ003-0111.wav|He was in consequence put out of the protection of their internal law, end quote. Their code was a subject of some curiosity.
+LJSpeech-1.1/wavs/LJ008-0258.wav|Let me retrace my steps, and speak more in detail of the treatment of the condemned in those bloodthirsty and brutally indifferent days,
+LJSpeech-1.1/wavs/LJ029-0022.wav|The original plan called for the President to spend only one day in the State, making whirlwind visits to Dallas, Fort Worth, San Antonio, and Houston.
+LJSpeech-1.1/wavs/LJ004-0045.wav|Mr. Sturges Bourne, Sir James Mackintosh, Sir James Scarlett, and William Wilberforce.
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/img/tacotron2_fp16_loss.png b/PyTorch/SpeechSynthesis/Tacotron2/img/tacotron2_fp16_loss.png
new file mode 100644
index 0000000000000000000000000000000000000000..fb96869414a5f6fb43d3c066c067180a6740fc56
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/inference.py b/PyTorch/SpeechSynthesis/Tacotron2/inference.py
new file mode 100644
index 00000000..b73682d9
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/inference.py
@@ -0,0 +1,214 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+from tacotron2.text import text_to_sequence
+import models
+import torch
+import argparse
+import numpy as np
+from scipy.io.wavfile import write
+
+import sys
+sys.path.append('waveglow/')
+
+import time
+from dllogger.logger import LOGGER
+import dllogger.logger as dllg
+from dllogger.autologging import log_hardware, log_args
+
+
+def parse_args(parser):
+    """
+    Parse commandline arguments.
+    """
+    parser.add_argument('-i', '--input', type=str, default="",
+                        help='full path to the input text (phareses separated by new line); \
+                        if not provided then use default text')
+    parser.add_argument('-o', '--output', required=True,
+                        help='output folder to save audio (file per phrase)')
+    parser.add_argument('--tacotron2', type=str, default="",
+                        help='full path to the Tacotron2 model checkpoint file')
+    parser.add_argument('--mel-file', type=str, default="",
+                        help='set if using mel spectrograms instead of Tacotron2 model')
+    parser.add_argument('--waveglow', required=True,
+                        help='full path to the WaveGlow model checkpoint file')
+    parser.add_argument('--old-waveglow', action='store_true',
+                        help='set if WaveGlow checkpoint is from GitHub.com/NVIDIA/waveglow')
+    parser.add_argument('-s', '--sigma-infer', default=0.6, type=float)
+    parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,
+                        help='Sampling rate')
+    parser.add_argument('--fp16-run', action='store_true',
+                        help='inference in fp16')
+    parser.add_argument('--log-file', type=str, default='nvlog.json',
+                        help='Filename for logging')
+
+
+    return parser
+
+
+def load_checkpoint(checkpoint_path, model_name):
+    assert os.path.isfile(checkpoint_path)
+
+    print("Loading checkpoint '{}'".format(checkpoint_path))
+    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+    model.load_state_dict(checkpoint_dict['state_dict'])
+    print("Loaded '{}' checkpoint '{}'" .format(model_name, checkpoint_path))
+    return model
+
+
+def checkpoint_from_distributed(state_dict):
+    """
+    Checks whether checkpoint was generated by DistributedDataParallel. DDP
+    wraps model in additional "module.", it needs to be unwrapped for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    ret = False
+    for key, _ in state_dict.items():
+        if key.find('module.') != -1:
+            ret = True
+            break
+    return ret
+
+
+def unwrap_distributed(state_dict):
+    """
+    Unwraps model from DistributedDataParallel.
+    DDP wraps model in additional "module.", it needs to be removed for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    new_state_dict = {}
+    for key, value in state_dict.items():
+        new_key = key.replace('module.', '')
+        new_state_dict[new_key] = value
+    return new_state_dict
+
+
+def load_and_setup_model(model_name, parser, checkpoint, fp16_run):
+    model_parser = models.parse_model_args(model_name, parser, add_help=False)
+    model_args, _ = model_parser.parse_known_args()
+
+    model_config = models.get_model_config(model_name, model_args)
+    model = models.get_model(model_name, model_config, to_fp16=fp16_run, to_cuda=True, training=False)
+
+    if checkpoint is not None:
+        state_dict = torch.load(checkpoint)['state_dict']
+        if checkpoint_from_distributed(state_dict):
+            state_dict = unwrap_distributed(state_dict)
+
+        model.load_state_dict(state_dict)
+    model.eval()
+
+    return model
+
+
+def main():
+    """
+    Launches text to speech (inference).
+    Inference is executed on a single GPU.
+    """
+    parser = argparse.ArgumentParser(
+        description='PyTorch Tacotron 2 Inference')
+    parser = parse_args(parser)
+    args, _ = parser.parse_known_args()
+
+    LOGGER.set_model_name("Tacotron2_PyT")
+    LOGGER.set_backends([
+        dllg.StdOutBackend(log_file=None,
+                           logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1),
+        dllg.JsonBackend(log_file=args.log_file,
+                         logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1)
+    ])
+    LOGGER.register_metric("tacotron2_items_per_sec", metric_scope=dllg.TRAIN_ITER_SCOPE)
+    LOGGER.register_metric("waveglow_items_per_sec", metric_scope=dllg.TRAIN_ITER_SCOPE)
+
+    log_hardware()
+    log_args(args)
+
+    # tacotron2 model filepath was specified
+    if args.tacotron2:
+        # Setup Tacotron2
+        tacotron2 = load_and_setup_model('Tacotron2', parser, args.tacotron2, args.fp16_run)
+    # file with mel spectrogram was specified
+    elif args.mel_file:
+        mel = torch.load(args.mel_file)
+        mel = torch.autograd.Variable(mel.cuda())
+        mel = torch.unsqueeze(mel, 0)
+
+    # Setup WaveGlow
+    if args.old_waveglow:
+        waveglow = torch.load(args.waveglow)['model']
+        waveglow = waveglow.remove_weightnorm(waveglow)
+        waveglow = waveglow.cuda()
+        waveglow.eval()
+    else:
+        waveglow = load_and_setup_model('WaveGlow', parser, args.waveglow, args.fp16_run)
+
+    texts = []
+    try:
+        f = open(args.input, 'r')
+        texts = f.readlines()
+    except:
+        print("Could not read file. Using default text.")
+        texts = ["The forms of printed letters should be beautiful, and\
+        that their arrangement on the page should be reasonable and\
+        a help to the shapeliness of the letters themselves."]
+
+    for i, text in enumerate(texts):
+
+        LOGGER.iteration_start()
+
+        sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
+        sequence = torch.autograd.Variable(
+            torch.from_numpy(sequence)).cuda().long()
+
+        if args.tacotron2:
+            tacotron2_t0 = time.time()
+            with torch.no_grad():
+                _, mel, _, _ = tacotron2.inference(sequence)
+            tacotron2_t1 = time.time()
+            tacotron2_infer_perf = sequence.size(1)/(tacotron2_t1-tacotron2_t0)
+            LOGGER.log(key="tacotron2_items_per_sec", value=tacotron2_infer_perf)
+
+        waveglow_t0 = time.time()
+        with torch.no_grad():
+            audio = waveglow.infer(mel, sigma=args.sigma_infer)
+            audio = audio.float()
+        waveglow_t1 = time.time()
+        waveglow_infer_perf = audio[0].size(0)/(waveglow_t1-waveglow_t0)
+
+        audio_path = args.output + "audio_"+str(i)+".wav"
+        write(audio_path, args.sampling_rate, audio[0].data.cpu().numpy())
+
+        LOGGER.log(key="waveglow_items_per_sec", value=waveglow_infer_perf)
+        LOGGER.iteration_stop()
+
+    LOGGER.finish()
+
+if __name__ == '__main__':
+    main()
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/inference_perf.py b/PyTorch/SpeechSynthesis/Tacotron2/inference_perf.py
new file mode 100644
index 00000000..20bf6096
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/inference_perf.py
@@ -0,0 +1,176 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+from tacotron2.text import text_to_sequence
+import models
+import torch
+import argparse
+import numpy as np
+from scipy.io.wavfile import write
+import json
+import time
+
+from inference import checkpoint_from_distributed, unwrap_distributed, load_and_setup_model
+
+from dllogger.logger import LOGGER
+import dllogger.logger as dllg
+from dllogger import tags
+from dllogger.autologging import log_hardware, log_args
+
+def parse_args(parser):
+    """
+    Parse commandline arguments.
+    """
+    parser.add_argument('-m', '--model-name', type=str, default='', required=True,
+                        help='Model to train')
+    parser.add_argument('--input-text', type=str, default=None,
+                        help='Path to tensor containing text (when running Tacotron2)')
+    parser.add_argument('--input-mels', type=str, default=None,
+                        help='Path to tensor containing mels (when running WaveGlow)')
+    parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,
+                        help='Sampling rate')
+    parser.add_argument('--fp16-run', action='store_true',
+                        help='inference in fp16')
+    parser.add_argument('-bs', '--batch-size', type=int, default=1)
+    parser.add_argument('--create-benchmark', action='store_true')
+
+    return parser
+
+
+def collate_text(batch):
+    """Collate's training batch from normalized text and mel-spectrogram
+    PARAMS
+    ------
+    batch: [text_normalized]
+    """
+    # Right zero-pad all one-hot text sequences to max input length
+    input_lengths, ids_sorted_decreasing = torch.sort(
+        torch.LongTensor([len(x) for x in batch]),
+        dim=0, descending=True)
+    max_input_len = input_lengths[0]
+
+    text_padded = torch.LongTensor(len(batch), max_input_len)
+    text_padded.zero_()
+    for i in range(len(ids_sorted_decreasing)):
+        text = batch[ids_sorted_decreasing[i]]
+        text_padded[i, :text.size(0)] = text
+
+    return text_padded, input_lengths
+
+
+def main():
+    """
+    Launches inference benchmark.
+    Inference is executed on a single GPU.
+    """
+    parser = argparse.ArgumentParser(
+        description='PyTorch Tacotron 2 Inference')
+    parser = parse_args(parser)
+    args, _ = parser.parse_known_args()
+
+    log_file = ("qa/baselines/" + args.model_name + "_inferbench_BS" + str(args.batch_size) + "_FP" + ("16" if args.fp16_run else "32") +
+                "_DGX1_16GB_1GPU_single" + ".json") \
+                if args.create_benchmark else \
+                   (args.model_name + "_infer_BS" + str(args.batch_size) + "_FP" + ("16" if args.fp16_run else "32") + \
+                   "_DGX1_16GB_1GPU_single" + ".json")
+
+    LOGGER.set_model_name("Tacotron2_PyT")
+    LOGGER.set_backends([
+        dllg.StdOutBackend(log_file=None,
+                           logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1),
+        dllg.JsonBackend(log_file,
+                         logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1)
+    ])
+    LOGGER.register_metric("items_per_sec",
+                           metric_scope=dllg.TRAIN_ITER_SCOPE)
+
+    log_hardware()
+    log_args(args)
+
+    # ## uncomment to generate new padded text
+    # texts = []
+    # f = open('qa/ljs_text_train_subset_2500.txt', 'r')
+    # texts = f.readlines()
+    # sequence = []
+    # for i, text in enumerate(texts):
+    #     sequence.append(torch.IntTensor(text_to_sequence(text, ['english_cleaners'])))
+
+    # text_padded, input_lengths = collate_text(sequence)
+    # text_padded = torch.autograd.Variable(text_padded).cuda().long()
+    # torch.save(text_padded, "qa/text_padded.pt")
+    # torch.save(input_lengths, "qa/input_lengths.pt")
+
+    model = load_and_setup_model(args.model_name, parser, None, args.fp16_run)
+
+    dry_runs = 3
+    num_iters = (16+dry_runs) if args.create_benchmark else (1+dry_runs)
+
+    for i in range(num_iters):
+        ## skipping the first inference which is slower
+        if i >= dry_runs:
+            LOGGER.iteration_start()
+
+        if args.model_name == 'Tacotron2':
+            text_padded = torch.load(args.input_text)
+            text_padded = text_padded[:args.batch_size]
+            text_padded = torch.autograd.Variable(text_padded).cuda().long()
+
+            t0 = time.time()
+            with torch.no_grad():
+                _, mels, _, _ = model.inference(text_padded)
+            t1 = time.time()
+            inference_time= t1 - t0
+            num_items = text_padded.size(0)*text_padded.size(1)
+
+            # # ## uncomment to generate new padded mels
+            # torch.save(mels, "qa/mel_padded.pt")
+
+        if args.model_name == 'WaveGlow':
+            mel_padded = torch.load(args.input_mels)
+            mel_padded = torch.cat((mel_padded, mel_padded, mel_padded, mel_padded))
+            mel_padded = mel_padded[:args.batch_size]
+            mel_padded = mel_padded.cuda()
+
+            if args.fp16_run:
+                mel_padded = mel_padded.half()
+
+            t0 = time.time()
+            with torch.no_grad():
+                audios = model.infer(mel_padded)
+                audios = audios.float()
+            t1 = time.time()
+            inference_time = t1 - t0
+            num_items = audios.size(0)*audios.size(1)
+
+        if i >= dry_runs:
+            LOGGER.log(key="items_per_sec", value=(num_items/inference_time))
+            LOGGER.iteration_stop()
+
+    LOGGER.finish()
+
+if __name__ == '__main__':
+    main()
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/loss_functions.py b/PyTorch/SpeechSynthesis/Tacotron2/loss_functions.py
new file mode 100644
index 00000000..768574ab
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/loss_functions.py
@@ -0,0 +1,44 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import torch
+import torch.nn as nn
+from tacotron2.loss_function import Tacotron2Loss
+from waveglow.loss_function import WaveGlowLoss
+
+
+def get_loss_function(loss_function, sigma=1.0):
+    if loss_function == 'Tacotron2':
+        loss = Tacotron2Loss()
+    elif loss_function == 'WaveGlow':
+        loss = WaveGlowLoss(sigma=sigma)
+    else:
+        raise NotImplementedError(
+            "unknown loss function requested: {}".format(loss_function))
+
+    loss.cuda()
+    return loss
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/models.py b/PyTorch/SpeechSynthesis/Tacotron2/models.py
new file mode 100644
index 00000000..6c3eb7fb
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/models.py
@@ -0,0 +1,134 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+from tacotron2.model import Tacotron2
+from waveglow.model import WaveGlow
+from tacotron2.arg_parser import parse_tacotron2_args
+from waveglow.arg_parser import parse_waveglow_args
+import torch
+
+
+def parse_model_args(model_name, parser, add_help=False):
+    if model_name == 'Tacotron2':
+        return parse_tacotron2_args(parser, add_help)
+    if model_name == 'WaveGlow':
+        return parse_waveglow_args(parser, add_help)
+    else:
+        raise NotImplementedError(model_name)
+
+
+def batchnorm_to_float(module):
+    """Converts LSTMCells to FP32"""
+    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+        module.float()
+    for child in module.children():
+        batchnorm_to_float(child)
+    return module
+
+
+def lstmcell_to_float(module):
+    """Converts batch norm modules to FP32"""
+    if isinstance(module, torch.nn.LSTMCell):
+        module.float()
+    for child in module.children():
+        lstmcell_to_float(child)
+    return module
+
+
+def get_model(model_name, model_config, to_fp16, to_cuda, training=True):
+    """ Code chooses a model based on name"""
+    model = None
+    if model_name == 'Tacotron2':
+        model = Tacotron2(**model_config)
+    elif model_name == 'WaveGlow':
+        model = WaveGlow(**model_config)
+    else:
+        raise NotImplementedError(model_name)
+    if to_fp16:
+        model = batchnorm_to_float(model.half())
+        if training:
+            model = lstmcell_to_float(model)
+        if model_name == "WaveGlow":
+            for k in model.convinv:
+                k.float()
+    if to_cuda:
+        model = model.cuda()
+    return model
+
+
+def get_model_config(model_name, args):
+    """ Code chooses a model based on name"""
+    if model_name == 'Tacotron2':
+        model_config = dict(
+            # optimization
+            mask_padding=args.mask_padding,
+            # audio
+            n_mel_channels=args.n_mel_channels,
+            # symbols
+            n_symbols=args.n_symbols,
+            symbols_embedding_dim=args.symbols_embedding_dim,
+            # encoder
+            encoder_kernel_size=args.encoder_kernel_size,
+            encoder_n_convolutions=args.encoder_n_convolutions,
+            encoder_embedding_dim=args.encoder_embedding_dim,
+            # attention
+            attention_rnn_dim=args.attention_rnn_dim,
+            attention_dim=args.attention_dim,
+            # attention location
+            attention_location_n_filters=args.attention_location_n_filters,
+            attention_location_kernel_size=args.attention_location_kernel_size,
+            # decoder
+            n_frames_per_step=args.n_frames_per_step,
+            decoder_rnn_dim=args.decoder_rnn_dim,
+            prenet_dim=args.prenet_dim,
+            max_decoder_steps=args.max_decoder_steps,
+            gate_threshold=args.gate_threshold,
+            p_attention_dropout=args.p_attention_dropout,
+            p_decoder_dropout=args.p_decoder_dropout,
+            # postnet
+            postnet_embedding_dim=args.postnet_embedding_dim,
+            postnet_kernel_size=args.postnet_kernel_size,
+            postnet_n_convolutions=args.postnet_n_convolutions,
+            decoder_no_early_stopping=args.decoder_no_early_stopping
+        )
+        return model_config
+    elif model_name == 'WaveGlow':
+        model_config = dict(
+            n_mel_channels=args.n_mel_channels,
+            n_flows=args.flows,
+            n_group=args.groups,
+            n_early_every=args.early_every,
+            n_early_size=args.early_size,
+            WN_config=dict(
+                n_layers=args.wn_layers,
+                kernel_size=args.wn_kernel_size,
+                n_channels=args.wn_channels
+            )
+        )
+        return model_config
+    else:
+        raise NotImplementedError(model_name)
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/multiproc.py b/PyTorch/SpeechSynthesis/Tacotron2/multiproc.py
new file mode 100644
index 00000000..8c5b6612
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/multiproc.py
@@ -0,0 +1,85 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import sys
+import subprocess
+
+import torch
+
+
+def main():
+    argslist = list(sys.argv)[1:]
+    world_size = torch.cuda.device_count()
+
+    if '--world-size' in argslist:
+        argslist[argslist.index('--world-size') + 1] = str(world_size)
+    else:
+        argslist.append('--world-size')
+        argslist.append(str(world_size))
+
+    workers = []
+
+    for i in range(world_size):
+        if '--rank' in argslist:
+            argslist[argslist.index('--rank') + 1] = str(i)
+        else:
+            argslist.append('--rank')
+            argslist.append(str(i))
+        stdout = None if i == 0 else subprocess.DEVNULL
+        worker = subprocess.Popen(
+            [str(sys.executable)] + argslist, stdout=stdout)
+        workers.append(worker)
+
+    returncode = 0
+    try:
+        pending = len(workers)
+        while pending > 0:
+            for worker in workers:
+                try:
+                    worker_returncode = worker.wait(1)
+                except subprocess.TimeoutExpired:
+                    continue
+                pending -= 1
+                if worker_returncode != 0:
+                    if returncode != 1:
+                        for worker in workers:
+                            worker.terminate()
+                    returncode = 1
+
+    except KeyboardInterrupt:
+        print('Pressed CTRL-C, TERMINATING')
+        for worker in workers:
+            worker.terminate()
+        for worker in workers:
+            worker.wait()
+        raise
+
+    sys.exit(returncode)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/phrase.txt b/PyTorch/SpeechSynthesis/Tacotron2/phrase.txt
new file mode 100644
index 00000000..8999934d
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/phrase.txt
@@ -0,0 +1 @@
+The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves.
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_1GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_1GPU.sh
new file mode 100644
index 00000000..1cc5fae9
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_1GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python train.py -m Tacotron2 -o output/ --fp16-run -lr 1e-3 --epochs 2001 -bs 80 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.3
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_4GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_4GPU.sh
new file mode 100644
index 00000000..7481deb5
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_4GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m Tacotron2 -o output/ --fp16-run -lr 1e-3 --epochs 2001 -bs 80 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.3
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_8GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_8GPU.sh
new file mode 100644
index 00000000..7481deb5
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP16_DGX1_16GB_8GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m Tacotron2 -o output/ --fp16-run -lr 1e-3 --epochs 2001 -bs 80 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.3
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_1GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_1GPU.sh
new file mode 100644
index 00000000..3467775b
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_1GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python train.py -m Tacotron2 -o output/ -lr 1e-3 --epochs 2001 -bs 48 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.1
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_4GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_4GPU.sh
new file mode 100644
index 00000000..2c32b2b1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_4GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m Tacotron2 -o output/ -lr 1e-3 --epochs 2001 -bs 48 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.1
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_8GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_8GPU.sh
new file mode 100644
index 00000000..2c32b2b1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_tacotron2_FP32_DGX1_16GB_8GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m Tacotron2 -o output/ -lr 1e-3 --epochs 2001 -bs 48 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.1
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_1GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_1GPU.sh
new file mode 100644
index 00000000..2c0c77c3
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_1GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python train.py -m WaveGlow -o output/ --fp16-run -lr 1e-4 --epochs 2001 -bs 8 --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_4GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_4GPU.sh
new file mode 100644
index 00000000..1aa37fc0
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_4GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m WaveGlow -o output/ --fp16-run -lr 1e-4 --epochs 2001 -bs 8 --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_8GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_8GPU.sh
new file mode 100644
index 00000000..1aa37fc0
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP16_DGX1_16GB_8GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m WaveGlow -o output/ --fp16-run -lr 1e-4 --epochs 2001 -bs 8 --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_1GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_1GPU.sh
new file mode 100644
index 00000000..bc4d3ac8
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_1GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python train.py -m WaveGlow -o output/ -lr 1e-4 --epochs 2001 -bs 4 --segment-length  8000 --weight-decay 0 --grad-clip-thresh 3.4028234663852886e+38 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_4GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_4GPU.sh
new file mode 100644
index 00000000..c689ab8c
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_4GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m WaveGlow -o output/ -lr 1e-4 --epochs 2001 -bs 4 --segment-length  8000 --weight-decay 0 --grad-clip-thresh 3.4028234663852886e+38 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_8GPU.sh b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_8GPU.sh
new file mode 100644
index 00000000..c689ab8c
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/platform/train_waveglow_FP32_DGX1_16GB_8GPU.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m WaveGlow -o output/ -lr 1e-4 --epochs 2001 -bs 4 --segment-length  8000 --weight-decay 0 --grad-clip-thresh 3.4028234663852886e+38 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file output/nvlog.json
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..55384d27
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 2 \
+    -bs 80 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.1 \
+    --epochs-per-checkpoint 250 \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp16-short-loss.json \
+    -p ./tacotron2_fp16-short-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..ea0f0999
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_epochtrain_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 2 \
+    -bs 48 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.1 \
+    --epochs-per-checkpoint 250
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp32-short-loss.json \
+    -p ./tacotron2_fp32-short-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP16_DGX1_16GB_1GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP16_DGX1_16GB_1GPU_single.sh
new file mode 100644
index 00000000..6f034fec
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP16_DGX1_16GB_1GPU_single.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -e
+
+## uncomment to generate new baseline; will be created in qa/baselines/ ##
+## python inference_perf.py -m Tacotron2 -bs=20 --input-text qa/text_padded.pt  --create-benchmark
+
+python inference_perf.py -m Tacotron2 -bs=1 --fp16-run --decoder-no-early-stopping --input-text qa/text_padded.pt
+python qa/check_curves.py \
+    -b qa/tacotron2_fp16-infer-bs1.json \
+    Tacotron2_infer_BS1_FP16_DGX1_16GB_1GPU_single.json \
+    -g "iter" \
+    -k "items_per_sec" \
+    --eps 0.001 \
+    --damping 1 \
+    --sigma 12.0 \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP32_DGX1_16GB_1GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP32_DGX1_16GB_1GPU_single.sh
new file mode 100644
index 00000000..031126c8
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_inferbench_FP32_DGX1_16GB_1GPU_single.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -e
+
+## uncomment to generate new baseline; will be created in qa/baselines/ ##
+## python inference_perf.py -m Tacotron2 -bs=20 --input-text qa/text_padded.pt  --create-benchmark
+
+python inference_perf.py -m Tacotron2 -bs=1 --decoder-no-early-stopping --input-text qa/text_padded.pt
+python qa/check_curves.py \
+    -b qa/tacotron2_fp32-infer-bs1.json \
+    Tacotron2_infer_BS1_FP32_DGX1_16GB_1GPU_single.json \
+    -g "iter" \
+    -k "items_per_sec" \
+    --eps 0.001 \
+    --damping 1 \
+    --sigma 12.0 \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..770f8b79
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 10 \
+    -bs 80 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.3 \
+    --epochs-per-checkpoint 250 \
+    --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt \
+    --dataset-path ./ \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp16-perf.json \
+    -g "epoch" \
+    -k "train_epoch_items/sec" \
+    --skip 9 \
+    --damping 1 \
+    --eps 0 \
+    ./nvlog.json \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..4dce13c2
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_Tacotron2_trainbench_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 10 \
+    -bs 48 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.3 \
+    --epochs-per-checkpoint 250 \
+    --training-files filelists/ljs_audio_text_train_subset_2500_filelist.txt \
+    --dataset-path ./
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp32-perf.json \
+    -g "epoch" \
+    -k "train_epoch_items/sec" \
+    --skip 9 \
+    --damping 1 \
+    --eps 0 \
+    ./nvlog.json \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..528643ab
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 2 \
+    -bs 8 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250 \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp16-short-loss.json \
+    -p ./waveglow_fp16-short-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..aa5f7a03
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_epochtrain_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 2 \
+    -bs 4 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp32-short-loss.json \
+    -p ./waveglow_fp32-short-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP16_DGX1_16GB_1GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP16_DGX1_16GB_1GPU_single.sh
new file mode 100644
index 00000000..77335a34
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP16_DGX1_16GB_1GPU_single.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -e
+
+## uncomment to generate new baseline; will be created in qa/baselines/ ##
+## python inference_perf.py -m Tacotron2 -bs=20 --input-text qa/text_padded.pt  --create-benchmark
+
+python inference_perf.py -m WaveGlow -bs=1 --fp16-run --input-mels qa/mel_padded.pt
+python qa/check_curves.py \
+    -b qa/waveglow_fp16-infer-bs1.json \
+    WaveGlow_infer_BS1_FP16_DGX1_16GB_1GPU_single.json \
+    -g "iter" \
+    -k "items_per_sec" \
+    --eps 0.001 \
+    --damping 1 \
+    --sigma 54.0 \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP32_DGX1_16GB_1GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP32_DGX1_16GB_1GPU_single.sh
new file mode 100644
index 00000000..34d30432
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_inferbench_FP32_DGX1_16GB_1GPU_single.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+set -e
+
+## uncomment to generate new baseline; will be created in qa/baselines/ ##
+## python inference_perf.py -m Tacotron2 -bs=20 --input-text qa/text_padded.pt  --create-benchmark
+
+python inference_perf.py -m WaveGlow -bs=1 --input-mels qa/mel_padded.pt
+python qa/check_curves.py \
+    -b qa/waveglow_fp32-infer-bs1.json \
+    WaveGlow_infer_BS1_FP32_DGX1_16GB_1GPU_single.json \
+    -g "iter" \
+    -k "items_per_sec" \
+    --eps 0.001 \
+    --damping 1 \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..a8832de1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 10 \
+    -bs 8 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250 \
+    --cudnn-benchmark=True \
+    --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt \
+    --dataset-path ./ \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp16-perf.json \
+    -g "epoch" \
+    -k "train_epoch_items/sec" \
+    --skip 9 \
+    --damping 1 \
+    --eps 0 \
+    ./nvlog.json \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..ad56ad29
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L2_WaveGlow_trainbench_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 10 \
+    -bs 4 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250 \
+    --cudnn-benchmark=True \
+    --training-files filelists/ljs_audio_text_train_subset_1250_filelist.txt \
+    --dataset-path ./
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp32-perf.json \
+    -g "epoch" \
+    -k "train_epoch_items/sec" \
+    --skip 9 \
+    --damping 1 \
+    --eps 0 \
+    ./nvlog.json \
+    --fail low
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..4585041c
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 1001 \
+    -bs 80 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.1 \
+    --epochs-per-checkpoint 250 \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp16-long-loss.json \
+    -p ./tacotron2_fp16-long-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..3faa3c16
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_Tacotron2_fulltrain_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m Tacotron2 \
+    -o ./ \
+    -lr 1e-3 \
+    --epochs 1001 \
+    -bs 48 \
+    --weight-decay 1e-6 \
+    --grad-clip-thresh 1.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --anneal-steps 500 1000 1500 \
+    --anneal-factor 0.1 \
+    --epochs-per-checkpoint 250
+
+python qa/check_curves.py \
+    -b qa/tacotron2_fp32-long-loss.json \
+    -p ./tacotron2_fp32-long-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP16_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP16_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..bceeffb3
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP16_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 1001 \
+    -bs 8 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250 \
+    --fp16-run
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp16-long-loss.json \
+    -p ./waveglow_fp16-long-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP32_DGX1_16GB_8GPU_single.sh b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP32_DGX1_16GB_8GPU_single.sh
new file mode 100755
index 00000000..cf0f5ab7
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/L3_WaveGlow_fulltrain_FP32_DGX1_16GB_8GPU_single.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+
+set -e
+
+bash scripts/prepare_dataset.sh
+
+python -m multiproc train.py \
+    -m WaveGlow \
+    -o ./ \
+    -lr 1e-4 \
+    --epochs 1001 \
+    -bs 4 \
+    --segment-length 8000 \
+    --weight-decay 0 \
+    --grad-clip-thresh 65504.0 \
+    --cudnn-benchmark=True \
+    --log-file ./nvlog.json \
+    --epochs-per-checkpoint 250
+
+python qa/check_curves.py \
+    -b qa/waveglow_fp32-long-loss.json \
+    -p ./waveglow_fp32-long-loss.png \
+    -g iter \
+    -k "train_iteration_loss" \
+    ./nvlog.json \
+    --fail high
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS1_FP16_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS1_FP16_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..78fbf164
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS1_FP16_DGX1_16GB_1GPU_single.json
@@ -0,0 +1,90 @@
+{
+    "run": {
+        "cpu_info": {
+            "num": 80,
+            "name": "Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz"
+        },
+        "mem_info": {
+            "ram": "503G"
+        },
+        "gpu_info": {
+            "driver_version": "410.79",
+            "num": 8,
+            "name": [
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB"
+            ],
+            "mem": [
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB",
+                "16130 MiB"
+            ]
+        },
+        "args": {
+            "model_name": "Tacotron2",
+            "input_text": "qa/text_padded.pt",
+            "input_mels": null,
+            "sampling_rate": 22050,
+            "fp16_run": true,
+            "batch_size": 1,
+            "create_benchmark": true
+        }
+    },
+    "epoch": {
+        "x": []
+    },
+    "iter": {
+        "x": [
+            [
+                0,
+                1,
+                2,
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+                9,
+                10,
+                11,
+                12,
+                13,
+                14,
+                15
+            ]
+        ],
+        "items_per_sec": [
+            [
+                156.32130205658873,
+                153.5082393743664,
+                148.03801851923782,
+                147.82735573646224,
+                146.59112327972724,
+                146.4482421373458,
+                148.1966524662319,
+                138.57728431172202,
+                144.63969615432546,
+                147.47893385206478,
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new file mode 100755
index 00000000..814ddc87
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS4_FP32_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS4_FP32_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..c9cf2a06
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS8_FP16_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/Tacotron2_inferbench_BS8_FP16_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..6520a93d
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS1_FP16_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS1_FP16_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..135ab054
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS1_FP32_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS1_FP32_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..e447b375
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS4_FP32_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS4_FP32_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..0bc6e574
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS8_FP16_DGX1_16GB_1GPU_single.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS8_FP16_DGX1_16GB_1GPU_single.json
new file mode 100755
index 00000000..eac8ae34
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+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/baselines/WaveGlow_inferbench_BS8_FP16_DGX1_16GB_1GPU_single.json
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+        },
+        "mem_info": {
+            "ram": "503G"
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+        "gpu_info": {
+            "driver_version": "410.79",
+            "num": 8,
+            "name": [
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
+                "Tesla V100-SXM2-16GB",
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/check_curves.py b/PyTorch/SpeechSynthesis/Tacotron2/qa/check_curves.py
new file mode 100644
index 00000000..d6ebb27e
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/check_curves.py
@@ -0,0 +1,170 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import argparse
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+
+
+def crop_length(*args):
+    min_len = min([len(a) for a in args])
+    return tuple([a[:min_len] for a in args])
+
+
+def load_benchmark(path):
+    with open(path) as file:
+        data = json.loads(file.read())
+    tile_size = data["tile_size"]
+    data = np.array([np.array(d) for d in data["data"]])
+    return data, tile_size
+
+
+def check_threshold(data, mean, std, sigma, skip, fail_high):
+    data = data[skip:]
+    mean = mean[skip:]
+    std = std[skip:]
+    normal = (data - mean) / std
+    aggr = np.argmax if fail_high else np.argmin
+    comp = (lambda v: v > sigma) if fail_high else (lambda v: v < -sigma)
+    ix = aggr(normal)
+    val = normal[ix]
+    if comp(val):
+        return False, (val, data[ix], mean[ix], std[ix])
+    return True, None
+
+
+def plot_data(data, mean, std, skip):
+    x = np.arange(skip, len(data))
+    data = data[skip:]
+    mean = mean[skip:]
+    std = std[skip:]
+    y = (data - mean) / std
+    plt.plot(x, y, color=(0.5, 0.5, 0.75, 0.25))
+
+
+def smooth_exp(data, damping):
+    mean_b = 0.
+    result = []
+    for v in data:
+        mean_b_ = mean_b * (1. - damping) + v * damping
+        result.append(mean_b_)
+        mean_b = mean_b_
+    result = np.array(result)
+    return result
+
+
+def tile_mean(d, tile_size):
+    d = np.array(d)
+    tile_n = len(d) // tile_size
+    d = d[:tile_n * tile_size]
+    d = d.reshape(tile_n, tile_size)
+    d = d.mean(-1)
+    return d
+
+
+def main():
+    parser = argparse.ArgumentParser(description="nvlog curve checker")
+    parser.add_argument("--benchmark", "-b", required=True,
+                        help="Path to benchmark data.")
+    parser.add_argument("--plot", "-p", type=str, required=False, default=None)
+    parser.add_argument("files", nargs="*")
+    parser.add_argument("--sigma", type=float, default=3.,
+                        help="Sigma threshold for failure")
+    parser.add_argument("--damping", type=float, default=0.001,
+                        help="Damping factor for exponential average")
+    parser.add_argument("--eps", type=float, default=0.001,
+                        help="Minimal value for std to avoid division by zero")
+    parser.add_argument("--key", "-k", type=str, required=True,
+                        help="Key to extract from nvlog[\"iter\"]")
+    parser.add_argument("--group", "-g", type=str, required=True,
+                        help="Which nvlog group to check for key", choices=("iter", "epoch"))
+    parser.add_argument("--skip", type=int, default=0,
+                        help="Number of iterations to skip during comparison")
+    parser.add_argument("--fail", "-f", type=str, required=True,
+                        help="Whether to check for values which are too low or too high", choices=("low", "high"))
+    args = parser.parse_args(sys.argv[1:])
+    fail_high = args.fail == "high"
+
+    benchmark, tile_size = load_benchmark(args.benchmark)
+    benchmark = np.array([
+        smooth_exp(v, args.damping)
+        for v in benchmark
+    ])
+    mean, std = benchmark.mean(0), benchmark.std(0) + args.eps
+
+    if args.plot is not None:
+        plt.figure(figsize=(16, 9), facecolor="white")
+
+    ok = True
+    outlier = None
+    for f in args.files:
+        with open(f) as file:
+            data = json.loads(file.read())
+        data = data[args.group][args.key]
+        if args.group == "iter":
+            data = np.array([v for l in data for v in l])
+        data = tile_mean(data, tile_size)
+        data = smooth_exp(data, args.damping)
+        data, mean_crop, std_crop = crop_length(data, mean, std)
+        ok_, outlier_ = check_threshold(
+            data, mean_crop, std_crop, args.sigma, args.skip, fail_high)
+        ok = ok and ok_
+        if not ok_:
+            if outlier is None:
+                outlier = outlier_
+            else:
+                if np.abs(outlier_[0]) > np.abs(outlier[0]):
+                    outlier = outlier_
+        if args.plot:
+            plot_data(data, mean_crop, std_crop, args.skip)
+
+    threshold = args.sigma if fail_high else -args.sigma
+    if args.plot is not None:
+        plt.plot((args.skip, len(mean) - 1), (threshold, threshold),
+                 color=(0.75, 0.5, 0.5, 1.0), label="threshold")
+        plt.xlabel("iteration")
+        plt.ylabel(f"excess above benchmark (in multiples of σ)")
+        plt.legend()
+        plt.savefig(args.plot)
+    if ok:
+        print("&&&& PASSED")
+        sys.exit(0)
+    else:
+        print(f"value: {outlier[1]}")
+        print(f"mean: {outlier[2]}")
+        print(f"std: {outlier[3]}")
+        comp_sign = ">" if fail_high else "<"
+        print(
+            f'(value - mean) / std = {outlier[0]} ({comp_sign} threshold {threshold})')
+        print("&&&& FAILED")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/input_lengths.pt b/PyTorch/SpeechSynthesis/Tacotron2/qa/input_lengths.pt
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-full-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-full-loss.json
new file mode 100644
index 00000000..7d0250db
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-full-loss.json
@@ -0,0 +1,16037 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs1.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs1.json
new file mode 100644
index 00000000..27cbacb1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs1.json
@@ -0,0 +1,53 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs8.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs8.json
new file mode 100644
index 00000000..33cce13a
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-infer-bs8.json
@@ -0,0 +1,53 @@
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\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-perf.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-perf.json
new file mode 100644
index 00000000..cc58507c
--- /dev/null
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-short-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-short-loss.json
new file mode 100644
index 00000000..9bd4985a
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp16-short-loss.json
@@ -0,0 +1,16037 @@
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\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-full-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-full-loss.json
new file mode 100644
index 00000000..853b12f0
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-full-loss.json
@@ -0,0 +1,16037 @@
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\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs1.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs1.json
new file mode 100644
index 00000000..b57e366f
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs1.json
@@ -0,0 +1,53 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs4.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs4.json
new file mode 100644
index 00000000..3a8f46b2
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-infer-bs4.json
@@ -0,0 +1,53 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-perf.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-perf.json
new file mode 100644
index 00000000..accb3f96
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-perf.json
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-short-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-short-loss.json
new file mode 100644
index 00000000..832ab529
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/tacotron2_fp32-short-loss.json
@@ -0,0 +1,16037 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/text_padded.pt b/PyTorch/SpeechSynthesis/Tacotron2/qa/text_padded.pt
new file mode 100644
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-full-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-full-loss.json
new file mode 100644
index 00000000..47ead7e8
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-full-loss.json
@@ -0,0 +1,16037 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs1.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs1.json
new file mode 100644
index 00000000..74c6ae57
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs1.json
@@ -0,0 +1,53 @@
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\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs8.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs8.json
new file mode 100644
index 00000000..af08e709
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-infer-bs8.json
@@ -0,0 +1,53 @@
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+}
\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-perf.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-perf.json
new file mode 100644
index 00000000..f56f56c0
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-perf.json
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-short-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-short-loss.json
new file mode 100644
index 00000000..de95252f
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp16-short-loss.json
@@ -0,0 +1,16037 @@
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\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-full-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-full-loss.json
new file mode 100644
index 00000000..cbf30f63
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-full-loss.json
@@ -0,0 +1,16037 @@
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-infer-bs1.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-infer-bs1.json
new file mode 100644
index 00000000..0fc8b449
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-infer-bs1.json
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-infer-bs4.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-infer-bs4.json
new file mode 100644
index 00000000..766d145d
--- /dev/null
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-perf.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-perf.json
new file mode 100644
index 00000000..32f73647
--- /dev/null
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diff --git a/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-short-loss.json b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-short-loss.json
new file mode 100644
index 00000000..7b3b54ba
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/qa/waveglow_fp32-short-loss.json
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+            -4.321140289306641,
+            -3.920367479324341,
+            -3.9998650550842285,
+            -3.954658269882202,
+            -4.076074123382568,
+            -4.141256332397461,
+            -4.0377678871154785,
+            -4.073906898498535,
+            -4.011902332305908,
+            -4.2708024978637695,
+            -4.094426155090332,
+            -4.2151198387146,
+            -4.3201494216918945,
+            -4.057845115661621,
+            -4.224267959594727,
+            -4.078060626983643,
+            -4.33290433883667,
+            -4.096004486083984,
+            -4.336763858795166,
+            -4.2156476974487305,
+            -4.116093158721924,
+            -4.122811317443848,
+            -4.220597743988037,
+            -4.272743225097656,
+            -4.124396324157715,
+            -4.408039093017578,
+            -4.068218231201172,
+            -4.353282928466797,
+            -4.16970682144165,
+            -4.272268295288086,
+            -4.307881832122803,
+            -4.250618934631348,
+            -4.358263969421387,
+            -4.299128532409668,
+            -4.289323806762695,
+            -4.317338466644287,
+            -3.8952431678771973,
+            -3.998185157775879,
+            -4.20953369140625,
+            -4.204343795776367,
+            -4.218940258026123,
+            -4.227054119110107,
+            -4.430464267730713,
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+            -3.9339776039123535,
+            -4.080575942993164,
+            -4.165947437286377,
+            -3.8939428329467773,
+            -4.179314136505127,
+            -4.236013412475586,
+            -4.06934928894043,
+            -3.9475715160369873,
+            -4.384787559509277,
+            -4.069636344909668,
+            -4.187922954559326,
+            -4.128095626831055,
+            -4.2017669677734375,
+            -4.080737590789795,
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+            -4.212646484375,
+            -4.081161975860596,
+            -3.885728597640991,
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+            -3.8932454586029053,
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+            -4.060749530792236,
+            -4.126290798187256,
+            -4.27032995223999,
+            -4.050243377685547,
+            -4.244298934936523,
+            -4.140697956085205,
+            -4.387523174285889,
+            -4.259603500366211,
+            -4.138591766357422,
+            -4.372316360473633,
+            -4.253814220428467,
+            -4.257273197174072,
+            -4.387909889221191,
+            -4.282580375671387,
+            -4.199916839599609,
+            -4.35098934173584,
+            -4.6534504890441895,
+            -4.252825736999512,
+            -4.257375717163086,
+            -3.939178705215454,
+            -4.0916314125061035,
+            -4.1250834465026855,
+            -4.109988212585449,
+            -4.053674697875977,
+            -4.154060363769531,
+            -4.024816989898682,
+            -4.166953086853027,
+            -4.219053745269775
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/requirements.txt b/PyTorch/SpeechSynthesis/Tacotron2/requirements.txt
new file mode 100644
index 00000000..3bcef4f6
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/requirements.txt
@@ -0,0 +1,6 @@
+matplotlib
+numpy
+inflect
+librosa
+scipy
+Unidecode
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/build.sh b/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/build.sh
new file mode 100644
index 00000000..1211ee2b
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/build.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build . --rm -t tacotron2
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/interactive.sh b/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/interactive.sh
new file mode 100644
index 00000000..af554a03
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/scripts/docker/interactive.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -it --rm --ipc=host -v $PWD:/workspace/tacotron2/ tacotron2 bash
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/scripts/prepare_dataset.sh b/PyTorch/SpeechSynthesis/Tacotron2/scripts/prepare_dataset.sh
new file mode 100755
index 00000000..3c6c13d1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/scripts/prepare_dataset.sh
@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+
+set -e
+
+DATADIR="LJSpeech-1.1"
+BZ2ARCHIVE="${DATADIR}.tar.bz2"
+ENDPOINT="http://data.keithito.com/data/speech/$BZ2ARCHIVE"
+FILELISTSDIR="filelists"
+FULLLIST="$FILELISTSDIR/ljs_audio_text_filelist.txt"
+TESTLIST="$FILELISTSDIR/ljs_audio_text_test_filelist.txt"
+TRAINLIST="$FILELISTSDIR/ljs_audio_text_train_filelist.txt"
+VALLIST="$FILELISTSDIR/ljs_audio_text_val_filelist.txt"
+SUBSETLIST="$FILELISTSDIR/ljs_audio_text_train_subset_filelist.txt"
+
+if [ ! -d "$DATADIR" ]; then
+  echo "dataset is missing, unpacking ..."
+  if [ ! -f "$BZ2ARCHIVE" ]; then
+    echo "dataset archive is missing, downloading ..."
+    wget "$ENDPOINT"
+  fi
+  tar jxvf "$BZ2ARCHIVE"
+fi
+
+if [ ! -d "$FILELISTSDIR" ]; then
+  echo "missing filelists directory, regenerating ..."
+  mkdir "$FILELISTSDIR"
+  if [ ! -f "$FULLLIST" ]; then
+    cat "$DATADIR/metadata.csv" | sed "s%^\([^|]*\)|[^|]*|\([^|]*\)$%$DATADIR/wavs/\1.wav|\2%g" \
+      > $FULLLIST
+  fi
+  if [ ! -f "$TRAINLIST" ]; then
+    head -n 12500 "$FULLLIST" > $TRAINLIST
+  fi
+  if [ ! -f "$TESTLIST" ]; then
+    head -n 13000 "$FULLLIST" | tail -n 500 "$FULLLIST" > $TESTLIST
+  fi
+  if [ ! -f "$VALLIST" ]; then
+    head -n 13100 "$FULLLIST" | tail -n 100 "$FULLLIST" > $VALLIST
+  fi
+  if [ ! -f "$SUBSETLIST" ]; then
+    head -n 1250 "$TRAINLIST" > $SUBSETLIST
+  fi
+fi
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_tacotron2.sh b/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_tacotron2.sh
new file mode 100644
index 00000000..6823a3b2
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_tacotron2.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m Tacotron2 -o ./output/ -lr 1e-3 --epochs 2001 -bs 80 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-benchmark=True --log-file ./output/nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.1 --fp16-run
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_waveglow.sh b/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_waveglow.sh
new file mode 100644
index 00000000..ca1d21f3
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/scripts/train_waveglow.sh
@@ -0,0 +1,2 @@
+mkdir -p output
+python -m multiproc train.py -m WaveGlow -o ./output/ -lr 1e-4 --epochs 2001 -bs 8 --segment-length  8000 --weight-decay 0 --grad-clip-thresh 65504.0 --epochs-per-checkpoint 50 --cudnn-benchmark=True --log-file ./output/nvlog.json --fp16-run
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/arg_parser.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/arg_parser.py
new file mode 100644
index 00000000..396273b1
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/arg_parser.py
@@ -0,0 +1,108 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import argparse
+
+from tacotron2.text import symbols
+
+
+def parse_tacotron2_args(parent, add_help=False):
+    """
+    Parse commandline arguments.
+    """
+    parser = argparse.ArgumentParser(parents=[parent], add_help=add_help)
+
+    # misc parameters
+    parser.add_argument('--mask-padding', default=False, type=bool,
+                        help='Use mask padding')
+    parser.add_argument('--n-mel-channels', default=80, type=int,
+                        help='Number of bins in mel-spectrograms')
+
+    # symbols parameters
+    global symbols
+    len_symbols = len(symbols)
+    symbols = parser.add_argument_group('symbols parameters')
+    symbols.add_argument('--n-symbols', default=len_symbols, type=int,
+                         help='Number of symbols in dictionary')
+    symbols.add_argument('--symbols-embedding-dim', default=512, type=int,
+                         help='Input embedding dimension')
+
+    # encoder parameters
+    encoder = parser.add_argument_group('encoder parameters')
+    encoder.add_argument('--encoder-kernel-size', default=5, type=int,
+                         help='Encoder kernel size')
+    encoder.add_argument('--encoder-n-convolutions', default=3, type=int,
+                         help='Number of encoder convolutions')
+    encoder.add_argument('--encoder-embedding-dim', default=512, type=int,
+                         help='Encoder embedding dimension')
+
+    # decoder parameters
+    decoder = parser.add_argument_group('decoder parameters')
+    decoder.add_argument('--n-frames-per-step', default=1,
+                         type=int,
+                         help='Number of frames processed per step') # currently only 1 is supported
+    decoder.add_argument('--decoder-rnn-dim', default=1024, type=int,
+                         help='Number of units in decoder LSTM')
+    decoder.add_argument('--prenet-dim', default=256, type=int,
+                         help='Number of ReLU units in prenet layers')
+    decoder.add_argument('--max-decoder-steps', default=1000, type=int,
+                         help='Maximum number of output mel spectrograms')
+    decoder.add_argument('--gate-threshold', default=0.5, type=float,
+                         help='Probability threshold for stop token')
+    decoder.add_argument('--p-attention-dropout', default=0.1, type=float,
+                         help='Dropout probability for attention LSTM')
+    decoder.add_argument('--p-decoder-dropout', default=0.1, type=float,
+                         help='Dropout probability for decoder LSTM')
+    decoder.add_argument('--decoder-no-early-stopping', action='store_true',
+                         help='Stop decoding once all samples are finished')
+
+    # attention parameters
+    attention = parser.add_argument_group('attention parameters')
+    attention.add_argument('--attention-rnn-dim', default=1024, type=int,
+                           help='Number of units in attention LSTM')
+    attention.add_argument('--attention-dim', default=128, type=int,
+                           help='Dimension of attention hidden representation')
+
+    # location layer parameters
+    location = parser.add_argument_group('location parameters')
+    location.add_argument(
+        '--attention-location-n-filters', default=32, type=int,
+        help='Number of filters for location-sensitive attention')
+    location.add_argument(
+        '--attention-location-kernel-size', default=31, type=int,
+        help='Kernel size for location-sensitive attention')
+
+    # Mel-post processing network parameters
+    postnet = parser.add_argument_group('postnet parameters')
+    postnet.add_argument('--postnet-embedding-dim', default=512, type=int,
+                         help='Postnet embedding dimension')
+    postnet.add_argument('--postnet-kernel-size', default=5, type=int,
+                         help='Postnet kernel size')
+    postnet.add_argument('--postnet-n-convolutions', default=5, type=int,
+                         help='Number of postnet convolutions')
+
+    return parser
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/data_function.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/data_function.py
new file mode 100644
index 00000000..0ee3da8a
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/data_function.py
@@ -0,0 +1,155 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import random
+import numpy as np
+import torch
+import torch.utils.data
+
+import common.layers as layers
+from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu
+from tacotron2.text import text_to_sequence
+
+class TextMelLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio,text pairs
+        2) normalizes text and converts them to sequences of one-hot vectors
+        3) computes mel-spectrograms from audio files.
+    """
+    def __init__(self, dataset_path, audiopaths_and_text, args):
+        self.audiopaths_and_text = load_filepaths_and_text(dataset_path, audiopaths_and_text)
+        self.text_cleaners = args.text_cleaners
+        self.max_wav_value = args.max_wav_value
+        self.sampling_rate = args.sampling_rate
+        self.load_mel_from_disk = args.load_mel_from_disk
+        self.stft = layers.TacotronSTFT(
+            args.filter_length, args.hop_length, args.win_length,
+            args.n_mel_channels, args.sampling_rate, args.mel_fmin,
+            args.mel_fmax)
+        random.seed(1234)
+        random.shuffle(self.audiopaths_and_text)
+
+    def get_mel_text_pair(self, audiopath_and_text):
+        # separate filename and text
+        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
+        len_text = len(text)
+        text = self.get_text(text)
+        mel = self.get_mel(audiopath)
+        return (text, mel, len_text)
+
+    def get_mel(self, filename):
+        if not self.load_mel_from_disk:
+            audio, sampling_rate = load_wav_to_torch(filename)
+            if sampling_rate != self.stft.sampling_rate:
+                raise ValueError("{} {} SR doesn't match target {} SR".format(
+                    sampling_rate, self.stft.sampling_rate))
+            audio_norm = audio / self.max_wav_value
+            audio_norm = audio_norm.unsqueeze(0)
+            audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+            melspec = self.stft.mel_spectrogram(audio_norm)
+            melspec = torch.squeeze(melspec, 0)
+        else:
+            melspec = torch.from_numpy(np.load(filename))
+            assert melspec.size(0) == self.stft.n_mel_channels, (
+                'Mel dimension mismatch: given {}, expected {}'.format(
+                    melspec.size(0), self.stft.n_mel_channels))
+
+        return melspec
+
+    def get_text(self, text):
+        text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
+        return text_norm
+
+    def __getitem__(self, index):
+        return self.get_mel_text_pair(self.audiopaths_and_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_and_text)
+
+
+class TextMelCollate():
+    """ Zero-pads model inputs and targets based on number of frames per setep
+    """
+    def __init__(self, n_frames_per_step):
+        self.n_frames_per_step = n_frames_per_step
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text and mel-spectrogram
+        PARAMS
+        ------
+        batch: [text_normalized, mel_normalized]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        input_lengths, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([len(x[0]) for x in batch]),
+            dim=0, descending=True)
+        max_input_len = input_lengths[0]
+
+        text_padded = torch.LongTensor(len(batch), max_input_len)
+        text_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            text = batch[ids_sorted_decreasing[i]][0]
+            text_padded[i, :text.size(0)] = text
+
+        # Right zero-pad mel-spec
+        num_mels = batch[0][1].size(0)
+        max_target_len = max([x[1].size(1) for x in batch])
+        if max_target_len % self.n_frames_per_step != 0:
+            max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
+            assert max_target_len % self.n_frames_per_step == 0
+
+        # include mel padded and gate padded
+        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
+        mel_padded.zero_()
+        gate_padded = torch.FloatTensor(len(batch), max_target_len)
+        gate_padded.zero_()
+        output_lengths = torch.LongTensor(len(batch))
+        for i in range(len(ids_sorted_decreasing)):
+            mel = batch[ids_sorted_decreasing[i]][1]
+            mel_padded[i, :, :mel.size(1)] = mel
+            gate_padded[i, mel.size(1)-1:] = 1
+            output_lengths[i] = mel.size(1)
+
+        # count number of items - characters in text
+        len_x = [x[2] for x in batch]
+        len_x = torch.Tensor(len_x)
+        return text_padded, input_lengths, mel_padded, gate_padded, \
+            output_lengths, len_x
+
+def batch_to_gpu(batch):
+    text_padded, input_lengths, mel_padded, gate_padded, \
+        output_lengths, len_x = batch
+    text_padded = to_gpu(text_padded).long()
+    input_lengths = to_gpu(input_lengths).long()
+    max_len = torch.max(input_lengths.data).item()
+    mel_padded = to_gpu(mel_padded).float()
+    gate_padded = to_gpu(gate_padded).float()
+    output_lengths = to_gpu(output_lengths).long()
+    x = (text_padded, input_lengths, mel_padded, max_len, output_lengths)
+    y = (mel_padded, gate_padded)
+    len_x = torch.sum(input_lengths)
+    return (x, y, len_x)
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/loss_function.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/loss_function.py
new file mode 100644
index 00000000..d489334a
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/loss_function.py
@@ -0,0 +1,46 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+from torch import nn
+
+
+class Tacotron2Loss(nn.Module):
+    def __init__(self):
+        super(Tacotron2Loss, self).__init__()
+
+    def forward(self, model_output, targets):
+        mel_target, gate_target = targets[0], targets[1]
+        mel_target.requires_grad = False
+        gate_target.requires_grad = False
+        gate_target = gate_target.view(-1, 1)
+
+        mel_out, mel_out_postnet, gate_out, _ = model_output
+        gate_out = gate_out.view(-1, 1)
+        mel_loss = nn.MSELoss()(mel_out, mel_target) + \
+            nn.MSELoss()(mel_out_postnet, mel_target)
+        gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
+        return mel_loss + gate_loss
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py
new file mode 100644
index 00000000..d4b939bb
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py
@@ -0,0 +1,663 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+from math import sqrt
+import torch
+from torch.autograd import Variable
+from torch import nn
+from torch.nn import functional as F
+from common.layers import ConvNorm, LinearNorm
+from common.utils import to_gpu, get_mask_from_lengths
+
+
+class LocationLayer(nn.Module):
+    def __init__(self, attention_n_filters, attention_kernel_size,
+                 attention_dim):
+        super(LocationLayer, self).__init__()
+        padding = int((attention_kernel_size - 1) / 2)
+        self.location_conv = ConvNorm(2, attention_n_filters,
+                                      kernel_size=attention_kernel_size,
+                                      padding=padding, bias=False, stride=1,
+                                      dilation=1)
+        self.location_dense = LinearNorm(attention_n_filters, attention_dim,
+                                         bias=False, w_init_gain='tanh')
+
+    def forward(self, attention_weights_cat):
+        processed_attention = self.location_conv(attention_weights_cat)
+        processed_attention = processed_attention.transpose(1, 2)
+        processed_attention = self.location_dense(processed_attention)
+        return processed_attention
+
+
+class Attention(nn.Module):
+    def __init__(self, attention_rnn_dim, embedding_dim,
+                 attention_dim, attention_location_n_filters,
+                 attention_location_kernel_size):
+        super(Attention, self).__init__()
+        self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
+                                      bias=False, w_init_gain='tanh')
+        self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
+                                       w_init_gain='tanh')
+        self.v = LinearNorm(attention_dim, 1, bias=False)
+        self.location_layer = LocationLayer(attention_location_n_filters,
+                                            attention_location_kernel_size,
+                                            attention_dim)
+        self.score_mask_value = -float("inf")
+
+    def get_alignment_energies(self, query, processed_memory,
+                               attention_weights_cat):
+        """
+        PARAMS
+        ------
+        query: decoder output (batch, n_mel_channels * n_frames_per_step)
+        processed_memory: processed encoder outputs (B, T_in, attention_dim)
+        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
+
+        RETURNS
+        -------
+        alignment (batch, max_time)
+        """
+
+        processed_query = self.query_layer(query.unsqueeze(1))
+        processed_attention_weights = self.location_layer(attention_weights_cat)
+        energies = self.v(torch.tanh(
+            processed_query + processed_attention_weights + processed_memory))
+
+        energies = energies.squeeze(-1)
+        return energies
+
+    def forward(self, attention_hidden_state, memory, processed_memory,
+                attention_weights_cat, mask):
+        """
+        PARAMS
+        ------
+        attention_hidden_state: attention rnn last output
+        memory: encoder outputs
+        processed_memory: processed encoder outputs
+        attention_weights_cat: previous and cummulative attention weights
+        mask: binary mask for padded data
+        """
+        alignment = self.get_alignment_energies(
+            attention_hidden_state, processed_memory, attention_weights_cat)
+
+        if mask is not None:
+            alignment.data.masked_fill_(mask, self.score_mask_value)
+
+        attention_weights = F.softmax(alignment, dim=1)
+        attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
+        attention_context = attention_context.squeeze(1)
+
+        return attention_context, attention_weights
+
+
+class Prenet(nn.Module):
+    def __init__(self, in_dim, sizes):
+        super(Prenet, self).__init__()
+        in_sizes = [in_dim] + sizes[:-1]
+        self.layers = nn.ModuleList(
+            [LinearNorm(in_size, out_size, bias=False)
+             for (in_size, out_size) in zip(in_sizes, sizes)])
+
+    def forward(self, x):
+        for linear in self.layers:
+            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
+        return x
+
+
+class Postnet(nn.Module):
+    """Postnet
+        - Five 1-d convolution with 512 channels and kernel size 5
+    """
+
+    def __init__(self, n_mel_channels, postnet_embedding_dim,
+                 postnet_kernel_size, postnet_n_convolutions):
+        super(Postnet, self).__init__()
+        self.convolutions = nn.ModuleList()
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(n_mel_channels, postnet_embedding_dim,
+                         kernel_size=postnet_kernel_size, stride=1,
+                         padding=int((postnet_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='tanh'),
+                nn.BatchNorm1d(postnet_embedding_dim))
+        )
+
+        for i in range(1, postnet_n_convolutions - 1):
+            self.convolutions.append(
+                nn.Sequential(
+                    ConvNorm(postnet_embedding_dim,
+                             postnet_embedding_dim,
+                             kernel_size=postnet_kernel_size, stride=1,
+                             padding=int((postnet_kernel_size - 1) / 2),
+                             dilation=1, w_init_gain='tanh'),
+                    nn.BatchNorm1d(postnet_embedding_dim))
+            )
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(postnet_embedding_dim, n_mel_channels,
+                         kernel_size=postnet_kernel_size, stride=1,
+                         padding=int((postnet_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='linear'),
+                nn.BatchNorm1d(n_mel_channels))
+        )
+
+    def forward(self, x):
+        for i in range(len(self.convolutions) - 1):
+            x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
+        x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
+
+        return x
+
+
+class Encoder(nn.Module):
+    """Encoder module:
+        - Three 1-d convolution banks
+        - Bidirectional LSTM
+    """
+    def __init__(self, encoder_n_convolutions,
+                 encoder_embedding_dim, encoder_kernel_size):
+        super(Encoder, self).__init__()
+
+        convolutions = []
+        for _ in range(encoder_n_convolutions):
+            conv_layer = nn.Sequential(
+                ConvNorm(encoder_embedding_dim,
+                         encoder_embedding_dim,
+                         kernel_size=encoder_kernel_size, stride=1,
+                         padding=int((encoder_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='relu'),
+                nn.BatchNorm1d(encoder_embedding_dim))
+            convolutions.append(conv_layer)
+            self.convolutions = nn.ModuleList(convolutions)
+
+        self.lstm = nn.LSTM(encoder_embedding_dim,
+                            int(encoder_embedding_dim / 2), 1,
+                            batch_first=True, bidirectional=True)
+
+    def forward(self, x, input_lengths):
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
+
+        x = x.transpose(1, 2)
+
+        # pytorch tensor are not reversible, hence the conversion
+        input_lengths = input_lengths.cpu().numpy()
+        x = nn.utils.rnn.pack_padded_sequence(
+            x, input_lengths, batch_first=True)
+
+        self.lstm.flatten_parameters()
+        outputs, _ = self.lstm(x)
+
+        outputs, _ = nn.utils.rnn.pad_packed_sequence(
+            outputs, batch_first=True)
+
+        return outputs
+
+    def inference(self, x):
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
+
+        x = x.transpose(1, 2)
+
+        self.lstm.flatten_parameters()
+        outputs, _ = self.lstm(x)
+
+        return outputs
+
+
+class Decoder(nn.Module):
+    def __init__(self, n_mel_channels, n_frames_per_step,
+                 encoder_embedding_dim, attention_dim,
+                 attention_location_n_filters,
+                 attention_location_kernel_size,
+                 attention_rnn_dim, decoder_rnn_dim,
+                 prenet_dim, max_decoder_steps, gate_threshold,
+                 p_attention_dropout, p_decoder_dropout,
+                 early_stopping):
+        super(Decoder, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.n_frames_per_step = n_frames_per_step
+        self.encoder_embedding_dim = encoder_embedding_dim
+        self.attention_rnn_dim = attention_rnn_dim
+        self.decoder_rnn_dim = decoder_rnn_dim
+        self.prenet_dim = prenet_dim
+        self.max_decoder_steps = max_decoder_steps
+        self.gate_threshold = gate_threshold
+        self.p_attention_dropout = p_attention_dropout
+        self.p_decoder_dropout = p_decoder_dropout
+        self.early_stopping = early_stopping
+
+        self.prenet = Prenet(
+            n_mel_channels * n_frames_per_step,
+            [prenet_dim, prenet_dim])
+
+        self.attention_rnn = nn.LSTMCell(
+            prenet_dim + encoder_embedding_dim,
+            attention_rnn_dim)
+
+        self.attention_layer = Attention(
+            attention_rnn_dim, encoder_embedding_dim,
+            attention_dim, attention_location_n_filters,
+            attention_location_kernel_size)
+
+        self.decoder_rnn = nn.LSTMCell(
+            attention_rnn_dim + encoder_embedding_dim,
+            decoder_rnn_dim, 1)
+
+        self.linear_projection = LinearNorm(
+            decoder_rnn_dim + encoder_embedding_dim,
+            n_mel_channels * n_frames_per_step)
+
+        self.gate_layer = LinearNorm(
+            decoder_rnn_dim + encoder_embedding_dim, 1,
+            bias=True, w_init_gain='sigmoid')
+
+    def get_go_frame(self, memory):
+        """ Gets all zeros frames to use as first decoder input
+        PARAMS
+        ------
+        memory: decoder outputs
+
+        RETURNS
+        -------
+        decoder_input: all zeros frames
+        """
+        B = memory.size(0)
+        decoder_input = Variable(memory.data.new(
+            B, self.n_mel_channels * self.n_frames_per_step).zero_())
+        return decoder_input
+
+    def initialize_decoder_states(self, memory, mask):
+        """ Initializes attention rnn states, decoder rnn states, attention
+        weights, attention cumulative weights, attention context, stores memory
+        and stores processed memory
+        PARAMS
+        ------
+        memory: Encoder outputs
+        mask: Mask for padded data if training, expects None for inference
+        """
+        B = memory.size(0)
+        MAX_TIME = memory.size(1)
+
+        self.attention_hidden = Variable(memory.data.new(
+            B, self.attention_rnn_dim).zero_())
+        self.attention_cell = Variable(memory.data.new(
+            B, self.attention_rnn_dim).zero_())
+
+        self.decoder_hidden = Variable(memory.data.new(
+            B, self.decoder_rnn_dim).zero_())
+        self.decoder_cell = Variable(memory.data.new(
+            B, self.decoder_rnn_dim).zero_())
+
+        self.attention_weights = Variable(memory.data.new(
+            B, MAX_TIME).zero_())
+        self.attention_weights_cum = Variable(memory.data.new(
+            B, MAX_TIME).zero_())
+        self.attention_context = Variable(memory.data.new(
+            B, self.encoder_embedding_dim).zero_())
+
+        self.memory = memory
+        self.processed_memory = self.attention_layer.memory_layer(memory)
+        self.mask = mask
+
+    def parse_decoder_inputs(self, decoder_inputs):
+        """ Prepares decoder inputs, i.e. mel outputs
+        PARAMS
+        ------
+        decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
+
+        RETURNS
+        -------
+        inputs: processed decoder inputs
+
+        """
+        # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
+        decoder_inputs = decoder_inputs.transpose(1, 2)
+        decoder_inputs = decoder_inputs.view(
+            decoder_inputs.size(0),
+            int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
+        # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
+        decoder_inputs = decoder_inputs.transpose(0, 1)
+        return decoder_inputs
+
+    def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
+        """ Prepares decoder outputs for output
+        PARAMS
+        ------
+        mel_outputs:
+        gate_outputs: gate output energies
+        alignments:
+
+        RETURNS
+        -------
+        mel_outputs:
+        gate_outpust: gate output energies
+        alignments:
+        """
+        # (T_out, B) -> (B, T_out)
+        alignments = torch.stack(alignments).transpose(0, 1)
+        # (T_out, B) -> (B, T_out)
+        gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
+        gate_outputs = gate_outputs.contiguous()
+        # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
+        mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
+        # decouple frames per step
+        mel_outputs = mel_outputs.view(
+            mel_outputs.size(0), -1, self.n_mel_channels)
+        # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
+        mel_outputs = mel_outputs.transpose(1, 2)
+
+        return mel_outputs, gate_outputs, alignments
+
+    def decode(self, decoder_input, is_infer=False):
+        """ Decoder step using stored states, attention and memory
+        PARAMS
+        ------
+        decoder_input: previous mel output
+
+        RETURNS
+        -------
+        mel_output:
+        gate_output: gate output energies
+        attention_weights:
+        """
+        cell_input = torch.cat((decoder_input, self.attention_context), -1)
+        attention_hidden_dtype = self.attention_hidden.dtype
+
+        if is_infer:
+            self.attention_hidden, self.attention_cell = self.attention_rnn(
+                cell_input, (self.attention_hidden, self.attention_cell))
+        else:
+            self.attention_hidden, self.attention_cell = self.attention_rnn(
+                cell_input.float(), (self.attention_hidden.float(), self.attention_cell.float()))
+
+        self.attention_hidden = F.dropout(
+            self.attention_hidden, self.p_attention_dropout, self.training)
+
+        if attention_hidden_dtype == torch.float16:
+            self.attention_hidden = self.attention_hidden.half()
+
+        self.attention_cell = F.dropout(
+            self.attention_cell, self.p_attention_dropout, self.training)
+
+        attention_weights_cat = torch.cat(
+            (self.attention_weights.unsqueeze(1),
+             self.attention_weights_cum.unsqueeze(1)), dim=1)
+        self.attention_context, self.attention_weights = self.attention_layer(
+            self.attention_hidden, self.memory, self.processed_memory,
+            attention_weights_cat, self.mask)
+
+        self.attention_weights_cum += self.attention_weights
+        decoder_input = torch.cat(
+            (self.attention_hidden, self.attention_context), -1)
+        decoder_hidden_dtype = self.decoder_hidden.dtype
+
+        if is_infer:
+            self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
+                decoder_input, (self.decoder_hidden, self.decoder_cell))
+        else:
+            self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
+                decoder_input.float(), (self.decoder_hidden.float(), self.decoder_cell.float()))
+            
+        self.decoder_hidden = F.dropout(
+            self.decoder_hidden, self.p_decoder_dropout, self.training)
+
+        if decoder_hidden_dtype == torch.float16:
+            self.decoder_hidden = self.decoder_hidden.half()
+
+        self.decoder_cell = F.dropout(
+            self.decoder_cell, self.p_decoder_dropout, self.training)
+
+        decoder_hidden_attention_context = torch.cat(
+            (self.decoder_hidden, self.attention_context), dim=1)
+        decoder_output = self.linear_projection(
+            decoder_hidden_attention_context)
+
+        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
+        return decoder_output, gate_prediction, self.attention_weights
+
+    def forward(self, memory, decoder_inputs, memory_lengths):
+        """ Decoder forward pass for training
+        PARAMS
+        ------
+        memory: Encoder outputs
+        decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
+        memory_lengths: Encoder output lengths for attention masking.
+
+        RETURNS
+        -------
+        mel_outputs: mel outputs from the decoder
+        gate_outputs: gate outputs from the decoder
+        alignments: sequence of attention weights from the decoder
+        """
+
+        decoder_input = self.get_go_frame(memory).unsqueeze(0)
+        decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
+        decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
+        decoder_inputs = self.prenet(decoder_inputs)
+
+        self.initialize_decoder_states(
+            memory, mask=~get_mask_from_lengths(memory_lengths))
+
+        mel_outputs, gate_outputs, alignments = [], [], []
+        while len(mel_outputs) < decoder_inputs.size(0) - 1:
+            decoder_input = decoder_inputs[len(mel_outputs)]
+            mel_output, gate_output, attention_weights = self.decode(
+                decoder_input)
+
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output.squeeze()]
+            alignments += [attention_weights]
+
+        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
+            mel_outputs, gate_outputs, alignments)
+
+        return mel_outputs, gate_outputs, alignments
+
+    # def inference(self, memory):
+    #     """ Decoder inference
+    #     PARAMS
+    #     ------
+    #     memory: Encoder outputs
+
+    #     RETURNS
+    #     -------
+    #     mel_outputs: mel outputs from the decoder
+    #     gate_outputs: gate outputs from the decoder
+    #     alignments: sequence of attention weights from the decoder
+    #     """
+    #     decoder_input = self.get_go_frame(memory)
+
+    #     self.initialize_decoder_states(memory, mask=None)
+
+    #     mel_outputs, gate_outputs, alignments = [], [], []
+    #     while True:
+    #         decoder_input = self.prenet(decoder_input)
+    #         mel_output, gate_output, alignment = self.decode(decoder_input)
+
+    #         mel_outputs += [mel_output.squeeze(1)]
+    #         gate_outputs += [gate_output]
+    #         alignments += [alignment]
+
+    #         if torch.sigmoid(gate_output.data) > self.gate_threshold:
+    #             break
+    #         elif len(mel_outputs) == self.max_decoder_steps:
+    #             print("Warning! Reached max decoder steps")
+    #             break
+
+    #         decoder_input = mel_output
+
+    #     mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
+    #         mel_outputs, gate_outputs, alignments)
+
+    #     return mel_outputs, gate_outputs, alignments
+
+    def inference(self, memory):
+        """ Decoder inference
+        PARAMS
+        ------
+        memory: Encoder outputs
+
+        RETURNS
+        -------
+        mel_outputs: mel outputs from the decoder
+        gate_outputs: gate outputs from the decoder
+        alignments: sequence of attention weights from the decoder
+        """
+        decoder_input = self.get_go_frame(memory)
+
+        self.initialize_decoder_states(memory, mask=None)
+
+        mel_lengths = torch.zeros([memory.size(0)], dtype=torch.int32).cuda()
+        not_finished = torch.ones([memory.size(0)], dtype=torch.int32).cuda()
+        mel_outputs, gate_outputs, alignments = [], [], []
+        while True:
+            decoder_input = self.prenet(decoder_input)
+            mel_output, gate_output, alignment = self.decode(decoder_input, is_infer=True)
+
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output]
+            alignments += [alignment]
+            dec = torch.le(torch.sigmoid(gate_output.data),
+                           self.gate_threshold).to(torch.int32).squeeze(1)
+
+            not_finished = not_finished*dec
+            mel_lengths += not_finished
+
+            if self.early_stopping and torch.sum(not_finished) == 0:
+                break
+            if len(mel_outputs) == self.max_decoder_steps:
+                print("Warning! Reached max decoder steps")
+                break
+
+            decoder_input = mel_output
+
+        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
+            mel_outputs, gate_outputs, alignments)
+
+        return mel_outputs, gate_outputs, alignments
+
+
+class Tacotron2(nn.Module):
+    def __init__(self, mask_padding, n_mel_channels,
+                 n_symbols, symbols_embedding_dim, encoder_kernel_size,
+                 encoder_n_convolutions, encoder_embedding_dim,
+                 attention_rnn_dim, attention_dim, attention_location_n_filters,
+                 attention_location_kernel_size, n_frames_per_step,
+                 decoder_rnn_dim, prenet_dim, max_decoder_steps, gate_threshold,
+                 p_attention_dropout, p_decoder_dropout,
+                 postnet_embedding_dim, postnet_kernel_size,
+                 postnet_n_convolutions, decoder_no_early_stopping):
+        super(Tacotron2, self).__init__()
+        self.mask_padding = mask_padding
+        self.n_mel_channels = n_mel_channels
+        self.n_frames_per_step = n_frames_per_step
+        self.embedding = nn.Embedding(
+            n_symbols, symbols_embedding_dim)
+        std = sqrt(2.0 / (n_symbols + symbols_embedding_dim))
+        val = sqrt(3.0) * std  # uniform bounds for std
+        self.embedding.weight.data.uniform_(-val, val)
+        self.encoder = Encoder(encoder_n_convolutions,
+                               encoder_embedding_dim,
+                               encoder_kernel_size)
+        self.decoder = Decoder(n_mel_channels, n_frames_per_step,
+                               encoder_embedding_dim, attention_dim,
+                               attention_location_n_filters,
+                               attention_location_kernel_size,
+                               attention_rnn_dim, decoder_rnn_dim,
+                               prenet_dim, max_decoder_steps,
+                               gate_threshold, p_attention_dropout,
+                               p_decoder_dropout,
+                               not decoder_no_early_stopping)
+        self.postnet = Postnet(n_mel_channels, postnet_embedding_dim,
+                               postnet_kernel_size,
+                               postnet_n_convolutions)
+
+    def parse_batch(self, batch):
+        text_padded, input_lengths, mel_padded, gate_padded, \
+            output_lengths = batch
+        text_padded = to_gpu(text_padded).long()
+        input_lengths = to_gpu(input_lengths).long()
+        max_len = torch.max(input_lengths.data).item()
+        mel_padded = to_gpu(mel_padded).float()
+        gate_padded = to_gpu(gate_padded).float()
+        output_lengths = to_gpu(output_lengths).long()
+
+        return (
+            (text_padded, input_lengths, mel_padded, max_len, output_lengths),
+            (mel_padded, gate_padded))
+
+    def parse_input(self, inputs):
+        return inputs
+
+    def parse_output(self, outputs, output_lengths=None):
+        if self.mask_padding and output_lengths is not None:
+            mask = ~get_mask_from_lengths(output_lengths)
+            mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
+            mask = mask.permute(1, 0, 2)
+
+            outputs[0].data.masked_fill_(mask, 0.0)
+            outputs[1].data.masked_fill_(mask, 0.0)
+            outputs[2].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
+
+        return outputs
+
+    def forward(self, inputs):
+        inputs, input_lengths, targets, max_len, \
+            output_lengths = self.parse_input(inputs)
+        input_lengths, output_lengths = input_lengths.data, output_lengths.data
+
+        embedded_inputs = self.embedding(inputs).transpose(1, 2)
+
+        encoder_outputs = self.encoder(embedded_inputs, input_lengths)
+
+        mel_outputs, gate_outputs, alignments = self.decoder(
+            encoder_outputs, targets, memory_lengths=input_lengths)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        return self.parse_output(
+            [mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
+            output_lengths)
+
+    def inference(self, inputs):
+        inputs = self.parse_input(inputs)
+        embedded_inputs = self.embedding(inputs).transpose(1, 2)
+        encoder_outputs = self.encoder.inference(embedded_inputs)
+        mel_outputs, gate_outputs, alignments = self.decoder.inference(
+            encoder_outputs)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        outputs = self.parse_output(
+            [mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
+
+        return outputs
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/LICENCE b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/LICENCE
new file mode 100644
index 00000000..8ac1abf2
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/LICENCE
@@ -0,0 +1,19 @@
+Copyright (c) 2017 Keith Ito
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
\ No newline at end of file
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/__init__.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/__init__.py
new file mode 100644
index 00000000..f81bab41
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/__init__.py
@@ -0,0 +1,74 @@
+""" from https://github.com/keithito/tacotron """
+import re
+from tacotron2.text import cleaners
+from tacotron2.text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+# Regular expression matching text enclosed in curly braces:
+_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
+
+
+def text_to_sequence(text, cleaner_names):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+
+    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
+    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
+
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = []
+
+  # Check for curly braces and treat their contents as ARPAbet:
+  while len(text):
+    m = _curly_re.match(text)
+    if not m:
+      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
+      break
+    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
+    sequence += _arpabet_to_sequence(m.group(2))
+    text = m.group(3)
+
+  return sequence
+
+
+def sequence_to_text(sequence):
+  '''Converts a sequence of IDs back to a string'''
+  result = ''
+  for symbol_id in sequence:
+    if symbol_id in _id_to_symbol:
+      s = _id_to_symbol[symbol_id]
+      # Enclose ARPAbet back in curly braces:
+      if len(s) > 1 and s[0] == '@':
+        s = '{%s}' % s[1:]
+      result += s
+  return result.replace('}{', ' ')
+
+
+def _clean_text(text, cleaner_names):
+  for name in cleaner_names:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text
+
+
+def _symbols_to_sequence(symbols):
+  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
+
+
+def _arpabet_to_sequence(text):
+  return _symbols_to_sequence(['@' + s for s in text.split()])
+
+
+def _should_keep_symbol(s):
+  return s in _symbol_to_id and s is not '_' and s is not '~'
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cleaners.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cleaners.py
new file mode 100644
index 00000000..e2e35c1a
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cleaners.py
@@ -0,0 +1,90 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+'''
+
+import re
+from unidecode import unidecode
+from .numbers import normalize_numbers
+
+
+# Regular expression matching whitespace:
+_whitespace_re = re.compile(r'\s+')
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('mrs', 'misess'),
+  ('mr', 'mister'),
+  ('dr', 'doctor'),
+  ('st', 'saint'),
+  ('co', 'company'),
+  ('jr', 'junior'),
+  ('maj', 'major'),
+  ('gen', 'general'),
+  ('drs', 'doctors'),
+  ('rev', 'reverend'),
+  ('lt', 'lieutenant'),
+  ('hon', 'honorable'),
+  ('sgt', 'sergeant'),
+  ('capt', 'captain'),
+  ('esq', 'esquire'),
+  ('ltd', 'limited'),
+  ('col', 'colonel'),
+  ('ft', 'fort'),
+]]
+
+
+def expand_abbreviations(text):
+  for regex, replacement in _abbreviations:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def expand_numbers(text):
+  return normalize_numbers(text)
+
+
+def lowercase(text):
+  return text.lower()
+
+
+def collapse_whitespace(text):
+  return re.sub(_whitespace_re, ' ', text)
+
+
+def convert_to_ascii(text):
+  return unidecode(text)
+
+
+def basic_cleaners(text):
+  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def transliteration_cleaners(text):
+  '''Pipeline for non-English text that transliterates to ASCII.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def english_cleaners(text):
+  '''Pipeline for English text, including number and abbreviation expansion.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_numbers(text)
+  text = expand_abbreviations(text)
+  text = collapse_whitespace(text)
+  return text
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cmudict.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cmudict.py
new file mode 100644
index 00000000..62bfef74
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/cmudict.py
@@ -0,0 +1,65 @@
+""" from https://github.com/keithito/tacotron """
+
+import re
+
+
+valid_symbols = [
+  'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
+  'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
+  'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
+  'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
+  'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
+  'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
+  'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
+]
+
+_valid_symbol_set = set(valid_symbols)
+
+
+class CMUDict:
+  '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
+  def __init__(self, file_or_path, keep_ambiguous=True):
+    if isinstance(file_or_path, str):
+      with open(file_or_path, encoding='latin-1') as f:
+        entries = _parse_cmudict(f)
+    else:
+      entries = _parse_cmudict(file_or_path)
+    if not keep_ambiguous:
+      entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
+    self._entries = entries
+
+
+  def __len__(self):
+    return len(self._entries)
+
+
+  def lookup(self, word):
+    '''Returns list of ARPAbet pronunciations of the given word.'''
+    return self._entries.get(word.upper())
+
+
+
+_alt_re = re.compile(r'\([0-9]+\)')
+
+
+def _parse_cmudict(file):
+  cmudict = {}
+  for line in file:
+    if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
+      parts = line.split('  ')
+      word = re.sub(_alt_re, '', parts[0])
+      pronunciation = _get_pronunciation(parts[1])
+      if pronunciation:
+        if word in cmudict:
+          cmudict[word].append(pronunciation)
+        else:
+          cmudict[word] = [pronunciation]
+  return cmudict
+
+
+def _get_pronunciation(s):
+  parts = s.strip().split(' ')
+  for part in parts:
+    if part not in _valid_symbol_set:
+      return None
+  return ' '.join(parts)
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/numbers.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/numbers.py
new file mode 100644
index 00000000..0d5f7fa8
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/numbers.py
@@ -0,0 +1,71 @@
+""" from https://github.com/keithito/tacotron """
+
+import inflect
+import re
+
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
+_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
+_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+_number_re = re.compile(r'[0-9]+')
+
+
+def _remove_commas(m):
+  return m.group(1).replace(',', '')
+
+
+def _expand_decimal_point(m):
+  return m.group(1).replace('.', ' point ')
+
+
+def _expand_dollars(m):
+  match = m.group(1)
+  parts = match.split('.')
+  if len(parts) > 2:
+    return match + ' dollars'  # Unexpected format
+  dollars = int(parts[0]) if parts[0] else 0
+  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+  if dollars and cents:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+  elif dollars:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    return '%s %s' % (dollars, dollar_unit)
+  elif cents:
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s' % (cents, cent_unit)
+  else:
+    return 'zero dollars'
+
+
+def _expand_ordinal(m):
+  return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+  num = int(m.group(0))
+  if num > 1000 and num < 3000:
+    if num == 2000:
+      return 'two thousand'
+    elif num > 2000 and num < 2010:
+      return 'two thousand ' + _inflect.number_to_words(num % 100)
+    elif num % 100 == 0:
+      return _inflect.number_to_words(num // 100) + ' hundred'
+    else:
+      return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+  else:
+    return _inflect.number_to_words(num, andword='')
+
+
+def normalize_numbers(text):
+  text = re.sub(_comma_number_re, _remove_commas, text)
+  text = re.sub(_pounds_re, r'\1 pounds', text)
+  text = re.sub(_dollars_re, _expand_dollars, text)
+  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+  text = re.sub(_ordinal_re, _expand_ordinal, text)
+  text = re.sub(_number_re, _expand_number, text)
+  return text
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py
new file mode 100644
index 00000000..175703cb
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py
@@ -0,0 +1,18 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Defines the set of symbols used in text input to the model.
+
+The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
+from tacotron2.text import cmudict
+
+_pad        = '_'
+_punctuation = '!\'(),.:;? '
+_special = '-'
+_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
+
+# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
+_arpabet = ['@' + s for s in cmudict.valid_symbols]
+
+# Export all symbols:
+symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/train.py b/PyTorch/SpeechSynthesis/Tacotron2/train.py
new file mode 100644
index 00000000..cbca9abc
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/train.py
@@ -0,0 +1,550 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import os
+import time
+import argparse
+import numpy as np
+from contextlib import contextmanager
+
+import torch
+from torch.utils.data import DataLoader
+from torch.autograd import Variable
+from torch.nn.parameter import Parameter
+
+import torch.distributed as dist
+from torch.utils.data.distributed import DistributedSampler
+
+from apex.fp16_utils import FP16_Optimizer
+from apex.parallel import DistributedDataParallel as DDP
+
+import models
+import loss_functions
+import data_functions
+
+from dllogger.logger import LOGGER
+import dllogger.logger as dllg
+from dllogger import tags
+from dllogger.autologging import log_hardware, log_args
+from scipy.io.wavfile import write as write_wav
+
+
+def parse_args(parser):
+    """
+    Parse commandline arguments.
+    """
+
+    parser.add_argument('-o', '--output_directory', type=str, required=True,
+                        help='Directory to save checkpoints')
+    parser.add_argument('-d', '--dataset-path', type=str,
+                        default='./', help='Path to dataset')
+    parser.add_argument('-m', '--model-name', type=str, default='', required=True,
+                        help='Model to train')
+    parser.add_argument('--log-file', type=str, default='nvlog.json',
+                        help='Filename for logging')
+    parser.add_argument('--phrase-path', type=str, default=None,
+                        help='Path to phrase sequence file used for sample generation')
+    parser.add_argument('--waveglow-checkpoint', type=str, default=None,
+                        help='Path to pre-trained WaveGlow checkpoint for sample generation')
+    parser.add_argument('--tacotron2-checkpoint', type=str, default=None,
+                        help='Path to pre-trained Tacotron2 checkpoint for sample generation')
+    parser.add_argument('--anneal-steps', nargs='*',
+                        help='Epochs after which decrease learning rate')
+    parser.add_argument('--anneal-factor', type=float, choices=[0.1, 0.3], default=0.1,
+                        help='Factor for annealing learning rate')
+
+    # training
+    training = parser.add_argument_group('training setup')
+    training.add_argument('--epochs', type=int, required=True,  # default=1500,
+                          help='Number of total epochs to run')
+    training.add_argument('--epochs-per-checkpoint', type=int, default=10,
+                          help='Number of epochs per checkpoint')
+    training.add_argument('--seed', type=int, default=1234,
+                          help='Seed for PyTorch random number generators')
+    training.add_argument('--dynamic-loss-scaling', type=bool, default=True,
+                          help='Enable dynamic loss scaling')
+    training.add_argument('--fp16-run', action='store_true',
+                          help='Enable fp16')
+    training.add_argument('--cudnn-enabled', type=bool, default=True,
+                          help='Enable cudnn')
+    training.add_argument('--cudnn-benchmark', type=bool, default=False,
+                          help='Run cudnn benchmark')
+
+    optimization = parser.add_argument_group('optimization setup')
+    optimization.add_argument(
+        '--use-saved-learning-rate', default=False, type=bool)
+    optimization.add_argument('-lr', '--learning-rate', type=float, required=True,
+                              help='Learing rate')
+    optimization.add_argument('--weight-decay', default=1e-6, type=float,
+                              help='Weight decay')
+    optimization.add_argument('--grad-clip-thresh', default=1.0, type=float,
+                              help='Clip threshold for gradients')
+    optimization.add_argument('-bs', '--batch-size', type=int, required=True,
+                              help='Batch size per GPU')
+    optimization.add_argument('--grad-clip', default=5.0, type=float,
+                              help='Enables gradient clipping and sets maximum gradient norm value')
+
+    # dataset parameters
+    dataset = parser.add_argument_group('dataset parameters')
+    dataset.add_argument('--load-mel-from-disk', default=False, type=bool,
+                         help='Loads mel spectrograms from disk instead of computing them on the fly')
+    dataset.add_argument('--training-files',
+                         default='filelists/ljs_audio_text_train_filelist.txt',
+                         type=str, help='Path to training filelist')
+    dataset.add_argument('--validation-files',
+                         default='filelists/ljs_audio_text_val_filelist.txt',
+                         type=str, help='Path to validation filelist')
+    dataset.add_argument('--text-cleaners', nargs='*',
+                         default=['english_cleaners'], type=str,
+                         help='Type of text cleaners for input text')
+
+    # audio parameters
+    audio = parser.add_argument_group('audio parameters')
+    audio.add_argument('--max-wav-value', default=32768.0, type=float,
+                       help='Maximum audiowave value')
+    audio.add_argument('--sampling-rate', default=22050, type=int,
+                       help='Sampling rate')
+    audio.add_argument('--filter-length', default=1024, type=int,
+                       help='Filter length')
+    audio.add_argument('--hop-length', default=256, type=int,
+                       help='Hop (stride) length')
+    audio.add_argument('--win-length', default=1024, type=int,
+                       help='Window length')
+    audio.add_argument('--mel-fmin', default=0.0, type=float,
+                       help='Minimum mel frequency')
+    audio.add_argument('--mel-fmax', default=8000.0, type=float,
+                       help='Maximum mel frequency')
+
+    distributed = parser.add_argument_group('distributed setup')
+    # distributed.add_argument('--distributed-run', default=True, type=bool,
+    #                          help='enable distributed run')
+    distributed.add_argument('--rank', default=0, type=int,
+                             help='Rank of the process, do not set! Done by multiproc module')
+    distributed.add_argument('--world-size', default=1, type=int,
+                             help='Number of processes, do not set! Done by multiproc module')
+    distributed.add_argument('--dist-url', type=str, default='tcp://localhost:23456',
+                             help='Url used to set up distributed training')
+    distributed.add_argument('--group-name', type=str, default='group_name',
+                             required=False, help='Distributed group name')
+    distributed.add_argument('--dist-backend', default='nccl', type=str, choices={'nccl'},
+                             help='Distributed run backend')
+
+    return parser
+
+
+def reduce_tensor(tensor, num_gpus):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.reduce_op.SUM)
+    rt /= num_gpus
+    return rt
+
+
+# shamelessly copied from apex/fp16_utils/fp16_optimizer.py
+# commit 34582381e289287e6d09490b69c0840718729f2e
+FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
+HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
+
+
+def conversion_helper(val, conversion):
+    """Apply conversion to val. Recursively apply conversion if `val` is a nested tuple/list structure."""
+    if not isinstance(val, (tuple, list)):
+        return conversion(val)
+    rtn = [conversion_helper(v, conversion) for v in val]
+    if isinstance(val, tuple):
+        rtn = tuple(rtn)
+    return rtn
+
+
+def fp32_to_fp16(val):
+    """Convert fp32 `val` to fp16"""
+    def half_conversion(val):
+        val_typecheck = val
+        if isinstance(val_typecheck, (Parameter, Variable)):
+            val_typecheck = val.data
+        if isinstance(val_typecheck, FLOAT_TYPES):
+            val = val.half()
+        return val
+    return conversion_helper(val, half_conversion)
+
+
+def fp16_to_fp32(val):
+    """Convert fp16 `val` to fp32"""
+    def float_conversion(val):
+        val_typecheck = val
+        if isinstance(val_typecheck, (Parameter, Variable)):
+            val_typecheck = val.data
+        if isinstance(val_typecheck, HALF_TYPES):
+            val = val.float()
+        return val
+    return conversion_helper(val, float_conversion)
+
+
+def init_distributed(args, world_size, rank, group_name):
+    assert torch.cuda.is_available(), "Distributed mode requires CUDA."
+    print("Initializing Distributed")
+
+    # Set cuda device so everything is done on the right GPU.
+    torch.cuda.set_device(rank % torch.cuda.device_count())
+
+    # Initialize distributed communication
+    dist.init_process_group(
+        backend=args.dist_backend, init_method=args.dist_url,
+        world_size=world_size, rank=rank, group_name=group_name)
+
+    print("Done initializing distributed")
+
+
+def save_checkpoint(model, epoch, config, filepath):
+    print("Saving model and optimizer state at epoch {} to {}".format(
+        epoch, filepath))
+    torch.save({'epoch': epoch,
+                'config': config,
+                'state_dict': model.state_dict()}, filepath)
+
+
+def save_sample(model_name, model, waveglow_path, tacotron2_path, phrase_path, filepath, sampling_rate, fp16):
+    if phrase_path is None:
+        return
+    phrase = torch.load(phrase_path, map_location='cpu')
+    if model_name == 'Tacotron2':
+        if waveglow_path is None:
+            raise Exception(
+                "WaveGlow checkpoint path is missing, could not generate sample")
+        with torch.no_grad():
+            checkpoint = torch.load(waveglow_path, map_location='cpu')
+            waveglow = models.get_model(
+                'WaveGlow', checkpoint['config'], to_fp16=False, to_cuda=False)
+            waveglow.eval()
+            model.eval()
+            mel = model.inference(phrase.cuda())[0].cpu()
+            model.train()
+            if fp16:
+                mel = mel.float()
+            audio = waveglow.infer(mel, sigma=0.6)
+    elif model_name == 'WaveGlow':
+        if tacotron2_path is None:
+            raise Exception(
+                "Tacotron2 checkpoint path is missing, could not generate sample")
+        with torch.no_grad():
+            checkpoint = torch.load(tacotron2_path, map_location='cpu')
+            tacotron2 = models.get_model(
+                'Tacotron2', checkpoint['config'], to_fp16=False, to_cuda=False)
+            tacotron2.eval()
+            mel = tacotron2.inference(phrase)[0].cuda()
+            model.eval()
+            if fp16:
+                mel = mel.half()
+            audio = model.infer(mel, sigma=0.6).cpu()
+            model.train()
+            if fp16:
+                audio = audio.float()
+    else:
+        raise NotImplementedError(
+            "unknown model requested: {}".format(model_name))
+    audio = audio[0].numpy()
+    audio = audio.astype('int16')
+    write_wav(filepath, sampling_rate, audio)
+
+# adapted from: https://discuss.pytorch.org/t/opinion-eval-should-be-a-context-manager/18998/3
+# Following snippet is licensed under MIT license
+
+
+@contextmanager
+def evaluating(model):
+    '''Temporarily switch to evaluation mode.'''
+    istrain = model.training
+    try:
+        model.eval()
+        yield model
+    finally:
+        if istrain:
+            model.train()
+
+
+def validate(model, criterion, valset, iteration, batch_size, world_size,
+             collate_fn, distributed_run, rank, batch_to_gpu, fp16_run):
+    """Handles all the validation scoring and printing"""
+    with evaluating(model), torch.no_grad():
+        val_sampler = DistributedSampler(valset) if distributed_run else None
+        val_loader = DataLoader(valset, num_workers=1, shuffle=False,
+                                sampler=val_sampler,
+                                batch_size=batch_size, pin_memory=False,
+                                collate_fn=collate_fn)
+
+        val_loss = 0.0
+        for i, batch in enumerate(val_loader):
+            x, y, len_x = batch_to_gpu(batch)
+            if fp16_run:
+                y_pred = model(fp32_to_fp16(x))
+                loss = criterion(fp16_to_fp32(y_pred), y)
+            else:
+                y_pred = model(x)
+                loss = criterion(y_pred, y)
+            if distributed_run:
+                reduced_val_loss = reduce_tensor(loss.data, world_size).item()
+            else:
+                reduced_val_loss = loss.item()
+            val_loss += reduced_val_loss
+        val_loss = val_loss / (i + 1)
+
+    LOGGER.log(key="val_iter_loss", value=reduced_val_loss)
+
+
+def adjust_learning_rate(epoch, optimizer, learning_rate,
+                         anneal_steps, anneal_factor):
+
+    p = 0
+    if anneal_steps is not None:
+        for i, a_step in enumerate(anneal_steps):
+            if epoch >= int(a_step):
+                p = p+1
+
+    if anneal_factor == 0.3:
+        lr = learning_rate*((0.1 ** (p//2))*(1.0 if p % 2 == 0 else 0.3))
+    else:
+        lr = learning_rate*(anneal_factor ** p)
+
+    if optimizer.param_groups[0]['lr'] != lr:
+        LOGGER.log_event("learning_rate changed",
+                         value=str(optimizer.param_groups[0]['lr']) + " -> " + str(lr))
+
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Training')
+    parser = parse_args(parser)
+    args, _ = parser.parse_known_args()
+
+    LOGGER.set_model_name("Tacotron2_PyT")
+    LOGGER.set_backends([
+        dllg.StdOutBackend(log_file=None,
+                           logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1),
+        dllg.JsonBackend(log_file=args.log_file if args.rank == 0 else None,
+                         logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1)
+    ])
+
+    LOGGER.timed_block_start("run")
+    LOGGER.register_metric(tags.TRAIN_ITERATION_LOSS,
+                           metric_scope=dllg.TRAIN_ITER_SCOPE)
+    LOGGER.register_metric("iter_time",
+                           metric_scope=dllg.TRAIN_ITER_SCOPE)
+    LOGGER.register_metric("epoch_time",
+                           metric_scope=dllg.EPOCH_SCOPE)
+    LOGGER.register_metric("run_time",
+                           metric_scope=dllg.RUN_SCOPE)
+    LOGGER.register_metric("val_iter_loss",
+                           metric_scope=dllg.EPOCH_SCOPE)
+    LOGGER.register_metric("train_epoch_items/sec",
+                           metric_scope=dllg.EPOCH_SCOPE)
+    LOGGER.register_metric("train_epoch_avg_loss",
+                           metric_scope=dllg.EPOCH_SCOPE)
+
+    log_hardware()
+
+    model_name = args.model_name
+    parser = models.parse_model_args(model_name, parser)
+    parser.parse_args()
+
+    args = parser.parse_args()
+
+    log_args(args)
+
+    torch.backends.cudnn.enabled = args.cudnn_enabled
+    torch.backends.cudnn.benchmark = args.cudnn_benchmark
+
+    distributed_run = args.world_size > 1
+    if distributed_run:
+        init_distributed(args, args.world_size, args.rank, args.group_name)
+
+    LOGGER.log(key=tags.RUN_START)
+    run_start_time = time.time()
+
+    model_config = models.get_model_config(model_name, args)
+    model = models.get_model(model_name, model_config,
+                             to_fp16=args.fp16_run, to_cuda=True)
+    if distributed_run:
+        model = DDP(model)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate,
+                                 weight_decay=args.weight_decay)
+
+    if args.fp16_run:
+        optimizer = FP16_Optimizer(
+            optimizer, dynamic_loss_scale=args.dynamic_loss_scaling)
+
+    try:
+        sigma = args.sigma
+    except AttributeError:
+        sigma = None
+
+    criterion = loss_functions.get_loss_function(model_name, sigma)
+
+    try:
+        n_frames_per_step = args.n_frames_per_step
+    except AttributeError:
+        n_frames_per_step = None
+
+    collate_fn = data_functions.get_collate_function(
+        model_name, n_frames_per_step)
+    trainset = data_functions.get_data_loader(
+        model_name, args.dataset_path, args.training_files, args)
+    train_sampler = DistributedSampler(trainset) if distributed_run else None
+    train_loader = DataLoader(trainset, num_workers=1, shuffle=False,
+                              sampler=train_sampler,
+                              batch_size=args.batch_size, pin_memory=False,
+                              drop_last=True, collate_fn=collate_fn)
+
+    valset = data_functions.get_data_loader(
+        model_name, args.dataset_path, args.validation_files, args)
+
+    batch_to_gpu = data_functions.get_batch_to_gpu(model_name)
+
+    iteration = 0
+    model.train()
+
+    LOGGER.log(key=tags.TRAIN_LOOP)
+
+    for epoch in range(args.epochs):
+        LOGGER.epoch_start()
+        epoch_start_time = time.time()
+        LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
+
+        # used to calculate avg items/sec over epoch
+        reduced_num_items_epoch = 0
+
+        # used to calculate avg loss over epoch
+        train_epoch_avg_loss = 0.0
+        num_iters = 0
+
+        # if overflow at the last iteration then do not save checkpoint
+        overflow = False
+
+        for i, batch in enumerate(train_loader):
+            LOGGER.iteration_start()
+            iter_start_time = time.time()
+            LOGGER.log(key=tags.TRAIN_ITER_START, value=i)
+            print("Batch: {}/{} epoch {}".format(i, len(train_loader), epoch))
+
+            start = time.perf_counter()
+            adjust_learning_rate(epoch, optimizer, args.learning_rate,
+                                 args.anneal_steps, args.anneal_factor)
+
+            model.zero_grad()
+            x, y, num_items = batch_to_gpu(batch)
+
+            if args.fp16_run:
+                y_pred = model(fp32_to_fp16(x))
+                loss = criterion(fp16_to_fp32(y_pred), y)
+            else:
+                y_pred = model(x)
+                loss = criterion(y_pred, y)
+
+            if distributed_run:
+                reduced_loss = reduce_tensor(loss.data, args.world_size).item()
+                reduced_num_items = reduce_tensor(num_items.data, 1).item()
+            else:
+                reduced_loss = loss.item()
+                reduced_num_items = num_items.item()
+            if np.isnan(reduced_loss):
+                raise Exception("loss is NaN")
+
+            LOGGER.log(key=tags.TRAIN_ITERATION_LOSS, value=reduced_loss)
+
+            train_epoch_avg_loss += reduced_loss
+            num_iters += 1
+
+            # accumulate number of items processed in this epoch
+            reduced_num_items_epoch += reduced_num_items
+
+            if args.fp16_run:
+                optimizer.backward(loss)
+                grad_norm = optimizer.clip_master_grads(args.grad_clip_thresh)
+            else:
+                loss.backward()
+                grad_norm = torch.nn.utils.clip_grad_norm_(
+                    model.parameters(), args.grad_clip_thresh)
+
+            optimizer.step()
+
+            overflow = optimizer.overflow if args.fp16_run else False
+            iteration += 1
+
+            LOGGER.log(key=tags.TRAIN_ITER_STOP, value=i)
+
+            iter_stop_time = time.time()
+            iter_time = iter_stop_time - iter_start_time
+            LOGGER.log(key="train_iter_items/sec",
+                       value=(reduced_num_items/iter_time))
+            LOGGER.log(key="iter_time", value=iter_time)
+            LOGGER.iteration_stop()
+
+        LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
+        epoch_stop_time = time.time()
+        epoch_time = epoch_stop_time - epoch_start_time
+
+        LOGGER.log(key="train_epoch_items/sec",
+                   value=(reduced_num_items_epoch/epoch_time))
+        LOGGER.log(key="train_epoch_avg_loss", value=(
+            train_epoch_avg_loss/num_iters if num_iters > 0 else 0.0))
+        LOGGER.log(key="epoch_time", value=epoch_time)
+
+        LOGGER.log(key=tags.EVAL_START, value=epoch)
+
+        validate(model, criterion, valset, iteration,
+                 args.batch_size, args.world_size, collate_fn,
+                 distributed_run, args.rank, batch_to_gpu, args.fp16_run)
+
+        LOGGER.log(key=tags.EVAL_STOP, value=epoch)
+
+        if not overflow and (epoch % args.epochs_per_checkpoint == 0) and args.rank == 0:
+            checkpoint_path = os.path.join(
+                args.output_directory, "checkpoint_{}_{}".format(model_name, epoch))
+            save_checkpoint(model, epoch, model_config, checkpoint_path)
+            save_sample(model_name, model, args.waveglow_checkpoint,
+                        args.tacotron2_checkpoint, args.phrase_path,
+                        os.path.join(args.output_directory, "sample_{}_{}.wav".format(model_name, iteration)), args.sampling_rate, args.fp16_run)
+
+        LOGGER.epoch_stop()
+
+    run_stop_time = time.time()
+    run_time = run_stop_time - run_start_time
+    LOGGER.log(key="run_time", value=run_time)
+    LOGGER.log(key=tags.RUN_FINAL)
+
+    print("training time", run_stop_time - run_start_time)
+
+    LOGGER.timed_block_stop("run")
+
+    if args.rank == 0:
+        LOGGER.finish()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/arg_parser.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/arg_parser.py
new file mode 100644
index 00000000..6cad6359
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/arg_parser.py
@@ -0,0 +1,65 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import argparse
+
+def parse_waveglow_args(parent, add_help=False):
+    """
+    Parse commandline arguments.
+    """
+    parser = argparse.ArgumentParser(parents=[parent], add_help=add_help)
+    
+    # misc parameters
+    parser.add_argument('--n-mel-channels', default=80, type=int,
+                        help='Number of bins in mel-spectrograms')
+
+    # glow parameters
+    parser.add_argument('--flows', default=12, type=int,
+                        help='Number of steps of flow')
+    parser.add_argument('--groups', default=8, type=int,
+                        help='Number of samples in a group processed by the steps of flow')
+    parser.add_argument('--early-every', default=4, type=int,
+                        help='Determines how often (i.e., after how many coupling layers) \
+                        a number of channels (defined by --early-size parameter) are output\
+                        to the loss function')
+    parser.add_argument('--early-size', default=2, type=int,
+                        help='Number of channels output to the loss function')
+    parser.add_argument('--sigma', default=1.0, type=float,
+                        help='Standard deviation used for sampling from Gaussian')
+    parser.add_argument('--segment-length', default=4000, type=int,
+                        help='Segment length (audio samples) processed per iteration')
+
+    # wavenet parameters
+    wavenet = parser.add_argument_group('WaveNet parameters')
+    wavenet.add_argument('--wn-kernel-size', default=3, type=int,
+                        help='Kernel size for dialted convolution in the affine coupling layer (WN)')
+    wavenet.add_argument('--wn-channels', default=512, type=int,
+                        help='Number of channels in WN')
+    wavenet.add_argument('--wn-layers', default=8, type=int,
+                        help='Number of layers in WN')
+
+    return parser
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/data_function.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/data_function.py
new file mode 100644
index 00000000..1a425189
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/data_function.py
@@ -0,0 +1,88 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************\
+
+import torch
+import random
+import common.layers as layers
+from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu
+
+
+class MelAudioLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio,text pairs
+        2) computes mel-spectrograms from audio files.
+    """
+
+    def __init__(self, dataset_path, audiopaths_and_text, args):
+        self.audiopaths_and_text = load_filepaths_and_text(dataset_path, audiopaths_and_text)
+        self.max_wav_value = args.max_wav_value
+        self.sampling_rate = args.sampling_rate
+        self.stft = layers.TacotronSTFT(
+            args.filter_length, args.hop_length, args.win_length,
+            args.n_mel_channels, args.sampling_rate, args.mel_fmin,
+            args.mel_fmax)
+        self.segment_length = args.segment_length
+        random.seed(1234)
+        random.shuffle(self.audiopaths_and_text)
+
+    def get_mel_audio_pair(self, filename):
+        audio, sampling_rate = load_wav_to_torch(filename)
+
+        if sampling_rate != self.stft.sampling_rate:
+            raise ValueError("{} {} SR doesn't match target {} SR".format(
+                sampling_rate, self.stft.sampling_rate))
+
+        # Take segment
+        if audio.size(0) >= self.segment_length:
+            max_audio_start = audio.size(0) - self.segment_length
+            audio_start = random.randint(0, max_audio_start)
+            audio = audio[audio_start:audio_start+self.segment_length]
+        else:
+            audio = torch.nn.functional.pad(
+                audio, (0, self.segment_length - audio.size(0)), 'constant').data
+
+        audio = audio / self.max_wav_value
+        audio_norm = audio.unsqueeze(0)
+        audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+        melspec = self.stft.mel_spectrogram(audio_norm)
+        melspec = melspec.squeeze(0)
+
+        return (melspec, audio, len(audio))
+
+    def __getitem__(self, index):
+        return self.get_mel_audio_pair(self.audiopaths_and_text[index][0])
+
+    def __len__(self):
+        return len(self.audiopaths_and_text)
+
+
+def batch_to_gpu(batch):
+    x, y, len_y = batch
+    x = to_gpu(x).float()
+    y = to_gpu(y).float()
+    len_y = to_gpu(torch.sum(len_y))
+    return ((x, y), y, len_y)
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow.py
new file mode 100644
index 00000000..1829f22f
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow.py
@@ -0,0 +1,311 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import copy
+import torch
+from torch.autograd import Variable
+import torch.nn.functional as F
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a+input_b
+    t_act = torch.nn.functional.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.nn.functional.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class WaveGlowLoss(torch.nn.Module):
+    def __init__(self, sigma=1.0):
+        super(WaveGlowLoss, self).__init__()
+        self.sigma = sigma
+
+    def forward(self, model_output):
+        z, log_s_list, log_det_W_list = model_output
+        for i, log_s in enumerate(log_s_list):
+            if i == 0:
+                log_s_total = torch.sum(log_s)
+                log_det_W_total = log_det_W_list[i]
+            else:
+                log_s_total = log_s_total + torch.sum(log_s)
+                log_det_W_total += log_det_W_list[i]
+
+        loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
+        return loss/(z.size(0)*z.size(1)*z.size(2))
+
+
+class Invertible1x1Conv(torch.nn.Module):
+    """
+    The layer outputs both the convolution, and the log determinant
+    of its weight matrix.  If reverse=True it does convolution with
+    inverse
+    """
+    def __init__(self, c):
+        super(Invertible1x1Conv, self).__init__()
+        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
+                                    bias=False)
+
+        # Sample a random orthonormal matrix to initialize weights
+        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
+
+        # Ensure determinant is 1.0 not -1.0
+        if torch.det(W) < 0:
+            W[:,0] = -1*W[:,0]
+        W = W.view(c, c, 1)
+        self.conv.weight.data = W
+
+    def forward(self, z, reverse=False):
+        # shape
+        batch_size, group_size, n_of_groups = z.size()
+
+        W = self.conv.weight.squeeze()
+
+        if reverse:
+            if not hasattr(self, 'W_inverse'):
+                # Reverse computation
+                W_inverse = W.inverse()
+                W_inverse = Variable(W_inverse[..., None])
+                if z.type() == 'torch.cuda.HalfTensor' or z.type() == 'torch.HalfTensor':                                    W_inverse = W_inverse.half()
+                self.W_inverse = W_inverse
+            z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
+            return z
+        else:
+            # Forward computation
+            log_det_W = batch_size * n_of_groups * torch.logdet(W)
+            z = self.conv(z)
+            return z, log_det_W
+
+
+class WN(torch.nn.Module):
+    """
+    This is the WaveNet like layer for the affine coupling.  The primary difference
+    from WaveNet is the convolutions need not be causal.  There is also no dilation
+    size reset.  The dilation only doubles on each layer
+    """
+    def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
+                 kernel_size):
+        super(WN, self).__init__()
+        assert(kernel_size % 2 == 1)
+        assert(n_channels % 2 == 0)
+        self.n_layers = n_layers
+        self.n_channels = n_channels
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.cond_layers = torch.nn.ModuleList()
+
+        start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
+        start = torch.nn.utils.weight_norm(start, name='weight')
+        self.start = start
+
+        # Initializing last layer to 0 makes the affine coupling layers
+        # do nothing at first.  This helps with training stability
+        end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
+        end.weight.data.zero_()
+        end.bias.data.zero_()
+        self.end = end
+
+        for i in range(n_layers):
+            dilation = 2 ** i
+            padding = int((kernel_size*dilation - dilation)/2)
+            in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1)
+            cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+            self.cond_layers.append(cond_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2*n_channels
+            else:
+                res_skip_channels = n_channels
+            res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, forward_input):
+        audio, spect = forward_input
+        audio = self.start(audio)
+
+        for i in range(self.n_layers):
+            acts = fused_add_tanh_sigmoid_multiply(
+                self.in_layers[i](audio),
+                self.cond_layers[i](spect),
+                torch.IntTensor([self.n_channels]))
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                audio = res_skip_acts[:,:self.n_channels,:] + audio
+                skip_acts = res_skip_acts[:,self.n_channels:,:]
+            else:
+                skip_acts = res_skip_acts
+
+            if i == 0:
+                output = skip_acts
+            else:
+                output = skip_acts + output
+        return self.end(output)
+
+
+class WaveGlow(torch.nn.Module):
+    def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
+                 n_early_size, WN_config):
+        super(WaveGlow, self).__init__()
+
+        self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
+                                                 n_mel_channels,
+                                                 1024, stride=256)
+        assert(n_group % 2 == 0)
+        self.n_flows = n_flows
+        self.n_group = n_group
+        self.n_early_every = n_early_every
+        self.n_early_size = n_early_size
+        self.WN = torch.nn.ModuleList()
+        self.convinv = torch.nn.ModuleList()
+
+        n_half = int(n_group/2)
+
+        # Set up layers with the right sizes based on how many dimensions
+        # have been output already
+        n_remaining_channels = n_group
+        for k in range(n_flows):
+            if k % self.n_early_every == 0 and k > 0:
+                n_half = n_half - int(self.n_early_size/2)
+                n_remaining_channels = n_remaining_channels - self.n_early_size
+            self.convinv.append(Invertible1x1Conv(n_remaining_channels))
+            self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
+        self.n_remaining_channels = n_remaining_channels  # Useful during inference
+
+    def forward(self, forward_input):
+        """
+        forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
+        forward_input[1] = audio: batch x time
+        """
+        spect, audio = forward_input
+
+        #  Upsample spectrogram to size of audio
+        spect = self.upsample(spect)
+        assert(spect.size(2) >= audio.size(1))
+        if spect.size(2) > audio.size(1):
+            spect = spect[:, :, :audio.size(1)]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
+
+        audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
+        output_audio = []
+        log_s_list = []
+        log_det_W_list = []
+
+        for k in range(self.n_flows):
+            if k % self.n_early_every == 0 and k > 0:
+                output_audio.append(audio[:,:self.n_early_size,:])
+                audio = audio[:,self.n_early_size:,:]
+
+            audio, log_det_W = self.convinv[k](audio)
+            log_det_W_list.append(log_det_W)
+
+            n_half = int(audio.size(1)/2)
+            audio_0 = audio[:,:n_half,:]
+            audio_1 = audio[:,n_half:,:]
+
+            output = self.WN[k]((audio_0, spect))
+            log_s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = torch.exp(log_s)*audio_1 + b
+            log_s_list.append(log_s)
+
+            audio = torch.cat([audio_0, audio_1],1)
+
+        output_audio.append(audio)
+        return torch.cat(output_audio,1), log_s_list, log_det_W_list
+
+    def infer(self, spect, sigma=1.0):
+        print("+++++++++++++++++++++glow.py")
+        spect = self.upsample(spect)
+        # trim conv artifacts. maybe pad spec to kernel multiple
+        time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
+        spect = spect[:, :, :-time_cutoff]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
+
+        if spect.type() == 'torch.cuda.HalfTensor':
+            audio = torch.cuda.HalfTensor(spect.size(0),
+                                          self.n_remaining_channels,
+                                          spect.size(2)).normal_()
+        else:
+            audio = torch.cuda.FloatTensor(spect.size(0),
+                                           self.n_remaining_channels,
+                                           spect.size(2)).normal_()
+
+        audio = torch.autograd.Variable(sigma*audio)
+
+        for k in reversed(range(self.n_flows)):
+            n_half = int(audio.size(1)/2)
+            audio_0 = audio[:,:n_half,:]
+            audio_1 = audio[:,n_half:,:]
+
+            output = self.WN[k]((audio_0, spect))
+            s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = (audio_1 - b)/torch.exp(s)
+            audio = torch.cat([audio_0, audio_1],1)
+
+            audio = self.convinv[k](audio, reverse=True)
+
+            if k % self.n_early_every == 0 and k > 0:
+                if spect.type() == 'torch.cuda.HalfTensor':
+                    z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
+                else:
+                    z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
+                audio = torch.cat((sigma*z, audio),1)
+
+        audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
+        return audio
+
+    @staticmethod
+    def remove_weightnorm(model):
+        waveglow = model
+        for WN in waveglow.WN:
+            WN.start = torch.nn.utils.remove_weight_norm(WN.start)
+            WN.in_layers = remove(WN.in_layers)
+            WN.cond_layers = remove(WN.cond_layers)
+            WN.res_skip_layers = remove(WN.res_skip_layers)
+        return waveglow
+
+
+def remove(conv_list):
+    new_conv_list = torch.nn.ModuleList()
+    for old_conv in conv_list:
+        old_conv = torch.nn.utils.remove_weight_norm(old_conv)
+        new_conv_list.append(old_conv)
+    return new_conv_list
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow_old.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow_old.py
new file mode 100644
index 00000000..c5d3e3d2
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/glow_old.py
@@ -0,0 +1,269 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import copy
+import torch
+from waveglow.glow import Invertible1x1Conv, remove
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a+input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class WN(torch.nn.Module):
+    """
+    This is the WaveNet like layer for the affine coupling.  The primary difference
+    from WaveNet is the convolutions need not be causal.  There is also no dilation
+    size reset.  The dilation only doubles on each layer
+    """
+    def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
+                 kernel_size):
+        super(WN, self).__init__()
+        assert(kernel_size % 2 == 1)
+        assert(n_channels % 2 == 0)
+        self.n_layers = n_layers
+        self.n_channels = n_channels
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.cond_layers = torch.nn.ModuleList()
+
+        start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
+        start = torch.nn.utils.weight_norm(start, name='weight')
+        self.start = start
+
+        # Initializing last layer to 0 makes the affine coupling layers
+        # do nothing at first.  This helps with training stability
+        end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
+        end.weight.data.zero_()
+        end.bias.data.zero_()
+        self.end = end
+
+        for i in range(n_layers):
+            dilation = 2 ** i
+            padding = int((kernel_size*dilation - dilation)/2)
+            in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1)
+            cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+            self.cond_layers.append(cond_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2*n_channels
+            else:
+                res_skip_channels = n_channels
+            res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, forward_input):
+        audio, spect = forward_input
+        audio = self.start(audio)
+
+        for i in range(self.n_layers):
+            acts = fused_add_tanh_sigmoid_multiply(
+                self.in_layers[i](audio),
+                self.cond_layers[i](spect),
+                torch.IntTensor([self.n_channels]))
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                audio = res_skip_acts[:,:self.n_channels,:] + audio
+                skip_acts = res_skip_acts[:,self.n_channels:,:]
+            else:
+                skip_acts = res_skip_acts
+
+            if i == 0:
+                output = skip_acts
+            else:
+                output = skip_acts + output
+        return self.end(output)
+
+
+class WaveGlow(torch.nn.Module):
+    def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
+                 n_early_size, WN_config):
+        super(WaveGlow, self).__init__()
+
+        self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
+                                                 n_mel_channels,
+                                                 1024, stride=256)
+        assert(n_group % 2 == 0)
+        self.n_flows = n_flows
+        self.n_group = n_group
+        self.n_early_every = n_early_every
+        self.n_early_size = n_early_size
+        self.WN = torch.nn.ModuleList()
+        self.convinv = torch.nn.ModuleList()
+
+        n_half = int(n_group/2)
+
+        # Set up layers with the right sizes based on how many dimensions
+        # have been output already
+        n_remaining_channels = n_group
+        for k in range(n_flows):
+            if k % self.n_early_every == 0 and k > 0:
+                n_half = n_half - int(self.n_early_size/2)
+                n_remaining_channels = n_remaining_channels - self.n_early_size
+            self.convinv.append(Invertible1x1Conv(n_remaining_channels))
+            self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
+        self.n_remaining_channels = n_remaining_channels  # Useful during inference
+
+    def forward(self, forward_input):
+        return None
+        """
+        forward_input[0] = audio: batch x time
+        forward_input[1] = upsamp_spectrogram:  batch x n_cond_channels x time
+        """
+        """
+        spect, audio = forward_input
+
+        #  Upsample spectrogram to size of audio
+        spect = self.upsample(spect)
+        assert(spect.size(2) >= audio.size(1))
+        if spect.size(2) > audio.size(1):
+            spect = spect[:, :, :audio.size(1)]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
+
+        audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
+        output_audio = []
+        s_list = []
+        s_conv_list = []
+
+        for k in range(self.n_flows):
+            if k%4 == 0 and k > 0:
+                output_audio.append(audio[:,:self.n_multi,:])
+                audio = audio[:,self.n_multi:,:]
+
+            # project to new basis
+            audio, s = self.convinv[k](audio)
+            s_conv_list.append(s)
+
+            n_half = int(audio.size(1)/2)
+            if k%2 == 0:
+                audio_0 = audio[:,:n_half,:]
+                audio_1 = audio[:,n_half:,:]
+            else:
+                audio_1 = audio[:,:n_half,:]
+                audio_0 = audio[:,n_half:,:]
+
+            output = self.nn[k]((audio_0, spect))
+            s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = torch.exp(s)*audio_1 + b
+            s_list.append(s)
+
+            if k%2 == 0:
+                audio = torch.cat([audio[:,:n_half,:], audio_1],1)
+            else:
+                audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
+        output_audio.append(audio)
+        return torch.cat(output_audio,1), s_list, s_conv_list
+        """
+
+    def infer(self, spect, sigma=1.0):
+        spect = self.upsample(spect)
+        # trim conv artifacts. maybe pad spec to kernel multiple
+        time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
+        spect = spect[:, :, :-time_cutoff]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
+
+        if spect.type() == 'torch.cuda.HalfTensor':
+            audio = torch.cuda.HalfTensor(spect.size(0),
+                                          self.n_remaining_channels,
+                                          spect.size(2)).normal_()
+        elif spect.type() == 'torch.cuda.FloatTensor':
+            audio = torch.cuda.FloatTensor(spect.size(0),
+                                           self.n_remaining_channels,
+                                           spect.size(2)).normal_()
+        else:
+            audio = torch.FloatTensor(spect.size(0),
+                                      self.n_remaining_channels,
+                                      spect.size(2)).normal_()
+            
+        audio = torch.autograd.Variable(sigma*audio)
+
+        for k in reversed(range(self.n_flows)):
+            n_half = int(audio.size(1)/2)
+            if k%2 == 0:
+                audio_0 = audio[:,:n_half,:]
+                audio_1 = audio[:,n_half:,:]
+            else:
+                audio_1 = audio[:,:n_half,:]
+                audio_0 = audio[:,n_half:,:]
+
+            output = self.WN[k]((audio_0, spect))
+            s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = (audio_1 - b)/torch.exp(s)
+            if k%2 == 0:
+                audio = torch.cat([audio[:,:n_half,:], audio_1],1)
+            else:
+                audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
+
+            audio = self.convinv[k](audio, reverse=True)
+
+            if k%4 == 0 and k > 0:
+                if spect.type() == 'torch.cuda.HalfTensor':
+                    z = torch.cuda.HalfTensor(spect.size(0),
+                                              self.n_early_size,
+                                              spect.size(2)).normal_()
+                elif spect.type() == 'torch.cuda.FloatTensor':
+                    z = torch.cuda.FloatTensor(spect.size(0),
+                                               self.n_early_size,
+                                               spect.size(2)).normal_()
+                else:
+                    z = torch.FloatTensor(spect.size(0),
+                                          self.n_early_size,
+                                          spect.size(2)).normal_()
+                    
+                audio = torch.cat((sigma*z, audio),1)
+
+        return audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
+
+    @staticmethod
+    def remove_weightnorm(model):
+        waveglow = model
+        for WN in waveglow.WN:
+            WN.start = torch.nn.utils.remove_weight_norm(WN.start)
+            WN.in_layers = remove(WN.in_layers)
+            WN.cond_layers = remove(WN.cond_layers)
+            WN.res_skip_layers = remove(WN.res_skip_layers)
+        return waveglow
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/loss_function.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/loss_function.py
new file mode 100644
index 00000000..efc00db7
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/loss_function.py
@@ -0,0 +1,48 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import torch
+
+class WaveGlowLoss(torch.nn.Module):
+    def __init__(self, sigma=1.0):
+        super(WaveGlowLoss, self).__init__()
+        self.sigma = sigma
+
+    def forward(self, model_output, clean_audio):
+        # clean_audio is unused;
+        z, log_s_list, log_det_W_list = model_output
+        for i, log_s in enumerate(log_s_list):
+            if i == 0:
+                log_s_total = torch.sum(log_s)
+                log_det_W_total = log_det_W_list[i]
+            else:
+                log_s_total = log_s_total + torch.sum(log_s)
+                log_det_W_total += log_det_W_list[i]
+
+        loss = torch.sum(
+            z * z) / (2 * self.sigma * self.sigma) - log_s_total - log_det_W_total  # noqa: E501
+        return loss / (z.size(0) * z.size(1) * z.size(2))
diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/model.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/model.py
new file mode 100644
index 00000000..cead885b
--- /dev/null
+++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/model.py
@@ -0,0 +1,294 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+import torch
+from torch.autograd import Variable
+import torch.nn.functional as F
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Invertible1x1Conv(torch.nn.Module):
+    """
+    The layer outputs both the convolution, and the log determinant
+    of its weight matrix.  If reverse=True it does convolution with
+    inverse
+    """
+
+    def __init__(self, c):
+        super(Invertible1x1Conv, self).__init__()
+        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
+                                    bias=False)
+
+        # Sample a random orthonormal matrix to initialize weights
+        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
+
+        # Ensure determinant is 1.0 not -1.0
+        if torch.det(W) < 0:
+            W[:, 0] = -1 * W[:, 0]
+        W = W.view(c, c, 1)
+        self.conv.weight.data = W
+
+    def forward(self, z, reverse=False):
+        # shape
+        batch_size, group_size, n_of_groups = z.size()
+
+        W = self.conv.weight.squeeze()
+
+        if reverse:
+            if not hasattr(self, 'W_inverse'):
+                # Reverse computation
+                W_inverse = W.inverse()
+                W_inverse = Variable(W_inverse[..., None])
+                if z.type() == 'torch.cuda.HalfTensor' or z.type() == 'torch.HalfTensor':
+                    W_inverse = W_inverse.half()
+                self.W_inverse = W_inverse
+            z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
+            return z
+        else:
+            # Forward computation
+            log_det_W = torch.logdet(W)
+            log_det_W = batch_size * n_of_groups * log_det_W
+            if z.dtype == torch.float16:
+                z = self.conv(z.float()).half()
+            else:
+                z = self.conv(z)
+            return z, log_det_W
+
+
+class WN(torch.nn.Module):
+    """
+    This is the WaveNet like layer for the affine coupling.  The primary
+    difference from WaveNet is the convolutions need not be causal.  There is
+    also no dilation size reset.  The dilation only doubles on each layer
+    """
+
+    def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
+                 kernel_size):
+        super(WN, self).__init__()
+        assert(kernel_size % 2 == 1)
+        assert(n_channels % 2 == 0)
+        self.n_layers = n_layers
+        self.n_channels = n_channels
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.cond_layers = torch.nn.ModuleList()
+
+        start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
+        start = torch.nn.utils.weight_norm(start, name='weight')
+        self.start = start
+
+        # Initializing last layer to 0 makes the affine coupling layers
+        # do nothing at first.  This helps with training stability
+        end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1)
+        end.weight.data.zero_()
+        end.bias.data.zero_()
+        self.end = end
+
+        for i in range(n_layers):
+            dilation = 2 ** i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(n_channels, 2 * n_channels, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels, 1)
+            cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+            self.cond_layers.append(cond_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * n_channels
+            else:
+                res_skip_channels = n_channels
+            res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(
+                res_skip_layer, name='weight')
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, forward_input):
+        audio, spect = forward_input
+        audio = self.start(audio)
+
+        for i in range(self.n_layers):
+            acts = fused_add_tanh_sigmoid_multiply(
+                self.in_layers[i](audio),
+                self.cond_layers[i](spect),
+                torch.IntTensor([self.n_channels]))
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                audio = res_skip_acts[:, :self.n_channels, :] + audio
+                skip_acts = res_skip_acts[:, self.n_channels:, :]
+            else:
+                skip_acts = res_skip_acts
+
+            if i == 0:
+                output = skip_acts
+            else:
+                output = skip_acts + output
+        return self.end(output)
+
+
+class WaveGlow(torch.nn.Module):
+    def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
+                 n_early_size, WN_config):
+        super(WaveGlow, self).__init__()
+
+        self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
+                                                 n_mel_channels,
+                                                 1024, stride=256)
+        assert(n_group % 2 == 0)
+        self.n_flows = n_flows
+        self.n_group = n_group
+        self.n_early_every = n_early_every
+        self.n_early_size = n_early_size
+        self.WN = torch.nn.ModuleList()
+        self.convinv = torch.nn.ModuleList()
+
+        n_half = int(n_group / 2)
+
+        # Set up layers with the right sizes based on how many dimensions
+        # have been output already
+        n_remaining_channels = n_group
+        for k in range(n_flows):
+            if k % self.n_early_every == 0 and k > 0:
+                n_half = n_half - int(self.n_early_size / 2)
+                n_remaining_channels = n_remaining_channels - self.n_early_size
+            self.convinv.append(Invertible1x1Conv(n_remaining_channels))
+            self.WN.append(WN(n_half, n_mel_channels * n_group, **WN_config))
+        self.n_remaining_channels = n_remaining_channels
+
+    def forward(self, forward_input):
+        """
+        forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
+        forward_input[1] = audio: batch x time
+        """
+        spect, audio = forward_input
+
+        #  Upsample spectrogram to size of audio
+        spect = self.upsample(spect)
+        assert(spect.size(2) >= audio.size(1))
+        if spect.size(2) > audio.size(1):
+            spect = spect[:, :, :audio.size(1)]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1)
+        spect = spect.permute(0, 2, 1)
+
+        audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
+        output_audio = []
+        log_s_list = []
+        log_det_W_list = []
+
+        for k in range(self.n_flows):
+            if k % self.n_early_every == 0 and k > 0:
+                output_audio.append(audio[:, :self.n_early_size, :])
+                audio = audio[:, self.n_early_size:, :]
+
+            audio, log_det_W = self.convinv[k](audio)
+            log_det_W_list.append(log_det_W)
+
+            n_half = int(audio.size(1) / 2)
+            audio_0 = audio[:, :n_half, :]
+            audio_1 = audio[:, n_half:, :]
+
+            output = self.WN[k]((audio_0, spect))
+            log_s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = torch.exp(log_s) * audio_1 + b
+            log_s_list.append(log_s)
+
+            audio = torch.cat([audio_0, audio_1], 1)
+
+        output_audio.append(audio)
+        return torch.cat(output_audio, 1), log_s_list, log_det_W_list
+
+    def infer(self, spect, sigma=1.0):
+        spect = self.upsample(spect)
+        # trim conv artifacts. maybe pad spec to kernel multiple
+        time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
+        spect = spect[:, :, :-time_cutoff]
+
+        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
+        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1)
+        spect = spect.permute(0, 2, 1)
+
+        audio = torch.randn(spect.size(0),
+                            self.n_remaining_channels,
+                            spect.size(2), device=spect.device).to(spect.dtype)
+
+        audio = torch.autograd.Variable(sigma * audio)
+
+        for k in reversed(range(self.n_flows)):
+            n_half = int(audio.size(1) / 2)
+            audio_0 = audio[:, :n_half, :]
+            audio_1 = audio[:, n_half:, :]
+
+            output = self.WN[k]((audio_0, spect))
+            s = output[:, n_half:, :]
+            b = output[:, :n_half, :]
+            audio_1 = (audio_1 - b) / torch.exp(s)
+            audio = torch.cat([audio_0, audio_1], 1)
+
+            audio = self.convinv[k](audio, reverse=True)
+
+            if k % self.n_early_every == 0 and k > 0:
+                z = torch.randn(spect.size(0), self.n_early_size, spect.size(
+                    2), device=spect.device).to(spect.dtype)
+                audio = torch.cat((sigma * z, audio), 1)
+
+        audio = audio.permute(
+            0, 2, 1).contiguous().view(
+            audio.size(0), -1).data
+        return audio
+
+    @staticmethod
+    def remove_weightnorm(model):
+        waveglow = model
+        for WN in waveglow.WN:
+            WN.start = torch.nn.utils.remove_weight_norm(WN.start)
+            WN.in_layers = remove(WN.in_layers)
+            WN.cond_layers = remove(WN.cond_layers)
+            WN.res_skip_layers = remove(WN.res_skip_layers)
+        return waveglow
+
+
+def remove(conv_list):
+    new_conv_list = torch.nn.ModuleList()
+    for old_conv in conv_list:
+        old_conv = torch.nn.utils.remove_weight_norm(old_conv)
+        new_conv_list.append(old_conv)
+    return new_conv_list
diff --git a/PyTorch/Translation/GNMT/seq2seq/models/decoder.py b/PyTorch/Translation/GNMT/seq2seq/models/decoder.py
index a7a3253a..ac86655a 100644
--- a/PyTorch/Translation/GNMT/seq2seq/models/decoder.py
+++ b/PyTorch/Translation/GNMT/seq2seq/models/decoder.py
@@ -141,7 +141,8 @@ class ResidualRecurrentDecoder(nn.Module):
         else:
             self.embedder = nn.Embedding(vocab_size, hidden_size,
                                          padding_idx=config.PAD)
-            nn.init.uniform_(embedder.weight.data, -init_weight, init_weight)
+            nn.init.uniform_(self.embedder.weight.data, -init_weight,
+                             init_weight)
 
         self.classifier = Classifier(hidden_size, vocab_size)
         self.dropout = nn.Dropout(p=dropout)
diff --git a/PyTorch/Translation/GNMT/seq2seq/models/encoder.py b/PyTorch/Translation/GNMT/seq2seq/models/encoder.py
index 92211820..16a7d7ef 100644
--- a/PyTorch/Translation/GNMT/seq2seq/models/encoder.py
+++ b/PyTorch/Translation/GNMT/seq2seq/models/encoder.py
@@ -60,7 +60,8 @@ class ResidualRecurrentEncoder(nn.Module):
         else:
             self.embedder = nn.Embedding(vocab_size, hidden_size,
                                          padding_idx=config.PAD)
-            nn.init.uniform_(embedder.weight.data, -init_weight, init_weight)
+            nn.init.uniform_(self.embedder.weight.data, -init_weight,
+                             init_weight)
 
     def forward(self, inputs, lengths):
         """
diff --git a/TensorFlow/Classification/RN50v1.5/.gitignore b/TensorFlow/Classification/RN50v1.5/.gitignore
new file mode 100644
index 00000000..e6df37d4
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/.gitignore
@@ -0,0 +1,126 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+*.whl
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Python IDEs
+.idea/
+.vscode/
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+models/
+
+# Testing
+tests/
+pytest.ini
+requirements_test.txt
diff --git a/TensorFlow/Classification/RN50v1.5/.style.yapf b/TensorFlow/Classification/RN50v1.5/.style.yapf
new file mode 100644
index 00000000..723fd02c
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/.style.yapf
@@ -0,0 +1,187 @@
+[style]
+based_on_style=google
+
+# The number of columns to use for indentation.
+indent_width = 4
+
+# The column limit.
+column_limit=120
+
+use_tabs = False
+
+# Align closing bracket with visual indentation.
+align_closing_bracket_with_visual_indent = False
+
+# Allow dictionary keys to exist on multiple lines. For example:
+# x = {
+#     ('this is the first element of a tuple',
+#      'this is the second element of a tuple'):
+#          value,
+# }
+allow_multiline_dictionary_keys = True
+
+# Allow splits before the dictionary value.
+allow_split_before_dict_value = True
+
+# Allow lambdas to be formatted on more than one line.
+allow_multiline_lambdas = True
+
+# Insert a blank line before a def or class immediately nested within another def or class. For example:
+#
+# class Foo:
+#                    # <------ this blank line
+#     def method():
+#         pass
+blank_line_before_nested_class_or_def = True
+
+# Insert a blank line before a module docstring.
+blank_line_before_module_docstring = True
+
+# Insert a blank line before a class-level docstring.
+blank_line_before_class_docstring = False
+
+# Sets the number of desired blank lines surrounding top-level function and class definitions. For example:
+#
+# class Foo:
+#     pass
+#                    # <------ having two blank lines here
+#                    # <------ is the default setting
+# class Bar:
+#     pass
+blank_lines_around_top_level_definition = 2
+
+# Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS is set
+coalesce_brackets = False
+
+# The style for continuation alignment. Possible values are:
+#
+# SPACE: Use spaces for continuation alignment. This is default behavior.
+#
+# FIXED: Use fixed number (CONTINUATION_INDENT_WIDTH) of columns (ie: CONTINUATION_INDENT_WIDTH/INDENT_WIDTH tabs) for continuation alignment.
+#
+# VALIGN-RIGHT: Vertically align continuation lines with indent characters. Slightly right (one more indent character) if cannot vertically align continuation lines with indent characters.
+#
+# For options FIXED, and VALIGN-RIGHT are only available when USE_TABS is enabled.
+continuation_align_style = SPACE
+
+# Indent width used for line continuations.
+continuation_indent_width = 4
+
+# Put closing brackets on a separate line, dedented, if the bracketed
+# expression can't fit in a single line. Applies to all kinds of brackets,
+# including function definitions and calls. For example:
+#
+#   config = {
+#       'key1': 'value1',
+#       'key2': 'value2',
+#   }        # <--- this bracket is dedented and on a separate line
+#
+#   time_series = self.remote_client.query_entity_counters(
+#       entity='dev3246.region1',
+#       key='dns.query_latency_tcp',
+#       transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
+#       start_ts=now()-timedelta(days=3),
+#       end_ts=now(),
+#   )        # <--- this bracket is dedented and on a separate line
+dedent_closing_brackets = True
+
+# Disable the heuristic which places each list element on a separate line if the list is comma-terminated.
+disable_ending_comma_heuristic = false
+
+# Place each dictionary entry onto its own line.
+each_dict_entry_on_separate_line = True
+
+# The regex for an internationalization comment.
+# The presence of this comment stops reformatting of that line,
+# because the comments are required to be next to the string they translate.
+i18n_comment = True
+
+# The internationalization function call names.
+# The presence of this function stops reformatting on that line,
+# because the string it has cannot be moved away from the i18n comment.
+i18n_function_call = True
+
+# Indent the dictionary value if it cannot fit on the same line as the dictionary key. For example:
+#
+# config = {
+#     'key1':
+#         'value1',
+#     'key2': value1 +
+#             value2,
+# }
+indent_dictionary_value = False
+
+# Join short lines into one line. E.g., single line if statements.
+join_multiple_lines = True
+
+# Do not include spaces around selected binary operators.
+# Example: 1 + 2 * 3 - 4 / 5     =>     1 + 2*3 - 4/5
+no_spaces_around_selected_binary_operators = True
+
+# Set to True to prefer spaces around the assignment operator for default or keyword arguments.
+spaces_around_default_or_named_assign = False
+
+# Set to True to prefer using spaces around **.
+spaces_around_power_operator = False
+
+# The number of spaces required before a trailing comment.
+spaces_before_comment = 2
+
+# Insert a space between the ending comma and closing bracket of a list, etc.
+space_between_ending_comma_and_closing_bracket = True
+
+# Split before arguments if the argument list is terminated by a comma.
+split_arguments_when_comma_terminated = True
+
+# If a comma separated list (dict, list, tuple, or function def) is on a line that is too long,
+# split such that all elements are on a single line.
+split_all_comma_separated_values = False
+
+# Set to True to prefer splitting before &, | or ^ rather than after.
+split_before_bitwise_operator = False
+
+# Split before the closing bracket if a list or dict literal doesn't fit on a single line.
+split_before_closing_bracket = True
+
+# Split before a dictionary or set generator (comp_for). For example, note the split before the for:
+#
+# foo = {
+#     variable: 'Hello world, have a nice day!'
+#     for variable in bar if variable != 42
+# }
+split_before_dict_set_generator = True
+
+# Split after the opening paren which surrounds an expression if it doesn't fit on a single line.
+split_before_expression_after_opening_paren = False
+
+# If an argument / parameter list is going to be split, then split before the first argument.
+split_before_first_argument = False
+
+# Set to True to prefer splitting before and or or rather than after.
+split_before_logical_operator  = False
+
+# Split named assignments onto individual lines.
+split_before_named_assigns = True
+
+# For list comprehensions and generator expressions with multiple clauses (e.g multiple "for" calls, "if" filter expressions) and which need to be reflowed, split each clause onto its own line. For example:
+#
+# result = [
+#     a_var + b_var for a_var in xrange(1000) for b_var in xrange(1000)
+#     if a_var % b_var]
+# would reformat to something like:
+#
+# result = [
+#     a_var + b_var
+#     for a_var in xrange(1000)
+#     for b_var in xrange(1000)
+#     if a_var % b_var]
+split_complex_comprehension = True
+
+split_penalty_after_opening_bracket = 500
+split_penalty_before_if_expr = 500
+split_penalty_bitwise_operator = 500
+split_penalty_comprehension = 500
+split_penalty_excess_character = 500
+split_penalty_for_added_line_split = 1000
+split_penalty_import_names = 500
+split_penalty_logical_operator = 500
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/Dockerfile b/TensorFlow/Classification/RN50v1.5/Dockerfile
new file mode 100644
index 00000000..3e9a796f
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/Dockerfile
@@ -0,0 +1,6 @@
+FROM nvcr.io/nvidia/tensorflow:19.01-py3
+## MAINTAINER Paweł Sołtysiak <psoltysiak@nvidia.com>
+
+ADD . /workspace/rn50v15_tf
+WORKDIR /workspace/rn50v15_tf
+RUN pip install -r requirements.txt
diff --git a/TensorFlow/Classification/RN50v1.5/LICENSE b/TensorFlow/Classification/RN50v1.5/LICENSE
new file mode 100644
index 00000000..3d1e9454
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/LICENSE
@@ -0,0 +1,11 @@
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/README.md b/TensorFlow/Classification/RN50v1.5/README.md
new file mode 100644
index 00000000..b57e06f4
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/README.md
@@ -0,0 +1,335 @@
+# ResNet-50 v1.5 for TensorFlow
+
+## Table Of Contents
+* [The model](#the-model)
+    * [Default configuration](#default-configuration)
+    * [Data Augmentation](#data-augmentation)
+    * [Other training recipes](#other-training-recipes)
+* [Setup](#setup)
+    * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+    * [Command line options](#command-line-options)
+    * [Training process](#training-process)
+    * [Enabling mixed precision](#enabling-mixed-precision)
+* [Benchmarking](#benchmarking)
+    * [Training performance benchmark](#training-performance-benchmark)
+    * [Inference performance benchmark](#inference-performance-benchmark)
+* [Results](#results)
+    * [Training accuracy results](#training-accuracy-results)
+    * [Training performance results](#training-performance-results)
+    * [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+# The model
+The ResNet50 v1.5 model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).
+
+The difference between v1 and v1.5 is in the bottleneck blocks which requires
+downsampling, for example, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
+
+This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1,
+but comes with a small performance drawback (~5% imgs/sec).
+
+The following features were implemented in this model:
+* Data-parallel multi-GPU training with Horovod
+* Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable. Tensor Core operations to maximize throughput using NVIDIA Volta GPUs.
+* Static loss scaling for Tensor Cores (mixed precision) training
+
+The following performance optimizations were implemented in this model:
+* XLA support (experimental)
+
+## Default configuration
+
+This model trains for 90 epochs, with default ResNet50 v1.5 setup:
+
+* SGD with momentum (0.9)
+
+* Learning rate = 0.1 for 256 batch size, for other batch sizes we linearly
+scale the learning rate.
+
+* Learning rate decay - multiply by 0.1 after 30, 60, and 80 epochs
+
+* For bigger batch sizes (512 and up) we use linear warmup of the learning rate
+during first 5 epochs according to [Training ImageNet in 1 hour](https://arxiv.org/abs/1706.02677).
+
+* Weight decay: 1e-4
+
+
+## Data Augmentation
+
+This model uses the following data augmentation:
+
+* During training, we perform the following augmentation techniques:
+  * Normalization
+  * Random resized crop to 224x224
+    * Scale from 8% to 100%
+    * Aspect ratio from 3/4 to 4/3
+  * Random horizontal flip
+
+* During inference, we perform the following augmentation techniques:
+  * Normalization
+  * Scale to 256x256
+  * Center crop to 224x224
+
+
+## Other training recipes
+
+This script does not target any specific benchmark.
+There are changes that others have made which can speed up convergence and/or increase accuracy.
+
+One of the more popular training recipes is provided by [fast.ai](https://github.com/fastai/imagenet-fast).
+
+The fast.ai recipe introduces many changes to the training procedure, one of which is progressive resizing of the training images.
+
+The first part of training uses 128px images, the middle part uses 224px images, and the last part uses 288px images.
+The final validation is performed on 288px images.
+
+The training script in this repository performs validation on 224px images, just like the original paper described.
+
+These two approaches can't be directly compared, since the fast.ai recipe requires validation on 288px images,
+and this recipe keeps the original assumption that validation is done on 224px images.
+
+Using 288px images means that a lot more FLOPs are needed during inference to reach the same accuracy.
+
+# Setup
+
+The following section list the requirements that you need to meet in order to use the ResNet50 v1.5 model.
+
+## Requirements
+This repository contains Dockerfile which extends the Tensorflow NGC container and encapsulates all dependencies.  Aside from these dependencies, ensure you have the following software:
+
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [TensorFlow 19.03-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
+* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html),
+* [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+* [Running Tensorflow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running).
+
+# Quick start guide
+To train your model using mixed precision with tensor cores, perform the following steps using the default parameters of the ResNet-50 v1.5 model on the [ImageNet](http://www.image-net.org/) dataset.
+
+## 1. Download and preprocess the dataset.
+The ResNet50 v1.5 script operates on ImageNet 1k, a widely popular image classification dataset from ILSVRC challenge.
+
+To download and preprocess the dataset, use the [Generate ImageNet for TensorFlow](https://github.com/tensorflow/models/blob/master/research/inception/inception/data/download_and_preprocess_imagenet.sh) script. The dataset will be downloaded to a directory specified as the first parameter of the script.
+
+
+## 2. Build the ResNet-50 v1.5 TensorFlow NGC container.
+```bash
+bash scripts/docker/build.sh
+```
+
+## 3. Start an interactive session in the NGC container to run training/inference.
+After you build the container image, you can start an interactive CLI session with
+```bash
+bash scripts/docker/interactive.sh
+```
+The interactive.sh script requires that the location on the dataset is specified.  For example, /data.
+
+## 4. Start training.
+To run training for a default configuration (as described in [Default configuration](#default-configuration), for example 1/4/8 GPUs, FP16/FP32), run one of the scripts in the ./scripts directory called `./scripts/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh`. Each of the scripts require three parameters: 
+* path to the root directory of the model as the first argument
+* path to the dataset as a second argument 
+* path to the results destination as a third argument
+
+For example:
+```bash
+./scripts/RN50_FP16_8GPU.sh <path to model> <path to dataset> <path to results>
+```
+
+## 5. Start validation/evaluation.
+
+Model evaluation on a checkpoint can be launched by running  one of the `./scripts/RN50_{FP16, FP32}_EVAL.sh` scripts in the `./scripts` directory. Each of the scripts requires three parameters: 
+* path to the root directory of the model as the first argument
+* path to the dataset as a second argument
+* path to the results destination as a third argument
+
+For example:
+```bash
+./scripts/RN50_FP16_EVAL.sh <path to model> <path to dataset> <path to results>
+```
+
+To run a non-default configuration, use:
+
+`python ./main.py --mode=evaluate --use_tf_amp --batch_size <batch size> --data_dir=<path to imagenet> --results_dir=<path to checkpoint>`
+
+
+# Details
+
+## Command line options
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for example:
+
+```
+python main.py --help
+```
+
+To summarize, the most important arguments are as follows:
+
+```
+  --mode {train,train_and_evaluate,evaluate,training_benchmark,inference_benchmark}
+                        The execution mode of the script.
+                        
+  --data_dir DATA_DIR   Path to dataset in TFRecord format. Files should be
+                        named 'train-*' and 'validation-*'.
+                        
+  --batch_size BATCH_SIZE
+                        Size of each minibatch per GPU.
+                                               
+  --num_iter NUM_ITER   Number of iterations to run.
+  
+  --iter_unit {epoch,batch}
+                        Unit of iterations.
+                        
+  --results_dir RESULTS_DIR
+                        Directory in which to write training logs, summaries
+                        and checkpoints.
+                        
+  --loss_scale LOSS_SCALE
+                        Loss scale for mixed precision training.
+                        
+  --use_auto_loss_scaling
+                        Use automatic loss scaling in fp32 AMP.
+                        
+  --use_xla             Enable XLA (Accelerated Linear Algebra) computation
+                        for improved performance.
+                        
+  --use_tf_amp          Enable Automatic Mixed Precision to speedup fp32
+                        computation using tensor cores.
+                                           
+```
+
+## Training process
+To run a configuration that is not based on the default parameters, use:
+
+* For 1 GPU
+    * FP32
+        `python ./main.py --batch_size=128 --data_dir=<path to imagenet> --results_dir=<path to results>`
+    * FP16
+        `python ./main.py --batch_size=256 --use_tf_amp --data_dir=<path to imagenet> --results_dir=<path to results>`
+
+* For multiple GPUs
+    * FP32
+        `mpiexec --allow-run-as-root --bind-to socket -np <num_gpus> python ./main.py --batch_size=128 --data_dir=<path to imagenet> --results_dir=<path to results>`
+    * FP16
+        `mpiexec --allow-run-as-root --bind-to socket -np <num_gpus> python ./main.py --batch_size=256 --use_tf_amp --data_dir=<path to imagenet> --results_dir=<path to results>`
+
+
+## Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network.  Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.  Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously required two steps:
+
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values. 
+
+This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code.  AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.
+
+In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
+
+For information about:
+ * How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+ * How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
+ * Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+
+
+# Benchmarking
+
+The following sections shows how to run benchmarks measuring the model performance in training and inference modes.
+
+## Training performance benchmark
+
+To benchmark the training performance on a specific batch size, run:
+
+* For 1 GPU
+    * FP32
+        `python ./main.py --mode=training_benchmark --warmup_steps 200 --batch_size <batch size> --data_dir=<path to imagenet> --results_dir=<path to results directory>`
+        
+    * FP16
+        `python ./main.py --mode=training_benchmark  --use_tf_amp --warmup_steps 200 --batch_size <batch size> --data_dir=<path to imagenet> --results_dir=<path to results directory>`
+        
+* For multiple GPUs
+    * FP32
+        `mpiexec --allow-run-as-root --bind-to socket -np <num_gpus> python ./main.py --mode=training_benchmark --batch_size <batch size> --data_dir=<path to imagenet> --results_dir=<path to results directory>`
+        
+    * FP16
+        `mpiexec --allow-run-as-root --bind-to socket -np <num_gpus> python ./main.py --mode=training_benchmark --use_tf_amp --batch_size <batch size> --data_dir=<path to imagenet> --results_dir=<path to results directory>`
+        
+        
+Each of these scripts runs 200 warm-up iterations and measures the first epoch.
+
+To control warmup and benchmark length, use `--warmup_steps`, `--num_iter` and `--iter_unit` flags.
+
+
+## Inference performance benchmark
+
+To benchmark the inference performance on a specific batch size, run:
+
+* FP32
+`python ./main.py --mode=inference_benchmark --precision=fp32 --warmup_steps 20 --train_iter 100 --iter_unit batch --batch_size <batch size> --data_dir=<path to imagenet> --log_dir=<path to results directory>`
+
+* FP16
+`python ./main.py --mode=inference_benchmark --precision=fp16 --warmup_steps 20 --train_iter 100 --iter_unit batch --batch_size <batch size> --data_dir=<path to imagenet> --log_dir=<path to results directory>`
+
+Each of these scripts, by default runs 20 warm-up iterations and measures the next 80 iterations.
+
+To control warm-up and benchmark length, use `--warmup_steps`, `--num_iter` and `--iter_unit` flags.
+
+# Results
+
+The following sections provide details on how we achieved our results in training accuracy, performance and inference performance.
+
+## Training accuracy results
+
+Our results were obtained by running the `./scripts/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh` script in
+the tensorflow-19.02-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPUs.
+
+
+| **number of GPUs** | **mixed precision top1** | **mixed precision training time** | **FP32 top1** | **FP32 training time** |
+|:------------------:|:------------------------:|:---------------------------------:|:-------------:|:----------------------:|
+| **1**                  | 76.18                    | 41.3h                             | 76.38         | 89.4h                  |
+| **4**                  | 76.30                    | 10.5h                             | 76.30         | 22.4h                  |
+| **8**                  | 76.18                    | 5.6h                              | 76.26         | 11.5h                  |
+
+
+
+## Training performance results
+
+Our results were obtained by running the `./scripts/benchmarking/DGX1V_trainbench_fp16.sh` and `./scripts/benchmarking/DGX1V_trainbench_fp32.sh` scripts in the tensorflow-19.02-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPUs.
+
+
+| **number of GPUs** | **mixed precision img/s** | **FP32 img/s** | **mixed precision speedup** | **mixed precision weak scaling** | **FP32 weak scaling** |
+|:------------------:|:-------------------------:|:--------------:|:---------------------------:|:--------------------------------:|:---------------------:|
+| **1**                  | 818.3                     | 362.5          | 2.25                        | 1.00                             | 1.00                  |
+| **4**                  | 3276.6                    | 1419.4         | 2.30                        | 4.00                             | 3.92                  |
+| **8**                  | 6508.4                    | 2832.2         | 2.30                        | 7.95                             | 7.81                  |
+
+Our results were obtained by running the `./scripts/benchmarking/DGX1V_inferbench_fp16.sh` and `./scripts/benchmarking/DGX1V_inferbench_fp32.sh` scripts in the tensorflow-19.02-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPUs.
+
+Those results can be improved when [XLA](https://www.tensorflow.org/xla) is used 
+in conjunction with mixed precision, delivering up to 3.3x speedup over FP32 on a single GPU (~1179 img/s).
+However XLA is still considered experimental.
+
+## Inference performance results
+
+| **batch size** | **mixed precision img/s** | **FP32 img/s** |
+|:--------------:|:-------------------------:|:--------------:|
+|         **1** |   177.2 |   170.8 |      
+|         **2** |   325.7 |   308.4 |
+|         **4** |   587.0 |   499.4 |         
+|         **8** |  1002.9 |   688.3 |         
+|        **16** |  1408.5 |   854.9 |        
+|        **32** |  1687.0 |   964.4 |        
+|        **64** |  1907.7 |  1045.1 |
+|       **128** |  2077.3 |  1100.1 |       
+|       **256** |  2129.3 |  N/A    |
+
+
+
+# Changelog
+1. March 1, 2019
+  * Initial release
+
+# Known issues
+There are no known issues with this model.
diff --git a/TensorFlow/Classification/RN50v1.5/dllogger/__init__.py b/TensorFlow/Classification/RN50v1.5/dllogger/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Classification/RN50v1.5/dllogger/autologging.py b/TensorFlow/Classification/RN50v1.5/dllogger/autologging.py
new file mode 100644
index 00000000..4fb47f0d
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/dllogger/autologging.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import subprocess
+import xml.etree.ElementTree as ET
+
+from dllogger.logger import LOGGER
+
+#TODO: print CUDA version, container version etc
+
+def log_hardware():
+    # TODO: asserts - what if you cannot launch those commands?
+    # number of CPU threads
+    cpu_info_command = 'cat /proc/cpuinfo'
+    cpu_info = subprocess.run(cpu_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    cpu_num_index = len(cpu_info) - cpu_info[::-1].index(b'processor') + 1
+    cpu_num = int(cpu_info[cpu_num_index]) + 1
+
+    # CPU name
+    cpu_name_begin_index = cpu_info.index(b'name')
+    cpu_name_end_index = cpu_info.index(b'stepping')
+    cpu_name = b' '.join(cpu_info[cpu_name_begin_index + 2:cpu_name_end_index]).decode('utf-8')
+
+    LOGGER.log(key='cpu_info', value={"num": cpu_num, "name": cpu_name})
+
+    # RAM memory
+    ram_info_command = 'free -m -h'
+    ram_info = subprocess.run(ram_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    ram_index = ram_info.index(b'Mem:') + 1
+    ram = ram_info[ram_index].decode('utf-8')
+
+    LOGGER.log(key='mem_info', value={"ram": ram})
+
+    # GPU
+    nvidia_smi_command = 'nvidia-smi -q -x'
+    nvidia_smi_output = subprocess.run(nvidia_smi_command.split(), stdout=subprocess.PIPE).stdout
+    nvidia_smi = ET.fromstring(nvidia_smi_output)
+    gpus = nvidia_smi.findall('gpu')
+    ver = nvidia_smi.findall('driver_version')
+
+    LOGGER.log(key="gpu_info",
+                 value={
+                      "driver_version": ver[0].text,
+                      "num": len(gpus),
+                      "name": [g.find('product_name').text for g in gpus],
+                      "mem": [g.find('fb_memory_usage').find('total').text for g in gpus]})
+
+def log_args(args):
+    LOGGER.log(key='args', value=vars(args))
diff --git a/TensorFlow/Classification/RN50v1.5/dllogger/logger.py b/TensorFlow/Classification/RN50v1.5/dllogger/logger.py
new file mode 100644
index 00000000..2bae7434
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/dllogger/logger.py
@@ -0,0 +1,429 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import json
+import logging
+import os
+import inspect
+import sys
+import re
+from contextlib import contextmanager
+import functools
+from collections import OrderedDict
+
+NVLOGGER_NAME = 'nv_dl_logger'
+NVLOGGER_VERSION = '0.2.3'
+NVLOGGER_TOKEN = ':::NVLOG'
+
+MLPERF_NAME = 'mlperf_logger'
+MLPERF_VERSION = '0.5.0'
+MLPERF_TOKEN = ':::MLP'
+
+DEFAULT_JSON_FILENAME = 'nvlog.json'
+
+RUN_SCOPE = 0
+EPOCH_SCOPE = 1
+TRAIN_ITER_SCOPE = 2
+
+_data = OrderedDict([
+    ('model', None),
+    ('epoch', -1),
+    ('iteration', -1),
+    ('total_iteration', -1),
+    ('metrics', OrderedDict()),
+    ('timed_blocks', OrderedDict()),
+    ('current_scope', RUN_SCOPE)
+    ])
+
+def get_caller(root_dir=None):
+    stack_files = [s.filename.split('/')[-1] for s in inspect.stack()]
+    stack_index = 0
+    while stack_index < len(stack_files) and stack_files[stack_index] != 'logger.py':
+        stack_index += 1
+    while (stack_index < len(stack_files) and 
+            stack_files[stack_index] in ['logger.py', 'autologging.py', 'contextlib.py']):
+        stack_index += 1
+
+    caller = inspect.stack()[stack_index]
+
+    return "%s:%d" % (stack_files[stack_index], caller.lineno)
+
+class StandardMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        pass
+
+    def record(self, value):
+        self.value = value
+
+    def get_value(self):
+        return self.value
+
+    def get_last(self):
+        return self.value
+
+class AverageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.count = 0
+        self.value = 0
+        self.last = 0
+
+    def record(self, value, n = 1):
+        self.last = value
+        self.count += n
+        self.value += value * n
+
+    def get_value(self):
+        return self.value / self.count
+
+    def get_last(self):
+        return self.last
+
+class JsonBackend(object):
+
+    def __init__(self, log_file=DEFAULT_JSON_FILENAME, logging_scope=TRAIN_ITER_SCOPE,
+            iteration_interval=1):
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.json_log = OrderedDict([
+            ('run', OrderedDict()),
+            ('epoch', OrderedDict()),
+            ('iter', OrderedDict()),
+            ('event', OrderedDict()),
+            ])
+        
+        self.json_log['epoch']['x'] = []
+        if self.logging_scope == TRAIN_ITER_SCOPE:
+            self.json_log['iter']['x'] = [[]]
+
+    def register_metric(self, key, metric_scope):
+        if (metric_scope == TRAIN_ITER_SCOPE and 
+                self.logging_scope == TRAIN_ITER_SCOPE):
+            if not key in self.json_log['iter'].keys():
+                self.json_log['iter'][key] = [[]]
+        if metric_scope == EPOCH_SCOPE:
+            if not key in self.json_log['epoch'].keys():
+                self.json_log['epoch'][key] = []
+
+    def log(self, key, value):
+        if _data['current_scope'] == RUN_SCOPE:
+            self.json_log['run'][key] = value
+        elif _data['current_scope'] == EPOCH_SCOPE: 
+            pass
+        elif _data['current_scope'] == TRAIN_ITER_SCOPE:
+            pass
+        else:
+            raise ValueError('log function for scope "', _data['current_scope'], 
+                    '" not implemented')
+    
+    def log_event(self, key, value):
+        if not key in self.json_log['event'].keys():
+            self.json_log['event'][key] = []
+        entry = OrderedDict()
+        entry['epoch'] = _data['epoch']
+        entry['iter'] = _data['iteration']
+        entry['timestamp'] = time.time()
+        if value:
+            entry['value'] = value
+        self.json_log['event'][key].append(entry)
+
+    def log_iteration_summary(self):
+        if (self.logging_scope == TRAIN_ITER_SCOPE and 
+                _data['total_iteration'] % self.iteration_interval == 0):
+            for key, m in _data['metrics'].items():
+                if m.metric_scope == TRAIN_ITER_SCOPE:
+                    self.json_log['iter'][key][-1].append(m.get_last())
+
+            # log x for iteration number
+            self.json_log['iter']['x'][-1].append(_data['iteration'])
+
+
+    def dump_json(self):
+        if self.log_file is None:
+            print(json.dumps(self.json_log, indent=4))
+        else:
+            with open(self.log_file, 'w') as f:
+                json.dump(self.json_log, fp=f, indent=4)
+
+    def log_epoch_summary(self):
+        for key, m in _data['metrics'].items():
+            if m.metric_scope == EPOCH_SCOPE:
+                self.json_log['epoch'][key].append(m.get_value())
+            elif (m.metric_scope == TRAIN_ITER_SCOPE and 
+                    self.logging_scope == TRAIN_ITER_SCOPE):
+                # create new sublists for each iter metric in the next epoch
+                self.json_log['iter'][key].append([])
+        
+        # log x for epoch number
+        self.json_log['epoch']['x'].append(_data['epoch'])
+
+        # create new sublist for iter's x in the next epoch
+        if self.logging_scope == TRAIN_ITER_SCOPE:
+            self.json_log['iter']['x'].append([])
+
+        self.dump_json()
+
+    def timed_block_start(self, name):
+        pass
+
+    def timed_block_stop(self, name):
+        pass
+
+    def finish(self):
+        self.dump_json()
+
+class _ParentStdOutBackend(object):
+
+    def __init__(self, name, token, version, log_file, logging_scope, iteration_interval):
+
+        self.root_dir = None
+        self.worker = [0]
+        self.prefix = ''
+
+        self.name = name
+        self.token = token
+        self.version = version
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.logger = logging.getLogger(self.name)
+        self.logger.setLevel(logging.DEBUG)
+        self.logger.handlers = []
+
+        if (self.log_file == None):
+            self.stream_handler = logging.StreamHandler(stream=sys.stdout)
+            self.stream_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.stream_handler)
+        else:
+            self.file_handler = logging.FileHandler(self.log_file, mode='w')
+            self.file_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.file_handler)
+
+    def register_metric(self, key, meter=None, metric_scope=EPOCH_SCOPE):
+        pass
+
+    def log_epoch_summary(self):
+        pass
+
+    def log_iteration_summary(self):
+        pass
+
+    def log(self, key, value):
+        if _data['current_scope'] > self.logging_scope:
+            pass
+        elif (_data['current_scope'] == TRAIN_ITER_SCOPE and 
+                _data['total_iteration'] % self.iteration_interval != 0):
+            pass
+        else:
+            self.log_stdout(key, value)
+
+    def log_event(self, key, value):
+        self.log_stdout(key, value)
+        
+    def log_stdout(self, key, value=None, forced=False):
+        # TODO: worker 0 
+        # only the 0-worker will log
+        #if not forced and self.worker != 0:
+        #    pass
+
+        if value is None:
+            msg = key
+        else:
+            str_json = json.dumps(value)
+            msg = '{key}: {value}'.format(key=key, value=str_json)
+
+        call_site = get_caller(root_dir=self.root_dir)
+        now = time.time()
+
+        message = '{prefix}{token}v{ver} {model} {secs:.9f} ({call_site}) {msg}'.format(
+            prefix=self.prefix, token=self.token, ver=self.version, secs=now, 
+            model=_data['model'],
+            call_site=call_site, msg=msg)
+
+        self.logger.debug(message)
+
+    def timed_block_start(self, name):
+        self.log_stdout(key=name + "_start")
+
+    def timed_block_stop(self, name):
+        self.log_stdout(key=name + "_stop")
+
+    def finish(self):
+        pass
+
+class StdOutBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=EPOCH_SCOPE, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=NVLOGGER_NAME, token=NVLOGGER_TOKEN, 
+                version=NVLOGGER_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+        
+class MLPerfBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=TRAIN_ITER_SCOPE, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=MLPERF_NAME, token=MLPERF_TOKEN, 
+                version=MLPERF_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+
+class JoCBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=EPOCH_SCOPE, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=NVLOGGER_NAME, token=NVLOGGER_TOKEN, 
+                version=NVLOGGER_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+    
+    def finish(self):
+        for k in _data['metrics'].keys():
+            if isinstance(_data['metrics'][k], AverageMeter):
+                super(JoCBackend, self).log('Average '+ k, _data['metrics'][k].get_value())
+        
+class _Logger(object):
+    def __init__(self):
+
+        self.backends = [
+                StdOutBackend(),
+                JsonBackend()
+                ]
+   
+    def set_model_name(self, name):
+        _data['model'] = name
+
+
+    def set_backends(self, backends):
+        self.backends = backends
+        
+    def register_metric(self, key, meter=None, metric_scope=EPOCH_SCOPE):
+        if meter == None:
+            meter = StandardMeter()
+        #TODO: move to argument of Meter?
+        meter.metric_scope = metric_scope
+        _data['metrics'][key] = meter
+        for b in self.backends:
+            b.register_metric(key, metric_scope)
+
+    def log(self, key, value=None, forced=False):
+        if _data['current_scope'] == TRAIN_ITER_SCOPE or _data['current_scope'] == EPOCH_SCOPE:
+            if key in _data['metrics'].keys():
+                if _data['metrics'][key].metric_scope == _data['current_scope']:
+                    _data['metrics'][key].record(value)
+        for b in self.backends:
+            b.log(key, value)
+
+    def log_event(self, key, value=None):
+        for b in self.backends:
+            b.log_event(key, value)
+    
+    def timed_block_start(self, name):
+        if not name in _data['timed_blocks']:
+            _data['timed_blocks'][name] = OrderedDict()
+        _data['timed_blocks'][name]['start'] = time.time()
+        for b in self.backends:
+            b.timed_block_start(name)
+    
+    def timed_block_stop(self, name):
+        if not name in _data['timed_blocks']:
+            raise ValueError('timed_block_stop called before timed_block_start for ' + name)
+        _data['timed_blocks'][name]['stop'] = time.time()
+        delta = _data['timed_blocks'][name]['stop'] - _data['timed_blocks'][name]['start']
+        self.log(name + '_time', delta)
+        for b in self.backends:
+            b.timed_block_stop(name)
+
+    def iteration_start(self):
+        _data['current_scope'] = TRAIN_ITER_SCOPE
+        _data['iteration'] += 1
+        _data['total_iteration'] += 1
+
+
+    def iteration_stop(self):
+        for b in self.backends:
+            b.log_iteration_summary()
+        _data['current_scope'] = EPOCH_SCOPE
+
+    def epoch_start(self):
+        _data['current_scope'] = EPOCH_SCOPE 
+        _data['epoch'] += 1
+        _data['iteration'] = -1
+
+        for n, m in _data['metrics'].items():
+            if m.metric_scope == TRAIN_ITER_SCOPE:
+                m.reset()
+
+    def epoch_stop(self):
+        for b in self.backends:
+            b.log_epoch_summary()
+        _data['current_scope'] = RUN_SCOPE
+
+    def finish(self):
+        for b in self.backends:
+            b.finish()
+
+    def iteration_generator_wrapper(self, gen):
+        for g in gen:
+            self.iteration_start()
+            yield g
+            self.iteration_stop()
+
+    def epoch_generator_wrapper(self, gen):
+        for g in gen:
+            self.epoch_start()
+            yield g
+            self.epoch_stop()
+
+LOGGER = _Logger()
+
+@contextmanager
+def timed_block(prefix, value=None, logger=LOGGER, forced=False):
+    """ This function helps with timed blocks
+        ----
+        Parameters:
+        prefix - one of items from TIMED_BLOCKS; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    if logger is None:
+        pass
+    logger.timed_block_start(prefix)
+    yield logger
+    logger.timed_block_stop(prefix)
+
+def timed_function(prefix, variable=None, forced=False):
+    """ This decorator helps with timed functions
+        ----
+        Parameters:
+        prefix - one of items from TIME_BLOCK; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    def timed_function_decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            logger = kwargs.get('logger', LOGGER)
+            value = kwargs.get(variable, next(iter(args), None))
+            with timed_block(prefix=prefix, logger=logger, value=value, forced=forced):
+                    func(*args, **kwargs)
+        return wrapper
+    return timed_function_decorator
+
diff --git a/TensorFlow/Classification/RN50v1.5/dllogger/tags.py b/TensorFlow/Classification/RN50v1.5/dllogger/tags.py
new file mode 100644
index 00000000..7a08a10c
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/dllogger/tags.py
@@ -0,0 +1,242 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Common values reported
+
+VALUE_EPOCH = "epoch"
+VALUE_ITERATION = "iteration"
+VALUE_ACCURACY = "accuracy"
+VALUE_BLEU = "bleu"
+VALUE_TOP1 = "top1"
+VALUE_TOP5 = "top5"
+VALUE_BBOX_MAP = "bbox_map"
+VALUE_MASK_MAP = "mask_map"
+VALUE_BCE = "binary_cross_entropy"
+
+
+# Timed blocks (used with timed_function & timed_block
+# For each there should be *_start and *_stop tags defined
+
+RUN_BLOCK = "run"
+SETUP_BLOCK = "setup"
+PREPROC_BLOCK = "preproc"
+
+TRAIN_BLOCK = "train"
+TRAIN_PREPROC_BLOCK = "train_preproc"
+TRAIN_EPOCH_BLOCK = "train_epoch"
+TRAIN_EPOCH_PREPROC_BLOCK = "train_epoch_preproc"
+TRAIN_CHECKPOINT_BLOCK = "train_checkpoint"
+TRAIN_ITER_BLOCK = "train_iteration"
+
+EVAL_BLOCK = "eval"
+EVAL_ITER_BLOCK = "eval_iteration"
+
+#TODO: to remove?
+TIMED_BLOCKS = {
+    RUN_BLOCK,
+    SETUP_BLOCK,
+    PREPROC_BLOCK,
+    TRAIN_BLOCK,
+    TRAIN_PREPROC_BLOCK,
+    TRAIN_EPOCH_BLOCK,
+    TRAIN_EPOCH_PREPROC_BLOCK,
+    TRAIN_CHECKPOINT_BLOCK,
+    TRAIN_ITER_BLOCK,
+    EVAL_BLOCK,
+    EVAL_ITER_BLOCK,
+}
+
+
+# Events
+
+RUN_INIT = "run_init"
+
+SETUP_START = "setup_start"
+SETUP_STOP = "setup_stop"
+
+PREPROC_START = "preproc_start"
+PREPROC_STOP = "preproc_stop"
+
+RUN_START = "run_start"
+RUN_STOP = "run_stop"
+RUN_FINAL = "run_final"
+
+TRAIN_CHECKPOINT_START = "train_checkpoint_start"
+TRAIN_CHECKPOINT_STOP = "train_checkpoint_stop"
+
+TRAIN_PREPROC_START = "train_preproc_start"
+TRAIN_PREPROC_STOP = "train_preproc_stop"
+
+TRAIN_EPOCH_PREPROC_START = "train_epoch_preproc_start"
+TRAIN_EPOCH_PREPROC_STOP = "train_epoch_preproc_stop"
+
+TRAIN_ITER_START = "train_iter_start"
+TRAIN_ITER_STOP = "train_iter_stop"
+
+TRAIN_EPOCH_START = "train_epoch_start"
+TRAIN_EPOCH_STOP = "train_epoch_stop"
+
+
+# MLPerf specific tags
+
+RUN_CLEAR_CACHES = "run_clear_caches"
+
+PREPROC_NUM_TRAIN_EXAMPLES = "preproc_num_train_examples"
+PREPROC_NUM_EVAL_EXAMPLES = "preproc_num_eval_examples"
+PREPROC_TOKENIZE_TRAINING = "preproc_tokenize_training"
+PREPROC_TOKENIZE_EVAL = "preproc_tokenize_eval"
+PREPROC_VOCAB_SIZE = "preproc_vocab_size"
+
+RUN_SET_RANDOM_SEED = "run_set_random_seed"
+
+INPUT_SIZE = "input_size"
+INPUT_BATCH_SIZE = "input_batch_size"
+INPUT_ORDER = "input_order"
+INPUT_SHARD = "input_shard"
+INPUT_BN_SPAN = "input_bn_span"
+
+INPUT_CENTRAL_CROP = "input_central_crop"
+INPUT_CROP_USES_BBOXES = "input_crop_uses_bboxes"
+INPUT_DISTORTED_CROP_MIN_OBJ_COV = "input_distorted_crop_min_object_covered"
+INPUT_DISTORTED_CROP_RATIO_RANGE = "input_distorted_crop_aspect_ratio_range"
+INPUT_DISTORTED_CROP_AREA_RANGE = "input_distorted_crop_area_range"
+INPUT_DISTORTED_CROP_MAX_ATTEMPTS = "input_distorted_crop_max_attempts"
+INPUT_MEAN_SUBTRACTION = "input_mean_subtraction"
+INPUT_RANDOM_FLIP = "input_random_flip"
+
+INPUT_RESIZE = "input_resize"
+INPUT_RESIZE_ASPECT_PRESERVING = "input_resize_aspect_preserving"
+
+
+# Opt
+
+OPT_NAME = "opt_name"
+
+OPT_LR = "opt_learning_rate"
+OPT_MOMENTUM = "opt_momentum"
+
+OPT_WEIGHT_DECAY = "opt_weight_decay"
+
+OPT_HP_ADAM_BETA1 = "opt_hp_Adam_beta1"
+OPT_HP_ADAM_BETA2 = "opt_hp_Adam_beta2"
+OPT_HP_ADAM_EPSILON = "opt_hp_Adam_epsilon"
+
+OPT_LR_WARMUP_STEPS = "opt_learning_rate_warmup_steps"
+
+
+#  Train
+
+TRAIN_LOOP = "train_loop"
+TRAIN_EPOCH = "train_epoch"
+TRAIN_CHECKPOINT = "train_checkpoint"
+TRAIN_LOSS = "train_loss"
+TRAIN_ITERATION_LOSS = "train_iteration_loss"
+
+
+# Eval
+
+EVAL_START = "eval_start"
+EVAL_SIZE = "eval_size"
+EVAL_TARGET = "eval_target"
+EVAL_ACCURACY = "eval_accuracy"
+EVAL_STOP = "eval_stop"
+
+
+# Perf
+
+PERF_IT_PER_SEC = "perf_it_per_sec"
+PERF_TIME_TO_TRAIN = "time_to_train"
+
+EVAL_ITERATION_ACCURACY = "eval_iteration_accuracy"
+
+
+# Model
+
+MODEL_HP_LOSS_FN = "model_hp_loss_fn"
+
+MODEL_HP_INITIAL_SHAPE = "model_hp_initial_shape"
+MODEL_HP_FINAL_SHAPE = "model_hp_final_shape"
+
+MODEL_L2_REGULARIZATION = "model_l2_regularization"
+MODEL_EXCLUDE_BN_FROM_L2 = "model_exclude_bn_from_l2"
+
+MODEL_HP_RELU = "model_hp_relu"
+MODEL_HP_CONV2D_FIXED_PADDING = "model_hp_conv2d_fixed_padding"
+MODEL_HP_BATCH_NORM = "model_hp_batch_norm"
+MODEL_HP_DENSE = "model_hp_dense"
+
+
+# GNMT specific
+
+MODEL_HP_LOSS_SMOOTHING = "model_hp_loss_smoothing"
+MODEL_HP_NUM_LAYERS = "model_hp_num_layers"
+MODEL_HP_HIDDEN_SIZE = "model_hp_hidden_size"
+MODEL_HP_DROPOUT = "model_hp_dropout"
+
+EVAL_HP_BEAM_SIZE = "eval_hp_beam_size"
+TRAIN_HP_MAX_SEQ_LEN = "train_hp_max_sequence_length"
+EVAL_HP_MAX_SEQ_LEN = "eval_hp_max_sequence_length"
+EVAL_HP_LEN_NORM_CONST = "eval_hp_length_normalization_constant"
+EVAL_HP_LEN_NORM_FACTOR = "eval_hp_length_normalization_factor"
+EVAL_HP_COV_PENALTY_FACTOR = "eval_hp_coverage_penalty_factor"
+
+
+# NCF specific
+
+PREPROC_HP_MIN_RATINGS = "preproc_hp_min_ratings"
+PREPROC_HP_NUM_EVAL = "preproc_hp_num_eval"
+PREPROC_HP_SAMPLE_EVAL_REPLACEMENT = "preproc_hp_sample_eval_replacement"
+
+INPUT_HP_NUM_NEG = "input_hp_num_neg"
+INPUT_HP_SAMPLE_TRAIN_REPLACEMENT = "input_hp_sample_train_replacement"
+INPUT_STEP_TRAIN_NEG_GEN = "input_step_train_neg_gen"
+INPUT_STEP_EVAL_NEG_GEN = "input_step_eval_neg_gen"
+
+EVAL_HP_NUM_USERS = "eval_hp_num_users"
+EVAL_HP_NUM_NEG = "eval_hp_num_neg"
+
+MODEL_HP_MF_DIM = "model_hp_mf_dim"
+MODEL_HP_MLP_LAYER_SIZES = "model_hp_mlp_layer_sizes"
+
+
+# RESNET specific
+
+EVAL_EPOCH_OFFSET = "eval_offset"
+
+MODEL_HP_INITIAL_MAX_POOL = "model_hp_initial_max_pool"
+MODEL_HP_BEGIN_BLOCK = "model_hp_begin_block"
+MODEL_HP_END_BLOCK = "model_hp_end_block"
+MODEL_HP_BLOCK_TYPE = "model_hp_block_type"
+MODEL_HP_PROJECTION_SHORTCUT = "model_hp_projection_shortcut"
+MODEL_HP_SHORTCUT_ADD = "model_hp_shorcut_add"
+MODEL_HP_RESNET_TOPOLOGY = "model_hp_resnet_topology"
+
+
+# Transformer specific
+
+INPUT_MAX_LENGTH = "input_max_length"
+
+MODEL_HP_INITIALIZER_GAIN = "model_hp_initializer_gain"
+MODEL_HP_VOCAB_SIZE = "model_hp_vocab_size"
+MODEL_HP_NUM_HIDDEN_LAYERS = "model_hp_hidden_layers"
+MODEL_HP_EMBEDDING_SHARED_WEIGHTS = "model_hp_embedding_shared_weights"
+MODEL_HP_ATTENTION_DENSE = "model_hp_attention_dense"
+MODEL_HP_ATTENTION_DROPOUT = "model_hp_attention_dropout"
+MODEL_HP_FFN_OUTPUT_DENSE = "model_hp_ffn_output_dense"
+MODEL_HP_FFN_FILTER_DENSE = "model_hp_ffn_filter_dense"
+MODEL_HP_RELU_DROPOUT = "model_hp_relu_dropout"
+MODEL_HP_LAYER_POSTPROCESS_DROPOUT = "model_hp_layer_postprocess_dropout"
+MODEL_HP_NORM = "model_hp_norm"
+MODEL_HP_SEQ_BEAM_SEARCH = "model_hp_sequence_beam_search"
+
diff --git a/TensorFlow/Classification/RN50v1.5/main.py b/TensorFlow/Classification/RN50v1.5/main.py
new file mode 100755
index 00000000..d64f6d80
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/main.py
@@ -0,0 +1,131 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import warnings
+warnings.simplefilter("ignore")
+
+import tensorflow as tf
+
+import horovod.tensorflow as hvd
+from utils import hvd_utils
+
+from runtime import Runner
+
+from utils.cmdline_helper import parse_cmdline
+
+if __name__ == "__main__":
+
+    tf.logging.set_verbosity(tf.logging.ERROR)
+
+    FLAGS = parse_cmdline()
+
+    RUNNING_CONFIG = tf.contrib.training.HParams(
+        mode=FLAGS.mode,
+
+        # ======= Directory HParams ======= #
+        log_dir=FLAGS.results_dir,
+        model_dir=FLAGS.results_dir,
+        summaries_dir=FLAGS.results_dir,
+        data_dir=FLAGS.data_dir,
+
+        # ========= Model HParams ========= #
+        n_classes=1001,
+        input_format='NHWC',
+        compute_format=FLAGS.data_format,
+        dtype=tf.float32 if FLAGS.precision == "fp32" else tf.float16,
+        height=224,
+        width=224,
+        n_channels=3,
+
+        # ======= Training HParams ======== #
+        iter_unit=FLAGS.iter_unit,
+        num_iter=FLAGS.num_iter,
+        warmup_steps=FLAGS.warmup_steps,
+        batch_size=FLAGS.batch_size,
+        log_every_n_steps=FLAGS.display_every,
+        learning_rate_init=FLAGS.lr_init,
+        weight_decay=FLAGS.weight_decay,
+        momentum=FLAGS.momentum,
+        loss_scale=FLAGS.loss_scale,
+        use_auto_loss_scaling=FLAGS.use_auto_loss_scaling,
+        distort_colors=False,
+
+        # ======= Optimization HParams ======== #
+        use_xla=FLAGS.use_xla,
+        use_tf_amp=FLAGS.use_tf_amp,
+        use_fast_math=FLAGS.use_fast_math,
+        
+        seed=FLAGS.seed,
+    )
+
+    # ===================================
+
+    runner = Runner(
+        # ========= Model HParams ========= #
+        n_classes=RUNNING_CONFIG.n_classes,
+        input_format=RUNNING_CONFIG.input_format,
+        compute_format=RUNNING_CONFIG.compute_format,
+        dtype=RUNNING_CONFIG.dtype,
+        n_channels=RUNNING_CONFIG.n_channels,
+        height=RUNNING_CONFIG.height,
+        width=RUNNING_CONFIG.width,
+        distort_colors=RUNNING_CONFIG.distort_colors,
+        log_dir=RUNNING_CONFIG.log_dir,
+        model_dir=RUNNING_CONFIG.model_dir,
+        data_dir=RUNNING_CONFIG.data_dir,
+
+        # ======= Optimization HParams ======== #
+        use_xla=RUNNING_CONFIG.use_xla,
+        use_tf_amp=RUNNING_CONFIG.use_tf_amp,
+        use_fast_math=RUNNING_CONFIG.use_fast_math,
+
+        seed=RUNNING_CONFIG.seed
+    )
+
+    if RUNNING_CONFIG.mode in ["train", "train_and_evaluate", "training_benchmark"]:
+
+        runner.train(
+            iter_unit=RUNNING_CONFIG.iter_unit,
+            num_iter=RUNNING_CONFIG.num_iter,
+            batch_size=RUNNING_CONFIG.batch_size,
+            warmup_steps=RUNNING_CONFIG.warmup_steps,
+            log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,
+            weight_decay=RUNNING_CONFIG.weight_decay,
+            learning_rate_init=RUNNING_CONFIG.learning_rate_init,
+            momentum=RUNNING_CONFIG.momentum,
+            loss_scale=RUNNING_CONFIG.loss_scale,
+            use_auto_loss_scaling=FLAGS.use_auto_loss_scaling,
+            is_benchmark=RUNNING_CONFIG.mode == 'training_benchmark',
+        )
+
+    if RUNNING_CONFIG.mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:
+
+        if RUNNING_CONFIG.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
+            raise NotImplementedError("Only single GPU inference is implemented.")
+
+        elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
+
+            runner.evaluate(
+                iter_unit=RUNNING_CONFIG.iter_unit if RUNNING_CONFIG.mode != "train_and_evaluate" else "epoch",
+                num_iter=RUNNING_CONFIG.num_iter if RUNNING_CONFIG.mode != "train_and_evaluate" else 1,
+                warmup_steps=RUNNING_CONFIG.warmup_steps,
+                batch_size=RUNNING_CONFIG.batch_size,
+                log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,
+                is_benchmark=RUNNING_CONFIG.mode == 'inference_benchmark'
+            )
diff --git a/TensorFlow/Classification/RN50v1.5/model/__init__.py b/TensorFlow/Classification/RN50v1.5/model/__init__.py
new file mode 100644
index 00000000..36734617
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from model import layers
+from model import blocks
+from model import resnet_v1_5
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/model/blocks/__init__.py b/TensorFlow/Classification/RN50v1.5/model/blocks/__init__.py
new file mode 100644
index 00000000..e546cb35
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/blocks/__init__.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from model.blocks.conv2d_block import conv2d_block
+from model.blocks.resnet_bottleneck_block import bottleneck_block
+
+__all__ = [
+
+    # conv + bn + act block
+    'conv2d_block',
+
+    # resnet blocks
+    'bottleneck_block'
+]
diff --git a/TensorFlow/Classification/RN50v1.5/model/blocks/conv2d_block.py b/TensorFlow/Classification/RN50v1.5/model/blocks/conv2d_block.py
new file mode 100644
index 00000000..a8902a11
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/blocks/conv2d_block.py
@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+from model import layers
+
+__all__ = ['conv2d_block']
+
+
+def conv2d_block(
+    inputs,
+    n_channels,
+    kernel_size=(3, 3),
+    strides=(2, 2),
+    mode='SAME',
+    use_batch_norm=True,
+    activation='relu',
+    is_training=True,
+    data_format='NHWC',
+    conv2d_hparams=None,
+    batch_norm_hparams=None,
+    name='conv2d'
+):
+
+    if not isinstance(conv2d_hparams, tf.contrib.training.HParams):
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    if not isinstance(batch_norm_hparams, tf.contrib.training.HParams) and use_batch_norm:
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    with tf.variable_scope(name):
+
+        if mode != 'SAME_RESNET':
+            net = layers.conv2d(
+                inputs,
+                n_channels=n_channels,
+                kernel_size=kernel_size,
+                strides=strides,
+                padding=mode,
+                data_format=data_format,
+                use_bias=not use_batch_norm,
+                trainable=is_training,
+                kernel_initializer=conv2d_hparams.kernel_initializer,
+                bias_initializer=conv2d_hparams.bias_initializer,
+            )
+
+        else:  # Special padding mode for ResNet models
+            if strides == (1, 1):
+
+                net = layers.conv2d(
+                    inputs,
+                    n_channels=n_channels,
+                    kernel_size=kernel_size,
+                    strides=strides,
+                    padding='SAME',
+                    data_format=data_format,
+                    use_bias=not use_batch_norm,
+                    trainable=is_training,
+                    kernel_initializer=conv2d_hparams.kernel_initializer,
+                    bias_initializer=conv2d_hparams.bias_initializer,
+                )
+
+            else:
+                rate = 1  # Unused (for 'a trous' convolutions)
+
+                kernel_height_effective = kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)
+
+                pad_h_beg = (kernel_height_effective - 1) // 2
+                pad_h_end = kernel_height_effective - 1 - pad_h_beg
+
+                kernel_width_effective = kernel_size[1] + (kernel_size[1] - 1) * (rate - 1)
+
+                pad_w_beg = (kernel_width_effective - 1) // 2
+                pad_w_end = kernel_width_effective - 1 - pad_w_beg
+
+                padding = [[0, 0], [pad_h_beg, pad_h_end], [pad_w_beg, pad_w_end], [0, 0]]
+
+                if data_format == 'NCHW':
+                    padding = [padding[0], padding[3], padding[1], padding[2]]
+
+                padded_inputs = tf.pad(inputs, padding)
+
+                net = layers.conv2d(
+                    padded_inputs,  # inputs,
+                    n_channels=n_channels,
+                    kernel_size=kernel_size,
+                    strides=strides,
+                    padding='VALID',
+                    data_format=data_format,
+                    use_bias=not use_batch_norm,
+                    trainable=is_training,
+                    kernel_initializer=conv2d_hparams.kernel_initializer,
+                    bias_initializer=conv2d_hparams.bias_initializer,
+                )
+
+        if use_batch_norm:
+            net = layers.batch_norm(
+                net,
+                decay=batch_norm_hparams.decay,
+                epsilon=batch_norm_hparams.epsilon,
+                scale=batch_norm_hparams.scale,
+                center=batch_norm_hparams.center,
+                is_training=is_training,
+                data_format=data_format,
+                param_initializers=batch_norm_hparams.param_initializers
+            )
+
+        if activation == 'relu':
+            net = layers.relu(net, name='relu')
+
+        elif activation == 'tanh':
+            net = layers.tanh(net, name='tanh')
+
+        elif activation != 'linear' and activation is not None:
+            raise KeyError('Invalid activation type: `%s`' % activation)
+
+        return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/blocks/resnet_bottleneck_block.py b/TensorFlow/Classification/RN50v1.5/model/blocks/resnet_bottleneck_block.py
new file mode 100644
index 00000000..a03f5d9b
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/blocks/resnet_bottleneck_block.py
@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+import tensorflow as tf
+
+from model import layers
+from model import blocks
+
+__all__ = ['bottleneck_block']
+
+
+def bottleneck_block(
+    inputs,
+    depth,
+    depth_bottleneck,
+    stride,
+    training=True,
+    data_format='NCHW',
+    conv2d_hparams=None,
+    batch_norm_hparams=None,
+    block_name="bottleneck_block"
+):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if not isinstance(conv2d_hparams, tf.contrib.training.HParams):
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    if not isinstance(batch_norm_hparams, tf.contrib.training.HParams):
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    in_shape = inputs.get_shape()
+
+    in_size = in_shape[1] if data_format == "NCHW" else in_shape[-1]
+
+    with tf.variable_scope(block_name):
+
+        with tf.variable_scope("shortcut"):
+            if depth == in_size:
+
+                if stride == 1:
+                    shortcut = tf.identity(inputs)
+
+                else:
+
+                    shortcut = layers.average_pooling2d(
+                        inputs,
+                        pool_size=(1, 1),
+                        strides=(stride, stride),
+                        padding='valid',
+                        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+                        name="average_pooling2d",
+                    )
+
+            else:
+
+                shortcut = blocks.conv2d_block(
+                    inputs,
+                    n_channels=depth,
+                    kernel_size=(1, 1),
+                    strides=(stride, stride),
+                    mode='SAME',
+                    use_batch_norm=True,
+                    activation=None,  # Applied at the end after addition with bottleneck
+                    is_training=training,
+                    data_format=data_format,
+                    conv2d_hparams=conv2d_hparams,
+                    batch_norm_hparams=batch_norm_hparams
+                )
+
+        bottleneck = blocks.conv2d_block(
+            inputs,
+            n_channels=depth_bottleneck,
+            kernel_size=(1, 1),
+            strides=(1, 1),
+            mode='SAME',
+            use_batch_norm=True,
+            activation='relu',
+            is_training=training,
+            data_format=data_format,
+            conv2d_hparams=conv2d_hparams,
+            batch_norm_hparams=batch_norm_hparams,
+            name='bottleneck_1'
+        )
+
+        bottleneck = blocks.conv2d_block(
+            bottleneck,
+            n_channels=depth_bottleneck,
+            kernel_size=(3, 3),
+            strides=(stride, stride),
+            mode='SAME_RESNET',
+            use_batch_norm=True,
+            activation='relu',
+            is_training=training,
+            data_format=data_format,
+            conv2d_hparams=conv2d_hparams,
+            batch_norm_hparams=batch_norm_hparams,
+            name='bottleneck_2'
+        )
+
+        bottleneck = blocks.conv2d_block(
+            bottleneck,
+            n_channels=depth,
+            kernel_size=(1, 1),
+            strides=(1, 1),
+            mode='SAME',
+            use_batch_norm=True,
+            activation=None,  # Applied at the end after addition with shortcut
+            is_training=training,
+            data_format=data_format,
+            conv2d_hparams=conv2d_hparams,
+            batch_norm_hparams=batch_norm_hparams,
+            name='bottleneck_3'
+        )
+
+        return layers.relu(shortcut + bottleneck, name='relu')
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/__init__.py b/TensorFlow/Classification/RN50v1.5/model/layers/__init__.py
new file mode 100644
index 00000000..8204ecfd
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/__init__.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from model.layers.activation import relu
+from model.layers.activation import softmax
+from model.layers.activation import tanh
+
+from model.layers.conv2d import conv2d
+
+from model.layers.dense import dense
+
+from model.layers.math_ops import reduce_mean
+
+from model.layers.normalization import batch_norm
+
+from model.layers.padding import pad
+
+from model.layers.pooling import average_pooling2d
+from model.layers.pooling import max_pooling2d
+
+__all__ = [
+
+    # activation layers
+    'relu',
+    'softmax',
+    'tanh',
+
+    # conv layers
+    'conv2d',
+
+    # dense layers
+    'dense',
+
+    # math_ops layers
+    'reduce_mean',
+
+    # normalization layers
+    'batch_norm',
+
+    # padding layers
+    'pad',
+
+    # pooling layers
+    'average_pooling2d',
+    'max_pooling2d'
+]
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/activation.py b/TensorFlow/Classification/RN50v1.5/model/layers/activation.py
new file mode 100644
index 00000000..aed2f822
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/activation.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+__all__ = ['relu', 'softmax', 'tanh']
+
+
+def relu(inputs, name='relu'):
+
+    net = tf.nn.relu(inputs, name=name)
+
+    return net
+
+
+def softmax(inputs, axis=None, name="softmax"):
+
+    net = tf.nn.softmax(
+        inputs,
+        axis=axis,
+        name=name,
+    )
+
+    return net
+
+
+def tanh(inputs, name='tanh'):
+
+    net = tf.math.tanh(inputs, name=name)
+
+    return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/conv2d.py b/TensorFlow/Classification/RN50v1.5/model/layers/conv2d.py
new file mode 100644
index 00000000..1c63f161
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/conv2d.py
@@ -0,0 +1,59 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+__all__ = ['conv2d']
+
+
+def conv2d(
+    inputs,
+    n_channels=8,
+    kernel_size=(3, 3),
+    strides=(1, 1),
+    padding='VALID',
+    data_format='NHWC',
+    dilation_rate=(1, 1),
+    use_bias=True,
+    kernel_initializer=tf.variance_scaling_initializer(),
+    bias_initializer=tf.zeros_initializer(),
+    trainable=True
+):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.upper() not in ['SAME', 'VALID']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['SAME', 'VALID'])" % padding.upper())
+
+    net = tf.layers.conv2d(
+        inputs,
+        filters=n_channels,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        dilation_rate=dilation_rate,
+        data_format='channels_last' if data_format == 'NHWC' else 'channels_first',
+        use_bias=use_bias,
+        kernel_initializer=kernel_initializer,
+        bias_initializer=bias_initializer,
+        trainable=trainable,
+        activation=None
+    )
+    
+    return net
+
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/dense.py b/TensorFlow/Classification/RN50v1.5/model/layers/dense.py
new file mode 100644
index 00000000..7e902e93
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/dense.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+
+__all__ = ['dense']
+
+
+def dense(
+    inputs,
+    units,
+    use_bias=True,
+    trainable=True,
+    kernel_initializer=tf.variance_scaling_initializer(),
+    bias_initializer=tf.zeros_initializer()
+):
+
+    net = tf.layers.dense(
+        inputs,
+        units=units,
+        activation=None,
+        use_bias=use_bias,
+        kernel_initializer=kernel_initializer,
+        bias_initializer=bias_initializer,
+        trainable=trainable
+    )
+
+    return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/math_ops.py b/TensorFlow/Classification/RN50v1.5/model/layers/math_ops.py
new file mode 100644
index 00000000..9fd35119
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/math_ops.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+__all__ = ['reduce_mean']
+
+
+def reduce_mean(inputs, keepdims=None, data_format='channels_last', name='spatial_mean'):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    axes = [1, 2] if data_format == 'NHWC' else [2, 3]
+
+    net = tf.math.reduce_mean(inputs, axis=axes, keepdims=keepdims, name=name)
+
+    return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/normalization.py b/TensorFlow/Classification/RN50v1.5/model/layers/normalization.py
new file mode 100644
index 00000000..bf518ac6
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/normalization.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+
+import tensorflow as tf
+from tensorflow.python.training import moving_averages
+
+__all__ = ['batch_norm']
+
+
+def batch_norm(
+    inputs,
+    decay=0.999,
+    epsilon=0.001,
+    scale=False,
+    center=True,
+    is_training=True,
+    data_format='NHWC',
+    param_initializers=None
+):
+    """Adds a Batch Normalization layer."""
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if param_initializers is not None:
+
+        for key, initializer in param_initializers.items():
+
+            if key not in ['beta', 'gamma', 'moving_mean', 'moving_variance']:
+                raise ValueError("Unknown key received: `%s`" % key)
+
+            if inspect.isclass(initializer):
+                initializer = initializer()
+                setattr(param_initializers, key, initializer)
+
+            if initializer.__class__.__module__ != 'tensorflow.python.ops.init_ops':
+                raise ValueError("The object `%s` is not a Tensor initializer" % str(initializer))
+
+    input_shape = inputs.get_shape()
+    input_rank = input_shape.ndims
+    input_channels = input_shape[1]
+
+    if input_rank == 2:
+
+        if data_format == 'NCHW':
+            new_shape = [-1, input_channels, 1, 1]
+        else:
+            new_shape = [-1, 1, 1, input_channels]
+
+        inputs = tf.reshape(inputs, new_shape)
+
+    net = tf.contrib.layers.batch_norm(
+        inputs,
+        decay=decay,
+        scale=scale,
+        epsilon=epsilon,
+        is_training=is_training,
+        trainable=is_training,
+        fused=True,
+        data_format=data_format,
+        center=center,
+        param_initializers=param_initializers
+    )
+
+    if input_rank == 2:
+        net = tf.reshape(net, [-1, input_channels])
+
+    return net
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/padding.py b/TensorFlow/Classification/RN50v1.5/model/layers/padding.py
new file mode 100644
index 00000000..280b1d08
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/padding.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+__all__ = ['pad']
+
+
+def pad(inputs, paddings, mode='CONSTANT', name='padding', constant_values=0):
+
+    if mode.upper() not in ['CONSTANT', 'REFLECT', 'SYMMETRIC']:
+        raise ValueError("Unknown padding mode: `%s` (accepted: ['CONSTANT', 'REFLECT', 'SYMMETRIC'])" % mode)
+
+    net = tf.pad(inputs, paddings=paddings, mode=mode, name=name, constant_values=constant_values)
+
+    return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/layers/pooling.py b/TensorFlow/Classification/RN50v1.5/model/layers/pooling.py
new file mode 100644
index 00000000..9f0c125b
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/layers/pooling.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+__all__ = ['average_pooling2d', 'max_pooling2d']
+
+
+def average_pooling2d(inputs, pool_size=(2, 2), strides=None, padding='valid', data_format=None, name="avg_pooling2d"):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.lower() not in ['same', 'valid']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['same', 'valid'])" % padding)
+
+    net = tf.layers.average_pooling2d(
+        inputs,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name
+    )
+
+    return net
+
+
+def max_pooling2d(inputs, pool_size=(2, 2), strides=None, padding='valid', data_format=None, name="max_pooling2d"):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.lower() not in ['same', 'valid']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['same', 'valid'])" % padding)
+
+    net = tf.layers.max_pooling2d(
+        inputs,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name
+    )
+
+    return net
diff --git a/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py b/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py
new file mode 100644
index 00000000..0016dfb8
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py
@@ -0,0 +1,446 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from __future__ import print_function
+
+import tensorflow as tf
+
+import horovod.tensorflow as hvd
+
+from model import layers
+from model import blocks
+
+from utils import var_storage
+from utils import hvd_utils
+
+from utils.data_utils import normalized_inputs
+
+from utils.learning_rate import learning_rate_scheduler
+from utils.optimizers import FixedLossScalerOptimizer
+
+from dllogger.logger import LOGGER
+
+__all__ = [
+    'ResnetModel',
+]
+
+
+class ResnetModel(object):
+    """Resnet cnn network configuration."""
+
+    def __init__(
+        self,
+        model_name,
+        n_classes,
+        compute_format='NCHW',
+        input_format='NHWC',
+        dtype=tf.float32,
+    ):
+
+        self.model_hparams = tf.contrib.training.HParams(
+            n_classes=n_classes,
+            compute_format=compute_format,
+            input_format=input_format,
+            dtype=dtype,
+            layer_counts=(3, 4, 6, 3),
+            model_name=model_name,
+        )
+
+        self.batch_norm_hparams = tf.contrib.training.HParams(
+            decay=0.9,
+            epsilon=1e-5,
+            scale=True,
+            center=True,
+            param_initializers={
+                'beta': tf.constant_initializer(0.0),
+                'gamma': tf.constant_initializer(1.0),
+                'moving_mean': tf.constant_initializer(0.0),
+                'moving_variance': tf.constant_initializer(1.0)
+            },
+        )
+
+        self.conv2d_hparams = tf.contrib.training.HParams(
+            kernel_initializer=tf.variance_scaling_initializer(
+                scale=2.0, distribution='truncated_normal', mode='fan_out'
+            ),
+            bias_initializer=tf.constant_initializer(0.0)
+        )
+
+        self.dense_hparams = tf.contrib.training.HParams(
+            kernel_initializer=tf.variance_scaling_initializer(
+                scale=2.0, distribution='truncated_normal', mode='fan_out'
+            ),
+            bias_initializer=tf.constant_initializer(0.0)
+        )
+        if hvd.rank() == 0:
+            LOGGER.log("Model HParams:")
+            LOGGER.log("Name", model_name)
+            LOGGER.log("Number of classes", n_classes)
+            LOGGER.log("Compute_format", compute_format)
+            LOGGER.log("Input_format", input_format)
+            LOGGER.log("dtype", str(dtype))
+
+
+    def __call__(self, features, labels, mode, params):
+
+        if mode == tf.estimator.ModeKeys.TRAIN:
+
+            if "batch_size" not in params.keys():
+                raise RuntimeError("Parameter `batch_size` is missing...")
+
+            if "learning_rate_init" not in params.keys():
+                raise RuntimeError("Parameter `learning_rate` is missing...")
+
+            if "num_gpus" not in params.keys():
+                raise RuntimeError("Parameter `num_gpus` is missing...")
+
+            if "steps_per_epoch" not in params.keys():
+                raise RuntimeError("Parameter `steps_per_epoch` is missing...")
+
+            if "momentum" not in params.keys():
+                raise RuntimeError("Parameter `momentum` is missing...")
+
+            if "weight_decay" not in params.keys():
+                raise RuntimeError("Parameter `weight_decay` is missing...")
+
+            if "loss_scale" not in params.keys():
+                raise RuntimeError("Parameter `loss_scale` is missing...")
+
+                
+        if mode == tf.estimator.ModeKeys.TRAIN:
+
+            with tf.device('/cpu:0'):
+                # Stage inputs on the host
+                cpu_prefetch_op, (features, labels) = ResnetModel._stage([features, labels])
+
+            with tf.device('/gpu:0'):
+                # Stage inputs to the device
+                gpu_prefetch_op, (features, labels) = ResnetModel._stage([features, labels])
+
+        with tf.device("/gpu:0"):
+
+            if features.dtype != self.model_hparams.dtype:
+                features = tf.cast(features, self.model_hparams.dtype)
+
+            # Subtract mean per channel
+            # and enforce values between [-1, 1]
+            features = normalized_inputs(features)
+
+            # Update Global Step
+            global_step = tf.train.get_or_create_global_step()
+            tf.identity(global_step, name="global_step_ref")
+
+            tf.identity(features, name="features_ref")
+            tf.identity(labels, name="labels_ref")
+
+            probs, logits = self.build_model(
+                features,
+                training=mode == tf.estimator.ModeKeys.TRAIN,
+                reuse=False
+            )
+
+            y_preds = tf.argmax(logits, axis=1, output_type=tf.int32)
+
+            # Check the output dtype, shall be FP32 in training
+            assert (probs.dtype == tf.float32)
+            assert (logits.dtype == tf.float32)
+            assert (y_preds.dtype == tf.int32)
+
+            tf.identity(logits, name="logits_ref")
+            tf.identity(probs, name="probs_ref")
+            tf.identity(y_preds, name="y_preds_ref")
+
+            if mode == tf.estimator.ModeKeys.TRAIN:
+                
+                assert (len(tf.trainable_variables()) == 161)
+
+            else:
+                
+                assert (len(tf.trainable_variables()) == 0)
+
+
+        if mode == tf.estimator.ModeKeys.PREDICT:
+
+            predictions = {'classes': y_preds, 'probabilities': probs}
+
+            return tf.estimator.EstimatorSpec(
+                mode=mode,
+                predictions=predictions,
+                export_outputs={'predict': tf.estimator.export.PredictOutput(predictions)}
+            )
+
+        else:
+
+            with tf.device("/gpu:0"):
+
+                if mode == tf.estimator.ModeKeys.TRAIN:
+                    acc_top1 = tf.nn.in_top_k(predictions=logits, targets=labels, k=1)
+                    acc_top5 = tf.nn.in_top_k(predictions=logits, targets=labels, k=5)
+
+                else:
+                    acc_top1, acc_top1_update_op = tf.metrics.mean(tf.nn.in_top_k(predictions=logits, targets=labels, k=1))
+                    acc_top5, acc_top5_update_op = tf.metrics.mean(tf.nn.in_top_k(predictions=logits, targets=labels, k=5))
+
+                tf.identity(acc_top1, name="acc_top1_ref")
+                tf.identity(acc_top5, name="acc_top5_ref")
+
+                predictions = {
+                    'classes': y_preds,
+                    'probabilities': probs,
+                    'accuracy_top1': acc_top1,
+                    'accuracy_top5': acc_top5
+                }
+
+                cross_entropy = tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)
+
+                assert (cross_entropy.dtype == tf.float32)
+                tf.identity(cross_entropy, name='cross_entropy_loss_ref')
+
+                def loss_filter_fn(name):
+                    """we don't need to compute L2 loss for BN and bias (eq. to add a cste)"""
+                    return all([
+                        tensor_name not in name.lower()
+                        # for tensor_name in ["batchnorm", "batch_norm", "batch_normalization", "bias"]
+                        for tensor_name in ["batchnorm", "batch_norm", "batch_normalization"]
+                    ])
+
+                filtered_params = [tf.cast(v, tf.float32) for v in tf.trainable_variables() if loss_filter_fn(v.name)]
+
+                if len(filtered_params) != 0:
+
+                    l2_loss_per_vars = [tf.nn.l2_loss(v) for v in filtered_params]
+                    l2_loss = tf.multiply(tf.add_n(l2_loss_per_vars), params["weight_decay"])
+
+                else:
+                    l2_loss = tf.zeros(shape=(), dtype=tf.float32)
+
+                assert (l2_loss.dtype == tf.float32)
+                tf.identity(l2_loss, name='l2_loss_ref')
+
+                total_loss = tf.add(cross_entropy, l2_loss, name="total_loss")
+
+                assert (total_loss.dtype == tf.float32)
+                tf.identity(total_loss, name='total_loss_ref')
+
+                tf.summary.scalar('cross_entropy', cross_entropy)
+                tf.summary.scalar('l2_loss', l2_loss)
+                tf.summary.scalar('total_loss', total_loss)
+                
+                if mode == tf.estimator.ModeKeys.TRAIN:
+
+                    with tf.device("/cpu:0"):
+
+                        learning_rate = learning_rate_scheduler(
+                            learning_rate_init=params["learning_rate_init"],
+                            global_step=global_step,
+                            batch_size=params["batch_size"],
+                            num_batches_per_epoch=params["steps_per_epoch"],
+                            num_gpus=params["num_gpus"]
+                        )
+
+                    tf.identity(learning_rate, name='learning_rate_ref')
+                    tf.summary.scalar('learning_rate', learning_rate)
+
+                    optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=params["momentum"])
+
+                    if params["apply_loss_scaling"]:
+
+                        optimizer = FixedLossScalerOptimizer(optimizer, scale=params["loss_scale"])
+
+                    if hvd_utils.is_using_hvd():
+                        
+                        optimizer = hvd.DistributedOptimizer(optimizer)
+
+                    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+                    if mode != tf.estimator.ModeKeys.TRAIN:
+                        update_ops += [acc_top1_update_op, acc_top5_update_op]
+                    
+                    deterministic = True
+                    gate_gradients = (tf.train.Optimizer.GATE_OP if deterministic else tf.train.Optimizer.GATE_NONE)
+
+                    backprop_op = optimizer.minimize(total_loss, gate_gradients=gate_gradients, global_step=global_step)
+
+                    train_ops = tf.group(backprop_op, cpu_prefetch_op, gpu_prefetch_op, update_ops, name='train_ops')
+
+                    return tf.estimator.EstimatorSpec(mode=mode, loss=total_loss, train_op=train_ops)
+
+                elif mode == tf.estimator.ModeKeys.EVAL:
+
+                    eval_metrics = {
+                        "top1_accuracy": (acc_top1, acc_top1_update_op),
+                        "top5_accuracy": (acc_top5, acc_top5_update_op)
+                    }
+
+                    return tf.estimator.EstimatorSpec(
+                        mode=mode,
+                        predictions=predictions,
+                        loss=total_loss,
+                        eval_metric_ops=eval_metrics
+                    )
+
+                else:
+                    raise NotImplementedError('Unknown mode {}'.format(mode))
+
+                
+    @staticmethod
+    def _stage(tensors):
+        """Stages the given tensors in a StagingArea for asynchronous put/get.
+        """
+        stage_area = tf.contrib.staging.StagingArea(
+            dtypes=[tensor.dtype for tensor in tensors],
+            shapes=[tensor.get_shape() for tensor in tensors]
+        )
+
+        put_op = stage_area.put(tensors)
+        get_tensors = stage_area.get()
+
+        tf.add_to_collection('STAGING_AREA_PUTS', put_op)
+
+        return put_op, get_tensors
+
+
+    def build_model(self, inputs, training=True, reuse=False):
+        
+        with var_storage.model_variable_scope(
+            self.model_hparams.model_name,
+            reuse=reuse,
+            dtype=self.model_hparams.dtype
+        ):
+
+            with tf.variable_scope("input_reshape"):
+
+                if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
+                    # Reshape inputs: NHWC => NCHW
+                    inputs = tf.transpose(inputs, [0, 3, 1, 2])
+
+                elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
+
+                    # Reshape inputs: NCHW => NHWC
+                    inputs = tf.transpose(inputs, [0, 2, 3, 1])
+
+            if self.model_hparams.dtype != inputs.dtype:
+                inputs = tf.cast(inputs, self.model_hparams.dtype)
+
+            net = blocks.conv2d_block(
+                inputs,
+                n_channels=64,
+                kernel_size=(7, 7),
+                strides=(2, 2),
+                mode='SAME_RESNET',
+                use_batch_norm=True,
+                activation='relu',
+                is_training=training,
+                data_format=self.model_hparams.compute_format,
+                conv2d_hparams=self.conv2d_hparams,
+                batch_norm_hparams=self.batch_norm_hparams,
+                name='conv2d'
+            )
+
+            net = layers.max_pooling2d(
+                net,
+                pool_size=(3, 3),
+                strides=(2, 2),
+                padding='SAME',
+                data_format=self.model_hparams.compute_format,
+                name="max_pooling2d",
+            )
+
+            for block_id, _ in enumerate(range(self.model_hparams.layer_counts[0])):
+
+                net = blocks.bottleneck_block(
+                    inputs=net,
+                    depth=256,
+                    depth_bottleneck=64,
+                    stride=1,
+                    training=training,
+                    data_format=self.model_hparams.compute_format,
+                    conv2d_hparams=self.conv2d_hparams,
+                    batch_norm_hparams=self.batch_norm_hparams,
+                    block_name="btlnck_block_1_%d" % (block_id + 1)
+                )
+
+            for block_id, i in enumerate(range(self.model_hparams.layer_counts[1])):
+
+                stride = 2 if i == 0 else 1
+                
+                net = blocks.bottleneck_block(
+                    inputs=net,
+                    depth=512,
+                    depth_bottleneck=128,
+                    stride=stride,
+                    training=training,
+                    data_format=self.model_hparams.compute_format,
+                    conv2d_hparams=self.conv2d_hparams,
+                    batch_norm_hparams=self.batch_norm_hparams,
+                    block_name="btlnck_block_2_%d" % (block_id + 1)
+                )
+
+            for block_id, i in enumerate(range(self.model_hparams.layer_counts[2])):
+
+                block_id += 1
+                stride = 2 if i == 0 else 1
+
+                net = blocks.bottleneck_block(
+                    inputs=net,
+                    depth=1024,
+                    depth_bottleneck=256,
+                    stride=stride,
+                    training=training,
+                    data_format=self.model_hparams.compute_format,
+                    conv2d_hparams=self.conv2d_hparams,
+                    batch_norm_hparams=self.batch_norm_hparams,
+                    block_name="btlnck_block_3_%d" % (block_id + 1)
+                )
+
+            for block_id, i in enumerate(range(self.model_hparams.layer_counts[3])):
+
+                stride = 2 if i == 0 else 1
+
+                net = blocks.bottleneck_block(
+                    inputs=net,
+                    depth=2048,
+                    depth_bottleneck=512,
+                    stride=stride,
+                    training=training,
+                    data_format=self.model_hparams.compute_format,
+                    conv2d_hparams=self.conv2d_hparams,
+                    batch_norm_hparams=self.batch_norm_hparams,
+                    block_name="btlnck_block_4_%d" % (block_id + 1)
+                )
+
+            with tf.variable_scope("output"):
+
+                net = layers.reduce_mean(
+                    net, keepdims=False, data_format=self.model_hparams.compute_format, name='spatial_mean'
+                )
+
+                logits = layers.dense(
+                    inputs=net,
+                    units=self.model_hparams.n_classes,
+                    use_bias=True,
+                    trainable=training,
+                    kernel_initializer=self.dense_hparams.kernel_initializer,
+                    bias_initializer=self.dense_hparams.bias_initializer
+                )
+
+                if logits.dtype != tf.float32:
+                    logits = tf.cast(logits, tf.float32)
+
+                probs = layers.softmax(logits, name="softmax", axis=1)
+
+            return probs, logits
diff --git a/TensorFlow/Classification/RN50v1.5/requirements.txt b/TensorFlow/Classification/RN50v1.5/requirements.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Classification/RN50v1.5/runtime/__init__.py b/TensorFlow/Classification/RN50v1.5/runtime/__init__.py
new file mode 100644
index 00000000..32cd392d
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/runtime/__init__.py
@@ -0,0 +1 @@
+from runtime.runner import Runner
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/runtime/runner.py b/TensorFlow/Classification/RN50v1.5/runtime/runner.py
new file mode 100644
index 00000000..0aaae996
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/runtime/runner.py
@@ -0,0 +1,558 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import os
+import multiprocessing
+import warnings
+
+import tensorflow as tf
+
+import horovod.tensorflow as hvd
+
+from model import resnet_v1_5
+
+from utils import hooks
+from utils import data_utils
+from utils import hvd_utils
+
+from runtime import runner_utils
+
+from dllogger.logger import LOGGER
+
+__all__ = [
+    'Runner',
+]
+
+class Runner(object):
+
+    def __init__(
+        self,
+        # ========= Model HParams ========= #
+        n_classes=1001,
+        input_format='NHWC',    # NCHW or NHWC
+        compute_format='NCHW',  # NCHW or NHWC
+        dtype=tf.float32,       # tf.float32 or tf.float16
+        n_channels=3,
+        height=224,
+        width=224,
+        distort_colors=False,
+        model_dir=None,
+        log_dir=None,
+        data_dir=None,
+
+        # ======= Optimization HParams ======== #
+        use_xla=False,
+        use_tf_amp=False,
+        use_fast_math=False,
+
+        # ======== Debug Flags ======== #
+        debug_verbosity=0,
+        seed=None
+    ):
+
+        if dtype not in [tf.float32, tf.float16]:
+            raise ValueError("Unknown dtype received: %s (allowed: `tf.float32` and `tf.float16`)" % dtype)
+
+        if compute_format not in ["NHWC", 'NCHW']:
+            raise ValueError("Unknown `compute_format` received: %s (allowed: ['NHWC', 'NCHW'])" % compute_format)
+
+        if input_format not in ["NHWC", 'NCHW']:
+            raise ValueError("Unknown `input_format` received: %s (allowed: ['NHWC', 'NCHW'])" % input_format)
+
+        if n_channels not in [1, 3]:
+            raise ValueError("Unsupported number of channels: %d (allowed: 1 (grayscale) and 3 (color))" % n_channels)
+
+        if data_dir is not None and not os.path.exists(data_dir):
+            raise ValueError("The `data_dir` received does not exists: %s" % data_dir)
+
+        hvd.init()
+        tf_seed = 2 * (seed + hvd.rank()) if seed is not None else None
+
+        # ============================================
+        # Optimsation Flags - Do not remove
+        # ============================================
+
+        os.environ['CUDA_CACHE_DISABLE'] = '0'
+
+        os.environ['HOROVOD_GPU_ALLREDUCE'] = 'NCCL'
+
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+        os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
+        os.environ['TF_GPU_THREAD_COUNT'] = '1' if not hvd_utils.is_using_hvd() else str(hvd.size())
+
+        os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
+
+        os.environ['TF_ADJUST_HUE_FUSED'] = '1'
+        os.environ['TF_ADJUST_SATURATION_FUSED'] = '1'
+        os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
+
+        os.environ['TF_SYNC_ON_FINISH'] = '0'
+        os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'
+        os.environ['TF_DISABLE_NVTX_RANGES'] = '1'
+
+        # ============================================
+        # TF-AMP and Fast Math Setup - Do not remove
+        # ============================================
+
+        if dtype == tf.float16:
+
+            if use_fast_math:
+                raise RuntimeError("Fast Math can not be activated for FP16 precision")
+
+            if use_tf_amp:
+                raise RuntimeError("TF AMP can not be activated for FP16 precision")
+
+        elif use_fast_math and use_tf_amp:
+            raise RuntimeError("TF AMP and Fast Math can not be activated simultaneously")
+
+        else:
+
+            if use_fast_math:
+                if hvd.rank() == 0:
+                    LOGGER.log("Fast Math computation is activated - Experimental Feature")
+
+                os.environ["TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32"] = "1"
+                os.environ["TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32"] = "1"
+                os.environ["TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32"] = "1"
+
+            elif use_tf_amp:
+                if hvd.rank() == 0:
+                    LOGGER.log("TF AMP is activated - Experimental Feature")
+                os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
+
+        # =================================================
+
+        model_hparams = tf.contrib.training.HParams(
+            width=height,
+            height=width,
+            n_channels=n_channels,
+            n_classes=n_classes,
+            dtype=dtype,
+            input_format=input_format,
+            compute_format=compute_format,
+            distort_colors=distort_colors,
+            seed=tf_seed
+        )
+
+        run_config_performance = tf.contrib.training.HParams(
+            num_preprocessing_threads=32,
+            use_fast_math=use_fast_math,
+            use_tf_amp=use_tf_amp,
+            use_xla=use_xla,
+        )
+
+        run_config_additional = tf.contrib.training.HParams(
+            model_dir=model_dir if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
+            log_dir=log_dir if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
+            data_dir=data_dir,
+            num_preprocessing_threads=32,
+        )
+
+        self.run_hparams = Runner._build_hparams(model_hparams, run_config_additional, run_config_performance)
+
+        self._model = resnet_v1_5.ResnetModel(
+            model_name="resnet50_v1.5",
+            n_classes=model_hparams.n_classes,
+            input_format=model_hparams.input_format,
+            compute_format=model_hparams.compute_format,
+            dtype=model_hparams.dtype,
+        )
+
+        if self.run_hparams.seed is not None:
+            if hvd.rank() == 0:
+                LOGGER.log("Deterministic Run - Seed: %d\n" % seed)
+            tf.set_random_seed(self.run_hparams.seed)
+            
+
+    @staticmethod
+    def _build_hparams(*args):
+
+        hparams = tf.contrib.training.HParams()
+
+        for _hparams in args:
+            if not isinstance(_hparams, tf.contrib.training.HParams):
+                raise ValueError("Non valid HParams argument object detected:", _hparams)
+
+            for key, val in _hparams.values().items():
+                try:
+                    hparams.add_hparam(name=key, value=val)
+
+                except ValueError:
+                    warnings.warn(
+                        "the parameter `{}` already exists - existing value: {} and duplicated value: {}".format(
+                            key, hparams.get(key), val
+                        )
+                    )
+
+        return hparams
+
+    @staticmethod
+    def _get_global_batch_size(worker_batch_size):
+
+        if hvd_utils.is_using_hvd():
+            return worker_batch_size * hvd.size()
+        else:
+            return worker_batch_size
+
+    @staticmethod
+    def _get_session_config(mode, use_xla):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        config = tf.ConfigProto()
+
+        config.allow_soft_placement = True
+        config.log_device_placement = False
+
+        config.gpu_options.allow_growth = True
+
+        if hvd_utils.is_using_hvd():
+            config.gpu_options.visible_device_list = str(hvd.local_rank())
+
+        if use_xla: 
+            LOGGER.log("XLA is activated - Experimental Feature")
+            config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+
+        config.gpu_options.force_gpu_compatible = True  # Force pinned memory
+
+        if mode == 'train':
+            config.intra_op_parallelism_threads = 1  # Avoid pool of Eigen threads
+
+            if hvd_utils.is_using_hvd():
+                config.inter_op_parallelism_threads = max(2, (multiprocessing.cpu_count() // hvd.size()) - 2)
+            else:
+                config.inter_op_parallelism_threads = 4
+
+        return config
+
+    @staticmethod
+    def _get_run_config(mode, model_dir, use_xla, seed=None):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        if seed is not None:
+            if hvd_utils.is_using_hvd():
+                tf_random_seed = 2 * (seed + hvd.rank())
+            else:
+                tf_random_seed = 2 * seed
+        else:
+            tf_random_seed = None
+
+        config = tf.estimator.RunConfig(
+            model_dir=model_dir,
+            tf_random_seed=tf_random_seed,
+            save_summary_steps=100 if mode in ['train', 'validation'] else 1e9,  # disabled in benchmark mode
+            save_checkpoints_steps=None,
+            save_checkpoints_secs=None,
+            session_config=Runner._get_session_config(mode=mode, use_xla=use_xla),
+            keep_checkpoint_max=5,
+            keep_checkpoint_every_n_hours=1e6,  # disabled
+            log_step_count_steps=1e9,
+            train_distribute=None,
+            device_fn=None,
+            protocol=None,
+            eval_distribute=None,
+            experimental_distribute=None
+        )
+
+        if mode == 'train':
+            if hvd_utils.is_using_hvd():
+                config = config.replace(
+                    save_checkpoints_steps=1000 if hvd.rank() == 0 else None, keep_checkpoint_every_n_hours=3
+                )
+            else:
+                config = config.replace(save_checkpoints_steps=1000, keep_checkpoint_every_n_hours=3)
+
+        return config
+
+    def _get_estimator(self, mode, run_params, use_xla):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        run_config = Runner._get_run_config(
+            mode=mode,
+            model_dir=self.run_hparams.model_dir,
+            use_xla=use_xla,
+            seed=self.run_hparams.seed
+        )
+
+        return tf.estimator.Estimator(
+            model_fn=self._model,
+            model_dir=self.run_hparams.model_dir,
+            config=run_config,
+            params=run_params
+        )
+
+    def train(
+        self,
+        iter_unit,
+        num_iter,
+        batch_size,
+        warmup_steps=50,
+        weight_decay=1e-4,
+        learning_rate_init=0.1,
+        momentum=0.9,
+        log_every_n_steps=1,
+        loss_scale=256,
+        use_auto_loss_scaling=False,
+        is_benchmark=False
+    ):
+
+        if iter_unit not in ["epoch", "batch"]:
+            raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
+
+        if self.run_hparams.data_dir is None and not is_benchmark:
+            raise ValueError('`data_dir` must be specified for training!')
+
+        if self.run_hparams.use_fast_math or self.run_hparams.use_tf_amp or self.run_hparams.dtype == tf.float16:
+            if use_auto_loss_scaling:
+                LOGGER.log("TF Loss Auto Scaling is activated - Experimental Feature")
+                os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1"
+
+            else:
+                os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "0"
+        else:
+            use_auto_loss_scaling = False  # Make sure it hasn't been set to True on FP32 training
+
+        num_gpus = 1 if not hvd_utils.is_using_hvd() else hvd.size()
+        global_batch_size = batch_size * num_gpus
+
+        if self.run_hparams.data_dir is not None:
+            filenames, num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
+                data_dir=self.run_hparams.data_dir,
+                mode="train",
+                iter_unit=iter_unit,
+                num_iter=num_iter,
+                global_batch_size=global_batch_size,
+            )
+
+            steps_per_epoch = num_steps / num_epochs
+
+
+        else:
+            num_epochs = 1
+            num_steps = num_iter
+            steps_per_epoch = num_steps
+            num_decay_steps = num_steps
+            num_samples = num_steps * batch_size
+
+        training_hooks = []
+      
+        if hvd.rank() == 0:
+            LOGGER.log('Starting Model Training...')
+            LOGGER.log("Training Epochs", num_epochs)
+            LOGGER.log("Total Steps", num_steps)
+            LOGGER.log("Steps per Epoch", steps_per_epoch)
+            LOGGER.log("Decay Steps", num_decay_steps)
+            LOGGER.log("Weight Decay Factor", weight_decay)
+            LOGGER.log("Init Learning Rate", learning_rate_init)
+            LOGGER.log("Momentum", momentum)
+            LOGGER.log("Num GPUs", num_gpus)
+            LOGGER.log("Per-GPU Batch Size", batch_size)
+
+            
+            if is_benchmark:
+
+                benchmark_logging_hook = hooks.BenchmarkLoggingHook(
+                    log_file_path=os.path.join(self.run_hparams.log_dir, "training_benchmark.json"),
+                    global_batch_size=global_batch_size,
+                    log_every=log_every_n_steps,
+                    warmup_steps=warmup_steps
+                )
+
+                training_hooks.append(benchmark_logging_hook)
+
+            else:
+
+                training_logging_hook = hooks.TrainingLoggingHook(
+                    log_file_path=os.path.join(self.run_hparams.log_dir, "training.json"),
+                    global_batch_size=global_batch_size,
+                    num_steps=num_steps,
+                    num_samples=num_samples,
+                    num_epochs=num_epochs,
+                    log_every=log_every_n_steps
+                )
+
+                training_hooks.append(training_logging_hook)
+
+        if hvd_utils.is_using_hvd():
+            bcast_hook = hvd.BroadcastGlobalVariablesHook(0)
+            training_hooks.append(bcast_hook)
+
+        training_hooks.append(hooks.PrefillStagingAreasHook())
+
+      
+        estimator_params = {
+            'batch_size': batch_size,
+            'steps_per_epoch': steps_per_epoch,
+            'num_gpus': num_gpus,
+            'momentum': momentum,
+            'learning_rate_init': learning_rate_init,
+            'weight_decay': weight_decay,
+            'loss_scale': loss_scale,
+            'apply_loss_scaling': not use_auto_loss_scaling
+        }
+
+        image_classifier = self._get_estimator(
+            mode='train',
+            run_params=estimator_params,
+            use_xla=self.run_hparams.use_xla
+        )
+
+        def training_data_fn():
+
+            if self.run_hparams.data_dir is not None:
+
+                return data_utils.get_tfrecords_input_fn(
+                    filenames=filenames,
+                    batch_size=batch_size,
+                    height=self.run_hparams.height,
+                    width=self.run_hparams.width,
+                    training=True,
+                    distort_color=self.run_hparams.distort_colors,
+                    num_threads=self.run_hparams.num_preprocessing_threads,
+                    deterministic=False if self.run_hparams.seed is None else True
+                )
+
+            else:
+                if hvd.rank() == 0:
+                    LOGGER.log("Using Synthetic Data ...")
+                return data_utils.get_synth_input_fn(
+                    batch_size=batch_size,
+                    height=self.run_hparams.height,
+                    width=self.run_hparams.width,
+                    num_channels=self.run_hparams.n_channels,
+                    data_format=self.run_hparams.input_format,
+                    num_classes=self.run_hparams.n_classes,
+                    dtype=self.run_hparams.dtype,
+                )
+
+
+        try:
+            image_classifier.train(
+                input_fn=training_data_fn,
+                steps=num_steps,
+                hooks=training_hooks,
+            )
+        except KeyboardInterrupt:
+            print("Keyboard interrupt")
+            
+        if hvd.rank() == 0:
+            LOGGER.log('Ending Model Training ...')
+
+    def evaluate(
+        self,
+        iter_unit,
+        num_iter,
+        batch_size,
+        warmup_steps=50,
+        log_every_n_steps=1,
+        is_benchmark=False
+    ):
+
+        if iter_unit not in ["epoch", "batch"]:
+            raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
+
+        if self.run_hparams.data_dir is None and not is_benchmark:
+            raise ValueError('`data_dir` must be specified for evaluation!')
+
+        if hvd_utils.is_using_hvd() and hvd.rank() != 0:
+            raise RuntimeError('Multi-GPU inference is not supported')
+
+        estimator_params = {}
+            
+        image_classifier = self._get_estimator(
+            mode='validation',
+            run_params=estimator_params,
+            use_xla=self.run_hparams.use_xla
+        )
+
+        if self.run_hparams.data_dir is not None:
+            filenames, num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
+                data_dir=self.run_hparams.data_dir,
+                mode="validation",
+                iter_unit=iter_unit,
+                num_iter=num_iter,
+                global_batch_size=batch_size,
+            )
+        
+        else:
+            num_epochs = 1
+            num_decay_steps = -1
+            num_steps = num_iter
+       
+        eval_hooks = []
+        
+        if hvd.rank() == 0:
+            if is_benchmark:
+                
+                benchmark_logging_hook = hooks.BenchmarkLoggingHook(
+                    log_file_path=os.path.join(self.run_hparams.log_dir, "eval_benchmark.json"),
+                    global_batch_size=batch_size,
+                    log_every=log_every_n_steps,
+                    warmup_steps=warmup_steps
+                )
+                eval_hooks.append(benchmark_logging_hook)
+
+            LOGGER.log('Starting Model Evaluation...')
+            LOGGER.log("Evaluation Epochs", num_epochs)
+            LOGGER.log("Evaluation Steps", num_steps)
+            LOGGER.log("Decay Steps", num_decay_steps)
+            LOGGER.log("Global Batch Size", batch_size)
+
+        def evaluation_data_fn():
+
+            if self.run_hparams.data_dir is not None:
+                return data_utils.get_tfrecords_input_fn(
+                        filenames=filenames,
+                        batch_size=batch_size,
+                        height=self.run_hparams.height,
+                        width=self.run_hparams.width,
+                        training=False,
+                        distort_color=self.run_hparams.distort_colors,
+                        num_threads=self.run_hparams.num_preprocessing_threads,
+                        deterministic=False if self.run_hparams.seed is None else True
+                    )
+
+            else:
+                LOGGER.log("Using Synthetic Data ...\n")
+                return data_utils.get_synth_input_fn(
+                    batch_size=batch_size,
+                    height=self.run_hparams.height,
+                    width=self.run_hparams.width,
+                    num_channels=self.run_hparams.n_channels,
+                    data_format=self.run_hparams.input_format,
+                    num_classes=self.run_hparams.n_classes,
+                    dtype=self.run_hparams.dtype,
+                )
+
+        try:
+            eval_results = image_classifier.evaluate(
+                input_fn=evaluation_data_fn,
+                steps=num_steps,
+                hooks=eval_hooks,
+            )
+            LOGGER.log('Top-1 Accuracy: %.3f' % float(eval_results['top1_accuracy'] * 100))
+            LOGGER.log('Top-5 Accuracy: %.3f' % float(eval_results['top5_accuracy'] * 100))
+
+        except KeyboardInterrupt:
+            print("Keyboard interrupt")
+
+        LOGGER.log('Ending Model Evaluation ...')
diff --git a/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py b/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py
new file mode 100644
index 00000000..965cd120
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py
@@ -0,0 +1,79 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import os
+import math
+
+import tensorflow as tf
+
+__all__ = ['count_steps', 'list_filenames_in_dataset', 'parse_tfrecords_dataset']
+
+
+def count_steps(iter_unit, num_samples, num_iter, global_batch_size):
+
+    if iter_unit not in ["batch", "epoch"]:
+        raise ValueError("Invalid `iter_unit` value: %s" % iter_unit)
+
+    if iter_unit == 'epoch':
+        num_steps = (num_samples // global_batch_size) * num_iter
+        num_epochs = num_iter
+        num_decay_steps = num_steps
+
+    else:
+        num_steps = num_iter
+        num_epochs = math.ceil(num_steps / (num_samples // global_batch_size))
+        num_decay_steps = 90 * num_samples // global_batch_size
+
+    return num_steps, num_epochs, num_decay_steps
+
+
+def list_filenames_in_dataset(data_dir, mode):
+
+    if mode not in ["train", 'validation']:
+        raise ValueError("Unknown mode received: %s" % mode)
+
+    if not os.path.exists(data_dir):
+        raise FileNotFoundError("The data directory: `%s` can't be found" % data_dir)
+
+    filename_pattern = os.path.join(data_dir, '%s-*' % mode)
+
+    file_list = sorted(tf.gfile.Glob(filename_pattern))
+
+    def count_records(tf_record_filename):
+        count = 0
+        for _ in tf.python_io.tf_record_iterator(tf_record_filename):
+            count += 1
+        return count
+
+    n_files = len(file_list)
+    num_samples = (count_records(file_list[0]) * (n_files - 1) + count_records(file_list[-1]))
+
+    return file_list, num_samples
+
+
+def parse_tfrecords_dataset(data_dir, mode, iter_unit, num_iter, global_batch_size):
+
+    if data_dir is not None:
+        filenames, num_samples = list_filenames_in_dataset(data_dir=data_dir, mode=mode)
+    else:
+        num_samples = 256000
+        filenames = []
+
+    num_steps, num_epochs, num_decay_steps = count_steps(
+        iter_unit=iter_unit, num_samples=num_samples, num_iter=num_iter, global_batch_size=global_batch_size
+    )
+
+    return filenames, num_samples, num_steps, num_epochs, num_decay_steps
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_1GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_1GPU.sh
new file mode 100755
index 00000000..2f2b4bf0
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_1GPU.sh
@@ -0,0 +1,18 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP16 on 1 GPUs using 256 batch size (256 per GPU)
+# Usage ./RN50_FP16_1GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2 --batch_size=256 --use_tf_amp --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_4GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_4GPU.sh
new file mode 100755
index 00000000..eed25f77
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_4GPU.sh
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP16 on 4 GPUs using 1024 batch size (256 per GPU)
+# Usage ./RN50_FP16_4GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+mpiexec --allow-run-as-root --bind-to socket -np 4 \
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2 --batch_size=256 --use_tf_amp --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_8GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_8GPU.sh
new file mode 100755
index 00000000..a189137d
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_8GPU.sh
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP16 on 8 GPUs using 2048 batch size (256 per GPU)
+# Usage ./RN50_FP16_8GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+mpiexec --allow-run-as-root --bind-to socket -np 8 \
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2 --batch_size=256 --use_tf_amp --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_EVAL.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_EVAL.sh
new file mode 100755
index 00000000..5fc68ec1
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP16_EVAL.sh
@@ -0,0 +1,18 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script evaluates ResNet50 model in FP16 using 256 batch size on 1 GPU
+# Usage: ./RN50_FP16_EVAL.sh <path to this repository> <path to dataset> <path to model directory>
+
+python $1/main.py --mode=evaluate --data_dir=$2 --batch_size=256 --num_iter=1 --iter_unit=epoch --use_tf_amp --results_dir=$3
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_1GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_1GPU.sh
new file mode 100755
index 00000000..f21c783b
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_1GPU.sh
@@ -0,0 +1,18 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP32 on 1 GPUs using 128 batch size (128 per GPU)
+# Usage ./RN50_FP32_1GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2  --batch_size=128 --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_4GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_4GPU.sh
new file mode 100755
index 00000000..8b7de255
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_4GPU.sh
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP32 on 4 GPUs using 512 batch size (128 per GPU)
+# Usage ./RN50_FP32_4GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+mpiexec --allow-run-as-root --bind-to socket -np 4 \
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2  --batch_size=128 --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_8GPU.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_8GPU.sh
new file mode 100755
index 00000000..84ae6a64
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_8GPU.sh
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches ResNet50 training in FP32 on 8 GPUs using 1024 batch size (128 per GPU)
+## Usage ./RN50_FP32_8GPU.sh <path to this repository> <path to dataset> <path to results directory>
+
+mpiexec --allow-run-as-root --bind-to socket -np 8 \
+python $1/main.py --num_iter=90 --iter_unit=epoch --data_dir=$2  --batch_size=128 --results_dir=$3
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_EVAL.sh b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_EVAL.sh
new file mode 100755
index 00000000..9ab9fc57
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/RN50_FP32_EVAL.sh
@@ -0,0 +1,18 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script evaluates ResNet50 model in FP32 using 128 batch size on 1 GPU
+# Usage: ./RN50_FP32_EVAL.sh <path to this repository> <path to dataset> <path to model directory>
+
+python $1/main.py --mode=evaluate --data_dir=$2 --batch_size=128 --num_iter=1 --iter_unit=epoch --results_dir=$3
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh
new file mode 100644
index 00000000..85d338f2
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+mkdir -p /tmp/results
+
+python ./scripts/benchmarking/benchmark.py --mode inference --use_tf_amp --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 16 32 64 128 256 --baseline ./scripts/benchmarking/baselines/RN50_tensorflow_infer_fp16.json  --data_dir $1 --results_dir $2
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp32.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp32.sh
new file mode 100644
index 00000000..f050043c
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp32.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+mkdir -p /tmp/results
+
+python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 16 32 64 128 --baseline ./scripts/benchmarking/baselines/RN50_tensorflow_infer_fp32.json --data_dir $1 --results_dir $2
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh
new file mode 100644
index 00000000..50556e3c
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+mkdir -p /tmp/results
+
+python ./scripts/benchmarking/benchmark.py --mode training --use_tf_amp --bench-warmup 200 --bench-iterations 500 --ngpus 1 4 8 --bs 64 128 256 --baseline ./scripts/benchmarking/baselines/RN50_tensorflow_train_fp16.json  --data_dir $1 --results_dir $2
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp32.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp32.sh
new file mode 100644
index 00000000..c58b9e7d
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp32.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+mkdir -p /tmp/results
+
+python ./scripts/benchmarking/benchmark.py --mode training --bench-warmup 200 --bench-iterations 500 --ngpus 1 4 8 --bs 32 64 128 --baseline ./scripts/benchmarking/baselines/RN50_tensorflow_train_fp32.json --data_dir $1 --results_dir $2
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp16.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp16.json
new file mode 100644
index 00000000..1ed28443
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp16.json
@@ -0,0 +1,51 @@
+{
+    "metric_keys": [
+        "total_ips"
+    ], 
+    "metrics": {
+        "1": {
+            "16": {
+                "total_ips": 1300.0
+            }, 
+            "32": {
+                "total_ips": 1600.0
+            }, 
+            "1": {
+                "total_ips": 160.0
+            }, 
+            "2": {
+                "total_ips": 320.0
+            }, 
+            "64": {
+                "total_ips": 1800.0
+            }, 
+            "4": {
+                "total_ips": 550.0
+            }, 
+            "128": {
+                "total_ips": 1950.0
+            }, 
+            "8": {
+                "total_ips": 950.0
+            }, 
+            "256": {
+                "total_ips": 2050.0
+            }
+        }
+    }, 
+    "model": "", 
+    "ngpus": [
+        1
+    ], 
+    "bs": [
+        1, 
+        2, 
+        4, 
+        8, 
+        16, 
+        32, 
+        64, 
+        128, 
+        256
+    ]
+}
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp32.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp32.json
new file mode 100644
index 00000000..b09cb5d1
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_infer_fp32.json
@@ -0,0 +1,47 @@
+{
+    "metric_keys": [
+        "total_ips"
+    ], 
+    "metrics": {
+        "1": {
+            "16": {
+                "total_ips": 800.0
+            }, 
+            "32": {
+                "total_ips": 920.0
+            }, 
+            "1": {
+                "total_ips": 150.0
+            }, 
+            "2": {
+                "total_ips": 270.0
+            }, 
+            "64": {
+                "total_ips": 1000.0
+            }, 
+            "4": {
+                "total_ips": 450.0
+            }, 
+            "128": {
+                "total_ips": 1075.0
+            }, 
+            "8": {
+                "total_ips": 650.0
+            }
+        }
+    }, 
+    "model": "", 
+    "ngpus": [
+        1
+    ], 
+    "bs": [
+        1, 
+        2, 
+        4, 
+        8, 
+        16, 
+        32, 
+        64, 
+        128
+    ]
+}
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp16.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp16.json
new file mode 100644
index 00000000..87ea1b05
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp16.json
@@ -0,0 +1,51 @@
+{
+    "metric_keys": [
+        "total_ips"
+    ], 
+    "metrics": {
+        "1": {
+            "64": {
+                "total_ips": 630.0
+            }, 
+            "128": {
+                "total_ips": 710.0
+            }, 
+            "256": {
+                "total_ips": 750.0
+            }
+        }, 
+        "4": {
+            "64": {
+                "total_ips": 2250.0
+            }, 
+            "128": {
+                "total_ips": 2600.0
+            }, 
+            "256": {
+                "total_ips": 2900.0
+            }
+        }, 
+        "8": {
+            "64": {
+                "total_ips": 4500.0
+            }, 
+            "128": {
+                "total_ips": 5300.0
+            }, 
+            "256": {
+                "total_ips": 5800.0
+            }
+        }
+    }, 
+    "model": "", 
+    "ngpus": [
+        1,
+        4,
+        8
+    ], 
+    "bs": [
+        64, 
+        128, 
+        256
+    ]
+}
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp32.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp32.json
new file mode 100644
index 00000000..9e2e8ec6
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/RN50_tensorflow_train_fp32.json
@@ -0,0 +1,51 @@
+{
+    "metric_keys": [
+        "total_ips"
+    ], 
+    "metrics": {
+        "1": {
+            "32": {
+                "total_ips": 300.0
+            }, 
+            "64": {
+                "total_ips": 330.0
+            }, 
+            "128": {
+                "total_ips": 350.0
+            }
+        }, 
+        "4": {
+            "32": {
+                "total_ips": 1050.0
+            }, 
+            "64": {
+                "total_ips": 1250.0
+            }, 
+            "128": {
+                "total_ips": 1350.0
+            }
+        }, 
+        "8": {
+            "32": {
+                "total_ips": 2100.0
+            }, 
+            "64": {
+                "total_ips": 2500.0
+            }, 
+            "128": {
+                "total_ips": 2700.0
+            }
+        }
+    }, 
+    "model": "", 
+    "ngpus": [
+        1,
+        4,
+        8
+    ], 
+    "bs": [
+        32, 
+        64, 
+        128
+    ]
+}
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py
new file mode 100644
index 00000000..d8caec49
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py
@@ -0,0 +1,190 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import sys
+import os
+import subprocess
+import numpy as np
+
+from collections import OrderedDict
+
+PERF_THR = 0.9
+
+parser = argparse.ArgumentParser(description='Tesnorflow Benchmark Tests')
+
+parser.add_argument('--bs', default=[1], type=int, nargs='+')
+parser.add_argument('--ngpus', default=[1], type=int, nargs='+')
+
+parser.add_argument(
+    '--mode',
+    default='training',
+    choices=['training', 'inference'],
+    help='benchmark training or inference (default: training)'
+)
+parser.add_argument(
+    '--bench-iterations',
+    type=int,
+    default=100,
+    metavar='N',
+    help='Run N iterations while benchmarking (ignored when training and validation)'
+)
+parser.add_argument(
+    '--bench-warmup', type=int, default=3, metavar='N', help='Number of warmup iterations for benchmarking'
+)
+
+parser.add_argument('--precision', default='fp32', choices=['fp16', 'fp32'], help=argparse.SUPPRESS)
+
+parser.add_argument("--use_tf_amp", action='store_true', required=False,  help="Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores.")
+
+parser.add_argument("--use_xla", action='store_true', required=False, help="Enable XLA (Accelerated Linear Algebra) computation for improved performance.")
+
+parser.add_argument('--baseline', type=str, default=None, metavar='FILE', help='path to the file with baselines')
+
+parser.add_argument('--data_dir', default="/data/imagenet", type=str, metavar='<PATH>', help='path to the dataset')
+parser.add_argument('--results_dir', default="/results", type=str, metavar='<PATH>', help='path to the results')
+
+args = parser.parse_args()
+
+command = "{{}} main.py --mode={mode}_benchmark --batch_size={batch_size} --warmup_steps={bench_warmup} --num_iter={num_iter} --precision={precision} --iter_unit=batch --data_dir={data_dir} --results_dir={results_dir}/{exp_name} {perf_args}"
+
+benchmark_filenames = {'training': 'training_benchmark.json', 'inference': 'eval_benchmark.json'}
+
+
+def benchmark(command, metrics, args):
+    sgpu = str(sys.executable)
+    mgpu = "mpiexec --allow-run-as-root --bind-to socket -np {ngpu} {sgpu}"
+
+    perf_args = []
+    if args.use_tf_amp:
+        perf_args.append('--use_tf_amp')
+        
+    if args.use_xla:
+        perf_args.append('--use_xla')
+    
+    table = {k: [] for k in metrics}
+    for ngpu in args.ngpus:
+
+        row = {k: [] for k in metrics}
+        for bspgpu in args.bs:
+
+            exp_name = "{}GPU_{}BS_{}bench".format(ngpu, bspgpu, args.mode)
+            results_path = os.path.join(args.results_dir, exp_name)
+            os.makedirs(results_path)
+
+            rfile = "{}/{}".format(results_path, benchmark_filenames[args.mode])
+            print('rfile', rfile)
+            mgpu_str = mgpu.format(ngpu=ngpu, sgpu=sgpu)
+            print(mgpu_str)
+            cmd = command.format(
+                mode=args.mode,
+                batch_size=bspgpu,
+                precision=args.precision,
+                bench_warmup=args.bench_warmup,
+                num_iter=args.bench_iterations,
+                data_dir=args.data_dir,
+                results_dir=args.results_dir,
+                exp_name=exp_name,
+                perf_args=' '.join(perf_args)
+            )
+            print(cmd)
+
+            cmd = cmd.format(sgpu if ngpu == 1 else mgpu_str)
+
+            print(cmd.split())
+
+            exit_code = subprocess.call(cmd.split())
+
+            if exit_code != 0:
+                print("CMD: \"{}\" exited with status {}".format("".join(cmd), exit_code))
+                assert False
+            else:
+                print("Job ended sucessfully")
+
+            raport = json.load(open(rfile, 'r'))
+
+            for m in metrics:
+                row[m].append((bspgpu, np.mean(raport['iter'][m])))
+
+        for m in metrics:
+            table[m].append((ngpu, OrderedDict(row[m])))
+
+    for m in metrics:
+        table[m] = OrderedDict(table[m])
+
+    def format_float(f):
+        return "{:>8.1f}".format(f)
+
+    def format_int(i):
+        return "{:>8}".format(i)
+
+    for m in metrics:
+        header = " {} |".format(m) + " |".join(map(format_int, args.ngpus)) + " |"
+
+        print(header)
+        print("-" * len(header))
+
+        for bspgpu in args.bs:
+            line = [format_int(bspgpu)]
+
+            for ngpu in args.ngpus:
+                line.append(format_float(table[m][ngpu][bspgpu]))
+
+            print(" " * (len(m) - 7) + " |".join(line) + " |")
+
+    return table
+
+
+def load_baseline_file(path):
+    with open(path, 'r') as f:
+        baseline = json.load(f)
+        return baseline['metrics']
+
+    return None
+
+
+def check(results, baseline, ngpus, bs, metrics):
+    allright = True
+    for m in metrics:
+        for n in ngpus:
+            for b in bs:
+                result = results[m][n][b]
+                reference = baseline[str(n)][str(b)][m]
+                if result < PERF_THR * reference:
+                    allright = False
+                    print(
+                        "Metric: {} NGPUs: {} BS: {} Result ( {} ) is more than {} times slower than reference ( {} )".
+                        format(m, n, b, result, PERF_THR, reference)
+                    )
+
+    return allright
+
+
+if args.mode == 'training':
+    metrics = ['total_ips']
+else:
+    metrics = ['total_ips']
+
+table = benchmark(command, metrics, args)
+
+if not args.baseline is None:
+    baseline = load_baseline_file(args.baseline)
+
+    if check(table, baseline, args.ngpus, args.bs, metrics):
+        print("&&&& PASSED")
+        exit(0)
+    else:
+        print("&&&& FAILED")
+        exit(1)
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/docker/build.sh b/TensorFlow/Classification/RN50v1.5/scripts/docker/build.sh
new file mode 100644
index 00000000..e5046190
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/docker/build.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build . --rm -t rn50v15_tf
diff --git a/TensorFlow/Classification/RN50v1.5/scripts/docker/interactive.sh b/TensorFlow/Classification/RN50v1.5/scripts/docker/interactive.sh
new file mode 100644
index 00000000..b971bc77
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/scripts/docker/interactive.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+nvidia-docker run -it --rm --ipc=host --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v $PWD:/workspace/rn50v15_tf/ rn50v15_tf bash
diff --git a/TensorFlow/Classification/RN50v1.5/utils/__init__.py b/TensorFlow/Classification/RN50v1.5/utils/__init__.py
new file mode 100644
index 00000000..6cc42ec3
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/__init__.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from utils import hooks
+
+from utils import var_storage
+
+from utils import cmdline_helper
+
+from utils import data_utils
+from utils import image_processing
+
+from utils import learning_rate
\ No newline at end of file
diff --git a/TensorFlow/Classification/RN50v1.5/utils/cmdline_helper.py b/TensorFlow/Classification/RN50v1.5/utils/cmdline_helper.py
new file mode 100644
index 00000000..35b9ca0c
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/cmdline_helper.py
@@ -0,0 +1,199 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+
+def _add_bool_argument(parser, name=None, default=False, required=False, help=None):
+
+    if not isinstance(default, bool):
+        raise ValueError()
+
+    feature_parser = parser.add_mutually_exclusive_group(required=required)
+
+    feature_parser.add_argument('--' + name, dest=name, action='store_true', help=help, default=default)
+    feature_parser.add_argument('--no' + name, dest=name, action='store_false')
+    feature_parser.set_defaults(name=default)
+
+
+def parse_cmdline():
+
+    p = argparse.ArgumentParser(description="JoC-RN50v1.5-TF")
+
+    p.add_argument(
+        '--mode',
+        choices=['train', 'train_and_evaluate', 'evaluate', 'training_benchmark', 'inference_benchmark'],
+        type=str,
+        default='train_and_evaluate',
+        required=False,
+        help="""The execution mode of the script."""
+    )
+
+    p.add_argument(
+        '--data_dir',
+        required=False,
+        default=None,
+        type=str,
+        help="Path to dataset in TFRecord format. Files should be named 'train-*' and 'validation-*'."
+    )
+
+    p.add_argument(
+        '--batch_size', 
+        type=int, 
+        required=True, 
+        help="""Size of each minibatch per GPU."""
+    )
+
+    p.add_argument(
+        '--num_iter',
+        type=int, 
+        required=True, 
+        help="""Number of iterations to run."""
+    )
+
+    p.add_argument(
+        '--iter_unit',
+        choices=['epoch', 'batch'],
+        type=str,
+        required=True,
+        help="""Unit of iterations."""
+    )
+
+    p.add_argument(
+        '--warmup_steps',
+        default=50,
+        type=int,
+        required=False,
+        help="""Number of steps considered as warmup and not taken into account for performance measurements."""
+    )
+
+    # Tensor format used for the computation.
+    p.add_argument(
+        '--data_format',
+        choices=['NHWC', 'NCHW'],
+        type=str,
+        default='NCHW',
+        required=False,
+        help=argparse.SUPPRESS 
+    )
+
+    p.add_argument(
+        '--results_dir',
+        type=str,
+        required=True,
+        help="""Directory in which to write training logs, summaries and checkpoints."""
+    )
+
+    p.add_argument(
+        '--display_every', 
+        default=10,
+        type=int, 
+        required=False, 
+        help="""How often (in batches) to print out running information."""
+    )
+
+    p.add_argument(
+        '--lr_init',
+        default=0.1,
+        type=float,
+        required=False,
+        help="""Initial value for the learning rate."""
+    )
+
+    p.add_argument(
+        '--weight_decay', 
+        default=1e-4,
+        type=float, 
+        required=False, 
+        help="""Weight Decay scale factor."""
+    )
+
+    p.add_argument(
+        '--momentum',
+        default=0.9,
+        type=float,
+        required=False,
+        help="""SGD momentum value for the Momentum optimizer."""
+    )
+    
+    #Select fp32 or non-AMP fp16 precision arithmetic.
+    p.add_argument(
+        '--precision',
+        choices=['fp32', 'fp16'],
+        type=str,
+        default='fp32',
+        required=False,
+        help=argparse.SUPPRESS 
+    )
+
+    p.add_argument(
+        '--loss_scale',
+        type=float,
+        default=256.0,
+        required=False,
+        help="""Loss scale for FP16 Training and Fast Math FP32."""
+    )
+
+    _add_bool_argument(
+        parser=p,
+        name="use_auto_loss_scaling",
+        default=False,
+        required=False,
+        help="Use AutoLossScaling in FP16, FP32 - Fast Math or FP32 AMP."
+    )
+
+    _add_bool_argument(
+        parser=p,
+        name="use_xla",
+        default=False,
+        required=False,
+        help="Enable XLA (Accelerated Linear Algebra) computation for improved performance."
+    )
+
+    #Enable FastMath Computation using TensorCores to speedup FP32 computation.
+    p.add_argument(
+        "--use_fast_math",
+        action='store_true',
+        required=False,
+        help=argparse.SUPPRESS 
+    )
+
+    _add_bool_argument(
+        parser=p,
+        name="use_tf_amp",
+        default=False,
+        required=False,
+        help="Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores."
+    )
+
+    p.add_argument(
+        '--seed', 
+        type=int, 
+        default=1, 
+        help="""Random seed."""
+    )
+
+    FLAGS, unknown_args = p.parse_known_args()
+
+    if len(unknown_args) > 0:
+
+        for bad_arg in unknown_args:
+            print("ERROR: Unknown command line arg: %s" % bad_arg)
+
+        raise ValueError("Invalid command line arg(s)")
+
+    return FLAGS
diff --git a/TensorFlow/Classification/RN50v1.5/utils/data_utils.py b/TensorFlow/Classification/RN50v1.5/utils/data_utils.py
new file mode 100644
index 00000000..aedf0409
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/data_utils.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from utils import image_processing
+from utils import hvd_utils
+
+__all__ = ["get_synth_input_fn", "normalized_inputs"]
+
+_R_MEAN = 123.68
+_G_MEAN = 116.78
+_B_MEAN = 103.94
+_CHANNEL_MEANS = [_R_MEAN, _G_MEAN, _B_MEAN]
+
+_NUM_CHANNELS = 3
+
+
+def get_synth_input_fn(batch_size, height, width, num_channels, data_format, num_classes, dtype=tf.float32):
+    """Returns an input function that returns a dataset with random data.
+
+    This input_fn returns a data set that iterates over a set of random data and
+    bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
+    copy is still included. This used to find the upper throughput bound when
+    tunning the full input pipeline.
+
+    Args:
+        height: Integer height that will be used to create a fake image tensor.
+        width: Integer width that will be used to create a fake image tensor.
+        num_channels: Integer depth that will be used to create a fake image tensor.
+        num_classes: Number of classes that should be represented in the fake labels
+            tensor
+        dtype: Data type for features/images.
+
+    Returns:
+        An input_fn that can be used in place of a real one to return a dataset
+        that can be used for iteration.
+    """
+
+    if data_format not in ["NHWC", "NCHW"]:
+        raise ValueError("Unknown data_format: %s" % str(data_format))
+
+    if data_format == "NHWC":
+        input_shape = [batch_size, height, width, num_channels]
+    else:
+        input_shape = [batch_size, num_channels, height, width]
+
+    # Convert the inputs to a Dataset.
+    inputs = tf.truncated_normal(input_shape, dtype=dtype, mean=127, stddev=60, name='synthetic_inputs')
+    labels = tf.random_uniform([batch_size], minval=0, maxval=num_classes - 1, dtype=tf.int32, name='synthetic_labels')
+
+    data = tf.data.Dataset.from_tensors((inputs, labels))
+
+    data = data.repeat()
+
+    data = data.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
+
+    return data
+
+
+def get_tfrecords_input_fn(filenames, batch_size, height, width, training, distort_color, num_threads, deterministic):
+
+    shuffle_buffer_size = 4096
+
+    if deterministic:
+        if hvd_utils.is_using_hvd():
+            seed = 13 * (1 + hvd.rank())
+        else:
+            seed = 13
+    else:
+        seed = None
+
+    ds = tf.data.Dataset.from_tensor_slices(filenames)
+
+    if hvd_utils.is_using_hvd() and training:
+        ds = ds.shard(hvd.size(), hvd.rank())
+
+    ds = ds.apply(
+        tf.data.experimental.parallel_interleave(
+            tf.data.TFRecordDataset,
+            cycle_length=4,
+            block_length=16,
+            sloppy=not deterministic,
+            prefetch_input_elements=16
+        )
+    )
+
+    counter = tf.data.Dataset.range(sys.maxsize)
+    ds = tf.data.Dataset.zip((ds, counter))
+
+    def preproc_func(record, counter_):
+        return image_processing.preprocess_image_record(record, height, width, _NUM_CHANNELS, training)
+
+    ds = ds.cache()
+
+    if training:
+
+        ds = ds.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=shuffle_buffer_size, seed=seed))
+
+    else:
+        ds = ds.repeat()
+
+    ds = ds.apply(
+        tf.data.experimental.map_and_batch(
+            map_func=preproc_func,
+            num_parallel_calls=num_threads,
+            batch_size=batch_size,
+            drop_remainder=True,
+        )
+    )
+
+    ds = ds.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
+
+    return ds
+
+
+def normalized_inputs(inputs):
+
+    num_channels = inputs.get_shape()[-1]
+
+    if inputs.get_shape().ndims != 4:
+        raise ValueError('Input must be of size [batch_size, height, width, C>0]')
+
+    if len(_CHANNEL_MEANS) != num_channels:
+        raise ValueError('len(means) must match the number of channels')
+
+    # We have a 1-D tensor of means; convert to 3-D.
+    means_per_channel = tf.reshape(_CHANNEL_MEANS, [1, 1, num_channels])
+    means_per_channel = tf.cast(means_per_channel, dtype=inputs.dtype)
+
+    inputs = tf.subtract(inputs, means_per_channel)
+
+    return tf.divide(inputs, 255.0)
diff --git a/TensorFlow/Classification/RN50v1.5/utils/hooks/__init__.py b/TensorFlow/Classification/RN50v1.5/utils/hooks/__init__.py
new file mode 100644
index 00000000..091760a5
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/hooks/__init__.py
@@ -0,0 +1,6 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from utils.hooks.training_hooks import *
+from utils.hooks.benchmark_hooks import *
+from utils.hooks.prefill_hook import *
diff --git a/TensorFlow/Classification/RN50v1.5/utils/hooks/benchmark_hooks.py b/TensorFlow/Classification/RN50v1.5/utils/hooks/benchmark_hooks.py
new file mode 100644
index 00000000..ea532cd4
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/hooks/benchmark_hooks.py
@@ -0,0 +1,66 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+import dllogger.logger as dllg
+from dllogger.logger import LOGGER
+
+__all__ = ['BenchmarkLoggingHook']
+
+
+class BenchmarkLoggingHook(tf.train.SessionRunHook):
+
+    def __init__(self, log_file_path, global_batch_size, log_every=10, warmup_steps=20):
+        LOGGER.set_model_name('resnet')
+
+        LOGGER.set_backends(
+            [
+                dllg.JsonBackend(
+                    log_file=log_file_path, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=log_every
+                ),
+                dllg.JoCBackend(log_file=None, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=log_every)
+            ]
+        )
+
+        LOGGER.register_metric("iteration", metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("total_ips", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+
+        self.warmup_steps = warmup_steps
+        self.global_batch_size = global_batch_size
+        self.current_step = 0
+
+    def before_run(self, run_context):
+        self.t0 = time.time()
+        if self.current_step >= self.warmup_steps:
+            LOGGER.iteration_start()
+
+    def after_run(self, run_context, run_values):
+        batch_time = time.time() - self.t0
+        ips = self.global_batch_size / batch_time
+        if self.current_step >= self.warmup_steps:
+            LOGGER.log("iteration", int(self.current_step))
+            LOGGER.log("total_ips", float(ips))
+            LOGGER.iteration_stop()
+
+        self.current_step += 1
+
+    def end(self, session):
+        LOGGER.finish()
diff --git a/TensorFlow/Classification/RN50v1.5/utils/hooks/prefill_hook.py b/TensorFlow/Classification/RN50v1.5/utils/hooks/prefill_hook.py
new file mode 100644
index 00000000..4e4db544
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/hooks/prefill_hook.py
@@ -0,0 +1,32 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+__all__ = ['PrefillStagingAreasHook']
+
+
+class PrefillStagingAreasHook(tf.train.SessionRunHook):
+
+    def after_create_session(self, session, coord):
+        # TODO: This assumes TF collections are ordered; is this safe?
+        enqueue_ops = tf.get_collection('STAGING_AREA_PUTS')
+        for i in range(len(enqueue_ops)):
+            session.run(enqueue_ops[:i + 1])
diff --git a/TensorFlow/Classification/RN50v1.5/utils/hooks/training_hooks.py b/TensorFlow/Classification/RN50v1.5/utils/hooks/training_hooks.py
new file mode 100644
index 00000000..78d17520
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/hooks/training_hooks.py
@@ -0,0 +1,112 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+import dllogger.logger as dllg
+from dllogger.logger import LOGGER
+
+__all__ = ['TrainingLoggingHook']
+
+
+class TrainingLoggingHook(tf.train.SessionRunHook):
+
+    def __init__(
+        self, log_file_path, global_batch_size, num_steps, num_samples, num_epochs, log_every=10, warmup_steps=20
+    ):
+        LOGGER.set_model_name('resnet')
+
+        LOGGER.set_backends(
+            [
+                dllg.JsonBackend(
+                    log_file=log_file_path, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=log_every
+                ),
+                dllg.StdOutBackend(log_file=None, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=log_every)
+            ]
+        )
+
+        #
+        # Set-up the train_iter scope metrics
+        LOGGER.register_metric("iteration", metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("imgs_per_sec", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("cross_entropy", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("l2_loss", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("total_loss", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+        LOGGER.register_metric("learning_rate", meter=dllg.AverageMeter(), metric_scope=dllg.TRAIN_ITER_SCOPE)
+
+        # Set up the eval-scope metrics
+        LOGGER.register_metric("epoch", metric_scope=dllg.EPOCH_SCOPE)
+        LOGGER.register_metric("final_cross_entropy", metric_scope=dllg.EPOCH_SCOPE)
+        LOGGER.register_metric("final_l2_loss", metric_scope=dllg.EPOCH_SCOPE)
+        LOGGER.register_metric("final_total_loss", metric_scope=dllg.EPOCH_SCOPE)
+        LOGGER.register_metric("final_learning_rate", metric_scope=dllg.EPOCH_SCOPE)
+
+        self.global_batch_size = global_batch_size
+        self.num_steps = num_steps
+        self.num_samples = num_samples
+        self.num_epochs = num_epochs
+        self.warmup_steps = warmup_steps
+
+        self.current_step = 0
+        self.current_epoch = 0
+
+    # Determines if its the last step of the epoch
+    def _last_step_of_epoch(self):
+        return (self.global_batch_size * (self.current_step + 1) > (self.current_epoch + 1) * self.num_samples)
+
+    def before_run(self, run_context):
+        LOGGER.iteration_start()
+        run_args = tf.train.SessionRunArgs(
+            fetches=[
+                tf.train.get_global_step(), 'cross_entropy_loss_ref:0', 'l2_loss_ref:0', 'total_loss_ref:0',
+                'learning_rate_ref:0'
+            ]
+        )
+        self.t0 = time.time()
+
+        return run_args
+
+    def after_run(self, run_context, run_values):
+        global_step, cross_entropy, l2_loss, total_loss, learning_rate = run_values.results
+        batch_time = time.time() - self.t0
+        ips = self.global_batch_size / batch_time
+
+        LOGGER.log("iteration", int(self.current_step))
+        LOGGER.log("imgs_per_sec", float(ips))
+        LOGGER.log("cross_entropy", float(cross_entropy))
+        LOGGER.log("l2_loss", float(l2_loss))
+        LOGGER.log("total_loss", float(total_loss))
+        LOGGER.log("learning_rate", float(learning_rate))
+        LOGGER.iteration_stop()
+
+        self.current_step += 1
+
+        if self._last_step_of_epoch():
+            LOGGER.epoch_start()
+            LOGGER.log("epoch", int(self.current_epoch))
+            LOGGER.log("final_cross_entropy", float(cross_entropy))
+            LOGGER.log("final_l2_loss", float(l2_loss))
+            LOGGER.log("final_total_loss", float(total_loss))
+            LOGGER.log("final_learning_rate", float(learning_rate))
+            LOGGER.epoch_stop()
+            self.current_epoch += 1
+
+    def end(self, session):
+        LOGGER.finish()
diff --git a/TensorFlow/Classification/RN50v1.5/utils/hvd_utils.py b/TensorFlow/Classification/RN50v1.5/utils/hvd_utils.py
new file mode 100644
index 00000000..c05106ff
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/hvd_utils.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+__all__ = ["is_using_hvd"]
+
+
+def is_using_hvd():
+    env_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"]
+
+    if all([var in os.environ for var in env_vars]):
+        return True
+    else:
+        return False
diff --git a/TensorFlow/Classification/RN50v1.5/utils/image_processing.py b/TensorFlow/Classification/RN50v1.5/utils/image_processing.py
new file mode 100644
index 00000000..ac1833d1
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/image_processing.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import tensorflow as tf
+
+_RESIZE_MIN = 256
+_DEFAULT_IMAGE_SIZE = 224
+
+__all__ = ['preprocess_image_record']
+
+
+def _deserialize_image_record(record):
+    feature_map = {
+        'image/encoded': tf.FixedLenFeature([], tf.string, ''),
+        'image/class/label': tf.FixedLenFeature([], tf.int64, -1),
+        'image/class/text': tf.FixedLenFeature([], tf.string, ''),
+        'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
+        'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
+        'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
+        'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32)
+    }
+    with tf.name_scope('deserialize_image_record'):
+        obj = tf.parse_single_example(record, feature_map)
+        imgdata = obj['image/encoded']
+        label = tf.cast(obj['image/class/label'], tf.int32)
+        bbox = tf.stack([obj['image/object/bbox/%s' % x].values for x in ['ymin', 'xmin', 'ymax', 'xmax']])
+        bbox = tf.transpose(tf.expand_dims(bbox, 0), [0, 2, 1])
+        text = obj['image/class/text']
+        return imgdata, label, bbox, text
+
+
+def _decode_jpeg(imgdata, channels=3):
+    return tf.image.decode_jpeg(imgdata, channels=channels, fancy_upscaling=False, dct_method='INTEGER_FAST')
+
+
+def _crop_and_filp(image, bbox, num_channels):
+    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bbox,
+        min_object_covered=0.1,
+        aspect_ratio_range=[0.75, 1.33],
+        area_range=[0.05, 1.0],
+        max_attempts=100,
+        use_image_if_no_bounding_boxes=True
+    )
+
+    bbox_begin, bbox_size, _ = sample_distorted_bounding_box
+    offset_y, offset_x, _ = tf.unstack(bbox_begin)
+    target_height, target_width, _ = tf.unstack(bbox_size)
+    cropped = tf.image.crop_to_bounding_box(image, offset_y, offset_x, target_height, target_width)
+    cropped = tf.image.random_flip_left_right(cropped)
+    return cropped
+
+
+def _central_crop(image, crop_height, crop_width):
+    shape = tf.shape(image)
+    height, width = shape[0], shape[1]
+
+    amount_to_be_cropped_h = (height - crop_height)
+    crop_top = amount_to_be_cropped_h // 2
+    amount_to_be_cropped_w = (width - crop_width)
+    crop_left = amount_to_be_cropped_w // 2
+    return tf.slice(image, [crop_top, crop_left, 0], [crop_height, crop_width, -1])
+
+
+def _smallest_size_at_least(height, width, resize_min):
+    resize_min = tf.cast(resize_min, tf.float32)
+
+    # Convert to floats to make subsequent calculations go smoothly.
+    height, width = tf.cast(height, tf.float32), tf.cast(width, tf.float32)
+
+    smaller_dim = tf.minimum(height, width)
+    scale_ratio = resize_min / smaller_dim
+
+    # Convert back to ints to make heights and widths that TF ops will accept.
+    new_height = tf.cast(height * scale_ratio, tf.int32)
+    new_width = tf.cast(width * scale_ratio, tf.int32)
+
+    return new_height, new_width
+
+
+def _aspect_preserving_resize(image, resize_min):
+    """Resize images preserving the original aspect ratio.
+
+    Args:
+    image: A 3-D image `Tensor`.
+    resize_min: A python integer or scalar `Tensor` indicating the size of
+      the smallest side after resize.
+
+    Returns:
+    resized_image: A 3-D tensor containing the resized image.
+    """
+    shape = tf.shape(image)
+    height, width = shape[0], shape[1]
+
+    new_height, new_width = _smallest_size_at_least(height, width, resize_min)
+
+    return _resize_image(image, new_height, new_width)
+
+
+def _resize_image(image, height, width):
+    """Simple wrapper around tf.resize_images.
+
+    This is primarily to make sure we use the same `ResizeMethod` and other
+    details each time.
+
+    Args:
+    image: A 3-D image `Tensor`.
+    height: The target height for the resized image.
+    width: The target width for the resized image.
+
+    Returns:
+    resized_image: A 3-D tensor containing the resized image. The first two
+      dimensions have the shape [height, width].
+    """
+    return tf.image.resize_images(image, [height, width], method=tf.image.ResizeMethod.BILINEAR, align_corners=False)
+
+
+def preprocess_image_record(record, height, width, num_channels, is_training=False):
+
+    imgdata, label, bbox, text = _deserialize_image_record(record)
+    label -= 1
+    try:
+        image = _decode_jpeg(imgdata, channels=3)
+    except:
+        image = tf.image.decode_png(imgdata, channels=3)
+
+    if is_training:
+        # For training, we want to randomize some of the distortions.
+        image = _crop_and_filp(image, bbox, num_channels)
+        image = _resize_image(image, height, width)
+    else:
+        image = _aspect_preserving_resize(image, _RESIZE_MIN)
+        image = _central_crop(image, height, width)
+
+    return image, label
diff --git a/TensorFlow/Classification/RN50v1.5/utils/learning_rate.py b/TensorFlow/Classification/RN50v1.5/utils/learning_rate.py
new file mode 100644
index 00000000..5d2cba46
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/learning_rate.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import tensorflow as tf
+
+__all__ = ['learning_rate_scheduler']
+
+
+def learning_rate_scheduler(learning_rate_init, global_step, batch_size, num_batches_per_epoch, num_gpus):
+
+    def get_scaled_base_learning_rate():
+        """Calculates base learning rate for creating lr schedule.
+        In replicated mode, gradients are summed rather than averaged which, with
+        the sgd and momentum optimizers, increases the effective learning rate by
+        lr * num_gpus. Dividing the base lr by num_gpus negates the increase.
+        Args:
+          batch_size: Total batch-size.
+        Returns:
+          Base learning rate to use to create lr schedule.
+        """
+
+        base_lr = learning_rate_init * num_gpus
+
+        # Starting LR = 0.1 with BS = 256, else linearly scale
+        return base_lr * (batch_size / 256.0)
+
+    rescaled_lr = get_scaled_base_learning_rate()
+
+    boundaries = [int(num_batches_per_epoch * x) for x in [30, 60, 80, 90]]
+
+    values = [1e0, 1e-1, 1e-2, 1e-3, 1e-4]
+    values = [rescaled_lr * v for v in values]
+
+    lr = tf.train.piecewise_constant(global_step, boundaries, values)
+
+    # First 5 Epochs are warmup
+    warmup_steps = int(num_batches_per_epoch * 5)
+
+    warmup_lr = (rescaled_lr * tf.cast(global_step, tf.float32) / tf.cast(warmup_steps, tf.float32))
+
+    return tf.cond(global_step < warmup_steps, lambda: warmup_lr, lambda: lr)
diff --git a/TensorFlow/Classification/RN50v1.5/utils/optimizers.py b/TensorFlow/Classification/RN50v1.5/utils/optimizers.py
new file mode 100755
index 00000000..23bb6380
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/optimizers.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import print_function
+
+import tensorflow as tf
+
+__all__ = ['FixedLossScalerOptimizer']
+
+
+class FixedLossScalerOptimizer(tf.train.Optimizer):
+    """An optimizer that scales loss and un-scales gradients for FP16 training."""
+
+    def __init__(self, optimizer, scale=None, name="LossScalingOptimizer", use_locking=False):
+
+        super(FixedLossScalerOptimizer, self).__init__(name=name, use_locking=use_locking)
+
+        self._optimizer = optimizer
+        self._scale = float(scale) if scale is not None else 1.0
+
+    def compute_gradients(self, loss, var_list=None, *args, **kwargs):
+
+        if self._scale != 1.0:
+            loss = tf.scalar_mul(self._scale, loss)
+
+        gradvar = self._optimizer.compute_gradients(loss, var_list, *args, **kwargs)
+        gradvar = [(tf.scalar_mul(1. / self._scale, g), v) for g, v in gradvar]
+
+        return gradvar
+
+    def apply_gradients(self, *args, **kwargs):
+        return self._optimizer.apply_gradients(*args, **kwargs)
diff --git a/TensorFlow/Classification/RN50v1.5/utils/var_storage.py b/TensorFlow/Classification/RN50v1.5/utils/var_storage.py
new file mode 100644
index 00000000..2f09ed6f
--- /dev/null
+++ b/TensorFlow/Classification/RN50v1.5/utils/var_storage.py
@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import tensorflow as tf
+
+__all__ = ['model_variable_scope']
+
+
+def model_variable_scope(name, reuse=False, dtype=tf.float32, *args, **kwargs):
+    """Returns a variable scope that the model should be created under.
+    If self.dtype is a castable type, model variable will be created in fp32
+    then cast to self.dtype before being used.
+    Returns:
+      A variable scope for the model.
+    """
+
+    def _custom_dtype_getter(getter, name, shape=None, dtype=None, trainable=True, regularizer=None, *args, **kwargs):
+        """Creates variables in fp32, then casts to fp16 if necessary.
+        This function is a custom getter. A custom getter is a function with the
+        same signature as tf.get_variable, except it has an additional getter
+        parameter. Custom getters can be passed as the `custom_getter` parameter of
+        tf.variable_scope. Then, tf.get_variable will call the custom getter,
+        instead of directly getting a variable itself. This can be used to change
+        the types of variables that are retrieved with tf.get_variable.
+        The `getter` parameter is the underlying variable getter, that would have
+        been called if no custom getter was used. Custom getters typically get a
+        variable with `getter`, then modify it in some way.
+        This custom getter will create an fp32 variable. If a low precision
+        (e.g. float16) variable was requested it will then cast the variable to the
+        requested dtype. The reason we do not directly create variables in low
+        precision dtypes is that applying small gradients to such variables may
+        cause the variable not to change.
+        Args:
+          getter: The underlying variable getter, that has the same signature as
+            tf.get_variable and returns a variable.
+          name: The name of the variable to get.
+          shape: The shape of the variable to get.
+          *args: Additional arguments to pass unmodified to getter.
+          **kwargs: Additional keyword arguments to pass unmodified to getter.
+        Returns:
+          A variable which is cast to fp16 if necessary.
+        """
+
+        storage_dtype = tf.float32 if dtype in [tf.float32, tf.float16] else dtype
+
+        variable = getter(
+            name,
+            shape,
+            dtype=storage_dtype,
+            trainable=trainable,
+            regularizer=(
+                regularizer if
+                (trainable and not any(l_name.lower() in name.lower()
+                                       for l_name in ['batchnorm', 'batch_norm'])) else None
+            ),
+            *args,
+            **kwargs
+        )
+
+        if dtype != tf.float32:
+            cast_name = name + '/fp16_cast'
+
+            try:
+                cast_variable = tf.get_default_graph().get_tensor_by_name(cast_name + ':0')
+
+            except KeyError:
+                cast_variable = tf.cast(variable, dtype, name=cast_name)
+
+            cast_variable._ref = variable._ref
+            variable = cast_variable
+
+        return variable
+
+    return tf.variable_scope(name, reuse=reuse, dtype=dtype, custom_getter=_custom_dtype_getter, *args, **kwargs)
diff --git a/TensorFlow/Classification/imagenet/README.md b/TensorFlow/Classification/imagenet/README.md
deleted file mode 100644
index ea62c3ba..00000000
--- a/TensorFlow/Classification/imagenet/README.md
+++ /dev/null
@@ -1,91 +0,0 @@
-
-# Convolutional neural network training scripts
-
-This script implements a number of popular CNN models and demonstrates
-efficient single-node training on multi-GPU systems. It can be used for
-benchmarking, training and evaluation of models.
-
-Uber's Horovod data-parallel framework is used for parallelization.
-
-
-## Imagenet data preprocessing
-
-See [this file](data_preprocessing/README.md) for instructions on downloading
-and preprocessing the imagenet data set.
-
-
-## ResNet50 training example
-
-The following command initiates training of the ResNet50 model distributed
-across 8 GPUs using fp16 arithmetic. We assume Imagenet is saved in TFRecord
-format at /data/imagenet_tfrecord.
-
-```
-    $ mpiexec -np 8 python nvcnn_hvd.py \
-                      --model=resnet50 \
-                      --data_dir=/data/imagenet_tfrecord \
-                      --batch_size=256 \
-                      --fp16 \
-                      --larc_mode=clip \
-                      --larc_eta=0.003 \
-                      --loss_scale=128 \
-                      --log_dir=./checkpoint-dir \
-                      --save_interval=3600 \
-                      --num_epochs=90 \
-                      --display_every=100
-                      --learning_rate=2.0
-```
-
-
-## Inception V3 training example
-
-The following command initiates training of the Inception V3 model distributed
-across 8 GPUs using fp16 arithmetic. We assume Imagenet is saved in TFRecord
-format at /data/imagenet_tfrecord.
-
-```
-    $ mpiexec -np 8 python nvcnn_hvd.py \
-                      --model=inception3 \
-                      --data_dir=/data/imagenet_tfrecord \
-                      --batch_size=128 \
-                      --fp16 \
-                      --larc_mode=clip \
-                      --larc_eta=0.003 \
-                      --loss_scale=128 \
-                      --log_dir=./checkpoint-dir \
-                      --save_interval=3600 \
-                      --num_epochs=90 \
-                      --display_every=100 \
-                      --learning_rate=1.0
-```
-
-
-## Evaluating accuracy with the test set
-
-Model parameters are stored in FP32 precision when training with either FP32 or
-FP16 arithmetic. Thus the `--fp16` flag is not needed for eval jobs. Also,
-evaluation is performed on a single GPU. The following command performs
-evaluation of a trained model.
-
-```
-    $ python nvcnn_hvd.py --model=<resnet50|inception3> \
-                          --data_dir=/data/imagenet_tfrecord \
-                          --batch_size=256 \
-                          --log_dir=./checkpoint-dir \
-                          --eval
-```
-
-After trianing, ResNet50 and Inception V3 should achieve top-1 accuracies of
-75.5% and 77.8%, respectively, on the imagenet validation set.
-
-
-##  Notes
-
-With the `--fp16` flag the model is trained using 16-bit floating-point
-operations. This provides optimized performance on Volta's TensorCores.
-For more information on training with FP16 arithmetic see
-[Training with Mixed Precision](http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html).
-
-If executing the training command above as root (for example in a Docker
-container), mpiexec requires an additional --allow-run-as-root flag.
-
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/README.md b/TensorFlow/Classification/imagenet/data_preprocessing/README.md
deleted file mode 100644
index da50ae45..00000000
--- a/TensorFlow/Classification/imagenet/data_preprocessing/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-ImageNet Dataset Build Scripts
-==========================
-
-What is this?
-------------------
-
-This directory includes all the scripts necessary to build the ImageNet dataset in a sharded protobuf representation, which is recommended by the TensorFlow team for performance reasons. The protobuf files will include a large number of JPEG images in one file, along with the image metadata (image class, bounding boxes, etc.). This will ensure good performance on both SSDs and magnetic hard drives. The protobufs will contain TFRecord data types, which are standard for TensorFlow.
-
-
-Performance considerations
-----------------------------------------
-
-This script is largely based on TensorFlow's ImageNet preprocessing script for the Inception v3 model (see [here](https://github.com/tensorflow/models/tree/master/inception/inception/data)). The advantages of the dataset preprocessed in this fashion are discussed here.
-
- 1. Protobufs containing many JPEGs are much faster to process than reading raw JPEGs, especially on magnetic disks, by avoiding seek time. This also tends to help on SSDs, because of sequential reads, which are still a bit faster than random reads. This was the case in the original TensorFlow preprocessing script.
- 2. This version of the preprocessing scripts is independent of [Bazel](https://bazel.build/), Google's build tool. The Bazel requirement to "build" the Python and shell scripts is unnecessary and is a heavy-weight step that can has been avoided here. The scripts work the same way as the public Google scripts, but one can run them immediately without needing to set up Bazel and going through incantations like ```bazel build inception/download_and_preprocess_imagenet``` (see [here](https://github.com/tensorflow/models/tree/master/inception)). This is a modification to the original Google script.
- 3. Pre-resizing while building the dataset is essential for good performance.The speedup from uniformly-sized images can be significant relative to original ImageNet, while running AlexNet-OWT (the gains are smaller for compute-heavy models such as Inception v3 and ResNet-50). Note that the current implementation does not preserve the aspect ratio while creating uniform-sized images. An alternative would be to resize while preserving the aspect ratio, then crop. This script is meant to help the user with preprocessing the model for performance reasons, but it may need to be tweaked to provide the best machine learning results. It's open-source so the user can modify it to their liking. This is a modification to the original Google script.
- 4. Efficient storage - the original JPEGs are stored with a [quality factor](https://en.wikipedia.org/wiki/JPEG) of 100, but the color distortions tend not to happen until Q drops below 85. These scripts store the images with a Q factor of 90 by default, which reduces the image size by 75% while causing minimal color distortions of no consequence for convolutional neural network training. The impact of this is very significant due to a reduction in I/O load, particularly in a multi-GPU setting, when more total disk accesses need to take place to feed more than one GPU. This is a modification to the original Google script.
-
-----------
-
-How to run the scripts?
---------------------------------
-
- 1. Create an ImageNet account at http://image-net.org. You will need a user ID
-    and the access key provided upon registration.
- 2. Run the download-imagenet.sh script. You will be asked for your ImageNet user ID, ImageNet password, and the directory in which to store the dataset. Future re-running of this script will be optimized in that if the tarballs containing the dataset are already available in the target directory, they won't be re-downloaded. However, the unzipping of the tarballs will still take place, so if you already ran this script, don't run it again.
- 3. Run the generate_tfrecord_protos.sh script. You will be asked about the location of the files downloaded in step 2, as well as the directory in which to store the protobuf files to be used by TensorFlow. Additional questions will pertain to whether original or pre-resized images are to be stored (it is strongly recommended that pre-resizing be chosen), the height and width of the images after resizing (if pre-resizing is chosen), and the JPEG Q factor (Q=90 is recommended).
-
-> **Note:**
-
-> Running this script requires a lot of memory. Make sure you have at least 16 GB RAM free on your machine, and preferably 32 GB. This is due to a bug in TensorFlow that is currently being investigaged. It has nothing to do with the script, rather with TensorFlow core. It shows up only in case of datasets with millions of files. Once the ImageNet dataset size is reduced to a few thousand files (as it will be after these scripts are run, due to storage of many JPEGs in one protobuf), we could no longer replicate it while training models.
-
-> If the script fails, examine the dmesg output - it likely failed due to an out of memory error. If that was the case, try freeing up memory. There is an included Python script called purge_mem_caches.py, which can be run on the host. This usually helps fix things that can't be resolved by just shutting down applications, such as purging virtual memory pages left by previous runs of TensorFlow, that weren't cleaned up by either the application or the OS. Note that running this script in the container itself, rather than the host, won't have any effect. Note also that this script has a dependency on psutil and pexpect, Python packages that can be installed using pip.gg
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/build_imagenet_data.py b/TensorFlow/Classification/imagenet/data_preprocessing/build_imagenet_data.py
deleted file mode 100644
index 75584f6f..00000000
--- a/TensorFlow/Classification/imagenet/data_preprocessing/build_imagenet_data.py
+++ /dev/null
@@ -1,455 +0,0 @@
-#!/usr/bin/python
-# Copyright 2016 Google Inc and 2017 NVIDIA Corporation. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Converts image data to TFRecords file format with Example protos.
-
-The image data set is expected to reside in JPEG files located in the
-following directory structure.
-
-  data_dir/label_0/image0.jpeg
-  data_dir/label_0/image1.jpg
-  ...
-  data_dir/label_1/weird-image.jpeg
-  data_dir/label_1/my-image.jpeg
-  ...
-
-where the sub-directory is the unique label associated with these images.
-
-This TensorFlow script converts the training and evaluation data into
-a sharded data set consisting of TFRecord files
-
-  train_directory/train-00000-of-01024
-  train_directory/train-00001-of-01024
-  ...
-  train_directory/train-00127-of-01024
-
-and
-
-  validation_directory/validation-00000-of-00128
-  validation_directory/validation-00001-of-00128
-  ...
-  validation_directory/validation-00127-of-00128
-
-where we have selected 1024 and 128 shards for each data set. Each record
-within the TFRecord file is a serialized Example proto. The Example proto
-contains the following fields:
-
-  image/encoded: string containing JPEG encoded image in RGB colorspace
-  image/height: integer, image height in pixels
-  image/width: integer, image width in pixels
-  image/colorspace: string, specifying the colorspace, always 'RGB'
-  image/channels: integer, specifying the number of channels, always 3
-  image/format: string, specifying the format, always'JPEG'
-
-  image/filename: string containing the basename of the image file
-            e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
-  image/class/label: integer specifying the index in a classification layer.
-    The label ranges from [0, num_labels] where 0 is unused and left as
-    the background class.
-  image/class/text: string specifying the human-readable version of the label
-    e.g. 'dog'
-
-If you data set involves bounding boxes, please look at build_imagenet_data.py.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from datetime import datetime
-import os
-import random
-import sys
-import threading
-
-
-import numpy as np
-import tensorflow as tf
-
-tf.app.flags.DEFINE_string('train_directory', '/tmp/',
-                           'Training data directory')
-tf.app.flags.DEFINE_string('validation_directory', '/tmp/',
-                           'Validation data directory')
-tf.app.flags.DEFINE_string('output_directory', '/tmp/',
-                           'Output data directory')
-
-tf.app.flags.DEFINE_integer('train_shards', 1024,
-                            'Number of shards in training TFRecord files.')
-tf.app.flags.DEFINE_integer('validation_shards', 128,
-                            'Number of shards in validation TFRecord files.')
-
-tf.app.flags.DEFINE_integer('num_threads', 4,
-                            'Number of threads to preprocess the images.')
-
-tf.app.flags.DEFINE_boolean('resize_images', True,
-                            'resize images to uniform size while creating protobufs')
-tf.app.flags.DEFINE_integer('new_height', 352, 
-                            'height after resizing')
-tf.app.flags.DEFINE_integer('new_width', 352,
-                            'width after resizing')
-tf.app.flags.DEFINE_integer('jpeg_q', 90,
-                            'JPEG Q factor (from 0 to 100)')
-
-# The labels file contains a list of valid labels are held in this file.
-# Assumes that the file contains entries as such:
-#   dog
-#   cat
-#   flower
-# where each line corresponds to a label. We map each label contained in
-# the file to an integer corresponding to the line number starting from 0.
-tf.app.flags.DEFINE_string('labels_file', '', 'Labels file')
-
-
-FLAGS = tf.app.flags.FLAGS
-
-
-def _int64_feature(value):
-  """Wrapper for inserting int64 features into Example proto."""
-  if not isinstance(value, list):
-    value = [value]
-  return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
-
-
-def _bytes_feature(value):
-  """Wrapper for inserting bytes features into Example proto."""
-  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
-
-def _convert_to_example(filename, image_buffer, label, text, height, width):
-  """Build an Example proto for an example.
-
-  Args:
-    filename: string, path to an image file, e.g., '/path/to/example.JPG'
-    image_buffer: string, JPEG encoding of RGB image
-    label: integer, identifier for the ground truth for the network
-    text: string, unique human-readable, e.g. 'dog'
-    height: integer, image height in pixels
-    width: integer, image width in pixels
-  Returns:
-    Example proto
-  """
-
-  colorspace = 'RGB'
-  channels = 3
-  image_format = 'JPEG'
-
-  example = tf.train.Example(features=tf.train.Features(feature={
-      'image/height': _int64_feature(height),
-      'image/width': _int64_feature(width),
-      'image/colorspace': _bytes_feature(tf.compat.as_bytes(colorspace)),
-      'image/channels': _int64_feature(channels),
-      'image/class/label': _int64_feature(label),
-      'image/class/text': _bytes_feature(tf.compat.as_bytes(text)),
-      'image/format': _bytes_feature(tf.compat.as_bytes(image_format)),
-      'image/filename': _bytes_feature(tf.compat.as_bytes(os.path.basename(filename))),
-      'image/encoded': _bytes_feature(tf.compat.as_bytes(image_buffer))}))
-  return example
-
-
-class ImageCoder(object):
-  """Helper class that provides TensorFlow image coding utilities."""
-
-  def __init__(self):
-    # Create a single Session to run all image coding calls.
-    self._sess = tf.Session()
-
-    # Initializes function that converts PNG to JPEG data.
-    self._png_data = tf.placeholder(dtype=tf.string)
-    image = tf.image.decode_png(self._png_data, channels=3)
-    self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
-
-    # Initializes function that decodes RGB JPEG data.
-    self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
-    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
-
-    # Resize
-    self._resize = tf.expand_dims(self._decode_jpeg, 0)
-    self._resize = tf.image.resize_bilinear(self._resize, [FLAGS.new_height, FLAGS.new_width])
-    self._resize = tf.squeeze(self._resize)
-    self._resize = tf.cast(self._resize, tf.uint8)
-
-    self._new_jpeg = tf.image.encode_jpeg(self._resize, format='rgb', quality=FLAGS.jpeg_q,
-      progressive=False, optimize_size=True, chroma_downsampling=True)
-
-  def png_to_jpeg(self, image_data):
-    return self._sess.run(self._png_to_jpeg,
-                          feed_dict={self._png_data: image_data})
-
-  def decode_jpeg(self, image_data):
-    if FLAGS.resize_images:
-      image = self._resize
-      image, img_buffer = self._sess.run([self._resize, self._new_jpeg], feed_dict={self._decode_jpeg_data: image_data})
-    else:
-      image = self._sess.run(self._decode_jpeg, feed_dict={self._decode_jpeg_data: image_data})
-      img_buffer = image_data
-    assert len(image.shape) == 3
-    assert image.shape[2] == 3
-    return image, img_buffer
-
-
-def _is_png(filename):
-  """Determine if a file contains a PNG format image.
-
-  Args:
-    filename: string, path of the image file.
-
-  Returns:
-    boolean indicating if the image is a PNG.
-  """
-  return '.png' in filename
-
-
-def _process_image(filename, coder):
-  """Process a single image file.
-
-  Args:
-    filename: string, path to an image file e.g., '/path/to/example.JPG'.
-    coder: instance of ImageCoder to provide TensorFlow image coding utils.
-  Returns:
-    image_buffer: string, JPEG encoding of RGB image.
-    height: integer, image height in pixels.
-    width: integer, image width in pixels.
-  """
-  # Read the image file.
-  with tf.gfile.FastGFile(filename, 'r') as f:
-    image_data = f.read()
-
-  # Convert any PNG to JPEG's for consistency.
-  if _is_png(filename):
-    print('Converting PNG to JPEG for %s' % filename)
-    image_data = coder.png_to_jpeg(image_data)
-
-  # Decode the RGB JPEG.
-  image, image_data = coder.decode_jpeg(image_data)
-
-  # Check that image converted to RGB
-  assert len(image.shape) == 3
-  height = image.shape[0]
-  width = image.shape[1]
-  assert image.shape[2] == 3
-
-  return image, image_data, height, width
-
-
-def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
-                               texts, labels, num_shards):
-  """Processes and saves list of images as TFRecord in 1 thread.
-
-  Args:
-    coder: instance of ImageCoder to provide TensorFlow image coding utils.
-    thread_index: integer, unique batch to run index is within [0, len(ranges)).
-    ranges: list of pairs of integers specifying ranges of each batches to
-      analyze in parallel.
-    name: string, unique identifier specifying the data set
-    filenames: list of strings; each string is a path to an image file
-    texts: list of strings; each string is human readable, e.g. 'dog'
-    labels: list of integer; each integer identifies the ground truth
-    num_shards: integer number of shards for this data set.
-  """
-  # Each thread produces N shards where N = int(num_shards / num_threads).
-  # For instance, if num_shards = 128, and the num_threads = 2, then the first
-  # thread would produce shards [0, 64).
-  num_threads = len(ranges)
-  assert not num_shards % num_threads
-  num_shards_per_batch = int(num_shards / num_threads)
-
-  shard_ranges = np.linspace(ranges[thread_index][0],
-                             ranges[thread_index][1],
-                             num_shards_per_batch + 1).astype(int)
-  num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
-
-  counter = 0
-  for s in range(num_shards_per_batch):
-    # Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
-    shard = thread_index * num_shards_per_batch + s
-    output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
-    output_file = os.path.join(FLAGS.output_directory, output_filename)
-    writer = tf.python_io.TFRecordWriter(output_file)
-
-    shard_counter = 0
-    files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
-    for i in files_in_shard:
-      filename = filenames[i]
-      label = labels[i]
-      text = texts[i]
-
-      decoded, image_buffer, height, width = _process_image(filename, coder)
-
-      example = _convert_to_example(filename, image_buffer, label,
-                                    text, height, width)
-      writer.write(example.SerializeToString())
-      shard_counter += 1
-      counter += 1
-
-      if not counter % 1000:
-        print('%s [thread %d]: Processed %d of %d images in thread batch.' %
-              (datetime.now(), thread_index, counter, num_files_in_thread))
-        sys.stdout.flush()
-
-    writer.close()
-    print('%s [thread %d]: Wrote %d images to %s' %
-          (datetime.now(), thread_index, shard_counter, output_file))
-    sys.stdout.flush()
-    shard_counter = 0
-  print('%s [thread %d]: Wrote %d images to %d shards.' %
-        (datetime.now(), thread_index, counter, num_files_in_thread))
-  sys.stdout.flush()
-
-
-def _process_image_files(name, filenames, texts, labels, num_shards):
-  """Process and save list of images as TFRecord of Example protos.
-
-  Args:
-    name: string, unique identifier specifying the data set
-    filenames: list of strings; each string is a path to an image file
-    texts: list of strings; each string is human readable, e.g. 'dog'
-    labels: list of integer; each integer identifies the ground truth
-    num_shards: integer number of shards for this data set.
-  """
-  assert len(filenames) == len(texts)
-  assert len(filenames) == len(labels)
-
-  # Break all images into batches with a [ranges[i][0], ranges[i][1]].
-  spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
-  ranges = []
-  for i in range(len(spacing) - 1):
-    ranges.append([spacing[i], spacing[i+1]])
-
-  # Launch a thread for each batch.
-  print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
-  sys.stdout.flush()
-
-  # Create a mechanism for monitoring when all threads are finished.
-  coord = tf.train.Coordinator()
-
-  # Create a generic TensorFlow-based utility for converting all image codings.
-  coder = ImageCoder()
-
-  threads = []
-  for thread_index in range(len(ranges)):
-    args = (coder, thread_index, ranges, name, filenames,
-            texts, labels, num_shards)
-    t = threading.Thread(target=_process_image_files_batch, args=args)
-    t.start()
-    threads.append(t)
-
-  # Wait for all the threads to terminate.
-  coord.join(threads)
-  print('%s: Finished writing all %d images in data set.' %
-        (datetime.now(), len(filenames)))
-  sys.stdout.flush()
-
-
-def _find_image_files(data_dir, labels_file):
-  """Build a list of all images files and labels in the data set.
-
-  Args:
-    data_dir: string, path to the root directory of images.
-
-      Assumes that the image data set resides in JPEG files located in
-      the following directory structure.
-
-        data_dir/dog/another-image.JPEG
-        data_dir/dog/my-image.jpg
-
-      where 'dog' is the label associated with these images.
-
-    labels_file: string, path to the labels file.
-
-      The list of valid labels are held in this file. Assumes that the file
-      contains entries as such:
-        dog
-        cat
-        flower
-      where each line corresponds to a label. We map each label contained in
-      the file to an integer starting with the integer 0 corresponding to the
-      label contained in the first line.
-
-  Returns:
-    filenames: list of strings; each string is a path to an image file.
-    texts: list of strings; each string is the class, e.g. 'dog'
-    labels: list of integer; each integer identifies the ground truth.
-  """
-  print('Determining list of input files and labels from %s.' % data_dir)
-  unique_labels = [l.strip() for l in tf.gfile.FastGFile(
-      labels_file, 'r').readlines()]
-
-  labels = []
-  filenames = []
-  texts = []
-
-  # Leave label index 0 empty as a background class.
-  label_index = 1
-
-  # Construct the list of JPEG files and labels.
-  for text in unique_labels:
-    jpeg_file_path = '%s/%s/*' % (data_dir, text)
-    matching_files = tf.gfile.Glob(jpeg_file_path)
-
-    labels.extend([label_index] * len(matching_files))
-    texts.extend([text] * len(matching_files))
-    filenames.extend(matching_files)
-
-    if not label_index % 100:
-      print('Finished finding files in %d of %d classes.' % (
-          label_index, len(labels)))
-    label_index += 1
-
-  # Shuffle the ordering of all image files in order to guarantee
-  # random ordering of the images with respect to label in the
-  # saved TFRecord files. Make the randomization repeatable.
-  shuffled_index = list(range(len(filenames)))
-  random.seed(12345)
-  random.shuffle(shuffled_index)
-
-  filenames = [filenames[i] for i in shuffled_index]
-  texts = [texts[i] for i in shuffled_index]
-  labels = [labels[i] for i in shuffled_index]
-
-  print('Found %d JPEG files across %d labels inside %s.' %
-        (len(filenames), len(unique_labels), data_dir))
-  return filenames, texts, labels
-
-
-def _process_dataset(name, directory, num_shards, labels_file):
-  """Process a complete data set and save it as a TFRecord.
-
-  Args:
-    name: string, unique identifier specifying the data set.
-    directory: string, root path to the data set.
-    num_shards: integer number of shards for this data set.
-    labels_file: string, path to the labels file.
-  """
-  filenames, texts, labels = _find_image_files(directory, labels_file)
-  _process_image_files(name, filenames, texts, labels, num_shards)
-
-
-def main(unused_argv):
-  assert not FLAGS.train_shards % FLAGS.num_threads, (
-      'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards')
-  assert not FLAGS.validation_shards % FLAGS.num_threads, (
-      'Please make the FLAGS.num_threads commensurate with '
-      'FLAGS.validation_shards')
-  print('Saving results to %s' % FLAGS.output_directory)
-
-  # Run it!
-  _process_dataset('validation', FLAGS.validation_directory,
-                   FLAGS.validation_shards, FLAGS.labels_file)
-  _process_dataset('train', FLAGS.train_directory,
-                   FLAGS.train_shards, FLAGS.labels_file)
-
-
-if __name__ == '__main__':
-  tf.app.run()
-
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/download_imagenet.sh b/TensorFlow/Classification/imagenet/data_preprocessing/download_imagenet.sh
deleted file mode 100755
index 301d7e3e..00000000
--- a/TensorFlow/Classification/imagenet/data_preprocessing/download_imagenet.sh
+++ /dev/null
@@ -1,165 +0,0 @@
-#"!/bin/bash
-# Copyright 2016 Google Inc. All Rights Reserved.
-# Copyright 2017 NVIDIA Corp. All Rights Reserved.  
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-# Script to download ImageNet Challenge 2012 training and validation data set.
-#
-# Downloads and decompresses raw images and bounding boxes.
-#
-# **IMPORTANT**
-# To download the raw images, the user must create an account with image-net.org
-# and generate a username and access_key. The latter two are required for
-# downloading the raw images.
-#
-# usage:
-#  ./download_imagenet.sh [dirname]
-set -e
-
-cat <<END
-
-In order to download the imagenet data, you have to create an account with
-image-net.org. This will get you a username and an access key. You can set the
-IMAGENET_USERNAME and IMAGENET_ACCESS_KEY environment variables, or you can
-enter the credentials interactively if env vars are not found.
- 
-END
-
-if [ "x$IMAGENET_USERNAME" == x ]; then
-  read -p "Username: " IMAGENET_USERNAME
-fi
-
-if [ "x$IMAGENET_ACCESS_KEY" == x ]; then
-  read -p "Access key: " IMAGENET_ACCESS_KEY
-fi
-
-if [ "x$IMAGENET_OUT_DIR" == x ]; then
-  read -p "Dataset output dir: " IMAGENET_OUT_DIR
-fi
-
-ORIG_DIR=$(pwd)
-
-# We need to create a data subdir under the ImageNet data dir
-# root to keep the tarballs at the root of the data dir.
-IMAGENET_DATA_DIR="${IMAGENET_OUT_DIR}/data"
-SYNSETS_FILE="${ORIG_DIR}/imagenet_lsvrc_2015_synsets.txt"
-
-echo "Saving downloaded files to $IMAGENET_DATA_DIR"
-mkdir -p "${IMAGENET_DATA_DIR}"
-BBOX_DIR="${IMAGENET_OUT_DIR}/bounding_boxes"
-mkdir -p "${BBOX_DIR}"
-cd "${IMAGENET_DATA_DIR}"
-
-# Download and process all of the ImageNet bounding boxes.
-BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nonpub"
-
-# See here for details: http://www.image-net.org/download-bboxes
-BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"
-BBOX_TAR_BALL="${BBOX_DIR}/annotations.tar.gz"
-if [ -e ${BBOX_TAR_BALL} ]; then
-  echo "${BBOX_TAR_BALL} exists. Re-using cached file."
-else
-  echo "Downloading bounding box annotations."
-  wget "${BOUNDING_BOX_ANNOTATIONS}" -O "${BBOX_TAR_BALL}" || BASE_URL_CHANGE=1
-  if [ $BASE_URL_CHANGE ]; then
-    BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nnoupb"
-    BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"
-    BBOX_TAR_BALL="${BBOX_DIR}/annotations.tar.gz"
-    wget "${BOUNDING_BOX_ANNOTATIONS}" -O "${BBOX_TAR_BALL}"
-  fi
-fi
-# TODO: check if uncompressed annotations are cached as well
-echo "Uncompressing bounding box annotations."
-
-python ${ORIG_DIR}/check_uncompressed.py ${BBOX_TAR_BALL} ${BBOX_DIR} || STATUS=$?
-if [[ $STATUS -eq 0 ]]; then
-  echo "Nothing to decompress. Using cached directories for ${BBOX_DIR}"
-else 
-  echo "Uncompressing ${BBOX_TAR_BALL}."
-  tar xzf "${BBOX_TAR_BALL}" -C "${BBOX_DIR}"
-fi
-
-LABELS_ANNOTATED="${BBOX_DIR}/*"
-NUM_XML=$(ls -1 ${LABELS_ANNOTATED} | wc -l)
-echo "Identified ${NUM_XML} bounding box annotations."
-
-# Download and uncompress all images from the ImageNet 2012 validation dataset.
-VALIDATION_TARBALL="ILSVRC2012_img_val.tar"
-OUTPUT_PATH="${IMAGENET_DATA_DIR}/validation/"
-mkdir -p "${OUTPUT_PATH}"
-cd "${IMAGENET_OUT_DIR}"
-if [ -e ${VALIDATION_TARBALL} ]; then
-  echo "${VALIDATION_TARBALL} exists. Re-using cached file."
-else
-  echo "Downloading ${VALIDATION_TARBALL} to ${IMAGENET_OUT_DIR}."
-  wget -nd -c "${BASE_URL}/${VALIDATION_TARBALL}"
-fi
-echo "Extracting ${VALIDATION_TARBALL} to ${OUTPUT_PATH}"
-tar xf "${VALIDATION_TARBALL}" -C "${OUTPUT_PATH}"
-
-# Download all images from the ImageNet 2012 train dataset.
-TRAIN_TARBALL="ILSVRC2012_img_train.tar"
-OUTPUT_PATH="${IMAGENET_DATA_DIR}/train/"
-mkdir -p "${OUTPUT_PATH}"
-cd "${IMAGENET_OUT_DIR}"
-if [ -e ${TRAIN_TARBALL} ]; then
-  echo "${TRAIN_TARBALL} exists. Re-using cached file."
-else
-  echo "Downloading ${TRAIN_TARBALL} to ${IMAGENET_OUT_DIR}."
-  wget -nd -c "${BASE_URL}/${TRAIN_TARBALL}"
-fi
-
-# Un-compress the individual tar-files within the train tar-file.
-echo "Uncompressing individual train tar-balls in the training data to ${OUTPUT_PATH}."
-
-SYNSET=${SYNSETS_FILE}
-
-while read SYNSET; do
- echo "Processing: ${SYNSET}"
-
-# Create a directory and delete anything there.
-  mkdir -p "${OUTPUT_PATH}/${SYNSET}"
-  rm -rf "${OUTPUT_PATH}/${SYNSET}/*"
-
-# Uncompress into the directory.
-  tar xf "${TRAIN_TARBALL}" "${SYNSET}.tar"
-  tar xf "${SYNSET}.tar" -C "${OUTPUT_PATH}/${SYNSET}/"
-  rm -f "${SYNSET}.tar"
-
-  echo "Finished processing: ${SYNSET}"
- done < "${SYNSETS_FILE}"
-
-# Bounding box label generation 
-
-TRAIN_DIR="${IMAGENET_DATA_DIR}/train"
-VALIDATION_DIR="${IMAGENET_DATA_DIR}/validation"
-
-# Convert the XML files for bounding box annotations into a single CSV.
-#echo "Extracting bounding box information from XML."
-BOUNDING_BOX_SCRIPT="./process_bounding_boxes.py"
-BOUNDING_BOX_FILE="${BBOX_DIR}/imagenet_2012_bounding_boxes.csv"
-
-LABELS_FILE="imagenet_lsvrc_2015_synsets.txt"
-
-cd ${ORIG_DIR}
-"${BOUNDING_BOX_SCRIPT}" "${BBOX_DIR}" "${LABELS_FILE}" \
- | sort >"${BOUNDING_BOX_FILE}"
-
-echo "Organizing the validation data into sub-directories."
-PREPROCESS_VAL_SCRIPT="${ORIG_DIR}/preprocess_imagenet_validation_data.py"
-VAL_LABELS_FILE="${ORIG_DIR}/imagenet_2012_validation_synset_labels.txt"
-
-"${PREPROCESS_VAL_SCRIPT}" "${VALIDATION_DIR}" "${VAL_LABELS_FILE}"
-
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/generate_tfrecord_protos.sh b/TensorFlow/Classification/imagenet/data_preprocessing/generate_tfrecord_protos.sh
deleted file mode 100755
index a6f53d7a..00000000
--- a/TensorFlow/Classification/imagenet/data_preprocessing/generate_tfrecord_protos.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/bin/bash
-# Copyright 2016 Google Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-
-if [ "x$PROTO_OUT_DIR" == x ]; then
-cat <<END
- Generate TFRecord protobufs. You can either set env vars such as
- IMAGENET_DATA_DIR, or enter them here.
-END
-  read -p "ImageNet data dir: " DATA_DIR
-  read -p "Proto out dir:  " PROTO_OUT_DIR
-  read -p "Pre-resize images to uniform size (Y/n): " RESIZE_IMAGES
-  if [ "${RESIZE_IMAGES,,}" = "y" ]; then
-    echo "We will need some information from you about resizing"
-    read -p "Image height after resizing (in pixels, recommended 352): " NEW_HEIGHT
-    read -p "Image width after resizing (in pixels, recommended 352): " NEW_WIDTH
-    read -p "JPEG Q after resizing (0 to 100, recommended 90): " JPEG_Q
-  else
-    RESIZE_IMAGES="false"
-    NEW_HEIGHT="352"
-    NEW_WIDTH="352"
-    JPEG_Q="90"
-  fi
-fi
-
-BUILD_SCRIPT="build_imagenet_data.py"
-IMAGENET_META_FILE="imagenet_metadata.txt"
-LABELS_FILE="imagenet_lsvrc_2015_synsets.txt"
-TRAIN_DIR="${DATA_DIR}/data/train"
-VALIDATION_DIR="${DATA_DIR}/data/validation"
-BBOX_FILE="${DATA_DIR}/bounding_boxes/imagenet_2012_bounding_boxes.csv"
-
-echo "Bounding boxes at ${BBOX_FILE}"
-
-  python "${BUILD_SCRIPT}" \
-  --train_directory="${TRAIN_DIR}" \
-  --validation_directory="${VALIDATION_DIR}" \
-  --output_directory="${PROTO_OUT_DIR}" \
-  --imagenet_metadata_file="${IMAGENET_META_FILE}" \
-  --labels_file="${LABELS_FILE}" \
-  --bounding_box_file="${BBOX_FILE}" \
-  --resize_images="${RESIZE_IMAGES}" \
-  --new_height="${NEW_HEIGHT}" \
-  --new_width="${NEW_WIDTH}" \
-  --jpeg_q="${JPEG_Q}"
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/purge_mem_caches.py b/TensorFlow/Classification/imagenet/data_preprocessing/purge_mem_caches.py
deleted file mode 100755
index e011db6f..00000000
--- a/TensorFlow/Classification/imagenet/data_preprocessing/purge_mem_caches.py
+++ /dev/null
@@ -1,45 +0,0 @@
-
-# Copyright 2017 NVIDIA Corporation. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-import os, subprocess, pexpect, psutil
-from getpass import getpass
-
-free_mem = lambda: psutil.virtual_memory().free / pow(1024, 3)
-
-print("Need to purge caches to safely run training set protobuf generation.")
-print("Free memory in GB before purging: %s" % free_mem())
-
-if free_mem() < 30.0:
-
-    cmd =  'sudo bash -c "echo 3 > /proc/sys/vm/drop_caches"'
-    user = pexpect.spawn('whoami').read().strip()
-    child = pexpect.spawn(cmd)
-
-    if user != 'root':
-        print("The memory is too full to process the training set.")
-        print("Please enter sudo password to purge unused virtual memory.")
-        print("If you choose to ignore this, purging will not happen and the app can fail at runtime.")
-        purge = raw_input("Would you like to purge caches? Y/n: ")
-        if purge.lower() == "y":
-            sudo_pass = getpass("Enter sudo password: ")
-            prompt = r'\[sudo\] password for %s: ' % user           
-            idx = child.expect([prompt, pexpect.EOF], 3) 
-            child.sendline(sudo_pass)
-        else:
-            print("Y/y not selected, will continue without purging, app may fail!")
-            child.kill(9)
-    child.expect(pexpect.EOF)
-
-print("Free memory now, in GB: %s" % free_mem())
diff --git a/TensorFlow/Classification/imagenet/nvcnn_hvd.py b/TensorFlow/Classification/imagenet/nvcnn_hvd.py
deleted file mode 100755
index 09716943..00000000
--- a/TensorFlow/Classification/imagenet/nvcnn_hvd.py
+++ /dev/null
@@ -1,1451 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""
-Changelog:
-1.1
-  - Center crop evaluation images
-  - Implement LARC learning rate control
-  - Set default learning rate policy to polynomial decay.
-  - Add cmd line options for checkpoint and summary intervals.
-  - Add loss scaling.
-  - Scale resnet learning rate by batch size.
-1.0
-  - Initial version based on nvcnn.py 1.4
-"""
-
-from __future__ import print_function
-from builtins import range
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.ops import data_flow_ops
-from tensorflow.python.ops import init_ops
-
-import sys
-import os
-import time
-import math
-from collections import defaultdict
-import argparse
-
-try:
-    import horovod.tensorflow as hvd
-except:
-    print("Failed to import horovod module. "
-          "%s is intended for use with Uber's Horovod distributed training "
-          "framework. To create a Docker image with Horovod support see "
-          "docker-examples/Dockerfile.horovod." % __file__)
-    raise
-
-__version__ = "1.1"
-
-def tensorflow_version_tuple():
-    v = tf.__version__
-    major, minor, patch = v.split('.')
-    return (int(major), int(minor), patch)
-
-hvd.init()
-
-def print_r0(*args, **kwargs):
-    if hvd.rank() == 0:
-        print(*args, **kwargs)
-
-def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
-                                    initializer=None, regularizer=None,
-                                    trainable=True,
-                                    *args, **kwargs):
-    storage_dtype = tf.float32 if trainable else dtype
-    variable = getter(name, shape, dtype=storage_dtype,
-                      initializer=initializer, regularizer=regularizer,
-                      trainable=trainable,
-                      *args, **kwargs)
-    if trainable and dtype != tf.float32:
-        variable = tf.cast(variable, dtype)
-    return variable
-
-class GPUNetworkBuilder(object):
-    """This class provides convenient methods for constructing feed-forward
-    networks with internal data layout of 'NCHW'.
-    """
-    def __init__(self,
-                 is_training,
-                 dtype=tf.float32,
-                 activation='RELU',
-                 use_batch_norm=True,
-                 batch_norm_config = {'decay':   0.9,
-                                      'epsilon': 1e-4,
-                                      'scale':   True,
-                                      'zero_debias_moving_mean': False}):
-        self.dtype             = dtype
-        self.activation_func   = activation
-        self.is_training       = is_training
-        self.use_batch_norm    = use_batch_norm
-        self.batch_norm_config = batch_norm_config
-        self._layer_counts     = defaultdict(lambda: 0)
-    def _count_layer(self, layer_type):
-        idx  = self._layer_counts[layer_type]
-        name = layer_type + str(idx)
-        self._layer_counts[layer_type] += 1
-        return name
-    def _get_variable(self, name, shape, dtype=None,
-                      initializer=None, seed=None):
-        if dtype is None:
-            dtype = self.dtype
-        if initializer is None:
-            initializer = init_ops.glorot_uniform_initializer(seed=seed)
-        elif (isinstance(initializer, float) or
-              isinstance(initializer, int)):
-            initializer = tf.constant_initializer(float(initializer))
-        return tf.get_variable(name, shape, dtype, initializer)
-    def _to_nhwc(self, x):
-        return tf.transpose(x, [0,2,3,1])
-    def _from_nhwc(self, x):
-        return tf.transpose(x, [0,3,1,2])
-    def _bias(self, input_layer):
-        num_outputs = input_layer.get_shape().as_list()[1]
-        biases = self._get_variable('biases', [num_outputs], input_layer.dtype,
-                                    initializer=0)
-        if len(input_layer.get_shape()) == 4:
-            return tf.nn.bias_add(input_layer, biases,
-                                  data_format='NCHW')
-        else:
-            return input_layer + biases
-    def _batch_norm(self, input_layer, scope):
-        return tf.contrib.layers.batch_norm(input_layer,
-                                            is_training=self.is_training,
-                                            scope=scope,
-                                            data_format='NCHW',
-                                            fused=True,
-                                            **self.batch_norm_config)
-    def _bias_or_batch_norm(self, input_layer, scope, use_batch_norm):
-        if use_batch_norm is None:
-            use_batch_norm = self.use_batch_norm
-        if use_batch_norm:
-            return self._batch_norm(input_layer, scope)
-        else:
-            return self._bias(input_layer)
-    def input_layer(self, input_layer):
-        """Converts input data into the internal format"""
-        x = self._from_nhwc(input_layer)
-        x = tf.cast(x, self.dtype)
-        # Rescale and shift to [-1,1]
-        x = x * (1./127.5) - 1
-        return x
-    def conv(self, input_layer, num_filters, filter_size,
-             filter_strides=(1,1), padding='SAME',
-             activation=None, use_batch_norm=None):
-        """Applies a 2D convolution layer that includes bias or batch-norm
-        and an activation function.
-        """
-        num_inputs = input_layer.get_shape().as_list()[1]
-        kernel_shape = [filter_size[0], filter_size[1],
-                        num_inputs, num_filters]
-        strides = [1, 1, filter_strides[0], filter_strides[1]]
-        with tf.variable_scope(self._count_layer('conv')) as scope:
-            kernel = self._get_variable('weights', kernel_shape,
-                                        input_layer.dtype)
-            if padding == 'SAME_RESNET': # ResNet models require custom padding
-                kh, kw = filter_size
-                rate = 1
-                kernel_size_effective = kh + (kw - 1) * (rate - 1)
-                pad_total = kernel_size_effective - 1
-                pad_beg = pad_total // 2
-                pad_end = pad_total - pad_beg
-                padding = [[0, 0], [0, 0],
-                           [pad_beg, pad_end], [pad_beg, pad_end]]
-                input_layer = tf.pad(input_layer, padding)
-                padding = 'VALID'
-            x = tf.nn.conv2d(input_layer, kernel, strides,
-                             padding=padding, data_format='NCHW')
-            x = self._bias_or_batch_norm(x, scope, use_batch_norm)
-            x = self.activate(x, activation)
-            return x
-    def deconv(self, input_layer, num_filters, filter_size,
-               filter_strides=(2,2), padding='SAME',
-               activation=None, use_batch_norm=None):
-        """Applies a 'transposed convolution' layer that includes bias or
-        batch-norm and an activation function.
-        """
-        num_inputs  = input_layer.get_shape().as_list()[1]
-        ih, iw      = input_layer.get_shape().as_list()[2:]
-        output_shape = [-1, num_filters,
-                        ih*filter_strides[0], iw*filter_strides[1]]
-        kernel_shape = [filter_size[0], filter_size[1],
-                        num_filters, num_inputs]
-        strides = [1, 1, filter_strides[0], filter_strides[1]]
-        with tf.variable_scope(self._count_layer('deconv')) as scope:
-            kernel = self._get_variable('weights', kernel_shape,
-                                        input_layer.dtype)
-            x = tf.nn.conv2d_transpose(input_layer, kernel, output_shape,
-                                       strides, padding=padding,
-                                       data_format='NCHW')
-            x = self._bias_or_batch_norm(x, scope, use_batch_norm)
-            x = self.activate(x, activation)
-            return x
-    def activate(self, input_layer, funcname=None):
-        """Applies an activation function"""
-        if isinstance(funcname, tuple):
-            funcname = funcname[0]
-            params = funcname[1:]
-        if funcname is None:
-            funcname = self.activation_func
-        if funcname == 'LINEAR':
-            return input_layer
-        activation_map = {
-            'RELU':    tf.nn.relu,
-            'RELU6':   tf.nn.relu6,
-            'ELU':     tf.nn.elu,
-            'SIGMOID': tf.nn.sigmoid,
-            'TANH':    tf.nn.tanh,
-            'LRELU':   lambda x, name: tf.maximum(params[0]*x, x, name=name)
-        }
-        return activation_map[funcname](input_layer, name=funcname.lower())
-    def pool(self, input_layer, funcname, window_size,
-                 window_strides=(2,2),
-                 padding='VALID'):
-        """Applies spatial pooling"""
-        pool_map = {
-            'MAX': tf.nn.max_pool,
-            'AVG': tf.nn.avg_pool
-        }
-        kernel_size    = [1, 1, window_size[0], window_size[1]]
-        kernel_strides = [1, 1, window_strides[0], window_strides[1]]
-        return pool_map[funcname](input_layer, kernel_size, kernel_strides,
-                                  padding, data_format='NCHW',
-                                  name=funcname.lower())
-    def project(self, input_layer, num_outputs, height, width,
-                activation=None):
-        """Linearly projects to an image-like tensor"""
-        with tf.variable_scope(self._count_layer('project')):
-            x = self.fully_connected(input_layer, num_outputs*height*width,
-                                     activation=activation)
-            x = tf.reshape(x, [-1, num_outputs, height, width])
-            return x
-    def flatten(self, input_layer):
-        """Flattens the spatial and channel dims into a single dim (4D->2D)"""
-        # Note: This ensures the output order matches that of NHWC networks
-        input_layer = self._to_nhwc(input_layer)
-        input_shape = input_layer.get_shape().as_list()
-        num_inputs  = input_shape[1]*input_shape[2]*input_shape[3]
-        return tf.reshape(input_layer, [-1, num_inputs], name='flatten')
-    def spatial_avg(self, input_layer):
-        """Averages over spatial dimensions (4D->2D)"""
-        return tf.reduce_mean(input_layer, [2, 3], name='spatial_avg')
-    def fully_connected(self, input_layer, num_outputs, activation=None):
-        """Applies a fully-connected set of weights"""
-        num_inputs = input_layer.get_shape().as_list()[1]
-        kernel_size = [num_inputs, num_outputs]
-        with tf.variable_scope(self._count_layer('fully_connected')):
-            kernel = self._get_variable('weights', kernel_size,
-                                        input_layer.dtype)
-            x = tf.matmul(input_layer, kernel)
-            x = self._bias(x)
-            x = self.activate(x, activation)
-            return x
-    def inception_module(self, input_layer, name, cols):
-        """Applies an inception module with a given form"""
-        with tf.name_scope(name):
-            col_layers      = []
-            col_layer_sizes = []
-            for c, col in enumerate(cols):
-                col_layers.append([])
-                col_layer_sizes.append([])
-                x = input_layer
-                for l, layer in enumerate(col):
-                    ltype, args = layer[0], layer[1:]
-                    if   ltype == 'conv': x = self.conv(x, *args)
-                    elif ltype == 'pool': x = self.pool(x, *args)
-                    elif ltype == 'share':
-                        # Share matching layer from previous column
-                        x = col_layers[c-1][l]
-                    else: raise KeyError("Invalid layer type for " +
-                                         "inception module: '%s'" % ltype)
-                    col_layers[c].append(x)
-            catdim  = 1
-            catvals = [layers[-1] for layers in col_layers]
-            x = tf.concat(catvals, catdim)
-            return x
-    def residual(self, input_layer, net, scale=1.0, activation='RELU'):
-        """Applies a residual layer"""
-        input_size     = input_layer.get_shape().as_list()
-        num_inputs     = input_size[1]
-        output_layer   = scale*net(self, input_layer)
-        output_size    = output_layer.get_shape().as_list()
-        num_outputs    = output_size[1]
-        kernel_strides = (input_size[2]//output_size[2],
-                          input_size[3]//output_size[3])
-        with tf.name_scope('residual'):
-            if (num_outputs != num_inputs or
-                kernel_strides[0] != 1 or
-                kernel_strides[1] != 1):
-                input_layer = self.conv(input_layer, num_outputs, [1, 1],
-                                        kernel_strides, activation='LINEAR')
-            x = self.activate(input_layer + output_layer, activation)
-            return x
-    def dropout(self, input_layer, keep_prob=0.5):
-        """Applies a dropout layer if is_training"""
-        if self.is_training:
-            dtype = input_layer.dtype
-            with tf.variable_scope(self._count_layer('dropout')):
-                keep_prob_tensor = tf.constant(keep_prob, dtype=dtype)
-                return tf.nn.dropout(input_layer, keep_prob_tensor)
-        else:
-            return input_layer
-
-def deserialize_image_record(record):
-    feature_map = {
-        'image/encoded':          tf.FixedLenFeature([ ], tf.string, ''),
-        'image/class/label':      tf.FixedLenFeature([1], tf.int64,  -1),
-        'image/class/text':       tf.FixedLenFeature([ ], tf.string, ''),
-        'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
-        'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
-        'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
-        'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32)
-    }
-    with tf.name_scope('deserialize_image_record'):
-        obj = tf.parse_single_example(record, feature_map)
-        imgdata = obj['image/encoded']
-        label   = tf.cast(obj['image/class/label'], tf.int32)
-        bbox    = tf.stack([obj['image/object/bbox/%s'%x].values
-                            for x in ['ymin', 'xmin', 'ymax', 'xmax']])
-        bbox = tf.transpose(tf.expand_dims(bbox, 0), [0,2,1])
-        text    = obj['image/class/text']
-        return imgdata, label, bbox, text
-
-def decode_jpeg(imgdata, channels=3):
-    return tf.image.decode_jpeg(imgdata, channels=channels,
-                                fancy_upscaling=False,
-                                dct_method='INTEGER_FAST')
-
-def decode_png(imgdata, channels=3):
-    return tf.image.decode_png(imgdata, channels=channels)
-
-def random_crop_and_resize_image(image, bbox, height, width):
-    with tf.name_scope('random_crop_and_resize'):
-        if FLAGS.eval:
-            image = tf.image.central_crop(image, 224./256.)
-        else:
-            bbox_begin, bbox_size, distorted_bbox = tf.image.sample_distorted_bounding_box(
-                tf.shape(image),
-                bounding_boxes=bbox,
-                min_object_covered=0.1,
-                aspect_ratio_range=[0.8, 1.25],
-                area_range=[0.1, 1.0],
-                max_attempts=100,
-                use_image_if_no_bounding_boxes=True)
-            # Crop the image to the distorted bounding box
-            image = tf.slice(image, bbox_begin, bbox_size)
-        # Resize to the desired output size
-        image = tf.image.resize_images(
-            image,
-            [height, width],
-            tf.image.ResizeMethod.BILINEAR,
-            align_corners=False)
-        image.set_shape([height, width, 3])
-        return image
-
-def distort_image_color(image, order):
-    with tf.name_scope('distort_color'):
-        image /= 255.
-        brightness = lambda img: tf.image.random_brightness(img, max_delta=32. / 255.)
-        saturation = lambda img: tf.image.random_saturation(img, lower=0.5, upper=1.5)
-        hue        = lambda img: tf.image.random_hue(img, max_delta=0.2)
-        contrast   = lambda img: tf.image.random_contrast(img, lower=0.5, upper=1.5)
-        if order == 0: ops = [brightness, saturation, hue, contrast]
-        else:          ops = [brightness, contrast, saturation, hue]
-        for op in ops:
-            image = op(image)
-        # The random_* ops do not necessarily clamp the output range
-        image = tf.clip_by_value(image, 0.0, 1.0)
-        # Restore the original scaling
-        image *= 255
-        return image
-
-class DummyPreprocessor(object):
-    def __init__(self, height, width, batch, nclass):
-        self.height = height
-        self.width  = width
-        self.batch = batch
-        self.nclass = nclass
-
-class ImagePreprocessor(object):
-    def __init__(self, height, width, subset='train', dtype=tf.uint8):
-        self.height = height
-        self.width  = width
-        self.subset = subset
-        self.dtype = dtype
-        self.nsummary = 10 # Max no. images to generate summaries for
-    def preprocess(self, imgdata, bbox, thread_id):
-        with tf.name_scope('preprocess_image'):
-            try:
-                image = decode_jpeg(imgdata)
-            except:
-                image = decode_png(imgdata)
-            if thread_id < self.nsummary:
-                image_with_bbox = tf.image.draw_bounding_boxes(
-                    tf.expand_dims(tf.to_float(image), 0), bbox)
-                tf.summary.image('original_image_and_bbox', image_with_bbox)
-            image = random_crop_and_resize_image(image, bbox,
-                                                 self.height, self.width)
-            if thread_id < self.nsummary:
-                tf.summary.image('cropped_resized_image',
-                                 tf.expand_dims(image, 0))
-            if not FLAGS.eval:
-                image = tf.image.random_flip_left_right(image)
-            if thread_id < self.nsummary:
-                tf.summary.image('flipped_image',
-                                 tf.expand_dims(image, 0))
-            if FLAGS.distort_color and not FLAGS.eval:
-                image = distort_image_color(image, order=thread_id%2)
-                if thread_id < self.nsummary:
-                    tf.summary.image('distorted_color_image',
-                                     tf.expand_dims(image, 0))
-        return image
-    def minibatch(self, batch_size):
-        record_input = data_flow_ops.RecordInput(
-            file_pattern=os.path.join(FLAGS.data_dir, '%s-*' % self.subset),
-            parallelism=64,
-            seed=301+hvd.rank(),
-            # Note: This causes deadlock during init if larger than dataset
-            buffer_size=FLAGS.input_buffer_size,
-            batch_size=batch_size)
-        records = record_input.get_yield_op()
-        # Split batch into individual images
-        records = tf.split(records, batch_size, 0)
-        records = [tf.reshape(record, []) for record in records]
-        # Deserialize and preprocess images into batches for each device
-        images = []
-        labels = []
-        with tf.name_scope('input_pipeline'):
-            for i, record in enumerate(records):
-                imgdata, label, bbox, text = deserialize_image_record(record)
-                image = self.preprocess(imgdata, bbox, thread_id=i)
-                label -= 1 # Change to 0-based (don't use background class)
-                images.append(image)
-                labels.append(label)
-            # Stack images back into a single tensor
-            images = tf.parallel_stack(images)
-            labels = tf.concat(labels, 0)
-            images = tf.reshape(images, [-1, self.height, self.width, 3])
-            images = tf.clip_by_value(images, 0., 255.)
-            images = tf.cast(images, self.dtype)
-        return images, labels
-
-def stage(tensors):
-    """Stages the given tensors in a StagingArea for asynchronous put/get.
-    """
-    stage_area = data_flow_ops.StagingArea(
-        dtypes=[tensor.dtype       for tensor in tensors],
-        shapes=[tensor.get_shape() for tensor in tensors])
-    put_op      = stage_area.put(tensors)
-    get_tensors = stage_area.get()
-
-    get_tensors = [tf.reshape(gt, t.get_shape())
-                   for (gt,t) in zip(get_tensors, tensors)]
-    return put_op, get_tensors
-
-
-class FeedForwardTrainer(object):
-    def __init__(self, preprocessor, loss_func, nstep_per_epoch=None):
-        self.image_preprocessor = preprocessor
-        self.loss_func          = loss_func
-        with tf.device('/cpu:0'):
-            self.global_step = tf.get_variable(
-                'global_step', [],
-                initializer=tf.constant_initializer(0),
-                dtype=tf.int64,
-                trainable=False)
-        if FLAGS.lr_decay_policy == 'poly':
-            self.learning_rate = tf.train.polynomial_decay(
-                FLAGS.learning_rate,
-                self.global_step,
-                decay_steps=FLAGS.num_epochs*nstep_per_epoch,
-                end_learning_rate=0.,
-                power=FLAGS.lr_poly_power,
-                cycle=False)
-        else:
-            self.learning_rate = tf.train.exponential_decay(
-                FLAGS.learning_rate,
-                self.global_step,
-                decay_steps=FLAGS.lr_decay_epochs*nstep_per_epoch,
-                decay_rate=FLAGS.lr_decay_rate,
-                staircase=True)
-    def training_step(self, batch_size):
-        if type(self.image_preprocessor) is not DummyPreprocessor:
-            with tf.device('/cpu:0'):
-                images, labels = self.image_preprocessor.minibatch(batch_size)
-                # Stage images on the host
-                preload_op, (images, labels) = stage([images, labels])
-            with tf.device('/gpu:0'):
-                # Copy images from host to device
-                gpucopy_op, (images, labels) = stage([images, labels])
-        else:
-            with tf.device('/gpu:0'):
-                input_shape = [self.image_preprocessor.batch,
-                               self.image_preprocessor.height,
-                               self.image_preprocessor.width,
-                               3]
-                images = tf.truncated_normal(
-                    input_shape,
-                    dtype=tf.float32,
-                    stddev=1.e-1,
-                    name='synthetic_images')
-                labels = tf.random_uniform(
-                    [self.image_preprocessor.batch],
-                    minval=0,
-                    maxval=self.image_preprocessor.nclass-1,
-                    dtype=tf.int32,
-                    name='synthetic_labels')
-                preload_op, (images, labels) = stage([images, labels])
-                gpucopy_op = None
-
-        with tf.device('/gpu:0'):
-            # Evaluate the loss and compute the gradients
-            with tf.variable_scope(
-                    'GPU_0',
-                    # Force all variables to be stored as float32
-                    custom_getter=float32_variable_storage_getter) as var_scope:
-                loss, logits = self.loss_func(images, labels, var_scope)
-
-        with tf.device('/cpu:0'): # No in_top_k implem on GPU
-            top1 = tf.reduce_mean(
-                tf.cast(tf.nn.in_top_k(logits, labels, 1), tf.float32))
-            top5 = tf.reduce_mean(
-                tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32))
-
-            averager = tf.train.ExponentialMovingAverage(0.90, name='loss_avg',
-                                                         zero_debias=True)
-            avg_op = averager.apply([loss])
-            loss_avg = averager.average(loss)
-            # Note: This must be done _after_ the averager.average() call
-            #         because it changes total_loss into a new object.
-            with tf.control_dependencies([avg_op]):
-                total_loss     = tf.identity(loss)
-                total_loss_avg = tf.identity(loss_avg)
-            tf.summary.scalar('total loss raw', total_loss)
-            tf.summary.scalar('total loss avg', total_loss_avg)
-            tf.summary.scalar('train accuracy top-1 %', 100.*top1)
-            tf.summary.scalar('train accuracy top-5 %', 100.*top5)
-            tf.summary.scalar('learning rate', self.learning_rate)
-
-        # Apply the gradients to optimize the loss function
-        with tf.device('/gpu:0'):
-            opt = tf.train.MomentumOptimizer(self.learning_rate, FLAGS.momentum,
-                                             use_nesterov=True)
-            opt = hvd.DistributedOptimizer(opt)
-            if FLAGS.loss_scale != 1.:
-                loss = loss * float(FLAGS.loss_scale)
-            gradvars = opt.compute_gradients(loss,
-                gate_gradients=tf.train.Optimizer.GATE_NONE)
-            if FLAGS.loss_scale != 1:
-                inv_scale = 1. / float(FLAGS.loss_scale)
-                gradvars = [(grad * inv_scale, var)
-                            for grad, var in gradvars]
-
-            if FLAGS.larc_eta is not None:
-                LARC_eta = float(FLAGS.larc_eta)
-                LARC_epsilon = float(FLAGS.larc_epsilon)
-                v_list = [tf.norm(tensor=v, ord=2) for _, v in gradvars]
-                g_list = [tf.norm(tensor=g, ord=2) if g is not None else 0.0
-                          for g, _ in gradvars ]
-                v_norms = tf.stack(v_list)
-                g_norms = tf.stack(g_list)
-                zeds = tf.zeros_like(v_norms)
-                cond = tf.logical_and(
-                    tf.not_equal(v_norms, zeds),
-                    tf.not_equal(g_norms, zeds))
-                true_vals = tf.scalar_mul(LARC_eta, tf.div(v_norms, g_norms))
-                false_vals = tf.fill(tf.shape(v_norms), LARC_epsilon)
-                larc_local_lr = tf.where(cond, true_vals, false_vals)
-                if FLAGS.larc_mode != "scale":
-                    ones = tf.ones_like(v_norms)
-                    lr = tf.fill(tf.shape(v_norms), self.learning_rate)
-                    larc_local_lr = tf.minimum(tf.div(larc_local_lr, lr), ones)
-
-                gradvars = [(tf.multiply(larc_local_lr[i], g), v)
-                            if g is not None else (None, v)
-                            for i, (g, v) in enumerate(gradvars) ]
-
-            train_op = opt.apply_gradients(gradvars, global_step=self.global_step)
-
-        self.enqueue_ops = []
-        self.enqueue_ops.append(preload_op)
-        if gpucopy_op is not None:
-            self.enqueue_ops.append(gpucopy_op)
-        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) or []
-        train_and_update_ops = tf.group(*([train_op] + update_ops))
-        all_training_ops = (self.enqueue_ops + [train_and_update_ops])
-        return total_loss_avg, self.learning_rate, all_training_ops
-    def init(self, sess):
-        init_op = tf.global_variables_initializer()
-        sess.run(init_op)
-    def sync(self, sess):
-        sync_op = hvd.broadcast_global_variables(0)
-        sess.run(sync_op)
-    def prefill_pipeline(self, sess):
-        # Pre-fill the input pipeline with data
-        for i in range(len(self.enqueue_ops)):
-            sess.run(self.enqueue_ops[:i+1])
-
-class FeedForwardEvaluator(object):
-    def __init__(self, preprocessor, eval_func):
-        self.eval_func          = eval_func
-        self.image_preprocessor = preprocessor
-    def evaluation_step(self, batch_size):
-        with tf.device('/cpu:0'):
-            images, labels = self.image_preprocessor.minibatch(batch_size)
-            # Stage images on the host
-            preload_op, (images, labels) = stage([images, labels])
-        with tf.device('/gpu:0'):
-            # Copy images from host to device
-            gpucopy_op, (images, labels) = stage([images, labels])
-            # Evaluate the loss and compute the gradients
-            with tf.variable_scope('GPU_0') as var_scope:
-                top1, top5 = self.eval_func(images, labels, var_scope)
-        self.enqueue_ops = [preload_op, gpucopy_op]
-        return top1, top5, self.enqueue_ops
-    def prefill_pipeline(self, sess):
-        # Pre-fill the input pipeline with data
-        for i in range(len(self.enqueue_ops)):
-            sess.run(self.enqueue_ops[:i+1])
-
-def inference_trivial(net, input_layer):
-    """A trivial model for benchmarking input pipeline performance"""
-    net.use_batch_norm = False
-    x = net.input_layer(input_layer)
-    x = net.flatten(x)
-    x = net.fully_connected(x, 1)
-    return x
-
-def inference_lenet5(net, input_layer):
-    """Tiny network matching TF's MNIST tutorial model"""
-    net.use_batch_norm = False
-    x = net.input_layer(input_layer)
-    x = net.conv(x, 32,    (5,5))
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.conv(x, 64,    (5,5))
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.flatten(x)
-    x = net.fully_connected(x, 512)
-    return x
-
-def inference_overfeat(net, input_layer):
-    net.use_batch_norm = False
-    x = net.input_layer(input_layer)
-    x = net.conv(x, 96,   (11,11), (4,4), 'VALID')
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.conv(x, 256,   (5,5), (1,1), 'VALID')
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.conv(x, 512,   (3,3))
-    x = net.conv(x, 1024,  (3,3))
-    x = net.conv(x, 1024,  (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.flatten(x)
-    x = net.fully_connected(x, 3072)
-    x = net.fully_connected(x, 4096)
-    return x
-
-def inference_alexnet_owt(net, input_layer):
-    """Alexnet One Weird Trick model
-    https://arxiv.org/abs/1404.5997
-    """
-    net.use_batch_norm = False
-    x = net.input_layer(input_layer)
-    # Note: VALID requires padding the images by 3 in width and height
-    x = net.conv(x, 64, (11,11), (4,4), 'VALID')
-    x = net.pool(x, 'MAX', (3,3))
-    x = net.conv(x, 192,   (5,5))
-    x = net.pool(x, 'MAX', (3,3))
-    x = net.conv(x, 384,   (3,3))
-    x = net.conv(x, 256,   (3,3))
-    x = net.conv(x, 256,   (3,3))
-    x = net.pool(x, 'MAX', (3,3))
-    x = net.flatten(x)
-    x = net.fully_connected(x, 4096)
-    x = net.dropout(x)
-    x = net.fully_connected(x, 4096)
-    x = net.dropout(x)
-    return x
-
-def inference_vgg_impl(net, input_layer, layer_counts):
-    net.use_batch_norm = False
-    x = net.input_layer(input_layer)
-    for _ in range(layer_counts[0]): x = net.conv(x,  64, (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    for _ in range(layer_counts[1]): x = net.conv(x, 128, (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    for _ in range(layer_counts[2]): x = net.conv(x, 256, (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    for _ in range(layer_counts[3]): x = net.conv(x, 512, (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    for _ in range(layer_counts[4]): x = net.conv(x, 512, (3,3))
-    x = net.pool(x, 'MAX', (2,2))
-    x = net.flatten(x)
-    x = net.fully_connected(x, 4096)
-    x = net.fully_connected(x, 4096)
-    return x
-def inference_vgg(net, input_layer, nlayer):
-    """Visual Geometry Group's family of models
-    https://arxiv.org/abs/1409.1556
-    """
-    if   nlayer == 11: return inference_vgg_impl(net, input_layer, [1,1,2,2,2]) # A
-    elif nlayer == 13: return inference_vgg_impl(net, input_layer, [2,2,2,2,2]) # B
-    elif nlayer == 16: return inference_vgg_impl(net, input_layer, [2,2,3,3,3]) # D
-    elif nlayer == 19: return inference_vgg_impl(net, input_layer, [2,2,4,4,4]) # E
-    else: raise ValueError("Invalid nlayer (%i); must be one of: 11,13,16,19" %
-                           nlayer)
-
-def inference_googlenet(net, input_layer):
-    """GoogLeNet model
-    https://arxiv.org/abs/1409.4842
-    """
-    net.use_batch_norm = False
-    def inception_v1(net, x, k, l, m, n, p, q):
-        cols = [[('conv', k, (1,1))],
-                [('conv', l, (1,1)), ('conv', m, (3,3))],
-                [('conv', n, (1,1)), ('conv', p, (5,5))],
-                [('pool', 'MAX', (3,3), (1,1), 'SAME'), ('conv', q, (1,1))]]
-        return net.inception_module(x, 'incept_v1', cols)
-    x = net.input_layer(input_layer)
-    x = net.conv(x,    64, (7,7), (2,2))
-    x = net.pool(x, 'MAX', (3,3), padding='SAME')
-    x = net.conv(x,    64, (1,1))
-    x = net.conv(x,   192, (3,3))
-    x = net.pool(x, 'MAX', (3,3), padding='SAME')
-    x = inception_v1(net, x,  64,  96, 128, 16,  32,  32)
-    x = inception_v1(net, x, 128, 128, 192, 32,  96,  64)
-    x = net.pool(x, 'MAX', (3,3), padding='SAME')
-    x = inception_v1(net, x, 192,  96, 208, 16,  48,  64)
-    x = inception_v1(net, x, 160, 112, 224, 24,  64,  64)
-    x = inception_v1(net, x, 128, 128, 256, 24,  64,  64)
-    x = inception_v1(net, x, 112, 144, 288, 32,  64,  64)
-    x = inception_v1(net, x, 256, 160, 320, 32, 128, 128)
-    x = net.pool(x, 'MAX', (3,3), padding='SAME')
-    x = inception_v1(net, x, 256, 160, 320, 32, 128, 128)
-    x = inception_v1(net, x, 384, 192, 384, 48, 128, 128)
-    x = net.spatial_avg(x)
-    return x
-
-def inference_inception_v3(net, input_layer):
-    """Google's Inception v3 model
-    https://arxiv.org/abs/1512.00567
-    """
-    def inception_v3_a(net, x, n):
-        cols = [[('conv',  64, (1,1))],
-                [('conv',  48, (1,1)), ('conv',  64, (5,5))],
-                [('conv',  64, (1,1)), ('conv',  96, (3,3)), ('conv',  96, (3,3))],
-                [('pool', 'AVG', (3,3), (1,1), 'SAME'), ('conv',   n, (1,1))]]
-        return net.inception_module(x, 'incept_v3_a', cols)
-    def inception_v3_b(net, x):
-        cols = [[('conv',  64, (1,1)), ('conv',  96, (3,3)), ('conv',  96, (3,3), (2,2), 'VALID')],
-                [('conv', 384, (3,3), (2,2), 'VALID')],
-                [('pool', 'MAX', (3,3), (2,2), 'VALID')]]
-        return net.inception_module(x, 'incept_v3_b', cols)
-    def inception_v3_c(net, x, n):
-        cols = [[('conv', 192, (1,1))],
-                [('conv',   n, (1,1)), ('conv',   n, (1,7)), ('conv', 192, (7,1))],
-                [('conv',   n, (1,1)), ('conv',   n, (7,1)), ('conv',   n, (1,7)), ('conv',   n, (7,1)), ('conv', 192, (1,7))],
-                [('pool', 'AVG', (3,3), (1,1), 'SAME'), ('conv', 192, (1,1))]]
-        return net.inception_module(x, 'incept_v3_c', cols)
-    def inception_v3_d(net, x):
-        cols = [[('conv', 192, (1,1)), ('conv', 320, (3,3), (2,2), 'VALID')],
-                [('conv', 192, (1,1)), ('conv', 192, (1,7)), ('conv', 192, (7,1)), ('conv', 192, (3,3), (2,2), 'VALID')],
-                [('pool', 'MAX', (3,3), (2,2), 'VALID')]]
-        return net.inception_module(x, 'incept_v3_d',cols)
-    def inception_v3_e(net, x, pooltype):
-        cols = [[('conv', 320, (1,1))],
-                [('conv', 384, (1,1)), ('conv', 384, (1,3))],
-                [('share',),           ('conv', 384, (3,1))],
-                [('conv', 448, (1,1)), ('conv', 384, (3,3)), ('conv', 384, (1,3))],
-                [('share',),          ('share',),            ('conv', 384, (3,1))],
-                [('pool', pooltype, (3,3), (1,1), 'SAME'),   ('conv', 192, (1,1))]]
-        return net.inception_module(x, 'incept_v3_e', cols)
-
-    # TODO: This does not include the extra 'arm' that forks off
-    #         from before the 3rd-last module (the arm is designed
-    #         to speed up training in the early stages).
-    net.use_batch_norm = True
-    x = net.input_layer(input_layer)
-    x = net.conv(x,    32, (3,3), (2,2), padding='VALID')
-    x = net.conv(x,    32, (3,3), (1,1), padding='VALID')
-    x = net.conv(x,    64, (3,3), (1,1), padding='SAME')
-    x = net.pool(x, 'MAX', (3,3))
-    x = net.conv(x,    80, (1,1), (1,1), padding='VALID')
-    x = net.conv(x,   192, (3,3), (1,1), padding='VALID')
-    x = net.pool(x, 'MAX', (3,3))
-    x = inception_v3_a(net, x, 32)
-    x = inception_v3_a(net, x, 64)
-    x = inception_v3_a(net, x, 64)
-    x = inception_v3_b(net, x)
-    x = inception_v3_c(net, x, 128)
-    x = inception_v3_c(net, x, 160)
-    x = inception_v3_c(net, x, 160)
-    x = inception_v3_c(net, x, 192)
-    x = inception_v3_d(net, x)
-    x = inception_v3_e(net, x, 'AVG')
-    x = inception_v3_e(net, x, 'MAX')
-    x = net.spatial_avg(x)
-    return x
-
-def resnet_bottleneck_v1(net, input_layer, depth, depth_bottleneck, stride,
-                         basic=False):
-    num_inputs = input_layer.get_shape().as_list()[1]
-    x = input_layer
-    s = stride
-    with tf.name_scope('resnet_v1'):
-        if depth == num_inputs:
-            if stride == 1:
-                shortcut = input_layer
-            else:
-                shortcut = net.pool(x, 'MAX', (1,1), (s,s))
-        else:
-            shortcut = net.conv(x, depth, (1,1), (s,s), activation='LINEAR')
-        if basic:
-            x = net.conv(x, depth_bottleneck, (3,3), (s,s), padding='SAME_RESNET')
-            x = net.conv(x, depth,            (3,3), activation='LINEAR')
-        else:
-            x = net.conv(x, depth_bottleneck, (1,1), (s,s))
-            x = net.conv(x, depth_bottleneck, (3,3), padding='SAME')
-            x = net.conv(x, depth,            (1,1), activation='LINEAR')
-        x = net.activate(x + shortcut)
-        return x
-
-def resnext_split_branch(net, input_layer, stride):
-    x = input_layer
-    with tf.name_scope('resnext_split_branch'):
-        x = net.conv(x, net.bottleneck_width, (1, 1), (stride, stride), activation='RELU', use_batch_norm=True)
-        x = net.conv(x, net.bottleneck_width, (3, 3), (1, 1), activation='RELU', use_batch_norm=True)
-    return x
-
-def resnext_shortcut(net, input_layer, stride, input_size, output_size):
-    x = input_layer
-    useConv = net.shortcut_type == 'C' or (net.shortcut_type == 'B' and input_size != output_size)
-    with tf.name_scope('resnext_shortcut'):
-        if useConv:
-            x = net.conv(x, output_size, (1,1), (stride, stride), use_batch_norm=True)
-        elif output_size == input_size:
-            if stride == 1:
-                x = input_layer
-            else:
-                x = net.pool(x, 'MAX', (1,1), (stride, stride))
-        else:
-            x = input_layer
-    return x
-
-def resnext_bottleneck_v1(net, input_layer, depth, depth_bottleneck, stride):
-    num_inputs = input_layer.get_shape().as_list()[1]
-    x = input_layer
-    with tf.name_scope('resnext_bottleneck_v1'):
-        shortcut = resnext_shortcut(net, x, stride, num_inputs, depth)
-        branches_list = []
-        for i in range(net.cardinality):
-            branch = resnext_split_branch(net, x, stride)
-            branches_list.append(branch)
-        concatenated_branches = tf.concat(values=branches_list, axis=1, name='concat')
-        bottleneck_depth = concatenated_branches.get_shape().as_list()[1]
-        x = net.conv(concatenated_branches, depth, (1, 1), (1, 1), activation=None)
-        x = net.activate(x + shortcut, 'RELU')
-    return x
-
-def inference_residual(net, input_layer, layer_counts, bottleneck_callback):
-    net.use_batch_norm = True
-    x = net.input_layer(input_layer)
-    x = net.conv(x, 64,    (7,7), (2,2), padding='SAME_RESNET')
-    x = net.pool(x, 'MAX', (3,3), (2,2), padding='SAME')
-    for i in range(layer_counts[0]):
-        x = bottleneck_callback(net, x,  256,  64, 1)
-    for i in range(layer_counts[1]):
-        x = bottleneck_callback(net, x, 512, 128, 2 if i==0 else 1)
-    for i in range(layer_counts[2]):
-        x = bottleneck_callback(net, x, 1024, 256, 2 if i==0 else 1)
-    for i in range(layer_counts[3]):
-        x = bottleneck_callback(net, x, 2048, 512, 2 if i==0 else 1)
-    x = net.spatial_avg(x)
-    return x
-
-def inference_resnet_v1_basic_impl(net, input_layer, layer_counts):
-    basic_resnet_bottleneck_callback = partial(resnet_bottleneck_v1, basic=True)
-    return inference_residual(net, input_layer, layer_counts, basic_resnet_bottleneck_callback)
-
-def inference_resnet_v1_impl(net, input_layer, layer_counts):
-    return inference_residual(net, input_layer, layer_counts, resnet_bottleneck_v1)
-
-def inference_resnext_v1_impl(net, input_layer, layer_counts):
-    return inference_residual(net, input_layer, layer_counts, resnext_bottleneck_v1)
-
-def inference_resnet_v1(net, input_layer, nlayer):
-    """Deep Residual Networks family of models
-    https://arxiv.org/abs/1512.03385
-    """
-    if   nlayer ==  18: return inference_resnet_v1_basic_impl(net, input_layer, [2,2, 2,2])
-    elif nlayer ==  34: return inference_resnet_v1_basic_impl(net, input_layer, [3,4, 6,3])
-    elif nlayer ==  50: return inference_resnet_v1_impl(net, input_layer, [3,4, 6,3])
-    elif nlayer == 101: return inference_resnet_v1_impl(net, input_layer, [3,4,23,3])
-    elif nlayer == 152: return inference_resnet_v1_impl(net, input_layer, [3,8,36,3])
-    else: raise ValueError("Invalid nlayer (%i); must be one of: 18,34,50,101,152" %
-                           nlayer)
-
-def inference_resnext_v1(net, input_layer, nlayer):
-    """Aggregated  Residual Networks family of models
-    https://arxiv.org/abs/1611.05431
-    """
-    cardinality_to_bottleneck_width = { 1:64, 2:40, 4:24, 8:14, 32:4 }
-    net.cardinality = 32
-    net.shortcut_type = 'B'
-    assert net.cardinality in cardinality_to_bottleneck_width.keys(), \
-    "Invalid  cardinality (%i); must be one of: 1,2,4,8,32" % net.cardinality
-    net.bottleneck_width = cardinality_to_bottleneck_width[net.cardinality]
-    if nlayer ==  50: return inference_resnext_v1_impl(net, input_layer, [3,4, 6,3])
-    elif nlayer == 101: return inference_resnext_v1_impl(net, input_layer, [3,4,23,3])
-    elif nlayer == 152: return inference_resnext_v1_impl(net, input_layer, [3,8,36,3])
-    else: raise ValueError("Invalid nlayer (%i); must be one of: 50,101,152" %
-                           nlayer)
-
-# Stem functions
-def inception_v4_sa(net, x):
-    cols = [[('pool', 'MAX', (3,3))],
-            [('conv',  96, (3,3), (2,2), 'VALID')]]
-    return net.inception_module(x, 'incept_v4_sa', cols)
-def inception_v4_sb(net, x):
-    cols = [[('conv',  64, (1,1)), ('conv',  96, (3,3), (1,1), 'VALID')],
-            [('conv',  64, (1,1)), ('conv',  64, (7,1)), ('conv',  64, (1,7)), ('conv',  96, (3,3), (1,1), 'VALID')]]
-    return net.inception_module(x, 'incept_v4_sb', cols)
-def inception_v4_sc(net, x):
-    cols = [[('conv', 192, (3,3), (2,2), 'VALID')],
-            [('pool', 'MAX', (3,3))]]
-    return net.inception_module(x, 'incept_v4_sc', cols)
-# Reduction functions
-def inception_v4_ra(net, x, k, l, m, n):
-    cols = [[('pool', 'MAX', (3,3))],
-            [('conv',   n, (3,3), (2,2), 'VALID')],
-            [('conv',   k, (1,1)), ('conv',   l, (3,3)), ('conv',   m, (3,3), (2,2), 'VALID')]]
-    return net.inception_module(x, 'incept_v4_ra', cols)
-def inception_v4_rb(net, x):
-    cols = [[('pool', 'MAX', (3,3))],
-            [('conv', 192, (1,1)), ('conv', 192, (3,3), (2,2), 'VALID')],
-            [('conv', 256, (1,1)), ('conv', 256, (1,7)), ('conv', 320, (7,1)), ('conv', 320, (3,3), (2,2), 'VALID')]]
-    return net.inception_module(x, 'incept_v4_rb', cols)
-def inception_resnet_v2_rb(net, x):
-    cols = [[('pool', 'MAX', (3,3))],
-            # Note: These match Facebook's Torch implem
-            [('conv', 256, (1,1)), ('conv', 384, (3,3), (2,2), 'VALID')],
-            [('conv', 256, (1,1)), ('conv', 256, (3,3), (2,2), 'VALID')],
-            [('conv', 256, (1,1)), ('conv', 256, (3,3)), ('conv', 256, (3,3), (2,2), 'VALID')]]
-    return net.inception_module(x, 'incept_resnet_v2_rb', cols)
-
-def inference_inception_v4(net, input_layer):
-    """Google's Inception v4 model
-    https://arxiv.org/abs/1602.07261
-    """
-    def inception_v4_a(net, x):
-        cols = [[('pool', 'AVG', (3,3), (1,1), 'SAME'), ('conv',  96, (1,1))],
-                [('conv',  96, (1,1))],
-                [('conv',  64, (1,1)), ('conv',  96, (3,3))],
-                [('conv',  64, (1,1)), ('conv',  96, (3,3)), ('conv',  96, (3,3))]]
-        return net.inception_module(x, 'incept_v4_a', cols)
-    def inception_v4_b(net, x):
-        cols = [[('pool', 'AVG', (3,3), (1,1), 'SAME'), ('conv', 128, (1,1))],
-                [('conv', 384, (1,1))],
-                [('conv', 192, (1,1)), ('conv', 224, (1,7)), ('conv', 256, (7,1))],
-                [('conv', 192, (1,1)), ('conv', 192, (1,7)), ('conv', 224, (7,1)), ('conv', 224, (1,7)), ('conv', 256, (7,1))]]
-        return net.inception_module(x, 'incept_v4_b', cols)
-    def inception_v4_c(net, x):
-        cols = [[('pool', 'AVG', (3,3), (1,1), 'SAME'), ('conv', 256, (1,1))],
-                [('conv', 256, (1,1))],
-                [('conv', 384, (1,1)), ('conv', 256, (1,3))],
-                [('share',),           ('conv', 256, (3,1))],
-                [('conv', 384, (1,1)), ('conv', 448, (1,3)), ('conv', 512, (3,1)), ('conv', 256, (3,1))],
-                [('share',),           ('share',),           ('share',),           ('conv', 256, (1,3))]]
-        return net.inception_module(x, 'incept_v4_c', cols)
-
-    net.use_batch_norm = True
-    x = net.input_layer(input_layer)
-    x = net.conv(x, 32, (3,3), (2,2), padding='VALID')
-    x = net.conv(x, 32, (3,3), (1,1), padding='VALID')
-    x = net.conv(x, 64, (3,3))
-    x = inception_v4_sa(net, x)
-    x = inception_v4_sb(net, x)
-    x = inception_v4_sc(net, x)
-    for _ in range(4):
-        x = inception_v4_a(net, x)
-    x = inception_v4_ra(net, x, 192, 224, 256, 384)
-    for _ in range(7):
-        x = inception_v4_b(net, x)
-    x = inception_v4_rb(net, x)
-    for _ in range(3):
-        x = inception_v4_c(net, x)
-    x = net.spatial_avg(x)
-    x = net.dropout(x, 0.8)
-    return x
-
-def inference_inception_resnet_v2(net, input_layer):
-    """Google's Inception-Resnet v2 model
-    https://arxiv.org/abs/1602.07261
-    """
-    def inception_resnet_v2_a(net, x):
-        cols = [[('conv',  32, (1,1))],
-                [('conv',  32, (1,1)), ('conv',  32, (3,3))],
-                [('conv',  32, (1,1)), ('conv',  48, (3,3)), ('conv',  64, (3,3))]]
-        x = net.inception_module(x, 'incept_resnet_v2_a', cols)
-        x = net.conv(x, 384, (1,1), activation='LINEAR')
-        return x
-    def inception_resnet_v2_b(net, x):
-        cols = [[('conv', 192, (1,1))],
-                [('conv', 128, (1,1)), ('conv', 160, (1,7)), ('conv', 192, (7,1))]]
-        x = net.inception_module(x, 'incept_resnet_v2_b', cols)
-        x = net.conv(x, 1152, (1,1), activation='LINEAR')
-        return x
-    def inception_resnet_v2_c(net, x):
-        cols = [[('conv', 192, (1,1))],
-                [('conv', 192, (1,1)), ('conv', 224, (1,3)), ('conv', 256, (3,1))]]
-        x = net.inception_module(x, 'incept_resnet_v2_c', cols)
-        x = net.conv(x, 2048, (1,1), activation='LINEAR')
-        return x
-
-    net.use_batch_norm = True
-    residual_scale = 0.2
-    x = net.input_layer(input_layer)
-    x = net.conv(x, 32, (3,3), (2,2), padding='VALID')
-    x = net.conv(x, 32, (3,3), (1,1), padding='VALID')
-    x = net.conv(x, 64, (3,3))
-    x = inception_v4_sa(net, x)
-    x = inception_v4_sb(net, x)
-    x = inception_v4_sc(net, x)
-    for _ in range(5):
-        x = net.residual(x, inception_resnet_v2_a, scale=residual_scale)
-    x = inception_v4_ra(net, x, 256, 256, 384, 384)
-    for _ in range(10):
-        x = net.residual(x, inception_resnet_v2_b, scale=residual_scale)
-    x = inception_resnet_v2_rb(net, x)
-    for _ in range(5):
-        x = net.residual(x, inception_resnet_v2_c, scale=residual_scale)
-    x = net.spatial_avg(x)
-    x = net.dropout(x, 0.8)
-    return x
-
-def run_evaluation(nstep, sess, top1_op, top5_op, enqueue_ops):
-    print("Evaluating")
-    top1s = []
-    top5s = []
-    print("  Step  Top-1  Top-5")
-    for step in range(nstep):
-        try:
-            top1, top5 = sess.run([top1_op, top5_op, enqueue_ops])[:2]
-            if step == 0 or (step+1) % FLAGS.display_every == 0:
-                print("% 6i %5.1f%% %5.1f%%" % (step+1, top1*100, top5*100))
-            top1s.append(top1)
-            top5s.append(top5)
-        except KeyboardInterrupt:
-            print("Keyboard interrupt")
-            break
-    nstep = len(top1s)
-    if nstep == 0:
-        return
-    top1s = np.asarray(top1s) * 100.
-    top5s = np.asarray(top5s) * 100.
-    top1_mean = np.mean(top1s)
-    top5_mean = np.mean(top5s)
-    if nstep > 2:
-        top1_uncertainty = np.std(top1s, ddof=1) / np.sqrt(float(nstep))
-        top5_uncertainty = np.std(top5s, ddof=1) / np.sqrt(float(nstep))
-    else:
-        top1_uncertainty = float('nan')
-        top5_uncertainty = float('nan')
-    top1_madstd = 1.4826*np.median(np.abs(top1s - np.median(top1s)))
-    top5_madstd = 1.4826*np.median(np.abs(top5s - np.median(top5s)))
-    print('-' * 64)
-    print('Validation Top-1: %.3f %% +/- %.2f (jitter = %.1f)' % (
-        top1_mean, top1_uncertainty, top1_madstd))
-    print('Validation Top-5: %.3f %% +/- %.2f (jitter = %.1f)' % (
-        top5_mean, top5_uncertainty, top5_madstd))
-    print('-' * 64)
-
-def get_num_records(tf_record_pattern):
-    def count_records(tf_record_filename):
-        count = 0
-        for _ in tf.python_io.tf_record_iterator(tf_record_filename):
-            count += 1
-        return count
-    filenames = sorted(tf.gfile.Glob(tf_record_pattern))
-    nfile = len(filenames)
-    return (count_records(filenames[0])*(nfile-1) +
-            count_records(filenames[-1]))
-
-def add_bool_argument(cmdline, shortname, longname=None, default=False, help=None):
-    if longname is None:
-        shortname, longname = None, shortname
-    elif default == True:
-        raise ValueError("""Boolean arguments that are True by default should not have short names.""")
-    name = longname[2:]
-    feature_parser = cmdline.add_mutually_exclusive_group(required=False)
-    if shortname is not None:
-        feature_parser.add_argument(shortname, '--'+name, dest=name, action='store_true', help=help, default=default)
-    else:
-        feature_parser.add_argument(           '--'+name, dest=name, action='store_true', help=help, default=default)
-    feature_parser.add_argument('--no'+name, dest=name, action='store_false')
-    return cmdline
-
-def main():
-    tf.set_random_seed(1234+hvd.rank())
-    np.random.seed(4321+hvd.rank())
-    cmdline = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    # Basic options
-    cmdline.add_argument('-m', '--model', required=True,
-                         help="""Name of model to run.""",
-                         choices=['inception3', 'resnet50'])
-#    cmdline.add_argument('-m', '--model', required=True,
-#                         help="""Name of model to run:
-#                         trivial, lenet,
-#                         alexnet, googlenet, vgg[11,13,16,19],
-#                         inception[3,4], resnet[18,34,50,101,152],
-#                         resnext[50,101,152], inception-resnet2.""")
-    cmdline.add_argument('--data_dir', default=None,
-                         help="""Path to dataset in TFRecord format
-                         (aka Example protobufs). Files should be
-                         named 'train-*' and 'validation-*'.""")
-    cmdline.add_argument('-b', '--batch_size', default=64, type=int,
-                         help="""Size of each minibatch.""")
-    cmdline.add_argument('--num_batches', default=50, type=int,
-                         help="""Number of batches to run.
-                         Ignored during eval.""")
-    cmdline.add_argument('--num_epochs', default=None, type=int,
-                         help="""Number of epochs to run.
-                         Overrides --num_batches. Ignored during eval.""")
-    cmdline.add_argument('--log_dir', default="",
-                         help="""Directory in which to write training
-                         summaries and checkpoints.""")
-    cmdline.add_argument('--display_every', default=1, type=int,
-                         help="""How often (in iterations) to print out
-                         running information.""")
-    cmdline.add_argument('--save_interval', default=43200, type=int,
-                         help="""Time in seconds between checkpoints.""")
-    cmdline.add_argument('--summary_interval', default=3600, type=int,
-                         help="""Time in seconds between saves of summary statistics.""")
-    cmdline.add_argument('--loss_scale', default=1., type=float,
-                         help="""Loss scaling factor. Set to 1 to disable.""")
-    cmdline.add_argument('--larc_eta', default=None, type=float,
-                         help="""LARC eta value. If not specified, LARC is
-                         disabled.""")
-    cmdline.add_argument('--larc_mode', default='clip',
-                         help="""LARC mode can be 'clip' or 'scale'.""")
-    cmdline.add_argument('--learning_rate', default=None, type=float,
-                         help="""Initial learning rate.""")
-    add_bool_argument(cmdline, '--eval',
-                      help="""Evaluate the top-1 and top-5 accuracy of
-                      a checkpointed model.""")
-    add_bool_argument(cmdline, '--fp16',
-                      help="""Train using float16 (half) precision instead
-                      of float32.""")
-
-    global FLAGS
-    FLAGS, unknown_args = cmdline.parse_known_args()
-    if len(unknown_args) > 0:
-        for bad_arg in unknown_args:
-            print("ERROR: Unknown command line arg: %s" % bad_arg)
-        raise ValueError("Invalid command line arg(s)")
-
-    nclass = 1000
-    batch_size = FLAGS.batch_size
-    subset = 'validation' if FLAGS.eval else 'train'
-
-    tfversion = tensorflow_version_tuple()
-    print_r0("TensorFlow:  %i.%i.%s" % tfversion)
-    print_r0("This script:", __file__, "v%s" % __version__)
-    print_r0("Cmd line args:")
-    print_r0('\n'.join(['  '+arg for arg in sys.argv[1:]]))
-
-    if FLAGS.data_dir is not None and FLAGS.data_dir != '':
-        nrecord = get_num_records(os.path.join(FLAGS.data_dir, '%s-*' % subset))
-    else:
-        nrecord = FLAGS.num_batches * batch_size * hvd.size()
-
-    # Training hyperparameters
-    FLAGS.momentum              = 0.9
-    FLAGS.lr_decay_policy       = 'poly'
-    FLAGS.lr_decay_epochs       = 30
-    FLAGS.lr_decay_rate         = 0.1
-    FLAGS.lr_poly_power         = 2.
-    FLAGS.weight_decay          = 1e-4
-    FLAGS.input_buffer_size     = min(10000, nrecord)
-    FLAGS.distort_color         = False
-    FLAGS.nstep_burnin          = 20
-    FLAGS.larc_epsilon          = 1.
-
-    if FLAGS.eval:
-        if FLAGS.data_dir is None:
-            raise ValueError("eval requires data_dir to be specified")
-        if hvd.size() > 1:
-            raise ValueError("Multi-GPU evaluation is not supported")
-        if FLAGS.fp16:
-            print("WARNING: eval supports only fp32 math. Ignoring --fp16 flag.")
-            FLAGS.fp16 = False
-
-    model_dtype = tf.float16 if FLAGS.fp16 else tf.float32
-
-    print_r0("Num ranks:  ", hvd.size())
-    print_r0("Num images: ", nrecord if FLAGS.data_dir is not None else 'Synthetic')
-    print_r0("Model:      ", FLAGS.model)
-    print_r0("Batch size: ", batch_size, 'per device')
-    print_r0("            ", batch_size * hvd.size(), 'total')
-    print_r0("Data format:", 'NCHW')
-    print_r0("Data type:  ", 'fp16' if model_dtype == tf.float16 else 'fp32')
-
-    if FLAGS.num_epochs is not None:
-        if FLAGS.data_dir is None:
-            raise ValueError("num_epochs requires data_dir to be specified")
-        nstep = nrecord * FLAGS.num_epochs // (batch_size * hvd.size())
-    else:
-        nstep = FLAGS.num_batches
-        FLAGS.num_epochs = max(nstep * batch_size * hvd.size() // nrecord, 1)
-
-    model_name = FLAGS.model
-    if   model_name == 'trivial':
-        height, width = 224, 224
-        model_func = inference_trivial
-    elif model_name == 'lenet':
-        height, width = 28, 28
-        model_func = inference_lenet5
-    elif model_name == 'alexnet':
-        height, width = 227, 227
-        model_func = inference_alexnet_owt
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.03
-    elif model_name == 'overfeat':
-        height, width = 231, 231
-        model_func = inference_overfeat
-    elif model_name.startswith('vgg'):
-        height, width = 224, 224
-        nlayer = int(model_name[len('vgg'):])
-        model_func = lambda net, images: inference_vgg(net, images, nlayer)
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.02
-    elif model_name == 'googlenet':
-        height, width = 224, 224
-        model_func = inference_googlenet
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.04
-    elif model_name == 'inception3':
-        height, width = 299, 299
-        model_func = inference_inception_v3
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.2
-    elif model_name.startswith('resnet'):
-        height, width = 224, 224
-        nlayer = int(model_name[len('resnet'):])
-        model_func = lambda net, images: inference_resnet_v1(net, images, nlayer)
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 1. * (batch_size * hvd.size() / 1024.0) if nlayer > 18 else 0.5
-    elif model_name.startswith('resnext'):
-        height, width = 224, 224
-        nlayer = int(model_name[len('resnext'):])
-        model_func = lambda net, images: inference_resnext_v1(net, images, nlayer)
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.1
-    elif model_name == 'inception4':
-        height, width = 299, 299
-        model_func = inference_inception_v4
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.045
-    elif model_name == 'inception-resnet2':
-        height, width = 299, 299
-        model_func = inference_inception_resnet_v2
-        if FLAGS.learning_rate == None:
-            FLAGS.learning_rate = 0.045
-    else:
-        raise ValueError("Invalid model type: %s" % model_name)
-
-    if FLAGS.learning_rate == None:
-        FLAGS.learning_rate = 0.001 # Model-specific or command line values are set above
-
-    if FLAGS.data_dir is None:
-        preprocessor = DummyPreprocessor(height, width, batch_size, nclass)
-    else:
-        preprocessor = ImagePreprocessor(height, width, subset)
-
-    def loss_func(images, labels, var_scope):
-        # Build the forward model
-        net = GPUNetworkBuilder(True, dtype=model_dtype)
-        output = model_func(net, images)
-        # Add final FC layer to produce nclass outputs
-        logits = net.fully_connected(output, nclass, activation='LINEAR')
-        if logits.dtype != tf.float32:
-            logits = tf.cast(logits, tf.float32)
-        loss = tf.losses.sparse_softmax_cross_entropy(
-            logits=logits, labels=labels)
-        # Add weight decay
-        if FLAGS.weight_decay is not None and FLAGS.weight_decay != 0.:
-            params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
-                                       scope=var_scope.name)
-            l2_loss = tf.add_n([tf.nn.l2_loss(w) for w in params])
-            if l2_loss.dtype != tf.float32:
-                l2_loss = tf.cast(l2_loss, tf.float32)
-            loss += FLAGS.weight_decay * l2_loss
-        return loss, logits
-    def eval_func(images, labels, var_scope):
-        net = GPUNetworkBuilder(False, dtype=model_dtype)
-        output = model_func(net, images)
-        logits = net.fully_connected(output, nclass, activation='LINEAR')
-        if logits.dtype != tf.float32:
-            logits = tf.cast(logits, tf.float32)
-        with tf.device('/cpu:0'):
-            top1 = tf.reduce_mean(
-                tf.cast(tf.nn.in_top_k(logits, labels, 1), tf.float32))
-            top5 = tf.reduce_mean(
-                tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32))
-        return top1, top5
-
-    if FLAGS.eval:
-        evaluator = FeedForwardEvaluator(preprocessor, eval_func)
-        print("Building evaluation graph")
-        top1_op, top5_op, enqueue_ops = evaluator.evaluation_step(batch_size)
-    else:
-        nstep_per_epoch = nrecord // (batch_size * hvd.size())
-        trainer = FeedForwardTrainer(preprocessor, loss_func, nstep_per_epoch)
-        print_r0("Building training graph")
-        total_loss, learning_rate, train_ops = trainer.training_step(
-            batch_size)
-
-    print_r0("Creating session")
-    config = tf.ConfigProto()
-    config.intra_op_parallelism_threads = 1
-    config.inter_op_parallelism_threads = 10
-    config.gpu_options.force_gpu_compatible = True
-    config.gpu_options.visible_device_list = str(hvd.local_rank())
-
-    sess = tf.Session(config=config)
-
-    train_writer = None
-    saver = None
-    summary_ops = None
-    if hvd.rank() == 0 and len(FLAGS.log_dir):
-        log_dir = FLAGS.log_dir
-        train_writer = tf.summary.FileWriter(log_dir, sess.graph)
-        summary_ops = tf.summary.merge_all()
-        last_summary_time = time.time()
-        saver = tf.train.Saver(keep_checkpoint_every_n_hours=3)
-        last_save_time = time.time()
-
-    if not FLAGS.eval:
-        print_r0("Initializing variables")
-        trainer.init(sess)
-
-    restored = False
-    if hvd.rank() == 0 and saver is not None:
-        ckpt = tf.train.get_checkpoint_state(log_dir)
-        checkpoint_file = os.path.join(log_dir, "checkpoint")
-        if ckpt and ckpt.model_checkpoint_path:
-            saver.restore(sess, ckpt.model_checkpoint_path)
-            restored = True
-            print("Restored session from checkpoint " + ckpt.model_checkpoint_path)
-        else:
-            if not os.path.exists(log_dir):
-                os.mkdir(log_dir)
-
-    if FLAGS.eval:
-        if not restored:
-            raise ValueError("No checkpoint found for evaluation")
-        else:
-            print("Pre-filling input pipeline")
-            evaluator.prefill_pipeline(sess)
-            nstep = nrecord // batch_size
-            run_evaluation(nstep, sess, top1_op, top5_op, enqueue_ops)
-            return
-
-    trainer.sync(sess)
-
-    if hvd.rank() == 0 and not restored:
-        if saver is not None:
-            save_path = saver.save(sess, checkpoint_file, global_step=0)
-            print("Checkpoint written to", save_path)
-
-    print_r0("Pre-filling input pipeline")
-    trainer.prefill_pipeline(sess)
-
-    print_r0("Training")
-    print_r0("  Step Epoch Img/sec   Loss   LR")
-    batch_times = []
-    oom = False
-    step0 = int(sess.run(trainer.global_step))
-    for step in range(step0, nstep):
-        ops_to_run = [total_loss, learning_rate] + train_ops
-        try:
-            start_time = time.time()
-            if (hvd.rank() == 0 and summary_ops is not None and
-                (step == 0 or step+1 == nstep or
-                 time.time() - last_summary_time > FLAGS.summary_interval)):
-                if step != 0:
-                    last_summary_time += FLAGS.summary_interval
-                print("Writing summaries to ", log_dir)
-                summary, loss, lr = sess.run([summary_ops] + ops_to_run)[:3]
-                train_writer.add_summary(summary, step)
-            else:
-                loss, lr = sess.run(ops_to_run)[:2]
-            elapsed = time.time() - start_time
-        except KeyboardInterrupt:
-            print("Keyboard interrupt")
-            break
-        except tf.errors.ResourceExhaustedError:
-            elapsed = -1.
-            loss    = 0.
-            lr      = -1
-            oom = True
-
-        if (hvd.rank() == 0 and saver is not None and
-            (time.time() - last_save_time > FLAGS.save_interval or step+1 == nstep)):
-            last_save_time += FLAGS.save_interval
-            save_path = saver.save(sess, checkpoint_file,
-                                   global_step=trainer.global_step)
-            print("Checkpoint written to", save_path)
-
-        if step >= FLAGS.nstep_burnin:
-            batch_times.append(elapsed)
-        img_per_sec = batch_size / elapsed
-        effective_accuracy = 100. / math.exp(min(loss,20.))
-        if step == 0 or (step+1) % FLAGS.display_every == 0:
-            epoch = step*batch_size*hvd.size() // nrecord
-            print_r0("%6i %5i %7.1f %7.3f %7.5f" % (
-                step+1, epoch+1, img_per_sec*hvd.size(), loss, lr))
-        if oom:
-            break
-    nstep = len(batch_times)
-    if nstep > 0:
-        batch_times = np.array(batch_times)
-        speeds = batch_size*hvd.size() / batch_times
-        speed_mean = np.mean(speeds)
-        if nstep > 2:
-            speed_uncertainty = np.std(speeds, ddof=1) / np.sqrt(float(nstep))
-        else:
-            speed_uncertainty = float('nan')
-        speed_madstd = 1.4826*np.median(np.abs(speeds - np.median(speeds)))
-        speed_jitter = speed_madstd
-        print_r0('-' * 64)
-        print_r0('Images/sec: %.1f +/- %.1f (jitter = %.1f)' % (
-            speed_mean, speed_uncertainty, speed_jitter))
-        print_r0('-' * 64)
-    else:
-        print_r0("No results, did not get past burn-in phase (%i steps)" %
-              FLAGS.nstep_burnin)
-
-    if train_writer is not None:
-        train_writer.close()
-
-    if oom:
-        print("Out of memory error detected, exiting")
-        sys.exit(-2)
-
-if __name__ == '__main__':
-    main()
diff --git a/TensorFlow/Detection/SSD/.dockerignore b/TensorFlow/Detection/SSD/.dockerignore
new file mode 100644
index 00000000..36097eab
--- /dev/null
+++ b/TensorFlow/Detection/SSD/.dockerignore
@@ -0,0 +1,10 @@
+.git
+!.git/HEAD
+!.git/refs/heads/
+
+models/
+!models/research/object_detection/
+!models/research/slim/
+**/*.jpg
+**/*.png
+**/*.pb
diff --git a/TensorFlow/Detection/SSD/.gitignore b/TensorFlow/Detection/SSD/.gitignore
new file mode 100644
index 00000000..ead84456
--- /dev/null
+++ b/TensorFlow/Detection/SSD/.gitignore
@@ -0,0 +1 @@
+**/*.swp
diff --git a/TensorFlow/Detection/SSD/Dockerfile b/TensorFlow/Detection/SSD/Dockerfile
new file mode 100644
index 00000000..8f1b53b0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/Dockerfile
@@ -0,0 +1,31 @@
+FROM nvcr.io/nvidia/tensorflow:19.03-py3 as base
+
+FROM base as sha
+
+RUN mkdir /sha
+RUN cat `cat HEAD | cut -d' ' -f2` > /sha/repo_sha
+
+FROM base as final
+
+WORKDIR /workdir
+
+RUN PROTOC_VERSION=3.0.0 && \
+    PROTOC_ZIP=protoc-${PROTOC_VERSION}-linux-x86_64.zip && \
+    curl -OL https://github.com/google/protobuf/releases/download/v$PROTOC_VERSION/$PROTOC_ZIP && \
+    unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
+    rm -f $PROTOC_ZIP
+
+COPY requirements.txt .
+RUN pip install Cython
+RUN pip install -r requirements.txt
+
+WORKDIR models/research/
+COPY models/research/ .
+RUN protoc object_detection/protos/*.proto --python_out=.
+ENV PYTHONPATH="/workdir/models/research/:/workdir/models/research/slim/:$PYTHONPATH"
+
+COPY examples/ examples
+COPY configs/ configs/
+COPY download_all.sh download_all.sh
+
+COPY --from=sha /sha .
diff --git a/TensorFlow/Detection/SSD/NOTICE b/TensorFlow/Detection/SSD/NOTICE
new file mode 100644
index 00000000..2f8a2ce2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/NOTICE
@@ -0,0 +1,6 @@
+SSD TensorFlow
+
+This repository includes software from https://github.com/tensorflow/models
+licensed under the Apache License, Version 2.0 (the "License")
+
+
diff --git a/TensorFlow/Detection/SSD/README.md b/TensorFlow/Detection/SSD/README.md
new file mode 100644
index 00000000..7f37722a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/README.md
@@ -0,0 +1,337 @@
+# SSD320 v1.2 For TensorFlow
+
+This repository provides a script and recipe to train SSD320 v1.2 to achieve state of the art accuracy, and is tested and maintained by NVIDIA.
+
+## Table Of Contents
+* [The model](#the-model)
+  * [Default configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+  * [Command line options](#command-line-options)
+  * [Getting the data](#getting-the-data)
+  * [Training process](#training-process)
+    * [Data preprocessing](#data-preprocessing)
+    * [Data augmentation](#data-augmentation)
+  * [Enabling mixed precision](#enabling-mixed-precision)
+* [Benchmarking](#benchmarking)
+  * [Training performance benchmark](#training-performance-benchmark)
+  * [Inference performance benchmark](#inference-performance-benchmark)
+* [Results](#results)
+  * [Training accuracy results](#training-accuracy-results)
+  * [Training performance results](#training-performance-results)
+  * [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+## The model
+
+The SSD320 v1.2 model is based on the [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which describes SSD as “a method for detecting objects in images using a single deep neural network”.
+
+We have altered the network in order to improve accuracy and increase throughput. Changes we have made include:
+- Replacing the VGG backbone with the more popular ResNet50.
+- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).
+- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).
+- Decreasing the input size to 320 x 320.
+
+Our implementation is based on the existing [model from the TensorFlow models repository](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config).
+
+This model trains with mixed precision tensor cores on NVIDIA Volta GPUs, therefore you can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
+
+The following features were implemented in this model:
+- Data-parallel multi-GPU training with Horovod.
+- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
+- Tensor Core operations to maximize throughput using NVIDIA Volta GPUs.
+- Dynamic loss scaling for tensor cores (mixed precision) training.
+
+Because of these enhancements, the SSD320 v1.2 model achieves higher accuracy.
+
+### Default configuration
+We trained the model for 12500 steps (27 epochs) with the following setup:
+- [SGDR](https://arxiv.org/pdf/1608.03983.pdf) with cosine decay learning rate
+- Learning rate base = 0.16 
+- Momentum = 0.9
+- Warm-up learning rate = 0.0693312
+- Warm-up steps = 1000
+- Batch size per GPU = 32
+- Number of GPUs = 8
+
+## Setup
+
+The following section list the requirements in order to start training the SSD320 v1.2 model.
+
+### Requirements
+This repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies.  Aside from these dependencies, ensure you have the following software:
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [TensorFlow 19.03-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) (or later) NGC container
+* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
+Documentation:
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+* [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)
+
+
+## Quick Start Guide
+To train your model using mixed precision with tensor cores or using FP32,
+perform the following steps using the default parameters of the SSD320 v1.2 model on the
+[COCO 2017](http://cocodataset.org/#download) dataset.
+
+
+
+### 1. Clone the repository.
+```
+git clone https://github.com/NVIDIA/DeepLearningExamples
+cd DeepLearningExamples/TensorFlow/Detection/SSD
+```
+
+### 2. Build the SSD320 v1.2 TensorFlow NGC container.
+
+```
+docker build . -t nvidia_ssd
+```
+
+### 3. Download and preprocess the dataset. 
+
+Extract the COCO 2017 dataset with:
+
+```
+download_all.sh nvidia_ssd <data_dir_path> <checkpoint_dir_path>
+```
+
+Data will be downloaded, preprocessed to tfrecords format and saved in the `<data_dir_path>` directory (on the host).
+Moreover the script will download pre-trained RN50 checkpoint in the `<checkpoint_dir_path>` directory
+
+
+### 4. Launch the NGC container to run training/inference.
+```
+nvidia-docker run --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v <data_dir_path>:/data -v <checkpoint_dir_path>:/checkpoints --ipc=host nvidia_ssd
+```
+
+### 5. Start training.
+
+The `./examples` directory provides several sample scripts for various GPU settings and act as wrappers around
+`object_detection/model_main.py` script. The example scripts can be modified by arguments: 
+- A path to directory for checkpoints
+- Additional arguments to `object_detection/model_main.py`
+
+If you want to run 8 GPUs, training with tensor cores acceleration and save checkpoints in `/checkpoints` directory, run:
+
+```
+bash ./examples/SSD320_FP16_8GPU.sh /checkpoints
+```
+
+
+### 6. Start validation/evaluation.
+
+
+The `model_main.py` training script automatically runs validation during training.
+The results from the validation are printed to `stdout`.
+
+Pycocotools’ open-sourced scripts provides a consistent way to evaluate models on the COCO dataset.
+We are using these scripts during validation to measure models performance in AP metric.
+Metrics below are evaluated using pycocotools’ methodology,
+in the following format:during validation to measure models performance in AP metric.
+Metrics below are evaluated using pycocotools’ methodology, in the following format:
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.273
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.423
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.291
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.257
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.398
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.427
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645
+```
+The metric reported in our results is present in the first row.
+
+To evaluate a checkpointed model saved in the previous step, you can use script from examples directory.
+If you want to run inference with tensor cores acceleration, run:
+
+```
+bash examples/SSD320_evaluate.sh <path to checkpoint>
+```
+
+## Details
+
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+### Command line options
+The SSD model training is conducted by the script from the object_detection library, `model_main.py`.
+Our experiments were done with settings described in the `examples` directory.
+If you would like to get more details about available arguments, please run:
+
+```
+python object_detection/model_main.py --help
+```
+
+### Getting the data
+
+The SSD320 v1.2 model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset.
+The `download_data.sh` script will preprocess the data to tfrecords format.
+
+This repository contains the `download_dataset.sh` script which will automatically download and preprocess the training,
+validation and test datasets. By default, data will be downloaded to the `/data` directory.
+
+### Training process
+Training the SSD model is implemented in the `object_detection/model_main.py` script. 
+
+All training parameters are set in the config files. Because evaluation is relatively time consuming,
+it does not run every epoch. By default, evaluation is executed only once at the end of the training.
+The model is evaluated using pycocotools distributed with the COCO dataset.
+The number of evaluations can be changed using the `eval_count` parameter.
+
+To run training with tensor cores, use `./examples/SSD320_FP16_{1,4,8}GPU.sh` scripts. For more details see [Enabling mixed precision](#enabling-mixed-precision) section below.
+
+#### Data preprocessing
+Before we feed data to the model, both during training and inference, we perform:
+*	Normalization
+*	Encoding bounding boxes
+*   Resize to 320x320
+
+#### Data augmentation
+During training we perform the following augmentation techniques:
+*	Random crop
+*	Random horizontal flip
+*	Color jitter
+
+
+
+### Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format,
+while storing minimal information in single-precision to retain as much information as possible in critical parts of the network.
+Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures,
+significant training speedups are experienced by switching to mixed precision --
+up to 3x overall speedup on the most arithmetically intense model architectures.
+Using [mixed precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+ 
+This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code.
+AMP enables mixed precision training on Volta and Turing GPUs automatically.
+The TensorFlow framework code makes all necessary model changes internally.
+
+In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,
+and the loss scaling is automatically applied inside of supported optimizers.
+AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization.
+It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
+
+
+For information about:
+- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
+- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+
+## Benchmarking
+The following section shows how to run benchmarks measuring the model performance in training and inference modes.
+
+### Training performance benchmark
+Training benchmark was run in various scenarios on V100 16G GPU. For each scenario, batch size was set to 32. 
+
+To benchmark training, run:
+
+```
+bash examples/SSD320_{PREC}_{NGPU}GPU_BENCHMARK.sh
+```
+
+
+Where the `{NGPU}` defines number of GPUs used in benchmark, and the `{PREC}` defines precision.
+The benchmark runs training with only 1200 steps and computes average training speed of last 300 steps.
+
+
+### Inference performance benchmark
+Inference benchmark was run with various batch-sizes on V100 16G GPU.
+For inference we are using single GPU setting. Examples are taken from the validation dataset.
+
+To benchmark inference, run:
+
+```
+bash examples/SSD320_FP{16,32}_inference.sh --batch_size <batch size> --checkpoint_dir <path to checkpoint>
+```
+
+Batch size for the inference benchmark is controlled by the `--batch_size` argument,
+while the checkpoint is provided to the script with the `--checkpoint_dir` argument.
+
+The benchmark script provides extra arguments for extra control over the experiment.
+We were using default values for the extra arguments during the experiments. For more details about them, please run:
+
+```
+bash examples/SSD320_FP16_inference.sh --help
+```
+
+## Results
+
+The following sections provide details on how we achieved our performance and accuracy in training and inference.
+
+### Training accuracy results
+Our results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+All the results are obtained with batch size set to 32.
+
+| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** |
+|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|
+| 1                  | 0.276                   | 7h 17min                               | 0.278        | 10h 20min                   |
+| 4                  | 0.277                   | 2h 15min                               | 0.275        | 2h 53min                    |
+| 8                  | 0.269                   | 1h 19min                               | 0.268        | 1h 37min                    |
+
+
+Here are example graphs of FP32 and FP16 training on 8 GPU configuration:
+
+![TrainingLoss](./img/training_loss.png)
+
+![ValidationAccuracy](./img/validation_accuracy.png)
+
+### Training performance results
+
+Our results were obtained by running:
+
+```
+python bash examples/SSD320_FP*GPU_BENCHMARK.sh
+```
+
+scripts in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with V100 16G GPUs. 
+
+
+| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **FP32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** |
+|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|
+| 1                  | 32                     |  124.97                   |   87.87        | 1.42                              | 1.00                                            | 1.00                                 |
+| 4                  | 32                     |  430.79                   |  330.35        | 1.39                              | 3.45                                            | 3.76                                 |
+| 8                  | 32                     |  752.04                   |  569.01        | 1.32                              | 6.02                                            | 6.48                                 |
+
+To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+Those results can be improved when [XLA](https://www.tensorflow.org/xla) is used 
+in conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (~179 img/s).
+However XLA is still considered experimental.
+
+### Inference performance results
+
+Our results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs.
+
+
+| **Batch size** | **Mixed precision img/s** | **FP32 img/s** |
+|:--------------:|:-------------------------:|:--------------:|
+|              1 |                    93.37  |        97.29   |
+|              2 |                   135.33  |       134.04   |
+|              4 |                   171.70  |       163.38   |
+|              8 |                   189.25  |       174.47   |
+|             16 |                   187.62  |       175.42   |
+|             32 |                   187.37  |       175.07   |
+|             64 |                   191.40  |       177.75   |
+
+To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+## Changelog
+
+March 2019
+ * Initial release
+
+## Known issues
+There are no known issues with this model.
+
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_bench.config b/TensorFlow/Detection/SSD/configs/ssd320_bench.config
new file mode 100644
index 00000000..0e9783b6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_bench.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 1250
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .02000000000000000000
+          total_steps: 1250
+          warmup_learning_rate: .00866640000000000000
+          warmup_steps: 400
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_double_1gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_double_1gpus.config
new file mode 100644
index 00000000..97025d13
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_double_1gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .02000000000000000000
+          total_steps: 200000
+          warmup_learning_rate: .00866640000000000000
+          warmup_steps: 8000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_double_4gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_double_4gpus.config
new file mode 100644
index 00000000..6c20f7af
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_double_4gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 50000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .08000000000000000000
+          total_steps: 50000
+          warmup_learning_rate: .03466560000000000000
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_double_8gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_double_8gpus.config
new file mode 100644
index 00000000..830b4f5f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_double_8gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 25000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .16000000000000000000
+          total_steps: 25000
+          warmup_learning_rate: .06933120000000000000
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_full_1gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_full_1gpus.config
new file mode 100644
index 00000000..dc3e2644
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_full_1gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 100000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .02000000000000000000
+          total_steps: 100000
+          warmup_learning_rate: .00866640000000000000
+          warmup_steps: 8000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_full_4gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_full_4gpus.config
new file mode 100644
index 00000000..477a2a4d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_full_4gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 25000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .08000000000000000000
+          total_steps: 25000
+          warmup_learning_rate: .03466560000000000000
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/configs/ssd320_full_8gpus.config b/TensorFlow/Detection/SSD/configs/ssd320_full_8gpus.config
new file mode 100644
index 00000000..574da18f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/configs/ssd320_full_8gpus.config
@@ -0,0 +1,193 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: true
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  batch_size: 32 
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 12500
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .16000000000000000000
+          total_steps: 12500
+          warmup_learning_rate: .06933120000000000000
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/download_all.sh b/TensorFlow/Detection/SSD/download_all.sh
new file mode 100755
index 00000000..acc19002
--- /dev/null
+++ b/TensorFlow/Detection/SSD/download_all.sh
@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Get COCO 2017 data sets
+if [ -z $1 ]; then echo "Docker container name is missing" && exit 1; fi
+CONTAINER=$1
+COCO_DIR=${2:-"/data"}
+CHECKPOINT_DIR=${3:-"/checkpoints"}
+mkdir $COCO_DIR 2> /dev/null;
+chmod 777 $COCO_DIR
+cd $COCO_DIR
+curl -O http://images.cocodataset.org/zips/train2017.zip; unzip train2017.zip
+curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
+curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
+# Download backbone checkpoint
+mkdir $CHECKPOINT_DIR 2> /dev/null;
+chmod 777 $CHECKPOINT_DIR
+cd $CHECKPOINT_DIR
+wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
+tar -xzf resnet_v1_50_2016_08_28.tar.gz
+mkdir resnet_v1_50
+mv resnet_v1_50.ckpt resnet_v1_50/model.ckpt
+nvidia-docker run --rm -it -u 123 -v $COCO_DIR:/data $CONTAINER bash -c '
+cd /data
+# Create TFRecords
+python /workdir/models/research/object_detection/dataset_tools/create_coco_tf_record.py \
+    --train_image_dir=`pwd`"/train2017" \
+    --val_image_dir=`pwd`"/val2017" \
+    --val_annotations_file=`pwd`"/annotations/instances_val2017.json" \
+    --train_annotations_file=`pwd`"/annotations/instances_train2017.json" \
+    --testdev_annotations_file=`pwd`"/annotations/instances_val2017.json" \
+    --test_image_dir=`pwd`"/val2017" \
+    --output_dir=`pwd`'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU.sh
new file mode 100644
index 00000000..789f77af
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU.sh
@@ -0,0 +1,15 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_1gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_1GPU"}
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time python -u /workdir/models/research/object_detection/model_main.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       --model_dir=${CKPT_DIR} \
+       --alsologtostder \
+       "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU_BENCHMARK.sh
new file mode 100644
index 00000000..21cc9c3c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_1GPU_BENCHMARK.sh
@@ -0,0 +1,17 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_1GPU"}
+GPUS=1
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "Single GPU mixed precision training performance: " && \
+python -u /workdir/models/research/object_detection/model_main.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       --model_dir=${CKPT_DIR} \
+       --alsologtostder \
+        "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU.sh
new file mode 100644
index 00000000..f6657d7b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU.sh
@@ -0,0 +1,26 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_4gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_4GPU"}
+GPUS=4
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU_BENCHMARK.sh
new file mode 100644
index 00000000..c160e65a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_4GPU_BENCHMARK.sh
@@ -0,0 +1,27 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_4GPU"}
+GPUS=4
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "$GPUS GPUs mixed precision training performance: " && \
+mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU.sh
new file mode 100644
index 00000000..01f23559
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU.sh
@@ -0,0 +1,26 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_8gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_8GPU"}
+GPUS=8
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU_BENCHMARK.sh
new file mode 100644
index 00000000..44167566
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_8GPU_BENCHMARK.sh
@@ -0,0 +1,27 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP16_8GPU"}
+GPUS=8
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "$GPUS GPUs mixed precision training performance: " && \
+mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP16_inference.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP16_inference.sh
new file mode 100644
index 00000000..8e1835ca
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP16_inference.sh
@@ -0,0 +1,11 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_1gpus.config
+
+export TF_ENABLE_AUTO_MIXED_PRECISION=1
+
+SCRIPT_DIR=$(dirname "${BASH_SOURCE[0]}")
+OBJECT_DETECTION=$(realpath $SCRIPT_DIR/../object_detection/)
+PYTHONPATH=$PYTHONPATH:$OBJECT_DETECTION
+
+python $SCRIPT_DIR/SSD320_inference.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       "$@"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU.sh
new file mode 100644
index 00000000..55febd25
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU.sh
@@ -0,0 +1,13 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_1gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_1GPU"}
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time python -u /workdir/models/research/object_detection/model_main.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       --model_dir=${CKPT_DIR} \
+       --alsologtostder \
+       "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU_BENCHMARK.sh
new file mode 100644
index 00000000..7fbf117c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_1GPU_BENCHMARK.sh
@@ -0,0 +1,15 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_1GPU"}
+GPUS=1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "Single GPU single precision training performance: " && \
+python -u /workdir/models/research/object_detection/model_main.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       --model_dir=${CKPT_DIR} \
+       --alsologtostder \
+        "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU.sh
new file mode 100644
index 00000000..a25d9a23
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU.sh
@@ -0,0 +1,24 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_4gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_4GPU"}
+GPUS=4
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU_BENCHMARK.sh
new file mode 100644
index 00000000..42be31b7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_4GPU_BENCHMARK.sh
@@ -0,0 +1,25 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_8GPU"}
+GPUS=4
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "$GPUS GPUs single precision training performance: " && \
+mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU.sh
new file mode 100644
index 00000000..45f2ae73
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU.sh
@@ -0,0 +1,24 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_8gpus.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_8GPU"}
+GPUS=8
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+time mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU_BENCHMARK.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU_BENCHMARK.sh
new file mode 100644
index 00000000..81582f70
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_8GPU_BENCHMARK.sh
@@ -0,0 +1,25 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_bench.config
+CKPT_DIR=${1:-"/results/SSD320_FP32_8GPU"}
+GPUS=8
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+echo -n "$GPUS GPUs single precision training performance: " && \
+mpirun --allow-run-as-root \
+       -np $GPUS \
+       -H localhost:$GPUS \
+       -bind-to none \
+       -map-by slot \
+       -x NCCL_DEBUG=INFO \
+       -x LD_LIBRARY_PATH \
+       -x PATH \
+       -mca pml ob1 \
+       -mca btl ^openib \
+        python -u /workdir/models/research/object_detection/model_main.py \
+               --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+               --model_dir=${CKPT_DIR} \
+               --alsologtostder \
+               "${@:2}" 2>&1 | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}'
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_FP32_inference.sh b/TensorFlow/Detection/SSD/examples/SSD320_FP32_inference.sh
new file mode 100644
index 00000000..965eeea8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_FP32_inference.sh
@@ -0,0 +1,9 @@
+PIPELINE_CONFIG_PATH=/workdir/models/research/configs/ssd320_full_1gpus.config
+
+SCRIPT_DIR=$(dirname "${BASH_SOURCE[0]}")
+OBJECT_DETECTION=$(realpath $SCRIPT_DIR/../object_detection/)
+PYTHONPATH=$PYTHONPATH:$OBJECT_DETECTION
+
+python $SCRIPT_DIR/SSD320_inference.py \
+       --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+       "$@"
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_evaluate.sh b/TensorFlow/Detection/SSD/examples/SSD320_evaluate.sh
new file mode 100644
index 00000000..d01f7f7a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_evaluate.sh
@@ -0,0 +1,8 @@
+CHECKPINT_DIR=$1
+
+TENSOR_OPS=0
+export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
+
+python object_detection/model_main.py --checkpoint_dir $CHECKPINT_DIR --model_dir /results --run_once --pipeline_config_path configs/ssd320_full_1gpus.config
diff --git a/TensorFlow/Detection/SSD/examples/SSD320_inference.py b/TensorFlow/Detection/SSD/examples/SSD320_inference.py
new file mode 100644
index 00000000..7970cc6d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/examples/SSD320_inference.py
@@ -0,0 +1,99 @@
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from absl import flags
+from time import time
+
+import tensorflow as tf
+
+from object_detection import model_hparams
+from object_detection import model_lib
+
+
+flags.DEFINE_string('checkpoint_dir', None, 'Path to directory holding a checkpoint.  If '
+                    '`checkpoint_dir` is not provided, benchmark is running on random model')
+flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config file.')
+flags.DEFINE_integer('warmup_iters', 100, 'Number of iterations skipped during benchmark')
+flags.DEFINE_integer('benchmark_iters', 300, 'Number of iterations measured by benchmark')
+flags.DEFINE_integer('batch_size', 1, 'Number of inputs processed paralelly')
+FLAGS = flags.FLAGS
+
+flags.mark_flag_as_required('pipeline_config_path')
+
+
+def build_estimator():
+    session_config = tf.ConfigProto()
+    config = tf.estimator.RunConfig(session_config=session_config)
+
+    train_and_eval_dict = model_lib.create_estimator_and_inputs(
+        run_config=config,
+        hparams=model_hparams.create_hparams(None),
+        pipeline_config_path=FLAGS.pipeline_config_path)
+    estimator = train_and_eval_dict['estimator']
+    eval_input_fns = train_and_eval_dict['eval_input_fns']
+    return  estimator, eval_input_fns[0]
+
+
+def build_benchmark_input_fn(input_fn):
+    def benchmark_input_fn(params={}):
+        params['batch_size'] = FLAGS.batch_size
+        return input_fn(params).repeat().take(FLAGS.warmup_iters + FLAGS.benchmark_iters)
+    return benchmark_input_fn
+
+
+class TimingHook(tf.train.SessionRunHook):
+    def __init__(self):
+        super(TimingHook, self).__init__()
+        self.times = []
+
+    def before_run(self, *args, **kwargs):
+        super(TimingHook, self).before_run(*args, **kwargs)
+        self.start_time = time()
+
+    def log_progress(self):
+        print(len(self.times) - FLAGS.warmup_iters, '/', FLAGS.benchmark_iters, ' '*10, end='\r')
+
+    def after_run(self, *args, **kwargs):
+        super(TimingHook, self).after_run(*args, **kwargs)
+        self.times.append(time() - self.start_time)
+        self.log_progress()
+
+    def collect_result(self):
+        return FLAGS.batch_size * FLAGS.benchmark_iters / sum(self.times[FLAGS.benchmark_iters:])
+
+    def end(self, *args, **kwargs):
+        super(TimingHook, self).end(*args, **kwargs)
+        print()
+        print('Benchmark result:', self.collect_result(), 'img/s')
+
+
+def main(unused_argv):
+    tf.logging.set_verbosity(tf.logging.INFO)
+
+    estimator, eval_input_fn = build_estimator()
+
+    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) \
+                    if FLAGS.checkpoint_dir \
+                    else None
+    results = estimator.predict(
+        input_fn=build_benchmark_input_fn(eval_input_fn),
+        checkpoint_path=checkpoint_path,
+        hooks=[ TimingHook() ],
+        yield_single_examples=False
+    )
+    list(results)
+
+if __name__ == '__main__':
+    tf.app.run()
diff --git a/TensorFlow/Detection/SSD/img/training_loss.png b/TensorFlow/Detection/SSD/img/training_loss.png
new file mode 100644
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diff --git a/TensorFlow/Detection/SSD/models/.gitignore b/TensorFlow/Detection/SSD/models/.gitignore
new file mode 100644
index 00000000..c9d58f66
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/.gitignore
@@ -0,0 +1,101 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# PyCharm
+.idea/
+
+# For mac
+.DS_Store
+
+# Training data for word embedding tutorial
+tutorials/embedding/text8
+tutorials/embedding/questions-words.txt
+
+samples/outreach/blogs/segmentation_blogpost/carvana-image-masking-challenge/
diff --git a/TensorFlow/Detection/SSD/models/.gitmodules b/TensorFlow/Detection/SSD/models/.gitmodules
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/AUTHORS b/TensorFlow/Detection/SSD/models/AUTHORS
new file mode 100644
index 00000000..0fa85c98
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/AUTHORS
@@ -0,0 +1,10 @@
+# This is the official list of authors for copyright purposes.
+# This file is distinct from the CONTRIBUTORS files.
+# See the latter for an explanation.
+
+# Names should be added to this file as:
+# Name or Organization <email address>
+# The email address is not required for organizations.
+
+Google Inc.
+David Dao <daviddao@broad.mit.edu>
diff --git a/TensorFlow/Detection/SSD/models/CODEOWNERS b/TensorFlow/Detection/SSD/models/CODEOWNERS
new file mode 100644
index 00000000..08671562
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/CODEOWNERS
@@ -0,0 +1,67 @@
+* @tensorflow/tf-garden-team
+/official/ @tensorflow/tf-garden-team @karmel
+/research/adversarial_crypto/ @dave-andersen
+/research/adversarial_logit_pairing/ @AlexeyKurakin
+/research/adversarial_text/ @rsepassi @a-dai
+/research/adv_imagenet_models/ @AlexeyKurakin
+/research/attention_ocr/ @alexgorban
+/research/audioset/ @plakal @dpwe
+/research/autoaugment/* @barretzoph
+/research/autoencoders/ @snurkabill
+/research/brain_coder/ @danabo
+/research/cognitive_mapping_and_planning/ @s-gupta
+/research/compression/ @nmjohn
+/research/cvt_text/ @clarkkev @lmthang
+/research/deep_contextual_bandits/ @rikel
+/research/deeplab/ @aquariusjay @yknzhu @gpapan
+/research/delf/ @andrefaraujo
+/research/differential_privacy/ @ilyamironov @ananthr
+/research/domain_adaptation/ @bousmalis @dmrd
+/research/efficient-hrl/ @ofirnachum
+/research/gan/ @joel-shor
+/research/global_objectives/ @mackeya-google
+/research/im2txt/ @cshallue
+/research/inception/ @shlens @vincentvanhoucke
+/research/keypointnet/ @mnorouzi
+/research/learned_optimizer/ @olganw @nirum
+/research/learning_to_remember_rare_events/ @lukaszkaiser @ofirnachum
+/research/learning_unsupervised_learning/ @lukemetz @nirum
+/research/lexnet_nc/ @vered1986 @waterson
+/research/lfads/ @jazcollins @susillo
+/research/lm_1b/ @oriolvinyals @panyx0718
+/research/lm_commonsense/ @thtrieu
+/research/lstm_object_detection/ @dreamdragon @masonliuw @yinxiaoli
+/research/marco/ @vincentvanhoucke
+/research/maskgan/ @a-dai
+/research/morph_net/ @gariel-google
+/research/namignizer/ @knathanieltucker
+/research/neural_gpu/ @lukaszkaiser
+/research/neural_programmer/ @arvind2505
+/research/next_frame_prediction/ @panyx0718
+/research/object_detection/ @jch1 @tombstone @derekjchow @jesu9 @dreamdragon @pkulzc
+/research/pcl_rl/ @ofirnachum
+/research/ptn/ @xcyan @arkanath @hellojas @honglaklee
+/research/real_nvp/ @laurent-dinh
+/research/rebar/ @gjtucker
+/research/resnet/ @panyx0718
+/research/seq2species/ @apbusia @depristo
+/research/skip_thoughts/ @cshallue
+/research/slim/ @sguada @nathansilberman
+/research/steve/ @buckman-google
+/research/street/ @theraysmith
+/research/struct2depth/ @aneliaangelova
+/research/swivel/ @waterson
+/research/syntaxnet/ @calberti @andorardo @bogatyy @markomernick
+/research/tcn/ @coreylynch @sermanet
+/research/tensorrt/ @karmel
+/research/textsum/ @panyx0718 @peterjliu
+/research/transformer/ @daviddao
+/research/vid2depth/ @rezama
+/research/video_prediction/ @cbfinn
+/research/fivo/ @dieterichlawson
+/samples/ @MarkDaoust @lamberta
+/samples/languages/java/ @asimshankar
+/tutorials/embedding/ @zffchen78 @a-dai
+/tutorials/image/ @sherrym @shlens
+/tutorials/image/cifar10_estimator/ @tfboyd @protoget
+/tutorials/rnn/ @lukaszkaiser @ebrevdo
diff --git a/TensorFlow/Detection/SSD/models/CONTRIBUTING.md b/TensorFlow/Detection/SSD/models/CONTRIBUTING.md
new file mode 100644
index 00000000..6053119c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/CONTRIBUTING.md
@@ -0,0 +1,23 @@
+# Contributing guidelines
+
+If you have created a model and would like to publish it here, please send us a
+pull request. For those just getting started with pull requests, GitHub has a
+[howto](https://help.github.com/articles/using-pull-requests/).
+
+The code for any model in this repository is licensed under the Apache License
+2.0.
+
+In order to accept our code, we have to make sure that we can publish your code:
+You have to sign a Contributor License Agreement (CLA).
+
+### Contributor License Agreements
+
+Please fill out either the individual or corporate Contributor License Agreement (CLA).
+
+  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
+  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
+
+Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.
+
+***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the repository.
+
diff --git a/TensorFlow/Detection/SSD/models/ISSUE_TEMPLATE.md b/TensorFlow/Detection/SSD/models/ISSUE_TEMPLATE.md
new file mode 100644
index 00000000..021d5aa8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/ISSUE_TEMPLATE.md
@@ -0,0 +1,37 @@
+Please go to Stack Overflow for help and support:
+
+http://stackoverflow.com/questions/tagged/tensorflow
+
+Also, please understand that many of the models included in this repository are experimental and research-style code. If you open a GitHub issue, here is our policy:
+
+1. It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead).
+2. The form below must be filled out.
+
+**Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
+
+------------------------
+
+### System information
+- **What is the top-level directory of the model you are using**:
+- **Have I written custom code (as opposed to using a stock example script provided in TensorFlow)**:
+- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
+- **TensorFlow installed from (source or binary)**:
+- **TensorFlow version (use command below)**:
+- **Bazel version (if compiling from source)**:
+- **CUDA/cuDNN version**:
+- **GPU model and memory**:
+- **Exact command to reproduce**:
+
+You can collect some of this information using our environment capture script:
+
+https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
+
+You can obtain the TensorFlow version with
+
+python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
+
+### Describe the problem
+Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
+
+### Source code / logs
+Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
diff --git a/TensorFlow/Detection/SSD/models/LICENSE b/TensorFlow/Detection/SSD/models/LICENSE
new file mode 100644
index 00000000..43fcf7bf
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/LICENSE
@@ -0,0 +1,203 @@
+Copyright 2016 The TensorFlow Authors.  All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
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diff --git a/TensorFlow/Detection/SSD/models/README.md b/TensorFlow/Detection/SSD/models/README.md
new file mode 100644
index 00000000..276bdb7d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/README.md
@@ -0,0 +1,19 @@
+# TensorFlow Models
+
+This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
+
+The [official models](https://github.com/tensorflow/models/tree/master/official) are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
+
+The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
+
+The [samples folder](https://github.com/tensorflow/models/tree/master/samples) contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
+
+The [tutorials folder](https://github.com/tensorflow/models/tree/master/tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
+
+## Contribution guidelines
+
+If you want to contribute to models, be sure to review the [contribution guidelines](CONTRIBUTING.md).
+
+## License
+
+[Apache License 2.0](LICENSE)
diff --git a/TensorFlow/Detection/SSD/models/WORKSPACE b/TensorFlow/Detection/SSD/models/WORKSPACE
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/CONTRIBUTING.md b/TensorFlow/Detection/SSD/models/research/object_detection/CONTRIBUTING.md
new file mode 100644
index 00000000..e3d87e3c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/CONTRIBUTING.md
@@ -0,0 +1,13 @@
+# Contributing to the Tensorflow Object Detection API
+
+Patches to Tensorflow Object Detection API are welcome!
+
+We require contributors to fill out either the individual or corporate
+Contributor License Agreement (CLA).
+
+  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
+  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
+
+Please follow the
+[Tensorflow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)
+when submitting pull requests.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/README.md b/TensorFlow/Detection/SSD/models/research/object_detection/README.md
new file mode 100644
index 00000000..2fd23408
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/README.md
@@ -0,0 +1,245 @@
+
+# Tensorflow Object Detection API
+Creating accurate machine learning models capable of localizing and identifying
+multiple objects in a single image remains a core challenge in computer vision.
+The TensorFlow Object Detection API is an open source framework built on top of
+TensorFlow that makes it easy to construct, train and deploy object detection
+models.  At Google we’ve certainly found this codebase to be useful for our
+computer vision needs, and we hope that you will as well.
+<p align="center">
+  <img src="g3doc/img/kites_detections_output.jpg" width=676 height=450>
+</p>
+Contributions to the codebase are welcome and we would love to hear back from
+you if you find this API useful.  Finally if you use the Tensorflow Object
+Detection API for a research publication, please consider citing:
+
+```
+"Speed/accuracy trade-offs for modern convolutional object detectors."
+Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z,
+Song Y, Guadarrama S, Murphy K, CVPR 2017
+```
+\[[link](https://arxiv.org/abs/1611.10012)\]\[[bibtex](
+https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAWUIIlnPZ_L9jxvPwcC49kDlELtaeIyU-&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)\]
+
+<p align="center">
+  <img src="g3doc/img/tf-od-api-logo.png" width=140 height=195>
+</p>
+
+## Maintainers
+
+* Jonathan Huang, github: [jch1](https://github.com/jch1)
+* Vivek Rathod, github: [tombstone](https://github.com/tombstone)
+* Ronny Votel, github: [ronnyvotel](https://github.com/ronnyvotel)
+* Derek Chow, github: [derekjchow](https://github.com/derekjchow)
+* Chen Sun, github: [jesu9](https://github.com/jesu9)
+* Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon)
+* Alireza Fathi, github: [afathi3](https://github.com/afathi3)
+* Zhichao Lu, github: [pkulzc](https://github.com/pkulzc)
+
+
+## Table of contents
+
+Setup:
+
+  * <a href='g3doc/installation.md'>Installation</a><br>
+
+Quick Start:
+
+  * <a href='object_detection_tutorial.ipynb'>
+      Quick Start: Jupyter notebook for off-the-shelf inference</a><br>
+  * <a href="g3doc/running_pets.md">Quick Start: Training a pet detector</a><br>
+
+Customizing a Pipeline:
+
+  * <a href='g3doc/configuring_jobs.md'>
+      Configuring an object detection pipeline</a><br>
+  * <a href='g3doc/preparing_inputs.md'>Preparing inputs</a><br>
+
+Running:
+
+  * <a href='g3doc/running_locally.md'>Running locally</a><br>
+  * <a href='g3doc/running_on_cloud.md'>Running on the cloud</a><br>
+
+Extras:
+
+  * <a href='g3doc/detection_model_zoo.md'>Tensorflow detection model zoo</a><br>
+  * <a href='g3doc/exporting_models.md'>
+      Exporting a trained model for inference</a><br>
+  * <a href='g3doc/defining_your_own_model.md'>
+      Defining your own model architecture</a><br>
+  * <a href='g3doc/using_your_own_dataset.md'>
+      Bringing in your own dataset</a><br>
+  * <a href='g3doc/evaluation_protocols.md'>
+      Supported object detection evaluation protocols</a><br>
+  * <a href='g3doc/oid_inference_and_evaluation.md'>
+      Inference and evaluation on the Open Images dataset</a><br>
+  * <a href='g3doc/instance_segmentation.md'>
+      Run an instance segmentation model</a><br>
+  * <a href='g3doc/challenge_evaluation.md'>
+      Run the evaluation for the Open Images Challenge 2018</a><br>
+  * <a href='g3doc/tpu_compatibility.md'>
+      TPU compatible detection pipelines</a><br>
+  * <a href='g3doc/running_on_mobile_tensorflowlite.md'>
+      Running object detection on mobile devices with TensorFlow Lite</a><br>
+
+## Getting Help
+
+To get help with issues you may encounter using the Tensorflow Object Detection
+API, create a new question on [StackOverflow](https://stackoverflow.com/) with
+the tags "tensorflow" and "object-detection".
+
+Please report bugs (actually broken code, not usage questions) to the
+tensorflow/models GitHub
+[issue tracker](https://github.com/tensorflow/models/issues), prefixing the
+issue name with "object_detection".
+
+Please check [FAQ](g3doc/faq.md) for frequently asked questions before
+reporting an issue.
+
+
+## Release information
+
+### Sep 17, 2018
+
+We have released Faster R-CNN detectors with ResNet-50 / ResNet-101 feature
+extractors trained on the [iNaturalist Species Detection Dataset](https://github.com/visipedia/inat_comp/blob/master/2017/README.md#bounding-boxes).
+The models are trained on the training split of the iNaturalist data for 4M
+iterations, they achieve 55% and 58% mean AP@.5 over 2854 classes respectively.
+For more details please refer to this [paper](https://arxiv.org/abs/1707.06642).
+
+<b>Thanks to contributors</b>: Chen Sun
+
+### July 13, 2018
+
+There are many new updates in this release, extending the functionality and
+capability of the API:
+
+* Moving from slim-based training to [Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)-based
+training.
+* Support for [RetinaNet](https://arxiv.org/abs/1708.02002), and a [MobileNet](https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html)
+adaptation of RetinaNet.
+* A novel SSD-based architecture called the [Pooling Pyramid Network](https://arxiv.org/abs/1807.03284) (PPN).
+* Releasing several [TPU](https://cloud.google.com/tpu/)-compatible models.
+These can be found in the `samples/configs/` directory with a comment in the
+pipeline configuration files indicating TPU compatibility.
+* Support for quantized training.
+* Updated documentation for new binaries, Cloud training, and [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/).
+
+See also our [expanded announcement blogpost](https://ai.googleblog.com/2018/07/accelerated-training-and-inference-with.html) and accompanying tutorial at the [TensorFlow blog](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193).
+
+<b>Thanks to contributors</b>: Sara Robinson, Aakanksha Chowdhery, Derek Chow,
+Pengchong Jin, Jonathan Huang, Vivek Rathod, Zhichao Lu, Ronny Votel
+
+
+### June 25, 2018
+
+Additional evaluation tools for the [Open Images Challenge 2018](https://storage.googleapis.com/openimages/web/challenge.html) are out.
+Check out our short tutorial on data preparation and running evaluation [here](g3doc/challenge_evaluation.md)!
+
+<b>Thanks to contributors</b>: Alina Kuznetsova
+
+### June 5, 2018
+
+We have released the implementation of evaluation metrics for both tracks of the [Open Images Challenge 2018](https://storage.googleapis.com/openimages/web/challenge.html) as a part of the Object Detection API - see the [evaluation protocols](g3doc/evaluation_protocols.md) for more details.
+Additionally, we have released a tool for hierarchical labels expansion for the Open Images Challenge: check out [oid_hierarchical_labels_expansion.py](dataset_tools/oid_hierarchical_labels_expansion.py).
+
+<b>Thanks to contributors</b>: Alina Kuznetsova, Vittorio Ferrari, Jasper Uijlings
+
+### April 30, 2018
+
+We have released a Faster R-CNN detector with ResNet-101 feature extractor trained on [AVA](https://research.google.com/ava/) v2.1.
+Compared with other commonly used object detectors, it changes the action classification loss function to per-class Sigmoid loss to handle boxes with multiple labels.
+The model is trained on the training split of AVA v2.1 for 1.5M iterations, it achieves mean AP of 11.25% over 60 classes on the validation split of AVA v2.1.
+For more details please refer to this [paper](https://arxiv.org/abs/1705.08421).
+
+<b>Thanks to contributors</b>: Chen Sun, David Ross
+
+### April 2, 2018
+
+Supercharge your mobile phones with the next generation mobile object detector!
+We are adding support for MobileNet V2 with SSDLite presented in
+[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381).
+This model is 35% faster than Mobilenet V1 SSD on a Google Pixel phone CPU (200ms vs. 270ms) at the same accuracy.
+Along with the model definition, we are also releasing a model checkpoint trained on the COCO dataset.
+
+<b>Thanks to contributors</b>: Menglong Zhu, Mark Sandler, Zhichao Lu, Vivek Rathod, Jonathan Huang
+
+### February 9, 2018
+
+We now support instance segmentation!!  In this API update we support a number of instance segmentation models similar to those discussed in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). For further details refer to
+[our slides](http://presentations.cocodataset.org/Places17-GMRI.pdf) from the 2017 Coco + Places Workshop.
+Refer to the section on [Running an Instance Segmentation Model](g3doc/instance_segmentation.md) for instructions on how to configure a model
+that predicts masks in addition to object bounding boxes.
+
+<b>Thanks to contributors</b>: Alireza Fathi, Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang
+
+### November 17, 2017
+
+As a part of the Open Images V3 release we have released:
+
+* An implementation of the Open Images evaluation metric and the [protocol](g3doc/evaluation_protocols.md#open-images).
+* Additional tools to separate inference of detection and evaluation (see [this tutorial](g3doc/oid_inference_and_evaluation.md)).
+* A new detection model trained on the Open Images V2 data release (see [Open Images model](g3doc/detection_model_zoo.md#open-images-models)).
+
+See more information on the [Open Images website](https://github.com/openimages/dataset)!
+
+<b>Thanks to contributors</b>: Stefan Popov, Alina Kuznetsova
+
+### November 6, 2017
+
+We have re-released faster versions of our (pre-trained) models in the
+<a href='g3doc/detection_model_zoo.md'>model zoo</a>.  In addition to what
+was available before, we are also adding Faster R-CNN models trained on COCO
+with Inception V2 and Resnet-50 feature extractors, as well as a Faster R-CNN
+with Resnet-101 model trained on the KITTI dataset.
+
+<b>Thanks to contributors</b>: Jonathan Huang, Vivek Rathod, Derek Chow,
+Tal Remez, Chen Sun.
+
+### October 31, 2017
+
+We have released a new state-of-the-art model for object detection using
+the Faster-RCNN with the
+[NASNet-A image featurization](https://arxiv.org/abs/1707.07012). This
+model achieves mAP of 43.1% on the test-dev validation dataset for COCO,
+improving on the best available model in the zoo by 6% in terms
+of absolute mAP.
+
+<b>Thanks to contributors</b>: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le
+
+### August 11, 2017
+
+We have released an update to the [Android Detect
+demo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android)
+which will now run models trained using the Tensorflow Object
+Detection API on an Android device.  By default, it currently runs a
+frozen SSD w/Mobilenet detector trained on COCO, but we encourage
+you to try out other detection models!
+
+<b>Thanks to contributors</b>: Jonathan Huang, Andrew Harp
+
+
+### June 15, 2017
+
+In addition to our base Tensorflow detection model definitions, this
+release includes:
+
+* A selection of trainable detection models, including:
+  * Single Shot Multibox Detector (SSD) with MobileNet,
+  * SSD with Inception V2,
+  * Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101,
+  * Faster RCNN with Resnet 101,
+  * Faster RCNN with Inception Resnet v2
+* Frozen weights (trained on the COCO dataset) for each of the above models to
+  be used for out-of-the-box inference purposes.
+* A [Jupyter notebook](object_detection_tutorial.ipynb) for performing
+  out-of-the-box inference with one of our released models
+* Convenient [local training](g3doc/running_locally.md) scripts as well as
+  distributed training and evaluation pipelines via
+  [Google Cloud](g3doc/running_on_cloud.md).
+
+
+<b>Thanks to contributors</b>: Jonathan Huang, Vivek Rathod, Derek Chow,
+Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings,
+Viacheslav Kovalevskyi, Kevin Murphy
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator.py
new file mode 100644
index 00000000..ba43f013
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator.py
@@ -0,0 +1,205 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Generates grid anchors on the fly as used in Faster RCNN.
+
+Generates grid anchors on the fly as described in:
+"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
+Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.
+"""
+
+import tensorflow as tf
+
+from object_detection.core import anchor_generator
+from object_detection.core import box_list
+from object_detection.utils import ops
+
+
+class GridAnchorGenerator(anchor_generator.AnchorGenerator):
+  """Generates a grid of anchors at given scales and aspect ratios."""
+
+  def __init__(self,
+               scales=(0.5, 1.0, 2.0),
+               aspect_ratios=(0.5, 1.0, 2.0),
+               base_anchor_size=None,
+               anchor_stride=None,
+               anchor_offset=None):
+    """Constructs a GridAnchorGenerator.
+
+    Args:
+      scales: a list of (float) scales, default=(0.5, 1.0, 2.0)
+      aspect_ratios: a list of (float) aspect ratios, default=(0.5, 1.0, 2.0)
+      base_anchor_size: base anchor size as height, width (
+                        (length-2 float32 list or tensor, default=[256, 256])
+      anchor_stride: difference in centers between base anchors for adjacent
+                     grid positions (length-2 float32 list or tensor,
+                     default=[16, 16])
+      anchor_offset: center of the anchor with scale and aspect ratio 1 for the
+                     upper left element of the grid, this should be zero for
+                     feature networks with only VALID padding and even receptive
+                     field size, but may need additional calculation if other
+                     padding is used (length-2 float32 list or tensor,
+                     default=[0, 0])
+    """
+    # Handle argument defaults
+    if base_anchor_size is None:
+      base_anchor_size = [256, 256]
+    base_anchor_size = tf.to_float(tf.convert_to_tensor(base_anchor_size))
+    if anchor_stride is None:
+      anchor_stride = [16, 16]
+    anchor_stride = tf.to_float(tf.convert_to_tensor(anchor_stride))
+    if anchor_offset is None:
+      anchor_offset = [0, 0]
+    anchor_offset = tf.to_float(tf.convert_to_tensor(anchor_offset))
+
+    self._scales = scales
+    self._aspect_ratios = aspect_ratios
+    self._base_anchor_size = base_anchor_size
+    self._anchor_stride = anchor_stride
+    self._anchor_offset = anchor_offset
+
+  def name_scope(self):
+    return 'GridAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    """Returns the number of anchors per spatial location.
+
+    Returns:
+      a list of integers, one for each expected feature map to be passed to
+      the `generate` function.
+    """
+    return [len(self._scales) * len(self._aspect_ratios)]
+
+  def _generate(self, feature_map_shape_list):
+    """Generates a collection of bounding boxes to be used as anchors.
+
+    Args:
+      feature_map_shape_list: list of pairs of convnet layer resolutions in the
+        format [(height_0, width_0)].  For example, setting
+        feature_map_shape_list=[(8, 8)] asks for anchors that correspond
+        to an 8x8 layer.  For this anchor generator, only lists of length 1 are
+        allowed.
+
+    Returns:
+      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+        the input feature map shapes.
+
+    Raises:
+      ValueError: if feature_map_shape_list, box_specs_list do not have the same
+        length.
+      ValueError: if feature_map_shape_list does not consist of pairs of
+        integers
+    """
+    if not (isinstance(feature_map_shape_list, list)
+            and len(feature_map_shape_list) == 1):
+      raise ValueError('feature_map_shape_list must be a list of length 1.')
+    if not all([isinstance(list_item, tuple) and len(list_item) == 2
+                for list_item in feature_map_shape_list]):
+      raise ValueError('feature_map_shape_list must be a list of pairs.')
+    grid_height, grid_width = feature_map_shape_list[0]
+    scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
+                                                   self._aspect_ratios)
+    scales_grid = tf.reshape(scales_grid, [-1])
+    aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
+    anchors = tile_anchors(grid_height,
+                           grid_width,
+                           scales_grid,
+                           aspect_ratios_grid,
+                           self._base_anchor_size,
+                           self._anchor_stride,
+                           self._anchor_offset)
+
+    num_anchors = anchors.num_boxes_static()
+    if num_anchors is None:
+      num_anchors = anchors.num_boxes()
+    anchor_indices = tf.zeros([num_anchors])
+    anchors.add_field('feature_map_index', anchor_indices)
+    return [anchors]
+
+
+def tile_anchors(grid_height,
+                 grid_width,
+                 scales,
+                 aspect_ratios,
+                 base_anchor_size,
+                 anchor_stride,
+                 anchor_offset):
+  """Create a tiled set of anchors strided along a grid in image space.
+
+  This op creates a set of anchor boxes by placing a "basis" collection of
+  boxes with user-specified scales and aspect ratios centered at evenly
+  distributed points along a grid.  The basis collection is specified via the
+  scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
+  and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
+  .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
+  and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
+  placing it over its respective center.
+
+  Grid points are specified via grid_height, grid_width parameters as well as
+  the anchor_stride and anchor_offset parameters.
+
+  Args:
+    grid_height: size of the grid in the y direction (int or int scalar tensor)
+    grid_width: size of the grid in the x direction (int or int scalar tensor)
+    scales: a 1-d  (float) tensor representing the scale of each box in the
+      basis set.
+    aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
+      box in the basis set.  The length of the scales and aspect_ratios tensors
+      must be equal.
+    base_anchor_size: base anchor size as [height, width]
+      (float tensor of shape [2])
+    anchor_stride: difference in centers between base anchors for adjacent grid
+                   positions (float tensor of shape [2])
+    anchor_offset: center of the anchor with scale and aspect ratio 1 for the
+                   upper left element of the grid, this should be zero for
+                   feature networks with only VALID padding and even receptive
+                   field size, but may need some additional calculation if other
+                   padding is used (float tensor of shape [2])
+  Returns:
+    a BoxList holding a collection of N anchor boxes
+  """
+  ratio_sqrts = tf.sqrt(aspect_ratios)
+  heights = scales / ratio_sqrts * base_anchor_size[0]
+  widths = scales * ratio_sqrts * base_anchor_size[1]
+
+  # Get a grid of box centers
+  y_centers = tf.to_float(tf.range(grid_height))
+  y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
+  x_centers = tf.to_float(tf.range(grid_width))
+  x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
+  x_centers, y_centers = ops.meshgrid(x_centers, y_centers)
+
+  widths_grid, x_centers_grid = ops.meshgrid(widths, x_centers)
+  heights_grid, y_centers_grid = ops.meshgrid(heights, y_centers)
+  bbox_centers = tf.stack([y_centers_grid, x_centers_grid], axis=3)
+  bbox_sizes = tf.stack([heights_grid, widths_grid], axis=3)
+  bbox_centers = tf.reshape(bbox_centers, [-1, 2])
+  bbox_sizes = tf.reshape(bbox_sizes, [-1, 2])
+  bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
+  return box_list.BoxList(bbox_corners)
+
+
+def _center_size_bbox_to_corners_bbox(centers, sizes):
+  """Converts bbox center-size representation to corners representation.
+
+  Args:
+    centers: a tensor with shape [N, 2] representing bounding box centers
+    sizes: a tensor with shape [N, 2] representing bounding boxes
+
+  Returns:
+    corners: tensor with shape [N, 4] representing bounding boxes in corners
+      representation
+  """
+  return tf.concat([centers - .5 * sizes, centers + .5 * sizes], 1)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator_test.py
new file mode 100644
index 00000000..8de74aa7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/grid_anchor_generator_test.py
@@ -0,0 +1,104 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.grid_anchor_generator."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.utils import test_case
+
+
+class GridAnchorGeneratorTest(test_case.TestCase):
+
+  def test_construct_single_anchor(self):
+    """Builds a 1x1 anchor grid to test the size of the output boxes."""
+    def graph_fn():
+      scales = [0.5, 1.0, 2.0]
+      aspect_ratios = [0.25, 1.0, 4.0]
+      anchor_offset = [7, -3]
+      anchor_generator = grid_anchor_generator.GridAnchorGenerator(
+          scales, aspect_ratios, anchor_offset=anchor_offset)
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
+      anchor_corners = anchors_list[0].get()
+      return (anchor_corners,)
+    exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
+                          [-505, -131, 519, 125], [-57, -67, 71, 61],
+                          [-121, -131, 135, 125], [-249, -259, 263, 253],
+                          [-25, -131, 39, 125], [-57, -259, 71, 253],
+                          [-121, -515, 135, 509]]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_anchor_grid(self):
+    def graph_fn():
+      base_anchor_size = [10, 10]
+      anchor_stride = [19, 19]
+      anchor_offset = [0, 0]
+      scales = [0.5, 1.0, 2.0]
+      aspect_ratios = [1.0]
+
+      anchor_generator = grid_anchor_generator.GridAnchorGenerator(
+          scales,
+          aspect_ratios,
+          base_anchor_size=base_anchor_size,
+          anchor_stride=anchor_stride,
+          anchor_offset=anchor_offset)
+
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
+      anchor_corners = anchors_list[0].get()
+      return (anchor_corners,)
+    exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
+                          [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
+                          [-5., 14., 5, 24], [-10., 9., 10, 29],
+                          [16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5],
+                          [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
+                          [14., 14., 24, 24], [9., 9., 29, 29]]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_anchor_grid_with_dynamic_feature_map_shapes(self):
+    def graph_fn(feature_map_height, feature_map_width):
+      base_anchor_size = [10, 10]
+      anchor_stride = [19, 19]
+      anchor_offset = [0, 0]
+      scales = [0.5, 1.0, 2.0]
+      aspect_ratios = [1.0]
+      anchor_generator = grid_anchor_generator.GridAnchorGenerator(
+          scales,
+          aspect_ratios,
+          base_anchor_size=base_anchor_size,
+          anchor_stride=anchor_stride,
+          anchor_offset=anchor_offset)
+
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list=[(feature_map_height, feature_map_width)])
+      anchor_corners = anchors_list[0].get()
+      return (anchor_corners,)
+
+    exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
+                          [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
+                          [-5., 14., 5, 24], [-10., 9., 10, 29],
+                          [16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5],
+                          [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
+                          [14., 14., 24, 24], [9., 9., 29, 29]]
+    anchor_corners_out = self.execute_cpu(graph_fn,
+                                          [np.array(2, dtype=np.int32),
+                                           np.array(2, dtype=np.int32)])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py
new file mode 100644
index 00000000..b870adce
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py
@@ -0,0 +1,334 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Generates grid anchors on the fly corresponding to multiple CNN layers.
+
+Generates grid anchors on the fly corresponding to multiple CNN layers as
+described in:
+"SSD: Single Shot MultiBox Detector"
+Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
+Cheng-Yang Fu, Alexander C. Berg
+(see Section 2.2: Choosing scales and aspect ratios for default boxes)
+"""
+
+import numpy as np
+
+import tensorflow as tf
+
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.core import anchor_generator
+from object_detection.core import box_list_ops
+
+
+class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
+  """Generate a grid of anchors for multiple CNN layers."""
+
+  def __init__(self,
+               box_specs_list,
+               base_anchor_size=None,
+               anchor_strides=None,
+               anchor_offsets=None,
+               clip_window=None):
+    """Constructs a MultipleGridAnchorGenerator.
+
+    To construct anchors, at multiple grid resolutions, one must provide a
+    list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
+    size, a corresponding list of (scale, aspect ratio) box specifications.
+
+    For example:
+    box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
+                      [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
+
+    To support the fully convolutional setting, we pass grid sizes in at
+    generation time, while scale and aspect ratios are fixed at construction
+    time.
+
+    Args:
+      box_specs_list: list of list of (scale, aspect ratio) pairs with the
+        outside list having the same number of entries as feature_map_shape_list
+        (which is passed in at generation time).
+      base_anchor_size: base anchor size as [height, width]
+                        (length-2 float tensor, default=[1.0, 1.0]).
+                        The height and width values are normalized to the
+                        minimum dimension of the input height and width, so that
+                        when the base anchor height equals the base anchor
+                        width, the resulting anchor is square even if the input
+                        image is not square.
+      anchor_strides: list of pairs of strides in pixels (in y and x directions
+        respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+        means that we want the anchors corresponding to the first layer to be
+        strided by 25 pixels and those in the second layer to be strided by 50
+        pixels in both y and x directions. If anchor_strides=None, they are set
+        to be the reciprocal of the corresponding feature map shapes.
+      anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+        respectively). The offset specifies where we want the center of the
+        (0, 0)-th anchor to lie for each layer. For example, setting
+        anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+        (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+        and likewise that we want the (0, 0)-th anchor of the second layer to
+        lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
+        set to be half of the corresponding anchor stride.
+      clip_window: a tensor of shape [4] specifying a window to which all
+        anchors should be clipped. If clip_window is None, then no clipping
+        is performed.
+
+    Raises:
+      ValueError: if box_specs_list is not a list of list of pairs
+      ValueError: if clip_window is not either None or a tensor of shape [4]
+    """
+    if isinstance(box_specs_list, list) and all(
+        [isinstance(list_item, list) for list_item in box_specs_list]):
+      self._box_specs = box_specs_list
+    else:
+      raise ValueError('box_specs_list is expected to be a '
+                       'list of lists of pairs')
+    if base_anchor_size is None:
+      base_anchor_size = tf.constant([256, 256], dtype=tf.float32)
+    self._base_anchor_size = base_anchor_size
+    self._anchor_strides = anchor_strides
+    self._anchor_offsets = anchor_offsets
+    if clip_window is not None and clip_window.get_shape().as_list() != [4]:
+      raise ValueError('clip_window must either be None or a shape [4] tensor')
+    self._clip_window = clip_window
+    self._scales = []
+    self._aspect_ratios = []
+    for box_spec in self._box_specs:
+      if not all([isinstance(entry, tuple) and len(entry) == 2
+                  for entry in box_spec]):
+        raise ValueError('box_specs_list is expected to be a '
+                         'list of lists of pairs')
+      scales, aspect_ratios = zip(*box_spec)
+      self._scales.append(scales)
+      self._aspect_ratios.append(aspect_ratios)
+
+    for arg, arg_name in zip([self._anchor_strides, self._anchor_offsets],
+                             ['anchor_strides', 'anchor_offsets']):
+      if arg and not (isinstance(arg, list) and
+                      len(arg) == len(self._box_specs)):
+        raise ValueError('%s must be a list with the same length '
+                         'as self._box_specs' % arg_name)
+      if arg and not all([
+          isinstance(list_item, tuple) and len(list_item) == 2
+          for list_item in arg
+      ]):
+        raise ValueError('%s must be a list of pairs.' % arg_name)
+
+  def name_scope(self):
+    return 'MultipleGridAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    """Returns the number of anchors per spatial location.
+
+    Returns:
+      a list of integers, one for each expected feature map to be passed to
+      the Generate function.
+    """
+    return [len(box_specs) for box_specs in self._box_specs]
+
+  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
+    """Generates a collection of bounding boxes to be used as anchors.
+
+    The number of anchors generated for a single grid with shape MxM where we
+    place k boxes over each grid center is k*M^2 and thus the total number of
+    anchors is the sum over all grids. In our box_specs_list example
+    (see the constructor docstring), we would place two boxes over each grid
+    point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
+    thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
+    output anchors follows the order of how the grid sizes and box_specs are
+    specified (with box_spec index varying the fastest, followed by width
+    index, then height index, then grid index).
+
+    Args:
+      feature_map_shape_list: list of pairs of convnet layer resolutions in the
+        format [(height_0, width_0), (height_1, width_1), ...]. For example,
+        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
+        correspond to an 8x8 layer followed by a 7x7 layer.
+      im_height: the height of the image to generate the grid for. If both
+        im_height and im_width are 1, the generated anchors default to
+        absolute coordinates, otherwise normalized coordinates are produced.
+      im_width: the width of the image to generate the grid for. If both
+        im_height and im_width are 1, the generated anchors default to
+        absolute coordinates, otherwise normalized coordinates are produced.
+
+    Returns:
+      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+        the input feature map shapes.
+
+    Raises:
+      ValueError: if feature_map_shape_list, box_specs_list do not have the same
+        length.
+      ValueError: if feature_map_shape_list does not consist of pairs of
+        integers
+    """
+    if not (isinstance(feature_map_shape_list, list)
+            and len(feature_map_shape_list) == len(self._box_specs)):
+      raise ValueError('feature_map_shape_list must be a list with the same '
+                       'length as self._box_specs')
+    if not all([isinstance(list_item, tuple) and len(list_item) == 2
+                for list_item in feature_map_shape_list]):
+      raise ValueError('feature_map_shape_list must be a list of pairs.')
+
+    im_height = tf.to_float(im_height)
+    im_width = tf.to_float(im_width)
+
+    if not self._anchor_strides:
+      anchor_strides = [(1.0 / tf.to_float(pair[0]), 1.0 / tf.to_float(pair[1]))
+                        for pair in feature_map_shape_list]
+    else:
+      anchor_strides = [(tf.to_float(stride[0]) / im_height,
+                         tf.to_float(stride[1]) / im_width)
+                        for stride in self._anchor_strides]
+    if not self._anchor_offsets:
+      anchor_offsets = [(0.5 * stride[0], 0.5 * stride[1])
+                        for stride in anchor_strides]
+    else:
+      anchor_offsets = [(tf.to_float(offset[0]) / im_height,
+                         tf.to_float(offset[1]) / im_width)
+                        for offset in self._anchor_offsets]
+
+    for arg, arg_name in zip([anchor_strides, anchor_offsets],
+                             ['anchor_strides', 'anchor_offsets']):
+      if not (isinstance(arg, list) and len(arg) == len(self._box_specs)):
+        raise ValueError('%s must be a list with the same length '
+                         'as self._box_specs' % arg_name)
+      if not all([isinstance(list_item, tuple) and len(list_item) == 2
+                  for list_item in arg]):
+        raise ValueError('%s must be a list of pairs.' % arg_name)
+
+    anchor_grid_list = []
+    min_im_shape = tf.minimum(im_height, im_width)
+    scale_height = min_im_shape / im_height
+    scale_width = min_im_shape / im_width
+    base_anchor_size = [
+        scale_height * self._base_anchor_size[0],
+        scale_width * self._base_anchor_size[1]
+    ]
+    for feature_map_index, (grid_size, scales, aspect_ratios, stride,
+                            offset) in enumerate(
+                                zip(feature_map_shape_list, self._scales,
+                                    self._aspect_ratios, anchor_strides,
+                                    anchor_offsets)):
+      tiled_anchors = grid_anchor_generator.tile_anchors(
+          grid_height=grid_size[0],
+          grid_width=grid_size[1],
+          scales=scales,
+          aspect_ratios=aspect_ratios,
+          base_anchor_size=base_anchor_size,
+          anchor_stride=stride,
+          anchor_offset=offset)
+      if self._clip_window is not None:
+        tiled_anchors = box_list_ops.clip_to_window(
+            tiled_anchors, self._clip_window, filter_nonoverlapping=False)
+      num_anchors_in_layer = tiled_anchors.num_boxes_static()
+      if num_anchors_in_layer is None:
+        num_anchors_in_layer = tiled_anchors.num_boxes()
+      anchor_indices = feature_map_index * tf.ones([num_anchors_in_layer])
+      tiled_anchors.add_field('feature_map_index', anchor_indices)
+      anchor_grid_list.append(tiled_anchors)
+
+    return anchor_grid_list
+
+
+def create_ssd_anchors(num_layers=6,
+                       min_scale=0.2,
+                       max_scale=0.95,
+                       scales=None,
+                       aspect_ratios=(1.0, 2.0, 3.0, 1.0 / 2, 1.0 / 3),
+                       interpolated_scale_aspect_ratio=1.0,
+                       base_anchor_size=None,
+                       anchor_strides=None,
+                       anchor_offsets=None,
+                       reduce_boxes_in_lowest_layer=True):
+  """Creates MultipleGridAnchorGenerator for SSD anchors.
+
+  This function instantiates a MultipleGridAnchorGenerator that reproduces
+  ``default box`` construction proposed by Liu et al in the SSD paper.
+  See Section 2.2 for details. Grid sizes are assumed to be passed in
+  at generation time from finest resolution to coarsest resolution --- this is
+  used to (linearly) interpolate scales of anchor boxes corresponding to the
+  intermediate grid sizes.
+
+  Anchors that are returned by calling the `generate` method on the returned
+  MultipleGridAnchorGenerator object are always in normalized coordinates
+  and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
+
+  Args:
+    num_layers: integer number of grid layers to create anchors for (actual
+      grid sizes passed in at generation time)
+    min_scale: scale of anchors corresponding to finest resolution (float)
+    max_scale: scale of anchors corresponding to coarsest resolution (float)
+    scales: As list of anchor scales to use. When not None and not empty,
+      min_scale and max_scale are not used.
+    aspect_ratios: list or tuple of (float) aspect ratios to place on each
+      grid point.
+    interpolated_scale_aspect_ratio: An additional anchor is added with this
+      aspect ratio and a scale interpolated between the scale for a layer
+      and the scale for the next layer (1.0 for the last layer).
+      This anchor is not included if this value is 0.
+    base_anchor_size: base anchor size as [height, width].
+      The height and width values are normalized to the minimum dimension of the
+      input height and width, so that when the base anchor height equals the
+      base anchor width, the resulting anchor is square even if the input image
+      is not square.
+    anchor_strides: list of pairs of strides in pixels (in y and x directions
+      respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+      means that we want the anchors corresponding to the first layer to be
+      strided by 25 pixels and those in the second layer to be strided by 50
+      pixels in both y and x directions. If anchor_strides=None, they are set to
+      be the reciprocal of the corresponding feature map shapes.
+    anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+      respectively). The offset specifies where we want the center of the
+      (0, 0)-th anchor to lie for each layer. For example, setting
+      anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+      (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+      and likewise that we want the (0, 0)-th anchor of the second layer to lie
+      at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
+      be half of the corresponding anchor stride.
+    reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
+      boxes per location is used in the lowest layer.
+
+  Returns:
+    a MultipleGridAnchorGenerator
+  """
+  if base_anchor_size is None:
+    base_anchor_size = [1.0, 1.0]
+  base_anchor_size = tf.constant(base_anchor_size, dtype=tf.float32)
+  box_specs_list = []
+  if scales is None or not scales:
+    scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
+              for i in range(num_layers)] + [1.0]
+  else:
+    # Add 1.0 to the end, which will only be used in scale_next below and used
+    # for computing an interpolated scale for the largest scale in the list.
+    scales += [1.0]
+
+  for layer, scale, scale_next in zip(
+      range(num_layers), scales[:-1], scales[1:]):
+    layer_box_specs = []
+    if layer == 0 and reduce_boxes_in_lowest_layer:
+      layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
+    else:
+      for aspect_ratio in aspect_ratios:
+        layer_box_specs.append((scale, aspect_ratio))
+      # Add one more anchor, with a scale between the current scale, and the
+      # scale for the next layer, with a specified aspect ratio (1.0 by
+      # default).
+      if interpolated_scale_aspect_ratio > 0.0:
+        layer_box_specs.append((np.sqrt(scale*scale_next),
+                                interpolated_scale_aspect_ratio))
+    box_specs_list.append(layer_box_specs)
+
+  return MultipleGridAnchorGenerator(box_specs_list, base_anchor_size,
+                                     anchor_strides, anchor_offsets)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py
new file mode 100644
index 00000000..070d81d3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py
@@ -0,0 +1,289 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for anchor_generators.multiple_grid_anchor_generator_test.py."""
+
+import numpy as np
+
+import tensorflow as tf
+
+from object_detection.anchor_generators import multiple_grid_anchor_generator as ag
+from object_detection.utils import test_case
+
+
+class MultipleGridAnchorGeneratorTest(test_case.TestCase):
+
+  def test_construct_single_anchor_grid(self):
+    """Builds a 1x1 anchor grid to test the size of the output boxes."""
+    def graph_fn():
+
+      box_specs_list = [[(.5, .25), (1.0, .25), (2.0, .25),
+                         (.5, 1.0), (1.0, 1.0), (2.0, 1.0),
+                         (.5, 4.0), (1.0, 4.0), (2.0, 4.0)]]
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([256, 256], dtype=tf.float32),
+          anchor_strides=[(16, 16)],
+          anchor_offsets=[(7, -3)])
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
+      return anchors_list[0].get()
+    exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
+                          [-505, -131, 519, 125], [-57, -67, 71, 61],
+                          [-121, -131, 135, 125], [-249, -259, 263, 253],
+                          [-25, -131, 39, 125], [-57, -259, 71, 253],
+                          [-121, -515, 135, 509]]
+
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_anchor_grid(self):
+    def graph_fn():
+      box_specs_list = [[(0.5, 1.0), (1.0, 1.0), (2.0, 1.0)]]
+
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([10, 10], dtype=tf.float32),
+          anchor_strides=[(19, 19)],
+          anchor_offsets=[(0, 0)])
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
+      return anchors_list[0].get()
+    exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
+                          [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
+                          [-5., 14., 5, 24], [-10., 9., 10, 29],
+                          [16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5],
+                          [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
+                          [14., 14., 24, 24], [9., 9., 29, 29]]
+
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_anchor_grid_non_square(self):
+
+    def graph_fn():
+      box_specs_list = [[(1.0, 1.0)]]
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list, base_anchor_size=tf.constant([1, 1],
+                                                       dtype=tf.float32))
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(
+          tf.constant(1, dtype=tf.int32), tf.constant(2, dtype=tf.int32))])
+      return anchors_list[0].get()
+
+    exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_dynamic_size_anchor_grid(self):
+
+    def graph_fn(height, width):
+      box_specs_list = [[(1.0, 1.0)]]
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list, base_anchor_size=tf.constant([1, 1],
+                                                       dtype=tf.float32))
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(height,
+                                                                        width)])
+      return anchors_list[0].get()
+
+    exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
+
+    anchor_corners_out = self.execute_cpu(graph_fn,
+                                          [np.array(1, dtype=np.int32),
+                                           np.array(2, dtype=np.int32)])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_anchor_grid_normalized(self):
+    def graph_fn():
+      box_specs_list = [[(1.0, 1.0)]]
+
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list, base_anchor_size=tf.constant([1, 1],
+                                                       dtype=tf.float32))
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list=[(tf.constant(1, dtype=tf.int32), tf.constant(
+              2, dtype=tf.int32))],
+          im_height=320,
+          im_width=640)
+      return anchors_list[0].get()
+
+    exp_anchor_corners = [[0., 0., 1., 0.5], [0., 0.5, 1., 1.]]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_multiple_grids(self):
+
+    def graph_fn():
+      box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
+                        [(1.0, 1.0), (1.0, 0.5)]]
+
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25), (.5, .5)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(4, 4), (
+          2, 2)])
+      return [anchors.get() for anchors in anchors_list]
+    # height and width of box with .5 aspect ratio
+    h = np.sqrt(2)
+    w = 1.0/np.sqrt(2)
+    exp_small_grid_corners = [[-.25, -.25, .75, .75],
+                              [.25-.5*h, .25-.5*w, .25+.5*h, .25+.5*w],
+                              [-.25, .25, .75, 1.25],
+                              [.25-.5*h, .75-.5*w, .25+.5*h, .75+.5*w],
+                              [.25, -.25, 1.25, .75],
+                              [.75-.5*h, .25-.5*w, .75+.5*h, .25+.5*w],
+                              [.25, .25, 1.25, 1.25],
+                              [.75-.5*h, .75-.5*w, .75+.5*h, .75+.5*w]]
+    # only test first entry of larger set of anchors
+    exp_big_grid_corners = [[.125-.5, .125-.5, .125+.5, .125+.5],
+                            [.125-1.0, .125-1.0, .125+1.0, .125+1.0],
+                            [.125-.5*h, .125-.5*w, .125+.5*h, .125+.5*w],]
+
+    anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
+    self.assertEquals(anchor_corners_out.shape, (56, 4))
+    big_grid_corners = anchor_corners_out[0:3, :]
+    small_grid_corners = anchor_corners_out[48:, :]
+    self.assertAllClose(small_grid_corners, exp_small_grid_corners)
+    self.assertAllClose(big_grid_corners, exp_big_grid_corners)
+
+  def test_construct_multiple_grids_with_clipping(self):
+
+    def graph_fn():
+      box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
+                        [(1.0, 1.0), (1.0, 0.5)]]
+
+      clip_window = tf.constant([0, 0, 1, 1], dtype=tf.float32)
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          clip_window=clip_window)
+      anchors_list = anchor_generator.generate(feature_map_shape_list=[(4, 4), (
+          2, 2)])
+      return [anchors.get() for anchors in anchors_list]
+    # height and width of box with .5 aspect ratio
+    h = np.sqrt(2)
+    w = 1.0/np.sqrt(2)
+    exp_small_grid_corners = [[0, 0, .75, .75],
+                              [0, 0, .25+.5*h, .25+.5*w],
+                              [0, .25, .75, 1],
+                              [0, .75-.5*w, .25+.5*h, 1],
+                              [.25, 0, 1, .75],
+                              [.75-.5*h, 0, 1, .25+.5*w],
+                              [.25, .25, 1, 1],
+                              [.75-.5*h, .75-.5*w, 1, 1]]
+
+    anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
+    small_grid_corners = anchor_corners_out[48:, :]
+    self.assertAllClose(small_grid_corners, exp_small_grid_corners)
+
+  def test_invalid_box_specs(self):
+    # not all box specs are pairs
+    box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
+                      [(1.0, 1.0), (1.0, 0.5, .3)]]
+    with self.assertRaises(ValueError):
+      ag.MultipleGridAnchorGenerator(box_specs_list)
+
+    # box_specs_list is not a list of lists
+    box_specs_list = [(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)]
+    with self.assertRaises(ValueError):
+      ag.MultipleGridAnchorGenerator(box_specs_list)
+
+  def test_invalid_generate_arguments(self):
+    box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
+                      [(1.0, 1.0), (1.0, 0.5)]]
+
+    # incompatible lengths with box_specs_list
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4, 4), (2, 2)])
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25), (.5, .5)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4, 4), (2, 2), (1, 1)])
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.5, .5)],
+          anchor_offsets=[(.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4, 4), (2, 2)])
+
+    # not pairs
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25), (.5, .5)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4, 4, 4), (2, 2)])
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25, .1), (.5, .5)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4, 4), (2, 2)])
+    with self.assertRaises(ValueError):
+      anchor_generator = ag.MultipleGridAnchorGenerator(
+          box_specs_list,
+          base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
+          anchor_strides=[(.25, .25), (.5, .5)],
+          anchor_offsets=[(.125, .125), (.25, .25)])
+      anchor_generator.generate(feature_map_shape_list=[(4), (2, 2)])
+
+
+class CreateSSDAnchorsTest(test_case.TestCase):
+
+  def test_create_ssd_anchors_returns_correct_shape(self):
+
+    def graph_fn1():
+      anchor_generator = ag.create_ssd_anchors(
+          num_layers=6,
+          min_scale=0.2,
+          max_scale=0.95,
+          aspect_ratios=(1.0, 2.0, 3.0, 1.0 / 2, 1.0 / 3),
+          reduce_boxes_in_lowest_layer=True)
+
+      feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
+                                (5, 5), (3, 3), (1, 1)]
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list=feature_map_shape_list)
+      return [anchors.get() for anchors in anchors_list]
+    anchor_corners_out = np.concatenate(self.execute(graph_fn1, []), axis=0)
+    self.assertEquals(anchor_corners_out.shape, (7308, 4))
+
+    def graph_fn2():
+      anchor_generator = ag.create_ssd_anchors(
+          num_layers=6, min_scale=0.2, max_scale=0.95,
+          aspect_ratios=(1.0, 2.0, 3.0, 1.0/2, 1.0/3),
+          reduce_boxes_in_lowest_layer=False)
+
+      feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
+                                (5, 5), (3, 3), (1, 1)]
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list=feature_map_shape_list)
+      return [anchors.get() for anchors in anchors_list]
+    anchor_corners_out = np.concatenate(self.execute(graph_fn2, []), axis=0)
+    self.assertEquals(anchor_corners_out.shape, (11640, 4))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py
new file mode 100644
index 00000000..cd2440a4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py
@@ -0,0 +1,145 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Generates grid anchors on the fly corresponding to multiple CNN layers.
+
+Generates grid anchors on the fly corresponding to multiple CNN layers as
+described in:
+"Focal Loss for Dense Object Detection" (https://arxiv.org/abs/1708.02002)
+T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar
+"""
+
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.core import anchor_generator
+from object_detection.core import box_list_ops
+
+
+class MultiscaleGridAnchorGenerator(anchor_generator.AnchorGenerator):
+  """Generate a grid of anchors for multiple CNN layers of different scale."""
+
+  def __init__(self, min_level, max_level, anchor_scale, aspect_ratios,
+               scales_per_octave, normalize_coordinates=True):
+    """Constructs a MultiscaleGridAnchorGenerator.
+
+    To construct anchors, at multiple scale resolutions, one must provide a
+    the minimum level and maximum levels on a scale pyramid. To define the size
+    of anchor, the anchor scale is provided to decide the size relatively to the
+    stride of the corresponding feature map. The generator allows one pixel
+    location on feature map maps to multiple anchors, that have different aspect
+    ratios and intermediate scales.
+
+    Args:
+      min_level: minimum level in feature pyramid.
+      max_level: maximum level in feature pyramid.
+      anchor_scale: anchor scale and feature stride define the size of the base
+        anchor on an image. For example, given a feature pyramid with strides
+        [2^3, ..., 2^7] and anchor scale 4. The base anchor size is
+        4 * [2^3, ..., 2^7].
+      aspect_ratios: list or tuple of (float) aspect ratios to place on each
+        grid point.
+      scales_per_octave: integer number of intermediate scales per scale octave.
+      normalize_coordinates: whether to produce anchors in normalized
+        coordinates. (defaults to True).
+    """
+    self._anchor_grid_info = []
+    self._aspect_ratios = aspect_ratios
+    self._scales_per_octave = scales_per_octave
+    self._normalize_coordinates = normalize_coordinates
+
+    scales = [2**(float(scale) / scales_per_octave)
+              for scale in range(scales_per_octave)]
+    aspects = list(aspect_ratios)
+
+    for level in range(min_level, max_level + 1):
+      anchor_stride = [2**level, 2**level]
+      base_anchor_size = [2**level * anchor_scale, 2**level * anchor_scale]
+      self._anchor_grid_info.append({
+          'level': level,
+          'info': [scales, aspects, base_anchor_size, anchor_stride]
+      })
+
+  def name_scope(self):
+    return 'MultiscaleGridAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    """Returns the number of anchors per spatial location.
+
+    Returns:
+      a list of integers, one for each expected feature map to be passed to
+      the Generate function.
+    """
+    return len(self._anchor_grid_info) * [
+        len(self._aspect_ratios) * self._scales_per_octave]
+
+  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
+    """Generates a collection of bounding boxes to be used as anchors.
+
+    Currently we require the input image shape to be statically defined.  That
+    is, im_height and im_width should be integers rather than tensors.
+
+    Args:
+      feature_map_shape_list: list of pairs of convnet layer resolutions in the
+        format [(height_0, width_0), (height_1, width_1), ...]. For example,
+        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
+        correspond to an 8x8 layer followed by a 7x7 layer.
+      im_height: the height of the image to generate the grid for. If both
+        im_height and im_width are 1, anchors can only be generated in
+        absolute coordinates.
+      im_width: the width of the image to generate the grid for. If both
+        im_height and im_width are 1, anchors can only be generated in
+        absolute coordinates.
+
+    Returns:
+      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+        the input feature map shapes.
+    Raises:
+      ValueError: if im_height and im_width are not integers.
+      ValueError: if im_height and im_width are 1, but normalized coordinates
+        were requested.
+    """
+    anchor_grid_list = []
+    for feat_shape, grid_info in zip(feature_map_shape_list,
+                                     self._anchor_grid_info):
+      # TODO(rathodv) check the feature_map_shape_list is consistent with
+      # self._anchor_grid_info
+      level = grid_info['level']
+      stride = 2**level
+      scales, aspect_ratios, base_anchor_size, anchor_stride = grid_info['info']
+      feat_h = feat_shape[0]
+      feat_w = feat_shape[1]
+      anchor_offset = [0, 0]
+      if isinstance(im_height, int) and isinstance(im_width, int):
+        if im_height % 2.0**level == 0 or im_height == 1:
+          anchor_offset[0] = stride / 2.0
+        if im_width % 2.0**level == 0 or im_width == 1:
+          anchor_offset[1] = stride / 2.0
+      ag = grid_anchor_generator.GridAnchorGenerator(
+          scales,
+          aspect_ratios,
+          base_anchor_size=base_anchor_size,
+          anchor_stride=anchor_stride,
+          anchor_offset=anchor_offset)
+      (anchor_grid,) = ag.generate(feature_map_shape_list=[(feat_h, feat_w)])
+
+      if self._normalize_coordinates:
+        if im_height == 1 or im_width == 1:
+          raise ValueError(
+              'Normalized coordinates were requested upon construction of the '
+              'MultiscaleGridAnchorGenerator, but a subsequent call to '
+              'generate did not supply dimension information.')
+        anchor_grid = box_list_ops.to_normalized_coordinates(
+            anchor_grid, im_height, im_width, check_range=False)
+      anchor_grid_list.append(anchor_grid)
+
+    return anchor_grid_list
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py
new file mode 100644
index 00000000..178705c1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py
@@ -0,0 +1,302 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for anchor_generators.multiscale_grid_anchor_generator_test.py."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.anchor_generators import multiscale_grid_anchor_generator as mg
+from object_detection.utils import test_case
+
+
+class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
+
+  def test_construct_single_anchor(self):
+    min_level = 5
+    max_level = 5
+    anchor_scale = 4.0
+    aspect_ratios = [1.0]
+    scales_per_octave = 1
+    im_height = 64
+    im_width = 64
+    feature_map_shape_list = [(2, 2)]
+    exp_anchor_corners = [[-48, -48, 80, 80],
+                          [-48, -16, 80, 112],
+                          [-16, -48, 112, 80],
+                          [-16, -16, 112, 112]]
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=False)
+    anchors_list = anchor_generator.generate(
+        feature_map_shape_list, im_height=im_height, im_width=im_width)
+    anchor_corners = anchors_list[0].get()
+
+    with self.test_session():
+      anchor_corners_out = anchor_corners.eval()
+      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchor_unit_dimensions(self):
+    min_level = 5
+    max_level = 5
+    anchor_scale = 1.0
+    aspect_ratios = [1.0]
+    scales_per_octave = 1
+    im_height = 1
+    im_width = 1
+    feature_map_shape_list = [(2, 2)]
+    # Positive offsets are produced.
+    exp_anchor_corners = [[0, 0, 32, 32],
+                          [0, 32, 32, 64],
+                          [32, 0, 64, 32],
+                          [32, 32, 64, 64]]
+
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=False)
+    anchors_list = anchor_generator.generate(
+        feature_map_shape_list, im_height=im_height, im_width=im_width)
+    anchor_corners = anchors_list[0].get()
+
+    with self.test_session():
+      anchor_corners_out = anchor_corners.eval()
+      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level=5, max_level=5, anchor_scale=1.0, aspect_ratios=[1.0],
+        scales_per_octave=1, normalize_coordinates=True)
+    with self.assertRaisesRegexp(ValueError, 'Normalized coordinates'):
+      anchor_generator.generate(
+          feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
+
+  def test_construct_single_anchor_in_normalized_coordinates(self):
+    min_level = 5
+    max_level = 5
+    anchor_scale = 4.0
+    aspect_ratios = [1.0]
+    scales_per_octave = 1
+    im_height = 64
+    im_width = 128
+    feature_map_shape_list = [(2, 2)]
+    exp_anchor_corners = [[-48./64, -48./128, 80./64, 80./128],
+                          [-48./64, -16./128, 80./64, 112./128],
+                          [-16./64, -48./128, 112./64, 80./128],
+                          [-16./64, -16./128, 112./64, 112./128]]
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=True)
+    anchors_list = anchor_generator.generate(
+        feature_map_shape_list, im_height=im_height, im_width=im_width)
+    anchor_corners = anchors_list[0].get()
+
+    with self.test_session():
+      anchor_corners_out = anchor_corners.eval()
+      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_num_anchors_per_location(self):
+    min_level = 5
+    max_level = 6
+    anchor_scale = 4.0
+    aspect_ratios = [1.0, 2.0]
+    scales_per_octave = 3
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=False)
+    self.assertEqual(anchor_generator.num_anchors_per_location(), [6, 6])
+
+  def test_construct_single_anchor_dynamic_size(self):
+    min_level = 5
+    max_level = 5
+    anchor_scale = 4.0
+    aspect_ratios = [1.0]
+    scales_per_octave = 1
+    im_height = tf.constant(64)
+    im_width = tf.constant(64)
+    feature_map_shape_list = [(2, 2)]
+    # Zero offsets are used.
+    exp_anchor_corners = [[-64, -64, 64, 64],
+                          [-64, -32, 64, 96],
+                          [-32, -64, 96, 64],
+                          [-32, -32, 96, 96]]
+
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=False)
+    anchors_list = anchor_generator.generate(
+        feature_map_shape_list, im_height=im_height, im_width=im_width)
+    anchor_corners = anchors_list[0].get()
+
+    with self.test_session():
+      anchor_corners_out = anchor_corners.eval()
+      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchor_with_odd_input_dimension(self):
+
+    def graph_fn():
+      min_level = 5
+      max_level = 5
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 65
+      im_width = 65
+      feature_map_shape_list = [(3, 3)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return (anchor_corners,)
+    anchor_corners_out = self.execute(graph_fn, [])
+    exp_anchor_corners = [[-64, -64, 64, 64],
+                          [-64, -32, 64, 96],
+                          [-64, 0, 64, 128],
+                          [-32, -64, 96, 64],
+                          [-32, -32, 96, 96],
+                          [-32, 0, 96, 128],
+                          [0, -64, 128, 64],
+                          [0, -32, 128, 96],
+                          [0, 0, 128, 128]]
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchor_on_two_feature_maps(self):
+
+    def graph_fn():
+      min_level = 5
+      max_level = 6
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(2, 2), (1, 1)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(feature_map_shape_list,
+                                               im_height=im_height,
+                                               im_width=im_width)
+      anchor_corners = [anchors.get() for anchors in anchors_list]
+      return anchor_corners
+
+    anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
+    exp_anchor_corners = [[-48, -48, 80, 80],
+                          [-48, -16, 80, 112],
+                          [-16, -48, 112, 80],
+                          [-16, -16, 112, 112],
+                          [-96, -96, 160, 160]]
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchor_with_two_scales_per_octave(self):
+
+    def graph_fn():
+      min_level = 6
+      max_level = 6
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 2
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(1, 1)]
+
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(feature_map_shape_list,
+                                               im_height=im_height,
+                                               im_width=im_width)
+      anchor_corners = [anchors.get() for anchors in anchors_list]
+      return anchor_corners
+    # There are 4 set of anchors in this configuration. The order is:
+    # [[2**0.0 intermediate scale + 1.0 aspect],
+    #  [2**0.5 intermediate scale + 1.0 aspect]]
+    exp_anchor_corners = [[-96., -96., 160., 160.],
+                          [-149.0193, -149.0193, 213.0193, 213.0193]]
+
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchor_with_two_scales_per_octave_and_aspect(self):
+    def graph_fn():
+      min_level = 6
+      max_level = 6
+      anchor_scale = 4.0
+      aspect_ratios = [1.0, 2.0]
+      scales_per_octave = 2
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(1, 1)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(feature_map_shape_list,
+                                               im_height=im_height,
+                                               im_width=im_width)
+      anchor_corners = [anchors.get() for anchors in anchors_list]
+      return anchor_corners
+    # There are 4 set of anchors in this configuration. The order is:
+    # [[2**0.0 intermediate scale + 1.0 aspect],
+    #  [2**0.5 intermediate scale + 1.0 aspect],
+    #  [2**0.0 intermediate scale + 2.0 aspect],
+    #  [2**0.5 intermediate scale + 2.0 aspect]]
+
+    exp_anchor_corners = [[-96., -96., 160., 160.],
+                          [-149.0193, -149.0193, 213.0193, 213.0193],
+                          [-58.50967, -149.0193, 122.50967, 213.0193],
+                          [-96., -224., 160., 288.]]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_single_anchors_on_feature_maps_with_dynamic_shape(self):
+
+    def graph_fn(feature_map1_height, feature_map1_width, feature_map2_height,
+                 feature_map2_width):
+      min_level = 5
+      max_level = 6
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(feature_map1_height, feature_map1_width),
+                                (feature_map2_height, feature_map2_width)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(feature_map_shape_list,
+                                               im_height=im_height,
+                                               im_width=im_width)
+      anchor_corners = [anchors.get() for anchors in anchors_list]
+      return anchor_corners
+
+    anchor_corners_out = np.concatenate(
+        self.execute_cpu(graph_fn, [
+            np.array(2, dtype=np.int32),
+            np.array(2, dtype=np.int32),
+            np.array(1, dtype=np.int32),
+            np.array(1, dtype=np.int32)
+        ]),
+        axis=0)
+    exp_anchor_corners = [[-48, -48, 80, 80],
+                          [-48, -16, 80, 112],
+                          [-16, -48, 112, 80],
+                          [-16, -16, 112, 112],
+                          [-96, -96, 160, 160]]
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder.py
new file mode 100644
index 00000000..af25e21a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder.py
@@ -0,0 +1,118 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Faster RCNN box coder.
+
+Faster RCNN box coder follows the coding schema described below:
+  ty = (y - ya) / ha
+  tx = (x - xa) / wa
+  th = log(h / ha)
+  tw = log(w / wa)
+  where x, y, w, h denote the box's center coordinates, width and height
+  respectively. Similarly, xa, ya, wa, ha denote the anchor's center
+  coordinates, width and height. tx, ty, tw and th denote the anchor-encoded
+  center, width and height respectively.
+
+  See http://arxiv.org/abs/1506.01497 for details.
+"""
+
+import tensorflow as tf
+
+from object_detection.core import box_coder
+from object_detection.core import box_list
+
+EPSILON = 1e-8
+
+
+class FasterRcnnBoxCoder(box_coder.BoxCoder):
+  """Faster RCNN box coder."""
+
+  def __init__(self, scale_factors=None):
+    """Constructor for FasterRcnnBoxCoder.
+
+    Args:
+      scale_factors: List of 4 positive scalars to scale ty, tx, th and tw.
+        If set to None, does not perform scaling. For Faster RCNN,
+        the open-source implementation recommends using [10.0, 10.0, 5.0, 5.0].
+    """
+    if scale_factors:
+      assert len(scale_factors) == 4
+      for scalar in scale_factors:
+        assert scalar > 0
+    self._scale_factors = scale_factors
+
+  @property
+  def code_size(self):
+    return 4
+
+  def _encode(self, boxes, anchors):
+    """Encode a box collection with respect to anchor collection.
+
+    Args:
+      boxes: BoxList holding N boxes to be encoded.
+      anchors: BoxList of anchors.
+
+    Returns:
+      a tensor representing N anchor-encoded boxes of the format
+      [ty, tx, th, tw].
+    """
+    # Convert anchors to the center coordinate representation.
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+    ycenter, xcenter, h, w = boxes.get_center_coordinates_and_sizes()
+    # Avoid NaN in division and log below.
+    ha += EPSILON
+    wa += EPSILON
+    h += EPSILON
+    w += EPSILON
+
+    tx = (xcenter - xcenter_a) / wa
+    ty = (ycenter - ycenter_a) / ha
+    tw = tf.log(w / wa)
+    th = tf.log(h / ha)
+    # Scales location targets as used in paper for joint training.
+    if self._scale_factors:
+      ty *= self._scale_factors[0]
+      tx *= self._scale_factors[1]
+      th *= self._scale_factors[2]
+      tw *= self._scale_factors[3]
+    return tf.transpose(tf.stack([ty, tx, th, tw]))
+
+  def _decode(self, rel_codes, anchors):
+    """Decode relative codes to boxes.
+
+    Args:
+      rel_codes: a tensor representing N anchor-encoded boxes.
+      anchors: BoxList of anchors.
+
+    Returns:
+      boxes: BoxList holding N bounding boxes.
+    """
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+
+    ty, tx, th, tw = tf.unstack(tf.transpose(rel_codes))
+    if self._scale_factors:
+      ty /= self._scale_factors[0]
+      tx /= self._scale_factors[1]
+      th /= self._scale_factors[2]
+      tw /= self._scale_factors[3]
+    w = tf.exp(tw) * wa
+    h = tf.exp(th) * ha
+    ycenter = ty * ha + ycenter_a
+    xcenter = tx * wa + xcenter_a
+    ymin = ycenter - h / 2.
+    xmin = xcenter - w / 2.
+    ymax = ycenter + h / 2.
+    xmax = xcenter + w / 2.
+    return box_list.BoxList(tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder_test.py
new file mode 100644
index 00000000..b2135f06
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/faster_rcnn_box_coder_test.py
@@ -0,0 +1,94 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.box_coder.faster_rcnn_box_coder."""
+
+import tensorflow as tf
+
+from object_detection.box_coders import faster_rcnn_box_coder
+from object_detection.core import box_list
+
+
+class FasterRcnnBoxCoderTest(tf.test.TestCase):
+
+  def test_get_correct_relative_codes_after_encoding(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    expected_rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321],
+                          [-0.083333, -0.222222, -0.693147, -1.098612]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_get_correct_relative_codes_after_encoding_with_scaling(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    scale_factors = [2, 3, 4, 5]
+    expected_rel_codes = [[-1., -1.25, -1.62186, -0.911608],
+                          [-0.166667, -0.666667, -2.772588, -5.493062]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+        scale_factors=scale_factors)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_get_correct_boxes_after_decoding(self):
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321],
+                 [-0.083333, -0.222222, -0.693147, -1.098612]]
+    expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      boxes_out, = sess.run([boxes.get()])
+      self.assertAllClose(boxes_out, expected_boxes)
+
+  def test_get_correct_boxes_after_decoding_with_scaling(self):
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [[-1., -1.25, -1.62186, -0.911608],
+                 [-0.166667, -0.666667, -2.772588, -5.493062]]
+    scale_factors = [2, 3, 4, 5]
+    expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+        scale_factors=scale_factors)
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      boxes_out, = sess.run([boxes.get()])
+      self.assertAllClose(boxes_out, expected_boxes)
+
+  def test_very_small_Width_nan_after_encoding(self):
+    boxes = [[10.0, 10.0, 10.0000001, 20.0]]
+    anchors = [[15.0, 12.0, 30.0, 18.0]]
+    expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder.py
new file mode 100644
index 00000000..67df3b82
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder.py
@@ -0,0 +1,171 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Keypoint box coder.
+
+The keypoint box coder follows the coding schema described below (this is
+similar to the FasterRcnnBoxCoder, except that it encodes keypoints in addition
+to box coordinates):
+  ty = (y - ya) / ha
+  tx = (x - xa) / wa
+  th = log(h / ha)
+  tw = log(w / wa)
+  tky0 = (ky0 - ya) / ha
+  tkx0 = (kx0 - xa) / wa
+  tky1 = (ky1 - ya) / ha
+  tkx1 = (kx1 - xa) / wa
+  ...
+  where x, y, w, h denote the box's center coordinates, width and height
+  respectively. Similarly, xa, ya, wa, ha denote the anchor's center
+  coordinates, width and height. tx, ty, tw and th denote the anchor-encoded
+  center, width and height respectively. ky0, kx0, ky1, kx1, ... denote the
+  keypoints' coordinates, and tky0, tkx0, tky1, tkx1, ... denote the
+  anchor-encoded keypoint coordinates.
+"""
+
+import tensorflow as tf
+
+from object_detection.core import box_coder
+from object_detection.core import box_list
+from object_detection.core import standard_fields as fields
+
+EPSILON = 1e-8
+
+
+class KeypointBoxCoder(box_coder.BoxCoder):
+  """Keypoint box coder."""
+
+  def __init__(self, num_keypoints, scale_factors=None):
+    """Constructor for KeypointBoxCoder.
+
+    Args:
+      num_keypoints: Number of keypoints to encode/decode.
+      scale_factors: List of 4 positive scalars to scale ty, tx, th and tw.
+        In addition to scaling ty and tx, the first 2 scalars are used to scale
+        the y and x coordinates of the keypoints as well. If set to None, does
+        not perform scaling.
+    """
+    self._num_keypoints = num_keypoints
+
+    if scale_factors:
+      assert len(scale_factors) == 4
+      for scalar in scale_factors:
+        assert scalar > 0
+    self._scale_factors = scale_factors
+    self._keypoint_scale_factors = None
+    if scale_factors is not None:
+      self._keypoint_scale_factors = tf.expand_dims(tf.tile(
+          [tf.to_float(scale_factors[0]), tf.to_float(scale_factors[1])],
+          [num_keypoints]), 1)
+
+  @property
+  def code_size(self):
+    return 4 + self._num_keypoints * 2
+
+  def _encode(self, boxes, anchors):
+    """Encode a box and keypoint collection with respect to anchor collection.
+
+    Args:
+      boxes: BoxList holding N boxes and keypoints to be encoded. Boxes are
+        tensors with the shape [N, 4], and keypoints are tensors with the shape
+        [N, num_keypoints, 2].
+      anchors: BoxList of anchors.
+
+    Returns:
+      a tensor representing N anchor-encoded boxes of the format
+      [ty, tx, th, tw, tky0, tkx0, tky1, tkx1, ...] where tky0 and tkx0
+      represent the y and x coordinates of the first keypoint, tky1 and tkx1
+      represent the y and x coordinates of the second keypoint, and so on.
+    """
+    # Convert anchors to the center coordinate representation.
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+    ycenter, xcenter, h, w = boxes.get_center_coordinates_and_sizes()
+    keypoints = boxes.get_field(fields.BoxListFields.keypoints)
+    keypoints = tf.transpose(tf.reshape(keypoints,
+                                        [-1, self._num_keypoints * 2]))
+    num_boxes = boxes.num_boxes()
+
+    # Avoid NaN in division and log below.
+    ha += EPSILON
+    wa += EPSILON
+    h += EPSILON
+    w += EPSILON
+
+    tx = (xcenter - xcenter_a) / wa
+    ty = (ycenter - ycenter_a) / ha
+    tw = tf.log(w / wa)
+    th = tf.log(h / ha)
+
+    tiled_anchor_centers = tf.tile(
+        tf.stack([ycenter_a, xcenter_a]), [self._num_keypoints, 1])
+    tiled_anchor_sizes = tf.tile(
+        tf.stack([ha, wa]), [self._num_keypoints, 1])
+    tkeypoints = (keypoints - tiled_anchor_centers) / tiled_anchor_sizes
+
+    # Scales location targets as used in paper for joint training.
+    if self._scale_factors:
+      ty *= self._scale_factors[0]
+      tx *= self._scale_factors[1]
+      th *= self._scale_factors[2]
+      tw *= self._scale_factors[3]
+      tkeypoints *= tf.tile(self._keypoint_scale_factors, [1, num_boxes])
+
+    tboxes = tf.stack([ty, tx, th, tw])
+    return tf.transpose(tf.concat([tboxes, tkeypoints], 0))
+
+  def _decode(self, rel_codes, anchors):
+    """Decode relative codes to boxes and keypoints.
+
+    Args:
+      rel_codes: a tensor with shape [N, 4 + 2 * num_keypoints] representing N
+        anchor-encoded boxes and keypoints
+      anchors: BoxList of anchors.
+
+    Returns:
+      boxes: BoxList holding N bounding boxes and keypoints.
+    """
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+
+    num_codes = tf.shape(rel_codes)[0]
+    result = tf.unstack(tf.transpose(rel_codes))
+    ty, tx, th, tw = result[:4]
+    tkeypoints = result[4:]
+    if self._scale_factors:
+      ty /= self._scale_factors[0]
+      tx /= self._scale_factors[1]
+      th /= self._scale_factors[2]
+      tw /= self._scale_factors[3]
+      tkeypoints /= tf.tile(self._keypoint_scale_factors, [1, num_codes])
+
+    w = tf.exp(tw) * wa
+    h = tf.exp(th) * ha
+    ycenter = ty * ha + ycenter_a
+    xcenter = tx * wa + xcenter_a
+    ymin = ycenter - h / 2.
+    xmin = xcenter - w / 2.
+    ymax = ycenter + h / 2.
+    xmax = xcenter + w / 2.
+    decoded_boxes_keypoints = box_list.BoxList(
+        tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
+
+    tiled_anchor_centers = tf.tile(
+        tf.stack([ycenter_a, xcenter_a]), [self._num_keypoints, 1])
+    tiled_anchor_sizes = tf.tile(
+        tf.stack([ha, wa]), [self._num_keypoints, 1])
+    keypoints = tkeypoints * tiled_anchor_sizes + tiled_anchor_centers
+    keypoints = tf.reshape(tf.transpose(keypoints),
+                           [-1, self._num_keypoints, 2])
+    decoded_boxes_keypoints.add_field(fields.BoxListFields.keypoints, keypoints)
+    return decoded_boxes_keypoints
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder_test.py
new file mode 100644
index 00000000..330641e5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/keypoint_box_coder_test.py
@@ -0,0 +1,140 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.box_coder.keypoint_box_coder."""
+
+import tensorflow as tf
+
+from object_detection.box_coders import keypoint_box_coder
+from object_detection.core import box_list
+from object_detection.core import standard_fields as fields
+
+
+class KeypointBoxCoderTest(tf.test.TestCase):
+
+  def test_get_correct_relative_codes_after_encoding(self):
+    boxes = [[10., 10., 20., 15.],
+             [0.2, 0.1, 0.5, 0.4]]
+    keypoints = [[[15., 12.], [10., 15.]],
+                 [[0.5, 0.3], [0.2, 0.4]]]
+    num_keypoints = len(keypoints[0])
+    anchors = [[15., 12., 30., 18.],
+               [0.1, 0.0, 0.7, 0.9]]
+    expected_rel_codes = [
+        [-0.5, -0.416666, -0.405465, -0.182321,
+         -0.5, -0.5, -0.833333, 0.],
+        [-0.083333, -0.222222, -0.693147, -1.098612,
+         0.166667, -0.166667, -0.333333, -0.055556]
+    ]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_get_correct_relative_codes_after_encoding_with_scaling(self):
+    boxes = [[10., 10., 20., 15.],
+             [0.2, 0.1, 0.5, 0.4]]
+    keypoints = [[[15., 12.], [10., 15.]],
+                 [[0.5, 0.3], [0.2, 0.4]]]
+    num_keypoints = len(keypoints[0])
+    anchors = [[15., 12., 30., 18.],
+               [0.1, 0.0, 0.7, 0.9]]
+    scale_factors = [2, 3, 4, 5]
+    expected_rel_codes = [
+        [-1., -1.25, -1.62186, -0.911608,
+         -1.0, -1.5, -1.666667, 0.],
+        [-0.166667, -0.666667, -2.772588, -5.493062,
+         0.333333, -0.5, -0.666667, -0.166667]
+    ]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = keypoint_box_coder.KeypointBoxCoder(
+        num_keypoints, scale_factors=scale_factors)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_get_correct_boxes_after_decoding(self):
+    anchors = [[15., 12., 30., 18.],
+               [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [
+        [-0.5, -0.416666, -0.405465, -0.182321,
+         -0.5, -0.5, -0.833333, 0.],
+        [-0.083333, -0.222222, -0.693147, -1.098612,
+         0.166667, -0.166667, -0.333333, -0.055556]
+    ]
+    expected_boxes = [[10., 10., 20., 15.],
+                      [0.2, 0.1, 0.5, 0.4]]
+    expected_keypoints = [[[15., 12.], [10., 15.]],
+                          [[0.5, 0.3], [0.2, 0.4]]]
+    num_keypoints = len(expected_keypoints[0])
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      boxes_out, keypoints_out = sess.run(
+          [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
+      self.assertAllClose(boxes_out, expected_boxes)
+      self.assertAllClose(keypoints_out, expected_keypoints)
+
+  def test_get_correct_boxes_after_decoding_with_scaling(self):
+    anchors = [[15., 12., 30., 18.],
+               [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [
+        [-1., -1.25, -1.62186, -0.911608,
+         -1.0, -1.5, -1.666667, 0.],
+        [-0.166667, -0.666667, -2.772588, -5.493062,
+         0.333333, -0.5, -0.666667, -0.166667]
+    ]
+    scale_factors = [2, 3, 4, 5]
+    expected_boxes = [[10., 10., 20., 15.],
+                      [0.2, 0.1, 0.5, 0.4]]
+    expected_keypoints = [[[15., 12.], [10., 15.]],
+                          [[0.5, 0.3], [0.2, 0.4]]]
+    num_keypoints = len(expected_keypoints[0])
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = keypoint_box_coder.KeypointBoxCoder(
+        num_keypoints, scale_factors=scale_factors)
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      boxes_out, keypoints_out = sess.run(
+          [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
+      self.assertAllClose(boxes_out, expected_boxes)
+      self.assertAllClose(keypoints_out, expected_keypoints)
+
+  def test_very_small_width_nan_after_encoding(self):
+    boxes = [[10., 10., 10.0000001, 20.]]
+    keypoints = [[[10., 10.], [10.0000001, 20.]]]
+    anchors = [[15., 12., 30., 18.]]
+    expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826,
+                           -0.833333, -0.833333, -0.833333, 0.833333]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = keypoint_box_coder.KeypointBoxCoder(2)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      rel_codes_out, = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder.py
new file mode 100644
index 00000000..256f53fd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder.py
@@ -0,0 +1,79 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Mean stddev box coder.
+
+This box coder use the following coding schema to encode boxes:
+rel_code = (box_corner - anchor_corner_mean) / anchor_corner_stddev.
+"""
+from object_detection.core import box_coder
+from object_detection.core import box_list
+
+
+class MeanStddevBoxCoder(box_coder.BoxCoder):
+  """Mean stddev box coder."""
+
+  def __init__(self, stddev=0.01):
+    """Constructor for MeanStddevBoxCoder.
+
+    Args:
+      stddev: The standard deviation used to encode and decode boxes.
+    """
+    self._stddev = stddev
+
+  @property
+  def code_size(self):
+    return 4
+
+  def _encode(self, boxes, anchors):
+    """Encode a box collection with respect to anchor collection.
+
+    Args:
+      boxes: BoxList holding N boxes to be encoded.
+      anchors: BoxList of N anchors.
+
+    Returns:
+      a tensor representing N anchor-encoded boxes
+
+    Raises:
+      ValueError: if the anchors still have deprecated stddev field.
+    """
+    box_corners = boxes.get()
+    if anchors.has_field('stddev'):
+      raise ValueError("'stddev' is a parameter of MeanStddevBoxCoder and "
+                       "should not be specified in the box list.")
+    means = anchors.get()
+    return (box_corners - means) / self._stddev
+
+  def _decode(self, rel_codes, anchors):
+    """Decode.
+
+    Args:
+      rel_codes: a tensor representing N anchor-encoded boxes.
+      anchors: BoxList of anchors.
+
+    Returns:
+      boxes: BoxList holding N bounding boxes
+
+    Raises:
+      ValueError: if the anchors still have deprecated stddev field and expects
+        the decode method to use stddev value from that field.
+    """
+    means = anchors.get()
+    if anchors.has_field('stddev'):
+      raise ValueError("'stddev' is a parameter of MeanStddevBoxCoder and "
+                       "should not be specified in the box list.")
+    box_corners = rel_codes * self._stddev + means
+    return box_list.BoxList(box_corners)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder_test.py
new file mode 100644
index 00000000..3e0eba93
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/mean_stddev_box_coder_test.py
@@ -0,0 +1,54 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.box_coder.mean_stddev_boxcoder."""
+
+import tensorflow as tf
+
+from object_detection.box_coders import mean_stddev_box_coder
+from object_detection.core import box_list
+
+
+class MeanStddevBoxCoderTest(tf.test.TestCase):
+
+  def testGetCorrectRelativeCodesAfterEncoding(self):
+    box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
+    boxes = box_list.BoxList(tf.constant(box_corners))
+    expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
+    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
+    priors = box_list.BoxList(prior_means)
+
+    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+    rel_codes = coder.encode(boxes, priors)
+    with self.test_session() as sess:
+      rel_codes_out = sess.run(rel_codes)
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def testGetCorrectBoxesAfterDecoding(self):
+    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
+    expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
+    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
+    priors = box_list.BoxList(prior_means)
+
+    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+    decoded_boxes = coder.decode(rel_codes, priors)
+    decoded_box_corners = decoded_boxes.get()
+    with self.test_session() as sess:
+      decoded_out = sess.run(decoded_box_corners)
+      self.assertAllClose(decoded_out, expected_box_corners)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder.py
new file mode 100644
index 00000000..ee46b689
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder.py
@@ -0,0 +1,126 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Square box coder.
+
+Square box coder follows the coding schema described below:
+l = sqrt(h * w)
+la = sqrt(ha * wa)
+ty = (y - ya) / la
+tx = (x - xa) / la
+tl = log(l / la)
+where x, y, w, h denote the box's center coordinates, width, and height,
+respectively. Similarly, xa, ya, wa, ha denote the anchor's center
+coordinates, width and height. tx, ty, tl denote the anchor-encoded
+center, and length, respectively. Because the encoded box is a square, only
+one length is encoded.
+
+This has shown to provide performance improvements over the Faster RCNN box
+coder when the objects being detected tend to be square (e.g. faces) and when
+the input images are not distorted via resizing.
+"""
+
+import tensorflow as tf
+
+from object_detection.core import box_coder
+from object_detection.core import box_list
+
+EPSILON = 1e-8
+
+
+class SquareBoxCoder(box_coder.BoxCoder):
+  """Encodes a 3-scalar representation of a square box."""
+
+  def __init__(self, scale_factors=None):
+    """Constructor for SquareBoxCoder.
+
+    Args:
+      scale_factors: List of 3 positive scalars to scale ty, tx, and tl.
+        If set to None, does not perform scaling. For faster RCNN,
+        the open-source implementation recommends using [10.0, 10.0, 5.0].
+
+    Raises:
+      ValueError: If scale_factors is not length 3 or contains values less than
+        or equal to 0.
+    """
+    if scale_factors:
+      if len(scale_factors) != 3:
+        raise ValueError('The argument scale_factors must be a list of length '
+                         '3.')
+      if any(scalar <= 0 for scalar in scale_factors):
+        raise ValueError('The values in scale_factors must all be greater '
+                         'than 0.')
+    self._scale_factors = scale_factors
+
+  @property
+  def code_size(self):
+    return 3
+
+  def _encode(self, boxes, anchors):
+    """Encodes a box collection with respect to an anchor collection.
+
+    Args:
+      boxes: BoxList holding N boxes to be encoded.
+      anchors: BoxList of anchors.
+
+    Returns:
+      a tensor representing N anchor-encoded boxes of the format
+      [ty, tx, tl].
+    """
+    # Convert anchors to the center coordinate representation.
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+    la = tf.sqrt(ha * wa)
+    ycenter, xcenter, h, w = boxes.get_center_coordinates_and_sizes()
+    l = tf.sqrt(h * w)
+    # Avoid NaN in division and log below.
+    la += EPSILON
+    l += EPSILON
+
+    tx = (xcenter - xcenter_a) / la
+    ty = (ycenter - ycenter_a) / la
+    tl = tf.log(l / la)
+    # Scales location targets for joint training.
+    if self._scale_factors:
+      ty *= self._scale_factors[0]
+      tx *= self._scale_factors[1]
+      tl *= self._scale_factors[2]
+    return tf.transpose(tf.stack([ty, tx, tl]))
+
+  def _decode(self, rel_codes, anchors):
+    """Decodes relative codes to boxes.
+
+    Args:
+      rel_codes: a tensor representing N anchor-encoded boxes.
+      anchors: BoxList of anchors.
+
+    Returns:
+      boxes: BoxList holding N bounding boxes.
+    """
+    ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
+    la = tf.sqrt(ha * wa)
+
+    ty, tx, tl = tf.unstack(tf.transpose(rel_codes))
+    if self._scale_factors:
+      ty /= self._scale_factors[0]
+      tx /= self._scale_factors[1]
+      tl /= self._scale_factors[2]
+    l = tf.exp(tl) * la
+    ycenter = ty * la + ycenter_a
+    xcenter = tx * la + xcenter_a
+    ymin = ycenter - l / 2.
+    xmin = xcenter - l / 2.
+    ymax = ycenter + l / 2.
+    xmax = xcenter + l / 2.
+    return box_list.BoxList(tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder_test.py
new file mode 100644
index 00000000..7f739c6b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/box_coders/square_box_coder_test.py
@@ -0,0 +1,97 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.box_coder.square_box_coder."""
+
+import tensorflow as tf
+
+from object_detection.box_coders import square_box_coder
+from object_detection.core import box_list
+
+
+class SquareBoxCoderTest(tf.test.TestCase):
+
+  def test_correct_relative_codes_with_default_scale(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    scale_factors = None
+    expected_rel_codes = [[-0.790569, -0.263523, -0.293893],
+                          [-0.068041, -0.272166, -0.89588]]
+
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      (rel_codes_out,) = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_correct_relative_codes_with_non_default_scale(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    scale_factors = [2, 3, 4]
+    expected_rel_codes = [[-1.581139, -0.790569, -1.175573],
+                          [-0.136083, -0.816497, -3.583519]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      (rel_codes_out,) = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_correct_relative_codes_with_small_width(self):
+    boxes = [[10.0, 10.0, 10.0000001, 20.0]]
+    anchors = [[15.0, 12.0, 30.0, 18.0]]
+    scale_factors = None
+    expected_rel_codes = [[-1.317616, 0., -20.670586]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+    rel_codes = coder.encode(boxes, anchors)
+    with self.test_session() as sess:
+      (rel_codes_out,) = sess.run([rel_codes])
+      self.assertAllClose(rel_codes_out, expected_rel_codes)
+
+  def test_correct_boxes_with_default_scale(self):
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [[-0.5, -0.416666, -0.405465],
+                 [-0.083333, -0.222222, -0.693147]]
+    scale_factors = None
+    expected_boxes = [[14.594306, 7.884875, 20.918861, 14.209432],
+                      [0.155051, 0.102989, 0.522474, 0.470412]]
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      (boxes_out,) = sess.run([boxes.get()])
+      self.assertAllClose(boxes_out, expected_boxes)
+
+  def test_correct_boxes_with_non_default_scale(self):
+    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    rel_codes = [[-1., -1.25, -1.62186], [-0.166667, -0.666667, -2.772588]]
+    scale_factors = [2, 3, 4]
+    expected_boxes = [[14.594306, 7.884875, 20.918861, 14.209432],
+                      [0.155051, 0.102989, 0.522474, 0.470412]]
+    anchors = box_list.BoxList(tf.constant(anchors))
+    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+    boxes = coder.decode(rel_codes, anchors)
+    with self.test_session() as sess:
+      (boxes_out,) = sess.run([boxes.get()])
+      self.assertAllClose(boxes_out, expected_boxes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder.py
new file mode 100644
index 00000000..54cec3a1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder.py
@@ -0,0 +1,94 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A function to build an object detection anchor generator from config."""
+
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.anchor_generators import multiple_grid_anchor_generator
+from object_detection.anchor_generators import multiscale_grid_anchor_generator
+from object_detection.protos import anchor_generator_pb2
+
+
+def build(anchor_generator_config):
+  """Builds an anchor generator based on the config.
+
+  Args:
+    anchor_generator_config: An anchor_generator.proto object containing the
+      config for the desired anchor generator.
+
+  Returns:
+    Anchor generator based on the config.
+
+  Raises:
+    ValueError: On empty anchor generator proto.
+  """
+  if not isinstance(anchor_generator_config,
+                    anchor_generator_pb2.AnchorGenerator):
+    raise ValueError('anchor_generator_config not of type '
+                     'anchor_generator_pb2.AnchorGenerator')
+  if anchor_generator_config.WhichOneof(
+      'anchor_generator_oneof') == 'grid_anchor_generator':
+    grid_anchor_generator_config = anchor_generator_config.grid_anchor_generator
+    return grid_anchor_generator.GridAnchorGenerator(
+        scales=[float(scale) for scale in grid_anchor_generator_config.scales],
+        aspect_ratios=[float(aspect_ratio)
+                       for aspect_ratio
+                       in grid_anchor_generator_config.aspect_ratios],
+        base_anchor_size=[grid_anchor_generator_config.height,
+                          grid_anchor_generator_config.width],
+        anchor_stride=[grid_anchor_generator_config.height_stride,
+                       grid_anchor_generator_config.width_stride],
+        anchor_offset=[grid_anchor_generator_config.height_offset,
+                       grid_anchor_generator_config.width_offset])
+  elif anchor_generator_config.WhichOneof(
+      'anchor_generator_oneof') == 'ssd_anchor_generator':
+    ssd_anchor_generator_config = anchor_generator_config.ssd_anchor_generator
+    anchor_strides = None
+    if ssd_anchor_generator_config.height_stride:
+      anchor_strides = zip(ssd_anchor_generator_config.height_stride,
+                           ssd_anchor_generator_config.width_stride)
+    anchor_offsets = None
+    if ssd_anchor_generator_config.height_offset:
+      anchor_offsets = zip(ssd_anchor_generator_config.height_offset,
+                           ssd_anchor_generator_config.width_offset)
+    return multiple_grid_anchor_generator.create_ssd_anchors(
+        num_layers=ssd_anchor_generator_config.num_layers,
+        min_scale=ssd_anchor_generator_config.min_scale,
+        max_scale=ssd_anchor_generator_config.max_scale,
+        scales=[float(scale) for scale in ssd_anchor_generator_config.scales],
+        aspect_ratios=ssd_anchor_generator_config.aspect_ratios,
+        interpolated_scale_aspect_ratio=(
+            ssd_anchor_generator_config.interpolated_scale_aspect_ratio),
+        base_anchor_size=[
+            ssd_anchor_generator_config.base_anchor_height,
+            ssd_anchor_generator_config.base_anchor_width
+        ],
+        anchor_strides=anchor_strides,
+        anchor_offsets=anchor_offsets,
+        reduce_boxes_in_lowest_layer=(
+            ssd_anchor_generator_config.reduce_boxes_in_lowest_layer))
+  elif anchor_generator_config.WhichOneof(
+      'anchor_generator_oneof') == 'multiscale_anchor_generator':
+    cfg = anchor_generator_config.multiscale_anchor_generator
+    return multiscale_grid_anchor_generator.MultiscaleGridAnchorGenerator(
+        cfg.min_level,
+        cfg.max_level,
+        cfg.anchor_scale,
+        [float(aspect_ratio) for aspect_ratio in cfg.aspect_ratios],
+        cfg.scales_per_octave,
+        cfg.normalize_coordinates
+    )
+  else:
+    raise ValueError('Empty anchor generator.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder_test.py
new file mode 100644
index 00000000..2a23c2d9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/anchor_generator_builder_test.py
@@ -0,0 +1,300 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for anchor_generator_builder."""
+
+import math
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.anchor_generators import multiple_grid_anchor_generator
+from object_detection.anchor_generators import multiscale_grid_anchor_generator
+from object_detection.builders import anchor_generator_builder
+from object_detection.protos import anchor_generator_pb2
+
+
+class AnchorGeneratorBuilderTest(tf.test.TestCase):
+
+  def assert_almost_list_equal(self, expected_list, actual_list, delta=None):
+    self.assertEqual(len(expected_list), len(actual_list))
+    for expected_item, actual_item in zip(expected_list, actual_list):
+      self.assertAlmostEqual(expected_item, actual_item, delta=delta)
+
+  def test_build_grid_anchor_generator_with_defaults(self):
+    anchor_generator_text_proto = """
+      grid_anchor_generator {
+      }
+     """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               grid_anchor_generator.GridAnchorGenerator))
+    self.assertListEqual(anchor_generator_object._scales, [])
+    self.assertListEqual(anchor_generator_object._aspect_ratios, [])
+    with self.test_session() as sess:
+      base_anchor_size, anchor_offset, anchor_stride = sess.run(
+          [anchor_generator_object._base_anchor_size,
+           anchor_generator_object._anchor_offset,
+           anchor_generator_object._anchor_stride])
+    self.assertAllEqual(anchor_offset, [0, 0])
+    self.assertAllEqual(anchor_stride, [16, 16])
+    self.assertAllEqual(base_anchor_size, [256, 256])
+
+  def test_build_grid_anchor_generator_with_non_default_parameters(self):
+    anchor_generator_text_proto = """
+      grid_anchor_generator {
+        height: 128
+        width: 512
+        height_stride: 10
+        width_stride: 20
+        height_offset: 30
+        width_offset: 40
+        scales: [0.4, 2.2]
+        aspect_ratios: [0.3, 4.5]
+      }
+     """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               grid_anchor_generator.GridAnchorGenerator))
+    self.assert_almost_list_equal(anchor_generator_object._scales,
+                                  [0.4, 2.2])
+    self.assert_almost_list_equal(anchor_generator_object._aspect_ratios,
+                                  [0.3, 4.5])
+    with self.test_session() as sess:
+      base_anchor_size, anchor_offset, anchor_stride = sess.run(
+          [anchor_generator_object._base_anchor_size,
+           anchor_generator_object._anchor_offset,
+           anchor_generator_object._anchor_stride])
+    self.assertAllEqual(anchor_offset, [30, 40])
+    self.assertAllEqual(anchor_stride, [10, 20])
+    self.assertAllEqual(base_anchor_size, [128, 512])
+
+  def test_build_ssd_anchor_generator_with_defaults(self):
+    anchor_generator_text_proto = """
+      ssd_anchor_generator {
+        aspect_ratios: [1.0]
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiple_grid_anchor_generator.
+                               MultipleGridAnchorGenerator))
+    for actual_scales, expected_scales in zip(
+        list(anchor_generator_object._scales),
+        [(0.1, 0.2, 0.2),
+         (0.35, 0.418),
+         (0.499, 0.570),
+         (0.649, 0.721),
+         (0.799, 0.871),
+         (0.949, 0.974)]):
+      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)
+    for actual_aspect_ratio, expected_aspect_ratio in zip(
+        list(anchor_generator_object._aspect_ratios),
+        [(1.0, 2.0, 0.5)] + 5 * [(1.0, 1.0)]):
+      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)
+
+    with self.test_session() as sess:
+      base_anchor_size = sess.run(anchor_generator_object._base_anchor_size)
+    self.assertAllClose(base_anchor_size, [1.0, 1.0])
+
+  def test_build_ssd_anchor_generator_with_custom_scales(self):
+    anchor_generator_text_proto = """
+      ssd_anchor_generator {
+        aspect_ratios: [1.0]
+        scales: [0.1, 0.15, 0.2, 0.4, 0.6, 0.8]
+        reduce_boxes_in_lowest_layer: false
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiple_grid_anchor_generator.
+                               MultipleGridAnchorGenerator))
+    for actual_scales, expected_scales in zip(
+        list(anchor_generator_object._scales),
+        [(0.1, math.sqrt(0.1 * 0.15)),
+         (0.15, math.sqrt(0.15 * 0.2)),
+         (0.2, math.sqrt(0.2 * 0.4)),
+         (0.4, math.sqrt(0.4 * 0.6)),
+         (0.6, math.sqrt(0.6 * 0.8)),
+         (0.8, math.sqrt(0.8 * 1.0))]):
+      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)
+
+  def test_build_ssd_anchor_generator_with_custom_interpolated_scale(self):
+    anchor_generator_text_proto = """
+      ssd_anchor_generator {
+        aspect_ratios: [0.5]
+        interpolated_scale_aspect_ratio: 0.5
+        reduce_boxes_in_lowest_layer: false
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiple_grid_anchor_generator.
+                               MultipleGridAnchorGenerator))
+    for actual_aspect_ratio, expected_aspect_ratio in zip(
+        list(anchor_generator_object._aspect_ratios),
+        6 * [(0.5, 0.5)]):
+      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)
+
+  def test_build_ssd_anchor_generator_without_reduced_boxes(self):
+    anchor_generator_text_proto = """
+      ssd_anchor_generator {
+        aspect_ratios: [1.0]
+        reduce_boxes_in_lowest_layer: false
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiple_grid_anchor_generator.
+                               MultipleGridAnchorGenerator))
+
+    for actual_scales, expected_scales in zip(
+        list(anchor_generator_object._scales),
+        [(0.2, 0.264),
+         (0.35, 0.418),
+         (0.499, 0.570),
+         (0.649, 0.721),
+         (0.799, 0.871),
+         (0.949, 0.974)]):
+      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)
+
+    for actual_aspect_ratio, expected_aspect_ratio in zip(
+        list(anchor_generator_object._aspect_ratios),
+        6 * [(1.0, 1.0)]):
+      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)
+
+    with self.test_session() as sess:
+      base_anchor_size = sess.run(anchor_generator_object._base_anchor_size)
+    self.assertAllClose(base_anchor_size, [1.0, 1.0])
+
+  def test_build_ssd_anchor_generator_with_non_default_parameters(self):
+    anchor_generator_text_proto = """
+      ssd_anchor_generator {
+        num_layers: 2
+        min_scale: 0.3
+        max_scale: 0.8
+        aspect_ratios: [2.0]
+        height_stride: 16
+        height_stride: 32
+        width_stride: 20
+        width_stride: 30
+        height_offset: 8
+        height_offset: 16
+        width_offset: 0
+        width_offset: 10
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiple_grid_anchor_generator.
+                               MultipleGridAnchorGenerator))
+
+    for actual_scales, expected_scales in zip(
+        list(anchor_generator_object._scales),
+        [(0.1, 0.3, 0.3), (0.8, 0.894)]):
+      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)
+
+    for actual_aspect_ratio, expected_aspect_ratio in zip(
+        list(anchor_generator_object._aspect_ratios),
+        [(1.0, 2.0, 0.5), (2.0, 1.0)]):
+      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)
+
+    for actual_strides, expected_strides in zip(
+        list(anchor_generator_object._anchor_strides), [(16, 20), (32, 30)]):
+      self.assert_almost_list_equal(expected_strides, actual_strides)
+
+    for actual_offsets, expected_offsets in zip(
+        list(anchor_generator_object._anchor_offsets), [(8, 0), (16, 10)]):
+      self.assert_almost_list_equal(expected_offsets, actual_offsets)
+
+    with self.test_session() as sess:
+      base_anchor_size = sess.run(anchor_generator_object._base_anchor_size)
+    self.assertAllClose(base_anchor_size, [1.0, 1.0])
+
+  def test_raise_value_error_on_empty_anchor_genertor(self):
+    anchor_generator_text_proto = """
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    with self.assertRaises(ValueError):
+      anchor_generator_builder.build(anchor_generator_proto)
+
+  def test_build_multiscale_anchor_generator_custom_aspect_ratios(self):
+    anchor_generator_text_proto = """
+      multiscale_anchor_generator {
+        aspect_ratios: [1.0]
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiscale_grid_anchor_generator.
+                               MultiscaleGridAnchorGenerator))
+    for level, anchor_grid_info in zip(
+        range(3, 8), anchor_generator_object._anchor_grid_info):
+      self.assertEqual(set(anchor_grid_info.keys()), set(['level', 'info']))
+      self.assertTrue(level, anchor_grid_info['level'])
+      self.assertEqual(len(anchor_grid_info['info']), 4)
+      self.assertAllClose(anchor_grid_info['info'][0], [2**0, 2**0.5])
+      self.assertTrue(anchor_grid_info['info'][1], 1.0)
+      self.assertAllClose(anchor_grid_info['info'][2],
+                          [4.0 * 2**level, 4.0 * 2**level])
+      self.assertAllClose(anchor_grid_info['info'][3], [2**level, 2**level])
+      self.assertTrue(anchor_generator_object._normalize_coordinates)
+
+  def test_build_multiscale_anchor_generator_with_anchors_in_pixel_coordinates(
+      self):
+    anchor_generator_text_proto = """
+      multiscale_anchor_generator {
+        aspect_ratios: [1.0]
+        normalize_coordinates: false
+      }
+    """
+    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
+    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
+    anchor_generator_object = anchor_generator_builder.build(
+        anchor_generator_proto)
+    self.assertTrue(isinstance(anchor_generator_object,
+                               multiscale_grid_anchor_generator.
+                               MultiscaleGridAnchorGenerator))
+    self.assertFalse(anchor_generator_object._normalize_coordinates)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder.py
new file mode 100644
index 00000000..cc13d5a2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder.py
@@ -0,0 +1,66 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A function to build an object detection box coder from configuration."""
+from object_detection.box_coders import faster_rcnn_box_coder
+from object_detection.box_coders import keypoint_box_coder
+from object_detection.box_coders import mean_stddev_box_coder
+from object_detection.box_coders import square_box_coder
+from object_detection.protos import box_coder_pb2
+
+
+def build(box_coder_config):
+  """Builds a box coder object based on the box coder config.
+
+  Args:
+    box_coder_config: A box_coder.proto object containing the config for the
+      desired box coder.
+
+  Returns:
+    BoxCoder based on the config.
+
+  Raises:
+    ValueError: On empty box coder proto.
+  """
+  if not isinstance(box_coder_config, box_coder_pb2.BoxCoder):
+    raise ValueError('box_coder_config not of type box_coder_pb2.BoxCoder.')
+
+  if box_coder_config.WhichOneof('box_coder_oneof') == 'faster_rcnn_box_coder':
+    return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[
+        box_coder_config.faster_rcnn_box_coder.y_scale,
+        box_coder_config.faster_rcnn_box_coder.x_scale,
+        box_coder_config.faster_rcnn_box_coder.height_scale,
+        box_coder_config.faster_rcnn_box_coder.width_scale
+    ])
+  if box_coder_config.WhichOneof('box_coder_oneof') == 'keypoint_box_coder':
+    return keypoint_box_coder.KeypointBoxCoder(
+        box_coder_config.keypoint_box_coder.num_keypoints,
+        scale_factors=[
+            box_coder_config.keypoint_box_coder.y_scale,
+            box_coder_config.keypoint_box_coder.x_scale,
+            box_coder_config.keypoint_box_coder.height_scale,
+            box_coder_config.keypoint_box_coder.width_scale
+        ])
+  if (box_coder_config.WhichOneof('box_coder_oneof') ==
+      'mean_stddev_box_coder'):
+    return mean_stddev_box_coder.MeanStddevBoxCoder(
+        stddev=box_coder_config.mean_stddev_box_coder.stddev)
+  if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder':
+    return square_box_coder.SquareBoxCoder(scale_factors=[
+        box_coder_config.square_box_coder.y_scale,
+        box_coder_config.square_box_coder.x_scale,
+        box_coder_config.square_box_coder.length_scale
+    ])
+  raise ValueError('Empty box coder.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder_test.py
new file mode 100644
index 00000000..286012e9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_coder_builder_test.py
@@ -0,0 +1,136 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for box_coder_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.box_coders import faster_rcnn_box_coder
+from object_detection.box_coders import keypoint_box_coder
+from object_detection.box_coders import mean_stddev_box_coder
+from object_detection.box_coders import square_box_coder
+from object_detection.builders import box_coder_builder
+from object_detection.protos import box_coder_pb2
+
+
+class BoxCoderBuilderTest(tf.test.TestCase):
+
+  def test_build_faster_rcnn_box_coder_with_defaults(self):
+    box_coder_text_proto = """
+      faster_rcnn_box_coder {
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertIsInstance(box_coder_object,
+                          faster_rcnn_box_coder.FasterRcnnBoxCoder)
+    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0])
+
+  def test_build_faster_rcnn_box_coder_with_non_default_parameters(self):
+    box_coder_text_proto = """
+      faster_rcnn_box_coder {
+        y_scale: 6.0
+        x_scale: 3.0
+        height_scale: 7.0
+        width_scale: 8.0
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertIsInstance(box_coder_object,
+                          faster_rcnn_box_coder.FasterRcnnBoxCoder)
+    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
+
+  def test_build_keypoint_box_coder_with_defaults(self):
+    box_coder_text_proto = """
+      keypoint_box_coder {
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
+    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0])
+
+  def test_build_keypoint_box_coder_with_non_default_parameters(self):
+    box_coder_text_proto = """
+      keypoint_box_coder {
+        num_keypoints: 6
+        y_scale: 6.0
+        x_scale: 3.0
+        height_scale: 7.0
+        width_scale: 8.0
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
+    self.assertEqual(box_coder_object._num_keypoints, 6)
+    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
+
+  def test_build_mean_stddev_box_coder(self):
+    box_coder_text_proto = """
+      mean_stddev_box_coder {
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertTrue(
+        isinstance(box_coder_object,
+                   mean_stddev_box_coder.MeanStddevBoxCoder))
+
+  def test_build_square_box_coder_with_defaults(self):
+    box_coder_text_proto = """
+      square_box_coder {
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertTrue(
+        isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
+    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0])
+
+  def test_build_square_box_coder_with_non_default_parameters(self):
+    box_coder_text_proto = """
+      square_box_coder {
+        y_scale: 6.0
+        x_scale: 3.0
+        length_scale: 7.0
+      }
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    box_coder_object = box_coder_builder.build(box_coder_proto)
+    self.assertTrue(
+        isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
+    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0])
+
+  def test_raise_error_on_empty_box_coder(self):
+    box_coder_text_proto = """
+    """
+    box_coder_proto = box_coder_pb2.BoxCoder()
+    text_format.Merge(box_coder_text_proto, box_coder_proto)
+    with self.assertRaises(ValueError):
+      box_coder_builder.build(box_coder_proto)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder.py
new file mode 100644
index 00000000..53a88daa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder.py
@@ -0,0 +1,633 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Function to build box predictor from configuration."""
+
+import collections
+import tensorflow as tf
+from object_detection.predictors import convolutional_box_predictor
+from object_detection.predictors import convolutional_keras_box_predictor
+from object_detection.predictors import mask_rcnn_box_predictor
+from object_detection.predictors import rfcn_box_predictor
+from object_detection.predictors.heads import box_head
+from object_detection.predictors.heads import class_head
+from object_detection.predictors.heads import keras_box_head
+from object_detection.predictors.heads import keras_class_head
+from object_detection.predictors.heads import mask_head
+from object_detection.protos import box_predictor_pb2
+
+
+def build_convolutional_box_predictor(is_training,
+                                      num_classes,
+                                      conv_hyperparams_fn,
+                                      min_depth,
+                                      max_depth,
+                                      num_layers_before_predictor,
+                                      use_dropout,
+                                      dropout_keep_prob,
+                                      kernel_size,
+                                      box_code_size,
+                                      apply_sigmoid_to_scores=False,
+                                      add_background_class=True,
+                                      class_prediction_bias_init=0.0,
+                                      use_depthwise=False,):
+  """Builds the ConvolutionalBoxPredictor from the arguments.
+
+  Args:
+    is_training: Indicates whether the BoxPredictor is in training mode.
+    num_classes: number of classes.  Note that num_classes *does not*
+      include the background category, so if groundtruth labels take values
+      in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+      assigned classification targets can range from {0,... K}).
+    conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+      hyperparameters for convolution ops.
+    min_depth: Minimum feature depth prior to predicting box encodings
+      and class predictions.
+    max_depth: Maximum feature depth prior to predicting box encodings
+      and class predictions. If max_depth is set to 0, no additional
+      feature map will be inserted before location and class predictions.
+    num_layers_before_predictor: Number of the additional conv layers before
+      the predictor.
+    use_dropout: Option to use dropout or not.  Note that a single dropout
+      op is applied here prior to both box and class predictions, which stands
+      in contrast to the ConvolutionalBoxPredictor below.
+    dropout_keep_prob: Keep probability for dropout.
+      This is only used if use_dropout is True.
+    kernel_size: Size of final convolution kernel.  If the
+      spatial resolution of the feature map is smaller than the kernel size,
+      then the kernel size is automatically set to be
+      min(feature_width, feature_height).
+    box_code_size: Size of encoding for each box.
+    apply_sigmoid_to_scores: If True, apply the sigmoid on the output
+      class_predictions.
+    add_background_class: Whether to add an implicit background class.
+    class_prediction_bias_init: Constant value to initialize bias of the last
+      conv2d layer before class prediction.
+    use_depthwise: Whether to use depthwise convolutions for prediction
+      steps. Default is False.
+
+  Returns:
+    A ConvolutionalBoxPredictor class.
+  """
+  box_prediction_head = box_head.ConvolutionalBoxHead(
+      is_training=is_training,
+      box_code_size=box_code_size,
+      kernel_size=kernel_size,
+      use_depthwise=use_depthwise)
+  class_prediction_head = class_head.ConvolutionalClassHead(
+      is_training=is_training,
+      num_class_slots=num_classes + 1 if add_background_class else num_classes,
+      use_dropout=use_dropout,
+      dropout_keep_prob=dropout_keep_prob,
+      kernel_size=kernel_size,
+      apply_sigmoid_to_scores=apply_sigmoid_to_scores,
+      class_prediction_bias_init=class_prediction_bias_init,
+      use_depthwise=use_depthwise)
+  other_heads = {}
+  return convolutional_box_predictor.ConvolutionalBoxPredictor(
+      is_training=is_training,
+      num_classes=num_classes,
+      box_prediction_head=box_prediction_head,
+      class_prediction_head=class_prediction_head,
+      other_heads=other_heads,
+      conv_hyperparams_fn=conv_hyperparams_fn,
+      num_layers_before_predictor=num_layers_before_predictor,
+      min_depth=min_depth,
+      max_depth=max_depth)
+
+
+def build_convolutional_keras_box_predictor(is_training,
+                                            num_classes,
+                                            conv_hyperparams,
+                                            freeze_batchnorm,
+                                            inplace_batchnorm_update,
+                                            num_predictions_per_location_list,
+                                            min_depth,
+                                            max_depth,
+                                            num_layers_before_predictor,
+                                            use_dropout,
+                                            dropout_keep_prob,
+                                            kernel_size,
+                                            box_code_size,
+                                            add_background_class=True,
+                                            class_prediction_bias_init=0.0,
+                                            use_depthwise=False,
+                                            name='BoxPredictor'):
+  """Builds the Keras ConvolutionalBoxPredictor from the arguments.
+
+  Args:
+    is_training: Indicates whether the BoxPredictor is in training mode.
+    num_classes: number of classes.  Note that num_classes *does not*
+      include the background category, so if groundtruth labels take values
+      in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+      assigned classification targets can range from {0,... K}).
+    conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+      containing hyperparameters for convolution ops.
+    freeze_batchnorm: Whether to freeze batch norm parameters during
+      training or not. When training with a small batch size (e.g. 1), it is
+      desirable to freeze batch norm update and use pretrained batch norm
+      params.
+    inplace_batchnorm_update: Whether to update batch norm moving average
+      values inplace. When this is false train op must add a control
+      dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+      batch norm statistics.
+    num_predictions_per_location_list: A list of integers representing the
+      number of box predictions to be made per spatial location for each
+      feature map.
+    min_depth: Minimum feature depth prior to predicting box encodings
+      and class predictions.
+    max_depth: Maximum feature depth prior to predicting box encodings
+      and class predictions. If max_depth is set to 0, no additional
+      feature map will be inserted before location and class predictions.
+    num_layers_before_predictor: Number of the additional conv layers before
+      the predictor.
+    use_dropout: Option to use dropout or not.  Note that a single dropout
+      op is applied here prior to both box and class predictions, which stands
+      in contrast to the ConvolutionalBoxPredictor below.
+    dropout_keep_prob: Keep probability for dropout.
+      This is only used if use_dropout is True.
+    kernel_size: Size of final convolution kernel.  If the
+      spatial resolution of the feature map is smaller than the kernel size,
+      then the kernel size is automatically set to be
+      min(feature_width, feature_height).
+    box_code_size: Size of encoding for each box.
+    add_background_class: Whether to add an implicit background class.
+    class_prediction_bias_init: constant value to initialize bias of the last
+      conv2d layer before class prediction.
+    use_depthwise: Whether to use depthwise convolutions for prediction
+      steps. Default is False.
+    name: A string name scope to assign to the box predictor. If `None`, Keras
+      will auto-generate one from the class name.
+
+  Returns:
+    A Keras ConvolutionalBoxPredictor class.
+  """
+  box_prediction_heads = []
+  class_prediction_heads = []
+  other_heads = {}
+
+  for stack_index, num_predictions_per_location in enumerate(
+      num_predictions_per_location_list):
+    box_prediction_heads.append(
+        keras_box_head.ConvolutionalBoxHead(
+            is_training=is_training,
+            box_code_size=box_code_size,
+            kernel_size=kernel_size,
+            conv_hyperparams=conv_hyperparams,
+            freeze_batchnorm=freeze_batchnorm,
+            num_predictions_per_location=num_predictions_per_location,
+            use_depthwise=use_depthwise,
+            name='ConvolutionalBoxHead_%d' % stack_index))
+    class_prediction_heads.append(
+        keras_class_head.ConvolutionalClassHead(
+            is_training=is_training,
+            num_class_slots=(
+                num_classes + 1 if add_background_class else num_classes),
+            use_dropout=use_dropout,
+            dropout_keep_prob=dropout_keep_prob,
+            kernel_size=kernel_size,
+            conv_hyperparams=conv_hyperparams,
+            freeze_batchnorm=freeze_batchnorm,
+            num_predictions_per_location=num_predictions_per_location,
+            class_prediction_bias_init=class_prediction_bias_init,
+            use_depthwise=use_depthwise,
+            name='ConvolutionalClassHead_%d' % stack_index))
+
+  return convolutional_keras_box_predictor.ConvolutionalBoxPredictor(
+      is_training=is_training,
+      num_classes=num_classes,
+      box_prediction_heads=box_prediction_heads,
+      class_prediction_heads=class_prediction_heads,
+      other_heads=other_heads,
+      conv_hyperparams=conv_hyperparams,
+      num_layers_before_predictor=num_layers_before_predictor,
+      min_depth=min_depth,
+      max_depth=max_depth,
+      freeze_batchnorm=freeze_batchnorm,
+      inplace_batchnorm_update=inplace_batchnorm_update,
+      name=name)
+
+
+def build_weight_shared_convolutional_box_predictor(
+    is_training,
+    num_classes,
+    conv_hyperparams_fn,
+    depth,
+    num_layers_before_predictor,
+    box_code_size,
+    kernel_size=3,
+    add_background_class=True,
+    class_prediction_bias_init=0.0,
+    use_dropout=False,
+    dropout_keep_prob=0.8,
+    share_prediction_tower=False,
+    apply_batch_norm=True,
+    use_depthwise=False,
+    score_converter_fn=tf.identity,
+    box_encodings_clip_range=None):
+  """Builds and returns a WeightSharedConvolutionalBoxPredictor class.
+
+  Args:
+    is_training: Indicates whether the BoxPredictor is in training mode.
+    num_classes: number of classes.  Note that num_classes *does not*
+      include the background category, so if groundtruth labels take values
+      in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+      assigned classification targets can range from {0,... K}).
+    conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+      hyperparameters for convolution ops.
+    depth: depth of conv layers.
+    num_layers_before_predictor: Number of the additional conv layers before
+      the predictor.
+    box_code_size: Size of encoding for each box.
+    kernel_size: Size of final convolution kernel.
+    add_background_class: Whether to add an implicit background class.
+    class_prediction_bias_init: constant value to initialize bias of the last
+      conv2d layer before class prediction.
+    use_dropout: Whether to apply dropout to class prediction head.
+    dropout_keep_prob: Probability of keeping activiations.
+    share_prediction_tower: Whether to share the multi-layer tower between box
+      prediction and class prediction heads.
+    apply_batch_norm: Whether to apply batch normalization to conv layers in
+      this predictor.
+    use_depthwise: Whether to use depthwise separable conv2d instead of conv2d.
+    score_converter_fn: Callable score converter to perform elementwise op on
+      class scores.
+    box_encodings_clip_range: Min and max values for clipping the box_encodings.
+
+  Returns:
+    A WeightSharedConvolutionalBoxPredictor class.
+  """
+  box_prediction_head = box_head.WeightSharedConvolutionalBoxHead(
+      box_code_size=box_code_size,
+      kernel_size=kernel_size,
+      use_depthwise=use_depthwise,
+      box_encodings_clip_range=box_encodings_clip_range)
+  class_prediction_head = (
+      class_head.WeightSharedConvolutionalClassHead(
+          num_class_slots=(
+              num_classes + 1 if add_background_class else num_classes),
+          kernel_size=kernel_size,
+          class_prediction_bias_init=class_prediction_bias_init,
+          use_dropout=use_dropout,
+          dropout_keep_prob=dropout_keep_prob,
+          use_depthwise=use_depthwise,
+          score_converter_fn=score_converter_fn))
+  other_heads = {}
+  return convolutional_box_predictor.WeightSharedConvolutionalBoxPredictor(
+      is_training=is_training,
+      num_classes=num_classes,
+      box_prediction_head=box_prediction_head,
+      class_prediction_head=class_prediction_head,
+      other_heads=other_heads,
+      conv_hyperparams_fn=conv_hyperparams_fn,
+      depth=depth,
+      num_layers_before_predictor=num_layers_before_predictor,
+      kernel_size=kernel_size,
+      apply_batch_norm=apply_batch_norm,
+      share_prediction_tower=share_prediction_tower,
+      use_depthwise=use_depthwise)
+
+
+def build_mask_rcnn_box_predictor(is_training,
+                                  num_classes,
+                                  fc_hyperparams_fn,
+                                  use_dropout,
+                                  dropout_keep_prob,
+                                  box_code_size,
+                                  add_background_class=True,
+                                  share_box_across_classes=False,
+                                  predict_instance_masks=False,
+                                  conv_hyperparams_fn=None,
+                                  mask_height=14,
+                                  mask_width=14,
+                                  mask_prediction_num_conv_layers=2,
+                                  mask_prediction_conv_depth=256,
+                                  masks_are_class_agnostic=False,
+                                  convolve_then_upsample_masks=False):
+  """Builds and returns a MaskRCNNBoxPredictor class.
+
+  Args:
+    is_training: Indicates whether the BoxPredictor is in training mode.
+    num_classes: number of classes.  Note that num_classes *does not*
+      include the background category, so if groundtruth labels take values
+      in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+      assigned classification targets can range from {0,... K}).
+    fc_hyperparams_fn: A function to generate tf-slim arg_scope with
+      hyperparameters for fully connected ops.
+    use_dropout: Option to use dropout or not.  Note that a single dropout
+      op is applied here prior to both box and class predictions, which stands
+      in contrast to the ConvolutionalBoxPredictor below.
+    dropout_keep_prob: Keep probability for dropout.
+      This is only used if use_dropout is True.
+    box_code_size: Size of encoding for each box.
+    add_background_class: Whether to add an implicit background class.
+    share_box_across_classes: Whether to share boxes across classes rather
+      than use a different box for each class.
+    predict_instance_masks: If True, will add a third stage mask prediction
+      to the returned class.
+    conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+      hyperparameters for convolution ops.
+    mask_height: Desired output mask height. The default value is 14.
+    mask_width: Desired output mask width. The default value is 14.
+    mask_prediction_num_conv_layers: Number of convolution layers applied to
+      the image_features in mask prediction branch.
+    mask_prediction_conv_depth: The depth for the first conv2d_transpose op
+      applied to the image_features in the mask prediction branch. If set
+      to 0, the depth of the convolution layers will be automatically chosen
+      based on the number of object classes and the number of channels in the
+      image features.
+    masks_are_class_agnostic: Boolean determining if the mask-head is
+      class-agnostic or not.
+    convolve_then_upsample_masks: Whether to apply convolutions on mask
+      features before upsampling using nearest neighbor resizing. Otherwise,
+      mask features are resized to [`mask_height`, `mask_width`] using
+      bilinear resizing before applying convolutions.
+
+  Returns:
+    A MaskRCNNBoxPredictor class.
+  """
+  box_prediction_head = box_head.MaskRCNNBoxHead(
+      is_training=is_training,
+      num_classes=num_classes,
+      fc_hyperparams_fn=fc_hyperparams_fn,
+      use_dropout=use_dropout,
+      dropout_keep_prob=dropout_keep_prob,
+      box_code_size=box_code_size,
+      share_box_across_classes=share_box_across_classes)
+  class_prediction_head = class_head.MaskRCNNClassHead(
+      is_training=is_training,
+      num_class_slots=num_classes + 1 if add_background_class else num_classes,
+      fc_hyperparams_fn=fc_hyperparams_fn,
+      use_dropout=use_dropout,
+      dropout_keep_prob=dropout_keep_prob)
+  third_stage_heads = {}
+  if predict_instance_masks:
+    third_stage_heads[
+        mask_rcnn_box_predictor.
+        MASK_PREDICTIONS] = mask_head.MaskRCNNMaskHead(
+            num_classes=num_classes,
+            conv_hyperparams_fn=conv_hyperparams_fn,
+            mask_height=mask_height,
+            mask_width=mask_width,
+            mask_prediction_num_conv_layers=mask_prediction_num_conv_layers,
+            mask_prediction_conv_depth=mask_prediction_conv_depth,
+            masks_are_class_agnostic=masks_are_class_agnostic,
+            convolve_then_upsample=convolve_then_upsample_masks)
+  return mask_rcnn_box_predictor.MaskRCNNBoxPredictor(
+      is_training=is_training,
+      num_classes=num_classes,
+      box_prediction_head=box_prediction_head,
+      class_prediction_head=class_prediction_head,
+      third_stage_heads=third_stage_heads)
+
+
+def build_score_converter(score_converter_config, is_training):
+  """Builds score converter based on the config.
+
+  Builds one of [tf.identity, tf.sigmoid] score converters based on the config
+  and whether the BoxPredictor is for training or inference.
+
+  Args:
+    score_converter_config:
+      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.score_converter.
+    is_training: Indicates whether the BoxPredictor is in training mode.
+
+  Returns:
+    Callable score converter op.
+
+  Raises:
+    ValueError: On unknown score converter.
+  """
+  if score_converter_config == (
+      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.IDENTITY):
+    return tf.identity
+  if score_converter_config == (
+      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.SIGMOID):
+    return tf.identity if is_training else tf.sigmoid
+  raise ValueError('Unknown score converter.')
+
+
+BoxEncodingsClipRange = collections.namedtuple('BoxEncodingsClipRange',
+                                               ['min', 'max'])
+
+
+def build(argscope_fn, box_predictor_config, is_training, num_classes,
+          add_background_class=True):
+  """Builds box predictor based on the configuration.
+
+  Builds box predictor based on the configuration. See box_predictor.proto for
+  configurable options. Also, see box_predictor.py for more details.
+
+  Args:
+    argscope_fn: A function that takes the following inputs:
+        * hyperparams_pb2.Hyperparams proto
+        * a boolean indicating if the model is in training mode.
+      and returns a tf slim argscope for Conv and FC hyperparameters.
+    box_predictor_config: box_predictor_pb2.BoxPredictor proto containing
+      configuration.
+    is_training: Whether the models is in training mode.
+    num_classes: Number of classes to predict.
+    add_background_class: Whether to add an implicit background class.
+
+  Returns:
+    box_predictor: box_predictor.BoxPredictor object.
+
+  Raises:
+    ValueError: On unknown box predictor.
+  """
+  if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor):
+    raise ValueError('box_predictor_config not of type '
+                     'box_predictor_pb2.BoxPredictor.')
+
+  box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof')
+
+  if  box_predictor_oneof == 'convolutional_box_predictor':
+    config_box_predictor = box_predictor_config.convolutional_box_predictor
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
+                                      is_training)
+    return build_convolutional_box_predictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        add_background_class=add_background_class,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+        box_code_size=config_box_predictor.box_code_size,
+        kernel_size=config_box_predictor.kernel_size,
+        num_layers_before_predictor=(
+            config_box_predictor.num_layers_before_predictor),
+        min_depth=config_box_predictor.min_depth,
+        max_depth=config_box_predictor.max_depth,
+        apply_sigmoid_to_scores=config_box_predictor.apply_sigmoid_to_scores,
+        class_prediction_bias_init=(
+            config_box_predictor.class_prediction_bias_init),
+        use_depthwise=config_box_predictor.use_depthwise)
+
+  if  box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
+    config_box_predictor = (
+        box_predictor_config.weight_shared_convolutional_box_predictor)
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
+                                      is_training)
+    apply_batch_norm = config_box_predictor.conv_hyperparams.HasField(
+        'batch_norm')
+    # During training phase, logits are used to compute the loss. Only apply
+    # sigmoid at inference to make the inference graph TPU friendly.
+    score_converter_fn = build_score_converter(
+        config_box_predictor.score_converter, is_training)
+    # Optionally apply clipping to box encodings, when box_encodings_clip_range
+    # is set.
+    box_encodings_clip_range = (
+        BoxEncodingsClipRange(
+            min=config_box_predictor.box_encodings_clip_range.min,
+            max=config_box_predictor.box_encodings_clip_range.max)
+        if config_box_predictor.HasField('box_encodings_clip_range') else None)
+
+    return build_weight_shared_convolutional_box_predictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        add_background_class=add_background_class,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        depth=config_box_predictor.depth,
+        num_layers_before_predictor=(
+            config_box_predictor.num_layers_before_predictor),
+        box_code_size=config_box_predictor.box_code_size,
+        kernel_size=config_box_predictor.kernel_size,
+        class_prediction_bias_init=(
+            config_box_predictor.class_prediction_bias_init),
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+        share_prediction_tower=config_box_predictor.share_prediction_tower,
+        apply_batch_norm=apply_batch_norm,
+        use_depthwise=config_box_predictor.use_depthwise,
+        score_converter_fn=score_converter_fn,
+        box_encodings_clip_range=box_encodings_clip_range)
+
+  if box_predictor_oneof == 'mask_rcnn_box_predictor':
+    config_box_predictor = box_predictor_config.mask_rcnn_box_predictor
+    fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams,
+                                    is_training)
+    conv_hyperparams_fn = None
+    if config_box_predictor.HasField('conv_hyperparams'):
+      conv_hyperparams_fn = argscope_fn(
+          config_box_predictor.conv_hyperparams, is_training)
+    return build_mask_rcnn_box_predictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        add_background_class=add_background_class,
+        fc_hyperparams_fn=fc_hyperparams_fn,
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+        box_code_size=config_box_predictor.box_code_size,
+        share_box_across_classes=(
+            config_box_predictor.share_box_across_classes),
+        predict_instance_masks=config_box_predictor.predict_instance_masks,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        mask_height=config_box_predictor.mask_height,
+        mask_width=config_box_predictor.mask_width,
+        mask_prediction_num_conv_layers=(
+            config_box_predictor.mask_prediction_num_conv_layers),
+        mask_prediction_conv_depth=(
+            config_box_predictor.mask_prediction_conv_depth),
+        masks_are_class_agnostic=(
+            config_box_predictor.masks_are_class_agnostic),
+        convolve_then_upsample_masks=(
+            config_box_predictor.convolve_then_upsample_masks))
+
+  if box_predictor_oneof == 'rfcn_box_predictor':
+    config_box_predictor = box_predictor_config.rfcn_box_predictor
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
+                                      is_training)
+    box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        crop_size=[config_box_predictor.crop_height,
+                   config_box_predictor.crop_width],
+        num_spatial_bins=[config_box_predictor.num_spatial_bins_height,
+                          config_box_predictor.num_spatial_bins_width],
+        depth=config_box_predictor.depth,
+        box_code_size=config_box_predictor.box_code_size)
+    return box_predictor_object
+  raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))
+
+
+def build_keras(conv_hyperparams_fn, freeze_batchnorm, inplace_batchnorm_update,
+                num_predictions_per_location_list, box_predictor_config,
+                is_training, num_classes, add_background_class=True):
+  """Builds a Keras-based box predictor based on the configuration.
+
+  Builds Keras-based box predictor based on the configuration.
+  See box_predictor.proto for configurable options. Also, see box_predictor.py
+  for more details.
+
+  Args:
+    conv_hyperparams_fn: A function that takes a hyperparams_pb2.Hyperparams
+      proto and returns a `hyperparams_builder.KerasLayerHyperparams`
+      for Conv or FC hyperparameters.
+    freeze_batchnorm: Whether to freeze batch norm parameters during
+      training or not. When training with a small batch size (e.g. 1), it is
+      desirable to freeze batch norm update and use pretrained batch norm
+      params.
+    inplace_batchnorm_update: Whether to update batch norm moving average
+      values inplace. When this is false train op must add a control
+      dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+      batch norm statistics.
+    num_predictions_per_location_list: A list of integers representing the
+      number of box predictions to be made per spatial location for each
+      feature map.
+    box_predictor_config: box_predictor_pb2.BoxPredictor proto containing
+      configuration.
+    is_training: Whether the models is in training mode.
+    num_classes: Number of classes to predict.
+    add_background_class: Whether to add an implicit background class.
+
+  Returns:
+    box_predictor: box_predictor.KerasBoxPredictor object.
+
+  Raises:
+    ValueError: On unknown box predictor, or one with no Keras box predictor.
+  """
+  if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor):
+    raise ValueError('box_predictor_config not of type '
+                     'box_predictor_pb2.BoxPredictor.')
+
+  box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof')
+
+  if box_predictor_oneof == 'convolutional_box_predictor':
+    config_box_predictor = box_predictor_config.convolutional_box_predictor
+    conv_hyperparams = conv_hyperparams_fn(
+        config_box_predictor.conv_hyperparams)
+    return build_convolutional_keras_box_predictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        add_background_class=add_background_class,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=freeze_batchnorm,
+        inplace_batchnorm_update=inplace_batchnorm_update,
+        num_predictions_per_location_list=num_predictions_per_location_list,
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+        box_code_size=config_box_predictor.box_code_size,
+        kernel_size=config_box_predictor.kernel_size,
+        num_layers_before_predictor=(
+            config_box_predictor.num_layers_before_predictor),
+        min_depth=config_box_predictor.min_depth,
+        max_depth=config_box_predictor.max_depth,
+        class_prediction_bias_init=(
+            config_box_predictor.class_prediction_bias_init),
+        use_depthwise=config_box_predictor.use_depthwise)
+
+  raise ValueError(
+      'Unknown box predictor for Keras: {}'.format(box_predictor_oneof))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder_test.py
new file mode 100644
index 00000000..08029df7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/box_predictor_builder_test.py
@@ -0,0 +1,658 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for box_predictor_builder."""
+
+import mock
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors import mask_rcnn_box_predictor
+from object_detection.protos import box_predictor_pb2
+from object_detection.protos import hyperparams_pb2
+
+
+class ConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
+
+  def test_box_predictor_calls_conv_argscope_fn(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.0003
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.3
+        }
+      }
+      activation: RELU_6
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.convolutional_box_predictor.conv_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10)
+    (conv_hyperparams_actual, is_training) = box_predictor._conv_hyperparams_fn
+    self.assertAlmostEqual((hyperparams_proto.regularizer.
+                            l1_regularizer.weight),
+                           (conv_hyperparams_actual.regularizer.l1_regularizer.
+                            weight))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.stddev),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.stddev))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.mean),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.mean))
+    self.assertEqual(hyperparams_proto.activation,
+                     conv_hyperparams_actual.activation)
+    self.assertFalse(is_training)
+
+  def test_construct_non_default_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      convolutional_box_predictor {
+        min_depth: 2
+        max_depth: 16
+        num_layers_before_predictor: 2
+        use_dropout: false
+        dropout_keep_probability: 0.4
+        kernel_size: 3
+        box_code_size: 3
+        apply_sigmoid_to_scores: true
+        class_prediction_bias_init: 4.0
+        use_depthwise: true
+      }
+    """
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor_proto.convolutional_box_predictor.conv_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10,
+        add_background_class=False)
+    class_head = box_predictor._class_prediction_head
+    self.assertEqual(box_predictor._min_depth, 2)
+    self.assertEqual(box_predictor._max_depth, 16)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 2)
+    self.assertFalse(class_head._use_dropout)
+    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.4)
+    self.assertTrue(class_head._apply_sigmoid_to_scores)
+    self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
+    self.assertEqual(class_head._num_class_slots, 10)
+    self.assertEqual(box_predictor.num_classes, 10)
+    self.assertFalse(box_predictor._is_training)
+    self.assertTrue(class_head._use_depthwise)
+
+  def test_construct_default_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    class_head = box_predictor._class_prediction_head
+    self.assertEqual(box_predictor._min_depth, 0)
+    self.assertEqual(box_predictor._max_depth, 0)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
+    self.assertTrue(class_head._use_dropout)
+    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.8)
+    self.assertFalse(class_head._apply_sigmoid_to_scores)
+    self.assertEqual(class_head._num_class_slots, 91)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertFalse(class_head._use_depthwise)
+
+
+class WeightSharedConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
+
+  def test_box_predictor_calls_conv_argscope_fn(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.0003
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.3
+        }
+      }
+      activation: RELU_6
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    (box_predictor_proto.weight_shared_convolutional_box_predictor
+     .conv_hyperparams.CopyFrom(hyperparams_proto))
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10)
+    (conv_hyperparams_actual, is_training) = box_predictor._conv_hyperparams_fn
+    self.assertAlmostEqual((hyperparams_proto.regularizer.
+                            l1_regularizer.weight),
+                           (conv_hyperparams_actual.regularizer.l1_regularizer.
+                            weight))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.stddev),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.stddev))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.mean),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.mean))
+    self.assertEqual(hyperparams_proto.activation,
+                     conv_hyperparams_actual.activation)
+    self.assertFalse(is_training)
+
+  def test_construct_non_default_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        depth: 2
+        num_layers_before_predictor: 2
+        kernel_size: 7
+        box_code_size: 3
+        class_prediction_bias_init: 4.0
+      }
+    """
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    (box_predictor_proto.weight_shared_convolutional_box_predictor.
+     conv_hyperparams.CopyFrom(hyperparams_proto))
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10,
+        add_background_class=False)
+    class_head = box_predictor._class_prediction_head
+    self.assertEqual(box_predictor._depth, 2)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 2)
+    self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
+    self.assertEqual(box_predictor.num_classes, 10)
+    self.assertFalse(box_predictor._is_training)
+    self.assertEqual(box_predictor._apply_batch_norm, False)
+
+  def test_construct_non_default_depthwise_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        depth: 2
+        num_layers_before_predictor: 2
+        kernel_size: 7
+        box_code_size: 3
+        class_prediction_bias_init: 4.0
+        use_depthwise: true
+      }
+    """
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    (box_predictor_proto.weight_shared_convolutional_box_predictor.
+     conv_hyperparams.CopyFrom(hyperparams_proto))
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10,
+        add_background_class=False)
+    class_head = box_predictor._class_prediction_head
+    self.assertEqual(box_predictor._depth, 2)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 2)
+    self.assertEqual(box_predictor._apply_batch_norm, False)
+    self.assertEqual(box_predictor._use_depthwise, True)
+    self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
+    self.assertEqual(box_predictor.num_classes, 10)
+    self.assertFalse(box_predictor._is_training)
+
+  def test_construct_default_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertEqual(box_predictor._depth, 0)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_predictor._apply_batch_norm, False)
+
+  def test_construct_default_conv_box_predictor_with_batch_norm(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          batch_norm {
+            train: true
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertEqual(box_predictor._depth, 0)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_predictor._apply_batch_norm, True)
+
+
+class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
+
+  def test_box_predictor_builder_calls_fc_argscope_fn(self):
+    fc_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.0003
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.3
+        }
+      }
+      activation: RELU_6
+      op: FC
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
+        hyperparams_proto)
+    mock_argscope_fn = mock.Mock(return_value='arg_scope')
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_argscope_fn,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10)
+    mock_argscope_fn.assert_called_with(hyperparams_proto, False)
+    self.assertEqual(box_predictor._box_prediction_head._fc_hyperparams_fn,
+                     'arg_scope')
+    self.assertEqual(box_predictor._class_prediction_head._fc_hyperparams_fn,
+                     'arg_scope')
+
+  def test_non_default_mask_rcnn_box_predictor(self):
+    fc_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+      op: FC
+    """
+    box_predictor_text_proto = """
+      mask_rcnn_box_predictor {
+        use_dropout: true
+        dropout_keep_probability: 0.8
+        box_code_size: 3
+        share_box_across_classes: true
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
+    def mock_fc_argscope_builder(fc_hyperparams_arg, is_training):
+      return (fc_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_fc_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    box_head = box_predictor._box_prediction_head
+    class_head = box_predictor._class_prediction_head
+    self.assertTrue(box_head._use_dropout)
+    self.assertTrue(class_head._use_dropout)
+    self.assertAlmostEqual(box_head._dropout_keep_prob, 0.8)
+    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.8)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_head._box_code_size, 3)
+    self.assertEqual(box_head._share_box_across_classes, True)
+
+  def test_build_default_mask_rcnn_box_predictor(self):
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
+        hyperparams_pb2.Hyperparams.FC)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock.Mock(return_value='arg_scope'),
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    box_head = box_predictor._box_prediction_head
+    class_head = box_predictor._class_prediction_head
+    self.assertFalse(box_head._use_dropout)
+    self.assertFalse(class_head._use_dropout)
+    self.assertAlmostEqual(box_head._dropout_keep_prob, 0.5)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_head._box_code_size, 4)
+    self.assertEqual(len(box_predictor._third_stage_heads.keys()), 0)
+
+  def test_build_box_predictor_with_mask_branch(self):
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
+        hyperparams_pb2.Hyperparams.FC)
+    box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams.op = (
+        hyperparams_pb2.Hyperparams.CONV)
+    box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
+    box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
+    box_predictor_proto.mask_rcnn_box_predictor.mask_height = 16
+    box_predictor_proto.mask_rcnn_box_predictor.mask_width = 16
+    mock_argscope_fn = mock.Mock(return_value='arg_scope')
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_argscope_fn,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    mock_argscope_fn.assert_has_calls(
+        [mock.call(box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams,
+                   True),
+         mock.call(box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams,
+                   True)], any_order=True)
+    box_head = box_predictor._box_prediction_head
+    class_head = box_predictor._class_prediction_head
+    third_stage_heads = box_predictor._third_stage_heads
+    self.assertFalse(box_head._use_dropout)
+    self.assertFalse(class_head._use_dropout)
+    self.assertAlmostEqual(box_head._dropout_keep_prob, 0.5)
+    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.5)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_head._box_code_size, 4)
+    self.assertTrue(
+        mask_rcnn_box_predictor.MASK_PREDICTIONS in third_stage_heads)
+    self.assertEqual(
+        third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
+        ._mask_prediction_conv_depth, 512)
+
+  def test_build_box_predictor_with_convlve_then_upsample_masks(self):
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
+        hyperparams_pb2.Hyperparams.FC)
+    box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams.op = (
+        hyperparams_pb2.Hyperparams.CONV)
+    box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
+    box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
+    box_predictor_proto.mask_rcnn_box_predictor.mask_height = 24
+    box_predictor_proto.mask_rcnn_box_predictor.mask_width = 24
+    box_predictor_proto.mask_rcnn_box_predictor.convolve_then_upsample_masks = (
+        True)
+
+    mock_argscope_fn = mock.Mock(return_value='arg_scope')
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_argscope_fn,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    mock_argscope_fn.assert_has_calls(
+        [mock.call(box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams,
+                   True),
+         mock.call(box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams,
+                   True)], any_order=True)
+    box_head = box_predictor._box_prediction_head
+    class_head = box_predictor._class_prediction_head
+    third_stage_heads = box_predictor._third_stage_heads
+    self.assertFalse(box_head._use_dropout)
+    self.assertFalse(class_head._use_dropout)
+    self.assertAlmostEqual(box_head._dropout_keep_prob, 0.5)
+    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.5)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_head._box_code_size, 4)
+    self.assertTrue(
+        mask_rcnn_box_predictor.MASK_PREDICTIONS in third_stage_heads)
+    self.assertEqual(
+        third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
+        ._mask_prediction_conv_depth, 512)
+    self.assertTrue(third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
+                    ._convolve_then_upsample)
+
+
+class RfcnBoxPredictorBuilderTest(tf.test.TestCase):
+
+  def test_box_predictor_calls_fc_argscope_fn(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.0003
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.3
+        }
+      }
+      activation: RELU_6
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10)
+    (conv_hyperparams_actual, is_training) = box_predictor._conv_hyperparams_fn
+    self.assertAlmostEqual((hyperparams_proto.regularizer.
+                            l1_regularizer.weight),
+                           (conv_hyperparams_actual.regularizer.l1_regularizer.
+                            weight))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.stddev),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.stddev))
+    self.assertAlmostEqual((hyperparams_proto.initializer.
+                            truncated_normal_initializer.mean),
+                           (conv_hyperparams_actual.initializer.
+                            truncated_normal_initializer.mean))
+    self.assertEqual(hyperparams_proto.activation,
+                     conv_hyperparams_actual.activation)
+    self.assertFalse(is_training)
+
+  def test_non_default_rfcn_box_predictor(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+    """
+    box_predictor_text_proto = """
+      rfcn_box_predictor {
+        num_spatial_bins_height: 4
+        num_spatial_bins_width: 4
+        depth: 4
+        box_code_size: 3
+        crop_height: 16
+        crop_width: 16
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_predictor._box_code_size, 3)
+    self.assertEqual(box_predictor._num_spatial_bins, [4, 4])
+    self.assertEqual(box_predictor._crop_size, [16, 16])
+
+  def test_default_rfcn_box_predictor(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
+        hyperparams_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertEqual(box_predictor.num_classes, 90)
+    self.assertTrue(box_predictor._is_training)
+    self.assertEqual(box_predictor._box_code_size, 4)
+    self.assertEqual(box_predictor._num_spatial_bins, [3, 3])
+    self.assertEqual(box_predictor._crop_size, [12, 12])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder.py
new file mode 100644
index 00000000..eac04fb5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder.py
@@ -0,0 +1,167 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""tf.data.Dataset builder.
+
+Creates data sources for DetectionModels from an InputReader config. See
+input_reader.proto for options.
+
+Note: If users wishes to also use their own InputReaders with the Object
+Detection configuration framework, they should define their own builder function
+that wraps the build function.
+"""
+import functools
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import input_reader_pb2
+
+
+def make_initializable_iterator(dataset):
+  """Creates an iterator, and initializes tables.
+
+  This is useful in cases where make_one_shot_iterator wouldn't work because
+  the graph contains a hash table that needs to be initialized.
+
+  Args:
+    dataset: A `tf.data.Dataset` object.
+
+  Returns:
+    A `tf.data.Iterator`.
+  """
+  iterator = dataset.make_initializable_iterator()
+  tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
+  return iterator
+
+
+def read_dataset(file_read_func, input_files, config):
+  """Reads a dataset, and handles repetition and shuffling.
+
+  Args:
+    file_read_func: Function to use in tf.contrib.data.parallel_interleave, to
+      read every individual file into a tf.data.Dataset.
+    input_files: A list of file paths to read.
+    config: A input_reader_builder.InputReader object.
+
+  Returns:
+    A tf.data.Dataset of (undecoded) tf-records based on config.
+  """
+  # Shard, shuffle, and read files.
+  filenames = tf.gfile.Glob(input_files)
+  num_readers = config.num_readers
+  if num_readers > len(filenames):
+    num_readers = len(filenames)
+    tf.logging.warning('num_readers has been reduced to %d to match input file '
+                       'shards.' % num_readers)
+  filename_dataset = tf.data.Dataset.from_tensor_slices(filenames)
+  if config.shuffle:
+    filename_dataset = filename_dataset.shuffle(
+        config.filenames_shuffle_buffer_size)
+  elif num_readers > 1:
+    tf.logging.warning('`shuffle` is false, but the input data stream is '
+                       'still slightly shuffled since `num_readers` > 1.')
+  filename_dataset = filename_dataset.repeat(config.num_epochs or None)
+  records_dataset = filename_dataset.apply(
+      tf.contrib.data.parallel_interleave(
+          file_read_func,
+          cycle_length=num_readers,
+          block_length=config.read_block_length,
+          sloppy=config.shuffle))
+  if config.shuffle:
+    records_dataset = records_dataset.shuffle(config.shuffle_buffer_size)
+  return records_dataset
+
+
+def build(input_reader_config, batch_size=None, transform_input_data_fn=None, multi_gpu=True):
+  """Builds a tf.data.Dataset.
+
+  Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
+  records. Applies a padded batch to the resulting dataset.
+
+  Args:
+    input_reader_config: A input_reader_pb2.InputReader object.
+    batch_size: Batch size. If batch size is None, no batching is performed.
+    transform_input_data_fn: Function to apply transformation to all records,
+      or None if no extra decoding is required.
+
+  Returns:
+    A tf.data.Dataset based on the input_reader_config.
+
+  Raises:
+    ValueError: On invalid input reader proto.
+    ValueError: If no input paths are specified.
+  """
+  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
+    raise ValueError('input_reader_config not of type '
+                     'input_reader_pb2.InputReader.')
+
+  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
+    config = input_reader_config.tf_record_input_reader
+    if not config.input_path:
+      raise ValueError('At least one input path must be specified in '
+                       '`input_reader_config`.')
+
+    label_map_proto_file = None
+    if input_reader_config.HasField('label_map_path'):
+      label_map_proto_file = input_reader_config.label_map_path
+    decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=input_reader_config.load_instance_masks,
+        instance_mask_type=input_reader_config.mask_type,
+        label_map_proto_file=label_map_proto_file,
+        use_display_name=input_reader_config.use_display_name,
+        num_additional_channels=input_reader_config.num_additional_channels)
+
+    def process_fn(value):
+      """Sets up tf graph that decodes, transforms and pads input data."""
+      processed_tensors = decoder.decode(value)
+      if transform_input_data_fn is not None:
+        processed_tensors = transform_input_data_fn(processed_tensors)
+      return processed_tensors
+
+    dataset = read_dataset(
+        functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
+        config.input_path[:], input_reader_config)
+    if multi_gpu:
+        dataset = dataset.shard(hvd.size(), hvd.rank())
+    # TODO(rathodv): make batch size a required argument once the old binaries
+    # are deleted.
+    if batch_size:
+      num_parallel_calls = batch_size * input_reader_config.num_parallel_batches
+    else:
+      num_parallel_calls = input_reader_config.num_parallel_map_calls
+    dataset = dataset.map(
+        process_fn,
+        num_parallel_calls=num_parallel_calls)
+    if batch_size:
+      dataset = dataset.apply(
+          tf.contrib.data.batch_and_drop_remainder(batch_size))
+    dataset = dataset.prefetch(input_reader_config.num_prefetch_batches)
+    return dataset
+
+  raise ValueError('Unsupported input_reader_config.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder_test.py
new file mode 100644
index 00000000..78677310
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/dataset_builder_test.py
@@ -0,0 +1,356 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for dataset_builder."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import dataset_builder
+from object_detection.core import standard_fields as fields
+from object_detection.protos import input_reader_pb2
+from object_detection.utils import dataset_util
+
+
+class DatasetBuilderTest(tf.test.TestCase):
+
+  def create_tf_record(self, has_additional_channels=False, num_examples=1):
+    path = os.path.join(self.get_temp_dir(), 'tfrecord')
+    writer = tf.python_io.TFRecordWriter(path)
+
+    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+    additional_channels_tensor = np.random.randint(
+        255, size=(4, 5, 1)).astype(np.uint8)
+    flat_mask = (4 * 5) * [1.0]
+    with self.test_session():
+      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
+          tf.constant(additional_channels_tensor)).eval()
+      for i in range(num_examples):
+        features = {
+            'image/source_id': dataset_util.bytes_feature(str(i)),
+            'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+            'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
+            'image/height': dataset_util.int64_feature(4),
+            'image/width': dataset_util.int64_feature(5),
+            'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
+            'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
+            'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
+            'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
+            'image/object/class/label': dataset_util.int64_list_feature([2]),
+            'image/object/mask': dataset_util.float_list_feature(flat_mask),
+        }
+        if has_additional_channels:
+          additional_channels_key = 'image/additional_channels/encoded'
+          features[additional_channels_key] = dataset_util.bytes_list_feature(
+              [encoded_additional_channels_jpeg] * 2)
+        example = tf.train.Example(features=tf.train.Features(feature=features))
+        writer.write(example.SerializeToString())
+      writer.close()
+
+    return path
+
+  def test_build_tf_record_input_reader(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertTrue(
+        fields.InputDataFields.groundtruth_instance_masks not in output_dict)
+    self.assertEquals((1, 4, 5, 3),
+                      output_dict[fields.InputDataFields.image].shape)
+    self.assertAllEqual([[2]],
+                        output_dict[fields.InputDataFields.groundtruth_classes])
+    self.assertEquals(
+        (1, 1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllEqual(
+        [0.0, 0.0, 1.0, 1.0],
+        output_dict[fields.InputDataFields.groundtruth_boxes][0][0])
+
+  def test_build_tf_record_input_reader_and_load_instance_masks(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      load_instance_masks: true
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+    self.assertAllEqual(
+        (1, 1, 4, 5),
+        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
+
+  def test_build_tf_record_input_reader_with_batch_size_two(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+
+    def one_hot_class_encoding_fn(tensor_dict):
+      tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
+          tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
+      return tensor_dict
+
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(
+            input_reader_proto,
+            transform_input_data_fn=one_hot_class_encoding_fn,
+            batch_size=2)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual([2, 4, 5, 3],
+                        output_dict[fields.InputDataFields.image].shape)
+    self.assertAllEqual(
+        [2, 1, 3],
+        output_dict[fields.InputDataFields.groundtruth_classes].shape)
+    self.assertAllEqual(
+        [2, 1, 4], output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllEqual([[[0.0, 0.0, 1.0, 1.0]], [[0.0, 0.0, 1.0, 1.0]]],
+                        output_dict[fields.InputDataFields.groundtruth_boxes])
+
+  def test_build_tf_record_input_reader_with_batch_size_two_and_masks(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      load_instance_masks: true
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+
+    def one_hot_class_encoding_fn(tensor_dict):
+      tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
+          tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
+      return tensor_dict
+
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(
+            input_reader_proto,
+            transform_input_data_fn=one_hot_class_encoding_fn,
+            batch_size=2)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        [2, 1, 4, 5],
+        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
+
+  def test_raises_error_with_no_input_paths(self):
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      load_instance_masks: true
+    """
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    with self.assertRaises(ValueError):
+      dataset_builder.build(input_reader_proto, batch_size=1)
+
+  def test_sample_all_data(self):
+    tf_record_path = self.create_tf_record(num_examples=2)
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      sample_1_of_n_examples: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
+      output_dict = sess.run(tensor_dict)
+      self.assertEquals(['1'], output_dict[fields.InputDataFields.source_id])
+
+  def test_sample_one_of_n_shards(self):
+    tf_record_path = self.create_tf_record(num_examples=4)
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      sample_1_of_n_examples: 2
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = dataset_builder.make_initializable_iterator(
+        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
+      output_dict = sess.run(tensor_dict)
+      self.assertEquals(['2'], output_dict[fields.InputDataFields.source_id])
+
+
+class ReadDatasetTest(tf.test.TestCase):
+
+  def setUp(self):
+    self._path_template = os.path.join(self.get_temp_dir(), 'examples_%s.txt')
+
+    for i in range(5):
+      path = self._path_template % i
+      with tf.gfile.Open(path, 'wb') as f:
+        f.write('\n'.join([str(i + 1), str((i + 1) * 10)]))
+
+    self._shuffle_path_template = os.path.join(self.get_temp_dir(),
+                                               'shuffle_%s.txt')
+    for i in range(2):
+      path = self._shuffle_path_template % i
+      with tf.gfile.Open(path, 'wb') as f:
+        f.write('\n'.join([str(i)] * 5))
+
+  def _get_dataset_next(self, files, config, batch_size):
+
+    def decode_func(value):
+      return [tf.string_to_number(value, out_type=tf.int32)]
+
+    dataset = dataset_builder.read_dataset(tf.data.TextLineDataset, files,
+                                           config)
+    dataset = dataset.map(decode_func)
+    dataset = dataset.batch(batch_size)
+    return dataset.make_one_shot_iterator().get_next()
+
+  def test_make_initializable_iterator_with_hashTable(self):
+    keys = [1, 0, -1]
+    dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
+    table = tf.contrib.lookup.HashTable(
+        initializer=tf.contrib.lookup.KeyValueTensorInitializer(
+            keys=keys, values=list(reversed(keys))),
+        default_value=100)
+    dataset = dataset.map(table.lookup)
+    data = dataset_builder.make_initializable_iterator(dataset).get_next()
+    init = tf.tables_initializer()
+
+    with self.test_session() as sess:
+      sess.run(init)
+      self.assertAllEqual(sess.run(data), [-1, 100, 1, 100])
+
+  def test_read_dataset(self):
+    config = input_reader_pb2.InputReader()
+    config.num_readers = 1
+    config.shuffle = False
+
+    data = self._get_dataset_next(
+        [self._path_template % '*'], config, batch_size=20)
+    with self.test_session() as sess:
+      self.assertAllEqual(
+          sess.run(data), [[
+              1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
+              50
+          ]])
+
+  def test_reduce_num_reader(self):
+    config = input_reader_pb2.InputReader()
+    config.num_readers = 10
+    config.shuffle = False
+
+    data = self._get_dataset_next(
+        [self._path_template % '*'], config, batch_size=20)
+    with self.test_session() as sess:
+      self.assertAllEqual(
+          sess.run(data), [[
+              1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
+              50
+          ]])
+
+  def test_enable_shuffle(self):
+    config = input_reader_pb2.InputReader()
+    config.num_readers = 1
+    config.shuffle = True
+
+    tf.set_random_seed(1)  # Set graph level seed.
+    data = self._get_dataset_next(
+        [self._shuffle_path_template % '*'], config, batch_size=10)
+    expected_non_shuffle_output = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+
+    with self.test_session() as sess:
+      self.assertTrue(
+          np.any(np.not_equal(sess.run(data), expected_non_shuffle_output)))
+
+  def test_disable_shuffle_(self):
+    config = input_reader_pb2.InputReader()
+    config.num_readers = 1
+    config.shuffle = False
+
+    data = self._get_dataset_next(
+        [self._shuffle_path_template % '*'], config, batch_size=10)
+    expected_non_shuffle_output = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+
+    with self.test_session() as sess:
+      self.assertAllEqual(sess.run(data), [expected_non_shuffle_output])
+
+  def test_read_dataset_single_epoch(self):
+    config = input_reader_pb2.InputReader()
+    config.num_epochs = 1
+    config.num_readers = 1
+    config.shuffle = False
+
+    data = self._get_dataset_next(
+        [self._path_template % '0'], config, batch_size=30)
+    with self.test_session() as sess:
+      # First batch will retrieve as much as it can, second batch will fail.
+      self.assertAllEqual(sess.run(data), [[1, 10]])
+      self.assertRaises(tf.errors.OutOfRangeError, sess.run, data)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder.py
new file mode 100644
index 00000000..77e60479
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder.py
@@ -0,0 +1,42 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for quantized training and evaluation."""
+
+import tensorflow as tf
+
+
+def build(graph_rewriter_config, is_training):
+  """Returns a function that modifies default graph based on options.
+
+  Args:
+    graph_rewriter_config: graph_rewriter_pb2.GraphRewriter proto.
+    is_training: whether in training of eval mode.
+  """
+  def graph_rewrite_fn():
+    """Function to quantize weights and activation of the default graph."""
+    if (graph_rewriter_config.quantization.weight_bits != 8 or
+        graph_rewriter_config.quantization.activation_bits != 8):
+      raise ValueError('Only 8bit quantization is supported')
+
+    # Quantize the graph by inserting quantize ops for weights and activations
+    if is_training:
+      tf.contrib.quantize.create_training_graph(
+          input_graph=tf.get_default_graph(),
+          quant_delay=graph_rewriter_config.quantization.delay)
+    else:
+      tf.contrib.quantize.create_eval_graph(input_graph=tf.get_default_graph())
+
+    tf.contrib.layers.summarize_collection('quant_vars')
+  return graph_rewrite_fn
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder_test.py
new file mode 100644
index 00000000..5f38d5a2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/graph_rewriter_builder_test.py
@@ -0,0 +1,57 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for graph_rewriter_builder."""
+import mock
+import tensorflow as tf
+from object_detection.builders import graph_rewriter_builder
+from object_detection.protos import graph_rewriter_pb2
+
+
+class QuantizationBuilderTest(tf.test.TestCase):
+
+  def testQuantizationBuilderSetsUpCorrectTrainArguments(self):
+    with mock.patch.object(
+        tf.contrib.quantize, 'create_training_graph') as mock_quant_fn:
+      with mock.patch.object(tf.contrib.layers,
+                             'summarize_collection') as mock_summarize_col:
+        graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_proto.quantization.delay = 10
+        graph_rewriter_proto.quantization.weight_bits = 8
+        graph_rewriter_proto.quantization.activation_bits = 8
+        graph_rewrite_fn = graph_rewriter_builder.build(
+            graph_rewriter_proto, is_training=True)
+        graph_rewrite_fn()
+        _, kwargs = mock_quant_fn.call_args
+        self.assertEqual(kwargs['input_graph'], tf.get_default_graph())
+        self.assertEqual(kwargs['quant_delay'], 10)
+        mock_summarize_col.assert_called_with('quant_vars')
+
+  def testQuantizationBuilderSetsUpCorrectEvalArguments(self):
+    with mock.patch.object(tf.contrib.quantize,
+                           'create_eval_graph') as mock_quant_fn:
+      with mock.patch.object(tf.contrib.layers,
+                             'summarize_collection') as mock_summarize_col:
+        graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_proto.quantization.delay = 10
+        graph_rewrite_fn = graph_rewriter_builder.build(
+            graph_rewriter_proto, is_training=False)
+        graph_rewrite_fn()
+        _, kwargs = mock_quant_fn.call_args
+        self.assertEqual(kwargs['input_graph'], tf.get_default_graph())
+        mock_summarize_col.assert_called_with('quant_vars')
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder.py
new file mode 100644
index 00000000..cd503e22
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder.py
@@ -0,0 +1,418 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Builder function to construct tf-slim arg_scope for convolution, fc ops."""
+import tensorflow as tf
+
+from object_detection.core import freezable_batch_norm
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import context_manager
+
+slim = tf.contrib.slim
+
+
+class KerasLayerHyperparams(object):
+  """
+  A hyperparameter configuration object for Keras layers used in
+  Object Detection models.
+  """
+
+  def __init__(self, hyperparams_config):
+    """Builds keras hyperparameter config for layers based on the proto config.
+
+    It automatically converts from Slim layer hyperparameter configs to
+    Keras layer hyperparameters. Namely, it:
+    - Builds Keras initializers/regularizers instead of Slim ones
+    - sets weights_regularizer/initializer to kernel_regularizer/initializer
+    - converts batchnorm decay to momentum
+    - converts Slim l2 regularizer weights to the equivalent Keras l2 weights
+
+    Contains a hyperparameter configuration for ops that specifies kernel
+    initializer, kernel regularizer, activation. Also contains parameters for
+    batch norm operators based on the configuration.
+
+    Note that if the batch_norm parameters are not specified in the config
+    (i.e. left to default) then batch norm is excluded from the config.
+
+    Args:
+      hyperparams_config: hyperparams.proto object containing
+        hyperparameters.
+
+    Raises:
+      ValueError: if hyperparams_config is not of type hyperparams.Hyperparams.
+    """
+    if not isinstance(hyperparams_config,
+                      hyperparams_pb2.Hyperparams):
+      raise ValueError('hyperparams_config not of type '
+                       'hyperparams_pb.Hyperparams.')
+
+    self._batch_norm_params = None
+    if hyperparams_config.HasField('batch_norm'):
+      self._batch_norm_params = _build_keras_batch_norm_params(
+          hyperparams_config.batch_norm)
+
+    self._activation_fn = _build_activation_fn(hyperparams_config.activation)
+    # TODO(kaftan): Unclear if these kwargs apply to separable & depthwise conv
+    # (Those might use depthwise_* instead of kernel_*)
+    # We should probably switch to using build_conv2d_layer and
+    # build_depthwise_conv2d_layer methods instead.
+    self._op_params = {
+        'kernel_regularizer': _build_keras_regularizer(
+            hyperparams_config.regularizer),
+        'kernel_initializer': _build_initializer(
+            hyperparams_config.initializer, build_for_keras=True),
+        'activation': _build_activation_fn(hyperparams_config.activation)
+    }
+
+  def use_batch_norm(self):
+    return self._batch_norm_params is not None
+
+  def batch_norm_params(self, **overrides):
+    """Returns a dict containing batchnorm layer construction hyperparameters.
+
+    Optionally overrides values in the batchnorm hyperparam dict. Overrides
+    only apply to individual calls of this method, and do not affect
+    future calls.
+
+    Args:
+      **overrides: keyword arguments to override in the hyperparams dictionary
+
+    Returns: dict containing the layer construction keyword arguments, with
+      values overridden by the `overrides` keyword arguments.
+    """
+    if self._batch_norm_params is None:
+      new_batch_norm_params = dict()
+    else:
+      new_batch_norm_params = self._batch_norm_params.copy()
+    new_batch_norm_params.update(overrides)
+    return new_batch_norm_params
+
+  def build_batch_norm(self, training=None, **overrides):
+    """Returns a Batch Normalization layer with the appropriate hyperparams.
+
+    If the hyperparams are configured to not use batch normalization,
+    this will return a Keras Lambda layer that only applies tf.Identity,
+    without doing any normalization.
+
+    Optionally overrides values in the batch_norm hyperparam dict. Overrides
+    only apply to individual calls of this method, and do not affect
+    future calls.
+
+    Args:
+      training: if True, the normalization layer will normalize using the batch
+       statistics. If False, the normalization layer will be frozen and will
+       act as if it is being used for inference. If None, the layer
+       will look up the Keras learning phase at `call` time to decide what to
+       do.
+      **overrides: batch normalization construction args to override from the
+        batch_norm hyperparams dictionary.
+
+    Returns: Either a FreezableBatchNorm layer (if use_batch_norm() is True),
+      or a Keras Lambda layer that applies the identity (if use_batch_norm()
+      is False)
+    """
+    if self.use_batch_norm():
+      return freezable_batch_norm.FreezableBatchNorm(
+          training=training,
+          **self.batch_norm_params(**overrides)
+      )
+    else:
+      return tf.keras.layers.Lambda(tf.identity)
+
+  def build_activation_layer(self, name='activation'):
+    """Returns a Keras layer that applies the desired activation function.
+
+    Args:
+      name: The name to assign the Keras layer.
+    Returns: A Keras lambda layer that applies the activation function
+      specified in the hyperparam config, or applies the identity if the
+      activation function is None.
+    """
+    if self._activation_fn:
+      return tf.keras.layers.Lambda(self._activation_fn, name=name)
+    else:
+      return tf.keras.layers.Lambda(tf.identity, name=name)
+
+  def params(self, include_activation=False, **overrides):
+    """Returns a dict containing the layer construction hyperparameters to use.
+
+    Optionally overrides values in the returned dict. Overrides
+    only apply to individual calls of this method, and do not affect
+    future calls.
+
+    Args:
+      include_activation: If False, activation in the returned dictionary will
+        be set to `None`, and the activation must be applied via a separate
+        layer created by `build_activation_layer`. If True, `activation` in the
+        output param dictionary will be set to the activation function
+        specified in the hyperparams config.
+      **overrides: keyword arguments to override in the hyperparams dictionary.
+
+    Returns: dict containing the layer construction keyword arguments, with
+      values overridden by the `overrides` keyword arguments.
+    """
+    new_params = self._op_params.copy()
+    new_params['activation'] = None
+    if include_activation:
+      new_params['activation'] = self._activation_fn
+    if self.use_batch_norm() and self.batch_norm_params()['center']:
+      new_params['use_bias'] = False
+    else:
+      new_params['use_bias'] = True
+    new_params.update(**overrides)
+    return new_params
+
+
+def build(hyperparams_config, is_training):
+  """Builds tf-slim arg_scope for convolution ops based on the config.
+
+  Returns an arg_scope to use for convolution ops containing weights
+  initializer, weights regularizer, activation function, batch norm function
+  and batch norm parameters based on the configuration.
+
+  Note that if no normalization parameters are specified in the config,
+  (i.e. left to default) then both batch norm and group norm are excluded
+  from the arg_scope.
+
+  The batch norm parameters are set for updates based on `is_training` argument
+  and conv_hyperparams_config.batch_norm.train parameter. During training, they
+  are updated only if batch_norm.train parameter is true. However, during eval,
+  no updates are made to the batch norm variables. In both cases, their current
+  values are used during forward pass.
+
+  Args:
+    hyperparams_config: hyperparams.proto object containing
+      hyperparameters.
+    is_training: Whether the network is in training mode.
+
+  Returns:
+    arg_scope_fn: A function to construct tf-slim arg_scope containing
+      hyperparameters for ops.
+
+  Raises:
+    ValueError: if hyperparams_config is not of type hyperparams.Hyperparams.
+  """
+  if not isinstance(hyperparams_config,
+                    hyperparams_pb2.Hyperparams):
+    raise ValueError('hyperparams_config not of type '
+                     'hyperparams_pb.Hyperparams.')
+
+  normalizer_fn = None
+  batch_norm_params = None
+  if hyperparams_config.HasField('batch_norm'):
+    normalizer_fn = slim.batch_norm
+    batch_norm_params = _build_batch_norm_params(
+        hyperparams_config.batch_norm, is_training)
+  if hyperparams_config.HasField('group_norm'):
+    normalizer_fn = tf.contrib.layers.group_norm
+  affected_ops = [slim.conv2d, slim.separable_conv2d, slim.conv2d_transpose]
+  if hyperparams_config.HasField('op') and (
+      hyperparams_config.op == hyperparams_pb2.Hyperparams.FC):
+    affected_ops = [slim.fully_connected]
+  def scope_fn():
+    with (slim.arg_scope([slim.batch_norm], **batch_norm_params)
+          if batch_norm_params is not None else
+          context_manager.IdentityContextManager()):
+      with slim.arg_scope(
+          affected_ops,
+          weights_regularizer=_build_slim_regularizer(
+              hyperparams_config.regularizer),
+          weights_initializer=_build_initializer(
+              hyperparams_config.initializer),
+          activation_fn=_build_activation_fn(hyperparams_config.activation),
+          normalizer_fn=normalizer_fn) as sc:
+        return sc
+
+  return scope_fn
+
+
+def _build_activation_fn(activation_fn):
+  """Builds a callable activation from config.
+
+  Args:
+    activation_fn: hyperparams_pb2.Hyperparams.activation
+
+  Returns:
+    Callable activation function.
+
+  Raises:
+    ValueError: On unknown activation function.
+  """
+  if activation_fn == hyperparams_pb2.Hyperparams.NONE:
+    return None
+  if activation_fn == hyperparams_pb2.Hyperparams.RELU:
+    return tf.nn.relu
+  if activation_fn == hyperparams_pb2.Hyperparams.RELU_6:
+    return tf.nn.relu6
+  raise ValueError('Unknown activation function: {}'.format(activation_fn))
+
+
+def _build_slim_regularizer(regularizer):
+  """Builds a tf-slim regularizer from config.
+
+  Args:
+    regularizer: hyperparams_pb2.Hyperparams.regularizer proto.
+
+  Returns:
+    tf-slim regularizer.
+
+  Raises:
+    ValueError: On unknown regularizer.
+  """
+  regularizer_oneof = regularizer.WhichOneof('regularizer_oneof')
+  if  regularizer_oneof == 'l1_regularizer':
+    return slim.l1_regularizer(scale=float(regularizer.l1_regularizer.weight))
+  if regularizer_oneof == 'l2_regularizer':
+    return slim.l2_regularizer(scale=float(regularizer.l2_regularizer.weight))
+  if regularizer_oneof is None:
+    return None
+  raise ValueError('Unknown regularizer function: {}'.format(regularizer_oneof))
+
+
+def _build_keras_regularizer(regularizer):
+  """Builds a keras regularizer from config.
+
+  Args:
+    regularizer: hyperparams_pb2.Hyperparams.regularizer proto.
+
+  Returns:
+    Keras regularizer.
+
+  Raises:
+    ValueError: On unknown regularizer.
+  """
+  regularizer_oneof = regularizer.WhichOneof('regularizer_oneof')
+  if  regularizer_oneof == 'l1_regularizer':
+    return tf.keras.regularizers.l1(float(regularizer.l1_regularizer.weight))
+  if regularizer_oneof == 'l2_regularizer':
+    # The Keras L2 regularizer weight differs from the Slim L2 regularizer
+    # weight by a factor of 2
+    return tf.keras.regularizers.l2(
+        float(regularizer.l2_regularizer.weight * 0.5))
+  raise ValueError('Unknown regularizer function: {}'.format(regularizer_oneof))
+
+
+def _build_initializer(initializer, build_for_keras=False):
+  """Build a tf initializer from config.
+
+  Args:
+    initializer: hyperparams_pb2.Hyperparams.regularizer proto.
+    build_for_keras: Whether the initializers should be built for Keras
+      operators. If false builds for Slim.
+
+  Returns:
+    tf initializer.
+
+  Raises:
+    ValueError: On unknown initializer.
+  """
+  initializer_oneof = initializer.WhichOneof('initializer_oneof')
+  if initializer_oneof == 'truncated_normal_initializer':
+    return tf.truncated_normal_initializer(
+        mean=initializer.truncated_normal_initializer.mean,
+        stddev=initializer.truncated_normal_initializer.stddev)
+  if initializer_oneof == 'random_normal_initializer':
+    return tf.random_normal_initializer(
+        mean=initializer.random_normal_initializer.mean,
+        stddev=initializer.random_normal_initializer.stddev)
+  if initializer_oneof == 'variance_scaling_initializer':
+    enum_descriptor = (hyperparams_pb2.VarianceScalingInitializer.
+                       DESCRIPTOR.enum_types_by_name['Mode'])
+    mode = enum_descriptor.values_by_number[initializer.
+                                            variance_scaling_initializer.
+                                            mode].name
+    if build_for_keras:
+      if initializer.variance_scaling_initializer.uniform:
+        return tf.variance_scaling_initializer(
+            scale=initializer.variance_scaling_initializer.factor,
+            mode=mode.lower(),
+            distribution='uniform')
+      else:
+        # In TF 1.9 release and earlier, the truncated_normal distribution was
+        # not supported correctly. So, in these earlier versions of tensorflow,
+        # the ValueError will be raised, and we manually truncate the
+        # distribution scale.
+        #
+        # It is insufficient to just set distribution to `normal` from the
+        # start, because the `normal` distribution in newer Tensorflow versions
+        # creates a truncated distribution, whereas it created untruncated
+        # distributions in older versions.
+        try:
+          return tf.variance_scaling_initializer(
+              scale=initializer.variance_scaling_initializer.factor,
+              mode=mode.lower(),
+              distribution='truncated_normal')
+        except ValueError:
+          truncate_constant = 0.87962566103423978
+          truncated_scale = initializer.variance_scaling_initializer.factor / (
+              truncate_constant * truncate_constant
+          )
+          return tf.variance_scaling_initializer(
+              scale=truncated_scale,
+              mode=mode.lower(),
+              distribution='normal')
+
+    else:
+      return slim.variance_scaling_initializer(
+          factor=initializer.variance_scaling_initializer.factor,
+          mode=mode,
+          uniform=initializer.variance_scaling_initializer.uniform)
+  raise ValueError('Unknown initializer function: {}'.format(
+      initializer_oneof))
+
+
+def _build_batch_norm_params(batch_norm, is_training):
+  """Build a dictionary of batch_norm params from config.
+
+  Args:
+    batch_norm: hyperparams_pb2.ConvHyperparams.batch_norm proto.
+    is_training: Whether the models is in training mode.
+
+  Returns:
+    A dictionary containing batch_norm parameters.
+  """
+  batch_norm_params = {
+      'decay': batch_norm.decay,
+      'center': batch_norm.center,
+      'scale': batch_norm.scale,
+      'epsilon': batch_norm.epsilon,
+      # Remove is_training parameter from here and deprecate it in the proto
+      # once we refactor Faster RCNN models to set is_training through an outer
+      # arg_scope in the meta architecture.
+      'is_training': is_training and batch_norm.train,
+  }
+  return batch_norm_params
+
+
+def _build_keras_batch_norm_params(batch_norm):
+  """Build a dictionary of Keras BatchNormalization params from config.
+
+  Args:
+    batch_norm: hyperparams_pb2.ConvHyperparams.batch_norm proto.
+
+  Returns:
+    A dictionary containing Keras BatchNormalization parameters.
+  """
+  # Note: Although decay is defined to be 1 - momentum in batch_norm,
+  # decay in the slim batch_norm layers was erroneously defined and is
+  # actually the same as momentum in the Keras batch_norm layers.
+  # For context, see: github.com/keras-team/keras/issues/6839
+  batch_norm_params = {
+      'momentum': batch_norm.decay,
+      'center': batch_norm.center,
+      'scale': batch_norm.scale,
+      'epsilon': batch_norm.epsilon,
+  }
+  return batch_norm_params
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder_test.py
new file mode 100644
index 00000000..a83b9eea
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/hyperparams_builder_test.py
@@ -0,0 +1,865 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests object_detection.core.hyperparams_builder."""
+
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import hyperparams_builder
+from object_detection.core import freezable_batch_norm
+from object_detection.protos import hyperparams_pb2
+
+slim = tf.contrib.slim
+
+
+def _get_scope_key(op):
+  return getattr(op, '_key_op', str(op))
+
+
+class HyperparamsBuilderTest(tf.test.TestCase):
+
+  def test_default_arg_scope_has_conv2d_op(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    self.assertTrue(_get_scope_key(slim.conv2d) in scope)
+
+  def test_default_arg_scope_has_separable_conv2d_op(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    self.assertTrue(_get_scope_key(slim.separable_conv2d) in scope)
+
+  def test_default_arg_scope_has_conv2d_transpose_op(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    self.assertTrue(_get_scope_key(slim.conv2d_transpose) in scope)
+
+  def test_explicit_fc_op_arg_scope_has_fully_connected_op(self):
+    conv_hyperparams_text_proto = """
+      op: FC
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    self.assertTrue(_get_scope_key(slim.fully_connected) in scope)
+
+  def test_separable_conv2d_and_conv2d_and_transpose_have_same_parameters(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    kwargs_1, kwargs_2, kwargs_3 = scope.values()
+    self.assertDictEqual(kwargs_1, kwargs_2)
+    self.assertDictEqual(kwargs_1, kwargs_3)
+
+  def test_return_l1_regularized_weights(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.5
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope.values()[0]
+    regularizer = conv_scope_arguments['weights_regularizer']
+    weights = np.array([1., -1, 4., 2.])
+    with self.test_session() as sess:
+      result = sess.run(regularizer(tf.constant(weights)))
+    self.assertAllClose(np.abs(weights).sum() * 0.5, result)
+
+  def test_return_l1_regularized_weights_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+          weight: 0.5
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+
+    regularizer = keras_config.params()['kernel_regularizer']
+    weights = np.array([1., -1, 4., 2.])
+    with self.test_session() as sess:
+      result = sess.run(regularizer(tf.constant(weights)))
+    self.assertAllClose(np.abs(weights).sum() * 0.5, result)
+
+  def test_return_l2_regularizer_weights(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+          weight: 0.42
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+
+    regularizer = conv_scope_arguments['weights_regularizer']
+    weights = np.array([1., -1, 4., 2.])
+    with self.test_session() as sess:
+      result = sess.run(regularizer(tf.constant(weights)))
+    self.assertAllClose(np.power(weights, 2).sum() / 2.0 * 0.42, result)
+
+  def test_return_l2_regularizer_weights_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+          weight: 0.42
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+
+    regularizer = keras_config.params()['kernel_regularizer']
+    weights = np.array([1., -1, 4., 2.])
+    with self.test_session() as sess:
+      result = sess.run(regularizer(tf.constant(weights)))
+    self.assertAllClose(np.power(weights, 2).sum() / 2.0 * 0.42, result)
+
+  def test_return_non_default_batch_norm_params_with_train_during_train(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        decay: 0.7
+        center: false
+        scale: true
+        epsilon: 0.03
+        train: true
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
+    batch_norm_params = scope[_get_scope_key(slim.batch_norm)]
+    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
+    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
+    self.assertFalse(batch_norm_params['center'])
+    self.assertTrue(batch_norm_params['scale'])
+    self.assertTrue(batch_norm_params['is_training'])
+
+  def test_return_non_default_batch_norm_params_keras(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        decay: 0.7
+        center: false
+        scale: true
+        epsilon: 0.03
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+
+    self.assertTrue(keras_config.use_batch_norm())
+    batch_norm_params = keras_config.batch_norm_params()
+    self.assertAlmostEqual(batch_norm_params['momentum'], 0.7)
+    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
+    self.assertFalse(batch_norm_params['center'])
+    self.assertTrue(batch_norm_params['scale'])
+
+    batch_norm_layer = keras_config.build_batch_norm()
+    self.assertTrue(isinstance(batch_norm_layer,
+                               freezable_batch_norm.FreezableBatchNorm))
+
+  def test_return_non_default_batch_norm_params_keras_override(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        decay: 0.7
+        center: false
+        scale: true
+        epsilon: 0.03
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+
+    self.assertTrue(keras_config.use_batch_norm())
+    batch_norm_params = keras_config.batch_norm_params(momentum=0.4)
+    self.assertAlmostEqual(batch_norm_params['momentum'], 0.4)
+    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
+    self.assertFalse(batch_norm_params['center'])
+    self.assertTrue(batch_norm_params['scale'])
+
+  def test_return_batch_norm_params_with_notrain_during_eval(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        decay: 0.7
+        center: false
+        scale: true
+        epsilon: 0.03
+        train: true
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=False)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
+    batch_norm_params = scope[_get_scope_key(slim.batch_norm)]
+    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
+    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
+    self.assertFalse(batch_norm_params['center'])
+    self.assertTrue(batch_norm_params['scale'])
+    self.assertFalse(batch_norm_params['is_training'])
+
+  def test_return_batch_norm_params_with_notrain_when_train_is_false(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        decay: 0.7
+        center: false
+        scale: true
+        epsilon: 0.03
+        train: false
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
+    batch_norm_params = scope[_get_scope_key(slim.batch_norm)]
+    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
+    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
+    self.assertFalse(batch_norm_params['center'])
+    self.assertTrue(batch_norm_params['scale'])
+    self.assertFalse(batch_norm_params['is_training'])
+
+  def test_do_not_use_batch_norm_if_default(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['normalizer_fn'], None)
+
+  def test_do_not_use_batch_norm_if_default_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    self.assertFalse(keras_config.use_batch_norm())
+    self.assertEqual(keras_config.batch_norm_params(), {})
+
+    # The batch norm builder should build an identity Lambda layer
+    identity_layer = keras_config.build_batch_norm()
+    self.assertTrue(isinstance(identity_layer,
+                               tf.keras.layers.Lambda))
+
+  def test_use_none_activation(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: NONE
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['activation_fn'], None)
+
+  def test_use_none_activation_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: NONE
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    self.assertEqual(keras_config.params()['activation'], None)
+    self.assertEqual(
+        keras_config.params(include_activation=True)['activation'], None)
+    activation_layer = keras_config.build_activation_layer()
+    self.assertTrue(isinstance(activation_layer, tf.keras.layers.Lambda))
+    self.assertEqual(activation_layer.function, tf.identity)
+
+  def test_use_relu_activation(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['activation_fn'], tf.nn.relu)
+
+  def test_use_relu_activation_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    self.assertEqual(keras_config.params()['activation'], None)
+    self.assertEqual(
+        keras_config.params(include_activation=True)['activation'], tf.nn.relu)
+    activation_layer = keras_config.build_activation_layer()
+    self.assertTrue(isinstance(activation_layer, tf.keras.layers.Lambda))
+    self.assertEqual(activation_layer.function, tf.nn.relu)
+
+  def test_use_relu_6_activation(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    self.assertEqual(conv_scope_arguments['activation_fn'], tf.nn.relu6)
+
+  def test_use_relu_6_activation_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    self.assertEqual(keras_config.params()['activation'], None)
+    self.assertEqual(
+        keras_config.params(include_activation=True)['activation'], tf.nn.relu6)
+    activation_layer = keras_config.build_activation_layer()
+    self.assertTrue(isinstance(activation_layer, tf.keras.layers.Lambda))
+    self.assertEqual(activation_layer.function, tf.nn.relu6)
+
+  def test_override_activation_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      activation: RELU_6
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    new_params = keras_config.params(activation=tf.nn.relu)
+    self.assertEqual(new_params['activation'], tf.nn.relu)
+
+  def _assert_variance_in_range(self, initializer, shape, variance,
+                                tol=1e-2):
+    with tf.Graph().as_default() as g:
+      with self.test_session(graph=g) as sess:
+        var = tf.get_variable(
+            name='test',
+            shape=shape,
+            dtype=tf.float32,
+            initializer=initializer)
+        sess.run(tf.global_variables_initializer())
+        values = sess.run(var)
+        self.assertAllClose(np.var(values), variance, tol, tol)
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_in(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_IN
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 100.)
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_in_keras(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_IN
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 100.)
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_out(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_OUT
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 40.)
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_out_keras(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_OUT
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 40.)
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_avg(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_AVG
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=4. / (100. + 40.))
+
+  def test_variance_in_range_with_variance_scaling_initializer_fan_avg_keras(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_AVG
+          uniform: false
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=4. / (100. + 40.))
+
+  def test_variance_in_range_with_variance_scaling_initializer_uniform(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_IN
+          uniform: true
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 100.)
+
+  def test_variance_in_range_with_variance_scaling_initializer_uniform_keras(
+      self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        variance_scaling_initializer {
+          factor: 2.0
+          mode: FAN_IN
+          uniform: true
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=2. / 100.)
+
+  def test_variance_in_range_with_truncated_normal_initializer(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.8
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=0.49, tol=1e-1)
+
+  def test_variance_in_range_with_truncated_normal_initializer_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          mean: 0.0
+          stddev: 0.8
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=0.49, tol=1e-1)
+
+  def test_variance_in_range_with_random_normal_initializer(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        random_normal_initializer {
+          mean: 0.0
+          stddev: 0.8
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    scope_fn = hyperparams_builder.build(conv_hyperparams_proto,
+                                         is_training=True)
+    scope = scope_fn()
+    conv_scope_arguments = scope[_get_scope_key(slim.conv2d)]
+    initializer = conv_scope_arguments['weights_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=0.64, tol=1e-1)
+
+  def test_variance_in_range_with_random_normal_initializer_keras(self):
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        random_normal_initializer {
+          mean: 0.0
+          stddev: 0.8
+        }
+      }
+    """
+    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
+    keras_config = hyperparams_builder.KerasLayerHyperparams(
+        conv_hyperparams_proto)
+    initializer = keras_config.params()['kernel_initializer']
+    self._assert_variance_in_range(initializer, shape=[100, 40],
+                                   variance=0.64, tol=1e-1)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder.py
new file mode 100644
index 00000000..243c84dd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder.py
@@ -0,0 +1,140 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Builder function for image resizing operations."""
+import functools
+import tensorflow as tf
+
+from object_detection.core import preprocessor
+from object_detection.protos import image_resizer_pb2
+
+
+def _tf_resize_method(resize_method):
+  """Maps image resize method from enumeration type to TensorFlow.
+
+  Args:
+    resize_method: The resize_method attribute of keep_aspect_ratio_resizer or
+      fixed_shape_resizer.
+
+  Returns:
+    method: The corresponding TensorFlow ResizeMethod.
+
+  Raises:
+    ValueError: if `resize_method` is of unknown type.
+  """
+  dict_method = {
+      image_resizer_pb2.BILINEAR:
+          tf.image.ResizeMethod.BILINEAR,
+      image_resizer_pb2.NEAREST_NEIGHBOR:
+          tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+      image_resizer_pb2.BICUBIC:
+          tf.image.ResizeMethod.BICUBIC,
+      image_resizer_pb2.AREA:
+          tf.image.ResizeMethod.AREA
+  }
+  if resize_method in dict_method:
+    return dict_method[resize_method]
+  else:
+    raise ValueError('Unknown resize_method')
+
+
+def build(image_resizer_config):
+  """Builds callable for image resizing operations.
+
+  Args:
+    image_resizer_config: image_resizer.proto object containing parameters for
+      an image resizing operation.
+
+  Returns:
+    image_resizer_fn: Callable for image resizing.  This callable always takes
+      a rank-3 image tensor (corresponding to a single image) and returns a
+      rank-3 image tensor, possibly with new spatial dimensions.
+
+  Raises:
+    ValueError: if `image_resizer_config` is of incorrect type.
+    ValueError: if `image_resizer_config.image_resizer_oneof` is of expected
+      type.
+    ValueError: if min_dimension > max_dimension when keep_aspect_ratio_resizer
+      is used.
+  """
+  if not isinstance(image_resizer_config, image_resizer_pb2.ImageResizer):
+    raise ValueError('image_resizer_config not of type '
+                     'image_resizer_pb2.ImageResizer.')
+
+  image_resizer_oneof = image_resizer_config.WhichOneof('image_resizer_oneof')
+  if image_resizer_oneof == 'keep_aspect_ratio_resizer':
+    keep_aspect_ratio_config = image_resizer_config.keep_aspect_ratio_resizer
+    if not (keep_aspect_ratio_config.min_dimension <=
+            keep_aspect_ratio_config.max_dimension):
+      raise ValueError('min_dimension > max_dimension')
+    method = _tf_resize_method(keep_aspect_ratio_config.resize_method)
+    per_channel_pad_value = (0, 0, 0)
+    if keep_aspect_ratio_config.per_channel_pad_value:
+      per_channel_pad_value = tuple(keep_aspect_ratio_config.
+                                    per_channel_pad_value)
+    image_resizer_fn = functools.partial(
+        preprocessor.resize_to_range,
+        min_dimension=keep_aspect_ratio_config.min_dimension,
+        max_dimension=keep_aspect_ratio_config.max_dimension,
+        method=method,
+        pad_to_max_dimension=keep_aspect_ratio_config.pad_to_max_dimension,
+        per_channel_pad_value=per_channel_pad_value)
+    if not keep_aspect_ratio_config.convert_to_grayscale:
+      return image_resizer_fn
+  elif image_resizer_oneof == 'fixed_shape_resizer':
+    fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer
+    method = _tf_resize_method(fixed_shape_resizer_config.resize_method)
+    image_resizer_fn = functools.partial(
+        preprocessor.resize_image,
+        new_height=fixed_shape_resizer_config.height,
+        new_width=fixed_shape_resizer_config.width,
+        method=method)
+    if not fixed_shape_resizer_config.convert_to_grayscale:
+      return image_resizer_fn
+  else:
+    raise ValueError(
+        'Invalid image resizer option: \'%s\'.' % image_resizer_oneof)
+
+  def grayscale_image_resizer(image, masks=None):
+    """Convert to grayscale before applying image_resizer_fn.
+
+    Args:
+      image: A 3D tensor of shape [height, width, 3]
+      masks: (optional) rank 3 float32 tensor with shape [num_instances, height,
+        width] containing instance masks.
+
+    Returns:
+    Note that the position of the resized_image_shape changes based on whether
+    masks are present.
+    resized_image: A 3D tensor of shape [new_height, new_width, 1],
+      where the image has been resized (with bilinear interpolation) so that
+      min(new_height, new_width) == min_dimension or
+      max(new_height, new_width) == max_dimension.
+    resized_masks: If masks is not None, also outputs masks. A 3D tensor of
+      shape [num_instances, new_height, new_width].
+    resized_image_shape: A 1D tensor of shape [3] containing shape of the
+      resized image.
+    """
+    # image_resizer_fn returns [resized_image, resized_image_shape] if
+    # mask==None, otherwise it returns
+    # [resized_image, resized_mask, resized_image_shape]. In either case, we
+    # only deal with first and last element of the returned list.
+    retval = image_resizer_fn(image, masks)
+    resized_image = retval[0]
+    resized_image_shape = retval[-1]
+    retval[0] = preprocessor.rgb_to_gray(resized_image)
+    retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0)
+    return retval
+
+  return functools.partial(grayscale_image_resizer)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder_test.py
new file mode 100644
index 00000000..f7da1912
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/image_resizer_builder_test.py
@@ -0,0 +1,141 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.builders.image_resizer_builder."""
+import numpy as np
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection.builders import image_resizer_builder
+from object_detection.protos import image_resizer_pb2
+
+
+class ImageResizerBuilderTest(tf.test.TestCase):
+
+  def _shape_of_resized_random_image_given_text_proto(self, input_shape,
+                                                      text_proto):
+    image_resizer_config = image_resizer_pb2.ImageResizer()
+    text_format.Merge(text_proto, image_resizer_config)
+    image_resizer_fn = image_resizer_builder.build(image_resizer_config)
+    images = tf.to_float(
+        tf.random_uniform(input_shape, minval=0, maxval=255, dtype=tf.int32))
+    resized_images, _ = image_resizer_fn(images)
+    with self.test_session() as sess:
+      return sess.run(resized_images).shape
+
+  def test_build_keep_aspect_ratio_resizer_returns_expected_shape(self):
+    image_resizer_text_proto = """
+      keep_aspect_ratio_resizer {
+        min_dimension: 10
+        max_dimension: 20
+      }
+    """
+    input_shape = (50, 25, 3)
+    expected_output_shape = (20, 10, 3)
+    output_shape = self._shape_of_resized_random_image_given_text_proto(
+        input_shape, image_resizer_text_proto)
+    self.assertEqual(output_shape, expected_output_shape)
+
+  def test_build_keep_aspect_ratio_resizer_grayscale(self):
+    image_resizer_text_proto = """
+      keep_aspect_ratio_resizer {
+        min_dimension: 10
+        max_dimension: 20
+        convert_to_grayscale: true
+      }
+    """
+    input_shape = (50, 25, 3)
+    expected_output_shape = (20, 10, 1)
+    output_shape = self._shape_of_resized_random_image_given_text_proto(
+        input_shape, image_resizer_text_proto)
+    self.assertEqual(output_shape, expected_output_shape)
+
+  def test_build_keep_aspect_ratio_resizer_with_padding(self):
+    image_resizer_text_proto = """
+      keep_aspect_ratio_resizer {
+        min_dimension: 10
+        max_dimension: 20
+        pad_to_max_dimension: true
+        per_channel_pad_value: 3
+        per_channel_pad_value: 4
+        per_channel_pad_value: 5
+      }
+    """
+    input_shape = (50, 25, 3)
+    expected_output_shape = (20, 20, 3)
+    output_shape = self._shape_of_resized_random_image_given_text_proto(
+        input_shape, image_resizer_text_proto)
+    self.assertEqual(output_shape, expected_output_shape)
+
+  def test_built_fixed_shape_resizer_returns_expected_shape(self):
+    image_resizer_text_proto = """
+      fixed_shape_resizer {
+        height: 10
+        width: 20
+      }
+    """
+    input_shape = (50, 25, 3)
+    expected_output_shape = (10, 20, 3)
+    output_shape = self._shape_of_resized_random_image_given_text_proto(
+        input_shape, image_resizer_text_proto)
+    self.assertEqual(output_shape, expected_output_shape)
+
+  def test_built_fixed_shape_resizer_grayscale(self):
+    image_resizer_text_proto = """
+      fixed_shape_resizer {
+        height: 10
+        width: 20
+        convert_to_grayscale: true
+      }
+    """
+    input_shape = (50, 25, 3)
+    expected_output_shape = (10, 20, 1)
+    output_shape = self._shape_of_resized_random_image_given_text_proto(
+        input_shape, image_resizer_text_proto)
+    self.assertEqual(output_shape, expected_output_shape)
+
+  def test_raises_error_on_invalid_input(self):
+    invalid_input = 'invalid_input'
+    with self.assertRaises(ValueError):
+      image_resizer_builder.build(invalid_input)
+
+  def _resized_image_given_text_proto(self, image, text_proto):
+    image_resizer_config = image_resizer_pb2.ImageResizer()
+    text_format.Merge(text_proto, image_resizer_config)
+    image_resizer_fn = image_resizer_builder.build(image_resizer_config)
+    image_placeholder = tf.placeholder(tf.uint8, [1, None, None, 3])
+    resized_image, _ = image_resizer_fn(image_placeholder)
+    with self.test_session() as sess:
+      return sess.run(resized_image, feed_dict={image_placeholder: image})
+
+  def test_fixed_shape_resizer_nearest_neighbor_method(self):
+    image_resizer_text_proto = """
+      fixed_shape_resizer {
+        height: 1
+        width: 1
+        resize_method: NEAREST_NEIGHBOR
+      }
+    """
+    image = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    image = np.expand_dims(image, axis=2)
+    image = np.tile(image, (1, 1, 3))
+    image = np.expand_dims(image, axis=0)
+    resized_image = self._resized_image_given_text_proto(
+        image, image_resizer_text_proto)
+    vals = np.unique(resized_image).tolist()
+    self.assertEqual(len(vals), 1)
+    self.assertEqual(vals[0], 1)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder.py
new file mode 100644
index 00000000..8cb5e2f0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder.py
@@ -0,0 +1,76 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Input reader builder.
+
+Creates data sources for DetectionModels from an InputReader config. See
+input_reader.proto for options.
+
+Note: If users wishes to also use their own InputReaders with the Object
+Detection configuration framework, they should define their own builder function
+that wraps the build function.
+"""
+
+import tensorflow as tf
+
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import input_reader_pb2
+
+parallel_reader = tf.contrib.slim.parallel_reader
+
+
+def build(input_reader_config):
+  """Builds a tensor dictionary based on the InputReader config.
+
+  Args:
+    input_reader_config: A input_reader_pb2.InputReader object.
+
+  Returns:
+    A tensor dict based on the input_reader_config.
+
+  Raises:
+    ValueError: On invalid input reader proto.
+    ValueError: If no input paths are specified.
+  """
+  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
+    raise ValueError('input_reader_config not of type '
+                     'input_reader_pb2.InputReader.')
+
+  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
+    config = input_reader_config.tf_record_input_reader
+    if not config.input_path:
+      raise ValueError('At least one input path must be specified in '
+                       '`input_reader_config`.')
+    _, string_tensor = parallel_reader.parallel_read(
+        config.input_path[:],  # Convert `RepeatedScalarContainer` to list.
+        reader_class=tf.TFRecordReader,
+        num_epochs=(input_reader_config.num_epochs
+                    if input_reader_config.num_epochs else None),
+        num_readers=input_reader_config.num_readers,
+        shuffle=input_reader_config.shuffle,
+        dtypes=[tf.string, tf.string],
+        capacity=input_reader_config.queue_capacity,
+        min_after_dequeue=input_reader_config.min_after_dequeue)
+
+    label_map_proto_file = None
+    if input_reader_config.HasField('label_map_path'):
+      label_map_proto_file = input_reader_config.label_map_path
+    decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=input_reader_config.load_instance_masks,
+        instance_mask_type=input_reader_config.mask_type,
+        label_map_proto_file=label_map_proto_file)
+    return decoder.decode(string_tensor)
+
+  raise ValueError('Unsupported input_reader_config.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder_test.py
new file mode 100644
index 00000000..c2c8ef4f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/input_reader_builder_test.py
@@ -0,0 +1,129 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for input_reader_builder."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import input_reader_builder
+from object_detection.core import standard_fields as fields
+from object_detection.protos import input_reader_pb2
+from object_detection.utils import dataset_util
+
+
+class InputReaderBuilderTest(tf.test.TestCase):
+
+  def create_tf_record(self):
+    path = os.path.join(self.get_temp_dir(), 'tfrecord')
+    writer = tf.python_io.TFRecordWriter(path)
+
+    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+    flat_mask = (4 * 5) * [1.0]
+    with self.test_session():
+      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+    example = tf.train.Example(features=tf.train.Features(feature={
+        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
+        'image/height': dataset_util.int64_feature(4),
+        'image/width': dataset_util.int64_feature(5),
+        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
+        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
+        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
+        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
+        'image/object/class/label': dataset_util.int64_list_feature([2]),
+        'image/object/mask': dataset_util.float_list_feature(flat_mask),
+    }))
+    writer.write(example.SerializeToString())
+    writer.close()
+
+    return path
+
+  def test_build_tf_record_input_reader(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = input_reader_builder.build(input_reader_proto)
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertTrue(fields.InputDataFields.groundtruth_instance_masks
+                    not in output_dict)
+    self.assertEquals(
+        (4, 5, 3), output_dict[fields.InputDataFields.image].shape)
+    self.assertEquals(
+        [2], output_dict[fields.InputDataFields.groundtruth_classes])
+    self.assertEquals(
+        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllEqual(
+        [0.0, 0.0, 1.0, 1.0],
+        output_dict[fields.InputDataFields.groundtruth_boxes][0])
+
+  def test_build_tf_record_input_reader_and_load_instance_masks(self):
+    tf_record_path = self.create_tf_record()
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      load_instance_masks: true
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    tensor_dict = input_reader_builder.build(input_reader_proto)
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertEquals(
+        (4, 5, 3), output_dict[fields.InputDataFields.image].shape)
+    self.assertEquals(
+        [2], output_dict[fields.InputDataFields.groundtruth_classes])
+    self.assertEquals(
+        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllEqual(
+        [0.0, 0.0, 1.0, 1.0],
+        output_dict[fields.InputDataFields.groundtruth_boxes][0])
+    self.assertAllEqual(
+        (1, 4, 5),
+        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
+
+  def test_raises_error_with_no_input_paths(self):
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      load_instance_masks: true
+    """
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    with self.assertRaises(ValueError):
+      input_reader_builder.build(input_reader_proto)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder.py
new file mode 100644
index 00000000..2b98d0aa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder.py
@@ -0,0 +1,252 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A function to build localization and classification losses from config."""
+
+import functools
+from object_detection.core import balanced_positive_negative_sampler as sampler
+from object_detection.core import losses
+from object_detection.protos import losses_pb2
+from object_detection.utils import ops
+
+
+def build(loss_config):
+  """Build losses based on the config.
+
+  Builds classification, localization losses and optionally a hard example miner
+  based on the config.
+
+  Args:
+    loss_config: A losses_pb2.Loss object.
+
+  Returns:
+    classification_loss: Classification loss object.
+    localization_loss: Localization loss object.
+    classification_weight: Classification loss weight.
+    localization_weight: Localization loss weight.
+    hard_example_miner: Hard example miner object.
+    random_example_sampler: BalancedPositiveNegativeSampler object.
+
+  Raises:
+    ValueError: If hard_example_miner is used with sigmoid_focal_loss.
+    ValueError: If random_example_sampler is getting non-positive value as
+      desired positive example fraction.
+  """
+  classification_loss = _build_classification_loss(
+      loss_config.classification_loss)
+  localization_loss = _build_localization_loss(
+      loss_config.localization_loss)
+  classification_weight = loss_config.classification_weight
+  localization_weight = loss_config.localization_weight
+  hard_example_miner = None
+  if loss_config.HasField('hard_example_miner'):
+    if (loss_config.classification_loss.WhichOneof('classification_loss') ==
+        'weighted_sigmoid_focal'):
+      raise ValueError('HardExampleMiner should not be used with sigmoid focal '
+                       'loss')
+    hard_example_miner = build_hard_example_miner(
+        loss_config.hard_example_miner,
+        classification_weight,
+        localization_weight)
+  random_example_sampler = None
+  if loss_config.HasField('random_example_sampler'):
+    if loss_config.random_example_sampler.positive_sample_fraction <= 0:
+      raise ValueError('RandomExampleSampler should not use non-positive'
+                       'value as positive sample fraction.')
+    random_example_sampler = sampler.BalancedPositiveNegativeSampler(
+        positive_fraction=loss_config.random_example_sampler.
+        positive_sample_fraction)
+
+  if loss_config.expected_loss_weights == loss_config.NONE:
+    expected_loss_weights_fn = None
+  elif loss_config.expected_loss_weights == loss_config.EXPECTED_SAMPLING:
+    expected_loss_weights_fn = functools.partial(
+        ops.expected_classification_loss_by_expected_sampling,
+        min_num_negative_samples=loss_config.min_num_negative_samples,
+        desired_negative_sampling_ratio=loss_config
+        .desired_negative_sampling_ratio)
+  elif (loss_config.expected_loss_weights == loss_config
+        .REWEIGHTING_UNMATCHED_ANCHORS):
+    expected_loss_weights_fn = functools.partial(
+        ops.expected_classification_loss_by_reweighting_unmatched_anchors,
+        min_num_negative_samples=loss_config.min_num_negative_samples,
+        desired_negative_sampling_ratio=loss_config
+        .desired_negative_sampling_ratio)
+  else:
+    raise ValueError('Not a valid value for expected_classification_loss.')
+
+  return (classification_loss, localization_loss, classification_weight,
+          localization_weight, hard_example_miner, random_example_sampler,
+          expected_loss_weights_fn)
+
+
+def build_hard_example_miner(config,
+                             classification_weight,
+                             localization_weight):
+  """Builds hard example miner based on the config.
+
+  Args:
+    config: A losses_pb2.HardExampleMiner object.
+    classification_weight: Classification loss weight.
+    localization_weight: Localization loss weight.
+
+  Returns:
+    Hard example miner.
+
+  """
+  loss_type = None
+  if config.loss_type == losses_pb2.HardExampleMiner.BOTH:
+    loss_type = 'both'
+  if config.loss_type == losses_pb2.HardExampleMiner.CLASSIFICATION:
+    loss_type = 'cls'
+  if config.loss_type == losses_pb2.HardExampleMiner.LOCALIZATION:
+    loss_type = 'loc'
+
+  max_negatives_per_positive = None
+  num_hard_examples = None
+  if config.max_negatives_per_positive > 0:
+    max_negatives_per_positive = config.max_negatives_per_positive
+  if config.num_hard_examples > 0:
+    num_hard_examples = config.num_hard_examples
+  hard_example_miner = losses.HardExampleMiner(
+      num_hard_examples=num_hard_examples,
+      iou_threshold=config.iou_threshold,
+      loss_type=loss_type,
+      cls_loss_weight=classification_weight,
+      loc_loss_weight=localization_weight,
+      max_negatives_per_positive=max_negatives_per_positive,
+      min_negatives_per_image=config.min_negatives_per_image)
+  return hard_example_miner
+
+
+def build_faster_rcnn_classification_loss(loss_config):
+  """Builds a classification loss for Faster RCNN based on the loss config.
+
+  Args:
+    loss_config: A losses_pb2.ClassificationLoss object.
+
+  Returns:
+    Loss based on the config.
+
+  Raises:
+    ValueError: On invalid loss_config.
+  """
+  if not isinstance(loss_config, losses_pb2.ClassificationLoss):
+    raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
+
+  loss_type = loss_config.WhichOneof('classification_loss')
+
+  if loss_type == 'weighted_sigmoid':
+    return losses.WeightedSigmoidClassificationLoss()
+  if loss_type == 'weighted_softmax':
+    config = loss_config.weighted_softmax
+    return losses.WeightedSoftmaxClassificationLoss(
+        logit_scale=config.logit_scale)
+  if loss_type == 'weighted_logits_softmax':
+    config = loss_config.weighted_logits_softmax
+    return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
+        logit_scale=config.logit_scale)
+  if loss_type == 'weighted_sigmoid_focal':
+    config = loss_config.weighted_sigmoid_focal
+    alpha = None
+    if config.HasField('alpha'):
+      alpha = config.alpha
+    return losses.SigmoidFocalClassificationLoss(
+        gamma=config.gamma,
+        alpha=alpha)
+
+  # By default, Faster RCNN second stage classifier uses Softmax loss
+  # with anchor-wise outputs.
+  config = loss_config.weighted_softmax
+  return losses.WeightedSoftmaxClassificationLoss(
+      logit_scale=config.logit_scale)
+
+
+def _build_localization_loss(loss_config):
+  """Builds a localization loss based on the loss config.
+
+  Args:
+    loss_config: A losses_pb2.LocalizationLoss object.
+
+  Returns:
+    Loss based on the config.
+
+  Raises:
+    ValueError: On invalid loss_config.
+  """
+  if not isinstance(loss_config, losses_pb2.LocalizationLoss):
+    raise ValueError('loss_config not of type losses_pb2.LocalizationLoss.')
+
+  loss_type = loss_config.WhichOneof('localization_loss')
+
+  if loss_type == 'weighted_l2':
+    return losses.WeightedL2LocalizationLoss()
+
+  if loss_type == 'weighted_smooth_l1':
+    return losses.WeightedSmoothL1LocalizationLoss(
+        loss_config.weighted_smooth_l1.delta)
+
+  if loss_type == 'weighted_iou':
+    return losses.WeightedIOULocalizationLoss()
+
+  raise ValueError('Empty loss config.')
+
+
+def _build_classification_loss(loss_config):
+  """Builds a classification loss based on the loss config.
+
+  Args:
+    loss_config: A losses_pb2.ClassificationLoss object.
+
+  Returns:
+    Loss based on the config.
+
+  Raises:
+    ValueError: On invalid loss_config.
+  """
+  if not isinstance(loss_config, losses_pb2.ClassificationLoss):
+    raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
+
+  loss_type = loss_config.WhichOneof('classification_loss')
+
+  if loss_type == 'weighted_sigmoid':
+    return losses.WeightedSigmoidClassificationLoss()
+
+  if loss_type == 'weighted_sigmoid_focal':
+    config = loss_config.weighted_sigmoid_focal
+    alpha = None
+    if config.HasField('alpha'):
+      alpha = config.alpha
+    return losses.SigmoidFocalClassificationLoss(
+        gamma=config.gamma,
+        alpha=alpha)
+
+  if loss_type == 'weighted_softmax':
+    config = loss_config.weighted_softmax
+    return losses.WeightedSoftmaxClassificationLoss(
+        logit_scale=config.logit_scale)
+
+  if loss_type == 'weighted_logits_softmax':
+    config = loss_config.weighted_logits_softmax
+    return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
+        logit_scale=config.logit_scale)
+
+  if loss_type == 'bootstrapped_sigmoid':
+    config = loss_config.bootstrapped_sigmoid
+    return losses.BootstrappedSigmoidClassificationLoss(
+        alpha=config.alpha,
+        bootstrap_type=('hard' if config.hard_bootstrap else 'soft'))
+
+  raise ValueError('Empty loss config.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder_test.py
new file mode 100644
index 00000000..24b96b40
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/losses_builder_test.py
@@ -0,0 +1,561 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for losses_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import losses_builder
+from object_detection.core import losses
+from object_detection.protos import losses_pb2
+from object_detection.utils import ops
+
+
+class LocalizationLossBuilderTest(tf.test.TestCase):
+
+  def test_build_weighted_l2_localization_loss(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedL2LocalizationLoss))
+
+  def test_build_weighted_smooth_l1_localization_loss_default_delta(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedSmoothL1LocalizationLoss))
+    self.assertAlmostEqual(localization_loss._delta, 1.0)
+
+  def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_smooth_l1 {
+          delta: 0.1
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedSmoothL1LocalizationLoss))
+    self.assertAlmostEqual(localization_loss._delta, 0.1)
+
+  def test_build_weighted_iou_localization_loss(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_iou {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedIOULocalizationLoss))
+
+  def test_anchorwise_output(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedSmoothL1LocalizationLoss))
+    predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
+    targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
+    weights = tf.constant([[1.0, 1.0]])
+    loss = localization_loss(predictions, targets, weights=weights)
+    self.assertEqual(loss.shape, [1, 2])
+
+  def test_raise_error_on_empty_localization_config(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    with self.assertRaises(ValueError):
+      losses_builder._build_localization_loss(losses_proto)
+
+
+class ClassificationLossBuilderTest(tf.test.TestCase):
+
+  def test_build_weighted_sigmoid_classification_loss(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSigmoidClassificationLoss))
+
+  def test_build_weighted_sigmoid_focal_classification_loss(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_sigmoid_focal {
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.SigmoidFocalClassificationLoss))
+    self.assertAlmostEqual(classification_loss._alpha, None)
+    self.assertAlmostEqual(classification_loss._gamma, 2.0)
+
+  def test_build_weighted_sigmoid_focal_loss_non_default(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 3.0
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.SigmoidFocalClassificationLoss))
+    self.assertAlmostEqual(classification_loss._alpha, 0.25)
+    self.assertAlmostEqual(classification_loss._gamma, 3.0)
+
+  def test_build_weighted_softmax_classification_loss(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSoftmaxClassificationLoss))
+
+  def test_build_weighted_logits_softmax_classification_loss(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_logits_softmax {
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(
+        isinstance(classification_loss,
+                   losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
+
+  def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_softmax {
+          logit_scale: 2.0
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSoftmaxClassificationLoss))
+
+  def test_build_bootstrapped_sigmoid_classification_loss(self):
+    losses_text_proto = """
+      classification_loss {
+        bootstrapped_sigmoid {
+          alpha: 0.5
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.BootstrappedSigmoidClassificationLoss))
+
+  def test_anchorwise_output(self):
+    losses_text_proto = """
+      classification_loss {
+        weighted_sigmoid {
+          anchorwise_output: true
+        }
+      }
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss, _, _, _, _, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSigmoidClassificationLoss))
+    predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
+    targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
+    weights = tf.constant([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]])
+    loss = classification_loss(predictions, targets, weights=weights)
+    self.assertEqual(loss.shape, [1, 2, 3])
+
+  def test_raise_error_on_empty_config(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    with self.assertRaises(ValueError):
+      losses_builder.build(losses_proto)
+
+
+class HardExampleMinerBuilderTest(tf.test.TestCase):
+
+  def test_do_not_build_hard_example_miner_by_default(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
+    self.assertEqual(hard_example_miner, None)
+
+  def test_build_hard_example_miner_for_classification_loss(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+        loss_type: CLASSIFICATION
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertEqual(hard_example_miner._loss_type, 'cls')
+
+  def test_build_hard_example_miner_for_localization_loss(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+        loss_type: LOCALIZATION
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertEqual(hard_example_miner._loss_type, 'loc')
+
+  def test_build_hard_example_miner_with_non_default_values(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 32
+        iou_threshold: 0.5
+        loss_type: LOCALIZATION
+        max_negatives_per_positive: 10
+        min_negatives_per_image: 3
+      }
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    _, _, _, _, hard_example_miner, _, _ = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertEqual(hard_example_miner._num_hard_examples, 32)
+    self.assertAlmostEqual(hard_example_miner._iou_threshold, 0.5)
+    self.assertEqual(hard_example_miner._max_negatives_per_positive, 10)
+    self.assertEqual(hard_example_miner._min_negatives_per_image, 3)
+
+
+class LossBuilderTest(tf.test.TestCase):
+
+  def test_build_all_loss_parameters(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+      }
+      classification_weight: 0.8
+      localization_weight: 0.2
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    (classification_loss, localization_loss, classification_weight,
+     localization_weight, hard_example_miner, _,
+     _) = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSoftmaxClassificationLoss))
+    self.assertTrue(isinstance(localization_loss,
+                               losses.WeightedL2LocalizationLoss))
+    self.assertAlmostEqual(classification_weight, 0.8)
+    self.assertAlmostEqual(localization_weight, 0.2)
+
+  def test_build_expected_sampling(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+      }
+      classification_weight: 0.8
+      localization_weight: 0.2
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    (classification_loss, localization_loss, classification_weight,
+     localization_weight, hard_example_miner, _,
+     _) = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertTrue(
+        isinstance(classification_loss,
+                   losses.WeightedSoftmaxClassificationLoss))
+    self.assertTrue(
+        isinstance(localization_loss, losses.WeightedL2LocalizationLoss))
+    self.assertAlmostEqual(classification_weight, 0.8)
+    self.assertAlmostEqual(localization_weight, 0.2)
+
+
+  def test_build_reweighting_unmatched_anchors(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_softmax {
+        }
+      }
+      hard_example_miner {
+      }
+      classification_weight: 0.8
+      localization_weight: 0.2
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    (classification_loss, localization_loss, classification_weight,
+     localization_weight, hard_example_miner, _,
+     _) = losses_builder.build(losses_proto)
+    self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
+    self.assertTrue(
+        isinstance(classification_loss,
+                   losses.WeightedSoftmaxClassificationLoss))
+    self.assertTrue(
+        isinstance(localization_loss, losses.WeightedL2LocalizationLoss))
+    self.assertAlmostEqual(classification_weight, 0.8)
+    self.assertAlmostEqual(localization_weight, 0.2)
+
+  def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):
+    losses_text_proto = """
+      localization_loss {
+        weighted_l2 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid_focal {
+        }
+      }
+      hard_example_miner {
+      }
+      classification_weight: 0.8
+      localization_weight: 0.2
+    """
+    losses_proto = losses_pb2.Loss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    with self.assertRaises(ValueError):
+      losses_builder.build(losses_proto)
+
+
+class FasterRcnnClassificationLossBuilderTest(tf.test.TestCase):
+
+  def test_build_sigmoid_loss(self):
+    losses_text_proto = """
+      weighted_sigmoid {
+      }
+    """
+    losses_proto = losses_pb2.ClassificationLoss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
+        losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSigmoidClassificationLoss))
+
+  def test_build_softmax_loss(self):
+    losses_text_proto = """
+      weighted_softmax {
+      }
+    """
+    losses_proto = losses_pb2.ClassificationLoss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
+        losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSoftmaxClassificationLoss))
+
+  def test_build_logits_softmax_loss(self):
+    losses_text_proto = """
+      weighted_logits_softmax {
+      }
+    """
+    losses_proto = losses_pb2.ClassificationLoss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
+        losses_proto)
+    self.assertTrue(
+        isinstance(classification_loss,
+                   losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
+
+  def test_build_sigmoid_focal_loss(self):
+    losses_text_proto = """
+      weighted_sigmoid_focal {
+      }
+    """
+    losses_proto = losses_pb2.ClassificationLoss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
+        losses_proto)
+    self.assertTrue(
+        isinstance(classification_loss,
+                   losses.SigmoidFocalClassificationLoss))
+
+  def test_build_softmax_loss_by_default(self):
+    losses_text_proto = """
+    """
+    losses_proto = losses_pb2.ClassificationLoss()
+    text_format.Merge(losses_text_proto, losses_proto)
+    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
+        losses_proto)
+    self.assertTrue(isinstance(classification_loss,
+                               losses.WeightedSoftmaxClassificationLoss))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder.py
new file mode 100644
index 00000000..d334f435
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder.py
@@ -0,0 +1,53 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A function to build an object detection matcher from configuration."""
+
+from object_detection.matchers import argmax_matcher
+from object_detection.matchers import bipartite_matcher
+from object_detection.protos import matcher_pb2
+
+
+def build(matcher_config):
+  """Builds a matcher object based on the matcher config.
+
+  Args:
+    matcher_config: A matcher.proto object containing the config for the desired
+      Matcher.
+
+  Returns:
+    Matcher based on the config.
+
+  Raises:
+    ValueError: On empty matcher proto.
+  """
+  if not isinstance(matcher_config, matcher_pb2.Matcher):
+    raise ValueError('matcher_config not of type matcher_pb2.Matcher.')
+  if matcher_config.WhichOneof('matcher_oneof') == 'argmax_matcher':
+    matcher = matcher_config.argmax_matcher
+    matched_threshold = unmatched_threshold = None
+    if not matcher.ignore_thresholds:
+      matched_threshold = matcher.matched_threshold
+      unmatched_threshold = matcher.unmatched_threshold
+    return argmax_matcher.ArgMaxMatcher(
+        matched_threshold=matched_threshold,
+        unmatched_threshold=unmatched_threshold,
+        negatives_lower_than_unmatched=matcher.negatives_lower_than_unmatched,
+        force_match_for_each_row=matcher.force_match_for_each_row,
+        use_matmul_gather=matcher.use_matmul_gather)
+  if matcher_config.WhichOneof('matcher_oneof') == 'bipartite_matcher':
+    matcher = matcher_config.bipartite_matcher
+    return bipartite_matcher.GreedyBipartiteMatcher(matcher.use_matmul_gather)
+  raise ValueError('Empty matcher.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder_test.py
new file mode 100644
index 00000000..66854491
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/matcher_builder_test.py
@@ -0,0 +1,99 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for matcher_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import matcher_builder
+from object_detection.matchers import argmax_matcher
+from object_detection.matchers import bipartite_matcher
+from object_detection.protos import matcher_pb2
+
+
+class MatcherBuilderTest(tf.test.TestCase):
+
+  def test_build_arg_max_matcher_with_defaults(self):
+    matcher_text_proto = """
+      argmax_matcher {
+      }
+    """
+    matcher_proto = matcher_pb2.Matcher()
+    text_format.Merge(matcher_text_proto, matcher_proto)
+    matcher_object = matcher_builder.build(matcher_proto)
+    self.assertTrue(isinstance(matcher_object, argmax_matcher.ArgMaxMatcher))
+    self.assertAlmostEqual(matcher_object._matched_threshold, 0.5)
+    self.assertAlmostEqual(matcher_object._unmatched_threshold, 0.5)
+    self.assertTrue(matcher_object._negatives_lower_than_unmatched)
+    self.assertFalse(matcher_object._force_match_for_each_row)
+
+  def test_build_arg_max_matcher_without_thresholds(self):
+    matcher_text_proto = """
+      argmax_matcher {
+        ignore_thresholds: true
+      }
+    """
+    matcher_proto = matcher_pb2.Matcher()
+    text_format.Merge(matcher_text_proto, matcher_proto)
+    matcher_object = matcher_builder.build(matcher_proto)
+    self.assertTrue(isinstance(matcher_object, argmax_matcher.ArgMaxMatcher))
+    self.assertEqual(matcher_object._matched_threshold, None)
+    self.assertEqual(matcher_object._unmatched_threshold, None)
+    self.assertTrue(matcher_object._negatives_lower_than_unmatched)
+    self.assertFalse(matcher_object._force_match_for_each_row)
+
+  def test_build_arg_max_matcher_with_non_default_parameters(self):
+    matcher_text_proto = """
+      argmax_matcher {
+        matched_threshold: 0.7
+        unmatched_threshold: 0.3
+        negatives_lower_than_unmatched: false
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    """
+    matcher_proto = matcher_pb2.Matcher()
+    text_format.Merge(matcher_text_proto, matcher_proto)
+    matcher_object = matcher_builder.build(matcher_proto)
+    self.assertTrue(isinstance(matcher_object, argmax_matcher.ArgMaxMatcher))
+    self.assertAlmostEqual(matcher_object._matched_threshold, 0.7)
+    self.assertAlmostEqual(matcher_object._unmatched_threshold, 0.3)
+    self.assertFalse(matcher_object._negatives_lower_than_unmatched)
+    self.assertTrue(matcher_object._force_match_for_each_row)
+    self.assertTrue(matcher_object._use_matmul_gather)
+
+  def test_build_bipartite_matcher(self):
+    matcher_text_proto = """
+      bipartite_matcher {
+      }
+    """
+    matcher_proto = matcher_pb2.Matcher()
+    text_format.Merge(matcher_text_proto, matcher_proto)
+    matcher_object = matcher_builder.build(matcher_proto)
+    self.assertTrue(
+        isinstance(matcher_object, bipartite_matcher.GreedyBipartiteMatcher))
+
+  def test_raise_error_on_empty_matcher(self):
+    matcher_text_proto = """
+    """
+    matcher_proto = matcher_pb2.Matcher()
+    text_format.Merge(matcher_text_proto, matcher_proto)
+    with self.assertRaises(ValueError):
+      matcher_builder.build(matcher_proto)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder.py
new file mode 100644
index 00000000..83b709ae
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder.py
@@ -0,0 +1,515 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A function to build a DetectionModel from configuration."""
+
+import functools
+
+from object_detection.builders import anchor_generator_builder
+from object_detection.builders import box_coder_builder
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.builders import image_resizer_builder
+from object_detection.builders import losses_builder
+from object_detection.builders import matcher_builder
+from object_detection.builders import post_processing_builder
+from object_detection.builders import region_similarity_calculator_builder as sim_calc
+from object_detection.core import balanced_positive_negative_sampler as sampler
+from object_detection.core import post_processing
+from object_detection.core import target_assigner
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.meta_architectures import rfcn_meta_arch
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
+from object_detection.models import faster_rcnn_inception_v2_feature_extractor as frcnn_inc_v2
+from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
+from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
+from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
+from object_detection.models import ssd_resnet_v1_fpn_feature_extractor as ssd_resnet_v1_fpn
+from object_detection.models import ssd_resnet_v1_ppn_feature_extractor as ssd_resnet_v1_ppn
+from object_detection.models.embedded_ssd_mobilenet_v1_feature_extractor import EmbeddedSSDMobileNetV1FeatureExtractor
+from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
+from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_fpn_feature_extractor import SSDMobileNetV1FpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_ppn_feature_extractor import SSDMobileNetV1PpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_feature_extractor import SSDMobileNetV2FeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
+from object_detection.models.ssd_pnasnet_feature_extractor import SSDPNASNetFeatureExtractor
+from object_detection.predictors import rfcn_box_predictor
+from object_detection.predictors.heads import mask_head
+from object_detection.protos import model_pb2
+from object_detection.utils import ops
+
+# A map of names to SSD feature extractors.
+SSD_FEATURE_EXTRACTOR_CLASS_MAP = {
+    'ssd_inception_v2': SSDInceptionV2FeatureExtractor,
+    'ssd_inception_v3': SSDInceptionV3FeatureExtractor,
+    'ssd_mobilenet_v1': SSDMobileNetV1FeatureExtractor,
+    'ssd_mobilenet_v1_fpn': SSDMobileNetV1FpnFeatureExtractor,
+    'ssd_mobilenet_v1_ppn': SSDMobileNetV1PpnFeatureExtractor,
+    'ssd_mobilenet_v2': SSDMobileNetV2FeatureExtractor,
+    'ssd_mobilenet_v2_fpn': SSDMobileNetV2FpnFeatureExtractor,
+    'ssd_resnet50_v1_fpn': ssd_resnet_v1_fpn.SSDResnet50V1FpnFeatureExtractor,
+    'ssd_resnet101_v1_fpn': ssd_resnet_v1_fpn.SSDResnet101V1FpnFeatureExtractor,
+    'ssd_resnet152_v1_fpn': ssd_resnet_v1_fpn.SSDResnet152V1FpnFeatureExtractor,
+    'ssd_resnet50_v1_ppn': ssd_resnet_v1_ppn.SSDResnet50V1PpnFeatureExtractor,
+    'ssd_resnet101_v1_ppn':
+        ssd_resnet_v1_ppn.SSDResnet101V1PpnFeatureExtractor,
+    'ssd_resnet152_v1_ppn':
+        ssd_resnet_v1_ppn.SSDResnet152V1PpnFeatureExtractor,
+    'embedded_ssd_mobilenet_v1': EmbeddedSSDMobileNetV1FeatureExtractor,
+    'ssd_pnasnet': SSDPNASNetFeatureExtractor,
+}
+
+SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
+    'ssd_mobilenet_v2_keras': SSDMobileNetV2KerasFeatureExtractor
+}
+
+# A map of names to Faster R-CNN feature extractors.
+FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP = {
+    'faster_rcnn_nas':
+    frcnn_nas.FasterRCNNNASFeatureExtractor,
+    'faster_rcnn_pnas':
+    frcnn_pnas.FasterRCNNPNASFeatureExtractor,
+    'faster_rcnn_inception_resnet_v2':
+    frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor,
+    'faster_rcnn_inception_v2':
+    frcnn_inc_v2.FasterRCNNInceptionV2FeatureExtractor,
+    'faster_rcnn_resnet50':
+    frcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
+    'faster_rcnn_resnet101':
+    frcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
+    'faster_rcnn_resnet152':
+    frcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor,
+}
+
+
+def build(model_config, is_training, add_summaries=True):
+  """Builds a DetectionModel based on the model config.
+
+  Args:
+    model_config: A model.proto object containing the config for the desired
+      DetectionModel.
+    is_training: True if this model is being built for training purposes.
+    add_summaries: Whether to add tensorflow summaries in the model graph.
+  Returns:
+    DetectionModel based on the config.
+
+  Raises:
+    ValueError: On invalid meta architecture or model.
+  """
+  if not isinstance(model_config, model_pb2.DetectionModel):
+    raise ValueError('model_config not of type model_pb2.DetectionModel.')
+  meta_architecture = model_config.WhichOneof('model')
+  if meta_architecture == 'ssd':
+    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
+  if meta_architecture == 'faster_rcnn':
+    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
+                                    add_summaries)
+  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
+
+
+def _build_ssd_feature_extractor(feature_extractor_config,
+                                 is_training,
+                                 freeze_batchnorm,
+                                 reuse_weights=None):
+  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
+
+  Args:
+    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
+    is_training: True if this feature extractor is being built for training.
+    freeze_batchnorm: Whether to freeze batch norm parameters during
+      training or not. When training with a small batch size (e.g. 1), it is
+      desirable to freeze batch norm update and use pretrained batch norm
+      params.
+    reuse_weights: if the feature extractor should reuse weights.
+
+  Returns:
+    ssd_meta_arch.SSDFeatureExtractor based on config.
+
+  Raises:
+    ValueError: On invalid feature extractor type.
+  """
+  feature_type = feature_extractor_config.type
+  is_keras_extractor = feature_type in SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP
+  depth_multiplier = feature_extractor_config.depth_multiplier
+  min_depth = feature_extractor_config.min_depth
+  pad_to_multiple = feature_extractor_config.pad_to_multiple
+  use_explicit_padding = feature_extractor_config.use_explicit_padding
+  use_depthwise = feature_extractor_config.use_depthwise
+
+  if is_keras_extractor:
+    conv_hyperparams = hyperparams_builder.KerasLayerHyperparams(
+        feature_extractor_config.conv_hyperparams)
+  else:
+    conv_hyperparams = hyperparams_builder.build(
+        feature_extractor_config.conv_hyperparams, is_training)
+  override_base_feature_extractor_hyperparams = (
+      feature_extractor_config.override_base_feature_extractor_hyperparams)
+
+  if (feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP) and (
+      not is_keras_extractor):
+    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
+
+  if is_keras_extractor:
+    feature_extractor_class = SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP[
+        feature_type]
+  else:
+    feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
+  kwargs = {
+      'is_training':
+          is_training,
+      'depth_multiplier':
+          depth_multiplier,
+      'min_depth':
+          min_depth,
+      'pad_to_multiple':
+          pad_to_multiple,
+      'use_explicit_padding':
+          use_explicit_padding,
+      'use_depthwise':
+          use_depthwise,
+      'override_base_feature_extractor_hyperparams':
+          override_base_feature_extractor_hyperparams
+  }
+
+  if is_keras_extractor:
+    kwargs.update({
+        'conv_hyperparams': conv_hyperparams,
+        'inplace_batchnorm_update': False,
+        'freeze_batchnorm': freeze_batchnorm
+    })
+  else:
+    kwargs.update({
+        'conv_hyperparams_fn': conv_hyperparams,
+        'reuse_weights': reuse_weights,
+    })
+
+  if feature_extractor_config.HasField('fpn'):
+    kwargs.update({
+        'fpn_min_level':
+            feature_extractor_config.fpn.min_level,
+        'fpn_max_level':
+            feature_extractor_config.fpn.max_level,
+        'additional_layer_depth':
+            feature_extractor_config.fpn.additional_layer_depth,
+    })
+
+  return feature_extractor_class(**kwargs)
+
+
+def _build_ssd_model(ssd_config, is_training, add_summaries):
+  """Builds an SSD detection model based on the model config.
+
+  Args:
+    ssd_config: A ssd.proto object containing the config for the desired
+      SSDMetaArch.
+    is_training: True if this model is being built for training purposes.
+    add_summaries: Whether to add tf summaries in the model.
+  Returns:
+    SSDMetaArch based on the config.
+
+  Raises:
+    ValueError: If ssd_config.type is not recognized (i.e. not registered in
+      model_class_map).
+  """
+  num_classes = ssd_config.num_classes
+
+  # Feature extractor
+  feature_extractor = _build_ssd_feature_extractor(
+      feature_extractor_config=ssd_config.feature_extractor,
+      freeze_batchnorm=ssd_config.freeze_batchnorm,
+      is_training=is_training)
+
+  box_coder = box_coder_builder.build(ssd_config.box_coder)
+  matcher = matcher_builder.build(ssd_config.matcher)
+  region_similarity_calculator = sim_calc.build(
+      ssd_config.similarity_calculator)
+  encode_background_as_zeros = ssd_config.encode_background_as_zeros
+  negative_class_weight = ssd_config.negative_class_weight
+  anchor_generator = anchor_generator_builder.build(
+      ssd_config.anchor_generator)
+  if feature_extractor.is_keras_model:
+    ssd_box_predictor = box_predictor_builder.build_keras(
+        conv_hyperparams_fn=hyperparams_builder.KerasLayerHyperparams,
+        freeze_batchnorm=ssd_config.freeze_batchnorm,
+        inplace_batchnorm_update=False,
+        num_predictions_per_location_list=anchor_generator
+        .num_anchors_per_location(),
+        box_predictor_config=ssd_config.box_predictor,
+        is_training=is_training,
+        num_classes=num_classes,
+        add_background_class=ssd_config.add_background_class)
+  else:
+    ssd_box_predictor = box_predictor_builder.build(
+        hyperparams_builder.build, ssd_config.box_predictor, is_training,
+        num_classes, ssd_config.add_background_class)
+  image_resizer_fn = image_resizer_builder.build(ssd_config.image_resizer)
+  non_max_suppression_fn, score_conversion_fn = post_processing_builder.build(
+      ssd_config.post_processing)
+  (classification_loss, localization_loss, classification_weight,
+   localization_weight, hard_example_miner, random_example_sampler,
+   expected_loss_weights_fn) = losses_builder.build(ssd_config.loss)
+  normalize_loss_by_num_matches = ssd_config.normalize_loss_by_num_matches
+  normalize_loc_loss_by_codesize = ssd_config.normalize_loc_loss_by_codesize
+
+  equalization_loss_config = ops.EqualizationLossConfig(
+      weight=ssd_config.loss.equalization_loss.weight,
+      exclude_prefixes=ssd_config.loss.equalization_loss.exclude_prefixes)
+
+  target_assigner_instance = target_assigner.TargetAssigner(
+      region_similarity_calculator,
+      matcher,
+      box_coder,
+      negative_class_weight=negative_class_weight)
+
+  ssd_meta_arch_fn = ssd_meta_arch.SSDMetaArch
+  kwargs = {}
+
+  return ssd_meta_arch_fn(
+      is_training=is_training,
+      anchor_generator=anchor_generator,
+      box_predictor=ssd_box_predictor,
+      box_coder=box_coder,
+      feature_extractor=feature_extractor,
+      encode_background_as_zeros=encode_background_as_zeros,
+      image_resizer_fn=image_resizer_fn,
+      non_max_suppression_fn=non_max_suppression_fn,
+      score_conversion_fn=score_conversion_fn,
+      classification_loss=classification_loss,
+      localization_loss=localization_loss,
+      classification_loss_weight=classification_weight,
+      localization_loss_weight=localization_weight,
+      normalize_loss_by_num_matches=normalize_loss_by_num_matches,
+      hard_example_miner=hard_example_miner,
+      target_assigner_instance=target_assigner_instance,
+      add_summaries=add_summaries,
+      normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize,
+      freeze_batchnorm=ssd_config.freeze_batchnorm,
+      inplace_batchnorm_update=ssd_config.inplace_batchnorm_update,
+      add_background_class=ssd_config.add_background_class,
+      explicit_background_class=ssd_config.explicit_background_class,
+      random_example_sampler=random_example_sampler,
+      expected_loss_weights_fn=expected_loss_weights_fn,
+      use_confidences_as_targets=ssd_config.use_confidences_as_targets,
+      implicit_example_weight=ssd_config.implicit_example_weight,
+      equalization_loss_config=equalization_loss_config,
+      **kwargs)
+
+
+def _build_faster_rcnn_feature_extractor(
+    feature_extractor_config, is_training, reuse_weights=None,
+    inplace_batchnorm_update=False):
+  """Builds a faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.
+
+  Args:
+    feature_extractor_config: A FasterRcnnFeatureExtractor proto config from
+      faster_rcnn.proto.
+    is_training: True if this feature extractor is being built for training.
+    reuse_weights: if the feature extractor should reuse weights.
+    inplace_batchnorm_update: Whether to update batch_norm inplace during
+      training. This is required for batch norm to work correctly on TPUs. When
+      this is false, user must add a control dependency on
+      tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+      norm moving average parameters.
+
+  Returns:
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.
+
+  Raises:
+    ValueError: On invalid feature extractor type.
+  """
+  if inplace_batchnorm_update:
+    raise ValueError('inplace batchnorm updates not supported.')
+  feature_type = feature_extractor_config.type
+  first_stage_features_stride = (
+      feature_extractor_config.first_stage_features_stride)
+  batch_norm_trainable = feature_extractor_config.batch_norm_trainable
+
+  if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
+    raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
+        feature_type))
+  feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
+      feature_type]
+  return feature_extractor_class(
+      is_training, first_stage_features_stride,
+      batch_norm_trainable, reuse_weights)
+
+
+def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
+  """Builds a Faster R-CNN or R-FCN detection model based on the model config.
+
+  Builds R-FCN model if the second_stage_box_predictor in the config is of type
+  `rfcn_box_predictor` else builds a Faster R-CNN model.
+
+  Args:
+    frcnn_config: A faster_rcnn.proto object containing the config for the
+      desired FasterRCNNMetaArch or RFCNMetaArch.
+    is_training: True if this model is being built for training purposes.
+    add_summaries: Whether to add tf summaries in the model.
+
+  Returns:
+    FasterRCNNMetaArch based on the config.
+
+  Raises:
+    ValueError: If frcnn_config.type is not recognized (i.e. not registered in
+      model_class_map).
+  """
+  num_classes = frcnn_config.num_classes
+  image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
+
+  feature_extractor = _build_faster_rcnn_feature_extractor(
+      frcnn_config.feature_extractor, is_training,
+      inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)
+
+  number_of_stages = frcnn_config.number_of_stages
+  first_stage_anchor_generator = anchor_generator_builder.build(
+      frcnn_config.first_stage_anchor_generator)
+
+  first_stage_target_assigner = target_assigner.create_target_assigner(
+      'FasterRCNN',
+      'proposal',
+      use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
+  first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
+  first_stage_box_predictor_arg_scope_fn = hyperparams_builder.build(
+      frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
+  first_stage_box_predictor_kernel_size = (
+      frcnn_config.first_stage_box_predictor_kernel_size)
+  first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
+  first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
+  use_static_shapes = frcnn_config.use_static_shapes and (
+      frcnn_config.use_static_shapes_for_eval or is_training)
+  first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
+      positive_fraction=frcnn_config.first_stage_positive_balance_fraction,
+      is_static=(frcnn_config.use_static_balanced_label_sampler and
+                 use_static_shapes))
+  first_stage_max_proposals = frcnn_config.first_stage_max_proposals
+  if (frcnn_config.first_stage_nms_iou_threshold < 0 or
+      frcnn_config.first_stage_nms_iou_threshold > 1.0):
+    raise ValueError('iou_threshold not in [0, 1.0].')
+  if (is_training and frcnn_config.second_stage_batch_size >
+      first_stage_max_proposals):
+    raise ValueError('second_stage_batch_size should be no greater than '
+                     'first_stage_max_proposals.')
+  first_stage_non_max_suppression_fn = functools.partial(
+      post_processing.batch_multiclass_non_max_suppression,
+      score_thresh=frcnn_config.first_stage_nms_score_threshold,
+      iou_thresh=frcnn_config.first_stage_nms_iou_threshold,
+      max_size_per_class=frcnn_config.first_stage_max_proposals,
+      max_total_size=frcnn_config.first_stage_max_proposals,
+      use_static_shapes=use_static_shapes)
+  first_stage_loc_loss_weight = (
+      frcnn_config.first_stage_localization_loss_weight)
+  first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
+
+  initial_crop_size = frcnn_config.initial_crop_size
+  maxpool_kernel_size = frcnn_config.maxpool_kernel_size
+  maxpool_stride = frcnn_config.maxpool_stride
+
+  second_stage_target_assigner = target_assigner.create_target_assigner(
+      'FasterRCNN',
+      'detection',
+      use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
+  second_stage_box_predictor = box_predictor_builder.build(
+      hyperparams_builder.build,
+      frcnn_config.second_stage_box_predictor,
+      is_training=is_training,
+      num_classes=num_classes)
+  second_stage_batch_size = frcnn_config.second_stage_batch_size
+  second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
+      positive_fraction=frcnn_config.second_stage_balance_fraction,
+      is_static=(frcnn_config.use_static_balanced_label_sampler and
+                 use_static_shapes))
+  (second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
+  ) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
+  second_stage_localization_loss_weight = (
+      frcnn_config.second_stage_localization_loss_weight)
+  second_stage_classification_loss = (
+      losses_builder.build_faster_rcnn_classification_loss(
+          frcnn_config.second_stage_classification_loss))
+  second_stage_classification_loss_weight = (
+      frcnn_config.second_stage_classification_loss_weight)
+  second_stage_mask_prediction_loss_weight = (
+      frcnn_config.second_stage_mask_prediction_loss_weight)
+
+  hard_example_miner = None
+  if frcnn_config.HasField('hard_example_miner'):
+    hard_example_miner = losses_builder.build_hard_example_miner(
+        frcnn_config.hard_example_miner,
+        second_stage_classification_loss_weight,
+        second_stage_localization_loss_weight)
+
+  crop_and_resize_fn = (
+      ops.matmul_crop_and_resize if frcnn_config.use_matmul_crop_and_resize
+      else ops.native_crop_and_resize)
+  clip_anchors_to_image = (
+      frcnn_config.clip_anchors_to_image)
+
+  common_kwargs = {
+      'is_training': is_training,
+      'num_classes': num_classes,
+      'image_resizer_fn': image_resizer_fn,
+      'feature_extractor': feature_extractor,
+      'number_of_stages': number_of_stages,
+      'first_stage_anchor_generator': first_stage_anchor_generator,
+      'first_stage_target_assigner': first_stage_target_assigner,
+      'first_stage_atrous_rate': first_stage_atrous_rate,
+      'first_stage_box_predictor_arg_scope_fn':
+      first_stage_box_predictor_arg_scope_fn,
+      'first_stage_box_predictor_kernel_size':
+      first_stage_box_predictor_kernel_size,
+      'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
+      'first_stage_minibatch_size': first_stage_minibatch_size,
+      'first_stage_sampler': first_stage_sampler,
+      'first_stage_non_max_suppression_fn': first_stage_non_max_suppression_fn,
+      'first_stage_max_proposals': first_stage_max_proposals,
+      'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
+      'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
+      'second_stage_target_assigner': second_stage_target_assigner,
+      'second_stage_batch_size': second_stage_batch_size,
+      'second_stage_sampler': second_stage_sampler,
+      'second_stage_non_max_suppression_fn':
+      second_stage_non_max_suppression_fn,
+      'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
+      'second_stage_localization_loss_weight':
+      second_stage_localization_loss_weight,
+      'second_stage_classification_loss':
+      second_stage_classification_loss,
+      'second_stage_classification_loss_weight':
+      second_stage_classification_loss_weight,
+      'hard_example_miner': hard_example_miner,
+      'add_summaries': add_summaries,
+      'crop_and_resize_fn': crop_and_resize_fn,
+      'clip_anchors_to_image': clip_anchors_to_image,
+      'use_static_shapes': use_static_shapes,
+      'resize_masks': frcnn_config.resize_masks
+  }
+
+  if isinstance(second_stage_box_predictor,
+                rfcn_box_predictor.RfcnBoxPredictor):
+    return rfcn_meta_arch.RFCNMetaArch(
+        second_stage_rfcn_box_predictor=second_stage_box_predictor,
+        **common_kwargs)
+  else:
+    return faster_rcnn_meta_arch.FasterRCNNMetaArch(
+        initial_crop_size=initial_crop_size,
+        maxpool_kernel_size=maxpool_kernel_size,
+        maxpool_stride=maxpool_stride,
+        second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
+        second_stage_mask_prediction_loss_weight=(
+            second_stage_mask_prediction_loss_weight),
+        **common_kwargs)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder_test.py
new file mode 100644
index 00000000..87ac0a8b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/model_builder_test.py
@@ -0,0 +1,1570 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.models.model_builder."""
+
+from absl.testing import parameterized
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import model_builder
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.meta_architectures import rfcn_meta_arch
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
+from object_detection.models import faster_rcnn_inception_v2_feature_extractor as frcnn_inc_v2
+from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
+from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
+from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
+from object_detection.models import ssd_resnet_v1_fpn_feature_extractor as ssd_resnet_v1_fpn
+from object_detection.models import ssd_resnet_v1_ppn_feature_extractor as ssd_resnet_v1_ppn
+from object_detection.models.embedded_ssd_mobilenet_v1_feature_extractor import EmbeddedSSDMobileNetV1FeatureExtractor
+from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
+from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_fpn_feature_extractor import SSDMobileNetV1FpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v1_ppn_feature_extractor import SSDMobileNetV1PpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_feature_extractor import SSDMobileNetV2FeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
+from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
+from object_detection.predictors import convolutional_box_predictor
+from object_detection.predictors import convolutional_keras_box_predictor
+from object_detection.protos import model_pb2
+
+FRCNN_RESNET_FEAT_MAPS = {
+    'faster_rcnn_resnet50':
+    frcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
+    'faster_rcnn_resnet101':
+    frcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
+    'faster_rcnn_resnet152':
+    frcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor
+}
+
+SSD_RESNET_V1_FPN_FEAT_MAPS = {
+    'ssd_resnet50_v1_fpn':
+    ssd_resnet_v1_fpn.SSDResnet50V1FpnFeatureExtractor,
+    'ssd_resnet101_v1_fpn':
+    ssd_resnet_v1_fpn.SSDResnet101V1FpnFeatureExtractor,
+    'ssd_resnet152_v1_fpn':
+    ssd_resnet_v1_fpn.SSDResnet152V1FpnFeatureExtractor,
+}
+
+SSD_RESNET_V1_PPN_FEAT_MAPS = {
+    'ssd_resnet50_v1_ppn':
+    ssd_resnet_v1_ppn.SSDResnet50V1PpnFeatureExtractor,
+    'ssd_resnet101_v1_ppn':
+    ssd_resnet_v1_ppn.SSDResnet101V1PpnFeatureExtractor,
+    'ssd_resnet152_v1_ppn':
+    ssd_resnet_v1_ppn.SSDResnet152V1PpnFeatureExtractor
+}
+
+
+class ModelBuilderTest(tf.test.TestCase, parameterized.TestCase):
+
+  def create_model(self, model_config):
+    """Builds a DetectionModel based on the model config.
+
+    Args:
+      model_config: A model.proto object containing the config for the desired
+        DetectionModel.
+
+    Returns:
+      DetectionModel based on the config.
+    """
+    return model_builder.build(model_config, is_training=True)
+
+  def test_create_ssd_inception_v2_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_inception_v2'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+          override_base_feature_extractor_hyperparams: true
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDInceptionV2FeatureExtractor)
+    self.assertIsNone(model._expected_loss_weights_fn)
+
+
+
+  def test_create_ssd_inception_v3_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_inception_v3'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+          override_base_feature_extractor_hyperparams: true
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDInceptionV3FeatureExtractor)
+
+  def test_create_ssd_resnet_v1_fpn_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_resnet50_v1_fpn'
+          fpn {
+            min_level: 3
+            max_level: 7
+          }
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        encode_background_as_zeros: true
+        anchor_generator {
+          multiscale_anchor_generator {
+            aspect_ratios: [1.0, 2.0, 0.5]
+            scales_per_octave: 2
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          weight_shared_convolutional_box_predictor {
+            depth: 32
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                random_normal_initializer {
+                }
+              }
+            }
+            num_layers_before_predictor: 1
+          }
+        }
+        normalize_loss_by_num_matches: true
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_sigmoid_focal {
+              alpha: 0.25
+              gamma: 2.0
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+              delta: 0.1
+            }
+          }
+          classification_weight: 1.0
+          localization_weight: 1.0
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+
+    for extractor_type, extractor_class in SSD_RESNET_V1_FPN_FEAT_MAPS.items():
+      model_proto.ssd.feature_extractor.type = extractor_type
+      model = model_builder.build(model_proto, is_training=True)
+      self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+      self.assertIsInstance(model._feature_extractor, extractor_class)
+
+  def test_create_ssd_resnet_v1_ppn_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_resnet_v1_50_ppn'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          mean_stddev_box_coder {
+          }
+        }
+        matcher {
+          bipartite_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          weight_shared_convolutional_box_predictor {
+            depth: 1024
+            class_prediction_bias_init: -4.6
+            conv_hyperparams {
+              activation: RELU_6,
+              regularizer {
+                l2_regularizer {
+                  weight: 0.0004
+                }
+              }
+              initializer {
+                variance_scaling_initializer {
+                }
+              }
+            }
+            num_layers_before_predictor: 2
+            kernel_size: 1
+          }
+        }
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_l2 {
+            }
+          }
+          classification_weight: 1.0
+          localization_weight: 1.0
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+
+    for extractor_type, extractor_class in SSD_RESNET_V1_PPN_FEAT_MAPS.items():
+      model_proto.ssd.feature_extractor.type = extractor_type
+      model = model_builder.build(model_proto, is_training=True)
+      self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+      self.assertIsInstance(model._feature_extractor, extractor_class)
+
+  def test_create_ssd_mobilenet_v1_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        freeze_batchnorm: true
+        inplace_batchnorm_update: true
+        feature_extractor {
+          type: 'ssd_mobilenet_v1'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV1FeatureExtractor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+    self.assertTrue(model._freeze_batchnorm)
+    self.assertTrue(model._inplace_batchnorm_update)
+
+  def test_create_ssd_mobilenet_v1_fpn_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        freeze_batchnorm: true
+        inplace_batchnorm_update: true
+        feature_extractor {
+          type: 'ssd_mobilenet_v1_fpn'
+          fpn {
+            min_level: 3
+            max_level: 7
+          }
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV1FpnFeatureExtractor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+    self.assertTrue(model._freeze_batchnorm)
+    self.assertTrue(model._inplace_batchnorm_update)
+
+  def test_create_ssd_mobilenet_v1_ppn_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        freeze_batchnorm: true
+        inplace_batchnorm_update: true
+        feature_extractor {
+          type: 'ssd_mobilenet_v1_ppn'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV1PpnFeatureExtractor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+    self.assertTrue(model._freeze_batchnorm)
+    self.assertTrue(model._inplace_batchnorm_update)
+
+  def test_create_ssd_mobilenet_v2_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_mobilenet_v2'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV2FeatureExtractor)
+    self.assertIsInstance(model._box_predictor,
+                          convolutional_box_predictor.ConvolutionalBoxPredictor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+
+  def test_create_ssd_mobilenet_v2_keras_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'ssd_mobilenet_v2_keras'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV2KerasFeatureExtractor)
+    self.assertIsInstance(
+        model._box_predictor,
+        convolutional_keras_box_predictor.ConvolutionalBoxPredictor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+
+  def test_create_ssd_mobilenet_v2_fpn_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        freeze_batchnorm: true
+        inplace_batchnorm_update: true
+        feature_extractor {
+          type: 'ssd_mobilenet_v2_fpn'
+          fpn {
+            min_level: 3
+            max_level: 7
+          }
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV2FpnFeatureExtractor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+    self.assertTrue(model._freeze_batchnorm)
+    self.assertTrue(model._inplace_batchnorm_update)
+
+  def test_create_ssd_mobilenet_v2_fpnlite_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        freeze_batchnorm: true
+        inplace_batchnorm_update: true
+        feature_extractor {
+          type: 'ssd_mobilenet_v2_fpn'
+          use_depthwise: true
+          fpn {
+            min_level: 3
+            max_level: 7
+            additional_layer_depth: 128
+          }
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 320
+            width: 320
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        normalize_loc_loss_by_codesize: true
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          SSDMobileNetV2FpnFeatureExtractor)
+    self.assertTrue(model._normalize_loc_loss_by_codesize)
+    self.assertTrue(model._freeze_batchnorm)
+    self.assertTrue(model._inplace_batchnorm_update)
+
+  def test_create_embedded_ssd_mobilenet_v1_model_from_config(self):
+    model_text_proto = """
+      ssd {
+        feature_extractor {
+          type: 'embedded_ssd_mobilenet_v1'
+          conv_hyperparams {
+            regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+          }
+        }
+        box_coder {
+          faster_rcnn_box_coder {
+          }
+        }
+        matcher {
+          argmax_matcher {
+          }
+        }
+        similarity_calculator {
+          iou_similarity {
+          }
+        }
+        anchor_generator {
+          ssd_anchor_generator {
+            aspect_ratios: 1.0
+          }
+        }
+        image_resizer {
+          fixed_shape_resizer {
+            height: 256
+            width: 256
+          }
+        }
+        box_predictor {
+          convolutional_box_predictor {
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        loss {
+          classification_loss {
+            weighted_softmax {
+            }
+          }
+          localization_loss {
+            weighted_smooth_l1 {
+            }
+          }
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = self.create_model(model_proto)
+    self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          EmbeddedSSDMobileNetV1FeatureExtractor)
+
+  def test_create_faster_rcnn_resnet_v1_models_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        inplace_batchnorm_update: false
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_resnet101'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 14
+        maxpool_kernel_size: 2
+        maxpool_stride: 2
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+
+    for extractor_type, extractor_class in FRCNN_RESNET_FEAT_MAPS.items():
+      model_proto.faster_rcnn.feature_extractor.type = extractor_type
+      model = model_builder.build(model_proto, is_training=True)
+      self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+      self.assertIsInstance(model._feature_extractor, extractor_class)
+
+  @parameterized.parameters(
+      {'use_matmul_crop_and_resize': False},
+      {'use_matmul_crop_and_resize': True},
+  )
+  def test_create_faster_rcnn_resnet101_with_mask_prediction_enabled(
+      self, use_matmul_crop_and_resize):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_resnet101'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 14
+        maxpool_kernel_size: 2
+        maxpool_stride: 2
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+            conv_hyperparams {
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+            predict_instance_masks: true
+          }
+        }
+        second_stage_mask_prediction_loss_weight: 3.0
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model_proto.faster_rcnn.use_matmul_crop_and_resize = (
+        use_matmul_crop_and_resize)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertAlmostEqual(model._second_stage_mask_loss_weight, 3.0)
+
+  def test_create_faster_rcnn_nas_model_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_nas'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 17
+        maxpool_kernel_size: 1
+        maxpool_stride: 1
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+    self.assertIsInstance(
+        model._feature_extractor,
+        frcnn_nas.FasterRCNNNASFeatureExtractor)
+
+  def test_create_faster_rcnn_pnas_model_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_pnas'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 17
+        maxpool_kernel_size: 1
+        maxpool_stride: 1
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+    self.assertIsInstance(
+        model._feature_extractor,
+        frcnn_pnas.FasterRCNNPNASFeatureExtractor)
+
+  def test_create_faster_rcnn_inception_resnet_v2_model_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_inception_resnet_v2'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 17
+        maxpool_kernel_size: 1
+        maxpool_stride: 1
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+    self.assertIsInstance(
+        model._feature_extractor,
+        frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor)
+
+  def test_create_faster_rcnn_inception_v2_model_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_inception_v2'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 14
+        maxpool_kernel_size: 2
+        maxpool_stride: 2
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+    self.assertIsInstance(model._feature_extractor,
+                          frcnn_inc_v2.FasterRCNNInceptionV2FeatureExtractor)
+
+  def test_create_faster_rcnn_model_from_config_with_example_miner(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        feature_extractor {
+          type: 'faster_rcnn_inception_resnet_v2'
+        }
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        second_stage_box_predictor {
+          mask_rcnn_box_predictor {
+            fc_hyperparams {
+              op: FC
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        hard_example_miner {
+          num_hard_examples: 10
+          iou_threshold: 0.99
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    model = model_builder.build(model_proto, is_training=True)
+    self.assertIsNotNone(model._hard_example_miner)
+
+  def test_create_rfcn_resnet_v1_model_from_config(self):
+    model_text_proto = """
+      faster_rcnn {
+        num_classes: 3
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 600
+            max_dimension: 1024
+          }
+        }
+        feature_extractor {
+          type: 'faster_rcnn_resnet101'
+        }
+        first_stage_anchor_generator {
+          grid_anchor_generator {
+            scales: [0.25, 0.5, 1.0, 2.0]
+            aspect_ratios: [0.5, 1.0, 2.0]
+            height_stride: 16
+            width_stride: 16
+          }
+        }
+        first_stage_box_predictor_conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+        initial_crop_size: 14
+        maxpool_kernel_size: 2
+        maxpool_stride: 2
+        second_stage_box_predictor {
+          rfcn_box_predictor {
+            conv_hyperparams {
+              op: CONV
+              regularizer {
+                l2_regularizer {
+                }
+              }
+              initializer {
+                truncated_normal_initializer {
+                }
+              }
+            }
+          }
+        }
+        second_stage_post_processing {
+          batch_non_max_suppression {
+            score_threshold: 0.01
+            iou_threshold: 0.6
+            max_detections_per_class: 100
+            max_total_detections: 300
+          }
+          score_converter: SOFTMAX
+        }
+      }"""
+    model_proto = model_pb2.DetectionModel()
+    text_format.Merge(model_text_proto, model_proto)
+    for extractor_type, extractor_class in FRCNN_RESNET_FEAT_MAPS.items():
+      model_proto.faster_rcnn.feature_extractor.type = extractor_type
+      model = model_builder.build(model_proto, is_training=True)
+      self.assertIsInstance(model, rfcn_meta_arch.RFCNMetaArch)
+      self.assertIsInstance(model._feature_extractor, extractor_class)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder.py
new file mode 100644
index 00000000..fdd3e221
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder.py
@@ -0,0 +1,144 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Functions to build DetectionModel training optimizers."""
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+from object_detection.utils import learning_schedules
+
+
+def build(optimizer_config):
+  """Create optimizer based on config.
+
+  Args:
+    optimizer_config: A Optimizer proto message.
+
+  Returns:
+    An optimizer and a list of variables for summary.
+
+  Raises:
+    ValueError: when using an unsupported input data type.
+  """
+  optimizer_type = optimizer_config.WhichOneof('optimizer')
+  optimizer = None
+
+  summary_vars = []
+  if optimizer_type == 'rms_prop_optimizer':
+    config = optimizer_config.rms_prop_optimizer
+    learning_rate = _create_learning_rate(config.learning_rate)
+    summary_vars.append(learning_rate)
+    optimizer = tf.train.RMSPropOptimizer(
+        learning_rate,
+        decay=config.decay,
+        momentum=config.momentum_optimizer_value,
+        epsilon=config.epsilon)
+
+  if optimizer_type == 'momentum_optimizer':
+    config = optimizer_config.momentum_optimizer
+    learning_rate = _create_learning_rate(config.learning_rate)
+    summary_vars.append(learning_rate)
+    optimizer = tf.train.MomentumOptimizer(
+        learning_rate,
+        momentum=config.momentum_optimizer_value)
+
+  if optimizer_type == 'adam_optimizer':
+    config = optimizer_config.adam_optimizer
+    learning_rate = _create_learning_rate(config.learning_rate)
+    summary_vars.append(learning_rate)
+    optimizer = tf.train.AdamOptimizer(learning_rate)
+
+  if optimizer is None:
+    raise ValueError('Optimizer %s not supported.' % optimizer_type)
+
+  optimizer = hvd.DistributedOptimizer(optimizer)
+
+  if optimizer_config.use_moving_average:
+    optimizer = tf.contrib.opt.MovingAverageOptimizer(
+        optimizer, average_decay=optimizer_config.moving_average_decay)
+
+  return optimizer, summary_vars
+
+
+def _create_learning_rate(learning_rate_config):
+  """Create optimizer learning rate based on config.
+
+  Args:
+    learning_rate_config: A LearningRate proto message.
+
+  Returns:
+    A learning rate.
+
+  Raises:
+    ValueError: when using an unsupported input data type.
+  """
+  learning_rate = None
+  learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
+  if learning_rate_type == 'constant_learning_rate':
+    config = learning_rate_config.constant_learning_rate
+    learning_rate = tf.constant(config.learning_rate, dtype=tf.float32,
+                                name='learning_rate')
+
+  if learning_rate_type == 'exponential_decay_learning_rate':
+    config = learning_rate_config.exponential_decay_learning_rate
+    learning_rate = learning_schedules.exponential_decay_with_burnin(
+        tf.train.get_or_create_global_step(),
+        config.initial_learning_rate,
+        config.decay_steps,
+        config.decay_factor,
+        burnin_learning_rate=config.burnin_learning_rate,
+        burnin_steps=config.burnin_steps,
+        min_learning_rate=config.min_learning_rate,
+        staircase=config.staircase)
+
+  if learning_rate_type == 'manual_step_learning_rate':
+    config = learning_rate_config.manual_step_learning_rate
+    if not config.schedule:
+      raise ValueError('Empty learning rate schedule.')
+    learning_rate_step_boundaries = [x.step for x in config.schedule]
+    learning_rate_sequence = [config.initial_learning_rate]
+    learning_rate_sequence += [x.learning_rate for x in config.schedule]
+    learning_rate = learning_schedules.manual_stepping(
+        tf.train.get_or_create_global_step(), learning_rate_step_boundaries,
+        learning_rate_sequence, config.warmup)
+
+  if learning_rate_type == 'cosine_decay_learning_rate':
+    config = learning_rate_config.cosine_decay_learning_rate
+    learning_rate = learning_schedules.cosine_decay_with_warmup(
+        tf.train.get_or_create_global_step(),
+        config.learning_rate_base,
+        config.total_steps,
+        config.warmup_learning_rate,
+        config.warmup_steps,
+        config.hold_base_rate_steps)
+
+  if learning_rate is None:
+    raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
+
+  return learning_rate
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder_test.py
new file mode 100644
index 00000000..343a858f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/optimizer_builder_test.py
@@ -0,0 +1,208 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for optimizer_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import optimizer_builder
+from object_detection.protos import optimizer_pb2
+
+
+class LearningRateBuilderTest(tf.test.TestCase):
+
+  def testBuildConstantLearningRate(self):
+    learning_rate_text_proto = """
+      constant_learning_rate {
+        learning_rate: 0.004
+      }
+    """
+    learning_rate_proto = optimizer_pb2.LearningRate()
+    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
+    learning_rate = optimizer_builder._create_learning_rate(
+        learning_rate_proto)
+    self.assertTrue(learning_rate.op.name.endswith('learning_rate'))
+    with self.test_session():
+      learning_rate_out = learning_rate.eval()
+    self.assertAlmostEqual(learning_rate_out, 0.004)
+
+  def testBuildExponentialDecayLearningRate(self):
+    learning_rate_text_proto = """
+      exponential_decay_learning_rate {
+        initial_learning_rate: 0.004
+        decay_steps: 99999
+        decay_factor: 0.85
+        staircase: false
+      }
+    """
+    learning_rate_proto = optimizer_pb2.LearningRate()
+    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
+    learning_rate = optimizer_builder._create_learning_rate(
+        learning_rate_proto)
+    self.assertTrue(learning_rate.op.name.endswith('learning_rate'))
+    self.assertTrue(isinstance(learning_rate, tf.Tensor))
+
+  def testBuildManualStepLearningRate(self):
+    learning_rate_text_proto = """
+      manual_step_learning_rate {
+        initial_learning_rate: 0.002
+        schedule {
+          step: 100
+          learning_rate: 0.006
+        }
+        schedule {
+          step: 90000
+          learning_rate: 0.00006
+        }
+        warmup: true
+      }
+    """
+    learning_rate_proto = optimizer_pb2.LearningRate()
+    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
+    learning_rate = optimizer_builder._create_learning_rate(
+        learning_rate_proto)
+    self.assertTrue(isinstance(learning_rate, tf.Tensor))
+
+  def testBuildCosineDecayLearningRate(self):
+    learning_rate_text_proto = """
+      cosine_decay_learning_rate {
+        learning_rate_base: 0.002
+        total_steps: 20000
+        warmup_learning_rate: 0.0001
+        warmup_steps: 1000
+        hold_base_rate_steps: 20000
+      }
+    """
+    learning_rate_proto = optimizer_pb2.LearningRate()
+    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
+    learning_rate = optimizer_builder._create_learning_rate(
+        learning_rate_proto)
+    self.assertTrue(isinstance(learning_rate, tf.Tensor))
+
+  def testRaiseErrorOnEmptyLearningRate(self):
+    learning_rate_text_proto = """
+    """
+    learning_rate_proto = optimizer_pb2.LearningRate()
+    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
+    with self.assertRaises(ValueError):
+      optimizer_builder._create_learning_rate(learning_rate_proto)
+
+
+class OptimizerBuilderTest(tf.test.TestCase):
+
+  def testBuildRMSPropOptimizer(self):
+    optimizer_text_proto = """
+      rms_prop_optimizer: {
+        learning_rate: {
+          exponential_decay_learning_rate {
+            initial_learning_rate: 0.004
+            decay_steps: 800720
+            decay_factor: 0.95
+          }
+        }
+        momentum_optimizer_value: 0.9
+        decay: 0.9
+        epsilon: 1.0
+      }
+      use_moving_average: false
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    optimizer, _ = optimizer_builder.build(optimizer_proto)
+    self.assertTrue(isinstance(optimizer, tf.train.RMSPropOptimizer))
+
+  def testBuildMomentumOptimizer(self):
+    optimizer_text_proto = """
+      momentum_optimizer: {
+        learning_rate: {
+          constant_learning_rate {
+            learning_rate: 0.001
+          }
+        }
+        momentum_optimizer_value: 0.99
+      }
+      use_moving_average: false
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    optimizer, _ = optimizer_builder.build(optimizer_proto)
+    self.assertTrue(isinstance(optimizer, tf.train.MomentumOptimizer))
+
+  def testBuildAdamOptimizer(self):
+    optimizer_text_proto = """
+      adam_optimizer: {
+        learning_rate: {
+          constant_learning_rate {
+            learning_rate: 0.002
+          }
+        }
+      }
+      use_moving_average: false
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    optimizer, _ = optimizer_builder.build(optimizer_proto)
+    self.assertTrue(isinstance(optimizer, tf.train.AdamOptimizer))
+
+  def testBuildMovingAverageOptimizer(self):
+    optimizer_text_proto = """
+      adam_optimizer: {
+        learning_rate: {
+          constant_learning_rate {
+            learning_rate: 0.002
+          }
+        }
+      }
+      use_moving_average: True
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    optimizer, _ = optimizer_builder.build(optimizer_proto)
+    self.assertTrue(
+        isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer))
+
+  def testBuildMovingAverageOptimizerWithNonDefaultDecay(self):
+    optimizer_text_proto = """
+      adam_optimizer: {
+        learning_rate: {
+          constant_learning_rate {
+            learning_rate: 0.002
+          }
+        }
+      }
+      use_moving_average: True
+      moving_average_decay: 0.2
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    optimizer, _ = optimizer_builder.build(optimizer_proto)
+    self.assertTrue(
+        isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer))
+    # TODO(rathodv): Find a way to not depend on the private members.
+    self.assertAlmostEqual(optimizer._ema._decay, 0.2)
+
+  def testBuildEmptyOptimizer(self):
+    optimizer_text_proto = """
+    """
+    optimizer_proto = optimizer_pb2.Optimizer()
+    text_format.Merge(optimizer_text_proto, optimizer_proto)
+    with self.assertRaises(ValueError):
+      optimizer_builder.build(optimizer_proto)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder.py
new file mode 100644
index 00000000..b48ed7ef
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder.py
@@ -0,0 +1,124 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Builder function for post processing operations."""
+import functools
+
+import tensorflow as tf
+from object_detection.core import post_processing
+from object_detection.protos import post_processing_pb2
+
+
+def build(post_processing_config):
+  """Builds callables for post-processing operations.
+
+  Builds callables for non-max suppression and score conversion based on the
+  configuration.
+
+  Non-max suppression callable takes `boxes`, `scores`, and optionally
+  `clip_window`, `parallel_iterations` `masks, and `scope` as inputs. It returns
+  `nms_boxes`, `nms_scores`, `nms_classes` `nms_masks` and `num_detections`. See
+  post_processing.batch_multiclass_non_max_suppression for the type and shape
+  of these tensors.
+
+  Score converter callable should be called with `input` tensor. The callable
+  returns the output from one of 3 tf operations based on the configuration -
+  tf.identity, tf.sigmoid or tf.nn.softmax. See tensorflow documentation for
+  argument and return value descriptions.
+
+  Args:
+    post_processing_config: post_processing.proto object containing the
+      parameters for the post-processing operations.
+
+  Returns:
+    non_max_suppressor_fn: Callable for non-max suppression.
+    score_converter_fn: Callable for score conversion.
+
+  Raises:
+    ValueError: if the post_processing_config is of incorrect type.
+  """
+  if not isinstance(post_processing_config, post_processing_pb2.PostProcessing):
+    raise ValueError('post_processing_config not of type '
+                     'post_processing_pb2.Postprocessing.')
+  non_max_suppressor_fn = _build_non_max_suppressor(
+      post_processing_config.batch_non_max_suppression)
+  score_converter_fn = _build_score_converter(
+      post_processing_config.score_converter,
+      post_processing_config.logit_scale)
+  return non_max_suppressor_fn, score_converter_fn
+
+
+def _build_non_max_suppressor(nms_config):
+  """Builds non-max suppresson based on the nms config.
+
+  Args:
+    nms_config: post_processing_pb2.PostProcessing.BatchNonMaxSuppression proto.
+
+  Returns:
+    non_max_suppressor_fn: Callable non-max suppressor.
+
+  Raises:
+    ValueError: On incorrect iou_threshold or on incompatible values of
+      max_total_detections and max_detections_per_class.
+  """
+  if nms_config.iou_threshold < 0 or nms_config.iou_threshold > 1.0:
+    raise ValueError('iou_threshold not in [0, 1.0].')
+  if nms_config.max_detections_per_class > nms_config.max_total_detections:
+    raise ValueError('max_detections_per_class should be no greater than '
+                     'max_total_detections.')
+
+  non_max_suppressor_fn = functools.partial(
+      post_processing.batch_multiclass_non_max_suppression,
+      score_thresh=nms_config.score_threshold,
+      iou_thresh=nms_config.iou_threshold,
+      max_size_per_class=nms_config.max_detections_per_class,
+      max_total_size=nms_config.max_total_detections,
+      use_static_shapes=nms_config.use_static_shapes)
+  return non_max_suppressor_fn
+
+
+def _score_converter_fn_with_logit_scale(tf_score_converter_fn, logit_scale):
+  """Create a function to scale logits then apply a Tensorflow function."""
+  def score_converter_fn(logits):
+    scaled_logits = tf.divide(logits, logit_scale, name='scale_logits')
+    return tf_score_converter_fn(scaled_logits, name='convert_scores')
+  score_converter_fn.__name__ = '%s_with_logit_scale' % (
+      tf_score_converter_fn.__name__)
+  return score_converter_fn
+
+
+def _build_score_converter(score_converter_config, logit_scale):
+  """Builds score converter based on the config.
+
+  Builds one of [tf.identity, tf.sigmoid, tf.softmax] score converters based on
+  the config.
+
+  Args:
+    score_converter_config: post_processing_pb2.PostProcessing.score_converter.
+    logit_scale: temperature to use for SOFTMAX score_converter.
+
+  Returns:
+    Callable score converter op.
+
+  Raises:
+    ValueError: On unknown score converter.
+  """
+  if score_converter_config == post_processing_pb2.PostProcessing.IDENTITY:
+    return _score_converter_fn_with_logit_scale(tf.identity, logit_scale)
+  if score_converter_config == post_processing_pb2.PostProcessing.SIGMOID:
+    return _score_converter_fn_with_logit_scale(tf.sigmoid, logit_scale)
+  if score_converter_config == post_processing_pb2.PostProcessing.SOFTMAX:
+    return _score_converter_fn_with_logit_scale(tf.nn.softmax, logit_scale)
+  raise ValueError('Unknown score converter.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder_test.py
new file mode 100644
index 00000000..c39fbfb4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/post_processing_builder_test.py
@@ -0,0 +1,107 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for post_processing_builder."""
+
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection.builders import post_processing_builder
+from object_detection.protos import post_processing_pb2
+
+
+class PostProcessingBuilderTest(tf.test.TestCase):
+
+  def test_build_non_max_suppressor_with_correct_parameters(self):
+    post_processing_text_proto = """
+      batch_non_max_suppression {
+        score_threshold: 0.7
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    non_max_suppressor, _ = post_processing_builder.build(
+        post_processing_config)
+    self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100)
+    self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300)
+    self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7)
+    self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6)
+
+  def test_build_identity_score_converter(self):
+    post_processing_text_proto = """
+      score_converter: IDENTITY
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    _, score_converter = post_processing_builder.build(post_processing_config)
+    self.assertEqual(score_converter.__name__, 'identity_with_logit_scale')
+
+    inputs = tf.constant([1, 1], tf.float32)
+    outputs = score_converter(inputs)
+    with self.test_session() as sess:
+      converted_scores = sess.run(outputs)
+      expected_converted_scores = sess.run(inputs)
+      self.assertAllClose(converted_scores, expected_converted_scores)
+
+  def test_build_identity_score_converter_with_logit_scale(self):
+    post_processing_text_proto = """
+      score_converter: IDENTITY
+      logit_scale: 2.0
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    _, score_converter = post_processing_builder.build(post_processing_config)
+    self.assertEqual(score_converter.__name__, 'identity_with_logit_scale')
+
+    inputs = tf.constant([1, 1], tf.float32)
+    outputs = score_converter(inputs)
+    with self.test_session() as sess:
+      converted_scores = sess.run(outputs)
+      expected_converted_scores = sess.run(tf.constant([.5, .5], tf.float32))
+      self.assertAllClose(converted_scores, expected_converted_scores)
+
+  def test_build_sigmoid_score_converter(self):
+    post_processing_text_proto = """
+      score_converter: SIGMOID
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    _, score_converter = post_processing_builder.build(post_processing_config)
+    self.assertEqual(score_converter.__name__, 'sigmoid_with_logit_scale')
+
+  def test_build_softmax_score_converter(self):
+    post_processing_text_proto = """
+      score_converter: SOFTMAX
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    _, score_converter = post_processing_builder.build(post_processing_config)
+    self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale')
+
+  def test_build_softmax_score_converter_with_temperature(self):
+    post_processing_text_proto = """
+      score_converter: SOFTMAX
+      logit_scale: 2.0
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+    _, score_converter = post_processing_builder.build(post_processing_config)
+    self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale')
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder.py
new file mode 100644
index 00000000..1d03b641
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder.py
@@ -0,0 +1,348 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Builder for preprocessing steps."""
+
+import tensorflow as tf
+
+from object_detection.core import preprocessor
+from object_detection.protos import preprocessor_pb2
+
+
+def _get_step_config_from_proto(preprocessor_step_config, step_name):
+  """Returns the value of a field named step_name from proto.
+
+  Args:
+    preprocessor_step_config: A preprocessor_pb2.PreprocessingStep object.
+    step_name: Name of the field to get value from.
+
+  Returns:
+    result_dict: a sub proto message from preprocessor_step_config which will be
+                 later converted to a dictionary.
+
+  Raises:
+    ValueError: If field does not exist in proto.
+  """
+  for field, value in preprocessor_step_config.ListFields():
+    if field.name == step_name:
+      return value
+
+  raise ValueError('Could not get field %s from proto!', step_name)
+
+
+def _get_dict_from_proto(config):
+  """Helper function to put all proto fields into a dictionary.
+
+  For many preprocessing steps, there's an trivial 1-1 mapping from proto fields
+  to function arguments. This function automatically populates a dictionary with
+  the arguments from the proto.
+
+  Protos that CANNOT be trivially populated include:
+  * nested messages.
+  * steps that check if an optional field is set (ie. where None != 0).
+  * protos that don't map 1-1 to arguments (ie. list should be reshaped).
+  * fields requiring additional validation (ie. repeated field has n elements).
+
+  Args:
+    config: A protobuf object that does not violate the conditions above.
+
+  Returns:
+    result_dict: |config| converted into a python dictionary.
+  """
+  result_dict = {}
+  for field, value in config.ListFields():
+    result_dict[field.name] = value
+  return result_dict
+
+
+# A map from a PreprocessingStep proto config field name to the preprocessing
+# function that should be used. The PreprocessingStep proto should be parsable
+# with _get_dict_from_proto.
+PREPROCESSING_FUNCTION_MAP = {
+    'normalize_image':
+        preprocessor.normalize_image,
+    'random_pixel_value_scale':
+        preprocessor.random_pixel_value_scale,
+    'random_image_scale':
+        preprocessor.random_image_scale,
+    'random_rgb_to_gray':
+        preprocessor.random_rgb_to_gray,
+    'random_adjust_brightness':
+        preprocessor.random_adjust_brightness,
+    'random_adjust_contrast':
+        preprocessor.random_adjust_contrast,
+    'random_adjust_hue':
+        preprocessor.random_adjust_hue,
+    'random_adjust_saturation':
+        preprocessor.random_adjust_saturation,
+    'random_distort_color':
+        preprocessor.random_distort_color,
+    'random_jitter_boxes':
+        preprocessor.random_jitter_boxes,
+    'random_crop_to_aspect_ratio':
+        preprocessor.random_crop_to_aspect_ratio,
+    'random_black_patches':
+        preprocessor.random_black_patches,
+    'rgb_to_gray':
+        preprocessor.rgb_to_gray,
+    'scale_boxes_to_pixel_coordinates': (
+        preprocessor.scale_boxes_to_pixel_coordinates),
+    'subtract_channel_mean':
+        preprocessor.subtract_channel_mean,
+    'convert_class_logits_to_softmax':
+        preprocessor.convert_class_logits_to_softmax,
+}
+
+
+# A map to convert from preprocessor_pb2.ResizeImage.Method enum to
+# tf.image.ResizeMethod.
+RESIZE_METHOD_MAP = {
+    preprocessor_pb2.ResizeImage.AREA: tf.image.ResizeMethod.AREA,
+    preprocessor_pb2.ResizeImage.BICUBIC: tf.image.ResizeMethod.BICUBIC,
+    preprocessor_pb2.ResizeImage.BILINEAR: tf.image.ResizeMethod.BILINEAR,
+    preprocessor_pb2.ResizeImage.NEAREST_NEIGHBOR: (
+        tf.image.ResizeMethod.NEAREST_NEIGHBOR),
+}
+
+
+def build(preprocessor_step_config):
+  """Builds preprocessing step based on the configuration.
+
+  Args:
+    preprocessor_step_config: PreprocessingStep configuration proto.
+
+  Returns:
+    function, argmap: A callable function and an argument map to call function
+                      with.
+
+  Raises:
+    ValueError: On invalid configuration.
+  """
+  step_type = preprocessor_step_config.WhichOneof('preprocessing_step')
+
+  if step_type in PREPROCESSING_FUNCTION_MAP:
+    preprocessing_function = PREPROCESSING_FUNCTION_MAP[step_type]
+    step_config = _get_step_config_from_proto(preprocessor_step_config,
+                                              step_type)
+    function_args = _get_dict_from_proto(step_config)
+    return (preprocessing_function, function_args)
+
+  if step_type == 'random_horizontal_flip':
+    config = preprocessor_step_config.random_horizontal_flip
+    return (preprocessor.random_horizontal_flip,
+            {
+                'keypoint_flip_permutation': tuple(
+                    config.keypoint_flip_permutation),
+            })
+
+  if step_type == 'random_vertical_flip':
+    config = preprocessor_step_config.random_vertical_flip
+    return (preprocessor.random_vertical_flip,
+            {
+                'keypoint_flip_permutation': tuple(
+                    config.keypoint_flip_permutation),
+            })
+
+  if step_type == 'random_rotation90':
+    return (preprocessor.random_rotation90, {})
+
+  if step_type == 'random_crop_image':
+    config = preprocessor_step_config.random_crop_image
+    return (preprocessor.random_crop_image,
+            {
+                'min_object_covered': config.min_object_covered,
+                'aspect_ratio_range': (config.min_aspect_ratio,
+                                       config.max_aspect_ratio),
+                'area_range': (config.min_area, config.max_area),
+                'overlap_thresh': config.overlap_thresh,
+                'clip_boxes': config.clip_boxes,
+                'random_coef': config.random_coef,
+            })
+
+  if step_type == 'random_pad_image':
+    config = preprocessor_step_config.random_pad_image
+    min_image_size = None
+    if (config.HasField('min_image_height') !=
+        config.HasField('min_image_width')):
+      raise ValueError('min_image_height and min_image_width should be either '
+                       'both set or both unset.')
+    if config.HasField('min_image_height'):
+      min_image_size = (config.min_image_height, config.min_image_width)
+
+    max_image_size = None
+    if (config.HasField('max_image_height') !=
+        config.HasField('max_image_width')):
+      raise ValueError('max_image_height and max_image_width should be either '
+                       'both set or both unset.')
+    if config.HasField('max_image_height'):
+      max_image_size = (config.max_image_height, config.max_image_width)
+
+    pad_color = config.pad_color or None
+    if pad_color:
+      if len(pad_color) == 3:
+        pad_color = tf.to_float([x for x in config.pad_color])
+      else:
+        raise ValueError('pad_color should have 3 elements (RGB) if set!')
+    return (preprocessor.random_pad_image,
+            {
+                'min_image_size': min_image_size,
+                'max_image_size': max_image_size,
+                'pad_color': pad_color,
+            })
+
+  if step_type == 'random_crop_pad_image':
+    config = preprocessor_step_config.random_crop_pad_image
+    min_padded_size_ratio = config.min_padded_size_ratio
+    if min_padded_size_ratio and len(min_padded_size_ratio) != 2:
+      raise ValueError('min_padded_size_ratio should have 2 elements if set!')
+    max_padded_size_ratio = config.max_padded_size_ratio
+    if max_padded_size_ratio and len(max_padded_size_ratio) != 2:
+      raise ValueError('max_padded_size_ratio should have 2 elements if set!')
+    pad_color = config.pad_color
+    if pad_color and len(pad_color) != 3:
+      raise ValueError('pad_color should have 3 elements if set!')
+    kwargs = {
+        'min_object_covered': config.min_object_covered,
+        'aspect_ratio_range': (config.min_aspect_ratio,
+                               config.max_aspect_ratio),
+        'area_range': (config.min_area, config.max_area),
+        'overlap_thresh': config.overlap_thresh,
+        'clip_boxes': config.clip_boxes,
+        'random_coef': config.random_coef,
+    }
+    if min_padded_size_ratio:
+      kwargs['min_padded_size_ratio'] = tuple(min_padded_size_ratio)
+    if max_padded_size_ratio:
+      kwargs['max_padded_size_ratio'] = tuple(max_padded_size_ratio)
+    if pad_color:
+      kwargs['pad_color'] = tuple(pad_color)
+    return (preprocessor.random_crop_pad_image, kwargs)
+
+  if step_type == 'random_resize_method':
+    config = preprocessor_step_config.random_resize_method
+    return (preprocessor.random_resize_method,
+            {
+                'target_size': [config.target_height, config.target_width],
+            })
+
+  if step_type == 'resize_image':
+    config = preprocessor_step_config.resize_image
+    method = RESIZE_METHOD_MAP[config.method]
+    return (preprocessor.resize_image,
+            {
+                'new_height': config.new_height,
+                'new_width': config.new_width,
+                'method': method
+            })
+
+  if step_type == 'ssd_random_crop':
+    config = preprocessor_step_config.ssd_random_crop
+    if config.operations:
+      min_object_covered = [op.min_object_covered for op in config.operations]
+      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
+                            for op in config.operations]
+      area_range = [(op.min_area, op.max_area) for op in config.operations]
+      overlap_thresh = [op.overlap_thresh for op in config.operations]
+      clip_boxes = [op.clip_boxes for op in config.operations]
+      random_coef = [op.random_coef for op in config.operations]
+      return (preprocessor.ssd_random_crop,
+              {
+                  'min_object_covered': min_object_covered,
+                  'aspect_ratio_range': aspect_ratio_range,
+                  'area_range': area_range,
+                  'overlap_thresh': overlap_thresh,
+                  'clip_boxes': clip_boxes,
+                  'random_coef': random_coef,
+              })
+    return (preprocessor.ssd_random_crop, {})
+
+  if step_type == 'ssd_random_crop_pad':
+    config = preprocessor_step_config.ssd_random_crop_pad
+    if config.operations:
+      min_object_covered = [op.min_object_covered for op in config.operations]
+      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
+                            for op in config.operations]
+      area_range = [(op.min_area, op.max_area) for op in config.operations]
+      overlap_thresh = [op.overlap_thresh for op in config.operations]
+      clip_boxes = [op.clip_boxes for op in config.operations]
+      random_coef = [op.random_coef for op in config.operations]
+      min_padded_size_ratio = [tuple(op.min_padded_size_ratio)
+                               for op in config.operations]
+      max_padded_size_ratio = [tuple(op.max_padded_size_ratio)
+                               for op in config.operations]
+      pad_color = [(op.pad_color_r, op.pad_color_g, op.pad_color_b)
+                   for op in config.operations]
+      return (preprocessor.ssd_random_crop_pad,
+              {
+                  'min_object_covered': min_object_covered,
+                  'aspect_ratio_range': aspect_ratio_range,
+                  'area_range': area_range,
+                  'overlap_thresh': overlap_thresh,
+                  'clip_boxes': clip_boxes,
+                  'random_coef': random_coef,
+                  'min_padded_size_ratio': min_padded_size_ratio,
+                  'max_padded_size_ratio': max_padded_size_ratio,
+                  'pad_color': pad_color,
+              })
+    return (preprocessor.ssd_random_crop_pad, {})
+
+  if step_type == 'ssd_random_crop_fixed_aspect_ratio':
+    config = preprocessor_step_config.ssd_random_crop_fixed_aspect_ratio
+    if config.operations:
+      min_object_covered = [op.min_object_covered for op in config.operations]
+      area_range = [(op.min_area, op.max_area) for op in config.operations]
+      overlap_thresh = [op.overlap_thresh for op in config.operations]
+      clip_boxes = [op.clip_boxes for op in config.operations]
+      random_coef = [op.random_coef for op in config.operations]
+      return (preprocessor.ssd_random_crop_fixed_aspect_ratio,
+              {
+                  'min_object_covered': min_object_covered,
+                  'aspect_ratio': config.aspect_ratio,
+                  'area_range': area_range,
+                  'overlap_thresh': overlap_thresh,
+                  'clip_boxes': clip_boxes,
+                  'random_coef': random_coef,
+              })
+    return (preprocessor.ssd_random_crop_fixed_aspect_ratio, {})
+
+  if step_type == 'ssd_random_crop_pad_fixed_aspect_ratio':
+    config = preprocessor_step_config.ssd_random_crop_pad_fixed_aspect_ratio
+    kwargs = {}
+    aspect_ratio = config.aspect_ratio
+    if aspect_ratio:
+      kwargs['aspect_ratio'] = aspect_ratio
+    min_padded_size_ratio = config.min_padded_size_ratio
+    if min_padded_size_ratio:
+      if len(min_padded_size_ratio) != 2:
+        raise ValueError('min_padded_size_ratio should have 2 elements if set!')
+      kwargs['min_padded_size_ratio'] = tuple(min_padded_size_ratio)
+    max_padded_size_ratio = config.max_padded_size_ratio
+    if max_padded_size_ratio:
+      if len(max_padded_size_ratio) != 2:
+        raise ValueError('max_padded_size_ratio should have 2 elements if set!')
+      kwargs['max_padded_size_ratio'] = tuple(max_padded_size_ratio)
+    if config.operations:
+      kwargs['min_object_covered'] = [op.min_object_covered
+                                      for op in config.operations]
+      kwargs['aspect_ratio_range'] = [(op.min_aspect_ratio, op.max_aspect_ratio)
+                                      for op in config.operations]
+      kwargs['area_range'] = [(op.min_area, op.max_area)
+                              for op in config.operations]
+      kwargs['overlap_thresh'] = [op.overlap_thresh for op in config.operations]
+      kwargs['clip_boxes'] = [op.clip_boxes for op in config.operations]
+      kwargs['random_coef'] = [op.random_coef for op in config.operations]
+    return (preprocessor.ssd_random_crop_pad_fixed_aspect_ratio, kwargs)
+
+  raise ValueError('Unknown preprocessing step.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder_test.py
new file mode 100644
index 00000000..89de2b43
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/preprocessor_builder_test.py
@@ -0,0 +1,598 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for preprocessor_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import preprocessor_builder
+from object_detection.core import preprocessor
+from object_detection.protos import preprocessor_pb2
+
+
+class PreprocessorBuilderTest(tf.test.TestCase):
+
+  def assert_dictionary_close(self, dict1, dict2):
+    """Helper to check if two dicts with floatst or integers are close."""
+    self.assertEqual(sorted(dict1.keys()), sorted(dict2.keys()))
+    for key in dict1:
+      value = dict1[key]
+      if isinstance(value, float):
+        self.assertAlmostEqual(value, dict2[key])
+      else:
+        self.assertEqual(value, dict2[key])
+
+  def test_build_normalize_image(self):
+    preprocessor_text_proto = """
+    normalize_image {
+      original_minval: 0.0
+      original_maxval: 255.0
+      target_minval: -1.0
+      target_maxval: 1.0
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.normalize_image)
+    self.assertEqual(args, {
+        'original_minval': 0.0,
+        'original_maxval': 255.0,
+        'target_minval': -1.0,
+        'target_maxval': 1.0,
+    })
+
+  def test_build_random_horizontal_flip(self):
+    preprocessor_text_proto = """
+    random_horizontal_flip {
+      keypoint_flip_permutation: 1
+      keypoint_flip_permutation: 0
+      keypoint_flip_permutation: 2
+      keypoint_flip_permutation: 3
+      keypoint_flip_permutation: 5
+      keypoint_flip_permutation: 4
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_horizontal_flip)
+    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)})
+
+  def test_build_random_vertical_flip(self):
+    preprocessor_text_proto = """
+    random_vertical_flip {
+      keypoint_flip_permutation: 1
+      keypoint_flip_permutation: 0
+      keypoint_flip_permutation: 2
+      keypoint_flip_permutation: 3
+      keypoint_flip_permutation: 5
+      keypoint_flip_permutation: 4
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_vertical_flip)
+    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)})
+
+  def test_build_random_rotation90(self):
+    preprocessor_text_proto = """
+    random_rotation90 {}
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_rotation90)
+    self.assertEqual(args, {})
+
+  def test_build_random_pixel_value_scale(self):
+    preprocessor_text_proto = """
+    random_pixel_value_scale {
+      minval: 0.8
+      maxval: 1.2
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_pixel_value_scale)
+    self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2})
+
+  def test_build_random_image_scale(self):
+    preprocessor_text_proto = """
+    random_image_scale {
+      min_scale_ratio: 0.8
+      max_scale_ratio: 2.2
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_image_scale)
+    self.assert_dictionary_close(args, {'min_scale_ratio': 0.8,
+                                        'max_scale_ratio': 2.2})
+
+  def test_build_random_rgb_to_gray(self):
+    preprocessor_text_proto = """
+    random_rgb_to_gray {
+      probability: 0.8
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_rgb_to_gray)
+    self.assert_dictionary_close(args, {'probability': 0.8})
+
+  def test_build_random_adjust_brightness(self):
+    preprocessor_text_proto = """
+    random_adjust_brightness {
+      max_delta: 0.2
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_adjust_brightness)
+    self.assert_dictionary_close(args, {'max_delta': 0.2})
+
+  def test_build_random_adjust_contrast(self):
+    preprocessor_text_proto = """
+    random_adjust_contrast {
+      min_delta: 0.7
+      max_delta: 1.1
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_adjust_contrast)
+    self.assert_dictionary_close(args, {'min_delta': 0.7, 'max_delta': 1.1})
+
+  def test_build_random_adjust_hue(self):
+    preprocessor_text_proto = """
+    random_adjust_hue {
+      max_delta: 0.01
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_adjust_hue)
+    self.assert_dictionary_close(args, {'max_delta': 0.01})
+
+  def test_build_random_adjust_saturation(self):
+    preprocessor_text_proto = """
+    random_adjust_saturation {
+      min_delta: 0.75
+      max_delta: 1.15
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_adjust_saturation)
+    self.assert_dictionary_close(args, {'min_delta': 0.75, 'max_delta': 1.15})
+
+  def test_build_random_distort_color(self):
+    preprocessor_text_proto = """
+    random_distort_color {
+      color_ordering: 1
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_distort_color)
+    self.assertEqual(args, {'color_ordering': 1})
+
+  def test_build_random_jitter_boxes(self):
+    preprocessor_text_proto = """
+    random_jitter_boxes {
+      ratio: 0.1
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_jitter_boxes)
+    self.assert_dictionary_close(args, {'ratio': 0.1})
+
+  def test_build_random_crop_image(self):
+    preprocessor_text_proto = """
+    random_crop_image {
+      min_object_covered: 0.75
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 1.5
+      min_area: 0.25
+      max_area: 0.875
+      overlap_thresh: 0.5
+      clip_boxes: False
+      random_coef: 0.125
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_crop_image)
+    self.assertEqual(args, {
+        'min_object_covered': 0.75,
+        'aspect_ratio_range': (0.75, 1.5),
+        'area_range': (0.25, 0.875),
+        'overlap_thresh': 0.5,
+        'clip_boxes': False,
+        'random_coef': 0.125,
+    })
+
+  def test_build_random_pad_image(self):
+    preprocessor_text_proto = """
+    random_pad_image {
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_pad_image)
+    self.assertEqual(args, {
+        'min_image_size': None,
+        'max_image_size': None,
+        'pad_color': None,
+    })
+
+  def test_build_random_crop_pad_image(self):
+    preprocessor_text_proto = """
+    random_crop_pad_image {
+      min_object_covered: 0.75
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 1.5
+      min_area: 0.25
+      max_area: 0.875
+      overlap_thresh: 0.5
+      clip_boxes: False
+      random_coef: 0.125
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_crop_pad_image)
+    self.assertEqual(args, {
+        'min_object_covered': 0.75,
+        'aspect_ratio_range': (0.75, 1.5),
+        'area_range': (0.25, 0.875),
+        'overlap_thresh': 0.5,
+        'clip_boxes': False,
+        'random_coef': 0.125,
+    })
+
+  def test_build_random_crop_pad_image_with_optional_parameters(self):
+    preprocessor_text_proto = """
+    random_crop_pad_image {
+      min_object_covered: 0.75
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 1.5
+      min_area: 0.25
+      max_area: 0.875
+      overlap_thresh: 0.5
+      clip_boxes: False
+      random_coef: 0.125
+      min_padded_size_ratio: 0.5
+      min_padded_size_ratio: 0.75
+      max_padded_size_ratio: 0.5
+      max_padded_size_ratio: 0.75
+      pad_color: 0.5
+      pad_color: 0.5
+      pad_color: 1.0
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_crop_pad_image)
+    self.assertEqual(args, {
+        'min_object_covered': 0.75,
+        'aspect_ratio_range': (0.75, 1.5),
+        'area_range': (0.25, 0.875),
+        'overlap_thresh': 0.5,
+        'clip_boxes': False,
+        'random_coef': 0.125,
+        'min_padded_size_ratio': (0.5, 0.75),
+        'max_padded_size_ratio': (0.5, 0.75),
+        'pad_color': (0.5, 0.5, 1.0)
+    })
+
+  def test_build_random_crop_to_aspect_ratio(self):
+    preprocessor_text_proto = """
+    random_crop_to_aspect_ratio {
+      aspect_ratio: 0.85
+      overlap_thresh: 0.35
+      clip_boxes: False
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio)
+    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
+                                        'overlap_thresh': 0.35,
+                                        'clip_boxes': False})
+
+  def test_build_random_black_patches(self):
+    preprocessor_text_proto = """
+    random_black_patches {
+      max_black_patches: 20
+      probability: 0.95
+      size_to_image_ratio: 0.12
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_black_patches)
+    self.assert_dictionary_close(args, {'max_black_patches': 20,
+                                        'probability': 0.95,
+                                        'size_to_image_ratio': 0.12})
+
+  def test_build_random_resize_method(self):
+    preprocessor_text_proto = """
+    random_resize_method {
+      target_height: 75
+      target_width: 100
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.random_resize_method)
+    self.assert_dictionary_close(args, {'target_size': [75, 100]})
+
+  def test_build_scale_boxes_to_pixel_coordinates(self):
+    preprocessor_text_proto = """
+    scale_boxes_to_pixel_coordinates {}
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.scale_boxes_to_pixel_coordinates)
+    self.assertEqual(args, {})
+
+  def test_build_resize_image(self):
+    preprocessor_text_proto = """
+    resize_image {
+      new_height: 75
+      new_width: 100
+      method: BICUBIC
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.resize_image)
+    self.assertEqual(args, {'new_height': 75,
+                            'new_width': 100,
+                            'method': tf.image.ResizeMethod.BICUBIC})
+
+  def test_build_rgb_to_gray(self):
+    preprocessor_text_proto = """
+    rgb_to_gray {}
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.rgb_to_gray)
+    self.assertEqual(args, {})
+
+  def test_build_subtract_channel_mean(self):
+    preprocessor_text_proto = """
+    subtract_channel_mean {
+      means: [1.0, 2.0, 3.0]
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.subtract_channel_mean)
+    self.assertEqual(args, {'means': [1.0, 2.0, 3.0]})
+
+  def test_build_ssd_random_crop(self):
+    preprocessor_text_proto = """
+    ssd_random_crop {
+      operations {
+        min_object_covered: 0.0
+        min_aspect_ratio: 0.875
+        max_aspect_ratio: 1.125
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.0
+        clip_boxes: False
+        random_coef: 0.375
+      }
+      operations {
+        min_object_covered: 0.25
+        min_aspect_ratio: 0.75
+        max_aspect_ratio: 1.5
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.25
+        clip_boxes: True
+        random_coef: 0.375
+      }
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.ssd_random_crop)
+    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
+                            'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
+                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
+                            'overlap_thresh': [0.0, 0.25],
+                            'clip_boxes': [False, True],
+                            'random_coef': [0.375, 0.375]})
+
+  def test_build_ssd_random_crop_empty_operations(self):
+    preprocessor_text_proto = """
+    ssd_random_crop {
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.ssd_random_crop)
+    self.assertEqual(args, {})
+
+  def test_build_ssd_random_crop_pad(self):
+    preprocessor_text_proto = """
+    ssd_random_crop_pad {
+      operations {
+        min_object_covered: 0.0
+        min_aspect_ratio: 0.875
+        max_aspect_ratio: 1.125
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.0
+        clip_boxes: False
+        random_coef: 0.375
+        min_padded_size_ratio: [1.0, 1.0]
+        max_padded_size_ratio: [2.0, 2.0]
+        pad_color_r: 0.5
+        pad_color_g: 0.5
+        pad_color_b: 0.5
+      }
+      operations {
+        min_object_covered: 0.25
+        min_aspect_ratio: 0.75
+        max_aspect_ratio: 1.5
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.25
+        clip_boxes: True
+        random_coef: 0.375
+        min_padded_size_ratio: [1.0, 1.0]
+        max_padded_size_ratio: [2.0, 2.0]
+        pad_color_r: 0.5
+        pad_color_g: 0.5
+        pad_color_b: 0.5
+      }
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.ssd_random_crop_pad)
+    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
+                            'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
+                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
+                            'overlap_thresh': [0.0, 0.25],
+                            'clip_boxes': [False, True],
+                            'random_coef': [0.375, 0.375],
+                            'min_padded_size_ratio': [(1.0, 1.0), (1.0, 1.0)],
+                            'max_padded_size_ratio': [(2.0, 2.0), (2.0, 2.0)],
+                            'pad_color': [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]})
+
+  def test_build_ssd_random_crop_fixed_aspect_ratio(self):
+    preprocessor_text_proto = """
+    ssd_random_crop_fixed_aspect_ratio {
+      operations {
+        min_object_covered: 0.0
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.0
+        clip_boxes: False
+        random_coef: 0.375
+      }
+      operations {
+        min_object_covered: 0.25
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.25
+        clip_boxes: True
+        random_coef: 0.375
+      }
+      aspect_ratio: 0.875
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.ssd_random_crop_fixed_aspect_ratio)
+    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
+                            'aspect_ratio': 0.875,
+                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
+                            'overlap_thresh': [0.0, 0.25],
+                            'clip_boxes': [False, True],
+                            'random_coef': [0.375, 0.375]})
+
+  def test_build_ssd_random_crop_pad_fixed_aspect_ratio(self):
+    preprocessor_text_proto = """
+    ssd_random_crop_pad_fixed_aspect_ratio {
+      operations {
+        min_object_covered: 0.0
+        min_aspect_ratio: 0.875
+        max_aspect_ratio: 1.125
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.0
+        clip_boxes: False
+        random_coef: 0.375
+      }
+      operations {
+        min_object_covered: 0.25
+        min_aspect_ratio: 0.75
+        max_aspect_ratio: 1.5
+        min_area: 0.5
+        max_area: 1.0
+        overlap_thresh: 0.25
+        clip_boxes: True
+        random_coef: 0.375
+      }
+      aspect_ratio: 0.875
+      min_padded_size_ratio: [1.0, 1.0]
+      max_padded_size_ratio: [2.0, 2.0]
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function,
+                     preprocessor.ssd_random_crop_pad_fixed_aspect_ratio)
+    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
+                            'aspect_ratio': 0.875,
+                            'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
+                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
+                            'overlap_thresh': [0.0, 0.25],
+                            'clip_boxes': [False, True],
+                            'random_coef': [0.375, 0.375],
+                            'min_padded_size_ratio': (1.0, 1.0),
+                            'max_padded_size_ratio': (2.0, 2.0)})
+
+  def test_build_normalize_image_convert_class_logits_to_softmax(self):
+    preprocessor_text_proto = """
+    convert_class_logits_to_softmax {
+        temperature: 2
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.convert_class_logits_to_softmax)
+    self.assertEqual(args, {'temperature': 2})
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder.py
new file mode 100644
index 00000000..157c94fb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder.py
@@ -0,0 +1,59 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Builder for region similarity calculators."""
+
+from object_detection.core import region_similarity_calculator
+from object_detection.protos import region_similarity_calculator_pb2
+
+
+def build(region_similarity_calculator_config):
+  """Builds region similarity calculator based on the configuration.
+
+  Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
+  core/region_similarity_calculator.proto for details.
+
+  Args:
+    region_similarity_calculator_config: RegionSimilarityCalculator
+      configuration proto.
+
+  Returns:
+    region_similarity_calculator: RegionSimilarityCalculator object.
+
+  Raises:
+    ValueError: On unknown region similarity calculator.
+  """
+
+  if not isinstance(
+      region_similarity_calculator_config,
+      region_similarity_calculator_pb2.RegionSimilarityCalculator):
+    raise ValueError(
+        'region_similarity_calculator_config not of type '
+        'region_similarity_calculator_pb2.RegionsSimilarityCalculator')
+
+  similarity_calculator = region_similarity_calculator_config.WhichOneof(
+      'region_similarity')
+  if similarity_calculator == 'iou_similarity':
+    return region_similarity_calculator.IouSimilarity()
+  if similarity_calculator == 'ioa_similarity':
+    return region_similarity_calculator.IoaSimilarity()
+  if similarity_calculator == 'neg_sq_dist_similarity':
+    return region_similarity_calculator.NegSqDistSimilarity()
+  if similarity_calculator == 'thresholded_iou_similarity':
+    return region_similarity_calculator.ThresholdedIouSimilarity(
+        region_similarity_calculator_config.thresholded_iou_similarity.threshold
+    )
+
+  raise ValueError('Unknown region similarity calculator.')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder_test.py
new file mode 100644
index 00000000..ca3a5512
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/builders/region_similarity_calculator_builder_test.py
@@ -0,0 +1,67 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for region_similarity_calculator_builder."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import region_similarity_calculator_builder
+from object_detection.core import region_similarity_calculator
+from object_detection.protos import region_similarity_calculator_pb2 as sim_calc_pb2
+
+
+class RegionSimilarityCalculatorBuilderTest(tf.test.TestCase):
+
+  def testBuildIoaSimilarityCalculator(self):
+    similarity_calc_text_proto = """
+      ioa_similarity {
+      }
+    """
+    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
+    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
+    similarity_calc = region_similarity_calculator_builder.build(
+        similarity_calc_proto)
+    self.assertTrue(isinstance(similarity_calc,
+                               region_similarity_calculator.IoaSimilarity))
+
+  def testBuildIouSimilarityCalculator(self):
+    similarity_calc_text_proto = """
+      iou_similarity {
+      }
+    """
+    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
+    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
+    similarity_calc = region_similarity_calculator_builder.build(
+        similarity_calc_proto)
+    self.assertTrue(isinstance(similarity_calc,
+                               region_similarity_calculator.IouSimilarity))
+
+  def testBuildNegSqDistSimilarityCalculator(self):
+    similarity_calc_text_proto = """
+      neg_sq_dist_similarity {
+      }
+    """
+    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
+    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
+    similarity_calc = region_similarity_calculator_builder.build(
+        similarity_calc_proto)
+    self.assertTrue(isinstance(similarity_calc,
+                               region_similarity_calculator.
+                               NegSqDistSimilarity))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/configs/mask_rcnn_resnet50_atrous_coco_8GPU.config b/TensorFlow/Detection/SSD/models/research/object_detection/configs/mask_rcnn_resnet50_atrous_coco_8GPU.config
new file mode 100644
index 00000000..194222b2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/configs/mask_rcnn_resnet50_atrous_coco_8GPU.config
@@ -0,0 +1,175 @@
+# Mask R-CNN with Resnet-50 (v1), Atrous version
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 800
+        max_dimension: 1365
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_resnet50'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        mask_height: 33
+        mask_width: 33
+        mask_prediction_conv_depth: 0
+        mask_prediction_num_conv_layers: 4
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_mask_prediction_loss_weight: 4.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0024
+          schedule {
+            step: 900000
+            learning_rate: .00024
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000024
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
+  fine_tune_checkpoint_type: "classification"
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_train.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "/data/coco_val.record*"
+  }
+  label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/anchor_generator.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/anchor_generator.py
new file mode 100644
index 00000000..f2797ef7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/anchor_generator.py
@@ -0,0 +1,150 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base anchor generator.
+
+The job of the anchor generator is to create (or load) a collection
+of bounding boxes to be used as anchors.
+
+Generated anchors are assumed to match some convolutional grid or list of grid
+shapes.  For example, we might want to generate anchors matching an 8x8
+feature map and a 4x4 feature map.  If we place 3 anchors per grid location
+on the first feature map and 6 anchors per grid location on the second feature
+map, then 3*8*8 + 6*4*4 = 288 anchors are generated in total.
+
+To support fully convolutional settings, feature map shapes are passed
+dynamically at generation time.  The number of anchors to place at each location
+is static --- implementations of AnchorGenerator must always be able return
+the number of anchors that it uses per location for each feature map.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+import tensorflow as tf
+
+
+class AnchorGenerator(object):
+  """Abstract base class for anchor generators."""
+  __metaclass__ = ABCMeta
+
+  @abstractmethod
+  def name_scope(self):
+    """Name scope.
+
+    Must be defined by implementations.
+
+    Returns:
+      a string representing the name scope of the anchor generation operation.
+    """
+    pass
+
+  @property
+  def check_num_anchors(self):
+    """Whether to dynamically check the number of anchors generated.
+
+    Can be overridden by implementations that would like to disable this
+    behavior.
+
+    Returns:
+      a boolean controlling whether the Generate function should dynamically
+      check the number of anchors generated against the mathematically
+      expected number of anchors.
+    """
+    return True
+
+  @abstractmethod
+  def num_anchors_per_location(self):
+    """Returns the number of anchors per spatial location.
+
+    Returns:
+      a list of integers, one for each expected feature map to be passed to
+      the `generate` function.
+    """
+    pass
+
+  def generate(self, feature_map_shape_list, **params):
+    """Generates a collection of bounding boxes to be used as anchors.
+
+    TODO(rathodv): remove **params from argument list and make stride and
+      offsets (for multiple_grid_anchor_generator) constructor arguments.
+
+    Args:
+      feature_map_shape_list: list of (height, width) pairs in the format
+        [(height_0, width_0), (height_1, width_1), ...] that the generated
+        anchors must align with.  Pairs can be provided as 1-dimensional
+        integer tensors of length 2 or simply as tuples of integers.
+      **params: parameters for anchor generation op
+
+    Returns:
+      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+        the input feature map shapes.
+
+    Raises:
+      ValueError: if the number of feature map shapes does not match the length
+        of NumAnchorsPerLocation.
+    """
+    if self.check_num_anchors and (
+        len(feature_map_shape_list) != len(self.num_anchors_per_location())):
+      raise ValueError('Number of feature maps is expected to equal the length '
+                       'of `num_anchors_per_location`.')
+    with tf.name_scope(self.name_scope()):
+      anchors_list = self._generate(feature_map_shape_list, **params)
+      if self.check_num_anchors:
+        with tf.control_dependencies([
+            self._assert_correct_number_of_anchors(
+                anchors_list, feature_map_shape_list)]):
+          for item in anchors_list:
+            item.set(tf.identity(item.get()))
+      return anchors_list
+
+  @abstractmethod
+  def _generate(self, feature_map_shape_list, **params):
+    """To be overridden by implementations.
+
+    Args:
+      feature_map_shape_list: list of (height, width) pairs in the format
+        [(height_0, width_0), (height_1, width_1), ...] that the generated
+        anchors must align with.
+      **params: parameters for anchor generation op
+
+    Returns:
+      boxes_list: a list of BoxList, each holding a collection of N anchor
+        boxes.
+    """
+    pass
+
+  def _assert_correct_number_of_anchors(self, anchors_list,
+                                        feature_map_shape_list):
+    """Assert that correct number of anchors was generated.
+
+    Args:
+      anchors_list: A list of box_list.BoxList object holding anchors generated.
+      feature_map_shape_list: list of (height, width) pairs in the format
+        [(height_0, width_0), (height_1, width_1), ...] that the generated
+        anchors must align with.
+    Returns:
+      Op that raises InvalidArgumentError if the number of anchors does not
+        match the number of expected anchors.
+    """
+    expected_num_anchors = 0
+    actual_num_anchors = 0
+    for num_anchors_per_location, feature_map_shape, anchors in zip(
+        self.num_anchors_per_location(), feature_map_shape_list, anchors_list):
+      expected_num_anchors += (num_anchors_per_location
+                               * feature_map_shape[0]
+                               * feature_map_shape[1])
+      actual_num_anchors += anchors.num_boxes()
+    return tf.assert_equal(expected_num_anchors, actual_num_anchors)
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler.py
new file mode 100644
index 00000000..a38f82f6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler.py
@@ -0,0 +1,264 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Class to subsample minibatches by balancing positives and negatives.
+
+Subsamples minibatches based on a pre-specified positive fraction in range
+[0,1]. The class presumes there are many more negatives than positive examples:
+if the desired batch_size cannot be achieved with the pre-specified positive
+fraction, it fills the rest with negative examples. If this is not sufficient
+for obtaining the desired batch_size, it returns fewer examples.
+
+The main function to call is Subsample(self, indicator, labels). For convenience
+one can also call SubsampleWeights(self, weights, labels) which is defined in
+the minibatch_sampler base class.
+
+When is_static is True, it implements a method that guarantees static shapes.
+It also ensures the length of output of the subsample is always batch_size, even
+when number of examples set to True in indicator is less than batch_size.
+"""
+
+import tensorflow as tf
+
+from object_detection.core import minibatch_sampler
+from object_detection.utils import ops
+
+
+class BalancedPositiveNegativeSampler(minibatch_sampler.MinibatchSampler):
+  """Subsamples minibatches to a desired balance of positives and negatives."""
+
+  def __init__(self, positive_fraction=0.5, is_static=False):
+    """Constructs a minibatch sampler.
+
+    Args:
+      positive_fraction: desired fraction of positive examples (scalar in [0,1])
+        in the batch.
+      is_static: If True, uses an implementation with static shape guarantees.
+
+    Raises:
+      ValueError: if positive_fraction < 0, or positive_fraction > 1
+    """
+    if positive_fraction < 0 or positive_fraction > 1:
+      raise ValueError('positive_fraction should be in range [0,1]. '
+                       'Received: %s.' % positive_fraction)
+    self._positive_fraction = positive_fraction
+    self._is_static = is_static
+
+  def _get_num_pos_neg_samples(self, sorted_indices_tensor, sample_size):
+    """Counts the number of positives and negatives numbers to be sampled.
+
+    Args:
+      sorted_indices_tensor: A sorted int32 tensor of shape [N] which contains
+        the signed indices of the examples where the sign is based on the label
+        value. The examples that cannot be sampled are set to 0. It samples
+        atmost sample_size*positive_fraction positive examples and remaining
+        from negative examples.
+      sample_size: Size of subsamples.
+
+    Returns:
+      A tuple containing the number of positive and negative labels in the
+      subsample.
+    """
+    input_length = tf.shape(sorted_indices_tensor)[0]
+    valid_positive_index = tf.greater(sorted_indices_tensor,
+                                      tf.zeros(input_length, tf.int32))
+    num_sampled_pos = tf.reduce_sum(tf.cast(valid_positive_index, tf.int32))
+    max_num_positive_samples = tf.constant(
+        int(sample_size * self._positive_fraction), tf.int32)
+    num_positive_samples = tf.minimum(max_num_positive_samples, num_sampled_pos)
+    num_negative_samples = tf.constant(sample_size,
+                                       tf.int32) - num_positive_samples
+
+    return num_positive_samples, num_negative_samples
+
+  def _get_values_from_start_and_end(self, input_tensor, num_start_samples,
+                                     num_end_samples, total_num_samples):
+    """slices num_start_samples and last num_end_samples from input_tensor.
+
+    Args:
+      input_tensor: An int32 tensor of shape [N] to be sliced.
+      num_start_samples: Number of examples to be sliced from the beginning
+        of the input tensor.
+      num_end_samples: Number of examples to be sliced from the end of the
+        input tensor.
+      total_num_samples: Sum of is num_start_samples and num_end_samples. This
+        should be a scalar.
+
+    Returns:
+      A tensor containing the first num_start_samples and last num_end_samples
+      from input_tensor.
+
+    """
+    input_length = tf.shape(input_tensor)[0]
+    start_positions = tf.less(tf.range(input_length), num_start_samples)
+    end_positions = tf.greater_equal(
+        tf.range(input_length), input_length - num_end_samples)
+    selected_positions = tf.logical_or(start_positions, end_positions)
+    selected_positions = tf.cast(selected_positions, tf.float32)
+    indexed_positions = tf.multiply(tf.cumsum(selected_positions),
+                                    selected_positions)
+    one_hot_selector = tf.one_hot(tf.cast(indexed_positions, tf.int32) - 1,
+                                  total_num_samples,
+                                  dtype=tf.float32)
+    return tf.cast(tf.tensordot(tf.cast(input_tensor, tf.float32),
+                                one_hot_selector, axes=[0, 0]), tf.int32)
+
+  def _static_subsample(self, indicator, batch_size, labels):
+    """Returns subsampled minibatch.
+
+    Args:
+      indicator: boolean tensor of shape [N] whose True entries can be sampled.
+        N should be a complie time constant.
+      batch_size: desired batch size. This scalar cannot be None.
+      labels: boolean tensor of shape [N] denoting positive(=True) and negative
+        (=False) examples. N should be a complie time constant.
+
+    Returns:
+      sampled_idx_indicator: boolean tensor of shape [N], True for entries which
+        are sampled. It ensures the length of output of the subsample is always
+        batch_size, even when number of examples set to True in indicator is
+        less than batch_size.
+
+    Raises:
+      ValueError: if labels and indicator are not 1D boolean tensors.
+    """
+    # Check if indicator and labels have a static size.
+    if not indicator.shape.is_fully_defined():
+      raise ValueError('indicator must be static in shape when is_static is'
+                       'True')
+    if not labels.shape.is_fully_defined():
+      raise ValueError('labels must be static in shape when is_static is'
+                       'True')
+    if not isinstance(batch_size, int):
+      raise ValueError('batch_size has to be an integer when is_static is'
+                       'True.')
+
+    input_length = tf.shape(indicator)[0]
+
+    # Set the number of examples set True in indicator to be at least
+    # batch_size.
+    num_true_sampled = tf.reduce_sum(tf.cast(indicator, tf.float32))
+    additional_false_sample = tf.less_equal(
+        tf.cumsum(tf.cast(tf.logical_not(indicator), tf.float32)),
+        batch_size - num_true_sampled)
+    indicator = tf.logical_or(indicator, additional_false_sample)
+
+    # Shuffle indicator and label. Need to store the permutation to restore the
+    # order post sampling.
+    permutation = tf.random_shuffle(tf.range(input_length))
+    indicator = ops.matmul_gather_on_zeroth_axis(
+        tf.cast(indicator, tf.float32), permutation)
+    labels = ops.matmul_gather_on_zeroth_axis(
+        tf.cast(labels, tf.float32), permutation)
+
+    # index (starting from 1) when indicator is True, 0 when False
+    indicator_idx = tf.where(
+        tf.cast(indicator, tf.bool), tf.range(1, input_length + 1),
+        tf.zeros(input_length, tf.int32))
+
+    # Replace -1 for negative, +1 for positive labels
+    signed_label = tf.where(
+        tf.cast(labels, tf.bool), tf.ones(input_length, tf.int32),
+        tf.scalar_mul(-1, tf.ones(input_length, tf.int32)))
+    # negative of index for negative label, positive index for positive label,
+    # 0 when indicator is False.
+    signed_indicator_idx = tf.multiply(indicator_idx, signed_label)
+    sorted_signed_indicator_idx = tf.nn.top_k(
+        signed_indicator_idx, input_length, sorted=True).values
+
+    [num_positive_samples,
+     num_negative_samples] = self._get_num_pos_neg_samples(
+         sorted_signed_indicator_idx, batch_size)
+
+    sampled_idx = self._get_values_from_start_and_end(
+        sorted_signed_indicator_idx, num_positive_samples,
+        num_negative_samples, batch_size)
+
+    # Shift the indices to start from 0 and remove any samples that are set as
+    # False.
+    sampled_idx = tf.abs(sampled_idx) - tf.ones(batch_size, tf.int32)
+    sampled_idx = tf.multiply(
+        tf.cast(tf.greater_equal(sampled_idx, tf.constant(0)), tf.int32),
+        sampled_idx)
+
+    sampled_idx_indicator = tf.cast(tf.reduce_sum(
+        tf.one_hot(sampled_idx, depth=input_length),
+        axis=0), tf.bool)
+
+    # project back the order based on stored permutations
+    reprojections = tf.one_hot(permutation, depth=input_length,
+                               dtype=tf.float32)
+    return tf.cast(tf.tensordot(
+        tf.cast(sampled_idx_indicator, tf.float32),
+        reprojections, axes=[0, 0]), tf.bool)
+
+  def subsample(self, indicator, batch_size, labels, scope=None):
+    """Returns subsampled minibatch.
+
+    Args:
+      indicator: boolean tensor of shape [N] whose True entries can be sampled.
+      batch_size: desired batch size. If None, keeps all positive samples and
+        randomly selects negative samples so that the positive sample fraction
+        matches self._positive_fraction. It cannot be None is is_static is True.
+      labels: boolean tensor of shape [N] denoting positive(=True) and negative
+          (=False) examples.
+      scope: name scope.
+
+    Returns:
+      sampled_idx_indicator: boolean tensor of shape [N], True for entries which
+        are sampled.
+
+    Raises:
+      ValueError: if labels and indicator are not 1D boolean tensors.
+    """
+    if len(indicator.get_shape().as_list()) != 1:
+      raise ValueError('indicator must be 1 dimensional, got a tensor of '
+                       'shape %s' % indicator.get_shape())
+    if len(labels.get_shape().as_list()) != 1:
+      raise ValueError('labels must be 1 dimensional, got a tensor of '
+                       'shape %s' % labels.get_shape())
+    if labels.dtype != tf.bool:
+      raise ValueError('labels should be of type bool. Received: %s' %
+                       labels.dtype)
+    if indicator.dtype != tf.bool:
+      raise ValueError('indicator should be of type bool. Received: %s' %
+                       indicator.dtype)
+    with tf.name_scope(scope, 'BalancedPositiveNegativeSampler'):
+      if self._is_static:
+        return self._static_subsample(indicator, batch_size, labels)
+
+      else:
+        # Only sample from indicated samples
+        negative_idx = tf.logical_not(labels)
+        positive_idx = tf.logical_and(labels, indicator)
+        negative_idx = tf.logical_and(negative_idx, indicator)
+
+        # Sample positive and negative samples separately
+        if batch_size is None:
+          max_num_pos = tf.reduce_sum(tf.to_int32(positive_idx))
+        else:
+          max_num_pos = int(self._positive_fraction * batch_size)
+        sampled_pos_idx = self.subsample_indicator(positive_idx, max_num_pos)
+        num_sampled_pos = tf.reduce_sum(tf.cast(sampled_pos_idx, tf.int32))
+        if batch_size is None:
+          negative_positive_ratio = (
+              1 - self._positive_fraction) / self._positive_fraction
+          max_num_neg = tf.to_int32(
+              negative_positive_ratio * tf.to_float(num_sampled_pos))
+        else:
+          max_num_neg = batch_size - num_sampled_pos
+        sampled_neg_idx = self.subsample_indicator(negative_idx, max_num_neg)
+
+        return tf.logical_or(sampled_pos_idx, sampled_neg_idx)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler_test.py
new file mode 100644
index 00000000..1df28e4c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/balanced_positive_negative_sampler_test.py
@@ -0,0 +1,204 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.balanced_positive_negative_sampler."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import balanced_positive_negative_sampler
+from object_detection.utils import test_case
+
+
+class BalancedPositiveNegativeSamplerTest(test_case.TestCase):
+
+  def test_subsample_all_examples_dynamic(self):
+    numpy_labels = np.random.permutation(300)
+    indicator = tf.constant(np.ones(300) == 1)
+    numpy_labels = (numpy_labels - 200) > 0
+
+    labels = tf.constant(numpy_labels)
+
+    sampler = (
+        balanced_positive_negative_sampler.BalancedPositiveNegativeSampler())
+    is_sampled = sampler.subsample(indicator, 64, labels)
+    with self.test_session() as sess:
+      is_sampled = sess.run(is_sampled)
+      self.assertTrue(sum(is_sampled) == 64)
+      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 32)
+      self.assertTrue(sum(np.logical_and(
+          np.logical_not(numpy_labels), is_sampled)) == 32)
+
+  def test_subsample_all_examples_static(self):
+    numpy_labels = np.random.permutation(300)
+    indicator = np.array(np.ones(300) == 1, np.bool)
+    numpy_labels = (numpy_labels - 200) > 0
+
+    labels = np.array(numpy_labels, np.bool)
+
+    def graph_fn(indicator, labels):
+      sampler = (
+          balanced_positive_negative_sampler.BalancedPositiveNegativeSampler(
+              is_static=True))
+      return sampler.subsample(indicator, 64, labels)
+
+    is_sampled = self.execute(graph_fn, [indicator, labels])
+    self.assertTrue(sum(is_sampled) == 64)
+    self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 32)
+    self.assertTrue(sum(np.logical_and(
+        np.logical_not(numpy_labels), is_sampled)) == 32)
+
+  def test_subsample_selection_dynamic(self):
+    # Test random sampling when only some examples can be sampled:
+    # 100 samples, 20 positives, 10 positives cannot be sampled
+    numpy_labels = np.arange(100)
+    numpy_indicator = numpy_labels < 90
+    indicator = tf.constant(numpy_indicator)
+    numpy_labels = (numpy_labels - 80) >= 0
+
+    labels = tf.constant(numpy_labels)
+
+    sampler = (
+        balanced_positive_negative_sampler.BalancedPositiveNegativeSampler())
+    is_sampled = sampler.subsample(indicator, 64, labels)
+    with self.test_session() as sess:
+      is_sampled = sess.run(is_sampled)
+      self.assertTrue(sum(is_sampled) == 64)
+      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 10)
+      self.assertTrue(sum(np.logical_and(
+          np.logical_not(numpy_labels), is_sampled)) == 54)
+      self.assertAllEqual(is_sampled, np.logical_and(is_sampled,
+                                                     numpy_indicator))
+
+  def test_subsample_selection_static(self):
+    # Test random sampling when only some examples can be sampled:
+    # 100 samples, 20 positives, 10 positives cannot be sampled.
+    numpy_labels = np.arange(100)
+    numpy_indicator = numpy_labels < 90
+    indicator = np.array(numpy_indicator, np.bool)
+    numpy_labels = (numpy_labels - 80) >= 0
+
+    labels = np.array(numpy_labels, np.bool)
+
+    def graph_fn(indicator, labels):
+      sampler = (
+          balanced_positive_negative_sampler.BalancedPositiveNegativeSampler(
+              is_static=True))
+      return sampler.subsample(indicator, 64, labels)
+
+    is_sampled = self.execute(graph_fn, [indicator, labels])
+    self.assertTrue(sum(is_sampled) == 64)
+    self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 10)
+    self.assertTrue(sum(np.logical_and(
+        np.logical_not(numpy_labels), is_sampled)) == 54)
+    self.assertAllEqual(is_sampled, np.logical_and(is_sampled, numpy_indicator))
+
+  def test_subsample_selection_larger_batch_size_dynamic(self):
+    # Test random sampling when total number of examples that can be sampled are
+    # less than batch size:
+    # 100 samples, 50 positives, 40 positives cannot be sampled, batch size 64.
+    numpy_labels = np.arange(100)
+    numpy_indicator = numpy_labels < 60
+    indicator = tf.constant(numpy_indicator)
+    numpy_labels = (numpy_labels - 50) >= 0
+
+    labels = tf.constant(numpy_labels)
+
+    sampler = (
+        balanced_positive_negative_sampler.BalancedPositiveNegativeSampler())
+    is_sampled = sampler.subsample(indicator, 64, labels)
+    with self.test_session() as sess:
+      is_sampled = sess.run(is_sampled)
+      self.assertTrue(sum(is_sampled) == 60)
+      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 10)
+      self.assertTrue(
+          sum(np.logical_and(np.logical_not(numpy_labels), is_sampled)) == 50)
+      self.assertAllEqual(is_sampled, np.logical_and(is_sampled,
+                                                     numpy_indicator))
+
+  def test_subsample_selection_larger_batch_size_static(self):
+    # Test random sampling when total number of examples that can be sampled are
+    # less than batch size:
+    # 100 samples, 50 positives, 40 positives cannot be sampled, batch size 64.
+    # It should still return 64 samples, with 4 of them that couldn't have been
+    # sampled.
+    numpy_labels = np.arange(100)
+    numpy_indicator = numpy_labels < 60
+    indicator = np.array(numpy_indicator, np.bool)
+    numpy_labels = (numpy_labels - 50) >= 0
+
+    labels = np.array(numpy_labels, np.bool)
+
+    def graph_fn(indicator, labels):
+      sampler = (
+          balanced_positive_negative_sampler.BalancedPositiveNegativeSampler(
+              is_static=True))
+      return sampler.subsample(indicator, 64, labels)
+
+    is_sampled = self.execute(graph_fn, [indicator, labels])
+    self.assertTrue(sum(is_sampled) == 64)
+    self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) >= 10)
+    self.assertTrue(
+        sum(np.logical_and(np.logical_not(numpy_labels), is_sampled)) >= 50)
+    self.assertTrue(sum(np.logical_and(is_sampled, numpy_indicator)) == 60)
+
+  def test_subsample_selection_no_batch_size(self):
+    # Test random sampling when only some examples can be sampled:
+    # 1000 samples, 6 positives (5 can be sampled).
+    numpy_labels = np.arange(1000)
+    numpy_indicator = numpy_labels < 999
+    indicator = tf.constant(numpy_indicator)
+    numpy_labels = (numpy_labels - 994) >= 0
+
+    labels = tf.constant(numpy_labels)
+
+    sampler = (balanced_positive_negative_sampler.
+               BalancedPositiveNegativeSampler(0.01))
+    is_sampled = sampler.subsample(indicator, None, labels)
+    with self.test_session() as sess:
+      is_sampled = sess.run(is_sampled)
+      self.assertTrue(sum(is_sampled) == 500)
+      self.assertTrue(sum(np.logical_and(numpy_labels, is_sampled)) == 5)
+      self.assertTrue(sum(np.logical_and(
+          np.logical_not(numpy_labels), is_sampled)) == 495)
+      self.assertAllEqual(is_sampled, np.logical_and(is_sampled,
+                                                     numpy_indicator))
+
+  def test_subsample_selection_no_batch_size_static(self):
+    labels = tf.constant([[True, False, False]])
+    indicator = tf.constant([True, False, True])
+    sampler = (
+        balanced_positive_negative_sampler.BalancedPositiveNegativeSampler())
+    with self.assertRaises(ValueError):
+      sampler.subsample(indicator, None, labels)
+
+  def test_raises_error_with_incorrect_label_shape(self):
+    labels = tf.constant([[True, False, False]])
+    indicator = tf.constant([True, False, True])
+    sampler = (balanced_positive_negative_sampler.
+               BalancedPositiveNegativeSampler())
+    with self.assertRaises(ValueError):
+      sampler.subsample(indicator, 64, labels)
+
+  def test_raises_error_with_incorrect_indicator_shape(self):
+    labels = tf.constant([True, False, False])
+    indicator = tf.constant([[True, False, True]])
+    sampler = (balanced_positive_negative_sampler.
+               BalancedPositiveNegativeSampler())
+    with self.assertRaises(ValueError):
+      sampler.subsample(indicator, 64, labels)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher.py
new file mode 100644
index 00000000..c5dfb712
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher.py
@@ -0,0 +1,136 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Provides functions to batch a dictionary of input tensors."""
+import collections
+
+import tensorflow as tf
+
+from object_detection.core import prefetcher
+
+rt_shape_str = '_runtime_shapes'
+
+
+class BatchQueue(object):
+  """BatchQueue class.
+
+  This class creates a batch queue to asynchronously enqueue tensors_dict.
+  It also adds a FIFO prefetcher so that the batches are readily available
+  for the consumers.  Dequeue ops for a BatchQueue object can be created via
+  the Dequeue method which evaluates to a batch of tensor_dict.
+
+  Example input pipeline with batching:
+  ------------------------------------
+  key, string_tensor = slim.parallel_reader.parallel_read(...)
+  tensor_dict = decoder.decode(string_tensor)
+  tensor_dict = preprocessor.preprocess(tensor_dict, ...)
+  batch_queue = batcher.BatchQueue(tensor_dict,
+                                   batch_size=32,
+                                   batch_queue_capacity=2000,
+                                   num_batch_queue_threads=8,
+                                   prefetch_queue_capacity=20)
+  tensor_dict = batch_queue.dequeue()
+  outputs = Model(tensor_dict)
+  ...
+  -----------------------------------
+
+  Notes:
+  -----
+  This class batches tensors of unequal sizes by zero padding and unpadding
+  them after generating a batch. This can be computationally expensive when
+  batching tensors (such as images) that are of vastly different sizes. So it is
+  recommended that the shapes of such tensors be fully defined in tensor_dict
+  while other lightweight tensors such as bounding box corners and class labels
+  can be of varying sizes. Use either crop or resize operations to fully define
+  the shape of an image in tensor_dict.
+
+  It is also recommended to perform any preprocessing operations on tensors
+  before passing to BatchQueue and subsequently calling the Dequeue method.
+
+  Another caveat is that this class does not read the last batch if it is not
+  full. The current implementation makes it hard to support that use case. So,
+  for evaluation, when it is critical to run all the examples through your
+  network use the input pipeline example mentioned in core/prefetcher.py.
+  """
+
+  def __init__(self, tensor_dict, batch_size, batch_queue_capacity,
+               num_batch_queue_threads, prefetch_queue_capacity):
+    """Constructs a batch queue holding tensor_dict.
+
+    Args:
+      tensor_dict: dictionary of tensors to batch.
+      batch_size: batch size.
+      batch_queue_capacity: max capacity of the queue from which the tensors are
+        batched.
+      num_batch_queue_threads: number of threads to use for batching.
+      prefetch_queue_capacity: max capacity of the queue used to prefetch
+        assembled batches.
+    """
+    # Remember static shapes to set shapes of batched tensors.
+    static_shapes = collections.OrderedDict(
+        {key: tensor.get_shape() for key, tensor in tensor_dict.items()})
+    # Remember runtime shapes to unpad tensors after batching.
+    runtime_shapes = collections.OrderedDict(
+        {(key + rt_shape_str): tf.shape(tensor)
+         for key, tensor in tensor_dict.items()})
+
+    all_tensors = tensor_dict
+    all_tensors.update(runtime_shapes)
+    batched_tensors = tf.train.batch(
+        all_tensors,
+        capacity=batch_queue_capacity,
+        batch_size=batch_size,
+        dynamic_pad=True,
+        num_threads=num_batch_queue_threads)
+
+    self._queue = prefetcher.prefetch(batched_tensors,
+                                      prefetch_queue_capacity)
+    self._static_shapes = static_shapes
+    self._batch_size = batch_size
+
+  def dequeue(self):
+    """Dequeues a batch of tensor_dict from the BatchQueue.
+
+    TODO: use allow_smaller_final_batch to allow running over the whole eval set
+
+    Returns:
+      A list of tensor_dicts of the requested batch_size.
+    """
+    batched_tensors = self._queue.dequeue()
+    # Separate input tensors from tensors containing their runtime shapes.
+    tensors = {}
+    shapes = {}
+    for key, batched_tensor in batched_tensors.items():
+      unbatched_tensor_list = tf.unstack(batched_tensor)
+      for i, unbatched_tensor in enumerate(unbatched_tensor_list):
+        if rt_shape_str in key:
+          shapes[(key[:-len(rt_shape_str)], i)] = unbatched_tensor
+        else:
+          tensors[(key, i)] = unbatched_tensor
+
+    # Undo that padding using shapes and create a list of size `batch_size` that
+    # contains tensor dictionaries.
+    tensor_dict_list = []
+    batch_size = self._batch_size
+    for batch_id in range(batch_size):
+      tensor_dict = {}
+      for key in self._static_shapes:
+        tensor_dict[key] = tf.slice(tensors[(key, batch_id)],
+                                    tf.zeros_like(shapes[(key, batch_id)]),
+                                    shapes[(key, batch_id)])
+        tensor_dict[key].set_shape(self._static_shapes[key])
+      tensor_dict_list.append(tensor_dict)
+
+    return tensor_dict_list
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher_test.py
new file mode 100644
index 00000000..61b4390b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/batcher_test.py
@@ -0,0 +1,158 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.batcher."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import batcher
+
+slim = tf.contrib.slim
+
+
+class BatcherTest(tf.test.TestCase):
+
+  def test_batch_and_unpad_2d_tensors_of_different_sizes_in_1st_dimension(self):
+    with self.test_session() as sess:
+      batch_size = 3
+      num_batches = 2
+      examples = tf.Variable(tf.constant(2, dtype=tf.int32))
+      counter = examples.count_up_to(num_batches * batch_size + 2)
+      boxes = tf.tile(
+          tf.reshape(tf.range(4), [1, 4]), tf.stack([counter, tf.constant(1)]))
+      batch_queue = batcher.BatchQueue(
+          tensor_dict={'boxes': boxes},
+          batch_size=batch_size,
+          batch_queue_capacity=100,
+          num_batch_queue_threads=1,
+          prefetch_queue_capacity=100)
+      batch = batch_queue.dequeue()
+
+      for tensor_dict in batch:
+        for tensor in tensor_dict.values():
+          self.assertAllEqual([None, 4], tensor.get_shape().as_list())
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        i = 2
+        for _ in range(num_batches):
+          batch_np = sess.run(batch)
+          for tensor_dict in batch_np:
+            for tensor in tensor_dict.values():
+              self.assertAllEqual(tensor, np.tile(np.arange(4), (i, 1)))
+              i += 1
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(batch)
+
+  def test_batch_and_unpad_2d_tensors_of_different_sizes_in_all_dimensions(
+      self):
+    with self.test_session() as sess:
+      batch_size = 3
+      num_batches = 2
+      examples = tf.Variable(tf.constant(2, dtype=tf.int32))
+      counter = examples.count_up_to(num_batches * batch_size + 2)
+      image = tf.reshape(
+          tf.range(counter * counter), tf.stack([counter, counter]))
+      batch_queue = batcher.BatchQueue(
+          tensor_dict={'image': image},
+          batch_size=batch_size,
+          batch_queue_capacity=100,
+          num_batch_queue_threads=1,
+          prefetch_queue_capacity=100)
+      batch = batch_queue.dequeue()
+
+      for tensor_dict in batch:
+        for tensor in tensor_dict.values():
+          self.assertAllEqual([None, None], tensor.get_shape().as_list())
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        i = 2
+        for _ in range(num_batches):
+          batch_np = sess.run(batch)
+          for tensor_dict in batch_np:
+            for tensor in tensor_dict.values():
+              self.assertAllEqual(tensor, np.arange(i * i).reshape((i, i)))
+              i += 1
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(batch)
+
+  def test_batch_and_unpad_2d_tensors_of_same_size_in_all_dimensions(self):
+    with self.test_session() as sess:
+      batch_size = 3
+      num_batches = 2
+      examples = tf.Variable(tf.constant(1, dtype=tf.int32))
+      counter = examples.count_up_to(num_batches * batch_size + 1)
+      image = tf.reshape(tf.range(1, 13), [4, 3]) * counter
+      batch_queue = batcher.BatchQueue(
+          tensor_dict={'image': image},
+          batch_size=batch_size,
+          batch_queue_capacity=100,
+          num_batch_queue_threads=1,
+          prefetch_queue_capacity=100)
+      batch = batch_queue.dequeue()
+
+      for tensor_dict in batch:
+        for tensor in tensor_dict.values():
+          self.assertAllEqual([4, 3], tensor.get_shape().as_list())
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        i = 1
+        for _ in range(num_batches):
+          batch_np = sess.run(batch)
+          for tensor_dict in batch_np:
+            for tensor in tensor_dict.values():
+              self.assertAllEqual(tensor, np.arange(1, 13).reshape((4, 3)) * i)
+              i += 1
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(batch)
+
+  def test_batcher_when_batch_size_is_one(self):
+    with self.test_session() as sess:
+      batch_size = 1
+      num_batches = 2
+      examples = tf.Variable(tf.constant(2, dtype=tf.int32))
+      counter = examples.count_up_to(num_batches * batch_size + 2)
+      image = tf.reshape(
+          tf.range(counter * counter), tf.stack([counter, counter]))
+      batch_queue = batcher.BatchQueue(
+          tensor_dict={'image': image},
+          batch_size=batch_size,
+          batch_queue_capacity=100,
+          num_batch_queue_threads=1,
+          prefetch_queue_capacity=100)
+      batch = batch_queue.dequeue()
+
+      for tensor_dict in batch:
+        for tensor in tensor_dict.values():
+          self.assertAllEqual([None, None], tensor.get_shape().as_list())
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        i = 2
+        for _ in range(num_batches):
+          batch_np = sess.run(batch)
+          for tensor_dict in batch_np:
+            for tensor in tensor_dict.values():
+              self.assertAllEqual(tensor, np.arange(i * i).reshape((i, i)))
+              i += 1
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(batch)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder.py
new file mode 100644
index 00000000..f20ac956
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder.py
@@ -0,0 +1,151 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base box coder.
+
+Box coders convert between coordinate frames, namely image-centric
+(with (0,0) on the top left of image) and anchor-centric (with (0,0) being
+defined by a specific anchor).
+
+Users of a BoxCoder can call two methods:
+ encode: which encodes a box with respect to a given anchor
+  (or rather, a tensor of boxes wrt a corresponding tensor of anchors) and
+ decode: which inverts this encoding with a decode operation.
+In both cases, the arguments are assumed to be in 1-1 correspondence already;
+it is not the job of a BoxCoder to perform matching.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+from abc import abstractproperty
+
+import tensorflow as tf
+
+
+# Box coder types.
+FASTER_RCNN = 'faster_rcnn'
+KEYPOINT = 'keypoint'
+MEAN_STDDEV = 'mean_stddev'
+SQUARE = 'square'
+
+
+class BoxCoder(object):
+  """Abstract base class for box coder."""
+  __metaclass__ = ABCMeta
+
+  @abstractproperty
+  def code_size(self):
+    """Return the size of each code.
+
+    This number is a constant and should agree with the output of the `encode`
+    op (e.g. if rel_codes is the output of self.encode(...), then it should have
+    shape [N, code_size()]).  This abstractproperty should be overridden by
+    implementations.
+
+    Returns:
+      an integer constant
+    """
+    pass
+
+  def encode(self, boxes, anchors):
+    """Encode a box list relative to an anchor collection.
+
+    Args:
+      boxes: BoxList holding N boxes to be encoded
+      anchors: BoxList of N anchors
+
+    Returns:
+      a tensor representing N relative-encoded boxes
+    """
+    with tf.name_scope('Encode'):
+      return self._encode(boxes, anchors)
+
+  def decode(self, rel_codes, anchors):
+    """Decode boxes that are encoded relative to an anchor collection.
+
+    Args:
+      rel_codes: a tensor representing N relative-encoded boxes
+      anchors: BoxList of anchors
+
+    Returns:
+      boxlist: BoxList holding N boxes encoded in the ordinary way (i.e.,
+        with corners y_min, x_min, y_max, x_max)
+    """
+    with tf.name_scope('Decode'):
+      return self._decode(rel_codes, anchors)
+
+  @abstractmethod
+  def _encode(self, boxes, anchors):
+    """Method to be overriden by implementations.
+
+    Args:
+      boxes: BoxList holding N boxes to be encoded
+      anchors: BoxList of N anchors
+
+    Returns:
+      a tensor representing N relative-encoded boxes
+    """
+    pass
+
+  @abstractmethod
+  def _decode(self, rel_codes, anchors):
+    """Method to be overriden by implementations.
+
+    Args:
+      rel_codes: a tensor representing N relative-encoded boxes
+      anchors: BoxList of anchors
+
+    Returns:
+      boxlist: BoxList holding N boxes encoded in the ordinary way (i.e.,
+        with corners y_min, x_min, y_max, x_max)
+    """
+    pass
+
+
+def batch_decode(encoded_boxes, box_coder, anchors):
+  """Decode a batch of encoded boxes.
+
+  This op takes a batch of encoded bounding boxes and transforms
+  them to a batch of bounding boxes specified by their corners in
+  the order of [y_min, x_min, y_max, x_max].
+
+  Args:
+    encoded_boxes: a float32 tensor of shape [batch_size, num_anchors,
+      code_size] representing the location of the objects.
+    box_coder: a BoxCoder object.
+    anchors: a BoxList of anchors used to encode `encoded_boxes`.
+
+  Returns:
+    decoded_boxes: a float32 tensor of shape [batch_size, num_anchors,
+      coder_size] representing the corners of the objects in the order
+      of [y_min, x_min, y_max, x_max].
+
+  Raises:
+    ValueError: if batch sizes of the inputs are inconsistent, or if
+    the number of anchors inferred from encoded_boxes and anchors are
+    inconsistent.
+  """
+  encoded_boxes.get_shape().assert_has_rank(3)
+  if encoded_boxes.get_shape()[1].value != anchors.num_boxes_static():
+    raise ValueError('The number of anchors inferred from encoded_boxes'
+                     ' and anchors are inconsistent: shape[1] of encoded_boxes'
+                     ' %s should be equal to the number of anchors: %s.' %
+                     (encoded_boxes.get_shape()[1].value,
+                      anchors.num_boxes_static()))
+
+  decoded_boxes = tf.stack([
+      box_coder.decode(boxes, anchors).get()
+      for boxes in tf.unstack(encoded_boxes)
+  ])
+  return decoded_boxes
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder_test.py
new file mode 100644
index 00000000..c087a325
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_coder_test.py
@@ -0,0 +1,61 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.box_coder."""
+
+import tensorflow as tf
+
+from object_detection.core import box_coder
+from object_detection.core import box_list
+
+
+class MockBoxCoder(box_coder.BoxCoder):
+  """Test BoxCoder that encodes/decodes using the multiply-by-two function."""
+
+  def code_size(self):
+    return 4
+
+  def _encode(self, boxes, anchors):
+    return 2.0 * boxes.get()
+
+  def _decode(self, rel_codes, anchors):
+    return box_list.BoxList(rel_codes / 2.0)
+
+
+class BoxCoderTest(tf.test.TestCase):
+
+  def test_batch_decode(self):
+    mock_anchor_corners = tf.constant(
+        [[0, 0.1, 0.2, 0.3], [0.2, 0.4, 0.4, 0.6]], tf.float32)
+    mock_anchors = box_list.BoxList(mock_anchor_corners)
+    mock_box_coder = MockBoxCoder()
+
+    expected_boxes = [[[0.0, 0.1, 0.5, 0.6], [0.5, 0.6, 0.7, 0.8]],
+                      [[0.1, 0.2, 0.3, 0.4], [0.7, 0.8, 0.9, 1.0]]]
+
+    encoded_boxes_list = [mock_box_coder.encode(
+        box_list.BoxList(tf.constant(boxes)), mock_anchors)
+                          for boxes in expected_boxes]
+    encoded_boxes = tf.stack(encoded_boxes_list)
+    decoded_boxes = box_coder.batch_decode(
+        encoded_boxes, mock_box_coder, mock_anchors)
+
+    with self.test_session() as sess:
+      decoded_boxes_result = sess.run(decoded_boxes)
+      self.assertAllClose(expected_boxes, decoded_boxes_result)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list.py
new file mode 100644
index 00000000..c0196f05
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list.py
@@ -0,0 +1,207 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Bounding Box List definition.
+
+BoxList represents a list of bounding boxes as tensorflow
+tensors, where each bounding box is represented as a row of 4 numbers,
+[y_min, x_min, y_max, x_max].  It is assumed that all bounding boxes
+within a given list correspond to a single image.  See also
+box_list_ops.py for common box related operations (such as area, iou, etc).
+
+Optionally, users can add additional related fields (such as weights).
+We assume the following things to be true about fields:
+* they correspond to boxes in the box_list along the 0th dimension
+* they have inferrable rank at graph construction time
+* all dimensions except for possibly the 0th can be inferred
+  (i.e., not None) at graph construction time.
+
+Some other notes:
+  * Following tensorflow conventions, we use height, width ordering,
+  and correspondingly, y,x (or ymin, xmin, ymax, xmax) ordering
+  * Tensors are always provided as (flat) [N, 4] tensors.
+"""
+
+import tensorflow as tf
+
+
+class BoxList(object):
+  """Box collection."""
+
+  def __init__(self, boxes):
+    """Constructs box collection.
+
+    Args:
+      boxes: a tensor of shape [N, 4] representing box corners
+
+    Raises:
+      ValueError: if invalid dimensions for bbox data or if bbox data is not in
+          float32 format.
+    """
+    if len(boxes.get_shape()) != 2 or boxes.get_shape()[-1] != 4:
+      raise ValueError('Invalid dimensions for box data.')
+    if boxes.dtype != tf.float32:
+      raise ValueError('Invalid tensor type: should be tf.float32')
+    self.data = {'boxes': boxes}
+
+  def num_boxes(self):
+    """Returns number of boxes held in collection.
+
+    Returns:
+      a tensor representing the number of boxes held in the collection.
+    """
+    return tf.shape(self.data['boxes'])[0]
+
+  def num_boxes_static(self):
+    """Returns number of boxes held in collection.
+
+    This number is inferred at graph construction time rather than run-time.
+
+    Returns:
+      Number of boxes held in collection (integer) or None if this is not
+        inferrable at graph construction time.
+    """
+    return self.data['boxes'].get_shape()[0].value
+
+  def get_all_fields(self):
+    """Returns all fields."""
+    return self.data.keys()
+
+  def get_extra_fields(self):
+    """Returns all non-box fields (i.e., everything not named 'boxes')."""
+    return [k for k in self.data.keys() if k != 'boxes']
+
+  def add_field(self, field, field_data):
+    """Add field to box list.
+
+    This method can be used to add related box data such as
+    weights/labels, etc.
+
+    Args:
+      field: a string key to access the data via `get`
+      field_data: a tensor containing the data to store in the BoxList
+    """
+    self.data[field] = field_data
+
+  def has_field(self, field):
+    return field in self.data
+
+  def get(self):
+    """Convenience function for accessing box coordinates.
+
+    Returns:
+      a tensor with shape [N, 4] representing box coordinates.
+    """
+    return self.get_field('boxes')
+
+  def set(self, boxes):
+    """Convenience function for setting box coordinates.
+
+    Args:
+      boxes: a tensor of shape [N, 4] representing box corners
+
+    Raises:
+      ValueError: if invalid dimensions for bbox data
+    """
+    if len(boxes.get_shape()) != 2 or boxes.get_shape()[-1] != 4:
+      raise ValueError('Invalid dimensions for box data.')
+    self.data['boxes'] = boxes
+
+  def get_field(self, field):
+    """Accesses a box collection and associated fields.
+
+    This function returns specified field with object; if no field is specified,
+    it returns the box coordinates.
+
+    Args:
+      field: this optional string parameter can be used to specify
+        a related field to be accessed.
+
+    Returns:
+      a tensor representing the box collection or an associated field.
+
+    Raises:
+      ValueError: if invalid field
+    """
+    if not self.has_field(field):
+      raise ValueError('field ' + str(field) + ' does not exist')
+    return self.data[field]
+
+  def set_field(self, field, value):
+    """Sets the value of a field.
+
+    Updates the field of a box_list with a given value.
+
+    Args:
+      field: (string) name of the field to set value.
+      value: the value to assign to the field.
+
+    Raises:
+      ValueError: if the box_list does not have specified field.
+    """
+    if not self.has_field(field):
+      raise ValueError('field %s does not exist' % field)
+    self.data[field] = value
+
+  def get_center_coordinates_and_sizes(self, scope=None):
+    """Computes the center coordinates, height and width of the boxes.
+
+    Args:
+      scope: name scope of the function.
+
+    Returns:
+      a list of 4 1-D tensors [ycenter, xcenter, height, width].
+    """
+    with tf.name_scope(scope, 'get_center_coordinates_and_sizes'):
+      box_corners = self.get()
+      ymin, xmin, ymax, xmax = tf.unstack(tf.transpose(box_corners))
+      width = xmax - xmin
+      height = ymax - ymin
+      ycenter = ymin + height / 2.
+      xcenter = xmin + width / 2.
+      return [ycenter, xcenter, height, width]
+
+  def transpose_coordinates(self, scope=None):
+    """Transpose the coordinate representation in a boxlist.
+
+    Args:
+      scope: name scope of the function.
+    """
+    with tf.name_scope(scope, 'transpose_coordinates'):
+      y_min, x_min, y_max, x_max = tf.split(
+          value=self.get(), num_or_size_splits=4, axis=1)
+      self.set(tf.concat([x_min, y_min, x_max, y_max], 1))
+
+  def as_tensor_dict(self, fields=None):
+    """Retrieves specified fields as a dictionary of tensors.
+
+    Args:
+      fields: (optional) list of fields to return in the dictionary.
+        If None (default), all fields are returned.
+
+    Returns:
+      tensor_dict: A dictionary of tensors specified by fields.
+
+    Raises:
+      ValueError: if specified field is not contained in boxlist.
+    """
+    tensor_dict = {}
+    if fields is None:
+      fields = self.get_all_fields()
+    for field in fields:
+      if not self.has_field(field):
+        raise ValueError('boxlist must contain all specified fields')
+      tensor_dict[field] = self.get_field(field)
+    return tensor_dict
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops.py
new file mode 100644
index 00000000..7c6d75c8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops.py
@@ -0,0 +1,1136 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Bounding Box List operations.
+
+Example box operations that are supported:
+  * areas: compute bounding box areas
+  * iou: pairwise intersection-over-union scores
+  * sq_dist: pairwise distances between bounding boxes
+
+Whenever box_list_ops functions output a BoxList, the fields of the incoming
+BoxList are retained unless documented otherwise.
+"""
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+
+
+class SortOrder(object):
+  """Enum class for sort order.
+
+  Attributes:
+    ascend: ascend order.
+    descend: descend order.
+  """
+  ascend = 1
+  descend = 2
+
+
+def area(boxlist, scope=None):
+  """Computes area of boxes.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N] representing box areas.
+  """
+  with tf.name_scope(scope, 'Area'):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    return tf.squeeze((y_max - y_min) * (x_max - x_min), [1])
+
+
+def height_width(boxlist, scope=None):
+  """Computes height and width of boxes in boxlist.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    scope: name scope.
+
+  Returns:
+    Height: A tensor with shape [N] representing box heights.
+    Width: A tensor with shape [N] representing box widths.
+  """
+  with tf.name_scope(scope, 'HeightWidth'):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    return tf.squeeze(y_max - y_min, [1]), tf.squeeze(x_max - x_min, [1])
+
+
+def scale(boxlist, y_scale, x_scale, scope=None):
+  """scale box coordinates in x and y dimensions.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    y_scale: (float) scalar tensor
+    x_scale: (float) scalar tensor
+    scope: name scope.
+
+  Returns:
+    boxlist: BoxList holding N boxes
+  """
+  with tf.name_scope(scope, 'Scale'):
+    y_scale = tf.cast(y_scale, tf.float32)
+    x_scale = tf.cast(x_scale, tf.float32)
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    y_min = y_scale * y_min
+    y_max = y_scale * y_max
+    x_min = x_scale * x_min
+    x_max = x_scale * x_max
+    scaled_boxlist = box_list.BoxList(
+        tf.concat([y_min, x_min, y_max, x_max], 1))
+    return _copy_extra_fields(scaled_boxlist, boxlist)
+
+
+def clip_to_window(boxlist, window, filter_nonoverlapping=True, scope=None):
+  """Clip bounding boxes to a window.
+
+  This op clips any input bounding boxes (represented by bounding box
+  corners) to a window, optionally filtering out boxes that do not
+  overlap at all with the window.
+
+  Args:
+    boxlist: BoxList holding M_in boxes
+    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
+      window to which the op should clip boxes.
+    filter_nonoverlapping: whether to filter out boxes that do not overlap at
+      all with the window.
+    scope: name scope.
+
+  Returns:
+    a BoxList holding M_out boxes where M_out <= M_in
+  """
+  with tf.name_scope(scope, 'ClipToWindow'):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
+    y_min_clipped = tf.maximum(tf.minimum(y_min, win_y_max), win_y_min)
+    y_max_clipped = tf.maximum(tf.minimum(y_max, win_y_max), win_y_min)
+    x_min_clipped = tf.maximum(tf.minimum(x_min, win_x_max), win_x_min)
+    x_max_clipped = tf.maximum(tf.minimum(x_max, win_x_max), win_x_min)
+    clipped = box_list.BoxList(
+        tf.concat([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped],
+                  1))
+    clipped = _copy_extra_fields(clipped, boxlist)
+    if filter_nonoverlapping:
+      areas = area(clipped)
+      nonzero_area_indices = tf.cast(
+          tf.reshape(tf.where(tf.greater(areas, 0.0)), [-1]), tf.int32)
+      clipped = gather(clipped, nonzero_area_indices)
+    return clipped
+
+
+def prune_outside_window(boxlist, window, scope=None):
+  """Prunes bounding boxes that fall outside a given window.
+
+  This function prunes bounding boxes that even partially fall outside the given
+  window. See also clip_to_window which only prunes bounding boxes that fall
+  completely outside the window, and clips any bounding boxes that partially
+  overflow.
+
+  Args:
+    boxlist: a BoxList holding M_in boxes.
+    window: a float tensor of shape [4] representing [ymin, xmin, ymax, xmax]
+      of the window
+    scope: name scope.
+
+  Returns:
+    pruned_corners: a tensor with shape [M_out, 4] where M_out <= M_in
+    valid_indices: a tensor with shape [M_out] indexing the valid bounding boxes
+     in the input tensor.
+  """
+  with tf.name_scope(scope, 'PruneOutsideWindow'):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
+    coordinate_violations = tf.concat([
+        tf.less(y_min, win_y_min), tf.less(x_min, win_x_min),
+        tf.greater(y_max, win_y_max), tf.greater(x_max, win_x_max)
+    ], 1)
+    valid_indices = tf.reshape(
+        tf.where(tf.logical_not(tf.reduce_any(coordinate_violations, 1))), [-1])
+    return gather(boxlist, valid_indices), valid_indices
+
+
+def prune_completely_outside_window(boxlist, window, scope=None):
+  """Prunes bounding boxes that fall completely outside of the given window.
+
+  The function clip_to_window prunes bounding boxes that fall
+  completely outside the window, but also clips any bounding boxes that
+  partially overflow. This function does not clip partially overflowing boxes.
+
+  Args:
+    boxlist: a BoxList holding M_in boxes.
+    window: a float tensor of shape [4] representing [ymin, xmin, ymax, xmax]
+      of the window
+    scope: name scope.
+
+  Returns:
+    pruned_boxlist: a new BoxList with all bounding boxes partially or fully in
+      the window.
+    valid_indices: a tensor with shape [M_out] indexing the valid bounding boxes
+     in the input tensor.
+  """
+  with tf.name_scope(scope, 'PruneCompleteleyOutsideWindow'):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=boxlist.get(), num_or_size_splits=4, axis=1)
+    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
+    coordinate_violations = tf.concat([
+        tf.greater_equal(y_min, win_y_max), tf.greater_equal(x_min, win_x_max),
+        tf.less_equal(y_max, win_y_min), tf.less_equal(x_max, win_x_min)
+    ], 1)
+    valid_indices = tf.reshape(
+        tf.where(tf.logical_not(tf.reduce_any(coordinate_violations, 1))), [-1])
+    return gather(boxlist, valid_indices), valid_indices
+
+
+def intersection(boxlist1, boxlist2, scope=None):
+  """Compute pairwise intersection areas between boxes.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing pairwise intersections
+  """
+  with tf.name_scope(scope, 'Intersection'):
+    y_min1, x_min1, y_max1, x_max1 = tf.split(
+        value=boxlist1.get(), num_or_size_splits=4, axis=1)
+    y_min2, x_min2, y_max2, x_max2 = tf.split(
+        value=boxlist2.get(), num_or_size_splits=4, axis=1)
+    all_pairs_min_ymax = tf.minimum(y_max1, tf.transpose(y_max2))
+    all_pairs_max_ymin = tf.maximum(y_min1, tf.transpose(y_min2))
+    intersect_heights = tf.maximum(0.0, all_pairs_min_ymax - all_pairs_max_ymin)
+    all_pairs_min_xmax = tf.minimum(x_max1, tf.transpose(x_max2))
+    all_pairs_max_xmin = tf.maximum(x_min1, tf.transpose(x_min2))
+    intersect_widths = tf.maximum(0.0, all_pairs_min_xmax - all_pairs_max_xmin)
+    return intersect_heights * intersect_widths
+
+
+def matched_intersection(boxlist1, boxlist2, scope=None):
+  """Compute intersection areas between corresponding boxes in two boxlists.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding N boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N] representing pairwise intersections
+  """
+  with tf.name_scope(scope, 'MatchedIntersection'):
+    y_min1, x_min1, y_max1, x_max1 = tf.split(
+        value=boxlist1.get(), num_or_size_splits=4, axis=1)
+    y_min2, x_min2, y_max2, x_max2 = tf.split(
+        value=boxlist2.get(), num_or_size_splits=4, axis=1)
+    min_ymax = tf.minimum(y_max1, y_max2)
+    max_ymin = tf.maximum(y_min1, y_min2)
+    intersect_heights = tf.maximum(0.0, min_ymax - max_ymin)
+    min_xmax = tf.minimum(x_max1, x_max2)
+    max_xmin = tf.maximum(x_min1, x_min2)
+    intersect_widths = tf.maximum(0.0, min_xmax - max_xmin)
+    return tf.reshape(intersect_heights * intersect_widths, [-1])
+
+
+def iou(boxlist1, boxlist2, scope=None):
+  """Computes pairwise intersection-over-union between box collections.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing pairwise iou scores.
+  """
+  with tf.name_scope(scope, 'IOU'):
+    intersections = intersection(boxlist1, boxlist2)
+    areas1 = area(boxlist1)
+    areas2 = area(boxlist2)
+    unions = (
+        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
+    return tf.where(
+        tf.equal(intersections, 0.0),
+        tf.zeros_like(intersections), tf.truediv(intersections, unions))
+
+
+def matched_iou(boxlist1, boxlist2, scope=None):
+  """Compute intersection-over-union between corresponding boxes in boxlists.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding N boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N] representing pairwise iou scores.
+  """
+  with tf.name_scope(scope, 'MatchedIOU'):
+    intersections = matched_intersection(boxlist1, boxlist2)
+    areas1 = area(boxlist1)
+    areas2 = area(boxlist2)
+    unions = areas1 + areas2 - intersections
+    return tf.where(
+        tf.equal(intersections, 0.0),
+        tf.zeros_like(intersections), tf.truediv(intersections, unions))
+
+
+def ioa(boxlist1, boxlist2, scope=None):
+  """Computes pairwise intersection-over-area between box collections.
+
+  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
+  their intersection area over box2's area. Note that ioa is not symmetric,
+  that is, ioa(box1, box2) != ioa(box2, box1).
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing pairwise ioa scores.
+  """
+  with tf.name_scope(scope, 'IOA'):
+    intersections = intersection(boxlist1, boxlist2)
+    areas = tf.expand_dims(area(boxlist2), 0)
+    return tf.truediv(intersections, areas)
+
+
+def prune_non_overlapping_boxes(
+    boxlist1, boxlist2, min_overlap=0.0, scope=None):
+  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.
+
+  For each box in boxlist1, we want its IOA to be more than minoverlap with
+  at least one of the boxes in boxlist2. If it does not, we remove it.
+
+  Args:
+    boxlist1: BoxList holding N boxes.
+    boxlist2: BoxList holding M boxes.
+    min_overlap: Minimum required overlap between boxes, to count them as
+                overlapping.
+    scope: name scope.
+
+  Returns:
+    new_boxlist1: A pruned boxlist with size [N', 4].
+    keep_inds: A tensor with shape [N'] indexing kept bounding boxes in the
+      first input BoxList `boxlist1`.
+  """
+  with tf.name_scope(scope, 'PruneNonOverlappingBoxes'):
+    ioa_ = ioa(boxlist2, boxlist1)  # [M, N] tensor
+    ioa_ = tf.reduce_max(ioa_, reduction_indices=[0])  # [N] tensor
+    keep_bool = tf.greater_equal(ioa_, tf.constant(min_overlap))
+    keep_inds = tf.squeeze(tf.where(keep_bool), squeeze_dims=[1])
+    new_boxlist1 = gather(boxlist1, keep_inds)
+    return new_boxlist1, keep_inds
+
+
+def prune_small_boxes(boxlist, min_side, scope=None):
+  """Prunes small boxes in the boxlist which have a side smaller than min_side.
+
+  Args:
+    boxlist: BoxList holding N boxes.
+    min_side: Minimum width AND height of box to survive pruning.
+    scope: name scope.
+
+  Returns:
+    A pruned boxlist.
+  """
+  with tf.name_scope(scope, 'PruneSmallBoxes'):
+    height, width = height_width(boxlist)
+    is_valid = tf.logical_and(tf.greater_equal(width, min_side),
+                              tf.greater_equal(height, min_side))
+    return gather(boxlist, tf.reshape(tf.where(is_valid), [-1]))
+
+
+def change_coordinate_frame(boxlist, window, scope=None):
+  """Change coordinate frame of the boxlist to be relative to window's frame.
+
+  Given a window of the form [ymin, xmin, ymax, xmax],
+  changes bounding box coordinates from boxlist to be relative to this window
+  (e.g., the min corner maps to (0,0) and the max corner maps to (1,1)).
+
+  An example use case is data augmentation: where we are given groundtruth
+  boxes (boxlist) and would like to randomly crop the image to some
+  window (window). In this case we need to change the coordinate frame of
+  each groundtruth box to be relative to this new window.
+
+  Args:
+    boxlist: A BoxList object holding N boxes.
+    window: A rank 1 tensor [4].
+    scope: name scope.
+
+  Returns:
+    Returns a BoxList object with N boxes.
+  """
+  with tf.name_scope(scope, 'ChangeCoordinateFrame'):
+    win_height = window[2] - window[0]
+    win_width = window[3] - window[1]
+    boxlist_new = scale(box_list.BoxList(
+        boxlist.get() - [window[0], window[1], window[0], window[1]]),
+                        1.0 / win_height, 1.0 / win_width)
+    boxlist_new = _copy_extra_fields(boxlist_new, boxlist)
+    return boxlist_new
+
+
+def sq_dist(boxlist1, boxlist2, scope=None):
+  """Computes the pairwise squared distances between box corners.
+
+  This op treats each box as if it were a point in a 4d Euclidean space and
+  computes pairwise squared distances.
+
+  Mathematically, we are given two matrices of box coordinates X and Y,
+  where X(i,:) is the i'th row of X, containing the 4 numbers defining the
+  corners of the i'th box in boxlist1. Similarly Y(j,:) corresponds to
+  boxlist2.  We compute
+  Z(i,j) = ||X(i,:) - Y(j,:)||^2
+         = ||X(i,:)||^2 + ||Y(j,:)||^2 - 2 X(i,:)' * Y(j,:),
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing pairwise distances
+  """
+  with tf.name_scope(scope, 'SqDist'):
+    sqnorm1 = tf.reduce_sum(tf.square(boxlist1.get()), 1, keep_dims=True)
+    sqnorm2 = tf.reduce_sum(tf.square(boxlist2.get()), 1, keep_dims=True)
+    innerprod = tf.matmul(boxlist1.get(), boxlist2.get(),
+                          transpose_a=False, transpose_b=True)
+    return sqnorm1 + tf.transpose(sqnorm2) - 2.0 * innerprod
+
+
+def boolean_mask(boxlist, indicator, fields=None, scope=None,
+                 use_static_shapes=False, indicator_sum=None):
+  """Select boxes from BoxList according to indicator and return new BoxList.
+
+  `boolean_mask` returns the subset of boxes that are marked as "True" by the
+  indicator tensor. By default, `boolean_mask` returns boxes corresponding to
+  the input index list, as well as all additional fields stored in the boxlist
+  (indexing into the first dimension).  However one can optionally only draw
+  from a subset of fields.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    indicator: a rank-1 boolean tensor
+    fields: (optional) list of fields to also gather from.  If None (default),
+      all fields are gathered from.  Pass an empty fields list to only gather
+      the box coordinates.
+    scope: name scope.
+    use_static_shapes: Whether to use an implementation with static shape
+      gurantees.
+    indicator_sum: An integer containing the sum of `indicator` vector. Only
+      required if `use_static_shape` is True.
+
+  Returns:
+    subboxlist: a BoxList corresponding to the subset of the input BoxList
+      specified by indicator
+  Raises:
+    ValueError: if `indicator` is not a rank-1 boolean tensor.
+  """
+  with tf.name_scope(scope, 'BooleanMask'):
+    if indicator.shape.ndims != 1:
+      raise ValueError('indicator should have rank 1')
+    if indicator.dtype != tf.bool:
+      raise ValueError('indicator should be a boolean tensor')
+    if use_static_shapes:
+      if not (indicator_sum and isinstance(indicator_sum, int)):
+        raise ValueError('`indicator_sum` must be a of type int')
+      selected_positions = tf.to_float(indicator)
+      indexed_positions = tf.cast(
+          tf.multiply(
+              tf.cumsum(selected_positions), selected_positions),
+          dtype=tf.int32)
+      one_hot_selector = tf.one_hot(
+          indexed_positions - 1, indicator_sum, dtype=tf.float32)
+      sampled_indices = tf.cast(
+          tf.tensordot(
+              tf.to_float(tf.range(tf.shape(indicator)[0])),
+              one_hot_selector,
+              axes=[0, 0]),
+          dtype=tf.int32)
+      return gather(boxlist, sampled_indices, use_static_shapes=True)
+    else:
+      subboxlist = box_list.BoxList(tf.boolean_mask(boxlist.get(), indicator))
+      if fields is None:
+        fields = boxlist.get_extra_fields()
+      for field in fields:
+        if not boxlist.has_field(field):
+          raise ValueError('boxlist must contain all specified fields')
+        subfieldlist = tf.boolean_mask(boxlist.get_field(field), indicator)
+        subboxlist.add_field(field, subfieldlist)
+      return subboxlist
+
+
+def gather(boxlist, indices, fields=None, scope=None, use_static_shapes=False):
+  """Gather boxes from BoxList according to indices and return new BoxList.
+
+  By default, `gather` returns boxes corresponding to the input index list, as
+  well as all additional fields stored in the boxlist (indexing into the
+  first dimension).  However one can optionally only gather from a
+  subset of fields.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    indices: a rank-1 tensor of type int32 / int64
+    fields: (optional) list of fields to also gather from.  If None (default),
+      all fields are gathered from.  Pass an empty fields list to only gather
+      the box coordinates.
+    scope: name scope.
+    use_static_shapes: Whether to use an implementation with static shape
+      gurantees.
+
+  Returns:
+    subboxlist: a BoxList corresponding to the subset of the input BoxList
+    specified by indices
+  Raises:
+    ValueError: if specified field is not contained in boxlist or if the
+      indices are not of type int32
+  """
+  with tf.name_scope(scope, 'Gather'):
+    if len(indices.shape.as_list()) != 1:
+      raise ValueError('indices should have rank 1')
+    if indices.dtype != tf.int32 and indices.dtype != tf.int64:
+      raise ValueError('indices should be an int32 / int64 tensor')
+    gather_op = tf.gather
+    if use_static_shapes:
+      gather_op = ops.matmul_gather_on_zeroth_axis
+    subboxlist = box_list.BoxList(gather_op(boxlist.get(), indices))
+    if fields is None:
+      fields = boxlist.get_extra_fields()
+    fields += ['boxes']
+    for field in fields:
+      if not boxlist.has_field(field):
+        raise ValueError('boxlist must contain all specified fields')
+      subfieldlist = gather_op(boxlist.get_field(field), indices)
+      subboxlist.add_field(field, subfieldlist)
+    return subboxlist
+
+
+def concatenate(boxlists, fields=None, scope=None):
+  """Concatenate list of BoxLists.
+
+  This op concatenates a list of input BoxLists into a larger BoxList.  It also
+  handles concatenation of BoxList fields as long as the field tensor shapes
+  are equal except for the first dimension.
+
+  Args:
+    boxlists: list of BoxList objects
+    fields: optional list of fields to also concatenate.  By default, all
+      fields from the first BoxList in the list are included in the
+      concatenation.
+    scope: name scope.
+
+  Returns:
+    a BoxList with number of boxes equal to
+      sum([boxlist.num_boxes() for boxlist in BoxList])
+  Raises:
+    ValueError: if boxlists is invalid (i.e., is not a list, is empty, or
+      contains non BoxList objects), or if requested fields are not contained in
+      all boxlists
+  """
+  with tf.name_scope(scope, 'Concatenate'):
+    if not isinstance(boxlists, list):
+      raise ValueError('boxlists should be a list')
+    if not boxlists:
+      raise ValueError('boxlists should have nonzero length')
+    for boxlist in boxlists:
+      if not isinstance(boxlist, box_list.BoxList):
+        raise ValueError('all elements of boxlists should be BoxList objects')
+    concatenated = box_list.BoxList(
+        tf.concat([boxlist.get() for boxlist in boxlists], 0))
+    if fields is None:
+      fields = boxlists[0].get_extra_fields()
+    for field in fields:
+      first_field_shape = boxlists[0].get_field(field).get_shape().as_list()
+      first_field_shape[0] = -1
+      if None in first_field_shape:
+        raise ValueError('field %s must have fully defined shape except for the'
+                         ' 0th dimension.' % field)
+      for boxlist in boxlists:
+        if not boxlist.has_field(field):
+          raise ValueError('boxlist must contain all requested fields')
+        field_shape = boxlist.get_field(field).get_shape().as_list()
+        field_shape[0] = -1
+        if field_shape != first_field_shape:
+          raise ValueError('field %s must have same shape for all boxlists '
+                           'except for the 0th dimension.' % field)
+      concatenated_field = tf.concat(
+          [boxlist.get_field(field) for boxlist in boxlists], 0)
+      concatenated.add_field(field, concatenated_field)
+    return concatenated
+
+
+def sort_by_field(boxlist, field, order=SortOrder.descend, scope=None):
+  """Sort boxes and associated fields according to a scalar field.
+
+  A common use case is reordering the boxes according to descending scores.
+
+  Args:
+    boxlist: BoxList holding N boxes.
+    field: A BoxList field for sorting and reordering the BoxList.
+    order: (Optional) descend or ascend. Default is descend.
+    scope: name scope.
+
+  Returns:
+    sorted_boxlist: A sorted BoxList with the field in the specified order.
+
+  Raises:
+    ValueError: if specified field does not exist
+    ValueError: if the order is not either descend or ascend
+  """
+  with tf.name_scope(scope, 'SortByField'):
+    if order != SortOrder.descend and order != SortOrder.ascend:
+      raise ValueError('Invalid sort order')
+
+    field_to_sort = boxlist.get_field(field)
+    if len(field_to_sort.shape.as_list()) != 1:
+      raise ValueError('Field should have rank 1')
+
+    num_boxes = boxlist.num_boxes()
+    num_entries = tf.size(field_to_sort)
+    length_assert = tf.Assert(
+        tf.equal(num_boxes, num_entries),
+        ['Incorrect field size: actual vs expected.', num_entries, num_boxes])
+
+    with tf.control_dependencies([length_assert]):
+      _, sorted_indices = tf.nn.top_k(field_to_sort, num_boxes, sorted=True)
+
+    if order == SortOrder.ascend:
+      sorted_indices = tf.reverse_v2(sorted_indices, [0])
+
+    return gather(boxlist, sorted_indices)
+
+
+def visualize_boxes_in_image(image, boxlist, normalized=False, scope=None):
+  """Overlay bounding box list on image.
+
+  Currently this visualization plots a 1 pixel thick red bounding box on top
+  of the image.  Note that tf.image.draw_bounding_boxes essentially is
+  1 indexed.
+
+  Args:
+    image: an image tensor with shape [height, width, 3]
+    boxlist: a BoxList
+    normalized: (boolean) specify whether corners are to be interpreted
+      as absolute coordinates in image space or normalized with respect to the
+      image size.
+    scope: name scope.
+
+  Returns:
+    image_and_boxes: an image tensor with shape [height, width, 3]
+  """
+  with tf.name_scope(scope, 'VisualizeBoxesInImage'):
+    if not normalized:
+      height, width, _ = tf.unstack(tf.shape(image))
+      boxlist = scale(boxlist,
+                      1.0 / tf.cast(height, tf.float32),
+                      1.0 / tf.cast(width, tf.float32))
+    corners = tf.expand_dims(boxlist.get(), 0)
+    image = tf.expand_dims(image, 0)
+    return tf.squeeze(tf.image.draw_bounding_boxes(image, corners), [0])
+
+
+def filter_field_value_equals(boxlist, field, value, scope=None):
+  """Filter to keep only boxes with field entries equal to the given value.
+
+  Args:
+    boxlist: BoxList holding N boxes.
+    field: field name for filtering.
+    value: scalar value.
+    scope: name scope.
+
+  Returns:
+    a BoxList holding M boxes where M <= N
+
+  Raises:
+    ValueError: if boxlist not a BoxList object or if it does not have
+      the specified field.
+  """
+  with tf.name_scope(scope, 'FilterFieldValueEquals'):
+    if not isinstance(boxlist, box_list.BoxList):
+      raise ValueError('boxlist must be a BoxList')
+    if not boxlist.has_field(field):
+      raise ValueError('boxlist must contain the specified field')
+    filter_field = boxlist.get_field(field)
+    gather_index = tf.reshape(tf.where(tf.equal(filter_field, value)), [-1])
+    return gather(boxlist, gather_index)
+
+
+def filter_greater_than(boxlist, thresh, scope=None):
+  """Filter to keep only boxes with score exceeding a given threshold.
+
+  This op keeps the collection of boxes whose corresponding scores are
+  greater than the input threshold.
+
+  TODO(jonathanhuang): Change function name to filter_scores_greater_than
+
+  Args:
+    boxlist: BoxList holding N boxes.  Must contain a 'scores' field
+      representing detection scores.
+    thresh: scalar threshold
+    scope: name scope.
+
+  Returns:
+    a BoxList holding M boxes where M <= N
+
+  Raises:
+    ValueError: if boxlist not a BoxList object or if it does not
+      have a scores field
+  """
+  with tf.name_scope(scope, 'FilterGreaterThan'):
+    if not isinstance(boxlist, box_list.BoxList):
+      raise ValueError('boxlist must be a BoxList')
+    if not boxlist.has_field('scores'):
+      raise ValueError('input boxlist must have \'scores\' field')
+    scores = boxlist.get_field('scores')
+    if len(scores.shape.as_list()) > 2:
+      raise ValueError('Scores should have rank 1 or 2')
+    if len(scores.shape.as_list()) == 2 and scores.shape.as_list()[1] != 1:
+      raise ValueError('Scores should have rank 1 or have shape '
+                       'consistent with [None, 1]')
+    high_score_indices = tf.cast(tf.reshape(
+        tf.where(tf.greater(scores, thresh)),
+        [-1]), tf.int32)
+    return gather(boxlist, high_score_indices)
+
+
+def non_max_suppression(boxlist, thresh, max_output_size, scope=None):
+  """Non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes.  Note that this only works for a single class ---
+  to apply NMS to multi-class predictions, use MultiClassNonMaxSuppression.
+
+  Args:
+    boxlist: BoxList holding N boxes.  Must contain a 'scores' field
+      representing detection scores.
+    thresh: scalar threshold
+    max_output_size: maximum number of retained boxes
+    scope: name scope.
+
+  Returns:
+    a BoxList holding M boxes where M <= max_output_size
+  Raises:
+    ValueError: if thresh is not in [0, 1]
+  """
+  with tf.name_scope(scope, 'NonMaxSuppression'):
+    if not 0 <= thresh <= 1.0:
+      raise ValueError('thresh must be between 0 and 1')
+    if not isinstance(boxlist, box_list.BoxList):
+      raise ValueError('boxlist must be a BoxList')
+    if not boxlist.has_field('scores'):
+      raise ValueError('input boxlist must have \'scores\' field')
+    selected_indices = tf.image.non_max_suppression(
+        boxlist.get(), boxlist.get_field('scores'),
+        max_output_size, iou_threshold=thresh)
+    return gather(boxlist, selected_indices)
+
+
+def _copy_extra_fields(boxlist_to_copy_to, boxlist_to_copy_from):
+  """Copies the extra fields of boxlist_to_copy_from to boxlist_to_copy_to.
+
+  Args:
+    boxlist_to_copy_to: BoxList to which extra fields are copied.
+    boxlist_to_copy_from: BoxList from which fields are copied.
+
+  Returns:
+    boxlist_to_copy_to with extra fields.
+  """
+  for field in boxlist_to_copy_from.get_extra_fields():
+    boxlist_to_copy_to.add_field(field, boxlist_to_copy_from.get_field(field))
+  return boxlist_to_copy_to
+
+
+def to_normalized_coordinates(boxlist, height, width,
+                              check_range=True, scope=None):
+  """Converts absolute box coordinates to normalized coordinates in [0, 1].
+
+  Usually one uses the dynamic shape of the image or conv-layer tensor:
+    boxlist = box_list_ops.to_normalized_coordinates(boxlist,
+                                                     tf.shape(images)[1],
+                                                     tf.shape(images)[2]),
+
+  This function raises an assertion failed error at graph execution time when
+  the maximum coordinate is smaller than 1.01 (which means that coordinates are
+  already normalized). The value 1.01 is to deal with small rounding errors.
+
+  Args:
+    boxlist: BoxList with coordinates in terms of pixel-locations.
+    height: Maximum value for height of absolute box coordinates.
+    width: Maximum value for width of absolute box coordinates.
+    check_range: If True, checks if the coordinates are normalized or not.
+    scope: name scope.
+
+  Returns:
+    boxlist with normalized coordinates in [0, 1].
+  """
+  with tf.name_scope(scope, 'ToNormalizedCoordinates'):
+    height = tf.cast(height, tf.float32)
+    width = tf.cast(width, tf.float32)
+
+    if check_range:
+      max_val = tf.reduce_max(boxlist.get())
+      max_assert = tf.Assert(tf.greater(max_val, 1.01),
+                             ['max value is lower than 1.01: ', max_val])
+      with tf.control_dependencies([max_assert]):
+        width = tf.identity(width)
+
+    return scale(boxlist, 1 / height, 1 / width)
+
+
+def to_absolute_coordinates(boxlist,
+                            height,
+                            width,
+                            check_range=True,
+                            maximum_normalized_coordinate=1.1,
+                            scope=None):
+  """Converts normalized box coordinates to absolute pixel coordinates.
+
+  This function raises an assertion failed error when the maximum box coordinate
+  value is larger than maximum_normalized_coordinate (in which case coordinates
+  are already absolute).
+
+  Args:
+    boxlist: BoxList with coordinates in range [0, 1].
+    height: Maximum value for height of absolute box coordinates.
+    width: Maximum value for width of absolute box coordinates.
+    check_range: If True, checks if the coordinates are normalized or not.
+    maximum_normalized_coordinate: Maximum coordinate value to be considered
+      as normalized, default to 1.1.
+    scope: name scope.
+
+  Returns:
+    boxlist with absolute coordinates in terms of the image size.
+
+  """
+  with tf.name_scope(scope, 'ToAbsoluteCoordinates'):
+    height = tf.cast(height, tf.float32)
+    width = tf.cast(width, tf.float32)
+
+    # Ensure range of input boxes is correct.
+    if check_range:
+      box_maximum = tf.reduce_max(boxlist.get())
+      max_assert = tf.Assert(
+          tf.greater_equal(maximum_normalized_coordinate, box_maximum),
+          ['maximum box coordinate value is larger '
+           'than %f: ' % maximum_normalized_coordinate, box_maximum])
+      with tf.control_dependencies([max_assert]):
+        width = tf.identity(width)
+
+    return scale(boxlist, height, width)
+
+
+def refine_boxes_multi_class(pool_boxes,
+                             num_classes,
+                             nms_iou_thresh,
+                             nms_max_detections,
+                             voting_iou_thresh=0.5):
+  """Refines a pool of boxes using non max suppression and box voting.
+
+  Box refinement is done independently for each class.
+
+  Args:
+    pool_boxes: (BoxList) A collection of boxes to be refined. pool_boxes must
+      have a rank 1 'scores' field and a rank 1 'classes' field.
+    num_classes: (int scalar) Number of classes.
+    nms_iou_thresh: (float scalar) iou threshold for non max suppression (NMS).
+    nms_max_detections: (int scalar) maximum output size for NMS.
+    voting_iou_thresh: (float scalar) iou threshold for box voting.
+
+  Returns:
+    BoxList of refined boxes.
+
+  Raises:
+    ValueError: if
+      a) nms_iou_thresh or voting_iou_thresh is not in [0, 1].
+      b) pool_boxes is not a BoxList.
+      c) pool_boxes does not have a scores and classes field.
+  """
+  if not 0.0 <= nms_iou_thresh <= 1.0:
+    raise ValueError('nms_iou_thresh must be between 0 and 1')
+  if not 0.0 <= voting_iou_thresh <= 1.0:
+    raise ValueError('voting_iou_thresh must be between 0 and 1')
+  if not isinstance(pool_boxes, box_list.BoxList):
+    raise ValueError('pool_boxes must be a BoxList')
+  if not pool_boxes.has_field('scores'):
+    raise ValueError('pool_boxes must have a \'scores\' field')
+  if not pool_boxes.has_field('classes'):
+    raise ValueError('pool_boxes must have a \'classes\' field')
+
+  refined_boxes = []
+  for i in range(num_classes):
+    boxes_class = filter_field_value_equals(pool_boxes, 'classes', i)
+    refined_boxes_class = refine_boxes(boxes_class, nms_iou_thresh,
+                                       nms_max_detections, voting_iou_thresh)
+    refined_boxes.append(refined_boxes_class)
+  return sort_by_field(concatenate(refined_boxes), 'scores')
+
+
+def refine_boxes(pool_boxes,
+                 nms_iou_thresh,
+                 nms_max_detections,
+                 voting_iou_thresh=0.5):
+  """Refines a pool of boxes using non max suppression and box voting.
+
+  Args:
+    pool_boxes: (BoxList) A collection of boxes to be refined. pool_boxes must
+      have a rank 1 'scores' field.
+    nms_iou_thresh: (float scalar) iou threshold for non max suppression (NMS).
+    nms_max_detections: (int scalar) maximum output size for NMS.
+    voting_iou_thresh: (float scalar) iou threshold for box voting.
+
+  Returns:
+    BoxList of refined boxes.
+
+  Raises:
+    ValueError: if
+      a) nms_iou_thresh or voting_iou_thresh is not in [0, 1].
+      b) pool_boxes is not a BoxList.
+      c) pool_boxes does not have a scores field.
+  """
+  if not 0.0 <= nms_iou_thresh <= 1.0:
+    raise ValueError('nms_iou_thresh must be between 0 and 1')
+  if not 0.0 <= voting_iou_thresh <= 1.0:
+    raise ValueError('voting_iou_thresh must be between 0 and 1')
+  if not isinstance(pool_boxes, box_list.BoxList):
+    raise ValueError('pool_boxes must be a BoxList')
+  if not pool_boxes.has_field('scores'):
+    raise ValueError('pool_boxes must have a \'scores\' field')
+
+  nms_boxes = non_max_suppression(
+      pool_boxes, nms_iou_thresh, nms_max_detections)
+  return box_voting(nms_boxes, pool_boxes, voting_iou_thresh)
+
+
+def box_voting(selected_boxes, pool_boxes, iou_thresh=0.5):
+  """Performs box voting as described in S. Gidaris and N. Komodakis, ICCV 2015.
+
+  Performs box voting as described in 'Object detection via a multi-region &
+  semantic segmentation-aware CNN model', Gidaris and Komodakis, ICCV 2015. For
+  each box 'B' in selected_boxes, we find the set 'S' of boxes in pool_boxes
+  with iou overlap >= iou_thresh. The location of B is set to the weighted
+  average location of boxes in S (scores are used for weighting). And the score
+  of B is set to the average score of boxes in S.
+
+  Args:
+    selected_boxes: BoxList containing a subset of boxes in pool_boxes. These
+      boxes are usually selected from pool_boxes using non max suppression.
+    pool_boxes: BoxList containing a set of (possibly redundant) boxes.
+    iou_thresh: (float scalar) iou threshold for matching boxes in
+      selected_boxes and pool_boxes.
+
+  Returns:
+    BoxList containing averaged locations and scores for each box in
+    selected_boxes.
+
+  Raises:
+    ValueError: if
+      a) selected_boxes or pool_boxes is not a BoxList.
+      b) if iou_thresh is not in [0, 1].
+      c) pool_boxes does not have a scores field.
+  """
+  if not 0.0 <= iou_thresh <= 1.0:
+    raise ValueError('iou_thresh must be between 0 and 1')
+  if not isinstance(selected_boxes, box_list.BoxList):
+    raise ValueError('selected_boxes must be a BoxList')
+  if not isinstance(pool_boxes, box_list.BoxList):
+    raise ValueError('pool_boxes must be a BoxList')
+  if not pool_boxes.has_field('scores'):
+    raise ValueError('pool_boxes must have a \'scores\' field')
+
+  iou_ = iou(selected_boxes, pool_boxes)
+  match_indicator = tf.to_float(tf.greater(iou_, iou_thresh))
+  num_matches = tf.reduce_sum(match_indicator, 1)
+  # TODO(kbanoop): Handle the case where some boxes in selected_boxes do not
+  # match to any boxes in pool_boxes. For such boxes without any matches, we
+  # should return the original boxes without voting.
+  match_assert = tf.Assert(
+      tf.reduce_all(tf.greater(num_matches, 0)),
+      ['Each box in selected_boxes must match with at least one box '
+       'in pool_boxes.'])
+
+  scores = tf.expand_dims(pool_boxes.get_field('scores'), 1)
+  scores_assert = tf.Assert(
+      tf.reduce_all(tf.greater_equal(scores, 0)),
+      ['Scores must be non negative.'])
+
+  with tf.control_dependencies([scores_assert, match_assert]):
+    sum_scores = tf.matmul(match_indicator, scores)
+  averaged_scores = tf.reshape(sum_scores, [-1]) / num_matches
+
+  box_locations = tf.matmul(match_indicator,
+                            pool_boxes.get() * scores) / sum_scores
+  averaged_boxes = box_list.BoxList(box_locations)
+  _copy_extra_fields(averaged_boxes, selected_boxes)
+  averaged_boxes.add_field('scores', averaged_scores)
+  return averaged_boxes
+
+
+def pad_or_clip_box_list(boxlist, num_boxes, scope=None):
+  """Pads or clips all fields of a BoxList.
+
+  Args:
+    boxlist: A BoxList with arbitrary of number of boxes.
+    num_boxes: First num_boxes in boxlist are kept.
+      The fields are zero-padded if num_boxes is bigger than the
+      actual number of boxes.
+    scope: name scope.
+
+  Returns:
+    BoxList with all fields padded or clipped.
+  """
+  with tf.name_scope(scope, 'PadOrClipBoxList'):
+    subboxlist = box_list.BoxList(shape_utils.pad_or_clip_tensor(
+        boxlist.get(), num_boxes))
+    for field in boxlist.get_extra_fields():
+      subfield = shape_utils.pad_or_clip_tensor(
+          boxlist.get_field(field), num_boxes)
+      subboxlist.add_field(field, subfield)
+    return subboxlist
+
+
+def select_random_box(boxlist,
+                      default_box=None,
+                      seed=None,
+                      scope=None):
+  """Selects a random bounding box from a `BoxList`.
+
+  Args:
+    boxlist: A BoxList.
+    default_box: A [1, 4] float32 tensor. If no boxes are present in `boxlist`,
+      this default box will be returned. If None, will use a default box of
+      [[-1., -1., -1., -1.]].
+    seed: Random seed.
+    scope: Name scope.
+
+  Returns:
+    bbox: A [1, 4] tensor with a random bounding box.
+    valid: A bool tensor indicating whether a valid bounding box is returned
+      (True) or whether the default box is returned (False).
+  """
+  with tf.name_scope(scope, 'SelectRandomBox'):
+    bboxes = boxlist.get()
+    combined_shape = shape_utils.combined_static_and_dynamic_shape(bboxes)
+    number_of_boxes = combined_shape[0]
+    default_box = default_box or tf.constant([[-1., -1., -1., -1.]])
+
+    def select_box():
+      random_index = tf.random_uniform([],
+                                       maxval=number_of_boxes,
+                                       dtype=tf.int32,
+                                       seed=seed)
+      return tf.expand_dims(bboxes[random_index], axis=0), tf.constant(True)
+
+  return tf.cond(
+      tf.greater_equal(number_of_boxes, 1),
+      true_fn=select_box,
+      false_fn=lambda: (default_box, tf.constant(False)))
+
+
+def get_minimal_coverage_box(boxlist,
+                             default_box=None,
+                             scope=None):
+  """Creates a single bounding box which covers all boxes in the boxlist.
+
+  Args:
+    boxlist: A Boxlist.
+    default_box: A [1, 4] float32 tensor. If no boxes are present in `boxlist`,
+      this default box will be returned. If None, will use a default box of
+      [[0., 0., 1., 1.]].
+    scope: Name scope.
+
+  Returns:
+    A [1, 4] float32 tensor with a bounding box that tightly covers all the
+    boxes in the box list. If the boxlist does not contain any boxes, the
+    default box is returned.
+  """
+  with tf.name_scope(scope, 'CreateCoverageBox'):
+    num_boxes = boxlist.num_boxes()
+
+    def coverage_box(bboxes):
+      y_min, x_min, y_max, x_max = tf.split(
+          value=bboxes, num_or_size_splits=4, axis=1)
+      y_min_coverage = tf.reduce_min(y_min, axis=0)
+      x_min_coverage = tf.reduce_min(x_min, axis=0)
+      y_max_coverage = tf.reduce_max(y_max, axis=0)
+      x_max_coverage = tf.reduce_max(x_max, axis=0)
+      return tf.stack(
+          [y_min_coverage, x_min_coverage, y_max_coverage, x_max_coverage],
+          axis=1)
+
+    default_box = default_box or tf.constant([[0., 0., 1., 1.]])
+    return tf.cond(
+        tf.greater_equal(num_boxes, 1),
+        true_fn=lambda: coverage_box(boxlist.get()),
+        false_fn=lambda: default_box)
+
+
+def sample_boxes_by_jittering(boxlist,
+                              num_boxes_to_sample,
+                              stddev=0.1,
+                              scope=None):
+  """Samples num_boxes_to_sample boxes by jittering around boxlist boxes.
+
+  It is possible that this function might generate boxes with size 0. The larger
+  the stddev, this is more probable. For a small stddev of 0.1 this probability
+  is very small.
+
+  Args:
+    boxlist: A boxlist containing N boxes in normalized coordinates.
+    num_boxes_to_sample: A positive integer containing the number of boxes to
+      sample.
+    stddev: Standard deviation. This is used to draw random offsets for the
+      box corners from a normal distribution. The offset is multiplied by the
+      box size so will be larger in terms of pixels for larger boxes.
+    scope: Name scope.
+
+  Returns:
+    sampled_boxlist: A boxlist containing num_boxes_to_sample boxes in
+      normalized coordinates.
+  """
+  with tf.name_scope(scope, 'SampleBoxesByJittering'):
+    num_boxes = boxlist.num_boxes()
+    box_indices = tf.random_uniform(
+        [num_boxes_to_sample],
+        minval=0,
+        maxval=num_boxes,
+        dtype=tf.int32)
+    sampled_boxes = tf.gather(boxlist.get(), box_indices)
+    sampled_boxes_height = sampled_boxes[:, 2] - sampled_boxes[:, 0]
+    sampled_boxes_width = sampled_boxes[:, 3] - sampled_boxes[:, 1]
+    rand_miny_gaussian = tf.random_normal([num_boxes_to_sample], stddev=stddev)
+    rand_minx_gaussian = tf.random_normal([num_boxes_to_sample], stddev=stddev)
+    rand_maxy_gaussian = tf.random_normal([num_boxes_to_sample], stddev=stddev)
+    rand_maxx_gaussian = tf.random_normal([num_boxes_to_sample], stddev=stddev)
+    miny = rand_miny_gaussian * sampled_boxes_height + sampled_boxes[:, 0]
+    minx = rand_minx_gaussian * sampled_boxes_width + sampled_boxes[:, 1]
+    maxy = rand_maxy_gaussian * sampled_boxes_height + sampled_boxes[:, 2]
+    maxx = rand_maxx_gaussian * sampled_boxes_width + sampled_boxes[:, 3]
+    maxy = tf.maximum(miny, maxy)
+    maxx = tf.maximum(minx, maxx)
+    sampled_boxes = tf.stack([miny, minx, maxy, maxx], axis=1)
+    sampled_boxes = tf.maximum(tf.minimum(sampled_boxes, 1.0), 0.0)
+    return box_list.BoxList(sampled_boxes)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops_test.py
new file mode 100644
index 00000000..727c198b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_ops_test.py
@@ -0,0 +1,1108 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.box_list_ops."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.utils import test_case
+
+
+class BoxListOpsTest(test_case.TestCase):
+  """Tests for common bounding box operations."""
+
+  def test_area(self):
+    corners = tf.constant([[0.0, 0.0, 10.0, 20.0], [1.0, 2.0, 3.0, 4.0]])
+    exp_output = [200.0, 4.0]
+    boxes = box_list.BoxList(corners)
+    areas = box_list_ops.area(boxes)
+    with self.test_session() as sess:
+      areas_output = sess.run(areas)
+      self.assertAllClose(areas_output, exp_output)
+
+  def test_height_width(self):
+    corners = tf.constant([[0.0, 0.0, 10.0, 20.0], [1.0, 2.0, 3.0, 4.0]])
+    exp_output_heights = [10., 2.]
+    exp_output_widths = [20., 2.]
+    boxes = box_list.BoxList(corners)
+    heights, widths = box_list_ops.height_width(boxes)
+    with self.test_session() as sess:
+      output_heights, output_widths = sess.run([heights, widths])
+      self.assertAllClose(output_heights, exp_output_heights)
+      self.assertAllClose(output_widths, exp_output_widths)
+
+  def test_scale(self):
+    corners = tf.constant([[0, 0, 100, 200], [50, 120, 100, 140]],
+                          dtype=tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.constant([[1], [2]]))
+
+    y_scale = tf.constant(1.0/100)
+    x_scale = tf.constant(1.0/200)
+    scaled_boxes = box_list_ops.scale(boxes, y_scale, x_scale)
+    exp_output = [[0, 0, 1, 1], [0.5, 0.6, 1.0, 0.7]]
+    with self.test_session() as sess:
+      scaled_corners_out = sess.run(scaled_boxes.get())
+      self.assertAllClose(scaled_corners_out, exp_output)
+      extra_data_out = sess.run(scaled_boxes.get_field('extra_data'))
+      self.assertAllEqual(extra_data_out, [[1], [2]])
+
+  def test_clip_to_window_filter_boxes_which_fall_outside_the_window(
+      self):
+    window = tf.constant([0, 0, 9, 14], tf.float32)
+    corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
+                           [-1.0, -2.0, 4.0, 5.0],
+                           [2.0, 3.0, 5.0, 9.0],
+                           [0.0, 0.0, 9.0, 14.0],
+                           [-100.0, -100.0, 300.0, 600.0],
+                           [-10.0, -10.0, -9.0, -9.0]])
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
+    exp_output = [[5.0, 5.0, 6.0, 6.0], [0.0, 0.0, 4.0, 5.0],
+                  [2.0, 3.0, 5.0, 9.0], [0.0, 0.0, 9.0, 14.0],
+                  [0.0, 0.0, 9.0, 14.0]]
+    pruned = box_list_ops.clip_to_window(
+        boxes, window, filter_nonoverlapping=True)
+    with self.test_session() as sess:
+      pruned_output = sess.run(pruned.get())
+      self.assertAllClose(pruned_output, exp_output)
+      extra_data_out = sess.run(pruned.get_field('extra_data'))
+      self.assertAllEqual(extra_data_out, [[1], [2], [3], [4], [5]])
+
+  def test_clip_to_window_without_filtering_boxes_which_fall_outside_the_window(
+      self):
+    window = tf.constant([0, 0, 9, 14], tf.float32)
+    corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
+                           [-1.0, -2.0, 4.0, 5.0],
+                           [2.0, 3.0, 5.0, 9.0],
+                           [0.0, 0.0, 9.0, 14.0],
+                           [-100.0, -100.0, 300.0, 600.0],
+                           [-10.0, -10.0, -9.0, -9.0]])
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
+    exp_output = [[5.0, 5.0, 6.0, 6.0], [0.0, 0.0, 4.0, 5.0],
+                  [2.0, 3.0, 5.0, 9.0], [0.0, 0.0, 9.0, 14.0],
+                  [0.0, 0.0, 9.0, 14.0], [0.0, 0.0, 0.0, 0.0]]
+    pruned = box_list_ops.clip_to_window(
+        boxes, window, filter_nonoverlapping=False)
+    with self.test_session() as sess:
+      pruned_output = sess.run(pruned.get())
+      self.assertAllClose(pruned_output, exp_output)
+      extra_data_out = sess.run(pruned.get_field('extra_data'))
+      self.assertAllEqual(extra_data_out, [[1], [2], [3], [4], [5], [6]])
+
+  def test_prune_outside_window_filters_boxes_which_fall_outside_the_window(
+      self):
+    window = tf.constant([0, 0, 9, 14], tf.float32)
+    corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
+                           [-1.0, -2.0, 4.0, 5.0],
+                           [2.0, 3.0, 5.0, 9.0],
+                           [0.0, 0.0, 9.0, 14.0],
+                           [-10.0, -10.0, -9.0, -9.0],
+                           [-100.0, -100.0, 300.0, 600.0]])
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
+    exp_output = [[5.0, 5.0, 6.0, 6.0],
+                  [2.0, 3.0, 5.0, 9.0],
+                  [0.0, 0.0, 9.0, 14.0]]
+    pruned, keep_indices = box_list_ops.prune_outside_window(boxes, window)
+    with self.test_session() as sess:
+      pruned_output = sess.run(pruned.get())
+      self.assertAllClose(pruned_output, exp_output)
+      keep_indices_out = sess.run(keep_indices)
+      self.assertAllEqual(keep_indices_out, [0, 2, 3])
+      extra_data_out = sess.run(pruned.get_field('extra_data'))
+      self.assertAllEqual(extra_data_out, [[1], [3], [4]])
+
+  def test_prune_completely_outside_window(self):
+    window = tf.constant([0, 0, 9, 14], tf.float32)
+    corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
+                           [-1.0, -2.0, 4.0, 5.0],
+                           [2.0, 3.0, 5.0, 9.0],
+                           [0.0, 0.0, 9.0, 14.0],
+                           [-10.0, -10.0, -9.0, -9.0],
+                           [-100.0, -100.0, 300.0, 600.0]])
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
+    exp_output = [[5.0, 5.0, 6.0, 6.0],
+                  [-1.0, -2.0, 4.0, 5.0],
+                  [2.0, 3.0, 5.0, 9.0],
+                  [0.0, 0.0, 9.0, 14.0],
+                  [-100.0, -100.0, 300.0, 600.0]]
+    pruned, keep_indices = box_list_ops.prune_completely_outside_window(boxes,
+                                                                        window)
+    with self.test_session() as sess:
+      pruned_output = sess.run(pruned.get())
+      self.assertAllClose(pruned_output, exp_output)
+      keep_indices_out = sess.run(keep_indices)
+      self.assertAllEqual(keep_indices_out, [0, 1, 2, 3, 5])
+      extra_data_out = sess.run(pruned.get_field('extra_data'))
+      self.assertAllEqual(extra_data_out, [[1], [2], [3], [4], [6]])
+
+  def test_prune_completely_outside_window_with_empty_boxlist(self):
+    window = tf.constant([0, 0, 9, 14], tf.float32)
+    corners = tf.zeros(shape=[0, 4], dtype=tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('extra_data', tf.zeros(shape=[0], dtype=tf.int32))
+    pruned, keep_indices = box_list_ops.prune_completely_outside_window(boxes,
+                                                                        window)
+    pruned_boxes = pruned.get()
+    extra = pruned.get_field('extra_data')
+
+    exp_pruned_boxes = np.zeros(shape=[0, 4], dtype=np.float32)
+    exp_extra = np.zeros(shape=[0], dtype=np.int32)
+    with self.test_session() as sess:
+      pruned_boxes_out, keep_indices_out, extra_out = sess.run(
+          [pruned_boxes, keep_indices, extra])
+      self.assertAllClose(exp_pruned_boxes, pruned_boxes_out)
+      self.assertAllEqual([], keep_indices_out)
+      self.assertAllEqual(exp_extra, extra_out)
+
+  def test_intersection(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    exp_output = [[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    intersect = box_list_ops.intersection(boxes1, boxes2)
+    with self.test_session() as sess:
+      intersect_output = sess.run(intersect)
+      self.assertAllClose(intersect_output, exp_output)
+
+  def test_matched_intersection(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
+    exp_output = [2.0, 0.0]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    intersect = box_list_ops.matched_intersection(boxes1, boxes2)
+    with self.test_session() as sess:
+      intersect_output = sess.run(intersect)
+      self.assertAllClose(intersect_output, exp_output)
+
+  def test_iou(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    iou = box_list_ops.iou(boxes1, boxes2)
+    with self.test_session() as sess:
+      iou_output = sess.run(iou)
+      self.assertAllClose(iou_output, exp_output)
+
+  def test_matched_iou(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
+    exp_output = [2.0 / 16.0, 0]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    iou = box_list_ops.matched_iou(boxes1, boxes2)
+    with self.test_session() as sess:
+      iou_output = sess.run(iou)
+      self.assertAllClose(iou_output, exp_output)
+
+  def test_iouworks_on_empty_inputs(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    boxes_empty = box_list.BoxList(tf.zeros((0, 4)))
+    iou_empty_1 = box_list_ops.iou(boxes1, boxes_empty)
+    iou_empty_2 = box_list_ops.iou(boxes_empty, boxes2)
+    iou_empty_3 = box_list_ops.iou(boxes_empty, boxes_empty)
+    with self.test_session() as sess:
+      iou_output_1, iou_output_2, iou_output_3 = sess.run(
+          [iou_empty_1, iou_empty_2, iou_empty_3])
+      self.assertAllEqual(iou_output_1.shape, (2, 0))
+      self.assertAllEqual(iou_output_2.shape, (0, 3))
+      self.assertAllEqual(iou_output_3.shape, (0, 0))
+
+  def test_ioa(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    exp_output_1 = [[2.0 / 12.0, 0, 6.0 / 400.0],
+                    [1.0 / 12.0, 0.0, 5.0 / 400.0]]
+    exp_output_2 = [[2.0 / 6.0, 1.0 / 5.0],
+                    [0, 0],
+                    [6.0 / 6.0, 5.0 / 5.0]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    ioa_1 = box_list_ops.ioa(boxes1, boxes2)
+    ioa_2 = box_list_ops.ioa(boxes2, boxes1)
+    with self.test_session() as sess:
+      ioa_output_1, ioa_output_2 = sess.run([ioa_1, ioa_2])
+      self.assertAllClose(ioa_output_1, exp_output_1)
+      self.assertAllClose(ioa_output_2, exp_output_2)
+
+  def test_prune_non_overlapping_boxes(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    minoverlap = 0.5
+
+    exp_output_1 = boxes1
+    exp_output_2 = box_list.BoxList(tf.constant(0.0, shape=[0, 4]))
+    output_1, keep_indices_1 = box_list_ops.prune_non_overlapping_boxes(
+        boxes1, boxes2, min_overlap=minoverlap)
+    output_2, keep_indices_2 = box_list_ops.prune_non_overlapping_boxes(
+        boxes2, boxes1, min_overlap=minoverlap)
+    with self.test_session() as sess:
+      (output_1_, keep_indices_1_, output_2_, keep_indices_2_, exp_output_1_,
+       exp_output_2_) = sess.run(
+           [output_1.get(), keep_indices_1,
+            output_2.get(), keep_indices_2,
+            exp_output_1.get(), exp_output_2.get()])
+      self.assertAllClose(output_1_, exp_output_1_)
+      self.assertAllClose(output_2_, exp_output_2_)
+      self.assertAllEqual(keep_indices_1_, [0, 1])
+      self.assertAllEqual(keep_indices_2_, [])
+
+  def test_prune_small_boxes(self):
+    boxes = tf.constant([[4.0, 3.0, 7.0, 5.0],
+                         [5.0, 6.0, 10.0, 7.0],
+                         [3.0, 4.0, 6.0, 8.0],
+                         [14.0, 14.0, 15.0, 15.0],
+                         [0.0, 0.0, 20.0, 20.0]])
+    exp_boxes = [[3.0, 4.0, 6.0, 8.0],
+                 [0.0, 0.0, 20.0, 20.0]]
+    boxes = box_list.BoxList(boxes)
+    pruned_boxes = box_list_ops.prune_small_boxes(boxes, 3)
+    with self.test_session() as sess:
+      pruned_boxes = sess.run(pruned_boxes.get())
+      self.assertAllEqual(pruned_boxes, exp_boxes)
+
+  def test_prune_small_boxes_prunes_boxes_with_negative_side(self):
+    boxes = tf.constant([[4.0, 3.0, 7.0, 5.0],
+                         [5.0, 6.0, 10.0, 7.0],
+                         [3.0, 4.0, 6.0, 8.0],
+                         [14.0, 14.0, 15.0, 15.0],
+                         [0.0, 0.0, 20.0, 20.0],
+                         [2.0, 3.0, 1.5, 7.0],  # negative height
+                         [2.0, 3.0, 5.0, 1.7]])  # negative width
+    exp_boxes = [[3.0, 4.0, 6.0, 8.0],
+                 [0.0, 0.0, 20.0, 20.0]]
+    boxes = box_list.BoxList(boxes)
+    pruned_boxes = box_list_ops.prune_small_boxes(boxes, 3)
+    with self.test_session() as sess:
+      pruned_boxes = sess.run(pruned_boxes.get())
+      self.assertAllEqual(pruned_boxes, exp_boxes)
+
+  def test_change_coordinate_frame(self):
+    corners = tf.constant([[0.25, 0.5, 0.75, 0.75], [0.5, 0.0, 1.0, 1.0]])
+    window = tf.constant([0.25, 0.25, 0.75, 0.75])
+    boxes = box_list.BoxList(corners)
+
+    expected_corners = tf.constant([[0, 0.5, 1.0, 1.0], [0.5, -0.5, 1.5, 1.5]])
+    expected_boxes = box_list.BoxList(expected_corners)
+    output = box_list_ops.change_coordinate_frame(boxes, window)
+
+    with self.test_session() as sess:
+      output_, expected_boxes_ = sess.run([output.get(), expected_boxes.get()])
+      self.assertAllClose(output_, expected_boxes_)
+
+  def test_ioaworks_on_empty_inputs(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    boxes_empty = box_list.BoxList(tf.zeros((0, 4)))
+    ioa_empty_1 = box_list_ops.ioa(boxes1, boxes_empty)
+    ioa_empty_2 = box_list_ops.ioa(boxes_empty, boxes2)
+    ioa_empty_3 = box_list_ops.ioa(boxes_empty, boxes_empty)
+    with self.test_session() as sess:
+      ioa_output_1, ioa_output_2, ioa_output_3 = sess.run(
+          [ioa_empty_1, ioa_empty_2, ioa_empty_3])
+      self.assertAllEqual(ioa_output_1.shape, (2, 0))
+      self.assertAllEqual(ioa_output_2.shape, (0, 3))
+      self.assertAllEqual(ioa_output_3.shape, (0, 0))
+
+  def test_pairwise_distances(self):
+    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
+                            [1.0, 1.0, 0.0, 2.0]])
+    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
+                            [-4.0, 0.0, 0.0, 3.0],
+                            [0.0, 0.0, 0.0, 0.0]])
+    exp_output = [[26, 25, 0], [18, 27, 6]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    dist_matrix = box_list_ops.sq_dist(boxes1, boxes2)
+    with self.test_session() as sess:
+      dist_output = sess.run(dist_matrix)
+      self.assertAllClose(dist_output, exp_output)
+
+  def test_boolean_mask(self):
+    corners = tf.constant(
+        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
+    indicator = tf.constant([True, False, True, False, True], tf.bool)
+    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    boxes = box_list.BoxList(corners)
+    subset = box_list_ops.boolean_mask(boxes, indicator)
+    with self.test_session() as sess:
+      subset_output = sess.run(subset.get())
+      self.assertAllClose(subset_output, expected_subset)
+
+  def test_static_boolean_mask_with_field(self):
+
+    def graph_fn(corners, weights, indicator):
+      boxes = box_list.BoxList(corners)
+      boxes.add_field('weights', weights)
+      subset = box_list_ops.boolean_mask(
+          boxes,
+          indicator, ['weights'],
+          use_static_shapes=True,
+          indicator_sum=3)
+      return (subset.get_field('boxes'), subset.get_field('weights'))
+
+    corners = np.array(
+        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]],
+        dtype=np.float32)
+    indicator = np.array([True, False, True, False, True], dtype=np.bool)
+    weights = np.array([[.1], [.3], [.5], [.7], [.9]], dtype=np.float32)
+    result_boxes, result_weights = self.execute(graph_fn,
+                                                [corners, weights, indicator])
+    expected_boxes = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    expected_weights = [[.1], [.5], [.9]]
+
+    self.assertAllClose(result_boxes, expected_boxes)
+    self.assertAllClose(result_weights, expected_weights)
+
+  def test_dynamic_boolean_mask_with_field(self):
+    corners = tf.placeholder(tf.float32, [None, 4])
+    indicator = tf.placeholder(tf.bool, [None])
+    weights = tf.placeholder(tf.float32, [None, 1])
+    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    expected_weights = [[.1], [.5], [.9]]
+
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('weights', weights)
+    subset = box_list_ops.boolean_mask(boxes, indicator, ['weights'])
+    with self.test_session() as sess:
+      subset_output, weights_output = sess.run(
+          [subset.get(), subset.get_field('weights')],
+          feed_dict={
+              corners:
+                  np.array(
+                      [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]]),
+              indicator:
+                  np.array([True, False, True, False, True]).astype(np.bool),
+              weights:
+                  np.array([[.1], [.3], [.5], [.7], [.9]])
+          })
+      self.assertAllClose(subset_output, expected_subset)
+      self.assertAllClose(weights_output, expected_weights)
+
+  def test_gather(self):
+    corners = tf.constant(
+        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
+    indices = tf.constant([0, 2, 4], tf.int32)
+    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    boxes = box_list.BoxList(corners)
+    subset = box_list_ops.gather(boxes, indices)
+    with self.test_session() as sess:
+      subset_output = sess.run(subset.get())
+      self.assertAllClose(subset_output, expected_subset)
+
+  def test_static_gather_with_field(self):
+
+    def graph_fn(corners, weights, indices):
+      boxes = box_list.BoxList(corners)
+      boxes.add_field('weights', weights)
+      subset = box_list_ops.gather(
+          boxes, indices, ['weights'], use_static_shapes=True)
+      return (subset.get_field('boxes'), subset.get_field('weights'))
+
+    corners = np.array([4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0],
+                        4 * [4.0]], dtype=np.float32)
+    weights = np.array([[.1], [.3], [.5], [.7], [.9]], dtype=np.float32)
+    indices = np.array([0, 2, 4], dtype=np.int32)
+
+    result_boxes, result_weights = self.execute(graph_fn,
+                                                [corners, weights, indices])
+    expected_boxes = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    expected_weights = [[.1], [.5], [.9]]
+    self.assertAllClose(result_boxes, expected_boxes)
+    self.assertAllClose(result_weights, expected_weights)
+
+  def test_dynamic_gather_with_field(self):
+    corners = tf.placeholder(tf.float32, [None, 4])
+    indices = tf.placeholder(tf.int32, [None])
+    weights = tf.placeholder(tf.float32, [None, 1])
+    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    expected_weights = [[.1], [.5], [.9]]
+
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('weights', weights)
+    subset = box_list_ops.gather(boxes, indices, ['weights'],
+                                 use_static_shapes=True)
+    with self.test_session() as sess:
+      subset_output, weights_output = sess.run(
+          [subset.get(), subset.get_field('weights')],
+          feed_dict={
+              corners:
+                  np.array(
+                      [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]]),
+              indices:
+                  np.array([0, 2, 4]).astype(np.int32),
+              weights:
+                  np.array([[.1], [.3], [.5], [.7], [.9]])
+          })
+      self.assertAllClose(subset_output, expected_subset)
+      self.assertAllClose(weights_output, expected_weights)
+
+  def test_gather_with_invalid_field(self):
+    corners = tf.constant([4 * [0.0], 4 * [1.0]])
+    indices = tf.constant([0, 1], tf.int32)
+    weights = tf.constant([[.1], [.3]], tf.float32)
+
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('weights', weights)
+    with self.assertRaises(ValueError):
+      box_list_ops.gather(boxes, indices, ['foo', 'bar'])
+
+  def test_gather_with_invalid_inputs(self):
+    corners = tf.constant(
+        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
+    indices_float32 = tf.constant([0, 2, 4], tf.float32)
+    boxes = box_list.BoxList(corners)
+    with self.assertRaises(ValueError):
+      _ = box_list_ops.gather(boxes, indices_float32)
+    indices_2d = tf.constant([[0, 2, 4]], tf.int32)
+    boxes = box_list.BoxList(corners)
+    with self.assertRaises(ValueError):
+      _ = box_list_ops.gather(boxes, indices_2d)
+
+  def test_gather_with_dynamic_indexing(self):
+    corners = tf.constant([4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]
+                          ])
+    weights = tf.constant([.5, .3, .7, .1, .9], tf.float32)
+    indices = tf.reshape(tf.where(tf.greater(weights, 0.4)), [-1])
+    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
+    expected_weights = [.5, .7, .9]
+
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('weights', weights)
+    subset = box_list_ops.gather(boxes, indices, ['weights'])
+    with self.test_session() as sess:
+      subset_output, weights_output = sess.run([subset.get(), subset.get_field(
+          'weights')])
+      self.assertAllClose(subset_output, expected_subset)
+      self.assertAllClose(weights_output, expected_weights)
+
+  def test_sort_by_field_ascending_order(self):
+    exp_corners = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
+                   [0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]]
+    exp_scores = [.95, .9, .75, .6, .5, .3]
+    exp_weights = [.2, .45, .6, .75, .8, .92]
+    shuffle = [2, 4, 0, 5, 1, 3]
+    corners = tf.constant([exp_corners[i] for i in shuffle], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant(
+        [exp_scores[i] for i in shuffle], tf.float32))
+    boxes.add_field('weights', tf.constant(
+        [exp_weights[i] for i in shuffle], tf.float32))
+    sort_by_weight = box_list_ops.sort_by_field(
+        boxes,
+        'weights',
+        order=box_list_ops.SortOrder.ascend)
+    with self.test_session() as sess:
+      corners_out, scores_out, weights_out = sess.run([
+          sort_by_weight.get(),
+          sort_by_weight.get_field('scores'),
+          sort_by_weight.get_field('weights')])
+      self.assertAllClose(corners_out, exp_corners)
+      self.assertAllClose(scores_out, exp_scores)
+      self.assertAllClose(weights_out, exp_weights)
+
+  def test_sort_by_field_descending_order(self):
+    exp_corners = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
+                   [0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]]
+    exp_scores = [.95, .9, .75, .6, .5, .3]
+    exp_weights = [.2, .45, .6, .75, .8, .92]
+    shuffle = [2, 4, 0, 5, 1, 3]
+
+    corners = tf.constant([exp_corners[i] for i in shuffle], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant(
+        [exp_scores[i] for i in shuffle], tf.float32))
+    boxes.add_field('weights', tf.constant(
+        [exp_weights[i] for i in shuffle], tf.float32))
+
+    sort_by_score = box_list_ops.sort_by_field(boxes, 'scores')
+    with self.test_session() as sess:
+      corners_out, scores_out, weights_out = sess.run([sort_by_score.get(
+      ), sort_by_score.get_field('scores'), sort_by_score.get_field('weights')])
+      self.assertAllClose(corners_out, exp_corners)
+      self.assertAllClose(scores_out, exp_scores)
+      self.assertAllClose(weights_out, exp_weights)
+
+  def test_sort_by_field_invalid_inputs(self):
+    corners = tf.constant([4 * [0.0], 4 * [0.5], 4 * [1.0], 4 * [2.0], 4 *
+                           [3.0], 4 * [4.0]])
+    misc = tf.constant([[.95, .9], [.5, .3]], tf.float32)
+    weights = tf.constant([.1, .2], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('misc', misc)
+    boxes.add_field('weights', weights)
+
+    with self.assertRaises(ValueError):
+      box_list_ops.sort_by_field(boxes, 'area')
+
+    with self.assertRaises(ValueError):
+      box_list_ops.sort_by_field(boxes, 'misc')
+
+    with self.assertRaises(ValueError):
+      box_list_ops.sort_by_field(boxes, 'weights')
+
+  def test_visualize_boxes_in_image(self):
+    image = tf.zeros((6, 4, 3))
+    corners = tf.constant([[0, 0, 5, 3],
+                           [0, 0, 3, 2]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    image_and_boxes = box_list_ops.visualize_boxes_in_image(image, boxes)
+    image_and_boxes_bw = tf.to_float(
+        tf.greater(tf.reduce_sum(image_and_boxes, 2), 0.0))
+    exp_result = [[1, 1, 1, 0],
+                  [1, 1, 1, 0],
+                  [1, 1, 1, 0],
+                  [1, 0, 1, 0],
+                  [1, 1, 1, 0],
+                  [0, 0, 0, 0]]
+    with self.test_session() as sess:
+      output = sess.run(image_and_boxes_bw)
+      self.assertAllEqual(output.astype(int), exp_result)
+
+  def test_filter_field_value_equals(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1],
+                           [0, -0.1, 1, 0.9],
+                           [0, 10, 1, 11],
+                           [0, 10.1, 1, 11.1],
+                           [0, 100, 1, 101]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('classes', tf.constant([1, 2, 1, 2, 2, 1]))
+    exp_output1 = [[0, 0, 1, 1], [0, -0.1, 1, 0.9], [0, 100, 1, 101]]
+    exp_output2 = [[0, 0.1, 1, 1.1], [0, 10, 1, 11], [0, 10.1, 1, 11.1]]
+
+    filtered_boxes1 = box_list_ops.filter_field_value_equals(
+        boxes, 'classes', 1)
+    filtered_boxes2 = box_list_ops.filter_field_value_equals(
+        boxes, 'classes', 2)
+    with self.test_session() as sess:
+      filtered_output1, filtered_output2 = sess.run([filtered_boxes1.get(),
+                                                     filtered_boxes2.get()])
+      self.assertAllClose(filtered_output1, exp_output1)
+      self.assertAllClose(filtered_output2, exp_output2)
+
+  def test_filter_greater_than(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1],
+                           [0, -0.1, 1, 0.9],
+                           [0, 10, 1, 11],
+                           [0, 10.1, 1, 11.1],
+                           [0, 100, 1, 101]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant([.1, .75, .9, .5, .5, .8]))
+    thresh = .6
+    exp_output = [[0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9], [0, 100, 1, 101]]
+
+    filtered_boxes = box_list_ops.filter_greater_than(boxes, thresh)
+    with self.test_session() as sess:
+      filtered_output = sess.run(filtered_boxes.get())
+      self.assertAllClose(filtered_output, exp_output)
+
+  def test_clip_box_list(self):
+    boxlist = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
+                     [0.6, 0.6, 0.8, 0.8], [0.2, 0.2, 0.3, 0.3]], tf.float32))
+    boxlist.add_field('classes', tf.constant([0, 0, 1, 1]))
+    boxlist.add_field('scores', tf.constant([0.75, 0.65, 0.3, 0.2]))
+    num_boxes = 2
+    clipped_boxlist = box_list_ops.pad_or_clip_box_list(boxlist, num_boxes)
+
+    expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]]
+    expected_classes = [0, 0]
+    expected_scores = [0.75, 0.65]
+    with self.test_session() as sess:
+      boxes_out, classes_out, scores_out = sess.run(
+          [clipped_boxlist.get(), clipped_boxlist.get_field('classes'),
+           clipped_boxlist.get_field('scores')])
+
+      self.assertAllClose(expected_boxes, boxes_out)
+      self.assertAllEqual(expected_classes, classes_out)
+      self.assertAllClose(expected_scores, scores_out)
+
+  def test_pad_box_list(self):
+    boxlist = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
+    boxlist.add_field('classes', tf.constant([0, 1]))
+    boxlist.add_field('scores', tf.constant([0.75, 0.2]))
+    num_boxes = 4
+    padded_boxlist = box_list_ops.pad_or_clip_box_list(boxlist, num_boxes)
+
+    expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
+                      [0, 0, 0, 0], [0, 0, 0, 0]]
+    expected_classes = [0, 1, 0, 0]
+    expected_scores = [0.75, 0.2, 0, 0]
+    with self.test_session() as sess:
+      boxes_out, classes_out, scores_out = sess.run(
+          [padded_boxlist.get(), padded_boxlist.get_field('classes'),
+           padded_boxlist.get_field('scores')])
+
+      self.assertAllClose(expected_boxes, boxes_out)
+      self.assertAllEqual(expected_classes, classes_out)
+      self.assertAllClose(expected_scores, scores_out)
+
+  def test_select_random_box(self):
+    boxes = [[0., 0., 1., 1.],
+             [0., 1., 2., 3.],
+             [0., 2., 3., 4.]]
+
+    corners = tf.constant(boxes, dtype=tf.float32)
+    boxlist = box_list.BoxList(corners)
+    random_bbox, valid = box_list_ops.select_random_box(boxlist)
+    with self.test_session() as sess:
+      random_bbox_out, valid_out = sess.run([random_bbox, valid])
+
+    norm_small = any(
+        [np.linalg.norm(random_bbox_out - box) < 1e-6 for box in boxes])
+
+    self.assertTrue(norm_small)
+    self.assertTrue(valid_out)
+
+  def test_select_random_box_with_empty_boxlist(self):
+    corners = tf.constant([], shape=[0, 4], dtype=tf.float32)
+    boxlist = box_list.BoxList(corners)
+    random_bbox, valid = box_list_ops.select_random_box(boxlist)
+    with self.test_session() as sess:
+      random_bbox_out, valid_out = sess.run([random_bbox, valid])
+
+    expected_bbox_out = np.array([[-1., -1., -1., -1.]], dtype=np.float32)
+    self.assertAllEqual(expected_bbox_out, random_bbox_out)
+    self.assertFalse(valid_out)
+
+  def test_get_minimal_coverage_box(self):
+    boxes = [[0., 0., 1., 1.],
+             [-1., 1., 2., 3.],
+             [0., 2., 3., 4.]]
+
+    expected_coverage_box = [[-1., 0., 3., 4.]]
+
+    corners = tf.constant(boxes, dtype=tf.float32)
+    boxlist = box_list.BoxList(corners)
+    coverage_box = box_list_ops.get_minimal_coverage_box(boxlist)
+    with self.test_session() as sess:
+      coverage_box_out = sess.run(coverage_box)
+
+    self.assertAllClose(expected_coverage_box, coverage_box_out)
+
+  def test_get_minimal_coverage_box_with_empty_boxlist(self):
+    corners = tf.constant([], shape=[0, 4], dtype=tf.float32)
+    boxlist = box_list.BoxList(corners)
+    coverage_box = box_list_ops.get_minimal_coverage_box(boxlist)
+    with self.test_session() as sess:
+      coverage_box_out = sess.run(coverage_box)
+
+    self.assertAllClose([[0.0, 0.0, 1.0, 1.0]], coverage_box_out)
+
+
+class ConcatenateTest(tf.test.TestCase):
+
+  def test_invalid_input_box_list_list(self):
+    with self.assertRaises(ValueError):
+      box_list_ops.concatenate(None)
+    with self.assertRaises(ValueError):
+      box_list_ops.concatenate([])
+    with self.assertRaises(ValueError):
+      corners = tf.constant([[0, 0, 0, 0]], tf.float32)
+      boxlist = box_list.BoxList(corners)
+      box_list_ops.concatenate([boxlist, 2])
+
+  def test_concatenate_with_missing_fields(self):
+    corners1 = tf.constant([[0, 0, 0, 0], [1, 2, 3, 4]], tf.float32)
+    scores1 = tf.constant([1.0, 2.1])
+    corners2 = tf.constant([[0, 3, 1, 6], [2, 4, 3, 8]], tf.float32)
+    boxlist1 = box_list.BoxList(corners1)
+    boxlist1.add_field('scores', scores1)
+    boxlist2 = box_list.BoxList(corners2)
+    with self.assertRaises(ValueError):
+      box_list_ops.concatenate([boxlist1, boxlist2])
+
+  def test_concatenate_with_incompatible_field_shapes(self):
+    corners1 = tf.constant([[0, 0, 0, 0], [1, 2, 3, 4]], tf.float32)
+    scores1 = tf.constant([1.0, 2.1])
+    corners2 = tf.constant([[0, 3, 1, 6], [2, 4, 3, 8]], tf.float32)
+    scores2 = tf.constant([[1.0, 1.0], [2.1, 3.2]])
+    boxlist1 = box_list.BoxList(corners1)
+    boxlist1.add_field('scores', scores1)
+    boxlist2 = box_list.BoxList(corners2)
+    boxlist2.add_field('scores', scores2)
+    with self.assertRaises(ValueError):
+      box_list_ops.concatenate([boxlist1, boxlist2])
+
+  def test_concatenate_is_correct(self):
+    corners1 = tf.constant([[0, 0, 0, 0], [1, 2, 3, 4]], tf.float32)
+    scores1 = tf.constant([1.0, 2.1])
+    corners2 = tf.constant([[0, 3, 1, 6], [2, 4, 3, 8], [1, 0, 5, 10]],
+                           tf.float32)
+    scores2 = tf.constant([1.0, 2.1, 5.6])
+
+    exp_corners = [[0, 0, 0, 0],
+                   [1, 2, 3, 4],
+                   [0, 3, 1, 6],
+                   [2, 4, 3, 8],
+                   [1, 0, 5, 10]]
+    exp_scores = [1.0, 2.1, 1.0, 2.1, 5.6]
+
+    boxlist1 = box_list.BoxList(corners1)
+    boxlist1.add_field('scores', scores1)
+    boxlist2 = box_list.BoxList(corners2)
+    boxlist2.add_field('scores', scores2)
+    result = box_list_ops.concatenate([boxlist1, boxlist2])
+    with self.test_session() as sess:
+      corners_output, scores_output = sess.run(
+          [result.get(), result.get_field('scores')])
+      self.assertAllClose(corners_output, exp_corners)
+      self.assertAllClose(scores_output, exp_scores)
+
+
+class NonMaxSuppressionTest(tf.test.TestCase):
+
+  def test_select_from_three_clusters(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1],
+                           [0, -0.1, 1, 0.9],
+                           [0, 10, 1, 11],
+                           [0, 10.1, 1, 11.1],
+                           [0, 100, 1, 101]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant([.9, .75, .6, .95, .5, .3]))
+    iou_thresh = .5
+    max_output_size = 3
+
+    exp_nms = [[0, 10, 1, 11],
+               [0, 0, 1, 1],
+               [0, 100, 1, 101]]
+    nms = box_list_ops.non_max_suppression(
+        boxes, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_select_at_most_two_boxes_from_three_clusters(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1],
+                           [0, -0.1, 1, 0.9],
+                           [0, 10, 1, 11],
+                           [0, 10.1, 1, 11.1],
+                           [0, 100, 1, 101]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant([.9, .75, .6, .95, .5, .3]))
+    iou_thresh = .5
+    max_output_size = 2
+
+    exp_nms = [[0, 10, 1, 11],
+               [0, 0, 1, 1]]
+    nms = box_list_ops.non_max_suppression(
+        boxes, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_select_at_most_thirty_boxes_from_three_clusters(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1],
+                           [0, -0.1, 1, 0.9],
+                           [0, 10, 1, 11],
+                           [0, 10.1, 1, 11.1],
+                           [0, 100, 1, 101]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant([.9, .75, .6, .95, .5, .3]))
+    iou_thresh = .5
+    max_output_size = 30
+
+    exp_nms = [[0, 10, 1, 11],
+               [0, 0, 1, 1],
+               [0, 100, 1, 101]]
+    nms = box_list_ops.non_max_suppression(
+        boxes, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_select_single_box(self):
+    corners = tf.constant([[0, 0, 1, 1]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant([.9]))
+    iou_thresh = .5
+    max_output_size = 3
+
+    exp_nms = [[0, 0, 1, 1]]
+    nms = box_list_ops.non_max_suppression(
+        boxes, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_select_from_ten_identical_boxes(self):
+    corners = tf.constant(10 * [[0, 0, 1, 1]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    boxes.add_field('scores', tf.constant(10 * [.9]))
+    iou_thresh = .5
+    max_output_size = 3
+
+    exp_nms = [[0, 0, 1, 1]]
+    nms = box_list_ops.non_max_suppression(
+        boxes, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_copy_extra_fields(self):
+    corners = tf.constant([[0, 0, 1, 1],
+                           [0, 0.1, 1, 1.1]], tf.float32)
+    boxes = box_list.BoxList(corners)
+    tensor1 = np.array([[1], [4]])
+    tensor2 = np.array([[1, 1], [2, 2]])
+    boxes.add_field('tensor1', tf.constant(tensor1))
+    boxes.add_field('tensor2', tf.constant(tensor2))
+    new_boxes = box_list.BoxList(tf.constant([[0, 0, 10, 10],
+                                              [1, 3, 5, 5]], tf.float32))
+    new_boxes = box_list_ops._copy_extra_fields(new_boxes, boxes)
+    with self.test_session() as sess:
+      self.assertAllClose(tensor1, sess.run(new_boxes.get_field('tensor1')))
+      self.assertAllClose(tensor2, sess.run(new_boxes.get_field('tensor2')))
+
+
+class CoordinatesConversionTest(tf.test.TestCase):
+
+  def test_to_normalized_coordinates(self):
+    coordinates = tf.constant([[0, 0, 100, 100],
+                               [25, 25, 75, 75]], tf.float32)
+    img = tf.ones((128, 100, 100, 3))
+    boxlist = box_list.BoxList(coordinates)
+    normalized_boxlist = box_list_ops.to_normalized_coordinates(
+        boxlist, tf.shape(img)[1], tf.shape(img)[2])
+    expected_boxes = [[0, 0, 1, 1],
+                      [0.25, 0.25, 0.75, 0.75]]
+
+    with self.test_session() as sess:
+      normalized_boxes = sess.run(normalized_boxlist.get())
+      self.assertAllClose(normalized_boxes, expected_boxes)
+
+  def test_to_normalized_coordinates_already_normalized(self):
+    coordinates = tf.constant([[0, 0, 1, 1],
+                               [0.25, 0.25, 0.75, 0.75]], tf.float32)
+    img = tf.ones((128, 100, 100, 3))
+    boxlist = box_list.BoxList(coordinates)
+    normalized_boxlist = box_list_ops.to_normalized_coordinates(
+        boxlist, tf.shape(img)[1], tf.shape(img)[2])
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('assertion failed'):
+        sess.run(normalized_boxlist.get())
+
+  def test_to_absolute_coordinates(self):
+    coordinates = tf.constant([[0, 0, 1, 1],
+                               [0.25, 0.25, 0.75, 0.75]], tf.float32)
+    img = tf.ones((128, 100, 100, 3))
+    boxlist = box_list.BoxList(coordinates)
+    absolute_boxlist = box_list_ops.to_absolute_coordinates(boxlist,
+                                                            tf.shape(img)[1],
+                                                            tf.shape(img)[2])
+    expected_boxes = [[0, 0, 100, 100],
+                      [25, 25, 75, 75]]
+
+    with self.test_session() as sess:
+      absolute_boxes = sess.run(absolute_boxlist.get())
+      self.assertAllClose(absolute_boxes, expected_boxes)
+
+  def test_to_absolute_coordinates_already_abolute(self):
+    coordinates = tf.constant([[0, 0, 100, 100],
+                               [25, 25, 75, 75]], tf.float32)
+    img = tf.ones((128, 100, 100, 3))
+    boxlist = box_list.BoxList(coordinates)
+    absolute_boxlist = box_list_ops.to_absolute_coordinates(boxlist,
+                                                            tf.shape(img)[1],
+                                                            tf.shape(img)[2])
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('assertion failed'):
+        sess.run(absolute_boxlist.get())
+
+  def test_convert_to_normalized_and_back(self):
+    coordinates = np.random.uniform(size=(100, 4))
+    coordinates = np.round(np.sort(coordinates) * 200)
+    coordinates[:, 2:4] += 1
+    coordinates[99, :] = [0, 0, 201, 201]
+    img = tf.ones((128, 202, 202, 3))
+
+    boxlist = box_list.BoxList(tf.constant(coordinates, tf.float32))
+    boxlist = box_list_ops.to_normalized_coordinates(boxlist,
+                                                     tf.shape(img)[1],
+                                                     tf.shape(img)[2])
+    boxlist = box_list_ops.to_absolute_coordinates(boxlist,
+                                                   tf.shape(img)[1],
+                                                   tf.shape(img)[2])
+
+    with self.test_session() as sess:
+      out = sess.run(boxlist.get())
+      self.assertAllClose(out, coordinates)
+
+  def test_convert_to_absolute_and_back(self):
+    coordinates = np.random.uniform(size=(100, 4))
+    coordinates = np.sort(coordinates)
+    coordinates[99, :] = [0, 0, 1, 1]
+    img = tf.ones((128, 202, 202, 3))
+
+    boxlist = box_list.BoxList(tf.constant(coordinates, tf.float32))
+    boxlist = box_list_ops.to_absolute_coordinates(boxlist,
+                                                   tf.shape(img)[1],
+                                                   tf.shape(img)[2])
+    boxlist = box_list_ops.to_normalized_coordinates(boxlist,
+                                                     tf.shape(img)[1],
+                                                     tf.shape(img)[2])
+
+    with self.test_session() as sess:
+      out = sess.run(boxlist.get())
+      self.assertAllClose(out, coordinates)
+
+  def test_to_absolute_coordinates_maximum_coordinate_check(self):
+    coordinates = tf.constant([[0, 0, 1.2, 1.2],
+                               [0.25, 0.25, 0.75, 0.75]], tf.float32)
+    img = tf.ones((128, 100, 100, 3))
+    boxlist = box_list.BoxList(coordinates)
+    absolute_boxlist = box_list_ops.to_absolute_coordinates(
+        boxlist,
+        tf.shape(img)[1],
+        tf.shape(img)[2],
+        maximum_normalized_coordinate=1.1)
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('assertion failed'):
+        sess.run(absolute_boxlist.get())
+
+
+class BoxRefinementTest(tf.test.TestCase):
+
+  def test_box_voting(self):
+    candidates = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.6, 0.6, 0.8, 0.8]], tf.float32))
+    candidates.add_field('ExtraField', tf.constant([1, 2]))
+    pool = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
+                     [0.6, 0.6, 0.8, 0.8]], tf.float32))
+    pool.add_field('scores', tf.constant([0.75, 0.25, 0.3]))
+    averaged_boxes = box_list_ops.box_voting(candidates, pool)
+    expected_boxes = [[0.1, 0.1, 0.425, 0.425], [0.6, 0.6, 0.8, 0.8]]
+    expected_scores = [0.5, 0.3]
+    with self.test_session() as sess:
+      boxes_out, scores_out, extra_field_out = sess.run(
+          [averaged_boxes.get(), averaged_boxes.get_field('scores'),
+           averaged_boxes.get_field('ExtraField')])
+
+      self.assertAllClose(expected_boxes, boxes_out)
+      self.assertAllClose(expected_scores, scores_out)
+      self.assertAllEqual(extra_field_out, [1, 2])
+
+  def test_box_voting_fails_with_negative_scores(self):
+    candidates = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4]], tf.float32))
+    pool = box_list.BoxList(tf.constant([[0.1, 0.1, 0.4, 0.4]], tf.float32))
+    pool.add_field('scores', tf.constant([-0.2]))
+    averaged_boxes = box_list_ops.box_voting(candidates, pool)
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('Scores must be non negative'):
+        sess.run([averaged_boxes.get()])
+
+  def test_box_voting_fails_when_unmatched(self):
+    candidates = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4]], tf.float32))
+    pool = box_list.BoxList(tf.constant([[0.6, 0.6, 0.8, 0.8]], tf.float32))
+    pool.add_field('scores', tf.constant([0.2]))
+    averaged_boxes = box_list_ops.box_voting(candidates, pool)
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('Each box in selected_boxes must match '
+                                    'with at least one box in pool_boxes.'):
+        sess.run([averaged_boxes.get()])
+
+  def test_refine_boxes(self):
+    pool = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
+                     [0.6, 0.6, 0.8, 0.8]], tf.float32))
+    pool.add_field('ExtraField', tf.constant([1, 2, 3]))
+    pool.add_field('scores', tf.constant([0.75, 0.25, 0.3]))
+    refined_boxes = box_list_ops.refine_boxes(pool, 0.5, 10)
+
+    expected_boxes = [[0.1, 0.1, 0.425, 0.425], [0.6, 0.6, 0.8, 0.8]]
+    expected_scores = [0.5, 0.3]
+    with self.test_session() as sess:
+      boxes_out, scores_out, extra_field_out = sess.run(
+          [refined_boxes.get(), refined_boxes.get_field('scores'),
+           refined_boxes.get_field('ExtraField')])
+
+      self.assertAllClose(expected_boxes, boxes_out)
+      self.assertAllClose(expected_scores, scores_out)
+      self.assertAllEqual(extra_field_out, [1, 3])
+
+  def test_refine_boxes_multi_class(self):
+    pool = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
+                     [0.6, 0.6, 0.8, 0.8], [0.2, 0.2, 0.3, 0.3]], tf.float32))
+    pool.add_field('classes', tf.constant([0, 0, 1, 1]))
+    pool.add_field('scores', tf.constant([0.75, 0.25, 0.3, 0.2]))
+    refined_boxes = box_list_ops.refine_boxes_multi_class(pool, 3, 0.5, 10)
+
+    expected_boxes = [[0.1, 0.1, 0.425, 0.425], [0.6, 0.6, 0.8, 0.8],
+                      [0.2, 0.2, 0.3, 0.3]]
+    expected_scores = [0.5, 0.3, 0.2]
+    with self.test_session() as sess:
+      boxes_out, scores_out, extra_field_out = sess.run(
+          [refined_boxes.get(), refined_boxes.get_field('scores'),
+           refined_boxes.get_field('classes')])
+
+      self.assertAllClose(expected_boxes, boxes_out)
+      self.assertAllClose(expected_scores, scores_out)
+      self.assertAllEqual(extra_field_out, [0, 1, 1])
+
+  def test_sample_boxes_by_jittering(self):
+    boxes = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4],
+                     [0.1, 0.1, 0.5, 0.5],
+                     [0.6, 0.6, 0.8, 0.8],
+                     [0.2, 0.2, 0.3, 0.3]], tf.float32))
+    sampled_boxes = box_list_ops.sample_boxes_by_jittering(
+        boxlist=boxes, num_boxes_to_sample=10)
+    iou = box_list_ops.iou(boxes, sampled_boxes)
+    iou_max = tf.reduce_max(iou, axis=0)
+    with self.test_session() as sess:
+      (np_sampled_boxes, np_iou_max) = sess.run([sampled_boxes.get(), iou_max])
+      self.assertAllEqual(np_sampled_boxes.shape, [10, 4])
+      self.assertAllGreater(np_iou_max, 0.5)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_test.py
new file mode 100644
index 00000000..edc00ebb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_list_test.py
@@ -0,0 +1,134 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.box_list."""
+
+import tensorflow as tf
+
+from object_detection.core import box_list
+
+
+class BoxListTest(tf.test.TestCase):
+  """Tests for BoxList class."""
+
+  def test_num_boxes(self):
+    data = tf.constant([[0, 0, 1, 1], [1, 1, 2, 3], [3, 4, 5, 5]], tf.float32)
+    expected_num_boxes = 3
+
+    boxes = box_list.BoxList(data)
+    with self.test_session() as sess:
+      num_boxes_output = sess.run(boxes.num_boxes())
+      self.assertEquals(num_boxes_output, expected_num_boxes)
+
+  def test_get_correct_center_coordinates_and_sizes(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    centers_sizes = boxes.get_center_coordinates_and_sizes()
+    expected_centers_sizes = [[15, 0.35], [12.5, 0.25], [10, 0.3], [5, 0.3]]
+    with self.test_session() as sess:
+      centers_sizes_out = sess.run(centers_sizes)
+      self.assertAllClose(centers_sizes_out, expected_centers_sizes)
+
+  def test_create_box_list_with_dynamic_shape(self):
+    data = tf.constant([[0, 0, 1, 1], [1, 1, 2, 3], [3, 4, 5, 5]], tf.float32)
+    indices = tf.reshape(tf.where(tf.greater([1, 0, 1], 0)), [-1])
+    data = tf.gather(data, indices)
+    assert data.get_shape().as_list() == [None, 4]
+    expected_num_boxes = 2
+
+    boxes = box_list.BoxList(data)
+    with self.test_session() as sess:
+      num_boxes_output = sess.run(boxes.num_boxes())
+      self.assertEquals(num_boxes_output, expected_num_boxes)
+
+  def test_transpose_coordinates(self):
+    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    boxes = box_list.BoxList(tf.constant(boxes))
+    boxes.transpose_coordinates()
+    expected_corners = [[10.0, 10.0, 15.0, 20.0], [0.1, 0.2, 0.4, 0.5]]
+    with self.test_session() as sess:
+      corners_out = sess.run(boxes.get())
+      self.assertAllClose(corners_out, expected_corners)
+
+  def test_box_list_invalid_inputs(self):
+    data0 = tf.constant([[[0, 0, 1, 1], [3, 4, 5, 5]]], tf.float32)
+    data1 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.float32)
+    data2 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.int32)
+
+    with self.assertRaises(ValueError):
+      _ = box_list.BoxList(data0)
+    with self.assertRaises(ValueError):
+      _ = box_list.BoxList(data1)
+    with self.assertRaises(ValueError):
+      _ = box_list.BoxList(data2)
+
+  def test_num_boxes_static(self):
+    box_corners = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
+    boxes = box_list.BoxList(tf.constant(box_corners))
+    self.assertEquals(boxes.num_boxes_static(), 2)
+    self.assertEquals(type(boxes.num_boxes_static()), int)
+
+  def test_num_boxes_static_for_uninferrable_shape(self):
+    placeholder = tf.placeholder(tf.float32, shape=[None, 4])
+    boxes = box_list.BoxList(placeholder)
+    self.assertEquals(boxes.num_boxes_static(), None)
+
+  def test_as_tensor_dict(self):
+    boxlist = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
+    boxlist.add_field('classes', tf.constant([0, 1]))
+    boxlist.add_field('scores', tf.constant([0.75, 0.2]))
+    tensor_dict = boxlist.as_tensor_dict()
+
+    expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]]
+    expected_classes = [0, 1]
+    expected_scores = [0.75, 0.2]
+
+    with self.test_session() as sess:
+      tensor_dict_out = sess.run(tensor_dict)
+      self.assertAllEqual(3, len(tensor_dict_out))
+      self.assertAllClose(expected_boxes, tensor_dict_out['boxes'])
+      self.assertAllEqual(expected_classes, tensor_dict_out['classes'])
+      self.assertAllClose(expected_scores, tensor_dict_out['scores'])
+
+  def test_as_tensor_dict_with_features(self):
+    boxlist = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
+    boxlist.add_field('classes', tf.constant([0, 1]))
+    boxlist.add_field('scores', tf.constant([0.75, 0.2]))
+    tensor_dict = boxlist.as_tensor_dict(['boxes', 'classes', 'scores'])
+
+    expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]]
+    expected_classes = [0, 1]
+    expected_scores = [0.75, 0.2]
+
+    with self.test_session() as sess:
+      tensor_dict_out = sess.run(tensor_dict)
+      self.assertAllEqual(3, len(tensor_dict_out))
+      self.assertAllClose(expected_boxes, tensor_dict_out['boxes'])
+      self.assertAllEqual(expected_classes, tensor_dict_out['classes'])
+      self.assertAllClose(expected_scores, tensor_dict_out['scores'])
+
+  def test_as_tensor_dict_missing_field(self):
+    boxlist = box_list.BoxList(
+        tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
+    boxlist.add_field('classes', tf.constant([0, 1]))
+    boxlist.add_field('scores', tf.constant([0.75, 0.2]))
+    with self.assertRaises(ValueError):
+      boxlist.as_tensor_dict(['foo', 'bar'])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/box_predictor.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_predictor.py
new file mode 100644
index 00000000..91a6647e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/box_predictor.py
@@ -0,0 +1,227 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Box predictor for object detectors.
+
+Box predictors are classes that take a high level
+image feature map as input and produce two predictions,
+(1) a tensor encoding box locations, and
+(2) a tensor encoding classes for each box.
+
+These components are passed directly to loss functions
+in our detection models.
+
+These modules are separated from the main model since the same
+few box predictor architectures are shared across many models.
+"""
+from abc import abstractmethod
+import tensorflow as tf
+
+BOX_ENCODINGS = 'box_encodings'
+CLASS_PREDICTIONS_WITH_BACKGROUND = 'class_predictions_with_background'
+MASK_PREDICTIONS = 'mask_predictions'
+
+
+class BoxPredictor(object):
+  """BoxPredictor."""
+
+  def __init__(self, is_training, num_classes):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+    """
+    self._is_training = is_training
+    self._num_classes = num_classes
+
+  @property
+  def is_keras_model(self):
+    return False
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def predict(self, image_features, num_predictions_per_location,
+              scope=None, **params):
+    """Computes encoded object locations and corresponding confidences.
+
+    Takes a list of high level image feature maps as input and produces a list
+    of box encodings and a list of class scores where each element in the output
+    lists correspond to the feature maps in the input list.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+      width_i, channels_i] containing features for a batch of images.
+      num_predictions_per_location: A list of integers representing the number
+        of box predictions to be made per spatial location for each feature map.
+      scope: Variable and Op scope name.
+      **params: Additional keyword arguments for specific implementations of
+              BoxPredictor.
+
+    Returns:
+      A dictionary containing at least the following tensors.
+        box_encodings: A list of float tensors. Each entry in the list
+          corresponds to a feature map in the input `image_features` list. All
+          tensors in the list have one of the two following shapes:
+          a. [batch_size, num_anchors_i, q, code_size] representing the location
+            of the objects, where q is 1 or the number of classes.
+          b. [batch_size, num_anchors_i, code_size].
+        class_predictions_with_background: A list of float tensors of shape
+          [batch_size, num_anchors_i, num_classes + 1] representing the class
+          predictions for the proposals. Each entry in the list corresponds to a
+          feature map in the input `image_features` list.
+
+    Raises:
+      ValueError: If length of `image_features` is not equal to length of
+        `num_predictions_per_location`.
+    """
+    if len(image_features) != len(num_predictions_per_location):
+      raise ValueError('image_feature and num_predictions_per_location must '
+                       'be of same length, found: {} vs {}'.
+                       format(len(image_features),
+                              len(num_predictions_per_location)))
+    if scope is not None:
+      with tf.variable_scope(scope):
+        return self._predict(image_features, num_predictions_per_location,
+                             **params)
+    return self._predict(image_features, num_predictions_per_location,
+                         **params)
+
+  # TODO(rathodv): num_predictions_per_location could be moved to constructor.
+  # This is currently only used by ConvolutionalBoxPredictor.
+  @abstractmethod
+  def _predict(self, image_features, num_predictions_per_location, **params):
+    """Implementations must override this method.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+        width_i, channels_i] containing features for a batch of images.
+      num_predictions_per_location: A list of integers representing the number
+        of box predictions to be made per spatial location for each feature map.
+      **params: Additional keyword arguments for specific implementations of
+              BoxPredictor.
+
+    Returns:
+      A dictionary containing at least the following tensors.
+        box_encodings: A list of float tensors. Each entry in the list
+          corresponds to a feature map in the input `image_features` list. All
+          tensors in the list have one of the two following shapes:
+          a. [batch_size, num_anchors_i, q, code_size] representing the location
+            of the objects, where q is 1 or the number of classes.
+          b. [batch_size, num_anchors_i, code_size].
+        class_predictions_with_background: A list of float tensors of shape
+          [batch_size, num_anchors_i, num_classes + 1] representing the class
+          predictions for the proposals. Each entry in the list corresponds to a
+          feature map in the input `image_features` list.
+    """
+    pass
+
+
+class KerasBoxPredictor(tf.keras.Model):
+  """Keras-based BoxPredictor."""
+
+  def __init__(self, is_training, num_classes, freeze_batchnorm,
+               inplace_batchnorm_update, name=None):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      freeze_batchnorm: Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      inplace_batchnorm_update: Whether to update batch norm moving average
+        values inplace. When this is false train op must add a control
+        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+        batch norm statistics.
+      name: A string name scope to assign to the model. If `None`, Keras
+        will auto-generate one from the class name.
+    """
+    super(KerasBoxPredictor, self).__init__(name=name)
+
+    self._is_training = is_training
+    self._num_classes = num_classes
+    self._freeze_batchnorm = freeze_batchnorm
+    self._inplace_batchnorm_update = inplace_batchnorm_update
+
+  @property
+  def is_keras_model(self):
+    return True
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def call(self, image_features, **kwargs):
+    """Computes encoded object locations and corresponding confidences.
+
+    Takes a list of high level image feature maps as input and produces a list
+    of box encodings and a list of class scores where each element in the output
+    lists correspond to the feature maps in the input list.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+      width_i, channels_i] containing features for a batch of images.
+      **kwargs: Additional keyword arguments for specific implementations of
+            BoxPredictor.
+
+    Returns:
+      A dictionary containing at least the following tensors.
+        box_encodings: A list of float tensors. Each entry in the list
+          corresponds to a feature map in the input `image_features` list. All
+          tensors in the list have one of the two following shapes:
+          a. [batch_size, num_anchors_i, q, code_size] representing the location
+            of the objects, where q is 1 or the number of classes.
+          b. [batch_size, num_anchors_i, code_size].
+        class_predictions_with_background: A list of float tensors of shape
+          [batch_size, num_anchors_i, num_classes + 1] representing the class
+          predictions for the proposals. Each entry in the list corresponds to a
+          feature map in the input `image_features` list.
+    """
+    return self._predict(image_features, **kwargs)
+
+  @abstractmethod
+  def _predict(self, image_features, **kwargs):
+    """Implementations must override this method.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+        width_i, channels_i] containing features for a batch of images.
+      **kwargs: Additional keyword arguments for specific implementations of
+              BoxPredictor.
+
+    Returns:
+      A dictionary containing at least the following tensors.
+        box_encodings: A list of float tensors. Each entry in the list
+          corresponds to a feature map in the input `image_features` list. All
+          tensors in the list have one of the two following shapes:
+          a. [batch_size, num_anchors_i, q, code_size] representing the location
+            of the objects, where q is 1 or the number of classes.
+          b. [batch_size, num_anchors_i, code_size].
+        class_predictions_with_background: A list of float tensors of shape
+          [batch_size, num_anchors_i, num_classes + 1] representing the class
+          predictions for the proposals. Each entry in the list corresponds to a
+          feature map in the input `image_features` list.
+    """
+    raise NotImplementedError
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/data_decoder.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/data_decoder.py
new file mode 100644
index 00000000..9ae18c1f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/data_decoder.py
@@ -0,0 +1,41 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Interface for data decoders.
+
+Data decoders decode the input data and return a dictionary of tensors keyed by
+the entries in core.reader.Fields.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+
+class DataDecoder(object):
+  """Interface for data decoders."""
+  __metaclass__ = ABCMeta
+
+  @abstractmethod
+  def decode(self, data):
+    """Return a single image and associated labels.
+
+    Args:
+      data: a string tensor holding a serialized protocol buffer corresponding
+        to data for a single image.
+
+    Returns:
+      tensor_dict: a dictionary containing tensors. Possible keys are defined in
+          reader.Fields.
+    """
+    pass
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/data_parser.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/data_parser.py
new file mode 100644
index 00000000..3dac4de2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/data_parser.py
@@ -0,0 +1,41 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Interface for data parsers.
+
+Data parser parses input data and returns a dictionary of numpy arrays
+keyed by the entries in standard_fields.py. Since the parser parses records
+to numpy arrays (materialized tensors) directly, it is used to read data for
+evaluation/visualization; to parse the data during training, DataDecoder should
+be used.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+
+class DataToNumpyParser(object):
+  __metaclass__ = ABCMeta
+
+  @abstractmethod
+  def parse(self, input_data):
+    """Parses input and returns a numpy array or a dictionary of numpy arrays.
+
+    Args:
+      input_data: an input data
+
+    Returns:
+      A numpy array or a dictionary of numpy arrays or None, if input
+      cannot be parsed.
+    """
+    pass
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm.py
new file mode 100644
index 00000000..68a56aa6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm.py
@@ -0,0 +1,70 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A freezable batch norm layer that uses Keras batch normalization."""
+import tensorflow as tf
+
+
+class FreezableBatchNorm(tf.keras.layers.BatchNormalization):
+  """Batch normalization layer (Ioffe and Szegedy, 2014).
+
+  This is a `freezable` batch norm layer that supports setting the `training`
+  parameter in the __init__ method rather than having to set it either via
+  the Keras learning phase or via the `call` method parameter. This layer will
+  forward all other parameters to the default Keras `BatchNormalization`
+  layer
+
+  This is class is necessary because Object Detection model training sometimes
+  requires batch normalization layers to be `frozen` and used as if it was
+  evaluation time, despite still training (and potentially using dropout layers)
+
+  Like the default Keras BatchNormalization layer, this will normalize the
+  activations of the previous layer at each batch,
+  i.e. applies a transformation that maintains the mean activation
+  close to 0 and the activation standard deviation close to 1.
+
+  Arguments:
+    training: Boolean or None. If True, the batch normalization layer will
+      normalize the input batch using the batch mean and standard deviation,
+      and update the total moving mean and standard deviations. If False, the
+      layer will normalize using the moving average and std. dev, without
+      updating the learned avg and std. dev.
+      If None, the layer will follow the keras BatchNormalization layer
+      strategy of checking the Keras learning phase at `call` time to decide
+      what to do.
+    **kwargs: The keyword arguments to forward to the keras BatchNormalization
+        layer constructor.
+
+  Input shape:
+      Arbitrary. Use the keyword argument `input_shape`
+      (tuple of integers, does not include the samples axis)
+      when using this layer as the first layer in a model.
+
+  Output shape:
+      Same shape as input.
+
+  References:
+      - [Batch Normalization: Accelerating Deep Network Training by Reducing
+        Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
+  """
+
+  def __init__(self, training=None, **kwargs):
+    super(FreezableBatchNorm, self).__init__(**kwargs)
+    self._training = training
+
+  def call(self, inputs, training=None):
+    if training is None:
+      training = self._training
+    return super(FreezableBatchNorm, self).call(inputs, training=training)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm_test.py
new file mode 100644
index 00000000..504b9e71
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/freezable_batch_norm_test.py
@@ -0,0 +1,121 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.freezable_batch_norm."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import freezable_batch_norm
+
+
+class FreezableBatchNormTest(tf.test.TestCase):
+  """Tests for FreezableBatchNorm operations."""
+
+  def _build_model(self, training=None):
+    model = tf.keras.models.Sequential()
+    norm = freezable_batch_norm.FreezableBatchNorm(training=training,
+                                                   input_shape=(10,),
+                                                   momentum=0.8)
+    model.add(norm)
+    return model, norm
+
+  def _train_freezable_batch_norm(self, training_mean, training_var):
+    model, _ = self._build_model()
+    model.compile(loss='mse', optimizer='sgd')
+
+    # centered on training_mean, variance training_var
+    train_data = np.random.normal(
+        loc=training_mean,
+        scale=training_var,
+        size=(1000, 10))
+    model.fit(train_data, train_data, epochs=4, verbose=0)
+    return model.weights
+
+  def test_batchnorm_freezing_training_true(self):
+    with self.test_session():
+      training_mean = 5.0
+      training_var = 10.0
+
+      testing_mean = -10.0
+      testing_var = 5.0
+
+      # Initially train the batch norm, and save the weights
+      trained_weights = self._train_freezable_batch_norm(training_mean,
+                                                         training_var)
+
+      # Load the batch norm weights, freezing training to True.
+      # Apply the batch norm layer to testing data and ensure it is normalized
+      # according to the batch statistics.
+      model, norm = self._build_model(training=True)
+      for trained_weight, blank_weight in zip(trained_weights, model.weights):
+        weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight))
+        tf.keras.backend.eval(weight_copy)
+
+      # centered on testing_mean, variance testing_var
+      test_data = np.random.normal(
+          loc=testing_mean,
+          scale=testing_var,
+          size=(1000, 10))
+
+      out_tensor = norm(tf.convert_to_tensor(test_data, dtype=tf.float32))
+      out = tf.keras.backend.eval(out_tensor)
+
+      out -= tf.keras.backend.eval(norm.beta)
+      out /= tf.keras.backend.eval(norm.gamma)
+
+      np.testing.assert_allclose(out.mean(), 0.0, atol=1.5e-1)
+      np.testing.assert_allclose(out.std(), 1.0, atol=1.5e-1)
+
+  def test_batchnorm_freezing_training_false(self):
+    with self.test_session():
+      training_mean = 5.0
+      training_var = 10.0
+
+      testing_mean = -10.0
+      testing_var = 5.0
+
+      # Initially train the batch norm, and save the weights
+      trained_weights = self._train_freezable_batch_norm(training_mean,
+                                                         training_var)
+
+      # Load the batch norm back up, freezing training to False.
+      # Apply the batch norm layer to testing data and ensure it is normalized
+      # according to the training data's statistics.
+      model, norm = self._build_model(training=False)
+      for trained_weight, blank_weight in zip(trained_weights, model.weights):
+        weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight))
+        tf.keras.backend.eval(weight_copy)
+
+      # centered on testing_mean, variance testing_var
+      test_data = np.random.normal(
+          loc=testing_mean,
+          scale=testing_var,
+          size=(1000, 10))
+
+      out_tensor = norm(tf.convert_to_tensor(test_data, dtype=tf.float32))
+      out = tf.keras.backend.eval(out_tensor)
+
+      out -= tf.keras.backend.eval(norm.beta)
+      out /= tf.keras.backend.eval(norm.gamma)
+
+      out *= training_var
+      out += (training_mean - testing_mean)
+      out /= testing_var
+
+      np.testing.assert_allclose(out.mean(), 0.0, atol=1.5e-1)
+      np.testing.assert_allclose(out.std(), 1.0, atol=1.5e-1)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops.py
new file mode 100644
index 00000000..e520845f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops.py
@@ -0,0 +1,282 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Keypoint operations.
+
+Keypoints are represented as tensors of shape [num_instances, num_keypoints, 2],
+where the last dimension holds rank 2 tensors of the form [y, x] representing
+the coordinates of the keypoint.
+"""
+import numpy as np
+import tensorflow as tf
+
+
+def scale(keypoints, y_scale, x_scale, scope=None):
+  """Scales keypoint coordinates in x and y dimensions.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    y_scale: (float) scalar tensor
+    x_scale: (float) scalar tensor
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'Scale'):
+    y_scale = tf.cast(y_scale, tf.float32)
+    x_scale = tf.cast(x_scale, tf.float32)
+    new_keypoints = keypoints * [[[y_scale, x_scale]]]
+    return new_keypoints
+
+
+def clip_to_window(keypoints, window, scope=None):
+  """Clips keypoints to a window.
+
+  This op clips any input keypoints to a window.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
+      window to which the op should clip the keypoints.
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'ClipToWindow'):
+    y, x = tf.split(value=keypoints, num_or_size_splits=2, axis=2)
+    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
+    y = tf.maximum(tf.minimum(y, win_y_max), win_y_min)
+    x = tf.maximum(tf.minimum(x, win_x_max), win_x_min)
+    new_keypoints = tf.concat([y, x], 2)
+    return new_keypoints
+
+
+def prune_outside_window(keypoints, window, scope=None):
+  """Prunes keypoints that fall outside a given window.
+
+  This function replaces keypoints that fall outside the given window with nan.
+  See also clip_to_window which clips any keypoints that fall outside the given
+  window.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
+      window outside of which the op should prune the keypoints.
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'PruneOutsideWindow'):
+    y, x = tf.split(value=keypoints, num_or_size_splits=2, axis=2)
+    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
+
+    valid_indices = tf.logical_and(
+        tf.logical_and(y >= win_y_min, y <= win_y_max),
+        tf.logical_and(x >= win_x_min, x <= win_x_max))
+
+    new_y = tf.where(valid_indices, y, np.nan * tf.ones_like(y))
+    new_x = tf.where(valid_indices, x, np.nan * tf.ones_like(x))
+    new_keypoints = tf.concat([new_y, new_x], 2)
+
+    return new_keypoints
+
+
+def change_coordinate_frame(keypoints, window, scope=None):
+  """Changes coordinate frame of the keypoints to be relative to window's frame.
+
+  Given a window of the form [y_min, x_min, y_max, x_max], changes keypoint
+  coordinates from keypoints of shape [num_instances, num_keypoints, 2]
+  to be relative to this window.
+
+  An example use case is data augmentation: where we are given groundtruth
+  keypoints and would like to randomly crop the image to some window. In this
+  case we need to change the coordinate frame of each groundtruth keypoint to be
+  relative to this new window.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
+      window we should change the coordinate frame to.
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'ChangeCoordinateFrame'):
+    win_height = window[2] - window[0]
+    win_width = window[3] - window[1]
+    new_keypoints = scale(keypoints - [window[0], window[1]], 1.0 / win_height,
+                          1.0 / win_width)
+    return new_keypoints
+
+
+def to_normalized_coordinates(keypoints, height, width,
+                              check_range=True, scope=None):
+  """Converts absolute keypoint coordinates to normalized coordinates in [0, 1].
+
+  Usually one uses the dynamic shape of the image or conv-layer tensor:
+    keypoints = keypoint_ops.to_normalized_coordinates(keypoints,
+                                                       tf.shape(images)[1],
+                                                       tf.shape(images)[2]),
+
+  This function raises an assertion failed error at graph execution time when
+  the maximum coordinate is smaller than 1.01 (which means that coordinates are
+  already normalized). The value 1.01 is to deal with small rounding errors.
+
+  Args:
+    keypoints: A tensor of shape [num_instances, num_keypoints, 2].
+    height: Maximum value for y coordinate of absolute keypoint coordinates.
+    width: Maximum value for x coordinate of absolute keypoint coordinates.
+    check_range: If True, checks if the coordinates are normalized.
+    scope: name scope.
+
+  Returns:
+    tensor of shape [num_instances, num_keypoints, 2] with normalized
+    coordinates in [0, 1].
+  """
+  with tf.name_scope(scope, 'ToNormalizedCoordinates'):
+    height = tf.cast(height, tf.float32)
+    width = tf.cast(width, tf.float32)
+
+    if check_range:
+      max_val = tf.reduce_max(keypoints)
+      max_assert = tf.Assert(tf.greater(max_val, 1.01),
+                             ['max value is lower than 1.01: ', max_val])
+      with tf.control_dependencies([max_assert]):
+        width = tf.identity(width)
+
+    return scale(keypoints, 1.0 / height, 1.0 / width)
+
+
+def to_absolute_coordinates(keypoints, height, width,
+                            check_range=True, scope=None):
+  """Converts normalized keypoint coordinates to absolute pixel coordinates.
+
+  This function raises an assertion failed error when the maximum keypoint
+  coordinate value is larger than 1.01 (in which case coordinates are already
+  absolute).
+
+  Args:
+    keypoints: A tensor of shape [num_instances, num_keypoints, 2]
+    height: Maximum value for y coordinate of absolute keypoint coordinates.
+    width: Maximum value for x coordinate of absolute keypoint coordinates.
+    check_range: If True, checks if the coordinates are normalized or not.
+    scope: name scope.
+
+  Returns:
+    tensor of shape [num_instances, num_keypoints, 2] with absolute coordinates
+    in terms of the image size.
+
+  """
+  with tf.name_scope(scope, 'ToAbsoluteCoordinates'):
+    height = tf.cast(height, tf.float32)
+    width = tf.cast(width, tf.float32)
+
+    # Ensure range of input keypoints is correct.
+    if check_range:
+      max_val = tf.reduce_max(keypoints)
+      max_assert = tf.Assert(tf.greater_equal(1.01, max_val),
+                             ['maximum keypoint coordinate value is larger '
+                              'than 1.01: ', max_val])
+      with tf.control_dependencies([max_assert]):
+        width = tf.identity(width)
+
+    return scale(keypoints, height, width)
+
+
+def flip_horizontal(keypoints, flip_point, flip_permutation, scope=None):
+  """Flips the keypoints horizontally around the flip_point.
+
+  This operation flips the x coordinate for each keypoint around the flip_point
+  and also permutes the keypoints in a manner specified by flip_permutation.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    flip_point:  (float) scalar tensor representing the x coordinate to flip the
+      keypoints around.
+    flip_permutation: rank 1 int32 tensor containing the keypoint flip
+      permutation. This specifies the mapping from original keypoint indices
+      to the flipped keypoint indices. This is used primarily for keypoints
+      that are not reflection invariant. E.g. Suppose there are 3 keypoints
+      representing ['head', 'right_eye', 'left_eye'], then a logical choice for
+      flip_permutation might be [0, 2, 1] since we want to swap the 'left_eye'
+      and 'right_eye' after a horizontal flip.
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'FlipHorizontal'):
+    keypoints = tf.transpose(keypoints, [1, 0, 2])
+    keypoints = tf.gather(keypoints, flip_permutation)
+    v, u = tf.split(value=keypoints, num_or_size_splits=2, axis=2)
+    u = flip_point * 2.0 - u
+    new_keypoints = tf.concat([v, u], 2)
+    new_keypoints = tf.transpose(new_keypoints, [1, 0, 2])
+    return new_keypoints
+
+
+def flip_vertical(keypoints, flip_point, flip_permutation, scope=None):
+  """Flips the keypoints vertically around the flip_point.
+
+  This operation flips the y coordinate for each keypoint around the flip_point
+  and also permutes the keypoints in a manner specified by flip_permutation.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    flip_point:  (float) scalar tensor representing the y coordinate to flip the
+      keypoints around.
+    flip_permutation: rank 1 int32 tensor containing the keypoint flip
+      permutation. This specifies the mapping from original keypoint indices
+      to the flipped keypoint indices. This is used primarily for keypoints
+      that are not reflection invariant. E.g. Suppose there are 3 keypoints
+      representing ['head', 'right_eye', 'left_eye'], then a logical choice for
+      flip_permutation might be [0, 2, 1] since we want to swap the 'left_eye'
+      and 'right_eye' after a horizontal flip.
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'FlipVertical'):
+    keypoints = tf.transpose(keypoints, [1, 0, 2])
+    keypoints = tf.gather(keypoints, flip_permutation)
+    v, u = tf.split(value=keypoints, num_or_size_splits=2, axis=2)
+    v = flip_point * 2.0 - v
+    new_keypoints = tf.concat([v, u], 2)
+    new_keypoints = tf.transpose(new_keypoints, [1, 0, 2])
+    return new_keypoints
+
+
+def rot90(keypoints, scope=None):
+  """Rotates the keypoints counter-clockwise by 90 degrees.
+
+  Args:
+    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+    scope: name scope.
+
+  Returns:
+    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope(scope, 'Rot90'):
+    keypoints = tf.transpose(keypoints, [1, 0, 2])
+    v, u = tf.split(value=keypoints[:, :, ::-1], num_or_size_splits=2, axis=2)
+    v = 1.0 - v
+    new_keypoints = tf.concat([v, u], 2)
+    new_keypoints = tf.transpose(new_keypoints, [1, 0, 2])
+    return new_keypoints
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops_test.py
new file mode 100644
index 00000000..1c09c55a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/keypoint_ops_test.py
@@ -0,0 +1,200 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.keypoint_ops."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import keypoint_ops
+
+
+class KeypointOpsTest(tf.test.TestCase):
+  """Tests for common keypoint operations."""
+
+  def test_scale(self):
+    keypoints = tf.constant([
+        [[0.0, 0.0], [100.0, 200.0]],
+        [[50.0, 120.0], [100.0, 140.0]]
+    ])
+    y_scale = tf.constant(1.0 / 100)
+    x_scale = tf.constant(1.0 / 200)
+
+    expected_keypoints = tf.constant([
+        [[0., 0.], [1.0, 1.0]],
+        [[0.5, 0.6], [1.0, 0.7]]
+    ])
+    output = keypoint_ops.scale(keypoints, y_scale, x_scale)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_clip_to_window(self):
+    keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+    window = tf.constant([0.25, 0.25, 0.75, 0.75])
+
+    expected_keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.25], [0.75, 0.75]]
+    ])
+    output = keypoint_ops.clip_to_window(keypoints, window)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_prune_outside_window(self):
+    keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+    window = tf.constant([0.25, 0.25, 0.75, 0.75])
+
+    expected_keypoints = tf.constant([[[0.25, 0.5], [0.75, 0.75]],
+                                      [[np.nan, np.nan], [np.nan, np.nan]]])
+    output = keypoint_ops.prune_outside_window(keypoints, window)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_change_coordinate_frame(self):
+    keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+    window = tf.constant([0.25, 0.25, 0.75, 0.75])
+
+    expected_keypoints = tf.constant([
+        [[0, 0.5], [1.0, 1.0]],
+        [[0.5, -0.5], [1.5, 1.5]]
+    ])
+    output = keypoint_ops.change_coordinate_frame(keypoints, window)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_to_normalized_coordinates(self):
+    keypoints = tf.constant([
+        [[10., 30.], [30., 45.]],
+        [[20., 0.], [40., 60.]]
+    ])
+    output = keypoint_ops.to_normalized_coordinates(
+        keypoints, 40, 60)
+    expected_keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_to_normalized_coordinates_already_normalized(self):
+    keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+    output = keypoint_ops.to_normalized_coordinates(
+        keypoints, 40, 60)
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('assertion failed'):
+        sess.run(output)
+
+  def test_to_absolute_coordinates(self):
+    keypoints = tf.constant([
+        [[0.25, 0.5], [0.75, 0.75]],
+        [[0.5, 0.0], [1.0, 1.0]]
+    ])
+    output = keypoint_ops.to_absolute_coordinates(
+        keypoints, 40, 60)
+    expected_keypoints = tf.constant([
+        [[10., 30.], [30., 45.]],
+        [[20., 0.], [40., 60.]]
+    ])
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_to_absolute_coordinates_already_absolute(self):
+    keypoints = tf.constant([
+        [[10., 30.], [30., 45.]],
+        [[20., 0.], [40., 60.]]
+    ])
+    output = keypoint_ops.to_absolute_coordinates(
+        keypoints, 40, 60)
+
+    with self.test_session() as sess:
+      with self.assertRaisesOpError('assertion failed'):
+        sess.run(output)
+
+  def test_flip_horizontal(self):
+    keypoints = tf.constant([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+        [[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]]
+    ])
+    flip_permutation = [0, 2, 1]
+
+    expected_keypoints = tf.constant([
+        [[0.1, 0.9], [0.3, 0.7], [0.2, 0.8]],
+        [[0.4, 0.6], [0.6, 0.4], [0.5, 0.5]],
+    ])
+    output = keypoint_ops.flip_horizontal(keypoints, 0.5, flip_permutation)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_flip_vertical(self):
+    keypoints = tf.constant([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+        [[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]]
+    ])
+    flip_permutation = [0, 2, 1]
+
+    expected_keypoints = tf.constant([
+        [[0.9, 0.1], [0.7, 0.3], [0.8, 0.2]],
+        [[0.6, 0.4], [0.4, 0.6], [0.5, 0.5]],
+    ])
+    output = keypoint_ops.flip_vertical(keypoints, 0.5, flip_permutation)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+  def test_rot90(self):
+    keypoints = tf.constant([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+        [[0.4, 0.6], [0.5, 0.6], [0.6, 0.7]]
+    ])
+    expected_keypoints = tf.constant([
+        [[0.9, 0.1], [0.8, 0.2], [0.7, 0.3]],
+        [[0.4, 0.4], [0.4, 0.5], [0.3, 0.6]],
+    ])
+    output = keypoint_ops.rot90(keypoints)
+
+    with self.test_session() as sess:
+      output_, expected_keypoints_ = sess.run([output, expected_keypoints])
+      self.assertAllClose(output_, expected_keypoints_)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/losses.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/losses.py
new file mode 100644
index 00000000..c7a85ac3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/losses.py
@@ -0,0 +1,674 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Classification and regression loss functions for object detection.
+
+Localization losses:
+ * WeightedL2LocalizationLoss
+ * WeightedSmoothL1LocalizationLoss
+ * WeightedIOULocalizationLoss
+
+Classification losses:
+ * WeightedSigmoidClassificationLoss
+ * WeightedSoftmaxClassificationLoss
+ * WeightedSoftmaxClassificationAgainstLogitsLoss
+ * BootstrappedSigmoidClassificationLoss
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.utils import ops
+
+slim = tf.contrib.slim
+
+
+class Loss(object):
+  """Abstract base class for loss functions."""
+  __metaclass__ = ABCMeta
+
+  def __call__(self,
+               prediction_tensor,
+               target_tensor,
+               ignore_nan_targets=False,
+               losses_mask=None,
+               scope=None,
+               **params):
+    """Call the loss function.
+
+    Args:
+      prediction_tensor: an N-d tensor of shape [batch, anchors, ...]
+        representing predicted quantities.
+      target_tensor: an N-d tensor of shape [batch, anchors, ...] representing
+        regression or classification targets.
+      ignore_nan_targets: whether to ignore nan targets in the loss computation.
+        E.g. can be used if the target tensor is missing groundtruth data that
+        shouldn't be factored into the loss.
+      losses_mask: A [batch] boolean tensor that indicates whether losses should
+        be applied to individual images in the batch. For elements that
+        are True, corresponding prediction, target, and weight tensors will be
+        removed prior to loss computation. If None, no filtering will take place
+        prior to loss computation.
+      scope: Op scope name. Defaults to 'Loss' if None.
+      **params: Additional keyword arguments for specific implementations of
+              the Loss.
+
+    Returns:
+      loss: a tensor representing the value of the loss function.
+    """
+    with tf.name_scope(scope, 'Loss',
+                       [prediction_tensor, target_tensor, params]) as scope:
+      if ignore_nan_targets:
+        target_tensor = tf.where(tf.is_nan(target_tensor),
+                                 prediction_tensor,
+                                 target_tensor)
+      if losses_mask is not None:
+        tensor_multiplier = self._get_loss_multiplier_for_tensor(
+            prediction_tensor,
+            losses_mask)
+        prediction_tensor *= tensor_multiplier
+        target_tensor *= tensor_multiplier
+
+        if 'weights' in params:
+          params['weights'] = tf.convert_to_tensor(params['weights'])
+          weights_multiplier = self._get_loss_multiplier_for_tensor(
+              params['weights'],
+              losses_mask)
+          params['weights'] *= weights_multiplier
+      return self._compute_loss(prediction_tensor, target_tensor, **params)
+
+  def _get_loss_multiplier_for_tensor(self, tensor, losses_mask):
+    loss_multiplier_shape = tf.stack([-1] + [1] * (len(tensor.shape) - 1))
+    return tf.cast(tf.reshape(losses_mask, loss_multiplier_shape), tf.float32)
+
+  @abstractmethod
+  def _compute_loss(self, prediction_tensor, target_tensor, **params):
+    """Method to be overridden by implementations.
+
+    Args:
+      prediction_tensor: a tensor representing predicted quantities
+      target_tensor: a tensor representing regression or classification targets
+      **params: Additional keyword arguments for specific implementations of
+              the Loss.
+
+    Returns:
+      loss: an N-d tensor of shape [batch, anchors, ...] containing the loss per
+        anchor
+    """
+    pass
+
+
+class WeightedL2LocalizationLoss(Loss):
+  """L2 localization loss function with anchorwise output support.
+
+  Loss[b,a] = .5 * ||weights[b,a] * (prediction[b,a,:] - target[b,a,:])||^2
+  """
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        code_size] representing the (encoded) predicted locations of objects.
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        code_size] representing the regression targets
+      weights: a float tensor of shape [batch_size, num_anchors]
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors] tensor
+        representing the value of the loss function.
+    """
+    weighted_diff = (prediction_tensor - target_tensor) * tf.expand_dims(
+        weights, 2)
+    square_diff = 0.5 * tf.square(weighted_diff)
+    return tf.reduce_sum(square_diff, 2)
+
+
+class WeightedSmoothL1LocalizationLoss(Loss):
+  """Smooth L1 localization loss function aka Huber Loss..
+
+  The smooth L1_loss is defined elementwise as .5 x^2 if |x| <= delta and
+  delta * (|x|- 0.5*delta) otherwise, where x is the difference between
+  predictions and target.
+
+  See also Equation (3) in the Fast R-CNN paper by Ross Girshick (ICCV 2015)
+  """
+
+  def __init__(self, delta=1.0):
+    """Constructor.
+
+    Args:
+      delta: delta for smooth L1 loss.
+    """
+    self._delta = delta
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        code_size] representing the (encoded) predicted locations of objects.
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        code_size] representing the regression targets
+      weights: a float tensor of shape [batch_size, num_anchors]
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors] tensor
+        representing the value of the loss function.
+    """
+    return tf.reduce_sum(tf.losses.huber_loss(
+        target_tensor,
+        prediction_tensor,
+        delta=self._delta,
+        weights=tf.expand_dims(weights, axis=2),
+        loss_collection=None,
+        reduction=tf.losses.Reduction.NONE
+    ), axis=2)
+
+
+class WeightedIOULocalizationLoss(Loss):
+  """IOU localization loss function.
+
+  Sums the IOU for corresponding pairs of predicted/groundtruth boxes
+  and for each pair assign a loss of 1 - IOU.  We then compute a weighted
+  sum over all pairs which is returned as the total loss.
+  """
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors, 4]
+        representing the decoded predicted boxes
+      target_tensor: A float tensor of shape [batch_size, num_anchors, 4]
+        representing the decoded target boxes
+      weights: a float tensor of shape [batch_size, num_anchors]
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors] tensor
+        representing the value of the loss function.
+    """
+    predicted_boxes = box_list.BoxList(tf.reshape(prediction_tensor, [-1, 4]))
+    target_boxes = box_list.BoxList(tf.reshape(target_tensor, [-1, 4]))
+    per_anchor_iou_loss = 1.0 - box_list_ops.matched_iou(predicted_boxes,
+                                                         target_boxes)
+    return tf.reshape(weights, [-1]) * per_anchor_iou_loss
+
+
+class WeightedSigmoidClassificationLoss(Loss):
+  """Sigmoid cross entropy classification loss function."""
+
+  def _compute_loss(self,
+                    prediction_tensor,
+                    target_tensor,
+                    weights,
+                    class_indices=None):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing the predicted logits for each class
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing one-hot encoded classification targets
+      weights: a float tensor of shape, either [batch_size, num_anchors,
+        num_classes] or [batch_size, num_anchors, 1]. If the shape is
+        [batch_size, num_anchors, 1], all the classses are equally weighted.
+      class_indices: (Optional) A 1-D integer tensor of class indices.
+        If provided, computes loss only for the specified class indices.
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors, num_classes]
+        representing the value of the loss function.
+    """
+    if class_indices is not None:
+      weights *= tf.reshape(
+          ops.indices_to_dense_vector(class_indices,
+                                      tf.shape(prediction_tensor)[2]),
+          [1, 1, -1])
+    per_entry_cross_ent = (tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=target_tensor, logits=prediction_tensor))
+    return per_entry_cross_ent * weights
+
+
+class SigmoidFocalClassificationLoss(Loss):
+  """Sigmoid focal cross entropy loss.
+
+  Focal loss down-weights well classified examples and focusses on the hard
+  examples. See https://arxiv.org/pdf/1708.02002.pdf for the loss definition.
+  """
+
+  def __init__(self, gamma=2.0, alpha=0.25):
+    """Constructor.
+
+    Args:
+      gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
+      alpha: optional alpha weighting factor to balance positives vs negatives.
+    """
+    self._alpha = alpha
+    self._gamma = gamma
+
+  def _compute_loss(self,
+                    prediction_tensor,
+                    target_tensor,
+                    weights,
+                    class_indices=None):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing the predicted logits for each class
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing one-hot encoded classification targets
+      weights: a float tensor of shape, either [batch_size, num_anchors,
+        num_classes] or [batch_size, num_anchors, 1]. If the shape is
+        [batch_size, num_anchors, 1], all the classses are equally weighted.
+      class_indices: (Optional) A 1-D integer tensor of class indices.
+        If provided, computes loss only for the specified class indices.
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors, num_classes]
+        representing the value of the loss function.
+    """
+    if class_indices is not None:
+      weights *= tf.reshape(
+          ops.indices_to_dense_vector(class_indices,
+                                      tf.shape(prediction_tensor)[2]),
+          [1, 1, -1])
+    per_entry_cross_ent = (tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=target_tensor, logits=prediction_tensor))
+    prediction_probabilities = tf.sigmoid(prediction_tensor)
+    p_t = ((target_tensor * prediction_probabilities) +
+           ((1 - target_tensor) * (1 - prediction_probabilities)))
+    modulating_factor = 1.0
+    if self._gamma:
+      modulating_factor = tf.pow(1.0 - p_t, self._gamma)
+    alpha_weight_factor = 1.0
+    if self._alpha is not None:
+      alpha_weight_factor = (target_tensor * self._alpha +
+                             (1 - target_tensor) * (1 - self._alpha))
+    focal_cross_entropy_loss = (modulating_factor * alpha_weight_factor *
+                                per_entry_cross_ent)
+    return focal_cross_entropy_loss * weights
+
+
+class WeightedSoftmaxClassificationLoss(Loss):
+  """Softmax loss function."""
+
+  def __init__(self, logit_scale=1.0):
+    """Constructor.
+
+    Args:
+      logit_scale: When this value is high, the prediction is "diffused" and
+                   when this value is low, the prediction is made peakier.
+                   (default 1.0)
+
+    """
+    self._logit_scale = logit_scale
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing the predicted logits for each class
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing one-hot encoded classification targets
+      weights: a float tensor of shape, either [batch_size, num_anchors,
+        num_classes] or [batch_size, num_anchors, 1]. If the shape is
+        [batch_size, num_anchors, 1], all the classses are equally weighted.
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors]
+        representing the value of the loss function.
+    """
+    weights = tf.reduce_mean(weights, axis=2)
+    num_classes = prediction_tensor.get_shape().as_list()[-1]
+    prediction_tensor = tf.divide(
+        prediction_tensor, self._logit_scale, name='scale_logit')
+    per_row_cross_ent = (tf.nn.softmax_cross_entropy_with_logits(
+        labels=tf.reshape(target_tensor, [-1, num_classes]),
+        logits=tf.reshape(prediction_tensor, [-1, num_classes])))
+    return tf.reshape(per_row_cross_ent, tf.shape(weights)) * weights
+
+
+class WeightedSoftmaxClassificationAgainstLogitsLoss(Loss):
+  """Softmax loss function against logits.
+
+   Targets are expected to be provided in logits space instead of "one hot" or
+   "probability distribution" space.
+  """
+
+  def __init__(self, logit_scale=1.0):
+    """Constructor.
+
+    Args:
+      logit_scale: When this value is high, the target is "diffused" and
+                   when this value is low, the target is made peakier.
+                   (default 1.0)
+
+    """
+    self._logit_scale = logit_scale
+
+  def _scale_and_softmax_logits(self, logits):
+    """Scale logits then apply softmax."""
+    scaled_logits = tf.divide(logits, self._logit_scale, name='scale_logits')
+    return tf.nn.softmax(scaled_logits, name='convert_scores')
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing the predicted logits for each class
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing logit classification targets
+      weights: a float tensor of shape, either [batch_size, num_anchors,
+        num_classes] or [batch_size, num_anchors, 1]. If the shape is
+        [batch_size, num_anchors, 1], all the classses are equally weighted.
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors]
+        representing the value of the loss function.
+    """
+    weights = tf.reduce_mean(weights, axis=2)
+    num_classes = prediction_tensor.get_shape().as_list()[-1]
+    target_tensor = self._scale_and_softmax_logits(target_tensor)
+    prediction_tensor = tf.divide(prediction_tensor, self._logit_scale,
+                                  name='scale_logits')
+
+    per_row_cross_ent = (tf.nn.softmax_cross_entropy_with_logits(
+        labels=tf.reshape(target_tensor, [-1, num_classes]),
+        logits=tf.reshape(prediction_tensor, [-1, num_classes])))
+    return tf.reshape(per_row_cross_ent, tf.shape(weights)) * weights
+
+
+class BootstrappedSigmoidClassificationLoss(Loss):
+  """Bootstrapped sigmoid cross entropy classification loss function.
+
+  This loss uses a convex combination of training labels and the current model's
+  predictions as training targets in the classification loss. The idea is that
+  as the model improves over time, its predictions can be trusted more and we
+  can use these predictions to mitigate the damage of noisy/incorrect labels,
+  because incorrect labels are likely to be eventually highly inconsistent with
+  other stimuli predicted to have the same label by the model.
+
+  In "soft" bootstrapping, we use all predicted class probabilities, whereas in
+  "hard" bootstrapping, we use the single class favored by the model.
+
+  See also Training Deep Neural Networks On Noisy Labels with Bootstrapping by
+  Reed et al. (ICLR 2015).
+  """
+
+  def __init__(self, alpha, bootstrap_type='soft'):
+    """Constructor.
+
+    Args:
+      alpha: a float32 scalar tensor between 0 and 1 representing interpolation
+        weight
+      bootstrap_type: set to either 'hard' or 'soft' (default)
+
+    Raises:
+      ValueError: if bootstrap_type is not either 'hard' or 'soft'
+    """
+    if bootstrap_type != 'hard' and bootstrap_type != 'soft':
+      raise ValueError('Unrecognized bootstrap_type: must be one of '
+                       '\'hard\' or \'soft.\'')
+    self._alpha = alpha
+    self._bootstrap_type = bootstrap_type
+
+  def _compute_loss(self, prediction_tensor, target_tensor, weights):
+    """Compute loss function.
+
+    Args:
+      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing the predicted logits for each class
+      target_tensor: A float tensor of shape [batch_size, num_anchors,
+        num_classes] representing one-hot encoded classification targets
+      weights: a float tensor of shape, either [batch_size, num_anchors,
+        num_classes] or [batch_size, num_anchors, 1]. If the shape is
+        [batch_size, num_anchors, 1], all the classses are equally weighted.
+
+    Returns:
+      loss: a float tensor of shape [batch_size, num_anchors, num_classes]
+        representing the value of the loss function.
+    """
+    if self._bootstrap_type == 'soft':
+      bootstrap_target_tensor = self._alpha * target_tensor + (
+          1.0 - self._alpha) * tf.sigmoid(prediction_tensor)
+    else:
+      bootstrap_target_tensor = self._alpha * target_tensor + (
+          1.0 - self._alpha) * tf.cast(
+              tf.sigmoid(prediction_tensor) > 0.5, tf.float32)
+    per_entry_cross_ent = (tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=bootstrap_target_tensor, logits=prediction_tensor))
+    return per_entry_cross_ent * weights
+
+
+class HardExampleMiner(object):
+  """Hard example mining for regions in a list of images.
+
+  Implements hard example mining to select a subset of regions to be
+  back-propagated. For each image, selects the regions with highest losses,
+  subject to the condition that a newly selected region cannot have
+  an IOU > iou_threshold with any of the previously selected regions.
+  This can be achieved by re-using a greedy non-maximum suppression algorithm.
+  A constraint on the number of negatives mined per positive region can also be
+  enforced.
+
+  Reference papers: "Training Region-based Object Detectors with Online
+  Hard Example Mining" (CVPR 2016) by Srivastava et al., and
+  "SSD: Single Shot MultiBox Detector" (ECCV 2016) by Liu et al.
+  """
+
+  def __init__(self,
+               num_hard_examples=64,
+               iou_threshold=0.7,
+               loss_type='both',
+               cls_loss_weight=0.05,
+               loc_loss_weight=0.06,
+               max_negatives_per_positive=None,
+               min_negatives_per_image=0):
+    """Constructor.
+
+    The hard example mining implemented by this class can replicate the behavior
+    in the two aforementioned papers (Srivastava et al., and Liu et al).
+    To replicate the A2 paper (Srivastava et al), num_hard_examples is set
+    to a fixed parameter (64 by default) and iou_threshold is set to .7 for
+    running non-max-suppression the predicted boxes prior to hard mining.
+    In order to replicate the SSD paper (Liu et al), num_hard_examples should
+    be set to None, max_negatives_per_positive should be 3 and iou_threshold
+    should be 1.0 (in order to effectively turn off NMS).
+
+    Args:
+      num_hard_examples: maximum number of hard examples to be
+        selected per image (prior to enforcing max negative to positive ratio
+        constraint).  If set to None, all examples obtained after NMS are
+        considered.
+      iou_threshold: minimum intersection over union for an example
+        to be discarded during NMS.
+      loss_type: use only classification losses ('cls', default),
+        localization losses ('loc') or both losses ('both').
+        In the last case, cls_loss_weight and loc_loss_weight are used to
+        compute weighted sum of the two losses.
+      cls_loss_weight: weight for classification loss.
+      loc_loss_weight: weight for location loss.
+      max_negatives_per_positive: maximum number of negatives to retain for
+        each positive anchor. By default, num_negatives_per_positive is None,
+        which means that we do not enforce a prespecified negative:positive
+        ratio.  Note also that num_negatives_per_positives can be a float
+        (and will be converted to be a float even if it is passed in otherwise).
+      min_negatives_per_image: minimum number of negative anchors to sample for
+        a given image. Setting this to a positive number allows sampling
+        negatives in an image without any positive anchors and thus not biased
+        towards at least one detection per image.
+    """
+    self._num_hard_examples = num_hard_examples
+    self._iou_threshold = iou_threshold
+    self._loss_type = loss_type
+    self._cls_loss_weight = cls_loss_weight
+    self._loc_loss_weight = loc_loss_weight
+    self._max_negatives_per_positive = max_negatives_per_positive
+    self._min_negatives_per_image = min_negatives_per_image
+    if self._max_negatives_per_positive is not None:
+      self._max_negatives_per_positive = float(self._max_negatives_per_positive)
+    self._num_positives_list = None
+    self._num_negatives_list = None
+
+  def __call__(self,
+               location_losses,
+               cls_losses,
+               decoded_boxlist_list,
+               match_list=None):
+    """Computes localization and classification losses after hard mining.
+
+    Args:
+      location_losses: a float tensor of shape [num_images, num_anchors]
+        representing anchorwise localization losses.
+      cls_losses: a float tensor of shape [num_images, num_anchors]
+        representing anchorwise classification losses.
+      decoded_boxlist_list: a list of decoded BoxList representing location
+        predictions for each image.
+      match_list: an optional list of matcher.Match objects encoding the match
+        between anchors and groundtruth boxes for each image of the batch,
+        with rows of the Match objects corresponding to groundtruth boxes
+        and columns corresponding to anchors.  Match objects in match_list are
+        used to reference which anchors are positive, negative or ignored.  If
+        self._max_negatives_per_positive exists, these are then used to enforce
+        a prespecified negative to positive ratio.
+
+    Returns:
+      mined_location_loss: a float scalar with sum of localization losses from
+        selected hard examples.
+      mined_cls_loss: a float scalar with sum of classification losses from
+        selected hard examples.
+    Raises:
+      ValueError: if location_losses, cls_losses and decoded_boxlist_list do
+        not have compatible shapes (i.e., they must correspond to the same
+        number of images).
+      ValueError: if match_list is specified but its length does not match
+        len(decoded_boxlist_list).
+    """
+    mined_location_losses = []
+    mined_cls_losses = []
+    location_losses = tf.unstack(location_losses)
+    cls_losses = tf.unstack(cls_losses)
+    num_images = len(decoded_boxlist_list)
+    if not match_list:
+      match_list = num_images * [None]
+    if not len(location_losses) == len(decoded_boxlist_list) == len(cls_losses):
+      raise ValueError('location_losses, cls_losses and decoded_boxlist_list '
+                       'do not have compatible shapes.')
+    if not isinstance(match_list, list):
+      raise ValueError('match_list must be a list.')
+    if len(match_list) != len(decoded_boxlist_list):
+      raise ValueError('match_list must either be None or have '
+                       'length=len(decoded_boxlist_list).')
+    num_positives_list = []
+    num_negatives_list = []
+    for ind, detection_boxlist in enumerate(decoded_boxlist_list):
+      box_locations = detection_boxlist.get()
+      match = match_list[ind]
+      image_losses = cls_losses[ind]
+      if self._loss_type == 'loc':
+        image_losses = location_losses[ind]
+      elif self._loss_type == 'both':
+        image_losses *= self._cls_loss_weight
+        image_losses += location_losses[ind] * self._loc_loss_weight
+      if self._num_hard_examples is not None:
+        num_hard_examples = self._num_hard_examples
+      else:
+        num_hard_examples = detection_boxlist.num_boxes()
+      selected_indices = tf.image.non_max_suppression(
+          box_locations, image_losses, num_hard_examples, self._iou_threshold)
+      if self._max_negatives_per_positive is not None and match:
+        (selected_indices, num_positives,
+         num_negatives) = self._subsample_selection_to_desired_neg_pos_ratio(
+             selected_indices, match, self._max_negatives_per_positive,
+             self._min_negatives_per_image)
+        num_positives_list.append(num_positives)
+        num_negatives_list.append(num_negatives)
+      mined_location_losses.append(
+          tf.reduce_sum(tf.gather(location_losses[ind], selected_indices)))
+      mined_cls_losses.append(
+          tf.reduce_sum(tf.gather(cls_losses[ind], selected_indices)))
+    location_loss = tf.reduce_sum(tf.stack(mined_location_losses))
+    cls_loss = tf.reduce_sum(tf.stack(mined_cls_losses))
+    if match and self._max_negatives_per_positive:
+      self._num_positives_list = num_positives_list
+      self._num_negatives_list = num_negatives_list
+    return (location_loss, cls_loss)
+
+  def summarize(self):
+    """Summarize the number of positives and negatives after mining."""
+    if self._num_positives_list and self._num_negatives_list:
+      avg_num_positives = tf.reduce_mean(tf.to_float(self._num_positives_list))
+      avg_num_negatives = tf.reduce_mean(tf.to_float(self._num_negatives_list))
+      tf.summary.scalar('HardExampleMiner/NumPositives', avg_num_positives)
+      tf.summary.scalar('HardExampleMiner/NumNegatives', avg_num_negatives)
+
+  def _subsample_selection_to_desired_neg_pos_ratio(self,
+                                                    indices,
+                                                    match,
+                                                    max_negatives_per_positive,
+                                                    min_negatives_per_image=0):
+    """Subsample a collection of selected indices to a desired neg:pos ratio.
+
+    This function takes a subset of M indices (indexing into a large anchor
+    collection of N anchors where M<N) which are labeled as positive/negative
+    via a Match object (matched indices are positive, unmatched indices
+    are negative).  It returns a subset of the provided indices retaining all
+    positives as well as up to the first K negatives, where:
+      K=floor(num_negative_per_positive * num_positives).
+
+    For example, if indices=[2, 4, 5, 7, 9, 10] (indexing into 12 anchors),
+    with positives=[2, 5] and negatives=[4, 7, 9, 10] and
+    num_negatives_per_positive=1, then the returned subset of indices
+    is [2, 4, 5, 7].
+
+    Args:
+      indices: An integer tensor of shape [M] representing a collection
+        of selected anchor indices
+      match: A matcher.Match object encoding the match between anchors and
+        groundtruth boxes for a given image, with rows of the Match objects
+        corresponding to groundtruth boxes and columns corresponding to anchors.
+      max_negatives_per_positive: (float) maximum number of negatives for
+        each positive anchor.
+      min_negatives_per_image: minimum number of negative anchors for a given
+        image. Allow sampling negatives in image without any positive anchors.
+
+    Returns:
+      selected_indices: An integer tensor of shape [M'] representing a
+        collection of selected anchor indices with M' <= M.
+      num_positives: An integer tensor representing the number of positive
+        examples in selected set of indices.
+      num_negatives: An integer tensor representing the number of negative
+        examples in selected set of indices.
+    """
+    positives_indicator = tf.gather(match.matched_column_indicator(), indices)
+    negatives_indicator = tf.gather(match.unmatched_column_indicator(), indices)
+    num_positives = tf.reduce_sum(tf.to_int32(positives_indicator))
+    max_negatives = tf.maximum(min_negatives_per_image,
+                               tf.to_int32(max_negatives_per_positive *
+                                           tf.to_float(num_positives)))
+    topk_negatives_indicator = tf.less_equal(
+        tf.cumsum(tf.to_int32(negatives_indicator)), max_negatives)
+    subsampled_selection_indices = tf.where(
+        tf.logical_or(positives_indicator, topk_negatives_indicator))
+    num_negatives = tf.size(subsampled_selection_indices) - num_positives
+    return (tf.reshape(tf.gather(indices, subsampled_selection_indices), [-1]),
+            num_positives, num_negatives)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/losses_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/losses_test.py
new file mode 100644
index 00000000..548d93cb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/losses_test.py
@@ -0,0 +1,1276 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for google3.research.vale.object_detection.losses."""
+import math
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import losses
+from object_detection.core import matcher
+
+
+class WeightedL2LocalizationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectWeightedLoss(self):
+    batch_size = 3
+    num_anchors = 10
+    code_size = 4
+    prediction_tensor = tf.ones([batch_size, num_anchors, code_size])
+    target_tensor = tf.zeros([batch_size, num_anchors, code_size])
+    weights = tf.constant([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], tf.float32)
+    loss_op = losses.WeightedL2LocalizationLoss()
+    loss = tf.reduce_sum(loss_op(prediction_tensor, target_tensor,
+                                 weights=weights))
+
+    expected_loss = (3 * 5 * 4) / 2.0
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, expected_loss)
+
+  def testReturnsCorrectAnchorwiseLoss(self):
+    batch_size = 3
+    num_anchors = 16
+    code_size = 4
+    prediction_tensor = tf.ones([batch_size, num_anchors, code_size])
+    target_tensor = tf.zeros([batch_size, num_anchors, code_size])
+    weights = tf.ones([batch_size, num_anchors])
+    loss_op = losses.WeightedL2LocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+
+    expected_loss = np.ones((batch_size, num_anchors)) * 2
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, expected_loss)
+
+  def testReturnsCorrectNanLoss(self):
+    batch_size = 3
+    num_anchors = 10
+    code_size = 4
+    prediction_tensor = tf.ones([batch_size, num_anchors, code_size])
+    target_tensor = tf.concat([
+        tf.zeros([batch_size, num_anchors, code_size / 2]),
+        tf.ones([batch_size, num_anchors, code_size / 2]) * np.nan
+    ], axis=2)
+    weights = tf.ones([batch_size, num_anchors])
+    loss_op = losses.WeightedL2LocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
+                   ignore_nan_targets=True)
+    loss = tf.reduce_sum(loss)
+
+    expected_loss = (3 * 5 * 4) / 2.0
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, expected_loss)
+
+  def testReturnsCorrectWeightedLossWithLossesMask(self):
+    batch_size = 4
+    num_anchors = 10
+    code_size = 4
+    prediction_tensor = tf.ones([batch_size, num_anchors, code_size])
+    target_tensor = tf.zeros([batch_size, num_anchors, code_size])
+    weights = tf.constant([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], tf.float32)
+    losses_mask = tf.constant([True, False, True, True], tf.bool)
+    loss_op = losses.WeightedL2LocalizationLoss()
+    loss = tf.reduce_sum(loss_op(prediction_tensor, target_tensor,
+                                 weights=weights, losses_mask=losses_mask))
+
+    expected_loss = (3 * 5 * 4) / 2.0
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, expected_loss)
+
+
+class WeightedSmoothL1LocalizationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLoss(self):
+    batch_size = 2
+    num_anchors = 3
+    code_size = 4
+    prediction_tensor = tf.constant([[[2.5, 0, .4, 0],
+                                      [0, 0, 0, 0],
+                                      [0, 2.5, 0, .4]],
+                                     [[3.5, 0, 0, 0],
+                                      [0, .4, 0, .9],
+                                      [0, 0, 1.5, 0]]], tf.float32)
+    target_tensor = tf.zeros([batch_size, num_anchors, code_size])
+    weights = tf.constant([[2, 1, 1],
+                           [0, 3, 0]], tf.float32)
+    loss_op = losses.WeightedSmoothL1LocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = 7.695
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossWithLossesMask(self):
+    batch_size = 3
+    num_anchors = 3
+    code_size = 4
+    prediction_tensor = tf.constant([[[2.5, 0, .4, 0],
+                                      [0, 0, 0, 0],
+                                      [0, 2.5, 0, .4]],
+                                     [[3.5, 0, 0, 0],
+                                      [0, .4, 0, .9],
+                                      [0, 0, 1.5, 0]],
+                                     [[3.5, 7., 0, 0],
+                                      [0, .4, 0, .9],
+                                      [2.2, 2.2, 1.5, 0]]], tf.float32)
+    target_tensor = tf.zeros([batch_size, num_anchors, code_size])
+    weights = tf.constant([[2, 1, 1],
+                           [0, 3, 0],
+                           [4, 3, 0]], tf.float32)
+    losses_mask = tf.constant([True, True, False], tf.bool)
+    loss_op = losses.WeightedSmoothL1LocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
+                   losses_mask=losses_mask)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = 7.695
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+
+class WeightedIOULocalizationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLoss(self):
+    prediction_tensor = tf.constant([[[1.5, 0, 2.4, 1],
+                                      [0, 0, 1, 1],
+                                      [0, 0, .5, .25]]])
+    target_tensor = tf.constant([[[1.5, 0, 2.4, 1],
+                                  [0, 0, 1, 1],
+                                  [50, 50, 500.5, 100.25]]])
+    weights = [[1.0, .5, 2.0]]
+    loss_op = losses.WeightedIOULocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+    exp_loss = 2.0
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossWithNoLabels(self):
+    prediction_tensor = tf.constant([[[1.5, 0, 2.4, 1],
+                                      [0, 0, 1, 1],
+                                      [0, 0, .5, .25]]])
+    target_tensor = tf.constant([[[1.5, 0, 2.4, 1],
+                                  [0, 0, 1, 1],
+                                  [50, 50, 500.5, 100.25]]])
+    weights = [[1.0, .5, 2.0]]
+    losses_mask = tf.constant([False], tf.bool)
+    loss_op = losses.WeightedIOULocalizationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
+                   losses_mask=losses_mask)
+    loss = tf.reduce_sum(loss)
+    exp_loss = 0.0
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+
+class WeightedSigmoidClassificationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    loss_op = losses.WeightedSigmoidClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = -2 * math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    loss_op = losses.WeightedSigmoidClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss, axis=2)
+
+    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
+                          [-math.log(.5), 0, 0, 0]])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossWithClassIndices(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100, 100],
+                                      [100, -100, -100, -100],
+                                      [100, 0, -100, 100],
+                                      [-100, -100, 100, -100]],
+                                     [[-100, 0, 100, 100],
+                                      [-100, 100, -100, 100],
+                                      [100, 100, 100, 100],
+                                      [0, 0, -1, 100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0, 0],
+                                  [1, 0, 0, 1],
+                                  [1, 0, 0, 0],
+                                  [0, 0, 1, 1]],
+                                 [[0, 0, 1, 0],
+                                  [0, 1, 0, 0],
+                                  [1, 1, 1, 0],
+                                  [1, 0, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1, 1],
+                            [1, 1, 1, 1],
+                            [1, 1, 1, 1],
+                            [1, 1, 1, 1]],
+                           [[1, 1, 1, 1],
+                            [1, 1, 1, 1],
+                            [1, 1, 1, 1],
+                            [0, 0, 0, 0]]], tf.float32)
+    # Ignores the last class.
+    class_indices = tf.constant([0, 1, 2], tf.int32)
+    loss_op = losses.WeightedSigmoidClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
+                   class_indices=class_indices)
+    loss = tf.reduce_sum(loss, axis=2)
+
+    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
+                          [-math.log(.5), 0, 0, 0]])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossWithLossesMask(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]],
+                                 [[0, 0, 0],
+                                  [0, 0, 0],
+                                  [0, 0, 0],
+                                  [0, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    losses_mask = tf.constant([True, True, False], tf.bool)
+
+    loss_op = losses.WeightedSigmoidClassificationLoss()
+    loss_per_anchor = loss_op(prediction_tensor, target_tensor, weights=weights,
+                              losses_mask=losses_mask)
+    loss = tf.reduce_sum(loss_per_anchor)
+
+    exp_loss = -2 * math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllEqual(prediction_tensor.shape.as_list(),
+                          loss_per_anchor.shape.as_list())
+      self.assertAllEqual(target_tensor.shape.as_list(),
+                          loss_per_anchor.shape.as_list())
+      self.assertAllClose(loss_output, exp_loss)
+
+
+def _logit(probability):
+  return math.log(probability / (1. - probability))
+
+
+class SigmoidFocalClassificationLossTest(tf.test.TestCase):
+
+  def testEasyExamplesProduceSmallLossComparedToSigmoidXEntropy(self):
+    prediction_tensor = tf.constant([[[_logit(0.97)],
+                                      [_logit(0.91)],
+                                      [_logit(0.73)],
+                                      [_logit(0.27)],
+                                      [_logit(0.09)],
+                                      [_logit(0.03)]]], tf.float32)
+    target_tensor = tf.constant([[[1],
+                                  [1],
+                                  [1],
+                                  [0],
+                                  [0],
+                                  [0]]], tf.float32)
+    weights = tf.constant([[[1], [1], [1], [1], [1], [1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=None)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights), axis=2)
+    sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor,
+                                                 target_tensor,
+                                                 weights=weights), axis=2)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      order_of_ratio = np.power(10,
+                                np.floor(np.log10(sigmoid_loss / focal_loss)))
+      self.assertAllClose(order_of_ratio, [[1000, 100, 10, 10, 100, 1000]])
+
+  def testHardExamplesProduceLossComparableToSigmoidXEntropy(self):
+    prediction_tensor = tf.constant([[[_logit(0.55)],
+                                      [_logit(0.52)],
+                                      [_logit(0.50)],
+                                      [_logit(0.48)],
+                                      [_logit(0.45)]]], tf.float32)
+    target_tensor = tf.constant([[[1],
+                                  [1],
+                                  [1],
+                                  [0],
+                                  [0]]], tf.float32)
+    weights = tf.constant([[[1], [1], [1], [1], [1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=None)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights), axis=2)
+    sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor,
+                                                 target_tensor,
+                                                 weights=weights), axis=2)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      order_of_ratio = np.power(10,
+                                np.floor(np.log10(sigmoid_loss / focal_loss)))
+      self.assertAllClose(order_of_ratio, [[1., 1., 1., 1., 1.]])
+
+  def testNonAnchorWiseOutputComparableToSigmoidXEntropy(self):
+    prediction_tensor = tf.constant([[[_logit(0.55)],
+                                      [_logit(0.52)],
+                                      [_logit(0.50)],
+                                      [_logit(0.48)],
+                                      [_logit(0.45)]]], tf.float32)
+    target_tensor = tf.constant([[[1],
+                                  [1],
+                                  [1],
+                                  [0],
+                                  [0]]], tf.float32)
+    weights = tf.constant([[[1], [1], [1], [1], [1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=None)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights))
+    sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor,
+                                                 target_tensor,
+                                                 weights=weights))
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      order_of_ratio = np.power(10,
+                                np.floor(np.log10(sigmoid_loss / focal_loss)))
+      self.assertAlmostEqual(order_of_ratio, 1.)
+
+  def testIgnoreNegativeExampleLossViaAlphaMultiplier(self):
+    prediction_tensor = tf.constant([[[_logit(0.55)],
+                                      [_logit(0.52)],
+                                      [_logit(0.50)],
+                                      [_logit(0.48)],
+                                      [_logit(0.45)]]], tf.float32)
+    target_tensor = tf.constant([[[1],
+                                  [1],
+                                  [1],
+                                  [0],
+                                  [0]]], tf.float32)
+    weights = tf.constant([[[1], [1], [1], [1], [1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=1.0)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights), axis=2)
+    sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor,
+                                                 target_tensor,
+                                                 weights=weights), axis=2)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      self.assertAllClose(focal_loss[0][3:], [0., 0.])
+      order_of_ratio = np.power(10,
+                                np.floor(np.log10(sigmoid_loss[0][:3] /
+                                                  focal_loss[0][:3])))
+      self.assertAllClose(order_of_ratio, [1., 1., 1.])
+
+  def testIgnorePositiveExampleLossViaAlphaMultiplier(self):
+    prediction_tensor = tf.constant([[[_logit(0.55)],
+                                      [_logit(0.52)],
+                                      [_logit(0.50)],
+                                      [_logit(0.48)],
+                                      [_logit(0.45)]]], tf.float32)
+    target_tensor = tf.constant([[[1],
+                                  [1],
+                                  [1],
+                                  [0],
+                                  [0]]], tf.float32)
+    weights = tf.constant([[[1], [1], [1], [1], [1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(gamma=2.0, alpha=0.0)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights), axis=2)
+    sigmoid_loss = tf.reduce_sum(sigmoid_loss_op(prediction_tensor,
+                                                 target_tensor,
+                                                 weights=weights), axis=2)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      self.assertAllClose(focal_loss[0][:3], [0., 0., 0.])
+      order_of_ratio = np.power(10,
+                                np.floor(np.log10(sigmoid_loss[0][3:] /
+                                                  focal_loss[0][3:])))
+      self.assertAllClose(order_of_ratio, [1., 1.])
+
+  def testSimilarToSigmoidXEntropyWithHalfAlphaAndZeroGammaUpToAScale(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=0.5, gamma=0.0)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = focal_loss_op(prediction_tensor, target_tensor,
+                               weights=weights)
+    sigmoid_loss = sigmoid_loss_op(prediction_tensor, target_tensor,
+                                   weights=weights)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      self.assertAllClose(sigmoid_loss, focal_loss * 2)
+
+  def testSameAsSigmoidXEntropyWithNoAlphaAndZeroGamma(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=None, gamma=0.0)
+    sigmoid_loss_op = losses.WeightedSigmoidClassificationLoss()
+    focal_loss = focal_loss_op(prediction_tensor, target_tensor,
+                               weights=weights)
+    sigmoid_loss = sigmoid_loss_op(prediction_tensor, target_tensor,
+                                   weights=weights)
+
+    with self.test_session() as sess:
+      sigmoid_loss, focal_loss = sess.run([sigmoid_loss, focal_loss])
+      self.assertAllClose(sigmoid_loss, focal_loss)
+
+  def testExpectedLossWithAlphaOneAndZeroGamma(self):
+    # All zeros correspond to 0.5 probability.
+    prediction_tensor = tf.constant([[[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]],
+                                     [[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=1.0, gamma=0.0)
+
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights))
+    with self.test_session() as sess:
+      focal_loss = sess.run(focal_loss)
+      self.assertAllClose(
+          (-math.log(.5) *  # x-entropy per class per anchor
+           1.0 *            # alpha
+           8),              # positives from 8 anchors
+          focal_loss)
+
+  def testExpectedLossWithAlpha75AndZeroGamma(self):
+    # All zeros correspond to 0.5 probability.
+    prediction_tensor = tf.constant([[[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]],
+                                     [[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=0.75, gamma=0.0)
+
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights))
+    with self.test_session() as sess:
+      focal_loss = sess.run(focal_loss)
+      self.assertAllClose(
+          (-math.log(.5) *  # x-entropy per class per anchor.
+           ((0.75 *         # alpha for positives.
+             8) +           # positives from 8 anchors.
+            (0.25 *         # alpha for negatives.
+             8 * 2))),      # negatives from 8 anchors for two classes.
+          focal_loss)
+
+  def testExpectedLossWithLossesMask(self):
+    # All zeros correspond to 0.5 probability.
+    prediction_tensor = tf.constant([[[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]],
+                                     [[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]],
+                                     [[0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0],
+                                      [0, 0, 0]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0]],
+                                 [[1, 0, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    losses_mask = tf.constant([True, True, False], tf.bool)
+    focal_loss_op = losses.SigmoidFocalClassificationLoss(alpha=0.75, gamma=0.0)
+
+    focal_loss = tf.reduce_sum(focal_loss_op(prediction_tensor, target_tensor,
+                                             weights=weights,
+                                             losses_mask=losses_mask))
+    with self.test_session() as sess:
+      focal_loss = sess.run(focal_loss)
+      self.assertAllClose(
+          (-math.log(.5) *  # x-entropy per class per anchor.
+           ((0.75 *         # alpha for positives.
+             8) +           # positives from 8 anchors.
+            (0.25 *         # alpha for negatives.
+             8 * 2))),      # negatives from 8 anchors for two classes.
+          focal_loss)
+
+
+class WeightedSoftmaxClassificationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [0, 1, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [0.5, 0.5, 0.5],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    loss_op = losses.WeightedSoftmaxClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = - 1.5 * math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [0, 1, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [0.5, 0.5, 0.5],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    loss_op = losses.WeightedSoftmaxClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+
+    exp_loss = np.matrix([[0, 0, - 0.5 * math.log(.5), 0],
+                          [-math.log(.5), 0, 0, 0]])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLossWithHighLogitScaleSetting(self):
+    """At very high logit_scale, all predictions will be ~0.33."""
+    # TODO(yonib): Also test logit_scale with anchorwise=False.
+    logit_scale = 10e16
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [0, 1, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    loss_op = losses.WeightedSoftmaxClassificationLoss(logit_scale=logit_scale)
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+
+    uniform_distribution_loss = - math.log(.33333333333)
+    exp_loss = np.matrix([[uniform_distribution_loss] * 4,
+                          [uniform_distribution_loss] * 4])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossWithLossesMask(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [0, 1, 0],
+                                  [1, 0, 0]],
+                                 [[1, 0, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [0.5, 0.5, 0.5],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]]], tf.float32)
+    losses_mask = tf.constant([True, True, False], tf.bool)
+    loss_op = losses.WeightedSoftmaxClassificationLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
+                   losses_mask=losses_mask)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = - 1.5 * math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+
+class WeightedSoftmaxClassificationAgainstLogitsLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+
+    target_tensor = tf.constant([[[-100, 100, -100],
+                                  [100, -100, -100],
+                                  [100, -100, -100],
+                                  [-100, -100, 100]],
+                                 [[-100, -100, 100],
+                                  [-100, 100, -100],
+                                  [-100, 100, -100],
+                                  [100, -100, -100]]], tf.float32)
+    weights = tf.constant([[1, 1, .5, 1],
+                           [1, 1, 1, 1]], tf.float32)
+    weights_shape = tf.shape(weights)
+    weights_multiple = tf.concat(
+        [tf.ones_like(weights_shape), tf.constant([3])],
+        axis=0)
+    weights = tf.tile(tf.expand_dims(weights, 2), weights_multiple)
+    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+
+    exp_loss = - 1.5 * math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[-100, 100, -100],
+                                  [100, -100, -100],
+                                  [100, -100, -100],
+                                  [-100, -100, 100]],
+                                 [[-100, -100, 100],
+                                  [-100, 100, -100],
+                                  [-100, 100, -100],
+                                  [100, -100, -100]]], tf.float32)
+    weights = tf.constant([[1, 1, .5, 1],
+                           [1, 1, 1, 0]], tf.float32)
+    weights_shape = tf.shape(weights)
+    weights_multiple = tf.concat(
+        [tf.ones_like(weights_shape), tf.constant([3])],
+        axis=0)
+    weights = tf.tile(tf.expand_dims(weights, 2), weights_multiple)
+    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+
+    exp_loss = np.matrix([[0, 0, - 0.5 * math.log(.5), 0],
+                          [-math.log(.5), 0, 0, 0]])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLossWithLogitScaleSetting(self):
+    logit_scale = 100.
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [0, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 0],
+                                      [-100, 100, -100],
+                                      [-100, 100, -100],
+                                      [100, -100, -100]]], tf.float32)
+    target_tensor = tf.constant([[[-100, 100, -100],
+                                  [100, -100, -100],
+                                  [0, 0, -100],
+                                  [-100, -100, 100]],
+                                 [[-100, 0, 0],
+                                  [-100, 100, -100],
+                                  [-100, 100, -100],
+                                  [100, -100, -100]]], tf.float32)
+    weights = tf.constant([[1, 1, .5, 1],
+                           [1, 1, 1, 0]], tf.float32)
+    weights_shape = tf.shape(weights)
+    weights_multiple = tf.concat(
+        [tf.ones_like(weights_shape), tf.constant([3])],
+        axis=0)
+    weights = tf.tile(tf.expand_dims(weights, 2), weights_multiple)
+    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
+        logit_scale=logit_scale)
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+
+    # find softmax of the two prediction types above
+    softmax_pred1 = [np.exp(-1), np.exp(-1), np.exp(1)]
+    softmax_pred1 /= sum(softmax_pred1)
+    softmax_pred2 = [np.exp(0), np.exp(0), np.exp(-1)]
+    softmax_pred2 /= sum(softmax_pred2)
+
+    # compute the expected cross entropy for perfect matches
+    exp_entropy1 = sum(
+        [-x*np.log(x) for x in softmax_pred1])
+    exp_entropy2 = sum(
+        [-x*np.log(x) for x in softmax_pred2])
+
+    # weighted expected losses
+    exp_loss = np.matrix(
+        [[exp_entropy1, exp_entropy1, exp_entropy2*.5, exp_entropy1],
+         [exp_entropy2, exp_entropy1, exp_entropy1, 0.]])
+
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+
+class BootstrappedSigmoidClassificationLossTest(tf.test.TestCase):
+
+  def testReturnsCorrectLossSoftBootstrapping(self):
+    prediction_tensor = tf.constant([[[-100, 100, 0],
+                                      [100, -100, -100],
+                                      [100, -100, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, -100, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    alpha = tf.constant(.5, tf.float32)
+    loss_op = losses.BootstrappedSigmoidClassificationLoss(
+        alpha, bootstrap_type='soft')
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+    exp_loss = -math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectLossHardBootstrapping(self):
+    prediction_tensor = tf.constant([[[-100, 100, 0],
+                                      [100, -100, -100],
+                                      [100, -100, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, -100, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    alpha = tf.constant(.5, tf.float32)
+    loss_op = losses.BootstrappedSigmoidClassificationLoss(
+        alpha, bootstrap_type='hard')
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss)
+    exp_loss = -math.log(.5)
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+  def testReturnsCorrectAnchorWiseLoss(self):
+    prediction_tensor = tf.constant([[[-100, 100, -100],
+                                      [100, -100, -100],
+                                      [100, 0, -100],
+                                      [-100, -100, 100]],
+                                     [[-100, 0, 100],
+                                      [-100, 100, -100],
+                                      [100, 100, 100],
+                                      [0, 0, -1]]], tf.float32)
+    target_tensor = tf.constant([[[0, 1, 0],
+                                  [1, 0, 0],
+                                  [1, 0, 0],
+                                  [0, 0, 1]],
+                                 [[0, 0, 1],
+                                  [0, 1, 0],
+                                  [1, 1, 1],
+                                  [1, 0, 0]]], tf.float32)
+    weights = tf.constant([[[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1]],
+                           [[1, 1, 1],
+                            [1, 1, 1],
+                            [1, 1, 1],
+                            [0, 0, 0]]], tf.float32)
+    alpha = tf.constant(.5, tf.float32)
+    loss_op = losses.BootstrappedSigmoidClassificationLoss(
+        alpha, bootstrap_type='hard')
+    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
+    loss = tf.reduce_sum(loss, axis=2)
+    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
+                          [-math.log(.5), 0, 0, 0]])
+    with self.test_session() as sess:
+      loss_output = sess.run(loss)
+      self.assertAllClose(loss_output, exp_loss)
+
+
+class HardExampleMinerTest(tf.test.TestCase):
+
+  def testHardMiningWithSingleLossType(self):
+    location_losses = tf.constant([[100, 90, 80, 0],
+                                   [0, 1, 2, 3]], tf.float32)
+    cls_losses = tf.constant([[0, 10, 50, 110],
+                              [9, 6, 3, 0]], tf.float32)
+    box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9]], tf.float32)
+    decoded_boxlist_list = []
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    # Uses only location loss to select hard examples
+    loss_op = losses.HardExampleMiner(num_hard_examples=1,
+                                      iou_threshold=0.0,
+                                      loss_type='loc',
+                                      cls_loss_weight=1,
+                                      loc_loss_weight=1)
+    (loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
+                                   decoded_boxlist_list)
+    exp_loc_loss = 100 + 3
+    exp_cls_loss = 0 + 0
+    with self.test_session() as sess:
+      loc_loss_output = sess.run(loc_loss)
+      self.assertAllClose(loc_loss_output, exp_loc_loss)
+      cls_loss_output = sess.run(cls_loss)
+      self.assertAllClose(cls_loss_output, exp_cls_loss)
+
+  def testHardMiningWithBothLossType(self):
+    location_losses = tf.constant([[100, 90, 80, 0],
+                                   [0, 1, 2, 3]], tf.float32)
+    cls_losses = tf.constant([[0, 10, 50, 110],
+                              [9, 6, 3, 0]], tf.float32)
+    box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9]], tf.float32)
+    decoded_boxlist_list = []
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    loss_op = losses.HardExampleMiner(num_hard_examples=1,
+                                      iou_threshold=0.0,
+                                      loss_type='both',
+                                      cls_loss_weight=1,
+                                      loc_loss_weight=1)
+    (loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
+                                   decoded_boxlist_list)
+    exp_loc_loss = 80 + 0
+    exp_cls_loss = 50 + 9
+    with self.test_session() as sess:
+      loc_loss_output = sess.run(loc_loss)
+      self.assertAllClose(loc_loss_output, exp_loc_loss)
+      cls_loss_output = sess.run(cls_loss)
+      self.assertAllClose(cls_loss_output, exp_cls_loss)
+
+  def testHardMiningNMS(self):
+    location_losses = tf.constant([[100, 90, 80, 0],
+                                   [0, 1, 2, 3]], tf.float32)
+    cls_losses = tf.constant([[0, 10, 50, 110],
+                              [9, 6, 3, 0]], tf.float32)
+    box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
+                               [0.9, 0.9, 0.99, 0.99],
+                               [0.1, 0.1, 0.9, 0.9],
+                               [0.1, 0.1, 0.9, 0.9]], tf.float32)
+    decoded_boxlist_list = []
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+    loss_op = losses.HardExampleMiner(num_hard_examples=2,
+                                      iou_threshold=0.5,
+                                      loss_type='cls',
+                                      cls_loss_weight=1,
+                                      loc_loss_weight=1)
+    (loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
+                                   decoded_boxlist_list)
+    exp_loc_loss = 0 + 90 + 0 + 1
+    exp_cls_loss = 110 + 10 + 9 + 6
+    with self.test_session() as sess:
+      loc_loss_output = sess.run(loc_loss)
+      self.assertAllClose(loc_loss_output, exp_loc_loss)
+      cls_loss_output = sess.run(cls_loss)
+      self.assertAllClose(cls_loss_output, exp_cls_loss)
+
+  def testEnforceNegativesPerPositiveRatio(self):
+    location_losses = tf.constant([[100, 90, 80, 0, 1, 2,
+                                    3, 10, 20, 100, 20, 3]], tf.float32)
+    cls_losses = tf.constant([[0, 0, 100, 0, 90, 70,
+                               0, 60, 0, 17, 13, 0]], tf.float32)
+    box_corners = tf.constant([[0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.5, 0.1],
+                               [0.0, 0.0, 0.6, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.8, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 1.0, 0.1],
+                               [0.0, 0.0, 1.1, 0.1],
+                               [0.0, 0.0, 0.2, 0.1]], tf.float32)
+    match_results = tf.constant([2, -1, 0, -1, -1, 1, -1, -1, -1, -1, -1, 3])
+    match_list = [matcher.Match(match_results)]
+    decoded_boxlist_list = []
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+
+    max_negatives_per_positive_list = [0.0, 0.5, 1.0, 1.5, 10]
+    exp_loc_loss_list = [80 + 2,
+                         80 + 1 + 2,
+                         80 + 1 + 2 + 10,
+                         80 + 1 + 2 + 10 + 100,
+                         80 + 1 + 2 + 10 + 100 + 20]
+    exp_cls_loss_list = [100 + 70,
+                         100 + 90 + 70,
+                         100 + 90 + 70 + 60,
+                         100 + 90 + 70 + 60 + 17,
+                         100 + 90 + 70 + 60 + 17 + 13]
+
+    for max_negatives_per_positive, exp_loc_loss, exp_cls_loss in zip(
+        max_negatives_per_positive_list, exp_loc_loss_list, exp_cls_loss_list):
+      loss_op = losses.HardExampleMiner(
+          num_hard_examples=None, iou_threshold=0.9999, loss_type='cls',
+          cls_loss_weight=1, loc_loss_weight=1,
+          max_negatives_per_positive=max_negatives_per_positive)
+      (loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
+                                     decoded_boxlist_list, match_list)
+      loss_op.summarize()
+
+      with self.test_session() as sess:
+        loc_loss_output = sess.run(loc_loss)
+        self.assertAllClose(loc_loss_output, exp_loc_loss)
+        cls_loss_output = sess.run(cls_loss)
+        self.assertAllClose(cls_loss_output, exp_cls_loss)
+
+  def testEnforceNegativesPerPositiveRatioWithMinNegativesPerImage(self):
+    location_losses = tf.constant([[100, 90, 80, 0, 1, 2,
+                                    3, 10, 20, 100, 20, 3]], tf.float32)
+    cls_losses = tf.constant([[0, 0, 100, 0, 90, 70,
+                               0, 60, 0, 17, 13, 0]], tf.float32)
+    box_corners = tf.constant([[0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.5, 0.1],
+                               [0.0, 0.0, 0.6, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 0.8, 0.1],
+                               [0.0, 0.0, 0.2, 0.1],
+                               [0.0, 0.0, 1.0, 0.1],
+                               [0.0, 0.0, 1.1, 0.1],
+                               [0.0, 0.0, 0.2, 0.1]], tf.float32)
+    match_results = tf.constant([-1] * 12)
+    match_list = [matcher.Match(match_results)]
+    decoded_boxlist_list = []
+    decoded_boxlist_list.append(box_list.BoxList(box_corners))
+
+    min_negatives_per_image_list = [0, 1, 2, 4, 5, 6]
+    exp_loc_loss_list = [0,
+                         80,
+                         80 + 1,
+                         80 + 1 + 2 + 10,
+                         80 + 1 + 2 + 10 + 100,
+                         80 + 1 + 2 + 10 + 100 + 20]
+    exp_cls_loss_list = [0,
+                         100,
+                         100 + 90,
+                         100 + 90 + 70 + 60,
+                         100 + 90 + 70 + 60 + 17,
+                         100 + 90 + 70 + 60 + 17 + 13]
+
+    for min_negatives_per_image, exp_loc_loss, exp_cls_loss in zip(
+        min_negatives_per_image_list, exp_loc_loss_list, exp_cls_loss_list):
+      loss_op = losses.HardExampleMiner(
+          num_hard_examples=None, iou_threshold=0.9999, loss_type='cls',
+          cls_loss_weight=1, loc_loss_weight=1,
+          max_negatives_per_positive=3,
+          min_negatives_per_image=min_negatives_per_image)
+      (loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
+                                     decoded_boxlist_list, match_list)
+      with self.test_session() as sess:
+        loc_loss_output = sess.run(loc_loss)
+        self.assertAllClose(loc_loss_output, exp_loc_loss)
+        cls_loss_output = sess.run(cls_loss)
+        self.assertAllClose(cls_loss_output, exp_cls_loss)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher.py
new file mode 100644
index 00000000..61b5da61
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher.py
@@ -0,0 +1,262 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Matcher interface and Match class.
+
+This module defines the Matcher interface and the Match object. The job of the
+matcher is to match row and column indices based on the similarity matrix and
+other optional parameters. Each column is matched to at most one row. There
+are three possibilities for the matching:
+
+1) match: A column matches a row.
+2) no_match: A column does not match any row.
+3) ignore: A column that is neither 'match' nor no_match.
+
+The ignore case is regularly encountered in object detection: when an anchor has
+a relatively small overlap with a ground-truth box, one neither wants to
+consider this box a positive example (match) nor a negative example (no match).
+
+The Match class is used to store the match results and it provides simple apis
+to query the results.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+import tensorflow as tf
+
+from object_detection.utils import ops
+
+
+class Match(object):
+  """Class to store results from the matcher.
+
+  This class is used to store the results from the matcher. It provides
+  convenient methods to query the matching results.
+  """
+
+  def __init__(self, match_results, use_matmul_gather=False):
+    """Constructs a Match object.
+
+    Args:
+      match_results: Integer tensor of shape [N] with (1) match_results[i]>=0,
+        meaning that column i is matched with row match_results[i].
+        (2) match_results[i]=-1, meaning that column i is not matched.
+        (3) match_results[i]=-2, meaning that column i is ignored.
+      use_matmul_gather: Use matrix multiplication based gather instead of
+        standard tf.gather. (Default: False).
+
+    Raises:
+      ValueError: if match_results does not have rank 1 or is not an
+        integer int32 scalar tensor
+    """
+    if match_results.shape.ndims != 1:
+      raise ValueError('match_results should have rank 1')
+    if match_results.dtype != tf.int32:
+      raise ValueError('match_results should be an int32 or int64 scalar '
+                       'tensor')
+    self._match_results = match_results
+    self._gather_op = tf.gather
+    if use_matmul_gather:
+      self._gather_op = ops.matmul_gather_on_zeroth_axis
+
+  @property
+  def match_results(self):
+    """The accessor for match results.
+
+    Returns:
+      the tensor which encodes the match results.
+    """
+    return self._match_results
+
+  def matched_column_indices(self):
+    """Returns column indices that match to some row.
+
+    The indices returned by this op are always sorted in increasing order.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return self._reshape_and_cast(tf.where(tf.greater(self._match_results, -1)))
+
+  def matched_column_indicator(self):
+    """Returns column indices that are matched.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return tf.greater_equal(self._match_results, 0)
+
+  def num_matched_columns(self):
+    """Returns number (int32 scalar tensor) of matched columns."""
+    return tf.size(self.matched_column_indices())
+
+  def unmatched_column_indices(self):
+    """Returns column indices that do not match any row.
+
+    The indices returned by this op are always sorted in increasing order.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return self._reshape_and_cast(tf.where(tf.equal(self._match_results, -1)))
+
+  def unmatched_column_indicator(self):
+    """Returns column indices that are unmatched.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return tf.equal(self._match_results, -1)
+
+  def num_unmatched_columns(self):
+    """Returns number (int32 scalar tensor) of unmatched columns."""
+    return tf.size(self.unmatched_column_indices())
+
+  def ignored_column_indices(self):
+    """Returns column indices that are ignored (neither Matched nor Unmatched).
+
+    The indices returned by this op are always sorted in increasing order.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return self._reshape_and_cast(tf.where(self.ignored_column_indicator()))
+
+  def ignored_column_indicator(self):
+    """Returns boolean column indicator where True means the colum is ignored.
+
+    Returns:
+      column_indicator: boolean vector which is True for all ignored column
+      indices.
+    """
+    return tf.equal(self._match_results, -2)
+
+  def num_ignored_columns(self):
+    """Returns number (int32 scalar tensor) of matched columns."""
+    return tf.size(self.ignored_column_indices())
+
+  def unmatched_or_ignored_column_indices(self):
+    """Returns column indices that are unmatched or ignored.
+
+    The indices returned by this op are always sorted in increasing order.
+
+    Returns:
+      column_indices: int32 tensor of shape [K] with column indices.
+    """
+    return self._reshape_and_cast(tf.where(tf.greater(0, self._match_results)))
+
+  def matched_row_indices(self):
+    """Returns row indices that match some column.
+
+    The indices returned by this op are ordered so as to be in correspondence
+    with the output of matched_column_indicator().  For example if
+    self.matched_column_indicator() is [0,2], and self.matched_row_indices() is
+    [7, 3], then we know that column 0 was matched to row 7 and column 2 was
+    matched to row 3.
+
+    Returns:
+      row_indices: int32 tensor of shape [K] with row indices.
+    """
+    return self._reshape_and_cast(
+        self._gather_op(self._match_results, self.matched_column_indices()))
+
+  def _reshape_and_cast(self, t):
+    return tf.cast(tf.reshape(t, [-1]), tf.int32)
+
+  def gather_based_on_match(self, input_tensor, unmatched_value,
+                            ignored_value):
+    """Gathers elements from `input_tensor` based on match results.
+
+    For columns that are matched to a row, gathered_tensor[col] is set to
+    input_tensor[match_results[col]]. For columns that are unmatched,
+    gathered_tensor[col] is set to unmatched_value. Finally, for columns that
+    are ignored gathered_tensor[col] is set to ignored_value.
+
+    Note that the input_tensor.shape[1:] must match with unmatched_value.shape
+    and ignored_value.shape
+
+    Args:
+      input_tensor: Tensor to gather values from.
+      unmatched_value: Constant tensor value for unmatched columns.
+      ignored_value: Constant tensor value for ignored columns.
+
+    Returns:
+      gathered_tensor: A tensor containing values gathered from input_tensor.
+        The shape of the gathered tensor is [match_results.shape[0]] +
+        input_tensor.shape[1:].
+    """
+    input_tensor = tf.concat(
+        [tf.stack([ignored_value, unmatched_value]),
+         tf.to_float(input_tensor)],
+        axis=0)
+    gather_indices = tf.maximum(self.match_results + 2, 0)
+    gathered_tensor = self._gather_op(input_tensor, gather_indices)
+    return gathered_tensor
+
+
+class Matcher(object):
+  """Abstract base class for matcher.
+  """
+  __metaclass__ = ABCMeta
+
+  def __init__(self, use_matmul_gather=False):
+    """Constructs a Matcher.
+
+    Args:
+      use_matmul_gather: Force constructed match objects to use matrix
+        multiplication based gather instead of standard tf.gather.
+        (Default: False).
+    """
+    self._use_matmul_gather = use_matmul_gather
+
+  def match(self, similarity_matrix, valid_rows=None, scope=None):
+    """Computes matches among row and column indices and returns the result.
+
+    Computes matches among the row and column indices based on the similarity
+    matrix and optional arguments.
+
+    Args:
+      similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
+        where higher value means more similar.
+      valid_rows: A boolean tensor of shape [N] indicating the rows that are
+        valid for matching.
+      scope: Op scope name. Defaults to 'Match' if None.
+
+    Returns:
+      A Match object with the results of matching.
+    """
+    with tf.name_scope(scope, 'Match') as scope:
+      if valid_rows is None:
+        valid_rows = tf.ones(tf.shape(similarity_matrix)[0], dtype=tf.bool)
+      return Match(self._match(similarity_matrix, valid_rows),
+                   self._use_matmul_gather)
+
+  @abstractmethod
+  def _match(self, similarity_matrix, valid_rows):
+    """Method to be overridden by implementations.
+
+    Args:
+      similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
+        where higher value means more similar.
+      valid_rows: A boolean tensor of shape [N] indicating the rows that are
+        valid for matching.
+    Returns:
+      match_results: Integer tensor of shape [M]: match_results[i]>=0 means
+        that column i is matched to row match_results[i], match_results[i]=-1
+        means that the column is not matched. match_results[i]=-2 means that
+        the column is ignored (usually this happens when there is a very weak
+        match which one neither wants as positive nor negative example).
+    """
+    pass
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher_test.py
new file mode 100644
index 00000000..05607834
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/matcher_test.py
@@ -0,0 +1,192 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.matcher."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import matcher
+
+
+class MatchTest(tf.test.TestCase):
+
+  def test_get_correct_matched_columnIndices(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indices = [0, 1, 3, 5]
+    matched_column_indices = match.matched_column_indices()
+    self.assertEquals(matched_column_indices.dtype, tf.int32)
+    with self.test_session() as sess:
+      matched_column_indices = sess.run(matched_column_indices)
+      self.assertAllEqual(matched_column_indices, expected_column_indices)
+
+  def test_get_correct_counts(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    exp_num_matched_columns = 4
+    exp_num_unmatched_columns = 2
+    exp_num_ignored_columns = 1
+    num_matched_columns = match.num_matched_columns()
+    num_unmatched_columns = match.num_unmatched_columns()
+    num_ignored_columns = match.num_ignored_columns()
+    self.assertEquals(num_matched_columns.dtype, tf.int32)
+    self.assertEquals(num_unmatched_columns.dtype, tf.int32)
+    self.assertEquals(num_ignored_columns.dtype, tf.int32)
+    with self.test_session() as sess:
+      (num_matched_columns_out, num_unmatched_columns_out,
+       num_ignored_columns_out) = sess.run(
+           [num_matched_columns, num_unmatched_columns, num_ignored_columns])
+      self.assertAllEqual(num_matched_columns_out, exp_num_matched_columns)
+      self.assertAllEqual(num_unmatched_columns_out, exp_num_unmatched_columns)
+      self.assertAllEqual(num_ignored_columns_out, exp_num_ignored_columns)
+
+  def testGetCorrectUnmatchedColumnIndices(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indices = [2, 4]
+    unmatched_column_indices = match.unmatched_column_indices()
+    self.assertEquals(unmatched_column_indices.dtype, tf.int32)
+    with self.test_session() as sess:
+      unmatched_column_indices = sess.run(unmatched_column_indices)
+      self.assertAllEqual(unmatched_column_indices, expected_column_indices)
+
+  def testGetCorrectMatchedRowIndices(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_row_indices = [3, 1, 0, 5]
+    matched_row_indices = match.matched_row_indices()
+    self.assertEquals(matched_row_indices.dtype, tf.int32)
+    with self.test_session() as sess:
+      matched_row_inds = sess.run(matched_row_indices)
+      self.assertAllEqual(matched_row_inds, expected_row_indices)
+
+  def test_get_correct_ignored_column_indices(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indices = [6]
+    ignored_column_indices = match.ignored_column_indices()
+    self.assertEquals(ignored_column_indices.dtype, tf.int32)
+    with self.test_session() as sess:
+      ignored_column_indices = sess.run(ignored_column_indices)
+      self.assertAllEqual(ignored_column_indices, expected_column_indices)
+
+  def test_get_correct_matched_column_indicator(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indicator = [True, True, False, True, False, True, False]
+    matched_column_indicator = match.matched_column_indicator()
+    self.assertEquals(matched_column_indicator.dtype, tf.bool)
+    with self.test_session() as sess:
+      matched_column_indicator = sess.run(matched_column_indicator)
+      self.assertAllEqual(matched_column_indicator, expected_column_indicator)
+
+  def test_get_correct_unmatched_column_indicator(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indicator = [False, False, True, False, True, False, False]
+    unmatched_column_indicator = match.unmatched_column_indicator()
+    self.assertEquals(unmatched_column_indicator.dtype, tf.bool)
+    with self.test_session() as sess:
+      unmatched_column_indicator = sess.run(unmatched_column_indicator)
+      self.assertAllEqual(unmatched_column_indicator, expected_column_indicator)
+
+  def test_get_correct_ignored_column_indicator(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indicator = [False, False, False, False, False, False, True]
+    ignored_column_indicator = match.ignored_column_indicator()
+    self.assertEquals(ignored_column_indicator.dtype, tf.bool)
+    with self.test_session() as sess:
+      ignored_column_indicator = sess.run(ignored_column_indicator)
+      self.assertAllEqual(ignored_column_indicator, expected_column_indicator)
+
+  def test_get_correct_unmatched_ignored_column_indices(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    match = matcher.Match(match_results)
+    expected_column_indices = [2, 4, 6]
+    unmatched_ignored_column_indices = (match.
+                                        unmatched_or_ignored_column_indices())
+    self.assertEquals(unmatched_ignored_column_indices.dtype, tf.int32)
+    with self.test_session() as sess:
+      unmatched_ignored_column_indices = sess.run(
+          unmatched_ignored_column_indices)
+      self.assertAllEqual(unmatched_ignored_column_indices,
+                          expected_column_indices)
+
+  def test_all_columns_accounted_for(self):
+    # Note: deliberately setting to small number so not always
+    # all possibilities appear (matched, unmatched, ignored)
+    num_matches = 10
+    match_results = tf.random_uniform(
+        [num_matches], minval=-2, maxval=5, dtype=tf.int32)
+    match = matcher.Match(match_results)
+    matched_column_indices = match.matched_column_indices()
+    unmatched_column_indices = match.unmatched_column_indices()
+    ignored_column_indices = match.ignored_column_indices()
+    with self.test_session() as sess:
+      matched, unmatched, ignored = sess.run([
+          matched_column_indices, unmatched_column_indices,
+          ignored_column_indices
+      ])
+      all_indices = np.hstack((matched, unmatched, ignored))
+      all_indices_sorted = np.sort(all_indices)
+      self.assertAllEqual(all_indices_sorted,
+                          np.arange(num_matches, dtype=np.int32))
+
+  def test_scalar_gather_based_on_match(self):
+    match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
+    input_tensor = tf.constant([0, 1, 2, 3, 4, 5, 6, 7], dtype=tf.float32)
+    expected_gathered_tensor = [3, 1, 100, 0, 100, 5, 200]
+    match = matcher.Match(match_results)
+    gathered_tensor = match.gather_based_on_match(input_tensor,
+                                                  unmatched_value=100.,
+                                                  ignored_value=200.)
+    self.assertEquals(gathered_tensor.dtype, tf.float32)
+    with self.test_session():
+      gathered_tensor_out = gathered_tensor.eval()
+    self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
+
+  def test_multidimensional_gather_based_on_match(self):
+    match_results = tf.constant([1, -1, -2])
+    input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
+                               dtype=tf.float32)
+    expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0], [0, 0, 0, 0]]
+    match = matcher.Match(match_results)
+    gathered_tensor = match.gather_based_on_match(input_tensor,
+                                                  unmatched_value=tf.zeros(4),
+                                                  ignored_value=tf.zeros(4))
+    self.assertEquals(gathered_tensor.dtype, tf.float32)
+    with self.test_session():
+      gathered_tensor_out = gathered_tensor.eval()
+    self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
+
+  def test_multidimensional_gather_based_on_match_with_matmul_gather_op(self):
+    match_results = tf.constant([1, -1, -2])
+    input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
+                               dtype=tf.float32)
+    expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0], [0, 0, 0, 0]]
+    match = matcher.Match(match_results, use_matmul_gather=True)
+    gathered_tensor = match.gather_based_on_match(input_tensor,
+                                                  unmatched_value=tf.zeros(4),
+                                                  ignored_value=tf.zeros(4))
+    self.assertEquals(gathered_tensor.dtype, tf.float32)
+    with self.test_session() as sess:
+      self.assertTrue(
+          all([op.name is not 'Gather' for op in sess.graph.get_operations()]))
+      gathered_tensor_out = gathered_tensor.eval()
+    self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler.py
new file mode 100644
index 00000000..dc622221
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler.py
@@ -0,0 +1,90 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base minibatch sampler module.
+
+The job of the minibatch_sampler is to subsample a minibatch based on some
+criterion.
+
+The main function call is:
+    subsample(indicator, batch_size, **params).
+Indicator is a 1d boolean tensor where True denotes which examples can be
+sampled. It returns a boolean indicator where True denotes an example has been
+sampled..
+
+Subclasses should implement the Subsample function and can make use of the
+@staticmethod SubsampleIndicator.
+"""
+
+from abc import ABCMeta
+from abc import abstractmethod
+
+import tensorflow as tf
+
+from object_detection.utils import ops
+
+
+class MinibatchSampler(object):
+  """Abstract base class for subsampling minibatches."""
+  __metaclass__ = ABCMeta
+
+  def __init__(self):
+    """Constructs a minibatch sampler."""
+    pass
+
+  @abstractmethod
+  def subsample(self, indicator, batch_size, **params):
+    """Returns subsample of entries in indicator.
+
+    Args:
+      indicator: boolean tensor of shape [N] whose True entries can be sampled.
+      batch_size: desired batch size.
+      **params: additional keyword arguments for specific implementations of
+          the MinibatchSampler.
+
+    Returns:
+      sample_indicator: boolean tensor of shape [N] whose True entries have been
+      sampled. If sum(indicator) >= batch_size, sum(is_sampled) = batch_size
+    """
+    pass
+
+  @staticmethod
+  def subsample_indicator(indicator, num_samples):
+    """Subsample indicator vector.
+
+    Given a boolean indicator vector with M elements set to `True`, the function
+    assigns all but `num_samples` of these previously `True` elements to
+    `False`. If `num_samples` is greater than M, the original indicator vector
+    is returned.
+
+    Args:
+      indicator: a 1-dimensional boolean tensor indicating which elements
+        are allowed to be sampled and which are not.
+      num_samples: int32 scalar tensor
+
+    Returns:
+      a boolean tensor with the same shape as input (indicator) tensor
+    """
+    indices = tf.where(indicator)
+    indices = tf.random_shuffle(indices)
+    indices = tf.reshape(indices, [-1])
+
+    num_samples = tf.minimum(tf.size(indices), num_samples)
+    selected_indices = tf.slice(indices, [0], tf.reshape(num_samples, [1]))
+
+    selected_indicator = ops.indices_to_dense_vector(selected_indices,
+                                                     tf.shape(indicator)[0])
+
+    return tf.equal(selected_indicator, 1)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler_test.py
new file mode 100644
index 00000000..7420ae5d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/minibatch_sampler_test.py
@@ -0,0 +1,82 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for google3.research.vale.object_detection.minibatch_sampler."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import minibatch_sampler
+
+
+class MinibatchSamplerTest(tf.test.TestCase):
+
+  def test_subsample_indicator_when_more_true_elements_than_num_samples(self):
+    np_indicator = [True, False, True, False, True, True, False]
+    indicator = tf.constant(np_indicator)
+    samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
+        indicator, 3)
+    with self.test_session() as sess:
+      samples_out = sess.run(samples)
+      self.assertTrue(np.sum(samples_out), 3)
+      self.assertAllEqual(samples_out,
+                          np.logical_and(samples_out, np_indicator))
+
+  def test_subsample_when_more_true_elements_than_num_samples_no_shape(self):
+    np_indicator = [True, False, True, False, True, True, False]
+    indicator = tf.placeholder(tf.bool)
+    feed_dict = {indicator: np_indicator}
+
+    samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
+        indicator, 3)
+    with self.test_session() as sess:
+      samples_out = sess.run(samples, feed_dict=feed_dict)
+      self.assertTrue(np.sum(samples_out), 3)
+      self.assertAllEqual(samples_out,
+                          np.logical_and(samples_out, np_indicator))
+
+  def test_subsample_indicator_when_less_true_elements_than_num_samples(self):
+    np_indicator = [True, False, True, False, True, True, False]
+    indicator = tf.constant(np_indicator)
+    samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
+        indicator, 5)
+    with self.test_session() as sess:
+      samples_out = sess.run(samples)
+      self.assertTrue(np.sum(samples_out), 4)
+      self.assertAllEqual(samples_out,
+                          np.logical_and(samples_out, np_indicator))
+
+  def test_subsample_indicator_when_num_samples_is_zero(self):
+    np_indicator = [True, False, True, False, True, True, False]
+    indicator = tf.constant(np_indicator)
+    samples_none = minibatch_sampler.MinibatchSampler.subsample_indicator(
+        indicator, 0)
+    with self.test_session() as sess:
+      samples_none_out = sess.run(samples_none)
+      self.assertAllEqual(
+          np.zeros_like(samples_none_out, dtype=bool),
+          samples_none_out)
+
+  def test_subsample_indicator_when_indicator_all_false(self):
+    indicator_empty = tf.zeros([0], dtype=tf.bool)
+    samples_empty = minibatch_sampler.MinibatchSampler.subsample_indicator(
+        indicator_empty, 4)
+    with self.test_session() as sess:
+      samples_empty_out = sess.run(samples_empty)
+      self.assertEqual(0, samples_empty_out.size)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/model.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/model.py
new file mode 100644
index 00000000..91e31813
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/model.py
@@ -0,0 +1,346 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Abstract detection model.
+
+This file defines a generic base class for detection models.  Programs that are
+designed to work with arbitrary detection models should only depend on this
+class.  We intend for the functions in this class to follow tensor-in/tensor-out
+design, thus all functions have tensors or lists/dictionaries holding tensors as
+inputs and outputs.
+
+Abstractly, detection models predict output tensors given input images
+which can be passed to a loss function at training time or passed to a
+postprocessing function at eval time.  The computation graphs at a high level
+consequently look as follows:
+
+Training time:
+inputs (images tensor) -> preprocess -> predict -> loss -> outputs (loss tensor)
+
+Evaluation time:
+inputs (images tensor) -> preprocess -> predict -> postprocess
+ -> outputs (boxes tensor, scores tensor, classes tensor, num_detections tensor)
+
+DetectionModels must thus implement four functions (1) preprocess, (2) predict,
+(3) postprocess and (4) loss.  DetectionModels should make no assumptions about
+the input size or aspect ratio --- they are responsible for doing any
+resize/reshaping necessary (see docstring for the preprocess function).
+Output classes are always integers in the range [0, num_classes).  Any mapping
+of these integers to semantic labels is to be handled outside of this class.
+
+Images are resized in the `preprocess` method. All of `preprocess`, `predict`,
+and `postprocess` should be reentrant.
+
+The `preprocess` method runs `image_resizer_fn` that returns resized_images and
+`true_image_shapes`. Since `image_resizer_fn` can pad the images with zeros,
+true_image_shapes indicate the slices that contain the image without padding.
+This is useful for padding images to be a fixed size for batching.
+
+The `postprocess` method uses the true image shapes to clip predictions that lie
+outside of images.
+
+By default, DetectionModels produce bounding box detections; However, we support
+a handful of auxiliary annotations associated with each bounding box, namely,
+instance masks and keypoints.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+from object_detection.core import standard_fields as fields
+
+
+class DetectionModel(object):
+  """Abstract base class for detection models."""
+  __metaclass__ = ABCMeta
+
+  def __init__(self, num_classes):
+    """Constructor.
+
+    Args:
+      num_classes: number of classes.  Note that num_classes *does not* include
+      background categories that might be implicitly predicted in various
+      implementations.
+    """
+    self._num_classes = num_classes
+    self._groundtruth_lists = {}
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def groundtruth_lists(self, field):
+    """Access list of groundtruth tensors.
+
+    Args:
+      field: a string key, options are
+        fields.BoxListFields.{boxes,classes,masks,keypoints} or
+        fields.InputDataFields.is_annotated.
+
+    Returns:
+      a list of tensors holding groundtruth information (see also
+      provide_groundtruth function below), with one entry for each image in the
+      batch.
+    Raises:
+      RuntimeError: if the field has not been provided via provide_groundtruth.
+    """
+    if field not in self._groundtruth_lists:
+      raise RuntimeError('Groundtruth tensor {} has not been provided'.format(
+          field))
+    return self._groundtruth_lists[field]
+
+  def groundtruth_has_field(self, field):
+    """Determines whether the groundtruth includes the given field.
+
+    Args:
+      field: a string key, options are
+        fields.BoxListFields.{boxes,classes,masks,keypoints} or
+        fields.InputDataFields.is_annotated.
+
+    Returns:
+      True if the groundtruth includes the given field, False otherwise.
+    """
+    return field in self._groundtruth_lists
+
+  @abstractmethod
+  def preprocess(self, inputs):
+    """Input preprocessing.
+
+    To be overridden by implementations.
+
+    This function is responsible for any scaling/shifting of input values that
+    is necessary prior to running the detector on an input image.
+    It is also responsible for any resizing, padding that might be necessary
+    as images are assumed to arrive in arbitrary sizes.  While this function
+    could conceivably be part of the predict method (below), it is often
+    convenient to keep these separate --- for example, we may want to preprocess
+    on one device, place onto a queue, and let another device (e.g., the GPU)
+    handle prediction.
+
+    A few important notes about the preprocess function:
+    + We assume that this operation does not have any trainable variables nor
+    does it affect the groundtruth annotations in any way (thus data
+    augmentation operations such as random cropping should be performed
+    externally).
+    + There is no assumption that the batchsize in this function is the same as
+    the batch size in the predict function.  In fact, we recommend calling the
+    preprocess function prior to calling any batching operations (which should
+    happen outside of the model) and thus assuming that batch sizes are equal
+    to 1 in the preprocess function.
+    + There is also no explicit assumption that the output resolutions
+    must be fixed across inputs --- this is to support "fully convolutional"
+    settings in which input images can have different shapes/resolutions.
+
+    Args:
+      inputs: a [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: a [batch, height_out, width_out, channels] float32
+        tensor representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+    """
+    pass
+
+  @abstractmethod
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    """Predict prediction tensors from inputs tensor.
+
+    Outputs of this function can be passed to loss or postprocess functions.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding prediction tensors to be
+        passed to the Loss or Postprocess functions.
+    """
+    pass
+
+  @abstractmethod
+  def postprocess(self, prediction_dict, true_image_shapes, **params):
+    """Convert predicted output tensors to final detections.
+
+    Outputs adhere to the following conventions:
+    * Classes are integers in [0, num_classes); background classes are removed
+      and the first non-background class is mapped to 0. If the model produces
+      class-agnostic detections, then no output is produced for classes.
+    * Boxes are to be interpreted as being in [y_min, x_min, y_max, x_max]
+      format and normalized relative to the image window.
+    * `num_detections` is provided for settings where detections are padded to a
+      fixed number of boxes.
+    * We do not specifically assume any kind of probabilistic interpretation
+      of the scores --- the only important thing is their relative ordering.
+      Thus implementations of the postprocess function are free to output
+      logits, probabilities, calibrated probabilities, or anything else.
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+      **params: Additional keyword arguments for specific implementations of
+        DetectionModel.
+
+    Returns:
+      detections: a dictionary containing the following fields
+        detection_boxes: [batch, max_detections, 4]
+        detection_scores: [batch, max_detections]
+        detection_classes: [batch, max_detections]
+          (If a model is producing class-agnostic detections, this field may be
+          missing)
+        instance_masks: [batch, max_detections, image_height, image_width]
+          (optional)
+        keypoints: [batch, max_detections, num_keypoints, 2] (optional)
+        num_detections: [batch]
+    """
+    pass
+
+  @abstractmethod
+  def loss(self, prediction_dict, true_image_shapes):
+    """Compute scalar loss tensors with respect to provided groundtruth.
+
+    Calling this function requires that groundtruth tensors have been
+    provided via the provide_groundtruth function.
+
+    Args:
+      prediction_dict: a dictionary holding predicted tensors
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      a dictionary mapping strings (loss names) to scalar tensors representing
+        loss values.
+    """
+    pass
+
+  def provide_groundtruth(self,
+                          groundtruth_boxes_list,
+                          groundtruth_classes_list,
+                          groundtruth_masks_list=None,
+                          groundtruth_keypoints_list=None,
+                          groundtruth_weights_list=None,
+                          groundtruth_confidences_list=None,
+                          groundtruth_is_crowd_list=None,
+                          is_annotated_list=None):
+    """Provide groundtruth tensors.
+
+    Args:
+      groundtruth_boxes_list: a list of 2-D tf.float32 tensors of shape
+        [num_boxes, 4] containing coordinates of the groundtruth boxes.
+          Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
+          format and assumed to be normalized and clipped
+          relative to the image window with y_min <= y_max and x_min <= x_max.
+      groundtruth_classes_list: a list of 2-D tf.float32 one-hot (or k-hot)
+        tensors of shape [num_boxes, num_classes] containing the class targets
+        with the 0th index assumed to map to the first non-background class.
+      groundtruth_masks_list: a list of 3-D tf.float32 tensors of
+        shape [num_boxes, height_in, width_in] containing instance
+        masks with values in {0, 1}.  If None, no masks are provided.
+        Mask resolution `height_in`x`width_in` must agree with the resolution
+        of the input image tensor provided to the `preprocess` function.
+      groundtruth_keypoints_list: a list of 3-D tf.float32 tensors of
+        shape [num_boxes, num_keypoints, 2] containing keypoints.
+        Keypoints are assumed to be provided in normalized coordinates and
+        missing keypoints should be encoded as NaN.
+      groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
+        [num_boxes] containing weights for groundtruth boxes.
+      groundtruth_confidences_list: A list of 2-D tf.float32 tensors of shape
+        [num_boxes, num_classes] containing class confidences for groundtruth
+        boxes.
+      groundtruth_is_crowd_list: A list of 1-D tf.bool tensors of shape
+        [num_boxes] containing is_crowd annotations
+      is_annotated_list: A list of scalar tf.bool tensors indicating whether
+        images have been labeled or not.
+    """
+    self._groundtruth_lists[fields.BoxListFields.boxes] = groundtruth_boxes_list
+    self._groundtruth_lists[
+        fields.BoxListFields.classes] = groundtruth_classes_list
+    if groundtruth_weights_list:
+      self._groundtruth_lists[fields.BoxListFields.
+                              weights] = groundtruth_weights_list
+    if groundtruth_confidences_list:
+      self._groundtruth_lists[fields.BoxListFields.
+                              confidences] = groundtruth_confidences_list
+    if groundtruth_masks_list:
+      self._groundtruth_lists[
+          fields.BoxListFields.masks] = groundtruth_masks_list
+    if groundtruth_keypoints_list:
+      self._groundtruth_lists[
+          fields.BoxListFields.keypoints] = groundtruth_keypoints_list
+    if groundtruth_is_crowd_list:
+      self._groundtruth_lists[
+          fields.BoxListFields.is_crowd] = groundtruth_is_crowd_list
+    if is_annotated_list:
+      self._groundtruth_lists[
+          fields.InputDataFields.is_annotated] = is_annotated_list
+
+  @abstractmethod
+  def regularization_losses(self):
+    """Returns a list of regularization losses for this model.
+
+    Returns a list of regularization losses for this model that the estimator
+    needs to use during training/optimization.
+
+    Returns:
+      A list of regularization loss tensors.
+    """
+    pass
+
+  @abstractmethod
+  def restore_map(self, fine_tune_checkpoint_type='detection'):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Returns a map of variable names to load from a checkpoint to variables in
+    the model graph. This enables the model to initialize based on weights from
+    another task. For example, the feature extractor variables from a
+    classification model can be used to bootstrap training of an object
+    detector. When loading from an object detection model, the checkpoint model
+    should have the same parameters as this detection model with exception of
+    the num_classes parameter.
+
+    Args:
+      fine_tune_checkpoint_type: whether to restore from a full detection
+        checkpoint (with compatible variable names) or to restore from a
+        classification checkpoint for initialization prior to training.
+        Valid values: `detection`, `classification`. Default 'detection'.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    pass
+
+  @abstractmethod
+  def updates(self):
+    """Returns a list of update operators for this model.
+
+    Returns a list of update operators for this model that must be executed at
+    each training step. The estimator's train op needs to have a control
+    dependency on these updates.
+
+    Returns:
+      A list of update operators.
+    """
+    pass
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing.py
new file mode 100644
index 00000000..20775859
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing.py
@@ -0,0 +1,498 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Post-processing operations on detected boxes."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.core import standard_fields as fields
+from object_detection.utils import shape_utils
+
+
+def multiclass_non_max_suppression(boxes,
+                                   scores,
+                                   score_thresh,
+                                   iou_thresh,
+                                   max_size_per_class,
+                                   max_total_size=0,
+                                   clip_window=None,
+                                   change_coordinate_frame=False,
+                                   masks=None,
+                                   boundaries=None,
+                                   pad_to_max_output_size=False,
+                                   additional_fields=None,
+                                   scope=None):
+  """Multi-class version of non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes.  It operates independently for each class for
+  which scores are provided (via the scores field of the input box_list),
+  pruning boxes with score less than a provided threshold prior to
+  applying NMS.
+
+  Please note that this operation is performed on *all* classes, therefore any
+  background classes should be removed prior to calling this function.
+
+  Selected boxes are guaranteed to be sorted in decreasing order by score (but
+  the sort is not guaranteed to be stable).
+
+  Args:
+    boxes: A [k, q, 4] float32 tensor containing k detections. `q` can be either
+      number of classes or 1 depending on whether a separate box is predicted
+      per class.
+    scores: A [k, num_classes] float32 tensor containing the scores for each of
+      the k detections. The scores have to be non-negative when
+      pad_to_max_output_size is True.
+    score_thresh: scalar threshold for score (low scoring boxes are removed).
+    iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
+      with previously selected boxes are removed).
+    max_size_per_class: maximum number of retained boxes per class.
+    max_total_size: maximum number of boxes retained over all classes. By
+      default returns all boxes retained after capping boxes per class.
+    clip_window: A float32 tensor of the form [y_min, x_min, y_max, x_max]
+      representing the window to clip and normalize boxes to before performing
+      non-max suppression.
+    change_coordinate_frame: Whether to normalize coordinates after clipping
+      relative to clip_window (this can only be set to True if a clip_window
+      is provided)
+    masks: (optional) a [k, q, mask_height, mask_width] float32 tensor
+      containing box masks. `q` can be either number of classes or 1 depending
+      on whether a separate mask is predicted per class.
+    boundaries: (optional) a [k, q, boundary_height, boundary_width] float32
+      tensor containing box boundaries. `q` can be either number of classes or 1
+      depending on whether a separate boundary is predicted per class.
+    pad_to_max_output_size: If true, the output nmsed boxes are padded to be of
+      length `max_size_per_class`. Defaults to false.
+    additional_fields: (optional) If not None, a dictionary that maps keys to
+      tensors whose first dimensions are all of size `k`. After non-maximum
+      suppression, all tensors corresponding to the selected boxes will be
+      added to resulting BoxList.
+    scope: name scope.
+
+  Returns:
+    A tuple of sorted_boxes and num_valid_nms_boxes. The sorted_boxes is a
+      BoxList holds M boxes with a rank-1 scores field representing
+      corresponding scores for each box with scores sorted in decreasing order
+      and a rank-1 classes field representing a class label for each box. The
+      num_valid_nms_boxes is a 0-D integer tensor representing the number of
+      valid elements in `BoxList`, with the valid elements appearing first.
+
+  Raises:
+    ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not have
+      a valid scores field.
+  """
+  if not 0 <= iou_thresh <= 1.0:
+    raise ValueError('iou_thresh must be between 0 and 1')
+  if scores.shape.ndims != 2:
+    raise ValueError('scores field must be of rank 2')
+  if scores.shape[1].value is None:
+    raise ValueError('scores must have statically defined second '
+                     'dimension')
+  if boxes.shape.ndims != 3:
+    raise ValueError('boxes must be of rank 3.')
+  if not (boxes.shape[1].value == scores.shape[1].value or
+          boxes.shape[1].value == 1):
+    raise ValueError('second dimension of boxes must be either 1 or equal '
+                     'to the second dimension of scores')
+  if boxes.shape[2].value != 4:
+    raise ValueError('last dimension of boxes must be of size 4.')
+  if change_coordinate_frame and clip_window is None:
+    raise ValueError('if change_coordinate_frame is True, then a clip_window'
+                     'must be specified.')
+
+  with tf.name_scope(scope, 'MultiClassNonMaxSuppression'):
+    num_scores = tf.shape(scores)[0]
+    num_classes = scores.get_shape()[1]
+
+    selected_boxes_list = []
+    num_valid_nms_boxes_cumulative = tf.constant(0)
+    per_class_boxes_list = tf.unstack(boxes, axis=1)
+    if masks is not None:
+      per_class_masks_list = tf.unstack(masks, axis=1)
+    if boundaries is not None:
+      per_class_boundaries_list = tf.unstack(boundaries, axis=1)
+    boxes_ids = (range(num_classes) if len(per_class_boxes_list) > 1
+                 else [0] * num_classes.value)
+    for class_idx, boxes_idx in zip(range(num_classes), boxes_ids):
+      per_class_boxes = per_class_boxes_list[boxes_idx]
+      boxlist_and_class_scores = box_list.BoxList(per_class_boxes)
+      class_scores = tf.reshape(
+          tf.slice(scores, [0, class_idx], tf.stack([num_scores, 1])), [-1])
+
+      boxlist_and_class_scores.add_field(fields.BoxListFields.scores,
+                                         class_scores)
+      if masks is not None:
+        per_class_masks = per_class_masks_list[boxes_idx]
+        boxlist_and_class_scores.add_field(fields.BoxListFields.masks,
+                                           per_class_masks)
+      if boundaries is not None:
+        per_class_boundaries = per_class_boundaries_list[boxes_idx]
+        boxlist_and_class_scores.add_field(fields.BoxListFields.boundaries,
+                                           per_class_boundaries)
+      if additional_fields is not None:
+        for key, tensor in additional_fields.items():
+          boxlist_and_class_scores.add_field(key, tensor)
+
+      if pad_to_max_output_size:
+        max_selection_size = max_size_per_class
+        selected_indices, num_valid_nms_boxes = (
+            tf.image.non_max_suppression_padded(
+                boxlist_and_class_scores.get(),
+                boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
+                max_selection_size,
+                iou_threshold=iou_thresh,
+                score_threshold=score_thresh,
+                pad_to_max_output_size=True))
+      else:
+        max_selection_size = tf.minimum(max_size_per_class,
+                                        boxlist_and_class_scores.num_boxes())
+        selected_indices = tf.image.non_max_suppression(
+            boxlist_and_class_scores.get(),
+            boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
+            max_selection_size,
+            iou_threshold=iou_thresh,
+            score_threshold=score_thresh)
+        num_valid_nms_boxes = tf.shape(selected_indices)[0]
+        selected_indices = tf.concat(
+            [selected_indices,
+             tf.zeros(max_selection_size-num_valid_nms_boxes, tf.int32)], 0)
+      nms_result = box_list_ops.gather(boxlist_and_class_scores,
+                                       selected_indices)
+      # Make the scores -1 for invalid boxes.
+      valid_nms_boxes_indx = tf.less(
+          tf.range(max_selection_size), num_valid_nms_boxes)
+      nms_scores = nms_result.get_field(fields.BoxListFields.scores)
+      nms_result.add_field(fields.BoxListFields.scores,
+                           tf.where(valid_nms_boxes_indx,
+                                    nms_scores, -1*tf.ones(max_selection_size)))
+      num_valid_nms_boxes_cumulative += num_valid_nms_boxes
+
+      nms_result.add_field(
+          fields.BoxListFields.classes, (tf.zeros_like(
+              nms_result.get_field(fields.BoxListFields.scores)) + class_idx))
+      selected_boxes_list.append(nms_result)
+    selected_boxes = box_list_ops.concatenate(selected_boxes_list)
+    sorted_boxes = box_list_ops.sort_by_field(selected_boxes,
+                                              fields.BoxListFields.scores)
+    if clip_window is not None:
+      # When pad_to_max_output_size is False, it prunes the boxes with zero
+      # area.
+      sorted_boxes = box_list_ops.clip_to_window(
+          sorted_boxes,
+          clip_window,
+          filter_nonoverlapping=not pad_to_max_output_size)
+      # Set the scores of boxes with zero area to -1 to keep the default
+      # behaviour of pruning out zero area boxes.
+      sorted_boxes_size = tf.shape(sorted_boxes.get())[0]
+      non_zero_box_area = tf.cast(box_list_ops.area(sorted_boxes), tf.bool)
+      sorted_boxes_scores = tf.where(
+          non_zero_box_area,
+          sorted_boxes.get_field(fields.BoxListFields.scores),
+          -1*tf.ones(sorted_boxes_size))
+      sorted_boxes.add_field(fields.BoxListFields.scores, sorted_boxes_scores)
+      num_valid_nms_boxes_cumulative = tf.reduce_sum(
+          tf.cast(tf.greater_equal(sorted_boxes_scores, 0), tf.int32))
+      sorted_boxes = box_list_ops.sort_by_field(sorted_boxes,
+                                                fields.BoxListFields.scores)
+      if change_coordinate_frame:
+        sorted_boxes = box_list_ops.change_coordinate_frame(
+            sorted_boxes, clip_window)
+
+    if max_total_size:
+      max_total_size = tf.minimum(max_total_size,
+                                  sorted_boxes.num_boxes())
+      sorted_boxes = box_list_ops.gather(sorted_boxes,
+                                         tf.range(max_total_size))
+      num_valid_nms_boxes_cumulative = tf.where(
+          max_total_size > num_valid_nms_boxes_cumulative,
+          num_valid_nms_boxes_cumulative, max_total_size)
+    # Select only the valid boxes if pad_to_max_output_size is False.
+    if not pad_to_max_output_size:
+      sorted_boxes = box_list_ops.gather(
+          sorted_boxes, tf.range(num_valid_nms_boxes_cumulative))
+
+    return sorted_boxes, num_valid_nms_boxes_cumulative
+
+
+def batch_multiclass_non_max_suppression(boxes,
+                                         scores,
+                                         score_thresh,
+                                         iou_thresh,
+                                         max_size_per_class,
+                                         max_total_size=0,
+                                         clip_window=None,
+                                         change_coordinate_frame=False,
+                                         num_valid_boxes=None,
+                                         masks=None,
+                                         additional_fields=None,
+                                         scope=None,
+                                         use_static_shapes=False,
+                                         parallel_iterations=32):
+  """Multi-class version of non maximum suppression that operates on a batch.
+
+  This op is similar to `multiclass_non_max_suppression` but operates on a batch
+  of boxes and scores. See documentation for `multiclass_non_max_suppression`
+  for details.
+
+  Args:
+    boxes: A [batch_size, num_anchors, q, 4] float32 tensor containing
+      detections. If `q` is 1 then same boxes are used for all classes
+        otherwise, if `q` is equal to number of classes, class-specific boxes
+        are used.
+    scores: A [batch_size, num_anchors, num_classes] float32 tensor containing
+      the scores for each of the `num_anchors` detections. The scores have to be
+      non-negative when use_static_shapes is set True.
+    score_thresh: scalar threshold for score (low scoring boxes are removed).
+    iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
+      with previously selected boxes are removed).
+    max_size_per_class: maximum number of retained boxes per class.
+    max_total_size: maximum number of boxes retained over all classes. By
+      default returns all boxes retained after capping boxes per class.
+    clip_window: A float32 tensor of shape [batch_size, 4]  where each entry is
+      of the form [y_min, x_min, y_max, x_max] representing the window to clip
+      boxes to before performing non-max suppression. This argument can also be
+      a tensor of shape [4] in which case, the same clip window is applied to
+      all images in the batch. If clip_widow is None, all boxes are used to
+      perform non-max suppression.
+    change_coordinate_frame: Whether to normalize coordinates after clipping
+      relative to clip_window (this can only be set to True if a clip_window
+      is provided)
+    num_valid_boxes: (optional) a Tensor of type `int32`. A 1-D tensor of shape
+      [batch_size] representing the number of valid boxes to be considered
+      for each image in the batch.  This parameter allows for ignoring zero
+      paddings.
+    masks: (optional) a [batch_size, num_anchors, q, mask_height, mask_width]
+      float32 tensor containing box masks. `q` can be either number of classes
+      or 1 depending on whether a separate mask is predicted per class.
+    additional_fields: (optional) If not None, a dictionary that maps keys to
+      tensors whose dimensions are [batch_size, num_anchors, ...].
+    scope: tf scope name.
+    use_static_shapes: If true, the output nmsed boxes are padded to be of
+      length `max_size_per_class` and it doesn't clip boxes to max_total_size.
+      Defaults to false.
+    parallel_iterations: (optional) number of batch items to process in
+      parallel.
+
+  Returns:
+    'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
+      containing the non-max suppressed boxes.
+    'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
+      the scores for the boxes.
+    'nmsed_classes': A [batch_size, max_detections] float32 tensor
+      containing the class for boxes.
+    'nmsed_masks': (optional) a
+      [batch_size, max_detections, mask_height, mask_width] float32 tensor
+      containing masks for each selected box. This is set to None if input
+      `masks` is None.
+    'nmsed_additional_fields': (optional) a dictionary of
+      [batch_size, max_detections, ...] float32 tensors corresponding to the
+      tensors specified in the input `additional_fields`. This is not returned
+      if input `additional_fields` is None.
+    'num_detections': A [batch_size] int32 tensor indicating the number of
+      valid detections per batch item. Only the top num_detections[i] entries in
+      nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
+      entries are zero paddings.
+
+  Raises:
+    ValueError: if `q` in boxes.shape is not 1 or not equal to number of
+      classes as inferred from scores.shape.
+  """
+  q = boxes.shape[2].value
+  num_classes = scores.shape[2].value
+  if q != 1 and q != num_classes:
+    raise ValueError('third dimension of boxes must be either 1 or equal '
+                     'to the third dimension of scores')
+  if change_coordinate_frame and clip_window is None:
+    raise ValueError('if change_coordinate_frame is True, then a clip_window'
+                     'must be specified.')
+  original_masks = masks
+  original_additional_fields = additional_fields
+  with tf.name_scope(scope, 'BatchMultiClassNonMaxSuppression'):
+    boxes_shape = boxes.shape
+    batch_size = boxes_shape[0].value
+    num_anchors = boxes_shape[1].value
+
+    if batch_size is None:
+      batch_size = tf.shape(boxes)[0]
+    if num_anchors is None:
+      num_anchors = tf.shape(boxes)[1]
+
+    # If num valid boxes aren't provided, create one and mark all boxes as
+    # valid.
+    if num_valid_boxes is None:
+      num_valid_boxes = tf.ones([batch_size], dtype=tf.int32) * num_anchors
+
+    # If masks aren't provided, create dummy masks so we can only have one copy
+    # of _single_image_nms_fn and discard the dummy masks after map_fn.
+    if masks is None:
+      masks_shape = tf.stack([batch_size, num_anchors, q, 1, 1])
+      masks = tf.zeros(masks_shape)
+
+    if clip_window is None:
+      clip_window = tf.stack([
+          tf.reduce_min(boxes[:, :, :, 0]),
+          tf.reduce_min(boxes[:, :, :, 1]),
+          tf.reduce_max(boxes[:, :, :, 2]),
+          tf.reduce_max(boxes[:, :, :, 3])
+      ])
+    if clip_window.shape.ndims == 1:
+      clip_window = tf.tile(tf.expand_dims(clip_window, 0), [batch_size, 1])
+
+    if additional_fields is None:
+      additional_fields = {}
+
+    def _single_image_nms_fn(args):
+      """Runs NMS on a single image and returns padded output.
+
+      Args:
+        args: A list of tensors consisting of the following:
+          per_image_boxes - A [num_anchors, q, 4] float32 tensor containing
+            detections. If `q` is 1 then same boxes are used for all classes
+            otherwise, if `q` is equal to number of classes, class-specific
+            boxes are used.
+          per_image_scores - A [num_anchors, num_classes] float32 tensor
+            containing the scores for each of the `num_anchors` detections.
+          per_image_masks - A [num_anchors, q, mask_height, mask_width] float32
+            tensor containing box masks. `q` can be either number of classes
+            or 1 depending on whether a separate mask is predicted per class.
+          per_image_clip_window - A 1D float32 tensor of the form
+            [ymin, xmin, ymax, xmax] representing the window to clip the boxes
+            to.
+          per_image_additional_fields - (optional) A variable number of float32
+            tensors each with size [num_anchors, ...].
+          per_image_num_valid_boxes - A tensor of type `int32`. A 1-D tensor of
+            shape [batch_size] representing the number of valid boxes to be
+            considered for each image in the batch.  This parameter allows for
+            ignoring zero paddings.
+
+      Returns:
+        'nmsed_boxes': A [max_detections, 4] float32 tensor containing the
+          non-max suppressed boxes.
+        'nmsed_scores': A [max_detections] float32 tensor containing the scores
+          for the boxes.
+        'nmsed_classes': A [max_detections] float32 tensor containing the class
+          for boxes.
+        'nmsed_masks': (optional) a [max_detections, mask_height, mask_width]
+          float32 tensor containing masks for each selected box. This is set to
+          None if input `masks` is None.
+        'nmsed_additional_fields':  (optional) A variable number of float32
+          tensors each with size [max_detections, ...] corresponding to the
+          input `per_image_additional_fields`.
+        'num_detections': A [batch_size] int32 tensor indicating the number of
+          valid detections per batch item. Only the top num_detections[i]
+          entries in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The
+          rest of the entries are zero paddings.
+      """
+      per_image_boxes = args[0]
+      per_image_scores = args[1]
+      per_image_masks = args[2]
+      per_image_clip_window = args[3]
+      per_image_additional_fields = {
+          key: value
+          for key, value in zip(additional_fields, args[4:-1])
+      }
+      per_image_num_valid_boxes = args[-1]
+      if use_static_shapes:
+        total_proposals = tf.shape(per_image_scores)
+        per_image_scores = tf.where(
+            tf.less(tf.range(total_proposals[0]), per_image_num_valid_boxes),
+            per_image_scores,
+            tf.fill(total_proposals, np.finfo('float32').min))
+      else:
+        per_image_boxes = tf.reshape(
+            tf.slice(per_image_boxes, 3 * [0],
+                     tf.stack([per_image_num_valid_boxes, -1, -1])), [-1, q, 4])
+        per_image_scores = tf.reshape(
+            tf.slice(per_image_scores, [0, 0],
+                     tf.stack([per_image_num_valid_boxes, -1])),
+            [-1, num_classes])
+        per_image_masks = tf.reshape(
+            tf.slice(per_image_masks, 4 * [0],
+                     tf.stack([per_image_num_valid_boxes, -1, -1, -1])),
+            [-1, q, per_image_masks.shape[2].value,
+             per_image_masks.shape[3].value])
+        if per_image_additional_fields is not None:
+          for key, tensor in per_image_additional_fields.items():
+            additional_field_shape = tensor.get_shape()
+            additional_field_dim = len(additional_field_shape)
+            per_image_additional_fields[key] = tf.reshape(
+                tf.slice(per_image_additional_fields[key],
+                         additional_field_dim * [0],
+                         tf.stack([per_image_num_valid_boxes] +
+                                  (additional_field_dim - 1) * [-1])),
+                [-1] + [dim.value for dim in additional_field_shape[1:]])
+
+      nmsed_boxlist, num_valid_nms_boxes = multiclass_non_max_suppression(
+          per_image_boxes,
+          per_image_scores,
+          score_thresh,
+          iou_thresh,
+          max_size_per_class,
+          max_total_size,
+          clip_window=per_image_clip_window,
+          change_coordinate_frame=change_coordinate_frame,
+          masks=per_image_masks,
+          pad_to_max_output_size=use_static_shapes,
+          additional_fields=per_image_additional_fields)
+
+      if not use_static_shapes:
+        nmsed_boxlist = box_list_ops.pad_or_clip_box_list(
+            nmsed_boxlist, max_total_size)
+      num_detections = num_valid_nms_boxes
+      nmsed_boxes = nmsed_boxlist.get()
+      nmsed_scores = nmsed_boxlist.get_field(fields.BoxListFields.scores)
+      nmsed_classes = nmsed_boxlist.get_field(fields.BoxListFields.classes)
+      nmsed_masks = nmsed_boxlist.get_field(fields.BoxListFields.masks)
+      nmsed_additional_fields = [
+          nmsed_boxlist.get_field(key) for key in per_image_additional_fields
+      ]
+      return ([nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks] +
+              nmsed_additional_fields + [num_detections])
+
+    num_additional_fields = 0
+    if additional_fields is not None:
+      num_additional_fields = len(additional_fields)
+    num_nmsed_outputs = 4 + num_additional_fields
+
+    batch_outputs = shape_utils.static_or_dynamic_map_fn(
+        _single_image_nms_fn,
+        elems=([boxes, scores, masks, clip_window] +
+               list(additional_fields.values()) + [num_valid_boxes]),
+        dtype=(num_nmsed_outputs * [tf.float32] + [tf.int32]),
+        parallel_iterations=parallel_iterations)
+
+    batch_nmsed_boxes = batch_outputs[0]
+    batch_nmsed_scores = batch_outputs[1]
+    batch_nmsed_classes = batch_outputs[2]
+    batch_nmsed_masks = batch_outputs[3]
+    batch_nmsed_additional_fields = {
+        key: value
+        for key, value in zip(additional_fields, batch_outputs[4:-1])
+    }
+    batch_num_detections = batch_outputs[-1]
+
+    if original_masks is None:
+      batch_nmsed_masks = None
+
+    if original_additional_fields is None:
+      batch_nmsed_additional_fields = None
+
+    return (batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
+            batch_nmsed_masks, batch_nmsed_additional_fields,
+            batch_num_detections)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing_test.py
new file mode 100644
index 00000000..c69aacd9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/post_processing_test.py
@@ -0,0 +1,1082 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for tensorflow_models.object_detection.core.post_processing."""
+import numpy as np
+import tensorflow as tf
+from object_detection.core import post_processing
+from object_detection.core import standard_fields as fields
+from object_detection.utils import test_case
+
+
+class MulticlassNonMaxSuppressionTest(test_case.TestCase):
+
+  def test_multiclass_nms_select_with_shared_boxes(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 1000, 1, 1002],
+                       [0, 100, 1, 101]]
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  # TODO(bhattad): Remove conditional after CMLE moves to TF 1.9
+
+  def test_multiclass_nms_select_with_shared_boxes_given_keypoints(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    num_keypoints = 6
+    keypoints = tf.tile(
+        tf.reshape(tf.range(8), [8, 1, 1]),
+        [1, num_keypoints, 2])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 1000, 1, 1002],
+                       [0, 100, 1, 101]]
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+    exp_nms_keypoints_tensor = tf.tile(
+        tf.reshape(tf.constant([3, 0, 6, 5], dtype=tf.float32), [4, 1, 1]),
+        [1, num_keypoints, 2])
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes,
+        scores,
+        score_thresh,
+        iou_thresh,
+        max_output_size,
+        additional_fields={fields.BoxListFields.keypoints: keypoints})
+
+    with self.test_session() as sess:
+      (nms_corners_output,
+       nms_scores_output,
+       nms_classes_output,
+       nms_keypoints,
+       exp_nms_keypoints) = sess.run([
+           nms.get(),
+           nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes),
+           nms.get_field(fields.BoxListFields.keypoints),
+           exp_nms_keypoints_tensor
+       ])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+      self.assertAllEqual(nms_keypoints, exp_nms_keypoints)
+
+  def test_multiclass_nms_with_shared_boxes_given_keypoint_heatmaps(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+
+    num_boxes = tf.shape(boxes)[0]
+    heatmap_height = 5
+    heatmap_width = 5
+    num_keypoints = 17
+    keypoint_heatmaps = tf.ones(
+        [num_boxes, heatmap_height, heatmap_width, num_keypoints],
+        dtype=tf.float32)
+
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 1000, 1, 1002],
+                       [0, 100, 1, 101]]
+
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+    exp_nms_keypoint_heatmaps = np.ones(
+        (4, heatmap_height, heatmap_width, num_keypoints), dtype=np.float32)
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes,
+        scores,
+        score_thresh,
+        iou_thresh,
+        max_output_size,
+        additional_fields={
+            fields.BoxListFields.keypoint_heatmaps: keypoint_heatmaps
+        })
+
+    with self.test_session() as sess:
+      (nms_corners_output,
+       nms_scores_output,
+       nms_classes_output,
+       nms_keypoint_heatmaps) = sess.run(
+           [nms.get(),
+            nms.get_field(fields.BoxListFields.scores),
+            nms.get_field(fields.BoxListFields.classes),
+            nms.get_field(fields.BoxListFields.keypoint_heatmaps)])
+
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+      self.assertAllEqual(nms_keypoint_heatmaps, exp_nms_keypoint_heatmaps)
+
+  def test_multiclass_nms_with_additional_fields(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+
+    coarse_boxes_key = 'coarse_boxes'
+    coarse_boxes = tf.constant([[0.1, 0.1, 1.1, 1.1],
+                                [0.1, 0.2, 1.1, 1.2],
+                                [0.1, -0.2, 1.1, 1.0],
+                                [0.1, 10.1, 1.1, 11.1],
+                                [0.1, 10.2, 1.1, 11.2],
+                                [0.1, 100.1, 1.1, 101.1],
+                                [0.1, 1000.1, 1.1, 1002.1],
+                                [0.1, 1000.1, 1.1, 1002.2]], tf.float32)
+
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[0, 10, 1, 11],
+                                [0, 0, 1, 1],
+                                [0, 1000, 1, 1002],
+                                [0, 100, 1, 101]], dtype=np.float32)
+
+    exp_nms_coarse_corners = np.array([[0.1, 10.1, 1.1, 11.1],
+                                       [0.1, 0.1, 1.1, 1.1],
+                                       [0.1, 1000.1, 1.1, 1002.1],
+                                       [0.1, 100.1, 1.1, 101.1]],
+                                      dtype=np.float32)
+
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes,
+        scores,
+        score_thresh,
+        iou_thresh,
+        max_output_size,
+        additional_fields={coarse_boxes_key: coarse_boxes})
+
+    with self.test_session() as sess:
+      (nms_corners_output,
+       nms_scores_output,
+       nms_classes_output,
+       nms_coarse_corners) = sess.run(
+           [nms.get(),
+            nms.get_field(fields.BoxListFields.scores),
+            nms.get_field(fields.BoxListFields.classes),
+            nms.get_field(coarse_boxes_key)])
+
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+      self.assertAllEqual(nms_coarse_corners, exp_nms_coarse_corners)
+
+  def test_multiclass_nms_select_with_shared_boxes_given_masks(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    num_classes = 2
+    mask_height = 3
+    mask_width = 3
+    masks = tf.tile(
+        tf.reshape(tf.range(8), [8, 1, 1, 1]),
+        [1, num_classes, mask_height, mask_width])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 1000, 1, 1002],
+                       [0, 100, 1, 101]]
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+    exp_nms_masks_tensor = tf.tile(
+        tf.reshape(tf.constant([3, 0, 6, 5], dtype=tf.float32), [4, 1, 1]),
+        [1, mask_height, mask_width])
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_output_size, masks=masks)
+    with self.test_session() as sess:
+      (nms_corners_output,
+       nms_scores_output,
+       nms_classes_output,
+       nms_masks,
+       exp_nms_masks) = sess.run([nms.get(),
+                                  nms.get_field(fields.BoxListFields.scores),
+                                  nms.get_field(fields.BoxListFields.classes),
+                                  nms.get_field(fields.BoxListFields.masks),
+                                  exp_nms_masks_tensor])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+      self.assertAllEqual(nms_masks, exp_nms_masks)
+
+  def test_multiclass_nms_select_with_clip_window(self):
+    boxes = tf.constant([[[0, 0, 10, 10]],
+                         [[1, 1, 11, 11]]], tf.float32)
+    scores = tf.constant([[.9], [.75]])
+    clip_window = tf.constant([5, 4, 8, 7], tf.float32)
+    score_thresh = 0.0
+    iou_thresh = 0.5
+    max_output_size = 100
+
+    exp_nms_corners = [[5, 4, 8, 7]]
+    exp_nms_scores = [.9]
+    exp_nms_classes = [0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes,
+        scores,
+        score_thresh,
+        iou_thresh,
+        max_output_size,
+        clip_window=clip_window)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  def test_multiclass_nms_select_with_clip_window_change_coordinate_frame(self):
+    boxes = tf.constant([[[0, 0, 10, 10]],
+                         [[1, 1, 11, 11]]], tf.float32)
+    scores = tf.constant([[.9], [.75]])
+    clip_window = tf.constant([5, 4, 8, 7], tf.float32)
+    score_thresh = 0.0
+    iou_thresh = 0.5
+    max_output_size = 100
+
+    exp_nms_corners = [[0, 0, 1, 1]]
+    exp_nms_scores = [.9]
+    exp_nms_classes = [0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes,
+        scores,
+        score_thresh,
+        iou_thresh,
+        max_output_size,
+        clip_window=clip_window,
+        change_coordinate_frame=True)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  def test_multiclass_nms_select_with_per_class_cap(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_size_per_class = 2
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 1000, 1, 1002]]
+    exp_nms_scores = [.95, .9, .85]
+    exp_nms_classes = [0, 0, 1]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_size_per_class)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  def test_multiclass_nms_select_with_total_cap(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_size_per_class = 4
+    max_total_size = 2
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1]]
+    exp_nms_scores = [.95, .9]
+    exp_nms_classes = [0, 0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_size_per_class,
+        max_total_size)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  def test_multiclass_nms_threshold_then_select_with_shared_boxes(self):
+    boxes = tf.constant([[[0, 0, 1, 1]],
+                         [[0, 0.1, 1, 1.1]],
+                         [[0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002]],
+                         [[0, 1000, 1, 1002.1]]], tf.float32)
+    scores = tf.constant([[.9], [.75], [.6], [.95], [.5], [.3], [.01], [.01]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 3
+
+    exp_nms = [[0, 10, 1, 11],
+               [0, 0, 1, 1],
+               [0, 100, 1, 101]]
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_output = sess.run(nms.get())
+      self.assertAllClose(nms_output, exp_nms)
+
+  def test_multiclass_nms_select_with_separate_boxes(self):
+    boxes = tf.constant([[[0, 0, 1, 1], [0, 0, 4, 5]],
+                         [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                         [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                         [[0, 10, 1, 11], [0, 10, 1, 11]],
+                         [[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                         [[0, 100, 1, 101], [0, 100, 1, 101]],
+                         [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                         [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]],
+                        tf.float32)
+    scores = tf.constant([[.9, 0.01], [.75, 0.05],
+                          [.6, 0.01], [.95, 0],
+                          [.5, 0.01], [.3, 0.01],
+                          [.01, .85], [.01, .5]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[0, 10, 1, 11],
+                       [0, 0, 1, 1],
+                       [0, 999, 2, 1004],
+                       [0, 100, 1, 101]]
+    exp_nms_scores = [.95, .9, .85, .3]
+    exp_nms_classes = [0, 0, 1, 0]
+
+    nms, _ = post_processing.multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh, max_output_size)
+    with self.test_session() as sess:
+      nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
+          [nms.get(), nms.get_field(fields.BoxListFields.scores),
+           nms.get_field(fields.BoxListFields.classes)])
+      self.assertAllClose(nms_corners_output, exp_nms_corners)
+      self.assertAllClose(nms_scores_output, exp_nms_scores)
+      self.assertAllClose(nms_classes_output, exp_nms_classes)
+
+  def test_batch_multiclass_nms_with_batch_size_1(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]],
+                          [[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0],
+                           [.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[[0, 10, 1, 11],
+                        [0, 0, 1, 1],
+                        [0, 999, 2, 1004],
+                        [0, 100, 1, 101]]]
+    exp_nms_scores = [[.95, .9, .85, .3]]
+    exp_nms_classes = [[0, 0, 1, 0]]
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size)
+
+    self.assertIsNone(nmsed_masks)
+    self.assertIsNone(nmsed_additional_fields)
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertEqual(num_detections, [4])
+
+  def test_batch_multiclass_nms_with_batch_size_2(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size)
+
+    self.assertIsNone(nmsed_masks)
+    self.assertIsNone(nmsed_additional_fields)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(),
+                        exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(),
+                        exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(),
+                        exp_nms_classes.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
+
+  def test_batch_multiclass_nms_with_per_batch_clip_window(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    clip_window = tf.constant([0., 0., 200., 200.])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.5, .3, 0, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [0, 0, 0, 0]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        clip_window=clip_window)
+
+    self.assertIsNone(nmsed_masks)
+    self.assertIsNone(nmsed_additional_fields)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(),
+                        exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(),
+                        exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(),
+                        exp_nms_classes.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 2])
+
+  def test_batch_multiclass_nms_with_per_image_clip_window(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    clip_window = tf.constant([[0., 0., 5., 5.],
+                               [0., 0., 200., 200.]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.9, 0., 0., 0.],
+                               [.5, .3, 0, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [0, 0, 0, 0]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        clip_window=clip_window)
+
+    self.assertIsNone(nmsed_masks)
+    self.assertIsNone(nmsed_additional_fields)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(),
+                        exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(),
+                        exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(),
+                        exp_nms_classes.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [1, 2])
+
+  def test_batch_multiclass_nms_with_masks(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    masks = tf.constant([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
+                          [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
+                          [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
+                          [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
+                         [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
+                          [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
+                          [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
+                          [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]],
+                        tf.float32)
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+    exp_nms_masks = np.array([[[[6, 7], [8, 9]],
+                               [[0, 1], [2, 3]],
+                               [[0, 0], [0, 0]],
+                               [[0, 0], [0, 0]]],
+                              [[[13, 14], [15, 16]],
+                               [[8, 9], [10, 11]],
+                               [[10, 11], [12, 13]],
+                               [[0, 0], [0, 0]]]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        masks=masks)
+
+    self.assertIsNone(nmsed_additional_fields)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(), exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(), exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(), exp_nms_classes.shape)
+    self.assertAllEqual(nmsed_masks.shape.as_list(), exp_nms_masks.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections])
+
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
+
+  def test_batch_multiclass_nms_with_additional_fields(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    additional_fields = {
+        'keypoints': tf.constant(
+            [[[[6, 7], [8, 9]],
+              [[0, 1], [2, 3]],
+              [[0, 0], [0, 0]],
+              [[0, 0], [0, 0]]],
+             [[[13, 14], [15, 16]],
+              [[8, 9], [10, 11]],
+              [[10, 11], [12, 13]],
+              [[0, 0], [0, 0]]]],
+            tf.float32)
+    }
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+    exp_nms_additional_fields = {
+        'keypoints': np.array([[[[0, 0], [0, 0]],
+                                [[6, 7], [8, 9]],
+                                [[0, 0], [0, 0]],
+                                [[0, 0], [0, 0]]],
+                               [[[10, 11], [12, 13]],
+                                [[13, 14], [15, 16]],
+                                [[8, 9], [10, 11]],
+                                [[0, 0], [0, 0]]]])
+    }
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        additional_fields=additional_fields)
+
+    self.assertIsNone(nmsed_masks)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(), exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(), exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(), exp_nms_classes.shape)
+    self.assertEqual(len(nmsed_additional_fields),
+                     len(exp_nms_additional_fields))
+    for key in exp_nms_additional_fields:
+      self.assertAllEqual(nmsed_additional_fields[key].shape.as_list(),
+                          exp_nms_additional_fields[key].shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_additional_fields,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_additional_fields, num_detections])
+
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      for key in exp_nms_additional_fields:
+        self.assertAllClose(nmsed_additional_fields[key],
+                            exp_nms_additional_fields[key])
+      self.assertAllClose(num_detections, [2, 3])
+
+  def test_batch_multiclass_nms_with_dynamic_batch_size(self):
+    boxes_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 4))
+    scores_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2))
+    masks_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 2, 2))
+
+    boxes = np.array([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                       [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                       [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                       [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                      [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                       [[0, 100, 1, 101], [0, 100, 1, 101]],
+                       [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                       [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]])
+    scores = np.array([[[.9, 0.01], [.75, 0.05],
+                        [.6, 0.01], [.95, 0]],
+                       [[.5, 0.01], [.3, 0.01],
+                        [.01, .85], [.01, .5]]])
+    masks = np.array([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
+                       [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
+                       [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
+                       [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
+                      [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
+                       [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
+                       [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
+                       [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+    exp_nms_masks = np.array([[[[6, 7], [8, 9]],
+                               [[0, 1], [2, 3]],
+                               [[0, 0], [0, 0]],
+                               [[0, 0], [0, 0]]],
+                              [[[13, 14], [15, 16]],
+                               [[8, 9], [10, 11]],
+                               [[10, 11], [12, 13]],
+                               [[0, 0], [0, 0]]]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes_placeholder, scores_placeholder, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        masks=masks_placeholder)
+
+    self.assertIsNone(nmsed_additional_fields)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(), [None, 4, 4])
+    self.assertAllEqual(nmsed_scores.shape.as_list(), [None, 4])
+    self.assertAllEqual(nmsed_classes.shape.as_list(), [None, 4])
+    self.assertAllEqual(nmsed_masks.shape.as_list(), [None, 4, 2, 2])
+    self.assertEqual(num_detections.shape.as_list(), [None])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections],
+                                  feed_dict={boxes_placeholder: boxes,
+                                             scores_placeholder: scores,
+                                             masks_placeholder: masks})
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
+
+  def test_batch_multiclass_nms_with_masks_and_num_valid_boxes(self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    masks = tf.constant([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
+                          [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
+                          [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
+                          [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
+                         [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
+                          [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
+                          [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
+                          [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]],
+                        tf.float32)
+    num_valid_boxes = tf.constant([1, 1], tf.int32)
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[[0, 0, 1, 1],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]],
+                       [[0, 10.1, 1, 11.1],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_nms_scores = [[.9, 0, 0, 0],
+                      [.5, 0, 0, 0]]
+    exp_nms_classes = [[0, 0, 0, 0],
+                       [0, 0, 0, 0]]
+    exp_nms_masks = [[[[0, 1], [2, 3]],
+                      [[0, 0], [0, 0]],
+                      [[0, 0], [0, 0]],
+                      [[0, 0], [0, 0]]],
+                     [[[8, 9], [10, 11]],
+                      [[0, 0], [0, 0]],
+                      [[0, 0], [0, 0]],
+                      [[0, 0], [0, 0]]]]
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        num_valid_boxes=num_valid_boxes, masks=masks)
+
+    self.assertIsNone(nmsed_additional_fields)
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [1, 1])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
+
+  def test_batch_multiclass_nms_with_additional_fields_and_num_valid_boxes(
+      self):
+    boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                          [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                          [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                          [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                         [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                          [[0, 100, 1, 101], [0, 100, 1, 101]],
+                          [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                          [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
+                        tf.float32)
+    scores = tf.constant([[[.9, 0.01], [.75, 0.05],
+                           [.6, 0.01], [.95, 0]],
+                          [[.5, 0.01], [.3, 0.01],
+                           [.01, .85], [.01, .5]]])
+    additional_fields = {
+        'keypoints': tf.constant(
+            [[[[6, 7], [8, 9]],
+              [[0, 1], [2, 3]],
+              [[0, 0], [0, 0]],
+              [[0, 0], [0, 0]]],
+             [[[13, 14], [15, 16]],
+              [[8, 9], [10, 11]],
+              [[10, 11], [12, 13]],
+              [[0, 0], [0, 0]]]],
+            tf.float32)
+    }
+    num_valid_boxes = tf.constant([1, 1], tf.int32)
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = [[[0, 0, 1, 1],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]],
+                       [[0, 10.1, 1, 11.1],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_nms_scores = [[.9, 0, 0, 0],
+                      [.5, 0, 0, 0]]
+    exp_nms_classes = [[0, 0, 0, 0],
+                       [0, 0, 0, 0]]
+    exp_nms_additional_fields = {
+        'keypoints': np.array([[[[6, 7], [8, 9]],
+                                [[0, 0], [0, 0]],
+                                [[0, 0], [0, 0]],
+                                [[0, 0], [0, 0]]],
+                               [[[13, 14], [15, 16]],
+                                [[0, 0], [0, 0]],
+                                [[0, 0], [0, 0]],
+                                [[0, 0], [0, 0]]]])
+    }
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     nmsed_additional_fields, num_detections
+    ) = post_processing.batch_multiclass_non_max_suppression(
+        boxes, scores, score_thresh, iou_thresh,
+        max_size_per_class=max_output_size, max_total_size=max_output_size,
+        num_valid_boxes=num_valid_boxes,
+        additional_fields=additional_fields)
+
+    self.assertIsNone(nmsed_masks)
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_additional_fields,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_additional_fields, num_detections])
+
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      for key in exp_nms_additional_fields:
+        self.assertAllClose(nmsed_additional_fields[key],
+                            exp_nms_additional_fields[key])
+      self.assertAllClose(num_detections, [1, 1])
+
+  # TODO(bhattad): Remove conditional after CMLE moves to TF 1.9
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher.py
new file mode 100644
index 00000000..e690c599
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher.py
@@ -0,0 +1,61 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Provides functions to prefetch tensors to feed into models."""
+import tensorflow as tf
+
+
+def prefetch(tensor_dict, capacity):
+  """Creates a prefetch queue for tensors.
+
+  Creates a FIFO queue to asynchronously enqueue tensor_dicts and returns a
+  dequeue op that evaluates to a tensor_dict. This function is useful in
+  prefetching preprocessed tensors so that the data is readily available for
+  consumers.
+
+  Example input pipeline when you don't need batching:
+  ----------------------------------------------------
+  key, string_tensor = slim.parallel_reader.parallel_read(...)
+  tensor_dict = decoder.decode(string_tensor)
+  tensor_dict = preprocessor.preprocess(tensor_dict, ...)
+  prefetch_queue = prefetcher.prefetch(tensor_dict, capacity=20)
+  tensor_dict = prefetch_queue.dequeue()
+  outputs = Model(tensor_dict)
+  ...
+  ----------------------------------------------------
+
+  For input pipelines with batching, refer to core/batcher.py
+
+  Args:
+    tensor_dict: a dictionary of tensors to prefetch.
+    capacity: the size of the prefetch queue.
+
+  Returns:
+    a FIFO prefetcher queue
+  """
+  names = list(tensor_dict.keys())
+  dtypes = [t.dtype for t in tensor_dict.values()]
+  shapes = [t.get_shape() for t in tensor_dict.values()]
+  prefetch_queue = tf.PaddingFIFOQueue(capacity, dtypes=dtypes,
+                                       shapes=shapes,
+                                       names=names,
+                                       name='prefetch_queue')
+  enqueue_op = prefetch_queue.enqueue(tensor_dict)
+  tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner(
+      prefetch_queue, [enqueue_op]))
+  tf.summary.scalar('queue/%s/fraction_of_%d_full' % (prefetch_queue.name,
+                                                      capacity),
+                    tf.to_float(prefetch_queue.size()) * (1. / capacity))
+  return prefetch_queue
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher_test.py
new file mode 100644
index 00000000..63f557e3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/prefetcher_test.py
@@ -0,0 +1,101 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.prefetcher."""
+import tensorflow as tf
+
+from object_detection.core import prefetcher
+
+slim = tf.contrib.slim
+
+
+class PrefetcherTest(tf.test.TestCase):
+
+  def test_prefetch_tensors_with_fully_defined_shapes(self):
+    with self.test_session() as sess:
+      batch_size = 10
+      image_size = 32
+      num_batches = 5
+      examples = tf.Variable(tf.constant(0, dtype=tf.int64))
+      counter = examples.count_up_to(num_batches)
+      image = tf.random_normal([batch_size, image_size,
+                                image_size, 3],
+                               dtype=tf.float32,
+                               name='images')
+      label = tf.random_uniform([batch_size, 1], 0, 10,
+                                dtype=tf.int32, name='labels')
+
+      prefetch_queue = prefetcher.prefetch(tensor_dict={'counter': counter,
+                                                        'image': image,
+                                                        'label': label},
+                                           capacity=100)
+      tensor_dict = prefetch_queue.dequeue()
+
+      self.assertAllEqual(tensor_dict['image'].get_shape().as_list(),
+                          [batch_size, image_size, image_size, 3])
+      self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
+                          [batch_size, 1])
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        for _ in range(num_batches):
+          results = sess.run(tensor_dict)
+          self.assertEquals(results['image'].shape,
+                            (batch_size, image_size, image_size, 3))
+          self.assertEquals(results['label'].shape, (batch_size, 1))
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(tensor_dict)
+
+  def test_prefetch_tensors_with_partially_defined_shapes(self):
+    with self.test_session() as sess:
+      batch_size = 10
+      image_size = 32
+      num_batches = 5
+      examples = tf.Variable(tf.constant(0, dtype=tf.int64))
+      counter = examples.count_up_to(num_batches)
+      image = tf.random_normal([batch_size,
+                                tf.Variable(image_size),
+                                tf.Variable(image_size), 3],
+                               dtype=tf.float32,
+                               name='image')
+      image.set_shape([batch_size, None, None, 3])
+      label = tf.random_uniform([batch_size, tf.Variable(1)], 0,
+                                10, dtype=tf.int32, name='label')
+      label.set_shape([batch_size, None])
+
+      prefetch_queue = prefetcher.prefetch(tensor_dict={'counter': counter,
+                                                        'image': image,
+                                                        'label': label},
+                                           capacity=100)
+      tensor_dict = prefetch_queue.dequeue()
+
+      self.assertAllEqual(tensor_dict['image'].get_shape().as_list(),
+                          [batch_size, None, None, 3])
+      self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
+                          [batch_size, None])
+
+      tf.initialize_all_variables().run()
+      with slim.queues.QueueRunners(sess):
+        for _ in range(num_batches):
+          results = sess.run(tensor_dict)
+          self.assertEquals(results['image'].shape,
+                            (batch_size, image_size, image_size, 3))
+          self.assertEquals(results['label'].shape, (batch_size, 1))
+        with self.assertRaises(tf.errors.OutOfRangeError):
+          sess.run(tensor_dict)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor.py
new file mode 100644
index 00000000..4d8f60b0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor.py
@@ -0,0 +1,3320 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Preprocess images and bounding boxes for detection.
+
+We perform two sets of operations in preprocessing stage:
+(a) operations that are applied to both training and testing data,
+(b) operations that are applied only to training data for the purpose of
+    data augmentation.
+
+A preprocessing function receives a set of inputs,
+e.g. an image and bounding boxes,
+performs an operation on them, and returns them.
+Some examples are: randomly cropping the image, randomly mirroring the image,
+                   randomly changing the brightness, contrast, hue and
+                   randomly jittering the bounding boxes.
+
+The preprocess function receives a tensor_dict which is a dictionary that maps
+different field names to their tensors. For example,
+tensor_dict[fields.InputDataFields.image] holds the image tensor.
+The image is a rank 4 tensor: [1, height, width, channels] with
+dtype=tf.float32. The groundtruth_boxes is a rank 2 tensor: [N, 4] where
+in each row there is a box with [ymin xmin ymax xmax].
+Boxes are in normalized coordinates meaning
+their coordinate values range in [0, 1]
+
+To preprocess multiple images with the same operations in cases where
+nondeterministic operations are used, a preprocessor_cache.PreprocessorCache
+object can be passed into the preprocess function or individual operations.
+All nondeterministic operations except random_jitter_boxes support caching.
+E.g.
+Let tensor_dict{1,2,3,4,5} be copies of the same inputs.
+Let preprocess_options contain nondeterministic operation(s) excluding
+random_jitter_boxes.
+
+cache1 = preprocessor_cache.PreprocessorCache()
+cache2 = preprocessor_cache.PreprocessorCache()
+a = preprocess(tensor_dict1, preprocess_options, preprocess_vars_cache=cache1)
+b = preprocess(tensor_dict2, preprocess_options, preprocess_vars_cache=cache1)
+c = preprocess(tensor_dict3, preprocess_options, preprocess_vars_cache=cache2)
+d = preprocess(tensor_dict4, preprocess_options, preprocess_vars_cache=cache2)
+e = preprocess(tensor_dict5, preprocess_options)
+
+Then correspondings tensors of object pairs (a,b) and (c,d)
+are guaranteed to be equal element-wise, but the equality of any other object
+pair cannot be determined.
+
+Important Note: In tensor_dict, images is a rank 4 tensor, but preprocessing
+functions receive a rank 3 tensor for processing the image. Thus, inside the
+preprocess function we squeeze the image to become a rank 3 tensor and then
+we pass it to the functions. At the end of the preprocess we expand the image
+back to rank 4.
+"""
+
+import functools
+import inspect
+import sys
+import tensorflow as tf
+
+from tensorflow.python.ops import control_flow_ops
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.core import keypoint_ops
+from object_detection.core import preprocessor_cache
+from object_detection.core import standard_fields as fields
+from object_detection.utils import shape_utils
+
+
+def _apply_with_random_selector(x,
+                                func,
+                                num_cases,
+                                preprocess_vars_cache=None,
+                                key=''):
+  """Computes func(x, sel), with sel sampled from [0...num_cases-1].
+
+  If both preprocess_vars_cache AND key are the same between two calls, sel will
+  be the same value in both calls.
+
+  Args:
+    x: input Tensor.
+    func: Python function to apply.
+    num_cases: Python int32, number of cases to sample sel from.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+    key: variable identifier for preprocess_vars_cache.
+
+  Returns:
+    The result of func(x, sel), where func receives the value of the
+    selector as a python integer, but sel is sampled dynamically.
+  """
+  generator_func = functools.partial(
+      tf.random_uniform, [], maxval=num_cases, dtype=tf.int32)
+  rand_sel = _get_or_create_preprocess_rand_vars(
+      generator_func, preprocessor_cache.PreprocessorCache.SELECTOR,
+      preprocess_vars_cache, key)
+
+  # Pass the real x only to one of the func calls.
+  return control_flow_ops.merge([func(
+      control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case)
+                                 for case in range(num_cases)])[0]
+
+
+def _apply_with_random_selector_tuples(x,
+                                       func,
+                                       num_cases,
+                                       preprocess_vars_cache=None,
+                                       key=''):
+  """Computes func(x, sel), with sel sampled from [0...num_cases-1].
+
+  If both preprocess_vars_cache AND key are the same between two calls, sel will
+  be the same value in both calls.
+
+  Args:
+    x: A tuple of input tensors.
+    func: Python function to apply.
+    num_cases: Python int32, number of cases to sample sel from.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+    key: variable identifier for preprocess_vars_cache.
+
+  Returns:
+    The result of func(x, sel), where func receives the value of the
+    selector as a python integer, but sel is sampled dynamically.
+  """
+  num_inputs = len(x)
+  generator_func = functools.partial(
+      tf.random_uniform, [], maxval=num_cases, dtype=tf.int32)
+  rand_sel = _get_or_create_preprocess_rand_vars(
+      generator_func, preprocessor_cache.PreprocessorCache.SELECTOR_TUPLES,
+      preprocess_vars_cache, key)
+
+  # Pass the real x only to one of the func calls.
+  tuples = [list() for t in x]
+  for case in range(num_cases):
+    new_x = [control_flow_ops.switch(t, tf.equal(rand_sel, case))[1] for t in x]
+    output = func(tuple(new_x), case)
+    for j in range(num_inputs):
+      tuples[j].append(output[j])
+
+  for i in range(num_inputs):
+    tuples[i] = control_flow_ops.merge(tuples[i])[0]
+  return tuple(tuples)
+
+
+def _get_or_create_preprocess_rand_vars(generator_func,
+                                        function_id,
+                                        preprocess_vars_cache,
+                                        key=''):
+  """Returns a tensor stored in preprocess_vars_cache or using generator_func.
+
+  If the tensor was previously generated and appears in the PreprocessorCache,
+  the previously generated tensor will be returned. Otherwise, a new tensor
+  is generated using generator_func and stored in the cache.
+
+  Args:
+    generator_func: A 0-argument function that generates a tensor.
+    function_id: identifier for the preprocessing function used.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+    key: identifier for the variable stored.
+  Returns:
+    The generated tensor.
+  """
+  if preprocess_vars_cache is not None:
+    var = preprocess_vars_cache.get(function_id, key)
+    if var is None:
+      var = generator_func()
+      preprocess_vars_cache.update(function_id, key, var)
+  else:
+    var = generator_func()
+  return var
+
+
+def _random_integer(minval, maxval, seed):
+  """Returns a random 0-D tensor between minval and maxval.
+
+  Args:
+    minval: minimum value of the random tensor.
+    maxval: maximum value of the random tensor.
+    seed: random seed.
+
+  Returns:
+    A random 0-D tensor between minval and maxval.
+  """
+  return tf.random_uniform(
+      [], minval=minval, maxval=maxval, dtype=tf.int32, seed=seed)
+
+
+# TODO(mttang): This method is needed because the current
+# tf.image.rgb_to_grayscale method does not support quantization. Replace with
+# tf.image.rgb_to_grayscale after quantization support is added.
+def _rgb_to_grayscale(images, name=None):
+  """Converts one or more images from RGB to Grayscale.
+
+  Outputs a tensor of the same `DType` and rank as `images`.  The size of the
+  last dimension of the output is 1, containing the Grayscale value of the
+  pixels.
+
+  Args:
+    images: The RGB tensor to convert. Last dimension must have size 3 and
+      should contain RGB values.
+    name: A name for the operation (optional).
+
+  Returns:
+    The converted grayscale image(s).
+  """
+  with tf.name_scope(name, 'rgb_to_grayscale', [images]) as name:
+    images = tf.convert_to_tensor(images, name='images')
+    # Remember original dtype to so we can convert back if needed
+    orig_dtype = images.dtype
+    flt_image = tf.image.convert_image_dtype(images, tf.float32)
+
+    # Reference for converting between RGB and grayscale.
+    # https://en.wikipedia.org/wiki/Luma_%28video%29
+    rgb_weights = [0.2989, 0.5870, 0.1140]
+    rank_1 = tf.expand_dims(tf.rank(images) - 1, 0)
+    gray_float = tf.reduce_sum(
+        flt_image * rgb_weights, rank_1, keep_dims=True)
+    gray_float.set_shape(images.get_shape()[:-1].concatenate([1]))
+    return tf.image.convert_image_dtype(gray_float, orig_dtype, name=name)
+
+
+def normalize_image(image, original_minval, original_maxval, target_minval,
+                    target_maxval):
+  """Normalizes pixel values in the image.
+
+  Moves the pixel values from the current [original_minval, original_maxval]
+  range to a the [target_minval, target_maxval] range.
+
+  Args:
+    image: rank 3 float32 tensor containing 1
+           image -> [height, width, channels].
+    original_minval: current image minimum value.
+    original_maxval: current image maximum value.
+    target_minval: target image minimum value.
+    target_maxval: target image maximum value.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('NormalizeImage', values=[image]):
+    original_minval = float(original_minval)
+    original_maxval = float(original_maxval)
+    target_minval = float(target_minval)
+    target_maxval = float(target_maxval)
+    image = tf.to_float(image)
+    image = tf.subtract(image, original_minval)
+    image = tf.multiply(image, (target_maxval - target_minval) /
+                        (original_maxval - original_minval))
+    image = tf.add(image, target_minval)
+    return image
+
+
+def retain_boxes_above_threshold(boxes,
+                                 labels,
+                                 label_weights,
+                                 label_confidences=None,
+                                 multiclass_scores=None,
+                                 masks=None,
+                                 keypoints=None,
+                                 threshold=0.0):
+  """Retains boxes whose label weight is above a given threshold.
+
+  If the label weight for a box is missing (represented by NaN), the box is
+  retained. The boxes that don't pass the threshold will not appear in the
+  returned tensor.
+
+  Args:
+    boxes: float32 tensor of shape [num_instance, 4] representing boxes
+      location in normalized coordinates.
+    labels: rank 1 int32 tensor of shape [num_instance] containing the object
+      classes.
+    label_weights: float32 tensor of shape [num_instance] representing the
+      weight for each box.
+    label_confidences: float32 tensor of shape [num_instance] representing the
+      confidence for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+      [num_instances, height, width] containing instance masks. The masks are of
+      the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+      [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
+      coordinates.
+    threshold: scalar python float.
+
+  Returns:
+    retained_boxes: [num_retained_instance, 4]
+    retianed_labels: [num_retained_instance]
+    retained_label_weights: [num_retained_instance]
+
+    If multiclass_scores, masks, or keypoints are not None, the function also
+      returns:
+
+    retained_multiclass_scores: [num_retained_instance, num_classes]
+    retained_masks: [num_retained_instance, height, width]
+    retained_keypoints: [num_retained_instance, num_keypoints, 2]
+  """
+  with tf.name_scope('RetainBoxesAboveThreshold',
+                     values=[boxes, labels, label_weights]):
+    indices = tf.where(
+        tf.logical_or(label_weights > threshold, tf.is_nan(label_weights)))
+    indices = tf.squeeze(indices, axis=1)
+    retained_boxes = tf.gather(boxes, indices)
+    retained_labels = tf.gather(labels, indices)
+    retained_label_weights = tf.gather(label_weights, indices)
+    result = [retained_boxes, retained_labels, retained_label_weights]
+
+    if label_confidences is not None:
+      retained_label_confidences = tf.gather(label_confidences, indices)
+      result.append(retained_label_confidences)
+
+    if multiclass_scores is not None:
+      retained_multiclass_scores = tf.gather(multiclass_scores, indices)
+      result.append(retained_multiclass_scores)
+
+    if masks is not None:
+      retained_masks = tf.gather(masks, indices)
+      result.append(retained_masks)
+
+    if keypoints is not None:
+      retained_keypoints = tf.gather(keypoints, indices)
+      result.append(retained_keypoints)
+
+    return result
+
+
+def _flip_boxes_left_right(boxes):
+  """Left-right flip the boxes.
+
+  Args:
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+
+  Returns:
+    Flipped boxes.
+  """
+  ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
+  flipped_xmin = tf.subtract(1.0, xmax)
+  flipped_xmax = tf.subtract(1.0, xmin)
+  flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)
+  return flipped_boxes
+
+
+def _flip_boxes_up_down(boxes):
+  """Up-down flip the boxes.
+
+  Args:
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+
+  Returns:
+    Flipped boxes.
+  """
+  ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
+  flipped_ymin = tf.subtract(1.0, ymax)
+  flipped_ymax = tf.subtract(1.0, ymin)
+  flipped_boxes = tf.concat([flipped_ymin, xmin, flipped_ymax, xmax], 1)
+  return flipped_boxes
+
+
+def _rot90_boxes(boxes):
+  """Rotate boxes counter-clockwise by 90 degrees.
+
+  Args:
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+
+  Returns:
+    Rotated boxes.
+  """
+  ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
+  rotated_ymin = tf.subtract(1.0, xmax)
+  rotated_ymax = tf.subtract(1.0, xmin)
+  rotated_xmin = ymin
+  rotated_xmax = ymax
+  rotated_boxes = tf.concat(
+      [rotated_ymin, rotated_xmin, rotated_ymax, rotated_xmax], 1)
+  return rotated_boxes
+
+
+def _flip_masks_left_right(masks):
+  """Left-right flip masks.
+
+  Args:
+    masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+
+  Returns:
+    flipped masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+  """
+  return masks[:, :, ::-1]
+
+
+def _flip_masks_up_down(masks):
+  """Up-down flip masks.
+
+  Args:
+    masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+
+  Returns:
+    flipped masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+  """
+  return masks[:, ::-1, :]
+
+
+def _rot90_masks(masks):
+  """Rotate masks counter-clockwise by 90 degrees.
+
+  Args:
+    masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+
+  Returns:
+    rotated masks: rank 3 float32 tensor with shape
+      [num_instances, height, width] representing instance masks.
+  """
+  masks = tf.transpose(masks, [0, 2, 1])
+  return masks[:, ::-1, :]
+
+
+def random_horizontal_flip(image,
+                           boxes=None,
+                           masks=None,
+                           keypoints=None,
+                           keypoint_flip_permutation=None,
+                           seed=None,
+                           preprocess_vars_cache=None):
+  """Randomly flips the image and detections horizontally.
+
+  The probability of flipping the image is 50%.
+
+  Args:
+    image: rank 3 float32 tensor with shape [height, width, channels].
+    boxes: (optional) rank 2 float32 tensor with shape [N, 4]
+           containing the bounding boxes.
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip
+                               permutation.
+    seed: random seed
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+
+    If boxes, masks, keypoints, and keypoint_flip_permutation are not None,
+    the function also returns the following tensors.
+
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+
+  Raises:
+    ValueError: if keypoints are provided but keypoint_flip_permutation is not.
+  """
+
+  def _flip_image(image):
+    # flip image
+    image_flipped = tf.image.flip_left_right(image)
+    return image_flipped
+
+  if keypoints is not None and keypoint_flip_permutation is None:
+    raise ValueError(
+        'keypoints are provided but keypoints_flip_permutation is not provided')
+
+  with tf.name_scope('RandomHorizontalFlip', values=[image, boxes]):
+    result = []
+    # random variable defining whether to do flip or not
+    generator_func = functools.partial(tf.random_uniform, [], seed=seed)
+    do_a_flip_random = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.HORIZONTAL_FLIP,
+        preprocess_vars_cache)
+    do_a_flip_random = tf.greater(do_a_flip_random, 0.5)
+
+    # flip image
+    image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
+    result.append(image)
+
+    # flip boxes
+    if boxes is not None:
+      boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_left_right(boxes),
+                      lambda: boxes)
+      result.append(boxes)
+
+    # flip masks
+    if masks is not None:
+      masks = tf.cond(do_a_flip_random, lambda: _flip_masks_left_right(masks),
+                      lambda: masks)
+      result.append(masks)
+
+    # flip keypoints
+    if keypoints is not None and keypoint_flip_permutation is not None:
+      permutation = keypoint_flip_permutation
+      keypoints = tf.cond(
+          do_a_flip_random,
+          lambda: keypoint_ops.flip_horizontal(keypoints, 0.5, permutation),
+          lambda: keypoints)
+      result.append(keypoints)
+
+    return tuple(result)
+
+
+def random_vertical_flip(image,
+                         boxes=None,
+                         masks=None,
+                         keypoints=None,
+                         keypoint_flip_permutation=None,
+                         seed=None,
+                         preprocess_vars_cache=None):
+  """Randomly flips the image and detections vertically.
+
+  The probability of flipping the image is 50%.
+
+  Args:
+    image: rank 3 float32 tensor with shape [height, width, channels].
+    boxes: (optional) rank 2 float32 tensor with shape [N, 4]
+           containing the bounding boxes.
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip
+                               permutation.
+    seed: random seed
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+
+    If boxes, masks, keypoints, and keypoint_flip_permutation are not None,
+    the function also returns the following tensors.
+
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+
+  Raises:
+    ValueError: if keypoints are provided but keypoint_flip_permutation is not.
+  """
+
+  def _flip_image(image):
+    # flip image
+    image_flipped = tf.image.flip_up_down(image)
+    return image_flipped
+
+  if keypoints is not None and keypoint_flip_permutation is None:
+    raise ValueError(
+        'keypoints are provided but keypoints_flip_permutation is not provided')
+
+  with tf.name_scope('RandomVerticalFlip', values=[image, boxes]):
+    result = []
+    # random variable defining whether to do flip or not
+    generator_func = functools.partial(tf.random_uniform, [], seed=seed)
+    do_a_flip_random = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.VERTICAL_FLIP,
+        preprocess_vars_cache)
+    do_a_flip_random = tf.greater(do_a_flip_random, 0.5)
+
+    # flip image
+    image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
+    result.append(image)
+
+    # flip boxes
+    if boxes is not None:
+      boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_up_down(boxes),
+                      lambda: boxes)
+      result.append(boxes)
+
+    # flip masks
+    if masks is not None:
+      masks = tf.cond(do_a_flip_random, lambda: _flip_masks_up_down(masks),
+                      lambda: masks)
+      result.append(masks)
+
+    # flip keypoints
+    if keypoints is not None and keypoint_flip_permutation is not None:
+      permutation = keypoint_flip_permutation
+      keypoints = tf.cond(
+          do_a_flip_random,
+          lambda: keypoint_ops.flip_vertical(keypoints, 0.5, permutation),
+          lambda: keypoints)
+      result.append(keypoints)
+
+    return tuple(result)
+
+
+def random_rotation90(image,
+                      boxes=None,
+                      masks=None,
+                      keypoints=None,
+                      seed=None,
+                      preprocess_vars_cache=None):
+  """Randomly rotates the image and detections 90 degrees counter-clockwise.
+
+  The probability of rotating the image is 50%. This can be combined with
+  random_horizontal_flip and random_vertical_flip to produce an output with a
+  uniform distribution of the eight possible 90 degree rotation / reflection
+  combinations.
+
+  Args:
+    image: rank 3 float32 tensor with shape [height, width, channels].
+    boxes: (optional) rank 2 float32 tensor with shape [N, 4]
+           containing the bounding boxes.
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    seed: random seed
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+
+    If boxes, masks, and keypoints, are not None,
+    the function also returns the following tensors.
+
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+
+  def _rot90_image(image):
+    # flip image
+    image_rotated = tf.image.rot90(image)
+    return image_rotated
+
+  with tf.name_scope('RandomRotation90', values=[image, boxes]):
+    result = []
+
+    # random variable defining whether to rotate by 90 degrees or not
+    generator_func = functools.partial(tf.random_uniform, [], seed=seed)
+    do_a_rot90_random = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.ROTATION90,
+        preprocess_vars_cache)
+    do_a_rot90_random = tf.greater(do_a_rot90_random, 0.5)
+
+    # flip image
+    image = tf.cond(do_a_rot90_random, lambda: _rot90_image(image),
+                    lambda: image)
+    result.append(image)
+
+    # flip boxes
+    if boxes is not None:
+      boxes = tf.cond(do_a_rot90_random, lambda: _rot90_boxes(boxes),
+                      lambda: boxes)
+      result.append(boxes)
+
+    # flip masks
+    if masks is not None:
+      masks = tf.cond(do_a_rot90_random, lambda: _rot90_masks(masks),
+                      lambda: masks)
+      result.append(masks)
+
+    # flip keypoints
+    if keypoints is not None:
+      keypoints = tf.cond(
+          do_a_rot90_random,
+          lambda: keypoint_ops.rot90(keypoints),
+          lambda: keypoints)
+      result.append(keypoints)
+
+    return tuple(result)
+
+
+def random_pixel_value_scale(image,
+                             minval=0.9,
+                             maxval=1.1,
+                             seed=None,
+                             preprocess_vars_cache=None):
+  """Scales each value in the pixels of the image.
+
+     This function scales each pixel independent of the other ones.
+     For each value in image tensor, draws a random number between
+     minval and maxval and multiples the values with them.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    minval: lower ratio of scaling pixel values.
+    maxval: upper ratio of scaling pixel values.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('RandomPixelValueScale', values=[image]):
+    generator_func = functools.partial(
+        tf.random_uniform, tf.shape(image),
+        minval=minval, maxval=maxval,
+        dtype=tf.float32, seed=seed)
+    color_coef = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.PIXEL_VALUE_SCALE,
+        preprocess_vars_cache)
+
+    image = tf.multiply(image, color_coef)
+    image = tf.clip_by_value(image, 0.0, 255.0)
+
+  return image
+
+
+def random_image_scale(image,
+                       masks=None,
+                       min_scale_ratio=0.5,
+                       max_scale_ratio=2.0,
+                       seed=None,
+                       preprocess_vars_cache=None):
+  """Scales the image size.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels].
+    masks: (optional) rank 3 float32 tensor containing masks with
+      size [height, width, num_masks]. The value is set to None if there are no
+      masks.
+    min_scale_ratio: minimum scaling ratio.
+    max_scale_ratio: maximum scaling ratio.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    masks: If masks is not none, resized masks which are the same rank as input
+      masks will be returned.
+  """
+  with tf.name_scope('RandomImageScale', values=[image]):
+    result = []
+    image_shape = tf.shape(image)
+    image_height = image_shape[0]
+    image_width = image_shape[1]
+    generator_func = functools.partial(
+        tf.random_uniform, [],
+        minval=min_scale_ratio, maxval=max_scale_ratio,
+        dtype=tf.float32, seed=seed)
+    size_coef = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.IMAGE_SCALE,
+        preprocess_vars_cache)
+
+    image_newysize = tf.to_int32(
+        tf.multiply(tf.to_float(image_height), size_coef))
+    image_newxsize = tf.to_int32(
+        tf.multiply(tf.to_float(image_width), size_coef))
+    image = tf.image.resize_images(
+        image, [image_newysize, image_newxsize], align_corners=True)
+    result.append(image)
+    if masks is not None:
+      masks = tf.image.resize_images(
+          masks, [image_newysize, image_newxsize],
+          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+          align_corners=True)
+      result.append(masks)
+    return tuple(result)
+
+
+def random_rgb_to_gray(image,
+                       probability=0.1,
+                       seed=None,
+                       preprocess_vars_cache=None):
+  """Changes the image from RGB to Grayscale with the given probability.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    probability: the probability of returning a grayscale image.
+            The probability should be a number between [0, 1].
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  def _image_to_gray(image):
+    image_gray1 = _rgb_to_grayscale(image)
+    image_gray3 = tf.image.grayscale_to_rgb(image_gray1)
+    return image_gray3
+
+  with tf.name_scope('RandomRGBtoGray', values=[image]):
+    # random variable defining whether to change to grayscale or not
+    generator_func = functools.partial(tf.random_uniform, [], seed=seed)
+    do_gray_random = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.RGB_TO_GRAY,
+        preprocess_vars_cache)
+
+    image = tf.cond(
+        tf.greater(do_gray_random, probability), lambda: image,
+        lambda: _image_to_gray(image))
+
+  return image
+
+
+def random_adjust_brightness(image,
+                             max_delta=0.2,
+                             seed=None,
+                             preprocess_vars_cache=None):
+  """Randomly adjusts brightness.
+
+  Makes sure the output image is still between 0 and 255.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    max_delta: how much to change the brightness. A value between [0, 1).
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+    boxes: boxes which is the same shape as input boxes.
+  """
+  with tf.name_scope('RandomAdjustBrightness', values=[image]):
+    generator_func = functools.partial(tf.random_uniform, [],
+                                       -max_delta, max_delta, seed=seed)
+    delta = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.ADJUST_BRIGHTNESS,
+        preprocess_vars_cache)
+
+    image = tf.image.adjust_brightness(image / 255, delta) * 255
+    image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
+    return image
+
+
+def random_adjust_contrast(image,
+                           min_delta=0.8,
+                           max_delta=1.25,
+                           seed=None,
+                           preprocess_vars_cache=None):
+  """Randomly adjusts contrast.
+
+  Makes sure the output image is still between 0 and 255.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    min_delta: see max_delta.
+    max_delta: how much to change the contrast. Contrast will change with a
+               value between min_delta and max_delta. This value will be
+               multiplied to the current contrast of the image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('RandomAdjustContrast', values=[image]):
+    generator_func = functools.partial(tf.random_uniform, [],
+                                       min_delta, max_delta, seed=seed)
+    contrast_factor = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.ADJUST_CONTRAST,
+        preprocess_vars_cache)
+    image = tf.image.adjust_contrast(image / 255, contrast_factor) * 255
+    image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
+    return image
+
+
+def random_adjust_hue(image,
+                      max_delta=0.02,
+                      seed=None,
+                      preprocess_vars_cache=None):
+  """Randomly adjusts hue.
+
+  Makes sure the output image is still between 0 and 255.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    max_delta: change hue randomly with a value between 0 and max_delta.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('RandomAdjustHue', values=[image]):
+    generator_func = functools.partial(tf.random_uniform, [],
+                                       -max_delta, max_delta, seed=seed)
+    delta = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.ADJUST_HUE,
+        preprocess_vars_cache)
+    image = tf.image.adjust_hue(image / 255, delta) * 255
+    image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
+    return image
+
+
+def random_adjust_saturation(image,
+                             min_delta=0.8,
+                             max_delta=1.25,
+                             seed=None,
+                             preprocess_vars_cache=None):
+  """Randomly adjusts saturation.
+
+  Makes sure the output image is still between 0 and 255.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    min_delta: see max_delta.
+    max_delta: how much to change the saturation. Saturation will change with a
+               value between min_delta and max_delta. This value will be
+               multiplied to the current saturation of the image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('RandomAdjustSaturation', values=[image]):
+    generator_func = functools.partial(tf.random_uniform, [],
+                                       min_delta, max_delta, seed=seed)
+    saturation_factor = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.ADJUST_SATURATION,
+        preprocess_vars_cache)
+    image = tf.image.adjust_saturation(image / 255, saturation_factor) * 255
+    image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
+    return image
+
+
+def random_distort_color(image, color_ordering=0, preprocess_vars_cache=None):
+  """Randomly distorts color.
+
+  Randomly distorts color using a combination of brightness, hue, contrast and
+  saturation changes. Makes sure the output image is still between 0 and 255.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    color_ordering: Python int, a type of distortion (valid values: 0, 1).
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same shape as input image.
+
+  Raises:
+    ValueError: if color_ordering is not in {0, 1}.
+  """
+  with tf.name_scope('RandomDistortColor', values=[image]):
+    if color_ordering == 0:
+      image = random_adjust_brightness(
+          image, max_delta=32. / 255.,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_saturation(
+          image, min_delta=0.5, max_delta=1.5,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_hue(
+          image, max_delta=0.2,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_contrast(
+          image, min_delta=0.5, max_delta=1.5,
+          preprocess_vars_cache=preprocess_vars_cache)
+
+    elif color_ordering == 1:
+      image = random_adjust_brightness(
+          image, max_delta=32. / 255.,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_contrast(
+          image, min_delta=0.5, max_delta=1.5,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_saturation(
+          image, min_delta=0.5, max_delta=1.5,
+          preprocess_vars_cache=preprocess_vars_cache)
+      image = random_adjust_hue(
+          image, max_delta=0.2,
+          preprocess_vars_cache=preprocess_vars_cache)
+    else:
+      raise ValueError('color_ordering must be in {0, 1}')
+    return image
+
+
+def random_jitter_boxes(boxes, ratio=0.05, seed=None):
+  """Randomly jitter boxes in image.
+
+  Args:
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    ratio: The ratio of the box width and height that the corners can jitter.
+           For example if the width is 100 pixels and ratio is 0.05,
+           the corners can jitter up to 5 pixels in the x direction.
+    seed: random seed.
+
+  Returns:
+    boxes: boxes which is the same shape as input boxes.
+  """
+  def random_jitter_box(box, ratio, seed):
+    """Randomly jitter box.
+
+    Args:
+      box: bounding box [1, 1, 4].
+      ratio: max ratio between jittered box and original box,
+      a number between [0, 0.5].
+      seed: random seed.
+
+    Returns:
+      jittered_box: jittered box.
+    """
+    rand_numbers = tf.random_uniform(
+        [1, 1, 4], minval=-ratio, maxval=ratio, dtype=tf.float32, seed=seed)
+    box_width = tf.subtract(box[0, 0, 3], box[0, 0, 1])
+    box_height = tf.subtract(box[0, 0, 2], box[0, 0, 0])
+    hw_coefs = tf.stack([box_height, box_width, box_height, box_width])
+    hw_rand_coefs = tf.multiply(hw_coefs, rand_numbers)
+    jittered_box = tf.add(box, hw_rand_coefs)
+    jittered_box = tf.clip_by_value(jittered_box, 0.0, 1.0)
+    return jittered_box
+
+  with tf.name_scope('RandomJitterBoxes', values=[boxes]):
+    # boxes are [N, 4]. Lets first make them [N, 1, 1, 4]
+    boxes_shape = tf.shape(boxes)
+    boxes = tf.expand_dims(boxes, 1)
+    boxes = tf.expand_dims(boxes, 2)
+
+    distorted_boxes = tf.map_fn(
+        lambda x: random_jitter_box(x, ratio, seed), boxes, dtype=tf.float32)
+
+    distorted_boxes = tf.reshape(distorted_boxes, boxes_shape)
+
+    return distorted_boxes
+
+
+def _strict_random_crop_image(image,
+                              boxes,
+                              labels,
+                              label_weights,
+                              label_confidences=None,
+                              multiclass_scores=None,
+                              masks=None,
+                              keypoints=None,
+                              min_object_covered=1.0,
+                              aspect_ratio_range=(0.75, 1.33),
+                              area_range=(0.1, 1.0),
+                              overlap_thresh=0.3,
+                              clip_boxes=True,
+                              preprocess_vars_cache=None):
+  """Performs random crop.
+
+  Note: Keypoint coordinates that are outside the crop will be set to NaN, which
+  is consistent with the original keypoint encoding for non-existing keypoints.
+  This function always crops the image and is supposed to be used by
+  `random_crop_image` function which sometimes returns the image unchanged.
+
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes with shape
+           [num_instances, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: (optional) float32 tensor of shape [num_instances]
+      representing the confidence for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If label_weights, multiclass_scores, masks, or keypoints is not None, the
+    function also returns:
+    label_weights: rank 1 float32 tensor with shape [num_instances].
+    multiclass_scores: rank 2 float32 tensor with shape
+                       [num_instances, num_classes]
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+  with tf.name_scope('RandomCropImage', values=[image, boxes]):
+    image_shape = tf.shape(image)
+
+    # boxes are [N, 4]. Lets first make them [N, 1, 4].
+    boxes_expanded = tf.expand_dims(
+        tf.clip_by_value(
+            boxes, clip_value_min=0.0, clip_value_max=1.0), 1)
+
+    generator_func = functools.partial(
+        tf.image.sample_distorted_bounding_box,
+        image_shape,
+        bounding_boxes=boxes_expanded,
+        min_object_covered=min_object_covered,
+        aspect_ratio_range=aspect_ratio_range,
+        area_range=area_range,
+        max_attempts=100,
+        use_image_if_no_bounding_boxes=True)
+
+    # for ssd cropping, each value of min_object_covered has its own
+    # cached random variable
+    sample_distorted_bounding_box = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.STRICT_CROP_IMAGE,
+        preprocess_vars_cache, key=min_object_covered)
+
+    im_box_begin, im_box_size, im_box = sample_distorted_bounding_box
+
+    new_image = tf.slice(image, im_box_begin, im_box_size)
+    new_image.set_shape([None, None, image.get_shape()[2]])
+
+    # [1, 4]
+    im_box_rank2 = tf.squeeze(im_box, squeeze_dims=[0])
+    # [4]
+    im_box_rank1 = tf.squeeze(im_box)
+
+    boxlist = box_list.BoxList(boxes)
+    boxlist.add_field('labels', labels)
+
+    if label_weights is not None:
+      boxlist.add_field('label_weights', label_weights)
+
+    if label_confidences is not None:
+      boxlist.add_field('label_confidences', label_confidences)
+
+    if multiclass_scores is not None:
+      boxlist.add_field('multiclass_scores', multiclass_scores)
+
+    im_boxlist = box_list.BoxList(im_box_rank2)
+
+    # remove boxes that are outside cropped image
+    boxlist, inside_window_ids = box_list_ops.prune_completely_outside_window(
+        boxlist, im_box_rank1)
+
+    # remove boxes that are outside image
+    overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes(
+        boxlist, im_boxlist, overlap_thresh)
+
+    # change the coordinate of the remaining boxes
+    new_labels = overlapping_boxlist.get_field('labels')
+    new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
+                                                       im_box_rank1)
+    new_boxes = new_boxlist.get()
+    if clip_boxes:
+      new_boxes = tf.clip_by_value(
+          new_boxes, clip_value_min=0.0, clip_value_max=1.0)
+
+    result = [new_image, new_boxes, new_labels]
+
+    if label_weights is not None:
+      new_label_weights = overlapping_boxlist.get_field('label_weights')
+      result.append(new_label_weights)
+
+    if label_confidences is not None:
+      new_label_confidences = overlapping_boxlist.get_field('label_confidences')
+      result.append(new_label_confidences)
+
+    if multiclass_scores is not None:
+      new_multiclass_scores = overlapping_boxlist.get_field('multiclass_scores')
+      result.append(new_multiclass_scores)
+
+    if masks is not None:
+      masks_of_boxes_inside_window = tf.gather(masks, inside_window_ids)
+      masks_of_boxes_completely_inside_window = tf.gather(
+          masks_of_boxes_inside_window, keep_ids)
+      masks_box_begin = [0, im_box_begin[0], im_box_begin[1]]
+      masks_box_size = [-1, im_box_size[0], im_box_size[1]]
+      new_masks = tf.slice(
+          masks_of_boxes_completely_inside_window,
+          masks_box_begin, masks_box_size)
+      result.append(new_masks)
+
+    if keypoints is not None:
+      keypoints_of_boxes_inside_window = tf.gather(keypoints, inside_window_ids)
+      keypoints_of_boxes_completely_inside_window = tf.gather(
+          keypoints_of_boxes_inside_window, keep_ids)
+      new_keypoints = keypoint_ops.change_coordinate_frame(
+          keypoints_of_boxes_completely_inside_window, im_box_rank1)
+      if clip_boxes:
+        new_keypoints = keypoint_ops.prune_outside_window(new_keypoints,
+                                                          [0.0, 0.0, 1.0, 1.0])
+      result.append(new_keypoints)
+
+    return tuple(result)
+
+
+def random_crop_image(image,
+                      boxes,
+                      labels,
+                      label_weights,
+                      label_confidences=None,
+                      multiclass_scores=None,
+                      masks=None,
+                      keypoints=None,
+                      min_object_covered=1.0,
+                      aspect_ratio_range=(0.75, 1.33),
+                      area_range=(0.1, 1.0),
+                      overlap_thresh=0.3,
+                      clip_boxes=True,
+                      random_coef=0.0,
+                      seed=None,
+                      preprocess_vars_cache=None):
+  """Randomly crops the image.
+
+  Given the input image and its bounding boxes, this op randomly
+  crops a subimage.  Given a user-provided set of input constraints,
+  the crop window is resampled until it satisfies these constraints.
+  If within 100 trials it is unable to find a valid crop, the original
+  image is returned. See the Args section for a description of the input
+  constraints. Both input boxes and returned Boxes are in normalized
+  form (e.g., lie in the unit square [0, 1]).
+  This function will return the original image with probability random_coef.
+
+  Note: Keypoint coordinates that are outside the crop will be set to NaN, which
+  is consistent with the original keypoint encoding for non-existing keypoints.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes with shape
+           [num_instances, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: (optional) float32 tensor of shape [num_instances].
+      representing the confidence for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: Image shape will be [new_height, new_width, channels].
+    boxes: boxes which is the same rank as input boxes. Boxes are in normalized
+           form.
+    labels: new labels.
+
+    If label_weights, multiclass_scores, masks, or keypoints is not None, the
+    function also returns:
+    label_weights: rank 1 float32 tensor with shape [num_instances].
+    multiclass_scores: rank 2 float32 tensor with shape
+                       [num_instances, num_classes]
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+
+  def strict_random_crop_image_fn():
+    return _strict_random_crop_image(
+        image,
+        boxes,
+        labels,
+        label_weights,
+        label_confidences=label_confidences,
+        multiclass_scores=multiclass_scores,
+        masks=masks,
+        keypoints=keypoints,
+        min_object_covered=min_object_covered,
+        aspect_ratio_range=aspect_ratio_range,
+        area_range=area_range,
+        overlap_thresh=overlap_thresh,
+        clip_boxes=clip_boxes,
+        preprocess_vars_cache=preprocess_vars_cache)
+
+  # avoids tf.cond to make faster RCNN training on borg. See b/140057645.
+  if random_coef < sys.float_info.min:
+    result = strict_random_crop_image_fn()
+  else:
+    generator_func = functools.partial(tf.random_uniform, [], seed=seed)
+    do_a_crop_random = _get_or_create_preprocess_rand_vars(
+        generator_func, preprocessor_cache.PreprocessorCache.CROP_IMAGE,
+        preprocess_vars_cache)
+    do_a_crop_random = tf.greater(do_a_crop_random, random_coef)
+
+    outputs = [image, boxes, labels]
+
+    if label_weights is not None:
+      outputs.append(label_weights)
+    if label_confidences is not None:
+      outputs.append(label_confidences)
+    if multiclass_scores is not None:
+      outputs.append(multiclass_scores)
+    if masks is not None:
+      outputs.append(masks)
+    if keypoints is not None:
+      outputs.append(keypoints)
+
+    result = tf.cond(do_a_crop_random, strict_random_crop_image_fn,
+                     lambda: tuple(outputs))
+  return result
+
+
+def random_pad_image(image,
+                     boxes,
+                     min_image_size=None,
+                     max_image_size=None,
+                     pad_color=None,
+                     seed=None,
+                     preprocess_vars_cache=None):
+  """Randomly pads the image.
+
+  This function randomly pads the image with zeros. The final size of the
+  padded image will be between min_image_size and max_image_size.
+  if min_image_size is smaller than the input image size, min_image_size will
+  be set to the input image size. The same for max_image_size. The input image
+  will be located at a uniformly random location inside the padded image.
+  The relative location of the boxes to the original image will remain the same.
+
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    min_image_size: a tensor of size [min_height, min_width], type tf.int32.
+                    If passed as None, will be set to image size
+                    [height, width].
+    max_image_size: a tensor of size [max_height, max_width], type tf.int32.
+                    If passed as None, will be set to twice the
+                    image [height * 2, width * 2].
+    pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
+               if set as None, it will be set to average color of the input
+               image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: Image shape will be [new_height, new_width, channels].
+    boxes: boxes which is the same rank as input boxes. Boxes are in normalized
+           form.
+  """
+  if pad_color is None:
+    pad_color = tf.reduce_mean(image, axis=[0, 1])
+
+  image_shape = tf.shape(image)
+  image_height = image_shape[0]
+  image_width = image_shape[1]
+
+  if max_image_size is None:
+    max_image_size = tf.stack([image_height * 2, image_width * 2])
+  max_image_size = tf.maximum(max_image_size,
+                              tf.stack([image_height, image_width]))
+
+  if min_image_size is None:
+    min_image_size = tf.stack([image_height, image_width])
+  min_image_size = tf.maximum(min_image_size,
+                              tf.stack([image_height, image_width]))
+
+  target_height = tf.cond(
+      max_image_size[0] > min_image_size[0],
+      lambda: _random_integer(min_image_size[0], max_image_size[0], seed),
+      lambda: max_image_size[0])
+
+  target_width = tf.cond(
+      max_image_size[1] > min_image_size[1],
+      lambda: _random_integer(min_image_size[1], max_image_size[1], seed),
+      lambda: max_image_size[1])
+
+  offset_height = tf.cond(
+      target_height > image_height,
+      lambda: _random_integer(0, target_height - image_height, seed),
+      lambda: tf.constant(0, dtype=tf.int32))
+
+  offset_width = tf.cond(
+      target_width > image_width,
+      lambda: _random_integer(0, target_width - image_width, seed),
+      lambda: tf.constant(0, dtype=tf.int32))
+
+  gen_func = lambda: (target_height, target_width, offset_height, offset_width)
+  params = _get_or_create_preprocess_rand_vars(
+      gen_func, preprocessor_cache.PreprocessorCache.PAD_IMAGE,
+      preprocess_vars_cache)
+  target_height, target_width, offset_height, offset_width = params
+
+  new_image = tf.image.pad_to_bounding_box(
+      image,
+      offset_height=offset_height,
+      offset_width=offset_width,
+      target_height=target_height,
+      target_width=target_width)
+
+  # Setting color of the padded pixels
+  image_ones = tf.ones_like(image)
+  image_ones_padded = tf.image.pad_to_bounding_box(
+      image_ones,
+      offset_height=offset_height,
+      offset_width=offset_width,
+      target_height=target_height,
+      target_width=target_width)
+  image_color_padded = (1.0 - image_ones_padded) * pad_color
+  new_image += image_color_padded
+
+  # setting boxes
+  new_window = tf.to_float(
+      tf.stack([
+          -offset_height, -offset_width, target_height - offset_height,
+          target_width - offset_width
+      ]))
+  new_window /= tf.to_float(
+      tf.stack([image_height, image_width, image_height, image_width]))
+  boxlist = box_list.BoxList(boxes)
+  new_boxlist = box_list_ops.change_coordinate_frame(boxlist, new_window)
+  new_boxes = new_boxlist.get()
+
+  return new_image, new_boxes
+
+
+def random_crop_pad_image(image,
+                          boxes,
+                          labels,
+                          label_weights,
+                          label_confidences=None,
+                          multiclass_scores=None,
+                          min_object_covered=1.0,
+                          aspect_ratio_range=(0.75, 1.33),
+                          area_range=(0.1, 1.0),
+                          overlap_thresh=0.3,
+                          clip_boxes=True,
+                          random_coef=0.0,
+                          min_padded_size_ratio=(1.0, 1.0),
+                          max_padded_size_ratio=(2.0, 2.0),
+                          pad_color=None,
+                          seed=None,
+                          preprocess_vars_cache=None):
+  """Randomly crops and pads the image.
+
+  Given an input image and its bounding boxes, this op first randomly crops
+  the image and then randomly pads the image with background values. Parameters
+  min_padded_size_ratio and max_padded_size_ratio, determine the range of the
+  final output image size.  Specifically, the final image size will have a size
+  in the range of min_padded_size_ratio * tf.shape(image) and
+  max_padded_size_ratio * tf.shape(image). Note that these ratios are with
+  respect to the size of the original image, so we can't capture the same
+  effect easily by independently applying RandomCropImage
+  followed by RandomPadImage.
+
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: rank 1 float32 containing the label weights.
+    label_confidences: rank 1 float32 containing the label confidences.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    min_padded_size_ratio: min ratio of padded image height and width to the
+                           input image's height and width.
+    max_padded_size_ratio: max ratio of padded image height and width to the
+                           input image's height and width.
+    pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
+               if set as None, it will be set to average color of the randomly
+               cropped image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    padded_image: padded image.
+    padded_boxes: boxes which is the same rank as input boxes. Boxes are in
+                  normalized form.
+    cropped_labels: cropped labels.
+    if label_weights is not None also returns:
+    cropped_label_weights: cropped label weights.
+    if multiclass_scores is not None also returns:
+    cropped_multiclass_scores: cropped_multiclass_scores.
+
+  """
+  image_size = tf.shape(image)
+  image_height = image_size[0]
+  image_width = image_size[1]
+  result = random_crop_image(
+      image=image,
+      boxes=boxes,
+      labels=labels,
+      label_weights=label_weights,
+      label_confidences=label_confidences,
+      multiclass_scores=multiclass_scores,
+      min_object_covered=min_object_covered,
+      aspect_ratio_range=aspect_ratio_range,
+      area_range=area_range,
+      overlap_thresh=overlap_thresh,
+      clip_boxes=clip_boxes,
+      random_coef=random_coef,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+
+  cropped_image, cropped_boxes, cropped_labels = result[:3]
+
+  min_image_size = tf.to_int32(
+      tf.to_float(tf.stack([image_height, image_width])) *
+      min_padded_size_ratio)
+  max_image_size = tf.to_int32(
+      tf.to_float(tf.stack([image_height, image_width])) *
+      max_padded_size_ratio)
+
+  padded_image, padded_boxes = random_pad_image(
+      cropped_image,
+      cropped_boxes,
+      min_image_size=min_image_size,
+      max_image_size=max_image_size,
+      pad_color=pad_color,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+
+  cropped_padded_output = (padded_image, padded_boxes, cropped_labels)
+
+  index = 3
+  if label_weights is not None:
+    cropped_label_weights = result[index]
+    cropped_padded_output += (cropped_label_weights,)
+    index += 1
+
+  if label_confidences is not None:
+    cropped_label_confidences = result[index]
+    cropped_padded_output += (cropped_label_confidences,)
+    index += 1
+
+  if multiclass_scores is not None:
+    cropped_multiclass_scores = result[index]
+    cropped_padded_output += (cropped_multiclass_scores,)
+
+  return cropped_padded_output
+
+
+def random_crop_to_aspect_ratio(image,
+                                boxes,
+                                labels,
+                                label_weights,
+                                label_confidences=None,
+                                multiclass_scores=None,
+                                masks=None,
+                                keypoints=None,
+                                aspect_ratio=1.0,
+                                overlap_thresh=0.3,
+                                clip_boxes=True,
+                                seed=None,
+                                preprocess_vars_cache=None):
+  """Randomly crops an image to the specified aspect ratio.
+
+  Randomly crops the a portion of the image such that the crop is of the
+  specified aspect ratio, and the crop is as large as possible. If the specified
+  aspect ratio is larger than the aspect ratio of the image, this op will
+  randomly remove rows from the top and bottom of the image. If the specified
+  aspect ratio is less than the aspect ratio of the image, this op will randomly
+  remove cols from the left and right of the image. If the specified aspect
+  ratio is the same as the aspect ratio of the image, this op will return the
+  image.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: (optional) float32 tensor of shape [num_instances]
+      representing the confidence for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    aspect_ratio: the aspect ratio of cropped image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If label_weights, masks, keypoints, or multiclass_scores is not None, the
+    function also returns:
+    label_weights: rank 1 float32 tensor with shape [num_instances].
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+    multiclass_scores: rank 2 float32 tensor with shape
+                       [num_instances, num_classes]
+
+  Raises:
+    ValueError: If image is not a 3D tensor.
+  """
+  if len(image.get_shape()) != 3:
+    raise ValueError('Image should be 3D tensor')
+
+  with tf.name_scope('RandomCropToAspectRatio', values=[image]):
+    image_shape = tf.shape(image)
+    orig_height = image_shape[0]
+    orig_width = image_shape[1]
+    orig_aspect_ratio = tf.to_float(orig_width) / tf.to_float(orig_height)
+    new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
+    def target_height_fn():
+      return tf.to_int32(tf.round(tf.to_float(orig_width) / new_aspect_ratio))
+
+    target_height = tf.cond(orig_aspect_ratio >= new_aspect_ratio,
+                            lambda: orig_height, target_height_fn)
+
+    def target_width_fn():
+      return tf.to_int32(tf.round(tf.to_float(orig_height) * new_aspect_ratio))
+
+    target_width = tf.cond(orig_aspect_ratio <= new_aspect_ratio,
+                           lambda: orig_width, target_width_fn)
+
+    # either offset_height = 0 and offset_width is randomly chosen from
+    # [0, offset_width - target_width), or else offset_width = 0 and
+    # offset_height is randomly chosen from [0, offset_height - target_height)
+    offset_height = _random_integer(0, orig_height - target_height + 1, seed)
+    offset_width = _random_integer(0, orig_width - target_width + 1, seed)
+
+    generator_func = lambda: (offset_height, offset_width)
+    offset_height, offset_width = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.CROP_TO_ASPECT_RATIO,
+        preprocess_vars_cache)
+
+    new_image = tf.image.crop_to_bounding_box(
+        image, offset_height, offset_width, target_height, target_width)
+
+    im_box = tf.stack([
+        tf.to_float(offset_height) / tf.to_float(orig_height),
+        tf.to_float(offset_width) / tf.to_float(orig_width),
+        tf.to_float(offset_height + target_height) / tf.to_float(orig_height),
+        tf.to_float(offset_width + target_width) / tf.to_float(orig_width)
+    ])
+
+    boxlist = box_list.BoxList(boxes)
+    boxlist.add_field('labels', labels)
+
+    boxlist.add_field('label_weights', label_weights)
+
+    if label_confidences is not None:
+      boxlist.add_field('label_confidences', label_confidences)
+
+    if multiclass_scores is not None:
+      boxlist.add_field('multiclass_scores', multiclass_scores)
+
+    im_boxlist = box_list.BoxList(tf.expand_dims(im_box, 0))
+
+    # remove boxes whose overlap with the image is less than overlap_thresh
+    overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes(
+        boxlist, im_boxlist, overlap_thresh)
+
+    # change the coordinate of the remaining boxes
+    new_labels = overlapping_boxlist.get_field('labels')
+    new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
+                                                       im_box)
+    if clip_boxes:
+      new_boxlist = box_list_ops.clip_to_window(
+          new_boxlist, tf.constant([0.0, 0.0, 1.0, 1.0], tf.float32))
+    new_boxes = new_boxlist.get()
+
+    result = [new_image, new_boxes, new_labels]
+
+    new_label_weights = overlapping_boxlist.get_field('label_weights')
+    result.append(new_label_weights)
+
+    if label_confidences is not None:
+      new_label_confidences = (
+          overlapping_boxlist.get_field('label_confidences'))
+      result.append(new_label_confidences)
+
+    if multiclass_scores is not None:
+      new_multiclass_scores = overlapping_boxlist.get_field('multiclass_scores')
+      result.append(new_multiclass_scores)
+
+    if masks is not None:
+      masks_inside_window = tf.gather(masks, keep_ids)
+      masks_box_begin = tf.stack([0, offset_height, offset_width])
+      masks_box_size = tf.stack([-1, target_height, target_width])
+      new_masks = tf.slice(masks_inside_window, masks_box_begin, masks_box_size)
+      result.append(new_masks)
+
+    if keypoints is not None:
+      keypoints_inside_window = tf.gather(keypoints, keep_ids)
+      new_keypoints = keypoint_ops.change_coordinate_frame(
+          keypoints_inside_window, im_box)
+      if clip_boxes:
+        new_keypoints = keypoint_ops.prune_outside_window(new_keypoints,
+                                                          [0.0, 0.0, 1.0, 1.0])
+      result.append(new_keypoints)
+
+    return tuple(result)
+
+
+def random_pad_to_aspect_ratio(image,
+                               boxes,
+                               masks=None,
+                               keypoints=None,
+                               aspect_ratio=1.0,
+                               min_padded_size_ratio=(1.0, 1.0),
+                               max_padded_size_ratio=(2.0, 2.0),
+                               seed=None,
+                               preprocess_vars_cache=None):
+  """Randomly zero pads an image to the specified aspect ratio.
+
+  Pads the image so that the resulting image will have the specified aspect
+  ratio without scaling less than the min_padded_size_ratio or more than the
+  max_padded_size_ratio. If the min_padded_size_ratio or max_padded_size_ratio
+  is lower than what is possible to maintain the aspect ratio, then this method
+  will use the least padding to achieve the specified aspect ratio.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    aspect_ratio: aspect ratio of the final image.
+    min_padded_size_ratio: min ratio of padded image height and width to the
+                           input image's height and width.
+    max_padded_size_ratio: max ratio of padded image height and width to the
+                           input image's height and width.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If masks, or keypoints is not None, the function also returns:
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+
+  Raises:
+    ValueError: If image is not a 3D tensor.
+  """
+  if len(image.get_shape()) != 3:
+    raise ValueError('Image should be 3D tensor')
+
+  with tf.name_scope('RandomPadToAspectRatio', values=[image]):
+    image_shape = tf.shape(image)
+    image_height = tf.to_float(image_shape[0])
+    image_width = tf.to_float(image_shape[1])
+    image_aspect_ratio = image_width / image_height
+    new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
+    target_height = tf.cond(
+        image_aspect_ratio <= new_aspect_ratio,
+        lambda: image_height,
+        lambda: image_width / new_aspect_ratio)
+    target_width = tf.cond(
+        image_aspect_ratio >= new_aspect_ratio,
+        lambda: image_width,
+        lambda: image_height * new_aspect_ratio)
+
+    min_height = tf.maximum(
+        min_padded_size_ratio[0] * image_height, target_height)
+    min_width = tf.maximum(
+        min_padded_size_ratio[1] * image_width, target_width)
+    max_height = tf.maximum(
+        max_padded_size_ratio[0] * image_height, target_height)
+    max_width = tf.maximum(
+        max_padded_size_ratio[1] * image_width, target_width)
+
+    max_scale = tf.minimum(max_height / target_height, max_width / target_width)
+    min_scale = tf.minimum(
+        max_scale,
+        tf.maximum(min_height / target_height, min_width / target_width))
+
+    generator_func = functools.partial(tf.random_uniform, [],
+                                       min_scale, max_scale, seed=seed)
+    scale = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.PAD_TO_ASPECT_RATIO,
+        preprocess_vars_cache)
+
+    target_height = tf.round(scale * target_height)
+    target_width = tf.round(scale * target_width)
+
+    new_image = tf.image.pad_to_bounding_box(
+        image, 0, 0, tf.to_int32(target_height), tf.to_int32(target_width))
+
+    im_box = tf.stack([
+        0.0,
+        0.0,
+        target_height / image_height,
+        target_width / image_width
+    ])
+    boxlist = box_list.BoxList(boxes)
+    new_boxlist = box_list_ops.change_coordinate_frame(boxlist, im_box)
+    new_boxes = new_boxlist.get()
+
+    result = [new_image, new_boxes]
+
+    if masks is not None:
+      new_masks = tf.expand_dims(masks, -1)
+      new_masks = tf.image.pad_to_bounding_box(new_masks, 0, 0,
+                                               tf.to_int32(target_height),
+                                               tf.to_int32(target_width))
+      new_masks = tf.squeeze(new_masks, [-1])
+      result.append(new_masks)
+
+    if keypoints is not None:
+      new_keypoints = keypoint_ops.change_coordinate_frame(keypoints, im_box)
+      result.append(new_keypoints)
+
+    return tuple(result)
+
+
+def random_black_patches(image,
+                         max_black_patches=10,
+                         probability=0.5,
+                         size_to_image_ratio=0.1,
+                         random_seed=None,
+                         preprocess_vars_cache=None):
+  """Randomly adds some black patches to the image.
+
+  This op adds up to max_black_patches square black patches of a fixed size
+  to the image where size is specified via the size_to_image_ratio parameter.
+
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    max_black_patches: number of times that the function tries to add a
+                       black box to the image.
+    probability: at each try, what is the chance of adding a box.
+    size_to_image_ratio: Determines the ratio of the size of the black patches
+                         to the size of the image.
+                         box_size = size_to_image_ratio *
+                                    min(image_width, image_height)
+    random_seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image
+  """
+  def add_black_patch_to_image(image, idx):
+    """Function for adding one patch to the image.
+
+    Args:
+      image: image
+      idx: counter for number of patches that could have been added
+
+    Returns:
+      image with a randomly added black box
+    """
+    image_shape = tf.shape(image)
+    image_height = image_shape[0]
+    image_width = image_shape[1]
+    box_size = tf.to_int32(
+        tf.multiply(
+            tf.minimum(tf.to_float(image_height), tf.to_float(image_width)),
+            size_to_image_ratio))
+
+    generator_func = functools.partial(tf.random_uniform, [], minval=0.0,
+                                       maxval=(1.0 - size_to_image_ratio),
+                                       seed=random_seed)
+    normalized_y_min = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.ADD_BLACK_PATCH,
+        preprocess_vars_cache, key=str(idx) + 'y')
+    normalized_x_min = _get_or_create_preprocess_rand_vars(
+        generator_func,
+        preprocessor_cache.PreprocessorCache.ADD_BLACK_PATCH,
+        preprocess_vars_cache, key=str(idx) + 'x')
+
+    y_min = tf.to_int32(normalized_y_min * tf.to_float(image_height))
+    x_min = tf.to_int32(normalized_x_min * tf.to_float(image_width))
+    black_box = tf.ones([box_size, box_size, 3], dtype=tf.float32)
+    mask = 1.0 - tf.image.pad_to_bounding_box(black_box, y_min, x_min,
+                                              image_height, image_width)
+    image = tf.multiply(image, mask)
+    return image
+
+  with tf.name_scope('RandomBlackPatchInImage', values=[image]):
+    for idx in range(max_black_patches):
+      generator_func = functools.partial(tf.random_uniform, [],
+                                         minval=0.0, maxval=1.0,
+                                         dtype=tf.float32, seed=random_seed)
+      random_prob = _get_or_create_preprocess_rand_vars(
+          generator_func,
+          preprocessor_cache.PreprocessorCache.BLACK_PATCHES,
+          preprocess_vars_cache, key=idx)
+      image = tf.cond(
+          tf.greater(random_prob, probability), lambda: image,
+          functools.partial(add_black_patch_to_image, image=image, idx=idx))
+    return image
+
+
+def image_to_float(image):
+  """Used in Faster R-CNN. Casts image pixel values to float.
+
+  Args:
+    image: input image which might be in tf.uint8 or sth else format
+
+  Returns:
+    image: image in tf.float32 format.
+  """
+  with tf.name_scope('ImageToFloat', values=[image]):
+    image = tf.to_float(image)
+    return image
+
+
+def random_resize_method(image, target_size, preprocess_vars_cache=None):
+  """Uses a random resize method to resize the image to target size.
+
+  Args:
+    image: a rank 3 tensor.
+    target_size: a list of [target_height, target_width]
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    resized image.
+  """
+
+  resized_image = _apply_with_random_selector(
+      image,
+      lambda x, method: tf.image.resize_images(x, target_size, method),
+      num_cases=4,
+      preprocess_vars_cache=preprocess_vars_cache,
+      key=preprocessor_cache.PreprocessorCache.RESIZE_METHOD)
+
+  return resized_image
+
+
+def _compute_new_static_size(image, min_dimension, max_dimension):
+  """Compute new static shape for resize_to_range method."""
+  image_shape = image.get_shape().as_list()
+  orig_height = image_shape[0]
+  orig_width = image_shape[1]
+  num_channels = image_shape[2]
+  orig_min_dim = min(orig_height, orig_width)
+  # Calculates the larger of the possible sizes
+  large_scale_factor = min_dimension / float(orig_min_dim)
+  # Scaling orig_(height|width) by large_scale_factor will make the smaller
+  # dimension equal to min_dimension, save for floating point rounding errors.
+  # For reasonably-sized images, taking the nearest integer will reliably
+  # eliminate this error.
+  large_height = int(round(orig_height * large_scale_factor))
+  large_width = int(round(orig_width * large_scale_factor))
+  large_size = [large_height, large_width]
+  if max_dimension:
+    # Calculates the smaller of the possible sizes, use that if the larger
+    # is too big.
+    orig_max_dim = max(orig_height, orig_width)
+    small_scale_factor = max_dimension / float(orig_max_dim)
+    # Scaling orig_(height|width) by small_scale_factor will make the larger
+    # dimension equal to max_dimension, save for floating point rounding
+    # errors. For reasonably-sized images, taking the nearest integer will
+    # reliably eliminate this error.
+    small_height = int(round(orig_height * small_scale_factor))
+    small_width = int(round(orig_width * small_scale_factor))
+    small_size = [small_height, small_width]
+    new_size = large_size
+    if max(large_size) > max_dimension:
+      new_size = small_size
+  else:
+    new_size = large_size
+  return tf.constant(new_size + [num_channels])
+
+
+def _compute_new_dynamic_size(image, min_dimension, max_dimension):
+  """Compute new dynamic shape for resize_to_range method."""
+  image_shape = tf.shape(image)
+  orig_height = tf.to_float(image_shape[0])
+  orig_width = tf.to_float(image_shape[1])
+  num_channels = image_shape[2]
+  orig_min_dim = tf.minimum(orig_height, orig_width)
+  # Calculates the larger of the possible sizes
+  min_dimension = tf.constant(min_dimension, dtype=tf.float32)
+  large_scale_factor = min_dimension / orig_min_dim
+  # Scaling orig_(height|width) by large_scale_factor will make the smaller
+  # dimension equal to min_dimension, save for floating point rounding errors.
+  # For reasonably-sized images, taking the nearest integer will reliably
+  # eliminate this error.
+  large_height = tf.to_int32(tf.round(orig_height * large_scale_factor))
+  large_width = tf.to_int32(tf.round(orig_width * large_scale_factor))
+  large_size = tf.stack([large_height, large_width])
+  if max_dimension:
+    # Calculates the smaller of the possible sizes, use that if the larger
+    # is too big.
+    orig_max_dim = tf.maximum(orig_height, orig_width)
+    max_dimension = tf.constant(max_dimension, dtype=tf.float32)
+    small_scale_factor = max_dimension / orig_max_dim
+    # Scaling orig_(height|width) by small_scale_factor will make the larger
+    # dimension equal to max_dimension, save for floating point rounding
+    # errors. For reasonably-sized images, taking the nearest integer will
+    # reliably eliminate this error.
+    small_height = tf.to_int32(tf.round(orig_height * small_scale_factor))
+    small_width = tf.to_int32(tf.round(orig_width * small_scale_factor))
+    small_size = tf.stack([small_height, small_width])
+    new_size = tf.cond(
+        tf.to_float(tf.reduce_max(large_size)) > max_dimension,
+        lambda: small_size, lambda: large_size)
+  else:
+    new_size = large_size
+  return tf.stack(tf.unstack(new_size) + [num_channels])
+
+
+def resize_to_range(image,
+                    masks=None,
+                    min_dimension=None,
+                    max_dimension=None,
+                    method=tf.image.ResizeMethod.BILINEAR,
+                    align_corners=False,
+                    pad_to_max_dimension=False,
+                    per_channel_pad_value=(0, 0, 0)):
+  """Resizes an image so its dimensions are within the provided value.
+
+  The output size can be described by two cases:
+  1. If the image can be rescaled so its minimum dimension is equal to the
+     provided value without the other dimension exceeding max_dimension,
+     then do so.
+  2. Otherwise, resize so the largest dimension is equal to max_dimension.
+
+  Args:
+    image: A 3D tensor of shape [height, width, channels]
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks.
+    min_dimension: (optional) (scalar) desired size of the smaller image
+                   dimension.
+    max_dimension: (optional) (scalar) maximum allowed size
+                   of the larger image dimension.
+    method: (optional) interpolation method used in resizing. Defaults to
+            BILINEAR.
+    align_corners: bool. If true, exactly align all 4 corners of the input
+                   and output. Defaults to False.
+    pad_to_max_dimension: Whether to resize the image and pad it with zeros
+      so the resulting image is of the spatial size
+      [max_dimension, max_dimension]. If masks are included they are padded
+      similarly.
+    per_channel_pad_value: A tuple of per-channel scalar value to use for
+      padding. By default pads zeros.
+
+  Returns:
+    Note that the position of the resized_image_shape changes based on whether
+    masks are present.
+    resized_image: A 3D tensor of shape [new_height, new_width, channels],
+      where the image has been resized (with bilinear interpolation) so that
+      min(new_height, new_width) == min_dimension or
+      max(new_height, new_width) == max_dimension.
+    resized_masks: If masks is not None, also outputs masks. A 3D tensor of
+      shape [num_instances, new_height, new_width].
+    resized_image_shape: A 1D tensor of shape [3] containing shape of the
+      resized image.
+
+  Raises:
+    ValueError: if the image is not a 3D tensor.
+  """
+  if len(image.get_shape()) != 3:
+    raise ValueError('Image should be 3D tensor')
+
+  with tf.name_scope('ResizeToRange', values=[image, min_dimension]):
+    if image.get_shape().is_fully_defined():
+      new_size = _compute_new_static_size(image, min_dimension, max_dimension)
+    else:
+      new_size = _compute_new_dynamic_size(image, min_dimension, max_dimension)
+    new_image = tf.image.resize_images(
+        image, new_size[:-1], method=method, align_corners=align_corners)
+
+    if pad_to_max_dimension:
+      channels = tf.unstack(new_image, axis=2)
+      if len(channels) != len(per_channel_pad_value):
+        raise ValueError('Number of channels must be equal to the length of '
+                         'per-channel pad value.')
+      new_image = tf.stack(
+          [
+              tf.pad(
+                  channels[i], [[0, max_dimension - new_size[0]],
+                                [0, max_dimension - new_size[1]]],
+                  constant_values=per_channel_pad_value[i])
+              for i in range(len(channels))
+          ],
+          axis=2)
+      new_image.set_shape([max_dimension, max_dimension, 3])
+
+    result = [new_image]
+    if masks is not None:
+      new_masks = tf.expand_dims(masks, 3)
+      new_masks = tf.image.resize_images(
+          new_masks,
+          new_size[:-1],
+          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+          align_corners=align_corners)
+      if pad_to_max_dimension:
+        new_masks = tf.image.pad_to_bounding_box(
+            new_masks, 0, 0, max_dimension, max_dimension)
+      new_masks = tf.squeeze(new_masks, 3)
+      result.append(new_masks)
+
+    result.append(new_size)
+    return result
+
+
+# TODO(alirezafathi): Make sure the static shapes are preserved.
+def resize_to_min_dimension(image, masks=None, min_dimension=600):
+  """Resizes image and masks given the min size maintaining the aspect ratio.
+
+  If one of the image dimensions is smaller that min_dimension, it will scale
+  the image such that its smallest dimension is equal to min_dimension.
+  Otherwise, will keep the image size as is.
+
+  Args:
+    image: a tensor of size [height, width, channels].
+    masks: (optional) a tensors of size [num_instances, height, width].
+    min_dimension: minimum image dimension.
+
+  Returns:
+    Note that the position of the resized_image_shape changes based on whether
+    masks are present.
+    resized_image: A tensor of size [new_height, new_width, channels].
+    resized_masks: If masks is not None, also outputs masks. A 3D tensor of
+      shape [num_instances, new_height, new_width]
+    resized_image_shape: A 1D tensor of shape [3] containing the shape of the
+      resized image.
+
+  Raises:
+    ValueError: if the image is not a 3D tensor.
+  """
+  if len(image.get_shape()) != 3:
+    raise ValueError('Image should be 3D tensor')
+
+  with tf.name_scope('ResizeGivenMinDimension', values=[image, min_dimension]):
+    image_height = tf.shape(image)[0]
+    image_width = tf.shape(image)[1]
+    num_channels = tf.shape(image)[2]
+    min_image_dimension = tf.minimum(image_height, image_width)
+    min_target_dimension = tf.maximum(min_image_dimension, min_dimension)
+    target_ratio = tf.to_float(min_target_dimension) / tf.to_float(
+        min_image_dimension)
+    target_height = tf.to_int32(tf.to_float(image_height) * target_ratio)
+    target_width = tf.to_int32(tf.to_float(image_width) * target_ratio)
+    image = tf.image.resize_bilinear(
+        tf.expand_dims(image, axis=0),
+        size=[target_height, target_width],
+        align_corners=True)
+    result = [tf.squeeze(image, axis=0)]
+
+    if masks is not None:
+      masks = tf.image.resize_nearest_neighbor(
+          tf.expand_dims(masks, axis=3),
+          size=[target_height, target_width],
+          align_corners=True)
+      result.append(tf.squeeze(masks, axis=3))
+
+    result.append(tf.stack([target_height, target_width, num_channels]))
+    return result
+
+
+def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None):
+  """Scales boxes from normalized to pixel coordinates.
+
+  Args:
+    image: A 3D float32 tensor of shape [height, width, channels].
+    boxes: A 2D float32 tensor of shape [num_boxes, 4] containing the bounding
+      boxes in normalized coordinates. Each row is of the form
+      [ymin, xmin, ymax, xmax].
+    keypoints: (optional) rank 3 float32 tensor with shape
+      [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
+      coordinates.
+
+  Returns:
+    image: unchanged input image.
+    scaled_boxes: a 2D float32 tensor of shape [num_boxes, 4] containing the
+      bounding boxes in pixel coordinates.
+    scaled_keypoints: a 3D float32 tensor with shape
+      [num_instances, num_keypoints, 2] containing the keypoints in pixel
+      coordinates.
+  """
+  boxlist = box_list.BoxList(boxes)
+  image_height = tf.shape(image)[0]
+  image_width = tf.shape(image)[1]
+  scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get()
+  result = [image, scaled_boxes]
+  if keypoints is not None:
+    scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width)
+    result.append(scaled_keypoints)
+  return tuple(result)
+
+
+# TODO(alirezafathi): Investigate if instead the function should return None if
+# masks is None.
+# pylint: disable=g-doc-return-or-yield
+def resize_image(image,
+                 masks=None,
+                 new_height=600,
+                 new_width=1024,
+                 method=tf.image.ResizeMethod.BILINEAR,
+                 align_corners=False):
+  """Resizes images to the given height and width.
+
+  Args:
+    image: A 3D tensor of shape [height, width, channels]
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks.
+    new_height: (optional) (scalar) desired height of the image.
+    new_width: (optional) (scalar) desired width of the image.
+    method: (optional) interpolation method used in resizing. Defaults to
+            BILINEAR.
+    align_corners: bool. If true, exactly align all 4 corners of the input
+                   and output. Defaults to False.
+
+  Returns:
+    Note that the position of the resized_image_shape changes based on whether
+    masks are present.
+    resized_image: A tensor of size [new_height, new_width, channels].
+    resized_masks: If masks is not None, also outputs masks. A 3D tensor of
+      shape [num_instances, new_height, new_width]
+    resized_image_shape: A 1D tensor of shape [3] containing the shape of the
+      resized image.
+  """
+  with tf.name_scope(
+      'ResizeImage',
+      values=[image, new_height, new_width, method, align_corners]):
+    new_image = tf.image.resize_images(
+        image, tf.stack([new_height, new_width]),
+        method=method,
+        align_corners=align_corners)
+    image_shape = shape_utils.combined_static_and_dynamic_shape(image)
+    result = [new_image]
+    if masks is not None:
+      num_instances = tf.shape(masks)[0]
+      new_size = tf.stack([new_height, new_width])
+      def resize_masks_branch():
+        new_masks = tf.expand_dims(masks, 3)
+        new_masks = tf.image.resize_nearest_neighbor(
+            new_masks, new_size, align_corners=align_corners)
+        new_masks = tf.squeeze(new_masks, axis=3)
+        return new_masks
+
+      def reshape_masks_branch():
+        # The shape function will be computed for both branches of the
+        # condition, regardless of which branch is actually taken. Make sure
+        # that we don't trigger an assertion in the shape function when trying
+        # to reshape a non empty tensor into an empty one.
+        new_masks = tf.reshape(masks, [-1, new_size[0], new_size[1]])
+        return new_masks
+
+      masks = tf.cond(num_instances > 0, resize_masks_branch,
+                      reshape_masks_branch)
+      result.append(masks)
+
+    result.append(tf.stack([new_height, new_width, image_shape[2]]))
+    return result
+
+
+def subtract_channel_mean(image, means=None):
+  """Normalizes an image by subtracting a mean from each channel.
+
+  Args:
+    image: A 3D tensor of shape [height, width, channels]
+    means: float list containing a mean for each channel
+  Returns:
+    normalized_images: a tensor of shape [height, width, channels]
+  Raises:
+    ValueError: if images is not a 4D tensor or if the number of means is not
+      equal to the number of channels.
+  """
+  with tf.name_scope('SubtractChannelMean', values=[image, means]):
+    if len(image.get_shape()) != 3:
+      raise ValueError('Input must be of size [height, width, channels]')
+    if len(means) != image.get_shape()[-1]:
+      raise ValueError('len(means) must match the number of channels')
+    return image - [[means]]
+
+
+def one_hot_encoding(labels, num_classes=None):
+  """One-hot encodes the multiclass labels.
+
+  Example usage:
+    labels = tf.constant([1, 4], dtype=tf.int32)
+    one_hot = OneHotEncoding(labels, num_classes=5)
+    one_hot.eval()    # evaluates to [0, 1, 0, 0, 1]
+
+  Args:
+    labels: A tensor of shape [None] corresponding to the labels.
+    num_classes: Number of classes in the dataset.
+  Returns:
+    onehot_labels: a tensor of shape [num_classes] corresponding to the one hot
+      encoding of the labels.
+  Raises:
+    ValueError: if num_classes is not specified.
+  """
+  with tf.name_scope('OneHotEncoding', values=[labels]):
+    if num_classes is None:
+      raise ValueError('num_classes must be specified')
+
+    labels = tf.one_hot(labels, num_classes, 1, 0)
+    return tf.reduce_max(labels, 0)
+
+
+def rgb_to_gray(image):
+  """Converts a 3 channel RGB image to a 1 channel grayscale image.
+
+  Args:
+    image: Rank 3 float32 tensor containing 1 image -> [height, width, 3]
+           with pixel values varying between [0, 1].
+
+  Returns:
+    image: A single channel grayscale image -> [image, height, 1].
+  """
+  return _rgb_to_grayscale(image)
+
+
+def ssd_random_crop(image,
+                    boxes,
+                    labels,
+                    label_weights,
+                    label_confidences=None,
+                    multiclass_scores=None,
+                    masks=None,
+                    keypoints=None,
+                    min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+                    aspect_ratio_range=((0.5, 2.0),) * 7,
+                    area_range=((0.1, 1.0),) * 7,
+                    overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+                    clip_boxes=(True,) * 7,
+                    random_coef=(0.15,) * 7,
+                    seed=None,
+                    preprocess_vars_cache=None):
+  """Random crop preprocessing with default parameters as in SSD paper.
+
+  Liu et al., SSD: Single shot multibox detector.
+  For further information on random crop preprocessing refer to RandomCrop
+  function above.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: rank 1 float32 tensor containing the weights.
+    label_confidences: rank 1 float32 tensor containing the confidences.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If label_weights, multiclass_scores, masks, or keypoints  is not None, the
+    function also returns:
+    label_weights: rank 1 float32 tensor with shape [num_instances].
+    multiclass_scores: rank 2 float32 tensor with shape
+                       [num_instances, num_classes]
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+
+  def random_crop_selector(selected_result, index):
+    """Applies random_crop_image to selected result.
+
+    Args:
+      selected_result: A tuple containing image, boxes, labels, keypoints (if
+                       not None), and masks (if not None).
+      index: The index that was randomly selected.
+
+    Returns: A tuple containing image, boxes, labels, keypoints (if not None),
+             and masks (if not None).
+    """
+
+    i = 3
+    image, boxes, labels = selected_result[:i]
+    selected_label_weights = None
+    selected_label_confidences = None
+    selected_multiclass_scores = None
+    selected_masks = None
+    selected_keypoints = None
+    if label_weights is not None:
+      selected_label_weights = selected_result[i]
+      i += 1
+    if label_confidences is not None:
+      selected_label_confidences = selected_result[i]
+      i += 1
+    if multiclass_scores is not None:
+      selected_multiclass_scores = selected_result[i]
+      i += 1
+    if masks is not None:
+      selected_masks = selected_result[i]
+      i += 1
+    if keypoints is not None:
+      selected_keypoints = selected_result[i]
+
+    return random_crop_image(
+        image=image,
+        boxes=boxes,
+        labels=labels,
+        label_weights=selected_label_weights,
+        label_confidences=selected_label_confidences,
+        multiclass_scores=selected_multiclass_scores,
+        masks=selected_masks,
+        keypoints=selected_keypoints,
+        min_object_covered=min_object_covered[index],
+        aspect_ratio_range=aspect_ratio_range[index],
+        area_range=area_range[index],
+        overlap_thresh=overlap_thresh[index],
+        clip_boxes=clip_boxes[index],
+        random_coef=random_coef[index],
+        seed=seed,
+        preprocess_vars_cache=preprocess_vars_cache)
+
+  result = _apply_with_random_selector_tuples(
+      tuple(
+          t for t in (image, boxes, labels, label_weights, label_confidences,
+                      multiclass_scores, masks, keypoints) if t is not None),
+      random_crop_selector,
+      num_cases=len(min_object_covered),
+      preprocess_vars_cache=preprocess_vars_cache,
+      key=preprocessor_cache.PreprocessorCache.SSD_CROP_SELECTOR_ID)
+  return result
+
+
+def ssd_random_crop_pad(image,
+                        boxes,
+                        labels,
+                        label_weights,
+                        label_confidences=None,
+                        multiclass_scores=None,
+                        min_object_covered=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+                        aspect_ratio_range=((0.5, 2.0),) * 6,
+                        area_range=((0.1, 1.0),) * 6,
+                        overlap_thresh=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+                        clip_boxes=(True,) * 6,
+                        random_coef=(0.15,) * 6,
+                        min_padded_size_ratio=((1.0, 1.0),) * 6,
+                        max_padded_size_ratio=((2.0, 2.0),) * 6,
+                        pad_color=(None,) * 6,
+                        seed=None,
+                        preprocess_vars_cache=None):
+  """Random crop preprocessing with default parameters as in SSD paper.
+
+  Liu et al., SSD: Single shot multibox detector.
+  For further information on random crop preprocessing refer to RandomCrop
+  function above.
+
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: float32 tensor of shape [num_instances] representing the
+      confidences for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    min_padded_size_ratio: min ratio of padded image height and width to the
+                           input image's height and width.
+    max_padded_size_ratio: max ratio of padded image height and width to the
+                           input image's height and width.
+    pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
+               if set as None, it will be set to average color of the randomly
+               cropped image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: Image shape will be [new_height, new_width, channels].
+    boxes: boxes which is the same rank as input boxes. Boxes are in normalized
+           form.
+    new_labels: new labels.
+    new_label_weights: new label weights.
+  """
+
+  def random_crop_pad_selector(image_boxes_labels, index):
+    """Random crop preprocessing helper."""
+    i = 3
+    image, boxes, labels = image_boxes_labels[:i]
+    selected_label_weights = None
+    selected_label_confidences = None
+    selected_multiclass_scores = None
+    if label_weights is not None:
+      selected_label_weights = image_boxes_labels[i]
+      i += 1
+    if label_confidences is not None:
+      selected_label_confidences = image_boxes_labels[i]
+      i += 1
+    if multiclass_scores is not None:
+      selected_multiclass_scores = image_boxes_labels[i]
+
+    return random_crop_pad_image(
+        image,
+        boxes,
+        labels,
+        label_weights=selected_label_weights,
+        label_confidences=selected_label_confidences,
+        multiclass_scores=selected_multiclass_scores,
+        min_object_covered=min_object_covered[index],
+        aspect_ratio_range=aspect_ratio_range[index],
+        area_range=area_range[index],
+        overlap_thresh=overlap_thresh[index],
+        clip_boxes=clip_boxes[index],
+        random_coef=random_coef[index],
+        min_padded_size_ratio=min_padded_size_ratio[index],
+        max_padded_size_ratio=max_padded_size_ratio[index],
+        pad_color=pad_color[index],
+        seed=seed,
+        preprocess_vars_cache=preprocess_vars_cache)
+
+  return _apply_with_random_selector_tuples(
+      tuple(t for t in (image, boxes, labels, label_weights, label_confidences,
+                        multiclass_scores) if t is not None),
+      random_crop_pad_selector,
+      num_cases=len(min_object_covered),
+      preprocess_vars_cache=preprocess_vars_cache,
+      key=preprocessor_cache.PreprocessorCache.SSD_CROP_PAD_SELECTOR_ID)
+
+
+def ssd_random_crop_fixed_aspect_ratio(
+    image,
+    boxes,
+    labels,
+    label_weights,
+    label_confidences=None,
+    multiclass_scores=None,
+    masks=None,
+    keypoints=None,
+    min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+    aspect_ratio=1.0,
+    area_range=((0.1, 1.0),) * 7,
+    overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+    clip_boxes=(True,) * 7,
+    random_coef=(0.15,) * 7,
+    seed=None,
+    preprocess_vars_cache=None):
+  """Random crop preprocessing with default parameters as in SSD paper.
+
+  Liu et al., SSD: Single shot multibox detector.
+  For further information on random crop preprocessing refer to RandomCrop
+  function above.
+
+  The only difference is that the aspect ratio of the crops are fixed.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: (optional) float32 tensor of shape [num_instances]
+      representing the confidences for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio: aspect ratio of the cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If mulitclass_scores, masks, or keypoints is not None, the function also
+      returns:
+
+    multiclass_scores: rank 2 float32 tensor with shape
+                       [num_instances, num_classes]
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+  aspect_ratio_range = ((aspect_ratio, aspect_ratio),) * len(area_range)
+
+  crop_result = ssd_random_crop(
+      image,
+      boxes,
+      labels,
+      label_weights=label_weights,
+      label_confidences=label_confidences,
+      multiclass_scores=multiclass_scores,
+      masks=masks,
+      keypoints=keypoints,
+      min_object_covered=min_object_covered,
+      aspect_ratio_range=aspect_ratio_range,
+      area_range=area_range,
+      overlap_thresh=overlap_thresh,
+      clip_boxes=clip_boxes,
+      random_coef=random_coef,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+  i = 3
+  new_image, new_boxes, new_labels = crop_result[:i]
+  new_label_weights = None
+  new_label_confidences = None
+  new_multiclass_scores = None
+  new_masks = None
+  new_keypoints = None
+  if label_weights is not None:
+    new_label_weights = crop_result[i]
+    i += 1
+  if label_confidences is not None:
+    new_label_confidences = crop_result[i]
+    i += 1
+  if multiclass_scores is not None:
+    new_multiclass_scores = crop_result[i]
+    i += 1
+  if masks is not None:
+    new_masks = crop_result[i]
+    i += 1
+  if keypoints is not None:
+    new_keypoints = crop_result[i]
+
+  result = random_crop_to_aspect_ratio(
+      new_image,
+      new_boxes,
+      new_labels,
+      label_weights=new_label_weights,
+      label_confidences=new_label_confidences,
+      multiclass_scores=new_multiclass_scores,
+      masks=new_masks,
+      keypoints=new_keypoints,
+      aspect_ratio=aspect_ratio,
+      clip_boxes=clip_boxes,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+
+  return result
+
+
+def ssd_random_crop_pad_fixed_aspect_ratio(
+    image,
+    boxes,
+    labels,
+    label_weights,
+    label_confidences=None,
+    multiclass_scores=None,
+    masks=None,
+    keypoints=None,
+    min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+    aspect_ratio=1.0,
+    aspect_ratio_range=((0.5, 2.0),) * 7,
+    area_range=((0.1, 1.0),) * 7,
+    overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
+    clip_boxes=(True,) * 7,
+    random_coef=(0.15,) * 7,
+    min_padded_size_ratio=(1.0, 1.0),
+    max_padded_size_ratio=(2.0, 2.0),
+    seed=None,
+    preprocess_vars_cache=None):
+  """Random crop and pad preprocessing with default parameters as in SSD paper.
+
+  Liu et al., SSD: Single shot multibox detector.
+  For further information on random crop preprocessing refer to RandomCrop
+  function above.
+
+  The only difference is that after the initial crop, images are zero-padded
+  to a fixed aspect ratio instead of being resized to that aspect ratio.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 1].
+    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
+           Boxes are in normalized form meaning their coordinates vary
+           between [0, 1].
+           Each row is in the form of [ymin, xmin, ymax, xmax].
+    labels: rank 1 int32 tensor containing the object classes.
+    label_weights: float32 tensor of shape [num_instances] representing the
+      weight for each box.
+    label_confidences: (optional) float32 tensor of shape [num_instances]
+      representing the confidence for each box.
+    multiclass_scores: (optional) float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    masks: (optional) rank 3 float32 tensor with shape
+           [num_instances, height, width] containing instance masks. The masks
+           are of the same height, width as the input `image`.
+    keypoints: (optional) rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]. The keypoints are in y-x
+               normalized coordinates.
+    min_object_covered: the cropped image must cover at least this fraction of
+                        at least one of the input bounding boxes.
+    aspect_ratio: the final aspect ratio to pad to.
+    aspect_ratio_range: allowed range for aspect ratio of cropped image.
+    area_range: allowed range for area ratio between cropped image and the
+                original image.
+    overlap_thresh: minimum overlap thresh with new cropped
+                    image to keep the box.
+    clip_boxes: whether to clip the boxes to the cropped image.
+    random_coef: a random coefficient that defines the chance of getting the
+                 original image. If random_coef is 0, we will always get the
+                 cropped image, and if it is 1.0, we will always get the
+                 original image.
+    min_padded_size_ratio: min ratio of padded image height and width to the
+                           input image's height and width.
+    max_padded_size_ratio: max ratio of padded image height and width to the
+                           input image's height and width.
+    seed: random seed.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    image: image which is the same rank as input image.
+    boxes: boxes which is the same rank as input boxes.
+           Boxes are in normalized form.
+    labels: new labels.
+
+    If multiclass_scores, masks, or keypoints is not None, the function also
+    returns:
+
+    multiclass_scores: rank 2 with shape [num_instances, num_classes]
+    masks: rank 3 float32 tensor with shape [num_instances, height, width]
+           containing instance masks.
+    keypoints: rank 3 float32 tensor with shape
+               [num_instances, num_keypoints, 2]
+  """
+  crop_result = ssd_random_crop(
+      image,
+      boxes,
+      labels,
+      label_weights=label_weights,
+      label_confidences=label_confidences,
+      multiclass_scores=multiclass_scores,
+      masks=masks,
+      keypoints=keypoints,
+      min_object_covered=min_object_covered,
+      aspect_ratio_range=aspect_ratio_range,
+      area_range=area_range,
+      overlap_thresh=overlap_thresh,
+      clip_boxes=clip_boxes,
+      random_coef=random_coef,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+  i = 3
+  new_image, new_boxes, new_labels = crop_result[:i]
+  new_label_weights = None
+  new_label_confidences = None
+  new_multiclass_scores = None
+  new_masks = None
+  new_keypoints = None
+  if label_weights is not None:
+    new_label_weights = crop_result[i]
+    i += 1
+  if label_confidences is not None:
+    new_label_confidences = crop_result[i]
+    i += 1
+  if multiclass_scores is not None:
+    new_multiclass_scores = crop_result[i]
+    i += 1
+  if masks is not None:
+    new_masks = crop_result[i]
+    i += 1
+  if keypoints is not None:
+    new_keypoints = crop_result[i]
+
+  result = random_pad_to_aspect_ratio(
+      new_image,
+      new_boxes,
+      masks=new_masks,
+      keypoints=new_keypoints,
+      aspect_ratio=aspect_ratio,
+      min_padded_size_ratio=min_padded_size_ratio,
+      max_padded_size_ratio=max_padded_size_ratio,
+      seed=seed,
+      preprocess_vars_cache=preprocess_vars_cache)
+
+  result = list(result)
+  i = 3
+  result.insert(2, new_labels)
+  if new_label_weights is not None:
+    result.insert(i, new_label_weights)
+    i += 1
+  if new_label_confidences is not None:
+    result.insert(i, new_label_confidences)
+    i += 1
+  if multiclass_scores is not None:
+    result.insert(i, new_multiclass_scores)
+  result = tuple(result)
+
+  return result
+
+
+def convert_class_logits_to_softmax(multiclass_scores, temperature=1.0):
+  """Converts multiclass logits to softmax scores after applying temperature.
+
+  Args:
+    multiclass_scores: float32 tensor of shape
+      [num_instances, num_classes] representing the score for each box for each
+      class.
+    temperature: Scale factor to use prior to applying softmax. Larger
+      temperatures give more uniform distruibutions after softmax.
+
+  Returns:
+    multiclass_scores: float32 tensor of shape
+      [num_instances, num_classes] with scaling and softmax applied.
+  """
+
+  # Multiclass scores must be stored as logits. Apply temp and softmax.
+  multiclass_scores_scaled = tf.divide(
+      multiclass_scores, temperature, name='scale_logits')
+  multiclass_scores = tf.nn.softmax(multiclass_scores_scaled, name='softmax')
+
+  return multiclass_scores
+
+
+def get_default_func_arg_map(include_label_weights=True,
+                             include_label_confidences=False,
+                             include_multiclass_scores=False,
+                             include_instance_masks=False,
+                             include_keypoints=False):
+  """Returns the default mapping from a preprocessor function to its args.
+
+  Args:
+    include_label_weights: If True, preprocessing functions will modify the
+      label weights, too.
+    include_label_confidences: If True, preprocessing functions will modify the
+      label confidences, too.
+    include_multiclass_scores: If True, preprocessing functions will modify the
+      multiclass scores, too.
+    include_instance_masks: If True, preprocessing functions will modify the
+      instance masks, too.
+    include_keypoints: If True, preprocessing functions will modify the
+      keypoints, too.
+
+  Returns:
+    A map from preprocessing functions to the arguments they receive.
+  """
+  groundtruth_label_weights = None
+  if include_label_weights:
+    groundtruth_label_weights = (
+        fields.InputDataFields.groundtruth_weights)
+
+  groundtruth_label_confidences = None
+  if include_label_confidences:
+    groundtruth_label_confidences = (
+        fields.InputDataFields.groundtruth_confidences)
+
+  multiclass_scores = None
+  if include_multiclass_scores:
+    multiclass_scores = (fields.InputDataFields.multiclass_scores)
+
+  groundtruth_instance_masks = None
+  if include_instance_masks:
+    groundtruth_instance_masks = (
+        fields.InputDataFields.groundtruth_instance_masks)
+
+  groundtruth_keypoints = None
+  if include_keypoints:
+    groundtruth_keypoints = fields.InputDataFields.groundtruth_keypoints
+
+  prep_func_arg_map = {
+      normalize_image: (fields.InputDataFields.image,),
+      random_horizontal_flip: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      random_vertical_flip: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      random_rotation90: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      random_pixel_value_scale: (fields.InputDataFields.image,),
+      random_image_scale: (
+          fields.InputDataFields.image,
+          groundtruth_instance_masks,
+      ),
+      random_rgb_to_gray: (fields.InputDataFields.image,),
+      random_adjust_brightness: (fields.InputDataFields.image,),
+      random_adjust_contrast: (fields.InputDataFields.image,),
+      random_adjust_hue: (fields.InputDataFields.image,),
+      random_adjust_saturation: (fields.InputDataFields.image,),
+      random_distort_color: (fields.InputDataFields.image,),
+      random_jitter_boxes: (fields.InputDataFields.groundtruth_boxes,),
+      random_crop_image: (fields.InputDataFields.image,
+                          fields.InputDataFields.groundtruth_boxes,
+                          fields.InputDataFields.groundtruth_classes,
+                          groundtruth_label_weights,
+                          groundtruth_label_confidences,
+                          multiclass_scores,
+                          groundtruth_instance_masks, groundtruth_keypoints),
+      random_pad_image: (fields.InputDataFields.image,
+                         fields.InputDataFields.groundtruth_boxes),
+      random_crop_pad_image: (fields.InputDataFields.image,
+                              fields.InputDataFields.groundtruth_boxes,
+                              fields.InputDataFields.groundtruth_classes,
+                              groundtruth_label_weights,
+                              groundtruth_label_confidences,
+                              multiclass_scores),
+      random_crop_to_aspect_ratio: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          fields.InputDataFields.groundtruth_classes,
+          groundtruth_label_weights,
+          groundtruth_label_confidences,
+          multiclass_scores,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      random_pad_to_aspect_ratio: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      random_black_patches: (fields.InputDataFields.image,),
+      retain_boxes_above_threshold: (
+          fields.InputDataFields.groundtruth_boxes,
+          fields.InputDataFields.groundtruth_classes,
+          groundtruth_label_weights,
+          groundtruth_label_confidences,
+          multiclass_scores,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      image_to_float: (fields.InputDataFields.image,),
+      random_resize_method: (fields.InputDataFields.image,),
+      resize_to_range: (
+          fields.InputDataFields.image,
+          groundtruth_instance_masks,
+      ),
+      resize_to_min_dimension: (
+          fields.InputDataFields.image,
+          groundtruth_instance_masks,
+      ),
+      scale_boxes_to_pixel_coordinates: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          groundtruth_keypoints,
+      ),
+      resize_image: (
+          fields.InputDataFields.image,
+          groundtruth_instance_masks,
+      ),
+      subtract_channel_mean: (fields.InputDataFields.image,),
+      one_hot_encoding: (fields.InputDataFields.groundtruth_image_classes,),
+      rgb_to_gray: (fields.InputDataFields.image,),
+      ssd_random_crop: (fields.InputDataFields.image,
+                        fields.InputDataFields.groundtruth_boxes,
+                        fields.InputDataFields.groundtruth_classes,
+                        groundtruth_label_weights,
+                        groundtruth_label_confidences,
+                        multiclass_scores,
+                        groundtruth_instance_masks,
+                        groundtruth_keypoints),
+      ssd_random_crop_pad: (fields.InputDataFields.image,
+                            fields.InputDataFields.groundtruth_boxes,
+                            fields.InputDataFields.groundtruth_classes,
+                            groundtruth_label_weights,
+                            groundtruth_label_confidences,
+                            multiclass_scores),
+      ssd_random_crop_fixed_aspect_ratio: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          fields.InputDataFields.groundtruth_classes,
+          groundtruth_label_weights,
+          groundtruth_label_confidences,
+          multiclass_scores,
+          groundtruth_instance_masks,
+          groundtruth_keypoints),
+      ssd_random_crop_pad_fixed_aspect_ratio: (
+          fields.InputDataFields.image,
+          fields.InputDataFields.groundtruth_boxes,
+          fields.InputDataFields.groundtruth_classes,
+          groundtruth_label_weights,
+          groundtruth_label_confidences,
+          multiclass_scores,
+          groundtruth_instance_masks,
+          groundtruth_keypoints,
+      ),
+      convert_class_logits_to_softmax: (multiclass_scores,),
+  }
+
+  return prep_func_arg_map
+
+
+def preprocess(tensor_dict,
+               preprocess_options,
+               func_arg_map=None,
+               preprocess_vars_cache=None):
+  """Preprocess images and bounding boxes.
+
+  Various types of preprocessing (to be implemented) based on the
+  preprocess_options dictionary e.g. "crop image" (affects image and possibly
+  boxes), "white balance image" (affects only image), etc. If self._options
+  is None, no preprocessing is done.
+
+  Args:
+    tensor_dict: dictionary that contains images, boxes, and can contain other
+                 things as well.
+                 images-> rank 4 float32 tensor contains
+                          1 image -> [1, height, width, 3].
+                          with pixel values varying between [0, 1]
+                 boxes-> rank 2 float32 tensor containing
+                         the bounding boxes -> [N, 4].
+                         Boxes are in normalized form meaning
+                         their coordinates vary between [0, 1].
+                         Each row is in the form
+                         of [ymin, xmin, ymax, xmax].
+    preprocess_options: It is a list of tuples, where each tuple contains a
+                        function and a dictionary that contains arguments and
+                        their values.
+    func_arg_map: mapping from preprocessing functions to arguments that they
+                  expect to receive and return.
+    preprocess_vars_cache: PreprocessorCache object that records previously
+                           performed augmentations. Updated in-place. If this
+                           function is called multiple times with the same
+                           non-null cache, it will perform deterministically.
+
+  Returns:
+    tensor_dict: which contains the preprocessed images, bounding boxes, etc.
+
+  Raises:
+    ValueError: (a) If the functions passed to Preprocess
+                    are not in func_arg_map.
+                (b) If the arguments that a function needs
+                    do not exist in tensor_dict.
+                (c) If image in tensor_dict is not rank 4
+  """
+  if func_arg_map is None:
+    func_arg_map = get_default_func_arg_map()
+
+  # changes the images to image (rank 4 to rank 3) since the functions
+  # receive rank 3 tensor for image
+  if fields.InputDataFields.image in tensor_dict:
+    images = tensor_dict[fields.InputDataFields.image]
+    if len(images.get_shape()) != 4:
+      raise ValueError('images in tensor_dict should be rank 4')
+    image = tf.squeeze(images, axis=0)
+    tensor_dict[fields.InputDataFields.image] = image
+
+  # Preprocess inputs based on preprocess_options
+  for option in preprocess_options:
+    func, params = option
+    if func not in func_arg_map:
+      raise ValueError('The function %s does not exist in func_arg_map' %
+                       (func.__name__))
+    arg_names = func_arg_map[func]
+    for a in arg_names:
+      if a is not None and a not in tensor_dict:
+        raise ValueError('The function %s requires argument %s' %
+                         (func.__name__, a))
+
+    def get_arg(key):
+      return tensor_dict[key] if key is not None else None
+
+    args = [get_arg(a) for a in arg_names]
+    if (preprocess_vars_cache is not None and
+        'preprocess_vars_cache' in inspect.getargspec(func).args):
+      params['preprocess_vars_cache'] = preprocess_vars_cache
+    results = func(*args, **params)
+    if not isinstance(results, (list, tuple)):
+      results = (results,)
+    # Removes None args since the return values will not contain those.
+    arg_names = [arg_name for arg_name in arg_names if arg_name is not None]
+    for res, arg_name in zip(results, arg_names):
+      tensor_dict[arg_name] = res
+
+  # changes the image to images (rank 3 to rank 4) to be compatible to what
+  # we received in the first place
+  if fields.InputDataFields.image in tensor_dict:
+    image = tensor_dict[fields.InputDataFields.image]
+    images = tf.expand_dims(image, 0)
+    tensor_dict[fields.InputDataFields.image] = images
+
+  return tensor_dict
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_cache.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_cache.py
new file mode 100644
index 00000000..4294ad1c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_cache.py
@@ -0,0 +1,102 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Records previous preprocessing operations and allows them to be repeated.
+
+Used with object_detection.core.preprocessor. Passing a PreprocessorCache
+into individual data augmentation functions or the general preprocess() function
+will store all randomly generated variables in the PreprocessorCache. When
+a preprocessor function is called multiple times with the same
+PreprocessorCache object, that function will perform the same augmentation
+on all calls.
+"""
+
+from collections import defaultdict
+
+
+class PreprocessorCache(object):
+  """Dictionary wrapper storing random variables generated during preprocessing.
+  """
+
+  # Constant keys representing different preprocessing functions
+  ROTATION90 = 'rotation90'
+  HORIZONTAL_FLIP = 'horizontal_flip'
+  VERTICAL_FLIP = 'vertical_flip'
+  PIXEL_VALUE_SCALE = 'pixel_value_scale'
+  IMAGE_SCALE = 'image_scale'
+  RGB_TO_GRAY = 'rgb_to_gray'
+  ADJUST_BRIGHTNESS = 'adjust_brightness'
+  ADJUST_CONTRAST = 'adjust_contrast'
+  ADJUST_HUE = 'adjust_hue'
+  ADJUST_SATURATION = 'adjust_saturation'
+  DISTORT_COLOR = 'distort_color'
+  STRICT_CROP_IMAGE = 'strict_crop_image'
+  CROP_IMAGE = 'crop_image'
+  PAD_IMAGE = 'pad_image'
+  CROP_TO_ASPECT_RATIO = 'crop_to_aspect_ratio'
+  RESIZE_METHOD = 'resize_method'
+  PAD_TO_ASPECT_RATIO = 'pad_to_aspect_ratio'
+  BLACK_PATCHES = 'black_patches'
+  ADD_BLACK_PATCH = 'add_black_patch'
+  SELECTOR = 'selector'
+  SELECTOR_TUPLES = 'selector_tuples'
+  SSD_CROP_SELECTOR_ID = 'ssd_crop_selector_id'
+  SSD_CROP_PAD_SELECTOR_ID = 'ssd_crop_pad_selector_id'
+
+  # 23 permitted function ids
+  _VALID_FNS = [ROTATION90, HORIZONTAL_FLIP, VERTICAL_FLIP, PIXEL_VALUE_SCALE,
+                IMAGE_SCALE, RGB_TO_GRAY, ADJUST_BRIGHTNESS, ADJUST_CONTRAST,
+                ADJUST_HUE, ADJUST_SATURATION, DISTORT_COLOR, STRICT_CROP_IMAGE,
+                CROP_IMAGE, PAD_IMAGE, CROP_TO_ASPECT_RATIO, RESIZE_METHOD,
+                PAD_TO_ASPECT_RATIO, BLACK_PATCHES, ADD_BLACK_PATCH, SELECTOR,
+                SELECTOR_TUPLES, SSD_CROP_SELECTOR_ID, SSD_CROP_PAD_SELECTOR_ID]
+
+  def __init__(self):
+    self._history = defaultdict(dict)
+
+  def clear(self):
+    """Resets cache."""
+    self._history = defaultdict(dict)
+
+  def get(self, function_id, key):
+    """Gets stored value given a function id and key.
+
+    Args:
+      function_id: identifier for the preprocessing function used.
+      key: identifier for the variable stored.
+    Returns:
+      value: the corresponding value, expected to be a tensor or
+             nested structure of tensors.
+    Raises:
+      ValueError: if function_id is not one of the 23 valid function ids.
+    """
+    if function_id not in self._VALID_FNS:
+      raise ValueError('Function id not recognized: %s.' % str(function_id))
+    return self._history[function_id].get(key)
+
+  def update(self, function_id, key, value):
+    """Adds a value to the dictionary.
+
+    Args:
+      function_id: identifier for the preprocessing function used.
+      key: identifier for the variable stored.
+      value: the value to store, expected to be a tensor or nested structure
+             of tensors.
+    Raises:
+      ValueError: if function_id is not one of the 23 valid function ids.
+    """
+    if function_id not in self._VALID_FNS:
+      raise ValueError('Function id not recognized: %s.' % str(function_id))
+    self._history[function_id][key] = value
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_test.py
new file mode 100644
index 00000000..4786f807
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/preprocessor_test.py
@@ -0,0 +1,2946 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.preprocessor."""
+
+import numpy as np
+import six
+
+import tensorflow as tf
+
+from object_detection.core import preprocessor
+from object_detection.core import preprocessor_cache
+from object_detection.core import standard_fields as fields
+
+if six.PY2:
+  import mock  # pylint: disable=g-import-not-at-top
+else:
+  from unittest import mock  # pylint: disable=g-import-not-at-top
+
+
+class PreprocessorTest(tf.test.TestCase):
+
+  def createColorfulTestImage(self):
+    ch255 = tf.fill([1, 100, 200, 1], tf.constant(255, dtype=tf.uint8))
+    ch128 = tf.fill([1, 100, 200, 1], tf.constant(128, dtype=tf.uint8))
+    ch0 = tf.fill([1, 100, 200, 1], tf.constant(0, dtype=tf.uint8))
+    imr = tf.concat([ch255, ch0, ch0], 3)
+    img = tf.concat([ch255, ch255, ch0], 3)
+    imb = tf.concat([ch255, ch0, ch255], 3)
+    imw = tf.concat([ch128, ch128, ch128], 3)
+    imu = tf.concat([imr, img], 2)
+    imd = tf.concat([imb, imw], 2)
+    im = tf.concat([imu, imd], 1)
+    return im
+
+  def createTestImages(self):
+    images_r = tf.constant([[[128, 128, 128, 128], [0, 0, 128, 128],
+                             [0, 128, 128, 128], [192, 192, 128, 128]]],
+                           dtype=tf.uint8)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[0, 0, 128, 128], [0, 0, 128, 128],
+                             [0, 128, 192, 192], [192, 192, 128, 192]]],
+                           dtype=tf.uint8)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[128, 128, 192, 0], [0, 0, 128, 192],
+                             [0, 128, 128, 0], [192, 192, 192, 128]]],
+                           dtype=tf.uint8)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def createEmptyTestBoxes(self):
+    boxes = tf.constant([[]], dtype=tf.float32)
+    return boxes
+
+  def createTestBoxes(self):
+    boxes = tf.constant(
+        [[0.0, 0.25, 0.75, 1.0], [0.25, 0.5, 0.75, 1.0]], dtype=tf.float32)
+    return boxes
+
+  def createTestGroundtruthWeights(self):
+    return tf.constant([1.0, 0.5], dtype=tf.float32)
+
+  def createTestMasks(self):
+    mask = np.array([
+        [[255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0]],
+        [[255.0, 255.0, 0.0],
+         [255.0, 255.0, 0.0],
+         [255.0, 255.0, 0.0]]])
+    return tf.constant(mask, dtype=tf.float32)
+
+  def createTestKeypoints(self):
+    keypoints = np.array([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+        [[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]],
+    ])
+    return tf.constant(keypoints, dtype=tf.float32)
+
+  def createTestKeypointsInsideCrop(self):
+    keypoints = np.array([
+        [[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]],
+        [[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]],
+    ])
+    return tf.constant(keypoints, dtype=tf.float32)
+
+  def createTestKeypointsOutsideCrop(self):
+    keypoints = np.array([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
+    ])
+    return tf.constant(keypoints, dtype=tf.float32)
+
+  def createKeypointFlipPermutation(self):
+    return np.array([0, 2, 1], dtype=np.int32)
+
+  def createTestLabels(self):
+    labels = tf.constant([1, 2], dtype=tf.int32)
+    return labels
+
+  def createTestBoxesOutOfImage(self):
+    boxes = tf.constant(
+        [[-0.1, 0.25, 0.75, 1], [0.25, 0.5, 0.75, 1.1]], dtype=tf.float32)
+    return boxes
+
+  def createTestMultiClassScores(self):
+    return tf.constant([[1.0, 0.0], [0.5, 0.5]], dtype=tf.float32)
+
+  def expectedImagesAfterNormalization(self):
+    images_r = tf.constant([[[0, 0, 0, 0], [-1, -1, 0, 0],
+                             [-1, 0, 0, 0], [0.5, 0.5, 0, 0]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[-1, -1, 0, 0], [-1, -1, 0, 0],
+                             [-1, 0, 0.5, 0.5], [0.5, 0.5, 0, 0.5]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[0, 0, 0.5, -1], [-1, -1, 0, 0.5],
+                             [-1, 0, 0, -1], [0.5, 0.5, 0.5, 0]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedMaxImageAfterColorScale(self):
+    images_r = tf.constant([[[0.1, 0.1, 0.1, 0.1], [-0.9, -0.9, 0.1, 0.1],
+                             [-0.9, 0.1, 0.1, 0.1], [0.6, 0.6, 0.1, 0.1]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[-0.9, -0.9, 0.1, 0.1], [-0.9, -0.9, 0.1, 0.1],
+                             [-0.9, 0.1, 0.6, 0.6], [0.6, 0.6, 0.1, 0.6]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[0.1, 0.1, 0.6, -0.9], [-0.9, -0.9, 0.1, 0.6],
+                             [-0.9, 0.1, 0.1, -0.9], [0.6, 0.6, 0.6, 0.1]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedMinImageAfterColorScale(self):
+    images_r = tf.constant([[[-0.1, -0.1, -0.1, -0.1], [-1, -1, -0.1, -0.1],
+                             [-1, -0.1, -0.1, -0.1], [0.4, 0.4, -0.1, -0.1]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[-1, -1, -0.1, -0.1], [-1, -1, -0.1, -0.1],
+                             [-1, -0.1, 0.4, 0.4], [0.4, 0.4, -0.1, 0.4]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[-0.1, -0.1, 0.4, -1], [-1, -1, -0.1, 0.4],
+                             [-1, -0.1, -0.1, -1], [0.4, 0.4, 0.4, -0.1]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedImagesAfterLeftRightFlip(self):
+    images_r = tf.constant([[[0, 0, 0, 0], [0, 0, -1, -1],
+                             [0, 0, 0, -1], [0, 0, 0.5, 0.5]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[0, 0, -1, -1], [0, 0, -1, -1],
+                             [0.5, 0.5, 0, -1], [0.5, 0, 0.5, 0.5]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[-1, 0.5, 0, 0], [0.5, 0, -1, -1],
+                             [-1, 0, 0, -1], [0, 0.5, 0.5, 0.5]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedImagesAfterUpDownFlip(self):
+    images_r = tf.constant([[[0.5, 0.5, 0, 0], [-1, 0, 0, 0],
+                             [-1, -1, 0, 0], [0, 0, 0, 0]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[0.5, 0.5, 0, 0.5], [-1, 0, 0.5, 0.5],
+                             [-1, -1, 0, 0], [-1, -1, 0, 0]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[0.5, 0.5, 0.5, 0], [-1, 0, 0, -1],
+                             [-1, -1, 0, 0.5], [0, 0, 0.5, -1]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedImagesAfterRot90(self):
+    images_r = tf.constant([[[0, 0, 0, 0], [0, 0, 0, 0],
+                             [0, -1, 0, 0.5], [0, -1, -1, 0.5]]],
+                           dtype=tf.float32)
+    images_r = tf.expand_dims(images_r, 3)
+    images_g = tf.constant([[[0, 0, 0.5, 0.5], [0, 0, 0.5, 0],
+                             [-1, -1, 0, 0.5], [-1, -1, -1, 0.5]]],
+                           dtype=tf.float32)
+    images_g = tf.expand_dims(images_g, 3)
+    images_b = tf.constant([[[-1, 0.5, -1, 0], [0.5, 0, 0, 0.5],
+                             [0, -1, 0, 0.5], [0, -1, -1, 0.5]]],
+                           dtype=tf.float32)
+    images_b = tf.expand_dims(images_b, 3)
+    images = tf.concat([images_r, images_g, images_b], 3)
+    return images
+
+  def expectedBoxesAfterLeftRightFlip(self):
+    boxes = tf.constant([[0.0, 0.0, 0.75, 0.75], [0.25, 0.0, 0.75, 0.5]],
+                        dtype=tf.float32)
+    return boxes
+
+  def expectedBoxesAfterUpDownFlip(self):
+    boxes = tf.constant([[0.25, 0.25, 1.0, 1.0], [0.25, 0.5, 0.75, 1.0]],
+                        dtype=tf.float32)
+    return boxes
+
+  def expectedBoxesAfterRot90(self):
+    boxes = tf.constant(
+        [[0.0, 0.0, 0.75, 0.75], [0.0, 0.25, 0.5, 0.75]], dtype=tf.float32)
+    return boxes
+
+  def expectedMasksAfterLeftRightFlip(self):
+    mask = np.array([
+        [[0.0, 0.0, 255.0],
+         [0.0, 0.0, 255.0],
+         [0.0, 0.0, 255.0]],
+        [[0.0, 255.0, 255.0],
+         [0.0, 255.0, 255.0],
+         [0.0, 255.0, 255.0]]])
+    return tf.constant(mask, dtype=tf.float32)
+
+  def expectedMasksAfterUpDownFlip(self):
+    mask = np.array([
+        [[255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0]],
+        [[255.0, 255.0, 0.0],
+         [255.0, 255.0, 0.0],
+         [255.0, 255.0, 0.0]]])
+    return tf.constant(mask, dtype=tf.float32)
+
+  def expectedMasksAfterRot90(self):
+    mask = np.array([
+        [[0.0, 0.0, 0.0],
+         [0.0, 0.0, 0.0],
+         [255.0, 255.0, 255.0]],
+        [[0.0, 0.0, 0.0],
+         [255.0, 255.0, 255.0],
+         [255.0, 255.0, 255.0]]])
+    return tf.constant(mask, dtype=tf.float32)
+
+  def expectedLabelScoresAfterThresholding(self):
+    return tf.constant([1.0], dtype=tf.float32)
+
+  def expectedBoxesAfterThresholding(self):
+    return tf.constant([[0.0, 0.25, 0.75, 1.0]], dtype=tf.float32)
+
+  def expectedLabelsAfterThresholding(self):
+    return tf.constant([1], dtype=tf.float32)
+
+  def expectedMultiClassScoresAfterThresholding(self):
+    return tf.constant([[1.0, 0.0]], dtype=tf.float32)
+
+  def expectedMasksAfterThresholding(self):
+    mask = np.array([
+        [[255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0],
+         [255.0, 0.0, 0.0]]])
+    return tf.constant(mask, dtype=tf.float32)
+
+  def expectedKeypointsAfterThresholding(self):
+    keypoints = np.array([
+        [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]
+    ])
+    return tf.constant(keypoints, dtype=tf.float32)
+
+  def expectedLabelScoresAfterThresholdingWithMissingScore(self):
+    return tf.constant([np.nan], dtype=tf.float32)
+
+  def expectedBoxesAfterThresholdingWithMissingScore(self):
+    return tf.constant([[0.25, 0.5, 0.75, 1]], dtype=tf.float32)
+
+  def expectedLabelsAfterThresholdingWithMissingScore(self):
+    return tf.constant([2], dtype=tf.float32)
+
+  def testRgbToGrayscale(self):
+    images = self.createTestImages()
+    grayscale_images = preprocessor._rgb_to_grayscale(images)
+    expected_images = tf.image.rgb_to_grayscale(images)
+    with self.test_session() as sess:
+      (grayscale_images, expected_images) = sess.run(
+          [grayscale_images, expected_images])
+      self.assertAllEqual(expected_images, grayscale_images)
+
+  def testNormalizeImage(self):
+    preprocess_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 256,
+        'target_minval': -1,
+        'target_maxval': 1
+    })]
+    images = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    images_expected = self.expectedImagesAfterNormalization()
+
+    with self.test_session() as sess:
+      (images_, images_expected_) = sess.run(
+          [images, images_expected])
+      images_shape_ = images_.shape
+      images_expected_shape_ = images_expected_.shape
+      expected_shape = [1, 4, 4, 3]
+      self.assertAllEqual(images_expected_shape_, images_shape_)
+      self.assertAllEqual(images_shape_, expected_shape)
+      self.assertAllClose(images_, images_expected_)
+
+  def testRetainBoxesAboveThreshold(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    (retained_boxes, retained_labels,
+     retained_weights) = preprocessor.retain_boxes_above_threshold(
+         boxes, labels, weights, threshold=0.6)
+    with self.test_session() as sess:
+      (retained_boxes_, retained_labels_, retained_weights_,
+       expected_retained_boxes_, expected_retained_labels_,
+       expected_retained_weights_) = sess.run([
+           retained_boxes, retained_labels, retained_weights,
+           self.expectedBoxesAfterThresholding(),
+           self.expectedLabelsAfterThresholding(),
+           self.expectedLabelScoresAfterThresholding()])
+      self.assertAllClose(
+          retained_boxes_, expected_retained_boxes_)
+      self.assertAllClose(
+          retained_labels_, expected_retained_labels_)
+      self.assertAllClose(
+          retained_weights_, expected_retained_weights_)
+
+  def testRetainBoxesAboveThresholdWithMultiClassScores(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    multiclass_scores = self.createTestMultiClassScores()
+    (_, _, _,
+     retained_multiclass_scores) = preprocessor.retain_boxes_above_threshold(
+         boxes,
+         labels,
+         weights,
+         multiclass_scores=multiclass_scores,
+         threshold=0.6)
+    with self.test_session() as sess:
+      (retained_multiclass_scores_,
+       expected_retained_multiclass_scores_) = sess.run([
+           retained_multiclass_scores,
+           self.expectedMultiClassScoresAfterThresholding()
+       ])
+
+      self.assertAllClose(retained_multiclass_scores_,
+                          expected_retained_multiclass_scores_)
+
+  def testRetainBoxesAboveThresholdWithMasks(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    masks = self.createTestMasks()
+    _, _, _, retained_masks = preprocessor.retain_boxes_above_threshold(
+        boxes, labels, weights, masks, threshold=0.6)
+    with self.test_session() as sess:
+      retained_masks_, expected_retained_masks_ = sess.run([
+          retained_masks, self.expectedMasksAfterThresholding()])
+
+      self.assertAllClose(
+          retained_masks_, expected_retained_masks_)
+
+  def testRetainBoxesAboveThresholdWithKeypoints(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypoints()
+    (_, _, _, retained_keypoints) = preprocessor.retain_boxes_above_threshold(
+        boxes, labels, weights, keypoints=keypoints, threshold=0.6)
+    with self.test_session() as sess:
+      (retained_keypoints_,
+       expected_retained_keypoints_) = sess.run([
+           retained_keypoints,
+           self.expectedKeypointsAfterThresholding()])
+
+      self.assertAllClose(
+          retained_keypoints_, expected_retained_keypoints_)
+
+  def testFlipBoxesLeftRight(self):
+    boxes = self.createTestBoxes()
+    flipped_boxes = preprocessor._flip_boxes_left_right(boxes)
+    expected_boxes = self.expectedBoxesAfterLeftRightFlip()
+    with self.test_session() as sess:
+      flipped_boxes, expected_boxes = sess.run([flipped_boxes, expected_boxes])
+      self.assertAllEqual(flipped_boxes.flatten(), expected_boxes.flatten())
+
+  def testFlipBoxesUpDown(self):
+    boxes = self.createTestBoxes()
+    flipped_boxes = preprocessor._flip_boxes_up_down(boxes)
+    expected_boxes = self.expectedBoxesAfterUpDownFlip()
+    with self.test_session() as sess:
+      flipped_boxes, expected_boxes = sess.run([flipped_boxes, expected_boxes])
+      self.assertAllEqual(flipped_boxes.flatten(), expected_boxes.flatten())
+
+  def testRot90Boxes(self):
+    boxes = self.createTestBoxes()
+    rotated_boxes = preprocessor._rot90_boxes(boxes)
+    expected_boxes = self.expectedBoxesAfterRot90()
+    with self.test_session() as sess:
+      rotated_boxes, expected_boxes = sess.run([rotated_boxes, expected_boxes])
+      self.assertAllEqual(rotated_boxes.flatten(), expected_boxes.flatten())
+
+  def testFlipMasksLeftRight(self):
+    test_mask = self.createTestMasks()
+    flipped_mask = preprocessor._flip_masks_left_right(test_mask)
+    expected_mask = self.expectedMasksAfterLeftRightFlip()
+    with self.test_session() as sess:
+      flipped_mask, expected_mask = sess.run([flipped_mask, expected_mask])
+      self.assertAllEqual(flipped_mask.flatten(), expected_mask.flatten())
+
+  def testFlipMasksUpDown(self):
+    test_mask = self.createTestMasks()
+    flipped_mask = preprocessor._flip_masks_up_down(test_mask)
+    expected_mask = self.expectedMasksAfterUpDownFlip()
+    with self.test_session() as sess:
+      flipped_mask, expected_mask = sess.run([flipped_mask, expected_mask])
+      self.assertAllEqual(flipped_mask.flatten(), expected_mask.flatten())
+
+  def testRot90Masks(self):
+    test_mask = self.createTestMasks()
+    rotated_mask = preprocessor._rot90_masks(test_mask)
+    expected_mask = self.expectedMasksAfterRot90()
+    with self.test_session() as sess:
+      rotated_mask, expected_mask = sess.run([rotated_mask, expected_mask])
+      self.assertAllEqual(rotated_mask.flatten(), expected_mask.flatten())
+
+  def _testPreprocessorCache(self,
+                             preprocess_options,
+                             test_boxes=False,
+                             test_masks=False,
+                             test_keypoints=False,
+                             num_runs=4):
+    cache = preprocessor_cache.PreprocessorCache()
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    weights = self.createTestGroundtruthWeights()
+    classes = self.createTestLabels()
+    masks = self.createTestMasks()
+    keypoints = self.createTestKeypoints()
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=test_masks, include_keypoints=test_keypoints)
+    out = []
+    for i in range(num_runs):
+      tensor_dict = {
+          fields.InputDataFields.image: images,
+          fields.InputDataFields.groundtruth_weights: weights
+      }
+      num_outputs = 1
+      if test_boxes:
+        tensor_dict[fields.InputDataFields.groundtruth_boxes] = boxes
+        tensor_dict[fields.InputDataFields.groundtruth_classes] = classes
+        num_outputs += 1
+      if test_masks:
+        tensor_dict[fields.InputDataFields.groundtruth_instance_masks] = masks
+        num_outputs += 1
+      if test_keypoints:
+        tensor_dict[fields.InputDataFields.groundtruth_keypoints] = keypoints
+        num_outputs += 1
+      out.append(preprocessor.preprocess(
+          tensor_dict, preprocess_options, preprocessor_arg_map, cache))
+
+    with self.test_session() as sess:
+      to_run = []
+      for i in range(num_runs):
+        to_run.append(out[i][fields.InputDataFields.image])
+        if test_boxes:
+          to_run.append(out[i][fields.InputDataFields.groundtruth_boxes])
+        if test_masks:
+          to_run.append(
+              out[i][fields.InputDataFields.groundtruth_instance_masks])
+        if test_keypoints:
+          to_run.append(out[i][fields.InputDataFields.groundtruth_keypoints])
+
+      out_array = sess.run(to_run)
+      for i in range(num_outputs, len(out_array)):
+        self.assertAllClose(out_array[i], out_array[i - num_outputs])
+
+  def testRandomHorizontalFlip(self):
+    preprocess_options = [(preprocessor.random_horizontal_flip, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterLeftRightFlip()
+    boxes_expected1 = self.expectedBoxesAfterLeftRightFlip()
+    images_expected2 = images
+    boxes_expected2 = boxes
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
+    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
+    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
+    boxes_diff_expected = tf.zeros_like(boxes_diff)
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_diff_,
+       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
+                                         boxes_diff, boxes_diff_expected])
+      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomHorizontalFlipWithEmptyBoxes(self):
+    preprocess_options = [(preprocessor.random_horizontal_flip, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createEmptyTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterLeftRightFlip()
+    boxes_expected = self.createEmptyTestBoxes()
+    images_expected2 = images
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_,
+       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
+                                    boxes_expected])
+      self.assertAllClose(boxes_, boxes_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomHorizontalFlipWithCache(self):
+    keypoint_flip_permutation = self.createKeypointFlipPermutation()
+    preprocess_options = [
+        (preprocessor.random_horizontal_flip,
+         {'keypoint_flip_permutation': keypoint_flip_permutation})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRunRandomHorizontalFlipWithMaskAndKeypoints(self):
+    preprocess_options = [(preprocessor.random_horizontal_flip, {})]
+    image_height = 3
+    image_width = 3
+    images = tf.random_uniform([1, image_height, image_width, 3])
+    boxes = self.createTestBoxes()
+    masks = self.createTestMasks()
+    keypoints = self.createTestKeypoints()
+    keypoint_flip_permutation = self.createKeypointFlipPermutation()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_instance_masks: masks,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+    preprocess_options = [
+        (preprocessor.random_horizontal_flip,
+         {'keypoint_flip_permutation': keypoint_flip_permutation})]
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True, include_keypoints=True)
+    tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
+    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
+    with self.test_session() as sess:
+      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
+      self.assertTrue(boxes is not None)
+      self.assertTrue(masks is not None)
+      self.assertTrue(keypoints is not None)
+
+  def testRandomVerticalFlip(self):
+    preprocess_options = [(preprocessor.random_vertical_flip, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterUpDownFlip()
+    boxes_expected1 = self.expectedBoxesAfterUpDownFlip()
+    images_expected2 = images
+    boxes_expected2 = boxes
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
+    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
+    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
+    boxes_diff_expected = tf.zeros_like(boxes_diff)
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_diff_,
+       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
+                                         boxes_diff, boxes_diff_expected])
+      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomVerticalFlipWithEmptyBoxes(self):
+    preprocess_options = [(preprocessor.random_vertical_flip, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createEmptyTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterUpDownFlip()
+    boxes_expected = self.createEmptyTestBoxes()
+    images_expected2 = images
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_,
+       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
+                                    boxes_expected])
+      self.assertAllClose(boxes_, boxes_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomVerticalFlipWithCache(self):
+    keypoint_flip_permutation = self.createKeypointFlipPermutation()
+    preprocess_options = [
+        (preprocessor.random_vertical_flip,
+         {'keypoint_flip_permutation': keypoint_flip_permutation})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRunRandomVerticalFlipWithMaskAndKeypoints(self):
+    preprocess_options = [(preprocessor.random_vertical_flip, {})]
+    image_height = 3
+    image_width = 3
+    images = tf.random_uniform([1, image_height, image_width, 3])
+    boxes = self.createTestBoxes()
+    masks = self.createTestMasks()
+    keypoints = self.createTestKeypoints()
+    keypoint_flip_permutation = self.createKeypointFlipPermutation()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_instance_masks: masks,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+    preprocess_options = [
+        (preprocessor.random_vertical_flip,
+         {'keypoint_flip_permutation': keypoint_flip_permutation})]
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True, include_keypoints=True)
+    tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
+    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
+    with self.test_session() as sess:
+      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
+      self.assertTrue(boxes is not None)
+      self.assertTrue(masks is not None)
+      self.assertTrue(keypoints is not None)
+
+  def testRandomRotation90(self):
+    preprocess_options = [(preprocessor.random_rotation90, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterRot90()
+    boxes_expected1 = self.expectedBoxesAfterRot90()
+    images_expected2 = images
+    boxes_expected2 = boxes
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
+    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
+    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
+    boxes_diff_expected = tf.zeros_like(boxes_diff)
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_diff_,
+       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
+                                         boxes_diff, boxes_diff_expected])
+      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomRotation90WithEmptyBoxes(self):
+    preprocess_options = [(preprocessor.random_rotation90, {})]
+    images = self.expectedImagesAfterNormalization()
+    boxes = self.createEmptyTestBoxes()
+    tensor_dict = {fields.InputDataFields.image: images,
+                   fields.InputDataFields.groundtruth_boxes: boxes}
+    images_expected1 = self.expectedImagesAfterRot90()
+    boxes_expected = self.createEmptyTestBoxes()
+    images_expected2 = images
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images = tensor_dict[fields.InputDataFields.image]
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+
+    images_diff1 = tf.squared_difference(images, images_expected1)
+    images_diff2 = tf.squared_difference(images, images_expected2)
+    images_diff = tf.multiply(images_diff1, images_diff2)
+    images_diff_expected = tf.zeros_like(images_diff)
+
+    with self.test_session() as sess:
+      (images_diff_, images_diff_expected_, boxes_,
+       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
+                                    boxes_expected])
+      self.assertAllClose(boxes_, boxes_expected_)
+      self.assertAllClose(images_diff_, images_diff_expected_)
+
+  def testRandomRotation90WithCache(self):
+    preprocess_options = [(preprocessor.random_rotation90, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRunRandomRotation90WithMaskAndKeypoints(self):
+    preprocess_options = [(preprocessor.random_rotation90, {})]
+    image_height = 3
+    image_width = 3
+    images = tf.random_uniform([1, image_height, image_width, 3])
+    boxes = self.createTestBoxes()
+    masks = self.createTestMasks()
+    keypoints = self.createTestKeypoints()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_instance_masks: masks,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True, include_keypoints=True)
+    tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
+    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
+    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
+    with self.test_session() as sess:
+      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
+      self.assertTrue(boxes is not None)
+      self.assertTrue(masks is not None)
+      self.assertTrue(keypoints is not None)
+
+  def testRandomPixelValueScale(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_pixel_value_scale, {}))
+    images = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images_min = tf.to_float(images) * 0.9 / 255.0
+    images_max = tf.to_float(images) * 1.1 / 255.0
+    images = tensor_dict[fields.InputDataFields.image]
+    values_greater = tf.greater_equal(images, images_min)
+    values_less = tf.less_equal(images, images_max)
+    values_true = tf.fill([1, 4, 4, 3], True)
+    with self.test_session() as sess:
+      (values_greater_, values_less_, values_true_) = sess.run(
+          [values_greater, values_less, values_true])
+      self.assertAllClose(values_greater_, values_true_)
+      self.assertAllClose(values_less_, values_true_)
+
+  def testRandomPixelValueScaleWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_pixel_value_scale, {}))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomImageScale(self):
+    preprocess_options = [(preprocessor.random_image_scale, {})]
+    images_original = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images_scaled = tensor_dict[fields.InputDataFields.image]
+    images_original_shape = tf.shape(images_original)
+    images_scaled_shape = tf.shape(images_scaled)
+    with self.test_session() as sess:
+      (images_original_shape_, images_scaled_shape_) = sess.run(
+          [images_original_shape, images_scaled_shape])
+      self.assertTrue(
+          images_original_shape_[1] * 0.5 <= images_scaled_shape_[1])
+      self.assertTrue(
+          images_original_shape_[1] * 2.0 >= images_scaled_shape_[1])
+      self.assertTrue(
+          images_original_shape_[2] * 0.5 <= images_scaled_shape_[2])
+      self.assertTrue(
+          images_original_shape_[2] * 2.0 >= images_scaled_shape_[2])
+
+  def testRandomImageScaleWithCache(self):
+    preprocess_options = [(preprocessor.random_image_scale, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomRGBtoGray(self):
+    preprocess_options = [(preprocessor.random_rgb_to_gray, {})]
+    images_original = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
+    images_gray = tensor_dict[fields.InputDataFields.image]
+    images_gray_r, images_gray_g, images_gray_b = tf.split(
+        value=images_gray, num_or_size_splits=3, axis=3)
+    images_r, images_g, images_b = tf.split(
+        value=images_original, num_or_size_splits=3, axis=3)
+    images_r_diff1 = tf.squared_difference(tf.to_float(images_r),
+                                           tf.to_float(images_gray_r))
+    images_r_diff2 = tf.squared_difference(tf.to_float(images_gray_r),
+                                           tf.to_float(images_gray_g))
+    images_r_diff = tf.multiply(images_r_diff1, images_r_diff2)
+    images_g_diff1 = tf.squared_difference(tf.to_float(images_g),
+                                           tf.to_float(images_gray_g))
+    images_g_diff2 = tf.squared_difference(tf.to_float(images_gray_g),
+                                           tf.to_float(images_gray_b))
+    images_g_diff = tf.multiply(images_g_diff1, images_g_diff2)
+    images_b_diff1 = tf.squared_difference(tf.to_float(images_b),
+                                           tf.to_float(images_gray_b))
+    images_b_diff2 = tf.squared_difference(tf.to_float(images_gray_b),
+                                           tf.to_float(images_gray_r))
+    images_b_diff = tf.multiply(images_b_diff1, images_b_diff2)
+    image_zero1 = tf.constant(0, dtype=tf.float32, shape=[1, 4, 4, 1])
+    with self.test_session() as sess:
+      (images_r_diff_, images_g_diff_, images_b_diff_, image_zero1_) = sess.run(
+          [images_r_diff, images_g_diff, images_b_diff, image_zero1])
+      self.assertAllClose(images_r_diff_, image_zero1_)
+      self.assertAllClose(images_g_diff_, image_zero1_)
+      self.assertAllClose(images_b_diff_, image_zero1_)
+
+  def testRandomRGBtoGrayWithCache(self):
+    preprocess_options = [(
+        preprocessor.random_rgb_to_gray, {'probability': 0.5})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomAdjustBrightness(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_adjust_brightness, {}))
+    images_original = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images_bright = tensor_dict[fields.InputDataFields.image]
+    image_original_shape = tf.shape(images_original)
+    image_bright_shape = tf.shape(images_bright)
+    with self.test_session() as sess:
+      (image_original_shape_, image_bright_shape_) = sess.run(
+          [image_original_shape, image_bright_shape])
+      self.assertAllEqual(image_original_shape_, image_bright_shape_)
+
+  def testRandomAdjustBrightnessWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_adjust_brightness, {}))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomAdjustContrast(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_adjust_contrast, {}))
+    images_original = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images_contrast = tensor_dict[fields.InputDataFields.image]
+    image_original_shape = tf.shape(images_original)
+    image_contrast_shape = tf.shape(images_contrast)
+    with self.test_session() as sess:
+      (image_original_shape_, image_contrast_shape_) = sess.run(
+          [image_original_shape, image_contrast_shape])
+      self.assertAllEqual(image_original_shape_, image_contrast_shape_)
+
+  def testRandomAdjustContrastWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_adjust_contrast, {}))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomAdjustHue(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_adjust_hue, {}))
+    images_original = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images_hue = tensor_dict[fields.InputDataFields.image]
+    image_original_shape = tf.shape(images_original)
+    image_hue_shape = tf.shape(images_hue)
+    with self.test_session() as sess:
+      (image_original_shape_, image_hue_shape_) = sess.run(
+          [image_original_shape, image_hue_shape])
+      self.assertAllEqual(image_original_shape_, image_hue_shape_)
+
+  def testRandomAdjustHueWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_adjust_hue, {}))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomDistortColor(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_distort_color, {}))
+    images_original = self.createTestImages()
+    images_original_shape = tf.shape(images_original)
+    tensor_dict = {fields.InputDataFields.image: images_original}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images_distorted_color = tensor_dict[fields.InputDataFields.image]
+    images_distorted_color_shape = tf.shape(images_distorted_color)
+    with self.test_session() as sess:
+      (images_original_shape_, images_distorted_color_shape_) = sess.run(
+          [images_original_shape, images_distorted_color_shape])
+      self.assertAllEqual(images_original_shape_, images_distorted_color_shape_)
+
+  def testRandomDistortColorWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_distort_color, {}))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=False,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomJitterBoxes(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.random_jitter_boxes, {}))
+    boxes = self.createTestBoxes()
+    boxes_shape = tf.shape(boxes)
+    tensor_dict = {fields.InputDataFields.groundtruth_boxes: boxes}
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    distorted_boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+    distorted_boxes_shape = tf.shape(distorted_boxes)
+
+    with self.test_session() as sess:
+      (boxes_shape_, distorted_boxes_shape_) = sess.run(
+          [boxes_shape, distorted_boxes_shape])
+      self.assertAllEqual(boxes_shape_, distorted_boxes_shape_)
+
+  def testRandomCropImage(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_crop_image, {}))
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    self.assertEqual(3, distorted_images.get_shape()[3])
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = sess.run([
+           boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank
+       ])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testRandomCropImageWithCache(self):
+    preprocess_options = [(preprocessor.random_rgb_to_gray,
+                           {'probability': 0.5}),
+                          (preprocessor.normalize_image, {
+                              'original_minval': 0,
+                              'original_maxval': 255,
+                              'target_minval': 0,
+                              'target_maxval': 1,
+                          }),
+                          (preprocessor.random_crop_image, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRandomCropImageGrayscale(self):
+    preprocessing_options = [(preprocessor.rgb_to_gray, {}),
+                             (preprocessor.normalize_image, {
+                                 'original_minval': 0,
+                                 'original_maxval': 255,
+                                 'target_minval': 0,
+                                 'target_maxval': 1,
+                             }),
+                             (preprocessor.random_crop_image, {})]
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    self.assertEqual(1, distorted_images.get_shape()[3])
+
+    with self.test_session() as sess:
+      session_results = sess.run([
+          boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank
+      ])
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = session_results
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testRandomCropImageWithBoxOutOfImage(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_crop_image, {}))
+    images = self.createTestImages()
+    boxes = self.createTestBoxesOutOfImage()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        }
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = sess.run(
+           [boxes_rank, distorted_boxes_rank, images_rank,
+            distorted_images_rank])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testRandomCropImageWithRandomCoefOne(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    })]
+
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_crop_image, {
+        'random_coef': 1.0
+    })]
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_labels = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_classes]
+    distorted_weights = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_weights]
+    boxes_shape = tf.shape(boxes)
+    distorted_boxes_shape = tf.shape(distorted_boxes)
+    images_shape = tf.shape(images)
+    distorted_images_shape = tf.shape(distorted_images)
+
+    with self.test_session() as sess:
+      (boxes_shape_, distorted_boxes_shape_, images_shape_,
+       distorted_images_shape_, images_, distorted_images_,
+       boxes_, distorted_boxes_, labels_, distorted_labels_,
+       weights_, distorted_weights_) = sess.run(
+           [boxes_shape, distorted_boxes_shape, images_shape,
+            distorted_images_shape, images, distorted_images,
+            boxes, distorted_boxes, labels, distorted_labels,
+            weights, distorted_weights])
+      self.assertAllEqual(boxes_shape_, distorted_boxes_shape_)
+      self.assertAllEqual(images_shape_, distorted_images_shape_)
+      self.assertAllClose(images_, distorted_images_)
+      self.assertAllClose(boxes_, distorted_boxes_)
+      self.assertAllEqual(labels_, distorted_labels_)
+      self.assertAllEqual(weights_, distorted_weights_)
+
+  def testRandomCropWithMockSampleDistortedBoundingBox(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    })]
+
+    images = self.createColorfulTestImage()
+    boxes = tf.constant([[0.1, 0.1, 0.8, 0.3],
+                         [0.2, 0.4, 0.75, 0.75],
+                         [0.3, 0.1, 0.4, 0.7]], dtype=tf.float32)
+    labels = tf.constant([1, 7, 11], dtype=tf.int32)
+    weights = tf.constant([1.0, 0.5, 0.6], dtype=tf.float32)
+
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_crop_image, {})]
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box') as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (tf.constant(
+          [6, 143, 0], dtype=tf.int32), tf.constant(
+              [190, 237, -1], dtype=tf.int32), tf.constant(
+                  [[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+
+      distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                      preprocessing_options)
+
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_weights = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_weights]
+      expected_boxes = tf.constant([[0.178947, 0.07173, 0.75789469, 0.66244733],
+                                    [0.28421, 0.0, 0.38947365, 0.57805908]],
+                                   dtype=tf.float32)
+      expected_labels = tf.constant([7, 11], dtype=tf.int32)
+      expected_weights = tf.constant([0.5, 0.6], dtype=tf.float32)
+
+      with self.test_session() as sess:
+        (distorted_boxes_, distorted_labels_, distorted_weights_,
+         expected_boxes_, expected_labels_, expected_weights_) = sess.run(
+             [distorted_boxes, distorted_labels, distorted_weights,
+              expected_boxes, expected_labels, expected_weights])
+        self.assertAllClose(distorted_boxes_, expected_boxes_)
+        self.assertAllEqual(distorted_labels_, expected_labels_)
+        self.assertAllEqual(distorted_weights_, expected_weights_)
+
+  def testRandomCropWithoutClipBoxes(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    })]
+
+    images = self.createColorfulTestImage()
+    boxes = tf.constant([[0.1, 0.1, 0.8, 0.3],
+                         [0.2, 0.4, 0.75, 0.75],
+                         [0.3, 0.1, 0.4, 0.7]], dtype=tf.float32)
+    keypoints = tf.constant([
+        [[0.1, 0.1], [0.8, 0.3]],
+        [[0.2, 0.4], [0.75, 0.75]],
+        [[0.3, 0.1], [0.4, 0.7]],
+    ], dtype=tf.float32)
+    labels = tf.constant([1, 7, 11], dtype=tf.int32)
+    weights = tf.constant([1.0, 0.5, 0.6], dtype=tf.float32)
+
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_keypoints: keypoints,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+
+    preprocessing_options = [(preprocessor.random_crop_image, {
+        'clip_boxes': False,
+    })]
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box') as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (tf.constant(
+          [6, 143, 0], dtype=tf.int32), tf.constant(
+              [190, 237, -1], dtype=tf.int32), tf.constant(
+                  [[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+
+      preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+          include_keypoints=True)
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_keypoints = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_keypoints]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_weights = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_weights]
+      expected_boxes = tf.constant(
+          [[0.178947, 0.07173, 0.75789469, 0.66244733],
+           [0.28421, -0.434599, 0.38947365, 0.57805908]],
+          dtype=tf.float32)
+      expected_keypoints = tf.constant(
+          [[[0.178947, 0.07173], [0.75789469, 0.66244733]],
+           [[0.28421, -0.434599], [0.38947365, 0.57805908]]],
+          dtype=tf.float32)
+      expected_labels = tf.constant([7, 11], dtype=tf.int32)
+      expected_weights = tf.constant([0.5, 0.6], dtype=tf.float32)
+
+      with self.test_session() as sess:
+        (distorted_boxes_, distorted_keypoints_, distorted_labels_,
+         distorted_weights_, expected_boxes_, expected_keypoints_,
+         expected_labels_, expected_weights_) = sess.run(
+             [distorted_boxes, distorted_keypoints, distorted_labels,
+              distorted_weights, expected_boxes, expected_keypoints,
+              expected_labels, expected_weights])
+        self.assertAllClose(distorted_boxes_, expected_boxes_)
+        self.assertAllClose(distorted_keypoints_, expected_keypoints_)
+        self.assertAllEqual(distorted_labels_, expected_labels_)
+        self.assertAllEqual(distorted_weights_, expected_weights_)
+
+  def testRandomCropImageWithMultiClassScores(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_crop_image, {}))
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    multiclass_scores = self.createTestMultiClassScores()
+
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.multiclass_scores: multiclass_scores
+    }
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_multiclass_scores = distorted_tensor_dict[
+        fields.InputDataFields.multiclass_scores]
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    multiclass_scores_rank = tf.rank(multiclass_scores)
+    distorted_multiclass_scores_rank = tf.rank(distorted_multiclass_scores)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_, multiclass_scores_rank_,
+       distorted_multiclass_scores_rank_,
+       distorted_multiclass_scores_) = sess.run([
+           boxes_rank, distorted_boxes, distorted_boxes_rank, images_rank,
+           distorted_images_rank, multiclass_scores_rank,
+           distorted_multiclass_scores_rank, distorted_multiclass_scores
+       ])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+      self.assertAllEqual(multiclass_scores_rank_,
+                          distorted_multiclass_scores_rank_)
+      self.assertAllEqual(distorted_boxes_.shape[0],
+                          distorted_multiclass_scores_.shape[0])
+
+  def testStrictRandomCropImageWithGroundtruthWeights(self):
+    image = self.createColorfulTestImage()[0]
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      new_image, new_boxes, new_labels, new_groundtruth_weights = (
+          preprocessor._strict_random_crop_image(
+              image, boxes, labels, weights))
+      with self.test_session() as sess:
+        new_image, new_boxes, new_labels, new_groundtruth_weights = (
+            sess.run(
+                [new_image, new_boxes, new_labels, new_groundtruth_weights])
+        )
+
+        expected_boxes = np.array(
+            [[0.0, 0.0, 0.75789469, 1.0],
+             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
+        self.assertAllEqual(new_image.shape, [190, 237, 3])
+        self.assertAllEqual(new_groundtruth_weights, [1.0, 0.5])
+        self.assertAllClose(
+            new_boxes.flatten(), expected_boxes.flatten())
+
+  def testStrictRandomCropImageWithMasks(self):
+    image = self.createColorfulTestImage()[0]
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      new_image, new_boxes, new_labels, new_weights, new_masks = (
+          preprocessor._strict_random_crop_image(
+              image, boxes, labels, weights, masks=masks))
+      with self.test_session() as sess:
+        new_image, new_boxes, new_labels, new_weights, new_masks = sess.run(
+            [new_image, new_boxes, new_labels, new_weights, new_masks])
+        expected_boxes = np.array(
+            [[0.0, 0.0, 0.75789469, 1.0],
+             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
+        self.assertAllEqual(new_image.shape, [190, 237, 3])
+        self.assertAllEqual(new_masks.shape, [2, 190, 237])
+        self.assertAllClose(
+            new_boxes.flatten(), expected_boxes.flatten())
+
+  def testStrictRandomCropImageWithKeypoints(self):
+    image = self.createColorfulTestImage()[0]
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypoints()
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      new_image, new_boxes, new_labels, new_weights, new_keypoints = (
+          preprocessor._strict_random_crop_image(
+              image, boxes, labels, weights, keypoints=keypoints))
+      with self.test_session() as sess:
+        new_image, new_boxes, new_labels, new_weights, new_keypoints = sess.run(
+            [new_image, new_boxes, new_labels, new_weights, new_keypoints])
+
+        expected_boxes = np.array([
+            [0.0, 0.0, 0.75789469, 1.0],
+            [0.23157893, 0.24050637, 0.75789469, 1.0],], dtype=np.float32)
+        expected_keypoints = np.array([
+            [[np.nan, np.nan],
+             [np.nan, np.nan],
+             [np.nan, np.nan]],
+            [[0.38947368, 0.07173],
+             [0.49473682, 0.24050637],
+             [0.60000002, 0.40928277]]
+        ], dtype=np.float32)
+        self.assertAllEqual(new_image.shape, [190, 237, 3])
+        self.assertAllClose(
+            new_boxes.flatten(), expected_boxes.flatten())
+        self.assertAllClose(
+            new_keypoints.flatten(), expected_keypoints.flatten())
+
+  def testRunRandomCropImageWithMasks(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_instance_masks: masks,
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True)
+
+    preprocessing_options = [(preprocessor.random_crop_image, {})]
+
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+      distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_masks = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_instance_masks]
+      with self.test_session() as sess:
+        (distorted_image_, distorted_boxes_, distorted_labels_,
+         distorted_masks_) = sess.run(
+             [distorted_image, distorted_boxes, distorted_labels,
+              distorted_masks])
+
+        expected_boxes = np.array([
+            [0.0, 0.0, 0.75789469, 1.0],
+            [0.23157893, 0.24050637, 0.75789469, 1.0],
+        ], dtype=np.float32)
+        self.assertAllEqual(distorted_image_.shape, [1, 190, 237, 3])
+        self.assertAllEqual(distorted_masks_.shape, [2, 190, 237])
+        self.assertAllEqual(distorted_labels_, [1, 2])
+        self.assertAllClose(
+            distorted_boxes_.flatten(), expected_boxes.flatten())
+
+  def testRunRandomCropImageWithKeypointsInsideCrop(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypointsInsideCrop()
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_keypoints: keypoints,
+        fields.InputDataFields.groundtruth_weights: weights
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_keypoints=True)
+
+    preprocessing_options = [(preprocessor.random_crop_image, {})]
+
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+      distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_keypoints = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_keypoints]
+      with self.test_session() as sess:
+        (distorted_image_, distorted_boxes_, distorted_labels_,
+         distorted_keypoints_) = sess.run(
+             [distorted_image, distorted_boxes, distorted_labels,
+              distorted_keypoints])
+
+        expected_boxes = np.array([
+            [0.0, 0.0, 0.75789469, 1.0],
+            [0.23157893, 0.24050637, 0.75789469, 1.0],
+        ], dtype=np.float32)
+        expected_keypoints = np.array([
+            [[0.38947368, 0.07173],
+             [0.49473682, 0.24050637],
+             [0.60000002, 0.40928277]],
+            [[0.38947368, 0.07173],
+             [0.49473682, 0.24050637],
+             [0.60000002, 0.40928277]]
+        ])
+        self.assertAllEqual(distorted_image_.shape, [1, 190, 237, 3])
+        self.assertAllEqual(distorted_labels_, [1, 2])
+        self.assertAllClose(
+            distorted_boxes_.flatten(), expected_boxes.flatten())
+        self.assertAllClose(
+            distorted_keypoints_.flatten(), expected_keypoints.flatten())
+
+  def testRunRandomCropImageWithKeypointsOutsideCrop(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypointsOutsideCrop()
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_keypoints=True)
+
+    preprocessing_options = [(preprocessor.random_crop_image, {})]
+
+    with mock.patch.object(
+        tf.image,
+        'sample_distorted_bounding_box'
+    ) as mock_sample_distorted_bounding_box:
+      mock_sample_distorted_bounding_box.return_value = (
+          tf.constant([6, 143, 0], dtype=tf.int32),
+          tf.constant([190, 237, -1], dtype=tf.int32),
+          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+      distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_keypoints = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_keypoints]
+      with self.test_session() as sess:
+        (distorted_image_, distorted_boxes_, distorted_labels_,
+         distorted_keypoints_) = sess.run(
+             [distorted_image, distorted_boxes, distorted_labels,
+              distorted_keypoints])
+
+        expected_boxes = np.array([
+            [0.0, 0.0, 0.75789469, 1.0],
+            [0.23157893, 0.24050637, 0.75789469, 1.0],
+        ], dtype=np.float32)
+        expected_keypoints = np.array([
+            [[np.nan, np.nan],
+             [np.nan, np.nan],
+             [np.nan, np.nan]],
+            [[np.nan, np.nan],
+             [np.nan, np.nan],
+             [np.nan, np.nan]],
+        ])
+        self.assertAllEqual(distorted_image_.shape, [1, 190, 237, 3])
+        self.assertAllEqual(distorted_labels_, [1, 2])
+        self.assertAllClose(
+            distorted_boxes_.flatten(), expected_boxes.flatten())
+        self.assertAllClose(
+            distorted_keypoints_.flatten(), expected_keypoints.flatten())
+
+  def testRunRetainBoxesAboveThreshold(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+
+    tensor_dict = {
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+
+    preprocessing_options = [
+        (preprocessor.retain_boxes_above_threshold, {'threshold': 0.6})
+    ]
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map()
+    retained_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    retained_boxes = retained_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    retained_labels = retained_tensor_dict[
+        fields.InputDataFields.groundtruth_classes]
+    retained_weights = retained_tensor_dict[
+        fields.InputDataFields.groundtruth_weights]
+
+    with self.test_session() as sess:
+      (retained_boxes_, retained_labels_,
+       retained_weights_, expected_retained_boxes_,
+       expected_retained_labels_, expected_retained_weights_) = sess.run(
+           [retained_boxes, retained_labels, retained_weights,
+            self.expectedBoxesAfterThresholding(),
+            self.expectedLabelsAfterThresholding(),
+            self.expectedLabelScoresAfterThresholding()])
+
+      self.assertAllClose(retained_boxes_, expected_retained_boxes_)
+      self.assertAllClose(retained_labels_, expected_retained_labels_)
+      self.assertAllClose(
+          retained_weights_, expected_retained_weights_)
+
+  def testRunRetainBoxesAboveThresholdWithMasks(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    masks = self.createTestMasks()
+
+    tensor_dict = {
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_instance_masks: masks
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_label_weights=True,
+        include_instance_masks=True)
+
+    preprocessing_options = [
+        (preprocessor.retain_boxes_above_threshold, {'threshold': 0.6})
+    ]
+
+    retained_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    retained_masks = retained_tensor_dict[
+        fields.InputDataFields.groundtruth_instance_masks]
+
+    with self.test_session() as sess:
+      (retained_masks_, expected_masks_) = sess.run(
+          [retained_masks,
+           self.expectedMasksAfterThresholding()])
+      self.assertAllClose(retained_masks_, expected_masks_)
+
+  def testRunRetainBoxesAboveThresholdWithKeypoints(self):
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypoints()
+
+    tensor_dict = {
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_keypoints=True)
+
+    preprocessing_options = [
+        (preprocessor.retain_boxes_above_threshold, {'threshold': 0.6})
+    ]
+
+    retained_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    retained_keypoints = retained_tensor_dict[
+        fields.InputDataFields.groundtruth_keypoints]
+
+    with self.test_session() as sess:
+      (retained_keypoints_, expected_keypoints_) = sess.run(
+          [retained_keypoints,
+           self.expectedKeypointsAfterThresholding()])
+      self.assertAllClose(retained_keypoints_, expected_keypoints_)
+
+  def testRandomCropToAspectRatioWithCache(self):
+    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testRunRandomCropToAspectRatioWithMasks(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_instance_masks: masks
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True)
+
+    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
+
+    with mock.patch.object(preprocessor,
+                           '_random_integer') as mock_random_integer:
+      mock_random_integer.return_value = tf.constant(0, dtype=tf.int32)
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+      distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_masks = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_instance_masks]
+      with self.test_session() as sess:
+        (distorted_image_, distorted_boxes_, distorted_labels_,
+         distorted_masks_) = sess.run([
+             distorted_image, distorted_boxes, distorted_labels, distorted_masks
+         ])
+
+        expected_boxes = np.array([0.0, 0.5, 0.75, 1.0], dtype=np.float32)
+        self.assertAllEqual(distorted_image_.shape, [1, 200, 200, 3])
+        self.assertAllEqual(distorted_labels_, [1])
+        self.assertAllClose(distorted_boxes_.flatten(),
+                            expected_boxes.flatten())
+        self.assertAllEqual(distorted_masks_.shape, [1, 200, 200])
+
+  def testRunRandomCropToAspectRatioWithKeypoints(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    keypoints = self.createTestKeypoints()
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_keypoints=True)
+
+    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
+
+    with mock.patch.object(preprocessor,
+                           '_random_integer') as mock_random_integer:
+      mock_random_integer.return_value = tf.constant(0, dtype=tf.int32)
+      distorted_tensor_dict = preprocessor.preprocess(
+          tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+      distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+      distorted_boxes = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_boxes]
+      distorted_labels = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_classes]
+      distorted_keypoints = distorted_tensor_dict[
+          fields.InputDataFields.groundtruth_keypoints]
+      with self.test_session() as sess:
+        (distorted_image_, distorted_boxes_, distorted_labels_,
+         distorted_keypoints_) = sess.run([
+             distorted_image, distorted_boxes, distorted_labels,
+             distorted_keypoints
+         ])
+
+        expected_boxes = np.array([0.0, 0.5, 0.75, 1.0], dtype=np.float32)
+        expected_keypoints = np.array(
+            [[0.1, 0.2], [0.2, 0.4], [0.3, 0.6]], dtype=np.float32)
+        self.assertAllEqual(distorted_image_.shape, [1, 200, 200, 3])
+        self.assertAllEqual(distorted_labels_, [1])
+        self.assertAllClose(distorted_boxes_.flatten(),
+                            expected_boxes.flatten())
+        self.assertAllClose(distorted_keypoints_.flatten(),
+                            expected_keypoints.flatten())
+
+  def testRandomPadToAspectRatioWithCache(self):
+    preprocess_options = [(preprocessor.random_pad_to_aspect_ratio, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRunRandomPadToAspectRatioWithMinMaxPaddedSizeRatios(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map()
+    preprocessing_options = [(preprocessor.random_pad_to_aspect_ratio,
+                              {'min_padded_size_ratio': (4.0, 4.0),
+                               'max_padded_size_ratio': (4.0, 4.0)})]
+
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_labels = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_classes]
+    with self.test_session() as sess:
+      distorted_image_, distorted_boxes_, distorted_labels_ = sess.run([
+          distorted_image, distorted_boxes, distorted_labels])
+
+      expected_boxes = np.array(
+          [[0.0, 0.125, 0.1875, 0.5], [0.0625, 0.25, 0.1875, 0.5]],
+          dtype=np.float32)
+      self.assertAllEqual(distorted_image_.shape, [1, 800, 800, 3])
+      self.assertAllEqual(distorted_labels_, [1, 2])
+      self.assertAllClose(distorted_boxes_.flatten(),
+                          expected_boxes.flatten())
+
+  def testRunRandomPadToAspectRatioWithMasks(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_instance_masks: masks
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_instance_masks=True)
+
+    preprocessing_options = [(preprocessor.random_pad_to_aspect_ratio, {})]
+
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_labels = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_classes]
+    distorted_masks = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_instance_masks]
+    with self.test_session() as sess:
+      (distorted_image_, distorted_boxes_, distorted_labels_,
+       distorted_masks_) = sess.run([
+           distorted_image, distorted_boxes, distorted_labels, distorted_masks
+       ])
+
+      expected_boxes = np.array(
+          [[0.0, 0.25, 0.375, 1.0], [0.125, 0.5, 0.375, 1.0]], dtype=np.float32)
+      self.assertAllEqual(distorted_image_.shape, [1, 400, 400, 3])
+      self.assertAllEqual(distorted_labels_, [1, 2])
+      self.assertAllClose(distorted_boxes_.flatten(),
+                          expected_boxes.flatten())
+      self.assertAllEqual(distorted_masks_.shape, [2, 400, 400])
+
+  def testRunRandomPadToAspectRatioWithKeypoints(self):
+    image = self.createColorfulTestImage()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    keypoints = self.createTestKeypoints()
+
+    tensor_dict = {
+        fields.InputDataFields.image: image,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_keypoints: keypoints
+    }
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_keypoints=True)
+
+    preprocessing_options = [(preprocessor.random_pad_to_aspect_ratio, {})]
+
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    distorted_image = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_labels = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_classes]
+    distorted_keypoints = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_keypoints]
+    with self.test_session() as sess:
+      (distorted_image_, distorted_boxes_, distorted_labels_,
+       distorted_keypoints_) = sess.run([
+           distorted_image, distorted_boxes, distorted_labels,
+           distorted_keypoints
+       ])
+
+      expected_boxes = np.array(
+          [[0.0, 0.25, 0.375, 1.0], [0.125, 0.5, 0.375, 1.0]], dtype=np.float32)
+      expected_keypoints = np.array([
+          [[0.05, 0.1], [0.1, 0.2], [0.15, 0.3]],
+          [[0.2, 0.4], [0.25, 0.5], [0.3, 0.6]],
+      ], dtype=np.float32)
+      self.assertAllEqual(distorted_image_.shape, [1, 400, 400, 3])
+      self.assertAllEqual(distorted_labels_, [1, 2])
+      self.assertAllClose(distorted_boxes_.flatten(),
+                          expected_boxes.flatten())
+      self.assertAllClose(distorted_keypoints_.flatten(),
+                          expected_keypoints.flatten())
+
+  def testRandomPadImageWithCache(self):
+    preprocess_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1,}), (preprocessor.random_pad_image, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRandomPadImage(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    })]
+
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_pad_image, {})]
+    padded_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                 preprocessing_options)
+
+    padded_images = padded_tensor_dict[fields.InputDataFields.image]
+    padded_boxes = padded_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_shape = tf.shape(boxes)
+    padded_boxes_shape = tf.shape(padded_boxes)
+    images_shape = tf.shape(images)
+    padded_images_shape = tf.shape(padded_images)
+
+    with self.test_session() as sess:
+      (boxes_shape_, padded_boxes_shape_, images_shape_,
+       padded_images_shape_, boxes_, padded_boxes_) = sess.run(
+           [boxes_shape, padded_boxes_shape, images_shape,
+            padded_images_shape, boxes, padded_boxes])
+      self.assertAllEqual(boxes_shape_, padded_boxes_shape_)
+      self.assertTrue((images_shape_[1] >= padded_images_shape_[1] * 0.5).all)
+      self.assertTrue((images_shape_[2] >= padded_images_shape_[2] * 0.5).all)
+      self.assertTrue((images_shape_[1] <= padded_images_shape_[1]).all)
+      self.assertTrue((images_shape_[2] <= padded_images_shape_[2]).all)
+      self.assertTrue(np.all((boxes_[:, 2] - boxes_[:, 0]) >= (
+          padded_boxes_[:, 2] - padded_boxes_[:, 0])))
+      self.assertTrue(np.all((boxes_[:, 3] - boxes_[:, 1]) >= (
+          padded_boxes_[:, 3] - padded_boxes_[:, 1])))
+
+  def testRandomCropPadImageWithCache(self):
+    preprocess_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1,}), (preprocessor.random_crop_pad_image, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRandomCropPadImageWithRandomCoefOne(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    })]
+
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_crop_pad_image, {
+        'random_coef': 1.0
+    })]
+    padded_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                 preprocessing_options)
+
+    padded_images = padded_tensor_dict[fields.InputDataFields.image]
+    padded_boxes = padded_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_shape = tf.shape(boxes)
+    padded_boxes_shape = tf.shape(padded_boxes)
+    images_shape = tf.shape(images)
+    padded_images_shape = tf.shape(padded_images)
+
+    with self.test_session() as sess:
+      (boxes_shape_, padded_boxes_shape_, images_shape_,
+       padded_images_shape_, boxes_, padded_boxes_) = sess.run(
+           [boxes_shape, padded_boxes_shape, images_shape,
+            padded_images_shape, boxes, padded_boxes])
+      self.assertAllEqual(boxes_shape_, padded_boxes_shape_)
+      self.assertTrue((images_shape_[1] >= padded_images_shape_[1] * 0.5).all)
+      self.assertTrue((images_shape_[2] >= padded_images_shape_[2] * 0.5).all)
+      self.assertTrue((images_shape_[1] <= padded_images_shape_[1]).all)
+      self.assertTrue((images_shape_[2] <= padded_images_shape_[2]).all)
+      self.assertTrue(np.all((boxes_[:, 2] - boxes_[:, 0]) >= (
+          padded_boxes_[:, 2] - padded_boxes_[:, 0])))
+      self.assertTrue(np.all((boxes_[:, 3] - boxes_[:, 1]) >= (
+          padded_boxes_[:, 3] - padded_boxes_[:, 1])))
+
+  def testRandomCropToAspectRatio(self):
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, [])
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
+        'aspect_ratio': 2.0
+    })]
+    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                  preprocessing_options)
+
+    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
+    cropped_boxes = cropped_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_shape = tf.shape(boxes)
+    cropped_boxes_shape = tf.shape(cropped_boxes)
+    images_shape = tf.shape(images)
+    cropped_images_shape = tf.shape(cropped_images)
+
+    with self.test_session() as sess:
+      (boxes_shape_, cropped_boxes_shape_, images_shape_,
+       cropped_images_shape_) = sess.run([
+           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
+       ])
+      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
+      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
+      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
+
+  def testRandomPadToAspectRatio(self):
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+    }
+    tensor_dict = preprocessor.preprocess(tensor_dict, [])
+    images = tensor_dict[fields.InputDataFields.image]
+
+    preprocessing_options = [(preprocessor.random_pad_to_aspect_ratio, {
+        'aspect_ratio': 2.0
+    })]
+    padded_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                 preprocessing_options)
+
+    padded_images = padded_tensor_dict[fields.InputDataFields.image]
+    padded_boxes = padded_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    boxes_shape = tf.shape(boxes)
+    padded_boxes_shape = tf.shape(padded_boxes)
+    images_shape = tf.shape(images)
+    padded_images_shape = tf.shape(padded_images)
+
+    with self.test_session() as sess:
+      (boxes_shape_, padded_boxes_shape_, images_shape_,
+       padded_images_shape_) = sess.run([
+           boxes_shape, padded_boxes_shape, images_shape, padded_images_shape
+       ])
+      self.assertAllEqual(boxes_shape_, padded_boxes_shape_)
+      self.assertEqual(images_shape_[1], padded_images_shape_[1])
+      self.assertEqual(2 * images_shape_[2], padded_images_shape_[2])
+
+  def testRandomBlackPatchesWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_black_patches, {
+        'size_to_image_ratio': 0.5
+    }))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRandomBlackPatches(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_black_patches, {
+        'size_to_image_ratio': 0.5
+    }))
+    images = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images}
+    blacked_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                  preprocessing_options)
+    blacked_images = blacked_tensor_dict[fields.InputDataFields.image]
+    images_shape = tf.shape(images)
+    blacked_images_shape = tf.shape(blacked_images)
+
+    with self.test_session() as sess:
+      (images_shape_, blacked_images_shape_) = sess.run(
+          [images_shape, blacked_images_shape])
+      self.assertAllEqual(images_shape_, blacked_images_shape_)
+
+  def testRandomResizeMethodWithCache(self):
+    preprocess_options = []
+    preprocess_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocess_options.append((preprocessor.random_resize_method, {
+        'target_size': (75, 150)
+    }))
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=True,
+                                test_keypoints=True)
+
+  def testRandomResizeMethod(self):
+    preprocessing_options = []
+    preprocessing_options.append((preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }))
+    preprocessing_options.append((preprocessor.random_resize_method, {
+        'target_size': (75, 150)
+    }))
+    images = self.createTestImages()
+    tensor_dict = {fields.InputDataFields.image: images}
+    resized_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                  preprocessing_options)
+    resized_images = resized_tensor_dict[fields.InputDataFields.image]
+    resized_images_shape = tf.shape(resized_images)
+    expected_images_shape = tf.constant([1, 75, 150, 3], dtype=tf.int32)
+
+    with self.test_session() as sess:
+      (expected_images_shape_, resized_images_shape_) = sess.run(
+          [expected_images_shape, resized_images_shape])
+      self.assertAllEqual(expected_images_shape_,
+                          resized_images_shape_)
+
+  def testResizeImageWithMasks(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
+    height = 50
+    width = 100
+    expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[15, 50, 100], [10, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_image(
+          in_image, in_masks, new_height=height, new_width=width)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape])
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeImageWithMasksTensorInputHeightAndWidth(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
+    height = tf.constant(50, dtype=tf.int32)
+    width = tf.constant(100, dtype=tf.int32)
+    expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[15, 50, 100], [10, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_image(
+          in_image, in_masks, new_height=height, new_width=width)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape])
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeImageWithNoInstanceMask(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
+    height = 50
+    width = 100
+    expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[0, 50, 100], [0, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_image(
+          in_image, in_masks, new_height=height, new_width=width)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape])
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToRangePreservesStaticSpatialShape(self):
+    """Tests image resizing, checking output sizes."""
+    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
+    min_dim = 50
+    max_dim = 100
+    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]
+
+    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
+      in_image = tf.random_uniform(in_shape)
+      out_image, _ = preprocessor.resize_to_range(
+          in_image, min_dimension=min_dim, max_dimension=max_dim)
+      self.assertAllEqual(out_image.get_shape().as_list(), expected_shape)
+
+  def testResizeToRangeWithDynamicSpatialShape(self):
+    """Tests image resizing, checking output sizes."""
+    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
+    min_dim = 50
+    max_dim = 100
+    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]
+
+    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
+      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+      out_image, _ = preprocessor.resize_to_range(
+          in_image, min_dimension=min_dim, max_dimension=max_dim)
+      out_image_shape = tf.shape(out_image)
+      with self.test_session() as sess:
+        out_image_shape = sess.run(out_image_shape,
+                                   feed_dict={in_image:
+                                              np.random.randn(*in_shape)})
+        self.assertAllEqual(out_image_shape, expected_shape)
+
+  def testResizeToRangeWithPadToMaxDimensionReturnsCorrectShapes(self):
+    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
+    min_dim = 50
+    max_dim = 100
+    expected_shape_list = [[100, 100, 3], [100, 100, 3], [100, 100, 3]]
+
+    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
+      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+      out_image, _ = preprocessor.resize_to_range(
+          in_image,
+          min_dimension=min_dim,
+          max_dimension=max_dim,
+          pad_to_max_dimension=True)
+      self.assertAllEqual(out_image.shape.as_list(), expected_shape)
+      out_image_shape = tf.shape(out_image)
+      with self.test_session() as sess:
+        out_image_shape = sess.run(
+            out_image_shape, feed_dict={in_image: np.random.randn(*in_shape)})
+        self.assertAllEqual(out_image_shape, expected_shape)
+
+  def testResizeToRangeWithPadToMaxDimensionReturnsCorrectTensor(self):
+    in_image_np = np.array([[[0, 1, 2]]], np.float32)
+    ex_image_np = np.array(
+        [[[0, 1, 2], [123.68, 116.779, 103.939]],
+         [[123.68, 116.779, 103.939], [123.68, 116.779, 103.939]]], np.float32)
+    min_dim = 1
+    max_dim = 2
+
+    in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+    out_image, _ = preprocessor.resize_to_range(
+        in_image,
+        min_dimension=min_dim,
+        max_dimension=max_dim,
+        pad_to_max_dimension=True,
+        per_channel_pad_value=(123.68, 116.779, 103.939))
+
+    with self.test_session() as sess:
+      out_image_np = sess.run(out_image, feed_dict={in_image: in_image_np})
+      self.assertAllClose(ex_image_np, out_image_np)
+
+  def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
+    min_dim = 50
+    max_dim = 100
+    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_to_range(
+          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
+      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
+      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
+
+  def testResizeToRangeWithMasksAndPadToMaxDimension(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
+    min_dim = 50
+    max_dim = 100
+    expected_image_shape_list = [[100, 100, 3], [100, 100, 3]]
+    expected_masks_shape_list = [[15, 100, 100], [10, 100, 100]]
+
+    for (in_image_shape,
+         expected_image_shape, in_masks_shape, expected_mask_shape) in zip(
+             in_image_shape_list, expected_image_shape_list,
+             in_masks_shape_list, expected_masks_shape_list):
+      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+      in_masks = tf.placeholder(tf.float32, shape=(None, None, None))
+      out_image, out_masks, _ = preprocessor.resize_to_range(
+          in_image,
+          in_masks,
+          min_dimension=min_dim,
+          max_dimension=max_dim,
+          pad_to_max_dimension=True)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape],
+            feed_dict={
+                in_image: np.random.randn(*in_image_shape),
+                in_masks: np.random.randn(*in_masks_shape)
+            })
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToRangeWithMasksAndDynamicSpatialShape(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
+    min_dim = 50
+    max_dim = 100
+    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+      in_masks = tf.placeholder(tf.float32, shape=(None, None, None))
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_to_range(
+          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape],
+            feed_dict={
+                in_image: np.random.randn(*in_image_shape),
+                in_masks: np.random.randn(*in_masks_shape)
+            })
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToRangeWithInstanceMasksTensorOfSizeZero(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
+    min_dim = 50
+    max_dim = 100
+    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[0, 75, 50], [0, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_to_range(
+          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape])
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToRange4DImageTensor(self):
+    image = tf.random_uniform([1, 200, 300, 3])
+    with self.assertRaises(ValueError):
+      preprocessor.resize_to_range(image, 500, 600)
+
+  def testResizeToRangeSameMinMax(self):
+    """Tests image resizing, checking output sizes."""
+    in_shape_list = [[312, 312, 3], [299, 299, 3]]
+    min_dim = 320
+    max_dim = 320
+    expected_shape_list = [[320, 320, 3], [320, 320, 3]]
+
+    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
+      in_image = tf.random_uniform(in_shape)
+      out_image, _ = preprocessor.resize_to_range(
+          in_image, min_dimension=min_dim, max_dimension=max_dim)
+      out_image_shape = tf.shape(out_image)
+
+      with self.test_session() as sess:
+        out_image_shape = sess.run(out_image_shape)
+        self.assertAllEqual(out_image_shape, expected_shape)
+
+  def testResizeToMinDimensionTensorShapes(self):
+    in_image_shape_list = [[60, 55, 3], [15, 30, 3]]
+    in_masks_shape_list = [[15, 60, 55], [10, 15, 30]]
+    min_dim = 50
+    expected_image_shape_list = [[60, 55, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[15, 60, 55], [10, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+      in_masks = tf.placeholder(tf.float32, shape=(None, None, None))
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_to_min_dimension(
+          in_image, in_masks, min_dimension=min_dim)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape],
+            feed_dict={
+                in_image: np.random.randn(*in_image_shape),
+                in_masks: np.random.randn(*in_masks_shape)
+            })
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToMinDimensionWithInstanceMasksTensorOfSizeZero(self):
+    """Tests image resizing, checking output sizes."""
+    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
+    in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
+    min_dim = 50
+    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
+    expected_masks_shape_list = [[0, 75, 50], [0, 50, 100]]
+
+    for (in_image_shape, expected_image_shape, in_masks_shape,
+         expected_mask_shape) in zip(in_image_shape_list,
+                                     expected_image_shape_list,
+                                     in_masks_shape_list,
+                                     expected_masks_shape_list):
+      in_image = tf.random_uniform(in_image_shape)
+      in_masks = tf.random_uniform(in_masks_shape)
+      out_image, out_masks, _ = preprocessor.resize_to_min_dimension(
+          in_image, in_masks, min_dimension=min_dim)
+      out_image_shape = tf.shape(out_image)
+      out_masks_shape = tf.shape(out_masks)
+
+      with self.test_session() as sess:
+        out_image_shape, out_masks_shape = sess.run(
+            [out_image_shape, out_masks_shape])
+        self.assertAllEqual(out_image_shape, expected_image_shape)
+        self.assertAllEqual(out_masks_shape, expected_mask_shape)
+
+  def testResizeToMinDimensionRaisesErrorOn4DImage(self):
+    image = tf.random_uniform([1, 200, 300, 3])
+    with self.assertRaises(ValueError):
+      preprocessor.resize_to_min_dimension(image, 500)
+
+  def testScaleBoxesToPixelCoordinates(self):
+    """Tests box scaling, checking scaled values."""
+    in_shape = [60, 40, 3]
+    in_boxes = [[0.1, 0.2, 0.4, 0.6],
+                [0.5, 0.3, 0.9, 0.7]]
+
+    expected_boxes = [[6., 8., 24., 24.],
+                      [30., 12., 54., 28.]]
+
+    in_image = tf.random_uniform(in_shape)
+    in_boxes = tf.constant(in_boxes)
+    _, out_boxes = preprocessor.scale_boxes_to_pixel_coordinates(
+        in_image, boxes=in_boxes)
+    with self.test_session() as sess:
+      out_boxes = sess.run(out_boxes)
+      self.assertAllClose(out_boxes, expected_boxes)
+
+  def testScaleBoxesToPixelCoordinatesWithKeypoints(self):
+    """Tests box and keypoint scaling, checking scaled values."""
+    in_shape = [60, 40, 3]
+    in_boxes = self.createTestBoxes()
+    in_keypoints = self.createTestKeypoints()
+
+    expected_boxes = [[0., 10., 45., 40.],
+                      [15., 20., 45., 40.]]
+    expected_keypoints = [
+        [[6., 4.], [12., 8.], [18., 12.]],
+        [[24., 16.], [30., 20.], [36., 24.]],
+    ]
+
+    in_image = tf.random_uniform(in_shape)
+    _, out_boxes, out_keypoints = preprocessor.scale_boxes_to_pixel_coordinates(
+        in_image, boxes=in_boxes, keypoints=in_keypoints)
+    with self.test_session() as sess:
+      out_boxes_, out_keypoints_ = sess.run([out_boxes, out_keypoints])
+      self.assertAllClose(out_boxes_, expected_boxes)
+      self.assertAllClose(out_keypoints_, expected_keypoints)
+
+  def testSubtractChannelMean(self):
+    """Tests whether channel means have been subtracted."""
+    with self.test_session():
+      image = tf.zeros((240, 320, 3))
+      means = [1, 2, 3]
+      actual = preprocessor.subtract_channel_mean(image, means=means)
+      actual = actual.eval()
+
+      self.assertTrue((actual[:, :, 0] == -1).all())
+      self.assertTrue((actual[:, :, 1] == -2).all())
+      self.assertTrue((actual[:, :, 2] == -3).all())
+
+  def testOneHotEncoding(self):
+    """Tests one hot encoding of multiclass labels."""
+    with self.test_session():
+      labels = tf.constant([1, 4, 2], dtype=tf.int32)
+      one_hot = preprocessor.one_hot_encoding(labels, num_classes=5)
+      one_hot = one_hot.eval()
+
+      self.assertAllEqual([0, 1, 1, 0, 1], one_hot)
+
+  def testSSDRandomCropWithCache(self):
+    preprocess_options = [
+        (preprocessor.normalize_image, {
+            'original_minval': 0,
+            'original_maxval': 255,
+            'target_minval': 0,
+            'target_maxval': 1
+        }),
+        (preprocessor.ssd_random_crop, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def testSSDRandomCrop(self):
+    preprocessing_options = [
+        (preprocessor.normalize_image, {
+            'original_minval': 0,
+            'original_maxval': 255,
+            'target_minval': 0,
+            'target_maxval': 1
+        }),
+        (preprocessor.ssd_random_crop, {})]
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = sess.run(
+           [boxes_rank, distorted_boxes_rank, images_rank,
+            distorted_images_rank])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testSSDRandomCropWithMultiClassScores(self):
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }), (preprocessor.ssd_random_crop, {})]
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    multiclass_scores = self.createTestMultiClassScores()
+
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.multiclass_scores: multiclass_scores,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_multiclass_scores=True)
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    distorted_multiclass_scores = distorted_tensor_dict[
+        fields.InputDataFields.multiclass_scores]
+
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+    multiclass_scores_rank = tf.rank(multiclass_scores)
+    distorted_multiclass_scores_rank = tf.rank(distorted_multiclass_scores)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_, multiclass_scores_rank_,
+       distorted_multiclass_scores_,
+       distorted_multiclass_scores_rank_) = sess.run([
+           boxes_rank, distorted_boxes, distorted_boxes_rank, images_rank,
+           distorted_images_rank, multiclass_scores_rank,
+           distorted_multiclass_scores, distorted_multiclass_scores_rank
+       ])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+      self.assertAllEqual(multiclass_scores_rank_,
+                          distorted_multiclass_scores_rank_)
+      self.assertAllEqual(distorted_boxes_.shape[0],
+                          distorted_multiclass_scores_.shape[0])
+
+  def testSSDRandomCropPad(self):
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    preprocessing_options = [
+        (preprocessor.normalize_image, {
+            'original_minval': 0,
+            'original_maxval': 255,
+            'target_minval': 0,
+            'target_maxval': 1
+        }),
+        (preprocessor.ssd_random_crop_pad, {})]
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights,
+    }
+    distorted_tensor_dict = preprocessor.preprocess(tensor_dict,
+                                                    preprocessing_options)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = sess.run([
+           boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank
+       ])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testSSDRandomCropFixedAspectRatioWithCache(self):
+    preprocess_options = [
+        (preprocessor.normalize_image, {
+            'original_minval': 0,
+            'original_maxval': 255,
+            'target_minval': 0,
+            'target_maxval': 1
+        }),
+        (preprocessor.ssd_random_crop_fixed_aspect_ratio, {})]
+    self._testPreprocessorCache(preprocess_options,
+                                test_boxes=True,
+                                test_masks=False,
+                                test_keypoints=False)
+
+  def _testSSDRandomCropFixedAspectRatio(self,
+                                         include_multiclass_scores,
+                                         include_instance_masks,
+                                         include_keypoints):
+    images = self.createTestImages()
+    boxes = self.createTestBoxes()
+    labels = self.createTestLabels()
+    weights = self.createTestGroundtruthWeights()
+    preprocessing_options = [(preprocessor.normalize_image, {
+        'original_minval': 0,
+        'original_maxval': 255,
+        'target_minval': 0,
+        'target_maxval': 1
+    }), (preprocessor.ssd_random_crop_fixed_aspect_ratio, {})]
+    tensor_dict = {
+        fields.InputDataFields.image: images,
+        fields.InputDataFields.groundtruth_boxes: boxes,
+        fields.InputDataFields.groundtruth_classes: labels,
+        fields.InputDataFields.groundtruth_weights: weights
+    }
+    if include_multiclass_scores:
+      multiclass_scores = self.createTestMultiClassScores()
+      tensor_dict[fields.InputDataFields.multiclass_scores] = (
+          multiclass_scores)
+    if include_instance_masks:
+      masks = self.createTestMasks()
+      tensor_dict[fields.InputDataFields.groundtruth_instance_masks] = masks
+    if include_keypoints:
+      keypoints = self.createTestKeypoints()
+      tensor_dict[fields.InputDataFields.groundtruth_keypoints] = keypoints
+
+    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
+        include_multiclass_scores=include_multiclass_scores,
+        include_instance_masks=include_instance_masks,
+        include_keypoints=include_keypoints)
+    distorted_tensor_dict = preprocessor.preprocess(
+        tensor_dict, preprocessing_options, func_arg_map=preprocessor_arg_map)
+    distorted_images = distorted_tensor_dict[fields.InputDataFields.image]
+    distorted_boxes = distorted_tensor_dict[
+        fields.InputDataFields.groundtruth_boxes]
+    images_rank = tf.rank(images)
+    distorted_images_rank = tf.rank(distorted_images)
+    boxes_rank = tf.rank(boxes)
+    distorted_boxes_rank = tf.rank(distorted_boxes)
+
+    with self.test_session() as sess:
+      (boxes_rank_, distorted_boxes_rank_, images_rank_,
+       distorted_images_rank_) = sess.run(
+           [boxes_rank, distorted_boxes_rank, images_rank,
+            distorted_images_rank])
+      self.assertAllEqual(boxes_rank_, distorted_boxes_rank_)
+      self.assertAllEqual(images_rank_, distorted_images_rank_)
+
+  def testSSDRandomCropFixedAspectRatio(self):
+    self._testSSDRandomCropFixedAspectRatio(include_multiclass_scores=False,
+                                            include_instance_masks=False,
+                                            include_keypoints=False)
+
+  def testSSDRandomCropFixedAspectRatioWithMultiClassScores(self):
+    self._testSSDRandomCropFixedAspectRatio(include_multiclass_scores=True,
+                                            include_instance_masks=False,
+                                            include_keypoints=False)
+
+  def testSSDRandomCropFixedAspectRatioWithMasksAndKeypoints(self):
+    self._testSSDRandomCropFixedAspectRatio(include_multiclass_scores=False,
+                                            include_instance_masks=True,
+                                            include_keypoints=True)
+
+  def testSSDRandomCropFixedAspectRatioWithLabelScoresMasksAndKeypoints(self):
+    self._testSSDRandomCropFixedAspectRatio(include_multiclass_scores=False,
+                                            include_instance_masks=True,
+                                            include_keypoints=True)
+
+  def testConvertClassLogitsToSoftmax(self):
+    multiclass_scores = tf.constant(
+        [[1.0, 0.0], [0.5, 0.5], [1000, 1]], dtype=tf.float32)
+    temperature = 2.0
+
+    converted_multiclass_scores = (
+        preprocessor.convert_class_logits_to_softmax(
+            multiclass_scores=multiclass_scores, temperature=temperature))
+
+    expected_converted_multiclass_scores = [[[0.62245935, 0.37754068],
+                                             [0.5, 0.5], [1, 0]]]
+
+    with self.test_session() as sess:
+      (converted_multiclass_scores_) = sess.run([converted_multiclass_scores])
+
+      self.assertAllClose(converted_multiclass_scores_,
+                          expected_converted_multiclass_scores)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator.py
new file mode 100644
index 00000000..793c7d38
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator.py
@@ -0,0 +1,154 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Region Similarity Calculators for BoxLists.
+
+Region Similarity Calculators compare a pairwise measure of similarity
+between the boxes in two BoxLists.
+"""
+from abc import ABCMeta
+from abc import abstractmethod
+
+import tensorflow as tf
+
+from object_detection.core import box_list_ops
+from object_detection.core import standard_fields as fields
+
+
+class RegionSimilarityCalculator(object):
+  """Abstract base class for region similarity calculator."""
+  __metaclass__ = ABCMeta
+
+  def compare(self, boxlist1, boxlist2, scope=None):
+    """Computes matrix of pairwise similarity between BoxLists.
+
+    This op (to be overridden) computes a measure of pairwise similarity between
+    the boxes in the given BoxLists. Higher values indicate more similarity.
+
+    Note that this method simply measures similarity and does not explicitly
+    perform a matching.
+
+    Args:
+      boxlist1: BoxList holding N boxes.
+      boxlist2: BoxList holding M boxes.
+      scope: Op scope name. Defaults to 'Compare' if None.
+
+    Returns:
+      a (float32) tensor of shape [N, M] with pairwise similarity score.
+    """
+    with tf.name_scope(scope, 'Compare', [boxlist1, boxlist2]) as scope:
+      return self._compare(boxlist1, boxlist2)
+
+  @abstractmethod
+  def _compare(self, boxlist1, boxlist2):
+    pass
+
+
+class IouSimilarity(RegionSimilarityCalculator):
+  """Class to compute similarity based on Intersection over Union (IOU) metric.
+
+  This class computes pairwise similarity between two BoxLists based on IOU.
+  """
+
+  def _compare(self, boxlist1, boxlist2):
+    """Compute pairwise IOU similarity between the two BoxLists.
+
+    Args:
+      boxlist1: BoxList holding N boxes.
+      boxlist2: BoxList holding M boxes.
+
+    Returns:
+      A tensor with shape [N, M] representing pairwise iou scores.
+    """
+    return box_list_ops.iou(boxlist1, boxlist2)
+
+
+class NegSqDistSimilarity(RegionSimilarityCalculator):
+  """Class to compute similarity based on the squared distance metric.
+
+  This class computes pairwise similarity between two BoxLists based on the
+  negative squared distance metric.
+  """
+
+  def _compare(self, boxlist1, boxlist2):
+    """Compute matrix of (negated) sq distances.
+
+    Args:
+      boxlist1: BoxList holding N boxes.
+      boxlist2: BoxList holding M boxes.
+
+    Returns:
+      A tensor with shape [N, M] representing negated pairwise squared distance.
+    """
+    return -1 * box_list_ops.sq_dist(boxlist1, boxlist2)
+
+
+class IoaSimilarity(RegionSimilarityCalculator):
+  """Class to compute similarity based on Intersection over Area (IOA) metric.
+
+  This class computes pairwise similarity between two BoxLists based on their
+  pairwise intersections divided by the areas of second BoxLists.
+  """
+
+  def _compare(self, boxlist1, boxlist2):
+    """Compute pairwise IOA similarity between the two BoxLists.
+
+    Args:
+      boxlist1: BoxList holding N boxes.
+      boxlist2: BoxList holding M boxes.
+
+    Returns:
+      A tensor with shape [N, M] representing pairwise IOA scores.
+    """
+    return box_list_ops.ioa(boxlist1, boxlist2)
+
+
+class ThresholdedIouSimilarity(RegionSimilarityCalculator):
+  """Class to compute similarity based on thresholded IOU and score.
+
+  This class computes pairwise similarity between two BoxLists based on IOU and
+  a 'score' present in boxlist1. If IOU > threshold, then the entry in the
+  output pairwise tensor will contain `score`, otherwise 0.
+  """
+
+  def __init__(self, iou_threshold=0):
+    """Initialize the ThresholdedIouSimilarity.
+
+    Args:
+      iou_threshold: For a given pair of boxes, if the IOU is > iou_threshold,
+        then the comparison result will be the foreground probability of
+        the first box, otherwise it will be zero.
+    """
+    self._iou_threshold = iou_threshold
+
+  def _compare(self, boxlist1, boxlist2):
+    """Compute pairwise IOU similarity between the two BoxLists and score.
+
+    Args:
+      boxlist1: BoxList holding N boxes. Must have a score field.
+      boxlist2: BoxList holding M boxes.
+
+    Returns:
+      A tensor with shape [N, M] representing scores threholded by pairwise
+      iou scores.
+    """
+    ious = box_list_ops.iou(boxlist1, boxlist2)
+    scores = boxlist1.get_field(fields.BoxListFields.scores)
+    scores = tf.expand_dims(scores, axis=1)
+    row_replicated_scores = tf.tile(scores, [1, tf.shape(ious)[-1]])
+    thresholded_ious = tf.where(ious > self._iou_threshold,
+                                row_replicated_scores, tf.zeros_like(ious))
+
+    return thresholded_ious
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator_test.py
new file mode 100644
index 00000000..1d0c26bb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/region_similarity_calculator_test.py
@@ -0,0 +1,95 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for region_similarity_calculator."""
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import region_similarity_calculator
+from object_detection.core import standard_fields as fields
+
+
+class RegionSimilarityCalculatorTest(tf.test.TestCase):
+
+  def test_get_correct_pairwise_similarity_based_on_iou(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    iou_similarity_calculator = region_similarity_calculator.IouSimilarity()
+    iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2)
+    with self.test_session() as sess:
+      iou_output = sess.run(iou_similarity)
+      self.assertAllClose(iou_output, exp_output)
+
+  def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
+    corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
+                            [1.0, 1.0, 0.0, 2.0]])
+    corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
+                            [-4.0, 0.0, 0.0, 3.0],
+                            [0.0, 0.0, 0.0, 0.0]])
+    exp_output = [[-26, -25, 0], [-18, -27, -6]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
+    dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
+    with self.test_session() as sess:
+      dist_output = sess.run(dist_similarity)
+      self.assertAllClose(dist_output, exp_output)
+
+  def test_get_correct_pairwise_similarity_based_on_ioa(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    exp_output_1 = [[2.0 / 12.0, 0, 6.0 / 400.0],
+                    [1.0 / 12.0, 0.0, 5.0 / 400.0]]
+    exp_output_2 = [[2.0 / 6.0, 1.0 / 5.0],
+                    [0, 0],
+                    [6.0 / 6.0, 5.0 / 5.0]]
+    boxes1 = box_list.BoxList(corners1)
+    boxes2 = box_list.BoxList(corners2)
+    ioa_similarity_calculator = region_similarity_calculator.IoaSimilarity()
+    ioa_similarity_1 = ioa_similarity_calculator.compare(boxes1, boxes2)
+    ioa_similarity_2 = ioa_similarity_calculator.compare(boxes2, boxes1)
+    with self.test_session() as sess:
+      iou_output_1, iou_output_2 = sess.run(
+          [ioa_similarity_1, ioa_similarity_2])
+      self.assertAllClose(iou_output_1, exp_output_1)
+      self.assertAllClose(iou_output_2, exp_output_2)
+
+  def test_get_correct_pairwise_similarity_based_on_thresholded_iou(self):
+    corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                            [0.0, 0.0, 20.0, 20.0]])
+    scores = tf.constant([.3, .6])
+    iou_threshold = .013
+
+    exp_output = tf.constant([[0.3, 0., 0.3], [0.6, 0., 0.]])
+    boxes1 = box_list.BoxList(corners1)
+    boxes1.add_field(fields.BoxListFields.scores, scores)
+    boxes2 = box_list.BoxList(corners2)
+    iou_similarity_calculator = (
+        region_similarity_calculator.ThresholdedIouSimilarity(
+            iou_threshold=iou_threshold))
+    iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2)
+    with self.test_session() as sess:
+      iou_output = sess.run(iou_similarity)
+      self.assertAllClose(iou_output, exp_output)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/standard_fields.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/standard_fields.py
new file mode 100644
index 00000000..cc0da39a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/standard_fields.py
@@ -0,0 +1,234 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Contains classes specifying naming conventions used for object detection.
+
+
+Specifies:
+  InputDataFields: standard fields used by reader/preprocessor/batcher.
+  DetectionResultFields: standard fields returned by object detector.
+  BoxListFields: standard field used by BoxList
+  TfExampleFields: standard fields for tf-example data format (go/tf-example).
+"""
+
+
+class InputDataFields(object):
+  """Names for the input tensors.
+
+  Holds the standard data field names to use for identifying input tensors. This
+  should be used by the decoder to identify keys for the returned tensor_dict
+  containing input tensors. And it should be used by the model to identify the
+  tensors it needs.
+
+  Attributes:
+    image: image.
+    image_additional_channels: additional channels.
+    original_image: image in the original input size.
+    original_image_spatial_shape: image in the original input size.
+    key: unique key corresponding to image.
+    source_id: source of the original image.
+    filename: original filename of the dataset (without common path).
+    groundtruth_image_classes: image-level class labels.
+    groundtruth_image_confidences: image-level class confidences.
+    groundtruth_boxes: coordinates of the ground truth boxes in the image.
+    groundtruth_classes: box-level class labels.
+    groundtruth_confidences: box-level class confidences. The shape should be
+      the same as the shape of groundtruth_classes.
+    groundtruth_label_types: box-level label types (e.g. explicit negative).
+    groundtruth_is_crowd: [DEPRECATED, use groundtruth_group_of instead]
+      is the groundtruth a single object or a crowd.
+    groundtruth_area: area of a groundtruth segment.
+    groundtruth_difficult: is a `difficult` object
+    groundtruth_group_of: is a `group_of` objects, e.g. multiple objects of the
+      same class, forming a connected group, where instances are heavily
+      occluding each other.
+    proposal_boxes: coordinates of object proposal boxes.
+    proposal_objectness: objectness score of each proposal.
+    groundtruth_instance_masks: ground truth instance masks.
+    groundtruth_instance_boundaries: ground truth instance boundaries.
+    groundtruth_instance_classes: instance mask-level class labels.
+    groundtruth_keypoints: ground truth keypoints.
+    groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
+    groundtruth_label_weights: groundtruth label weights.
+    groundtruth_weights: groundtruth weight factor for bounding boxes.
+    num_groundtruth_boxes: number of groundtruth boxes.
+    is_annotated: whether an image has been labeled or not.
+    true_image_shapes: true shapes of images in the resized images, as resized
+      images can be padded with zeros.
+    multiclass_scores: the label score per class for each box.
+  """
+  image = 'image'
+  image_additional_channels = 'image_additional_channels'
+  original_image = 'original_image'
+  original_image_spatial_shape = 'original_image_spatial_shape'
+  key = 'key'
+  source_id = 'source_id'
+  filename = 'filename'
+  groundtruth_image_classes = 'groundtruth_image_classes'
+  groundtruth_image_confidences = 'groundtruth_image_confidences'
+  groundtruth_boxes = 'groundtruth_boxes'
+  groundtruth_classes = 'groundtruth_classes'
+  groundtruth_confidences = 'groundtruth_confidences'
+  groundtruth_label_types = 'groundtruth_label_types'
+  groundtruth_is_crowd = 'groundtruth_is_crowd'
+  groundtruth_area = 'groundtruth_area'
+  groundtruth_difficult = 'groundtruth_difficult'
+  groundtruth_group_of = 'groundtruth_group_of'
+  proposal_boxes = 'proposal_boxes'
+  proposal_objectness = 'proposal_objectness'
+  groundtruth_instance_masks = 'groundtruth_instance_masks'
+  groundtruth_instance_boundaries = 'groundtruth_instance_boundaries'
+  groundtruth_instance_classes = 'groundtruth_instance_classes'
+  groundtruth_keypoints = 'groundtruth_keypoints'
+  groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'
+  groundtruth_label_weights = 'groundtruth_label_weights'
+  groundtruth_weights = 'groundtruth_weights'
+  num_groundtruth_boxes = 'num_groundtruth_boxes'
+  is_annotated = 'is_annotated'
+  true_image_shape = 'true_image_shape'
+  multiclass_scores = 'multiclass_scores'
+
+
+class DetectionResultFields(object):
+  """Naming conventions for storing the output of the detector.
+
+  Attributes:
+    source_id: source of the original image.
+    key: unique key corresponding to image.
+    detection_boxes: coordinates of the detection boxes in the image.
+    detection_scores: detection scores for the detection boxes in the image.
+    detection_classes: detection-level class labels.
+    detection_masks: contains a segmentation mask for each detection box.
+    detection_boundaries: contains an object boundary for each detection box.
+    detection_keypoints: contains detection keypoints for each detection box.
+    num_detections: number of detections in the batch.
+  """
+
+  source_id = 'source_id'
+  key = 'key'
+  detection_boxes = 'detection_boxes'
+  detection_scores = 'detection_scores'
+  detection_classes = 'detection_classes'
+  detection_masks = 'detection_masks'
+  detection_boundaries = 'detection_boundaries'
+  detection_keypoints = 'detection_keypoints'
+  num_detections = 'num_detections'
+
+
+class BoxListFields(object):
+  """Naming conventions for BoxLists.
+
+  Attributes:
+    boxes: bounding box coordinates.
+    classes: classes per bounding box.
+    scores: scores per bounding box.
+    weights: sample weights per bounding box.
+    objectness: objectness score per bounding box.
+    masks: masks per bounding box.
+    boundaries: boundaries per bounding box.
+    keypoints: keypoints per bounding box.
+    keypoint_heatmaps: keypoint heatmaps per bounding box.
+    is_crowd: is_crowd annotation per bounding box.
+  """
+  boxes = 'boxes'
+  classes = 'classes'
+  scores = 'scores'
+  weights = 'weights'
+  confidences = 'confidences'
+  objectness = 'objectness'
+  masks = 'masks'
+  boundaries = 'boundaries'
+  keypoints = 'keypoints'
+  keypoint_heatmaps = 'keypoint_heatmaps'
+  is_crowd = 'is_crowd'
+
+
+class TfExampleFields(object):
+  """TF-example proto feature names for object detection.
+
+  Holds the standard feature names to load from an Example proto for object
+  detection.
+
+  Attributes:
+    image_encoded: JPEG encoded string
+    image_format: image format, e.g. "JPEG"
+    filename: filename
+    channels: number of channels of image
+    colorspace: colorspace, e.g. "RGB"
+    height: height of image in pixels, e.g. 462
+    width: width of image in pixels, e.g. 581
+    source_id: original source of the image
+    image_class_text: image-level label in text format
+    image_class_label: image-level label in numerical format
+    object_class_text: labels in text format, e.g. ["person", "cat"]
+    object_class_label: labels in numbers, e.g. [16, 8]
+    object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
+    object_bbox_xmax: xmax coordinates of groundtruth box, e.g. 50, 40
+    object_bbox_ymin: ymin coordinates of groundtruth box, e.g. 40, 50
+    object_bbox_ymax: ymax coordinates of groundtruth box, e.g. 80, 70
+    object_view: viewpoint of object, e.g. ["frontal", "left"]
+    object_truncated: is object truncated, e.g. [true, false]
+    object_occluded: is object occluded, e.g. [true, false]
+    object_difficult: is object difficult, e.g. [true, false]
+    object_group_of: is object a single object or a group of objects
+    object_depiction: is object a depiction
+    object_is_crowd: [DEPRECATED, use object_group_of instead]
+      is the object a single object or a crowd
+    object_segment_area: the area of the segment.
+    object_weight: a weight factor for the object's bounding box.
+    instance_masks: instance segmentation masks.
+    instance_boundaries: instance boundaries.
+    instance_classes: Classes for each instance segmentation mask.
+    detection_class_label: class label in numbers.
+    detection_bbox_ymin: ymin coordinates of a detection box.
+    detection_bbox_xmin: xmin coordinates of a detection box.
+    detection_bbox_ymax: ymax coordinates of a detection box.
+    detection_bbox_xmax: xmax coordinates of a detection box.
+    detection_score: detection score for the class label and box.
+  """
+  image_encoded = 'image/encoded'
+  image_format = 'image/format'  # format is reserved keyword
+  filename = 'image/filename'
+  channels = 'image/channels'
+  colorspace = 'image/colorspace'
+  height = 'image/height'
+  width = 'image/width'
+  source_id = 'image/source_id'
+  image_class_text = 'image/class/text'
+  image_class_label = 'image/class/label'
+  object_class_text = 'image/object/class/text'
+  object_class_label = 'image/object/class/label'
+  object_bbox_ymin = 'image/object/bbox/ymin'
+  object_bbox_xmin = 'image/object/bbox/xmin'
+  object_bbox_ymax = 'image/object/bbox/ymax'
+  object_bbox_xmax = 'image/object/bbox/xmax'
+  object_view = 'image/object/view'
+  object_truncated = 'image/object/truncated'
+  object_occluded = 'image/object/occluded'
+  object_difficult = 'image/object/difficult'
+  object_group_of = 'image/object/group_of'
+  object_depiction = 'image/object/depiction'
+  object_is_crowd = 'image/object/is_crowd'
+  object_segment_area = 'image/object/segment/area'
+  object_weight = 'image/object/weight'
+  instance_masks = 'image/segmentation/object'
+  instance_boundaries = 'image/boundaries/object'
+  instance_classes = 'image/segmentation/object/class'
+  detection_class_label = 'image/detection/label'
+  detection_bbox_ymin = 'image/detection/bbox/ymin'
+  detection_bbox_xmin = 'image/detection/bbox/xmin'
+  detection_bbox_ymax = 'image/detection/bbox/ymax'
+  detection_bbox_xmax = 'image/detection/bbox/xmax'
+  detection_score = 'image/detection/score'
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner.py
new file mode 100644
index 00000000..0bb3b613
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner.py
@@ -0,0 +1,634 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base target assigner module.
+
+The job of a TargetAssigner is, for a given set of anchors (bounding boxes) and
+groundtruth detections (bounding boxes), to assign classification and regression
+targets to each anchor as well as weights to each anchor (specifying, e.g.,
+which anchors should not contribute to training loss).
+
+It assigns classification/regression targets by performing the following steps:
+1) Computing pairwise similarity between anchors and groundtruth boxes using a
+  provided RegionSimilarity Calculator
+2) Computing a matching based on the similarity matrix using a provided Matcher
+3) Assigning regression targets based on the matching and a provided BoxCoder
+4) Assigning classification targets based on the matching and groundtruth labels
+
+Note that TargetAssigners only operate on detections from a single
+image at a time, so any logic for applying a TargetAssigner to multiple
+images must be handled externally.
+"""
+import tensorflow as tf
+
+from object_detection.box_coders import faster_rcnn_box_coder
+from object_detection.box_coders import mean_stddev_box_coder
+from object_detection.core import box_coder as bcoder
+from object_detection.core import box_list
+from object_detection.core import matcher as mat
+from object_detection.core import region_similarity_calculator as sim_calc
+from object_detection.core import standard_fields as fields
+from object_detection.matchers import argmax_matcher
+from object_detection.matchers import bipartite_matcher
+from object_detection.utils import shape_utils
+
+
+class TargetAssigner(object):
+  """Target assigner to compute classification and regression targets."""
+
+  def __init__(self,
+               similarity_calc,
+               matcher,
+               box_coder,
+               negative_class_weight=1.0):
+    """Construct Object Detection Target Assigner.
+
+    Args:
+      similarity_calc: a RegionSimilarityCalculator
+      matcher: an object_detection.core.Matcher used to match groundtruth to
+        anchors.
+      box_coder: an object_detection.core.BoxCoder used to encode matching
+        groundtruth boxes with respect to anchors.
+      negative_class_weight: classification weight to be associated to negative
+        anchors (default: 1.0). The weight must be in [0., 1.].
+
+    Raises:
+      ValueError: if similarity_calc is not a RegionSimilarityCalculator or
+        if matcher is not a Matcher or if box_coder is not a BoxCoder
+    """
+    if not isinstance(similarity_calc, sim_calc.RegionSimilarityCalculator):
+      raise ValueError('similarity_calc must be a RegionSimilarityCalculator')
+    if not isinstance(matcher, mat.Matcher):
+      raise ValueError('matcher must be a Matcher')
+    if not isinstance(box_coder, bcoder.BoxCoder):
+      raise ValueError('box_coder must be a BoxCoder')
+    self._similarity_calc = similarity_calc
+    self._matcher = matcher
+    self._box_coder = box_coder
+    self._negative_class_weight = negative_class_weight
+
+  @property
+  def box_coder(self):
+    return self._box_coder
+
+  # TODO(rathodv): move labels, scores, and weights to groundtruth_boxes fields.
+  def assign(self,
+             anchors,
+             groundtruth_boxes,
+             groundtruth_labels=None,
+             unmatched_class_label=None,
+             groundtruth_weights=None):
+    """Assign classification and regression targets to each anchor.
+
+    For a given set of anchors and groundtruth detections, match anchors
+    to groundtruth_boxes and assign classification and regression targets to
+    each anchor as well as weights based on the resulting match (specifying,
+    e.g., which anchors should not contribute to training loss).
+
+    Anchors that are not matched to anything are given a classification target
+    of self._unmatched_cls_target which can be specified via the constructor.
+
+    Args:
+      anchors: a BoxList representing N anchors
+      groundtruth_boxes: a BoxList representing M groundtruth boxes
+      groundtruth_labels:  a tensor of shape [M, d_1, ... d_k]
+        with labels for each of the ground_truth boxes. The subshape
+        [d_1, ... d_k] can be empty (corresponding to scalar inputs).  When set
+        to None, groundtruth_labels assumes a binary problem where all
+        ground_truth boxes get a positive label (of 1).
+      unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
+        which is consistent with the classification target for each
+        anchor (and can be empty for scalar targets).  This shape must thus be
+        compatible with the groundtruth labels that are passed to the "assign"
+        function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
+        If set to None, unmatched_cls_target is set to be [0] for each anchor.
+      groundtruth_weights: a float tensor of shape [M] indicating the weight to
+        assign to all anchors match to a particular groundtruth box. The weights
+        must be in [0., 1.]. If None, all weights are set to 1. Generally no
+        groundtruth boxes with zero weight match to any anchors as matchers are
+        aware of groundtruth weights. Additionally, `cls_weights` and
+        `reg_weights` are calculated using groundtruth weights as an added
+        safety.
+
+    Returns:
+      cls_targets: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k],
+        where the subshape [d_1, ..., d_k] is compatible with groundtruth_labels
+        which has shape [num_gt_boxes, d_1, d_2, ... d_k].
+      cls_weights: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k],
+        representing weights for each element in cls_targets.
+      reg_targets: a float32 tensor with shape [num_anchors, box_code_dimension]
+      reg_weights: a float32 tensor with shape [num_anchors]
+      match: a matcher.Match object encoding the match between anchors and
+        groundtruth boxes, with rows corresponding to groundtruth boxes
+        and columns corresponding to anchors.
+
+    Raises:
+      ValueError: if anchors or groundtruth_boxes are not of type
+        box_list.BoxList
+    """
+    if not isinstance(anchors, box_list.BoxList):
+      raise ValueError('anchors must be an BoxList')
+    if not isinstance(groundtruth_boxes, box_list.BoxList):
+      raise ValueError('groundtruth_boxes must be an BoxList')
+
+    if unmatched_class_label is None:
+      unmatched_class_label = tf.constant([0], tf.float32)
+
+    if groundtruth_labels is None:
+      groundtruth_labels = tf.ones(tf.expand_dims(groundtruth_boxes.num_boxes(),
+                                                  0))
+      groundtruth_labels = tf.expand_dims(groundtruth_labels, -1)
+
+    unmatched_shape_assert = shape_utils.assert_shape_equal(
+        shape_utils.combined_static_and_dynamic_shape(groundtruth_labels)[1:],
+        shape_utils.combined_static_and_dynamic_shape(unmatched_class_label))
+    labels_and_box_shapes_assert = shape_utils.assert_shape_equal(
+        shape_utils.combined_static_and_dynamic_shape(
+            groundtruth_labels)[:1],
+        shape_utils.combined_static_and_dynamic_shape(
+            groundtruth_boxes.get())[:1])
+
+    if groundtruth_weights is None:
+      num_gt_boxes = groundtruth_boxes.num_boxes_static()
+      if not num_gt_boxes:
+        num_gt_boxes = groundtruth_boxes.num_boxes()
+      groundtruth_weights = tf.ones([num_gt_boxes], dtype=tf.float32)
+
+    with tf.control_dependencies(
+        [unmatched_shape_assert, labels_and_box_shapes_assert]):
+      match_quality_matrix = self._similarity_calc.compare(groundtruth_boxes,
+                                                           anchors)
+      match = self._matcher.match(match_quality_matrix,
+                                  valid_rows=tf.greater(groundtruth_weights, 0))
+      reg_targets = self._create_regression_targets(anchors,
+                                                    groundtruth_boxes,
+                                                    match)
+      cls_targets = self._create_classification_targets(groundtruth_labels,
+                                                        unmatched_class_label,
+                                                        match)
+      reg_weights = self._create_regression_weights(match, groundtruth_weights)
+
+      cls_weights = self._create_classification_weights(match,
+                                                        groundtruth_weights)
+      # convert cls_weights from per-anchor to per-class.
+      class_label_shape = tf.shape(cls_targets)[1:]
+      weights_shape = tf.shape(cls_weights)
+      weights_multiple = tf.concat(
+          [tf.ones_like(weights_shape), class_label_shape],
+          axis=0)
+      for _ in range(len(cls_targets.get_shape()[1:])):
+        cls_weights = tf.expand_dims(cls_weights, -1)
+      cls_weights = tf.tile(cls_weights, weights_multiple)
+
+    num_anchors = anchors.num_boxes_static()
+    if num_anchors is not None:
+      reg_targets = self._reset_target_shape(reg_targets, num_anchors)
+      cls_targets = self._reset_target_shape(cls_targets, num_anchors)
+      reg_weights = self._reset_target_shape(reg_weights, num_anchors)
+      cls_weights = self._reset_target_shape(cls_weights, num_anchors)
+
+    return cls_targets, cls_weights, reg_targets, reg_weights, match
+
+  def _reset_target_shape(self, target, num_anchors):
+    """Sets the static shape of the target.
+
+    Args:
+      target: the target tensor. Its first dimension will be overwritten.
+      num_anchors: the number of anchors, which is used to override the target's
+        first dimension.
+
+    Returns:
+      A tensor with the shape info filled in.
+    """
+    target_shape = target.get_shape().as_list()
+    target_shape[0] = num_anchors
+    target.set_shape(target_shape)
+    return target
+
+  def _create_regression_targets(self, anchors, groundtruth_boxes, match):
+    """Returns a regression target for each anchor.
+
+    Args:
+      anchors: a BoxList representing N anchors
+      groundtruth_boxes: a BoxList representing M groundtruth_boxes
+      match: a matcher.Match object
+
+    Returns:
+      reg_targets: a float32 tensor with shape [N, box_code_dimension]
+    """
+    matched_gt_boxes = match.gather_based_on_match(
+        groundtruth_boxes.get(),
+        unmatched_value=tf.zeros(4),
+        ignored_value=tf.zeros(4))
+    matched_gt_boxlist = box_list.BoxList(matched_gt_boxes)
+    if groundtruth_boxes.has_field(fields.BoxListFields.keypoints):
+      groundtruth_keypoints = groundtruth_boxes.get_field(
+          fields.BoxListFields.keypoints)
+      matched_keypoints = match.gather_based_on_match(
+          groundtruth_keypoints,
+          unmatched_value=tf.zeros(groundtruth_keypoints.get_shape()[1:]),
+          ignored_value=tf.zeros(groundtruth_keypoints.get_shape()[1:]))
+      matched_gt_boxlist.add_field(fields.BoxListFields.keypoints,
+                                   matched_keypoints)
+    matched_reg_targets = self._box_coder.encode(matched_gt_boxlist, anchors)
+    match_results_shape = shape_utils.combined_static_and_dynamic_shape(
+        match.match_results)
+
+    # Zero out the unmatched and ignored regression targets.
+    unmatched_ignored_reg_targets = tf.tile(
+        self._default_regression_target(), [match_results_shape[0], 1])
+    matched_anchors_mask = match.matched_column_indicator()
+    reg_targets = tf.where(matched_anchors_mask,
+                           matched_reg_targets,
+                           unmatched_ignored_reg_targets)
+    return reg_targets
+
+  def _default_regression_target(self):
+    """Returns the default target for anchors to regress to.
+
+    Default regression targets are set to zero (though in
+    this implementation what these targets are set to should
+    not matter as the regression weight of any box set to
+    regress to the default target is zero).
+
+    Returns:
+      default_target: a float32 tensor with shape [1, box_code_dimension]
+    """
+    return tf.constant([self._box_coder.code_size*[0]], tf.float32)
+
+  def _create_classification_targets(self, groundtruth_labels,
+                                     unmatched_class_label, match):
+    """Create classification targets for each anchor.
+
+    Assign a classification target of for each anchor to the matching
+    groundtruth label that is provided by match.  Anchors that are not matched
+    to anything are given the target self._unmatched_cls_target
+
+    Args:
+      groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
+        with labels for each of the ground_truth boxes. The subshape
+        [d_1, ... d_k] can be empty (corresponding to scalar labels).
+      unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
+        which is consistent with the classification target for each
+        anchor (and can be empty for scalar targets).  This shape must thus be
+        compatible with the groundtruth labels that are passed to the "assign"
+        function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
+      match: a matcher.Match object that provides a matching between anchors
+        and groundtruth boxes.
+
+    Returns:
+      a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
+      subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
+      shape [num_gt_boxes, d_1, d_2, ... d_k].
+    """
+    return match.gather_based_on_match(
+        groundtruth_labels,
+        unmatched_value=unmatched_class_label,
+        ignored_value=unmatched_class_label)
+
+  def _create_regression_weights(self, match, groundtruth_weights):
+    """Set regression weight for each anchor.
+
+    Only positive anchors are set to contribute to the regression loss, so this
+    method returns a weight of 1 for every positive anchor and 0 for every
+    negative anchor.
+
+    Args:
+      match: a matcher.Match object that provides a matching between anchors
+        and groundtruth boxes.
+      groundtruth_weights: a float tensor of shape [M] indicating the weight to
+        assign to all anchors match to a particular groundtruth box.
+
+    Returns:
+      a float32 tensor with shape [num_anchors] representing regression weights.
+    """
+    return match.gather_based_on_match(
+        groundtruth_weights, ignored_value=0., unmatched_value=0.)
+
+  def _create_classification_weights(self,
+                                     match,
+                                     groundtruth_weights):
+    """Create classification weights for each anchor.
+
+    Positive (matched) anchors are associated with a weight of
+    positive_class_weight and negative (unmatched) anchors are associated with
+    a weight of negative_class_weight. When anchors are ignored, weights are set
+    to zero. By default, both positive/negative weights are set to 1.0,
+    but they can be adjusted to handle class imbalance (which is almost always
+    the case in object detection).
+
+    Args:
+      match: a matcher.Match object that provides a matching between anchors
+        and groundtruth boxes.
+      groundtruth_weights: a float tensor of shape [M] indicating the weight to
+        assign to all anchors match to a particular groundtruth box.
+
+    Returns:
+      a float32 tensor with shape [num_anchors] representing classification
+      weights.
+    """
+    return match.gather_based_on_match(
+        groundtruth_weights,
+        ignored_value=0.,
+        unmatched_value=self._negative_class_weight)
+
+  def get_box_coder(self):
+    """Get BoxCoder of this TargetAssigner.
+
+    Returns:
+      BoxCoder object.
+    """
+    return self._box_coder
+
+
+# TODO(rathodv): This method pulls in all the implementation dependencies into
+# core. Therefore its best to have this factory method outside of core.
+def create_target_assigner(reference, stage=None,
+                           negative_class_weight=1.0, use_matmul_gather=False):
+  """Factory function for creating standard target assigners.
+
+  Args:
+    reference: string referencing the type of TargetAssigner.
+    stage: string denoting stage: {proposal, detection}.
+    negative_class_weight: classification weight to be associated to negative
+      anchors (default: 1.0)
+    use_matmul_gather: whether to use matrix multiplication based gather which
+      are better suited for TPUs.
+
+  Returns:
+    TargetAssigner: desired target assigner.
+
+  Raises:
+    ValueError: if combination reference+stage is invalid.
+  """
+  if reference == 'Multibox' and stage == 'proposal':
+    similarity_calc = sim_calc.NegSqDistSimilarity()
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
+
+  elif reference == 'FasterRCNN' and stage == 'proposal':
+    similarity_calc = sim_calc.IouSimilarity()
+    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7,
+                                           unmatched_threshold=0.3,
+                                           force_match_for_each_row=True,
+                                           use_matmul_gather=use_matmul_gather)
+    box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+        scale_factors=[10.0, 10.0, 5.0, 5.0])
+
+  elif reference == 'FasterRCNN' and stage == 'detection':
+    similarity_calc = sim_calc.IouSimilarity()
+    # Uses all proposals with IOU < 0.5 as candidate negatives.
+    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                           negatives_lower_than_unmatched=True,
+                                           use_matmul_gather=use_matmul_gather)
+    box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+        scale_factors=[10.0, 10.0, 5.0, 5.0])
+
+  elif reference == 'FastRCNN':
+    similarity_calc = sim_calc.IouSimilarity()
+    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                           unmatched_threshold=0.1,
+                                           force_match_for_each_row=False,
+                                           negatives_lower_than_unmatched=False,
+                                           use_matmul_gather=use_matmul_gather)
+    box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+
+  else:
+    raise ValueError('No valid combination of reference and stage.')
+
+  return TargetAssigner(similarity_calc, matcher, box_coder,
+                        negative_class_weight=negative_class_weight)
+
+
+def batch_assign_targets(target_assigner,
+                         anchors_batch,
+                         gt_box_batch,
+                         gt_class_targets_batch,
+                         unmatched_class_label=None,
+                         gt_weights_batch=None):
+  """Batched assignment of classification and regression targets.
+
+  Args:
+    target_assigner: a target assigner.
+    anchors_batch: BoxList representing N box anchors or list of BoxList objects
+      with length batch_size representing anchor sets.
+    gt_box_batch: a list of BoxList objects with length batch_size
+      representing groundtruth boxes for each image in the batch
+    gt_class_targets_batch: a list of tensors with length batch_size, where
+      each tensor has shape [num_gt_boxes_i, classification_target_size] and
+      num_gt_boxes_i is the number of boxes in the ith boxlist of
+      gt_box_batch.
+    unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
+      which is consistent with the classification target for each
+      anchor (and can be empty for scalar targets).  This shape must thus be
+      compatible with the groundtruth labels that are passed to the "assign"
+      function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
+    gt_weights_batch: A list of 1-D tf.float32 tensors of shape
+      [num_boxes] containing weights for groundtruth boxes.
+
+  Returns:
+    batch_cls_targets: a tensor with shape [batch_size, num_anchors,
+      num_classes],
+    batch_cls_weights: a tensor with shape [batch_size, num_anchors,
+      num_classes],
+    batch_reg_targets: a tensor with shape [batch_size, num_anchors,
+      box_code_dimension]
+    batch_reg_weights: a tensor with shape [batch_size, num_anchors],
+    match_list: a list of matcher.Match objects encoding the match between
+      anchors and groundtruth boxes for each image of the batch,
+      with rows of the Match objects corresponding to groundtruth boxes
+      and columns corresponding to anchors.
+  Raises:
+    ValueError: if input list lengths are inconsistent, i.e.,
+      batch_size == len(gt_box_batch) == len(gt_class_targets_batch)
+        and batch_size == len(anchors_batch) unless anchors_batch is a single
+        BoxList.
+  """
+  if not isinstance(anchors_batch, list):
+    anchors_batch = len(gt_box_batch) * [anchors_batch]
+  if not all(
+      isinstance(anchors, box_list.BoxList) for anchors in anchors_batch):
+    raise ValueError('anchors_batch must be a BoxList or list of BoxLists.')
+  if not (len(anchors_batch)
+          == len(gt_box_batch)
+          == len(gt_class_targets_batch)):
+    raise ValueError('batch size incompatible with lengths of anchors_batch, '
+                     'gt_box_batch and gt_class_targets_batch.')
+  cls_targets_list = []
+  cls_weights_list = []
+  reg_targets_list = []
+  reg_weights_list = []
+  match_list = []
+  if gt_weights_batch is None:
+    gt_weights_batch = [None] * len(gt_class_targets_batch)
+  for anchors, gt_boxes, gt_class_targets, gt_weights in zip(
+      anchors_batch, gt_box_batch, gt_class_targets_batch, gt_weights_batch):
+    (cls_targets, cls_weights,
+     reg_targets, reg_weights, match) = target_assigner.assign(
+         anchors, gt_boxes, gt_class_targets, unmatched_class_label, gt_weights)
+    cls_targets_list.append(cls_targets)
+    cls_weights_list.append(cls_weights)
+    reg_targets_list.append(reg_targets)
+    reg_weights_list.append(reg_weights)
+    match_list.append(match)
+  batch_cls_targets = tf.stack(cls_targets_list)
+  batch_cls_weights = tf.stack(cls_weights_list)
+  batch_reg_targets = tf.stack(reg_targets_list)
+  batch_reg_weights = tf.stack(reg_weights_list)
+  return (batch_cls_targets, batch_cls_weights, batch_reg_targets,
+          batch_reg_weights, match_list)
+
+
+def batch_assign_confidences(target_assigner,
+                             anchors_batch,
+                             gt_box_batch,
+                             gt_class_confidences_batch,
+                             gt_weights_batch=None,
+                             unmatched_class_label=None,
+                             include_background_class=True,
+                             implicit_class_weight=1.0):
+  """Batched assignment of classification and regression targets.
+
+  This differences between batch_assign_confidences and batch_assign_targets:
+   - 'batch_assign_targets' supports scalar (agnostic), vector (multiclass) and
+     tensor (high-dimensional) targets. 'batch_assign_confidences' only support
+     scalar (agnostic) and vector (multiclass) targets.
+   - 'batch_assign_targets' assumes the input class tensor using the binary
+     one/K-hot encoding. 'batch_assign_confidences' takes the class confidence
+     scores as the input, where 1 means positive classes, 0 means implicit
+     negative classes, and -1 means explicit negative classes.
+   - 'batch_assign_confidences' assigns the targets in the similar way as
+     'batch_assign_targets' except that it gives different weights for implicit
+     and explicit classes. This allows user to control the negative gradients
+     pushed differently for implicit and explicit examples during the training.
+
+  Args:
+    target_assigner: a target assigner.
+    anchors_batch: BoxList representing N box anchors or list of BoxList objects
+      with length batch_size representing anchor sets.
+    gt_box_batch: a list of BoxList objects with length batch_size
+      representing groundtruth boxes for each image in the batch
+    gt_class_confidences_batch: a list of tensors with length batch_size, where
+      each tensor has shape [num_gt_boxes_i, classification_target_size] and
+      num_gt_boxes_i is the number of boxes in the ith boxlist of
+      gt_box_batch. Note that in this tensor, 1 means explicit positive class,
+      -1 means explicit negative class, and 0 means implicit negative class.
+    gt_weights_batch: A list of 1-D tf.float32 tensors of shape
+      [num_gt_boxes_i] containing weights for groundtruth boxes.
+    unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
+      which is consistent with the classification target for each
+      anchor (and can be empty for scalar targets).  This shape must thus be
+      compatible with the groundtruth labels that are passed to the "assign"
+      function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
+    include_background_class: whether or not gt_class_confidences_batch includes
+      the background class.
+    implicit_class_weight: the weight assigned to implicit examples.
+
+  Returns:
+    batch_cls_targets: a tensor with shape [batch_size, num_anchors,
+      num_classes],
+    batch_cls_weights: a tensor with shape [batch_size, num_anchors,
+      num_classes],
+    batch_reg_targets: a tensor with shape [batch_size, num_anchors,
+      box_code_dimension]
+    batch_reg_weights: a tensor with shape [batch_size, num_anchors],
+    match_list: a list of matcher.Match objects encoding the match between
+      anchors and groundtruth boxes for each image of the batch,
+      with rows of the Match objects corresponding to groundtruth boxes
+      and columns corresponding to anchors.
+
+  Raises:
+    ValueError: if input list lengths are inconsistent, i.e.,
+      batch_size == len(gt_box_batch) == len(gt_class_targets_batch)
+      and batch_size == len(anchors_batch) unless anchors_batch is a single
+      BoxList, or if any element in gt_class_confidences_batch has rank > 2.
+  """
+  if not isinstance(anchors_batch, list):
+    anchors_batch = len(gt_box_batch) * [anchors_batch]
+  if not all(
+      isinstance(anchors, box_list.BoxList) for anchors in anchors_batch):
+    raise ValueError('anchors_batch must be a BoxList or list of BoxLists.')
+  if not (len(anchors_batch)
+          == len(gt_box_batch)
+          == len(gt_class_confidences_batch)):
+    raise ValueError('batch size incompatible with lengths of anchors_batch, '
+                     'gt_box_batch and gt_class_confidences_batch.')
+
+  cls_targets_list = []
+  cls_weights_list = []
+  reg_targets_list = []
+  reg_weights_list = []
+  match_list = []
+  if gt_weights_batch is None:
+    gt_weights_batch = [None] * len(gt_class_confidences_batch)
+  for anchors, gt_boxes, gt_class_confidences, gt_weights in zip(
+      anchors_batch, gt_box_batch, gt_class_confidences_batch,
+      gt_weights_batch):
+
+    if (gt_class_confidences is not None and
+        len(gt_class_confidences.get_shape().as_list()) > 2):
+      raise ValueError('The shape of the class target is not supported. ',
+                       gt_class_confidences.get_shape())
+
+    cls_targets, _, reg_targets, _, match = target_assigner.assign(
+        anchors, gt_boxes, gt_class_confidences, unmatched_class_label,
+        groundtruth_weights=gt_weights)
+
+    if include_background_class:
+      cls_targets_without_background = tf.slice(
+          cls_targets, [0, 1], [-1, -1])
+    else:
+      cls_targets_without_background = cls_targets
+
+    positive_mask = tf.greater(cls_targets_without_background, 0.0)
+    negative_mask = tf.less(cls_targets_without_background, 0.0)
+    explicit_example_mask = tf.logical_or(positive_mask, negative_mask)
+    positive_anchors = tf.reduce_any(positive_mask, axis=-1)
+
+    regression_weights = tf.to_float(positive_anchors)
+    regression_targets = (
+        reg_targets * tf.expand_dims(regression_weights, axis=-1))
+    regression_weights_expanded = tf.expand_dims(regression_weights, axis=-1)
+
+    cls_targets_without_background = (
+        cls_targets_without_background * (1 - tf.to_float(negative_mask)))
+    cls_weights_without_background = (
+        (1 - implicit_class_weight) * tf.to_float(explicit_example_mask)
+        + implicit_class_weight)
+
+    if include_background_class:
+      cls_weights_background = (
+          (1 - implicit_class_weight) * regression_weights_expanded
+          + implicit_class_weight)
+      classification_weights = tf.concat(
+          [cls_weights_background, cls_weights_without_background], axis=-1)
+      cls_targets_background = 1 - regression_weights_expanded
+      classification_targets = tf.concat(
+          [cls_targets_background, cls_targets_without_background], axis=-1)
+    else:
+      classification_targets = cls_targets_without_background
+      classification_weights = cls_weights_without_background
+
+    cls_targets_list.append(classification_targets)
+    cls_weights_list.append(classification_weights)
+    reg_targets_list.append(regression_targets)
+    reg_weights_list.append(regression_weights)
+    match_list.append(match)
+  batch_cls_targets = tf.stack(cls_targets_list)
+  batch_cls_weights = tf.stack(cls_weights_list)
+  batch_reg_targets = tf.stack(reg_targets_list)
+  batch_reg_weights = tf.stack(reg_weights_list)
+  return (batch_cls_targets, batch_cls_weights, batch_reg_targets,
+          batch_reg_weights, match_list)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner_test.py
new file mode 100644
index 00000000..443c33aa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/core/target_assigner_test.py
@@ -0,0 +1,1178 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.target_assigner."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.box_coders import keypoint_box_coder
+from object_detection.box_coders import mean_stddev_box_coder
+from object_detection.core import box_list
+from object_detection.core import region_similarity_calculator
+from object_detection.core import standard_fields as fields
+from object_detection.core import target_assigner as targetassigner
+from object_detection.matchers import argmax_matcher
+from object_detection.matchers import bipartite_matcher
+from object_detection.utils import test_case
+
+
+class TargetAssignerTest(test_case.TestCase):
+
+  def test_assign_agnostic(self):
+    def graph_fn(anchor_means, groundtruth_box_corners):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      result = target_assigner.assign(
+          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0, 0.5, .5, 1.0]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.9, 0.9]],
+                                       dtype=np.float32)
+    exp_cls_targets = [[1], [1], [0]]
+    exp_cls_weights = [[1], [1], [1]]
+    exp_reg_targets = [[0, 0, 0, 0],
+                       [0, 0, -1, 1],
+                       [0, 0, 0, 0]]
+    exp_reg_weights = [1, 1, 0]
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_class_agnostic_with_ignored_matches(self):
+    # Note: test is very similar to above. The third box matched with an IOU
+    # of 0.35, which is between the matched and unmatched threshold. This means
+    # That like above the expected classification targets are [1, 1, 0].
+    # Unlike above, the third target is ignored and therefore expected
+    # classification weights are [1, 1, 0].
+    def graph_fn(anchor_means, groundtruth_box_corners):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.3)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      result = target_assigner.assign(
+          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0.0, 0.5, .9, 1.0]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.9, 0.9]], dtype=np.float32)
+    exp_cls_targets = [[1], [1], [0]]
+    exp_cls_weights = [[1], [1], [0]]
+    exp_reg_targets = [[0, 0, 0, 0],
+                       [0, 0, -1, 1],
+                       [0, 0, 0, 0]]
+    exp_reg_weights = [1, 1, 0]
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_agnostic_with_keypoints(self):
+    def graph_fn(anchor_means, groundtruth_box_corners,
+                 groundtruth_keypoints):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = keypoint_box_coder.KeypointBoxCoder(
+          num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0])
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      groundtruth_boxlist.add_field(fields.BoxListFields.keypoints,
+                                    groundtruth_keypoints)
+      result = target_assigner.assign(
+          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 1.0],
+                             [0.0, 0.5, .9, 1.0]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.45, 0.45, 0.95, 0.95]],
+                                       dtype=np.float32)
+    groundtruth_keypoints = np.array(
+        [[[0.1, 0.2], [0.1, 0.3], [0.2, 0.2], [0.2, 0.2], [0.1, 0.1], [0.9, 0]],
+         [[0, 0.3], [0.2, 0.4], [0.5, 0.6], [0, 0.6], [0.8, 0.2], [0.2, 0.4]]],
+        dtype=np.float32)
+    exp_cls_targets = [[1], [1], [0]]
+    exp_cls_weights = [[1], [1], [1]]
+    exp_reg_targets = [[0, 0, 0, 0, -3, -1, -3, 1, -1, -1, -1, -1, -3, -3, 13,
+                        -5],
+                       [-1, -1, 0, 0, -15, -9, -11, -7, -5, -3, -15, -3, 1, -11,
+                        -11, -7],
+                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+    exp_reg_weights = [1, 1, 0]
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [anchor_means,
+                                                groundtruth_box_corners,
+                                                groundtruth_keypoints])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_class_agnostic_with_keypoints_and_ignored_matches(self):
+    # Note: test is very similar to above. The third box matched with an IOU
+    # of 0.35, which is between the matched and unmatched threshold. This means
+    # That like above the expected classification targets are [1, 1, 0].
+    # Unlike above, the third target is ignored and therefore expected
+    # classification weights are [1, 1, 0].
+    def graph_fn(anchor_means, groundtruth_box_corners,
+                 groundtruth_keypoints):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = keypoint_box_coder.KeypointBoxCoder(
+          num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0])
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      groundtruth_boxlist.add_field(fields.BoxListFields.keypoints,
+                                    groundtruth_keypoints)
+      result = target_assigner.assign(
+          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 1.0],
+                             [0.0, 0.5, .9, 1.0]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.45, 0.45, 0.95, 0.95]],
+                                       dtype=np.float32)
+    groundtruth_keypoints = np.array(
+        [[[0.1, 0.2], [0.1, 0.3], [0.2, 0.2], [0.2, 0.2], [0.1, 0.1], [0.9, 0]],
+         [[0, 0.3], [0.2, 0.4], [0.5, 0.6], [0, 0.6], [0.8, 0.2], [0.2, 0.4]]],
+        dtype=np.float32)
+    exp_cls_targets = [[1], [1], [0]]
+    exp_cls_weights = [[1], [1], [1]]
+    exp_reg_targets = [[0, 0, 0, 0, -3, -1, -3, 1, -1, -1, -1, -1, -3, -3, 13,
+                        -5],
+                       [-1, -1, 0, 0, -15, -9, -11, -7, -5, -3, -15, -3, 1, -11,
+                        -11, -7],
+                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+    exp_reg_weights = [1, 1, 0]
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [anchor_means,
+                                                groundtruth_box_corners,
+                                                groundtruth_keypoints])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_multiclass(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      result = target_assigner.assign(
+          anchors_boxlist,
+          groundtruth_boxlist,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0, 0.5, .5, 1.0],
+                             [.75, 0, 1.0, .25]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.9, 0.9],
+                                        [.75, 0, .95, .27]], dtype=np.float32)
+    groundtruth_labels = np.array([[0, 1, 0, 0, 0, 0, 0],
+                                   [0, 0, 0, 0, 0, 1, 0],
+                                   [0, 0, 0, 1, 0, 0, 0]], dtype=np.float32)
+
+    exp_cls_targets = [[0, 1, 0, 0, 0, 0, 0],
+                       [0, 0, 0, 0, 0, 1, 0],
+                       [1, 0, 0, 0, 0, 0, 0],
+                       [0, 0, 0, 1, 0, 0, 0]]
+    exp_cls_weights = [[1, 1, 1, 1, 1, 1, 1],
+                       [1, 1, 1, 1, 1, 1, 1],
+                       [1, 1, 1, 1, 1, 1, 1],
+                       [1, 1, 1, 1, 1, 1, 1]]
+    exp_reg_targets = [[0, 0, 0, 0],
+                       [0, 0, -1, 1],
+                       [0, 0, 0, 0],
+                       [0, 0, -.5, .2]]
+    exp_reg_weights = [1, 1, 0, 1]
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners, groundtruth_labels])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_multiclass_with_groundtruth_weights(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels,
+                 groundtruth_weights):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      result = target_assigner.assign(
+          anchors_boxlist,
+          groundtruth_boxlist,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label,
+          groundtruth_weights=groundtruth_weights)
+      (_, cls_weights, _, reg_weights, _) = result
+      return (cls_weights, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0, 0.5, .5, 1.0],
+                             [.75, 0, 1.0, .25]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.9, 0.9],
+                                        [.75, 0, .95, .27]], dtype=np.float32)
+    groundtruth_labels = np.array([[0, 1, 0, 0, 0, 0, 0],
+                                   [0, 0, 0, 0, 0, 1, 0],
+                                   [0, 0, 0, 1, 0, 0, 0]], dtype=np.float32)
+    groundtruth_weights = np.array([0.3, 0., 0.5], dtype=np.float32)
+
+    # background class gets weight of 1.
+    exp_cls_weights = [[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
+                       [0, 0, 0, 0, 0, 0, 0],
+                       [1, 1, 1, 1, 1, 1, 1],
+                       [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]]
+    exp_reg_weights = [0.3, 0., 0., 0.5]  # background class gets weight of 0.
+
+    (cls_weights_out, reg_weights_out) = self.execute(graph_fn, [
+        anchor_means, groundtruth_box_corners, groundtruth_labels,
+        groundtruth_weights
+    ])
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_assign_multidimensional_class_targets(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+
+      unmatched_class_label = tf.constant([[0, 0], [0, 0]], tf.float32)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      result = target_assigner.assign(
+          anchors_boxlist,
+          groundtruth_boxlist,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0, 0.5, .5, 1.0],
+                             [.75, 0, 1.0, .25]], dtype=np.float32)
+    groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.9, 0.9],
+                                        [.75, 0, .95, .27]], dtype=np.float32)
+
+    groundtruth_labels = np.array([[[0, 1], [1, 0]],
+                                   [[1, 0], [0, 1]],
+                                   [[0, 1], [1, .5]]], np.float32)
+
+    exp_cls_targets = [[[0, 1], [1, 0]],
+                       [[1, 0], [0, 1]],
+                       [[0, 0], [0, 0]],
+                       [[0, 1], [1, .5]]]
+    exp_cls_weights = [[[1, 1], [1, 1]],
+                       [[1, 1], [1, 1]],
+                       [[1, 1], [1, 1]],
+                       [[1, 1], [1, 1]]]
+    exp_reg_targets = [[0, 0, 0, 0],
+                       [0, 0, -1, 1],
+                       [0, 0, 0, 0],
+                       [0, 0, -.5, .2]]
+    exp_reg_weights = [1, 1, 0, 1]
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners, groundtruth_labels])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_assign_empty_groundtruth(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels):
+      similarity_calc = region_similarity_calculator.IouSimilarity()
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                             unmatched_threshold=0.5)
+      box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      unmatched_class_label = tf.constant([0, 0, 0], tf.float32)
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      target_assigner = targetassigner.TargetAssigner(
+          similarity_calc, matcher, box_coder)
+      result = target_assigner.assign(
+          anchors_boxlist,
+          groundtruth_boxlist,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label)
+      (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_box_corners = np.zeros((0, 4), dtype=np.float32)
+    groundtruth_labels = np.zeros((0, 3), dtype=np.float32)
+    anchor_means = np.array([[0.0, 0.0, 0.5, 0.5],
+                             [0.5, 0.5, 1.0, 0.8],
+                             [0, 0.5, .5, 1.0],
+                             [.75, 0, 1.0, .25]],
+                            dtype=np.float32)
+    exp_cls_targets = [[0, 0, 0],
+                       [0, 0, 0],
+                       [0, 0, 0],
+                       [0, 0, 0]]
+    exp_cls_weights = [[1, 1, 1],
+                       [1, 1, 1],
+                       [1, 1, 1],
+                       [1, 1, 1]]
+    exp_reg_targets = [[0, 0, 0, 0],
+                       [0, 0, 0, 0],
+                       [0, 0, 0, 0],
+                       [0, 0, 0, 0]]
+    exp_reg_weights = [0, 0, 0, 0]
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners, groundtruth_labels])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+    self.assertEquals(cls_targets_out.dtype, np.float32)
+    self.assertEquals(cls_weights_out.dtype, np.float32)
+    self.assertEquals(reg_targets_out.dtype, np.float32)
+    self.assertEquals(reg_weights_out.dtype, np.float32)
+
+  def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self):
+    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
+    unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
+    target_assigner = targetassigner.TargetAssigner(
+        similarity_calc, matcher, box_coder)
+
+    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
+                               [0.5, 0.5, 1.0, 0.8],
+                               [0, 0.5, .5, 1.0],
+                               [.75, 0, 1.0, .25]])
+    priors = box_list.BoxList(prior_means)
+
+    box_corners = [[0.0, 0.0, 0.5, 0.5],
+                   [0.0, 0.0, 0.5, 0.8],
+                   [0.5, 0.5, 0.9, 0.9],
+                   [.75, 0, .95, .27]]
+    boxes = box_list.BoxList(tf.constant(box_corners))
+
+    groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
+                                      [0, 0, 0, 0, 0, 1, 0],
+                                      [0, 0, 0, 1, 0, 0, 0]], tf.float32)
+    with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
+      target_assigner.assign(
+          priors,
+          boxes,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label)
+
+  def test_raises_error_on_invalid_groundtruth_labels(self):
+    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=1.0)
+    unmatched_class_label = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
+    target_assigner = targetassigner.TargetAssigner(
+        similarity_calc, matcher, box_coder)
+
+    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
+    priors = box_list.BoxList(prior_means)
+
+    box_corners = [[0.0, 0.0, 0.5, 0.5],
+                   [0.5, 0.5, 0.9, 0.9],
+                   [.75, 0, .95, .27]]
+    boxes = box_list.BoxList(tf.constant(box_corners))
+    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)
+
+    with self.assertRaises(ValueError):
+      target_assigner.assign(
+          priors,
+          boxes,
+          groundtruth_labels,
+          unmatched_class_label=unmatched_class_label)
+
+
+class BatchTargetAssignerTest(test_case.TestCase):
+
+  def _get_target_assigner(self):
+    similarity_calc = region_similarity_calculator.IouSimilarity()
+    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                           unmatched_threshold=0.5)
+    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+    return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
+
+  def test_batch_assign_targets(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_targets = [None, None]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      agnostic_target_assigner = self._get_target_assigner()
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_targets(
+           agnostic_target_assigner, anchors_boxlist, gt_box_batch,
+           gt_class_targets)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    exp_cls_targets = [[[1], [0], [0], [0]],
+                       [[0], [1], [1], [0]]]
+    exp_cls_weights = [[[1], [1], [1], [1]],
+                       [[1], [1], [1], [1]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0.15789001, -0.01500003, 0.57889998, -1.15799987],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 1, 0]]
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_boxlist1, groundtruth_boxlist2])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_multiclass_targets(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_targets = [class_targets1, class_targets2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      num_classes = 3
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_targets(
+           multiclass_target_assigner, anchors_boxlist, gt_box_batch,
+           gt_class_targets, unmatched_class_label)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, 1, 0]], dtype=np.float32)
+
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+    exp_cls_targets = [[[0, 1, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]],
+                       [[1, 0, 0, 0],
+                        [0, 0, 0, 1],
+                        [0, 0, 1, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1]],
+                       [[1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0.15789001, -0.01500003, 0.57889998, -1.15799987],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 1, 0]]
+
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [
+         anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+         class_targets1, class_targets2
+     ])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_multiclass_targets_with_padded_groundtruth(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2, groundtruth_weights1,
+                 groundtruth_weights2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_targets = [class_targets1, class_targets2]
+      gt_weights = [groundtruth_weights1, groundtruth_weights2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      num_classes = 3
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_targets(
+           multiclass_target_assigner, anchors_boxlist, gt_box_batch,
+           gt_class_targets, unmatched_class_label, gt_weights)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2],
+                                     [0., 0., 0., 0.]], dtype=np.float32)
+    groundtruth_weights1 = np.array([1, 0], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842],
+                                     [0, 0, 0, 0]],
+                                    dtype=np.float32)
+    groundtruth_weights2 = np.array([1, 1, 0], dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, 1, 0],
+                               [0, 0, 0, 0]], dtype=np.float32)
+
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    exp_cls_targets = [[[0, 1, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]],
+                       [[1, 0, 0, 0],
+                        [0, 0, 0, 1],
+                        [0, 0, 1, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1]],
+                       [[1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0.15789001, -0.01500003, 0.57889998, -1.15799987],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 1, 0]]
+
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [
+         anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+         class_targets1, class_targets2, groundtruth_weights1,
+         groundtruth_weights2
+     ])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_multidimensional_targets(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_targets = [class_targets1, class_targets2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      target_dimensions = (2, 3)
+      unmatched_class_label = tf.constant(np.zeros(target_dimensions),
+                                          tf.float32)
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_targets(
+           multiclass_target_assigner, anchors_boxlist, gt_box_batch,
+           gt_class_targets, unmatched_class_label)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, 1, 0]], dtype=np.float32)
+    class_targets1 = np.array([[[0, 1, 1],
+                                [1, 1, 0]]], dtype=np.float32)
+    class_targets2 = np.array([[[0, 1, 1],
+                                [1, 1, 0]],
+                               [[0, 0, 1],
+                                [0, 0, 1]]], dtype=np.float32)
+
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    exp_cls_targets = [[[[0., 1., 1.],
+                         [1., 1., 0.]],
+                        [[0., 0., 0.],
+                         [0., 0., 0.]],
+                        [[0., 0., 0.],
+                         [0., 0., 0.]],
+                        [[0., 0., 0.],
+                         [0., 0., 0.]]],
+                       [[[0., 0., 0.],
+                         [0., 0., 0.]],
+                        [[0., 1., 1.],
+                         [1., 1., 0.]],
+                        [[0., 0., 1.],
+                         [0., 0., 1.]],
+                        [[0., 0., 0.],
+                         [0., 0., 0.]]]]
+    exp_cls_weights = [[[[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]]],
+                       [[[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.]]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0.15789001, -0.01500003, 0.57889998, -1.15799987],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 1, 0]]
+
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [
+         anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+         class_targets1, class_targets2
+     ])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_empty_groundtruth(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, gt_class_targets):
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      gt_box_batch = [groundtruth_boxlist]
+      gt_class_targets_batch = [gt_class_targets]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+
+      multiclass_target_assigner = self._get_target_assigner()
+      num_classes = 3
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_targets(
+           multiclass_target_assigner, anchors_boxlist,
+           gt_box_batch, gt_class_targets_batch, unmatched_class_label)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_box_corners = np.zeros((0, 4), dtype=np.float32)
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1]], dtype=np.float32)
+    exp_cls_targets = [[[1, 0, 0, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[1, 1, 1, 1],
+                        [1, 1, 1, 1]]]
+    exp_reg_targets = [[[0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[0, 0]]
+    num_classes = 3
+    pad = 1
+    gt_class_targets = np.zeros((0, num_classes + pad), dtype=np.float32)
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_box_corners, gt_class_targets])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+
+class BatchTargetAssignConfidencesTest(test_case.TestCase):
+
+  def _get_target_assigner(self):
+    similarity_calc = region_similarity_calculator.IouSimilarity()
+    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
+                                           unmatched_threshold=0.5)
+    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+    return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
+
+  def test_batch_assign_empty_groundtruth(self):
+
+    def graph_fn(anchor_means, groundtruth_box_corners, gt_class_confidences):
+      groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
+      gt_box_batch = [groundtruth_boxlist]
+      gt_class_confidences_batch = [gt_class_confidences]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+
+      num_classes = 3
+      implicit_class_weight = 0.5
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      multiclass_target_assigner = self._get_target_assigner()
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_confidences(
+           multiclass_target_assigner,
+           anchors_boxlist,
+           gt_box_batch,
+           gt_class_confidences_batch,
+           unmatched_class_label=unmatched_class_label,
+           include_background_class=True,
+           implicit_class_weight=implicit_class_weight)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_box_corners = np.zeros((0, 4), dtype=np.float32)
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1]], dtype=np.float32)
+    num_classes = 3
+    pad = 1
+    gt_class_confidences = np.zeros((0, num_classes + pad), dtype=np.float32)
+
+    exp_cls_targets = [[[1, 0, 0, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[0.5, 0.5, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5]]]
+    exp_reg_targets = [[[0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[0, 0]]
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn,
+         [anchor_means, groundtruth_box_corners, gt_class_confidences])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_confidences_agnostic(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_confidences_batch = [None, None]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      agnostic_target_assigner = self._get_target_assigner()
+      implicit_class_weight = 0.5
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_confidences(
+           agnostic_target_assigner,
+           anchors_boxlist,
+           gt_box_batch,
+           gt_class_confidences_batch,
+           include_background_class=False,
+           implicit_class_weight=implicit_class_weight)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    exp_cls_targets = [[[1], [0], [0], [0]],
+                       [[0], [1], [1], [0]]]
+    exp_cls_weights = [[[1], [0.5], [0.5], [0.5]],
+                       [[0.5], [1], [1], [0.5]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0.15789001, -0.01500003, 0.57889998, -1.15799987],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 1, 0]]
+
+    (cls_targets_out,
+     cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
+         graph_fn, [anchor_means, groundtruth_boxlist1, groundtruth_boxlist2])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_confidences_multiclass(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_confidences_batch = [class_targets1, class_targets2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      num_classes = 3
+      implicit_class_weight = 0.5
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_confidences(
+           multiclass_target_assigner,
+           anchors_boxlist,
+           gt_box_batch,
+           gt_class_confidences_batch,
+           unmatched_class_label=unmatched_class_label,
+           include_background_class=True,
+           implicit_class_weight=implicit_class_weight)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, -1, 0]], dtype=np.float32)
+
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+    exp_cls_targets = [[[0, 1, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]],
+                       [[1, 0, 0, 0],
+                        [0, 0, 0, 1],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[1, 1, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5]],
+                       [[0.5, 0.5, 0.5, 0.5],
+                        [1, 0.5, 0.5, 1],
+                        [0.5, 0.5, 1, 0.5],
+                        [0.5, 0.5, 0.5, 0.5]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 0, 0]]
+
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [
+         anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+         class_targets1, class_targets2
+     ])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_confidences_multiclass_with_padded_groundtruth(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2, groundtruth_weights1,
+                 groundtruth_weights2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_confidences_batch = [class_targets1, class_targets2]
+      gt_weights = [groundtruth_weights1, groundtruth_weights2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      num_classes = 3
+      unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
+      implicit_class_weight = 0.5
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_confidences(
+           multiclass_target_assigner,
+           anchors_boxlist,
+           gt_box_batch,
+           gt_class_confidences_batch,
+           gt_weights,
+           unmatched_class_label=unmatched_class_label,
+           include_background_class=True,
+           implicit_class_weight=implicit_class_weight)
+
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2],
+                                     [0., 0., 0., 0.]], dtype=np.float32)
+    groundtruth_weights1 = np.array([1, 0], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842],
+                                     [0, 0, 0, 0]],
+                                    dtype=np.float32)
+    groundtruth_weights2 = np.array([1, 1, 0], dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, -1, 0],
+                               [0, 0, 0, 0]], dtype=np.float32)
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    exp_cls_targets = [[[0, 1, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]],
+                       [[1, 0, 0, 0],
+                        [0, 0, 0, 1],
+                        [1, 0, 0, 0],
+                        [1, 0, 0, 0]]]
+    exp_cls_weights = [[[1, 1, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5],
+                        [0.5, 0.5, 0.5, 0.5]],
+                       [[0.5, 0.5, 0.5, 0.5],
+                        [1, 0.5, 0.5, 1],
+                        [0.5, 0.5, 1, 0.5],
+                        [0.5, 0.5, 0.5, 0.5]]]
+    exp_reg_targets = [[[0, 0, -0.5, -0.5],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0,],
+                        [0, 0, 0, 0,],],
+                       [[0, 0, 0, 0,],
+                        [0, 0.01231521, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0]]]
+    exp_reg_weights = [[1, 0, 0, 0],
+                       [0, 1, 0, 0]]
+
+    (cls_targets_out, cls_weights_out, reg_targets_out,
+     reg_weights_out) = self.execute(graph_fn, [
+         anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+         class_targets1, class_targets2, groundtruth_weights1,
+         groundtruth_weights2
+     ])
+    self.assertAllClose(cls_targets_out, exp_cls_targets)
+    self.assertAllClose(cls_weights_out, exp_cls_weights)
+    self.assertAllClose(reg_targets_out, exp_reg_targets)
+    self.assertAllClose(reg_weights_out, exp_reg_weights)
+
+  def test_batch_assign_confidences_multidimensional(self):
+
+    def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+                 class_targets1, class_targets2):
+      box_list1 = box_list.BoxList(groundtruth_boxlist1)
+      box_list2 = box_list.BoxList(groundtruth_boxlist2)
+      gt_box_batch = [box_list1, box_list2]
+      gt_class_confidences_batch = [class_targets1, class_targets2]
+      anchors_boxlist = box_list.BoxList(anchor_means)
+      multiclass_target_assigner = self._get_target_assigner()
+      target_dimensions = (2, 3)
+      unmatched_class_label = tf.constant(np.zeros(target_dimensions),
+                                          tf.float32)
+      implicit_class_weight = 0.5
+      (cls_targets, cls_weights, reg_targets, reg_weights,
+       _) = targetassigner.batch_assign_confidences(
+           multiclass_target_assigner,
+           anchors_boxlist,
+           gt_box_batch,
+           gt_class_confidences_batch,
+           unmatched_class_label=unmatched_class_label,
+           include_background_class=True,
+           implicit_class_weight=implicit_class_weight)
+      return (cls_targets, cls_weights, reg_targets, reg_weights)
+
+    groundtruth_boxlist1 = np.array([[0., 0., 0.2, 0.2]], dtype=np.float32)
+    groundtruth_boxlist2 = np.array([[0, 0.25123152, 1, 1],
+                                     [0.015789, 0.0985, 0.55789, 0.3842]],
+                                    dtype=np.float32)
+    class_targets1 = np.array([[0, 1, 0, 0]], dtype=np.float32)
+    class_targets2 = np.array([[0, 0, 0, 1],
+                               [0, 0, 1, 0]], dtype=np.float32)
+    class_targets1 = np.array([[[0, 1, 1],
+                                [1, 1, 0]]], dtype=np.float32)
+    class_targets2 = np.array([[[0, 1, 1],
+                                [1, 1, 0]],
+                               [[0, 0, 1],
+                                [0, 0, 1]]], dtype=np.float32)
+
+    anchor_means = np.array([[0, 0, .25, .25],
+                             [0, .25, 1, 1],
+                             [0, .1, .5, .5],
+                             [.75, .75, 1, 1]], dtype=np.float32)
+
+    with self.assertRaises(ValueError):
+      _, _, _, _ = self.execute(graph_fn, [
+          anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
+          class_targets1, class_targets2
+      ])
+
+
+class CreateTargetAssignerTest(tf.test.TestCase):
+
+  def test_create_target_assigner(self):
+    """Tests that named constructor gives working target assigners.
+
+    TODO(rathodv): Make this test more general.
+    """
+    corners = [[0.0, 0.0, 1.0, 1.0]]
+    groundtruth = box_list.BoxList(tf.constant(corners))
+
+    priors = box_list.BoxList(tf.constant(corners))
+    multibox_ta = (targetassigner
+                   .create_target_assigner('Multibox', stage='proposal'))
+    multibox_ta.assign(priors, groundtruth)
+    # No tests on output, as that may vary arbitrarily as new target assigners
+    # are added. As long as it is constructed correctly and runs without errors,
+    # tests on the individual assigners cover correctness of the assignments.
+
+    anchors = box_list.BoxList(tf.constant(corners))
+    faster_rcnn_proposals_ta = (targetassigner
+                                .create_target_assigner('FasterRCNN',
+                                                        stage='proposal'))
+    faster_rcnn_proposals_ta.assign(anchors, groundtruth)
+
+    fast_rcnn_ta = (targetassigner
+                    .create_target_assigner('FastRCNN'))
+    fast_rcnn_ta.assign(anchors, groundtruth)
+
+    faster_rcnn_detection_ta = (targetassigner
+                                .create_target_assigner('FasterRCNN',
+                                                        stage='detection'))
+    faster_rcnn_detection_ta.assign(anchors, groundtruth)
+
+    with self.assertRaises(ValueError):
+      targetassigner.create_target_assigner('InvalidDetector',
+                                            stage='invalid_stage')
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/ava_label_map_v2.1.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/ava_label_map_v2.1.pbtxt
new file mode 100644
index 00000000..5e2c4856
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/ava_label_map_v2.1.pbtxt
@@ -0,0 +1,240 @@
+item {
+  name: "bend/bow (at the waist)"
+  id: 1
+}
+item {
+  name: "crouch/kneel"
+  id: 3
+}
+item {
+  name: "dance"
+  id: 4
+}
+item {
+  name: "fall down"
+  id: 5
+}
+item {
+  name: "get up"
+  id: 6
+}
+item {
+  name: "jump/leap"
+  id: 7
+}
+item {
+  name: "lie/sleep"
+  id: 8
+}
+item {
+  name: "martial art"
+  id: 9
+}
+item {
+  name: "run/jog"
+  id: 10
+}
+item {
+  name: "sit"
+  id: 11
+}
+item {
+  name: "stand"
+  id: 12
+}
+item {
+  name: "swim"
+  id: 13
+}
+item {
+  name: "walk"
+  id: 14
+}
+item {
+  name: "answer phone"
+  id: 15
+}
+item {
+  name: "carry/hold (an object)"
+  id: 17
+}
+item {
+  name: "climb (e.g., a mountain)"
+  id: 20
+}
+item {
+  name: "close (e.g., a door, a box)"
+  id: 22
+}
+item {
+  name: "cut"
+  id: 24
+}
+item {
+  name: "dress/put on clothing"
+  id: 26
+}
+item {
+  name: "drink"
+  id: 27
+}
+item {
+  name: "drive (e.g., a car, a truck)"
+  id: 28
+}
+item {
+  name: "eat"
+  id: 29
+}
+item {
+  name: "enter"
+  id: 30
+}
+item {
+  name: "hit (an object)"
+  id: 34
+}
+item {
+  name: "lift/pick up"
+  id: 36
+}
+item {
+  name: "listen (e.g., to music)"
+  id: 37
+}
+item {
+  name: "open (e.g., a window, a car door)"
+  id: 38
+}
+item {
+  name: "play musical instrument"
+  id: 41
+}
+item {
+  name: "point to (an object)"
+  id: 43
+}
+item {
+  name: "pull (an object)"
+  id: 45
+}
+item {
+  name: "push (an object)"
+  id: 46
+}
+item {
+  name: "put down"
+  id: 47
+}
+item {
+  name: "read"
+  id: 48
+}
+item {
+  name: "ride (e.g., a bike, a car, a horse)"
+  id: 49
+}
+item {
+  name: "sail boat"
+  id: 51
+}
+item {
+  name: "shoot"
+  id: 52
+}
+item {
+  name: "smoke"
+  id: 54
+}
+item {
+  name: "take a photo"
+  id: 56
+}
+item {
+  name: "text on/look at a cellphone"
+  id: 57
+}
+item {
+  name: "throw"
+  id: 58
+}
+item {
+  name: "touch (an object)"
+  id: 59
+}
+item {
+  name: "turn (e.g., a screwdriver)"
+  id: 60
+}
+item {
+  name: "watch (e.g., TV)"
+  id: 61
+}
+item {
+  name: "work on a computer"
+  id: 62
+}
+item {
+  name: "write"
+  id: 63
+}
+item {
+  name: "fight/hit (a person)"
+  id: 64
+}
+item {
+  name: "give/serve (an object) to (a person)"
+  id: 65
+}
+item {
+  name: "grab (a person)"
+  id: 66
+}
+item {
+  name: "hand clap"
+  id: 67
+}
+item {
+  name: "hand shake"
+  id: 68
+}
+item {
+  name: "hand wave"
+  id: 69
+}
+item {
+  name: "hug (a person)"
+  id: 70
+}
+item {
+  name: "kiss (a person)"
+  id: 72
+}
+item {
+  name: "lift (a person)"
+  id: 73
+}
+item {
+  name: "listen to (a person)"
+  id: 74
+}
+item {
+  name: "push (another person)"
+  id: 76
+}
+item {
+  name: "sing to (e.g., self, a person, a group)"
+  id: 77
+}
+item {
+  name: "take (an object) from (a person)"
+  id: 78
+}
+item {
+  name: "talk to (e.g., self, a person, a group)"
+  id: 79
+}
+item {
+  name: "watch (a person)"
+  id: 80
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/face_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/face_label_map.pbtxt
new file mode 100644
index 00000000..1c7355db
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/face_label_map.pbtxt
@@ -0,0 +1,6 @@
+item {
+  name: "face"
+  id: 1
+  display_name: "face"
+}
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/fgvc_2854_classes_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/fgvc_2854_classes_label_map.pbtxt
new file mode 100644
index 00000000..009797f0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/fgvc_2854_classes_label_map.pbtxt
@@ -0,0 +1,14270 @@
+item {
+  name: "147457"
+  id: 1
+  display_name: "Nicrophorus tomentosus"
+}
+item {
+  name: "81923"
+  id: 2
+  display_name: "Halyomorpha halys"
+}
+item {
+  name: "7"
+  id: 3
+  display_name: "Aramus guarauna"
+}
+item {
+  name: "201041"
+  id: 4
+  display_name: "Rupornis magnirostris"
+}
+item {
+  name: "65551"
+  id: 5
+  display_name: "Hyla eximia"
+}
+item {
+  name: "106516"
+  id: 6
+  display_name: "Nannothemis bella"
+}
+item {
+  name: "154287"
+  id: 7
+  display_name: "Acalymma vittatum"
+}
+item {
+  name: "32798"
+  id: 8
+  display_name: "Ramphotyphlops braminus"
+}
+item {
+  name: "8229"
+  id: 9
+  display_name: "Cyanocitta cristata"
+}
+item {
+  name: "73766"
+  id: 10
+  display_name: "Drymarchon melanurus"
+}
+item {
+  name: "409639"
+  id: 11
+  display_name: "Aenetus virescens"
+}
+item {
+  name: "8234"
+  id: 12
+  display_name: "Cyanocitta stelleri"
+}
+item {
+  name: "228593"
+  id: 13
+  display_name: "Polygrammate hebraeicum"
+}
+item {
+  name: "53"
+  id: 14
+  display_name: "Balearica regulorum"
+}
+item {
+  name: "57399"
+  id: 15
+  display_name: "Fistularia commersonii"
+}
+item {
+  name: "81979"
+  id: 16
+  display_name: "Syritta pipiens"
+}
+item {
+  name: "73788"
+  id: 17
+  display_name: "Plestiodon fasciatus"
+}
+item {
+  name: "73790"
+  id: 18
+  display_name: "Plestiodon inexpectatus"
+}
+item {
+  name: "16447"
+  id: 19
+  display_name: "Pyrocephalus rubinus"
+}
+item {
+  name: "73792"
+  id: 20
+  display_name: "Plestiodon laticeps"
+}
+item {
+  name: "49219"
+  id: 21
+  display_name: "Anguilla rostrata"
+}
+item {
+  name: "73797"
+  id: 22
+  display_name: "Plestiodon obsoletus"
+}
+item {
+  name: "73803"
+  id: 23
+  display_name: "Plestiodon tetragrammus"
+}
+item {
+  name: "122956"
+  id: 24
+  display_name: "Syntomoides imaon"
+}
+item {
+  name: "82003"
+  id: 25
+  display_name: "Arion ater"
+}
+item {
+  name: "32854"
+  id: 26
+  display_name: "Chamaeleo dilepis"
+}
+item {
+  name: "42341"
+  id: 27
+  display_name: "Tragelaphus scriptus"
+}
+item {
+  name: "82018"
+  id: 28
+  display_name: "Taeniopoda eques"
+}
+item {
+  name: "57443"
+  id: 29
+  display_name: "Libellula quadrimaculata"
+}
+item {
+  name: "4885"
+  id: 30
+  display_name: "Recurvirostra americana"
+}
+item {
+  name: "178403"
+  id: 31
+  display_name: "Phalaenophana pyramusalis"
+}
+item {
+  name: "135027"
+  id: 32
+  display_name: "Agalychnis dacnicolor"
+}
+item {
+  name: "49262"
+  id: 33
+  display_name: "Haemulon sciurus"
+}
+item {
+  name: "98417"
+  id: 34
+  display_name: "Cordulegaster diastatops"
+}
+item {
+  name: "57458"
+  id: 35
+  display_name: "Ladona julia"
+}
+item {
+  name: "115"
+  id: 36
+  display_name: "Ardeotis kori"
+}
+item {
+  name: "49269"
+  id: 37
+  display_name: "Diodon holocanthus"
+}
+item {
+  name: "57463"
+  id: 38
+  display_name: "Papilio canadensis"
+}
+item {
+  name: "82043"
+  id: 39
+  display_name: "Monochamus scutellatus"
+}
+item {
+  name: "147580"
+  id: 40
+  display_name: "Ceratotherium simum simum"
+}
+item {
+  name: "98430"
+  id: 41
+  display_name: "Cordulia shurtleffii"
+}
+item {
+  name: "8319"
+  id: 42
+  display_name: "Pica nuttalli"
+}
+item {
+  name: "43712"
+  id: 43
+  display_name: "Dasyprocta punctata"
+}
+item {
+  name: "8335"
+  id: 44
+  display_name: "Perisoreus canadensis"
+}
+item {
+  name: "508048"
+  id: 45
+  display_name: "Antigone canadensis"
+}
+item {
+  name: "49297"
+  id: 46
+  display_name: "Aetobatus narinari"
+}
+item {
+  name: "82069"
+  id: 47
+  display_name: "Phyciodes pulchella"
+}
+item {
+  name: "73149"
+  id: 48
+  display_name: "Parkesia noveboracensis"
+}
+item {
+  name: "180379"
+  id: 49
+  display_name: "Ardea herodias occidentalis"
+}
+item {
+  name: "73884"
+  id: 50
+  display_name: "Pantherophis emoryi"
+}
+item {
+  name: "106653"
+  id: 51
+  display_name: "Nehalennia irene"
+}
+item {
+  name: "73887"
+  id: 52
+  display_name: "Pantherophis guttatus"
+}
+item {
+  name: "73888"
+  id: 53
+  display_name: "Pantherophis obsoletus"
+}
+item {
+  name: "162"
+  id: 54
+  display_name: "Porzana carolina"
+}
+item {
+  name: "245925"
+  id: 55
+  display_name: "Siproeta stelenes biplagiata"
+}
+item {
+  name: "117302"
+  id: 56
+  display_name: "Physalia physalis"
+}
+item {
+  name: "57516"
+  id: 57
+  display_name: "Bombus terrestris"
+}
+item {
+  name: "204995"
+  id: 58
+  display_name: "Anas platyrhynchos diazi"
+}
+item {
+  name: "49348"
+  id: 59
+  display_name: "Hyles lineata"
+}
+item {
+  name: "82117"
+  id: 60
+  display_name: "Dolomedes tenebrosus"
+}
+item {
+  name: "114891"
+  id: 61
+  display_name: "Varanus salvator"
+}
+item {
+  name: "319695"
+  id: 62
+  display_name: "Epilachna mexicana"
+}
+item {
+  name: "41168"
+  id: 63
+  display_name: "Desmodus rotundus"
+}
+item {
+  name: "13688"
+  id: 64
+  display_name: "Motacilla cinerea"
+}
+item {
+  name: "57556"
+  id: 65
+  display_name: "Papio ursinus"
+}
+item {
+  name: "16598"
+  id: 66
+  display_name: "Empidonax difficilis"
+}
+item {
+  name: "16602"
+  id: 67
+  display_name: "Empidonax minimus"
+}
+item {
+  name: "16604"
+  id: 68
+  display_name: "Empidonax fulvifrons"
+}
+item {
+  name: "409181"
+  id: 69
+  display_name: "Trite planiceps"
+}
+item {
+  name: "82144"
+  id: 70
+  display_name: "Hemileuca eglanterina"
+}
+item {
+  name: "16611"
+  id: 71
+  display_name: "Empidonax traillii"
+}
+item {
+  name: "82153"
+  id: 72
+  display_name: "Ceratomia undulosa"
+}
+item {
+  name: "82155"
+  id: 73
+  display_name: "Bittacomorpha clavipes"
+}
+item {
+  name: "205036"
+  id: 74
+  display_name: "Xanthorhoe lacustrata"
+}
+item {
+  name: "16624"
+  id: 75
+  display_name: "Empidonax hammondii"
+}
+item {
+  name: "16625"
+  id: 76
+  display_name: "Empidonax occidentalis"
+}
+item {
+  name: "243"
+  id: 77
+  display_name: "Rallus limicola"
+}
+item {
+  name: "41"
+  id: 78
+  display_name: "Grus grus"
+}
+item {
+  name: "49402"
+  id: 79
+  display_name: "Abudefduf saxatilis"
+}
+item {
+  name: "58550"
+  id: 80
+  display_name: "Callophrys niphon"
+}
+item {
+  name: "205055"
+  id: 81
+  display_name: "Zopherus nodulosus haldemani"
+}
+item {
+  name: "82177"
+  id: 82
+  display_name: "Hermetia illucens"
+}
+item {
+  name: "9601"
+  id: 83
+  display_name: "Quiscalus major"
+}
+item {
+  name: "7101"
+  id: 84
+  display_name: "Branta leucopsis"
+}
+item {
+  name: "8470"
+  id: 85
+  display_name: "Cyanocorax yucatanicus"
+}
+item {
+  name: "74009"
+  id: 86
+  display_name: "Zamenis longissimus"
+}
+item {
+  name: "8474"
+  id: 87
+  display_name: "Cyanocorax yncas"
+}
+item {
+  name: "82204"
+  id: 88
+  display_name: "Nadata gibbosa"
+}
+item {
+  name: "123168"
+  id: 89
+  display_name: "Ensatina eschscholtzii xanthoptica"
+}
+item {
+  name: "82210"
+  id: 90
+  display_name: "Heterocampa biundata"
+}
+item {
+  name: "48284"
+  id: 91
+  display_name: "Oniscus asellus"
+}
+item {
+  name: "4146"
+  id: 92
+  display_name: "Oceanites oceanicus"
+}
+item {
+  name: "82225"
+  id: 93
+  display_name: "Lophocampa caryae"
+}
+item {
+  name: "9609"
+  id: 94
+  display_name: "Quiscalus niger"
+}
+item {
+  name: "65849"
+  id: 95
+  display_name: "Incilius nebulifer"
+}
+item {
+  name: "207583"
+  id: 96
+  display_name: "Miomantis caffra"
+}
+item {
+  name: "491839"
+  id: 97
+  display_name: "Pyrausta insequalis"
+}
+item {
+  name: "74048"
+  id: 98
+  display_name: "Alces americanus"
+}
+item {
+  name: "57665"
+  id: 99
+  display_name: "Cotinis mutabilis"
+}
+item {
+  name: "65860"
+  id: 100
+  display_name: "Incilius valliceps"
+}
+item {
+  name: "52911"
+  id: 101
+  display_name: "Dolichovespula maculata"
+}
+item {
+  name: "8524"
+  id: 102
+  display_name: "Psilorhinus morio"
+}
+item {
+  name: "49491"
+  id: 103
+  display_name: "Thalassoma bifasciatum"
+}
+item {
+  name: "41301"
+  id: 104
+  display_name: "Tadarida brasiliensis"
+}
+item {
+  name: "57687"
+  id: 105
+  display_name: "Xylocopa varipuncta"
+}
+item {
+  name: "57689"
+  id: 106
+  display_name: "Bombus vosnesenskii"
+}
+item {
+  name: "57690"
+  id: 107
+  display_name: "Bombus sonorus"
+}
+item {
+  name: "33118"
+  id: 108
+  display_name: "Basiliscus vittatus"
+}
+item {
+  name: "205151"
+  id: 109
+  display_name: "Phlogophora meticulosa"
+}
+item {
+  name: "49504"
+  id: 110
+  display_name: "Callinectes sapidus"
+}
+item {
+  name: "16737"
+  id: 111
+  display_name: "Megarynchus pitangua"
+}
+item {
+  name: "357"
+  id: 112
+  display_name: "Gallinula tenebrosa"
+}
+item {
+  name: "82278"
+  id: 113
+  display_name: "Ameiurus melas"
+}
+item {
+  name: "82279"
+  id: 114
+  display_name: "Automeris io"
+}
+item {
+  name: "505478"
+  id: 115
+  display_name: "Gallus gallus domesticus"
+}
+item {
+  name: "33135"
+  id: 116
+  display_name: "Crotaphytus collaris"
+}
+item {
+  name: "41328"
+  id: 117
+  display_name: "Lavia frons"
+}
+item {
+  name: "196979"
+  id: 118
+  display_name: "Anaxyrus boreas halophilus"
+}
+item {
+  name: "44902"
+  id: 119
+  display_name: "Sigmodon hispidus"
+}
+item {
+  name: "1428"
+  id: 120
+  display_name: "Numida meleagris"
+}
+item {
+  name: "119153"
+  id: 121
+  display_name: "Junco hyemalis caniceps"
+}
+item {
+  name: "49539"
+  id: 122
+  display_name: "Pisaster brevispinus"
+}
+item {
+  name: "328068"
+  id: 123
+  display_name: "Belocaulus angustipes"
+}
+item {
+  name: "120214"
+  id: 124
+  display_name: "Clostera albosigma"
+}
+item {
+  name: "16779"
+  id: 125
+  display_name: "Tyrannus vociferans"
+}
+item {
+  name: "16782"
+  id: 126
+  display_name: "Tyrannus tyrannus"
+}
+item {
+  name: "16783"
+  id: 127
+  display_name: "Tyrannus forficatus"
+}
+item {
+  name: "16784"
+  id: 128
+  display_name: "Tyrannus crassirostris"
+}
+item {
+  name: "57745"
+  id: 129
+  display_name: "Linckia laevigata"
+}
+item {
+  name: "205202"
+  id: 130
+  display_name: "Ecliptopera silaceata"
+}
+item {
+  name: "205203"
+  id: 131
+  display_name: "Dyspteris abortivaria"
+}
+item {
+  name: "16791"
+  id: 132
+  display_name: "Tyrannus verticalis"
+}
+item {
+  name: "16793"
+  id: 133
+  display_name: "Tyrannus savana"
+}
+item {
+  name: "205213"
+  id: 134
+  display_name: "Caripeta divisata"
+}
+item {
+  name: "49566"
+  id: 135
+  display_name: "Cicindela sexguttata"
+}
+item {
+  name: "491935"
+  id: 136
+  display_name: "Thylacodes squamigerus"
+}
+item {
+  name: "205216"
+  id: 137
+  display_name: "Cerma cerintha"
+}
+item {
+  name: "39665"
+  id: 138
+  display_name: "Caretta caretta"
+}
+item {
+  name: "147881"
+  id: 139
+  display_name: "Trichechus manatus latirostris"
+}
+item {
+  name: "28743"
+  id: 140
+  display_name: "Salvadora hexalepis"
+}
+item {
+  name: "205231"
+  id: 141
+  display_name: "Idaea dimidiata"
+}
+item {
+  name: "205233"
+  id: 142
+  display_name: "Iridopsis larvaria"
+}
+item {
+  name: "205235"
+  id: 143
+  display_name: "Leuconycta diphteroides"
+}
+item {
+  name: "436"
+  id: 144
+  display_name: "Gallirallus australis"
+}
+item {
+  name: "205238"
+  id: 145
+  display_name: "Metanema inatomaria"
+}
+item {
+  name: "49591"
+  id: 146
+  display_name: "Lepomis macrochirus"
+}
+item {
+  name: "229817"
+  id: 147
+  display_name: "Raphia frater"
+}
+item {
+  name: "49594"
+  id: 148
+  display_name: "Pomoxis nigromaculatus"
+}
+item {
+  name: "65979"
+  id: 149
+  display_name: "Lithobates catesbeianus"
+}
+item {
+  name: "49596"
+  id: 150
+  display_name: "Salvelinus fontinalis"
+}
+item {
+  name: "65982"
+  id: 151
+  display_name: "Lithobates clamitans"
+}
+item {
+  name: "8649"
+  id: 152
+  display_name: "Calocitta formosa"
+}
+item {
+  name: "8650"
+  id: 153
+  display_name: "Calocitta colliei"
+}
+item {
+  name: "82379"
+  id: 154
+  display_name: "Hemaris thysbe"
+}
+item {
+  name: "49614"
+  id: 155
+  display_name: "Lepomis gibbosus"
+}
+item {
+  name: "63028"
+  id: 156
+  display_name: "Hypercompe scribonia"
+}
+item {
+  name: "39672"
+  id: 157
+  display_name: "Eretmochelys imbricata"
+}
+item {
+  name: "66003"
+  id: 158
+  display_name: "Lithobates pipiens"
+}
+item {
+  name: "197077"
+  id: 159
+  display_name: "Vanessa kershawi"
+}
+item {
+  name: "473"
+  id: 160
+  display_name: "Fulica americana"
+}
+item {
+  name: "147930"
+  id: 161
+  display_name: "Rabidosa rabida"
+}
+item {
+  name: "147931"
+  id: 162
+  display_name: "Panoquina ocola"
+}
+item {
+  name: "66012"
+  id: 163
+  display_name: "Lithobates sylvaticus"
+}
+item {
+  name: "8671"
+  id: 164
+  display_name: "Pachyramphus aglaiae"
+}
+item {
+  name: "41440"
+  id: 165
+  display_name: "Phocoena phocoena"
+}
+item {
+  name: "27388"
+  id: 166
+  display_name: "Carphophis amoenus"
+}
+item {
+  name: "82418"
+  id: 167
+  display_name: "Cicindela punctulata"
+}
+item {
+  name: "25078"
+  id: 168
+  display_name: "Gastrophryne carolinensis"
+}
+item {
+  name: "82425"
+  id: 169
+  display_name: "Cicindela repanda"
+}
+item {
+  name: "143446"
+  id: 170
+  display_name: "Paonias myops"
+}
+item {
+  name: "41478"
+  id: 171
+  display_name: "Eschrichtius robustus"
+}
+item {
+  name: "5200"
+  id: 172
+  display_name: "Buteo lagopus"
+}
+item {
+  name: "148908"
+  id: 173
+  display_name: "Chrysodeixis includens"
+}
+item {
+  name: "41482"
+  id: 174
+  display_name: "Tursiops truncatus"
+}
+item {
+  name: "6914"
+  id: 175
+  display_name: "Cygnus atratus"
+}
+item {
+  name: "464301"
+  id: 176
+  display_name: "Philesturnus rufusater"
+}
+item {
+  name: "129226"
+  id: 177
+  display_name: "Chytolita morbidalis"
+}
+item {
+  name: "180759"
+  id: 178
+  display_name: "Aphonopelma iodius"
+}
+item {
+  name: "135318"
+  id: 179
+  display_name: "Apantesis phalerata"
+}
+item {
+  name: "49699"
+  id: 180
+  display_name: "Pisaster ochraceus"
+}
+item {
+  name: "49700"
+  id: 181
+  display_name: "Coluber lateralis lateralis"
+}
+item {
+  name: "61532"
+  id: 182
+  display_name: "Propylea quatuordecimpunctata"
+}
+item {
+  name: "4368"
+  id: 183
+  display_name: "Larus marinus"
+}
+item {
+  name: "41521"
+  id: 184
+  display_name: "Orcinus orca"
+}
+item {
+  name: "49716"
+  id: 185
+  display_name: "Paonias excaecata"
+}
+item {
+  name: "41526"
+  id: 186
+  display_name: "Delphinus delphis"
+}
+item {
+  name: "49723"
+  id: 187
+  display_name: "Pugettia producta"
+}
+item {
+  name: "16956"
+  id: 188
+  display_name: "Pitangus sulphuratus"
+}
+item {
+  name: "210607"
+  id: 189
+  display_name: "Diastictis fracturalis"
+}
+item {
+  name: "148030"
+  id: 190
+  display_name: "Equus asinus"
+}
+item {
+  name: "6924"
+  id: 191
+  display_name: "Anas rubripes"
+}
+item {
+  name: "30844"
+  id: 192
+  display_name: "Bothriechis schlegelii"
+}
+item {
+  name: "123628"
+  id: 193
+  display_name: "Argynnis paphia"
+}
+item {
+  name: "131676"
+  id: 194
+  display_name: "Anthus novaeseelandiae novaeseelandiae"
+}
+item {
+  name: "41566"
+  id: 195
+  display_name: "Megaptera novaeangliae"
+}
+item {
+  name: "49759"
+  id: 196
+  display_name: "Pyrgus oileus"
+}
+item {
+  name: "49761"
+  id: 197
+  display_name: "Anartia jatrophae"
+}
+item {
+  name: "49766"
+  id: 198
+  display_name: "Heliconius charithonia"
+}
+item {
+  name: "33383"
+  id: 199
+  display_name: "Coleonyx brevis"
+}
+item {
+  name: "33384"
+  id: 200
+  display_name: "Coleonyx elegans"
+}
+item {
+  name: "312764"
+  id: 201
+  display_name: "Euptoieta hegesia meridiania"
+}
+item {
+  name: "82538"
+  id: 202
+  display_name: "Vanessa gonerilla"
+}
+item {
+  name: "33387"
+  id: 203
+  display_name: "Coleonyx variegatus"
+}
+item {
+  name: "56082"
+  id: 204
+  display_name: "Aeshna canadensis"
+}
+item {
+  name: "17008"
+  id: 205
+  display_name: "Sayornis phoebe"
+}
+item {
+  name: "200808"
+  id: 206
+  display_name: "Sceloporus graciosus vandenburgianus"
+}
+item {
+  name: "17013"
+  id: 207
+  display_name: "Sayornis nigricans"
+}
+item {
+  name: "122381"
+  id: 208
+  display_name: "Cupido comyntas"
+}
+item {
+  name: "123516"
+  id: 209
+  display_name: "Mydas clavatus"
+}
+item {
+  name: "8834"
+  id: 210
+  display_name: "Tityra semifasciata"
+}
+item {
+  name: "146199"
+  id: 211
+  display_name: "Lampropeltis californiae"
+}
+item {
+  name: "17858"
+  id: 212
+  display_name: "Dryocopus lineatus"
+}
+item {
+  name: "334616"
+  id: 213
+  display_name: "Battus philenor hirsuta"
+}
+item {
+  name: "82582"
+  id: 214
+  display_name: "Labidomera clivicollis"
+}
+item {
+  name: "204699"
+  id: 215
+  display_name: "Pseudothyatira cymatophoroides"
+}
+item {
+  name: "41638"
+  id: 216
+  display_name: "Ursus americanus"
+}
+item {
+  name: "27420"
+  id: 217
+  display_name: "Desmognathus fuscus"
+}
+item {
+  name: "81584"
+  id: 218
+  display_name: "Anisota virginiensis"
+}
+item {
+  name: "49848"
+  id: 219
+  display_name: "Navanax inermis"
+}
+item {
+  name: "143476"
+  id: 220
+  display_name: "Calledapteryx dryopterata"
+}
+item {
+  name: "41663"
+  id: 221
+  display_name: "Procyon lotor"
+}
+item {
+  name: "49857"
+  id: 222
+  display_name: "Aplysia vaccaria"
+}
+item {
+  name: "41673"
+  id: 223
+  display_name: "Nasua narica"
+}
+item {
+  name: "41676"
+  id: 224
+  display_name: "Bassariscus astutus"
+}
+item {
+  name: "27427"
+  id: 225
+  display_name: "Aneides lugubris"
+}
+item {
+  name: "418530"
+  id: 226
+  display_name: "Porphyrio melanotus"
+}
+item {
+  name: "311419"
+  id: 227
+  display_name: "Neobernaya spadicea"
+}
+item {
+  name: "113502"
+  id: 228
+  display_name: "Sympetrum costiferum"
+}
+item {
+  name: "66278"
+  id: 229
+  display_name: "Oophaga pumilio"
+}
+item {
+  name: "6951"
+  id: 230
+  display_name: "Anas bahamensis"
+}
+item {
+  name: "213740"
+  id: 231
+  display_name: "Antaeotricha schlaegeri"
+}
+item {
+  name: "143485"
+  id: 232
+  display_name: "Xanthorhoe ferrugata"
+}
+item {
+  name: "120275"
+  id: 233
+  display_name: "Euphyia intermediata"
+}
+item {
+  name: "48035"
+  id: 234
+  display_name: "Strongylocentrotus purpuratus"
+}
+item {
+  name: "41728"
+  id: 235
+  display_name: "Mirounga angustirostris"
+}
+item {
+  name: "41733"
+  id: 236
+  display_name: "Halichoerus grypus"
+}
+item {
+  name: "41740"
+  id: 237
+  display_name: "Zalophus californianus"
+}
+item {
+  name: "118914"
+  id: 238
+  display_name: "Echinargus isola"
+}
+item {
+  name: "4936"
+  id: 239
+  display_name: "Egretta novaehollandiae"
+}
+item {
+  name: "131862"
+  id: 240
+  display_name: "Typocerus velutinus"
+}
+item {
+  name: "55401"
+  id: 241
+  display_name: "Pieris brassicae"
+}
+item {
+  name: "41752"
+  id: 242
+  display_name: "Arctocephalus forsteri"
+}
+item {
+  name: "41755"
+  id: 243
+  display_name: "Eumetopias jubatus"
+}
+item {
+  name: "123676"
+  id: 244
+  display_name: "Anas crecca carolinensis"
+}
+item {
+  name: "41763"
+  id: 245
+  display_name: "Phocarctos hookeri"
+}
+item {
+  name: "181034"
+  id: 246
+  display_name: "Cervus elaphus canadensis"
+}
+item {
+  name: "49964"
+  id: 247
+  display_name: "Ginglymostoma cirratum"
+}
+item {
+  name: "213809"
+  id: 248
+  display_name: "Anticarsia gemmatalis"
+}
+item {
+  name: "49972"
+  id: 249
+  display_name: "Battus philenor"
+}
+item {
+  name: "205623"
+  id: 250
+  display_name: "Microstylum morosum"
+}
+item {
+  name: "336697"
+  id: 251
+  display_name: "Arctia villica"
+}
+item {
+  name: "41789"
+  id: 252
+  display_name: "Taxidea taxus"
+}
+item {
+  name: "48724"
+  id: 253
+  display_name: "Phidiana hiltoni"
+}
+item {
+  name: "123713"
+  id: 254
+  display_name: "Neoscona oaxacensis"
+}
+item {
+  name: "33602"
+  id: 255
+  display_name: "Tarentola mauritanica"
+}
+item {
+  name: "846"
+  id: 256
+  display_name: "Alectoris chukar"
+}
+item {
+  name: "41808"
+  id: 257
+  display_name: "Mustela erminea"
+}
+item {
+  name: "50001"
+  id: 258
+  display_name: "Terrapene carolina carolina"
+}
+item {
+  name: "41810"
+  id: 259
+  display_name: "Mustela frenata"
+}
+item {
+  name: "82774"
+  id: 260
+  display_name: "Oryctes nasicornis"
+}
+item {
+  name: "41815"
+  id: 261
+  display_name: "Mustela nivalis"
+}
+item {
+  name: "4239"
+  id: 262
+  display_name: "Tachybaptus dominicus"
+}
+item {
+  name: "344926"
+  id: 263
+  display_name: "Artemisiospiza belli"
+}
+item {
+  name: "82792"
+  id: 264
+  display_name: "Celastrina neglecta"
+}
+item {
+  name: "41841"
+  id: 265
+  display_name: "Meles meles"
+}
+item {
+  name: "882"
+  id: 266
+  display_name: "Gallus gallus"
+}
+item {
+  name: "125758"
+  id: 267
+  display_name: "Mercenaria mercenaria"
+}
+item {
+  name: "9081"
+  id: 268
+  display_name: "Cardinalis sinuatus"
+}
+item {
+  name: "9083"
+  id: 269
+  display_name: "Cardinalis cardinalis"
+}
+item {
+  name: "9092"
+  id: 270
+  display_name: "Melospiza lincolnii"
+}
+item {
+  name: "4246"
+  id: 271
+  display_name: "Podilymbus podiceps"
+}
+item {
+  name: "9096"
+  id: 272
+  display_name: "Melospiza georgiana"
+}
+item {
+  name: "906"
+  id: 273
+  display_name: "Meleagris gallopavo"
+}
+item {
+  name: "50059"
+  id: 274
+  display_name: "Limacia cockerelli"
+}
+item {
+  name: "394124"
+  id: 275
+  display_name: "Orthodera novaezealandiae"
+}
+item {
+  name: "82832"
+  id: 276
+  display_name: "Cosmopepla lintneriana"
+}
+item {
+  name: "913"
+  id: 277
+  display_name: "Meleagris ocellata"
+}
+item {
+  name: "41877"
+  id: 278
+  display_name: "Conepatus leuconotus"
+}
+item {
+  name: "196419"
+  id: 279
+  display_name: "Euborellia annulipes"
+}
+item {
+  name: "50071"
+  id: 280
+  display_name: "Erynnis horatius"
+}
+item {
+  name: "41880"
+  id: 281
+  display_name: "Mephitis mephitis"
+}
+item {
+  name: "50073"
+  id: 282
+  display_name: "Dryas iulia"
+}
+item {
+  name: "173793"
+  id: 283
+  display_name: "Diphthera festiva"
+}
+item {
+  name: "41886"
+  id: 284
+  display_name: "Crocuta crocuta"
+}
+item {
+  name: "30683"
+  id: 285
+  display_name: "Agkistrodon contortrix contortrix"
+}
+item {
+  name: "931"
+  id: 286
+  display_name: "Lagopus lagopus"
+}
+item {
+  name: "41901"
+  id: 287
+  display_name: "Herpestes javanicus"
+}
+item {
+  name: "143517"
+  id: 288
+  display_name: "Biston betularia"
+}
+item {
+  name: "9139"
+  id: 289
+  display_name: "Spizella atrogularis"
+}
+item {
+  name: "8350"
+  id: 290
+  display_name: "Pyrrhocorax graculus"
+}
+item {
+  name: "9144"
+  id: 291
+  display_name: "Spizella breweri"
+}
+item {
+  name: "12936"
+  id: 292
+  display_name: "Sialia currucoides"
+}
+item {
+  name: "9152"
+  id: 293
+  display_name: "Spizella pusilla"
+}
+item {
+  name: "68229"
+  id: 294
+  display_name: "Tramea carolina"
+}
+item {
+  name: "6987"
+  id: 295
+  display_name: "Anas superciliosa"
+}
+item {
+  name: "9156"
+  id: 296
+  display_name: "Passerella iliaca"
+}
+item {
+  name: "202315"
+  id: 297
+  display_name: "Romaleon antennarium"
+}
+item {
+  name: "4257"
+  id: 298
+  display_name: "Phoenicopterus ruber"
+}
+item {
+  name: "25545"
+  id: 299
+  display_name: "Rana aurora"
+}
+item {
+  name: "15282"
+  id: 300
+  display_name: "Sylvia atricapilla"
+}
+item {
+  name: "103927"
+  id: 301
+  display_name: "Ladona deplanata"
+}
+item {
+  name: "17356"
+  id: 302
+  display_name: "Vireo bellii"
+}
+item {
+  name: "26765"
+  id: 303
+  display_name: "Ambystoma mavortium"
+}
+item {
+  name: "205777"
+  id: 304
+  display_name: "Plectrodera scalator"
+}
+item {
+  name: "17362"
+  id: 305
+  display_name: "Vireo plumbeus"
+}
+item {
+  name: "99283"
+  id: 306
+  display_name: "Didymops transversa"
+}
+item {
+  name: "17364"
+  id: 307
+  display_name: "Vireo philadelphicus"
+}
+item {
+  name: "17365"
+  id: 308
+  display_name: "Vireo flavifrons"
+}
+item {
+  name: "17366"
+  id: 309
+  display_name: "Vireo olivaceus"
+}
+item {
+  name: "9182"
+  id: 310
+  display_name: "Zonotrichia querula"
+}
+item {
+  name: "17375"
+  id: 311
+  display_name: "Vireo huttoni"
+}
+item {
+  name: "9184"
+  id: 312
+  display_name: "Zonotrichia albicollis"
+}
+item {
+  name: "9185"
+  id: 313
+  display_name: "Zonotrichia atricapilla"
+}
+item {
+  name: "50147"
+  id: 314
+  display_name: "Celithemis eponina"
+}
+item {
+  name: "47585"
+  id: 315
+  display_name: "Crassostrea virginica"
+}
+item {
+  name: "9195"
+  id: 316
+  display_name: "Emberiza citrinella"
+}
+item {
+  name: "41964"
+  id: 317
+  display_name: "Panthera leo"
+}
+item {
+  name: "6994"
+  id: 318
+  display_name: "Bucephala islandica"
+}
+item {
+  name: "52506"
+  id: 319
+  display_name: "Adalia bipunctata"
+}
+item {
+  name: "9201"
+  id: 320
+  display_name: "Emberiza schoeniclus"
+}
+item {
+  name: "17394"
+  id: 321
+  display_name: "Vireo gilvus"
+}
+item {
+  name: "25591"
+  id: 322
+  display_name: "Rana temporaria"
+}
+item {
+  name: "41976"
+  id: 323
+  display_name: "Lynx rufus"
+}
+item {
+  name: "214015"
+  id: 324
+  display_name: "Apoda y-inversum"
+}
+item {
+  name: "50176"
+  id: 325
+  display_name: "Enallagma vesperum"
+}
+item {
+  name: "99331"
+  id: 326
+  display_name: "Diplacodes trivialis"
+}
+item {
+  name: "50181"
+  id: 327
+  display_name: "Loxosceles reclusa"
+}
+item {
+  name: "74758"
+  id: 328
+  display_name: "Neovison vison"
+}
+item {
+  name: "123912"
+  id: 329
+  display_name: "Charaxes jasius"
+}
+item {
+  name: "41997"
+  id: 330
+  display_name: "Leopardus pardalis"
+}
+item {
+  name: "123920"
+  id: 331
+  display_name: "Dorcus parallelipipedus"
+}
+item {
+  name: "132334"
+  id: 332
+  display_name: "Urbanus procne"
+}
+item {
+  name: "123922"
+  id: 333
+  display_name: "Abudefduf sordidus"
+}
+item {
+  name: "9236"
+  id: 334
+  display_name: "Serinus serinus"
+}
+item {
+  name: "42007"
+  id: 335
+  display_name: "Puma concolor"
+}
+item {
+  name: "9240"
+  id: 336
+  display_name: "Serinus mozambicus"
+}
+item {
+  name: "148506"
+  id: 337
+  display_name: "Melanis pixe"
+}
+item {
+  name: "58399"
+  id: 338
+  display_name: "Urosalpinx cinerea"
+}
+item {
+  name: "312353"
+  id: 339
+  display_name: "Leptophobia aripa elodia"
+}
+item {
+  name: "148517"
+  id: 340
+  display_name: "Heliopetes laviana"
+}
+item {
+  name: "73905"
+  id: 341
+  display_name: "Phrynosoma cornutum"
+}
+item {
+  name: "39772"
+  id: 342
+  display_name: "Chrysemys picta marginata"
+}
+item {
+  name: "25646"
+  id: 343
+  display_name: "Rana boylii"
+}
+item {
+  name: "62984"
+  id: 344
+  display_name: "Aedes albopictus"
+}
+item {
+  name: "123959"
+  id: 345
+  display_name: "Ensatina eschscholtzii oregonensis"
+}
+item {
+  name: "1081"
+  id: 346
+  display_name: "Lophura leucomelanos"
+}
+item {
+  name: "39775"
+  id: 347
+  display_name: "Chrysemys picta picta"
+}
+item {
+  name: "42046"
+  id: 348
+  display_name: "Canis mesomelas"
+}
+item {
+  name: "42048"
+  id: 349
+  display_name: "Canis lupus"
+}
+item {
+  name: "42051"
+  id: 350
+  display_name: "Canis latrans"
+}
+item {
+  name: "9284"
+  id: 351
+  display_name: "Euphonia elegantissima"
+}
+item {
+  name: "25669"
+  id: 352
+  display_name: "Rana dalmatina"
+}
+item {
+  name: "9287"
+  id: 353
+  display_name: "Euphonia hirundinacea"
+}
+item {
+  name: "9291"
+  id: 354
+  display_name: "Euphonia affinis"
+}
+item {
+  name: "222284"
+  id: 355
+  display_name: "Iridopsis defectaria"
+}
+item {
+  name: "74832"
+  id: 356
+  display_name: "Papio anubis"
+}
+item {
+  name: "148563"
+  id: 357
+  display_name: "Myscelia ethusa"
+}
+item {
+  name: "42069"
+  id: 358
+  display_name: "Vulpes vulpes"
+}
+item {
+  name: "9743"
+  id: 359
+  display_name: "Agelaius tricolor"
+}
+item {
+  name: "42076"
+  id: 360
+  display_name: "Urocyon cinereoargenteus"
+}
+item {
+  name: "509025"
+  id: 361
+  display_name: "Momotus lessonii"
+}
+item {
+  name: "17506"
+  id: 362
+  display_name: "Zosterops japonicus"
+}
+item {
+  name: "4283"
+  id: 363
+  display_name: "Phalacrocorax pelagicus"
+}
+item {
+  name: "58469"
+  id: 364
+  display_name: "Thorybes pylades"
+}
+item {
+  name: "9319"
+  id: 365
+  display_name: "Icterus cucullatus"
+}
+item {
+  name: "58473"
+  id: 366
+  display_name: "Erynnis icelus"
+}
+item {
+  name: "58475"
+  id: 367
+  display_name: "Erynnis juvenalis"
+}
+item {
+  name: "42093"
+  id: 368
+  display_name: "Lycaon pictus"
+}
+item {
+  name: "58478"
+  id: 369
+  display_name: "Erynnis baptisiae"
+}
+item {
+  name: "9328"
+  id: 370
+  display_name: "Icterus graduacauda"
+}
+item {
+  name: "58481"
+  id: 371
+  display_name: "Ancyloxypha numitor"
+}
+item {
+  name: "132210"
+  id: 372
+  display_name: "Deloyala guttata"
+}
+item {
+  name: "58484"
+  id: 373
+  display_name: "Thymelicus lineola"
+}
+item {
+  name: "13701"
+  id: 374
+  display_name: "Motacilla aguimp"
+}
+item {
+  name: "410743"
+  id: 375
+  display_name: "Anas superciliosa \303\227 platyrhynchos"
+}
+item {
+  name: "9336"
+  id: 376
+  display_name: "Icterus pustulatus"
+}
+item {
+  name: "9339"
+  id: 377
+  display_name: "Icterus gularis"
+}
+item {
+  name: "124031"
+  id: 378
+  display_name: "Agrius convolvuli"
+}
+item {
+  name: "42113"
+  id: 379
+  display_name: "Pecari tajacu"
+}
+item {
+  name: "132227"
+  id: 380
+  display_name: "Lethe appalachia"
+}
+item {
+  name: "113516"
+  id: 381
+  display_name: "Sympetrum madidum"
+}
+item {
+  name: "58509"
+  id: 382
+  display_name: "Anatrytone logan"
+}
+item {
+  name: "83086"
+  id: 383
+  display_name: "Eurytides marcellus"
+}
+item {
+  name: "58511"
+  id: 384
+  display_name: "Poanes viator"
+}
+item {
+  name: "83090"
+  id: 385
+  display_name: "Epimecis hortaria"
+}
+item {
+  name: "115859"
+  id: 386
+  display_name: "Micrurus tener tener"
+}
+item {
+  name: "129902"
+  id: 387
+  display_name: "Camponotus pennsylvanicus"
+}
+item {
+  name: "42134"
+  id: 388
+  display_name: "Sus scrofa"
+}
+item {
+  name: "58519"
+  id: 389
+  display_name: "Pompeius verna"
+}
+item {
+  name: "205977"
+  id: 390
+  display_name: "Coccinella undecimpunctata"
+}
+item {
+  name: "58523"
+  id: 391
+  display_name: "Papilio polyxenes"
+}
+item {
+  name: "58525"
+  id: 392
+  display_name: "Papilio troilus"
+}
+item {
+  name: "410783"
+  id: 393
+  display_name: "Hypoblemum albovittatum"
+}
+item {
+  name: "9376"
+  id: 394
+  display_name: "Carduelis cannabina"
+}
+item {
+  name: "58531"
+  id: 395
+  display_name: "Colias philodice"
+}
+item {
+  name: "50340"
+  id: 396
+  display_name: "Hylephila phyleus"
+}
+item {
+  name: "42149"
+  id: 397
+  display_name: "Hippopotamus amphibius"
+}
+item {
+  name: "50342"
+  id: 398
+  display_name: "Erythrodiplax umbrata"
+}
+item {
+  name: "12883"
+  id: 399
+  display_name: "Catharus minimus"
+}
+item {
+  name: "28557"
+  id: 400
+  display_name: "Storeria occipitomaculata"
+}
+item {
+  name: "199"
+  id: 401
+  display_name: "Amaurornis phoenicurus"
+}
+item {
+  name: "58541"
+  id: 402
+  display_name: "Satyrium liparops"
+}
+item {
+  name: "58543"
+  id: 403
+  display_name: "Callophrys augustinus"
+}
+item {
+  name: "42161"
+  id: 404
+  display_name: "Dama dama"
+}
+item {
+  name: "61508"
+  id: 405
+  display_name: "Ischnura elegans"
+}
+item {
+  name: "1204"
+  id: 406
+  display_name: "Pavo cristatus"
+}
+item {
+  name: "42166"
+  id: 407
+  display_name: "Axis axis"
+}
+item {
+  name: "146797"
+  id: 408
+  display_name: "Platynota idaeusalis"
+}
+item {
+  name: "58556"
+  id: 409
+  display_name: "Celastrina ladon"
+}
+item {
+  name: "367477"
+  id: 410
+  display_name: "Rallus crepitans"
+}
+item {
+  name: "58561"
+  id: 411
+  display_name: "Libytheana carinenta"
+}
+item {
+  name: "58563"
+  id: 412
+  display_name: "Speyeria aphrodite"
+}
+item {
+  name: "58564"
+  id: 413
+  display_name: "Boloria bellona"
+}
+item {
+  name: "413489"
+  id: 414
+  display_name: "Nestor meridionalis septentrionalis"
+}
+item {
+  name: "42184"
+  id: 415
+  display_name: "Capreolus capreolus"
+}
+item {
+  name: "9419"
+  id: 416
+  display_name: "Pipilo chlorurus"
+}
+item {
+  name: "9420"
+  id: 417
+  display_name: "Pipilo maculatus"
+}
+item {
+  name: "9424"
+  id: 418
+  display_name: "Pipilo erythrophthalmus"
+}
+item {
+  name: "99539"
+  id: 419
+  display_name: "Dorocordulia libera"
+}
+item {
+  name: "58580"
+  id: 420
+  display_name: "Polygonia progne"
+}
+item {
+  name: "58581"
+  id: 421
+  display_name: "Nymphalis vaualbum"
+}
+item {
+  name: "42199"
+  id: 422
+  display_name: "Rangifer tarandus"
+}
+item {
+  name: "58586"
+  id: 423
+  display_name: "Limenitis archippus"
+}
+item {
+  name: "58587"
+  id: 424
+  display_name: "Asterocampa clyton"
+}
+item {
+  name: "42206"
+  id: 425
+  display_name: "Cervus elaphus"
+}
+item {
+  name: "312543"
+  id: 426
+  display_name: "Anartia jatrophae luteipicta"
+}
+item {
+  name: "204094"
+  id: 427
+  display_name: "Cairina moschata domestica"
+}
+item {
+  name: "4304"
+  id: 428
+  display_name: "Phalacrocorax varius"
+}
+item {
+  name: "42210"
+  id: 429
+  display_name: "Cervus nippon"
+}
+item {
+  name: "17638"
+  id: 430
+  display_name: "Picoides dorsalis"
+}
+item {
+  name: "132330"
+  id: 431
+  display_name: "Chlosyne janais"
+}
+item {
+  name: "58603"
+  id: 432
+  display_name: "Megisto cymela"
+}
+item {
+  name: "42220"
+  id: 433
+  display_name: "Odocoileus hemionus"
+}
+item {
+  name: "17645"
+  id: 434
+  display_name: "Picoides nuttallii"
+}
+item {
+  name: "58606"
+  id: 435
+  display_name: "Cercyonis pegala"
+}
+item {
+  name: "42223"
+  id: 436
+  display_name: "Odocoileus virginianus"
+}
+item {
+  name: "58609"
+  id: 437
+  display_name: "Lepisosteus osseus"
+}
+item {
+  name: "17650"
+  id: 438
+  display_name: "Picoides scalaris"
+}
+item {
+  name: "132339"
+  id: 439
+  display_name: "Anthanassa texana"
+}
+item {
+  name: "58612"
+  id: 440
+  display_name: "Carassius auratus"
+}
+item {
+  name: "1406"
+  id: 441
+  display_name: "Callipepla gambelii"
+}
+item {
+  name: "9462"
+  id: 442
+  display_name: "Pyrrhula pyrrhula"
+}
+item {
+  name: "4308"
+  id: 443
+  display_name: "Phalacrocorax brasilianus"
+}
+item {
+  name: "17660"
+  id: 444
+  display_name: "Picoides pubescens"
+}
+item {
+  name: "1280"
+  id: 445
+  display_name: "Colinus virginianus"
+}
+item {
+  name: "129920"
+  id: 446
+  display_name: "Calliostoma ligatum"
+}
+item {
+  name: "58627"
+  id: 447
+  display_name: "Perca flavescens"
+}
+item {
+  name: "148742"
+  id: 448
+  display_name: "Hamadryas februa"
+}
+item {
+  name: "39809"
+  id: 449
+  display_name: "Terrapene ornata ornata"
+}
+item {
+  name: "115979"
+  id: 450
+  display_name: "Plestiodon skiltonianus skiltonianus"
+}
+item {
+  name: "9484"
+  id: 451
+  display_name: "Sporophila torqueola"
+}
+item {
+  name: "17678"
+  id: 452
+  display_name: "Picoides villosus"
+}
+item {
+  name: "3862"
+  id: 453
+  display_name: "Calidris pusilla"
+}
+item {
+  name: "70421"
+  id: 454
+  display_name: "Acris blanchardi"
+}
+item {
+  name: "124183"
+  id: 455
+  display_name: "Phlogophora periculosa"
+}
+item {
+  name: "124184"
+  id: 456
+  display_name: "Plodia interpunctella"
+}
+item {
+  name: "99609"
+  id: 457
+  display_name: "Dromogomphus spinosus"
+}
+item {
+  name: "99610"
+  id: 458
+  display_name: "Dromogomphus spoliatus"
+}
+item {
+  name: "17694"
+  id: 459
+  display_name: "Picoides arcticus"
+}
+item {
+  name: "113521"
+  id: 460
+  display_name: "Sympetrum pallipes"
+}
+item {
+  name: "320801"
+  id: 461
+  display_name: "Aspidoscelis tesselata"
+}
+item {
+  name: "7047"
+  id: 462
+  display_name: "Aythya marila"
+}
+item {
+  name: "4317"
+  id: 463
+  display_name: "Phaethon aethereus"
+}
+item {
+  name: "81606"
+  id: 464
+  display_name: "Littorina littorea"
+}
+item {
+  name: "99891"
+  id: 465
+  display_name: "Enallagma aspersum"
+}
+item {
+  name: "9528"
+  id: 466
+  display_name: "Sturnella magna"
+}
+item {
+  name: "99641"
+  id: 467
+  display_name: "Dythemis fugax"
+}
+item {
+  name: "99644"
+  id: 468
+  display_name: "Dythemis nigrescens"
+}
+item {
+  name: "39818"
+  id: 469
+  display_name: "Terrapene carolina triunguis"
+}
+item {
+  name: "99647"
+  id: 470
+  display_name: "Dythemis velox"
+}
+item {
+  name: "148800"
+  id: 471
+  display_name: "Chioides albofasciatus"
+}
+item {
+  name: "19339"
+  id: 472
+  display_name: "Melopsittacus undulatus"
+}
+item {
+  name: "47509"
+  id: 473
+  display_name: "Diaulula sandiegensis"
+}
+item {
+  name: "148810"
+  id: 474
+  display_name: "Anaea aidea"
+}
+item {
+  name: "123070"
+  id: 475
+  display_name: "Capra hircus"
+}
+item {
+  name: "7054"
+  id: 476
+  display_name: "Aythya affinis"
+}
+item {
+  name: "99897"
+  id: 477
+  display_name: "Enallagma civile"
+}
+item {
+  name: "42328"
+  id: 478
+  display_name: "Kobus ellipsiprymnus"
+}
+item {
+  name: "48328"
+  id: 479
+  display_name: "Aurelia aurita"
+}
+item {
+  name: "132445"
+  id: 480
+  display_name: "Conchylodes ovulalis"
+}
+item {
+  name: "215271"
+  id: 481
+  display_name: "Bleptina caradrinalis"
+}
+item {
+  name: "83297"
+  id: 482
+  display_name: "Scarus rubroviolaceus"
+}
+item {
+  name: "42347"
+  id: 483
+  display_name: "Rupicapra rupicapra"
+}
+item {
+  name: "7058"
+  id: 484
+  display_name: "Aythya novaeseelandiae"
+}
+item {
+  name: "52457"
+  id: 485
+  display_name: "Chaetodon auriga"
+}
+item {
+  name: "1392"
+  id: 486
+  display_name: "Cyrtonyx montezumae"
+}
+item {
+  name: "4328"
+  id: 487
+  display_name: "Pelecanus occidentalis"
+}
+item {
+  name: "7647"
+  id: 488
+  display_name: "Cinclus cinclus"
+}
+item {
+  name: "148856"
+  id: 489
+  display_name: "Anteos clorinde"
+}
+item {
+  name: "7060"
+  id: 490
+  display_name: "Chen rossii"
+}
+item {
+  name: "58750"
+  id: 491
+  display_name: "Nomophila nearctica"
+}
+item {
+  name: "1409"
+  id: 492
+  display_name: "Callipepla californica"
+}
+item {
+  name: "9602"
+  id: 493
+  display_name: "Quiscalus quiscula"
+}
+item {
+  name: "296326"
+  id: 494
+  display_name: "Oncopeltus sexmaculatus"
+}
+item {
+  name: "9607"
+  id: 495
+  display_name: "Quiscalus mexicanus"
+}
+item {
+  name: "319724"
+  id: 496
+  display_name: "Euphoria kernii"
+}
+item {
+  name: "1419"
+  id: 497
+  display_name: "Callipepla squamata"
+}
+item {
+  name: "148883"
+  id: 498
+  display_name: "Eantis tamenund"
+}
+item {
+  name: "42391"
+  id: 499
+  display_name: "Ovis canadensis"
+}
+item {
+  name: "107937"
+  id: 500
+  display_name: "Orthemis discolor"
+}
+item {
+  name: "42405"
+  id: 501
+  display_name: "Syncerus caffer"
+}
+item {
+  name: "42408"
+  id: 502
+  display_name: "Bison bison"
+}
+item {
+  name: "116137"
+  id: 503
+  display_name: "Sceloporus cowlesi"
+}
+item {
+  name: "326296"
+  id: 504
+  display_name: "Bufo bufo"
+}
+item {
+  name: "148907"
+  id: 505
+  display_name: "Cydia latiferreana"
+}
+item {
+  name: "42414"
+  id: 506
+  display_name: "Oreamnos americanus"
+}
+item {
+  name: "116143"
+  id: 507
+  display_name: "Sceloporus tristichus"
+}
+item {
+  name: "99912"
+  id: 508
+  display_name: "Enallagma geminatum"
+}
+item {
+  name: "226889"
+  id: 509
+  display_name: "Pangrapta decoralis"
+}
+item {
+  name: "42429"
+  id: 510
+  display_name: "Antilocapra americana"
+}
+item {
+  name: "17855"
+  id: 511
+  display_name: "Dryocopus pileatus"
+}
+item {
+  name: "107974"
+  id: 512
+  display_name: "Orthetrum sabina"
+}
+item {
+  name: "56225"
+  id: 513
+  display_name: "Polygonia c-album"
+}
+item {
+  name: "67016"
+  id: 514
+  display_name: "Rana draytonii"
+}
+item {
+  name: "132553"
+  id: 515
+  display_name: "Strymon istapa"
+}
+item {
+  name: "73155"
+  id: 516
+  display_name: "Passerina caerulea"
+}
+item {
+  name: "26074"
+  id: 517
+  display_name: "Crocodylus moreletii"
+}
+item {
+  name: "171903"
+  id: 518
+  display_name: "Oligyra orbiculata"
+}
+item {
+  name: "26085"
+  id: 519
+  display_name: "Crocodylus acutus"
+}
+item {
+  name: "143613"
+  id: 520
+  display_name: "Homophoberia apicosa"
+}
+item {
+  name: "5715"
+  id: 521
+  display_name: "Amazilia beryllina"
+}
+item {
+  name: "9721"
+  id: 522
+  display_name: "Geothlypis trichas"
+}
+item {
+  name: "154446"
+  id: 523
+  display_name: "Lambdina fiscellaria"
+}
+item {
+  name: "236841"
+  id: 524
+  display_name: "Lichanura orcutti"
+}
+item {
+  name: "20737"
+  id: 525
+  display_name: "Trogon melanocephalus"
+}
+item {
+  name: "124431"
+  id: 526
+  display_name: "Cycloneda sanguinea"
+}
+item {
+  name: "124432"
+  id: 527
+  display_name: "Deroceras reticulatum"
+}
+item {
+  name: "39566"
+  id: 528
+  display_name: "Apalone ferox"
+}
+item {
+  name: "149017"
+  id: 529
+  display_name: "Chlorochlamys chloroleucaria"
+}
+item {
+  name: "15281"
+  id: 530
+  display_name: "Sylvia communis"
+}
+item {
+  name: "312873"
+  id: 531
+  display_name: "Anartia fatima fatima"
+}
+item {
+  name: "9771"
+  id: 532
+  display_name: "Pinicola enucleator"
+}
+item {
+  name: "39858"
+  id: 533
+  display_name: "Graptemys geographica"
+}
+item {
+  name: "26159"
+  id: 534
+  display_name: "Alligator mississippiensis"
+}
+item {
+  name: "304690"
+  id: 535
+  display_name: "Naupactus cervinus"
+}
+item {
+  name: "124467"
+  id: 536
+  display_name: "Pseudosphinx tetrio"
+}
+item {
+  name: "99892"
+  id: 537
+  display_name: "Enallagma basidens"
+}
+item {
+  name: "99895"
+  id: 538
+  display_name: "Enallagma carunculatum"
+}
+item {
+  name: "67129"
+  id: 539
+  display_name: "Rhinella marina"
+}
+item {
+  name: "83515"
+  id: 540
+  display_name: "Oxybelis aeneus"
+}
+item {
+  name: "81681"
+  id: 541
+  display_name: "Campaea perlata"
+}
+item {
+  name: "99901"
+  id: 542
+  display_name: "Enallagma cyathigerum"
+}
+item {
+  name: "99911"
+  id: 543
+  display_name: "Enallagma exsulans"
+}
+item {
+  name: "9800"
+  id: 544
+  display_name: "Coccothraustes vespertinus"
+}
+item {
+  name: "9801"
+  id: 545
+  display_name: "Coccothraustes coccothraustes"
+}
+item {
+  name: "154551"
+  id: 546
+  display_name: "Leptoglossus zonatus"
+}
+item {
+  name: "9807"
+  id: 547
+  display_name: "Vermivora chrysoptera"
+}
+item {
+  name: "61157"
+  id: 548
+  display_name: "Trichodes ornatus"
+}
+item {
+  name: "99924"
+  id: 549
+  display_name: "Enallagma signatum"
+}
+item {
+  name: "1626"
+  id: 550
+  display_name: "Opisthocomus hoazin"
+}
+item {
+  name: "132704"
+  id: 551
+  display_name: "Setophaga coronata coronata"
+}
+item {
+  name: "119056"
+  id: 552
+  display_name: "Centruroides vittatus"
+}
+item {
+  name: "50786"
+  id: 553
+  display_name: "Vanessa annabella"
+}
+item {
+  name: "60347"
+  id: 554
+  display_name: "Pituophis catenifer sayi"
+}
+item {
+  name: "9833"
+  id: 555
+  display_name: "Diglossa baritula"
+}
+item {
+  name: "132718"
+  id: 556
+  display_name: "Scathophaga stercoraria"
+}
+item {
+  name: "132719"
+  id: 557
+  display_name: "Calopteron reticulatum"
+}
+item {
+  name: "116340"
+  id: 558
+  display_name: "Dreissena polymorpha"
+}
+item {
+  name: "134078"
+  id: 559
+  display_name: "Scoliopteryx libatrix"
+}
+item {
+  name: "9850"
+  id: 560
+  display_name: "Saltator coerulescens"
+}
+item {
+  name: "117695"
+  id: 561
+  display_name: "Cucumaria miniata"
+}
+item {
+  name: "9854"
+  id: 562
+  display_name: "Saltator atriceps"
+}
+item {
+  name: "132736"
+  id: 563
+  display_name: "Urola nivalis"
+}
+item {
+  name: "34435"
+  id: 564
+  display_name: "Hemidactylus turcicus"
+}
+item {
+  name: "9864"
+  id: 565
+  display_name: "Sicalis flaveola"
+}
+item {
+  name: "7106"
+  id: 566
+  display_name: "Aix galericulata"
+}
+item {
+  name: "485010"
+  id: 567
+  display_name: "Chinavia hilaris"
+}
+item {
+  name: "132764"
+  id: 568
+  display_name: "Junco hyemalis hyemalis"
+}
+item {
+  name: "367558"
+  id: 569
+  display_name: "Eupsittula canicularis"
+}
+item {
+  name: "370351"
+  id: 570
+  display_name: "Microcarbo melanoleucos"
+}
+item {
+  name: "50867"
+  id: 571
+  display_name: "Argiope bruennichi"
+}
+item {
+  name: "67252"
+  id: 572
+  display_name: "Trachycephalus typhonius"
+}
+item {
+  name: "132789"
+  id: 573
+  display_name: "Clepsis peritana"
+}
+item {
+  name: "9915"
+  id: 574
+  display_name: "Piranga rubra"
+}
+item {
+  name: "50880"
+  id: 575
+  display_name: "Limenitis lorquini"
+}
+item {
+  name: "9921"
+  id: 576
+  display_name: "Piranga olivacea"
+}
+item {
+  name: "100034"
+  id: 577
+  display_name: "Epiaeschna heros"
+}
+item {
+  name: "9924"
+  id: 578
+  display_name: "Piranga flava"
+}
+item {
+  name: "42339"
+  id: 579
+  display_name: "Tragelaphus strepsiceros"
+}
+item {
+  name: "50892"
+  id: 580
+  display_name: "Euphydryas chalcedona"
+}
+item {
+  name: "130348"
+  id: 581
+  display_name: "Dione moneta"
+}
+item {
+  name: "394966"
+  id: 582
+  display_name: "Phaulacridium marginale"
+}
+item {
+  name: "9943"
+  id: 583
+  display_name: "Amphispiza bilineata"
+}
+item {
+  name: "4388"
+  id: 584
+  display_name: "Larus dominicanus"
+}
+item {
+  name: "1758"
+  id: 585
+  display_name: "Piaya cayana"
+}
+item {
+  name: "50913"
+  id: 586
+  display_name: "Hyalophora euryalus"
+}
+item {
+  name: "9958"
+  id: 587
+  display_name: "Aimophila ruficeps"
+}
+item {
+  name: "59115"
+  id: 588
+  display_name: "Gambusia affinis"
+}
+item {
+  name: "64346"
+  id: 589
+  display_name: "Natrix tessellata"
+}
+item {
+  name: "59119"
+  id: 590
+  display_name: "Pontia protodice"
+}
+item {
+  name: "18160"
+  id: 591
+  display_name: "Melanerpes lewis"
+}
+item {
+  name: "18161"
+  id: 592
+  display_name: "Melanerpes uropygialis"
+}
+item {
+  name: "50931"
+  id: 593
+  display_name: "Strymon melinus"
+}
+item {
+  name: "59124"
+  id: 594
+  display_name: "Anthocharis sara"
+}
+item {
+  name: "59127"
+  id: 595
+  display_name: "Lycaena helloides"
+}
+item {
+  name: "59128"
+  id: 596
+  display_name: "Atlides halesus"
+}
+item {
+  name: "67324"
+  id: 597
+  display_name: "Eurema daira"
+}
+item {
+  name: "9981"
+  id: 598
+  display_name: "Passerculus sandwichensis"
+}
+item {
+  name: "59134"
+  id: 599
+  display_name: "Satyrium sylvinus"
+}
+item {
+  name: "67327"
+  id: 600
+  display_name: "Schistocerca obscura"
+}
+item {
+  name: "67328"
+  id: 601
+  display_name: "Pholcus phalangioides"
+}
+item {
+  name: "59138"
+  id: 602
+  display_name: "Satyrium saepium"
+}
+item {
+  name: "132867"
+  id: 603
+  display_name: "Microtia elva"
+}
+item {
+  name: "18181"
+  id: 604
+  display_name: "Melanerpes pucherani"
+}
+item {
+  name: "7486"
+  id: 605
+  display_name: "Salpinctes obsoletus"
+}
+item {
+  name: "108303"
+  id: 606
+  display_name: "Paltothemis lineatipes"
+}
+item {
+  name: "59152"
+  id: 607
+  display_name: "Leptotes marina"
+}
+item {
+  name: "132881"
+  id: 608
+  display_name: "Catocala ultronia"
+}
+item {
+  name: "143662"
+  id: 609
+  display_name: "Orthosoma brunneum"
+}
+item {
+  name: "59164"
+  id: 610
+  display_name: "Plebejus icarioides"
+}
+item {
+  name: "18205"
+  id: 611
+  display_name: "Melanerpes carolinus"
+}
+item {
+  name: "18206"
+  id: 612
+  display_name: "Melanerpes chrysogenys"
+}
+item {
+  name: "83744"
+  id: 613
+  display_name: "Amblyomma americanum"
+}
+item {
+  name: "18209"
+  id: 614
+  display_name: "Melanerpes formicivorus"
+}
+item {
+  name: "116517"
+  id: 615
+  display_name: "Caiman crocodilus"
+}
+item {
+  name: "59176"
+  id: 616
+  display_name: "Phyciodes mylitta"
+}
+item {
+  name: "59182"
+  id: 617
+  display_name: "Euphydryas editha"
+}
+item {
+  name: "43997"
+  id: 618
+  display_name: "Myocastor coypus"
+}
+item {
+  name: "59185"
+  id: 619
+  display_name: "Coenonympha tullia"
+}
+item {
+  name: "59187"
+  id: 620
+  display_name: "Erynnis propertius"
+}
+item {
+  name: "59188"
+  id: 621
+  display_name: "Erynnis funeralis"
+}
+item {
+  name: "59189"
+  id: 622
+  display_name: "Erynnis tristis"
+}
+item {
+  name: "59190"
+  id: 623
+  display_name: "Heliopetes ericetorum"
+}
+item {
+  name: "34615"
+  id: 624
+  display_name: "Gekko gecko"
+}
+item {
+  name: "42808"
+  id: 625
+  display_name: "Trichosurus vulpecula"
+}
+item {
+  name: "59194"
+  id: 626
+  display_name: "Ochlodes sylvanoides"
+}
+item {
+  name: "59195"
+  id: 627
+  display_name: "Lerodea eufala"
+}
+item {
+  name: "18236"
+  id: 628
+  display_name: "Colaptes auratus"
+}
+item {
+  name: "10045"
+  id: 629
+  display_name: "Basileuterus rufifrons"
+}
+item {
+  name: "59202"
+  id: 630
+  display_name: "Larus michahellis"
+}
+item {
+  name: "10053"
+  id: 631
+  display_name: "Ramphocelus passerinii"
+}
+item {
+  name: "19975"
+  id: 632
+  display_name: "Athene cunicularia"
+}
+item {
+  name: "82231"
+  id: 633
+  display_name: "Periplaneta americana"
+}
+item {
+  name: "67409"
+  id: 634
+  display_name: "Gobiesox maeandricus"
+}
+item {
+  name: "83795"
+  id: 635
+  display_name: "Cipangopaludina chinensis"
+}
+item {
+  name: "59220"
+  id: 636
+  display_name: "Branta hutchinsii"
+}
+item {
+  name: "10069"
+  id: 637
+  display_name: "Fringilla montifringilla"
+}
+item {
+  name: "10070"
+  id: 638
+  display_name: "Fringilla coelebs"
+}
+item {
+  name: "83802"
+  id: 639
+  display_name: "Megacyllene robiniae"
+}
+item {
+  name: "83804"
+  id: 640
+  display_name: "Dynastes tityus"
+}
+item {
+  name: "51039"
+  id: 641
+  display_name: "Cepaea hortensis"
+}
+item {
+  name: "68062"
+  id: 642
+  display_name: "Menemerus bivittatus"
+}
+item {
+  name: "47527"
+  id: 643
+  display_name: "Ostracion meleagris"
+}
+item {
+  name: "67435"
+  id: 644
+  display_name: "Urbanus proteus"
+}
+item {
+  name: "10094"
+  id: 645
+  display_name: "Junco hyemalis"
+}
+item {
+  name: "67440"
+  id: 646
+  display_name: "Utetheisa ornatrix"
+}
+item {
+  name: "100210"
+  id: 647
+  display_name: "Epitheca canis"
+}
+item {
+  name: "1907"
+  id: 648
+  display_name: "Cuculus canorus"
+}
+item {
+  name: "100215"
+  id: 649
+  display_name: "Epitheca princeps"
+}
+item {
+  name: "27826"
+  id: 650
+  display_name: "Taricha granulosa"
+}
+item {
+  name: "129147"
+  id: 651
+  display_name: "Ammophila procera"
+}
+item {
+  name: "10111"
+  id: 652
+  display_name: "Junco phaeonotus"
+}
+item {
+  name: "83844"
+  id: 653
+  display_name: "Oxyopes salticus"
+}
+item {
+  name: "144107"
+  id: 654
+  display_name: "Tetracis crocallata"
+}
+item {
+  name: "51097"
+  id: 655
+  display_name: "Papilio zelicaon"
+}
+item {
+  name: "10138"
+  id: 656
+  display_name: "Ammodramus nelsoni"
+}
+item {
+  name: "10139"
+  id: 657
+  display_name: "Ammodramus savannarum"
+}
+item {
+  name: "10147"
+  id: 658
+  display_name: "Ammodramus maritimus"
+}
+item {
+  name: "59300"
+  id: 659
+  display_name: "Anagrapha falcifera"
+}
+item {
+  name: "51110"
+  id: 660
+  display_name: "Xylocopa virginica"
+}
+item {
+  name: "1960"
+  id: 661
+  display_name: "Coccyzus erythropthalmus"
+}
+item {
+  name: "42652"
+  id: 662
+  display_name: "Didelphis virginiana"
+}
+item {
+  name: "428606"
+  id: 663
+  display_name: "Heraclides rumiko"
+}
+item {
+  name: "127303"
+  id: 664
+  display_name: "Callophrys henrici"
+}
+item {
+  name: "1964"
+  id: 665
+  display_name: "Coccyzus minor"
+}
+item {
+  name: "1965"
+  id: 666
+  display_name: "Coccyzus americanus"
+}
+item {
+  name: "8520"
+  id: 667
+  display_name: "Nucifraga columbiana"
+}
+item {
+  name: "116658"
+  id: 668
+  display_name: "Siphanta acuta"
+}
+item {
+  name: "1972"
+  id: 669
+  display_name: "Crotophaga sulcirostris"
+}
+item {
+  name: "10168"
+  id: 670
+  display_name: "Pooecetes gramineus"
+}
+item {
+  name: "53893"
+  id: 671
+  display_name: "Chlosyne palla"
+}
+item {
+  name: "10173"
+  id: 672
+  display_name: "Arremonops rufivirgatus"
+}
+item {
+  name: "1986"
+  id: 673
+  display_name: "Geococcyx californianus"
+}
+item {
+  name: "1987"
+  id: 674
+  display_name: "Geococcyx velox"
+}
+item {
+  name: "116680"
+  id: 675
+  display_name: "Tabanus atratus"
+}
+item {
+  name: "116681"
+  id: 676
+  display_name: "Atteva aurea"
+}
+item {
+  name: "124875"
+  id: 677
+  display_name: "Spodoptera litura"
+}
+item {
+  name: "26575"
+  id: 678
+  display_name: "Diadophis punctatus"
+}
+item {
+  name: "10199"
+  id: 679
+  display_name: "Coereba flaveola"
+}
+item {
+  name: "26591"
+  id: 680
+  display_name: "Diadophis punctatus edwardsii"
+}
+item {
+  name: "59360"
+  id: 681
+  display_name: "Neverita duplicata"
+}
+item {
+  name: "68263"
+  id: 682
+  display_name: "Papilio multicaudata"
+}
+item {
+  name: "26598"
+  id: 683
+  display_name: "Diadophis punctatus amabilis"
+}
+item {
+  name: "42983"
+  id: 684
+  display_name: "Phascolarctos cinereus"
+}
+item {
+  name: "67560"
+  id: 685
+  display_name: "Adelpha californica"
+}
+item {
+  name: "10224"
+  id: 686
+  display_name: "Passerina ciris"
+}
+item {
+  name: "2038"
+  id: 687
+  display_name: "Alectura lathami"
+}
+item {
+  name: "10232"
+  id: 688
+  display_name: "Passerina leclancherii"
+}
+item {
+  name: "10234"
+  id: 689
+  display_name: "Passerina amoena"
+}
+item {
+  name: "10243"
+  id: 690
+  display_name: "Icteria virens"
+}
+item {
+  name: "2052"
+  id: 691
+  display_name: "Crax rubra"
+}
+item {
+  name: "94551"
+  id: 692
+  display_name: "Argia immunda"
+}
+item {
+  name: "2062"
+  id: 693
+  display_name: "Penelope purpurascens"
+}
+item {
+  name: "204490"
+  id: 694
+  display_name: "Copsychus malabaricus"
+}
+item {
+  name: "10257"
+  id: 695
+  display_name: "Paroaria capitata"
+}
+item {
+  name: "51221"
+  id: 696
+  display_name: "Procambarus clarkii"
+}
+item {
+  name: "10262"
+  id: 697
+  display_name: "Cyanerpes cyaneus"
+}
+item {
+  name: "508249"
+  id: 698
+  display_name: "Microcarbo melanoleucos brevirostris"
+}
+item {
+  name: "18460"
+  id: 699
+  display_name: "Sphyrapicus thyroideus"
+}
+item {
+  name: "10271"
+  id: 700
+  display_name: "Pheucticus ludovicianus"
+}
+item {
+  name: "18464"
+  id: 701
+  display_name: "Sphyrapicus ruber"
+}
+item {
+  name: "10274"
+  id: 702
+  display_name: "Pheucticus melanocephalus"
+}
+item {
+  name: "18467"
+  id: 703
+  display_name: "Sphyrapicus nuchalis"
+}
+item {
+  name: "100391"
+  id: 704
+  display_name: "Erythrodiplax berenice"
+}
+item {
+  name: "2089"
+  id: 705
+  display_name: "Ortalis poliocephala"
+}
+item {
+  name: "2090"
+  id: 706
+  display_name: "Ortalis vetula"
+}
+item {
+  name: "8038"
+  id: 707
+  display_name: "Corvus albus"
+}
+item {
+  name: "67629"
+  id: 708
+  display_name: "Oligocottus maculosus"
+}
+item {
+  name: "10286"
+  id: 709
+  display_name: "Mniotilta varia"
+}
+item {
+  name: "10288"
+  id: 710
+  display_name: "Volatinia jacarina"
+}
+item {
+  name: "100403"
+  id: 711
+  display_name: "Erythrodiplax minuscula"
+}
+item {
+  name: "84023"
+  id: 712
+  display_name: "Amorpha juglandis"
+}
+item {
+  name: "84024"
+  id: 713
+  display_name: "Galasa nigrinodis"
+}
+item {
+  name: "10297"
+  id: 714
+  display_name: "Thraupis palmarum"
+}
+item {
+  name: "67642"
+  id: 715
+  display_name: "Pantherophis spiloides"
+}
+item {
+  name: "67653"
+  id: 716
+  display_name: "Phoebis agarithe"
+}
+item {
+  name: "84038"
+  id: 717
+  display_name: "Haploa lecontei"
+}
+item {
+  name: "26695"
+  id: 718
+  display_name: "Scaphiopus holbrookii"
+}
+item {
+  name: "84040"
+  id: 719
+  display_name: "Chauliognathus marginatus"
+}
+item {
+  name: "51275"
+  id: 720
+  display_name: "Pentatoma rufipes"
+}
+item {
+  name: "2124"
+  id: 721
+  display_name: "Momotus mexicanus"
+}
+item {
+  name: "26702"
+  id: 722
+  display_name: "Spea hammondii"
+}
+item {
+  name: "10325"
+  id: 723
+  display_name: "Euphagus cyanocephalus"
+}
+item {
+  name: "43102"
+  id: 724
+  display_name: "Sylvilagus palustris"
+}
+item {
+  name: "49509"
+  id: 725
+  display_name: "Lutjanus griseus"
+}
+item {
+  name: "116834"
+  id: 726
+  display_name: "Cacatua galerita"
+}
+item {
+  name: "127188"
+  id: 727
+  display_name: "Junco hyemalis oreganus"
+}
+item {
+  name: "26725"
+  id: 728
+  display_name: "Ambystoma jeffersonianum"
+}
+item {
+  name: "43111"
+  id: 729
+  display_name: "Sylvilagus floridanus"
+}
+item {
+  name: "43112"
+  id: 730
+  display_name: "Sylvilagus bachmani"
+}
+item {
+  name: "67691"
+  id: 731
+  display_name: "Lophocampa maculata"
+}
+item {
+  name: "51311"
+  id: 732
+  display_name: "Urbanus dorantes"
+}
+item {
+  name: "67700"
+  id: 733
+  display_name: "Caracolus caracolla"
+}
+item {
+  name: "43128"
+  id: 734
+  display_name: "Lepus europaeus"
+}
+item {
+  name: "26745"
+  id: 735
+  display_name: "Ambystoma texanum"
+}
+item {
+  name: "67706"
+  id: 736
+  display_name: "Argiope argentata"
+}
+item {
+  name: "26747"
+  id: 737
+  display_name: "Ambystoma gracile"
+}
+item {
+  name: "67708"
+  id: 738
+  display_name: "Argiope trifasciata"
+}
+item {
+  name: "26749"
+  id: 739
+  display_name: "Ambystoma tigrinum"
+}
+item {
+  name: "4896"
+  id: 740
+  display_name: "Pluvialis fulva"
+}
+item {
+  name: "10369"
+  id: 741
+  display_name: "Molothrus aeneus"
+}
+item {
+  name: "26754"
+  id: 742
+  display_name: "Ambystoma macrodactylum"
+}
+item {
+  name: "10373"
+  id: 743
+  display_name: "Molothrus ater"
+}
+item {
+  name: "2185"
+  id: 744
+  display_name: "Merops pusillus"
+}
+item {
+  name: "84109"
+  id: 745
+  display_name: "Pisaurina mira"
+}
+item {
+  name: "67726"
+  id: 746
+  display_name: "Aeshna palmata"
+}
+item {
+  name: "2191"
+  id: 747
+  display_name: "Merops apiaster"
+}
+item {
+  name: "67731"
+  id: 748
+  display_name: "Anax junius"
+}
+item {
+  name: "198804"
+  id: 749
+  display_name: "Satyrium titus"
+}
+item {
+  name: "51349"
+  id: 750
+  display_name: "Pyrgus communis"
+}
+item {
+  name: "18584"
+  id: 751
+  display_name: "Pteroglossus torquatus"
+}
+item {
+  name: "67737"
+  id: 752
+  display_name: "Rhionaeschna multicolor"
+}
+item {
+  name: "198812"
+  id: 753
+  display_name: "Lethe anthedon"
+}
+item {
+  name: "321697"
+  id: 754
+  display_name: "Melanchroia chephise"
+}
+item {
+  name: "198821"
+  id: 755
+  display_name: "Pieris oleracea"
+}
+item {
+  name: "26790"
+  id: 756
+  display_name: "Ambystoma maculatum"
+}
+item {
+  name: "10411"
+  id: 757
+  display_name: "Loxia curvirostra"
+}
+item {
+  name: "133295"
+  id: 758
+  display_name: "Melitaea didyma"
+}
+item {
+  name: "67760"
+  id: 759
+  display_name: "Popillia japonica"
+}
+item {
+  name: "43188"
+  id: 760
+  display_name: "Ochotona princeps"
+}
+item {
+  name: "2229"
+  id: 761
+  display_name: "Merops orientalis"
+}
+item {
+  name: "10423"
+  id: 762
+  display_name: "Loxia leucoptera"
+}
+item {
+  name: "67771"
+  id: 763
+  display_name: "Leptoglossus occidentalis"
+}
+item {
+  name: "84162"
+  id: 764
+  display_name: "Chrysochus auratus"
+}
+item {
+  name: "26822"
+  id: 765
+  display_name: "Dicamptodon tenebrosus"
+}
+item {
+  name: "26823"
+  id: 766
+  display_name: "Dicamptodon ensatus"
+}
+item {
+  name: "51402"
+  id: 767
+  display_name: "Megalops atlanticus"
+}
+item {
+  name: "67725"
+  id: 768
+  display_name: "Aeshna interrupta"
+}
+item {
+  name: "411858"
+  id: 769
+  display_name: "Vanessa gonerilla gonerilla"
+}
+item {
+  name: "26835"
+  id: 770
+  display_name: "Drymobius margaritiferus"
+}
+item {
+  name: "84185"
+  id: 771
+  display_name: "Megalopyge opercularis"
+}
+item {
+  name: "2266"
+  id: 772
+  display_name: "Coracias garrulus"
+}
+item {
+  name: "141531"
+  id: 773
+  display_name: "Lethe eurydice"
+}
+item {
+  name: "2269"
+  id: 774
+  display_name: "Coracias caudatus"
+}
+item {
+  name: "133346"
+  id: 775
+  display_name: "Melittia cucurbitae"
+}
+item {
+  name: "2275"
+  id: 776
+  display_name: "Coracias benghalensis"
+}
+item {
+  name: "84196"
+  id: 777
+  display_name: "Pontania californica"
+}
+item {
+  name: "10470"
+  id: 778
+  display_name: "Xanthocephalus xanthocephalus"
+}
+item {
+  name: "10479"
+  id: 779
+  display_name: "Chondestes grammacus"
+}
+item {
+  name: "51440"
+  id: 780
+  display_name: "Pituophis catenifer catenifer"
+}
+item {
+  name: "54087"
+  id: 781
+  display_name: "Pieris napi"
+}
+item {
+  name: "59635"
+  id: 782
+  display_name: "Phragmatopoma californica"
+}
+item {
+  name: "10487"
+  id: 783
+  display_name: "Dolichonyx oryzivorus"
+}
+item {
+  name: "67835"
+  id: 784
+  display_name: "Danaus chrysippus"
+}
+item {
+  name: "59644"
+  id: 785
+  display_name: "Pantherophis alleghaniensis"
+}
+item {
+  name: "59646"
+  id: 786
+  display_name: "Pantherophis bairdi"
+}
+item {
+  name: "116999"
+  id: 787
+  display_name: "Pandion haliaetus"
+}
+item {
+  name: "117002"
+  id: 788
+  display_name: "Phainopepla nitens"
+}
+item {
+  name: "16770"
+  id: 789
+  display_name: "Tyrannus couchii"
+}
+item {
+  name: "84239"
+  id: 790
+  display_name: "Callophrys gryneus"
+}
+item {
+  name: "104553"
+  id: 791
+  display_name: "Leucorrhinia proxima"
+}
+item {
+  name: "117016"
+  id: 792
+  display_name: "Phylloscopus collybita"
+}
+item {
+  name: "49540"
+  id: 793
+  display_name: "Gasteracantha cancriformis"
+}
+item {
+  name: "59675"
+  id: 794
+  display_name: "Pyrrharctia isabella"
+}
+item {
+  name: "469277"
+  id: 795
+  display_name: "Neotibicen superbus"
+}
+item {
+  name: "236973"
+  id: 796
+  display_name: "Circus cyaneus hudsonius"
+}
+item {
+  name: "59683"
+  id: 797
+  display_name: "Porpita porpita"
+}
+item {
+  name: "26916"
+  id: 798
+  display_name: "Contia tenuis"
+}
+item {
+  name: "51493"
+  id: 799
+  display_name: "Trimerotropis pallidipennis"
+}
+item {
+  name: "51495"
+  id: 800
+  display_name: "Anthocharis cardamines"
+}
+item {
+  name: "133416"
+  id: 801
+  display_name: "Phoebis philea"
+}
+item {
+  name: "8583"
+  id: 802
+  display_name: "Grallina cyanoleuca"
+}
+item {
+  name: "395569"
+  id: 803
+  display_name: "Prionoplus reticularis"
+}
+item {
+  name: "59698"
+  id: 804
+  display_name: "Velella velella"
+}
+item {
+  name: "141626"
+  id: 805
+  display_name: "Lygaeus turcicus"
+}
+item {
+  name: "84286"
+  id: 806
+  display_name: "Diapheromera femorata"
+}
+item {
+  name: "117059"
+  id: 807
+  display_name: "Plectrophenax nivalis"
+}
+item {
+  name: "133447"
+  id: 808
+  display_name: "Crambus agitatellus"
+}
+item {
+  name: "133448"
+  id: 809
+  display_name: "Climaciella brunnea"
+}
+item {
+  name: "51534"
+  id: 810
+  display_name: "Leptotes cassius"
+}
+item {
+  name: "205197"
+  id: 811
+  display_name: "Eutrapela clemataria"
+}
+item {
+  name: "51536"
+  id: 812
+  display_name: "Ascia monuste"
+}
+item {
+  name: "10585"
+  id: 813
+  display_name: "Calamospiza melanocorys"
+}
+item {
+  name: "49552"
+  id: 814
+  display_name: "Scutigera coleoptrata"
+}
+item {
+  name: "51555"
+  id: 815
+  display_name: "Sympetrum illotum"
+}
+item {
+  name: "51557"
+  id: 816
+  display_name: "Bombylius major"
+}
+item {
+  name: "117095"
+  id: 817
+  display_name: "Regulus calendula"
+}
+item {
+  name: "117097"
+  id: 818
+  display_name: "Regulus ignicapilla"
+}
+item {
+  name: "117099"
+  id: 819
+  display_name: "Regulus regulus"
+}
+item {
+  name: "117100"
+  id: 820
+  display_name: "Regulus satrapa"
+}
+item {
+  name: "84333"
+  id: 821
+  display_name: "Eudryas grata"
+}
+item {
+  name: "215409"
+  id: 822
+  display_name: "Bradybaena similaris"
+}
+item {
+  name: "16787"
+  id: 823
+  display_name: "Tyrannus melancholicus"
+}
+item {
+  name: "46225"
+  id: 824
+  display_name: "Tamias dorsalis"
+}
+item {
+  name: "59774"
+  id: 825
+  display_name: "Pachydiplax longipennis"
+}
+item {
+  name: "59776"
+  id: 826
+  display_name: "Perithemis tenera"
+}
+item {
+  name: "119014"
+  id: 827
+  display_name: "Argia fumipennis violacea"
+}
+item {
+  name: "4326"
+  id: 828
+  display_name: "Pelecanus conspicillatus"
+}
+item {
+  name: "18833"
+  id: 829
+  display_name: "Aulacorhynchus prasinus"
+}
+item {
+  name: "43411"
+  id: 830
+  display_name: "Ateles geoffroyi"
+}
+item {
+  name: "141725"
+  id: 831
+  display_name: "Nezara viridula"
+}
+item {
+  name: "51614"
+  id: 832
+  display_name: "Eurema hecabe"
+}
+item {
+  name: "125343"
+  id: 833
+  display_name: "Crepidula fornicata"
+}
+item {
+  name: "2464"
+  id: 834
+  display_name: "Todiramphus sanctus"
+}
+item {
+  name: "43432"
+  id: 835
+  display_name: "Cebus capucinus"
+}
+item {
+  name: "43436"
+  id: 836
+  display_name: "Alouatta palliata"
+}
+item {
+  name: "43439"
+  id: 837
+  display_name: "Alouatta pigra"
+}
+item {
+  name: "9357"
+  id: 838
+  display_name: "Icterus bullockii"
+}
+item {
+  name: "84403"
+  id: 839
+  display_name: "Phyllopalpus pulchellus"
+}
+item {
+  name: "10676"
+  id: 840
+  display_name: "Spiza americana"
+}
+item {
+  name: "16798"
+  id: 841
+  display_name: "Tyrannus dominicensis"
+}
+item {
+  name: "141752"
+  id: 842
+  display_name: "Biblis hyperia"
+}
+item {
+  name: "4512"
+  id: 843
+  display_name: "Chlidonias niger"
+}
+item {
+  name: "43460"
+  id: 844
+  display_name: "Macaca mulatta"
+}
+item {
+  name: "51654"
+  id: 845
+  display_name: "Junonia almana"
+}
+item {
+  name: "51659"
+  id: 846
+  display_name: "Anthopleura xanthogrammica"
+}
+item {
+  name: "84428"
+  id: 847
+  display_name: "Drepana arcuata"
+}
+item {
+  name: "10702"
+  id: 848
+  display_name: "Oriturus superciliosus"
+}
+item {
+  name: "68047"
+  id: 849
+  display_name: "Psarocolius montezuma"
+}
+item {
+  name: "12707"
+  id: 850
+  display_name: "Turdus pilaris"
+}
+item {
+  name: "84437"
+  id: 851
+  display_name: "Nicrophorus orbicollis"
+}
+item {
+  name: "84438"
+  id: 852
+  display_name: "Platyprepia virginalis"
+}
+item {
+  name: "117209"
+  id: 853
+  display_name: "Notiomystis cincta"
+}
+item {
+  name: "343393"
+  id: 854
+  display_name: "Hypsopygia olinalis"
+}
+item {
+  name: "27101"
+  id: 855
+  display_name: "Eurycea longicauda"
+}
+item {
+  name: "117214"
+  id: 856
+  display_name: "Sagittarius serpentarius"
+}
+item {
+  name: "18911"
+  id: 857
+  display_name: "Psittacula krameri"
+}
+item {
+  name: "117218"
+  id: 858
+  display_name: "Verrucosa arenata"
+}
+item {
+  name: "117221"
+  id: 859
+  display_name: "Dasymutilla occidentalis"
+}
+item {
+  name: "35303"
+  id: 860
+  display_name: "Ctenosaura similis"
+}
+item {
+  name: "18920"
+  id: 861
+  display_name: "Platycercus eximius"
+}
+item {
+  name: "10729"
+  id: 862
+  display_name: "Protonotaria citrea"
+}
+item {
+  name: "35306"
+  id: 863
+  display_name: "Ctenosaura pectinata"
+}
+item {
+  name: "109650"
+  id: 864
+  display_name: "Platycnemis pennipes"
+}
+item {
+  name: "27120"
+  id: 865
+  display_name: "Eurycea bislineata"
+}
+item {
+  name: "27123"
+  id: 866
+  display_name: "Eurycea lucifuga"
+}
+item {
+  name: "51702"
+  id: 867
+  display_name: "Coccinella septempunctata"
+}
+item {
+  name: "2552"
+  id: 868
+  display_name: "Megaceryle torquata"
+}
+item {
+  name: "133625"
+  id: 869
+  display_name: "Zanclognatha jacchusalis"
+}
+item {
+  name: "18943"
+  id: 870
+  display_name: "Nestor meridionalis"
+}
+item {
+  name: "84481"
+  id: 871
+  display_name: "Calopteryx maculata"
+}
+item {
+  name: "35330"
+  id: 872
+  display_name: "Sauromalus ater"
+}
+item {
+  name: "27140"
+  id: 873
+  display_name: "Coluber constrictor priapus"
+}
+item {
+  name: "199179"
+  id: 874
+  display_name: "Polistes chinensis"
+}
+item {
+  name: "51724"
+  id: 875
+  display_name: "Mopalia lignosa"
+}
+item {
+  name: "27149"
+  id: 876
+  display_name: "Coluber constrictor constrictor"
+}
+item {
+  name: "35342"
+  id: 877
+  display_name: "Iguana iguana"
+}
+item {
+  name: "27153"
+  id: 878
+  display_name: "Coluber constrictor flaviventris"
+}
+item {
+  name: "35347"
+  id: 879
+  display_name: "Amblyrhynchus cristatus"
+}
+item {
+  name: "125461"
+  id: 880
+  display_name: "Ursus arctos horribilis"
+}
+item {
+  name: "84507"
+  id: 881
+  display_name: "Lygus lineolaris"
+}
+item {
+  name: "35356"
+  id: 882
+  display_name: "Dipsosaurus dorsalis"
+}
+item {
+  name: "51743"
+  id: 883
+  display_name: "Danaus gilippus"
+}
+item {
+  name: "18976"
+  id: 884
+  display_name: "Amazona viridigenalis"
+}
+item {
+  name: "125475"
+  id: 885
+  display_name: "Plusiodonta compressipalpis"
+}
+item {
+  name: "51748"
+  id: 886
+  display_name: "Danaus gilippus thersippus"
+}
+item {
+  name: "68137"
+  id: 887
+  display_name: "Chlorocebus pygerythrus"
+}
+item {
+  name: "133675"
+  id: 888
+  display_name: "Coenobita clypeatus"
+}
+item {
+  name: "215596"
+  id: 889
+  display_name: "Buprestis aurulenta"
+}
+item {
+  name: "117293"
+  id: 890
+  display_name: "Oecophylla smaragdina"
+}
+item {
+  name: "68142"
+  id: 891
+  display_name: "Prenolepis imparis"
+}
+item {
+  name: "27184"
+  id: 892
+  display_name: "Plethodon glutinosus"
+}
+item {
+  name: "27186"
+  id: 893
+  display_name: "Plethodon cinereus"
+}
+item {
+  name: "18995"
+  id: 894
+  display_name: "Amazona albifrons"
+}
+item {
+  name: "51765"
+  id: 895
+  display_name: "Poanes melane"
+}
+item {
+  name: "18998"
+  id: 896
+  display_name: "Amazona oratrix"
+}
+item {
+  name: "41396"
+  id: 897
+  display_name: "Rhynchonycteris naso"
+}
+item {
+  name: "27194"
+  id: 898
+  display_name: "Plethodon vehiculum"
+}
+item {
+  name: "51773"
+  id: 899
+  display_name: "Nathalis iole"
+}
+item {
+  name: "12908"
+  id: 900
+  display_name: "Saxicola rubetra"
+}
+item {
+  name: "68165"
+  id: 901
+  display_name: "Linepithema humile"
+}
+item {
+  name: "154721"
+  id: 902
+  display_name: "Brachygastra mellifica"
+}
+item {
+  name: "338504"
+  id: 903
+  display_name: "Xanthocnemis zealandica"
+}
+item {
+  name: "338505"
+  id: 904
+  display_name: "Melangyna novaezelandiae"
+}
+item {
+  name: "27093"
+  id: 905
+  display_name: "Eurycea cirrigera"
+}
+item {
+  name: "65975"
+  id: 906
+  display_name: "Lithobates berlandieri"
+}
+item {
+  name: "19020"
+  id: 907
+  display_name: "Ara militaris"
+}
+item {
+  name: "474210"
+  id: 908
+  display_name: "Spizelloides arborea"
+}
+item {
+  name: "205240"
+  id: 909
+  display_name: "Pantographa limata"
+}
+item {
+  name: "27226"
+  id: 910
+  display_name: "Plethodon albagula"
+}
+item {
+  name: "318545"
+  id: 911
+  display_name: "Coreus marginatus"
+}
+item {
+  name: "2662"
+  id: 912
+  display_name: "Ceryle rudis"
+}
+item {
+  name: "109161"
+  id: 913
+  display_name: "Perithemis intensa"
+}
+item {
+  name: "51824"
+  id: 914
+  display_name: "Calopteryx splendens"
+}
+item {
+  name: "27250"
+  id: 915
+  display_name: "Ensatina eschscholtzii"
+}
+item {
+  name: "2676"
+  id: 916
+  display_name: "Chloroceryle aenea"
+}
+item {
+  name: "2679"
+  id: 917
+  display_name: "Chloroceryle amazona"
+}
+item {
+  name: "84602"
+  id: 918
+  display_name: "Zale lunata"
+}
+item {
+  name: "133756"
+  id: 919
+  display_name: "Leptoglossus oppositus"
+}
+item {
+  name: "35453"
+  id: 920
+  display_name: "Zootoca vivipara"
+}
+item {
+  name: "84612"
+  id: 921
+  display_name: "Polyphylla decemlineata"
+}
+item {
+  name: "133765"
+  id: 922
+  display_name: "Eumenes fraternus"
+}
+item {
+  name: "68230"
+  id: 923
+  display_name: "Brachymesia gravida"
+}
+item {
+  name: "49601"
+  id: 924
+  display_name: "Mola mola"
+}
+item {
+  name: "68232"
+  id: 925
+  display_name: "Papilio palamedes"
+}
+item {
+  name: "68233"
+  id: 926
+  display_name: "Orthemis ferruginea"
+}
+item {
+  name: "68239"
+  id: 927
+  display_name: "Parnassius clodius"
+}
+item {
+  name: "68240"
+  id: 928
+  display_name: "Chlosyne lacinia"
+}
+item {
+  name: "68244"
+  id: 929
+  display_name: "Euptoieta claudia"
+}
+item {
+  name: "68249"
+  id: 930
+  display_name: "Dymasia dymas"
+}
+item {
+  name: "68251"
+  id: 931
+  display_name: "Limenitis weidemeyerii"
+}
+item {
+  name: "133790"
+  id: 932
+  display_name: "Chalybion californicum"
+}
+item {
+  name: "84644"
+  id: 933
+  display_name: "Phalangium opilio"
+}
+item {
+  name: "68262"
+  id: 934
+  display_name: "Polygonia faunus"
+}
+item {
+  name: "133799"
+  id: 935
+  display_name: "Xenox tigrinus"
+}
+item {
+  name: "68264"
+  id: 936
+  display_name: "Asterocampa celtis"
+}
+item {
+  name: "132892"
+  id: 937
+  display_name: "Anacridium aegyptium"
+}
+item {
+  name: "68268"
+  id: 938
+  display_name: "Euptoieta hegesia"
+}
+item {
+  name: "68269"
+  id: 939
+  display_name: "Aglais milberti"
+}
+item {
+  name: "43694"
+  id: 940
+  display_name: "Loxodonta africana"
+}
+item {
+  name: "59165"
+  id: 941
+  display_name: "Apodemia mormo"
+}
+item {
+  name: "68274"
+  id: 942
+  display_name: "Phyciodes phaon"
+}
+item {
+  name: "68275"
+  id: 943
+  display_name: "Battus polydamas"
+}
+item {
+  name: "84662"
+  id: 944
+  display_name: "Celastrina lucia"
+}
+item {
+  name: "16842"
+  id: 945
+  display_name: "Myiozetetes similis"
+}
+item {
+  name: "133826"
+  id: 946
+  display_name: "Zelus longipes"
+}
+item {
+  name: "14912"
+  id: 947
+  display_name: "Toxostoma curvirostre"
+}
+item {
+  name: "53708"
+  id: 948
+  display_name: "Pacifastacus leniusculus"
+}
+item {
+  name: "117452"
+  id: 949
+  display_name: "Sphinx kalmiae"
+}
+item {
+  name: "182997"
+  id: 950
+  display_name: "Megisto rubricata"
+}
+item {
+  name: "223965"
+  id: 951
+  display_name: "Lithacodia musta"
+}
+item {
+  name: "125663"
+  id: 952
+  display_name: "Kelletia kelletii"
+}
+item {
+  name: "125669"
+  id: 953
+  display_name: "Rumina decollata"
+}
+item {
+  name: "68328"
+  id: 954
+  display_name: "Oxythyrea funesta"
+}
+item {
+  name: "179324"
+  id: 955
+  display_name: "Dactylotum bicolor"
+}
+item {
+  name: "68330"
+  id: 956
+  display_name: "Arctia caja"
+}
+item {
+  name: "2548"
+  id: 957
+  display_name: "Megaceryle alcyon"
+}
+item {
+  name: "207600"
+  id: 958
+  display_name: "Thasus neocalifornicus"
+}
+item {
+  name: "207601"
+  id: 959
+  display_name: "Palpita quadristigmalis"
+}
+item {
+  name: "51954"
+  id: 960
+  display_name: "Sphecius speciosus"
+}
+item {
+  name: "207603"
+  id: 961
+  display_name: "Prolimacodes badia"
+}
+item {
+  name: "7294"
+  id: 962
+  display_name: "Eremophila alpestris"
+}
+item {
+  name: "19196"
+  id: 963
+  display_name: "Alisterus scapularis"
+}
+item {
+  name: "145194"
+  id: 964
+  display_name: "Cinnyris jugularis"
+}
+item {
+  name: "27390"
+  id: 965
+  display_name: "Desmognathus ochrophaeus"
+}
+item {
+  name: "207615"
+  id: 966
+  display_name: "Polistes apachus"
+}
+item {
+  name: "63275"
+  id: 967
+  display_name: "Tremex columba"
+}
+item {
+  name: "61910"
+  id: 968
+  display_name: "Orgyia antiqua"
+}
+item {
+  name: "199438"
+  id: 969
+  display_name: "Orgyia postica"
+}
+item {
+  name: "43794"
+  id: 970
+  display_name: "Castor canadensis"
+}
+item {
+  name: "84755"
+  id: 971
+  display_name: "Arion rufus"
+}
+item {
+  name: "51996"
+  id: 972
+  display_name: "Daphnis nerii"
+}
+item {
+  name: "194075"
+  id: 973
+  display_name: "Drymarchon melanurus erebennus"
+}
+item {
+  name: "133923"
+  id: 974
+  display_name: "Mermiria bivittata"
+}
+item {
+  name: "84778"
+  id: 975
+  display_name: "Leptinotarsa decemlineata"
+}
+item {
+  name: "11051"
+  id: 976
+  display_name: "Xiphorhynchus flavigaster"
+}
+item {
+  name: "121992"
+  id: 977
+  display_name: "Cervus elaphus roosevelti"
+}
+item {
+  name: "27459"
+  id: 978
+  display_name: "Batrachoseps attenuatus"
+}
+item {
+  name: "84806"
+  id: 979
+  display_name: "Acanalonia conica"
+}
+item {
+  name: "52043"
+  id: 980
+  display_name: "Spoladea recurvalis"
+}
+item {
+  name: "27468"
+  id: 981
+  display_name: "Batrachoseps major"
+}
+item {
+  name: "133966"
+  id: 982
+  display_name: "Lomographa vestaliata"
+}
+item {
+  name: "27474"
+  id: 983
+  display_name: "Batrachoseps nigriventris"
+}
+item {
+  name: "101204"
+  id: 984
+  display_name: "Gambusia holbrooki"
+}
+item {
+  name: "52055"
+  id: 985
+  display_name: "Crocothemis servilia"
+}
+item {
+  name: "4580"
+  id: 986
+  display_name: "Jacana jacana"
+}
+item {
+  name: "346970"
+  id: 987
+  display_name: "Callophrys dumetorum"
+}
+item {
+  name: "27486"
+  id: 988
+  display_name: "Pseudotriton ruber"
+}
+item {
+  name: "52075"
+  id: 989
+  display_name: "Atalopedes campestris"
+}
+item {
+  name: "27500"
+  id: 990
+  display_name: "Gyrinophilus porphyriticus"
+}
+item {
+  name: "73203"
+  id: 991
+  display_name: "Phalaropus fulicarius"
+}
+item {
+  name: "322417"
+  id: 992
+  display_name: "Limacus flavus"
+}
+item {
+  name: "40083"
+  id: 993
+  display_name: "Gopherus berlandieri"
+}
+item {
+  name: "68469"
+  id: 994
+  display_name: "Papilio demodocus"
+}
+item {
+  name: "2938"
+  id: 995
+  display_name: "Streptopelia turtur"
+}
+item {
+  name: "117633"
+  id: 996
+  display_name: "Mopalia muscosa"
+}
+item {
+  name: "117641"
+  id: 997
+  display_name: "Nucella lamellosa"
+}
+item {
+  name: "322443"
+  id: 998
+  display_name: "Thasus gigas"
+}
+item {
+  name: "68492"
+  id: 999
+  display_name: "Hemidactylus mabouia"
+}
+item {
+  name: "143853"
+  id: 1000
+  display_name: "Pica hudsonia"
+}
+item {
+  name: "144757"
+  id: 1001
+  display_name: "Corvus cornix"
+}
+item {
+  name: "117650"
+  id: 1002
+  display_name: "Mytilus edulis"
+}
+item {
+  name: "19349"
+  id: 1003
+  display_name: "Myiopsitta monachus"
+}
+item {
+  name: "2969"
+  id: 1004
+  display_name: "Streptopelia decaocto"
+}
+item {
+  name: "9919"
+  id: 1005
+  display_name: "Piranga ludoviciana"
+}
+item {
+  name: "5009"
+  id: 1006
+  display_name: "Ixobrychus exilis"
+}
+item {
+  name: "117666"
+  id: 1007
+  display_name: "Pleuroncodes planipes"
+}
+item {
+  name: "7603"
+  id: 1008
+  display_name: "Auriparus flaviceps"
+}
+item {
+  name: "117674"
+  id: 1009
+  display_name: "Ligia occidentalis"
+}
+item {
+  name: "145223"
+  id: 1010
+  display_name: "Geothlypis tolmiei"
+}
+item {
+  name: "60341"
+  id: 1011
+  display_name: "Lithobates sphenocephalus"
+}
+item {
+  name: "60342"
+  id: 1012
+  display_name: "Thamnophis proximus"
+}
+item {
+  name: "52155"
+  id: 1013
+  display_name: "Dermacentor variabilis"
+}
+item {
+  name: "60349"
+  id: 1014
+  display_name: "Scincella lateralis"
+}
+item {
+  name: "52158"
+  id: 1015
+  display_name: "Schistocerca nitens"
+}
+item {
+  name: "117696"
+  id: 1016
+  display_name: "Dendraster excentricus"
+}
+item {
+  name: "232391"
+  id: 1017
+  display_name: "Tetracha carolina"
+}
+item {
+  name: "3017"
+  id: 1018
+  display_name: "Columba livia"
+}
+item {
+  name: "145229"
+  id: 1019
+  display_name: "Setophaga citrina"
+}
+item {
+  name: "84950"
+  id: 1020
+  display_name: "Alypia octomaculata"
+}
+item {
+  name: "52188"
+  id: 1021
+  display_name: "Rhincodon typus"
+}
+item {
+  name: "494559"
+  id: 1022
+  display_name: "Polydrusus formosus"
+}
+item {
+  name: "145232"
+  id: 1023
+  display_name: "Setophaga cerulea"
+}
+item {
+  name: "3048"
+  id: 1024
+  display_name: "Columba palumbus"
+}
+item {
+  name: "9922"
+  id: 1025
+  display_name: "Piranga bidentata"
+}
+item {
+  name: "44026"
+  id: 1026
+  display_name: "Erethizon dorsatum"
+}
+item {
+  name: "61505"
+  id: 1027
+  display_name: "Manduca sexta"
+}
+item {
+  name: "84994"
+  id: 1028
+  display_name: "Acanthocephala declivis"
+}
+item {
+  name: "27652"
+  id: 1029
+  display_name: "Hemidactylium scutatum"
+}
+item {
+  name: "117767"
+  id: 1030
+  display_name: "Cervus elaphus nannodes"
+}
+item {
+  name: "494603"
+  id: 1031
+  display_name: "Hermissenda opalescens"
+}
+item {
+  name: "39819"
+  id: 1032
+  display_name: "Terrapene carolina bauri"
+}
+item {
+  name: "3093"
+  id: 1033
+  display_name: "Patagioenas leucocephala"
+}
+item {
+  name: "205316"
+  id: 1034
+  display_name: "Aidemona azteca"
+}
+item {
+  name: "216093"
+  id: 1035
+  display_name: "Caracolus marginella"
+}
+item {
+  name: "44062"
+  id: 1036
+  display_name: "Thomomys bottae"
+}
+item {
+  name: "85024"
+  id: 1037
+  display_name: "Heraclides cresphontes"
+}
+item {
+  name: "3108"
+  id: 1038
+  display_name: "Patagioenas fasciata"
+}
+item {
+  name: "213510"
+  id: 1039
+  display_name: "Anageshna primordialis"
+}
+item {
+  name: "85030"
+  id: 1040
+  display_name: "Crocothemis erythraea"
+}
+item {
+  name: "85034"
+  id: 1041
+  display_name: "Neoscona crucifera"
+}
+item {
+  name: "3117"
+  id: 1042
+  display_name: "Patagioenas flavirostris"
+}
+item {
+  name: "207924"
+  id: 1043
+  display_name: "Synchlora frondaria"
+}
+item {
+  name: "35900"
+  id: 1044
+  display_name: "Lacerta bilineata"
+}
+item {
+  name: "24382"
+  id: 1045
+  display_name: "Osteopilus septentrionalis"
+}
+item {
+  name: "145249"
+  id: 1046
+  display_name: "Setophaga discolor"
+}
+item {
+  name: "52297"
+  id: 1047
+  display_name: "Triakis semifasciata"
+}
+item {
+  name: "27726"
+  id: 1048
+  display_name: "Salamandra salamandra"
+}
+item {
+  name: "27727"
+  id: 1049
+  display_name: "Bogertophis subocularis"
+}
+item {
+  name: "143043"
+  id: 1050
+  display_name: "Cycnia tenera"
+}
+item {
+  name: "52313"
+  id: 1051
+  display_name: "Diodon hystrix"
+}
+item {
+  name: "143316"
+  id: 1052
+  display_name: "Schinia florida"
+}
+item {
+  name: "61968"
+  id: 1053
+  display_name: "Graphosoma lineatum"
+}
+item {
+  name: "502885"
+  id: 1054
+  display_name: "Lissachatina fulica"
+}
+item {
+  name: "71029"
+  id: 1055
+  display_name: "Crotalus cerastes cerastes"
+}
+item {
+  name: "207977"
+  id: 1056
+  display_name: "Aglais io"
+}
+item {
+  name: "19577"
+  id: 1057
+  display_name: "Chordeiles minor"
+}
+item {
+  name: "93312"
+  id: 1058
+  display_name: "Acropora palmata"
+}
+item {
+  name: "52354"
+  id: 1059
+  display_name: "Ambystoma laterale"
+}
+item {
+  name: "19587"
+  id: 1060
+  display_name: "Chordeiles acutipennis"
+}
+item {
+  name: "58585"
+  id: 1061
+  display_name: "Limenitis arthemis astyanax"
+}
+item {
+  name: "134277"
+  id: 1062
+  display_name: "Gastrophryne olivacea"
+}
+item {
+  name: "60551"
+  id: 1063
+  display_name: "Papilio glaucus"
+}
+item {
+  name: "3731"
+  id: 1064
+  display_name: "Platalea leucorodia"
+}
+item {
+  name: "232593"
+  id: 1065
+  display_name: "Thyris sepulchralis"
+}
+item {
+  name: "19609"
+  id: 1066
+  display_name: "Phalaenoptilus nuttallii"
+}
+item {
+  name: "126106"
+  id: 1067
+  display_name: "Haploa clymene"
+}
+item {
+  name: "27805"
+  id: 1068
+  display_name: "Notophthalmus viridescens"
+}
+item {
+  name: "199840"
+  id: 1069
+  display_name: "Haemorhous mexicanus"
+}
+item {
+  name: "199841"
+  id: 1070
+  display_name: "Haemorhous purpureus"
+}
+item {
+  name: "219719"
+  id: 1071
+  display_name: "Eudryas unio"
+}
+item {
+  name: "27818"
+  id: 1072
+  display_name: "Taricha torosa"
+}
+item {
+  name: "19627"
+  id: 1073
+  display_name: "Nyctidromus albicollis"
+}
+item {
+  name: "28750"
+  id: 1074
+  display_name: "Salvadora grahamiae lineata"
+}
+item {
+  name: "27824"
+  id: 1075
+  display_name: "Taricha rivularis"
+}
+item {
+  name: "146632"
+  id: 1076
+  display_name: "Toxomerus politus"
+}
+item {
+  name: "52402"
+  id: 1077
+  display_name: "Cetonia aurata"
+}
+item {
+  name: "18291"
+  id: 1078
+  display_name: "Campephilus guatemalensis"
+}
+item {
+  name: "60598"
+  id: 1079
+  display_name: "Ixodes scapularis"
+}
+item {
+  name: "199870"
+  id: 1080
+  display_name: "Pyralis farinalis"
+}
+item {
+  name: "60607"
+  id: 1081
+  display_name: "Limenitis arthemis"
+}
+item {
+  name: "205241"
+  id: 1082
+  display_name: "Plagodis phlogosaria"
+}
+item {
+  name: "14898"
+  id: 1083
+  display_name: "Toxostoma rufum"
+}
+item {
+  name: "126153"
+  id: 1084
+  display_name: "Amphion floridensis"
+}
+item {
+  name: "126155"
+  id: 1085
+  display_name: "Vespula germanica"
+}
+item {
+  name: "51392"
+  id: 1086
+  display_name: "Morone saxatilis"
+}
+item {
+  name: "3280"
+  id: 1087
+  display_name: "Leptotila verreauxi"
+}
+item {
+  name: "19670"
+  id: 1088
+  display_name: "Nyctibius jamaicensis"
+}
+item {
+  name: "6929"
+  id: 1089
+  display_name: "Anas penelope"
+}
+item {
+  name: "97738"
+  id: 1090
+  display_name: "Chromagrion conditum"
+}
+item {
+  name: "52449"
+  id: 1091
+  display_name: "Rhinecanthus rectangulus"
+}
+item {
+  name: "52451"
+  id: 1092
+  display_name: "Naso lituratus"
+}
+item {
+  name: "56529"
+  id: 1093
+  display_name: "Papilio machaon"
+}
+item {
+  name: "199913"
+  id: 1094
+  display_name: "Buteo plagiatus"
+}
+item {
+  name: "199914"
+  id: 1095
+  display_name: "Selasphorus calliope"
+}
+item {
+  name: "85227"
+  id: 1096
+  display_name: "Hemideina crassidens"
+}
+item {
+  name: "36076"
+  id: 1097
+  display_name: "Cophosaurus texanus"
+}
+item {
+  name: "36077"
+  id: 1098
+  display_name: "Cophosaurus texanus texanus"
+}
+item {
+  name: "208112"
+  id: 1099
+  display_name: "Palpita magniferalis"
+}
+item {
+  name: "85235"
+  id: 1100
+  display_name: "Deinacrida rugosa"
+}
+item {
+  name: "93429"
+  id: 1101
+  display_name: "Aeshna constricta"
+}
+item {
+  name: "36086"
+  id: 1102
+  display_name: "Callisaurus draconoides rhodostictus"
+}
+item {
+  name: "126204"
+  id: 1103
+  display_name: "Synchlora aerata"
+}
+item {
+  name: "93437"
+  id: 1104
+  display_name: "Aeshna mixta"
+}
+item {
+  name: "126207"
+  id: 1105
+  display_name: "Schizura unicornis"
+}
+item {
+  name: "126209"
+  id: 1106
+  display_name: "Metcalfa pruinosa"
+}
+item {
+  name: "126211"
+  id: 1107
+  display_name: "Poecilocapsus lineatus"
+}
+item {
+  name: "36100"
+  id: 1108
+  display_name: "Uta stansburiana elegans"
+}
+item {
+  name: "48342"
+  id: 1109
+  display_name: "Hemigrapsus nudus"
+}
+item {
+  name: "199942"
+  id: 1110
+  display_name: "Strategus aloeus"
+}
+item {
+  name: "126215"
+  id: 1111
+  display_name: "Monobia quadridens"
+}
+item {
+  name: "101640"
+  id: 1112
+  display_name: "Gomphaeschna furcillata"
+}
+item {
+  name: "126217"
+  id: 1113
+  display_name: "Pyrausta orphisalis"
+}
+item {
+  name: "36107"
+  id: 1114
+  display_name: "Urosaurus ornatus"
+}
+item {
+  name: "51940"
+  id: 1115
+  display_name: "Hemidactylus frenatus"
+}
+item {
+  name: "36121"
+  id: 1116
+  display_name: "Urosaurus graciosus"
+}
+item {
+  name: "19743"
+  id: 1117
+  display_name: "Megascops kennicottii"
+}
+item {
+  name: "68901"
+  id: 1118
+  display_name: "Salticus scenicus"
+}
+item {
+  name: "44326"
+  id: 1119
+  display_name: "Microtus californicus"
+}
+item {
+  name: "82481"
+  id: 1120
+  display_name: "Pieris marginalis"
+}
+item {
+  name: "474332"
+  id: 1121
+  display_name: "Porphyrio poliocephalus"
+}
+item {
+  name: "81674"
+  id: 1122
+  display_name: "Rivula propinqualis"
+}
+item {
+  name: "126252"
+  id: 1123
+  display_name: "Mastigoproctus giganteus"
+}
+item {
+  name: "36142"
+  id: 1124
+  display_name: "Sceloporus undulatus"
+}
+item {
+  name: "68911"
+  id: 1125
+  display_name: "Libellula needhami"
+}
+item {
+  name: "68912"
+  id: 1126
+  display_name: "Dysdera crocata"
+}
+item {
+  name: "42888"
+  id: 1127
+  display_name: "Macropus giganteus"
+}
+item {
+  name: "19765"
+  id: 1128
+  display_name: "Megascops asio"
+}
+item {
+  name: "68918"
+  id: 1129
+  display_name: "Poecilanthrax lucifer"
+}
+item {
+  name: "333705"
+  id: 1130
+  display_name: "Pantherophis obsoletus lindheimeri"
+}
+item {
+  name: "126267"
+  id: 1131
+  display_name: "Coleomegilla maculata"
+}
+item {
+  name: "101693"
+  id: 1132
+  display_name: "Gomphus vastus"
+}
+item {
+  name: "85221"
+  id: 1133
+  display_name: "Hemideina thoracica"
+}
+item {
+  name: "126276"
+  id: 1134
+  display_name: "Agrotis ipsilon"
+}
+item {
+  name: "85317"
+  id: 1135
+  display_name: "Eurosta solidaginis"
+}
+item {
+  name: "36169"
+  id: 1136
+  display_name: "Sceloporus spinosus"
+}
+item {
+  name: "60752"
+  id: 1137
+  display_name: "Hermeuptychia sosybius"
+}
+item {
+  name: "60754"
+  id: 1138
+  display_name: "Pyromorpha dimidiata"
+}
+item {
+  name: "126291"
+  id: 1139
+  display_name: "Prosapia bicincta"
+}
+item {
+  name: "52564"
+  id: 1140
+  display_name: "Anthopleura elegantissima"
+}
+item {
+  name: "126293"
+  id: 1141
+  display_name: "Prionoxystus robiniae"
+}
+item {
+  name: "120719"
+  id: 1142
+  display_name: "Pseudacris hypochondriaca"
+}
+item {
+  name: "36189"
+  id: 1143
+  display_name: "Sceloporus poinsettii"
+}
+item {
+  name: "52576"
+  id: 1144
+  display_name: "Uroctonus mordax"
+}
+item {
+  name: "36198"
+  id: 1145
+  display_name: "Sceloporus orcutti"
+}
+item {
+  name: "52584"
+  id: 1146
+  display_name: "Pantala hymenaea"
+}
+item {
+  name: "44395"
+  id: 1147
+  display_name: "Peromyscus leucopus"
+}
+item {
+  name: "36204"
+  id: 1148
+  display_name: "Sceloporus occidentalis"
+}
+item {
+  name: "52589"
+  id: 1149
+  display_name: "Coenonympha pamphilus"
+}
+item {
+  name: "3439"
+  id: 1150
+  display_name: "Zenaida auriculata"
+}
+item {
+  name: "36208"
+  id: 1151
+  display_name: "Sceloporus occidentalis bocourtii"
+}
+item {
+  name: "72936"
+  id: 1152
+  display_name: "Hymenolaimus malacorhynchos"
+}
+item {
+  name: "85362"
+  id: 1153
+  display_name: "Sphex ichneumoneus"
+}
+item {
+  name: "36217"
+  id: 1154
+  display_name: "Sceloporus merriami"
+}
+item {
+  name: "68993"
+  id: 1155
+  display_name: "Liometopum occidentale"
+}
+item {
+  name: "199916"
+  id: 1156
+  display_name: "Setophaga caerulescens"
+}
+item {
+  name: "52620"
+  id: 1157
+  display_name: "Cicindela oregona"
+}
+item {
+  name: "36243"
+  id: 1158
+  display_name: "Sceloporus jarrovii"
+}
+item {
+  name: "52628"
+  id: 1159
+  display_name: "Araneus diadematus"
+}
+item {
+  name: "180007"
+  id: 1160
+  display_name: "Otospermophilus beecheyi"
+}
+item {
+  name: "85408"
+  id: 1161
+  display_name: "Erythemis collocata"
+}
+item {
+  name: "36262"
+  id: 1162
+  display_name: "Sceloporus grammicus"
+}
+item {
+  name: "60839"
+  id: 1163
+  display_name: "Spilosoma virginica"
+}
+item {
+  name: "16968"
+  id: 1164
+  display_name: "Camptostoma imberbe"
+}
+item {
+  name: "4715"
+  id: 1165
+  display_name: "Caracara plancus"
+}
+item {
+  name: "313246"
+  id: 1166
+  display_name: "Olla v-nigrum"
+}
+item {
+  name: "126393"
+  id: 1167
+  display_name: "Stomolophus meleagris"
+}
+item {
+  name: "126397"
+  id: 1168
+  display_name: "Halysidota harrisii"
+}
+item {
+  name: "64221"
+  id: 1169
+  display_name: "Bipalium kewense"
+}
+item {
+  name: "28102"
+  id: 1170
+  display_name: "Virginia striatula"
+}
+item {
+  name: "150985"
+  id: 1171
+  display_name: "Planorbella trivolvis"
+}
+item {
+  name: "36306"
+  id: 1172
+  display_name: "Phrynosoma modestum"
+}
+item {
+  name: "36307"
+  id: 1173
+  display_name: "Phrynosoma orbiculare"
+}
+item {
+  name: "199929"
+  id: 1174
+  display_name: "Plagiometriona clavata"
+}
+item {
+  name: "3545"
+  id: 1175
+  display_name: "Columbina passerina"
+}
+item {
+  name: "36315"
+  id: 1176
+  display_name: "Phrynosoma hernandesi"
+}
+item {
+  name: "367556"
+  id: 1177
+  display_name: "Eupsittula nana"
+}
+item {
+  name: "371963"
+  id: 1178
+  display_name: "Lampropeltis multifasciata"
+}
+item {
+  name: "36339"
+  id: 1179
+  display_name: "Holbrookia propinqua"
+}
+item {
+  name: "36094"
+  id: 1180
+  display_name: "Uta stansburiana"
+}
+item {
+  name: "36343"
+  id: 1181
+  display_name: "Holbrookia maculata"
+}
+item {
+  name: "52766"
+  id: 1182
+  display_name: "Megaphasma denticrus"
+}
+item {
+  name: "18941"
+  id: 1183
+  display_name: "Nestor notabilis"
+}
+item {
+  name: "3580"
+  id: 1184
+  display_name: "Columbina talpacoti"
+}
+item {
+  name: "123690"
+  id: 1185
+  display_name: "Caranx melampygus"
+}
+item {
+  name: "52482"
+  id: 1186
+  display_name: "Episyrphus balteatus"
+}
+item {
+  name: "28762"
+  id: 1187
+  display_name: "Rhinocheilus lecontei"
+}
+item {
+  name: "3607"
+  id: 1188
+  display_name: "Geopelia striata"
+}
+item {
+  name: "52484"
+  id: 1189
+  display_name: "Celastrina echo"
+}
+item {
+  name: "61293"
+  id: 1190
+  display_name: "Thaumetopoea pityocampa"
+}
+item {
+  name: "19998"
+  id: 1191
+  display_name: "Athene noctua"
+}
+item {
+  name: "44575"
+  id: 1192
+  display_name: "Rattus rattus"
+}
+item {
+  name: "44576"
+  id: 1193
+  display_name: "Rattus norvegicus"
+}
+item {
+  name: "133250"
+  id: 1194
+  display_name: "Tettigonia viridissima"
+}
+item {
+  name: "52774"
+  id: 1195
+  display_name: "Bombus fervidus"
+}
+item {
+  name: "49756"
+  id: 1196
+  display_name: "Nephila clavipes"
+}
+item {
+  name: "52779"
+  id: 1197
+  display_name: "Bombus bimaculatus"
+}
+item {
+  name: "52782"
+  id: 1198
+  display_name: "Melissodes bimaculata"
+}
+item {
+  name: "126513"
+  id: 1199
+  display_name: "Larinioides cornutus"
+}
+item {
+  name: "69170"
+  id: 1200
+  display_name: "Hemigrapsus oregonensis"
+}
+item {
+  name: "1971"
+  id: 1201
+  display_name: "Crotophaga ani"
+}
+item {
+  name: "12942"
+  id: 1202
+  display_name: "Sialia sialis"
+}
+item {
+  name: "126532"
+  id: 1203
+  display_name: "Toxomerus geminatus"
+}
+item {
+  name: "216649"
+  id: 1204
+  display_name: "Chauliognathus pensylvanicus"
+}
+item {
+  name: "3734"
+  id: 1205
+  display_name: "Platalea alba"
+}
+item {
+  name: "216651"
+  id: 1206
+  display_name: "Chelinidea vittiger"
+}
+item {
+  name: "20044"
+  id: 1207
+  display_name: "Bubo virginianus"
+}
+item {
+  name: "11855"
+  id: 1208
+  display_name: "Petrochelidon fulva"
+}
+item {
+  name: "28246"
+  id: 1209
+  display_name: "Arizona elegans"
+}
+item {
+  name: "224855"
+  id: 1210
+  display_name: "Melipotis indomita"
+}
+item {
+  name: "11867"
+  id: 1211
+  display_name: "Progne subis"
+}
+item {
+  name: "126562"
+  id: 1212
+  display_name: "Setophaga coronata auduboni"
+}
+item {
+  name: "126568"
+  id: 1213
+  display_name: "Manduca rustica"
+}
+item {
+  name: "11882"
+  id: 1214
+  display_name: "Hirundo neoxena"
+}
+item {
+  name: "11901"
+  id: 1215
+  display_name: "Hirundo rustica"
+}
+item {
+  name: "52865"
+  id: 1216
+  display_name: "Tramea lacerata"
+}
+item {
+  name: "142978"
+  id: 1217
+  display_name: "Simyra insularis"
+}
+item {
+  name: "123499"
+  id: 1218
+  display_name: "Notophthalmus viridescens viridescens"
+}
+item {
+  name: "339592"
+  id: 1219
+  display_name: "Calidris virgata"
+}
+item {
+  name: "339593"
+  id: 1220
+  display_name: "Calidris pugnax"
+}
+item {
+  name: "44311"
+  id: 1221
+  display_name: "Microtus pennsylvanicus"
+}
+item {
+  name: "142988"
+  id: 1222
+  display_name: "Lerema accius"
+}
+item {
+  name: "142990"
+  id: 1223
+  display_name: "Autographa precationis"
+}
+item {
+  name: "142995"
+  id: 1224
+  display_name: "Hymenia perspectalis"
+}
+item {
+  name: "129423"
+  id: 1225
+  display_name: "Zelus luridus"
+}
+item {
+  name: "3733"
+  id: 1226
+  display_name: "Platalea regia"
+}
+item {
+  name: "470678"
+  id: 1227
+  display_name: "Cerithideopsis californica"
+}
+item {
+  name: "146713"
+  id: 1228
+  display_name: "Elaphria grata"
+}
+item {
+  name: "143002"
+  id: 1229
+  display_name: "Orthonama obstipata"
+}
+item {
+  name: "11931"
+  id: 1230
+  display_name: "Tachycineta thalassina"
+}
+item {
+  name: "143005"
+  id: 1231
+  display_name: "Costaconvexa centrostrigaria"
+}
+item {
+  name: "3743"
+  id: 1232
+  display_name: "Bostrychia hagedash"
+}
+item {
+  name: "143009"
+  id: 1233
+  display_name: "Ectropis crepuscularia"
+}
+item {
+  name: "36514"
+  id: 1234
+  display_name: "Anolis carolinensis"
+}
+item {
+  name: "143012"
+  id: 1235
+  display_name: "Zanclognatha pedipilalis"
+}
+item {
+  name: "11941"
+  id: 1236
+  display_name: "Riparia riparia"
+}
+item {
+  name: "52902"
+  id: 1237
+  display_name: "Palthis asopialis"
+}
+item {
+  name: "3751"
+  id: 1238
+  display_name: "Eudocimus albus"
+}
+item {
+  name: "52906"
+  id: 1239
+  display_name: "Chytonix palliatricula"
+}
+item {
+  name: "3756"
+  id: 1240
+  display_name: "Plegadis falcinellus"
+}
+item {
+  name: "3759"
+  id: 1241
+  display_name: "Plegadis chihi"
+}
+item {
+  name: "143024"
+  id: 1242
+  display_name: "Eusarca confusaria"
+}
+item {
+  name: "62067"
+  id: 1243
+  display_name: "Orthetrum cancellatum"
+}
+item {
+  name: "28340"
+  id: 1244
+  display_name: "Thamnophis sauritus"
+}
+item {
+  name: "28345"
+  id: 1245
+  display_name: "Thamnophis cyrtopsis"
+}
+item {
+  name: "143034"
+  id: 1246
+  display_name: "Hippodamia variegata"
+}
+item {
+  name: "28347"
+  id: 1247
+  display_name: "Thamnophis cyrtopsis ocellatus"
+}
+item {
+  name: "52925"
+  id: 1248
+  display_name: "Phyciodes tharos"
+}
+item {
+  name: "8010"
+  id: 1249
+  display_name: "Corvus corax"
+}
+item {
+  name: "11970"
+  id: 1250
+  display_name: "Stelgidopteryx serripennis"
+}
+item {
+  name: "28362"
+  id: 1251
+  display_name: "Thamnophis sirtalis"
+}
+item {
+  name: "3788"
+  id: 1252
+  display_name: "Sula dactylatra"
+}
+item {
+  name: "44749"
+  id: 1253
+  display_name: "Neotoma fuscipes"
+}
+item {
+  name: "52943"
+  id: 1254
+  display_name: "Trichodezia albovittata"
+}
+item {
+  name: "3793"
+  id: 1255
+  display_name: "Sula sula"
+}
+item {
+  name: "101667"
+  id: 1256
+  display_name: "Gomphus exilis"
+}
+item {
+  name: "3797"
+  id: 1257
+  display_name: "Sula leucogaster"
+}
+item {
+  name: "118486"
+  id: 1258
+  display_name: "Macaria aemulataria"
+}
+item {
+  name: "3801"
+  id: 1259
+  display_name: "Morus serrator"
+}
+item {
+  name: "28378"
+  id: 1260
+  display_name: "Thamnophis radix"
+}
+item {
+  name: "118492"
+  id: 1261
+  display_name: "Helicoverpa zea"
+}
+item {
+  name: "148793"
+  id: 1262
+  display_name: "Asterocampa leilia"
+}
+item {
+  name: "28384"
+  id: 1263
+  display_name: "Thamnophis proximus rubrilineatus"
+}
+item {
+  name: "257761"
+  id: 1264
+  display_name: "Phocides polybius"
+}
+item {
+  name: "28387"
+  id: 1265
+  display_name: "Thamnophis proximus orarius"
+}
+item {
+  name: "28390"
+  id: 1266
+  display_name: "Thamnophis marcianus"
+}
+item {
+  name: "118503"
+  id: 1267
+  display_name: "Darapsa myron"
+}
+item {
+  name: "3817"
+  id: 1268
+  display_name: "Eudyptula minor"
+}
+item {
+  name: "36135"
+  id: 1269
+  display_name: "Uma scoparia"
+}
+item {
+  name: "28396"
+  id: 1270
+  display_name: "Thamnophis hammondii"
+}
+item {
+  name: "28400"
+  id: 1271
+  display_name: "Thamnophis elegans elegans"
+}
+item {
+  name: "118513"
+  id: 1272
+  display_name: "Hypena scabra"
+}
+item {
+  name: "28403"
+  id: 1273
+  display_name: "Thamnophis elegans vagrans"
+}
+item {
+  name: "201342"
+  id: 1274
+  display_name: "Chalcoela iphitalis"
+}
+item {
+  name: "3831"
+  id: 1275
+  display_name: "Megadyptes antipodes"
+}
+item {
+  name: "126712"
+  id: 1276
+  display_name: "Corydalus cornutus"
+}
+item {
+  name: "30676"
+  id: 1277
+  display_name: "Agkistrodon piscivorus leucostoma"
+}
+item {
+  name: "3834"
+  id: 1278
+  display_name: "Scopus umbretta"
+}
+item {
+  name: "213631"
+  id: 1279
+  display_name: "Anicla infecta"
+}
+item {
+  name: "143105"
+  id: 1280
+  display_name: "Pleuroprucha insulsaria"
+}
+item {
+  name: "28418"
+  id: 1281
+  display_name: "Thamnophis atratus"
+}
+item {
+  name: "118531"
+  id: 1282
+  display_name: "Parallelia bistriaris"
+}
+item {
+  name: "145363"
+  id: 1283
+  display_name: "Troglodytes troglodytes"
+}
+item {
+  name: "3845"
+  id: 1284
+  display_name: "Calidris canutus"
+}
+item {
+  name: "12038"
+  id: 1285
+  display_name: "Lanius collurio"
+}
+item {
+  name: "143114"
+  id: 1286
+  display_name: "Phragmatobia fuliginosa"
+}
+item {
+  name: "3851"
+  id: 1287
+  display_name: "Calidris bairdii"
+}
+item {
+  name: "324226"
+  id: 1288
+  display_name: "Meleagris gallopavo intermedia"
+}
+item {
+  name: "143118"
+  id: 1289
+  display_name: "Pseudeustrotia carneola"
+}
+item {
+  name: "3855"
+  id: 1290
+  display_name: "Calidris mauri"
+}
+item {
+  name: "3856"
+  id: 1291
+  display_name: "Calidris maritima"
+}
+item {
+  name: "3857"
+  id: 1292
+  display_name: "Calidris alpina"
+}
+item {
+  name: "143124"
+  id: 1293
+  display_name: "Parapediasia teterrella"
+}
+item {
+  name: "143125"
+  id: 1294
+  display_name: "Hypena madefactalis"
+}
+item {
+  name: "3863"
+  id: 1295
+  display_name: "Calidris ferruginea"
+}
+item {
+  name: "118552"
+  id: 1296
+  display_name: "Felis catus"
+}
+item {
+  name: "3865"
+  id: 1297
+  display_name: "Calidris melanotos"
+}
+item {
+  name: "3869"
+  id: 1298
+  display_name: "Limnodromus griseus"
+}
+item {
+  name: "118558"
+  id: 1299
+  display_name: "Manduca quinquemaculata"
+}
+item {
+  name: "118559"
+  id: 1300
+  display_name: "Tetraopes tetrophthalmus"
+}
+item {
+  name: "12065"
+  id: 1301
+  display_name: "Malurus cyaneus"
+}
+item {
+  name: "3878"
+  id: 1302
+  display_name: "Tringa nebularia"
+}
+item {
+  name: "101681"
+  id: 1303
+  display_name: "Gomphus militaris"
+}
+item {
+  name: "413483"
+  id: 1304
+  display_name: "Todiramphus sanctus vagans"
+}
+item {
+  name: "3885"
+  id: 1305
+  display_name: "Tringa ochropus"
+}
+item {
+  name: "3888"
+  id: 1306
+  display_name: "Tringa glareola"
+}
+item {
+  name: "126770"
+  id: 1307
+  display_name: "Vulpes vulpes fulvus"
+}
+item {
+  name: "3892"
+  id: 1308
+  display_name: "Tringa melanoleuca"
+}
+item {
+  name: "3893"
+  id: 1309
+  display_name: "Tringa flavipes"
+}
+item {
+  name: "126775"
+  id: 1310
+  display_name: "Cervus elaphus nelsoni"
+}
+item {
+  name: "3896"
+  id: 1311
+  display_name: "Numenius arquata"
+}
+item {
+  name: "126777"
+  id: 1312
+  display_name: "Peucetia viridans"
+}
+item {
+  name: "3901"
+  id: 1313
+  display_name: "Numenius phaeopus"
+}
+item {
+  name: "32058"
+  id: 1314
+  display_name: "Elgaria multicarinata webbii"
+}
+item {
+  name: "413506"
+  id: 1315
+  display_name: "Phalacrocorax carbo novaehollandiae"
+}
+item {
+  name: "413508"
+  id: 1316
+  display_name: "Petroica macrocephala macrocephala"
+}
+item {
+  name: "413512"
+  id: 1317
+  display_name: "Petroica australis longipes"
+}
+item {
+  name: "61258"
+  id: 1318
+  display_name: "Junonia evarete"
+}
+item {
+  name: "28493"
+  id: 1319
+  display_name: "Tantilla nigriceps"
+}
+item {
+  name: "413522"
+  id: 1320
+  display_name: "Prosthemadera novaeseelandiae novaeseelandiae"
+}
+item {
+  name: "58506"
+  id: 1321
+  display_name: "Polites themistocles"
+}
+item {
+  name: "28505"
+  id: 1322
+  display_name: "Tantilla gracilis"
+}
+item {
+  name: "20315"
+  id: 1323
+  display_name: "Asio flammeus"
+}
+item {
+  name: "143196"
+  id: 1324
+  display_name: "Schinia arcigera"
+}
+item {
+  name: "413533"
+  id: 1325
+  display_name: "Rhipidura fuliginosa fuliginosa"
+}
+item {
+  name: "3936"
+  id: 1326
+  display_name: "Scolopax minor"
+}
+item {
+  name: "3938"
+  id: 1327
+  display_name: "Arenaria interpres"
+}
+item {
+  name: "3941"
+  id: 1328
+  display_name: "Arenaria melanocephala"
+}
+item {
+  name: "413543"
+  id: 1329
+  display_name: "Rhipidura fuliginosa placabilis"
+}
+item {
+  name: "3947"
+  id: 1330
+  display_name: "Limosa limosa"
+}
+item {
+  name: "3950"
+  id: 1331
+  display_name: "Limosa haemastica"
+}
+item {
+  name: "126269"
+  id: 1332
+  display_name: "Austrolestes colensonis"
+}
+item {
+  name: "3954"
+  id: 1333
+  display_name: "Limosa fedoa"
+}
+item {
+  name: "199998"
+  id: 1334
+  display_name: "Pedicia albivitta"
+}
+item {
+  name: "3959"
+  id: 1335
+  display_name: "Phalaropus lobatus"
+}
+item {
+  name: "3962"
+  id: 1336
+  display_name: "Bartramia longicauda"
+}
+item {
+  name: "199999"
+  id: 1337
+  display_name: "Callopistria mollissima"
+}
+item {
+  name: "104426"
+  id: 1338
+  display_name: "Lestes disjunctus"
+}
+item {
+  name: "126848"
+  id: 1339
+  display_name: "Delphinia picta"
+}
+item {
+  name: "3951"
+  id: 1340
+  display_name: "Limosa lapponica"
+}
+item {
+  name: "20356"
+  id: 1341
+  display_name: "Aegolius acadicus"
+}
+item {
+  name: "121792"
+  id: 1342
+  display_name: "Polistes carolina"
+}
+item {
+  name: "3978"
+  id: 1343
+  display_name: "Actitis hypoleucos"
+}
+item {
+  name: "53911"
+  id: 1344
+  display_name: "Cyprinus carpio"
+}
+item {
+  name: "135055"
+  id: 1345
+  display_name: "Bufotes balearicus"
+}
+item {
+  name: "19121"
+  id: 1346
+  display_name: "Trichoglossus haematodus"
+}
+item {
+  name: "28562"
+  id: 1347
+  display_name: "Storeria dekayi"
+}
+item {
+  name: "28563"
+  id: 1348
+  display_name: "Storeria dekayi texana"
+}
+item {
+  name: "20372"
+  id: 1349
+  display_name: "Surnia ulula"
+}
+item {
+  name: "135064"
+  id: 1350
+  display_name: "Bufotes viridis"
+}
+item {
+  name: "28570"
+  id: 1351
+  display_name: "Storeria dekayi dekayi"
+}
+item {
+  name: "61341"
+  id: 1352
+  display_name: "Narceus americanus"
+}
+item {
+  name: "7493"
+  id: 1353
+  display_name: "Polioptila caerulea"
+}
+item {
+  name: "29339"
+  id: 1354
+  display_name: "Natrix natrix"
+}
+item {
+  name: "9135"
+  id: 1355
+  display_name: "Spizella passerina"
+}
+item {
+  name: "126889"
+  id: 1356
+  display_name: "Toxomerus marginatus"
+}
+item {
+  name: "143274"
+  id: 1357
+  display_name: "Gluphisia septentrionis"
+}
+item {
+  name: "343021"
+  id: 1358
+  display_name: "Anguis fragilis"
+}
+item {
+  name: "14591"
+  id: 1359
+  display_name: "Pycnonotus jocosus"
+}
+item {
+  name: "10227"
+  id: 1360
+  display_name: "Passerina cyanea"
+}
+item {
+  name: "10228"
+  id: 1361
+  display_name: "Passerina versicolor"
+}
+item {
+  name: "61371"
+  id: 1362
+  display_name: "Panulirus interruptus"
+}
+item {
+  name: "143294"
+  id: 1363
+  display_name: "Colias croceus"
+}
+item {
+  name: "135104"
+  id: 1364
+  display_name: "Ichthyosaura alpestris"
+}
+item {
+  name: "83958"
+  id: 1365
+  display_name: "Phryganidia californica"
+}
+item {
+  name: "143302"
+  id: 1366
+  display_name: "Megapallifera mutabilis"
+}
+item {
+  name: "12231"
+  id: 1367
+  display_name: "Manorina melanocephala"
+}
+item {
+  name: "200661"
+  id: 1368
+  display_name: "Coluber constrictor mormon"
+}
+item {
+  name: "3681"
+  id: 1369
+  display_name: "Ocyphaps lophotes"
+}
+item {
+  name: "4773"
+  id: 1370
+  display_name: "Jabiru mycteria"
+}
+item {
+  name: "135140"
+  id: 1371
+  display_name: "Taricha sierrae"
+}
+item {
+  name: "28649"
+  id: 1372
+  display_name: "Sonora semiannulata"
+}
+item {
+  name: "53226"
+  id: 1373
+  display_name: "Boisea rubrolineata"
+}
+item {
+  name: "53227"
+  id: 1374
+  display_name: "Boisea trivittata"
+}
+item {
+  name: "14593"
+  id: 1375
+  display_name: "Pycnonotus cafer"
+}
+item {
+  name: "61428"
+  id: 1376
+  display_name: "Arion subfuscus"
+}
+item {
+  name: "333822"
+  id: 1377
+  display_name: "Anser cygnoides domesticus"
+}
+item {
+  name: "41641"
+  id: 1378
+  display_name: "Ursus arctos"
+}
+item {
+  name: "56602"
+  id: 1379
+  display_name: "Plebejus lupini"
+}
+item {
+  name: "55295"
+  id: 1380
+  display_name: "Grapsus grapsus"
+}
+item {
+  name: "36181"
+  id: 1381
+  display_name: "Sceloporus cyanogenys"
+}
+item {
+  name: "41708"
+  id: 1382
+  display_name: "Phoca vitulina"
+}
+item {
+  name: "118788"
+  id: 1383
+  display_name: "Desmia funeralis"
+}
+item {
+  name: "61445"
+  id: 1384
+  display_name: "Acanthocephala terminalis"
+}
+item {
+  name: "30721"
+  id: 1385
+  display_name: "Crotalus triseriatus"
+}
+item {
+  name: "180010"
+  id: 1386
+  display_name: "Callospermophilus lateralis"
+}
+item {
+  name: "53875"
+  id: 1387
+  display_name: "Ocypode quadrata"
+}
+item {
+  name: "18358"
+  id: 1388
+  display_name: "Picus viridis"
+}
+item {
+  name: "143390"
+  id: 1389
+  display_name: "Oxidus gracilis"
+}
+item {
+  name: "55785"
+  id: 1390
+  display_name: "Ochlodes agricola"
+}
+item {
+  name: "4141"
+  id: 1391
+  display_name: "Phoebastria nigripes"
+}
+item {
+  name: "20526"
+  id: 1392
+  display_name: "Struthio camelus"
+}
+item {
+  name: "32093"
+  id: 1393
+  display_name: "Boa constrictor"
+}
+item {
+  name: "4144"
+  id: 1394
+  display_name: "Phoebastria immutabilis"
+}
+item {
+  name: "74442"
+  id: 1395
+  display_name: "Hydrochoerus hydrochaeris"
+}
+item {
+  name: "61492"
+  id: 1396
+  display_name: "Chrysopilus thoracicus"
+}
+item {
+  name: "61495"
+  id: 1397
+  display_name: "Erythemis simplicicollis"
+}
+item {
+  name: "389177"
+  id: 1398
+  display_name: "Eriophora pustulosa"
+}
+item {
+  name: "61503"
+  id: 1399
+  display_name: "Ascalapha odorata"
+}
+item {
+  name: "118855"
+  id: 1400
+  display_name: "Calosoma scrutator"
+}
+item {
+  name: "61513"
+  id: 1401
+  display_name: "Adelges tsugae"
+}
+item {
+  name: "28749"
+  id: 1402
+  display_name: "Salvadora grahamiae"
+}
+item {
+  name: "143440"
+  id: 1403
+  display_name: "Ceratomia catalpae"
+}
+item {
+  name: "61523"
+  id: 1404
+  display_name: "Helix pomatia"
+}
+item {
+  name: "4180"
+  id: 1405
+  display_name: "Fulmarus glacialis"
+}
+item {
+  name: "143445"
+  id: 1406
+  display_name: "Pachysphinx modesta"
+}
+item {
+  name: "233560"
+  id: 1407
+  display_name: "Vespula squamosa"
+}
+item {
+  name: "126308"
+  id: 1408
+  display_name: "Marpesia chiron"
+}
+item {
+  name: "61536"
+  id: 1409
+  display_name: "Calopteryx virgo"
+}
+item {
+  name: "685"
+  id: 1410
+  display_name: "Francolinus pondicerianus"
+}
+item {
+  name: "60774"
+  id: 1411
+  display_name: "Psychomorpha epimenis"
+}
+item {
+  name: "135271"
+  id: 1412
+  display_name: "Amphibolips confluenta"
+}
+item {
+  name: "69736"
+  id: 1413
+  display_name: "Schistocerca americana"
+}
+item {
+  name: "69737"
+  id: 1414
+  display_name: "Xylophanes tersa"
+}
+item {
+  name: "6141"
+  id: 1415
+  display_name: "Cynanthus latirostris"
+}
+item {
+  name: "4205"
+  id: 1416
+  display_name: "Podiceps nigricollis"
+}
+item {
+  name: "69743"
+  id: 1417
+  display_name: "Wallengrenia otho"
+}
+item {
+  name: "4208"
+  id: 1418
+  display_name: "Podiceps cristatus"
+}
+item {
+  name: "4209"
+  id: 1419
+  display_name: "Podiceps auritus"
+}
+item {
+  name: "118901"
+  id: 1420
+  display_name: "Hyles gallii"
+}
+item {
+  name: "17871"
+  id: 1421
+  display_name: "Dendrocopos major"
+}
+item {
+  name: "143484"
+  id: 1422
+  display_name: "Blepharomastix ranalis"
+}
+item {
+  name: "4224"
+  id: 1423
+  display_name: "Podiceps grisegena"
+}
+item {
+  name: "200834"
+  id: 1424
+  display_name: "Sphenodon punctatus"
+}
+item {
+  name: "179995"
+  id: 1425
+  display_name: "Urocitellus beldingi"
+}
+item {
+  name: "322024"
+  id: 1426
+  display_name: "Apatura ilia"
+}
+item {
+  name: "44396"
+  id: 1427
+  display_name: "Peromyscus maniculatus"
+}
+item {
+  name: "4237"
+  id: 1428
+  display_name: "Tachybaptus ruficollis"
+}
+item {
+  name: "118930"
+  id: 1429
+  display_name: "Spodoptera ornithogalli"
+}
+item {
+  name: "118936"
+  id: 1430
+  display_name: "Euplagia quadripunctaria"
+}
+item {
+  name: "4804"
+  id: 1431
+  display_name: "Charadrius montanus"
+}
+item {
+  name: "127133"
+  id: 1432
+  display_name: "Hyphantria cunea"
+}
+item {
+  name: "143518"
+  id: 1433
+  display_name: "Prochoerodes lineola"
+}
+item {
+  name: "52592"
+  id: 1434
+  display_name: "Pararge aegeria"
+}
+item {
+  name: "36149"
+  id: 1435
+  display_name: "Sceloporus torquatus"
+}
+item {
+  name: "118951"
+  id: 1436
+  display_name: "Pterophylla camellifolia"
+}
+item {
+  name: "4265"
+  id: 1437
+  display_name: "Phalacrocorax auritus"
+}
+item {
+  name: "4270"
+  id: 1438
+  display_name: "Phalacrocorax carbo"
+}
+item {
+  name: "446640"
+  id: 1439
+  display_name: "Neomonachus schauinslandi"
+}
+item {
+  name: "118961"
+  id: 1440
+  display_name: "Conocephalus brevipennis"
+}
+item {
+  name: "28850"
+  id: 1441
+  display_name: "Regina septemvittata"
+}
+item {
+  name: "4277"
+  id: 1442
+  display_name: "Phalacrocorax penicillatus"
+}
+item {
+  name: "4234"
+  id: 1443
+  display_name: "Aechmophorus clarkii"
+}
+item {
+  name: "118967"
+  id: 1444
+  display_name: "Psyllobora vigintimaculata"
+}
+item {
+  name: "118968"
+  id: 1445
+  display_name: "Allograpta obliqua"
+}
+item {
+  name: "118970"
+  id: 1446
+  display_name: "Bombus impatiens"
+}
+item {
+  name: "123594"
+  id: 1447
+  display_name: "Anaxyrus americanus americanus"
+}
+item {
+  name: "69838"
+  id: 1448
+  display_name: "Cyanea capillata"
+}
+item {
+  name: "69844"
+  id: 1449
+  display_name: "Anthocharis midea"
+}
+item {
+  name: "48505"
+  id: 1450
+  display_name: "Junonia coenia"
+}
+item {
+  name: "151769"
+  id: 1451
+  display_name: "Diaphania hyalinata"
+}
+item {
+  name: "151770"
+  id: 1452
+  display_name: "Peridea angulosa"
+}
+item {
+  name: "53467"
+  id: 1453
+  display_name: "Leucauge venusta"
+}
+item {
+  name: "119013"
+  id: 1454
+  display_name: "Ctenucha virginica"
+}
+item {
+  name: "4327"
+  id: 1455
+  display_name: "Pelecanus onocrotalus"
+}
+item {
+  name: "143592"
+  id: 1456
+  display_name: "Spragueia leo"
+}
+item {
+  name: "200938"
+  id: 1457
+  display_name: "Diaethria anna"
+}
+item {
+  name: "4334"
+  id: 1458
+  display_name: "Pelecanus erythrorhynchos"
+}
+item {
+  name: "151794"
+  id: 1459
+  display_name: "Atta texana"
+}
+item {
+  name: "3454"
+  id: 1460
+  display_name: "Zenaida macroura"
+}
+item {
+  name: "4872"
+  id: 1461
+  display_name: "Vanellus miles"
+}
+item {
+  name: "4345"
+  id: 1462
+  display_name: "Larus occidentalis"
+}
+item {
+  name: "143610"
+  id: 1463
+  display_name: "Besma quercivoraria"
+}
+item {
+  name: "20733"
+  id: 1464
+  display_name: "Trogon massena"
+}
+item {
+  name: "143615"
+  id: 1465
+  display_name: "Udea rubigalis"
+}
+item {
+  name: "4352"
+  id: 1466
+  display_name: "Larus thayeri"
+}
+item {
+  name: "4353"
+  id: 1467
+  display_name: "Larus heermanni"
+}
+item {
+  name: "4354"
+  id: 1468
+  display_name: "Larus livens"
+}
+item {
+  name: "4356"
+  id: 1469
+  display_name: "Larus canus"
+}
+item {
+  name: "220826"
+  id: 1470
+  display_name: "Habrosyne scripta"
+}
+item {
+  name: "4361"
+  id: 1471
+  display_name: "Larus glaucoides"
+}
+item {
+  name: "4364"
+  id: 1472
+  display_name: "Larus delawarensis"
+}
+item {
+  name: "102672"
+  id: 1473
+  display_name: "Hetaerina titia"
+}
+item {
+  name: "20754"
+  id: 1474
+  display_name: "Trogon collaris"
+}
+item {
+  name: "479512"
+  id: 1475
+  display_name: "Acronicta fallax"
+}
+item {
+  name: "3460"
+  id: 1476
+  display_name: "Zenaida asiatica"
+}
+item {
+  name: "119066"
+  id: 1477
+  display_name: "Idia lubricalis"
+}
+item {
+  name: "119068"
+  id: 1478
+  display_name: "Apodemia virgulti"
+}
+item {
+  name: "4381"
+  id: 1479
+  display_name: "Larus fuscus"
+}
+item {
+  name: "4385"
+  id: 1480
+  display_name: "Larus californicus"
+}
+item {
+  name: "69922"
+  id: 1481
+  display_name: "Oncorhynchus nerka"
+}
+item {
+  name: "12580"
+  id: 1482
+  display_name: "Prosthemadera novaeseelandiae"
+}
+item {
+  name: "69925"
+  id: 1483
+  display_name: "Clinocardium nuttallii"
+}
+item {
+  name: "20781"
+  id: 1484
+  display_name: "Trogon elegans"
+}
+item {
+  name: "4399"
+  id: 1485
+  display_name: "Larus glaucescens"
+}
+item {
+  name: "94513"
+  id: 1486
+  display_name: "Archilestes grandis"
+}
+item {
+  name: "119090"
+  id: 1487
+  display_name: "Eremnophila aureonotata"
+}
+item {
+  name: "20787"
+  id: 1488
+  display_name: "Trogon citreolus"
+}
+item {
+  name: "69940"
+  id: 1489
+  display_name: "Hemiargus ceraunus"
+}
+item {
+  name: "61749"
+  id: 1490
+  display_name: "Lucanus cervus"
+}
+item {
+  name: "4415"
+  id: 1491
+  display_name: "Cepphus columba"
+}
+item {
+  name: "4832"
+  id: 1492
+  display_name: "Himantopus leucocephalus"
+}
+item {
+  name: "4418"
+  id: 1493
+  display_name: "Cepphus grylle"
+}
+item {
+  name: "12612"
+  id: 1494
+  display_name: "Anthornis melanura"
+}
+item {
+  name: "125627"
+  id: 1495
+  display_name: "Ellychnia corrusca"
+}
+item {
+  name: "201031"
+  id: 1496
+  display_name: "Leptoptilos crumenifer"
+}
+item {
+  name: "201032"
+  id: 1497
+  display_name: "Threskiornis moluccus"
+}
+item {
+  name: "60812"
+  id: 1498
+  display_name: "Lucanus capreolus"
+}
+item {
+  name: "10295"
+  id: 1499
+  display_name: "Thraupis episcopus"
+}
+item {
+  name: "209233"
+  id: 1500
+  display_name: "Equus caballus"
+}
+item {
+  name: "119122"
+  id: 1501
+  display_name: "Araneus trifolium"
+}
+item {
+  name: "201043"
+  id: 1502
+  display_name: "Geranoaetus albicaudatus"
+}
+item {
+  name: "61781"
+  id: 1503
+  display_name: "Ochlodes sylvanus"
+}
+item {
+  name: "49133"
+  id: 1504
+  display_name: "Vanessa atalanta"
+}
+item {
+  name: "94556"
+  id: 1505
+  display_name: "Argia lugens"
+}
+item {
+  name: "94557"
+  id: 1506
+  display_name: "Argia moesta"
+}
+item {
+  name: "61524"
+  id: 1507
+  display_name: "Forficula auricularia"
+}
+item {
+  name: "4449"
+  id: 1508
+  display_name: "Sterna paradisaea"
+}
+item {
+  name: "4450"
+  id: 1509
+  display_name: "Sterna hirundo"
+}
+item {
+  name: "348515"
+  id: 1510
+  display_name: "Nyctemera annulata"
+}
+item {
+  name: "110625"
+  id: 1511
+  display_name: "Progomphus obscurus"
+}
+item {
+  name: "94566"
+  id: 1512
+  display_name: "Argia plana"
+}
+item {
+  name: "4457"
+  id: 1513
+  display_name: "Sterna forsteri"
+}
+item {
+  name: "94571"
+  id: 1514
+  display_name: "Argia sedula"
+}
+item {
+  name: "61804"
+  id: 1515
+  display_name: "Olivella biplicata"
+}
+item {
+  name: "204532"
+  id: 1516
+  display_name: "Lanius excubitor"
+}
+item {
+  name: "29038"
+  id: 1517
+  display_name: "Pituophis deppei"
+}
+item {
+  name: "143728"
+  id: 1518
+  display_name: "Choristoneura rosaceana"
+}
+item {
+  name: "94577"
+  id: 1519
+  display_name: "Argia translata"
+}
+item {
+  name: "130451"
+  id: 1520
+  display_name: "Dione juno"
+}
+item {
+  name: "29044"
+  id: 1521
+  display_name: "Pituophis catenifer"
+}
+item {
+  name: "70005"
+  id: 1522
+  display_name: "Ilyanassa obsoleta"
+}
+item {
+  name: "143734"
+  id: 1523
+  display_name: "Eupithecia miserulata"
+}
+item {
+  name: "20856"
+  id: 1524
+  display_name: "Pharomachrus mocinno"
+}
+item {
+  name: "29049"
+  id: 1525
+  display_name: "Pituophis catenifer deserticola"
+}
+item {
+  name: "29052"
+  id: 1526
+  display_name: "Pituophis catenifer affinis"
+}
+item {
+  name: "29053"
+  id: 1527
+  display_name: "Pituophis catenifer annectens"
+}
+item {
+  name: "4478"
+  id: 1528
+  display_name: "Sterna striata"
+}
+item {
+  name: "407459"
+  id: 1529
+  display_name: "Dolomedes minor"
+}
+item {
+  name: "4489"
+  id: 1530
+  display_name: "Stercorarius parasiticus"
+}
+item {
+  name: "4491"
+  id: 1531
+  display_name: "Stercorarius pomarinus"
+}
+item {
+  name: "6969"
+  id: 1532
+  display_name: "Anas gracilis"
+}
+item {
+  name: "4494"
+  id: 1533
+  display_name: "Rissa tridactyla"
+}
+item {
+  name: "4496"
+  id: 1534
+  display_name: "Rynchops niger"
+}
+item {
+  name: "4501"
+  id: 1535
+  display_name: "Alca torda"
+}
+item {
+  name: "4504"
+  id: 1536
+  display_name: "Fratercula arctica"
+}
+item {
+  name: "4509"
+  id: 1537
+  display_name: "Fratercula cirrhata"
+}
+item {
+  name: "26693"
+  id: 1538
+  display_name: "Scaphiopus hurterii"
+}
+item {
+  name: "94624"
+  id: 1539
+  display_name: "Arigomphus submedianus"
+}
+item {
+  name: "94625"
+  id: 1540
+  display_name: "Arigomphus villosipes"
+}
+item {
+  name: "120720"
+  id: 1541
+  display_name: "Pseudacris sierra"
+}
+item {
+  name: "70057"
+  id: 1542
+  display_name: "Agrilus planipennis"
+}
+item {
+  name: "127402"
+  id: 1543
+  display_name: "Grammia virgo"
+}
+item {
+  name: "51271"
+  id: 1544
+  display_name: "Trachemys scripta elegans"
+}
+item {
+  name: "12716"
+  id: 1545
+  display_name: "Turdus merula"
+}
+item {
+  name: "12718"
+  id: 1546
+  display_name: "Turdus plumbeus"
+}
+item {
+  name: "12720"
+  id: 1547
+  display_name: "Turdus grayi"
+}
+item {
+  name: "63697"
+  id: 1548
+  display_name: "Metacarcinus magister"
+}
+item {
+  name: "12727"
+  id: 1549
+  display_name: "Turdus migratorius"
+}
+item {
+  name: "26698"
+  id: 1550
+  display_name: "Spea multiplicata"
+}
+item {
+  name: "12735"
+  id: 1551
+  display_name: "Turdus viscivorus"
+}
+item {
+  name: "26699"
+  id: 1552
+  display_name: "Spea bombifrons"
+}
+item {
+  name: "127431"
+  id: 1553
+  display_name: "Emmelina monodactyla"
+}
+item {
+  name: "4553"
+  id: 1554
+  display_name: "Cerorhinca monocerata"
+}
+item {
+  name: "12748"
+  id: 1555
+  display_name: "Turdus philomelos"
+}
+item {
+  name: "233933"
+  id: 1556
+  display_name: "Zale horrida"
+}
+item {
+  name: "1468"
+  id: 1557
+  display_name: "Galbula ruficauda"
+}
+item {
+  name: "111055"
+  id: 1558
+  display_name: "Pseudoleon superbus"
+}
+item {
+  name: "61908"
+  id: 1559
+  display_name: "Orgyia vetusta"
+}
+item {
+  name: "43086"
+  id: 1560
+  display_name: "Procavia capensis"
+}
+item {
+  name: "143830"
+  id: 1561
+  display_name: "Eumorpha vitis"
+}
+item {
+  name: "67663"
+  id: 1562
+  display_name: "Leptysma marginicollis"
+}
+item {
+  name: "127457"
+  id: 1563
+  display_name: "Idia americalis"
+}
+item {
+  name: "4578"
+  id: 1564
+  display_name: "Jacana spinosa"
+}
+item {
+  name: "127460"
+  id: 1565
+  display_name: "Idia aemula"
+}
+item {
+  name: "201192"
+  id: 1566
+  display_name: "Saxicola rubicola"
+}
+item {
+  name: "20969"
+  id: 1567
+  display_name: "Upupa epops"
+}
+item {
+  name: "94699"
+  id: 1568
+  display_name: "Aspidoscelis marmorata"
+}
+item {
+  name: "10322"
+  id: 1569
+  display_name: "Euphagus carolinus"
+}
+item {
+  name: "53743"
+  id: 1570
+  display_name: "Uca pugilator"
+}
+item {
+  name: "61256"
+  id: 1571
+  display_name: "Leptoglossus phyllopus"
+}
+item {
+  name: "29438"
+  id: 1572
+  display_name: "Coluber flagellum piceus"
+}
+item {
+  name: "53750"
+  id: 1573
+  display_name: "Lottia gigantea"
+}
+item {
+  name: "143865"
+  id: 1574
+  display_name: "Odocoileus hemionus hemionus"
+}
+item {
+  name: "143867"
+  id: 1575
+  display_name: "Protoboarmia porcelaria"
+}
+item {
+  name: "209405"
+  id: 1576
+  display_name: "Cenopis reticulatana"
+}
+item {
+  name: "49920"
+  id: 1577
+  display_name: "Nymphalis californica"
+}
+item {
+  name: "53762"
+  id: 1578
+  display_name: "Scolopendra polymorpha"
+}
+item {
+  name: "127492"
+  id: 1579
+  display_name: "Megalographa biloba"
+}
+item {
+  name: "62470"
+  id: 1580
+  display_name: "Limax maximus"
+}
+item {
+  name: "4621"
+  id: 1581
+  display_name: "Gavia pacifica"
+}
+item {
+  name: "14884"
+  id: 1582
+  display_name: "Mimus gilvus"
+}
+item {
+  name: "29200"
+  id: 1583
+  display_name: "Opheodrys aestivus"
+}
+item {
+  name: "201233"
+  id: 1584
+  display_name: "Passer italiae"
+}
+item {
+  name: "4626"
+  id: 1585
+  display_name: "Gavia immer"
+}
+item {
+  name: "4627"
+  id: 1586
+  display_name: "Gavia stellata"
+}
+item {
+  name: "12822"
+  id: 1587
+  display_name: "Oenanthe oenanthe"
+}
+item {
+  name: "4631"
+  id: 1588
+  display_name: "Fregata magnificens"
+}
+item {
+  name: "4636"
+  id: 1589
+  display_name: "Fregata minor"
+}
+item {
+  name: "70174"
+  id: 1590
+  display_name: "Hypolimnas bolina"
+}
+item {
+  name: "4643"
+  id: 1591
+  display_name: "Falco subbuteo"
+}
+item {
+  name: "4644"
+  id: 1592
+  display_name: "Falco mexicanus"
+}
+item {
+  name: "4645"
+  id: 1593
+  display_name: "Falco femoralis"
+}
+item {
+  name: "4647"
+  id: 1594
+  display_name: "Falco peregrinus"
+}
+item {
+  name: "119340"
+  id: 1595
+  display_name: "Amphipyra pyramidoides"
+}
+item {
+  name: "61997"
+  id: 1596
+  display_name: "Steatoda grossa"
+}
+item {
+  name: "70191"
+  id: 1597
+  display_name: "Ischnura ramburii"
+}
+item {
+  name: "53809"
+  id: 1598
+  display_name: "Phidippus audax"
+}
+item {
+  name: "143213"
+  id: 1599
+  display_name: "Frontinella communis"
+}
+item {
+  name: "4664"
+  id: 1600
+  display_name: "Falco rufigularis"
+}
+item {
+  name: "4665"
+  id: 1601
+  display_name: "Falco sparverius"
+}
+item {
+  name: "19893"
+  id: 1602
+  display_name: "Strix varia"
+}
+item {
+  name: "4672"
+  id: 1603
+  display_name: "Falco columbarius"
+}
+item {
+  name: "201281"
+  id: 1604
+  display_name: "Phyllodesma americana"
+}
+item {
+  name: "201282"
+  id: 1605
+  display_name: "Gallinula chloropus"
+}
+item {
+  name: "152131"
+  id: 1606
+  display_name: "Bagrada hilaris"
+}
+item {
+  name: "145276"
+  id: 1607
+  display_name: "Cardellina pusilla"
+}
+item {
+  name: "12878"
+  id: 1608
+  display_name: "Catharus ustulatus"
+}
+item {
+  name: "4690"
+  id: 1609
+  display_name: "Falco novaeseelandiae"
+}
+item {
+  name: "53843"
+  id: 1610
+  display_name: "Brephidium exilis"
+}
+item {
+  name: "36281"
+  id: 1611
+  display_name: "Sceloporus clarkii"
+}
+item {
+  name: "12890"
+  id: 1612
+  display_name: "Catharus guttatus"
+}
+item {
+  name: "62045"
+  id: 1613
+  display_name: "Lygaeus kalmii"
+}
+item {
+  name: "47075"
+  id: 1614
+  display_name: "Dasypus novemcinctus"
+}
+item {
+  name: "12901"
+  id: 1615
+  display_name: "Catharus fuscescens"
+}
+item {
+  name: "4714"
+  id: 1616
+  display_name: "Caracara cheriway"
+}
+item {
+  name: "53867"
+  id: 1617
+  display_name: "Erythemis plebeja"
+}
+item {
+  name: "62060"
+  id: 1618
+  display_name: "Palomena prasina"
+}
+item {
+  name: "53869"
+  id: 1619
+  display_name: "Ocypus olens"
+}
+item {
+  name: "4719"
+  id: 1620
+  display_name: "Herpetotheres cachinnans"
+}
+item {
+  name: "116840"
+  id: 1621
+  display_name: "Calcarius lapponicus"
+}
+item {
+  name: "4726"
+  id: 1622
+  display_name: "Milvago chimachima"
+}
+item {
+  name: "29304"
+  id: 1623
+  display_name: "Nerodia taxispilota"
+}
+item {
+  name: "29305"
+  id: 1624
+  display_name: "Nerodia sipedon"
+}
+item {
+  name: "29306"
+  id: 1625
+  display_name: "Nerodia sipedon sipedon"
+}
+item {
+  name: "142783"
+  id: 1626
+  display_name: "Myodocha serripes"
+}
+item {
+  name: "4733"
+  id: 1627
+  display_name: "Ciconia ciconia"
+}
+item {
+  name: "29310"
+  id: 1628
+  display_name: "Nerodia rhombifer"
+}
+item {
+  name: "201343"
+  id: 1629
+  display_name: "Lithacodes fasciola"
+}
+item {
+  name: "21121"
+  id: 1630
+  display_name: "Dendrobates auratus"
+}
+item {
+  name: "127618"
+  id: 1631
+  display_name: "Epirrhoe alternata"
+}
+item {
+  name: "43115"
+  id: 1632
+  display_name: "Sylvilagus audubonii"
+}
+item {
+  name: "29317"
+  id: 1633
+  display_name: "Nerodia fasciata"
+}
+item {
+  name: "4742"
+  id: 1634
+  display_name: "Mycteria americana"
+}
+item {
+  name: "53895"
+  id: 1635
+  display_name: "Stenopelmatus fuscus"
+}
+item {
+  name: "4744"
+  id: 1636
+  display_name: "Mycteria ibis"
+}
+item {
+  name: "12937"
+  id: 1637
+  display_name: "Sialia mexicana"
+}
+item {
+  name: "29322"
+  id: 1638
+  display_name: "Nerodia fasciata confluens"
+}
+item {
+  name: "29324"
+  id: 1639
+  display_name: "Nerodia clarkii clarkii"
+}
+item {
+  name: "29327"
+  id: 1640
+  display_name: "Nerodia cyclopion"
+}
+item {
+  name: "29328"
+  id: 1641
+  display_name: "Nerodia erythrogaster"
+}
+item {
+  name: "53905"
+  id: 1642
+  display_name: "Mantis religiosa"
+}
+item {
+  name: "4754"
+  id: 1643
+  display_name: "Ephippiorhynchus senegalensis"
+}
+item {
+  name: "127635"
+  id: 1644
+  display_name: "Plecia nearctica"
+}
+item {
+  name: "4756"
+  id: 1645
+  display_name: "Cathartes aura"
+}
+item {
+  name: "29334"
+  id: 1646
+  display_name: "Nerodia erythrogaster flavigaster"
+}
+item {
+  name: "12951"
+  id: 1647
+  display_name: "Myadestes townsendi"
+}
+item {
+  name: "4761"
+  id: 1648
+  display_name: "Cathartes burrovianus"
+}
+item {
+  name: "4763"
+  id: 1649
+  display_name: "Sarcoramphus papa"
+}
+item {
+  name: "4765"
+  id: 1650
+  display_name: "Coragyps atratus"
+}
+item {
+  name: "19890"
+  id: 1651
+  display_name: "Strix nebulosa"
+}
+item {
+  name: "26736"
+  id: 1652
+  display_name: "Ambystoma opacum"
+}
+item {
+  name: "66331"
+  id: 1653
+  display_name: "Pelophylax perezi"
+}
+item {
+  name: "4776"
+  id: 1654
+  display_name: "Anastomus lamelligerus"
+}
+item {
+  name: "4892"
+  id: 1655
+  display_name: "Pluvialis squatarola"
+}
+item {
+  name: "4778"
+  id: 1656
+  display_name: "Gymnogyps californianus"
+}
+item {
+  name: "12971"
+  id: 1657
+  display_name: "Muscicapa striata"
+}
+item {
+  name: "56776"
+  id: 1658
+  display_name: "Glaucopsyche lygdamus"
+}
+item {
+  name: "127669"
+  id: 1659
+  display_name: "Jadera haematoloma"
+}
+item {
+  name: "4793"
+  id: 1660
+  display_name: "Charadrius vociferus"
+}
+item {
+  name: "209594"
+  id: 1661
+  display_name: "Scantius aegyptius"
+}
+item {
+  name: "4795"
+  id: 1662
+  display_name: "Charadrius wilsonia"
+}
+item {
+  name: "48586"
+  id: 1663
+  display_name: "Cepaea nemoralis"
+}
+item {
+  name: "4798"
+  id: 1664
+  display_name: "Charadrius melodus"
+}
+item {
+  name: "12992"
+  id: 1665
+  display_name: "Phoenicurus phoenicurus"
+}
+item {
+  name: "45763"
+  id: 1666
+  display_name: "Ondatra zibethicus"
+}
+item {
+  name: "119492"
+  id: 1667
+  display_name: "Smerinthus cerisyi"
+}
+item {
+  name: "13000"
+  id: 1668
+  display_name: "Phoenicurus ochruros"
+}
+item {
+  name: "4811"
+  id: 1669
+  display_name: "Charadrius dubius"
+}
+item {
+  name: "64973"
+  id: 1670
+  display_name: "Anaxyrus cognatus"
+}
+item {
+  name: "2168"
+  id: 1671
+  display_name: "Eumomota superciliosa"
+}
+item {
+  name: "6980"
+  id: 1672
+  display_name: "Anas querquedula"
+}
+item {
+  name: "64975"
+  id: 1673
+  display_name: "Anaxyrus debilis"
+}
+item {
+  name: "43130"
+  id: 1674
+  display_name: "Lepus californicus"
+}
+item {
+  name: "67707"
+  id: 1675
+  display_name: "Argiope aurantia"
+}
+item {
+  name: "4836"
+  id: 1676
+  display_name: "Himantopus mexicanus"
+}
+item {
+  name: "4838"
+  id: 1677
+  display_name: "Haematopus bachmani"
+}
+item {
+  name: "43132"
+  id: 1678
+  display_name: "Lepus americanus"
+}
+item {
+  name: "144106"
+  id: 1679
+  display_name: "Pica pica"
+}
+item {
+  name: "4843"
+  id: 1680
+  display_name: "Haematopus ostralegus"
+}
+item {
+  name: "67709"
+  id: 1681
+  display_name: "Antrodiaetus riversi"
+}
+item {
+  name: "4848"
+  id: 1682
+  display_name: "Haematopus unicolor"
+}
+item {
+  name: "4857"
+  id: 1683
+  display_name: "Vanellus vanellus"
+}
+item {
+  name: "29435"
+  id: 1684
+  display_name: "Coluber flagellum testaceus"
+}
+item {
+  name: "119550"
+  id: 1685
+  display_name: "Feltia jaculifera"
+}
+item {
+  name: "4866"
+  id: 1686
+  display_name: "Vanellus spinosus"
+}
+item {
+  name: "4870"
+  id: 1687
+  display_name: "Vanellus armatus"
+}
+item {
+  name: "54024"
+  id: 1688
+  display_name: "Satyrium californica"
+}
+item {
+  name: "13071"
+  id: 1689
+  display_name: "Luscinia svecica"
+}
+item {
+  name: "3544"
+  id: 1690
+  display_name: "Columbina inca"
+}
+item {
+  name: "4883"
+  id: 1691
+  display_name: "Recurvirostra avosetta"
+}
+item {
+  name: "204701"
+  id: 1692
+  display_name: "Melanchra adjuncta"
+}
+item {
+  name: "56083"
+  id: 1693
+  display_name: "Armadillidium vulgare"
+}
+item {
+  name: "981"
+  id: 1694
+  display_name: "Phasianus colchicus"
+}
+item {
+  name: "4893"
+  id: 1695
+  display_name: "Pluvialis dominica"
+}
+item {
+  name: "103200"
+  id: 1696
+  display_name: "Hypsiglena jani"
+}
+item {
+  name: "127777"
+  id: 1697
+  display_name: "Vespula vulgaris"
+}
+item {
+  name: "7643"
+  id: 1698
+  display_name: "Cinclus mexicanus"
+}
+item {
+  name: "13094"
+  id: 1699
+  display_name: "Erithacus rubecula"
+}
+item {
+  name: "41777"
+  id: 1700
+  display_name: "Lontra canadensis"
+}
+item {
+  name: "64988"
+  id: 1701
+  display_name: "Anaxyrus terrestris"
+}
+item {
+  name: "18167"
+  id: 1702
+  display_name: "Melanerpes aurifrons"
+}
+item {
+  name: "54064"
+  id: 1703
+  display_name: "Polygonia comma"
+}
+item {
+  name: "209713"
+  id: 1704
+  display_name: "Phigalia titea"
+}
+item {
+  name: "54068"
+  id: 1705
+  display_name: "Boloria selene"
+}
+item {
+  name: "104585"
+  id: 1706
+  display_name: "Libellula semifasciata"
+}
+item {
+  name: "119608"
+  id: 1707
+  display_name: "Theba pisana"
+}
+item {
+  name: "4801"
+  id: 1708
+  display_name: "Charadrius hiaticula"
+}
+item {
+  name: "104586"
+  id: 1709
+  display_name: "Libellula vibrans"
+}
+item {
+  name: "4935"
+  id: 1710
+  display_name: "Egretta gularis"
+}
+item {
+  name: "4937"
+  id: 1711
+  display_name: "Egretta caerulea"
+}
+item {
+  name: "4938"
+  id: 1712
+  display_name: "Egretta tricolor"
+}
+item {
+  name: "4940"
+  id: 1713
+  display_name: "Egretta thula"
+}
+item {
+  name: "340813"
+  id: 1714
+  display_name: "Hyalymenus tarsatus"
+}
+item {
+  name: "4943"
+  id: 1715
+  display_name: "Egretta garzetta"
+}
+item {
+  name: "4947"
+  id: 1716
+  display_name: "Egretta sacra"
+}
+item {
+  name: "13141"
+  id: 1717
+  display_name: "Monticola solitarius"
+}
+item {
+  name: "4952"
+  id: 1718
+  display_name: "Ardea cocoi"
+}
+item {
+  name: "4954"
+  id: 1719
+  display_name: "Ardea cinerea"
+}
+item {
+  name: "67727"
+  id: 1720
+  display_name: "Aeshna umbrosa"
+}
+item {
+  name: "4956"
+  id: 1721
+  display_name: "Ardea herodias"
+}
+item {
+  name: "144223"
+  id: 1722
+  display_name: "Chlosyne theona"
+}
+item {
+  name: "201568"
+  id: 1723
+  display_name: "Diabrotica undecimpunctata undecimpunctata"
+}
+item {
+  name: "47383"
+  id: 1724
+  display_name: "Latrodectus geometricus"
+}
+item {
+  name: "119664"
+  id: 1725
+  display_name: "Cacyreus marshalli"
+}
+item {
+  name: "62321"
+  id: 1726
+  display_name: "Rutpela maculata"
+}
+item {
+  name: "217970"
+  id: 1727
+  display_name: "Cyclophora pendulinaria"
+}
+item {
+  name: "4981"
+  id: 1728
+  display_name: "Nycticorax nycticorax"
+}
+item {
+  name: "12714"
+  id: 1729
+  display_name: "Turdus rufopalliatus"
+}
+item {
+  name: "4994"
+  id: 1730
+  display_name: "Ardeola ralloides"
+}
+item {
+  name: "4999"
+  id: 1731
+  display_name: "Nyctanassa violacea"
+}
+item {
+  name: "37769"
+  id: 1732
+  display_name: "Plestiodon skiltonianus"
+}
+item {
+  name: "213826"
+  id: 1733
+  display_name: "Apamea amputatrix"
+}
+item {
+  name: "67736"
+  id: 1734
+  display_name: "Rhionaeschna californica"
+}
+item {
+  name: "155380"
+  id: 1735
+  display_name: "Andricus crystallinus"
+}
+item {
+  name: "144280"
+  id: 1736
+  display_name: "Aramides cajaneus"
+}
+item {
+  name: "5017"
+  id: 1737
+  display_name: "Bubulcus ibis"
+}
+item {
+  name: "5020"
+  id: 1738
+  display_name: "Butorides virescens"
+}
+item {
+  name: "144285"
+  id: 1739
+  display_name: "Porphyrio martinicus"
+}
+item {
+  name: "81729"
+  id: 1740
+  display_name: "Feniseca tarquinius"
+}
+item {
+  name: "127905"
+  id: 1741
+  display_name: "Bombus ternarius"
+}
+item {
+  name: "5034"
+  id: 1742
+  display_name: "Botaurus lentiginosus"
+}
+item {
+  name: "29330"
+  id: 1743
+  display_name: "Nerodia erythrogaster transversa"
+}
+item {
+  name: "5036"
+  id: 1744
+  display_name: "Cochlearius cochlearius"
+}
+item {
+  name: "46001"
+  id: 1745
+  display_name: "Sciurus vulgaris"
+}
+item {
+  name: "46005"
+  id: 1746
+  display_name: "Sciurus variegatoides"
+}
+item {
+  name: "127928"
+  id: 1747
+  display_name: "Autochton cellus"
+}
+item {
+  name: "340923"
+  id: 1748
+  display_name: "Scolypopa australis"
+}
+item {
+  name: "46017"
+  id: 1749
+  display_name: "Sciurus carolinensis"
+}
+item {
+  name: "46018"
+  id: 1750
+  display_name: "Sciurus aberti"
+}
+item {
+  name: "447427"
+  id: 1751
+  display_name: "Neverita lewisii"
+}
+item {
+  name: "46020"
+  id: 1752
+  display_name: "Sciurus niger"
+}
+item {
+  name: "5061"
+  id: 1753
+  display_name: "Anhinga novaehollandiae"
+}
+item {
+  name: "46023"
+  id: 1754
+  display_name: "Sciurus griseus"
+}
+item {
+  name: "122375"
+  id: 1755
+  display_name: "Carterocephalus palaemon"
+}
+item {
+  name: "5066"
+  id: 1756
+  display_name: "Anhinga rufa"
+}
+item {
+  name: "145289"
+  id: 1757
+  display_name: "Melozone fusca"
+}
+item {
+  name: "5074"
+  id: 1758
+  display_name: "Aquila chrysaetos"
+}
+item {
+  name: "49998"
+  id: 1759
+  display_name: "Thamnophis sirtalis infernalis"
+}
+item {
+  name: "13270"
+  id: 1760
+  display_name: "Hylocichla mustelina"
+}
+item {
+  name: "62423"
+  id: 1761
+  display_name: "Cimbex americana"
+}
+item {
+  name: "62424"
+  id: 1762
+  display_name: "Sitochroa palealis"
+}
+item {
+  name: "111578"
+  id: 1763
+  display_name: "Regina grahamii"
+}
+item {
+  name: "144207"
+  id: 1764
+  display_name: "Aphelocoma wollweberi"
+}
+item {
+  name: "62429"
+  id: 1765
+  display_name: "Pyronia tithonus"
+}
+item {
+  name: "47934"
+  id: 1766
+  display_name: "Libellula luctuosa"
+}
+item {
+  name: "50000"
+  id: 1767
+  display_name: "Clemmys guttata"
+}
+item {
+  name: "5097"
+  id: 1768
+  display_name: "Accipiter striatus"
+}
+item {
+  name: "119789"
+  id: 1769
+  display_name: "Cisseps fulvicollis"
+}
+item {
+  name: "5106"
+  id: 1770
+  display_name: "Accipiter nisus"
+}
+item {
+  name: "5108"
+  id: 1771
+  display_name: "Accipiter gentilis"
+}
+item {
+  name: "62456"
+  id: 1772
+  display_name: "Rhagonycha fulva"
+}
+item {
+  name: "4948"
+  id: 1773
+  display_name: "Egretta rufescens"
+}
+item {
+  name: "46082"
+  id: 1774
+  display_name: "Marmota marmota"
+}
+item {
+  name: "6990"
+  id: 1775
+  display_name: "Bucephala clangula"
+}
+item {
+  name: "4535"
+  id: 1776
+  display_name: "Anous stolidus"
+}
+item {
+  name: "46087"
+  id: 1777
+  display_name: "Marmota caligata"
+}
+item {
+  name: "72458"
+  id: 1778
+  display_name: "Actitis macularius"
+}
+item {
+  name: "4951"
+  id: 1779
+  display_name: "Ardea purpurea"
+}
+item {
+  name: "128012"
+  id: 1780
+  display_name: "Eumorpha fasciatus"
+}
+item {
+  name: "472078"
+  id: 1781
+  display_name: "Todiramphus chloris"
+}
+item {
+  name: "46095"
+  id: 1782
+  display_name: "Marmota monax"
+}
+item {
+  name: "34"
+  id: 1783
+  display_name: "Grus americana"
+}
+item {
+  name: "4835"
+  id: 1784
+  display_name: "Himantopus himantopus"
+}
+item {
+  name: "122374"
+  id: 1785
+  display_name: "Eurema mexicana"
+}
+item {
+  name: "19812"
+  id: 1786
+  display_name: "Glaucidium gnoma"
+}
+item {
+  name: "73823"
+  id: 1787
+  display_name: "Hierophis viridiflavus"
+}
+item {
+  name: "5168"
+  id: 1788
+  display_name: "Circus approximans"
+}
+item {
+  name: "143110"
+  id: 1789
+  display_name: "Hypagyrtis unipunctata"
+}
+item {
+  name: "65976"
+  id: 1790
+  display_name: "Lithobates blairi"
+}
+item {
+  name: "5173"
+  id: 1791
+  display_name: "Circus aeruginosus"
+}
+item {
+  name: "54327"
+  id: 1792
+  display_name: "Vespa crabro"
+}
+item {
+  name: "4273"
+  id: 1793
+  display_name: "Phalacrocorax sulcirostris"
+}
+item {
+  name: "5180"
+  id: 1794
+  display_name: "Buteo albonotatus"
+}
+item {
+  name: "103485"
+  id: 1795
+  display_name: "Ischnura denticollis"
+}
+item {
+  name: "62528"
+  id: 1796
+  display_name: "Butorides striata"
+}
+item {
+  name: "62529"
+  id: 1797
+  display_name: "Platalea ajaja"
+}
+item {
+  name: "5186"
+  id: 1798
+  display_name: "Buteo brachyurus"
+}
+item {
+  name: "103494"
+  id: 1799
+  display_name: "Ischnura hastata"
+}
+item {
+  name: "144455"
+  id: 1800
+  display_name: "Ardea alba"
+}
+item {
+  name: "103497"
+  id: 1801
+  display_name: "Ischnura perparva"
+}
+item {
+  name: "103498"
+  id: 1802
+  display_name: "Ischnura posita"
+}
+item {
+  name: "5196"
+  id: 1803
+  display_name: "Buteo swainsoni"
+}
+item {
+  name: "128079"
+  id: 1804
+  display_name: "Grammia ornata"
+}
+item {
+  name: "29777"
+  id: 1805
+  display_name: "Lampropeltis triangulum"
+}
+item {
+  name: "867"
+  id: 1806
+  display_name: "Alectoris rufa"
+}
+item {
+  name: "5206"
+  id: 1807
+  display_name: "Buteo lineatus"
+}
+item {
+  name: "29783"
+  id: 1808
+  display_name: "Lampropeltis triangulum triangulum"
+}
+item {
+  name: "122383"
+  id: 1809
+  display_name: "Plebejus melissa"
+}
+item {
+  name: "5212"
+  id: 1810
+  display_name: "Buteo jamaicensis"
+}
+item {
+  name: "81495"
+  id: 1811
+  display_name: "Libellula pulchella"
+}
+item {
+  name: "35003"
+  id: 1812
+  display_name: "Heloderma suspectum"
+}
+item {
+  name: "46180"
+  id: 1813
+  display_name: "Cynomys gunnisoni"
+}
+item {
+  name: "144485"
+  id: 1814
+  display_name: "Charadrius nivosus"
+}
+item {
+  name: "144490"
+  id: 1815
+  display_name: "Tringa incana"
+}
+item {
+  name: "144491"
+  id: 1816
+  display_name: "Tringa semipalmata"
+}
+item {
+  name: "25185"
+  id: 1817
+  display_name: "Hypopachus variolosus"
+}
+item {
+  name: "5231"
+  id: 1818
+  display_name: "Terathopius ecaudatus"
+}
+item {
+  name: "144496"
+  id: 1819
+  display_name: "Gallinago delicata"
+}
+item {
+  name: "5233"
+  id: 1820
+  display_name: "Buteogallus anthracinus"
+}
+item {
+  name: "211035"
+  id: 1821
+  display_name: "Speranza pustularia"
+}
+item {
+  name: "29813"
+  id: 1822
+  display_name: "Lampropeltis getula"
+}
+item {
+  name: "144502"
+  id: 1823
+  display_name: "Chroicocephalus philadelphia"
+}
+item {
+  name: "5242"
+  id: 1824
+  display_name: "Circaetus gallicus"
+}
+item {
+  name: "144507"
+  id: 1825
+  display_name: "Chroicocephalus novaehollandiae"
+}
+item {
+  name: "144510"
+  id: 1826
+  display_name: "Chroicocephalus ridibundus"
+}
+item {
+  name: "52757"
+  id: 1827
+  display_name: "Polistes fuscatus"
+}
+item {
+  name: "144514"
+  id: 1828
+  display_name: "Leucophaeus atricilla"
+}
+item {
+  name: "144515"
+  id: 1829
+  display_name: "Leucophaeus pipixcan"
+}
+item {
+  name: "46217"
+  id: 1830
+  display_name: "Tamias striatus"
+}
+item {
+  name: "144525"
+  id: 1831
+  display_name: "Onychoprion fuscatus"
+}
+item {
+  name: "46222"
+  id: 1832
+  display_name: "Tamias minimus"
+}
+item {
+  name: "144530"
+  id: 1833
+  display_name: "Sternula antillarum"
+}
+item {
+  name: "46230"
+  id: 1834
+  display_name: "Tamias merriami"
+}
+item {
+  name: "144537"
+  id: 1835
+  display_name: "Hydroprogne caspia"
+}
+item {
+  name: "144539"
+  id: 1836
+  display_name: "Thalasseus maximus"
+}
+item {
+  name: "144540"
+  id: 1837
+  display_name: "Thalasseus bergii"
+}
+item {
+  name: "5277"
+  id: 1838
+  display_name: "Elanus leucurus"
+}
+item {
+  name: "324766"
+  id: 1839
+  display_name: "Epicallima argenticinctella"
+}
+item {
+  name: "72486"
+  id: 1840
+  display_name: "Alopochen aegyptiaca"
+}
+item {
+  name: "62229"
+  id: 1841
+  display_name: "Ischnura cervula"
+}
+item {
+  name: "144550"
+  id: 1842
+  display_name: "Streptopelia senegalensis"
+}
+item {
+  name: "46256"
+  id: 1843
+  display_name: "Ammospermophilus harrisii"
+}
+item {
+  name: "94559"
+  id: 1844
+  display_name: "Argia nahuana"
+}
+item {
+  name: "46259"
+  id: 1845
+  display_name: "Tamiasciurus douglasii"
+}
+item {
+  name: "46260"
+  id: 1846
+  display_name: "Tamiasciurus hudsonicus"
+}
+item {
+  name: "119989"
+  id: 1847
+  display_name: "Stagmomantis carolina"
+}
+item {
+  name: "13494"
+  id: 1848
+  display_name: "Gerygone igata"
+}
+item {
+  name: "5305"
+  id: 1849
+  display_name: "Haliaeetus leucocephalus"
+}
+item {
+  name: "7596"
+  id: 1850
+  display_name: "Cistothorus platensis"
+}
+item {
+  name: "5308"
+  id: 1851
+  display_name: "Haliaeetus vocifer"
+}
+item {
+  name: "218301"
+  id: 1852
+  display_name: "Diacme elealis"
+}
+item {
+  name: "95422"
+  id: 1853
+  display_name: "Basiaeschna janata"
+}
+item {
+  name: "46272"
+  id: 1854
+  display_name: "Glaucomys volans"
+}
+item {
+  name: "120010"
+  id: 1855
+  display_name: "Polistes metricus"
+}
+item {
+  name: "144594"
+  id: 1856
+  display_name: "Bubo scandiacus"
+}
+item {
+  name: "52771"
+  id: 1857
+  display_name: "Gonepteryx rhamni"
+}
+item {
+  name: "144597"
+  id: 1858
+  display_name: "Ciccaba virgata"
+}
+item {
+  name: "890"
+  id: 1859
+  display_name: "Bonasa umbellus"
+}
+item {
+  name: "52773"
+  id: 1860
+  display_name: "Poanes zabulon"
+}
+item {
+  name: "120033"
+  id: 1861
+  display_name: "Lapara bombycoides"
+}
+item {
+  name: "5346"
+  id: 1862
+  display_name: "Busarellus nigricollis"
+}
+item {
+  name: "5349"
+  id: 1863
+  display_name: "Rostrhamus sociabilis"
+}
+item {
+  name: "36391"
+  id: 1864
+  display_name: "Anolis equestris"
+}
+item {
+  name: "46316"
+  id: 1865
+  display_name: "Trichechus manatus"
+}
+item {
+  name: "5267"
+  id: 1866
+  display_name: "Milvus milvus"
+}
+item {
+  name: "128241"
+  id: 1867
+  display_name: "Darapsa choerilus"
+}
+item {
+  name: "128242"
+  id: 1868
+  display_name: "Palthis angulalis"
+}
+item {
+  name: "5366"
+  id: 1869
+  display_name: "Gyps fulvus"
+}
+item {
+  name: "204512"
+  id: 1870
+  display_name: "Ficedula hypoleuca"
+}
+item {
+  name: "54526"
+  id: 1871
+  display_name: "Crassadoma gigantea"
+}
+item {
+  name: "144642"
+  id: 1872
+  display_name: "Momotus coeruliceps"
+}
+item {
+  name: "120070"
+  id: 1873
+  display_name: "Strongylocentrotus droebachiensis"
+}
+item {
+  name: "54538"
+  id: 1874
+  display_name: "Syngnathus leptorhynchus"
+}
+item {
+  name: "81746"
+  id: 1875
+  display_name: "Necrophila americana"
+}
+item {
+  name: "300301"
+  id: 1876
+  display_name: "Pseudomyrmex gracilis"
+}
+item {
+  name: "202003"
+  id: 1877
+  display_name: "Apiomerus spissipes"
+}
+item {
+  name: "41860"
+  id: 1878
+  display_name: "Enhydra lutris"
+}
+item {
+  name: "4817"
+  id: 1879
+  display_name: "Charadrius semipalmatus"
+}
+item {
+  name: "36145"
+  id: 1880
+  display_name: "Sceloporus variabilis"
+}
+item {
+  name: "202012"
+  id: 1881
+  display_name: "Steatoda capensis"
+}
+item {
+  name: "62749"
+  id: 1882
+  display_name: "Iphiclides podalirius"
+}
+item {
+  name: "5406"
+  id: 1883
+  display_name: "Haliastur indus"
+}
+item {
+  name: "62751"
+  id: 1884
+  display_name: "Andricus kingi"
+}
+item {
+  name: "5363"
+  id: 1885
+  display_name: "Gyps africanus"
+}
+item {
+  name: "5416"
+  id: 1886
+  display_name: "Ictinia mississippiensis"
+}
+item {
+  name: "62766"
+  id: 1887
+  display_name: "Issoria lathonia"
+}
+item {
+  name: "62768"
+  id: 1888
+  display_name: "Scolia dubia"
+}
+item {
+  name: "126206"
+  id: 1889
+  display_name: "Dissosteira carolina"
+}
+item {
+  name: "269875"
+  id: 1890
+  display_name: "Mallodon dasystomus"
+}
+item {
+  name: "155030"
+  id: 1891
+  display_name: "Limenitis reducta"
+}
+item {
+  name: "62345"
+  id: 1892
+  display_name: "Duttaphrynus melanostictus"
+}
+item {
+  name: "52519"
+  id: 1893
+  display_name: "Aeshna cyanea"
+}
+item {
+  name: "10001"
+  id: 1894
+  display_name: "Dives dives"
+}
+item {
+  name: "460365"
+  id: 1895
+  display_name: "Tegula funebralis"
+}
+item {
+  name: "13631"
+  id: 1896
+  display_name: "Baeolophus atricristatus"
+}
+item {
+  name: "13632"
+  id: 1897
+  display_name: "Baeolophus bicolor"
+}
+item {
+  name: "13633"
+  id: 1898
+  display_name: "Baeolophus inornatus"
+}
+item {
+  name: "9100"
+  id: 1899
+  display_name: "Melospiza melodia"
+}
+item {
+  name: "62796"
+  id: 1900
+  display_name: "Crotaphytus bicinctores"
+}
+item {
+  name: "62797"
+  id: 1901
+  display_name: "Gambelia wislizenii"
+}
+item {
+  name: "46009"
+  id: 1902
+  display_name: "Sciurus aureogaster"
+}
+item {
+  name: "112867"
+  id: 1903
+  display_name: "Sparisoma viride"
+}
+item {
+  name: "70997"
+  id: 1904
+  display_name: "Pelecinus polyturator"
+}
+item {
+  name: "62806"
+  id: 1905
+  display_name: "Mytilus californianus"
+}
+item {
+  name: "120156"
+  id: 1906
+  display_name: "Musca domestica"
+}
+item {
+  name: "136548"
+  id: 1907
+  display_name: "Euclea delphinii"
+}
+item {
+  name: "50065"
+  id: 1908
+  display_name: "Danaus eresimus"
+}
+item {
+  name: "43239"
+  id: 1909
+  display_name: "Tachyglossus aculeatus"
+}
+item {
+  name: "145303"
+  id: 1910
+  display_name: "Spinus spinus"
+}
+item {
+  name: "120183"
+  id: 1911
+  display_name: "Araneus marmoreus"
+}
+item {
+  name: "71032"
+  id: 1912
+  display_name: "Crotalus scutulatus scutulatus"
+}
+item {
+  name: "71034"
+  id: 1913
+  display_name: "Tenodera sinensis"
+}
+item {
+  name: "143121"
+  id: 1914
+  display_name: "Ochropleura implecta"
+}
+item {
+  name: "13695"
+  id: 1915
+  display_name: "Motacilla alba"
+}
+item {
+  name: "7458"
+  id: 1916
+  display_name: "Certhia americana"
+}
+item {
+  name: "38293"
+  id: 1917
+  display_name: "Lampropholis delicata"
+}
+item {
+  name: "144281"
+  id: 1918
+  display_name: "Bucorvus leadbeateri"
+}
+item {
+  name: "120217"
+  id: 1919
+  display_name: "Halysidota tessellaris"
+}
+item {
+  name: "226718"
+  id: 1920
+  display_name: "Otiorhynchus sulcatus"
+}
+item {
+  name: "464287"
+  id: 1921
+  display_name: "Anteaeolidiella oliviae"
+}
+item {
+  name: "226720"
+  id: 1922
+  display_name: "Oxychilus draparnaudi"
+}
+item {
+  name: "13729"
+  id: 1923
+  display_name: "Anthus pratensis"
+}
+item {
+  name: "13732"
+  id: 1924
+  display_name: "Anthus rubescens"
+}
+item {
+  name: "11930"
+  id: 1925
+  display_name: "Tachycineta albilinea"
+}
+item {
+  name: "71085"
+  id: 1926
+  display_name: "Varanus niloticus"
+}
+item {
+  name: "144814"
+  id: 1927
+  display_name: "Poecile carolinensis"
+}
+item {
+  name: "144815"
+  id: 1928
+  display_name: "Poecile atricapillus"
+}
+item {
+  name: "144816"
+  id: 1929
+  display_name: "Poecile gambeli"
+}
+item {
+  name: "144820"
+  id: 1930
+  display_name: "Poecile rufescens"
+}
+item {
+  name: "144823"
+  id: 1931
+  display_name: "Periparus ater"
+}
+item {
+  name: "10485"
+  id: 1932
+  display_name: "Chlorophanes spiza"
+}
+item {
+  name: "40523"
+  id: 1933
+  display_name: "Lasiurus cinereus"
+}
+item {
+  name: "47719"
+  id: 1934
+  display_name: "Datana ministra"
+}
+item {
+  name: "13770"
+  id: 1935
+  display_name: "Estrilda astrild"
+}
+item {
+  name: "144849"
+  id: 1936
+  display_name: "Cyanistes caeruleus"
+}
+item {
+  name: "218587"
+  id: 1937
+  display_name: "Discus rotundatus"
+}
+item {
+  name: "47105"
+  id: 1938
+  display_name: "Tamandua mexicana"
+}
+item {
+  name: "18463"
+  id: 1939
+  display_name: "Sphyrapicus varius"
+}
+item {
+  name: "11858"
+  id: 1940
+  display_name: "Petrochelidon pyrrhonota"
+}
+item {
+  name: "144882"
+  id: 1941
+  display_name: "Troglodytes pacificus"
+}
+item {
+  name: "144883"
+  id: 1942
+  display_name: "Troglodytes hiemalis"
+}
+item {
+  name: "153076"
+  id: 1943
+  display_name: "Nephelodes minians"
+}
+item {
+  name: "62978"
+  id: 1944
+  display_name: "Chlosyne nycteis"
+}
+item {
+  name: "128517"
+  id: 1945
+  display_name: "Catocala ilia"
+}
+item {
+  name: "153102"
+  id: 1946
+  display_name: "Dysphania militaris"
+}
+item {
+  name: "59651"
+  id: 1947
+  display_name: "Aquarius remigis"
+}
+item {
+  name: "13851"
+  id: 1948
+  display_name: "Passer montanus"
+}
+item {
+  name: "13858"
+  id: 1949
+  display_name: "Passer domesticus"
+}
+item {
+  name: "39742"
+  id: 1950
+  display_name: "Kinosternon flavescens"
+}
+item {
+  name: "506118"
+  id: 1951
+  display_name: "Aphelocoma californica"
+}
+item {
+  name: "5672"
+  id: 1952
+  display_name: "Amazilia yucatanensis"
+}
+item {
+  name: "5676"
+  id: 1953
+  display_name: "Amazilia tzacatl"
+}
+item {
+  name: "204503"
+  id: 1954
+  display_name: "Dicrurus adsimilis"
+}
+item {
+  name: "52785"
+  id: 1955
+  display_name: "Megachile sculpturalis"
+}
+item {
+  name: "126905"
+  id: 1956
+  display_name: "Harrisina americana"
+}
+item {
+  name: "55773"
+  id: 1957
+  display_name: "Promachus hinei"
+}
+item {
+  name: "84752"
+  id: 1958
+  display_name: "Microcentrum rhombifolium"
+}
+item {
+  name: "5698"
+  id: 1959
+  display_name: "Amazilia violiceps"
+}
+item {
+  name: "145539"
+  id: 1960
+  display_name: "Ovis canadensis nelsoni"
+}
+item {
+  name: "104004"
+  id: 1961
+  display_name: "Lampropeltis splendida"
+}
+item {
+  name: "13893"
+  id: 1962
+  display_name: "Lonchura punctulata"
+}
+item {
+  name: "63048"
+  id: 1963
+  display_name: "Nuttallina californica"
+}
+item {
+  name: "226901"
+  id: 1964
+  display_name: "Panopoda rufimargo"
+}
+item {
+  name: "194134"
+  id: 1965
+  display_name: "Anthanassa tulcis"
+}
+item {
+  name: "5049"
+  id: 1966
+  display_name: "Tigrisoma mexicanum"
+}
+item {
+  name: "407130"
+  id: 1967
+  display_name: "Porphyrio melanotus melanotus"
+}
+item {
+  name: "226910"
+  id: 1968
+  display_name: "Panthea furcilla"
+}
+item {
+  name: "130661"
+  id: 1969
+  display_name: "Catasticta nimbice"
+}
+item {
+  name: "120215"
+  id: 1970
+  display_name: "Bombus griseocollis"
+}
+item {
+  name: "144220"
+  id: 1971
+  display_name: "Melanitta americana"
+}
+item {
+  name: "9148"
+  id: 1972
+  display_name: "Spizella pallida"
+}
+item {
+  name: "320610"
+  id: 1973
+  display_name: "Sceloporus magister"
+}
+item {
+  name: "54900"
+  id: 1974
+  display_name: "Papilio polyxenes asterius"
+}
+item {
+  name: "36080"
+  id: 1975
+  display_name: "Callisaurus draconoides"
+}
+item {
+  name: "5758"
+  id: 1976
+  display_name: "Amazilia rutila"
+}
+item {
+  name: "3465"
+  id: 1977
+  display_name: "Zenaida aurita"
+}
+item {
+  name: "116461"
+  id: 1978
+  display_name: "Anolis sagrei"
+}
+item {
+  name: "61295"
+  id: 1979
+  display_name: "Aporia crataegi"
+}
+item {
+  name: "131673"
+  id: 1980
+  display_name: "Tetracis cachexiata"
+}
+item {
+  name: "63113"
+  id: 1981
+  display_name: "Blarina brevicauda"
+}
+item {
+  name: "26904"
+  id: 1982
+  display_name: "Coronella austriaca"
+}
+item {
+  name: "94575"
+  id: 1983
+  display_name: "Argia tibialis"
+}
+item {
+  name: "237166"
+  id: 1984
+  display_name: "Lycaena phlaeas hypophlaeas"
+}
+item {
+  name: "129305"
+  id: 1985
+  display_name: "Melanoplus bivittatus"
+}
+item {
+  name: "63128"
+  id: 1986
+  display_name: "Speyeria atlantis"
+}
+item {
+  name: "113514"
+  id: 1987
+  display_name: "Sympetrum internum"
+}
+item {
+  name: "48757"
+  id: 1988
+  display_name: "Echinothrix calamaris"
+}
+item {
+  name: "128670"
+  id: 1989
+  display_name: "Bombus vagans"
+}
+item {
+  name: "13988"
+  id: 1990
+  display_name: "Prunella modularis"
+}
+item {
+  name: "54951"
+  id: 1991
+  display_name: "Anartia fatima"
+}
+item {
+  name: "54952"
+  id: 1992
+  display_name: "Cardisoma guanhumi"
+}
+item {
+  name: "325295"
+  id: 1993
+  display_name: "Cydalima perspectalis"
+}
+item {
+  name: "63160"
+  id: 1994
+  display_name: "Celithemis elisa"
+}
+item {
+  name: "210615"
+  id: 1995
+  display_name: "Pyrausta volupialis"
+}
+item {
+  name: "472766"
+  id: 1996
+  display_name: "Falco tinnunculus"
+}
+item {
+  name: "29927"
+  id: 1997
+  display_name: "Heterodon nasicus"
+}
+item {
+  name: "145088"
+  id: 1998
+  display_name: "Ixoreus naevius"
+}
+item {
+  name: "6432"
+  id: 1999
+  display_name: "Archilochus colubris"
+}
+item {
+  name: "5827"
+  id: 2000
+  display_name: "Lampornis clemenciae"
+}
+item {
+  name: "15990"
+  id: 2001
+  display_name: "Myiarchus tuberculifer"
+}
+item {
+  name: "128712"
+  id: 2002
+  display_name: "Coccinella californica"
+}
+item {
+  name: "67559"
+  id: 2003
+  display_name: "Adelpha eulalia"
+}
+item {
+  name: "128719"
+  id: 2004
+  display_name: "Echinometra mathaei"
+}
+item {
+  name: "10247"
+  id: 2005
+  display_name: "Setophaga ruticilla"
+}
+item {
+  name: "202451"
+  id: 2006
+  display_name: "Copaeodes minima"
+}
+item {
+  name: "95958"
+  id: 2007
+  display_name: "Boyeria vinosa"
+}
+item {
+  name: "16016"
+  id: 2008
+  display_name: "Myiarchus tyrannulus"
+}
+item {
+  name: "36202"
+  id: 2009
+  display_name: "Sceloporus olivaceus"
+}
+item {
+  name: "95982"
+  id: 2010
+  display_name: "Brachymesia furcata"
+}
+item {
+  name: "126589"
+  id: 2011
+  display_name: "Calycopis isobeon"
+}
+item {
+  name: "120578"
+  id: 2012
+  display_name: "Micrathena sagittata"
+}
+item {
+  name: "194690"
+  id: 2013
+  display_name: "Pogonomyrmex barbatus"
+}
+item {
+  name: "120583"
+  id: 2014
+  display_name: "Parasteatoda tepidariorum"
+}
+item {
+  name: "202505"
+  id: 2015
+  display_name: "Zosterops lateralis"
+}
+item {
+  name: "38671"
+  id: 2016
+  display_name: "Aspidoscelis tigris"
+}
+item {
+  name: "38672"
+  id: 2017
+  display_name: "Aspidoscelis tigris stejnegeri"
+}
+item {
+  name: "9176"
+  id: 2018
+  display_name: "Zonotrichia leucophrys"
+}
+item {
+  name: "120596"
+  id: 2019
+  display_name: "Aphonopelma hentzi"
+}
+item {
+  name: "9744"
+  id: 2020
+  display_name: "Agelaius phoeniceus"
+}
+item {
+  name: "38684"
+  id: 2021
+  display_name: "Aspidoscelis tigris mundus"
+}
+item {
+  name: "62426"
+  id: 2022
+  display_name: "Aphantopus hyperantus"
+}
+item {
+  name: "30494"
+  id: 2023
+  display_name: "Micrurus tener"
+}
+item {
+  name: "58578"
+  id: 2024
+  display_name: "Euphydryas phaeton"
+}
+item {
+  name: "96036"
+  id: 2025
+  display_name: "Brechmorhoga mendax"
+}
+item {
+  name: "333608"
+  id: 2026
+  display_name: "Leukoma staminea"
+}
+item {
+  name: "38703"
+  id: 2027
+  display_name: "Aspidoscelis sexlineata sexlineata"
+}
+item {
+  name: "126600"
+  id: 2028
+  display_name: "Chortophaga viridifasciata"
+}
+item {
+  name: "63287"
+  id: 2029
+  display_name: "Megalorchestia californiana"
+}
+item {
+  name: "128824"
+  id: 2030
+  display_name: "Lucilia sericata"
+}
+item {
+  name: "104249"
+  id: 2031
+  display_name: "Lepisosteus oculatus"
+}
+item {
+  name: "203153"
+  id: 2032
+  display_name: "Parus major"
+}
+item {
+  name: "9183"
+  id: 2033
+  display_name: "Zonotrichia capensis"
+}
+item {
+  name: "82201"
+  id: 2034
+  display_name: "Hypena baltimoralis"
+}
+item {
+  name: "145217"
+  id: 2035
+  display_name: "Oreothlypis peregrina"
+}
+item {
+  name: "145218"
+  id: 2036
+  display_name: "Oreothlypis celata"
+}
+item {
+  name: "145221"
+  id: 2037
+  display_name: "Oreothlypis ruficapilla"
+}
+item {
+  name: "145224"
+  id: 2038
+  display_name: "Geothlypis philadelphia"
+}
+item {
+  name: "145225"
+  id: 2039
+  display_name: "Geothlypis formosa"
+}
+item {
+  name: "448331"
+  id: 2040
+  display_name: "Ambigolimax valentianus"
+}
+item {
+  name: "128845"
+  id: 2041
+  display_name: "Copestylum mexicanum"
+}
+item {
+  name: "145231"
+  id: 2042
+  display_name: "Setophaga tigrina"
+}
+item {
+  name: "145233"
+  id: 2043
+  display_name: "Setophaga americana"
+}
+item {
+  name: "145235"
+  id: 2044
+  display_name: "Setophaga magnolia"
+}
+item {
+  name: "145236"
+  id: 2045
+  display_name: "Setophaga castanea"
+}
+item {
+  name: "145237"
+  id: 2046
+  display_name: "Setophaga fusca"
+}
+item {
+  name: "145238"
+  id: 2047
+  display_name: "Setophaga petechia"
+}
+item {
+  name: "145240"
+  id: 2048
+  display_name: "Setophaga striata"
+}
+item {
+  name: "145242"
+  id: 2049
+  display_name: "Setophaga palmarum"
+}
+item {
+  name: "179855"
+  id: 2050
+  display_name: "Polites vibex"
+}
+item {
+  name: "145244"
+  id: 2051
+  display_name: "Setophaga pinus"
+}
+item {
+  name: "145245"
+  id: 2052
+  display_name: "Setophaga coronata"
+}
+item {
+  name: "145246"
+  id: 2053
+  display_name: "Setophaga dominica"
+}
+item {
+  name: "5987"
+  id: 2054
+  display_name: "Campylopterus hemileucurus"
+}
+item {
+  name: "17382"
+  id: 2055
+  display_name: "Vireo cassinii"
+}
+item {
+  name: "145254"
+  id: 2056
+  display_name: "Setophaga nigrescens"
+}
+item {
+  name: "145255"
+  id: 2057
+  display_name: "Setophaga townsendi"
+}
+item {
+  name: "145256"
+  id: 2058
+  display_name: "Setophaga occidentalis"
+}
+item {
+  name: "145257"
+  id: 2059
+  display_name: "Setophaga chrysoparia"
+}
+item {
+  name: "145258"
+  id: 2060
+  display_name: "Setophaga virens"
+}
+item {
+  name: "48786"
+  id: 2061
+  display_name: "Pollicipes polymerus"
+}
+item {
+  name: "36207"
+  id: 2062
+  display_name: "Sceloporus occidentalis longipes"
+}
+item {
+  name: "22392"
+  id: 2063
+  display_name: "Eleutherodactylus marnockii"
+}
+item {
+  name: "22393"
+  id: 2064
+  display_name: "Eleutherodactylus cystignathoides"
+}
+item {
+  name: "145275"
+  id: 2065
+  display_name: "Cardellina canadensis"
+}
+item {
+  name: "145277"
+  id: 2066
+  display_name: "Cardellina rubra"
+}
+item {
+  name: "7829"
+  id: 2067
+  display_name: "Aphelocoma coerulescens"
+}
+item {
+  name: "41963"
+  id: 2068
+  display_name: "Panthera pardus"
+}
+item {
+  name: "142998"
+  id: 2069
+  display_name: "Pyrausta acrionalis"
+}
+item {
+  name: "18204"
+  id: 2070
+  display_name: "Melanerpes erythrocephalus"
+}
+item {
+  name: "47425"
+  id: 2071
+  display_name: "Tonicella lineata"
+}
+item {
+  name: "148460"
+  id: 2072
+  display_name: "Charadra deridens"
+}
+item {
+  name: "145291"
+  id: 2073
+  display_name: "Emberiza calandra"
+}
+item {
+  name: "52523"
+  id: 2074
+  display_name: "Carcinus maenas"
+}
+item {
+  name: "46994"
+  id: 2075
+  display_name: "Scapanus latimanus"
+}
+item {
+  name: "114314"
+  id: 2076
+  display_name: "Tramea onusta"
+}
+item {
+  name: "145300"
+  id: 2077
+  display_name: "Acanthis flammea"
+}
+item {
+  name: "63382"
+  id: 2078
+  display_name: "Dermasterias imbricata"
+}
+item {
+  name: "126772"
+  id: 2079
+  display_name: "Ursus americanus californiensis"
+}
+item {
+  name: "145304"
+  id: 2080
+  display_name: "Spinus pinus"
+}
+item {
+  name: "10294"
+  id: 2081
+  display_name: "Thraupis abbas"
+}
+item {
+  name: "145308"
+  id: 2082
+  display_name: "Spinus psaltria"
+}
+item {
+  name: "145309"
+  id: 2083
+  display_name: "Spinus lawrencei"
+}
+item {
+  name: "145310"
+  id: 2084
+  display_name: "Spinus tristis"
+}
+item {
+  name: "3739"
+  id: 2085
+  display_name: "Threskiornis aethiopicus"
+}
+item {
+  name: "47014"
+  id: 2086
+  display_name: "Scalopus aquaticus"
+}
+item {
+  name: "4566"
+  id: 2087
+  display_name: "Gygis alba"
+}
+item {
+  name: "43335"
+  id: 2088
+  display_name: "Equus quagga"
+}
+item {
+  name: "41970"
+  id: 2089
+  display_name: "Panthera onca"
+}
+item {
+  name: "128950"
+  id: 2090
+  display_name: "Lycomorpha pholus"
+}
+item {
+  name: "11935"
+  id: 2091
+  display_name: "Tachycineta bicolor"
+}
+item {
+  name: "333759"
+  id: 2092
+  display_name: "Larus dominicanus dominicanus"
+}
+item {
+  name: "143008"
+  id: 2093
+  display_name: "Herpetogramma pertextalis"
+}
+item {
+  name: "235341"
+  id: 2094
+  display_name: "Coenonympha tullia california"
+}
+item {
+  name: "44705"
+  id: 2095
+  display_name: "Mus musculus"
+}
+item {
+  name: "145352"
+  id: 2096
+  display_name: "Lonchura oryzivora"
+}
+item {
+  name: "4840"
+  id: 2097
+  display_name: "Haematopus palliatus"
+}
+item {
+  name: "244845"
+  id: 2098
+  display_name: "Apiomerus californicus"
+}
+item {
+  name: "145360"
+  id: 2099
+  display_name: "Chloris chloris"
+}
+item {
+  name: "5112"
+  id: 2100
+  display_name: "Accipiter cooperii"
+}
+item {
+  name: "30675"
+  id: 2101
+  display_name: "Agkistrodon piscivorus"
+}
+item {
+  name: "341972"
+  id: 2102
+  display_name: "Crocodylus niloticus"
+}
+item {
+  name: "30677"
+  id: 2103
+  display_name: "Agkistrodon piscivorus conanti"
+}
+item {
+  name: "30678"
+  id: 2104
+  display_name: "Agkistrodon contortrix"
+}
+item {
+  name: "52900"
+  id: 2105
+  display_name: "Caenurgina crassiuscula"
+}
+item {
+  name: "30682"
+  id: 2106
+  display_name: "Agkistrodon contortrix laticinctus"
+}
+item {
+  name: "47067"
+  id: 2107
+  display_name: "Bradypus variegatus"
+}
+item {
+  name: "55260"
+  id: 2108
+  display_name: "Erythemis vesiculosa"
+}
+item {
+  name: "17402"
+  id: 2109
+  display_name: "Vireo solitarius"
+}
+item {
+  name: "6369"
+  id: 2110
+  display_name: "Selasphorus platycercus"
+}
+item {
+  name: "104416"
+  id: 2111
+  display_name: "Lestes alacer"
+}
+item {
+  name: "128993"
+  id: 2112
+  display_name: "Narceus annularus"
+}
+item {
+  name: "104422"
+  id: 2113
+  display_name: "Lestes congener"
+}
+item {
+  name: "227307"
+  id: 2114
+  display_name: "Patalene olyzonaria"
+}
+item {
+  name: "104429"
+  id: 2115
+  display_name: "Lestes dryas"
+}
+item {
+  name: "194542"
+  id: 2116
+  display_name: "Phyciodes graphica"
+}
+item {
+  name: "52904"
+  id: 2117
+  display_name: "Microcrambus elegans"
+}
+item {
+  name: "129363"
+  id: 2118
+  display_name: "Calephelis nemesis"
+}
+item {
+  name: "144506"
+  id: 2119
+  display_name: "Chroicocephalus scopulinus"
+}
+item {
+  name: "30713"
+  id: 2120
+  display_name: "Crotalus oreganus helleri"
+}
+item {
+  name: "47101"
+  id: 2121
+  display_name: "Choloepus hoffmanni"
+}
+item {
+  name: "210942"
+  id: 2122
+  display_name: "Caedicia simplex"
+}
+item {
+  name: "30719"
+  id: 2123
+  display_name: "Crotalus scutulatus"
+}
+item {
+  name: "30724"
+  id: 2124
+  display_name: "Crotalus ruber"
+}
+item {
+  name: "47110"
+  id: 2125
+  display_name: "Triopha maculata"
+}
+item {
+  name: "4235"
+  id: 2126
+  display_name: "Aechmophorus occidentalis"
+}
+item {
+  name: "30731"
+  id: 2127
+  display_name: "Crotalus molossus"
+}
+item {
+  name: "30733"
+  id: 2128
+  display_name: "Crotalus molossus nigrescens"
+}
+item {
+  name: "30735"
+  id: 2129
+  display_name: "Crotalus mitchellii"
+}
+item {
+  name: "30740"
+  id: 2130
+  display_name: "Crotalus lepidus"
+}
+item {
+  name: "30746"
+  id: 2131
+  display_name: "Crotalus horridus"
+}
+item {
+  name: "63518"
+  id: 2132
+  display_name: "Melanoplus differentialis"
+}
+item {
+  name: "30751"
+  id: 2133
+  display_name: "Crotalus cerastes"
+}
+item {
+  name: "126640"
+  id: 2134
+  display_name: "Caenurgina erechtea"
+}
+item {
+  name: "46086"
+  id: 2135
+  display_name: "Marmota flaviventris"
+}
+item {
+  name: "194599"
+  id: 2136
+  display_name: "Heliomata cycladata"
+}
+item {
+  name: "30764"
+  id: 2137
+  display_name: "Crotalus atrox"
+}
+item {
+  name: "204520"
+  id: 2138
+  display_name: "Hemiphaga novaeseelandiae"
+}
+item {
+  name: "128141"
+  id: 2139
+  display_name: "Crepidula adunca"
+}
+item {
+  name: "121183"
+  id: 2140
+  display_name: "Mythimna unipuncta"
+}
+item {
+  name: "40827"
+  id: 2141
+  display_name: "Eidolon helvum"
+}
+item {
+  name: "4571"
+  id: 2142
+  display_name: "Xema sabini"
+}
+item {
+  name: "211007"
+  id: 2143
+  display_name: "Nepytia canosaria"
+}
+item {
+  name: "47171"
+  id: 2144
+  display_name: "Flabellina iodinea"
+}
+item {
+  name: "211012"
+  id: 2145
+  display_name: "Maliattha synochitis"
+}
+item {
+  name: "30798"
+  id: 2146
+  display_name: "Bothrops asper"
+}
+item {
+  name: "47188"
+  id: 2147
+  display_name: "Pachygrapsus crassipes"
+}
+item {
+  name: "55387"
+  id: 2148
+  display_name: "Esox lucius"
+}
+item {
+  name: "58583"
+  id: 2149
+  display_name: "Limenitis arthemis arthemis"
+}
+item {
+  name: "104548"
+  id: 2150
+  display_name: "Leucorrhinia frigida"
+}
+item {
+  name: "104550"
+  id: 2151
+  display_name: "Leucorrhinia hudsonica"
+}
+item {
+  name: "104551"
+  id: 2152
+  display_name: "Leucorrhinia intacta"
+}
+item {
+  name: "47209"
+  id: 2153
+  display_name: "Hermissenda crassicornis"
+}
+item {
+  name: "55655"
+  id: 2154
+  display_name: "Lycaena phlaeas"
+}
+item {
+  name: "202861"
+  id: 2155
+  display_name: "Otala lactea"
+}
+item {
+  name: "143037"
+  id: 2156
+  display_name: "Lineodes integra"
+}
+item {
+  name: "47219"
+  id: 2157
+  display_name: "Apis mellifera"
+}
+item {
+  name: "24254"
+  id: 2158
+  display_name: "Pseudacris cadaverina"
+}
+item {
+  name: "47226"
+  id: 2159
+  display_name: "Papilio rutulus"
+}
+item {
+  name: "104572"
+  id: 2160
+  display_name: "Libellula comanche"
+}
+item {
+  name: "104574"
+  id: 2161
+  display_name: "Libellula croceipennis"
+}
+item {
+  name: "104575"
+  id: 2162
+  display_name: "Libellula cyanea"
+}
+item {
+  name: "145538"
+  id: 2163
+  display_name: "Ovis canadensis canadensis"
+}
+item {
+  name: "104580"
+  id: 2164
+  display_name: "Libellula incesta"
+}
+item {
+  name: "24257"
+  id: 2165
+  display_name: "Pseudacris streckeri"
+}
+item {
+  name: "53866"
+  id: 2166
+  display_name: "Calpodes ethlius"
+}
+item {
+  name: "18796"
+  id: 2167
+  display_name: "Ramphastos sulfuratus"
+}
+item {
+  name: "2413"
+  id: 2168
+  display_name: "Dacelo novaeguineae"
+}
+item {
+  name: "482"
+  id: 2169
+  display_name: "Fulica atra"
+}
+item {
+  name: "47251"
+  id: 2170
+  display_name: "Sphyraena barracuda"
+}
+item {
+  name: "358549"
+  id: 2171
+  display_name: "Hemaris diffinis"
+}
+item {
+  name: "81526"
+  id: 2172
+  display_name: "Crotalus viridis"
+}
+item {
+  name: "342169"
+  id: 2173
+  display_name: "Hirundo rustica erythrogaster"
+}
+item {
+  name: "39280"
+  id: 2174
+  display_name: "Leiocephalus carinatus"
+}
+item {
+  name: "47269"
+  id: 2175
+  display_name: "Dasyatis americana"
+}
+item {
+  name: "55467"
+  id: 2176
+  display_name: "Sabulodes aegrotata"
+}
+item {
+  name: "6316"
+  id: 2177
+  display_name: "Calypte costae"
+}
+item {
+  name: "6317"
+  id: 2178
+  display_name: "Calypte anna"
+}
+item {
+  name: "47280"
+  id: 2179
+  display_name: "Pterois volitans"
+}
+item {
+  name: "81608"
+  id: 2180
+  display_name: "Geukensia demissa"
+}
+item {
+  name: "121012"
+  id: 2181
+  display_name: "Euglandina rosea"
+}
+item {
+  name: "236980"
+  id: 2182
+  display_name: "Colaptes auratus cafer"
+}
+item {
+  name: "38673"
+  id: 2183
+  display_name: "Aspidoscelis tigris tigris"
+}
+item {
+  name: "3786"
+  id: 2184
+  display_name: "Sula nebouxii"
+}
+item {
+  name: "55487"
+  id: 2185
+  display_name: "Diabrotica undecimpunctata"
+}
+item {
+  name: "243904"
+  id: 2186
+  display_name: "Phrynosoma platyrhinos"
+}
+item {
+  name: "55489"
+  id: 2187
+  display_name: "Cycloneda munda"
+}
+item {
+  name: "204491"
+  id: 2188
+  display_name: "Copsychus saularis"
+}
+item {
+  name: "55492"
+  id: 2189
+  display_name: "Cycloneda polita"
+}
+item {
+  name: "129222"
+  id: 2190
+  display_name: "Heterophleps triguttaria"
+}
+item {
+  name: "129223"
+  id: 2191
+  display_name: "Pasiphila rectangulata"
+}
+item {
+  name: "28365"
+  id: 2192
+  display_name: "Thamnophis sirtalis sirtalis"
+}
+item {
+  name: "47316"
+  id: 2193
+  display_name: "Chaetodon lunula"
+}
+item {
+  name: "6359"
+  id: 2194
+  display_name: "Selasphorus sasin"
+}
+item {
+  name: "62500"
+  id: 2195
+  display_name: "Leptophobia aripa"
+}
+item {
+  name: "6363"
+  id: 2196
+  display_name: "Selasphorus rufus"
+}
+item {
+  name: "96480"
+  id: 2197
+  display_name: "Calopteryx aequabilis"
+}
+item {
+  name: "55521"
+  id: 2198
+  display_name: "Papilio eurymedon"
+}
+item {
+  name: "6371"
+  id: 2199
+  display_name: "Calothorax lucifer"
+}
+item {
+  name: "129263"
+  id: 2200
+  display_name: "Syrbula admirabilis"
+}
+item {
+  name: "28371"
+  id: 2201
+  display_name: "Thamnophis sirtalis fitchi"
+}
+item {
+  name: "243962"
+  id: 2202
+  display_name: "Charina bottae"
+}
+item {
+  name: "145659"
+  id: 2203
+  display_name: "Acronicta americana"
+}
+item {
+  name: "14588"
+  id: 2204
+  display_name: "Pycnonotus barbatus"
+}
+item {
+  name: "480298"
+  id: 2205
+  display_name: "Cornu aspersum"
+}
+item {
+  name: "51584"
+  id: 2206
+  display_name: "Melanitis leda"
+}
+item {
+  name: "243970"
+  id: 2207
+  display_name: "Larus glaucescens \303\227 occidentalis"
+}
+item {
+  name: "55556"
+  id: 2208
+  display_name: "Oncopeltus fasciatus"
+}
+item {
+  name: "506117"
+  id: 2209
+  display_name: "Aphelocoma woodhouseii"
+}
+item {
+  name: "63750"
+  id: 2210
+  display_name: "Anavitrinella pampinaria"
+}
+item {
+  name: "30983"
+  id: 2211
+  display_name: "Sistrurus miliarius"
+}
+item {
+  name: "211210"
+  id: 2212
+  display_name: "Holocnemus pluchei"
+}
+item {
+  name: "49587"
+  id: 2213
+  display_name: "Micropterus salmoides"
+}
+item {
+  name: "6417"
+  id: 2214
+  display_name: "Florisuga mellivora"
+}
+item {
+  name: "47381"
+  id: 2215
+  display_name: "Latrodectus mactans"
+}
+item {
+  name: "47382"
+  id: 2216
+  display_name: "Latrodectus hesperus"
+}
+item {
+  name: "4851"
+  id: 2217
+  display_name: "Haematopus finschi"
+}
+item {
+  name: "51588"
+  id: 2218
+  display_name: "Papilio polytes"
+}
+item {
+  name: "144431"
+  id: 2219
+  display_name: "Falcipennis canadensis"
+}
+item {
+  name: "118490"
+  id: 2220
+  display_name: "Haematopis grataria"
+}
+item {
+  name: "6433"
+  id: 2221
+  display_name: "Archilochus alexandri"
+}
+item {
+  name: "52956"
+  id: 2222
+  display_name: "Chaetodon capistratus"
+}
+item {
+  name: "203050"
+  id: 2223
+  display_name: "Junonia genoveva"
+}
+item {
+  name: "5170"
+  id: 2224
+  display_name: "Circus cyaneus"
+}
+item {
+  name: "84332"
+  id: 2225
+  display_name: "Panorpa nuptialis"
+}
+item {
+  name: "47414"
+  id: 2226
+  display_name: "Emerita analoga"
+}
+item {
+  name: "129335"
+  id: 2227
+  display_name: "Gibbifer californicus"
+}
+item {
+  name: "55610"
+  id: 2228
+  display_name: "Pyrrhocoris apterus"
+}
+item {
+  name: "58421"
+  id: 2229
+  display_name: "Phidippus johnsoni"
+}
+item {
+  name: "208608"
+  id: 2230
+  display_name: "Trachymela sloanei"
+}
+item {
+  name: "68138"
+  id: 2231
+  display_name: "Sympetrum corruptum"
+}
+item {
+  name: "129350"
+  id: 2232
+  display_name: "Photinus pyralis"
+}
+item {
+  name: "55625"
+  id: 2233
+  display_name: "Sympetrum striolatum"
+}
+item {
+  name: "55626"
+  id: 2234
+  display_name: "Pieris rapae"
+}
+item {
+  name: "203084"
+  id: 2235
+  display_name: "Ardea alba modesta"
+}
+item {
+  name: "129362"
+  id: 2236
+  display_name: "Zerene cesonia"
+}
+item {
+  name: "55638"
+  id: 2237
+  display_name: "Anania hortulata"
+}
+item {
+  name: "148537"
+  id: 2238
+  display_name: "Astraptes fulgerator"
+}
+item {
+  name: "55640"
+  id: 2239
+  display_name: "Celastrina argiolus"
+}
+item {
+  name: "55641"
+  id: 2240
+  display_name: "Polyommatus icarus"
+}
+item {
+  name: "16028"
+  id: 2241
+  display_name: "Myiarchus crinitus"
+}
+item {
+  name: "55643"
+  id: 2242
+  display_name: "Araschnia levana"
+}
+item {
+  name: "121180"
+  id: 2243
+  display_name: "Megastraea undosa"
+}
+item {
+  name: "47454"
+  id: 2244
+  display_name: "Triopha catalinae"
+}
+item {
+  name: "28389"
+  id: 2245
+  display_name: "Thamnophis ordinoides"
+}
+item {
+  name: "68139"
+  id: 2246
+  display_name: "Sympetrum vicinum"
+}
+item {
+  name: "55651"
+  id: 2247
+  display_name: "Autographa gamma"
+}
+item {
+  name: "55653"
+  id: 2248
+  display_name: "Maniola jurtina"
+}
+item {
+  name: "84369"
+  id: 2249
+  display_name: "Libellula forensis"
+}
+item {
+  name: "47135"
+  id: 2250
+  display_name: "Badumna longinqua"
+}
+item {
+  name: "48213"
+  id: 2251
+  display_name: "Ariolimax californicus"
+}
+item {
+  name: "121196"
+  id: 2252
+  display_name: "Acanthurus coeruleus"
+}
+item {
+  name: "47469"
+  id: 2253
+  display_name: "Doris montereyensis"
+}
+item {
+  name: "5181"
+  id: 2254
+  display_name: "Buteo regalis"
+}
+item {
+  name: "47472"
+  id: 2255
+  display_name: "Acanthodoris lutea"
+}
+item {
+  name: "129415"
+  id: 2256
+  display_name: "Copaeodes aurantiaca"
+}
+item {
+  name: "47505"
+  id: 2257
+  display_name: "Geitodoris heathi"
+}
+item {
+  name: "28398"
+  id: 2258
+  display_name: "Thamnophis elegans"
+}
+item {
+  name: "6553"
+  id: 2259
+  display_name: "Aeronautes saxatalis"
+}
+item {
+  name: "47516"
+  id: 2260
+  display_name: "Oncorhynchus mykiss"
+}
+item {
+  name: "6557"
+  id: 2261
+  display_name: "Chaetura vauxi"
+}
+item {
+  name: "47518"
+  id: 2262
+  display_name: "Salmo trutta"
+}
+item {
+  name: "55711"
+  id: 2263
+  display_name: "Ladona depressa"
+}
+item {
+  name: "55719"
+  id: 2264
+  display_name: "Eristalis tenax"
+}
+item {
+  name: "6571"
+  id: 2265
+  display_name: "Chaetura pelagica"
+}
+item {
+  name: "119881"
+  id: 2266
+  display_name: "Chrysochus cobaltinus"
+}
+item {
+  name: "145239"
+  id: 2267
+  display_name: "Setophaga pensylvanica"
+}
+item {
+  name: "154043"
+  id: 2268
+  display_name: "Bombus huntii"
+}
+item {
+  name: "41955"
+  id: 2269
+  display_name: "Acinonyx jubatus"
+}
+item {
+  name: "55746"
+  id: 2270
+  display_name: "Misumena vatia"
+}
+item {
+  name: "12024"
+  id: 2271
+  display_name: "Lanius ludovicianus"
+}
+item {
+  name: "5063"
+  id: 2272
+  display_name: "Anhinga anhinga"
+}
+item {
+  name: "59892"
+  id: 2273
+  display_name: "Prionus californicus"
+}
+item {
+  name: "52986"
+  id: 2274
+  display_name: "Largus californicus"
+}
+item {
+  name: "204454"
+  id: 2275
+  display_name: "Acridotheres tristis"
+}
+item {
+  name: "14816"
+  id: 2276
+  display_name: "Sitta pygmaea"
+}
+item {
+  name: "148560"
+  id: 2277
+  display_name: "Mestra amymone"
+}
+item {
+  name: "4585"
+  id: 2278
+  display_name: "Actophilornis africanus"
+}
+item {
+  name: "47590"
+  id: 2279
+  display_name: "Phloeodes diabolicus"
+}
+item {
+  name: "14823"
+  id: 2280
+  display_name: "Sitta canadensis"
+}
+item {
+  name: "14824"
+  id: 2281
+  display_name: "Sitta europaea"
+}
+item {
+  name: "14825"
+  id: 2282
+  display_name: "Sitta pusilla"
+}
+item {
+  name: "67598"
+  id: 2283
+  display_name: "Solenopsis invicta"
+}
+item {
+  name: "6638"
+  id: 2284
+  display_name: "Apus apus"
+}
+item {
+  name: "301557"
+  id: 2285
+  display_name: "Euphoria basalis"
+}
+item {
+  name: "132070"
+  id: 2286
+  display_name: "Phaneroptera nana"
+}
+item {
+  name: "14850"
+  id: 2287
+  display_name: "Sturnus vulgaris"
+}
+item {
+  name: "62550"
+  id: 2288
+  display_name: "Seiurus aurocapilla"
+}
+item {
+  name: "64006"
+  id: 2289
+  display_name: "Corbicula fluminea"
+}
+item {
+  name: "204545"
+  id: 2290
+  display_name: "Motacilla flava"
+}
+item {
+  name: "47632"
+  id: 2291
+  display_name: "Katharina tunicata"
+}
+item {
+  name: "325309"
+  id: 2292
+  display_name: "Chortophaga viridifasciata viridifasciata"
+}
+item {
+  name: "104993"
+  id: 2293
+  display_name: "Macrodiplax balteata"
+}
+item {
+  name: "17408"
+  id: 2294
+  display_name: "Vireo griseus"
+}
+item {
+  name: "14895"
+  id: 2295
+  display_name: "Toxostoma longirostre"
+}
+item {
+  name: "47664"
+  id: 2296
+  display_name: "Henricia leviuscula"
+}
+item {
+  name: "31281"
+  id: 2297
+  display_name: "Calotes versicolor"
+}
+item {
+  name: "119086"
+  id: 2298
+  display_name: "Agrius cingulata"
+}
+item {
+  name: "3849"
+  id: 2299
+  display_name: "Calidris alba"
+}
+item {
+  name: "14906"
+  id: 2300
+  display_name: "Toxostoma redivivum"
+}
+item {
+  name: "144479"
+  id: 2301
+  display_name: "Gallinula galeata"
+}
+item {
+  name: "3850"
+  id: 2302
+  display_name: "Calidris himantopus"
+}
+item {
+  name: "117520"
+  id: 2303
+  display_name: "Enhydra lutris nereis"
+}
+item {
+  name: "51491"
+  id: 2304
+  display_name: "Myliobatis californica"
+}
+item {
+  name: "121612"
+  id: 2305
+  display_name: "Estigmene acrea"
+}
+item {
+  name: "105034"
+  id: 2306
+  display_name: "Macromia illinoiensis"
+}
+item {
+  name: "6498"
+  id: 2307
+  display_name: "Eugenes fulgens"
+}
+item {
+  name: "46179"
+  id: 2308
+  display_name: "Cynomys ludovicianus"
+}
+item {
+  name: "105049"
+  id: 2309
+  display_name: "Macromia taeniolata"
+}
+item {
+  name: "94045"
+  id: 2310
+  display_name: "Anax longipes"
+}
+item {
+  name: "143119"
+  id: 2311
+  display_name: "Galgula partita"
+}
+item {
+  name: "9317"
+  id: 2312
+  display_name: "Icterus wagleri"
+}
+item {
+  name: "122704"
+  id: 2313
+  display_name: "Nucella ostrina"
+}
+item {
+  name: "146709"
+  id: 2314
+  display_name: "Grylloprociphilus imbricator"
+}
+item {
+  name: "9318"
+  id: 2315
+  display_name: "Icterus parisorum"
+}
+item {
+  name: "85333"
+  id: 2316
+  display_name: "Micrathena gracilis"
+}
+item {
+  name: "126737"
+  id: 2317
+  display_name: "Anania funebris"
+}
+item {
+  name: "49053"
+  id: 2318
+  display_name: "Cryptochiton stelleri"
+}
+item {
+  name: "47721"
+  id: 2319
+  display_name: "Parastichopus californicus"
+}
+item {
+  name: "34050"
+  id: 2320
+  display_name: "Phelsuma laticauda"
+}
+item {
+  name: "154219"
+  id: 2321
+  display_name: "Notarctia proxima"
+}
+item {
+  name: "51781"
+  id: 2322
+  display_name: "Tyria jacobaeae"
+}
+item {
+  name: "24230"
+  id: 2323
+  display_name: "Acris crepitans"
+}
+item {
+  name: "146032"
+  id: 2324
+  display_name: "Coluber flagellum"
+}
+item {
+  name: "146033"
+  id: 2325
+  display_name: "Coluber flagellum flagellum"
+}
+item {
+  name: "244340"
+  id: 2326
+  display_name: "Hordnia atropunctata"
+}
+item {
+  name: "146037"
+  id: 2327
+  display_name: "Coluber taeniatus"
+}
+item {
+  name: "244344"
+  id: 2328
+  display_name: "Scopula rubraria"
+}
+item {
+  name: "47737"
+  id: 2329
+  display_name: "Harpaphe haydeniana"
+}
+item {
+  name: "5227"
+  id: 2330
+  display_name: "Buteo platypterus"
+}
+item {
+  name: "39556"
+  id: 2331
+  display_name: "Apalone spinifera"
+}
+item {
+  name: "39560"
+  id: 2332
+  display_name: "Apalone spinifera emoryi"
+}
+item {
+  name: "318836"
+  id: 2333
+  display_name: "Gallinago gallinago"
+}
+item {
+  name: "105098"
+  id: 2334
+  display_name: "Magicicada septendecim"
+}
+item {
+  name: "96907"
+  id: 2335
+  display_name: "Celithemis fasciata"
+}
+item {
+  name: "9325"
+  id: 2336
+  display_name: "Icterus spurius"
+}
+item {
+  name: "3864"
+  id: 2337
+  display_name: "Calidris minutilla"
+}
+item {
+  name: "14995"
+  id: 2338
+  display_name: "Dumetella carolinensis"
+}
+item {
+  name: "424597"
+  id: 2339
+  display_name: "Porphyrio hochstetteri"
+}
+item {
+  name: "47768"
+  id: 2340
+  display_name: "Doriopsilla albopunctata"
+}
+item {
+  name: "498116"
+  id: 2341
+  display_name: "Aeolidia papillosa"
+}
+item {
+  name: "244378"
+  id: 2342
+  display_name: "Mallophora fautrix"
+}
+item {
+  name: "3866"
+  id: 2343
+  display_name: "Calidris fuscicollis"
+}
+item {
+  name: "47776"
+  id: 2344
+  display_name: "Ariolimax columbianus"
+}
+item {
+  name: "144497"
+  id: 2345
+  display_name: "Phalaropus tricolor"
+}
+item {
+  name: "39824"
+  id: 2346
+  display_name: "Pseudemys nelsoni"
+}
+item {
+  name: "236979"
+  id: 2347
+  display_name: "Colaptes auratus auratus"
+}
+item {
+  name: "55990"
+  id: 2348
+  display_name: "Podarcis muralis"
+}
+item {
+  name: "244407"
+  id: 2349
+  display_name: "Zelus renardii"
+}
+item {
+  name: "47802"
+  id: 2350
+  display_name: "Lymantria dispar"
+}
+item {
+  name: "15035"
+  id: 2351
+  display_name: "Melanotis caerulescens"
+}
+item {
+  name: "51658"
+  id: 2352
+  display_name: "Anthopleura artemisia"
+}
+item {
+  name: "121534"
+  id: 2353
+  display_name: "Oreta rosea"
+}
+item {
+  name: "73504"
+  id: 2354
+  display_name: "Tiaris olivaceus"
+}
+item {
+  name: "15045"
+  id: 2355
+  display_name: "Oreoscoptes montanus"
+}
+item {
+  name: "3873"
+  id: 2356
+  display_name: "Limnodromus scolopaceus"
+}
+item {
+  name: "47673"
+  id: 2357
+  display_name: "Pycnopodia helianthoides"
+}
+item {
+  name: "47817"
+  id: 2358
+  display_name: "Libellula saturata"
+}
+item {
+  name: "56644"
+  id: 2359
+  display_name: "Polygonia satyrus"
+}
+item {
+  name: "47826"
+  id: 2360
+  display_name: "Cancer productus"
+}
+item {
+  name: "3875"
+  id: 2361
+  display_name: "Tringa solitaria"
+}
+item {
+  name: "39782"
+  id: 2362
+  display_name: "Trachemys scripta"
+}
+item {
+  name: "143140"
+  id: 2363
+  display_name: "Cyllopsis gemma"
+}
+item {
+  name: "29818"
+  id: 2364
+  display_name: "Lampropeltis holbrooki"
+}
+item {
+  name: "56293"
+  id: 2365
+  display_name: "Macroglossum stellatarum"
+}
+item {
+  name: "154340"
+  id: 2366
+  display_name: "Gryllodes sigillatus"
+}
+item {
+  name: "14801"
+  id: 2367
+  display_name: "Sitta carolinensis"
+}
+item {
+  name: "121578"
+  id: 2368
+  display_name: "Ovis aries"
+}
+item {
+  name: "3879"
+  id: 2369
+  display_name: "Tringa totanus"
+}
+item {
+  name: "6893"
+  id: 2370
+  display_name: "Dendrocygna autumnalis"
+}
+item {
+  name: "154353"
+  id: 2371
+  display_name: "Sunira bicolorago"
+}
+item {
+  name: "6898"
+  id: 2372
+  display_name: "Dendrocygna viduata"
+}
+item {
+  name: "6899"
+  id: 2373
+  display_name: "Dendrocygna bicolor"
+}
+item {
+  name: "9342"
+  id: 2374
+  display_name: "Icterus abeillei"
+}
+item {
+  name: "39670"
+  id: 2375
+  display_name: "Lepidochelys olivacea"
+}
+item {
+  name: "4867"
+  id: 2376
+  display_name: "Vanellus chilensis"
+}
+item {
+  name: "39677"
+  id: 2377
+  display_name: "Dermochelys coriacea"
+}
+item {
+  name: "113407"
+  id: 2378
+  display_name: "Stylurus plagiatus"
+}
+item {
+  name: "39682"
+  id: 2379
+  display_name: "Chelydra serpentina"
+}
+item {
+  name: "6915"
+  id: 2380
+  display_name: "Cygnus buccinator"
+}
+item {
+  name: "6916"
+  id: 2381
+  display_name: "Cygnus cygnus"
+}
+item {
+  name: "6917"
+  id: 2382
+  display_name: "Cygnus columbianus"
+}
+item {
+  name: "29825"
+  id: 2383
+  display_name: "Lampropeltis calligaster calligaster"
+}
+item {
+  name: "6921"
+  id: 2384
+  display_name: "Cygnus olor"
+}
+item {
+  name: "146186"
+  id: 2385
+  display_name: "Intellagama lesueurii"
+}
+item {
+  name: "9346"
+  id: 2386
+  display_name: "Icterus galbula"
+}
+item {
+  name: "126765"
+  id: 2387
+  display_name: "Plutella xylostella"
+}
+item {
+  name: "71154"
+  id: 2388
+  display_name: "Aphis nerii"
+}
+item {
+  name: "6930"
+  id: 2389
+  display_name: "Anas platyrhynchos"
+}
+item {
+  name: "6933"
+  id: 2390
+  display_name: "Anas acuta"
+}
+item {
+  name: "39703"
+  id: 2391
+  display_name: "Sternotherus odoratus"
+}
+item {
+  name: "6937"
+  id: 2392
+  display_name: "Anas crecca"
+}
+item {
+  name: "64287"
+  id: 2393
+  display_name: "Lottia digitalis"
+}
+item {
+  name: "6944"
+  id: 2394
+  display_name: "Anas cyanoptera"
+}
+item {
+  name: "39713"
+  id: 2395
+  display_name: "Kinosternon subrubrum"
+}
+item {
+  name: "26691"
+  id: 2396
+  display_name: "Scaphiopus couchii"
+}
+item {
+  name: "6948"
+  id: 2397
+  display_name: "Anas fulvigula"
+}
+item {
+  name: "6953"
+  id: 2398
+  display_name: "Anas discors"
+}
+item {
+  name: "47914"
+  id: 2399
+  display_name: "Eumorpha pandorus"
+}
+item {
+  name: "47916"
+  id: 2400
+  display_name: "Actias luna"
+}
+item {
+  name: "6957"
+  id: 2401
+  display_name: "Anas strepera"
+}
+item {
+  name: "47919"
+  id: 2402
+  display_name: "Antheraea polyphemus"
+}
+item {
+  name: "119953"
+  id: 2403
+  display_name: "Hypoprepia fucosa"
+}
+item {
+  name: "6961"
+  id: 2404
+  display_name: "Anas clypeata"
+}
+item {
+  name: "134119"
+  id: 2405
+  display_name: "Anisomorpha buprestoides"
+}
+item {
+  name: "51678"
+  id: 2406
+  display_name: "Coenagrion puella"
+}
+item {
+  name: "72502"
+  id: 2407
+  display_name: "Anas chlorotis"
+}
+item {
+  name: "49060"
+  id: 2408
+  display_name: "Epiactis prolifera"
+}
+item {
+  name: "42122"
+  id: 2409
+  display_name: "Phacochoerus africanus"
+}
+item {
+  name: "58507"
+  id: 2410
+  display_name: "Poanes hobomok"
+}
+item {
+  name: "121669"
+  id: 2411
+  display_name: "Stenopus hispidus"
+}
+item {
+  name: "8143"
+  id: 2412
+  display_name: "Rhipidura leucophrys"
+}
+item {
+  name: "6985"
+  id: 2413
+  display_name: "Anas americana"
+}
+item {
+  name: "6993"
+  id: 2414
+  display_name: "Bucephala albeola"
+}
+item {
+  name: "121682"
+  id: 2415
+  display_name: "Tetraclita rubescens"
+}
+item {
+  name: "6996"
+  id: 2416
+  display_name: "Mergus serrator"
+}
+item {
+  name: "113498"
+  id: 2417
+  display_name: "Sympetrum ambiguum"
+}
+item {
+  name: "39771"
+  id: 2418
+  display_name: "Chrysemys picta"
+}
+item {
+  name: "7004"
+  id: 2419
+  display_name: "Mergus merganser"
+}
+item {
+  name: "39773"
+  id: 2420
+  display_name: "Chrysemys picta bellii"
+}
+item {
+  name: "113503"
+  id: 2421
+  display_name: "Sympetrum danae"
+}
+item {
+  name: "113507"
+  id: 2422
+  display_name: "Sympetrum fonscolombii"
+}
+item {
+  name: "154469"
+  id: 2423
+  display_name: "Isa textula"
+}
+item {
+  name: "47975"
+  id: 2424
+  display_name: "Argia apicalis"
+}
+item {
+  name: "7018"
+  id: 2425
+  display_name: "Anser anser"
+}
+item {
+  name: "7019"
+  id: 2426
+  display_name: "Anser albifrons"
+}
+item {
+  name: "47980"
+  id: 2427
+  display_name: "Speyeria cybele"
+}
+item {
+  name: "58514"
+  id: 2428
+  display_name: "Euphyes vestris"
+}
+item {
+  name: "113519"
+  id: 2429
+  display_name: "Sympetrum obtrusum"
+}
+item {
+  name: "7024"
+  id: 2430
+  display_name: "Somateria mollissima"
+}
+item {
+  name: "39793"
+  id: 2431
+  display_name: "Trachemys scripta scripta"
+}
+item {
+  name: "367475"
+  id: 2432
+  display_name: "Rallus obsoletus"
+}
+item {
+  name: "121716"
+  id: 2433
+  display_name: "Uresiphita reversalis"
+}
+item {
+  name: "113525"
+  id: 2434
+  display_name: "Sympetrum sanguineum"
+}
+item {
+  name: "113526"
+  id: 2435
+  display_name: "Sympetrum semicinctum"
+}
+item {
+  name: "18921"
+  id: 2436
+  display_name: "Platycercus elegans"
+}
+item {
+  name: "7032"
+  id: 2437
+  display_name: "Melanitta fusca"
+}
+item {
+  name: "5268"
+  id: 2438
+  display_name: "Milvus migrans"
+}
+item {
+  name: "144536"
+  id: 2439
+  display_name: "Gelochelidon nilotica"
+}
+item {
+  name: "413503"
+  id: 2440
+  display_name: "Ninox novaeseelandiae novaeseelandiae"
+}
+item {
+  name: "7036"
+  id: 2441
+  display_name: "Melanitta perspicillata"
+}
+item {
+  name: "64382"
+  id: 2442
+  display_name: "Lissotriton vulgaris"
+}
+item {
+  name: "39807"
+  id: 2443
+  display_name: "Terrapene ornata"
+}
+item {
+  name: "39808"
+  id: 2444
+  display_name: "Terrapene ornata luteola"
+}
+item {
+  name: "7044"
+  id: 2445
+  display_name: "Aythya collaris"
+}
+item {
+  name: "7045"
+  id: 2446
+  display_name: "Aythya ferina"
+}
+item {
+  name: "7046"
+  id: 2447
+  display_name: "Aythya fuligula"
+}
+item {
+  name: "146314"
+  id: 2448
+  display_name: "Opheodrys vernalis"
+}
+item {
+  name: "3906"
+  id: 2449
+  display_name: "Numenius americanus"
+}
+item {
+  name: "39823"
+  id: 2450
+  display_name: "Pseudemys gorzugi"
+}
+item {
+  name: "178991"
+  id: 2451
+  display_name: "Sypharochiton pelliserpentis"
+}
+item {
+  name: "7061"
+  id: 2452
+  display_name: "Chen caerulescens"
+}
+item {
+  name: "39830"
+  id: 2453
+  display_name: "Pseudemys concinna"
+}
+item {
+  name: "127490"
+  id: 2454
+  display_name: "Parrhasius m-album"
+}
+item {
+  name: "15256"
+  id: 2455
+  display_name: "Chamaea fasciata"
+}
+item {
+  name: "39836"
+  id: 2456
+  display_name: "Malaclemys terrapin"
+}
+item {
+  name: "133764"
+  id: 2457
+  display_name: "Trichopoda pennipes"
+}
+item {
+  name: "334753"
+  id: 2458
+  display_name: "Hypselonotus punctiventris"
+}
+item {
+  name: "58611"
+  id: 2459
+  display_name: "Amia calva"
+}
+item {
+  name: "56240"
+  id: 2460
+  display_name: "Argia vivida"
+}
+item {
+  name: "7089"
+  id: 2461
+  display_name: "Branta canadensis"
+}
+item {
+  name: "146354"
+  id: 2462
+  display_name: "Phrynosoma blainvillii"
+}
+item {
+  name: "56243"
+  id: 2463
+  display_name: "Plebejus acmon"
+}
+item {
+  name: "144542"
+  id: 2464
+  display_name: "Thalasseus elegans"
+}
+item {
+  name: "121783"
+  id: 2465
+  display_name: "Lithobates clamitans melanota"
+}
+item {
+  name: "39865"
+  id: 2466
+  display_name: "Glyptemys insculpta"
+}
+item {
+  name: "39867"
+  id: 2467
+  display_name: "Emys orbicularis"
+}
+item {
+  name: "7104"
+  id: 2468
+  display_name: "Branta sandvicensis"
+}
+item {
+  name: "50336"
+  id: 2469
+  display_name: "Siproeta stelenes"
+}
+item {
+  name: "7056"
+  id: 2470
+  display_name: "Aythya americana"
+}
+item {
+  name: "7107"
+  id: 2471
+  display_name: "Aix sponsa"
+}
+item {
+  name: "7109"
+  id: 2472
+  display_name: "Lophodytes cucullatus"
+}
+item {
+  name: "7111"
+  id: 2473
+  display_name: "Histrionicus histrionicus"
+}
+item {
+  name: "367562"
+  id: 2474
+  display_name: "Aratinga nenday"
+}
+item {
+  name: "39885"
+  id: 2475
+  display_name: "Emydoidea blandingii"
+}
+item {
+  name: "367566"
+  id: 2476
+  display_name: "Psittacara holochlorus"
+}
+item {
+  name: "143181"
+  id: 2477
+  display_name: "Marimatha nigrofimbria"
+}
+item {
+  name: "7120"
+  id: 2478
+  display_name: "Cairina moschata"
+}
+item {
+  name: "7122"
+  id: 2479
+  display_name: "Netta rufina"
+}
+item {
+  name: "130003"
+  id: 2480
+  display_name: "Phaeoura quernaria"
+}
+item {
+  name: "367572"
+  id: 2481
+  display_name: "Psittacara erythrogenys"
+}
+item {
+  name: "17009"
+  id: 2482
+  display_name: "Sayornis saya"
+}
+item {
+  name: "154582"
+  id: 2483
+  display_name: "Ennomos magnaria"
+}
+item {
+  name: "58532"
+  id: 2484
+  display_name: "Colias eurytheme"
+}
+item {
+  name: "121821"
+  id: 2485
+  display_name: "Sceliphron caementarium"
+}
+item {
+  name: "48094"
+  id: 2486
+  display_name: "Dryocampa rubicunda"
+}
+item {
+  name: "7057"
+  id: 2487
+  display_name: "Aythya valisineria"
+}
+item {
+  name: "17646"
+  id: 2488
+  display_name: "Picoides albolarvatus"
+}
+item {
+  name: "201551"
+  id: 2489
+  display_name: "Procyon lotor lotor"
+}
+item {
+  name: "58534"
+  id: 2490
+  display_name: "Lycaena hyllus"
+}
+item {
+  name: "73553"
+  id: 2491
+  display_name: "Vermivora cyanoptera"
+}
+item {
+  name: "359401"
+  id: 2492
+  display_name: "Exomala orientalis"
+}
+item {
+  name: "8018"
+  id: 2493
+  display_name: "Corvus caurinus"
+}
+item {
+  name: "490478"
+  id: 2494
+  display_name: "Tegula brunnea"
+}
+item {
+  name: "20307"
+  id: 2495
+  display_name: "Asio otus"
+}
+item {
+  name: "227466"
+  id: 2496
+  display_name: "Peridea ferruginea"
+}
+item {
+  name: "122172"
+  id: 2497
+  display_name: "Pyrisitia lisa"
+}
+item {
+  name: "133631"
+  id: 2498
+  display_name: "Polites peckius"
+}
+item {
+  name: "8021"
+  id: 2499
+  display_name: "Corvus brachyrhynchos"
+}
+item {
+  name: "7170"
+  id: 2500
+  display_name: "Clangula hyemalis"
+}
+item {
+  name: "58539"
+  id: 2501
+  display_name: "Satyrium calanus"
+}
+item {
+  name: "27137"
+  id: 2502
+  display_name: "Coluber constrictor"
+}
+item {
+  name: "7176"
+  id: 2503
+  display_name: "Chenonetta jubata"
+}
+item {
+  name: "42157"
+  id: 2504
+  display_name: "Giraffa camelopardalis"
+}
+item {
+  name: "144541"
+  id: 2505
+  display_name: "Thalasseus sandvicensis"
+}
+item {
+  name: "23572"
+  id: 2506
+  display_name: "Litoria aurea"
+}
+item {
+  name: "354820"
+  id: 2507
+  display_name: "Patiriella regularis"
+}
+item {
+  name: "55887"
+  id: 2508
+  display_name: "Andricus quercuscalifornicus"
+}
+item {
+  name: "46255"
+  id: 2509
+  display_name: "Ammospermophilus leucurus"
+}
+item {
+  name: "334341"
+  id: 2510
+  display_name: "Oryctolagus cuniculus domesticus"
+}
+item {
+  name: "144560"
+  id: 2511
+  display_name: "Eolophus roseicapilla"
+}
+item {
+  name: "94043"
+  id: 2512
+  display_name: "Anax imperator"
+}
+item {
+  name: "425004"
+  id: 2513
+  display_name: "Dryas iulia moderata"
+}
+item {
+  name: "269359"
+  id: 2514
+  display_name: "Cactophagus spinolae"
+}
+item {
+  name: "72755"
+  id: 2515
+  display_name: "Colaptes rubiginosus"
+}
+item {
+  name: "319123"
+  id: 2516
+  display_name: "Meleagris gallopavo silvestris"
+}
+item {
+  name: "130846"
+  id: 2517
+  display_name: "Lyssa zampa"
+}
+item {
+  name: "203831"
+  id: 2518
+  display_name: "Nemoria bistriaria"
+}
+item {
+  name: "367678"
+  id: 2519
+  display_name: "Ptiliogonys cinereus"
+}
+item {
+  name: "5301"
+  id: 2520
+  display_name: "Elanoides forficatus"
+}
+item {
+  name: "9398"
+  id: 2521
+  display_name: "Carduelis carduelis"
+}
+item {
+  name: "143201"
+  id: 2522
+  display_name: "Coryphista meadii"
+}
+item {
+  name: "104419"
+  id: 2523
+  display_name: "Lestes australis"
+}
+item {
+  name: "367693"
+  id: 2524
+  display_name: "Cassiculus melanicterus"
+}
+item {
+  name: "143452"
+  id: 2525
+  display_name: "Deidamia inscriptum"
+}
+item {
+  name: "466003"
+  id: 2526
+  display_name: "Romalea microptera"
+}
+item {
+  name: "84494"
+  id: 2527
+  display_name: "Paraphidippus aurantius"
+}
+item {
+  name: "203866"
+  id: 2528
+  display_name: "Rabdophaga strobiloides"
+}
+item {
+  name: "72797"
+  id: 2529
+  display_name: "Dendragapus fuliginosus"
+}
+item {
+  name: "7266"
+  id: 2530
+  display_name: "Psaltriparus minimus"
+}
+item {
+  name: "120920"
+  id: 2531
+  display_name: "Odocoileus virginianus clavium"
+}
+item {
+  name: "7278"
+  id: 2532
+  display_name: "Aegithalos caudatus"
+}
+item {
+  name: "30681"
+  id: 2533
+  display_name: "Agkistrodon contortrix mokasen"
+}
+item {
+  name: "413547"
+  id: 2534
+  display_name: "Zosterops lateralis lateralis"
+}
+item {
+  name: "48262"
+  id: 2535
+  display_name: "Apatelodes torrefacta"
+}
+item {
+  name: "121993"
+  id: 2536
+  display_name: "Lampides boeticus"
+}
+item {
+  name: "48267"
+  id: 2537
+  display_name: "Crotalus oreganus oreganus"
+}
+item {
+  name: "48268"
+  id: 2538
+  display_name: "Crotalus oreganus"
+}
+item {
+  name: "147309"
+  id: 2539
+  display_name: "Feltia herilis"
+}
+item {
+  name: "146413"
+  id: 2540
+  display_name: "Sceloporus consobrinus"
+}
+item {
+  name: "326764"
+  id: 2541
+  display_name: "Cyprinus carpio haematopterus"
+}
+item {
+  name: "5315"
+  id: 2542
+  display_name: "Haliaeetus leucogaster"
+}
+item {
+  name: "4519"
+  id: 2543
+  display_name: "Uria aalge"
+}
+item {
+  name: "40085"
+  id: 2544
+  display_name: "Gopherus polyphemus"
+}
+item {
+  name: "23702"
+  id: 2545
+  display_name: "Agalychnis callidryas"
+}
+item {
+  name: "210116"
+  id: 2546
+  display_name: "Tringa semipalmata inornatus"
+}
+item {
+  name: "40092"
+  id: 2547
+  display_name: "Stigmochelys pardalis"
+}
+item {
+  name: "59931"
+  id: 2548
+  display_name: "Acanthurus triostegus"
+}
+item {
+  name: "48292"
+  id: 2549
+  display_name: "Philoscia muscorum"
+}
+item {
+  name: "146601"
+  id: 2550
+  display_name: "Scolopendra heros"
+}
+item {
+  name: "244906"
+  id: 2551
+  display_name: "Panchlora nivea"
+}
+item {
+  name: "48302"
+  id: 2552
+  display_name: "Limulus polyphemus"
+}
+item {
+  name: "180008"
+  id: 2553
+  display_name: "Otospermophilus variegatus"
+}
+item {
+  name: "7347"
+  id: 2554
+  display_name: "Alauda arvensis"
+}
+item {
+  name: "43459"
+  id: 2555
+  display_name: "Macaca fascicularis"
+}
+item {
+  name: "113846"
+  id: 2556
+  display_name: "Telebasis salva"
+}
+item {
+  name: "7356"
+  id: 2557
+  display_name: "Galerida cristata"
+}
+item {
+  name: "64705"
+  id: 2558
+  display_name: "Delichon urbicum"
+}
+item {
+  name: "145932"
+  id: 2559
+  display_name: "Aspidoscelis hyperythra beldingi"
+}
+item {
+  name: "72912"
+  id: 2560
+  display_name: "Helmitheros vermivorum"
+}
+item {
+  name: "69805"
+  id: 2561
+  display_name: "Octogomphus specularis"
+}
+item {
+  name: "129572"
+  id: 2562
+  display_name: "Aphomia sociella"
+}
+item {
+  name: "31964"
+  id: 2563
+  display_name: "Barisia imbricata"
+}
+item {
+  name: "244625"
+  id: 2564
+  display_name: "Halmus chalybeus"
+}
+item {
+  name: "58576"
+  id: 2565
+  display_name: "Phyciodes cocyta"
+}
+item {
+  name: "72931"
+  id: 2566
+  display_name: "Hylocharis leucotis"
+}
+item {
+  name: "104449"
+  id: 2567
+  display_name: "Lestes rectangularis"
+}
+item {
+  name: "14886"
+  id: 2568
+  display_name: "Mimus polyglottos"
+}
+item {
+  name: "23783"
+  id: 2569
+  display_name: "Hyla versicolor"
+}
+item {
+  name: "23784"
+  id: 2570
+  display_name: "Hyla plicata"
+}
+item {
+  name: "8575"
+  id: 2571
+  display_name: "Gymnorhina tibicen"
+}
+item {
+  name: "2599"
+  id: 2572
+  display_name: "Alcedo atthis"
+}
+item {
+  name: "61152"
+  id: 2573
+  display_name: "Pyrrhosoma nymphula"
+}
+item {
+  name: "58579"
+  id: 2574
+  display_name: "Polygonia interrogationis"
+}
+item {
+  name: "31993"
+  id: 2575
+  display_name: "Ophisaurus attenuatus attenuatus"
+}
+item {
+  name: "53985"
+  id: 2576
+  display_name: "Odocoileus hemionus californicus"
+}
+item {
+  name: "144549"
+  id: 2577
+  display_name: "Streptopelia chinensis"
+}
+item {
+  name: "105730"
+  id: 2578
+  display_name: "Micrathyria hagenii"
+}
+item {
+  name: "7428"
+  id: 2579
+  display_name: "Bombycilla cedrorum"
+}
+item {
+  name: "7429"
+  id: 2580
+  display_name: "Bombycilla garrulus"
+}
+item {
+  name: "50391"
+  id: 2581
+  display_name: "Polygonia gracilis"
+}
+item {
+  name: "7067"
+  id: 2582
+  display_name: "Tadorna tadorna"
+}
+item {
+  name: "413513"
+  id: 2583
+  display_name: "Petroica australis australis"
+}
+item {
+  name: "39469"
+  id: 2584
+  display_name: "Varanus varius"
+}
+item {
+  name: "58479"
+  id: 2585
+  display_name: "Pholisora catullus"
+}
+item {
+  name: "127929"
+  id: 2586
+  display_name: "Achalarus lyciades"
+}
+item {
+  name: "48403"
+  id: 2587
+  display_name: "Gasterosteus aculeatus"
+}
+item {
+  name: "18990"
+  id: 2588
+  display_name: "Amazona autumnalis"
+}
+item {
+  name: "1241"
+  id: 2589
+  display_name: "Dendragapus obscurus"
+}
+item {
+  name: "228634"
+  id: 2590
+  display_name: "Ponometia erastrioides"
+}
+item {
+  name: "64806"
+  id: 2591
+  display_name: "Pelophylax"
+}
+item {
+  name: "51761"
+  id: 2592
+  display_name: "Hetaerina americana"
+}
+item {
+  name: "7464"
+  id: 2593
+  display_name: "Catherpes mexicanus"
+}
+item {
+  name: "318761"
+  id: 2594
+  display_name: "Sceloporus uniformis"
+}
+item {
+  name: "7068"
+  id: 2595
+  display_name: "Tadorna ferruginea"
+}
+item {
+  name: "204077"
+  id: 2596
+  display_name: "Achyra rantalis"
+}
+item {
+  name: "7470"
+  id: 2597
+  display_name: "Campylorhynchus brunneicapillus"
+}
+item {
+  name: "32048"
+  id: 2598
+  display_name: "Gerrhonotus infernalis"
+}
+item {
+  name: "204081"
+  id: 2599
+  display_name: "Pyrausta laticlavia"
+}
+item {
+  name: "7476"
+  id: 2600
+  display_name: "Campylorhynchus rufinucha"
+}
+item {
+  name: "32055"
+  id: 2601
+  display_name: "Elgaria multicarinata"
+}
+item {
+  name: "244276"
+  id: 2602
+  display_name: "Rhipidura fuliginosa"
+}
+item {
+  name: "144187"
+  id: 2603
+  display_name: "Pyrisitia proterpia"
+}
+item {
+  name: "32059"
+  id: 2604
+  display_name: "Elgaria multicarinata multicarinata"
+}
+item {
+  name: "32061"
+  id: 2605
+  display_name: "Elgaria kingii"
+}
+item {
+  name: "146750"
+  id: 2606
+  display_name: "Lascoria ambigualis"
+}
+item {
+  name: "32064"
+  id: 2607
+  display_name: "Elgaria coerulea"
+}
+item {
+  name: "23873"
+  id: 2608
+  display_name: "Hyla squirella"
+}
+item {
+  name: "48450"
+  id: 2609
+  display_name: "Peltodoris nobilis"
+}
+item {
+  name: "64146"
+  id: 2610
+  display_name: "Fissurella volcano"
+}
+item {
+  name: "48259"
+  id: 2611
+  display_name: "Pelidnota punctata"
+}
+item {
+  name: "122185"
+  id: 2612
+  display_name: "Pantherophis alleghaniensis quadrivittata"
+}
+item {
+  name: "7498"
+  id: 2613
+  display_name: "Polioptila melanura"
+}
+item {
+  name: "56652"
+  id: 2614
+  display_name: "Haliotis rufescens"
+}
+item {
+  name: "122191"
+  id: 2615
+  display_name: "Pelecanus occidentalis carolinensis"
+}
+item {
+  name: "73041"
+  id: 2616
+  display_name: "Melozone aberti"
+}
+item {
+  name: "199381"
+  id: 2617
+  display_name: "Homalodisca vitripennis"
+}
+item {
+  name: "73044"
+  id: 2618
+  display_name: "Melozone crissalis"
+}
+item {
+  name: "83290"
+  id: 2619
+  display_name: "Zanclus cornutus"
+}
+item {
+  name: "7513"
+  id: 2620
+  display_name: "Thryothorus ludovicianus"
+}
+item {
+  name: "28559"
+  id: 2621
+  display_name: "Storeria occipitomaculata occipitomaculata"
+}
+item {
+  name: "24255"
+  id: 2622
+  display_name: "Pseudacris maculata"
+}
+item {
+  name: "130398"
+  id: 2623
+  display_name: "Melanargia galathea"
+}
+item {
+  name: "29925"
+  id: 2624
+  display_name: "Heterodon platirhinos"
+}
+item {
+  name: "48484"
+  id: 2625
+  display_name: "Harmonia axyridis"
+}
+item {
+  name: "122214"
+  id: 2626
+  display_name: "Odontotaenius disjunctus"
+}
+item {
+  name: "39484"
+  id: 2627
+  display_name: "Xantusia vigilis"
+}
+item {
+  name: "73919"
+  id: 2628
+  display_name: "Podarcis sicula"
+}
+item {
+  name: "154553"
+  id: 2629
+  display_name: "Leptoglossus clypealis"
+}
+item {
+  name: "23922"
+  id: 2630
+  display_name: "Hyla intermedia"
+}
+item {
+  name: "122228"
+  id: 2631
+  display_name: "Acharia stimulea"
+}
+item {
+  name: "108344"
+  id: 2632
+  display_name: "Pantala flavescens"
+}
+item {
+  name: "118538"
+  id: 2633
+  display_name: "Cotinis nitida"
+}
+item {
+  name: "23930"
+  id: 2634
+  display_name: "Hyla chrysoscelis"
+}
+item {
+  name: "23933"
+  id: 2635
+  display_name: "Hyla arenicolor"
+}
+item {
+  name: "122238"
+  id: 2636
+  display_name: "Porcellio scaber"
+}
+item {
+  name: "479803"
+  id: 2637
+  display_name: "Dioprosopa clavata"
+}
+item {
+  name: "5355"
+  id: 2638
+  display_name: "Parabuteo unicinctus"
+}
+item {
+  name: "146822"
+  id: 2639
+  display_name: "Texola elada"
+}
+item {
+  name: "236935"
+  id: 2640
+  display_name: "Anas platyrhynchos domesticus"
+}
+item {
+  name: "7562"
+  id: 2641
+  display_name: "Troglodytes aedon"
+}
+item {
+  name: "339444"
+  id: 2642
+  display_name: "Buteo lineatus elegans"
+}
+item {
+  name: "42221"
+  id: 2643
+  display_name: "Odocoileus hemionus columbianus"
+}
+item {
+  name: "15764"
+  id: 2644
+  display_name: "Thamnophilus doliatus"
+}
+item {
+  name: "122261"
+  id: 2645
+  display_name: "Cucullia convexipennis"
+}
+item {
+  name: "122262"
+  id: 2646
+  display_name: "Brachystola magna"
+}
+item {
+  name: "7576"
+  id: 2647
+  display_name: "Thryomanes bewickii"
+}
+item {
+  name: "143015"
+  id: 2648
+  display_name: "Eubaphe mendica"
+}
+item {
+  name: "73592"
+  id: 2649
+  display_name: "Actinemys marmorata"
+}
+item {
+  name: "84549"
+  id: 2650
+  display_name: "Plathemis lydia"
+}
+item {
+  name: "23969"
+  id: 2651
+  display_name: "Hyla cinerea"
+}
+item {
+  name: "318882"
+  id: 2652
+  display_name: "Ancistrocerus gazella"
+}
+item {
+  name: "7072"
+  id: 2653
+  display_name: "Tadorna variegata"
+}
+item {
+  name: "48548"
+  id: 2654
+  display_name: "Vanessa cardui"
+}
+item {
+  name: "48549"
+  id: 2655
+  display_name: "Vanessa virginiensis"
+}
+item {
+  name: "122278"
+  id: 2656
+  display_name: "Pomacea canaliculata"
+}
+item {
+  name: "9457"
+  id: 2657
+  display_name: "Myioborus miniatus"
+}
+item {
+  name: "122280"
+  id: 2658
+  display_name: "Pyrgus albescens"
+}
+item {
+  name: "122281"
+  id: 2659
+  display_name: "Calycopis cecrops"
+}
+item {
+  name: "130474"
+  id: 2660
+  display_name: "Achlyodes pallida"
+}
+item {
+  name: "338503"
+  id: 2661
+  display_name: "Phalacrocorax varius varius"
+}
+item {
+  name: "9458"
+  id: 2662
+  display_name: "Myioborus pictus"
+}
+item {
+  name: "73629"
+  id: 2663
+  display_name: "Anolis nebulosus"
+}
+item {
+  name: "122291"
+  id: 2664
+  display_name: "Larus argentatus smithsonianus"
+}
+item {
+  name: "56756"
+  id: 2665
+  display_name: "Murgantia histrionica"
+}
+item {
+  name: "73148"
+  id: 2666
+  display_name: "Parkesia motacilla"
+}
+item {
+  name: "48575"
+  id: 2667
+  display_name: "Okenia rosacea"
+}
+item {
+  name: "56768"
+  id: 2668
+  display_name: "Sula granti"
+}
+item {
+  name: "48578"
+  id: 2669
+  display_name: "Anteos maerula"
+}
+item {
+  name: "64968"
+  id: 2670
+  display_name: "Anaxyrus americanus"
+}
+item {
+  name: "64970"
+  id: 2671
+  display_name: "Anaxyrus boreas"
+}
+item {
+  name: "115549"
+  id: 2672
+  display_name: "Crotalus lepidus lepidus"
+}
+item {
+  name: "64977"
+  id: 2673
+  display_name: "Anaxyrus fowleri"
+}
+item {
+  name: "19022"
+  id: 2674
+  display_name: "Ara macao"
+}
+item {
+  name: "24259"
+  id: 2675
+  display_name: "Pseudacris regilla"
+}
+item {
+  name: "64984"
+  id: 2676
+  display_name: "Anaxyrus punctatus"
+}
+item {
+  name: "64985"
+  id: 2677
+  display_name: "Anaxyrus quercicus"
+}
+item {
+  name: "73178"
+  id: 2678
+  display_name: "Peucaea ruficauda"
+}
+item {
+  name: "64987"
+  id: 2679
+  display_name: "Anaxyrus speciosus"
+}
+item {
+  name: "64989"
+  id: 2680
+  display_name: "Anaxyrus woodhousii"
+}
+item {
+  name: "339596"
+  id: 2681
+  display_name: "Calidris subruficollis"
+}
+item {
+  name: "56552"
+  id: 2682
+  display_name: "Carabus nemoralis"
+}
+item {
+  name: "84722"
+  id: 2683
+  display_name: "Ischnura verticalis"
+}
+item {
+  name: "122356"
+  id: 2684
+  display_name: "Eumorpha achemon"
+}
+item {
+  name: "318965"
+  id: 2685
+  display_name: "Chrysolina bankii"
+}
+item {
+  name: "228855"
+  id: 2686
+  display_name: "Protodeltote muscosula"
+}
+item {
+  name: "146940"
+  id: 2687
+  display_name: "Agriphila vulgivagella"
+}
+item {
+  name: "56832"
+  id: 2688
+  display_name: "Nymphalis antiopa"
+}
+item {
+  name: "61355"
+  id: 2689
+  display_name: "Vespula pensylvanica"
+}
+item {
+  name: "48645"
+  id: 2690
+  display_name: "Megathura crenulata"
+}
+item {
+  name: "73222"
+  id: 2691
+  display_name: "Phoenicopterus roseus"
+}
+item {
+  name: "363354"
+  id: 2692
+  display_name: "Lobatus gigas"
+}
+item {
+  name: "3802"
+  id: 2693
+  display_name: "Morus bassanus"
+}
+item {
+  name: "62722"
+  id: 2694
+  display_name: "Apalone spinifera spinifera"
+}
+item {
+  name: "48655"
+  id: 2695
+  display_name: "Aplysia californica"
+}
+item {
+  name: "54468"
+  id: 2696
+  display_name: "Aglais urticae"
+}
+item {
+  name: "48662"
+  id: 2697
+  display_name: "Danaus plexippus"
+}
+item {
+  name: "49071"
+  id: 2698
+  display_name: "Metridium senile"
+}
+item {
+  name: "228899"
+  id: 2699
+  display_name: "Psamatodes abydata"
+}
+item {
+  name: "133102"
+  id: 2700
+  display_name: "Oncometopia orbona"
+}
+item {
+  name: "39659"
+  id: 2701
+  display_name: "Chelonia mydas"
+}
+item {
+  name: "121437"
+  id: 2702
+  display_name: "Dolomedes triton"
+}
+item {
+  name: "94545"
+  id: 2703
+  display_name: "Argia fumipennis"
+}
+item {
+  name: "56887"
+  id: 2704
+  display_name: "Bombus pensylvanicus"
+}
+item {
+  name: "40509"
+  id: 2705
+  display_name: "Eptesicus fuscus"
+}
+item {
+  name: "58635"
+  id: 2706
+  display_name: "Lepomis megalotis"
+}
+item {
+  name: "100369"
+  id: 2707
+  display_name: "Erpetogomphus designatus"
+}
+item {
+  name: "58636"
+  id: 2708
+  display_name: "Lepomis cyanellus"
+}
+item {
+  name: "40522"
+  id: 2709
+  display_name: "Lasiurus borealis"
+}
+item {
+  name: "102006"
+  id: 2710
+  display_name: "Hagenius brevistylus"
+}
+item {
+  name: "50283"
+  id: 2711
+  display_name: "Marpesia petreus"
+}
+item {
+  name: "123829"
+  id: 2712
+  display_name: "Pelecanus occidentalis californicus"
+}
+item {
+  name: "62453"
+  id: 2713
+  display_name: "Anthidium manicatum"
+}
+item {
+  name: "56925"
+  id: 2714
+  display_name: "Graphocephala coccinea"
+}
+item {
+  name: "48738"
+  id: 2715
+  display_name: "Sphex pensylvanicus"
+}
+item {
+  name: "43151"
+  id: 2716
+  display_name: "Oryctolagus cuniculus"
+}
+item {
+  name: "19822"
+  id: 2717
+  display_name: "Glaucidium brasilianum"
+}
+item {
+  name: "48750"
+  id: 2718
+  display_name: "Lottia scabra"
+}
+item {
+  name: "335071"
+  id: 2719
+  display_name: "Elophila obliteralis"
+}
+item {
+  name: "81521"
+  id: 2720
+  display_name: "Vipera berus"
+}
+item {
+  name: "43697"
+  id: 2721
+  display_name: "Elephas maximus"
+}
+item {
+  name: "7079"
+  id: 2722
+  display_name: "Oxyura jamaicensis"
+}
+item {
+  name: "43042"
+  id: 2723
+  display_name: "Erinaceus europaeus"
+}
+item {
+  name: "40086"
+  id: 2724
+  display_name: "Gopherus agassizii"
+}
+item {
+  name: "81545"
+  id: 2725
+  display_name: "Lumbricus terrestris"
+}
+item {
+  name: "16010"
+  id: 2726
+  display_name: "Myiarchus cinerascens"
+}
+item {
+  name: "2669"
+  id: 2727
+  display_name: "Chloroceryle americana"
+}
+item {
+  name: "9535"
+  id: 2728
+  display_name: "Sturnella neglecta"
+}
+item {
+  name: "81554"
+  id: 2729
+  display_name: "Ictalurus punctatus"
+}
+item {
+  name: "339907"
+  id: 2730
+  display_name: "Ramphastos ambiguus"
+}
+item {
+  name: "39814"
+  id: 2731
+  display_name: "Terrapene carolina"
+}
+item {
+  name: "10254"
+  id: 2732
+  display_name: "Paroaria coronata"
+}
+item {
+  name: "40614"
+  id: 2733
+  display_name: "Antrozous pallidus"
+}
+item {
+  name: "502385"
+  id: 2734
+  display_name: "Probole amicaria"
+}
+item {
+  name: "24233"
+  id: 2735
+  display_name: "Acris gryllus"
+}
+item {
+  name: "81579"
+  id: 2736
+  display_name: "Steatoda triangulosa"
+}
+item {
+  name: "81580"
+  id: 2737
+  display_name: "Callosamia promethea"
+}
+item {
+  name: "146034"
+  id: 2738
+  display_name: "Coluber lateralis"
+}
+item {
+  name: "81582"
+  id: 2739
+  display_name: "Hyalophora cecropia"
+}
+item {
+  name: "81583"
+  id: 2740
+  display_name: "Anisota senatoria"
+}
+item {
+  name: "66002"
+  id: 2741
+  display_name: "Lithobates palustris"
+}
+item {
+  name: "81586"
+  id: 2742
+  display_name: "Citheronia regalis"
+}
+item {
+  name: "40629"
+  id: 2743
+  display_name: "Lasionycteris noctivagans"
+}
+item {
+  name: "81590"
+  id: 2744
+  display_name: "Eacles imperialis"
+}
+item {
+  name: "204472"
+  id: 2745
+  display_name: "Buteo buteo"
+}
+item {
+  name: "65212"
+  id: 2746
+  display_name: "Craugastor augusti"
+}
+item {
+  name: "48830"
+  id: 2747
+  display_name: "Patiria miniata"
+}
+item {
+  name: "48833"
+  id: 2748
+  display_name: "Pisaster giganteus"
+}
+item {
+  name: "16071"
+  id: 2749
+  display_name: "Myiodynastes luteiventris"
+}
+item {
+  name: "81610"
+  id: 2750
+  display_name: "Balanus glandula"
+}
+item {
+  name: "24268"
+  id: 2751
+  display_name: "Pseudacris crucifer"
+}
+item {
+  name: "16079"
+  id: 2752
+  display_name: "Contopus sordidulus"
+}
+item {
+  name: "204496"
+  id: 2753
+  display_name: "Corvus corone"
+}
+item {
+  name: "204498"
+  id: 2754
+  display_name: "Cyanoramphus novaezelandiae"
+}
+item {
+  name: "24277"
+  id: 2755
+  display_name: "Smilisca baudinii"
+}
+item {
+  name: "22631"
+  id: 2756
+  display_name: "Eleutherodactylus planirostris"
+}
+item {
+  name: "16100"
+  id: 2757
+  display_name: "Contopus virens"
+}
+item {
+  name: "42278"
+  id: 2758
+  display_name: "Aepyceros melampus"
+}
+item {
+  name: "16106"
+  id: 2759
+  display_name: "Contopus pertinax"
+}
+item {
+  name: "16110"
+  id: 2760
+  display_name: "Contopus cooperi"
+}
+item {
+  name: "42280"
+  id: 2761
+  display_name: "Connochaetes taurinus"
+}
+item {
+  name: "47455"
+  id: 2762
+  display_name: "Octopus rubescens"
+}
+item {
+  name: "204533"
+  id: 2763
+  display_name: "Larus argentatus"
+}
+item {
+  name: "81656"
+  id: 2764
+  display_name: "Nematocampa resistaria"
+}
+item {
+  name: "81657"
+  id: 2765
+  display_name: "Lacinipolia renigera"
+}
+item {
+  name: "204519"
+  id: 2766
+  display_name: "Halcyon smyrnensis"
+}
+item {
+  name: "62762"
+  id: 2767
+  display_name: "Cordulegaster dorsalis"
+}
+item {
+  name: "81663"
+  id: 2768
+  display_name: "Malacosoma disstria"
+}
+item {
+  name: "32512"
+  id: 2769
+  display_name: "Rena dulcis"
+}
+item {
+  name: "81665"
+  id: 2770
+  display_name: "Orgyia leucostigma"
+}
+item {
+  name: "130821"
+  id: 2771
+  display_name: "Haploa confusa"
+}
+item {
+  name: "81672"
+  id: 2772
+  display_name: "Clemensia albata"
+}
+item {
+  name: "204554"
+  id: 2773
+  display_name: "Onychognathus morio"
+}
+item {
+  name: "81677"
+  id: 2774
+  display_name: "Euchaetes egle"
+}
+item {
+  name: "81680"
+  id: 2775
+  display_name: "Scopula limboundata"
+}
+item {
+  name: "318497"
+  id: 2776
+  display_name: "Hemipenthes sinuosa"
+}
+item {
+  name: "179987"
+  id: 2777
+  display_name: "Ictidomys parvidens"
+}
+item {
+  name: "179988"
+  id: 2778
+  display_name: "Ictidomys tridecemlineatus"
+}
+item {
+  name: "81685"
+  id: 2779
+  display_name: "Evergestis pallidata"
+}
+item {
+  name: "81687"
+  id: 2780
+  display_name: "Noctua pronuba"
+}
+item {
+  name: "179992"
+  id: 2781
+  display_name: "Xerospermophilus spilosoma"
+}
+item {
+  name: "179994"
+  id: 2782
+  display_name: "Urocitellus armatus"
+}
+item {
+  name: "9519"
+  id: 2783
+  display_name: "Cyanocompsa parellina"
+}
+item {
+  name: "179998"
+  id: 2784
+  display_name: "Urocitellus columbianus"
+}
+item {
+  name: "114463"
+  id: 2785
+  display_name: "Trithemis annulata"
+}
+item {
+  name: "199169"
+  id: 2786
+  display_name: "Catocala maestosa"
+}
+item {
+  name: "143323"
+  id: 2787
+  display_name: "Tolype velleda"
+}
+item {
+  name: "120113"
+  id: 2788
+  display_name: "Anthrenus verbasci"
+}
+item {
+  name: "7601"
+  id: 2789
+  display_name: "Cistothorus palustris"
+}
+item {
+  name: "81706"
+  id: 2790
+  display_name: "Alaus oculatus"
+}
+item {
+  name: "220974"
+  id: 2791
+  display_name: "Harrisimemna trisignata"
+}
+item {
+  name: "20445"
+  id: 2792
+  display_name: "Tyto alba"
+}
+item {
+  name: "73523"
+  id: 2793
+  display_name: "Trogon caligatus"
+}
+item {
+  name: "49590"
+  id: 2794
+  display_name: "Micropterus dolomieu"
+}
+item {
+  name: "41729"
+  id: 2795
+  display_name: "Mirounga leonina"
+}
+item {
+  name: "48957"
+  id: 2796
+  display_name: "Arilus cristatus"
+}
+item {
+  name: "81727"
+  id: 2797
+  display_name: "Abaeis nicippe"
+}
+item {
+  name: "8000"
+  id: 2798
+  display_name: "Corvus monedula"
+}
+item {
+  name: "8001"
+  id: 2799
+  display_name: "Corvus ossifragus"
+}
+item {
+  name: "171843"
+  id: 2800
+  display_name: "Rabdotus dealbatus"
+}
+item {
+  name: "81734"
+  id: 2801
+  display_name: "Neophasia menapia"
+}
+item {
+  name: "258813"
+  id: 2802
+  display_name: "Clogmia albipunctata"
+}
+item {
+  name: "332243"
+  id: 2803
+  display_name: "Lepturobosca chrysocoma"
+}
+item {
+  name: "81744"
+  id: 2804
+  display_name: "Heliconius erato"
+}
+item {
+  name: "218424"
+  id: 2805
+  display_name: "Dicymolomia julianalis"
+}
+item {
+  name: "3813"
+  id: 2806
+  display_name: "Spheniscus demersus"
+}
+item {
+  name: "81749"
+  id: 2807
+  display_name: "Malacosoma americanum"
+}
+item {
+  name: "81752"
+  id: 2808
+  display_name: "Pyrausta tyralis"
+}
+item {
+  name: "48987"
+  id: 2809
+  display_name: "Hippodamia convergens"
+}
+item {
+  name: "8029"
+  id: 2810
+  display_name: "Corvus frugilegus"
+}
+item {
+  name: "8031"
+  id: 2811
+  display_name: "Corvus splendens"
+}
+item {
+  name: "147298"
+  id: 2812
+  display_name: "Lasiommata megera"
+}
+item {
+  name: "7087"
+  id: 2813
+  display_name: "Branta bernicla"
+}
+item {
+  name: "48550"
+  id: 2814
+  display_name: "Phoebis sennae"
+}
+item {
+  name: "4349"
+  id: 2815
+  display_name: "Larus hyperboreus"
+}
+item {
+  name: "84027"
+  id: 2816
+  display_name: "Trigonopeltastes delta"
+}
+item {
+  name: "194762"
+  id: 2817
+  display_name: "Vanessa itea"
+}
+item {
+  name: "311163"
+  id: 2818
+  display_name: "Pseudomops septentrionalis"
+}
+item {
+  name: "55957"
+  id: 2819
+  display_name: "Scudderia furcata"
+}
+item {
+  name: "39822"
+  id: 2820
+  display_name: "Pseudemys texana"
+}
+item {
+  name: "204685"
+  id: 2821
+  display_name: "Chlosyne ehrenbergii"
+}
+item {
+  name: "122767"
+  id: 2822
+  display_name: "Columba livia domestica"
+}
+item {
+  name: "55960"
+  id: 2823
+  display_name: "Sceloporus graciosus"
+}
+item {
+  name: "121823"
+  id: 2824
+  display_name: "Autographa californica"
+}
+item {
+  name: "8088"
+  id: 2825
+  display_name: "Garrulus glandarius"
+}
+item {
+  name: "65433"
+  id: 2826
+  display_name: "Ecnomiohyla miotympanum"
+}
+item {
+  name: "49051"
+  id: 2827
+  display_name: "Anthopleura sola"
+}
+item {
+  name: "125815"
+  id: 2828
+  display_name: "Coenonympha arcania"
+}
+item {
+  name: "55963"
+  id: 2829
+  display_name: "Malacosoma californicum"
+}
+item {
+  name: "120479"
+  id: 2830
+  display_name: "Anser anser domesticus"
+}
+item {
+  name: "133788"
+  id: 2831
+  display_name: "Xylocopa micans"
+}
+item {
+  name: "81559"
+  id: 2832
+  display_name: "Epargyreus clarus"
+}
+item {
+  name: "81839"
+  id: 2833
+  display_name: "Platycryptus undatus"
+}
+item {
+  name: "133791"
+  id: 2834
+  display_name: "Polistes exclamans"
+}
+item {
+  name: "84640"
+  id: 2835
+  display_name: "Polistes dominula"
+}
+item {
+  name: "73666"
+  id: 2836
+  display_name: "Aspidoscelis exsanguis"
+}
+item {
+  name: "73669"
+  id: 2837
+  display_name: "Aspidoscelis gularis"
+}
+item {
+  name: "16326"
+  id: 2838
+  display_name: "Mitrephanes phaeocercus"
+}
+item {
+  name: "49095"
+  id: 2839
+  display_name: "Pagurus samuelis"
+}
+item {
+  name: "73672"
+  id: 2840
+  display_name: "Aspidoscelis hyperythra"
+}
+item {
+  name: "59192"
+  id: 2841
+  display_name: "Polites sabuleti"
+}
+item {
+  name: "81561"
+  id: 2842
+  display_name: "Anaea andria"
+}
+item {
+  name: "81881"
+  id: 2843
+  display_name: "Amphipsalta zelandica"
+}
+item {
+  name: "73690"
+  id: 2844
+  display_name: "Aspidoscelis sexlineata"
+}
+item {
+  name: "73694"
+  id: 2845
+  display_name: "Aspidoscelis velox"
+}
+item {
+  name: "335840"
+  id: 2846
+  display_name: "Pyrausta inornatalis"
+}
+item {
+  name: "49126"
+  id: 2847
+  display_name: "Strongylocentrotus franciscanus"
+}
+item {
+  name: "204775"
+  id: 2848
+  display_name: "Kricogonia lyside"
+}
+item {
+  name: "475115"
+  id: 2849
+  display_name: "Ardenna creatopus"
+}
+item {
+  name: "475120"
+  id: 2850
+  display_name: "Ardenna gravis"
+}
+item {
+  name: "62803"
+  id: 2851
+  display_name: "Monadenia fidelis"
+}
+item {
+  name: "49150"
+  id: 2852
+  display_name: "Agraulis vanillae"
+}
+item {
+  name: "83929"
+  id: 2853
+  display_name: "Phanaeus vindex"
+}
+item {
+  name: "199839"
+  id: 2854
+  display_name: "Haemorhous cassinii"
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/kitti_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/kitti_label_map.pbtxt
new file mode 100644
index 00000000..0afcc693
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/kitti_label_map.pbtxt
@@ -0,0 +1,9 @@
+item {
+  id: 1
+  name: 'car'
+}
+
+item {
+  id: 2
+  name: 'pedestrian'
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_complete_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_complete_label_map.pbtxt
new file mode 100644
index 00000000..d73fc065
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_complete_label_map.pbtxt
@@ -0,0 +1,455 @@
+item {
+  name: "background"
+  id: 0
+  display_name: "background"
+}
+item {
+  name: "/m/01g317"
+  id: 1
+  display_name: "person"
+}
+item {
+  name: "/m/0199g"
+  id: 2
+  display_name: "bicycle"
+}
+item {
+  name: "/m/0k4j"
+  id: 3
+  display_name: "car"
+}
+item {
+  name: "/m/04_sv"
+  id: 4
+  display_name: "motorcycle"
+}
+item {
+  name: "/m/05czz6l"
+  id: 5
+  display_name: "airplane"
+}
+item {
+  name: "/m/01bjv"
+  id: 6
+  display_name: "bus"
+}
+item {
+  name: "/m/07jdr"
+  id: 7
+  display_name: "train"
+}
+item {
+  name: "/m/07r04"
+  id: 8
+  display_name: "truck"
+}
+item {
+  name: "/m/019jd"
+  id: 9
+  display_name: "boat"
+}
+item {
+  name: "/m/015qff"
+  id: 10
+  display_name: "traffic light"
+}
+item {
+  name: "/m/01pns0"
+  id: 11
+  display_name: "fire hydrant"
+}
+item {
+  name: "12"
+  id: 12
+  display_name: "12"
+}
+item {
+  name: "/m/02pv19"
+  id: 13
+  display_name: "stop sign"
+}
+item {
+  name: "/m/015qbp"
+  id: 14
+  display_name: "parking meter"
+}
+item {
+  name: "/m/0cvnqh"
+  id: 15
+  display_name: "bench"
+}
+item {
+  name: "/m/015p6"
+  id: 16
+  display_name: "bird"
+}
+item {
+  name: "/m/01yrx"
+  id: 17
+  display_name: "cat"
+}
+item {
+  name: "/m/0bt9lr"
+  id: 18
+  display_name: "dog"
+}
+item {
+  name: "/m/03k3r"
+  id: 19
+  display_name: "horse"
+}
+item {
+  name: "/m/07bgp"
+  id: 20
+  display_name: "sheep"
+}
+item {
+  name: "/m/01xq0k1"
+  id: 21
+  display_name: "cow"
+}
+item {
+  name: "/m/0bwd_0j"
+  id: 22
+  display_name: "elephant"
+}
+item {
+  name: "/m/01dws"
+  id: 23
+  display_name: "bear"
+}
+item {
+  name: "/m/0898b"
+  id: 24
+  display_name: "zebra"
+}
+item {
+  name: "/m/03bk1"
+  id: 25
+  display_name: "giraffe"
+}
+item {
+  name: "26"
+  id: 26
+  display_name: "26"
+}
+item {
+  name: "/m/01940j"
+  id: 27
+  display_name: "backpack"
+}
+item {
+  name: "/m/0hnnb"
+  id: 28
+  display_name: "umbrella"
+}
+item {
+  name: "29"
+  id: 29
+  display_name: "29"
+}
+item {
+  name: "30"
+  id: 30
+  display_name: "30"
+}
+item {
+  name: "/m/080hkjn"
+  id: 31
+  display_name: "handbag"
+}
+item {
+  name: "/m/01rkbr"
+  id: 32
+  display_name: "tie"
+}
+item {
+  name: "/m/01s55n"
+  id: 33
+  display_name: "suitcase"
+}
+item {
+  name: "/m/02wmf"
+  id: 34
+  display_name: "frisbee"
+}
+item {
+  name: "/m/071p9"
+  id: 35
+  display_name: "skis"
+}
+item {
+  name: "/m/06__v"
+  id: 36
+  display_name: "snowboard"
+}
+item {
+  name: "/m/018xm"
+  id: 37
+  display_name: "sports ball"
+}
+item {
+  name: "/m/02zt3"
+  id: 38
+  display_name: "kite"
+}
+item {
+  name: "/m/03g8mr"
+  id: 39
+  display_name: "baseball bat"
+}
+item {
+  name: "/m/03grzl"
+  id: 40
+  display_name: "baseball glove"
+}
+item {
+  name: "/m/06_fw"
+  id: 41
+  display_name: "skateboard"
+}
+item {
+  name: "/m/019w40"
+  id: 42
+  display_name: "surfboard"
+}
+item {
+  name: "/m/0dv9c"
+  id: 43
+  display_name: "tennis racket"
+}
+item {
+  name: "/m/04dr76w"
+  id: 44
+  display_name: "bottle"
+}
+item {
+  name: "45"
+  id: 45
+  display_name: "45"
+}
+item {
+  name: "/m/09tvcd"
+  id: 46
+  display_name: "wine glass"
+}
+item {
+  name: "/m/08gqpm"
+  id: 47
+  display_name: "cup"
+}
+item {
+  name: "/m/0dt3t"
+  id: 48
+  display_name: "fork"
+}
+item {
+  name: "/m/04ctx"
+  id: 49
+  display_name: "knife"
+}
+item {
+  name: "/m/0cmx8"
+  id: 50
+  display_name: "spoon"
+}
+item {
+  name: "/m/04kkgm"
+  id: 51
+  display_name: "bowl"
+}
+item {
+  name: "/m/09qck"
+  id: 52
+  display_name: "banana"
+}
+item {
+  name: "/m/014j1m"
+  id: 53
+  display_name: "apple"
+}
+item {
+  name: "/m/0l515"
+  id: 54
+  display_name: "sandwich"
+}
+item {
+  name: "/m/0cyhj_"
+  id: 55
+  display_name: "orange"
+}
+item {
+  name: "/m/0hkxq"
+  id: 56
+  display_name: "broccoli"
+}
+item {
+  name: "/m/0fj52s"
+  id: 57
+  display_name: "carrot"
+}
+item {
+  name: "/m/01b9xk"
+  id: 58
+  display_name: "hot dog"
+}
+item {
+  name: "/m/0663v"
+  id: 59
+  display_name: "pizza"
+}
+item {
+  name: "/m/0jy4k"
+  id: 60
+  display_name: "donut"
+}
+item {
+  name: "/m/0fszt"
+  id: 61
+  display_name: "cake"
+}
+item {
+  name: "/m/01mzpv"
+  id: 62
+  display_name: "chair"
+}
+item {
+  name: "/m/02crq1"
+  id: 63
+  display_name: "couch"
+}
+item {
+  name: "/m/03fp41"
+  id: 64
+  display_name: "potted plant"
+}
+item {
+  name: "/m/03ssj5"
+  id: 65
+  display_name: "bed"
+}
+item {
+  name: "66"
+  id: 66
+  display_name: "66"
+}
+item {
+  name: "/m/04bcr3"
+  id: 67
+  display_name: "dining table"
+}
+item {
+  name: "68"
+  id: 68
+  display_name: "68"
+}
+item {
+  name: "69"
+  id: 69
+  display_name: "69"
+}
+item {
+  name: "/m/09g1w"
+  id: 70
+  display_name: "toilet"
+}
+item {
+  name: "71"
+  id: 71
+  display_name: "71"
+}
+item {
+  name: "/m/07c52"
+  id: 72
+  display_name: "tv"
+}
+item {
+  name: "/m/01c648"
+  id: 73
+  display_name: "laptop"
+}
+item {
+  name: "/m/020lf"
+  id: 74
+  display_name: "mouse"
+}
+item {
+  name: "/m/0qjjc"
+  id: 75
+  display_name: "remote"
+}
+item {
+  name: "/m/01m2v"
+  id: 76
+  display_name: "keyboard"
+}
+item {
+  name: "/m/050k8"
+  id: 77
+  display_name: "cell phone"
+}
+item {
+  name: "/m/0fx9l"
+  id: 78
+  display_name: "microwave"
+}
+item {
+  name: "/m/029bxz"
+  id: 79
+  display_name: "oven"
+}
+item {
+  name: "/m/01k6s3"
+  id: 80
+  display_name: "toaster"
+}
+item {
+  name: "/m/0130jx"
+  id: 81
+  display_name: "sink"
+}
+item {
+  name: "/m/040b_t"
+  id: 82
+  display_name: "refrigerator"
+}
+item {
+  name: "83"
+  id: 83
+  display_name: "83"
+}
+item {
+  name: "/m/0bt_c3"
+  id: 84
+  display_name: "book"
+}
+item {
+  name: "/m/01x3z"
+  id: 85
+  display_name: "clock"
+}
+item {
+  name: "/m/02s195"
+  id: 86
+  display_name: "vase"
+}
+item {
+  name: "/m/01lsmm"
+  id: 87
+  display_name: "scissors"
+}
+item {
+  name: "/m/0kmg4"
+  id: 88
+  display_name: "teddy bear"
+}
+item {
+  name: "/m/03wvsk"
+  id: 89
+  display_name: "hair drier"
+}
+item {
+  name: "/m/012xff"
+  id: 90
+  display_name: "toothbrush"
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_label_map.pbtxt
new file mode 100644
index 00000000..1f4872bd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_label_map.pbtxt
@@ -0,0 +1,400 @@
+item {
+  name: "/m/01g317"
+  id: 1
+  display_name: "person"
+}
+item {
+  name: "/m/0199g"
+  id: 2
+  display_name: "bicycle"
+}
+item {
+  name: "/m/0k4j"
+  id: 3
+  display_name: "car"
+}
+item {
+  name: "/m/04_sv"
+  id: 4
+  display_name: "motorcycle"
+}
+item {
+  name: "/m/05czz6l"
+  id: 5
+  display_name: "airplane"
+}
+item {
+  name: "/m/01bjv"
+  id: 6
+  display_name: "bus"
+}
+item {
+  name: "/m/07jdr"
+  id: 7
+  display_name: "train"
+}
+item {
+  name: "/m/07r04"
+  id: 8
+  display_name: "truck"
+}
+item {
+  name: "/m/019jd"
+  id: 9
+  display_name: "boat"
+}
+item {
+  name: "/m/015qff"
+  id: 10
+  display_name: "traffic light"
+}
+item {
+  name: "/m/01pns0"
+  id: 11
+  display_name: "fire hydrant"
+}
+item {
+  name: "/m/02pv19"
+  id: 13
+  display_name: "stop sign"
+}
+item {
+  name: "/m/015qbp"
+  id: 14
+  display_name: "parking meter"
+}
+item {
+  name: "/m/0cvnqh"
+  id: 15
+  display_name: "bench"
+}
+item {
+  name: "/m/015p6"
+  id: 16
+  display_name: "bird"
+}
+item {
+  name: "/m/01yrx"
+  id: 17
+  display_name: "cat"
+}
+item {
+  name: "/m/0bt9lr"
+  id: 18
+  display_name: "dog"
+}
+item {
+  name: "/m/03k3r"
+  id: 19
+  display_name: "horse"
+}
+item {
+  name: "/m/07bgp"
+  id: 20
+  display_name: "sheep"
+}
+item {
+  name: "/m/01xq0k1"
+  id: 21
+  display_name: "cow"
+}
+item {
+  name: "/m/0bwd_0j"
+  id: 22
+  display_name: "elephant"
+}
+item {
+  name: "/m/01dws"
+  id: 23
+  display_name: "bear"
+}
+item {
+  name: "/m/0898b"
+  id: 24
+  display_name: "zebra"
+}
+item {
+  name: "/m/03bk1"
+  id: 25
+  display_name: "giraffe"
+}
+item {
+  name: "/m/01940j"
+  id: 27
+  display_name: "backpack"
+}
+item {
+  name: "/m/0hnnb"
+  id: 28
+  display_name: "umbrella"
+}
+item {
+  name: "/m/080hkjn"
+  id: 31
+  display_name: "handbag"
+}
+item {
+  name: "/m/01rkbr"
+  id: 32
+  display_name: "tie"
+}
+item {
+  name: "/m/01s55n"
+  id: 33
+  display_name: "suitcase"
+}
+item {
+  name: "/m/02wmf"
+  id: 34
+  display_name: "frisbee"
+}
+item {
+  name: "/m/071p9"
+  id: 35
+  display_name: "skis"
+}
+item {
+  name: "/m/06__v"
+  id: 36
+  display_name: "snowboard"
+}
+item {
+  name: "/m/018xm"
+  id: 37
+  display_name: "sports ball"
+}
+item {
+  name: "/m/02zt3"
+  id: 38
+  display_name: "kite"
+}
+item {
+  name: "/m/03g8mr"
+  id: 39
+  display_name: "baseball bat"
+}
+item {
+  name: "/m/03grzl"
+  id: 40
+  display_name: "baseball glove"
+}
+item {
+  name: "/m/06_fw"
+  id: 41
+  display_name: "skateboard"
+}
+item {
+  name: "/m/019w40"
+  id: 42
+  display_name: "surfboard"
+}
+item {
+  name: "/m/0dv9c"
+  id: 43
+  display_name: "tennis racket"
+}
+item {
+  name: "/m/04dr76w"
+  id: 44
+  display_name: "bottle"
+}
+item {
+  name: "/m/09tvcd"
+  id: 46
+  display_name: "wine glass"
+}
+item {
+  name: "/m/08gqpm"
+  id: 47
+  display_name: "cup"
+}
+item {
+  name: "/m/0dt3t"
+  id: 48
+  display_name: "fork"
+}
+item {
+  name: "/m/04ctx"
+  id: 49
+  display_name: "knife"
+}
+item {
+  name: "/m/0cmx8"
+  id: 50
+  display_name: "spoon"
+}
+item {
+  name: "/m/04kkgm"
+  id: 51
+  display_name: "bowl"
+}
+item {
+  name: "/m/09qck"
+  id: 52
+  display_name: "banana"
+}
+item {
+  name: "/m/014j1m"
+  id: 53
+  display_name: "apple"
+}
+item {
+  name: "/m/0l515"
+  id: 54
+  display_name: "sandwich"
+}
+item {
+  name: "/m/0cyhj_"
+  id: 55
+  display_name: "orange"
+}
+item {
+  name: "/m/0hkxq"
+  id: 56
+  display_name: "broccoli"
+}
+item {
+  name: "/m/0fj52s"
+  id: 57
+  display_name: "carrot"
+}
+item {
+  name: "/m/01b9xk"
+  id: 58
+  display_name: "hot dog"
+}
+item {
+  name: "/m/0663v"
+  id: 59
+  display_name: "pizza"
+}
+item {
+  name: "/m/0jy4k"
+  id: 60
+  display_name: "donut"
+}
+item {
+  name: "/m/0fszt"
+  id: 61
+  display_name: "cake"
+}
+item {
+  name: "/m/01mzpv"
+  id: 62
+  display_name: "chair"
+}
+item {
+  name: "/m/02crq1"
+  id: 63
+  display_name: "couch"
+}
+item {
+  name: "/m/03fp41"
+  id: 64
+  display_name: "potted plant"
+}
+item {
+  name: "/m/03ssj5"
+  id: 65
+  display_name: "bed"
+}
+item {
+  name: "/m/04bcr3"
+  id: 67
+  display_name: "dining table"
+}
+item {
+  name: "/m/09g1w"
+  id: 70
+  display_name: "toilet"
+}
+item {
+  name: "/m/07c52"
+  id: 72
+  display_name: "tv"
+}
+item {
+  name: "/m/01c648"
+  id: 73
+  display_name: "laptop"
+}
+item {
+  name: "/m/020lf"
+  id: 74
+  display_name: "mouse"
+}
+item {
+  name: "/m/0qjjc"
+  id: 75
+  display_name: "remote"
+}
+item {
+  name: "/m/01m2v"
+  id: 76
+  display_name: "keyboard"
+}
+item {
+  name: "/m/050k8"
+  id: 77
+  display_name: "cell phone"
+}
+item {
+  name: "/m/0fx9l"
+  id: 78
+  display_name: "microwave"
+}
+item {
+  name: "/m/029bxz"
+  id: 79
+  display_name: "oven"
+}
+item {
+  name: "/m/01k6s3"
+  id: 80
+  display_name: "toaster"
+}
+item {
+  name: "/m/0130jx"
+  id: 81
+  display_name: "sink"
+}
+item {
+  name: "/m/040b_t"
+  id: 82
+  display_name: "refrigerator"
+}
+item {
+  name: "/m/0bt_c3"
+  id: 84
+  display_name: "book"
+}
+item {
+  name: "/m/01x3z"
+  id: 85
+  display_name: "clock"
+}
+item {
+  name: "/m/02s195"
+  id: 86
+  display_name: "vase"
+}
+item {
+  name: "/m/01lsmm"
+  id: 87
+  display_name: "scissors"
+}
+item {
+  name: "/m/0kmg4"
+  id: 88
+  display_name: "teddy bear"
+}
+item {
+  name: "/m/03wvsk"
+  id: 89
+  display_name: "hair drier"
+}
+item {
+  name: "/m/012xff"
+  id: 90
+  display_name: "toothbrush"
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_minival_ids.txt b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_minival_ids.txt
new file mode 100644
index 00000000..5bbff3c1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/mscoco_minival_ids.txt
@@ -0,0 +1,8059 @@
+25096
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diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt
new file mode 100644
index 00000000..863e4f31
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt
@@ -0,0 +1,2725 @@
+item {
+  name: "/m/01g317"
+  id: 1
+  display_name: "Person"
+}
+item {
+  name: "/m/09j2d"
+  id: 2
+  display_name: "Clothing"
+}
+item {
+  name: "/m/04yx4"
+  id: 3
+  display_name: "Man"
+}
+item {
+  name: "/m/0dzct"
+  id: 4
+  display_name: "Face"
+}
+item {
+  name: "/m/07j7r"
+  id: 5
+  display_name: "Tree"
+}
+item {
+  name: "/m/05s2s"
+  id: 6
+  display_name: "Plant"
+}
+item {
+  name: "/m/03bt1vf"
+  id: 7
+  display_name: "Woman"
+}
+item {
+  name: "/m/07yv9"
+  id: 8
+  display_name: "Vehicle"
+}
+item {
+  name: "/m/0cgh4"
+  id: 9
+  display_name: "Building"
+}
+item {
+  name: "/m/01prls"
+  id: 10
+  display_name: "Land vehicle"
+}
+item {
+  name: "/m/09j5n"
+  id: 11
+  display_name: "Footwear"
+}
+item {
+  name: "/m/05r655"
+  id: 12
+  display_name: "Girl"
+}
+item {
+  name: "/m/0jbk"
+  id: 13
+  display_name: "Animal"
+}
+item {
+  name: "/m/0k4j"
+  id: 14
+  display_name: "Car"
+}
+item {
+  name: "/m/02wbm"
+  id: 15
+  display_name: "Food"
+}
+item {
+  name: "/m/083wq"
+  id: 16
+  display_name: "Wheel"
+}
+item {
+  name: "/m/0c9ph5"
+  id: 17
+  display_name: "Flower"
+}
+item {
+  name: "/m/0c_jw"
+  id: 18
+  display_name: "Furniture"
+}
+item {
+  name: "/m/0d4v4"
+  id: 19
+  display_name: "Window"
+}
+item {
+  name: "/m/03jm5"
+  id: 20
+  display_name: "House"
+}
+item {
+  name: "/m/01bl7v"
+  id: 21
+  display_name: "Boy"
+}
+item {
+  name: "/m/0463sg"
+  id: 22
+  display_name: "Fashion accessory"
+}
+item {
+  name: "/m/04bcr3"
+  id: 23
+  display_name: "Table"
+}
+item {
+  name: "/m/0jyfg"
+  id: 24
+  display_name: "Glasses"
+}
+item {
+  name: "/m/01xyhv"
+  id: 25
+  display_name: "Suit"
+}
+item {
+  name: "/m/08dz3q"
+  id: 26
+  display_name: "Auto part"
+}
+item {
+  name: "/m/015p6"
+  id: 27
+  display_name: "Bird"
+}
+item {
+  name: "/m/05y5lj"
+  id: 28
+  display_name: "Sports equipment"
+}
+item {
+  name: "/m/01d40f"
+  id: 29
+  display_name: "Dress"
+}
+item {
+  name: "/m/0bt9lr"
+  id: 30
+  display_name: "Dog"
+}
+item {
+  name: "/m/01lrl"
+  id: 31
+  display_name: "Carnivore"
+}
+item {
+  name: "/m/02p0tk3"
+  id: 32
+  display_name: "Human body"
+}
+item {
+  name: "/m/0fly7"
+  id: 33
+  display_name: "Jeans"
+}
+item {
+  name: "/m/04szw"
+  id: 34
+  display_name: "Musical instrument"
+}
+item {
+  name: "/m/0271t"
+  id: 35
+  display_name: "Drink"
+}
+item {
+  name: "/m/019jd"
+  id: 36
+  display_name: "Boat"
+}
+item {
+  name: "/m/03q69"
+  id: 37
+  display_name: "Hair"
+}
+item {
+  name: "/m/0h9mv"
+  id: 38
+  display_name: "Tire"
+}
+item {
+  name: "/m/04hgtk"
+  id: 39
+  display_name: "Head"
+}
+item {
+  name: "/m/01yrx"
+  id: 40
+  display_name: "Cat"
+}
+item {
+  name: "/m/01rzcn"
+  id: 41
+  display_name: "Watercraft"
+}
+item {
+  name: "/m/01mzpv"
+  id: 42
+  display_name: "Chair"
+}
+item {
+  name: "/m/0199g"
+  id: 43
+  display_name: "Bike"
+}
+item {
+  name: "/m/01fdzj"
+  id: 44
+  display_name: "Tower"
+}
+item {
+  name: "/m/04rky"
+  id: 45
+  display_name: "Mammal"
+}
+item {
+  name: "/m/079cl"
+  id: 46
+  display_name: "Skyscraper"
+}
+item {
+  name: "/m/0dzf4"
+  id: 47
+  display_name: "Arm"
+}
+item {
+  name: "/m/0138tl"
+  id: 48
+  display_name: "Toy"
+}
+item {
+  name: "/m/06msq"
+  id: 49
+  display_name: "Sculpture"
+}
+item {
+  name: "/m/03xxp"
+  id: 50
+  display_name: "Invertebrate"
+}
+item {
+  name: "/m/0hg7b"
+  id: 51
+  display_name: "Microphone"
+}
+item {
+  name: "/m/01n5jq"
+  id: 52
+  display_name: "Poster"
+}
+item {
+  name: "/m/03vt0"
+  id: 53
+  display_name: "Insect"
+}
+item {
+  name: "/m/0342h"
+  id: 54
+  display_name: "Guitar"
+}
+item {
+  name: "/m/0k0pj"
+  id: 55
+  display_name: "Nose"
+}
+item {
+  name: "/m/02dl1y"
+  id: 56
+  display_name: "Hat"
+}
+item {
+  name: "/m/04brg2"
+  id: 57
+  display_name: "Tableware"
+}
+item {
+  name: "/m/02dgv"
+  id: 58
+  display_name: "Door"
+}
+item {
+  name: "/m/01bqk0"
+  id: 59
+  display_name: "Bicycle wheel"
+}
+item {
+  name: "/m/017ftj"
+  id: 60
+  display_name: "Sunglasses"
+}
+item {
+  name: "/m/052lwg6"
+  id: 61
+  display_name: "Baked goods"
+}
+item {
+  name: "/m/014sv8"
+  id: 62
+  display_name: "Eye"
+}
+item {
+  name: "/m/0270h"
+  id: 63
+  display_name: "Dessert"
+}
+item {
+  name: "/m/0283dt1"
+  id: 64
+  display_name: "Mouth"
+}
+item {
+  name: "/m/0k5j"
+  id: 65
+  display_name: "Aircraft"
+}
+item {
+  name: "/m/0cmf2"
+  id: 66
+  display_name: "Airplane"
+}
+item {
+  name: "/m/07jdr"
+  id: 67
+  display_name: "Train"
+}
+item {
+  name: "/m/032b3c"
+  id: 68
+  display_name: "Jacket"
+}
+item {
+  name: "/m/033rq4"
+  id: 69
+  display_name: "Street light"
+}
+item {
+  name: "/m/0k65p"
+  id: 70
+  display_name: "Hand"
+}
+item {
+  name: "/m/01ww8y"
+  id: 71
+  display_name: "Snack"
+}
+item {
+  name: "/m/0zvk5"
+  id: 72
+  display_name: "Helmet"
+}
+item {
+  name: "/m/07mhn"
+  id: 73
+  display_name: "Trousers"
+}
+item {
+  name: "/m/04dr76w"
+  id: 74
+  display_name: "Bottle"
+}
+item {
+  name: "/m/03fp41"
+  id: 75
+  display_name: "Houseplant"
+}
+item {
+  name: "/m/03k3r"
+  id: 76
+  display_name: "Horse"
+}
+item {
+  name: "/m/01y9k5"
+  id: 77
+  display_name: "Desk"
+}
+item {
+  name: "/m/0cdl1"
+  id: 78
+  display_name: "Palm tree"
+}
+item {
+  name: "/m/0f4s2w"
+  id: 79
+  display_name: "Vegetable"
+}
+item {
+  name: "/m/02xwb"
+  id: 80
+  display_name: "Fruit"
+}
+item {
+  name: "/m/035r7c"
+  id: 81
+  display_name: "Leg"
+}
+item {
+  name: "/m/0bt_c3"
+  id: 82
+  display_name: "Book"
+}
+item {
+  name: "/m/01_bhs"
+  id: 83
+  display_name: "Fast food"
+}
+item {
+  name: "/m/01599"
+  id: 84
+  display_name: "Beer"
+}
+item {
+  name: "/m/03120"
+  id: 85
+  display_name: "Flag"
+}
+item {
+  name: "/m/026t6"
+  id: 86
+  display_name: "Drum"
+}
+item {
+  name: "/m/01bjv"
+  id: 87
+  display_name: "Bus"
+}
+item {
+  name: "/m/07r04"
+  id: 88
+  display_name: "Truck"
+}
+item {
+  name: "/m/018xm"
+  id: 89
+  display_name: "Ball"
+}
+item {
+  name: "/m/01rkbr"
+  id: 90
+  display_name: "Tie"
+}
+item {
+  name: "/m/0fm3zh"
+  id: 91
+  display_name: "Flowerpot"
+}
+item {
+  name: "/m/02_n6y"
+  id: 92
+  display_name: "Goggles"
+}
+item {
+  name: "/m/04_sv"
+  id: 93
+  display_name: "Motorcycle"
+}
+item {
+  name: "/m/06z37_"
+  id: 94
+  display_name: "Picture frame"
+}
+item {
+  name: "/m/01bfm9"
+  id: 95
+  display_name: "Shorts"
+}
+item {
+  name: "/m/0h8mhzd"
+  id: 96
+  display_name: "Sports uniform"
+}
+item {
+  name: "/m/0d_2m"
+  id: 97
+  display_name: "Moths and butterflies"
+}
+item {
+  name: "/m/0gjbg72"
+  id: 98
+  display_name: "Shelf"
+}
+item {
+  name: "/m/01n4qj"
+  id: 99
+  display_name: "Shirt"
+}
+item {
+  name: "/m/0ch_cf"
+  id: 100
+  display_name: "Fish"
+}
+item {
+  name: "/m/06m11"
+  id: 101
+  display_name: "Rose"
+}
+item {
+  name: "/m/01jfm_"
+  id: 102
+  display_name: "Licence plate"
+}
+item {
+  name: "/m/02crq1"
+  id: 103
+  display_name: "Couch"
+}
+item {
+  name: "/m/083kb"
+  id: 104
+  display_name: "Weapon"
+}
+item {
+  name: "/m/01c648"
+  id: 105
+  display_name: "Laptop"
+}
+item {
+  name: "/m/09tvcd"
+  id: 106
+  display_name: "Wine glass"
+}
+item {
+  name: "/m/0h2r6"
+  id: 107
+  display_name: "Van"
+}
+item {
+  name: "/m/081qc"
+  id: 108
+  display_name: "Wine"
+}
+item {
+  name: "/m/09ddx"
+  id: 109
+  display_name: "Duck"
+}
+item {
+  name: "/m/03p3bw"
+  id: 110
+  display_name: "Bicycle helmet"
+}
+item {
+  name: "/m/0cyf8"
+  id: 111
+  display_name: "Butterfly"
+}
+item {
+  name: "/m/0b_rs"
+  id: 112
+  display_name: "Swimming pool"
+}
+item {
+  name: "/m/039xj_"
+  id: 113
+  display_name: "Ear"
+}
+item {
+  name: "/m/021sj1"
+  id: 114
+  display_name: "Office"
+}
+item {
+  name: "/m/0dv5r"
+  id: 115
+  display_name: "Camera"
+}
+item {
+  name: "/m/01lynh"
+  id: 116
+  display_name: "Stairs"
+}
+item {
+  name: "/m/06bt6"
+  id: 117
+  display_name: "Reptile"
+}
+item {
+  name: "/m/01226z"
+  id: 118
+  display_name: "Football"
+}
+item {
+  name: "/m/0fszt"
+  id: 119
+  display_name: "Cake"
+}
+item {
+  name: "/m/050k8"
+  id: 120
+  display_name: "Mobile phone"
+}
+item {
+  name: "/m/02wbtzl"
+  id: 121
+  display_name: "Sun hat"
+}
+item {
+  name: "/m/02p5f1q"
+  id: 122
+  display_name: "Coffee cup"
+}
+item {
+  name: "/m/025nd"
+  id: 123
+  display_name: "Christmas tree"
+}
+item {
+  name: "/m/02522"
+  id: 124
+  display_name: "Computer monitor"
+}
+item {
+  name: "/m/09ct_"
+  id: 125
+  display_name: "Helicopter"
+}
+item {
+  name: "/m/0cvnqh"
+  id: 126
+  display_name: "Bench"
+}
+item {
+  name: "/m/0d5gx"
+  id: 127
+  display_name: "Castle"
+}
+item {
+  name: "/m/01xygc"
+  id: 128
+  display_name: "Coat"
+}
+item {
+  name: "/m/04m6gz"
+  id: 129
+  display_name: "Porch"
+}
+item {
+  name: "/m/01gkx_"
+  id: 130
+  display_name: "Swimwear"
+}
+item {
+  name: "/m/01s105"
+  id: 131
+  display_name: "Cabinetry"
+}
+item {
+  name: "/m/01j61q"
+  id: 132
+  display_name: "Tent"
+}
+item {
+  name: "/m/0hnnb"
+  id: 133
+  display_name: "Umbrella"
+}
+item {
+  name: "/m/01j51"
+  id: 134
+  display_name: "Balloon"
+}
+item {
+  name: "/m/01knjb"
+  id: 135
+  display_name: "Billboard"
+}
+item {
+  name: "/m/03__z0"
+  id: 136
+  display_name: "Bookcase"
+}
+item {
+  name: "/m/01m2v"
+  id: 137
+  display_name: "Computer keyboard"
+}
+item {
+  name: "/m/0167gd"
+  id: 138
+  display_name: "Doll"
+}
+item {
+  name: "/m/0284d"
+  id: 139
+  display_name: "Dairy"
+}
+item {
+  name: "/m/03ssj5"
+  id: 140
+  display_name: "Bed"
+}
+item {
+  name: "/m/02fq_6"
+  id: 141
+  display_name: "Fedora"
+}
+item {
+  name: "/m/06nwz"
+  id: 142
+  display_name: "Seafood"
+}
+item {
+  name: "/m/0220r2"
+  id: 143
+  display_name: "Fountain"
+}
+item {
+  name: "/m/01mqdt"
+  id: 144
+  display_name: "Traffic sign"
+}
+item {
+  name: "/m/0268lbt"
+  id: 145
+  display_name: "Hiking equipment"
+}
+item {
+  name: "/m/07c52"
+  id: 146
+  display_name: "Television"
+}
+item {
+  name: "/m/0grw1"
+  id: 147
+  display_name: "Salad"
+}
+item {
+  name: "/m/01h3n"
+  id: 148
+  display_name: "Bee"
+}
+item {
+  name: "/m/078n6m"
+  id: 149
+  display_name: "Coffee table"
+}
+item {
+  name: "/m/01xq0k1"
+  id: 150
+  display_name: "Cattle"
+}
+item {
+  name: "/m/0gd2v"
+  id: 151
+  display_name: "Marine mammal"
+}
+item {
+  name: "/m/0dbvp"
+  id: 152
+  display_name: "Goose"
+}
+item {
+  name: "/m/03rszm"
+  id: 153
+  display_name: "Curtain"
+}
+item {
+  name: "/m/0h8n5zk"
+  id: 154
+  display_name: "Kitchen & dining room table"
+}
+item {
+  name: "/m/019dx1"
+  id: 155
+  display_name: "Home appliance"
+}
+item {
+  name: "/m/03hl4l9"
+  id: 156
+  display_name: "Marine invertebrates"
+}
+item {
+  name: "/m/0b3fp9"
+  id: 157
+  display_name: "Countertop"
+}
+item {
+  name: "/m/02rdsp"
+  id: 158
+  display_name: "Office supplies"
+}
+item {
+  name: "/m/0hf58v5"
+  id: 159
+  display_name: "Luggage and bags"
+}
+item {
+  name: "/m/04h7h"
+  id: 160
+  display_name: "Lighthouse"
+}
+item {
+  name: "/m/024g6"
+  id: 161
+  display_name: "Cocktail"
+}
+item {
+  name: "/m/0cffdh"
+  id: 162
+  display_name: "Maple"
+}
+item {
+  name: "/m/03q5c7"
+  id: 163
+  display_name: "Saucer"
+}
+item {
+  name: "/m/014y4n"
+  id: 164
+  display_name: "Paddle"
+}
+item {
+  name: "/m/01yx86"
+  id: 165
+  display_name: "Bronze sculpture"
+}
+item {
+  name: "/m/020jm"
+  id: 166
+  display_name: "Beetle"
+}
+item {
+  name: "/m/025dyy"
+  id: 167
+  display_name: "Box"
+}
+item {
+  name: "/m/01llwg"
+  id: 168
+  display_name: "Necklace"
+}
+item {
+  name: "/m/08pbxl"
+  id: 169
+  display_name: "Monkey"
+}
+item {
+  name: "/m/02d9qx"
+  id: 170
+  display_name: "Whiteboard"
+}
+item {
+  name: "/m/02pkr5"
+  id: 171
+  display_name: "Plumbing fixture"
+}
+item {
+  name: "/m/0h99cwc"
+  id: 172
+  display_name: "Kitchen appliance"
+}
+item {
+  name: "/m/050gv4"
+  id: 173
+  display_name: "Plate"
+}
+item {
+  name: "/m/02vqfm"
+  id: 174
+  display_name: "Coffee"
+}
+item {
+  name: "/m/09kx5"
+  id: 175
+  display_name: "Deer"
+}
+item {
+  name: "/m/019w40"
+  id: 176
+  display_name: "Surfboard"
+}
+item {
+  name: "/m/09dzg"
+  id: 177
+  display_name: "Turtle"
+}
+item {
+  name: "/m/07k1x"
+  id: 178
+  display_name: "Tool"
+}
+item {
+  name: "/m/080hkjn"
+  id: 179
+  display_name: "Handbag"
+}
+item {
+  name: "/m/07qxg_"
+  id: 180
+  display_name: "Football helmet"
+}
+item {
+  name: "/m/0ph39"
+  id: 181
+  display_name: "Canoe"
+}
+item {
+  name: "/m/018p4k"
+  id: 182
+  display_name: "Cart"
+}
+item {
+  name: "/m/02h19r"
+  id: 183
+  display_name: "Scarf"
+}
+item {
+  name: "/m/015h_t"
+  id: 184
+  display_name: "Beard"
+}
+item {
+  name: "/m/0fqfqc"
+  id: 185
+  display_name: "Drawer"
+}
+item {
+  name: "/m/025rp__"
+  id: 186
+  display_name: "Cowboy hat"
+}
+item {
+  name: "/m/01x3z"
+  id: 187
+  display_name: "Clock"
+}
+item {
+  name: "/m/0crjs"
+  id: 188
+  display_name: "Convenience store"
+}
+item {
+  name: "/m/0l515"
+  id: 189
+  display_name: "Sandwich"
+}
+item {
+  name: "/m/015qff"
+  id: 190
+  display_name: "Traffic light"
+}
+item {
+  name: "/m/09kmb"
+  id: 191
+  display_name: "Spider"
+}
+item {
+  name: "/m/09728"
+  id: 192
+  display_name: "Bread"
+}
+item {
+  name: "/m/071qp"
+  id: 193
+  display_name: "Squirrel"
+}
+item {
+  name: "/m/02s195"
+  id: 194
+  display_name: "Vase"
+}
+item {
+  name: "/m/06c54"
+  id: 195
+  display_name: "Rifle"
+}
+item {
+  name: "/m/01xqw"
+  id: 196
+  display_name: "Cello"
+}
+item {
+  name: "/m/05zsy"
+  id: 197
+  display_name: "Pumpkin"
+}
+item {
+  name: "/m/0bwd_0j"
+  id: 198
+  display_name: "Elephant"
+}
+item {
+  name: "/m/04m9y"
+  id: 199
+  display_name: "Lizard"
+}
+item {
+  name: "/m/052sf"
+  id: 200
+  display_name: "Mushroom"
+}
+item {
+  name: "/m/03grzl"
+  id: 201
+  display_name: "Baseball glove"
+}
+item {
+  name: "/m/01z1kdw"
+  id: 202
+  display_name: "Juice"
+}
+item {
+  name: "/m/02wv6h6"
+  id: 203
+  display_name: "Skirt"
+}
+item {
+  name: "/m/016m2d"
+  id: 204
+  display_name: "Skull"
+}
+item {
+  name: "/m/0dtln"
+  id: 205
+  display_name: "Lamp"
+}
+item {
+  name: "/m/057cc"
+  id: 206
+  display_name: "Musical keyboard"
+}
+item {
+  name: "/m/06k2mb"
+  id: 207
+  display_name: "High heels"
+}
+item {
+  name: "/m/0f6wt"
+  id: 208
+  display_name: "Falcon"
+}
+item {
+  name: "/m/0cxn2"
+  id: 209
+  display_name: "Ice cream"
+}
+item {
+  name: "/m/02jvh9"
+  id: 210
+  display_name: "Mug"
+}
+item {
+  name: "/m/0gjkl"
+  id: 211
+  display_name: "Watch"
+}
+item {
+  name: "/m/01b638"
+  id: 212
+  display_name: "Boot"
+}
+item {
+  name: "/m/071p9"
+  id: 213
+  display_name: "Ski"
+}
+item {
+  name: "/m/0pg52"
+  id: 214
+  display_name: "Taxi"
+}
+item {
+  name: "/m/0ftb8"
+  id: 215
+  display_name: "Sunflower"
+}
+item {
+  name: "/m/0hnyx"
+  id: 216
+  display_name: "Pastry"
+}
+item {
+  name: "/m/02jz0l"
+  id: 217
+  display_name: "Tap"
+}
+item {
+  name: "/m/04kkgm"
+  id: 218
+  display_name: "Bowl"
+}
+item {
+  name: "/m/0174n1"
+  id: 219
+  display_name: "Glove"
+}
+item {
+  name: "/m/0gv1x"
+  id: 220
+  display_name: "Parrot"
+}
+item {
+  name: "/m/09csl"
+  id: 221
+  display_name: "Eagle"
+}
+item {
+  name: "/m/02jnhm"
+  id: 222
+  display_name: "Tin can"
+}
+item {
+  name: "/m/099ssp"
+  id: 223
+  display_name: "Platter"
+}
+item {
+  name: "/m/03nfch"
+  id: 224
+  display_name: "Sandal"
+}
+item {
+  name: "/m/07y_7"
+  id: 225
+  display_name: "Violin"
+}
+item {
+  name: "/m/05z6w"
+  id: 226
+  display_name: "Penguin"
+}
+item {
+  name: "/m/03m3pdh"
+  id: 227
+  display_name: "Sofa bed"
+}
+item {
+  name: "/m/09ld4"
+  id: 228
+  display_name: "Frog"
+}
+item {
+  name: "/m/09b5t"
+  id: 229
+  display_name: "Chicken"
+}
+item {
+  name: "/m/054xkw"
+  id: 230
+  display_name: "Lifejacket"
+}
+item {
+  name: "/m/0130jx"
+  id: 231
+  display_name: "Sink"
+}
+item {
+  name: "/m/07fbm7"
+  id: 232
+  display_name: "Strawberry"
+}
+item {
+  name: "/m/01dws"
+  id: 233
+  display_name: "Bear"
+}
+item {
+  name: "/m/01tcjp"
+  id: 234
+  display_name: "Muffin"
+}
+item {
+  name: "/m/0dftk"
+  id: 235
+  display_name: "Swan"
+}
+item {
+  name: "/m/0c06p"
+  id: 236
+  display_name: "Candle"
+}
+item {
+  name: "/m/034c16"
+  id: 237
+  display_name: "Pillow"
+}
+item {
+  name: "/m/09d5_"
+  id: 238
+  display_name: "Owl"
+}
+item {
+  name: "/m/03hlz0c"
+  id: 239
+  display_name: "Kitchen utensil"
+}
+item {
+  name: "/m/0ft9s"
+  id: 240
+  display_name: "Dragonfly"
+}
+item {
+  name: "/m/011k07"
+  id: 241
+  display_name: "Tortoise"
+}
+item {
+  name: "/m/054_l"
+  id: 242
+  display_name: "Mirror"
+}
+item {
+  name: "/m/0jqgx"
+  id: 243
+  display_name: "Lily"
+}
+item {
+  name: "/m/0663v"
+  id: 244
+  display_name: "Pizza"
+}
+item {
+  name: "/m/0242l"
+  id: 245
+  display_name: "Coin"
+}
+item {
+  name: "/m/014trl"
+  id: 246
+  display_name: "Cosmetics"
+}
+item {
+  name: "/m/05r5c"
+  id: 247
+  display_name: "Piano"
+}
+item {
+  name: "/m/07j87"
+  id: 248
+  display_name: "Tomato"
+}
+item {
+  name: "/m/05kyg_"
+  id: 249
+  display_name: "Chest of drawers"
+}
+item {
+  name: "/m/0kmg4"
+  id: 250
+  display_name: "Teddy bear"
+}
+item {
+  name: "/m/07cmd"
+  id: 251
+  display_name: "Tank"
+}
+item {
+  name: "/m/0dv77"
+  id: 252
+  display_name: "Squash"
+}
+item {
+  name: "/m/096mb"
+  id: 253
+  display_name: "Lion"
+}
+item {
+  name: "/m/01gmv2"
+  id: 254
+  display_name: "Brassiere"
+}
+item {
+  name: "/m/07bgp"
+  id: 255
+  display_name: "Sheep"
+}
+item {
+  name: "/m/0cmx8"
+  id: 256
+  display_name: "Spoon"
+}
+item {
+  name: "/m/029tx"
+  id: 257
+  display_name: "Dinosaur"
+}
+item {
+  name: "/m/073bxn"
+  id: 258
+  display_name: "Tripod"
+}
+item {
+  name: "/m/0bh9flk"
+  id: 259
+  display_name: "Tablet computer"
+}
+item {
+  name: "/m/06mf6"
+  id: 260
+  display_name: "Rabbit"
+}
+item {
+  name: "/m/06_fw"
+  id: 261
+  display_name: "Skateboard"
+}
+item {
+  name: "/m/078jl"
+  id: 262
+  display_name: "Snake"
+}
+item {
+  name: "/m/0fbdv"
+  id: 263
+  display_name: "Shellfish"
+}
+item {
+  name: "/m/0h23m"
+  id: 264
+  display_name: "Sparrow"
+}
+item {
+  name: "/m/014j1m"
+  id: 265
+  display_name: "Apple"
+}
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+  name: "/m/03fwl"
+  id: 266
+  display_name: "Goat"
+}
+item {
+  name: "/m/02y6n"
+  id: 267
+  display_name: "French fries"
+}
+item {
+  name: "/m/06c7f7"
+  id: 268
+  display_name: "Lipstick"
+}
+item {
+  name: "/m/026qbn5"
+  id: 269
+  display_name: "studio couch"
+}
+item {
+  name: "/m/0cdn1"
+  id: 270
+  display_name: "Hamburger"
+}
+item {
+  name: "/m/07clx"
+  id: 271
+  display_name: "Tea"
+}
+item {
+  name: "/m/07cx4"
+  id: 272
+  display_name: "Telephone"
+}
+item {
+  name: "/m/03g8mr"
+  id: 273
+  display_name: "Baseball bat"
+}
+item {
+  name: "/m/0cnyhnx"
+  id: 274
+  display_name: "Bull"
+}
+item {
+  name: "/m/01b7fy"
+  id: 275
+  display_name: "Headphones"
+}
+item {
+  name: "/m/04gth"
+  id: 276
+  display_name: "Lavender"
+}
+item {
+  name: "/m/0cyfs"
+  id: 277
+  display_name: "Parachute"
+}
+item {
+  name: "/m/021mn"
+  id: 278
+  display_name: "Cookie"
+}
+item {
+  name: "/m/07dm6"
+  id: 279
+  display_name: "Tiger"
+}
+item {
+  name: "/m/0k1tl"
+  id: 280
+  display_name: "Pen"
+}
+item {
+  name: "/m/0dv9c"
+  id: 281
+  display_name: "Racket"
+}
+item {
+  name: "/m/0dt3t"
+  id: 282
+  display_name: "Fork"
+}
+item {
+  name: "/m/04yqq2"
+  id: 283
+  display_name: "Bust"
+}
+item {
+  name: "/m/01cmb2"
+  id: 284
+  display_name: "Miniskirt"
+}
+item {
+  name: "/m/0gd36"
+  id: 285
+  display_name: "Sea lion"
+}
+item {
+  name: "/m/033cnk"
+  id: 286
+  display_name: "Egg"
+}
+item {
+  name: "/m/06ncr"
+  id: 287
+  display_name: "Saxophone"
+}
+item {
+  name: "/m/03bk1"
+  id: 288
+  display_name: "Giraffe"
+}
+item {
+  name: "/m/0bjyj5"
+  id: 289
+  display_name: "Waste container"
+}
+item {
+  name: "/m/06__v"
+  id: 290
+  display_name: "Snowboard"
+}
+item {
+  name: "/m/0qmmr"
+  id: 291
+  display_name: "Wheelchair"
+}
+item {
+  name: "/m/01xgg_"
+  id: 292
+  display_name: "Medical equipment"
+}
+item {
+  name: "/m/0czz2"
+  id: 293
+  display_name: "Antelope"
+}
+item {
+  name: "/m/02l8p9"
+  id: 294
+  display_name: "Harbor seal"
+}
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+  name: "/m/09g1w"
+  id: 295
+  display_name: "Toilet"
+}
+item {
+  name: "/m/0ll1f78"
+  id: 296
+  display_name: "Shrimp"
+}
+item {
+  name: "/m/0cyhj_"
+  id: 297
+  display_name: "Orange"
+}
+item {
+  name: "/m/0642b4"
+  id: 298
+  display_name: "Cupboard"
+}
+item {
+  name: "/m/0h8mzrc"
+  id: 299
+  display_name: "Wall clock"
+}
+item {
+  name: "/m/068zj"
+  id: 300
+  display_name: "Pig"
+}
+item {
+  name: "/m/02z51p"
+  id: 301
+  display_name: "Nightstand"
+}
+item {
+  name: "/m/0h8nr_l"
+  id: 302
+  display_name: "Bathroom accessory"
+}
+item {
+  name: "/m/0388q"
+  id: 303
+  display_name: "Grape"
+}
+item {
+  name: "/m/02hj4"
+  id: 304
+  display_name: "Dolphin"
+}
+item {
+  name: "/m/01jfsr"
+  id: 305
+  display_name: "Lantern"
+}
+item {
+  name: "/m/07gql"
+  id: 306
+  display_name: "Trumpet"
+}
+item {
+  name: "/m/0h8my_4"
+  id: 307
+  display_name: "Tennis racket"
+}
+item {
+  name: "/m/0n28_"
+  id: 308
+  display_name: "Crab"
+}
+item {
+  name: "/m/0120dh"
+  id: 309
+  display_name: "Sea turtle"
+}
+item {
+  name: "/m/020kz"
+  id: 310
+  display_name: "Cannon"
+}
+item {
+  name: "/m/0mkg"
+  id: 311
+  display_name: "Accordion"
+}
+item {
+  name: "/m/03c7gz"
+  id: 312
+  display_name: "Door handle"
+}
+item {
+  name: "/m/09k_b"
+  id: 313
+  display_name: "Lemon"
+}
+item {
+  name: "/m/031n1"
+  id: 314
+  display_name: "Foot"
+}
+item {
+  name: "/m/04rmv"
+  id: 315
+  display_name: "Mouse"
+}
+item {
+  name: "/m/084rd"
+  id: 316
+  display_name: "Wok"
+}
+item {
+  name: "/m/02rgn06"
+  id: 317
+  display_name: "Volleyball"
+}
+item {
+  name: "/m/05z55"
+  id: 318
+  display_name: "Pasta"
+}
+item {
+  name: "/m/01r546"
+  id: 319
+  display_name: "Earrings"
+}
+item {
+  name: "/m/09qck"
+  id: 320
+  display_name: "Banana"
+}
+item {
+  name: "/m/012w5l"
+  id: 321
+  display_name: "Ladder"
+}
+item {
+  name: "/m/01940j"
+  id: 322
+  display_name: "Backpack"
+}
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+  name: "/m/09f_2"
+  id: 323
+  display_name: "Crocodile"
+}
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+  name: "/m/02p3w7d"
+  id: 324
+  display_name: "Roller skates"
+}
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+  name: "/m/057p5t"
+  id: 325
+  display_name: "Scoreboard"
+}
+item {
+  name: "/m/0d8zb"
+  id: 326
+  display_name: "Jellyfish"
+}
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+  name: "/m/01nq26"
+  id: 327
+  display_name: "Sock"
+}
+item {
+  name: "/m/01x_v"
+  id: 328
+  display_name: "Camel"
+}
+item {
+  name: "/m/05gqfk"
+  id: 329
+  display_name: "Plastic bag"
+}
+item {
+  name: "/m/0cydv"
+  id: 330
+  display_name: "Caterpillar"
+}
+item {
+  name: "/m/07030"
+  id: 331
+  display_name: "Sushi"
+}
+item {
+  name: "/m/084zz"
+  id: 332
+  display_name: "Whale"
+}
+item {
+  name: "/m/0c29q"
+  id: 333
+  display_name: "Leopard"
+}
+item {
+  name: "/m/02zn6n"
+  id: 334
+  display_name: "Barrel"
+}
+item {
+  name: "/m/03tw93"
+  id: 335
+  display_name: "Fireplace"
+}
+item {
+  name: "/m/0fqt361"
+  id: 336
+  display_name: "Stool"
+}
+item {
+  name: "/m/0f9_l"
+  id: 337
+  display_name: "Snail"
+}
+item {
+  name: "/m/0gm28"
+  id: 338
+  display_name: "Candy"
+}
+item {
+  name: "/m/09rvcxw"
+  id: 339
+  display_name: "Rocket"
+}
+item {
+  name: "/m/01nkt"
+  id: 340
+  display_name: "Cheese"
+}
+item {
+  name: "/m/04p0qw"
+  id: 341
+  display_name: "Billiard table"
+}
+item {
+  name: "/m/03hj559"
+  id: 342
+  display_name: "Mixing bowl"
+}
+item {
+  name: "/m/07pj7bq"
+  id: 343
+  display_name: "Bowling equipment"
+}
+item {
+  name: "/m/04ctx"
+  id: 344
+  display_name: "Knife"
+}
+item {
+  name: "/m/0703r8"
+  id: 345
+  display_name: "Loveseat"
+}
+item {
+  name: "/m/03qrc"
+  id: 346
+  display_name: "Hamster"
+}
+item {
+  name: "/m/020lf"
+  id: 347
+  display_name: "Mouse"
+}
+item {
+  name: "/m/0by6g"
+  id: 348
+  display_name: "Shark"
+}
+item {
+  name: "/m/01fh4r"
+  id: 349
+  display_name: "Teapot"
+}
+item {
+  name: "/m/07c6l"
+  id: 350
+  display_name: "Trombone"
+}
+item {
+  name: "/m/03bj1"
+  id: 351
+  display_name: "Panda"
+}
+item {
+  name: "/m/0898b"
+  id: 352
+  display_name: "Zebra"
+}
+item {
+  name: "/m/02x984l"
+  id: 353
+  display_name: "Mechanical fan"
+}
+item {
+  name: "/m/0fj52s"
+  id: 354
+  display_name: "Carrot"
+}
+item {
+  name: "/m/0cd4d"
+  id: 355
+  display_name: "Cheetah"
+}
+item {
+  name: "/m/02068x"
+  id: 356
+  display_name: "Gondola"
+}
+item {
+  name: "/m/01vbnl"
+  id: 357
+  display_name: "Bidet"
+}
+item {
+  name: "/m/0449p"
+  id: 358
+  display_name: "Jaguar"
+}
+item {
+  name: "/m/0gj37"
+  id: 359
+  display_name: "Ladybug"
+}
+item {
+  name: "/m/0nl46"
+  id: 360
+  display_name: "Crown"
+}
+item {
+  name: "/m/0152hh"
+  id: 361
+  display_name: "Snowman"
+}
+item {
+  name: "/m/03dnzn"
+  id: 362
+  display_name: "Bathtub"
+}
+item {
+  name: "/m/05_5p_0"
+  id: 363
+  display_name: "Table tennis racket"
+}
+item {
+  name: "/m/02jfl0"
+  id: 364
+  display_name: "Sombrero"
+}
+item {
+  name: "/m/01dxs"
+  id: 365
+  display_name: "Brown bear"
+}
+item {
+  name: "/m/0cjq5"
+  id: 366
+  display_name: "Lobster"
+}
+item {
+  name: "/m/040b_t"
+  id: 367
+  display_name: "Refrigerator"
+}
+item {
+  name: "/m/0_cp5"
+  id: 368
+  display_name: "Oyster"
+}
+item {
+  name: "/m/0gxl3"
+  id: 369
+  display_name: "Handgun"
+}
+item {
+  name: "/m/029bxz"
+  id: 370
+  display_name: "Oven"
+}
+item {
+  name: "/m/02zt3"
+  id: 371
+  display_name: "Kite"
+}
+item {
+  name: "/m/03d443"
+  id: 372
+  display_name: "Rhinoceros"
+}
+item {
+  name: "/m/0306r"
+  id: 373
+  display_name: "Fox"
+}
+item {
+  name: "/m/0h8l4fh"
+  id: 374
+  display_name: "Light bulb"
+}
+item {
+  name: "/m/0633h"
+  id: 375
+  display_name: "Polar bear"
+}
+item {
+  name: "/m/01s55n"
+  id: 376
+  display_name: "Suitcase"
+}
+item {
+  name: "/m/0hkxq"
+  id: 377
+  display_name: "Broccoli"
+}
+item {
+  name: "/m/0cn6p"
+  id: 378
+  display_name: "Otter"
+}
+item {
+  name: "/m/0dbzx"
+  id: 379
+  display_name: "Mule"
+}
+item {
+  name: "/m/01dy8n"
+  id: 380
+  display_name: "Woodpecker"
+}
+item {
+  name: "/m/01h8tj"
+  id: 381
+  display_name: "Starfish"
+}
+item {
+  name: "/m/03s_tn"
+  id: 382
+  display_name: "Kettle"
+}
+item {
+  name: "/m/01xs3r"
+  id: 383
+  display_name: "Jet ski"
+}
+item {
+  name: "/m/031b6r"
+  id: 384
+  display_name: "Window blind"
+}
+item {
+  name: "/m/06j2d"
+  id: 385
+  display_name: "Raven"
+}
+item {
+  name: "/m/0hqkz"
+  id: 386
+  display_name: "Grapefruit"
+}
+item {
+  name: "/m/01_5g"
+  id: 387
+  display_name: "Chopsticks"
+}
+item {
+  name: "/m/02zvsm"
+  id: 388
+  display_name: "Tart"
+}
+item {
+  name: "/m/0kpqd"
+  id: 389
+  display_name: "Watermelon"
+}
+item {
+  name: "/m/015x4r"
+  id: 390
+  display_name: "Cucumber"
+}
+item {
+  name: "/m/061hd_"
+  id: 391
+  display_name: "Infant bed"
+}
+item {
+  name: "/m/04ylt"
+  id: 392
+  display_name: "Missile"
+}
+item {
+  name: "/m/02wv84t"
+  id: 393
+  display_name: "Gas stove"
+}
+item {
+  name: "/m/04y4h8h"
+  id: 394
+  display_name: "Bathroom cabinet"
+}
+item {
+  name: "/m/01gllr"
+  id: 395
+  display_name: "Beehive"
+}
+item {
+  name: "/m/0pcr"
+  id: 396
+  display_name: "Alpaca"
+}
+item {
+  name: "/m/0jy4k"
+  id: 397
+  display_name: "Doughnut"
+}
+item {
+  name: "/m/09f20"
+  id: 398
+  display_name: "Hippopotamus"
+}
+item {
+  name: "/m/0mcx2"
+  id: 399
+  display_name: "Ipod"
+}
+item {
+  name: "/m/04c0y"
+  id: 400
+  display_name: "Kangaroo"
+}
+item {
+  name: "/m/0_k2"
+  id: 401
+  display_name: "Ant"
+}
+item {
+  name: "/m/0jg57"
+  id: 402
+  display_name: "Bell pepper"
+}
+item {
+  name: "/m/03fj2"
+  id: 403
+  display_name: "Goldfish"
+}
+item {
+  name: "/m/03ldnb"
+  id: 404
+  display_name: "Ceiling fan"
+}
+item {
+  name: "/m/06nrc"
+  id: 405
+  display_name: "Shotgun"
+}
+item {
+  name: "/m/01btn"
+  id: 406
+  display_name: "Barge"
+}
+item {
+  name: "/m/05vtc"
+  id: 407
+  display_name: "Potato"
+}
+item {
+  name: "/m/08hvt4"
+  id: 408
+  display_name: "Jug"
+}
+item {
+  name: "/m/0fx9l"
+  id: 409
+  display_name: "Microwave oven"
+}
+item {
+  name: "/m/01h44"
+  id: 410
+  display_name: "Bat"
+}
+item {
+  name: "/m/05n4y"
+  id: 411
+  display_name: "Ostrich"
+}
+item {
+  name: "/m/0jly1"
+  id: 412
+  display_name: "Turkey"
+}
+item {
+  name: "/m/06y5r"
+  id: 413
+  display_name: "Sword"
+}
+item {
+  name: "/m/05ctyq"
+  id: 414
+  display_name: "Tennis ball"
+}
+item {
+  name: "/m/0fp6w"
+  id: 415
+  display_name: "Pineapple"
+}
+item {
+  name: "/m/0d4w1"
+  id: 416
+  display_name: "Closet"
+}
+item {
+  name: "/m/02pv19"
+  id: 417
+  display_name: "Stop sign"
+}
+item {
+  name: "/m/07crc"
+  id: 418
+  display_name: "Taco"
+}
+item {
+  name: "/m/01dwwc"
+  id: 419
+  display_name: "Pancake"
+}
+item {
+  name: "/m/01b9xk"
+  id: 420
+  display_name: "Hot dog"
+}
+item {
+  name: "/m/013y1f"
+  id: 421
+  display_name: "Organ"
+}
+item {
+  name: "/m/0m53l"
+  id: 422
+  display_name: "Rays and skates"
+}
+item {
+  name: "/m/0174k2"
+  id: 423
+  display_name: "Washing machine"
+}
+item {
+  name: "/m/01dwsz"
+  id: 424
+  display_name: "Waffle"
+}
+item {
+  name: "/m/04vv5k"
+  id: 425
+  display_name: "Snowplow"
+}
+item {
+  name: "/m/04cp_"
+  id: 426
+  display_name: "Koala"
+}
+item {
+  name: "/m/0fz0h"
+  id: 427
+  display_name: "Honeycomb"
+}
+item {
+  name: "/m/0llzx"
+  id: 428
+  display_name: "Sewing machine"
+}
+item {
+  name: "/m/0319l"
+  id: 429
+  display_name: "Horn"
+}
+item {
+  name: "/m/04v6l4"
+  id: 430
+  display_name: "Frying pan"
+}
+item {
+  name: "/m/0dkzw"
+  id: 431
+  display_name: "Seat belt"
+}
+item {
+  name: "/m/027pcv"
+  id: 432
+  display_name: "Zucchini"
+}
+item {
+  name: "/m/0323sq"
+  id: 433
+  display_name: "Golf cart"
+}
+item {
+  name: "/m/054fyh"
+  id: 434
+  display_name: "Pitcher"
+}
+item {
+  name: "/m/01pns0"
+  id: 435
+  display_name: "Fire hydrant"
+}
+item {
+  name: "/m/012n7d"
+  id: 436
+  display_name: "Ambulance"
+}
+item {
+  name: "/m/044r5d"
+  id: 437
+  display_name: "Golf ball"
+}
+item {
+  name: "/m/01krhy"
+  id: 438
+  display_name: "Tiara"
+}
+item {
+  name: "/m/0dq75"
+  id: 439
+  display_name: "Raccoon"
+}
+item {
+  name: "/m/0176mf"
+  id: 440
+  display_name: "Belt"
+}
+item {
+  name: "/m/0h8lkj8"
+  id: 441
+  display_name: "Corded phone"
+}
+item {
+  name: "/m/04tn4x"
+  id: 442
+  display_name: "Swim cap"
+}
+item {
+  name: "/m/06l9r"
+  id: 443
+  display_name: "Red panda"
+}
+item {
+  name: "/m/0cjs7"
+  id: 444
+  display_name: "Asparagus"
+}
+item {
+  name: "/m/01lsmm"
+  id: 445
+  display_name: "Scissors"
+}
+item {
+  name: "/m/01lcw4"
+  id: 446
+  display_name: "Limousine"
+}
+item {
+  name: "/m/047j0r"
+  id: 447
+  display_name: "Filing cabinet"
+}
+item {
+  name: "/m/01fb_0"
+  id: 448
+  display_name: "Bagel"
+}
+item {
+  name: "/m/04169hn"
+  id: 449
+  display_name: "Wood-burning stove"
+}
+item {
+  name: "/m/076bq"
+  id: 450
+  display_name: "Segway"
+}
+item {
+  name: "/m/0hdln"
+  id: 451
+  display_name: "Ruler"
+}
+item {
+  name: "/m/01g3x7"
+  id: 452
+  display_name: "Bow and arrow"
+}
+item {
+  name: "/m/0l3ms"
+  id: 453
+  display_name: "Balance beam"
+}
+item {
+  name: "/m/058qzx"
+  id: 454
+  display_name: "Kitchen knife"
+}
+item {
+  name: "/m/0h8n6ft"
+  id: 455
+  display_name: "Cake stand"
+}
+item {
+  name: "/m/018j2"
+  id: 456
+  display_name: "Banjo"
+}
+item {
+  name: "/m/0l14j_"
+  id: 457
+  display_name: "Flute"
+}
+item {
+  name: "/m/0wdt60w"
+  id: 458
+  display_name: "Rugby ball"
+}
+item {
+  name: "/m/02gzp"
+  id: 459
+  display_name: "Dagger"
+}
+item {
+  name: "/m/0h8n6f9"
+  id: 460
+  display_name: "Dog bed"
+}
+item {
+  name: "/m/0fbw6"
+  id: 461
+  display_name: "Cabbage"
+}
+item {
+  name: "/m/07kng9"
+  id: 462
+  display_name: "Picnic basket"
+}
+item {
+  name: "/m/0dj6p"
+  id: 463
+  display_name: "Peach"
+}
+item {
+  name: "/m/06pcq"
+  id: 464
+  display_name: "Submarine sandwich"
+}
+item {
+  name: "/m/061_f"
+  id: 465
+  display_name: "Pear"
+}
+item {
+  name: "/m/04g2r"
+  id: 466
+  display_name: "Lynx"
+}
+item {
+  name: "/m/0jwn_"
+  id: 467
+  display_name: "Pomegranate"
+}
+item {
+  name: "/m/02f9f_"
+  id: 468
+  display_name: "Shower"
+}
+item {
+  name: "/m/01f8m5"
+  id: 469
+  display_name: "Blue jay"
+}
+item {
+  name: "/m/01m4t"
+  id: 470
+  display_name: "Printer"
+}
+item {
+  name: "/m/0cl4p"
+  id: 471
+  display_name: "Hedgehog"
+}
+item {
+  name: "/m/07xyvk"
+  id: 472
+  display_name: "Coffeemaker"
+}
+item {
+  name: "/m/084hf"
+  id: 473
+  display_name: "Worm"
+}
+item {
+  name: "/m/03v5tg"
+  id: 474
+  display_name: "Drinking straw"
+}
+item {
+  name: "/m/0qjjc"
+  id: 475
+  display_name: "Remote control"
+}
+item {
+  name: "/m/015x5n"
+  id: 476
+  display_name: "Radish"
+}
+item {
+  name: "/m/0ccs93"
+  id: 477
+  display_name: "Canary"
+}
+item {
+  name: "/m/0nybt"
+  id: 478
+  display_name: "Seahorse"
+}
+item {
+  name: "/m/02vkqh8"
+  id: 479
+  display_name: "Wardrobe"
+}
+item {
+  name: "/m/09gtd"
+  id: 480
+  display_name: "Toilet paper"
+}
+item {
+  name: "/m/019h78"
+  id: 481
+  display_name: "Centipede"
+}
+item {
+  name: "/m/015wgc"
+  id: 482
+  display_name: "Croissant"
+}
+item {
+  name: "/m/01x3jk"
+  id: 483
+  display_name: "Snowmobile"
+}
+item {
+  name: "/m/01j3zr"
+  id: 484
+  display_name: "Burrito"
+}
+item {
+  name: "/m/0c568"
+  id: 485
+  display_name: "Porcupine"
+}
+item {
+  name: "/m/02pdsw"
+  id: 486
+  display_name: "Cutting board"
+}
+item {
+  name: "/m/029b3"
+  id: 487
+  display_name: "Dice"
+}
+item {
+  name: "/m/03q5t"
+  id: 488
+  display_name: "Harpsichord"
+}
+item {
+  name: "/m/0p833"
+  id: 489
+  display_name: "Perfume"
+}
+item {
+  name: "/m/01d380"
+  id: 490
+  display_name: "Drill"
+}
+item {
+  name: "/m/024d2"
+  id: 491
+  display_name: "Calculator"
+}
+item {
+  name: "/m/0mw_6"
+  id: 492
+  display_name: "Willow"
+}
+item {
+  name: "/m/01f91_"
+  id: 493
+  display_name: "Pretzel"
+}
+item {
+  name: "/m/02g30s"
+  id: 494
+  display_name: "Guacamole"
+}
+item {
+  name: "/m/01hrv5"
+  id: 495
+  display_name: "Popcorn"
+}
+item {
+  name: "/m/03m5k"
+  id: 496
+  display_name: "Harp"
+}
+item {
+  name: "/m/0162_1"
+  id: 497
+  display_name: "Towel"
+}
+item {
+  name: "/m/063rgb"
+  id: 498
+  display_name: "Mixer"
+}
+item {
+  name: "/m/06_72j"
+  id: 499
+  display_name: "Digital clock"
+}
+item {
+  name: "/m/046dlr"
+  id: 500
+  display_name: "Alarm clock"
+}
+item {
+  name: "/m/047v4b"
+  id: 501
+  display_name: "Artichoke"
+}
+item {
+  name: "/m/04zpv"
+  id: 502
+  display_name: "Milk"
+}
+item {
+  name: "/m/043nyj"
+  id: 503
+  display_name: "Common fig"
+}
+item {
+  name: "/m/03bbps"
+  id: 504
+  display_name: "Power plugs and sockets"
+}
+item {
+  name: "/m/02w3r3"
+  id: 505
+  display_name: "Paper towel"
+}
+item {
+  name: "/m/02pjr4"
+  id: 506
+  display_name: "Blender"
+}
+item {
+  name: "/m/0755b"
+  id: 507
+  display_name: "Scorpion"
+}
+item {
+  name: "/m/02lbcq"
+  id: 508
+  display_name: "Stretcher"
+}
+item {
+  name: "/m/0fldg"
+  id: 509
+  display_name: "Mango"
+}
+item {
+  name: "/m/012074"
+  id: 510
+  display_name: "Magpie"
+}
+item {
+  name: "/m/035vxb"
+  id: 511
+  display_name: "Isopod"
+}
+item {
+  name: "/m/02w3_ws"
+  id: 512
+  display_name: "Personal care"
+}
+item {
+  name: "/m/0f6nr"
+  id: 513
+  display_name: "Unicycle"
+}
+item {
+  name: "/m/0420v5"
+  id: 514
+  display_name: "Punching bag"
+}
+item {
+  name: "/m/0frqm"
+  id: 515
+  display_name: "Envelope"
+}
+item {
+  name: "/m/03txqz"
+  id: 516
+  display_name: "Scale"
+}
+item {
+  name: "/m/0271qf7"
+  id: 517
+  display_name: "Wine rack"
+}
+item {
+  name: "/m/074d1"
+  id: 518
+  display_name: "Submarine"
+}
+item {
+  name: "/m/08p92x"
+  id: 519
+  display_name: "Cream"
+}
+item {
+  name: "/m/01j4z9"
+  id: 520
+  display_name: "Chainsaw"
+}
+item {
+  name: "/m/0kpt_"
+  id: 521
+  display_name: "Cantaloupe"
+}
+item {
+  name: "/m/0h8n27j"
+  id: 522
+  display_name: "Serving tray"
+}
+item {
+  name: "/m/03y6mg"
+  id: 523
+  display_name: "Food processor"
+}
+item {
+  name: "/m/04h8sr"
+  id: 524
+  display_name: "Dumbbell"
+}
+item {
+  name: "/m/065h6l"
+  id: 525
+  display_name: "Jacuzzi"
+}
+item {
+  name: "/m/02tsc9"
+  id: 526
+  display_name: "Slow cooker"
+}
+item {
+  name: "/m/012ysf"
+  id: 527
+  display_name: "Syringe"
+}
+item {
+  name: "/m/0ky7b"
+  id: 528
+  display_name: "Dishwasher"
+}
+item {
+  name: "/m/02wg_p"
+  id: 529
+  display_name: "Tree house"
+}
+item {
+  name: "/m/0584n8"
+  id: 530
+  display_name: "Briefcase"
+}
+item {
+  name: "/m/03kt2w"
+  id: 531
+  display_name: "Stationary bicycle"
+}
+item {
+  name: "/m/05kms"
+  id: 532
+  display_name: "Oboe"
+}
+item {
+  name: "/m/030610"
+  id: 533
+  display_name: "Treadmill"
+}
+item {
+  name: "/m/0lt4_"
+  id: 534
+  display_name: "Binoculars"
+}
+item {
+  name: "/m/076lb9"
+  id: 535
+  display_name: "Bench"
+}
+item {
+  name: "/m/02ctlc"
+  id: 536
+  display_name: "Cricket ball"
+}
+item {
+  name: "/m/02x8cch"
+  id: 537
+  display_name: "Salt and pepper shakers"
+}
+item {
+  name: "/m/09gys"
+  id: 538
+  display_name: "Squid"
+}
+item {
+  name: "/m/03jbxj"
+  id: 539
+  display_name: "Light switch"
+}
+item {
+  name: "/m/012xff"
+  id: 540
+  display_name: "Toothbrush"
+}
+item {
+  name: "/m/0h8kx63"
+  id: 541
+  display_name: "Spice rack"
+}
+item {
+  name: "/m/073g6"
+  id: 542
+  display_name: "Stethoscope"
+}
+item {
+  name: "/m/02cvgx"
+  id: 543
+  display_name: "Winter melon"
+}
+item {
+  name: "/m/027rl48"
+  id: 544
+  display_name: "Ladle"
+}
+item {
+  name: "/m/01kb5b"
+  id: 545
+  display_name: "Flashlight"
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt
new file mode 100644
index 00000000..044f6d4c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt
@@ -0,0 +1,2500 @@
+item {
+  name: "/m/061hd_"
+  id: 1
+  display_name: "Infant bed"
+}
+item {
+  name: "/m/06m11"
+  id: 2
+  display_name: "Rose"
+}
+item {
+  name: "/m/03120"
+  id: 3
+  display_name: "Flag"
+}
+item {
+  name: "/m/01kb5b"
+  id: 4
+  display_name: "Flashlight"
+}
+item {
+  name: "/m/0120dh"
+  id: 5
+  display_name: "Sea turtle"
+}
+item {
+  name: "/m/0dv5r"
+  id: 6
+  display_name: "Camera"
+}
+item {
+  name: "/m/0jbk"
+  id: 7
+  display_name: "Animal"
+}
+item {
+  name: "/m/0174n1"
+  id: 8
+  display_name: "Glove"
+}
+item {
+  name: "/m/09f_2"
+  id: 9
+  display_name: "Crocodile"
+}
+item {
+  name: "/m/01xq0k1"
+  id: 10
+  display_name: "Cattle"
+}
+item {
+  name: "/m/03jm5"
+  id: 11
+  display_name: "House"
+}
+item {
+  name: "/m/02g30s"
+  id: 12
+  display_name: "Guacamole"
+}
+item {
+  name: "/m/05z6w"
+  id: 13
+  display_name: "Penguin"
+}
+item {
+  name: "/m/01jfm_"
+  id: 14
+  display_name: "Vehicle registration plate"
+}
+item {
+  name: "/m/076lb9"
+  id: 15
+  display_name: "Training bench"
+}
+item {
+  name: "/m/0gj37"
+  id: 16
+  display_name: "Ladybug"
+}
+item {
+  name: "/m/0k0pj"
+  id: 17
+  display_name: "Human nose"
+}
+item {
+  name: "/m/0kpqd"
+  id: 18
+  display_name: "Watermelon"
+}
+item {
+  name: "/m/0l14j_"
+  id: 19
+  display_name: "Flute"
+}
+item {
+  name: "/m/0cyf8"
+  id: 20
+  display_name: "Butterfly"
+}
+item {
+  name: "/m/0174k2"
+  id: 21
+  display_name: "Washing machine"
+}
+item {
+  name: "/m/0dq75"
+  id: 22
+  display_name: "Raccoon"
+}
+item {
+  name: "/m/076bq"
+  id: 23
+  display_name: "Segway"
+}
+item {
+  name: "/m/07crc"
+  id: 24
+  display_name: "Taco"
+}
+item {
+  name: "/m/0d8zb"
+  id: 25
+  display_name: "Jellyfish"
+}
+item {
+  name: "/m/0fszt"
+  id: 26
+  display_name: "Cake"
+}
+item {
+  name: "/m/0k1tl"
+  id: 27
+  display_name: "Pen"
+}
+item {
+  name: "/m/020kz"
+  id: 28
+  display_name: "Cannon"
+}
+item {
+  name: "/m/09728"
+  id: 29
+  display_name: "Bread"
+}
+item {
+  name: "/m/07j7r"
+  id: 30
+  display_name: "Tree"
+}
+item {
+  name: "/m/0fbdv"
+  id: 31
+  display_name: "Shellfish"
+}
+item {
+  name: "/m/03ssj5"
+  id: 32
+  display_name: "Bed"
+}
+item {
+  name: "/m/03qrc"
+  id: 33
+  display_name: "Hamster"
+}
+item {
+  name: "/m/02dl1y"
+  id: 34
+  display_name: "Hat"
+}
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+  name: "/m/01k6s3"
+  id: 35
+  display_name: "Toaster"
+}
+item {
+  name: "/m/02jfl0"
+  id: 36
+  display_name: "Sombrero"
+}
+item {
+  name: "/m/01krhy"
+  id: 37
+  display_name: "Tiara"
+}
+item {
+  name: "/m/04kkgm"
+  id: 38
+  display_name: "Bowl"
+}
+item {
+  name: "/m/0ft9s"
+  id: 39
+  display_name: "Dragonfly"
+}
+item {
+  name: "/m/0d_2m"
+  id: 40
+  display_name: "Moths and butterflies"
+}
+item {
+  name: "/m/0czz2"
+  id: 41
+  display_name: "Antelope"
+}
+item {
+  name: "/m/0f4s2w"
+  id: 42
+  display_name: "Vegetable"
+}
+item {
+  name: "/m/07dd4"
+  id: 43
+  display_name: "Torch"
+}
+item {
+  name: "/m/0cgh4"
+  id: 44
+  display_name: "Building"
+}
+item {
+  name: "/m/03bbps"
+  id: 45
+  display_name: "Power plugs and sockets"
+}
+item {
+  name: "/m/02pjr4"
+  id: 46
+  display_name: "Blender"
+}
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+  name: "/m/04p0qw"
+  id: 47
+  display_name: "Billiard table"
+}
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+  name: "/m/02pdsw"
+  id: 48
+  display_name: "Cutting board"
+}
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+  name: "/m/01yx86"
+  id: 49
+  display_name: "Bronze sculpture"
+}
+item {
+  name: "/m/09dzg"
+  id: 50
+  display_name: "Turtle"
+}
+item {
+  name: "/m/0hkxq"
+  id: 51
+  display_name: "Broccoli"
+}
+item {
+  name: "/m/07dm6"
+  id: 52
+  display_name: "Tiger"
+}
+item {
+  name: "/m/054_l"
+  id: 53
+  display_name: "Mirror"
+}
+item {
+  name: "/m/01dws"
+  id: 54
+  display_name: "Bear"
+}
+item {
+  name: "/m/027pcv"
+  id: 55
+  display_name: "Zucchini"
+}
+item {
+  name: "/m/01d40f"
+  id: 56
+  display_name: "Dress"
+}
+item {
+  name: "/m/02rgn06"
+  id: 57
+  display_name: "Volleyball"
+}
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+  name: "/m/0342h"
+  id: 58
+  display_name: "Guitar"
+}
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+  name: "/m/06bt6"
+  id: 59
+  display_name: "Reptile"
+}
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+  name: "/m/0323sq"
+  id: 60
+  display_name: "Golf cart"
+}
+item {
+  name: "/m/02zvsm"
+  id: 61
+  display_name: "Tart"
+}
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+  name: "/m/02fq_6"
+  id: 62
+  display_name: "Fedora"
+}
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+  name: "/m/01lrl"
+  id: 63
+  display_name: "Carnivore"
+}
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+  name: "/m/0k4j"
+  id: 64
+  display_name: "Car"
+}
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+  name: "/m/04h7h"
+  id: 65
+  display_name: "Lighthouse"
+}
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+  name: "/m/07xyvk"
+  id: 66
+  display_name: "Coffeemaker"
+}
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+  name: "/m/03y6mg"
+  id: 67
+  display_name: "Food processor"
+}
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+  name: "/m/07r04"
+  id: 68
+  display_name: "Truck"
+}
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+  name: "/m/03__z0"
+  id: 69
+  display_name: "Bookcase"
+}
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+  name: "/m/019w40"
+  id: 70
+  display_name: "Surfboard"
+}
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+  name: "/m/09j5n"
+  id: 71
+  display_name: "Footwear"
+}
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+  name: "/m/0cvnqh"
+  id: 72
+  display_name: "Bench"
+}
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+  name: "/m/01llwg"
+  id: 73
+  display_name: "Necklace"
+}
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+  name: "/m/0c9ph5"
+  id: 74
+  display_name: "Flower"
+}
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+  name: "/m/015x5n"
+  id: 75
+  display_name: "Radish"
+}
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+  name: "/m/0gd2v"
+  id: 76
+  display_name: "Marine mammal"
+}
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+  id: 77
+  display_name: "Frying pan"
+}
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+  name: "/m/02jz0l"
+  id: 78
+  display_name: "Tap"
+}
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+  name: "/m/0dj6p"
+  id: 79
+  display_name: "Peach"
+}
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+  name: "/m/04ctx"
+  id: 80
+  display_name: "Knife"
+}
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+  name: "/m/080hkjn"
+  id: 81
+  display_name: "Handbag"
+}
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+  name: "/m/01c648"
+  id: 82
+  display_name: "Laptop"
+}
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+  name: "/m/01j61q"
+  id: 83
+  display_name: "Tent"
+}
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+  name: "/m/012n7d"
+  id: 84
+  display_name: "Ambulance"
+}
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+  name: "/m/025nd"
+  id: 85
+  display_name: "Christmas tree"
+}
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+  name: "/m/09csl"
+  id: 86
+  display_name: "Eagle"
+}
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+  name: "/m/01lcw4"
+  id: 87
+  display_name: "Limousine"
+}
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+  id: 88
+  display_name: "Kitchen & dining room table"
+}
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+  name: "/m/0633h"
+  id: 89
+  display_name: "Polar bear"
+}
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+  id: 90
+  display_name: "Tower"
+}
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+  name: "/m/01226z"
+  id: 91
+  display_name: "Football"
+}
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+  name: "/m/0mw_6"
+  id: 92
+  display_name: "Willow"
+}
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+  name: "/m/04hgtk"
+  id: 93
+  display_name: "Human head"
+}
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+  id: 94
+  display_name: "Stop sign"
+}
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+  id: 95
+  display_name: "Banana"
+}
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+  name: "/m/063rgb"
+  id: 96
+  display_name: "Mixer"
+}
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+  name: "/m/0lt4_"
+  id: 97
+  display_name: "Binoculars"
+}
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+  id: 98
+  display_name: "Dessert"
+}
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+  id: 99
+  display_name: "Bee"
+}
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+  name: "/m/01mzpv"
+  id: 100
+  display_name: "Chair"
+}
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+  name: "/m/04169hn"
+  id: 101
+  display_name: "Wood-burning stove"
+}
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+  name: "/m/0fm3zh"
+  id: 102
+  display_name: "Flowerpot"
+}
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+  id: 103
+  display_name: "Beaker"
+}
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+  id: 104
+  display_name: "Oyster"
+}
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+  id: 105
+  display_name: "Woodpecker"
+}
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+  id: 106
+  display_name: "Harp"
+}
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+  name: "/m/03dnzn"
+  id: 107
+  display_name: "Bathtub"
+}
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+  name: "/m/0h8mzrc"
+  id: 108
+  display_name: "Wall clock"
+}
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+  name: "/m/0h8mhzd"
+  id: 109
+  display_name: "Sports uniform"
+}
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+  id: 110
+  display_name: "Rhinoceros"
+}
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+  id: 111
+  display_name: "Beehive"
+}
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+  id: 112
+  display_name: "Cupboard"
+}
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+  name: "/m/09b5t"
+  id: 113
+  display_name: "Chicken"
+}
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+  name: "/m/04yx4"
+  id: 114
+  display_name: "Man"
+}
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+  name: "/m/01f8m5"
+  id: 115
+  display_name: "Blue jay"
+}
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+  id: 116
+  display_name: "Cucumber"
+}
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+  id: 117
+  display_name: "Balloon"
+}
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+  id: 118
+  display_name: "Kite"
+}
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+  name: "/m/03tw93"
+  id: 119
+  display_name: "Fireplace"
+}
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+  id: 120
+  display_name: "Lantern"
+}
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+  id: 121
+  display_name: "Missile"
+}
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+  id: 122
+  display_name: "Book"
+}
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+  id: 123
+  display_name: "Spoon"
+}
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+  id: 124
+  display_name: "Grapefruit"
+}
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+  id: 125
+  display_name: "Squirrel"
+}
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+  id: 126
+  display_name: "Orange"
+}
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+  name: "/m/01xygc"
+  id: 127
+  display_name: "Coat"
+}
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+  id: 128
+  display_name: "Punching bag"
+}
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+  id: 129
+  display_name: "Zebra"
+}
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+  name: "/m/01knjb"
+  id: 130
+  display_name: "Billboard"
+}
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+  id: 131
+  display_name: "Bicycle"
+}
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+  id: 132
+  display_name: "Door handle"
+}
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+  id: 133
+  display_name: "Mechanical fan"
+}
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+  id: 134
+  display_name: "Ring binder"
+}
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+  id: 135
+  display_name: "Table"
+}
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+  id: 136
+  display_name: "Parrot"
+}
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+  display_name: "Sock"
+}
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+  id: 138
+  display_name: "Vase"
+}
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+  display_name: "Weapon"
+}
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+  id: 140
+  display_name: "Shotgun"
+}
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+  id: 141
+  display_name: "Glasses"
+}
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+  id: 142
+  display_name: "Seahorse"
+}
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+  id: 143
+  display_name: "Belt"
+}
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+  display_name: "Watercraft"
+}
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+  id: 145
+  display_name: "Window"
+}
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+  name: "/m/03bk1"
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+  display_name: "Giraffe"
+}
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+  display_name: "Lion"
+}
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+  id: 148
+  display_name: "Tire"
+}
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+  id: 149
+  display_name: "Vehicle"
+}
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+  id: 150
+  display_name: "Canoe"
+}
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+  id: 151
+  display_name: "Tie"
+}
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+  display_name: "Shelf"
+}
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+  display_name: "Picture frame"
+}
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+  id: 154
+  display_name: "Printer"
+}
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+  id: 155
+  display_name: "Human leg"
+}
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+  id: 156
+  display_name: "Boat"
+}
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+  id: 157
+  display_name: "Slow cooker"
+}
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+  display_name: "Croissant"
+}
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+  display_name: "Candle"
+}
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+  display_name: "Pancake"
+}
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+  display_name: "Pillow"
+}
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+  id: 162
+  display_name: "Coin"
+}
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+  display_name: "Stretcher"
+}
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+  id: 164
+  display_name: "Sandal"
+}
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+  display_name: "Woman"
+}
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+  display_name: "Stairs"
+}
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+  display_name: "Harpsichord"
+}
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+  id: 168
+  display_name: "Stool"
+}
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+  display_name: "Bus"
+}
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+  display_name: "Suitcase"
+}
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+  id: 171
+  display_name: "Human mouth"
+}
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+  id: 172
+  display_name: "Juice"
+}
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+  id: 173
+  display_name: "Skull"
+}
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+  id: 174
+  display_name: "Door"
+}
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+  id: 175
+  display_name: "Violin"
+}
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+  display_name: "Chopsticks"
+}
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+  id: 177
+  display_name: "Digital clock"
+}
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+  display_name: "Sunflower"
+}
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+  id: 179
+  display_name: "Leopard"
+}
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+  id: 180
+  display_name: "Bell pepper"
+}
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+  id: 181
+  display_name: "Harbor seal"
+}
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+  name: "/m/078jl"
+  id: 182
+  display_name: "Snake"
+}
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+  id: 183
+  display_name: "Sewing machine"
+}
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+  id: 184
+  display_name: "Goose"
+}
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+  id: 185
+  display_name: "Helicopter"
+}
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+  display_name: "Seat belt"
+}
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+  id: 187
+  display_name: "Coffee cup"
+}
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+  id: 188
+  display_name: "Microwave oven"
+}
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+  name: "/m/01b9xk"
+  id: 189
+  display_name: "Hot dog"
+}
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+  name: "/m/0b3fp9"
+  id: 190
+  display_name: "Countertop"
+}
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+  name: "/m/0h8n27j"
+  id: 191
+  display_name: "Serving tray"
+}
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+  name: "/m/0h8n6f9"
+  id: 192
+  display_name: "Dog bed"
+}
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+  id: 193
+  display_name: "Beer"
+}
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+  name: "/m/017ftj"
+  id: 194
+  display_name: "Sunglasses"
+}
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+  name: "/m/044r5d"
+  id: 195
+  display_name: "Golf ball"
+}
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+  name: "/m/01dwsz"
+  id: 196
+  display_name: "Waffle"
+}
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+  name: "/m/0cdl1"
+  id: 197
+  display_name: "Palm tree"
+}
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+  name: "/m/07gql"
+  id: 198
+  display_name: "Trumpet"
+}
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+  name: "/m/0hdln"
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+  display_name: "Ruler"
+}
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+  id: 200
+  display_name: "Helmet"
+}
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+  id: 201
+  display_name: "Ladder"
+}
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+  name: "/m/021sj1"
+  id: 202
+  display_name: "Office building"
+}
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+  name: "/m/0bh9flk"
+  id: 203
+  display_name: "Tablet computer"
+}
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+  name: "/m/09gtd"
+  id: 204
+  display_name: "Toilet paper"
+}
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+  name: "/m/0jwn_"
+  id: 205
+  display_name: "Pomegranate"
+}
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+  name: "/m/02wv6h6"
+  id: 206
+  display_name: "Skirt"
+}
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+  name: "/m/02wv84t"
+  id: 207
+  display_name: "Gas stove"
+}
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+  name: "/m/021mn"
+  id: 208
+  display_name: "Cookie"
+}
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+  name: "/m/018p4k"
+  id: 209
+  display_name: "Cart"
+}
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+  name: "/m/06j2d"
+  id: 210
+  display_name: "Raven"
+}
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+  name: "/m/033cnk"
+  id: 211
+  display_name: "Egg"
+}
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+  id: 212
+  display_name: "Burrito"
+}
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+  name: "/m/03fwl"
+  id: 213
+  display_name: "Goat"
+}
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+  name: "/m/058qzx"
+  id: 214
+  display_name: "Kitchen knife"
+}
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+  name: "/m/06_fw"
+  id: 215
+  display_name: "Skateboard"
+}
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+  name: "/m/02x8cch"
+  id: 216
+  display_name: "Salt and pepper shakers"
+}
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+  id: 217
+  display_name: "Lynx"
+}
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+  id: 218
+  display_name: "Boot"
+}
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+  display_name: "Platter"
+}
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+  display_name: "Ski"
+}
+item {
+  name: "/m/01gkx_"
+  id: 221
+  display_name: "Swimwear"
+}
+item {
+  name: "/m/0b_rs"
+  id: 222
+  display_name: "Swimming pool"
+}
+item {
+  name: "/m/03v5tg"
+  id: 223
+  display_name: "Drinking straw"
+}
+item {
+  name: "/m/01j5ks"
+  id: 224
+  display_name: "Wrench"
+}
+item {
+  name: "/m/026t6"
+  id: 225
+  display_name: "Drum"
+}
+item {
+  name: "/m/0_k2"
+  id: 226
+  display_name: "Ant"
+}
+item {
+  name: "/m/039xj_"
+  id: 227
+  display_name: "Human ear"
+}
+item {
+  name: "/m/01b7fy"
+  id: 228
+  display_name: "Headphones"
+}
+item {
+  name: "/m/0220r2"
+  id: 229
+  display_name: "Fountain"
+}
+item {
+  name: "/m/015p6"
+  id: 230
+  display_name: "Bird"
+}
+item {
+  name: "/m/0fly7"
+  id: 231
+  display_name: "Jeans"
+}
+item {
+  name: "/m/07c52"
+  id: 232
+  display_name: "Television"
+}
+item {
+  name: "/m/0n28_"
+  id: 233
+  display_name: "Crab"
+}
+item {
+  name: "/m/0hg7b"
+  id: 234
+  display_name: "Microphone"
+}
+item {
+  name: "/m/019dx1"
+  id: 235
+  display_name: "Home appliance"
+}
+item {
+  name: "/m/04vv5k"
+  id: 236
+  display_name: "Snowplow"
+}
+item {
+  name: "/m/020jm"
+  id: 237
+  display_name: "Beetle"
+}
+item {
+  name: "/m/047v4b"
+  id: 238
+  display_name: "Artichoke"
+}
+item {
+  name: "/m/01xs3r"
+  id: 239
+  display_name: "Jet ski"
+}
+item {
+  name: "/m/03kt2w"
+  id: 240
+  display_name: "Stationary bicycle"
+}
+item {
+  name: "/m/03q69"
+  id: 241
+  display_name: "Human hair"
+}
+item {
+  name: "/m/01dxs"
+  id: 242
+  display_name: "Brown bear"
+}
+item {
+  name: "/m/01h8tj"
+  id: 243
+  display_name: "Starfish"
+}
+item {
+  name: "/m/0dt3t"
+  id: 244
+  display_name: "Fork"
+}
+item {
+  name: "/m/0cjq5"
+  id: 245
+  display_name: "Lobster"
+}
+item {
+  name: "/m/0h8lkj8"
+  id: 246
+  display_name: "Corded phone"
+}
+item {
+  name: "/m/0271t"
+  id: 247
+  display_name: "Drink"
+}
+item {
+  name: "/m/03q5c7"
+  id: 248
+  display_name: "Saucer"
+}
+item {
+  name: "/m/0fj52s"
+  id: 249
+  display_name: "Carrot"
+}
+item {
+  name: "/m/03vt0"
+  id: 250
+  display_name: "Insect"
+}
+item {
+  name: "/m/01x3z"
+  id: 251
+  display_name: "Clock"
+}
+item {
+  name: "/m/0d5gx"
+  id: 252
+  display_name: "Castle"
+}
+item {
+  name: "/m/0h8my_4"
+  id: 253
+  display_name: "Tennis racket"
+}
+item {
+  name: "/m/03ldnb"
+  id: 254
+  display_name: "Ceiling fan"
+}
+item {
+  name: "/m/0cjs7"
+  id: 255
+  display_name: "Asparagus"
+}
+item {
+  name: "/m/0449p"
+  id: 256
+  display_name: "Jaguar"
+}
+item {
+  name: "/m/04szw"
+  id: 257
+  display_name: "Musical instrument"
+}
+item {
+  name: "/m/07jdr"
+  id: 258
+  display_name: "Train"
+}
+item {
+  name: "/m/01yrx"
+  id: 259
+  display_name: "Cat"
+}
+item {
+  name: "/m/06c54"
+  id: 260
+  display_name: "Rifle"
+}
+item {
+  name: "/m/04h8sr"
+  id: 261
+  display_name: "Dumbbell"
+}
+item {
+  name: "/m/050k8"
+  id: 262
+  display_name: "Mobile phone"
+}
+item {
+  name: "/m/0pg52"
+  id: 263
+  display_name: "Taxi"
+}
+item {
+  name: "/m/02f9f_"
+  id: 264
+  display_name: "Shower"
+}
+item {
+  name: "/m/054fyh"
+  id: 265
+  display_name: "Pitcher"
+}
+item {
+  name: "/m/09k_b"
+  id: 266
+  display_name: "Lemon"
+}
+item {
+  name: "/m/03xxp"
+  id: 267
+  display_name: "Invertebrate"
+}
+item {
+  name: "/m/0jly1"
+  id: 268
+  display_name: "Turkey"
+}
+item {
+  name: "/m/06k2mb"
+  id: 269
+  display_name: "High heels"
+}
+item {
+  name: "/m/04yqq2"
+  id: 270
+  display_name: "Bust"
+}
+item {
+  name: "/m/0bwd_0j"
+  id: 271
+  display_name: "Elephant"
+}
+item {
+  name: "/m/02h19r"
+  id: 272
+  display_name: "Scarf"
+}
+item {
+  name: "/m/02zn6n"
+  id: 273
+  display_name: "Barrel"
+}
+item {
+  name: "/m/07c6l"
+  id: 274
+  display_name: "Trombone"
+}
+item {
+  name: "/m/05zsy"
+  id: 275
+  display_name: "Pumpkin"
+}
+item {
+  name: "/m/025dyy"
+  id: 276
+  display_name: "Box"
+}
+item {
+  name: "/m/07j87"
+  id: 277
+  display_name: "Tomato"
+}
+item {
+  name: "/m/09ld4"
+  id: 278
+  display_name: "Frog"
+}
+item {
+  name: "/m/01vbnl"
+  id: 279
+  display_name: "Bidet"
+}
+item {
+  name: "/m/0dzct"
+  id: 280
+  display_name: "Human face"
+}
+item {
+  name: "/m/03fp41"
+  id: 281
+  display_name: "Houseplant"
+}
+item {
+  name: "/m/0h2r6"
+  id: 282
+  display_name: "Van"
+}
+item {
+  name: "/m/0by6g"
+  id: 283
+  display_name: "Shark"
+}
+item {
+  name: "/m/0cxn2"
+  id: 284
+  display_name: "Ice cream"
+}
+item {
+  name: "/m/04tn4x"
+  id: 285
+  display_name: "Swim cap"
+}
+item {
+  name: "/m/0f6wt"
+  id: 286
+  display_name: "Falcon"
+}
+item {
+  name: "/m/05n4y"
+  id: 287
+  display_name: "Ostrich"
+}
+item {
+  name: "/m/0gxl3"
+  id: 288
+  display_name: "Handgun"
+}
+item {
+  name: "/m/02d9qx"
+  id: 289
+  display_name: "Whiteboard"
+}
+item {
+  name: "/m/04m9y"
+  id: 290
+  display_name: "Lizard"
+}
+item {
+  name: "/m/05z55"
+  id: 291
+  display_name: "Pasta"
+}
+item {
+  name: "/m/01x3jk"
+  id: 292
+  display_name: "Snowmobile"
+}
+item {
+  name: "/m/0h8l4fh"
+  id: 293
+  display_name: "Light bulb"
+}
+item {
+  name: "/m/031b6r"
+  id: 294
+  display_name: "Window blind"
+}
+item {
+  name: "/m/01tcjp"
+  id: 295
+  display_name: "Muffin"
+}
+item {
+  name: "/m/01f91_"
+  id: 296
+  display_name: "Pretzel"
+}
+item {
+  name: "/m/02522"
+  id: 297
+  display_name: "Computer monitor"
+}
+item {
+  name: "/m/0319l"
+  id: 298
+  display_name: "Horn"
+}
+item {
+  name: "/m/0c_jw"
+  id: 299
+  display_name: "Furniture"
+}
+item {
+  name: "/m/0l515"
+  id: 300
+  display_name: "Sandwich"
+}
+item {
+  name: "/m/0306r"
+  id: 301
+  display_name: "Fox"
+}
+item {
+  name: "/m/0crjs"
+  id: 302
+  display_name: "Convenience store"
+}
+item {
+  name: "/m/0ch_cf"
+  id: 303
+  display_name: "Fish"
+}
+item {
+  name: "/m/02xwb"
+  id: 304
+  display_name: "Fruit"
+}
+item {
+  name: "/m/01r546"
+  id: 305
+  display_name: "Earrings"
+}
+item {
+  name: "/m/03rszm"
+  id: 306
+  display_name: "Curtain"
+}
+item {
+  name: "/m/0388q"
+  id: 307
+  display_name: "Grape"
+}
+item {
+  name: "/m/03m3pdh"
+  id: 308
+  display_name: "Sofa bed"
+}
+item {
+  name: "/m/03k3r"
+  id: 309
+  display_name: "Horse"
+}
+item {
+  name: "/m/0hf58v5"
+  id: 310
+  display_name: "Luggage and bags"
+}
+item {
+  name: "/m/01y9k5"
+  id: 311
+  display_name: "Desk"
+}
+item {
+  name: "/m/05441v"
+  id: 312
+  display_name: "Crutch"
+}
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+  name: "/m/03p3bw"
+  id: 313
+  display_name: "Bicycle helmet"
+}
+item {
+  name: "/m/0175cv"
+  id: 314
+  display_name: "Tick"
+}
+item {
+  name: "/m/0cmf2"
+  id: 315
+  display_name: "Airplane"
+}
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+  name: "/m/0ccs93"
+  id: 316
+  display_name: "Canary"
+}
+item {
+  name: "/m/02d1br"
+  id: 317
+  display_name: "Spatula"
+}
+item {
+  name: "/m/0gjkl"
+  id: 318
+  display_name: "Watch"
+}
+item {
+  name: "/m/0jqgx"
+  id: 319
+  display_name: "Lily"
+}
+item {
+  name: "/m/0h99cwc"
+  id: 320
+  display_name: "Kitchen appliance"
+}
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+  name: "/m/047j0r"
+  id: 321
+  display_name: "Filing cabinet"
+}
+item {
+  name: "/m/0k5j"
+  id: 322
+  display_name: "Aircraft"
+}
+item {
+  name: "/m/0h8n6ft"
+  id: 323
+  display_name: "Cake stand"
+}
+item {
+  name: "/m/0gm28"
+  id: 324
+  display_name: "Candy"
+}
+item {
+  name: "/m/0130jx"
+  id: 325
+  display_name: "Sink"
+}
+item {
+  name: "/m/04rmv"
+  id: 326
+  display_name: "Mouse"
+}
+item {
+  name: "/m/081qc"
+  id: 327
+  display_name: "Wine"
+}
+item {
+  name: "/m/0qmmr"
+  id: 328
+  display_name: "Wheelchair"
+}
+item {
+  name: "/m/03fj2"
+  id: 329
+  display_name: "Goldfish"
+}
+item {
+  name: "/m/040b_t"
+  id: 330
+  display_name: "Refrigerator"
+}
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+  name: "/m/02y6n"
+  id: 331
+  display_name: "French fries"
+}
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+  name: "/m/0fqfqc"
+  id: 332
+  display_name: "Drawer"
+}
+item {
+  name: "/m/030610"
+  id: 333
+  display_name: "Treadmill"
+}
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+  name: "/m/07kng9"
+  id: 334
+  display_name: "Picnic basket"
+}
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+  name: "/m/029b3"
+  id: 335
+  display_name: "Dice"
+}
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+  name: "/m/0fbw6"
+  id: 336
+  display_name: "Cabbage"
+}
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+  name: "/m/07qxg_"
+  id: 337
+  display_name: "Football helmet"
+}
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+  name: "/m/068zj"
+  id: 338
+  display_name: "Pig"
+}
+item {
+  name: "/m/01g317"
+  id: 339
+  display_name: "Person"
+}
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+  name: "/m/01bfm9"
+  id: 340
+  display_name: "Shorts"
+}
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+  name: "/m/02068x"
+  id: 341
+  display_name: "Gondola"
+}
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+  name: "/m/0fz0h"
+  id: 342
+  display_name: "Honeycomb"
+}
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+  name: "/m/0jy4k"
+  id: 343
+  display_name: "Doughnut"
+}
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+  name: "/m/05kyg_"
+  id: 344
+  display_name: "Chest of drawers"
+}
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+  name: "/m/01prls"
+  id: 345
+  display_name: "Land vehicle"
+}
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+  name: "/m/01h44"
+  id: 346
+  display_name: "Bat"
+}
+item {
+  name: "/m/08pbxl"
+  id: 347
+  display_name: "Monkey"
+}
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+  name: "/m/02gzp"
+  id: 348
+  display_name: "Dagger"
+}
+item {
+  name: "/m/04brg2"
+  id: 349
+  display_name: "Tableware"
+}
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+  name: "/m/031n1"
+  id: 350
+  display_name: "Human foot"
+}
+item {
+  name: "/m/02jvh9"
+  id: 351
+  display_name: "Mug"
+}
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+  name: "/m/046dlr"
+  id: 352
+  display_name: "Alarm clock"
+}
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+  name: "/m/0h8ntjv"
+  id: 353
+  display_name: "Pressure cooker"
+}
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+  name: "/m/0k65p"
+  id: 354
+  display_name: "Human hand"
+}
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+  name: "/m/011k07"
+  id: 355
+  display_name: "Tortoise"
+}
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+  name: "/m/03grzl"
+  id: 356
+  display_name: "Baseball glove"
+}
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+  name: "/m/06y5r"
+  id: 357
+  display_name: "Sword"
+}
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+  name: "/m/061_f"
+  id: 358
+  display_name: "Pear"
+}
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+  name: "/m/01cmb2"
+  id: 359
+  display_name: "Miniskirt"
+}
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+  name: "/m/01mqdt"
+  id: 360
+  display_name: "Traffic sign"
+}
+item {
+  name: "/m/05r655"
+  id: 361
+  display_name: "Girl"
+}
+item {
+  name: "/m/02p3w7d"
+  id: 362
+  display_name: "Roller skates"
+}
+item {
+  name: "/m/029tx"
+  id: 363
+  display_name: "Dinosaur"
+}
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+  name: "/m/04m6gz"
+  id: 364
+  display_name: "Porch"
+}
+item {
+  name: "/m/015h_t"
+  id: 365
+  display_name: "Human beard"
+}
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+  name: "/m/06pcq"
+  id: 366
+  display_name: "Submarine sandwich"
+}
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+  name: "/m/01bms0"
+  id: 367
+  display_name: "Screwdriver"
+}
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+  name: "/m/07fbm7"
+  id: 368
+  display_name: "Strawberry"
+}
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+  name: "/m/09tvcd"
+  id: 369
+  display_name: "Wine glass"
+}
+item {
+  name: "/m/06nwz"
+  id: 370
+  display_name: "Seafood"
+}
+item {
+  name: "/m/0dv9c"
+  id: 371
+  display_name: "Racket"
+}
+item {
+  name: "/m/083wq"
+  id: 372
+  display_name: "Wheel"
+}
+item {
+  name: "/m/0gd36"
+  id: 373
+  display_name: "Sea lion"
+}
+item {
+  name: "/m/0138tl"
+  id: 374
+  display_name: "Toy"
+}
+item {
+  name: "/m/07clx"
+  id: 375
+  display_name: "Tea"
+}
+item {
+  name: "/m/05ctyq"
+  id: 376
+  display_name: "Tennis ball"
+}
+item {
+  name: "/m/0bjyj5"
+  id: 377
+  display_name: "Waste container"
+}
+item {
+  name: "/m/0dbzx"
+  id: 378
+  display_name: "Mule"
+}
+item {
+  name: "/m/02ctlc"
+  id: 379
+  display_name: "Cricket ball"
+}
+item {
+  name: "/m/0fp6w"
+  id: 380
+  display_name: "Pineapple"
+}
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+  name: "/m/0djtd"
+  id: 381
+  display_name: "Coconut"
+}
+item {
+  name: "/m/0167gd"
+  id: 382
+  display_name: "Doll"
+}
+item {
+  name: "/m/078n6m"
+  id: 383
+  display_name: "Coffee table"
+}
+item {
+  name: "/m/0152hh"
+  id: 384
+  display_name: "Snowman"
+}
+item {
+  name: "/m/04gth"
+  id: 385
+  display_name: "Lavender"
+}
+item {
+  name: "/m/0ll1f78"
+  id: 386
+  display_name: "Shrimp"
+}
+item {
+  name: "/m/0cffdh"
+  id: 387
+  display_name: "Maple"
+}
+item {
+  name: "/m/025rp__"
+  id: 388
+  display_name: "Cowboy hat"
+}
+item {
+  name: "/m/02_n6y"
+  id: 389
+  display_name: "Goggles"
+}
+item {
+  name: "/m/0wdt60w"
+  id: 390
+  display_name: "Rugby ball"
+}
+item {
+  name: "/m/0cydv"
+  id: 391
+  display_name: "Caterpillar"
+}
+item {
+  name: "/m/01n5jq"
+  id: 392
+  display_name: "Poster"
+}
+item {
+  name: "/m/09rvcxw"
+  id: 393
+  display_name: "Rocket"
+}
+item {
+  name: "/m/013y1f"
+  id: 394
+  display_name: "Organ"
+}
+item {
+  name: "/m/06ncr"
+  id: 395
+  display_name: "Saxophone"
+}
+item {
+  name: "/m/015qff"
+  id: 396
+  display_name: "Traffic light"
+}
+item {
+  name: "/m/024g6"
+  id: 397
+  display_name: "Cocktail"
+}
+item {
+  name: "/m/05gqfk"
+  id: 398
+  display_name: "Plastic bag"
+}
+item {
+  name: "/m/0dv77"
+  id: 399
+  display_name: "Squash"
+}
+item {
+  name: "/m/052sf"
+  id: 400
+  display_name: "Mushroom"
+}
+item {
+  name: "/m/0cdn1"
+  id: 401
+  display_name: "Hamburger"
+}
+item {
+  name: "/m/03jbxj"
+  id: 402
+  display_name: "Light switch"
+}
+item {
+  name: "/m/0cyfs"
+  id: 403
+  display_name: "Parachute"
+}
+item {
+  name: "/m/0kmg4"
+  id: 404
+  display_name: "Teddy bear"
+}
+item {
+  name: "/m/02cvgx"
+  id: 405
+  display_name: "Winter melon"
+}
+item {
+  name: "/m/09kx5"
+  id: 406
+  display_name: "Deer"
+}
+item {
+  name: "/m/057cc"
+  id: 407
+  display_name: "Musical keyboard"
+}
+item {
+  name: "/m/02pkr5"
+  id: 408
+  display_name: "Plumbing fixture"
+}
+item {
+  name: "/m/057p5t"
+  id: 409
+  display_name: "Scoreboard"
+}
+item {
+  name: "/m/03g8mr"
+  id: 410
+  display_name: "Baseball bat"
+}
+item {
+  name: "/m/0frqm"
+  id: 411
+  display_name: "Envelope"
+}
+item {
+  name: "/m/03m3vtv"
+  id: 412
+  display_name: "Adhesive tape"
+}
+item {
+  name: "/m/0584n8"
+  id: 413
+  display_name: "Briefcase"
+}
+item {
+  name: "/m/014y4n"
+  id: 414
+  display_name: "Paddle"
+}
+item {
+  name: "/m/01g3x7"
+  id: 415
+  display_name: "Bow and arrow"
+}
+item {
+  name: "/m/07cx4"
+  id: 416
+  display_name: "Telephone"
+}
+item {
+  name: "/m/07bgp"
+  id: 417
+  display_name: "Sheep"
+}
+item {
+  name: "/m/032b3c"
+  id: 418
+  display_name: "Jacket"
+}
+item {
+  name: "/m/01bl7v"
+  id: 419
+  display_name: "Boy"
+}
+item {
+  name: "/m/0663v"
+  id: 420
+  display_name: "Pizza"
+}
+item {
+  name: "/m/0cn6p"
+  id: 421
+  display_name: "Otter"
+}
+item {
+  name: "/m/02rdsp"
+  id: 422
+  display_name: "Office supplies"
+}
+item {
+  name: "/m/02crq1"
+  id: 423
+  display_name: "Couch"
+}
+item {
+  name: "/m/01xqw"
+  id: 424
+  display_name: "Cello"
+}
+item {
+  name: "/m/0cnyhnx"
+  id: 425
+  display_name: "Bull"
+}
+item {
+  name: "/m/01x_v"
+  id: 426
+  display_name: "Camel"
+}
+item {
+  name: "/m/018xm"
+  id: 427
+  display_name: "Ball"
+}
+item {
+  name: "/m/09ddx"
+  id: 428
+  display_name: "Duck"
+}
+item {
+  name: "/m/084zz"
+  id: 429
+  display_name: "Whale"
+}
+item {
+  name: "/m/01n4qj"
+  id: 430
+  display_name: "Shirt"
+}
+item {
+  name: "/m/07cmd"
+  id: 431
+  display_name: "Tank"
+}
+item {
+  name: "/m/04_sv"
+  id: 432
+  display_name: "Motorcycle"
+}
+item {
+  name: "/m/0mkg"
+  id: 433
+  display_name: "Accordion"
+}
+item {
+  name: "/m/09d5_"
+  id: 434
+  display_name: "Owl"
+}
+item {
+  name: "/m/0c568"
+  id: 435
+  display_name: "Porcupine"
+}
+item {
+  name: "/m/02wbtzl"
+  id: 436
+  display_name: "Sun hat"
+}
+item {
+  name: "/m/05bm6"
+  id: 437
+  display_name: "Nail"
+}
+item {
+  name: "/m/01lsmm"
+  id: 438
+  display_name: "Scissors"
+}
+item {
+  name: "/m/0dftk"
+  id: 439
+  display_name: "Swan"
+}
+item {
+  name: "/m/0dtln"
+  id: 440
+  display_name: "Lamp"
+}
+item {
+  name: "/m/0nl46"
+  id: 441
+  display_name: "Crown"
+}
+item {
+  name: "/m/05r5c"
+  id: 442
+  display_name: "Piano"
+}
+item {
+  name: "/m/06msq"
+  id: 443
+  display_name: "Sculpture"
+}
+item {
+  name: "/m/0cd4d"
+  id: 444
+  display_name: "Cheetah"
+}
+item {
+  name: "/m/05kms"
+  id: 445
+  display_name: "Oboe"
+}
+item {
+  name: "/m/02jnhm"
+  id: 446
+  display_name: "Tin can"
+}
+item {
+  name: "/m/0fldg"
+  id: 447
+  display_name: "Mango"
+}
+item {
+  name: "/m/073bxn"
+  id: 448
+  display_name: "Tripod"
+}
+item {
+  name: "/m/029bxz"
+  id: 449
+  display_name: "Oven"
+}
+item {
+  name: "/m/020lf"
+  id: 450
+  display_name: "Computer mouse"
+}
+item {
+  name: "/m/01btn"
+  id: 451
+  display_name: "Barge"
+}
+item {
+  name: "/m/02vqfm"
+  id: 452
+  display_name: "Coffee"
+}
+item {
+  name: "/m/06__v"
+  id: 453
+  display_name: "Snowboard"
+}
+item {
+  name: "/m/043nyj"
+  id: 454
+  display_name: "Common fig"
+}
+item {
+  name: "/m/0grw1"
+  id: 455
+  display_name: "Salad"
+}
+item {
+  name: "/m/03hl4l9"
+  id: 456
+  display_name: "Marine invertebrates"
+}
+item {
+  name: "/m/0hnnb"
+  id: 457
+  display_name: "Umbrella"
+}
+item {
+  name: "/m/04c0y"
+  id: 458
+  display_name: "Kangaroo"
+}
+item {
+  name: "/m/0dzf4"
+  id: 459
+  display_name: "Human arm"
+}
+item {
+  name: "/m/07v9_z"
+  id: 460
+  display_name: "Measuring cup"
+}
+item {
+  name: "/m/0f9_l"
+  id: 461
+  display_name: "Snail"
+}
+item {
+  name: "/m/0703r8"
+  id: 462
+  display_name: "Loveseat"
+}
+item {
+  name: "/m/01xyhv"
+  id: 463
+  display_name: "Suit"
+}
+item {
+  name: "/m/01fh4r"
+  id: 464
+  display_name: "Teapot"
+}
+item {
+  name: "/m/04dr76w"
+  id: 465
+  display_name: "Bottle"
+}
+item {
+  name: "/m/0pcr"
+  id: 466
+  display_name: "Alpaca"
+}
+item {
+  name: "/m/03s_tn"
+  id: 467
+  display_name: "Kettle"
+}
+item {
+  name: "/m/07mhn"
+  id: 468
+  display_name: "Trousers"
+}
+item {
+  name: "/m/01hrv5"
+  id: 469
+  display_name: "Popcorn"
+}
+item {
+  name: "/m/019h78"
+  id: 470
+  display_name: "Centipede"
+}
+item {
+  name: "/m/09kmb"
+  id: 471
+  display_name: "Spider"
+}
+item {
+  name: "/m/0h23m"
+  id: 472
+  display_name: "Sparrow"
+}
+item {
+  name: "/m/050gv4"
+  id: 473
+  display_name: "Plate"
+}
+item {
+  name: "/m/01fb_0"
+  id: 474
+  display_name: "Bagel"
+}
+item {
+  name: "/m/02w3_ws"
+  id: 475
+  display_name: "Personal care"
+}
+item {
+  name: "/m/014j1m"
+  id: 476
+  display_name: "Apple"
+}
+item {
+  name: "/m/01gmv2"
+  id: 477
+  display_name: "Brassiere"
+}
+item {
+  name: "/m/04y4h8h"
+  id: 478
+  display_name: "Bathroom cabinet"
+}
+item {
+  name: "/m/026qbn5"
+  id: 479
+  display_name: "Studio couch"
+}
+item {
+  name: "/m/01m2v"
+  id: 480
+  display_name: "Computer keyboard"
+}
+item {
+  name: "/m/05_5p_0"
+  id: 481
+  display_name: "Table tennis racket"
+}
+item {
+  name: "/m/07030"
+  id: 482
+  display_name: "Sushi"
+}
+item {
+  name: "/m/01s105"
+  id: 483
+  display_name: "Cabinetry"
+}
+item {
+  name: "/m/033rq4"
+  id: 484
+  display_name: "Street light"
+}
+item {
+  name: "/m/0162_1"
+  id: 485
+  display_name: "Towel"
+}
+item {
+  name: "/m/02z51p"
+  id: 486
+  display_name: "Nightstand"
+}
+item {
+  name: "/m/06mf6"
+  id: 487
+  display_name: "Rabbit"
+}
+item {
+  name: "/m/02hj4"
+  id: 488
+  display_name: "Dolphin"
+}
+item {
+  name: "/m/0bt9lr"
+  id: 489
+  display_name: "Dog"
+}
+item {
+  name: "/m/08hvt4"
+  id: 490
+  display_name: "Jug"
+}
+item {
+  name: "/m/084rd"
+  id: 491
+  display_name: "Wok"
+}
+item {
+  name: "/m/01pns0"
+  id: 492
+  display_name: "Fire hydrant"
+}
+item {
+  name: "/m/014sv8"
+  id: 493
+  display_name: "Human eye"
+}
+item {
+  name: "/m/079cl"
+  id: 494
+  display_name: "Skyscraper"
+}
+item {
+  name: "/m/01940j"
+  id: 495
+  display_name: "Backpack"
+}
+item {
+  name: "/m/05vtc"
+  id: 496
+  display_name: "Potato"
+}
+item {
+  name: "/m/02w3r3"
+  id: 497
+  display_name: "Paper towel"
+}
+item {
+  name: "/m/054xkw"
+  id: 498
+  display_name: "Lifejacket"
+}
+item {
+  name: "/m/01bqk0"
+  id: 499
+  display_name: "Bicycle wheel"
+}
+item {
+  name: "/m/09g1w"
+  id: 500
+  display_name: "Toilet"
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/pascal_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/pascal_label_map.pbtxt
new file mode 100644
index 00000000..c9e9e2af
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/pascal_label_map.pbtxt
@@ -0,0 +1,99 @@
+item {
+  id: 1
+  name: 'aeroplane'
+}
+
+item {
+  id: 2
+  name: 'bicycle'
+}
+
+item {
+  id: 3
+  name: 'bird'
+}
+
+item {
+  id: 4
+  name: 'boat'
+}
+
+item {
+  id: 5
+  name: 'bottle'
+}
+
+item {
+  id: 6
+  name: 'bus'
+}
+
+item {
+  id: 7
+  name: 'car'
+}
+
+item {
+  id: 8
+  name: 'cat'
+}
+
+item {
+  id: 9
+  name: 'chair'
+}
+
+item {
+  id: 10
+  name: 'cow'
+}
+
+item {
+  id: 11
+  name: 'diningtable'
+}
+
+item {
+  id: 12
+  name: 'dog'
+}
+
+item {
+  id: 13
+  name: 'horse'
+}
+
+item {
+  id: 14
+  name: 'motorbike'
+}
+
+item {
+  id: 15
+  name: 'person'
+}
+
+item {
+  id: 16
+  name: 'pottedplant'
+}
+
+item {
+  id: 17
+  name: 'sheep'
+}
+
+item {
+  id: 18
+  name: 'sofa'
+}
+
+item {
+  id: 19
+  name: 'train'
+}
+
+item {
+  id: 20
+  name: 'tvmonitor'
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data/pet_label_map.pbtxt b/TensorFlow/Detection/SSD/models/research/object_detection/data/pet_label_map.pbtxt
new file mode 100644
index 00000000..54d7d351
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data/pet_label_map.pbtxt
@@ -0,0 +1,184 @@
+item {
+  id: 1
+  name: 'Abyssinian'
+}
+
+item {
+  id: 2
+  name: 'american_bulldog'
+}
+
+item {
+  id: 3
+  name: 'american_pit_bull_terrier'
+}
+
+item {
+  id: 4
+  name: 'basset_hound'
+}
+
+item {
+  id: 5
+  name: 'beagle'
+}
+
+item {
+  id: 6
+  name: 'Bengal'
+}
+
+item {
+  id: 7
+  name: 'Birman'
+}
+
+item {
+  id: 8
+  name: 'Bombay'
+}
+
+item {
+  id: 9
+  name: 'boxer'
+}
+
+item {
+  id: 10
+  name: 'British_Shorthair'
+}
+
+item {
+  id: 11
+  name: 'chihuahua'
+}
+
+item {
+  id: 12
+  name: 'Egyptian_Mau'
+}
+
+item {
+  id: 13
+  name: 'english_cocker_spaniel'
+}
+
+item {
+  id: 14
+  name: 'english_setter'
+}
+
+item {
+  id: 15
+  name: 'german_shorthaired'
+}
+
+item {
+  id: 16
+  name: 'great_pyrenees'
+}
+
+item {
+  id: 17
+  name: 'havanese'
+}
+
+item {
+  id: 18
+  name: 'japanese_chin'
+}
+
+item {
+  id: 19
+  name: 'keeshond'
+}
+
+item {
+  id: 20
+  name: 'leonberger'
+}
+
+item {
+  id: 21
+  name: 'Maine_Coon'
+}
+
+item {
+  id: 22
+  name: 'miniature_pinscher'
+}
+
+item {
+  id: 23
+  name: 'newfoundland'
+}
+
+item {
+  id: 24
+  name: 'Persian'
+}
+
+item {
+  id: 25
+  name: 'pomeranian'
+}
+
+item {
+  id: 26
+  name: 'pug'
+}
+
+item {
+  id: 27
+  name: 'Ragdoll'
+}
+
+item {
+  id: 28
+  name: 'Russian_Blue'
+}
+
+item {
+  id: 29
+  name: 'saint_bernard'
+}
+
+item {
+  id: 30
+  name: 'samoyed'
+}
+
+item {
+  id: 31
+  name: 'scottish_terrier'
+}
+
+item {
+  id: 32
+  name: 'shiba_inu'
+}
+
+item {
+  id: 33
+  name: 'Siamese'
+}
+
+item {
+  id: 34
+  name: 'Sphynx'
+}
+
+item {
+  id: 35
+  name: 'staffordshire_bull_terrier'
+}
+
+item {
+  id: 36
+  name: 'wheaten_terrier'
+}
+
+item {
+  id: 37
+  name: 'yorkshire_terrier'
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder.py b/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder.py
new file mode 100644
index 00000000..c844e3dc
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder.py
@@ -0,0 +1,466 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tensorflow Example proto decoder for object detection.
+
+A decoder to decode string tensors containing serialized tensorflow.Example
+protos for object detection.
+"""
+import tensorflow as tf
+
+from object_detection.core import data_decoder
+from object_detection.core import standard_fields as fields
+from object_detection.protos import input_reader_pb2
+from object_detection.utils import label_map_util
+
+slim_example_decoder = tf.contrib.slim.tfexample_decoder
+
+
+class _ClassTensorHandler(slim_example_decoder.Tensor):
+  """An ItemHandler to fetch class ids from class text."""
+
+  def __init__(self,
+               tensor_key,
+               label_map_proto_file,
+               shape_keys=None,
+               shape=None,
+               default_value=''):
+    """Initializes the LookupTensor handler.
+
+    Simply calls a vocabulary (most often, a label mapping) lookup.
+
+    Args:
+      tensor_key: the name of the `TFExample` feature to read the tensor from.
+      label_map_proto_file: File path to a text format LabelMapProto message
+        mapping class text to id.
+      shape_keys: Optional name or list of names of the TF-Example feature in
+        which the tensor shape is stored. If a list, then each corresponds to
+        one dimension of the shape.
+      shape: Optional output shape of the `Tensor`. If provided, the `Tensor` is
+        reshaped accordingly.
+      default_value: The value used when the `tensor_key` is not found in a
+        particular `TFExample`.
+
+    Raises:
+      ValueError: if both `shape_keys` and `shape` are specified.
+    """
+    name_to_id = label_map_util.get_label_map_dict(
+        label_map_proto_file, use_display_name=False)
+    # We use a default_value of -1, but we expect all labels to be contained
+    # in the label map.
+    name_to_id_table = tf.contrib.lookup.HashTable(
+        initializer=tf.contrib.lookup.KeyValueTensorInitializer(
+            keys=tf.constant(list(name_to_id.keys())),
+            values=tf.constant(list(name_to_id.values()), dtype=tf.int64)),
+        default_value=-1)
+    display_name_to_id = label_map_util.get_label_map_dict(
+        label_map_proto_file, use_display_name=True)
+    # We use a default_value of -1, but we expect all labels to be contained
+    # in the label map.
+    display_name_to_id_table = tf.contrib.lookup.HashTable(
+        initializer=tf.contrib.lookup.KeyValueTensorInitializer(
+            keys=tf.constant(list(display_name_to_id.keys())),
+            values=tf.constant(
+                list(display_name_to_id.values()), dtype=tf.int64)),
+        default_value=-1)
+
+    self._name_to_id_table = name_to_id_table
+    self._display_name_to_id_table = display_name_to_id_table
+    super(_ClassTensorHandler, self).__init__(tensor_key, shape_keys, shape,
+                                              default_value)
+
+  def tensors_to_item(self, keys_to_tensors):
+    unmapped_tensor = super(_ClassTensorHandler,
+                            self).tensors_to_item(keys_to_tensors)
+    return tf.maximum(self._name_to_id_table.lookup(unmapped_tensor),
+                      self._display_name_to_id_table.lookup(unmapped_tensor))
+
+
+class _BackupHandler(slim_example_decoder.ItemHandler):
+  """An ItemHandler that tries two ItemHandlers in order."""
+
+  def __init__(self, handler, backup):
+    """Initializes the BackupHandler handler.
+
+    If the first Handler's tensors_to_item returns a Tensor with no elements,
+    the second Handler is used.
+
+    Args:
+      handler: The primary ItemHandler.
+      backup: The backup ItemHandler.
+
+    Raises:
+      ValueError: if either is not an ItemHandler.
+    """
+    if not isinstance(handler, slim_example_decoder.ItemHandler):
+      raise ValueError('Primary handler is of type %s instead of ItemHandler' %
+                       type(handler))
+    if not isinstance(backup, slim_example_decoder.ItemHandler):
+      raise ValueError(
+          'Backup handler is of type %s instead of ItemHandler' % type(backup))
+    self._handler = handler
+    self._backup = backup
+    super(_BackupHandler, self).__init__(handler.keys + backup.keys)
+
+  def tensors_to_item(self, keys_to_tensors):
+    item = self._handler.tensors_to_item(keys_to_tensors)
+    return tf.cond(
+        pred=tf.equal(tf.reduce_prod(tf.shape(item)), 0),
+        true_fn=lambda: self._backup.tensors_to_item(keys_to_tensors),
+        false_fn=lambda: item)
+
+
+class TfExampleDecoder(data_decoder.DataDecoder):
+  """Tensorflow Example proto decoder."""
+
+  def __init__(self,
+               load_instance_masks=False,
+               instance_mask_type=input_reader_pb2.NUMERICAL_MASKS,
+               label_map_proto_file=None,
+               use_display_name=False,
+               dct_method='',
+               num_keypoints=0,
+               num_additional_channels=0):
+    """Constructor sets keys_to_features and items_to_handlers.
+
+    Args:
+      load_instance_masks: whether or not to load and handle instance masks.
+      instance_mask_type: type of instance masks. Options are provided in
+        input_reader.proto. This is only used if `load_instance_masks` is True.
+      label_map_proto_file: a file path to a
+        object_detection.protos.StringIntLabelMap proto. If provided, then the
+        mapped IDs of 'image/object/class/text' will take precedence over the
+        existing 'image/object/class/label' ID.  Also, if provided, it is
+        assumed that 'image/object/class/text' will be in the data.
+      use_display_name: whether or not to use the `display_name` for label
+        mapping (instead of `name`).  Only used if label_map_proto_file is
+        provided.
+      dct_method: An optional string. Defaults to None. It only takes
+        effect when image format is jpeg, used to specify a hint about the
+        algorithm used for jpeg decompression. Currently valid values
+        are ['INTEGER_FAST', 'INTEGER_ACCURATE']. The hint may be ignored, for
+        example, the jpeg library does not have that specific option.
+      num_keypoints: the number of keypoints per object.
+      num_additional_channels: how many additional channels to use.
+
+    Raises:
+      ValueError: If `instance_mask_type` option is not one of
+        input_reader_pb2.DEFAULT, input_reader_pb2.NUMERICAL, or
+        input_reader_pb2.PNG_MASKS.
+    """
+    # TODO(rathodv): delete unused `use_display_name` argument once we change
+    # other decoders to handle label maps similarly.
+    del use_display_name
+    self.keys_to_features = {
+        'image/encoded':
+            tf.FixedLenFeature((), tf.string, default_value=''),
+        'image/format':
+            tf.FixedLenFeature((), tf.string, default_value='jpeg'),
+        'image/filename':
+            tf.FixedLenFeature((), tf.string, default_value=''),
+        'image/key/sha256':
+            tf.FixedLenFeature((), tf.string, default_value=''),
+        'image/source_id':
+            tf.FixedLenFeature((), tf.string, default_value=''),
+        'image/height':
+            tf.FixedLenFeature((), tf.int64, default_value=1),
+        'image/width':
+            tf.FixedLenFeature((), tf.int64, default_value=1),
+        # Image-level labels.
+        'image/class/text':
+            tf.VarLenFeature(tf.string),
+        'image/class/label':
+            tf.VarLenFeature(tf.int64),
+        # Object boxes and classes.
+        'image/object/bbox/xmin':
+            tf.VarLenFeature(tf.float32),
+        'image/object/bbox/xmax':
+            tf.VarLenFeature(tf.float32),
+        'image/object/bbox/ymin':
+            tf.VarLenFeature(tf.float32),
+        'image/object/bbox/ymax':
+            tf.VarLenFeature(tf.float32),
+        'image/object/class/label':
+            tf.VarLenFeature(tf.int64),
+        'image/object/class/text':
+            tf.VarLenFeature(tf.string),
+        'image/object/area':
+            tf.VarLenFeature(tf.float32),
+        'image/object/is_crowd':
+            tf.VarLenFeature(tf.int64),
+        'image/object/difficult':
+            tf.VarLenFeature(tf.int64),
+        'image/object/group_of':
+            tf.VarLenFeature(tf.int64),
+        'image/object/weight':
+            tf.VarLenFeature(tf.float32),
+    }
+    # We are checking `dct_method` instead of passing it directly in order to
+    # ensure TF version 1.6 compatibility.
+    if dct_method:
+      image = slim_example_decoder.Image(
+          image_key='image/encoded',
+          format_key='image/format',
+          channels=3,
+          dct_method=dct_method)
+      additional_channel_image = slim_example_decoder.Image(
+          image_key='image/additional_channels/encoded',
+          format_key='image/format',
+          channels=1,
+          repeated=True,
+          dct_method=dct_method)
+    else:
+      image = slim_example_decoder.Image(
+          image_key='image/encoded', format_key='image/format', channels=3)
+      additional_channel_image = slim_example_decoder.Image(
+          image_key='image/additional_channels/encoded',
+          format_key='image/format',
+          channels=1,
+          repeated=True)
+    self.items_to_handlers = {
+        fields.InputDataFields.image:
+            image,
+        fields.InputDataFields.source_id: (
+            slim_example_decoder.Tensor('image/source_id')),
+        fields.InputDataFields.key: (
+            slim_example_decoder.Tensor('image/key/sha256')),
+        fields.InputDataFields.filename: (
+            slim_example_decoder.Tensor('image/filename')),
+        # Object boxes and classes.
+        fields.InputDataFields.groundtruth_boxes: (
+            slim_example_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
+                                             'image/object/bbox/')),
+        fields.InputDataFields.groundtruth_area:
+            slim_example_decoder.Tensor('image/object/area'),
+        fields.InputDataFields.groundtruth_is_crowd: (
+            slim_example_decoder.Tensor('image/object/is_crowd')),
+        fields.InputDataFields.groundtruth_difficult: (
+            slim_example_decoder.Tensor('image/object/difficult')),
+        fields.InputDataFields.groundtruth_group_of: (
+            slim_example_decoder.Tensor('image/object/group_of')),
+        fields.InputDataFields.groundtruth_weights: (
+            slim_example_decoder.Tensor('image/object/weight')),
+    }
+    if num_additional_channels > 0:
+      self.keys_to_features[
+          'image/additional_channels/encoded'] = tf.FixedLenFeature(
+              (num_additional_channels,), tf.string)
+      self.items_to_handlers[
+          fields.InputDataFields.
+          image_additional_channels] = additional_channel_image
+    self._num_keypoints = num_keypoints
+    if num_keypoints > 0:
+      self.keys_to_features['image/object/keypoint/x'] = (
+          tf.VarLenFeature(tf.float32))
+      self.keys_to_features['image/object/keypoint/y'] = (
+          tf.VarLenFeature(tf.float32))
+      self.items_to_handlers[fields.InputDataFields.groundtruth_keypoints] = (
+          slim_example_decoder.ItemHandlerCallback(
+              ['image/object/keypoint/y', 'image/object/keypoint/x'],
+              self._reshape_keypoints))
+    if load_instance_masks:
+      if instance_mask_type in (input_reader_pb2.DEFAULT,
+                                input_reader_pb2.NUMERICAL_MASKS):
+        self.keys_to_features['image/object/mask'] = (
+            tf.VarLenFeature(tf.float32))
+        self.items_to_handlers[
+            fields.InputDataFields.groundtruth_instance_masks] = (
+                slim_example_decoder.ItemHandlerCallback(
+                    ['image/object/mask', 'image/height', 'image/width'],
+                    self._reshape_instance_masks))
+      elif instance_mask_type == input_reader_pb2.PNG_MASKS:
+        self.keys_to_features['image/object/mask'] = tf.VarLenFeature(tf.string)
+        self.items_to_handlers[
+            fields.InputDataFields.groundtruth_instance_masks] = (
+                slim_example_decoder.ItemHandlerCallback(
+                    ['image/object/mask', 'image/height', 'image/width'],
+                    self._decode_png_instance_masks))
+      else:
+        raise ValueError('Did not recognize the `instance_mask_type` option.')
+    if label_map_proto_file:
+      # If the label_map_proto is provided, try to use it in conjunction with
+      # the class text, and fall back to a materialized ID.
+      label_handler = _BackupHandler(
+          _ClassTensorHandler(
+              'image/object/class/text', label_map_proto_file,
+              default_value=''),
+          slim_example_decoder.Tensor('image/object/class/label'))
+      image_label_handler = _BackupHandler(
+          _ClassTensorHandler(
+              fields.TfExampleFields.image_class_text,
+              label_map_proto_file,
+              default_value=''),
+          slim_example_decoder.Tensor(fields.TfExampleFields.image_class_label))
+    else:
+      label_handler = slim_example_decoder.Tensor('image/object/class/label')
+      image_label_handler = slim_example_decoder.Tensor(
+          fields.TfExampleFields.image_class_label)
+    self.items_to_handlers[
+        fields.InputDataFields.groundtruth_classes] = label_handler
+    self.items_to_handlers[
+        fields.InputDataFields.groundtruth_image_classes] = image_label_handler
+
+  def decode(self, tf_example_string_tensor):
+    """Decodes serialized tensorflow example and returns a tensor dictionary.
+
+    Args:
+      tf_example_string_tensor: a string tensor holding a serialized tensorflow
+        example proto.
+
+    Returns:
+      A dictionary of the following tensors.
+      fields.InputDataFields.image - 3D uint8 tensor of shape [None, None, 3]
+        containing image.
+      fields.InputDataFields.original_image_spatial_shape - 1D int32 tensor of
+        shape [2] containing shape of the image.
+      fields.InputDataFields.source_id - string tensor containing original
+        image id.
+      fields.InputDataFields.key - string tensor with unique sha256 hash key.
+      fields.InputDataFields.filename - string tensor with original dataset
+        filename.
+      fields.InputDataFields.groundtruth_boxes - 2D float32 tensor of shape
+        [None, 4] containing box corners.
+      fields.InputDataFields.groundtruth_classes - 1D int64 tensor of shape
+        [None] containing classes for the boxes.
+      fields.InputDataFields.groundtruth_weights - 1D float32 tensor of
+        shape [None] indicating the weights of groundtruth boxes.
+      fields.InputDataFields.groundtruth_area - 1D float32 tensor of shape
+        [None] containing containing object mask area in pixel squared.
+      fields.InputDataFields.groundtruth_is_crowd - 1D bool tensor of shape
+        [None] indicating if the boxes enclose a crowd.
+
+    Optional:
+      fields.InputDataFields.image_additional_channels - 3D uint8 tensor of
+        shape [None, None, num_additional_channels]. 1st dim is height; 2nd dim
+        is width; 3rd dim is the number of additional channels.
+      fields.InputDataFields.groundtruth_difficult - 1D bool tensor of shape
+        [None] indicating if the boxes represent `difficult` instances.
+      fields.InputDataFields.groundtruth_group_of - 1D bool tensor of shape
+        [None] indicating if the boxes represent `group_of` instances.
+      fields.InputDataFields.groundtruth_keypoints - 3D float32 tensor of
+        shape [None, None, 2] containing keypoints, where the coordinates of
+        the keypoints are ordered (y, x).
+      fields.InputDataFields.groundtruth_instance_masks - 3D float32 tensor of
+        shape [None, None, None] containing instance masks.
+      fields.InputDataFields.groundtruth_image_classes - 1D uint64 of shape
+        [None] containing classes for the boxes.
+    """
+    serialized_example = tf.reshape(tf_example_string_tensor, shape=[])
+    decoder = slim_example_decoder.TFExampleDecoder(self.keys_to_features,
+                                                    self.items_to_handlers)
+    keys = decoder.list_items()
+    tensors = decoder.decode(serialized_example, items=keys)
+    tensor_dict = dict(zip(keys, tensors))
+    is_crowd = fields.InputDataFields.groundtruth_is_crowd
+    tensor_dict[is_crowd] = tf.cast(tensor_dict[is_crowd], dtype=tf.bool)
+    tensor_dict[fields.InputDataFields.image].set_shape([None, None, 3])
+    tensor_dict[fields.InputDataFields.original_image_spatial_shape] = tf.shape(
+        tensor_dict[fields.InputDataFields.image])[:2]
+
+    if fields.InputDataFields.image_additional_channels in tensor_dict:
+      channels = tensor_dict[fields.InputDataFields.image_additional_channels]
+      channels = tf.squeeze(channels, axis=3)
+      channels = tf.transpose(channels, perm=[1, 2, 0])
+      tensor_dict[fields.InputDataFields.image_additional_channels] = channels
+
+    def default_groundtruth_weights():
+      return tf.ones(
+          [tf.shape(tensor_dict[fields.InputDataFields.groundtruth_boxes])[0]],
+          dtype=tf.float32)
+
+    tensor_dict[fields.InputDataFields.groundtruth_weights] = tf.cond(
+        tf.greater(
+            tf.shape(
+                tensor_dict[fields.InputDataFields.groundtruth_weights])[0],
+            0), lambda: tensor_dict[fields.InputDataFields.groundtruth_weights],
+        default_groundtruth_weights)
+    return tensor_dict
+
+  def _reshape_keypoints(self, keys_to_tensors):
+    """Reshape keypoints.
+
+    The instance segmentation masks are reshaped to [num_instances,
+    num_keypoints, 2].
+
+    Args:
+      keys_to_tensors: a dictionary from keys to tensors.
+
+    Returns:
+      A 3-D float tensor of shape [num_instances, num_keypoints, 2] with values
+        in {0, 1}.
+    """
+    y = keys_to_tensors['image/object/keypoint/y']
+    if isinstance(y, tf.SparseTensor):
+      y = tf.sparse_tensor_to_dense(y)
+    y = tf.expand_dims(y, 1)
+    x = keys_to_tensors['image/object/keypoint/x']
+    if isinstance(x, tf.SparseTensor):
+      x = tf.sparse_tensor_to_dense(x)
+    x = tf.expand_dims(x, 1)
+    keypoints = tf.concat([y, x], 1)
+    keypoints = tf.reshape(keypoints, [-1, self._num_keypoints, 2])
+    return keypoints
+
+  def _reshape_instance_masks(self, keys_to_tensors):
+    """Reshape instance segmentation masks.
+
+    The instance segmentation masks are reshaped to [num_instances, height,
+    width].
+
+    Args:
+      keys_to_tensors: a dictionary from keys to tensors.
+
+    Returns:
+      A 3-D float tensor of shape [num_instances, height, width] with values
+        in {0, 1}.
+    """
+    height = keys_to_tensors['image/height']
+    width = keys_to_tensors['image/width']
+    to_shape = tf.cast(tf.stack([-1, height, width]), tf.int32)
+    masks = keys_to_tensors['image/object/mask']
+    if isinstance(masks, tf.SparseTensor):
+      masks = tf.sparse_tensor_to_dense(masks)
+    masks = tf.reshape(tf.to_float(tf.greater(masks, 0.0)), to_shape)
+    return tf.cast(masks, tf.float32)
+
+  def _decode_png_instance_masks(self, keys_to_tensors):
+    """Decode PNG instance segmentation masks and stack into dense tensor.
+
+    The instance segmentation masks are reshaped to [num_instances, height,
+    width].
+
+    Args:
+      keys_to_tensors: a dictionary from keys to tensors.
+
+    Returns:
+      A 3-D float tensor of shape [num_instances, height, width] with values
+        in {0, 1}.
+    """
+
+    def decode_png_mask(image_buffer):
+      image = tf.squeeze(
+          tf.image.decode_image(image_buffer, channels=1), axis=2)
+      image.set_shape([None, None])
+      image = tf.to_float(tf.greater(image, 0))
+      return image
+
+    png_masks = keys_to_tensors['image/object/mask']
+    height = keys_to_tensors['image/height']
+    width = keys_to_tensors['image/width']
+    if isinstance(png_masks, tf.SparseTensor):
+      png_masks = tf.sparse_tensor_to_dense(png_masks, default_value='')
+    return tf.cond(
+        tf.greater(tf.size(png_masks), 0),
+        lambda: tf.map_fn(decode_png_mask, png_masks, dtype=tf.float32),
+        lambda: tf.zeros(tf.to_int32(tf.stack([0, height, width]))))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder_test.py
new file mode 100644
index 00000000..91fa8693
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/data_decoders/tf_example_decoder_test.py
@@ -0,0 +1,839 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.data_decoders.tf_example_decoder."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.framework import test_util
+from object_detection.core import standard_fields as fields
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import input_reader_pb2
+from object_detection.utils import dataset_util
+
+slim_example_decoder = tf.contrib.slim.tfexample_decoder
+
+
+class TfExampleDecoderTest(tf.test.TestCase):
+
+  def _EncodeImage(self, image_tensor, encoding_type='jpeg'):
+    with self.test_session():
+      if encoding_type == 'jpeg':
+        image_encoded = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+      elif encoding_type == 'png':
+        image_encoded = tf.image.encode_png(tf.constant(image_tensor)).eval()
+      else:
+        raise ValueError('Invalid encoding type.')
+    return image_encoded
+
+  def _DecodeImage(self, image_encoded, encoding_type='jpeg'):
+    with self.test_session():
+      if encoding_type == 'jpeg':
+        image_decoded = tf.image.decode_jpeg(tf.constant(image_encoded)).eval()
+      elif encoding_type == 'png':
+        image_decoded = tf.image.decode_png(tf.constant(image_encoded)).eval()
+      else:
+        raise ValueError('Invalid encoding type.')
+    return image_decoded
+
+  def testDecodeAdditionalChannels(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+
+    additional_channel_tensor = np.random.randint(
+        256, size=(4, 5, 1)).astype(np.uint8)
+    encoded_additional_channel = self._EncodeImage(additional_channel_tensor)
+    decoded_additional_channel = self._DecodeImage(encoded_additional_channel)
+
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/additional_channels/encoded':
+                    dataset_util.bytes_list_feature(
+                        [encoded_additional_channel] * 2),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/source_id':
+                    dataset_util.bytes_feature('image_id'),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        num_additional_channels=2)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+      self.assertAllEqual(
+          np.concatenate([decoded_additional_channel] * 2, axis=2),
+          tensor_dict[fields.InputDataFields.image_additional_channels])
+
+  def testDecodeJpegImage(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    decoded_jpeg = self._DecodeImage(encoded_jpeg)
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+                'image/format': dataset_util.bytes_feature('jpeg'),
+                'image/source_id': dataset_util.bytes_feature('image_id'),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
+                         get_shape().as_list()), [None, None, 3])
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.
+                                     original_image_spatial_shape].
+                         get_shape().as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(decoded_jpeg, tensor_dict[fields.InputDataFields.image])
+    self.assertAllEqual([4, 5], tensor_dict[fields.InputDataFields.
+                                            original_image_spatial_shape])
+    self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
+
+  def testDecodeImageKeyAndFilename(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+                'image/key/sha256': dataset_util.bytes_feature('abc'),
+                'image/filename': dataset_util.bytes_feature('filename')
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertEqual('abc', tensor_dict[fields.InputDataFields.key])
+    self.assertEqual('filename', tensor_dict[fields.InputDataFields.filename])
+
+  def testDecodePngImage(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_png = self._EncodeImage(image_tensor, encoding_type='png')
+    decoded_png = self._DecodeImage(encoded_png, encoding_type='png')
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded': dataset_util.bytes_feature(encoded_png),
+                'image/format': dataset_util.bytes_feature('png'),
+                'image/source_id': dataset_util.bytes_feature('image_id')
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
+                         get_shape().as_list()), [None, None, 3])
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.
+                                     original_image_spatial_shape].
+                         get_shape().as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
+    self.assertAllEqual([4, 5], tensor_dict[fields.InputDataFields.
+                                            original_image_spatial_shape])
+    self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
+
+  def testDecodePngInstanceMasks(self):
+    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
+    mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
+    encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
+    decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
+    encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
+    decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
+    encoded_masks = [encoded_png_1, encoded_png_2]
+    decoded_masks = np.stack([decoded_png_1, decoded_png_2])
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/mask':
+                    dataset_util.bytes_list_feature(encoded_masks)
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        decoded_masks,
+        tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
+
+  def testDecodeEmptyPngInstanceMasks(self):
+    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    encoded_masks = []
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/mask':
+                    dataset_util.bytes_list_feature(encoded_masks),
+                'image/height':
+                    dataset_util.int64_feature(10),
+                'image/width':
+                    dataset_util.int64_feature(10),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+      self.assertAllEqual(
+          tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape,
+          [0, 10, 10])
+
+  def testDecodeBoundingBox(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_ymins = [0.0, 4.0]
+    bbox_xmins = [1.0, 5.0]
+    bbox_ymaxs = [2.0, 6.0]
+    bbox_xmaxs = [3.0, 7.0]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/bbox/ymin':
+                    dataset_util.float_list_feature(bbox_ymins),
+                'image/object/bbox/xmin':
+                    dataset_util.float_list_feature(bbox_xmins),
+                'image/object/bbox/ymax':
+                    dataset_util.float_list_feature(bbox_ymaxs),
+                'image/object/bbox/xmax':
+                    dataset_util.float_list_feature(bbox_xmaxs),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes]
+                         .get_shape().as_list()), [None, 4])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    expected_boxes = np.vstack([bbox_ymins, bbox_xmins, bbox_ymaxs,
+                                bbox_xmaxs]).transpose()
+    self.assertAllEqual(expected_boxes,
+                        tensor_dict[fields.InputDataFields.groundtruth_boxes])
+
+  @test_util.enable_c_shapes
+  def testDecodeKeypoint(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_ymins = [0.0, 4.0]
+    bbox_xmins = [1.0, 5.0]
+    bbox_ymaxs = [2.0, 6.0]
+    bbox_xmaxs = [3.0, 7.0]
+    keypoint_ys = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
+    keypoint_xs = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/bbox/ymin':
+                    dataset_util.float_list_feature(bbox_ymins),
+                'image/object/bbox/xmin':
+                    dataset_util.float_list_feature(bbox_xmins),
+                'image/object/bbox/ymax':
+                    dataset_util.float_list_feature(bbox_ymaxs),
+                'image/object/bbox/xmax':
+                    dataset_util.float_list_feature(bbox_xmaxs),
+                'image/object/keypoint/y':
+                    dataset_util.float_list_feature(keypoint_ys),
+                'image/object/keypoint/x':
+                    dataset_util.float_list_feature(keypoint_xs),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(num_keypoints=3)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes]
+                         .get_shape().as_list()), [None, 4])
+    self.assertAllEqual(
+        (tensor_dict[fields.InputDataFields.groundtruth_keypoints].get_shape()
+         .as_list()), [2, 3, 2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    expected_boxes = np.vstack([bbox_ymins, bbox_xmins, bbox_ymaxs,
+                                bbox_xmaxs]).transpose()
+    self.assertAllEqual(expected_boxes,
+                        tensor_dict[fields.InputDataFields.groundtruth_boxes])
+
+    expected_keypoints = (
+        np.vstack([keypoint_ys, keypoint_xs]).transpose().reshape((2, 3, 2)))
+    self.assertAllEqual(
+        expected_keypoints,
+        tensor_dict[fields.InputDataFields.groundtruth_keypoints])
+
+  def testDecodeDefaultGroundtruthWeights(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_ymins = [0.0, 4.0]
+    bbox_xmins = [1.0, 5.0]
+    bbox_ymaxs = [2.0, 6.0]
+    bbox_xmaxs = [3.0, 7.0]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/bbox/ymin':
+                    dataset_util.float_list_feature(bbox_ymins),
+                'image/object/bbox/xmin':
+                    dataset_util.float_list_feature(bbox_xmins),
+                'image/object/bbox/ymax':
+                    dataset_util.float_list_feature(bbox_ymaxs),
+                'image/object/bbox/xmax':
+                    dataset_util.float_list_feature(bbox_xmaxs),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes]
+                         .get_shape().as_list()), [None, 4])
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllClose(tensor_dict[fields.InputDataFields.groundtruth_weights],
+                        np.ones(2, dtype=np.float32))
+
+  @test_util.enable_c_shapes
+  def testDecodeObjectLabel(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes = [0, 1]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/class/label':
+                    dataset_util.int64_list_feature(bbox_classes),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [2])
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(bbox_classes,
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  def testDecodeObjectLabelNoText(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes = [1, 2]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/class/label':
+                    dataset_util.int64_list_feature(bbox_classes),
+            })).SerializeToString()
+    label_map_string = """
+      item {
+        id:1
+        name:'cat'
+      }
+      item {
+        id:2
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [None])
+
+    init = tf.tables_initializer()
+    with self.test_session() as sess:
+      sess.run(init)
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(bbox_classes,
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  def testDecodeObjectLabelUnrecognizedName(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes_text = ['cat', 'cheetah']
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/class/text':
+                    dataset_util.bytes_list_feature(bbox_classes_text),
+            })).SerializeToString()
+
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [None])
+
+    with self.test_session() as sess:
+      sess.run(tf.tables_initializer())
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual([2, -1],
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  def testDecodeObjectLabelWithMappingWithDisplayName(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes_text = ['cat', 'dog']
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/class/text':
+                    dataset_util.bytes_list_feature(bbox_classes_text),
+            })).SerializeToString()
+
+    label_map_string = """
+      item {
+        id:3
+        display_name:'cat'
+      }
+      item {
+        id:1
+        display_name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [None])
+
+    with self.test_session() as sess:
+      sess.run(tf.tables_initializer())
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual([3, 1],
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  def testDecodeObjectLabelWithMappingWithName(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes_text = ['cat', 'dog']
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/class/text':
+                    dataset_util.bytes_list_feature(bbox_classes_text),
+            })).SerializeToString()
+
+    label_map_string = """
+      item {
+        id:3
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [None])
+
+    with self.test_session() as sess:
+      sess.run(tf.tables_initializer())
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual([3, 1],
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  @test_util.enable_c_shapes
+  def testDecodeObjectArea(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_area = [100., 174.]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/area':
+                    dataset_util.float_list_feature(object_area),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area]
+                         .get_shape().as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(object_area,
+                        tensor_dict[fields.InputDataFields.groundtruth_area])
+
+  @test_util.enable_c_shapes
+  def testDecodeObjectIsCrowd(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_is_crowd = [0, 1]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/is_crowd':
+                    dataset_util.int64_list_feature(object_is_crowd),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual(
+        (tensor_dict[fields.InputDataFields.groundtruth_is_crowd].get_shape()
+         .as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        [bool(item) for item in object_is_crowd],
+        tensor_dict[fields.InputDataFields.groundtruth_is_crowd])
+
+  @test_util.enable_c_shapes
+  def testDecodeObjectDifficult(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_difficult = [0, 1]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/difficult':
+                    dataset_util.int64_list_feature(object_difficult),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual(
+        (tensor_dict[fields.InputDataFields.groundtruth_difficult].get_shape()
+         .as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        [bool(item) for item in object_difficult],
+        tensor_dict[fields.InputDataFields.groundtruth_difficult])
+
+  @test_util.enable_c_shapes
+  def testDecodeObjectGroupOf(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_group_of = [0, 1]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/group_of':
+                    dataset_util.int64_list_feature(object_group_of),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual(
+        (tensor_dict[fields.InputDataFields.groundtruth_group_of].get_shape()
+         .as_list()), [2])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        [bool(item) for item in object_group_of],
+        tensor_dict[fields.InputDataFields.groundtruth_group_of])
+
+  def testDecodeObjectWeight(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_weights = [0.75, 1.0]
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/object/weight':
+                    dataset_util.float_list_feature(object_weights),
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_weights]
+                         .get_shape().as_list()), [None])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(object_weights,
+                        tensor_dict[fields.InputDataFields.groundtruth_weights])
+
+  @test_util.enable_c_shapes
+  def testDecodeInstanceSegmentation(self):
+    num_instances = 4
+    image_height = 5
+    image_width = 3
+
+    # Randomly generate image.
+    image_tensor = np.random.randint(
+        256, size=(image_height, image_width, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+
+    # Randomly generate instance segmentation masks.
+    instance_masks = (
+        np.random.randint(2, size=(num_instances, image_height,
+                                   image_width)).astype(np.float32))
+    instance_masks_flattened = np.reshape(instance_masks, [-1])
+
+    # Randomly generate class labels for each instance.
+    object_classes = np.random.randint(
+        100, size=(num_instances)).astype(np.int64)
+
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/height':
+                    dataset_util.int64_feature(image_height),
+                'image/width':
+                    dataset_util.int64_feature(image_width),
+                'image/object/mask':
+                    dataset_util.float_list_feature(instance_masks_flattened),
+                'image/object/class/label':
+                    dataset_util.int64_list_feature(object_classes)
+            })).SerializeToString()
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=True)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual(
+        (tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
+         .get_shape().as_list()), [4, 5, 3])
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [4])
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        instance_masks.astype(np.float32),
+        tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
+    self.assertAllEqual(object_classes,
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  def testInstancesNotAvailableByDefault(self):
+    num_instances = 4
+    image_height = 5
+    image_width = 3
+    # Randomly generate image.
+    image_tensor = np.random.randint(
+        256, size=(image_height, image_width, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+
+    # Randomly generate instance segmentation masks.
+    instance_masks = (
+        np.random.randint(2, size=(num_instances, image_height,
+                                   image_width)).astype(np.float32))
+    instance_masks_flattened = np.reshape(instance_masks, [-1])
+
+    # Randomly generate class labels for each instance.
+    object_classes = np.random.randint(
+        100, size=(num_instances)).astype(np.int64)
+
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/height':
+                    dataset_util.int64_feature(image_height),
+                'image/width':
+                    dataset_util.int64_feature(image_width),
+                'image/object/mask':
+                    dataset_util.float_list_feature(instance_masks_flattened),
+                'image/object/class/label':
+                    dataset_util.int64_list_feature(object_classes)
+            })).SerializeToString()
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+    self.assertTrue(
+        fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
+
+  def testDecodeImageLabels(self):
+    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+                'image/format': dataset_util.bytes_feature('jpeg'),
+                'image/class/label': dataset_util.int64_list_feature([1, 2]),
+            })).SerializeToString()
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+    self.assertTrue(
+        fields.InputDataFields.groundtruth_image_classes in tensor_dict)
+    self.assertAllEqual(
+        tensor_dict[fields.InputDataFields.groundtruth_image_classes],
+        np.array([1, 2]))
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    dataset_util.bytes_feature(encoded_jpeg),
+                'image/format':
+                    dataset_util.bytes_feature('jpeg'),
+                'image/class/text':
+                    dataset_util.bytes_list_feature(['dog', 'cat']),
+            })).SerializeToString()
+    label_map_string = """
+      item {
+        id:3
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+    with self.test_session() as sess:
+      sess.run(tf.tables_initializer())
+      tensor_dict = sess.run(tensor_dict)
+    self.assertTrue(
+        fields.InputDataFields.groundtruth_image_classes in tensor_dict)
+    self.assertAllEqual(
+        tensor_dict[fields.InputDataFields.groundtruth_image_classes],
+        np.array([1, 3]))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record.py
new file mode 100644
index 00000000..7f2bd1fb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record.py
@@ -0,0 +1,282 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Convert raw COCO dataset to TFRecord for object_detection.
+
+Please note that this tool creates sharded output files.
+
+Example usage:
+    python create_coco_tf_record.py --logtostderr \
+      --train_image_dir="${TRAIN_IMAGE_DIR}" \
+      --val_image_dir="${VAL_IMAGE_DIR}" \
+      --test_image_dir="${TEST_IMAGE_DIR}" \
+      --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \
+      --val_annotations_file="${VAL_ANNOTATIONS_FILE}" \
+      --testdev_annotations_file="${TESTDEV_ANNOTATIONS_FILE}" \
+      --output_dir="${OUTPUT_DIR}"
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import hashlib
+import io
+import json
+import os
+import contextlib2
+import numpy as np
+import PIL.Image
+
+from pycocotools import mask
+import tensorflow as tf
+
+from object_detection.dataset_tools import tf_record_creation_util
+from object_detection.utils import dataset_util
+from object_detection.utils import label_map_util
+
+
+flags = tf.app.flags
+tf.flags.DEFINE_boolean('include_masks', False,
+                        'Whether to include instance segmentations masks '
+                        '(PNG encoded) in the result. default: False.')
+tf.flags.DEFINE_string('train_image_dir', '',
+                       'Training image directory.')
+tf.flags.DEFINE_string('val_image_dir', '',
+                       'Validation image directory.')
+tf.flags.DEFINE_string('test_image_dir', '',
+                       'Test image directory.')
+tf.flags.DEFINE_string('train_annotations_file', '',
+                       'Training annotations JSON file.')
+tf.flags.DEFINE_string('val_annotations_file', '',
+                       'Validation annotations JSON file.')
+tf.flags.DEFINE_string('testdev_annotations_file', '',
+                       'Test-dev annotations JSON file.')
+tf.flags.DEFINE_string('output_dir', '/tmp/', 'Output data directory.')
+
+FLAGS = flags.FLAGS
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+
+def create_tf_example(image,
+                      annotations_list,
+                      image_dir,
+                      category_index,
+                      include_masks=False):
+  """Converts image and annotations to a tf.Example proto.
+
+  Args:
+    image: dict with keys:
+      [u'license', u'file_name', u'coco_url', u'height', u'width',
+      u'date_captured', u'flickr_url', u'id']
+    annotations_list:
+      list of dicts with keys:
+      [u'segmentation', u'area', u'iscrowd', u'image_id',
+      u'bbox', u'category_id', u'id']
+      Notice that bounding box coordinates in the official COCO dataset are
+      given as [x, y, width, height] tuples using absolute coordinates where
+      x, y represent the top-left (0-indexed) corner.  This function converts
+      to the format expected by the Tensorflow Object Detection API (which is
+      which is [ymin, xmin, ymax, xmax] with coordinates normalized relative
+      to image size).
+    image_dir: directory containing the image files.
+    category_index: a dict containing COCO category information keyed
+      by the 'id' field of each category.  See the
+      label_map_util.create_category_index function.
+    include_masks: Whether to include instance segmentations masks
+      (PNG encoded) in the result. default: False.
+  Returns:
+    example: The converted tf.Example
+    num_annotations_skipped: Number of (invalid) annotations that were ignored.
+
+  Raises:
+    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
+  """
+  image_height = image['height']
+  image_width = image['width']
+  filename = image['file_name']
+  image_id = image['id']
+
+  full_path = os.path.join(image_dir, filename)
+  with tf.gfile.GFile(full_path, 'rb') as fid:
+    encoded_jpg = fid.read()
+  encoded_jpg_io = io.BytesIO(encoded_jpg)
+  image = PIL.Image.open(encoded_jpg_io)
+  key = hashlib.sha256(encoded_jpg).hexdigest()
+
+  xmin = []
+  xmax = []
+  ymin = []
+  ymax = []
+  is_crowd = []
+  category_names = []
+  category_ids = []
+  area = []
+  encoded_mask_png = []
+  num_annotations_skipped = 0
+  for object_annotations in annotations_list:
+    (x, y, width, height) = tuple(object_annotations['bbox'])
+    if width <= 0 or height <= 0:
+      num_annotations_skipped += 1
+      continue
+    if x + width > image_width or y + height > image_height:
+      num_annotations_skipped += 1
+      continue
+    xmin.append(float(x) / image_width)
+    xmax.append(float(x + width) / image_width)
+    ymin.append(float(y) / image_height)
+    ymax.append(float(y + height) / image_height)
+    is_crowd.append(object_annotations['iscrowd'])
+    category_id = int(object_annotations['category_id'])
+    category_ids.append(category_id)
+    category_names.append(category_index[category_id]['name'].encode('utf8'))
+    area.append(object_annotations['area'])
+
+    if include_masks:
+      run_len_encoding = mask.frPyObjects(object_annotations['segmentation'],
+                                          image_height, image_width)
+      binary_mask = mask.decode(run_len_encoding)
+      if not object_annotations['iscrowd']:
+        binary_mask = np.amax(binary_mask, axis=2)
+      pil_image = PIL.Image.fromarray(binary_mask)
+      output_io = io.BytesIO()
+      pil_image.save(output_io, format='PNG')
+      encoded_mask_png.append(output_io.getvalue())
+  feature_dict = {
+      'image/height':
+          dataset_util.int64_feature(image_height),
+      'image/width':
+          dataset_util.int64_feature(image_width),
+      'image/filename':
+          dataset_util.bytes_feature(filename.encode('utf8')),
+      'image/source_id':
+          dataset_util.bytes_feature(str(image_id).encode('utf8')),
+      'image/key/sha256':
+          dataset_util.bytes_feature(key.encode('utf8')),
+      'image/encoded':
+          dataset_util.bytes_feature(encoded_jpg),
+      'image/format':
+          dataset_util.bytes_feature('jpeg'.encode('utf8')),
+      'image/object/bbox/xmin':
+          dataset_util.float_list_feature(xmin),
+      'image/object/bbox/xmax':
+          dataset_util.float_list_feature(xmax),
+      'image/object/bbox/ymin':
+          dataset_util.float_list_feature(ymin),
+      'image/object/bbox/ymax':
+          dataset_util.float_list_feature(ymax),
+      'image/object/class/text':
+          dataset_util.bytes_list_feature(category_names),
+      'image/object/is_crowd':
+          dataset_util.int64_list_feature(is_crowd),
+      'image/object/area':
+          dataset_util.float_list_feature(area),
+  }
+  if include_masks:
+    feature_dict['image/object/mask'] = (
+        dataset_util.bytes_list_feature(encoded_mask_png))
+  example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
+  return key, example, num_annotations_skipped
+
+
+def _create_tf_record_from_coco_annotations(
+    annotations_file, image_dir, output_path, include_masks, num_shards):
+  """Loads COCO annotation json files and converts to tf.Record format.
+
+  Args:
+    annotations_file: JSON file containing bounding box annotations.
+    image_dir: Directory containing the image files.
+    output_path: Path to output tf.Record file.
+    include_masks: Whether to include instance segmentations masks
+      (PNG encoded) in the result. default: False.
+    num_shards: number of output file shards.
+  """
+  with contextlib2.ExitStack() as tf_record_close_stack, \
+      tf.gfile.GFile(annotations_file, 'r') as fid:
+    output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
+        tf_record_close_stack, output_path, num_shards)
+    groundtruth_data = json.load(fid)
+    images = groundtruth_data['images']
+    category_index = label_map_util.create_category_index(
+        groundtruth_data['categories'])
+
+    annotations_index = {}
+    if 'annotations' in groundtruth_data:
+      tf.logging.info(
+          'Found groundtruth annotations. Building annotations index.')
+      for annotation in groundtruth_data['annotations']:
+        image_id = annotation['image_id']
+        if image_id not in annotations_index:
+          annotations_index[image_id] = []
+        annotations_index[image_id].append(annotation)
+    missing_annotation_count = 0
+    for image in images:
+      image_id = image['id']
+      if image_id not in annotations_index:
+        missing_annotation_count += 1
+        annotations_index[image_id] = []
+    tf.logging.info('%d images are missing annotations.',
+                    missing_annotation_count)
+
+    total_num_annotations_skipped = 0
+    for idx, image in enumerate(images):
+      if idx % 100 == 0:
+        tf.logging.info('On image %d of %d', idx, len(images))
+      annotations_list = annotations_index[image['id']]
+      _, tf_example, num_annotations_skipped = create_tf_example(
+          image, annotations_list, image_dir, category_index, include_masks)
+      total_num_annotations_skipped += num_annotations_skipped
+      shard_idx = idx % num_shards
+      output_tfrecords[shard_idx].write(tf_example.SerializeToString())
+    tf.logging.info('Finished writing, skipped %d annotations.',
+                    total_num_annotations_skipped)
+
+
+def main(_):
+  assert FLAGS.train_image_dir, '`train_image_dir` missing.'
+  assert FLAGS.val_image_dir, '`val_image_dir` missing.'
+  assert FLAGS.test_image_dir, '`test_image_dir` missing.'
+  assert FLAGS.train_annotations_file, '`train_annotations_file` missing.'
+  assert FLAGS.val_annotations_file, '`val_annotations_file` missing.'
+  assert FLAGS.testdev_annotations_file, '`testdev_annotations_file` missing.'
+
+  if not tf.gfile.IsDirectory(FLAGS.output_dir):
+    tf.gfile.MakeDirs(FLAGS.output_dir)
+  train_output_path = os.path.join(FLAGS.output_dir, 'coco_train.record')
+  val_output_path = os.path.join(FLAGS.output_dir, 'coco_val.record')
+  testdev_output_path = os.path.join(FLAGS.output_dir, 'coco_testdev.record')
+
+  _create_tf_record_from_coco_annotations(
+      FLAGS.train_annotations_file,
+      FLAGS.train_image_dir,
+      train_output_path,
+      FLAGS.include_masks,
+      num_shards=100)
+  _create_tf_record_from_coco_annotations(
+      FLAGS.val_annotations_file,
+      FLAGS.val_image_dir,
+      val_output_path,
+      FLAGS.include_masks,
+      num_shards=10)
+  _create_tf_record_from_coco_annotations(
+      FLAGS.testdev_annotations_file,
+      FLAGS.test_image_dir,
+      testdev_output_path,
+      FLAGS.include_masks,
+      num_shards=100)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record_test.py
new file mode 100644
index 00000000..b99fd12b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_coco_tf_record_test.py
@@ -0,0 +1,251 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for create_coco_tf_record.py."""
+
+import io
+import json
+import os
+
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+from object_detection.dataset_tools import create_coco_tf_record
+
+
+class CreateCocoTFRecordTest(tf.test.TestCase):
+
+  def _assertProtoEqual(self, proto_field, expectation):
+    """Helper function to assert if a proto field equals some value.
+
+    Args:
+      proto_field: The protobuf field to compare.
+      expectation: The expected value of the protobuf field.
+    """
+    proto_list = [p for p in proto_field]
+    self.assertListEqual(proto_list, expectation)
+
+  def test_create_tf_example(self):
+    image_file_name = 'tmp_image.jpg'
+    image_data = np.random.rand(256, 256, 3)
+    tmp_dir = self.get_temp_dir()
+    save_path = os.path.join(tmp_dir, image_file_name)
+    image = PIL.Image.fromarray(image_data, 'RGB')
+    image.save(save_path)
+
+    image = {
+        'file_name': image_file_name,
+        'height': 256,
+        'width': 256,
+        'id': 11,
+    }
+
+    annotations_list = [{
+        'area': .5,
+        'iscrowd': False,
+        'image_id': 11,
+        'bbox': [64, 64, 128, 128],
+        'category_id': 2,
+        'id': 1000,
+    }]
+
+    image_dir = tmp_dir
+    category_index = {
+        1: {
+            'name': 'dog',
+            'id': 1
+        },
+        2: {
+            'name': 'cat',
+            'id': 2
+        },
+        3: {
+            'name': 'human',
+            'id': 3
+        }
+    }
+
+    (_, example,
+     num_annotations_skipped) = create_coco_tf_record.create_tf_example(
+         image, annotations_list, image_dir, category_index)
+
+    self.assertEqual(num_annotations_skipped, 0)
+    self._assertProtoEqual(
+        example.features.feature['image/height'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/width'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/filename'].bytes_list.value,
+        [image_file_name])
+    self._assertProtoEqual(
+        example.features.feature['image/source_id'].bytes_list.value,
+        [str(image['id'])])
+    self._assertProtoEqual(
+        example.features.feature['image/format'].bytes_list.value, ['jpeg'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/text'].bytes_list.value,
+        ['cat'])
+
+  def test_create_tf_example_with_instance_masks(self):
+    image_file_name = 'tmp_image.jpg'
+    image_data = np.random.rand(8, 8, 3)
+    tmp_dir = self.get_temp_dir()
+    save_path = os.path.join(tmp_dir, image_file_name)
+    image = PIL.Image.fromarray(image_data, 'RGB')
+    image.save(save_path)
+
+    image = {
+        'file_name': image_file_name,
+        'height': 8,
+        'width': 8,
+        'id': 11,
+    }
+
+    annotations_list = [{
+        'area': .5,
+        'iscrowd': False,
+        'image_id': 11,
+        'bbox': [0, 0, 8, 8],
+        'segmentation': [[4, 0, 0, 0, 0, 4], [8, 4, 4, 8, 8, 8]],
+        'category_id': 1,
+        'id': 1000,
+    }]
+
+    image_dir = tmp_dir
+    category_index = {
+        1: {
+            'name': 'dog',
+            'id': 1
+        },
+    }
+
+    (_, example,
+     num_annotations_skipped) = create_coco_tf_record.create_tf_example(
+         image, annotations_list, image_dir, category_index, include_masks=True)
+
+    self.assertEqual(num_annotations_skipped, 0)
+    self._assertProtoEqual(
+        example.features.feature['image/height'].int64_list.value, [8])
+    self._assertProtoEqual(
+        example.features.feature['image/width'].int64_list.value, [8])
+    self._assertProtoEqual(
+        example.features.feature['image/filename'].bytes_list.value,
+        [image_file_name])
+    self._assertProtoEqual(
+        example.features.feature['image/source_id'].bytes_list.value,
+        [str(image['id'])])
+    self._assertProtoEqual(
+        example.features.feature['image/format'].bytes_list.value, ['jpeg'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmin'].float_list.value,
+        [0])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymin'].float_list.value,
+        [0])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmax'].float_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymax'].float_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/text'].bytes_list.value,
+        ['dog'])
+    encoded_mask_pngs = [
+        io.BytesIO(encoded_masks) for encoded_masks in example.features.feature[
+            'image/object/mask'].bytes_list.value
+    ]
+    pil_masks = [
+        np.array(PIL.Image.open(encoded_mask_png))
+        for encoded_mask_png in encoded_mask_pngs
+    ]
+    self.assertTrue(len(pil_masks) == 1)
+    self.assertAllEqual(pil_masks[0],
+                        [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
+                         [1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 1],
+                         [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1]])
+
+  def test_create_sharded_tf_record(self):
+    tmp_dir = self.get_temp_dir()
+    image_paths = ['tmp1_image.jpg', 'tmp2_image.jpg']
+    for image_path in image_paths:
+      image_data = np.random.rand(256, 256, 3)
+      save_path = os.path.join(tmp_dir, image_path)
+      image = PIL.Image.fromarray(image_data, 'RGB')
+      image.save(save_path)
+
+    images = [{
+        'file_name': image_paths[0],
+        'height': 256,
+        'width': 256,
+        'id': 11,
+    }, {
+        'file_name': image_paths[1],
+        'height': 256,
+        'width': 256,
+        'id': 12,
+    }]
+
+    annotations = [{
+        'area': .5,
+        'iscrowd': False,
+        'image_id': 11,
+        'bbox': [64, 64, 128, 128],
+        'category_id': 2,
+        'id': 1000,
+    }]
+
+    category_index = [{
+        'name': 'dog',
+        'id': 1
+    }, {
+        'name': 'cat',
+        'id': 2
+    }, {
+        'name': 'human',
+        'id': 3
+    }]
+    groundtruth_data = {'images': images, 'annotations': annotations,
+                        'categories': category_index}
+    annotation_file = os.path.join(tmp_dir, 'annotation.json')
+    with open(annotation_file, 'w') as annotation_fid:
+      json.dump(groundtruth_data, annotation_fid)
+
+    output_path = os.path.join(tmp_dir, 'out.record')
+    create_coco_tf_record._create_tf_record_from_coco_annotations(
+        annotation_file,
+        tmp_dir,
+        output_path,
+        False,
+        2)
+    self.assertTrue(os.path.exists(output_path + '-00000-of-00002'))
+    self.assertTrue(os.path.exists(output_path + '-00001-of-00002'))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record.py
new file mode 100644
index 00000000..c612db99
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record.py
@@ -0,0 +1,310 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Convert raw KITTI detection dataset to TFRecord for object_detection.
+
+Converts KITTI detection dataset to TFRecords with a standard format allowing
+  to use this dataset to train object detectors. The raw dataset can be
+  downloaded from:
+  http://kitti.is.tue.mpg.de/kitti/data_object_image_2.zip.
+  http://kitti.is.tue.mpg.de/kitti/data_object_label_2.zip
+  Permission can be requested at the main website.
+
+  KITTI detection dataset contains 7481 training images. Using this code with
+  the default settings will set aside the first 500 images as a validation set.
+  This can be altered using the flags, see details below.
+
+Example usage:
+    python object_detection/dataset_tools/create_kitti_tf_record.py \
+        --data_dir=/home/user/kitti \
+        --output_path=/home/user/kitti.record
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import hashlib
+import io
+import os
+
+import numpy as np
+import PIL.Image as pil
+import tensorflow as tf
+
+from object_detection.utils import dataset_util
+from object_detection.utils import label_map_util
+from object_detection.utils.np_box_ops import iou
+
+tf.app.flags.DEFINE_string('data_dir', '', 'Location of root directory for the '
+                           'data. Folder structure is assumed to be:'
+                           '<data_dir>/training/label_2 (annotations) and'
+                           '<data_dir>/data_object_image_2/training/image_2'
+                           '(images).')
+tf.app.flags.DEFINE_string('output_path', '', 'Path to which TFRecord files'
+                           'will be written. The TFRecord with the training set'
+                           'will be located at: <output_path>_train.tfrecord.'
+                           'And the TFRecord with the validation set will be'
+                           'located at: <output_path>_val.tfrecord')
+tf.app.flags.DEFINE_string('classes_to_use', 'car,pedestrian,dontcare',
+                           'Comma separated list of class names that will be'
+                           'used. Adding the dontcare class will remove all'
+                           'bboxs in the dontcare regions.')
+tf.app.flags.DEFINE_string('label_map_path', 'data/kitti_label_map.pbtxt',
+                           'Path to label map proto.')
+tf.app.flags.DEFINE_integer('validation_set_size', '500', 'Number of images to'
+                            'be used as a validation set.')
+FLAGS = tf.app.flags.FLAGS
+
+
+def convert_kitti_to_tfrecords(data_dir, output_path, classes_to_use,
+                               label_map_path, validation_set_size):
+  """Convert the KITTI detection dataset to TFRecords.
+
+  Args:
+    data_dir: The full path to the unzipped folder containing the unzipped data
+      from data_object_image_2 and data_object_label_2.zip.
+      Folder structure is assumed to be: data_dir/training/label_2 (annotations)
+      and data_dir/data_object_image_2/training/image_2 (images).
+    output_path: The path to which TFRecord files will be written. The TFRecord
+      with the training set will be located at: <output_path>_train.tfrecord
+      And the TFRecord with the validation set will be located at:
+      <output_path>_val.tfrecord
+    classes_to_use: List of strings naming the classes for which data should be
+      converted. Use the same names as presented in the KIITI README file.
+      Adding dontcare class will remove all other bounding boxes that overlap
+      with areas marked as dontcare regions.
+    label_map_path: Path to label map proto
+    validation_set_size: How many images should be left as the validation set.
+      (Ffirst `validation_set_size` examples are selected to be in the
+      validation set).
+  """
+  label_map_dict = label_map_util.get_label_map_dict(label_map_path)
+  train_count = 0
+  val_count = 0
+
+  annotation_dir = os.path.join(data_dir,
+                                'training',
+                                'label_2')
+
+  image_dir = os.path.join(data_dir,
+                           'data_object_image_2',
+                           'training',
+                           'image_2')
+
+  train_writer = tf.python_io.TFRecordWriter('%s_train.tfrecord'%
+                                             output_path)
+  val_writer = tf.python_io.TFRecordWriter('%s_val.tfrecord'%
+                                           output_path)
+
+  images = sorted(tf.gfile.ListDirectory(image_dir))
+  for img_name in images:
+    img_num = int(img_name.split('.')[0])
+    is_validation_img = img_num < validation_set_size
+    img_anno = read_annotation_file(os.path.join(annotation_dir,
+                                                 str(img_num).zfill(6)+'.txt'))
+
+    image_path = os.path.join(image_dir, img_name)
+
+    # Filter all bounding boxes of this frame that are of a legal class, and
+    # don't overlap with a dontcare region.
+    # TODO(talremez) filter out targets that are truncated or heavily occluded.
+    annotation_for_image = filter_annotations(img_anno, classes_to_use)
+
+    example = prepare_example(image_path, annotation_for_image, label_map_dict)
+    if is_validation_img:
+      val_writer.write(example.SerializeToString())
+      val_count += 1
+    else:
+      train_writer.write(example.SerializeToString())
+      train_count += 1
+
+  train_writer.close()
+  val_writer.close()
+
+
+def prepare_example(image_path, annotations, label_map_dict):
+  """Converts a dictionary with annotations for an image to tf.Example proto.
+
+  Args:
+    image_path: The complete path to image.
+    annotations: A dictionary representing the annotation of a single object
+      that appears in the image.
+    label_map_dict: A map from string label names to integer ids.
+
+  Returns:
+    example: The converted tf.Example.
+  """
+  with tf.gfile.GFile(image_path, 'rb') as fid:
+    encoded_png = fid.read()
+  encoded_png_io = io.BytesIO(encoded_png)
+  image = pil.open(encoded_png_io)
+  image = np.asarray(image)
+
+  key = hashlib.sha256(encoded_png).hexdigest()
+
+  width = int(image.shape[1])
+  height = int(image.shape[0])
+
+  xmin_norm = annotations['2d_bbox_left'] / float(width)
+  ymin_norm = annotations['2d_bbox_top'] / float(height)
+  xmax_norm = annotations['2d_bbox_right'] / float(width)
+  ymax_norm = annotations['2d_bbox_bottom'] / float(height)
+
+  difficult_obj = [0]*len(xmin_norm)
+
+  example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': dataset_util.int64_feature(height),
+      'image/width': dataset_util.int64_feature(width),
+      'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')),
+      'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')),
+      'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
+      'image/encoded': dataset_util.bytes_feature(encoded_png),
+      'image/format': dataset_util.bytes_feature('png'.encode('utf8')),
+      'image/object/bbox/xmin': dataset_util.float_list_feature(xmin_norm),
+      'image/object/bbox/xmax': dataset_util.float_list_feature(xmax_norm),
+      'image/object/bbox/ymin': dataset_util.float_list_feature(ymin_norm),
+      'image/object/bbox/ymax': dataset_util.float_list_feature(ymax_norm),
+      'image/object/class/text': dataset_util.bytes_list_feature(
+          [x.encode('utf8') for x in annotations['type']]),
+      'image/object/class/label': dataset_util.int64_list_feature(
+          [label_map_dict[x] for x in annotations['type']]),
+      'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
+      'image/object/truncated': dataset_util.float_list_feature(
+          annotations['truncated']),
+      'image/object/alpha': dataset_util.float_list_feature(
+          annotations['alpha']),
+      'image/object/3d_bbox/height': dataset_util.float_list_feature(
+          annotations['3d_bbox_height']),
+      'image/object/3d_bbox/width': dataset_util.float_list_feature(
+          annotations['3d_bbox_width']),
+      'image/object/3d_bbox/length': dataset_util.float_list_feature(
+          annotations['3d_bbox_length']),
+      'image/object/3d_bbox/x': dataset_util.float_list_feature(
+          annotations['3d_bbox_x']),
+      'image/object/3d_bbox/y': dataset_util.float_list_feature(
+          annotations['3d_bbox_y']),
+      'image/object/3d_bbox/z': dataset_util.float_list_feature(
+          annotations['3d_bbox_z']),
+      'image/object/3d_bbox/rot_y': dataset_util.float_list_feature(
+          annotations['3d_bbox_rot_y']),
+  }))
+
+  return example
+
+
+def filter_annotations(img_all_annotations, used_classes):
+  """Filters out annotations from the unused classes and dontcare regions.
+
+  Filters out the annotations that belong to classes we do now wish to use and
+  (optionally) also removes all boxes that overlap with dontcare regions.
+
+  Args:
+    img_all_annotations: A list of annotation dictionaries. See documentation of
+      read_annotation_file for more details about the format of the annotations.
+    used_classes: A list of strings listing the classes we want to keep, if the
+    list contains "dontcare", all bounding boxes with overlapping with dont
+    care regions will also be filtered out.
+
+  Returns:
+    img_filtered_annotations: A list of annotation dictionaries that have passed
+      the filtering.
+  """
+
+  img_filtered_annotations = {}
+
+  # Filter the type of the objects.
+  relevant_annotation_indices = [
+      i for i, x in enumerate(img_all_annotations['type']) if x in used_classes
+  ]
+
+  for key in img_all_annotations.keys():
+    img_filtered_annotations[key] = (
+        img_all_annotations[key][relevant_annotation_indices])
+
+  if 'dontcare' in used_classes:
+    dont_care_indices = [i for i,
+                         x in enumerate(img_filtered_annotations['type'])
+                         if x == 'dontcare']
+
+    # bounding box format [y_min, x_min, y_max, x_max]
+    all_boxes = np.stack([img_filtered_annotations['2d_bbox_top'],
+                          img_filtered_annotations['2d_bbox_left'],
+                          img_filtered_annotations['2d_bbox_bottom'],
+                          img_filtered_annotations['2d_bbox_right']],
+                         axis=1)
+
+    ious = iou(boxes1=all_boxes,
+               boxes2=all_boxes[dont_care_indices])
+
+    # Remove all bounding boxes that overlap with a dontcare region.
+    if ious.size > 0:
+      boxes_to_remove = np.amax(ious, axis=1) > 0.0
+      for key in img_all_annotations.keys():
+        img_filtered_annotations[key] = (
+            img_filtered_annotations[key][np.logical_not(boxes_to_remove)])
+
+  return img_filtered_annotations
+
+
+def read_annotation_file(filename):
+  """Reads a KITTI annotation file.
+
+  Converts a KITTI annotation file into a dictionary containing all the
+  relevant information.
+
+  Args:
+    filename: the path to the annotataion text file.
+
+  Returns:
+    anno: A dictionary with the converted annotation information. See annotation
+    README file for details on the different fields.
+  """
+  with open(filename) as f:
+    content = f.readlines()
+  content = [x.strip().split(' ') for x in content]
+
+  anno = {}
+  anno['type'] = np.array([x[0].lower() for x in content])
+  anno['truncated'] = np.array([float(x[1]) for x in content])
+  anno['occluded'] = np.array([int(x[2]) for x in content])
+  anno['alpha'] = np.array([float(x[3]) for x in content])
+
+  anno['2d_bbox_left'] = np.array([float(x[4]) for x in content])
+  anno['2d_bbox_top'] = np.array([float(x[5]) for x in content])
+  anno['2d_bbox_right'] = np.array([float(x[6]) for x in content])
+  anno['2d_bbox_bottom'] = np.array([float(x[7]) for x in content])
+
+  anno['3d_bbox_height'] = np.array([float(x[8]) for x in content])
+  anno['3d_bbox_width'] = np.array([float(x[9]) for x in content])
+  anno['3d_bbox_length'] = np.array([float(x[10]) for x in content])
+  anno['3d_bbox_x'] = np.array([float(x[11]) for x in content])
+  anno['3d_bbox_y'] = np.array([float(x[12]) for x in content])
+  anno['3d_bbox_z'] = np.array([float(x[13]) for x in content])
+  anno['3d_bbox_rot_y'] = np.array([float(x[14]) for x in content])
+
+  return anno
+
+
+def main(_):
+  convert_kitti_to_tfrecords(
+      data_dir=FLAGS.data_dir,
+      output_path=FLAGS.output_path,
+      classes_to_use=FLAGS.classes_to_use.split(','),
+      label_map_path=FLAGS.label_map_path,
+      validation_set_size=FLAGS.validation_set_size)
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record_test.py
new file mode 100644
index 00000000..37ac4b8b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_kitti_tf_record_test.py
@@ -0,0 +1,130 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Test for create_kitti_tf_record.py."""
+
+import os
+
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+from object_detection.dataset_tools import create_kitti_tf_record
+
+
+class CreateKittiTFRecordTest(tf.test.TestCase):
+
+  def _assertProtoEqual(self, proto_field, expectation):
+    """Helper function to assert if a proto field equals some value.
+
+    Args:
+      proto_field: The protobuf field to compare.
+      expectation: The expected value of the protobuf field.
+    """
+    proto_list = [p for p in proto_field]
+    self.assertListEqual(proto_list, expectation)
+
+  def test_dict_to_tf_example(self):
+    image_file_name = 'tmp_image.jpg'
+    image_data = np.random.rand(256, 256, 3)
+    save_path = os.path.join(self.get_temp_dir(), image_file_name)
+    image = PIL.Image.fromarray(image_data, 'RGB')
+    image.save(save_path)
+
+    annotations = {}
+    annotations['2d_bbox_left'] = np.array([64])
+    annotations['2d_bbox_top'] = np.array([64])
+    annotations['2d_bbox_right'] = np.array([192])
+    annotations['2d_bbox_bottom'] = np.array([192])
+    annotations['type'] = ['car']
+    annotations['truncated'] = np.array([1])
+    annotations['alpha'] = np.array([2])
+    annotations['3d_bbox_height'] = np.array([10])
+    annotations['3d_bbox_width'] = np.array([11])
+    annotations['3d_bbox_length'] = np.array([12])
+    annotations['3d_bbox_x'] = np.array([13])
+    annotations['3d_bbox_y'] = np.array([14])
+    annotations['3d_bbox_z'] = np.array([15])
+    annotations['3d_bbox_rot_y'] = np.array([4])
+
+    label_map_dict = {
+        'background': 0,
+        'car': 1,
+    }
+
+    example = create_kitti_tf_record.prepare_example(
+        save_path,
+        annotations,
+        label_map_dict)
+
+    self._assertProtoEqual(
+        example.features.feature['image/height'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/width'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/filename'].bytes_list.value,
+        [save_path])
+    self._assertProtoEqual(
+        example.features.feature['image/source_id'].bytes_list.value,
+        [save_path])
+    self._assertProtoEqual(
+        example.features.feature['image/format'].bytes_list.value, ['png'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/text'].bytes_list.value,
+        ['car'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/label'].int64_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/truncated'].float_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/alpha'].float_list.value,
+        [2])
+    self._assertProtoEqual(example.features.feature[
+        'image/object/3d_bbox/height'].float_list.value, [10])
+    self._assertProtoEqual(
+        example.features.feature['image/object/3d_bbox/width'].float_list.value,
+        [11])
+    self._assertProtoEqual(example.features.feature[
+        'image/object/3d_bbox/length'].float_list.value, [12])
+    self._assertProtoEqual(
+        example.features.feature['image/object/3d_bbox/x'].float_list.value,
+        [13])
+    self._assertProtoEqual(
+        example.features.feature['image/object/3d_bbox/y'].float_list.value,
+        [14])
+    self._assertProtoEqual(
+        example.features.feature['image/object/3d_bbox/z'].float_list.value,
+        [15])
+    self._assertProtoEqual(
+        example.features.feature['image/object/3d_bbox/rot_y'].float_list.value,
+        [4])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_oid_tf_record.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_oid_tf_record.py
new file mode 100644
index 00000000..26d9699c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_oid_tf_record.py
@@ -0,0 +1,117 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Creates TFRecords of Open Images dataset for object detection.
+
+Example usage:
+  python object_detection/dataset_tools/create_oid_tf_record.py \
+    --input_box_annotations_csv=/path/to/input/annotations-human-bbox.csv \
+    --input_image_label_annotations_csv=/path/to/input/annotations-label.csv \
+    --input_images_directory=/path/to/input/image_pixels_directory \
+    --input_label_map=/path/to/input/labels_bbox_545.labelmap \
+    --output_tf_record_path_prefix=/path/to/output/prefix.tfrecord
+
+CSVs with bounding box annotations and image metadata (including the image URLs)
+can be downloaded from the Open Images GitHub repository:
+https://github.com/openimages/dataset
+
+This script will include every image found in the input_images_directory in the
+output TFRecord, even if the image has no corresponding bounding box annotations
+in the input_annotations_csv. If input_image_label_annotations_csv is specified,
+it will add image-level labels as well. Note that the information of whether a
+label is positivelly or negativelly verified is NOT added to tfrecord.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import contextlib2
+import pandas as pd
+import tensorflow as tf
+
+from object_detection.dataset_tools import oid_tfrecord_creation
+from object_detection.dataset_tools import tf_record_creation_util
+from object_detection.utils import label_map_util
+
+tf.flags.DEFINE_string('input_box_annotations_csv', None,
+                       'Path to CSV containing image bounding box annotations')
+tf.flags.DEFINE_string('input_images_directory', None,
+                       'Directory containing the image pixels '
+                       'downloaded from the OpenImages GitHub repository.')
+tf.flags.DEFINE_string('input_image_label_annotations_csv', None,
+                       'Path to CSV containing image-level labels annotations')
+tf.flags.DEFINE_string('input_label_map', None, 'Path to the label map proto')
+tf.flags.DEFINE_string(
+    'output_tf_record_path_prefix', None,
+    'Path to the output TFRecord. The shard index and the number of shards '
+    'will be appended for each output shard.')
+tf.flags.DEFINE_integer('num_shards', 100, 'Number of TFRecord shards')
+
+FLAGS = tf.flags.FLAGS
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  required_flags = [
+      'input_box_annotations_csv', 'input_images_directory', 'input_label_map',
+      'output_tf_record_path_prefix'
+  ]
+  for flag_name in required_flags:
+    if not getattr(FLAGS, flag_name):
+      raise ValueError('Flag --{} is required'.format(flag_name))
+
+  label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
+  all_box_annotations = pd.read_csv(FLAGS.input_box_annotations_csv)
+  if FLAGS.input_image_label_annotations_csv:
+    all_label_annotations = pd.read_csv(FLAGS.input_image_label_annotations_csv)
+    all_label_annotations.rename(
+        columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
+  else:
+    all_label_annotations = None
+  all_images = tf.gfile.Glob(
+      os.path.join(FLAGS.input_images_directory, '*.jpg'))
+  all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
+  all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
+  all_annotations = pd.concat(
+      [all_box_annotations, all_image_ids, all_label_annotations])
+
+  tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
+
+  with contextlib2.ExitStack() as tf_record_close_stack:
+    output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
+        tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
+        FLAGS.num_shards)
+
+    for counter, image_data in enumerate(all_annotations.groupby('ImageID')):
+      tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
+                             counter)
+
+      image_id, image_annotations = image_data
+      # In OID image file names are formed by appending ".jpg" to the image ID.
+      image_path = os.path.join(FLAGS.input_images_directory, image_id + '.jpg')
+      with tf.gfile.Open(image_path) as image_file:
+        encoded_image = image_file.read()
+
+      tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
+          image_annotations, label_map, encoded_image)
+      if tf_example:
+        shard_idx = int(image_id, 16) % FLAGS.num_shards
+        output_tfrecords[shard_idx].write(tf_example.SerializeToString())
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record.py
new file mode 100644
index 00000000..813071c9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record.py
@@ -0,0 +1,185 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Convert raw PASCAL dataset to TFRecord for object_detection.
+
+Example usage:
+    python object_detection/dataset_tools/create_pascal_tf_record.py \
+        --data_dir=/home/user/VOCdevkit \
+        --year=VOC2012 \
+        --output_path=/home/user/pascal.record
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import hashlib
+import io
+import logging
+import os
+
+from lxml import etree
+import PIL.Image
+import tensorflow as tf
+
+from object_detection.utils import dataset_util
+from object_detection.utils import label_map_util
+
+
+flags = tf.app.flags
+flags.DEFINE_string('data_dir', '', 'Root directory to raw PASCAL VOC dataset.')
+flags.DEFINE_string('set', 'train', 'Convert training set, validation set or '
+                    'merged set.')
+flags.DEFINE_string('annotations_dir', 'Annotations',
+                    '(Relative) path to annotations directory.')
+flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.')
+flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
+flags.DEFINE_string('label_map_path', 'data/pascal_label_map.pbtxt',
+                    'Path to label map proto')
+flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore '
+                     'difficult instances')
+FLAGS = flags.FLAGS
+
+SETS = ['train', 'val', 'trainval', 'test']
+YEARS = ['VOC2007', 'VOC2012', 'merged']
+
+
+def dict_to_tf_example(data,
+                       dataset_directory,
+                       label_map_dict,
+                       ignore_difficult_instances=False,
+                       image_subdirectory='JPEGImages'):
+  """Convert XML derived dict to tf.Example proto.
+
+  Notice that this function normalizes the bounding box coordinates provided
+  by the raw data.
+
+  Args:
+    data: dict holding PASCAL XML fields for a single image (obtained by
+      running dataset_util.recursive_parse_xml_to_dict)
+    dataset_directory: Path to root directory holding PASCAL dataset
+    label_map_dict: A map from string label names to integers ids.
+    ignore_difficult_instances: Whether to skip difficult instances in the
+      dataset  (default: False).
+    image_subdirectory: String specifying subdirectory within the
+      PASCAL dataset directory holding the actual image data.
+
+  Returns:
+    example: The converted tf.Example.
+
+  Raises:
+    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
+  """
+  img_path = os.path.join(data['folder'], image_subdirectory, data['filename'])
+  full_path = os.path.join(dataset_directory, img_path)
+  with tf.gfile.GFile(full_path, 'rb') as fid:
+    encoded_jpg = fid.read()
+  encoded_jpg_io = io.BytesIO(encoded_jpg)
+  image = PIL.Image.open(encoded_jpg_io)
+  if image.format != 'JPEG':
+    raise ValueError('Image format not JPEG')
+  key = hashlib.sha256(encoded_jpg).hexdigest()
+
+  width = int(data['size']['width'])
+  height = int(data['size']['height'])
+
+  xmin = []
+  ymin = []
+  xmax = []
+  ymax = []
+  classes = []
+  classes_text = []
+  truncated = []
+  poses = []
+  difficult_obj = []
+  if 'object' in data:
+    for obj in data['object']:
+      difficult = bool(int(obj['difficult']))
+      if ignore_difficult_instances and difficult:
+        continue
+
+      difficult_obj.append(int(difficult))
+
+      xmin.append(float(obj['bndbox']['xmin']) / width)
+      ymin.append(float(obj['bndbox']['ymin']) / height)
+      xmax.append(float(obj['bndbox']['xmax']) / width)
+      ymax.append(float(obj['bndbox']['ymax']) / height)
+      classes_text.append(obj['name'].encode('utf8'))
+      classes.append(label_map_dict[obj['name']])
+      truncated.append(int(obj['truncated']))
+      poses.append(obj['pose'].encode('utf8'))
+
+  example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': dataset_util.int64_feature(height),
+      'image/width': dataset_util.int64_feature(width),
+      'image/filename': dataset_util.bytes_feature(
+          data['filename'].encode('utf8')),
+      'image/source_id': dataset_util.bytes_feature(
+          data['filename'].encode('utf8')),
+      'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
+      'image/encoded': dataset_util.bytes_feature(encoded_jpg),
+      'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
+      'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
+      'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
+      'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
+      'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
+      'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
+      'image/object/class/label': dataset_util.int64_list_feature(classes),
+      'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
+      'image/object/truncated': dataset_util.int64_list_feature(truncated),
+      'image/object/view': dataset_util.bytes_list_feature(poses),
+  }))
+  return example
+
+
+def main(_):
+  if FLAGS.set not in SETS:
+    raise ValueError('set must be in : {}'.format(SETS))
+  if FLAGS.year not in YEARS:
+    raise ValueError('year must be in : {}'.format(YEARS))
+
+  data_dir = FLAGS.data_dir
+  years = ['VOC2007', 'VOC2012']
+  if FLAGS.year != 'merged':
+    years = [FLAGS.year]
+
+  writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
+
+  label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)
+
+  for year in years:
+    logging.info('Reading from PASCAL %s dataset.', year)
+    examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',
+                                 'aeroplane_' + FLAGS.set + '.txt')
+    annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir)
+    examples_list = dataset_util.read_examples_list(examples_path)
+    for idx, example in enumerate(examples_list):
+      if idx % 100 == 0:
+        logging.info('On image %d of %d', idx, len(examples_list))
+      path = os.path.join(annotations_dir, example + '.xml')
+      with tf.gfile.GFile(path, 'r') as fid:
+        xml_str = fid.read()
+      xml = etree.fromstring(xml_str)
+      data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
+
+      tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict,
+                                      FLAGS.ignore_difficult_instances)
+      writer.write(tf_example.SerializeToString())
+
+  writer.close()
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record_test.py
new file mode 100644
index 00000000..66929bd4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pascal_tf_record_test.py
@@ -0,0 +1,118 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Test for create_pascal_tf_record.py."""
+
+import os
+
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+from object_detection.dataset_tools import create_pascal_tf_record
+
+
+class CreatePascalTFRecordTest(tf.test.TestCase):
+
+  def _assertProtoEqual(self, proto_field, expectation):
+    """Helper function to assert if a proto field equals some value.
+
+    Args:
+      proto_field: The protobuf field to compare.
+      expectation: The expected value of the protobuf field.
+    """
+    proto_list = [p for p in proto_field]
+    self.assertListEqual(proto_list, expectation)
+
+  def test_dict_to_tf_example(self):
+    image_file_name = 'tmp_image.jpg'
+    image_data = np.random.rand(256, 256, 3)
+    save_path = os.path.join(self.get_temp_dir(), image_file_name)
+    image = PIL.Image.fromarray(image_data, 'RGB')
+    image.save(save_path)
+
+    data = {
+        'folder': '',
+        'filename': image_file_name,
+        'size': {
+            'height': 256,
+            'width': 256,
+        },
+        'object': [
+            {
+                'difficult': 1,
+                'bndbox': {
+                    'xmin': 64,
+                    'ymin': 64,
+                    'xmax': 192,
+                    'ymax': 192,
+                },
+                'name': 'person',
+                'truncated': 0,
+                'pose': '',
+            },
+        ],
+    }
+
+    label_map_dict = {
+        'background': 0,
+        'person': 1,
+        'notperson': 2,
+    }
+
+    example = create_pascal_tf_record.dict_to_tf_example(
+        data, self.get_temp_dir(), label_map_dict, image_subdirectory='')
+    self._assertProtoEqual(
+        example.features.feature['image/height'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/width'].int64_list.value, [256])
+    self._assertProtoEqual(
+        example.features.feature['image/filename'].bytes_list.value,
+        [image_file_name])
+    self._assertProtoEqual(
+        example.features.feature['image/source_id'].bytes_list.value,
+        [image_file_name])
+    self._assertProtoEqual(
+        example.features.feature['image/format'].bytes_list.value, ['jpeg'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymin'].float_list.value,
+        [0.25])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/xmax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/bbox/ymax'].float_list.value,
+        [0.75])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/text'].bytes_list.value,
+        ['person'])
+    self._assertProtoEqual(
+        example.features.feature['image/object/class/label'].int64_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/difficult'].int64_list.value,
+        [1])
+    self._assertProtoEqual(
+        example.features.feature['image/object/truncated'].int64_list.value,
+        [0])
+    self._assertProtoEqual(
+        example.features.feature['image/object/view'].bytes_list.value, [''])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pet_tf_record.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pet_tf_record.py
new file mode 100644
index 00000000..9b3b55c6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pet_tf_record.py
@@ -0,0 +1,318 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Convert the Oxford pet dataset to TFRecord for object_detection.
+
+See: O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar
+     Cats and Dogs
+     IEEE Conference on Computer Vision and Pattern Recognition, 2012
+     http://www.robots.ox.ac.uk/~vgg/data/pets/
+
+Example usage:
+    python object_detection/dataset_tools/create_pet_tf_record.py \
+        --data_dir=/home/user/pet \
+        --output_dir=/home/user/pet/output
+"""
+
+import hashlib
+import io
+import logging
+import os
+import random
+import re
+
+import contextlib2
+from lxml import etree
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+from object_detection.dataset_tools import tf_record_creation_util
+from object_detection.utils import dataset_util
+from object_detection.utils import label_map_util
+
+flags = tf.app.flags
+flags.DEFINE_string('data_dir', '', 'Root directory to raw pet dataset.')
+flags.DEFINE_string('output_dir', '', 'Path to directory to output TFRecords.')
+flags.DEFINE_string('label_map_path', 'data/pet_label_map.pbtxt',
+                    'Path to label map proto')
+flags.DEFINE_boolean('faces_only', True, 'If True, generates bounding boxes '
+                     'for pet faces.  Otherwise generates bounding boxes (as '
+                     'well as segmentations for full pet bodies).  Note that '
+                     'in the latter case, the resulting files are much larger.')
+flags.DEFINE_string('mask_type', 'png', 'How to represent instance '
+                    'segmentation masks. Options are "png" or "numerical".')
+flags.DEFINE_integer('num_shards', 10, 'Number of TFRecord shards')
+
+FLAGS = flags.FLAGS
+
+
+def get_class_name_from_filename(file_name):
+  """Gets the class name from a file.
+
+  Args:
+    file_name: The file name to get the class name from.
+               ie. "american_pit_bull_terrier_105.jpg"
+
+  Returns:
+    A string of the class name.
+  """
+  match = re.match(r'([A-Za-z_]+)(_[0-9]+\.jpg)', file_name, re.I)
+  return match.groups()[0]
+
+
+def dict_to_tf_example(data,
+                       mask_path,
+                       label_map_dict,
+                       image_subdirectory,
+                       ignore_difficult_instances=False,
+                       faces_only=True,
+                       mask_type='png'):
+  """Convert XML derived dict to tf.Example proto.
+
+  Notice that this function normalizes the bounding box coordinates provided
+  by the raw data.
+
+  Args:
+    data: dict holding PASCAL XML fields for a single image (obtained by
+      running dataset_util.recursive_parse_xml_to_dict)
+    mask_path: String path to PNG encoded mask.
+    label_map_dict: A map from string label names to integers ids.
+    image_subdirectory: String specifying subdirectory within the
+      Pascal dataset directory holding the actual image data.
+    ignore_difficult_instances: Whether to skip difficult instances in the
+      dataset  (default: False).
+    faces_only: If True, generates bounding boxes for pet faces.  Otherwise
+      generates bounding boxes (as well as segmentations for full pet bodies).
+    mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
+      smaller file sizes.
+
+  Returns:
+    example: The converted tf.Example.
+
+  Raises:
+    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
+  """
+  img_path = os.path.join(image_subdirectory, data['filename'])
+  with tf.gfile.GFile(img_path, 'rb') as fid:
+    encoded_jpg = fid.read()
+  encoded_jpg_io = io.BytesIO(encoded_jpg)
+  image = PIL.Image.open(encoded_jpg_io)
+  if image.format != 'JPEG':
+    raise ValueError('Image format not JPEG')
+  key = hashlib.sha256(encoded_jpg).hexdigest()
+
+  with tf.gfile.GFile(mask_path, 'rb') as fid:
+    encoded_mask_png = fid.read()
+  encoded_png_io = io.BytesIO(encoded_mask_png)
+  mask = PIL.Image.open(encoded_png_io)
+  if mask.format != 'PNG':
+    raise ValueError('Mask format not PNG')
+
+  mask_np = np.asarray(mask)
+  nonbackground_indices_x = np.any(mask_np != 2, axis=0)
+  nonbackground_indices_y = np.any(mask_np != 2, axis=1)
+  nonzero_x_indices = np.where(nonbackground_indices_x)
+  nonzero_y_indices = np.where(nonbackground_indices_y)
+
+  width = int(data['size']['width'])
+  height = int(data['size']['height'])
+
+  xmins = []
+  ymins = []
+  xmaxs = []
+  ymaxs = []
+  classes = []
+  classes_text = []
+  truncated = []
+  poses = []
+  difficult_obj = []
+  masks = []
+  if 'object' in data:
+    for obj in data['object']:
+      difficult = bool(int(obj['difficult']))
+      if ignore_difficult_instances and difficult:
+        continue
+      difficult_obj.append(int(difficult))
+
+      if faces_only:
+        xmin = float(obj['bndbox']['xmin'])
+        xmax = float(obj['bndbox']['xmax'])
+        ymin = float(obj['bndbox']['ymin'])
+        ymax = float(obj['bndbox']['ymax'])
+      else:
+        xmin = float(np.min(nonzero_x_indices))
+        xmax = float(np.max(nonzero_x_indices))
+        ymin = float(np.min(nonzero_y_indices))
+        ymax = float(np.max(nonzero_y_indices))
+
+      xmins.append(xmin / width)
+      ymins.append(ymin / height)
+      xmaxs.append(xmax / width)
+      ymaxs.append(ymax / height)
+      class_name = get_class_name_from_filename(data['filename'])
+      classes_text.append(class_name.encode('utf8'))
+      classes.append(label_map_dict[class_name])
+      truncated.append(int(obj['truncated']))
+      poses.append(obj['pose'].encode('utf8'))
+      if not faces_only:
+        mask_remapped = (mask_np != 2).astype(np.uint8)
+        masks.append(mask_remapped)
+
+  feature_dict = {
+      'image/height': dataset_util.int64_feature(height),
+      'image/width': dataset_util.int64_feature(width),
+      'image/filename': dataset_util.bytes_feature(
+          data['filename'].encode('utf8')),
+      'image/source_id': dataset_util.bytes_feature(
+          data['filename'].encode('utf8')),
+      'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
+      'image/encoded': dataset_util.bytes_feature(encoded_jpg),
+      'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
+      'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
+      'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
+      'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
+      'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
+      'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
+      'image/object/class/label': dataset_util.int64_list_feature(classes),
+      'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
+      'image/object/truncated': dataset_util.int64_list_feature(truncated),
+      'image/object/view': dataset_util.bytes_list_feature(poses),
+  }
+  if not faces_only:
+    if mask_type == 'numerical':
+      mask_stack = np.stack(masks).astype(np.float32)
+      masks_flattened = np.reshape(mask_stack, [-1])
+      feature_dict['image/object/mask'] = (
+          dataset_util.float_list_feature(masks_flattened.tolist()))
+    elif mask_type == 'png':
+      encoded_mask_png_list = []
+      for mask in masks:
+        img = PIL.Image.fromarray(mask)
+        output = io.BytesIO()
+        img.save(output, format='PNG')
+        encoded_mask_png_list.append(output.getvalue())
+      feature_dict['image/object/mask'] = (
+          dataset_util.bytes_list_feature(encoded_mask_png_list))
+
+  example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
+  return example
+
+
+def create_tf_record(output_filename,
+                     num_shards,
+                     label_map_dict,
+                     annotations_dir,
+                     image_dir,
+                     examples,
+                     faces_only=True,
+                     mask_type='png'):
+  """Creates a TFRecord file from examples.
+
+  Args:
+    output_filename: Path to where output file is saved.
+    num_shards: Number of shards for output file.
+    label_map_dict: The label map dictionary.
+    annotations_dir: Directory where annotation files are stored.
+    image_dir: Directory where image files are stored.
+    examples: Examples to parse and save to tf record.
+    faces_only: If True, generates bounding boxes for pet faces.  Otherwise
+      generates bounding boxes (as well as segmentations for full pet bodies).
+    mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
+      smaller file sizes.
+  """
+  with contextlib2.ExitStack() as tf_record_close_stack:
+    output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
+        tf_record_close_stack, output_filename, num_shards)
+    for idx, example in enumerate(examples):
+      if idx % 100 == 0:
+        logging.info('On image %d of %d', idx, len(examples))
+      xml_path = os.path.join(annotations_dir, 'xmls', example + '.xml')
+      mask_path = os.path.join(annotations_dir, 'trimaps', example + '.png')
+
+      if not os.path.exists(xml_path):
+        logging.warning('Could not find %s, ignoring example.', xml_path)
+        continue
+      with tf.gfile.GFile(xml_path, 'r') as fid:
+        xml_str = fid.read()
+      xml = etree.fromstring(xml_str)
+      data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
+
+      try:
+        tf_example = dict_to_tf_example(
+            data,
+            mask_path,
+            label_map_dict,
+            image_dir,
+            faces_only=faces_only,
+            mask_type=mask_type)
+        if tf_example:
+          shard_idx = idx % num_shards
+          output_tfrecords[shard_idx].write(tf_example.SerializeToString())
+      except ValueError:
+        logging.warning('Invalid example: %s, ignoring.', xml_path)
+
+
+# TODO(derekjchow): Add test for pet/PASCAL main files.
+def main(_):
+  data_dir = FLAGS.data_dir
+  label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)
+
+  logging.info('Reading from Pet dataset.')
+  image_dir = os.path.join(data_dir, 'images')
+  annotations_dir = os.path.join(data_dir, 'annotations')
+  examples_path = os.path.join(annotations_dir, 'trainval.txt')
+  examples_list = dataset_util.read_examples_list(examples_path)
+
+  # Test images are not included in the downloaded data set, so we shall perform
+  # our own split.
+  random.seed(42)
+  random.shuffle(examples_list)
+  num_examples = len(examples_list)
+  num_train = int(0.7 * num_examples)
+  train_examples = examples_list[:num_train]
+  val_examples = examples_list[num_train:]
+  logging.info('%d training and %d validation examples.',
+               len(train_examples), len(val_examples))
+
+  train_output_path = os.path.join(FLAGS.output_dir, 'pet_faces_train.record')
+  val_output_path = os.path.join(FLAGS.output_dir, 'pet_faces_val.record')
+  if not FLAGS.faces_only:
+    train_output_path = os.path.join(FLAGS.output_dir,
+                                     'pets_fullbody_with_masks_train.record')
+    val_output_path = os.path.join(FLAGS.output_dir,
+                                   'pets_fullbody_with_masks_val.record')
+  create_tf_record(
+      train_output_path,
+      FLAGS.num_shards,
+      label_map_dict,
+      annotations_dir,
+      image_dir,
+      train_examples,
+      faces_only=FLAGS.faces_only,
+      mask_type=FLAGS.mask_type)
+  create_tf_record(
+      val_output_path,
+      FLAGS.num_shards,
+      label_map_dict,
+      annotations_dir,
+      image_dir,
+      val_examples,
+      faces_only=FLAGS.faces_only,
+      mask_type=FLAGS.mask_type)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pycocotools_package.sh b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pycocotools_package.sh
new file mode 100644
index 00000000..511df185
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/create_pycocotools_package.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download pycocotools and make package for CMLE jobs.
+#
+# usage:
+#  bash object_detection/dataset_tools/create_pycocotools_package.sh \
+#    /tmp/pycocotools
+set -e
+
+if [ -z "$1" ]; then
+  echo "usage create_pycocotools_package.sh [output dir]"
+  exit
+fi
+
+# Create the output directory.
+OUTPUT_DIR="${1%/}"
+SCRATCH_DIR="${OUTPUT_DIR}/raw"
+mkdir -p "${OUTPUT_DIR}"
+mkdir -p "${SCRATCH_DIR}"
+
+cd ${SCRATCH_DIR}
+git clone https://github.com/cocodataset/cocoapi.git
+cd cocoapi/PythonAPI && mv ../common ./
+
+sed "s/\.\.\/common/common/g" setup.py > setup.py.updated
+cp -f setup.py.updated setup.py
+rm setup.py.updated
+
+sed "s/\.\.\/common/common/g" pycocotools/_mask.pyx > _mask.pyx.updated
+cp -f _mask.pyx.updated pycocotools/_mask.pyx
+rm _mask.pyx.updated
+
+sed "s/import matplotlib\.pyplot as plt/import matplotlib\nmatplotlib\.use\(\'Agg\'\)\nimport matplotlib\.pyplot as plt/g" pycocotools/coco.py > coco.py.updated
+cp -f coco.py.updated pycocotools/coco.py
+rm coco.py.updated
+
+cd "${OUTPUT_DIR}"
+tar -czf pycocotools-2.0.tar.gz -C "${SCRATCH_DIR}/cocoapi/" PythonAPI/
+rm -rf ${SCRATCH_DIR}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/download_and_preprocess_mscoco.sh b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/download_and_preprocess_mscoco.sh
new file mode 100644
index 00000000..843ba869
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/download_and_preprocess_mscoco.sh
@@ -0,0 +1,106 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download and preprocess the MSCOCO data set for detection.
+#
+# The outputs of this script are TFRecord files containing serialized
+# tf.Example protocol buffers. See create_coco_tf_record.py for details of how
+# the tf.Example protocol buffers are constructed and see
+# http://cocodataset.org/#overview for an overview of the dataset.
+#
+# usage:
+#  bash object_detection/dataset_tools/download_and_preprocess_mscoco.sh \
+#    /tmp/mscoco
+set -e
+
+if [ -z "$1" ]; then
+  echo "usage download_and_preprocess_mscoco.sh [data dir]"
+  exit
+fi
+
+if [ "$(uname)" == "Darwin" ]; then
+  UNZIP="tar -xf"
+else
+  UNZIP="unzip -nq"
+fi
+
+# Create the output directories.
+OUTPUT_DIR="${1%/}"
+SCRATCH_DIR="${OUTPUT_DIR}/raw-data"
+mkdir -p "${OUTPUT_DIR}"
+mkdir -p "${SCRATCH_DIR}"
+CURRENT_DIR=$(pwd)
+
+# Helper function to download and unpack a .zip file.
+function download_and_unzip() {
+  local BASE_URL=${1}
+  local FILENAME=${2}
+
+  if [ ! -f ${FILENAME} ]; then
+    echo "Downloading ${FILENAME} to $(pwd)"
+    wget -nd -c "${BASE_URL}/${FILENAME}"
+  else
+    echo "Skipping download of ${FILENAME}"
+  fi
+  echo "Unzipping ${FILENAME}"
+  ${UNZIP} ${FILENAME}
+}
+
+cd ${SCRATCH_DIR}
+
+# Download the images.
+BASE_IMAGE_URL="http://images.cocodataset.org/zips"
+
+TRAIN_IMAGE_FILE="train2017.zip"
+download_and_unzip ${BASE_IMAGE_URL} ${TRAIN_IMAGE_FILE}
+TRAIN_IMAGE_DIR="${SCRATCH_DIR}/train2017"
+
+VAL_IMAGE_FILE="val2017.zip"
+download_and_unzip ${BASE_IMAGE_URL} ${VAL_IMAGE_FILE}
+VAL_IMAGE_DIR="${SCRATCH_DIR}/val2017"
+
+TEST_IMAGE_FILE="test2017.zip"
+download_and_unzip ${BASE_IMAGE_URL} ${TEST_IMAGE_FILE}
+TEST_IMAGE_DIR="${SCRATCH_DIR}/test2017"
+
+# Download the annotations.
+BASE_INSTANCES_URL="http://images.cocodataset.org/annotations"
+INSTANCES_FILE="annotations_trainval2017.zip"
+download_and_unzip ${BASE_INSTANCES_URL} ${INSTANCES_FILE}
+
+TRAIN_ANNOTATIONS_FILE="${SCRATCH_DIR}/annotations/instances_train2017.json"
+VAL_ANNOTATIONS_FILE="${SCRATCH_DIR}/annotations/instances_val2017.json"
+
+# Download the test image info.
+BASE_IMAGE_INFO_URL="http://images.cocodataset.org/annotations"
+IMAGE_INFO_FILE="image_info_test2017.zip"
+download_and_unzip ${BASE_IMAGE_INFO_URL} ${IMAGE_INFO_FILE}
+
+TESTDEV_ANNOTATIONS_FILE="${SCRATCH_DIR}/annotations/image_info_test-dev2017.json"
+
+# Build TFRecords of the image data.
+cd "${CURRENT_DIR}"
+python object_detection/dataset_tools/create_coco_tf_record.py \
+  --logtostderr \
+  --include_masks \
+  --train_image_dir="${TRAIN_IMAGE_DIR}" \
+  --val_image_dir="${VAL_IMAGE_DIR}" \
+  --test_image_dir="${TEST_IMAGE_DIR}" \
+  --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \
+  --val_annotations_file="${VAL_ANNOTATIONS_FILE}" \
+  --testdev_annotations_file="${TESTDEV_ANNOTATIONS_FILE}" \
+  --output_dir="${OUTPUT_DIR}"
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py
new file mode 100644
index 00000000..61d6ed7f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py
@@ -0,0 +1,201 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""An executable to expand hierarchically image-level labels and boxes.
+
+Example usage:
+python models/research/object_detection/dataset_tools/\
+oid_hierarchical_labels_expansion.py \
+--json_hierarchy_file=<path to JSON hierarchy> \
+--input_annotations=<input csv file> \
+--output_annotations=<output csv file> \
+--annotation_type=<1 (for boxes) or 2 (for image-level labels)>
+"""
+
+from __future__ import print_function
+
+import argparse
+import json
+
+
+def _update_dict(initial_dict, update):
+  """Updates dictionary with update content.
+
+  Args:
+   initial_dict: initial dictionary.
+   update: updated dictionary.
+  """
+
+  for key, value_list in update.iteritems():
+    if key in initial_dict:
+      initial_dict[key].extend(value_list)
+    else:
+      initial_dict[key] = value_list
+
+
+def _build_plain_hierarchy(hierarchy, skip_root=False):
+  """Expands tree hierarchy representation to parent-child dictionary.
+
+  Args:
+   hierarchy: labels hierarchy as JSON file.
+   skip_root: if true skips root from the processing (done for the case when all
+     classes under hierarchy are collected under virtual node).
+
+  Returns:
+    keyed_parent - dictionary of parent - all its children nodes.
+    keyed_child  - dictionary of children - all its parent nodes
+    children - all children of the current node.
+  """
+  all_children = []
+  all_keyed_parent = {}
+  all_keyed_child = {}
+  if 'Subcategory' in hierarchy:
+    for node in hierarchy['Subcategory']:
+      keyed_parent, keyed_child, children = _build_plain_hierarchy(node)
+      # Update is not done through dict.update() since some children have multi-
+      # ple parents in the hiearchy.
+      _update_dict(all_keyed_parent, keyed_parent)
+      _update_dict(all_keyed_child, keyed_child)
+      all_children.extend(children)
+
+  if not skip_root:
+    all_keyed_parent[hierarchy['LabelName']] = all_children
+    all_children = [hierarchy['LabelName']] + all_children
+    for child, _ in all_keyed_child.iteritems():
+      all_keyed_child[child].append(hierarchy['LabelName'])
+    all_keyed_child[hierarchy['LabelName']] = []
+
+  return all_keyed_parent, all_keyed_child, all_children
+
+
+class OIDHierarchicalLabelsExpansion(object):
+  """ Main class to perform labels hierachical expansion."""
+
+  def __init__(self, hierarchy):
+    """Constructor.
+
+    Args:
+      hierarchy: labels hierarchy as JSON object.
+    """
+
+    self._hierarchy_keyed_parent, self._hierarchy_keyed_child, _ = (
+        _build_plain_hierarchy(hierarchy, skip_root=True))
+
+  def expand_boxes_from_csv(self, csv_row):
+    """Expands a row containing bounding boxes from CSV file.
+
+    Args:
+      csv_row: a single row of Open Images released groundtruth file.
+
+    Returns:
+      a list of strings (including the initial row) corresponding to the ground
+      truth expanded to multiple annotation for evaluation with Open Images
+      Challenge 2018 metric.
+    """
+    # Row header is expected to be exactly:
+    # ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,
+    # IsTruncated,IsGroupOf,IsDepiction,IsInside
+    cvs_row_splitted = csv_row.split(',')
+    assert len(cvs_row_splitted) == 13
+    result = [csv_row]
+    assert cvs_row_splitted[2] in self._hierarchy_keyed_child
+    parent_nodes = self._hierarchy_keyed_child[cvs_row_splitted[2]]
+    for parent_node in parent_nodes:
+      cvs_row_splitted[2] = parent_node
+      result.append(','.join(cvs_row_splitted))
+    return result
+
+  def expand_labels_from_csv(self, csv_row):
+    """Expands a row containing bounding boxes from CSV file.
+
+    Args:
+      csv_row: a single row of Open Images released groundtruth file.
+
+    Returns:
+      a list of strings (including the initial row) corresponding to the ground
+      truth expanded to multiple annotation for evaluation with Open Images
+      Challenge 2018 metric.
+    """
+    # Row header is expected to be exactly:
+    # ImageID,Source,LabelName,Confidence
+    cvs_row_splited = csv_row.split(',')
+    assert len(cvs_row_splited) == 4
+    result = [csv_row]
+    if int(cvs_row_splited[3]) == 1:
+      assert cvs_row_splited[2] in self._hierarchy_keyed_child
+      parent_nodes = self._hierarchy_keyed_child[cvs_row_splited[2]]
+      for parent_node in parent_nodes:
+        cvs_row_splited[2] = parent_node
+        result.append(','.join(cvs_row_splited))
+    else:
+      assert cvs_row_splited[2] in self._hierarchy_keyed_parent
+      child_nodes = self._hierarchy_keyed_parent[cvs_row_splited[2]]
+      for child_node in child_nodes:
+        cvs_row_splited[2] = child_node
+        result.append(','.join(cvs_row_splited))
+    return result
+
+
+def main(parsed_args):
+
+  with open(parsed_args.json_hierarchy_file) as f:
+    hierarchy = json.load(f)
+  expansion_generator = OIDHierarchicalLabelsExpansion(hierarchy)
+  labels_file = False
+  if parsed_args.annotation_type == 2:
+    labels_file = True
+  elif parsed_args.annotation_type != 1:
+    print('--annotation_type expected value is 1 or 2.')
+    return -1
+  with open(parsed_args.input_annotations, 'r') as source:
+    with open(parsed_args.output_annotations, 'w') as target:
+      header = None
+      for line in source:
+        if not header:
+          header = line
+          target.writelines(header)
+          continue
+        if labels_file:
+          expanded_lines = expansion_generator.expand_labels_from_csv(line)
+        else:
+          expanded_lines = expansion_generator.expand_boxes_from_csv(line)
+        target.writelines(expanded_lines)
+
+
+if __name__ == '__main__':
+
+  parser = argparse.ArgumentParser(
+      description='Hierarchically expand annotations (excluding root node).')
+  parser.add_argument(
+      '--json_hierarchy_file',
+      required=True,
+      help='Path to the file containing label hierarchy in JSON format.')
+  parser.add_argument(
+      '--input_annotations',
+      required=True,
+      help="""Path to Open Images annotations file (either bounding boxes or
+      image-level labels).""")
+  parser.add_argument(
+      '--output_annotations',
+      required=True,
+      help="""Path to the output file.""")
+  parser.add_argument(
+      '--annotation_type',
+      type=int,
+      required=True,
+      help="""Type of the input annotations: 1 - boxes, 2 - image-level
+      labels"""
+  )
+  args = parser.parse_args()
+  main(args)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py
new file mode 100644
index 00000000..cd62b9cf
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py
@@ -0,0 +1,88 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the OpenImages label expansion (OIDHierarchicalLabelsExpansion)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from object_detection.dataset_tools import oid_hierarchical_labels_expansion
+
+
+def create_test_data():
+  hierarchy = {
+      'LabelName':
+          'a',
+      'Subcategory': [{
+          'LabelName': 'b'
+      }, {
+          'LabelName': 'c',
+          'Subcategory': [{
+              'LabelName': 'd'
+          }, {
+              'LabelName': 'e'
+          }]
+      }, {
+          'LabelName': 'f',
+          'Subcategory': [{
+              'LabelName': 'd'
+          },]
+      }]
+  }
+  bbox_rows = [
+      '123,xclick,b,1,0.1,0.2,0.1,0.2,1,1,0,0,0',
+      '123,xclick,d,1,0.2,0.3,0.1,0.2,1,1,0,0,0'
+  ]
+  label_rows = [
+      '123,verification,b,0', '123,verification,c,0', '124,verification,d,1'
+  ]
+  return hierarchy, bbox_rows, label_rows
+
+
+class HierarchicalLabelsExpansionTest(tf.test.TestCase):
+
+  def test_bbox_expansion(self):
+    hierarchy, bbox_rows, _ = create_test_data()
+    expansion_generator = (
+        oid_hierarchical_labels_expansion.OIDHierarchicalLabelsExpansion(
+            hierarchy))
+    all_result_rows = []
+    for row in bbox_rows:
+      all_result_rows.extend(expansion_generator.expand_boxes_from_csv(row))
+    self.assertItemsEqual([
+        '123,xclick,b,1,0.1,0.2,0.1,0.2,1,1,0,0,0',
+        '123,xclick,d,1,0.2,0.3,0.1,0.2,1,1,0,0,0',
+        '123,xclick,f,1,0.2,0.3,0.1,0.2,1,1,0,0,0',
+        '123,xclick,c,1,0.2,0.3,0.1,0.2,1,1,0,0,0'
+    ], all_result_rows)
+
+  def test_labels_expansion(self):
+    hierarchy, _, label_rows = create_test_data()
+    expansion_generator = (
+        oid_hierarchical_labels_expansion.OIDHierarchicalLabelsExpansion(
+            hierarchy))
+    all_result_rows = []
+    for row in label_rows:
+      all_result_rows.extend(expansion_generator.expand_labels_from_csv(row))
+    self.assertItemsEqual([
+        '123,verification,b,0', '123,verification,c,0', '123,verification,d,0',
+        '123,verification,e,0', '124,verification,d,1', '124,verification,f,1',
+        '124,verification,c,1'
+    ], all_result_rows)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation.py
new file mode 100644
index 00000000..70628098
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation.py
@@ -0,0 +1,106 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Utilities for creating TFRecords of TF examples for the Open Images dataset.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.utils import dataset_util
+
+
+def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
+                                           encoded_image):
+  """Populates a TF Example message with image annotations from a data frame.
+
+  Args:
+    annotations_data_frame: Data frame containing the annotations for a single
+      image.
+    label_map: String to integer label map.
+    encoded_image: The encoded image string
+
+  Returns:
+    The populated TF Example, if the label of at least one object is present in
+    label_map. Otherwise, returns None.
+  """
+
+  filtered_data_frame = annotations_data_frame[
+      annotations_data_frame.LabelName.isin(label_map)]
+  filtered_data_frame_boxes = filtered_data_frame[
+      ~filtered_data_frame.YMin.isnull()]
+  filtered_data_frame_labels = filtered_data_frame[
+      filtered_data_frame.YMin.isnull()]
+  image_id = annotations_data_frame.ImageID.iloc[0]
+
+  feature_map = {
+      standard_fields.TfExampleFields.object_bbox_ymin:
+          dataset_util.float_list_feature(
+              filtered_data_frame_boxes.YMin.as_matrix()),
+      standard_fields.TfExampleFields.object_bbox_xmin:
+          dataset_util.float_list_feature(
+              filtered_data_frame_boxes.XMin.as_matrix()),
+      standard_fields.TfExampleFields.object_bbox_ymax:
+          dataset_util.float_list_feature(
+              filtered_data_frame_boxes.YMax.as_matrix()),
+      standard_fields.TfExampleFields.object_bbox_xmax:
+          dataset_util.float_list_feature(
+              filtered_data_frame_boxes.XMax.as_matrix()),
+      standard_fields.TfExampleFields.object_class_text:
+          dataset_util.bytes_list_feature(
+              filtered_data_frame_boxes.LabelName.as_matrix()),
+      standard_fields.TfExampleFields.object_class_label:
+          dataset_util.int64_list_feature(
+              filtered_data_frame_boxes.LabelName.map(lambda x: label_map[x])
+              .as_matrix()),
+      standard_fields.TfExampleFields.filename:
+          dataset_util.bytes_feature('{}.jpg'.format(image_id)),
+      standard_fields.TfExampleFields.source_id:
+          dataset_util.bytes_feature(image_id),
+      standard_fields.TfExampleFields.image_encoded:
+          dataset_util.bytes_feature(encoded_image),
+  }
+
+  if 'IsGroupOf' in filtered_data_frame.columns:
+    feature_map[standard_fields.TfExampleFields.
+                object_group_of] = dataset_util.int64_list_feature(
+                    filtered_data_frame_boxes.IsGroupOf.as_matrix().astype(int))
+  if 'IsOccluded' in filtered_data_frame.columns:
+    feature_map[standard_fields.TfExampleFields.
+                object_occluded] = dataset_util.int64_list_feature(
+                    filtered_data_frame_boxes.IsOccluded.as_matrix().astype(
+                        int))
+  if 'IsTruncated' in filtered_data_frame.columns:
+    feature_map[standard_fields.TfExampleFields.
+                object_truncated] = dataset_util.int64_list_feature(
+                    filtered_data_frame_boxes.IsTruncated.as_matrix().astype(
+                        int))
+  if 'IsDepiction' in filtered_data_frame.columns:
+    feature_map[standard_fields.TfExampleFields.
+                object_depiction] = dataset_util.int64_list_feature(
+                    filtered_data_frame_boxes.IsDepiction.as_matrix().astype(
+                        int))
+
+  if 'ConfidenceImageLabel' in filtered_data_frame_labels.columns:
+    feature_map[standard_fields.TfExampleFields.
+                image_class_label] = dataset_util.int64_list_feature(
+                    filtered_data_frame_labels.LabelName.map(
+                        lambda x: label_map[x]).as_matrix())
+    feature_map[standard_fields.TfExampleFields.
+                image_class_text] = dataset_util.bytes_list_feature(
+                    filtered_data_frame_labels.LabelName.as_matrix()),
+  return tf.train.Example(features=tf.train.Features(feature=feature_map))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation_test.py
new file mode 100644
index 00000000..44ef8521
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/oid_tfrecord_creation_test.py
@@ -0,0 +1,200 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for oid_tfrecord_creation.py."""
+
+import pandas as pd
+import tensorflow as tf
+
+from object_detection.dataset_tools import oid_tfrecord_creation
+
+
+def create_test_data():
+  data = {
+      'ImageID': ['i1', 'i1', 'i1', 'i1', 'i1', 'i2', 'i2'],
+      'LabelName': ['a', 'a', 'b', 'b', 'c', 'b', 'c'],
+      'YMin': [0.3, 0.6, 0.8, 0.1, None, 0.0, 0.0],
+      'XMin': [0.1, 0.3, 0.7, 0.0, None, 0.1, 0.1],
+      'XMax': [0.2, 0.3, 0.8, 0.5, None, 0.9, 0.9],
+      'YMax': [0.3, 0.6, 1, 0.8, None, 0.8, 0.8],
+      'IsOccluded': [0, 1, 1, 0, None, 0, 0],
+      'IsTruncated': [0, 0, 0, 1, None, 0, 0],
+      'IsGroupOf': [0, 0, 0, 0, None, 0, 1],
+      'IsDepiction': [1, 0, 0, 0, None, 0, 0],
+      'ConfidenceImageLabel': [None, None, None, None, 0, None, None],
+  }
+  df = pd.DataFrame(data=data)
+  label_map = {'a': 0, 'b': 1, 'c': 2}
+  return label_map, df
+
+
+class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
+
+  def test_simple(self):
+    label_map, df = create_test_data()
+
+    tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
+        df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
+    self.assertProtoEquals(
+        """
+        features {
+          feature {
+            key: "image/encoded"
+            value { bytes_list { value: "encoded_image_test" } } }
+          feature {
+            key: "image/filename"
+            value { bytes_list { value: "i1.jpg" } } }
+          feature {
+            key: "image/object/bbox/ymin"
+            value { float_list { value: [0.3, 0.6, 0.8, 0.1] } } }
+          feature {
+            key: "image/object/bbox/xmin"
+            value { float_list { value: [0.1, 0.3, 0.7, 0.0] } } }
+          feature {
+            key: "image/object/bbox/ymax"
+            value { float_list { value: [0.3, 0.6, 1.0, 0.8] } } }
+          feature {
+            key: "image/object/bbox/xmax"
+            value { float_list { value: [0.2, 0.3, 0.8, 0.5] } } }
+          feature {
+            key: "image/object/class/label"
+            value { int64_list { value: [0, 0, 1, 1] } } }
+          feature {
+            key: "image/object/class/text"
+            value { bytes_list { value: ["a", "a", "b", "b"] } } }
+          feature {
+            key: "image/source_id"
+            value { bytes_list { value: "i1" } } }
+          feature {
+            key: "image/object/depiction"
+            value { int64_list { value: [1, 0, 0, 0] } } }
+          feature {
+            key: "image/object/group_of"
+            value { int64_list { value: [0, 0, 0, 0] } } }
+          feature {
+            key: "image/object/occluded"
+            value { int64_list { value: [0, 1, 1, 0] } } }
+          feature {
+            key: "image/object/truncated"
+            value { int64_list { value: [0, 0, 0, 1] } } }
+          feature {
+            key: "image/class/label"
+            value { int64_list { value: [2] } } }
+          feature {
+            key: "image/class/text"
+            value { bytes_list { value: ["c"] } } } }
+    """, tf_example)
+
+  def test_no_attributes(self):
+    label_map, df = create_test_data()
+
+    del df['IsDepiction']
+    del df['IsGroupOf']
+    del df['IsOccluded']
+    del df['IsTruncated']
+    del df['ConfidenceImageLabel']
+
+    tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
+        df[df.ImageID == 'i2'], label_map, 'encoded_image_test')
+    self.assertProtoEquals("""
+        features {
+          feature {
+            key: "image/encoded"
+            value { bytes_list { value: "encoded_image_test" } } }
+          feature {
+            key: "image/filename"
+            value { bytes_list { value: "i2.jpg" } } }
+          feature {
+            key: "image/object/bbox/ymin"
+            value { float_list { value: [0.0, 0.0] } } }
+          feature {
+            key: "image/object/bbox/xmin"
+            value { float_list { value: [0.1, 0.1] } } }
+          feature {
+            key: "image/object/bbox/ymax"
+            value { float_list { value: [0.8, 0.8] } } }
+          feature {
+            key: "image/object/bbox/xmax"
+            value { float_list { value: [0.9, 0.9] } } }
+          feature {
+            key: "image/object/class/label"
+            value { int64_list { value: [1, 2] } } }
+          feature {
+            key: "image/object/class/text"
+            value { bytes_list { value: ["b", "c"] } } }
+          feature {
+            key: "image/source_id"
+           value { bytes_list { value: "i2" } } } }
+    """, tf_example)
+
+  def test_label_filtering(self):
+    label_map, df = create_test_data()
+
+    label_map = {'a': 0}
+
+    tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
+        df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
+    self.assertProtoEquals(
+        """
+        features {
+          feature {
+            key: "image/encoded"
+            value { bytes_list { value: "encoded_image_test" } } }
+          feature {
+            key: "image/filename"
+            value { bytes_list { value: "i1.jpg" } } }
+          feature {
+            key: "image/object/bbox/ymin"
+            value { float_list { value: [0.3, 0.6] } } }
+          feature {
+            key: "image/object/bbox/xmin"
+            value { float_list { value: [0.1, 0.3] } } }
+          feature {
+            key: "image/object/bbox/ymax"
+            value { float_list { value: [0.3, 0.6] } } }
+          feature {
+            key: "image/object/bbox/xmax"
+            value { float_list { value: [0.2, 0.3] } } }
+          feature {
+            key: "image/object/class/label"
+            value { int64_list { value: [0, 0] } } }
+          feature {
+            key: "image/object/class/text"
+            value { bytes_list { value: ["a", "a"] } } }
+          feature {
+            key: "image/source_id"
+            value { bytes_list { value: "i1" } } }
+          feature {
+            key: "image/object/depiction"
+            value { int64_list { value: [1, 0] } } }
+          feature {
+            key: "image/object/group_of"
+            value { int64_list { value: [0, 0] } } }
+          feature {
+            key: "image/object/occluded"
+            value { int64_list { value: [0, 1] } } }
+          feature {
+            key: "image/object/truncated"
+            value { int64_list { value: [0, 0] } } }
+          feature {
+            key: "image/class/label"
+            value { int64_list { } } }
+          feature {
+            key: "image/class/text"
+            value { bytes_list { } } } }
+    """, tf_example)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util.py
new file mode 100644
index 00000000..e8da2291
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util.py
@@ -0,0 +1,46 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Utilities for creating TFRecords of TF examples for the Open Images dataset.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+
+def open_sharded_output_tfrecords(exit_stack, base_path, num_shards):
+  """Opens all TFRecord shards for writing and adds them to an exit stack.
+
+  Args:
+    exit_stack: A context2.ExitStack used to automatically closed the TFRecords
+      opened in this function.
+    base_path: The base path for all shards
+    num_shards: The number of shards
+
+  Returns:
+    The list of opened TFRecords. Position k in the list corresponds to shard k.
+  """
+  tf_record_output_filenames = [
+      '{}-{:05d}-of-{:05d}'.format(base_path, idx, num_shards)
+      for idx in range(num_shards)
+  ]
+
+  tfrecords = [
+      exit_stack.enter_context(tf.python_io.TFRecordWriter(file_name))
+      for file_name in tf_record_output_filenames
+  ]
+
+  return tfrecords
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util_test.py
new file mode 100644
index 00000000..f1231f8b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools/tf_record_creation_util_test.py
@@ -0,0 +1,42 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tf_record_creation_util.py."""
+
+import os
+import contextlib2
+import tensorflow as tf
+
+from object_detection.dataset_tools import tf_record_creation_util
+
+
+class OpenOutputTfrecordsTests(tf.test.TestCase):
+
+  def test_sharded_tfrecord_writes(self):
+    with contextlib2.ExitStack() as tf_record_close_stack:
+      output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
+          tf_record_close_stack,
+          os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), 10)
+      for idx in range(10):
+        output_tfrecords[idx].write('test_{}'.format(idx))
+
+    for idx in range(10):
+      tf_record_path = '{}-{:05d}-of-00010'.format(
+          os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), idx)
+      records = list(tf.python_io.tf_record_iterator(tf_record_path))
+      self.assertAllEqual(records, ['test_{}'.format(idx)])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/Dockerfile b/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/Dockerfile
new file mode 100644
index 00000000..cb5e0ba9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/Dockerfile
@@ -0,0 +1,136 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# #==========================================================================
+
+FROM tensorflow/tensorflow:nightly-devel
+
+# Get the tensorflow models research directory, and move it into tensorflow
+# source folder to match recommendation of installation
+RUN git clone --depth 1 https://github.com/tensorflow/models.git && \
+    mv models /tensorflow/models
+
+
+# Install gcloud and gsutil commands
+# https://cloud.google.com/sdk/docs/quickstart-debian-ubuntu
+RUN export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
+    echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - && \
+    apt-get update -y && apt-get install google-cloud-sdk -y
+
+
+# Install the Tensorflow Object Detection API from here
+# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md
+
+# Install object detection api dependencies
+RUN apt-get install -y protobuf-compiler python-pil python-lxml python-tk && \
+    pip install Cython && \
+    pip install contextlib2 && \
+    pip install jupyter && \
+    pip install matplotlib
+
+# Install pycocoapi
+RUN git clone --depth 1 https://github.com/cocodataset/cocoapi.git && \
+    cd cocoapi/PythonAPI && \
+    make -j8 && \
+    cp -r pycocotools /tensorflow/models/research && \
+    cd ../../ && \
+    rm -rf cocoapi
+
+# Get protoc 3.0.0, rather than the old version already in the container
+RUN curl -OL "https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip" && \
+    unzip protoc-3.0.0-linux-x86_64.zip -d proto3 && \
+    mv proto3/bin/* /usr/local/bin && \
+    mv proto3/include/* /usr/local/include && \
+    rm -rf proto3 protoc-3.0.0-linux-x86_64.zip
+
+# Run protoc on the object detection repo
+RUN cd /tensorflow/models/research && \
+    protoc object_detection/protos/*.proto --python_out=.
+
+# Set the PYTHONPATH to finish installing the API
+ENV PYTHONPATH $PYTHONPATH:/tensorflow/models/research:/tensorflow/models/research/slim
+
+
+# Install wget (to make life easier below) and editors (to allow people to edit
+# the files inside the container)
+RUN apt-get install -y wget vim emacs nano
+
+
+# Grab various data files which are used throughout the demo: dataset,
+# pretrained model, and pretrained TensorFlow Lite model. Install these all in
+# the same directories as recommended by the blog post.
+
+# Pets example dataset
+RUN mkdir -p /tmp/pet_faces_tfrecord/ && \
+    cd /tmp/pet_faces_tfrecord && \
+    curl "http://download.tensorflow.org/models/object_detection/pet_faces_tfrecord.tar.gz" | tar xzf -
+
+# Pretrained model
+# This one doesn't need its own directory, since it comes in a folder.
+RUN cd /tmp && \
+    curl -O "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03.tar.gz" && \
+    tar xzf ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03.tar.gz && \
+    rm ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03.tar.gz
+
+# Trained TensorFlow Lite model. This should get replaced by one generated from
+# export_tflite_ssd_graph.py when that command is called.
+RUN cd /tmp && \
+    curl -L -o tflite.zip \
+    https://storage.googleapis.com/download.tensorflow.org/models/tflite/frozengraphs_ssd_mobilenet_v1_0.75_quant_pets_2018_06_29.zip && \
+    unzip tflite.zip -d tflite && \
+    rm tflite.zip
+
+
+# Install Android development tools
+# Inspired by the following sources:
+# https://github.com/bitrise-docker/android/blob/master/Dockerfile
+# https://github.com/reddit/docker-android-build/blob/master/Dockerfile
+
+# Set environment variables
+ENV ANDROID_HOME /opt/android-sdk-linux
+ENV ANDROID_NDK_HOME /opt/android-ndk-r14b
+ENV PATH ${PATH}:${ANDROID_HOME}/tools:${ANDROID_HOME}/tools/bin:${ANDROID_HOME}/platform-tools
+
+# Install SDK tools
+RUN cd /opt && \
+    curl -OL https://dl.google.com/android/repository/sdk-tools-linux-4333796.zip && \
+    unzip sdk-tools-linux-4333796.zip -d ${ANDROID_HOME} && \
+    rm sdk-tools-linux-4333796.zip
+
+# Accept licenses before installing components, no need to echo y for each component
+# License is valid for all the standard components in versions installed from this file
+# Non-standard components: MIPS system images, preview versions, GDK (Google Glass) and Android Google TV require separate licenses, not accepted there
+RUN yes | sdkmanager --licenses
+
+# Install platform tools, SDK platform, and other build tools
+RUN yes | sdkmanager \
+    "tools" \
+    "platform-tools" \
+    "platforms;android-27" \
+    "platforms;android-23" \
+    "build-tools;27.0.3" \
+    "build-tools;23.0.3"
+
+# Install Android NDK (r14b)
+RUN cd /opt && \
+    curl -L -o android-ndk.zip http://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \
+    unzip -q android-ndk.zip && \
+    rm -f android-ndk.zip
+
+# Configure the build to use the things we just downloaded
+RUN cd /tensorflow && \
+    printf '\n\nn\ny\nn\nn\nn\ny\nn\nn\nn\nn\nn\nn\n\ny\n%s\n\n\n' ${ANDROID_HOME}|./configure
+
+
+WORKDIR /tensorflow
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/README.md b/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/README.md
new file mode 100644
index 00000000..107a0db8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/dockerfiles/android/README.md
@@ -0,0 +1,69 @@
+# Dockerfile for the TPU and TensorFlow Lite Object Detection tutorial
+
+This Docker image automates the setup involved with training
+object detection models on Google Cloud and building the Android TensorFlow Lite
+demo app. We recommend using this container if you decide to work through our
+tutorial on ["Training and serving a real-time mobile object detector in
+30 minutes with Cloud TPUs"](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193), though of course it may be useful even if you would
+like to use the Object Detection API outside the context of the tutorial.
+
+A couple words of warning:
+
+1. Docker containers do not have persistent storage. This means that any changes
+   you make to files inside the container will not persist if you restart
+   the container. When running through the tutorial,
+   **do not close the container**.
+2. To be able to deploy the [Android app](
+   https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/android/app)
+   (which you will build at the end of the tutorial),
+   you will need to kill any instances of `adb` running on the host machine. You
+   can accomplish this by closing all instances of Android Studio, and then
+   running `adb kill-server`.
+
+You can install Docker by following the [instructions here](
+https://docs.docker.com/install/).
+
+## Running The Container
+
+From this directory, build the Dockerfile as follows (this takes a while):
+
+```
+docker build --tag detect-tf .
+```
+
+Run the container:
+
+```
+docker run --rm -it --privileged -p 6006:6006 detect-tf
+```
+
+When running the container, you will find yourself inside the `/tensorflow`
+directory, which is the path to the TensorFlow [source
+tree](https://github.com/tensorflow/tensorflow).
+
+## Text Editing
+
+The tutorial also
+requires you to occasionally edit files inside the source tree.
+This Docker images comes with `vim`, `nano`, and `emacs` preinstalled for your
+convenience.
+
+## What's In This Container
+
+This container is derived from the nightly build of TensorFlow, and contains the
+sources for TensorFlow at `/tensorflow`, as well as the
+[TensorFlow Models](https://github.com/tensorflow/models) which are available at
+`/tensorflow/models` (and contain the Object Detection API as a subdirectory
+at `/tensorflow/models/research/object_detection`).
+The Oxford-IIIT Pets dataset, the COCO pre-trained SSD + MobileNet (v1)
+checkpoint, and example
+trained model are all available in `/tmp` in their respective folders.
+
+This container also has the `gsutil` and `gcloud` utilities, the `bazel` build
+tool, and all dependencies necessary to use the Object Detection API, and
+compile and install the TensorFlow Lite Android demo app.
+
+At various points throughout the tutorial, you may see references to the
+*research directory*.  This refers to the `research` folder within the
+models repository, located at
+`/tensorflow/models/resesarch`.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/eval_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/eval_util.py
new file mode 100644
index 00000000..f7145511
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/eval_util.py
@@ -0,0 +1,907 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Common utility functions for evaluation."""
+import collections
+import os
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.core import keypoint_ops
+from object_detection.core import standard_fields as fields
+from object_detection.metrics import coco_evaluation
+from object_detection.utils import label_map_util
+from object_detection.utils import object_detection_evaluation
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from object_detection.utils import visualization_utils as vis_utils
+
+slim = tf.contrib.slim
+
+# A dictionary of metric names to classes that implement the metric. The classes
+# in the dictionary must implement
+# utils.object_detection_evaluation.DetectionEvaluator interface.
+EVAL_METRICS_CLASS_DICT = {
+    'coco_detection_metrics':
+        coco_evaluation.CocoDetectionEvaluator,
+    'coco_mask_metrics':
+        coco_evaluation.CocoMaskEvaluator,
+    'oid_challenge_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionChallengeEvaluator,
+    'pascal_voc_detection_metrics':
+        object_detection_evaluation.PascalDetectionEvaluator,
+    'weighted_pascal_voc_detection_metrics':
+        object_detection_evaluation.WeightedPascalDetectionEvaluator,
+    'pascal_voc_instance_segmentation_metrics':
+        object_detection_evaluation.PascalInstanceSegmentationEvaluator,
+    'weighted_pascal_voc_instance_segmentation_metrics':
+        object_detection_evaluation.WeightedPascalInstanceSegmentationEvaluator,
+    'oid_V2_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionEvaluator,
+}
+
+EVAL_DEFAULT_METRIC = 'coco_detection_metrics'
+
+
+def write_metrics(metrics, global_step, summary_dir):
+  """Write metrics to a summary directory.
+
+  Args:
+    metrics: A dictionary containing metric names and values.
+    global_step: Global step at which the metrics are computed.
+    summary_dir: Directory to write tensorflow summaries to.
+  """
+  tf.logging.info('Writing metrics to tf summary.')
+  summary_writer = tf.summary.FileWriterCache.get(summary_dir)
+  for key in sorted(metrics):
+    summary = tf.Summary(value=[
+        tf.Summary.Value(tag=key, simple_value=metrics[key]),
+    ])
+    summary_writer.add_summary(summary, global_step)
+    tf.logging.info('%s: %f', key, metrics[key])
+  tf.logging.info('Metrics written to tf summary.')
+
+
+# TODO(rathodv): Add tests.
+def visualize_detection_results(result_dict,
+                                tag,
+                                global_step,
+                                categories,
+                                summary_dir='',
+                                export_dir='',
+                                agnostic_mode=False,
+                                show_groundtruth=False,
+                                groundtruth_box_visualization_color='black',
+                                min_score_thresh=.5,
+                                max_num_predictions=20,
+                                skip_scores=False,
+                                skip_labels=False,
+                                keep_image_id_for_visualization_export=False):
+  """Visualizes detection results and writes visualizations to image summaries.
+
+  This function visualizes an image with its detected bounding boxes and writes
+  to image summaries which can be viewed on tensorboard.  It optionally also
+  writes images to a directory. In the case of missing entry in the label map,
+  unknown class name in the visualization is shown as "N/A".
+
+  Args:
+    result_dict: a dictionary holding groundtruth and detection
+      data corresponding to each image being evaluated.  The following keys
+      are required:
+        'original_image': a numpy array representing the image with shape
+          [1, height, width, 3] or [1, height, width, 1]
+        'detection_boxes': a numpy array of shape [N, 4]
+        'detection_scores': a numpy array of shape [N]
+        'detection_classes': a numpy array of shape [N]
+      The following keys are optional:
+        'groundtruth_boxes': a numpy array of shape [N, 4]
+        'groundtruth_keypoints': a numpy array of shape [N, num_keypoints, 2]
+      Detections are assumed to be provided in decreasing order of score and for
+      display, and we assume that scores are probabilities between 0 and 1.
+    tag: tensorboard tag (string) to associate with image.
+    global_step: global step at which the visualization are generated.
+    categories: a list of dictionaries representing all possible categories.
+      Each dict in this list has the following keys:
+          'id': (required) an integer id uniquely identifying this category
+          'name': (required) string representing category name
+            e.g., 'cat', 'dog', 'pizza'
+          'supercategory': (optional) string representing the supercategory
+            e.g., 'animal', 'vehicle', 'food', etc
+    summary_dir: the output directory to which the image summaries are written.
+    export_dir: the output directory to which images are written.  If this is
+      empty (default), then images are not exported.
+    agnostic_mode: boolean (default: False) controlling whether to evaluate in
+      class-agnostic mode or not.
+    show_groundtruth: boolean (default: False) controlling whether to show
+      groundtruth boxes in addition to detected boxes
+    groundtruth_box_visualization_color: box color for visualizing groundtruth
+      boxes
+    min_score_thresh: minimum score threshold for a box to be visualized
+    max_num_predictions: maximum number of detections to visualize
+    skip_scores: whether to skip score when drawing a single detection
+    skip_labels: whether to skip label when drawing a single detection
+    keep_image_id_for_visualization_export: whether to keep image identifier in
+      filename when exported to export_dir
+  Raises:
+    ValueError: if result_dict does not contain the expected keys (i.e.,
+      'original_image', 'detection_boxes', 'detection_scores',
+      'detection_classes')
+  """
+  detection_fields = fields.DetectionResultFields
+  input_fields = fields.InputDataFields
+  if not set([
+      input_fields.original_image,
+      detection_fields.detection_boxes,
+      detection_fields.detection_scores,
+      detection_fields.detection_classes,
+  ]).issubset(set(result_dict.keys())):
+    raise ValueError('result_dict does not contain all expected keys.')
+  if show_groundtruth and input_fields.groundtruth_boxes not in result_dict:
+    raise ValueError('If show_groundtruth is enabled, result_dict must contain '
+                     'groundtruth_boxes.')
+  tf.logging.info('Creating detection visualizations.')
+  category_index = label_map_util.create_category_index(categories)
+
+  image = np.squeeze(result_dict[input_fields.original_image], axis=0)
+  if image.shape[2] == 1:  # If one channel image, repeat in RGB.
+    image = np.tile(image, [1, 1, 3])
+  detection_boxes = result_dict[detection_fields.detection_boxes]
+  detection_scores = result_dict[detection_fields.detection_scores]
+  detection_classes = np.int32((result_dict[
+      detection_fields.detection_classes]))
+  detection_keypoints = result_dict.get(detection_fields.detection_keypoints)
+  detection_masks = result_dict.get(detection_fields.detection_masks)
+  detection_boundaries = result_dict.get(detection_fields.detection_boundaries)
+
+  # Plot groundtruth underneath detections
+  if show_groundtruth:
+    groundtruth_boxes = result_dict[input_fields.groundtruth_boxes]
+    groundtruth_keypoints = result_dict.get(input_fields.groundtruth_keypoints)
+    vis_utils.visualize_boxes_and_labels_on_image_array(
+        image=image,
+        boxes=groundtruth_boxes,
+        classes=None,
+        scores=None,
+        category_index=category_index,
+        keypoints=groundtruth_keypoints,
+        use_normalized_coordinates=False,
+        max_boxes_to_draw=None,
+        groundtruth_box_visualization_color=groundtruth_box_visualization_color)
+  vis_utils.visualize_boxes_and_labels_on_image_array(
+      image,
+      detection_boxes,
+      detection_classes,
+      detection_scores,
+      category_index,
+      instance_masks=detection_masks,
+      instance_boundaries=detection_boundaries,
+      keypoints=detection_keypoints,
+      use_normalized_coordinates=False,
+      max_boxes_to_draw=max_num_predictions,
+      min_score_thresh=min_score_thresh,
+      agnostic_mode=agnostic_mode,
+      skip_scores=skip_scores,
+      skip_labels=skip_labels)
+
+  if export_dir:
+    if keep_image_id_for_visualization_export and result_dict[fields.
+                                                              InputDataFields()
+                                                              .key]:
+      export_path = os.path.join(export_dir, 'export-{}-{}.png'.format(
+          tag, result_dict[fields.InputDataFields().key]))
+    else:
+      export_path = os.path.join(export_dir, 'export-{}.png'.format(tag))
+    vis_utils.save_image_array_as_png(image, export_path)
+
+  summary = tf.Summary(value=[
+      tf.Summary.Value(
+          tag=tag,
+          image=tf.Summary.Image(
+              encoded_image_string=vis_utils.encode_image_array_as_png_str(
+                  image)))
+  ])
+  summary_writer = tf.summary.FileWriterCache.get(summary_dir)
+  summary_writer.add_summary(summary, global_step)
+
+  tf.logging.info('Detection visualizations written to summary with tag %s.',
+                  tag)
+
+
+def _run_checkpoint_once(tensor_dict,
+                         evaluators=None,
+                         batch_processor=None,
+                         checkpoint_dirs=None,
+                         variables_to_restore=None,
+                         restore_fn=None,
+                         num_batches=1,
+                         master='',
+                         save_graph=False,
+                         save_graph_dir='',
+                         losses_dict=None,
+                         eval_export_path=None):
+  """Evaluates metrics defined in evaluators and returns summaries.
+
+  This function loads the latest checkpoint in checkpoint_dirs and evaluates
+  all metrics defined in evaluators. The metrics are processed in batch by the
+  batch_processor.
+
+  Args:
+    tensor_dict: a dictionary holding tensors representing a batch of detections
+      and corresponding groundtruth annotations.
+    evaluators: a list of object of type DetectionEvaluator to be used for
+      evaluation. Note that the metric names produced by different evaluators
+      must be unique.
+    batch_processor: a function taking four arguments:
+      1. tensor_dict: the same tensor_dict that is passed in as the first
+        argument to this function.
+      2. sess: a tensorflow session
+      3. batch_index: an integer representing the index of the batch amongst
+        all batches
+      By default, batch_processor is None, which defaults to running:
+        return sess.run(tensor_dict)
+      To skip an image, it suffices to return an empty dictionary in place of
+      result_dict.
+    checkpoint_dirs: list of directories to load into an EnsembleModel. If it
+      has only one directory, EnsembleModel will not be used --
+        a DetectionModel
+      will be instantiated directly. Not used if restore_fn is set.
+    variables_to_restore: None, or a dictionary mapping variable names found in
+      a checkpoint to model variables. The dictionary would normally be
+      generated by creating a tf.train.ExponentialMovingAverage object and
+      calling its variables_to_restore() method. Not used if restore_fn is set.
+    restore_fn: None, or a function that takes a tf.Session object and correctly
+      restores all necessary variables from the correct checkpoint file. If
+      None, attempts to restore from the first directory in checkpoint_dirs.
+    num_batches: the number of batches to use for evaluation.
+    master: the location of the Tensorflow session.
+    save_graph: whether or not the Tensorflow graph is stored as a pbtxt file.
+    save_graph_dir: where to store the Tensorflow graph on disk. If save_graph
+      is True this must be non-empty.
+    losses_dict: optional dictionary of scalar detection losses.
+    eval_export_path: Path for saving a json file that contains the detection
+      results in json format.
+
+  Returns:
+    global_step: the count of global steps.
+    all_evaluator_metrics: A dictionary containing metric names and values.
+
+  Raises:
+    ValueError: if restore_fn is None and checkpoint_dirs doesn't have at least
+      one element.
+    ValueError: if save_graph is True and save_graph_dir is not defined.
+  """
+  if save_graph and not save_graph_dir:
+    raise ValueError('`save_graph_dir` must be defined.')
+  sess = tf.Session(master, graph=tf.get_default_graph())
+  sess.run(tf.global_variables_initializer())
+  sess.run(tf.local_variables_initializer())
+  sess.run(tf.tables_initializer())
+  if restore_fn:
+    restore_fn(sess)
+  else:
+    if not checkpoint_dirs:
+      raise ValueError('`checkpoint_dirs` must have at least one entry.')
+    checkpoint_file = tf.train.latest_checkpoint(checkpoint_dirs[0])
+    saver = tf.train.Saver(variables_to_restore)
+    saver.restore(sess, checkpoint_file)
+
+  if save_graph:
+    tf.train.write_graph(sess.graph_def, save_graph_dir, 'eval.pbtxt')
+
+  counters = {'skipped': 0, 'success': 0}
+  aggregate_result_losses_dict = collections.defaultdict(list)
+  with tf.contrib.slim.queues.QueueRunners(sess):
+    try:
+      for batch in range(int(num_batches)):
+        if (batch + 1) % 100 == 0:
+          tf.logging.info('Running eval ops batch %d/%d', batch + 1,
+                          num_batches)
+        if not batch_processor:
+          try:
+            if not losses_dict:
+              losses_dict = {}
+            result_dict, result_losses_dict = sess.run([tensor_dict,
+                                                        losses_dict])
+            counters['success'] += 1
+          except tf.errors.InvalidArgumentError:
+            tf.logging.info('Skipping image')
+            counters['skipped'] += 1
+            result_dict = {}
+        else:
+          result_dict, result_losses_dict = batch_processor(
+              tensor_dict, sess, batch, counters, losses_dict=losses_dict)
+        if not result_dict:
+          continue
+        for key, value in iter(result_losses_dict.items()):
+          aggregate_result_losses_dict[key].append(value)
+        for evaluator in evaluators:
+          # TODO(b/65130867): Use image_id tensor once we fix the input data
+          # decoders to return correct image_id.
+          # TODO(akuznetsa): result_dict contains batches of images, while
+          # add_single_ground_truth_image_info expects a single image. Fix
+          if (isinstance(result_dict, dict) and
+              fields.InputDataFields.key in result_dict and
+              result_dict[fields.InputDataFields.key]):
+            image_id = result_dict[fields.InputDataFields.key]
+          else:
+            image_id = batch
+          evaluator.add_single_ground_truth_image_info(
+              image_id=image_id, groundtruth_dict=result_dict)
+          evaluator.add_single_detected_image_info(
+              image_id=image_id, detections_dict=result_dict)
+      tf.logging.info('Running eval batches done.')
+    except tf.errors.OutOfRangeError:
+      tf.logging.info('Done evaluating -- epoch limit reached')
+    finally:
+      # When done, ask the threads to stop.
+      tf.logging.info('# success: %d', counters['success'])
+      tf.logging.info('# skipped: %d', counters['skipped'])
+      all_evaluator_metrics = {}
+      if eval_export_path and eval_export_path is not None:
+        for evaluator in evaluators:
+          if (isinstance(evaluator, coco_evaluation.CocoDetectionEvaluator) or
+              isinstance(evaluator, coco_evaluation.CocoMaskEvaluator)):
+            tf.logging.info('Started dumping to json file.')
+            evaluator.dump_detections_to_json_file(
+                json_output_path=eval_export_path)
+            tf.logging.info('Finished dumping to json file.')
+      for evaluator in evaluators:
+        metrics = evaluator.evaluate()
+        evaluator.clear()
+        if any(key in all_evaluator_metrics for key in metrics):
+          raise ValueError('Metric names between evaluators must not collide.')
+        all_evaluator_metrics.update(metrics)
+      global_step = tf.train.global_step(sess, tf.train.get_global_step())
+
+      for key, value in iter(aggregate_result_losses_dict.items()):
+        all_evaluator_metrics['Losses/' + key] = np.mean(value)
+  sess.close()
+  return (global_step, all_evaluator_metrics)
+
+
+# TODO(rathodv): Add tests.
+def repeated_checkpoint_run(tensor_dict,
+                            summary_dir,
+                            evaluators,
+                            batch_processor=None,
+                            checkpoint_dirs=None,
+                            variables_to_restore=None,
+                            restore_fn=None,
+                            num_batches=1,
+                            eval_interval_secs=120,
+                            max_number_of_evaluations=None,
+                            master='',
+                            save_graph=False,
+                            save_graph_dir='',
+                            losses_dict=None,
+                            eval_export_path=None):
+  """Periodically evaluates desired tensors using checkpoint_dirs or restore_fn.
+
+  This function repeatedly loads a checkpoint and evaluates a desired
+  set of tensors (provided by tensor_dict) and hands the resulting numpy
+  arrays to a function result_processor which can be used to further
+  process/save/visualize the results.
+
+  Args:
+    tensor_dict: a dictionary holding tensors representing a batch of detections
+      and corresponding groundtruth annotations.
+    summary_dir: a directory to write metrics summaries.
+    evaluators: a list of object of type DetectionEvaluator to be used for
+      evaluation. Note that the metric names produced by different evaluators
+      must be unique.
+    batch_processor: a function taking three arguments:
+      1. tensor_dict: the same tensor_dict that is passed in as the first
+        argument to this function.
+      2. sess: a tensorflow session
+      3. batch_index: an integer representing the index of the batch amongst
+        all batches
+      By default, batch_processor is None, which defaults to running:
+        return sess.run(tensor_dict)
+    checkpoint_dirs: list of directories to load into a DetectionModel or an
+      EnsembleModel if restore_fn isn't set. Also used to determine when to run
+      next evaluation. Must have at least one element.
+    variables_to_restore: None, or a dictionary mapping variable names found in
+      a checkpoint to model variables. The dictionary would normally be
+      generated by creating a tf.train.ExponentialMovingAverage object and
+      calling its variables_to_restore() method. Not used if restore_fn is set.
+    restore_fn: a function that takes a tf.Session object and correctly restores
+      all necessary variables from the correct checkpoint file.
+    num_batches: the number of batches to use for evaluation.
+    eval_interval_secs: the number of seconds between each evaluation run.
+    max_number_of_evaluations: the max number of iterations of the evaluation.
+      If the value is left as None the evaluation continues indefinitely.
+    master: the location of the Tensorflow session.
+    save_graph: whether or not the Tensorflow graph is saved as a pbtxt file.
+    save_graph_dir: where to save on disk the Tensorflow graph. If store_graph
+      is True this must be non-empty.
+    losses_dict: optional dictionary of scalar detection losses.
+    eval_export_path: Path for saving a json file that contains the detection
+      results in json format.
+
+  Returns:
+    metrics: A dictionary containing metric names and values in the latest
+      evaluation.
+
+  Raises:
+    ValueError: if max_num_of_evaluations is not None or a positive number.
+    ValueError: if checkpoint_dirs doesn't have at least one element.
+  """
+  if max_number_of_evaluations and max_number_of_evaluations <= 0:
+    raise ValueError(
+        '`number_of_steps` must be either None or a positive number.')
+
+  if not checkpoint_dirs:
+    raise ValueError('`checkpoint_dirs` must have at least one entry.')
+
+  last_evaluated_model_path = None
+  number_of_evaluations = 0
+  while True:
+    start = time.time()
+    tf.logging.info('Starting evaluation at ' + time.strftime(
+        '%Y-%m-%d-%H:%M:%S', time.gmtime()))
+    model_path = tf.train.latest_checkpoint(checkpoint_dirs[0])
+    if not model_path:
+      tf.logging.info('No model found in %s. Will try again in %d seconds',
+                      checkpoint_dirs[0], eval_interval_secs)
+    elif model_path == last_evaluated_model_path:
+      tf.logging.info('Found already evaluated checkpoint. Will try again in '
+                      '%d seconds', eval_interval_secs)
+    else:
+      last_evaluated_model_path = model_path
+      global_step, metrics = _run_checkpoint_once(
+          tensor_dict,
+          evaluators,
+          batch_processor,
+          checkpoint_dirs,
+          variables_to_restore,
+          restore_fn,
+          num_batches,
+          master,
+          save_graph,
+          save_graph_dir,
+          losses_dict=losses_dict,
+          eval_export_path=eval_export_path)
+      write_metrics(metrics, global_step, summary_dir)
+    number_of_evaluations += 1
+
+    if (max_number_of_evaluations and
+        number_of_evaluations >= max_number_of_evaluations):
+      tf.logging.info('Finished evaluation!')
+      break
+    time_to_next_eval = start + eval_interval_secs - time.time()
+    if time_to_next_eval > 0:
+      time.sleep(time_to_next_eval)
+
+  return metrics
+
+
+def _scale_box_to_absolute(args):
+  boxes, image_shape = args
+  return box_list_ops.to_absolute_coordinates(
+      box_list.BoxList(boxes), image_shape[0], image_shape[1]).get()
+
+
+def _resize_detection_masks(args):
+  detection_boxes, detection_masks, image_shape = args
+  detection_masks_reframed = ops.reframe_box_masks_to_image_masks(
+      detection_masks, detection_boxes, image_shape[0], image_shape[1])
+  return tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)
+
+
+def _resize_groundtruth_masks(args):
+  mask, image_shape = args
+  mask = tf.expand_dims(mask, 3)
+  mask = tf.image.resize_images(
+      mask,
+      image_shape,
+      method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+      align_corners=True)
+  return tf.cast(tf.squeeze(mask, 3), tf.uint8)
+
+
+def _scale_keypoint_to_absolute(args):
+  keypoints, image_shape = args
+  return keypoint_ops.scale(keypoints, image_shape[0], image_shape[1])
+
+
+def result_dict_for_single_example(image,
+                                   key,
+                                   detections,
+                                   groundtruth=None,
+                                   class_agnostic=False,
+                                   scale_to_absolute=False):
+  """Merges all detection and groundtruth information for a single example.
+
+  Note that evaluation tools require classes that are 1-indexed, and so this
+  function performs the offset. If `class_agnostic` is True, all output classes
+  have label 1.
+
+  Args:
+    image: A single 4D uint8 image tensor of shape [1, H, W, C].
+    key: A single string tensor identifying the image.
+    detections: A dictionary of detections, returned from
+      DetectionModel.postprocess().
+    groundtruth: (Optional) Dictionary of groundtruth items, with fields:
+      'groundtruth_boxes': [num_boxes, 4] float32 tensor of boxes, in
+        normalized coordinates.
+      'groundtruth_classes': [num_boxes] int64 tensor of 1-indexed classes.
+      'groundtruth_area': [num_boxes] float32 tensor of bbox area. (Optional)
+      'groundtruth_is_crowd': [num_boxes] int64 tensor. (Optional)
+      'groundtruth_difficult': [num_boxes] int64 tensor. (Optional)
+      'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
+      'groundtruth_instance_masks': 3D int64 tensor of instance masks
+        (Optional).
+    class_agnostic: Boolean indicating whether the detections are class-agnostic
+      (i.e. binary). Default False.
+    scale_to_absolute: Boolean indicating whether boxes and keypoints should be
+      scaled to absolute coordinates. Note that for IoU based evaluations, it
+      does not matter whether boxes are expressed in absolute or relative
+      coordinates. Default False.
+
+  Returns:
+    A dictionary with:
+    'original_image': A [1, H, W, C] uint8 image tensor.
+    'key': A string tensor with image identifier.
+    'detection_boxes': [max_detections, 4] float32 tensor of boxes, in
+      normalized or absolute coordinates, depending on the value of
+      `scale_to_absolute`.
+    'detection_scores': [max_detections] float32 tensor of scores.
+    'detection_classes': [max_detections] int64 tensor of 1-indexed classes.
+    'detection_masks': [max_detections, H, W] float32 tensor of binarized
+      masks, reframed to full image masks.
+    'groundtruth_boxes': [num_boxes, 4] float32 tensor of boxes, in
+      normalized or absolute coordinates, depending on the value of
+      `scale_to_absolute`. (Optional)
+    'groundtruth_classes': [num_boxes] int64 tensor of 1-indexed classes.
+      (Optional)
+    'groundtruth_area': [num_boxes] float32 tensor of bbox area. (Optional)
+    'groundtruth_is_crowd': [num_boxes] int64 tensor. (Optional)
+    'groundtruth_difficult': [num_boxes] int64 tensor. (Optional)
+    'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
+    'groundtruth_instance_masks': 3D int64 tensor of instance masks
+      (Optional).
+
+  """
+
+  if groundtruth:
+    max_gt_boxes = tf.shape(
+        groundtruth[fields.InputDataFields.groundtruth_boxes])[0]
+    for gt_key in groundtruth:
+      # expand groundtruth dict along the batch dimension.
+      groundtruth[gt_key] = tf.expand_dims(groundtruth[gt_key], 0)
+
+  for detection_key in detections:
+    detections[detection_key] = tf.expand_dims(
+        detections[detection_key][0], axis=0)
+
+  batched_output_dict = result_dict_for_batched_example(
+      image,
+      tf.expand_dims(key, 0),
+      detections,
+      groundtruth,
+      class_agnostic,
+      scale_to_absolute,
+      max_gt_boxes=max_gt_boxes)
+
+  exclude_keys = [
+      fields.InputDataFields.original_image,
+      fields.DetectionResultFields.num_detections,
+      fields.InputDataFields.num_groundtruth_boxes
+  ]
+
+  output_dict = {
+      fields.InputDataFields.original_image:
+          batched_output_dict[fields.InputDataFields.original_image]
+  }
+
+  for key in batched_output_dict:
+    # remove the batch dimension.
+    if key not in exclude_keys:
+      output_dict[key] = tf.squeeze(batched_output_dict[key], 0)
+  return output_dict
+
+
+def result_dict_for_batched_example(images,
+                                    keys,
+                                    detections,
+                                    groundtruth=None,
+                                    class_agnostic=False,
+                                    scale_to_absolute=False,
+                                    original_image_spatial_shapes=None,
+                                    true_image_shapes=None,
+                                    max_gt_boxes=None):
+  """Merges all detection and groundtruth information for a single example.
+
+  Note that evaluation tools require classes that are 1-indexed, and so this
+  function performs the offset. If `class_agnostic` is True, all output classes
+  have label 1.
+
+  Args:
+    images: A single 4D uint8 image tensor of shape [batch_size, H, W, C].
+    keys: A [batch_size] string tensor with image identifier.
+    detections: A dictionary of detections, returned from
+      DetectionModel.postprocess().
+    groundtruth: (Optional) Dictionary of groundtruth items, with fields:
+      'groundtruth_boxes': [batch_size, max_number_of_boxes, 4] float32 tensor
+        of boxes, in normalized coordinates.
+      'groundtruth_classes':  [batch_size, max_number_of_boxes] int64 tensor of
+        1-indexed classes.
+      'groundtruth_area': [batch_size, max_number_of_boxes] float32 tensor of
+        bbox area. (Optional)
+      'groundtruth_is_crowd':[batch_size, max_number_of_boxes] int64
+        tensor. (Optional)
+      'groundtruth_difficult': [batch_size, max_number_of_boxes] int64
+        tensor. (Optional)
+      'groundtruth_group_of': [batch_size, max_number_of_boxes] int64
+        tensor. (Optional)
+      'groundtruth_instance_masks': 4D int64 tensor of instance
+        masks (Optional).
+    class_agnostic: Boolean indicating whether the detections are class-agnostic
+      (i.e. binary). Default False.
+    scale_to_absolute: Boolean indicating whether boxes and keypoints should be
+      scaled to absolute coordinates. Note that for IoU based evaluations, it
+      does not matter whether boxes are expressed in absolute or relative
+      coordinates. Default False.
+    original_image_spatial_shapes: A 2D int32 tensor of shape [batch_size, 2]
+      used to resize the image. When set to None, the image size is retained.
+    true_image_shapes: A 2D int32 tensor of shape [batch_size, 3]
+      containing the size of the unpadded original_image.
+    max_gt_boxes: [batch_size] tensor representing the maximum number of
+      groundtruth boxes to pad.
+
+  Returns:
+    A dictionary with:
+    'original_image': A [batch_size, H, W, C] uint8 image tensor.
+    'original_image_spatial_shape': A [batch_size, 2] tensor containing the
+      original image sizes.
+    'true_image_shape': A [batch_size, 3] tensor containing the size of
+      the unpadded original_image.
+    'key': A [batch_size] string tensor with image identifier.
+    'detection_boxes': [batch_size, max_detections, 4] float32 tensor of boxes,
+      in normalized or absolute coordinates, depending on the value of
+      `scale_to_absolute`.
+    'detection_scores': [batch_size, max_detections] float32 tensor of scores.
+    'detection_classes': [batch_size, max_detections] int64 tensor of 1-indexed
+      classes.
+    'detection_masks': [batch_size, max_detections, H, W] float32 tensor of
+      binarized masks, reframed to full image masks.
+    'num_detections': [batch_size] int64 tensor containing number of valid
+      detections.
+    'groundtruth_boxes': [batch_size, num_boxes, 4] float32 tensor of boxes, in
+      normalized or absolute coordinates, depending on the value of
+      `scale_to_absolute`. (Optional)
+    'groundtruth_classes': [batch_size, num_boxes] int64 tensor of 1-indexed
+      classes. (Optional)
+    'groundtruth_area': [batch_size, num_boxes] float32 tensor of bbox
+      area. (Optional)
+    'groundtruth_is_crowd': [batch_size, num_boxes] int64 tensor. (Optional)
+    'groundtruth_difficult': [batch_size, num_boxes] int64 tensor. (Optional)
+    'groundtruth_group_of': [batch_size, num_boxes] int64 tensor. (Optional)
+    'groundtruth_instance_masks': 4D int64 tensor of instance masks
+      (Optional).
+    'num_groundtruth_boxes': [batch_size] tensor containing the maximum number
+      of groundtruth boxes per image.
+
+  Raises:
+    ValueError: if original_image_spatial_shape is not 2D int32 tensor of shape
+      [2].
+    ValueError: if true_image_shapes is not 2D int32 tensor of shape
+      [3].
+  """
+  label_id_offset = 1  # Applying label id offset (b/63711816)
+
+  input_data_fields = fields.InputDataFields
+  if original_image_spatial_shapes is None:
+    original_image_spatial_shapes = tf.tile(
+        tf.expand_dims(tf.shape(images)[1:3], axis=0),
+        multiples=[tf.shape(images)[0], 1])
+  else:
+    if (len(original_image_spatial_shapes.shape) != 2 and
+        original_image_spatial_shapes.shape[1] != 2):
+      raise ValueError(
+          '`original_image_spatial_shape` should be a 2D tensor of shape '
+          '[batch_size, 2].')
+
+  if true_image_shapes is None:
+    true_image_shapes = tf.tile(
+        tf.expand_dims(tf.shape(images)[1:4], axis=0),
+        multiples=[tf.shape(images)[0], 1])
+  else:
+    if (len(true_image_shapes.shape) != 2
+        and true_image_shapes.shape[1] != 3):
+      raise ValueError('`true_image_shapes` should be a 2D tensor of '
+                       'shape [batch_size, 3].')
+
+  output_dict = {
+      input_data_fields.original_image:
+          images,
+      input_data_fields.key:
+          keys,
+      input_data_fields.original_image_spatial_shape: (
+          original_image_spatial_shapes),
+      input_data_fields.true_image_shape:
+          true_image_shapes
+  }
+
+  detection_fields = fields.DetectionResultFields
+  detection_boxes = detections[detection_fields.detection_boxes]
+  detection_scores = detections[detection_fields.detection_scores]
+  num_detections = tf.to_int32(detections[detection_fields.num_detections])
+
+  if class_agnostic:
+    detection_classes = tf.ones_like(detection_scores, dtype=tf.int64)
+  else:
+    detection_classes = (
+        tf.to_int64(detections[detection_fields.detection_classes]) +
+        label_id_offset)
+
+  if scale_to_absolute:
+    output_dict[detection_fields.detection_boxes] = (
+        shape_utils.static_or_dynamic_map_fn(
+            _scale_box_to_absolute,
+            elems=[detection_boxes, original_image_spatial_shapes],
+            dtype=tf.float32))
+  else:
+    output_dict[detection_fields.detection_boxes] = detection_boxes
+  output_dict[detection_fields.detection_classes] = detection_classes
+  output_dict[detection_fields.detection_scores] = detection_scores
+  output_dict[detection_fields.num_detections] = num_detections
+
+  if detection_fields.detection_masks in detections:
+    detection_masks = detections[detection_fields.detection_masks]
+    # TODO(rathodv): This should be done in model's postprocess
+    # function ideally.
+    output_dict[detection_fields.detection_masks] = (
+        shape_utils.static_or_dynamic_map_fn(
+            _resize_detection_masks,
+            elems=[detection_boxes, detection_masks,
+                   original_image_spatial_shapes],
+            dtype=tf.uint8))
+
+  if detection_fields.detection_keypoints in detections:
+    detection_keypoints = detections[detection_fields.detection_keypoints]
+    output_dict[detection_fields.detection_keypoints] = detection_keypoints
+    if scale_to_absolute:
+      output_dict[detection_fields.detection_keypoints] = (
+          shape_utils.static_or_dynamic_map_fn(
+              _scale_keypoint_to_absolute,
+              elems=[detection_keypoints, original_image_spatial_shapes],
+              dtype=tf.float32))
+
+  if groundtruth:
+    if max_gt_boxes is None:
+      if input_data_fields.num_groundtruth_boxes in groundtruth:
+        max_gt_boxes = groundtruth[input_data_fields.num_groundtruth_boxes]
+      else:
+        raise ValueError(
+            'max_gt_boxes must be provided when processing batched examples.')
+
+    if input_data_fields.groundtruth_instance_masks in groundtruth:
+      masks = groundtruth[input_data_fields.groundtruth_instance_masks]
+      groundtruth[input_data_fields.groundtruth_instance_masks] = (
+          shape_utils.static_or_dynamic_map_fn(
+              _resize_groundtruth_masks,
+              elems=[masks, original_image_spatial_shapes],
+              dtype=tf.uint8))
+
+    output_dict.update(groundtruth)
+    if scale_to_absolute:
+      groundtruth_boxes = groundtruth[input_data_fields.groundtruth_boxes]
+      output_dict[input_data_fields.groundtruth_boxes] = (
+          shape_utils.static_or_dynamic_map_fn(
+              _scale_box_to_absolute,
+              elems=[groundtruth_boxes, original_image_spatial_shapes],
+              dtype=tf.float32))
+
+    # For class-agnostic models, groundtruth classes all become 1.
+    if class_agnostic:
+      groundtruth_classes = groundtruth[input_data_fields.groundtruth_classes]
+      groundtruth_classes = tf.ones_like(groundtruth_classes, dtype=tf.int64)
+      output_dict[input_data_fields.groundtruth_classes] = groundtruth_classes
+
+    output_dict[input_data_fields.num_groundtruth_boxes] = max_gt_boxes
+
+  return output_dict
+
+
+def get_evaluators(eval_config, categories, evaluator_options=None):
+  """Returns the evaluator class according to eval_config, valid for categories.
+
+  Args:
+    eval_config: An `eval_pb2.EvalConfig`.
+    categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+    evaluator_options: A dictionary of metric names (see
+      EVAL_METRICS_CLASS_DICT) to `DetectionEvaluator` initialization
+      keyword arguments. For example:
+      evalator_options = {
+        'coco_detection_metrics': {'include_metrics_per_category': True}
+      }
+
+  Returns:
+    An list of instances of DetectionEvaluator.
+
+  Raises:
+    ValueError: if metric is not in the metric class dictionary.
+  """
+  evaluator_options = evaluator_options or {}
+  eval_metric_fn_keys = eval_config.metrics_set
+  if not eval_metric_fn_keys:
+    eval_metric_fn_keys = [EVAL_DEFAULT_METRIC]
+  evaluators_list = []
+  for eval_metric_fn_key in eval_metric_fn_keys:
+    if eval_metric_fn_key not in EVAL_METRICS_CLASS_DICT:
+      raise ValueError('Metric not found: {}'.format(eval_metric_fn_key))
+    kwargs_dict = (evaluator_options[eval_metric_fn_key] if eval_metric_fn_key
+                   in evaluator_options else {})
+    evaluators_list.append(EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](
+        categories,
+        **kwargs_dict))
+  return evaluators_list
+
+
+def get_eval_metric_ops_for_evaluators(eval_config,
+                                       categories,
+                                       eval_dict):
+  """Returns eval metrics ops to use with `tf.estimator.EstimatorSpec`.
+
+  Args:
+    eval_config: An `eval_pb2.EvalConfig`.
+    categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+    eval_dict: An evaluation dictionary, returned from
+      result_dict_for_single_example().
+
+  Returns:
+    A dictionary of metric names to tuple of value_op and update_op that can be
+    used as eval metric ops in tf.EstimatorSpec.
+  """
+  eval_metric_ops = {}
+  evaluator_options = evaluator_options_from_eval_config(eval_config)
+  evaluators_list = get_evaluators(eval_config, categories, evaluator_options)
+  for evaluator in evaluators_list:
+    eval_metric_ops.update(evaluator.get_estimator_eval_metric_ops(
+        eval_dict))
+  return eval_metric_ops
+
+
+def evaluator_options_from_eval_config(eval_config):
+  """Produces a dictionary of evaluation options for each eval metric.
+
+  Args:
+    eval_config: An `eval_pb2.EvalConfig`.
+
+  Returns:
+    evaluator_options: A dictionary of metric names (see
+      EVAL_METRICS_CLASS_DICT) to `DetectionEvaluator` initialization
+      keyword arguments. For example:
+      evalator_options = {
+        'coco_detection_metrics': {'include_metrics_per_category': True}
+      }
+  """
+  eval_metric_fn_keys = eval_config.metrics_set
+  evaluator_options = {}
+  for eval_metric_fn_key in eval_metric_fn_keys:
+    if eval_metric_fn_key in ('coco_detection_metrics', 'coco_mask_metrics'):
+      evaluator_options[eval_metric_fn_key] = {
+          'include_metrics_per_category': (
+              eval_config.include_metrics_per_category)
+      }
+  return evaluator_options
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/eval_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/eval_util_test.py
new file mode 100644
index 00000000..98f5ab75
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/eval_util_test.py
@@ -0,0 +1,234 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for eval_util."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+import tensorflow as tf
+
+from object_detection import eval_util
+from object_detection.core import standard_fields as fields
+from object_detection.protos import eval_pb2
+from object_detection.utils import test_case
+
+
+class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
+
+  def _get_categories_list(self):
+    return [{'id': 0, 'name': 'person'},
+            {'id': 1, 'name': 'dog'},
+            {'id': 2, 'name': 'cat'}]
+
+  def _make_evaluation_dict(self,
+                            resized_groundtruth_masks=False,
+                            batch_size=1,
+                            max_gt_boxes=None,
+                            scale_to_absolute=False):
+    input_data_fields = fields.InputDataFields
+    detection_fields = fields.DetectionResultFields
+
+    image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
+    if batch_size == 1:
+      key = tf.constant('image1')
+    else:
+      key = tf.constant([str(i) for i in range(batch_size)])
+    detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
+                              multiples=[batch_size, 1, 1])
+    detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
+    detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
+    detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
+                              multiples=[batch_size, 1, 1, 1])
+    num_detections = tf.ones([batch_size])
+    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
+    groundtruth_classes = tf.constant([1])
+    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
+    if resized_groundtruth_masks:
+      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
+
+    if batch_size > 1:
+      groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
+                                  multiples=[batch_size, 1, 1])
+      groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
+                                    multiples=[batch_size, 1])
+      groundtruth_instance_masks = tf.tile(
+          tf.expand_dims(groundtruth_instance_masks, 0),
+          multiples=[batch_size, 1, 1, 1])
+
+    detections = {
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes,
+        detection_fields.detection_masks: detection_masks,
+        detection_fields.num_detections: num_detections
+    }
+    groundtruth = {
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
+    }
+    if batch_size > 1:
+      return eval_util.result_dict_for_batched_example(
+          image, key, detections, groundtruth,
+          scale_to_absolute=scale_to_absolute,
+          max_gt_boxes=max_gt_boxes)
+    else:
+      return eval_util.result_dict_for_single_example(
+          image, key, detections, groundtruth,
+          scale_to_absolute=scale_to_absolute)
+
+  @parameterized.parameters(
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
+  )
+  def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
+                                                   max_gt_boxes=None,
+                                                   scale_to_absolute=False):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(['coco_detection_metrics'])
+    categories = self._get_categories_list()
+    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
+                                           max_gt_boxes=max_gt_boxes,
+                                           scale_to_absolute=scale_to_absolute)
+    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
+        eval_config, categories, eval_dict)
+    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      metrics = {}
+      for key, (value_op, _) in metric_ops.iteritems():
+        metrics[key] = value_op
+      sess.run(update_op)
+      metrics = sess.run(metrics)
+      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
+      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
+
+  @parameterized.parameters(
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
+  )
+  def test_get_eval_metric_ops_for_coco_detections_and_masks(
+      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(
+        ['coco_detection_metrics', 'coco_mask_metrics'])
+    categories = self._get_categories_list()
+    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
+                                           max_gt_boxes=max_gt_boxes,
+                                           scale_to_absolute=scale_to_absolute)
+    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
+        eval_config, categories, eval_dict)
+    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
+    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
+
+    with self.test_session() as sess:
+      metrics = {}
+      for key, (value_op, _) in metric_ops.iteritems():
+        metrics[key] = value_op
+      sess.run(update_op_boxes)
+      sess.run(update_op_masks)
+      metrics = sess.run(metrics)
+      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
+      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
+
+  @parameterized.parameters(
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
+      {'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
+      {'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
+  )
+  def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
+      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(
+        ['coco_detection_metrics', 'coco_mask_metrics'])
+    categories = self._get_categories_list()
+    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
+                                           max_gt_boxes=max_gt_boxes,
+                                           scale_to_absolute=scale_to_absolute,
+                                           resized_groundtruth_masks=True)
+    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
+        eval_config, categories, eval_dict)
+    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
+    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
+
+    with self.test_session() as sess:
+      metrics = {}
+      for key, (value_op, _) in metric_ops.iteritems():
+        metrics[key] = value_op
+      sess.run(update_op_boxes)
+      sess.run(update_op_masks)
+      metrics = sess.run(metrics)
+      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
+      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
+
+  def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(['unsupported_metric'])
+    categories = self._get_categories_list()
+    eval_dict = self._make_evaluation_dict()
+    with self.assertRaises(ValueError):
+      eval_util.get_eval_metric_ops_for_evaluators(
+          eval_config, categories, eval_dict)
+
+  def test_get_eval_metric_ops_for_evaluators(self):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(
+        ['coco_detection_metrics', 'coco_mask_metrics'])
+    eval_config.include_metrics_per_category = True
+
+    evaluator_options = eval_util.evaluator_options_from_eval_config(
+        eval_config)
+    self.assertTrue(evaluator_options['coco_detection_metrics'][
+        'include_metrics_per_category'])
+    self.assertTrue(evaluator_options['coco_mask_metrics'][
+        'include_metrics_per_category'])
+
+  def test_get_evaluator_with_evaluator_options(self):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(['coco_detection_metrics'])
+    eval_config.include_metrics_per_category = True
+    categories = self._get_categories_list()
+
+    evaluator_options = eval_util.evaluator_options_from_eval_config(
+        eval_config)
+    evaluator = eval_util.get_evaluators(
+        eval_config, categories, evaluator_options)
+
+    self.assertTrue(evaluator[0]._include_metrics_per_category)
+
+  def test_get_evaluator_with_no_evaluator_options(self):
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.metrics_set.extend(['coco_detection_metrics'])
+    eval_config.include_metrics_per_category = True
+    categories = self._get_categories_list()
+
+    evaluator = eval_util.get_evaluators(
+        eval_config, categories, evaluator_options=None)
+
+    # Even though we are setting eval_config.include_metrics_per_category = True
+    # this option is never passed into the DetectionEvaluator constructor (via
+    # `evaluator_options`).
+    self.assertFalse(evaluator[0]._include_metrics_per_category)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/export_inference_graph.py b/TensorFlow/Detection/SSD/models/research/object_detection/export_inference_graph.py
new file mode 100644
index 00000000..6b5257be
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/export_inference_graph.py
@@ -0,0 +1,150 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Tool to export an object detection model for inference.
+
+Prepares an object detection tensorflow graph for inference using model
+configuration and a trained checkpoint. Outputs inference
+graph, associated checkpoint files, a frozen inference graph and a
+SavedModel (https://tensorflow.github.io/serving/serving_basic.html).
+
+The inference graph contains one of three input nodes depending on the user
+specified option.
+  * `image_tensor`: Accepts a uint8 4-D tensor of shape [None, None, None, 3]
+  * `encoded_image_string_tensor`: Accepts a 1-D string tensor of shape [None]
+    containing encoded PNG or JPEG images. Image resolutions are expected to be
+    the same if more than 1 image is provided.
+  * `tf_example`: Accepts a 1-D string tensor of shape [None] containing
+    serialized TFExample protos. Image resolutions are expected to be the same
+    if more than 1 image is provided.
+
+and the following output nodes returned by the model.postprocess(..):
+  * `num_detections`: Outputs float32 tensors of the form [batch]
+      that specifies the number of valid boxes per image in the batch.
+  * `detection_boxes`: Outputs float32 tensors of the form
+      [batch, num_boxes, 4] containing detected boxes.
+  * `detection_scores`: Outputs float32 tensors of the form
+      [batch, num_boxes] containing class scores for the detections.
+  * `detection_classes`: Outputs float32 tensors of the form
+      [batch, num_boxes] containing classes for the detections.
+  * `detection_masks`: Outputs float32 tensors of the form
+      [batch, num_boxes, mask_height, mask_width] containing predicted instance
+      masks for each box if its present in the dictionary of postprocessed
+      tensors returned by the model.
+
+Notes:
+ * This tool uses `use_moving_averages` from eval_config to decide which
+   weights to freeze.
+
+Example Usage:
+--------------
+python export_inference_graph \
+    --input_type image_tensor \
+    --pipeline_config_path path/to/ssd_inception_v2.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory
+
+The expected output would be in the directory
+path/to/exported_model_directory (which is created if it does not exist)
+with contents:
+ - inference_graph.pbtxt
+ - model.ckpt.data-00000-of-00001
+ - model.ckpt.info
+ - model.ckpt.meta
+ - frozen_inference_graph.pb
+ + saved_model (a directory)
+
+Config overrides (see the `config_override` flag) are text protobufs
+(also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
+certain fields in the provided pipeline_config_path.  These are useful for
+making small changes to the inference graph that differ from the training or
+eval config.
+
+Example Usage (in which we change the second stage post-processing score
+threshold to be 0.5):
+
+python export_inference_graph \
+    --input_type image_tensor \
+    --pipeline_config_path path/to/ssd_inception_v2.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory \
+    --config_override " \
+            model{ \
+              faster_rcnn { \
+                second_stage_post_processing { \
+                  batch_non_max_suppression { \
+                    score_threshold: 0.5 \
+                  } \
+                } \
+              } \
+            }"
+"""
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection import exporter
+from object_detection.protos import pipeline_pb2
+
+slim = tf.contrib.slim
+flags = tf.app.flags
+
+flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
+                    'one of [`image_tensor`, `encoded_image_string_tensor`, '
+                    '`tf_example`]')
+flags.DEFINE_string('input_shape', None,
+                    'If input_type is `image_tensor`, this can explicitly set '
+                    'the shape of this input tensor to a fixed size. The '
+                    'dimensions are to be provided as a comma-separated list '
+                    'of integers. A value of -1 can be used for unknown '
+                    'dimensions. If not specified, for an `image_tensor, the '
+                    'default shape will be partially specified as '
+                    '`[None, None, None, 3]`.')
+flags.DEFINE_string('pipeline_config_path', None,
+                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
+                    'file.')
+flags.DEFINE_string('trained_checkpoint_prefix', None,
+                    'Path to trained checkpoint, typically of the form '
+                    'path/to/model.ckpt')
+flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
+flags.DEFINE_string('config_override', '',
+                    'pipeline_pb2.TrainEvalPipelineConfig '
+                    'text proto to override pipeline_config_path.')
+flags.DEFINE_boolean('write_inference_graph', False,
+                     'If true, writes inference graph to disk.')
+tf.app.flags.mark_flag_as_required('pipeline_config_path')
+tf.app.flags.mark_flag_as_required('trained_checkpoint_prefix')
+tf.app.flags.mark_flag_as_required('output_directory')
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
+    text_format.Merge(f.read(), pipeline_config)
+  text_format.Merge(FLAGS.config_override, pipeline_config)
+  if FLAGS.input_shape:
+    input_shape = [
+        int(dim) if dim != '-1' else None
+        for dim in FLAGS.input_shape.split(',')
+    ]
+  else:
+    input_shape = None
+  exporter.export_inference_graph(
+      FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix,
+      FLAGS.output_directory, input_shape=input_shape,
+      write_inference_graph=FLAGS.write_inference_graph)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph.py b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph.py
new file mode 100644
index 00000000..b7ed428d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph.py
@@ -0,0 +1,143 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Exports an SSD detection model to use with tf-lite.
+
+Outputs file:
+* A tflite compatible frozen graph - $output_directory/tflite_graph.pb
+
+The exported graph has the following input and output nodes.
+
+Inputs:
+'normalized_input_image_tensor': a float32 tensor of shape
+[1, height, width, 3] containing the normalized input image. Note that the
+height and width must be compatible with the height and width configured in
+the fixed_shape_image resizer options in the pipeline config proto.
+
+In floating point Mobilenet model, 'normalized_image_tensor' has values
+between [-1,1). This typically means mapping each pixel (linearly)
+to a value between [-1, 1]. Input image
+values between 0 and 255 are scaled by (1/128.0) and then a value of
+-1 is added to them to ensure the range is [-1,1).
+In quantized Mobilenet model, 'normalized_image_tensor' has values between [0,
+255].
+In general, see the `preprocess` function defined in the feature extractor class
+in the object_detection/models directory.
+
+Outputs:
+If add_postprocessing_op is true: frozen graph adds a
+  TFLite_Detection_PostProcess custom op node has four outputs:
+  detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
+  locations
+  detection_classes: a float32 tensor of shape [1, num_boxes]
+  with class indices
+  detection_scores: a float32 tensor of shape [1, num_boxes]
+  with class scores
+  num_boxes: a float32 tensor of size 1 containing the number of detected boxes
+else:
+  the graph has two outputs:
+   'raw_outputs/box_encodings': a float32 tensor of shape [1, num_anchors, 4]
+    containing the encoded box predictions.
+   'raw_outputs/class_predictions': a float32 tensor of shape
+    [1, num_anchors, num_classes] containing the class scores for each anchor
+    after applying score conversion.
+
+Example Usage:
+--------------
+python object_detection/export_tflite_ssd_graph \
+    --pipeline_config_path path/to/ssd_mobilenet.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory
+
+The expected output would be in the directory
+path/to/exported_model_directory (which is created if it does not exist)
+with contents:
+ - tflite_graph.pbtxt
+ - tflite_graph.pb
+Config overrides (see the `config_override` flag) are text protobufs
+(also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
+certain fields in the provided pipeline_config_path.  These are useful for
+making small changes to the inference graph that differ from the training or
+eval config.
+
+Example Usage (in which we change the NMS iou_threshold to be 0.5 and
+NMS score_threshold to be 0.0):
+python object_detection/export_tflite_ssd_graph \
+    --pipeline_config_path path/to/ssd_mobilenet.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory
+    --config_override " \
+            model{ \
+            ssd{ \
+              post_processing { \
+                batch_non_max_suppression { \
+                        score_threshold: 0.0 \
+                        iou_threshold: 0.5 \
+                } \
+             } \
+          } \
+       } \
+       "
+"""
+
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection import export_tflite_ssd_graph_lib
+from object_detection.protos import pipeline_pb2
+
+flags = tf.app.flags
+flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
+flags.DEFINE_string(
+    'pipeline_config_path', None,
+    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
+    'file.')
+flags.DEFINE_string('trained_checkpoint_prefix', None, 'Checkpoint prefix.')
+flags.DEFINE_integer('max_detections', 10,
+                     'Maximum number of detections (boxes) to show.')
+flags.DEFINE_integer('max_classes_per_detection', 1,
+                     'Number of classes to display per detection box.')
+flags.DEFINE_integer(
+    'detections_per_class', 100,
+    'Number of anchors used per class in Regular Non-Max-Suppression.')
+flags.DEFINE_bool('add_postprocessing_op', True,
+                  'Add TFLite custom op for postprocessing to the graph.')
+flags.DEFINE_bool(
+    'use_regular_nms', False,
+    'Flag to set postprocessing op to use Regular NMS instead of Fast NMS.')
+flags.DEFINE_string(
+    'config_override', '', 'pipeline_pb2.TrainEvalPipelineConfig '
+    'text proto to override pipeline_config_path.')
+
+FLAGS = flags.FLAGS
+
+
+def main(argv):
+  del argv  # Unused.
+  flags.mark_flag_as_required('output_directory')
+  flags.mark_flag_as_required('pipeline_config_path')
+  flags.mark_flag_as_required('trained_checkpoint_prefix')
+
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+
+  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
+    text_format.Merge(f.read(), pipeline_config)
+  text_format.Merge(FLAGS.config_override, pipeline_config)
+  export_tflite_ssd_graph_lib.export_tflite_graph(
+      pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory,
+      FLAGS.add_postprocessing_op, FLAGS.max_detections,
+      FLAGS.max_classes_per_detection, FLAGS.use_regular_nms)
+
+
+if __name__ == '__main__':
+  tf.app.run(main)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib.py b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib.py
new file mode 100644
index 00000000..05075f17
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib.py
@@ -0,0 +1,312 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Exports an SSD detection model to use with tf-lite.
+
+See export_tflite_ssd_graph.py for usage.
+"""
+import os
+import tempfile
+import numpy as np
+import tensorflow as tf
+from tensorflow.core.framework import attr_value_pb2
+from tensorflow.core.framework import types_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.tools.graph_transforms import TransformGraph
+from object_detection import exporter
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.builders import post_processing_builder
+from object_detection.core import box_list
+
+_DEFAULT_NUM_CHANNELS = 3
+_DEFAULT_NUM_COORD_BOX = 4
+
+
+def get_const_center_size_encoded_anchors(anchors):
+  """Exports center-size encoded anchors as a constant tensor.
+
+  Args:
+    anchors: a float32 tensor of shape [num_anchors, 4] containing the anchor
+      boxes
+
+  Returns:
+    encoded_anchors: a float32 constant tensor of shape [num_anchors, 4]
+    containing the anchor boxes.
+  """
+  anchor_boxlist = box_list.BoxList(anchors)
+  y, x, h, w = anchor_boxlist.get_center_coordinates_and_sizes()
+  num_anchors = y.get_shape().as_list()
+
+  with tf.Session() as sess:
+    y_out, x_out, h_out, w_out = sess.run([y, x, h, w])
+  encoded_anchors = tf.constant(
+      np.transpose(np.stack((y_out, x_out, h_out, w_out))),
+      dtype=tf.float32,
+      shape=[num_anchors[0], _DEFAULT_NUM_COORD_BOX],
+      name='anchors')
+  return encoded_anchors
+
+
+def append_postprocessing_op(frozen_graph_def,
+                             max_detections,
+                             max_classes_per_detection,
+                             nms_score_threshold,
+                             nms_iou_threshold,
+                             num_classes,
+                             scale_values,
+                             detections_per_class=100,
+                             use_regular_nms=False):
+  """Appends postprocessing custom op.
+
+  Args:
+    frozen_graph_def: Frozen GraphDef for SSD model after freezing the
+      checkpoint
+    max_detections: Maximum number of detections (boxes) to show
+    max_classes_per_detection: Number of classes to display per detection
+    nms_score_threshold: Score threshold used in Non-maximal suppression in
+      post-processing
+    nms_iou_threshold: Intersection-over-union threshold used in Non-maximal
+      suppression in post-processing
+    num_classes: number of classes in SSD detector
+    scale_values: scale values is a dict with following key-value pairs
+      {y_scale: 10, x_scale: 10, h_scale: 5, w_scale: 5} that are used in decode
+      centersize boxes
+    detections_per_class: In regular NonMaxSuppression, number of anchors used
+    for NonMaxSuppression per class
+    use_regular_nms: Flag to set postprocessing op to use Regular NMS instead
+      of Fast NMS.
+
+  Returns:
+    transformed_graph_def: Frozen GraphDef with postprocessing custom op
+    appended
+    TFLite_Detection_PostProcess custom op node has four outputs:
+    detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
+    locations
+    detection_classes: a float32 tensor of shape [1, num_boxes]
+    with class indices
+    detection_scores: a float32 tensor of shape [1, num_boxes]
+    with class scores
+    num_boxes: a float32 tensor of size 1 containing the number of detected
+    boxes
+  """
+  new_output = frozen_graph_def.node.add()
+  new_output.op = 'TFLite_Detection_PostProcess'
+  new_output.name = 'TFLite_Detection_PostProcess'
+  new_output.attr['_output_quantized'].CopyFrom(
+      attr_value_pb2.AttrValue(b=True))
+  new_output.attr['_output_types'].list.type.extend([
+      types_pb2.DT_FLOAT, types_pb2.DT_FLOAT, types_pb2.DT_FLOAT,
+      types_pb2.DT_FLOAT
+  ])
+  new_output.attr['_support_output_type_float_in_quantized_op'].CopyFrom(
+      attr_value_pb2.AttrValue(b=True))
+  new_output.attr['max_detections'].CopyFrom(
+      attr_value_pb2.AttrValue(i=max_detections))
+  new_output.attr['max_classes_per_detection'].CopyFrom(
+      attr_value_pb2.AttrValue(i=max_classes_per_detection))
+  new_output.attr['nms_score_threshold'].CopyFrom(
+      attr_value_pb2.AttrValue(f=nms_score_threshold.pop()))
+  new_output.attr['nms_iou_threshold'].CopyFrom(
+      attr_value_pb2.AttrValue(f=nms_iou_threshold.pop()))
+  new_output.attr['num_classes'].CopyFrom(
+      attr_value_pb2.AttrValue(i=num_classes))
+
+  new_output.attr['y_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['y_scale'].pop()))
+  new_output.attr['x_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['x_scale'].pop()))
+  new_output.attr['h_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['h_scale'].pop()))
+  new_output.attr['w_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['w_scale'].pop()))
+  new_output.attr['detections_per_class'].CopyFrom(
+      attr_value_pb2.AttrValue(i=detections_per_class))
+  new_output.attr['use_regular_nms'].CopyFrom(
+      attr_value_pb2.AttrValue(b=use_regular_nms))
+
+  new_output.input.extend(
+      ['raw_outputs/box_encodings', 'raw_outputs/class_predictions', 'anchors'])
+  # Transform the graph to append new postprocessing op
+  input_names = []
+  output_names = ['TFLite_Detection_PostProcess']
+  transforms = ['strip_unused_nodes']
+  transformed_graph_def = TransformGraph(frozen_graph_def, input_names,
+                                         output_names, transforms)
+  return transformed_graph_def
+
+
+def export_tflite_graph(pipeline_config,
+                        trained_checkpoint_prefix,
+                        output_dir,
+                        add_postprocessing_op,
+                        max_detections,
+                        max_classes_per_detection,
+                        detections_per_class=100,
+                        use_regular_nms=False):
+  """Exports a tflite compatible graph and anchors for ssd detection model.
+
+  Anchors are written to a tensor and tflite compatible graph
+  is written to output_dir/tflite_graph.pb.
+
+  Args:
+    pipeline_config: a pipeline.proto object containing the configuration for
+      SSD model to export.
+    trained_checkpoint_prefix: a file prefix for the checkpoint containing the
+      trained parameters of the SSD model.
+    output_dir: A directory to write the tflite graph and anchor file to.
+    add_postprocessing_op: If add_postprocessing_op is true: frozen graph adds a
+      TFLite_Detection_PostProcess custom op
+    max_detections: Maximum number of detections (boxes) to show
+    max_classes_per_detection: Number of classes to display per detection
+    detections_per_class: In regular NonMaxSuppression, number of anchors used
+    for NonMaxSuppression per class
+    use_regular_nms: Flag to set postprocessing op to use Regular NMS instead
+      of Fast NMS.
+
+  Raises:
+    ValueError: if the pipeline config contains models other than ssd or uses an
+      fixed_shape_resizer and provides a shape as well.
+  """
+  tf.gfile.MakeDirs(output_dir)
+  if pipeline_config.model.WhichOneof('model') != 'ssd':
+    raise ValueError('Only ssd models are supported in tflite. '
+                     'Found {} in config'.format(
+                         pipeline_config.model.WhichOneof('model')))
+
+  num_classes = pipeline_config.model.ssd.num_classes
+  nms_score_threshold = {
+      pipeline_config.model.ssd.post_processing.batch_non_max_suppression.
+      score_threshold
+  }
+  nms_iou_threshold = {
+      pipeline_config.model.ssd.post_processing.batch_non_max_suppression.
+      iou_threshold
+  }
+  scale_values = {}
+  scale_values['y_scale'] = {
+      pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale
+  }
+  scale_values['x_scale'] = {
+      pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale
+  }
+  scale_values['h_scale'] = {
+      pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale
+  }
+  scale_values['w_scale'] = {
+      pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale
+  }
+
+  image_resizer_config = pipeline_config.model.ssd.image_resizer
+  image_resizer = image_resizer_config.WhichOneof('image_resizer_oneof')
+  num_channels = _DEFAULT_NUM_CHANNELS
+  if image_resizer == 'fixed_shape_resizer':
+    height = image_resizer_config.fixed_shape_resizer.height
+    width = image_resizer_config.fixed_shape_resizer.width
+    if image_resizer_config.fixed_shape_resizer.convert_to_grayscale:
+      num_channels = 1
+    shape = [1, height, width, num_channels]
+  else:
+    raise ValueError(
+        'Only fixed_shape_resizer'
+        'is supported with tflite. Found {}'.format(
+            image_resizer_config.WhichOneof('image_resizer_oneof')))
+
+  image = tf.placeholder(
+      tf.float32, shape=shape, name='normalized_input_image_tensor')
+
+  detection_model = model_builder.build(
+      pipeline_config.model, is_training=False)
+  predicted_tensors = detection_model.predict(image, true_image_shapes=None)
+  # The score conversion occurs before the post-processing custom op
+  _, score_conversion_fn = post_processing_builder.build(
+      pipeline_config.model.ssd.post_processing)
+  class_predictions = score_conversion_fn(
+      predicted_tensors['class_predictions_with_background'])
+
+  with tf.name_scope('raw_outputs'):
+    # 'raw_outputs/box_encodings': a float32 tensor of shape [1, num_anchors, 4]
+    #  containing the encoded box predictions. Note that these are raw
+    #  predictions and no Non-Max suppression is applied on them and
+    #  no decode center size boxes is applied to them.
+    tf.identity(predicted_tensors['box_encodings'], name='box_encodings')
+    # 'raw_outputs/class_predictions': a float32 tensor of shape
+    #  [1, num_anchors, num_classes] containing the class scores for each anchor
+    #  after applying score conversion.
+    tf.identity(class_predictions, name='class_predictions')
+  # 'anchors': a float32 tensor of shape
+  #   [4, num_anchors] containing the anchors as a constant node.
+  tf.identity(
+      get_const_center_size_encoded_anchors(predicted_tensors['anchors']),
+      name='anchors')
+
+  # Add global step to the graph, so we know the training step number when we
+  # evaluate the model.
+  tf.train.get_or_create_global_step()
+
+  # graph rewriter
+  is_quantized = pipeline_config.HasField('graph_rewriter')
+  if is_quantized:
+    graph_rewriter_config = pipeline_config.graph_rewriter
+    graph_rewriter_fn = graph_rewriter_builder.build(
+        graph_rewriter_config, is_training=False)
+    graph_rewriter_fn()
+
+  if pipeline_config.model.ssd.feature_extractor.HasField('fpn'):
+    exporter.rewrite_nn_resize_op(is_quantized)
+
+  # freeze the graph
+  saver_kwargs = {}
+  if pipeline_config.eval_config.use_moving_averages:
+    saver_kwargs['write_version'] = saver_pb2.SaverDef.V1
+    moving_average_checkpoint = tempfile.NamedTemporaryFile()
+    exporter.replace_variable_values_with_moving_averages(
+        tf.get_default_graph(), trained_checkpoint_prefix,
+        moving_average_checkpoint.name)
+    checkpoint_to_use = moving_average_checkpoint.name
+  else:
+    checkpoint_to_use = trained_checkpoint_prefix
+
+  saver = tf.train.Saver(**saver_kwargs)
+  input_saver_def = saver.as_saver_def()
+  frozen_graph_def = exporter.freeze_graph_with_def_protos(
+      input_graph_def=tf.get_default_graph().as_graph_def(),
+      input_saver_def=input_saver_def,
+      input_checkpoint=checkpoint_to_use,
+      output_node_names=','.join([
+          'raw_outputs/box_encodings', 'raw_outputs/class_predictions',
+          'anchors'
+      ]),
+      restore_op_name='save/restore_all',
+      filename_tensor_name='save/Const:0',
+      clear_devices=True,
+      output_graph='',
+      initializer_nodes='')
+
+  # Add new operation to do post processing in a custom op (TF Lite only)
+  if add_postprocessing_op:
+    transformed_graph_def = append_postprocessing_op(
+        frozen_graph_def, max_detections, max_classes_per_detection,
+        nms_score_threshold, nms_iou_threshold, num_classes, scale_values,
+        detections_per_class, use_regular_nms)
+  else:
+    # Return frozen without adding post-processing custom op
+    transformed_graph_def = frozen_graph_def
+
+  binary_graph = os.path.join(output_dir, 'tflite_graph.pb')
+  with tf.gfile.GFile(binary_graph, 'wb') as f:
+    f.write(transformed_graph_def.SerializeToString())
+  txt_graph = os.path.join(output_dir, 'tflite_graph.pbtxt')
+  with tf.gfile.GFile(txt_graph, 'w') as f:
+    f.write(str(transformed_graph_def))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib_test.py
new file mode 100644
index 00000000..cb93a87d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/export_tflite_ssd_graph_lib_test.py
@@ -0,0 +1,369 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.export_tflite_ssd_graph."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import numpy as np
+import six
+import tensorflow as tf
+from tensorflow.core.framework import types_pb2
+from object_detection import export_tflite_ssd_graph_lib
+from object_detection import exporter
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.core import model
+from object_detection.protos import graph_rewriter_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.protos import post_processing_pb2
+
+
+if six.PY2:
+  import mock  # pylint: disable=g-import-not-at-top
+else:
+  from unittest import mock  # pylint: disable=g-import-not-at-top
+
+
+class FakeModel(model.DetectionModel):
+
+  def __init__(self, add_detection_masks=False):
+    self._add_detection_masks = add_detection_masks
+
+  def preprocess(self, inputs):
+    pass
+
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    features = tf.contrib.slim.conv2d(preprocessed_inputs, 3, 1)
+    with tf.control_dependencies([features]):
+      prediction_tensors = {
+          'box_encodings':
+              tf.constant([[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]],
+                          tf.float32),
+          'class_predictions_with_background':
+              tf.constant([[[0.7, 0.6], [0.9, 0.0]]], tf.float32),
+      }
+    with tf.control_dependencies(
+        [tf.convert_to_tensor(features.get_shape().as_list()[1:3])]):
+      prediction_tensors['anchors'] = tf.constant(
+          [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]], tf.float32)
+    return prediction_tensors
+
+  def postprocess(self, prediction_tensors, true_image_shapes):
+    pass
+
+  def restore_map(self, checkpoint_path, from_detection_checkpoint):
+    pass
+
+  def loss(self, prediction_dict, true_image_shapes):
+    pass
+
+  def regularization_losses(self):
+    pass
+
+  def updates(self):
+    pass
+
+
+class ExportTfliteGraphTest(tf.test.TestCase):
+
+  def _save_checkpoint_from_mock_model(self,
+                                       checkpoint_path,
+                                       use_moving_averages,
+                                       quantize=False,
+                                       num_channels=3):
+    g = tf.Graph()
+    with g.as_default():
+      mock_model = FakeModel()
+      inputs = tf.placeholder(tf.float32, shape=[1, 10, 10, num_channels])
+      mock_model.predict(inputs, true_image_shapes=None)
+      if use_moving_averages:
+        tf.train.ExponentialMovingAverage(0.0).apply()
+      tf.train.get_or_create_global_step()
+      if quantize:
+        graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_config.quantization.delay = 500000
+        graph_rewriter_fn = graph_rewriter_builder.build(
+            graph_rewriter_config, is_training=False)
+        graph_rewriter_fn()
+
+      saver = tf.train.Saver()
+      init = tf.global_variables_initializer()
+      with self.test_session() as sess:
+        sess.run(init)
+        saver.save(sess, checkpoint_path)
+
+  def _assert_quant_vars_exists(self, tflite_graph_file):
+    with tf.gfile.Open(tflite_graph_file) as f:
+      graph_string = f.read()
+      print(graph_string)
+      self.assertTrue('quant' in graph_string)
+
+  def _import_graph_and_run_inference(self, tflite_graph_file, num_channels=3):
+    """Imports a tflite graph, runs single inference and returns outputs."""
+    graph = tf.Graph()
+    with graph.as_default():
+      graph_def = tf.GraphDef()
+      with tf.gfile.Open(tflite_graph_file) as f:
+        graph_def.ParseFromString(f.read())
+      tf.import_graph_def(graph_def, name='')
+      input_tensor = graph.get_tensor_by_name('normalized_input_image_tensor:0')
+      box_encodings = graph.get_tensor_by_name('raw_outputs/box_encodings:0')
+      class_predictions = graph.get_tensor_by_name(
+          'raw_outputs/class_predictions:0')
+      with self.test_session(graph) as sess:
+        [box_encodings_np, class_predictions_np] = sess.run(
+            [box_encodings, class_predictions],
+            feed_dict={input_tensor: np.random.rand(1, 10, 10, num_channels)})
+    return box_encodings_np, class_predictions_np
+
+  def _export_graph(self, pipeline_config, num_channels=3):
+    """Exports a tflite graph."""
+    output_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(output_dir, 'model.ckpt')
+    tflite_graph_file = os.path.join(output_dir, 'tflite_graph.pb')
+
+    quantize = pipeline_config.HasField('graph_rewriter')
+    self._save_checkpoint_from_mock_model(
+        trained_checkpoint_prefix,
+        use_moving_averages=pipeline_config.eval_config.use_moving_averages,
+        quantize=quantize,
+        num_channels=num_channels)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+
+      with tf.Graph().as_default():
+        export_tflite_ssd_graph_lib.export_tflite_graph(
+            pipeline_config=pipeline_config,
+            trained_checkpoint_prefix=trained_checkpoint_prefix,
+            output_dir=output_dir,
+            add_postprocessing_op=False,
+            max_detections=10,
+            max_classes_per_detection=1)
+    return tflite_graph_file
+
+  def _export_graph_with_postprocessing_op(self,
+                                           pipeline_config,
+                                           num_channels=3):
+    """Exports a tflite graph with custom postprocessing op."""
+    output_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(output_dir, 'model.ckpt')
+    tflite_graph_file = os.path.join(output_dir, 'tflite_graph.pb')
+
+    quantize = pipeline_config.HasField('graph_rewriter')
+    self._save_checkpoint_from_mock_model(
+        trained_checkpoint_prefix,
+        use_moving_averages=pipeline_config.eval_config.use_moving_averages,
+        quantize=quantize,
+        num_channels=num_channels)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+
+      with tf.Graph().as_default():
+        export_tflite_ssd_graph_lib.export_tflite_graph(
+            pipeline_config=pipeline_config,
+            trained_checkpoint_prefix=trained_checkpoint_prefix,
+            output_dir=output_dir,
+            add_postprocessing_op=True,
+            max_detections=10,
+            max_classes_per_detection=1)
+    return tflite_graph_file
+
+  def test_export_tflite_graph_with_moving_averages(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = True
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+
+    (box_encodings_np, class_predictions_np
+    ) = self._import_graph_and_run_inference(tflite_graph_file)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
+
+  def test_export_tflite_graph_without_moving_averages(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    (box_encodings_np, class_predictions_np
+    ) = self._import_graph_and_run_inference(tflite_graph_file)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
+
+  def test_export_tflite_graph_grayscale(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    (pipeline_config.model.ssd.image_resizer.fixed_shape_resizer
+    ).convert_to_grayscale = True
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config, num_channels=1)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    (box_encodings_np,
+     class_predictions_np) = self._import_graph_and_run_inference(
+         tflite_graph_file, num_channels=1)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
+
+  def test_export_tflite_graph_with_quantization(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.graph_rewriter.quantization.delay = 500000
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    self._assert_quant_vars_exists(tflite_graph_file)
+    (box_encodings_np, class_predictions_np
+    ) = self._import_graph_and_run_inference(tflite_graph_file)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
+
+  def test_export_tflite_graph_with_softmax_score_conversion(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.post_processing.score_converter = (
+        post_processing_pb2.PostProcessing.SOFTMAX)
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    (box_encodings_np, class_predictions_np
+    ) = self._import_graph_and_run_inference(tflite_graph_file)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np,
+                        [[[0.524979, 0.475021], [0.710949, 0.28905]]])
+
+  def test_export_tflite_graph_with_sigmoid_score_conversion(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.post_processing.score_converter = (
+        post_processing_pb2.PostProcessing.SIGMOID)
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph(pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    (box_encodings_np, class_predictions_np
+    ) = self._import_graph_and_run_inference(tflite_graph_file)
+    self.assertAllClose(box_encodings_np,
+                        [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
+    self.assertAllClose(class_predictions_np,
+                        [[[0.668188, 0.645656], [0.710949, 0.5]]])
+
+  def test_export_tflite_graph_with_postprocessing_op(self):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = False
+    pipeline_config.model.ssd.post_processing.score_converter = (
+        post_processing_pb2.PostProcessing.SIGMOID)
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.num_classes = 2
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
+    pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
+    tflite_graph_file = self._export_graph_with_postprocessing_op(
+        pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    graph = tf.Graph()
+    with graph.as_default():
+      graph_def = tf.GraphDef()
+      with tf.gfile.Open(tflite_graph_file) as f:
+        graph_def.ParseFromString(f.read())
+      all_op_names = [node.name for node in graph_def.node]
+      self.assertTrue('TFLite_Detection_PostProcess' in all_op_names)
+      for node in graph_def.node:
+        if node.name == 'TFLite_Detection_PostProcess':
+          self.assertTrue(node.attr['_output_quantized'].b is True)
+          self.assertTrue(
+              node.attr['_support_output_type_float_in_quantized_op'].b is True)
+          self.assertTrue(node.attr['y_scale'].f == 10.0)
+          self.assertTrue(node.attr['x_scale'].f == 10.0)
+          self.assertTrue(node.attr['h_scale'].f == 5.0)
+          self.assertTrue(node.attr['w_scale'].f == 5.0)
+          self.assertTrue(node.attr['num_classes'].i == 2)
+          self.assertTrue(
+              all([
+                  t == types_pb2.DT_FLOAT
+                  for t in node.attr['_output_types'].list.type
+              ]))
+
+  @mock.patch.object(exporter, 'rewrite_nn_resize_op')
+  def test_export_with_nn_resize_op_not_called_without_fpn(self, mock_get):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    tflite_graph_file = self._export_graph_with_postprocessing_op(
+        pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    mock_get.assert_not_called()
+
+  @mock.patch.object(exporter, 'rewrite_nn_resize_op')
+  def test_export_with_nn_resize_op_called_with_fpn(self, mock_get):
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
+    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
+    pipeline_config.model.ssd.feature_extractor.fpn.min_level = 3
+    pipeline_config.model.ssd.feature_extractor.fpn.max_level = 7
+    tflite_graph_file = self._export_graph_with_postprocessing_op(
+        pipeline_config)
+    self.assertTrue(os.path.exists(tflite_graph_file))
+    mock_get.assert_called_once()
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/exporter.py b/TensorFlow/Detection/SSD/models/research/object_detection/exporter.py
new file mode 100644
index 00000000..9aaf012b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/exporter.py
@@ -0,0 +1,489 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Functions to export object detection inference graph."""
+import os
+import tempfile
+import tensorflow as tf
+from tensorflow.contrib.quantize.python import graph_matcher
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.client import session
+from tensorflow.python.platform import gfile
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.tools import freeze_graph
+from tensorflow.python.training import saver as saver_lib
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.core import standard_fields as fields
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.utils import config_util
+from object_detection.utils import shape_utils
+
+slim = tf.contrib.slim
+
+freeze_graph_with_def_protos = freeze_graph.freeze_graph_with_def_protos
+
+
+def rewrite_nn_resize_op(is_quantized=False):
+  """Replaces a custom nearest-neighbor resize op with the Tensorflow version.
+
+  Some graphs use this custom version for TPU-compatibility.
+
+  Args:
+    is_quantized: True if the default graph is quantized.
+  """
+  input_pattern = graph_matcher.OpTypePattern(
+      'FakeQuantWithMinMaxVars' if is_quantized else '*')
+  reshape_1_pattern = graph_matcher.OpTypePattern(
+      'Reshape', inputs=[input_pattern, 'Const'], ordered_inputs=False)
+  mul_pattern = graph_matcher.OpTypePattern(
+      'Mul', inputs=[reshape_1_pattern, 'Const'], ordered_inputs=False)
+  # The quantization script may or may not insert a fake quant op after the
+  # Mul. In either case, these min/max vars are not needed once replaced with
+  # the TF version of NN resize.
+  fake_quant_pattern = graph_matcher.OpTypePattern(
+      'FakeQuantWithMinMaxVars',
+      inputs=[mul_pattern, 'Identity', 'Identity'],
+      ordered_inputs=False)
+  reshape_2_pattern = graph_matcher.OpTypePattern(
+      'Reshape',
+      inputs=[graph_matcher.OneofPattern([fake_quant_pattern, mul_pattern]),
+              'Const'],
+      ordered_inputs=False)
+  add_pattern = graph_matcher.OpTypePattern(
+      'Add', inputs=[reshape_2_pattern, '*'], ordered_inputs=False)
+
+  matcher = graph_matcher.GraphMatcher(add_pattern)
+  for match in matcher.match_graph(tf.get_default_graph()):
+    projection_op = match.get_op(input_pattern)
+    reshape_2_op = match.get_op(reshape_2_pattern)
+    add_op = match.get_op(add_pattern)
+    nn_resize = tf.image.resize_nearest_neighbor(
+        projection_op.outputs[0],
+        add_op.outputs[0].shape.dims[1:3],
+        align_corners=False)
+
+    for index, op_input in enumerate(add_op.inputs):
+      if op_input == reshape_2_op.outputs[0]:
+        add_op._update_input(index, nn_resize)  # pylint: disable=protected-access
+        break
+
+
+def replace_variable_values_with_moving_averages(graph,
+                                                 current_checkpoint_file,
+                                                 new_checkpoint_file):
+  """Replaces variable values in the checkpoint with their moving averages.
+
+  If the current checkpoint has shadow variables maintaining moving averages of
+  the variables defined in the graph, this function generates a new checkpoint
+  where the variables contain the values of their moving averages.
+
+  Args:
+    graph: a tf.Graph object.
+    current_checkpoint_file: a checkpoint containing both original variables and
+      their moving averages.
+    new_checkpoint_file: file path to write a new checkpoint.
+  """
+  with graph.as_default():
+    variable_averages = tf.train.ExponentialMovingAverage(0.0)
+    ema_variables_to_restore = variable_averages.variables_to_restore()
+    with tf.Session() as sess:
+      read_saver = tf.train.Saver(ema_variables_to_restore)
+      read_saver.restore(sess, current_checkpoint_file)
+      write_saver = tf.train.Saver()
+      write_saver.save(sess, new_checkpoint_file)
+
+
+def _image_tensor_input_placeholder(input_shape=None):
+  """Returns input placeholder and a 4-D uint8 image tensor."""
+  if input_shape is None:
+    input_shape = (None, None, None, 3)
+  input_tensor = tf.placeholder(
+      dtype=tf.uint8, shape=input_shape, name='image_tensor')
+  return input_tensor, input_tensor
+
+
+def _tf_example_input_placeholder():
+  """Returns input that accepts a batch of strings with tf examples.
+
+  Returns:
+    a tuple of input placeholder and the output decoded images.
+  """
+  batch_tf_example_placeholder = tf.placeholder(
+      tf.string, shape=[None], name='tf_example')
+  def decode(tf_example_string_tensor):
+    tensor_dict = tf_example_decoder.TfExampleDecoder().decode(
+        tf_example_string_tensor)
+    image_tensor = tensor_dict[fields.InputDataFields.image]
+    return image_tensor
+  return (batch_tf_example_placeholder,
+          shape_utils.static_or_dynamic_map_fn(
+              decode,
+              elems=batch_tf_example_placeholder,
+              dtype=tf.uint8,
+              parallel_iterations=32,
+              back_prop=False))
+
+
+def _encoded_image_string_tensor_input_placeholder():
+  """Returns input that accepts a batch of PNG or JPEG strings.
+
+  Returns:
+    a tuple of input placeholder and the output decoded images.
+  """
+  batch_image_str_placeholder = tf.placeholder(
+      dtype=tf.string,
+      shape=[None],
+      name='encoded_image_string_tensor')
+  def decode(encoded_image_string_tensor):
+    image_tensor = tf.image.decode_image(encoded_image_string_tensor,
+                                         channels=3)
+    image_tensor.set_shape((None, None, 3))
+    return image_tensor
+  return (batch_image_str_placeholder,
+          tf.map_fn(
+              decode,
+              elems=batch_image_str_placeholder,
+              dtype=tf.uint8,
+              parallel_iterations=32,
+              back_prop=False))
+
+
+input_placeholder_fn_map = {
+    'image_tensor': _image_tensor_input_placeholder,
+    'encoded_image_string_tensor':
+    _encoded_image_string_tensor_input_placeholder,
+    'tf_example': _tf_example_input_placeholder,
+}
+
+
+def add_output_tensor_nodes(postprocessed_tensors,
+                            output_collection_name='inference_op'):
+  """Adds output nodes for detection boxes and scores.
+
+  Adds the following nodes for output tensors -
+    * num_detections: float32 tensor of shape [batch_size].
+    * detection_boxes: float32 tensor of shape [batch_size, num_boxes, 4]
+      containing detected boxes.
+    * detection_scores: float32 tensor of shape [batch_size, num_boxes]
+      containing scores for the detected boxes.
+    * detection_classes: float32 tensor of shape [batch_size, num_boxes]
+      containing class predictions for the detected boxes.
+    * detection_keypoints: (Optional) float32 tensor of shape
+      [batch_size, num_boxes, num_keypoints, 2] containing keypoints for each
+      detection box.
+    * detection_masks: (Optional) float32 tensor of shape
+      [batch_size, num_boxes, mask_height, mask_width] containing masks for each
+      detection box.
+
+  Args:
+    postprocessed_tensors: a dictionary containing the following fields
+      'detection_boxes': [batch, max_detections, 4]
+      'detection_scores': [batch, max_detections]
+      'detection_classes': [batch, max_detections]
+      'detection_masks': [batch, max_detections, mask_height, mask_width]
+        (optional).
+      'detection_keypoints': [batch, max_detections, num_keypoints, 2]
+        (optional).
+      'num_detections': [batch]
+    output_collection_name: Name of collection to add output tensors to.
+
+  Returns:
+    A tensor dict containing the added output tensor nodes.
+  """
+  detection_fields = fields.DetectionResultFields
+  label_id_offset = 1
+  boxes = postprocessed_tensors.get(detection_fields.detection_boxes)
+  scores = postprocessed_tensors.get(detection_fields.detection_scores)
+  classes = postprocessed_tensors.get(
+      detection_fields.detection_classes) + label_id_offset
+  keypoints = postprocessed_tensors.get(detection_fields.detection_keypoints)
+  masks = postprocessed_tensors.get(detection_fields.detection_masks)
+  num_detections = postprocessed_tensors.get(detection_fields.num_detections)
+  outputs = {}
+  outputs[detection_fields.detection_boxes] = tf.identity(
+      boxes, name=detection_fields.detection_boxes)
+  outputs[detection_fields.detection_scores] = tf.identity(
+      scores, name=detection_fields.detection_scores)
+  outputs[detection_fields.detection_classes] = tf.identity(
+      classes, name=detection_fields.detection_classes)
+  outputs[detection_fields.num_detections] = tf.identity(
+      num_detections, name=detection_fields.num_detections)
+  if keypoints is not None:
+    outputs[detection_fields.detection_keypoints] = tf.identity(
+        keypoints, name=detection_fields.detection_keypoints)
+  if masks is not None:
+    outputs[detection_fields.detection_masks] = tf.identity(
+        masks, name=detection_fields.detection_masks)
+  for output_key in outputs:
+    tf.add_to_collection(output_collection_name, outputs[output_key])
+
+  return outputs
+
+
+def write_saved_model(saved_model_path,
+                      frozen_graph_def,
+                      inputs,
+                      outputs):
+  """Writes SavedModel to disk.
+
+  If checkpoint_path is not None bakes the weights into the graph thereby
+  eliminating the need of checkpoint files during inference. If the model
+  was trained with moving averages, setting use_moving_averages to true
+  restores the moving averages, otherwise the original set of variables
+  is restored.
+
+  Args:
+    saved_model_path: Path to write SavedModel.
+    frozen_graph_def: tf.GraphDef holding frozen graph.
+    inputs: The input placeholder tensor.
+    outputs: A tensor dictionary containing the outputs of a DetectionModel.
+  """
+  with tf.Graph().as_default():
+    with session.Session() as sess:
+
+      tf.import_graph_def(frozen_graph_def, name='')
+
+      builder = tf.saved_model.builder.SavedModelBuilder(saved_model_path)
+
+      tensor_info_inputs = {
+          'inputs': tf.saved_model.utils.build_tensor_info(inputs)}
+      tensor_info_outputs = {}
+      for k, v in outputs.items():
+        tensor_info_outputs[k] = tf.saved_model.utils.build_tensor_info(v)
+
+      detection_signature = (
+          tf.saved_model.signature_def_utils.build_signature_def(
+              inputs=tensor_info_inputs,
+              outputs=tensor_info_outputs,
+              method_name=signature_constants.PREDICT_METHOD_NAME))
+
+      builder.add_meta_graph_and_variables(
+          sess, [tf.saved_model.tag_constants.SERVING],
+          signature_def_map={
+              signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+                  detection_signature,
+          },
+      )
+      builder.save()
+
+
+def write_graph_and_checkpoint(inference_graph_def,
+                               model_path,
+                               input_saver_def,
+                               trained_checkpoint_prefix):
+  """Writes the graph and the checkpoint into disk."""
+  for node in inference_graph_def.node:
+    node.device = ''
+  with tf.Graph().as_default():
+    tf.import_graph_def(inference_graph_def, name='')
+    with session.Session() as sess:
+      saver = saver_lib.Saver(saver_def=input_saver_def,
+                              save_relative_paths=True)
+      saver.restore(sess, trained_checkpoint_prefix)
+      saver.save(sess, model_path)
+
+
+def _get_outputs_from_inputs(input_tensors, detection_model,
+                             output_collection_name):
+  inputs = tf.to_float(input_tensors)
+  preprocessed_inputs, true_image_shapes = detection_model.preprocess(inputs)
+  output_tensors = detection_model.predict(
+      preprocessed_inputs, true_image_shapes)
+  postprocessed_tensors = detection_model.postprocess(
+      output_tensors, true_image_shapes)
+  return add_output_tensor_nodes(postprocessed_tensors,
+                                 output_collection_name)
+
+
+def _build_detection_graph(input_type, detection_model, input_shape,
+                           output_collection_name, graph_hook_fn):
+  """Build the detection graph."""
+  if input_type not in input_placeholder_fn_map:
+    raise ValueError('Unknown input type: {}'.format(input_type))
+  placeholder_args = {}
+  if input_shape is not None:
+    if input_type != 'image_tensor':
+      raise ValueError('Can only specify input shape for `image_tensor` '
+                       'inputs.')
+    placeholder_args['input_shape'] = input_shape
+  placeholder_tensor, input_tensors = input_placeholder_fn_map[input_type](
+      **placeholder_args)
+  outputs = _get_outputs_from_inputs(
+      input_tensors=input_tensors,
+      detection_model=detection_model,
+      output_collection_name=output_collection_name)
+
+  # Add global step to the graph.
+  slim.get_or_create_global_step()
+
+  if graph_hook_fn: graph_hook_fn()
+
+  return outputs, placeholder_tensor
+
+
+def _export_inference_graph(input_type,
+                            detection_model,
+                            use_moving_averages,
+                            trained_checkpoint_prefix,
+                            output_directory,
+                            additional_output_tensor_names=None,
+                            input_shape=None,
+                            output_collection_name='inference_op',
+                            graph_hook_fn=None,
+                            write_inference_graph=False):
+  """Export helper."""
+  tf.gfile.MakeDirs(output_directory)
+  frozen_graph_path = os.path.join(output_directory,
+                                   'frozen_inference_graph.pb')
+  saved_model_path = os.path.join(output_directory, 'saved_model')
+  model_path = os.path.join(output_directory, 'model.ckpt')
+
+  outputs, placeholder_tensor = _build_detection_graph(
+      input_type=input_type,
+      detection_model=detection_model,
+      input_shape=input_shape,
+      output_collection_name=output_collection_name,
+      graph_hook_fn=graph_hook_fn)
+
+  profile_inference_graph(tf.get_default_graph())
+  saver_kwargs = {}
+  if use_moving_averages:
+    # This check is to be compatible with both version of SaverDef.
+    if os.path.isfile(trained_checkpoint_prefix):
+      saver_kwargs['write_version'] = saver_pb2.SaverDef.V1
+      temp_checkpoint_prefix = tempfile.NamedTemporaryFile().name
+    else:
+      temp_checkpoint_prefix = tempfile.mkdtemp()
+    replace_variable_values_with_moving_averages(
+        tf.get_default_graph(), trained_checkpoint_prefix,
+        temp_checkpoint_prefix)
+    checkpoint_to_use = temp_checkpoint_prefix
+  else:
+    checkpoint_to_use = trained_checkpoint_prefix
+
+  saver = tf.train.Saver(**saver_kwargs)
+  input_saver_def = saver.as_saver_def()
+
+  write_graph_and_checkpoint(
+      inference_graph_def=tf.get_default_graph().as_graph_def(),
+      model_path=model_path,
+      input_saver_def=input_saver_def,
+      trained_checkpoint_prefix=checkpoint_to_use)
+  if write_inference_graph:
+    inference_graph_def = tf.get_default_graph().as_graph_def()
+    inference_graph_path = os.path.join(output_directory,
+                                        'inference_graph.pbtxt')
+    for node in inference_graph_def.node:
+      node.device = ''
+    with gfile.GFile(inference_graph_path, 'wb') as f:
+      f.write(str(inference_graph_def))
+
+  if additional_output_tensor_names is not None:
+    output_node_names = ','.join(outputs.keys()+additional_output_tensor_names)
+  else:
+    output_node_names = ','.join(outputs.keys())
+
+  frozen_graph_def = freeze_graph.freeze_graph_with_def_protos(
+      input_graph_def=tf.get_default_graph().as_graph_def(),
+      input_saver_def=input_saver_def,
+      input_checkpoint=checkpoint_to_use,
+      output_node_names=output_node_names,
+      restore_op_name='save/restore_all',
+      filename_tensor_name='save/Const:0',
+      output_graph=frozen_graph_path,
+      clear_devices=True,
+      initializer_nodes='')
+
+  write_saved_model(saved_model_path, frozen_graph_def,
+                    placeholder_tensor, outputs)
+
+
+def export_inference_graph(input_type,
+                           pipeline_config,
+                           trained_checkpoint_prefix,
+                           output_directory,
+                           input_shape=None,
+                           output_collection_name='inference_op',
+                           additional_output_tensor_names=None,
+                           write_inference_graph=False):
+  """Exports inference graph for the model specified in the pipeline config.
+
+  Args:
+    input_type: Type of input for the graph. Can be one of ['image_tensor',
+      'encoded_image_string_tensor', 'tf_example'].
+    pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
+    trained_checkpoint_prefix: Path to the trained checkpoint file.
+    output_directory: Path to write outputs.
+    input_shape: Sets a fixed shape for an `image_tensor` input. If not
+      specified, will default to [None, None, None, 3].
+    output_collection_name: Name of collection to add output tensors to.
+      If None, does not add output tensors to a collection.
+    additional_output_tensor_names: list of additional output
+      tensors to include in the frozen graph.
+    write_inference_graph: If true, writes inference graph to disk.
+  """
+  detection_model = model_builder.build(pipeline_config.model,
+                                        is_training=False)
+  graph_rewriter_fn = None
+  if pipeline_config.HasField('graph_rewriter'):
+    graph_rewriter_config = pipeline_config.graph_rewriter
+    graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
+                                                     is_training=False)
+  _export_inference_graph(
+      input_type,
+      detection_model,
+      pipeline_config.eval_config.use_moving_averages,
+      trained_checkpoint_prefix,
+      output_directory,
+      additional_output_tensor_names,
+      input_shape,
+      output_collection_name,
+      graph_hook_fn=graph_rewriter_fn,
+      write_inference_graph=write_inference_graph)
+  pipeline_config.eval_config.use_moving_averages = False
+  config_util.save_pipeline_config(pipeline_config, output_directory)
+
+
+def profile_inference_graph(graph):
+  """Profiles the inference graph.
+
+  Prints model parameters and computation FLOPs given an inference graph.
+  BatchNorms are excluded from the parameter count due to the fact that
+  BatchNorms are usually folded. BatchNorm, Initializer, Regularizer
+  and BiasAdd are not considered in FLOP count.
+
+  Args:
+    graph: the inference graph.
+  """
+  tfprof_vars_option = (
+      tf.contrib.tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+  tfprof_flops_option = tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS
+
+  # Batchnorm is usually folded during inference.
+  tfprof_vars_option['trim_name_regexes'] = ['.*BatchNorm.*']
+  # Initializer and Regularizer are only used in training.
+  tfprof_flops_option['trim_name_regexes'] = [
+      '.*BatchNorm.*', '.*Initializer.*', '.*Regularizer.*', '.*BiasAdd.*'
+  ]
+
+  tf.contrib.tfprof.model_analyzer.print_model_analysis(
+      graph,
+      tfprof_options=tfprof_vars_option)
+
+  tf.contrib.tfprof.model_analyzer.print_model_analysis(
+      graph,
+      tfprof_options=tfprof_flops_option)
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/exporter_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/exporter_test.py
new file mode 100644
index 00000000..5d2bd9ba
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/exporter_test.py
@@ -0,0 +1,988 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.export_inference_graph."""
+import os
+import numpy as np
+import six
+import tensorflow as tf
+from google.protobuf import text_format
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from object_detection import exporter
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.core import model
+from object_detection.protos import graph_rewriter_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.utils import ops
+
+if six.PY2:
+  import mock  # pylint: disable=g-import-not-at-top
+else:
+  from unittest import mock  # pylint: disable=g-import-not-at-top
+
+slim = tf.contrib.slim
+
+
+class FakeModel(model.DetectionModel):
+
+  def __init__(self, add_detection_keypoints=False, add_detection_masks=False):
+    self._add_detection_keypoints = add_detection_keypoints
+    self._add_detection_masks = add_detection_masks
+
+  def preprocess(self, inputs):
+    true_image_shapes = []  # Doesn't matter for the fake model.
+    return tf.identity(inputs), true_image_shapes
+
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    return {'image': tf.layers.conv2d(preprocessed_inputs, 3, 1)}
+
+  def postprocess(self, prediction_dict, true_image_shapes):
+    with tf.control_dependencies(prediction_dict.values()):
+      postprocessed_tensors = {
+          'detection_boxes': tf.constant([[[0.0, 0.0, 0.5, 0.5],
+                                           [0.5, 0.5, 0.8, 0.8]],
+                                          [[0.5, 0.5, 1.0, 1.0],
+                                           [0.0, 0.0, 0.0, 0.0]]], tf.float32),
+          'detection_scores': tf.constant([[0.7, 0.6],
+                                           [0.9, 0.0]], tf.float32),
+          'detection_classes': tf.constant([[0, 1],
+                                            [1, 0]], tf.float32),
+          'num_detections': tf.constant([2, 1], tf.float32)
+      }
+      if self._add_detection_keypoints:
+        postprocessed_tensors['detection_keypoints'] = tf.constant(
+            np.arange(48).reshape([2, 2, 6, 2]), tf.float32)
+      if self._add_detection_masks:
+        postprocessed_tensors['detection_masks'] = tf.constant(
+            np.arange(64).reshape([2, 2, 4, 4]), tf.float32)
+    return postprocessed_tensors
+
+  def restore_map(self, checkpoint_path, fine_tune_checkpoint_type):
+    pass
+
+  def loss(self, prediction_dict, true_image_shapes):
+    pass
+
+  def regularization_losses(self):
+    pass
+
+  def updates(self):
+    pass
+
+
+class ExportInferenceGraphTest(tf.test.TestCase):
+
+  def _save_checkpoint_from_mock_model(self,
+                                       checkpoint_path,
+                                       use_moving_averages,
+                                       enable_quantization=False):
+    g = tf.Graph()
+    with g.as_default():
+      mock_model = FakeModel()
+      preprocessed_inputs, true_image_shapes = mock_model.preprocess(
+          tf.placeholder(tf.float32, shape=[None, None, None, 3]))
+      predictions = mock_model.predict(preprocessed_inputs, true_image_shapes)
+      mock_model.postprocess(predictions, true_image_shapes)
+      if use_moving_averages:
+        tf.train.ExponentialMovingAverage(0.0).apply()
+      tf.train.get_or_create_global_step()
+      if enable_quantization:
+        graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_config.quantization.delay = 500000
+        graph_rewriter_fn = graph_rewriter_builder.build(
+            graph_rewriter_config, is_training=False)
+        graph_rewriter_fn()
+      saver = tf.train.Saver()
+      init = tf.global_variables_initializer()
+      with self.test_session() as sess:
+        sess.run(init)
+        saver.save(sess, checkpoint_path)
+
+  def _load_inference_graph(self, inference_graph_path, is_binary=True):
+    od_graph = tf.Graph()
+    with od_graph.as_default():
+      od_graph_def = tf.GraphDef()
+      with tf.gfile.GFile(inference_graph_path) as fid:
+        if is_binary:
+          od_graph_def.ParseFromString(fid.read())
+        else:
+          text_format.Parse(fid.read(), od_graph_def)
+        tf.import_graph_def(od_graph_def, name='')
+    return od_graph
+
+  def _create_tf_example(self, image_array):
+    with self.test_session():
+      encoded_image = tf.image.encode_jpeg(tf.constant(image_array)).eval()
+    def _bytes_feature(value):
+      return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+    example = tf.train.Example(features=tf.train.Features(feature={
+        'image/encoded': _bytes_feature(encoded_image),
+        'image/format': _bytes_feature('jpg'),
+        'image/source_id': _bytes_feature('image_id')
+    })).SerializeToString()
+    return example
+
+  def test_export_graph_with_image_tensor_input(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      output_directory = os.path.join(tmp_dir, 'output')
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+      self.assertTrue(os.path.exists(os.path.join(
+          output_directory, 'saved_model', 'saved_model.pb')))
+
+  def test_write_inference_graph(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      output_directory = os.path.join(tmp_dir, 'output')
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory,
+          write_inference_graph=True)
+      self.assertTrue(os.path.exists(os.path.join(
+          output_directory, 'inference_graph.pbtxt')))
+
+  def test_export_graph_with_fixed_size_image_tensor_input(self):
+    input_shape = [1, 320, 320, 3]
+
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(
+        trained_checkpoint_prefix, use_moving_averages=False)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      output_directory = os.path.join(tmp_dir, 'output')
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory,
+          input_shape=input_shape)
+      saved_model_path = os.path.join(output_directory, 'saved_model')
+      self.assertTrue(
+          os.path.exists(os.path.join(saved_model_path, 'saved_model.pb')))
+
+    with tf.Graph().as_default() as od_graph:
+      with self.test_session(graph=od_graph) as sess:
+        meta_graph = tf.saved_model.loader.load(
+            sess, [tf.saved_model.tag_constants.SERVING], saved_model_path)
+        signature = meta_graph.signature_def['serving_default']
+        input_tensor_name = signature.inputs['inputs'].name
+        image_tensor = od_graph.get_tensor_by_name(input_tensor_name)
+        self.assertSequenceEqual(image_tensor.get_shape().as_list(),
+                                 input_shape)
+
+  def test_export_graph_with_tf_example_input(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      output_directory = os.path.join(tmp_dir, 'output')
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='tf_example',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+      self.assertTrue(os.path.exists(os.path.join(
+          output_directory, 'saved_model', 'saved_model.pb')))
+
+  def test_export_graph_with_encoded_image_string_input(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      output_directory = os.path.join(tmp_dir, 'output')
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='encoded_image_string_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+      self.assertTrue(os.path.exists(os.path.join(
+          output_directory, 'saved_model', 'saved_model.pb')))
+
+  def _get_variables_in_checkpoint(self, checkpoint_file):
+    return set([
+        var_name
+        for var_name, _ in tf.train.list_variables(checkpoint_file)])
+
+  def test_replace_variable_values_with_moving_averages(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    graph = tf.Graph()
+    with graph.as_default():
+      fake_model = FakeModel()
+      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
+          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
+      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
+      fake_model.postprocess(predictions, true_image_shapes)
+      exporter.replace_variable_values_with_moving_averages(
+          graph, trained_checkpoint_prefix, new_checkpoint_prefix)
+
+    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
+    variables_in_old_ckpt = self._get_variables_in_checkpoint(
+        trained_checkpoint_prefix)
+    self.assertIn('conv2d/bias/ExponentialMovingAverage',
+                  variables_in_old_ckpt)
+    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
+                  variables_in_old_ckpt)
+    variables_in_new_ckpt = self._get_variables_in_checkpoint(
+        new_checkpoint_prefix)
+    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
+    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
+                     variables_in_new_ckpt)
+    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
+                     variables_in_new_ckpt)
+
+  def test_export_graph_with_moving_averages(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = True
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+      self.assertTrue(os.path.exists(os.path.join(
+          output_directory, 'saved_model', 'saved_model.pb')))
+    expected_variables = set(['conv2d/bias', 'conv2d/kernel', 'global_step'])
+    actual_variables = set(
+        [var_name for var_name, _ in tf.train.list_variables(output_directory)])
+    self.assertTrue(expected_variables.issubset(actual_variables))
+
+  def test_export_model_with_quantization_nodes(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(
+        trained_checkpoint_prefix,
+        use_moving_averages=False,
+        enable_quantization=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'inference_graph.pbtxt')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      text_format.Merge(
+          """graph_rewriter {
+               quantization {
+                 delay: 50000
+                 activation_bits: 8
+                 weight_bits: 8
+               }
+             }""", pipeline_config)
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory,
+          write_inference_graph=True)
+    self._load_inference_graph(inference_graph_path, is_binary=False)
+    has_quant_nodes = False
+    for v in tf.global_variables():
+      if v.op.name.endswith('act_quant/min'):
+        has_quant_nodes = True
+        break
+    self.assertTrue(has_quant_nodes)
+
+  def test_export_model_with_all_output_nodes(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    with self.test_session(graph=inference_graph):
+      inference_graph.get_tensor_by_name('image_tensor:0')
+      inference_graph.get_tensor_by_name('detection_boxes:0')
+      inference_graph.get_tensor_by_name('detection_scores:0')
+      inference_graph.get_tensor_by_name('detection_classes:0')
+      inference_graph.get_tensor_by_name('detection_keypoints:0')
+      inference_graph.get_tensor_by_name('detection_masks:0')
+      inference_graph.get_tensor_by_name('num_detections:0')
+
+  def test_export_model_with_detection_only_nodes(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(add_detection_masks=False)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    with self.test_session(graph=inference_graph):
+      inference_graph.get_tensor_by_name('image_tensor:0')
+      inference_graph.get_tensor_by_name('detection_boxes:0')
+      inference_graph.get_tensor_by_name('detection_scores:0')
+      inference_graph.get_tensor_by_name('detection_classes:0')
+      inference_graph.get_tensor_by_name('num_detections:0')
+      with self.assertRaises(KeyError):
+        inference_graph.get_tensor_by_name('detection_keypoints:0')
+        inference_graph.get_tensor_by_name('detection_masks:0')
+
+  def test_export_and_run_inference_with_image_tensor(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    with self.test_session(graph=inference_graph) as sess:
+      image_tensor = inference_graph.get_tensor_by_name('image_tensor:0')
+      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
+      scores = inference_graph.get_tensor_by_name('detection_scores:0')
+      classes = inference_graph.get_tensor_by_name('detection_classes:0')
+      keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
+      masks = inference_graph.get_tensor_by_name('detection_masks:0')
+      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
+      (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+       num_detections_np) = sess.run(
+           [boxes, scores, classes, keypoints, masks, num_detections],
+           feed_dict={image_tensor: np.ones((2, 4, 4, 3)).astype(np.uint8)})
+      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                      [0.5, 0.5, 0.8, 0.8]],
+                                     [[0.5, 0.5, 1.0, 1.0],
+                                      [0.0, 0.0, 0.0, 0.0]]])
+      self.assertAllClose(scores_np, [[0.7, 0.6],
+                                      [0.9, 0.0]])
+      self.assertAllClose(classes_np, [[1, 2],
+                                       [2, 1]])
+      self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+      self.assertAllClose(num_detections_np, [2, 1])
+
+  def _create_encoded_image_string(self, image_array_np, encoding_format):
+    od_graph = tf.Graph()
+    with od_graph.as_default():
+      if encoding_format == 'jpg':
+        encoded_string = tf.image.encode_jpeg(image_array_np)
+      elif encoding_format == 'png':
+        encoded_string = tf.image.encode_png(image_array_np)
+      else:
+        raise ValueError('Supports only the following formats: `jpg`, `png`')
+    with self.test_session(graph=od_graph):
+      return encoded_string.eval()
+
+  def test_export_and_run_inference_with_encoded_image_string_tensor(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='encoded_image_string_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    jpg_image_str = self._create_encoded_image_string(
+        np.ones((4, 4, 3)).astype(np.uint8), 'jpg')
+    png_image_str = self._create_encoded_image_string(
+        np.ones((4, 4, 3)).astype(np.uint8), 'png')
+    with self.test_session(graph=inference_graph) as sess:
+      image_str_tensor = inference_graph.get_tensor_by_name(
+          'encoded_image_string_tensor:0')
+      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
+      scores = inference_graph.get_tensor_by_name('detection_scores:0')
+      classes = inference_graph.get_tensor_by_name('detection_classes:0')
+      keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
+      masks = inference_graph.get_tensor_by_name('detection_masks:0')
+      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
+      for image_str in [jpg_image_str, png_image_str]:
+        image_str_batch_np = np.hstack([image_str]* 2)
+        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+         num_detections_np) = sess.run(
+             [boxes, scores, classes, keypoints, masks, num_detections],
+             feed_dict={image_str_tensor: image_str_batch_np})
+        self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.8, 0.8]],
+                                       [[0.5, 0.5, 1.0, 1.0],
+                                        [0.0, 0.0, 0.0, 0.0]]])
+        self.assertAllClose(scores_np, [[0.7, 0.6],
+                                        [0.9, 0.0]])
+        self.assertAllClose(classes_np, [[1, 2],
+                                         [2, 1]])
+        self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+        self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_raise_runtime_error_on_images_with_different_sizes(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='encoded_image_string_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    large_image = self._create_encoded_image_string(
+        np.ones((4, 4, 3)).astype(np.uint8), 'jpg')
+    small_image = self._create_encoded_image_string(
+        np.ones((2, 2, 3)).astype(np.uint8), 'jpg')
+
+    image_str_batch_np = np.hstack([large_image, small_image])
+    with self.test_session(graph=inference_graph) as sess:
+      image_str_tensor = inference_graph.get_tensor_by_name(
+          'encoded_image_string_tensor:0')
+      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
+      scores = inference_graph.get_tensor_by_name('detection_scores:0')
+      classes = inference_graph.get_tensor_by_name('detection_classes:0')
+      keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
+      masks = inference_graph.get_tensor_by_name('detection_masks:0')
+      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
+      with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
+                                   'TensorArray.*shape'):
+        sess.run(
+            [boxes, scores, classes, keypoints, masks, num_detections],
+            feed_dict={image_str_tensor: image_str_batch_np})
+
+  def test_export_and_run_inference_with_tf_example(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='tf_example',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    tf_example_np = np.expand_dims(self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8)), axis=0)
+    with self.test_session(graph=inference_graph) as sess:
+      tf_example = inference_graph.get_tensor_by_name('tf_example:0')
+      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
+      scores = inference_graph.get_tensor_by_name('detection_scores:0')
+      classes = inference_graph.get_tensor_by_name('detection_classes:0')
+      keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
+      masks = inference_graph.get_tensor_by_name('detection_masks:0')
+      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
+      (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+       num_detections_np) = sess.run(
+           [boxes, scores, classes, keypoints, masks, num_detections],
+           feed_dict={tf_example: tf_example_np})
+      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                      [0.5, 0.5, 0.8, 0.8]],
+                                     [[0.5, 0.5, 1.0, 1.0],
+                                      [0.0, 0.0, 0.0, 0.0]]])
+      self.assertAllClose(scores_np, [[0.7, 0.6],
+                                      [0.9, 0.0]])
+      self.assertAllClose(classes_np, [[1, 2],
+                                       [2, 1]])
+      self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+      self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_write_frozen_graph(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    inference_graph_path = os.path.join(output_directory,
+                                        'frozen_inference_graph.pb')
+    tf.gfile.MakeDirs(output_directory)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      detection_model = model_builder.build(pipeline_config.model,
+                                            is_training=False)
+      outputs, _ = exporter._build_detection_graph(
+          input_type='tf_example',
+          detection_model=detection_model,
+          input_shape=None,
+          output_collection_name='inference_op',
+          graph_hook_fn=None)
+      output_node_names = ','.join(outputs.keys())
+      saver = tf.train.Saver()
+      input_saver_def = saver.as_saver_def()
+      exporter.freeze_graph_with_def_protos(
+          input_graph_def=tf.get_default_graph().as_graph_def(),
+          input_saver_def=input_saver_def,
+          input_checkpoint=trained_checkpoint_prefix,
+          output_node_names=output_node_names,
+          restore_op_name='save/restore_all',
+          filename_tensor_name='save/Const:0',
+          output_graph=inference_graph_path,
+          clear_devices=True,
+          initializer_nodes='')
+
+    inference_graph = self._load_inference_graph(inference_graph_path)
+    tf_example_np = np.expand_dims(self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8)), axis=0)
+    with self.test_session(graph=inference_graph) as sess:
+      tf_example = inference_graph.get_tensor_by_name('tf_example:0')
+      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
+      scores = inference_graph.get_tensor_by_name('detection_scores:0')
+      classes = inference_graph.get_tensor_by_name('detection_classes:0')
+      keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
+      masks = inference_graph.get_tensor_by_name('detection_masks:0')
+      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
+      (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+       num_detections_np) = sess.run(
+           [boxes, scores, classes, keypoints, masks, num_detections],
+           feed_dict={tf_example: tf_example_np})
+      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                      [0.5, 0.5, 0.8, 0.8]],
+                                     [[0.5, 0.5, 1.0, 1.0],
+                                      [0.0, 0.0, 0.0, 0.0]]])
+      self.assertAllClose(scores_np, [[0.7, 0.6],
+                                      [0.9, 0.0]])
+      self.assertAllClose(classes_np, [[1, 2],
+                                       [2, 1]])
+      self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+      self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_export_graph_saves_pipeline_file(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=True)
+    output_directory = os.path.join(tmp_dir, 'output')
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel()
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      exporter.export_inference_graph(
+          input_type='image_tensor',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+      expected_pipeline_path = os.path.join(
+          output_directory, 'pipeline.config')
+      self.assertTrue(os.path.exists(expected_pipeline_path))
+
+      written_pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      with tf.gfile.GFile(expected_pipeline_path, 'r') as f:
+        proto_str = f.read()
+        text_format.Merge(proto_str, written_pipeline_config)
+        self.assertProtoEquals(pipeline_config, written_pipeline_config)
+
+  def test_export_saved_model_and_run_inference(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    output_directory = os.path.join(tmp_dir, 'output')
+    saved_model_path = os.path.join(output_directory, 'saved_model')
+
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='tf_example',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    tf_example_np = np.hstack([self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
+    with tf.Graph().as_default() as od_graph:
+      with self.test_session(graph=od_graph) as sess:
+        meta_graph = tf.saved_model.loader.load(
+            sess, [tf.saved_model.tag_constants.SERVING], saved_model_path)
+
+        signature = meta_graph.signature_def['serving_default']
+        input_tensor_name = signature.inputs['inputs'].name
+        tf_example = od_graph.get_tensor_by_name(input_tensor_name)
+
+        boxes = od_graph.get_tensor_by_name(
+            signature.outputs['detection_boxes'].name)
+        scores = od_graph.get_tensor_by_name(
+            signature.outputs['detection_scores'].name)
+        classes = od_graph.get_tensor_by_name(
+            signature.outputs['detection_classes'].name)
+        keypoints = od_graph.get_tensor_by_name(
+            signature.outputs['detection_keypoints'].name)
+        masks = od_graph.get_tensor_by_name(
+            signature.outputs['detection_masks'].name)
+        num_detections = od_graph.get_tensor_by_name(
+            signature.outputs['num_detections'].name)
+
+        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+         num_detections_np) = sess.run(
+             [boxes, scores, classes, keypoints, masks, num_detections],
+             feed_dict={tf_example: tf_example_np})
+        self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.8, 0.8]],
+                                       [[0.5, 0.5, 1.0, 1.0],
+                                        [0.0, 0.0, 0.0, 0.0]]])
+        self.assertAllClose(scores_np, [[0.7, 0.6],
+                                        [0.9, 0.0]])
+        self.assertAllClose(classes_np, [[1, 2],
+                                         [2, 1]])
+        self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+        self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_write_saved_model(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    output_directory = os.path.join(tmp_dir, 'output')
+    saved_model_path = os.path.join(output_directory, 'saved_model')
+    tf.gfile.MakeDirs(output_directory)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      detection_model = model_builder.build(pipeline_config.model,
+                                            is_training=False)
+      outputs, placeholder_tensor = exporter._build_detection_graph(
+          input_type='tf_example',
+          detection_model=detection_model,
+          input_shape=None,
+          output_collection_name='inference_op',
+          graph_hook_fn=None)
+      output_node_names = ','.join(outputs.keys())
+      saver = tf.train.Saver()
+      input_saver_def = saver.as_saver_def()
+      frozen_graph_def = exporter.freeze_graph_with_def_protos(
+          input_graph_def=tf.get_default_graph().as_graph_def(),
+          input_saver_def=input_saver_def,
+          input_checkpoint=trained_checkpoint_prefix,
+          output_node_names=output_node_names,
+          restore_op_name='save/restore_all',
+          filename_tensor_name='save/Const:0',
+          output_graph='',
+          clear_devices=True,
+          initializer_nodes='')
+      exporter.write_saved_model(
+          saved_model_path=saved_model_path,
+          frozen_graph_def=frozen_graph_def,
+          inputs=placeholder_tensor,
+          outputs=outputs)
+
+    tf_example_np = np.hstack([self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
+    with tf.Graph().as_default() as od_graph:
+      with self.test_session(graph=od_graph) as sess:
+        meta_graph = tf.saved_model.loader.load(
+            sess, [tf.saved_model.tag_constants.SERVING], saved_model_path)
+
+        signature = meta_graph.signature_def['serving_default']
+        input_tensor_name = signature.inputs['inputs'].name
+        tf_example = od_graph.get_tensor_by_name(input_tensor_name)
+
+        boxes = od_graph.get_tensor_by_name(
+            signature.outputs['detection_boxes'].name)
+        scores = od_graph.get_tensor_by_name(
+            signature.outputs['detection_scores'].name)
+        classes = od_graph.get_tensor_by_name(
+            signature.outputs['detection_classes'].name)
+        keypoints = od_graph.get_tensor_by_name(
+            signature.outputs['detection_keypoints'].name)
+        masks = od_graph.get_tensor_by_name(
+            signature.outputs['detection_masks'].name)
+        num_detections = od_graph.get_tensor_by_name(
+            signature.outputs['num_detections'].name)
+
+        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+         num_detections_np) = sess.run(
+             [boxes, scores, classes, keypoints, masks, num_detections],
+             feed_dict={tf_example: tf_example_np})
+        self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.8, 0.8]],
+                                       [[0.5, 0.5, 1.0, 1.0],
+                                        [0.0, 0.0, 0.0, 0.0]]])
+        self.assertAllClose(scores_np, [[0.7, 0.6],
+                                        [0.9, 0.0]])
+        self.assertAllClose(classes_np, [[1, 2],
+                                         [2, 1]])
+        self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+        self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_export_checkpoint_and_run_inference(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    output_directory = os.path.join(tmp_dir, 'output')
+    model_path = os.path.join(output_directory, 'model.ckpt')
+    meta_graph_path = model_path + '.meta'
+
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      exporter.export_inference_graph(
+          input_type='tf_example',
+          pipeline_config=pipeline_config,
+          trained_checkpoint_prefix=trained_checkpoint_prefix,
+          output_directory=output_directory)
+
+    tf_example_np = np.hstack([self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
+    with tf.Graph().as_default() as od_graph:
+      with self.test_session(graph=od_graph) as sess:
+        new_saver = tf.train.import_meta_graph(meta_graph_path)
+        new_saver.restore(sess, model_path)
+
+        tf_example = od_graph.get_tensor_by_name('tf_example:0')
+        boxes = od_graph.get_tensor_by_name('detection_boxes:0')
+        scores = od_graph.get_tensor_by_name('detection_scores:0')
+        classes = od_graph.get_tensor_by_name('detection_classes:0')
+        keypoints = od_graph.get_tensor_by_name('detection_keypoints:0')
+        masks = od_graph.get_tensor_by_name('detection_masks:0')
+        num_detections = od_graph.get_tensor_by_name('num_detections:0')
+        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+         num_detections_np) = sess.run(
+             [boxes, scores, classes, keypoints, masks, num_detections],
+             feed_dict={tf_example: tf_example_np})
+        self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.8, 0.8]],
+                                       [[0.5, 0.5, 1.0, 1.0],
+                                        [0.0, 0.0, 0.0, 0.0]]])
+        self.assertAllClose(scores_np, [[0.7, 0.6],
+                                        [0.9, 0.0]])
+        self.assertAllClose(classes_np, [[1, 2],
+                                         [2, 1]])
+        self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+        self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_write_graph_and_checkpoint(self):
+    tmp_dir = self.get_temp_dir()
+    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
+    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
+                                          use_moving_averages=False)
+    output_directory = os.path.join(tmp_dir, 'output')
+    model_path = os.path.join(output_directory, 'model.ckpt')
+    meta_graph_path = model_path + '.meta'
+    tf.gfile.MakeDirs(output_directory)
+    with mock.patch.object(
+        model_builder, 'build', autospec=True) as mock_builder:
+      mock_builder.return_value = FakeModel(
+          add_detection_keypoints=True, add_detection_masks=True)
+      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+      pipeline_config.eval_config.use_moving_averages = False
+      detection_model = model_builder.build(pipeline_config.model,
+                                            is_training=False)
+      exporter._build_detection_graph(
+          input_type='tf_example',
+          detection_model=detection_model,
+          input_shape=None,
+          output_collection_name='inference_op',
+          graph_hook_fn=None)
+      saver = tf.train.Saver()
+      input_saver_def = saver.as_saver_def()
+      exporter.write_graph_and_checkpoint(
+          inference_graph_def=tf.get_default_graph().as_graph_def(),
+          model_path=model_path,
+          input_saver_def=input_saver_def,
+          trained_checkpoint_prefix=trained_checkpoint_prefix)
+
+    tf_example_np = np.hstack([self._create_tf_example(
+        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
+    with tf.Graph().as_default() as od_graph:
+      with self.test_session(graph=od_graph) as sess:
+        new_saver = tf.train.import_meta_graph(meta_graph_path)
+        new_saver.restore(sess, model_path)
+
+        tf_example = od_graph.get_tensor_by_name('tf_example:0')
+        boxes = od_graph.get_tensor_by_name('detection_boxes:0')
+        scores = od_graph.get_tensor_by_name('detection_scores:0')
+        classes = od_graph.get_tensor_by_name('detection_classes:0')
+        keypoints = od_graph.get_tensor_by_name('detection_keypoints:0')
+        masks = od_graph.get_tensor_by_name('detection_masks:0')
+        num_detections = od_graph.get_tensor_by_name('num_detections:0')
+        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
+         num_detections_np) = sess.run(
+             [boxes, scores, classes, keypoints, masks, num_detections],
+             feed_dict={tf_example: tf_example_np})
+        self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
+                                        [0.5, 0.5, 0.8, 0.8]],
+                                       [[0.5, 0.5, 1.0, 1.0],
+                                        [0.0, 0.0, 0.0, 0.0]]])
+        self.assertAllClose(scores_np, [[0.7, 0.6],
+                                        [0.9, 0.0]])
+        self.assertAllClose(classes_np, [[1, 2],
+                                         [2, 1]])
+        self.assertAllClose(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
+        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
+        self.assertAllClose(num_detections_np, [2, 1])
+
+  def test_rewrite_nn_resize_op(self):
+    g = tf.Graph()
+    with g.as_default():
+      x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
+      y = array_ops.placeholder(dtypes.float32, shape=(8, 20, 20, 8))
+      s = ops.nearest_neighbor_upsampling(x, 2)
+      t = s + y
+      exporter.rewrite_nn_resize_op()
+
+    resize_op_found = False
+    for op in g.get_operations():
+      if op.type == 'ResizeNearestNeighbor':
+        resize_op_found = True
+        self.assertEqual(op.inputs[0], x)
+        self.assertEqual(op.outputs[0].consumers()[0], t.op)
+        break
+
+    self.assertTrue(resize_op_found)
+
+  def test_rewrite_nn_resize_op_quantized(self):
+    g = tf.Graph()
+    with g.as_default():
+      x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
+      x_conv = tf.contrib.slim.conv2d(x, 8, 1)
+      y = array_ops.placeholder(dtypes.float32, shape=(8, 20, 20, 8))
+      s = ops.nearest_neighbor_upsampling(x_conv, 2)
+      t = s + y
+
+      graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
+      graph_rewriter_config.quantization.delay = 500000
+      graph_rewriter_fn = graph_rewriter_builder.build(
+          graph_rewriter_config, is_training=False)
+      graph_rewriter_fn()
+
+      exporter.rewrite_nn_resize_op(is_quantized=True)
+
+    resize_op_found = False
+    for op in g.get_operations():
+      if op.type == 'ResizeNearestNeighbor':
+        resize_op_found = True
+        self.assertEqual(op.inputs[0].op.type, 'FakeQuantWithMinMaxVars')
+        self.assertEqual(op.outputs[0].consumers()[0], t.op)
+        break
+
+    self.assertTrue(resize_op_found)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/challenge_evaluation.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/challenge_evaluation.md
new file mode 100644
index 00000000..4a60b0fc
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/challenge_evaluation.md
@@ -0,0 +1,151 @@
+# Open Images Challenge Evaluation
+
+The Object Detection API is currently supporting several evaluation metrics used in the [Open Images Challenge 2018](https://storage.googleapis.com/openimages/web/challenge.html).
+In addition, several data processing tools are available. Detailed instructions on using the tools for each track are available below.
+
+**NOTE**: links to the external website in this tutorial may change after the Open Images Challenge 2018 is finished.
+
+## Object Detection Track
+
+The [Object Detection metric](https://storage.googleapis.com/openimages/web/object_detection_metric.html) protocol requires a pre-processing of the released data to ensure correct evaluation. The released data contains only leaf-most bounding box annotations and image-level labels.
+The evaluation metric implementation is available in the class `OpenImagesDetectionChallengeEvaluator`.
+
+1. Download class hierarchy of Open Images Challenge 2018 in JSON format from [here](https://storage.googleapis.com/openimages/challenge_2018/bbox_labels_500_hierarchy.json).
+2. Download ground-truth [boundling boxes](https://storage.googleapis.com/openimages/challenge_2018/train/challenge-2018-train-annotations-bbox.csv) and [image-level labels](https://storage.googleapis.com/openimages/challenge_2018/train/challenge-2018-train-annotations-human-imagelabels.csv).
+3. Filter the rows corresponding to the validation set images you want to use and store the results in the same CSV format.
+4. Run the following command to create hierarchical expansion of the bounding boxes annotations:
+
+```
+HIERARCHY_FILE=/path/to/bbox_labels_500_hierarchy.json
+BOUNDING_BOXES=/path/to/challenge-2018-train-annotations-bbox
+IMAGE_LABELS=/path/to/challenge-2018-train-annotations-human-imagelabels
+
+python object_detection/dataset_tools/oid_hierarchical_labels_expansion.py \
+    --json_hierarchy_file=${HIERARCHY_FILE} \
+    --input_annotations=${BOUNDING_BOXES}.csv \
+    --output_annotations=${BOUNDING_BOXES}_expanded.csv \
+    --annotation_type=1
+
+python object_detection/dataset_tools/oid_hierarchical_labels_expansion.py \
+    --json_hierarchy_file=${HIERARCHY_FILE} \
+    --input_annotations=${IMAGE_LABELS}.csv \
+    --output_annotations=${IMAGE_LABELS}_expanded.csv \
+    --annotation_type=2
+```
+
+After step 4 you will have produced the ground-truth files suitable for running 'OID Challenge Object Detection Metric 2018' evaluation.
+
+```
+INPUT_PREDICTIONS=/path/to/detection_predictions.csv
+OUTPUT_METRICS=/path/to/output/metrics/file
+
+python models/research/object_detection/metrics/oid_od_challenge_evaluation.py \
+    --input_annotations_boxes=${BOUNDING_BOXES}_expanded.csv \
+    --input_annotations_labels=${IMAGE_LABELS}_expanded.csv \
+    --input_class_labelmap=object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt \
+    --input_predictions=${INPUT_PREDICTIONS} \
+    --output_metrics=${OUTPUT_METRICS} \
+```
+
+### Running evaluation on CSV files directly
+
+5. If you are not using Tensorflow, you can run evaluation directly using your algorithm's output and generated ground-truth files. {value=5}
+
+
+### Running evaluation using TF Object Detection API
+
+5. Produce tf.Example files suitable for running inference: {value=5}
+
+```
+RAW_IMAGES_DIR=/path/to/raw_images_location
+OUTPUT_DIR=/path/to/output_tfrecords
+
+python object_detection/dataset_tools/create_oid_tf_record.py \
+    --input_box_annotations_csv ${BOUNDING_BOXES}_expanded.csv \
+    --input_image_label_annotations_csv ${IMAGE_LABELS}_expanded.csv \
+    --input_images_directory ${RAW_IMAGES_DIR} \
+    --input_label_map object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt \
+    --output_tf_record_path_prefix ${OUTPUT_DIR} \
+    --num_shards=100
+```
+
+6. Run inference of your model and fill corresponding fields in tf.Example: see [this tutorial](object_detection/g3doc/oid_inference_and_evaluation.md) on running the inference with Tensorflow Object Detection API models. {value=6}
+
+7. Finally, run the evaluation script to produce the final evaluation result.
+
+```
+INPUT_TFRECORDS_WITH_DETECTIONS=/path/to/tf_records_with_detections
+OUTPUT_CONFIG_DIR=/path/to/configs
+
+echo "
+label_map_path: 'object_detection/data/oid_object_detection_challenge_500_label_map.pbtxt'
+tf_record_input_reader: { input_path: '${INPUT_TFRECORDS_WITH_DETECTIONS}' }
+" > ${OUTPUT_CONFIG_DIR}/input_config.pbtxt
+
+echo "
+metrics_set: 'oid_challenge_detection_metrics'
+" > ${OUTPUT_CONFIG_DIR}/eval_config.pbtxt
+
+OUTPUT_METRICS_DIR=/path/to/metrics_csv
+
+python object_detection/metrics/offline_eval_map_corloc.py \
+    --eval_dir=${OUTPUT_METRICS_DIR} \
+    --eval_config_path=${OUTPUT_CONFIG_DIR}/eval_config.pbtxt \
+    --input_config_path=${OUTPUT_CONFIG_DIR}/input_config.pbtxt
+```
+
+The result of the evaluation will be stored in `${OUTPUT_METRICS_DIR}/metrics.csv`
+
+For the Object Detection Track, the participants will be ranked on:
+
+- "OpenImagesChallenge2018_Precision/mAP@0.5IOU"
+
+## Visual Relationships Detection Track
+
+The [Visual Relationships Detection metrics](https://storage.googleapis.com/openimages/web/vrd_detection_metric.html) can be directly evaluated using the ground-truth data and model predictions. The evaluation metric implementation is available in the class `VRDRelationDetectionEvaluator`,`VRDPhraseDetectionEvaluator`.
+
+1. Download the ground-truth [visual relationships annotations](https://storage.googleapis.com/openimages/challenge_2018/train/challenge-2018-train-vrd.csv) and [image-level labels](https://storage.googleapis.com/openimages/challenge_2018/train/challenge-2018-train-vrd-labels.csv).
+2. Filter the rows corresponding to the validation set images you want to use and store the results in the same CSV format.
+3. Run the follwing command to produce final metrics:
+
+```
+INPUT_ANNOTATIONS_BOXES=/path/to/challenge-2018-train-vrd.csv
+INPUT_ANNOTATIONS_LABELS=/path/to/challenge-2018-train-vrd-labels.csv
+INPUT_PREDICTIONS=/path/to/predictions.csv
+INPUT_CLASS_LABELMAP=/path/to/oid_object_detection_challenge_500_label_map.pbtxt
+INPUT_RELATIONSHIP_LABELMAP=/path/to/relationships_labelmap.pbtxt
+OUTPUT_METRICS=/path/to/output/metrics/file
+
+echo "item { name: '/m/02gy9n' id: 602 display_name: 'Transparent' }
+item { name: '/m/05z87' id: 603 display_name: 'Plastic' }
+item { name: '/m/0dnr7' id: 604 display_name: '(made of)Textile' }
+item { name: '/m/04lbp' id: 605 display_name: '(made of)Leather' }
+item { name: '/m/083vt' id: 606 display_name: 'Wooden'}
+">>${INPUT_CLASS_LABELMAP}
+
+echo "item { name: 'at' id: 1 display_name: 'at' }
+item { name: 'on' id: 2 display_name: 'on (top of)' }
+item { name: 'holds' id: 3 display_name: 'holds' }
+item { name: 'plays' id: 4 display_name: 'plays' }
+item { name: 'interacts_with' id: 5 display_name: 'interacts with' }
+item { name: 'wears' id: 6 display_name: 'wears' }
+item { name: 'is' id: 7 display_name: 'is' }
+item { name: 'inside_of' id: 8 display_name: 'inside of' }
+item { name: 'under' id: 9 display_name: 'under' }
+item { name: 'hits' id: 10 display_name: 'hits' }
+"> ${INPUT_RELATIONSHIP_LABELMAP}
+
+python object_detection/metrics/oid_vrd_challenge_evaluation.py \
+    --input_annotations_boxes=${INPUT_ANNOTATIONS_BOXES} \
+    --input_annotations_labels=${INPUT_ANNOTATIONS_LABELS} \
+    --input_predictions=${INPUT_PREDICTIONS} \
+    --input_class_labelmap=${INPUT_CLASS_LABELMAP} \
+    --input_relationship_labelmap=${INPUT_RELATIONSHIP_LABELMAP} \
+    --output_metrics=${OUTPUT_METRICS}
+```
+
+The participants of the challenge will be evaluated by weighted average of the following three metrics:
+
+- "VRDMetric_Relationships_mAP@0.5IOU"
+- "VRDMetric_Relationships_Recall@50@0.5IOU"
+- "VRDMetric_Phrases_mAP@0.5IOU"
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/configuring_jobs.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/configuring_jobs.md
new file mode 100644
index 00000000..c088169b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/configuring_jobs.md
@@ -0,0 +1,165 @@
+# Configuring the Object Detection Training Pipeline
+
+## Overview
+
+The Tensorflow Object Detection API uses protobuf files to configure the
+training and evaluation process. The schema for the training pipeline can be
+found in object_detection/protos/pipeline.proto. At a high level, the config
+file is split into 5 parts:
+
+1. The `model` configuration. This defines what type of model will be trained
+(ie. meta-architecture, feature extractor).
+2. The `train_config`, which decides what parameters should be used to train
+model parameters (ie. SGD parameters, input preprocessing and feature extractor
+initialization values).
+3. The `eval_config`, which determines what set of metrics will be reported for
+evaluation.
+4. The `train_input_config`, which defines what dataset the model should be
+trained on.
+5. The `eval_input_config`, which defines what dataset the model will be
+evaluated on. Typically this should be different than the training input
+dataset.
+
+A skeleton configuration file is shown below:
+
+```
+model {
+(... Add model config here...)
+}
+
+train_config : {
+(... Add train_config here...)
+}
+
+train_input_reader: {
+(... Add train_input configuration here...)
+}
+
+eval_config: {
+}
+
+eval_input_reader: {
+(... Add eval_input configuration here...)
+}
+```
+
+## Picking Model Parameters
+
+There are a large number of model parameters to configure. The best settings
+will depend on your given application. Faster R-CNN models are better suited to
+cases where high accuracy is desired and latency is of lower priority.
+Conversely, if processing time is the most important factor, SSD models are
+recommended. Read [our paper](https://arxiv.org/abs/1611.10012) for a more
+detailed discussion on the speed vs accuracy tradeoff.
+
+To help new users get started, sample model configurations have been provided
+in the object_detection/samples/configs folder. The contents of these
+configuration files can be pasted into `model` field of the skeleton
+configuration. Users should note that the `num_classes` field should be changed
+to a value suited for the dataset the user is training on.
+
+## Defining Inputs
+
+The Tensorflow Object Detection API accepts inputs in the TFRecord file format.
+Users must specify the locations of both the training and evaluation files.
+Additionally, users should also specify a label map, which define the mapping
+between a class id and class name. The label map should be identical between
+training and evaluation datasets.
+
+An example input configuration looks as follows:
+
+```
+tf_record_input_reader {
+  input_path: "/usr/home/username/data/train.record"
+}
+label_map_path: "/usr/home/username/data/label_map.pbtxt"
+```
+
+Users should substitute the `input_path` and `label_map_path` arguments and
+insert the input configuration into the `train_input_reader` and
+`eval_input_reader` fields in the skeleton configuration. Note that the paths
+can also point to Google Cloud Storage buckets (ie.
+"gs://project_bucket/train.record") for use on Google Cloud.
+
+## Configuring the Trainer
+
+The `train_config` defines parts of the training process:
+
+1. Model parameter initialization.
+2. Input preprocessing.
+3. SGD parameters.
+
+A sample `train_config` is below:
+
+```
+batch_size: 1
+optimizer {
+  momentum_optimizer: {
+    learning_rate: {
+      manual_step_learning_rate {
+        initial_learning_rate: 0.0002
+        schedule {
+          step: 0
+          learning_rate: .0002
+        }
+        schedule {
+          step: 900000
+          learning_rate: .00002
+        }
+        schedule {
+          step: 1200000
+          learning_rate: .000002
+        }
+      }
+    }
+    momentum_optimizer_value: 0.9
+  }
+  use_moving_average: false
+}
+fine_tune_checkpoint: "/usr/home/username/tmp/model.ckpt-#####"
+from_detection_checkpoint: true
+load_all_detection_checkpoint_vars: true
+gradient_clipping_by_norm: 10.0
+data_augmentation_options {
+  random_horizontal_flip {
+  }
+}
+```
+
+### Model Parameter Initialization
+
+While optional, it is highly recommended that users utilize other object
+detection checkpoints. Training an object detector from scratch can take days.
+To speed up the training process, it is recommended that users re-use the
+feature extractor parameters from a pre-existing image classification or
+object detection checkpoint. `train_config` provides two fields to specify
+pre-existing checkpoints: `fine_tune_checkpoint` and
+`from_detection_checkpoint`. `fine_tune_checkpoint` should provide a path to
+the pre-existing checkpoint
+(ie:"/usr/home/username/checkpoint/model.ckpt-#####").
+`from_detection_checkpoint` is a boolean value. If false, it assumes the
+checkpoint was from an object classification checkpoint. Note that starting
+from a detection checkpoint will usually result in a faster training job than
+a classification checkpoint.
+
+The list of provided checkpoints can be found [here](detection_model_zoo.md).
+
+### Input Preprocessing
+
+The `data_augmentation_options` in `train_config` can be used to specify
+how training data can be modified. This field is optional.
+
+### SGD Parameters
+
+The remainings parameters in `train_config` are hyperparameters for gradient
+descent. Please note that the optimal learning rates provided in these
+configuration files may depend on the specifics of the training setup (e.g.
+number of workers, gpu type).
+
+## Configuring the Evaluator
+
+The main components to set in `eval_config` are `num_examples` and
+`metrics_set`. The parameter `num_examples` indicates the number of batches (
+currently of batch size 1) used for an evaluation cycle, and often is the total
+size of the evaluation dataset. The parameter `metrics_set` indicates which
+metrics to run during evaluation (i.e. `"coco_detection_metrics"`).
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/defining_your_own_model.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/defining_your_own_model.md
new file mode 100644
index 00000000..865f6af1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/defining_your_own_model.md
@@ -0,0 +1,137 @@
+# So you want to create a new model!
+
+In this section, we discuss some of the abstractions that we use
+for defining detection models. If you would like to define a new model
+architecture for detection and use it in the Tensorflow Detection API,
+then this section should also serve as a high level guide to the files that you
+will need to edit to get your new model working.
+
+## DetectionModels (`object_detection/core/model.py`)
+
+In order to be trained, evaluated, and exported for serving  using our
+provided binaries, all models under the Tensorflow Object Detection API must
+implement the `DetectionModel` interface (see the full definition in `object_detection/core/model.py`).  In particular,
+each of these models are responsible for implementing 5 functions:
+
+* `preprocess`: Run any preprocessing (e.g., scaling/shifting/reshaping) of
+  input values that is necessary prior to running the detector on an input
+  image.
+* `predict`: Produce “raw” prediction tensors that can be passed to loss or
+  postprocess functions.
+* `postprocess`: Convert predicted output tensors to final detections.
+* `loss`: Compute scalar loss tensors with respect to provided groundtruth.
+* `restore`: Load a checkpoint into the Tensorflow graph.
+
+Given a `DetectionModel` at training time, we pass each image batch through
+the following sequence of functions to compute a loss which can be optimized via
+SGD:
+
+```
+inputs (images tensor) -> preprocess -> predict -> loss -> outputs (loss tensor)
+```
+
+And at eval time, we pass each image batch through the following sequence of
+functions to produce a set of detections:
+
+```
+inputs (images tensor) -> preprocess -> predict -> postprocess ->
+  outputs (boxes tensor, scores tensor, classes tensor, num_detections tensor)
+```
+
+Some conventions to be aware of:
+
+* `DetectionModel`s should make no assumptions about the input size or aspect
+  ratio --- they are responsible for doing any resize/reshaping necessary
+  (see docstring for the `preprocess` function).
+* Output classes are always integers in the range `[0, num_classes)`.
+  Any mapping of these integers to semantic labels is to be handled outside
+  of this class.  We never explicitly emit a “background class” --- thus 0 is
+  the first non-background class and any logic of predicting and removing
+  implicit background classes must be handled internally by the implementation.
+* Detected boxes are to be interpreted as being in
+  `[y_min, x_min, y_max, x_max]` format and normalized relative to the
+  image window.
+* We do not specifically assume any kind of probabilistic interpretation of the
+  scores --- the only important thing is their relative ordering. Thus
+  implementations of the postprocess function are free to output logits,
+  probabilities, calibrated probabilities, or anything else.
+
+## Defining a new Faster R-CNN or SSD Feature Extractor
+
+In most cases, you probably will not implement a `DetectionModel` from scratch
+--- instead you might create a new feature extractor to be used by one of the
+SSD or Faster R-CNN meta-architectures.  (We think of meta-architectures as
+classes that define entire families of models using the `DetectionModel`
+abstraction).
+
+Note: For the following discussion to make sense, we recommend first becoming
+familiar with the [Faster R-CNN](https://arxiv.org/abs/1506.01497) paper.
+
+Let’s now imagine that you have invented a brand new network architecture
+(say, “InceptionV100”) for classification and want to see how InceptionV100
+would behave as a feature extractor for detection (say, with Faster R-CNN).
+A similar procedure would hold for SSD models, but we’ll discuss Faster R-CNN.
+
+To use InceptionV100, we will have to define a new
+`FasterRCNNFeatureExtractor` and pass it to our `FasterRCNNMetaArch`
+constructor as input.  See
+`object_detection/meta_architectures/faster_rcnn_meta_arch.py` for definitions
+of `FasterRCNNFeatureExtractor` and `FasterRCNNMetaArch`, respectively.
+A `FasterRCNNFeatureExtractor` must define a few
+functions:
+
+* `preprocess`: Run any preprocessing of input values that is necessary prior
+  to running the detector on an input image.
+* `_extract_proposal_features`: Extract first stage Region Proposal Network
+  (RPN) features.
+* `_extract_box_classifier_features`: Extract second stage Box Classifier
+  features.
+* `restore_from_classification_checkpoint_fn`: Load a checkpoint into the
+  Tensorflow graph.
+
+See the `object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py`
+definition as one example. Some remarks:
+
+* We typically initialize the weights of this feature extractor
+  using those from the
+  [Slim Resnet-101 classification checkpoint](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models),
+  and we know
+  that images were preprocessed when training this checkpoint
+  by subtracting a channel mean from each input
+  image.  Thus, we implement the preprocess function to replicate the same
+  channel mean subtraction behavior.
+* The “full” resnet classification network defined in slim is cut into two
+  parts --- all but the last “resnet block” is put into the
+  `_extract_proposal_features` function and the final block is separately
+  defined in the `_extract_box_classifier_features function`.  In general,
+  some experimentation may be required to decide on an optimal layer at
+  which to “cut” your feature extractor into these two pieces for Faster R-CNN.
+
+## Register your model for configuration
+
+Assuming that your new feature extractor does not require nonstandard
+configuration, you will want to ideally be able to simply change the
+“feature_extractor.type” fields in your configuration protos to point to a
+new feature extractor.  In order for our API to know how to understand this
+new type though, you will first have to register your new feature
+extractor with the model builder (`object_detection/builders/model_builder.py`),
+whose job is to create models from config protos..
+
+Registration is simple --- just add a pointer to the new Feature Extractor
+class that you have defined in one of the SSD or Faster R-CNN Feature
+Extractor Class maps at the top of the
+`object_detection/builders/model_builder.py` file.
+We recommend adding a test in `object_detection/builders/model_builder_test.py`
+to make sure that parsing your proto will work as expected.
+
+## Taking your new model for a spin
+
+After registration you are ready to go with your model!  Some final tips:
+
+* To save time debugging, try running your configuration file locally first
+  (both training and evaluation).
+* Do a sweep of learning rates to figure out which learning rate is best
+  for your model.
+* A small but often important detail: you may find it necessary to disable
+  batchnorm training (that is, load the batch norm parameters from the
+  classification checkpoint, but do not update them during gradient descent).
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/detection_model_zoo.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/detection_model_zoo.md
new file mode 100644
index 00000000..ed8d698f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/detection_model_zoo.md
@@ -0,0 +1,136 @@
+# Tensorflow detection model zoo
+
+We provide a collection of detection models pre-trained on the [COCO
+dataset](http://mscoco.org), the [Kitti dataset](http://www.cvlibs.net/datasets/kitti/),
+the [Open Images dataset](https://github.com/openimages/dataset), the
+[AVA v2.1 dataset](https://research.google.com/ava/) and the
+[iNaturalist Species Detection Dataset](https://github.com/visipedia/inat_comp/blob/master/2017/README.md#bounding-boxes).
+These models can be useful for out-of-the-box inference if you are interested in
+categories already in those datasets. They are also useful for initializing your
+models when training on novel datasets.
+
+In the table below, we list each such pre-trained model including:
+
+* a model name that corresponds to a config file that was used to train this
+  model in the `samples/configs` directory,
+* a download link to a tar.gz file containing the pre-trained model,
+* model speed --- we report running time in ms per 600x600 image (including all
+  pre and post-processing), but please be
+  aware that these timings depend highly on one's specific hardware
+  configuration (these timings were performed using an Nvidia
+  GeForce GTX TITAN X card) and should be treated more as relative timings in
+  many cases. Also note that desktop GPU timing does not always reflect mobile
+  run time. For example Mobilenet V2 is faster on mobile devices than Mobilenet
+  V1, but is slightly slower on desktop GPU.
+* detector performance on subset of the COCO validation set or Open Images test split as measured by the dataset-specific mAP measure.
+  Here, higher is better, and we only report bounding box mAP rounded to the
+  nearest integer.
+* Output types (`Boxes`, and `Masks` if applicable )
+
+You can un-tar each tar.gz file via, e.g.,:
+
+```
+tar -xzvf ssd_mobilenet_v1_coco.tar.gz
+```
+
+Inside the un-tar'ed directory, you will find:
+
+* a graph proto (`graph.pbtxt`)
+* a checkpoint
+  (`model.ckpt.data-00000-of-00001`, `model.ckpt.index`, `model.ckpt.meta`)
+* a frozen graph proto with weights baked into the graph as constants
+  (`frozen_inference_graph.pb`) to be used for out of the box inference
+    (try this out in the Jupyter notebook!)
+* a config file (`pipeline.config`) which was used to generate the graph.  These
+  directly correspond to a config file in the
+  [samples/configs](https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs)) directory but often with a modified score threshold.  In the case
+  of the heavier Faster R-CNN models, we also provide a version of the model
+  that uses a highly reduced number of proposals for speed.
+
+Some remarks on frozen inference graphs:
+
+* If you try to evaluate the frozen graph, you may find performance numbers for
+  some of the models to be slightly lower than what we report in the below
+  tables.  This is because we discard detections with scores below a
+  threshold (typically 0.3) when creating the frozen graph.  This corresponds
+  effectively to picking a point on the precision recall curve of
+  a detector (and discarding the part past that point), which negatively impacts
+  standard mAP metrics.
+* Our frozen inference graphs are generated using the
+  [v1.8.0](https://github.com/tensorflow/tensorflow/tree/v1.8.0)
+  release version of Tensorflow and we do not guarantee that these will work
+  with other versions; this being said, each frozen inference graph can be
+  regenerated using your current version of Tensorflow by re-running the
+  [exporter](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/exporting_models.md),
+  pointing it at the model directory as well as the corresponding config file in
+  [samples/configs](https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs).
+
+
+## COCO-trained models
+
+| Model name  | Speed (ms) | COCO mAP[^1] | Outputs |
+| ------------ | :--------------: | :--------------: | :-------------: |
+| [ssd_mobilenet_v1_coco](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) | 30 | 21 | Boxes |
+| [ssd_mobilenet_v1_0.75_depth_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03.tar.gz) | 26 | 18 | Boxes |
+| [ssd_mobilenet_v1_quantized_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz) | 29 | 18 | Boxes |
+| [ssd_mobilenet_v1_0.75_depth_quantized_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18.tar.gz) | 29 | 16 | Boxes |
+| [ssd_mobilenet_v1_ppn_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz) | 26 | 20 | Boxes |
+| [ssd_mobilenet_v1_fpn_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz) | 56 | 32 | Boxes |
+| [ssd_resnet_50_fpn_coco ☆](http://download.tensorflow.org/models/object_detection/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz) | 76 | 35 | Boxes |
+| [ssd_mobilenet_v2_coco](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz) | 31 | 22 | Boxes |
+| [ssd_mobilenet_v2_quantized_coco](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2018_09_14.tar.gz) | 29 | 22 | Boxes |
+| [ssdlite_mobilenet_v2_coco](http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz) | 27 | 22 | Boxes |
+| [ssd_inception_v2_coco](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2018_01_28.tar.gz) | 42 | 24 | Boxes |
+| [faster_rcnn_inception_v2_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) | 58 | 28 | Boxes |
+| [faster_rcnn_resnet50_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz) | 89 | 30 | Boxes |
+| [faster_rcnn_resnet50_lowproposals_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_lowproposals_coco_2018_01_28.tar.gz) | 64 |  | Boxes |
+| [rfcn_resnet101_coco](http://download.tensorflow.org/models/object_detection/rfcn_resnet101_coco_2018_01_28.tar.gz)  | 92 | 30 | Boxes |
+| [faster_rcnn_resnet101_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_2018_01_28.tar.gz) | 106 | 32 | Boxes |
+| [faster_rcnn_resnet101_lowproposals_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_lowproposals_coco_2018_01_28.tar.gz) | 82 |  | Boxes |
+| [faster_rcnn_inception_resnet_v2_atrous_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz) | 620 | 37 | Boxes |
+| [faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco_2018_01_28.tar.gz) | 241 |  | Boxes |
+| [faster_rcnn_nas](http://download.tensorflow.org/models/object_detection/faster_rcnn_nas_coco_2018_01_28.tar.gz) | 1833 | 43 | Boxes |
+| [faster_rcnn_nas_lowproposals_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_nas_lowproposals_coco_2018_01_28.tar.gz) | 540 |  | Boxes |
+| [mask_rcnn_inception_resnet_v2_atrous_coco](http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz) | 771 | 36 | Masks |
+| [mask_rcnn_inception_v2_coco](http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz) | 79 | 25 | Masks |
+| [mask_rcnn_resnet101_atrous_coco](http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz) | 470 | 33 | Masks |
+| [mask_rcnn_resnet50_atrous_coco](http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz) | 343 | 29 | Masks |
+
+Note: The asterisk (☆) at the end of model name indicates that this model supports TPU training.
+
+Note: If you download the tar.gz file of quantized models and un-tar, you will get different set of files - a checkpoint, a config file and tflite frozen graphs (txt/binary).
+
+## Kitti-trained models
+
+Model name                                                                                                                                                        | Speed (ms) | Pascal mAP@0.5 | Outputs
+----------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---: | :-------------: | :-----:
+[faster_rcnn_resnet101_kitti](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_kitti_2018_01_28.tar.gz) | 79  | 87              | Boxes
+
+## Open Images-trained models
+
+Model name                                                                                                                                                                                    | Speed (ms) | Open Images mAP@0.5[^2] | Outputs
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------: | :---------------------: | :-----:
+[faster_rcnn_inception_resnet_v2_atrous_oidv2](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz)                           | 727        | 37                      | Boxes
+[faster_rcnn_inception_resnet_v2_atrous_lowproposals_oidv2](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_lowproposals_oid_2018_01_28.tar.gz) | 347        |                         | Boxes
+[facessd_mobilenet_v2_quantized_open_image_v4](http://download.tensorflow.org/models/object_detection/facessd_mobilenet_v2_quantized_320x320_open_image_v4.tar.gz) [^3]                       | 20         | 73 (faces)              | Boxes
+
+## iNaturalist Species-trained models
+
+Model name                                                                                                                                                        | Speed (ms) | Pascal mAP@0.5 | Outputs
+----------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---: | :-------------: | :-----:
+[faster_rcnn_resnet101_fgvc](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_fgvc_2018_07_19.tar.gz) | 395  | 58              | Boxes
+[faster_rcnn_resnet50_fgvc](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_fgvc_2018_07_19.tar.gz) | 366  | 55             | Boxes
+
+
+## AVA v2.1 trained models
+
+Model name                                                                                                                                                        | Speed (ms) | Pascal mAP@0.5 | Outputs
+----------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---: | :-------------: | :-----:
+[faster_rcnn_resnet101_ava_v2.1](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_ava_v2.1_2018_04_30.tar.gz) | 93  | 11              | Boxes
+
+
+[^1]: See [MSCOCO evaluation protocol](http://cocodataset.org/#detections-eval).
+
+[^2]: This is PASCAL mAP with a slightly different way of true positives computation: see [Open Images evaluation protocol](evaluation_protocols.md#open-images).
+[^3]: Non-face boxes are dropped during training and non-face groundtruth boxes are ignored when evaluating.
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/evaluation_protocols.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/evaluation_protocols.md
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index 00000000..7746f112
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+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/evaluation_protocols.md
@@ -0,0 +1,156 @@
+# Supported object detection evaluation protocols
+
+The Tensorflow Object Detection API currently supports three evaluation protocols,
+that can be configured in `EvalConfig` by setting `metrics_set` to the
+corresponding value.
+
+## PASCAL VOC 2010 detection metric
+
+`EvalConfig.metrics_set='pascal_voc_detection_metrics'`
+
+The commonly used mAP metric for evaluating the quality of object detectors,
+computed according to the protocol of the PASCAL VOC Challenge 2010-2012. The
+protocol is available
+[here](http://host.robots.ox.ac.uk/pascal/VOC/voc2010/devkit_doc_08-May-2010.pdf).
+
+## Weighted PASCAL VOC detection metric
+
+`EvalConfig.metrics_set='weighted_pascal_voc_detection_metrics'`
+
+The weighted PASCAL metric computes the mean average precision as the average
+precision when treating all classes as a single class. In comparison,
+PASCAL metrics computes the mean average precision as the mean of the
+per-class average precisions.
+
+For example, the test set consists of two classes, "cat" and "dog", and there
+are ten times more boxes of "cat" than those of "dog". According to PASCAL VOC
+2010 metric, performance on each of the two classes would contribute equally
+towards the final mAP value, while for the Weighted PASCAL VOC metric the final
+mAP value will be influenced by frequency of each class.
+
+## PASCAL VOC 2010 instance segmentation metric
+
+`EvalConfig.metrics_set='pascal_voc_instance_segmentation_metrics'`
+
+Similar to Pascal VOC 2010 detection metric, but computes the intersection over
+union based on the object masks instead of object boxes.
+
+## Weighted PASCAL VOC instance segmentation metric
+
+`EvalConfig.metrics_set='weighted_pascal_voc_instance_segmentation_metrics'`
+
+Similar to the weighted pascal voc 2010 detection metric, but computes the
+intersection over union based on the object masks instead of object boxes.
+
+
+## COCO detection metrics
+
+`EvalConfig.metrics_set='coco_detection_metrics'`
+
+The COCO metrics are the official detection metrics used to score the
+[COCO competition](http://cocodataset.org/) and are similar to Pascal VOC
+metrics but have a slightly different implementation and report additional
+statistics such as mAP at IOU thresholds of .5:.95, and precision/recall
+statistics for small, medium, and large objects.
+See the
+[pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI)
+repository for more details.
+
+## COCO mask metrics
+
+`EvalConfig.metrics_set='coco_mask_metrics'`
+
+Similar to the COCO detection metrics, but computes the
+intersection over union based on the object masks instead of object boxes.
+
+## Open Images V2 detection metric
+
+`EvalConfig.metrics_set='oid_V2_detection_metrics'`
+
+This metric is defined originally for evaluating detector performance on [Open
+Images V2 dataset](https://github.com/openimages/dataset) and is fairly similar
+to the PASCAL VOC 2010 metric mentioned above. It computes interpolated average
+precision (AP) for each class and averages it among all classes (mAP).
+
+The difference to the PASCAL VOC 2010 metric is the following: Open Images
+annotations contain `group-of` ground-truth boxes (see [Open Images data
+description](https://github.com/openimages/dataset#annotations-human-bboxcsv)),
+that are treated differently for the purpose of deciding whether detections are
+"true positives", "ignored", "false positives". Here we define these three
+cases:
+
+A detection is a "true positive" if there is a non-group-of ground-truth box,
+such that:
+
+*   The detection box and the ground-truth box are of the same class, and
+    intersection-over-union (IoU) between the detection box and the ground-truth
+    box is greater than the IoU threshold (default value 0.5). \
+    Illustration of handling non-group-of boxes: \
+    ![alt
+    groupof_case_eval](img/nongroupof_case_eval.png "illustration of handling non-group-of boxes: yellow box - ground truth bounding box; green box - true positive; red box - false positives.")
+
+    *   yellow box - ground-truth box;
+    *   green box - true positive;
+    *   red boxes - false positives.
+
+*   This is the highest scoring detection for this ground truth box that
+    satisfies the criteria above.
+
+A detection is "ignored" if it is not a true positive, and there is a `group-of`
+ground-truth box such that:
+
+*   The detection box and the ground-truth box are of the same class, and the
+    area of intersection between the detection box and the ground-truth box
+    divided by the area of the detection is greater than 0.5. This is intended
+    to measure whether the detection box is approximately inside the group-of
+    ground-truth box. \
+    Illustration of handling `group-of` boxes: \
+    ![alt
+    groupof_case_eval](img/groupof_case_eval.png "illustration of handling group-of boxes: yellow box - ground truth bounding box; grey boxes - two detections of cars, that are ignored; red box - false positive.")
+
+    *   yellow box - ground-truth box;
+    *   grey boxes - two detections on cars, that are ignored;
+    *   red box - false positive.
+
+A detection is a "false positive" if it is neither a "true positive" nor
+"ignored".
+
+Precision and recall are defined as:
+
+* Precision = number-of-true-positives/(number-of-true-positives + number-of-false-positives)
+* Recall = number-of-true-positives/number-of-non-group-of-boxes
+
+Note that detections ignored as firing on a `group-of` ground-truth box do not
+contribute to the number of true positives.
+
+The labels in Open Images are organized in a
+[hierarchy](https://storage.googleapis.com/openimages/2017_07/bbox_labels_vis/bbox_labels_vis.html).
+Ground-truth bounding-boxes are annotated with the most specific class available
+in the hierarchy. For example, "car" has two children "limousine" and "van". Any
+other kind of car is annotated as "car" (for example, a sedan). Given this
+convention, the evaluation software treats all classes independently, ignoring
+the hierarchy. To achieve high performance values, object detectors should
+output bounding-boxes labelled in the same manner.
+
+The old metric name is DEPRECATED.
+`EvalConfig.metrics_set='open_images_V2_detection_metrics'`
+
+## OID Challenge Object Detection Metric 2018
+
+`EvalConfig.metrics_set='oid_challenge_detection_metrics'`
+
+The metric for the OID Challenge Object Detection Metric 2018, Object Detection
+track. The description is provided on the [Open Images Challenge
+website](https://storage.googleapis.com/openimages/web/challenge.html).
+
+The old metric name is DEPRECATED.
+`EvalConfig.metrics_set='oid_challenge_object_detection_metrics'`
+
+## OID Challenge Visual Relationship Detection Metric 2018
+
+The metric for the OID Challenge Visual Relationship Detection Metric 2018, Visual
+Relationship Detection track. The description is provided on the [Open Images
+Challenge
+website](https://storage.googleapis.com/openimages/web/challenge.html). Note:
+this is currently a stand-alone metric, that can be used only through the
+`metrics/oid_vrd_challenge_evaluation.py` util.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/exporting_models.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/exporting_models.md
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index 00000000..c6440830
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/exporting_models.md
@@ -0,0 +1,36 @@
+# Exporting a trained model for inference
+
+After your model has been trained, you should export it to a Tensorflow
+graph proto. A checkpoint will typically consist of three files:
+
+* model.ckpt-${CHECKPOINT_NUMBER}.data-00000-of-00001
+* model.ckpt-${CHECKPOINT_NUMBER}.index
+* model.ckpt-${CHECKPOINT_NUMBER}.meta
+
+After you've identified a candidate checkpoint to export, run the following
+command from tensorflow/models/research:
+
+``` bash
+# From tensorflow/models/research/
+INPUT_TYPE=image_tensor
+PIPELINE_CONFIG_PATH={path to pipeline config file}
+TRAINED_CKPT_PREFIX={path to model.ckpt}
+EXPORT_DIR={path to folder that will be used for export}
+python object_detection/export_inference_graph.py \
+    --input_type=${INPUT_TYPE} \
+    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+    --trained_checkpoint_prefix=${TRAINED_CKPT_PREFIX} \
+    --output_directory=${EXPORT_DIR}
+```
+
+NOTE: We are configuring our exported model to ingest 4-D image tensors. We can
+also configure the exported model to take encoded images or serialized
+`tf.Example`s.
+
+After export, you should see the directory ${EXPORT_DIR} containing the following:
+
+* saved_model/, a directory containing the saved model format of the exported model
+* frozen_inference_graph.pb, the frozen graph format of the exported model
+* model.ckpt.*, the model checkpoints used for exporting
+* checkpoint, a file specifying to restore included checkpoint files
+* pipeline.config, pipeline config file for the exported model
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/faq.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/faq.md
new file mode 100644
index 00000000..faa94b39
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+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/faq.md
@@ -0,0 +1,27 @@
+# Frequently Asked Questions
+
+## Q: How can I ensure that all the groundtruth boxes are used during train and eval?
+A: For the object detecion framework to be TPU-complient, we must pad our input
+tensors to static shapes. This means that we must pad to a fixed number of
+bounding boxes, configured by `InputReader.max_number_of_boxes`. It is
+important to set this value to a number larger than the maximum number of
+groundtruth boxes in the dataset. If an image is encountered with more
+bounding boxes, the excess boxes will be clipped.
+
+## Q: AttributeError: 'module' object has no attribute 'BackupHandler'
+A: This BackupHandler (tf.contrib.slim.tfexample_decoder.BackupHandler) was
+introduced in tensorflow 1.5.0 so runing with earlier versions may cause this
+issue. It now has been replaced by
+object_detection.data_decoders.tf_example_decoder.BackupHandler. Whoever sees
+this issue should be able to resolve it by syncing your fork to HEAD.
+Same for LookupTensor.
+
+## Q: AttributeError: 'module' object has no attribute 'LookupTensor'
+A: Similar to BackupHandler, syncing your fork to HEAD should make it work.
+
+## Q: Why can't I get the inference time as reported in model zoo?
+A: The inference time reported in model zoo is mean time of testing hundreds of
+images with an internal machine. As mentioned in
+[Tensorflow detection model zoo](detection_model_zoo.md), this speed depends
+highly on one's specific hardware configuration and should be treated more as
+relative timing.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/installation.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/installation.md
new file mode 100644
index 00000000..206304d1
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+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/installation.md
@@ -0,0 +1,152 @@
+# Installation
+
+## Dependencies
+
+Tensorflow Object Detection API depends on the following libraries:
+
+*   Protobuf 3.0.0
+*   Python-tk
+*   Pillow 1.0
+*   lxml
+*   tf Slim (which is included in the "tensorflow/models/research/" checkout)
+*   Jupyter notebook
+*   Matplotlib
+*   Tensorflow (>=1.9.0)
+*   Cython
+*   contextlib2
+*   cocoapi
+
+For detailed steps to install Tensorflow, follow the [Tensorflow installation
+instructions](https://www.tensorflow.org/install/). A typical user can install
+Tensorflow using one of the following commands:
+
+``` bash
+# For CPU
+pip install tensorflow
+# For GPU
+pip install tensorflow-gpu
+```
+
+The remaining libraries can be installed on Ubuntu 16.04 using via apt-get:
+
+``` bash
+sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
+pip install --user Cython
+pip install --user contextlib2
+pip install --user jupyter
+pip install --user matplotlib
+```
+
+Alternatively, users can install dependencies using pip:
+
+``` bash
+pip install --user Cython
+pip install --user contextlib2
+pip install --user pillow
+pip install --user lxml
+pip install --user jupyter
+pip install --user matplotlib
+```
+
+<!-- common_typos_disable -->
+**Note**: sometimes "sudo apt-get install protobuf-compiler" will install
+Protobuf 3+ versions for you and some users have issues when using 3.5.
+If that is your case, try the [manual](#Manual-protobuf-compiler-installation-and-usage) installation.
+
+## COCO API installation
+
+Download the
+[cocoapi](https://github.com/cocodataset/cocoapi) and
+copy the pycocotools subfolder to the tensorflow/models/research directory if
+you are interested in using COCO evaluation metrics. The default metrics are
+based on those used in Pascal VOC evaluation. To use the COCO object detection
+metrics add `metrics_set: "coco_detection_metrics"` to the `eval_config` message
+in the config file. To use the COCO instance segmentation metrics add
+`metrics_set: "coco_mask_metrics"` to the `eval_config` message in the config
+file.
+
+```bash
+git clone https://github.com/cocodataset/cocoapi.git
+cd cocoapi/PythonAPI
+make
+cp -r pycocotools <path_to_tensorflow>/models/research/
+```
+
+## Protobuf Compilation
+
+The Tensorflow Object Detection API uses Protobufs to configure model and
+training parameters. Before the framework can be used, the Protobuf libraries
+must be compiled. This should be done by running the following command from
+the tensorflow/models/research/ directory:
+
+
+``` bash
+# From tensorflow/models/research/
+protoc object_detection/protos/*.proto --python_out=.
+```
+
+**Note**: If you're getting errors while compiling, you might be using an incompatible protobuf compiler. If that's the case, use the following manual installation
+
+## Manual protobuf-compiler installation and usage
+
+**If you are on linux:**
+
+Download and install the 3.0 release of protoc, then unzip the file.
+
+```bash
+# From tensorflow/models/research/
+wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip
+unzip protobuf.zip
+```
+
+Run the compilation process again, but use the downloaded version of protoc
+
+```bash
+# From tensorflow/models/research/
+./bin/protoc object_detection/protos/*.proto --python_out=.
+```
+
+**If you are on MacOS:**
+
+If you have homebrew, download and install the protobuf with
+```brew install protobuf```
+
+Alternately, run:
+```PROTOC_ZIP=protoc-3.3.0-osx-x86_64.zip
+curl -OL https://github.com/google/protobuf/releases/download/v3.3.0/$PROTOC_ZIP
+sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
+rm -f $PROTOC_ZIP
+```
+
+Run the compilation process again:
+
+``` bash
+# From tensorflow/models/research/
+protoc object_detection/protos/*.proto --python_out=.
+```
+
+## Add Libraries to PYTHONPATH
+
+When running locally, the tensorflow/models/research/ and slim directories
+should be appended to PYTHONPATH. This can be done by running the following from
+tensorflow/models/research/:
+
+
+``` bash
+# From tensorflow/models/research/
+export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
+```
+
+Note: This command needs to run from every new terminal you start. If you wish
+to avoid running this manually, you can add it as a new line to the end of your
+~/.bashrc file, replacing \`pwd\` with the absolute path of
+tensorflow/models/research on your system.
+
+# Testing the Installation
+
+You can test that you have correctly installed the Tensorflow Object Detection\
+API by running the following command:
+
+```bash
+python object_detection/builders/model_builder_test.py
+```
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/instance_segmentation.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/instance_segmentation.md
new file mode 100644
index 00000000..8ebf7d8c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/instance_segmentation.md
@@ -0,0 +1,105 @@
+## Run an Instance Segmentation Model
+
+For some applications it isn't adequate enough to localize an object with a
+simple bounding box. For instance, you might want to segment an object region
+once it is detected. This class of problems is called **instance segmentation**.
+
+<p align="center">
+  <img src="img/kites_with_segment_overlay.png" width=676 height=450>
+</p>
+
+### Materializing data for instance segmentation {#materializing-instance-seg}
+
+Instance segmentation is an extension of object detection, where a binary mask
+(i.e. object vs. background) is associated with every bounding box. This allows
+for more fine-grained information about the extent of the object within the box.
+To train an instance segmentation model, a groundtruth mask must be supplied for
+every groundtruth bounding box. In additional to the proto fields listed in the
+section titled [Using your own dataset](using_your_own_dataset.md), one must
+also supply `image/object/mask`, which can either be a repeated list of
+single-channel encoded PNG strings, or a single dense 3D binary tensor where
+masks corresponding to each object are stacked along the first dimension. Each
+is described in more detail below.
+
+#### PNG Instance Segmentation Masks
+
+Instance segmentation masks can be supplied as serialized PNG images.
+
+```shell
+image/object/mask = ["\x89PNG\r\n\x1A\n\x00\x00\x00\rIHDR\...", ...]
+```
+
+These masks are whole-image masks, one for each object instance. The spatial
+dimensions of each mask must agree with the image. Each mask has only a single
+channel, and the pixel values are either 0 (background) or 1 (object mask).
+**PNG masks are the preferred parameterization since they offer considerable
+space savings compared to dense numerical masks.**
+
+#### Dense Numerical Instance Segmentation Masks
+
+Masks can also be specified via a dense numerical tensor.
+
+```shell
+image/object/mask = [0.0, 0.0, 1.0, 1.0, 0.0, ...]
+```
+
+For an image with dimensions `H` x `W` and `num_boxes` groundtruth boxes, the
+mask corresponds to a [`num_boxes`, `H`, `W`] float32 tensor, flattened into a
+single vector of shape `num_boxes` * `H` * `W`. In TensorFlow, examples are read
+in row-major format, so the elements are organized as:
+
+```shell
+... mask 0 row 0 ... mask 0 row 1 ... // ... mask 0 row H-1 ... mask 1 row 0 ...
+```
+
+where each row has W contiguous binary values.
+
+To see an example tf-records with mask labels, see the examples under the
+[Preparing Inputs](preparing_inputs.md) section.
+
+### Pre-existing config files
+
+We provide four instance segmentation config files that you can use to train
+your own models:
+
+1.  <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/mask_rcnn_inception_resnet_v2_atrous_coco.config" target=_blank>mask_rcnn_inception_resnet_v2_atrous_coco</a>
+1.  <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/mask_rcnn_resnet101_atrous_coco.config" target=_blank>mask_rcnn_resnet101_atrous_coco</a>
+1.  <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/mask_rcnn_resnet50_atrous_coco.config" target=_blank>mask_rcnn_resnet50_atrous_coco</a>
+1.  <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/mask_rcnn_inception_v2_coco.config" target=_blank>mask_rcnn_inception_v2_coco</a>
+
+For more details see the [detection model zoo](detection_model_zoo.md).
+
+### Updating a Faster R-CNN config file
+
+Currently, the only supported instance segmentation model is [Mask
+R-CNN](https://arxiv.org/abs/1703.06870), which requires Faster R-CNN as the
+backbone object detector.
+
+Once you have a baseline Faster R-CNN pipeline configuration, you can make the
+following modifications in order to convert it into a Mask R-CNN model.
+
+1.  Within `train_input_reader` and `eval_input_reader`, set
+    `load_instance_masks` to `True`. If using PNG masks, set `mask_type` to
+    `PNG_MASKS`, otherwise you can leave it as the default 'NUMERICAL_MASKS'.
+1.  Within the `faster_rcnn` config, use a `MaskRCNNBoxPredictor` as the
+    `second_stage_box_predictor`.
+1.  Within the `MaskRCNNBoxPredictor` message, set `predict_instance_masks` to
+    `True`. You must also define `conv_hyperparams`.
+1.  Within the `faster_rcnn` message, set `number_of_stages` to `3`.
+1.  Add instance segmentation metrics to the set of metrics:
+    `'coco_mask_metrics'`.
+1.  Update the `input_path`s to point at your data.
+
+Please refer to the section on [Running the pets dataset](running_pets.md) for
+additional details.
+
+> Note: The mask prediction branch consists of a sequence of convolution layers.
+> You can set the number of convolution layers and their depth as follows:
+>
+> 1.  Within the `MaskRCNNBoxPredictor` message, set the
+>     `mask_prediction_conv_depth` to your value of interest. The default value
+>     is 256. If you set it to `0` (recommended), the depth is computed
+>     automatically based on the number of classes in the dataset.
+> 1.  Within the `MaskRCNNBoxPredictor` message, set the
+>     `mask_prediction_num_conv_layers` to your value of interest. The default
+>     value is 2.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/oid_inference_and_evaluation.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/oid_inference_and_evaluation.md
new file mode 100644
index 00000000..360497f6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/oid_inference_and_evaluation.md
@@ -0,0 +1,257 @@
+# Inference and evaluation on the Open Images dataset
+
+This page presents a tutorial for running object detector inference and
+evaluation measure computations on the [Open Images
+dataset](https://github.com/openimages/dataset), using tools from the
+[TensorFlow Object Detection
+API](https://github.com/tensorflow/models/tree/master/research/object_detection).
+It shows how to download the images and annotations for the validation and test
+sets of Open Images; how to package the downloaded data in a format understood
+by the Object Detection API; where to find a trained object detector model for
+Open Images; how to run inference; and how to compute evaluation measures on the
+inferred detections.
+
+Inferred detections will look like the following:
+
+![](img/oid_bus_72e19c28aac34ed8.jpg)
+![](img/oid_monkey_3b4168c89cecbc5b.jpg)
+
+On the validation set of Open Images, this tutorial requires 27GB of free disk
+space and the inference step takes approximately 9 hours on a single NVIDIA
+Tesla P100 GPU. On the test set -- 75GB and 27 hours respectively. All other
+steps require less than two hours in total on both sets.
+
+## Installing TensorFlow, the Object Detection API, and Google Cloud SDK
+
+Please run through the [installation instructions](installation.md) to install
+TensorFlow and all its dependencies. Ensure the Protobuf libraries are compiled
+and the library directories are added to `PYTHONPATH`. You will also need to
+`pip` install `pandas` and `contextlib2`.
+
+Some of the data used in this tutorial lives in Google Cloud buckets. To access
+it, you will have to [install the Google Cloud
+SDK](https://cloud.google.com/sdk/downloads) on your workstation or laptop.
+
+## Preparing the Open Images validation and test sets
+
+In order to run inference and subsequent evaluation measure computations, we
+require a dataset of images and ground truth boxes, packaged as TFRecords of
+TFExamples. To create such a dataset for Open Images, you will need to first
+download ground truth boxes from the [Open Images
+website](https://github.com/openimages/dataset):
+
+```bash
+# From tensorflow/models/research
+mkdir oid
+cd oid
+wget https://storage.googleapis.com/openimages/2017_07/annotations_human_bbox_2017_07.tar.gz
+tar -xvf annotations_human_bbox_2017_07.tar.gz
+```
+
+Next, download the images. In this tutorial, we will use lower resolution images
+provided by [CVDF](http://www.cvdfoundation.org). Please follow the instructions
+on [CVDF's Open Images repository
+page](https://github.com/cvdfoundation/open-images-dataset) in order to gain
+access to the cloud bucket with the images. Then run:
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # Set SPLIT to "test" to download the images in the test set
+mkdir raw_images_${SPLIT}
+gsutil -m rsync -r gs://open-images-dataset/$SPLIT raw_images_${SPLIT}
+```
+
+Another option for downloading the images is to follow the URLs contained in the
+[image URLs and metadata CSV
+files](https://storage.googleapis.com/openimages/2017_07/images_2017_07.tar.gz)
+on the Open Images website.
+
+At this point, your `tensorflow/models/research/oid` directory should appear as
+follows:
+
+```lang-none
+|-- 2017_07
+|   |-- test
+|   |   `-- annotations-human-bbox.csv
+|   |-- train
+|   |   `-- annotations-human-bbox.csv
+|   `-- validation
+|       `-- annotations-human-bbox.csv
+|-- raw_images_validation (if you downloaded the validation split)
+|   `-- ... (41,620 files matching regex "[0-9a-f]{16}.jpg")
+|-- raw_images_test (if you downloaded the test split)
+|   `-- ... (125,436 files matching regex "[0-9a-f]{16}.jpg")
+`-- annotations_human_bbox_2017_07.tar.gz
+```
+
+Next, package the data into TFRecords of TFExamples by running:
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # Set SPLIT to "test" to create TFRecords for the test split
+mkdir ${SPLIT}_tfrecords
+
+PYTHONPATH=$PYTHONPATH:$(readlink -f ..) \
+python -m object_detection/dataset_tools/create_oid_tf_record \
+  --input_box_annotations_csv 2017_07/$SPLIT/annotations-human-bbox.csv \
+  --input_images_directory raw_images_${SPLIT} \
+  --input_label_map ../object_detection/data/oid_bbox_trainable_label_map.pbtxt \
+  --output_tf_record_path_prefix ${SPLIT}_tfrecords/$SPLIT.tfrecord \
+  --num_shards=100
+```
+
+To add image-level labels, use the `--input_image_label_annotations_csv` flag.
+
+This results in 100 TFRecord files (shards), written to
+`oid/${SPLIT}_tfrecords`, with filenames matching
+`${SPLIT}.tfrecord-000[0-9][0-9]-of-00100`. Each shard contains approximately
+the same number of images and is defacto a representative random sample of the
+input data. [This enables](#accelerating_inference) a straightforward work
+division scheme for distributing inference and also approximate measure
+computations on subsets of the validation and test sets.
+
+## Inferring detections
+
+Inference requires a trained object detection model. In this tutorial we will
+use a model from the [detections model zoo](detection_model_zoo.md), which can
+be downloaded and unpacked by running the commands below. More information about
+the model, such as its architecture and how it was trained, is available in the
+[model zoo page](detection_model_zoo.md).
+
+```bash
+# From tensorflow/models/research/oid
+wget http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_14_10_2017.tar.gz
+tar -zxvf faster_rcnn_inception_resnet_v2_atrous_oid_14_10_2017.tar.gz
+```
+
+At this point, data is packed into TFRecords and we have an object detector
+model. We can run inference using:
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # or test
+TF_RECORD_FILES=$(ls -1 ${SPLIT}_tfrecords/* | tr '\n' ',')
+
+PYTHONPATH=$PYTHONPATH:$(readlink -f ..) \
+python -m object_detection/inference/infer_detections \
+  --input_tfrecord_paths=$TF_RECORD_FILES \
+  --output_tfrecord_path=${SPLIT}_detections.tfrecord-00000-of-00001 \
+  --inference_graph=faster_rcnn_inception_resnet_v2_atrous_oid/frozen_inference_graph.pb \
+  --discard_image_pixels
+```
+
+Inference preserves all fields of the input TFExamples, and adds new fields to
+store the inferred detections. This allows [computing evaluation
+measures](#compute_evaluation_measures) on the output TFRecord alone, as ground
+truth boxes are preserved as well. Since measure computations don't require
+access to the images, `infer_detections` can optionally discard them with the
+`--discard_image_pixels` flag. Discarding the images drastically reduces the
+size of the output TFRecord.
+
+### Accelerating inference
+
+Running inference on the whole validation or test set can take a long time to
+complete due to the large number of images present in these sets (41,620 and
+125,436 respectively). For quick but approximate evaluation, inference and the
+subsequent measure computations can be run on a small number of shards. To run
+for example on 2% of all the data, it is enough to set `TF_RECORD_FILES` as
+shown below before running `infer_detections`:
+
+```bash
+TF_RECORD_FILES=$(ls ${SPLIT}_tfrecords/${SPLIT}.tfrecord-0000[0-1]-of-00100 | tr '\n' ',')
+```
+
+Please note that computing evaluation measures on a small subset of the data
+introduces variance and bias, since some classes of objects won't be seen during
+evaluation. In the example above, this leads to 13.2% higher mAP on the first
+two shards of the validation set compared to the mAP for the full set ([see mAP
+results](#expected-maps)).
+
+Another way to accelerate inference is to run it in parallel on multiple
+TensorFlow devices on possibly multiple machines. The script below uses
+[tmux](https://github.com/tmux/tmux/wiki) to run a separate `infer_detections`
+process for each GPU on different partition of the input data.
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # or test
+NUM_GPUS=4
+NUM_SHARDS=100
+
+tmux new-session -d -s "inference"
+function tmux_start { tmux new-window -d -n "inference:GPU$1" "${*:2}; exec bash"; }
+for gpu_index in $(seq 0 $(($NUM_GPUS-1))); do
+  start_shard=$(( $gpu_index * $NUM_SHARDS / $NUM_GPUS ))
+  end_shard=$(( ($gpu_index + 1) * $NUM_SHARDS / $NUM_GPUS - 1))
+  TF_RECORD_FILES=$(seq -s, -f "${SPLIT}_tfrecords/${SPLIT}.tfrecord-%05.0f-of-$(printf '%05d' $NUM_SHARDS)" $start_shard $end_shard)
+  tmux_start ${gpu_index} \
+  PYTHONPATH=$PYTHONPATH:$(readlink -f ..) CUDA_VISIBLE_DEVICES=$gpu_index \
+  python -m object_detection/inference/infer_detections \
+    --input_tfrecord_paths=$TF_RECORD_FILES \
+    --output_tfrecord_path=${SPLIT}_detections.tfrecord-$(printf "%05d" $gpu_index)-of-$(printf "%05d" $NUM_GPUS) \
+    --inference_graph=faster_rcnn_inception_resnet_v2_atrous_oid/frozen_inference_graph.pb \
+    --discard_image_pixels
+done
+```
+
+After all `infer_detections` processes finish, `tensorflow/models/research/oid`
+will contain one output TFRecord from each process, with name matching
+`validation_detections.tfrecord-0000[0-3]-of-00004`.
+
+## Computing evaluation measures
+
+To compute evaluation measures on the inferred detections you first need to
+create the appropriate configuration files:
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # or test
+NUM_SHARDS=1  # Set to NUM_GPUS if using the parallel evaluation script above
+
+mkdir -p ${SPLIT}_eval_metrics
+
+echo "
+label_map_path: '../object_detection/data/oid_bbox_trainable_label_map.pbtxt'
+tf_record_input_reader: { input_path: '${SPLIT}_detections.tfrecord@${NUM_SHARDS}' }
+" > ${SPLIT}_eval_metrics/${SPLIT}_input_config.pbtxt
+
+echo "
+metrics_set: 'oid_V2_detection_metrics'
+" > ${SPLIT}_eval_metrics/${SPLIT}_eval_config.pbtxt
+```
+
+And then run:
+
+```bash
+# From tensorflow/models/research/oid
+SPLIT=validation  # or test
+
+PYTHONPATH=$PYTHONPATH:$(readlink -f ..) \
+python -m object_detection/metrics/offline_eval_map_corloc \
+  --eval_dir=${SPLIT}_eval_metrics \
+  --eval_config_path=${SPLIT}_eval_metrics/${SPLIT}_eval_config.pbtxt \
+  --input_config_path=${SPLIT}_eval_metrics/${SPLIT}_input_config.pbtxt
+```
+
+The first configuration file contains an `object_detection.protos.InputReader`
+message that describes the location of the necessary input files. The second
+file contains an `object_detection.protos.EvalConfig` message that describes the
+evaluation metric. For more information about these protos see the corresponding
+source files.
+
+### Expected mAPs
+
+The result of running `offline_eval_map_corloc` is a CSV file located at
+`${SPLIT}_eval_metrics/metrics.csv`. With the above configuration, the file will
+contain average precision at IoU≥0.5 for each of the classes present in the
+dataset. It will also contain the mAP@IoU≥0.5. Both the per-class average
+precisions and the mAP are computed according to the [Open Images evaluation
+protocol](evaluation_protocols.md). The expected mAPs for the validation and
+test sets of Open Images in this case are:
+
+Set        | Fraction of data | Images  | mAP@IoU≥0.5
+---------: | :--------------: | :-----: | -----------
+validation | everything       | 41,620  | 39.2%
+validation | first 2 shards   | 884     | 52.4%
+test       | everything       | 125,436 | 37.7%
+test       | first 2 shards   | 2,476   | 50.8%
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/preparing_inputs.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/preparing_inputs.md
new file mode 100644
index 00000000..cd51e109
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/preparing_inputs.md
@@ -0,0 +1,59 @@
+# Preparing Inputs
+
+Tensorflow Object Detection API reads data using the TFRecord file format. Two
+sample scripts (`create_pascal_tf_record.py` and `create_pet_tf_record.py`) are
+provided to convert from the PASCAL VOC dataset and Oxford-IIIT Pet dataset to
+TFRecords.
+
+## Generating the PASCAL VOC TFRecord files.
+
+The raw 2012 PASCAL VOC data set is located
+[here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar).
+To download, extract and convert it to TFRecords, run the following commands
+below:
+
+```bash
+# From tensorflow/models/research/
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+tar -xvf VOCtrainval_11-May-2012.tar
+python object_detection/dataset_tools/create_pascal_tf_record.py \
+    --label_map_path=object_detection/data/pascal_label_map.pbtxt \
+    --data_dir=VOCdevkit --year=VOC2012 --set=train \
+    --output_path=pascal_train.record
+python object_detection/dataset_tools/create_pascal_tf_record.py \
+    --label_map_path=object_detection/data/pascal_label_map.pbtxt \
+    --data_dir=VOCdevkit --year=VOC2012 --set=val \
+    --output_path=pascal_val.record
+```
+
+You should end up with two TFRecord files named `pascal_train.record` and
+`pascal_val.record` in the `tensorflow/models/research/` directory.
+
+The label map for the PASCAL VOC data set can be found at
+`object_detection/data/pascal_label_map.pbtxt`.
+
+## Generating the Oxford-IIIT Pet TFRecord files.
+
+The Oxford-IIIT Pet data set is located
+[here](http://www.robots.ox.ac.uk/~vgg/data/pets/). To download, extract and
+convert it to TFRecrods, run the following commands below:
+
+```bash
+# From tensorflow/models/research/
+wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
+wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
+tar -xvf annotations.tar.gz
+tar -xvf images.tar.gz
+python object_detection/dataset_tools/create_pet_tf_record.py \
+    --label_map_path=object_detection/data/pet_label_map.pbtxt \
+    --data_dir=`pwd` \
+    --output_dir=`pwd`
+```
+
+You should end up with two 10-sharded TFRecord files named
+`pet_faces_train.record-?????-of-00010` and
+`pet_faces_val.record-?????-of-00010` in the `tensorflow/models/research/`
+directory.
+
+The label map for the Pet dataset can be found at
+`object_detection/data/pet_label_map.pbtxt`.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_locally.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_locally.md
new file mode 100644
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+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_locally.md
@@ -0,0 +1,66 @@
+# Running Locally
+
+This page walks through the steps required to train an object detection model
+on a local machine. It assumes the reader has completed the
+following prerequisites:
+
+1. The Tensorflow Object Detection API has been installed as documented in the
+[installation instructions](installation.md). This includes installing library
+dependencies, compiling the configuration protobufs and setting up the Python
+environment.
+2. A valid data set has been created. See [this page](preparing_inputs.md) for
+instructions on how to generate a dataset for the PASCAL VOC challenge or the
+Oxford-IIIT Pet dataset.
+3. A Object Detection pipeline configuration has been written. See
+[this page](configuring_jobs.md) for details on how to write a pipeline configuration.
+
+## Recommended Directory Structure for Training and Evaluation
+
+```
++data
+  -label_map file
+  -train TFRecord file
+  -eval TFRecord file
++models
+  + model
+    -pipeline config file
+    +train
+    +eval
+```
+
+## Running the Training Job
+
+A local training job can be run with the following command:
+
+```bash
+# From the tensorflow/models/research/ directory
+PIPELINE_CONFIG_PATH={path to pipeline config file}
+MODEL_DIR={path to model directory}
+NUM_TRAIN_STEPS=50000
+SAMPLE_1_OF_N_EVAL_EXAMPLES=1
+python object_detection/model_main.py \
+    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
+    --model_dir=${MODEL_DIR} \
+    --num_train_steps=${NUM_TRAIN_STEPS} \
+    --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
+    --alsologtostderr
+```
+
+where `${PIPELINE_CONFIG_PATH}` points to the pipeline config and
+`${MODEL_DIR}` points to the directory in which training checkpoints
+and events will be written to. Note that this binary will interleave both
+training and evaluation.
+
+## Running Tensorboard
+
+Progress for training and eval jobs can be inspected using Tensorboard. If
+using the recommended directory structure, Tensorboard can be run using the
+following command:
+
+```bash
+tensorboard --logdir=${MODEL_DIR}
+```
+
+where `${MODEL_DIR}` points to the directory that contains the
+train and eval directories. Please note it may take Tensorboard a couple minutes
+to populate with data.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_notebook.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_notebook.md
new file mode 100644
index 00000000..c2b8ad18
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_notebook.md
@@ -0,0 +1,15 @@
+# Quick Start: Jupyter notebook for off-the-shelf inference
+
+If you'd like to hit the ground running and run detection on a few example
+images right out of the box, we recommend trying out the Jupyter notebook demo.
+To run the Jupyter notebook, run the following command from
+`tensorflow/models/research/object_detection`:
+
+```
+# From tensorflow/models/research/object_detection
+jupyter notebook
+```
+
+The notebook should open in your favorite web browser. Click the
+[`object_detection_tutorial.ipynb`](../object_detection_tutorial.ipynb) link to
+open the demo.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_cloud.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_cloud.md
new file mode 100644
index 00000000..fb2dd53c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_cloud.md
@@ -0,0 +1,170 @@
+# Running on Google Cloud ML Engine
+
+The Tensorflow Object Detection API supports distributed training on Google
+Cloud ML Engine. This section documents instructions on how to train and
+evaluate your model using Cloud ML. The reader should complete the following
+prerequistes:
+
+1. The reader has created and configured a project on Google Cloud Platform.
+See [the Cloud ML quick start guide](https://cloud.google.com/ml-engine/docs/quickstarts/command-line).
+2. The reader has installed the Tensorflow Object Detection API as documented
+in the [installation instructions](installation.md).
+3. The reader has a valid data set and stored it in a Google Cloud Storage
+bucket. See [this page](preparing_inputs.md) for instructions on how to generate
+a dataset for the PASCAL VOC challenge or the Oxford-IIIT Pet dataset.
+4. The reader has configured a valid Object Detection pipeline, and stored it
+in a Google Cloud Storage bucket. See [this page](configuring_jobs.md) for
+details on how to write a pipeline configuration.
+
+Additionally, it is recommended users test their job by running training and
+evaluation jobs for a few iterations
+[locally on their own machines](running_locally.md).
+
+## Packaging
+
+In order to run the Tensorflow Object Detection API on Cloud ML, it must be
+packaged (along with it's TF-Slim dependency and the
+[pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools)
+library). The required packages can be created with the following command
+
+``` bash
+# From tensorflow/models/research/
+bash object_detection/dataset_tools/create_pycocotools_package.sh /tmp/pycocotools
+python setup.py sdist
+(cd slim && python setup.py sdist)
+```
+
+This will create python packages dist/object_detection-0.1.tar.gz,
+slim/dist/slim-0.1.tar.gz, and /tmp/pycocotools/pycocotools-2.0.tar.gz.
+
+## Running a Multiworker (GPU) Training Job on CMLE
+
+Google Cloud ML requires a YAML configuration file for a multiworker training
+job using GPUs. A sample YAML file is given below:
+
+```
+trainingInput:
+  runtimeVersion: "1.9"
+  scaleTier: CUSTOM
+  masterType: standard_gpu
+  workerCount: 9
+  workerType: standard_gpu
+  parameterServerCount: 3
+  parameterServerType: standard
+
+
+```
+
+Please keep the following guidelines in mind when writing the YAML
+configuration:
+
+* A job with n workers will have n + 1 training machines (n workers + 1 master).
+* The number of parameters servers used should be an odd number to prevent
+  a parameter server from storing only weight variables or only bias variables
+  (due to round robin parameter scheduling).
+* The learning rate in the training config should be decreased when using a
+  larger number of workers. Some experimentation is required to find the
+  optimal learning rate.
+
+The YAML file should be saved on the local machine (not on GCP). Once it has
+been written, a user can start a training job on Cloud ML Engine using the
+following command:
+
+```bash
+# From tensorflow/models/research/
+gcloud ml-engine jobs submit training object_detection_`date +%m_%d_%Y_%H_%M_%S` \
+    --runtime-version 1.9 \
+    --job-dir=gs://${MODEL_DIR} \
+    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+    --module-name object_detection.model_main \
+    --region us-central1 \
+    --config ${PATH_TO_LOCAL_YAML_FILE} \
+    -- \
+    --model_dir=gs://${MODEL_DIR} \
+    --pipeline_config_path=gs://${PIPELINE_CONFIG_PATH}
+```
+
+Where `${PATH_TO_LOCAL_YAML_FILE}` is the local path to the YAML configuration,
+`gs://${MODEL_DIR}` specifies the directory on Google Cloud Storage where the
+training checkpoints and events will be written to and
+`gs://${PIPELINE_CONFIG_PATH}` points to the pipeline configuration stored on
+Google Cloud Storage.
+
+Users can monitor the progress of their training job on the [ML Engine
+Dashboard](https://console.cloud.google.com/mlengine/jobs).
+
+Note: This sample is supported for use with 1.9 runtime version.
+
+## Running a TPU Training Job on CMLE
+
+Launching a training job with a TPU compatible pipeline config requires using a
+similar command:
+
+```bash
+gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%m_%d_%Y_%H_%M_%S` \
+--job-dir=gs://${MODEL_DIR} \
+--packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+--module-name object_detection.model_tpu_main \
+--runtime-version 1.9 \
+--scale-tier BASIC_TPU \
+--region us-central1 \
+-- \
+--tpu_zone us-central1 \
+--model_dir=gs://${MODEL_DIR} \
+--pipeline_config_path=gs://${PIPELINE_CONFIG_PATH}
+```
+
+In contrast with the GPU training command, there is no need to specify a YAML
+file and we point to the *object_detection.model_tpu_main* binary instead of
+*object_detection.model_main*. We must also now set `scale-tier` to be
+`BASIC_TPU` and provide a `tpu_zone`. Finally as before `pipeline_config_path`
+points to a points to the pipeline configuration stored on Google Cloud Storage
+(but is now must be a TPU compatible model).
+
+## Running an Evaluation Job on CMLE
+
+Note: You only need to do this when using TPU for training as it does not
+interleave evaluation during training as in the case of Multiworker GPU
+training.
+
+Evaluation jobs run on a single machine, so it is not necessary to write a YAML
+configuration for evaluation. Run the following command to start the evaluation
+job:
+
+```bash
+gcloud ml-engine jobs submit training object_detection_eval_`date +%m_%d_%Y_%H_%M_%S` \
+    --runtime-version 1.9 \
+    --job-dir=gs://${MODEL_DIR} \
+    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+    --module-name object_detection.model_main \
+    --region us-central1 \
+    --scale-tier BASIC_GPU \
+    -- \
+    --model_dir=gs://${MODEL_DIR} \
+    --pipeline_config_path=gs://${PIPELINE_CONFIG_PATH} \
+    --checkpoint_dir=gs://${MODEL_DIR}
+```
+
+Where `gs://${MODEL_DIR}` points to the directory on Google Cloud Storage where
+training checkpoints are saved (same as the training job), as well as
+to where evaluation events will be saved on Google Cloud Storage and
+`gs://${PIPELINE_CONFIG_PATH}` points to where the pipeline configuration is
+stored on Google Cloud Storage.
+
+Typically one starts an evaluation job concurrently with the training job.
+Note that we do not support running evaluation on TPU, so the above command
+line for launching evaluation jobs is the same whether you are training
+on GPU or TPU.
+
+## Running Tensorboard
+
+You can run Tensorboard locally on your own machine to view progress of your
+training and eval jobs on Google Cloud ML. Run the following command to start
+Tensorboard:
+
+``` bash
+tensorboard --logdir=gs://${YOUR_CLOUD_BUCKET}
+```
+
+Note it may Tensorboard a few minutes to populate with results.
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md
new file mode 100644
index 00000000..a86c3792
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md
@@ -0,0 +1,163 @@
+# Running on mobile with TensorFlow Lite
+
+In this section, we will show you how to use [TensorFlow
+Lite](https://www.tensorflow.org/mobile/tflite/) to get a smaller model and
+allow you take advantage of ops that have been optimized for mobile devices.
+TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded
+devices. It enables on-device machine learning inference with low latency and a
+small binary size. TensorFlow Lite uses many techniques for this such as
+quantized kernels that allow smaller and faster (fixed-point math) models.
+
+For this section, you will need to build [TensorFlow from
+source](https://www.tensorflow.org/install/install_sources) to get the
+TensorFlow Lite support for the SSD model. At this time only SSD models are supported.
+Models like faster_rcnn are not supported at this time. You will also need to install the
+[bazel build
+tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#bazel).
+
+To make these commands easier to run, let’s set up some environment variables:
+
+```shell
+export CONFIG_FILE=PATH_TO_BE_CONFIGURED/pipeline.config
+export CHECKPOINT_PATH=PATH_TO_BE_CONFIGURED/model.ckpt
+export OUTPUT_DIR=/tmp/tflite
+```
+
+We start with a checkpoint and get a TensorFlow frozen graph with compatible ops
+that we can use with TensorFlow Lite. First, you’ll need to install these
+[python
+libraries](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md).
+Then to get the frozen graph, run the export_tflite_ssd_graph.py script from the
+`models/research` directory with this command:
+
+```shell
+object_detection/export_tflite_ssd_graph.py \
+--pipeline_config_path=$CONFIG_FILE \
+--trained_checkpoint_prefix=$CHECKPOINT_PATH \
+--output_directory=$OUTPUT_DIR \
+--add_postprocessing_op=true
+```
+
+In the /tmp/tflite directory, you should now see two files: tflite_graph.pb and
+tflite_graph.pbtxt. Note that the add_postprocessing flag enables the model to
+take advantage of a custom optimized detection post-processing operation which
+can be thought of as a replacement for
+[tf.image.non_max_suppression](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression).
+Make sure not to confuse export_tflite_ssd_graph with export_inference_graph in
+the same directory. Both scripts output frozen graphs: export_tflite_ssd_graph
+will output the frozen graph that we can input to TensorFlow Lite directly and
+is the one we’ll be using.
+
+Next we’ll use TensorFlow Lite to get the optimized model by using
+[TOCO](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/toco),
+the TensorFlow Lite Optimizing Converter. This will convert the resulting frozen
+graph (tflite_graph.pb) to the TensorFlow Lite flatbuffer format (detect.tflite)
+via the following command. For a quantized model, run this from the tensorflow/
+directory:
+
+```shell
+bazel run --config=opt tensorflow/lite/toco:toco -- \
+--input_file=$OUTPUT_DIR/tflite_graph.pb \
+--output_file=$OUTPUT_DIR/detect.tflite \
+--input_shapes=1,300,300,3 \
+--input_arrays=normalized_input_image_tensor \
+--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \
+--inference_type=QUANTIZED_UINT8 \
+--mean_values=128 \
+--std_values=128 \
+--change_concat_input_ranges=false \
+--allow_custom_ops
+```
+
+This command takes the input tensor normalized_input_image_tensor after resizing
+each camera image frame to 300x300 pixels. The outputs of the quantized model
+are named 'TFLite_Detection_PostProcess', 'TFLite_Detection_PostProcess:1',
+'TFLite_Detection_PostProcess:2', and 'TFLite_Detection_PostProcess:3' and
+represent four arrays: detection_boxes, detection_classes, detection_scores, and
+num_detections. The documentation for other flags used in this command is
+[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/convert/cmdline_reference.md).
+If things ran successfully, you should now see a third file in the /tmp/tflite
+directory called detect.tflite. This file contains the graph and all model
+parameters and can be run via the TensorFlow Lite interpreter on the Android
+device. For a floating point model, run this from the tensorflow/ directory:
+
+```shell
+bazel run --config=opt tensorflow/lite/toco:toco -- \
+--input_file=$OUTPUT_DIR/tflite_graph.pb \
+--output_file=$OUTPUT_DIR/detect.tflite \
+--input_shapes=1,300,300,3 \
+--input_arrays=normalized_input_image_tensor \
+--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3'  \
+--inference_type=FLOAT \
+--allow_custom_ops
+```
+
+# Running our model on Android
+
+To run our TensorFlow Lite model on device, we will need to install the Android
+NDK and SDK. The current recommended Android NDK version is 14b and can be found
+on the [NDK
+Archives](https://developer.android.com/ndk/downloads/older_releases.html#ndk-14b-downloads)
+page. Android SDK and build tools can be [downloaded
+separately](https://developer.android.com/tools/revisions/build-tools.html) or
+used as part of [Android
+Studio](https://developer.android.com/studio/index.html). To build the
+TensorFlow Lite Android demo, build tools require API >= 23 (but it will run on
+devices with API >= 21). Additional details are available on the [TensorFlow
+Lite Android App
+page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/java/demo/README.md).
+
+Next we need to point the app to our new detect.tflite file and give it the
+names of our new labels. Specifically, we will copy our TensorFlow Lite
+flatbuffer to the app assets directory with the following command:
+
+```shell
+cp /tmp/tflite/detect.tflite \
+//tensorflow/lite/examples/android/app/src/main/assets
+```
+
+You will also need to copy your new labelmap labels_list.txt to the assets
+directory.
+
+We will now edit the BUILD file to point to this new model. First, open the
+BUILD file tensorflow/lite/examples/android/BUILD. Then find the assets
+section, and replace the line “@tflite_mobilenet_ssd_quant//:detect.tflite”
+(which by default points to a COCO pretrained model) with the path to your new
+TFLite model
+“//tensorflow/lite/examples/android/app/src/main/assets:detect.tflite”.
+Finally, change the last line in assets section to use the new label map as
+well.
+
+We will also need to tell our app to use the new label map. In order to do this,
+open up the
+tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java
+file in a text editor and find the definition of TF_OD_API_LABELS_FILE. Update
+this path to point to your new label map file:
+"file:///android_asset/labels_list.txt". Note that if your model is quantized,
+the flag TF_OD_API_IS_QUANTIZED is set to true, and if your model is floating
+point, the flag TF_OD_API_IS_QUANTIZED is set to false. This new section of
+DetectorActivity.java should now look as follows for a quantized model:
+
+```shell
+  private static final boolean TF_OD_API_IS_QUANTIZED = true;
+  private static final String TF_OD_API_MODEL_FILE = "detect.tflite";
+  private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/labels_list.txt";
+```
+
+Once you’ve copied the TensorFlow Lite file and edited your BUILD and
+DetectorActivity.java files, you can build the demo app, run this bazel command
+from the tensorflow directory:
+
+```shell
+ bazel build -c opt --config=android_arm{,64} --cxxopt='--std=c++11'
+"//tensorflow/lite/examples/android:tflite_demo"
+```
+
+Now install the demo on a
+[debug-enabled](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#install)
+Android phone via [Android Debug
+Bridge](https://developer.android.com/studio/command-line/adb) (adb):
+
+```shell
+adb install bazel-bin/tensorflow/lite/examples/android/tflite_demo.apk
+```
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_pets.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_pets.md
new file mode 100644
index 00000000..bbfcdf7d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/running_pets.md
@@ -0,0 +1,319 @@
+# Quick Start: Distributed Training on the Oxford-IIIT Pets Dataset on Google Cloud
+
+This page is a walkthrough for training an object detector using the Tensorflow
+Object Detection API. In this tutorial, we'll be training on the Oxford-IIIT Pets
+dataset to build a system to detect various breeds of cats and dogs. The output
+of the detector will look like the following:
+
+![](img/oxford_pet.png)
+
+## Setting up a Project on Google Cloud
+
+To accelerate the process, we'll run training and evaluation on [Google Cloud
+ML Engine](https://cloud.google.com/ml-engine/) to leverage multiple GPUs. To
+begin, you will have to set up Google Cloud via the following steps (if you have
+already done this, feel free to skip to the next section):
+
+1. [Create a GCP project](https://cloud.google.com/resource-manager/docs/creating-managing-projects).
+2. [Install the Google Cloud SDK](https://cloud.google.com/sdk/downloads) on
+your workstation or laptop.
+This will provide the tools you need to upload files to Google Cloud Storage and
+start ML training jobs.
+3. [Enable the ML Engine
+APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component&_ga=1.73374291.1570145678.1496689256).
+By default, a new GCP project does not enable APIs to start ML Engine training
+jobs. Use the above link to explicitly enable them.
+4. [Set up a Google Cloud Storage (GCS)
+bucket](https://cloud.google.com/storage/docs/creating-buckets). ML Engine
+training jobs can only access files on a Google Cloud Storage bucket. In this
+tutorial, we'll be required to upload our dataset and configuration to GCS.
+
+Please remember the name of your GCS bucket, as we will reference it multiple
+times in this document. Substitute `${YOUR_GCS_BUCKET}` with the name of
+your bucket in this document. For your convenience, you should define the
+environment variable below:
+
+``` bash
+export YOUR_GCS_BUCKET=${YOUR_GCS_BUCKET}
+```
+
+It is also possible to run locally by following
+[the running locally instructions](running_locally.md).
+
+## Installing Tensorflow and the Tensorflow Object Detection API
+
+Please run through the [installation instructions](installation.md) to install
+Tensorflow and all it dependencies. Ensure the Protobuf libraries are
+compiled and the library directories are added to `PYTHONPATH`.
+
+## Getting the Oxford-IIIT Pets Dataset and Uploading it to Google Cloud Storage
+
+In order to train a detector, we require a dataset of images, bounding boxes and
+classifications. For this demo, we'll use the Oxford-IIIT Pets dataset. The raw
+dataset for Oxford-IIIT Pets lives
+[here](http://www.robots.ox.ac.uk/~vgg/data/pets/). You will need to download
+both the image dataset [`images.tar.gz`](http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz)
+and the groundtruth data [`annotations.tar.gz`](http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz)
+to the `tensorflow/models/research/` directory and unzip them. This may take
+some time.
+
+``` bash
+# From tensorflow/models/research/
+wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
+wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
+tar -xvf images.tar.gz
+tar -xvf annotations.tar.gz
+```
+
+After downloading the tarballs, your `tensorflow/models/research/` directory
+should appear as follows:
+
+```lang-none
+- images.tar.gz
+- annotations.tar.gz
++ images/
++ annotations/
++ object_detection/
+... other files and directories
+```
+
+The Tensorflow Object Detection API expects data to be in the TFRecord format,
+so we'll now run the `create_pet_tf_record` script to convert from the raw
+Oxford-IIIT Pet dataset into TFRecords. Run the following commands from the
+`tensorflow/models/research/` directory:
+
+``` bash
+# From tensorflow/models/research/
+python object_detection/dataset_tools/create_pet_tf_record.py \
+    --label_map_path=object_detection/data/pet_label_map.pbtxt \
+    --data_dir=`pwd` \
+    --output_dir=`pwd`
+```
+
+Note: It is normal to see some warnings when running this script. You may ignore
+them.
+
+Two 10-sharded TFRecord files named `pet_faces_train.record-*` and
+`pet_faces_val.record-*` should be generated in the
+`tensorflow/models/research/` directory.
+
+Now that the data has been generated, we'll need to upload it to Google Cloud
+Storage so the data can be accessed by ML Engine. Run the following command to
+copy the files into your GCS bucket (substituting `${YOUR_GCS_BUCKET}`):
+
+```bash
+# From tensorflow/models/research/
+gsutil cp pet_faces_train.record-* gs://${YOUR_GCS_BUCKET}/data/
+gsutil cp pet_faces_val.record-* gs://${YOUR_GCS_BUCKET}/data/
+gsutil cp object_detection/data/pet_label_map.pbtxt gs://${YOUR_GCS_BUCKET}/data/pet_label_map.pbtxt
+```
+
+Please remember the path where you upload the data to, as we will need this
+information when configuring the pipeline in a following step.
+
+## Downloading a COCO-pretrained Model for Transfer Learning
+
+Training a state of the art object detector from scratch can take days, even
+when using multiple GPUs! In order to speed up training, we'll take an object
+detector trained on a different dataset (COCO), and reuse some of it's
+parameters to initialize our new model.
+
+Download our [COCO-pretrained Faster R-CNN with Resnet-101
+model](http://storage.googleapis.com/download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_11_06_2017.tar.gz).
+Unzip the contents of the folder and copy the `model.ckpt*` files into your GCS
+Bucket.
+
+``` bash
+wget http://storage.googleapis.com/download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_11_06_2017.tar.gz
+tar -xvf faster_rcnn_resnet101_coco_11_06_2017.tar.gz
+gsutil cp faster_rcnn_resnet101_coco_11_06_2017/model.ckpt.* gs://${YOUR_GCS_BUCKET}/data/
+```
+
+Remember the path where you uploaded the model checkpoint to, as we will need it
+in the following step.
+
+## Configuring the Object Detection Pipeline
+
+In the Tensorflow Object Detection API, the model parameters, training
+parameters and eval parameters are all defined by a config file. More details
+can be found [here](configuring_jobs.md). For this tutorial, we will use some
+predefined templates provided with the source code. In the
+`object_detection/samples/configs` folder, there are skeleton object_detection
+configuration files. We will use `faster_rcnn_resnet101_pets.config` as a
+starting point for configuring the pipeline. Open the file with your favourite
+text editor.
+
+We'll need to configure some paths in order for the template to work. Search the
+file for instances of `PATH_TO_BE_CONFIGURED` and replace them with the
+appropriate value (typically `gs://${YOUR_GCS_BUCKET}/data/`). Afterwards
+upload your edited file onto GCS, making note of the path it was uploaded to
+(we'll need it when starting the training/eval jobs).
+
+``` bash
+# From tensorflow/models/research/
+
+# Edit the faster_rcnn_resnet101_pets.config template. Please note that there
+# are multiple places where PATH_TO_BE_CONFIGURED needs to be set.
+sed -i "s|PATH_TO_BE_CONFIGURED|"gs://${YOUR_GCS_BUCKET}"/data|g" \
+    object_detection/samples/configs/faster_rcnn_resnet101_pets.config
+
+# Copy edited template to cloud.
+gsutil cp object_detection/samples/configs/faster_rcnn_resnet101_pets.config \
+    gs://${YOUR_GCS_BUCKET}/data/faster_rcnn_resnet101_pets.config
+```
+
+## Checking Your Google Cloud Storage Bucket
+
+At this point in the tutorial, you should have uploaded the training/validation
+datasets (including label map), our COCO trained FasterRCNN finetune checkpoint and your job
+configuration to your Google Cloud Storage Bucket. Your bucket should look like
+the following:
+
+```lang-none
++ ${YOUR_GCS_BUCKET}/
+  + data/
+    - faster_rcnn_resnet101_pets.config
+    - model.ckpt.index
+    - model.ckpt.meta
+    - model.ckpt.data-00000-of-00001
+    - pet_label_map.pbtxt
+    - pet_faces_train.record-*
+    - pet_faces_val.record-*
+```
+
+You can inspect your bucket using the [Google Cloud Storage
+browser](https://console.cloud.google.com/storage/browser).
+
+## Starting Training and Evaluation Jobs on Google Cloud ML Engine
+
+Before we can start a job on Google Cloud ML Engine, we must:
+
+1. Package the Tensorflow Object Detection code.
+2. Write a cluster configuration for our Google Cloud ML job.
+
+To package the Tensorflow Object Detection code, run the following commands from
+the `tensorflow/models/research/` directory:
+
+```bash
+# From tensorflow/models/research/
+bash object_detection/dataset_tools/create_pycocotools_package.sh /tmp/pycocotools
+python setup.py sdist
+(cd slim && python setup.py sdist)
+```
+
+This will create python packages dist/object_detection-0.1.tar.gz,
+slim/dist/slim-0.1.tar.gz, and /tmp/pycocotools/pycocotools-2.0.tar.gz.
+
+For running the training Cloud ML job, we'll configure the cluster to use 5
+training jobs and three parameters servers. The
+configuration file can be found at `object_detection/samples/cloud/cloud.yml`.
+
+Note: The code sample below is supported for use with 1.9 runtime version.
+
+To start training and evaluation, execute the following command from the
+`tensorflow/models/research/` directory:
+
+```bash
+# From tensorflow/models/research/
+gcloud ml-engine jobs submit training `whoami`_object_detection_pets_`date +%m_%d_%Y_%H_%M_%S` \
+    --runtime-version 1.9 \
+    --job-dir=gs://${YOUR_GCS_BUCKET}/model_dir \
+    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+    --module-name object_detection.model_main \
+    --region us-central1 \
+    --config object_detection/samples/cloud/cloud.yml \
+    -- \
+    --model_dir=gs://${YOUR_GCS_BUCKET}/model_dir \
+    --pipeline_config_path=gs://${YOUR_GCS_BUCKET}/data/faster_rcnn_resnet101_pets.config
+```
+
+Users can monitor and stop training and evaluation jobs on the [ML Engine
+Dashboard](https://console.cloud.google.com/mlengine/jobs).
+
+## Monitoring Progress with Tensorboard
+
+You can monitor progress of the training and eval jobs by running Tensorboard on
+your local machine:
+
+```bash
+# This command needs to be run once to allow your local machine to access your
+# GCS bucket.
+gcloud auth application-default login
+
+tensorboard --logdir=gs://${YOUR_GCS_BUCKET}/model_dir
+```
+
+Once Tensorboard is running, navigate to `localhost:6006` from your favourite
+web browser. You should see something similar to the following:
+
+![](img/tensorboard.png)
+
+Make sure your Tensorboard version is the same minor version as your Tensorflow (1.x)
+
+You will also want to click on the images tab to see example detections made by
+the model while it trains. After about an hour and a half of training, you can
+expect to see something like this:
+
+![](img/tensorboard2.png)
+
+Note: It takes roughly 10 minutes for a job to get started on ML Engine, and
+roughly an hour for the system to evaluate the validation dataset. It may take
+some time to populate the dashboards. If you do not see any entries after half
+an hour, check the logs from the [ML Engine
+Dashboard](https://console.cloud.google.com/mlengine/jobs). Note that by default
+the training jobs are configured to go for much longer than is necessary for
+convergence.  To save money, we recommend killing your jobs once you've seen
+that they've converged.
+
+## Exporting the Tensorflow Graph
+
+After your model has been trained, you should export it to a Tensorflow graph
+proto. First, you need to identify a candidate checkpoint to export. You can
+search your bucket using the [Google Cloud Storage
+Browser](https://console.cloud.google.com/storage/browser). The file should be
+stored under `${YOUR_GCS_BUCKET}/model_dir`. The checkpoint will typically
+consist of three files:
+
+* `model.ckpt-${CHECKPOINT_NUMBER}.data-00000-of-00001`
+* `model.ckpt-${CHECKPOINT_NUMBER}.index`
+* `model.ckpt-${CHECKPOINT_NUMBER}.meta`
+
+After you've identified a candidate checkpoint to export, run the following
+command from `tensorflow/models/research/`:
+
+```bash
+# From tensorflow/models/research/
+gsutil cp gs://${YOUR_GCS_BUCKET}/model_dir/model.ckpt-${CHECKPOINT_NUMBER}.* .
+python object_detection/export_inference_graph.py \
+    --input_type image_tensor \
+    --pipeline_config_path object_detection/samples/configs/faster_rcnn_resnet101_pets.config \
+    --trained_checkpoint_prefix model.ckpt-${CHECKPOINT_NUMBER} \
+    --output_directory exported_graphs
+```
+
+Afterwards, you should see a directory named `exported_graphs` containing the
+SavedModel and frozen graph.
+
+## Configuring the Instance Segmentation Pipeline
+
+Mask prediction can be turned on for an object detection config by adding
+`predict_instance_masks: true` within the `MaskRCNNBoxPredictor`. Other
+parameters such as mask size, number of convolutions in the mask layer, and the
+convolution hyper parameters can be defined. We will use
+`mask_rcnn_resnet101_pets.config` as a starting point for configuring the
+instance segmentation pipeline. Everything above that was mentioned about object
+detection holds true for instance segmentation. Instance segmentation consists
+of an object detection model with an additional head that predicts the object
+mask inside each predicted box once we remove the training and other details.
+Please refer to the section on [Running an Instance Segmentation
+Model](instance_segmentation.md) for instructions on how to configure a model
+that predicts masks in addition to object bounding boxes.
+
+## What's Next
+
+Congratulations, you have now trained an object detector for various cats and
+dogs! There different things you can do now:
+
+1. [Test your exported model using the provided Jupyter notebook.](running_notebook.md)
+2. [Experiment with different model configurations.](configuring_jobs.md)
+3. Train an object detector using your own data.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/tpu_compatibility.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/tpu_compatibility.md
new file mode 100644
index 00000000..f64a5001
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/tpu_compatibility.md
@@ -0,0 +1,206 @@
+# TPU compatible detection pipelines
+
+[TOC]
+
+The Tensorflow Object Detection API supports TPU training for some models. To
+make models TPU compatible you need to make a few tweaks to the model config as
+mentioned below. We also provide several sample configs that you can use as a
+template.
+
+## TPU compatibility
+
+### Static shaped tensors
+
+TPU training currently requires all tensors in the Tensorflow Graph to have
+static shapes. However, most of the sample configs in Object Detection API have
+a few different tensors that are dynamically shaped. Fortunately, we provide
+simple alternatives in the model configuration that modifies these tensors to
+have static shape:
+
+*   **Image tensors with static shape** - This can be achieved either by using a
+    `fixed_shape_resizer` that resizes images to a fixed spatial shape or by
+    setting `pad_to_max_dimension: true` in `keep_aspect_ratio_resizer` which
+    pads the resized images with zeros to the bottom and right. Padded image
+    tensors are correctly handled internally within the model.
+
+    ```
+    image_resizer {
+      fixed_shape_resizer {
+        height: 640
+        width: 640
+      }
+    }
+    ```
+
+    or
+
+    ```
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 640
+        max_dimension: 640
+        pad_to_max_dimension: true
+      }
+    }
+    ```
+
+*   **Groundtruth tensors with static shape** - Images in a typical detection
+    dataset have variable number of groundtruth boxes and associated classes.
+    Setting `max_number_of_boxes` to a large enough number in the
+    `train_input_reader` and `eval_input_reader` pads the groundtruth tensors
+    with zeros to a static shape. Padded groundtruth tensors are correctly
+    handled internally within the model.
+
+    ```
+    train_input_reader: {
+      tf_record_input_reader {
+        input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+      }
+      label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+      max_number_of_boxes: 200
+    }
+
+    eval_input_reader: {
+      tf_record_input_reader {
+        input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-0010"
+      }
+      label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+      max_number_of_boxes: 200
+    }
+    ```
+
+### TPU friendly ops
+
+Although TPU supports a vast number of tensorflow ops, a few used in the
+Tensorflow Object Detection API are unsupported. We list such ops below and
+recommend compatible substitutes.
+
+*   **Anchor sampling** - Typically we use hard example mining in standard SSD
+    pipeliens to balance positive and negative anchors that contribute to the
+    loss. Hard Example mining uses non max suppression as a subroutine and since
+    non max suppression is not currently supported on TPUs we cannot use hard
+    example mining. Fortunately, we provide an implementation of focal loss that
+    can be used instead of hard example mining. Remove `hard_example_miner` from
+    the config and substitute `weighted_sigmoid` classification loss with
+    `weighted_sigmoid_focal` loss.
+
+    ```
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    ```
+
+*   **Target Matching** - Object detection API provides two choices for matcher
+    used in target assignment: `argmax_matcher` and `bipartite_matcher`.
+    Bipartite matcher is not currently supported on TPU, therefore we must
+    modify the configs to use `argmax_matcher`. Additionally, set
+    `use_matmul_gather: true` for efficiency on TPU.
+
+    ```
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    ```
+
+### TPU training hyperparameters
+
+Object Detection training on TPU uses synchronous SGD. On a typical cloud TPU
+with 8 cores we recommend batch sizes that are 8x large when compared to a GPU
+config that uses asynchronous SGD. We also use fewer training steps (~ 1/100 x)
+due to the large batch size. This necessitates careful tuning of some other
+training parameters as listed below.
+
+*   **Batch size** - Use the largest batch size that can fit on cloud TPU.
+
+    ```
+    train_config {
+      batch_size: 1024
+    }
+    ```
+
+*   **Training steps** - Typically only 10s of thousands.
+
+    ```
+    train_config {
+      num_steps: 25000
+    }
+    ```
+
+*   **Batch norm decay** - Use smaller decay constants (0.97 or 0.997) since we
+    take fewer training steps.
+
+    ```
+    batch_norm {
+      scale: true,
+      decay: 0.97,
+      epsilon: 0.001,
+    }
+    ```
+
+*   **Learning rate** - Use large learning rate with warmup. Scale learning rate
+    linearly with batch size. See `cosine_decay_learning_rate` or
+    `manual_step_learning_rate` for examples.
+
+    ```
+    learning_rate: {
+      cosine_decay_learning_rate {
+        learning_rate_base: .04
+        total_steps: 25000
+        warmup_learning_rate: .013333
+        warmup_steps: 2000
+      }
+    }
+    ```
+
+    or
+
+    ```
+     learning_rate: {
+      manual_step_learning_rate {
+        warmup: true
+        initial_learning_rate: .01333
+        schedule {
+          step: 2000
+          learning_rate: 0.04
+        }
+        schedule {
+          step: 15000
+          learning_rate: 0.004
+        }
+      }
+    }
+    ```
+
+## Example TPU compatible configs
+
+We provide example config files that you can use to train your own models on TPU
+
+*   <a href='https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_300x300_coco14_sync.config'>ssd_mobilenet_v1_300x300</a> <br>
+*   <a href='https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync.config'>ssd_mobilenet_v1_ppn_300x300</a> <br>
+*   <a href='https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config'>ssd_mobilenet_v1_fpn_640x640
+    (mobilenet based retinanet)</a> <br>
+*   <a href='https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config'>ssd_resnet50_v1_fpn_640x640
+    (retinanet)</a> <br>
+
+## Supported Meta architectures
+
+Currently, `SSDMetaArch` models are supported on TPUs. `FasterRCNNMetaArch` is
+going to be supported soon.
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/using_your_own_dataset.md b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/using_your_own_dataset.md
new file mode 100644
index 00000000..077b6543
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/g3doc/using_your_own_dataset.md
@@ -0,0 +1,209 @@
+# Preparing Inputs
+
+[TOC]
+
+To use your own dataset in Tensorflow Object Detection API, you must convert it
+into the [TFRecord file format](https://www.tensorflow.org/api_guides/python/python_io#tfrecords_format_details).
+This document outlines how to write a script to generate the TFRecord file.
+
+## Label Maps
+
+Each dataset is required to have a label map associated with it. This label map
+defines a mapping from string class names to integer class Ids. The label map
+should be a `StringIntLabelMap` text protobuf. Sample label maps can be found in
+object_detection/data. Label maps should always start from id 1.
+
+## Dataset Requirements
+
+For every example in your dataset, you should have the following information:
+
+1. An RGB image for the dataset encoded as jpeg or png.
+2. A list of bounding boxes for the image. Each bounding box should contain:
+    1. A bounding box coordinates (with origin in top left corner) defined by 4
+       floating point numbers [ymin, xmin, ymax, xmax]. Note that we store the
+       _normalized_ coordinates (x / width, y / height) in the TFRecord dataset.
+    2. The class of the object in the bounding box.
+
+# Example Image
+
+Consider the following image:
+
+![Example Image](img/example_cat.jpg "Example Image")
+
+with the following label map:
+
+```
+item {
+  id: 1
+  name: 'Cat'
+}
+
+
+item {
+  id: 2
+  name: 'Dog'
+}
+```
+
+We can generate a tf.Example proto for this image using the following code:
+
+```python
+
+def create_cat_tf_example(encoded_cat_image_data):
+   """Creates a tf.Example proto from sample cat image.
+
+  Args:
+    encoded_cat_image_data: The jpg encoded data of the cat image.
+
+  Returns:
+    example: The created tf.Example.
+  """
+
+  height = 1032.0
+  width = 1200.0
+  filename = 'example_cat.jpg'
+  image_format = b'jpg'
+
+  xmins = [322.0 / 1200.0]
+  xmaxs = [1062.0 / 1200.0]
+  ymins = [174.0 / 1032.0]
+  ymaxs = [761.0 / 1032.0]
+  classes_text = ['Cat']
+  classes = [1]
+
+  tf_example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': dataset_util.int64_feature(height),
+      'image/width': dataset_util.int64_feature(width),
+      'image/filename': dataset_util.bytes_feature(filename),
+      'image/source_id': dataset_util.bytes_feature(filename),
+      'image/encoded': dataset_util.bytes_feature(encoded_cat_image_data),
+      'image/format': dataset_util.bytes_feature(image_format),
+      'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
+      'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
+      'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
+      'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
+      'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
+      'image/object/class/label': dataset_util.int64_list_feature(classes),
+  }))
+  return tf_example
+```
+
+## Conversion Script Outline {#conversion-script-outline}
+
+A typical conversion script will look like the following:
+
+```python
+
+import tensorflow as tf
+
+from object_detection.utils import dataset_util
+
+
+flags = tf.app.flags
+flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
+FLAGS = flags.FLAGS
+
+
+def create_tf_example(example):
+  # TODO(user): Populate the following variables from your example.
+  height = None # Image height
+  width = None # Image width
+  filename = None # Filename of the image. Empty if image is not from file
+  encoded_image_data = None # Encoded image bytes
+  image_format = None # b'jpeg' or b'png'
+
+  xmins = [] # List of normalized left x coordinates in bounding box (1 per box)
+  xmaxs = [] # List of normalized right x coordinates in bounding box
+             # (1 per box)
+  ymins = [] # List of normalized top y coordinates in bounding box (1 per box)
+  ymaxs = [] # List of normalized bottom y coordinates in bounding box
+             # (1 per box)
+  classes_text = [] # List of string class name of bounding box (1 per box)
+  classes = [] # List of integer class id of bounding box (1 per box)
+
+  tf_example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': dataset_util.int64_feature(height),
+      'image/width': dataset_util.int64_feature(width),
+      'image/filename': dataset_util.bytes_feature(filename),
+      'image/source_id': dataset_util.bytes_feature(filename),
+      'image/encoded': dataset_util.bytes_feature(encoded_image_data),
+      'image/format': dataset_util.bytes_feature(image_format),
+      'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
+      'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
+      'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
+      'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
+      'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
+      'image/object/class/label': dataset_util.int64_list_feature(classes),
+  }))
+  return tf_example
+
+
+def main(_):
+  writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
+
+  # TODO(user): Write code to read in your dataset to examples variable
+
+  for example in examples:
+    tf_example = create_tf_example(example)
+    writer.write(tf_example.SerializeToString())
+
+  writer.close()
+
+
+if __name__ == '__main__':
+  tf.app.run()
+
+```
+
+Note: You may notice additional fields in some other datasets. They are
+currently unused by the API and are optional.
+
+Note: Please refer to the section on [Running an Instance Segmentation
+Model](instance_segmentation.md) for instructions on how to configure a model
+that predicts masks in addition to object bounding boxes.
+
+## Sharding datasets
+
+When you have more than a few thousand examples, it is beneficial to shard your
+dataset into multiple files:
+
+*   tf.data.Dataset API can read input examples in parallel improving
+    throughput.
+*   tf.data.Dataset API can shuffle the examples better with sharded files which
+    improves performance of the model slightly.
+
+Instead of writing all tf.Example protos to a single file as shown in
+[conversion script outline](#conversion-script-outline), use the snippet below.
+
+```python
+import contextlib2
+from object_detection.dataset_tools import tf_record_creation_util
+
+num_shards=10
+output_filebase='/path/to/train_dataset.record'
+
+with contextlib2.ExitStack() as tf_record_close_stack:
+  output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
+      tf_record_close_stack, output_filebase, num_shards)
+  for index, example in examples:
+    tf_example = create_tf_example(example)
+    output_shard_index = index % num_shards
+    output_tfrecords[output_shard_index].write(tf_example.SerializeToString())
+```
+
+This will produce the following output files
+
+```bash
+/path/to/train_dataset.record-00000-00010
+/path/to/train_dataset.record-00001-00010
+...
+/path/to/train_dataset.record-00009-00010
+```
+
+which can then be used in the config file as below.
+
+```bash
+tf_record_input_reader {
+  input_path: "/path/to/train_dataset.record-?????-of-00010"
+}
+```
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inference/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/inference/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference.py b/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference.py
new file mode 100644
index 00000000..ccd8b3ae
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference.py
@@ -0,0 +1,141 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility functions for detection inference."""
+from __future__ import division
+
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+
+
+def build_input(tfrecord_paths):
+  """Builds the graph's input.
+
+  Args:
+    tfrecord_paths: List of paths to the input TFRecords
+
+  Returns:
+    serialized_example_tensor: The next serialized example. String scalar Tensor
+    image_tensor: The decoded image of the example. Uint8 tensor,
+        shape=[1, None, None,3]
+  """
+  filename_queue = tf.train.string_input_producer(
+      tfrecord_paths, shuffle=False, num_epochs=1)
+
+  tf_record_reader = tf.TFRecordReader()
+  _, serialized_example_tensor = tf_record_reader.read(filename_queue)
+  features = tf.parse_single_example(
+      serialized_example_tensor,
+      features={
+          standard_fields.TfExampleFields.image_encoded:
+              tf.FixedLenFeature([], tf.string),
+      })
+  encoded_image = features[standard_fields.TfExampleFields.image_encoded]
+  image_tensor = tf.image.decode_image(encoded_image, channels=3)
+  image_tensor.set_shape([None, None, 3])
+  image_tensor = tf.expand_dims(image_tensor, 0)
+
+  return serialized_example_tensor, image_tensor
+
+
+def build_inference_graph(image_tensor, inference_graph_path):
+  """Loads the inference graph and connects it to the input image.
+
+  Args:
+    image_tensor: The input image. uint8 tensor, shape=[1, None, None, 3]
+    inference_graph_path: Path to the inference graph with embedded weights
+
+  Returns:
+    detected_boxes_tensor: Detected boxes. Float tensor,
+        shape=[num_detections, 4]
+    detected_scores_tensor: Detected scores. Float tensor,
+        shape=[num_detections]
+    detected_labels_tensor: Detected labels. Int64 tensor,
+        shape=[num_detections]
+  """
+  with tf.gfile.Open(inference_graph_path, 'rb') as graph_def_file:
+    graph_content = graph_def_file.read()
+  graph_def = tf.GraphDef()
+  graph_def.MergeFromString(graph_content)
+
+  tf.import_graph_def(
+      graph_def, name='', input_map={'image_tensor': image_tensor})
+
+  g = tf.get_default_graph()
+
+  num_detections_tensor = tf.squeeze(
+      g.get_tensor_by_name('num_detections:0'), 0)
+  num_detections_tensor = tf.cast(num_detections_tensor, tf.int32)
+
+  detected_boxes_tensor = tf.squeeze(
+      g.get_tensor_by_name('detection_boxes:0'), 0)
+  detected_boxes_tensor = detected_boxes_tensor[:num_detections_tensor]
+
+  detected_scores_tensor = tf.squeeze(
+      g.get_tensor_by_name('detection_scores:0'), 0)
+  detected_scores_tensor = detected_scores_tensor[:num_detections_tensor]
+
+  detected_labels_tensor = tf.squeeze(
+      g.get_tensor_by_name('detection_classes:0'), 0)
+  detected_labels_tensor = tf.cast(detected_labels_tensor, tf.int64)
+  detected_labels_tensor = detected_labels_tensor[:num_detections_tensor]
+
+  return detected_boxes_tensor, detected_scores_tensor, detected_labels_tensor
+
+
+def infer_detections_and_add_to_example(
+    serialized_example_tensor, detected_boxes_tensor, detected_scores_tensor,
+    detected_labels_tensor, discard_image_pixels):
+  """Runs the supplied tensors and adds the inferred detections to the example.
+
+  Args:
+    serialized_example_tensor: Serialized TF example. Scalar string tensor
+    detected_boxes_tensor: Detected boxes. Float tensor,
+        shape=[num_detections, 4]
+    detected_scores_tensor: Detected scores. Float tensor,
+        shape=[num_detections]
+    detected_labels_tensor: Detected labels. Int64 tensor,
+        shape=[num_detections]
+    discard_image_pixels: If true, discards the image from the result
+  Returns:
+    The de-serialized TF example augmented with the inferred detections.
+  """
+  tf_example = tf.train.Example()
+  (serialized_example, detected_boxes, detected_scores,
+   detected_classes) = tf.get_default_session().run([
+       serialized_example_tensor, detected_boxes_tensor, detected_scores_tensor,
+       detected_labels_tensor
+   ])
+  detected_boxes = detected_boxes.T
+
+  tf_example.ParseFromString(serialized_example)
+  feature = tf_example.features.feature
+  feature[standard_fields.TfExampleFields.
+          detection_score].float_list.value[:] = detected_scores
+  feature[standard_fields.TfExampleFields.
+          detection_bbox_ymin].float_list.value[:] = detected_boxes[0]
+  feature[standard_fields.TfExampleFields.
+          detection_bbox_xmin].float_list.value[:] = detected_boxes[1]
+  feature[standard_fields.TfExampleFields.
+          detection_bbox_ymax].float_list.value[:] = detected_boxes[2]
+  feature[standard_fields.TfExampleFields.
+          detection_bbox_xmax].float_list.value[:] = detected_boxes[3]
+  feature[standard_fields.TfExampleFields.
+          detection_class_label].int64_list.value[:] = detected_classes
+
+  if discard_image_pixels:
+    del feature[standard_fields.TfExampleFields.image_encoded]
+
+  return tf_example
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference_test.py
new file mode 100644
index 00000000..eabb6b47
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/inference/detection_inference_test.py
@@ -0,0 +1,176 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Tests for detection_inference.py."""
+
+import os
+import StringIO
+
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.inference import detection_inference
+from object_detection.utils import dataset_util
+
+
+def get_mock_tfrecord_path():
+  return os.path.join(tf.test.get_temp_dir(), 'mock.tfrec')
+
+
+def create_mock_tfrecord():
+  pil_image = Image.fromarray(np.array([[[123, 0, 0]]], dtype=np.uint8), 'RGB')
+  image_output_stream = StringIO.StringIO()
+  pil_image.save(image_output_stream, format='png')
+  encoded_image = image_output_stream.getvalue()
+
+  feature_map = {
+      'test_field':
+          dataset_util.float_list_feature([1, 2, 3, 4]),
+      standard_fields.TfExampleFields.image_encoded:
+          dataset_util.bytes_feature(encoded_image),
+  }
+
+  tf_example = tf.train.Example(features=tf.train.Features(feature=feature_map))
+  with tf.python_io.TFRecordWriter(get_mock_tfrecord_path()) as writer:
+    writer.write(tf_example.SerializeToString())
+
+
+def get_mock_graph_path():
+  return os.path.join(tf.test.get_temp_dir(), 'mock_graph.pb')
+
+
+def create_mock_graph():
+  g = tf.Graph()
+  with g.as_default():
+    in_image_tensor = tf.placeholder(
+        tf.uint8, shape=[1, None, None, 3], name='image_tensor')
+    tf.constant([2.0], name='num_detections')
+    tf.constant(
+        [[[0, 0.8, 0.7, 1], [0.1, 0.2, 0.8, 0.9], [0.2, 0.3, 0.4, 0.5]]],
+        name='detection_boxes')
+    tf.constant([[0.1, 0.2, 0.3]], name='detection_scores')
+    tf.identity(
+        tf.constant([[1.0, 2.0, 3.0]]) *
+        tf.reduce_sum(tf.cast(in_image_tensor, dtype=tf.float32)),
+        name='detection_classes')
+    graph_def = g.as_graph_def()
+
+  with tf.gfile.Open(get_mock_graph_path(), 'w') as fl:
+    fl.write(graph_def.SerializeToString())
+
+
+class InferDetectionsTests(tf.test.TestCase):
+
+  def test_simple(self):
+    create_mock_graph()
+    create_mock_tfrecord()
+
+    serialized_example_tensor, image_tensor = detection_inference.build_input(
+        [get_mock_tfrecord_path()])
+    self.assertAllEqual(image_tensor.get_shape().as_list(), [1, None, None, 3])
+
+    (detected_boxes_tensor, detected_scores_tensor,
+     detected_labels_tensor) = detection_inference.build_inference_graph(
+         image_tensor, get_mock_graph_path())
+
+    with self.test_session(use_gpu=False) as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(tf.local_variables_initializer())
+      tf.train.start_queue_runners()
+
+      tf_example = detection_inference.infer_detections_and_add_to_example(
+          serialized_example_tensor, detected_boxes_tensor,
+          detected_scores_tensor, detected_labels_tensor, False)
+
+    self.assertProtoEquals(r"""
+        features {
+          feature {
+            key: "image/detection/bbox/ymin"
+            value { float_list { value: [0.0, 0.1] } } }
+          feature {
+            key: "image/detection/bbox/xmin"
+            value { float_list { value: [0.8, 0.2] } } }
+          feature {
+            key: "image/detection/bbox/ymax"
+            value { float_list { value: [0.7, 0.8] } } }
+          feature {
+            key: "image/detection/bbox/xmax"
+            value { float_list { value: [1.0, 0.9] } } }
+          feature {
+            key: "image/detection/label"
+            value { int64_list { value: [123, 246] } } }
+          feature {
+            key: "image/detection/score"
+            value { float_list { value: [0.1, 0.2] } } }
+          feature {
+            key: "image/encoded"
+            value { bytes_list { value:
+              "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\001\000\000"
+              "\000\001\010\002\000\000\000\220wS\336\000\000\000\022IDATx"
+              "\234b\250f`\000\000\000\000\377\377\003\000\001u\000|gO\242"
+              "\213\000\000\000\000IEND\256B`\202" } } }
+          feature {
+            key: "test_field"
+            value { float_list { value: [1.0, 2.0, 3.0, 4.0] } } } }
+    """, tf_example)
+
+  def test_discard_image(self):
+    create_mock_graph()
+    create_mock_tfrecord()
+
+    serialized_example_tensor, image_tensor = detection_inference.build_input(
+        [get_mock_tfrecord_path()])
+    (detected_boxes_tensor, detected_scores_tensor,
+     detected_labels_tensor) = detection_inference.build_inference_graph(
+         image_tensor, get_mock_graph_path())
+
+    with self.test_session(use_gpu=False) as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(tf.local_variables_initializer())
+      tf.train.start_queue_runners()
+
+      tf_example = detection_inference.infer_detections_and_add_to_example(
+          serialized_example_tensor, detected_boxes_tensor,
+          detected_scores_tensor, detected_labels_tensor, True)
+
+    self.assertProtoEquals(r"""
+        features {
+          feature {
+            key: "image/detection/bbox/ymin"
+            value { float_list { value: [0.0, 0.1] } } }
+          feature {
+            key: "image/detection/bbox/xmin"
+            value { float_list { value: [0.8, 0.2] } } }
+          feature {
+            key: "image/detection/bbox/ymax"
+            value { float_list { value: [0.7, 0.8] } } }
+          feature {
+            key: "image/detection/bbox/xmax"
+            value { float_list { value: [1.0, 0.9] } } }
+          feature {
+            key: "image/detection/label"
+            value { int64_list { value: [123, 246] } } }
+          feature {
+            key: "image/detection/score"
+            value { float_list { value: [0.1, 0.2] } } }
+          feature {
+            key: "test_field"
+            value { float_list { value: [1.0, 2.0, 3.0, 4.0] } } } }
+    """, tf_example)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inference/infer_detections.py b/TensorFlow/Detection/SSD/models/research/object_detection/inference/infer_detections.py
new file mode 100644
index 00000000..a251009e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/inference/infer_detections.py
@@ -0,0 +1,96 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Infers detections on a TFRecord of TFExamples given an inference graph.
+
+Example usage:
+  ./infer_detections \
+    --input_tfrecord_paths=/path/to/input/tfrecord1,/path/to/input/tfrecord2 \
+    --output_tfrecord_path_prefix=/path/to/output/detections.tfrecord \
+    --inference_graph=/path/to/frozen_weights_inference_graph.pb
+
+The output is a TFRecord of TFExamples. Each TFExample from the input is first
+augmented with detections from the inference graph and then copied to the
+output.
+
+The input and output nodes of the inference graph are expected to have the same
+types, shapes, and semantics, as the input and output nodes of graphs produced
+by export_inference_graph.py, when run with --input_type=image_tensor.
+
+The script can also discard the image pixels in the output. This greatly
+reduces the output size and can potentially accelerate reading data in
+subsequent processing steps that don't require the images (e.g. computing
+metrics).
+"""
+
+import itertools
+import tensorflow as tf
+from object_detection.inference import detection_inference
+
+tf.flags.DEFINE_string('input_tfrecord_paths', None,
+                       'A comma separated list of paths to input TFRecords.')
+tf.flags.DEFINE_string('output_tfrecord_path', None,
+                       'Path to the output TFRecord.')
+tf.flags.DEFINE_string('inference_graph', None,
+                       'Path to the inference graph with embedded weights.')
+tf.flags.DEFINE_boolean('discard_image_pixels', False,
+                        'Discards the images in the output TFExamples. This'
+                        ' significantly reduces the output size and is useful'
+                        ' if the subsequent tools don\'t need access to the'
+                        ' images (e.g. when computing evaluation measures).')
+
+FLAGS = tf.flags.FLAGS
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  required_flags = ['input_tfrecord_paths', 'output_tfrecord_path',
+                    'inference_graph']
+  for flag_name in required_flags:
+    if not getattr(FLAGS, flag_name):
+      raise ValueError('Flag --{} is required'.format(flag_name))
+
+  with tf.Session() as sess:
+    input_tfrecord_paths = [
+        v for v in FLAGS.input_tfrecord_paths.split(',') if v]
+    tf.logging.info('Reading input from %d files', len(input_tfrecord_paths))
+    serialized_example_tensor, image_tensor = detection_inference.build_input(
+        input_tfrecord_paths)
+    tf.logging.info('Reading graph and building model...')
+    (detected_boxes_tensor, detected_scores_tensor,
+     detected_labels_tensor) = detection_inference.build_inference_graph(
+         image_tensor, FLAGS.inference_graph)
+
+    tf.logging.info('Running inference and writing output to {}'.format(
+        FLAGS.output_tfrecord_path))
+    sess.run(tf.local_variables_initializer())
+    tf.train.start_queue_runners()
+    with tf.python_io.TFRecordWriter(
+        FLAGS.output_tfrecord_path) as tf_record_writer:
+      try:
+        for counter in itertools.count():
+          tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 10,
+                                 counter)
+          tf_example = detection_inference.infer_detections_and_add_to_example(
+              serialized_example_tensor, detected_boxes_tensor,
+              detected_scores_tensor, detected_labels_tensor,
+              FLAGS.discard_image_pixels)
+          tf_record_writer.write(tf_example.SerializeToString())
+      except tf.errors.OutOfRangeError:
+        tf.logging.info('Finished processing records')
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inputs.py b/TensorFlow/Detection/SSD/models/research/object_detection/inputs.py
new file mode 100644
index 00000000..cb1dd96c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/inputs.py
@@ -0,0 +1,650 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Model input function for tf-learn object detection model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+import tensorflow as tf
+from object_detection.builders import dataset_builder
+from object_detection.builders import image_resizer_builder
+from object_detection.builders import model_builder
+from object_detection.builders import preprocessor_builder
+from object_detection.core import preprocessor
+from object_detection.core import standard_fields as fields
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import eval_pb2
+from object_detection.protos import input_reader_pb2
+from object_detection.protos import model_pb2
+from object_detection.protos import train_pb2
+from object_detection.utils import config_util
+from object_detection.utils import ops as util_ops
+from object_detection.utils import shape_utils
+
+HASH_KEY = 'hash'
+HASH_BINS = 1 << 31
+SERVING_FED_EXAMPLE_KEY = 'serialized_example'
+
+# A map of names to methods that help build the input pipeline.
+INPUT_BUILDER_UTIL_MAP = {
+    'dataset_build': dataset_builder.build,
+}
+
+
+def transform_input_data(tensor_dict,
+                         model_preprocess_fn,
+                         image_resizer_fn,
+                         num_classes,
+                         data_augmentation_fn=None,
+                         merge_multiple_boxes=False,
+                         retain_original_image=False,
+                         use_bfloat16=False):
+  """A single function that is responsible for all input data transformations.
+
+  Data transformation functions are applied in the following order.
+  1. If key fields.InputDataFields.image_additional_channels is present in
+     tensor_dict, the additional channels will be merged into
+     fields.InputDataFields.image.
+  2. data_augmentation_fn (optional): applied on tensor_dict.
+  3. model_preprocess_fn: applied only on image tensor in tensor_dict.
+  4. image_resizer_fn: applied on original image and instance mask tensor in
+     tensor_dict.
+  5. one_hot_encoding: applied to classes tensor in tensor_dict.
+  6. merge_multiple_boxes (optional): when groundtruth boxes are exactly the
+     same they can be merged into a single box with an associated k-hot class
+     label.
+
+  Args:
+    tensor_dict: dictionary containing input tensors keyed by
+      fields.InputDataFields.
+    model_preprocess_fn: model's preprocess function to apply on image tensor.
+      This function must take in a 4-D float tensor and return a 4-D preprocess
+      float tensor and a tensor containing the true image shape.
+    image_resizer_fn: image resizer function to apply on groundtruth instance
+      `masks. This function must take a 3-D float tensor of an image and a 3-D
+      tensor of instance masks and return a resized version of these along with
+      the true shapes.
+    num_classes: number of max classes to one-hot (or k-hot) encode the class
+      labels.
+    data_augmentation_fn: (optional) data augmentation function to apply on
+      input `tensor_dict`.
+    merge_multiple_boxes: (optional) whether to merge multiple groundtruth boxes
+      and classes for a given image if the boxes are exactly the same.
+    retain_original_image: (optional) whether to retain original image in the
+      output dictionary.
+    use_bfloat16: (optional) a bool, whether to use bfloat16 in training.
+
+  Returns:
+    A dictionary keyed by fields.InputDataFields containing the tensors obtained
+    after applying all the transformations.
+  """
+  if fields.InputDataFields.groundtruth_boxes in tensor_dict:
+    tensor_dict = util_ops.filter_groundtruth_with_nan_box_coordinates(
+        tensor_dict)
+  if fields.InputDataFields.image_additional_channels in tensor_dict:
+    channels = tensor_dict[fields.InputDataFields.image_additional_channels]
+    tensor_dict[fields.InputDataFields.image] = tf.concat(
+        [tensor_dict[fields.InputDataFields.image], channels], axis=2)
+
+  if retain_original_image:
+    tensor_dict[fields.InputDataFields.original_image] = tf.cast(
+        image_resizer_fn(tensor_dict[fields.InputDataFields.image], None)[0],
+        tf.uint8)
+
+  # Apply data augmentation ops.
+  if data_augmentation_fn is not None:
+    tensor_dict = data_augmentation_fn(tensor_dict)
+
+  # Apply model preprocessing ops and resize instance masks.
+  image = tensor_dict[fields.InputDataFields.image]
+  preprocessed_resized_image, true_image_shape = model_preprocess_fn(
+      tf.expand_dims(tf.to_float(image), axis=0))
+  if use_bfloat16:
+    preprocessed_resized_image = tf.cast(
+        preprocessed_resized_image, tf.bfloat16)
+  tensor_dict[fields.InputDataFields.image] = tf.squeeze(
+      preprocessed_resized_image, axis=0)
+  tensor_dict[fields.InputDataFields.true_image_shape] = tf.squeeze(
+      true_image_shape, axis=0)
+  if fields.InputDataFields.groundtruth_instance_masks in tensor_dict:
+    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
+    _, resized_masks, _ = image_resizer_fn(image, masks)
+    if use_bfloat16:
+      resized_masks = tf.cast(resized_masks, tf.bfloat16)
+    tensor_dict[fields.InputDataFields.
+                groundtruth_instance_masks] = resized_masks
+
+  # Transform groundtruth classes to one hot encodings.
+  label_offset = 1
+  zero_indexed_groundtruth_classes = tensor_dict[
+      fields.InputDataFields.groundtruth_classes] - label_offset
+  tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
+      zero_indexed_groundtruth_classes, num_classes)
+
+  if fields.InputDataFields.groundtruth_confidences in tensor_dict:
+    groundtruth_confidences = tensor_dict[
+        fields.InputDataFields.groundtruth_confidences]
+    # Map the confidences to the one-hot encoding of classes
+    tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
+        tf.reshape(groundtruth_confidences, [-1, 1]) *
+        tensor_dict[fields.InputDataFields.groundtruth_classes])
+  else:
+    groundtruth_confidences = tf.ones_like(
+        zero_indexed_groundtruth_classes, dtype=tf.float32)
+    tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
+        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
+  if merge_multiple_boxes:
+    merged_boxes, merged_classes, merged_confidences, _ = (
+        util_ops.merge_boxes_with_multiple_labels(
+            tensor_dict[fields.InputDataFields.groundtruth_boxes],
+            zero_indexed_groundtruth_classes,
+            groundtruth_confidences,
+            num_classes))
+    merged_classes = tf.cast(merged_classes, tf.float32)
+    tensor_dict[fields.InputDataFields.groundtruth_boxes] = merged_boxes
+    tensor_dict[fields.InputDataFields.groundtruth_classes] = merged_classes
+    tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
+        merged_confidences)
+  if fields.InputDataFields.groundtruth_boxes in tensor_dict:
+    tensor_dict[fields.InputDataFields.num_groundtruth_boxes] = tf.shape(
+        tensor_dict[fields.InputDataFields.groundtruth_boxes])[0]
+
+  return tensor_dict
+
+
+def pad_input_data_to_static_shapes(tensor_dict, max_num_boxes, num_classes,
+                                    spatial_image_shape=None):
+  """Pads input tensors to static shapes.
+
+  Args:
+    tensor_dict: Tensor dictionary of input data
+    max_num_boxes: Max number of groundtruth boxes needed to compute shapes for
+      padding.
+    num_classes: Number of classes in the dataset needed to compute shapes for
+      padding.
+    spatial_image_shape: A list of two integers of the form [height, width]
+      containing expected spatial shape of the image.
+
+  Returns:
+    A dictionary keyed by fields.InputDataFields containing padding shapes for
+    tensors in the dataset.
+
+  Raises:
+    ValueError: If groundtruth classes is neither rank 1 nor rank 2.
+  """
+
+  if not spatial_image_shape or spatial_image_shape == [-1, -1]:
+    height, width = None, None
+  else:
+    height, width = spatial_image_shape  # pylint: disable=unpacking-non-sequence
+
+  num_additional_channels = 0
+  if fields.InputDataFields.image_additional_channels in tensor_dict:
+    num_additional_channels = tensor_dict[
+        fields.InputDataFields.image_additional_channels].shape[2].value
+  num_image_channels = 3
+  if fields.InputDataFields.image in tensor_dict:
+    num_image_channels = tensor_dict[fields.InputDataFields
+                                     .image].shape[2].value
+  padding_shapes = {
+      # Additional channels are merged before batching.
+      fields.InputDataFields.image: [
+          height, width, num_image_channels + num_additional_channels
+      ],
+      fields.InputDataFields.original_image_spatial_shape: [2],
+      fields.InputDataFields.image_additional_channels: [
+          height, width, num_additional_channels
+      ],
+      fields.InputDataFields.source_id: [],
+      fields.InputDataFields.filename: [],
+      fields.InputDataFields.key: [],
+      fields.InputDataFields.groundtruth_difficult: [max_num_boxes],
+      fields.InputDataFields.groundtruth_boxes: [max_num_boxes, 4],
+      fields.InputDataFields.groundtruth_classes: [max_num_boxes, num_classes],
+      fields.InputDataFields.groundtruth_instance_masks: [
+          max_num_boxes, height, width
+      ],
+      fields.InputDataFields.groundtruth_is_crowd: [max_num_boxes],
+      fields.InputDataFields.groundtruth_group_of: [max_num_boxes],
+      fields.InputDataFields.groundtruth_area: [max_num_boxes],
+      fields.InputDataFields.groundtruth_weights: [max_num_boxes],
+      fields.InputDataFields.groundtruth_confidences: [
+          max_num_boxes, num_classes
+      ],
+      fields.InputDataFields.num_groundtruth_boxes: [],
+      fields.InputDataFields.groundtruth_label_types: [max_num_boxes],
+      fields.InputDataFields.groundtruth_label_weights: [max_num_boxes],
+      fields.InputDataFields.true_image_shape: [3],
+      fields.InputDataFields.multiclass_scores: [
+          max_num_boxes, num_classes + 1 if num_classes is not None else None
+      ],
+      fields.InputDataFields.groundtruth_image_classes: [num_classes],
+      fields.InputDataFields.groundtruth_image_confidences: [num_classes],
+  }
+
+  if fields.InputDataFields.original_image in tensor_dict:
+    padding_shapes[fields.InputDataFields.original_image] = [
+        height, width, num_image_channels + num_additional_channels
+    ]
+  if fields.InputDataFields.groundtruth_keypoints in tensor_dict:
+    tensor_shape = (
+        tensor_dict[fields.InputDataFields.groundtruth_keypoints].shape)
+    padding_shape = [max_num_boxes, tensor_shape[1].value,
+                     tensor_shape[2].value]
+    padding_shapes[fields.InputDataFields.groundtruth_keypoints] = padding_shape
+  if fields.InputDataFields.groundtruth_keypoint_visibilities in tensor_dict:
+    tensor_shape = tensor_dict[fields.InputDataFields.
+                               groundtruth_keypoint_visibilities].shape
+    padding_shape = [max_num_boxes, tensor_shape[1].value]
+    padding_shapes[fields.InputDataFields.
+                   groundtruth_keypoint_visibilities] = padding_shape
+
+  padded_tensor_dict = {}
+  for tensor_name in tensor_dict:
+    padded_tensor_dict[tensor_name] = shape_utils.pad_or_clip_nd(
+        tensor_dict[tensor_name], padding_shapes[tensor_name])
+
+  # Make sure that the number of groundtruth boxes now reflects the
+  # padded/clipped tensors.
+  if fields.InputDataFields.num_groundtruth_boxes in padded_tensor_dict:
+    padded_tensor_dict[fields.InputDataFields.num_groundtruth_boxes] = (
+        tf.minimum(
+            padded_tensor_dict[fields.InputDataFields.num_groundtruth_boxes],
+            max_num_boxes))
+  return padded_tensor_dict
+
+
+def augment_input_data(tensor_dict, data_augmentation_options):
+  """Applies data augmentation ops to input tensors.
+
+  Args:
+    tensor_dict: A dictionary of input tensors keyed by fields.InputDataFields.
+    data_augmentation_options: A list of tuples, where each tuple contains a
+      function and a dictionary that contains arguments and their values.
+      Usually, this is the output of core/preprocessor.build.
+
+  Returns:
+    A dictionary of tensors obtained by applying data augmentation ops to the
+    input tensor dictionary.
+  """
+  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
+      tf.to_float(tensor_dict[fields.InputDataFields.image]), 0)
+
+  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
+                            in tensor_dict)
+  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
+                       in tensor_dict)
+  include_label_weights = (fields.InputDataFields.groundtruth_weights
+                           in tensor_dict)
+  include_label_confidences = (fields.InputDataFields.groundtruth_confidences
+                               in tensor_dict)
+  tensor_dict = preprocessor.preprocess(
+      tensor_dict, data_augmentation_options,
+      func_arg_map=preprocessor.get_default_func_arg_map(
+          include_label_weights=include_label_weights,
+          include_label_confidences=include_label_confidences,
+          include_instance_masks=include_instance_masks,
+          include_keypoints=include_keypoints))
+  tensor_dict[fields.InputDataFields.image] = tf.squeeze(
+      tensor_dict[fields.InputDataFields.image], axis=0)
+  return tensor_dict
+
+
+def _get_labels_dict(input_dict):
+  """Extracts labels dict from input dict."""
+  required_label_keys = [
+      fields.InputDataFields.num_groundtruth_boxes,
+      fields.InputDataFields.groundtruth_boxes,
+      fields.InputDataFields.groundtruth_classes,
+      fields.InputDataFields.groundtruth_weights,
+  ]
+  labels_dict = {}
+  for key in required_label_keys:
+    labels_dict[key] = input_dict[key]
+
+  optional_label_keys = [
+      fields.InputDataFields.groundtruth_confidences,
+      fields.InputDataFields.groundtruth_keypoints,
+      fields.InputDataFields.groundtruth_instance_masks,
+      fields.InputDataFields.groundtruth_area,
+      fields.InputDataFields.groundtruth_is_crowd,
+      fields.InputDataFields.groundtruth_difficult
+  ]
+
+  for key in optional_label_keys:
+    if key in input_dict:
+      labels_dict[key] = input_dict[key]
+  if fields.InputDataFields.groundtruth_difficult in labels_dict:
+    labels_dict[fields.InputDataFields.groundtruth_difficult] = tf.cast(
+        labels_dict[fields.InputDataFields.groundtruth_difficult], tf.int32)
+  return labels_dict
+
+
+def _replace_empty_string_with_random_number(string_tensor):
+  """Returns string unchanged if non-empty, and random string tensor otherwise.
+
+  The random string is an integer 0 and 2**63 - 1, casted as string.
+
+
+  Args:
+    string_tensor: A tf.tensor of dtype string.
+
+  Returns:
+    out_string: A tf.tensor of dtype string. If string_tensor contains the empty
+      string, out_string will contain a random integer casted to a string.
+      Otherwise string_tensor is returned unchanged.
+
+  """
+
+  empty_string = tf.constant('', dtype=tf.string, name='EmptyString')
+
+  random_source_id = tf.as_string(
+      tf.random_uniform(shape=[], maxval=2**63 - 1, dtype=tf.int64))
+
+  out_string = tf.cond(
+      tf.equal(string_tensor, empty_string),
+      true_fn=lambda: random_source_id,
+      false_fn=lambda: string_tensor)
+
+  return out_string
+
+
+def _get_features_dict(input_dict):
+  """Extracts features dict from input dict."""
+
+  source_id = _replace_empty_string_with_random_number(
+      input_dict[fields.InputDataFields.source_id])
+
+  hash_from_source_id = tf.string_to_hash_bucket_fast(source_id, HASH_BINS)
+  features = {
+      fields.InputDataFields.image:
+          input_dict[fields.InputDataFields.image],
+      HASH_KEY: tf.cast(hash_from_source_id, tf.int32),
+      fields.InputDataFields.true_image_shape:
+          input_dict[fields.InputDataFields.true_image_shape],
+      fields.InputDataFields.original_image_spatial_shape:
+          input_dict[fields.InputDataFields.original_image_spatial_shape]
+  }
+  if fields.InputDataFields.original_image in input_dict:
+    features[fields.InputDataFields.original_image] = input_dict[
+        fields.InputDataFields.original_image]
+  return features
+
+
+def create_train_input_fn(train_config, train_input_config,
+                          model_config):
+  """Creates a train `input` function for `Estimator`.
+
+  Args:
+    train_config: A train_pb2.TrainConfig.
+    train_input_config: An input_reader_pb2.InputReader.
+    model_config: A model_pb2.DetectionModel.
+
+  Returns:
+    `input_fn` for `Estimator` in TRAIN mode.
+  """
+
+  def _train_input_fn(params=None):
+    """Returns `features` and `labels` tensor dictionaries for training.
+
+    Args:
+      params: Parameter dictionary passed from the estimator.
+
+    Returns:
+      A tf.data.Dataset that holds (features, labels) tuple.
+
+      features: Dictionary of feature tensors.
+        features[fields.InputDataFields.image] is a [batch_size, H, W, C]
+          float32 tensor with preprocessed images.
+        features[HASH_KEY] is a [batch_size] int32 tensor representing unique
+          identifiers for the images.
+        features[fields.InputDataFields.true_image_shape] is a [batch_size, 3]
+          int32 tensor representing the true image shapes, as preprocessed
+          images could be padded.
+        features[fields.InputDataFields.original_image] (optional) is a
+          [batch_size, H, W, C] float32 tensor with original images.
+      labels: Dictionary of groundtruth tensors.
+        labels[fields.InputDataFields.num_groundtruth_boxes] is a [batch_size]
+          int32 tensor indicating the number of groundtruth boxes.
+        labels[fields.InputDataFields.groundtruth_boxes] is a
+          [batch_size, num_boxes, 4] float32 tensor containing the corners of
+          the groundtruth boxes.
+        labels[fields.InputDataFields.groundtruth_classes] is a
+          [batch_size, num_boxes, num_classes] float32 one-hot tensor of
+          classes.
+        labels[fields.InputDataFields.groundtruth_weights] is a
+          [batch_size, num_boxes] float32 tensor containing groundtruth weights
+          for the boxes.
+        -- Optional --
+        labels[fields.InputDataFields.groundtruth_instance_masks] is a
+          [batch_size, num_boxes, H, W] float32 tensor containing only binary
+          values, which represent instance masks for objects.
+        labels[fields.InputDataFields.groundtruth_keypoints] is a
+          [batch_size, num_boxes, num_keypoints, 2] float32 tensor containing
+          keypoints for each box.
+
+    Raises:
+      TypeError: if the `train_config`, `train_input_config` or `model_config`
+        are not of the correct type.
+    """
+    if not isinstance(train_config, train_pb2.TrainConfig):
+      raise TypeError('For training mode, the `train_config` must be a '
+                      'train_pb2.TrainConfig.')
+    if not isinstance(train_input_config, input_reader_pb2.InputReader):
+      raise TypeError('The `train_input_config` must be a '
+                      'input_reader_pb2.InputReader.')
+    if not isinstance(model_config, model_pb2.DetectionModel):
+      raise TypeError('The `model_config` must be a '
+                      'model_pb2.DetectionModel.')
+
+    def transform_and_pad_input_data_fn(tensor_dict):
+      """Combines transform and pad operation."""
+      data_augmentation_options = [
+          preprocessor_builder.build(step)
+          for step in train_config.data_augmentation_options
+      ]
+      data_augmentation_fn = functools.partial(
+          augment_input_data,
+          data_augmentation_options=data_augmentation_options)
+      model = model_builder.build(model_config, is_training=True)
+      image_resizer_config = config_util.get_image_resizer_config(model_config)
+      image_resizer_fn = image_resizer_builder.build(image_resizer_config)
+      transform_data_fn = functools.partial(
+          transform_input_data, model_preprocess_fn=model.preprocess,
+          image_resizer_fn=image_resizer_fn,
+          num_classes=config_util.get_number_of_classes(model_config),
+          data_augmentation_fn=data_augmentation_fn,
+          merge_multiple_boxes=train_config.merge_multiple_label_boxes,
+          retain_original_image=train_config.retain_original_images,
+          use_bfloat16=train_config.use_bfloat16)
+
+      tensor_dict = pad_input_data_to_static_shapes(
+          tensor_dict=transform_data_fn(tensor_dict),
+          max_num_boxes=train_input_config.max_number_of_boxes,
+          num_classes=config_util.get_number_of_classes(model_config),
+          spatial_image_shape=config_util.get_spatial_image_size(
+              image_resizer_config))
+      return (_get_features_dict(tensor_dict), _get_labels_dict(tensor_dict))
+
+    dataset = INPUT_BUILDER_UTIL_MAP['dataset_build'](
+        train_input_config,
+        transform_input_data_fn=transform_and_pad_input_data_fn,
+        batch_size=params['batch_size'] if params else train_config.batch_size,
+        multi_gpu=True)
+    return dataset
+
+  return _train_input_fn
+
+
+def create_eval_input_fn(eval_config, eval_input_config, model_config):
+  """Creates an eval `input` function for `Estimator`.
+
+  Args:
+    eval_config: An eval_pb2.EvalConfig.
+    eval_input_config: An input_reader_pb2.InputReader.
+    model_config: A model_pb2.DetectionModel.
+
+  Returns:
+    `input_fn` for `Estimator` in EVAL mode.
+  """
+
+  def _eval_input_fn(params=None):
+    """Returns `features` and `labels` tensor dictionaries for evaluation.
+
+    Args:
+      params: Parameter dictionary passed from the estimator.
+
+    Returns:
+      A tf.data.Dataset that holds (features, labels) tuple.
+
+      features: Dictionary of feature tensors.
+        features[fields.InputDataFields.image] is a [1, H, W, C] float32 tensor
+          with preprocessed images.
+        features[HASH_KEY] is a [1] int32 tensor representing unique
+          identifiers for the images.
+        features[fields.InputDataFields.true_image_shape] is a [1, 3]
+          int32 tensor representing the true image shapes, as preprocessed
+          images could be padded.
+        features[fields.InputDataFields.original_image] is a [1, H', W', C]
+          float32 tensor with the original image.
+      labels: Dictionary of groundtruth tensors.
+        labels[fields.InputDataFields.groundtruth_boxes] is a [1, num_boxes, 4]
+          float32 tensor containing the corners of the groundtruth boxes.
+        labels[fields.InputDataFields.groundtruth_classes] is a
+          [num_boxes, num_classes] float32 one-hot tensor of classes.
+        labels[fields.InputDataFields.groundtruth_area] is a [1, num_boxes]
+          float32 tensor containing object areas.
+        labels[fields.InputDataFields.groundtruth_is_crowd] is a [1, num_boxes]
+          bool tensor indicating if the boxes enclose a crowd.
+        labels[fields.InputDataFields.groundtruth_difficult] is a [1, num_boxes]
+          int32 tensor indicating if the boxes represent difficult instances.
+        -- Optional --
+        labels[fields.InputDataFields.groundtruth_instance_masks] is a
+          [1, num_boxes, H, W] float32 tensor containing only binary values,
+          which represent instance masks for objects.
+
+    Raises:
+      TypeError: if the `eval_config`, `eval_input_config` or `model_config`
+        are not of the correct type.
+    """
+    params = params or {}
+    if not isinstance(eval_config, eval_pb2.EvalConfig):
+      raise TypeError('For eval mode, the `eval_config` must be a '
+                      'train_pb2.EvalConfig.')
+    if not isinstance(eval_input_config, input_reader_pb2.InputReader):
+      raise TypeError('The `eval_input_config` must be a '
+                      'input_reader_pb2.InputReader.')
+    if not isinstance(model_config, model_pb2.DetectionModel):
+      raise TypeError('The `model_config` must be a '
+                      'model_pb2.DetectionModel.')
+
+    def transform_and_pad_input_data_fn(tensor_dict):
+      """Combines transform and pad operation."""
+      num_classes = config_util.get_number_of_classes(model_config)
+      model = model_builder.build(model_config, is_training=False)
+      image_resizer_config = config_util.get_image_resizer_config(model_config)
+      image_resizer_fn = image_resizer_builder.build(image_resizer_config)
+
+      transform_data_fn = functools.partial(
+          transform_input_data, model_preprocess_fn=model.preprocess,
+          image_resizer_fn=image_resizer_fn,
+          num_classes=num_classes,
+          data_augmentation_fn=None,
+          retain_original_image=eval_config.retain_original_images)
+      tensor_dict = pad_input_data_to_static_shapes(
+          tensor_dict=transform_data_fn(tensor_dict),
+          max_num_boxes=eval_input_config.max_number_of_boxes,
+          num_classes=config_util.get_number_of_classes(model_config),
+          spatial_image_shape=config_util.get_spatial_image_size(
+              image_resizer_config))
+      return (_get_features_dict(tensor_dict), _get_labels_dict(tensor_dict))
+    dataset = INPUT_BUILDER_UTIL_MAP['dataset_build'](
+        eval_input_config,
+        batch_size=params['batch_size'] if params else eval_config.batch_size,
+        transform_input_data_fn=transform_and_pad_input_data_fn,
+        multi_gpu=False)
+    return dataset
+
+  return _eval_input_fn
+
+
+def create_predict_input_fn(model_config, predict_input_config):
+  """Creates a predict `input` function for `Estimator`.
+
+  Args:
+    model_config: A model_pb2.DetectionModel.
+    predict_input_config: An input_reader_pb2.InputReader.
+
+  Returns:
+    `input_fn` for `Estimator` in PREDICT mode.
+  """
+
+  def _predict_input_fn(params=None):
+    """Decodes serialized tf.Examples and returns `ServingInputReceiver`.
+
+    Args:
+      params: Parameter dictionary passed from the estimator.
+
+    Returns:
+      `ServingInputReceiver`.
+    """
+    del params
+    example = tf.placeholder(dtype=tf.string, shape=[], name='tf_example')
+
+    num_classes = config_util.get_number_of_classes(model_config)
+    model = model_builder.build(model_config, is_training=False)
+    image_resizer_config = config_util.get_image_resizer_config(model_config)
+    image_resizer_fn = image_resizer_builder.build(image_resizer_config)
+
+    transform_fn = functools.partial(
+        transform_input_data, model_preprocess_fn=model.preprocess,
+        image_resizer_fn=image_resizer_fn,
+        num_classes=num_classes,
+        data_augmentation_fn=None)
+
+    decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=False,
+        num_additional_channels=predict_input_config.num_additional_channels)
+    input_dict = transform_fn(decoder.decode(example))
+    images = tf.to_float(input_dict[fields.InputDataFields.image])
+    images = tf.expand_dims(images, axis=0)
+    true_image_shape = tf.expand_dims(
+        input_dict[fields.InputDataFields.true_image_shape], axis=0)
+
+    return tf.estimator.export.ServingInputReceiver(
+        features={
+            fields.InputDataFields.image: images,
+            fields.InputDataFields.true_image_shape: true_image_shape},
+        receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})
+
+  return _predict_input_fn
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/inputs_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/inputs_test.py
new file mode 100644
index 00000000..e0a85301
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/inputs_test.py
@@ -0,0 +1,963 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.tflearn.inputs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import os
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection import inputs
+from object_detection.core import preprocessor
+from object_detection.core import standard_fields as fields
+from object_detection.utils import config_util
+from object_detection.utils import test_case
+
+FLAGS = tf.flags.FLAGS
+
+
+def _get_configs_for_model(model_name):
+  """Returns configurations for model."""
+  fname = os.path.join(tf.resource_loader.get_data_files_path(),
+                       'samples/configs/' + model_name + '.config')
+  label_map_path = os.path.join(tf.resource_loader.get_data_files_path(),
+                                'data/pet_label_map.pbtxt')
+  data_path = os.path.join(tf.resource_loader.get_data_files_path(),
+                           'test_data/pets_examples.record')
+  configs = config_util.get_configs_from_pipeline_file(fname)
+  override_dict = {
+      'train_input_path': data_path,
+      'eval_input_path': data_path,
+      'label_map_path': label_map_path
+  }
+  return config_util.merge_external_params_with_configs(
+      configs, kwargs_dict=override_dict)
+
+
+def _make_initializable_iterator(dataset):
+  """Creates an iterator, and initializes tables.
+
+  Args:
+    dataset: A `tf.data.Dataset` object.
+
+  Returns:
+    A `tf.data.Iterator`.
+  """
+  iterator = dataset.make_initializable_iterator()
+  tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
+  return iterator
+
+
+class InputsTest(test_case.TestCase, parameterized.TestCase):
+
+  def test_faster_rcnn_resnet50_train_input(self):
+    """Tests the training input function for FasterRcnnResnet50."""
+    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
+    model_config = configs['model']
+    model_config.faster_rcnn.num_classes = 37
+    train_input_fn = inputs.create_train_input_fn(
+        configs['train_config'], configs['train_input_config'], model_config)
+    features, labels = _make_initializable_iterator(train_input_fn()).get_next()
+
+    self.assertAllEqual([1, None, None, 3],
+                        features[fields.InputDataFields.image].shape.as_list())
+    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
+    self.assertAllEqual([1],
+                        features[inputs.HASH_KEY].shape.as_list())
+    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
+    self.assertAllEqual(
+        [1, 100, 4],
+        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [1, 100, model_config.faster_rcnn.num_classes],
+        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_classes].dtype)
+    self.assertAllEqual(
+        [1, 100],
+        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_weights].dtype)
+    self.assertAllEqual(
+        [1, 100, model_config.faster_rcnn.num_classes],
+        labels[fields.InputDataFields.groundtruth_confidences].shape.as_list())
+    self.assertEqual(
+        tf.float32,
+        labels[fields.InputDataFields.groundtruth_confidences].dtype)
+
+  @parameterized.parameters(
+      {'eval_batch_size': 1},
+      {'eval_batch_size': 8}
+  )
+  def test_faster_rcnn_resnet50_eval_input(self, eval_batch_size=1):
+    """Tests the eval input function for FasterRcnnResnet50."""
+    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
+    model_config = configs['model']
+    model_config.faster_rcnn.num_classes = 37
+    eval_config = configs['eval_config']
+    eval_config.batch_size = eval_batch_size
+    eval_input_fn = inputs.create_eval_input_fn(
+        eval_config, configs['eval_input_configs'][0], model_config)
+    features, labels = _make_initializable_iterator(eval_input_fn()).get_next()
+    self.assertAllEqual([eval_batch_size, None, None, 3],
+                        features[fields.InputDataFields.image].shape.as_list())
+    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, None, None, 3],
+        features[fields.InputDataFields.original_image].shape.as_list())
+    self.assertEqual(tf.uint8,
+                     features[fields.InputDataFields.original_image].dtype)
+    self.assertAllEqual([eval_batch_size],
+                        features[inputs.HASH_KEY].shape.as_list())
+    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100, 4],
+        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100, model_config.faster_rcnn.num_classes],
+        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_classes].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
+    self.assertEqual(
+        tf.float32,
+        labels[fields.InputDataFields.groundtruth_weights].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_area].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_area].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_is_crowd].shape.as_list())
+    self.assertEqual(
+        tf.bool, labels[fields.InputDataFields.groundtruth_is_crowd].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_difficult].shape.as_list())
+    self.assertEqual(
+        tf.int32, labels[fields.InputDataFields.groundtruth_difficult].dtype)
+
+  def test_ssd_inceptionV2_train_input(self):
+    """Tests the training input function for SSDInceptionV2."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    model_config = configs['model']
+    model_config.ssd.num_classes = 37
+    batch_size = configs['train_config'].batch_size
+    train_input_fn = inputs.create_train_input_fn(
+        configs['train_config'], configs['train_input_config'], model_config)
+    features, labels = _make_initializable_iterator(train_input_fn()).get_next()
+
+    self.assertAllEqual([batch_size, 300, 300, 3],
+                        features[fields.InputDataFields.image].shape.as_list())
+    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
+    self.assertAllEqual([batch_size],
+                        features[inputs.HASH_KEY].shape.as_list())
+    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
+    self.assertAllEqual(
+        [batch_size],
+        labels[fields.InputDataFields.num_groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.int32,
+                     labels[fields.InputDataFields.num_groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [batch_size, 100, 4],
+        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [batch_size, 100, model_config.ssd.num_classes],
+        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_classes].dtype)
+    self.assertAllEqual(
+        [batch_size, 100],
+        labels[
+            fields.InputDataFields.groundtruth_weights].shape.as_list())
+    self.assertEqual(
+        tf.float32,
+        labels[fields.InputDataFields.groundtruth_weights].dtype)
+
+  @parameterized.parameters(
+      {'eval_batch_size': 1},
+      {'eval_batch_size': 8}
+  )
+  def test_ssd_inceptionV2_eval_input(self, eval_batch_size=1):
+    """Tests the eval input function for SSDInceptionV2."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    model_config = configs['model']
+    model_config.ssd.num_classes = 37
+    eval_config = configs['eval_config']
+    eval_config.batch_size = eval_batch_size
+    eval_input_fn = inputs.create_eval_input_fn(
+        eval_config, configs['eval_input_configs'][0], model_config)
+    features, labels = _make_initializable_iterator(eval_input_fn()).get_next()
+    self.assertAllEqual([eval_batch_size, 300, 300, 3],
+                        features[fields.InputDataFields.image].shape.as_list())
+    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 300, 300, 3],
+        features[fields.InputDataFields.original_image].shape.as_list())
+    self.assertEqual(tf.uint8,
+                     features[fields.InputDataFields.original_image].dtype)
+    self.assertAllEqual([eval_batch_size],
+                        features[inputs.HASH_KEY].shape.as_list())
+    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100, 4],
+        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100, model_config.ssd.num_classes],
+        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_classes].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[
+            fields.InputDataFields.groundtruth_weights].shape.as_list())
+    self.assertEqual(
+        tf.float32,
+        labels[fields.InputDataFields.groundtruth_weights].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_area].shape.as_list())
+    self.assertEqual(tf.float32,
+                     labels[fields.InputDataFields.groundtruth_area].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_is_crowd].shape.as_list())
+    self.assertEqual(
+        tf.bool, labels[fields.InputDataFields.groundtruth_is_crowd].dtype)
+    self.assertAllEqual(
+        [eval_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_difficult].shape.as_list())
+    self.assertEqual(
+        tf.int32, labels[fields.InputDataFields.groundtruth_difficult].dtype)
+
+  def test_predict_input(self):
+    """Tests the predict input function."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    predict_input_fn = inputs.create_predict_input_fn(
+        model_config=configs['model'],
+        predict_input_config=configs['eval_input_configs'][0])
+    serving_input_receiver = predict_input_fn()
+
+    image = serving_input_receiver.features[fields.InputDataFields.image]
+    receiver_tensors = serving_input_receiver.receiver_tensors[
+        inputs.SERVING_FED_EXAMPLE_KEY]
+    self.assertEqual([1, 300, 300, 3], image.shape.as_list())
+    self.assertEqual(tf.float32, image.dtype)
+    self.assertEqual(tf.string, receiver_tensors.dtype)
+
+  def test_predict_input_with_additional_channels(self):
+    """Tests the predict input function with additional channels."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['eval_input_configs'][0].num_additional_channels = 2
+    predict_input_fn = inputs.create_predict_input_fn(
+        model_config=configs['model'],
+        predict_input_config=configs['eval_input_configs'][0])
+    serving_input_receiver = predict_input_fn()
+
+    image = serving_input_receiver.features[fields.InputDataFields.image]
+    receiver_tensors = serving_input_receiver.receiver_tensors[
+        inputs.SERVING_FED_EXAMPLE_KEY]
+    # RGB + 2 additional channels = 5 channels.
+    self.assertEqual([1, 300, 300, 5], image.shape.as_list())
+    self.assertEqual(tf.float32, image.dtype)
+    self.assertEqual(tf.string, receiver_tensors.dtype)
+
+  def test_error_with_bad_train_config(self):
+    """Tests that a TypeError is raised with improper train config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    train_input_fn = inputs.create_train_input_fn(
+        train_config=configs['eval_config'],  # Expecting `TrainConfig`.
+        train_input_config=configs['train_input_config'],
+        model_config=configs['model'])
+    with self.assertRaises(TypeError):
+      train_input_fn()
+
+  def test_error_with_bad_train_input_config(self):
+    """Tests that a TypeError is raised with improper train input config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    train_input_fn = inputs.create_train_input_fn(
+        train_config=configs['train_config'],
+        train_input_config=configs['model'],  # Expecting `InputReader`.
+        model_config=configs['model'])
+    with self.assertRaises(TypeError):
+      train_input_fn()
+
+  def test_error_with_bad_train_model_config(self):
+    """Tests that a TypeError is raised with improper train model config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    train_input_fn = inputs.create_train_input_fn(
+        train_config=configs['train_config'],
+        train_input_config=configs['train_input_config'],
+        model_config=configs['train_config'])  # Expecting `DetectionModel`.
+    with self.assertRaises(TypeError):
+      train_input_fn()
+
+  def test_error_with_bad_eval_config(self):
+    """Tests that a TypeError is raised with improper eval config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    eval_input_fn = inputs.create_eval_input_fn(
+        eval_config=configs['train_config'],  # Expecting `EvalConfig`.
+        eval_input_config=configs['eval_input_configs'][0],
+        model_config=configs['model'])
+    with self.assertRaises(TypeError):
+      eval_input_fn()
+
+  def test_error_with_bad_eval_input_config(self):
+    """Tests that a TypeError is raised with improper eval input config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    eval_input_fn = inputs.create_eval_input_fn(
+        eval_config=configs['eval_config'],
+        eval_input_config=configs['model'],  # Expecting `InputReader`.
+        model_config=configs['model'])
+    with self.assertRaises(TypeError):
+      eval_input_fn()
+
+  def test_error_with_bad_eval_model_config(self):
+    """Tests that a TypeError is raised with improper eval model config."""
+    configs = _get_configs_for_model('ssd_inception_v2_pets')
+    configs['model'].ssd.num_classes = 37
+    eval_input_fn = inputs.create_eval_input_fn(
+        eval_config=configs['eval_config'],
+        eval_input_config=configs['eval_input_configs'][0],
+        model_config=configs['eval_config'])  # Expecting `DetectionModel`.
+    with self.assertRaises(TypeError):
+      eval_input_fn()
+
+  def test_output_equal_in_replace_empty_string_with_random_number(self):
+    string_placeholder = tf.placeholder(tf.string, shape=[])
+    replaced_string = inputs._replace_empty_string_with_random_number(
+        string_placeholder)
+
+    test_string = 'hello world'
+    feed_dict = {string_placeholder: test_string}
+
+    with self.test_session() as sess:
+      out_string = sess.run(replaced_string, feed_dict=feed_dict)
+
+    self.assertEqual(test_string, out_string)
+
+  def test_output_is_integer_in_replace_empty_string_with_random_number(self):
+
+    string_placeholder = tf.placeholder(tf.string, shape=[])
+    replaced_string = inputs._replace_empty_string_with_random_number(
+        string_placeholder)
+
+    empty_string = ''
+    feed_dict = {string_placeholder: empty_string}
+
+    tf.set_random_seed(0)
+
+    with self.test_session() as sess:
+      out_string = sess.run(replaced_string, feed_dict=feed_dict)
+
+    # Test whether out_string is a string which represents an integer.
+    int(out_string)  # throws an error if out_string is not castable to int.
+
+    self.assertEqual(out_string, '2798129067578209328')
+
+
+class DataAugmentationFnTest(test_case.TestCase):
+
+  def test_apply_image_and_box_augmentation(self):
+    data_augmentation_options = [
+        (preprocessor.resize_image, {
+            'new_height': 20,
+            'new_width': 20,
+            'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        }),
+        (preprocessor.scale_boxes_to_pixel_coordinates, {}),
+    ]
+    data_augmentation_fn = functools.partial(
+        inputs.augment_input_data,
+        data_augmentation_options=data_augmentation_options)
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[.5, .5, 1., 1.]], np.float32))
+    }
+    augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
+    with self.test_session() as sess:
+      augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
+
+    self.assertAllEqual(
+        augmented_tensor_dict_out[fields.InputDataFields.image].shape,
+        [20, 20, 3]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
+        [[10, 10, 20, 20]]
+    )
+
+  def test_apply_image_and_box_augmentation_with_scores(self):
+    data_augmentation_options = [
+        (preprocessor.resize_image, {
+            'new_height': 20,
+            'new_width': 20,
+            'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        }),
+        (preprocessor.scale_boxes_to_pixel_coordinates, {}),
+    ]
+    data_augmentation_fn = functools.partial(
+        inputs.augment_input_data,
+        data_augmentation_options=data_augmentation_options)
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[.5, .5, 1., 1.]], np.float32)),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([1.0], np.float32)),
+        fields.InputDataFields.groundtruth_weights:
+            tf.constant(np.array([0.8], np.float32)),
+    }
+    augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
+    with self.test_session() as sess:
+      augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
+
+    self.assertAllEqual(
+        augmented_tensor_dict_out[fields.InputDataFields.image].shape,
+        [20, 20, 3]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
+        [[10, 10, 20, 20]]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[fields.InputDataFields.groundtruth_classes],
+        [1.0]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[
+            fields.InputDataFields.groundtruth_weights],
+        [0.8]
+    )
+
+  def test_include_masks_in_data_augmentation(self):
+    data_augmentation_options = [
+        (preprocessor.resize_image, {
+            'new_height': 20,
+            'new_width': 20,
+            'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        })
+    ]
+    data_augmentation_fn = functools.partial(
+        inputs.augment_input_data,
+        data_augmentation_options=data_augmentation_options)
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_instance_masks:
+            tf.constant(np.zeros([2, 10, 10], np.uint8))
+    }
+    augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
+    with self.test_session() as sess:
+      augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
+
+    self.assertAllEqual(
+        augmented_tensor_dict_out[fields.InputDataFields.image].shape,
+        [20, 20, 3])
+    self.assertAllEqual(augmented_tensor_dict_out[
+        fields.InputDataFields.groundtruth_instance_masks].shape, [2, 20, 20])
+
+  def test_include_keypoints_in_data_augmentation(self):
+    data_augmentation_options = [
+        (preprocessor.resize_image, {
+            'new_height': 20,
+            'new_width': 20,
+            'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        }),
+        (preprocessor.scale_boxes_to_pixel_coordinates, {}),
+    ]
+    data_augmentation_fn = functools.partial(
+        inputs.augment_input_data,
+        data_augmentation_options=data_augmentation_options)
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[.5, .5, 1., 1.]], np.float32)),
+        fields.InputDataFields.groundtruth_keypoints:
+            tf.constant(np.array([[[0.5, 1.0], [0.5, 0.5]]], np.float32))
+    }
+    augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
+    with self.test_session() as sess:
+      augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
+
+    self.assertAllEqual(
+        augmented_tensor_dict_out[fields.InputDataFields.image].shape,
+        [20, 20, 3]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
+        [[10, 10, 20, 20]]
+    )
+    self.assertAllClose(
+        augmented_tensor_dict_out[fields.InputDataFields.groundtruth_keypoints],
+        [[[10, 20], [10, 10]]]
+    )
+
+
+def _fake_model_preprocessor_fn(image):
+  return (image, tf.expand_dims(tf.shape(image)[1:], axis=0))
+
+
+def _fake_image_resizer_fn(image, mask):
+  return (image, mask, tf.shape(image))
+
+
+class DataTransformationFnTest(test_case.TestCase):
+
+  def test_combine_additional_channels_if_present(self):
+    image = np.random.rand(4, 4, 3).astype(np.float32)
+    additional_channels = np.random.rand(4, 4, 2).astype(np.float32)
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(image),
+        fields.InputDataFields.image_additional_channels:
+            tf.constant(additional_channels),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([1, 1], np.int32))
+    }
+
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=1)
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+    self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].dtype,
+                        tf.float32)
+    self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].shape,
+                        [4, 4, 5])
+    self.assertAllClose(transformed_inputs[fields.InputDataFields.image],
+                        np.concatenate((image, additional_channels), axis=2))
+
+  def test_returns_correct_class_label_encodings(self):
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[0, 0, 1, 1], [.5, .5, 1, 1]], np.float32)),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32))
+    }
+    num_classes = 3
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes)
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_classes],
+        [[0, 0, 1], [1, 0, 0]])
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_confidences],
+        [[0, 0, 1], [1, 0, 0]])
+
+  def test_returns_correct_merged_boxes(self):
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[.5, .5, 1, 1], [.5, .5, 1, 1]], np.float32)),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32))
+    }
+
+    num_classes = 3
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes,
+        merge_multiple_boxes=True)
+
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_boxes],
+        [[.5, .5, 1., 1.]])
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_classes],
+        [[1, 0, 1]])
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_confidences],
+        [[1, 0, 1]])
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.num_groundtruth_boxes],
+        1)
+
+  def test_returns_correct_groundtruth_confidences_when_input_present(self):
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.constant(np.array([[0, 0, 1, 1], [.5, .5, 1, 1]], np.float32)),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32)),
+        fields.InputDataFields.groundtruth_confidences:
+            tf.constant(np.array([1.0, -1.0], np.float32))
+    }
+    num_classes = 3
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes)
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_classes],
+        [[0, 0, 1], [1, 0, 0]])
+    self.assertAllClose(
+        transformed_inputs[fields.InputDataFields.groundtruth_confidences],
+        [[0, 0, 1], [-1, 0, 0]])
+
+  def test_returns_resized_masks(self):
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
+        fields.InputDataFields.groundtruth_instance_masks:
+            tf.constant(np.random.rand(2, 4, 4).astype(np.float32)),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32)),
+        fields.InputDataFields.original_image_spatial_shape:
+            tf.constant(np.array([4, 4], np.int32))
+    }
+
+    def fake_image_resizer_fn(image, masks=None):
+      resized_image = tf.image.resize_images(image, [8, 8])
+      results = [resized_image]
+      if masks is not None:
+        resized_masks = tf.transpose(
+            tf.image.resize_images(tf.transpose(masks, [1, 2, 0]), [8, 8]),
+            [2, 0, 1])
+        results.append(resized_masks)
+      results.append(tf.shape(resized_image))
+      return results
+
+    num_classes = 3
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=fake_image_resizer_fn,
+        num_classes=num_classes,
+        retain_original_image=True)
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+    self.assertAllEqual(transformed_inputs[
+        fields.InputDataFields.original_image].dtype, tf.uint8)
+    self.assertAllEqual(transformed_inputs[
+        fields.InputDataFields.original_image_spatial_shape], [4, 4])
+    self.assertAllEqual(transformed_inputs[
+        fields.InputDataFields.original_image].shape, [8, 8, 3])
+    self.assertAllEqual(transformed_inputs[
+        fields.InputDataFields.groundtruth_instance_masks].shape, [2, 8, 8])
+
+  def test_applies_model_preprocess_fn_to_image_tensor(self):
+    np_image = np.random.randint(256, size=(4, 4, 3))
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np_image),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32))
+    }
+
+    def fake_model_preprocessor_fn(image):
+      return (image / 255., tf.expand_dims(tf.shape(image)[1:], axis=0))
+
+    num_classes = 3
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes)
+
+    with self.test_session() as sess:
+      transformed_inputs = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+    self.assertAllClose(transformed_inputs[fields.InputDataFields.image],
+                        np_image / 255.)
+    self.assertAllClose(transformed_inputs[fields.InputDataFields.
+                                           true_image_shape],
+                        [4, 4, 3])
+
+  def test_applies_data_augmentation_fn_to_tensor_dict(self):
+    np_image = np.random.randint(256, size=(4, 4, 3))
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np_image),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32))
+    }
+
+    def add_one_data_augmentation_fn(tensor_dict):
+      return {key: value + 1 for key, value in tensor_dict.items()}
+
+    num_classes = 4
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=_fake_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes,
+        data_augmentation_fn=add_one_data_augmentation_fn)
+    with self.test_session() as sess:
+      augmented_tensor_dict = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+
+    self.assertAllEqual(augmented_tensor_dict[fields.InputDataFields.image],
+                        np_image + 1)
+    self.assertAllEqual(
+        augmented_tensor_dict[fields.InputDataFields.groundtruth_classes],
+        [[0, 0, 0, 1], [0, 1, 0, 0]])
+
+  def test_applies_data_augmentation_fn_before_model_preprocess_fn(self):
+    np_image = np.random.randint(256, size=(4, 4, 3))
+    tensor_dict = {
+        fields.InputDataFields.image:
+            tf.constant(np_image),
+        fields.InputDataFields.groundtruth_classes:
+            tf.constant(np.array([3, 1], np.int32))
+    }
+
+    def mul_two_model_preprocessor_fn(image):
+      return (image * 2, tf.expand_dims(tf.shape(image)[1:], axis=0))
+
+    def add_five_to_image_data_augmentation_fn(tensor_dict):
+      tensor_dict[fields.InputDataFields.image] += 5
+      return tensor_dict
+
+    num_classes = 4
+    input_transformation_fn = functools.partial(
+        inputs.transform_input_data,
+        model_preprocess_fn=mul_two_model_preprocessor_fn,
+        image_resizer_fn=_fake_image_resizer_fn,
+        num_classes=num_classes,
+        data_augmentation_fn=add_five_to_image_data_augmentation_fn)
+    with self.test_session() as sess:
+      augmented_tensor_dict = sess.run(
+          input_transformation_fn(tensor_dict=tensor_dict))
+
+    self.assertAllEqual(augmented_tensor_dict[fields.InputDataFields.image],
+                        (np_image + 5) * 2)
+
+
+class PadInputDataToStaticShapesFnTest(test_case.TestCase):
+
+  def test_pad_images_boxes_and_classes(self):
+    input_tensor_dict = {
+        fields.InputDataFields.image:
+            tf.placeholder(tf.float32, [None, None, 3]),
+        fields.InputDataFields.groundtruth_boxes:
+            tf.placeholder(tf.float32, [None, 4]),
+        fields.InputDataFields.groundtruth_classes:
+            tf.placeholder(tf.int32, [None, 3]),
+        fields.InputDataFields.true_image_shape:
+            tf.placeholder(tf.int32, [3]),
+        fields.InputDataFields.original_image_spatial_shape:
+            tf.placeholder(tf.int32, [2])
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
+        [5, 6, 3])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.true_image_shape]
+        .shape.as_list(), [3])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.original_image_spatial_shape]
+        .shape.as_list(), [2])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.groundtruth_boxes]
+        .shape.as_list(), [3, 4])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.groundtruth_classes]
+        .shape.as_list(), [3, 3])
+
+  def test_clip_boxes_and_classes(self):
+    input_tensor_dict = {
+        fields.InputDataFields.groundtruth_boxes:
+            tf.placeholder(tf.float32, [None, 4]),
+        fields.InputDataFields.groundtruth_classes:
+            tf.placeholder(tf.int32, [None, 3]),
+        fields.InputDataFields.num_groundtruth_boxes:
+            tf.placeholder(tf.int32, [])
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.groundtruth_boxes]
+        .shape.as_list(), [3, 4])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.groundtruth_classes]
+        .shape.as_list(), [3, 3])
+
+    with self.test_session() as sess:
+      out_tensor_dict = sess.run(
+          padded_tensor_dict,
+          feed_dict={
+              input_tensor_dict[fields.InputDataFields.groundtruth_boxes]:
+                  np.random.rand(5, 4),
+              input_tensor_dict[fields.InputDataFields.groundtruth_classes]:
+                  np.random.rand(2, 3),
+              input_tensor_dict[fields.InputDataFields.num_groundtruth_boxes]:
+                  5,
+          })
+
+    self.assertAllEqual(
+        out_tensor_dict[fields.InputDataFields.groundtruth_boxes].shape, [3, 4])
+    self.assertAllEqual(
+        out_tensor_dict[fields.InputDataFields.groundtruth_classes].shape,
+        [3, 3])
+    self.assertEqual(
+        out_tensor_dict[fields.InputDataFields.num_groundtruth_boxes],
+        3)
+
+  def test_do_not_pad_dynamic_images(self):
+    input_tensor_dict = {
+        fields.InputDataFields.image:
+            tf.placeholder(tf.float32, [None, None, 3]),
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[None, None])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
+        [None, None, 3])
+
+  def test_images_and_additional_channels(self):
+    input_tensor_dict = {
+        fields.InputDataFields.image:
+            tf.placeholder(tf.float32, [None, None, 3]),
+        fields.InputDataFields.image_additional_channels:
+            tf.placeholder(tf.float32, [None, None, 2]),
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
+        [5, 6, 5])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image_additional_channels]
+        .shape.as_list(), [5, 6, 2])
+
+  def test_gray_images(self):
+    input_tensor_dict = {
+        fields.InputDataFields.image:
+            tf.placeholder(tf.float32, [None, None, 1]),
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
+        [5, 6, 1])
+
+  def test_gray_images_and_additional_channels(self):
+    input_tensor_dict = {
+        fields.InputDataFields.image:
+            tf.placeholder(tf.float32, [None, None, 1]),
+        fields.InputDataFields.image_additional_channels:
+            tf.placeholder(tf.float32, [None, None, 2]),
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
+        [5, 6, 3])
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.image_additional_channels]
+        .shape.as_list(), [5, 6, 2])
+
+  def test_keypoints(self):
+    input_tensor_dict = {
+        fields.InputDataFields.groundtruth_keypoints:
+            tf.placeholder(tf.float32, [None, 16, 4]),
+        fields.InputDataFields.groundtruth_keypoint_visibilities:
+            tf.placeholder(tf.bool, [None, 16]),
+    }
+    padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
+        tensor_dict=input_tensor_dict,
+        max_num_boxes=3,
+        num_classes=3,
+        spatial_image_shape=[5, 6])
+
+    self.assertAllEqual(
+        padded_tensor_dict[fields.InputDataFields.groundtruth_keypoints]
+        .shape.as_list(), [3, 16, 4])
+    self.assertAllEqual(
+        padded_tensor_dict[
+            fields.InputDataFields.groundtruth_keypoint_visibilities]
+        .shape.as_list(), [3, 16])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/eval.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/eval.py
new file mode 100644
index 00000000..73ab06f6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/eval.py
@@ -0,0 +1,142 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Evaluation executable for detection models.
+
+This executable is used to evaluate DetectionModels. There are two ways of
+configuring the eval job.
+
+1) A single pipeline_pb2.TrainEvalPipelineConfig file maybe specified instead.
+In this mode, the --eval_training_data flag may be given to force the pipeline
+to evaluate on training data instead.
+
+Example usage:
+    ./eval \
+        --logtostderr \
+        --checkpoint_dir=path/to/checkpoint_dir \
+        --eval_dir=path/to/eval_dir \
+        --pipeline_config_path=pipeline_config.pbtxt
+
+2) Three configuration files may be provided: a model_pb2.DetectionModel
+configuration file to define what type of DetectionModel is being evaluated, an
+input_reader_pb2.InputReader file to specify what data the model is evaluating
+and an eval_pb2.EvalConfig file to configure evaluation parameters.
+
+Example usage:
+    ./eval \
+        --logtostderr \
+        --checkpoint_dir=path/to/checkpoint_dir \
+        --eval_dir=path/to/eval_dir \
+        --eval_config_path=eval_config.pbtxt \
+        --model_config_path=model_config.pbtxt \
+        --input_config_path=eval_input_config.pbtxt
+"""
+import functools
+import os
+import tensorflow as tf
+
+from object_detection.builders import dataset_builder
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.legacy import evaluator
+from object_detection.utils import config_util
+from object_detection.utils import label_map_util
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+flags = tf.app.flags
+flags.DEFINE_boolean('eval_training_data', False,
+                     'If training data should be evaluated for this job.')
+flags.DEFINE_string(
+    'checkpoint_dir', '',
+    'Directory containing checkpoints to evaluate, typically '
+    'set to `train_dir` used in the training job.')
+flags.DEFINE_string('eval_dir', '', 'Directory to write eval summaries to.')
+flags.DEFINE_string(
+    'pipeline_config_path', '',
+    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
+    'file. If provided, other configs are ignored')
+flags.DEFINE_string('eval_config_path', '',
+                    'Path to an eval_pb2.EvalConfig config file.')
+flags.DEFINE_string('input_config_path', '',
+                    'Path to an input_reader_pb2.InputReader config file.')
+flags.DEFINE_string('model_config_path', '',
+                    'Path to a model_pb2.DetectionModel config file.')
+flags.DEFINE_boolean(
+    'run_once', False, 'Option to only run a single pass of '
+    'evaluation. Overrides the `max_evals` parameter in the '
+    'provided config.')
+FLAGS = flags.FLAGS
+
+
+@tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
+def main(unused_argv):
+  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
+  assert FLAGS.eval_dir, '`eval_dir` is missing.'
+  tf.gfile.MakeDirs(FLAGS.eval_dir)
+  if FLAGS.pipeline_config_path:
+    configs = config_util.get_configs_from_pipeline_file(
+        FLAGS.pipeline_config_path)
+    tf.gfile.Copy(
+        FLAGS.pipeline_config_path,
+        os.path.join(FLAGS.eval_dir, 'pipeline.config'),
+        overwrite=True)
+  else:
+    configs = config_util.get_configs_from_multiple_files(
+        model_config_path=FLAGS.model_config_path,
+        eval_config_path=FLAGS.eval_config_path,
+        eval_input_config_path=FLAGS.input_config_path)
+    for name, config in [('model.config', FLAGS.model_config_path),
+                         ('eval.config', FLAGS.eval_config_path),
+                         ('input.config', FLAGS.input_config_path)]:
+      tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True)
+
+  model_config = configs['model']
+  eval_config = configs['eval_config']
+  input_config = configs['eval_input_config']
+  if FLAGS.eval_training_data:
+    input_config = configs['train_input_config']
+
+  model_fn = functools.partial(
+      model_builder.build, model_config=model_config, is_training=False)
+
+  def get_next(config):
+    return dataset_builder.make_initializable_iterator(
+        dataset_builder.build(config)).get_next()
+
+  create_input_dict_fn = functools.partial(get_next, input_config)
+
+  categories = label_map_util.create_categories_from_labelmap(
+      input_config.label_map_path)
+
+  if FLAGS.run_once:
+    eval_config.max_evals = 1
+
+  graph_rewriter_fn = None
+  if 'graph_rewriter_config' in configs:
+    graph_rewriter_fn = graph_rewriter_builder.build(
+        configs['graph_rewriter_config'], is_training=False)
+
+  evaluator.evaluate(
+      create_input_dict_fn,
+      model_fn,
+      eval_config,
+      categories,
+      FLAGS.checkpoint_dir,
+      FLAGS.eval_dir,
+      graph_hook_fn=graph_rewriter_fn)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/evaluator.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/evaluator.py
new file mode 100644
index 00000000..10c73a32
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/evaluator.py
@@ -0,0 +1,295 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Detection model evaluator.
+
+This file provides a generic evaluation method that can be used to evaluate a
+DetectionModel.
+"""
+
+import logging
+import tensorflow as tf
+
+from object_detection import eval_util
+from object_detection.core import prefetcher
+from object_detection.core import standard_fields as fields
+from object_detection.metrics import coco_evaluation
+from object_detection.utils import object_detection_evaluation
+
+# A dictionary of metric names to classes that implement the metric. The classes
+# in the dictionary must implement
+# utils.object_detection_evaluation.DetectionEvaluator interface.
+EVAL_METRICS_CLASS_DICT = {
+    'pascal_voc_detection_metrics':
+        object_detection_evaluation.PascalDetectionEvaluator,
+    'weighted_pascal_voc_detection_metrics':
+        object_detection_evaluation.WeightedPascalDetectionEvaluator,
+    'pascal_voc_instance_segmentation_metrics':
+        object_detection_evaluation.PascalInstanceSegmentationEvaluator,
+    'weighted_pascal_voc_instance_segmentation_metrics':
+        object_detection_evaluation.WeightedPascalInstanceSegmentationEvaluator,
+    'oid_V2_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionEvaluator,
+    # DEPRECATED: please use oid_V2_detection_metrics instead
+    'open_images_V2_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionEvaluator,
+    'coco_detection_metrics':
+        coco_evaluation.CocoDetectionEvaluator,
+    'coco_mask_metrics':
+        coco_evaluation.CocoMaskEvaluator,
+    'oid_challenge_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionChallengeEvaluator,
+    # DEPRECATED: please use oid_challenge_detection_metrics instead
+    'oid_challenge_object_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionChallengeEvaluator,
+}
+
+EVAL_DEFAULT_METRIC = 'pascal_voc_detection_metrics'
+
+
+def _extract_predictions_and_losses(model,
+                                    create_input_dict_fn,
+                                    ignore_groundtruth=False):
+  """Constructs tensorflow detection graph and returns output tensors.
+
+  Args:
+    model: model to perform predictions with.
+    create_input_dict_fn: function to create input tensor dictionaries.
+    ignore_groundtruth: whether groundtruth should be ignored.
+
+  Returns:
+    prediction_groundtruth_dict: A dictionary with postprocessed tensors (keyed
+      by standard_fields.DetectionResultsFields) and optional groundtruth
+      tensors (keyed by standard_fields.InputDataFields).
+    losses_dict: A dictionary containing detection losses. This is empty when
+      ignore_groundtruth is true.
+  """
+  input_dict = create_input_dict_fn()
+  prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
+  input_dict = prefetch_queue.dequeue()
+  original_image = tf.expand_dims(input_dict[fields.InputDataFields.image], 0)
+  preprocessed_image, true_image_shapes = model.preprocess(
+      tf.to_float(original_image))
+  prediction_dict = model.predict(preprocessed_image, true_image_shapes)
+  detections = model.postprocess(prediction_dict, true_image_shapes)
+
+  groundtruth = None
+  losses_dict = {}
+  if not ignore_groundtruth:
+    groundtruth = {
+        fields.InputDataFields.groundtruth_boxes:
+            input_dict[fields.InputDataFields.groundtruth_boxes],
+        fields.InputDataFields.groundtruth_classes:
+            input_dict[fields.InputDataFields.groundtruth_classes],
+        fields.InputDataFields.groundtruth_area:
+            input_dict[fields.InputDataFields.groundtruth_area],
+        fields.InputDataFields.groundtruth_is_crowd:
+            input_dict[fields.InputDataFields.groundtruth_is_crowd],
+        fields.InputDataFields.groundtruth_difficult:
+            input_dict[fields.InputDataFields.groundtruth_difficult]
+    }
+    if fields.InputDataFields.groundtruth_group_of in input_dict:
+      groundtruth[fields.InputDataFields.groundtruth_group_of] = (
+          input_dict[fields.InputDataFields.groundtruth_group_of])
+    groundtruth_masks_list = None
+    if fields.DetectionResultFields.detection_masks in detections:
+      groundtruth[fields.InputDataFields.groundtruth_instance_masks] = (
+          input_dict[fields.InputDataFields.groundtruth_instance_masks])
+      groundtruth_masks_list = [
+          input_dict[fields.InputDataFields.groundtruth_instance_masks]]
+    groundtruth_keypoints_list = None
+    if fields.DetectionResultFields.detection_keypoints in detections:
+      groundtruth[fields.InputDataFields.groundtruth_keypoints] = (
+          input_dict[fields.InputDataFields.groundtruth_keypoints])
+      groundtruth_keypoints_list = [
+          input_dict[fields.InputDataFields.groundtruth_keypoints]]
+    label_id_offset = 1
+    model.provide_groundtruth(
+        [input_dict[fields.InputDataFields.groundtruth_boxes]],
+        [tf.one_hot(input_dict[fields.InputDataFields.groundtruth_classes]
+                    - label_id_offset, depth=model.num_classes)],
+        groundtruth_masks_list, groundtruth_keypoints_list)
+    losses_dict.update(model.loss(prediction_dict, true_image_shapes))
+
+  result_dict = eval_util.result_dict_for_single_example(
+      original_image,
+      input_dict[fields.InputDataFields.source_id],
+      detections,
+      groundtruth,
+      class_agnostic=(
+          fields.DetectionResultFields.detection_classes not in detections),
+      scale_to_absolute=True)
+  return result_dict, losses_dict
+
+
+def get_evaluators(eval_config, categories):
+  """Returns the evaluator class according to eval_config, valid for categories.
+
+  Args:
+    eval_config: evaluation configurations.
+    categories: a list of categories to evaluate.
+  Returns:
+    An list of instances of DetectionEvaluator.
+
+  Raises:
+    ValueError: if metric is not in the metric class dictionary.
+  """
+  eval_metric_fn_keys = eval_config.metrics_set
+  if not eval_metric_fn_keys:
+    eval_metric_fn_keys = [EVAL_DEFAULT_METRIC]
+  evaluators_list = []
+  for eval_metric_fn_key in eval_metric_fn_keys:
+    if eval_metric_fn_key not in EVAL_METRICS_CLASS_DICT:
+      raise ValueError('Metric not found: {}'.format(eval_metric_fn_key))
+    if eval_metric_fn_key == 'oid_challenge_object_detection_metrics':
+      logging.warning(
+          'oid_challenge_object_detection_metrics is deprecated; '
+          'use oid_challenge_detection_metrics instead'
+      )
+    if eval_metric_fn_key == 'oid_V2_detection_metrics':
+      logging.warning(
+          'open_images_V2_detection_metrics is deprecated; '
+          'use oid_V2_detection_metrics instead'
+      )
+    evaluators_list.append(
+        EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](categories=categories))
+  return evaluators_list
+
+
+def evaluate(create_input_dict_fn, create_model_fn, eval_config, categories,
+             checkpoint_dir, eval_dir, graph_hook_fn=None, evaluator_list=None):
+  """Evaluation function for detection models.
+
+  Args:
+    create_input_dict_fn: a function to create a tensor input dictionary.
+    create_model_fn: a function that creates a DetectionModel.
+    eval_config: a eval_pb2.EvalConfig protobuf.
+    categories: a list of category dictionaries. Each dict in the list should
+                have an integer 'id' field and string 'name' field.
+    checkpoint_dir: directory to load the checkpoints to evaluate from.
+    eval_dir: directory to write evaluation metrics summary to.
+    graph_hook_fn: Optional function that is called after the training graph is
+      completely built. This is helpful to perform additional changes to the
+      training graph such as optimizing batchnorm. The function should modify
+      the default graph.
+    evaluator_list: Optional list of instances of DetectionEvaluator. If not
+      given, this list of metrics is created according to the eval_config.
+
+  Returns:
+    metrics: A dictionary containing metric names and values from the latest
+      run.
+  """
+
+  model = create_model_fn()
+
+  if eval_config.ignore_groundtruth and not eval_config.export_path:
+    logging.fatal('If ignore_groundtruth=True then an export_path is '
+                  'required. Aborting!!!')
+
+  tensor_dict, losses_dict = _extract_predictions_and_losses(
+      model=model,
+      create_input_dict_fn=create_input_dict_fn,
+      ignore_groundtruth=eval_config.ignore_groundtruth)
+
+  def _process_batch(tensor_dict, sess, batch_index, counters,
+                     losses_dict=None):
+    """Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
+
+    This function calls sess.run on tensor_dict, evaluating the original_image
+    tensor only on the first K examples and visualizing detections overlaid
+    on this original_image.
+
+    Args:
+      tensor_dict: a dictionary of tensors
+      sess: tensorflow session
+      batch_index: the index of the batch amongst all batches in the run.
+      counters: a dictionary holding 'success' and 'skipped' fields which can
+        be updated to keep track of number of successful and failed runs,
+        respectively.  If these fields are not updated, then the success/skipped
+        counter values shown at the end of evaluation will be incorrect.
+      losses_dict: Optional dictonary of scalar loss tensors.
+
+    Returns:
+      result_dict: a dictionary of numpy arrays
+      result_losses_dict: a dictionary of scalar losses. This is empty if input
+        losses_dict is None.
+    """
+    try:
+      if not losses_dict:
+        losses_dict = {}
+      result_dict, result_losses_dict = sess.run([tensor_dict, losses_dict])
+      counters['success'] += 1
+    except tf.errors.InvalidArgumentError:
+      logging.info('Skipping image')
+      counters['skipped'] += 1
+      return {}, {}
+    global_step = tf.train.global_step(sess, tf.train.get_global_step())
+    if batch_index < eval_config.num_visualizations:
+      tag = 'image-{}'.format(batch_index)
+      eval_util.visualize_detection_results(
+          result_dict,
+          tag,
+          global_step,
+          categories=categories,
+          summary_dir=eval_dir,
+          export_dir=eval_config.visualization_export_dir,
+          show_groundtruth=eval_config.visualize_groundtruth_boxes,
+          groundtruth_box_visualization_color=eval_config.
+          groundtruth_box_visualization_color,
+          min_score_thresh=eval_config.min_score_threshold,
+          max_num_predictions=eval_config.max_num_boxes_to_visualize,
+          skip_scores=eval_config.skip_scores,
+          skip_labels=eval_config.skip_labels,
+          keep_image_id_for_visualization_export=eval_config.
+          keep_image_id_for_visualization_export)
+    return result_dict, result_losses_dict
+
+  if graph_hook_fn: graph_hook_fn()
+
+  variables_to_restore = tf.global_variables()
+  global_step = tf.train.get_or_create_global_step()
+  variables_to_restore.append(global_step)
+
+  if eval_config.use_moving_averages:
+    variable_averages = tf.train.ExponentialMovingAverage(0.0)
+    variables_to_restore = variable_averages.variables_to_restore()
+  saver = tf.train.Saver(variables_to_restore)
+
+  def _restore_latest_checkpoint(sess):
+    latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
+    saver.restore(sess, latest_checkpoint)
+
+  if not evaluator_list:
+    evaluator_list = get_evaluators(eval_config, categories)
+
+  metrics = eval_util.repeated_checkpoint_run(
+      tensor_dict=tensor_dict,
+      summary_dir=eval_dir,
+      evaluators=evaluator_list,
+      batch_processor=_process_batch,
+      checkpoint_dirs=[checkpoint_dir],
+      variables_to_restore=None,
+      restore_fn=_restore_latest_checkpoint,
+      num_batches=eval_config.num_examples,
+      eval_interval_secs=eval_config.eval_interval_secs,
+      max_number_of_evaluations=(1 if eval_config.ignore_groundtruth else
+                                 eval_config.max_evals
+                                 if eval_config.max_evals else None),
+      master=eval_config.eval_master,
+      save_graph=eval_config.save_graph,
+      save_graph_dir=(eval_dir if eval_config.save_graph else ''),
+      losses_dict=losses_dict,
+      eval_export_path=eval_config.export_path)
+
+  return metrics
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/train.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/train.py
new file mode 100644
index 00000000..33b9a9cf
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/train.py
@@ -0,0 +1,184 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Training executable for detection models.
+
+This executable is used to train DetectionModels. There are two ways of
+configuring the training job:
+
+1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file
+can be specified by --pipeline_config_path.
+
+Example usage:
+    ./train \
+        --logtostderr \
+        --train_dir=path/to/train_dir \
+        --pipeline_config_path=pipeline_config.pbtxt
+
+2) Three configuration files can be provided: a model_pb2.DetectionModel
+configuration file to define what type of DetectionModel is being trained, an
+input_reader_pb2.InputReader file to specify what training data will be used and
+a train_pb2.TrainConfig file to configure training parameters.
+
+Example usage:
+    ./train \
+        --logtostderr \
+        --train_dir=path/to/train_dir \
+        --model_config_path=model_config.pbtxt \
+        --train_config_path=train_config.pbtxt \
+        --input_config_path=train_input_config.pbtxt
+"""
+
+import functools
+import json
+import os
+import tensorflow as tf
+
+from object_detection.builders import dataset_builder
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.legacy import trainer
+from object_detection.utils import config_util
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+flags = tf.app.flags
+flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
+flags.DEFINE_integer('task', 0, 'task id')
+flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')
+flags.DEFINE_boolean('clone_on_cpu', False,
+                     'Force clones to be deployed on CPU.  Note that even if '
+                     'set to False (allowing ops to run on gpu), some ops may '
+                     'still be run on the CPU if they have no GPU kernel.')
+flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '
+                     'replicas.')
+flags.DEFINE_integer('ps_tasks', 0,
+                     'Number of parameter server tasks. If None, does not use '
+                     'a parameter server.')
+flags.DEFINE_string('train_dir', '',
+                    'Directory to save the checkpoints and training summaries.')
+
+flags.DEFINE_string('pipeline_config_path', '',
+                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
+                    'file. If provided, other configs are ignored')
+
+flags.DEFINE_string('train_config_path', '',
+                    'Path to a train_pb2.TrainConfig config file.')
+flags.DEFINE_string('input_config_path', '',
+                    'Path to an input_reader_pb2.InputReader config file.')
+flags.DEFINE_string('model_config_path', '',
+                    'Path to a model_pb2.DetectionModel config file.')
+
+FLAGS = flags.FLAGS
+
+
+@tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
+def main(_):
+  assert FLAGS.train_dir, '`train_dir` is missing.'
+  if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
+  if FLAGS.pipeline_config_path:
+    configs = config_util.get_configs_from_pipeline_file(
+        FLAGS.pipeline_config_path)
+    if FLAGS.task == 0:
+      tf.gfile.Copy(FLAGS.pipeline_config_path,
+                    os.path.join(FLAGS.train_dir, 'pipeline.config'),
+                    overwrite=True)
+  else:
+    configs = config_util.get_configs_from_multiple_files(
+        model_config_path=FLAGS.model_config_path,
+        train_config_path=FLAGS.train_config_path,
+        train_input_config_path=FLAGS.input_config_path)
+    if FLAGS.task == 0:
+      for name, config in [('model.config', FLAGS.model_config_path),
+                           ('train.config', FLAGS.train_config_path),
+                           ('input.config', FLAGS.input_config_path)]:
+        tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
+                      overwrite=True)
+
+  model_config = configs['model']
+  train_config = configs['train_config']
+  input_config = configs['train_input_config']
+
+  model_fn = functools.partial(
+      model_builder.build,
+      model_config=model_config,
+      is_training=True)
+
+  def get_next(config):
+    return dataset_builder.make_initializable_iterator(
+        dataset_builder.build(config)).get_next()
+
+  create_input_dict_fn = functools.partial(get_next, input_config)
+
+  env = json.loads(os.environ.get('TF_CONFIG', '{}'))
+  cluster_data = env.get('cluster', None)
+  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None
+  task_data = env.get('task', None) or {'type': 'master', 'index': 0}
+  task_info = type('TaskSpec', (object,), task_data)
+
+  # Parameters for a single worker.
+  ps_tasks = 0
+  worker_replicas = 1
+  worker_job_name = 'lonely_worker'
+  task = 0
+  is_chief = True
+  master = ''
+
+  if cluster_data and 'worker' in cluster_data:
+    # Number of total worker replicas include "worker"s and the "master".
+    worker_replicas = len(cluster_data['worker']) + 1
+  if cluster_data and 'ps' in cluster_data:
+    ps_tasks = len(cluster_data['ps'])
+
+  if worker_replicas > 1 and ps_tasks < 1:
+    raise ValueError('At least 1 ps task is needed for distributed training.')
+
+  if worker_replicas >= 1 and ps_tasks > 0:
+    # Set up distributed training.
+    server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',
+                             job_name=task_info.type,
+                             task_index=task_info.index)
+    if task_info.type == 'ps':
+      server.join()
+      return
+
+    worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)
+    task = task_info.index
+    is_chief = (task_info.type == 'master')
+    master = server.target
+
+  graph_rewriter_fn = None
+  if 'graph_rewriter_config' in configs:
+    graph_rewriter_fn = graph_rewriter_builder.build(
+        configs['graph_rewriter_config'], is_training=True)
+
+  trainer.train(
+      create_input_dict_fn,
+      model_fn,
+      train_config,
+      master,
+      task,
+      FLAGS.num_clones,
+      worker_replicas,
+      FLAGS.clone_on_cpu,
+      ps_tasks,
+      worker_job_name,
+      is_chief,
+      FLAGS.train_dir,
+      graph_hook_fn=graph_rewriter_fn)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer.py
new file mode 100644
index 00000000..a4b46859
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer.py
@@ -0,0 +1,416 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Detection model trainer.
+
+This file provides a generic training method that can be used to train a
+DetectionModel.
+"""
+
+import functools
+
+import tensorflow as tf
+
+from object_detection.builders import optimizer_builder
+from object_detection.builders import preprocessor_builder
+from object_detection.core import batcher
+from object_detection.core import preprocessor
+from object_detection.core import standard_fields as fields
+from object_detection.utils import ops as util_ops
+from object_detection.utils import variables_helper
+from deployment import model_deploy
+
+slim = tf.contrib.slim
+
+
+def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
+                       batch_queue_capacity, num_batch_queue_threads,
+                       prefetch_queue_capacity, data_augmentation_options):
+  """Sets up reader, prefetcher and returns input queue.
+
+  Args:
+    batch_size_per_clone: batch size to use per clone.
+    create_tensor_dict_fn: function to create tensor dictionary.
+    batch_queue_capacity: maximum number of elements to store within a queue.
+    num_batch_queue_threads: number of threads to use for batching.
+    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
+                             assembled batches.
+    data_augmentation_options: a list of tuples, where each tuple contains a
+      data augmentation function and a dictionary containing arguments and their
+      values (see preprocessor.py).
+
+  Returns:
+    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
+      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
+      call input_queue.Dequeue().
+  """
+  tensor_dict = create_tensor_dict_fn()
+
+  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
+      tensor_dict[fields.InputDataFields.image], 0)
+
+  images = tensor_dict[fields.InputDataFields.image]
+  float_images = tf.to_float(images)
+  tensor_dict[fields.InputDataFields.image] = float_images
+
+  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
+                            in tensor_dict)
+  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
+                       in tensor_dict)
+  include_multiclass_scores = (fields.InputDataFields.multiclass_scores
+                               in tensor_dict)
+  if data_augmentation_options:
+    tensor_dict = preprocessor.preprocess(
+        tensor_dict, data_augmentation_options,
+        func_arg_map=preprocessor.get_default_func_arg_map(
+            include_label_weights=True,
+            include_multiclass_scores=include_multiclass_scores,
+            include_instance_masks=include_instance_masks,
+            include_keypoints=include_keypoints))
+
+  input_queue = batcher.BatchQueue(
+      tensor_dict,
+      batch_size=batch_size_per_clone,
+      batch_queue_capacity=batch_queue_capacity,
+      num_batch_queue_threads=num_batch_queue_threads,
+      prefetch_queue_capacity=prefetch_queue_capacity)
+  return input_queue
+
+
+def get_inputs(input_queue,
+               num_classes,
+               merge_multiple_label_boxes=False,
+               use_multiclass_scores=False):
+  """Dequeues batch and constructs inputs to object detection model.
+
+  Args:
+    input_queue: BatchQueue object holding enqueued tensor_dicts.
+    num_classes: Number of classes.
+    merge_multiple_label_boxes: Whether to merge boxes with multiple labels
+      or not. Defaults to false. Merged boxes are represented with a single
+      box and a k-hot encoding of the multiple labels associated with the
+      boxes.
+    use_multiclass_scores: Whether to use multiclass scores instead of
+      groundtruth_classes.
+
+  Returns:
+    images: a list of 3-D float tensor of images.
+    image_keys: a list of string keys for the images.
+    locations_list: a list of tensors of shape [num_boxes, 4]
+      containing the corners of the groundtruth boxes.
+    classes_list: a list of padded one-hot (or K-hot) float32 tensors containing
+      target classes.
+    masks_list: a list of 3-D float tensors of shape [num_boxes, image_height,
+      image_width] containing instance masks for objects if present in the
+      input_queue. Else returns None.
+    keypoints_list: a list of 3-D float tensors of shape [num_boxes,
+      num_keypoints, 2] containing keypoints for objects if present in the
+      input queue. Else returns None.
+    weights_lists: a list of 1-D float32 tensors of shape [num_boxes]
+      containing groundtruth weight for each box.
+  """
+  read_data_list = input_queue.dequeue()
+  label_id_offset = 1
+  def extract_images_and_targets(read_data):
+    """Extract images and targets from the input dict."""
+    image = read_data[fields.InputDataFields.image]
+    key = ''
+    if fields.InputDataFields.source_id in read_data:
+      key = read_data[fields.InputDataFields.source_id]
+    location_gt = read_data[fields.InputDataFields.groundtruth_boxes]
+    classes_gt = tf.cast(read_data[fields.InputDataFields.groundtruth_classes],
+                         tf.int32)
+    classes_gt -= label_id_offset
+
+    if merge_multiple_label_boxes and use_multiclass_scores:
+      raise ValueError(
+          'Using both merge_multiple_label_boxes and use_multiclass_scores is'
+          'not supported'
+      )
+
+    if merge_multiple_label_boxes:
+      location_gt, classes_gt, _ = util_ops.merge_boxes_with_multiple_labels(
+          location_gt, classes_gt, num_classes)
+      classes_gt = tf.cast(classes_gt, tf.float32)
+    elif use_multiclass_scores:
+      classes_gt = tf.cast(read_data[fields.InputDataFields.multiclass_scores],
+                           tf.float32)
+    else:
+      classes_gt = util_ops.padded_one_hot_encoding(
+          indices=classes_gt, depth=num_classes, left_pad=0)
+    masks_gt = read_data.get(fields.InputDataFields.groundtruth_instance_masks)
+    keypoints_gt = read_data.get(fields.InputDataFields.groundtruth_keypoints)
+    if (merge_multiple_label_boxes and (
+        masks_gt is not None or keypoints_gt is not None)):
+      raise NotImplementedError('Multi-label support is only for boxes.')
+    weights_gt = read_data.get(
+        fields.InputDataFields.groundtruth_weights)
+    return (image, key, location_gt, classes_gt, masks_gt, keypoints_gt,
+            weights_gt)
+
+  return zip(*map(extract_images_and_targets, read_data_list))
+
+
+def _create_losses(input_queue, create_model_fn, train_config):
+  """Creates loss function for a DetectionModel.
+
+  Args:
+    input_queue: BatchQueue object holding enqueued tensor_dicts.
+    create_model_fn: A function to create the DetectionModel.
+    train_config: a train_pb2.TrainConfig protobuf.
+  """
+  detection_model = create_model_fn()
+  (images, _, groundtruth_boxes_list, groundtruth_classes_list,
+   groundtruth_masks_list, groundtruth_keypoints_list,
+   groundtruth_weights_list) = get_inputs(
+       input_queue,
+       detection_model.num_classes,
+       train_config.merge_multiple_label_boxes,
+       train_config.use_multiclass_scores)
+
+  preprocessed_images = []
+  true_image_shapes = []
+  for image in images:
+    resized_image, true_image_shape = detection_model.preprocess(image)
+    preprocessed_images.append(resized_image)
+    true_image_shapes.append(true_image_shape)
+
+  images = tf.concat(preprocessed_images, 0)
+  true_image_shapes = tf.concat(true_image_shapes, 0)
+
+  if any(mask is None for mask in groundtruth_masks_list):
+    groundtruth_masks_list = None
+  if any(keypoints is None for keypoints in groundtruth_keypoints_list):
+    groundtruth_keypoints_list = None
+
+  detection_model.provide_groundtruth(
+      groundtruth_boxes_list,
+      groundtruth_classes_list,
+      groundtruth_masks_list,
+      groundtruth_keypoints_list,
+      groundtruth_weights_list=groundtruth_weights_list)
+  prediction_dict = detection_model.predict(images, true_image_shapes)
+
+  losses_dict = detection_model.loss(prediction_dict, true_image_shapes)
+  for loss_tensor in losses_dict.values():
+    tf.losses.add_loss(loss_tensor)
+
+
+def train(create_tensor_dict_fn,
+          create_model_fn,
+          train_config,
+          master,
+          task,
+          num_clones,
+          worker_replicas,
+          clone_on_cpu,
+          ps_tasks,
+          worker_job_name,
+          is_chief,
+          train_dir,
+          graph_hook_fn=None):
+  """Training function for detection models.
+
+  Args:
+    create_tensor_dict_fn: a function to create a tensor input dictionary.
+    create_model_fn: a function that creates a DetectionModel and generates
+                     losses.
+    train_config: a train_pb2.TrainConfig protobuf.
+    master: BNS name of the TensorFlow master to use.
+    task: The task id of this training instance.
+    num_clones: The number of clones to run per machine.
+    worker_replicas: The number of work replicas to train with.
+    clone_on_cpu: True if clones should be forced to run on CPU.
+    ps_tasks: Number of parameter server tasks.
+    worker_job_name: Name of the worker job.
+    is_chief: Whether this replica is the chief replica.
+    train_dir: Directory to write checkpoints and training summaries to.
+    graph_hook_fn: Optional function that is called after the inference graph is
+      built (before optimization). This is helpful to perform additional changes
+      to the training graph such as adding FakeQuant ops. The function should
+      modify the default graph.
+
+  Raises:
+    ValueError: If both num_clones > 1 and train_config.sync_replicas is true.
+  """
+
+  detection_model = create_model_fn()
+  data_augmentation_options = [
+      preprocessor_builder.build(step)
+      for step in train_config.data_augmentation_options]
+
+  with tf.Graph().as_default():
+    # Build a configuration specifying multi-GPU and multi-replicas.
+    deploy_config = model_deploy.DeploymentConfig(
+        num_clones=num_clones,
+        clone_on_cpu=clone_on_cpu,
+        replica_id=task,
+        num_replicas=worker_replicas,
+        num_ps_tasks=ps_tasks,
+        worker_job_name=worker_job_name)
+
+    # Place the global step on the device storing the variables.
+    with tf.device(deploy_config.variables_device()):
+      global_step = slim.create_global_step()
+
+    if num_clones != 1 and train_config.sync_replicas:
+      raise ValueError('In Synchronous SGD mode num_clones must ',
+                       'be 1. Found num_clones: {}'.format(num_clones))
+    batch_size = train_config.batch_size // num_clones
+    if train_config.sync_replicas:
+      batch_size //= train_config.replicas_to_aggregate
+
+    with tf.device(deploy_config.inputs_device()):
+      input_queue = create_input_queue(
+          batch_size, create_tensor_dict_fn,
+          train_config.batch_queue_capacity,
+          train_config.num_batch_queue_threads,
+          train_config.prefetch_queue_capacity, data_augmentation_options)
+
+    # Gather initial summaries.
+    # TODO(rathodv): See if summaries can be added/extracted from global tf
+    # collections so that they don't have to be passed around.
+    summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
+    global_summaries = set([])
+
+    model_fn = functools.partial(_create_losses,
+                                 create_model_fn=create_model_fn,
+                                 train_config=train_config)
+    clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
+    first_clone_scope = clones[0].scope
+
+    if graph_hook_fn:
+      with tf.device(deploy_config.variables_device()):
+        graph_hook_fn()
+
+    # Gather update_ops from the first clone. These contain, for example,
+    # the updates for the batch_norm variables created by model_fn.
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
+
+    with tf.device(deploy_config.optimizer_device()):
+      training_optimizer, optimizer_summary_vars = optimizer_builder.build(
+          train_config.optimizer)
+      for var in optimizer_summary_vars:
+        tf.summary.scalar(var.op.name, var, family='LearningRate')
+
+    sync_optimizer = None
+    if train_config.sync_replicas:
+      training_optimizer = tf.train.SyncReplicasOptimizer(
+          training_optimizer,
+          replicas_to_aggregate=train_config.replicas_to_aggregate,
+          total_num_replicas=worker_replicas)
+      sync_optimizer = training_optimizer
+
+    with tf.device(deploy_config.optimizer_device()):
+      regularization_losses = (None if train_config.add_regularization_loss
+                               else [])
+      total_loss, grads_and_vars = model_deploy.optimize_clones(
+          clones, training_optimizer,
+          regularization_losses=regularization_losses)
+      total_loss = tf.check_numerics(total_loss, 'LossTensor is inf or nan.')
+
+      # Optionally multiply bias gradients by train_config.bias_grad_multiplier.
+      if train_config.bias_grad_multiplier:
+        biases_regex_list = ['.*/biases']
+        grads_and_vars = variables_helper.multiply_gradients_matching_regex(
+            grads_and_vars,
+            biases_regex_list,
+            multiplier=train_config.bias_grad_multiplier)
+
+      # Optionally freeze some layers by setting their gradients to be zero.
+      if train_config.freeze_variables:
+        grads_and_vars = variables_helper.freeze_gradients_matching_regex(
+            grads_and_vars, train_config.freeze_variables)
+
+      # Optionally clip gradients
+      if train_config.gradient_clipping_by_norm > 0:
+        with tf.name_scope('clip_grads'):
+          grads_and_vars = slim.learning.clip_gradient_norms(
+              grads_and_vars, train_config.gradient_clipping_by_norm)
+
+      # Create gradient updates.
+      grad_updates = training_optimizer.apply_gradients(grads_and_vars,
+                                                        global_step=global_step)
+      update_ops.append(grad_updates)
+      update_op = tf.group(*update_ops, name='update_barrier')
+      with tf.control_dependencies([update_op]):
+        train_tensor = tf.identity(total_loss, name='train_op')
+
+    # Add summaries.
+    for model_var in slim.get_model_variables():
+      global_summaries.add(tf.summary.histogram('ModelVars/' +
+                                                model_var.op.name, model_var))
+    for loss_tensor in tf.losses.get_losses():
+      global_summaries.add(tf.summary.scalar('Losses/' + loss_tensor.op.name,
+                                             loss_tensor))
+    global_summaries.add(
+        tf.summary.scalar('Losses/TotalLoss', tf.losses.get_total_loss()))
+
+    # Add the summaries from the first clone. These contain the summaries
+    # created by model_fn and either optimize_clones() or _gather_clone_loss().
+    summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
+                                       first_clone_scope))
+    summaries |= global_summaries
+
+    # Merge all summaries together.
+    summary_op = tf.summary.merge(list(summaries), name='summary_op')
+
+    # Soft placement allows placing on CPU ops without GPU implementation.
+    session_config = tf.ConfigProto(allow_soft_placement=True,
+                                    log_device_placement=False)
+
+    # Save checkpoints regularly.
+    keep_checkpoint_every_n_hours = train_config.keep_checkpoint_every_n_hours
+    saver = tf.train.Saver(
+        keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
+
+    # Create ops required to initialize the model from a given checkpoint.
+    init_fn = None
+    if train_config.fine_tune_checkpoint:
+      if not train_config.fine_tune_checkpoint_type:
+        # train_config.from_detection_checkpoint field is deprecated. For
+        # backward compatibility, fine_tune_checkpoint_type is set based on
+        # from_detection_checkpoint.
+        if train_config.from_detection_checkpoint:
+          train_config.fine_tune_checkpoint_type = 'detection'
+        else:
+          train_config.fine_tune_checkpoint_type = 'classification'
+      var_map = detection_model.restore_map(
+          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
+          load_all_detection_checkpoint_vars=(
+              train_config.load_all_detection_checkpoint_vars))
+      available_var_map = (variables_helper.
+                           get_variables_available_in_checkpoint(
+                               var_map, train_config.fine_tune_checkpoint,
+                               include_global_step=False))
+      init_saver = tf.train.Saver(available_var_map)
+      def initializer_fn(sess):
+        init_saver.restore(sess, train_config.fine_tune_checkpoint)
+      init_fn = initializer_fn
+
+    slim.learning.train(
+        train_tensor,
+        logdir=train_dir,
+        master=master,
+        is_chief=is_chief,
+        session_config=session_config,
+        startup_delay_steps=train_config.startup_delay_steps,
+        init_fn=init_fn,
+        summary_op=summary_op,
+        number_of_steps=(
+            train_config.num_steps if train_config.num_steps else None),
+        save_summaries_secs=120,
+        sync_optimizer=sync_optimizer,
+        saver=saver)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer_test.py
new file mode 100644
index 00000000..3c06e07e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/legacy/trainer_test.py
@@ -0,0 +1,291 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.trainer."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.core import losses
+from object_detection.core import model
+from object_detection.core import standard_fields as fields
+from object_detection.legacy import trainer
+from object_detection.protos import train_pb2
+
+
+NUMBER_OF_CLASSES = 2
+
+
+def get_input_function():
+  """A function to get test inputs. Returns an image with one box."""
+  image = tf.random_uniform([32, 32, 3], dtype=tf.float32)
+  key = tf.constant('image_000000')
+  class_label = tf.random_uniform(
+      [1], minval=0, maxval=NUMBER_OF_CLASSES, dtype=tf.int32)
+  box_label = tf.random_uniform(
+      [1, 4], minval=0.4, maxval=0.6, dtype=tf.float32)
+  multiclass_scores = tf.random_uniform(
+      [1, NUMBER_OF_CLASSES], minval=0.4, maxval=0.6, dtype=tf.float32)
+
+  return {
+      fields.InputDataFields.image: image,
+      fields.InputDataFields.key: key,
+      fields.InputDataFields.groundtruth_classes: class_label,
+      fields.InputDataFields.groundtruth_boxes: box_label,
+      fields.InputDataFields.multiclass_scores: multiclass_scores
+  }
+
+
+class FakeDetectionModel(model.DetectionModel):
+  """A simple (and poor) DetectionModel for use in test."""
+
+  def __init__(self):
+    super(FakeDetectionModel, self).__init__(num_classes=NUMBER_OF_CLASSES)
+    self._classification_loss = losses.WeightedSigmoidClassificationLoss()
+    self._localization_loss = losses.WeightedSmoothL1LocalizationLoss()
+
+  def preprocess(self, inputs):
+    """Input preprocessing, resizes images to 28x28.
+
+    Args:
+      inputs: a [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: a [batch, 28, 28, channels] float32 tensor.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+    """
+    true_image_shapes = [inputs.shape[:-1].as_list()
+                         for _ in range(inputs.shape[-1])]
+    return tf.image.resize_images(inputs, [28, 28]), true_image_shapes
+
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    """Prediction tensors from inputs tensor.
+
+    Args:
+      preprocessed_inputs: a [batch, 28, 28, channels] float32 tensor.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding prediction tensors to be
+        passed to the Loss or Postprocess functions.
+    """
+    flattened_inputs = tf.contrib.layers.flatten(preprocessed_inputs)
+    class_prediction = tf.contrib.layers.fully_connected(
+        flattened_inputs, self._num_classes)
+    box_prediction = tf.contrib.layers.fully_connected(flattened_inputs, 4)
+
+    return {
+        'class_predictions_with_background': tf.reshape(
+            class_prediction, [-1, 1, self._num_classes]),
+        'box_encodings': tf.reshape(box_prediction, [-1, 1, 4])
+    }
+
+  def postprocess(self, prediction_dict, true_image_shapes, **params):
+    """Convert predicted output tensors to final detections. Unused.
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+      **params: Additional keyword arguments for specific implementations of
+        DetectionModel.
+
+    Returns:
+      detections: a dictionary with empty fields.
+    """
+    return {
+        'detection_boxes': None,
+        'detection_scores': None,
+        'detection_classes': None,
+        'num_detections': None
+    }
+
+  def loss(self, prediction_dict, true_image_shapes):
+    """Compute scalar loss tensors with respect to provided groundtruth.
+
+    Calling this function requires that groundtruth tensors have been
+    provided via the provide_groundtruth function.
+
+    Args:
+      prediction_dict: a dictionary holding predicted tensors
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      a dictionary mapping strings (loss names) to scalar tensors representing
+        loss values.
+    """
+    batch_reg_targets = tf.stack(
+        self.groundtruth_lists(fields.BoxListFields.boxes))
+    batch_cls_targets = tf.stack(
+        self.groundtruth_lists(fields.BoxListFields.classes))
+    weights = tf.constant(
+        1.0, dtype=tf.float32,
+        shape=[len(self.groundtruth_lists(fields.BoxListFields.boxes)), 1])
+
+    location_losses = self._localization_loss(
+        prediction_dict['box_encodings'], batch_reg_targets,
+        weights=weights)
+    cls_losses = self._classification_loss(
+        prediction_dict['class_predictions_with_background'], batch_cls_targets,
+        weights=weights)
+
+    loss_dict = {
+        'localization_loss': tf.reduce_sum(location_losses),
+        'classification_loss': tf.reduce_sum(cls_losses),
+    }
+    return loss_dict
+
+  def regularization_losses(self):
+    """Returns a list of regularization losses for this model.
+
+    Returns a list of regularization losses for this model that the estimator
+    needs to use during training/optimization.
+
+    Returns:
+      A list of regularization loss tensors.
+    """
+    pass
+
+  def restore_map(self, fine_tune_checkpoint_type='detection'):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Args:
+      fine_tune_checkpoint_type: whether to restore from a full detection
+        checkpoint (with compatible variable names) or to restore from a
+        classification checkpoint for initialization prior to training.
+        Valid values: `detection`, `classification`. Default 'detection'.
+
+    Returns:
+      A dict mapping variable names to variables.
+    """
+    return {var.op.name: var for var in tf.global_variables()}
+
+  def updates(self):
+    """Returns a list of update operators for this model.
+
+    Returns a list of update operators for this model that must be executed at
+    each training step. The estimator's train op needs to have a control
+    dependency on these updates.
+
+    Returns:
+      A list of update operators.
+    """
+    pass
+
+
+class TrainerTest(tf.test.TestCase):
+
+  def test_configure_trainer_and_train_two_steps(self):
+    train_config_text_proto = """
+    optimizer {
+      adam_optimizer {
+        learning_rate {
+          constant_learning_rate {
+            learning_rate: 0.01
+          }
+        }
+      }
+    }
+    data_augmentation_options {
+      random_adjust_brightness {
+        max_delta: 0.2
+      }
+    }
+    data_augmentation_options {
+      random_adjust_contrast {
+        min_delta: 0.7
+        max_delta: 1.1
+      }
+    }
+    num_steps: 2
+    """
+    train_config = train_pb2.TrainConfig()
+    text_format.Merge(train_config_text_proto, train_config)
+
+    train_dir = self.get_temp_dir()
+
+    trainer.train(
+        create_tensor_dict_fn=get_input_function,
+        create_model_fn=FakeDetectionModel,
+        train_config=train_config,
+        master='',
+        task=0,
+        num_clones=1,
+        worker_replicas=1,
+        clone_on_cpu=True,
+        ps_tasks=0,
+        worker_job_name='worker',
+        is_chief=True,
+        train_dir=train_dir)
+
+  def test_configure_trainer_with_multiclass_scores_and_train_two_steps(self):
+    train_config_text_proto = """
+    optimizer {
+      adam_optimizer {
+        learning_rate {
+          constant_learning_rate {
+            learning_rate: 0.01
+          }
+        }
+      }
+    }
+    data_augmentation_options {
+      random_adjust_brightness {
+        max_delta: 0.2
+      }
+    }
+    data_augmentation_options {
+      random_adjust_contrast {
+        min_delta: 0.7
+        max_delta: 1.1
+      }
+    }
+    num_steps: 2
+    use_multiclass_scores: true
+    """
+    train_config = train_pb2.TrainConfig()
+    text_format.Merge(train_config_text_proto, train_config)
+
+    train_dir = self.get_temp_dir()
+
+    trainer.train(create_tensor_dict_fn=get_input_function,
+                  create_model_fn=FakeDetectionModel,
+                  train_config=train_config,
+                  master='',
+                  task=0,
+                  num_clones=1,
+                  worker_replicas=1,
+                  clone_on_cpu=True,
+                  ps_tasks=0,
+                  worker_job_name='worker',
+                  is_chief=True,
+                  train_dir=train_dir)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/matchers/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher.py b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher.py
new file mode 100644
index 00000000..07e22403
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher.py
@@ -0,0 +1,208 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Argmax matcher implementation.
+
+This class takes a similarity matrix and matches columns to rows based on the
+maximum value per column. One can specify matched_thresholds and
+to prevent columns from matching to rows (generally resulting in a negative
+training example) and unmatched_theshold to ignore the match (generally
+resulting in neither a positive or negative training example).
+
+This matcher is used in Fast(er)-RCNN.
+
+Note: matchers are used in TargetAssigners. There is a create_target_assigner
+factory function for popular implementations.
+"""
+import tensorflow as tf
+
+from object_detection.core import matcher
+from object_detection.utils import shape_utils
+
+
+class ArgMaxMatcher(matcher.Matcher):
+  """Matcher based on highest value.
+
+  This class computes matches from a similarity matrix. Each column is matched
+  to a single row.
+
+  To support object detection target assignment this class enables setting both
+  matched_threshold (upper threshold) and unmatched_threshold (lower thresholds)
+  defining three categories of similarity which define whether examples are
+  positive, negative, or ignored:
+  (1) similarity >= matched_threshold: Highest similarity. Matched/Positive!
+  (2) matched_threshold > similarity >= unmatched_threshold: Medium similarity.
+          Depending on negatives_lower_than_unmatched, this is either
+          Unmatched/Negative OR Ignore.
+  (3) unmatched_threshold > similarity: Lowest similarity. Depending on flag
+          negatives_lower_than_unmatched, either Unmatched/Negative OR Ignore.
+  For ignored matches this class sets the values in the Match object to -2.
+  """
+
+  def __init__(self,
+               matched_threshold,
+               unmatched_threshold=None,
+               negatives_lower_than_unmatched=True,
+               force_match_for_each_row=False,
+               use_matmul_gather=False):
+    """Construct ArgMaxMatcher.
+
+    Args:
+      matched_threshold: Threshold for positive matches. Positive if
+        sim >= matched_threshold, where sim is the maximum value of the
+        similarity matrix for a given column. Set to None for no threshold.
+      unmatched_threshold: Threshold for negative matches. Negative if
+        sim < unmatched_threshold. Defaults to matched_threshold
+        when set to None.
+      negatives_lower_than_unmatched: Boolean which defaults to True. If True
+        then negative matches are the ones below the unmatched_threshold,
+        whereas ignored matches are in between the matched and umatched
+        threshold. If False, then negative matches are in between the matched
+        and unmatched threshold, and everything lower than unmatched is ignored.
+      force_match_for_each_row: If True, ensures that each row is matched to
+        at least one column (which is not guaranteed otherwise if the
+        matched_threshold is high). Defaults to False. See
+        argmax_matcher_test.testMatcherForceMatch() for an example.
+      use_matmul_gather: Force constructed match objects to use matrix
+        multiplication based gather instead of standard tf.gather.
+        (Default: False).
+
+    Raises:
+      ValueError: if unmatched_threshold is set but matched_threshold is not set
+        or if unmatched_threshold > matched_threshold.
+    """
+    super(ArgMaxMatcher, self).__init__(use_matmul_gather=use_matmul_gather)
+    if (matched_threshold is None) and (unmatched_threshold is not None):
+      raise ValueError('Need to also define matched_threshold when'
+                       'unmatched_threshold is defined')
+    self._matched_threshold = matched_threshold
+    if unmatched_threshold is None:
+      self._unmatched_threshold = matched_threshold
+    else:
+      if unmatched_threshold > matched_threshold:
+        raise ValueError('unmatched_threshold needs to be smaller or equal'
+                         'to matched_threshold')
+      self._unmatched_threshold = unmatched_threshold
+    if not negatives_lower_than_unmatched:
+      if self._unmatched_threshold == self._matched_threshold:
+        raise ValueError('When negatives are in between matched and '
+                         'unmatched thresholds, these cannot be of equal '
+                         'value. matched: {}, unmatched: {}'.format(
+                             self._matched_threshold,
+                             self._unmatched_threshold))
+    self._force_match_for_each_row = force_match_for_each_row
+    self._negatives_lower_than_unmatched = negatives_lower_than_unmatched
+
+  def _match(self, similarity_matrix, valid_rows):
+    """Tries to match each column of the similarity matrix to a row.
+
+    Args:
+      similarity_matrix: tensor of shape [N, M] representing any similarity
+        metric.
+      valid_rows: a boolean tensor of shape [N] indicating valid rows.
+
+    Returns:
+      Match object with corresponding matches for each of M columns.
+    """
+
+    def _match_when_rows_are_empty():
+      """Performs matching when the rows of similarity matrix are empty.
+
+      When the rows are empty, all detections are false positives. So we return
+      a tensor of -1's to indicate that the columns do not match to any rows.
+
+      Returns:
+        matches:  int32 tensor indicating the row each column matches to.
+      """
+      similarity_matrix_shape = shape_utils.combined_static_and_dynamic_shape(
+          similarity_matrix)
+      return -1 * tf.ones([similarity_matrix_shape[1]], dtype=tf.int32)
+
+    def _match_when_rows_are_non_empty():
+      """Performs matching when the rows of similarity matrix are non empty.
+
+      Returns:
+        matches:  int32 tensor indicating the row each column matches to.
+      """
+      # Matches for each column
+      matches = tf.argmax(similarity_matrix, 0, output_type=tf.int32)
+
+      # Deal with matched and unmatched threshold
+      if self._matched_threshold is not None:
+        # Get logical indices of ignored and unmatched columns as tf.int64
+        matched_vals = tf.reduce_max(similarity_matrix, 0)
+        below_unmatched_threshold = tf.greater(self._unmatched_threshold,
+                                               matched_vals)
+        between_thresholds = tf.logical_and(
+            tf.greater_equal(matched_vals, self._unmatched_threshold),
+            tf.greater(self._matched_threshold, matched_vals))
+
+        if self._negatives_lower_than_unmatched:
+          matches = self._set_values_using_indicator(matches,
+                                                     below_unmatched_threshold,
+                                                     -1)
+          matches = self._set_values_using_indicator(matches,
+                                                     between_thresholds,
+                                                     -2)
+        else:
+          matches = self._set_values_using_indicator(matches,
+                                                     below_unmatched_threshold,
+                                                     -2)
+          matches = self._set_values_using_indicator(matches,
+                                                     between_thresholds,
+                                                     -1)
+
+      if self._force_match_for_each_row:
+        similarity_matrix_shape = shape_utils.combined_static_and_dynamic_shape(
+            similarity_matrix)
+        force_match_column_ids = tf.argmax(similarity_matrix, 1,
+                                           output_type=tf.int32)
+        force_match_column_indicators = (
+            tf.one_hot(
+                force_match_column_ids, depth=similarity_matrix_shape[1]) *
+            tf.cast(tf.expand_dims(valid_rows, axis=-1), dtype=tf.float32))
+        force_match_row_ids = tf.argmax(force_match_column_indicators, 0,
+                                        output_type=tf.int32)
+        force_match_column_mask = tf.cast(
+            tf.reduce_max(force_match_column_indicators, 0), tf.bool)
+        final_matches = tf.where(force_match_column_mask,
+                                 force_match_row_ids, matches)
+        return final_matches
+      else:
+        return matches
+
+    if similarity_matrix.shape.is_fully_defined():
+      if similarity_matrix.shape[0].value == 0:
+        return _match_when_rows_are_empty()
+      else:
+        return _match_when_rows_are_non_empty()
+    else:
+      return tf.cond(
+          tf.greater(tf.shape(similarity_matrix)[0], 0),
+          _match_when_rows_are_non_empty, _match_when_rows_are_empty)
+
+  def _set_values_using_indicator(self, x, indicator, val):
+    """Set the indicated fields of x to val.
+
+    Args:
+      x: tensor.
+      indicator: boolean with same shape as x.
+      val: scalar with value to set.
+
+    Returns:
+      modified tensor.
+    """
+    indicator = tf.cast(indicator, x.dtype)
+    return tf.add(tf.multiply(x, 1 - indicator), val * indicator)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher_test.py
new file mode 100644
index 00000000..4b73efb8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/argmax_matcher_test.py
@@ -0,0 +1,235 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.matchers.argmax_matcher."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.matchers import argmax_matcher
+from object_detection.utils import test_case
+
+
+class ArgMaxMatcherTest(test_case.TestCase):
+
+  def test_return_correct_matches_with_default_thresholds(self):
+
+    def graph_fn(similarity_matrix):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None)
+      match = matcher.match(similarity_matrix)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1., 1, 1, 3, 1],
+                           [2, -1, 2, 0, 4],
+                           [3, 0, -1, 0, 0]], dtype=np.float32)
+    expected_matched_rows = np.array([2, 0, 1, 0, 1])
+    (res_matched_cols, res_unmatched_cols,
+     res_match_results) = self.execute(graph_fn, [similarity])
+
+    self.assertAllEqual(res_match_results[res_matched_cols],
+                        expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], [0, 1, 2, 3, 4])
+    self.assertFalse(np.all(res_unmatched_cols))
+
+  def test_return_correct_matches_with_empty_rows(self):
+
+    def graph_fn(similarity_matrix):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None)
+      match = matcher.match(similarity_matrix)
+      return match.unmatched_column_indicator()
+    similarity = 0.2 * np.ones([0, 5], dtype=np.float32)
+    res_unmatched_cols = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], np.arange(5))
+
+  def test_return_correct_matches_with_matched_threshold(self):
+
+    def graph_fn(similarity):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.)
+      match = matcher.match(similarity)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [2, -1, 2, 0, 4],
+                           [3, 0, -1, 0, 0]], dtype=np.float32)
+    expected_matched_cols = np.array([0, 3, 4])
+    expected_matched_rows = np.array([2, 0, 1])
+    expected_unmatched_cols = np.array([1, 2])
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_return_correct_matches_with_matched_and_unmatched_threshold(self):
+
+    def graph_fn(similarity):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.,
+                                             unmatched_threshold=2.)
+      match = matcher.match(similarity)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [2, -1, 2, 0, 4],
+                           [3, 0, -1, 0, 0]], dtype=np.float32)
+    expected_matched_cols = np.array([0, 3, 4])
+    expected_matched_rows = np.array([2, 0, 1])
+    expected_unmatched_cols = np.array([1])  # col 2 has too high maximum val
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_return_correct_matches_negatives_lower_than_unmatched_false(self):
+
+    def graph_fn(similarity):
+      matcher = argmax_matcher.ArgMaxMatcher(
+          matched_threshold=3.,
+          unmatched_threshold=2.,
+          negatives_lower_than_unmatched=False)
+      match = matcher.match(similarity)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [2, -1, 2, 0, 4],
+                           [3, 0, -1, 0, 0]], dtype=np.float32)
+    expected_matched_cols = np.array([0, 3, 4])
+    expected_matched_rows = np.array([2, 0, 1])
+    expected_unmatched_cols = np.array([2])  # col 1 has too low maximum val
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_return_correct_matches_unmatched_row_not_using_force_match(self):
+
+    def graph_fn(similarity):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.,
+                                             unmatched_threshold=2.)
+      match = matcher.match(similarity)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [-1, 0, -2, -2, -1],
+                           [3, 0, -1, 2, 0]], dtype=np.float32)
+    expected_matched_cols = np.array([0, 3])
+    expected_matched_rows = np.array([2, 0])
+    expected_unmatched_cols = np.array([1, 2, 4])
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_return_correct_matches_unmatched_row_while_using_force_match(self):
+    def graph_fn(similarity):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.,
+                                             unmatched_threshold=2.,
+                                             force_match_for_each_row=True)
+      match = matcher.match(similarity)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [-1, 0, -2, -2, -1],
+                           [3, 0, -1, 2, 0]], dtype=np.float32)
+    expected_matched_cols = np.array([0, 1, 3])
+    expected_matched_rows = np.array([2, 1, 0])
+    expected_unmatched_cols = np.array([2, 4])  # col 2 has too high max val
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_return_correct_matches_using_force_match_padded_groundtruth(self):
+    def graph_fn(similarity, valid_rows):
+      matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.,
+                                             unmatched_threshold=2.,
+                                             force_match_for_each_row=True)
+      match = matcher.match(similarity, valid_rows)
+      matched_cols = match.matched_column_indicator()
+      unmatched_cols = match.unmatched_column_indicator()
+      match_results = match.match_results
+      return (matched_cols, unmatched_cols, match_results)
+
+    similarity = np.array([[1, 1, 1, 3, 1],
+                           [-1, 0, -2, -2, -1],
+                           [0, 0, 0, 0, 0],
+                           [3, 0, -1, 2, 0],
+                           [0, 0, 0, 0, 0]], dtype=np.float32)
+    valid_rows = np.array([True, True, False, True, False])
+    expected_matched_cols = np.array([0, 1, 3])
+    expected_matched_rows = np.array([3, 1, 0])
+    expected_unmatched_cols = np.array([2, 4])  # col 2 has too high max val
+
+    (res_matched_cols, res_unmatched_cols,
+     match_results) = self.execute(graph_fn, [similarity, valid_rows])
+    self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows)
+    self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols)
+    self.assertAllEqual(np.nonzero(res_unmatched_cols)[0],
+                        expected_unmatched_cols)
+
+  def test_valid_arguments_corner_case(self):
+    argmax_matcher.ArgMaxMatcher(matched_threshold=1,
+                                 unmatched_threshold=1)
+
+  def test_invalid_arguments_corner_case_negatives_lower_than_thres_false(self):
+    with self.assertRaises(ValueError):
+      argmax_matcher.ArgMaxMatcher(matched_threshold=1,
+                                   unmatched_threshold=1,
+                                   negatives_lower_than_unmatched=False)
+
+  def test_invalid_arguments_no_matched_threshold(self):
+    with self.assertRaises(ValueError):
+      argmax_matcher.ArgMaxMatcher(matched_threshold=None,
+                                   unmatched_threshold=4)
+
+  def test_invalid_arguments_unmatched_thres_larger_than_matched_thres(self):
+    with self.assertRaises(ValueError):
+      argmax_matcher.ArgMaxMatcher(matched_threshold=1,
+                                   unmatched_threshold=2)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher.py b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher.py
new file mode 100644
index 00000000..56cff85a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher.py
@@ -0,0 +1,70 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Bipartite matcher implementation."""
+
+import tensorflow as tf
+
+from tensorflow.contrib.image.python.ops import image_ops
+from object_detection.core import matcher
+
+
+class GreedyBipartiteMatcher(matcher.Matcher):
+  """Wraps a Tensorflow greedy bipartite matcher."""
+
+  def __init__(self, use_matmul_gather=False):
+    """Constructs a Matcher.
+
+    Args:
+      use_matmul_gather: Force constructed match objects to use matrix
+        multiplication based gather instead of standard tf.gather.
+        (Default: False).
+    """
+    super(GreedyBipartiteMatcher, self).__init__(
+        use_matmul_gather=use_matmul_gather)
+
+  def _match(self, similarity_matrix, valid_rows):
+    """Bipartite matches a collection rows and columns. A greedy bi-partite.
+
+    TODO(rathodv): Add num_valid_columns options to match only that many columns
+    with all the rows.
+
+    Args:
+      similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
+        where higher values mean more similar.
+      valid_rows: A boolean tensor of shape [N] indicating the rows that are
+        valid.
+
+    Returns:
+      match_results: int32 tensor of shape [M] with match_results[i]=-1
+        meaning that column i is not matched and otherwise that it is matched to
+        row match_results[i].
+    """
+    valid_row_sim_matrix = tf.gather(similarity_matrix,
+                                     tf.squeeze(tf.where(valid_rows), axis=-1))
+    invalid_row_sim_matrix = tf.gather(
+        similarity_matrix,
+        tf.squeeze(tf.where(tf.logical_not(valid_rows)), axis=-1))
+    similarity_matrix = tf.concat(
+        [valid_row_sim_matrix, invalid_row_sim_matrix], axis=0)
+    # Convert similarity matrix to distance matrix as tf.image.bipartite tries
+    # to find minimum distance matches.
+    distance_matrix = -1 * similarity_matrix
+    num_valid_rows = tf.reduce_sum(tf.to_float(valid_rows))
+    _, match_results = image_ops.bipartite_match(
+        distance_matrix, num_valid_rows=num_valid_rows)
+    match_results = tf.reshape(match_results, [-1])
+    match_results = tf.cast(match_results, tf.int32)
+    return match_results
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher_test.py
new file mode 100644
index 00000000..c0f693a4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/matchers/bipartite_matcher_test.py
@@ -0,0 +1,81 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.core.bipartite_matcher."""
+
+import tensorflow as tf
+
+from object_detection.matchers import bipartite_matcher
+
+
+class GreedyBipartiteMatcherTest(tf.test.TestCase):
+
+  def test_get_expected_matches_when_all_rows_are_valid(self):
+    similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]])
+    valid_rows = tf.ones([2], dtype=tf.bool)
+    expected_match_results = [-1, 1, 0]
+
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    match = matcher.match(similarity_matrix, valid_rows=valid_rows)
+    with self.test_session() as sess:
+      match_results_out = sess.run(match._match_results)
+      self.assertAllEqual(match_results_out, expected_match_results)
+
+  def test_get_expected_matches_with_all_rows_be_default(self):
+    similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]])
+    expected_match_results = [-1, 1, 0]
+
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    match = matcher.match(similarity_matrix)
+    with self.test_session() as sess:
+      match_results_out = sess.run(match._match_results)
+      self.assertAllEqual(match_results_out, expected_match_results)
+
+  def test_get_no_matches_with_zero_valid_rows(self):
+    similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]])
+    valid_rows = tf.zeros([2], dtype=tf.bool)
+    expected_match_results = [-1, -1, -1]
+
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    match = matcher.match(similarity_matrix, valid_rows)
+    with self.test_session() as sess:
+      match_results_out = sess.run(match._match_results)
+      self.assertAllEqual(match_results_out, expected_match_results)
+
+  def test_get_expected_matches_with_only_one_valid_row(self):
+    similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]])
+    valid_rows = tf.constant([True, False], dtype=tf.bool)
+    expected_match_results = [-1, -1, 0]
+
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    match = matcher.match(similarity_matrix, valid_rows)
+    with self.test_session() as sess:
+      match_results_out = sess.run(match._match_results)
+      self.assertAllEqual(match_results_out, expected_match_results)
+
+  def test_get_expected_matches_with_only_one_valid_row_at_bottom(self):
+    similarity_matrix = tf.constant([[0.15, 0.2, 0.3], [0.50, 0.1, 0.8]])
+    valid_rows = tf.constant([False, True], dtype=tf.bool)
+    expected_match_results = [-1, -1, 0]
+
+    matcher = bipartite_matcher.GreedyBipartiteMatcher()
+    match = matcher.match(similarity_matrix, valid_rows)
+    with self.test_session() as sess:
+      match_results_out = sess.run(match._match_results)
+      self.assertAllEqual(match_results_out, expected_match_results)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch.py
new file mode 100644
index 00000000..95cbba07
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch.py
@@ -0,0 +1,2259 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Faster R-CNN meta-architecture definition.
+
+General tensorflow implementation of Faster R-CNN detection models.
+
+See Faster R-CNN: Ren, Shaoqing, et al.
+"Faster R-CNN: Towards real-time object detection with region proposal
+networks." Advances in neural information processing systems. 2015.
+
+We allow for three modes: number_of_stages={1, 2, 3}. In case of 1 stage,
+all of the user facing methods (e.g., predict, postprocess, loss) can be used as
+if the model consisted only of the RPN, returning class agnostic proposals
+(these can be thought of as approximate detections with no associated class
+information).  In case of 2 stages, proposals are computed, then passed
+through a second stage "box classifier" to yield (multi-class) detections.
+Finally, in case of 3 stages which is only used during eval, proposals are
+computed, then passed through a second stage "box classifier" that will compute
+refined boxes and classes, and then features are pooled from the refined and
+non-maximum suppressed boxes and are passed through the box classifier again. If
+number of stages is 3 during training it will be reduced to two automatically.
+
+Implementations of Faster R-CNN models must define a new
+FasterRCNNFeatureExtractor and override three methods: `preprocess`,
+`_extract_proposal_features` (the first stage of the model), and
+`_extract_box_classifier_features` (the second stage of the model). Optionally,
+the `restore_fn` method can be overridden.  See tests for an example.
+
+A few important notes:
++ Batching conventions:  We support batched inference and training where
+all images within a batch have the same resolution.  Batch sizes are determined
+dynamically via the shape of the input tensors (rather than being specified
+directly as, e.g., a model constructor).
+
+A complication is that due to non-max suppression, we are not guaranteed to get
+the same number of proposals from the first stage RPN (region proposal network)
+for each image (though in practice, we should often get the same number of
+proposals).  For this reason we pad to a max number of proposals per image
+within a batch. This `self.max_num_proposals` property is set to the
+`first_stage_max_proposals` parameter at inference time and the
+`second_stage_batch_size` at training time since we subsample the batch to
+be sent through the box classifier during training.
+
+For the second stage of the pipeline, we arrange the proposals for all images
+within the batch along a single batch dimension.  For example, the input to
+_extract_box_classifier_features is a tensor of shape
+`[total_num_proposals, crop_height, crop_width, depth]` where
+total_num_proposals is batch_size * self.max_num_proposals.  (And note that per
+the above comment, a subset of these entries correspond to zero paddings.)
+
++ Coordinate representations:
+Following the API (see model.DetectionModel definition), our outputs after
+postprocessing operations are always normalized boxes however, internally, we
+sometimes convert to absolute --- e.g. for loss computation.  In particular,
+anchors and proposal_boxes are both represented as absolute coordinates.
+
+Images are resized in the `preprocess` method.
+
+The Faster R-CNN meta architecture has two post-processing methods
+`_postprocess_rpn` which is applied after first stage and
+`_postprocess_box_classifier` which is applied after second stage. There are
+three different ways post-processing can happen depending on number_of_stages
+configured in the meta architecture:
+
+1. When number_of_stages is 1:
+  `_postprocess_rpn` is run as part of the `postprocess` method where
+  true_image_shapes is used to clip proposals, perform non-max suppression and
+  normalize them.
+2. When number of stages is 2:
+  `_postprocess_rpn` is run as part of the `_predict_second_stage` method where
+  `resized_image_shapes` is used to clip proposals, perform non-max suppression
+  and normalize them. In this case `postprocess` method skips `_postprocess_rpn`
+  and only runs `_postprocess_box_classifier` using `true_image_shapes` to clip
+  detections, perform non-max suppression and normalize them.
+3. When number of stages is 3:
+  `_postprocess_rpn` is run as part of the `_predict_second_stage` using
+  `resized_image_shapes` to clip proposals, perform non-max suppression and
+  normalize them. Subsequently, `_postprocess_box_classifier` is run as part of
+  `_predict_third_stage` using `true_image_shapes` to clip detections, peform
+  non-max suppression and normalize them. In this case, the `postprocess` method
+  skips both `_postprocess_rpn` and `_postprocess_box_classifier`.
+"""
+from abc import abstractmethod
+from functools import partial
+import tensorflow as tf
+
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.builders import box_predictor_builder
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.core import box_predictor
+from object_detection.core import losses
+from object_detection.core import model
+from object_detection.core import standard_fields as fields
+from object_detection.core import target_assigner
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+
+slim = tf.contrib.slim
+
+
+class FasterRCNNFeatureExtractor(object):
+  """Faster R-CNN Feature Extractor definition."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: A boolean indicating whether the training version of the
+        computation graph should be constructed.
+      first_stage_features_stride: Output stride of extracted RPN feature map.
+      batch_norm_trainable: Whether to update batch norm parameters during
+        training or not. When training with a relative large batch size
+        (e.g. 8), it could be desirable to enable batch norm update.
+      reuse_weights: Whether to reuse variables. Default is None.
+      weight_decay: float weight decay for feature extractor (default: 0.0).
+    """
+    self._is_training = is_training
+    self._first_stage_features_stride = first_stage_features_stride
+    self._train_batch_norm = (batch_norm_trainable and is_training)
+    self._reuse_weights = reuse_weights
+    self._weight_decay = weight_decay
+
+  @abstractmethod
+  def preprocess(self, resized_inputs):
+    """Feature-extractor specific preprocessing (minus image resizing)."""
+    pass
+
+  def extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    This function is responsible for extracting feature maps from preprocessed
+    images.  These features are used by the region proposal network (RPN) to
+    predict proposals.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      activations: A dictionary mapping activation tensor names to tensors.
+    """
+    with tf.variable_scope(scope, values=[preprocessed_inputs]):
+      return self._extract_proposal_features(preprocessed_inputs, scope)
+
+  @abstractmethod
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features, to be overridden."""
+    pass
+
+  def extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name.
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    with tf.variable_scope(
+        scope, values=[proposal_feature_maps], reuse=tf.AUTO_REUSE):
+      return self._extract_box_classifier_features(proposal_feature_maps, scope)
+
+  @abstractmethod
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features, to be overridden."""
+    pass
+
+  def restore_from_classification_checkpoint_fn(
+      self,
+      first_stage_feature_extractor_scope,
+      second_stage_feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Args:
+      first_stage_feature_extractor_scope: A scope name for the first stage
+        feature extractor.
+      second_stage_feature_extractor_scope: A scope name for the second stage
+        feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      for scope_name in [first_stage_feature_extractor_scope,
+                         second_stage_feature_extractor_scope]:
+        if variable.op.name.startswith(scope_name):
+          var_name = variable.op.name.replace(scope_name + '/', '')
+          variables_to_restore[var_name] = variable
+    return variables_to_restore
+
+
+class FasterRCNNMetaArch(model.DetectionModel):
+  """Faster R-CNN Meta-architecture definition."""
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               image_resizer_fn,
+               feature_extractor,
+               number_of_stages,
+               first_stage_anchor_generator,
+               first_stage_target_assigner,
+               first_stage_atrous_rate,
+               first_stage_box_predictor_arg_scope_fn,
+               first_stage_box_predictor_kernel_size,
+               first_stage_box_predictor_depth,
+               first_stage_minibatch_size,
+               first_stage_sampler,
+               first_stage_non_max_suppression_fn,
+               first_stage_max_proposals,
+               first_stage_localization_loss_weight,
+               first_stage_objectness_loss_weight,
+               crop_and_resize_fn,
+               initial_crop_size,
+               maxpool_kernel_size,
+               maxpool_stride,
+               second_stage_target_assigner,
+               second_stage_mask_rcnn_box_predictor,
+               second_stage_batch_size,
+               second_stage_sampler,
+               second_stage_non_max_suppression_fn,
+               second_stage_score_conversion_fn,
+               second_stage_localization_loss_weight,
+               second_stage_classification_loss_weight,
+               second_stage_classification_loss,
+               second_stage_mask_prediction_loss_weight=1.0,
+               hard_example_miner=None,
+               parallel_iterations=16,
+               add_summaries=True,
+               clip_anchors_to_image=False,
+               use_static_shapes=False,
+               resize_masks=True):
+    """FasterRCNNMetaArch Constructor.
+
+    Args:
+      is_training: A boolean indicating whether the training version of the
+        computation graph should be constructed.
+      num_classes: Number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      image_resizer_fn: A callable for image resizing.  This callable
+        takes a rank-3 image tensor of shape [height, width, channels]
+        (corresponding to a single image), an optional rank-3 instance mask
+        tensor of shape [num_masks, height, width] and returns a resized rank-3
+        image tensor, a resized mask tensor if one was provided in the input. In
+        addition this callable must also return a 1-D tensor of the form
+        [height, width, channels] containing the size of the true image, as the
+        image resizer can perform zero padding. See protos/image_resizer.proto.
+      feature_extractor: A FasterRCNNFeatureExtractor object.
+      number_of_stages:  An integer values taking values in {1, 2, 3}. If
+        1, the function will construct only the Region Proposal Network (RPN)
+        part of the model. If 2, the function will perform box refinement and
+        other auxiliary predictions all in the second stage. If 3, it will
+        extract features from refined boxes and perform the auxiliary
+        predictions on the non-maximum suppressed refined boxes.
+        If is_training is true and the value of number_of_stages is 3, it is
+        reduced to 2 since all the model heads are trained in parallel in second
+        stage during training.
+      first_stage_anchor_generator: An anchor_generator.AnchorGenerator object
+        (note that currently we only support
+        grid_anchor_generator.GridAnchorGenerator objects)
+      first_stage_target_assigner: Target assigner to use for first stage of
+        Faster R-CNN (RPN).
+      first_stage_atrous_rate: A single integer indicating the atrous rate for
+        the single convolution op which is applied to the `rpn_features_to_crop`
+        tensor to obtain a tensor to be used for box prediction. Some feature
+        extractors optionally allow for producing feature maps computed at
+        denser resolutions.  The atrous rate is used to compensate for the
+        denser feature maps by using an effectively larger receptive field.
+        (This should typically be set to 1).
+      first_stage_box_predictor_arg_scope_fn: A function to construct tf-slim
+        arg_scope for conv2d, separable_conv2d and fully_connected ops for the
+        RPN box predictor.
+      first_stage_box_predictor_kernel_size: Kernel size to use for the
+        convolution op just prior to RPN box predictions.
+      first_stage_box_predictor_depth: Output depth for the convolution op
+        just prior to RPN box predictions.
+      first_stage_minibatch_size: The "batch size" to use for computing the
+        objectness and location loss of the region proposal network. This
+        "batch size" refers to the number of anchors selected as contributing
+        to the loss function for any given image within the image batch and is
+        only called "batch_size" due to terminology from the Faster R-CNN paper.
+      first_stage_sampler: Sampler to use for first stage loss (RPN loss).
+      first_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
+        callable that takes `boxes`, `scores` and optional `clip_window`(with
+        all other inputs already set) and returns a dictionary containing
+        tensors with keys: `detection_boxes`, `detection_scores`,
+        `detection_classes`, `num_detections`. This is used to perform non max
+        suppression  on the boxes predicted by the Region Proposal Network
+        (RPN).
+        See `post_processing.batch_multiclass_non_max_suppression` for the type
+        and shape of these tensors.
+      first_stage_max_proposals: Maximum number of boxes to retain after
+        performing Non-Max Suppression (NMS) on the boxes predicted by the
+        Region Proposal Network (RPN).
+      first_stage_localization_loss_weight: A float
+      first_stage_objectness_loss_weight: A float
+      crop_and_resize_fn: A differentiable resampler to use for cropping RPN
+        proposal features.
+      initial_crop_size: A single integer indicating the output size
+        (width and height are set to be the same) of the initial bilinear
+        interpolation based cropping during ROI pooling.
+      maxpool_kernel_size: A single integer indicating the kernel size of the
+        max pool op on the cropped feature map during ROI pooling.
+      maxpool_stride: A single integer indicating the stride of the max pool
+        op on the cropped feature map during ROI pooling.
+      second_stage_target_assigner: Target assigner to use for second stage of
+        Faster R-CNN. If the model is configured with multiple prediction heads,
+        this target assigner is used to generate targets for all heads (with the
+        correct `unmatched_class_label`).
+      second_stage_mask_rcnn_box_predictor: Mask R-CNN box predictor to use for
+        the second stage.
+      second_stage_batch_size: The batch size used for computing the
+        classification and refined location loss of the box classifier.  This
+        "batch size" refers to the number of proposals selected as contributing
+        to the loss function for any given image within the image batch and is
+        only called "batch_size" due to terminology from the Faster R-CNN paper.
+      second_stage_sampler:  Sampler to use for second stage loss (box
+        classifier loss).
+      second_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
+        callable that takes `boxes`, `scores`, optional `clip_window` and
+        optional (kwarg) `mask` inputs (with all other inputs already set)
+        and returns a dictionary containing tensors with keys:
+        `detection_boxes`, `detection_scores`, `detection_classes`,
+        `num_detections`, and (optionally) `detection_masks`. See
+        `post_processing.batch_multiclass_non_max_suppression` for the type and
+        shape of these tensors.
+      second_stage_score_conversion_fn: Callable elementwise nonlinearity
+        (that takes tensors as inputs and returns tensors).  This is usually
+        used to convert logits to probabilities.
+      second_stage_localization_loss_weight: A float indicating the scale factor
+        for second stage localization loss.
+      second_stage_classification_loss_weight: A float indicating the scale
+        factor for second stage classification loss.
+      second_stage_classification_loss: Classification loss used by the second
+        stage classifier. Either losses.WeightedSigmoidClassificationLoss or
+        losses.WeightedSoftmaxClassificationLoss.
+      second_stage_mask_prediction_loss_weight: A float indicating the scale
+        factor for second stage mask prediction loss. This is applicable only if
+        second stage box predictor is configured to predict masks.
+      hard_example_miner:  A losses.HardExampleMiner object (can be None).
+      parallel_iterations: (Optional) The number of iterations allowed to run
+        in parallel for calls to tf.map_fn.
+      add_summaries: boolean (default: True) controlling whether summary ops
+        should be added to tensorflow graph.
+      clip_anchors_to_image: Normally, anchors generated for a given image size
+        are pruned during training if they lie outside the image window. This
+        option clips the anchors to be within the image instead of pruning.
+      use_static_shapes: If True, uses implementation of ops with static shape
+        guarantees.
+      resize_masks: Indicates whether the masks presend in the groundtruth
+        should be resized in the model with `image_resizer_fn`
+
+    Raises:
+      ValueError: If `second_stage_batch_size` > `first_stage_max_proposals` at
+        training time.
+      ValueError: If first_stage_anchor_generator is not of type
+        grid_anchor_generator.GridAnchorGenerator.
+    """
+    # TODO(rathodv): add_summaries is currently unused. Respect that directive
+    # in the future.
+    super(FasterRCNNMetaArch, self).__init__(num_classes=num_classes)
+
+    if not isinstance(first_stage_anchor_generator,
+                      grid_anchor_generator.GridAnchorGenerator):
+      raise ValueError('first_stage_anchor_generator must be of type '
+                       'grid_anchor_generator.GridAnchorGenerator.')
+
+    self._is_training = is_training
+    self._image_resizer_fn = image_resizer_fn
+    self._resize_masks = resize_masks
+    self._feature_extractor = feature_extractor
+    self._number_of_stages = number_of_stages
+
+    self._proposal_target_assigner = first_stage_target_assigner
+    self._detector_target_assigner = second_stage_target_assigner
+    # Both proposal and detector target assigners use the same box coder
+    self._box_coder = self._proposal_target_assigner.box_coder
+
+    # (First stage) Region proposal network parameters
+    self._first_stage_anchor_generator = first_stage_anchor_generator
+    self._first_stage_atrous_rate = first_stage_atrous_rate
+    self._first_stage_box_predictor_arg_scope_fn = (
+        first_stage_box_predictor_arg_scope_fn)
+    self._first_stage_box_predictor_kernel_size = (
+        first_stage_box_predictor_kernel_size)
+    self._first_stage_box_predictor_depth = first_stage_box_predictor_depth
+    self._first_stage_minibatch_size = first_stage_minibatch_size
+    self._first_stage_sampler = first_stage_sampler
+    self._first_stage_box_predictor = (
+        box_predictor_builder.build_convolutional_box_predictor(
+            is_training=self._is_training,
+            num_classes=1,
+            conv_hyperparams_fn=self._first_stage_box_predictor_arg_scope_fn,
+            use_dropout=False,
+            dropout_keep_prob=1.0,
+            box_code_size=self._box_coder.code_size,
+            kernel_size=1,
+            num_layers_before_predictor=0,
+            min_depth=0,
+            max_depth=0))
+
+    self._first_stage_nms_fn = first_stage_non_max_suppression_fn
+    self._first_stage_max_proposals = first_stage_max_proposals
+    self._use_static_shapes = use_static_shapes
+
+    self._first_stage_localization_loss = (
+        losses.WeightedSmoothL1LocalizationLoss())
+    self._first_stage_objectness_loss = (
+        losses.WeightedSoftmaxClassificationLoss())
+    self._first_stage_loc_loss_weight = first_stage_localization_loss_weight
+    self._first_stage_obj_loss_weight = first_stage_objectness_loss_weight
+
+    # Per-region cropping parameters
+    self._crop_and_resize_fn = crop_and_resize_fn
+    self._initial_crop_size = initial_crop_size
+    self._maxpool_kernel_size = maxpool_kernel_size
+    self._maxpool_stride = maxpool_stride
+
+    self._mask_rcnn_box_predictor = second_stage_mask_rcnn_box_predictor
+
+    self._second_stage_batch_size = second_stage_batch_size
+    self._second_stage_sampler = second_stage_sampler
+
+    self._second_stage_nms_fn = second_stage_non_max_suppression_fn
+    self._second_stage_score_conversion_fn = second_stage_score_conversion_fn
+
+    self._second_stage_localization_loss = (
+        losses.WeightedSmoothL1LocalizationLoss())
+    self._second_stage_classification_loss = second_stage_classification_loss
+    self._second_stage_mask_loss = (
+        losses.WeightedSigmoidClassificationLoss())
+    self._second_stage_loc_loss_weight = second_stage_localization_loss_weight
+    self._second_stage_cls_loss_weight = second_stage_classification_loss_weight
+    self._second_stage_mask_loss_weight = (
+        second_stage_mask_prediction_loss_weight)
+    self._hard_example_miner = hard_example_miner
+    self._parallel_iterations = parallel_iterations
+
+    self.clip_anchors_to_image = clip_anchors_to_image
+
+    if self._number_of_stages <= 0 or self._number_of_stages > 3:
+      raise ValueError('Number of stages should be a value in {1, 2, 3}.')
+
+  @property
+  def first_stage_feature_extractor_scope(self):
+    return 'FirstStageFeatureExtractor'
+
+  @property
+  def second_stage_feature_extractor_scope(self):
+    return 'SecondStageFeatureExtractor'
+
+  @property
+  def first_stage_box_predictor_scope(self):
+    return 'FirstStageBoxPredictor'
+
+  @property
+  def second_stage_box_predictor_scope(self):
+    return 'SecondStageBoxPredictor'
+
+  @property
+  def max_num_proposals(self):
+    """Max number of proposals (to pad to) for each image in the input batch.
+
+    At training time, this is set to be the `second_stage_batch_size` if hard
+    example miner is not configured, else it is set to
+    `first_stage_max_proposals`. At inference time, this is always set to
+    `first_stage_max_proposals`.
+
+    Returns:
+      A positive integer.
+    """
+    if self._is_training and not self._hard_example_miner:
+      return self._second_stage_batch_size
+    return self._first_stage_max_proposals
+
+  @property
+  def anchors(self):
+    if not self._anchors:
+      raise RuntimeError('anchors have not been constructed yet!')
+    if not isinstance(self._anchors, box_list.BoxList):
+      raise RuntimeError('anchors should be a BoxList object, but is not.')
+    return self._anchors
+
+  def preprocess(self, inputs):
+    """Feature-extractor specific preprocessing.
+
+    See base class.
+
+    For Faster R-CNN, we perform image resizing in the base class --- each
+    class subclassing FasterRCNNMetaArch is responsible for any additional
+    preprocessing (e.g., scaling pixel values to be in [-1, 1]).
+
+    Args:
+      inputs: a [batch, height_in, width_in, channels] float tensor representing
+        a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: a [batch, height_out, width_out, channels] float
+        tensor representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+    Raises:
+      ValueError: if inputs tensor does not have type tf.float32
+    """
+    if inputs.dtype is not tf.float32:
+      raise ValueError('`preprocess` expects a tf.float32 tensor')
+    with tf.name_scope('Preprocessor'):
+      outputs = shape_utils.static_or_dynamic_map_fn(
+          self._image_resizer_fn,
+          elems=inputs,
+          dtype=[tf.float32, tf.int32],
+          parallel_iterations=self._parallel_iterations)
+      resized_inputs = outputs[0]
+      true_image_shapes = outputs[1]
+      return (self._feature_extractor.preprocess(resized_inputs),
+              true_image_shapes)
+
+  def _compute_clip_window(self, image_shapes):
+    """Computes clip window for non max suppression based on image shapes.
+
+    This function assumes that the clip window's left top corner is at (0, 0).
+
+    Args:
+      image_shapes: A 2-D int32 tensor of shape [batch_size, 3] containing
+      shapes of images in the batch. Each row represents [height, width,
+      channels] of an image.
+
+    Returns:
+      A 2-D float32 tensor of shape [batch_size, 4] containing the clip window
+      for each image in the form [ymin, xmin, ymax, xmax].
+    """
+    clip_heights = image_shapes[:, 0]
+    clip_widths = image_shapes[:, 1]
+    clip_window = tf.to_float(tf.stack([tf.zeros_like(clip_heights),
+                                        tf.zeros_like(clip_heights),
+                                        clip_heights, clip_widths], axis=1))
+    return clip_window
+
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    """Predicts unpostprocessed tensors from input tensor.
+
+    This function takes an input batch of images and runs it through the
+    forward pass of the network to yield "raw" un-postprocessed predictions.
+    If `number_of_stages` is 1, this function only returns first stage
+    RPN predictions (un-postprocessed).  Otherwise it returns both
+    first stage RPN predictions as well as second stage box classifier
+    predictions.
+
+    Other remarks:
+    + Anchor pruning vs. clipping: following the recommendation of the Faster
+    R-CNN paper, we prune anchors that venture outside the image window at
+    training time and clip anchors to the image window at inference time.
+    + Proposal padding: as described at the top of the file, proposals are
+    padded to self._max_num_proposals and flattened so that proposals from all
+    images within the input batch are arranged along the same batch dimension.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding "raw" prediction tensors:
+        1) rpn_box_predictor_features: A 4-D float32 tensor with shape
+          [batch_size, height, width, depth] to be used for predicting proposal
+          boxes and corresponding objectness scores.
+        2) rpn_features_to_crop: A 4-D float32 tensor with shape
+          [batch_size, height, width, depth] representing image features to crop
+          using the proposal boxes predicted by the RPN.
+        3) image_shape: a 1-D tensor of shape [4] representing the input
+          image shape.
+        4) rpn_box_encodings:  3-D float tensor of shape
+          [batch_size, num_anchors, self._box_coder.code_size] containing
+          predicted boxes.
+        5) rpn_objectness_predictions_with_background: 3-D float tensor of shape
+          [batch_size, num_anchors, 2] containing class
+          predictions (logits) for each of the anchors.  Note that this
+          tensor *includes* background class predictions (at class index 0).
+        6) anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
+          for the first stage RPN (in absolute coordinates).  Note that
+          `num_anchors` can differ depending on whether the model is created in
+          training or inference mode.
+
+        (and if number_of_stages > 1):
+        7) refined_box_encodings: a 3-D tensor with shape
+          [total_num_proposals, num_classes, self._box_coder.code_size]
+          representing predicted (final) refined box encodings, where
+          total_num_proposals=batch_size*self._max_num_proposals. If using
+          a shared box across classes the shape will instead be
+          [total_num_proposals, 1, self._box_coder.code_size].
+        8) class_predictions_with_background: a 3-D tensor with shape
+          [total_num_proposals, num_classes + 1] containing class
+          predictions (logits) for each of the anchors, where
+          total_num_proposals=batch_size*self._max_num_proposals.
+          Note that this tensor *includes* background class predictions
+          (at class index 0).
+        9) num_proposals: An int32 tensor of shape [batch_size] representing the
+          number of proposals generated by the RPN.  `num_proposals` allows us
+          to keep track of which entries are to be treated as zero paddings and
+          which are not since we always pad the number of proposals to be
+          `self.max_num_proposals` for each image.
+        10) proposal_boxes: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing
+          decoded proposal bounding boxes in absolute coordinates.
+        11) mask_predictions: (optional) a 4-D tensor with shape
+          [total_num_padded_proposals, num_classes, mask_height, mask_width]
+          containing instance mask predictions.
+
+    Raises:
+      ValueError: If `predict` is called before `preprocess`.
+    """
+    (rpn_box_predictor_features, rpn_features_to_crop, anchors_boxlist,
+     image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
+    (rpn_box_encodings, rpn_objectness_predictions_with_background
+    ) = self._predict_rpn_proposals(rpn_box_predictor_features)
+
+    # The Faster R-CNN paper recommends pruning anchors that venture outside
+    # the image window at training time and clipping at inference time.
+    clip_window = tf.to_float(tf.stack([0, 0, image_shape[1], image_shape[2]]))
+    if self._is_training:
+      if self.clip_anchors_to_image:
+        anchors_boxlist = box_list_ops.clip_to_window(
+            anchors_boxlist, clip_window, filter_nonoverlapping=False)
+      else:
+        (rpn_box_encodings, rpn_objectness_predictions_with_background,
+         anchors_boxlist) = self._remove_invalid_anchors_and_predictions(
+             rpn_box_encodings, rpn_objectness_predictions_with_background,
+             anchors_boxlist, clip_window)
+    else:
+      anchors_boxlist = box_list_ops.clip_to_window(
+          anchors_boxlist, clip_window,
+          filter_nonoverlapping=not self._use_static_shapes)
+
+    self._anchors = anchors_boxlist
+    prediction_dict = {
+        'rpn_box_predictor_features': rpn_box_predictor_features,
+        'rpn_features_to_crop': rpn_features_to_crop,
+        'image_shape': image_shape,
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'anchors': self._anchors.get()
+    }
+
+    if self._number_of_stages >= 2:
+      # If mixed-precision training on TPU is enabled, rpn_box_encodings and
+      # rpn_objectness_predictions_with_background are bfloat16 tensors.
+      # Considered prediction results, they need to be casted to float32
+      # tensors for correct postprocess_rpn computation in predict_second_stage.
+      prediction_dict.update(self._predict_second_stage(
+          tf.to_float(rpn_box_encodings),
+          tf.to_float(rpn_objectness_predictions_with_background),
+          rpn_features_to_crop,
+          self._anchors.get(), image_shape, true_image_shapes))
+
+    if self._number_of_stages == 3:
+      prediction_dict = self._predict_third_stage(
+          prediction_dict, true_image_shapes)
+
+    return prediction_dict
+
+  def _image_batch_shape_2d(self, image_batch_shape_1d):
+    """Takes a 1-D image batch shape tensor and converts it to a 2-D tensor.
+
+    Example:
+    If 1-D image batch shape tensor is [2, 300, 300, 3]. The corresponding 2-D
+    image batch tensor would be [[300, 300, 3], [300, 300, 3]]
+
+    Args:
+      image_batch_shape_1d: 1-D tensor of the form [batch_size, height,
+        width, channels].
+
+    Returns:
+      image_batch_shape_2d: 2-D tensor of shape [batch_size, 3] were each row is
+        of the form [height, width, channels].
+    """
+    return tf.tile(tf.expand_dims(image_batch_shape_1d[1:], 0),
+                   [image_batch_shape_1d[0], 1])
+
+  def _predict_second_stage(self, rpn_box_encodings,
+                            rpn_objectness_predictions_with_background,
+                            rpn_features_to_crop,
+                            anchors,
+                            image_shape,
+                            true_image_shapes):
+    """Predicts the output tensors from second stage of Faster R-CNN.
+
+    Args:
+      rpn_box_encodings: 4-D float tensor of shape
+        [batch_size, num_valid_anchors, self._box_coder.code_size] containing
+        predicted boxes.
+      rpn_objectness_predictions_with_background: 2-D float tensor of shape
+        [batch_size, num_valid_anchors, 2] containing class
+        predictions (logits) for each of the anchors.  Note that this
+        tensor *includes* background class predictions (at class index 0).
+      rpn_features_to_crop: A 4-D float32 or bfloat16 tensor with shape
+        [batch_size, height, width, depth] representing image features to crop
+        using the proposal boxes predicted by the RPN.
+      anchors: 2-D float tensor of shape
+        [num_anchors, self._box_coder.code_size].
+      image_shape: A 1D int32 tensors of size [4] containing the image shape.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding "raw" prediction tensors:
+        1) refined_box_encodings: a 3-D tensor with shape
+          [total_num_proposals, num_classes, self._box_coder.code_size]
+          representing predicted (final) refined box encodings, where
+          total_num_proposals=batch_size*self._max_num_proposals. If using a
+          shared box across classes the shape will instead be
+          [total_num_proposals, 1, self._box_coder.code_size].
+        2) class_predictions_with_background: a 3-D tensor with shape
+          [total_num_proposals, num_classes + 1] containing class
+          predictions (logits) for each of the anchors, where
+          total_num_proposals=batch_size*self._max_num_proposals.
+          Note that this tensor *includes* background class predictions
+          (at class index 0).
+        3) num_proposals: An int32 tensor of shape [batch_size] representing the
+          number of proposals generated by the RPN.  `num_proposals` allows us
+          to keep track of which entries are to be treated as zero paddings and
+          which are not since we always pad the number of proposals to be
+          `self.max_num_proposals` for each image.
+        4) proposal_boxes: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing
+          decoded proposal bounding boxes in absolute coordinates.
+        5) proposal_boxes_normalized: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing decoded proposal
+          bounding boxes in normalized coordinates. Can be used to override the
+          boxes proposed by the RPN, thus enabling one to extract features and
+          get box classification and prediction for externally selected areas
+          of the image.
+        6) box_classifier_features: a 4-D float32 or bfloat16 tensor
+          representing the features for each proposal.
+    """
+    image_shape_2d = self._image_batch_shape_2d(image_shape)
+    proposal_boxes_normalized, _, num_proposals = self._postprocess_rpn(
+        rpn_box_encodings, rpn_objectness_predictions_with_background,
+        anchors, image_shape_2d, true_image_shapes)
+
+    # If mixed-precision training on TPU is enabled, the dtype of
+    # rpn_features_to_crop is bfloat16, otherwise it is float32. tf.cast is
+    # used to match the dtype of proposal_boxes_normalized to that of
+    # rpn_features_to_crop for further computation.
+    flattened_proposal_feature_maps = (
+        self._compute_second_stage_input_feature_maps(
+            rpn_features_to_crop,
+            tf.cast(proposal_boxes_normalized, rpn_features_to_crop.dtype)))
+
+    box_classifier_features = (
+        self._feature_extractor.extract_box_classifier_features(
+            flattened_proposal_feature_maps,
+            scope=self.second_stage_feature_extractor_scope))
+
+    if self._mask_rcnn_box_predictor.is_keras_model:
+      box_predictions = self._mask_rcnn_box_predictor(
+          [box_classifier_features],
+          prediction_stage=2)
+    else:
+      box_predictions = self._mask_rcnn_box_predictor.predict(
+          [box_classifier_features],
+          num_predictions_per_location=[1],
+          scope=self.second_stage_box_predictor_scope,
+          prediction_stage=2)
+
+    refined_box_encodings = tf.squeeze(
+        box_predictions[box_predictor.BOX_ENCODINGS],
+        axis=1, name='all_refined_box_encodings')
+    class_predictions_with_background = tf.squeeze(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1, name='all_class_predictions_with_background')
+
+    absolute_proposal_boxes = ops.normalized_to_image_coordinates(
+        proposal_boxes_normalized, image_shape, self._parallel_iterations)
+
+    prediction_dict = {
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background':
+        class_predictions_with_background,
+        'num_proposals': num_proposals,
+        'proposal_boxes': absolute_proposal_boxes,
+        'box_classifier_features': box_classifier_features,
+        'proposal_boxes_normalized': proposal_boxes_normalized,
+    }
+
+    return prediction_dict
+
+  def _predict_third_stage(self, prediction_dict, image_shapes):
+    """Predicts non-box, non-class outputs using refined detections.
+
+    For training, masks as predicted directly on the box_classifier_features,
+    which are region-features from the initial anchor boxes.
+    For inference, this happens after calling the post-processing stage, such
+    that masks are only calculated for the top scored boxes.
+
+    Args:
+     prediction_dict: a dictionary holding "raw" prediction tensors:
+        1) refined_box_encodings: a 3-D tensor with shape
+          [total_num_proposals, num_classes, self._box_coder.code_size]
+          representing predicted (final) refined box encodings, where
+          total_num_proposals=batch_size*self._max_num_proposals. If using a
+          shared box across classes the shape will instead be
+          [total_num_proposals, 1, self._box_coder.code_size].
+        2) class_predictions_with_background: a 3-D tensor with shape
+          [total_num_proposals, num_classes + 1] containing class
+          predictions (logits) for each of the anchors, where
+          total_num_proposals=batch_size*self._max_num_proposals.
+          Note that this tensor *includes* background class predictions
+          (at class index 0).
+        3) num_proposals: An int32 tensor of shape [batch_size] representing the
+          number of proposals generated by the RPN.  `num_proposals` allows us
+          to keep track of which entries are to be treated as zero paddings and
+          which are not since we always pad the number of proposals to be
+          `self.max_num_proposals` for each image.
+        4) proposal_boxes: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing
+          decoded proposal bounding boxes in absolute coordinates.
+        5) box_classifier_features: a 4-D float32 tensor representing the
+          features for each proposal.
+      image_shapes: A 2-D int32 tensors of shape [batch_size, 3] containing
+        shapes of images in the batch.
+
+    Returns:
+      prediction_dict: a dictionary that in addition to the input predictions
+      does hold the following predictions as well:
+        1) mask_predictions: a 4-D tensor with shape
+          [batch_size, max_detection, mask_height, mask_width] containing
+          instance mask predictions.
+    """
+    if self._is_training:
+      curr_box_classifier_features = prediction_dict['box_classifier_features']
+      detection_classes = prediction_dict['class_predictions_with_background']
+      if self._mask_rcnn_box_predictor.is_keras_model:
+        mask_predictions = self._mask_rcnn_box_predictor(
+            [curr_box_classifier_features],
+            prediction_stage=3)
+      else:
+        mask_predictions = self._mask_rcnn_box_predictor.predict(
+            [curr_box_classifier_features],
+            num_predictions_per_location=[1],
+            scope=self.second_stage_box_predictor_scope,
+            prediction_stage=3)
+      prediction_dict['mask_predictions'] = tf.squeeze(mask_predictions[
+          box_predictor.MASK_PREDICTIONS], axis=1)
+    else:
+      detections_dict = self._postprocess_box_classifier(
+          prediction_dict['refined_box_encodings'],
+          prediction_dict['class_predictions_with_background'],
+          prediction_dict['proposal_boxes'],
+          prediction_dict['num_proposals'],
+          image_shapes)
+      prediction_dict.update(detections_dict)
+      detection_boxes = detections_dict[
+          fields.DetectionResultFields.detection_boxes]
+      detection_classes = detections_dict[
+          fields.DetectionResultFields.detection_classes]
+      rpn_features_to_crop = prediction_dict['rpn_features_to_crop']
+      batch_size = tf.shape(detection_boxes)[0]
+      max_detection = tf.shape(detection_boxes)[1]
+      flattened_detected_feature_maps = (
+          self._compute_second_stage_input_feature_maps(
+              rpn_features_to_crop, detection_boxes))
+      curr_box_classifier_features = (
+          self._feature_extractor.extract_box_classifier_features(
+              flattened_detected_feature_maps,
+              scope=self.second_stage_feature_extractor_scope))
+
+      if self._mask_rcnn_box_predictor.is_keras_model:
+        mask_predictions = self._mask_rcnn_box_predictor(
+            [curr_box_classifier_features],
+            prediction_stage=3)
+      else:
+        mask_predictions = self._mask_rcnn_box_predictor.predict(
+            [curr_box_classifier_features],
+            num_predictions_per_location=[1],
+            scope=self.second_stage_box_predictor_scope,
+            prediction_stage=3)
+
+      detection_masks = tf.squeeze(mask_predictions[
+          box_predictor.MASK_PREDICTIONS], axis=1)
+
+      _, num_classes, mask_height, mask_width = (
+          detection_masks.get_shape().as_list())
+      _, max_detection = detection_classes.get_shape().as_list()
+      prediction_dict['mask_predictions'] = tf.reshape(
+          detection_masks, [-1, num_classes, mask_height, mask_width])
+      if num_classes > 1:
+        detection_masks = self._gather_instance_masks(
+            detection_masks, detection_classes)
+
+      prediction_dict[fields.DetectionResultFields.detection_masks] = (
+          tf.reshape(tf.sigmoid(detection_masks),
+                     [batch_size, max_detection, mask_height, mask_width]))
+
+    return prediction_dict
+
+  def _gather_instance_masks(self, instance_masks, classes):
+    """Gathers the masks that correspond to classes.
+
+    Args:
+      instance_masks: A 4-D float32 tensor with shape
+        [K, num_classes, mask_height, mask_width].
+      classes: A 2-D int32 tensor with shape [batch_size, max_detection].
+
+    Returns:
+      masks: a 3-D float32 tensor with shape [K, mask_height, mask_width].
+    """
+    _, num_classes, height, width = instance_masks.get_shape().as_list()
+    k = tf.shape(instance_masks)[0]
+    instance_masks = tf.reshape(instance_masks, [-1, height, width])
+    classes = tf.to_int32(tf.reshape(classes, [-1]))
+    gather_idx = tf.range(k) * num_classes + classes
+    return tf.gather(instance_masks, gather_idx)
+
+  def _extract_rpn_feature_maps(self, preprocessed_inputs):
+    """Extracts RPN features.
+
+    This function extracts two feature maps: a feature map to be directly
+    fed to a box predictor (to predict location and objectness scores for
+    proposals) and a feature map from which to crop regions which will then
+    be sent to the second stage box classifier.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] image tensor.
+
+    Returns:
+      rpn_box_predictor_features: A 4-D float32 tensor with shape
+        [batch, height, width, depth] to be used for predicting proposal boxes
+        and corresponding objectness scores.
+      rpn_features_to_crop: A 4-D float32 tensor with shape
+        [batch, height, width, depth] representing image features to crop using
+        the proposals boxes.
+      anchors: A BoxList representing anchors (for the RPN) in
+        absolute coordinates.
+      image_shape: A 1-D tensor representing the input image shape.
+    """
+    image_shape = tf.shape(preprocessed_inputs)
+
+    rpn_features_to_crop, self.endpoints = (
+        self._feature_extractor.extract_proposal_features(
+            preprocessed_inputs,
+            scope=self.first_stage_feature_extractor_scope))
+
+    feature_map_shape = tf.shape(rpn_features_to_crop)
+    anchors = box_list_ops.concatenate(
+        self._first_stage_anchor_generator.generate([(feature_map_shape[1],
+                                                      feature_map_shape[2])]))
+    with slim.arg_scope(self._first_stage_box_predictor_arg_scope_fn()):
+      kernel_size = self._first_stage_box_predictor_kernel_size
+      reuse = tf.get_variable_scope().reuse
+      rpn_box_predictor_features = slim.conv2d(
+          rpn_features_to_crop,
+          self._first_stage_box_predictor_depth,
+          kernel_size=[kernel_size, kernel_size],
+          rate=self._first_stage_atrous_rate,
+          activation_fn=tf.nn.relu6,
+          scope='Conv',
+          reuse=reuse)
+    return (rpn_box_predictor_features, rpn_features_to_crop,
+            anchors, image_shape)
+
+  def _predict_rpn_proposals(self, rpn_box_predictor_features):
+    """Adds box predictors to RPN feature map to predict proposals.
+
+    Note resulting tensors will not have been postprocessed.
+
+    Args:
+      rpn_box_predictor_features: A 4-D float32 tensor with shape
+        [batch, height, width, depth] to be used for predicting proposal boxes
+        and corresponding objectness scores.
+
+    Returns:
+      box_encodings: 3-D float tensor of shape
+        [batch_size, num_anchors, self._box_coder.code_size] containing
+        predicted boxes.
+      objectness_predictions_with_background: 3-D float tensor of shape
+        [batch_size, num_anchors, 2] containing class
+        predictions (logits) for each of the anchors.  Note that this
+        tensor *includes* background class predictions (at class index 0).
+
+    Raises:
+      RuntimeError: if the anchor generator generates anchors corresponding to
+        multiple feature maps.  We currently assume that a single feature map
+        is generated for the RPN.
+    """
+    num_anchors_per_location = (
+        self._first_stage_anchor_generator.num_anchors_per_location())
+    if len(num_anchors_per_location) != 1:
+      raise RuntimeError('anchor_generator is expected to generate anchors '
+                         'corresponding to a single feature map.')
+    if self._first_stage_box_predictor.is_keras_model:
+      box_predictions = self._first_stage_box_predictor(
+          [rpn_box_predictor_features])
+    else:
+      box_predictions = self._first_stage_box_predictor.predict(
+          [rpn_box_predictor_features],
+          num_anchors_per_location,
+          scope=self.first_stage_box_predictor_scope)
+
+    box_encodings = tf.concat(
+        box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+    objectness_predictions_with_background = tf.concat(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1)
+    return (tf.squeeze(box_encodings, axis=2),
+            objectness_predictions_with_background)
+
+  def _remove_invalid_anchors_and_predictions(
+      self,
+      box_encodings,
+      objectness_predictions_with_background,
+      anchors_boxlist,
+      clip_window):
+    """Removes anchors that (partially) fall outside an image.
+
+    Also removes associated box encodings and objectness predictions.
+
+    Args:
+      box_encodings: 3-D float tensor of shape
+        [batch_size, num_anchors, self._box_coder.code_size] containing
+        predicted boxes.
+      objectness_predictions_with_background: 3-D float tensor of shape
+        [batch_size, num_anchors, 2] containing class
+        predictions (logits) for each of the anchors.  Note that this
+        tensor *includes* background class predictions (at class index 0).
+      anchors_boxlist: A BoxList representing num_anchors anchors (for the RPN)
+        in absolute coordinates.
+      clip_window: a 1-D tensor representing the [ymin, xmin, ymax, xmax]
+        extent of the window to clip/prune to.
+
+    Returns:
+      box_encodings: 4-D float tensor of shape
+        [batch_size, num_valid_anchors, self._box_coder.code_size] containing
+        predicted boxes, where num_valid_anchors <= num_anchors
+      objectness_predictions_with_background: 2-D float tensor of shape
+        [batch_size, num_valid_anchors, 2] containing class
+        predictions (logits) for each of the anchors, where
+        num_valid_anchors <= num_anchors.  Note that this
+        tensor *includes* background class predictions (at class index 0).
+      anchors: A BoxList representing num_valid_anchors anchors (for the RPN) in
+        absolute coordinates.
+    """
+    pruned_anchors_boxlist, keep_indices = box_list_ops.prune_outside_window(
+        anchors_boxlist, clip_window)
+    def _batch_gather_kept_indices(predictions_tensor):
+      return shape_utils.static_or_dynamic_map_fn(
+          partial(tf.gather, indices=keep_indices),
+          elems=predictions_tensor,
+          dtype=tf.float32,
+          parallel_iterations=self._parallel_iterations,
+          back_prop=True)
+    return (_batch_gather_kept_indices(box_encodings),
+            _batch_gather_kept_indices(objectness_predictions_with_background),
+            pruned_anchors_boxlist)
+
+  def _flatten_first_two_dimensions(self, inputs):
+    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.
+
+    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
+    [A * B, ..., depth].
+
+    Args:
+      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
+        two and last dimensions must be statically defined.
+    Returns:
+      A float tensor with shape [A * B, ..., depth] (where the first and last
+        dimension are statically defined.
+    """
+    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
+    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
+                               combined_shape[2:])
+    return tf.reshape(inputs, flattened_shape)
+
+  def postprocess(self, prediction_dict, true_image_shapes):
+    """Convert prediction tensors to final detections.
+
+    This function converts raw predictions tensors to final detection results.
+    See base class for output format conventions.  Note also that by default,
+    scores are to be interpreted as logits, but if a score_converter is used,
+    then scores are remapped (and may thus have a different interpretation).
+
+    If number_of_stages=1, the returned results represent proposals from the
+    first stage RPN and are padded to have self.max_num_proposals for each
+    image; otherwise, the results can be interpreted as multiclass detections
+    from the full two-stage model and are padded to self._max_detections.
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors (see the
+        documentation for the predict method.  If number_of_stages=1, we
+        expect prediction_dict to contain `rpn_box_encodings`,
+        `rpn_objectness_predictions_with_background`, `rpn_features_to_crop`,
+        and `anchors` fields.  Otherwise we expect prediction_dict to
+        additionally contain `refined_box_encodings`,
+        `class_predictions_with_background`, `num_proposals`,
+        `proposal_boxes` and, optionally, `mask_predictions` fields.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      detections: a dictionary containing the following fields
+        detection_boxes: [batch, max_detection, 4]
+        detection_scores: [batch, max_detections]
+        detection_classes: [batch, max_detections]
+          (this entry is only created if rpn_mode=False)
+        num_detections: [batch]
+
+    Raises:
+      ValueError: If `predict` is called before `preprocess`.
+    """
+
+    with tf.name_scope('FirstStagePostprocessor'):
+      if self._number_of_stages == 1:
+        proposal_boxes, proposal_scores, num_proposals = self._postprocess_rpn(
+            prediction_dict['rpn_box_encodings'],
+            prediction_dict['rpn_objectness_predictions_with_background'],
+            prediction_dict['anchors'],
+            true_image_shapes,
+            true_image_shapes)
+        return {
+            fields.DetectionResultFields.detection_boxes: proposal_boxes,
+            fields.DetectionResultFields.detection_scores: proposal_scores,
+            fields.DetectionResultFields.num_detections:
+                tf.to_float(num_proposals),
+        }
+
+    # TODO(jrru): Remove mask_predictions from _post_process_box_classifier.
+    if (self._number_of_stages == 2 or
+        (self._number_of_stages == 3 and self._is_training)):
+      with tf.name_scope('SecondStagePostprocessor'):
+        mask_predictions = prediction_dict.get(box_predictor.MASK_PREDICTIONS)
+        detections_dict = self._postprocess_box_classifier(
+            prediction_dict['refined_box_encodings'],
+            prediction_dict['class_predictions_with_background'],
+            prediction_dict['proposal_boxes'],
+            prediction_dict['num_proposals'],
+            true_image_shapes,
+            mask_predictions=mask_predictions)
+
+      if 'rpn_features_to_crop' in prediction_dict and self._initial_crop_size:
+        self._add_detection_features_output_node(
+            detections_dict[fields.DetectionResultFields.detection_boxes],
+            prediction_dict['rpn_features_to_crop'])
+
+      return detections_dict
+
+    if self._number_of_stages == 3:
+      # Post processing is already performed in 3rd stage. We need to transfer
+      # postprocessed tensors from `prediction_dict` to `detections_dict`.
+      return prediction_dict
+
+  def _add_detection_features_output_node(self, detection_boxes,
+                                          rpn_features_to_crop):
+    """Add the detection features to the output node.
+
+    The detection features are from cropping rpn_features with boxes.
+    Each bounding box has one feature vector of length depth, which comes from
+    mean_pooling of the cropped rpn_features.
+
+    Args:
+      detection_boxes: a 3-D float32 tensor of shape
+        [batch_size, max_detection, 4] which represents the bounding boxes.
+      rpn_features_to_crop: A 4-D float32 tensor with shape
+        [batch, height, width, depth] representing image features to crop using
+        the proposals boxes.
+    """
+    with tf.name_scope('SecondStageDetectionFeaturesExtract'):
+      flattened_detected_feature_maps = (
+          self._compute_second_stage_input_feature_maps(
+              rpn_features_to_crop, detection_boxes))
+      detection_features_unpooled = (
+          self._feature_extractor.extract_box_classifier_features(
+              flattened_detected_feature_maps,
+              scope=self.second_stage_feature_extractor_scope))
+
+      batch_size = tf.shape(detection_boxes)[0]
+      max_detection = tf.shape(detection_boxes)[1]
+      detection_features_pool = tf.reduce_mean(
+          detection_features_unpooled, axis=[1, 2])
+      detection_features = tf.reshape(
+          detection_features_pool,
+          [batch_size, max_detection, tf.shape(detection_features_pool)[-1]])
+
+    detection_features = tf.identity(
+        detection_features, 'detection_features')
+
+  def _postprocess_rpn(self,
+                       rpn_box_encodings_batch,
+                       rpn_objectness_predictions_with_background_batch,
+                       anchors,
+                       image_shapes,
+                       true_image_shapes):
+    """Converts first stage prediction tensors from the RPN to proposals.
+
+    This function decodes the raw RPN predictions, runs non-max suppression
+    on the result.
+
+    Note that the behavior of this function is slightly modified during
+    training --- specifically, we stop the gradient from passing through the
+    proposal boxes and we only return a balanced sampled subset of proposals
+    with size `second_stage_batch_size`.
+
+    Args:
+      rpn_box_encodings_batch: A 3-D float32 tensor of shape
+        [batch_size, num_anchors, self._box_coder.code_size] containing
+        predicted proposal box encodings.
+      rpn_objectness_predictions_with_background_batch: A 3-D float tensor of
+        shape [batch_size, num_anchors, 2] containing objectness predictions
+        (logits) for each of the anchors with 0 corresponding to background
+        and 1 corresponding to object.
+      anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
+        for the first stage RPN.  Note that `num_anchors` can differ depending
+        on whether the model is created in training or inference mode.
+      image_shapes: A 2-D tensor of shape [batch, 3] containing the shapes of
+        images in the batch.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      proposal_boxes: A float tensor with shape
+        [batch_size, max_num_proposals, 4] representing the (potentially zero
+        padded) proposal boxes for all images in the batch.  These boxes are
+        represented as normalized coordinates.
+      proposal_scores:  A float tensor with shape
+        [batch_size, max_num_proposals] representing the (potentially zero
+        padded) proposal objectness scores for all images in the batch.
+      num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
+        representing the number of proposals predicted for each image in
+        the batch.
+    """
+    rpn_box_encodings_batch = tf.expand_dims(rpn_box_encodings_batch, axis=2)
+    rpn_encodings_shape = shape_utils.combined_static_and_dynamic_shape(
+        rpn_box_encodings_batch)
+    tiled_anchor_boxes = tf.tile(
+        tf.expand_dims(anchors, 0), [rpn_encodings_shape[0], 1, 1])
+    proposal_boxes = self._batch_decode_boxes(rpn_box_encodings_batch,
+                                              tiled_anchor_boxes)
+    proposal_boxes = tf.squeeze(proposal_boxes, axis=2)
+    rpn_objectness_softmax_without_background = tf.nn.softmax(
+        rpn_objectness_predictions_with_background_batch)[:, :, 1]
+    clip_window = self._compute_clip_window(image_shapes)
+    (proposal_boxes, proposal_scores, _, _, _,
+     num_proposals) = self._first_stage_nms_fn(
+         tf.expand_dims(proposal_boxes, axis=2),
+         tf.expand_dims(rpn_objectness_softmax_without_background, axis=2),
+         clip_window=clip_window)
+    if self._is_training:
+      proposal_boxes = tf.stop_gradient(proposal_boxes)
+      if not self._hard_example_miner:
+        (groundtruth_boxlists, groundtruth_classes_with_background_list, _,
+         groundtruth_weights_list
+        ) = self._format_groundtruth_data(true_image_shapes)
+        (proposal_boxes, proposal_scores,
+         num_proposals) = self._sample_box_classifier_batch(
+             proposal_boxes, proposal_scores, num_proposals,
+             groundtruth_boxlists, groundtruth_classes_with_background_list,
+             groundtruth_weights_list)
+    # normalize proposal boxes
+    def normalize_boxes(args):
+      proposal_boxes_per_image = args[0]
+      image_shape = args[1]
+      normalized_boxes_per_image = box_list_ops.to_normalized_coordinates(
+          box_list.BoxList(proposal_boxes_per_image), image_shape[0],
+          image_shape[1], check_range=False).get()
+      return normalized_boxes_per_image
+    normalized_proposal_boxes = shape_utils.static_or_dynamic_map_fn(
+        normalize_boxes, elems=[proposal_boxes, image_shapes], dtype=tf.float32)
+    return normalized_proposal_boxes, proposal_scores, num_proposals
+
+  def _sample_box_classifier_batch(
+      self,
+      proposal_boxes,
+      proposal_scores,
+      num_proposals,
+      groundtruth_boxlists,
+      groundtruth_classes_with_background_list,
+      groundtruth_weights_list):
+    """Samples a minibatch for second stage.
+
+    Args:
+      proposal_boxes: A float tensor with shape
+        [batch_size, num_proposals, 4] representing the (potentially zero
+        padded) proposal boxes for all images in the batch.  These boxes are
+        represented in absolute coordinates.
+      proposal_scores:  A float tensor with shape
+        [batch_size, num_proposals] representing the (potentially zero
+        padded) proposal objectness scores for all images in the batch.
+      num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
+        representing the number of proposals predicted for each image in
+        the batch.
+      groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
+        of the groundtruth boxes.
+      groundtruth_classes_with_background_list: A list of 2-D one-hot
+        (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
+        class targets with the 0th index assumed to map to the background class.
+      groundtruth_weights_list: A list of 1-D tensors of shape [num_boxes]
+        indicating the weight associated with the groundtruth boxes.
+
+    Returns:
+      proposal_boxes: A float tensor with shape
+        [batch_size, second_stage_batch_size, 4] representing the (potentially
+        zero padded) proposal boxes for all images in the batch.  These boxes
+        are represented in absolute coordinates.
+      proposal_scores:  A float tensor with shape
+        [batch_size, second_stage_batch_size] representing the (potentially zero
+        padded) proposal objectness scores for all images in the batch.
+      num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
+        representing the number of proposals predicted for each image in
+        the batch.
+    """
+    single_image_proposal_box_sample = []
+    single_image_proposal_score_sample = []
+    single_image_num_proposals_sample = []
+    for (single_image_proposal_boxes,
+         single_image_proposal_scores,
+         single_image_num_proposals,
+         single_image_groundtruth_boxlist,
+         single_image_groundtruth_classes_with_background,
+         single_image_groundtruth_weights) in zip(
+             tf.unstack(proposal_boxes),
+             tf.unstack(proposal_scores),
+             tf.unstack(num_proposals),
+             groundtruth_boxlists,
+             groundtruth_classes_with_background_list,
+             groundtruth_weights_list):
+      single_image_boxlist = box_list.BoxList(single_image_proposal_boxes)
+      single_image_boxlist.add_field(fields.BoxListFields.scores,
+                                     single_image_proposal_scores)
+      sampled_boxlist = self._sample_box_classifier_minibatch_single_image(
+          single_image_boxlist,
+          single_image_num_proposals,
+          single_image_groundtruth_boxlist,
+          single_image_groundtruth_classes_with_background,
+          single_image_groundtruth_weights)
+      sampled_padded_boxlist = box_list_ops.pad_or_clip_box_list(
+          sampled_boxlist,
+          num_boxes=self._second_stage_batch_size)
+      single_image_num_proposals_sample.append(tf.minimum(
+          sampled_boxlist.num_boxes(),
+          self._second_stage_batch_size))
+      bb = sampled_padded_boxlist.get()
+      single_image_proposal_box_sample.append(bb)
+      single_image_proposal_score_sample.append(
+          sampled_padded_boxlist.get_field(fields.BoxListFields.scores))
+    return (tf.stack(single_image_proposal_box_sample),
+            tf.stack(single_image_proposal_score_sample),
+            tf.stack(single_image_num_proposals_sample))
+
+  def _format_groundtruth_data(self, true_image_shapes):
+    """Helper function for preparing groundtruth data for target assignment.
+
+    In order to be consistent with the model.DetectionModel interface,
+    groundtruth boxes are specified in normalized coordinates and classes are
+    specified as label indices with no assumed background category.  To prepare
+    for target assignment, we:
+    1) convert boxes to absolute coordinates,
+    2) add a background class at class index 0
+    3) groundtruth instance masks, if available, are resized to match
+       image_shape.
+
+    Args:
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
+        of the groundtruth boxes.
+      groundtruth_classes_with_background_list: A list of 2-D one-hot
+        (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
+        class targets with the 0th index assumed to map to the background class.
+      groundtruth_masks_list: If present, a list of 3-D tf.float32 tensors of
+        shape [num_boxes, image_height, image_width] containing instance masks.
+        This is set to None if no masks exist in the provided groundtruth.
+    """
+    groundtruth_boxlists = [
+        box_list_ops.to_absolute_coordinates(
+            box_list.BoxList(boxes), true_image_shapes[i, 0],
+            true_image_shapes[i, 1])
+        for i, boxes in enumerate(
+            self.groundtruth_lists(fields.BoxListFields.boxes))
+    ]
+    groundtruth_classes_with_background_list = [
+        tf.to_float(
+            tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT'))
+        for one_hot_encoding in self.groundtruth_lists(
+            fields.BoxListFields.classes)]
+
+    groundtruth_masks_list = self._groundtruth_lists.get(
+        fields.BoxListFields.masks)
+    # TODO(rathodv): Remove mask resizing once the legacy pipeline is deleted.
+    if groundtruth_masks_list is not None and self._resize_masks:
+      resized_masks_list = []
+      for mask in groundtruth_masks_list:
+
+        _, resized_mask, _ = self._image_resizer_fn(
+            # Reuse the given `image_resizer_fn` to resize groundtruth masks.
+            # `mask` tensor for an image is of the shape [num_masks,
+            # image_height, image_width]. Below we create a dummy image of the
+            # the shape [image_height, image_width, 1] to use with
+            # `image_resizer_fn`.
+            image=tf.zeros(tf.stack([tf.shape(mask)[1],
+                                     tf.shape(mask)[2], 1])),
+            masks=mask)
+        resized_masks_list.append(resized_mask)
+
+      groundtruth_masks_list = resized_masks_list
+    if self.groundtruth_has_field(fields.BoxListFields.weights):
+      groundtruth_weights_list = self.groundtruth_lists(
+          fields.BoxListFields.weights)
+    else:
+      # Set weights for all batch elements equally to 1.0
+      groundtruth_weights_list = []
+      for groundtruth_classes in groundtruth_classes_with_background_list:
+        num_gt = tf.shape(groundtruth_classes)[0]
+        groundtruth_weights = tf.ones(num_gt)
+        groundtruth_weights_list.append(groundtruth_weights)
+
+    return (groundtruth_boxlists, groundtruth_classes_with_background_list,
+            groundtruth_masks_list, groundtruth_weights_list)
+
+  def _sample_box_classifier_minibatch_single_image(
+      self, proposal_boxlist, num_valid_proposals, groundtruth_boxlist,
+      groundtruth_classes_with_background, groundtruth_weights):
+    """Samples a mini-batch of proposals to be sent to the box classifier.
+
+    Helper function for self._postprocess_rpn.
+
+    Args:
+      proposal_boxlist: A BoxList containing K proposal boxes in absolute
+        coordinates.
+      num_valid_proposals: Number of valid proposals in the proposal boxlist.
+      groundtruth_boxlist: A Boxlist containing N groundtruth object boxes in
+        absolute coordinates.
+      groundtruth_classes_with_background: A tensor with shape
+        `[N, self.num_classes + 1]` representing groundtruth classes. The
+        classes are assumed to be k-hot encoded, and include background as the
+        zero-th class.
+      groundtruth_weights: Weights attached to the groundtruth_boxes.
+
+    Returns:
+      a BoxList contained sampled proposals.
+    """
+    (cls_targets, cls_weights, _, _, _) = self._detector_target_assigner.assign(
+        proposal_boxlist,
+        groundtruth_boxlist,
+        groundtruth_classes_with_background,
+        unmatched_class_label=tf.constant(
+            [1] + self._num_classes * [0], dtype=tf.float32),
+        groundtruth_weights=groundtruth_weights)
+    # Selects all boxes as candidates if none of them is selected according
+    # to cls_weights. This could happen as boxes within certain IOU ranges
+    # are ignored. If triggered, the selected boxes will still be ignored
+    # during loss computation.
+    cls_weights = tf.reduce_mean(cls_weights, axis=-1)
+    positive_indicator = tf.greater(tf.argmax(cls_targets, axis=1), 0)
+    valid_indicator = tf.logical_and(
+        tf.range(proposal_boxlist.num_boxes()) < num_valid_proposals,
+        cls_weights > 0
+    )
+    selected_positions = self._second_stage_sampler.subsample(
+        valid_indicator,
+        self._second_stage_batch_size,
+        positive_indicator)
+    return box_list_ops.boolean_mask(
+        proposal_boxlist,
+        selected_positions,
+        use_static_shapes=self._use_static_shapes,
+        indicator_sum=(self._second_stage_batch_size
+                       if self._use_static_shapes else None))
+
+  def _compute_second_stage_input_feature_maps(self, features_to_crop,
+                                               proposal_boxes_normalized):
+    """Crops to a set of proposals from the feature map for a batch of images.
+
+    Helper function for self._postprocess_rpn. This function calls
+    `tf.image.crop_and_resize` to create the feature map to be passed to the
+    second stage box classifier for each proposal.
+
+    Args:
+      features_to_crop: A float32 tensor with shape
+        [batch_size, height, width, depth]
+      proposal_boxes_normalized: A float32 tensor with shape [batch_size,
+        num_proposals, box_code_size] containing proposal boxes in
+        normalized coordinates.
+
+    Returns:
+      A float32 tensor with shape [K, new_height, new_width, depth].
+    """
+    cropped_regions = self._flatten_first_two_dimensions(
+        self._crop_and_resize_fn(
+            features_to_crop, proposal_boxes_normalized,
+            [self._initial_crop_size, self._initial_crop_size]))
+    return slim.max_pool2d(
+        cropped_regions,
+        [self._maxpool_kernel_size, self._maxpool_kernel_size],
+        stride=self._maxpool_stride)
+
+  def _postprocess_box_classifier(self,
+                                  refined_box_encodings,
+                                  class_predictions_with_background,
+                                  proposal_boxes,
+                                  num_proposals,
+                                  image_shapes,
+                                  mask_predictions=None):
+    """Converts predictions from the second stage box classifier to detections.
+
+    Args:
+      refined_box_encodings: a 3-D float tensor with shape
+        [total_num_padded_proposals, num_classes, self._box_coder.code_size]
+        representing predicted (final) refined box encodings. If using a shared
+        box across classes the shape will instead be
+        [total_num_padded_proposals, 1, 4]
+      class_predictions_with_background: a 3-D tensor float with shape
+        [total_num_padded_proposals, num_classes + 1] containing class
+        predictions (logits) for each of the proposals.  Note that this tensor
+        *includes* background class predictions (at class index 0).
+      proposal_boxes: a 3-D float tensor with shape
+        [batch_size, self.max_num_proposals, 4] representing decoded proposal
+        bounding boxes in absolute coordinates.
+      num_proposals: a 1-D int32 tensor of shape [batch] representing the number
+        of proposals predicted for each image in the batch.
+      image_shapes: a 2-D int32 tensor containing shapes of input image in the
+        batch.
+      mask_predictions: (optional) a 4-D float tensor with shape
+        [total_num_padded_proposals, num_classes, mask_height, mask_width]
+        containing instance mask prediction logits.
+
+    Returns:
+      A dictionary containing:
+        `detection_boxes`: [batch, max_detection, 4] in normalized co-ordinates.
+        `detection_scores`: [batch, max_detections]
+        `detection_classes`: [batch, max_detections]
+        `num_detections`: [batch]
+        `detection_masks`:
+          (optional) [batch, max_detections, mask_height, mask_width]. Note
+          that a pixel-wise sigmoid score converter is applied to the detection
+          masks.
+    """
+    refined_box_encodings_batch = tf.reshape(
+        refined_box_encodings,
+        [-1,
+         self.max_num_proposals,
+         refined_box_encodings.shape[1],
+         self._box_coder.code_size])
+    class_predictions_with_background_batch = tf.reshape(
+        class_predictions_with_background,
+        [-1, self.max_num_proposals, self.num_classes + 1]
+    )
+    refined_decoded_boxes_batch = self._batch_decode_boxes(
+        refined_box_encodings_batch, proposal_boxes)
+    class_predictions_with_background_batch = (
+        self._second_stage_score_conversion_fn(
+            class_predictions_with_background_batch))
+    class_predictions_batch = tf.reshape(
+        tf.slice(class_predictions_with_background_batch,
+                 [0, 0, 1], [-1, -1, -1]),
+        [-1, self.max_num_proposals, self.num_classes])
+    clip_window = self._compute_clip_window(image_shapes)
+    mask_predictions_batch = None
+    if mask_predictions is not None:
+      mask_height = mask_predictions.shape[2].value
+      mask_width = mask_predictions.shape[3].value
+      mask_predictions = tf.sigmoid(mask_predictions)
+      mask_predictions_batch = tf.reshape(
+          mask_predictions, [-1, self.max_num_proposals,
+                             self.num_classes, mask_height, mask_width])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, _,
+     num_detections) = self._second_stage_nms_fn(
+         refined_decoded_boxes_batch,
+         class_predictions_batch,
+         clip_window=clip_window,
+         change_coordinate_frame=True,
+         num_valid_boxes=num_proposals,
+         masks=mask_predictions_batch)
+    detections = {
+        fields.DetectionResultFields.detection_boxes: nmsed_boxes,
+        fields.DetectionResultFields.detection_scores: nmsed_scores,
+        fields.DetectionResultFields.detection_classes: nmsed_classes,
+        fields.DetectionResultFields.num_detections: tf.to_float(num_detections)
+    }
+    if nmsed_masks is not None:
+      detections[fields.DetectionResultFields.detection_masks] = nmsed_masks
+    return detections
+
+  def _batch_decode_boxes(self, box_encodings, anchor_boxes):
+    """Decodes box encodings with respect to the anchor boxes.
+
+    Args:
+      box_encodings: a 4-D tensor with shape
+        [batch_size, num_anchors, num_classes, self._box_coder.code_size]
+        representing box encodings.
+      anchor_boxes: [batch_size, num_anchors, self._box_coder.code_size]
+        representing decoded bounding boxes. If using a shared box across
+        classes the shape will instead be
+        [total_num_proposals, 1, self._box_coder.code_size].
+
+    Returns:
+      decoded_boxes: a
+        [batch_size, num_anchors, num_classes, self._box_coder.code_size]
+        float tensor representing bounding box predictions (for each image in
+        batch, proposal and class). If using a shared box across classes the
+        shape will instead be
+        [batch_size, num_anchors, 1, self._box_coder.code_size].
+    """
+    combined_shape = shape_utils.combined_static_and_dynamic_shape(
+        box_encodings)
+    num_classes = combined_shape[2]
+    tiled_anchor_boxes = tf.tile(
+        tf.expand_dims(anchor_boxes, 2), [1, 1, num_classes, 1])
+    tiled_anchors_boxlist = box_list.BoxList(
+        tf.reshape(tiled_anchor_boxes, [-1, 4]))
+    decoded_boxes = self._box_coder.decode(
+        tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
+        tiled_anchors_boxlist)
+    return tf.reshape(decoded_boxes.get(),
+                      tf.stack([combined_shape[0], combined_shape[1],
+                                num_classes, 4]))
+
+  def loss(self, prediction_dict, true_image_shapes, scope=None):
+    """Compute scalar loss tensors given prediction tensors.
+
+    If number_of_stages=1, only RPN related losses are computed (i.e.,
+    `rpn_localization_loss` and `rpn_objectness_loss`).  Otherwise all
+    losses are computed.
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors (see the
+        documentation for the predict method.  If number_of_stages=1, we
+        expect prediction_dict to contain `rpn_box_encodings`,
+        `rpn_objectness_predictions_with_background`, `rpn_features_to_crop`,
+        `image_shape`, and `anchors` fields.  Otherwise we expect
+        prediction_dict to additionally contain `refined_box_encodings`,
+        `class_predictions_with_background`, `num_proposals`, and
+        `proposal_boxes` fields.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+      scope: Optional scope name.
+
+    Returns:
+      a dictionary mapping loss keys (`first_stage_localization_loss`,
+        `first_stage_objectness_loss`, 'second_stage_localization_loss',
+        'second_stage_classification_loss') to scalar tensors representing
+        corresponding loss values.
+    """
+    with tf.name_scope(scope, 'Loss', prediction_dict.values()):
+      (groundtruth_boxlists, groundtruth_classes_with_background_list,
+       groundtruth_masks_list, groundtruth_weights_list
+      ) = self._format_groundtruth_data(true_image_shapes)
+      loss_dict = self._loss_rpn(
+          prediction_dict['rpn_box_encodings'],
+          prediction_dict['rpn_objectness_predictions_with_background'],
+          prediction_dict['anchors'], groundtruth_boxlists,
+          groundtruth_classes_with_background_list, groundtruth_weights_list)
+      if self._number_of_stages > 1:
+        loss_dict.update(
+            self._loss_box_classifier(
+                prediction_dict['refined_box_encodings'],
+                prediction_dict['class_predictions_with_background'],
+                prediction_dict['proposal_boxes'],
+                prediction_dict['num_proposals'], groundtruth_boxlists,
+                groundtruth_classes_with_background_list,
+                groundtruth_weights_list, prediction_dict['image_shape'],
+                prediction_dict.get('mask_predictions'), groundtruth_masks_list,
+                prediction_dict.get(
+                    fields.DetectionResultFields.detection_boxes),
+                prediction_dict.get(
+                    fields.DetectionResultFields.num_detections)))
+    return loss_dict
+
+  def _loss_rpn(self, rpn_box_encodings,
+                rpn_objectness_predictions_with_background, anchors,
+                groundtruth_boxlists, groundtruth_classes_with_background_list,
+                groundtruth_weights_list):
+    """Computes scalar RPN loss tensors.
+
+    Uses self._proposal_target_assigner to obtain regression and classification
+    targets for the first stage RPN, samples a "minibatch" of anchors to
+    participate in the loss computation, and returns the RPN losses.
+
+    Args:
+      rpn_box_encodings: A 4-D float tensor of shape
+        [batch_size, num_anchors, self._box_coder.code_size] containing
+        predicted proposal box encodings.
+      rpn_objectness_predictions_with_background: A 2-D float tensor of shape
+        [batch_size, num_anchors, 2] containing objectness predictions
+        (logits) for each of the anchors with 0 corresponding to background
+        and 1 corresponding to object.
+      anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
+        for the first stage RPN.  Note that `num_anchors` can differ depending
+        on whether the model is created in training or inference mode.
+      groundtruth_boxlists: A list of BoxLists containing coordinates of the
+        groundtruth boxes.
+      groundtruth_classes_with_background_list: A list of 2-D one-hot
+        (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
+        class targets with the 0th index assumed to map to the background class.
+      groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
+        [num_boxes] containing weights for groundtruth boxes.
+
+    Returns:
+      a dictionary mapping loss keys (`first_stage_localization_loss`,
+        `first_stage_objectness_loss`) to scalar tensors representing
+        corresponding loss values.
+    """
+    with tf.name_scope('RPNLoss'):
+      (batch_cls_targets, batch_cls_weights, batch_reg_targets,
+       batch_reg_weights, _) = target_assigner.batch_assign_targets(
+           target_assigner=self._proposal_target_assigner,
+           anchors_batch=box_list.BoxList(anchors),
+           gt_box_batch=groundtruth_boxlists,
+           gt_class_targets_batch=(len(groundtruth_boxlists) * [None]),
+           gt_weights_batch=groundtruth_weights_list)
+      batch_cls_weights = tf.reduce_mean(batch_cls_weights, axis=2)
+      batch_cls_targets = tf.squeeze(batch_cls_targets, axis=2)
+
+      def _minibatch_subsample_fn(inputs):
+        cls_targets, cls_weights = inputs
+        return self._first_stage_sampler.subsample(
+            tf.cast(cls_weights, tf.bool),
+            self._first_stage_minibatch_size, tf.cast(cls_targets, tf.bool))
+      batch_sampled_indices = tf.to_float(shape_utils.static_or_dynamic_map_fn(
+          _minibatch_subsample_fn,
+          [batch_cls_targets, batch_cls_weights],
+          dtype=tf.bool,
+          parallel_iterations=self._parallel_iterations,
+          back_prop=True))
+
+      # Normalize by number of examples in sampled minibatch
+      normalizer = tf.reduce_sum(batch_sampled_indices, axis=1)
+      batch_one_hot_targets = tf.one_hot(
+          tf.to_int32(batch_cls_targets), depth=2)
+      sampled_reg_indices = tf.multiply(batch_sampled_indices,
+                                        batch_reg_weights)
+
+      losses_mask = None
+      if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
+        losses_mask = tf.stack(self.groundtruth_lists(
+            fields.InputDataFields.is_annotated))
+      localization_losses = self._first_stage_localization_loss(
+          rpn_box_encodings, batch_reg_targets, weights=sampled_reg_indices,
+          losses_mask=losses_mask)
+      objectness_losses = self._first_stage_objectness_loss(
+          rpn_objectness_predictions_with_background,
+          batch_one_hot_targets,
+          weights=tf.expand_dims(batch_sampled_indices, axis=-1),
+          losses_mask=losses_mask)
+      localization_loss = tf.reduce_mean(
+          tf.reduce_sum(localization_losses, axis=1) / normalizer)
+      objectness_loss = tf.reduce_mean(
+          tf.reduce_sum(objectness_losses, axis=1) / normalizer)
+
+      localization_loss = tf.multiply(self._first_stage_loc_loss_weight,
+                                      localization_loss,
+                                      name='localization_loss')
+      objectness_loss = tf.multiply(self._first_stage_obj_loss_weight,
+                                    objectness_loss, name='objectness_loss')
+      loss_dict = {localization_loss.op.name: localization_loss,
+                   objectness_loss.op.name: objectness_loss}
+    return loss_dict
+
+  def _loss_box_classifier(self,
+                           refined_box_encodings,
+                           class_predictions_with_background,
+                           proposal_boxes,
+                           num_proposals,
+                           groundtruth_boxlists,
+                           groundtruth_classes_with_background_list,
+                           groundtruth_weights_list,
+                           image_shape,
+                           prediction_masks=None,
+                           groundtruth_masks_list=None,
+                           detection_boxes=None,
+                           num_detections=None):
+    """Computes scalar box classifier loss tensors.
+
+    Uses self._detector_target_assigner to obtain regression and classification
+    targets for the second stage box classifier, optionally performs
+    hard mining, and returns losses.  All losses are computed independently
+    for each image and then averaged across the batch.
+    Please note that for boxes and masks with multiple labels, the box
+    regression and mask prediction losses are only computed for one label.
+
+    This function assumes that the proposal boxes in the "padded" regions are
+    actually zero (and thus should not be matched to).
+
+
+    Args:
+      refined_box_encodings: a 3-D tensor with shape
+        [total_num_proposals, num_classes, box_coder.code_size] representing
+        predicted (final) refined box encodings. If using a shared box across
+        classes this will instead have shape
+        [total_num_proposals, 1, box_coder.code_size].
+      class_predictions_with_background: a 2-D tensor with shape
+        [total_num_proposals, num_classes + 1] containing class
+        predictions (logits) for each of the anchors.  Note that this tensor
+        *includes* background class predictions (at class index 0).
+      proposal_boxes: [batch_size, self.max_num_proposals, 4] representing
+        decoded proposal bounding boxes.
+      num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
+        representing the number of proposals predicted for each image in
+        the batch.
+      groundtruth_boxlists: a list of BoxLists containing coordinates of the
+        groundtruth boxes.
+      groundtruth_classes_with_background_list: a list of 2-D one-hot
+        (or k-hot) tensors of shape [num_boxes, num_classes + 1] containing the
+        class targets with the 0th index assumed to map to the background class.
+      groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
+        [num_boxes] containing weights for groundtruth boxes.
+      image_shape: a 1-D tensor of shape [4] representing the image shape.
+      prediction_masks: an optional 4-D tensor with shape [total_num_proposals,
+        num_classes, mask_height, mask_width] containing the instance masks for
+        each box.
+      groundtruth_masks_list: an optional list of 3-D tensors of shape
+        [num_boxes, image_height, image_width] containing the instance masks for
+        each of the boxes.
+      detection_boxes: 3-D float tensor of shape [batch,
+        max_total_detections, 4] containing post-processed detection boxes in
+        normalized co-ordinates.
+      num_detections: 1-D int32 tensor of shape [batch] containing number of
+        valid detections in `detection_boxes`.
+
+    Returns:
+      a dictionary mapping loss keys ('second_stage_localization_loss',
+        'second_stage_classification_loss') to scalar tensors representing
+        corresponding loss values.
+
+    Raises:
+      ValueError: if `predict_instance_masks` in
+        second_stage_mask_rcnn_box_predictor is True and
+        `groundtruth_masks_list` is not provided.
+    """
+    with tf.name_scope('BoxClassifierLoss'):
+      paddings_indicator = self._padded_batched_proposals_indicator(
+          num_proposals, proposal_boxes.shape[1])
+      proposal_boxlists = [
+          box_list.BoxList(proposal_boxes_single_image)
+          for proposal_boxes_single_image in tf.unstack(proposal_boxes)]
+      batch_size = len(proposal_boxlists)
+
+      num_proposals_or_one = tf.to_float(tf.expand_dims(
+          tf.maximum(num_proposals, tf.ones_like(num_proposals)), 1))
+      normalizer = tf.tile(num_proposals_or_one,
+                           [1, self.max_num_proposals]) * batch_size
+
+      (batch_cls_targets_with_background, batch_cls_weights, batch_reg_targets,
+       batch_reg_weights, _) = target_assigner.batch_assign_targets(
+           target_assigner=self._detector_target_assigner,
+           anchors_batch=proposal_boxlists,
+           gt_box_batch=groundtruth_boxlists,
+           gt_class_targets_batch=groundtruth_classes_with_background_list,
+           unmatched_class_label=tf.constant(
+               [1] + self._num_classes * [0], dtype=tf.float32),
+           gt_weights_batch=groundtruth_weights_list)
+
+      class_predictions_with_background = tf.reshape(
+          class_predictions_with_background,
+          [batch_size, self.max_num_proposals, -1])
+
+      flat_cls_targets_with_background = tf.reshape(
+          batch_cls_targets_with_background,
+          [batch_size * self.max_num_proposals, -1])
+      one_hot_flat_cls_targets_with_background = tf.argmax(
+          flat_cls_targets_with_background, axis=1)
+      one_hot_flat_cls_targets_with_background = tf.one_hot(
+          one_hot_flat_cls_targets_with_background,
+          flat_cls_targets_with_background.get_shape()[1])
+
+      # If using a shared box across classes use directly
+      if refined_box_encodings.shape[1] == 1:
+        reshaped_refined_box_encodings = tf.reshape(
+            refined_box_encodings,
+            [batch_size, self.max_num_proposals, self._box_coder.code_size])
+      # For anchors with multiple labels, picks refined_location_encodings
+      # for just one class to avoid over-counting for regression loss and
+      # (optionally) mask loss.
+      else:
+        reshaped_refined_box_encodings = (
+            self._get_refined_encodings_for_postitive_class(
+                refined_box_encodings,
+                one_hot_flat_cls_targets_with_background, batch_size))
+
+      losses_mask = None
+      if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
+        losses_mask = tf.stack(self.groundtruth_lists(
+            fields.InputDataFields.is_annotated))
+      second_stage_loc_losses = self._second_stage_localization_loss(
+          reshaped_refined_box_encodings,
+          batch_reg_targets,
+          weights=batch_reg_weights,
+          losses_mask=losses_mask) / normalizer
+      second_stage_cls_losses = ops.reduce_sum_trailing_dimensions(
+          self._second_stage_classification_loss(
+              class_predictions_with_background,
+              batch_cls_targets_with_background,
+              weights=batch_cls_weights,
+              losses_mask=losses_mask),
+          ndims=2) / normalizer
+
+      second_stage_loc_loss = tf.reduce_sum(
+          second_stage_loc_losses * tf.to_float(paddings_indicator))
+      second_stage_cls_loss = tf.reduce_sum(
+          second_stage_cls_losses * tf.to_float(paddings_indicator))
+
+      if self._hard_example_miner:
+        (second_stage_loc_loss, second_stage_cls_loss
+        ) = self._unpad_proposals_and_apply_hard_mining(
+            proposal_boxlists, second_stage_loc_losses,
+            second_stage_cls_losses, num_proposals)
+      localization_loss = tf.multiply(self._second_stage_loc_loss_weight,
+                                      second_stage_loc_loss,
+                                      name='localization_loss')
+
+      classification_loss = tf.multiply(self._second_stage_cls_loss_weight,
+                                        second_stage_cls_loss,
+                                        name='classification_loss')
+
+      loss_dict = {localization_loss.op.name: localization_loss,
+                   classification_loss.op.name: classification_loss}
+      second_stage_mask_loss = None
+      if prediction_masks is not None:
+        if groundtruth_masks_list is None:
+          raise ValueError('Groundtruth instance masks not provided. '
+                           'Please configure input reader.')
+
+        if not self._is_training:
+          (proposal_boxes, proposal_boxlists, paddings_indicator,
+           one_hot_flat_cls_targets_with_background
+          ) = self._get_mask_proposal_boxes_and_classes(
+              detection_boxes, num_detections, image_shape,
+              groundtruth_boxlists, groundtruth_classes_with_background_list,
+              groundtruth_weights_list)
+        unmatched_mask_label = tf.zeros(image_shape[1:3], dtype=tf.float32)
+        (batch_mask_targets, _, _, batch_mask_target_weights,
+         _) = target_assigner.batch_assign_targets(
+             target_assigner=self._detector_target_assigner,
+             anchors_batch=proposal_boxlists,
+             gt_box_batch=groundtruth_boxlists,
+             gt_class_targets_batch=groundtruth_masks_list,
+             unmatched_class_label=unmatched_mask_label,
+             gt_weights_batch=groundtruth_weights_list)
+
+        # Pad the prediction_masks with to add zeros for background class to be
+        # consistent with class predictions.
+        if prediction_masks.get_shape().as_list()[1] == 1:
+          # Class agnostic masks or masks for one-class prediction. Logic for
+          # both cases is the same since background predictions are ignored
+          # through the batch_mask_target_weights.
+          prediction_masks_masked_by_class_targets = prediction_masks
+        else:
+          prediction_masks_with_background = tf.pad(
+              prediction_masks, [[0, 0], [1, 0], [0, 0], [0, 0]])
+          prediction_masks_masked_by_class_targets = tf.boolean_mask(
+              prediction_masks_with_background,
+              tf.greater(one_hot_flat_cls_targets_with_background, 0))
+
+        mask_height = prediction_masks.shape[2].value
+        mask_width = prediction_masks.shape[3].value
+        reshaped_prediction_masks = tf.reshape(
+            prediction_masks_masked_by_class_targets,
+            [batch_size, -1, mask_height * mask_width])
+
+        batch_mask_targets_shape = tf.shape(batch_mask_targets)
+        flat_gt_masks = tf.reshape(batch_mask_targets,
+                                   [-1, batch_mask_targets_shape[2],
+                                    batch_mask_targets_shape[3]])
+
+        # Use normalized proposals to crop mask targets from image masks.
+        flat_normalized_proposals = box_list_ops.to_normalized_coordinates(
+            box_list.BoxList(tf.reshape(proposal_boxes, [-1, 4])),
+            image_shape[1], image_shape[2]).get()
+
+        flat_cropped_gt_mask = self._crop_and_resize_fn(
+            tf.expand_dims(flat_gt_masks, -1),
+            tf.expand_dims(flat_normalized_proposals, axis=1),
+            [mask_height, mask_width])
+        # Without stopping gradients into cropped groundtruth masks the
+        # performance with 100-padded groundtruth masks when batch size > 1 is
+        # about 4% worse.
+        # TODO(rathodv): Investigate this since we don't expect any variables
+        # upstream of flat_cropped_gt_mask.
+        flat_cropped_gt_mask = tf.stop_gradient(flat_cropped_gt_mask)
+
+        batch_cropped_gt_mask = tf.reshape(
+            flat_cropped_gt_mask,
+            [batch_size, -1, mask_height * mask_width])
+
+        mask_losses_weights = (
+            batch_mask_target_weights * tf.to_float(paddings_indicator))
+        mask_losses = self._second_stage_mask_loss(
+            reshaped_prediction_masks,
+            batch_cropped_gt_mask,
+            weights=tf.expand_dims(mask_losses_weights, axis=-1),
+            losses_mask=losses_mask)
+        total_mask_loss = tf.reduce_sum(mask_losses)
+        normalizer = tf.maximum(
+            tf.reduce_sum(mask_losses_weights * mask_height * mask_width), 1.0)
+        second_stage_mask_loss = total_mask_loss / normalizer
+
+      if second_stage_mask_loss is not None:
+        mask_loss = tf.multiply(self._second_stage_mask_loss_weight,
+                                second_stage_mask_loss, name='mask_loss')
+        loss_dict[mask_loss.op.name] = mask_loss
+    return loss_dict
+
+  def _get_mask_proposal_boxes_and_classes(
+      self, detection_boxes, num_detections, image_shape, groundtruth_boxlists,
+      groundtruth_classes_with_background_list, groundtruth_weights_list):
+    """Returns proposal boxes and class targets to compute evaluation mask loss.
+
+    During evaluation, detection boxes are used to extract features for mask
+    prediction. Therefore, to compute mask loss during evaluation detection
+    boxes must be used to compute correct class and mask targets. This function
+    returns boxes and classes in the correct format for computing mask targets
+    during evaluation.
+
+    Args:
+      detection_boxes: A 3-D float tensor of shape [batch, max_detection_boxes,
+        4] containing detection boxes in normalized co-ordinates.
+      num_detections: A 1-D float tensor of shape [batch] containing number of
+        valid boxes in `detection_boxes`.
+      image_shape: A 1-D tensor of shape [4] containing image tensor shape.
+      groundtruth_boxlists: A list of groundtruth boxlists.
+      groundtruth_classes_with_background_list: A list of groundtruth classes.
+      groundtruth_weights_list: A list of groundtruth weights.
+    Return:
+      mask_proposal_boxes: detection boxes to use for mask proposals in absolute
+        co-ordinates.
+      mask_proposal_boxlists: `mask_proposal_boxes` in a list of BoxLists in
+        absolute co-ordinates.
+      mask_proposal_paddings_indicator: a tensor indicating valid boxes.
+      mask_proposal_one_hot_flat_cls_targets_with_background: Class targets
+        computed using detection boxes.
+    """
+    batch, max_num_detections, _ = detection_boxes.shape.as_list()
+    proposal_boxes = tf.reshape(box_list_ops.to_absolute_coordinates(
+        box_list.BoxList(tf.reshape(detection_boxes, [-1, 4])), image_shape[1],
+        image_shape[2]).get(), [batch, max_num_detections, 4])
+    proposal_boxlists = [
+        box_list.BoxList(detection_boxes_single_image)
+        for detection_boxes_single_image in tf.unstack(proposal_boxes)
+    ]
+    paddings_indicator = self._padded_batched_proposals_indicator(
+        tf.to_int32(num_detections), detection_boxes.shape[1])
+    (batch_cls_targets_with_background, _, _, _,
+     _) = target_assigner.batch_assign_targets(
+         target_assigner=self._detector_target_assigner,
+         anchors_batch=proposal_boxlists,
+         gt_box_batch=groundtruth_boxlists,
+         gt_class_targets_batch=groundtruth_classes_with_background_list,
+         unmatched_class_label=tf.constant(
+             [1] + self._num_classes * [0], dtype=tf.float32),
+         gt_weights_batch=groundtruth_weights_list)
+    flat_cls_targets_with_background = tf.reshape(
+        batch_cls_targets_with_background, [-1, self._num_classes + 1])
+    one_hot_flat_cls_targets_with_background = tf.argmax(
+        flat_cls_targets_with_background, axis=1)
+    one_hot_flat_cls_targets_with_background = tf.one_hot(
+        one_hot_flat_cls_targets_with_background,
+        flat_cls_targets_with_background.get_shape()[1])
+    return (proposal_boxes, proposal_boxlists, paddings_indicator,
+            one_hot_flat_cls_targets_with_background)
+
+  def _get_refined_encodings_for_postitive_class(
+      self, refined_box_encodings, flat_cls_targets_with_background,
+      batch_size):
+    # We only predict refined location encodings for the non background
+    # classes, but we now pad it to make it compatible with the class
+    # predictions
+    refined_box_encodings_with_background = tf.pad(refined_box_encodings,
+                                                   [[0, 0], [1, 0], [0, 0]])
+    refined_box_encodings_masked_by_class_targets = (
+        box_list_ops.boolean_mask(
+            box_list.BoxList(
+                tf.reshape(refined_box_encodings_with_background,
+                           [-1, self._box_coder.code_size])),
+            tf.reshape(tf.greater(flat_cls_targets_with_background, 0), [-1]),
+            use_static_shapes=self._use_static_shapes,
+            indicator_sum=batch_size * self.max_num_proposals
+            if self._use_static_shapes else None).get())
+    return tf.reshape(
+        refined_box_encodings_masked_by_class_targets, [
+            batch_size, self.max_num_proposals,
+            self._box_coder.code_size
+        ])
+
+  def _padded_batched_proposals_indicator(self,
+                                          num_proposals,
+                                          max_num_proposals):
+    """Creates indicator matrix of non-pad elements of padded batch proposals.
+
+    Args:
+      num_proposals: Tensor of type tf.int32 with shape [batch_size].
+      max_num_proposals: Maximum number of proposals per image (integer).
+
+    Returns:
+      A Tensor of type tf.bool with shape [batch_size, max_num_proposals].
+    """
+    batch_size = tf.size(num_proposals)
+    tiled_num_proposals = tf.tile(
+        tf.expand_dims(num_proposals, 1), [1, max_num_proposals])
+    tiled_proposal_index = tf.tile(
+        tf.expand_dims(tf.range(max_num_proposals), 0), [batch_size, 1])
+    return tf.greater(tiled_num_proposals, tiled_proposal_index)
+
+  def _unpad_proposals_and_apply_hard_mining(self,
+                                             proposal_boxlists,
+                                             second_stage_loc_losses,
+                                             second_stage_cls_losses,
+                                             num_proposals):
+    """Unpads proposals and applies hard mining.
+
+    Args:
+      proposal_boxlists: A list of `batch_size` BoxLists each representing
+        `self.max_num_proposals` representing decoded proposal bounding boxes
+        for each image.
+      second_stage_loc_losses: A Tensor of type `float32`. A tensor of shape
+        `[batch_size, self.max_num_proposals]` representing per-anchor
+        second stage localization loss values.
+      second_stage_cls_losses: A Tensor of type `float32`. A tensor of shape
+        `[batch_size, self.max_num_proposals]` representing per-anchor
+        second stage classification loss values.
+      num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
+        representing the number of proposals predicted for each image in
+        the batch.
+
+    Returns:
+      second_stage_loc_loss: A scalar float32 tensor representing the second
+        stage localization loss.
+      second_stage_cls_loss: A scalar float32 tensor representing the second
+        stage classification loss.
+    """
+    for (proposal_boxlist, single_image_loc_loss, single_image_cls_loss,
+         single_image_num_proposals) in zip(
+             proposal_boxlists,
+             tf.unstack(second_stage_loc_losses),
+             tf.unstack(second_stage_cls_losses),
+             tf.unstack(num_proposals)):
+      proposal_boxlist = box_list.BoxList(
+          tf.slice(proposal_boxlist.get(),
+                   [0, 0], [single_image_num_proposals, -1]))
+      single_image_loc_loss = tf.slice(single_image_loc_loss,
+                                       [0], [single_image_num_proposals])
+      single_image_cls_loss = tf.slice(single_image_cls_loss,
+                                       [0], [single_image_num_proposals])
+      return self._hard_example_miner(
+          location_losses=tf.expand_dims(single_image_loc_loss, 0),
+          cls_losses=tf.expand_dims(single_image_cls_loss, 0),
+          decoded_boxlist_list=[proposal_boxlist])
+
+  def regularization_losses(self):
+    """Returns a list of regularization losses for this model.
+
+    Returns a list of regularization losses for this model that the estimator
+    needs to use during training/optimization.
+
+    Returns:
+      A list of regularization loss tensors.
+    """
+    return tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+
+  def restore_map(self,
+                  fine_tune_checkpoint_type='detection',
+                  load_all_detection_checkpoint_vars=False):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    See parent class for details.
+
+    Args:
+      fine_tune_checkpoint_type: whether to restore from a full detection
+        checkpoint (with compatible variable names) or to restore from a
+        classification checkpoint for initialization prior to training.
+        Valid values: `detection`, `classification`. Default 'detection'.
+       load_all_detection_checkpoint_vars: whether to load all variables (when
+         `fine_tune_checkpoint_type` is `detection`). If False, only variables
+         within the feature extractor scopes are included. Default False.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    Raises:
+      ValueError: if fine_tune_checkpoint_type is neither `classification`
+        nor `detection`.
+    """
+    if fine_tune_checkpoint_type not in ['detection', 'classification']:
+      raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format(
+          fine_tune_checkpoint_type))
+    if fine_tune_checkpoint_type == 'classification':
+      return self._feature_extractor.restore_from_classification_checkpoint_fn(
+          self.first_stage_feature_extractor_scope,
+          self.second_stage_feature_extractor_scope)
+
+    variables_to_restore = tf.global_variables()
+    variables_to_restore.append(slim.get_or_create_global_step())
+    # Only load feature extractor variables to be consistent with loading from
+    # a classification checkpoint.
+    include_patterns = None
+    if not load_all_detection_checkpoint_vars:
+      include_patterns = [
+          self.first_stage_feature_extractor_scope,
+          self.second_stage_feature_extractor_scope
+      ]
+    feature_extractor_variables = tf.contrib.framework.filter_variables(
+        variables_to_restore, include_patterns=include_patterns)
+    return {var.op.name: var for var in feature_extractor_variables}
+
+  def updates(self):
+    """Returns a list of update operators for this model.
+
+    Returns a list of update operators for this model that must be executed at
+    each training step. The estimator's train op needs to have a control
+    dependency on these updates.
+
+    Returns:
+      A list of update operators.
+    """
+    return tf.get_collection(tf.GraphKeys.UPDATE_OPS)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test.py
new file mode 100644
index 00000000..2c701d28
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test.py
@@ -0,0 +1,378 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.meta_architectures.faster_rcnn_meta_arch."""
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch_test_lib
+
+
+class FasterRCNNMetaArchTest(
+    faster_rcnn_meta_arch_test_lib.FasterRCNNMetaArchTestBase,
+    parameterized.TestCase):
+
+  def test_postprocess_second_stage_only_inference_mode_with_masks(self):
+    model = self._build_model(
+        is_training=False, number_of_stages=2, second_stage_batch_size=6)
+
+    batch_size = 2
+    total_num_padded_proposals = batch_size * model.max_num_proposals
+    proposal_boxes = tf.constant(
+        [[[1, 1, 2, 3],
+          [0, 0, 1, 1],
+          [.5, .5, .6, .6],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0]],
+         [[2, 3, 6, 8],
+          [1, 2, 5, 3],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0], 4*[0]]], dtype=tf.float32)
+    num_proposals = tf.constant([3, 2], dtype=tf.int32)
+    refined_box_encodings = tf.zeros(
+        [total_num_padded_proposals, model.num_classes, 4], dtype=tf.float32)
+    class_predictions_with_background = tf.ones(
+        [total_num_padded_proposals, model.num_classes+1], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 36, 48, 3], dtype=tf.int32)
+
+    mask_height = 2
+    mask_width = 2
+    mask_predictions = 30. * tf.ones(
+        [total_num_padded_proposals, model.num_classes,
+         mask_height, mask_width], dtype=tf.float32)
+    exp_detection_masks = np.array([[[[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]]],
+                                    [[[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[1, 1], [1, 1]],
+                                     [[0, 0], [0, 0]]]])
+
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    detections = model.postprocess({
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'num_proposals': num_proposals,
+        'proposal_boxes': proposal_boxes,
+        'image_shape': image_shape,
+        'mask_predictions': mask_predictions
+    }, true_image_shapes)
+    with self.test_session() as sess:
+      detections_out = sess.run(detections)
+      self.assertAllEqual(detections_out['detection_boxes'].shape, [2, 5, 4])
+      self.assertAllClose(detections_out['detection_scores'],
+                          [[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]])
+      self.assertAllClose(detections_out['detection_classes'],
+                          [[0, 0, 0, 1, 1], [0, 0, 1, 1, 0]])
+      self.assertAllClose(detections_out['num_detections'], [5, 4])
+      self.assertAllClose(detections_out['detection_masks'],
+                          exp_detection_masks)
+      self.assertTrue(np.amax(detections_out['detection_masks'] <= 1.0))
+      self.assertTrue(np.amin(detections_out['detection_masks'] >= 0.0))
+
+  def test_postprocess_second_stage_only_inference_mode_with_shared_boxes(self):
+    model = self._build_model(
+        is_training=False, number_of_stages=2, second_stage_batch_size=6)
+
+    batch_size = 2
+    total_num_padded_proposals = batch_size * model.max_num_proposals
+    proposal_boxes = tf.constant(
+        [[[1, 1, 2, 3],
+          [0, 0, 1, 1],
+          [.5, .5, .6, .6],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0]],
+         [[2, 3, 6, 8],
+          [1, 2, 5, 3],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0], 4*[0]]], dtype=tf.float32)
+    num_proposals = tf.constant([3, 2], dtype=tf.int32)
+
+    # This has 1 box instead of one for each class.
+    refined_box_encodings = tf.zeros(
+        [total_num_padded_proposals, 1, 4], dtype=tf.float32)
+    class_predictions_with_background = tf.ones(
+        [total_num_padded_proposals, model.num_classes+1], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 36, 48, 3], dtype=tf.int32)
+
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    detections = model.postprocess({
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'num_proposals': num_proposals,
+        'proposal_boxes': proposal_boxes,
+        'image_shape': image_shape,
+    }, true_image_shapes)
+    with self.test_session() as sess:
+      detections_out = sess.run(detections)
+      self.assertAllEqual(detections_out['detection_boxes'].shape, [2, 5, 4])
+      self.assertAllClose(detections_out['detection_scores'],
+                          [[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]])
+      self.assertAllClose(detections_out['detection_classes'],
+                          [[0, 0, 0, 1, 1], [0, 0, 1, 1, 0]])
+      self.assertAllClose(detections_out['num_detections'], [5, 4])
+
+  @parameterized.parameters(
+      {'masks_are_class_agnostic': False},
+      {'masks_are_class_agnostic': True},
+  )
+  def test_predict_correct_shapes_in_inference_mode_three_stages_with_masks(
+      self, masks_are_class_agnostic):
+    batch_size = 2
+    image_size = 10
+    max_num_proposals = 8
+    initial_crop_size = 3
+    maxpool_stride = 1
+
+    input_shapes = [(batch_size, image_size, image_size, 3),
+                    (None, image_size, image_size, 3),
+                    (batch_size, None, None, 3),
+                    (None, None, None, 3)]
+    expected_num_anchors = image_size * image_size * 3 * 3
+    expected_shapes = {
+        'rpn_box_predictor_features':
+        (2, image_size, image_size, 512),
+        'rpn_features_to_crop': (2, image_size, image_size, 3),
+        'image_shape': (4,),
+        'rpn_box_encodings': (2, expected_num_anchors, 4),
+        'rpn_objectness_predictions_with_background':
+        (2, expected_num_anchors, 2),
+        'anchors': (expected_num_anchors, 4),
+        'refined_box_encodings': (2 * max_num_proposals, 2, 4),
+        'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
+        'num_proposals': (2,),
+        'proposal_boxes': (2, max_num_proposals, 4),
+        'proposal_boxes_normalized': (2, max_num_proposals, 4),
+        'box_classifier_features':
+        self._get_box_classifier_features_shape(image_size,
+                                                batch_size,
+                                                max_num_proposals,
+                                                initial_crop_size,
+                                                maxpool_stride,
+                                                3)
+    }
+
+    for input_shape in input_shapes:
+      test_graph = tf.Graph()
+      with test_graph.as_default():
+        model = self._build_model(
+            is_training=False,
+            number_of_stages=3,
+            second_stage_batch_size=2,
+            predict_masks=True,
+            masks_are_class_agnostic=masks_are_class_agnostic)
+        preprocessed_inputs = tf.placeholder(tf.float32, shape=input_shape)
+        _, true_image_shapes = model.preprocess(preprocessed_inputs)
+        result_tensor_dict = model.predict(preprocessed_inputs,
+                                           true_image_shapes)
+        init_op = tf.global_variables_initializer()
+      with self.test_session(graph=test_graph) as sess:
+        sess.run(init_op)
+        tensor_dict_out = sess.run(result_tensor_dict, feed_dict={
+            preprocessed_inputs:
+            np.zeros((batch_size, image_size, image_size, 3))})
+      self.assertEqual(
+          set(tensor_dict_out.keys()),
+          set(expected_shapes.keys()).union(
+              set([
+                  'detection_boxes', 'detection_scores', 'detection_classes',
+                  'detection_masks', 'num_detections', 'mask_predictions',
+              ])))
+      for key in expected_shapes:
+        self.assertAllEqual(tensor_dict_out[key].shape, expected_shapes[key])
+      self.assertAllEqual(tensor_dict_out['detection_boxes'].shape, [2, 5, 4])
+      self.assertAllEqual(tensor_dict_out['detection_masks'].shape,
+                          [2, 5, 14, 14])
+      self.assertAllEqual(tensor_dict_out['detection_classes'].shape, [2, 5])
+      self.assertAllEqual(tensor_dict_out['detection_scores'].shape, [2, 5])
+      self.assertAllEqual(tensor_dict_out['num_detections'].shape, [2])
+      num_classes = 1 if masks_are_class_agnostic else 2
+      self.assertAllEqual(tensor_dict_out['mask_predictions'].shape,
+                          [10, num_classes, 14, 14])
+
+  @parameterized.parameters(
+      {'masks_are_class_agnostic': False},
+      {'masks_are_class_agnostic': True},
+  )
+  def test_predict_gives_correct_shapes_in_train_mode_both_stages_with_masks(
+      self, masks_are_class_agnostic):
+    test_graph = tf.Graph()
+    with test_graph.as_default():
+      model = self._build_model(
+          is_training=True,
+          number_of_stages=3,
+          second_stage_batch_size=7,
+          predict_masks=True,
+          masks_are_class_agnostic=masks_are_class_agnostic)
+      batch_size = 2
+      image_size = 10
+      max_num_proposals = 7
+      initial_crop_size = 3
+      maxpool_stride = 1
+
+      image_shape = (batch_size, image_size, image_size, 3)
+      preprocessed_inputs = tf.zeros(image_shape, dtype=tf.float32)
+      groundtruth_boxes_list = [
+          tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
+          tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32)
+      ]
+      groundtruth_classes_list = [
+          tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
+          tf.constant([[1, 0], [1, 0]], dtype=tf.float32)
+      ]
+      groundtruth_weights_list = [
+          tf.constant([1, 1], dtype=tf.float32),
+          tf.constant([1, 1], dtype=tf.float32)]
+      _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+      model.provide_groundtruth(
+          groundtruth_boxes_list,
+          groundtruth_classes_list,
+          groundtruth_weights_list=groundtruth_weights_list)
+
+      result_tensor_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      mask_shape_1 = 1 if masks_are_class_agnostic else model._num_classes
+      expected_shapes = {
+          'rpn_box_predictor_features': (2, image_size, image_size, 512),
+          'rpn_features_to_crop': (2, image_size, image_size, 3),
+          'image_shape': (4,),
+          'refined_box_encodings': (2 * max_num_proposals, 2, 4),
+          'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
+          'num_proposals': (2,),
+          'proposal_boxes': (2, max_num_proposals, 4),
+          'proposal_boxes_normalized': (2, max_num_proposals, 4),
+          'box_classifier_features':
+              self._get_box_classifier_features_shape(
+                  image_size, batch_size, max_num_proposals, initial_crop_size,
+                  maxpool_stride, 3),
+          'mask_predictions': (2 * max_num_proposals, mask_shape_1, 14, 14)
+      }
+
+      init_op = tf.global_variables_initializer()
+      with self.test_session(graph=test_graph) as sess:
+        sess.run(init_op)
+        tensor_dict_out = sess.run(result_tensor_dict)
+        self.assertEqual(
+            set(tensor_dict_out.keys()),
+            set(expected_shapes.keys()).union(
+                set([
+                    'rpn_box_encodings',
+                    'rpn_objectness_predictions_with_background',
+                    'anchors',
+                ])))
+        for key in expected_shapes:
+          self.assertAllEqual(tensor_dict_out[key].shape, expected_shapes[key])
+
+        anchors_shape_out = tensor_dict_out['anchors'].shape
+        self.assertEqual(2, len(anchors_shape_out))
+        self.assertEqual(4, anchors_shape_out[1])
+        num_anchors_out = anchors_shape_out[0]
+        self.assertAllEqual(tensor_dict_out['rpn_box_encodings'].shape,
+                            (2, num_anchors_out, 4))
+        self.assertAllEqual(
+            tensor_dict_out['rpn_objectness_predictions_with_background'].shape,
+            (2, num_anchors_out, 2))
+
+  def test_postprocess_third_stage_only_inference_mode(self):
+    num_proposals_shapes = [(2), (None)]
+    refined_box_encodings_shapes = [(16, 2, 4), (None, 2, 4)]
+    class_predictions_with_background_shapes = [(16, 3), (None, 3)]
+    proposal_boxes_shapes = [(2, 8, 4), (None, 8, 4)]
+    batch_size = 2
+    image_shape = np.array((2, 36, 48, 3), dtype=np.int32)
+    for (num_proposals_shape, refined_box_encoding_shape,
+         class_predictions_with_background_shape,
+         proposal_boxes_shape) in zip(num_proposals_shapes,
+                                      refined_box_encodings_shapes,
+                                      class_predictions_with_background_shapes,
+                                      proposal_boxes_shapes):
+      tf_graph = tf.Graph()
+      with tf_graph.as_default():
+        model = self._build_model(
+            is_training=False, number_of_stages=3,
+            second_stage_batch_size=6, predict_masks=True)
+        total_num_padded_proposals = batch_size * model.max_num_proposals
+        proposal_boxes = np.array(
+            [[[1, 1, 2, 3],
+              [0, 0, 1, 1],
+              [.5, .5, .6, .6],
+              4*[0], 4*[0], 4*[0], 4*[0], 4*[0]],
+             [[2, 3, 6, 8],
+              [1, 2, 5, 3],
+              4*[0], 4*[0], 4*[0], 4*[0], 4*[0], 4*[0]]])
+        num_proposals = np.array([3, 2], dtype=np.int32)
+        refined_box_encodings = np.zeros(
+            [total_num_padded_proposals, model.num_classes, 4])
+        class_predictions_with_background = np.ones(
+            [total_num_padded_proposals, model.num_classes+1])
+
+        num_proposals_placeholder = tf.placeholder(tf.int32,
+                                                   shape=num_proposals_shape)
+        refined_box_encodings_placeholder = tf.placeholder(
+            tf.float32, shape=refined_box_encoding_shape)
+        class_predictions_with_background_placeholder = tf.placeholder(
+            tf.float32, shape=class_predictions_with_background_shape)
+        proposal_boxes_placeholder = tf.placeholder(
+            tf.float32, shape=proposal_boxes_shape)
+        image_shape_placeholder = tf.placeholder(tf.int32, shape=(4))
+        _, true_image_shapes = model.preprocess(
+            tf.zeros(image_shape_placeholder))
+        detections = model.postprocess({
+            'refined_box_encodings': refined_box_encodings_placeholder,
+            'class_predictions_with_background':
+            class_predictions_with_background_placeholder,
+            'num_proposals': num_proposals_placeholder,
+            'proposal_boxes': proposal_boxes_placeholder,
+            'image_shape': image_shape_placeholder,
+            'detection_boxes': tf.zeros([2, 5, 4]),
+            'detection_masks': tf.zeros([2, 5, 14, 14]),
+            'detection_scores': tf.zeros([2, 5]),
+            'detection_classes': tf.zeros([2, 5]),
+            'num_detections': tf.zeros([2]),
+        }, true_image_shapes)
+      with self.test_session(graph=tf_graph) as sess:
+        detections_out = sess.run(
+            detections,
+            feed_dict={
+                refined_box_encodings_placeholder: refined_box_encodings,
+                class_predictions_with_background_placeholder:
+                class_predictions_with_background,
+                num_proposals_placeholder: num_proposals,
+                proposal_boxes_placeholder: proposal_boxes,
+                image_shape_placeholder: image_shape
+            })
+      self.assertAllEqual(detections_out['detection_boxes'].shape, [2, 5, 4])
+      self.assertAllEqual(detections_out['detection_masks'].shape,
+                          [2, 5, 14, 14])
+      self.assertAllClose(detections_out['detection_scores'].shape, [2, 5])
+      self.assertAllClose(detections_out['detection_classes'].shape, [2, 5])
+      self.assertAllClose(detections_out['num_detections'].shape, [2])
+      self.assertTrue(np.amax(detections_out['detection_masks'] <= 1.0))
+      self.assertTrue(np.amin(detections_out['detection_masks'] >= 0.0))
+
+  def _get_box_classifier_features_shape(self,
+                                         image_size,
+                                         batch_size,
+                                         max_num_proposals,
+                                         initial_crop_size,
+                                         maxpool_stride,
+                                         num_features):
+    return (batch_size * max_num_proposals,
+            initial_crop_size/maxpool_stride,
+            initial_crop_size/maxpool_stride,
+            num_features)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py
new file mode 100644
index 00000000..655a44fe
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py
@@ -0,0 +1,1707 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.meta_architectures.faster_rcnn_meta_arch."""
+import functools
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.anchor_generators import grid_anchor_generator
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.builders import post_processing_builder
+from object_detection.core import balanced_positive_negative_sampler as sampler
+from object_detection.core import losses
+from object_detection.core import post_processing
+from object_detection.core import target_assigner
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.protos import box_predictor_pb2
+from object_detection.protos import hyperparams_pb2
+from object_detection.protos import post_processing_pb2
+from object_detection.utils import ops
+from object_detection.utils import test_case
+from object_detection.utils import test_utils
+
+slim = tf.contrib.slim
+BOX_CODE_SIZE = 4
+
+
+class FakeFasterRCNNFeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Fake feature extracture to use in tests."""
+
+  def __init__(self):
+    super(FakeFasterRCNNFeatureExtractor, self).__init__(
+        is_training=False,
+        first_stage_features_stride=32,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def preprocess(self, resized_inputs):
+    return tf.identity(resized_inputs)
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    with tf.variable_scope('mock_model'):
+      proposal_features = 0 * slim.conv2d(
+          preprocessed_inputs, num_outputs=3, kernel_size=1, scope='layer1')
+      return proposal_features, {}
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    with tf.variable_scope('mock_model'):
+      return 0 * slim.conv2d(proposal_feature_maps,
+                             num_outputs=3, kernel_size=1, scope='layer2')
+
+
+class FasterRCNNMetaArchTestBase(test_case.TestCase, parameterized.TestCase):
+  """Base class to test Faster R-CNN and R-FCN meta architectures."""
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        hyperparams_text_proto,
+                                        is_training):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    return hyperparams_builder.build(hyperparams, is_training=is_training)
+
+  def _get_second_stage_box_predictor_text_proto(self):
+    box_predictor_text_proto = """
+      mask_rcnn_box_predictor {
+        fc_hyperparams {
+          op: FC
+          activation: NONE
+          regularizer {
+            l2_regularizer {
+              weight: 0.0005
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    """
+    return box_predictor_text_proto
+
+  def _add_mask_to_second_stage_box_predictor_text_proto(
+      self, masks_are_class_agnostic=False):
+    agnostic = 'true' if masks_are_class_agnostic else 'false'
+    box_predictor_text_proto = """
+      mask_rcnn_box_predictor {
+        predict_instance_masks: true
+        masks_are_class_agnostic: """ + agnostic + """
+        mask_height: 14
+        mask_width: 14
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    """
+    return box_predictor_text_proto
+
+  def _get_second_stage_box_predictor(self, num_classes, is_training,
+                                      predict_masks, masks_are_class_agnostic):
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
+                      box_predictor_proto)
+    if predict_masks:
+      text_format.Merge(
+          self._add_mask_to_second_stage_box_predictor_text_proto(
+              masks_are_class_agnostic),
+          box_predictor_proto)
+
+    return box_predictor_builder.build(
+        hyperparams_builder.build,
+        box_predictor_proto,
+        num_classes=num_classes,
+        is_training=is_training)
+
+  def _get_model(self, box_predictor, **common_kwargs):
+    return faster_rcnn_meta_arch.FasterRCNNMetaArch(
+        initial_crop_size=3,
+        maxpool_kernel_size=1,
+        maxpool_stride=1,
+        second_stage_mask_rcnn_box_predictor=box_predictor,
+        **common_kwargs)
+
+  def _build_model(self,
+                   is_training,
+                   number_of_stages,
+                   second_stage_batch_size,
+                   first_stage_max_proposals=8,
+                   num_classes=2,
+                   hard_mining=False,
+                   softmax_second_stage_classification_loss=True,
+                   predict_masks=False,
+                   pad_to_max_dimension=None,
+                   masks_are_class_agnostic=False,
+                   use_matmul_crop_and_resize=False,
+                   clip_anchors_to_image=False,
+                   use_matmul_gather_in_matcher=False,
+                   use_static_shapes=False):
+
+    def image_resizer_fn(image, masks=None):
+      """Fake image resizer function."""
+      resized_inputs = []
+      resized_image = tf.identity(image)
+      if pad_to_max_dimension is not None:
+        resized_image = tf.image.pad_to_bounding_box(image, 0, 0,
+                                                     pad_to_max_dimension,
+                                                     pad_to_max_dimension)
+      resized_inputs.append(resized_image)
+      if masks is not None:
+        resized_masks = tf.identity(masks)
+        if pad_to_max_dimension is not None:
+          resized_masks = tf.image.pad_to_bounding_box(tf.transpose(masks,
+                                                                    [1, 2, 0]),
+                                                       0, 0,
+                                                       pad_to_max_dimension,
+                                                       pad_to_max_dimension)
+          resized_masks = tf.transpose(resized_masks, [2, 0, 1])
+        resized_inputs.append(resized_masks)
+      resized_inputs.append(tf.shape(image))
+      return resized_inputs
+
+    # anchors in this test are designed so that a subset of anchors are inside
+    # the image and a subset of anchors are outside.
+    first_stage_anchor_scales = (0.001, 0.005, 0.1)
+    first_stage_anchor_aspect_ratios = (0.5, 1.0, 2.0)
+    first_stage_anchor_strides = (1, 1)
+    first_stage_anchor_generator = grid_anchor_generator.GridAnchorGenerator(
+        first_stage_anchor_scales,
+        first_stage_anchor_aspect_ratios,
+        anchor_stride=first_stage_anchor_strides)
+    first_stage_target_assigner = target_assigner.create_target_assigner(
+        'FasterRCNN',
+        'proposal',
+        use_matmul_gather=use_matmul_gather_in_matcher)
+
+    fake_feature_extractor = FakeFasterRCNNFeatureExtractor()
+
+    first_stage_box_predictor_hyperparams_text_proto = """
+      op: CONV
+      activation: RELU
+      regularizer {
+        l2_regularizer {
+          weight: 0.00004
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.03
+        }
+      }
+    """
+    first_stage_box_predictor_arg_scope_fn = (
+        self._build_arg_scope_with_hyperparams(
+            first_stage_box_predictor_hyperparams_text_proto, is_training))
+
+    first_stage_box_predictor_kernel_size = 3
+    first_stage_atrous_rate = 1
+    first_stage_box_predictor_depth = 512
+    first_stage_minibatch_size = 3
+    first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
+        positive_fraction=0.5, is_static=use_static_shapes)
+
+    first_stage_nms_score_threshold = -1.0
+    first_stage_nms_iou_threshold = 1.0
+    first_stage_max_proposals = first_stage_max_proposals
+    first_stage_non_max_suppression_fn = functools.partial(
+        post_processing.batch_multiclass_non_max_suppression,
+        score_thresh=first_stage_nms_score_threshold,
+        iou_thresh=first_stage_nms_iou_threshold,
+        max_size_per_class=first_stage_max_proposals,
+        max_total_size=first_stage_max_proposals,
+        use_static_shapes=use_static_shapes)
+
+    first_stage_localization_loss_weight = 1.0
+    first_stage_objectness_loss_weight = 1.0
+
+    post_processing_text_proto = """
+      batch_non_max_suppression {
+        score_threshold: -20.0
+        iou_threshold: 1.0
+        max_detections_per_class: 5
+        max_total_detections: 5
+        use_static_shapes: """ +'{}'.format(use_static_shapes) + """
+      }
+    """
+    post_processing_config = post_processing_pb2.PostProcessing()
+    text_format.Merge(post_processing_text_proto, post_processing_config)
+
+    second_stage_target_assigner = target_assigner.create_target_assigner(
+        'FasterRCNN', 'detection',
+        use_matmul_gather=use_matmul_gather_in_matcher)
+    second_stage_non_max_suppression_fn, _ = post_processing_builder.build(
+        post_processing_config)
+    second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
+        positive_fraction=1.0, is_static=use_static_shapes)
+
+    second_stage_score_conversion_fn = tf.identity
+    second_stage_localization_loss_weight = 1.0
+    second_stage_classification_loss_weight = 1.0
+    if softmax_second_stage_classification_loss:
+      second_stage_classification_loss = (
+          losses.WeightedSoftmaxClassificationLoss())
+    else:
+      second_stage_classification_loss = (
+          losses.WeightedSigmoidClassificationLoss())
+
+    hard_example_miner = None
+    if hard_mining:
+      hard_example_miner = losses.HardExampleMiner(
+          num_hard_examples=1,
+          iou_threshold=0.99,
+          loss_type='both',
+          cls_loss_weight=second_stage_classification_loss_weight,
+          loc_loss_weight=second_stage_localization_loss_weight,
+          max_negatives_per_positive=None)
+
+    crop_and_resize_fn = (
+        ops.matmul_crop_and_resize
+        if use_matmul_crop_and_resize else ops.native_crop_and_resize)
+    common_kwargs = {
+        'is_training': is_training,
+        'num_classes': num_classes,
+        'image_resizer_fn': image_resizer_fn,
+        'feature_extractor': fake_feature_extractor,
+        'number_of_stages': number_of_stages,
+        'first_stage_anchor_generator': first_stage_anchor_generator,
+        'first_stage_target_assigner': first_stage_target_assigner,
+        'first_stage_atrous_rate': first_stage_atrous_rate,
+        'first_stage_box_predictor_arg_scope_fn':
+        first_stage_box_predictor_arg_scope_fn,
+        'first_stage_box_predictor_kernel_size':
+        first_stage_box_predictor_kernel_size,
+        'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
+        'first_stage_minibatch_size': first_stage_minibatch_size,
+        'first_stage_sampler': first_stage_sampler,
+        'first_stage_non_max_suppression_fn':
+        first_stage_non_max_suppression_fn,
+        'first_stage_max_proposals': first_stage_max_proposals,
+        'first_stage_localization_loss_weight':
+        first_stage_localization_loss_weight,
+        'first_stage_objectness_loss_weight':
+        first_stage_objectness_loss_weight,
+        'second_stage_target_assigner': second_stage_target_assigner,
+        'second_stage_batch_size': second_stage_batch_size,
+        'second_stage_sampler': second_stage_sampler,
+        'second_stage_non_max_suppression_fn':
+        second_stage_non_max_suppression_fn,
+        'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
+        'second_stage_localization_loss_weight':
+        second_stage_localization_loss_weight,
+        'second_stage_classification_loss_weight':
+        second_stage_classification_loss_weight,
+        'second_stage_classification_loss':
+        second_stage_classification_loss,
+        'hard_example_miner': hard_example_miner,
+        'crop_and_resize_fn': crop_and_resize_fn,
+        'clip_anchors_to_image': clip_anchors_to_image,
+        'use_static_shapes': use_static_shapes,
+        'resize_masks': True,
+    }
+
+    return self._get_model(
+        self._get_second_stage_box_predictor(
+            num_classes=num_classes,
+            is_training=is_training,
+            predict_masks=predict_masks,
+            masks_are_class_agnostic=masks_are_class_agnostic), **common_kwargs)
+
+  def test_predict_gives_correct_shapes_in_inference_mode_first_stage_only(
+      self, use_static_shapes=False):
+    batch_size = 2
+    height = 10
+    width = 12
+    input_image_shape = (batch_size, height, width, 3)
+
+    def graph_fn(images):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=False,
+          number_of_stages=1,
+          second_stage_batch_size=2,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes)
+      preprocessed_inputs, true_image_shapes = model.preprocess(images)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      return (prediction_dict['rpn_box_predictor_features'],
+              prediction_dict['rpn_features_to_crop'],
+              prediction_dict['image_shape'],
+              prediction_dict['rpn_box_encodings'],
+              prediction_dict['rpn_objectness_predictions_with_background'],
+              prediction_dict['anchors'])
+
+    images = np.zeros(input_image_shape, dtype=np.float32)
+
+    # In inference mode, anchors are clipped to the image window, but not
+    # pruned.  Since MockFasterRCNN.extract_proposal_features returns a
+    # tensor with the same shape as its input, the expected number of anchors
+    # is height * width * the number of anchors per location (i.e. 3x3).
+    expected_num_anchors = height * width * 3 * 3
+    expected_output_shapes = {
+        'rpn_box_predictor_features': (batch_size, height, width, 512),
+        'rpn_features_to_crop': (batch_size, height, width, 3),
+        'rpn_box_encodings': (batch_size, expected_num_anchors, 4),
+        'rpn_objectness_predictions_with_background':
+        (batch_size, expected_num_anchors, 2),
+        'anchors': (expected_num_anchors, 4)
+    }
+
+    if use_static_shapes:
+      results = self.execute(graph_fn, [images])
+    else:
+      results = self.execute_cpu(graph_fn, [images])
+
+    self.assertAllEqual(results[0].shape,
+                        expected_output_shapes['rpn_box_predictor_features'])
+    self.assertAllEqual(results[1].shape,
+                        expected_output_shapes['rpn_features_to_crop'])
+    self.assertAllEqual(results[2],
+                        input_image_shape)
+    self.assertAllEqual(results[3].shape,
+                        expected_output_shapes['rpn_box_encodings'])
+    self.assertAllEqual(
+        results[4].shape,
+        expected_output_shapes['rpn_objectness_predictions_with_background'])
+    self.assertAllEqual(results[5].shape,
+                        expected_output_shapes['anchors'])
+
+    # Check that anchors are clipped to window.
+    anchors = results[5]
+    self.assertTrue(np.all(np.greater_equal(anchors, 0)))
+    self.assertTrue(np.all(np.less_equal(anchors[:, 0], height)))
+    self.assertTrue(np.all(np.less_equal(anchors[:, 1], width)))
+    self.assertTrue(np.all(np.less_equal(anchors[:, 2], height)))
+    self.assertTrue(np.all(np.less_equal(anchors[:, 3], width)))
+
+  def test_predict_gives_valid_anchors_in_training_mode_first_stage_only(self):
+    test_graph = tf.Graph()
+    with test_graph.as_default():
+      model = self._build_model(
+          is_training=True, number_of_stages=1, second_stage_batch_size=2)
+      batch_size = 2
+      height = 10
+      width = 12
+      input_image_shape = (batch_size, height, width, 3)
+      _, true_image_shapes = model.preprocess(tf.zeros(input_image_shape))
+      preprocessed_inputs = tf.placeholder(
+          dtype=tf.float32, shape=(batch_size, None, None, 3))
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+
+      expected_output_keys = set([
+          'rpn_box_predictor_features', 'rpn_features_to_crop', 'image_shape',
+          'rpn_box_encodings', 'rpn_objectness_predictions_with_background',
+          'anchors'])
+      # At training time, anchors that exceed image bounds are pruned.  Thus
+      # the `expected_num_anchors` in the above inference mode test is now
+      # a strict upper bound on the number of anchors.
+      num_anchors_strict_upper_bound = height * width * 3 * 3
+
+      init_op = tf.global_variables_initializer()
+      with self.test_session(graph=test_graph) as sess:
+        sess.run(init_op)
+        prediction_out = sess.run(prediction_dict,
+                                  feed_dict={
+                                      preprocessed_inputs:
+                                      np.zeros(input_image_shape)
+                                  })
+
+        self.assertEqual(set(prediction_out.keys()), expected_output_keys)
+        self.assertAllEqual(prediction_out['image_shape'], input_image_shape)
+
+        # Check that anchors have less than the upper bound and
+        # are clipped to window.
+        anchors = prediction_out['anchors']
+        self.assertTrue(len(anchors.shape) == 2 and anchors.shape[1] == 4)
+        num_anchors_out = anchors.shape[0]
+        self.assertLess(num_anchors_out, num_anchors_strict_upper_bound)
+
+        self.assertTrue(np.all(np.greater_equal(anchors, 0)))
+        self.assertTrue(np.all(np.less_equal(anchors[:, 0], height)))
+        self.assertTrue(np.all(np.less_equal(anchors[:, 1], width)))
+        self.assertTrue(np.all(np.less_equal(anchors[:, 2], height)))
+        self.assertTrue(np.all(np.less_equal(anchors[:, 3], width)))
+
+        self.assertAllEqual(prediction_out['rpn_box_encodings'].shape,
+                            (batch_size, num_anchors_out, 4))
+        self.assertAllEqual(
+            prediction_out['rpn_objectness_predictions_with_background'].shape,
+            (batch_size, num_anchors_out, 2))
+
+  def test_predict_correct_shapes_in_inference_mode_two_stages(
+      self, use_static_shapes=False):
+
+    def compare_results(results, expected_output_shapes):
+      """Checks if the shape of the predictions are as expected."""
+      self.assertAllEqual(results[0].shape,
+                          expected_output_shapes['rpn_box_predictor_features'])
+      self.assertAllEqual(results[1].shape,
+                          expected_output_shapes['rpn_features_to_crop'])
+      self.assertAllEqual(results[2].shape,
+                          expected_output_shapes['image_shape'])
+      self.assertAllEqual(results[3].shape,
+                          expected_output_shapes['rpn_box_encodings'])
+      self.assertAllEqual(
+          results[4].shape,
+          expected_output_shapes['rpn_objectness_predictions_with_background'])
+      self.assertAllEqual(results[5].shape,
+                          expected_output_shapes['anchors'])
+      self.assertAllEqual(results[6].shape,
+                          expected_output_shapes['refined_box_encodings'])
+      self.assertAllEqual(
+          results[7].shape,
+          expected_output_shapes['class_predictions_with_background'])
+      self.assertAllEqual(results[8].shape,
+                          expected_output_shapes['num_proposals'])
+      self.assertAllEqual(results[9].shape,
+                          expected_output_shapes['proposal_boxes'])
+      self.assertAllEqual(results[10].shape,
+                          expected_output_shapes['proposal_boxes_normalized'])
+      self.assertAllEqual(results[11].shape,
+                          expected_output_shapes['box_classifier_features'])
+
+    batch_size = 2
+    image_size = 10
+    max_num_proposals = 8
+    initial_crop_size = 3
+    maxpool_stride = 1
+
+    input_shapes = [(batch_size, image_size, image_size, 3),
+                    (None, image_size, image_size, 3),
+                    (batch_size, None, None, 3),
+                    (None, None, None, 3)]
+
+    def graph_fn_tpu(images):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=False,
+          number_of_stages=2,
+          second_stage_batch_size=2,
+          predict_masks=False,
+          use_matmul_crop_and_resize=use_static_shapes,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes)
+      preprocessed_inputs, true_image_shapes = model.preprocess(images)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      return (prediction_dict['rpn_box_predictor_features'],
+              prediction_dict['rpn_features_to_crop'],
+              prediction_dict['image_shape'],
+              prediction_dict['rpn_box_encodings'],
+              prediction_dict['rpn_objectness_predictions_with_background'],
+              prediction_dict['anchors'],
+              prediction_dict['refined_box_encodings'],
+              prediction_dict['class_predictions_with_background'],
+              prediction_dict['num_proposals'],
+              prediction_dict['proposal_boxes'],
+              prediction_dict['proposal_boxes_normalized'],
+              prediction_dict['box_classifier_features'])
+
+    expected_num_anchors = image_size * image_size * 3 * 3
+    expected_shapes = {
+        'rpn_box_predictor_features':
+        (2, image_size, image_size, 512),
+        'rpn_features_to_crop': (2, image_size, image_size, 3),
+        'image_shape': (4,),
+        'rpn_box_encodings': (2, expected_num_anchors, 4),
+        'rpn_objectness_predictions_with_background':
+        (2, expected_num_anchors, 2),
+        'anchors': (expected_num_anchors, 4),
+        'refined_box_encodings': (2 * max_num_proposals, 2, 4),
+        'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
+        'num_proposals': (2,),
+        'proposal_boxes': (2, max_num_proposals, 4),
+        'proposal_boxes_normalized': (2, max_num_proposals, 4),
+        'box_classifier_features':
+        self._get_box_classifier_features_shape(image_size,
+                                                batch_size,
+                                                max_num_proposals,
+                                                initial_crop_size,
+                                                maxpool_stride,
+                                                3)
+    }
+
+    if use_static_shapes:
+      input_shape = (batch_size, image_size, image_size, 3)
+      images = np.zeros(input_shape, dtype=np.float32)
+      results = self.execute(graph_fn_tpu, [images])
+      compare_results(results, expected_shapes)
+    else:
+      for input_shape in input_shapes:
+        test_graph = tf.Graph()
+        with test_graph.as_default():
+          model = self._build_model(
+              is_training=False,
+              number_of_stages=2,
+              second_stage_batch_size=2,
+              predict_masks=False)
+          preprocessed_inputs = tf.placeholder(tf.float32, shape=input_shape)
+          _, true_image_shapes = model.preprocess(preprocessed_inputs)
+          result_tensor_dict = model.predict(
+              preprocessed_inputs, true_image_shapes)
+          init_op = tf.global_variables_initializer()
+        with self.test_session(graph=test_graph) as sess:
+          sess.run(init_op)
+          tensor_dict_out = sess.run(result_tensor_dict, feed_dict={
+              preprocessed_inputs:
+              np.zeros((batch_size, image_size, image_size, 3))})
+        self.assertEqual(set(tensor_dict_out.keys()),
+                         set(expected_shapes.keys()))
+        for key in expected_shapes:
+          self.assertAllEqual(tensor_dict_out[key].shape, expected_shapes[key])
+
+  def test_predict_gives_correct_shapes_in_train_mode_both_stages(
+      self,
+      use_static_shapes=False):
+    batch_size = 2
+    image_size = 10
+    max_num_proposals = 7
+    initial_crop_size = 3
+    maxpool_stride = 1
+
+    def graph_fn(images, gt_boxes, gt_classes, gt_weights):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=True,
+          number_of_stages=2,
+          second_stage_batch_size=7,
+          predict_masks=False,
+          use_matmul_crop_and_resize=use_static_shapes,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes)
+
+      preprocessed_inputs, true_image_shapes = model.preprocess(images)
+      model.provide_groundtruth(
+          groundtruth_boxes_list=tf.unstack(gt_boxes),
+          groundtruth_classes_list=tf.unstack(gt_classes),
+          groundtruth_weights_list=tf.unstack(gt_weights))
+      result_tensor_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      return (result_tensor_dict['refined_box_encodings'],
+              result_tensor_dict['class_predictions_with_background'],
+              result_tensor_dict['proposal_boxes'],
+              result_tensor_dict['proposal_boxes_normalized'],
+              result_tensor_dict['anchors'],
+              result_tensor_dict['rpn_box_encodings'],
+              result_tensor_dict['rpn_objectness_predictions_with_background'],
+              result_tensor_dict['rpn_features_to_crop'],
+              result_tensor_dict['rpn_box_predictor_features'],
+             )
+
+    image_shape = (batch_size, image_size, image_size, 3)
+    images = np.zeros(image_shape, dtype=np.float32)
+    gt_boxes = np.stack([
+        np.array([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=np.float32),
+        np.array([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=np.float32)
+    ])
+    gt_classes = np.stack([
+        np.array([[1, 0], [0, 1]], dtype=np.float32),
+        np.array([[1, 0], [1, 0]], dtype=np.float32)
+    ])
+    gt_weights = np.stack([
+        np.array([1, 1], dtype=np.float32),
+        np.array([1, 1], dtype=np.float32)
+    ])
+    if use_static_shapes:
+      results = self.execute(graph_fn,
+                             [images, gt_boxes, gt_classes, gt_weights])
+    else:
+      results = self.execute_cpu(graph_fn,
+                                 [images, gt_boxes, gt_classes, gt_weights])
+
+    expected_shapes = {
+        'rpn_box_predictor_features': (2, image_size, image_size, 512),
+        'rpn_features_to_crop': (2, image_size, image_size, 3),
+        'refined_box_encodings': (2 * max_num_proposals, 2, 4),
+        'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
+        'proposal_boxes': (2, max_num_proposals, 4),
+        'rpn_box_encodings': (2, image_size * image_size * 9, 4),
+        'proposal_boxes_normalized': (2, max_num_proposals, 4),
+        'box_classifier_features':
+            self._get_box_classifier_features_shape(
+                image_size, batch_size, max_num_proposals, initial_crop_size,
+                maxpool_stride, 3),
+        'rpn_objectness_predictions_with_background':
+        (2, image_size * image_size * 9, 2)
+    }
+    # TODO(rathodv): Possibly change utils/test_case.py to accept dictionaries
+    # and return dicionaries so don't have to rely on the order of tensors.
+    self.assertAllEqual(results[0].shape,
+                        expected_shapes['refined_box_encodings'])
+    self.assertAllEqual(results[1].shape,
+                        expected_shapes['class_predictions_with_background'])
+    self.assertAllEqual(results[2].shape, expected_shapes['proposal_boxes'])
+    self.assertAllEqual(results[3].shape,
+                        expected_shapes['proposal_boxes_normalized'])
+    anchors_shape = results[4].shape
+    self.assertAllEqual(results[5].shape,
+                        [batch_size, anchors_shape[0], 4])
+    self.assertAllEqual(results[6].shape,
+                        [batch_size, anchors_shape[0], 2])
+    self.assertAllEqual(results[7].shape,
+                        expected_shapes['rpn_features_to_crop'])
+    self.assertAllEqual(results[8].shape,
+                        expected_shapes['rpn_box_predictor_features'])
+
+  def test_postprocess_first_stage_only_inference_mode(
+      self, use_static_shapes=False, pad_to_max_dimension=None):
+    batch_size = 2
+    first_stage_max_proposals = 4 if use_static_shapes else 8
+
+    def graph_fn(images,
+                 rpn_box_encodings,
+                 rpn_objectness_predictions_with_background,
+                 rpn_features_to_crop,
+                 anchors):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=False, number_of_stages=1, second_stage_batch_size=6,
+          use_matmul_crop_and_resize=use_static_shapes,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes,
+          use_matmul_gather_in_matcher=use_static_shapes,
+          first_stage_max_proposals=first_stage_max_proposals,
+          pad_to_max_dimension=pad_to_max_dimension)
+      _, true_image_shapes = model.preprocess(images)
+      proposals = model.postprocess({
+          'rpn_box_encodings': rpn_box_encodings,
+          'rpn_objectness_predictions_with_background':
+          rpn_objectness_predictions_with_background,
+          'rpn_features_to_crop': rpn_features_to_crop,
+          'anchors': anchors}, true_image_shapes)
+      return (proposals['num_detections'],
+              proposals['detection_boxes'],
+              proposals['detection_scores'])
+
+    anchors = np.array(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=np.float32)
+    rpn_box_encodings = np.zeros(
+        (batch_size, anchors.shape[0], BOX_CODE_SIZE), dtype=np.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = np.array([
+        [[-10, 13],
+         [10, -10],
+         [10, -11],
+         [-10, 12]],
+        [[10, -10],
+         [-10, 13],
+         [-10, 12],
+         [10, -11]]], dtype=np.float32)
+    rpn_features_to_crop = np.ones((batch_size, 8, 8, 10), dtype=np.float32)
+    image_shape = (batch_size, 32, 32, 3)
+    images = np.zeros(image_shape, dtype=np.float32)
+
+    if use_static_shapes:
+      results = self.execute(graph_fn,
+                             [images, rpn_box_encodings,
+                              rpn_objectness_predictions_with_background,
+                              rpn_features_to_crop, anchors])
+    else:
+      results = self.execute_cpu(graph_fn,
+                                 [images, rpn_box_encodings,
+                                  rpn_objectness_predictions_with_background,
+                                  rpn_features_to_crop, anchors])
+
+    expected_proposal_boxes = [
+        [[0, 0, .5, .5], [.5, .5, 1, 1], [0, .5, .5, 1], [.5, 0, 1.0, .5]]
+        + 4 * [4 * [0]],
+        [[0, .5, .5, 1], [.5, 0, 1.0, .5], [0, 0, .5, .5], [.5, .5, 1, 1]]
+        + 4 * [4 * [0]]]
+    expected_proposal_scores = [[1, 1, 0, 0, 0, 0, 0, 0],
+                                [1, 1, 0, 0, 0, 0, 0, 0]]
+    expected_num_proposals = [4, 4]
+
+    self.assertAllClose(results[0], expected_num_proposals)
+    for indx, num_proposals in enumerate(expected_num_proposals):
+      self.assertAllClose(results[1][indx][0:num_proposals],
+                          expected_proposal_boxes[indx][0:num_proposals])
+      self.assertAllClose(results[2][indx][0:num_proposals],
+                          expected_proposal_scores[indx][0:num_proposals])
+
+  def _test_postprocess_first_stage_only_train_mode(self,
+                                                    pad_to_max_dimension=None):
+    model = self._build_model(
+        is_training=True, number_of_stages=1, second_stage_batch_size=2,
+        pad_to_max_dimension=pad_to_max_dimension)
+    batch_size = 2
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size, anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant([
+        [[-10, 13],
+         [-10, 12],
+         [-10, 11],
+         [-10, 10]],
+        [[-10, 13],
+         [-10, 12],
+         [-10, 11],
+         [-10, 10]]], dtype=tf.float32)
+    rpn_features_to_crop = tf.ones((batch_size, 8, 8, 10), dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+    groundtruth_boxes_list = [
+        tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
+        tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32)]
+    groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
+                                tf.constant([[1, 0], [1, 0]], dtype=tf.float32)]
+    groundtruth_weights_list = [
+        tf.constant([1, 1], dtype=tf.float32),
+        tf.constant([1, 1], dtype=tf.float32)
+    ]
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(
+        groundtruth_boxes_list,
+        groundtruth_classes_list,
+        groundtruth_weights_list=groundtruth_weights_list)
+    proposals = model.postprocess({
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'rpn_features_to_crop': rpn_features_to_crop,
+        'anchors': anchors}, true_image_shapes)
+    expected_proposal_boxes = [
+        [[0, 0, .5, .5], [.5, .5, 1, 1]], [[0, .5, .5, 1], [.5, 0, 1, .5]]]
+    expected_proposal_scores = [[1, 1],
+                                [1, 1]]
+    expected_num_proposals = [2, 2]
+
+    expected_output_keys = set(['detection_boxes', 'detection_scores',
+                                'num_detections'])
+    self.assertEqual(set(proposals.keys()), expected_output_keys)
+
+    with self.test_session() as sess:
+      proposals_out = sess.run(proposals)
+      for image_idx in range(batch_size):
+        self.assertTrue(
+            test_utils.first_rows_close_as_set(
+                proposals_out['detection_boxes'][image_idx].tolist(),
+                expected_proposal_boxes[image_idx]))
+      self.assertAllClose(proposals_out['detection_scores'],
+                          expected_proposal_scores)
+      self.assertAllEqual(proposals_out['num_detections'],
+                          expected_num_proposals)
+
+  def test_postprocess_first_stage_only_train_mode(self):
+    self._test_postprocess_first_stage_only_train_mode()
+
+  def test_postprocess_first_stage_only_train_mode_padded_image(self):
+    self._test_postprocess_first_stage_only_train_mode(pad_to_max_dimension=56)
+
+  def test_postprocess_second_stage_only_inference_mode(
+      self, use_static_shapes=False, pad_to_max_dimension=None):
+    batch_size = 2
+    num_classes = 2
+    image_shape = np.array((2, 36, 48, 3), dtype=np.int32)
+    first_stage_max_proposals = 8
+    total_num_padded_proposals = batch_size * first_stage_max_proposals
+
+    def graph_fn(images,
+                 refined_box_encodings,
+                 class_predictions_with_background,
+                 num_proposals,
+                 proposal_boxes):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=False, number_of_stages=2,
+          second_stage_batch_size=6,
+          use_matmul_crop_and_resize=use_static_shapes,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes,
+          use_matmul_gather_in_matcher=use_static_shapes,
+          pad_to_max_dimension=pad_to_max_dimension)
+      _, true_image_shapes = model.preprocess(images)
+      detections = model.postprocess({
+          'refined_box_encodings': refined_box_encodings,
+          'class_predictions_with_background':
+          class_predictions_with_background,
+          'num_proposals': num_proposals,
+          'proposal_boxes': proposal_boxes,
+      }, true_image_shapes)
+      return (detections['num_detections'],
+              detections['detection_boxes'],
+              detections['detection_scores'],
+              detections['detection_classes'])
+
+    proposal_boxes = np.array(
+        [[[1, 1, 2, 3],
+          [0, 0, 1, 1],
+          [.5, .5, .6, .6],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0]],
+         [[2, 3, 6, 8],
+          [1, 2, 5, 3],
+          4*[0], 4*[0], 4*[0], 4*[0], 4*[0], 4*[0]]], dtype=np.float32)
+    num_proposals = np.array([3, 2], dtype=np.int32)
+    refined_box_encodings = np.zeros(
+        [total_num_padded_proposals, num_classes, 4], dtype=np.float32)
+    class_predictions_with_background = np.ones(
+        [total_num_padded_proposals, num_classes+1], dtype=np.float32)
+    images = np.zeros(image_shape, dtype=np.float32)
+
+    if use_static_shapes:
+      results = self.execute(graph_fn,
+                             [images, refined_box_encodings,
+                              class_predictions_with_background,
+                              num_proposals, proposal_boxes])
+    else:
+      results = self.execute_cpu(graph_fn,
+                                 [images, refined_box_encodings,
+                                  class_predictions_with_background,
+                                  num_proposals, proposal_boxes])
+    expected_num_detections = [5, 4]
+    expected_detection_classes = [[0, 0, 0, 1, 1], [0, 0, 1, 1, 0]]
+    expected_detection_scores = [[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]]
+
+    self.assertAllClose(results[0], expected_num_detections)
+
+    for indx, num_proposals in enumerate(expected_num_detections):
+      self.assertAllClose(results[2][indx][0:num_proposals],
+                          expected_detection_scores[indx][0:num_proposals])
+      self.assertAllClose(results[3][indx][0:num_proposals],
+                          expected_detection_classes[indx][0:num_proposals])
+
+    if not use_static_shapes:
+      self.assertAllEqual(results[1].shape, [2, 5, 4])
+
+  def test_preprocess_preserves_input_shapes(self):
+    image_shapes = [(3, None, None, 3),
+                    (None, 10, 10, 3),
+                    (None, None, None, 3)]
+    for image_shape in image_shapes:
+      model = self._build_model(
+          is_training=False, number_of_stages=2, second_stage_batch_size=6)
+      image_placeholder = tf.placeholder(tf.float32, shape=image_shape)
+      preprocessed_inputs, _ = model.preprocess(image_placeholder)
+      self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape)
+
+  # TODO(rathodv): Split test into two - with and without masks.
+  def test_loss_first_stage_only_mode(self):
+    model = self._build_model(
+        is_training=True, number_of_stages=1, second_stage_batch_size=6)
+    batch_size = 2
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant([
+        [[-10, 13],
+         [10, -10],
+         [10, -11],
+         [-10, 12]],
+        [[10, -10],
+         [-10, 13],
+         [-10, 12],
+         [10, -11]]], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    groundtruth_boxes_list = [
+        tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
+        tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32)]
+    groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
+                                tf.constant([[1, 0], [1, 0]], dtype=tf.float32)]
+
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(groundtruth_boxes_list,
+                              groundtruth_classes_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
+      self.assertNotIn('Loss/BoxClassifierLoss/localization_loss',
+                       loss_dict_out)
+      self.assertNotIn('Loss/BoxClassifierLoss/classification_loss',
+                       loss_dict_out)
+
+  # TODO(rathodv): Split test into two - with and without masks.
+  def test_loss_full(self):
+    model = self._build_model(
+        is_training=True, number_of_stages=2, second_stage_batch_size=6)
+    batch_size = 3
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant(
+        [[[-10, 13], [10, -10], [10, -11], [-10, 12]], [[10, -10], [-10, 13], [
+            -10, 12
+        ], [10, -11]], [[10, -10], [-10, 13], [-10, 12], [10, -11]]],
+        dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    num_proposals = tf.constant([6, 6, 6], dtype=tf.int32)
+    proposal_boxes = tf.constant(
+        3 * [[[0, 0, 16, 16], [0, 16, 16, 32], [16, 0, 32, 16],
+              [16, 16, 32, 32], [0, 0, 16, 16], [0, 16, 16, 32]]],
+        dtype=tf.float32)
+    refined_box_encodings = tf.zeros(
+        (batch_size * model.max_num_proposals,
+         model.num_classes,
+         BOX_CODE_SIZE), dtype=tf.float32)
+    class_predictions_with_background = tf.constant(
+        [
+            [-10, 10, -10],  # first image
+            [10, -10, -10],
+            [10, -10, -10],
+            [-10, -10, 10],
+            [-10, 10, -10],
+            [10, -10, -10],
+            [10, -10, -10],  # second image
+            [-10, 10, -10],
+            [-10, 10, -10],
+            [10, -10, -10],
+            [10, -10, -10],
+            [-10, 10, -10],
+            [10, -10, -10],  # third image
+            [-10, 10, -10],
+            [-10, 10, -10],
+            [10, -10, -10],
+            [10, -10, -10],
+            [-10, 10, -10]
+        ],
+        dtype=tf.float32)
+
+    mask_predictions_logits = 20 * tf.ones((batch_size *
+                                            model.max_num_proposals,
+                                            model.num_classes,
+                                            14, 14),
+                                           dtype=tf.float32)
+
+    groundtruth_boxes_list = [
+        tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
+        tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32),
+        tf.constant([[0, .5, .5, 1], [.5, 0, 1, 1]], dtype=tf.float32)
+    ]
+    groundtruth_classes_list = [
+        tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
+        tf.constant([[1, 0], [1, 0]], dtype=tf.float32),
+        tf.constant([[1, 0], [0, 1]], dtype=tf.float32)
+    ]
+
+    # Set all elements of groundtruth mask to 1.0. In this case all proposal
+    # crops of the groundtruth masks should return a mask that covers the entire
+    # proposal. Thus, if mask_predictions_logits element values are all greater
+    # than 20, the loss should be zero.
+    groundtruth_masks_list = [
+        tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32),
+        tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32),
+        tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32)
+    ]
+    groundtruth_weights_list = [
+        tf.constant([1, 1], dtype=tf.float32),
+        tf.constant([1, 1], dtype=tf.float32),
+        tf.constant([1, 0], dtype=tf.float32)
+    ]
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors,
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'proposal_boxes': proposal_boxes,
+        'num_proposals': num_proposals,
+        'mask_predictions': mask_predictions_logits
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(
+        groundtruth_boxes_list,
+        groundtruth_classes_list,
+        groundtruth_masks_list,
+        groundtruth_weights_list=groundtruth_weights_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/classification_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
+
+  def test_loss_full_zero_padded_proposals(self):
+    model = self._build_model(
+        is_training=True, number_of_stages=2, second_stage_batch_size=6)
+    batch_size = 1
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant([
+        [[-10, 13],
+         [10, -10],
+         [10, -11],
+         [10, -12]],], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    # box_classifier_batch_size is 6, but here we assume that the number of
+    # actual proposals (not counting zero paddings) is fewer (3).
+    num_proposals = tf.constant([3], dtype=tf.int32)
+    proposal_boxes = tf.constant(
+        [[[0, 0, 16, 16],
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]]], dtype=tf.float32)
+
+    refined_box_encodings = tf.zeros(
+        (batch_size * model.max_num_proposals,
+         model.num_classes,
+         BOX_CODE_SIZE), dtype=tf.float32)
+    class_predictions_with_background = tf.constant(
+        [[-10, 10, -10],
+         [10, -10, -10],
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0]], dtype=tf.float32)
+
+    mask_predictions_logits = 20 * tf.ones((batch_size *
+                                            model.max_num_proposals,
+                                            model.num_classes,
+                                            14, 14),
+                                           dtype=tf.float32)
+
+    groundtruth_boxes_list = [
+        tf.constant([[0, 0, .5, .5]], dtype=tf.float32)]
+    groundtruth_classes_list = [tf.constant([[1, 0]], dtype=tf.float32)]
+
+    # Set all elements of groundtruth mask to 1.0. In this case all proposal
+    # crops of the groundtruth masks should return a mask that covers the entire
+    # proposal. Thus, if mask_predictions_logits element values are all greater
+    # than 20, the loss should be zero.
+    groundtruth_masks_list = [tf.convert_to_tensor(np.ones((1, 32, 32)),
+                                                   dtype=tf.float32)]
+
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors,
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'proposal_boxes': proposal_boxes,
+        'num_proposals': num_proposals,
+        'mask_predictions': mask_predictions_logits
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(groundtruth_boxes_list,
+                              groundtruth_classes_list,
+                              groundtruth_masks_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/classification_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
+
+  def test_loss_full_multiple_label_groundtruth(self):
+    model = self._build_model(
+        is_training=True, number_of_stages=2, second_stage_batch_size=6,
+        softmax_second_stage_classification_loss=False)
+    batch_size = 1
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant([
+        [[-10, 13],
+         [10, -10],
+         [10, -11],
+         [10, -12]],], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    # box_classifier_batch_size is 6, but here we assume that the number of
+    # actual proposals (not counting zero paddings) is fewer (3).
+    num_proposals = tf.constant([3], dtype=tf.int32)
+    proposal_boxes = tf.constant(
+        [[[0, 0, 16, 16],
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]]], dtype=tf.float32)
+
+    # second_stage_localization_loss should only be computed for predictions
+    # that match groundtruth. For multiple label groundtruth boxes, the loss
+    # should only be computed once for the label with the smaller index.
+    refined_box_encodings = tf.constant(
+        [[[0, 0, 0, 0], [1, 1, -1, -1]],
+         [[1, 1, -1, -1], [1, 1, 1, 1]],
+         [[1, 1, -1, -1], [1, 1, 1, 1]],
+         [[1, 1, -1, -1], [1, 1, 1, 1]],
+         [[1, 1, -1, -1], [1, 1, 1, 1]],
+         [[1, 1, -1, -1], [1, 1, 1, 1]]], dtype=tf.float32)
+    class_predictions_with_background = tf.constant(
+        [[-100, 100, 100],
+         [100, -100, -100],
+         [100, -100, -100],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0]], dtype=tf.float32)
+
+    mask_predictions_logits = 20 * tf.ones((batch_size *
+                                            model.max_num_proposals,
+                                            model.num_classes,
+                                            14, 14),
+                                           dtype=tf.float32)
+
+    groundtruth_boxes_list = [
+        tf.constant([[0, 0, .5, .5]], dtype=tf.float32)]
+    # Box contains two ground truth labels.
+    groundtruth_classes_list = [tf.constant([[1, 1]], dtype=tf.float32)]
+
+    # Set all elements of groundtruth mask to 1.0. In this case all proposal
+    # crops of the groundtruth masks should return a mask that covers the entire
+    # proposal. Thus, if mask_predictions_logits element values are all greater
+    # than 20, the loss should be zero.
+    groundtruth_masks_list = [tf.convert_to_tensor(np.ones((1, 32, 32)),
+                                                   dtype=tf.float32)]
+
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors,
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'proposal_boxes': proposal_boxes,
+        'num_proposals': num_proposals,
+        'mask_predictions': mask_predictions_logits
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(groundtruth_boxes_list,
+                              groundtruth_classes_list,
+                              groundtruth_masks_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/localization_loss'], 0)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/classification_loss'], 0)
+      self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
+
+  def test_loss_full_zero_padded_proposals_nonzero_loss_with_two_images(
+      self, use_static_shapes=False, shared_boxes=False):
+    batch_size = 2
+    first_stage_max_proposals = 8
+    second_stage_batch_size = 6
+    num_classes = 2
+    def graph_fn(anchors, rpn_box_encodings,
+                 rpn_objectness_predictions_with_background, images,
+                 num_proposals, proposal_boxes, refined_box_encodings,
+                 class_predictions_with_background, groundtruth_boxes,
+                 groundtruth_classes):
+      """Function to construct tf graph for the test."""
+      model = self._build_model(
+          is_training=True, number_of_stages=2,
+          second_stage_batch_size=second_stage_batch_size,
+          first_stage_max_proposals=first_stage_max_proposals,
+          num_classes=num_classes,
+          use_matmul_crop_and_resize=use_static_shapes,
+          clip_anchors_to_image=use_static_shapes,
+          use_static_shapes=use_static_shapes)
+
+      prediction_dict = {
+          'rpn_box_encodings': rpn_box_encodings,
+          'rpn_objectness_predictions_with_background':
+          rpn_objectness_predictions_with_background,
+          'image_shape': tf.shape(images),
+          'anchors': anchors,
+          'refined_box_encodings': refined_box_encodings,
+          'class_predictions_with_background':
+          class_predictions_with_background,
+          'proposal_boxes': proposal_boxes,
+          'num_proposals': num_proposals
+      }
+      _, true_image_shapes = model.preprocess(images)
+      model.provide_groundtruth(tf.unstack(groundtruth_boxes),
+                                tf.unstack(groundtruth_classes))
+      loss_dict = model.loss(prediction_dict, true_image_shapes)
+      return (loss_dict['Loss/RPNLoss/localization_loss'],
+              loss_dict['Loss/RPNLoss/objectness_loss'],
+              loss_dict['Loss/BoxClassifierLoss/localization_loss'],
+              loss_dict['Loss/BoxClassifierLoss/classification_loss'])
+
+    anchors = np.array(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=np.float32)
+    rpn_box_encodings = np.zeros(
+        [batch_size, anchors.shape[1], BOX_CODE_SIZE], dtype=np.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = np.array(
+        [[[-10, 13],
+          [10, -10],
+          [10, -11],
+          [10, -12]],
+         [[-10, 13],
+          [10, -10],
+          [10, -11],
+          [10, -12]]], dtype=np.float32)
+    images = np.zeros([batch_size, 32, 32, 3], dtype=np.float32)
+
+    # box_classifier_batch_size is 6, but here we assume that the number of
+    # actual proposals (not counting zero paddings) is fewer.
+    num_proposals = np.array([3, 2], dtype=np.int32)
+    proposal_boxes = np.array(
+        [[[0, 0, 16, 16],
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]],
+         [[0, 0, 16, 16],
+          [0, 16, 16, 32],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]]], dtype=np.float32)
+
+    refined_box_encodings = np.zeros(
+        (batch_size * second_stage_batch_size, 1
+         if shared_boxes else num_classes, BOX_CODE_SIZE),
+        dtype=np.float32)
+    class_predictions_with_background = np.array(
+        [[-10, 10, -10],  # first image
+         [10, -10, -10],
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0],
+         [-10, -10, 10],  # second image
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0],
+         [0, 0, 0],], dtype=np.float32)
+
+    # The first groundtruth box is 4/5 of the anchor size in both directions
+    # experiencing a loss of:
+    # 2 * SmoothL1(5 * log(4/5)) / num_proposals
+    #   = 2 * (abs(5 * log(1/2)) - .5) / 3
+    # The second groundtruth box is identical to the prediction and thus
+    # experiences zero loss.
+    # Total average loss is (abs(5 * log(1/2)) - .5) / 3.
+    groundtruth_boxes = np.stack([
+        np.array([[0.05, 0.05, 0.45, 0.45]], dtype=np.float32),
+        np.array([[0.0, 0.0, 0.5, 0.5]], dtype=np.float32)])
+    groundtruth_classes = np.stack([np.array([[1, 0]], dtype=np.float32),
+                                    np.array([[0, 1]], dtype=np.float32)])
+
+    execute_fn = self.execute_cpu
+    if use_static_shapes:
+      execute_fn = self.execute
+
+    results = execute_fn(graph_fn, [
+        anchors, rpn_box_encodings, rpn_objectness_predictions_with_background,
+        images, num_proposals, proposal_boxes, refined_box_encodings,
+        class_predictions_with_background, groundtruth_boxes,
+        groundtruth_classes
+    ])
+
+    exp_loc_loss = (-5 * np.log(.8) - 0.5) / 3.0
+
+    self.assertAllClose(results[0], exp_loc_loss, rtol=1e-4, atol=1e-4)
+    self.assertAllClose(results[1], 0.0)
+    self.assertAllClose(results[2], exp_loc_loss, rtol=1e-4, atol=1e-4)
+    self.assertAllClose(results[3], 0.0)
+
+  def test_loss_with_hard_mining(self):
+    model = self._build_model(is_training=True,
+                              number_of_stages=2,
+                              second_stage_batch_size=None,
+                              first_stage_max_proposals=6,
+                              hard_mining=True)
+    batch_size = 1
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant(
+        [[[-10, 13],
+          [-10, 12],
+          [10, -11],
+          [10, -12]]], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    # box_classifier_batch_size is 6, but here we assume that the number of
+    # actual proposals (not counting zero paddings) is fewer (3).
+    num_proposals = tf.constant([3], dtype=tf.int32)
+    proposal_boxes = tf.constant(
+        [[[0, 0, 16, 16],
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]]], dtype=tf.float32)
+
+    refined_box_encodings = tf.zeros(
+        (batch_size * model.max_num_proposals,
+         model.num_classes,
+         BOX_CODE_SIZE), dtype=tf.float32)
+    class_predictions_with_background = tf.constant(
+        [[-10, 10, -10],  # first image
+         [-10, -10, 10],
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0]], dtype=tf.float32)
+
+    # The first groundtruth box is 4/5 of the anchor size in both directions
+    # experiencing a loss of:
+    # 2 * SmoothL1(5 * log(4/5)) / num_proposals
+    #   = 2 * (abs(5 * log(1/2)) - .5) / 3
+    # The second groundtruth box is 46/50 of the anchor size in both directions
+    # experiencing a loss of:
+    # 2 * SmoothL1(5 * log(42/50)) / num_proposals
+    #   = 2 * (.5(5 * log(.92))^2 - .5) / 3.
+    # Since the first groundtruth box experiences greater loss, and we have
+    # set num_hard_examples=1 in the HardMiner, the final localization loss
+    # corresponds to that of the first groundtruth box.
+    groundtruth_boxes_list = [
+        tf.constant([[0.05, 0.05, 0.45, 0.45],
+                     [0.02, 0.52, 0.48, 0.98],], dtype=tf.float32)]
+    groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32)]
+    exp_loc_loss = 2 * (-5 * np.log(.8) - 0.5) / 3.0
+
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors,
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'proposal_boxes': proposal_boxes,
+        'num_proposals': num_proposals
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(groundtruth_boxes_list,
+                              groundtruth_classes_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/localization_loss'], exp_loc_loss)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/classification_loss'], 0)
+
+  def test_loss_with_hard_mining_and_losses_mask(self):
+    model = self._build_model(is_training=True,
+                              number_of_stages=2,
+                              second_stage_batch_size=None,
+                              first_stage_max_proposals=6,
+                              hard_mining=True)
+    batch_size = 2
+    number_of_proposals = 3
+    anchors = tf.constant(
+        [[0, 0, 16, 16],
+         [0, 16, 16, 32],
+         [16, 0, 32, 16],
+         [16, 16, 32, 32]], dtype=tf.float32)
+    rpn_box_encodings = tf.zeros(
+        [batch_size,
+         anchors.get_shape().as_list()[0],
+         BOX_CODE_SIZE], dtype=tf.float32)
+    # use different numbers for the objectness category to break ties in
+    # order of boxes returned by NMS
+    rpn_objectness_predictions_with_background = tf.constant(
+        [[[-10, 13],
+          [-10, 12],
+          [10, -11],
+          [10, -12]],
+         [[-10, 13],
+          [-10, 12],
+          [10, -11],
+          [10, -12]]], dtype=tf.float32)
+    image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
+
+    # box_classifier_batch_size is 6, but here we assume that the number of
+    # actual proposals (not counting zero paddings) is fewer (3).
+    num_proposals = tf.constant([number_of_proposals, number_of_proposals],
+                                dtype=tf.int32)
+    proposal_boxes = tf.constant(
+        [[[0, 0, 16, 16],  # first image
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]],
+         [[0, 0, 16, 16],  # second image
+          [0, 16, 16, 32],
+          [16, 0, 32, 16],
+          [0, 0, 0, 0],  # begin paddings
+          [0, 0, 0, 0],
+          [0, 0, 0, 0]]], dtype=tf.float32)
+
+    refined_box_encodings = tf.zeros(
+        (batch_size * model.max_num_proposals,
+         model.num_classes,
+         BOX_CODE_SIZE), dtype=tf.float32)
+    class_predictions_with_background = tf.constant(
+        [[-10, 10, -10],  # first image
+         [-10, -10, 10],
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0],
+         [-10, 10, -10],  # second image
+         [-10, -10, 10],
+         [10, -10, -10],
+         [0, 0, 0],  # begin paddings
+         [0, 0, 0],
+         [0, 0, 0]], dtype=tf.float32)
+
+    # The first groundtruth box is 4/5 of the anchor size in both directions
+    # experiencing a loss of:
+    # 2 * SmoothL1(5 * log(4/5)) / (num_proposals * batch_size)
+    #   = 2 * (abs(5 * log(1/2)) - .5) / 3
+    # The second groundtruth box is 46/50 of the anchor size in both directions
+    # experiencing a loss of:
+    # 2 * SmoothL1(5 * log(42/50)) / (num_proposals * batch_size)
+    #   = 2 * (.5(5 * log(.92))^2 - .5) / 3.
+    # Since the first groundtruth box experiences greater loss, and we have
+    # set num_hard_examples=1 in the HardMiner, the final localization loss
+    # corresponds to that of the first groundtruth box.
+    groundtruth_boxes_list = [
+        tf.constant([[0.05, 0.05, 0.45, 0.45],
+                     [0.02, 0.52, 0.48, 0.98]], dtype=tf.float32),
+        tf.constant([[0.05, 0.05, 0.45, 0.45],
+                     [0.02, 0.52, 0.48, 0.98]], dtype=tf.float32)]
+    groundtruth_classes_list = [
+        tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
+        tf.constant([[1, 0], [0, 1]], dtype=tf.float32)]
+    is_annotated_list = [tf.constant(True, dtype=tf.bool),
+                         tf.constant(False, dtype=tf.bool)]
+    exp_loc_loss = (2 * (-5 * np.log(.8) - 0.5) /
+                    (number_of_proposals * batch_size))
+
+    prediction_dict = {
+        'rpn_box_encodings': rpn_box_encodings,
+        'rpn_objectness_predictions_with_background':
+        rpn_objectness_predictions_with_background,
+        'image_shape': image_shape,
+        'anchors': anchors,
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background': class_predictions_with_background,
+        'proposal_boxes': proposal_boxes,
+        'num_proposals': num_proposals
+    }
+    _, true_image_shapes = model.preprocess(tf.zeros(image_shape))
+    model.provide_groundtruth(groundtruth_boxes_list,
+                              groundtruth_classes_list,
+                              is_annotated_list=is_annotated_list)
+    loss_dict = model.loss(prediction_dict, true_image_shapes)
+
+    with self.test_session() as sess:
+      loss_dict_out = sess.run(loss_dict)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/localization_loss'], exp_loc_loss)
+      self.assertAllClose(loss_dict_out[
+          'Loss/BoxClassifierLoss/classification_loss'], 0)
+
+  def test_restore_map_for_classification_ckpt(self):
+    # Define mock tensorflow classification graph and save variables.
+    test_graph_classification = tf.Graph()
+    with test_graph_classification.as_default():
+      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
+      with tf.variable_scope('mock_model'):
+        net = slim.conv2d(image, num_outputs=3, kernel_size=1, scope='layer1')
+        slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')
+
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver()
+      save_path = self.get_temp_dir()
+      with self.test_session(graph=test_graph_classification) as sess:
+        sess.run(init_op)
+        saved_model_path = saver.save(sess, save_path)
+
+    # Create tensorflow detection graph and load variables from
+    # classification checkpoint.
+    test_graph_detection = tf.Graph()
+    with test_graph_detection.as_default():
+      model = self._build_model(
+          is_training=False, number_of_stages=2, second_stage_batch_size=6)
+
+      inputs_shape = (2, 20, 20, 3)
+      inputs = tf.to_float(tf.random_uniform(
+          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      model.postprocess(prediction_dict, true_image_shapes)
+      var_map = model.restore_map(fine_tune_checkpoint_type='classification')
+      self.assertIsInstance(var_map, dict)
+      saver = tf.train.Saver(var_map)
+      with self.test_session(graph=test_graph_classification) as sess:
+        saver.restore(sess, saved_model_path)
+        for var in sess.run(tf.report_uninitialized_variables()):
+          self.assertNotIn(model.first_stage_feature_extractor_scope, var)
+          self.assertNotIn(model.second_stage_feature_extractor_scope, var)
+
+  def test_restore_map_for_detection_ckpt(self):
+    # Define first detection graph and save variables.
+    test_graph_detection1 = tf.Graph()
+    with test_graph_detection1.as_default():
+      model = self._build_model(
+          is_training=False, number_of_stages=2, second_stage_batch_size=6)
+      inputs_shape = (2, 20, 20, 3)
+      inputs = tf.to_float(tf.random_uniform(
+          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      model.postprocess(prediction_dict, true_image_shapes)
+      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver()
+      save_path = self.get_temp_dir()
+      with self.test_session(graph=test_graph_detection1) as sess:
+        sess.run(init_op)
+        saved_model_path = saver.save(sess, save_path)
+
+    # Define second detection graph and restore variables.
+    test_graph_detection2 = tf.Graph()
+    with test_graph_detection2.as_default():
+      model2 = self._build_model(is_training=False, number_of_stages=2,
+                                 second_stage_batch_size=6, num_classes=42)
+
+      inputs_shape2 = (2, 20, 20, 3)
+      inputs2 = tf.to_float(tf.random_uniform(
+          inputs_shape2, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs2, true_image_shapes = model2.preprocess(inputs2)
+      prediction_dict2 = model2.predict(preprocessed_inputs2, true_image_shapes)
+      model2.postprocess(prediction_dict2, true_image_shapes)
+      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
+      var_map = model2.restore_map(fine_tune_checkpoint_type='detection')
+      self.assertIsInstance(var_map, dict)
+      saver = tf.train.Saver(var_map)
+      with self.test_session(graph=test_graph_detection2) as sess:
+        saver.restore(sess, saved_model_path)
+        uninitialized_vars_list = sess.run(tf.report_uninitialized_variables())
+        self.assertIn('another_variable', uninitialized_vars_list)
+        for var in uninitialized_vars_list:
+          self.assertNotIn(model2.first_stage_feature_extractor_scope, var)
+          self.assertNotIn(model2.second_stage_feature_extractor_scope, var)
+
+  def test_load_all_det_checkpoint_vars(self):
+    test_graph_detection = tf.Graph()
+    with test_graph_detection.as_default():
+      model = self._build_model(
+          is_training=False,
+          number_of_stages=2,
+          second_stage_batch_size=6,
+          num_classes=42)
+
+      inputs_shape = (2, 20, 20, 3)
+      inputs = tf.to_float(
+          tf.random_uniform(inputs_shape, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      model.postprocess(prediction_dict, true_image_shapes)
+      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
+      var_map = model.restore_map(
+          fine_tune_checkpoint_type='detection',
+          load_all_detection_checkpoint_vars=True)
+      self.assertIsInstance(var_map, dict)
+      self.assertIn('another_variable', var_map)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch.py
new file mode 100644
index 00000000..0c0052fe
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch.py
@@ -0,0 +1,331 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""R-FCN meta-architecture definition.
+
+R-FCN: Dai, Jifeng, et al. "R-FCN: Object Detection via Region-based
+Fully Convolutional Networks." arXiv preprint arXiv:1605.06409 (2016).
+
+The R-FCN meta architecture is similar to Faster R-CNN and only differs in the
+second stage. Hence this class inherits FasterRCNNMetaArch and overrides only
+the `_predict_second_stage` method.
+
+Similar to Faster R-CNN we allow for two modes: number_of_stages=1 and
+number_of_stages=2.  In the former setting, all of the user facing methods
+(e.g., predict, postprocess, loss) can be used as if the model consisted
+only of the RPN, returning class agnostic proposals (these can be thought of as
+approximate detections with no associated class information).  In the latter
+setting, proposals are computed, then passed through a second stage
+"box classifier" to yield (multi-class) detections.
+
+Implementations of R-FCN models must define a new FasterRCNNFeatureExtractor and
+override three methods: `preprocess`, `_extract_proposal_features` (the first
+stage of the model), and `_extract_box_classifier_features` (the second stage of
+the model). Optionally, the `restore_fn` method can be overridden.  See tests
+for an example.
+
+See notes in the documentation of Faster R-CNN meta-architecture as they all
+apply here.
+"""
+import tensorflow as tf
+
+from object_detection.core import box_predictor
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.utils import ops
+
+
+class RFCNMetaArch(faster_rcnn_meta_arch.FasterRCNNMetaArch):
+  """R-FCN Meta-architecture definition."""
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               image_resizer_fn,
+               feature_extractor,
+               number_of_stages,
+               first_stage_anchor_generator,
+               first_stage_target_assigner,
+               first_stage_atrous_rate,
+               first_stage_box_predictor_arg_scope_fn,
+               first_stage_box_predictor_kernel_size,
+               first_stage_box_predictor_depth,
+               first_stage_minibatch_size,
+               first_stage_sampler,
+               first_stage_non_max_suppression_fn,
+               first_stage_max_proposals,
+               first_stage_localization_loss_weight,
+               first_stage_objectness_loss_weight,
+               crop_and_resize_fn,
+               second_stage_target_assigner,
+               second_stage_rfcn_box_predictor,
+               second_stage_batch_size,
+               second_stage_sampler,
+               second_stage_non_max_suppression_fn,
+               second_stage_score_conversion_fn,
+               second_stage_localization_loss_weight,
+               second_stage_classification_loss_weight,
+               second_stage_classification_loss,
+               hard_example_miner,
+               parallel_iterations=16,
+               add_summaries=True,
+               clip_anchors_to_image=False,
+               use_static_shapes=False,
+               resize_masks=False):
+    """RFCNMetaArch Constructor.
+
+    Args:
+      is_training: A boolean indicating whether the training version of the
+        computation graph should be constructed.
+      num_classes: Number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      image_resizer_fn: A callable for image resizing.  This callable always
+        takes a rank-3 image tensor (corresponding to a single image) and
+        returns a rank-3 image tensor, possibly with new spatial dimensions.
+        See builders/image_resizer_builder.py.
+      feature_extractor: A FasterRCNNFeatureExtractor object.
+      number_of_stages:  Valid values are {1, 2}. If 1 will only construct the
+        Region Proposal Network (RPN) part of the model.
+      first_stage_anchor_generator: An anchor_generator.AnchorGenerator object
+        (note that currently we only support
+        grid_anchor_generator.GridAnchorGenerator objects)
+      first_stage_target_assigner: Target assigner to use for first stage of
+        R-FCN (RPN).
+      first_stage_atrous_rate: A single integer indicating the atrous rate for
+        the single convolution op which is applied to the `rpn_features_to_crop`
+        tensor to obtain a tensor to be used for box prediction. Some feature
+        extractors optionally allow for producing feature maps computed at
+        denser resolutions.  The atrous rate is used to compensate for the
+        denser feature maps by using an effectively larger receptive field.
+        (This should typically be set to 1).
+      first_stage_box_predictor_arg_scope_fn: A function to generate tf-slim
+        arg_scope for conv2d, separable_conv2d and fully_connected ops for the
+        RPN box predictor.
+      first_stage_box_predictor_kernel_size: Kernel size to use for the
+        convolution op just prior to RPN box predictions.
+      first_stage_box_predictor_depth: Output depth for the convolution op
+        just prior to RPN box predictions.
+      first_stage_minibatch_size: The "batch size" to use for computing the
+        objectness and location loss of the region proposal network. This
+        "batch size" refers to the number of anchors selected as contributing
+        to the loss function for any given image within the image batch and is
+        only called "batch_size" due to terminology from the Faster R-CNN paper.
+      first_stage_sampler: The sampler for the boxes used to calculate the RPN
+        loss after the first stage.
+      first_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
+        callable that takes `boxes`, `scores` and optional `clip_window`(with
+        all other inputs already set) and returns a dictionary containing
+        tensors with keys: `detection_boxes`, `detection_scores`,
+        `detection_classes`, `num_detections`. This is used to perform non max
+        suppression  on the boxes predicted by the Region Proposal Network
+        (RPN).
+        See `post_processing.batch_multiclass_non_max_suppression` for the type
+        and shape of these tensors.
+      first_stage_max_proposals: Maximum number of boxes to retain after
+        performing Non-Max Suppression (NMS) on the boxes predicted by the
+        Region Proposal Network (RPN).
+      first_stage_localization_loss_weight: A float
+      first_stage_objectness_loss_weight: A float
+      crop_and_resize_fn: A differentiable resampler to use for cropping RPN
+        proposal features.
+      second_stage_target_assigner: Target assigner to use for second stage of
+        R-FCN. If the model is configured with multiple prediction heads, this
+        target assigner is used to generate targets for all heads (with the
+        correct `unmatched_class_label`).
+      second_stage_rfcn_box_predictor: RFCN box predictor to use for
+        second stage.
+      second_stage_batch_size: The batch size used for computing the
+        classification and refined location loss of the box classifier.  This
+        "batch size" refers to the number of proposals selected as contributing
+        to the loss function for any given image within the image batch and is
+        only called "batch_size" due to terminology from the Faster R-CNN paper.
+      second_stage_sampler: The sampler for the boxes used for second stage
+        box classifier.
+      second_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
+        callable that takes `boxes`, `scores`, optional `clip_window` and
+        optional (kwarg) `mask` inputs (with all other inputs already set)
+        and returns a dictionary containing tensors with keys:
+        `detection_boxes`, `detection_scores`, `detection_classes`,
+        `num_detections`, and (optionally) `detection_masks`. See
+        `post_processing.batch_multiclass_non_max_suppression` for the type and
+        shape of these tensors.
+      second_stage_score_conversion_fn: Callable elementwise nonlinearity
+        (that takes tensors as inputs and returns tensors).  This is usually
+        used to convert logits to probabilities.
+      second_stage_localization_loss_weight: A float
+      second_stage_classification_loss_weight: A float
+      second_stage_classification_loss: A string indicating which loss function
+        to use, supports 'softmax' and 'sigmoid'.
+      hard_example_miner:  A losses.HardExampleMiner object (can be None).
+      parallel_iterations: (Optional) The number of iterations allowed to run
+        in parallel for calls to tf.map_fn.
+      add_summaries: boolean (default: True) controlling whether summary ops
+        should be added to tensorflow graph.
+      clip_anchors_to_image: The anchors generated are clip to the
+        window size without filtering the nonoverlapping anchors. This generates
+        a static number of anchors. This argument is unused.
+      use_static_shapes: If True, uses implementation of ops with static shape
+        guarantees.
+      resize_masks: Indicates whether the masks presend in the groundtruth
+        should be resized in the model with `image_resizer_fn`
+
+    Raises:
+      ValueError: If `second_stage_batch_size` > `first_stage_max_proposals`
+      ValueError: If first_stage_anchor_generator is not of type
+        grid_anchor_generator.GridAnchorGenerator.
+    """
+    # TODO(rathodv): add_summaries and crop_and_resize_fn is currently
+    # unused. Respect that directive in the future.
+    super(RFCNMetaArch, self).__init__(
+        is_training,
+        num_classes,
+        image_resizer_fn,
+        feature_extractor,
+        number_of_stages,
+        first_stage_anchor_generator,
+        first_stage_target_assigner,
+        first_stage_atrous_rate,
+        first_stage_box_predictor_arg_scope_fn,
+        first_stage_box_predictor_kernel_size,
+        first_stage_box_predictor_depth,
+        first_stage_minibatch_size,
+        first_stage_sampler,
+        first_stage_non_max_suppression_fn,
+        first_stage_max_proposals,
+        first_stage_localization_loss_weight,
+        first_stage_objectness_loss_weight,
+        crop_and_resize_fn,
+        None,  # initial_crop_size is not used in R-FCN
+        None,  # maxpool_kernel_size is not use in R-FCN
+        None,  # maxpool_stride is not use in R-FCN
+        second_stage_target_assigner,
+        None,  # fully_connected_box_predictor is not used in R-FCN.
+        second_stage_batch_size,
+        second_stage_sampler,
+        second_stage_non_max_suppression_fn,
+        second_stage_score_conversion_fn,
+        second_stage_localization_loss_weight,
+        second_stage_classification_loss_weight,
+        second_stage_classification_loss,
+        1.0,  # second stage mask prediction loss weight isn't used in R-FCN.
+        hard_example_miner,
+        parallel_iterations,
+        add_summaries,
+        clip_anchors_to_image,
+        use_static_shapes,
+        resize_masks)
+
+    self._rfcn_box_predictor = second_stage_rfcn_box_predictor
+
+  def _predict_second_stage(self, rpn_box_encodings,
+                            rpn_objectness_predictions_with_background,
+                            rpn_features,
+                            anchors,
+                            image_shape,
+                            true_image_shapes):
+    """Predicts the output tensors from 2nd stage of R-FCN.
+
+    Args:
+      rpn_box_encodings: 3-D float tensor of shape
+        [batch_size, num_valid_anchors, self._box_coder.code_size] containing
+        predicted boxes.
+      rpn_objectness_predictions_with_background: 3-D float tensor of shape
+        [batch_size, num_valid_anchors, 2] containing class
+        predictions (logits) for each of the anchors.  Note that this
+        tensor *includes* background class predictions (at class index 0).
+      rpn_features: A 4-D float32 tensor with shape
+        [batch_size, height, width, depth] representing image features from the
+        RPN.
+      anchors: 2-D float tensor of shape
+        [num_anchors, self._box_coder.code_size].
+      image_shape: A 1D int32 tensors of size [4] containing the image shape.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding "raw" prediction tensors:
+        1) refined_box_encodings: a 3-D tensor with shape
+          [total_num_proposals, num_classes, 4] representing predicted
+          (final) refined box encodings, where
+          total_num_proposals=batch_size*self._max_num_proposals
+        2) class_predictions_with_background: a 2-D tensor with shape
+          [total_num_proposals, num_classes + 1] containing class
+          predictions (logits) for each of the anchors, where
+          total_num_proposals=batch_size*self._max_num_proposals.
+          Note that this tensor *includes* background class predictions
+          (at class index 0).
+        3) num_proposals: An int32 tensor of shape [batch_size] representing the
+          number of proposals generated by the RPN. `num_proposals` allows us
+          to keep track of which entries are to be treated as zero paddings and
+          which are not since we always pad the number of proposals to be
+          `self.max_num_proposals` for each image.
+        4) proposal_boxes: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing
+          decoded proposal bounding boxes (in absolute coordinates).
+        5) proposal_boxes_normalized: A float32 tensor of shape
+          [batch_size, self.max_num_proposals, 4] representing decoded proposal
+          bounding boxes (in normalized coordinates). Can be used to override
+          the boxes proposed by the RPN, thus enabling one to extract box
+          classification and prediction for externally selected areas of the
+          image.
+        6) box_classifier_features: a 4-D float32 tensor, of shape
+          [batch_size, feature_map_height, feature_map_width, depth],
+          representing the box classifier features.
+    """
+    image_shape_2d = tf.tile(tf.expand_dims(image_shape[1:], 0),
+                             [image_shape[0], 1])
+    proposal_boxes_normalized, _, num_proposals = self._postprocess_rpn(
+        rpn_box_encodings, rpn_objectness_predictions_with_background,
+        anchors, image_shape_2d, true_image_shapes)
+
+    box_classifier_features = (
+        self._feature_extractor.extract_box_classifier_features(
+            rpn_features,
+            scope=self.second_stage_feature_extractor_scope))
+
+    if self._rfcn_box_predictor.is_keras_model:
+      box_predictions = self._rfcn_box_predictor(
+          [box_classifier_features],
+          proposal_boxes=proposal_boxes_normalized)
+    else:
+      box_predictions = self._rfcn_box_predictor.predict(
+          [box_classifier_features],
+          num_predictions_per_location=[1],
+          scope=self.second_stage_box_predictor_scope,
+          proposal_boxes=proposal_boxes_normalized)
+    refined_box_encodings = tf.squeeze(
+        tf.concat(box_predictions[box_predictor.BOX_ENCODINGS], axis=1), axis=1)
+    class_predictions_with_background = tf.squeeze(
+        tf.concat(
+            box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+            axis=1),
+        axis=1)
+
+    absolute_proposal_boxes = ops.normalized_to_image_coordinates(
+        proposal_boxes_normalized, image_shape,
+        parallel_iterations=self._parallel_iterations)
+
+    prediction_dict = {
+        'refined_box_encodings': refined_box_encodings,
+        'class_predictions_with_background':
+        class_predictions_with_background,
+        'num_proposals': num_proposals,
+        'proposal_boxes': absolute_proposal_boxes,
+        'box_classifier_features': box_classifier_features,
+        'proposal_boxes_normalized': proposal_boxes_normalized,
+    }
+    return prediction_dict
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch_test.py
new file mode 100644
index 00000000..829140ac
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/rfcn_meta_arch_test.py
@@ -0,0 +1,65 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.meta_architectures.rfcn_meta_arch."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch_test_lib
+from object_detection.meta_architectures import rfcn_meta_arch
+
+
+class RFCNMetaArchTest(
+    faster_rcnn_meta_arch_test_lib.FasterRCNNMetaArchTestBase):
+
+  def _get_second_stage_box_predictor_text_proto(self):
+    box_predictor_text_proto = """
+      rfcn_box_predictor {
+        conv_hyperparams {
+          op: CONV
+          activation: NONE
+          regularizer {
+            l2_regularizer {
+              weight: 0.0005
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    """
+    return box_predictor_text_proto
+
+  def _get_model(self, box_predictor, **common_kwargs):
+    return rfcn_meta_arch.RFCNMetaArch(
+        second_stage_rfcn_box_predictor=box_predictor, **common_kwargs)
+
+  def _get_box_classifier_features_shape(self,
+                                         image_size,
+                                         batch_size,
+                                         max_num_proposals,
+                                         initial_crop_size,
+                                         maxpool_stride,
+                                         num_features):
+    return (batch_size, image_size, image_size, num_features)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch.py
new file mode 100644
index 00000000..b618dae9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch.py
@@ -0,0 +1,1193 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SSD Meta-architecture definition.
+
+General tensorflow implementation of convolutional Multibox/SSD detection
+models.
+"""
+from abc import abstractmethod
+
+import tensorflow as tf
+
+from object_detection.core import box_list
+from object_detection.core import box_list_ops
+from object_detection.core import model
+from object_detection.core import standard_fields as fields
+from object_detection.core import target_assigner
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from object_detection.utils import visualization_utils
+
+slim = tf.contrib.slim
+
+
+class SSDFeatureExtractor(object):
+  """SSD Slim Feature Extractor definition."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """Constructor.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    self._is_training = is_training
+    self._depth_multiplier = depth_multiplier
+    self._min_depth = min_depth
+    self._pad_to_multiple = pad_to_multiple
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._reuse_weights = reuse_weights
+    self._use_explicit_padding = use_explicit_padding
+    self._use_depthwise = use_depthwise
+    self._override_base_feature_extractor_hyperparams = (
+        override_base_feature_extractor_hyperparams)
+
+  @property
+  def is_keras_model(self):
+    return False
+
+  @abstractmethod
+  def preprocess(self, resized_inputs):
+    """Preprocesses images for feature extraction (minus image resizing).
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+    """
+    pass
+
+  @abstractmethod
+  def extract_features(self, preprocessed_inputs):
+    """Extracts features from preprocessed inputs.
+
+    This function is responsible for extracting feature maps from preprocessed
+    images.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    raise NotImplementedError
+
+  def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Args:
+      feature_extractor_scope: A scope name for the feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      var_name = variable.op.name
+      if var_name.startswith(feature_extractor_scope + '/'):
+        var_name = var_name.replace(feature_extractor_scope + '/', '')
+        variables_to_restore[var_name] = variable
+
+    return variables_to_restore
+
+
+class SSDKerasFeatureExtractor(tf.keras.Model):
+  """SSD Feature Extractor definition."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams,
+               freeze_batchnorm,
+               inplace_batchnorm_update,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False,
+               name=None):
+    """Constructor.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
+        containing convolution hyperparameters for the layers added on top of
+        the base feature extractor.
+      freeze_batchnorm: Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      inplace_batchnorm_update: Whether to update batch norm moving average
+        values inplace. When this is false train op must add a control
+        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+        batch norm statistics.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_config`.
+      name: A string name scope to assign to the model. If 'None', Keras
+        will auto-generate one from the class name.
+    """
+    super(SSDKerasFeatureExtractor, self).__init__(name=name)
+
+    self._is_training = is_training
+    self._depth_multiplier = depth_multiplier
+    self._min_depth = min_depth
+    self._pad_to_multiple = pad_to_multiple
+    self._conv_hyperparams = conv_hyperparams
+    self._freeze_batchnorm = freeze_batchnorm
+    self._inplace_batchnorm_update = inplace_batchnorm_update
+    self._use_explicit_padding = use_explicit_padding
+    self._use_depthwise = use_depthwise
+    self._override_base_feature_extractor_hyperparams = (
+        override_base_feature_extractor_hyperparams)
+
+  @property
+  def is_keras_model(self):
+    return True
+
+  @abstractmethod
+  def preprocess(self, resized_inputs):
+    """Preprocesses images for feature extraction (minus image resizing).
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+    """
+    raise NotImplementedError
+
+  @abstractmethod
+  def _extract_features(self, preprocessed_inputs):
+    """Extracts features from preprocessed inputs.
+
+    This function is responsible for extracting feature maps from preprocessed
+    images.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    raise NotImplementedError
+
+  # This overrides the keras.Model `call` method with the _extract_features
+  # method.
+  def call(self, inputs, **kwargs):
+    return self._extract_features(inputs)
+
+  def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Args:
+      feature_extractor_scope: A scope name for the feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      var_name = variable.op.name
+      if var_name.startswith(feature_extractor_scope + '/'):
+        var_name = var_name.replace(feature_extractor_scope + '/', '')
+        variables_to_restore[var_name] = variable
+
+    return variables_to_restore
+
+
+class SSDMetaArch(model.DetectionModel):
+  """SSD Meta-architecture definition."""
+
+  def __init__(self,
+               is_training,
+               anchor_generator,
+               box_predictor,
+               box_coder,
+               feature_extractor,
+               encode_background_as_zeros,
+               image_resizer_fn,
+               non_max_suppression_fn,
+               score_conversion_fn,
+               classification_loss,
+               localization_loss,
+               classification_loss_weight,
+               localization_loss_weight,
+               normalize_loss_by_num_matches,
+               hard_example_miner,
+               target_assigner_instance,
+               add_summaries=True,
+               normalize_loc_loss_by_codesize=False,
+               freeze_batchnorm=False,
+               inplace_batchnorm_update=False,
+               add_background_class=True,
+               explicit_background_class=False,
+               random_example_sampler=None,
+               expected_loss_weights_fn=None,
+               use_confidences_as_targets=False,
+               implicit_example_weight=0.5,
+               equalization_loss_config=None):
+    """SSDMetaArch Constructor.
+
+    TODO(rathodv,jonathanhuang): group NMS parameters + score converter into
+    a class and loss parameters into a class and write config protos for
+    postprocessing and losses.
+
+    Args:
+      is_training: A boolean indicating whether the training version of the
+        computation graph should be constructed.
+      anchor_generator: an anchor_generator.AnchorGenerator object.
+      box_predictor: a box_predictor.BoxPredictor object.
+      box_coder: a box_coder.BoxCoder object.
+      feature_extractor: a SSDFeatureExtractor object.
+      encode_background_as_zeros: boolean determining whether background
+        targets are to be encoded as an all zeros vector or a one-hot
+        vector (where background is the 0th class).
+      image_resizer_fn: a callable for image resizing.  This callable always
+        takes a rank-3 image tensor (corresponding to a single image) and
+        returns a rank-3 image tensor, possibly with new spatial dimensions and
+        a 1-D tensor of shape [3] indicating shape of true image within
+        the resized image tensor as the resized image tensor could be padded.
+        See builders/image_resizer_builder.py.
+      non_max_suppression_fn: batch_multiclass_non_max_suppression
+        callable that takes `boxes`, `scores` and optional `clip_window`
+        inputs (with all other inputs already set) and returns a dictionary
+        hold tensors with keys: `detection_boxes`, `detection_scores`,
+        `detection_classes` and `num_detections`. See `post_processing.
+        batch_multiclass_non_max_suppression` for the type and shape of these
+        tensors.
+      score_conversion_fn: callable elementwise nonlinearity (that takes tensors
+        as inputs and returns tensors).  This is usually used to convert logits
+        to probabilities.
+      classification_loss: an object_detection.core.losses.Loss object.
+      localization_loss: a object_detection.core.losses.Loss object.
+      classification_loss_weight: float
+      localization_loss_weight: float
+      normalize_loss_by_num_matches: boolean
+      hard_example_miner: a losses.HardExampleMiner object (can be None)
+      target_assigner_instance: target_assigner.TargetAssigner instance to use.
+      add_summaries: boolean (default: True) controlling whether summary ops
+        should be added to tensorflow graph.
+      normalize_loc_loss_by_codesize: whether to normalize localization loss
+        by code size of the box encoder.
+      freeze_batchnorm: Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      inplace_batchnorm_update: Whether to update batch norm moving average
+        values inplace. When this is false train op must add a control
+        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+        batch norm statistics.
+      add_background_class: Whether to add an implicit background class to
+        one-hot encodings of groundtruth labels. Set to false if training a
+        single class model or using groundtruth labels with an explicit
+        background class.
+      explicit_background_class: Set to true if using groundtruth labels with an
+        explicit background class, as in multiclass scores.
+      random_example_sampler: a BalancedPositiveNegativeSampler object that can
+        perform random example sampling when computing loss. If None, random
+        sampling process is skipped. Note that random example sampler and hard
+        example miner can both be applied to the model. In that case, random
+        sampler will take effect first and hard example miner can only process
+        the random sampled examples.
+      expected_loss_weights_fn: If not None, use to calculate
+        loss by background/foreground weighting. Should take batch_cls_targets
+        as inputs and return foreground_weights, background_weights. See
+        expected_classification_loss_by_expected_sampling and
+        expected_classification_loss_by_reweighting_unmatched_anchors in
+        third_party/tensorflow_models/object_detection/utils/ops.py as examples.
+      use_confidences_as_targets: Whether to use groundtruth_condifences field
+        to assign the targets.
+      implicit_example_weight: a float number that specifies the weight used
+        for the implicit negative examples.
+      equalization_loss_config: a namedtuple that specifies configs for
+        computing equalization loss.
+    """
+    super(SSDMetaArch, self).__init__(num_classes=box_predictor.num_classes)
+    self._is_training = is_training
+    self._freeze_batchnorm = freeze_batchnorm
+    self._inplace_batchnorm_update = inplace_batchnorm_update
+
+    self._anchor_generator = anchor_generator
+    self._box_predictor = box_predictor
+
+    self._box_coder = box_coder
+    self._feature_extractor = feature_extractor
+    self._add_background_class = add_background_class
+    self._explicit_background_class = explicit_background_class
+
+    if add_background_class and explicit_background_class:
+      raise ValueError("Cannot have both 'add_background_class' and"
+                       " 'explicit_background_class' true.")
+
+    # Needed for fine-tuning from classification checkpoints whose
+    # variables do not have the feature extractor scope.
+    if self._feature_extractor.is_keras_model:
+      # Keras feature extractors will have a name they implicitly use to scope.
+      # So, all contained variables are prefixed by this name.
+      # To load from classification checkpoints, need to filter out this name.
+      self._extract_features_scope = feature_extractor.name
+    else:
+      # Slim feature extractors get an explicit naming scope
+      self._extract_features_scope = 'FeatureExtractor'
+
+    if encode_background_as_zeros:
+      background_class = [0]
+    else:
+      background_class = [1]
+
+    if self._add_background_class:
+      num_foreground_classes = self.num_classes
+    else:
+      num_foreground_classes = self.num_classes - 1
+
+    self._unmatched_class_label = tf.constant(
+        background_class + num_foreground_classes * [0], tf.float32)
+
+    self._target_assigner = target_assigner_instance
+
+    self._classification_loss = classification_loss
+    self._localization_loss = localization_loss
+    self._classification_loss_weight = classification_loss_weight
+    self._localization_loss_weight = localization_loss_weight
+    self._normalize_loss_by_num_matches = normalize_loss_by_num_matches
+    self._normalize_loc_loss_by_codesize = normalize_loc_loss_by_codesize
+    self._hard_example_miner = hard_example_miner
+    self._random_example_sampler = random_example_sampler
+    self._parallel_iterations = 16
+
+    self._image_resizer_fn = image_resizer_fn
+    self._non_max_suppression_fn = non_max_suppression_fn
+    self._score_conversion_fn = score_conversion_fn
+
+    self._anchors = None
+    self._add_summaries = add_summaries
+    self._batched_prediction_tensor_names = []
+    self._expected_loss_weights_fn = expected_loss_weights_fn
+    self._use_confidences_as_targets = use_confidences_as_targets
+    self._implicit_example_weight = implicit_example_weight
+
+    self._equalization_loss_config = equalization_loss_config
+
+  @property
+  def anchors(self):
+    if not self._anchors:
+      raise RuntimeError('anchors have not been constructed yet!')
+    if not isinstance(self._anchors, box_list.BoxList):
+      raise RuntimeError('anchors should be a BoxList object, but is not.')
+    return self._anchors
+
+  @property
+  def batched_prediction_tensor_names(self):
+    if not self._batched_prediction_tensor_names:
+      raise RuntimeError('Must call predict() method to get batched prediction '
+                         'tensor names.')
+    return self._batched_prediction_tensor_names
+
+  def preprocess(self, inputs):
+    """Feature-extractor specific preprocessing.
+
+    SSD meta architecture uses a default clip_window of [0, 0, 1, 1] during
+    post-processing. On calling `preprocess` method, clip_window gets updated
+    based on `true_image_shapes` returned by `image_resizer_fn`.
+
+    Args:
+      inputs: a [batch, height_in, width_in, channels] float tensor representing
+        a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: a [batch, height_out, width_out, channels] float
+        tensor representing a batch of images.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Raises:
+      ValueError: if inputs tensor does not have type tf.float32
+    """
+    if inputs.dtype is not tf.float32:
+      raise ValueError('`preprocess` expects a tf.float32 tensor')
+    with tf.name_scope('Preprocessor'):
+      # TODO(jonathanhuang): revisit whether to always use batch size as
+      # the number of parallel iterations vs allow for dynamic batching.
+      outputs = shape_utils.static_or_dynamic_map_fn(
+          self._image_resizer_fn,
+          elems=inputs,
+          dtype=[tf.float32, tf.int32])
+      resized_inputs = outputs[0]
+      true_image_shapes = outputs[1]
+
+      return (self._feature_extractor.preprocess(resized_inputs),
+              true_image_shapes)
+
+  def _compute_clip_window(self, preprocessed_images, true_image_shapes):
+    """Computes clip window to use during post_processing.
+
+    Computes a new clip window to use during post-processing based on
+    `resized_image_shapes` and `true_image_shapes` only if `preprocess` method
+    has been called. Otherwise returns a default clip window of [0, 0, 1, 1].
+
+    Args:
+      preprocessed_images: the [batch, height, width, channels] image
+          tensor.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros. Or None if the clip window should cover the full image.
+
+    Returns:
+      a 2-D float32 tensor of the form [batch_size, 4] containing the clip
+      window for each image in the batch in normalized coordinates (relative to
+      the resized dimensions) where each clip window is of the form [ymin, xmin,
+      ymax, xmax] or a default clip window of [0, 0, 1, 1].
+
+    """
+    if true_image_shapes is None:
+      return tf.constant([0, 0, 1, 1], dtype=tf.float32)
+
+    resized_inputs_shape = shape_utils.combined_static_and_dynamic_shape(
+        preprocessed_images)
+    true_heights, true_widths, _ = tf.unstack(
+        tf.to_float(true_image_shapes), axis=1)
+    padded_height = tf.to_float(resized_inputs_shape[1])
+    padded_width = tf.to_float(resized_inputs_shape[2])
+    return tf.stack(
+        [
+            tf.zeros_like(true_heights),
+            tf.zeros_like(true_widths), true_heights / padded_height,
+            true_widths / padded_width
+        ],
+        axis=1)
+
+  def predict(self, preprocessed_inputs, true_image_shapes):
+    """Predicts unpostprocessed tensors from input tensor.
+
+    This function takes an input batch of images and runs it through the forward
+    pass of the network to yield unpostprocessesed predictions.
+
+    A side effect of calling the predict method is that self._anchors is
+    populated with a box_list.BoxList of anchors.  These anchors must be
+    constructed before the postprocess or loss functions can be called.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] image tensor.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+
+    Returns:
+      prediction_dict: a dictionary holding "raw" prediction tensors:
+        1) preprocessed_inputs: the [batch, height, width, channels] image
+          tensor.
+        2) box_encodings: 4-D float tensor of shape [batch_size, num_anchors,
+          box_code_dimension] containing predicted boxes.
+        3) class_predictions_with_background: 3-D float tensor of shape
+          [batch_size, num_anchors, num_classes+1] containing class predictions
+          (logits) for each of the anchors.  Note that this tensor *includes*
+          background class predictions (at class index 0).
+        4) feature_maps: a list of tensors where the ith tensor has shape
+          [batch, height_i, width_i, depth_i].
+        5) anchors: 2-D float tensor of shape [num_anchors, 4] containing
+          the generated anchors in normalized coordinates.
+    """
+    batchnorm_updates_collections = (None if self._inplace_batchnorm_update
+                                     else tf.GraphKeys.UPDATE_OPS)
+    if self._feature_extractor.is_keras_model:
+      feature_maps = self._feature_extractor(preprocessed_inputs)
+    else:
+      with slim.arg_scope([slim.batch_norm],
+                          is_training=(self._is_training and
+                                       not self._freeze_batchnorm),
+                          updates_collections=batchnorm_updates_collections):
+        with tf.variable_scope(None, self._extract_features_scope,
+                               [preprocessed_inputs]):
+          feature_maps = self._feature_extractor.extract_features(
+              preprocessed_inputs)
+
+    feature_map_spatial_dims = self._get_feature_map_spatial_dims(
+        feature_maps)
+    image_shape = shape_utils.combined_static_and_dynamic_shape(
+        preprocessed_inputs)
+    self._anchors = box_list_ops.concatenate(
+        self._anchor_generator.generate(
+            feature_map_spatial_dims,
+            im_height=image_shape[1],
+            im_width=image_shape[2]))
+    if self._box_predictor.is_keras_model:
+      predictor_results_dict = self._box_predictor(feature_maps)
+    else:
+      with slim.arg_scope([slim.batch_norm],
+                          is_training=(self._is_training and
+                                       not self._freeze_batchnorm),
+                          updates_collections=batchnorm_updates_collections):
+        predictor_results_dict = self._box_predictor.predict(
+            feature_maps, self._anchor_generator.num_anchors_per_location())
+    predictions_dict = {
+        'preprocessed_inputs': preprocessed_inputs,
+        'feature_maps': feature_maps,
+        'anchors': self._anchors.get()
+    }
+    for prediction_key, prediction_list in iter(predictor_results_dict.items()):
+      prediction = tf.concat(prediction_list, axis=1)
+      if (prediction_key == 'box_encodings' and prediction.shape.ndims == 4 and
+          prediction.shape[2] == 1):
+        prediction = tf.squeeze(prediction, axis=2)
+      predictions_dict[prediction_key] = prediction
+    self._batched_prediction_tensor_names = [x for x in predictions_dict
+                                             if x != 'anchors']
+    return predictions_dict
+
+  def _get_feature_map_spatial_dims(self, feature_maps):
+    """Return list of spatial dimensions for each feature map in a list.
+
+    Args:
+      feature_maps: a list of tensors where the ith tensor has shape
+          [batch, height_i, width_i, depth_i].
+
+    Returns:
+      a list of pairs (height, width) for each feature map in feature_maps
+    """
+    feature_map_shapes = [
+        shape_utils.combined_static_and_dynamic_shape(
+            feature_map) for feature_map in feature_maps
+    ]
+    return [(shape[1], shape[2]) for shape in feature_map_shapes]
+
+  def postprocess(self, prediction_dict, true_image_shapes):
+    """Converts prediction tensors to final detections.
+
+    This function converts raw predictions tensors to final detection results by
+    slicing off the background class, decoding box predictions and applying
+    non max suppression and clipping to the image window.
+
+    See base class for output format conventions.  Note also that by default,
+    scores are to be interpreted as logits, but if a score_conversion_fn is
+    used, then scores are remapped (and may thus have a different
+    interpretation).
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors with
+        1) preprocessed_inputs: a [batch, height, width, channels] image
+          tensor.
+        2) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
+          box_code_dimension] containing predicted boxes.
+        3) class_predictions_with_background: 3-D float tensor of shape
+          [batch_size, num_anchors, num_classes+1] containing class predictions
+          (logits) for each of the anchors.  Note that this tensor *includes*
+          background class predictions.
+        4) mask_predictions: (optional) a 5-D float tensor of shape
+          [batch_size, num_anchors, q, mask_height, mask_width]. `q` can be
+          either number of classes or 1 depending on whether a separate mask is
+          predicted per class.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros. Or None, if the clip window should cover the full image.
+
+    Returns:
+      detections: a dictionary containing the following fields
+        detection_boxes: [batch, max_detections, 4]
+        detection_scores: [batch, max_detections]
+        detection_classes: [batch, max_detections]
+        detection_keypoints: [batch, max_detections, num_keypoints, 2] (if
+          encoded in the prediction_dict 'box_encodings')
+        detection_masks: [batch_size, max_detections, mask_height, mask_width]
+          (optional)
+        num_detections: [batch]
+    Raises:
+      ValueError: if prediction_dict does not contain `box_encodings` or
+        `class_predictions_with_background` fields.
+    """
+    if ('box_encodings' not in prediction_dict or
+        'class_predictions_with_background' not in prediction_dict):
+      raise ValueError('prediction_dict does not contain expected entries.')
+    with tf.name_scope('Postprocessor'):
+      preprocessed_images = prediction_dict['preprocessed_inputs']
+      box_encodings = prediction_dict['box_encodings']
+      box_encodings = tf.identity(box_encodings, 'raw_box_encodings')
+      class_predictions = prediction_dict['class_predictions_with_background']
+      detection_boxes, detection_keypoints = self._batch_decode(box_encodings)
+      detection_boxes = tf.identity(detection_boxes, 'raw_box_locations')
+      detection_boxes = tf.expand_dims(detection_boxes, axis=2)
+
+      detection_scores = self._score_conversion_fn(class_predictions)
+      detection_scores = tf.identity(detection_scores, 'raw_box_scores')
+      if self._add_background_class or self._explicit_background_class:
+        detection_scores = tf.slice(detection_scores, [0, 0, 1], [-1, -1, -1])
+      additional_fields = None
+
+      batch_size = (
+          shape_utils.combined_static_and_dynamic_shape(preprocessed_images)[0])
+
+      if 'feature_maps' in prediction_dict:
+        feature_map_list = []
+        for feature_map in prediction_dict['feature_maps']:
+          feature_map_list.append(tf.reshape(feature_map, [batch_size, -1]))
+        box_features = tf.concat(feature_map_list, 1)
+        box_features = tf.identity(box_features, 'raw_box_features')
+
+      if detection_keypoints is not None:
+        additional_fields = {
+            fields.BoxListFields.keypoints: detection_keypoints}
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       nmsed_additional_fields, num_detections) = self._non_max_suppression_fn(
+           detection_boxes,
+           detection_scores,
+           clip_window=self._compute_clip_window(preprocessed_images,
+                                                 true_image_shapes),
+           additional_fields=additional_fields,
+           masks=prediction_dict.get('mask_predictions'))
+      detection_dict = {
+          fields.DetectionResultFields.detection_boxes: nmsed_boxes,
+          fields.DetectionResultFields.detection_scores: nmsed_scores,
+          fields.DetectionResultFields.detection_classes: nmsed_classes,
+          fields.DetectionResultFields.num_detections:
+              tf.to_float(num_detections)
+      }
+      if (nmsed_additional_fields is not None and
+          fields.BoxListFields.keypoints in nmsed_additional_fields):
+        detection_dict[fields.DetectionResultFields.detection_keypoints] = (
+            nmsed_additional_fields[fields.BoxListFields.keypoints])
+      if nmsed_masks is not None:
+        detection_dict[
+            fields.DetectionResultFields.detection_masks] = nmsed_masks
+      return detection_dict
+
+  def loss(self, prediction_dict, true_image_shapes, scope=None):
+    """Compute scalar loss tensors with respect to provided groundtruth.
+
+    Calling this function requires that groundtruth tensors have been
+    provided via the provide_groundtruth function.
+
+    Args:
+      prediction_dict: a dictionary holding prediction tensors with
+        1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
+          box_code_dimension] containing predicted boxes.
+        2) class_predictions_with_background: 3-D float tensor of shape
+          [batch_size, num_anchors, num_classes+1] containing class predictions
+          (logits) for each of the anchors. Note that this tensor *includes*
+          background class predictions.
+      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
+        of the form [height, width, channels] indicating the shapes
+        of true images in the resized images, as resized images can be padded
+        with zeros.
+      scope: Optional scope name.
+
+    Returns:
+      a dictionary mapping loss keys (`localization_loss` and
+        `classification_loss`) to scalar tensors representing corresponding loss
+        values.
+    """
+    with tf.name_scope(scope, 'Loss', prediction_dict.values()):
+      keypoints = None
+      if self.groundtruth_has_field(fields.BoxListFields.keypoints):
+        keypoints = self.groundtruth_lists(fields.BoxListFields.keypoints)
+      weights = None
+      if self.groundtruth_has_field(fields.BoxListFields.weights):
+        weights = self.groundtruth_lists(fields.BoxListFields.weights)
+      confidences = None
+      if self.groundtruth_has_field(fields.BoxListFields.confidences):
+        confidences = self.groundtruth_lists(fields.BoxListFields.confidences)
+      (batch_cls_targets, batch_cls_weights, batch_reg_targets,
+       batch_reg_weights, match_list) = self._assign_targets(
+           self.groundtruth_lists(fields.BoxListFields.boxes),
+           self.groundtruth_lists(fields.BoxListFields.classes),
+           keypoints, weights, confidences)
+      if self._add_summaries:
+        self._summarize_target_assignment(
+            self.groundtruth_lists(fields.BoxListFields.boxes), match_list)
+
+      if self._random_example_sampler:
+        batch_cls_per_anchor_weights = tf.reduce_mean(
+            batch_cls_weights, axis=-1)
+        batch_sampled_indicator = tf.to_float(
+            shape_utils.static_or_dynamic_map_fn(
+                self._minibatch_subsample_fn,
+                [batch_cls_targets, batch_cls_per_anchor_weights],
+                dtype=tf.bool,
+                parallel_iterations=self._parallel_iterations,
+                back_prop=True))
+        batch_reg_weights = tf.multiply(batch_sampled_indicator,
+                                        batch_reg_weights)
+        batch_cls_weights = tf.multiply(
+            tf.expand_dims(batch_sampled_indicator, -1),
+            batch_cls_weights)
+
+      losses_mask = None
+      if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
+        losses_mask = tf.stack(self.groundtruth_lists(
+            fields.InputDataFields.is_annotated))
+      location_losses = self._localization_loss(
+          prediction_dict['box_encodings'],
+          batch_reg_targets,
+          ignore_nan_targets=True,
+          weights=batch_reg_weights,
+          losses_mask=losses_mask)
+
+      cls_losses = self._classification_loss(
+          prediction_dict['class_predictions_with_background'],
+          batch_cls_targets,
+          weights=batch_cls_weights,
+          losses_mask=losses_mask)
+
+      if self._expected_loss_weights_fn:
+        # Need to compute losses for assigned targets against the
+        # unmatched_class_label as well as their assigned targets.
+        # simplest thing (but wasteful) is just to calculate all losses
+        # twice
+        batch_size, num_anchors, num_classes = batch_cls_targets.get_shape()
+        unmatched_targets = tf.ones([batch_size, num_anchors, 1
+                                    ]) * self._unmatched_class_label
+
+        unmatched_cls_losses = self._classification_loss(
+            prediction_dict['class_predictions_with_background'],
+            unmatched_targets,
+            weights=batch_cls_weights,
+            losses_mask=losses_mask)
+
+        if cls_losses.get_shape().ndims == 3:
+          batch_size, num_anchors, num_classes = cls_losses.get_shape()
+          cls_losses = tf.reshape(cls_losses, [batch_size, -1])
+          unmatched_cls_losses = tf.reshape(unmatched_cls_losses,
+                                            [batch_size, -1])
+          batch_cls_targets = tf.reshape(
+              batch_cls_targets, [batch_size, num_anchors * num_classes, -1])
+          batch_cls_targets = tf.concat(
+              [1 - batch_cls_targets, batch_cls_targets], axis=-1)
+
+          location_losses = tf.tile(location_losses, [1, num_classes])
+
+        foreground_weights, background_weights = (
+            self._expected_loss_weights_fn(batch_cls_targets))
+
+        cls_losses = (
+            foreground_weights * cls_losses +
+            background_weights * unmatched_cls_losses)
+
+        location_losses *= foreground_weights
+
+        classification_loss = tf.reduce_sum(cls_losses)
+        localization_loss = tf.reduce_sum(location_losses)
+      elif self._hard_example_miner:
+        cls_losses = ops.reduce_sum_trailing_dimensions(cls_losses, ndims=2)
+        (localization_loss, classification_loss) = self._apply_hard_mining(
+            location_losses, cls_losses, prediction_dict, match_list)
+        if self._add_summaries:
+          self._hard_example_miner.summarize()
+      else:
+        cls_losses = ops.reduce_sum_trailing_dimensions(cls_losses, ndims=2)
+        localization_loss = tf.reduce_sum(location_losses)
+        classification_loss = tf.reduce_sum(cls_losses)
+
+      # Optionally normalize by number of positive matches
+      normalizer = tf.constant(1.0, dtype=tf.float32)
+      if self._normalize_loss_by_num_matches:
+        normalizer = tf.maximum(tf.to_float(tf.reduce_sum(batch_reg_weights)),
+                                1.0)
+
+      localization_loss_normalizer = normalizer
+      if self._normalize_loc_loss_by_codesize:
+        localization_loss_normalizer *= self._box_coder.code_size
+      localization_loss = tf.multiply((self._localization_loss_weight /
+                                       localization_loss_normalizer),
+                                      localization_loss,
+                                      name='localization_loss')
+      classification_loss = tf.multiply((self._classification_loss_weight /
+                                         normalizer), classification_loss,
+                                        name='classification_loss')
+
+      loss_dict = {
+          str(localization_loss.op.name): localization_loss,
+          str(classification_loss.op.name): classification_loss
+      }
+
+
+    return loss_dict
+
+  def _minibatch_subsample_fn(self, inputs):
+    """Randomly samples anchors for one image.
+
+    Args:
+      inputs: a list of 2 inputs. First one is a tensor of shape [num_anchors,
+        num_classes] indicating targets assigned to each anchor. Second one
+        is a tensor of shape [num_anchors] indicating the class weight of each
+        anchor.
+
+    Returns:
+      batch_sampled_indicator: bool tensor of shape [num_anchors] indicating
+        whether the anchor should be selected for loss computation.
+    """
+    cls_targets, cls_weights = inputs
+    if self._add_background_class:
+      # Set background_class bits to 0 so that the positives_indicator
+      # computation would not consider background class.
+      background_class = tf.zeros_like(tf.slice(cls_targets, [0, 0], [-1, 1]))
+      regular_class = tf.slice(cls_targets, [0, 1], [-1, -1])
+      cls_targets = tf.concat([background_class, regular_class], 1)
+    positives_indicator = tf.reduce_sum(cls_targets, axis=1)
+    return self._random_example_sampler.subsample(
+        tf.cast(cls_weights, tf.bool),
+        batch_size=None,
+        labels=tf.cast(positives_indicator, tf.bool))
+
+  def _summarize_anchor_classification_loss(self, class_ids, cls_losses):
+    positive_indices = tf.where(tf.greater(class_ids, 0))
+    positive_anchor_cls_loss = tf.squeeze(
+        tf.gather(cls_losses, positive_indices), axis=1)
+    visualization_utils.add_cdf_image_summary(positive_anchor_cls_loss,
+                                              'PositiveAnchorLossCDF')
+    negative_indices = tf.where(tf.equal(class_ids, 0))
+    negative_anchor_cls_loss = tf.squeeze(
+        tf.gather(cls_losses, negative_indices), axis=1)
+    visualization_utils.add_cdf_image_summary(negative_anchor_cls_loss,
+                                              'NegativeAnchorLossCDF')
+
+  def _assign_targets(self,
+                      groundtruth_boxes_list,
+                      groundtruth_classes_list,
+                      groundtruth_keypoints_list=None,
+                      groundtruth_weights_list=None,
+                      groundtruth_confidences_list=None):
+    """Assign groundtruth targets.
+
+    Adds a background class to each one-hot encoding of groundtruth classes
+    and uses target assigner to obtain regression and classification targets.
+
+    Args:
+      groundtruth_boxes_list: a list of 2-D tensors of shape [num_boxes, 4]
+        containing coordinates of the groundtruth boxes.
+          Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
+          format and assumed to be normalized and clipped
+          relative to the image window with y_min <= y_max and x_min <= x_max.
+      groundtruth_classes_list: a list of 2-D one-hot (or k-hot) tensors of
+        shape [num_boxes, num_classes] containing the class targets with the 0th
+        index assumed to map to the first non-background class.
+      groundtruth_keypoints_list: (optional) a list of 3-D tensors of shape
+        [num_boxes, num_keypoints, 2]
+      groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
+        [num_boxes] containing weights for groundtruth boxes.
+      groundtruth_confidences_list: A list of 2-D tf.float32 tensors of shape
+        [num_boxes, num_classes] containing class confidences for
+        groundtruth boxes.
+
+    Returns:
+      batch_cls_targets: a tensor with shape [batch_size, num_anchors,
+        num_classes],
+      batch_cls_weights: a tensor with shape [batch_size, num_anchors],
+      batch_reg_targets: a tensor with shape [batch_size, num_anchors,
+        box_code_dimension]
+      batch_reg_weights: a tensor with shape [batch_size, num_anchors],
+      match_list: a list of matcher.Match objects encoding the match between
+        anchors and groundtruth boxes for each image of the batch,
+        with rows of the Match objects corresponding to groundtruth boxes
+        and columns corresponding to anchors.
+    """
+    groundtruth_boxlists = [
+        box_list.BoxList(boxes) for boxes in groundtruth_boxes_list
+    ]
+    train_using_confidences = (self._is_training and
+                               self._use_confidences_as_targets)
+    if self._add_background_class:
+      groundtruth_classes_with_background_list = [
+          tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT')
+          for one_hot_encoding in groundtruth_classes_list
+      ]
+      if train_using_confidences:
+        groundtruth_confidences_with_background_list = [
+            tf.pad(groundtruth_confidences, [[0, 0], [1, 0]], mode='CONSTANT')
+            for groundtruth_confidences in groundtruth_confidences_list
+        ]
+    else:
+      groundtruth_classes_with_background_list = groundtruth_classes_list
+
+    if groundtruth_keypoints_list is not None:
+      for boxlist, keypoints in zip(
+          groundtruth_boxlists, groundtruth_keypoints_list):
+        boxlist.add_field(fields.BoxListFields.keypoints, keypoints)
+    if train_using_confidences:
+      return target_assigner.batch_assign_confidences(
+          self._target_assigner,
+          self.anchors,
+          groundtruth_boxlists,
+          groundtruth_confidences_with_background_list,
+          groundtruth_weights_list,
+          self._unmatched_class_label,
+          self._add_background_class,
+          self._implicit_example_weight)
+    else:
+      return target_assigner.batch_assign_targets(
+          self._target_assigner,
+          self.anchors,
+          groundtruth_boxlists,
+          groundtruth_classes_with_background_list,
+          self._unmatched_class_label,
+          groundtruth_weights_list)
+
+  def _summarize_target_assignment(self, groundtruth_boxes_list, match_list):
+    """Creates tensorflow summaries for the input boxes and anchors.
+
+    This function creates four summaries corresponding to the average
+    number (over images in a batch) of (1) groundtruth boxes, (2) anchors
+    marked as positive, (3) anchors marked as negative, and (4) anchors marked
+    as ignored.
+
+    Args:
+      groundtruth_boxes_list: a list of 2-D tensors of shape [num_boxes, 4]
+        containing corners of the groundtruth boxes.
+      match_list: a list of matcher.Match objects encoding the match between
+        anchors and groundtruth boxes for each image of the batch,
+        with rows of the Match objects corresponding to groundtruth boxes
+        and columns corresponding to anchors.
+    """
+    num_boxes_per_image = tf.stack(
+        [tf.shape(x)[0] for x in groundtruth_boxes_list])
+    pos_anchors_per_image = tf.stack(
+        [match.num_matched_columns() for match in match_list])
+    neg_anchors_per_image = tf.stack(
+        [match.num_unmatched_columns() for match in match_list])
+    ignored_anchors_per_image = tf.stack(
+        [match.num_ignored_columns() for match in match_list])
+    tf.summary.scalar('AvgNumGroundtruthBoxesPerImage',
+                      tf.reduce_mean(tf.to_float(num_boxes_per_image)),
+                      family='TargetAssignment')
+    tf.summary.scalar('AvgNumPositiveAnchorsPerImage',
+                      tf.reduce_mean(tf.to_float(pos_anchors_per_image)),
+                      family='TargetAssignment')
+    tf.summary.scalar('AvgNumNegativeAnchorsPerImage',
+                      tf.reduce_mean(tf.to_float(neg_anchors_per_image)),
+                      family='TargetAssignment')
+    tf.summary.scalar('AvgNumIgnoredAnchorsPerImage',
+                      tf.reduce_mean(tf.to_float(ignored_anchors_per_image)),
+                      family='TargetAssignment')
+
+  def _apply_hard_mining(self, location_losses, cls_losses, prediction_dict,
+                         match_list):
+    """Applies hard mining to anchorwise losses.
+
+    Args:
+      location_losses: Float tensor of shape [batch_size, num_anchors]
+        representing anchorwise location losses.
+      cls_losses: Float tensor of shape [batch_size, num_anchors]
+        representing anchorwise classification losses.
+      prediction_dict: p a dictionary holding prediction tensors with
+        1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
+          box_code_dimension] containing predicted boxes.
+        2) class_predictions_with_background: 3-D float tensor of shape
+          [batch_size, num_anchors, num_classes+1] containing class predictions
+          (logits) for each of the anchors.  Note that this tensor *includes*
+          background class predictions.
+      match_list: a list of matcher.Match objects encoding the match between
+        anchors and groundtruth boxes for each image of the batch,
+        with rows of the Match objects corresponding to groundtruth boxes
+        and columns corresponding to anchors.
+
+    Returns:
+      mined_location_loss: a float scalar with sum of localization losses from
+        selected hard examples.
+      mined_cls_loss: a float scalar with sum of classification losses from
+        selected hard examples.
+    """
+    class_predictions = tf.slice(
+        prediction_dict['class_predictions_with_background'], [0, 0,
+                                                               1], [-1, -1, -1])
+
+    decoded_boxes, _ = self._batch_decode(prediction_dict['box_encodings'])
+    decoded_box_tensors_list = tf.unstack(decoded_boxes)
+    class_prediction_list = tf.unstack(class_predictions)
+    decoded_boxlist_list = []
+    for box_location, box_score in zip(decoded_box_tensors_list,
+                                       class_prediction_list):
+      decoded_boxlist = box_list.BoxList(box_location)
+      decoded_boxlist.add_field('scores', box_score)
+      decoded_boxlist_list.append(decoded_boxlist)
+    return self._hard_example_miner(
+        location_losses=location_losses,
+        cls_losses=cls_losses,
+        decoded_boxlist_list=decoded_boxlist_list,
+        match_list=match_list)
+
+  def _batch_decode(self, box_encodings):
+    """Decodes a batch of box encodings with respect to the anchors.
+
+    Args:
+      box_encodings: A float32 tensor of shape
+        [batch_size, num_anchors, box_code_size] containing box encodings.
+
+    Returns:
+      decoded_boxes: A float32 tensor of shape
+        [batch_size, num_anchors, 4] containing the decoded boxes.
+      decoded_keypoints: A float32 tensor of shape
+        [batch_size, num_anchors, num_keypoints, 2] containing the decoded
+        keypoints if present in the input `box_encodings`, None otherwise.
+    """
+    combined_shape = shape_utils.combined_static_and_dynamic_shape(
+        box_encodings)
+    batch_size = combined_shape[0]
+    tiled_anchor_boxes = tf.tile(
+        tf.expand_dims(self.anchors.get(), 0), [batch_size, 1, 1])
+    tiled_anchors_boxlist = box_list.BoxList(
+        tf.reshape(tiled_anchor_boxes, [-1, 4]))
+    decoded_boxes = self._box_coder.decode(
+        tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
+        tiled_anchors_boxlist)
+    decoded_keypoints = None
+    if decoded_boxes.has_field(fields.BoxListFields.keypoints):
+      decoded_keypoints = decoded_boxes.get_field(
+          fields.BoxListFields.keypoints)
+      num_keypoints = decoded_keypoints.get_shape()[1]
+      decoded_keypoints = tf.reshape(
+          decoded_keypoints,
+          tf.stack([combined_shape[0], combined_shape[1], num_keypoints, 2]))
+    decoded_boxes = tf.reshape(decoded_boxes.get(), tf.stack(
+        [combined_shape[0], combined_shape[1], 4]))
+    return decoded_boxes, decoded_keypoints
+
+  def regularization_losses(self):
+    """Returns a list of regularization losses for this model.
+
+    Returns a list of regularization losses for this model that the estimator
+    needs to use during training/optimization.
+
+    Returns:
+      A list of regularization loss tensors.
+    """
+    losses = []
+    slim_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+    # Copy the slim losses to avoid modifying the collection
+    if slim_losses:
+      losses.extend(slim_losses)
+    if self._box_predictor.is_keras_model:
+      losses.extend(self._box_predictor.losses)
+    if self._feature_extractor.is_keras_model:
+      losses.extend(self._feature_extractor.losses)
+    return losses
+
+  def restore_map(self,
+                  fine_tune_checkpoint_type='detection',
+                  load_all_detection_checkpoint_vars=False):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    See parent class for details.
+
+    Args:
+      fine_tune_checkpoint_type: whether to restore from a full detection
+        checkpoint (with compatible variable names) or to restore from a
+        classification checkpoint for initialization prior to training.
+        Valid values: `detection`, `classification`. Default 'detection'.
+      load_all_detection_checkpoint_vars: whether to load all variables (when
+         `fine_tune_checkpoint_type='detection'`). If False, only variables
+         within the appropriate scopes are included. Default False.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    Raises:
+      ValueError: if fine_tune_checkpoint_type is neither `classification`
+        nor `detection`.
+    """
+    if fine_tune_checkpoint_type not in ['detection', 'classification']:
+      raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format(
+          fine_tune_checkpoint_type))
+
+    if fine_tune_checkpoint_type == 'classification':
+      return self._feature_extractor.restore_from_classification_checkpoint_fn(
+          self._extract_features_scope)
+
+    if fine_tune_checkpoint_type == 'detection':
+      variables_to_restore = {}
+      for variable in tf.global_variables():
+        var_name = variable.op.name
+        if load_all_detection_checkpoint_vars:
+          variables_to_restore[var_name] = variable
+        else:
+          if var_name.startswith(self._extract_features_scope):
+            variables_to_restore[var_name] = variable
+
+    return variables_to_restore
+
+  def updates(self):
+    """Returns a list of update operators for this model.
+
+    Returns a list of update operators for this model that must be executed at
+    each training step. The estimator's train op needs to have a control
+    dependency on these updates.
+
+    Returns:
+      A list of update operators.
+    """
+    update_ops = []
+    slim_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    # Copy the slim ops to avoid modifying the collection
+    if slim_update_ops:
+      update_ops.extend(slim_update_ops)
+    if self._box_predictor.is_keras_model:
+      update_ops.extend(self._box_predictor.get_updates_for(None))
+      update_ops.extend(self._box_predictor.get_updates_for(
+          self._box_predictor.inputs))
+    if self._feature_extractor.is_keras_model:
+      update_ops.extend(self._feature_extractor.get_updates_for(None))
+      update_ops.extend(self._feature_extractor.get_updates_for(
+          self._feature_extractor.inputs))
+    return update_ops
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test.py
new file mode 100644
index 00000000..a4014574
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test.py
@@ -0,0 +1,538 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.meta_architectures.ssd_meta_arch."""
+
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.meta_architectures import ssd_meta_arch_test_lib
+from object_detection.protos import model_pb2
+from object_detection.utils import test_utils
+
+slim = tf.contrib.slim
+keras = tf.keras.layers
+
+
+@parameterized.parameters(
+    {'use_keras': False},
+    {'use_keras': True},
+)
+class SsdMetaArchTest(ssd_meta_arch_test_lib.SSDMetaArchTestBase,
+                      parameterized.TestCase):
+
+  def _create_model(
+      self,
+      apply_hard_mining=True,
+      normalize_loc_loss_by_codesize=False,
+      add_background_class=True,
+      random_example_sampling=False,
+      expected_loss_weights=model_pb2.DetectionModel().ssd.loss.NONE,
+      min_num_negative_samples=1,
+      desired_negative_sampling_ratio=3,
+      use_keras=False,
+      predict_mask=False,
+      use_static_shapes=False,
+      nms_max_size_per_class=5):
+    return super(SsdMetaArchTest, self)._create_model(
+        model_fn=ssd_meta_arch.SSDMetaArch,
+        apply_hard_mining=apply_hard_mining,
+        normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize,
+        add_background_class=add_background_class,
+        random_example_sampling=random_example_sampling,
+        expected_loss_weights=expected_loss_weights,
+        min_num_negative_samples=min_num_negative_samples,
+        desired_negative_sampling_ratio=desired_negative_sampling_ratio,
+        use_keras=use_keras,
+        predict_mask=predict_mask,
+        use_static_shapes=use_static_shapes,
+        nms_max_size_per_class=nms_max_size_per_class)
+
+  def test_preprocess_preserves_shapes_with_dynamic_input_image(
+      self, use_keras):
+    image_shapes = [(3, None, None, 3),
+                    (None, 10, 10, 3),
+                    (None, None, None, 3)]
+    model, _, _, _ = self._create_model(use_keras=use_keras)
+    for image_shape in image_shapes:
+      image_placeholder = tf.placeholder(tf.float32, shape=image_shape)
+      preprocessed_inputs, _ = model.preprocess(image_placeholder)
+      self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape)
+
+  def test_preprocess_preserves_shape_with_static_input_image(self, use_keras):
+    def graph_fn(input_image):
+      model, _, _, _ = self._create_model(use_keras=use_keras)
+      return model.preprocess(input_image)
+    input_image = np.random.rand(2, 3, 3, 3).astype(np.float32)
+    preprocessed_inputs, _ = self.execute(graph_fn, [input_image])
+    self.assertAllEqual(preprocessed_inputs.shape, [2, 3, 3, 3])
+
+  def test_predict_result_shapes_on_image_with_dynamic_shape(self, use_keras):
+    batch_size = 3
+    image_size = 2
+    input_shapes = [(None, image_size, image_size, 3),
+                    (batch_size, None, None, 3),
+                    (None, None, None, 3)]
+
+    for input_shape in input_shapes:
+      tf_graph = tf.Graph()
+      with tf_graph.as_default():
+        model, num_classes, num_anchors, code_size = self._create_model(
+            use_keras=use_keras)
+        preprocessed_input_placeholder = tf.placeholder(tf.float32,
+                                                        shape=input_shape)
+        prediction_dict = model.predict(
+            preprocessed_input_placeholder, true_image_shapes=None)
+
+        self.assertIn('box_encodings', prediction_dict)
+        self.assertIn('class_predictions_with_background', prediction_dict)
+        self.assertIn('feature_maps', prediction_dict)
+        self.assertIn('anchors', prediction_dict)
+
+        init_op = tf.global_variables_initializer()
+      with self.test_session(graph=tf_graph) as sess:
+        sess.run(init_op)
+        prediction_out = sess.run(prediction_dict,
+                                  feed_dict={
+                                      preprocessed_input_placeholder:
+                                      np.random.uniform(
+                                          size=(batch_size, 2, 2, 3))})
+      expected_box_encodings_shape_out = (batch_size, num_anchors, code_size)
+      expected_class_predictions_with_background_shape_out = (batch_size,
+                                                              num_anchors,
+                                                              num_classes + 1)
+
+      self.assertAllEqual(prediction_out['box_encodings'].shape,
+                          expected_box_encodings_shape_out)
+      self.assertAllEqual(
+          prediction_out['class_predictions_with_background'].shape,
+          expected_class_predictions_with_background_shape_out)
+
+  def test_predict_result_shapes_on_image_with_static_shape(self, use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, num_anchors, code_size = self._create_model(
+          use_keras=use_keras)
+
+    def graph_fn(input_image):
+      model, _, _, _ = self._create_model()
+      predictions = model.predict(input_image, true_image_shapes=None)
+      return (predictions['box_encodings'],
+              predictions['class_predictions_with_background'],
+              predictions['feature_maps'],
+              predictions['anchors'])
+    batch_size = 3
+    image_size = 2
+    channels = 3
+    input_image = np.random.rand(batch_size, image_size, image_size,
+                                 channels).astype(np.float32)
+    expected_box_encodings_shape = (batch_size, num_anchors, code_size)
+    expected_class_predictions_shape = (batch_size, num_anchors, num_classes+1)
+    (box_encodings, class_predictions, _, _) = self.execute(graph_fn,
+                                                            [input_image])
+    self.assertAllEqual(box_encodings.shape, expected_box_encodings_shape)
+    self.assertAllEqual(class_predictions.shape,
+                        expected_class_predictions_shape)
+
+  def test_postprocess_results_are_correct(self, use_keras):
+    batch_size = 2
+    image_size = 2
+    input_shapes = [(batch_size, image_size, image_size, 3),
+                    (None, image_size, image_size, 3),
+                    (batch_size, None, None, 3),
+                    (None, None, None, 3)]
+
+    expected_boxes = [
+        [
+            [0, 0, .5, .5],
+            [0, .5, .5, 1],
+            [.5, 0, 1, .5],
+            [0, 0, 0, 0],  # pruned prediction
+            [0, 0, 0, 0]
+        ],  # padding
+        [
+            [0, 0, .5, .5],
+            [0, .5, .5, 1],
+            [.5, 0, 1, .5],
+            [0, 0, 0, 0],  # pruned prediction
+            [0, 0, 0, 0]
+        ]
+    ]  # padding
+    expected_scores = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
+    expected_classes = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
+    expected_num_detections = np.array([3, 3])
+
+    for input_shape in input_shapes:
+      tf_graph = tf.Graph()
+      with tf_graph.as_default():
+        model, _, _, _ = self._create_model(use_keras=use_keras)
+        input_placeholder = tf.placeholder(tf.float32, shape=input_shape)
+        preprocessed_inputs, true_image_shapes = model.preprocess(
+            input_placeholder)
+        prediction_dict = model.predict(preprocessed_inputs,
+                                        true_image_shapes)
+        detections = model.postprocess(prediction_dict, true_image_shapes)
+        self.assertIn('detection_boxes', detections)
+        self.assertIn('detection_scores', detections)
+        self.assertIn('detection_classes', detections)
+        self.assertIn('num_detections', detections)
+        init_op = tf.global_variables_initializer()
+      with self.test_session(graph=tf_graph) as sess:
+        sess.run(init_op)
+        detections_out = sess.run(detections,
+                                  feed_dict={
+                                      input_placeholder:
+                                      np.random.uniform(
+                                          size=(batch_size, 2, 2, 3))})
+      for image_idx in range(batch_size):
+        self.assertTrue(
+            test_utils.first_rows_close_as_set(
+                detections_out['detection_boxes'][image_idx].tolist(),
+                expected_boxes[image_idx]))
+      self.assertAllClose(detections_out['detection_scores'], expected_scores)
+      self.assertAllClose(detections_out['detection_classes'], expected_classes)
+      self.assertAllClose(detections_out['num_detections'],
+                          expected_num_detections)
+
+
+  def test_loss_results_are_correct(self, use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, num_anchors, _ = self._create_model(use_keras=use_keras)
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_classes1, groundtruth_classes2):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
+      model, _, _, _ = self._create_model(apply_hard_mining=False)
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list)
+      prediction_dict = model.predict(preprocessed_tensor,
+                                      true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (self._get_value_for_matching_key(loss_dict,
+                                               'Loss/localization_loss'),
+              self._get_value_for_matching_key(loss_dict,
+                                               'Loss/classification_loss'))
+
+    batch_size = 2
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.0
+    expected_classification_loss = (batch_size * num_anchors
+                                    * (num_classes+1) * np.log(2.0))
+    (localization_loss,
+     classification_loss) = self.execute(graph_fn, [preprocessed_input,
+                                                    groundtruth_boxes1,
+                                                    groundtruth_boxes2,
+                                                    groundtruth_classes1,
+                                                    groundtruth_classes2])
+    self.assertAllClose(localization_loss, expected_localization_loss)
+    self.assertAllClose(classification_loss, expected_classification_loss)
+
+  def test_loss_results_are_correct_with_normalize_by_codesize_true(
+      self, use_keras):
+
+    with tf.Graph().as_default():
+      _, _, _, _ = self._create_model(use_keras=use_keras)
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_classes1, groundtruth_classes2):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
+      model, _, _, _ = self._create_model(apply_hard_mining=False,
+                                          normalize_loc_loss_by_codesize=True,
+                                          use_keras=use_keras)
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list)
+      prediction_dict = model.predict(preprocessed_tensor,
+                                      true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (self._get_value_for_matching_key(loss_dict,
+                                               'Loss/localization_loss'),)
+
+    batch_size = 2
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, 1, 1]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.5 / 4
+    localization_loss = self.execute(graph_fn, [preprocessed_input,
+                                                groundtruth_boxes1,
+                                                groundtruth_boxes2,
+                                                groundtruth_classes1,
+                                                groundtruth_classes2])
+    self.assertAllClose(localization_loss, expected_localization_loss)
+
+  def test_loss_results_are_correct_with_hard_example_mining(self, use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, num_anchors, _ = self._create_model(use_keras=use_keras)
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_classes1, groundtruth_classes2):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
+      model, _, _, _ = self._create_model()
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list)
+      prediction_dict = model.predict(preprocessed_tensor,
+                                      true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (self._get_value_for_matching_key(loss_dict,
+                                               'Loss/localization_loss'),
+              self._get_value_for_matching_key(loss_dict,
+                                               'Loss/classification_loss'))
+
+    batch_size = 2
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.0
+    expected_classification_loss = (batch_size * num_anchors
+                                    * (num_classes+1) * np.log(2.0))
+    (localization_loss, classification_loss) = self.execute_cpu(
+        graph_fn, [
+            preprocessed_input, groundtruth_boxes1, groundtruth_boxes2,
+            groundtruth_classes1, groundtruth_classes2
+        ])
+    self.assertAllClose(localization_loss, expected_localization_loss)
+    self.assertAllClose(classification_loss, expected_classification_loss)
+
+  def test_loss_results_are_correct_without_add_background_class(
+      self, use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, num_anchors, _ = self._create_model(
+          add_background_class=False, use_keras=use_keras)
+
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_classes1, groundtruth_classes2):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
+      model, _, _, _ = self._create_model(
+          apply_hard_mining=False, add_background_class=False,
+          use_keras=use_keras)
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list)
+      prediction_dict = model.predict(
+          preprocessed_tensor, true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (loss_dict['Loss/localization_loss'],
+              loss_dict['Loss/classification_loss'])
+
+    batch_size = 2
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.0
+    expected_classification_loss = (
+        batch_size * num_anchors * num_classes * np.log(2.0))
+    (localization_loss, classification_loss) = self.execute(
+        graph_fn, [
+            preprocessed_input, groundtruth_boxes1, groundtruth_boxes2,
+            groundtruth_classes1, groundtruth_classes2
+        ])
+
+    self.assertAllClose(localization_loss, expected_localization_loss)
+    self.assertAllClose(classification_loss, expected_classification_loss)
+
+
+  def test_loss_results_are_correct_with_losses_mask(self, use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, num_anchors, _ = self._create_model(use_keras=use_keras)
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_boxes3, groundtruth_classes1, groundtruth_classes2,
+                 groundtruth_classes3):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2,
+                                groundtruth_boxes3]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2,
+                                  groundtruth_classes3]
+      is_annotated_list = [tf.constant(True), tf.constant(True),
+                           tf.constant(False)]
+      model, _, _, _ = self._create_model(apply_hard_mining=False)
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list,
+                                is_annotated_list=is_annotated_list)
+      prediction_dict = model.predict(preprocessed_tensor,
+                                      true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (self._get_value_for_matching_key(loss_dict,
+                                               'Loss/localization_loss'),
+              self._get_value_for_matching_key(loss_dict,
+                                               'Loss/classification_loss'))
+
+    batch_size = 3
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes3 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes3 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.0
+    # Note that we are subtracting 1 from batch_size, since the final image is
+    # not annotated.
+    expected_classification_loss = ((batch_size - 1) * num_anchors
+                                    * (num_classes+1) * np.log(2.0))
+    (localization_loss,
+     classification_loss) = self.execute(graph_fn, [preprocessed_input,
+                                                    groundtruth_boxes1,
+                                                    groundtruth_boxes2,
+                                                    groundtruth_boxes3,
+                                                    groundtruth_classes1,
+                                                    groundtruth_classes2,
+                                                    groundtruth_classes3])
+    self.assertAllClose(localization_loss, expected_localization_loss)
+    self.assertAllClose(classification_loss, expected_classification_loss)
+
+  def test_restore_map_for_detection_ckpt(self, use_keras):
+    model, _, _, _ = self._create_model(use_keras=use_keras)
+    model.predict(tf.constant(np.array([[[[0, 0], [1, 1]], [[1, 0], [0, 1]]]],
+                                       dtype=np.float32)),
+                  true_image_shapes=None)
+    init_op = tf.global_variables_initializer()
+    saver = tf.train.Saver()
+    save_path = self.get_temp_dir()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      saved_model_path = saver.save(sess, save_path)
+      var_map = model.restore_map(
+          fine_tune_checkpoint_type='detection',
+          load_all_detection_checkpoint_vars=False)
+      self.assertIsInstance(var_map, dict)
+      saver = tf.train.Saver(var_map)
+      saver.restore(sess, saved_model_path)
+      for var in sess.run(tf.report_uninitialized_variables()):
+        self.assertNotIn('FeatureExtractor', var)
+
+  def test_restore_map_for_classification_ckpt(self, use_keras):
+    # Define mock tensorflow classification graph and save variables.
+    test_graph_classification = tf.Graph()
+    with test_graph_classification.as_default():
+      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
+      if use_keras:
+        with tf.name_scope('mock_model'):
+          layer_one = keras.Conv2D(32, kernel_size=1, name='layer1')
+          net = layer_one(image)
+          layer_two = keras.Conv2D(3, kernel_size=1, name='layer2')
+          layer_two(net)
+      else:
+        with tf.variable_scope('mock_model'):
+          net = slim.conv2d(image, num_outputs=32, kernel_size=1,
+                            scope='layer1')
+          slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')
+
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver()
+      save_path = self.get_temp_dir()
+      with self.test_session(graph=test_graph_classification) as sess:
+        sess.run(init_op)
+        saved_model_path = saver.save(sess, save_path)
+
+    # Create tensorflow detection graph and load variables from
+    # classification checkpoint.
+    test_graph_detection = tf.Graph()
+    with test_graph_detection.as_default():
+      model, _, _, _ = self._create_model(use_keras=use_keras)
+      inputs_shape = [2, 2, 2, 3]
+      inputs = tf.to_float(tf.random_uniform(
+          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      model.postprocess(prediction_dict, true_image_shapes)
+      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
+      var_map = model.restore_map(fine_tune_checkpoint_type='classification')
+      self.assertNotIn('another_variable', var_map)
+      self.assertIsInstance(var_map, dict)
+      saver = tf.train.Saver(var_map)
+      with self.test_session(graph=test_graph_detection) as sess:
+        saver.restore(sess, saved_model_path)
+        for var in sess.run(tf.report_uninitialized_variables()):
+          self.assertNotIn('FeatureExtractor', var)
+
+  def test_load_all_det_checkpoint_vars(self, use_keras):
+    test_graph_detection = tf.Graph()
+    with test_graph_detection.as_default():
+      model, _, _, _ = self._create_model(use_keras=use_keras)
+      inputs_shape = [2, 2, 2, 3]
+      inputs = tf.to_float(
+          tf.random_uniform(inputs_shape, minval=0, maxval=255, dtype=tf.int32))
+      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
+      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
+      model.postprocess(prediction_dict, true_image_shapes)
+      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
+      var_map = model.restore_map(
+          fine_tune_checkpoint_type='detection',
+          load_all_detection_checkpoint_vars=True)
+      self.assertIsInstance(var_map, dict)
+      self.assertIn('another_variable', var_map)
+
+  def test_loss_results_are_correct_with_random_example_sampling(
+      self,
+      use_keras):
+
+    with tf.Graph().as_default():
+      _, num_classes, _, _ = self._create_model(
+          random_example_sampling=True, use_keras=use_keras)
+
+    def graph_fn(preprocessed_tensor, groundtruth_boxes1, groundtruth_boxes2,
+                 groundtruth_classes1, groundtruth_classes2):
+      groundtruth_boxes_list = [groundtruth_boxes1, groundtruth_boxes2]
+      groundtruth_classes_list = [groundtruth_classes1, groundtruth_classes2]
+      model, _, _, _ = self._create_model(random_example_sampling=True,
+                                          use_keras=use_keras)
+      model.provide_groundtruth(groundtruth_boxes_list,
+                                groundtruth_classes_list)
+      prediction_dict = model.predict(
+          preprocessed_tensor, true_image_shapes=None)
+      loss_dict = model.loss(prediction_dict, true_image_shapes=None)
+      return (self._get_value_for_matching_key(loss_dict,
+                                               'Loss/localization_loss'),
+              self._get_value_for_matching_key(loss_dict,
+                                               'Loss/classification_loss'))
+
+    batch_size = 2
+    preprocessed_input = np.random.rand(batch_size, 2, 2, 3).astype(np.float32)
+    groundtruth_boxes1 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_boxes2 = np.array([[0, 0, .5, .5]], dtype=np.float32)
+    groundtruth_classes1 = np.array([[1]], dtype=np.float32)
+    groundtruth_classes2 = np.array([[1]], dtype=np.float32)
+    expected_localization_loss = 0.0
+    # Among 4 anchors (1 positive, 3 negative) in this test, only 2 anchors are
+    # selected (1 positive, 1 negative) since random sampler will adjust number
+    # of negative examples to make sure positive example fraction in the batch
+    # is 0.5.
+    expected_classification_loss = (
+        batch_size * 2 * (num_classes + 1) * np.log(2.0))
+    (localization_loss, classification_loss) = self.execute_cpu(
+        graph_fn, [
+            preprocessed_input, groundtruth_boxes1, groundtruth_boxes2,
+            groundtruth_classes1, groundtruth_classes2
+        ])
+    self.assertAllClose(localization_loss, expected_localization_loss)
+    self.assertAllClose(classification_loss, expected_classification_loss)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test_lib.py b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test_lib.py
new file mode 100644
index 00000000..9d9adc6b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures/ssd_meta_arch_test_lib.py
@@ -0,0 +1,230 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Helper functions for SSD models meta architecture tests."""
+
+import functools
+import tensorflow as tf
+
+from object_detection.core import anchor_generator
+from object_detection.core import balanced_positive_negative_sampler as sampler
+from object_detection.core import box_list
+from object_detection.core import losses
+from object_detection.core import post_processing
+from object_detection.core import region_similarity_calculator as sim_calc
+from object_detection.core import target_assigner
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.protos import model_pb2
+from object_detection.utils import ops
+from object_detection.utils import test_case
+from object_detection.utils import test_utils
+
+slim = tf.contrib.slim
+keras = tf.keras.layers
+
+
+class FakeSSDFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """Fake ssd feature extracture for ssd meta arch tests."""
+
+  def __init__(self):
+    super(FakeSSDFeatureExtractor, self).__init__(
+        is_training=True,
+        depth_multiplier=0,
+        min_depth=0,
+        pad_to_multiple=1,
+        conv_hyperparams_fn=None)
+
+  def preprocess(self, resized_inputs):
+    return tf.identity(resized_inputs)
+
+  def extract_features(self, preprocessed_inputs):
+    with tf.variable_scope('mock_model'):
+      features = slim.conv2d(
+          inputs=preprocessed_inputs,
+          num_outputs=32,
+          kernel_size=1,
+          scope='layer1')
+      return [features]
+
+
+class FakeSSDKerasFeatureExtractor(ssd_meta_arch.SSDKerasFeatureExtractor):
+  """Fake keras based ssd feature extracture for ssd meta arch tests."""
+
+  def __init__(self):
+    with tf.name_scope('mock_model'):
+      super(FakeSSDKerasFeatureExtractor, self).__init__(
+          is_training=True,
+          depth_multiplier=0,
+          min_depth=0,
+          pad_to_multiple=1,
+          conv_hyperparams=None,
+          freeze_batchnorm=False,
+          inplace_batchnorm_update=False,
+      )
+
+      self._conv = keras.Conv2D(filters=32, kernel_size=1, name='layer1')
+
+  def preprocess(self, resized_inputs):
+    return tf.identity(resized_inputs)
+
+  def _extract_features(self, preprocessed_inputs, **kwargs):
+    with tf.name_scope('mock_model'):
+      return [self._conv(preprocessed_inputs)]
+
+
+class MockAnchorGenerator2x2(anchor_generator.AnchorGenerator):
+  """A simple 2x2 anchor grid on the unit square used for test only."""
+
+  def name_scope(self):
+    return 'MockAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    return [1]
+
+  def _generate(self, feature_map_shape_list, im_height, im_width):
+    return [
+        box_list.BoxList(
+            tf.constant(
+                [
+                    [0, 0, .5, .5],
+                    [0, .5, .5, 1],
+                    [.5, 0, 1, .5],
+                    [1., 1., 1.5, 1.5]  # Anchor that is outside clip_window.
+                ],
+                tf.float32))
+    ]
+
+  def num_anchors(self):
+    return 4
+
+
+class SSDMetaArchTestBase(test_case.TestCase):
+  """Base class to test SSD based meta architectures."""
+
+  def _create_model(
+      self,
+      model_fn=ssd_meta_arch.SSDMetaArch,
+      apply_hard_mining=True,
+      normalize_loc_loss_by_codesize=False,
+      add_background_class=True,
+      random_example_sampling=False,
+      expected_loss_weights=model_pb2.DetectionModel().ssd.loss.NONE,
+      min_num_negative_samples=1,
+      desired_negative_sampling_ratio=3,
+      use_keras=False,
+      predict_mask=False,
+      use_static_shapes=False,
+      nms_max_size_per_class=5):
+    is_training = False
+    num_classes = 1
+    mock_anchor_generator = MockAnchorGenerator2x2()
+    if use_keras:
+      mock_box_predictor = test_utils.MockKerasBoxPredictor(
+          is_training, num_classes, add_background_class=add_background_class)
+    else:
+      mock_box_predictor = test_utils.MockBoxPredictor(
+          is_training, num_classes, add_background_class=add_background_class)
+    mock_box_coder = test_utils.MockBoxCoder()
+    if use_keras:
+      fake_feature_extractor = FakeSSDKerasFeatureExtractor()
+    else:
+      fake_feature_extractor = FakeSSDFeatureExtractor()
+    mock_matcher = test_utils.MockMatcher()
+    region_similarity_calculator = sim_calc.IouSimilarity()
+    encode_background_as_zeros = False
+
+    def image_resizer_fn(image):
+      return [tf.identity(image), tf.shape(image)]
+
+    classification_loss = losses.WeightedSigmoidClassificationLoss()
+    localization_loss = losses.WeightedSmoothL1LocalizationLoss()
+    non_max_suppression_fn = functools.partial(
+        post_processing.batch_multiclass_non_max_suppression,
+        score_thresh=-20.0,
+        iou_thresh=1.0,
+        max_size_per_class=nms_max_size_per_class,
+        max_total_size=nms_max_size_per_class,
+        use_static_shapes=use_static_shapes)
+    classification_loss_weight = 1.0
+    localization_loss_weight = 1.0
+    negative_class_weight = 1.0
+    normalize_loss_by_num_matches = False
+
+    hard_example_miner = None
+    if apply_hard_mining:
+      # This hard example miner is expected to be a no-op.
+      hard_example_miner = losses.HardExampleMiner(
+          num_hard_examples=None, iou_threshold=1.0)
+
+    random_example_sampler = None
+    if random_example_sampling:
+      random_example_sampler = sampler.BalancedPositiveNegativeSampler(
+          positive_fraction=0.5)
+
+    target_assigner_instance = target_assigner.TargetAssigner(
+        region_similarity_calculator,
+        mock_matcher,
+        mock_box_coder,
+        negative_class_weight=negative_class_weight)
+
+    model_config = model_pb2.DetectionModel()
+    if expected_loss_weights == model_config.ssd.loss.NONE:
+      expected_loss_weights_fn = None
+    else:
+      raise ValueError('Not a valid value for expected_loss_weights.')
+
+    code_size = 4
+
+    kwargs = {}
+    if predict_mask:
+      kwargs.update({
+          'mask_prediction_fn': test_utils.MockMaskHead(num_classes=1).predict,
+      })
+
+    model = model_fn(
+        is_training=is_training,
+        anchor_generator=mock_anchor_generator,
+        box_predictor=mock_box_predictor,
+        box_coder=mock_box_coder,
+        feature_extractor=fake_feature_extractor,
+        encode_background_as_zeros=encode_background_as_zeros,
+        image_resizer_fn=image_resizer_fn,
+        non_max_suppression_fn=non_max_suppression_fn,
+        score_conversion_fn=tf.identity,
+        classification_loss=classification_loss,
+        localization_loss=localization_loss,
+        classification_loss_weight=classification_loss_weight,
+        localization_loss_weight=localization_loss_weight,
+        normalize_loss_by_num_matches=normalize_loss_by_num_matches,
+        hard_example_miner=hard_example_miner,
+        target_assigner_instance=target_assigner_instance,
+        add_summaries=False,
+        normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize,
+        freeze_batchnorm=False,
+        inplace_batchnorm_update=False,
+        add_background_class=add_background_class,
+        random_example_sampler=random_example_sampler,
+        expected_loss_weights_fn=expected_loss_weights_fn,
+        **kwargs)
+    return model, num_classes, mock_anchor_generator.num_anchors(), code_size
+
+  def _get_value_for_matching_key(self, dictionary, suffix):
+    for key in dictionary.keys():
+      if key.endswith(suffix):
+        return dictionary[key]
+    raise ValueError('key not found {}'.format(suffix))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation.py
new file mode 100644
index 00000000..cefbd6ab
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation.py
@@ -0,0 +1,736 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Class for evaluating object detections with COCO metrics."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.metrics import coco_tools
+from object_detection.utils import json_utils
+from object_detection.utils import object_detection_evaluation
+
+
+class CocoDetectionEvaluator(object_detection_evaluation.DetectionEvaluator):
+  """Class to evaluate COCO detection metrics."""
+
+  def __init__(self,
+               categories,
+               include_metrics_per_category=False,
+               all_metrics_per_category=False):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+      include_metrics_per_category: If True, include metrics for each category.
+      all_metrics_per_category: Whether to include all the summary metrics for
+        each category in per_category_ap. Be careful with setting it to true if
+        you have more than handful of categories, because it will pollute
+        your mldash.
+    """
+    super(CocoDetectionEvaluator, self).__init__(categories)
+    # _image_ids is a dictionary that maps unique image ids to Booleans which
+    # indicate whether a corresponding detection has been added.
+    self._image_ids = {}
+    self._groundtruth_list = []
+    self._detection_boxes_list = []
+    self._category_id_set = set([cat['id'] for cat in self._categories])
+    self._annotation_id = 1
+    self._metrics = None
+    self._include_metrics_per_category = include_metrics_per_category
+    self._all_metrics_per_category = all_metrics_per_category
+
+  def clear(self):
+    """Clears the state to prepare for a fresh evaluation."""
+    self._image_ids.clear()
+    self._groundtruth_list = []
+    self._detection_boxes_list = []
+
+  def add_single_ground_truth_image_info(self,
+                                         image_id,
+                                         groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    If the image has already been added, a warning is logged, and groundtruth is
+    ignored.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        InputDataFields.groundtruth_boxes: float32 numpy array of shape
+          [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
+          [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        InputDataFields.groundtruth_classes: integer numpy array of shape
+          [num_boxes] containing 1-indexed groundtruth classes for the boxes.
+        InputDataFields.groundtruth_is_crowd (optional): integer numpy array of
+          shape [num_boxes] containing iscrowd flag for groundtruth boxes.
+    """
+    if image_id in self._image_ids:
+      tf.logging.warning('Ignoring ground truth with image id %s since it was '
+                         'previously added', image_id)
+      return
+
+    groundtruth_is_crowd = groundtruth_dict.get(
+        standard_fields.InputDataFields.groundtruth_is_crowd)
+    # Drop groundtruth_is_crowd if empty tensor.
+    if groundtruth_is_crowd is not None and not groundtruth_is_crowd.shape[0]:
+      groundtruth_is_crowd = None
+
+    self._groundtruth_list.extend(
+        coco_tools.ExportSingleImageGroundtruthToCoco(
+            image_id=image_id,
+            next_annotation_id=self._annotation_id,
+            category_id_set=self._category_id_set,
+            groundtruth_boxes=groundtruth_dict[
+                standard_fields.InputDataFields.groundtruth_boxes],
+            groundtruth_classes=groundtruth_dict[
+                standard_fields.InputDataFields.groundtruth_classes],
+            groundtruth_is_crowd=groundtruth_is_crowd))
+    self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
+                                            groundtruth_boxes].shape[0]
+    # Boolean to indicate whether a detection has been added for this image.
+    self._image_ids[image_id] = False
+
+  def add_single_detected_image_info(self,
+                                     image_id,
+                                     detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    If a detection has already been added for this image id, a warning is
+    logged, and the detection is skipped.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary containing -
+        DetectionResultFields.detection_boxes: float32 numpy array of shape
+          [num_boxes, 4] containing `num_boxes` detection boxes of the format
+          [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        DetectionResultFields.detection_scores: float32 numpy array of shape
+          [num_boxes] containing detection scores for the boxes.
+        DetectionResultFields.detection_classes: integer numpy array of shape
+          [num_boxes] containing 1-indexed detection classes for the boxes.
+
+    Raises:
+      ValueError: If groundtruth for the image_id is not available.
+    """
+    if image_id not in self._image_ids:
+      raise ValueError('Missing groundtruth for image id: {}'.format(image_id))
+
+    if self._image_ids[image_id]:
+      tf.logging.warning('Ignoring detection with image id %s since it was '
+                         'previously added', image_id)
+      return
+
+    self._detection_boxes_list.extend(
+        coco_tools.ExportSingleImageDetectionBoxesToCoco(
+            image_id=image_id,
+            category_id_set=self._category_id_set,
+            detection_boxes=detections_dict[standard_fields.
+                                            DetectionResultFields
+                                            .detection_boxes],
+            detection_scores=detections_dict[standard_fields.
+                                             DetectionResultFields.
+                                             detection_scores],
+            detection_classes=detections_dict[standard_fields.
+                                              DetectionResultFields.
+                                              detection_classes]))
+    self._image_ids[image_id] = True
+
+  def dump_detections_to_json_file(self, json_output_path):
+    """Saves the detections into json_output_path in the format used by MS COCO.
+
+    Args:
+      json_output_path: String containing the output file's path. It can be also
+        None. In that case nothing will be written to the output file.
+    """
+    if json_output_path and json_output_path is not None:
+      with tf.gfile.GFile(json_output_path, 'w') as fid:
+        tf.logging.info('Dumping detections to output json file.')
+        json_utils.Dump(
+            obj=self._detection_boxes_list, fid=fid, float_digits=4, indent=2)
+
+  def evaluate(self):
+    """Evaluates the detection boxes and returns a dictionary of coco metrics.
+
+    Returns:
+      A dictionary holding -
+
+      1. summary_metrics:
+      'DetectionBoxes_Precision/mAP': mean average precision over classes
+        averaged over IOU thresholds ranging from .5 to .95 with .05
+        increments.
+      'DetectionBoxes_Precision/mAP@.50IOU': mean average precision at 50% IOU
+      'DetectionBoxes_Precision/mAP@.75IOU': mean average precision at 75% IOU
+      'DetectionBoxes_Precision/mAP (small)': mean average precision for small
+        objects (area < 32^2 pixels).
+      'DetectionBoxes_Precision/mAP (medium)': mean average precision for
+        medium sized objects (32^2 pixels < area < 96^2 pixels).
+      'DetectionBoxes_Precision/mAP (large)': mean average precision for large
+        objects (96^2 pixels < area < 10000^2 pixels).
+      'DetectionBoxes_Recall/AR@1': average recall with 1 detection.
+      'DetectionBoxes_Recall/AR@10': average recall with 10 detections.
+      'DetectionBoxes_Recall/AR@100': average recall with 100 detections.
+      'DetectionBoxes_Recall/AR@100 (small)': average recall for small objects
+        with 100.
+      'DetectionBoxes_Recall/AR@100 (medium)': average recall for medium objects
+        with 100.
+      'DetectionBoxes_Recall/AR@100 (large)': average recall for large objects
+        with 100 detections.
+
+      2. per_category_ap: if include_metrics_per_category is True, category
+      specific results with keys of the form:
+      'Precision mAP ByCategory/category' (without the supercategory part if
+      no supercategories exist). For backward compatibility
+      'PerformanceByCategory' is included in the output regardless of
+      all_metrics_per_category.
+    """
+    groundtruth_dict = {
+        'annotations': self._groundtruth_list,
+        'images': [{'id': image_id} for image_id in self._image_ids],
+        'categories': self._categories
+    }
+    coco_wrapped_groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+    coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(
+        self._detection_boxes_list)
+    box_evaluator = coco_tools.COCOEvalWrapper(
+        coco_wrapped_groundtruth, coco_wrapped_detections, agnostic_mode=False)
+    box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
+        include_metrics_per_category=self._include_metrics_per_category,
+        all_metrics_per_category=self._all_metrics_per_category)
+    box_metrics.update(box_per_category_ap)
+    box_metrics = {'DetectionBoxes_'+ key: value
+                   for key, value in iter(box_metrics.items())}
+    return box_metrics
+
+  def get_estimator_eval_metric_ops(self, eval_dict):
+    """Returns a dictionary of eval metric ops.
+
+    Note that once value_op is called, the detections and groundtruth added via
+    update_op are cleared.
+
+    This function can take in groundtruth and detections for a batch of images,
+    or for a single image. For the latter case, the batch dimension for input
+    tensors need not be present.
+
+    Args:
+      eval_dict: A dictionary that holds tensors for evaluating object detection
+        performance. For single-image evaluation, this dictionary may be
+        produced from eval_util.result_dict_for_single_example(). If multi-image
+        evaluation, `eval_dict` should contain the fields
+        'num_groundtruth_boxes_per_image' and 'num_det_boxes_per_image' to
+        properly unpad the tensors from the batch.
+
+    Returns:
+      a dictionary of metric names to tuple of value_op and update_op that can
+      be used as eval metric ops in tf.estimator.EstimatorSpec. Note that all
+      update ops must be run together and similarly all value ops must be run
+      together to guarantee correct behaviour.
+    """
+    def update_op(
+        image_id_batched,
+        groundtruth_boxes_batched,
+        groundtruth_classes_batched,
+        groundtruth_is_crowd_batched,
+        num_gt_boxes_per_image,
+        detection_boxes_batched,
+        detection_scores_batched,
+        detection_classes_batched,
+        num_det_boxes_per_image,
+        is_annotated_batched):
+      """Update operation for adding batch of images to Coco evaluator."""
+
+      for (image_id, gt_box, gt_class, gt_is_crowd, num_gt_box, det_box,
+           det_score, det_class, num_det_box, is_annotated) in zip(
+               image_id_batched, groundtruth_boxes_batched,
+               groundtruth_classes_batched, groundtruth_is_crowd_batched,
+               num_gt_boxes_per_image,
+               detection_boxes_batched, detection_scores_batched,
+               detection_classes_batched, num_det_boxes_per_image,
+               is_annotated_batched):
+        if is_annotated:
+          self.add_single_ground_truth_image_info(
+              image_id, {
+                  'groundtruth_boxes': gt_box[:num_gt_box],
+                  'groundtruth_classes': gt_class[:num_gt_box],
+                  'groundtruth_is_crowd': gt_is_crowd[:num_gt_box]
+              })
+          self.add_single_detected_image_info(
+              image_id,
+              {'detection_boxes': det_box[:num_det_box],
+               'detection_scores': det_score[:num_det_box],
+               'detection_classes': det_class[:num_det_box]})
+
+    # Unpack items from the evaluation dictionary.
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    image_id = eval_dict[input_data_fields.key]
+    groundtruth_boxes = eval_dict[input_data_fields.groundtruth_boxes]
+    groundtruth_classes = eval_dict[input_data_fields.groundtruth_classes]
+    groundtruth_is_crowd = eval_dict.get(
+        input_data_fields.groundtruth_is_crowd, None)
+    detection_boxes = eval_dict[detection_fields.detection_boxes]
+    detection_scores = eval_dict[detection_fields.detection_scores]
+    detection_classes = eval_dict[detection_fields.detection_classes]
+    num_gt_boxes_per_image = eval_dict.get(
+        'num_groundtruth_boxes_per_image', None)
+    num_det_boxes_per_image = eval_dict.get('num_det_boxes_per_image', None)
+    is_annotated = eval_dict.get('is_annotated', None)
+
+    if groundtruth_is_crowd is None:
+      groundtruth_is_crowd = tf.zeros_like(groundtruth_classes, dtype=tf.bool)
+    if not image_id.shape.as_list():
+      # Apply a batch dimension to all tensors.
+      image_id = tf.expand_dims(image_id, 0)
+      groundtruth_boxes = tf.expand_dims(groundtruth_boxes, 0)
+      groundtruth_classes = tf.expand_dims(groundtruth_classes, 0)
+      groundtruth_is_crowd = tf.expand_dims(groundtruth_is_crowd, 0)
+      detection_boxes = tf.expand_dims(detection_boxes, 0)
+      detection_scores = tf.expand_dims(detection_scores, 0)
+      detection_classes = tf.expand_dims(detection_classes, 0)
+
+      if num_gt_boxes_per_image is None:
+        num_gt_boxes_per_image = tf.shape(groundtruth_boxes)[1:2]
+      else:
+        num_gt_boxes_per_image = tf.expand_dims(num_gt_boxes_per_image, 0)
+
+      if num_det_boxes_per_image is None:
+        num_det_boxes_per_image = tf.shape(detection_boxes)[1:2]
+      else:
+        num_det_boxes_per_image = tf.expand_dims(num_det_boxes_per_image, 0)
+
+      if is_annotated is None:
+        is_annotated = tf.constant([True])
+      else:
+        is_annotated = tf.expand_dims(is_annotated, 0)
+    else:
+      if num_gt_boxes_per_image is None:
+        num_gt_boxes_per_image = tf.tile(
+            tf.shape(groundtruth_boxes)[1:2],
+            multiples=tf.shape(groundtruth_boxes)[0:1])
+      if num_det_boxes_per_image is None:
+        num_det_boxes_per_image = tf.tile(
+            tf.shape(detection_boxes)[1:2],
+            multiples=tf.shape(detection_boxes)[0:1])
+      if is_annotated is None:
+        is_annotated = tf.ones_like(image_id, dtype=tf.bool)
+
+    update_op = tf.py_func(update_op, [image_id,
+                                       groundtruth_boxes,
+                                       groundtruth_classes,
+                                       groundtruth_is_crowd,
+                                       num_gt_boxes_per_image,
+                                       detection_boxes,
+                                       detection_scores,
+                                       detection_classes,
+                                       num_det_boxes_per_image,
+                                       is_annotated], [])
+    metric_names = ['DetectionBoxes_Precision/mAP',
+                    'DetectionBoxes_Precision/mAP@.50IOU',
+                    'DetectionBoxes_Precision/mAP@.75IOU',
+                    'DetectionBoxes_Precision/mAP (large)',
+                    'DetectionBoxes_Precision/mAP (medium)',
+                    'DetectionBoxes_Precision/mAP (small)',
+                    'DetectionBoxes_Recall/AR@1',
+                    'DetectionBoxes_Recall/AR@10',
+                    'DetectionBoxes_Recall/AR@100',
+                    'DetectionBoxes_Recall/AR@100 (large)',
+                    'DetectionBoxes_Recall/AR@100 (medium)',
+                    'DetectionBoxes_Recall/AR@100 (small)']
+    if self._include_metrics_per_category:
+      for category_dict in self._categories:
+        metric_names.append('DetectionBoxes_PerformanceByCategory/mAP/' +
+                            category_dict['name'])
+
+    def first_value_func():
+      self._metrics = self.evaluate()
+      self.clear()
+      return np.float32(self._metrics[metric_names[0]])
+
+    def value_func_factory(metric_name):
+      def value_func():
+        return np.float32(self._metrics[metric_name])
+      return value_func
+
+    # Ensure that the metrics are only evaluated once.
+    first_value_op = tf.py_func(first_value_func, [], tf.float32)
+    eval_metric_ops = {metric_names[0]: (first_value_op, update_op)}
+    with tf.control_dependencies([first_value_op]):
+      for metric_name in metric_names[1:]:
+        eval_metric_ops[metric_name] = (tf.py_func(
+            value_func_factory(metric_name), [], np.float32), update_op)
+    return eval_metric_ops
+
+
+def _check_mask_type_and_value(array_name, masks):
+  """Checks whether mask dtype is uint8 and the values are either 0 or 1."""
+  if masks.dtype != np.uint8:
+    raise ValueError('{} must be of type np.uint8. Found {}.'.format(
+        array_name, masks.dtype))
+  if np.any(np.logical_and(masks != 0, masks != 1)):
+    raise ValueError('{} elements can only be either 0 or 1.'.format(
+        array_name))
+
+
+class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
+  """Class to evaluate COCO detection metrics."""
+
+  def __init__(self, categories, include_metrics_per_category=False):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+      include_metrics_per_category: If True, include metrics for each category.
+    """
+    super(CocoMaskEvaluator, self).__init__(categories)
+    self._image_id_to_mask_shape_map = {}
+    self._image_ids_with_detections = set([])
+    self._groundtruth_list = []
+    self._detection_masks_list = []
+    self._category_id_set = set([cat['id'] for cat in self._categories])
+    self._annotation_id = 1
+    self._include_metrics_per_category = include_metrics_per_category
+
+  def clear(self):
+    """Clears the state to prepare for a fresh evaluation."""
+    self._image_id_to_mask_shape_map.clear()
+    self._image_ids_with_detections.clear()
+    self._groundtruth_list = []
+    self._detection_masks_list = []
+
+  def add_single_ground_truth_image_info(self,
+                                         image_id,
+                                         groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    If the image has already been added, a warning is logged, and groundtruth is
+    ignored.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        InputDataFields.groundtruth_boxes: float32 numpy array of shape
+          [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
+          [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        InputDataFields.groundtruth_classes: integer numpy array of shape
+          [num_boxes] containing 1-indexed groundtruth classes for the boxes.
+        InputDataFields.groundtruth_instance_masks: uint8 numpy array of shape
+          [num_boxes, image_height, image_width] containing groundtruth masks
+          corresponding to the boxes. The elements of the array must be in
+          {0, 1}.
+    """
+    if image_id in self._image_id_to_mask_shape_map:
+      tf.logging.warning('Ignoring ground truth with image id %s since it was '
+                         'previously added', image_id)
+      return
+
+    groundtruth_instance_masks = groundtruth_dict[
+        standard_fields.InputDataFields.groundtruth_instance_masks]
+    _check_mask_type_and_value(standard_fields.InputDataFields.
+                               groundtruth_instance_masks,
+                               groundtruth_instance_masks)
+    self._groundtruth_list.extend(
+        coco_tools.
+        ExportSingleImageGroundtruthToCoco(
+            image_id=image_id,
+            next_annotation_id=self._annotation_id,
+            category_id_set=self._category_id_set,
+            groundtruth_boxes=groundtruth_dict[standard_fields.InputDataFields.
+                                               groundtruth_boxes],
+            groundtruth_classes=groundtruth_dict[standard_fields.
+                                                 InputDataFields.
+                                                 groundtruth_classes],
+            groundtruth_masks=groundtruth_instance_masks))
+    self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
+                                            groundtruth_boxes].shape[0]
+    self._image_id_to_mask_shape_map[image_id] = groundtruth_dict[
+        standard_fields.InputDataFields.groundtruth_instance_masks].shape
+
+  def add_single_detected_image_info(self,
+                                     image_id,
+                                     detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    If a detection has already been added for this image id, a warning is
+    logged, and the detection is skipped.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary containing -
+        DetectionResultFields.detection_scores: float32 numpy array of shape
+          [num_boxes] containing detection scores for the boxes.
+        DetectionResultFields.detection_classes: integer numpy array of shape
+          [num_boxes] containing 1-indexed detection classes for the boxes.
+        DetectionResultFields.detection_masks: optional uint8 numpy array of
+          shape [num_boxes, image_height, image_width] containing instance
+          masks corresponding to the boxes. The elements of the array must be
+          in {0, 1}.
+
+    Raises:
+      ValueError: If groundtruth for the image_id is not available or if
+        spatial shapes of groundtruth_instance_masks and detection_masks are
+        incompatible.
+    """
+    if image_id not in self._image_id_to_mask_shape_map:
+      raise ValueError('Missing groundtruth for image id: {}'.format(image_id))
+
+    if image_id in self._image_ids_with_detections:
+      tf.logging.warning('Ignoring detection with image id %s since it was '
+                         'previously added', image_id)
+      return
+
+    groundtruth_masks_shape = self._image_id_to_mask_shape_map[image_id]
+    detection_masks = detections_dict[standard_fields.DetectionResultFields.
+                                      detection_masks]
+    if groundtruth_masks_shape[1:] != detection_masks.shape[1:]:
+      raise ValueError('Spatial shape of groundtruth masks and detection masks '
+                       'are incompatible: {} vs {}'.format(
+                           groundtruth_masks_shape,
+                           detection_masks.shape))
+    _check_mask_type_and_value(standard_fields.DetectionResultFields.
+                               detection_masks,
+                               detection_masks)
+    self._detection_masks_list.extend(
+        coco_tools.ExportSingleImageDetectionMasksToCoco(
+            image_id=image_id,
+            category_id_set=self._category_id_set,
+            detection_masks=detection_masks,
+            detection_scores=detections_dict[standard_fields.
+                                             DetectionResultFields.
+                                             detection_scores],
+            detection_classes=detections_dict[standard_fields.
+                                              DetectionResultFields.
+                                              detection_classes]))
+    self._image_ids_with_detections.update([image_id])
+
+  def dump_detections_to_json_file(self, json_output_path):
+    """Saves the detections into json_output_path in the format used by MS COCO.
+
+    Args:
+      json_output_path: String containing the output file's path. It can be also
+        None. In that case nothing will be written to the output file.
+    """
+    if json_output_path and json_output_path is not None:
+      tf.logging.info('Dumping detections to output json file.')
+      with tf.gfile.GFile(json_output_path, 'w') as fid:
+        json_utils.Dump(
+            obj=self._detection_masks_list, fid=fid, float_digits=4, indent=2)
+
+  def evaluate(self):
+    """Evaluates the detection masks and returns a dictionary of coco metrics.
+
+    Returns:
+      A dictionary holding -
+
+      1. summary_metrics:
+      'DetectionMasks_Precision/mAP': mean average precision over classes
+        averaged over IOU thresholds ranging from .5 to .95 with .05 increments.
+      'DetectionMasks_Precision/mAP@.50IOU': mean average precision at 50% IOU.
+      'DetectionMasks_Precision/mAP@.75IOU': mean average precision at 75% IOU.
+      'DetectionMasks_Precision/mAP (small)': mean average precision for small
+        objects (area < 32^2 pixels).
+      'DetectionMasks_Precision/mAP (medium)': mean average precision for medium
+        sized objects (32^2 pixels < area < 96^2 pixels).
+      'DetectionMasks_Precision/mAP (large)': mean average precision for large
+        objects (96^2 pixels < area < 10000^2 pixels).
+      'DetectionMasks_Recall/AR@1': average recall with 1 detection.
+      'DetectionMasks_Recall/AR@10': average recall with 10 detections.
+      'DetectionMasks_Recall/AR@100': average recall with 100 detections.
+      'DetectionMasks_Recall/AR@100 (small)': average recall for small objects
+        with 100 detections.
+      'DetectionMasks_Recall/AR@100 (medium)': average recall for medium objects
+        with 100 detections.
+      'DetectionMasks_Recall/AR@100 (large)': average recall for large objects
+        with 100 detections.
+
+      2. per_category_ap: if include_metrics_per_category is True, category
+      specific results with keys of the form:
+      'Precision mAP ByCategory/category' (without the supercategory part if
+      no supercategories exist). For backward compatibility
+      'PerformanceByCategory' is included in the output regardless of
+      all_metrics_per_category.
+    """
+    groundtruth_dict = {
+        'annotations': self._groundtruth_list,
+        'images': [{'id': image_id, 'height': shape[1], 'width': shape[2]}
+                   for image_id, shape in self._image_id_to_mask_shape_map.
+                   iteritems()],
+        'categories': self._categories
+    }
+    coco_wrapped_groundtruth = coco_tools.COCOWrapper(
+        groundtruth_dict, detection_type='segmentation')
+    coco_wrapped_detection_masks = coco_wrapped_groundtruth.LoadAnnotations(
+        self._detection_masks_list)
+    mask_evaluator = coco_tools.COCOEvalWrapper(
+        coco_wrapped_groundtruth, coco_wrapped_detection_masks,
+        agnostic_mode=False, iou_type='segm')
+    mask_metrics, mask_per_category_ap = mask_evaluator.ComputeMetrics(
+        include_metrics_per_category=self._include_metrics_per_category)
+    mask_metrics.update(mask_per_category_ap)
+    mask_metrics = {'DetectionMasks_'+ key: value
+                    for key, value in mask_metrics.iteritems()}
+    return mask_metrics
+
+  def get_estimator_eval_metric_ops(self, eval_dict):
+    """Returns a dictionary of eval metric ops.
+
+    Note that once value_op is called, the detections and groundtruth added via
+    update_op are cleared.
+
+    Args:
+      eval_dict: A dictionary that holds tensors for evaluating object detection
+        performance. For single-image evaluation, this dictionary may be
+        produced from eval_util.result_dict_for_single_example(). If multi-image
+        evaluation, `eval_dict` should contain the fields
+        'num_groundtruth_boxes_per_image' and 'num_det_boxes_per_image' to
+        properly unpad the tensors from the batch.
+
+    Returns:
+      a dictionary of metric names to tuple of value_op and update_op that can
+      be used as eval metric ops in tf.estimator.EstimatorSpec. Note that all
+      update ops  must be run together and similarly all value ops must be run
+      together to guarantee correct behaviour.
+    """
+
+    def update_op(image_id_batched, groundtruth_boxes_batched,
+                  groundtruth_classes_batched,
+                  groundtruth_instance_masks_batched,
+                  groundtruth_is_crowd_batched, num_gt_boxes_per_image,
+                  detection_scores_batched, detection_classes_batched,
+                  detection_masks_batched, num_det_boxes_per_image):
+      """Update op for metrics."""
+
+      for (image_id, groundtruth_boxes, groundtruth_classes,
+           groundtruth_instance_masks, groundtruth_is_crowd, num_gt_box,
+           detection_scores, detection_classes,
+           detection_masks, num_det_box) in zip(
+               image_id_batched, groundtruth_boxes_batched,
+               groundtruth_classes_batched, groundtruth_instance_masks_batched,
+               groundtruth_is_crowd_batched, num_gt_boxes_per_image,
+               detection_scores_batched, detection_classes_batched,
+               detection_masks_batched, num_det_boxes_per_image):
+        self.add_single_ground_truth_image_info(
+            image_id, {
+                'groundtruth_boxes':
+                    groundtruth_boxes[:num_gt_box],
+                'groundtruth_classes':
+                    groundtruth_classes[:num_gt_box],
+                'groundtruth_instance_masks':
+                    groundtruth_instance_masks[:num_gt_box],
+                'groundtruth_is_crowd':
+                    groundtruth_is_crowd[:num_gt_box]
+            })
+        self.add_single_detected_image_info(
+            image_id, {
+                'detection_scores': detection_scores[:num_det_box],
+                'detection_classes': detection_classes[:num_det_box],
+                'detection_masks': detection_masks[:num_det_box]
+            })
+
+    # Unpack items from the evaluation dictionary.
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    image_id = eval_dict[input_data_fields.key]
+    groundtruth_boxes = eval_dict[input_data_fields.groundtruth_boxes]
+    groundtruth_classes = eval_dict[input_data_fields.groundtruth_classes]
+    groundtruth_instance_masks = eval_dict[
+        input_data_fields.groundtruth_instance_masks]
+    groundtruth_is_crowd = eval_dict.get(
+        input_data_fields.groundtruth_is_crowd, None)
+    num_gt_boxes_per_image = eval_dict.get(
+        input_data_fields.num_groundtruth_boxes, None)
+    detection_scores = eval_dict[detection_fields.detection_scores]
+    detection_classes = eval_dict[detection_fields.detection_classes]
+    detection_masks = eval_dict[detection_fields.detection_masks]
+    num_det_boxes_per_image = eval_dict.get(detection_fields.num_detections,
+                                            None)
+
+    if groundtruth_is_crowd is None:
+      groundtruth_is_crowd = tf.zeros_like(groundtruth_classes, dtype=tf.bool)
+
+    if not image_id.shape.as_list():
+      # Apply a batch dimension to all tensors.
+      image_id = tf.expand_dims(image_id, 0)
+      groundtruth_boxes = tf.expand_dims(groundtruth_boxes, 0)
+      groundtruth_classes = tf.expand_dims(groundtruth_classes, 0)
+      groundtruth_instance_masks = tf.expand_dims(groundtruth_instance_masks, 0)
+      groundtruth_is_crowd = tf.expand_dims(groundtruth_is_crowd, 0)
+      detection_scores = tf.expand_dims(detection_scores, 0)
+      detection_classes = tf.expand_dims(detection_classes, 0)
+      detection_masks = tf.expand_dims(detection_masks, 0)
+
+      if num_gt_boxes_per_image is None:
+        num_gt_boxes_per_image = tf.shape(groundtruth_boxes)[1:2]
+      else:
+        num_gt_boxes_per_image = tf.expand_dims(num_gt_boxes_per_image, 0)
+
+      if num_det_boxes_per_image is None:
+        num_det_boxes_per_image = tf.shape(detection_scores)[1:2]
+      else:
+        num_det_boxes_per_image = tf.expand_dims(num_det_boxes_per_image, 0)
+    else:
+      if num_gt_boxes_per_image is None:
+        num_gt_boxes_per_image = tf.tile(
+            tf.shape(groundtruth_boxes)[1:2],
+            multiples=tf.shape(groundtruth_boxes)[0:1])
+      if num_det_boxes_per_image is None:
+        num_det_boxes_per_image = tf.tile(
+            tf.shape(detection_scores)[1:2],
+            multiples=tf.shape(detection_scores)[0:1])
+
+    update_op = tf.py_func(update_op, [
+        image_id, groundtruth_boxes, groundtruth_classes,
+        groundtruth_instance_masks, groundtruth_is_crowd,
+        num_gt_boxes_per_image, detection_scores, detection_classes,
+        detection_masks, num_det_boxes_per_image
+    ], [])
+
+    metric_names = ['DetectionMasks_Precision/mAP',
+                    'DetectionMasks_Precision/mAP@.50IOU',
+                    'DetectionMasks_Precision/mAP@.75IOU',
+                    'DetectionMasks_Precision/mAP (large)',
+                    'DetectionMasks_Precision/mAP (medium)',
+                    'DetectionMasks_Precision/mAP (small)',
+                    'DetectionMasks_Recall/AR@1',
+                    'DetectionMasks_Recall/AR@10',
+                    'DetectionMasks_Recall/AR@100',
+                    'DetectionMasks_Recall/AR@100 (large)',
+                    'DetectionMasks_Recall/AR@100 (medium)',
+                    'DetectionMasks_Recall/AR@100 (small)']
+    if self._include_metrics_per_category:
+      for category_dict in self._categories:
+        metric_names.append('DetectionMasks_PerformanceByCategory/mAP/' +
+                            category_dict['name'])
+
+    def first_value_func():
+      self._metrics = self.evaluate()
+      self.clear()
+      return np.float32(self._metrics[metric_names[0]])
+
+    def value_func_factory(metric_name):
+      def value_func():
+        return np.float32(self._metrics[metric_name])
+      return value_func
+
+    # Ensure that the metrics are only evaluated once.
+    first_value_op = tf.py_func(first_value_func, [], tf.float32)
+    eval_metric_ops = {metric_names[0]: (first_value_op, update_op)}
+    with tf.control_dependencies([first_value_op]):
+      for metric_name in metric_names[1:]:
+        eval_metric_ops[metric_name] = (tf.py_func(
+            value_func_factory(metric_name), [], np.float32), update_op)
+    return eval_metric_ops
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation_test.py
new file mode 100644
index 00000000..0a567c51
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_evaluation_test.py
@@ -0,0 +1,951 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow_models.object_detection.metrics.coco_evaluation."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+from object_detection.core import standard_fields
+from object_detection.metrics import coco_evaluation
+
+
+def _get_categories_list():
+  return [{
+      'id': 1,
+      'name': 'person'
+  }, {
+      'id': 2,
+      'name': 'dog'
+  }, {
+      'id': 3,
+      'name': 'cat'
+  }]
+
+
+class CocoDetectionEvaluationTest(tf.test.TestCase):
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
+    """Tests that mAP is calculated correctly on GT and Detections."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image1',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1])
+        })
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image2',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[50., 50., 100., 100.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image2',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[50., 50., 100., 100.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1])
+        })
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image3',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[25., 25., 50., 50.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image3',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[25., 25., 50., 50.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1])
+        })
+    metrics = coco_evaluator.evaluate()
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsSkipCrowd(self):
+    """Tests computing mAP with is_crowd GT boxes skipped."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image1',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+                np.array([[100., 100., 200., 200.], [99., 99., 200., 200.]]),
+            standard_fields.InputDataFields.groundtruth_classes:
+                np.array([1, 2]),
+            standard_fields.InputDataFields.groundtruth_is_crowd:
+                np.array([0, 1])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+                np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+                np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+                np.array([1])
+        })
+    metrics = coco_evaluator.evaluate()
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsEmptyCrowd(self):
+    """Tests computing mAP with empty is_crowd array passed in."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image1',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+                np.array([[100., 100., 200., 200.]]),
+            standard_fields.InputDataFields.groundtruth_classes:
+                np.array([1]),
+            standard_fields.InputDataFields.groundtruth_is_crowd:
+                np.array([])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+                np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+                np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+                np.array([1])
+        })
+    metrics = coco_evaluator.evaluate()
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+
+  def testRejectionOnDuplicateGroundtruth(self):
+    """Tests that groundtruth cannot be added more than once for an image."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    #  Add groundtruth
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    coco_evaluator.add_single_ground_truth_image_info(image_key1, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes1,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels1
+    })
+    groundtruth_lists_len = len(coco_evaluator._groundtruth_list)
+
+    # Add groundtruth with the same image id.
+    coco_evaluator.add_single_ground_truth_image_info(image_key1, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes1,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels1
+    })
+    self.assertEqual(groundtruth_lists_len,
+                     len(coco_evaluator._groundtruth_list))
+
+  def testRejectionOnDuplicateDetections(self):
+    """Tests that detections cannot be added more than once for an image."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    #  Add groundtruth
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image1',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[99., 100., 200., 200.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1])
+        })
+    detections_lists_len = len(coco_evaluator._detection_boxes_list)
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',  # Note that this image id was previously added.
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1])
+        })
+    self.assertEqual(detections_lists_len,
+                     len(coco_evaluator._detection_boxes_list))
+
+  def testExceptionRaisedWithMissingGroundtruth(self):
+    """Tests that exception is raised for detection with missing groundtruth."""
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    with self.assertRaises(ValueError):
+      coco_evaluator.add_single_detected_image_info(
+          image_id='image1',
+          detections_dict={
+              standard_fields.DetectionResultFields.detection_boxes:
+                  np.array([[100., 100., 200., 200.]]),
+              standard_fields.DetectionResultFields.detection_scores:
+                  np.array([.8]),
+              standard_fields.DetectionResultFields.detection_classes:
+                  np.array([1])
+          })
+
+
+class CocoEvaluationPyFuncTest(tf.test.TestCase):
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    image_id = tf.placeholder(tf.string, shape=())
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
+    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    detection_scores = tf.placeholder(tf.float32, shape=(None))
+    detection_classes = tf.placeholder(tf.float32, shape=(None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image1',
+                   groundtruth_boxes: np.array([[100., 100., 200., 200.]]),
+                   groundtruth_classes: np.array([1]),
+                   detection_boxes: np.array([[100., 100., 200., 200.]]),
+                   detection_scores: np.array([.8]),
+                   detection_classes: np.array([1])
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image2',
+                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
+                   groundtruth_classes: np.array([3]),
+                   detection_boxes: np.array([[50., 50., 100., 100.]]),
+                   detection_scores: np.array([.7]),
+                   detection_classes: np.array([3])
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image3',
+                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
+                   groundtruth_classes: np.array([2]),
+                   detection_boxes: np.array([[25., 25., 50., 50.]]),
+                   detection_scores: np.array([.9]),
+                   detection_classes: np.array([2])
+               })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_boxes_list)
+    self.assertFalse(coco_evaluator._image_ids)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsIsAnnotated(self):
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    image_id = tf.placeholder(tf.string, shape=())
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
+    is_annotated = tf.placeholder(tf.bool, shape=())
+    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    detection_scores = tf.placeholder(tf.float32, shape=(None))
+    detection_classes = tf.placeholder(tf.float32, shape=(None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        'is_annotated': is_annotated,
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image1',
+                   groundtruth_boxes: np.array([[100., 100., 200., 200.]]),
+                   groundtruth_classes: np.array([1]),
+                   is_annotated: True,
+                   detection_boxes: np.array([[100., 100., 200., 200.]]),
+                   detection_scores: np.array([.8]),
+                   detection_classes: np.array([1])
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image2',
+                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
+                   groundtruth_classes: np.array([3]),
+                   is_annotated: True,
+                   detection_boxes: np.array([[50., 50., 100., 100.]]),
+                   detection_scores: np.array([.7]),
+                   detection_classes: np.array([3])
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image3',
+                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
+                   groundtruth_classes: np.array([2]),
+                   is_annotated: True,
+                   detection_boxes: np.array([[25., 25., 50., 50.]]),
+                   detection_scores: np.array([.9]),
+                   detection_classes: np.array([2])
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image4',
+                   groundtruth_boxes: np.zeros((0, 4)),
+                   groundtruth_classes: np.zeros((0)),
+                   is_annotated: False,  # Note that this image isn't annotated.
+                   detection_boxes: np.array([[25., 25., 50., 50.],
+                                              [25., 25., 70., 50.],
+                                              [25., 25., 80., 50.],
+                                              [25., 25., 90., 50.]]),
+                   detection_scores: np.array([0.6, 0.7, 0.8, 0.9]),
+                   detection_classes: np.array([1, 2, 2, 3])
+               })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_boxes_list)
+    self.assertFalse(coco_evaluator._image_ids)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsPadded(self):
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    image_id = tf.placeholder(tf.string, shape=())
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
+    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    detection_scores = tf.placeholder(tf.float32, shape=(None))
+    detection_classes = tf.placeholder(tf.float32, shape=(None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id:
+                  'image1',
+              groundtruth_boxes:
+                  np.array([[100., 100., 200., 200.], [-1, -1, -1, -1]]),
+              groundtruth_classes:
+                  np.array([1, -1]),
+              detection_boxes:
+                  np.array([[100., 100., 200., 200.], [0., 0., 0., 0.]]),
+              detection_scores:
+                  np.array([.8, 0.]),
+              detection_classes:
+                  np.array([1, -1])
+          })
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id:
+                  'image2',
+              groundtruth_boxes:
+                  np.array([[50., 50., 100., 100.], [-1, -1, -1, -1]]),
+              groundtruth_classes:
+                  np.array([3, -1]),
+              detection_boxes:
+                  np.array([[50., 50., 100., 100.], [0., 0., 0., 0.]]),
+              detection_scores:
+                  np.array([.7, 0.]),
+              detection_classes:
+                  np.array([3, -1])
+          })
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id:
+                  'image3',
+              groundtruth_boxes:
+                  np.array([[25., 25., 50., 50.], [10., 10., 15., 15.]]),
+              groundtruth_classes:
+                  np.array([2, 2]),
+              detection_boxes:
+                  np.array([[25., 25., 50., 50.], [10., 10., 15., 15.]]),
+              detection_scores:
+                  np.array([.95, .9]),
+              detection_classes:
+                  np.array([2, 2])
+          })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 0.83333331)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_boxes_list)
+    self.assertFalse(coco_evaluator._image_ids)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsBatched(self):
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    batch_size = 3
+    image_id = tf.placeholder(tf.string, shape=(batch_size))
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+    detection_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
+    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
+    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(update_op,
+               feed_dict={
+                   image_id: ['image1', 'image2', 'image3'],
+                   groundtruth_boxes: np.array([[[100., 100., 200., 200.]],
+                                                [[50., 50., 100., 100.]],
+                                                [[25., 25., 50., 50.]]]),
+                   groundtruth_classes: np.array([[1], [3], [2]]),
+                   detection_boxes: np.array([[[100., 100., 200., 200.]],
+                                              [[50., 50., 100., 100.]],
+                                              [[25., 25., 50., 50.]]]),
+                   detection_scores: np.array([[.8], [.7], [.9]]),
+                   detection_classes: np.array([[1], [3], [2]])
+               })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_boxes_list)
+    self.assertFalse(coco_evaluator._image_ids)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsPaddedBatches(self):
+    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
+        _get_categories_list())
+    batch_size = 3
+    image_id = tf.placeholder(tf.string, shape=(batch_size))
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+    num_gt_boxes_per_image = tf.placeholder(tf.int32, shape=(None))
+    detection_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
+    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
+    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+    num_det_boxes_per_image = tf.placeholder(tf.int32, shape=(None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes,
+        'num_groundtruth_boxes_per_image': num_gt_boxes_per_image,
+        'num_det_boxes_per_image': num_det_boxes_per_image
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id: ['image1', 'image2', 'image3'],
+              groundtruth_boxes:
+                  np.array([[[100., 100., 200., 200.], [-1, -1, -1, -1]],
+                            [[50., 50., 100., 100.], [-1, -1, -1, -1]],
+                            [[25., 25., 50., 50.], [10., 10., 15., 15.]]]),
+              groundtruth_classes:
+                  np.array([[1, -1], [3, -1], [2, 2]]),
+              num_gt_boxes_per_image:
+                  np.array([1, 1, 2]),
+              detection_boxes:
+                  np.array([[[100., 100., 200., 200.],
+                             [0., 0., 0., 0.],
+                             [0., 0., 0., 0.]],
+                            [[50., 50., 100., 100.],
+                             [0., 0., 0., 0.],
+                             [0., 0., 0., 0.]],
+                            [[25., 25., 50., 50.],
+                             [10., 10., 15., 15.],
+                             [10., 10., 15., 15.]]]),
+              detection_scores:
+                  np.array([[.8, 0., 0.], [.7, 0., 0.], [.95, .9, 0.9]]),
+              detection_classes:
+                  np.array([[1, -1, -1], [3, -1, -1], [2, 2, 2]]),
+              num_det_boxes_per_image:
+                  np.array([1, 1, 3]),
+          })
+
+    # Check the number of bounding boxes added.
+    self.assertEqual(len(coco_evaluator._groundtruth_list), 4)
+    self.assertEqual(len(coco_evaluator._detection_boxes_list), 5)
+
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 0.83333331)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_boxes_list)
+    self.assertFalse(coco_evaluator._image_ids)
+
+
+class CocoMaskEvaluationTest(tf.test.TestCase):
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
+    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image1',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+            np.pad(np.ones([1, 100, 100], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image1',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[100., 100., 200., 200.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1]),
+            standard_fields.DetectionResultFields.detection_masks:
+            np.pad(np.ones([1, 100, 100], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image2',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[50., 50., 100., 100.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+            np.pad(np.ones([1, 50, 50], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image2',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[50., 50., 100., 100.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1]),
+            standard_fields.DetectionResultFields.detection_masks:
+            np.pad(np.ones([1, 50, 50], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    coco_evaluator.add_single_ground_truth_image_info(
+        image_id='image3',
+        groundtruth_dict={
+            standard_fields.InputDataFields.groundtruth_boxes:
+            np.array([[25., 25., 50., 50.]]),
+            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+            np.pad(np.ones([1, 25, 25], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    coco_evaluator.add_single_detected_image_info(
+        image_id='image3',
+        detections_dict={
+            standard_fields.DetectionResultFields.detection_boxes:
+            np.array([[25., 25., 50., 50.]]),
+            standard_fields.DetectionResultFields.detection_scores:
+            np.array([.8]),
+            standard_fields.DetectionResultFields.detection_classes:
+            np.array([1]),
+            standard_fields.DetectionResultFields.detection_masks:
+            np.pad(np.ones([1, 25, 25], dtype=np.uint8),
+                   ((0, 0), (10, 10), (10, 10)), mode='constant')
+        })
+    metrics = coco_evaluator.evaluate()
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
+    coco_evaluator.clear()
+    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
+    self.assertFalse(coco_evaluator._image_ids_with_detections)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._detection_masks_list)
+
+
+class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
+    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
+    image_id = tf.placeholder(tf.string, shape=())
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
+    groundtruth_masks = tf.placeholder(tf.uint8, shape=(None, None, None))
+    detection_scores = tf.placeholder(tf.float32, shape=(None))
+    detection_classes = tf.placeholder(tf.float32, shape=(None))
+    detection_masks = tf.placeholder(tf.uint8, shape=(None, None, None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        input_data_fields.groundtruth_instance_masks: groundtruth_masks,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes,
+        detection_fields.detection_masks: detection_masks,
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionMasks_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id:
+                  'image1',
+              groundtruth_boxes:
+                  np.array([[100., 100., 200., 200.], [50., 50., 100., 100.]]),
+              groundtruth_classes:
+                  np.array([1, 2]),
+              groundtruth_masks:
+                  np.stack([
+                      np.pad(
+                          np.ones([100, 100], dtype=np.uint8), ((10, 10),
+                                                                (10, 10)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([50, 50], dtype=np.uint8), ((0, 70), (0, 70)),
+                          mode='constant')
+                  ]),
+              detection_scores:
+                  np.array([.9, .8]),
+              detection_classes:
+                  np.array([2, 1]),
+              detection_masks:
+                  np.stack([
+                      np.pad(
+                          np.ones([50, 50], dtype=np.uint8), ((0, 70), (0, 70)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([100, 100], dtype=np.uint8), ((10, 10),
+                                                                (10, 10)),
+                          mode='constant'),
+                  ])
+          })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image2',
+                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
+                   groundtruth_classes: np.array([1]),
+                   groundtruth_masks: np.pad(np.ones([1, 50, 50],
+                                                     dtype=np.uint8),
+                                             ((0, 0), (10, 10), (10, 10)),
+                                             mode='constant'),
+                   detection_scores: np.array([.8]),
+                   detection_classes: np.array([1]),
+                   detection_masks: np.pad(np.ones([1, 50, 50], dtype=np.uint8),
+                                           ((0, 0), (10, 10), (10, 10)),
+                                           mode='constant')
+               })
+      sess.run(update_op,
+               feed_dict={
+                   image_id: 'image3',
+                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
+                   groundtruth_classes: np.array([1]),
+                   groundtruth_masks: np.pad(np.ones([1, 25, 25],
+                                                     dtype=np.uint8),
+                                             ((0, 0), (10, 10), (10, 10)),
+                                             mode='constant'),
+                   detection_scores: np.array([.8]),
+                   detection_classes: np.array([1]),
+                   detection_masks: np.pad(np.ones([1, 25, 25],
+                                                   dtype=np.uint8),
+                                           ((0, 0), (10, 10), (10, 10)),
+                                           mode='constant')
+               })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@1'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._image_ids_with_detections)
+    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
+    self.assertFalse(coco_evaluator._detection_masks_list)
+
+  def testGetOneMAPWithMatchingGroundtruthAndDetectionsBatched(self):
+    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
+    batch_size = 3
+    image_id = tf.placeholder(tf.string, shape=(batch_size))
+    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
+    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+    groundtruth_masks = tf.placeholder(
+        tf.uint8, shape=(batch_size, None, None, None))
+    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
+    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
+    detection_masks = tf.placeholder(
+        tf.uint8, shape=(batch_size, None, None, None))
+
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+    eval_dict = {
+        input_data_fields.key: image_id,
+        input_data_fields.groundtruth_boxes: groundtruth_boxes,
+        input_data_fields.groundtruth_classes: groundtruth_classes,
+        input_data_fields.groundtruth_instance_masks: groundtruth_masks,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes,
+        detection_fields.detection_masks: detection_masks,
+    }
+
+    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)
+
+    _, update_op = eval_metric_ops['DetectionMasks_Precision/mAP']
+
+    with self.test_session() as sess:
+      sess.run(
+          update_op,
+          feed_dict={
+              image_id: ['image1', 'image2', 'image3'],
+              groundtruth_boxes:
+                  np.array([[[100., 100., 200., 200.]],
+                            [[50., 50., 100., 100.]],
+                            [[25., 25., 50., 50.]]]),
+              groundtruth_classes:
+                  np.array([[1], [1], [1]]),
+              groundtruth_masks:
+                  np.stack([
+                      np.pad(
+                          np.ones([1, 100, 100], dtype=np.uint8),
+                          ((0, 0), (0, 0), (0, 0)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([1, 50, 50], dtype=np.uint8),
+                          ((0, 0), (25, 25), (25, 25)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([1, 25, 25], dtype=np.uint8),
+                          ((0, 0), (37, 38), (37, 38)),
+                          mode='constant')
+                  ],
+                           axis=0),
+              detection_scores:
+                  np.array([[.8], [.8], [.8]]),
+              detection_classes:
+                  np.array([[1], [1], [1]]),
+              detection_masks:
+                  np.stack([
+                      np.pad(
+                          np.ones([1, 100, 100], dtype=np.uint8),
+                          ((0, 0), (0, 0), (0, 0)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([1, 50, 50], dtype=np.uint8),
+                          ((0, 0), (25, 25), (25, 25)),
+                          mode='constant'),
+                      np.pad(
+                          np.ones([1, 25, 25], dtype=np.uint8),
+                          ((0, 0), (37, 38), (37, 38)),
+                          mode='constant')
+                  ],
+                           axis=0)
+          })
+    metrics = {}
+    for key, (value_op, _) in eval_metric_ops.iteritems():
+      metrics[key] = value_op
+    metrics = sess.run(metrics)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.50IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.75IOU'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (small)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@1'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@10'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (large)'], 1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (medium)'],
+                           1.0)
+    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (small)'], 1.0)
+    self.assertFalse(coco_evaluator._groundtruth_list)
+    self.assertFalse(coco_evaluator._image_ids_with_detections)
+    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
+    self.assertFalse(coco_evaluator._detection_masks_list)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py
new file mode 100644
index 00000000..71b747bc
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py
@@ -0,0 +1,850 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Wrappers for third party pycocotools to be used within object_detection.
+
+Note that nothing in this file is tensorflow related and thus cannot
+be called directly as a slim metric, for example.
+
+TODO(jonathanhuang): wrap as a slim metric in metrics.py
+
+
+Usage example: given a set of images with ids in the list image_ids
+and corresponding lists of numpy arrays encoding groundtruth (boxes and classes)
+and detections (boxes, scores and classes), where elements of each list
+correspond to detections/annotations of a single image,
+then evaluation (in multi-class mode) can be invoked as follows:
+
+  groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
+      image_ids, groundtruth_boxes_list, groundtruth_classes_list,
+      max_num_classes, output_path=None)
+  detections_list = coco_tools.ExportDetectionsToCOCO(
+      image_ids, detection_boxes_list, detection_scores_list,
+      detection_classes_list, output_path=None)
+  groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+  detections = groundtruth.LoadAnnotations(detections_list)
+  evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                         agnostic_mode=False)
+  metrics = evaluator.ComputeMetrics()
+
+"""
+from collections import OrderedDict
+import copy
+import time
+import numpy as np
+
+from pycocotools import coco
+from pycocotools import cocoeval
+from pycocotools import mask
+
+import tensorflow as tf
+
+from object_detection.utils import json_utils
+
+
+class COCOWrapper(coco.COCO):
+  """Wrapper for the pycocotools COCO class."""
+
+  def __init__(self, dataset, detection_type='bbox'):
+    """COCOWrapper constructor.
+
+    See http://mscoco.org/dataset/#format for a description of the format.
+    By default, the coco.COCO class constructor reads from a JSON file.
+    This function duplicates the same behavior but loads from a dictionary,
+    allowing us to perform evaluation without writing to external storage.
+
+    Args:
+      dataset: a dictionary holding bounding box annotations in the COCO format.
+      detection_type: type of detections being wrapped. Can be one of ['bbox',
+        'segmentation']
+
+    Raises:
+      ValueError: if detection_type is unsupported.
+    """
+    supported_detection_types = ['bbox', 'segmentation']
+    if detection_type not in supported_detection_types:
+      raise ValueError('Unsupported detection type: {}. '
+                       'Supported values are: {}'.format(
+                           detection_type, supported_detection_types))
+    self._detection_type = detection_type
+    coco.COCO.__init__(self)
+    self.dataset = dataset
+    self.createIndex()
+
+  def LoadAnnotations(self, annotations):
+    """Load annotations dictionary into COCO datastructure.
+
+    See http://mscoco.org/dataset/#format for a description of the annotations
+    format.  As above, this function replicates the default behavior of the API
+    but does not require writing to external storage.
+
+    Args:
+      annotations: python list holding object detection results where each
+        detection is encoded as a dict with required keys ['image_id',
+        'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
+        `detection_type`.
+
+    Returns:
+      a coco.COCO datastructure holding object detection annotations results
+
+    Raises:
+      ValueError: if annotations is not a list
+      ValueError: if annotations do not correspond to the images contained
+        in self.
+    """
+    results = coco.COCO()
+    results.dataset['images'] = [img for img in self.dataset['images']]
+
+    tf.logging.info('Loading and preparing annotation results...')
+    tic = time.time()
+
+    if not isinstance(annotations, list):
+      raise ValueError('annotations is not a list of objects')
+    annotation_img_ids = [ann['image_id'] for ann in annotations]
+    if (set(annotation_img_ids) != (set(annotation_img_ids)
+                                    & set(self.getImgIds()))):
+      raise ValueError('Results do not correspond to current coco set')
+    results.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+    if self._detection_type == 'bbox':
+      for idx, ann in enumerate(annotations):
+        bb = ann['bbox']
+        ann['area'] = bb[2] * bb[3]
+        ann['id'] = idx + 1
+        ann['iscrowd'] = 0
+    elif self._detection_type == 'segmentation':
+      for idx, ann in enumerate(annotations):
+        ann['area'] = mask.area(ann['segmentation'])
+        ann['bbox'] = mask.toBbox(ann['segmentation'])
+        ann['id'] = idx + 1
+        ann['iscrowd'] = 0
+    tf.logging.info('DONE (t=%0.2fs)', (time.time() - tic))
+
+    results.dataset['annotations'] = annotations
+    results.createIndex()
+    return results
+
+
+class COCOEvalWrapper(cocoeval.COCOeval):
+  """Wrapper for the pycocotools COCOeval class.
+
+  To evaluate, create two objects (groundtruth_dict and detections_list)
+  using the conventions listed at http://mscoco.org/dataset/#format.
+  Then call evaluation as follows:
+
+    groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+    detections = groundtruth.LoadAnnotations(detections_list)
+    evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                           agnostic_mode=False)
+
+    metrics = evaluator.ComputeMetrics()
+  """
+
+  def __init__(self, groundtruth=None, detections=None, agnostic_mode=False,
+               iou_type='bbox'):
+    """COCOEvalWrapper constructor.
+
+    Note that for the area-based metrics to be meaningful, detection and
+    groundtruth boxes must be in image coordinates measured in pixels.
+
+    Args:
+      groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
+        groundtruth annotations
+      detections: a coco.COCO (or coco_tools.COCOWrapper) object holding
+        detections
+      agnostic_mode: boolean (default: False).  If True, evaluation ignores
+        class labels, treating all detections as proposals.
+      iou_type: IOU type to use for evaluation. Supports `bbox` or `segm`.
+    """
+    cocoeval.COCOeval.__init__(self, groundtruth, detections,
+                               iouType=iou_type)
+    if agnostic_mode:
+      self.params.useCats = 0
+
+  def GetCategory(self, category_id):
+    """Fetches dictionary holding category information given category id.
+
+    Args:
+      category_id: integer id
+    Returns:
+      dictionary holding 'id', 'name'.
+    """
+    return self.cocoGt.cats[category_id]
+
+  def GetAgnosticMode(self):
+    """Returns true if COCO Eval is configured to evaluate in agnostic mode."""
+    return self.params.useCats == 0
+
+  def GetCategoryIdList(self):
+    """Returns list of valid category ids."""
+    return self.params.catIds
+
+  def ComputeMetrics(self,
+                     include_metrics_per_category=False,
+                     all_metrics_per_category=False):
+    """Computes detection metrics.
+
+    Args:
+      include_metrics_per_category: If True, will include metrics per category.
+      all_metrics_per_category: If true, include all the summery metrics for
+        each category in per_category_ap. Be careful with setting it to true if
+        you have more than handful of categories, because it will pollute
+        your mldash.
+
+    Returns:
+      1. summary_metrics: a dictionary holding:
+        'Precision/mAP': mean average precision over classes averaged over IOU
+          thresholds ranging from .5 to .95 with .05 increments
+        'Precision/mAP@.50IOU': mean average precision at 50% IOU
+        'Precision/mAP@.75IOU': mean average precision at 75% IOU
+        'Precision/mAP (small)': mean average precision for small objects
+                        (area < 32^2 pixels)
+        'Precision/mAP (medium)': mean average precision for medium sized
+                        objects (32^2 pixels < area < 96^2 pixels)
+        'Precision/mAP (large)': mean average precision for large objects
+                        (96^2 pixels < area < 10000^2 pixels)
+        'Recall/AR@1': average recall with 1 detection
+        'Recall/AR@10': average recall with 10 detections
+        'Recall/AR@100': average recall with 100 detections
+        'Recall/AR@100 (small)': average recall for small objects with 100
+          detections
+        'Recall/AR@100 (medium)': average recall for medium objects with 100
+          detections
+        'Recall/AR@100 (large)': average recall for large objects with 100
+          detections
+      2. per_category_ap: a dictionary holding category specific results with
+        keys of the form: 'Precision mAP ByCategory/category'
+        (without the supercategory part if no supercategories exist).
+        For backward compatibility 'PerformanceByCategory' is included in the
+        output regardless of all_metrics_per_category.
+        If evaluating class-agnostic mode, per_category_ap is an empty
+        dictionary.
+
+    Raises:
+      ValueError: If category_stats does not exist.
+    """
+    self.evaluate()
+    self.accumulate()
+    self.summarize()
+
+    summary_metrics = OrderedDict([
+        ('Precision/mAP', self.stats[0]),
+        ('Precision/mAP@.50IOU', self.stats[1]),
+        ('Precision/mAP@.75IOU', self.stats[2]),
+        ('Precision/mAP (small)', self.stats[3]),
+        ('Precision/mAP (medium)', self.stats[4]),
+        ('Precision/mAP (large)', self.stats[5]),
+        ('Recall/AR@1', self.stats[6]),
+        ('Recall/AR@10', self.stats[7]),
+        ('Recall/AR@100', self.stats[8]),
+        ('Recall/AR@100 (small)', self.stats[9]),
+        ('Recall/AR@100 (medium)', self.stats[10]),
+        ('Recall/AR@100 (large)', self.stats[11])
+    ])
+    if not include_metrics_per_category:
+      return summary_metrics, {}
+    if not hasattr(self, 'category_stats'):
+      raise ValueError('Category stats do not exist')
+    per_category_ap = OrderedDict([])
+    if self.GetAgnosticMode():
+      return summary_metrics, per_category_ap
+    for category_index, category_id in enumerate(self.GetCategoryIdList()):
+      category = self.GetCategory(category_id)['name']
+      # Kept for backward compatilbility
+      per_category_ap['PerformanceByCategory/mAP/{}'.format(
+          category)] = self.category_stats[0][category_index]
+      if all_metrics_per_category:
+        per_category_ap['Precision mAP ByCategory/{}'.format(
+            category)] = self.category_stats[0][category_index]
+        per_category_ap['Precision mAP@.50IOU ByCategory/{}'.format(
+            category)] = self.category_stats[1][category_index]
+        per_category_ap['Precision mAP@.75IOU ByCategory/{}'.format(
+            category)] = self.category_stats[2][category_index]
+        per_category_ap['Precision mAP (small) ByCategory/{}'.format(
+            category)] = self.category_stats[3][category_index]
+        per_category_ap['Precision mAP (medium) ByCategory/{}'.format(
+            category)] = self.category_stats[4][category_index]
+        per_category_ap['Precision mAP (large) ByCategory/{}'.format(
+            category)] = self.category_stats[5][category_index]
+        per_category_ap['Recall AR@1 ByCategory/{}'.format(
+            category)] = self.category_stats[6][category_index]
+        per_category_ap['Recall AR@10 ByCategory/{}'.format(
+            category)] = self.category_stats[7][category_index]
+        per_category_ap['Recall AR@100 ByCategory/{}'.format(
+            category)] = self.category_stats[8][category_index]
+        per_category_ap['Recall AR@100 (small) ByCategory/{}'.format(
+            category)] = self.category_stats[9][category_index]
+        per_category_ap['Recall AR@100 (medium) ByCategory/{}'.format(
+            category)] = self.category_stats[10][category_index]
+        per_category_ap['Recall AR@100 (large) ByCategory/{}'.format(
+            category)] = self.category_stats[11][category_index]
+
+    return summary_metrics, per_category_ap
+
+
+def _ConvertBoxToCOCOFormat(box):
+  """Converts a box in [ymin, xmin, ymax, xmax] format to COCO format.
+
+  This is a utility function for converting from our internal
+  [ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
+  i.e., [xmin, ymin, width, height].
+
+  Args:
+    box: a [ymin, xmin, ymax, xmax] numpy array
+
+  Returns:
+    a list of floats representing [xmin, ymin, width, height]
+  """
+  return [float(box[1]), float(box[0]), float(box[3] - box[1]),
+          float(box[2] - box[0])]
+
+
+def _RleCompress(masks):
+  """Compresses mask using Run-length encoding provided by pycocotools.
+
+  Args:
+    masks: uint8 numpy array of shape [mask_height, mask_width] with values in
+    {0, 1}.
+
+  Returns:
+    A pycocotools Run-length encoding of the mask.
+  """
+  return mask.encode(np.asfortranarray(masks))
+
+
+def ExportSingleImageGroundtruthToCoco(image_id,
+                                       next_annotation_id,
+                                       category_id_set,
+                                       groundtruth_boxes,
+                                       groundtruth_classes,
+                                       groundtruth_masks=None,
+                                       groundtruth_is_crowd=None):
+  """Export groundtruth of a single image to COCO format.
+
+  This function converts groundtruth detection annotations represented as numpy
+  arrays to dictionaries that can be ingested by the COCO evaluation API. Note
+  that the image_ids provided here must match the ones given to
+  ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in
+  correspondence - that is: groundtruth_boxes[i, :], and
+  groundtruth_classes[i] are associated with the same groundtruth annotation.
+
+  In the exported result, "area" fields are always set to the area of the
+  groundtruth bounding box.
+
+  Args:
+    image_id: a unique image identifier either of type integer or string.
+    next_annotation_id: integer specifying the first id to use for the
+      groundtruth annotations. All annotations are assigned a continuous integer
+      id starting from this value.
+    category_id_set: A set of valid class ids. Groundtruth with classes not in
+      category_id_set are dropped.
+    groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
+    groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
+    groundtruth_masks: optional uint8 numpy array of shape [num_detections,
+      image_height, image_width] containing detection_masks.
+    groundtruth_is_crowd: optional numpy array (int) with shape [num_gt_boxes]
+      indicating whether groundtruth boxes are crowd.
+
+  Returns:
+    a list of groundtruth annotations for a single image in the COCO format.
+
+  Raises:
+    ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
+      right lengths or (2) if each of the elements inside these lists do not
+      have the correct shapes or (3) if image_ids are not integers
+  """
+
+  if len(groundtruth_classes.shape) != 1:
+    raise ValueError('groundtruth_classes is '
+                     'expected to be of rank 1.')
+  if len(groundtruth_boxes.shape) != 2:
+    raise ValueError('groundtruth_boxes is expected to be of '
+                     'rank 2.')
+  if groundtruth_boxes.shape[1] != 4:
+    raise ValueError('groundtruth_boxes should have '
+                     'shape[1] == 4.')
+  num_boxes = groundtruth_classes.shape[0]
+  if num_boxes != groundtruth_boxes.shape[0]:
+    raise ValueError('Corresponding entries in groundtruth_classes, '
+                     'and groundtruth_boxes should have '
+                     'compatible shapes (i.e., agree on the 0th dimension).'
+                     'Classes shape: %d. Boxes shape: %d. Image ID: %s' % (
+                         groundtruth_classes.shape[0],
+                         groundtruth_boxes.shape[0], image_id))
+  has_is_crowd = groundtruth_is_crowd is not None
+  if has_is_crowd and len(groundtruth_is_crowd.shape) != 1:
+    raise ValueError('groundtruth_is_crowd is expected to be of rank 1.')
+  groundtruth_list = []
+  for i in range(num_boxes):
+    if groundtruth_classes[i] in category_id_set:
+      iscrowd = groundtruth_is_crowd[i] if has_is_crowd else 0
+      export_dict = {
+          'id':
+              next_annotation_id + i,
+          'image_id':
+              image_id,
+          'category_id':
+              int(groundtruth_classes[i]),
+          'bbox':
+              list(_ConvertBoxToCOCOFormat(groundtruth_boxes[i, :])),
+          'area':
+              float((groundtruth_boxes[i, 2] - groundtruth_boxes[i, 0]) *
+                    (groundtruth_boxes[i, 3] - groundtruth_boxes[i, 1])),
+          'iscrowd':
+              iscrowd
+      }
+      if groundtruth_masks is not None:
+        export_dict['segmentation'] = _RleCompress(groundtruth_masks[i])
+      groundtruth_list.append(export_dict)
+  return groundtruth_list
+
+
+def ExportGroundtruthToCOCO(image_ids,
+                            groundtruth_boxes,
+                            groundtruth_classes,
+                            categories,
+                            output_path=None):
+  """Export groundtruth detection annotations in numpy arrays to COCO API.
+
+  This function converts a set of groundtruth detection annotations represented
+  as numpy arrays to dictionaries that can be ingested by the COCO API.
+  Inputs to this function are three lists: image ids for each groundtruth image,
+  groundtruth boxes for each image and groundtruth classes respectively.
+  Note that the image_ids provided here must match the ones given to the
+  ExportDetectionsToCOCO function in order for evaluation to work properly.
+  We assume that for each image, boxes, scores and classes are in
+  correspondence --- that is: image_id[i], groundtruth_boxes[i, :] and
+  groundtruth_classes[i] are associated with the same groundtruth annotation.
+
+  In the exported result, "area" fields are always set to the area of the
+  groundtruth bounding box and "iscrowd" fields are always set to 0.
+  TODO(jonathanhuang): pass in "iscrowd" array for evaluating on COCO dataset.
+
+  Args:
+    image_ids: a list of unique image identifier either of type integer or
+      string.
+    groundtruth_boxes: list of numpy arrays with shape [num_gt_boxes, 4]
+      (note that num_gt_boxes can be different for each entry in the list)
+    groundtruth_classes: list of numpy arrays (int) with shape [num_gt_boxes]
+      (note that num_gt_boxes can be different for each entry in the list)
+    categories: a list of dictionaries representing all possible categories.
+        Each dict in this list has the following keys:
+          'id': (required) an integer id uniquely identifying this category
+          'name': (required) string representing category name
+            e.g., 'cat', 'dog', 'pizza'
+          'supercategory': (optional) string representing the supercategory
+            e.g., 'animal', 'vehicle', 'food', etc
+    output_path: (optional) path for exporting result to JSON
+  Returns:
+    dictionary that can be read by COCO API
+  Raises:
+    ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
+      right lengths or (2) if each of the elements inside these lists do not
+      have the correct shapes or (3) if image_ids are not integers
+  """
+  category_id_set = set([cat['id'] for cat in categories])
+  groundtruth_export_list = []
+  image_export_list = []
+  if not len(image_ids) == len(groundtruth_boxes) == len(groundtruth_classes):
+    raise ValueError('Input lists must have the same length')
+
+  # For reasons internal to the COCO API, it is important that annotation ids
+  # are not equal to zero; we thus start counting from 1.
+  annotation_id = 1
+  for image_id, boxes, classes in zip(image_ids, groundtruth_boxes,
+                                      groundtruth_classes):
+    image_export_list.append({'id': image_id})
+    groundtruth_export_list.extend(ExportSingleImageGroundtruthToCoco(
+        image_id,
+        annotation_id,
+        category_id_set,
+        boxes,
+        classes))
+    num_boxes = classes.shape[0]
+    annotation_id += num_boxes
+
+  groundtruth_dict = {
+      'annotations': groundtruth_export_list,
+      'images': image_export_list,
+      'categories': categories
+  }
+  if output_path:
+    with tf.gfile.GFile(output_path, 'w') as fid:
+      json_utils.Dump(groundtruth_dict, fid, float_digits=4, indent=2)
+  return groundtruth_dict
+
+
+def ExportSingleImageDetectionBoxesToCoco(image_id,
+                                          category_id_set,
+                                          detection_boxes,
+                                          detection_scores,
+                                          detection_classes):
+  """Export detections of a single image to COCO format.
+
+  This function converts detections represented as numpy arrays to dictionaries
+  that can be ingested by the COCO evaluation API. Note that the image_ids
+  provided here must match the ones given to the
+  ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in
+  correspondence - that is: boxes[i, :], and classes[i]
+  are associated with the same groundtruth annotation.
+
+  Args:
+    image_id: unique image identifier either of type integer or string.
+    category_id_set: A set of valid class ids. Detections with classes not in
+      category_id_set are dropped.
+    detection_boxes: float numpy array of shape [num_detections, 4] containing
+      detection boxes.
+    detection_scores: float numpy array of shape [num_detections] containing
+      scored for the detection boxes.
+    detection_classes: integer numpy array of shape [num_detections] containing
+      the classes for detection boxes.
+
+  Returns:
+    a list of detection annotations for a single image in the COCO format.
+
+  Raises:
+    ValueError: if (1) detection_boxes, detection_scores and detection_classes
+      do not have the right lengths or (2) if each of the elements inside these
+      lists do not have the correct shapes or (3) if image_ids are not integers.
+  """
+
+  if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+    raise ValueError('All entries in detection_classes and detection_scores'
+                     'expected to be of rank 1.')
+  if len(detection_boxes.shape) != 2:
+    raise ValueError('All entries in detection_boxes expected to be of '
+                     'rank 2.')
+  if detection_boxes.shape[1] != 4:
+    raise ValueError('All entries in detection_boxes should have '
+                     'shape[1] == 4.')
+  num_boxes = detection_classes.shape[0]
+  if not num_boxes == detection_boxes.shape[0] == detection_scores.shape[0]:
+    raise ValueError('Corresponding entries in detection_classes, '
+                     'detection_scores and detection_boxes should have '
+                     'compatible shapes (i.e., agree on the 0th dimension). '
+                     'Classes shape: %d. Boxes shape: %d. '
+                     'Scores shape: %d' % (
+                         detection_classes.shape[0], detection_boxes.shape[0],
+                         detection_scores.shape[0]
+                     ))
+  detections_list = []
+  for i in range(num_boxes):
+    if detection_classes[i] in category_id_set:
+      detections_list.append({
+          'image_id': image_id,
+          'category_id': int(detection_classes[i]),
+          'bbox': list(_ConvertBoxToCOCOFormat(detection_boxes[i, :])),
+          'score': float(detection_scores[i])
+      })
+  return detections_list
+
+
+def ExportSingleImageDetectionMasksToCoco(image_id,
+                                          category_id_set,
+                                          detection_masks,
+                                          detection_scores,
+                                          detection_classes):
+  """Export detection masks of a single image to COCO format.
+
+  This function converts detections represented as numpy arrays to dictionaries
+  that can be ingested by the COCO evaluation API. We assume that
+  detection_masks, detection_scores, and detection_classes are in correspondence
+  - that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
+    are associated with the same annotation.
+
+  Args:
+    image_id: unique image identifier either of type integer or string.
+    category_id_set: A set of valid class ids. Detections with classes not in
+      category_id_set are dropped.
+    detection_masks: uint8 numpy array of shape [num_detections, image_height,
+      image_width] containing detection_masks.
+    detection_scores: float numpy array of shape [num_detections] containing
+      scores for detection masks.
+    detection_classes: integer numpy array of shape [num_detections] containing
+      the classes for detection masks.
+
+  Returns:
+    a list of detection mask annotations for a single image in the COCO format.
+
+  Raises:
+    ValueError: if (1) detection_masks, detection_scores and detection_classes
+      do not have the right lengths or (2) if each of the elements inside these
+      lists do not have the correct shapes or (3) if image_ids are not integers.
+  """
+
+  if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+    raise ValueError('All entries in detection_classes and detection_scores'
+                     'expected to be of rank 1.')
+  num_boxes = detection_classes.shape[0]
+  if not num_boxes == len(detection_masks) == detection_scores.shape[0]:
+    raise ValueError('Corresponding entries in detection_classes, '
+                     'detection_scores and detection_masks should have '
+                     'compatible lengths and shapes '
+                     'Classes length: %d.  Masks length: %d. '
+                     'Scores length: %d' % (
+                         detection_classes.shape[0], len(detection_masks),
+                         detection_scores.shape[0]
+                     ))
+  detections_list = []
+  for i in range(num_boxes):
+    if detection_classes[i] in category_id_set:
+      detections_list.append({
+          'image_id': image_id,
+          'category_id': int(detection_classes[i]),
+          'segmentation': _RleCompress(detection_masks[i]),
+          'score': float(detection_scores[i])
+      })
+  return detections_list
+
+
+def ExportDetectionsToCOCO(image_ids,
+                           detection_boxes,
+                           detection_scores,
+                           detection_classes,
+                           categories,
+                           output_path=None):
+  """Export detection annotations in numpy arrays to COCO API.
+
+  This function converts a set of predicted detections represented
+  as numpy arrays to dictionaries that can be ingested by the COCO API.
+  Inputs to this function are lists, consisting of boxes, scores and
+  classes, respectively, corresponding to each image for which detections
+  have been produced.  Note that the image_ids provided here must
+  match the ones given to the ExportGroundtruthToCOCO function in order
+  for evaluation to work properly.
+
+  We assume that for each image, boxes, scores and classes are in
+  correspondence --- that is: detection_boxes[i, :], detection_scores[i] and
+  detection_classes[i] are associated with the same detection.
+
+  Args:
+    image_ids: a list of unique image identifier either of type integer or
+      string.
+    detection_boxes: list of numpy arrays with shape [num_detection_boxes, 4]
+    detection_scores: list of numpy arrays (float) with shape
+      [num_detection_boxes]. Note that num_detection_boxes can be different
+      for each entry in the list.
+    detection_classes: list of numpy arrays (int) with shape
+      [num_detection_boxes]. Note that num_detection_boxes can be different
+      for each entry in the list.
+    categories: a list of dictionaries representing all possible categories.
+      Each dict in this list must have an integer 'id' key uniquely identifying
+      this category.
+    output_path: (optional) path for exporting result to JSON
+
+  Returns:
+    list of dictionaries that can be read by COCO API, where each entry
+    corresponds to a single detection and has keys from:
+    ['image_id', 'category_id', 'bbox', 'score'].
+  Raises:
+    ValueError: if (1) detection_boxes and detection_classes do not have the
+      right lengths or (2) if each of the elements inside these lists do not
+      have the correct shapes or (3) if image_ids are not integers.
+  """
+  category_id_set = set([cat['id'] for cat in categories])
+  detections_export_list = []
+  if not (len(image_ids) == len(detection_boxes) == len(detection_scores) ==
+          len(detection_classes)):
+    raise ValueError('Input lists must have the same length')
+  for image_id, boxes, scores, classes in zip(image_ids, detection_boxes,
+                                              detection_scores,
+                                              detection_classes):
+    detections_export_list.extend(ExportSingleImageDetectionBoxesToCoco(
+        image_id,
+        category_id_set,
+        boxes,
+        scores,
+        classes))
+  if output_path:
+    with tf.gfile.GFile(output_path, 'w') as fid:
+      json_utils.Dump(detections_export_list, fid, float_digits=4, indent=2)
+  return detections_export_list
+
+
+def ExportSegmentsToCOCO(image_ids,
+                         detection_masks,
+                         detection_scores,
+                         detection_classes,
+                         categories,
+                         output_path=None):
+  """Export segmentation masks in numpy arrays to COCO API.
+
+  This function converts a set of predicted instance masks represented
+  as numpy arrays to dictionaries that can be ingested by the COCO API.
+  Inputs to this function are lists, consisting of segments, scores and
+  classes, respectively, corresponding to each image for which detections
+  have been produced.
+
+  Note this function is recommended to use for small dataset.
+  For large dataset, it should be used with a merge function
+  (e.g. in map reduce), otherwise the memory consumption is large.
+
+  We assume that for each image, masks, scores and classes are in
+  correspondence --- that is: detection_masks[i, :, :, :], detection_scores[i]
+  and detection_classes[i] are associated with the same detection.
+
+  Args:
+    image_ids: list of image ids (typically ints or strings)
+    detection_masks: list of numpy arrays with shape [num_detection, h, w, 1]
+      and type uint8. The height and width should match the shape of
+      corresponding image.
+    detection_scores: list of numpy arrays (float) with shape
+      [num_detection]. Note that num_detection can be different
+      for each entry in the list.
+    detection_classes: list of numpy arrays (int) with shape
+      [num_detection]. Note that num_detection can be different
+      for each entry in the list.
+    categories: a list of dictionaries representing all possible categories.
+      Each dict in this list must have an integer 'id' key uniquely identifying
+      this category.
+    output_path: (optional) path for exporting result to JSON
+
+  Returns:
+    list of dictionaries that can be read by COCO API, where each entry
+    corresponds to a single detection and has keys from:
+    ['image_id', 'category_id', 'segmentation', 'score'].
+
+  Raises:
+    ValueError: if detection_masks and detection_classes do not have the
+      right lengths or if each of the elements inside these lists do not
+      have the correct shapes.
+  """
+  if not (len(image_ids) == len(detection_masks) == len(detection_scores) ==
+          len(detection_classes)):
+    raise ValueError('Input lists must have the same length')
+
+  segment_export_list = []
+  for image_id, masks, scores, classes in zip(image_ids, detection_masks,
+                                              detection_scores,
+                                              detection_classes):
+
+    if len(classes.shape) != 1 or len(scores.shape) != 1:
+      raise ValueError('All entries in detection_classes and detection_scores'
+                       'expected to be of rank 1.')
+    if len(masks.shape) != 4:
+      raise ValueError('All entries in masks expected to be of '
+                       'rank 4. Given {}'.format(masks.shape))
+
+    num_boxes = classes.shape[0]
+    if not num_boxes == masks.shape[0] == scores.shape[0]:
+      raise ValueError('Corresponding entries in segment_classes, '
+                       'detection_scores and detection_boxes should have '
+                       'compatible shapes (i.e., agree on the 0th dimension).')
+
+    category_id_set = set([cat['id'] for cat in categories])
+    segment_export_list.extend(ExportSingleImageDetectionMasksToCoco(
+        image_id, category_id_set, np.squeeze(masks, axis=3), scores, classes))
+
+  if output_path:
+    with tf.gfile.GFile(output_path, 'w') as fid:
+      json_utils.Dump(segment_export_list, fid, float_digits=4, indent=2)
+  return segment_export_list
+
+
+def ExportKeypointsToCOCO(image_ids,
+                          detection_keypoints,
+                          detection_scores,
+                          detection_classes,
+                          categories,
+                          output_path=None):
+  """Exports keypoints in numpy arrays to COCO API.
+
+  This function converts a set of predicted keypoints represented
+  as numpy arrays to dictionaries that can be ingested by the COCO API.
+  Inputs to this function are lists, consisting of keypoints, scores and
+  classes, respectively, corresponding to each image for which detections
+  have been produced.
+
+  We assume that for each image, keypoints, scores and classes are in
+  correspondence --- that is: detection_keypoints[i, :, :, :],
+  detection_scores[i] and detection_classes[i] are associated with the same
+  detection.
+
+  Args:
+    image_ids: list of image ids (typically ints or strings)
+    detection_keypoints: list of numpy arrays with shape
+      [num_detection, num_keypoints, 2] and type float32 in absolute
+      x-y coordinates.
+    detection_scores: list of numpy arrays (float) with shape
+      [num_detection]. Note that num_detection can be different
+      for each entry in the list.
+    detection_classes: list of numpy arrays (int) with shape
+      [num_detection]. Note that num_detection can be different
+      for each entry in the list.
+    categories: a list of dictionaries representing all possible categories.
+      Each dict in this list must have an integer 'id' key uniquely identifying
+      this category and an integer 'num_keypoints' key specifying the number of
+      keypoints the category has.
+    output_path: (optional) path for exporting result to JSON
+
+  Returns:
+    list of dictionaries that can be read by COCO API, where each entry
+    corresponds to a single detection and has keys from:
+    ['image_id', 'category_id', 'keypoints', 'score'].
+
+  Raises:
+    ValueError: if detection_keypoints and detection_classes do not have the
+      right lengths or if each of the elements inside these lists do not
+      have the correct shapes.
+  """
+  if not (len(image_ids) == len(detection_keypoints) ==
+          len(detection_scores) == len(detection_classes)):
+    raise ValueError('Input lists must have the same length')
+
+  keypoints_export_list = []
+  for image_id, keypoints, scores, classes in zip(
+      image_ids, detection_keypoints, detection_scores, detection_classes):
+
+    if len(classes.shape) != 1 or len(scores.shape) != 1:
+      raise ValueError('All entries in detection_classes and detection_scores'
+                       'expected to be of rank 1.')
+    if len(keypoints.shape) != 3:
+      raise ValueError('All entries in keypoints expected to be of '
+                       'rank 3. Given {}'.format(keypoints.shape))
+
+    num_boxes = classes.shape[0]
+    if not num_boxes == keypoints.shape[0] == scores.shape[0]:
+      raise ValueError('Corresponding entries in detection_classes, '
+                       'detection_keypoints, and detection_scores should have '
+                       'compatible shapes (i.e., agree on the 0th dimension).')
+
+    category_id_set = set([cat['id'] for cat in categories])
+    category_id_to_num_keypoints_map = {
+        cat['id']: cat['num_keypoints'] for cat in categories
+        if 'num_keypoints' in cat}
+
+    for i in range(num_boxes):
+      if classes[i] not in category_id_set:
+        raise ValueError('class id should be in category_id_set\n')
+
+      if classes[i] in category_id_to_num_keypoints_map:
+        num_keypoints = category_id_to_num_keypoints_map[classes[i]]
+        # Adds extra ones to indicate the visibility for each keypoint as is
+        # recommended by MSCOCO.
+        instance_keypoints = np.concatenate(
+            [keypoints[i, 0:num_keypoints, :],
+             np.expand_dims(np.ones(num_keypoints), axis=1)],
+            axis=1).astype(int)
+
+        instance_keypoints = instance_keypoints.flatten().tolist()
+        keypoints_export_list.append({
+            'image_id': image_id,
+            'category_id': int(classes[i]),
+            'keypoints': instance_keypoints,
+            'score': float(scores[i])
+        })
+
+  if output_path:
+    with tf.gfile.GFile(output_path, 'w') as fid:
+      json_utils.Dump(keypoints_export_list, fid, float_digits=4, indent=2)
+  return keypoints_export_list
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools_test.py
new file mode 100644
index 00000000..cfb73d8c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools_test.py
@@ -0,0 +1,295 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow_model.object_detection.metrics.coco_tools."""
+import json
+import os
+import re
+import numpy as np
+
+from pycocotools import mask
+
+import tensorflow as tf
+
+from object_detection.metrics import coco_tools
+
+
+class CocoToolsTest(tf.test.TestCase):
+
+  def setUp(self):
+    groundtruth_annotations_list = [
+        {
+            'id': 1,
+            'image_id': 'first',
+            'category_id': 1,
+            'bbox': [100., 100., 100., 100.],
+            'area': 100.**2,
+            'iscrowd': 0
+        },
+        {
+            'id': 2,
+            'image_id': 'second',
+            'category_id': 1,
+            'bbox': [50., 50., 50., 50.],
+            'area': 50.**2,
+            'iscrowd': 0
+        },
+    ]
+    image_list = [{'id': 'first'}, {'id': 'second'}]
+    category_list = [{'id': 0, 'name': 'person'},
+                     {'id': 1, 'name': 'cat'},
+                     {'id': 2, 'name': 'dog'}]
+    self._groundtruth_dict = {
+        'annotations': groundtruth_annotations_list,
+        'images': image_list,
+        'categories': category_list
+    }
+
+    self._detections_list = [
+        {
+            'image_id': 'first',
+            'category_id': 1,
+            'bbox': [100., 100., 100., 100.],
+            'score': .8
+        },
+        {
+            'image_id': 'second',
+            'category_id': 1,
+            'bbox': [50., 50., 50., 50.],
+            'score': .7
+        },
+    ]
+
+  def testCocoWrappers(self):
+    groundtruth = coco_tools.COCOWrapper(self._groundtruth_dict)
+    detections = groundtruth.LoadAnnotations(self._detections_list)
+    evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections)
+    summary_metrics, _ = evaluator.ComputeMetrics()
+    self.assertAlmostEqual(1.0, summary_metrics['Precision/mAP'])
+
+  def testExportGroundtruthToCOCO(self):
+    image_ids = ['first', 'second']
+    groundtruth_boxes = [np.array([[100, 100, 200, 200]], np.float),
+                         np.array([[50, 50, 100, 100]], np.float)]
+    groundtruth_classes = [np.array([1], np.int32), np.array([1], np.int32)]
+    categories = [{'id': 0, 'name': 'person'},
+                  {'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'}]
+    output_path = os.path.join(tf.test.get_temp_dir(), 'groundtruth.json')
+    result = coco_tools.ExportGroundtruthToCOCO(
+        image_ids,
+        groundtruth_boxes,
+        groundtruth_classes,
+        categories,
+        output_path=output_path)
+    self.assertDictEqual(result, self._groundtruth_dict)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      written_result = f.read()
+      # The json output should have floats written to 4 digits of precision.
+      matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
+      self.assertTrue(matcher.findall(written_result))
+      written_result = json.loads(written_result)
+      self.assertAlmostEqual(result, written_result)
+
+  def testExportDetectionsToCOCO(self):
+    image_ids = ['first', 'second']
+    detections_boxes = [np.array([[100, 100, 200, 200]], np.float),
+                        np.array([[50, 50, 100, 100]], np.float)]
+    detections_scores = [np.array([.8], np.float), np.array([.7], np.float)]
+    detections_classes = [np.array([1], np.int32), np.array([1], np.int32)]
+    categories = [{'id': 0, 'name': 'person'},
+                  {'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'}]
+    output_path = os.path.join(tf.test.get_temp_dir(), 'detections.json')
+    result = coco_tools.ExportDetectionsToCOCO(
+        image_ids,
+        detections_boxes,
+        detections_scores,
+        detections_classes,
+        categories,
+        output_path=output_path)
+    self.assertListEqual(result, self._detections_list)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      written_result = f.read()
+      # The json output should have floats written to 4 digits of precision.
+      matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
+      self.assertTrue(matcher.findall(written_result))
+      written_result = json.loads(written_result)
+      self.assertAlmostEqual(result, written_result)
+
+  def testExportSegmentsToCOCO(self):
+    image_ids = ['first', 'second']
+    detection_masks = [np.array(
+        [[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
+        dtype=np.uint8), np.array(
+            [[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
+            dtype=np.uint8)]
+
+    for i, detection_mask in enumerate(detection_masks):
+      detection_masks[i] = detection_mask[:, :, :, None]
+
+    detection_scores = [np.array([.8], np.float), np.array([.7], np.float)]
+    detection_classes = [np.array([1], np.int32), np.array([1], np.int32)]
+
+    categories = [{'id': 0, 'name': 'person'},
+                  {'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'}]
+    output_path = os.path.join(tf.test.get_temp_dir(), 'segments.json')
+    result = coco_tools.ExportSegmentsToCOCO(
+        image_ids,
+        detection_masks,
+        detection_scores,
+        detection_classes,
+        categories,
+        output_path=output_path)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      written_result = f.read()
+      written_result = json.loads(written_result)
+      mask_load = mask.decode([written_result[0]['segmentation']])
+      self.assertTrue(np.allclose(mask_load, detection_masks[0]))
+      self.assertAlmostEqual(result, written_result)
+
+  def testExportKeypointsToCOCO(self):
+    image_ids = ['first', 'second']
+    detection_keypoints = [
+        np.array(
+            [[[100, 200], [300, 400], [500, 600]],
+             [[50, 150], [250, 350], [450, 550]]], dtype=np.int32),
+        np.array(
+            [[[110, 210], [310, 410], [510, 610]],
+             [[60, 160], [260, 360], [460, 560]]], dtype=np.int32)]
+
+    detection_scores = [np.array([.8, 0.2], np.float),
+                        np.array([.7, 0.3], np.float)]
+    detection_classes = [np.array([1, 1], np.int32), np.array([1, 1], np.int32)]
+
+    categories = [{'id': 1, 'name': 'person', 'num_keypoints': 3},
+                  {'id': 2, 'name': 'cat'},
+                  {'id': 3, 'name': 'dog'}]
+
+    output_path = os.path.join(tf.test.get_temp_dir(), 'keypoints.json')
+    result = coco_tools.ExportKeypointsToCOCO(
+        image_ids,
+        detection_keypoints,
+        detection_scores,
+        detection_classes,
+        categories,
+        output_path=output_path)
+
+    with tf.gfile.GFile(output_path, 'r') as f:
+      written_result = f.read()
+      written_result = json.loads(written_result)
+      self.assertAlmostEqual(result, written_result)
+
+  def testSingleImageDetectionBoxesExport(self):
+    boxes = np.array([[0, 0, 1, 1],
+                      [0, 0, .5, .5],
+                      [.5, .5, 1, 1]], dtype=np.float32)
+    classes = np.array([1, 2, 3], dtype=np.int32)
+    scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
+    coco_boxes = np.array([[0, 0, 1, 1],
+                           [0, 0, .5, .5],
+                           [.5, .5, .5, .5]], dtype=np.float32)
+    coco_annotations = coco_tools.ExportSingleImageDetectionBoxesToCoco(
+        image_id='first_image',
+        category_id_set=set([1, 2, 3]),
+        detection_boxes=boxes,
+        detection_classes=classes,
+        detection_scores=scores)
+    for i, annotation in enumerate(coco_annotations):
+      self.assertEqual(annotation['image_id'], 'first_image')
+      self.assertEqual(annotation['category_id'], classes[i])
+      self.assertAlmostEqual(annotation['score'], scores[i])
+      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
+
+  def testSingleImageDetectionMaskExport(self):
+    masks = np.array(
+        [[[1, 1,], [1, 1]],
+         [[0, 0], [0, 1]],
+         [[0, 0], [0, 0]]], dtype=np.uint8)
+    classes = np.array([1, 2, 3], dtype=np.int32)
+    scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
+    coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco(
+        image_id='first_image',
+        category_id_set=set([1, 2, 3]),
+        detection_classes=classes,
+        detection_scores=scores,
+        detection_masks=masks)
+    expected_counts = ['04', '31', '4']
+    for i, mask_annotation in enumerate(coco_annotations):
+      self.assertEqual(mask_annotation['segmentation']['counts'],
+                       expected_counts[i])
+      self.assertTrue(np.all(np.equal(mask.decode(
+          mask_annotation['segmentation']), masks[i])))
+      self.assertEqual(mask_annotation['image_id'], 'first_image')
+      self.assertEqual(mask_annotation['category_id'], classes[i])
+      self.assertAlmostEqual(mask_annotation['score'], scores[i])
+
+  def testSingleImageGroundtruthExport(self):
+    masks = np.array(
+        [[[1, 1,], [1, 1]],
+         [[0, 0], [0, 1]],
+         [[0, 0], [0, 0]]], dtype=np.uint8)
+    boxes = np.array([[0, 0, 1, 1],
+                      [0, 0, .5, .5],
+                      [.5, .5, 1, 1]], dtype=np.float32)
+    coco_boxes = np.array([[0, 0, 1, 1],
+                           [0, 0, .5, .5],
+                           [.5, .5, .5, .5]], dtype=np.float32)
+    classes = np.array([1, 2, 3], dtype=np.int32)
+    is_crowd = np.array([0, 1, 0], dtype=np.int32)
+    next_annotation_id = 1
+    expected_counts = ['04', '31', '4']
+
+    # Tests exporting without passing in is_crowd (for backward compatibility).
+    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
+        image_id='first_image',
+        category_id_set=set([1, 2, 3]),
+        next_annotation_id=next_annotation_id,
+        groundtruth_boxes=boxes,
+        groundtruth_classes=classes,
+        groundtruth_masks=masks)
+    for i, annotation in enumerate(coco_annotations):
+      self.assertEqual(annotation['segmentation']['counts'],
+                       expected_counts[i])
+      self.assertTrue(np.all(np.equal(mask.decode(
+          annotation['segmentation']), masks[i])))
+      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
+      self.assertEqual(annotation['image_id'], 'first_image')
+      self.assertEqual(annotation['category_id'], classes[i])
+      self.assertEqual(annotation['id'], i + next_annotation_id)
+
+    # Tests exporting with is_crowd.
+    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
+        image_id='first_image',
+        category_id_set=set([1, 2, 3]),
+        next_annotation_id=next_annotation_id,
+        groundtruth_boxes=boxes,
+        groundtruth_classes=classes,
+        groundtruth_masks=masks,
+        groundtruth_is_crowd=is_crowd)
+    for i, annotation in enumerate(coco_annotations):
+      self.assertEqual(annotation['segmentation']['counts'],
+                       expected_counts[i])
+      self.assertTrue(np.all(np.equal(mask.decode(
+          annotation['segmentation']), masks[i])))
+      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
+      self.assertEqual(annotation['image_id'], 'first_image')
+      self.assertEqual(annotation['category_id'], classes[i])
+      self.assertEqual(annotation['iscrowd'], is_crowd[i])
+      self.assertEqual(annotation['id'], i + next_annotation_id)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/io_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/io_utils.py
new file mode 100644
index 00000000..900584de
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/io_utils.py
@@ -0,0 +1,34 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Common IO utils used in offline metric computation.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import csv
+
+
+def write_csv(fid, metrics):
+  """Writes metrics key-value pairs to CSV file.
+
+  Args:
+    fid: File identifier of an opened file.
+    metrics: A dictionary with metrics to be written.
+  """
+  metrics_writer = csv.writer(fid, delimiter=',')
+  for metric_name, metric_value in metrics.items():
+    metrics_writer.writerow([metric_name, str(metric_value)])
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc.py
new file mode 100644
index 00000000..b5514be1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc.py
@@ -0,0 +1,171 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Evaluation executable for detection data.
+
+This executable evaluates precomputed detections produced by a detection
+model and writes the evaluation results into csv file metrics.csv, stored
+in the directory, specified by --eval_dir.
+
+The evaluation metrics set is supplied in object_detection.protos.EvalConfig
+in metrics_set field.
+Currently two set of metrics are supported:
+- pascal_voc_metrics: standard PASCAL VOC 2007 metric
+- open_images_detection_metrics: Open Image V2 metric
+All other field of object_detection.protos.EvalConfig are ignored.
+
+Example usage:
+    ./compute_metrics \
+        --eval_dir=path/to/eval_dir \
+        --eval_config_path=path/to/evaluation/configuration/file \
+        --input_config_path=path/to/input/configuration/file
+"""
+import csv
+import os
+import re
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.legacy import evaluator
+from object_detection.metrics import tf_example_parser
+from object_detection.utils import config_util
+from object_detection.utils import label_map_util
+
+flags = tf.app.flags
+tf.logging.set_verbosity(tf.logging.INFO)
+
+flags.DEFINE_string('eval_dir', None, 'Directory to write eval summaries to.')
+flags.DEFINE_string('eval_config_path', None,
+                    'Path to an eval_pb2.EvalConfig config file.')
+flags.DEFINE_string('input_config_path', None,
+                    'Path to an eval_pb2.InputConfig config file.')
+
+FLAGS = flags.FLAGS
+
+
+def _generate_sharded_filenames(filename):
+  m = re.search(r'@(\d{1,})', filename)
+  if m:
+    num_shards = int(m.group(1))
+    return [
+        re.sub(r'@(\d{1,})', '-%.5d-of-%.5d' % (i, num_shards), filename)
+        for i in range(num_shards)
+    ]
+  else:
+    return [filename]
+
+
+def _generate_filenames(filenames):
+  result = []
+  for filename in filenames:
+    result += _generate_sharded_filenames(filename)
+  return result
+
+
+def read_data_and_evaluate(input_config, eval_config):
+  """Reads pre-computed object detections and groundtruth from tf_record.
+
+  Args:
+    input_config: input config proto of type
+      object_detection.protos.InputReader.
+    eval_config: evaluation config proto of type
+      object_detection.protos.EvalConfig.
+
+  Returns:
+    Evaluated detections metrics.
+
+  Raises:
+    ValueError: if input_reader type is not supported or metric type is unknown.
+  """
+  if input_config.WhichOneof('input_reader') == 'tf_record_input_reader':
+    input_paths = input_config.tf_record_input_reader.input_path
+
+    categories = label_map_util.create_categories_from_labelmap(
+        input_config.label_map_path)
+
+    object_detection_evaluators = evaluator.get_evaluators(
+        eval_config, categories)
+    # Support a single evaluator
+    object_detection_evaluator = object_detection_evaluators[0]
+
+    skipped_images = 0
+    processed_images = 0
+    for input_path in _generate_filenames(input_paths):
+      tf.logging.info('Processing file: {0}'.format(input_path))
+
+      record_iterator = tf.python_io.tf_record_iterator(path=input_path)
+      data_parser = tf_example_parser.TfExampleDetectionAndGTParser()
+
+      for string_record in record_iterator:
+        tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
+                               processed_images)
+        processed_images += 1
+
+        example = tf.train.Example()
+        example.ParseFromString(string_record)
+        decoded_dict = data_parser.parse(example)
+
+        if decoded_dict:
+          object_detection_evaluator.add_single_ground_truth_image_info(
+              decoded_dict[standard_fields.DetectionResultFields.key],
+              decoded_dict)
+          object_detection_evaluator.add_single_detected_image_info(
+              decoded_dict[standard_fields.DetectionResultFields.key],
+              decoded_dict)
+        else:
+          skipped_images += 1
+          tf.logging.info('Skipped images: {0}'.format(skipped_images))
+
+    return object_detection_evaluator.evaluate()
+
+  raise ValueError('Unsupported input_reader_config.')
+
+
+def write_metrics(metrics, output_dir):
+  """Write metrics to the output directory.
+
+  Args:
+    metrics: A dictionary containing metric names and values.
+    output_dir: Directory to write metrics to.
+  """
+  tf.logging.info('Writing metrics.')
+
+  with open(os.path.join(output_dir, 'metrics.csv'), 'w') as csvfile:
+    metrics_writer = csv.writer(csvfile, delimiter=',')
+    for metric_name, metric_value in metrics.items():
+      metrics_writer.writerow([metric_name, str(metric_value)])
+
+
+def main(argv):
+  del argv
+  required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
+  for flag_name in required_flags:
+    if not getattr(FLAGS, flag_name):
+      raise ValueError('Flag --{} is required'.format(flag_name))
+
+  configs = config_util.get_configs_from_multiple_files(
+      eval_input_config_path=FLAGS.input_config_path,
+      eval_config_path=FLAGS.eval_config_path)
+
+  eval_config = configs['eval_config']
+  input_config = configs['eval_input_config']
+
+  metrics = read_data_and_evaluate(input_config, eval_config)
+
+  # Save metrics
+  write_metrics(metrics, FLAGS.eval_dir)
+
+
+if __name__ == '__main__':
+  tf.app.run(main)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc_test.py
new file mode 100644
index 00000000..68ac3893
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/offline_eval_map_corloc_test.py
@@ -0,0 +1,58 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for utilities in offline_eval_map_corloc binary."""
+
+import tensorflow as tf
+
+from object_detection.metrics import offline_eval_map_corloc as offline_eval
+
+
+class OfflineEvalMapCorlocTest(tf.test.TestCase):
+
+  def test_generateShardedFilenames(self):
+    test_filename = '/path/to/file'
+    result = offline_eval._generate_sharded_filenames(test_filename)
+    self.assertEqual(result, [test_filename])
+
+    test_filename = '/path/to/file-00000-of-00050'
+    result = offline_eval._generate_sharded_filenames(test_filename)
+    self.assertEqual(result, [test_filename])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/@3.record')
+    self.assertEqual(result, [
+        '/path/to/-00000-of-00003.record', '/path/to/-00001-of-00003.record',
+        '/path/to/-00002-of-00003.record'
+    ])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/abc@3')
+    self.assertEqual(result, [
+        '/path/to/abc-00000-of-00003', '/path/to/abc-00001-of-00003',
+        '/path/to/abc-00002-of-00003'
+    ])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/@1')
+    self.assertEqual(result, ['/path/to/-00000-of-00001'])
+
+  def test_generateFilenames(self):
+    test_filenames = ['/path/to/file', '/path/to/@3.record']
+    result = offline_eval._generate_filenames(test_filenames)
+    self.assertEqual(result, [
+        '/path/to/file', '/path/to/-00000-of-00003.record',
+        '/path/to/-00001-of-00003.record', '/path/to/-00002-of-00003.record'
+    ])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation.py
new file mode 100644
index 00000000..6e6e7ac3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation.py
@@ -0,0 +1,128 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Runs evaluation using OpenImages groundtruth and predictions.
+
+Example usage:
+python models/research/object_detection/metrics/oid_od_challenge_evaluation.py \
+    --input_annotations_boxes=/path/to/input/annotations-human-bbox.csv \
+    --input_annotations_labels=/path/to/input/annotations-label.csv \
+    --input_class_labelmap=/path/to/input/class_labelmap.pbtxt \
+    --input_predictions=/path/to/input/predictions.csv \
+    --output_metrics=/path/to/output/metric.csv \
+
+CSVs with bounding box annotations and image label (including the image URLs)
+can be downloaded from the Open Images Challenge website:
+https://storage.googleapis.com/openimages/web/challenge.html
+The format of the input csv and the metrics itself are described on the
+challenge website.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import pandas as pd
+from google.protobuf import text_format
+
+from object_detection.metrics import io_utils
+from object_detection.metrics import oid_od_challenge_evaluation_utils as utils
+from object_detection.protos import string_int_label_map_pb2
+from object_detection.utils import object_detection_evaluation
+
+
+def _load_labelmap(labelmap_path):
+  """Loads labelmap from the labelmap path.
+
+  Args:
+    labelmap_path: Path to the labelmap.
+
+  Returns:
+    A dictionary mapping class name to class numerical id
+    A list with dictionaries, one dictionary per category.
+  """
+
+  label_map = string_int_label_map_pb2.StringIntLabelMap()
+  with open(labelmap_path, 'r') as fid:
+    label_map_string = fid.read()
+    text_format.Merge(label_map_string, label_map)
+  labelmap_dict = {}
+  categories = []
+  for item in label_map.item:
+    labelmap_dict[item.name] = item.id
+    categories.append({'id': item.id, 'name': item.name})
+  return labelmap_dict, categories
+
+
+def main(parsed_args):
+  all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
+  all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
+  all_label_annotations.rename(
+      columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
+  all_annotations = pd.concat([all_box_annotations, all_label_annotations])
+
+  class_label_map, categories = _load_labelmap(parsed_args.input_class_labelmap)
+  challenge_evaluator = (
+      object_detection_evaluation.OpenImagesDetectionChallengeEvaluator(
+          categories))
+
+  for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
+    image_id, image_groundtruth = groundtruth
+    groundtruth_dictionary = utils.build_groundtruth_boxes_dictionary(
+        image_groundtruth, class_label_map)
+    challenge_evaluator.add_single_ground_truth_image_info(
+        image_id, groundtruth_dictionary)
+
+  all_predictions = pd.read_csv(parsed_args.input_predictions)
+  for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
+    image_id, image_predictions = prediction_data
+    prediction_dictionary = utils.build_predictions_dictionary(
+        image_predictions, class_label_map)
+    challenge_evaluator.add_single_detected_image_info(image_id,
+                                                       prediction_dictionary)
+
+  metrics = challenge_evaluator.evaluate()
+
+  with open(parsed_args.output_metrics, 'w') as fid:
+    io_utils.write_csv(fid, metrics)
+
+
+if __name__ == '__main__':
+
+  parser = argparse.ArgumentParser(
+      description='Evaluate Open Images Object Detection Challenge predictions.'
+  )
+  parser.add_argument(
+      '--input_annotations_boxes',
+      required=True,
+      help='File with groundtruth boxes annotations.')
+  parser.add_argument(
+      '--input_annotations_labels',
+      required=True,
+      help='File with groundtruth labels annotations')
+  parser.add_argument(
+      '--input_predictions',
+      required=True,
+      help="""File with detection predictions; NOTE: no postprocessing is
+      applied in the evaluation script.""")
+  parser.add_argument(
+      '--input_class_labelmap',
+      required=True,
+      help='Open Images Challenge labelmap.')
+  parser.add_argument(
+      '--output_metrics', required=True, help='Output file with csv metrics')
+
+  args = parser.parse_args()
+  main(args)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils.py
new file mode 100644
index 00000000..bfd3ee3f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils.py
@@ -0,0 +1,90 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Converts data from CSV to the OpenImagesDetectionChallengeEvaluator format.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from object_detection.core import standard_fields
+
+
+def build_groundtruth_boxes_dictionary(data, class_label_map):
+  """Builds a groundtruth dictionary from groundtruth data in CSV file.
+
+  Args:
+    data: Pandas DataFrame with the groundtruth data for a single image.
+    class_label_map: Class labelmap from string label name to an integer.
+
+  Returns:
+    A dictionary with keys suitable for passing to
+    OpenImagesDetectionChallengeEvaluator.add_single_ground_truth_image_info:
+        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
+          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
+          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
+          of shape [num_boxes] containing 1-indexed groundtruth classes for the
+          boxes.
+        standard_fields.InputDataFields.verified_labels: integer 1D numpy array
+          containing all classes for which labels are verified.
+        standard_fields.InputDataFields.groundtruth_group_of: Optional length
+          M numpy boolean array denoting whether a groundtruth box contains a
+          group of instances.
+  """
+  data_boxes = data[data.ConfidenceImageLabel.isnull()]
+  data_labels = data[data.XMin.isnull()]
+
+  return {
+      standard_fields.InputDataFields.groundtruth_boxes:
+          data_boxes[['YMin', 'XMin', 'YMax', 'XMax']].as_matrix(),
+      standard_fields.InputDataFields.groundtruth_classes:
+          data_boxes['LabelName'].map(lambda x: class_label_map[x]).as_matrix(),
+      standard_fields.InputDataFields.groundtruth_group_of:
+          data_boxes['IsGroupOf'].as_matrix().astype(int),
+      standard_fields.InputDataFields.groundtruth_image_classes:
+          data_labels['LabelName'].map(lambda x: class_label_map[x])
+          .as_matrix(),
+  }
+
+
+def build_predictions_dictionary(data, class_label_map):
+  """Builds a predictions dictionary from predictions data in CSV file.
+
+  Args:
+    data: Pandas DataFrame with the predictions data for a single image.
+    class_label_map: Class labelmap from string label name to an integer.
+
+  Returns:
+    Dictionary with keys suitable for passing to
+    OpenImagesDetectionChallengeEvaluator.add_single_detected_image_info:
+        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
+          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
+          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.DetectionResultFields.detection_scores: float32 numpy
+          array of shape [num_boxes] containing detection scores for the boxes.
+        standard_fields.DetectionResultFields.detection_classes: integer numpy
+          array of shape [num_boxes] containing 1-indexed detection classes for
+          the boxes.
+
+  """
+  return {
+      standard_fields.DetectionResultFields.detection_boxes:
+          data[['YMin', 'XMin', 'YMax', 'XMax']].as_matrix(),
+      standard_fields.DetectionResultFields.detection_classes:
+          data['LabelName'].map(lambda x: class_label_map[x]).as_matrix(),
+      standard_fields.DetectionResultFields.detection_scores:
+          data['Score'].as_matrix()
+  }
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils_test.py
new file mode 100644
index 00000000..a7a1a585
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_od_challenge_evaluation_utils_test.py
@@ -0,0 +1,103 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for oid_od_challenge_evaluation_util."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from object_detection.core import standard_fields
+from object_detection.metrics import oid_od_challenge_evaluation_utils as utils
+
+
+class OidOdChallengeEvaluationUtilTest(tf.test.TestCase):
+
+  def testBuildGroundtruthDictionary(self):
+    np_data = pd.DataFrame(
+        [['fe58ec1b06db2bb7', '/m/04bcr3', 0.0, 0.3, 0.5, 0.6, 1, None], [
+            'fe58ec1b06db2bb7', '/m/02gy9n', 0.1, 0.2, 0.3, 0.4, 0, None
+        ], ['fe58ec1b06db2bb7', '/m/04bcr3', None, None, None, None, None, 1], [
+            'fe58ec1b06db2bb7', '/m/083vt', None, None, None, None, None, 0
+        ], ['fe58ec1b06db2bb7', '/m/02gy9n', None, None, None, None, None, 1]],
+        columns=[
+            'ImageID', 'LabelName', 'XMin', 'XMax', 'YMin', 'YMax', 'IsGroupOf',
+            'ConfidenceImageLabel'
+        ])
+    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
+    groundtruth_dictionary = utils.build_groundtruth_boxes_dictionary(
+        np_data, class_label_map)
+
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_boxes in
+                    groundtruth_dictionary)
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_classes in
+                    groundtruth_dictionary)
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_group_of in
+                    groundtruth_dictionary)
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_image_classes in
+                    groundtruth_dictionary)
+
+    self.assertAllEqual(
+        np.array([1, 3]), groundtruth_dictionary[
+            standard_fields.InputDataFields.groundtruth_classes])
+    self.assertAllEqual(
+        np.array([1, 0]), groundtruth_dictionary[
+            standard_fields.InputDataFields.groundtruth_group_of])
+
+    expected_boxes_data = np.array([[0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2]])
+
+    self.assertNDArrayNear(
+        expected_boxes_data, groundtruth_dictionary[
+            standard_fields.InputDataFields.groundtruth_boxes], 1e-5)
+    self.assertAllEqual(
+        np.array([1, 2, 3]), groundtruth_dictionary[
+            standard_fields.InputDataFields.groundtruth_image_classes])
+
+  def testBuildPredictionDictionary(self):
+    np_data = pd.DataFrame(
+        [['fe58ec1b06db2bb7', '/m/04bcr3', 0.0, 0.3, 0.5, 0.6, 0.1], [
+            'fe58ec1b06db2bb7', '/m/02gy9n', 0.1, 0.2, 0.3, 0.4, 0.2
+        ], ['fe58ec1b06db2bb7', '/m/04bcr3', 0.0, 0.1, 0.2, 0.3, 0.3]],
+        columns=[
+            'ImageID', 'LabelName', 'XMin', 'XMax', 'YMin', 'YMax', 'Score'
+        ])
+    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
+    prediction_dictionary = utils.build_predictions_dictionary(
+        np_data, class_label_map)
+
+    self.assertTrue(standard_fields.DetectionResultFields.detection_boxes in
+                    prediction_dictionary)
+    self.assertTrue(standard_fields.DetectionResultFields.detection_classes in
+                    prediction_dictionary)
+    self.assertTrue(standard_fields.DetectionResultFields.detection_scores in
+                    prediction_dictionary)
+
+    self.assertAllEqual(
+        np.array([1, 3, 1]), prediction_dictionary[
+            standard_fields.DetectionResultFields.detection_classes])
+    expected_boxes_data = np.array([[0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2],
+                                    [0.2, 0.0, 0.3, 0.1]])
+    self.assertNDArrayNear(
+        expected_boxes_data, prediction_dictionary[
+            standard_fields.DetectionResultFields.detection_boxes], 1e-5)
+    self.assertNDArrayNear(
+        np.array([0.1, 0.2, 0.3]), prediction_dictionary[
+            standard_fields.DetectionResultFields.detection_scores], 1e-5)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation.py
new file mode 100644
index 00000000..20b93e66
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation.py
@@ -0,0 +1,154 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Runs evaluation using OpenImages groundtruth and predictions.
+
+Example usage:
+python \
+models/research/object_detection/metrics/oid_vrd_challenge_evaluation.py \
+    --input_annotations_boxes=/path/to/input/annotations-human-bbox.csv \
+    --input_annotations_labels=/path/to/input/annotations-label.csv \
+    --input_class_labelmap=/path/to/input/class_labelmap.pbtxt \
+    --input_relationship_labelmap=/path/to/input/relationship_labelmap.pbtxt \
+    --input_predictions=/path/to/input/predictions.csv \
+    --output_metrics=/path/to/output/metric.csv \
+
+CSVs with bounding box annotations and image label (including the image URLs)
+can be downloaded from the Open Images Challenge website:
+https://storage.googleapis.com/openimages/web/challenge.html
+The format of the input csv and the metrics itself are described on the
+challenge website.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import pandas as pd
+from google.protobuf import text_format
+
+from object_detection.metrics import io_utils
+from object_detection.metrics import oid_vrd_challenge_evaluation_utils as utils
+from object_detection.protos import string_int_label_map_pb2
+from object_detection.utils import vrd_evaluation
+
+
+def _load_labelmap(labelmap_path):
+  """Loads labelmap from the labelmap path.
+
+  Args:
+    labelmap_path: Path to the labelmap.
+
+  Returns:
+    A dictionary mapping class name to class numerical id.
+  """
+
+  label_map = string_int_label_map_pb2.StringIntLabelMap()
+  with open(labelmap_path, 'r') as fid:
+    label_map_string = fid.read()
+    text_format.Merge(label_map_string, label_map)
+  labelmap_dict = {}
+  for item in label_map.item:
+    labelmap_dict[item.name] = item.id
+  return labelmap_dict
+
+
+def _swap_labelmap_dict(labelmap_dict):
+  """Swaps keys and labels in labelmap.
+
+  Args:
+    labelmap_dict: Input dictionary.
+
+  Returns:
+    A dictionary mapping class name to class numerical id.
+  """
+  return dict((v, k) for k, v in labelmap_dict.iteritems())
+
+
+def main(parsed_args):
+  all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
+  all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
+  all_annotations = pd.concat([all_box_annotations, all_label_annotations])
+
+  class_label_map = _load_labelmap(parsed_args.input_class_labelmap)
+  relationship_label_map = _load_labelmap(
+      parsed_args.input_relationship_labelmap)
+
+  relation_evaluator = vrd_evaluation.VRDRelationDetectionEvaluator()
+  phrase_evaluator = vrd_evaluation.VRDPhraseDetectionEvaluator()
+
+  for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
+    image_id, image_groundtruth = groundtruth
+    groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
+        image_groundtruth, class_label_map, relationship_label_map)
+
+    relation_evaluator.add_single_ground_truth_image_info(
+        image_id, groundtruth_dictionary)
+    phrase_evaluator.add_single_ground_truth_image_info(image_id,
+                                                        groundtruth_dictionary)
+
+  all_predictions = pd.read_csv(parsed_args.input_predictions)
+  for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
+    image_id, image_predictions = prediction_data
+    prediction_dictionary = utils.build_predictions_vrd_dictionary(
+        image_predictions, class_label_map, relationship_label_map)
+
+    relation_evaluator.add_single_detected_image_info(image_id,
+                                                      prediction_dictionary)
+    phrase_evaluator.add_single_detected_image_info(image_id,
+                                                    prediction_dictionary)
+
+  relation_metrics = relation_evaluator.evaluate(
+      relationships=_swap_labelmap_dict(relationship_label_map))
+  phrase_metrics = phrase_evaluator.evaluate(
+      relationships=_swap_labelmap_dict(relationship_label_map))
+
+  with open(parsed_args.output_metrics, 'w') as fid:
+    io_utils.write_csv(fid, relation_metrics)
+    io_utils.write_csv(fid, phrase_metrics)
+
+
+if __name__ == '__main__':
+
+  parser = argparse.ArgumentParser(
+      description=
+      'Evaluate Open Images Visual Relationship Detection predictions.')
+  parser.add_argument(
+      '--input_annotations_boxes',
+      required=True,
+      help='File with groundtruth vrd annotations.')
+  parser.add_argument(
+      '--input_annotations_labels',
+      required=True,
+      help='File with groundtruth labels annotations')
+  parser.add_argument(
+      '--input_predictions',
+      required=True,
+      help="""File with detection predictions; NOTE: no postprocessing is
+      applied in the evaluation script.""")
+  parser.add_argument(
+      '--input_class_labelmap',
+      required=True,
+      help="""OpenImages Challenge labelmap; note: it is expected to include
+      attributes.""")
+  parser.add_argument(
+      '--input_relationship_labelmap',
+      required=True,
+      help="""OpenImages Challenge relationship labelmap.""")
+  parser.add_argument(
+      '--output_metrics', required=True, help='Output file with csv metrics')
+
+  args = parser.parse_args()
+  main(args)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils.py
new file mode 100644
index 00000000..34be018c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils.py
@@ -0,0 +1,125 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Converts data from CSV format to the VRDDetectionEvaluator format."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from object_detection.core import standard_fields
+from object_detection.utils import vrd_evaluation
+
+
+def build_groundtruth_vrd_dictionary(data, class_label_map,
+                                     relationship_label_map):
+  """Builds a groundtruth dictionary from groundtruth data in CSV file.
+
+  Args:
+    data: Pandas DataFrame with the groundtruth data for a single image.
+    class_label_map: Class labelmap from string label name to an integer.
+    relationship_label_map: Relationship type labelmap from string name to an
+      integer.
+
+  Returns:
+    A dictionary with keys suitable for passing to
+    VRDDetectionEvaluator.add_single_ground_truth_image_info:
+        standard_fields.InputDataFields.groundtruth_boxes: A numpy array
+          of structures with the shape [M, 1], representing M tuples, each tuple
+          containing the same number of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max] (see
+          datatype vrd_box_data_type, single_box_data_type above).
+        standard_fields.InputDataFields.groundtruth_classes: A numpy array of
+          structures shape [M, 1], representing  the class labels of the
+          corresponding bounding boxes and possibly additional classes (see
+          datatype label_data_type above).
+        standard_fields.InputDataFields.verified_labels: numpy array
+          of shape [K] containing verified labels.
+  """
+  data_boxes = data[data.LabelName.isnull()]
+  data_labels = data[data.LabelName1.isnull()]
+
+  boxes = np.zeros(data_boxes.shape[0], dtype=vrd_evaluation.vrd_box_data_type)
+  boxes['subject'] = data_boxes[['YMin1', 'XMin1', 'YMax1',
+                                 'XMax1']].as_matrix()
+  boxes['object'] = data_boxes[['YMin2', 'XMin2', 'YMax2', 'XMax2']].as_matrix()
+
+  labels = np.zeros(data_boxes.shape[0], dtype=vrd_evaluation.label_data_type)
+  labels['subject'] = data_boxes['LabelName1'].map(
+      lambda x: class_label_map[x]).as_matrix()
+  labels['object'] = data_boxes['LabelName2'].map(
+      lambda x: class_label_map[x]).as_matrix()
+  labels['relation'] = data_boxes['RelationshipLabel'].map(
+      lambda x: relationship_label_map[x]).as_matrix()
+
+  return {
+      standard_fields.InputDataFields.groundtruth_boxes:
+          boxes,
+      standard_fields.InputDataFields.groundtruth_classes:
+          labels,
+      standard_fields.InputDataFields.groundtruth_image_classes:
+          data_labels['LabelName'].map(lambda x: class_label_map[x])
+          .as_matrix(),
+  }
+
+
+def build_predictions_vrd_dictionary(data, class_label_map,
+                                     relationship_label_map):
+  """Builds a predictions dictionary from predictions data in CSV file.
+
+  Args:
+    data: Pandas DataFrame with the predictions data for a single image.
+    class_label_map: Class labelmap from string label name to an integer.
+    relationship_label_map: Relationship type labelmap from string name to an
+      integer.
+
+  Returns:
+    Dictionary with keys suitable for passing to
+    VRDDetectionEvaluator.add_single_detected_image_info:
+        standard_fields.DetectionResultFields.detection_boxes: A numpy array of
+          structures with shape [N, 1], representing N tuples, each tuple
+          containing the same number of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max] (as an example
+          see datatype vrd_box_data_type, single_box_data_type above).
+        standard_fields.DetectionResultFields.detection_scores: float32 numpy
+          array of shape [N] containing detection scores for the boxes.
+        standard_fields.DetectionResultFields.detection_classes: A numpy array
+          of structures shape [N, 1], representing the class labels of the
+          corresponding bounding boxes and possibly additional classes (see
+          datatype label_data_type above).
+  """
+  data_boxes = data
+
+  boxes = np.zeros(data_boxes.shape[0], dtype=vrd_evaluation.vrd_box_data_type)
+  boxes['subject'] = data_boxes[['YMin1', 'XMin1', 'YMax1',
+                                 'XMax1']].as_matrix()
+  boxes['object'] = data_boxes[['YMin2', 'XMin2', 'YMax2', 'XMax2']].as_matrix()
+
+  labels = np.zeros(data_boxes.shape[0], dtype=vrd_evaluation.label_data_type)
+  labels['subject'] = data_boxes['LabelName1'].map(
+      lambda x: class_label_map[x]).as_matrix()
+  labels['object'] = data_boxes['LabelName2'].map(
+      lambda x: class_label_map[x]).as_matrix()
+  labels['relation'] = data_boxes['RelationshipLabel'].map(
+      lambda x: relationship_label_map[x]).as_matrix()
+
+  return {
+      standard_fields.DetectionResultFields.detection_boxes:
+          boxes,
+      standard_fields.DetectionResultFields.detection_classes:
+          labels,
+      standard_fields.DetectionResultFields.detection_scores:
+          data_boxes['Score'].as_matrix()
+  }
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils_test.py
new file mode 100644
index 00000000..73818288
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/oid_vrd_challenge_evaluation_utils_test.py
@@ -0,0 +1,149 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for oid_vrd_challenge_evaluation_utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from object_detection.core import standard_fields
+from object_detection.metrics import oid_vrd_challenge_evaluation_utils as utils
+from object_detection.utils import vrd_evaluation
+
+
+class OidVrdChallengeEvaluationUtilsTest(tf.test.TestCase):
+
+  def testBuildGroundtruthDictionary(self):
+    np_data = pd.DataFrame(
+        [[
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.3, 0.5, 0.6,
+            0.0, 0.3, 0.5, 0.6, 'is', None, None
+        ], [
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/02gy9n', 0.0, 0.3, 0.5, 0.6,
+            0.1, 0.2, 0.3, 0.4, 'under', None, None
+        ], [
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.1, 0.2, 0.3,
+            0.0, 0.1, 0.2, 0.3, 'is', None, None
+        ], [
+            'fe58ec1b06db2bb7', '/m/083vt', '/m/04bcr3', 0.1, 0.2, 0.3, 0.4,
+            0.5, 0.6, 0.7, 0.8, 'at', None, None
+        ], [
+            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
+            None, None, None, '/m/04bcr3', 1.0
+        ], [
+            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
+            None, None, None, '/m/083vt', 0.0
+        ], [
+            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
+            None, None, None, '/m/02gy9n', 0.0
+        ]],
+        columns=[
+            'ImageID', 'LabelName1', 'LabelName2', 'XMin1', 'XMax1', 'YMin1',
+            'YMax1', 'XMin2', 'XMax2', 'YMin2', 'YMax2', 'RelationshipLabel',
+            'LabelName', 'Confidence'
+        ])
+    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
+    relationship_label_map = {'is': 1, 'under': 2, 'at': 3}
+    groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
+        np_data, class_label_map, relationship_label_map)
+
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_boxes in
+                    groundtruth_dictionary)
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_classes in
+                    groundtruth_dictionary)
+    self.assertTrue(standard_fields.InputDataFields.groundtruth_image_classes in
+                    groundtruth_dictionary)
+
+    self.assertAllEqual(
+        np.array(
+            [(1, 2, 1), (1, 3, 2), (1, 2, 1), (2, 1, 3)],
+            dtype=vrd_evaluation.label_data_type), groundtruth_dictionary[
+                standard_fields.InputDataFields.groundtruth_classes])
+    expected_vrd_data = np.array(
+        [
+            ([0.5, 0.0, 0.6, 0.3], [0.5, 0.0, 0.6, 0.3]),
+            ([0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2]),
+            ([0.2, 0.0, 0.3, 0.1], [0.2, 0.0, 0.3, 0.1]),
+            ([0.3, 0.1, 0.4, 0.2], [0.7, 0.5, 0.8, 0.6]),
+        ],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    for field in expected_vrd_data.dtype.fields:
+      self.assertNDArrayNear(
+          expected_vrd_data[field], groundtruth_dictionary[
+              standard_fields.InputDataFields.groundtruth_boxes][field], 1e-5)
+    self.assertAllEqual(
+        np.array([1, 2, 3]), groundtruth_dictionary[
+            standard_fields.InputDataFields.groundtruth_image_classes])
+
+  def testBuildPredictionDictionary(self):
+    np_data = pd.DataFrame(
+        [[
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.3, 0.5, 0.6,
+            0.0, 0.3, 0.5, 0.6, 'is', 0.1
+        ], [
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/02gy9n', 0.0, 0.3, 0.5, 0.6,
+            0.1, 0.2, 0.3, 0.4, 'under', 0.2
+        ], [
+            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.1, 0.2, 0.3,
+            0.0, 0.1, 0.2, 0.3, 'is', 0.3
+        ], [
+            'fe58ec1b06db2bb7', '/m/083vt', '/m/04bcr3', 0.1, 0.2, 0.3, 0.4,
+            0.5, 0.6, 0.7, 0.8, 'at', 0.4
+        ]],
+        columns=[
+            'ImageID', 'LabelName1', 'LabelName2', 'XMin1', 'XMax1', 'YMin1',
+            'YMax1', 'XMin2', 'XMax2', 'YMin2', 'YMax2', 'RelationshipLabel',
+            'Score'
+        ])
+    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
+    relationship_label_map = {'is': 1, 'under': 2, 'at': 3}
+    prediction_dictionary = utils.build_predictions_vrd_dictionary(
+        np_data, class_label_map, relationship_label_map)
+
+    self.assertTrue(standard_fields.DetectionResultFields.detection_boxes in
+                    prediction_dictionary)
+    self.assertTrue(standard_fields.DetectionResultFields.detection_classes in
+                    prediction_dictionary)
+    self.assertTrue(standard_fields.DetectionResultFields.detection_scores in
+                    prediction_dictionary)
+
+    self.assertAllEqual(
+        np.array(
+            [(1, 2, 1), (1, 3, 2), (1, 2, 1), (2, 1, 3)],
+            dtype=vrd_evaluation.label_data_type), prediction_dictionary[
+                standard_fields.DetectionResultFields.detection_classes])
+    expected_vrd_data = np.array(
+        [
+            ([0.5, 0.0, 0.6, 0.3], [0.5, 0.0, 0.6, 0.3]),
+            ([0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2]),
+            ([0.2, 0.0, 0.3, 0.1], [0.2, 0.0, 0.3, 0.1]),
+            ([0.3, 0.1, 0.4, 0.2], [0.7, 0.5, 0.8, 0.6]),
+        ],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    for field in expected_vrd_data.dtype.fields:
+      self.assertNDArrayNear(
+          expected_vrd_data[field], prediction_dictionary[
+              standard_fields.DetectionResultFields.detection_boxes][field],
+          1e-5)
+    self.assertNDArrayNear(
+        np.array([0.1, 0.2, 0.3, 0.4]), prediction_dictionary[
+            standard_fields.DetectionResultFields.detection_scores], 1e-5)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser.py
new file mode 100644
index 00000000..9a5f130f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser.py
@@ -0,0 +1,159 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tensorflow Example proto parser for data loading.
+
+A parser to decode data containing serialized tensorflow.Example
+protos into materialized tensors (numpy arrays).
+"""
+
+import numpy as np
+
+from object_detection.core import data_parser
+from object_detection.core import standard_fields as fields
+
+
+class FloatParser(data_parser.DataToNumpyParser):
+  """Tensorflow Example float parser."""
+
+  def __init__(self, field_name):
+    self.field_name = field_name
+
+  def parse(self, tf_example):
+    return np.array(
+        tf_example.features.feature[self.field_name].float_list.value,
+        dtype=np.float).transpose() if tf_example.features.feature[
+            self.field_name].HasField("float_list") else None
+
+
+class StringParser(data_parser.DataToNumpyParser):
+  """Tensorflow Example string parser."""
+
+  def __init__(self, field_name):
+    self.field_name = field_name
+
+  def parse(self, tf_example):
+    return "".join(tf_example.features.feature[self.field_name]
+                   .bytes_list.value) if tf_example.features.feature[
+                       self.field_name].HasField("bytes_list") else None
+
+
+class Int64Parser(data_parser.DataToNumpyParser):
+  """Tensorflow Example int64 parser."""
+
+  def __init__(self, field_name):
+    self.field_name = field_name
+
+  def parse(self, tf_example):
+    return np.array(
+        tf_example.features.feature[self.field_name].int64_list.value,
+        dtype=np.int64).transpose() if tf_example.features.feature[
+            self.field_name].HasField("int64_list") else None
+
+
+class BoundingBoxParser(data_parser.DataToNumpyParser):
+  """Tensorflow Example bounding box parser."""
+
+  def __init__(self, xmin_field_name, ymin_field_name, xmax_field_name,
+               ymax_field_name):
+    self.field_names = [
+        ymin_field_name, xmin_field_name, ymax_field_name, xmax_field_name
+    ]
+
+  def parse(self, tf_example):
+    result = []
+    parsed = True
+    for field_name in self.field_names:
+      result.append(tf_example.features.feature[field_name].float_list.value)
+      parsed &= (
+          tf_example.features.feature[field_name].HasField("float_list"))
+
+    return np.array(result).transpose() if parsed else None
+
+
+class TfExampleDetectionAndGTParser(data_parser.DataToNumpyParser):
+  """Tensorflow Example proto parser."""
+
+  def __init__(self):
+    self.items_to_handlers = {
+        fields.DetectionResultFields.key:
+            StringParser(fields.TfExampleFields.source_id),
+        # Object ground truth boxes and classes.
+        fields.InputDataFields.groundtruth_boxes: (BoundingBoxParser(
+            fields.TfExampleFields.object_bbox_xmin,
+            fields.TfExampleFields.object_bbox_ymin,
+            fields.TfExampleFields.object_bbox_xmax,
+            fields.TfExampleFields.object_bbox_ymax)),
+        fields.InputDataFields.groundtruth_classes: (
+            Int64Parser(fields.TfExampleFields.object_class_label)),
+        # Object detections.
+        fields.DetectionResultFields.detection_boxes: (BoundingBoxParser(
+            fields.TfExampleFields.detection_bbox_xmin,
+            fields.TfExampleFields.detection_bbox_ymin,
+            fields.TfExampleFields.detection_bbox_xmax,
+            fields.TfExampleFields.detection_bbox_ymax)),
+        fields.DetectionResultFields.detection_classes: (
+            Int64Parser(fields.TfExampleFields.detection_class_label)),
+        fields.DetectionResultFields.detection_scores: (
+            FloatParser(fields.TfExampleFields.detection_score)),
+    }
+
+    self.optional_items_to_handlers = {
+        fields.InputDataFields.groundtruth_difficult:
+            Int64Parser(fields.TfExampleFields.object_difficult),
+        fields.InputDataFields.groundtruth_group_of:
+            Int64Parser(fields.TfExampleFields.object_group_of),
+        fields.InputDataFields.groundtruth_image_classes:
+            Int64Parser(fields.TfExampleFields.image_class_label),
+    }
+
+  def parse(self, tf_example):
+    """Parses tensorflow example and returns a tensor dictionary.
+
+    Args:
+      tf_example: a tf.Example object.
+
+    Returns:
+      A dictionary of the following numpy arrays:
+      fields.DetectionResultFields.source_id - string containing original image
+      id.
+      fields.InputDataFields.groundtruth_boxes - a numpy array containing
+      groundtruth boxes.
+      fields.InputDataFields.groundtruth_classes - a numpy array containing
+      groundtruth classes.
+      fields.InputDataFields.groundtruth_group_of - a numpy array containing
+      groundtruth group of flag (optional, None if not specified).
+      fields.InputDataFields.groundtruth_difficult - a numpy array containing
+      groundtruth difficult flag (optional, None if not specified).
+      fields.InputDataFields.groundtruth_image_classes - a numpy array
+      containing groundtruth image-level labels.
+      fields.DetectionResultFields.detection_boxes - a numpy array containing
+      detection boxes.
+      fields.DetectionResultFields.detection_classes - a numpy array containing
+      detection class labels.
+      fields.DetectionResultFields.detection_scores - a numpy array containing
+      detection scores.
+      Returns None if tf.Example was not parsed or non-optional fields were not
+      found.
+    """
+    results_dict = {}
+    parsed = True
+    for key, parser in self.items_to_handlers.items():
+      results_dict[key] = parser.parse(tf_example)
+      parsed &= (results_dict[key] is not None)
+
+    for key, parser in self.optional_items_to_handlers.items():
+      results_dict[key] = parser.parse(tf_example)
+
+    return results_dict if parsed else None
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser_test.py
new file mode 100644
index 00000000..7d265cc2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/metrics/tf_example_parser_test.py
@@ -0,0 +1,197 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.data_decoders.tf_example_parser."""
+
+import numpy as np
+import numpy.testing as np_testing
+import tensorflow as tf
+
+from object_detection.core import standard_fields as fields
+from object_detection.metrics import tf_example_parser
+
+
+class TfExampleDecoderTest(tf.test.TestCase):
+
+  def _Int64Feature(self, value):
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+  def _FloatFeature(self, value):
+    return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+  def _BytesFeature(self, value):
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+  def testParseDetectionsAndGT(self):
+    source_id = 'abc.jpg'
+    # y_min, x_min, y_max, x_max
+    object_bb = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6], [1.0, 0.6, 0.8],
+                          [1.0, 0.6, 0.7]]).transpose()
+    detection_bb = np.array([[0.1, 0.2], [0.0, 0.8], [1.0, 0.6],
+                             [1.0, 0.85]]).transpose()
+
+    object_class_label = [1, 1, 2]
+    object_difficult = [1, 0, 0]
+    object_group_of = [0, 0, 1]
+    verified_labels = [1, 2, 3, 4]
+    detection_class_label = [2, 1]
+    detection_score = [0.5, 0.3]
+    features = {
+        fields.TfExampleFields.source_id:
+            self._BytesFeature(source_id),
+        fields.TfExampleFields.object_bbox_ymin:
+            self._FloatFeature(object_bb[:, 0].tolist()),
+        fields.TfExampleFields.object_bbox_xmin:
+            self._FloatFeature(object_bb[:, 1].tolist()),
+        fields.TfExampleFields.object_bbox_ymax:
+            self._FloatFeature(object_bb[:, 2].tolist()),
+        fields.TfExampleFields.object_bbox_xmax:
+            self._FloatFeature(object_bb[:, 3].tolist()),
+        fields.TfExampleFields.detection_bbox_ymin:
+            self._FloatFeature(detection_bb[:, 0].tolist()),
+        fields.TfExampleFields.detection_bbox_xmin:
+            self._FloatFeature(detection_bb[:, 1].tolist()),
+        fields.TfExampleFields.detection_bbox_ymax:
+            self._FloatFeature(detection_bb[:, 2].tolist()),
+        fields.TfExampleFields.detection_bbox_xmax:
+            self._FloatFeature(detection_bb[:, 3].tolist()),
+        fields.TfExampleFields.detection_class_label:
+            self._Int64Feature(detection_class_label),
+        fields.TfExampleFields.detection_score:
+            self._FloatFeature(detection_score),
+    }
+
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+    parser = tf_example_parser.TfExampleDetectionAndGTParser()
+
+    results_dict = parser.parse(example)
+    self.assertIsNone(results_dict)
+
+    features[fields.TfExampleFields.object_class_label] = (
+        self._Int64Feature(object_class_label))
+    features[fields.TfExampleFields.object_difficult] = (
+        self._Int64Feature(object_difficult))
+
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+    results_dict = parser.parse(example)
+
+    self.assertIsNotNone(results_dict)
+    self.assertEqual(source_id, results_dict[fields.DetectionResultFields.key])
+    np_testing.assert_almost_equal(
+        object_bb, results_dict[fields.InputDataFields.groundtruth_boxes])
+    np_testing.assert_almost_equal(
+        detection_bb,
+        results_dict[fields.DetectionResultFields.detection_boxes])
+    np_testing.assert_almost_equal(
+        detection_score,
+        results_dict[fields.DetectionResultFields.detection_scores])
+    np_testing.assert_almost_equal(
+        detection_class_label,
+        results_dict[fields.DetectionResultFields.detection_classes])
+    np_testing.assert_almost_equal(
+        object_difficult,
+        results_dict[fields.InputDataFields.groundtruth_difficult])
+    np_testing.assert_almost_equal(
+        object_class_label,
+        results_dict[fields.InputDataFields.groundtruth_classes])
+
+    parser = tf_example_parser.TfExampleDetectionAndGTParser()
+
+    features[fields.TfExampleFields.object_group_of] = (
+        self._Int64Feature(object_group_of))
+
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+    results_dict = parser.parse(example)
+    self.assertIsNotNone(results_dict)
+    np_testing.assert_equal(
+        object_group_of,
+        results_dict[fields.InputDataFields.groundtruth_group_of])
+
+    features[fields.TfExampleFields.image_class_label] = (
+        self._Int64Feature(verified_labels))
+
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+    results_dict = parser.parse(example)
+    self.assertIsNotNone(results_dict)
+    np_testing.assert_equal(
+        verified_labels,
+        results_dict[fields.InputDataFields.groundtruth_image_classes])
+
+  def testParseString(self):
+    string_val = 'abc'
+    features = {'string': self._BytesFeature(string_val)}
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    parser = tf_example_parser.StringParser('string')
+    result = parser.parse(example)
+    self.assertIsNotNone(result)
+    self.assertEqual(result, string_val)
+
+    parser = tf_example_parser.StringParser('another_string')
+    result = parser.parse(example)
+    self.assertIsNone(result)
+
+  def testParseFloat(self):
+    float_array_val = [1.5, 1.4, 2.0]
+    features = {'floats': self._FloatFeature(float_array_val)}
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    parser = tf_example_parser.FloatParser('floats')
+    result = parser.parse(example)
+    self.assertIsNotNone(result)
+    np_testing.assert_almost_equal(result, float_array_val)
+
+    parser = tf_example_parser.StringParser('another_floats')
+    result = parser.parse(example)
+    self.assertIsNone(result)
+
+  def testInt64Parser(self):
+    int_val = [1, 2, 3]
+    features = {'ints': self._Int64Feature(int_val)}
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    parser = tf_example_parser.Int64Parser('ints')
+    result = parser.parse(example)
+    self.assertIsNotNone(result)
+    np_testing.assert_almost_equal(result, int_val)
+
+    parser = tf_example_parser.Int64Parser('another_ints')
+    result = parser.parse(example)
+    self.assertIsNone(result)
+
+  def testBoundingBoxParser(self):
+    bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
+                               [1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
+    features = {
+        'ymin': self._FloatFeature(bounding_boxes[:, 0]),
+        'xmin': self._FloatFeature(bounding_boxes[:, 1]),
+        'ymax': self._FloatFeature(bounding_boxes[:, 2]),
+        'xmax': self._FloatFeature(bounding_boxes[:, 3])
+    }
+
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
+    result = parser.parse(example)
+    self.assertIsNotNone(result)
+    np_testing.assert_almost_equal(result, bounding_boxes)
+
+    parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
+                                                 'another_ymax')
+    result = parser.parse(example)
+    self.assertIsNone(result)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/model_hparams.py b/TensorFlow/Detection/SSD/models/research/object_detection/model_hparams.py
new file mode 100644
index 00000000..b0d12fce
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/model_hparams.py
@@ -0,0 +1,44 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Hyperparameters for the object detection model in TF.learn.
+
+This file consolidates and documents the hyperparameters used by the model.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+
+def create_hparams(hparams_overrides=None):
+  """Returns hyperparameters, including any flag value overrides.
+
+  Args:
+    hparams_overrides: Optional hparams overrides, represented as a
+      string containing comma-separated hparam_name=value pairs.
+
+  Returns:
+    The hyperparameters as a tf.HParams object.
+  """
+  hparams = tf.contrib.training.HParams(
+      # Whether a fine tuning checkpoint (provided in the pipeline config)
+      # should be loaded for training.
+      load_pretrained=True)
+  # Override any of the preceding hyperparameter values.
+  if hparams_overrides:
+    hparams = hparams.parse(hparams_overrides)
+  return hparams
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/model_lib.py b/TensorFlow/Detection/SSD/models/research/object_detection/model_lib.py
new file mode 100644
index 00000000..e85b92b7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/model_lib.py
@@ -0,0 +1,839 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+r"""Constructs model, inputs, and training environment."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import functools
+import os
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from object_detection import eval_util
+from object_detection import exporter as exporter_lib
+from object_detection import inputs
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import model_builder
+from object_detection.builders import optimizer_builder
+from object_detection.core import standard_fields as fields
+from object_detection.utils import config_util
+from object_detection.utils import label_map_util
+from object_detection.utils import shape_utils
+from object_detection.utils import variables_helper
+from object_detection.utils import visualization_utils as vis_utils
+
+# A map of names to methods that help build the model.
+MODEL_BUILD_UTIL_MAP = {
+    'get_configs_from_pipeline_file':
+        config_util.get_configs_from_pipeline_file,
+    'create_pipeline_proto_from_configs':
+        config_util.create_pipeline_proto_from_configs,
+    'merge_external_params_with_configs':
+        config_util.merge_external_params_with_configs,
+    'create_train_input_fn':
+        inputs.create_train_input_fn,
+    'create_eval_input_fn':
+        inputs.create_eval_input_fn,
+    'create_predict_input_fn':
+        inputs.create_predict_input_fn,
+}
+
+
+def _prepare_groundtruth_for_eval(detection_model, class_agnostic,
+                                  max_number_of_boxes):
+  """Extracts groundtruth data from detection_model and prepares it for eval.
+
+  Args:
+    detection_model: A `DetectionModel` object.
+    class_agnostic: Whether the detections are class_agnostic.
+    max_number_of_boxes: Max number of groundtruth boxes.
+
+  Returns:
+    A tuple of:
+    groundtruth: Dictionary with the following fields:
+      'groundtruth_boxes': [batch_size, num_boxes, 4] float32 tensor of boxes,
+        in normalized coordinates.
+      'groundtruth_classes': [batch_size, num_boxes] int64 tensor of 1-indexed
+        classes.
+      'groundtruth_masks': 4D float32 tensor of instance masks (if provided in
+        groundtruth)
+      'groundtruth_is_crowd': [batch_size, num_boxes] bool tensor indicating
+        is_crowd annotations (if provided in groundtruth).
+      'num_groundtruth_boxes': [batch_size] tensor containing the maximum number
+        of groundtruth boxes per image..
+    class_agnostic: Boolean indicating whether detections are class agnostic.
+  """
+  input_data_fields = fields.InputDataFields()
+  groundtruth_boxes = tf.stack(
+      detection_model.groundtruth_lists(fields.BoxListFields.boxes))
+  groundtruth_boxes_shape = tf.shape(groundtruth_boxes)
+  # For class-agnostic models, groundtruth one-hot encodings collapse to all
+  # ones.
+  if class_agnostic:
+    groundtruth_classes_one_hot = tf.ones(
+        [groundtruth_boxes_shape[0], groundtruth_boxes_shape[1], 1])
+  else:
+    groundtruth_classes_one_hot = tf.stack(
+        detection_model.groundtruth_lists(fields.BoxListFields.classes))
+  label_id_offset = 1  # Applying label id offset (b/63711816)
+  groundtruth_classes = (
+      tf.argmax(groundtruth_classes_one_hot, axis=2) + label_id_offset)
+  groundtruth = {
+      input_data_fields.groundtruth_boxes: groundtruth_boxes,
+      input_data_fields.groundtruth_classes: groundtruth_classes
+  }
+  if detection_model.groundtruth_has_field(fields.BoxListFields.masks):
+    groundtruth[input_data_fields.groundtruth_instance_masks] = tf.stack(
+        detection_model.groundtruth_lists(fields.BoxListFields.masks))
+
+  if detection_model.groundtruth_has_field(fields.BoxListFields.is_crowd):
+    groundtruth[input_data_fields.groundtruth_is_crowd] = tf.stack(
+        detection_model.groundtruth_lists(fields.BoxListFields.is_crowd))
+
+  groundtruth[input_data_fields.num_groundtruth_boxes] = (
+      tf.tile([max_number_of_boxes], multiples=[groundtruth_boxes_shape[0]]))
+  return groundtruth
+
+
+def unstack_batch(tensor_dict, unpad_groundtruth_tensors=True):
+  """Unstacks all tensors in `tensor_dict` along 0th dimension.
+
+  Unstacks tensor from the tensor dict along 0th dimension and returns a
+  tensor_dict containing values that are lists of unstacked, unpadded tensors.
+
+  Tensors in the `tensor_dict` are expected to be of one of the three shapes:
+  1. [batch_size]
+  2. [batch_size, height, width, channels]
+  3. [batch_size, num_boxes, d1, d2, ... dn]
+
+  When unpad_groundtruth_tensors is set to true, unstacked tensors of form 3
+  above are sliced along the `num_boxes` dimension using the value in tensor
+  field.InputDataFields.num_groundtruth_boxes.
+
+  Note that this function has a static list of input data fields and has to be
+  kept in sync with the InputDataFields defined in core/standard_fields.py
+
+  Args:
+    tensor_dict: A dictionary of batched groundtruth tensors.
+    unpad_groundtruth_tensors: Whether to remove padding along `num_boxes`
+      dimension of the groundtruth tensors.
+
+  Returns:
+    A dictionary where the keys are from fields.InputDataFields and values are
+    a list of unstacked (optionally unpadded) tensors.
+
+  Raises:
+    ValueError: If unpad_tensors is True and `tensor_dict` does not contain
+      `num_groundtruth_boxes` tensor.
+  """
+  unbatched_tensor_dict = {
+      key: tf.unstack(tensor) for key, tensor in tensor_dict.items()
+  }
+  if unpad_groundtruth_tensors:
+    if (fields.InputDataFields.num_groundtruth_boxes not in
+        unbatched_tensor_dict):
+      raise ValueError('`num_groundtruth_boxes` not found in tensor_dict. '
+                       'Keys available: {}'.format(
+                           unbatched_tensor_dict.keys()))
+    unbatched_unpadded_tensor_dict = {}
+    unpad_keys = set([
+        # List of input data fields that are padded along the num_boxes
+        # dimension. This list has to be kept in sync with InputDataFields in
+        # standard_fields.py.
+        fields.InputDataFields.groundtruth_instance_masks,
+        fields.InputDataFields.groundtruth_classes,
+        fields.InputDataFields.groundtruth_boxes,
+        fields.InputDataFields.groundtruth_keypoints,
+        fields.InputDataFields.groundtruth_group_of,
+        fields.InputDataFields.groundtruth_difficult,
+        fields.InputDataFields.groundtruth_is_crowd,
+        fields.InputDataFields.groundtruth_area,
+        fields.InputDataFields.groundtruth_weights
+    ]).intersection(set(unbatched_tensor_dict.keys()))
+
+    for key in unpad_keys:
+      unpadded_tensor_list = []
+      for num_gt, padded_tensor in zip(
+          unbatched_tensor_dict[fields.InputDataFields.num_groundtruth_boxes],
+          unbatched_tensor_dict[key]):
+        tensor_shape = shape_utils.combined_static_and_dynamic_shape(
+            padded_tensor)
+        slice_begin = tf.zeros([len(tensor_shape)], dtype=tf.int32)
+        slice_size = tf.stack(
+            [num_gt] + [-1 if dim is None else dim for dim in tensor_shape[1:]])
+        unpadded_tensor = tf.slice(padded_tensor, slice_begin, slice_size)
+        unpadded_tensor_list.append(unpadded_tensor)
+      unbatched_unpadded_tensor_dict[key] = unpadded_tensor_list
+    unbatched_tensor_dict.update(unbatched_unpadded_tensor_dict)
+
+  return unbatched_tensor_dict
+
+
+def create_model_fn(detection_model_fn, configs, hparams, use_tpu=False):
+  """Creates a model function for `Estimator`.
+
+  Args:
+    detection_model_fn: Function that returns a `DetectionModel` instance.
+    configs: Dictionary of pipeline config objects.
+    hparams: `HParams` object.
+    use_tpu: Boolean indicating whether model should be constructed for
+        use on TPU.
+
+  Returns:
+    `model_fn` for `Estimator`.
+  """
+  train_config = configs['train_config']
+  eval_input_config = configs['eval_input_config']
+  eval_config = configs['eval_config']
+
+  def model_fn(features, labels, mode, params=None):
+    """Constructs the object detection model.
+
+    Args:
+      features: Dictionary of feature tensors, returned from `input_fn`.
+      labels: Dictionary of groundtruth tensors if mode is TRAIN or EVAL,
+        otherwise None.
+      mode: Mode key from tf.estimator.ModeKeys.
+      params: Parameter dictionary passed from the estimator.
+
+    Returns:
+      An `EstimatorSpec` that encapsulates the model and its serving
+        configurations.
+    """
+    params = params or {}
+    total_loss, train_op, detections, export_outputs = None, None, None, None
+    is_training = mode == tf.estimator.ModeKeys.TRAIN
+
+    # Make sure to set the Keras learning phase. True during training,
+    # False for inference.
+    tf.keras.backend.set_learning_phase(is_training)
+    detection_model = detection_model_fn(
+        is_training=is_training, add_summaries=(not use_tpu))
+    scaffold_fn = None
+
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      labels = unstack_batch(
+          labels,
+          unpad_groundtruth_tensors=train_config.unpad_groundtruth_tensors)
+    elif mode == tf.estimator.ModeKeys.EVAL:
+      # For evaling on train data, it is necessary to check whether groundtruth
+      # must be unpadded.
+      boxes_shape = (
+          labels[fields.InputDataFields.groundtruth_boxes].get_shape()
+          .as_list())
+      unpad_groundtruth_tensors = boxes_shape[1] is not None and not use_tpu
+      labels = unstack_batch(
+          labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
+
+    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
+      gt_boxes_list = labels[fields.InputDataFields.groundtruth_boxes]
+      gt_classes_list = labels[fields.InputDataFields.groundtruth_classes]
+      gt_masks_list = None
+      if fields.InputDataFields.groundtruth_instance_masks in labels:
+        gt_masks_list = labels[
+            fields.InputDataFields.groundtruth_instance_masks]
+      gt_keypoints_list = None
+      if fields.InputDataFields.groundtruth_keypoints in labels:
+        gt_keypoints_list = labels[fields.InputDataFields.groundtruth_keypoints]
+      gt_weights_list = None
+      if fields.InputDataFields.groundtruth_weights in labels:
+        gt_weights_list = labels[fields.InputDataFields.groundtruth_weights]
+      gt_confidences_list = None
+      if fields.InputDataFields.groundtruth_confidences in labels:
+        gt_confidences_list = labels[
+            fields.InputDataFields.groundtruth_confidences]
+      gt_is_crowd_list = None
+      if fields.InputDataFields.groundtruth_is_crowd in labels:
+        gt_is_crowd_list = labels[fields.InputDataFields.groundtruth_is_crowd]
+      detection_model.provide_groundtruth(
+          groundtruth_boxes_list=gt_boxes_list,
+          groundtruth_classes_list=gt_classes_list,
+          groundtruth_confidences_list=gt_confidences_list,
+          groundtruth_masks_list=gt_masks_list,
+          groundtruth_keypoints_list=gt_keypoints_list,
+          groundtruth_weights_list=gt_weights_list,
+          groundtruth_is_crowd_list=gt_is_crowd_list)
+
+    preprocessed_images = features[fields.InputDataFields.image]
+    if use_tpu and train_config.use_bfloat16:
+      with tf.contrib.tpu.bfloat16_scope():
+        prediction_dict = detection_model.predict(
+            preprocessed_images,
+            features[fields.InputDataFields.true_image_shape])
+        for k, v in prediction_dict.items():
+          if v.dtype == tf.bfloat16:
+            prediction_dict[k] = tf.cast(v, tf.float32)
+    else:
+      prediction_dict = detection_model.predict(
+          preprocessed_images,
+          features[fields.InputDataFields.true_image_shape])
+    if mode in (tf.estimator.ModeKeys.EVAL, tf.estimator.ModeKeys.PREDICT):
+      detections = detection_model.postprocess(
+          prediction_dict, features[fields.InputDataFields.true_image_shape])
+
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      if train_config.fine_tune_checkpoint and hparams.load_pretrained:
+        if not train_config.fine_tune_checkpoint_type:
+          # train_config.from_detection_checkpoint field is deprecated. For
+          # backward compatibility, set train_config.fine_tune_checkpoint_type
+          # based on train_config.from_detection_checkpoint.
+          if train_config.from_detection_checkpoint:
+            train_config.fine_tune_checkpoint_type = 'detection'
+          else:
+            train_config.fine_tune_checkpoint_type = 'classification'
+        asg_map = detection_model.restore_map(
+            fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
+            load_all_detection_checkpoint_vars=(
+                train_config.load_all_detection_checkpoint_vars))
+        available_var_map = (
+            variables_helper.get_variables_available_in_checkpoint(
+                asg_map,
+                train_config.fine_tune_checkpoint,
+                include_global_step=False))
+        if use_tpu:
+
+          def tpu_scaffold():
+            tf.train.init_from_checkpoint(train_config.fine_tune_checkpoint,
+                                          available_var_map)
+            return tf.train.Scaffold()
+
+          scaffold_fn = tpu_scaffold
+        else:
+          tf.train.init_from_checkpoint(train_config.fine_tune_checkpoint,
+                                        available_var_map)
+
+    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
+      losses_dict = detection_model.loss(
+          prediction_dict, features[fields.InputDataFields.true_image_shape])
+      losses = [loss_tensor for loss_tensor in losses_dict.values()]
+      if train_config.add_regularization_loss:
+        regularization_losses = detection_model.regularization_losses()
+        if regularization_losses:
+          regularization_loss = tf.add_n(
+              regularization_losses, name='regularization_loss')
+          losses.append(regularization_loss)
+          losses_dict['Loss/regularization_loss'] = regularization_loss
+      total_loss = tf.add_n(losses, name='total_loss')
+      losses_dict['Loss/total_loss'] = total_loss
+
+      if 'graph_rewriter_config' in configs:
+        graph_rewriter_fn = graph_rewriter_builder.build(
+            configs['graph_rewriter_config'], is_training=is_training)
+        graph_rewriter_fn()
+
+      # TODO(rathodv): Stop creating optimizer summary vars in EVAL mode once we
+      # can write learning rate summaries on TPU without host calls.
+      global_step = tf.train.get_or_create_global_step()
+      training_optimizer, optimizer_summary_vars = optimizer_builder.build(
+          train_config.optimizer)
+
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      if use_tpu:
+        training_optimizer = tf.contrib.tpu.CrossShardOptimizer(
+            training_optimizer)
+
+      # Optionally freeze some layers by setting their gradients to be zero.
+      trainable_variables = None
+      include_variables = (
+          train_config.update_trainable_variables
+          if train_config.update_trainable_variables else None)
+      exclude_variables = (
+          train_config.freeze_variables
+          if train_config.freeze_variables else None)
+      trainable_variables = tf.contrib.framework.filter_variables(
+          tf.trainable_variables(),
+          include_patterns=include_variables,
+          exclude_patterns=exclude_variables)
+
+      clip_gradients_value = None
+      if train_config.gradient_clipping_by_norm > 0:
+        clip_gradients_value = train_config.gradient_clipping_by_norm
+
+      if not use_tpu:
+        for var in optimizer_summary_vars:
+          tf.summary.scalar(var.op.name, var)
+      summaries = [] if use_tpu else None
+      if train_config.summarize_gradients:
+        summaries = ['gradients', 'gradient_norm', 'global_gradient_norm']
+      train_op = tf.contrib.layers.optimize_loss(
+          loss=total_loss,
+          global_step=global_step,
+          learning_rate=None,
+          clip_gradients=clip_gradients_value,
+          optimizer=training_optimizer,
+          update_ops=detection_model.updates(),
+          variables=trainable_variables,
+          summaries=summaries,
+          name='')  # Preventing scope prefix on all variables.
+
+    if mode == tf.estimator.ModeKeys.PREDICT:
+      exported_output = exporter_lib.add_output_tensor_nodes(detections)
+      export_outputs = {
+          tf.saved_model.signature_constants.PREDICT_METHOD_NAME:
+              tf.estimator.export.PredictOutput(exported_output)
+      }
+
+    eval_metric_ops = None
+    scaffold = None
+    if mode == tf.estimator.ModeKeys.EVAL:
+      class_agnostic = (
+          fields.DetectionResultFields.detection_classes not in detections)
+      groundtruth = _prepare_groundtruth_for_eval(
+          detection_model, class_agnostic,
+          eval_input_config.max_number_of_boxes)
+      use_original_images = fields.InputDataFields.original_image in features
+      if use_original_images:
+        eval_images = features[fields.InputDataFields.original_image]
+        true_image_shapes = tf.slice(
+            features[fields.InputDataFields.true_image_shape], [0, 0], [-1, 3])
+        original_image_spatial_shapes = features[fields.InputDataFields
+                                                 .original_image_spatial_shape]
+      else:
+        eval_images = features[fields.InputDataFields.image]
+        true_image_shapes = None
+        original_image_spatial_shapes = None
+
+      eval_dict = eval_util.result_dict_for_batched_example(
+          eval_images,
+          features[inputs.HASH_KEY],
+          detections,
+          groundtruth,
+          class_agnostic=class_agnostic,
+          scale_to_absolute=True,
+          original_image_spatial_shapes=original_image_spatial_shapes,
+          true_image_shapes=true_image_shapes)
+
+      if class_agnostic:
+        category_index = label_map_util.create_class_agnostic_category_index()
+      else:
+        category_index = label_map_util.create_category_index_from_labelmap(
+            eval_input_config.label_map_path)
+      vis_metric_ops = None
+      if not use_tpu and use_original_images:
+        eval_metric_op_vis = vis_utils.VisualizeSingleFrameDetections(
+            category_index,
+            max_examples_to_draw=eval_config.num_visualizations,
+            max_boxes_to_draw=eval_config.max_num_boxes_to_visualize,
+            min_score_thresh=eval_config.min_score_threshold,
+            use_normalized_coordinates=False)
+        vis_metric_ops = eval_metric_op_vis.get_estimator_eval_metric_ops(
+            eval_dict)
+
+      # Eval metrics on a single example.
+      eval_metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
+          eval_config, list(category_index.values()), eval_dict)
+      for loss_key, loss_tensor in iter(losses_dict.items()):
+        eval_metric_ops[loss_key] = tf.metrics.mean(loss_tensor)
+      for var in optimizer_summary_vars:
+        eval_metric_ops[var.op.name] = (var, tf.no_op())
+      if vis_metric_ops is not None:
+        eval_metric_ops.update(vis_metric_ops)
+      eval_metric_ops = {str(k): v for k, v in eval_metric_ops.items()}
+
+      if eval_config.use_moving_averages:
+        variable_averages = tf.train.ExponentialMovingAverage(0.0)
+        variables_to_restore = variable_averages.variables_to_restore()
+        keep_checkpoint_every_n_hours = (
+            train_config.keep_checkpoint_every_n_hours)
+        saver = tf.train.Saver(
+            variables_to_restore,
+            keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
+        scaffold = tf.train.Scaffold(saver=saver)
+
+    # EVAL executes on CPU, so use regular non-TPU EstimatorSpec.
+    if use_tpu and mode != tf.estimator.ModeKeys.EVAL:
+      return tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          scaffold_fn=scaffold_fn,
+          predictions=detections,
+          loss=total_loss,
+          train_op=train_op,
+          eval_metrics=eval_metric_ops,
+          export_outputs=export_outputs)
+    else:
+      if scaffold is None:
+        keep_checkpoint_every_n_hours = (
+            train_config.keep_checkpoint_every_n_hours)
+        saver = tf.train.Saver(
+            sharded=True,
+            keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
+            save_relative_paths=True)
+        tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
+        scaffold = tf.train.Scaffold(saver=saver)
+      return tf.estimator.EstimatorSpec(
+          mode=mode,
+          predictions=detections,
+          loss=total_loss,
+          train_op=train_op,
+          eval_metric_ops=eval_metric_ops,
+          export_outputs=export_outputs,
+          scaffold=scaffold)
+
+  return model_fn
+
+
+def create_estimator_and_inputs(run_config,
+                                hparams,
+                                pipeline_config_path,
+                                eval_count=1,
+                                config_override=None,
+                                train_steps=None,
+                                sample_1_of_n_eval_examples=1,
+                                sample_1_of_n_eval_on_train_examples=1,
+                                model_fn_creator=create_model_fn,
+                                use_tpu_estimator=False,
+                                use_tpu=False,
+                                num_shards=1,
+                                params=None,
+                                override_eval_num_epochs=True,
+                                save_final_config=False,
+                                **kwargs):
+  """Creates `Estimator`, input functions, and steps.
+
+  Args:
+    run_config: A `RunConfig`.
+    hparams: A `HParams`.
+    pipeline_config_path: A path to a pipeline config file.
+    config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to
+      override the config from `pipeline_config_path`.
+    train_steps: Number of training steps. If None, the number of training steps
+      is set from the `TrainConfig` proto.
+    sample_1_of_n_eval_examples: Integer representing how often an eval example
+      should be sampled. If 1, will sample all examples.
+    sample_1_of_n_eval_on_train_examples: Similar to
+      `sample_1_of_n_eval_examples`, except controls the sampling of training
+      data for evaluation.
+    model_fn_creator: A function that creates a `model_fn` for `Estimator`.
+      Follows the signature:
+
+      * Args:
+        * `detection_model_fn`: Function that returns `DetectionModel` instance.
+        * `configs`: Dictionary of pipeline config objects.
+        * `hparams`: `HParams` object.
+      * Returns:
+        `model_fn` for `Estimator`.
+
+    use_tpu_estimator: Whether a `TPUEstimator` should be returned. If False,
+      an `Estimator` will be returned.
+    use_tpu: Boolean, whether training and evaluation should run on TPU. Only
+      used if `use_tpu_estimator` is True.
+    num_shards: Number of shards (TPU cores). Only used if `use_tpu_estimator`
+      is True.
+    params: Parameter dictionary passed from the estimator. Only used if
+      `use_tpu_estimator` is True.
+    override_eval_num_epochs: Whether to overwrite the number of epochs to
+      1 for eval_input.
+    save_final_config: Whether to save final config (obtained after applying
+      overrides) to `estimator.model_dir`.
+    **kwargs: Additional keyword arguments for configuration override.
+
+  Returns:
+    A dictionary with the following fields:
+    'estimator': An `Estimator` or `TPUEstimator`.
+    'train_input_fn': A training input function.
+    'eval_input_fns': A list of all evaluation input functions.
+    'eval_input_names': A list of names for each evaluation input.
+    'eval_on_train_input_fn': An evaluation-on-train input function.
+    'predict_input_fn': A prediction input function.
+    'train_steps': Number of training steps. Either directly from input or from
+      configuration.
+  """
+  get_configs_from_pipeline_file = MODEL_BUILD_UTIL_MAP[
+      'get_configs_from_pipeline_file']
+  merge_external_params_with_configs = MODEL_BUILD_UTIL_MAP[
+      'merge_external_params_with_configs']
+  create_pipeline_proto_from_configs = MODEL_BUILD_UTIL_MAP[
+      'create_pipeline_proto_from_configs']
+  create_train_input_fn = MODEL_BUILD_UTIL_MAP['create_train_input_fn']
+  create_eval_input_fn = MODEL_BUILD_UTIL_MAP['create_eval_input_fn']
+  create_predict_input_fn = MODEL_BUILD_UTIL_MAP['create_predict_input_fn']
+
+  configs = get_configs_from_pipeline_file(pipeline_config_path,
+                                           config_override=config_override)
+  kwargs.update({
+      'train_steps': train_steps,
+      'sample_1_of_n_eval_examples': sample_1_of_n_eval_examples
+  })
+  if override_eval_num_epochs:
+    kwargs.update({'eval_num_epochs': 1})
+    tf.logging.warning(
+        'Forced number of epochs for all eval validations to be 1.')
+  configs = merge_external_params_with_configs(
+      configs, hparams, kwargs_dict=kwargs)
+  model_config = configs['model']
+  train_config = configs['train_config']
+  train_input_config = configs['train_input_config']
+  eval_config = configs['eval_config']
+  eval_input_configs = configs['eval_input_configs']
+  eval_on_train_input_config = copy.deepcopy(train_input_config)
+  eval_on_train_input_config.sample_1_of_n_examples = (
+      sample_1_of_n_eval_on_train_examples)
+  if override_eval_num_epochs and eval_on_train_input_config.num_epochs != 1:
+    tf.logging.warning('Expected number of evaluation epochs is 1, but '
+                       'instead encountered `eval_on_train_input_config'
+                       '.num_epochs` = '
+                       '{}. Overwriting `num_epochs` to 1.'.format(
+                           eval_on_train_input_config.num_epochs))
+    eval_on_train_input_config.num_epochs = 1
+
+  # update train_steps from config but only when non-zero value is provided
+  if train_steps is None and train_config.num_steps != 0:
+    train_steps = train_config.num_steps
+
+  detection_model_fn = functools.partial(
+      model_builder.build, model_config=model_config)
+
+  # Create the input functions for TRAIN/EVAL/PREDICT.
+  train_input_fn = create_train_input_fn(
+      train_config=train_config,
+      train_input_config=train_input_config,
+      model_config=model_config)
+  eval_input_fns = [
+      create_eval_input_fn(
+          eval_config=eval_config,
+          eval_input_config=eval_input_config,
+          model_config=model_config) for eval_input_config in eval_input_configs
+  ]
+  eval_input_names = [
+      eval_input_config.name for eval_input_config in eval_input_configs
+  ]
+  eval_on_train_input_fn = create_eval_input_fn(
+      eval_config=eval_config,
+      eval_input_config=eval_on_train_input_config,
+      model_config=model_config)
+  predict_input_fn = create_predict_input_fn(
+      model_config=model_config, predict_input_config=eval_input_configs[0])
+
+  export_to_tpu = hparams.get('export_to_tpu', False)
+  tf.logging.info('create_estimator_and_inputs: use_tpu %s, export_to_tpu %s',
+                  use_tpu, export_to_tpu)
+  model_fn = model_fn_creator(detection_model_fn, configs, hparams, use_tpu)
+  run_config = tf.estimator.RunConfig(model_dir=run_config.model_dir,
+                                      session_config=run_config.session_config,
+                                      save_checkpoints_steps=train_steps // eval_count)
+  if use_tpu_estimator:
+    estimator = tf.contrib.tpu.TPUEstimator(
+        model_fn=model_fn,
+        train_batch_size=train_config.batch_size,
+        # For each core, only batch size 1 is supported for eval.
+        eval_batch_size=num_shards * 1 if use_tpu else 1,
+        use_tpu=use_tpu,
+        config=run_config,
+        # TODO(lzc): Remove conditional after CMLE moves to TF 1.9
+        params=params if params else {})
+  else:
+    estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)
+
+  # Write the as-run pipeline config to disk.
+  if run_config.is_chief and save_final_config:
+    pipeline_config_final = create_pipeline_proto_from_configs(configs)
+    config_util.save_pipeline_config(pipeline_config_final, estimator.model_dir)
+
+  return dict(
+      estimator=estimator,
+      train_input_fn=train_input_fn,
+      eval_input_fns=eval_input_fns,
+      eval_input_names=eval_input_names,
+      eval_on_train_input_fn=eval_on_train_input_fn,
+      predict_input_fn=predict_input_fn,
+      train_steps=train_steps)
+
+
+def create_train_and_eval_specs(train_input_fn,
+                                eval_input_fns,
+                                eval_on_train_input_fn,
+                                predict_input_fn,
+                                train_steps,
+                                eval_on_train_data=False,
+                                final_exporter_name='Servo',
+                                eval_spec_names=None):
+  """Creates a `TrainSpec` and `EvalSpec`s.
+
+  Args:
+    train_input_fn: Function that produces features and labels on train data.
+    eval_input_fns: A list of functions that produce features and labels on eval
+      data.
+    eval_on_train_input_fn: Function that produces features and labels for
+      evaluation on train data.
+    predict_input_fn: Function that produces features for inference.
+    train_steps: Number of training steps.
+    eval_on_train_data: Whether to evaluate model on training data. Default is
+      False.
+    final_exporter_name: String name given to `FinalExporter`.
+    eval_spec_names: A list of string names for each `EvalSpec`.
+
+  Returns:
+    Tuple of `TrainSpec` and list of `EvalSpecs`. If `eval_on_train_data` is
+    True, the last `EvalSpec` in the list will correspond to training data. The
+    rest EvalSpecs in the list are evaluation datas.
+  """
+  train_spec = tf.estimator.TrainSpec(
+      input_fn=train_input_fn,
+      max_steps=train_steps // hvd.size(), # no `steps' attribute; only max_steps available
+      hooks=[hvd.BroadcastGlobalVariablesHook(0)])
+
+  if eval_spec_names is None:
+    eval_spec_names = [str(i) for i in range(len(eval_input_fns))]
+
+  eval_specs = []
+  for index, (eval_spec_name, eval_input_fn) in enumerate(
+      zip(eval_spec_names, eval_input_fns)):
+    # Uses final_exporter_name as exporter_name for the first eval spec for
+    # backward compatibility.
+    if index == 0:
+      exporter_name = final_exporter_name
+    else:
+      exporter_name = '{}_{}'.format(final_exporter_name, eval_spec_name)
+    exporter = tf.estimator.FinalExporter(
+        name=exporter_name, serving_input_receiver_fn=predict_input_fn)
+    eval_specs.append(
+        tf.estimator.EvalSpec(
+            name=eval_spec_name,
+            input_fn=eval_input_fn,
+            steps=None,
+            exporters=exporter))
+
+  if eval_on_train_data:
+    eval_specs.append(
+        tf.estimator.EvalSpec(
+            name='eval_on_train', input_fn=eval_on_train_input_fn, steps=None))
+
+  return train_spec, eval_specs
+
+
+def continuous_eval(estimator, model_dir, input_fn, train_steps, name):
+  """Perform continuous evaluation on checkpoints written to a model directory.
+
+  Args:
+    estimator: Estimator object to use for evaluation.
+    model_dir: Model directory to read checkpoints for continuous evaluation.
+    input_fn: Input function to use for evaluation.
+    train_steps: Number of training steps. This is used to infer the last
+      checkpoint and stop evaluation loop.
+    name: Namescope for eval summary.
+  """
+
+  def terminate_eval():
+    tf.logging.info('Terminating eval after 180 seconds of no checkpoints')
+    return True
+
+  for ckpt in tf.contrib.training.checkpoints_iterator(
+      model_dir, min_interval_secs=180, timeout=None,
+      timeout_fn=terminate_eval):
+
+    tf.logging.info('Starting Evaluation.')
+    try:
+      eval_results = estimator.evaluate(
+          input_fn=input_fn, steps=None, checkpoint_path=ckpt, name=name)
+      tf.logging.info('Eval results: %s' % eval_results)
+
+      # Terminate eval job when final checkpoint is reached
+      current_step = int(os.path.basename(ckpt).split('-')[1])
+      if current_step >= train_steps:
+        tf.logging.info(
+            'Evaluation finished after training step %d' % current_step)
+        break
+
+    except tf.errors.NotFoundError:
+      tf.logging.info(
+          'Checkpoint %s no longer exists, skipping checkpoint' % ckpt)
+
+
+def populate_experiment(run_config,
+                        hparams,
+                        pipeline_config_path,
+                        train_steps=None,
+                        eval_steps=None,
+                        model_fn_creator=create_model_fn,
+                        **kwargs):
+  """Populates an `Experiment` object.
+
+  EXPERIMENT CLASS IS DEPRECATED. Please switch to
+  tf.estimator.train_and_evaluate. As an example, see model_main.py.
+
+  Args:
+    run_config: A `RunConfig`.
+    hparams: A `HParams`.
+    pipeline_config_path: A path to a pipeline config file.
+    train_steps: Number of training steps. If None, the number of training steps
+      is set from the `TrainConfig` proto.
+    eval_steps: Number of evaluation steps per evaluation cycle. If None, the
+      number of evaluation steps is set from the `EvalConfig` proto.
+    model_fn_creator: A function that creates a `model_fn` for `Estimator`.
+      Follows the signature:
+
+      * Args:
+        * `detection_model_fn`: Function that returns `DetectionModel` instance.
+        * `configs`: Dictionary of pipeline config objects.
+        * `hparams`: `HParams` object.
+      * Returns:
+        `model_fn` for `Estimator`.
+
+    **kwargs: Additional keyword arguments for configuration override.
+
+  Returns:
+    An `Experiment` that defines all aspects of training, evaluation, and
+    export.
+  """
+  tf.logging.warning('Experiment is being deprecated. Please use '
+                     'tf.estimator.train_and_evaluate(). See model_main.py for '
+                     'an example.')
+  train_and_eval_dict = create_estimator_and_inputs(
+      run_config,
+      hparams,
+      pipeline_config_path,
+      train_steps=train_steps,
+      eval_steps=eval_steps,
+      model_fn_creator=model_fn_creator,
+      save_final_config=True,
+      **kwargs)
+  estimator = train_and_eval_dict['estimator']
+  train_input_fn = train_and_eval_dict['train_input_fn']
+  eval_input_fns = train_and_eval_dict['eval_input_fns']
+  predict_input_fn = train_and_eval_dict['predict_input_fn']
+  train_steps = train_and_eval_dict['train_steps']
+
+  export_strategies = [
+      tf.contrib.learn.utils.saved_model_export_utils.make_export_strategy(
+          serving_input_fn=predict_input_fn)
+  ]
+
+  return tf.contrib.learn.Experiment(
+      estimator=estimator,
+      train_input_fn=train_input_fn,
+      eval_input_fn=eval_input_fns[0],
+      train_steps=train_steps,
+      eval_steps=None,
+      export_strategies=export_strategies,
+      eval_delay_secs=120,
+  )
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/model_lib_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/model_lib_test.py
new file mode 100644
index 00000000..c61fbb6e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/model_lib_test.py
@@ -0,0 +1,430 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object detection model library."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.contrib.tpu.python.tpu import tpu_config
+from tensorflow.contrib.tpu.python.tpu import tpu_estimator
+
+from object_detection import inputs
+from object_detection import model_hparams
+from object_detection import model_lib
+from object_detection.builders import model_builder
+from object_detection.core import standard_fields as fields
+from object_detection.utils import config_util
+
+
+# Model for test. Options are:
+# 'ssd_inception_v2_pets', 'faster_rcnn_resnet50_pets'
+MODEL_NAME_FOR_TEST = 'ssd_inception_v2_pets'
+
+
+def _get_data_path():
+  """Returns an absolute path to TFRecord file."""
+  return os.path.join(tf.resource_loader.get_data_files_path(), 'test_data',
+                      'pets_examples.record')
+
+
+def get_pipeline_config_path(model_name):
+  """Returns path to the local pipeline config file."""
+  return os.path.join(tf.resource_loader.get_data_files_path(), 'samples',
+                      'configs', model_name + '.config')
+
+
+def _get_labelmap_path():
+  """Returns an absolute path to label map file."""
+  return os.path.join(tf.resource_loader.get_data_files_path(), 'data',
+                      'pet_label_map.pbtxt')
+
+
+def _get_configs_for_model(model_name):
+  """Returns configurations for model."""
+  filename = get_pipeline_config_path(model_name)
+  data_path = _get_data_path()
+  label_map_path = _get_labelmap_path()
+  configs = config_util.get_configs_from_pipeline_file(filename)
+  override_dict = {
+      'train_input_path': data_path,
+      'eval_input_path': data_path,
+      'label_map_path': label_map_path
+  }
+  configs = config_util.merge_external_params_with_configs(
+      configs, kwargs_dict=override_dict)
+  return configs
+
+
+def _make_initializable_iterator(dataset):
+  """Creates an iterator, and initializes tables.
+
+  Args:
+    dataset: A `tf.data.Dataset` object.
+
+  Returns:
+    A `tf.data.Iterator`.
+  """
+  iterator = dataset.make_initializable_iterator()
+  tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
+  return iterator
+
+
+class ModelLibTest(tf.test.TestCase):
+
+  @classmethod
+  def setUpClass(cls):
+    tf.reset_default_graph()
+
+  def _assert_model_fn_for_train_eval(self, configs, mode,
+                                      class_agnostic=False):
+    model_config = configs['model']
+    train_config = configs['train_config']
+    with tf.Graph().as_default():
+      if mode == 'train':
+        features, labels = _make_initializable_iterator(
+            inputs.create_train_input_fn(configs['train_config'],
+                                         configs['train_input_config'],
+                                         configs['model'])()).get_next()
+        model_mode = tf.estimator.ModeKeys.TRAIN
+        batch_size = train_config.batch_size
+      elif mode == 'eval':
+        features, labels = _make_initializable_iterator(
+            inputs.create_eval_input_fn(configs['eval_config'],
+                                        configs['eval_input_config'],
+                                        configs['model'])()).get_next()
+        model_mode = tf.estimator.ModeKeys.EVAL
+        batch_size = 1
+      elif mode == 'eval_on_train':
+        features, labels = _make_initializable_iterator(
+            inputs.create_eval_input_fn(configs['eval_config'],
+                                        configs['train_input_config'],
+                                        configs['model'])()).get_next()
+        model_mode = tf.estimator.ModeKeys.EVAL
+        batch_size = 1
+
+      detection_model_fn = functools.partial(
+          model_builder.build, model_config=model_config, is_training=True)
+
+      hparams = model_hparams.create_hparams(
+          hparams_overrides='load_pretrained=false')
+
+      model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
+      estimator_spec = model_fn(features, labels, model_mode)
+
+      self.assertIsNotNone(estimator_spec.loss)
+      self.assertIsNotNone(estimator_spec.predictions)
+      if mode == 'eval' or mode == 'eval_on_train':
+        if class_agnostic:
+          self.assertNotIn('detection_classes', estimator_spec.predictions)
+        else:
+          detection_classes = estimator_spec.predictions['detection_classes']
+          self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
+          self.assertEqual(tf.float32, detection_classes.dtype)
+        detection_boxes = estimator_spec.predictions['detection_boxes']
+        detection_scores = estimator_spec.predictions['detection_scores']
+        num_detections = estimator_spec.predictions['num_detections']
+        self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
+        self.assertEqual(tf.float32, detection_boxes.dtype)
+        self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
+        self.assertEqual(tf.float32, detection_scores.dtype)
+        self.assertEqual(tf.float32, num_detections.dtype)
+        if mode == 'eval':
+          self.assertIn('Detections_Left_Groundtruth_Right/0',
+                        estimator_spec.eval_metric_ops)
+      if model_mode == tf.estimator.ModeKeys.TRAIN:
+        self.assertIsNotNone(estimator_spec.train_op)
+      return estimator_spec
+
+  def _assert_model_fn_for_predict(self, configs):
+    model_config = configs['model']
+
+    with tf.Graph().as_default():
+      features, _ = _make_initializable_iterator(
+          inputs.create_eval_input_fn(configs['eval_config'],
+                                      configs['eval_input_config'],
+                                      configs['model'])()).get_next()
+      detection_model_fn = functools.partial(
+          model_builder.build, model_config=model_config, is_training=False)
+
+      hparams = model_hparams.create_hparams(
+          hparams_overrides='load_pretrained=false')
+
+      model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
+      estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)
+
+      self.assertIsNone(estimator_spec.loss)
+      self.assertIsNone(estimator_spec.train_op)
+      self.assertIsNotNone(estimator_spec.predictions)
+      self.assertIsNotNone(estimator_spec.export_outputs)
+      self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
+                    estimator_spec.export_outputs)
+
+  def test_model_fn_in_train_mode(self):
+    """Tests the model function in TRAIN mode."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    self._assert_model_fn_for_train_eval(configs, 'train')
+
+  def test_model_fn_in_train_mode_freeze_all_variables(self):
+    """Tests model_fn TRAIN mode with all variables frozen."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    configs['train_config'].freeze_variables.append('.*')
+    with self.assertRaisesRegexp(ValueError, 'No variables to optimize'):
+      self._assert_model_fn_for_train_eval(configs, 'train')
+
+  def test_model_fn_in_train_mode_freeze_all_included_variables(self):
+    """Tests model_fn TRAIN mode with all included variables frozen."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    train_config = configs['train_config']
+    train_config.update_trainable_variables.append('FeatureExtractor')
+    train_config.freeze_variables.append('.*')
+    with self.assertRaisesRegexp(ValueError, 'No variables to optimize'):
+      self._assert_model_fn_for_train_eval(configs, 'train')
+
+  def test_model_fn_in_train_mode_freeze_box_predictor(self):
+    """Tests model_fn TRAIN mode with FeatureExtractor variables frozen."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    train_config = configs['train_config']
+    train_config.update_trainable_variables.append('FeatureExtractor')
+    train_config.update_trainable_variables.append('BoxPredictor')
+    train_config.freeze_variables.append('FeatureExtractor')
+    self._assert_model_fn_for_train_eval(configs, 'train')
+
+  def test_model_fn_in_eval_mode(self):
+    """Tests the model function in EVAL mode."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    self._assert_model_fn_for_train_eval(configs, 'eval')
+
+  def test_model_fn_in_eval_on_train_mode(self):
+    """Tests the model function in EVAL mode with train data."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    self._assert_model_fn_for_train_eval(configs, 'eval_on_train')
+
+  def test_model_fn_in_predict_mode(self):
+    """Tests the model function in PREDICT mode."""
+    configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
+    self._assert_model_fn_for_predict(configs)
+
+  def test_create_estimator_and_inputs(self):
+    """Tests that Estimator and input function are constructed correctly."""
+    run_config = tf.estimator.RunConfig()
+    hparams = model_hparams.create_hparams(
+        hparams_overrides='load_pretrained=false')
+    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
+    train_steps = 20
+    train_and_eval_dict = model_lib.create_estimator_and_inputs(
+        run_config,
+        hparams,
+        pipeline_config_path,
+        train_steps=train_steps)
+    estimator = train_and_eval_dict['estimator']
+    train_steps = train_and_eval_dict['train_steps']
+    self.assertIsInstance(estimator, tf.estimator.Estimator)
+    self.assertEqual(20, train_steps)
+    self.assertIn('train_input_fn', train_and_eval_dict)
+    self.assertIn('eval_input_fns', train_and_eval_dict)
+    self.assertIn('eval_on_train_input_fn', train_and_eval_dict)
+
+  def test_create_estimator_with_default_train_eval_steps(self):
+    """Tests that number of train/eval defaults to config values."""
+    run_config = tf.estimator.RunConfig()
+    hparams = model_hparams.create_hparams(
+        hparams_overrides='load_pretrained=false')
+    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    config_train_steps = configs['train_config'].num_steps
+    train_and_eval_dict = model_lib.create_estimator_and_inputs(
+        run_config, hparams, pipeline_config_path)
+    estimator = train_and_eval_dict['estimator']
+    train_steps = train_and_eval_dict['train_steps']
+
+    self.assertIsInstance(estimator, tf.estimator.Estimator)
+    self.assertEqual(config_train_steps, train_steps)
+
+  def test_create_tpu_estimator_and_inputs(self):
+    """Tests that number of train/eval defaults to config values."""
+
+    run_config = tpu_config.RunConfig()
+    hparams = model_hparams.create_hparams(
+        hparams_overrides='load_pretrained=false')
+    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
+    train_steps = 20
+    train_and_eval_dict = model_lib.create_estimator_and_inputs(
+        run_config,
+        hparams,
+        pipeline_config_path,
+        train_steps=train_steps,
+        use_tpu_estimator=True)
+    estimator = train_and_eval_dict['estimator']
+    train_steps = train_and_eval_dict['train_steps']
+
+    self.assertIsInstance(estimator, tpu_estimator.TPUEstimator)
+    self.assertEqual(20, train_steps)
+
+  def test_create_train_and_eval_specs(self):
+    """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
+    run_config = tf.estimator.RunConfig()
+    hparams = model_hparams.create_hparams(
+        hparams_overrides='load_pretrained=false')
+    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
+    train_steps = 20
+    train_and_eval_dict = model_lib.create_estimator_and_inputs(
+        run_config,
+        hparams,
+        pipeline_config_path,
+        train_steps=train_steps)
+    train_input_fn = train_and_eval_dict['train_input_fn']
+    eval_input_fns = train_and_eval_dict['eval_input_fns']
+    eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
+    predict_input_fn = train_and_eval_dict['predict_input_fn']
+    train_steps = train_and_eval_dict['train_steps']
+
+    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
+        train_input_fn,
+        eval_input_fns,
+        eval_on_train_input_fn,
+        predict_input_fn,
+        train_steps,
+        eval_on_train_data=True,
+        final_exporter_name='exporter',
+        eval_spec_names=['holdout'])
+    self.assertEqual(train_steps, train_spec.max_steps)
+    self.assertEqual(2, len(eval_specs))
+    self.assertEqual(None, eval_specs[0].steps)
+    self.assertEqual('holdout', eval_specs[0].name)
+    self.assertEqual('exporter', eval_specs[0].exporters[0].name)
+    self.assertEqual(None, eval_specs[1].steps)
+    self.assertEqual('eval_on_train', eval_specs[1].name)
+
+  def test_experiment(self):
+    """Tests that the `Experiment` object is constructed correctly."""
+    run_config = tf.estimator.RunConfig()
+    hparams = model_hparams.create_hparams(
+        hparams_overrides='load_pretrained=false')
+    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
+    experiment = model_lib.populate_experiment(
+        run_config,
+        hparams,
+        pipeline_config_path,
+        train_steps=10,
+        eval_steps=20)
+    self.assertEqual(10, experiment.train_steps)
+    self.assertEqual(None, experiment.eval_steps)
+
+
+class UnbatchTensorsTest(tf.test.TestCase):
+
+  def test_unbatch_without_unpadding(self):
+    image_placeholder = tf.placeholder(tf.float32, [2, None, None, None])
+    groundtruth_boxes_placeholder = tf.placeholder(tf.float32, [2, None, None])
+    groundtruth_classes_placeholder = tf.placeholder(tf.float32,
+                                                     [2, None, None])
+    groundtruth_weights_placeholder = tf.placeholder(tf.float32, [2, None])
+
+    tensor_dict = {
+        fields.InputDataFields.image:
+            image_placeholder,
+        fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes_placeholder,
+        fields.InputDataFields.groundtruth_classes:
+            groundtruth_classes_placeholder,
+        fields.InputDataFields.groundtruth_weights:
+            groundtruth_weights_placeholder
+    }
+    unbatched_tensor_dict = model_lib.unstack_batch(
+        tensor_dict, unpad_groundtruth_tensors=False)
+
+    with self.test_session() as sess:
+      unbatched_tensor_dict_out = sess.run(
+          unbatched_tensor_dict,
+          feed_dict={
+              image_placeholder:
+                  np.random.rand(2, 4, 4, 3).astype(np.float32),
+              groundtruth_boxes_placeholder:
+                  np.random.rand(2, 5, 4).astype(np.float32),
+              groundtruth_classes_placeholder:
+                  np.random.rand(2, 5, 6).astype(np.float32),
+              groundtruth_weights_placeholder:
+                  np.random.rand(2, 5).astype(np.float32)
+          })
+    for image_out in unbatched_tensor_dict_out[fields.InputDataFields.image]:
+      self.assertAllEqual(image_out.shape, [4, 4, 3])
+    for groundtruth_boxes_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_boxes]:
+      self.assertAllEqual(groundtruth_boxes_out.shape, [5, 4])
+    for groundtruth_classes_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_classes]:
+      self.assertAllEqual(groundtruth_classes_out.shape, [5, 6])
+    for groundtruth_weights_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_weights]:
+      self.assertAllEqual(groundtruth_weights_out.shape, [5])
+
+  def test_unbatch_and_unpad_groundtruth_tensors(self):
+    image_placeholder = tf.placeholder(tf.float32, [2, None, None, None])
+    groundtruth_boxes_placeholder = tf.placeholder(tf.float32, [2, 5, None])
+    groundtruth_classes_placeholder = tf.placeholder(tf.float32, [2, 5, None])
+    groundtruth_weights_placeholder = tf.placeholder(tf.float32, [2, 5])
+    num_groundtruth_placeholder = tf.placeholder(tf.int32, [2])
+
+    tensor_dict = {
+        fields.InputDataFields.image:
+            image_placeholder,
+        fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes_placeholder,
+        fields.InputDataFields.groundtruth_classes:
+            groundtruth_classes_placeholder,
+        fields.InputDataFields.groundtruth_weights:
+            groundtruth_weights_placeholder,
+        fields.InputDataFields.num_groundtruth_boxes:
+            num_groundtruth_placeholder
+    }
+    unbatched_tensor_dict = model_lib.unstack_batch(
+        tensor_dict, unpad_groundtruth_tensors=True)
+    with self.test_session() as sess:
+      unbatched_tensor_dict_out = sess.run(
+          unbatched_tensor_dict,
+          feed_dict={
+              image_placeholder:
+                  np.random.rand(2, 4, 4, 3).astype(np.float32),
+              groundtruth_boxes_placeholder:
+                  np.random.rand(2, 5, 4).astype(np.float32),
+              groundtruth_classes_placeholder:
+                  np.random.rand(2, 5, 6).astype(np.float32),
+              groundtruth_weights_placeholder:
+                  np.random.rand(2, 5).astype(np.float32),
+              num_groundtruth_placeholder:
+                  np.array([3, 3], np.int32)
+          })
+    for image_out in unbatched_tensor_dict_out[fields.InputDataFields.image]:
+      self.assertAllEqual(image_out.shape, [4, 4, 3])
+    for groundtruth_boxes_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_boxes]:
+      self.assertAllEqual(groundtruth_boxes_out.shape, [3, 4])
+    for groundtruth_classes_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_classes]:
+      self.assertAllEqual(groundtruth_classes_out.shape, [3, 6])
+    for groundtruth_weights_out in unbatched_tensor_dict_out[
+        fields.InputDataFields.groundtruth_weights]:
+      self.assertAllEqual(groundtruth_weights_out.shape, [3])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/model_main.py b/TensorFlow/Detection/SSD/models/research/object_detection/model_main.py
new file mode 100644
index 00000000..0f1bef7f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/model_main.py
@@ -0,0 +1,149 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Binary to run train and evaluation on object detection model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl import flags
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from object_detection import model_hparams
+from object_detection import model_lib
+
+flags.DEFINE_string(
+    'model_dir', None, 'Path to output model directory '
+    'where event and checkpoint files will be written.')
+flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
+                    'file.')
+flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
+flags.DEFINE_boolean('eval_training_data', False,
+                     'If training data should be evaluated for this job. Note '
+                     'that one call only use this in eval-only mode, and '
+                     '`checkpoint_dir` must be supplied.')
+flags.DEFINE_integer('sample_1_of_n_eval_examples', 1, 'Will sample one of '
+                     'every n eval input examples, where n is provided.')
+flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
+                     'one of every n train input examples for evaluation, '
+                     'where n is provided. This is only used if '
+                     '`eval_training_data` is True.')
+flags.DEFINE_integer('eval_count', 1, 'How many times the evaluation should be run')
+flags.DEFINE_string(
+    'hparams_overrides', None, 'Hyperparameter overrides, '
+    'represented as a string containing comma-separated '
+    'hparam_name=value pairs.')
+flags.DEFINE_string(
+    'checkpoint_dir', None, 'Path to directory holding a checkpoint.  If '
+    '`checkpoint_dir` is provided, this binary operates in eval-only mode, '
+    'writing resulting metrics to `model_dir`.')
+flags.DEFINE_boolean(
+    'allow_xla', False, 'Enable XLA compilation')
+flags.DEFINE_boolean(
+    'run_once', False, 'If running in eval-only mode, whether to run just '
+    'one round of eval vs running continuously (default).'
+)
+FLAGS = flags.FLAGS
+
+
+def main(unused_argv):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  hvd.init()
+
+  flags.mark_flag_as_required('model_dir')
+  flags.mark_flag_as_required('pipeline_config_path')
+  session_config = tf.ConfigProto()
+  session_config.gpu_options.allow_growth = True
+  session_config.gpu_options.visible_device_list = str(hvd.local_rank())
+  if FLAGS.allow_xla:
+      session_config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+  model_dir = FLAGS.model_dir if hvd.rank() == 0 else None
+  config = tf.estimator.RunConfig(model_dir=model_dir, session_config=session_config)
+
+  train_and_eval_dict = model_lib.create_estimator_and_inputs(
+      run_config=config,
+      eval_count=FLAGS.eval_count,
+      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
+      pipeline_config_path=FLAGS.pipeline_config_path,
+      train_steps=FLAGS.num_train_steps,
+      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
+      sample_1_of_n_eval_on_train_examples=(
+          FLAGS.sample_1_of_n_eval_on_train_examples))
+  estimator = train_and_eval_dict['estimator']
+  train_input_fn = train_and_eval_dict['train_input_fn']
+  eval_input_fns = train_and_eval_dict['eval_input_fns']
+  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
+  predict_input_fn = train_and_eval_dict['predict_input_fn']
+  train_steps = train_and_eval_dict['train_steps']
+
+  if FLAGS.checkpoint_dir:
+    if FLAGS.eval_training_data:
+      name = 'training_data'
+      input_fn = eval_on_train_input_fn
+    else:
+      name = 'validation_data'
+      # The first eval input will be evaluated.
+      input_fn = eval_input_fns[0]
+    if FLAGS.run_once:
+      estimator.evaluate(input_fn,
+                         steps=None,
+                         checkpoint_path=tf.train.latest_checkpoint(
+                             FLAGS.checkpoint_dir))
+    else:
+      model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
+                                train_steps, name)
+  else:
+    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
+        train_input_fn,
+        eval_input_fns,
+        eval_on_train_input_fn,
+        predict_input_fn,
+        train_steps,
+        eval_on_train_data=False)
+
+    train_hooks = [hvd.BroadcastGlobalVariablesHook(0)]
+    eval_hooks = []
+    
+    for x in range(FLAGS.eval_count):
+        estimator.train(train_input_fn,
+                        hooks=train_hooks,
+                        steps=train_steps // FLAGS.eval_count)
+
+
+        if hvd.rank() == 0:
+            eval_input_fn = eval_input_fns[0]
+            results = estimator.evaluate(eval_input_fn,
+                               steps=None,
+                               hooks=eval_hooks)
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/model_tpu_main.py b/TensorFlow/Detection/SSD/models/research/object_detection/model_tpu_main.py
new file mode 100644
index 00000000..67a62fe3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/model_tpu_main.py
@@ -0,0 +1,142 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Creates and runs `Estimator` for object detection model on TPUs.
+
+This uses the TPUEstimator API to define and run a model in TRAIN/EVAL modes.
+"""
+# pylint: enable=line-too-long
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl import flags
+import tensorflow as tf
+
+
+from object_detection import model_hparams
+from object_detection import model_lib
+
+tf.flags.DEFINE_bool('use_tpu', True, 'Use TPUs rather than plain CPUs')
+
+# Cloud TPU Cluster Resolvers
+flags.DEFINE_string(
+    'gcp_project',
+    default=None,
+    help='Project name for the Cloud TPU-enabled project. If not specified, we '
+    'will attempt to automatically detect the GCE project from metadata.')
+flags.DEFINE_string(
+    'tpu_zone',
+    default=None,
+    help='GCE zone where the Cloud TPU is located in. If not specified, we '
+    'will attempt to automatically detect the GCE project from metadata.')
+flags.DEFINE_string(
+    'tpu_name',
+    default=None,
+    help='Name of the Cloud TPU for Cluster Resolvers.')
+
+flags.DEFINE_integer('num_shards', 8, 'Number of shards (TPU cores).')
+flags.DEFINE_integer('iterations_per_loop', 100,
+                     'Number of iterations per TPU training loop.')
+# For mode=train_and_eval, evaluation occurs after training is finished.
+# Note: independently of steps_per_checkpoint, estimator will save the most
+# recent checkpoint every 10 minutes by default for train_and_eval
+flags.DEFINE_string('mode', 'train',
+                    'Mode to run: train, eval')
+flags.DEFINE_integer('train_batch_size', None, 'Batch size for training. If '
+                     'this is not provided, batch size is read from training '
+                     'config.')
+
+flags.DEFINE_string(
+    'hparams_overrides', None, 'Comma-separated list of '
+    'hyperparameters to override defaults.')
+flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
+flags.DEFINE_boolean('eval_training_data', False,
+                     'If training data should be evaluated for this job.')
+flags.DEFINE_integer('sample_1_of_n_eval_examples', 1, 'Will sample one of '
+                     'every n eval input examples, where n is provided.')
+flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
+                     'one of every n train input examples for evaluation, '
+                     'where n is provided. This is only used if '
+                     '`eval_training_data` is True.')
+flags.DEFINE_string(
+    'model_dir', None, 'Path to output model directory '
+    'where event and checkpoint files will be written.')
+flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
+                    'file.')
+
+FLAGS = tf.flags.FLAGS
+
+
+def main(unused_argv):
+  flags.mark_flag_as_required('model_dir')
+  flags.mark_flag_as_required('pipeline_config_path')
+
+  tpu_cluster_resolver = (
+      tf.contrib.cluster_resolver.TPUClusterResolver(
+          tpu=[FLAGS.tpu_name],
+          zone=FLAGS.tpu_zone,
+          project=FLAGS.gcp_project))
+  tpu_grpc_url = tpu_cluster_resolver.get_master()
+
+  config = tf.contrib.tpu.RunConfig(
+      master=tpu_grpc_url,
+      evaluation_master=tpu_grpc_url,
+      model_dir=FLAGS.model_dir,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations_per_loop,
+          num_shards=FLAGS.num_shards))
+
+  kwargs = {}
+  if FLAGS.train_batch_size:
+    kwargs['batch_size'] = FLAGS.train_batch_size
+
+  train_and_eval_dict = model_lib.create_estimator_and_inputs(
+      run_config=config,
+      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
+      pipeline_config_path=FLAGS.pipeline_config_path,
+      train_steps=FLAGS.num_train_steps,
+      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
+      sample_1_of_n_eval_on_train_examples=(
+          FLAGS.sample_1_of_n_eval_on_train_examples),
+      use_tpu_estimator=True,
+      use_tpu=FLAGS.use_tpu,
+      num_shards=FLAGS.num_shards,
+      save_final_config=FLAGS.mode == 'train',
+      **kwargs)
+  estimator = train_and_eval_dict['estimator']
+  train_input_fn = train_and_eval_dict['train_input_fn']
+  eval_input_fns = train_and_eval_dict['eval_input_fns']
+  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
+  train_steps = train_and_eval_dict['train_steps']
+
+  if FLAGS.mode == 'train':
+    estimator.train(input_fn=train_input_fn, max_steps=train_steps)
+
+  # Continuously evaluating.
+  if FLAGS.mode == 'eval':
+    if FLAGS.eval_training_data:
+      name = 'training_data'
+      input_fn = eval_on_train_input_fn
+    else:
+      name = 'validation_data'
+      # Currently only a single eval input is allowed.
+      input_fn = eval_input_fns[0]
+    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, train_steps,
+                              name)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py
new file mode 100644
index 00000000..f0cad235
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py
@@ -0,0 +1,165 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Embedded-friendly SSDFeatureExtractor for MobilenetV1 features."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+class EmbeddedSSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """Embedded-friendly SSD Feature Extractor using MobilenetV1 features.
+
+  This feature extractor is similar to SSD MobileNetV1 feature extractor, and
+  it fixes input resolution to be 256x256, reduces the number of feature maps
+  used for box prediction and ensures convolution kernel to be no larger
+  than input tensor in spatial dimensions.
+
+  This feature extractor requires support of the following ops if used in
+  embedded devices:
+  - Conv
+  - DepthwiseConv
+  - Relu6
+
+  All conv/depthwiseconv use SAME padding, and no additional spatial padding is
+  needed.
+  """
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """MobileNetV1 Feature Extractor for Embedded-friendly SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to. For EmbeddedSSD it must be set to 1.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+
+    Raises:
+      ValueError: upon invalid `pad_to_multiple` values.
+    """
+    if pad_to_multiple != 1:
+      raise ValueError('Embedded-specific SSD only supports `pad_to_multiple` '
+                       'of 1.')
+
+    super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise,
+        override_base_feature_extractor_hyperparams)
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+
+    Raises:
+      ValueError: if image height or width are not 256 pixels.
+    """
+    image_shape = preprocessed_inputs.get_shape()
+    image_shape.assert_has_rank(4)
+    image_height = image_shape[1].value
+    image_width = image_shape[2].value
+
+    if image_height is None or image_width is None:
+      shape_assert = tf.Assert(
+          tf.logical_and(tf.equal(tf.shape(preprocessed_inputs)[1], 256),
+                         tf.equal(tf.shape(preprocessed_inputs)[2], 256)),
+          ['image size must be 256 in both height and width.'])
+      with tf.control_dependencies([shape_assert]):
+        preprocessed_inputs = tf.identity(preprocessed_inputs)
+    elif image_height != 256 or image_width != 256:
+      raise ValueError('image size must be = 256 in both height and width;'
+                       ' image dim = %d,%d' % (image_height, image_width))
+
+    feature_map_layout = {
+        'from_layer': [
+            'Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '', ''
+        ],
+        'layer_depth': [-1, -1, 512, 256, 256],
+        'conv_kernel_size': [-1, -1, 3, 3, 2],
+        'use_explicit_padding': self._use_explicit_padding,
+        'use_depthwise': self._use_depthwise,
+    }
+
+    with tf.variable_scope('MobilenetV1',
+                           reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams
+              else context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v1.mobilenet_v1_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='Conv2d_13_pointwise',
+              min_depth=self._min_depth,
+              depth_multiplier=self._depth_multiplier,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        feature_maps = feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            image_features=image_features)
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_test.py
new file mode 100644
index 00000000..1fee66c8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_test.py
@@ -0,0 +1,129 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for embedded_ssd_mobilenet_v1_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import embedded_ssd_mobilenet_v1_feature_extractor
+from object_detection.models import ssd_feature_extractor_test
+
+
+class EmbeddedSSDMobileNetV1FeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return (embedded_ssd_mobilenet_v1_feature_extractor.
+            EmbeddedSSDMobileNetV1FeatureExtractor(
+                is_training, depth_multiplier, min_depth, pad_to_multiple,
+                self.conv_hyperparams_fn,
+                override_base_feature_extractor_hyperparams=True))
+
+  def test_extract_features_returns_correct_shapes_256(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
+                                  (2, 4, 4, 512), (2, 2, 2, 256),
+                                  (2, 1, 1, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
+                                  (2, 4, 4, 512), (2, 2, 2, 256),
+                                  (2, 1, 1, 256)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 16, 16, 32), (2, 8, 8, 32), (2, 4, 4, 32),
+                                  (2, 2, 2, 32), (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple_of_1(
+      self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
+                                  (2, 4, 4, 512), (2, 2, 2, 256),
+                                  (2, 1, 1, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_raises_error_with_pad_to_multiple_not_1(self):
+    depth_multiplier = 1.0
+    pad_to_multiple = 2
+    with self.assertRaises(ValueError):
+      _ = self._create_feature_extractor(depth_multiplier, pad_to_multiple)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(4, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV1'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py
new file mode 100644
index 00000000..30b3dd4e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py
@@ -0,0 +1,213 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Inception Resnet v2 Faster R-CNN implementation.
+
+See "Inception-v4, Inception-ResNet and the Impact of Residual Connections on
+Learning" by Szegedy et al. (https://arxiv.org/abs/1602.07261)
+as well as
+"Speed/accuracy trade-offs for modern convolutional object detectors" by
+Huang et al. (https://arxiv.org/abs/1611.10012)
+"""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from nets import inception_resnet_v2
+
+slim = tf.contrib.slim
+
+
+class FasterRCNNInceptionResnetV2FeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN with Inception Resnet v2 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16.
+    """
+    if first_stage_features_stride != 8 and first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 8 or 16.')
+    super(FasterRCNNInceptionResnetV2FeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN with Inception Resnet v2 preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: A [batch, height_out, width_out, channels] float32
+        tensor representing a batch of images.
+
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Extracts features using the first half of the Inception Resnet v2 network.
+    We construct the network in `align_feature_maps=True` mode, which means
+    that all VALID paddings in the network are changed to SAME padding so that
+    the feature maps are aligned.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+    Raises:
+      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
+        (height or width) is less than 33.
+      ValueError: If the created network is missing the required activation.
+    """
+    if len(preprocessed_inputs.get_shape().as_list()) != 4:
+      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
+                       'tensor of shape %s' % preprocessed_inputs.get_shape())
+
+    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
+        weight_decay=self._weight_decay)):
+      # Forces is_training to False to disable batch norm update.
+      with slim.arg_scope([slim.batch_norm],
+                          is_training=self._train_batch_norm):
+        with tf.variable_scope('InceptionResnetV2',
+                               reuse=self._reuse_weights) as scope:
+          return inception_resnet_v2.inception_resnet_v2_base(
+              preprocessed_inputs, final_endpoint='PreAuxLogits',
+              scope=scope, output_stride=self._first_stage_features_stride,
+              align_feature_maps=True)
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    This function reconstructs the "second half" of the Inception ResNet v2
+    network after the part defined in `_extract_proposal_features`.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name.
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    with tf.variable_scope('InceptionResnetV2', reuse=self._reuse_weights):
+      with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope(
+          weight_decay=self._weight_decay)):
+        # Forces is_training to False to disable batch norm update.
+        with slim.arg_scope([slim.batch_norm],
+                            is_training=self._train_batch_norm):
+          with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                              stride=1, padding='SAME'):
+            with tf.variable_scope('Mixed_7a'):
+              with tf.variable_scope('Branch_0'):
+                tower_conv = slim.conv2d(proposal_feature_maps,
+                                         256, 1, scope='Conv2d_0a_1x1')
+                tower_conv_1 = slim.conv2d(
+                    tower_conv, 384, 3, stride=2,
+                    padding='VALID', scope='Conv2d_1a_3x3')
+              with tf.variable_scope('Branch_1'):
+                tower_conv1 = slim.conv2d(
+                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
+                tower_conv1_1 = slim.conv2d(
+                    tower_conv1, 288, 3, stride=2,
+                    padding='VALID', scope='Conv2d_1a_3x3')
+              with tf.variable_scope('Branch_2'):
+                tower_conv2 = slim.conv2d(
+                    proposal_feature_maps, 256, 1, scope='Conv2d_0a_1x1')
+                tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
+                                            scope='Conv2d_0b_3x3')
+                tower_conv2_2 = slim.conv2d(
+                    tower_conv2_1, 320, 3, stride=2,
+                    padding='VALID', scope='Conv2d_1a_3x3')
+              with tf.variable_scope('Branch_3'):
+                tower_pool = slim.max_pool2d(
+                    proposal_feature_maps, 3, stride=2, padding='VALID',
+                    scope='MaxPool_1a_3x3')
+              net = tf.concat(
+                  [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
+            net = slim.repeat(net, 9, inception_resnet_v2.block8, scale=0.20)
+            net = inception_resnet_v2.block8(net, activation_fn=None)
+            proposal_classifier_features = slim.conv2d(
+                net, 1536, 1, scope='Conv2d_7b_1x1')
+        return proposal_classifier_features
+
+  def restore_from_classification_checkpoint_fn(
+      self,
+      first_stage_feature_extractor_scope,
+      second_stage_feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Note that this overrides the default implementation in
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
+    InceptionResnetV2 checkpoints.
+
+    TODO(jonathanhuang,rathodv): revisit whether it's possible to force the
+    `Repeat` namescope as created in `_extract_box_classifier_features` to
+    start counting at 2 (e.g. `Repeat_2`) so that the default restore_fn can
+    be used.
+
+    Args:
+      first_stage_feature_extractor_scope: A scope name for the first stage
+        feature extractor.
+      second_stage_feature_extractor_scope: A scope name for the second stage
+        feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      if variable.op.name.startswith(
+          first_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            first_stage_feature_extractor_scope + '/', '')
+        variables_to_restore[var_name] = variable
+      if variable.op.name.startswith(
+          second_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            second_stage_feature_extractor_scope
+            + '/InceptionResnetV2/Repeat', 'InceptionResnetV2/Repeat_2')
+        var_name = var_name.replace(
+            second_stage_feature_extractor_scope + '/', '')
+        variables_to_restore[var_name] = variable
+    return variables_to_restore
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor_test.py
new file mode 100644
index 00000000..1d9f088f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor_test.py
@@ -0,0 +1,109 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for models.faster_rcnn_inception_resnet_v2_feature_extractor."""
+
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
+
+
+class FasterRcnnInceptionResnetV2FeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 299, 299, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 19, 19, 1088])
+
+  def test_extract_proposal_features_stride_eight(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=8)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 28, 28, 1088])
+
+  def test_extract_proposal_features_half_size_input(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 1088])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [2, 17, 17, 1088], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [2, 8, 8, 1536])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor.py
new file mode 100644
index 00000000..fe067516
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor.py
@@ -0,0 +1,254 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Inception V2 Faster R-CNN implementation.
+
+See "Rethinking the Inception Architecture for Computer Vision"
+https://arxiv.org/abs/1512.00567
+"""
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from nets import inception_v2
+
+slim = tf.contrib.slim
+
+
+def _batch_norm_arg_scope(list_ops,
+                          use_batch_norm=True,
+                          batch_norm_decay=0.9997,
+                          batch_norm_epsilon=0.001,
+                          batch_norm_scale=False,
+                          train_batch_norm=False):
+  """Slim arg scope for InceptionV2 batch norm."""
+  if use_batch_norm:
+    batch_norm_params = {
+        'is_training': train_batch_norm,
+        'scale': batch_norm_scale,
+        'decay': batch_norm_decay,
+        'epsilon': batch_norm_epsilon
+    }
+    normalizer_fn = slim.batch_norm
+  else:
+    normalizer_fn = None
+    batch_norm_params = None
+
+  return slim.arg_scope(list_ops,
+                        normalizer_fn=normalizer_fn,
+                        normalizer_params=batch_norm_params)
+
+
+class FasterRCNNInceptionV2FeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN Inception V2 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0,
+               depth_multiplier=1.0,
+               min_depth=16):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16.
+    """
+    if first_stage_features_stride != 8 and first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 8 or 16.')
+    self._depth_multiplier = depth_multiplier
+    self._min_depth = min_depth
+    super(FasterRCNNInceptionV2FeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN Inception V2 preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      activations: A dictionary mapping feature extractor tensor names to
+        tensors
+
+    Raises:
+      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
+        (height or width) is less than 33.
+      ValueError: If the created network is missing the required activation.
+    """
+
+    preprocessed_inputs.get_shape().assert_has_rank(4)
+    shape_assert = tf.Assert(
+        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
+                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
+        ['image size must at least be 33 in both height and width.'])
+
+    with tf.control_dependencies([shape_assert]):
+      with tf.variable_scope('InceptionV2',
+                             reuse=self._reuse_weights) as scope:
+        with _batch_norm_arg_scope([slim.conv2d, slim.separable_conv2d],
+                                   batch_norm_scale=True,
+                                   train_batch_norm=self._train_batch_norm):
+          _, activations = inception_v2.inception_v2_base(
+              preprocessed_inputs,
+              final_endpoint='Mixed_4e',
+              min_depth=self._min_depth,
+              depth_multiplier=self._depth_multiplier,
+              scope=scope)
+
+    return activations['Mixed_4e'], activations
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name (unused).
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    net = proposal_feature_maps
+
+    depth = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
+    trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+    data_format = 'NHWC'
+    concat_dim = 3 if data_format == 'NHWC' else 1
+
+    with tf.variable_scope('InceptionV2', reuse=self._reuse_weights):
+      with slim.arg_scope(
+          [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+          stride=1,
+          padding='SAME',
+          data_format=data_format):
+        with _batch_norm_arg_scope([slim.conv2d, slim.separable_conv2d],
+                                   batch_norm_scale=True,
+                                   train_batch_norm=self._train_batch_norm):
+
+          with tf.variable_scope('Mixed_5a'):
+            with tf.variable_scope('Branch_0'):
+              branch_0 = slim.conv2d(
+                  net, depth(128), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2,
+                                     scope='Conv2d_1a_3x3')
+            with tf.variable_scope('Branch_1'):
+              branch_1 = slim.conv2d(
+                  net, depth(192), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_1 = slim.conv2d(branch_1, depth(256), [3, 3],
+                                     scope='Conv2d_0b_3x3')
+              branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2,
+                                     scope='Conv2d_1a_3x3')
+            with tf.variable_scope('Branch_2'):
+              branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
+                                         scope='MaxPool_1a_3x3')
+            net = tf.concat([branch_0, branch_1, branch_2], concat_dim)
+
+          with tf.variable_scope('Mixed_5b'):
+            with tf.variable_scope('Branch_0'):
+              branch_0 = slim.conv2d(net, depth(352), [1, 1],
+                                     scope='Conv2d_0a_1x1')
+            with tf.variable_scope('Branch_1'):
+              branch_1 = slim.conv2d(
+                  net, depth(192), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
+                                     scope='Conv2d_0b_3x3')
+            with tf.variable_scope('Branch_2'):
+              branch_2 = slim.conv2d(
+                  net, depth(160), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                     scope='Conv2d_0b_3x3')
+              branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                     scope='Conv2d_0c_3x3')
+            with tf.variable_scope('Branch_3'):
+              branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+              branch_3 = slim.conv2d(
+                  branch_3, depth(128), [1, 1],
+                  weights_initializer=trunc_normal(0.1),
+                  scope='Conv2d_0b_1x1')
+            net = tf.concat([branch_0, branch_1, branch_2, branch_3],
+                            concat_dim)
+
+          with tf.variable_scope('Mixed_5c'):
+            with tf.variable_scope('Branch_0'):
+              branch_0 = slim.conv2d(net, depth(352), [1, 1],
+                                     scope='Conv2d_0a_1x1')
+            with tf.variable_scope('Branch_1'):
+              branch_1 = slim.conv2d(
+                  net, depth(192), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
+                                     scope='Conv2d_0b_3x3')
+            with tf.variable_scope('Branch_2'):
+              branch_2 = slim.conv2d(
+                  net, depth(192), [1, 1],
+                  weights_initializer=trunc_normal(0.09),
+                  scope='Conv2d_0a_1x1')
+              branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                     scope='Conv2d_0b_3x3')
+              branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                     scope='Conv2d_0c_3x3')
+            with tf.variable_scope('Branch_3'):
+              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+              branch_3 = slim.conv2d(
+                  branch_3, depth(128), [1, 1],
+                  weights_initializer=trunc_normal(0.1),
+                  scope='Conv2d_0b_1x1')
+            proposal_classifier_features = tf.concat(
+                [branch_0, branch_1, branch_2, branch_3], concat_dim)
+
+    return proposal_classifier_features
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor_test.py
new file mode 100644
index 00000000..6b5bc2f9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_inception_v2_feature_extractor_test.py
@@ -0,0 +1,126 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for faster_rcnn_inception_v2_feature_extractor."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_inception_v2_feature_extractor as faster_rcnn_inception_v2
+
+
+class FasterRcnnInceptionV2FeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return faster_rcnn_inception_v2.FasterRCNNInceptionV2FeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [4, 14, 14, 576])
+
+  def test_extract_proposal_features_stride_eight(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=8)
+    preprocessed_inputs = tf.random_uniform(
+        [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [4, 14, 14, 576])
+
+  def test_extract_proposal_features_half_size_input(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 576])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_on_very_small_images(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(
+            features_shape,
+            feed_dict={preprocessed_inputs: np.random.rand(4, 32, 32, 3)})
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [3, 14, 14, 576], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [3, 7, 7, 1024])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py
new file mode 100644
index 00000000..52c744b8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py
@@ -0,0 +1,194 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Mobilenet v1 Faster R-CNN implementation."""
+import numpy as np
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.utils import shape_utils
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
+  if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
+    raise ValueError(
+        'Only the following ratio percentages are supported: 25, 50, 75, 100')
+  conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
+  channels = np.array([
+      32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
+  ], dtype=np.float32)
+  channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
+  return [
+      mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
+      mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
+  ]
+
+
+class FasterRCNNMobilenetV1FeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN Mobilenet V1 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0,
+               depth_multiplier=1.0,
+               min_depth=16,
+               skip_last_stride=False,
+               conv_depth_ratio_in_percentage=100):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      skip_last_stride: Skip the last stride if True.
+      conv_depth_ratio_in_percentage: Conv depth ratio in percentage. Only
+        applied if skip_last_stride is True.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16.
+    """
+    if first_stage_features_stride != 8 and first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 8 or 16.')
+    self._depth_multiplier = depth_multiplier
+    self._min_depth = min_depth
+    self._skip_last_stride = skip_last_stride
+    self._conv_depth_ratio_in_percentage = conv_depth_ratio_in_percentage
+    super(FasterRCNNMobilenetV1FeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN Mobilenet V1 preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      activations: A dictionary mapping feature extractor tensor names to
+        tensors
+
+    Raises:
+      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
+        (height or width) is less than 33.
+      ValueError: If the created network is missing the required activation.
+    """
+
+    preprocessed_inputs.get_shape().assert_has_rank(4)
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        min_dim=33, image_tensor=preprocessed_inputs)
+
+    with slim.arg_scope(
+        mobilenet_v1.mobilenet_v1_arg_scope(
+            is_training=self._train_batch_norm,
+            weight_decay=self._weight_decay)):
+      with tf.variable_scope('MobilenetV1',
+                             reuse=self._reuse_weights) as scope:
+        params = {}
+        if self._skip_last_stride:
+          params['conv_defs'] = _get_mobilenet_conv_no_last_stride_defs(
+              conv_depth_ratio_in_percentage=self.
+              _conv_depth_ratio_in_percentage)
+        _, activations = mobilenet_v1.mobilenet_v1_base(
+            preprocessed_inputs,
+            final_endpoint='Conv2d_11_pointwise',
+            min_depth=self._min_depth,
+            depth_multiplier=self._depth_multiplier,
+            scope=scope,
+            **params)
+    return activations['Conv2d_11_pointwise'], activations
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name (unused).
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    net = proposal_feature_maps
+
+    conv_depth = 1024
+    if self._skip_last_stride:
+      conv_depth_ratio = float(self._conv_depth_ratio_in_percentage) / 100.0
+      conv_depth = int(float(conv_depth) * conv_depth_ratio)
+
+    depth = lambda d: max(int(d * 1.0), 16)
+    with tf.variable_scope('MobilenetV1', reuse=self._reuse_weights):
+      with slim.arg_scope(
+          mobilenet_v1.mobilenet_v1_arg_scope(
+              is_training=self._train_batch_norm,
+              weight_decay=self._weight_decay)):
+        with slim.arg_scope(
+            [slim.conv2d, slim.separable_conv2d], padding='SAME'):
+          net = slim.separable_conv2d(
+              net,
+              depth(conv_depth), [3, 3],
+              depth_multiplier=1,
+              stride=2,
+              scope='Conv2d_12_pointwise')
+          return slim.separable_conv2d(
+              net,
+              depth(conv_depth), [3, 3],
+              depth_multiplier=1,
+              stride=1,
+              scope='Conv2d_13_pointwise')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_test.py
new file mode 100644
index 00000000..fcefe616
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_test.py
@@ -0,0 +1,126 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for faster_rcnn_mobilenet_v1_feature_extractor."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_mobilenet_v1_feature_extractor as faster_rcnn_mobilenet_v1
+
+
+class FasterRcnnMobilenetV1FeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return faster_rcnn_mobilenet_v1.FasterRCNNMobilenetV1FeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [4, 14, 14, 512])
+
+  def test_extract_proposal_features_stride_eight(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=8)
+    preprocessed_inputs = tf.random_uniform(
+        [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [4, 14, 14, 512])
+
+  def test_extract_proposal_features_half_size_input(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 512])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_on_very_small_images(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(
+            features_shape,
+            feed_dict={preprocessed_inputs: np.random.rand(4, 32, 32, 3)})
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [3, 14, 14, 576], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [3, 7, 7, 1024])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor.py
new file mode 100644
index 00000000..5fa6bf75
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor.py
@@ -0,0 +1,324 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""NASNet Faster R-CNN implementation.
+
+Learning Transferable Architectures for Scalable Image Recognition
+Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
+https://arxiv.org/abs/1707.07012
+"""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from nets.nasnet import nasnet
+from nets.nasnet import nasnet_utils
+
+arg_scope = tf.contrib.framework.arg_scope
+slim = tf.contrib.slim
+
+
+def nasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
+  """Defines the default arg scope for the NASNet-A Large for object detection.
+
+  This provides a small edit to switch batch norm training on and off.
+
+  Args:
+    is_batch_norm_training: Boolean indicating whether to train with batch norm.
+
+  Returns:
+    An `arg_scope` to use for the NASNet Large Model.
+  """
+  imagenet_scope = nasnet.nasnet_large_arg_scope()
+  with arg_scope(imagenet_scope):
+    with arg_scope([slim.batch_norm], is_training=is_batch_norm_training) as sc:
+      return sc
+
+
+# Note: This is largely a copy of _build_nasnet_base inside nasnet.py but
+# with special edits to remove instantiation of the stem and the special
+# ability to receive as input a pair of hidden states.
+def _build_nasnet_base(hidden_previous,
+                       hidden,
+                       normal_cell,
+                       reduction_cell,
+                       hparams,
+                       true_cell_num,
+                       start_cell_num):
+  """Constructs a NASNet image model."""
+
+  # Find where to place the reduction cells or stride normal cells
+  reduction_indices = nasnet_utils.calc_reduction_layers(
+      hparams.num_cells, hparams.num_reduction_layers)
+
+  # Note: The None is prepended to match the behavior of _imagenet_stem()
+  cell_outputs = [None, hidden_previous, hidden]
+  net = hidden
+
+  # NOTE: In the nasnet.py code, filter_scaling starts at 1.0. We instead
+  # start at 2.0 because 1 reduction cell has been created which would
+  # update the filter_scaling to 2.0.
+  filter_scaling = 2.0
+
+  # Run the cells
+  for cell_num in range(start_cell_num, hparams.num_cells):
+    stride = 1
+    if hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    if cell_num in reduction_indices:
+      filter_scaling *= hparams.filter_scaling_rate
+      net = reduction_cell(
+          net,
+          scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
+          filter_scaling=filter_scaling,
+          stride=2,
+          prev_layer=cell_outputs[-2],
+          cell_num=true_cell_num)
+      true_cell_num += 1
+      cell_outputs.append(net)
+    if not hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    net = normal_cell(
+        net,
+        scope='cell_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=stride,
+        prev_layer=prev_layer,
+        cell_num=true_cell_num)
+    true_cell_num += 1
+    cell_outputs.append(net)
+
+  # Final nonlinearity.
+  # Note that we have dropped the final pooling, dropout and softmax layers
+  # from the default nasnet version.
+  with tf.variable_scope('final_layer'):
+    net = tf.nn.relu(net)
+  return net
+
+
+# TODO(shlens): Only fixed_shape_resizer is currently supported for NASNet
+# featurization. The reason for this is that nasnet.py only supports
+# inputs with fully known shapes. We need to update nasnet.py to handle
+# shapes not known at compile time.
+class FasterRCNNNASFeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN with NASNet-A feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 16.
+    """
+    if first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 16.')
+    super(FasterRCNNNASFeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN with NAS preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: A [batch, height_out, width_out, channels] float32
+        tensor representing a batch of images.
+
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Extracts features using the first half of the NASNet network.
+    We construct the network in `align_feature_maps=True` mode, which means
+    that all VALID paddings in the network are changed to SAME padding so that
+    the feature maps are aligned.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      end_points: A dictionary mapping feature extractor tensor names to tensors
+
+    Raises:
+      ValueError: If the created network is missing the required activation.
+    """
+    del scope
+
+    if len(preprocessed_inputs.get_shape().as_list()) != 4:
+      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
+                       'tensor of shape %s' % preprocessed_inputs.get_shape())
+
+    with slim.arg_scope(nasnet_large_arg_scope_for_detection(
+        is_batch_norm_training=self._train_batch_norm)):
+      with arg_scope([slim.conv2d,
+                      slim.batch_norm,
+                      slim.separable_conv2d],
+                     reuse=self._reuse_weights):
+        _, end_points = nasnet.build_nasnet_large(
+            preprocessed_inputs, num_classes=None,
+            is_training=self._is_training,
+            final_endpoint='Cell_11')
+
+    # Note that both 'Cell_10' and 'Cell_11' have equal depth = 2016.
+    rpn_feature_map = tf.concat([end_points['Cell_10'],
+                                 end_points['Cell_11']], 3)
+
+    # nasnet.py does not maintain the batch size in the first dimension.
+    # This work around permits us retaining the batch for below.
+    batch = preprocessed_inputs.get_shape().as_list()[0]
+    shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
+    rpn_feature_map_shape = [batch] + shape_without_batch
+    rpn_feature_map.set_shape(rpn_feature_map_shape)
+
+    return rpn_feature_map, end_points
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    This function reconstructs the "second half" of the NASNet-A
+    network after the part defined in `_extract_proposal_features`.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name.
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    del scope
+
+    # Note that we always feed into 2 layers of equal depth
+    # where the first N channels corresponds to previous hidden layer
+    # and the second N channels correspond to the final hidden layer.
+    hidden_previous, hidden = tf.split(proposal_feature_maps, 2, axis=3)
+
+    # Note that what follows is largely a copy of build_nasnet_large() within
+    # nasnet.py. We are copying to minimize code pollution in slim.
+
+    # TODO(shlens,skornblith): Determine the appropriate drop path schedule.
+    # For now the schedule is the default (1.0->0.7 over 250,000 train steps).
+    hparams = nasnet.large_imagenet_config()
+    if not self._is_training:
+      hparams.set_hparam('drop_path_keep_prob', 1.0)
+
+    # Calculate the total number of cells in the network
+    # -- Add 2 for the reduction cells.
+    total_num_cells = hparams.num_cells + 2
+    # -- And add 2 for the stem cells for ImageNet training.
+    total_num_cells += 2
+
+    normal_cell = nasnet_utils.NasNetANormalCell(
+        hparams.num_conv_filters, hparams.drop_path_keep_prob,
+        total_num_cells, hparams.total_training_steps)
+    reduction_cell = nasnet_utils.NasNetAReductionCell(
+        hparams.num_conv_filters, hparams.drop_path_keep_prob,
+        total_num_cells, hparams.total_training_steps)
+    with arg_scope([slim.dropout, nasnet_utils.drop_path],
+                   is_training=self._is_training):
+      with arg_scope([slim.batch_norm], is_training=self._train_batch_norm):
+        with arg_scope([slim.avg_pool2d,
+                        slim.max_pool2d,
+                        slim.conv2d,
+                        slim.batch_norm,
+                        slim.separable_conv2d,
+                        nasnet_utils.factorized_reduction,
+                        nasnet_utils.global_avg_pool,
+                        nasnet_utils.get_channel_index,
+                        nasnet_utils.get_channel_dim],
+                       data_format=hparams.data_format):
+
+          # This corresponds to the cell number just past 'Cell_11' used by
+          # by _extract_proposal_features().
+          start_cell_num = 12
+          # Note that this number equals:
+          #  start_cell_num + 2 stem cells + 1 reduction cell
+          true_cell_num = 15
+
+          with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+            net = _build_nasnet_base(hidden_previous,
+                                     hidden,
+                                     normal_cell=normal_cell,
+                                     reduction_cell=reduction_cell,
+                                     hparams=hparams,
+                                     true_cell_num=true_cell_num,
+                                     start_cell_num=start_cell_num)
+
+    proposal_classifier_features = net
+    return proposal_classifier_features
+
+  def restore_from_classification_checkpoint_fn(
+      self,
+      first_stage_feature_extractor_scope,
+      second_stage_feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Note that this overrides the default implementation in
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
+    NASNet-A checkpoints.
+
+    Args:
+      first_stage_feature_extractor_scope: A scope name for the first stage
+        feature extractor.
+      second_stage_feature_extractor_scope: A scope name for the second stage
+        feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    # Note that the NAS checkpoint only contains the moving average version of
+    # the Variables so we need to generate an appropriate dictionary mapping.
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      if variable.op.name.startswith(
+          first_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            first_stage_feature_extractor_scope + '/', '')
+        var_name += '/ExponentialMovingAverage'
+        variables_to_restore[var_name] = variable
+      if variable.op.name.startswith(
+          second_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            second_stage_feature_extractor_scope + '/', '')
+        var_name += '/ExponentialMovingAverage'
+        variables_to_restore[var_name] = variable
+    return variables_to_restore
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor_test.py
new file mode 100644
index 00000000..cecfc4f8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_nas_feature_extractor_test.py
@@ -0,0 +1,109 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for models.faster_rcnn_nas_feature_extractor."""
+
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
+
+
+class FasterRcnnNASFeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return frcnn_nas.FasterRCNNNASFeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 299, 299, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 19, 19, 4032])
+
+  def test_extract_proposal_features_input_size_224(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 14, 14, 4032])
+
+  def test_extract_proposal_features_input_size_112(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 4032])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [2, 17, 17, 1088], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [2, 9, 9, 4032])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor.py
new file mode 100644
index 00000000..b5d0f43a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor.py
@@ -0,0 +1,318 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""PNASNet Faster R-CNN implementation.
+
+Based on PNASNet model: https://arxiv.org/abs/1712.00559
+"""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from nets.nasnet import nasnet_utils
+from nets.nasnet import pnasnet
+
+arg_scope = tf.contrib.framework.arg_scope
+slim = tf.contrib.slim
+
+
+def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
+  """Defines the default arg scope for the PNASNet Large for object detection.
+
+  This provides a small edit to switch batch norm training on and off.
+
+  Args:
+    is_batch_norm_training: Boolean indicating whether to train with batch norm.
+
+  Returns:
+    An `arg_scope` to use for the PNASNet Large Model.
+  """
+  imagenet_scope = pnasnet.pnasnet_large_arg_scope()
+  with arg_scope(imagenet_scope):
+    with arg_scope([slim.batch_norm], is_training=is_batch_norm_training) as sc:
+      return sc
+
+
+def _filter_scaling(reduction_indices, start_cell_num):
+  """Compute the expected filter scaling at given PNASNet cell start_cell_num.
+
+  In the pnasnet.py code, filter_scaling starts at 1.0. We instead
+  adapt filter scaling to depend on the starting cell.
+  At first cells, before any reduction, filter_scalling is 1.0. With passing
+  any reduction cell, the filter_scaling is multiplied by 2.
+
+  Args:
+    reduction_indices: list of int indices.
+    start_cell_num: int.
+  Returns:
+    filter_scaling: float.
+  """
+  filter_scaling = 1.0
+  for ind in reduction_indices:
+    if ind < start_cell_num:
+      filter_scaling *= 2.0
+  return filter_scaling
+
+
+# Note: This is largely a copy of _build_pnasnet_base inside pnasnet.py but
+# with special edits to remove instantiation of the stem and the special
+# ability to receive as input a pair of hidden states. It constructs only
+# a sub-network from the original PNASNet model, starting from the
+# start_cell_num cell and with modified final layer.
+def _build_pnasnet_base(
+    hidden_previous, hidden, normal_cell, hparams, true_cell_num,
+    start_cell_num):
+  """Constructs a PNASNet image model for proposal classifier features."""
+
+  # Find where to place the reduction cells or stride normal cells
+  reduction_indices = nasnet_utils.calc_reduction_layers(
+      hparams.num_cells, hparams.num_reduction_layers)
+  filter_scaling = _filter_scaling(reduction_indices, start_cell_num)
+
+  # Note: The None is prepended to match the behavior of _imagenet_stem()
+  cell_outputs = [None, hidden_previous, hidden]
+  net = hidden
+
+  # Run the cells
+  for cell_num in range(start_cell_num, hparams.num_cells):
+    is_reduction = cell_num in reduction_indices
+    stride = 2 if is_reduction else 1
+    if is_reduction: filter_scaling *= hparams.filter_scaling_rate
+    prev_layer = cell_outputs[-2]
+    net = normal_cell(
+        net,
+        scope='cell_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=stride,
+        prev_layer=prev_layer,
+        cell_num=true_cell_num)
+    true_cell_num += 1
+    cell_outputs.append(net)
+
+  # Final nonlinearity.
+  # Note that we have dropped the final pooling, dropout and softmax layers
+  # from the default pnasnet version.
+  with tf.variable_scope('final_layer'):
+    net = tf.nn.relu(net)
+  return net
+
+
+# TODO(shlens): Only fixed_shape_resizer is currently supported for PNASNet
+# featurization. The reason for this is that pnasnet.py only supports
+# inputs with fully known shapes. We need to update pnasnet.py to handle
+# shapes not known at compile time.
+class FasterRCNNPNASFeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN with PNASNet feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 16.
+    """
+    if first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 16.')
+    super(FasterRCNNPNASFeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN with PNAS preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: A [batch, height_out, width_out, channels] float32
+        tensor representing a batch of images.
+
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Extracts features using the first half of the PNASNet network.
+    We construct the network in `align_feature_maps=True` mode, which means
+    that all VALID paddings in the network are changed to SAME padding so that
+    the feature maps are aligned.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      end_points: A dictionary mapping feature extractor tensor names to tensors
+
+    Raises:
+      ValueError: If the created network is missing the required activation.
+    """
+    del scope
+
+    if len(preprocessed_inputs.get_shape().as_list()) != 4:
+      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
+                       'tensor of shape %s' % preprocessed_inputs.get_shape())
+
+    with slim.arg_scope(pnasnet_large_arg_scope_for_detection(
+        is_batch_norm_training=self._train_batch_norm)):
+      with arg_scope([slim.conv2d,
+                      slim.batch_norm,
+                      slim.separable_conv2d],
+                     reuse=self._reuse_weights):
+        _, end_points = pnasnet.build_pnasnet_large(
+            preprocessed_inputs, num_classes=None,
+            is_training=self._is_training,
+            final_endpoint='Cell_7')
+
+    # Note that both 'Cell_6' and 'Cell_7' have equal depth = 2160.
+    # Cell_7 is the last cell before second reduction.
+    rpn_feature_map = tf.concat([end_points['Cell_6'],
+                                 end_points['Cell_7']], 3)
+
+    # pnasnet.py does not maintain the batch size in the first dimension.
+    # This work around permits us retaining the batch for below.
+    batch = preprocessed_inputs.get_shape().as_list()[0]
+    shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
+    rpn_feature_map_shape = [batch] + shape_without_batch
+    rpn_feature_map.set_shape(rpn_feature_map_shape)
+
+    return rpn_feature_map, end_points
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    This function reconstructs the "second half" of the PNASNet
+    network after the part defined in `_extract_proposal_features`.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name.
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    del scope
+
+    # Number of used stem cells.
+    num_stem_cells = 2
+
+    # Note that we always feed into 2 layers of equal depth
+    # where the first N channels corresponds to previous hidden layer
+    # and the second N channels correspond to the final hidden layer.
+    hidden_previous, hidden = tf.split(proposal_feature_maps, 2, axis=3)
+
+    # Note that what follows is largely a copy of build_pnasnet_large() within
+    # pnasnet.py. We are copying to minimize code pollution in slim.
+
+    # TODO(shlens,skornblith): Determine the appropriate drop path schedule.
+    # For now the schedule is the default (1.0->0.7 over 250,000 train steps).
+    hparams = pnasnet.large_imagenet_config()
+    if not self._is_training:
+      hparams.set_hparam('drop_path_keep_prob', 1.0)
+
+    # Calculate the total number of cells in the network
+    total_num_cells = hparams.num_cells + num_stem_cells
+
+    normal_cell = pnasnet.PNasNetNormalCell(
+        hparams.num_conv_filters, hparams.drop_path_keep_prob,
+        total_num_cells, hparams.total_training_steps)
+    with arg_scope([slim.dropout, nasnet_utils.drop_path],
+                   is_training=self._is_training):
+      with arg_scope([slim.batch_norm], is_training=self._train_batch_norm):
+        with arg_scope([slim.avg_pool2d,
+                        slim.max_pool2d,
+                        slim.conv2d,
+                        slim.batch_norm,
+                        slim.separable_conv2d,
+                        nasnet_utils.factorized_reduction,
+                        nasnet_utils.global_avg_pool,
+                        nasnet_utils.get_channel_index,
+                        nasnet_utils.get_channel_dim],
+                       data_format=hparams.data_format):
+
+          # This corresponds to the cell number just past 'Cell_7' used by
+          # _extract_proposal_features().
+          start_cell_num = 8
+          true_cell_num = start_cell_num + num_stem_cells
+
+          with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+            net = _build_pnasnet_base(
+                hidden_previous,
+                hidden,
+                normal_cell=normal_cell,
+                hparams=hparams,
+                true_cell_num=true_cell_num,
+                start_cell_num=start_cell_num)
+
+    proposal_classifier_features = net
+    return proposal_classifier_features
+
+  def restore_from_classification_checkpoint_fn(
+      self,
+      first_stage_feature_extractor_scope,
+      second_stage_feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Note that this overrides the default implementation in
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
+    PNASNet checkpoints.
+
+    Args:
+      first_stage_feature_extractor_scope: A scope name for the first stage
+        feature extractor.
+      second_stage_feature_extractor_scope: A scope name for the second stage
+        feature extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      if variable.op.name.startswith(
+          first_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            first_stage_feature_extractor_scope + '/', '')
+        var_name += '/ExponentialMovingAverage'
+        variables_to_restore[var_name] = variable
+      if variable.op.name.startswith(
+          second_stage_feature_extractor_scope):
+        var_name = variable.op.name.replace(
+            second_stage_feature_extractor_scope + '/', '')
+        var_name += '/ExponentialMovingAverage'
+        variables_to_restore[var_name] = variable
+    return variables_to_restore
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor_test.py
new file mode 100644
index 00000000..6bb36804
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_pnas_feature_extractor_test.py
@@ -0,0 +1,122 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for models.faster_rcnn_pnas_feature_extractor."""
+
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
+
+
+class FasterRcnnPNASFeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return frcnn_pnas.FasterRCNNPNASFeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 299, 299, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 19, 19, 4320])
+
+  def test_extract_proposal_features_input_size_224(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 14, 14, 4320])
+
+  def test_extract_proposal_features_input_size_112(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 4320])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [2, 17, 17, 1088], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [2, 9, 9, 4320])
+
+  def test_filter_scaling_computation(self):
+    expected_filter_scaling = {
+        ((4, 8), 2): 1.0,
+        ((4, 8), 7): 2.0,
+        ((4, 8), 8): 2.0,
+        ((4, 8), 9): 4.0
+    }
+    for args, filter_scaling in expected_filter_scaling.items():
+      reduction_indices, start_cell_num = args
+      self.assertAlmostEqual(
+          frcnn_pnas._filter_scaling(reduction_indices, start_cell_num),
+          filter_scaling)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py
new file mode 100644
index 00000000..8f89b7c0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py
@@ -0,0 +1,258 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Resnet V1 Faster R-CNN implementation.
+
+See "Deep Residual Learning for Image Recognition" by He et al., 2015.
+https://arxiv.org/abs/1512.03385
+
+Note: this implementation assumes that the classification checkpoint used
+to finetune this model is trained using the same configuration as that of
+the MSRA provided checkpoints
+(see https://github.com/KaimingHe/deep-residual-networks), e.g., with
+same preprocessing, batch norm scaling, etc.
+"""
+import tensorflow as tf
+
+from object_detection.meta_architectures import faster_rcnn_meta_arch
+from nets import resnet_utils
+from nets import resnet_v1
+
+slim = tf.contrib.slim
+
+
+class FasterRCNNResnetV1FeatureExtractor(
+    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+  """Faster R-CNN Resnet V1 feature extractor implementation."""
+
+  def __init__(self,
+               architecture,
+               resnet_model,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      architecture: Architecture name of the Resnet V1 model.
+      resnet_model: Definition of the Resnet V1 model.
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16.
+    """
+    if first_stage_features_stride != 8 and first_stage_features_stride != 16:
+      raise ValueError('`first_stage_features_stride` must be 8 or 16.')
+    self._architecture = architecture
+    self._resnet_model = resnet_model
+    super(FasterRCNNResnetV1FeatureExtractor, self).__init__(
+        is_training, first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+  def preprocess(self, resized_inputs):
+    """Faster R-CNN Resnet V1 preprocessing.
+
+    VGG style channel mean subtraction as described here:
+    https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
+    Note that if the number of channels is not equal to 3, the mean subtraction
+    will be skipped and the original resized_inputs will be returned.
+
+    Args:
+      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
+        representing a batch of images with values between 0 and 255.0.
+
+    Returns:
+      preprocessed_inputs: A [batch, height_out, width_out, channels] float32
+        tensor representing a batch of images.
+
+    """
+    if resized_inputs.shape.as_list()[3] == 3:
+      channel_means = [123.68, 116.779, 103.939]
+      return resized_inputs - [[channel_means]]
+    else:
+      return resized_inputs
+
+  def _extract_proposal_features(self, preprocessed_inputs, scope):
+    """Extracts first stage RPN features.
+
+    Args:
+      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
+        representing a batch of images.
+      scope: A scope name.
+
+    Returns:
+      rpn_feature_map: A tensor with shape [batch, height, width, depth]
+      activations: A dictionary mapping feature extractor tensor names to
+        tensors
+
+    Raises:
+      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
+        (height or width) is less than 33.
+      ValueError: If the created network is missing the required activation.
+    """
+    if len(preprocessed_inputs.get_shape().as_list()) != 4:
+      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
+                       'tensor of shape %s' % preprocessed_inputs.get_shape())
+    shape_assert = tf.Assert(
+        tf.logical_and(
+            tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
+            tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
+        ['image size must at least be 33 in both height and width.'])
+
+    with tf.control_dependencies([shape_assert]):
+      # Disables batchnorm for fine-tuning with smaller batch sizes.
+      # TODO(chensun): Figure out if it is needed when image
+      # batch size is bigger.
+      with slim.arg_scope(
+          resnet_utils.resnet_arg_scope(
+              batch_norm_epsilon=1e-5,
+              batch_norm_scale=True,
+              weight_decay=self._weight_decay)):
+        with tf.variable_scope(
+            self._architecture, reuse=self._reuse_weights) as var_scope:
+          _, activations = self._resnet_model(
+              preprocessed_inputs,
+              num_classes=None,
+              is_training=self._train_batch_norm,
+              global_pool=False,
+              output_stride=self._first_stage_features_stride,
+              spatial_squeeze=False,
+              scope=var_scope)
+
+    handle = scope + '/%s/block3' % self._architecture
+    return activations[handle], activations
+
+  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
+    """Extracts second stage box classifier features.
+
+    Args:
+      proposal_feature_maps: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
+        representing the feature map cropped to each proposal.
+      scope: A scope name (unused).
+
+    Returns:
+      proposal_classifier_features: A 4-D float tensor with shape
+        [batch_size * self.max_num_proposals, height, width, depth]
+        representing box classifier features for each proposal.
+    """
+    with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
+      with slim.arg_scope(
+          resnet_utils.resnet_arg_scope(
+              batch_norm_epsilon=1e-5,
+              batch_norm_scale=True,
+              weight_decay=self._weight_decay)):
+        with slim.arg_scope([slim.batch_norm],
+                            is_training=self._train_batch_norm):
+          blocks = [
+              resnet_utils.Block('block4', resnet_v1.bottleneck, [{
+                  'depth': 2048,
+                  'depth_bottleneck': 512,
+                  'stride': 1
+              }] * 3)
+          ]
+          proposal_classifier_features = resnet_utils.stack_blocks_dense(
+              proposal_feature_maps, blocks)
+    return proposal_classifier_features
+
+
+class FasterRCNNResnet50FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
+  """Faster R-CNN Resnet 50 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16,
+        or if `architecture` is not supported.
+    """
+    super(FasterRCNNResnet50FeatureExtractor, self).__init__(
+        'resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
+        first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+
+class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
+  """Faster R-CNN Resnet 101 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16,
+        or if `architecture` is not supported.
+    """
+    super(FasterRCNNResnet101FeatureExtractor, self).__init__(
+        'resnet_v1_101', resnet_v1.resnet_v1_101, is_training,
+        first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
+
+
+class FasterRCNNResnet152FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
+  """Faster R-CNN Resnet 152 feature extractor implementation."""
+
+  def __init__(self,
+               is_training,
+               first_stage_features_stride,
+               batch_norm_trainable=False,
+               reuse_weights=None,
+               weight_decay=0.0):
+    """Constructor.
+
+    Args:
+      is_training: See base class.
+      first_stage_features_stride: See base class.
+      batch_norm_trainable: See base class.
+      reuse_weights: See base class.
+      weight_decay: See base class.
+
+    Raises:
+      ValueError: If `first_stage_features_stride` is not 8 or 16,
+        or if `architecture` is not supported.
+    """
+    super(FasterRCNNResnet152FeatureExtractor, self).__init__(
+        'resnet_v1_152', resnet_v1.resnet_v1_152, is_training,
+        first_stage_features_stride, batch_norm_trainable,
+        reuse_weights, weight_decay)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor_test.py
new file mode 100644
index 00000000..e2a336f0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor_test.py
@@ -0,0 +1,137 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.models.faster_rcnn_resnet_v1_feature_extractor."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as faster_rcnn_resnet_v1
+
+
+class FasterRcnnResnetV1FeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self,
+                               first_stage_features_stride,
+                               architecture='resnet_v1_101'):
+    feature_extractor_map = {
+        'resnet_v1_50':
+            faster_rcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
+        'resnet_v1_101':
+            faster_rcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
+        'resnet_v1_152':
+            faster_rcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor
+    }
+    return feature_extractor_map[architecture](
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    for architecture in ['resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152']:
+      feature_extractor = self._build_feature_extractor(
+          first_stage_features_stride=16, architecture=architecture)
+      preprocessed_inputs = tf.random_uniform(
+          [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+      rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+      features_shape = tf.shape(rpn_feature_map)
+
+      init_op = tf.global_variables_initializer()
+      with self.test_session() as sess:
+        sess.run(init_op)
+        features_shape_out = sess.run(features_shape)
+        self.assertAllEqual(features_shape_out, [4, 14, 14, 1024])
+
+  def test_extract_proposal_features_stride_eight(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=8)
+    preprocessed_inputs = tf.random_uniform(
+        [4, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [4, 28, 28, 1024])
+
+  def test_extract_proposal_features_half_size_input(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 1024])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_on_very_small_images(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(
+            features_shape,
+            feed_dict={preprocessed_inputs: np.random.rand(4, 32, 32, 3)})
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [3, 7, 7, 1024], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [3, 7, 7, 2048])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators.py
new file mode 100644
index 00000000..4c2fe68d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators.py
@@ -0,0 +1,556 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Functions to generate a list of feature maps based on image features.
+
+Provides several feature map generators that can be used to build object
+detection feature extractors.
+
+Object detection feature extractors usually are built by stacking two components
+- A base feature extractor such as Inception V3 and a feature map generator.
+Feature map generators build on the base feature extractors and produce a list
+of final feature maps.
+"""
+import collections
+import functools
+import tensorflow as tf
+from object_detection.utils import ops
+slim = tf.contrib.slim
+
+
+def get_depth_fn(depth_multiplier, min_depth):
+  """Builds a callable to compute depth (output channels) of conv filters.
+
+  Args:
+    depth_multiplier: a multiplier for the nominal depth.
+    min_depth: a lower bound on the depth of filters.
+
+  Returns:
+    A callable that takes in a nominal depth and returns the depth to use.
+  """
+  def multiply_depth(depth):
+    new_depth = int(depth * depth_multiplier)
+    return max(new_depth, min_depth)
+  return multiply_depth
+
+
+class KerasMultiResolutionFeatureMaps(tf.keras.Model):
+  """Generates multi resolution feature maps from input image features.
+
+  A Keras model that generates multi-scale feature maps for detection as in the
+  SSD papers by Liu et al: https://arxiv.org/pdf/1512.02325v2.pdf, See Sec 2.1.
+
+  More specifically, when called on inputs it performs the following two tasks:
+  1) If a layer name is provided in the configuration, returns that layer as a
+     feature map.
+  2) If a layer name is left as an empty string, constructs a new feature map
+     based on the spatial shape and depth configuration. Note that the current
+     implementation only supports generating new layers using convolution of
+     stride 2 resulting in a spatial resolution reduction by a factor of 2.
+     By default convolution kernel size is set to 3, and it can be customized
+     by caller.
+
+  An example of the configuration for Inception V3:
+  {
+    'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+    'layer_depth': [-1, -1, -1, 512, 256, 128]
+  }
+
+  When this feature generator object is called on input image_features:
+    Args:
+      image_features: A dictionary of handles to activation tensors from the
+        base feature extractor.
+
+    Returns:
+      feature_maps: an OrderedDict mapping keys (feature map names) to
+        tensors where each tensor has shape [batch, height_i, width_i, depth_i].
+  """
+
+  def __init__(self,
+               feature_map_layout,
+               depth_multiplier,
+               min_depth,
+               insert_1x1_conv,
+               is_training,
+               conv_hyperparams,
+               freeze_batchnorm,
+               name=None):
+    """Constructor.
+
+    Args:
+      feature_map_layout: Dictionary of specifications for the feature map
+        layouts in the following format (Inception V2/V3 respectively):
+        {
+          'from_layer': ['Mixed_3c', 'Mixed_4c', 'Mixed_5c', '', '', ''],
+          'layer_depth': [-1, -1, -1, 512, 256, 128]
+        }
+        or
+        {
+          'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+          'layer_depth': [-1, -1, -1, 512, 256, 128]
+        }
+        If 'from_layer' is specified, the specified feature map is directly used
+        as a box predictor layer, and the layer_depth is directly infered from
+        the feature map (instead of using the provided 'layer_depth' parameter).
+        In this case, our convention is to set 'layer_depth' to -1 for clarity.
+        Otherwise, if 'from_layer' is an empty string, then the box predictor
+        layer will be built from the previous layer using convolution
+        operations. Note that the current implementation only supports
+        generating new layers using convolutions of stride 2 (resulting in a
+        spatial resolution reduction by a factor of 2), and will be extended to
+        a more flexible design. Convolution kernel size is set to 3 by default,
+        and can be customized by 'conv_kernel_size' parameter (similarily,
+        'conv_kernel_size' should be set to -1 if 'from_layer' is specified).
+        The created convolution operation will be a normal 2D convolution by
+        default, and a depthwise convolution followed by 1x1 convolution if
+        'use_depthwise' is set to True.
+      depth_multiplier: Depth multiplier for convolutional layers.
+      min_depth: Minimum depth for convolutional layers.
+      insert_1x1_conv: A boolean indicating whether an additional 1x1
+        convolution should be inserted before shrinking the feature map.
+      is_training: Indicates whether the feature generator is in training mode.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops.
+      freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      name: A string name scope to assign to the model. If 'None', Keras
+        will auto-generate one from the class name.
+    """
+    super(KerasMultiResolutionFeatureMaps, self).__init__(name=name)
+
+    self.feature_map_layout = feature_map_layout
+    self.convolutions = []
+
+    depth_fn = get_depth_fn(depth_multiplier, min_depth)
+
+    base_from_layer = ''
+    use_explicit_padding = False
+    if 'use_explicit_padding' in feature_map_layout:
+      use_explicit_padding = feature_map_layout['use_explicit_padding']
+    use_depthwise = False
+    if 'use_depthwise' in feature_map_layout:
+      use_depthwise = feature_map_layout['use_depthwise']
+    for index, from_layer in enumerate(feature_map_layout['from_layer']):
+      net = []
+      layer_depth = feature_map_layout['layer_depth'][index]
+      conv_kernel_size = 3
+      if 'conv_kernel_size' in feature_map_layout:
+        conv_kernel_size = feature_map_layout['conv_kernel_size'][index]
+      if from_layer:
+        base_from_layer = from_layer
+      else:
+        if insert_1x1_conv:
+          layer_name = '{}_1_Conv2d_{}_1x1_{}'.format(
+              base_from_layer, index, depth_fn(layer_depth / 2))
+          net.append(tf.keras.layers.Conv2D(depth_fn(layer_depth / 2),
+                                            [1, 1],
+                                            padding='SAME',
+                                            strides=1,
+                                            name=layer_name + '_conv',
+                                            **conv_hyperparams.params()))
+          net.append(
+              conv_hyperparams.build_batch_norm(
+                  training=(is_training and not freeze_batchnorm),
+                  name=layer_name + '_batchnorm'))
+          net.append(
+              conv_hyperparams.build_activation_layer(
+                  name=layer_name))
+
+        layer_name = '{}_2_Conv2d_{}_{}x{}_s2_{}'.format(
+            base_from_layer, index, conv_kernel_size, conv_kernel_size,
+            depth_fn(layer_depth))
+        stride = 2
+        padding = 'SAME'
+        if use_explicit_padding:
+          padding = 'VALID'
+          # We define this function here while capturing the value of
+          # conv_kernel_size, to avoid holding a reference to the loop variable
+          # conv_kernel_size inside of a lambda function
+          def fixed_padding(features, kernel_size=conv_kernel_size):
+            return ops.fixed_padding(features, kernel_size)
+          net.append(tf.keras.layers.Lambda(fixed_padding))
+        # TODO(rathodv): Add some utilities to simplify the creation of
+        # Depthwise & non-depthwise convolutions w/ normalization & activations
+        if use_depthwise:
+          net.append(tf.keras.layers.DepthwiseConv2D(
+              [conv_kernel_size, conv_kernel_size],
+              depth_multiplier=1,
+              padding=padding,
+              strides=stride,
+              name=layer_name + '_depthwise_conv',
+              **conv_hyperparams.params()))
+          net.append(
+              conv_hyperparams.build_batch_norm(
+                  training=(is_training and not freeze_batchnorm),
+                  name=layer_name + '_depthwise_batchnorm'))
+          net.append(
+              conv_hyperparams.build_activation_layer(
+                  name=layer_name + '_depthwise'))
+
+          net.append(tf.keras.layers.Conv2D(depth_fn(layer_depth), [1, 1],
+                                            padding='SAME',
+                                            strides=1,
+                                            name=layer_name + '_conv',
+                                            **conv_hyperparams.params()))
+          net.append(
+              conv_hyperparams.build_batch_norm(
+                  training=(is_training and not freeze_batchnorm),
+                  name=layer_name + '_batchnorm'))
+          net.append(
+              conv_hyperparams.build_activation_layer(
+                  name=layer_name))
+
+        else:
+          net.append(tf.keras.layers.Conv2D(
+              depth_fn(layer_depth),
+              [conv_kernel_size, conv_kernel_size],
+              padding=padding,
+              strides=stride,
+              name=layer_name + '_conv',
+              **conv_hyperparams.params()))
+          net.append(
+              conv_hyperparams.build_batch_norm(
+                  training=(is_training and not freeze_batchnorm),
+                  name=layer_name + '_batchnorm'))
+          net.append(
+              conv_hyperparams.build_activation_layer(
+                  name=layer_name))
+
+      # Until certain bugs are fixed in checkpointable lists,
+      # this net must be appended only once it's been filled with layers
+      self.convolutions.append(net)
+
+  def call(self, image_features):
+    """Generate the multi-resolution feature maps.
+
+    Executed when calling the `.__call__` method on input.
+
+    Args:
+      image_features: A dictionary of handles to activation tensors from the
+        base feature extractor.
+
+    Returns:
+      feature_maps: an OrderedDict mapping keys (feature map names) to
+        tensors where each tensor has shape [batch, height_i, width_i, depth_i].
+    """
+    feature_maps = []
+    feature_map_keys = []
+
+    for index, from_layer in enumerate(self.feature_map_layout['from_layer']):
+      if from_layer:
+        feature_map = image_features[from_layer]
+        feature_map_keys.append(from_layer)
+      else:
+        feature_map = feature_maps[-1]
+        for layer in self.convolutions[index]:
+          feature_map = layer(feature_map)
+        layer_name = self.convolutions[index][-1].name
+        feature_map_keys.append(layer_name)
+      feature_maps.append(feature_map)
+    return collections.OrderedDict(
+        [(x, y) for (x, y) in zip(feature_map_keys, feature_maps)])
+
+
+def multi_resolution_feature_maps(feature_map_layout, depth_multiplier,
+                                  min_depth, insert_1x1_conv, image_features,
+                                  pool_residual=False):
+  """Generates multi resolution feature maps from input image features.
+
+  Generates multi-scale feature maps for detection as in the SSD papers by
+  Liu et al: https://arxiv.org/pdf/1512.02325v2.pdf, See Sec 2.1.
+
+  More specifically, it performs the following two tasks:
+  1) If a layer name is provided in the configuration, returns that layer as a
+     feature map.
+  2) If a layer name is left as an empty string, constructs a new feature map
+     based on the spatial shape and depth configuration. Note that the current
+     implementation only supports generating new layers using convolution of
+     stride 2 resulting in a spatial resolution reduction by a factor of 2.
+     By default convolution kernel size is set to 3, and it can be customized
+     by caller.
+
+  An example of the configuration for Inception V3:
+  {
+    'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+    'layer_depth': [-1, -1, -1, 512, 256, 128]
+  }
+
+  Args:
+    feature_map_layout: Dictionary of specifications for the feature map
+      layouts in the following format (Inception V2/V3 respectively):
+      {
+        'from_layer': ['Mixed_3c', 'Mixed_4c', 'Mixed_5c', '', '', ''],
+        'layer_depth': [-1, -1, -1, 512, 256, 128]
+      }
+      or
+      {
+        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+        'layer_depth': [-1, -1, -1, 512, 256, 128]
+      }
+      If 'from_layer' is specified, the specified feature map is directly used
+      as a box predictor layer, and the layer_depth is directly infered from the
+      feature map (instead of using the provided 'layer_depth' parameter). In
+      this case, our convention is to set 'layer_depth' to -1 for clarity.
+      Otherwise, if 'from_layer' is an empty string, then the box predictor
+      layer will be built from the previous layer using convolution operations.
+      Note that the current implementation only supports generating new layers
+      using convolutions of stride 2 (resulting in a spatial resolution
+      reduction by a factor of 2), and will be extended to a more flexible
+      design. Convolution kernel size is set to 3 by default, and can be
+      customized by 'conv_kernel_size' parameter (similarily, 'conv_kernel_size'
+      should be set to -1 if 'from_layer' is specified). The created convolution
+      operation will be a normal 2D convolution by default, and a depthwise
+      convolution followed by 1x1 convolution if 'use_depthwise' is set to True.
+    depth_multiplier: Depth multiplier for convolutional layers.
+    min_depth: Minimum depth for convolutional layers.
+    insert_1x1_conv: A boolean indicating whether an additional 1x1 convolution
+      should be inserted before shrinking the feature map.
+    image_features: A dictionary of handles to activation tensors from the
+      base feature extractor.
+    pool_residual: Whether to add an average pooling layer followed by a
+      residual connection between subsequent feature maps when the channel
+      depth match. For example, with option 'layer_depth': [-1, 512, 256, 256],
+      a pooling and residual layer is added between the third and forth feature
+      map. This option is better used with Weight Shared Convolution Box
+      Predictor when all feature maps have the same channel depth to encourage
+      more consistent features across multi-scale feature maps.
+
+  Returns:
+    feature_maps: an OrderedDict mapping keys (feature map names) to
+      tensors where each tensor has shape [batch, height_i, width_i, depth_i].
+
+  Raises:
+    ValueError: if the number entries in 'from_layer' and
+      'layer_depth' do not match.
+    ValueError: if the generated layer does not have the same resolution
+      as specified.
+  """
+  depth_fn = get_depth_fn(depth_multiplier, min_depth)
+
+  feature_map_keys = []
+  feature_maps = []
+  base_from_layer = ''
+  use_explicit_padding = False
+  if 'use_explicit_padding' in feature_map_layout:
+    use_explicit_padding = feature_map_layout['use_explicit_padding']
+  use_depthwise = False
+  if 'use_depthwise' in feature_map_layout:
+    use_depthwise = feature_map_layout['use_depthwise']
+  for index, from_layer in enumerate(feature_map_layout['from_layer']):
+    layer_depth = feature_map_layout['layer_depth'][index]
+    conv_kernel_size = 3
+    if 'conv_kernel_size' in feature_map_layout:
+      conv_kernel_size = feature_map_layout['conv_kernel_size'][index]
+    if from_layer:
+      feature_map = image_features[from_layer]
+      base_from_layer = from_layer
+      feature_map_keys.append(from_layer)
+    else:
+      pre_layer = feature_maps[-1]
+      pre_layer_depth = pre_layer.get_shape().as_list()[3]
+      intermediate_layer = pre_layer
+      if insert_1x1_conv:
+        layer_name = '{}_1_Conv2d_{}_1x1_{}'.format(
+            base_from_layer, index, depth_fn(layer_depth / 2))
+        intermediate_layer = slim.conv2d(
+            pre_layer,
+            depth_fn(layer_depth / 2), [1, 1],
+            padding='SAME',
+            stride=1,
+            scope=layer_name)
+      layer_name = '{}_2_Conv2d_{}_{}x{}_s2_{}'.format(
+          base_from_layer, index, conv_kernel_size, conv_kernel_size,
+          depth_fn(layer_depth))
+      stride = 2
+      padding = 'SAME'
+      if use_explicit_padding:
+        padding = 'VALID'
+        intermediate_layer = ops.fixed_padding(
+            intermediate_layer, conv_kernel_size)
+      if use_depthwise:
+        feature_map = slim.separable_conv2d(
+            intermediate_layer,
+            None, [conv_kernel_size, conv_kernel_size],
+            depth_multiplier=1,
+            padding=padding,
+            stride=stride,
+            scope=layer_name + '_depthwise')
+        feature_map = slim.conv2d(
+            feature_map,
+            depth_fn(layer_depth), [1, 1],
+            padding='SAME',
+            stride=1,
+            scope=layer_name)
+        if pool_residual and pre_layer_depth == depth_fn(layer_depth):
+          feature_map += slim.avg_pool2d(
+              pre_layer, [3, 3],
+              padding='SAME',
+              stride=2,
+              scope=layer_name + '_pool')
+      else:
+        feature_map = slim.conv2d(
+            intermediate_layer,
+            depth_fn(layer_depth), [conv_kernel_size, conv_kernel_size],
+            padding=padding,
+            stride=stride,
+            scope=layer_name)
+      feature_map_keys.append(layer_name)
+    feature_maps.append(feature_map)
+  return collections.OrderedDict(
+      [(x, y) for (x, y) in zip(feature_map_keys, feature_maps)])
+
+
+def fpn_top_down_feature_maps(image_features,
+                              depth,
+                              use_depthwise=False,
+                              use_explicit_padding=False,
+                              scope=None):
+  """Generates `top-down` feature maps for Feature Pyramid Networks.
+
+  See https://arxiv.org/abs/1612.03144 for details.
+
+  Args:
+    image_features: list of tuples of (tensor_name, image_feature_tensor).
+      Spatial resolutions of succesive tensors must reduce exactly by a factor
+      of 2.
+    depth: depth of output feature maps.
+    use_depthwise: whether to use depthwise separable conv instead of regular
+      conv.
+    use_explicit_padding: whether to use explicit padding.
+    scope: A scope name to wrap this op under.
+
+  Returns:
+    feature_maps: an OrderedDict mapping keys (feature map names) to
+      tensors where each tensor has shape [batch, height_i, width_i, depth_i].
+  """
+  with tf.name_scope(scope, 'top_down'):
+    num_levels = len(image_features)
+    output_feature_maps_list = []
+    output_feature_map_keys = []
+    padding = 'VALID' if use_explicit_padding else 'SAME'
+    kernel_size = 3
+    with slim.arg_scope(
+        [slim.conv2d, slim.separable_conv2d], padding=padding, stride=1):
+      top_down = slim.conv2d(
+          image_features[-1][1],
+          depth, [1, 1], activation_fn=None, normalizer_fn=None,
+          scope='projection_%d' % num_levels)
+      output_feature_maps_list.append(top_down)
+      output_feature_map_keys.append(
+          'top_down_%s' % image_features[-1][0])
+
+      for level in reversed(range(num_levels - 1)):
+        top_down = ops.nearest_neighbor_upsampling(top_down, 2)
+        residual = slim.conv2d(
+            image_features[level][1], depth, [1, 1],
+            activation_fn=None, normalizer_fn=None,
+            scope='projection_%d' % (level + 1))
+        if use_explicit_padding:
+          # slice top_down to the same shape as residual
+          residual_shape = tf.shape(residual)
+          top_down = top_down[:, :residual_shape[1], :residual_shape[2], :]
+        top_down += residual
+        if use_depthwise:
+          conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
+        else:
+          conv_op = slim.conv2d
+        if use_explicit_padding:
+          top_down = ops.fixed_padding(top_down, kernel_size)
+        output_feature_maps_list.append(conv_op(
+            top_down,
+            depth, [kernel_size, kernel_size],
+            scope='smoothing_%d' % (level + 1)))
+        output_feature_map_keys.append('top_down_%s' % image_features[level][0])
+      return collections.OrderedDict(reversed(
+          list(zip(output_feature_map_keys, output_feature_maps_list))))
+
+
+def pooling_pyramid_feature_maps(base_feature_map_depth, num_layers,
+                                 image_features, replace_pool_with_conv=False):
+  """Generates pooling pyramid feature maps.
+
+  The pooling pyramid feature maps is motivated by
+  multi_resolution_feature_maps. The main difference are that it is simpler and
+  reduces the number of free parameters.
+
+  More specifically:
+   - Instead of using convolutions to shrink the feature map, it uses max
+     pooling, therefore totally gets rid of the parameters in convolution.
+   - By pooling feature from larger map up to a single cell, it generates
+     features in the same feature space.
+   - Instead of independently making box predictions from individual maps, it
+     shares the same classifier across different feature maps, therefore reduces
+     the "mis-calibration" across different scales.
+
+  See go/ppn-detection for more details.
+
+  Args:
+    base_feature_map_depth: Depth of the base feature before the max pooling.
+    num_layers: Number of layers used to make predictions. They are pooled
+      from the base feature.
+    image_features: A dictionary of handles to activation tensors from the
+      feature extractor.
+    replace_pool_with_conv: Whether or not to replace pooling operations with
+      convolutions in the PPN. Default is False.
+
+  Returns:
+    feature_maps: an OrderedDict mapping keys (feature map names) to
+      tensors where each tensor has shape [batch, height_i, width_i, depth_i].
+  Raises:
+    ValueError: image_features does not contain exactly one entry
+  """
+  if len(image_features) != 1:
+    raise ValueError('image_features should be a dictionary of length 1.')
+  image_features = image_features[image_features.keys()[0]]
+
+  feature_map_keys = []
+  feature_maps = []
+  feature_map_key = 'Base_Conv2d_1x1_%d' % base_feature_map_depth
+  if base_feature_map_depth > 0:
+    image_features = slim.conv2d(
+        image_features,
+        base_feature_map_depth,
+        [1, 1],  # kernel size
+        padding='SAME', stride=1, scope=feature_map_key)
+    # Add a 1x1 max-pooling node (a no op node) immediately after the conv2d for
+    # TPU v1 compatibility.  Without the following dummy op, TPU runtime
+    # compiler will combine the convolution with one max-pooling below into a
+    # single cycle, so getting the conv2d feature becomes impossible.
+    image_features = slim.max_pool2d(
+        image_features, [1, 1], padding='SAME', stride=1, scope=feature_map_key)
+  feature_map_keys.append(feature_map_key)
+  feature_maps.append(image_features)
+  feature_map = image_features
+  if replace_pool_with_conv:
+    with slim.arg_scope([slim.conv2d], padding='SAME', stride=2):
+      for i in range(num_layers - 1):
+        feature_map_key = 'Conv2d_{}_3x3_s2_{}'.format(i,
+                                                       base_feature_map_depth)
+        feature_map = slim.conv2d(
+            feature_map, base_feature_map_depth, [3, 3], scope=feature_map_key)
+        feature_map_keys.append(feature_map_key)
+        feature_maps.append(feature_map)
+  else:
+    with slim.arg_scope([slim.max_pool2d], padding='SAME', stride=2):
+      for i in range(num_layers - 1):
+        feature_map_key = 'MaxPool2d_%d_2x2' % i
+        feature_map = slim.max_pool2d(
+            feature_map, [2, 2], padding='SAME', scope=feature_map_key)
+        feature_map_keys.append(feature_map_key)
+        feature_maps.append(feature_map)
+  return collections.OrderedDict(
+      [(x, y) for (x, y) in zip(feature_map_keys, feature_maps)])
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators_test.py
new file mode 100644
index 00000000..f7ac0cc0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/feature_map_generators_test.py
@@ -0,0 +1,459 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for feature map generators."""
+
+from absl.testing import parameterized
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import hyperparams_builder
+from object_detection.models import feature_map_generators
+from object_detection.protos import hyperparams_pb2
+
+INCEPTION_V2_LAYOUT = {
+    'from_layer': ['Mixed_3c', 'Mixed_4c', 'Mixed_5c', '', '', ''],
+    'layer_depth': [-1, -1, -1, 512, 256, 256],
+    'anchor_strides': [16, 32, 64, -1, -1, -1],
+    'layer_target_norm': [20.0, -1, -1, -1, -1, -1],
+}
+
+INCEPTION_V3_LAYOUT = {
+    'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+    'layer_depth': [-1, -1, -1, 512, 256, 128],
+    'anchor_strides': [16, 32, 64, -1, -1, -1],
+    'aspect_ratios': [1.0, 2.0, 1.0/2, 3.0, 1.0/3]
+}
+
+EMBEDDED_SSD_MOBILENET_V1_LAYOUT = {
+    'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '', ''],
+    'layer_depth': [-1, -1, 512, 256, 256],
+    'conv_kernel_size': [-1, -1, 3, 3, 2],
+}
+
+SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT = {
+    'from_layer': ['Conv2d_13_pointwise', '', '', ''],
+    'layer_depth': [-1, 256, 256, 256],
+}
+
+
+@parameterized.parameters(
+    {'use_keras': False},
+    {'use_keras': True},
+)
+class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def _build_feature_map_generator(self, feature_map_layout, use_keras,
+                                   pool_residual=False):
+    if use_keras:
+      return feature_map_generators.KerasMultiResolutionFeatureMaps(
+          feature_map_layout=feature_map_layout,
+          depth_multiplier=1,
+          min_depth=32,
+          insert_1x1_conv=True,
+          freeze_batchnorm=False,
+          is_training=True,
+          conv_hyperparams=self._build_conv_hyperparams(),
+          name='FeatureMaps'
+      )
+    else:
+      def feature_map_generator(image_features):
+        return feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=1,
+            min_depth=32,
+            insert_1x1_conv=True,
+            image_features=image_features,
+            pool_residual=pool_residual)
+      return feature_map_generator
+
+  def test_get_expected_feature_map_shapes_with_inception_v2(self, use_keras):
+    image_features = {
+        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
+        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
+        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
+    }
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=INCEPTION_V2_LAYOUT,
+        use_keras=use_keras
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_feature_map_shapes = {
+        'Mixed_3c': (4, 28, 28, 256),
+        'Mixed_4c': (4, 14, 14, 576),
+        'Mixed_5c': (4, 7, 7, 1024),
+        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
+        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
+        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = dict(
+          (key, value.shape) for key, value in out_feature_maps.items())
+      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+  def test_get_expected_feature_map_shapes_use_explicit_padding(
+      self, use_keras):
+    image_features = {
+        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
+        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
+        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
+    }
+    layout_copy = INCEPTION_V2_LAYOUT.copy()
+    layout_copy['use_explicit_padding'] = True
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=layout_copy,
+        use_keras=use_keras
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_feature_map_shapes = {
+        'Mixed_3c': (4, 28, 28, 256),
+        'Mixed_4c': (4, 14, 14, 576),
+        'Mixed_5c': (4, 7, 7, 1024),
+        'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
+        'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
+        'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = dict(
+          (key, value.shape) for key, value in out_feature_maps.items())
+      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
+
+  def test_get_expected_feature_map_shapes_with_inception_v3(self, use_keras):
+    image_features = {
+        'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
+        'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
+        'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
+    }
+
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=INCEPTION_V3_LAYOUT,
+        use_keras=use_keras
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_feature_map_shapes = {
+        'Mixed_5d': (4, 35, 35, 256),
+        'Mixed_6e': (4, 17, 17, 576),
+        'Mixed_7c': (4, 8, 8, 1024),
+        'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
+        'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
+        'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = dict(
+          (key, value.shape) for key, value in out_feature_maps.items())
+      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
+
+  def test_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(
+      self, use_keras):
+    image_features = {
+        'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
+                                                 dtype=tf.float32),
+        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
+                                                 dtype=tf.float32),
+    }
+
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
+        use_keras=use_keras
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_feature_map_shapes = {
+        'Conv2d_11_pointwise': (4, 16, 16, 512),
+        'Conv2d_13_pointwise': (4, 8, 8, 1024),
+        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),
+        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),
+        'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = dict(
+          (key, value.shape) for key, value in out_feature_maps.items())
+      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
+
+  def test_feature_map_shapes_with_pool_residual_ssd_mobilenet_v1(
+      self, use_keras):
+    image_features = {
+        'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
+                                                 dtype=tf.float32),
+    }
+
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT,
+        use_keras=use_keras,
+        pool_residual=True
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_feature_map_shapes = {
+        'Conv2d_13_pointwise': (4, 8, 8, 1024),
+        'Conv2d_13_pointwise_2_Conv2d_1_3x3_s2_256': (4, 4, 4, 256),
+        'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_256': (4, 2, 2, 256),
+        'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 1, 1, 256)}
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = dict(
+          (key, value.shape) for key, value in out_feature_maps.items())
+      self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
+
+  def test_get_expected_variable_names_with_inception_v2(self, use_keras):
+    image_features = {
+        'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
+        'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
+        'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
+    }
+    feature_map_generator = self._build_feature_map_generator(
+        feature_map_layout=INCEPTION_V2_LAYOUT,
+        use_keras=use_keras
+    )
+    feature_maps = feature_map_generator(image_features)
+
+    expected_slim_variables = set([
+        'Mixed_5c_1_Conv2d_3_1x1_256/weights',
+        'Mixed_5c_1_Conv2d_3_1x1_256/biases',
+        'Mixed_5c_2_Conv2d_3_3x3_s2_512/weights',
+        'Mixed_5c_2_Conv2d_3_3x3_s2_512/biases',
+        'Mixed_5c_1_Conv2d_4_1x1_128/weights',
+        'Mixed_5c_1_Conv2d_4_1x1_128/biases',
+        'Mixed_5c_2_Conv2d_4_3x3_s2_256/weights',
+        'Mixed_5c_2_Conv2d_4_3x3_s2_256/biases',
+        'Mixed_5c_1_Conv2d_5_1x1_128/weights',
+        'Mixed_5c_1_Conv2d_5_1x1_128/biases',
+        'Mixed_5c_2_Conv2d_5_3x3_s2_256/weights',
+        'Mixed_5c_2_Conv2d_5_3x3_s2_256/biases',
+    ])
+
+    expected_keras_variables = set([
+        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/kernel',
+        'FeatureMaps/Mixed_5c_1_Conv2d_3_1x1_256_conv/bias',
+        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/kernel',
+        'FeatureMaps/Mixed_5c_2_Conv2d_3_3x3_s2_512_conv/bias',
+        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/kernel',
+        'FeatureMaps/Mixed_5c_1_Conv2d_4_1x1_128_conv/bias',
+        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/kernel',
+        'FeatureMaps/Mixed_5c_2_Conv2d_4_3x3_s2_256_conv/bias',
+        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/kernel',
+        'FeatureMaps/Mixed_5c_1_Conv2d_5_1x1_128_conv/bias',
+        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/kernel',
+        'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',
+    ])
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      sess.run(feature_maps)
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+      if use_keras:
+        self.assertSetEqual(expected_keras_variables, actual_variable_set)
+      else:
+        self.assertSetEqual(expected_slim_variables, actual_variable_set)
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+
+class FPNFeatureMapGeneratorTest(tf.test.TestCase):
+
+  def test_get_expected_feature_map_shapes(self):
+    image_features = [
+        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
+        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
+        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
+        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
+    ]
+    feature_maps = feature_map_generators.fpn_top_down_feature_maps(
+        image_features=image_features, depth=128)
+
+    expected_feature_map_shapes = {
+        'top_down_block2': (4, 8, 8, 128),
+        'top_down_block3': (4, 4, 4, 128),
+        'top_down_block4': (4, 2, 2, 128),
+        'top_down_block5': (4, 1, 1, 128)
+    }
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = {key: value.shape
+                                for key, value in out_feature_maps.items()}
+      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
+
+  def test_get_expected_feature_map_shapes_with_depthwise(self):
+    image_features = [
+        ('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
+        ('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
+        ('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
+        ('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
+    ]
+    feature_maps = feature_map_generators.fpn_top_down_feature_maps(
+        image_features=image_features, depth=128, use_depthwise=True)
+
+    expected_feature_map_shapes = {
+        'top_down_block2': (4, 8, 8, 128),
+        'top_down_block3': (4, 4, 4, 128),
+        'top_down_block4': (4, 2, 2, 128),
+        'top_down_block5': (4, 1, 1, 128)
+    }
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = {key: value.shape
+                                for key, value in out_feature_maps.items()}
+      self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
+
+
+class GetDepthFunctionTest(tf.test.TestCase):
+
+  def test_return_min_depth_when_multiplier_is_small(self):
+    depth_fn = feature_map_generators.get_depth_fn(depth_multiplier=0.5,
+                                                   min_depth=16)
+    self.assertEqual(depth_fn(16), 16)
+
+  def test_return_correct_depth_with_multiplier(self):
+    depth_fn = feature_map_generators.get_depth_fn(depth_multiplier=0.5,
+                                                   min_depth=16)
+    self.assertEqual(depth_fn(64), 32)
+
+
+@parameterized.parameters(
+    {'replace_pool_with_conv': False},
+    {'replace_pool_with_conv': True},
+)
+class PoolingPyramidFeatureMapGeneratorTest(tf.test.TestCase):
+
+  def test_get_expected_feature_map_shapes(self, replace_pool_with_conv):
+    image_features = {
+        'image_features': tf.random_uniform([4, 19, 19, 1024])
+    }
+    feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
+        base_feature_map_depth=1024,
+        num_layers=6,
+        image_features=image_features,
+        replace_pool_with_conv=replace_pool_with_conv)
+
+    expected_pool_feature_map_shapes = {
+        'Base_Conv2d_1x1_1024': (4, 19, 19, 1024),
+        'MaxPool2d_0_2x2': (4, 10, 10, 1024),
+        'MaxPool2d_1_2x2': (4, 5, 5, 1024),
+        'MaxPool2d_2_2x2': (4, 3, 3, 1024),
+        'MaxPool2d_3_2x2': (4, 2, 2, 1024),
+        'MaxPool2d_4_2x2': (4, 1, 1, 1024),
+    }
+
+    expected_conv_feature_map_shapes = {
+        'Base_Conv2d_1x1_1024': (4, 19, 19, 1024),
+        'Conv2d_0_3x3_s2_1024': (4, 10, 10, 1024),
+        'Conv2d_1_3x3_s2_1024': (4, 5, 5, 1024),
+        'Conv2d_2_3x3_s2_1024': (4, 3, 3, 1024),
+        'Conv2d_3_3x3_s2_1024': (4, 2, 2, 1024),
+        'Conv2d_4_3x3_s2_1024': (4, 1, 1, 1024),
+    }
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      out_feature_maps = sess.run(feature_maps)
+      out_feature_map_shapes = {key: value.shape
+                                for key, value in out_feature_maps.items()}
+      if replace_pool_with_conv:
+        self.assertDictEqual(expected_conv_feature_map_shapes,
+                             out_feature_map_shapes)
+      else:
+        self.assertDictEqual(expected_pool_feature_map_shapes,
+                             out_feature_map_shapes)
+
+  def test_get_expected_variable_names(self, replace_pool_with_conv):
+    image_features = {
+        'image_features': tf.random_uniform([4, 19, 19, 1024])
+    }
+    feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
+        base_feature_map_depth=1024,
+        num_layers=6,
+        image_features=image_features,
+        replace_pool_with_conv=replace_pool_with_conv)
+
+    expected_pool_variables = set([
+        'Base_Conv2d_1x1_1024/weights',
+        'Base_Conv2d_1x1_1024/biases',
+    ])
+
+    expected_conv_variables = set([
+        'Base_Conv2d_1x1_1024/weights',
+        'Base_Conv2d_1x1_1024/biases',
+        'Conv2d_0_3x3_s2_1024/weights',
+        'Conv2d_0_3x3_s2_1024/biases',
+        'Conv2d_1_3x3_s2_1024/weights',
+        'Conv2d_1_3x3_s2_1024/biases',
+        'Conv2d_2_3x3_s2_1024/weights',
+        'Conv2d_2_3x3_s2_1024/biases',
+        'Conv2d_3_3x3_s2_1024/weights',
+        'Conv2d_3_3x3_s2_1024/biases',
+        'Conv2d_4_3x3_s2_1024/weights',
+        'Conv2d_4_3x3_s2_1024/biases',
+    ])
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      sess.run(feature_maps)
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+      if replace_pool_with_conv:
+        self.assertSetEqual(expected_conv_variables, actual_variable_set)
+      else:
+        self.assertSetEqual(expected_pool_variables, actual_variable_set)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2.py
new file mode 100644
index 00000000..5969b23d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2.py
@@ -0,0 +1,314 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A wrapper around the MobileNet v2 models for Keras, for object detection."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from object_detection.core import freezable_batch_norm
+from object_detection.utils import ops
+
+
+# pylint: disable=invalid-name
+# This method copied from the slim mobilenet base network code (same license)
+def _make_divisible(v, divisor, min_value=None):
+  if min_value is None:
+    min_value = divisor
+  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+  # Make sure that round down does not go down by more than 10%.
+  if new_v < 0.9 * v:
+    new_v += divisor
+  return new_v
+
+
+class _LayersOverride(object):
+  """Alternative Keras layers interface for the Keras MobileNetV2."""
+
+  def __init__(self,
+               batchnorm_training,
+               default_batchnorm_momentum=0.999,
+               conv_hyperparams=None,
+               use_explicit_padding=False,
+               alpha=1.0,
+               min_depth=None):
+    """Alternative tf.keras.layers interface, for use by the Keras MobileNetV2.
+
+    It is used by the Keras applications kwargs injection API to
+    modify the Mobilenet v2 Keras application with changes required by
+    the Object Detection API.
+
+    These injected interfaces make the following changes to the network:
+
+    - Applies the Object Detection hyperparameter configuration
+    - Supports FreezableBatchNorms
+    - Adds support for a min number of filters for each layer
+    - Makes the `alpha` parameter affect the final convolution block even if it
+        is less than 1.0
+    - Adds support for explicit padding of convolutions
+
+    Args:
+      batchnorm_training: Bool. Assigned to Batch norm layer `training` param
+        when constructing `freezable_batch_norm.FreezableBatchNorm` layers.
+      default_batchnorm_momentum: Float. When 'conv_hyperparams' is None,
+        batch norm layers will be constructed using this value as the momentum.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops. Optionally set to `None`
+        to use default mobilenet_v2 layer builders.
+      use_explicit_padding: If True, use 'valid' padding for convolutions,
+        but explicitly pre-pads inputs so that the output dimensions are the
+        same as if 'same' padding were used. Off by default.
+      alpha: The width multiplier referenced in the MobileNetV2 paper. It
+        modifies the number of filters in each convolutional layer.
+      min_depth: Minimum number of filters in the convolutional layers.
+    """
+    self._alpha = alpha
+    self._batchnorm_training = batchnorm_training
+    self._default_batchnorm_momentum = default_batchnorm_momentum
+    self._conv_hyperparams = conv_hyperparams
+    self._use_explicit_padding = use_explicit_padding
+    self._min_depth = min_depth
+    self.regularizer = tf.keras.regularizers.l2(0.00004 * 0.5)
+    self.initializer = tf.truncated_normal_initializer(stddev=0.09)
+
+  def _FixedPaddingLayer(self, kernel_size):
+    return tf.keras.layers.Lambda(lambda x: ops.fixed_padding(x, kernel_size))
+
+  def Conv2D(self, filters, **kwargs):
+    """Builds a Conv2D layer according to the current Object Detection config.
+
+    Overrides the Keras MobileNetV2 application's convolutions with ones that
+    follow the spec specified by the Object Detection hyperparameters.
+
+    Args:
+      filters: The number of filters to use for the convolution.
+      **kwargs: Keyword args specified by the Keras application for
+        constructing the convolution.
+
+    Returns:
+      A one-arg callable that will either directly apply a Keras Conv2D layer to
+      the input argument, or that will first pad the input then apply a Conv2D
+      layer.
+    """
+    # Make sure 'alpha' is always applied to the last convolution block's size
+    # (This overrides the Keras application's functionality)
+    if kwargs.get('name') == 'Conv_1' and self._alpha < 1.0:
+      filters = _make_divisible(1280 * self._alpha, 8)
+
+    # Apply the minimum depth to the convolution layers
+    if (self._min_depth and (filters < self._min_depth)
+        and not kwargs.get('name').endswith('expand')):
+      filters = self._min_depth
+
+    if self._conv_hyperparams:
+      kwargs = self._conv_hyperparams.params(**kwargs)
+    else:
+      kwargs['kernel_regularizer'] = self.regularizer
+      kwargs['kernel_initializer'] = self.initializer
+
+    kwargs['padding'] = 'same'
+    kernel_size = kwargs.get('kernel_size')
+    if self._use_explicit_padding and kernel_size > 1:
+      kwargs['padding'] = 'valid'
+      def padded_conv(features):
+        padded_features = self._FixedPaddingLayer(kernel_size)(features)
+        return tf.keras.layers.Conv2D(filters, **kwargs)(padded_features)
+
+      return padded_conv
+    else:
+      return tf.keras.layers.Conv2D(filters, **kwargs)
+
+  def DepthwiseConv2D(self, **kwargs):
+    """Builds a DepthwiseConv2D according to the Object Detection config.
+
+    Overrides the Keras MobileNetV2 application's convolutions with ones that
+    follow the spec specified by the Object Detection hyperparameters.
+
+    Args:
+      **kwargs: Keyword args specified by the Keras application for
+        constructing the convolution.
+
+    Returns:
+      A one-arg callable that will either directly apply a Keras DepthwiseConv2D
+      layer to the input argument, or that will first pad the input then apply
+      the depthwise convolution.
+    """
+    if self._conv_hyperparams:
+      kwargs = self._conv_hyperparams.params(**kwargs)
+    else:
+      kwargs['depthwise_initializer'] = self.initializer
+
+    kwargs['padding'] = 'same'
+    kernel_size = kwargs.get('kernel_size')
+    if self._use_explicit_padding and kernel_size > 1:
+      kwargs['padding'] = 'valid'
+      def padded_depthwise_conv(features):
+        padded_features = self._FixedPaddingLayer(kernel_size)(features)
+        return tf.keras.layers.DepthwiseConv2D(**kwargs)(padded_features)
+
+      return padded_depthwise_conv
+    else:
+      return tf.keras.layers.DepthwiseConv2D(**kwargs)
+
+  def BatchNormalization(self, **kwargs):
+    """Builds a normalization layer.
+
+    Overrides the Keras application batch norm with the norm specified by the
+    Object Detection configuration.
+
+    Args:
+      **kwargs: Only the name is used, all other params ignored.
+        Required for matching `layers.BatchNormalization` calls in the Keras
+        application.
+
+    Returns:
+      A normalization layer specified by the Object Detection hyperparameter
+      configurations.
+    """
+    name = kwargs.get('name')
+    if self._conv_hyperparams:
+      return self._conv_hyperparams.build_batch_norm(
+          training=self._batchnorm_training,
+          name=name)
+    else:
+      return freezable_batch_norm.FreezableBatchNorm(
+          training=self._batchnorm_training,
+          epsilon=1e-3,
+          momentum=self._default_batchnorm_momentum,
+          name=name)
+
+  def Input(self, shape):
+    """Builds an Input layer.
+
+    Overrides the Keras application Input layer with one that uses a
+    tf.placeholder_with_default instead of a tf.placeholder. This is necessary
+    to ensure the application works when run on a TPU.
+
+    Args:
+      shape: The shape for the input layer to use. (Does not include a dimension
+        for the batch size).
+    Returns:
+      An input layer for the specified shape that internally uses a
+      placeholder_with_default.
+    """
+    default_size = 224
+    default_batch_size = 1
+    shape = list(shape)
+    default_shape = [default_size if dim is None else dim for dim in shape]
+
+    input_tensor = tf.constant(0.0, shape=[default_batch_size] + default_shape)
+
+    placeholder_with_default = tf.placeholder_with_default(
+        input=input_tensor, shape=[None] + shape)
+    return tf.keras.layers.Input(tensor=placeholder_with_default)
+
+  # pylint: disable=unused-argument
+  def ReLU(self, *args, **kwargs):
+    """Builds an activation layer.
+
+    Overrides the Keras application ReLU with the activation specified by the
+    Object Detection configuration.
+
+    Args:
+      *args: Ignored, required to match the `tf.keras.ReLU` interface
+      **kwargs: Only the name is used,
+        required to match `tf.keras.ReLU` interface
+
+    Returns:
+      An activation layer specified by the Object Detection hyperparameter
+      configurations.
+    """
+    name = kwargs.get('name')
+    if self._conv_hyperparams:
+      return self._conv_hyperparams.build_activation_layer(name=name)
+    else:
+      return tf.keras.layers.Lambda(tf.nn.relu6, name=name)
+  # pylint: enable=unused-argument
+
+  # pylint: disable=unused-argument
+  def ZeroPadding2D(self, **kwargs):
+    """Replaces explicit padding in the Keras application with a no-op.
+
+    Args:
+      **kwargs: Ignored, required to match the Keras applications usage.
+
+    Returns:
+      A no-op identity lambda.
+    """
+    return lambda x: x
+  # pylint: enable=unused-argument
+
+  # Forward all non-overridden methods to the keras layers
+  def __getattr__(self, item):
+    return getattr(tf.keras.layers, item)
+
+
+def mobilenet_v2(batchnorm_training,
+                 default_batchnorm_momentum=0.9997,
+                 conv_hyperparams=None,
+                 use_explicit_padding=False,
+                 alpha=1.0,
+                 min_depth=None,
+                 **kwargs):
+  """Instantiates the MobileNetV2 architecture, modified for object detection.
+
+  This wraps the MobileNetV2 tensorflow Keras application, but uses the
+  Keras application's kwargs-based monkey-patching API to override the Keras
+  architecture with the following changes:
+
+  - Changes the default batchnorm momentum to 0.9997
+  - Applies the Object Detection hyperparameter configuration
+  - Supports FreezableBatchNorms
+  - Adds support for a min number of filters for each layer
+  - Makes the `alpha` parameter affect the final convolution block even if it
+      is less than 1.0
+  - Adds support for explicit padding of convolutions
+  - Makes the Input layer use a tf.placeholder_with_default instead of a
+      tf.placeholder, to work on TPUs.
+
+  Args:
+      batchnorm_training: Bool. Assigned to Batch norm layer `training` param
+        when constructing `freezable_batch_norm.FreezableBatchNorm` layers.
+      default_batchnorm_momentum: Float. When 'conv_hyperparams' is None,
+        batch norm layers will be constructed using this value as the momentum.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops. Optionally set to `None`
+        to use default mobilenet_v2 layer builders.
+      use_explicit_padding: If True, use 'valid' padding for convolutions,
+        but explicitly pre-pads inputs so that the output dimensions are the
+        same as if 'same' padding were used. Off by default.
+      alpha: The width multiplier referenced in the MobileNetV2 paper. It
+        modifies the number of filters in each convolutional layer.
+      min_depth: Minimum number of filters in the convolutional layers.
+      **kwargs: Keyword arguments forwarded directly to the
+        `tf.keras.applications.MobilenetV2` method that constructs the Keras
+        model.
+
+  Returns:
+      A Keras model instance.
+  """
+  layers_override = _LayersOverride(
+      batchnorm_training,
+      default_batchnorm_momentum=default_batchnorm_momentum,
+      conv_hyperparams=conv_hyperparams,
+      use_explicit_padding=use_explicit_padding,
+      min_depth=min_depth,
+      alpha=alpha)
+  return tf.keras.applications.MobileNetV2(alpha=alpha,
+                                           layers=layers_override,
+                                           **kwargs)
+# pylint: enable=invalid-name
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2_test.py
new file mode 100644
index 00000000..dafa4bc4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/keras_applications/mobilenet_v2_test.py
@@ -0,0 +1,467 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for mobilenet_v2."""
+import itertools
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import hyperparams_builder
+from object_detection.models.keras_applications import mobilenet_v2
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+_layers_to_check = [
+    'Conv1_relu',
+    'block_1_expand_relu', 'block_1_depthwise_relu', 'block_1_project_BN',
+    'block_2_expand_relu', 'block_2_depthwise_relu', 'block_2_project_BN',
+    'block_3_expand_relu', 'block_3_depthwise_relu', 'block_3_project_BN',
+    'block_4_expand_relu', 'block_4_depthwise_relu', 'block_4_project_BN',
+    'block_5_expand_relu', 'block_5_depthwise_relu', 'block_5_project_BN',
+    'block_6_expand_relu', 'block_6_depthwise_relu', 'block_6_project_BN',
+    'block_7_expand_relu', 'block_7_depthwise_relu', 'block_7_project_BN',
+    'block_8_expand_relu', 'block_8_depthwise_relu', 'block_8_project_BN',
+    'block_9_expand_relu', 'block_9_depthwise_relu', 'block_9_project_BN',
+    'block_10_expand_relu', 'block_10_depthwise_relu', 'block_10_project_BN',
+    'block_11_expand_relu', 'block_11_depthwise_relu', 'block_11_project_BN',
+    'block_12_expand_relu', 'block_12_depthwise_relu', 'block_12_project_BN',
+    'block_13_expand_relu', 'block_13_depthwise_relu', 'block_13_project_BN',
+    'block_14_expand_relu', 'block_14_depthwise_relu', 'block_14_project_BN',
+    'block_15_expand_relu', 'block_15_depthwise_relu', 'block_15_project_BN',
+    'block_16_expand_relu', 'block_16_depthwise_relu', 'block_16_project_BN',
+    'out_relu']
+
+
+class MobilenetV2Test(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        train: true,
+        scale: false,
+        center: true,
+        decay: 0.2,
+        epsilon: 0.1,
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def _create_application_with_layer_outputs(
+      self, layer_names, batchnorm_training,
+      conv_hyperparams=None,
+      use_explicit_padding=False,
+      alpha=1.0,
+      min_depth=None):
+    """Constructs Keras mobilenetv2 that extracts intermediate layer outputs."""
+    if not layer_names:
+      layer_names = _layers_to_check
+    full_model = mobilenet_v2.mobilenet_v2(
+        batchnorm_training=batchnorm_training,
+        conv_hyperparams=conv_hyperparams,
+        weights=None,
+        use_explicit_padding=use_explicit_padding,
+        alpha=alpha,
+        min_depth=min_depth,
+        include_top=False)
+    layer_outputs = [full_model.get_layer(name=layer).output
+                     for layer in layer_names]
+    return tf.keras.Model(
+        inputs=full_model.inputs,
+        outputs=layer_outputs)
+
+  def _check_returns_correct_shape(
+      self, batch_size, image_height, image_width, depth_multiplier,
+      expected_feature_map_shapes, use_explicit_padding=False, min_depth=None,
+      layer_names=None):
+    def graph_fn(image_tensor):
+      model = self._create_application_with_layer_outputs(
+          layer_names=layer_names,
+          batchnorm_training=False, use_explicit_padding=use_explicit_padding,
+          min_depth=min_depth,
+          alpha=depth_multiplier)
+      return model(image_tensor)
+
+    image_tensor = np.random.rand(batch_size, image_height, image_width,
+                                  3).astype(np.float32)
+    feature_maps = self.execute(graph_fn, [image_tensor])
+
+    for feature_map, expected_shape in itertools.izip(
+        feature_maps, expected_feature_map_shapes):
+      self.assertAllEqual(feature_map.shape, expected_shape)
+
+  def _check_returns_correct_shapes_with_dynamic_inputs(
+      self, batch_size, image_height, image_width, depth_multiplier,
+      expected_feature_map_shapes, use_explicit_padding=False,
+      layer_names=None):
+    def graph_fn(image_height, image_width):
+      image_tensor = tf.random_uniform([batch_size, image_height, image_width,
+                                        3], dtype=tf.float32)
+      model = self._create_application_with_layer_outputs(
+          layer_names=layer_names,
+          batchnorm_training=False, use_explicit_padding=use_explicit_padding,
+          alpha=depth_multiplier)
+      return model(image_tensor)
+
+    feature_maps = self.execute_cpu(graph_fn, [
+        np.array(image_height, dtype=np.int32),
+        np.array(image_width, dtype=np.int32)
+    ])
+
+    for feature_map, expected_shape in itertools.izip(
+        feature_maps, expected_feature_map_shapes):
+      self.assertAllEqual(feature_map.shape, expected_shape)
+
+  def _get_variables(self, depth_multiplier, layer_names=None):
+    g = tf.Graph()
+    with g.as_default():
+      preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+      model = self._create_application_with_layer_outputs(
+          layer_names=layer_names,
+          batchnorm_training=False, use_explicit_padding=False,
+          alpha=depth_multiplier)
+      model(preprocessed_inputs)
+      return g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+
+  def test_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    expected_feature_map_shape = [(2, 64, 64, 32),
+                                  (2, 64, 64, 96),
+                                  (2, 32, 32, 96),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 16, 16, 144),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 8, 8, 192),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 4, 4, 576),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 320),
+                                  (2, 4, 4, 1280)]
+
+    self._check_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier,
+        expected_feature_map_shape)
+
+  def test_returns_correct_shapes_128_explicit_padding(
+      self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    expected_feature_map_shape = [(2, 64, 64, 32),
+                                  (2, 64, 64, 96),
+                                  (2, 32, 32, 96),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 16, 16, 144),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 8, 8, 192),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 4, 4, 576),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 320),
+                                  (2, 4, 4, 1280)]
+    self._check_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_returns_correct_shapes_with_dynamic_inputs(
+      self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    expected_feature_map_shape = [(2, 64, 64, 32),
+                                  (2, 64, 64, 96),
+                                  (2, 32, 32, 96),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 144),
+                                  (2, 32, 32, 24),
+                                  (2, 32, 32, 144),
+                                  (2, 16, 16, 144),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 192),
+                                  (2, 16, 16, 32),
+                                  (2, 16, 16, 192),
+                                  (2, 8, 8, 192),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 64),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 384),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 576),
+                                  (2, 8, 8, 96),
+                                  (2, 8, 8, 576),
+                                  (2, 4, 4, 576),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 160),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 960),
+                                  (2, 4, 4, 320),
+                                  (2, 4, 4, 1280)]
+    self._check_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier,
+        expected_feature_map_shape)
+
+  def test_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    expected_feature_map_shape = [(2, 150, 150, 32),
+                                  (2, 150, 150, 96),
+                                  (2, 75, 75, 96),
+                                  (2, 75, 75, 24),
+                                  (2, 75, 75, 144),
+                                  (2, 75, 75, 144),
+                                  (2, 75, 75, 24),
+                                  (2, 75, 75, 144),
+                                  (2, 38, 38, 144),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 64),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 64),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 64),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 64),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 384),
+                                  (2, 19, 19, 96),
+                                  (2, 19, 19, 576),
+                                  (2, 19, 19, 576),
+                                  (2, 19, 19, 96),
+                                  (2, 19, 19, 576),
+                                  (2, 19, 19, 576),
+                                  (2, 19, 19, 96),
+                                  (2, 19, 19, 576),
+                                  (2, 10, 10, 576),
+                                  (2, 10, 10, 160),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 160),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 160),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 960),
+                                  (2, 10, 10, 320),
+                                  (2, 10, 10, 1280)]
+
+    self._check_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier,
+        expected_feature_map_shape)
+
+  def test_returns_correct_shapes_enforcing_min_depth(
+      self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 0.5**12
+    expected_feature_map_shape = [(2, 150, 150, 32),
+                                  (2, 150, 150, 192),
+                                  (2, 75, 75, 192),
+                                  (2, 75, 75, 32),
+                                  (2, 75, 75, 192),
+                                  (2, 75, 75, 192),
+                                  (2, 75, 75, 32),
+                                  (2, 75, 75, 192),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 192),
+                                  (2, 38, 38, 32),
+                                  (2, 38, 38, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 192),
+                                  (2, 19, 19, 32),
+                                  (2, 19, 19, 192),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 32),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 32),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 32),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 192),
+                                  (2, 10, 10, 32),
+                                  (2, 10, 10, 32)]
+    self._check_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier,
+        expected_feature_map_shape, min_depth=32)
+
+  def test_hyperparam_override(self):
+    hyperparams = self._build_conv_hyperparams()
+    model = mobilenet_v2.mobilenet_v2(
+        batchnorm_training=True,
+        conv_hyperparams=hyperparams,
+        weights=None,
+        use_explicit_padding=False,
+        alpha=1.0,
+        min_depth=32,
+        include_top=False)
+    hyperparams.params()
+    bn_layer = model.get_layer(name='block_5_project_BN')
+    self.assertAllClose(bn_layer.momentum, 0.2)
+    self.assertAllClose(bn_layer.epsilon, 0.1)
+
+  def test_variable_count(self):
+    depth_multiplier = 1
+    variables = self._get_variables(depth_multiplier)
+    self.assertEqual(len(variables), 260)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_feature_extractor_test.py
new file mode 100644
index 00000000..36699b63
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_feature_extractor_test.py
@@ -0,0 +1,163 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base test class SSDFeatureExtractors."""
+
+from abc import abstractmethod
+
+import itertools
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class SsdFeatureExtractorTestBase(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+      batch_norm {
+        scale: false
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def conv_hyperparams_fn(self):
+    with tf.contrib.slim.arg_scope([]) as sc:
+      return sc
+
+  @abstractmethod
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False, use_keras=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+      use_keras: if True builds a keras-based feature extractor, if False builds
+        a slim-based one.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor or an
+      ssd_meta_arch.SSDKerasFeatureExtractor object.
+    """
+    pass
+
+  def _extract_features(self, image_tensor, depth_multiplier, pad_to_multiple,
+                        use_explicit_padding=False, use_keras=False):
+    try:
+      feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                         pad_to_multiple,
+                                                         use_explicit_padding,
+                                                         use_keras=use_keras)
+    # If the unit test does not support a use_keras arg, it raises an error:
+    except TypeError:
+      feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                         pad_to_multiple,
+                                                         use_explicit_padding)
+    if use_keras:
+      feature_maps = feature_extractor(image_tensor)
+    else:
+      feature_maps = feature_extractor.extract_features(image_tensor)
+    return feature_maps
+
+  def check_extract_features_returns_correct_shape(
+      self, batch_size, image_height, image_width, depth_multiplier,
+      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False,
+      use_keras=False):
+    def graph_fn(image_tensor):
+      return self._extract_features(image_tensor,
+                                    depth_multiplier,
+                                    pad_to_multiple,
+                                    use_explicit_padding,
+                                    use_keras=use_keras)
+
+    image_tensor = np.random.rand(batch_size, image_height, image_width,
+                                  3).astype(np.float32)
+    feature_maps = self.execute(graph_fn, [image_tensor])
+    for feature_map, expected_shape in itertools.izip(
+        feature_maps, expected_feature_map_shapes):
+      self.assertAllEqual(feature_map.shape, expected_shape)
+
+  def check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+      self, batch_size, image_height, image_width, depth_multiplier,
+      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False,
+      use_keras=False):
+    def graph_fn(image_height, image_width):
+      image_tensor = tf.random_uniform([batch_size, image_height, image_width,
+                                        3], dtype=tf.float32)
+      return self._extract_features(image_tensor,
+                                    depth_multiplier,
+                                    pad_to_multiple,
+                                    use_explicit_padding,
+                                    use_keras=use_keras)
+
+    feature_maps = self.execute_cpu(graph_fn, [
+        np.array(image_height, dtype=np.int32),
+        np.array(image_width, dtype=np.int32)
+    ])
+    for feature_map, expected_shape in itertools.izip(
+        feature_maps, expected_feature_map_shapes):
+      self.assertAllEqual(feature_map.shape, expected_shape)
+
+  def check_extract_features_raises_error_with_invalid_image_size(
+      self, image_height, image_width, depth_multiplier, pad_to_multiple,
+      use_keras=False):
+    preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+    feature_maps = self._extract_features(preprocessed_inputs,
+                                          depth_multiplier,
+                                          pad_to_multiple,
+                                          use_keras=use_keras)
+    test_preprocessed_image = np.random.rand(4, image_height, image_width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(feature_maps,
+                 feed_dict={preprocessed_inputs: test_preprocessed_image})
+
+  def check_feature_extractor_variables_under_scope(
+      self, depth_multiplier, pad_to_multiple, scope_name, use_keras=False):
+    variables = self.get_feature_extractor_variables(
+        depth_multiplier, pad_to_multiple, use_keras)
+    for variable in variables:
+      self.assertTrue(variable.name.startswith(scope_name))
+
+  def get_feature_extractor_variables(
+      self, depth_multiplier, pad_to_multiple, use_keras=False):
+    g = tf.Graph()
+    with g.as_default():
+      preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+      self._extract_features(preprocessed_inputs,
+                             depth_multiplier,
+                             pad_to_multiple,
+                             use_keras=use_keras)
+      return g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor.py
new file mode 100644
index 00000000..f7e97527
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor.py
@@ -0,0 +1,133 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for InceptionV2 features."""
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import inception_v2
+
+slim = tf.contrib.slim
+
+
+class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using InceptionV2 features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """InceptionV2 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+
+    Raises:
+      ValueError: If `override_base_feature_extractor_hyperparams` is False.
+    """
+    super(SSDInceptionV2FeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+    if not self._override_base_feature_extractor_hyperparams:
+      raise ValueError('SSD Inception V2 feature extractor always uses'
+                       'scope returned by `conv_hyperparams_fn` for both the '
+                       'base feature extractor and the additional layers '
+                       'added since there is no arg_scope defined for the base '
+                       'feature extractor.')
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    feature_map_layout = {
+        'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
+        'layer_depth': [-1, -1, 512, 256, 256, 128],
+        'use_explicit_padding': self._use_explicit_padding,
+        'use_depthwise': self._use_depthwise,
+    }
+
+    with slim.arg_scope(self._conv_hyperparams_fn()):
+      with tf.variable_scope('InceptionV2',
+                             reuse=self._reuse_weights) as scope:
+        _, image_features = inception_v2.inception_v2_base(
+            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+            final_endpoint='Mixed_5c',
+            min_depth=self._min_depth,
+            depth_multiplier=self._depth_multiplier,
+            scope=scope)
+        feature_maps = feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            image_features=image_features)
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor_test.py
new file mode 100644
index 00000000..054dcc4e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v2_feature_extractor_test.py
@@ -0,0 +1,134 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.models.ssd_inception_v2_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_inception_v2_feature_extractor
+
+
+class SsdInceptionV2FeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True):
+    """Constructs a SsdInceptionV2FeatureExtractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+
+    Returns:
+      an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor.
+    """
+    min_depth = 32
+    return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        self.conv_hyperparams_fn,
+        override_base_feature_extractor_hyperparams=True)
+
+  def test_extract_features_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1024),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1024),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 576), (2, 10, 10, 1024),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 128), (2, 10, 10, 128),
+                                  (2, 5, 5, 32), (2, 3, 3, 32),
+                                  (2, 2, 2, 32), (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 20, 20, 576), (2, 10, 10, 1024),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(4, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'InceptionV2'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor.py
new file mode 100644
index 00000000..b0ff9ab6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor.py
@@ -0,0 +1,133 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for InceptionV3 features."""
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import inception_v3
+
+slim = tf.contrib.slim
+
+
+class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using InceptionV3 features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """InceptionV3 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+
+    Raises:
+      ValueError: If `override_base_feature_extractor_hyperparams` is False.
+    """
+    super(SSDInceptionV3FeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+    if not self._override_base_feature_extractor_hyperparams:
+      raise ValueError('SSD Inception V3 feature extractor always uses'
+                       'scope returned by `conv_hyperparams_fn` for both the '
+                       'base feature extractor and the additional layers '
+                       'added since there is no arg_scope defined for the base '
+                       'feature extractor.')
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    feature_map_layout = {
+        'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
+        'layer_depth': [-1, -1, -1, 512, 256, 128],
+        'use_explicit_padding': self._use_explicit_padding,
+        'use_depthwise': self._use_depthwise,
+    }
+
+    with slim.arg_scope(self._conv_hyperparams_fn()):
+      with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
+        _, image_features = inception_v3.inception_v3_base(
+            ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+            final_endpoint='Mixed_7c',
+            min_depth=self._min_depth,
+            depth_multiplier=self._depth_multiplier,
+            scope=scope)
+        feature_maps = feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            image_features=image_features)
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor_test.py
new file mode 100644
index 00000000..fe3d3520
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_inception_v3_feature_extractor_test.py
@@ -0,0 +1,134 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.models.ssd_inception_v3_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_inception_v3_feature_extractor
+
+
+class SsdInceptionV3FeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True):
+    """Constructs a SsdInceptionV3FeatureExtractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+
+    Returns:
+      an ssd_inception_v3_feature_extractor.SsdInceptionV3FeatureExtractor.
+    """
+    min_depth = 32
+    return ssd_inception_v3_feature_extractor.SSDInceptionV3FeatureExtractor(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        self.conv_hyperparams_fn,
+        override_base_feature_extractor_hyperparams=True)
+
+  def test_extract_features_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768),
+                                  (2, 2, 2, 2048), (2, 1, 1, 512),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768),
+                                  (2, 2, 2, 2048), (2, 1, 1, 512),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 35, 35, 288), (2, 17, 17, 768),
+                                  (2, 8, 8, 2048), (2, 4, 4, 512),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 35, 35, 128), (2, 17, 17, 128),
+                                  (2, 8, 8, 192), (2, 4, 4, 32),
+                                  (2, 2, 2, 32), (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 37, 37, 288), (2, 18, 18, 768),
+                                  (2, 8, 8, 2048), (2, 4, 4, 512),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(4, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'InceptionV3'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py
new file mode 100644
index 00000000..3b859ab0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py
@@ -0,0 +1,135 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for MobilenetV1 features."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV1 features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """MobileNetV1 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDMobileNetV1FeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    feature_map_layout = {
+        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
+                       '', ''],
+        'layer_depth': [-1, -1, 512, 256, 256, 128],
+        'use_explicit_padding': self._use_explicit_padding,
+        'use_depthwise': self._use_depthwise,
+    }
+
+    with tf.variable_scope('MobilenetV1',
+                           reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v1.mobilenet_v1_arg_scope(
+              is_training=None, regularize_depthwise=True)):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams
+              else context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v1.mobilenet_v1_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='Conv2d_13_pointwise',
+              min_depth=self._min_depth,
+              depth_multiplier=self._depth_multiplier,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        feature_maps = feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            image_features=image_features)
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py
new file mode 100644
index 00000000..d3a9542b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py
@@ -0,0 +1,166 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_mobilenet_v1_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_mobilenet_v1_feature_extractor
+
+slim = tf.contrib.slim
+
+
+class SsdMobilenetV1FeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, use_explicit_padding=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        self.conv_hyperparams_fn,
+        use_explicit_padding=use_explicit_padding)
+
+  def test_extract_features_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 512), (2, 4, 4, 1024),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 512), (2, 10, 10, 1024),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_with_dynamic_image_shape(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 512), (2, 4, 4, 1024),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 32), (2, 10, 10, 32),
+                                  (2, 5, 5, 32), (2, 3, 3, 32),
+                                  (2, 2, 2, 32), (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 1024),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(2, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV1'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+  def test_has_fused_batchnorm(self):
+    image_height = 40
+    image_width = 40
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(any(op.type == 'FusedBatchNorm'
+                        for op in tf.get_default_graph().get_operations()))
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py
new file mode 100644
index 00000000..b0c149ae
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py
@@ -0,0 +1,192 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSD MobilenetV1 FPN Feature Extractor."""
+
+import copy
+import functools
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+# A modified config of mobilenet v1 that makes it more detection friendly,
+def _create_modified_mobilenet_config():
+  conv_defs = copy.deepcopy(mobilenet_v1.MOBILENETV1_CONV_DEFS)
+  conv_defs[-2] = mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=512)
+  conv_defs[-1] = mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=256)
+  return conv_defs
+
+
+class SSDMobileNetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV1 FPN features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD FPN feature extractor based on Mobilenet v1 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the base
+        feature extractor.
+      fpn_min_level: the highest resolution feature map to use in FPN. The valid
+        values are {2, 3, 4, 5} which map to MobileNet v1 layers
+        {Conv2d_3_pointwise, Conv2d_5_pointwise, Conv2d_11_pointwise,
+        Conv2d_13_pointwise}, respectively.
+      fpn_max_level: the smallest resolution feature map to construct or use in
+        FPN. FPN constructions uses features maps starting from fpn_min_level
+        upto the fpn_max_level. In the case that there are not enough feature
+        maps in the backbone network, additional feature maps are created by
+        applying stride 2 convolutions until we get the desired number of fpn
+        levels.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDMobileNetV1FpnFeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+    self._fpn_min_level = fpn_min_level
+    self._fpn_max_level = fpn_max_level
+    self._additional_layer_depth = additional_layer_depth
+    self._conv_defs = None
+    if self._use_depthwise:
+      self._conv_defs = _create_modified_mobilenet_config()
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    with tf.variable_scope('MobilenetV1',
+                           reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v1.mobilenet_v1_arg_scope(
+              is_training=None, regularize_depthwise=True)):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams
+              else context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v1.mobilenet_v1_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='Conv2d_13_pointwise',
+              min_depth=self._min_depth,
+              depth_multiplier=self._depth_multiplier,
+              conv_defs=self._conv_defs,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+
+      depth_fn = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        with tf.variable_scope('fpn', reuse=self._reuse_weights):
+          feature_blocks = [
+              'Conv2d_3_pointwise', 'Conv2d_5_pointwise', 'Conv2d_11_pointwise',
+              'Conv2d_13_pointwise'
+          ]
+          base_fpn_max_level = min(self._fpn_max_level, 5)
+          feature_block_list = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_block_list.append(feature_blocks[level - 2])
+          fpn_features = feature_map_generators.fpn_top_down_feature_maps(
+              [(key, image_features[key]) for key in feature_block_list],
+              depth=depth_fn(self._additional_layer_depth),
+              use_depthwise=self._use_depthwise,
+              use_explicit_padding=self._use_explicit_padding)
+          feature_maps = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_maps.append(fpn_features['top_down_{}'.format(
+                feature_blocks[level - 2])])
+          last_feature_map = fpn_features['top_down_{}'.format(
+              feature_blocks[base_fpn_max_level - 2])]
+          # Construct coarse features
+          padding = 'VALID' if self._use_explicit_padding else 'SAME'
+          kernel_size = 3
+          for i in range(base_fpn_max_level + 1, self._fpn_max_level + 1):
+            if self._use_depthwise:
+              conv_op = functools.partial(
+                  slim.separable_conv2d, depth_multiplier=1)
+            else:
+              conv_op = slim.conv2d
+            if self._use_explicit_padding:
+              last_feature_map = ops.fixed_padding(
+                  last_feature_map, kernel_size)
+            last_feature_map = conv_op(
+                last_feature_map,
+                num_outputs=depth_fn(self._additional_layer_depth),
+                kernel_size=[kernel_size, kernel_size],
+                stride=2,
+                padding=padding,
+                scope='bottom_up_Conv2d_{}'.format(i - base_fpn_max_level + 13))
+            feature_maps.append(last_feature_map)
+    return feature_maps
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_test.py
new file mode 100644
index 00000000..72771ceb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_test.py
@@ -0,0 +1,172 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_mobilenet_v1_fpn_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_mobilenet_v1_fpn_feature_extractor
+
+slim = tf.contrib.slim
+
+
+class SsdMobilenetV1FpnFeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, use_explicit_padding=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return (ssd_mobilenet_v1_fpn_feature_extractor.
+            SSDMobileNetV1FpnFeatureExtractor(
+                is_training,
+                depth_multiplier,
+                min_depth,
+                pad_to_multiple,
+                self.conv_hyperparams_fn,
+                use_explicit_padding=use_explicit_padding))
+
+  def test_extract_features_returns_correct_shapes_256(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_384(self):
+    image_height = 320
+    image_width = 320
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256),
+                                  (2, 10, 10, 256), (2, 5, 5, 256),
+                                  (2, 3, 3, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_with_dynamic_image_shape(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256),
+                                  (2, 10, 10, 256), (2, 5, 5, 256),
+                                  (2, 3, 3, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 32), (2, 16, 16, 32),
+                                  (2, 8, 8, 32), (2, 4, 4, 32),
+                                  (2, 2, 2, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(2, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV1'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+  def test_fused_batchnorm(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(
+        any(op.type == 'FusedBatchNorm'
+            for op in tf.get_default_graph().get_operations()))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor.py
new file mode 100644
index 00000000..34e9cb4e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor.py
@@ -0,0 +1,84 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for MobilenetV1 PPN features."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+class SSDMobileNetV1PpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV1 PPN features."""
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    with tf.variable_scope('MobilenetV1',
+                           reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v1.mobilenet_v1_arg_scope(
+              is_training=None, regularize_depthwise=True)):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams
+              else context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v1.mobilenet_v1_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='Conv2d_13_pointwise',
+              min_depth=self._min_depth,
+              depth_multiplier=self._depth_multiplier,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
+            base_feature_map_depth=0,
+            num_layers=6,
+            image_features={
+                'image_features': image_features['Conv2d_11_pointwise']
+            })
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor_test.py
new file mode 100644
index 00000000..1fb17df2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor_test.py
@@ -0,0 +1,185 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_mobilenet_v1_ppn_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_mobilenet_v1_ppn_feature_extractor
+
+slim = tf.contrib.slim
+
+
+class SsdMobilenetV1PpnFeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, use_explicit_padding=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return (ssd_mobilenet_v1_ppn_feature_extractor.
+            SSDMobileNetV1PpnFeatureExtractor(
+                is_training,
+                depth_multiplier,
+                min_depth,
+                pad_to_multiple,
+                self.conv_hyperparams_fn,
+                use_explicit_padding=use_explicit_padding))
+
+  def test_extract_features_returns_correct_shapes_320(self):
+    image_height = 320
+    image_width = 320
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 512),
+                                  (2, 5, 5, 512), (2, 3, 3, 512),
+                                  (2, 2, 2, 512), (2, 1, 1, 512)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_300(self):
+    image_height = 300
+    image_width = 300
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 512), (2, 10, 10, 512),
+                                  (2, 5, 5, 512), (2, 3, 3, 512),
+                                  (2, 2, 2, 512), (2, 1, 1, 512)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_640(self):
+    image_height = 640
+    image_width = 640
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 40, 40, 512), (2, 20, 20, 512),
+                                  (2, 10, 10, 512), (2, 5, 5, 512),
+                                  (2, 3, 3, 512), (2, 2, 2, 512)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_with_dynamic_image_shape(self):
+    image_height = 320
+    image_width = 320
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 512),
+                                  (2, 5, 5, 512), (2, 3, 3, 512),
+                                  (2, 2, 2, 512), (2, 1, 1, 512)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 512),
+                                  (2, 5, 5, 512), (2, 3, 3, 512),
+                                  (2, 2, 2, 512)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 16, 16, 32), (2, 8, 8, 32),
+                                  (2, 4, 4, 32), (2, 2, 2, 32),
+                                  (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(2, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV1'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+  def test_has_fused_batchnorm(self):
+    image_height = 320
+    image_width = 320
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(any(op.type == 'FusedBatchNorm'
+                        for op in tf.get_default_graph().get_operations()))
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor.py
new file mode 100644
index 00000000..d8e5ccc9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor.py
@@ -0,0 +1,136 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for MobilenetV2 features."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets.mobilenet import mobilenet
+from nets.mobilenet import mobilenet_v2
+
+slim = tf.contrib.slim
+
+
+class SSDMobileNetV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV2 features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """MobileNetV2 Feature Extractor for SSD Models.
+
+    Mobilenet v2 (experimental), designed by sandler@. More details can be found
+    in //knowledge/cerebra/brain/compression/mobilenet/mobilenet_experimental.py
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDMobileNetV2FeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    feature_map_layout = {
+        'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
+        'layer_depth': [-1, -1, 512, 256, 256, 128],
+        'use_depthwise': self._use_depthwise,
+        'use_explicit_padding': self._use_explicit_padding,
+    }
+
+    with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
+          slim.arg_scope(
+              [mobilenet.depth_multiplier], min_depth=self._min_depth):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams else
+              context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v2.mobilenet_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='layer_19',
+              depth_multiplier=self._depth_multiplier,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+        with slim.arg_scope(self._conv_hyperparams_fn()):
+          feature_maps = feature_map_generators.multi_resolution_feature_maps(
+              feature_map_layout=feature_map_layout,
+              depth_multiplier=self._depth_multiplier,
+              min_depth=self._min_depth,
+              insert_1x1_conv=True,
+              image_features=image_features)
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor_test.py
new file mode 100644
index 00000000..fb52a43f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_feature_extractor_test.py
@@ -0,0 +1,207 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_mobilenet_v2_feature_extractor."""
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_mobilenet_v2_feature_extractor
+from object_detection.models import ssd_mobilenet_v2_keras_feature_extractor
+
+slim = tf.contrib.slim
+
+
+@parameterized.parameters(
+    {'use_keras': False},
+    {'use_keras': True},
+)
+class SsdMobilenetV2FeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False, use_keras=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+      use_keras: if True builds a keras-based feature extractor, if False builds
+        a slim-based one.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    if use_keras:
+      return (ssd_mobilenet_v2_keras_feature_extractor.
+              SSDMobileNetV2KerasFeatureExtractor(
+                  is_training=False,
+                  depth_multiplier=depth_multiplier,
+                  min_depth=min_depth,
+                  pad_to_multiple=pad_to_multiple,
+                  conv_hyperparams=self._build_conv_hyperparams(),
+                  freeze_batchnorm=False,
+                  inplace_batchnorm_update=False,
+                  use_explicit_padding=use_explicit_padding,
+                  name='MobilenetV2'))
+    else:
+      return ssd_mobilenet_v2_feature_extractor.SSDMobileNetV2FeatureExtractor(
+          False,
+          depth_multiplier,
+          min_depth,
+          pad_to_multiple,
+          self.conv_hyperparams_fn,
+          use_explicit_padding=use_explicit_padding)
+
+  def test_extract_features_returns_correct_shapes_128(self, use_keras):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1280),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_keras=use_keras)
+
+  def test_extract_features_returns_correct_shapes_128_explicit_padding(
+      self, use_keras):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1280),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True,
+        use_keras=use_keras)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(
+      self, use_keras):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 576), (2, 4, 4, 1280),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_keras=use_keras)
+
+  def test_extract_features_returns_correct_shapes_299(self, use_keras):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 576), (2, 10, 10, 1280),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_keras=use_keras)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(
+      self, use_keras):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 192), (2, 10, 10, 32),
+                                  (2, 5, 5, 32), (2, 3, 3, 32),
+                                  (2, 2, 2, 32), (2, 1, 1, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_keras=use_keras)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(
+      self, use_keras):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 20, 20, 576), (2, 10, 10, 1280),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_keras=use_keras)
+
+  def test_extract_features_raises_error_with_invalid_image_size(
+      self, use_keras):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple,
+        use_keras=use_keras)
+
+  def test_preprocess_returns_correct_value_range(self, use_keras):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(4, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple,
+                                                       use_keras=use_keras)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self, use_keras):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV2'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name, use_keras=use_keras)
+
+  def test_variable_count(self, use_keras):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    variables = self.get_feature_extractor_variables(
+        depth_multiplier, pad_to_multiple, use_keras=use_keras)
+    self.assertEqual(len(variables), 292)
+
+  def test_has_fused_batchnorm(self, use_keras):
+    image_height = 40
+    image_width = 40
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple,
+                                                       use_keras=use_keras)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    if use_keras:
+      _ = feature_extractor(preprocessed_image)
+    else:
+      _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(any(op.type == 'FusedBatchNorm'
+                        for op in tf.get_default_graph().get_operations()))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor.py
new file mode 100644
index 00000000..d3c1f7a2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor.py
@@ -0,0 +1,189 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSD MobilenetV2 FPN Feature Extractor."""
+
+import copy
+import functools
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets.mobilenet import mobilenet
+from nets.mobilenet import mobilenet_v2
+
+slim = tf.contrib.slim
+
+
+# A modified config of mobilenet v2 that makes it more detection friendly.
+def _create_modified_mobilenet_config():
+  conv_defs = copy.deepcopy(mobilenet_v2.V2_DEF)
+  conv_defs['spec'][-1] = mobilenet.op(
+      slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=256)
+  return conv_defs
+
+
+class SSDMobileNetV2FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV2 FPN features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD FPN feature extractor based on Mobilenet v2 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the base
+        feature extractor.
+      fpn_min_level: the highest resolution feature map to use in FPN. The valid
+        values are {2, 3, 4, 5} which map to MobileNet v2 layers
+        {layer_4, layer_7, layer_14, layer_19}, respectively.
+      fpn_max_level: the smallest resolution feature map to construct or use in
+        FPN. FPN constructions uses features maps starting from fpn_min_level
+        upto the fpn_max_level. In the case that there are not enough feature
+        maps in the backbone network, additional feature maps are created by
+        applying stride 2 convolutions until we get the desired number of fpn
+        levels.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDMobileNetV2FpnFeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+    self._fpn_min_level = fpn_min_level
+    self._fpn_max_level = fpn_max_level
+    self._additional_layer_depth = additional_layer_depth
+    self._conv_defs = None
+    if self._use_depthwise:
+      self._conv_defs = _create_modified_mobilenet_config()
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(
+          mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
+          slim.arg_scope(
+              [mobilenet.depth_multiplier], min_depth=self._min_depth):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams else
+              context_manager.IdentityContextManager()):
+          _, image_features = mobilenet_v2.mobilenet_base(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              final_endpoint='layer_19',
+              depth_multiplier=self._depth_multiplier,
+              conv_defs=self._conv_defs,
+              use_explicit_padding=self._use_explicit_padding,
+              scope=scope)
+      depth_fn = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        with tf.variable_scope('fpn', reuse=self._reuse_weights):
+          feature_blocks = [
+              'layer_4', 'layer_7', 'layer_14', 'layer_19'
+          ]
+          base_fpn_max_level = min(self._fpn_max_level, 5)
+          feature_block_list = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_block_list.append(feature_blocks[level - 2])
+          fpn_features = feature_map_generators.fpn_top_down_feature_maps(
+              [(key, image_features[key]) for key in feature_block_list],
+              depth=depth_fn(self._additional_layer_depth),
+              use_depthwise=self._use_depthwise,
+              use_explicit_padding=self._use_explicit_padding)
+          feature_maps = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_maps.append(fpn_features['top_down_{}'.format(
+                feature_blocks[level - 2])])
+          last_feature_map = fpn_features['top_down_{}'.format(
+              feature_blocks[base_fpn_max_level - 2])]
+          # Construct coarse features
+          padding = 'VALID' if self._use_explicit_padding else 'SAME'
+          kernel_size = 3
+          for i in range(base_fpn_max_level + 1, self._fpn_max_level + 1):
+            if self._use_depthwise:
+              conv_op = functools.partial(
+                  slim.separable_conv2d, depth_multiplier=1)
+            else:
+              conv_op = slim.conv2d
+            if self._use_explicit_padding:
+              last_feature_map = ops.fixed_padding(
+                  last_feature_map, kernel_size)
+            last_feature_map = conv_op(
+                last_feature_map,
+                num_outputs=depth_fn(self._additional_layer_depth),
+                kernel_size=[kernel_size, kernel_size],
+                stride=2,
+                padding=padding,
+                scope='bottom_up_Conv2d_{}'.format(i - base_fpn_max_level + 19))
+            feature_maps.append(last_feature_map)
+    return feature_maps
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_test.py
new file mode 100644
index 00000000..c37b06b3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_test.py
@@ -0,0 +1,206 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_mobilenet_v2_fpn_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_mobilenet_v2_fpn_feature_extractor
+
+slim = tf.contrib.slim
+
+
+class SsdMobilenetV2FpnFeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, use_explicit_padding=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return (ssd_mobilenet_v2_fpn_feature_extractor.
+            SSDMobileNetV2FpnFeatureExtractor(
+                is_training,
+                depth_multiplier,
+                min_depth,
+                pad_to_multiple,
+                self.conv_hyperparams_fn,
+                use_explicit_padding=use_explicit_padding))
+
+  def test_extract_features_returns_correct_shapes_256(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_384(self):
+    image_height = 320
+    image_width = 320
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256),
+                                  (2, 10, 10, 256), (2, 5, 5, 256),
+                                  (2, 3, 3, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_with_dynamic_image_shape(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256),
+                                  (2, 10, 10, 256), (2, 5, 5, 256),
+                                  (2, 3, 3, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 0.5**12
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 32), (2, 16, 16, 32),
+                                  (2, 8, 8, 32), (2, 4, 4, 32),
+                                  (2, 2, 2, 32)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(2, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    scope_name = 'MobilenetV2'
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
+
+  def test_fused_batchnorm(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(
+        any(op.type == 'FusedBatchNorm'
+            for op in tf.get_default_graph().get_operations()))
+
+  def test_get_expected_feature_map_variable_names(self):
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+
+    expected_feature_maps_variables = set([
+        # Mobilenet V2 feature maps
+        'MobilenetV2/expanded_conv_4/depthwise/depthwise_weights',
+        'MobilenetV2/expanded_conv_7/depthwise/depthwise_weights',
+        'MobilenetV2/expanded_conv_14/depthwise/depthwise_weights',
+        'MobilenetV2/Conv_1/weights',
+        # FPN layers
+        'MobilenetV2/fpn/bottom_up_Conv2d_20/weights',
+        'MobilenetV2/fpn/bottom_up_Conv2d_21/weights',
+        'MobilenetV2/fpn/smoothing_1/weights',
+        'MobilenetV2/fpn/smoothing_2/weights',
+        'MobilenetV2/fpn/projection_1/weights',
+        'MobilenetV2/fpn/projection_2/weights',
+        'MobilenetV2/fpn/projection_3/weights',
+    ])
+
+    g = tf.Graph()
+    with g.as_default():
+      preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+      feature_extractor = self._create_feature_extractor(
+          depth_multiplier, pad_to_multiple)
+      feature_extractor.extract_features(preprocessed_inputs)
+      actual_variable_set = set([
+          var.op.name for var in g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+      ])
+      variable_intersection = expected_feature_maps_variables.intersection(
+          actual_variable_set)
+      self.assertSetEqual(expected_feature_maps_variables,
+                          variable_intersection)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py
new file mode 100644
index 00000000..9bf560eb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py
@@ -0,0 +1,163 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for MobilenetV2 features."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.models.keras_applications import mobilenet_v2
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+
+
+class SSDMobileNetV2KerasFeatureExtractor(
+    ssd_meta_arch.SSDKerasFeatureExtractor):
+  """SSD Feature Extractor using MobilenetV2 features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams,
+               freeze_batchnorm,
+               inplace_batchnorm_update,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False,
+               name=None):
+    """MobileNetV2 Feature Extractor for SSD Models.
+
+    Mobilenet v2 (experimental), designed by sandler@. More details can be found
+    in //knowledge/cerebra/brain/compression/mobilenet/mobilenet_experimental.py
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor (Functions
+        as a width multiplier for the mobilenet_v2 network itself).
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
+        containing convolution hyperparameters for the layers added on top of
+        the base feature extractor.
+      freeze_batchnorm: Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      inplace_batchnorm_update: Whether to update batch norm moving average
+        values inplace. When this is false train op must add a control
+        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+        batch norm statistics.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+      name: A string name scope to assign to the model. If 'None', Keras
+        will auto-generate one from the class name.
+    """
+    super(SSDMobileNetV2KerasFeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=freeze_batchnorm,
+        inplace_batchnorm_update=inplace_batchnorm_update,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams,
+        name=name)
+    self._feature_map_layout = {
+        'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
+        'layer_depth': [-1, -1, 512, 256, 256, 128],
+        'use_depthwise': self._use_depthwise,
+        'use_explicit_padding': self._use_explicit_padding,
+    }
+
+    self.mobilenet_v2 = None
+    self.feature_map_generator = None
+
+  def build(self, input_shape):
+    full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
+        batchnorm_training=(self._is_training and not self._freeze_batchnorm),
+        conv_hyperparams=(self._conv_hyperparams
+                          if self._override_base_feature_extractor_hyperparams
+                          else None),
+        weights=None,
+        use_explicit_padding=self._use_explicit_padding,
+        alpha=self._depth_multiplier,
+        min_depth=self._min_depth,
+        include_top=False)
+    conv2d_11_pointwise = full_mobilenet_v2.get_layer(
+        name='block_13_expand_relu').output
+    conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
+    self.mobilenet_v2 = tf.keras.Model(
+        inputs=full_mobilenet_v2.inputs,
+        outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
+    self.feature_map_generator = (
+        feature_map_generators.KerasMultiResolutionFeatureMaps(
+            feature_map_layout=self._feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            is_training=self._is_training,
+            conv_hyperparams=self._conv_hyperparams,
+            freeze_batchnorm=self._freeze_batchnorm,
+            name='FeatureMaps'))
+    self.built = True
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def _extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        33, preprocessed_inputs)
+
+    image_features = self.mobilenet_v2(
+        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
+
+    feature_maps = self.feature_map_generator({
+        'layer_15/expansion_output': image_features[0],
+        'layer_19': image_features[1]})
+
+    return feature_maps.values()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor.py
new file mode 100644
index 00000000..4f697c70
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor.py
@@ -0,0 +1,175 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""SSDFeatureExtractor for PNASNet features.
+
+Based on PNASNet ImageNet model: https://arxiv.org/abs/1712.00559
+"""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from nets.nasnet import pnasnet
+
+slim = tf.contrib.slim
+
+
+def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
+  """Defines the default arg scope for the PNASNet Large for object detection.
+
+  This provides a small edit to switch batch norm training on and off.
+
+  Args:
+    is_batch_norm_training: Boolean indicating whether to train with batch norm.
+    Default is False.
+
+  Returns:
+    An `arg_scope` to use for the PNASNet Large Model.
+  """
+  imagenet_scope = pnasnet.pnasnet_large_arg_scope()
+  with slim.arg_scope(imagenet_scope):
+    with slim.arg_scope([slim.batch_norm],
+                        is_training=is_batch_norm_training) as sc:
+      return sc
+
+
+class SSDPNASNetFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD Feature Extractor using PNASNet features."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """PNASNet Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+      use_depthwise: Whether to use depthwise convolutions.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDPNASNetFeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    Maps pixel values to the range [-1, 1].
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    return (2.0 / 255.0) * resized_inputs - 1.0
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+    """
+
+    feature_map_layout = {
+        'from_layer': ['Cell_7', 'Cell_11', '', '', '', ''],
+        'layer_depth': [-1, -1, 512, 256, 256, 128],
+        'use_explicit_padding': self._use_explicit_padding,
+        'use_depthwise': self._use_depthwise,
+    }
+
+    with slim.arg_scope(
+        pnasnet_large_arg_scope_for_detection(
+            is_batch_norm_training=self._is_training)):
+      with slim.arg_scope([slim.conv2d, slim.batch_norm, slim.separable_conv2d],
+                          reuse=self._reuse_weights):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams else
+              context_manager.IdentityContextManager()):
+          _, image_features = pnasnet.build_pnasnet_large(
+              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
+              num_classes=None,
+              is_training=self._is_training,
+              final_endpoint='Cell_11')
+    with tf.variable_scope('SSD_feature_maps', reuse=self._reuse_weights):
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        feature_maps = feature_map_generators.multi_resolution_feature_maps(
+            feature_map_layout=feature_map_layout,
+            depth_multiplier=self._depth_multiplier,
+            min_depth=self._min_depth,
+            insert_1x1_conv=True,
+            image_features=image_features)
+
+    return feature_maps.values()
+
+  def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
+    """Returns a map of variables to load from a foreign checkpoint.
+
+    Note that this overrides the default implementation in
+    ssd_meta_arch.SSDFeatureExtractor which does not work for PNASNet
+    checkpoints.
+
+    Args:
+      feature_extractor_scope: A scope name for the first stage feature
+        extractor.
+
+    Returns:
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
+    """
+    variables_to_restore = {}
+    for variable in tf.global_variables():
+      if variable.op.name.startswith(feature_extractor_scope):
+        var_name = variable.op.name.replace(feature_extractor_scope + '/', '')
+        var_name += '/ExponentialMovingAverage'
+        variables_to_restore[var_name] = variable
+    return variables_to_restore
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor_test.py
new file mode 100644
index 00000000..6646c2f9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_pnasnet_feature_extractor_test.py
@@ -0,0 +1,87 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for ssd_pnas_feature_extractor."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+from object_detection.models import ssd_pnasnet_feature_extractor
+
+slim = tf.contrib.slim
+
+
+class SsdPnasNetFeatureExtractorTest(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, use_explicit_padding=False):
+    """Constructs a new feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return ssd_pnasnet_feature_extractor.SSDPNASNetFeatureExtractor(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        self.conv_hyperparams_fn,
+        use_explicit_padding=use_explicit_padding)
+
+  def test_extract_features_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 8, 8, 2160), (2, 4, 4, 4320),
+                                  (2, 2, 2, 512), (2, 1, 1, 256),
+                                  (2, 1, 1, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 2160), (2, 10, 10, 4320),
+                                  (2, 5, 5, 512), (2, 3, 3, 256),
+                                  (2, 2, 2, 256), (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(2, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py
new file mode 100644
index 00000000..a7bc806a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py
@@ -0,0 +1,379 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SSD Feature Pyramid Network (FPN) feature extractors based on Resnet v1.
+
+See https://arxiv.org/abs/1708.02002 for details.
+"""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import resnet_v1
+
+slim = tf.contrib.slim
+
+
+class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD FPN feature extractor based on Resnet v1 architecture."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               resnet_base_fn,
+               resnet_scope_name,
+               fpn_scope_name,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD FPN feature extractor based on Resnet v1 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+        UNUSED currently.
+      min_depth: minimum feature extractor depth. UNUSED Currently.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      resnet_base_fn: base resnet network to use.
+      resnet_scope_name: scope name under which to construct resnet
+      fpn_scope_name: scope name under which to construct the feature pyramid
+        network.
+      fpn_min_level: the highest resolution feature map to use in FPN. The valid
+        values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
+        respectively.
+      fpn_max_level: the smallest resolution feature map to construct or use in
+        FPN. FPN constructions uses features maps starting from fpn_min_level
+        upto the fpn_max_level. In the case that there are not enough feature
+        maps in the backbone network, additional feature maps are created by
+        applying stride 2 convolutions until we get the desired number of fpn
+        levels.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False. UNUSED currently.
+      use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+
+    Raises:
+      ValueError: On supplying invalid arguments for unused arguments.
+    """
+    super(_SSDResnetV1FpnFeatureExtractor, self).__init__(
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+    if self._depth_multiplier != 1.0:
+      raise ValueError('Only depth 1.0 is supported, found: {}'.
+                       format(self._depth_multiplier))
+    if self._use_explicit_padding is True:
+      raise ValueError('Explicit padding is not a valid option.')
+    self._resnet_base_fn = resnet_base_fn
+    self._resnet_scope_name = resnet_scope_name
+    self._fpn_scope_name = fpn_scope_name
+    self._fpn_min_level = fpn_min_level
+    self._fpn_max_level = fpn_max_level
+    self._additional_layer_depth = additional_layer_depth
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    VGG style channel mean subtraction as described here:
+    https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-mdnge.
+    Note that if the number of channels is not equal to 3, the mean subtraction
+    will be skipped and the original resized_inputs will be returned.
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    if resized_inputs.shape.as_list()[3] == 3:
+      channel_means = [123.68, 116.779, 103.939]
+      return resized_inputs - [[channel_means]]
+    else:
+      return resized_inputs
+
+  def _filter_features(self, image_features):
+    # TODO(rathodv): Change resnet endpoint to strip scope prefixes instead
+    # of munging the scope here.
+    filtered_image_features = dict({})
+    for key, feature in image_features.items():
+      feature_name = key.split('/')[-1]
+      if feature_name in ['block1', 'block2', 'block3', 'block4']:
+        filtered_image_features[feature_name] = feature
+    return filtered_image_features
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+
+    Raises:
+      ValueError: depth multiplier is not supported.
+    """
+    if self._depth_multiplier != 1.0:
+      raise ValueError('Depth multiplier not supported.')
+
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        129, preprocessed_inputs)
+
+    with tf.variable_scope(
+        self._resnet_scope_name, reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams else
+              context_manager.IdentityContextManager()):
+          _, image_features = self._resnet_base_fn(
+              inputs=ops.pad_to_multiple(preprocessed_inputs,
+                                         self._pad_to_multiple),
+              num_classes=None,
+              is_training=None,
+              global_pool=False,
+              output_stride=None,
+              store_non_strided_activations=True,
+              scope=scope)
+          image_features = self._filter_features(image_features)
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        with tf.variable_scope(self._fpn_scope_name,
+                               reuse=self._reuse_weights):
+          base_fpn_max_level = min(self._fpn_max_level, 5)
+          feature_block_list = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_block_list.append('block{}'.format(level - 1))
+          fpn_features = feature_map_generators.fpn_top_down_feature_maps(
+              [(key, image_features[key]) for key in feature_block_list],
+              depth=self._additional_layer_depth)
+          feature_maps = []
+          for level in range(self._fpn_min_level, base_fpn_max_level + 1):
+            feature_maps.append(
+                fpn_features['top_down_block{}'.format(level - 1)])
+          last_feature_map = fpn_features['top_down_block{}'.format(
+              base_fpn_max_level - 1)]
+          # Construct coarse features
+          for i in range(base_fpn_max_level, self._fpn_max_level):
+            last_feature_map = slim.conv2d(
+                last_feature_map,
+                num_outputs=self._additional_layer_depth,
+                kernel_size=[3, 3],
+                stride=2,
+                padding='SAME',
+                scope='bottom_up_block{}'.format(i))
+            feature_maps.append(last_feature_map)
+    return feature_maps
+
+
+class SSDResnet50V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
+  """SSD Resnet50 V1 FPN feature extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD Resnet50 V1 FPN feature extractor based on Resnet v1 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+        UNUSED currently.
+      min_depth: minimum feature extractor depth. UNUSED Currently.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      fpn_min_level: the minimum level in feature pyramid networks.
+      fpn_max_level: the maximum level in feature pyramid networks.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False. UNUSED currently.
+      use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet50V1FpnFeatureExtractor, self).__init__(
+        is_training,
+        depth_multiplier,
+        min_depth,
+        pad_to_multiple,
+        conv_hyperparams_fn,
+        resnet_v1.resnet_v1_50,
+        'resnet_v1_50',
+        'fpn',
+        fpn_min_level,
+        fpn_max_level,
+        additional_layer_depth,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+
+class SSDResnet101V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
+  """SSD Resnet101 V1 FPN feature extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD Resnet101 V1 FPN feature extractor based on Resnet v1 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+        UNUSED currently.
+      min_depth: minimum feature extractor depth. UNUSED Currently.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      fpn_min_level: the minimum level in feature pyramid networks.
+      fpn_max_level: the maximum level in feature pyramid networks.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False. UNUSED currently.
+      use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet101V1FpnFeatureExtractor, self).__init__(
+        is_training,
+        depth_multiplier,
+        min_depth,
+        pad_to_multiple,
+        conv_hyperparams_fn,
+        resnet_v1.resnet_v1_101,
+        'resnet_v1_101',
+        'fpn',
+        fpn_min_level,
+        fpn_max_level,
+        additional_layer_depth,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
+
+
+class SSDResnet152V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
+  """SSD Resnet152 V1 FPN feature extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               fpn_min_level=3,
+               fpn_max_level=7,
+               additional_layer_depth=256,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """SSD Resnet152 V1 FPN feature extractor based on Resnet v1 architecture.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+        UNUSED currently.
+      min_depth: minimum feature extractor depth. UNUSED Currently.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      fpn_min_level: the minimum level in feature pyramid networks.
+      fpn_max_level: the maximum level in feature pyramid networks.
+      additional_layer_depth: additional feature map layer channel depth.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False. UNUSED currently.
+      use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet152V1FpnFeatureExtractor, self).__init__(
+        is_training,
+        depth_multiplier,
+        min_depth,
+        pad_to_multiple,
+        conv_hyperparams_fn,
+        resnet_v1.resnet_v1_152,
+        'resnet_v1_152',
+        'fpn',
+        fpn_min_level,
+        fpn_max_level,
+        additional_layer_depth,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=
+        override_base_feature_extractor_hyperparams)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py
new file mode 100644
index 00000000..5f406359
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py
@@ -0,0 +1,84 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ssd resnet v1 FPN feature extractors."""
+import tensorflow as tf
+
+from object_detection.models import ssd_resnet_v1_fpn_feature_extractor
+from object_detection.models import ssd_resnet_v1_fpn_feature_extractor_testbase
+
+
+class SSDResnet50V1FeatureExtractorTest(
+    ssd_resnet_v1_fpn_feature_extractor_testbase.
+    SSDResnetFPNFeatureExtractorTestBase):
+  """SSDResnet50v1Fpn feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return ssd_resnet_v1_fpn_feature_extractor.SSDResnet50V1FpnFeatureExtractor(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        self.conv_hyperparams_fn, use_explicit_padding=use_explicit_padding)
+
+  def _resnet_scope_name(self):
+    return 'resnet_v1_50'
+
+
+class SSDResnet101V1FeatureExtractorTest(
+    ssd_resnet_v1_fpn_feature_extractor_testbase.
+    SSDResnetFPNFeatureExtractorTestBase):
+  """SSDResnet101v1Fpn feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return (
+        ssd_resnet_v1_fpn_feature_extractor.SSDResnet101V1FpnFeatureExtractor(
+            is_training,
+            depth_multiplier,
+            min_depth,
+            pad_to_multiple,
+            self.conv_hyperparams_fn,
+            use_explicit_padding=use_explicit_padding))
+
+  def _resnet_scope_name(self):
+    return 'resnet_v1_101'
+
+
+class SSDResnet152V1FeatureExtractorTest(
+    ssd_resnet_v1_fpn_feature_extractor_testbase.
+    SSDResnetFPNFeatureExtractorTestBase):
+  """SSDResnet152v1Fpn feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return (
+        ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
+            is_training,
+            depth_multiplier,
+            min_depth,
+            pad_to_multiple,
+            self.conv_hyperparams_fn,
+            use_explicit_padding=use_explicit_padding))
+
+  def _resnet_scope_name(self):
+    return 'resnet_v1_152'
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py
new file mode 100644
index 00000000..fd8d9f6d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py
@@ -0,0 +1,108 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ssd resnet v1 FPN feature extractors."""
+import abc
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+
+
+class SSDResnetFPNFeatureExtractorTestBase(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+  """Helper test class for SSD Resnet v1 FPN feature extractors."""
+
+  @abc.abstractmethod
+  def _resnet_scope_name(self):
+    pass
+
+  @abc.abstractmethod
+  def _fpn_scope_name(self):
+    return 'fpn'
+
+  def test_extract_features_returns_correct_shapes_256(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
+    image_height = 256
+    image_width = 256
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 254
+    image_width = 254
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256),
+                                  (2, 8, 8, 256), (2, 4, 4, 256),
+                                  (2, 2, 2, 256)]
+
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = tf.constant(np.random.rand(4, image_height, image_width, 3))
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    with self.test_session() as sess:
+      test_image_out, preprocessed_image_out = sess.run(
+          [test_image, preprocessed_image])
+      self.assertAllClose(preprocessed_image_out,
+                          test_image_out - [[123.68, 116.779, 103.939]])
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    g = tf.Graph()
+    with g.as_default():
+      feature_extractor = self._create_feature_extractor(
+          depth_multiplier, pad_to_multiple)
+      preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
+      feature_extractor.extract_features(preprocessed_inputs)
+      variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+      for variable in variables:
+        self.assertTrue(
+            variable.name.startswith(self._resnet_scope_name())
+            or variable.name.startswith(self._fpn_scope_name()))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor.py
new file mode 100644
index 00000000..d275852a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor.py
@@ -0,0 +1,284 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SSD feature extractors based on Resnet v1 and PPN architectures."""
+
+import tensorflow as tf
+
+from object_detection.meta_architectures import ssd_meta_arch
+from object_detection.models import feature_map_generators
+from object_detection.utils import context_manager
+from object_detection.utils import ops
+from object_detection.utils import shape_utils
+from nets import resnet_v1
+
+slim = tf.contrib.slim
+
+
+class _SSDResnetPpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
+  """SSD feature extractor based on resnet architecture and PPN."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               resnet_base_fn,
+               resnet_scope_name,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               base_feature_map_depth=1024,
+               num_layers=6,
+               override_base_feature_extractor_hyperparams=False,
+               use_bounded_activations=False):
+    """Resnet based PPN Feature Extractor for SSD Models.
+
+    See go/pooling-pyramid for more details about PPN.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      resnet_base_fn: base resnet network to use.
+      resnet_scope_name: scope name to construct resnet
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      base_feature_map_depth: Depth of the base feature before the max pooling.
+      num_layers: Number of layers used to make predictions. They are pooled
+        from the base feature.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+      use_bounded_activations: Whether or not to use bounded activations for
+        resnet v1 bottleneck residual unit. Bounded activations better lend
+        themselves to quantized inference.
+    """
+    super(_SSDResnetPpnFeatureExtractor, self).__init__(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise,
+        override_base_feature_extractor_hyperparams)
+    self._resnet_base_fn = resnet_base_fn
+    self._resnet_scope_name = resnet_scope_name
+    self._base_feature_map_depth = base_feature_map_depth
+    self._num_layers = num_layers
+    self._use_bounded_activations = use_bounded_activations
+
+  def _filter_features(self, image_features):
+    # TODO(rathodv): Change resnet endpoint to strip scope prefixes instead
+    # of munging the scope here.
+    filtered_image_features = dict({})
+    for key, feature in image_features.items():
+      feature_name = key.split('/')[-1]
+      if feature_name in ['block2', 'block3', 'block4']:
+        filtered_image_features[feature_name] = feature
+    return filtered_image_features
+
+  def preprocess(self, resized_inputs):
+    """SSD preprocessing.
+
+    VGG style channel mean subtraction as described here:
+    https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-mdnge.
+    Note that if the number of channels is not equal to 3, the mean subtraction
+    will be skipped and the original resized_inputs will be returned.
+
+    Args:
+      resized_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+    """
+    if resized_inputs.shape.as_list()[3] == 3:
+      channel_means = [123.68, 116.779, 103.939]
+      return resized_inputs - [[channel_means]]
+    else:
+      return resized_inputs
+
+  def extract_features(self, preprocessed_inputs):
+    """Extract features from preprocessed inputs.
+
+    Args:
+      preprocessed_inputs: a [batch, height, width, channels] float tensor
+        representing a batch of images.
+
+    Returns:
+      feature_maps: a list of tensors where the ith tensor has shape
+        [batch, height_i, width_i, depth_i]
+
+    Raises:
+      ValueError: depth multiplier is not supported.
+    """
+    if self._depth_multiplier != 1.0:
+      raise ValueError('Depth multiplier not supported.')
+
+    preprocessed_inputs = shape_utils.check_min_image_dim(
+        129, preprocessed_inputs)
+
+    with tf.variable_scope(
+        self._resnet_scope_name, reuse=self._reuse_weights) as scope:
+      with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+        with (slim.arg_scope(self._conv_hyperparams_fn())
+              if self._override_base_feature_extractor_hyperparams else
+              context_manager.IdentityContextManager()):
+          with slim.arg_scope(
+              [resnet_v1.bottleneck],
+              use_bounded_activations=self._use_bounded_activations):
+            _, activations = self._resnet_base_fn(
+                inputs=ops.pad_to_multiple(preprocessed_inputs,
+                                           self._pad_to_multiple),
+                num_classes=None,
+                is_training=None,
+                global_pool=False,
+                output_stride=None,
+                store_non_strided_activations=True,
+                scope=scope)
+
+      with slim.arg_scope(self._conv_hyperparams_fn()):
+        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
+            base_feature_map_depth=self._base_feature_map_depth,
+            num_layers=self._num_layers,
+            image_features={
+                'image_features': self._filter_features(activations)['block3']
+            })
+    return feature_maps.values()
+
+
+class SSDResnet50V1PpnFeatureExtractor(_SSDResnetPpnFeatureExtractor):
+  """PPN Resnet50 v1 Feature Extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """Resnet50 v1 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet50V1PpnFeatureExtractor, self).__init__(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams_fn, resnet_v1.resnet_v1_50, 'resnet_v1_50',
+        reuse_weights, use_explicit_padding, use_depthwise,
+        override_base_feature_extractor_hyperparams=(
+            override_base_feature_extractor_hyperparams))
+
+
+class SSDResnet101V1PpnFeatureExtractor(_SSDResnetPpnFeatureExtractor):
+  """PPN Resnet101 v1 Feature Extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """Resnet101 v1 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet101V1PpnFeatureExtractor, self).__init__(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams_fn, resnet_v1.resnet_v1_101, 'resnet_v1_101',
+        reuse_weights, use_explicit_padding, use_depthwise,
+        override_base_feature_extractor_hyperparams=(
+            override_base_feature_extractor_hyperparams))
+
+
+class SSDResnet152V1PpnFeatureExtractor(_SSDResnetPpnFeatureExtractor):
+  """PPN Resnet152 v1 Feature Extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False):
+    """Resnet152 v1 Feature Extractor for SSD Models.
+
+    Args:
+      is_training: whether the network is in training mode.
+      depth_multiplier: float depth multiplier for feature extractor.
+      min_depth: minimum feature extractor depth.
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
+        and separable_conv2d ops in the layers that are added on top of the
+        base feature extractor.
+      reuse_weights: Whether to reuse variables. Default is None.
+      use_explicit_padding: Whether to use explicit padding when extracting
+        features. Default is False.
+      use_depthwise: Whether to use depthwise convolutions. Default is False.
+      override_base_feature_extractor_hyperparams: Whether to override
+        hyperparameters of the base feature extractor with the one from
+        `conv_hyperparams_fn`.
+    """
+    super(SSDResnet152V1PpnFeatureExtractor, self).__init__(
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams_fn, resnet_v1.resnet_v1_152, 'resnet_v1_152',
+        reuse_weights, use_explicit_padding, use_depthwise,
+        override_base_feature_extractor_hyperparams=(
+            override_base_feature_extractor_hyperparams))
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_test.py
new file mode 100644
index 00000000..c47cd120
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_test.py
@@ -0,0 +1,88 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ssd resnet v1 feature extractors."""
+import tensorflow as tf
+
+from object_detection.models import ssd_resnet_v1_ppn_feature_extractor
+from object_detection.models import ssd_resnet_v1_ppn_feature_extractor_testbase
+
+
+class SSDResnet50V1PpnFeatureExtractorTest(
+    ssd_resnet_v1_ppn_feature_extractor_testbase.
+    SSDResnetPpnFeatureExtractorTestBase):
+  """SSDResnet50v1 feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return ssd_resnet_v1_ppn_feature_extractor.SSDResnet50V1PpnFeatureExtractor(
+        is_training,
+        depth_multiplier,
+        min_depth,
+        pad_to_multiple,
+        self.conv_hyperparams_fn,
+        use_explicit_padding=use_explicit_padding)
+
+  def _scope_name(self):
+    return 'resnet_v1_50'
+
+
+class SSDResnet101V1PpnFeatureExtractorTest(
+    ssd_resnet_v1_ppn_feature_extractor_testbase.
+    SSDResnetPpnFeatureExtractorTestBase):
+  """SSDResnet101v1 feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return (
+        ssd_resnet_v1_ppn_feature_extractor.SSDResnet101V1PpnFeatureExtractor(
+            is_training,
+            depth_multiplier,
+            min_depth,
+            pad_to_multiple,
+            self.conv_hyperparams_fn,
+            use_explicit_padding=use_explicit_padding))
+
+  def _scope_name(self):
+    return 'resnet_v1_101'
+
+
+class SSDResnet152V1PpnFeatureExtractorTest(
+    ssd_resnet_v1_ppn_feature_extractor_testbase.
+    SSDResnetPpnFeatureExtractorTestBase):
+  """SSDResnet152v1 feature extractor test."""
+
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
+    min_depth = 32
+    is_training = True
+    return (
+        ssd_resnet_v1_ppn_feature_extractor.SSDResnet152V1PpnFeatureExtractor(
+            is_training,
+            depth_multiplier,
+            min_depth,
+            pad_to_multiple,
+            self.conv_hyperparams_fn,
+            use_explicit_padding=use_explicit_padding))
+
+  def _scope_name(self):
+    return 'resnet_v1_152'
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py
new file mode 100644
index 00000000..3857fc70
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py
@@ -0,0 +1,82 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ssd resnet v1 feature extractors."""
+import abc
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+
+
+class SSDResnetPpnFeatureExtractorTestBase(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+  """Helper test class for SSD Resnet PPN feature extractors."""
+
+  @abc.abstractmethod
+  def _scope_name(self):
+    pass
+
+  def test_extract_features_returns_correct_shapes_289(self):
+    image_height = 289
+    image_width = 289
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 1024), (2, 10, 10, 1024),
+                                  (2, 5, 5, 1024), (2, 3, 3, 1024),
+                                  (2, 2, 2, 1024), (2, 1, 1, 1024)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
+    image_height = 289
+    image_width = 289
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    expected_feature_map_shape = [(2, 19, 19, 1024), (2, 10, 10, 1024),
+                                  (2, 5, 5, 1024), (2, 3, 3, 1024),
+                                  (2, 2, 2, 1024), (2, 1, 1, 1024)]
+    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_raises_error_with_invalid_image_size(self):
+    image_height = 32
+    image_width = 32
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    self.check_extract_features_raises_error_with_invalid_image_size(
+        image_height, image_width, depth_multiplier, pad_to_multiple)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = tf.constant(np.random.rand(4, image_height, image_width, 3))
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    with self.test_session() as sess:
+      test_image_out, preprocessed_image_out = sess.run(
+          [test_image, preprocessed_image])
+      self.assertAllClose(preprocessed_image_out,
+                          test_image_out - [[123.68, 116.779, 103.939]])
+
+  def test_variables_only_created_in_scope(self):
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, self._scope_name())
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/object_detection_tutorial.ipynb b/TensorFlow/Detection/SSD/models/research/object_detection/object_detection_tutorial.ipynb
new file mode 100644
index 00000000..ddbe0dca
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/object_detection_tutorial.ipynb
@@ -0,0 +1,471 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "V8-yl-s-WKMG"
+      },
+      "source": [
+        "# Object Detection Demo\n",
+        "Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "kFSqkTCdWKMI"
+      },
+      "source": [
+        "# Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "hV4P5gyTWKMI"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import six.moves.urllib as urllib\n",
+        "import sys\n",
+        "import tarfile\n",
+        "import tensorflow as tf\n",
+        "import zipfile\n",
+        "\n",
+        "from distutils.version import StrictVersion\n",
+        "from collections import defaultdict\n",
+        "from io import StringIO\n",
+        "from matplotlib import pyplot as plt\n",
+        "from PIL import Image\n",
+        "\n",
+        "# This is needed since the notebook is stored in the object_detection folder.\n",
+        "sys.path.append(\"..\")\n",
+        "from object_detection.utils import ops as utils_ops\n",
+        "\n",
+        "if StrictVersion(tf.__version__) \u003c StrictVersion('1.9.0'):\n",
+        "  raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "Wy72mWwAWKMK"
+      },
+      "source": [
+        "## Env setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "v7m_NY_aWKMK"
+      },
+      "outputs": [],
+      "source": [
+        "# This is needed to display the images.\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "r5FNuiRPWKMN"
+      },
+      "source": [
+        "## Object detection imports\n",
+        "Here are the imports from the object detection module."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "bm0_uNRnWKMN"
+      },
+      "outputs": [],
+      "source": [
+        "from utils import label_map_util\n",
+        "\n",
+        "from utils import visualization_utils as vis_util"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "cfn_tRFOWKMO"
+      },
+      "source": [
+        "# Model preparation "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "X_sEBLpVWKMQ"
+      },
+      "source": [
+        "## Variables\n",
+        "\n",
+        "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.  \n",
+        "\n",
+        "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "VyPz_t8WWKMQ"
+      },
+      "outputs": [],
+      "source": [
+        "# What model to download.\n",
+        "MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\n",
+        "MODEL_FILE = MODEL_NAME + '.tar.gz'\n",
+        "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
+        "\n",
+        "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
+        "PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'\n",
+        "\n",
+        "# List of the strings that is used to add correct label for each box.\n",
+        "PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "7ai8pLZZWKMS"
+      },
+      "source": [
+        "## Download Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "KILYnwR5WKMS"
+      },
+      "outputs": [],
+      "source": [
+        "opener = urllib.request.URLopener()\n",
+        "opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
+        "tar_file = tarfile.open(MODEL_FILE)\n",
+        "for file in tar_file.getmembers():\n",
+        "  file_name = os.path.basename(file.name)\n",
+        "  if 'frozen_inference_graph.pb' in file_name:\n",
+        "    tar_file.extract(file, os.getcwd())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "YBcB9QHLWKMU"
+      },
+      "source": [
+        "## Load a (frozen) Tensorflow model into memory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "KezjCRVvWKMV"
+      },
+      "outputs": [],
+      "source": [
+        "detection_graph = tf.Graph()\n",
+        "with detection_graph.as_default():\n",
+        "  od_graph_def = tf.GraphDef()\n",
+        "  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:\n",
+        "    serialized_graph = fid.read()\n",
+        "    od_graph_def.ParseFromString(serialized_graph)\n",
+        "    tf.import_graph_def(od_graph_def, name='')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "_1MVVTcLWKMW"
+      },
+      "source": [
+        "## Loading label map\n",
+        "Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "hDbpHkiWWKMX"
+      },
+      "outputs": [],
+      "source": [
+        "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "EFsoUHvbWKMZ"
+      },
+      "source": [
+        "## Helper code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "aSlYc3JkWKMa"
+      },
+      "outputs": [],
+      "source": [
+        "def load_image_into_numpy_array(image):\n",
+        "  (im_width, im_height) = image.size\n",
+        "  return np.array(image.getdata()).reshape(\n",
+        "      (im_height, im_width, 3)).astype(np.uint8)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "H0_1AGhrWKMc"
+      },
+      "source": [
+        "# Detection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "jG-zn5ykWKMd"
+      },
+      "outputs": [],
+      "source": [
+        "# For the sake of simplicity we will use only 2 images:\n",
+        "# image1.jpg\n",
+        "# image2.jpg\n",
+        "# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\n",
+        "PATH_TO_TEST_IMAGES_DIR = 'test_images'\n",
+        "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]\n",
+        "\n",
+        "# Size, in inches, of the output images.\n",
+        "IMAGE_SIZE = (12, 8)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "92BHxzcNWKMf"
+      },
+      "outputs": [],
+      "source": [
+        "def run_inference_for_single_image(image, graph):\n",
+        "  with graph.as_default():\n",
+        "    with tf.Session() as sess:\n",
+        "      # Get handles to input and output tensors\n",
+        "      ops = tf.get_default_graph().get_operations()\n",
+        "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
+        "      tensor_dict = {}\n",
+        "      for key in [\n",
+        "          'num_detections', 'detection_boxes', 'detection_scores',\n",
+        "          'detection_classes', 'detection_masks'\n",
+        "      ]:\n",
+        "        tensor_name = key + ':0'\n",
+        "        if tensor_name in all_tensor_names:\n",
+        "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
+        "              tensor_name)\n",
+        "      if 'detection_masks' in tensor_dict:\n",
+        "        # The following processing is only for single image\n",
+        "        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
+        "        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
+        "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
+        "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
+        "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
+        "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
+        "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
+        "            detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
+        "        detection_masks_reframed = tf.cast(\n",
+        "            tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
+        "        # Follow the convention by adding back the batch dimension\n",
+        "        tensor_dict['detection_masks'] = tf.expand_dims(\n",
+        "            detection_masks_reframed, 0)\n",
+        "      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
+        "\n",
+        "      # Run inference\n",
+        "      output_dict = sess.run(tensor_dict,\n",
+        "                             feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
+        "\n",
+        "      # all outputs are float32 numpy arrays, so convert types as appropriate\n",
+        "      output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
+        "      output_dict['detection_classes'] = output_dict[\n",
+        "          'detection_classes'][0].astype(np.uint8)\n",
+        "      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
+        "      output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
+        "      if 'detection_masks' in output_dict:\n",
+        "        output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
+        "  return output_dict"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "3a5wMHN8WKMh"
+      },
+      "outputs": [],
+      "source": [
+        "for image_path in TEST_IMAGE_PATHS:\n",
+        "  image = Image.open(image_path)\n",
+        "  # the array based representation of the image will be used later in order to prepare the\n",
+        "  # result image with boxes and labels on it.\n",
+        "  image_np = load_image_into_numpy_array(image)\n",
+        "  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
+        "  image_np_expanded = np.expand_dims(image_np, axis=0)\n",
+        "  # Actual detection.\n",
+        "  output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
+        "  # Visualization of the results of a detection.\n",
+        "  vis_util.visualize_boxes_and_labels_on_image_array(\n",
+        "      image_np,\n",
+        "      output_dict['detection_boxes'],\n",
+        "      output_dict['detection_classes'],\n",
+        "      output_dict['detection_scores'],\n",
+        "      category_index,\n",
+        "      instance_masks=output_dict.get('detection_masks'),\n",
+        "      use_normalized_coordinates=True,\n",
+        "      line_thickness=8)\n",
+        "  plt.figure(figsize=IMAGE_SIZE)\n",
+        "  plt.imshow(image_np)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "LQSEnEsPWKMj"
+      },
+      "outputs": [],
+      "source": [
+        ""
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "default_view": {},
+      "name": "object_detection_tutorial.ipynb?workspaceId=ronnyvotel:python_inference::citc",
+      "provenance": [],
+      "version": "0.3.2",
+      "views": {}
+    },
+    "kernelspec": {
+      "display_name": "Python 2",
+      "language": "python",
+      "name": "python2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor.py
new file mode 100644
index 00000000..628f7bc0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor.py
@@ -0,0 +1,421 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Convolutional Box Predictors with and without weight sharing."""
+import functools
+import tensorflow as tf
+from object_detection.core import box_predictor
+from object_detection.utils import static_shape
+
+slim = tf.contrib.slim
+
+BOX_ENCODINGS = box_predictor.BOX_ENCODINGS
+CLASS_PREDICTIONS_WITH_BACKGROUND = (
+    box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND)
+MASK_PREDICTIONS = box_predictor.MASK_PREDICTIONS
+
+
+class _NoopVariableScope(object):
+  """A dummy class that does not push any scope."""
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exc_type, exc_value, traceback):
+    return False
+
+
+class ConvolutionalBoxPredictor(box_predictor.BoxPredictor):
+  """Convolutional Box Predictor.
+
+  Optionally add an intermediate 1x1 convolutional layer after features and
+  predict in parallel branches box_encodings and
+  class_predictions_with_background.
+
+  Currently this box predictor assumes that predictions are "shared" across
+  classes --- that is each anchor makes box predictions which do not depend
+  on class.
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               box_prediction_head,
+               class_prediction_head,
+               other_heads,
+               conv_hyperparams_fn,
+               num_layers_before_predictor,
+               min_depth,
+               max_depth):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      box_prediction_head: The head that predicts the boxes.
+      class_prediction_head: The head that predicts the classes.
+      other_heads: A dictionary mapping head names to convolutional
+        head classes.
+      conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for convolution ops.
+      num_layers_before_predictor: Number of the additional conv layers before
+        the predictor.
+      min_depth: Minimum feature depth prior to predicting box encodings
+        and class predictions.
+      max_depth: Maximum feature depth prior to predicting box encodings
+        and class predictions. If max_depth is set to 0, no additional
+        feature map will be inserted before location and class predictions.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalBoxPredictor, self).__init__(is_training, num_classes)
+    self._box_prediction_head = box_prediction_head
+    self._class_prediction_head = class_prediction_head
+    self._other_heads = other_heads
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._min_depth = min_depth
+    self._max_depth = max_depth
+    self._num_layers_before_predictor = num_layers_before_predictor
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def _predict(self, image_features, num_predictions_per_location_list):
+    """Computes encoded object locations and corresponding confidences.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+        width_i, channels_i] containing features for a batch of images.
+      num_predictions_per_location_list: A list of integers representing the
+        number of box predictions to be made per spatial location for each
+        feature map.
+
+    Returns:
+      box_encodings: A list of float tensors of shape
+        [batch_size, num_anchors_i, q, code_size] representing the location of
+        the objects, where q is 1 or the number of classes. Each entry in the
+        list corresponds to a feature map in the input `image_features` list.
+      class_predictions_with_background: A list of float tensors of shape
+        [batch_size, num_anchors_i, num_classes + 1] representing the class
+        predictions for the proposals. Each entry in the list corresponds to a
+        feature map in the input `image_features` list.
+    """
+    predictions = {
+        BOX_ENCODINGS: [],
+        CLASS_PREDICTIONS_WITH_BACKGROUND: [],
+    }
+    for head_name in self._other_heads.keys():
+      predictions[head_name] = []
+    # TODO(rathodv): Come up with a better way to generate scope names
+    # in box predictor once we have time to retrain all models in the zoo.
+    # The following lines create scope names to be backwards compatible with the
+    # existing checkpoints.
+    box_predictor_scopes = [_NoopVariableScope()]
+    if len(image_features) > 1:
+      box_predictor_scopes = [
+          tf.variable_scope('BoxPredictor_{}'.format(i))
+          for i in range(len(image_features))
+      ]
+    for (image_feature,
+         num_predictions_per_location, box_predictor_scope) in zip(
+             image_features, num_predictions_per_location_list,
+             box_predictor_scopes):
+      net = image_feature
+      with box_predictor_scope:
+        with slim.arg_scope(self._conv_hyperparams_fn()):
+          with slim.arg_scope([slim.dropout], is_training=self._is_training):
+            # Add additional conv layers before the class predictor.
+            features_depth = static_shape.get_depth(image_feature.get_shape())
+            depth = max(min(features_depth, self._max_depth), self._min_depth)
+            tf.logging.info('depth of additional conv before box predictor: {}'.
+                            format(depth))
+            if depth > 0 and self._num_layers_before_predictor > 0:
+              for i in range(self._num_layers_before_predictor):
+                net = slim.conv2d(
+                    net,
+                    depth, [1, 1],
+                    reuse=tf.AUTO_REUSE,
+                    scope='Conv2d_%d_1x1_%d' % (i, depth))
+            sorted_keys = sorted(self._other_heads.keys())
+            sorted_keys.append(BOX_ENCODINGS)
+            sorted_keys.append(CLASS_PREDICTIONS_WITH_BACKGROUND)
+            for head_name in sorted_keys:
+              if head_name == BOX_ENCODINGS:
+                head_obj = self._box_prediction_head
+              elif head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
+                head_obj = self._class_prediction_head
+              else:
+                head_obj = self._other_heads[head_name]
+              prediction = head_obj.predict(
+                  features=net,
+                  num_predictions_per_location=num_predictions_per_location)
+              predictions[head_name].append(prediction)
+    return predictions
+
+
+# TODO(rathodv): Replace with slim.arg_scope_func_key once its available
+# externally.
+def _arg_scope_func_key(op):
+  """Returns a key that can be used to index arg_scope dictionary."""
+  return getattr(op, '_key_op', str(op))
+
+
+# TODO(rathodv): Merge the implementation with ConvolutionalBoxPredictor above
+# since they are very similar.
+class WeightSharedConvolutionalBoxPredictor(box_predictor.BoxPredictor):
+  """Convolutional Box Predictor with weight sharing.
+
+  Defines the box predictor as defined in
+  https://arxiv.org/abs/1708.02002. This class differs from
+  ConvolutionalBoxPredictor in that it shares weights and biases while
+  predicting from different feature maps. However, batch_norm parameters are not
+  shared because the statistics of the activations vary among the different
+  feature maps.
+
+  Also note that separate multi-layer towers are constructed for the box
+  encoding and class predictors respectively.
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               box_prediction_head,
+               class_prediction_head,
+               other_heads,
+               conv_hyperparams_fn,
+               depth,
+               num_layers_before_predictor,
+               kernel_size=3,
+               apply_batch_norm=False,
+               share_prediction_tower=False,
+               use_depthwise=False):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      box_prediction_head: The head that predicts the boxes.
+      class_prediction_head: The head that predicts the classes.
+      other_heads: A dictionary mapping head names to convolutional
+        head classes.
+      conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for convolution ops.
+      depth: depth of conv layers.
+      num_layers_before_predictor: Number of the additional conv layers before
+        the predictor.
+      kernel_size: Size of final convolution kernel.
+      apply_batch_norm: Whether to apply batch normalization to conv layers in
+        this predictor.
+      share_prediction_tower: Whether to share the multi-layer tower between box
+        prediction and class prediction heads.
+      use_depthwise: Whether to use depthwise separable conv2d instead of
+       regular conv2d.
+    """
+    super(WeightSharedConvolutionalBoxPredictor, self).__init__(is_training,
+                                                                num_classes)
+    self._box_prediction_head = box_prediction_head
+    self._class_prediction_head = class_prediction_head
+    self._other_heads = other_heads
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._depth = depth
+    self._num_layers_before_predictor = num_layers_before_predictor
+    self._kernel_size = kernel_size
+    self._apply_batch_norm = apply_batch_norm
+    self._share_prediction_tower = share_prediction_tower
+    self._use_depthwise = use_depthwise
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def _insert_additional_projection_layer(self, image_feature,
+                                          inserted_layer_counter,
+                                          target_channel):
+    if inserted_layer_counter < 0:
+      return image_feature, inserted_layer_counter
+    image_feature = slim.conv2d(
+        image_feature,
+        target_channel, [1, 1],
+        stride=1,
+        padding='SAME',
+        activation_fn=None,
+        normalizer_fn=(tf.identity if self._apply_batch_norm else None),
+        scope='ProjectionLayer/conv2d_{}'.format(
+            inserted_layer_counter))
+    if self._apply_batch_norm:
+      image_feature = slim.batch_norm(
+          image_feature,
+          scope='ProjectionLayer/conv2d_{}/BatchNorm'.format(
+              inserted_layer_counter))
+    inserted_layer_counter += 1
+    return image_feature, inserted_layer_counter
+
+  def _compute_base_tower(self, tower_name_scope, image_feature, feature_index,
+                          has_different_feature_channels, target_channel,
+                          inserted_layer_counter):
+    net = image_feature
+    for i in range(self._num_layers_before_predictor):
+      if self._use_depthwise:
+        conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
+      else:
+        conv_op = slim.conv2d
+      net = conv_op(
+          net,
+          self._depth, [self._kernel_size, self._kernel_size],
+          stride=1,
+          padding='SAME',
+          activation_fn=None,
+          normalizer_fn=(tf.identity if self._apply_batch_norm else None),
+          scope='{}/conv2d_{}'.format(tower_name_scope, i))
+      if self._apply_batch_norm:
+        net = slim.batch_norm(
+            net,
+            scope='{}/conv2d_{}/BatchNorm/feature_{}'.
+            format(tower_name_scope, i, feature_index))
+      net = tf.nn.relu6(net)
+    return net
+
+  def _predict_head(self, head_name, head_obj, image_feature, box_tower_feature,
+                    feature_index, has_different_feature_channels,
+                    target_channel, inserted_layer_counter,
+                    num_predictions_per_location):
+    if head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
+      tower_name_scope = 'ClassPredictionTower'
+    else:
+      raise ValueError('Unknown head')
+    if self._share_prediction_tower:
+      head_tower_feature = box_tower_feature
+    else:
+      head_tower_feature = self._compute_base_tower(
+          tower_name_scope=tower_name_scope,
+          image_feature=image_feature,
+          feature_index=feature_index,
+          has_different_feature_channels=has_different_feature_channels,
+          target_channel=target_channel,
+          inserted_layer_counter=inserted_layer_counter)
+    return head_obj.predict(
+        features=head_tower_feature,
+        num_predictions_per_location=num_predictions_per_location)
+
+  def _predict(self, image_features, num_predictions_per_location_list):
+    """Computes encoded object locations and corresponding confidences.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+        width_i, channels] containing features for a batch of images. Note that
+        when not all tensors in the list have the same number of channels, an
+        additional projection layer will be added on top the tensor to generate
+        feature map with number of channels consitent with the majority.
+      num_predictions_per_location_list: A list of integers representing the
+        number of box predictions to be made per spatial location for each
+        feature map. Note that all values must be the same since the weights are
+        shared.
+
+    Returns:
+      A dictionary containing:
+        box_encodings: A list of float tensors of shape
+          [batch_size, num_anchors_i, code_size] representing the location of
+          the objects. Each entry in the list corresponds to a feature map in
+          the input `image_features` list.
+        class_predictions_with_background: A list of float tensors of shape
+          [batch_size, num_anchors_i, num_classes + 1] representing the class
+          predictions for the proposals. Each entry in the list corresponds to a
+          feature map in the input `image_features` list.
+        (optional) mask_predictions: A list of float tensors of shape
+          [batch_size, num_anchord_i, num_classes, mask_height, mask_width].
+
+
+    Raises:
+      ValueError: If the image feature maps do not have the same number of
+        channels or if the num predictions per locations is differs between the
+        feature maps.
+    """
+    if len(set(num_predictions_per_location_list)) > 1:
+      raise ValueError('num predictions per location must be same for all'
+                       'feature maps, found: {}'.format(
+                           num_predictions_per_location_list))
+    feature_channels = [
+        image_feature.shape[3].value for image_feature in image_features
+    ]
+    has_different_feature_channels = len(set(feature_channels)) > 1
+    if has_different_feature_channels:
+      inserted_layer_counter = 0
+      target_channel = max(set(feature_channels), key=feature_channels.count)
+      tf.logging.info('Not all feature maps have the same number of '
+                      'channels, found: {}, addition project layers '
+                      'to bring all feature maps to uniform channels '
+                      'of {}'.format(feature_channels, target_channel))
+    else:
+      # Place holder variables if has_different_feature_channels is False.
+      target_channel = -1
+      inserted_layer_counter = -1
+    predictions = {
+        BOX_ENCODINGS: [],
+        CLASS_PREDICTIONS_WITH_BACKGROUND: [],
+    }
+    for head_name in self._other_heads.keys():
+      predictions[head_name] = []
+    for feature_index, (image_feature,
+                        num_predictions_per_location) in enumerate(
+                            zip(image_features,
+                                num_predictions_per_location_list)):
+      with tf.variable_scope('WeightSharedConvolutionalBoxPredictor',
+                             reuse=tf.AUTO_REUSE):
+        with slim.arg_scope(self._conv_hyperparams_fn()):
+          (image_feature,
+           inserted_layer_counter) = self._insert_additional_projection_layer(
+               image_feature, inserted_layer_counter, target_channel)
+          if self._share_prediction_tower:
+            box_tower_scope = 'PredictionTower'
+          else:
+            box_tower_scope = 'BoxPredictionTower'
+          box_tower_feature = self._compute_base_tower(
+              tower_name_scope=box_tower_scope,
+              image_feature=image_feature,
+              feature_index=feature_index,
+              has_different_feature_channels=has_different_feature_channels,
+              target_channel=target_channel,
+              inserted_layer_counter=inserted_layer_counter)
+          box_encodings = self._box_prediction_head.predict(
+              features=box_tower_feature,
+              num_predictions_per_location=num_predictions_per_location)
+          predictions[BOX_ENCODINGS].append(box_encodings)
+          sorted_keys = sorted(self._other_heads.keys())
+          sorted_keys.append(CLASS_PREDICTIONS_WITH_BACKGROUND)
+          for head_name in sorted_keys:
+            if head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
+              head_obj = self._class_prediction_head
+            else:
+              head_obj = self._other_heads[head_name]
+            prediction = self._predict_head(
+                head_name=head_name,
+                head_obj=head_obj,
+                image_feature=image_feature,
+                box_tower_feature=box_tower_feature,
+                feature_index=feature_index,
+                has_different_feature_channels=has_different_feature_channels,
+                target_channel=target_channel,
+                inserted_layer_counter=inserted_layer_counter,
+                num_predictions_per_location=num_predictions_per_location)
+            predictions[head_name].append(prediction)
+    return predictions
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor_test.py
new file mode 100644
index 00000000..3ae1dbf2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_box_predictor_test.py
@@ -0,0 +1,856 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.convolutional_box_predictor."""
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors import convolutional_box_predictor as box_predictor
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class ConvolutionalBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.build(conv_hyperparams, is_training=True)
+
+  def test_get_boxes_for_five_aspect_ratios_per_location(self):
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_box_predictor(
+              is_training=False,
+              num_classes=0,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features], num_predictions_per_location=[5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      objectness_predictions = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, objectness_predictions)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, objectness_predictions) = self.execute(graph_fn,
+                                                           [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
+    self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
+
+  def test_get_boxes_for_one_aspect_ratio_per_location(self):
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_box_predictor(
+              is_training=False,
+              num_classes=0,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features], num_predictions_per_location=[1],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      objectness_predictions = tf.concat(box_predictions[
+          box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+      return (box_encodings, objectness_predictions)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, objectness_predictions) = self.execute(graph_fn,
+                                                           [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
+    self.assertAllEqual(objectness_predictions.shape, [4, 64, 1])
+
+  def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
+      self):
+    num_classes_without_background = 6
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features],
+          num_predictions_per_location=[5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+    (box_encodings,
+     class_predictions_with_background) = self.execute(graph_fn,
+                                                       [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
+    self.assertAllEqual(class_predictions_with_background.shape,
+                        [4, 320, num_classes_without_background+1])
+
+  def test_get_predictions_with_feature_maps_of_dynamic_shape(
+      self):
+    image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
+    conv_box_predictor = (
+        box_predictor_builder.build_convolutional_box_predictor(
+            is_training=False,
+            num_classes=0,
+            conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+            min_depth=0,
+            max_depth=32,
+            num_layers_before_predictor=1,
+            use_dropout=True,
+            dropout_keep_prob=0.8,
+            kernel_size=1,
+            box_code_size=4))
+    box_predictions = conv_box_predictor.predict(
+        [image_features], num_predictions_per_location=[5],
+        scope='BoxPredictor')
+    box_encodings = tf.concat(
+        box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+    objectness_predictions = tf.concat(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1)
+    init_op = tf.global_variables_initializer()
+
+    resolution = 32
+    expected_num_anchors = resolution*resolution*5
+    with self.test_session() as sess:
+      sess.run(init_op)
+      (box_encodings_shape,
+       objectness_predictions_shape) = sess.run(
+           [tf.shape(box_encodings), tf.shape(objectness_predictions)],
+           feed_dict={image_features:
+                      np.random.rand(4, resolution, resolution, 64)})
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+      self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
+      self.assertAllEqual(objectness_predictions_shape,
+                          [4, expected_num_anchors, 1])
+    expected_variable_set = set([
+        'BoxPredictor/Conv2d_0_1x1_32/biases',
+        'BoxPredictor/Conv2d_0_1x1_32/weights',
+        'BoxPredictor/BoxEncodingPredictor/biases',
+        'BoxPredictor/BoxEncodingPredictor/weights',
+        'BoxPredictor/ClassPredictor/biases',
+        'BoxPredictor/ClassPredictor/weights'])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_use_depthwise_convolution(self):
+    image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
+    conv_box_predictor = (
+        box_predictor_builder.build_convolutional_box_predictor(
+            is_training=False,
+            num_classes=0,
+            conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+            min_depth=0,
+            max_depth=32,
+            num_layers_before_predictor=1,
+            dropout_keep_prob=0.8,
+            kernel_size=1,
+            box_code_size=4,
+            use_dropout=True,
+            use_depthwise=True))
+    box_predictions = conv_box_predictor.predict(
+        [image_features], num_predictions_per_location=[5],
+        scope='BoxPredictor')
+    box_encodings = tf.concat(
+        box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+    objectness_predictions = tf.concat(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1)
+    init_op = tf.global_variables_initializer()
+
+    resolution = 32
+    expected_num_anchors = resolution*resolution*5
+    with self.test_session() as sess:
+      sess.run(init_op)
+      (box_encodings_shape,
+       objectness_predictions_shape) = sess.run(
+           [tf.shape(box_encodings), tf.shape(objectness_predictions)],
+           feed_dict={image_features:
+                      np.random.rand(4, resolution, resolution, 64)})
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
+    self.assertAllEqual(objectness_predictions_shape,
+                        [4, expected_num_anchors, 1])
+    expected_variable_set = set([
+        'BoxPredictor/Conv2d_0_1x1_32/biases',
+        'BoxPredictor/Conv2d_0_1x1_32/weights',
+        'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
+        'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
+        'BoxPredictor/BoxEncodingPredictor/biases',
+        'BoxPredictor/BoxEncodingPredictor/weights',
+        'BoxPredictor/ClassPredictor_depthwise/biases',
+        'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
+        'BoxPredictor/ClassPredictor/biases',
+        'BoxPredictor/ClassPredictor/weights'])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_no_dangling_outputs(self):
+    image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
+    conv_box_predictor = (
+        box_predictor_builder.build_convolutional_box_predictor(
+            is_training=False,
+            num_classes=0,
+            conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+            min_depth=0,
+            max_depth=32,
+            num_layers_before_predictor=1,
+            dropout_keep_prob=0.8,
+            kernel_size=1,
+            box_code_size=4,
+            use_dropout=True,
+            use_depthwise=True))
+    box_predictions = conv_box_predictor.predict(
+        [image_features], num_predictions_per_location=[5],
+        scope='BoxPredictor')
+    tf.concat(
+        box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+    tf.concat(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1)
+
+    bad_dangling_ops = []
+    types_safe_to_dangle = set(['Assign', 'Mul', 'Const'])
+    for op in tf.get_default_graph().get_operations():
+      if (not op.outputs) or (not op.outputs[0].consumers()):
+        if 'BoxPredictor' in op.name:
+          if op.type not in types_safe_to_dangle:
+            bad_dangling_ops.append(op)
+
+    self.assertEqual(bad_dangling_ops, [])
+
+
+class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        random_normal_initializer {
+          stddev: 0.01
+          mean: 0.0
+        }
+      }
+      batch_norm {
+        train: true,
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.build(conv_hyperparams, is_training=True)
+
+  def _build_conv_arg_scope_no_batch_norm(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        random_normal_initializer {
+          stddev: 0.01
+          mean: 0.0
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.build(conv_hyperparams, is_training=True)
+
+  def test_get_boxes_for_five_aspect_ratios_per_location(self):
+
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=0,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features], num_predictions_per_location=[5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      objectness_predictions = tf.concat(box_predictions[
+          box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+      return (box_encodings, objectness_predictions)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, objectness_predictions) = self.execute(
+        graph_fn, [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 4])
+    self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
+
+  def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
+
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=True,
+              num_classes=2,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=1,
+              class_prediction_bias_init=-4.6,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features], num_predictions_per_location=[5],
+          scope='BoxPredictor')
+      class_predictions = tf.concat(box_predictions[
+          box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+      return (tf.nn.sigmoid(class_predictions),)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    class_predictions = self.execute(graph_fn, [image_features])
+    self.assertAlmostEqual(np.mean(class_predictions), 0.01, places=3)
+
+  def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
+      self):
+
+    num_classes_without_background = 6
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features],
+          num_predictions_per_location=[5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(box_predictions[
+          box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, class_predictions_with_background) = self.execute(
+        graph_fn, [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 4])
+    self.assertAllEqual(class_predictions_with_background.shape,
+                        [4, 320, num_classes_without_background+1])
+
+  def test_get_multi_class_predictions_from_two_feature_maps(
+      self):
+
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, class_predictions_with_background) = self.execute(
+        graph_fn, [image_features1, image_features2])
+    self.assertAllEqual(box_encodings.shape, [4, 640, 4])
+    self.assertAllEqual(class_predictions_with_background.shape,
+                        [4, 640, num_classes_without_background+1])
+
+  def test_get_multi_class_predictions_from_feature_maps_of_different_depth(
+      self):
+
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2, image_features3):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=1,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2, image_features3],
+          num_predictions_per_location=[5, 5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    image_features3 = np.random.rand(4, 8, 8, 32).astype(np.float32)
+    (box_encodings, class_predictions_with_background) = self.execute(
+        graph_fn, [image_features1, image_features2, image_features3])
+    self.assertAllEqual(box_encodings.shape, [4, 960, 4])
+    self.assertAllEqual(class_predictions_with_background.shape,
+                        [4, 960, num_classes_without_background+1])
+
+  def test_predictions_multiple_feature_maps_share_weights_separate_batchnorm(
+      self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Box prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_predictions_multiple_feature_maps_share_weights_without_batchnorm(
+      self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4,
+              apply_batch_norm=False))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Box prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/biases'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/biases'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_predictions_multiple_feature_maps_share_weights_with_depthwise(
+      self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4,
+              apply_batch_norm=False,
+              use_depthwise=True))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Box prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/biases'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/biases'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/depthwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/pointwise_weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_no_batchnorm_params_when_batchnorm_is_not_configured(self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_conv_arg_scope_no_batch_norm(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4,
+              apply_batch_norm=False))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Box prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictionTower/conv2d_1/biases'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictionTower/conv2d_1/biases'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_predictions_share_weights_share_tower_separate_batchnorm(
+      self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4,
+              share_prediction_tower=True))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Shared prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_predictions_share_weights_share_tower_without_batchnorm(
+      self):
+    num_classes_without_background = 6
+    def graph_fn(image_features1, image_features2):
+      conv_box_predictor = (
+          box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+              depth=32,
+              num_layers_before_predictor=2,
+              box_code_size=4,
+              share_prediction_tower=True,
+              apply_batch_norm=False))
+      box_predictions = conv_box_predictor.predict(
+          [image_features1, image_features2],
+          num_predictions_per_location=[5, 5],
+          scope='BoxPredictor')
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    with self.test_session(graph=tf.Graph()):
+      graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
+               tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+    expected_variable_set = set([
+        # Shared prediction tower
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_0/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_0/biases'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_1/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'PredictionTower/conv2d_1/biases'),
+        # Box prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'BoxPredictor/biases'),
+        # Class prediction head
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/weights'),
+        ('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
+         'ClassPredictor/biases')])
+
+    self.assertEqual(expected_variable_set, actual_variable_set)
+
+  def test_get_predictions_with_feature_maps_of_dynamic_shape(
+      self):
+    image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
+    conv_box_predictor = (
+        box_predictor_builder.build_weight_shared_convolutional_box_predictor(
+            is_training=False,
+            num_classes=0,
+            conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+            depth=32,
+            num_layers_before_predictor=1,
+            box_code_size=4))
+    box_predictions = conv_box_predictor.predict(
+        [image_features], num_predictions_per_location=[5],
+        scope='BoxPredictor')
+    box_encodings = tf.concat(box_predictions[box_predictor.BOX_ENCODINGS],
+                              axis=1)
+    objectness_predictions = tf.concat(box_predictions[
+        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+    init_op = tf.global_variables_initializer()
+
+    resolution = 32
+    expected_num_anchors = resolution*resolution*5
+    with self.test_session() as sess:
+      sess.run(init_op)
+      (box_encodings_shape,
+       objectness_predictions_shape) = sess.run(
+           [tf.shape(box_encodings), tf.shape(objectness_predictions)],
+           feed_dict={image_features:
+                      np.random.rand(4, resolution, resolution, 64)})
+      self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 4])
+      self.assertAllEqual(objectness_predictions_shape,
+                          [4, expected_num_anchors, 1])
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor.py
new file mode 100644
index 00000000..f8e981a3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor.py
@@ -0,0 +1,199 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Convolutional Box Predictors with and without weight sharing."""
+import collections
+
+import tensorflow as tf
+
+from object_detection.core import box_predictor
+from object_detection.utils import static_shape
+
+keras = tf.keras.layers
+
+BOX_ENCODINGS = box_predictor.BOX_ENCODINGS
+CLASS_PREDICTIONS_WITH_BACKGROUND = (
+    box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND)
+MASK_PREDICTIONS = box_predictor.MASK_PREDICTIONS
+
+
+class _NoopVariableScope(object):
+  """A dummy class that does not push any scope."""
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exc_type, exc_value, traceback):
+    return False
+
+
+class ConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
+  """Convolutional Keras Box Predictor.
+
+  Optionally add an intermediate 1x1 convolutional layer after features and
+  predict in parallel branches box_encodings and
+  class_predictions_with_background.
+
+  Currently this box predictor assumes that predictions are "shared" across
+  classes --- that is each anchor makes box predictions which do not depend
+  on class.
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               box_prediction_heads,
+               class_prediction_heads,
+               other_heads,
+               conv_hyperparams,
+               num_layers_before_predictor,
+               min_depth,
+               max_depth,
+               freeze_batchnorm,
+               inplace_batchnorm_update,
+               name=None):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      box_prediction_heads: A list of heads that predict the boxes.
+      class_prediction_heads: A list of heads that predict the classes.
+      other_heads: A dictionary mapping head names to lists of convolutional
+        heads.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops.
+      num_layers_before_predictor: Number of the additional conv layers before
+        the predictor.
+      min_depth: Minimum feature depth prior to predicting box encodings
+        and class predictions.
+      max_depth: Maximum feature depth prior to predicting box encodings
+        and class predictions. If max_depth is set to 0, no additional
+        feature map will be inserted before location and class predictions.
+      freeze_batchnorm: Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      inplace_batchnorm_update: Whether to update batch norm moving average
+        values inplace. When this is false train op must add a control
+        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
+        batch norm statistics.
+      name: A string name scope to assign to the model. If `None`, Keras
+        will auto-generate one from the class name.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalBoxPredictor, self).__init__(
+        is_training, num_classes, freeze_batchnorm=freeze_batchnorm,
+        inplace_batchnorm_update=inplace_batchnorm_update,
+        name=name)
+    if min_depth > max_depth:
+      raise ValueError('min_depth should be less than or equal to max_depth')
+    if len(box_prediction_heads) != len(class_prediction_heads):
+      raise ValueError('All lists of heads must be the same length.')
+    for other_head_list in other_heads.values():
+      if len(box_prediction_heads) != len(other_head_list):
+        raise ValueError('All lists of heads must be the same length.')
+
+    self._prediction_heads = {
+        BOX_ENCODINGS: box_prediction_heads,
+        CLASS_PREDICTIONS_WITH_BACKGROUND: class_prediction_heads,
+    }
+
+    if other_heads:
+      self._prediction_heads.update(other_heads)
+
+    # We generate a consistent ordering for the prediction head names,
+    # So that all workers build the model in the exact same order
+    self._sorted_head_names = sorted(self._prediction_heads.keys())
+
+    self._conv_hyperparams = conv_hyperparams
+    self._min_depth = min_depth
+    self._max_depth = max_depth
+    self._num_layers_before_predictor = num_layers_before_predictor
+
+    self._shared_nets = []
+
+  def build(self, input_shapes):
+    """Creates the variables of the layer."""
+    if len(input_shapes) != len(self._prediction_heads[BOX_ENCODINGS]):
+      raise ValueError('This box predictor was constructed with %d heads,'
+                       'but there are %d inputs.' %
+                       (len(self._prediction_heads[BOX_ENCODINGS]),
+                        len(input_shapes)))
+    for stack_index, input_shape in enumerate(input_shapes):
+      net = []
+
+      # Add additional conv layers before the class predictor.
+      features_depth = static_shape.get_depth(input_shape)
+      depth = max(min(features_depth, self._max_depth), self._min_depth)
+      tf.logging.info(
+          'depth of additional conv before box predictor: {}'.format(depth))
+
+      if depth > 0 and self._num_layers_before_predictor > 0:
+        for i in range(self._num_layers_before_predictor):
+          net.append(keras.Conv2D(depth, [1, 1],
+                                  name='SharedConvolutions_%d/Conv2d_%d_1x1_%d'
+                                  % (stack_index, i, depth),
+                                  padding='SAME',
+                                  **self._conv_hyperparams.params()))
+          net.append(self._conv_hyperparams.build_batch_norm(
+              training=(self._is_training and not self._freeze_batchnorm),
+              name='SharedConvolutions_%d/Conv2d_%d_1x1_%d_norm'
+              % (stack_index, i, depth)))
+          net.append(self._conv_hyperparams.build_activation_layer(
+              name='SharedConvolutions_%d/Conv2d_%d_1x1_%d_activation'
+              % (stack_index, i, depth),
+          ))
+      # Until certain bugs are fixed in checkpointable lists,
+      # this net must be appended only once it's been filled with layers
+      self._shared_nets.append(net)
+    self.built = True
+
+  def _predict(self, image_features):
+    """Computes encoded object locations and corresponding confidences.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+        width_i, channels_i] containing features for a batch of images.
+
+    Returns:
+      box_encodings: A list of float tensors of shape
+        [batch_size, num_anchors_i, q, code_size] representing the location of
+        the objects, where q is 1 or the number of classes. Each entry in the
+        list corresponds to a feature map in the input `image_features` list.
+      class_predictions_with_background: A list of float tensors of shape
+        [batch_size, num_anchors_i, num_classes + 1] representing the class
+        predictions for the proposals. Each entry in the list corresponds to a
+        feature map in the input `image_features` list.
+    """
+    predictions = collections.defaultdict(list)
+
+    for (index, net) in enumerate(image_features):
+
+      # Apply shared conv layers before the head predictors.
+      for layer in self._shared_nets[index]:
+        net = layer(net)
+
+      for head_name in self._sorted_head_names:
+        head_obj = self._prediction_heads[head_name][index]
+        prediction = head_obj(net)
+        predictions[head_name].append(prediction)
+
+    return predictions
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor_test.py
new file mode 100644
index 00000000..dff2a36c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/convolutional_keras_box_predictor_test.py
@@ -0,0 +1,197 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.convolutional_keras_box_predictor."""
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors import convolutional_keras_box_predictor as box_predictor
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      activation: RELU_6
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def test_get_boxes_for_five_aspect_ratios_per_location(self):
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_keras_box_predictor(
+              is_training=False,
+              num_classes=0,
+              conv_hyperparams=self._build_conv_hyperparams(),
+              freeze_batchnorm=False,
+              inplace_batchnorm_update=False,
+              num_predictions_per_location_list=[5],
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4
+          ))
+      box_predictions = conv_box_predictor([image_features])
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      objectness_predictions = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, objectness_predictions)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, objectness_predictions) = self.execute(graph_fn,
+                                                           [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
+    self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
+
+  def test_get_boxes_for_one_aspect_ratio_per_location(self):
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_keras_box_predictor(
+              is_training=False,
+              num_classes=0,
+              conv_hyperparams=self._build_conv_hyperparams(),
+              freeze_batchnorm=False,
+              inplace_batchnorm_update=False,
+              num_predictions_per_location_list=[1],
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4
+          ))
+      box_predictions = conv_box_predictor([image_features])
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      objectness_predictions = tf.concat(box_predictions[
+          box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
+      return (box_encodings, objectness_predictions)
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    (box_encodings, objectness_predictions) = self.execute(graph_fn,
+                                                           [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
+    self.assertAllEqual(objectness_predictions.shape, [4, 64, 1])
+
+  def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
+      self):
+    num_classes_without_background = 6
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    def graph_fn(image_features):
+      conv_box_predictor = (
+          box_predictor_builder.build_convolutional_keras_box_predictor(
+              is_training=False,
+              num_classes=num_classes_without_background,
+              conv_hyperparams=self._build_conv_hyperparams(),
+              freeze_batchnorm=False,
+              inplace_batchnorm_update=False,
+              num_predictions_per_location_list=[5],
+              min_depth=0,
+              max_depth=32,
+              num_layers_before_predictor=1,
+              use_dropout=True,
+              dropout_keep_prob=0.8,
+              kernel_size=1,
+              box_code_size=4
+          ))
+      box_predictions = conv_box_predictor([image_features])
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+    (box_encodings,
+     class_predictions_with_background) = self.execute(graph_fn,
+                                                       [image_features])
+    self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
+    self.assertAllEqual(class_predictions_with_background.shape,
+                        [4, 320, num_classes_without_background+1])
+
+  def test_get_predictions_with_feature_maps_of_dynamic_shape(
+      self):
+    image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
+    conv_box_predictor = (
+        box_predictor_builder.build_convolutional_keras_box_predictor(
+            is_training=False,
+            num_classes=0,
+            conv_hyperparams=self._build_conv_hyperparams(),
+            freeze_batchnorm=False,
+            inplace_batchnorm_update=False,
+            num_predictions_per_location_list=[5],
+            min_depth=0,
+            max_depth=32,
+            num_layers_before_predictor=1,
+            use_dropout=True,
+            dropout_keep_prob=0.8,
+            kernel_size=1,
+            box_code_size=4
+        ))
+    box_predictions = conv_box_predictor([image_features])
+    box_encodings = tf.concat(
+        box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+    objectness_predictions = tf.concat(
+        box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+        axis=1)
+    init_op = tf.global_variables_initializer()
+
+    resolution = 32
+    expected_num_anchors = resolution*resolution*5
+    with self.test_session() as sess:
+      sess.run(init_op)
+      (box_encodings_shape,
+       objectness_predictions_shape) = sess.run(
+           [tf.shape(box_encodings), tf.shape(objectness_predictions)],
+           feed_dict={image_features:
+                      np.random.rand(4, resolution, resolution, 64)})
+      actual_variable_set = set(
+          [var.op.name for var in tf.trainable_variables()])
+      self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
+      self.assertAllEqual(objectness_predictions_shape,
+                          [4, expected_num_anchors, 1])
+    expected_variable_set = set([
+        'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/bias',
+        'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/kernel',
+        'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/bias',
+        'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/kernel',
+        'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias',
+        'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/kernel'])
+    self.assertEqual(expected_variable_set, actual_variable_set)
+    self.assertEqual(conv_box_predictor._sorted_head_names,
+                     ['box_encodings', 'class_predictions_with_background'])
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head.py
new file mode 100644
index 00000000..3adfecb7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head.py
@@ -0,0 +1,253 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Box Head.
+
+Contains Box prediction head classes for different meta architectures.
+All the box prediction heads have a predict function that receives the
+`features` as the first argument and returns `box_encodings`.
+"""
+import functools
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+
+slim = tf.contrib.slim
+
+
+class MaskRCNNBoxHead(head.Head):
+  """Box prediction head.
+
+  Please refer to Mask RCNN paper:
+  https://arxiv.org/abs/1703.06870
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               fc_hyperparams_fn,
+               use_dropout,
+               dropout_keep_prob,
+               box_code_size,
+               share_box_across_classes=False):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      fc_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for fully connected ops.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+      box_code_size: Size of encoding for each box.
+      share_box_across_classes: Whether to share boxes across classes rather
+        than use a different box for each class.
+    """
+    super(MaskRCNNBoxHead, self).__init__()
+    self._is_training = is_training
+    self._num_classes = num_classes
+    self._fc_hyperparams_fn = fc_hyperparams_fn
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._box_code_size = box_code_size
+    self._share_box_across_classes = share_box_across_classes
+
+  def predict(self, features, num_predictions_per_location=1):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width,
+        channels] containing features for a batch of images.
+      num_predictions_per_location: Int containing number of predictions per
+        location.
+
+    Returns:
+      box_encodings: A float tensor of shape
+        [batch_size, 1, num_classes, code_size] representing the location of the
+        objects.
+
+    Raises:
+      ValueError: If num_predictions_per_location is not 1.
+    """
+    if num_predictions_per_location != 1:
+      raise ValueError('Only num_predictions_per_location=1 is supported')
+    spatial_averaged_roi_pooled_features = tf.reduce_mean(
+        features, [1, 2], keep_dims=True, name='AvgPool')
+    flattened_roi_pooled_features = slim.flatten(
+        spatial_averaged_roi_pooled_features)
+    if self._use_dropout:
+      flattened_roi_pooled_features = slim.dropout(
+          flattened_roi_pooled_features,
+          keep_prob=self._dropout_keep_prob,
+          is_training=self._is_training)
+    number_of_boxes = 1
+    if not self._share_box_across_classes:
+      number_of_boxes = self._num_classes
+
+    with slim.arg_scope(self._fc_hyperparams_fn()):
+      box_encodings = slim.fully_connected(
+          flattened_roi_pooled_features,
+          number_of_boxes * self._box_code_size,
+          activation_fn=None,
+          scope='BoxEncodingPredictor')
+    box_encodings = tf.reshape(box_encodings,
+                               [-1, 1, number_of_boxes, self._box_code_size])
+    return box_encodings
+
+
+class ConvolutionalBoxHead(head.Head):
+  """Convolutional box prediction head."""
+
+  def __init__(self,
+               is_training,
+               box_code_size,
+               kernel_size,
+               use_depthwise=False):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      box_code_size: Size of encoding for each box.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalBoxHead, self).__init__()
+    self._is_training = is_training
+    self._box_code_size = box_code_size
+    self._kernel_size = kernel_size
+    self._use_depthwise = use_depthwise
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location. Int specifying number of boxes per location.
+
+    Returns:
+      box_encodings: A float tensors of shape
+        [batch_size, num_anchors, q, code_size] representing the location of
+        the objects, where q is 1 or the number of classes.
+    """
+    net = features
+    if self._use_depthwise:
+      box_encodings = slim.separable_conv2d(
+          net, None, [self._kernel_size, self._kernel_size],
+          padding='SAME', depth_multiplier=1, stride=1,
+          rate=1, scope='BoxEncodingPredictor_depthwise')
+      box_encodings = slim.conv2d(
+          box_encodings,
+          num_predictions_per_location * self._box_code_size, [1, 1],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='BoxEncodingPredictor')
+    else:
+      box_encodings = slim.conv2d(
+          net, num_predictions_per_location * self._box_code_size,
+          [self._kernel_size, self._kernel_size],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='BoxEncodingPredictor')
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    box_encodings = tf.reshape(box_encodings,
+                               [batch_size, -1, 1, self._box_code_size])
+    return box_encodings
+
+
+# TODO(alirezafathi): See if possible to unify Weight Shared with regular
+# convolutional box head.
+class WeightSharedConvolutionalBoxHead(head.Head):
+  """Weight shared convolutional box prediction head.
+
+  This head allows sharing the same set of parameters (weights) when called more
+  then once on different feature maps.
+  """
+
+  def __init__(self,
+               box_code_size,
+               kernel_size=3,
+               use_depthwise=False,
+               box_encodings_clip_range=None):
+    """Constructor.
+
+    Args:
+      box_code_size: Size of encoding for each box.
+      kernel_size: Size of final convolution kernel.
+      use_depthwise: Whether to use depthwise convolutions for prediction steps.
+        Default is False.
+      box_encodings_clip_range: Min and max values for clipping box_encodings.
+    """
+    super(WeightSharedConvolutionalBoxHead, self).__init__()
+    self._box_code_size = box_code_size
+    self._kernel_size = kernel_size
+    self._use_depthwise = use_depthwise
+    self._box_encodings_clip_range = box_encodings_clip_range
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location.
+
+    Returns:
+      box_encodings: A float tensor of shape
+        [batch_size, num_anchors, code_size] representing the location of
+        the objects.
+    """
+    box_encodings_net = features
+    if self._use_depthwise:
+      conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
+    else:
+      conv_op = slim.conv2d
+    box_encodings = conv_op(
+        box_encodings_net,
+        num_predictions_per_location * self._box_code_size,
+        [self._kernel_size, self._kernel_size],
+        activation_fn=None, stride=1, padding='SAME',
+        normalizer_fn=None,
+        scope='BoxPredictor')
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    # Clipping the box encodings to make the inference graph TPU friendly.
+    if self._box_encodings_clip_range is not None:
+      box_encodings = tf.clip_by_value(
+          box_encodings, self._box_encodings_clip_range.min,
+          self._box_encodings_clip_range.max)
+    box_encodings = tf.reshape(box_encodings,
+                               [batch_size, -1, self._box_code_size])
+    return box_encodings
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head_test.py
new file mode 100644
index 00000000..34df8f43
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/box_head_test.py
@@ -0,0 +1,127 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.box_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import box_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class MaskRCNNBoxHeadTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        op_type=hyperparams_pb2.Hyperparams.FC):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    box_prediction_head = box_head.MaskRCNNBoxHead(
+        is_training=False,
+        num_classes=20,
+        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        box_code_size=4,
+        share_box_across_classes=False)
+    roi_pooled_features = tf.random_uniform(
+        [64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    prediction = box_prediction_head.predict(
+        features=roi_pooled_features, num_predictions_per_location=1)
+    self.assertAllEqual([64, 1, 20, 4], prediction.get_shape().as_list())
+
+
+class ConvolutionalBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    box_prediction_head = box_head.ConvolutionalBoxHead(
+        is_training=True,
+        box_code_size=4,
+        kernel_size=3)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    box_encodings = box_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 1, 4], box_encodings.get_shape().as_list())
+
+
+class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    box_prediction_head = box_head.WeightSharedConvolutionalBoxHead(
+        box_code_size=4)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    box_encodings = box_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 4], box_encodings.get_shape().as_list())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head.py
new file mode 100644
index 00000000..ad41203b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head.py
@@ -0,0 +1,284 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Class Head.
+
+Contains Class prediction head classes for different meta architectures.
+All the class prediction heads have a predict function that receives the
+`features` as the first argument and returns class predictions with background.
+"""
+import functools
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+
+slim = tf.contrib.slim
+
+
+class MaskRCNNClassHead(head.Head):
+  """Mask RCNN class prediction head.
+
+  Please refer to Mask RCNN paper:
+  https://arxiv.org/abs/1703.06870
+  """
+
+  def __init__(self,
+               is_training,
+               num_class_slots,
+               fc_hyperparams_fn,
+               use_dropout,
+               dropout_keep_prob):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_class_slots: number of class slots. Note that num_class_slots may or
+        may not include an implicit background category.
+      fc_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for fully connected ops.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+    """
+    super(MaskRCNNClassHead, self).__init__()
+    self._is_training = is_training
+    self._num_class_slots = num_class_slots
+    self._fc_hyperparams_fn = fc_hyperparams_fn
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+
+  def predict(self, features, num_predictions_per_location=1):
+    """Predicts boxes and class scores.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing features for a batch of images.
+      num_predictions_per_location: Int containing number of predictions per
+        location.
+
+    Returns:
+      class_predictions_with_background: A float tensor of shape
+        [batch_size, 1, num_class_slots] representing the class predictions for
+        the proposals.
+
+    Raises:
+      ValueError: If num_predictions_per_location is not 1.
+    """
+    if num_predictions_per_location != 1:
+      raise ValueError('Only num_predictions_per_location=1 is supported')
+    spatial_averaged_roi_pooled_features = tf.reduce_mean(
+        features, [1, 2], keep_dims=True, name='AvgPool')
+    flattened_roi_pooled_features = slim.flatten(
+        spatial_averaged_roi_pooled_features)
+    if self._use_dropout:
+      flattened_roi_pooled_features = slim.dropout(
+          flattened_roi_pooled_features,
+          keep_prob=self._dropout_keep_prob,
+          is_training=self._is_training)
+
+    with slim.arg_scope(self._fc_hyperparams_fn()):
+      class_predictions_with_background = slim.fully_connected(
+          flattened_roi_pooled_features,
+          self._num_class_slots,
+          activation_fn=None,
+          scope='ClassPredictor')
+    class_predictions_with_background = tf.reshape(
+        class_predictions_with_background,
+        [-1, 1, self._num_class_slots])
+    return class_predictions_with_background
+
+
+class ConvolutionalClassHead(head.Head):
+  """Convolutional class prediction head."""
+
+  def __init__(self,
+               is_training,
+               num_class_slots,
+               use_dropout,
+               dropout_keep_prob,
+               kernel_size,
+               apply_sigmoid_to_scores=False,
+               class_prediction_bias_init=0.0,
+               use_depthwise=False):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_class_slots: number of class slots. Note that num_class_slots may or
+        may not include an implicit background category.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      apply_sigmoid_to_scores: if True, apply the sigmoid on the output
+        class_predictions.
+      class_prediction_bias_init: constant value to initialize bias of the last
+        conv2d layer before class prediction.
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalClassHead, self).__init__()
+    self._is_training = is_training
+    self._num_class_slots = num_class_slots
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._kernel_size = kernel_size
+    self._apply_sigmoid_to_scores = apply_sigmoid_to_scores
+    self._class_prediction_bias_init = class_prediction_bias_init
+    self._use_depthwise = use_depthwise
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location.
+
+    Returns:
+      class_predictions_with_background: A float tensors of shape
+        [batch_size, num_anchors, num_class_slots] representing the class
+        predictions for the proposals.
+    """
+    net = features
+    if self._use_dropout:
+      net = slim.dropout(net, keep_prob=self._dropout_keep_prob)
+    if self._use_depthwise:
+      class_predictions_with_background = slim.separable_conv2d(
+          net, None, [self._kernel_size, self._kernel_size],
+          padding='SAME', depth_multiplier=1, stride=1,
+          rate=1, scope='ClassPredictor_depthwise')
+      class_predictions_with_background = slim.conv2d(
+          class_predictions_with_background,
+          num_predictions_per_location * self._num_class_slots, [1, 1],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='ClassPredictor')
+    else:
+      class_predictions_with_background = slim.conv2d(
+          net,
+          num_predictions_per_location * self._num_class_slots,
+          [self._kernel_size, self._kernel_size],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='ClassPredictor',
+          biases_initializer=tf.constant_initializer(
+              self._class_prediction_bias_init))
+    if self._apply_sigmoid_to_scores:
+      class_predictions_with_background = tf.sigmoid(
+          class_predictions_with_background)
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    class_predictions_with_background = tf.reshape(
+        class_predictions_with_background,
+        [batch_size, -1, self._num_class_slots])
+    return class_predictions_with_background
+
+
+# TODO(alirezafathi): See if possible to unify Weight Shared with regular
+# convolutional class head.
+class WeightSharedConvolutionalClassHead(head.Head):
+  """Weight shared convolutional class prediction head.
+
+  This head allows sharing the same set of parameters (weights) when called more
+  then once on different feature maps.
+  """
+
+  def __init__(self,
+               num_class_slots,
+               kernel_size=3,
+               class_prediction_bias_init=0.0,
+               use_dropout=False,
+               dropout_keep_prob=0.8,
+               use_depthwise=False,
+               score_converter_fn=tf.identity):
+    """Constructor.
+
+    Args:
+      num_class_slots: number of class slots. Note that num_class_slots may or
+        may not include an implicit background category.
+      kernel_size: Size of final convolution kernel.
+      class_prediction_bias_init: constant value to initialize bias of the last
+        conv2d layer before class prediction.
+      use_dropout: Whether to apply dropout to class prediction head.
+      dropout_keep_prob: Probability of keeping activiations.
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+      score_converter_fn: Callable elementwise nonlinearity (that takes tensors
+        as inputs and returns tensors).
+    """
+    super(WeightSharedConvolutionalClassHead, self).__init__()
+    self._num_class_slots = num_class_slots
+    self._kernel_size = kernel_size
+    self._class_prediction_bias_init = class_prediction_bias_init
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._use_depthwise = use_depthwise
+    self._score_converter_fn = score_converter_fn
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location.
+
+    Returns:
+      class_predictions_with_background: A tensor of shape
+        [batch_size, num_anchors, num_class_slots] representing the class
+        predictions for the proposals.
+    """
+    class_predictions_net = features
+    if self._use_dropout:
+      class_predictions_net = slim.dropout(
+          class_predictions_net, keep_prob=self._dropout_keep_prob)
+    if self._use_depthwise:
+      conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
+    else:
+      conv_op = slim.conv2d
+    class_predictions_with_background = conv_op(
+        class_predictions_net,
+        num_predictions_per_location * self._num_class_slots,
+        [self._kernel_size, self._kernel_size],
+        activation_fn=None, stride=1, padding='SAME',
+        normalizer_fn=None,
+        biases_initializer=tf.constant_initializer(
+            self._class_prediction_bias_init),
+        scope='ClassPredictor')
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    class_predictions_with_background = self._score_converter_fn(
+        class_predictions_with_background)
+    class_predictions_with_background = tf.reshape(
+        class_predictions_with_background,
+        [batch_size, -1, self._num_class_slots])
+    return class_predictions_with_background
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head_test.py
new file mode 100644
index 00000000..270dc5d1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/class_head_test.py
@@ -0,0 +1,128 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.class_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import class_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class MaskRCNNClassHeadTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        op_type=hyperparams_pb2.Hyperparams.FC):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    class_prediction_head = class_head.MaskRCNNClassHead(
+        is_training=False,
+        num_class_slots=20,
+        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+        use_dropout=True,
+        dropout_keep_prob=0.5)
+    roi_pooled_features = tf.random_uniform(
+        [64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    prediction = class_prediction_head.predict(
+        features=roi_pooled_features, num_predictions_per_location=1)
+    self.assertAllEqual([64, 1, 20], prediction.get_shape().as_list())
+
+
+class ConvolutionalClassPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    class_prediction_head = class_head.ConvolutionalClassHead(
+        is_training=True,
+        num_class_slots=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    class_predictions = class_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 20],
+                        class_predictions.get_shape().as_list())
+
+
+class WeightSharedConvolutionalClassPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    class_prediction_head = (
+        class_head.WeightSharedConvolutionalClassHead(num_class_slots=20))
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    class_predictions = class_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 20], class_predictions.get_shape().as_list())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/head.py
new file mode 100644
index 00000000..cd1607ae
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/head.py
@@ -0,0 +1,81 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Base head class.
+
+All the different kinds of prediction heads in different models will inherit
+from this class. What is in common between all head classes is that they have a
+`predict` function that receives `features` as its first argument.
+
+How to add a new prediction head to an existing meta architecture?
+For example, how can we add a `3d shape` prediction head to Mask RCNN?
+
+We have to take the following steps to add a new prediction head to an
+existing meta arch:
+(a) Add a class for predicting the head. This class should inherit from the
+`Head` class below and have a `predict` function that receives the features
+and predicts the output. The output is always a tf.float32 tensor.
+(b) Add the head to the meta architecture. For example in case of Mask RCNN,
+go to box_predictor_builder and put in the logic for adding the new head to the
+Mask RCNN box predictor.
+(c) Add the logic for computing the loss for the new head.
+(d) Add the necessary metrics for the new head.
+(e) (optional) Add visualization for the new head.
+"""
+from abc import abstractmethod
+
+import tensorflow as tf
+
+
+class Head(object):
+  """Mask RCNN head base class."""
+
+  def __init__(self):
+    """Constructor."""
+    pass
+
+  @abstractmethod
+  def predict(self, features, num_predictions_per_location):
+    """Returns the head's predictions.
+
+    Args:
+      features: A float tensor of features.
+      num_predictions_per_location: Int containing number of predictions per
+        location.
+
+    Returns:
+      A tf.float32 tensor.
+    """
+    pass
+
+
+class KerasHead(tf.keras.Model):
+  """Keras head base class."""
+
+  def call(self, features):
+    """The Keras model call will delegate to the `_predict` method."""
+    return self._predict(features)
+
+  @abstractmethod
+  def _predict(self, features):
+    """Returns the head's predictions.
+
+    Args:
+      features: A float tensor of features.
+
+    Returns:
+      A tf.float32 tensor.
+    """
+    pass
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head.py
new file mode 100644
index 00000000..da311478
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head.py
@@ -0,0 +1,124 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Box Head.
+
+Contains Box prediction head classes for different meta architectures.
+All the box prediction heads have a _predict function that receives the
+`features` as the first argument and returns `box_encodings`.
+"""
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+
+
+class ConvolutionalBoxHead(head.KerasHead):
+  """Convolutional box prediction head."""
+
+  def __init__(self,
+               is_training,
+               box_code_size,
+               kernel_size,
+               num_predictions_per_location,
+               conv_hyperparams,
+               freeze_batchnorm,
+               use_depthwise=True,
+               name=None):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      box_code_size: Size of encoding for each box.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location. Int specifying number of boxes per location.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops.
+      freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+      name: A string name scope to assign to the model. If `None`, Keras
+        will auto-generate one from the class name.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalBoxHead, self).__init__(name=name)
+    self._is_training = is_training
+    self._box_code_size = box_code_size
+    self._kernel_size = kernel_size
+    self._num_predictions_per_location = num_predictions_per_location
+    self._use_depthwise = use_depthwise
+
+    self._box_encoder_layers = []
+
+    if self._use_depthwise:
+      self._box_encoder_layers.append(
+          tf.keras.layers.DepthwiseConv2D(
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              depth_multiplier=1,
+              strides=1,
+              dilation_rate=1,
+              name='BoxEncodingPredictor_depthwise',
+              **conv_hyperparams.params()))
+      self._box_encoder_layers.append(
+          conv_hyperparams.build_batch_norm(
+              training=(is_training and not freeze_batchnorm),
+              name='BoxEncodingPredictor_depthwise_batchnorm'))
+      self._box_encoder_layers.append(
+          conv_hyperparams.build_activation_layer(
+              name='BoxEncodingPredictor_depthwise_activation'))
+      self._box_encoder_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * self._box_code_size, [1, 1],
+              name='BoxEncodingPredictor',
+              **conv_hyperparams.params(use_bias=True)))
+    else:
+      self._box_encoder_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * self._box_code_size,
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              name='BoxEncodingPredictor',
+              **conv_hyperparams.params(use_bias=True)))
+
+  def _predict(self, features):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+
+    Returns:
+      box_encodings: A float tensor of shape
+        [batch_size, num_anchors, q, code_size] representing the location of
+        the objects, where q is 1 or the number of classes.
+    """
+    box_encodings = features
+    for layer in self._box_encoder_layers:
+      box_encodings = layer(box_encodings)
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    box_encodings = tf.reshape(box_encodings,
+                               [batch_size, -1, 1, self._box_code_size])
+    return box_encodings
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head_test.py
new file mode 100644
index 00000000..b4057378
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_box_head_test.py
@@ -0,0 +1,62 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.box_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import keras_box_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class ConvolutionalKerasBoxHeadTest(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+    activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def test_prediction_size_depthwise_false(self):
+    conv_hyperparams = self._build_conv_hyperparams()
+    box_prediction_head = keras_box_head.ConvolutionalBoxHead(
+        is_training=True,
+        box_code_size=4,
+        kernel_size=3,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=False,
+        num_predictions_per_location=1,
+        use_depthwise=False)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    box_encodings = box_prediction_head(image_feature)
+    self.assertAllEqual([64, 323, 1, 4], box_encodings.get_shape().as_list())
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head.py
new file mode 100644
index 00000000..f157254e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head.py
@@ -0,0 +1,148 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Class Head.
+
+Contains Class prediction head classes for different meta architectures.
+All the class prediction heads have a predict function that receives the
+`features` as the first argument and returns class predictions with background.
+"""
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+
+
+class ConvolutionalClassHead(head.KerasHead):
+  """Convolutional class prediction head."""
+
+  def __init__(self,
+               is_training,
+               num_class_slots,
+               use_dropout,
+               dropout_keep_prob,
+               kernel_size,
+               num_predictions_per_location,
+               conv_hyperparams,
+               freeze_batchnorm,
+               class_prediction_bias_init=0.0,
+               use_depthwise=False,
+               name=None):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_class_slots: number of class slots. Note that num_class_slots may or
+        may not include an implicit background category.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location. Int specifying number of boxes per location.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops.
+      freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      class_prediction_bias_init: constant value to initialize bias of the last
+        conv2d layer before class prediction.
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+      name: A string name scope to assign to the model. If `None`, Keras
+        will auto-generate one from the class name.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalClassHead, self).__init__(name=name)
+    self._is_training = is_training
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._kernel_size = kernel_size
+    self._class_prediction_bias_init = class_prediction_bias_init
+    self._use_depthwise = use_depthwise
+    self._num_class_slots = num_class_slots
+
+    self._class_predictor_layers = []
+
+    if self._use_dropout:
+      self._class_predictor_layers.append(
+          # The Dropout layer's `training` parameter for the call method must
+          # be set implicitly by the Keras set_learning_phase. The object
+          # detection training code takes care of this.
+          tf.keras.layers.Dropout(rate=1.0 - self._dropout_keep_prob))
+    if self._use_depthwise:
+      self._class_predictor_layers.append(
+          tf.keras.layers.DepthwiseConv2D(
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              depth_multiplier=1,
+              strides=1,
+              dilation_rate=1,
+              name='ClassPredictor_depthwise',
+              **conv_hyperparams.params()))
+      self._class_predictor_layers.append(
+          conv_hyperparams.build_batch_norm(
+              training=(is_training and not freeze_batchnorm),
+              name='ClassPredictor_depthwise_batchnorm'))
+      self._class_predictor_layers.append(
+          conv_hyperparams.build_activation_layer(
+              name='ClassPredictor_depthwise_activation'))
+      self._class_predictor_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * self._num_class_slots, [1, 1],
+              name='ClassPredictor',
+              **conv_hyperparams.params(use_bias=True)))
+    else:
+      self._class_predictor_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * self._num_class_slots,
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              name='ClassPredictor',
+              bias_initializer=tf.constant_initializer(
+                  self._class_prediction_bias_init),
+              **conv_hyperparams.params(use_bias=True)))
+
+  def _predict(self, features):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+
+    Returns:
+      class_predictions_with_background: A float tensor of shape
+        [batch_size, num_anchors, num_class_slots] representing the class
+        predictions for the proposals.
+    """
+    # Add a slot for the background class.
+    class_predictions_with_background = features
+    for layer in self._class_predictor_layers:
+      class_predictions_with_background = layer(
+          class_predictions_with_background)
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    class_predictions_with_background = tf.reshape(
+        class_predictions_with_background,
+        [batch_size, -1, self._num_class_slots])
+    return class_predictions_with_background
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head_test.py
new file mode 100644
index 00000000..125fcdf8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_class_head_test.py
@@ -0,0 +1,65 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.class_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import keras_class_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class ConvolutionalKerasClassPredictorTest(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+    activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def test_prediction_size_depthwise_false(self):
+    conv_hyperparams = self._build_conv_hyperparams()
+    class_prediction_head = keras_class_head.ConvolutionalClassHead(
+        is_training=True,
+        num_class_slots=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=False,
+        num_predictions_per_location=1,
+        use_depthwise=False)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    class_predictions = class_prediction_head(image_feature,)
+    self.assertAllEqual([64, 323, 20],
+                        class_predictions.get_shape().as_list())
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head.py
new file mode 100644
index 00000000..fa4b1d1e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head.py
@@ -0,0 +1,158 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Keras Mask Heads.
+
+Contains Mask prediction head classes for different meta architectures.
+All the mask prediction heads have a predict function that receives the
+`features` as the first argument and returns `mask_predictions`.
+"""
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+
+
+class ConvolutionalMaskHead(head.KerasHead):
+  """Convolutional class prediction head."""
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               use_dropout,
+               dropout_keep_prob,
+               kernel_size,
+               num_predictions_per_location,
+               conv_hyperparams,
+               freeze_batchnorm,
+               use_depthwise=False,
+               mask_height=7,
+               mask_width=7,
+               masks_are_class_agnostic=False,
+               name=None):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: Number of classes.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location. Int specifying number of boxes per location.
+      conv_hyperparams: A `hyperparams_builder.KerasLayerHyperparams` object
+        containing hyperparameters for convolution ops.
+      freeze_batchnorm: Bool. Whether to freeze batch norm parameters during
+        training or not. When training with a small batch size (e.g. 1), it is
+        desirable to freeze batch norm update and use pretrained batch norm
+        params.
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+      mask_height: Desired output mask height. The default value is 7.
+      mask_width: Desired output mask width. The default value is 7.
+      masks_are_class_agnostic: Boolean determining if the mask-head is
+        class-agnostic or not.
+      name: A string name scope to assign to the model. If `None`, Keras
+        will auto-generate one from the class name.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalMaskHead, self).__init__(name=name)
+    self._is_training = is_training
+    self._num_classes = num_classes
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._kernel_size = kernel_size
+    self._num_predictions_per_location = num_predictions_per_location
+    self._use_depthwise = use_depthwise
+    self._mask_height = mask_height
+    self._mask_width = mask_width
+    self._masks_are_class_agnostic = masks_are_class_agnostic
+
+    self._mask_predictor_layers = []
+
+    # Add a slot for the background class.
+    if self._masks_are_class_agnostic:
+      self._num_masks = 1
+    else:
+      self._num_masks = self._num_classes
+
+    num_mask_channels = self._num_masks * self._mask_height * self._mask_width
+
+    if self._use_dropout:
+      self._mask_predictor_layers.append(
+          # The Dropout layer's `training` parameter for the call method must
+          # be set implicitly by the Keras set_learning_phase. The object
+          # detection training code takes care of this.
+          tf.keras.layers.Dropout(rate=1.0 - self._dropout_keep_prob))
+    if self._use_depthwise:
+      self._mask_predictor_layers.append(
+          tf.keras.layers.DepthwiseConv2D(
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              depth_multiplier=1,
+              strides=1,
+              dilation_rate=1,
+              name='MaskPredictor_depthwise',
+              **conv_hyperparams.params()))
+      self._mask_predictor_layers.append(
+          conv_hyperparams.build_batch_norm(
+              training=(is_training and not freeze_batchnorm),
+              name='MaskPredictor_depthwise_batchnorm'))
+      self._mask_predictor_layers.append(
+          conv_hyperparams.build_activation_layer(
+              name='MaskPredictor_depthwise_activation'))
+      self._mask_predictor_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * num_mask_channels, [1, 1],
+              name='MaskPredictor',
+              **conv_hyperparams.params(use_bias=True)))
+    else:
+      self._mask_predictor_layers.append(
+          tf.keras.layers.Conv2D(
+              num_predictions_per_location * num_mask_channels,
+              [self._kernel_size, self._kernel_size],
+              padding='SAME',
+              name='MaskPredictor',
+              **conv_hyperparams.params(use_bias=True)))
+
+  def _predict(self, features):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+
+    Returns:
+      mask_predictions: A float tensors of shape
+        [batch_size, num_anchors, num_masks, mask_height, mask_width]
+        representing the mask predictions for the proposals.
+    """
+    mask_predictions = features
+    for layer in self._mask_predictor_layers:
+      mask_predictions = layer(mask_predictions)
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    mask_predictions = tf.reshape(
+        mask_predictions,
+        [batch_size, -1, self._num_masks, self._mask_height, self._mask_width])
+    return mask_predictions
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head_test.py
new file mode 100644
index 00000000..67274d50
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keras_mask_head_test.py
@@ -0,0 +1,90 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.mask_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import keras_mask_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class ConvolutionalMaskPredictorTest(test_case.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+    activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def test_prediction_size_use_depthwise_false(self):
+    conv_hyperparams = self._build_conv_hyperparams()
+    mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
+        is_training=True,
+        num_classes=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=False,
+        num_predictions_per_location=1,
+        use_depthwise=False,
+        mask_height=7,
+        mask_width=7)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head(image_feature)
+    self.assertAllEqual([64, 323, 20, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+  def test_class_agnostic_prediction_size_use_depthwise_false(self):
+    conv_hyperparams = self._build_conv_hyperparams()
+    mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
+        is_training=True,
+        num_classes=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3,
+        conv_hyperparams=conv_hyperparams,
+        freeze_batchnorm=False,
+        num_predictions_per_location=1,
+        use_depthwise=False,
+        mask_height=7,
+        mask_width=7,
+        masks_are_class_agnostic=True)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head(image_feature)
+    self.assertAllEqual([64, 323, 1, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+  # TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head.py
new file mode 100644
index 00000000..f02d92c7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head.py
@@ -0,0 +1,109 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Keypoint Head.
+
+Contains Keypoint prediction head classes for different meta architectures.
+All the keypoint prediction heads have a predict function that receives the
+`features` as the first argument and returns `keypoint_predictions`.
+Keypoints could be used to represent the human body joint locations as in
+Mask RCNN paper. Or they could be used to represent different part locations of
+objects.
+"""
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+slim = tf.contrib.slim
+
+
+class MaskRCNNKeypointHead(head.Head):
+  """Mask RCNN keypoint prediction head.
+
+  Please refer to Mask RCNN paper:
+  https://arxiv.org/abs/1703.06870
+  """
+
+  def __init__(self,
+               num_keypoints=17,
+               conv_hyperparams_fn=None,
+               keypoint_heatmap_height=56,
+               keypoint_heatmap_width=56,
+               keypoint_prediction_num_conv_layers=8,
+               keypoint_prediction_conv_depth=512):
+    """Constructor.
+
+    Args:
+      num_keypoints: (int scalar) number of keypoints.
+      conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for convolution ops.
+      keypoint_heatmap_height: Desired output mask height. The default value
+        is 14.
+      keypoint_heatmap_width: Desired output mask width. The default value
+        is 14.
+      keypoint_prediction_num_conv_layers: Number of convolution layers applied
+        to the image_features in mask prediction branch.
+      keypoint_prediction_conv_depth: The depth for the first conv2d_transpose
+        op applied to the image_features in the mask prediction branch. If set
+        to 0, the depth of the convolution layers will be automatically chosen
+        based on the number of object classes and the number of channels in the
+        image features.
+    """
+    super(MaskRCNNKeypointHead, self).__init__()
+    self._num_keypoints = num_keypoints
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._keypoint_heatmap_height = keypoint_heatmap_height
+    self._keypoint_heatmap_width = keypoint_heatmap_width
+    self._keypoint_prediction_num_conv_layers = (
+        keypoint_prediction_num_conv_layers)
+    self._keypoint_prediction_conv_depth = keypoint_prediction_conv_depth
+
+  def predict(self, features, num_predictions_per_location=1):
+    """Performs keypoint prediction.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width,
+        channels] containing features for a batch of images.
+      num_predictions_per_location: Int containing number of predictions per
+        location.
+
+    Returns:
+      instance_masks: A float tensor of shape
+          [batch_size, 1, num_keypoints, heatmap_height, heatmap_width].
+
+    Raises:
+      ValueError: If num_predictions_per_location is not 1.
+    """
+    if num_predictions_per_location != 1:
+      raise ValueError('Only num_predictions_per_location=1 is supported')
+    with slim.arg_scope(self._conv_hyperparams_fn()):
+      net = slim.conv2d(
+          features,
+          self._keypoint_prediction_conv_depth, [3, 3],
+          scope='conv_1')
+      for i in range(1, self._keypoint_prediction_num_conv_layers):
+        net = slim.conv2d(
+            net,
+            self._keypoint_prediction_conv_depth, [3, 3],
+            scope='conv_%d' % (i + 1))
+      net = slim.conv2d_transpose(
+          net, self._num_keypoints, [2, 2], scope='deconv1')
+      heatmaps_mask = tf.image.resize_bilinear(
+          net, [self._keypoint_heatmap_height, self._keypoint_heatmap_width],
+          align_corners=True,
+          name='upsample')
+      return tf.expand_dims(
+          tf.transpose(heatmaps_mask, perm=[0, 3, 1, 2]),
+          axis=1,
+          name='KeypointPredictor')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head_test.py
new file mode 100644
index 00000000..626d59c4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/keypoint_head_test.py
@@ -0,0 +1,57 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.keypoint_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import keypoint_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class MaskRCNNKeypointHeadTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        op_type=hyperparams_pb2.Hyperparams.FC):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    keypoint_prediction_head = keypoint_head.MaskRCNNKeypointHead(
+        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams())
+    roi_pooled_features = tf.random_uniform(
+        [64, 14, 14, 1024], minval=-2.0, maxval=2.0, dtype=tf.float32)
+    prediction = keypoint_prediction_head.predict(
+        features=roi_pooled_features, num_predictions_per_location=1)
+    self.assertAllEqual([64, 1, 17, 56, 56], prediction.get_shape().as_list())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head.py
new file mode 100644
index 00000000..c97d08eb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head.py
@@ -0,0 +1,355 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Mask Head.
+
+Contains Mask prediction head classes for different meta architectures.
+All the mask prediction heads have a predict function that receives the
+`features` as the first argument and returns `mask_predictions`.
+"""
+import math
+import tensorflow as tf
+
+from object_detection.predictors.heads import head
+from object_detection.utils import ops
+
+slim = tf.contrib.slim
+
+
+class MaskRCNNMaskHead(head.Head):
+  """Mask RCNN mask prediction head.
+
+  Please refer to Mask RCNN paper:
+  https://arxiv.org/abs/1703.06870
+  """
+
+  def __init__(self,
+               num_classes,
+               conv_hyperparams_fn=None,
+               mask_height=14,
+               mask_width=14,
+               mask_prediction_num_conv_layers=2,
+               mask_prediction_conv_depth=256,
+               masks_are_class_agnostic=False,
+               convolve_then_upsample=False):
+    """Constructor.
+
+    Args:
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      conv_hyperparams_fn: A function to generate tf-slim arg_scope with
+        hyperparameters for convolution ops.
+      mask_height: Desired output mask height. The default value is 14.
+      mask_width: Desired output mask width. The default value is 14.
+      mask_prediction_num_conv_layers: Number of convolution layers applied to
+        the image_features in mask prediction branch.
+      mask_prediction_conv_depth: The depth for the first conv2d_transpose op
+        applied to the image_features in the mask prediction branch. If set
+        to 0, the depth of the convolution layers will be automatically chosen
+        based on the number of object classes and the number of channels in the
+        image features.
+      masks_are_class_agnostic: Boolean determining if the mask-head is
+        class-agnostic or not.
+      convolve_then_upsample: Whether to apply convolutions on mask features
+        before upsampling using nearest neighbor resizing. Otherwise, mask
+        features are resized to [`mask_height`, `mask_width`] using bilinear
+        resizing before applying convolutions.
+
+    Raises:
+      ValueError: conv_hyperparams_fn is None.
+    """
+    super(MaskRCNNMaskHead, self).__init__()
+    self._num_classes = num_classes
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._mask_height = mask_height
+    self._mask_width = mask_width
+    self._mask_prediction_num_conv_layers = mask_prediction_num_conv_layers
+    self._mask_prediction_conv_depth = mask_prediction_conv_depth
+    self._masks_are_class_agnostic = masks_are_class_agnostic
+    self._convolve_then_upsample = convolve_then_upsample
+    if conv_hyperparams_fn is None:
+      raise ValueError('conv_hyperparams_fn is None.')
+
+  def _get_mask_predictor_conv_depth(self,
+                                     num_feature_channels,
+                                     num_classes,
+                                     class_weight=3.0,
+                                     feature_weight=2.0):
+    """Computes the depth of the mask predictor convolutions.
+
+    Computes the depth of the mask predictor convolutions given feature channels
+    and number of classes by performing a weighted average of the two in
+    log space to compute the number of convolution channels. The weights that
+    are used for computing the weighted average do not need to sum to 1.
+
+    Args:
+      num_feature_channels: An integer containing the number of feature
+        channels.
+      num_classes: An integer containing the number of classes.
+      class_weight: Class weight used in computing the weighted average.
+      feature_weight: Feature weight used in computing the weighted average.
+
+    Returns:
+      An integer containing the number of convolution channels used by mask
+        predictor.
+    """
+    num_feature_channels_log = math.log(float(num_feature_channels), 2.0)
+    num_classes_log = math.log(float(num_classes), 2.0)
+    weighted_num_feature_channels_log = (
+        num_feature_channels_log * feature_weight)
+    weighted_num_classes_log = num_classes_log * class_weight
+    total_weight = feature_weight + class_weight
+    num_conv_channels_log = round(
+        (weighted_num_feature_channels_log + weighted_num_classes_log) /
+        total_weight)
+    return int(math.pow(2.0, num_conv_channels_log))
+
+  def predict(self, features, num_predictions_per_location=1):
+    """Performs mask prediction.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing features for a batch of images.
+      num_predictions_per_location: Int containing number of predictions per
+        location.
+
+    Returns:
+      instance_masks: A float tensor of shape
+          [batch_size, 1, num_classes, mask_height, mask_width].
+
+    Raises:
+      ValueError: If num_predictions_per_location is not 1.
+    """
+    if num_predictions_per_location != 1:
+      raise ValueError('Only num_predictions_per_location=1 is supported')
+    num_conv_channels = self._mask_prediction_conv_depth
+    if num_conv_channels == 0:
+      num_feature_channels = features.get_shape().as_list()[3]
+      num_conv_channels = self._get_mask_predictor_conv_depth(
+          num_feature_channels, self._num_classes)
+    with slim.arg_scope(self._conv_hyperparams_fn()):
+      if not self._convolve_then_upsample:
+        features = tf.image.resize_bilinear(
+            features, [self._mask_height, self._mask_width],
+            align_corners=True)
+      for _ in range(self._mask_prediction_num_conv_layers - 1):
+        features = slim.conv2d(
+            features,
+            num_outputs=num_conv_channels,
+            kernel_size=[3, 3])
+      if self._convolve_then_upsample:
+        # Replace Transposed Convolution with a Nearest Neighbor upsampling step
+        # followed by 3x3 convolution.
+        height_scale = self._mask_height / features.shape[1].value
+        width_scale = self._mask_width / features.shape[2].value
+        features = ops.nearest_neighbor_upsampling(
+            features, height_scale=height_scale, width_scale=width_scale)
+        features = slim.conv2d(
+            features,
+            num_outputs=num_conv_channels,
+            kernel_size=[3, 3])
+
+      num_masks = 1 if self._masks_are_class_agnostic else self._num_classes
+      mask_predictions = slim.conv2d(
+          features,
+          num_outputs=num_masks,
+          activation_fn=None,
+          normalizer_fn=None,
+          kernel_size=[3, 3])
+      return tf.expand_dims(
+          tf.transpose(mask_predictions, perm=[0, 3, 1, 2]),
+          axis=1,
+          name='MaskPredictor')
+
+
+class ConvolutionalMaskHead(head.Head):
+  """Convolutional class prediction head."""
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               use_dropout,
+               dropout_keep_prob,
+               kernel_size,
+               use_depthwise=False,
+               mask_height=7,
+               mask_width=7,
+               masks_are_class_agnostic=False):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: Number of classes.
+      use_dropout: Option to use dropout or not.  Note that a single dropout
+        op is applied here prior to both box and class predictions, which stands
+        in contrast to the ConvolutionalBoxPredictor below.
+      dropout_keep_prob: Keep probability for dropout.
+        This is only used if use_dropout is True.
+      kernel_size: Size of final convolution kernel.  If the
+        spatial resolution of the feature map is smaller than the kernel size,
+        then the kernel size is automatically set to be
+        min(feature_width, feature_height).
+      use_depthwise: Whether to use depthwise convolutions for prediction
+        steps. Default is False.
+      mask_height: Desired output mask height. The default value is 7.
+      mask_width: Desired output mask width. The default value is 7.
+      masks_are_class_agnostic: Boolean determining if the mask-head is
+        class-agnostic or not.
+
+    Raises:
+      ValueError: if min_depth > max_depth.
+    """
+    super(ConvolutionalMaskHead, self).__init__()
+    self._is_training = is_training
+    self._num_classes = num_classes
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._kernel_size = kernel_size
+    self._use_depthwise = use_depthwise
+    self._mask_height = mask_height
+    self._mask_width = mask_width
+    self._masks_are_class_agnostic = masks_are_class_agnostic
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location.
+
+    Returns:
+      mask_predictions: A float tensors of shape
+        [batch_size, num_anchors, num_masks, mask_height, mask_width]
+        representing the mask predictions for the proposals.
+    """
+    image_feature = features
+    # Add a slot for the background class.
+    if self._masks_are_class_agnostic:
+      num_masks = 1
+    else:
+      num_masks = self._num_classes
+    num_mask_channels = num_masks * self._mask_height * self._mask_width
+    net = image_feature
+    if self._use_dropout:
+      net = slim.dropout(net, keep_prob=self._dropout_keep_prob)
+    if self._use_depthwise:
+      mask_predictions = slim.separable_conv2d(
+          net, None, [self._kernel_size, self._kernel_size],
+          padding='SAME', depth_multiplier=1, stride=1,
+          rate=1, scope='MaskPredictor_depthwise')
+      mask_predictions = slim.conv2d(
+          mask_predictions,
+          num_predictions_per_location * num_mask_channels,
+          [1, 1],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='MaskPredictor')
+    else:
+      mask_predictions = slim.conv2d(
+          net,
+          num_predictions_per_location * num_mask_channels,
+          [self._kernel_size, self._kernel_size],
+          activation_fn=None,
+          normalizer_fn=None,
+          normalizer_params=None,
+          scope='MaskPredictor')
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    mask_predictions = tf.reshape(
+        mask_predictions,
+        [batch_size, -1, num_masks, self._mask_height, self._mask_width])
+    return mask_predictions
+
+
+# TODO(alirezafathi): See if possible to unify Weight Shared with regular
+# convolutional mask head.
+class WeightSharedConvolutionalMaskHead(head.Head):
+  """Weight shared convolutional mask prediction head."""
+
+  def __init__(self,
+               num_classes,
+               kernel_size=3,
+               use_dropout=False,
+               dropout_keep_prob=0.8,
+               mask_height=7,
+               mask_width=7,
+               masks_are_class_agnostic=False):
+    """Constructor.
+
+    Args:
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      kernel_size: Size of final convolution kernel.
+      use_dropout: Whether to apply dropout to class prediction head.
+      dropout_keep_prob: Probability of keeping activiations.
+      mask_height: Desired output mask height. The default value is 7.
+      mask_width: Desired output mask width. The default value is 7.
+      masks_are_class_agnostic: Boolean determining if the mask-head is
+        class-agnostic or not.
+    """
+    super(WeightSharedConvolutionalMaskHead, self).__init__()
+    self._num_classes = num_classes
+    self._kernel_size = kernel_size
+    self._use_dropout = use_dropout
+    self._dropout_keep_prob = dropout_keep_prob
+    self._mask_height = mask_height
+    self._mask_width = mask_width
+    self._masks_are_class_agnostic = masks_are_class_agnostic
+
+  def predict(self, features, num_predictions_per_location):
+    """Predicts boxes.
+
+    Args:
+      features: A float tensor of shape [batch_size, height, width, channels]
+        containing image features.
+      num_predictions_per_location: Number of box predictions to be made per
+        spatial location.
+
+    Returns:
+      mask_predictions: A tensor of shape
+        [batch_size, num_anchors, num_classes, mask_height, mask_width]
+        representing the mask predictions for the proposals.
+    """
+    mask_predictions_net = features
+    if self._masks_are_class_agnostic:
+      num_masks = 1
+    else:
+      num_masks = self._num_classes
+    num_mask_channels = num_masks * self._mask_height * self._mask_width
+    if self._use_dropout:
+      mask_predictions_net = slim.dropout(
+          mask_predictions_net, keep_prob=self._dropout_keep_prob)
+    mask_predictions = slim.conv2d(
+        mask_predictions_net,
+        num_predictions_per_location * num_mask_channels,
+        [self._kernel_size, self._kernel_size],
+        activation_fn=None, stride=1, padding='SAME',
+        normalizer_fn=None,
+        scope='MaskPredictor')
+    batch_size = features.get_shape().as_list()[0]
+    if batch_size is None:
+      batch_size = tf.shape(features)[0]
+    mask_predictions = tf.reshape(
+        mask_predictions,
+        [batch_size, -1, num_masks, self._mask_height, self._mask_width])
+    return mask_predictions
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head_test.py
new file mode 100644
index 00000000..ae46d6ad
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads/mask_head_test.py
@@ -0,0 +1,185 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.heads.mask_head."""
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors.heads import mask_head
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class MaskRCNNMaskHeadTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        op_type=hyperparams_pb2.Hyperparams.FC):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    mask_prediction_head = mask_head.MaskRCNNMaskHead(
+        num_classes=20,
+        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+        mask_height=14,
+        mask_width=14,
+        mask_prediction_num_conv_layers=2,
+        mask_prediction_conv_depth=256,
+        masks_are_class_agnostic=False)
+    roi_pooled_features = tf.random_uniform(
+        [64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    prediction = mask_prediction_head.predict(
+        features=roi_pooled_features, num_predictions_per_location=1)
+    self.assertAllEqual([64, 1, 20, 14, 14], prediction.get_shape().as_list())
+
+  def test_prediction_size_with_convolve_then_upsample(self):
+    mask_prediction_head = mask_head.MaskRCNNMaskHead(
+        num_classes=20,
+        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+        mask_height=28,
+        mask_width=28,
+        mask_prediction_num_conv_layers=2,
+        mask_prediction_conv_depth=256,
+        masks_are_class_agnostic=True,
+        convolve_then_upsample=True)
+    roi_pooled_features = tf.random_uniform(
+        [64, 14, 14, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    prediction = mask_prediction_head.predict(
+        features=roi_pooled_features, num_predictions_per_location=1)
+    self.assertAllEqual([64, 1, 1, 28, 28], prediction.get_shape().as_list())
+
+
+class ConvolutionalMaskPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    mask_prediction_head = mask_head.ConvolutionalMaskHead(
+        is_training=True,
+        num_classes=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3,
+        mask_height=7,
+        mask_width=7)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 20, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+  def test_class_agnostic_prediction_size(self):
+    mask_prediction_head = mask_head.ConvolutionalMaskHead(
+        is_training=True,
+        num_classes=20,
+        use_dropout=True,
+        dropout_keep_prob=0.5,
+        kernel_size=3,
+        mask_height=7,
+        mask_width=7,
+        masks_are_class_agnostic=True)
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 1, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+
+class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(
+      self, op_type=hyperparams_pb2.Hyperparams.CONV):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_prediction_size(self):
+    mask_prediction_head = (
+        mask_head.WeightSharedConvolutionalMaskHead(
+            num_classes=20,
+            mask_height=7,
+            mask_width=7))
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 20, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+  def test_class_agnostic_prediction_size(self):
+    mask_prediction_head = (
+        mask_head.WeightSharedConvolutionalMaskHead(
+            num_classes=20,
+            mask_height=7,
+            mask_width=7,
+            masks_are_class_agnostic=True))
+    image_feature = tf.random_uniform(
+        [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
+    mask_predictions = mask_prediction_head.predict(
+        features=image_feature,
+        num_predictions_per_location=1)
+    self.assertAllEqual([64, 323, 1, 7, 7],
+                        mask_predictions.get_shape().as_list())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor.py
new file mode 100644
index 00000000..2fd1b692
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor.py
@@ -0,0 +1,144 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Mask R-CNN Box Predictor."""
+import tensorflow as tf
+
+from object_detection.core import box_predictor
+
+slim = tf.contrib.slim
+
+BOX_ENCODINGS = box_predictor.BOX_ENCODINGS
+CLASS_PREDICTIONS_WITH_BACKGROUND = (
+    box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND)
+MASK_PREDICTIONS = box_predictor.MASK_PREDICTIONS
+
+
+class MaskRCNNBoxPredictor(box_predictor.BoxPredictor):
+  """Mask R-CNN Box Predictor.
+
+  See Mask R-CNN: He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017).
+  Mask R-CNN. arXiv preprint arXiv:1703.06870.
+
+  This is used for the second stage of the Mask R-CNN detector where proposals
+  cropped from an image are arranged along the batch dimension of the input
+  image_features tensor. Notice that locations are *not* shared across classes,
+  thus for each anchor, a separate prediction is made for each class.
+
+  In addition to predicting boxes and classes, optionally this class allows
+  predicting masks and/or keypoints inside detection boxes.
+
+  Currently this box predictor makes per-class predictions; that is, each
+  anchor makes a separate box prediction for each class.
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               box_prediction_head,
+               class_prediction_head,
+               third_stage_heads):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      box_prediction_head: The head that predicts the boxes in second stage.
+      class_prediction_head: The head that predicts the classes in second stage.
+      third_stage_heads: A dictionary mapping head names to mask rcnn head
+        classes.
+    """
+    super(MaskRCNNBoxPredictor, self).__init__(is_training, num_classes)
+    self._box_prediction_head = box_prediction_head
+    self._class_prediction_head = class_prediction_head
+    self._third_stage_heads = third_stage_heads
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def get_second_stage_prediction_heads(self):
+    return BOX_ENCODINGS, CLASS_PREDICTIONS_WITH_BACKGROUND
+
+  def get_third_stage_prediction_heads(self):
+    return sorted(self._third_stage_heads.keys())
+
+  def _predict(self,
+               image_features,
+               num_predictions_per_location,
+               prediction_stage=2):
+    """Optionally computes encoded object locations, confidences, and masks.
+
+    Predicts the heads belonging to the given prediction stage.
+
+    Args:
+      image_features: A list of float tensors of shape
+        [batch_size, height_i, width_i, channels_i] containing roi pooled
+        features for each image. The length of the list should be 1 otherwise
+        a ValueError will be raised.
+      num_predictions_per_location: A list of integers representing the number
+        of box predictions to be made per spatial location for each feature map.
+        Currently, this must be set to [1], or an error will be raised.
+      prediction_stage: Prediction stage. Acceptable values are 2 and 3.
+
+    Returns:
+      A dictionary containing the predicted tensors that are listed in
+      self._prediction_heads. A subset of the following keys will exist in the
+      dictionary:
+        BOX_ENCODINGS: A float tensor of shape
+          [batch_size, 1, num_classes, code_size] representing the
+          location of the objects.
+        CLASS_PREDICTIONS_WITH_BACKGROUND: A float tensor of shape
+          [batch_size, 1, num_classes + 1] representing the class
+          predictions for the proposals.
+        MASK_PREDICTIONS: A float tensor of shape
+          [batch_size, 1, num_classes, image_height, image_width]
+
+    Raises:
+      ValueError: If num_predictions_per_location is not 1 or if
+        len(image_features) is not 1.
+      ValueError: if prediction_stage is not 2 or 3.
+    """
+    if (len(num_predictions_per_location) != 1 or
+        num_predictions_per_location[0] != 1):
+      raise ValueError('Currently FullyConnectedBoxPredictor only supports '
+                       'predicting a single box per class per location.')
+    if len(image_features) != 1:
+      raise ValueError('length of `image_features` must be 1. Found {}'.format(
+          len(image_features)))
+    image_feature = image_features[0]
+    predictions_dict = {}
+
+    if prediction_stage == 2:
+      predictions_dict[BOX_ENCODINGS] = self._box_prediction_head.predict(
+          features=image_feature,
+          num_predictions_per_location=num_predictions_per_location[0])
+      predictions_dict[CLASS_PREDICTIONS_WITH_BACKGROUND] = (
+          self._class_prediction_head.predict(
+              features=image_feature,
+              num_predictions_per_location=num_predictions_per_location[0]))
+    elif prediction_stage == 3:
+      for prediction_head in self.get_third_stage_prediction_heads():
+        head_object = self._third_stage_heads[prediction_head]
+        predictions_dict[prediction_head] = head_object.predict(
+            features=image_feature,
+            num_predictions_per_location=num_predictions_per_location[0])
+    else:
+      raise ValueError('prediction_stage should be either 2 or 3.')
+
+    return predictions_dict
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor_test.py
new file mode 100644
index 00000000..77042b5b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/mask_rcnn_box_predictor_test.py
@@ -0,0 +1,151 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.mask_rcnn_box_predictor."""
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import box_predictor_builder
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors import mask_rcnn_box_predictor as box_predictor
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class MaskRCNNBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_hyperparams(self,
+                                        op_type=hyperparams_pb2.Hyperparams.FC):
+    hyperparams = hyperparams_pb2.Hyperparams()
+    hyperparams_text_proto = """
+      activation: NONE
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(hyperparams_text_proto, hyperparams)
+    hyperparams.op = op_type
+    return hyperparams_builder.build(hyperparams, is_training=True)
+
+  def test_get_boxes_with_five_classes(self):
+    def graph_fn(image_features):
+      mask_box_predictor = box_predictor_builder.build_mask_rcnn_box_predictor(
+          is_training=False,
+          num_classes=5,
+          fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+          use_dropout=False,
+          dropout_keep_prob=0.5,
+          box_code_size=4,
+      )
+      box_predictions = mask_box_predictor.predict(
+          [image_features],
+          num_predictions_per_location=[1],
+          scope='BoxPredictor',
+          prediction_stage=2)
+      return (box_predictions[box_predictor.BOX_ENCODINGS],
+              box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND])
+    image_features = np.random.rand(2, 7, 7, 3).astype(np.float32)
+    (box_encodings,
+     class_predictions_with_background) = self.execute(graph_fn,
+                                                       [image_features])
+    self.assertAllEqual(box_encodings.shape, [2, 1, 5, 4])
+    self.assertAllEqual(class_predictions_with_background.shape, [2, 1, 6])
+
+  def test_get_boxes_with_five_classes_share_box_across_classes(self):
+    def graph_fn(image_features):
+      mask_box_predictor = box_predictor_builder.build_mask_rcnn_box_predictor(
+          is_training=False,
+          num_classes=5,
+          fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+          use_dropout=False,
+          dropout_keep_prob=0.5,
+          box_code_size=4,
+          share_box_across_classes=True
+      )
+      box_predictions = mask_box_predictor.predict(
+          [image_features],
+          num_predictions_per_location=[1],
+          scope='BoxPredictor',
+          prediction_stage=2)
+      return (box_predictions[box_predictor.BOX_ENCODINGS],
+              box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND])
+    image_features = np.random.rand(2, 7, 7, 3).astype(np.float32)
+    (box_encodings,
+     class_predictions_with_background) = self.execute(graph_fn,
+                                                       [image_features])
+    self.assertAllEqual(box_encodings.shape, [2, 1, 1, 4])
+    self.assertAllEqual(class_predictions_with_background.shape, [2, 1, 6])
+
+  def test_value_error_on_predict_instance_masks_with_no_conv_hyperparms(self):
+    with self.assertRaises(ValueError):
+      box_predictor_builder.build_mask_rcnn_box_predictor(
+          is_training=False,
+          num_classes=5,
+          fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+          use_dropout=False,
+          dropout_keep_prob=0.5,
+          box_code_size=4,
+          predict_instance_masks=True)
+
+  def test_get_instance_masks(self):
+    def graph_fn(image_features):
+      mask_box_predictor = box_predictor_builder.build_mask_rcnn_box_predictor(
+          is_training=False,
+          num_classes=5,
+          fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+          use_dropout=False,
+          dropout_keep_prob=0.5,
+          box_code_size=4,
+          conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(
+              op_type=hyperparams_pb2.Hyperparams.CONV),
+          predict_instance_masks=True)
+      box_predictions = mask_box_predictor.predict(
+          [image_features],
+          num_predictions_per_location=[1],
+          scope='BoxPredictor',
+          prediction_stage=3)
+      return (box_predictions[box_predictor.MASK_PREDICTIONS],)
+    image_features = np.random.rand(2, 7, 7, 3).astype(np.float32)
+    mask_predictions = self.execute(graph_fn, [image_features])
+    self.assertAllEqual(mask_predictions.shape, [2, 1, 5, 14, 14])
+
+  def test_do_not_return_instance_masks_without_request(self):
+    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
+    mask_box_predictor = box_predictor_builder.build_mask_rcnn_box_predictor(
+        is_training=False,
+        num_classes=5,
+        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
+        use_dropout=False,
+        dropout_keep_prob=0.5,
+        box_code_size=4)
+    box_predictions = mask_box_predictor.predict(
+        [image_features],
+        num_predictions_per_location=[1],
+        scope='BoxPredictor',
+        prediction_stage=2)
+    self.assertEqual(len(box_predictions), 2)
+    self.assertTrue(box_predictor.BOX_ENCODINGS in box_predictions)
+    self.assertTrue(box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND
+                    in box_predictions)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor.py
new file mode 100644
index 00000000..d16de044
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor.py
@@ -0,0 +1,159 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""RFCN Box Predictor."""
+import tensorflow as tf
+from object_detection.core import box_predictor
+from object_detection.utils import ops
+
+slim = tf.contrib.slim
+
+BOX_ENCODINGS = box_predictor.BOX_ENCODINGS
+CLASS_PREDICTIONS_WITH_BACKGROUND = (
+    box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND)
+MASK_PREDICTIONS = box_predictor.MASK_PREDICTIONS
+
+
+class RfcnBoxPredictor(box_predictor.BoxPredictor):
+  """RFCN Box Predictor.
+
+  Applies a position sensitive ROI pooling on position sensitive feature maps to
+  predict classes and refined locations. See https://arxiv.org/abs/1605.06409
+  for details.
+
+  This is used for the second stage of the RFCN meta architecture. Notice that
+  locations are *not* shared across classes, thus for each anchor, a separate
+  prediction is made for each class.
+  """
+
+  def __init__(self,
+               is_training,
+               num_classes,
+               conv_hyperparams_fn,
+               num_spatial_bins,
+               depth,
+               crop_size,
+               box_code_size):
+    """Constructor.
+
+    Args:
+      is_training: Indicates whether the BoxPredictor is in training mode.
+      num_classes: number of classes.  Note that num_classes *does not*
+        include the background category, so if groundtruth labels take values
+        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
+        assigned classification targets can range from {0,... K}).
+      conv_hyperparams_fn: A function to construct tf-slim arg_scope with
+        hyperparameters for convolutional layers.
+      num_spatial_bins: A list of two integers `[spatial_bins_y,
+        spatial_bins_x]`.
+      depth: Target depth to reduce the input feature maps to.
+      crop_size: A list of two integers `[crop_height, crop_width]`.
+      box_code_size: Size of encoding for each box.
+    """
+    super(RfcnBoxPredictor, self).__init__(is_training, num_classes)
+    self._conv_hyperparams_fn = conv_hyperparams_fn
+    self._num_spatial_bins = num_spatial_bins
+    self._depth = depth
+    self._crop_size = crop_size
+    self._box_code_size = box_code_size
+
+  @property
+  def num_classes(self):
+    return self._num_classes
+
+  def _predict(self, image_features, num_predictions_per_location,
+               proposal_boxes):
+    """Computes encoded object locations and corresponding confidences.
+
+    Args:
+      image_features: A list of float tensors of shape [batch_size, height_i,
+      width_i, channels_i] containing features for a batch of images.
+      num_predictions_per_location: A list of integers representing the number
+        of box predictions to be made per spatial location for each feature map.
+        Currently, this must be set to [1], or an error will be raised.
+      proposal_boxes: A float tensor of shape [batch_size, num_proposals,
+        box_code_size].
+
+    Returns:
+      box_encodings: A list of float tensors of shape
+        [batch_size, num_anchors_i, q, code_size] representing the location of
+        the objects, where q is 1 or the number of classes. Each entry in the
+        list corresponds to a feature map in the input `image_features` list.
+      class_predictions_with_background: A list of float tensors of shape
+        [batch_size, num_anchors_i, num_classes + 1] representing the class
+        predictions for the proposals. Each entry in the list corresponds to a
+        feature map in the input `image_features` list.
+
+    Raises:
+      ValueError: if num_predictions_per_location is not 1 or if
+        len(image_features) is not 1.
+    """
+    if (len(num_predictions_per_location) != 1 or
+        num_predictions_per_location[0] != 1):
+      raise ValueError('Currently RfcnBoxPredictor only supports '
+                       'predicting a single box per class per location.')
+    if len(image_features) != 1:
+      raise ValueError('length of `image_features` must be 1. Found {}'.
+                       format(len(image_features)))
+    image_feature = image_features[0]
+    num_predictions_per_location = num_predictions_per_location[0]
+    batch_size = tf.shape(proposal_boxes)[0]
+    num_boxes = tf.shape(proposal_boxes)[1]
+    net = image_feature
+    with slim.arg_scope(self._conv_hyperparams_fn()):
+      net = slim.conv2d(net, self._depth, [1, 1], scope='reduce_depth')
+      # Location predictions.
+      location_feature_map_depth = (self._num_spatial_bins[0] *
+                                    self._num_spatial_bins[1] *
+                                    self.num_classes *
+                                    self._box_code_size)
+      location_feature_map = slim.conv2d(net, location_feature_map_depth,
+                                         [1, 1], activation_fn=None,
+                                         scope='refined_locations')
+      box_encodings = ops.batch_position_sensitive_crop_regions(
+          location_feature_map,
+          boxes=proposal_boxes,
+          crop_size=self._crop_size,
+          num_spatial_bins=self._num_spatial_bins,
+          global_pool=True)
+      box_encodings = tf.squeeze(box_encodings, squeeze_dims=[2, 3])
+      box_encodings = tf.reshape(box_encodings,
+                                 [batch_size * num_boxes, 1, self.num_classes,
+                                  self._box_code_size])
+
+      # Class predictions.
+      total_classes = self.num_classes + 1  # Account for background class.
+      class_feature_map_depth = (self._num_spatial_bins[0] *
+                                 self._num_spatial_bins[1] *
+                                 total_classes)
+      class_feature_map = slim.conv2d(net, class_feature_map_depth, [1, 1],
+                                      activation_fn=None,
+                                      scope='class_predictions')
+      class_predictions_with_background = (
+          ops.batch_position_sensitive_crop_regions(
+              class_feature_map,
+              boxes=proposal_boxes,
+              crop_size=self._crop_size,
+              num_spatial_bins=self._num_spatial_bins,
+              global_pool=True))
+      class_predictions_with_background = tf.squeeze(
+          class_predictions_with_background, squeeze_dims=[2, 3])
+      class_predictions_with_background = tf.reshape(
+          class_predictions_with_background,
+          [batch_size * num_boxes, 1, total_classes])
+
+    return {BOX_ENCODINGS: [box_encodings],
+            CLASS_PREDICTIONS_WITH_BACKGROUND:
+            [class_predictions_with_background]}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor_test.py
new file mode 100644
index 00000000..104246d0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/predictors/rfcn_box_predictor_test.py
@@ -0,0 +1,77 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.predictors.rfcn_box_predictor."""
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import hyperparams_builder
+from object_detection.predictors import rfcn_box_predictor as box_predictor
+from object_detection.protos import hyperparams_pb2
+from object_detection.utils import test_case
+
+
+class RfcnBoxPredictorTest(test_case.TestCase):
+
+  def _build_arg_scope_with_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.build(conv_hyperparams, is_training=True)
+
+  def test_get_correct_box_encoding_and_class_prediction_shapes(self):
+
+    def graph_fn(image_features, proposal_boxes):
+      rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
+          is_training=False,
+          num_classes=2,
+          conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
+          num_spatial_bins=[3, 3],
+          depth=4,
+          crop_size=[12, 12],
+          box_code_size=4
+      )
+      box_predictions = rfcn_box_predictor.predict(
+          [image_features], num_predictions_per_location=[1],
+          scope='BoxPredictor',
+          proposal_boxes=proposal_boxes)
+      box_encodings = tf.concat(
+          box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
+      class_predictions_with_background = tf.concat(
+          box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
+          axis=1)
+      return (box_encodings, class_predictions_with_background)
+
+    image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
+    proposal_boxes = np.random.rand(4, 2, 4).astype(np.float32)
+    (box_encodings, class_predictions_with_background) = self.execute(
+        graph_fn, [image_features, proposal_boxes])
+
+    self.assertAllEqual(box_encodings.shape, [8, 1, 2, 4])
+    self.assertAllEqual(class_predictions_with_background.shape, [8, 1, 3])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/protos/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/anchor_generator.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/anchor_generator.proto
new file mode 100644
index 00000000..c47b558f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/anchor_generator.proto
@@ -0,0 +1,17 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/grid_anchor_generator.proto";
+import "object_detection/protos/ssd_anchor_generator.proto";
+import "object_detection/protos/multiscale_anchor_generator.proto";
+
+// Configuration proto for the anchor generator to use in the object detection
+// pipeline. See core/anchor_generator.py for details.
+message AnchorGenerator {
+  oneof anchor_generator_oneof {
+    GridAnchorGenerator grid_anchor_generator = 1;
+    SsdAnchorGenerator ssd_anchor_generator = 2;
+    MultiscaleAnchorGenerator multiscale_anchor_generator = 3;
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/argmax_matcher.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/argmax_matcher.proto
new file mode 100644
index 00000000..947fcb98
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/argmax_matcher.proto
@@ -0,0 +1,29 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for ArgMaxMatcher. See
+// matchers/argmax_matcher.py for details.
+message ArgMaxMatcher {
+  // Threshold for positive matches.
+  optional float matched_threshold = 1 [default = 0.5];
+
+  // Threshold for negative matches.
+  optional float unmatched_threshold = 2 [default = 0.5];
+
+  // Whether to construct ArgMaxMatcher without thresholds.
+  optional bool ignore_thresholds = 3 [default = false];
+
+  // If True then negative matches are the ones below the unmatched_threshold,
+  // whereas ignored matches are in between the matched and umatched
+  // threshold. If False, then negative matches are in between the matched
+  // and unmatched threshold, and everything lower than unmatched is ignored.
+  optional bool negatives_lower_than_unmatched = 4 [default = true];
+
+  // Whether to ensure each row is matched to at least one column.
+  optional bool force_match_for_each_row = 5 [default = false];
+
+  // Force constructed match objects to use matrix multiplication based gather
+  // instead of standard tf.gather
+  optional bool use_matmul_gather = 6 [default = false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/bipartite_matcher.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/bipartite_matcher.proto
new file mode 100644
index 00000000..175ecdd1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/bipartite_matcher.proto
@@ -0,0 +1,11 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for bipartite matcher. See
+// matchers/bipartite_matcher.py for details.
+message BipartiteMatcher {
+  // Force constructed match objects to use matrix multiplication based gather
+  // instead of standard tf.gather
+  optional bool use_matmul_gather = 6 [default = false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_coder.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_coder.proto
new file mode 100644
index 00000000..79b81812
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_coder.proto
@@ -0,0 +1,19 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/faster_rcnn_box_coder.proto";
+import "object_detection/protos/keypoint_box_coder.proto";
+import "object_detection/protos/mean_stddev_box_coder.proto";
+import "object_detection/protos/square_box_coder.proto";
+
+// Configuration proto for the box coder to be used in the object detection
+// pipeline. See core/box_coder.py for details.
+message BoxCoder {
+  oneof box_coder_oneof {
+    FasterRcnnBoxCoder faster_rcnn_box_coder = 1;
+    MeanStddevBoxCoder mean_stddev_box_coder = 2;
+    SquareBoxCoder square_box_coder = 3;
+    KeypointBoxCoder keypoint_box_coder = 4;
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_predictor.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_predictor.proto
new file mode 100644
index 00000000..25d5b1fc
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/box_predictor.proto
@@ -0,0 +1,191 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/hyperparams.proto";
+
+// Configuration proto for box predictor. See core/box_predictor.py for details.
+message BoxPredictor {
+  oneof box_predictor_oneof {
+    ConvolutionalBoxPredictor convolutional_box_predictor = 1;
+    MaskRCNNBoxPredictor mask_rcnn_box_predictor = 2;
+    RfcnBoxPredictor rfcn_box_predictor = 3;
+    WeightSharedConvolutionalBoxPredictor
+        weight_shared_convolutional_box_predictor = 4;
+  }
+}
+
+
+// Configuration proto for Convolutional box predictor.
+// Next id: 13
+message ConvolutionalBoxPredictor {
+  // Hyperparameters for convolution ops used in the box predictor.
+  optional Hyperparams conv_hyperparams = 1;
+
+  // Minimum feature depth prior to predicting box encodings and class
+  // predictions.
+  optional int32 min_depth = 2 [default = 0];
+
+  // Maximum feature depth prior to predicting box encodings and class
+  // predictions. If max_depth is set to 0, no additional feature map will be
+  // inserted before location and class predictions.
+  optional int32 max_depth = 3 [default = 0];
+
+  // Number of the additional conv layers before the predictor.
+  optional int32 num_layers_before_predictor = 4 [default = 0];
+
+  // Whether to use dropout for class prediction.
+  optional bool use_dropout = 5 [default = true];
+
+  // Keep probability for dropout
+  optional float dropout_keep_probability = 6 [default = 0.8];
+
+  // Size of final convolution kernel. If the spatial resolution of the feature
+  // map is smaller than the kernel size, then the kernel size is set to
+  // min(feature_width, feature_height).
+  optional int32 kernel_size = 7 [default = 1];
+
+  // Size of the encoding for boxes.
+  optional int32 box_code_size = 8 [default = 4];
+
+  // Whether to apply sigmoid to the output of class predictions.
+  // TODO(jonathanhuang): Do we need this since we have a post processing
+  // module.?
+  optional bool apply_sigmoid_to_scores = 9 [default = false];
+
+  optional float class_prediction_bias_init = 10 [default = 0.0];
+
+  // Whether to use depthwise separable convolution for box predictor layers.
+  optional bool use_depthwise = 11 [default = false];
+}
+
+// Configuration proto for weight shared convolutional box predictor.
+// Next id: 18
+message WeightSharedConvolutionalBoxPredictor {
+  // Hyperparameters for convolution ops used in the box predictor.
+  optional Hyperparams conv_hyperparams = 1;
+
+  // Number of the additional conv layers before the predictor.
+  optional int32 num_layers_before_predictor = 4 [default = 0];
+
+  // Output depth for the convolution ops prior to predicting box encodings
+  // and class predictions.
+  optional int32 depth = 2 [default = 0];
+
+  // Size of final convolution kernel. If the spatial resolution of the feature
+  // map is smaller than the kernel size, then the kernel size is set to
+  // min(feature_width, feature_height).
+  optional int32 kernel_size = 7 [default = 3];
+
+  // Size of the encoding for boxes.
+  optional int32 box_code_size = 8 [default = 4];
+
+  // Bias initialization for class prediction. It has been show to stabilize
+  // training where there are large number of negative boxes. See
+  // https://arxiv.org/abs/1708.02002 for details.
+  optional float class_prediction_bias_init = 10 [default = 0.0];
+
+  // Whether to use dropout for class prediction.
+  optional bool use_dropout = 11 [default = false];
+
+  // Keep probability for dropout.
+  optional float dropout_keep_probability = 12 [default = 0.8];
+
+  // Whether to share the multi-layer tower between box prediction and class
+  // prediction heads.
+  optional bool share_prediction_tower = 13 [default = false];
+
+  // Whether to use depthwise separable convolution for box predictor layers.
+  optional bool use_depthwise = 14 [default = false];
+
+  // Enum to specify how to convert the detection scores at inference time.
+  enum ScoreConverter {
+    // Input scores equals output scores.
+    IDENTITY = 0;
+
+    // Applies a sigmoid on input scores.
+    SIGMOID = 1;
+  }
+
+  // Callable elementwise score converter at inference time.
+  optional ScoreConverter score_converter = 16 [default = IDENTITY];
+
+  // If specified, apply clipping to box encodings.
+  message BoxEncodingsClipRange {
+    optional float min = 1;
+    optional float max = 2;
+  }
+  optional BoxEncodingsClipRange box_encodings_clip_range = 17;
+}
+
+// TODO(alirezafathi): Refactor the proto file to be able to configure mask rcnn
+// head easily.
+// Next id: 15
+message MaskRCNNBoxPredictor {
+  // Hyperparameters for fully connected ops used in the box predictor.
+  optional Hyperparams fc_hyperparams = 1;
+
+  // Whether to use dropout op prior to the both box and class predictions.
+  optional bool use_dropout = 2 [default = false];
+
+  // Keep probability for dropout. This is only used if use_dropout is true.
+  optional float dropout_keep_probability = 3 [default = 0.5];
+
+  // Size of the encoding for the boxes.
+  optional int32 box_code_size = 4 [default = 4];
+
+  // Hyperparameters for convolution ops used in the box predictor.
+  optional Hyperparams conv_hyperparams = 5;
+
+  // Whether to predict instance masks inside detection boxes.
+  optional bool predict_instance_masks = 6 [default = false];
+
+  // The depth for the first conv2d_transpose op applied to the
+  // image_features in the mask prediction branch. If set to 0, the value
+  // will be set automatically based on the number of channels in the image
+  // features and the number of classes.
+  optional int32 mask_prediction_conv_depth = 7 [default = 256];
+
+  // Whether to predict keypoints inside detection boxes.
+  optional bool predict_keypoints = 8 [default = false];
+
+  // The height and the width of the predicted mask.
+  optional int32 mask_height = 9 [default = 15];
+  optional int32 mask_width = 10 [default = 15];
+
+  // The number of convolutions applied to image_features in the mask prediction
+  // branch.
+  optional int32 mask_prediction_num_conv_layers = 11 [default = 2];
+  optional bool masks_are_class_agnostic = 12 [default = false];
+
+  // Whether to use one box for all classes rather than a different box for each
+  // class.
+  optional bool share_box_across_classes = 13 [default = false];
+
+  // Whether to apply convolutions on mask features before upsampling using
+  // nearest neighbor resizing.
+  // By default, mask features are resized to [`mask_height`, `mask_width`]
+  // before applying convolutions and predicting masks.
+  optional bool convolve_then_upsample_masks = 14 [default = false];
+}
+
+message RfcnBoxPredictor {
+  // Hyperparameters for convolution ops used in the box predictor.
+  optional Hyperparams conv_hyperparams = 1;
+
+  // Bin sizes for RFCN crops.
+  optional int32 num_spatial_bins_height = 2 [default = 3];
+
+  optional int32 num_spatial_bins_width = 3 [default = 3];
+
+  // Target depth to reduce the input image features to.
+  optional int32 depth = 4 [default = 1024];
+
+  // Size of the encoding for the boxes.
+  optional int32 box_code_size = 5 [default = 4];
+
+  // Size to resize the rfcn crops to.
+  optional int32 crop_height = 6 [default = 12];
+
+  optional int32 crop_width = 7 [default = 12];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/eval.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/eval.proto
new file mode 100644
index 00000000..ed5a3a9c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/eval.proto
@@ -0,0 +1,79 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Message for configuring DetectionModel evaluation jobs (eval.py).
+message EvalConfig {
+  optional uint32 batch_size = 25 [default=1];
+  // Number of visualization images to generate.
+  optional uint32 num_visualizations = 1 [default=10];
+
+  // Number of examples to process of evaluation.
+  optional uint32 num_examples = 2 [default=5000, deprecated=true];
+
+  // How often to run evaluation.
+  optional uint32 eval_interval_secs = 3 [default=300];
+
+  // Maximum number of times to run evaluation. If set to 0, will run forever.
+  optional uint32 max_evals = 4 [default=0, deprecated=true];
+
+  // Whether the TensorFlow graph used for evaluation should be saved to disk.
+  optional bool save_graph = 5 [default=false];
+
+  // Path to directory to store visualizations in. If empty, visualization
+  // images are not exported (only shown on Tensorboard).
+  optional string visualization_export_dir = 6 [default=""];
+
+  // BNS name of the TensorFlow master.
+  optional string eval_master = 7 [default=""];
+
+  // Type of metrics to use for evaluation.
+  repeated string metrics_set = 8;
+
+  // Path to export detections to COCO compatible JSON format.
+  optional string export_path = 9 [default=''];
+
+  // Option to not read groundtruth labels and only export detections to
+  // COCO-compatible JSON file.
+  optional bool ignore_groundtruth = 10 [default=false];
+
+  // Use exponential moving averages of variables for evaluation.
+  // TODO(rathodv): When this is false make sure the model is constructed
+  // without moving averages in restore_fn.
+  optional bool use_moving_averages = 11 [default=false];
+
+  // Whether to evaluate instance masks.
+  // Note that since there is no evaluation code currently for instance
+  // segmenation this option is unused.
+  optional bool eval_instance_masks = 12 [default=false];
+
+  // Minimum score threshold for a detected object box to be visualized
+  optional float min_score_threshold = 13 [default=0.5];
+
+  // Maximum number of detections to visualize
+  optional int32 max_num_boxes_to_visualize = 14 [default=20];
+
+  // When drawing a single detection, each label is by default visualized as
+  // <label name> : <label score>. One can skip the name or/and score using the
+  // following fields:
+  optional bool skip_scores = 15 [default=false];
+  optional bool skip_labels = 16 [default=false];
+
+  // Whether to show groundtruth boxes in addition to detected boxes in
+  // visualizations.
+  optional bool visualize_groundtruth_boxes = 17 [default=false];
+
+  // Box color for visualizing groundtruth boxes.
+  optional string groundtruth_box_visualization_color = 18 [default="black"];
+
+  // Whether to keep image identifier in filename when exported to
+  // visualization_export_dir.
+  optional bool keep_image_id_for_visualization_export = 19 [default=false];
+
+  // Whether to retain original images (i.e. not pre-processed) in the tensor
+  // dictionary, so that they can be displayed in Tensorboard.
+  optional bool retain_original_images = 23 [default=true];
+
+  // If True, additionally include per-category metrics.
+  optional bool include_metrics_per_category = 24 [default=false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn.proto
new file mode 100644
index 00000000..be5a61a1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn.proto
@@ -0,0 +1,186 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/anchor_generator.proto";
+import "object_detection/protos/box_predictor.proto";
+import "object_detection/protos/hyperparams.proto";
+import "object_detection/protos/image_resizer.proto";
+import "object_detection/protos/losses.proto";
+import "object_detection/protos/post_processing.proto";
+
+// Configuration for Faster R-CNN models.
+// See meta_architectures/faster_rcnn_meta_arch.py and models/model_builder.py
+//
+// Naming conventions:
+// Faster R-CNN models have two stages: a first stage region proposal network
+// (or RPN) and a second stage box classifier.  We thus use the prefixes
+// `first_stage_` and `second_stage_` to indicate the stage to which each
+// parameter pertains when relevant.
+message FasterRcnn {
+
+  // Whether to construct only the Region Proposal Network (RPN).
+  optional int32 number_of_stages = 1 [default=2];
+
+  // Number of classes to predict.
+  optional int32 num_classes = 3;
+
+  // Image resizer for preprocessing the input image.
+  optional ImageResizer image_resizer = 4;
+
+  // Feature extractor config.
+  optional FasterRcnnFeatureExtractor feature_extractor = 5;
+
+
+  // (First stage) region proposal network (RPN) parameters.
+
+  // Anchor generator to compute RPN anchors.
+  optional AnchorGenerator first_stage_anchor_generator = 6;
+
+  // Atrous rate for the convolution op applied to the
+  // `first_stage_features_to_crop` tensor to obtain box predictions.
+  optional int32 first_stage_atrous_rate = 7 [default=1];
+
+  // Hyperparameters for the convolutional RPN box predictor.
+  optional Hyperparams first_stage_box_predictor_conv_hyperparams = 8;
+
+  // Kernel size to use for the convolution op just prior to RPN box
+  // predictions.
+  optional int32 first_stage_box_predictor_kernel_size = 9 [default=3];
+
+  // Output depth for the convolution op just prior to RPN box predictions.
+  optional int32 first_stage_box_predictor_depth = 10 [default=512];
+
+  // The batch size to use for computing the first stage objectness and
+  // location losses.
+  optional int32 first_stage_minibatch_size = 11 [default=256];
+
+  // Fraction of positive examples per image for the RPN.
+  optional float first_stage_positive_balance_fraction = 12 [default=0.5];
+
+  // Non max suppression score threshold applied to first stage RPN proposals.
+  optional float first_stage_nms_score_threshold = 13 [default=0.0];
+
+  // Non max suppression IOU threshold applied to first stage RPN proposals.
+  optional float first_stage_nms_iou_threshold = 14 [default=0.7];
+
+  // Maximum number of RPN proposals retained after first stage postprocessing.
+  optional int32 first_stage_max_proposals = 15 [default=300];
+
+  // First stage RPN localization loss weight.
+  optional float first_stage_localization_loss_weight = 16 [default=1.0];
+
+  // First stage RPN objectness loss weight.
+  optional float first_stage_objectness_loss_weight = 17 [default=1.0];
+
+
+  // Per-region cropping parameters.
+  // Note that if a R-FCN model is constructed the per region cropping
+  // parameters below are ignored.
+
+  // Output size (width and height are set to be the same) of the initial
+  // bilinear interpolation based cropping during ROI pooling.
+  optional int32 initial_crop_size = 18;
+
+  // Kernel size of the max pool op on the cropped feature map during
+  // ROI pooling.
+  optional int32 maxpool_kernel_size = 19;
+
+  // Stride of the max pool op on the cropped feature map during ROI pooling.
+  optional int32 maxpool_stride = 20;
+
+
+  // (Second stage) box classifier parameters
+
+  // Hyperparameters for the second stage box predictor. If box predictor type
+  // is set to rfcn_box_predictor, a R-FCN model is constructed, otherwise a
+  // Faster R-CNN model is constructed.
+  optional BoxPredictor second_stage_box_predictor  = 21;
+
+  // The batch size per image used for computing the classification and refined
+  // location loss of the box classifier.
+  // Note that this field is ignored if `hard_example_miner` is configured.
+  optional int32 second_stage_batch_size = 22 [default=64];
+
+  // Fraction of positive examples to use per image for the box classifier.
+  optional float second_stage_balance_fraction = 23 [default=0.25];
+
+  // Post processing to apply on the second stage box classifier predictions.
+  // Note: the `score_converter` provided to the FasterRCNNMetaArch constructor
+  // is taken from this `second_stage_post_processing` proto.
+  optional PostProcessing second_stage_post_processing = 24;
+
+  // Second stage refined localization loss weight.
+  optional float second_stage_localization_loss_weight = 25 [default=1.0];
+
+  // Second stage classification loss weight
+  optional float second_stage_classification_loss_weight = 26 [default=1.0];
+
+  // Second stage instance mask loss weight. Note that this is only applicable
+  // when `MaskRCNNBoxPredictor` is selected for second stage and configured to
+  // predict instance masks.
+  optional float second_stage_mask_prediction_loss_weight = 27 [default=1.0];
+
+  // If not left to default, applies hard example mining only to classification
+  // and localization loss..
+  optional HardExampleMiner hard_example_miner = 28;
+
+  // Loss for second stage box classifers, supports Softmax and Sigmoid.
+  // Note that score converter must be consistent with loss type.
+  // When there are multiple labels assigned to the same boxes, recommend
+  // to use sigmoid loss and enable merge_multiple_label_boxes.
+  // If not specified, Softmax loss is used as default.
+  optional ClassificationLoss second_stage_classification_loss = 29;
+
+  // Whether to update batch_norm inplace during training. This is required
+  // for batch norm to work correctly on TPUs. When this is false, user must add
+  // a control dependency on tf.GraphKeys.UPDATE_OPS for train/loss op in order
+  // to update the batch norm moving average parameters.
+  optional bool inplace_batchnorm_update = 30 [default = false];
+
+  // Force the use of matrix multiplication based crop and resize instead of
+  // standard tf.image.crop_and_resize while computing second stage input
+  // feature maps.
+  optional bool use_matmul_crop_and_resize = 31 [default = false];
+
+  // Normally, anchors generated for a given image size are pruned during
+  // training if they lie outside the image window. Setting this option to true,
+  // clips the anchors to be within the image instead of pruning.
+  optional bool clip_anchors_to_image = 32 [default = false];
+
+  // After peforming matching between anchors and targets, in order to pull out
+  // targets for training Faster R-CNN meta architecture we perform a gather
+  // operation. This options specifies whether to use an alternate
+  // implementation of tf.gather that is faster on TPUs.
+  optional bool use_matmul_gather_in_matcher = 33 [default = false];
+
+  // Whether to use the balanced positive negative sampler implementation with
+  // static shape guarantees.
+  optional bool use_static_balanced_label_sampler = 34 [default = false];
+
+  // If True, uses implementation of ops with static shape guarantees.
+  optional bool use_static_shapes = 35 [default = false];
+
+  // Whether the masks present in groundtruth should be resized in the model to
+  // match the image size.
+  optional bool resize_masks = 36 [default = true];
+
+  // If True, uses implementation of ops with static shape guarantees when
+  // running evaluation (specifically not is_training if False).
+  optional bool use_static_shapes_for_eval = 37 [default = false];
+}
+
+
+message FasterRcnnFeatureExtractor {
+  // Type of Faster R-CNN model (e.g., 'faster_rcnn_resnet101';
+  // See builders/model_builder.py for expected types).
+  optional string type = 1;
+
+  // Output stride of extracted RPN feature map.
+  optional int32 first_stage_features_stride = 2 [default=16];
+
+  // Whether to update batch norm parameters during training or not.
+  // When training with a relative large batch size (e.g. 8), it could be
+  // desirable to enable batch norm update.
+  optional bool batch_norm_trainable = 3 [default=false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn_box_coder.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn_box_coder.proto
new file mode 100644
index 00000000..512a20a1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/faster_rcnn_box_coder.proto
@@ -0,0 +1,17 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for FasterRCNNBoxCoder. See
+// box_coders/faster_rcnn_box_coder.py for details.
+message FasterRcnnBoxCoder {
+  // Scale factor for anchor encoded box center.
+  optional float y_scale = 1 [default = 10.0];
+  optional float x_scale = 2 [default = 10.0];
+
+  // Scale factor for anchor encoded box height.
+  optional float height_scale = 3 [default = 5.0];
+
+  // Scale factor for anchor encoded box width.
+  optional float width_scale = 4 [default = 5.0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/graph_rewriter.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/graph_rewriter.proto
new file mode 100644
index 00000000..8828f9f0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/graph_rewriter.proto
@@ -0,0 +1,23 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Message to configure graph rewriter for the tf graph.
+message GraphRewriter {
+  optional Quantization quantization = 1;
+}
+
+// Message for quantization options. See
+// tensorflow/contrib/quantize/python/quantize.py for details.
+message Quantization {
+  // Number of steps to delay before quantization takes effect during training.
+  optional int32 delay = 1 [default = 500000];
+
+  // Number of bits to use for quantizing weights.
+  // Only 8 bit is supported for now.
+  optional int32 weight_bits = 2 [default = 8];
+
+  // Number of bits to use for quantizing activations.
+  // Only 8 bit is supported for now.
+  optional int32 activation_bits = 3 [default = 8];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/grid_anchor_generator.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/grid_anchor_generator.proto
new file mode 100644
index 00000000..85168f8f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/grid_anchor_generator.proto
@@ -0,0 +1,34 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for GridAnchorGenerator. See
+// anchor_generators/grid_anchor_generator.py for details.
+message GridAnchorGenerator {
+   // Anchor height in pixels.
+  optional int32 height = 1 [default = 256];
+
+  // Anchor width in pixels.
+  optional int32 width = 2 [default = 256];
+
+  // Anchor stride in height dimension in pixels.
+  optional int32 height_stride = 3 [default = 16];
+
+  // Anchor stride in width dimension in pixels.
+  optional int32 width_stride = 4 [default = 16];
+
+  // Anchor height offset in pixels.
+  optional int32 height_offset = 5 [default = 0];
+
+  // Anchor width offset in pixels.
+  optional int32 width_offset = 6 [default = 0];
+
+  // At any given location, len(scales) * len(aspect_ratios) anchors are
+  // generated with all possible combinations of scales and aspect ratios.
+
+  // List of scales for the anchors.
+  repeated float scales = 7;
+
+  // List of aspect ratios for the anchors.
+  repeated float aspect_ratios = 8;
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/hyperparams.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/hyperparams.proto
new file mode 100644
index 00000000..eb8a2252
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/hyperparams.proto
@@ -0,0 +1,127 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for the convolution op hyperparameters to use in the
+// object detection pipeline.
+message Hyperparams {
+
+  // Operations affected by hyperparameters.
+  enum Op {
+    // Convolution, Separable Convolution, Convolution transpose.
+    CONV = 1;
+
+    // Fully connected
+    FC = 2;
+  }
+  optional Op op = 1 [default = CONV];
+
+  // Regularizer for the weights of the convolution op.
+  optional Regularizer regularizer = 2;
+
+  // Initializer for the weights of the convolution op.
+  optional Initializer initializer = 3;
+
+  // Type of activation to apply after convolution.
+  enum Activation {
+    // Use None (no activation)
+    NONE = 0;
+
+    // Use tf.nn.relu
+    RELU = 1;
+
+    // Use tf.nn.relu6
+    RELU_6 = 2;
+  }
+  optional Activation activation = 4 [default = RELU];
+
+  oneof normalizer_oneof {
+    // Note that if nothing below is selected, then no normalization is applied
+    // BatchNorm hyperparameters.
+    BatchNorm batch_norm = 5;
+    // GroupNorm hyperparameters. This is only supported on a subset of models.
+    // Note that the current implementation of group norm instantiated in
+    // tf.contrib.group.layers.group_norm() only supports fixed_size_resizer
+    // for image preprocessing.
+    GroupNorm group_norm = 7;
+  }
+
+  // Whether depthwise convolutions should be regularized. If this parameter is
+  // NOT set then the conv hyperparams will default to the parent scope.
+  optional bool regularize_depthwise = 6 [default = false];
+}
+
+// Proto with one-of field for regularizers.
+message Regularizer {
+  oneof regularizer_oneof {
+    L1Regularizer l1_regularizer = 1;
+    L2Regularizer l2_regularizer = 2;
+  }
+}
+
+// Configuration proto for L1 Regularizer.
+// See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l1_regularizer
+message L1Regularizer {
+  optional float weight = 1 [default = 1.0];
+}
+
+// Configuration proto for L2 Regularizer.
+// See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l2_regularizer
+message L2Regularizer {
+  optional float weight = 1 [default = 1.0];
+}
+
+// Proto with one-of field for initializers.
+message Initializer {
+  oneof initializer_oneof {
+    TruncatedNormalInitializer truncated_normal_initializer = 1;
+    VarianceScalingInitializer variance_scaling_initializer = 2;
+    RandomNormalInitializer random_normal_initializer = 3;
+  }
+}
+
+// Configuration proto for truncated normal initializer. See
+// https://www.tensorflow.org/api_docs/python/tf/truncated_normal_initializer
+message TruncatedNormalInitializer {
+  optional float mean = 1 [default = 0.0];
+  optional float stddev = 2 [default = 1.0];
+}
+
+// Configuration proto for variance scaling initializer. See
+// https://www.tensorflow.org/api_docs/python/tf/contrib/layers/
+// variance_scaling_initializer
+message VarianceScalingInitializer {
+  optional float factor = 1 [default = 2.0];
+  optional bool uniform = 2 [default = false];
+  enum Mode {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    FAN_AVG = 2;
+  }
+  optional Mode mode = 3 [default = FAN_IN];
+}
+
+// Configuration proto for random normal initializer. See
+// https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer
+message RandomNormalInitializer {
+  optional float mean = 1 [default = 0.0];
+  optional float stddev = 2 [default = 1.0];
+}
+
+// Configuration proto for batch norm to apply after convolution op. See
+// https://www.tensorflow.org/api_docs/python/tf/contrib/layers/batch_norm
+message BatchNorm {
+  optional float decay = 1 [default = 0.999];
+  optional bool center = 2 [default = true];
+  optional bool scale = 3 [default = false];
+  optional float epsilon = 4 [default = 0.001];
+  // Whether to train the batch norm variables. If this is set to false during
+  // training, the current value of the batch_norm variables are used for
+  // forward pass but they are never updated.
+  optional bool train = 5 [default = true];
+}
+
+// Configuration proto for group normalization to apply after convolution op.
+// https://arxiv.org/abs/1803.08494
+message GroupNorm {
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/image_resizer.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/image_resizer.proto
new file mode 100644
index 00000000..f8b3f660
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/image_resizer.proto
@@ -0,0 +1,59 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for image resizing operations.
+// See builders/image_resizer_builder.py for details.
+message ImageResizer {
+  oneof image_resizer_oneof {
+    KeepAspectRatioResizer keep_aspect_ratio_resizer = 1;
+    FixedShapeResizer fixed_shape_resizer = 2;
+  }
+}
+
+// Enumeration type for image resizing methods provided in TensorFlow.
+enum ResizeType {
+  BILINEAR = 0; // Corresponds to tf.image.ResizeMethod.BILINEAR
+  NEAREST_NEIGHBOR = 1; // Corresponds to tf.image.ResizeMethod.NEAREST_NEIGHBOR
+  BICUBIC = 2; // Corresponds to tf.image.ResizeMethod.BICUBIC
+  AREA = 3; // Corresponds to tf.image.ResizeMethod.AREA
+}
+
+// Configuration proto for image resizer that keeps aspect ratio.
+message KeepAspectRatioResizer {
+  // Desired size of the smaller image dimension in pixels.
+  optional int32 min_dimension = 1 [default = 600];
+
+  // Desired size of the larger image dimension in pixels.
+  optional int32 max_dimension = 2 [default = 1024];
+
+  // Desired method when resizing image.
+  optional ResizeType resize_method = 3 [default = BILINEAR];
+
+  // Whether to pad the image with zeros so the output spatial size is
+  // [max_dimension, max_dimension]. Note that the zeros are padded to the
+  // bottom and the right of the resized image.
+  optional bool pad_to_max_dimension = 4 [default = false];
+
+  // Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
+  optional bool convert_to_grayscale = 5 [default = false];
+
+  // Per-channel pad value. This is only used when pad_to_max_dimension is True.
+  // If unspecified, a default pad value of 0 is applied to all channels.
+  repeated float per_channel_pad_value = 6;
+}
+
+// Configuration proto for image resizer that resizes to a fixed shape.
+message FixedShapeResizer {
+  // Desired height of image in pixels.
+  optional int32 height = 1 [default = 300];
+
+  // Desired width of image in pixels.
+  optional int32 width = 2 [default = 300];
+
+  // Desired method when resizing image.
+  optional ResizeType resize_method = 3 [default = BILINEAR];
+
+  // Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
+  optional bool convert_to_grayscale = 4 [default = false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/input_reader.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/input_reader.proto
new file mode 100644
index 00000000..38848f6b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/input_reader.proto
@@ -0,0 +1,123 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for defining input readers that generate Object Detection
+// Examples from input sources. Input readers are expected to generate a
+// dictionary of tensors, with the following fields populated:
+//
+// 'image': an [image_height, image_width, channels] image tensor that detection
+//    will be run on.
+// 'groundtruth_classes': a [num_boxes] int32 tensor storing the class
+//    labels of detected boxes in the image.
+// 'groundtruth_boxes': a [num_boxes, 4] float tensor storing the coordinates of
+//    detected boxes in the image.
+// 'groundtruth_instance_masks': (Optional), a [num_boxes, image_height,
+//    image_width] float tensor storing binary mask of the objects in boxes.
+
+// Instance mask format. Note that PNG masks are much more space efficient.
+enum InstanceMaskType {
+  DEFAULT = 0;          // Default implementation, currently NUMERICAL_MASKS
+  NUMERICAL_MASKS = 1;  // [num_masks, H, W] float32 binary masks.
+  PNG_MASKS = 2;        // Encoded PNG masks.
+}
+
+// Next id: 24
+message InputReader {
+  // Name of input reader. Typically used to describe the dataset that is read
+  // by this input reader.
+  optional string name = 23 [default=""];
+
+  // Path to StringIntLabelMap pbtxt file specifying the mapping from string
+  // labels to integer ids.
+  optional string label_map_path = 1 [default=""];
+
+  // Whether data should be processed in the order they are read in, or
+  // shuffled randomly.
+  optional bool shuffle = 2 [default=true];
+
+  // Buffer size to be used when shuffling.
+  optional uint32 shuffle_buffer_size = 11 [default = 2048];
+
+  // Buffer size to be used when shuffling file names.
+  optional uint32 filenames_shuffle_buffer_size = 12 [default = 100];
+
+  // The number of times a data source is read. If set to zero, the data source
+  // will be reused indefinitely.
+  optional uint32 num_epochs = 5 [default=0];
+
+  // Integer representing how often an example should be sampled. To feed
+  // only 1/3 of your data into your model, set `sample_1_of_n_examples` to 3.
+  // This is particularly useful for evaluation, where you might not prefer to
+  // evaluate all of your samples.
+  optional uint32 sample_1_of_n_examples = 22 [default=1];
+
+  // Number of file shards to read in parallel.
+  optional uint32 num_readers = 6 [default=64];
+
+  // Number of batches to produce in parallel. If this is run on a 2x2 TPU set
+  // this to 8.
+  optional uint32 num_parallel_batches = 19 [default=8];
+
+  // Number of batches to prefetch. Prefetch decouples input pipeline and
+  // model so they can be pipelined resulting in higher throughput. Set this
+  // to a small constant and increment linearly until the improvements become
+  // marginal or you exceed your cpu memory budget. Setting this to -1,
+  // automatically tunes this value for you.
+  optional int32 num_prefetch_batches = 20 [default=2];
+
+  // Maximum number of records to keep in reader queue.
+  optional uint32 queue_capacity = 3 [default=2000, deprecated=true];
+
+  // Minimum number of records to keep in reader queue. A large value is needed
+  // to generate a good random shuffle.
+  optional uint32 min_after_dequeue = 4 [default=1000, deprecated=true];
+
+  // Number of records to read from each reader at once.
+  optional uint32 read_block_length = 15 [default=32];
+
+  // Number of decoded records to prefetch before batching.
+  optional uint32 prefetch_size = 13 [default = 512, deprecated=true];
+
+  // Number of parallel decode ops to apply.
+  optional uint32 num_parallel_map_calls = 14 [default = 64, deprecated=true];
+
+  // If positive, TfExampleDecoder will try to decode rasters of additional
+  // channels from tf.Examples.
+  optional int32 num_additional_channels = 18 [default = 0];
+
+  // Number of groundtruth keypoints per object.
+  optional uint32 num_keypoints = 16 [default = 0];
+
+  // Maximum number of boxes to pad to during training / evaluation.
+  // Set this to at least the maximum amount of boxes in the input data,
+  // otherwise some groundtruth boxes may be clipped.
+  optional int32 max_number_of_boxes = 21 [default=100];
+
+  // Whether to load groundtruth instance masks.
+  optional bool load_instance_masks = 7 [default = false];
+
+  // Type of instance mask.
+  optional InstanceMaskType mask_type = 10 [default = NUMERICAL_MASKS];
+
+  // Whether to use the display name when decoding examples. This is only used
+  // when mapping class text strings to integers.
+  optional bool use_display_name = 17 [default = false];
+
+  oneof input_reader {
+    TFRecordInputReader tf_record_input_reader = 8;
+    ExternalInputReader external_input_reader = 9;
+  }
+}
+
+// An input reader that reads TF Example protos from local TFRecord files.
+message TFRecordInputReader {
+  // Path(s) to `TFRecordFile`s.
+  repeated string input_path = 1;
+}
+
+// An externally defined input reader. Users may define an extension to this
+// proto to interface their own input readers.
+message ExternalInputReader {
+  extensions 1 to 999;
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/keypoint_box_coder.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/keypoint_box_coder.proto
new file mode 100644
index 00000000..542ebbfb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/keypoint_box_coder.proto
@@ -0,0 +1,19 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for KeypointBoxCoder. See
+// box_coders/keypoint_box_coder.py for details.
+message KeypointBoxCoder {
+  optional int32 num_keypoints = 1;
+
+  // Scale factor for anchor encoded box center and keypoints.
+  optional float y_scale = 2 [default = 10.0];
+  optional float x_scale = 3 [default = 10.0];
+
+  // Scale factor for anchor encoded box height.
+  optional float height_scale = 4 [default = 5.0];
+
+  // Scale factor for anchor encoded box width.
+  optional float width_scale = 5 [default = 5.0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/losses.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/losses.proto
new file mode 100644
index 00000000..ed856588
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/losses.proto
@@ -0,0 +1,202 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Message for configuring the localization loss, classification loss and hard
+// example miner used for training object detection models. See core/losses.py
+// for details
+message Loss {
+  // Localization loss to use.
+  optional LocalizationLoss localization_loss = 1;
+
+  // Classification loss to use.
+  optional ClassificationLoss classification_loss = 2;
+
+  // If not left to default, applies hard example mining.
+  optional HardExampleMiner hard_example_miner = 3;
+
+  // Classification loss weight.
+  optional float classification_weight = 4 [default=1.0];
+
+  // Localization loss weight.
+  optional float localization_weight = 5 [default=1.0];
+
+  // If not left to default, applies random example sampling.
+  optional RandomExampleSampler random_example_sampler = 6;
+
+  // Equalization loss.
+  message EqualizationLoss {
+    // Weight equalization loss strength.
+    optional float weight = 1 [default=0.0];
+
+    // When computing equalization loss, ops that start with
+    // equalization_exclude_prefixes will be ignored. Only used when
+    // equalization_weight > 0.
+    repeated string exclude_prefixes = 2;
+  }
+
+  optional EqualizationLoss equalization_loss = 7;
+
+  enum ExpectedLossWeights {
+    NONE = 0;
+    // Use expected_classification_loss_by_expected_sampling
+    // from third_party/tensorflow_models/object_detection/utils/ops.py
+    EXPECTED_SAMPLING = 1;
+    // Use expected_classification_loss_by_reweighting_unmatched_anchors
+    // from third_party/tensorflow_models/object_detection/utils/ops.py
+    REWEIGHTING_UNMATCHED_ANCHORS = 2;
+  }
+
+  // Method to compute expected loss weights with respect to balanced
+  // positive/negative sampling scheme. If NONE, use explicit sampling.
+  // TODO(birdbrain): Move under ExpectedLossWeights.
+  optional ExpectedLossWeights expected_loss_weights = 18 [default = NONE];
+
+  // Minimum number of effective negative samples.
+  // Only applies if expected_loss_weights is not NONE.
+  // TODO(birdbrain): Move under ExpectedLossWeights.
+  optional float min_num_negative_samples = 19 [default=0];
+
+  // Desired number of effective negative samples per positive sample.
+  // Only applies if expected_loss_weights is not NONE.
+  // TODO(birdbrain): Move under ExpectedLossWeights.
+  optional float desired_negative_sampling_ratio = 20 [default=3];
+}
+
+// Configuration for bounding box localization loss function.
+message LocalizationLoss {
+  oneof localization_loss {
+    WeightedL2LocalizationLoss weighted_l2 = 1;
+    WeightedSmoothL1LocalizationLoss weighted_smooth_l1 = 2;
+    WeightedIOULocalizationLoss weighted_iou = 3;
+  }
+}
+
+// L2 location loss: 0.5 * ||weight * (a - b)|| ^ 2
+message WeightedL2LocalizationLoss {
+  // DEPRECATED, do not use.
+  // Output loss per anchor.
+  optional bool anchorwise_output = 1 [default=false];
+}
+
+// SmoothL1 (Huber) location loss.
+// The smooth L1_loss is defined elementwise as .5 x^2 if |x| <= delta and
+// delta * (|x|-0.5*delta) otherwise, where x is the difference between
+// predictions and target.
+message WeightedSmoothL1LocalizationLoss {
+  // DEPRECATED, do not use.
+  // Output loss per anchor.
+  optional bool anchorwise_output = 1 [default=false];
+
+  // Delta value for huber loss.
+  optional float delta = 2 [default=1.0];
+}
+
+// Intersection over union location loss: 1 - IOU
+message WeightedIOULocalizationLoss {
+}
+
+// Configuration for class prediction loss function.
+message ClassificationLoss {
+  oneof classification_loss {
+    WeightedSigmoidClassificationLoss weighted_sigmoid = 1;
+    WeightedSoftmaxClassificationLoss weighted_softmax = 2;
+    WeightedSoftmaxClassificationAgainstLogitsLoss weighted_logits_softmax = 5;
+    BootstrappedSigmoidClassificationLoss bootstrapped_sigmoid = 3;
+    SigmoidFocalClassificationLoss weighted_sigmoid_focal = 4;
+  }
+}
+
+// Classification loss using a sigmoid function over class predictions.
+message WeightedSigmoidClassificationLoss {
+  // DEPRECATED, do not use.
+  // Output loss per anchor.
+  optional bool anchorwise_output = 1 [default=false];
+}
+
+// Sigmoid Focal cross entropy loss as described in
+// https://arxiv.org/abs/1708.02002
+message SigmoidFocalClassificationLoss {
+  // DEPRECATED, do not use.
+  optional bool anchorwise_output = 1 [default = false];
+  // modulating factor for the loss.
+  optional float gamma = 2 [default = 2.0];
+  // alpha weighting factor for the loss.
+  optional float alpha = 3;
+}
+
+// Classification loss using a softmax function over class predictions.
+message WeightedSoftmaxClassificationLoss {
+  // DEPRECATED, do not use.
+  // Output loss per anchor.
+  optional bool anchorwise_output = 1 [default=false];
+  // Scale logit (input) value before calculating softmax classification loss.
+  // Typically used for softmax distillation.
+  optional float logit_scale = 2 [default = 1.0];
+}
+
+// Classification loss using a softmax function over class predictions and
+// a softmax function over the groundtruth labels (assumed to be logits).
+message WeightedSoftmaxClassificationAgainstLogitsLoss {
+  // DEPRECATED, do not use.
+  optional bool anchorwise_output = 1 [default = false];
+  // Scale and softmax groundtruth logits before calculating softmax
+  // classification loss. Typically used for softmax distillation with teacher
+  // annotations stored as logits.
+  optional float logit_scale = 2 [default = 1.0];
+}
+
+// Classification loss using a sigmoid function over the class prediction with
+// the highest prediction score.
+message BootstrappedSigmoidClassificationLoss {
+  // Interpolation weight between 0 and 1.
+  optional float alpha = 1;
+
+  // Whether hard boot strapping should be used or not. If true, will only use
+  // one class favored by model. Othewise, will use all predicted class
+  // probabilities.
+  optional bool hard_bootstrap = 2 [default=false];
+
+  // DEPRECATED, do not use.
+  // Output loss per anchor.
+  optional bool anchorwise_output = 3 [default=false];
+}
+
+// Configuration for hard example miner.
+message HardExampleMiner {
+  // Maximum number of hard examples to be selected per image (prior to
+  // enforcing max negative to positive ratio constraint).  If set to 0,
+  // all examples obtained after NMS are considered.
+  optional int32 num_hard_examples = 1 [default=64];
+
+  // Minimum intersection over union for an example to be discarded during NMS.
+  optional float iou_threshold = 2 [default=0.7];
+
+  // Whether to use classification losses ('cls', default), localization losses
+  // ('loc') or both losses ('both'). In the case of 'both', cls_loss_weight and
+  // loc_loss_weight are used to compute weighted sum of the two losses.
+  enum LossType {
+    BOTH = 0;
+    CLASSIFICATION = 1;
+    LOCALIZATION = 2;
+  }
+  optional LossType loss_type = 3 [default=BOTH];
+
+  // Maximum number of negatives to retain for each positive anchor. If
+  // num_negatives_per_positive is 0 no prespecified negative:positive ratio is
+  // enforced.
+  optional int32 max_negatives_per_positive = 4 [default=0];
+
+  // Minimum number of negative anchors to sample for a given image. Setting
+  // this to a positive number samples negatives in an image without any
+  // positive anchors and thus not bias the model towards having at least one
+  // detection per image.
+  optional int32 min_negatives_per_image = 5 [default=0];
+}
+
+// Configuration for random example sampler.
+message RandomExampleSampler {
+  // The desired fraction of positive samples in batch when applying random
+  // example sampling.
+  optional float positive_sample_fraction = 1 [default = 0.01];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/matcher.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/matcher.proto
new file mode 100644
index 00000000..b47de56c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/matcher.proto
@@ -0,0 +1,15 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/argmax_matcher.proto";
+import "object_detection/protos/bipartite_matcher.proto";
+
+// Configuration proto for the matcher to be used in the object detection
+// pipeline. See core/matcher.py for details.
+message Matcher {
+  oneof matcher_oneof {
+    ArgMaxMatcher argmax_matcher = 1;
+    BipartiteMatcher bipartite_matcher = 2;
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/mean_stddev_box_coder.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/mean_stddev_box_coder.proto
new file mode 100644
index 00000000..eb2d97ca
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/mean_stddev_box_coder.proto
@@ -0,0 +1,10 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for MeanStddevBoxCoder. See
+// box_coders/mean_stddev_box_coder.py for details.
+message MeanStddevBoxCoder {
+  // The standard deviation used to encode and decode boxes.
+  optional float stddev = 1 [default=0.01];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/model.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/model.proto
new file mode 100644
index 00000000..b699c17b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/model.proto
@@ -0,0 +1,14 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/faster_rcnn.proto";
+import "object_detection/protos/ssd.proto";
+
+// Top level configuration for DetectionModels.
+message DetectionModel {
+  oneof model {
+    FasterRcnn faster_rcnn = 1;
+    Ssd ssd = 2;
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/multiscale_anchor_generator.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/multiscale_anchor_generator.proto
new file mode 100644
index 00000000..799db98a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/multiscale_anchor_generator.proto
@@ -0,0 +1,26 @@
+  syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for RetinaNet anchor generator described in
+// https://arxiv.org/abs/1708.02002. See
+// anchor_generators/multiscale_grid_anchor_generator.py for details.
+message MultiscaleAnchorGenerator {
+  // minimum level in feature pyramid
+  optional int32 min_level = 1 [default = 3];
+
+  // maximum level in feature pyramid
+  optional int32 max_level = 2 [default = 7];
+
+  // Scale of anchor to feature stride
+  optional float anchor_scale = 3 [default = 4.0];
+
+  // Aspect ratios for anchors at each grid point.
+  repeated float aspect_ratios = 4;
+
+  // Number of intermediate scale each scale octave
+  optional int32 scales_per_octave = 5 [default = 2];
+
+  // Whether to produce anchors in normalized coordinates.
+  optional bool normalize_coordinates = 6 [default = true];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/optimizer.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/optimizer.proto
new file mode 100644
index 00000000..8bbebe45
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/optimizer.proto
@@ -0,0 +1,91 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Messages for configuring the optimizing strategy for training object
+// detection models.
+
+// Top level optimizer message.
+message Optimizer {
+  oneof optimizer {
+    RMSPropOptimizer rms_prop_optimizer = 1;
+    MomentumOptimizer momentum_optimizer = 2;
+    AdamOptimizer adam_optimizer = 3;
+  }
+  optional bool use_moving_average = 4 [default = true];
+  optional float moving_average_decay = 5 [default = 0.9999];
+}
+
+// Configuration message for the RMSPropOptimizer
+// See: https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer
+message RMSPropOptimizer {
+  optional LearningRate learning_rate = 1;
+  optional float momentum_optimizer_value = 2 [default = 0.9];
+  optional float decay = 3 [default = 0.9];
+  optional float epsilon = 4 [default = 1.0];
+}
+
+// Configuration message for the MomentumOptimizer
+// See: https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer
+message MomentumOptimizer {
+  optional LearningRate learning_rate = 1;
+  optional float momentum_optimizer_value = 2 [default = 0.9];
+}
+
+// Configuration message for the AdamOptimizer
+// See: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
+message AdamOptimizer {
+  optional LearningRate learning_rate = 1;
+}
+
+// Configuration message for optimizer learning rate.
+message LearningRate {
+  oneof learning_rate {
+    ConstantLearningRate constant_learning_rate = 1;
+    ExponentialDecayLearningRate exponential_decay_learning_rate = 2;
+    ManualStepLearningRate manual_step_learning_rate = 3;
+    CosineDecayLearningRate cosine_decay_learning_rate = 4;
+  }
+}
+
+// Configuration message for a constant learning rate.
+message ConstantLearningRate {
+  optional float learning_rate = 1 [default = 0.002];
+}
+
+// Configuration message for an exponentially decaying learning rate.
+// See https://www.tensorflow.org/versions/master/api_docs/python/train/ \
+//     decaying_the_learning_rate#exponential_decay
+message ExponentialDecayLearningRate {
+  optional float initial_learning_rate = 1 [default = 0.002];
+  optional uint32 decay_steps = 2 [default = 4000000];
+  optional float decay_factor = 3 [default = 0.95];
+  optional bool staircase = 4 [default = true];
+  optional float burnin_learning_rate = 5 [default = 0.0];
+  optional uint32 burnin_steps =  6 [default = 0];
+  optional float min_learning_rate =  7 [default = 0.0];
+}
+
+// Configuration message for a manually defined learning rate schedule.
+message ManualStepLearningRate {
+  optional float initial_learning_rate = 1 [default = 0.002];
+  message LearningRateSchedule {
+    optional uint32 step = 1;
+    optional float learning_rate = 2 [default = 0.002];
+  }
+  repeated LearningRateSchedule schedule = 2;
+
+  // Whether to linearly interpolate learning rates for steps in
+  // [0, schedule[0].step].
+  optional bool warmup = 3 [default = false];
+}
+
+// Configuration message for a cosine decaying learning rate as defined in
+// object_detection/utils/learning_schedules.py
+message CosineDecayLearningRate {
+  optional float learning_rate_base = 1 [default = 0.002];
+  optional uint32 total_steps = 2 [default = 4000000];
+  optional float warmup_learning_rate = 3 [default = 0.0002];
+  optional uint32 warmup_steps = 4 [default = 10000];
+  optional uint32 hold_base_rate_steps = 5 [default = 0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/pipeline.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/pipeline.proto
new file mode 100644
index 00000000..0b3cd150
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/pipeline.proto
@@ -0,0 +1,22 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/eval.proto";
+import "object_detection/protos/graph_rewriter.proto";
+import "object_detection/protos/input_reader.proto";
+import "object_detection/protos/model.proto";
+import "object_detection/protos/train.proto";
+
+// Convenience message for configuring a training and eval pipeline. Allows all
+// of the pipeline parameters to be configured from one file.
+// Next id: 7
+message TrainEvalPipelineConfig {
+  optional DetectionModel model = 1;
+  optional TrainConfig train_config = 2;
+  optional InputReader train_input_reader = 3;
+  optional EvalConfig eval_config = 4;
+  repeated InputReader eval_input_reader = 5;
+  optional GraphRewriter graph_rewriter = 6;
+  extensions 1000 to max;
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/post_processing.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/post_processing.proto
new file mode 100644
index 00000000..3d501b6d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/post_processing.proto
@@ -0,0 +1,49 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for non-max-suppression operation on a batch of
+// detections.
+message BatchNonMaxSuppression {
+  // Scalar threshold for score (low scoring boxes are removed).
+  optional float score_threshold = 1 [default = 0.0];
+
+  // Scalar threshold for IOU (boxes that have high IOU overlap
+  // with previously selected boxes are removed).
+  optional float iou_threshold = 2 [default = 0.6];
+
+  // Maximum number of detections to retain per class.
+  optional int32 max_detections_per_class = 3 [default = 100];
+
+  // Maximum number of detections to retain across all classes.
+  optional int32 max_total_detections = 5 [default = 100];
+
+  // Whether to use the implementation of NMS that guarantees static shapes.
+  optional bool use_static_shapes = 6 [default = false];
+}
+
+// Configuration proto for post-processing predicted boxes and
+// scores.
+message PostProcessing {
+  // Non max suppression parameters.
+  optional BatchNonMaxSuppression batch_non_max_suppression = 1;
+
+  // Enum to specify how to convert the detection scores.
+  enum ScoreConverter {
+    // Input scores equals output scores.
+    IDENTITY = 0;
+
+    // Applies a sigmoid on input scores.
+    SIGMOID = 1;
+
+    // Applies a softmax on input scores
+    SOFTMAX = 2;
+  }
+
+  // Score converter to use.
+  optional ScoreConverter score_converter = 2 [default = IDENTITY];
+  // Scale logit (input) value before conversion in post-processing step.
+  // Typically used for softmax distillation, though can be used to scale for
+  // other reasons.
+  optional float logit_scale = 3 [default = 1.0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/preprocessor.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/preprocessor.proto
new file mode 100644
index 00000000..4905bf4b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/preprocessor.proto
@@ -0,0 +1,441 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Message for defining a preprocessing operation on input data.
+// See: //third_party/tensorflow_models/object_detection/core/preprocessor.py
+message PreprocessingStep {
+  oneof preprocessing_step {
+    NormalizeImage normalize_image = 1;
+    RandomHorizontalFlip random_horizontal_flip = 2;
+    RandomPixelValueScale random_pixel_value_scale = 3;
+    RandomImageScale random_image_scale = 4;
+    RandomRGBtoGray random_rgb_to_gray = 5;
+    RandomAdjustBrightness random_adjust_brightness = 6;
+    RandomAdjustContrast random_adjust_contrast = 7;
+    RandomAdjustHue random_adjust_hue = 8;
+    RandomAdjustSaturation random_adjust_saturation = 9;
+    RandomDistortColor random_distort_color = 10;
+    RandomJitterBoxes random_jitter_boxes = 11;
+    RandomCropImage random_crop_image = 12;
+    RandomPadImage random_pad_image = 13;
+    RandomCropPadImage random_crop_pad_image = 14;
+    RandomCropToAspectRatio random_crop_to_aspect_ratio = 15;
+    RandomBlackPatches random_black_patches = 16;
+    RandomResizeMethod random_resize_method = 17;
+    ScaleBoxesToPixelCoordinates scale_boxes_to_pixel_coordinates = 18;
+    ResizeImage resize_image = 19;
+    SubtractChannelMean subtract_channel_mean = 20;
+    SSDRandomCrop ssd_random_crop = 21;
+    SSDRandomCropPad ssd_random_crop_pad = 22;
+    SSDRandomCropFixedAspectRatio ssd_random_crop_fixed_aspect_ratio = 23;
+    SSDRandomCropPadFixedAspectRatio ssd_random_crop_pad_fixed_aspect_ratio = 24;
+    RandomVerticalFlip random_vertical_flip = 25;
+    RandomRotation90 random_rotation90 = 26;
+    RGBtoGray rgb_to_gray = 27;
+    ConvertClassLogitsToSoftmax convert_class_logits_to_softmax = 28;
+  }
+}
+
+// Normalizes pixel values in an image.
+// For every channel in the image, moves the pixel values from the range
+// [original_minval, original_maxval] to [target_minval, target_maxval].
+message NormalizeImage {
+  optional float original_minval = 1;
+  optional float original_maxval = 2;
+  optional float target_minval = 3 [default=0];
+  optional float target_maxval = 4 [default=1];
+}
+
+// Randomly horizontally flips the image and detections 50% of the time.
+message RandomHorizontalFlip {
+  // Specifies a mapping from the original keypoint indices to horizontally
+  // flipped indices. This is used in the event that keypoints are specified,
+  // in which case when the image is horizontally flipped the keypoints will
+  // need to be permuted. E.g. for keypoints representing left_eye, right_eye,
+  // nose_tip, mouth, left_ear, right_ear (in that order), one might specify
+  // the keypoint_flip_permutation below:
+  // keypoint_flip_permutation: 1
+  // keypoint_flip_permutation: 0
+  // keypoint_flip_permutation: 2
+  // keypoint_flip_permutation: 3
+  // keypoint_flip_permutation: 5
+  // keypoint_flip_permutation: 4
+  repeated int32 keypoint_flip_permutation = 1;
+}
+
+// Randomly vertically flips the image and detections 50% of the time.
+message RandomVerticalFlip {
+  // Specifies a mapping from the original keypoint indices to vertically
+  // flipped indices. This is used in the event that keypoints are specified,
+  // in which case when the image is vertically flipped the keypoints will
+  // need to be permuted. E.g. for keypoints representing left_eye, right_eye,
+  // nose_tip, mouth, left_ear, right_ear (in that order), one might specify
+  // the keypoint_flip_permutation below:
+  // keypoint_flip_permutation: 1
+  // keypoint_flip_permutation: 0
+  // keypoint_flip_permutation: 2
+  // keypoint_flip_permutation: 3
+  // keypoint_flip_permutation: 5
+  // keypoint_flip_permutation: 4
+  repeated int32 keypoint_flip_permutation = 1;
+}
+
+// Randomly rotates the image and detections by 90 degrees counter-clockwise
+// 50% of the time.
+message RandomRotation90 {}
+
+// Randomly scales the values of all pixels in the image by some constant value
+// between [minval, maxval], then clip the value to a range between [0, 1.0].
+message RandomPixelValueScale {
+  optional float minval = 1 [default=0.9];
+  optional float maxval = 2 [default=1.1];
+}
+
+// Randomly enlarges or shrinks image (keeping aspect ratio).
+message RandomImageScale {
+  optional float min_scale_ratio = 1 [default=0.5];
+  optional float max_scale_ratio = 2 [default=2.0];
+}
+
+// Randomly convert entire image to grey scale.
+message RandomRGBtoGray {
+  optional float probability = 1 [default=0.1];
+}
+
+// Randomly changes image brightness by up to max_delta. Image outputs will be
+// saturated between 0 and 1.
+message RandomAdjustBrightness {
+  optional float max_delta=1 [default=0.2];
+}
+
+// Randomly scales contract by a value between [min_delta, max_delta].
+message RandomAdjustContrast {
+  optional float min_delta = 1 [default=0.8];
+  optional float max_delta = 2 [default=1.25];
+}
+
+// Randomly alters hue by a value of up to max_delta.
+message RandomAdjustHue {
+  optional float max_delta = 1 [default=0.02];
+}
+
+// Randomly changes saturation by a value between [min_delta, max_delta].
+message RandomAdjustSaturation {
+  optional float min_delta = 1 [default=0.8];
+  optional float max_delta = 2 [default=1.25];
+}
+
+// Performs a random color distortion. color_orderings should either be 0 or 1.
+message RandomDistortColor {
+  optional int32 color_ordering = 1;
+}
+
+// Randomly jitters corners of boxes in the image determined by ratio.
+// ie. If a box is [100, 200] and ratio is 0.02, the corners can move by [1, 4].
+message RandomJitterBoxes {
+  optional float ratio = 1 [default=0.05];
+}
+
+// Randomly crops the image and bounding boxes.
+message RandomCropImage {
+  // Cropped image must cover at least one box by this fraction.
+  optional float min_object_covered = 1 [default=1.0];
+
+  // Aspect ratio bounds of cropped image.
+  optional float min_aspect_ratio = 2 [default=0.75];
+  optional float max_aspect_ratio = 3 [default=1.33];
+
+  // Allowed area ratio of cropped image to original image.
+  optional float min_area = 4 [default=0.1];
+  optional float max_area = 5 [default=1.0];
+
+  // Minimum overlap threshold of cropped boxes to keep in new image. If the
+  // ratio between a cropped bounding box and the original is less than this
+  // value, it is removed from the new image.
+  optional float overlap_thresh = 6 [default=0.3];
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 8 [default=true];
+
+  // Probability of keeping the original image.
+  optional float random_coef = 7 [default=0.0];
+}
+
+// Randomly adds padding to the image.
+message RandomPadImage {
+  // Minimum dimensions for padded image. If unset, will use original image
+  // dimension as a lower bound.
+  optional int32 min_image_height = 1;
+  optional int32 min_image_width = 2;
+
+  // Maximum dimensions for padded image. If unset, will use double the original
+  // image dimension as a lower bound.
+  optional int32 max_image_height = 3;
+  optional int32 max_image_width = 4;
+
+  // Color of the padding. If unset, will pad using average color of the input
+  // image.
+  repeated float pad_color = 5;
+}
+
+// Randomly crops an image followed by a random pad.
+message RandomCropPadImage {
+  // Cropping operation must cover at least one box by this fraction.
+  optional float min_object_covered = 1 [default=1.0];
+
+  // Aspect ratio bounds of image after cropping operation.
+  optional float min_aspect_ratio = 2 [default=0.75];
+  optional float max_aspect_ratio = 3 [default=1.33];
+
+  // Allowed area ratio of image after cropping operation.
+  optional float min_area = 4 [default=0.1];
+  optional float max_area = 5 [default=1.0];
+
+  // Minimum overlap threshold of cropped boxes to keep in new image. If the
+  // ratio between a cropped bounding box and the original is less than this
+  // value, it is removed from the new image.
+  optional float overlap_thresh = 6 [default=0.3];
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 11 [default=true];
+
+  // Probability of keeping the original image during the crop operation.
+  optional float random_coef = 7 [default=0.0];
+
+  // Maximum dimensions for padded image. If unset, will use double the original
+  // image dimension as a lower bound. Both of the following fields should be
+  // length 2.
+  repeated float min_padded_size_ratio = 8;
+  repeated float max_padded_size_ratio = 9;
+
+  // Color of the padding. If unset, will pad using average color of the input
+  // image. This field should be of length 3.
+  repeated float pad_color = 10;
+}
+
+// Randomly crops an iamge to a given aspect ratio.
+message RandomCropToAspectRatio {
+  // Aspect ratio.
+  optional float aspect_ratio = 1 [default=1.0];
+
+  // Minimum overlap threshold of cropped boxes to keep in new image. If the
+  // ratio between a cropped bounding box and the original is less than this
+  // value, it is removed from the new image.
+  optional float overlap_thresh = 2 [default=0.3];
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 3 [default=true];
+}
+
+// Randomly adds black square patches to an image.
+message RandomBlackPatches {
+  // The maximum number of black patches to add.
+  optional int32 max_black_patches = 1 [default=10];
+
+  // The probability of a black patch being added to an image.
+  optional float probability = 2 [default=0.5];
+
+  // Ratio between the dimension of the black patch to the minimum dimension of
+  // the image (patch_width = patch_height = min(image_height, image_width)).
+  optional float size_to_image_ratio = 3 [default=0.1];
+}
+
+// Randomly resizes the image up to [target_height, target_width].
+message RandomResizeMethod {
+  optional float target_height = 1;
+  optional float target_width = 2;
+}
+
+// Converts the RGB image to a grayscale image. This also converts the image
+// depth from 3 to 1, unlike RandomRGBtoGray which does not change the image
+// depth.
+message RGBtoGray {}
+
+// Scales boxes from normalized coordinates to pixel coordinates.
+message ScaleBoxesToPixelCoordinates {
+}
+
+// Resizes images to [new_height, new_width].
+message ResizeImage {
+  optional int32 new_height = 1;
+  optional int32 new_width = 2;
+  enum Method {
+    AREA=1;
+    BICUBIC=2;
+    BILINEAR=3;
+    NEAREST_NEIGHBOR=4;
+  }
+  optional Method method = 3 [default=BILINEAR];
+}
+
+// Normalizes an image by subtracting a mean from each channel.
+message SubtractChannelMean {
+  // The mean to subtract from each channel. Should be of same dimension of
+  // channels in the input image.
+  repeated float means = 1;
+}
+
+message SSDRandomCropOperation {
+  // Cropped image must cover at least this fraction of one original bounding
+  // box.
+  optional float min_object_covered = 1;
+
+  // The aspect ratio of the cropped image must be within the range of
+  // [min_aspect_ratio, max_aspect_ratio].
+  optional float min_aspect_ratio = 2;
+  optional float max_aspect_ratio = 3;
+
+  // The area of the cropped image must be within the range of
+  // [min_area, max_area].
+  optional float min_area = 4;
+  optional float max_area = 5;
+
+  // Cropped box area ratio must be above this threhold to be kept.
+  optional float overlap_thresh = 6;
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 8 [default=true];
+
+  // Probability a crop operation is skipped.
+  optional float random_coef = 7;
+}
+
+// Randomly crops a image according to:
+//     Liu et al., SSD: Single shot multibox detector.
+// This preprocessing step defines multiple SSDRandomCropOperations. Only one
+// operation (chosen at random) is actually performed on an image.
+message SSDRandomCrop {
+  repeated SSDRandomCropOperation operations = 1;
+}
+
+message SSDRandomCropPadOperation {
+  // Cropped image must cover at least this fraction of one original bounding
+  // box.
+  optional float min_object_covered = 1;
+
+  // The aspect ratio of the cropped image must be within the range of
+  // [min_aspect_ratio, max_aspect_ratio].
+  optional float min_aspect_ratio = 2;
+  optional float max_aspect_ratio = 3;
+
+  // The area of the cropped image must be within the range of
+  // [min_area, max_area].
+  optional float min_area = 4;
+  optional float max_area = 5;
+
+  // Cropped box area ratio must be above this threhold to be kept.
+  optional float overlap_thresh = 6;
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 13 [default=true];
+
+  // Probability a crop operation is skipped.
+  optional float random_coef = 7;
+
+  // Min ratio of padded image height and width to the input image's height and
+  // width. Two entries per operation.
+  repeated float min_padded_size_ratio = 8;
+
+  // Max ratio of padded image height and width to the input image's height and
+  // width. Two entries per operation.
+  repeated float max_padded_size_ratio = 9;
+
+  // Padding color.
+  optional float pad_color_r = 10;
+  optional float pad_color_g = 11;
+  optional float pad_color_b = 12;
+}
+
+// Randomly crops and pads an image according to:
+//     Liu et al., SSD: Single shot multibox detector.
+// This preprocessing step defines multiple SSDRandomCropPadOperations. Only one
+// operation (chosen at random) is actually performed on an image.
+message SSDRandomCropPad {
+  repeated SSDRandomCropPadOperation operations = 1;
+}
+
+message SSDRandomCropFixedAspectRatioOperation {
+  // Cropped image must cover at least this fraction of one original bounding
+  // box.
+  optional float min_object_covered = 1;
+
+  // The area of the cropped image must be within the range of
+  // [min_area, max_area].
+  optional float min_area = 4;
+  optional float max_area = 5;
+
+  // Cropped box area ratio must be above this threhold to be kept.
+  optional float overlap_thresh = 6;
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 8 [default=true];
+
+  // Probability a crop operation is skipped.
+  optional float random_coef = 7;
+}
+
+// Randomly crops a image to a fixed aspect ratio according to:
+//     Liu et al., SSD: Single shot multibox detector.
+// Multiple SSDRandomCropFixedAspectRatioOperations are defined by this
+// preprocessing step. Only one operation (chosen at random) is actually
+// performed on an image.
+message SSDRandomCropFixedAspectRatio {
+  repeated SSDRandomCropFixedAspectRatioOperation operations = 1;
+
+  // Aspect ratio to crop to. This value is used for all crop operations.
+  optional float aspect_ratio = 2 [default=1.0];
+}
+
+message SSDRandomCropPadFixedAspectRatioOperation {
+  // Cropped image must cover at least this fraction of one original bounding
+  // box.
+  optional float min_object_covered = 1;
+
+  // The aspect ratio of the cropped image must be within the range of
+  // [min_aspect_ratio, max_aspect_ratio].
+  optional float min_aspect_ratio = 2;
+  optional float max_aspect_ratio = 3;
+
+  // The area of the cropped image must be within the range of
+  // [min_area, max_area].
+  optional float min_area = 4;
+  optional float max_area = 5;
+
+  // Cropped box area ratio must be above this threhold to be kept.
+  optional float overlap_thresh = 6;
+
+  // Whether to clip the boxes to the cropped image.
+  optional bool clip_boxes = 8 [default=true];
+
+  // Probability a crop operation is skipped.
+  optional float random_coef = 7;
+}
+
+// Randomly crops and pads an image to a fixed aspect ratio according to:
+//     Liu et al., SSD: Single shot multibox detector.
+// Multiple SSDRandomCropPadFixedAspectRatioOperations are defined by this
+// preprocessing step. Only one operation (chosen at random) is actually
+// performed on an image.
+message SSDRandomCropPadFixedAspectRatio {
+  repeated SSDRandomCropPadFixedAspectRatioOperation operations = 1;
+
+  // Aspect ratio to pad to. This value is used for all crop and pad operations.
+  optional float aspect_ratio = 2 [default=1.0];
+
+  // Min ratio of padded image height and width to the input image's height and
+  // width. Two entries per operation.
+  repeated float min_padded_size_ratio = 3;
+
+  // Max ratio of padded image height and width to the input image's height and
+  // width. Two entries per operation.
+  repeated float max_padded_size_ratio = 4;
+}
+
+// Converts class logits to softmax optionally scaling the values by temperature
+// first.
+message ConvertClassLogitsToSoftmax {
+
+  // Scale to use on logits before applying softmax.
+  optional float temperature = 1 [default=1.0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/region_similarity_calculator.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/region_similarity_calculator.proto
new file mode 100644
index 00000000..2fc70c01
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/region_similarity_calculator.proto
@@ -0,0 +1,33 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for region similarity calculators. See
+// core/region_similarity_calculator.py for details.
+message RegionSimilarityCalculator {
+  oneof region_similarity {
+    NegSqDistSimilarity neg_sq_dist_similarity = 1;
+    IouSimilarity iou_similarity = 2;
+    IoaSimilarity ioa_similarity = 3;
+    ThresholdedIouSimilarity thresholded_iou_similarity = 4;
+  }
+}
+
+// Configuration for negative squared distance similarity calculator.
+message NegSqDistSimilarity {
+}
+
+// Configuration for intersection-over-union (IOU) similarity calculator.
+message IouSimilarity {
+}
+
+// Configuration for intersection-over-area (IOA) similarity calculator.
+message IoaSimilarity {
+}
+
+// Configuration for thresholded-intersection-over-union similarity calculator.
+message ThresholdedIouSimilarity {
+
+  // IOU threshold used for filtering scores.
+  optional float iou_threshold = 1 [default = 0.5];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/square_box_coder.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/square_box_coder.proto
new file mode 100644
index 00000000..41575eb4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/square_box_coder.proto
@@ -0,0 +1,14 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for SquareBoxCoder. See
+// box_coders/square_box_coder.py for details.
+message SquareBoxCoder {
+  // Scale factor for anchor encoded box center.
+  optional float y_scale = 1 [default = 10.0];
+  optional float x_scale = 2 [default = 10.0];
+
+  // Scale factor for anchor encoded box length.
+  optional float length_scale = 3 [default = 5.0];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd.proto
new file mode 100644
index 00000000..21313836
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd.proto
@@ -0,0 +1,213 @@
+syntax = "proto2";
+package object_detection.protos;
+
+import "object_detection/protos/anchor_generator.proto";
+import "object_detection/protos/box_coder.proto";
+import "object_detection/protos/box_predictor.proto";
+import "object_detection/protos/hyperparams.proto";
+import "object_detection/protos/image_resizer.proto";
+import "object_detection/protos/matcher.proto";
+import "object_detection/protos/losses.proto";
+import "object_detection/protos/post_processing.proto";
+import "object_detection/protos/region_similarity_calculator.proto";
+
+// Configuration for Single Shot Detection (SSD) models.
+// Next id: 26
+message Ssd {
+
+  // Number of classes to predict.
+  optional int32 num_classes = 1;
+
+  // Image resizer for preprocessing the input image.
+  optional ImageResizer image_resizer = 2;
+
+  // Feature extractor config.
+  optional SsdFeatureExtractor feature_extractor = 3;
+
+  // Box coder to encode the boxes.
+  optional BoxCoder box_coder = 4;
+
+  // Matcher to match groundtruth with anchors.
+  optional Matcher matcher = 5;
+
+  // Region similarity calculator to compute similarity of boxes.
+  optional RegionSimilarityCalculator similarity_calculator = 6;
+
+  // Whether background targets are to be encoded as an all
+  // zeros vector or a one-hot vector (where background is the 0th class).
+  optional bool encode_background_as_zeros = 12 [default = false];
+
+  // classification weight to be associated to negative
+  // anchors (default: 1.0). The weight must be in [0., 1.].
+  optional float negative_class_weight = 13 [default = 1.0];
+
+  // Box predictor to attach to the features.
+  optional BoxPredictor box_predictor = 7;
+
+  // Anchor generator to compute anchors.
+  optional AnchorGenerator anchor_generator = 8;
+
+  // Post processing to apply on the predictions.
+  optional PostProcessing post_processing = 9;
+
+  // Whether to normalize the loss by number of groundtruth boxes that match to
+  // the anchors.
+  optional bool normalize_loss_by_num_matches = 10 [default = true];
+
+  // Whether to normalize the localization loss by the code size of the box
+  // encodings. This is applied along with other normalization factors.
+  optional bool normalize_loc_loss_by_codesize = 14 [default = false];
+
+  // Loss configuration for training.
+  optional Loss loss = 11;
+
+  // Whether to update batch norm parameters during training or not.
+  // When training with a relative small batch size (e.g. 1), it is
+  // desirable to disable batch norm update and use pretrained batch norm
+  // params.
+  //
+  // Note: Some feature extractors are used with canned arg_scopes
+  // (e.g resnet arg scopes).  In these cases training behavior of batch norm
+  // variables may depend on both values of `batch_norm_trainable` and
+  // `is_training`.
+  //
+  // When canned arg_scopes are used with feature extractors `conv_hyperparams`
+  // will apply only to the additional layers that are added and are outside the
+  // canned arg_scope.
+  optional bool freeze_batchnorm = 16 [default = false];
+
+  // Whether to update batch_norm inplace during training. This is required
+  // for batch norm to work correctly on TPUs. When this is false, user must add
+  // a control dependency on tf.GraphKeys.UPDATE_OPS for train/loss op in order
+  // to update the batch norm moving average parameters.
+  optional bool inplace_batchnorm_update = 15 [default = false];
+
+  // Whether to add an implicit background class to one-hot encodings of
+  // groundtruth labels. Set to false if training a single
+  // class model or using an explicit background class.
+  optional bool add_background_class = 21 [default = true];
+
+  // Whether to use an explicit background class. Set to true if using
+  // groundtruth labels with an explicit background class, as in multiclass
+  // scores.
+  optional bool explicit_background_class = 24 [default = false];
+
+  optional bool use_confidences_as_targets = 22 [default = false];
+
+  optional float implicit_example_weight = 23 [default = 1.0];
+
+  // Configuration proto for MaskHead.
+  // Next id: 11
+  message MaskHead {
+    // The height and the width of the predicted mask. Only used when
+    // predict_instance_masks is true.
+    optional int32 mask_height = 1 [default = 15];
+    optional int32 mask_width = 2 [default = 15];
+
+    // Whether to predict class agnostic masks. Only used when
+    // predict_instance_masks is true.
+    optional bool masks_are_class_agnostic = 3 [default = true];
+
+    // The depth for the first conv2d_transpose op applied to the
+    // image_features in the mask prediction branch. If set to 0, the value
+    // will be set automatically based on the number of channels in the image
+    // features and the number of classes.
+    optional int32 mask_prediction_conv_depth = 4 [default = 256];
+
+    // The number of convolutions applied to image_features in the mask prediction
+    // branch.
+    optional int32 mask_prediction_num_conv_layers = 5 [default = 2];
+
+    // Whether to apply convolutions on mask features before upsampling using
+    // nearest neighbor resizing.
+    // By default, mask features are resized to [`mask_height`, `mask_width`]
+    // before applying convolutions and predicting masks.
+    optional bool convolve_then_upsample_masks = 6 [default = false];
+
+    // Mask loss weight.
+    optional float mask_loss_weight = 7 [default=5.0];
+
+    // Number of boxes to be generated at training time for computing mask loss.
+    optional int32 mask_loss_sample_size = 8 [default=16];
+
+    // Hyperparameters for convolution ops used in the box predictor.
+    optional Hyperparams conv_hyperparams = 9;
+
+    // Output size (width and height are set to be the same) of the initial
+    // bilinear interpolation based cropping during ROI pooling. Only used when
+    // we have second stage prediction head enabled (e.g. mask head).
+    optional int32 initial_crop_size = 10 [default = 15];
+  }
+
+  // Configs for mask head.
+  optional MaskHead mask_head_config = 25;
+}
+
+message SsdFeatureExtractor {
+  reserved 6;
+
+  // Type of ssd feature extractor.
+  optional string type = 1;
+
+  // The factor to alter the depth of the channels in the feature extractor.
+  optional float depth_multiplier = 2 [default = 1.0];
+
+  // Minimum number of the channels in the feature extractor.
+  optional int32 min_depth = 3 [default = 16];
+
+  // Hyperparameters that affect the layers of feature extractor added on top
+  // of the base feature extractor.
+  optional Hyperparams conv_hyperparams = 4;
+
+  // Normally, SSD feature extractors are constructed by reusing an existing
+  // base feature extractor (that has its own hyperparams) and adding new layers
+  // on top of it. `conv_hyperparams` above normally applies only to the new
+  // layers while base feature extractor uses its own default hyperparams. If
+  // this value is set to true, the base feature extractor's hyperparams will be
+  // overridden with the `conv_hyperparams`.
+  optional bool override_base_feature_extractor_hyperparams = 9
+      [default = false];
+
+  // The nearest multiple to zero-pad the input height and width dimensions to.
+  // For example, if pad_to_multiple = 2, input dimensions are zero-padded
+  // until the resulting dimensions are even.
+  optional int32 pad_to_multiple = 5 [default = 1];
+
+  // Whether to use explicit padding when extracting SSD multiresolution
+  // features. This will also apply to the base feature extractor if a MobileNet
+  // architecture is used.
+  optional bool use_explicit_padding = 7 [default = false];
+
+  // Whether to use depthwise separable convolutions for to extract additional
+  // feature maps added by SSD.
+  optional bool use_depthwise = 8 [default = false];
+
+  // Feature Pyramid Networks config.
+  optional FeaturePyramidNetworks fpn = 10;
+}
+
+// Configuration for Feature Pyramid Networks.
+message FeaturePyramidNetworks {
+  // We recommend to use multi_resolution_feature_map_generator with FPN, and
+  // the levels there must match the levels defined below for better
+  // performance.
+  // Correspondence from FPN levels to Resnet/Mobilenet V1 feature maps:
+  // FPN Level        Resnet Feature Map      Mobilenet-V1 Feature Map
+  //     2               Block 1                Conv2d_3_pointwise
+  //     3               Block 2                Conv2d_5_pointwise
+  //     4               Block 3                Conv2d_11_pointwise
+  //     5               Block 4                Conv2d_13_pointwise
+  //     6               Bottomup_5             bottom_up_Conv2d_14
+  //     7               Bottomup_6             bottom_up_Conv2d_15
+  //     8               Bottomup_7             bottom_up_Conv2d_16
+  //     9               Bottomup_8             bottom_up_Conv2d_17
+
+  // minimum level in feature pyramid
+  optional int32 min_level = 1 [default = 3];
+
+  // maximum level in feature pyramid
+  optional int32 max_level = 2 [default = 7];
+
+  // channel depth for additional coarse feature layers.
+  optional int32 additional_layer_depth = 3 [default = 256];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd_anchor_generator.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd_anchor_generator.proto
new file mode 100644
index 00000000..d969ddf7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/ssd_anchor_generator.proto
@@ -0,0 +1,55 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration proto for SSD anchor generator described in
+// https://arxiv.org/abs/1512.02325. See
+// anchor_generators/multiple_grid_anchor_generator.py for details.
+message SsdAnchorGenerator {
+  // Number of grid layers to create anchors for.
+  optional int32 num_layers = 1 [default = 6];
+
+  // Scale of anchors corresponding to finest resolution.
+  optional float min_scale = 2 [default = 0.2];
+
+  // Scale of anchors corresponding to coarsest resolution
+  optional float max_scale = 3 [default = 0.95];
+
+  // Can be used to override min_scale->max_scale, with an explicitly defined
+  // set of scales.  If empty, then min_scale->max_scale is used.
+  repeated float scales = 12;
+
+  // Aspect ratios for anchors at each grid point.
+  repeated float aspect_ratios = 4;
+
+  // When this aspect ratio is greater than 0, then an additional
+  // anchor, with an interpolated scale is added with this aspect ratio.
+  optional float interpolated_scale_aspect_ratio = 13 [default = 1.0];
+
+  // Whether to use the following aspect ratio and scale combination for the
+  // layer with the finest resolution : (scale=0.1, aspect_ratio=1.0),
+  // (scale=min_scale, aspect_ration=2.0), (scale=min_scale, aspect_ratio=0.5).
+  optional bool reduce_boxes_in_lowest_layer = 5 [default = true];
+
+  // The base anchor size in height dimension.
+  optional float base_anchor_height = 6 [default = 1.0];
+
+  // The base anchor size in width dimension.
+  optional float base_anchor_width = 7 [default = 1.0];
+
+  // Anchor stride in height dimension in pixels for each layer. The length of
+  // this field is expected to be equal to the value of num_layers.
+  repeated int32 height_stride = 8;
+
+  // Anchor stride in width dimension in pixels for each layer. The length of
+  // this field is expected to be equal to the value of num_layers.
+  repeated int32 width_stride = 9;
+
+  // Anchor height offset in pixels for each layer. The length of this field is
+  // expected to be equal to the value of num_layers.
+  repeated int32 height_offset = 10;
+
+  // Anchor width offset in pixels for each layer. The length of this field is
+  // expected to be equal to the value of num_layers.
+  repeated int32 width_offset = 11;
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/string_int_label_map.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/string_int_label_map.proto
new file mode 100644
index 00000000..0894183b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/string_int_label_map.proto
@@ -0,0 +1,24 @@
+// Message to store the mapping from class label strings to class id. Datasets
+// use string labels to represent classes while the object detection framework
+// works with class ids. This message maps them so they can be converted back
+// and forth as needed.
+syntax = "proto2";
+
+package object_detection.protos;
+
+message StringIntLabelMapItem {
+  // String name. The most common practice is to set this to a MID or synsets
+  // id.
+  optional string name = 1;
+
+  // Integer id that maps to the string name above. Label ids should start from
+  // 1.
+  optional int32 id = 2;
+
+  // Human readable string label.
+  optional string display_name = 3;
+};
+
+message StringIntLabelMap {
+  repeated StringIntLabelMapItem item = 1;
+};
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/protos/train.proto b/TensorFlow/Detection/SSD/models/research/object_detection/protos/train.proto
new file mode 100644
index 00000000..0d2a7e62
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/protos/train.proto
@@ -0,0 +1,121 @@
+syntax = "proto2";
+
+package object_detection.protos;
+
+import "object_detection/protos/optimizer.proto";
+import "object_detection/protos/preprocessor.proto";
+
+// Message for configuring DetectionModel training jobs (train.py).
+// Next id: 28
+message TrainConfig {
+  // Effective batch size to use for training.
+  // For TPU (or sync SGD jobs), the batch size per core (or GPU) is going to be
+  // `batch_size` / number of cores (or `batch_size` / number of GPUs).
+  optional uint32 batch_size = 1 [default=32];
+
+  // Data augmentation options.
+  repeated PreprocessingStep data_augmentation_options = 2;
+
+  // Whether to synchronize replicas during training.
+  optional bool sync_replicas = 3 [default=false];
+
+  // How frequently to keep checkpoints.
+  optional float keep_checkpoint_every_n_hours = 4 [default=10000.0];
+
+  // Optimizer used to train the DetectionModel.
+  optional Optimizer optimizer = 5;
+
+  // If greater than 0, clips gradients by this value.
+  optional float gradient_clipping_by_norm = 6 [default=0.0];
+
+  // Checkpoint to restore variables from. Typically used to load feature
+  // extractor variables trained outside of object detection.
+  optional string fine_tune_checkpoint = 7 [default=""];
+
+  // Type of checkpoint to restore variables from, e.g. 'classification' or
+  // 'detection'. Provides extensibility to from_detection_checkpoint.
+  // Typically used to load feature extractor variables from trained models.
+  optional string fine_tune_checkpoint_type = 22 [default=""];
+
+  // [Deprecated]: use fine_tune_checkpoint_type instead.
+  // Specifies if the finetune checkpoint is from an object detection model.
+  // If from an object detection model, the model being trained should have
+  // the same parameters with the exception of the num_classes parameter.
+  // If false, it assumes the checkpoint was a object classification model.
+  optional bool from_detection_checkpoint = 8 [default=false, deprecated=true];
+
+  // Whether to load all checkpoint vars that match model variable names and
+  // sizes. This option is only available if `from_detection_checkpoint` is
+  // True.
+  optional bool load_all_detection_checkpoint_vars = 19 [default = false];
+
+  // Number of steps to train the DetectionModel for. If 0, will train the model
+  // indefinitely.
+  optional uint32 num_steps = 9 [default=0];
+
+  // Number of training steps between replica startup.
+  // This flag must be set to 0 if sync_replicas is set to true.
+  optional float startup_delay_steps = 10 [default=15];
+
+  // If greater than 0, multiplies the gradient of bias variables by this
+  // amount.
+  optional float bias_grad_multiplier = 11 [default=0];
+
+  // Variables that should be updated during training. Note that variables which
+  // also match the patterns in freeze_variables will be excluded.
+  repeated string update_trainable_variables = 25;
+
+  // Variables that should not be updated during training. If
+  // update_trainable_variables is not empty, only eliminates the included
+  // variables according to freeze_variables patterns.
+  repeated string freeze_variables = 12;
+
+  // Number of replicas to aggregate before making parameter updates.
+  optional int32 replicas_to_aggregate = 13 [default=1];
+
+  // Maximum number of elements to store within a queue.
+  optional int32 batch_queue_capacity = 14 [default=150];
+
+  // Number of threads to use for batching.
+  optional int32 num_batch_queue_threads = 15 [default=8];
+
+  // Maximum capacity of the queue used to prefetch assembled batches.
+  optional int32 prefetch_queue_capacity = 16 [default=5];
+
+  // If true, boxes with the same coordinates will be merged together.
+  // This is useful when each box can have multiple labels.
+  // Note that only Sigmoid classification losses should be used.
+  optional bool merge_multiple_label_boxes = 17 [default=false];
+
+  // If true, will use multiclass scores from object annotations as ground
+  // truth. Currently only compatible with annotated image inputs.
+  optional bool use_multiclass_scores = 24 [default = false];
+
+  // Whether to add regularization loss to `total_loss`. This is true by
+  // default and adds all regularization losses defined in the model to
+  // `total_loss`.
+  // Setting this option to false is very useful while debugging the model and
+  // losses.
+  optional bool add_regularization_loss = 18 [default=true];
+
+  // Maximum number of boxes used during training.
+  // Set this to at least the maximum amount of boxes in the input data.
+  // Otherwise, it may cause "Data loss: Attempted to pad to a smaller size
+  // than the input element" errors.
+  optional int32 max_number_of_boxes = 20 [default=100, deprecated=true];
+
+  // Whether to remove padding along `num_boxes` dimension of the groundtruth
+  // tensors.
+  optional bool unpad_groundtruth_tensors = 21 [default=true];
+
+  // Whether to retain original images (i.e. not pre-processed) in the tensor
+  // dictionary, so that they can be displayed in Tensorboard. Note that this
+  // will lead to a larger memory footprint.
+  optional bool retain_original_images = 23 [default=false];
+
+  // Whether to use bfloat16 for training.
+  optional bool use_bfloat16 = 26 [default=false];
+
+  // Whether to summarize gradients.
+  optional bool summarize_gradients = 27 [default=false];
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/cloud/cloud.yml b/TensorFlow/Detection/SSD/models/research/object_detection/samples/cloud/cloud.yml
new file mode 100644
index 00000000..3dde6bd1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/cloud/cloud.yml
@@ -0,0 +1,11 @@
+trainingInput:
+  runtimeVersion: "1.9"
+  scaleTier: CUSTOM
+  masterType: standard_gpu
+  workerCount: 5
+  workerType: standard_gpu
+  parameterServerCount: 3
+  parameterServerType: standard
+
+
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/embedded_ssd_mobilenet_v1_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/embedded_ssd_mobilenet_v1_coco.config
new file mode 100644
index 00000000..eb6baf0a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/embedded_ssd_mobilenet_v1_coco.config
@@ -0,0 +1,186 @@
+# Embedded SSD with Mobilenet v1 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 5
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 256
+        width: 256
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'embedded_ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 0.125
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 32
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "/PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  use_moving_averages: true
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/facessd_mobilenet_v2_quantized_320x320_open_image_v4.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/facessd_mobilenet_v2_quantized_320x320_open_image_v4.config
new file mode 100644
index 00000000..8cf59027
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/facessd_mobilenet_v2_quantized_320x320_open_image_v4.config
@@ -0,0 +1,211 @@
+# Quantized trained SSD with Mobilenet v2 on Open Images v4.
+# Non-face boxes are dropped during training and non-face groundtruth boxes are
+# ignored when evaluating.
+#
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 1
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    feature_extractor {
+      type: "ssd_mobilenet_v2"
+      depth_multiplier: 1.0
+      min_depth: 16
+      conv_hyperparams {
+        regularizer {
+          l2_regularizer {
+            weight: 4.0e-05
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            mean: 0.0
+            stddev: 0.03
+          }
+        }
+        activation: RELU_6
+        batch_norm {
+          decay: 0.9997
+          center: true
+          scale: true
+          epsilon: 0.001
+          train: true
+        }
+      }
+      pad_to_multiple: 32
+      use_explicit_padding: true
+    }
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+              weight: 4.0e-05
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              mean: 0.0
+              stddev: 0.03
+            }
+          }
+          activation: RELU_6
+          batch_norm {
+            decay: 0.9997
+            center: true
+            scale: true
+            epsilon: 0.001
+            train: true
+          }
+        }
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        kernel_size: 3
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+        height_stride: 16
+        height_stride: 32
+        height_stride: 64
+        height_stride: 128
+        height_stride: 256
+        height_stride: 512
+        width_stride: 16
+        width_stride: 32
+        width_stride: 64
+        width_stride: 128
+        width_stride: 256
+        width_stride: 512
+      }
+    }
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1.0e-08
+        iou_threshold: 0.5
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+    normalize_loss_by_num_matches: true
+    loss {
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 10
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+  }
+}
+train_config {
+  batch_size: 32
+  data_augmentation_options {
+    random_horizontal_flip {
+      keypoint_flip_permutation: 1
+      keypoint_flip_permutation: 0
+      keypoint_flip_permutation: 2
+      keypoint_flip_permutation: 3
+      keypoint_flip_permutation: 5
+      keypoint_flip_permutation: 4
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop_fixed_aspect_ratio {
+    }
+  }
+  optimizer {
+    rms_prop_optimizer {
+      learning_rate {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: ""
+}
+train_input_reader {
+  label_map_path: "PATH_TO_BE_CONFIGURED/face_label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/face_train.record-?????-of-00100"
+  }
+}
+eval_config {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: true
+}
+eval_input_reader {
+  label_map_path: "PATH_TO_BE_CONFIGURED/face_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/face_val.record-?????-of-00010"
+  }
+}
+graph_rewriter {
+  quantization {
+    delay: 500000
+    weight_bits: 8
+    activation_bits: 8
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_coco.config
new file mode 100644
index 00000000..e198ed08
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_coco.config
@@ -0,0 +1,142 @@
+# Faster R-CNN with Inception Resnet v2, Atrous version;
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_resnet_v2'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_cosine_lr_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_cosine_lr_coco.config
new file mode 100644
index 00000000..0d650f48
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_cosine_lr_coco.config
@@ -0,0 +1,132 @@
+# Faster R-CNN with Inception Resnet v2, Atrous version, with Cosine
+# Learning Rate schedule.
+# Trained on COCO, initialized from Imagenet classification checkpoint
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_resnet_v2'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.0006
+          total_steps: 1200000
+          warmup_learning_rate: 0.00006
+          warmup_steps: 20000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_oid.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_oid.config
new file mode 100644
index 00000000..69770460
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_oid.config
@@ -0,0 +1,142 @@
+# Faster R-CNN with Inception Resnet v2, Atrous version;
+# Configured for Open Images Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 546
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_resnet_v2'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.00006
+          schedule {
+            step: 6000000
+            learning_rate: .000006
+          }
+          schedule {
+            step: 7000000
+            learning_rate: .0000006
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  # Note: The below line limits the training process to 800K steps, which we
+  # empirically found to be sufficient enough to train the Open Images dataset.
+  # This effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 8000000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_train.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "open_images_metrics"
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_val.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config
new file mode 100644
index 00000000..cce5fe4a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config
@@ -0,0 +1,141 @@
+# Faster R-CNN with Inception Resnet v2, Atrous version;
+# Configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_resnet_v2'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_coco.config
new file mode 100644
index 00000000..9623d877
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_coco.config
@@ -0,0 +1,141 @@
+# Faster R-CNN with Inception v2, configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_v2'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0002
+          schedule {
+            step: 900000
+            learning_rate: .00002
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000002
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the COCO dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_pets.config
new file mode 100644
index 00000000..0b3ddbd0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_pets.config
@@ -0,0 +1,140 @@
+# Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_inception_v2'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0002
+          schedule {
+            step: 900000
+            learning_rate: .00002
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000002
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_nas_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_nas_coco.config
new file mode 100644
index 00000000..61915d12
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_nas_coco.config
@@ -0,0 +1,142 @@
+# Faster R-CNN with NASNet-A featurization
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      # TODO(shlens): Only fixed_shape_resizer is currently supported for NASNet
+      # featurization. The reason for this is that nasnet.py only supports
+      # inputs with fully known shapes. We need to update nasnet.py to handle
+      # shapes not known at compile time.
+      fixed_shape_resizer {
+        height: 1200
+        width: 1200
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_nas'
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 500
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_atrous_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_atrous_coco.config
new file mode 100644
index 00000000..1a34a435
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_atrous_coco.config
@@ -0,0 +1,136 @@
+# Faster R-CNN with Resnet-101 (v1), Atrous version
+# Trained on COCO, initialized from Imagenet classification checkpoint
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_ava_v2.1.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_ava_v2.1.config
new file mode 100644
index 00000000..5ad9b36f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_ava_v2.1.config
@@ -0,0 +1,138 @@
+# Faster R-CNN with Resnet-101 (v1), configuration for AVA v2.1.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 80
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SIGMOID
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_classification_loss {
+      weighted_sigmoid {
+        anchorwise_output: true
+      }
+    }
+  }
+}
+
+train_config: {
+  batch_size: 1
+  num_steps: 1500000
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 1200000
+            learning_rate: .00003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  merge_multiple_label_boxes: true
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  max_number_of_boxes: 100
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/ava_train.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/ava_label_map_v2.1.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "pascal_voc_detection_metrics"
+  use_moving_averages: false
+  num_examples: 57371
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/ava_val.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/ava_label_map_v2.1.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_coco.config
new file mode 100644
index 00000000..a04b0cb2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_coco.config
@@ -0,0 +1,135 @@
+# Faster R-CNN with Resnet-101 (v1), configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_fgvc.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_fgvc.config
new file mode 100644
index 00000000..7ec8a250
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_fgvc.config
@@ -0,0 +1,135 @@
+# Faster R-CNN with Resnet-101 (v1)
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 2854
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 32
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_batch_size: 32
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 5
+        max_total_detections: 5
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  num_steps: 4000000
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 3000000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 3500000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/fgvc_2854_classes_label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/animal_2854_train.record"
+  }
+}
+
+eval_config: {
+  metrics_set: "pascal_voc_detection_metrics"
+  use_moving_averages: false
+  num_examples: 48736
+}
+
+eval_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/fgvc_2854_classes_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/animal_2854_val.record"
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_kitti.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_kitti.config
new file mode 100644
index 00000000..799f508e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_kitti.config
@@ -0,0 +1,138 @@
+# Faster R-CNN with Resnet-101 (v1)
+# Trained on KITTI dataset (cars and pedestrian), initialized from COCO
+# detection checkpoint.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 2
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        # Raw KITTI images have a resolution of 1242x375, if we wish to resize
+        # them to have a height of 600 then their width should be
+        # 1242/(375/600)=1987.2
+        min_dimension: 600
+        max_dimension: 1987
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0001
+          schedule {
+            step: 500000
+            learning_rate: .00001
+          }
+          schedule {
+            step: 700000
+            learning_rate: .000001
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  num_steps: 800000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/kitti_label_map.pbtxt"
+  tf_record_input_reader: {
+    input_path: "PATH_TO_BE_CONFIGURED/kitti_train.tfrecord"
+  }
+}
+
+eval_config: {
+  use_moving_averages: false
+  num_examples: 500
+}
+
+eval_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/kitti_label_map.pbtxt"
+  tf_record_input_reader: {
+    input_path: "PATH_TO_BE_CONFIGURED/kitti_val.tfrecord"
+  }
+}
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_pets.config
new file mode 100644
index 00000000..078b9511
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_pets.config
@@ -0,0 +1,139 @@
+# Faster R-CNN with Resnet-101 (v1) configured for the Oxford-IIIT Pet Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config
new file mode 100644
index 00000000..7458f4a5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config
@@ -0,0 +1,133 @@
+# Faster R-CNN with Resnet-101 (v1), configured for Pascal VOC Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 20
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0001
+          schedule {
+            step: 500000
+            learning_rate: .00001
+          }
+          schedule {
+            step: 700000
+            learning_rate: .000001
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  num_steps: 800000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pascal_train.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pascal_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 4952
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pascal_val.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pascal_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_coco.config
new file mode 100644
index 00000000..1d0e7d50
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_coco.config
@@ -0,0 +1,140 @@
+# Faster R-CNN with Resnet-152 (v1), configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet152'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_pets.config
new file mode 100644
index 00000000..757858ac
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet152_pets.config
@@ -0,0 +1,139 @@
+# Faster R-CNN with Resnet-152 (v1), configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet152'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_coco.config
new file mode 100644
index 00000000..28eb1f2c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_coco.config
@@ -0,0 +1,140 @@
+# Faster R-CNN with Resnet-50 (v1), configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet50'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_fgvc.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_fgvc.config
new file mode 100644
index 00000000..1c4ca719
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_fgvc.config
@@ -0,0 +1,135 @@
+# Faster R-CNN with Resnet-50 (v1)
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 2854
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet50'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 32
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_batch_size: 32
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 5
+        max_total_detections: 5
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  num_steps: 4000000
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 3000000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 3500000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/fgvc_2854_classes_label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/animal_2854_train.record"
+  }
+}
+
+eval_config: {
+  metrics_set: "pascal_voc_detection_metrics"
+  use_moving_averages: false
+  num_examples: 10
+}
+
+eval_input_reader: {
+  label_map_path: "PATH_TO_BE_CONFIGURED/fgvc_2854_classes_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/animal_2854_val.record"
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_pets.config
new file mode 100644
index 00000000..8f9e5776
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/faster_rcnn_resnet50_pets.config
@@ -0,0 +1,140 @@
+# Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet50'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  max_number_of_boxes: 50
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_resnet_v2_atrous_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_resnet_v2_atrous_coco.config
new file mode 100644
index 00000000..7906bca4
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_resnet_v2_atrous_coco.config
@@ -0,0 +1,166 @@
+# Mask R-CNN with Inception Resnet v2, Atrous version
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 800
+        max_dimension: 1365
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_inception_resnet_v2'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 17
+    maxpool_kernel_size: 1
+    maxpool_stride: 1
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        mask_height: 33
+        mask_width: 33
+        mask_prediction_conv_depth: 0
+        mask_prediction_num_conv_layers: 4
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_mask_prediction_loss_weight: 4.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_v2_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_v2_coco.config
new file mode 100644
index 00000000..22fc1d4f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_inception_v2_coco.config
@@ -0,0 +1,165 @@
+# Mask R-CNN with Inception V2
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 800
+        max_dimension: 1365
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_inception_v2'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        mask_height: 15
+        mask_width: 15
+        mask_prediction_conv_depth: 0
+        mask_prediction_num_conv_layers: 2
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_mask_prediction_loss_weight: 4.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0002
+          schedule {
+            step: 900000
+            learning_rate: .00002
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000002
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_atrous_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_atrous_coco.config
new file mode 100644
index 00000000..21421384
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_atrous_coco.config
@@ -0,0 +1,166 @@
+# Mask R-CNN with Resnet-101 (v1), Atrous version
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 800
+        max_dimension: 1365
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        mask_height: 33
+        mask_width: 33
+        mask_prediction_conv_depth: 0
+        mask_prediction_num_conv_layers: 4
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_mask_prediction_loss_weight: 4.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_pets.config
new file mode 100644
index 00000000..106e6915
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet101_pets.config
@@ -0,0 +1,156 @@
+# Mask R-CNN with Resnet-101 (v1) configured for the Oxford-IIIT Pet Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0007
+          schedule {
+            step: 15000
+            learning_rate: 0.00007
+          }
+          schedule {
+            step: 30000
+            learning_rate: 0.000007
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_fullbody_with_masks_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  load_instance_masks: true
+}
+
+eval_config: {
+  metrics_set: "coco_mask_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_fullbody_with_masks_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  load_instance_masks: true
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet50_atrous_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet50_atrous_coco.config
new file mode 100644
index 00000000..04ab5cf6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/mask_rcnn_resnet50_atrous_coco.config
@@ -0,0 +1,166 @@
+# Mask R-CNN with Resnet-50 (v1), Atrous version
+# Configured for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 800
+        max_dimension: 1365
+      }
+    }
+    number_of_stages: 3
+    feature_extractor {
+      type: 'faster_rcnn_resnet50'
+      first_stage_features_stride: 8
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 8
+        width_stride: 8
+      }
+    }
+    first_stage_atrous_rate: 2
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        predict_instance_masks: true
+        mask_height: 33
+        mask_width: 33
+        mask_prediction_conv_depth: 0
+        mask_prediction_num_conv_layers: 4
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
+            }
+          }
+        }
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+    second_stage_mask_prediction_loss_weight: 4.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  load_instance_masks: true
+  mask_type: PNG_MASKS
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_coco.config
new file mode 100644
index 00000000..541ae8c5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_coco.config
@@ -0,0 +1,137 @@
+# R-FCN with Resnet-101 (v1),  configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 90
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    second_stage_box_predictor {
+      rfcn_box_predictor {
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+        crop_height: 18
+        crop_width: 18
+        num_spatial_bins_height: 3
+        num_spatial_bins_width: 3
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_pets.config
new file mode 100644
index 00000000..3da5c21e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/rfcn_resnet101_pets.config
@@ -0,0 +1,136 @@
+# R-FCN with Resnet-101 (v1),  configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 37
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
+        }
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    second_stage_box_predictor {
+      rfcn_box_predictor {
+        conv_hyperparams {
+          op: CONV
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.01
+            }
+          }
+        }
+        crop_height: 18
+        crop_width: 18
+        num_spatial_bins_height: 3
+        num_spatial_bins_width: 3
+      }
+    }
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
+      }
+      score_converter: SOFTMAX
+    }
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0003
+          schedule {
+            step: 900000
+            learning_rate: .00003
+          }
+          schedule {
+            step: 1200000
+            learning_rate: .000003
+          }
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_coco.config
new file mode 100644
index 00000000..e1d3cd82
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_coco.config
@@ -0,0 +1,189 @@
+# SSD with Inception v2 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+        reduce_boxes_in_lowest_layer: true
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_inception_v2'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_pets.config
new file mode 100644
index 00000000..e36c5e8f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v2_pets.config
@@ -0,0 +1,189 @@
+# SSD with Inception v2 configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 37
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+        reduce_boxes_in_lowest_layer: true
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_inception_v2'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  max_number_of_boxes: 50
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v3_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v3_pets.config
new file mode 100644
index 00000000..3cf3aa60
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_inception_v3_pets.config
@@ -0,0 +1,188 @@
+# SSD with Inception v2 configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 37
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+        reduce_boxes_in_lowest_layer: true
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_inception_v3'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.1
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.01,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync.config
new file mode 100644
index 00000000..3d180719
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync.config
@@ -0,0 +1,198 @@
+# SSD with Mobilenet v1 0.75 depth multiplied feature extractor and focal loss.
+# Trained on COCO14, initialized from Imagenet classification checkpoint
+
+# Achieves 17.5 mAP on COCO14 minival dataset. Doubling the number of training
+# steps gets to 18.4.
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.97,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 0.75
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.97,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75,
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+          delta: 1.0
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 2048
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 10000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.9
+          total_steps: 10000
+          warmup_learning_rate: 0.3
+          warmup_steps: 300
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync.config
new file mode 100644
index 00000000..25cc0bf1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync.config
@@ -0,0 +1,201 @@
+# SSD with Mobilenet v1 with quantized training.
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+# Achieves 18.2 mAP on coco14 minival dataset.
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            center: true,
+            decay: 0.97,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 0.75
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            stddev: 0.01
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          center: true,
+          decay: 0.97,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75,
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 128
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 50000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .2
+          total_steps: 50000
+          warmup_learning_rate: 0.06
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
+
+graph_rewriter {
+  quantization {
+    delay: 48000
+    activation_bits: 8
+    weight_bits: 8
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_pets_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_pets_sync.config
new file mode 100644
index 00000000..d15fd313
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_0.75_depth_quantized_300x300_pets_sync.config
@@ -0,0 +1,204 @@
+# SSD with Mobilenet v1 0.75 depth multiplied feature extractor, focal loss and
+# quantized training.
+# Trained on IIIT-Oxford pets, initialized from COCO detection checkpoint
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 37
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 0.75
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          center: true,
+          decay: 0.9,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75,
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+          delta: 1.0
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  fine_tune_checkpoint_type: "detection"
+  load_all_detection_checkpoint_vars: true
+  batch_size: 128
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 2000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.2
+          total_steps: 2000
+          warmup_steps: 0
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 1100
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
+
+graph_rewriter {
+  quantization {
+    delay: 1800
+    activation_bits: 8
+    weight_bits: 8
+  }
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_300x300_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_300x300_coco14_sync.config
new file mode 100644
index 00000000..c21a1fab
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_300x300_coco14_sync.config
@@ -0,0 +1,197 @@
+# SSD with Mobilenet v1 feature extractor and focal loss.
+# Trained on COCO14, initialized from Imagenet classification checkpoint
+
+# Achieves 20.5 mAP on COCO14 minival dataset.
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.97,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.97,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75,
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+          delta: 1.0
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 1024
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 20000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.9
+          total_steps: 20000
+          warmup_learning_rate: 0.3
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config
new file mode 100644
index 00000000..a80117b5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config
@@ -0,0 +1,194 @@
+# SSD with Mobilenet v1 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_focal_loss_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_focal_loss_pets.config
new file mode 100644
index 00000000..4623660f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_focal_loss_pets.config
@@ -0,0 +1,191 @@
+# SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+# TPU-compatible
+
+model {
+  ssd {
+    num_classes: 37
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  max_number_of_boxes: 50
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1101
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config
new file mode 100644
index 00000000..a2abb6b1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config
@@ -0,0 +1,196 @@
+# SSD with Mobilenet v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+# Achieves 29.7 mAP on COCO14 minival dataset.
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 640
+        width: 640
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            stddev: 0.01
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 64
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 25000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .04
+          total_steps: 25000
+          warmup_learning_rate: .013333
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config
new file mode 100644
index 00000000..11e91d40
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config
@@ -0,0 +1,193 @@
+# SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 37
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  load_all_detection_checkpoint_vars: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_train.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  num_examples: 1100
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pet_faces_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync.config
new file mode 100644
index 00000000..92c9e6ed
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync.config
@@ -0,0 +1,191 @@
+# SSD with Mobilenet v1 PPN feature extractor.
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+# Achieves 19.7 mAP on COCO14 minival dataset.
+
+# This config is TPU compatible.
+
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.15
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+        reduce_boxes_in_lowest_layer: false
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 512
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true
+            center: true
+            train: true
+            decay: 0.97
+            epsilon: 0.001
+          }
+        }
+        num_layers_before_predictor: 1
+        kernel_size: 1
+        share_prediction_tower: true
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1_ppn'
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            stddev: 0.01
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true
+          center: true
+          decay: 0.97
+          epsilon: 0.001
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.5
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 512
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 50000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.7
+          total_steps: 50000
+          warmup_learning_rate: 0.1333
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_quantized_300x300_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_quantized_300x300_coco14_sync.config
new file mode 100644
index 00000000..a79d7726
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v1_quantized_300x300_coco14_sync.config
@@ -0,0 +1,201 @@
+# SSD with Mobilenet v1 with quantized training.
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+# Achieves 18.2 mAP on coco14 minival dataset.
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            center: true,
+            decay: 0.97,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            stddev: 0.01
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          center: true,
+          decay: 0.97,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.75,
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 128
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 50000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .2
+          total_steps: 50000
+          warmup_learning_rate: 0.06
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
+
+graph_rewriter {
+  quantization {
+    delay: 48000
+    activation_bits: 8
+    weight_bits: 8
+  }
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config
new file mode 100644
index 00000000..e1adf269
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config
@@ -0,0 +1,194 @@
+# SSD with Mobilenet v2 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v2'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 3
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  fine_tune_checkpoint_type:  "detection"
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config
new file mode 100644
index 00000000..e8d8f9d5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config
@@ -0,0 +1,202 @@
+# Quantized trained SSD with Mobilenet v2 on MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 1
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v2'
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 3
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  fine_tune_checkpoint_type:  "detection"
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
+
+graph_rewriter {
+  quantization {
+    delay: 48000
+    weight_bits: 8
+    activation_bits: 8
+  }
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config
new file mode 100644
index 00000000..503f8ecd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config
@@ -0,0 +1,197 @@
+# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
+# loss (a.k.a Retinanet).
+# See Lin et al, https://arxiv.org/abs/1708.02002
+# Trained on COCO, initialized from Imagenet classification checkpoint
+
+# Achieves 35.2 mAP on COCO14 minival dataset. Doubling the number of training
+# steps to 50k gets 36.9 mAP
+
+# This config is TPU compatible
+
+model {
+  ssd {
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    encode_background_as_zeros: true
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: [1.0, 2.0, 0.5]
+        scales_per_octave: 2
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 640
+        width: 640
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        depth: 256
+        class_prediction_bias_init: -4.6
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.0004
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              stddev: 0.01
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            scale: true,
+            decay: 0.997,
+            epsilon: 0.001,
+          }
+        }
+        num_layers_before_predictor: 4
+        kernel_size: 3
+      }
+    }
+    feature_extractor {
+      type: 'ssd_resnet50_v1_fpn'
+      fpn {
+        min_level: 3
+        max_level: 7
+      }
+      min_depth: 16
+      depth_multiplier: 1.0
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.0004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          scale: true,
+          decay: 0.997,
+          epsilon: 0.001,
+        }
+      }
+      override_base_feature_extractor_hyperparams: true
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid_focal {
+          alpha: 0.25
+          gamma: 2.0
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    normalize_loc_loss_by_codesize: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  batch_size: 64
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 25000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        cosine_decay_learning_rate {
+          learning_rate_base: .04
+          total_steps: 25000
+          warmup_learning_rate: .013333
+          warmup_steps: 2000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+  num_examples: 8000
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v1_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v1_coco.config
new file mode 100644
index 00000000..b6c8fb09
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v1_coco.config
@@ -0,0 +1,196 @@
+# SSDLite with Mobilenet v1 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        use_depthwise: true
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v1'
+      min_depth: 16
+      depth_multiplier: 1.0
+      use_depthwise: true
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v2_coco.config b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v2_coco.config
new file mode 100644
index 00000000..09222600
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/samples/configs/ssdlite_mobilenet_v2_coco.config
@@ -0,0 +1,196 @@
+# SSDLite with Mobilenet v2 configuration for MSCOCO Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  ssd {
+    num_classes: 90
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 6
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 300
+        width: 300
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 0
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        use_depthwise: true
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'ssd_mobilenet_v2'
+      min_depth: 16
+      depth_multiplier: 1.0
+      use_depthwise: true
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 3
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-8
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 24
+  optimizer {
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.004
+          decay_steps: 800720
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  fine_tune_checkpoint_type:  "detection"
+  # Note: The below line limits the training process to 200K steps, which we
+  # empirically found to be sufficient enough to train the pets dataset. This
+  # effectively bypasses the learning rate schedule (the learning rate will
+  # never decay). Remove the below line to train indefinitely.
+  num_steps: 200000
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    ssd_random_crop {
+    }
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 8000
+  # Note: The below line limits the evaluation process to 10 evaluations.
+  # Remove the below line to evaluate indefinitely.
+  max_evals: 10
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}
\ No newline at end of file
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/test_data/pets_examples.record b/TensorFlow/Detection/SSD/models/research/object_detection/test_data/pets_examples.record
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diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/test_images/image_info.txt b/TensorFlow/Detection/SSD/models/research/object_detection/test_images/image_info.txt
new file mode 100644
index 00000000..6f805cbc
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/test_images/image_info.txt
@@ -0,0 +1,6 @@
+
+Image provenance:
+image1.jpg: https://commons.wikimedia.org/wiki/File:Baegle_dwa.jpg
+image2.jpg: Michael Miley,
+  https://www.flickr.com/photos/mike_miley/4678754542/in/photolist-88rQHL-88oBVp-88oC2B-88rS6J-88rSqm-88oBLv-88oBC4
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/__init__.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util.py
new file mode 100644
index 00000000..fdd9c1c1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util.py
@@ -0,0 +1,72 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Functions for importing/exporting Object Detection categories."""
+import csv
+
+import tensorflow as tf
+
+
+def load_categories_from_csv_file(csv_path):
+  """Loads categories from a csv file.
+
+  The CSV file should have one comma delimited numeric category id and string
+  category name pair per line. For example:
+
+  0,"cat"
+  1,"dog"
+  2,"bird"
+  ...
+
+  Args:
+    csv_path: Path to the csv file to be parsed into categories.
+  Returns:
+    categories: A list of dictionaries representing all possible categories.
+                The categories will contain an integer 'id' field and a string
+                'name' field.
+  Raises:
+    ValueError: If the csv file is incorrectly formatted.
+  """
+  categories = []
+
+  with tf.gfile.Open(csv_path, 'r') as csvfile:
+    reader = csv.reader(csvfile, delimiter=',', quotechar='"')
+    for row in reader:
+      if not row:
+        continue
+
+      if len(row) != 2:
+        raise ValueError('Expected 2 fields per row in csv: %s' % ','.join(row))
+
+      category_id = int(row[0])
+      category_name = row[1]
+      categories.append({'id': category_id, 'name': category_name})
+
+  return categories
+
+
+def save_categories_to_csv_file(categories, csv_path):
+  """Saves categories to a csv file.
+
+  Args:
+    categories: A list of dictionaries representing categories to save to file.
+                Each category must contain an 'id' and 'name' field.
+    csv_path: Path to the csv file to be parsed into categories.
+  """
+  categories.sort(key=lambda x: x['id'])
+  with tf.gfile.Open(csv_path, 'w') as csvfile:
+    writer = csv.writer(csvfile, delimiter=',', quotechar='"')
+    for category in categories:
+      writer.writerow([category['id'], category['name']])
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util_test.py
new file mode 100644
index 00000000..9c99079e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/category_util_test.py
@@ -0,0 +1,54 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.category_util."""
+import os
+
+import tensorflow as tf
+
+from object_detection.utils import category_util
+
+
+class EvalUtilTest(tf.test.TestCase):
+
+  def test_load_categories_from_csv_file(self):
+    csv_data = """
+        0,"cat"
+        1,"dog"
+        2,"bird"
+    """.strip(' ')
+    csv_path = os.path.join(self.get_temp_dir(), 'test.csv')
+    with tf.gfile.Open(csv_path, 'wb') as f:
+      f.write(csv_data)
+
+    categories = category_util.load_categories_from_csv_file(csv_path)
+    self.assertTrue({'id': 0, 'name': 'cat'} in categories)
+    self.assertTrue({'id': 1, 'name': 'dog'} in categories)
+    self.assertTrue({'id': 2, 'name': 'bird'} in categories)
+
+  def test_save_categories_to_csv_file(self):
+    categories = [
+        {'id': 0, 'name': 'cat'},
+        {'id': 1, 'name': 'dog'},
+        {'id': 2, 'name': 'bird'},
+    ]
+    csv_path = os.path.join(self.get_temp_dir(), 'test.csv')
+    category_util.save_categories_to_csv_file(categories, csv_path)
+    saved_categories = category_util.load_categories_from_csv_file(csv_path)
+    self.assertEqual(saved_categories, categories)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util.py
new file mode 100644
index 00000000..69da3bf9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util.py
@@ -0,0 +1,934 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for reading and updating configuration files."""
+
+import os
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from tensorflow.python.lib.io import file_io
+
+from object_detection.protos import eval_pb2
+from object_detection.protos import graph_rewriter_pb2
+from object_detection.protos import input_reader_pb2
+from object_detection.protos import model_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.protos import train_pb2
+
+
+def get_image_resizer_config(model_config):
+  """Returns the image resizer config from a model config.
+
+  Args:
+    model_config: A model_pb2.DetectionModel.
+
+  Returns:
+    An image_resizer_pb2.ImageResizer.
+
+  Raises:
+    ValueError: If the model type is not recognized.
+  """
+  meta_architecture = model_config.WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    return model_config.faster_rcnn.image_resizer
+  if meta_architecture == "ssd":
+    return model_config.ssd.image_resizer
+
+  raise ValueError("Unknown model type: {}".format(meta_architecture))
+
+
+def get_spatial_image_size(image_resizer_config):
+  """Returns expected spatial size of the output image from a given config.
+
+  Args:
+    image_resizer_config: An image_resizer_pb2.ImageResizer.
+
+  Returns:
+    A list of two integers of the form [height, width]. `height` and `width` are
+    set  -1 if they cannot be determined during graph construction.
+
+  Raises:
+    ValueError: If the model type is not recognized.
+  """
+  if image_resizer_config.HasField("fixed_shape_resizer"):
+    return [
+        image_resizer_config.fixed_shape_resizer.height,
+        image_resizer_config.fixed_shape_resizer.width
+    ]
+  if image_resizer_config.HasField("keep_aspect_ratio_resizer"):
+    if image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension:
+      return [image_resizer_config.keep_aspect_ratio_resizer.max_dimension] * 2
+    else:
+      return [-1, -1]
+  raise ValueError("Unknown image resizer type.")
+
+
+def get_configs_from_pipeline_file(pipeline_config_path, config_override=None):
+  """Reads config from a file containing pipeline_pb2.TrainEvalPipelineConfig.
+
+  Args:
+    pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text
+      proto.
+    config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to
+      override pipeline_config_path.
+
+  Returns:
+    Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_config`. Value are the
+      corresponding config objects.
+  """
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+  with tf.gfile.GFile(pipeline_config_path, "r") as f:
+    proto_str = f.read()
+    text_format.Merge(proto_str, pipeline_config)
+  if config_override:
+    text_format.Merge(config_override, pipeline_config)
+  return create_configs_from_pipeline_proto(pipeline_config)
+
+
+def create_configs_from_pipeline_proto(pipeline_config):
+  """Creates a configs dictionary from pipeline_pb2.TrainEvalPipelineConfig.
+
+  Args:
+    pipeline_config: pipeline_pb2.TrainEvalPipelineConfig proto object.
+
+  Returns:
+    Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_configs`. Value are
+      the corresponding config objects or list of config objects (only for
+      eval_input_configs).
+  """
+  configs = {}
+  configs["model"] = pipeline_config.model
+  configs["train_config"] = pipeline_config.train_config
+  configs["train_input_config"] = pipeline_config.train_input_reader
+  configs["eval_config"] = pipeline_config.eval_config
+  configs["eval_input_configs"] = pipeline_config.eval_input_reader
+  # Keeps eval_input_config only for backwards compatibility. All clients should
+  # read eval_input_configs instead.
+  if configs["eval_input_configs"]:
+    configs["eval_input_config"] = configs["eval_input_configs"][0]
+  if pipeline_config.HasField("graph_rewriter"):
+    configs["graph_rewriter_config"] = pipeline_config.graph_rewriter
+
+  return configs
+
+
+def get_graph_rewriter_config_from_file(graph_rewriter_config_file):
+  """Parses config for graph rewriter.
+
+  Args:
+    graph_rewriter_config_file: file path to the graph rewriter config.
+
+  Returns:
+    graph_rewriter_pb2.GraphRewriter proto
+  """
+  graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
+  with tf.gfile.GFile(graph_rewriter_config_file, "r") as f:
+    text_format.Merge(f.read(), graph_rewriter_config)
+  return graph_rewriter_config
+
+
+def create_pipeline_proto_from_configs(configs):
+  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.
+
+  This function performs the inverse operation of
+  create_configs_from_pipeline_proto().
+
+  Args:
+    configs: Dictionary of configs. See get_configs_from_pipeline_file().
+
+  Returns:
+    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
+  """
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+  pipeline_config.model.CopyFrom(configs["model"])
+  pipeline_config.train_config.CopyFrom(configs["train_config"])
+  pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"])
+  pipeline_config.eval_config.CopyFrom(configs["eval_config"])
+  pipeline_config.eval_input_reader.extend(configs["eval_input_configs"])
+  if "graph_rewriter_config" in configs:
+    pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"])
+  return pipeline_config
+
+
+def save_pipeline_config(pipeline_config, directory):
+  """Saves a pipeline config text file to disk.
+
+  Args:
+    pipeline_config: A pipeline_pb2.TrainEvalPipelineConfig.
+    directory: The model directory into which the pipeline config file will be
+      saved.
+  """
+  if not file_io.file_exists(directory):
+    file_io.recursive_create_dir(directory)
+  pipeline_config_path = os.path.join(directory, "pipeline.config")
+  config_text = text_format.MessageToString(pipeline_config)
+  with tf.gfile.Open(pipeline_config_path, "wb") as f:
+    tf.logging.info("Writing pipeline config file to %s",
+                    pipeline_config_path)
+    f.write(config_text)
+
+
+def get_configs_from_multiple_files(model_config_path="",
+                                    train_config_path="",
+                                    train_input_config_path="",
+                                    eval_config_path="",
+                                    eval_input_config_path="",
+                                    graph_rewriter_config_path=""):
+  """Reads training configuration from multiple config files.
+
+  Args:
+    model_config_path: Path to model_pb2.DetectionModel.
+    train_config_path: Path to train_pb2.TrainConfig.
+    train_input_config_path: Path to input_reader_pb2.InputReader.
+    eval_config_path: Path to eval_pb2.EvalConfig.
+    eval_input_config_path: Path to input_reader_pb2.InputReader.
+    graph_rewriter_config_path: Path to graph_rewriter_pb2.GraphRewriter.
+
+  Returns:
+    Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_config`. Key/Values are
+        returned only for valid (non-empty) strings.
+  """
+  configs = {}
+  if model_config_path:
+    model_config = model_pb2.DetectionModel()
+    with tf.gfile.GFile(model_config_path, "r") as f:
+      text_format.Merge(f.read(), model_config)
+      configs["model"] = model_config
+
+  if train_config_path:
+    train_config = train_pb2.TrainConfig()
+    with tf.gfile.GFile(train_config_path, "r") as f:
+      text_format.Merge(f.read(), train_config)
+      configs["train_config"] = train_config
+
+  if train_input_config_path:
+    train_input_config = input_reader_pb2.InputReader()
+    with tf.gfile.GFile(train_input_config_path, "r") as f:
+      text_format.Merge(f.read(), train_input_config)
+      configs["train_input_config"] = train_input_config
+
+  if eval_config_path:
+    eval_config = eval_pb2.EvalConfig()
+    with tf.gfile.GFile(eval_config_path, "r") as f:
+      text_format.Merge(f.read(), eval_config)
+      configs["eval_config"] = eval_config
+
+  if eval_input_config_path:
+    eval_input_config = input_reader_pb2.InputReader()
+    with tf.gfile.GFile(eval_input_config_path, "r") as f:
+      text_format.Merge(f.read(), eval_input_config)
+      configs["eval_input_configs"] = [eval_input_config]
+
+  if graph_rewriter_config_path:
+    configs["graph_rewriter_config"] = get_graph_rewriter_config_from_file(
+        graph_rewriter_config_path)
+
+  return configs
+
+
+def get_number_of_classes(model_config):
+  """Returns the number of classes for a detection model.
+
+  Args:
+    model_config: A model_pb2.DetectionModel.
+
+  Returns:
+    Number of classes.
+
+  Raises:
+    ValueError: If the model type is not recognized.
+  """
+  meta_architecture = model_config.WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    return model_config.faster_rcnn.num_classes
+  if meta_architecture == "ssd":
+    return model_config.ssd.num_classes
+
+  raise ValueError("Expected the model to be one of 'faster_rcnn' or 'ssd'.")
+
+
+def get_optimizer_type(train_config):
+  """Returns the optimizer type for training.
+
+  Args:
+    train_config: A train_pb2.TrainConfig.
+
+  Returns:
+    The type of the optimizer
+  """
+  return train_config.optimizer.WhichOneof("optimizer")
+
+
+def get_learning_rate_type(optimizer_config):
+  """Returns the learning rate type for training.
+
+  Args:
+    optimizer_config: An optimizer_pb2.Optimizer.
+
+  Returns:
+    The type of the learning rate.
+  """
+  return optimizer_config.learning_rate.WhichOneof("learning_rate")
+
+
+def _is_generic_key(key):
+  """Determines whether the key starts with a generic config dictionary key."""
+  for prefix in [
+      "graph_rewriter_config",
+      "model",
+      "train_input_config",
+      "train_config",
+      "eval_config"]:
+    if key.startswith(prefix + "."):
+      return True
+  return False
+
+
+def _check_and_convert_legacy_input_config_key(key):
+  """Checks key and converts legacy input config update to specific update.
+
+  Args:
+    key: string indicates the target of update operation.
+
+  Returns:
+    is_valid_input_config_key: A boolean indicating whether the input key is to
+      update input config(s).
+    key_name: 'eval_input_configs' or 'train_input_config' string if
+      is_valid_input_config_key is true. None if is_valid_input_config_key is
+      false.
+    input_name: always returns None since legacy input config key never
+      specifies the target input config. Keeping this output only to match the
+      output form defined for input config update.
+    field_name: the field name in input config. `key` itself if
+      is_valid_input_config_key is false.
+  """
+  key_name = None
+  input_name = None
+  field_name = key
+  is_valid_input_config_key = True
+  if field_name == "train_shuffle":
+    key_name = "train_input_config"
+    field_name = "shuffle"
+  elif field_name == "eval_shuffle":
+    key_name = "eval_input_configs"
+    field_name = "shuffle"
+  elif field_name == "train_input_path":
+    key_name = "train_input_config"
+    field_name = "input_path"
+  elif field_name == "eval_input_path":
+    key_name = "eval_input_configs"
+    field_name = "input_path"
+  elif field_name == "append_train_input_path":
+    key_name = "train_input_config"
+    field_name = "input_path"
+  elif field_name == "append_eval_input_path":
+    key_name = "eval_input_configs"
+    field_name = "input_path"
+  else:
+    is_valid_input_config_key = False
+
+  return is_valid_input_config_key, key_name, input_name, field_name
+
+
+def check_and_parse_input_config_key(configs, key):
+  """Checks key and returns specific fields if key is valid input config update.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    key: string indicates the target of update operation.
+
+  Returns:
+    is_valid_input_config_key: A boolean indicate whether the input key is to
+      update input config(s).
+    key_name: 'eval_input_configs' or 'train_input_config' string if
+      is_valid_input_config_key is true. None if is_valid_input_config_key is
+      false.
+    input_name: the name of the input config to be updated. None if
+      is_valid_input_config_key is false.
+    field_name: the field name in input config. `key` itself if
+      is_valid_input_config_key is false.
+
+  Raises:
+    ValueError: when the input key format doesn't match any known formats.
+    ValueError: if key_name doesn't match 'eval_input_configs' or
+      'train_input_config'.
+    ValueError: if input_name doesn't match any name in train or eval input
+      configs.
+    ValueError: if field_name doesn't match any supported fields.
+  """
+  key_name = None
+  input_name = None
+  field_name = None
+  fields = key.split(":")
+  if len(fields) == 1:
+    field_name = key
+    return _check_and_convert_legacy_input_config_key(key)
+  elif len(fields) == 3:
+    key_name = fields[0]
+    input_name = fields[1]
+    field_name = fields[2]
+  else:
+    raise ValueError("Invalid key format when overriding configs.")
+
+  # Checks if key_name is valid for specific update.
+  if key_name not in ["eval_input_configs", "train_input_config"]:
+    raise ValueError("Invalid key_name when overriding input config.")
+
+  # Checks if input_name is valid for specific update. For train input config it
+  # should match configs[key_name].name, for eval input configs it should match
+  # the name field of one of the eval_input_configs.
+  if isinstance(configs[key_name], input_reader_pb2.InputReader):
+    is_valid_input_name = configs[key_name].name == input_name
+  else:
+    is_valid_input_name = input_name in [
+        eval_input_config.name for eval_input_config in configs[key_name]
+    ]
+  if not is_valid_input_name:
+    raise ValueError("Invalid input_name when overriding input config.")
+
+  # Checks if field_name is valid for specific update.
+  if field_name not in [
+      "input_path", "label_map_path", "shuffle", "mask_type",
+      "sample_1_of_n_examples"
+  ]:
+    raise ValueError("Invalid field_name when overriding input config.")
+
+  return True, key_name, input_name, field_name
+
+
+def merge_external_params_with_configs(configs, hparams=None, kwargs_dict=None):
+  """Updates `configs` dictionary based on supplied parameters.
+
+  This utility is for modifying specific fields in the object detection configs.
+  Say that one would like to experiment with different learning rates, momentum
+  values, or batch sizes. Rather than creating a new config text file for each
+  experiment, one can use a single base config file, and update particular
+  values.
+
+  There are two types of field overrides:
+  1. Strategy-based overrides, which update multiple relevant configuration
+  options. For example, updating `learning_rate` will update both the warmup and
+  final learning rates.
+  In this case key can be one of the following formats:
+      1. legacy update: single string that indicates the attribute to be
+        updated. E.g. 'label_map_path', 'eval_input_path', 'shuffle'.
+        Note that when updating fields (e.g. eval_input_path, eval_shuffle) in
+        eval_input_configs, the override will only be applied when
+        eval_input_configs has exactly 1 element.
+      2. specific update: colon separated string that indicates which field in
+        which input_config to update. It should have 3 fields:
+        - key_name: Name of the input config we should update, either
+          'train_input_config' or 'eval_input_configs'
+        - input_name: a 'name' that can be used to identify elements, especially
+          when configs[key_name] is a repeated field.
+        - field_name: name of the field that you want to override.
+        For example, given configs dict as below:
+          configs = {
+            'model': {...}
+            'train_config': {...}
+            'train_input_config': {...}
+            'eval_config': {...}
+            'eval_input_configs': [{ name:"eval_coco", ...},
+                                   { name:"eval_voc", ... }]
+          }
+        Assume we want to update the input_path of the eval_input_config
+        whose name is 'eval_coco'. The `key` would then be:
+        'eval_input_configs:eval_coco:input_path'
+  2. Generic key/value, which update a specific parameter based on namespaced
+  configuration keys. For example,
+  `model.ssd.loss.hard_example_miner.max_negatives_per_positive` will update the
+  hard example miner configuration for an SSD model config. Generic overrides
+  are automatically detected based on the namespaced keys.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    hparams: A `HParams`.
+    kwargs_dict: Extra keyword arguments that are treated the same way as
+      attribute/value pairs in `hparams`. Note that hyperparameters with the
+      same names will override keyword arguments.
+
+  Returns:
+    `configs` dictionary.
+
+  Raises:
+    ValueError: when the key string doesn't match any of its allowed formats.
+  """
+
+  if kwargs_dict is None:
+    kwargs_dict = {}
+  if hparams:
+    kwargs_dict.update(hparams.values())
+  for key, value in kwargs_dict.items():
+    tf.logging.info("Maybe overwriting %s: %s", key, value)
+    # pylint: disable=g-explicit-bool-comparison
+    if value == "" or value is None:
+      continue
+    # pylint: enable=g-explicit-bool-comparison
+    elif _maybe_update_config_with_key_value(configs, key, value):
+      continue
+    elif _is_generic_key(key):
+      _update_generic(configs, key, value)
+    else:
+      tf.logging.info("Ignoring config override key: %s", key)
+  return configs
+
+
+def _maybe_update_config_with_key_value(configs, key, value):
+  """Checks key type and updates `configs` with the key value pair accordingly.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    key: String indicates the field(s) to be updated.
+    value: Value used to override existing field value.
+
+  Returns:
+    A boolean value that indicates whether the override succeeds.
+
+  Raises:
+    ValueError: when the key string doesn't match any of the formats above.
+  """
+  is_valid_input_config_key, key_name, input_name, field_name = (
+      check_and_parse_input_config_key(configs, key))
+  if is_valid_input_config_key:
+    update_input_reader_config(
+        configs,
+        key_name=key_name,
+        input_name=input_name,
+        field_name=field_name,
+        value=value)
+  elif field_name == "learning_rate":
+    _update_initial_learning_rate(configs, value)
+  elif field_name == "batch_size":
+    _update_batch_size(configs, value)
+  elif field_name == "momentum_optimizer_value":
+    _update_momentum_optimizer_value(configs, value)
+  elif field_name == "classification_localization_weight_ratio":
+    # Localization weight is fixed to 1.0.
+    _update_classification_localization_weight_ratio(configs, value)
+  elif field_name == "focal_loss_gamma":
+    _update_focal_loss_gamma(configs, value)
+  elif field_name == "focal_loss_alpha":
+    _update_focal_loss_alpha(configs, value)
+  elif field_name == "train_steps":
+    _update_train_steps(configs, value)
+  elif field_name == "label_map_path":
+    _update_label_map_path(configs, value)
+  elif field_name == "mask_type":
+    _update_mask_type(configs, value)
+  elif field_name == "sample_1_of_n_eval_examples":
+    _update_all_eval_input_configs(configs, "sample_1_of_n_examples", value)
+  elif field_name == "eval_num_epochs":
+    _update_all_eval_input_configs(configs, "num_epochs", value)
+  elif field_name == "eval_with_moving_averages":
+    _update_use_moving_averages(configs, value)
+  elif field_name == "retain_original_images_in_eval":
+    _update_retain_original_images(configs["eval_config"], value)
+  elif field_name == "use_bfloat16":
+    _update_use_bfloat16(configs, value)
+  else:
+    return False
+  return True
+
+
+def _update_tf_record_input_path(input_config, input_path):
+  """Updates input configuration to reflect a new input path.
+
+  The input_config object is updated in place, and hence not returned.
+
+  Args:
+    input_config: A input_reader_pb2.InputReader.
+    input_path: A path to data or list of paths.
+
+  Raises:
+    TypeError: if input reader type is not `tf_record_input_reader`.
+  """
+  input_reader_type = input_config.WhichOneof("input_reader")
+  if input_reader_type == "tf_record_input_reader":
+    input_config.tf_record_input_reader.ClearField("input_path")
+    if isinstance(input_path, list):
+      input_config.tf_record_input_reader.input_path.extend(input_path)
+    else:
+      input_config.tf_record_input_reader.input_path.append(input_path)
+  else:
+    raise TypeError("Input reader type must be `tf_record_input_reader`.")
+
+
+def update_input_reader_config(configs,
+                               key_name=None,
+                               input_name=None,
+                               field_name=None,
+                               value=None,
+                               path_updater=_update_tf_record_input_path):
+  """Updates specified input reader config field.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    key_name: Name of the input config we should update, either
+      'train_input_config' or 'eval_input_configs'
+    input_name: String name used to identify input config to update with. Should
+      be either None or value of the 'name' field in one of the input reader
+      configs.
+    field_name: Field name in input_reader_pb2.InputReader.
+    value: Value used to override existing field value.
+    path_updater: helper function used to update the input path. Only used when
+      field_name is "input_path".
+
+  Raises:
+    ValueError: when input field_name is None.
+    ValueError: when input_name is None and number of eval_input_readers does
+      not equal to 1.
+  """
+  if isinstance(configs[key_name], input_reader_pb2.InputReader):
+    # Updates singular input_config object.
+    target_input_config = configs[key_name]
+    if field_name == "input_path":
+      path_updater(input_config=target_input_config, input_path=value)
+    else:
+      setattr(target_input_config, field_name, value)
+  elif input_name is None and len(configs[key_name]) == 1:
+    # Updates first (and the only) object of input_config list.
+    target_input_config = configs[key_name][0]
+    if field_name == "input_path":
+      path_updater(input_config=target_input_config, input_path=value)
+    else:
+      setattr(target_input_config, field_name, value)
+  elif input_name is not None and len(configs[key_name]):
+    # Updates input_config whose name matches input_name.
+    update_count = 0
+    for input_config in configs[key_name]:
+      if input_config.name == input_name:
+        setattr(input_config, field_name, value)
+        update_count = update_count + 1
+    if not update_count:
+      raise ValueError(
+          "Input name {} not found when overriding.".format(input_name))
+    elif update_count > 1:
+      raise ValueError("Duplicate input name found when overriding.")
+  else:
+    key_name = "None" if key_name is None else key_name
+    input_name = "None" if input_name is None else input_name
+    field_name = "None" if field_name is None else field_name
+    raise ValueError("Unknown input config overriding: "
+                     "key_name:{}, input_name:{}, field_name:{}.".format(
+                         key_name, input_name, field_name))
+
+
+def _update_initial_learning_rate(configs, learning_rate):
+  """Updates `configs` to reflect the new initial learning rate.
+
+  This function updates the initial learning rate. For learning rate schedules,
+  all other defined learning rates in the pipeline config are scaled to maintain
+  their same ratio with the initial learning rate.
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    learning_rate: Initial learning rate for optimizer.
+
+  Raises:
+    TypeError: if optimizer type is not supported, or if learning rate type is
+      not supported.
+  """
+
+  optimizer_type = get_optimizer_type(configs["train_config"])
+  if optimizer_type == "rms_prop_optimizer":
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+  elif optimizer_type == "momentum_optimizer":
+    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
+  elif optimizer_type == "adam_optimizer":
+    optimizer_config = configs["train_config"].optimizer.adam_optimizer
+  else:
+    raise TypeError("Optimizer %s is not supported." % optimizer_type)
+
+  learning_rate_type = get_learning_rate_type(optimizer_config)
+  if learning_rate_type == "constant_learning_rate":
+    constant_lr = optimizer_config.learning_rate.constant_learning_rate
+    constant_lr.learning_rate = learning_rate
+  elif learning_rate_type == "exponential_decay_learning_rate":
+    exponential_lr = (
+        optimizer_config.learning_rate.exponential_decay_learning_rate)
+    exponential_lr.initial_learning_rate = learning_rate
+  elif learning_rate_type == "manual_step_learning_rate":
+    manual_lr = optimizer_config.learning_rate.manual_step_learning_rate
+    original_learning_rate = manual_lr.initial_learning_rate
+    learning_rate_scaling = float(learning_rate) / original_learning_rate
+    manual_lr.initial_learning_rate = learning_rate
+    for schedule in manual_lr.schedule:
+      schedule.learning_rate *= learning_rate_scaling
+  elif learning_rate_type == "cosine_decay_learning_rate":
+    cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate
+    learning_rate_base = cosine_lr.learning_rate_base
+    warmup_learning_rate = cosine_lr.warmup_learning_rate
+    warmup_scale_factor = warmup_learning_rate / learning_rate_base
+    cosine_lr.learning_rate_base = learning_rate
+    cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate
+  else:
+    raise TypeError("Learning rate %s is not supported." % learning_rate_type)
+
+
+def _update_batch_size(configs, batch_size):
+  """Updates `configs` to reflect the new training batch size.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    batch_size: Batch size to use for training (Ideally a power of 2). Inputs
+      are rounded, and capped to be 1 or greater.
+  """
+  configs["train_config"].batch_size = max(1, int(round(batch_size)))
+
+
+def _validate_message_has_field(message, field):
+  if not message.HasField(field):
+    raise ValueError("Expecting message to have field %s" % field)
+
+
+def _update_generic(configs, key, value):
+  """Update a pipeline configuration parameter based on a generic key/value.
+
+  Args:
+    configs: Dictionary of pipeline configuration protos.
+    key: A string key, dot-delimited to represent the argument key.
+      e.g. "model.ssd.train_config.batch_size"
+    value: A value to set the argument to. The type of the value must match the
+      type for the protocol buffer. Note that setting the wrong type will
+      result in a TypeError.
+      e.g. 42
+
+  Raises:
+    ValueError if the message key does not match the existing proto fields.
+    TypeError the value type doesn't match the protobuf field type.
+  """
+  fields = key.split(".")
+  first_field = fields.pop(0)
+  last_field = fields.pop()
+  message = configs[first_field]
+  for field in fields:
+    _validate_message_has_field(message, field)
+    message = getattr(message, field)
+  _validate_message_has_field(message, last_field)
+  setattr(message, last_field, value)
+
+
+def _update_momentum_optimizer_value(configs, momentum):
+  """Updates `configs` to reflect the new momentum value.
+
+  Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any
+  other optimizer, no changes take place. The configs dictionary is updated in
+  place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    momentum: New momentum value. Values are clipped at 0.0 and 1.0.
+
+  Raises:
+    TypeError: If the optimizer type is not `rms_prop_optimizer` or
+    `momentum_optimizer`.
+  """
+  optimizer_type = get_optimizer_type(configs["train_config"])
+  if optimizer_type == "rms_prop_optimizer":
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+  elif optimizer_type == "momentum_optimizer":
+    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
+  else:
+    raise TypeError("Optimizer type must be one of `rms_prop_optimizer` or "
+                    "`momentum_optimizer`.")
+
+  optimizer_config.momentum_optimizer_value = min(max(0.0, momentum), 1.0)
+
+
+def _update_classification_localization_weight_ratio(configs, ratio):
+  """Updates the classification/localization weight loss ratio.
+
+  Detection models usually define a loss weight for both classification and
+  objectness. This function updates the weights such that the ratio between
+  classification weight to localization weight is the ratio provided.
+  Arbitrarily, localization weight is set to 1.0.
+
+  Note that in the case of Faster R-CNN, this same ratio is applied to the first
+  stage objectness loss weight relative to localization loss weight.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    ratio: Desired ratio of classification (and/or objectness) loss weight to
+      localization loss weight.
+  """
+  meta_architecture = configs["model"].WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    model = configs["model"].faster_rcnn
+    model.first_stage_localization_loss_weight = 1.0
+    model.first_stage_objectness_loss_weight = ratio
+    model.second_stage_localization_loss_weight = 1.0
+    model.second_stage_classification_loss_weight = ratio
+  if meta_architecture == "ssd":
+    model = configs["model"].ssd
+    model.loss.localization_weight = 1.0
+    model.loss.classification_weight = ratio
+
+
+def _get_classification_loss(model_config):
+  """Returns the classification loss for a model."""
+  meta_architecture = model_config.WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    model = model_config.faster_rcnn
+    classification_loss = model.second_stage_classification_loss
+  elif meta_architecture == "ssd":
+    model = model_config.ssd
+    classification_loss = model.loss.classification_loss
+  else:
+    raise TypeError("Did not recognize the model architecture.")
+  return classification_loss
+
+
+def _update_focal_loss_gamma(configs, gamma):
+  """Updates the gamma value for a sigmoid focal loss.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    gamma: Exponent term in focal loss.
+
+  Raises:
+    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
+  """
+  classification_loss = _get_classification_loss(configs["model"])
+  classification_loss_type = classification_loss.WhichOneof(
+      "classification_loss")
+  if classification_loss_type != "weighted_sigmoid_focal":
+    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
+  classification_loss.weighted_sigmoid_focal.gamma = gamma
+
+
+def _update_focal_loss_alpha(configs, alpha):
+  """Updates the alpha value for a sigmoid focal loss.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    alpha: Class weight multiplier for sigmoid loss.
+
+  Raises:
+    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
+  """
+  classification_loss = _get_classification_loss(configs["model"])
+  classification_loss_type = classification_loss.WhichOneof(
+      "classification_loss")
+  if classification_loss_type != "weighted_sigmoid_focal":
+    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
+  classification_loss.weighted_sigmoid_focal.alpha = alpha
+
+
+def _update_train_steps(configs, train_steps):
+  """Updates `configs` to reflect new number of training steps."""
+  configs["train_config"].num_steps = int(train_steps)
+
+
+def _update_eval_steps(configs, eval_steps):
+  """Updates `configs` to reflect new number of eval steps per evaluation."""
+  configs["eval_config"].num_examples = int(eval_steps)
+
+
+def _update_all_eval_input_configs(configs, field, value):
+  """Updates the content of `field` with `value` for all eval input configs."""
+  for eval_input_config in configs["eval_input_configs"]:
+    setattr(eval_input_config, field, value)
+
+
+def _update_label_map_path(configs, label_map_path):
+  """Updates the label map path for both train and eval input readers.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    label_map_path: New path to `StringIntLabelMap` pbtxt file.
+  """
+  configs["train_input_config"].label_map_path = label_map_path
+  _update_all_eval_input_configs(configs, "label_map_path", label_map_path)
+
+
+def _update_mask_type(configs, mask_type):
+  """Updates the mask type for both train and eval input readers.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    mask_type: A string name representing a value of
+      input_reader_pb2.InstanceMaskType
+  """
+  configs["train_input_config"].mask_type = mask_type
+  _update_all_eval_input_configs(configs, "mask_type", mask_type)
+
+
+def _update_use_moving_averages(configs, use_moving_averages):
+  """Updates the eval config option to use or not use moving averages.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    use_moving_averages: Boolean indicating whether moving average variables
+      should be loaded during evaluation.
+  """
+  configs["eval_config"].use_moving_averages = use_moving_averages
+
+
+def _update_retain_original_images(eval_config, retain_original_images):
+  """Updates eval config with option to retain original images.
+
+  The eval_config object is updated in place, and hence not returned.
+
+  Args:
+    eval_config: A eval_pb2.EvalConfig.
+    retain_original_images: Boolean indicating whether to retain original images
+      in eval mode.
+  """
+  eval_config.retain_original_images = retain_original_images
+
+
+def _update_use_bfloat16(configs, use_bfloat16):
+  """Updates `configs` to reflect the new setup on whether to use bfloat16.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    use_bfloat16: A bool, indicating whether to use bfloat16 for training.
+  """
+  configs["train_config"].use_bfloat16 = use_bfloat16
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util_test.py
new file mode 100644
index 00000000..92679e92
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/config_util_test.py
@@ -0,0 +1,866 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.utils.config_util."""
+
+import os
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.protos import eval_pb2
+from object_detection.protos import image_resizer_pb2
+from object_detection.protos import input_reader_pb2
+from object_detection.protos import model_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.protos import train_pb2
+from object_detection.utils import config_util
+
+
+def _write_config(config, config_path):
+  """Writes a config object to disk."""
+  config_text = text_format.MessageToString(config)
+  with tf.gfile.Open(config_path, "wb") as f:
+    f.write(config_text)
+
+
+def _update_optimizer_with_constant_learning_rate(optimizer, learning_rate):
+  """Adds a new constant learning rate."""
+  constant_lr = optimizer.learning_rate.constant_learning_rate
+  constant_lr.learning_rate = learning_rate
+
+
+def _update_optimizer_with_exponential_decay_learning_rate(
+    optimizer, learning_rate):
+  """Adds a new exponential decay learning rate."""
+  exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
+  exponential_lr.initial_learning_rate = learning_rate
+
+
+def _update_optimizer_with_manual_step_learning_rate(
+    optimizer, initial_learning_rate, learning_rate_scaling):
+  """Adds a learning rate schedule."""
+  manual_lr = optimizer.learning_rate.manual_step_learning_rate
+  manual_lr.initial_learning_rate = initial_learning_rate
+  for i in range(3):
+    schedule = manual_lr.schedule.add()
+    schedule.learning_rate = initial_learning_rate * learning_rate_scaling**i
+
+
+def _update_optimizer_with_cosine_decay_learning_rate(
+    optimizer, learning_rate, warmup_learning_rate):
+  """Adds a new cosine decay learning rate."""
+  cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
+  cosine_lr.learning_rate_base = learning_rate
+  cosine_lr.warmup_learning_rate = warmup_learning_rate
+
+
+class ConfigUtilTest(tf.test.TestCase):
+
+  def _create_and_load_test_configs(self, pipeline_config):
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    _write_config(pipeline_config, pipeline_config_path)
+    return config_util.get_configs_from_pipeline_file(pipeline_config_path)
+
+  def test_get_configs_from_pipeline_file(self):
+    """Test that proto configs can be read from pipeline config file."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 10
+    pipeline_config.train_config.batch_size = 32
+    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
+    pipeline_config.eval_config.num_examples = 20
+    pipeline_config.eval_input_reader.add().queue_capacity = 100
+
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    self.assertProtoEquals(pipeline_config.model, configs["model"])
+    self.assertProtoEquals(pipeline_config.train_config,
+                           configs["train_config"])
+    self.assertProtoEquals(pipeline_config.train_input_reader,
+                           configs["train_input_config"])
+    self.assertProtoEquals(pipeline_config.eval_config,
+                           configs["eval_config"])
+    self.assertProtoEquals(pipeline_config.eval_input_reader,
+                           configs["eval_input_configs"])
+
+  def test_create_configs_from_pipeline_proto(self):
+    """Tests creating configs dictionary from pipeline proto."""
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 10
+    pipeline_config.train_config.batch_size = 32
+    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
+    pipeline_config.eval_config.num_examples = 20
+    pipeline_config.eval_input_reader.add().queue_capacity = 100
+
+    configs = config_util.create_configs_from_pipeline_proto(pipeline_config)
+    self.assertProtoEquals(pipeline_config.model, configs["model"])
+    self.assertProtoEquals(pipeline_config.train_config,
+                           configs["train_config"])
+    self.assertProtoEquals(pipeline_config.train_input_reader,
+                           configs["train_input_config"])
+    self.assertProtoEquals(pipeline_config.eval_config, configs["eval_config"])
+    self.assertProtoEquals(pipeline_config.eval_input_reader,
+                           configs["eval_input_configs"])
+
+  def test_create_pipeline_proto_from_configs(self):
+    """Tests that proto can be reconstructed from configs dictionary."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 10
+    pipeline_config.train_config.batch_size = 32
+    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
+    pipeline_config.eval_config.num_examples = 20
+    pipeline_config.eval_input_reader.add().queue_capacity = 100
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    pipeline_config_reconstructed = (
+        config_util.create_pipeline_proto_from_configs(configs))
+    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
+
+  def test_save_pipeline_config(self):
+    """Tests that the pipeline config is properly saved to disk."""
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 10
+    pipeline_config.train_config.batch_size = 32
+    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
+    pipeline_config.eval_config.num_examples = 20
+    pipeline_config.eval_input_reader.add().queue_capacity = 100
+
+    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
+    configs = config_util.get_configs_from_pipeline_file(
+        os.path.join(self.get_temp_dir(), "pipeline.config"))
+    pipeline_config_reconstructed = (
+        config_util.create_pipeline_proto_from_configs(configs))
+
+    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
+
+  def test_get_configs_from_multiple_files(self):
+    """Tests that proto configs can be read from multiple files."""
+    temp_dir = self.get_temp_dir()
+
+    # Write model config file.
+    model_config_path = os.path.join(temp_dir, "model.config")
+    model = model_pb2.DetectionModel()
+    model.faster_rcnn.num_classes = 10
+    _write_config(model, model_config_path)
+
+    # Write train config file.
+    train_config_path = os.path.join(temp_dir, "train.config")
+    train_config = train_config = train_pb2.TrainConfig()
+    train_config.batch_size = 32
+    _write_config(train_config, train_config_path)
+
+    # Write train input config file.
+    train_input_config_path = os.path.join(temp_dir, "train_input.config")
+    train_input_config = input_reader_pb2.InputReader()
+    train_input_config.label_map_path = "path/to/label_map"
+    _write_config(train_input_config, train_input_config_path)
+
+    # Write eval config file.
+    eval_config_path = os.path.join(temp_dir, "eval.config")
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.num_examples = 20
+    _write_config(eval_config, eval_config_path)
+
+    # Write eval input config file.
+    eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
+    eval_input_config = input_reader_pb2.InputReader()
+    eval_input_config.label_map_path = "path/to/another/label_map"
+    _write_config(eval_input_config, eval_input_config_path)
+
+    configs = config_util.get_configs_from_multiple_files(
+        model_config_path=model_config_path,
+        train_config_path=train_config_path,
+        train_input_config_path=train_input_config_path,
+        eval_config_path=eval_config_path,
+        eval_input_config_path=eval_input_config_path)
+    self.assertProtoEquals(model, configs["model"])
+    self.assertProtoEquals(train_config, configs["train_config"])
+    self.assertProtoEquals(train_input_config,
+                           configs["train_input_config"])
+    self.assertProtoEquals(eval_config, configs["eval_config"])
+    self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
+
+  def _assertOptimizerWithNewLearningRate(self, optimizer_name):
+    """Asserts successful updating of all learning rate schemes."""
+    original_learning_rate = 0.7
+    learning_rate_scaling = 0.1
+    warmup_learning_rate = 0.07
+    hparams = tf.contrib.training.HParams(learning_rate=0.15)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    # Constant learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_constant_learning_rate(optimizer,
+                                                  original_learning_rate)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    constant_lr = optimizer.learning_rate.constant_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate, constant_lr.learning_rate)
+
+    # Exponential decay learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_exponential_decay_learning_rate(
+        optimizer, original_learning_rate)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate,
+                           exponential_lr.initial_learning_rate)
+
+    # Manual step learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_manual_step_learning_rate(
+        optimizer, original_learning_rate, learning_rate_scaling)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    manual_lr = optimizer.learning_rate.manual_step_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate,
+                           manual_lr.initial_learning_rate)
+    for i, schedule in enumerate(manual_lr.schedule):
+      self.assertAlmostEqual(hparams.learning_rate * learning_rate_scaling**i,
+                             schedule.learning_rate)
+
+    # Cosine decay learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_cosine_decay_learning_rate(optimizer,
+                                                      original_learning_rate,
+                                                      warmup_learning_rate)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
+
+    self.assertAlmostEqual(hparams.learning_rate, cosine_lr.learning_rate_base)
+    warmup_scale_factor = warmup_learning_rate / original_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate * warmup_scale_factor,
+                           cosine_lr.warmup_learning_rate)
+
+  def testRMSPropWithNewLearingRate(self):
+    """Tests new learning rates for RMSProp Optimizer."""
+    self._assertOptimizerWithNewLearningRate("rms_prop_optimizer")
+
+  def testMomentumOptimizerWithNewLearningRate(self):
+    """Tests new learning rates for Momentum Optimizer."""
+    self._assertOptimizerWithNewLearningRate("momentum_optimizer")
+
+  def testAdamOptimizerWithNewLearningRate(self):
+    """Tests new learning rates for Adam Optimizer."""
+    self._assertOptimizerWithNewLearningRate("adam_optimizer")
+
+  def testGenericConfigOverride(self):
+    """Tests generic config overrides for all top-level configs."""
+    # Set one parameter for each of the top-level pipeline configs:
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.ssd.num_classes = 1
+    pipeline_config.train_config.batch_size = 1
+    pipeline_config.eval_config.num_visualizations = 1
+    pipeline_config.train_input_reader.label_map_path = "/some/path"
+    pipeline_config.eval_input_reader.add().label_map_path = "/some/path"
+    pipeline_config.graph_rewriter.quantization.weight_bits = 1
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    _write_config(pipeline_config, pipeline_config_path)
+
+    # Override each of the parameters:
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    hparams = tf.contrib.training.HParams(
+        **{
+            "model.ssd.num_classes": 2,
+            "train_config.batch_size": 2,
+            "train_input_config.label_map_path": "/some/other/path",
+            "eval_config.num_visualizations": 2,
+            "graph_rewriter_config.quantization.weight_bits": 2
+        })
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+
+    # Ensure that the parameters have the overridden values:
+    self.assertEqual(2, configs["model"].ssd.num_classes)
+    self.assertEqual(2, configs["train_config"].batch_size)
+    self.assertEqual("/some/other/path",
+                     configs["train_input_config"].label_map_path)
+    self.assertEqual(2, configs["eval_config"].num_visualizations)
+    self.assertEqual(2,
+                     configs["graph_rewriter_config"].quantization.weight_bits)
+
+  def testNewBatchSize(self):
+    """Tests that batch size is updated appropriately."""
+    original_batch_size = 2
+    hparams = tf.contrib.training.HParams(batch_size=16)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = original_batch_size
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    new_batch_size = configs["train_config"].batch_size
+    self.assertEqual(16, new_batch_size)
+
+  def testNewBatchSizeWithClipping(self):
+    """Tests that batch size is clipped to 1 from below."""
+    original_batch_size = 2
+    hparams = tf.contrib.training.HParams(batch_size=0.5)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = original_batch_size
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    new_batch_size = configs["train_config"].batch_size
+    self.assertEqual(1, new_batch_size)  # Clipped to 1.0.
+
+  def testOverwriteBatchSizeWithKeyValue(self):
+    """Tests that batch size is overwritten based on key/value."""
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = 2
+    configs = self._create_and_load_test_configs(pipeline_config)
+    hparams = tf.contrib.training.HParams(**{"train_config.batch_size": 10})
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    new_batch_size = configs["train_config"].batch_size
+    self.assertEqual(10, new_batch_size)
+
+  def testKeyValueOverrideBadKey(self):
+    """Tests that overwriting with a bad key causes an exception."""
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    configs = self._create_and_load_test_configs(pipeline_config)
+    hparams = tf.contrib.training.HParams(**{"train_config.no_such_field": 10})
+    with self.assertRaises(ValueError):
+      config_util.merge_external_params_with_configs(configs, hparams)
+
+  def testOverwriteBatchSizeWithBadValueType(self):
+    """Tests that overwriting with a bad valuye type causes an exception."""
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = 2
+    configs = self._create_and_load_test_configs(pipeline_config)
+    # Type should be an integer, but we're passing a string "10".
+    hparams = tf.contrib.training.HParams(**{"train_config.batch_size": "10"})
+    with self.assertRaises(TypeError):
+      config_util.merge_external_params_with_configs(configs, hparams)
+
+  def testNewMomentumOptimizerValue(self):
+    """Tests that new momentum value is updated appropriately."""
+    original_momentum_value = 0.4
+    hparams = tf.contrib.training.HParams(momentum_optimizer_value=1.1)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
+    optimizer_config.momentum_optimizer_value = original_momentum_value
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+    new_momentum_value = optimizer_config.momentum_optimizer_value
+    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0.
+
+  def testNewClassificationLocalizationWeightRatio(self):
+    """Tests that the loss weight ratio is updated appropriately."""
+    original_localization_weight = 0.1
+    original_classification_weight = 0.2
+    new_weight_ratio = 5.0
+    hparams = tf.contrib.training.HParams(
+        classification_localization_weight_ratio=new_weight_ratio)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.ssd.loss.localization_weight = (
+        original_localization_weight)
+    pipeline_config.model.ssd.loss.classification_weight = (
+        original_classification_weight)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    loss = configs["model"].ssd.loss
+    self.assertAlmostEqual(1.0, loss.localization_weight)
+    self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
+
+  def testNewFocalLossParameters(self):
+    """Tests that the loss weight ratio is updated appropriately."""
+    original_alpha = 1.0
+    original_gamma = 1.0
+    new_alpha = 0.3
+    new_gamma = 2.0
+    hparams = tf.contrib.training.HParams(
+        focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    classification_loss = pipeline_config.model.ssd.loss.classification_loss
+    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
+    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    classification_loss = configs["model"].ssd.loss.classification_loss
+    self.assertAlmostEqual(new_alpha,
+                           classification_loss.weighted_sigmoid_focal.alpha)
+    self.assertAlmostEqual(new_gamma,
+                           classification_loss.weighted_sigmoid_focal.gamma)
+
+  def testMergingKeywordArguments(self):
+    """Tests that keyword arguments get merged as do hyperparameters."""
+    original_num_train_steps = 100
+    desired_num_train_steps = 10
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.num_steps = original_num_train_steps
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"train_steps": desired_num_train_steps}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    train_steps = configs["train_config"].num_steps
+    self.assertEqual(desired_num_train_steps, train_steps)
+
+  def testGetNumberOfClasses(self):
+    """Tests that number of classes can be retrieved."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 20
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    number_of_classes = config_util.get_number_of_classes(configs["model"])
+    self.assertEqual(20, number_of_classes)
+
+  def testNewTrainInputPath(self):
+    """Tests that train input path can be overwritten with single file."""
+    original_train_path = ["path/to/data"]
+    new_train_path = "another/path/to/data"
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
+    reader_config.input_path.extend(original_train_path)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"train_input_path": new_train_path}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    reader_config = configs["train_input_config"].tf_record_input_reader
+    final_path = reader_config.input_path
+    self.assertEqual([new_train_path], final_path)
+
+  def testNewTrainInputPathList(self):
+    """Tests that train input path can be overwritten with multiple files."""
+    original_train_path = ["path/to/data"]
+    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
+    reader_config.input_path.extend(original_train_path)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"train_input_path": new_train_path}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    reader_config = configs["train_input_config"].tf_record_input_reader
+    final_path = reader_config.input_path
+    self.assertEqual(new_train_path, final_path)
+
+  def testNewLabelMapPath(self):
+    """Tests that label map path can be overwritten in input readers."""
+    original_label_map_path = "path/to/original/label_map"
+    new_label_map_path = "path//to/new/label_map"
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_input_reader = pipeline_config.train_input_reader
+    train_input_reader.label_map_path = original_label_map_path
+    eval_input_reader = pipeline_config.eval_input_reader.add()
+    eval_input_reader.label_map_path = original_label_map_path
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"label_map_path": new_label_map_path}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    self.assertEqual(new_label_map_path,
+                     configs["train_input_config"].label_map_path)
+    for eval_input_config in configs["eval_input_configs"]:
+      self.assertEqual(new_label_map_path, eval_input_config.label_map_path)
+
+  def testDontOverwriteEmptyLabelMapPath(self):
+    """Tests that label map path will not by overwritten with empty string."""
+    original_label_map_path = "path/to/original/label_map"
+    new_label_map_path = ""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_input_reader = pipeline_config.train_input_reader
+    train_input_reader.label_map_path = original_label_map_path
+    eval_input_reader = pipeline_config.eval_input_reader.add()
+    eval_input_reader.label_map_path = original_label_map_path
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"label_map_path": new_label_map_path}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    self.assertEqual(original_label_map_path,
+                     configs["train_input_config"].label_map_path)
+    self.assertEqual(original_label_map_path,
+                     configs["eval_input_configs"][0].label_map_path)
+
+  def testNewMaskType(self):
+    """Tests that mask type can be overwritten in input readers."""
+    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
+    new_mask_type = input_reader_pb2.PNG_MASKS
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_input_reader = pipeline_config.train_input_reader
+    train_input_reader.mask_type = original_mask_type
+    eval_input_reader = pipeline_config.eval_input_reader.add()
+    eval_input_reader.mask_type = original_mask_type
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"mask_type": new_mask_type}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
+    self.assertEqual(new_mask_type, configs["eval_input_configs"][0].mask_type)
+
+  def testUseMovingAverageForEval(self):
+    use_moving_averages_orig = False
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.use_moving_averages = use_moving_averages_orig
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"eval_with_moving_averages": True}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    self.assertEqual(True, configs["eval_config"].use_moving_averages)
+
+  def  testGetImageResizerConfig(self):
+    """Tests that number of classes can be retrieved."""
+    model_config = model_pb2.DetectionModel()
+    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
+    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
+    image_resizer_config = config_util.get_image_resizer_config(model_config)
+    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
+    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
+
+  def testGetSpatialImageSizeFromFixedShapeResizerConfig(self):
+    image_resizer_config = image_resizer_pb2.ImageResizer()
+    image_resizer_config.fixed_shape_resizer.height = 100
+    image_resizer_config.fixed_shape_resizer.width = 200
+    image_shape = config_util.get_spatial_image_size(image_resizer_config)
+    self.assertAllEqual(image_shape, [100, 200])
+
+  def testGetSpatialImageSizeFromAspectPreservingResizerConfig(self):
+    image_resizer_config = image_resizer_pb2.ImageResizer()
+    image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
+    image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
+    image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension = True
+    image_shape = config_util.get_spatial_image_size(image_resizer_config)
+    self.assertAllEqual(image_shape, [600, 600])
+
+  def testGetSpatialImageSizeFromAspectPreservingResizerDynamic(self):
+    image_resizer_config = image_resizer_pb2.ImageResizer()
+    image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
+    image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
+    image_shape = config_util.get_spatial_image_size(image_resizer_config)
+    self.assertAllEqual(image_shape, [-1, -1])
+
+  def testEvalShuffle(self):
+    """Tests that `eval_shuffle` keyword arguments are applied correctly."""
+    original_shuffle = True
+    desired_shuffle = False
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_input_reader.add().shuffle = original_shuffle
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"eval_shuffle": desired_shuffle}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    self.assertEqual(desired_shuffle, configs["eval_input_configs"][0].shuffle)
+
+  def testTrainShuffle(self):
+    """Tests that `train_shuffle` keyword arguments are applied correctly."""
+    original_shuffle = True
+    desired_shuffle = False
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_input_reader.shuffle = original_shuffle
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"train_shuffle": desired_shuffle}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    train_shuffle = configs["train_input_config"].shuffle
+    self.assertEqual(desired_shuffle, train_shuffle)
+
+  def testOverWriteRetainOriginalImages(self):
+    """Tests that `train_shuffle` keyword arguments are applied correctly."""
+    original_retain_original_images = True
+    desired_retain_original_images = False
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_config.retain_original_images = (
+        original_retain_original_images)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {
+        "retain_original_images_in_eval": desired_retain_original_images
+    }
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    retain_original_images = configs["eval_config"].retain_original_images
+    self.assertEqual(desired_retain_original_images, retain_original_images)
+
+  def testOverwriteAllEvalSampling(self):
+    original_num_eval_examples = 1
+    new_num_eval_examples = 10
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
+        original_num_eval_examples)
+    pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
+        original_num_eval_examples)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"sample_1_of_n_eval_examples": new_num_eval_examples}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    for eval_input_config in configs["eval_input_configs"]:
+      self.assertEqual(new_num_eval_examples,
+                       eval_input_config.sample_1_of_n_examples)
+
+  def testOverwriteAllEvalNumEpochs(self):
+    original_num_epochs = 10
+    new_num_epochs = 1
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
+    pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"eval_num_epochs": new_num_epochs}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+    for eval_input_config in configs["eval_input_configs"]:
+      self.assertEqual(new_num_epochs, eval_input_config.num_epochs)
+
+  def testUpdateMaskTypeForAllInputConfigs(self):
+    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
+    new_mask_type = input_reader_pb2.PNG_MASKS
+
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_config = pipeline_config.train_input_reader
+    train_config.mask_type = original_mask_type
+    eval_1 = pipeline_config.eval_input_reader.add()
+    eval_1.mask_type = original_mask_type
+    eval_1.name = "eval_1"
+    eval_2 = pipeline_config.eval_input_reader.add()
+    eval_2.mask_type = original_mask_type
+    eval_2.name = "eval_2"
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"mask_type": new_mask_type}
+    configs = config_util.merge_external_params_with_configs(
+        configs, kwargs_dict=override_dict)
+
+    self.assertEqual(configs["train_input_config"].mask_type, new_mask_type)
+    for eval_input_config in configs["eval_input_configs"]:
+      self.assertEqual(eval_input_config.mask_type, new_mask_type)
+
+  def testErrorOverwritingMultipleInputConfig(self):
+    original_shuffle = False
+    new_shuffle = True
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    eval_1 = pipeline_config.eval_input_reader.add()
+    eval_1.shuffle = original_shuffle
+    eval_1.name = "eval_1"
+    eval_2 = pipeline_config.eval_input_reader.add()
+    eval_2.shuffle = original_shuffle
+    eval_2.name = "eval_2"
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    override_dict = {"eval_shuffle": new_shuffle}
+    with self.assertRaises(ValueError):
+      configs = config_util.merge_external_params_with_configs(
+          configs, kwargs_dict=override_dict)
+
+  def testCheckAndParseInputConfigKey(self):
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_input_reader.add().name = "eval_1"
+    pipeline_config.eval_input_reader.add().name = "eval_2"
+    _write_config(pipeline_config, pipeline_config_path)
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+
+    specific_shuffle_update_key = "eval_input_configs:eval_2:shuffle"
+    is_valid_input_config_key, key_name, input_name, field_name = (
+        config_util.check_and_parse_input_config_key(
+            configs, specific_shuffle_update_key))
+    self.assertTrue(is_valid_input_config_key)
+    self.assertEqual(key_name, "eval_input_configs")
+    self.assertEqual(input_name, "eval_2")
+    self.assertEqual(field_name, "shuffle")
+
+    legacy_shuffle_update_key = "eval_shuffle"
+    is_valid_input_config_key, key_name, input_name, field_name = (
+        config_util.check_and_parse_input_config_key(configs,
+                                                     legacy_shuffle_update_key))
+    self.assertTrue(is_valid_input_config_key)
+    self.assertEqual(key_name, "eval_input_configs")
+    self.assertEqual(input_name, None)
+    self.assertEqual(field_name, "shuffle")
+
+    non_input_config_update_key = "label_map_path"
+    is_valid_input_config_key, key_name, input_name, field_name = (
+        config_util.check_and_parse_input_config_key(
+            configs, non_input_config_update_key))
+    self.assertFalse(is_valid_input_config_key)
+    self.assertEqual(key_name, None)
+    self.assertEqual(input_name, None)
+    self.assertEqual(field_name, "label_map_path")
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "Invalid key format when overriding configs."):
+      config_util.check_and_parse_input_config_key(
+          configs, "train_input_config:shuffle")
+
+    with self.assertRaisesRegexp(
+        ValueError, "Invalid key_name when overriding input config."):
+      config_util.check_and_parse_input_config_key(
+          configs, "invalid_key_name:train_name:shuffle")
+
+    with self.assertRaisesRegexp(
+        ValueError, "Invalid input_name when overriding input config."):
+      config_util.check_and_parse_input_config_key(
+          configs, "eval_input_configs:unknown_eval_name:shuffle")
+
+    with self.assertRaisesRegexp(
+        ValueError, "Invalid field_name when overriding input config."):
+      config_util.check_and_parse_input_config_key(
+          configs, "eval_input_configs:eval_2:unknown_field_name")
+
+  def testUpdateInputReaderConfigSuccess(self):
+    original_shuffle = False
+    new_shuffle = True
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_input_reader.shuffle = original_shuffle
+    _write_config(pipeline_config, pipeline_config_path)
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+
+    config_util.update_input_reader_config(
+        configs,
+        key_name="train_input_config",
+        input_name=None,
+        field_name="shuffle",
+        value=new_shuffle)
+    self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
+
+    config_util.update_input_reader_config(
+        configs,
+        key_name="train_input_config",
+        input_name=None,
+        field_name="shuffle",
+        value=new_shuffle)
+    self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
+
+  def testUpdateInputReaderConfigErrors(self):
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.eval_input_reader.add().name = "same_eval_name"
+    pipeline_config.eval_input_reader.add().name = "same_eval_name"
+    _write_config(pipeline_config, pipeline_config_path)
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "Duplicate input name found when overriding."):
+      config_util.update_input_reader_config(
+          configs,
+          key_name="eval_input_configs",
+          input_name="same_eval_name",
+          field_name="shuffle",
+          value=False)
+
+    with self.assertRaisesRegexp(
+        ValueError, "Input name name_not_exist not found when overriding."):
+      config_util.update_input_reader_config(
+          configs,
+          key_name="eval_input_configs",
+          input_name="name_not_exist",
+          field_name="shuffle",
+          value=False)
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "Unknown input config overriding."):
+      config_util.update_input_reader_config(
+          configs,
+          key_name="eval_input_configs",
+          input_name=None,
+          field_name="shuffle",
+          value=False)
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager.py
new file mode 100644
index 00000000..0ee8723d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager.py
@@ -0,0 +1,40 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Python context management helper."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+class IdentityContextManager(object):
+  """Returns an identity context manager that does nothing.
+
+  This is helpful in setting up conditional `with` statement as below:
+
+  with slim.arg_scope(x) if use_slim_scope else IdentityContextManager():
+    do_stuff()
+
+  """
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exec_type, exec_value, traceback):
+    del exec_type
+    del exec_value
+    del traceback
+    return False
+
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/check_uncompressed.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager_test.py
old mode 100755
new mode 100644
similarity index 50%
rename from TensorFlow/Classification/imagenet/data_preprocessing/check_uncompressed.py
rename to TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager_test.py
index 3e1d93c0..b8da6b10
--- a/TensorFlow/Classification/imagenet/data_preprocessing/check_uncompressed.py
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/context_manager_test.py
@@ -1,5 +1,4 @@
-#!/usr/bin/python
-# Copyright 2017 NVIDIA Corporation. All Rights Reserved.
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,21 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import glob
-import re
-import sys
-import tarfile
+"""Tests for tensorflow_models.object_detection.utils.context_manager."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from object_detection.utils import context_manager
+
+
+class ContextManagerTest(tf.test.TestCase):
+
+  def test_identity_context_manager(self):
+    with context_manager.IdentityContextManager() as identity_context:
+      self.assertIsNone(identity_context)
+
 
 if __name__ == '__main__':
-  tar_file_name = sys.argv[1]
-  path_to_check = sys.argv[2]
-  tar_file = tarfile.open(tar_file_name)
-  tar_paths = set([_i.path for _i in tar_file])
-  uncompressed_paths = files = [re.sub('(\.?\/)?(.+)', '\g<2>', _f) for _f in glob.glob(path_to_check + "/*")]
-  for _f in tar_paths:
-    if _f not in uncompressed_paths:
-      print("%s is not fully uncompressed" % tar_file_name)
-      print("Missing file: %s in %s" % (_f, path_to_check))
-      sys.exit(1)
-  print("%s is already fully uncompressed" % tar_file_name)
-
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util.py
new file mode 100644
index 00000000..77b46a2d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util.py
@@ -0,0 +1,88 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utility functions for creating TFRecord data sets."""
+
+import tensorflow as tf
+
+
+def int64_feature(value):
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+
+def int64_list_feature(value):
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+
+def bytes_feature(value):
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def bytes_list_feature(value):
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
+
+
+def float_list_feature(value):
+  return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+
+def read_examples_list(path):
+  """Read list of training or validation examples.
+
+  The file is assumed to contain a single example per line where the first
+  token in the line is an identifier that allows us to find the image and
+  annotation xml for that example.
+
+  For example, the line:
+  xyz 3
+  would allow us to find files xyz.jpg and xyz.xml (the 3 would be ignored).
+
+  Args:
+    path: absolute path to examples list file.
+
+  Returns:
+    list of example identifiers (strings).
+  """
+  with tf.gfile.GFile(path) as fid:
+    lines = fid.readlines()
+  return [line.strip().split(' ')[0] for line in lines]
+
+
+def recursive_parse_xml_to_dict(xml):
+  """Recursively parses XML contents to python dict.
+
+  We assume that `object` tags are the only ones that can appear
+  multiple times at the same level of a tree.
+
+  Args:
+    xml: xml tree obtained by parsing XML file contents using lxml.etree
+
+  Returns:
+    Python dictionary holding XML contents.
+  """
+  if not xml:
+    return {xml.tag: xml.text}
+  result = {}
+  for child in xml:
+    child_result = recursive_parse_xml_to_dict(child)
+    if child.tag != 'object':
+      result[child.tag] = child_result[child.tag]
+    else:
+      if child.tag not in result:
+        result[child.tag] = []
+      result[child.tag].append(child_result[child.tag])
+  return {xml.tag: result}
+
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util_test.py
new file mode 100644
index 00000000..99cfb2cd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/dataset_util_test.py
@@ -0,0 +1,37 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.dataset_util."""
+
+import os
+import tensorflow as tf
+
+from object_detection.utils import dataset_util
+
+
+class DatasetUtilTest(tf.test.TestCase):
+
+  def test_read_examples_list(self):
+    example_list_data = """example1 1\nexample2 2"""
+    example_list_path = os.path.join(self.get_temp_dir(), 'examples.txt')
+    with tf.gfile.Open(example_list_path, 'wb') as f:
+      f.write(example_list_data)
+
+    examples = dataset_util.read_examples_list(example_list_path)
+    self.assertListEqual(['example1', 'example2'], examples)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils.py
new file mode 100644
index 00000000..c8d09eb0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils.py
@@ -0,0 +1,87 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for dealing with writing json strings.
+
+json_utils wraps json.dump and json.dumps so that they can be used to safely
+control the precision of floats when writing to json strings or files.
+"""
+import json
+from json import encoder
+
+
+def Dump(obj, fid, float_digits=-1, **params):
+  """Wrapper of json.dump that allows specifying the float precision used.
+
+  Args:
+    obj: The object to dump.
+    fid: The file id to write to.
+    float_digits: The number of digits of precision when writing floats out.
+    **params: Additional parameters to pass to json.dumps.
+  """
+  original_encoder = encoder.FLOAT_REPR
+  if float_digits >= 0:
+    encoder.FLOAT_REPR = lambda o: format(o, '.%df' % float_digits)
+  try:
+    json.dump(obj, fid, **params)
+  finally:
+    encoder.FLOAT_REPR = original_encoder
+
+
+def Dumps(obj, float_digits=-1, **params):
+  """Wrapper of json.dumps that allows specifying the float precision used.
+
+  Args:
+    obj: The object to dump.
+    float_digits: The number of digits of precision when writing floats out.
+    **params: Additional parameters to pass to json.dumps.
+
+  Returns:
+    output: JSON string representation of obj.
+  """
+  original_encoder = encoder.FLOAT_REPR
+  original_c_make_encoder = encoder.c_make_encoder
+  if float_digits >= 0:
+    encoder.FLOAT_REPR = lambda o: format(o, '.%df' % float_digits)
+    encoder.c_make_encoder = None
+  try:
+    output = json.dumps(obj, **params)
+  finally:
+    encoder.FLOAT_REPR = original_encoder
+    encoder.c_make_encoder = original_c_make_encoder
+
+  return output
+
+
+def PrettyParams(**params):
+  """Returns parameters for use with Dump and Dumps to output pretty json.
+
+  Example usage:
+    ```json_str = json_utils.Dumps(obj, **json_utils.PrettyParams())```
+    ```json_str = json_utils.Dumps(
+                      obj, **json_utils.PrettyParams(allow_nans=False))```
+
+  Args:
+    **params: Additional params to pass to json.dump or json.dumps.
+
+  Returns:
+    params: Parameters that are compatible with json_utils.Dump and
+      json_utils.Dumps.
+  """
+  params['float_digits'] = 4
+  params['sort_keys'] = True
+  params['indent'] = 2
+  params['separators'] = (',', ': ')
+  return params
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils_test.py
new file mode 100644
index 00000000..5e379043
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/json_utils_test.py
@@ -0,0 +1,97 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for google3.image.understanding.object_detection.utils.json_utils."""
+import os
+
+import tensorflow as tf
+
+from object_detection.utils import json_utils
+
+
+class JsonUtilsTest(tf.test.TestCase):
+
+  def testDumpReasonablePrecision(self):
+    output_path = os.path.join(tf.test.get_temp_dir(), 'test.json')
+    with tf.gfile.GFile(output_path, 'w') as f:
+      json_utils.Dump(1.0, f, float_digits=2)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      self.assertEqual(f.read(), '1.00')
+
+  def testDumpPassExtraParams(self):
+    output_path = os.path.join(tf.test.get_temp_dir(), 'test.json')
+    with tf.gfile.GFile(output_path, 'w') as f:
+      json_utils.Dump([1.0], f, float_digits=2, indent=3)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      self.assertEqual(f.read(), '[\n   1.00\n]')
+
+  def testDumpZeroPrecision(self):
+    output_path = os.path.join(tf.test.get_temp_dir(), 'test.json')
+    with tf.gfile.GFile(output_path, 'w') as f:
+      json_utils.Dump(1.0, f, float_digits=0, indent=3)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      self.assertEqual(f.read(), '1')
+
+  def testDumpUnspecifiedPrecision(self):
+    output_path = os.path.join(tf.test.get_temp_dir(), 'test.json')
+    with tf.gfile.GFile(output_path, 'w') as f:
+      json_utils.Dump(1.012345, f)
+    with tf.gfile.GFile(output_path, 'r') as f:
+      self.assertEqual(f.read(), '1.012345')
+
+  def testDumpsReasonablePrecision(self):
+    s = json_utils.Dumps(1.0, float_digits=2)
+    self.assertEqual(s, '1.00')
+
+  def testDumpsPassExtraParams(self):
+    s = json_utils.Dumps([1.0], float_digits=2, indent=3)
+    self.assertEqual(s, '[\n   1.00\n]')
+
+  def testDumpsZeroPrecision(self):
+    s = json_utils.Dumps(1.0, float_digits=0)
+    self.assertEqual(s, '1')
+
+  def testDumpsUnspecifiedPrecision(self):
+    s = json_utils.Dumps(1.012345)
+    self.assertEqual(s, '1.012345')
+
+  def testPrettyParams(self):
+    s = json_utils.Dumps({'v': 1.012345, 'n': 2}, **json_utils.PrettyParams())
+    self.assertEqual(s, '{\n  "n": 2,\n  "v": 1.0123\n}')
+
+  def testPrettyParamsExtraParamsInside(self):
+    s = json_utils.Dumps(
+        {'v': 1.012345,
+         'n': float('nan')}, **json_utils.PrettyParams(allow_nan=True))
+    self.assertEqual(s, '{\n  "n": NaN,\n  "v": 1.0123\n}')
+
+    with self.assertRaises(ValueError):
+      s = json_utils.Dumps(
+          {'v': 1.012345,
+           'n': float('nan')}, **json_utils.PrettyParams(allow_nan=False))
+
+  def testPrettyParamsExtraParamsOutside(self):
+    s = json_utils.Dumps(
+        {'v': 1.012345,
+         'n': float('nan')}, allow_nan=True, **json_utils.PrettyParams())
+    self.assertEqual(s, '{\n  "n": NaN,\n  "v": 1.0123\n}')
+
+    with self.assertRaises(ValueError):
+      s = json_utils.Dumps(
+          {'v': 1.012345,
+           'n': float('nan')}, allow_nan=False, **json_utils.PrettyParams())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util.py
new file mode 100644
index 00000000..c395a716
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util.py
@@ -0,0 +1,235 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Label map utility functions."""
+
+import logging
+
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection.protos import string_int_label_map_pb2
+
+
+def _validate_label_map(label_map):
+  """Checks if a label map is valid.
+
+  Args:
+    label_map: StringIntLabelMap to validate.
+
+  Raises:
+    ValueError: if label map is invalid.
+  """
+  for item in label_map.item:
+    if item.id < 0:
+      raise ValueError('Label map ids should be >= 0.')
+    if (item.id == 0 and item.name != 'background' and
+        item.display_name != 'background'):
+      raise ValueError('Label map id 0 is reserved for the background label')
+
+
+def create_category_index(categories):
+  """Creates dictionary of COCO compatible categories keyed by category id.
+
+  Args:
+    categories: a list of dicts, each of which has the following keys:
+      'id': (required) an integer id uniquely identifying this category.
+      'name': (required) string representing category name
+        e.g., 'cat', 'dog', 'pizza'.
+
+  Returns:
+    category_index: a dict containing the same entries as categories, but keyed
+      by the 'id' field of each category.
+  """
+  category_index = {}
+  for cat in categories:
+    category_index[cat['id']] = cat
+  return category_index
+
+
+def get_max_label_map_index(label_map):
+  """Get maximum index in label map.
+
+  Args:
+    label_map: a StringIntLabelMapProto
+
+  Returns:
+    an integer
+  """
+  return max([item.id for item in label_map.item])
+
+
+def convert_label_map_to_categories(label_map,
+                                    max_num_classes,
+                                    use_display_name=True):
+  """Given label map proto returns categories list compatible with eval.
+
+  This function converts label map proto and returns a list of dicts, each of
+  which  has the following keys:
+    'id': (required) an integer id uniquely identifying this category.
+    'name': (required) string representing category name
+      e.g., 'cat', 'dog', 'pizza'.
+  We only allow class into the list if its id-label_id_offset is
+  between 0 (inclusive) and max_num_classes (exclusive).
+  If there are several items mapping to the same id in the label map,
+  we will only keep the first one in the categories list.
+
+  Args:
+    label_map: a StringIntLabelMapProto or None.  If None, a default categories
+      list is created with max_num_classes categories.
+    max_num_classes: maximum number of (consecutive) label indices to include.
+    use_display_name: (boolean) choose whether to load 'display_name' field as
+      category name.  If False or if the display_name field does not exist, uses
+      'name' field as category names instead.
+
+  Returns:
+    categories: a list of dictionaries representing all possible categories.
+  """
+  categories = []
+  list_of_ids_already_added = []
+  if not label_map:
+    label_id_offset = 1
+    for class_id in range(max_num_classes):
+      categories.append({
+          'id': class_id + label_id_offset,
+          'name': 'category_{}'.format(class_id + label_id_offset)
+      })
+    return categories
+  for item in label_map.item:
+    if not 0 < item.id <= max_num_classes:
+      logging.info(
+          'Ignore item %d since it falls outside of requested '
+          'label range.', item.id)
+      continue
+    if use_display_name and item.HasField('display_name'):
+      name = item.display_name
+    else:
+      name = item.name
+    if item.id not in list_of_ids_already_added:
+      list_of_ids_already_added.append(item.id)
+      categories.append({'id': item.id, 'name': name})
+  return categories
+
+
+def load_labelmap(path):
+  """Loads label map proto.
+
+  Args:
+    path: path to StringIntLabelMap proto text file.
+  Returns:
+    a StringIntLabelMapProto
+  """
+  with tf.gfile.GFile(path, 'r') as fid:
+    label_map_string = fid.read()
+    label_map = string_int_label_map_pb2.StringIntLabelMap()
+    try:
+      text_format.Merge(label_map_string, label_map)
+    except text_format.ParseError:
+      label_map.ParseFromString(label_map_string)
+  _validate_label_map(label_map)
+  return label_map
+
+
+def get_label_map_dict(label_map_path,
+                       use_display_name=False,
+                       fill_in_gaps_and_background=False):
+  """Reads a label map and returns a dictionary of label names to id.
+
+  Args:
+    label_map_path: path to StringIntLabelMap proto text file.
+    use_display_name: whether to use the label map items' display names as keys.
+    fill_in_gaps_and_background: whether to fill in gaps and background with
+    respect to the id field in the proto. The id: 0 is reserved for the
+    'background' class and will be added if it is missing. All other missing
+    ids in range(1, max(id)) will be added with a dummy class name
+    ("class_<id>") if they are missing.
+
+  Returns:
+    A dictionary mapping label names to id.
+
+  Raises:
+    ValueError: if fill_in_gaps_and_background and label_map has non-integer or
+    negative values.
+  """
+  label_map = load_labelmap(label_map_path)
+  label_map_dict = {}
+  for item in label_map.item:
+    if use_display_name:
+      label_map_dict[item.display_name] = item.id
+    else:
+      label_map_dict[item.name] = item.id
+
+  if fill_in_gaps_and_background:
+    values = set(label_map_dict.values())
+
+    if 0 not in values:
+      label_map_dict['background'] = 0
+    if not all(isinstance(value, int) for value in values):
+      raise ValueError('The values in label map must be integers in order to'
+                       'fill_in_gaps_and_background.')
+    if not all(value >= 0 for value in values):
+      raise ValueError('The values in the label map must be positive.')
+
+    if len(values) != max(values) + 1:
+      # there are gaps in the labels, fill in gaps.
+      for value in range(1, max(values)):
+        if value not in values:
+          label_map_dict['class_' + str(value)] = value
+
+  return label_map_dict
+
+
+def create_categories_from_labelmap(label_map_path, use_display_name=True):
+  """Reads a label map and returns categories list compatible with eval.
+
+  This function converts label map proto and returns a list of dicts, each of
+  which  has the following keys:
+    'id': an integer id uniquely identifying this category.
+    'name': string representing category name e.g., 'cat', 'dog'.
+
+  Args:
+    label_map_path: Path to `StringIntLabelMap` proto text file.
+    use_display_name: (boolean) choose whether to load 'display_name' field
+      as category name.  If False or if the display_name field does not exist,
+      uses 'name' field as category names instead.
+
+  Returns:
+    categories: a list of dictionaries representing all possible categories.
+  """
+  label_map = load_labelmap(label_map_path)
+  max_num_classes = max(item.id for item in label_map.item)
+  return convert_label_map_to_categories(label_map, max_num_classes,
+                                         use_display_name)
+
+
+def create_category_index_from_labelmap(label_map_path, use_display_name=True):
+  """Reads a label map and returns a category index.
+
+  Args:
+    label_map_path: Path to `StringIntLabelMap` proto text file.
+    use_display_name: (boolean) choose whether to load 'display_name' field
+      as category name.  If False or if the display_name field does not exist,
+      uses 'name' field as category names instead.
+
+  Returns:
+    A category index, which is a dictionary that maps integer ids to dicts
+    containing categories, e.g.
+    {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
+  """
+  categories = create_categories_from_labelmap(label_map_path, use_display_name)
+  return create_category_index(categories)
+
+
+def create_class_agnostic_category_index():
+  """Creates a category index with a single `object` class."""
+  return {1: {'id': 1, 'name': 'object'}}
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util_test.py
new file mode 100644
index 00000000..e5f69f91
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/label_map_util_test.py
@@ -0,0 +1,334 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.utils.label_map_util."""
+
+import os
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.protos import string_int_label_map_pb2
+from object_detection.utils import label_map_util
+
+
+class LabelMapUtilTest(tf.test.TestCase):
+
+  def _generate_label_map(self, num_classes):
+    label_map_proto = string_int_label_map_pb2.StringIntLabelMap()
+    for i in range(1, num_classes + 1):
+      item = label_map_proto.item.add()
+      item.id = i
+      item.name = 'label_' + str(i)
+      item.display_name = str(i)
+    return label_map_proto
+
+  def test_get_label_map_dict(self):
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    label_map_dict = label_map_util.get_label_map_dict(label_map_path)
+    self.assertEqual(label_map_dict['dog'], 1)
+    self.assertEqual(label_map_dict['cat'], 2)
+
+  def test_get_label_map_dict_display(self):
+    label_map_string = """
+      item {
+        id:2
+        display_name:'cat'
+      }
+      item {
+        id:1
+        display_name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    label_map_dict = label_map_util.get_label_map_dict(
+        label_map_path, use_display_name=True)
+    self.assertEqual(label_map_dict['dog'], 1)
+    self.assertEqual(label_map_dict['cat'], 2)
+
+  def test_load_bad_label_map(self):
+    label_map_string = """
+      item {
+        id:0
+        name:'class that should not be indexed at zero'
+      }
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    with self.assertRaises(ValueError):
+      label_map_util.load_labelmap(label_map_path)
+
+  def test_load_label_map_with_background(self):
+    label_map_string = """
+      item {
+        id:0
+        name:'background'
+      }
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    label_map_dict = label_map_util.get_label_map_dict(label_map_path)
+    self.assertEqual(label_map_dict['background'], 0)
+    self.assertEqual(label_map_dict['dog'], 1)
+    self.assertEqual(label_map_dict['cat'], 2)
+
+  def test_get_label_map_dict_with_fill_in_gaps_and_background(self):
+    label_map_string = """
+      item {
+        id:3
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    label_map_dict = label_map_util.get_label_map_dict(
+        label_map_path, fill_in_gaps_and_background=True)
+
+    self.assertEqual(label_map_dict['background'], 0)
+    self.assertEqual(label_map_dict['dog'], 1)
+    self.assertEqual(label_map_dict['class_2'], 2)
+    self.assertEqual(label_map_dict['cat'], 3)
+    self.assertEqual(len(label_map_dict), max(label_map_dict.values()) + 1)
+
+  def test_keep_categories_with_unique_id(self):
+    label_map_proto = string_int_label_map_pb2.StringIntLabelMap()
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'child'
+      }
+      item {
+        id:1
+        name:'person'
+      }
+      item {
+        id:1
+        name:'n00007846'
+      }
+    """
+    text_format.Merge(label_map_string, label_map_proto)
+    categories = label_map_util.convert_label_map_to_categories(
+        label_map_proto, max_num_classes=3)
+    self.assertListEqual([{
+        'id': 2,
+        'name': u'cat'
+    }, {
+        'id': 1,
+        'name': u'child'
+    }], categories)
+
+  def test_convert_label_map_to_categories_no_label_map(self):
+    categories = label_map_util.convert_label_map_to_categories(
+        None, max_num_classes=3)
+    expected_categories_list = [{
+        'name': u'category_1',
+        'id': 1
+    }, {
+        'name': u'category_2',
+        'id': 2
+    }, {
+        'name': u'category_3',
+        'id': 3
+    }]
+    self.assertListEqual(expected_categories_list, categories)
+
+  def test_convert_label_map_to_categories(self):
+    label_map_proto = self._generate_label_map(num_classes=4)
+    categories = label_map_util.convert_label_map_to_categories(
+        label_map_proto, max_num_classes=3)
+    expected_categories_list = [{
+        'name': u'1',
+        'id': 1
+    }, {
+        'name': u'2',
+        'id': 2
+    }, {
+        'name': u'3',
+        'id': 3
+    }]
+    self.assertListEqual(expected_categories_list, categories)
+
+  def test_convert_label_map_to_categories_with_few_classes(self):
+    label_map_proto = self._generate_label_map(num_classes=4)
+    cat_no_offset = label_map_util.convert_label_map_to_categories(
+        label_map_proto, max_num_classes=2)
+    expected_categories_list = [{
+        'name': u'1',
+        'id': 1
+    }, {
+        'name': u'2',
+        'id': 2
+    }]
+    self.assertListEqual(expected_categories_list, cat_no_offset)
+
+  def test_get_max_label_map_index(self):
+    num_classes = 4
+    label_map_proto = self._generate_label_map(num_classes=num_classes)
+    max_index = label_map_util.get_max_label_map_index(label_map_proto)
+    self.assertEqual(num_classes, max_index)
+
+  def test_create_category_index(self):
+    categories = [{'name': u'1', 'id': 1}, {'name': u'2', 'id': 2}]
+    category_index = label_map_util.create_category_index(categories)
+    self.assertDictEqual({
+        1: {
+            'name': u'1',
+            'id': 1
+        },
+        2: {
+            'name': u'2',
+            'id': 2
+        }
+    }, category_index)
+
+  def test_create_categories_from_labelmap(self):
+    label_map_string = """
+      item {
+        id:1
+        name:'dog'
+      }
+      item {
+        id:2
+        name:'cat'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    categories = label_map_util.create_categories_from_labelmap(label_map_path)
+    self.assertListEqual([{
+        'name': u'dog',
+        'id': 1
+    }, {
+        'name': u'cat',
+        'id': 2
+    }], categories)
+
+  def test_create_category_index_from_labelmap(self):
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    category_index = label_map_util.create_category_index_from_labelmap(
+        label_map_path)
+    self.assertDictEqual({
+        1: {
+            'name': u'dog',
+            'id': 1
+        },
+        2: {
+            'name': u'cat',
+            'id': 2
+        }
+    }, category_index)
+
+  def test_create_category_index_from_labelmap_display(self):
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+        display_name:'meow'
+      }
+      item {
+        id:1
+        name:'dog'
+        display_name:'woof'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    self.assertDictEqual({
+        1: {
+            'name': u'dog',
+            'id': 1
+        },
+        2: {
+            'name': u'cat',
+            'id': 2
+        }
+    }, label_map_util.create_category_index_from_labelmap(
+        label_map_path, False))
+
+    self.assertDictEqual({
+        1: {
+            'name': u'woof',
+            'id': 1
+        },
+        2: {
+            'name': u'meow',
+            'id': 2
+        }
+    }, label_map_util.create_category_index_from_labelmap(label_map_path))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules.py
new file mode 100644
index 00000000..8b62f709
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules.py
@@ -0,0 +1,175 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Library of common learning rate schedules."""
+
+import numpy as np
+import tensorflow as tf
+
+
+def exponential_decay_with_burnin(global_step,
+                                  learning_rate_base,
+                                  learning_rate_decay_steps,
+                                  learning_rate_decay_factor,
+                                  burnin_learning_rate=0.0,
+                                  burnin_steps=0,
+                                  min_learning_rate=0.0,
+                                  staircase=True):
+  """Exponential decay schedule with burn-in period.
+
+  In this schedule, learning rate is fixed at burnin_learning_rate
+  for a fixed period, before transitioning to a regular exponential
+  decay schedule.
+
+  Args:
+    global_step: int tensor representing global step.
+    learning_rate_base: base learning rate.
+    learning_rate_decay_steps: steps to take between decaying the learning rate.
+      Note that this includes the number of burn-in steps.
+    learning_rate_decay_factor: multiplicative factor by which to decay
+      learning rate.
+    burnin_learning_rate: initial learning rate during burn-in period.  If
+      0.0 (which is the default), then the burn-in learning rate is simply
+      set to learning_rate_base.
+    burnin_steps: number of steps to use burnin learning rate.
+    min_learning_rate: the minimum learning rate.
+    staircase: whether use staircase decay.
+
+  Returns:
+    a (scalar) float tensor representing learning rate
+  """
+  if burnin_learning_rate == 0:
+    burnin_learning_rate = learning_rate_base
+  post_burnin_learning_rate = tf.train.exponential_decay(
+      learning_rate_base,
+      global_step - burnin_steps,
+      learning_rate_decay_steps,
+      learning_rate_decay_factor,
+      staircase=staircase)
+  return tf.maximum(tf.where(
+      tf.less(tf.cast(global_step, tf.int32), tf.constant(burnin_steps)),
+      tf.constant(burnin_learning_rate),
+      post_burnin_learning_rate), min_learning_rate, name='learning_rate')
+
+
+def cosine_decay_with_warmup(global_step,
+                             learning_rate_base,
+                             total_steps,
+                             warmup_learning_rate=0.0,
+                             warmup_steps=0,
+                             hold_base_rate_steps=0):
+  """Cosine decay schedule with warm up period.
+
+  Cosine annealing learning rate as described in:
+    Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts.
+    ICLR 2017. https://arxiv.org/abs/1608.03983
+  In this schedule, the learning rate grows linearly from warmup_learning_rate
+  to learning_rate_base for warmup_steps, then transitions to a cosine decay
+  schedule.
+
+  Args:
+    global_step: int64 (scalar) tensor representing global step.
+    learning_rate_base: base learning rate.
+    total_steps: total number of training steps.
+    warmup_learning_rate: initial learning rate for warm up.
+    warmup_steps: number of warmup steps.
+    hold_base_rate_steps: Optional number of steps to hold base learning rate
+      before decaying.
+
+  Returns:
+    a (scalar) float tensor representing learning rate.
+
+  Raises:
+    ValueError: if warmup_learning_rate is larger than learning_rate_base,
+      or if warmup_steps is larger than total_steps.
+  """
+  if total_steps < warmup_steps:
+    raise ValueError('total_steps must be larger or equal to '
+                     'warmup_steps.')
+  learning_rate = 0.5 * learning_rate_base * (1 + tf.cos(
+      np.pi *
+      (tf.cast(global_step, tf.float32) - warmup_steps - hold_base_rate_steps
+      ) / float(total_steps - warmup_steps - hold_base_rate_steps)))
+  if hold_base_rate_steps > 0:
+    learning_rate = tf.where(global_step > warmup_steps + hold_base_rate_steps,
+                             learning_rate, learning_rate_base)
+  if warmup_steps > 0:
+    if learning_rate_base < warmup_learning_rate:
+      raise ValueError('learning_rate_base must be larger or equal to '
+                       'warmup_learning_rate.')
+    slope = (learning_rate_base - warmup_learning_rate) / warmup_steps
+    warmup_rate = slope * tf.cast(global_step,
+                                  tf.float32) + warmup_learning_rate
+    learning_rate = tf.where(global_step < warmup_steps, warmup_rate,
+                             learning_rate)
+  return tf.where(global_step > total_steps, 0.0, learning_rate,
+                  name='learning_rate')
+
+
+def manual_stepping(global_step, boundaries, rates, warmup=False):
+  """Manually stepped learning rate schedule.
+
+  This function provides fine grained control over learning rates.  One must
+  specify a sequence of learning rates as well as a set of integer steps
+  at which the current learning rate must transition to the next.  For example,
+  if boundaries = [5, 10] and rates = [.1, .01, .001], then the learning
+  rate returned by this function is .1 for global_step=0,...,4, .01 for
+  global_step=5...9, and .001 for global_step=10 and onward.
+
+  Args:
+    global_step: int64 (scalar) tensor representing global step.
+    boundaries: a list of global steps at which to switch learning
+      rates.  This list is assumed to consist of increasing positive integers.
+    rates: a list of (float) learning rates corresponding to intervals between
+      the boundaries.  The length of this list must be exactly
+      len(boundaries) + 1.
+    warmup: Whether to linearly interpolate learning rate for steps in
+      [0, boundaries[0]].
+
+  Returns:
+    a (scalar) float tensor representing learning rate
+  Raises:
+    ValueError: if one of the following checks fails:
+      1. boundaries is a strictly increasing list of positive integers
+      2. len(rates) == len(boundaries) + 1
+      3. boundaries[0] != 0
+  """
+  if any([b < 0 for b in boundaries]) or any(
+      [not isinstance(b, int) for b in boundaries]):
+    raise ValueError('boundaries must be a list of positive integers')
+  if any([bnext <= b for bnext, b in zip(boundaries[1:], boundaries[:-1])]):
+    raise ValueError('Entries in boundaries must be strictly increasing.')
+  if any([not isinstance(r, float) for r in rates]):
+    raise ValueError('Learning rates must be floats')
+  if len(rates) != len(boundaries) + 1:
+    raise ValueError('Number of provided learning rates must exceed '
+                     'number of boundary points by exactly 1.')
+
+  if boundaries and boundaries[0] == 0:
+    raise ValueError('First step cannot be zero.')
+
+  if warmup and boundaries:
+    slope = (rates[1] - rates[0]) * 1.0 / boundaries[0]
+    warmup_steps = range(boundaries[0])
+    warmup_rates = [rates[0] + slope * step for step in warmup_steps]
+    boundaries = warmup_steps + boundaries
+    rates = warmup_rates + rates[1:]
+  else:
+    boundaries = [0] + boundaries
+  num_boundaries = len(boundaries)
+  rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries),
+                                      list(range(num_boundaries)),
+                                      [0] * num_boundaries))
+  return tf.reduce_sum(rates * tf.one_hot(rate_index, depth=num_boundaries),
+                       name='learning_rate')
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules_test.py
new file mode 100644
index 00000000..b78ca775
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/learning_schedules_test.py
@@ -0,0 +1,156 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.learning_schedules."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import learning_schedules
+from object_detection.utils import test_case
+
+
+class LearningSchedulesTest(test_case.TestCase):
+
+  def testExponentialDecayWithBurnin(self):
+    def graph_fn(global_step):
+      learning_rate_base = 1.0
+      learning_rate_decay_steps = 3
+      learning_rate_decay_factor = .1
+      burnin_learning_rate = .5
+      burnin_steps = 2
+      min_learning_rate = .05
+      learning_rate = learning_schedules.exponential_decay_with_burnin(
+          global_step, learning_rate_base, learning_rate_decay_steps,
+          learning_rate_decay_factor, burnin_learning_rate, burnin_steps,
+          min_learning_rate)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+
+    output_rates = [
+        self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9)
+    ]
+
+    exp_rates = [.5, .5, 1, 1, 1, .1, .1, .1, .05]
+    self.assertAllClose(output_rates, exp_rates, rtol=1e-4)
+
+  def testCosineDecayWithWarmup(self):
+    def graph_fn(global_step):
+      learning_rate_base = 1.0
+      total_steps = 100
+      warmup_learning_rate = 0.1
+      warmup_steps = 9
+      learning_rate = learning_schedules.cosine_decay_with_warmup(
+          global_step, learning_rate_base, total_steps,
+          warmup_learning_rate, warmup_steps)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+    exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
+    input_global_steps = [0, 4, 8, 9, 100]
+    output_rates = [
+        self.execute(graph_fn, [np.array(step).astype(np.int64)])
+        for step in input_global_steps
+    ]
+    self.assertAllClose(output_rates, exp_rates)
+
+  def testCosineDecayAfterTotalSteps(self):
+    def graph_fn(global_step):
+      learning_rate_base = 1.0
+      total_steps = 100
+      warmup_learning_rate = 0.1
+      warmup_steps = 9
+      learning_rate = learning_schedules.cosine_decay_with_warmup(
+          global_step, learning_rate_base, total_steps,
+          warmup_learning_rate, warmup_steps)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+    exp_rates = [0]
+    input_global_steps = [101]
+    output_rates = [
+        self.execute(graph_fn, [np.array(step).astype(np.int64)])
+        for step in input_global_steps
+    ]
+    self.assertAllClose(output_rates, exp_rates)
+
+  def testCosineDecayWithHoldBaseLearningRateSteps(self):
+    def graph_fn(global_step):
+      learning_rate_base = 1.0
+      total_steps = 120
+      warmup_learning_rate = 0.1
+      warmup_steps = 9
+      hold_base_rate_steps = 20
+      learning_rate = learning_schedules.cosine_decay_with_warmup(
+          global_step, learning_rate_base, total_steps,
+          warmup_learning_rate, warmup_steps, hold_base_rate_steps)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+    exp_rates = [0.1, 0.5, 0.9, 1.0, 1.0, 1.0, 0.999702, 0.874255, 0.577365,
+                 0.0]
+    input_global_steps = [0, 4, 8, 9, 10, 29, 30, 50, 70, 120]
+    output_rates = [
+        self.execute(graph_fn, [np.array(step).astype(np.int64)])
+        for step in input_global_steps
+    ]
+    self.assertAllClose(output_rates, exp_rates)
+
+  def testManualStepping(self):
+    def graph_fn(global_step):
+      boundaries = [2, 3, 7]
+      rates = [1.0, 2.0, 3.0, 4.0]
+      learning_rate = learning_schedules.manual_stepping(
+          global_step, boundaries, rates)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+
+    output_rates = [
+        self.execute(graph_fn, [np.array(i).astype(np.int64)])
+        for i in range(10)
+    ]
+    exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
+    self.assertAllClose(output_rates, exp_rates)
+
+  def testManualSteppingWithWarmup(self):
+    def graph_fn(global_step):
+      boundaries = [4, 6, 8]
+      rates = [0.02, 0.10, 0.01, 0.001]
+      learning_rate = learning_schedules.manual_stepping(
+          global_step, boundaries, rates, warmup=True)
+      assert learning_rate.op.name.endswith('learning_rate')
+      return (learning_rate,)
+
+    output_rates = [
+        self.execute(graph_fn, [np.array(i).astype(np.int64)])
+        for i in range(9)
+    ]
+    exp_rates = [0.02, 0.04, 0.06, 0.08, 0.10, 0.10, 0.01, 0.01, 0.001]
+    self.assertAllClose(output_rates, exp_rates)
+
+  def testManualSteppingWithZeroBoundaries(self):
+    def graph_fn(global_step):
+      boundaries = []
+      rates = [0.01]
+      learning_rate = learning_schedules.manual_stepping(
+          global_step, boundaries, rates)
+      return (learning_rate,)
+
+    output_rates = [
+        self.execute(graph_fn, [np.array(i).astype(np.int64)])
+        for i in range(4)
+    ]
+    exp_rates = [0.01] * 4
+    self.assertAllClose(output_rates, exp_rates)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics.py
new file mode 100644
index 00000000..98441129
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics.py
@@ -0,0 +1,190 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for computing metrics like precision, recall, CorLoc and etc."""
+from __future__ import division
+
+import numpy as np
+
+
+def compute_precision_recall(scores, labels, num_gt):
+  """Compute precision and recall.
+
+  Args:
+    scores: A float numpy array representing detection score
+    labels: A float numpy array representing weighted true/false positive labels
+    num_gt: Number of ground truth instances
+
+  Raises:
+    ValueError: if the input is not of the correct format
+
+  Returns:
+    precision: Fraction of positive instances over detected ones. This value is
+      None if no ground truth labels are present.
+    recall: Fraction of detected positive instance over all positive instances.
+      This value is None if no ground truth labels are present.
+
+  """
+  if not isinstance(labels, np.ndarray) or len(labels.shape) != 1:
+    raise ValueError("labels must be single dimension numpy array")
+
+  if labels.dtype != np.float and labels.dtype != np.bool:
+    raise ValueError("labels type must be either bool or float")
+
+  if not isinstance(scores, np.ndarray) or len(scores.shape) != 1:
+    raise ValueError("scores must be single dimension numpy array")
+
+  if num_gt < np.sum(labels):
+    raise ValueError("Number of true positives must be smaller than num_gt.")
+
+  if len(scores) != len(labels):
+    raise ValueError("scores and labels must be of the same size.")
+
+  if num_gt == 0:
+    return None, None
+
+  sorted_indices = np.argsort(scores)
+  sorted_indices = sorted_indices[::-1]
+  true_positive_labels = labels[sorted_indices]
+  false_positive_labels = (true_positive_labels <= 0).astype(float)
+  cum_true_positives = np.cumsum(true_positive_labels)
+  cum_false_positives = np.cumsum(false_positive_labels)
+  precision = cum_true_positives.astype(float) / (
+      cum_true_positives + cum_false_positives)
+  recall = cum_true_positives.astype(float) / num_gt
+  return precision, recall
+
+
+def compute_average_precision(precision, recall):
+  """Compute Average Precision according to the definition in VOCdevkit.
+
+  Precision is modified to ensure that it does not decrease as recall
+  decrease.
+
+  Args:
+    precision: A float [N, 1] numpy array of precisions
+    recall: A float [N, 1] numpy array of recalls
+
+  Raises:
+    ValueError: if the input is not of the correct format
+
+  Returns:
+    average_precison: The area under the precision recall curve. NaN if
+      precision and recall are None.
+
+  """
+  if precision is None:
+    if recall is not None:
+      raise ValueError("If precision is None, recall must also be None")
+    return np.NAN
+
+  if not isinstance(precision, np.ndarray) or not isinstance(
+      recall, np.ndarray):
+    raise ValueError("precision and recall must be numpy array")
+  if precision.dtype != np.float or recall.dtype != np.float:
+    raise ValueError("input must be float numpy array.")
+  if len(precision) != len(recall):
+    raise ValueError("precision and recall must be of the same size.")
+  if not precision.size:
+    return 0.0
+  if np.amin(precision) < 0 or np.amax(precision) > 1:
+    raise ValueError("Precision must be in the range of [0, 1].")
+  if np.amin(recall) < 0 or np.amax(recall) > 1:
+    raise ValueError("recall must be in the range of [0, 1].")
+  if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
+    raise ValueError("recall must be a non-decreasing array")
+
+  recall = np.concatenate([[0], recall, [1]])
+  precision = np.concatenate([[0], precision, [0]])
+
+  # Preprocess precision to be a non-decreasing array
+  for i in range(len(precision) - 2, -1, -1):
+    precision[i] = np.maximum(precision[i], precision[i + 1])
+
+  indices = np.where(recall[1:] != recall[:-1])[0] + 1
+  average_precision = np.sum(
+      (recall[indices] - recall[indices - 1]) * precision[indices])
+  return average_precision
+
+
+def compute_cor_loc(num_gt_imgs_per_class,
+                    num_images_correctly_detected_per_class):
+  """Compute CorLoc according to the definition in the following paper.
+
+  https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf
+
+  Returns nans if there are no ground truth images for a class.
+
+  Args:
+    num_gt_imgs_per_class: 1D array, representing number of images containing
+        at least one object instance of a particular class
+    num_images_correctly_detected_per_class: 1D array, representing number of
+        images that are correctly detected at least one object instance of a
+        particular class
+
+  Returns:
+    corloc_per_class: A float numpy array represents the corloc score of each
+      class
+  """
+  return np.where(
+      num_gt_imgs_per_class == 0, np.nan,
+      num_images_correctly_detected_per_class / num_gt_imgs_per_class)
+
+
+def compute_median_rank_at_k(tp_fp_list, k):
+  """Computes MedianRank@k, where k is the top-scoring labels.
+
+  Args:
+    tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
+        detection on a single image, where the detections are sorted by score in
+        descending order. Further, each numpy array element can have boolean or
+        float values. True positive elements have either value >0.0 or True;
+        any other value is considered false positive.
+    k: number of top-scoring proposals to take.
+
+  Returns:
+    median_rank: median rank of all true positive proposals among top k by
+      score.
+  """
+  ranks = []
+  for i in range(len(tp_fp_list)):
+    ranks.append(
+        np.where(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])] > 0)[0])
+  concatenated_ranks = np.concatenate(ranks)
+  return np.median(concatenated_ranks)
+
+
+def compute_recall_at_k(tp_fp_list, num_gt, k):
+  """Computes Recall@k, MedianRank@k, where k is the top-scoring labels.
+
+  Args:
+    tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
+        detection on a single image, where the detections are sorted by score in
+        descending order. Further, each numpy array element can have boolean or
+        float values. True positive elements have either value >0.0 or True;
+        any other value is considered false positive.
+    num_gt: number of groundtruth anotations.
+    k: number of top-scoring proposals to take.
+
+  Returns:
+    recall: recall evaluated on the top k by score detections.
+  """
+
+  tp_fp_eval = []
+  for i in range(len(tp_fp_list)):
+    tp_fp_eval.append(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])])
+
+  tp_fp_eval = np.concatenate(tp_fp_eval)
+
+  return np.sum(tp_fp_eval) / num_gt
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics_test.py
new file mode 100644
index 00000000..51726f47
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/metrics_test.py
@@ -0,0 +1,143 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.metrics."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import metrics
+
+
+class MetricsTest(tf.test.TestCase):
+
+  def test_compute_cor_loc(self):
+    num_gt_imgs_per_class = np.array([100, 1, 5, 1, 1], dtype=int)
+    num_images_correctly_detected_per_class = np.array(
+        [10, 0, 1, 0, 0], dtype=int)
+    corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
+                                     num_images_correctly_detected_per_class)
+    expected_corloc = np.array([0.1, 0, 0.2, 0, 0], dtype=float)
+    self.assertTrue(np.allclose(corloc, expected_corloc))
+
+  def test_compute_cor_loc_nans(self):
+    num_gt_imgs_per_class = np.array([100, 0, 0, 1, 1], dtype=int)
+    num_images_correctly_detected_per_class = np.array(
+        [10, 0, 1, 0, 0], dtype=int)
+    corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
+                                     num_images_correctly_detected_per_class)
+    expected_corloc = np.array([0.1, np.nan, np.nan, 0, 0], dtype=float)
+    self.assertAllClose(corloc, expected_corloc)
+
+  def test_compute_precision_recall(self):
+    num_gt = 10
+    scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float)
+    labels = np.array([0, 1, 1, 0, 0, 1], dtype=bool)
+    labels_float_type = np.array([0, 1, 1, 0, 0, 1], dtype=float)
+    accumulated_tp_count = np.array([0, 1, 1, 2, 2, 3], dtype=float)
+    expected_precision = accumulated_tp_count / np.array([1, 2, 3, 4, 5, 6])
+    expected_recall = accumulated_tp_count / num_gt
+
+    precision, recall = metrics.compute_precision_recall(scores, labels, num_gt)
+    precision_float_type, recall_float_type = metrics.compute_precision_recall(
+        scores, labels_float_type, num_gt)
+
+    self.assertAllClose(precision, expected_precision)
+    self.assertAllClose(recall, expected_recall)
+    self.assertAllClose(precision_float_type, expected_precision)
+    self.assertAllClose(recall_float_type, expected_recall)
+
+  def test_compute_precision_recall_float(self):
+    num_gt = 10
+    scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float)
+    labels_float = np.array([0, 1, 1, 0.5, 0, 1], dtype=float)
+    expected_precision = np.array(
+        [0., 0.5, 0.33333333, 0.5, 0.55555556, 0.63636364], dtype=float)
+    expected_recall = np.array([0., 0.1, 0.1, 0.2, 0.25, 0.35], dtype=float)
+    precision, recall = metrics.compute_precision_recall(
+        scores, labels_float, num_gt)
+    self.assertAllClose(precision, expected_precision)
+    self.assertAllClose(recall, expected_recall)
+
+  def test_compute_average_precision(self):
+    precision = np.array([0.8, 0.76, 0.9, 0.65, 0.7, 0.5, 0.55, 0], dtype=float)
+    recall = np.array([0.3, 0.3, 0.4, 0.4, 0.45, 0.45, 0.5, 0.5], dtype=float)
+    processed_precision = np.array(
+        [0.9, 0.9, 0.9, 0.7, 0.7, 0.55, 0.55, 0], dtype=float)
+    recall_interval = np.array([0.3, 0, 0.1, 0, 0.05, 0, 0.05, 0], dtype=float)
+    expected_mean_ap = np.sum(recall_interval * processed_precision)
+    mean_ap = metrics.compute_average_precision(precision, recall)
+    self.assertAlmostEqual(expected_mean_ap, mean_ap)
+
+  def test_compute_precision_recall_and_ap_no_groundtruth(self):
+    num_gt = 0
+    scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float)
+    labels = np.array([0, 0, 0, 0, 0, 0], dtype=bool)
+    expected_precision = None
+    expected_recall = None
+    precision, recall = metrics.compute_precision_recall(scores, labels, num_gt)
+    self.assertEquals(precision, expected_precision)
+    self.assertEquals(recall, expected_recall)
+    ap = metrics.compute_average_precision(precision, recall)
+    self.assertTrue(np.isnan(ap))
+
+  def test_compute_recall_at_k(self):
+    num_gt = 4
+    tp_fp = [
+        np.array([1, 0, 0], dtype=float),
+        np.array([0, 1], dtype=float),
+        np.array([0, 0, 0, 0, 0], dtype=float)
+    ]
+    tp_fp_bool = [
+        np.array([True, False, False], dtype=bool),
+        np.array([False, True], dtype=float),
+        np.array([False, False, False, False, False], dtype=float)
+    ]
+
+    recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
+    recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
+    recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
+
+    recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
+
+    self.assertAlmostEqual(recall_1, 0.25)
+    self.assertAlmostEqual(recall_3, 0.5)
+    self.assertAlmostEqual(recall_3_bool, 0.5)
+    self.assertAlmostEqual(recall_5, 0.5)
+
+  def test_compute_median_rank_at_k(self):
+    tp_fp = [
+        np.array([1, 0, 0], dtype=float),
+        np.array([0, 0.1], dtype=float),
+        np.array([0, 0, 0, 0, 0], dtype=float)
+    ]
+    tp_fp_bool = [
+        np.array([True, False, False], dtype=bool),
+        np.array([False, True], dtype=float),
+        np.array([False, False, False, False, False], dtype=float)
+    ]
+
+    median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
+    median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
+    median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
+    median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
+
+    self.assertEquals(median_ranks_1, 0)
+    self.assertEquals(median_ranks_3, 0.5)
+    self.assertEquals(median_ranks_3_bool, 0.5)
+    self.assertEquals(median_ranks_5, 0.5)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list.py
new file mode 100644
index 00000000..70263ca3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list.py
@@ -0,0 +1,133 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Numpy BoxList classes and functions."""
+
+import numpy as np
+
+
+class BoxList(object):
+  """Box collection.
+
+  BoxList represents a list of bounding boxes as numpy array, where each
+  bounding box is represented as a row of 4 numbers,
+  [y_min, x_min, y_max, x_max].  It is assumed that all bounding boxes within a
+  given list correspond to a single image.
+
+  Optionally, users can add additional related fields (such as
+  objectness/classification scores).
+  """
+
+  def __init__(self, data):
+    """Constructs box collection.
+
+    Args:
+      data: a numpy array of shape [N, 4] representing box coordinates
+
+    Raises:
+      ValueError: if bbox data is not a numpy array
+      ValueError: if invalid dimensions for bbox data
+    """
+    if not isinstance(data, np.ndarray):
+      raise ValueError('data must be a numpy array.')
+    if len(data.shape) != 2 or data.shape[1] != 4:
+      raise ValueError('Invalid dimensions for box data.')
+    if data.dtype != np.float32 and data.dtype != np.float64:
+      raise ValueError('Invalid data type for box data: float is required.')
+    if not self._is_valid_boxes(data):
+      raise ValueError('Invalid box data. data must be a numpy array of '
+                       'N*[y_min, x_min, y_max, x_max]')
+    self.data = {'boxes': data}
+
+  def num_boxes(self):
+    """Return number of boxes held in collections."""
+    return self.data['boxes'].shape[0]
+
+  def get_extra_fields(self):
+    """Return all non-box fields."""
+    return [k for k in self.data.keys() if k != 'boxes']
+
+  def has_field(self, field):
+    return field in self.data
+
+  def add_field(self, field, field_data):
+    """Add data to a specified field.
+
+    Args:
+      field: a string parameter used to speficy a related field to be accessed.
+      field_data: a numpy array of [N, ...] representing the data associated
+          with the field.
+    Raises:
+      ValueError: if the field is already exist or the dimension of the field
+          data does not matches the number of boxes.
+    """
+    if self.has_field(field):
+      raise ValueError('Field ' + field + 'already exists')
+    if len(field_data.shape) < 1 or field_data.shape[0] != self.num_boxes():
+      raise ValueError('Invalid dimensions for field data')
+    self.data[field] = field_data
+
+  def get(self):
+    """Convenience function for accesssing box coordinates.
+
+    Returns:
+      a numpy array of shape [N, 4] representing box corners
+    """
+    return self.get_field('boxes')
+
+  def get_field(self, field):
+    """Accesses data associated with the specified field in the box collection.
+
+    Args:
+      field: a string parameter used to speficy a related field to be accessed.
+
+    Returns:
+      a numpy 1-d array representing data of an associated field
+
+    Raises:
+      ValueError: if invalid field
+    """
+    if not self.has_field(field):
+      raise ValueError('field {} does not exist'.format(field))
+    return self.data[field]
+
+  def get_coordinates(self):
+    """Get corner coordinates of boxes.
+
+    Returns:
+     a list of 4 1-d numpy arrays [y_min, x_min, y_max, x_max]
+    """
+    box_coordinates = self.get()
+    y_min = box_coordinates[:, 0]
+    x_min = box_coordinates[:, 1]
+    y_max = box_coordinates[:, 2]
+    x_max = box_coordinates[:, 3]
+    return [y_min, x_min, y_max, x_max]
+
+  def _is_valid_boxes(self, data):
+    """Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin].
+
+    Args:
+      data: a numpy array of shape [N, 4] representing box coordinates
+
+    Returns:
+      a boolean indicating whether all ymax of boxes are equal or greater than
+          ymin, and all xmax of boxes are equal or greater than xmin.
+    """
+    if data.shape[0] > 0:
+      for i in range(data.shape[0]):
+        if data[i, 0] > data[i, 2] or data[i, 1] > data[i, 3]:
+          return False
+    return True
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops.py
new file mode 100644
index 00000000..9ed8ebfb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops.py
@@ -0,0 +1,554 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Bounding Box List operations for Numpy BoxLists.
+
+Example box operations that are supported:
+  * Areas: compute bounding box areas
+  * IOU: pairwise intersection-over-union scores
+"""
+import numpy as np
+
+from object_detection.utils import np_box_list
+from object_detection.utils import np_box_ops
+
+
+class SortOrder(object):
+  """Enum class for sort order.
+
+  Attributes:
+    ascend: ascend order.
+    descend: descend order.
+  """
+  ASCEND = 1
+  DESCEND = 2
+
+
+def area(boxlist):
+  """Computes area of boxes.
+
+  Args:
+    boxlist: BoxList holding N boxes
+
+  Returns:
+    a numpy array with shape [N*1] representing box areas
+  """
+  y_min, x_min, y_max, x_max = boxlist.get_coordinates()
+  return (y_max - y_min) * (x_max - x_min)
+
+
+def intersection(boxlist1, boxlist2):
+  """Compute pairwise intersection areas between boxes.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+
+  Returns:
+    a numpy array with shape [N*M] representing pairwise intersection area
+  """
+  return np_box_ops.intersection(boxlist1.get(), boxlist2.get())
+
+
+def iou(boxlist1, boxlist2):
+  """Computes pairwise intersection-over-union between box collections.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise iou scores.
+  """
+  return np_box_ops.iou(boxlist1.get(), boxlist2.get())
+
+
+def ioa(boxlist1, boxlist2):
+  """Computes pairwise intersection-over-area between box collections.
+
+  Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
+  their intersection area over box2's area. Note that ioa is not symmetric,
+  that is, IOA(box1, box2) != IOA(box2, box1).
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise ioa scores.
+  """
+  return np_box_ops.ioa(boxlist1.get(), boxlist2.get())
+
+
+def gather(boxlist, indices, fields=None):
+  """Gather boxes from BoxList according to indices and return new BoxList.
+
+  By default, gather returns boxes corresponding to the input index list, as
+  well as all additional fields stored in the boxlist (indexing into the
+  first dimension).  However one can optionally only gather from a
+  subset of fields.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    indices: a 1-d numpy array of type int_
+    fields: (optional) list of fields to also gather from.  If None (default),
+        all fields are gathered from.  Pass an empty fields list to only gather
+        the box coordinates.
+
+  Returns:
+    subboxlist: a BoxList corresponding to the subset of the input BoxList
+        specified by indices
+
+  Raises:
+    ValueError: if specified field is not contained in boxlist or if the
+        indices are not of type int_
+  """
+  if indices.size:
+    if np.amax(indices) >= boxlist.num_boxes() or np.amin(indices) < 0:
+      raise ValueError('indices are out of valid range.')
+  subboxlist = np_box_list.BoxList(boxlist.get()[indices, :])
+  if fields is None:
+    fields = boxlist.get_extra_fields()
+  for field in fields:
+    extra_field_data = boxlist.get_field(field)
+    subboxlist.add_field(field, extra_field_data[indices, ...])
+  return subboxlist
+
+
+def sort_by_field(boxlist, field, order=SortOrder.DESCEND):
+  """Sort boxes and associated fields according to a scalar field.
+
+  A common use case is reordering the boxes according to descending scores.
+
+  Args:
+    boxlist: BoxList holding N boxes.
+    field: A BoxList field for sorting and reordering the BoxList.
+    order: (Optional) 'descend' or 'ascend'. Default is descend.
+
+  Returns:
+    sorted_boxlist: A sorted BoxList with the field in the specified order.
+
+  Raises:
+    ValueError: if specified field does not exist or is not of single dimension.
+    ValueError: if the order is not either descend or ascend.
+  """
+  if not boxlist.has_field(field):
+    raise ValueError('Field ' + field + ' does not exist')
+  if len(boxlist.get_field(field).shape) != 1:
+    raise ValueError('Field ' + field + 'should be single dimension.')
+  if order != SortOrder.DESCEND and order != SortOrder.ASCEND:
+    raise ValueError('Invalid sort order')
+
+  field_to_sort = boxlist.get_field(field)
+  sorted_indices = np.argsort(field_to_sort)
+  if order == SortOrder.DESCEND:
+    sorted_indices = sorted_indices[::-1]
+  return gather(boxlist, sorted_indices)
+
+
+def non_max_suppression(boxlist,
+                        max_output_size=10000,
+                        iou_threshold=1.0,
+                        score_threshold=-10.0):
+  """Non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes. In each iteration, the detected bounding box with
+  highest score in the available pool is selected.
+
+  Args:
+    boxlist: BoxList holding N boxes.  Must contain a 'scores' field
+      representing detection scores. All scores belong to the same class.
+    max_output_size: maximum number of retained boxes
+    iou_threshold: intersection over union threshold.
+    score_threshold: minimum score threshold. Remove the boxes with scores
+                     less than this value. Default value is set to -10. A very
+                     low threshold to pass pretty much all the boxes, unless
+                     the user sets a different score threshold.
+
+  Returns:
+    a BoxList holding M boxes where M <= max_output_size
+  Raises:
+    ValueError: if 'scores' field does not exist
+    ValueError: if threshold is not in [0, 1]
+    ValueError: if max_output_size < 0
+  """
+  if not boxlist.has_field('scores'):
+    raise ValueError('Field scores does not exist')
+  if iou_threshold < 0. or iou_threshold > 1.0:
+    raise ValueError('IOU threshold must be in [0, 1]')
+  if max_output_size < 0:
+    raise ValueError('max_output_size must be bigger than 0.')
+
+  boxlist = filter_scores_greater_than(boxlist, score_threshold)
+  if boxlist.num_boxes() == 0:
+    return boxlist
+
+  boxlist = sort_by_field(boxlist, 'scores')
+
+  # Prevent further computation if NMS is disabled.
+  if iou_threshold == 1.0:
+    if boxlist.num_boxes() > max_output_size:
+      selected_indices = np.arange(max_output_size)
+      return gather(boxlist, selected_indices)
+    else:
+      return boxlist
+
+  boxes = boxlist.get()
+  num_boxes = boxlist.num_boxes()
+  # is_index_valid is True only for all remaining valid boxes,
+  is_index_valid = np.full(num_boxes, 1, dtype=bool)
+  selected_indices = []
+  num_output = 0
+  for i in range(num_boxes):
+    if num_output < max_output_size:
+      if is_index_valid[i]:
+        num_output += 1
+        selected_indices.append(i)
+        is_index_valid[i] = False
+        valid_indices = np.where(is_index_valid)[0]
+        if valid_indices.size == 0:
+          break
+
+        intersect_over_union = np_box_ops.iou(
+            np.expand_dims(boxes[i, :], axis=0), boxes[valid_indices, :])
+        intersect_over_union = np.squeeze(intersect_over_union, axis=0)
+        is_index_valid[valid_indices] = np.logical_and(
+            is_index_valid[valid_indices],
+            intersect_over_union <= iou_threshold)
+  return gather(boxlist, np.array(selected_indices))
+
+
+def multi_class_non_max_suppression(boxlist, score_thresh, iou_thresh,
+                                    max_output_size):
+  """Multi-class version of non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes.  It operates independently for each class for
+  which scores are provided (via the scores field of the input box_list),
+  pruning boxes with score less than a provided threshold prior to
+  applying NMS.
+
+  Args:
+    boxlist: BoxList holding N boxes.  Must contain a 'scores' field
+      representing detection scores.  This scores field is a tensor that can
+      be 1 dimensional (in the case of a single class) or 2-dimensional, which
+      which case we assume that it takes the shape [num_boxes, num_classes].
+      We further assume that this rank is known statically and that
+      scores.shape[1] is also known (i.e., the number of classes is fixed
+      and known at graph construction time).
+    score_thresh: scalar threshold for score (low scoring boxes are removed).
+    iou_thresh: scalar threshold for IOU (boxes that that high IOU overlap
+      with previously selected boxes are removed).
+    max_output_size: maximum number of retained boxes per class.
+
+  Returns:
+    a BoxList holding M boxes with a rank-1 scores field representing
+      corresponding scores for each box with scores sorted in decreasing order
+      and a rank-1 classes field representing a class label for each box.
+  Raises:
+    ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not have
+      a valid scores field.
+  """
+  if not 0 <= iou_thresh <= 1.0:
+    raise ValueError('thresh must be between 0 and 1')
+  if not isinstance(boxlist, np_box_list.BoxList):
+    raise ValueError('boxlist must be a BoxList')
+  if not boxlist.has_field('scores'):
+    raise ValueError('input boxlist must have \'scores\' field')
+  scores = boxlist.get_field('scores')
+  if len(scores.shape) == 1:
+    scores = np.reshape(scores, [-1, 1])
+  elif len(scores.shape) == 2:
+    if scores.shape[1] is None:
+      raise ValueError('scores field must have statically defined second '
+                       'dimension')
+  else:
+    raise ValueError('scores field must be of rank 1 or 2')
+  num_boxes = boxlist.num_boxes()
+  num_scores = scores.shape[0]
+  num_classes = scores.shape[1]
+
+  if num_boxes != num_scores:
+    raise ValueError('Incorrect scores field length: actual vs expected.')
+
+  selected_boxes_list = []
+  for class_idx in range(num_classes):
+    boxlist_and_class_scores = np_box_list.BoxList(boxlist.get())
+    class_scores = np.reshape(scores[0:num_scores, class_idx], [-1])
+    boxlist_and_class_scores.add_field('scores', class_scores)
+    boxlist_filt = filter_scores_greater_than(boxlist_and_class_scores,
+                                              score_thresh)
+    nms_result = non_max_suppression(boxlist_filt,
+                                     max_output_size=max_output_size,
+                                     iou_threshold=iou_thresh,
+                                     score_threshold=score_thresh)
+    nms_result.add_field(
+        'classes', np.zeros_like(nms_result.get_field('scores')) + class_idx)
+    selected_boxes_list.append(nms_result)
+  selected_boxes = concatenate(selected_boxes_list)
+  sorted_boxes = sort_by_field(selected_boxes, 'scores')
+  return sorted_boxes
+
+
+def scale(boxlist, y_scale, x_scale):
+  """Scale box coordinates in x and y dimensions.
+
+  Args:
+    boxlist: BoxList holding N boxes
+    y_scale: float
+    x_scale: float
+
+  Returns:
+    boxlist: BoxList holding N boxes
+  """
+  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
+  y_min = y_scale * y_min
+  y_max = y_scale * y_max
+  x_min = x_scale * x_min
+  x_max = x_scale * x_max
+  scaled_boxlist = np_box_list.BoxList(np.hstack([y_min, x_min, y_max, x_max]))
+
+  fields = boxlist.get_extra_fields()
+  for field in fields:
+    extra_field_data = boxlist.get_field(field)
+    scaled_boxlist.add_field(field, extra_field_data)
+
+  return scaled_boxlist
+
+
+def clip_to_window(boxlist, window):
+  """Clip bounding boxes to a window.
+
+  This op clips input bounding boxes (represented by bounding box
+  corners) to a window, optionally filtering out boxes that do not
+  overlap at all with the window.
+
+  Args:
+    boxlist: BoxList holding M_in boxes
+    window: a numpy array of shape [4] representing the
+            [y_min, x_min, y_max, x_max] window to which the op
+            should clip boxes.
+
+  Returns:
+    a BoxList holding M_out boxes where M_out <= M_in
+  """
+  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
+  win_y_min = window[0]
+  win_x_min = window[1]
+  win_y_max = window[2]
+  win_x_max = window[3]
+  y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min)
+  y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min)
+  x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min)
+  x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min)
+  clipped = np_box_list.BoxList(
+      np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped]))
+  clipped = _copy_extra_fields(clipped, boxlist)
+  areas = area(clipped)
+  nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)),
+                                    [-1]).astype(np.int32)
+  return gather(clipped, nonzero_area_indices)
+
+
+def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
+  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.
+
+  For each box in boxlist1, we want its IOA to be more than minoverlap with
+  at least one of the boxes in boxlist2. If it does not, we remove it.
+
+  Args:
+    boxlist1: BoxList holding N boxes.
+    boxlist2: BoxList holding M boxes.
+    minoverlap: Minimum required overlap between boxes, to count them as
+                overlapping.
+
+  Returns:
+    A pruned boxlist with size [N', 4].
+  """
+  intersection_over_area = ioa(boxlist2, boxlist1)  # [M, N] tensor
+  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
+  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
+  keep_inds = np.nonzero(keep_bool)[0]
+  new_boxlist1 = gather(boxlist1, keep_inds)
+  return new_boxlist1
+
+
+def prune_outside_window(boxlist, window):
+  """Prunes bounding boxes that fall outside a given window.
+
+  This function prunes bounding boxes that even partially fall outside the given
+  window. See also ClipToWindow which only prunes bounding boxes that fall
+  completely outside the window, and clips any bounding boxes that partially
+  overflow.
+
+  Args:
+    boxlist: a BoxList holding M_in boxes.
+    window: a numpy array of size 4, representing [ymin, xmin, ymax, xmax]
+            of the window.
+
+  Returns:
+    pruned_corners: a tensor with shape [M_out, 4] where M_out <= M_in.
+    valid_indices: a tensor with shape [M_out] indexing the valid bounding boxes
+     in the input tensor.
+  """
+
+  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
+  win_y_min = window[0]
+  win_x_min = window[1]
+  win_y_max = window[2]
+  win_x_max = window[3]
+  coordinate_violations = np.hstack([np.less(y_min, win_y_min),
+                                     np.less(x_min, win_x_min),
+                                     np.greater(y_max, win_y_max),
+                                     np.greater(x_max, win_x_max)])
+  valid_indices = np.reshape(
+      np.where(np.logical_not(np.max(coordinate_violations, axis=1))), [-1])
+  return gather(boxlist, valid_indices), valid_indices
+
+
+def concatenate(boxlists, fields=None):
+  """Concatenate list of BoxLists.
+
+  This op concatenates a list of input BoxLists into a larger BoxList.  It also
+  handles concatenation of BoxList fields as long as the field tensor shapes
+  are equal except for the first dimension.
+
+  Args:
+    boxlists: list of BoxList objects
+    fields: optional list of fields to also concatenate.  By default, all
+      fields from the first BoxList in the list are included in the
+      concatenation.
+
+  Returns:
+    a BoxList with number of boxes equal to
+      sum([boxlist.num_boxes() for boxlist in BoxList])
+  Raises:
+    ValueError: if boxlists is invalid (i.e., is not a list, is empty, or
+      contains non BoxList objects), or if requested fields are not contained in
+      all boxlists
+  """
+  if not isinstance(boxlists, list):
+    raise ValueError('boxlists should be a list')
+  if not boxlists:
+    raise ValueError('boxlists should have nonzero length')
+  for boxlist in boxlists:
+    if not isinstance(boxlist, np_box_list.BoxList):
+      raise ValueError('all elements of boxlists should be BoxList objects')
+  concatenated = np_box_list.BoxList(
+      np.vstack([boxlist.get() for boxlist in boxlists]))
+  if fields is None:
+    fields = boxlists[0].get_extra_fields()
+  for field in fields:
+    first_field_shape = boxlists[0].get_field(field).shape
+    first_field_shape = first_field_shape[1:]
+    for boxlist in boxlists:
+      if not boxlist.has_field(field):
+        raise ValueError('boxlist must contain all requested fields')
+      field_shape = boxlist.get_field(field).shape
+      field_shape = field_shape[1:]
+      if field_shape != first_field_shape:
+        raise ValueError('field %s must have same shape for all boxlists '
+                         'except for the 0th dimension.' % field)
+    concatenated_field = np.concatenate(
+        [boxlist.get_field(field) for boxlist in boxlists], axis=0)
+    concatenated.add_field(field, concatenated_field)
+  return concatenated
+
+
+def filter_scores_greater_than(boxlist, thresh):
+  """Filter to keep only boxes with score exceeding a given threshold.
+
+  This op keeps the collection of boxes whose corresponding scores are
+  greater than the input threshold.
+
+  Args:
+    boxlist: BoxList holding N boxes.  Must contain a 'scores' field
+      representing detection scores.
+    thresh: scalar threshold
+
+  Returns:
+    a BoxList holding M boxes where M <= N
+
+  Raises:
+    ValueError: if boxlist not a BoxList object or if it does not
+      have a scores field
+  """
+  if not isinstance(boxlist, np_box_list.BoxList):
+    raise ValueError('boxlist must be a BoxList')
+  if not boxlist.has_field('scores'):
+    raise ValueError('input boxlist must have \'scores\' field')
+  scores = boxlist.get_field('scores')
+  if len(scores.shape) > 2:
+    raise ValueError('Scores should have rank 1 or 2')
+  if len(scores.shape) == 2 and scores.shape[1] != 1:
+    raise ValueError('Scores should have rank 1 or have shape '
+                     'consistent with [None, 1]')
+  high_score_indices = np.reshape(np.where(np.greater(scores, thresh)),
+                                  [-1]).astype(np.int32)
+  return gather(boxlist, high_score_indices)
+
+
+def change_coordinate_frame(boxlist, window):
+  """Change coordinate frame of the boxlist to be relative to window's frame.
+
+  Given a window of the form [ymin, xmin, ymax, xmax],
+  changes bounding box coordinates from boxlist to be relative to this window
+  (e.g., the min corner maps to (0,0) and the max corner maps to (1,1)).
+
+  An example use case is data augmentation: where we are given groundtruth
+  boxes (boxlist) and would like to randomly crop the image to some
+  window (window). In this case we need to change the coordinate frame of
+  each groundtruth box to be relative to this new window.
+
+  Args:
+    boxlist: A BoxList object holding N boxes.
+    window: a size 4 1-D numpy array.
+
+  Returns:
+    Returns a BoxList object with N boxes.
+  """
+  win_height = window[2] - window[0]
+  win_width = window[3] - window[1]
+  boxlist_new = scale(
+      np_box_list.BoxList(boxlist.get() -
+                          [window[0], window[1], window[0], window[1]]),
+      1.0 / win_height, 1.0 / win_width)
+  _copy_extra_fields(boxlist_new, boxlist)
+
+  return boxlist_new
+
+
+def _copy_extra_fields(boxlist_to_copy_to, boxlist_to_copy_from):
+  """Copies the extra fields of boxlist_to_copy_from to boxlist_to_copy_to.
+
+  Args:
+    boxlist_to_copy_to: BoxList to which extra fields are copied.
+    boxlist_to_copy_from: BoxList from which fields are copied.
+
+  Returns:
+    boxlist_to_copy_to with extra fields.
+  """
+  for field in boxlist_to_copy_from.get_extra_fields():
+    boxlist_to_copy_to.add_field(field, boxlist_to_copy_from.get_field(field))
+  return boxlist_to_copy_to
+
+
+def _update_valid_indices_by_removing_high_iou_boxes(
+    selected_indices, is_index_valid, intersect_over_union, threshold):
+  max_iou = np.max(intersect_over_union[:, selected_indices], axis=1)
+  return np.logical_and(is_index_valid, max_iou <= threshold)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops_test.py
new file mode 100644
index 00000000..24a2cc8c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_ops_test.py
@@ -0,0 +1,414 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.np_box_list_ops."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_box_list
+from object_detection.utils import np_box_list_ops
+
+
+class AreaRelatedTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
+                      dtype=float)
+    boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                       [0.0, 0.0, 20.0, 20.0]],
+                      dtype=float)
+    self.boxlist1 = np_box_list.BoxList(boxes1)
+    self.boxlist2 = np_box_list.BoxList(boxes2)
+
+  def test_area(self):
+    areas = np_box_list_ops.area(self.boxlist1)
+    expected_areas = np.array([6.0, 5.0], dtype=float)
+    self.assertAllClose(expected_areas, areas)
+
+  def test_intersection(self):
+    intersection = np_box_list_ops.intersection(self.boxlist1, self.boxlist2)
+    expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]],
+                                     dtype=float)
+    self.assertAllClose(intersection, expected_intersection)
+
+  def test_iou(self):
+    iou = np_box_list_ops.iou(self.boxlist1, self.boxlist2)
+    expected_iou = np.array([[2.0 / 16.0, 0.0, 6.0 / 400.0],
+                             [1.0 / 16.0, 0.0, 5.0 / 400.0]],
+                            dtype=float)
+    self.assertAllClose(iou, expected_iou)
+
+  def test_ioa(self):
+    boxlist1 = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    boxlist2 = np_box_list.BoxList(
+        np.array(
+            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
+    ioa21 = np_box_list_ops.ioa(boxlist2, boxlist1)
+    expected_ioa21 = np.array([[0.5, 0.0],
+                               [1.0, 1.0]],
+                              dtype=np.float32)
+    self.assertAllClose(ioa21, expected_ioa21)
+
+  def test_scale(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    boxlist_scaled = np_box_list_ops.scale(boxlist, 2.0, 3.0)
+    expected_boxlist_scaled = np_box_list.BoxList(
+        np.array(
+            [[0.5, 0.75, 1.5, 2.25], [0.0, 0.0, 1.0, 2.25]], dtype=np.float32))
+    self.assertAllClose(expected_boxlist_scaled.get(), boxlist_scaled.get())
+
+  def test_clip_to_window(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+             [-0.2, -0.3, 0.7, 1.5]],
+            dtype=np.float32))
+    boxlist_clipped = np_box_list_ops.clip_to_window(boxlist,
+                                                     [0.0, 0.0, 1.0, 1.0])
+    expected_boxlist_clipped = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+             [0.0, 0.0, 0.7, 1.0]],
+            dtype=np.float32))
+    self.assertAllClose(expected_boxlist_clipped.get(), boxlist_clipped.get())
+
+  def test_prune_outside_window(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+             [-0.2, -0.3, 0.7, 1.5]],
+            dtype=np.float32))
+    boxlist_pruned, _ = np_box_list_ops.prune_outside_window(
+        boxlist, [0.0, 0.0, 1.0, 1.0])
+    expected_boxlist_pruned = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    self.assertAllClose(expected_boxlist_pruned.get(), boxlist_pruned.get())
+
+  def test_concatenate(self):
+    boxlist1 = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    boxlist2 = np_box_list.BoxList(
+        np.array(
+            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
+    boxlists = [boxlist1, boxlist2]
+    boxlist_concatenated = np_box_list_ops.concatenate(boxlists)
+    boxlist_concatenated_expected = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+             [0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
+            dtype=np.float32))
+    self.assertAllClose(boxlist_concatenated_expected.get(),
+                        boxlist_concatenated.get())
+
+  def test_change_coordinate_frame(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    boxlist_coord = np_box_list_ops.change_coordinate_frame(
+        boxlist, np.array([0, 0, 0.5, 0.5], dtype=np.float32))
+    expected_boxlist_coord = np_box_list.BoxList(
+        np.array([[0.5, 0.5, 1.5, 1.5], [0, 0, 1.0, 1.5]], dtype=np.float32))
+    self.assertAllClose(boxlist_coord.get(), expected_boxlist_coord.get())
+
+  def test_filter_scores_greater_than(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
+            np.float32))
+    boxlist.add_field('scores', np.array([0.8, 0.2], np.float32))
+    boxlist_greater = np_box_list_ops.filter_scores_greater_than(boxlist, 0.5)
+
+    expected_boxlist_greater = np_box_list.BoxList(
+        np.array([[0.25, 0.25, 0.75, 0.75]], dtype=np.float32))
+
+    self.assertAllClose(boxlist_greater.get(), expected_boxlist_greater.get())
+
+
+class GatherOpsTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    self.boxlist = np_box_list.BoxList(boxes)
+    self.boxlist.add_field('scores', np.array([0.5, 0.7, 0.9], dtype=float))
+    self.boxlist.add_field('labels',
+                           np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
+                                     [0, 0, 0, 0, 1]],
+                                    dtype=int))
+
+  def test_gather_with_out_of_range_indices(self):
+    indices = np.array([3, 1], dtype=int)
+    boxlist = self.boxlist
+    with self.assertRaises(ValueError):
+      np_box_list_ops.gather(boxlist, indices)
+
+  def test_gather_with_invalid_multidimensional_indices(self):
+    indices = np.array([[0, 1], [1, 2]], dtype=int)
+    boxlist = self.boxlist
+    with self.assertRaises(ValueError):
+      np_box_list_ops.gather(boxlist, indices)
+
+  def test_gather_without_fields_specified(self):
+    indices = np.array([2, 0, 1], dtype=int)
+    boxlist = self.boxlist
+    subboxlist = np_box_list_ops.gather(boxlist, indices)
+
+    expected_scores = np.array([0.9, 0.5, 0.7], dtype=float)
+    self.assertAllClose(expected_scores, subboxlist.get_field('scores'))
+
+    expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
+                               [14.0, 14.0, 15.0, 15.0]],
+                              dtype=float)
+    self.assertAllClose(expected_boxes, subboxlist.get())
+
+    expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
+                                [0, 1, 0, 0, 0]],
+                               dtype=int)
+    self.assertAllClose(expected_labels, subboxlist.get_field('labels'))
+
+  def test_gather_with_invalid_field_specified(self):
+    indices = np.array([2, 0, 1], dtype=int)
+    boxlist = self.boxlist
+
+    with self.assertRaises(ValueError):
+      np_box_list_ops.gather(boxlist, indices, 'labels')
+
+    with self.assertRaises(ValueError):
+      np_box_list_ops.gather(boxlist, indices, ['objectness'])
+
+  def test_gather_with_fields_specified(self):
+    indices = np.array([2, 0, 1], dtype=int)
+    boxlist = self.boxlist
+    subboxlist = np_box_list_ops.gather(boxlist, indices, ['labels'])
+
+    self.assertFalse(subboxlist.has_field('scores'))
+
+    expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
+                               [14.0, 14.0, 15.0, 15.0]],
+                              dtype=float)
+    self.assertAllClose(expected_boxes, subboxlist.get())
+
+    expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
+                                [0, 1, 0, 0, 0]],
+                               dtype=int)
+    self.assertAllClose(expected_labels, subboxlist.get_field('labels'))
+
+
+class SortByFieldTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    self.boxlist = np_box_list.BoxList(boxes)
+    self.boxlist.add_field('scores', np.array([0.5, 0.9, 0.4], dtype=float))
+    self.boxlist.add_field('labels',
+                           np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
+                                     [0, 0, 0, 0, 1]],
+                                    dtype=int))
+
+  def test_with_invalid_field(self):
+    with self.assertRaises(ValueError):
+      np_box_list_ops.sort_by_field(self.boxlist, 'objectness')
+    with self.assertRaises(ValueError):
+      np_box_list_ops.sort_by_field(self.boxlist, 'labels')
+
+  def test_with_invalid_sorting_order(self):
+    with self.assertRaises(ValueError):
+      np_box_list_ops.sort_by_field(self.boxlist, 'scores', 'Descending')
+
+  def test_with_descending_sorting(self):
+    sorted_boxlist = np_box_list_ops.sort_by_field(self.boxlist, 'scores')
+
+    expected_boxes = np.array([[14.0, 14.0, 15.0, 15.0], [3.0, 4.0, 6.0, 8.0],
+                               [0.0, 0.0, 20.0, 20.0]],
+                              dtype=float)
+    self.assertAllClose(expected_boxes, sorted_boxlist.get())
+
+    expected_scores = np.array([0.9, 0.5, 0.4], dtype=float)
+    self.assertAllClose(expected_scores, sorted_boxlist.get_field('scores'))
+
+  def test_with_ascending_sorting(self):
+    sorted_boxlist = np_box_list_ops.sort_by_field(
+        self.boxlist, 'scores', np_box_list_ops.SortOrder.ASCEND)
+
+    expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0],
+                               [3.0, 4.0, 6.0, 8.0],
+                               [14.0, 14.0, 15.0, 15.0],],
+                              dtype=float)
+    self.assertAllClose(expected_boxes, sorted_boxlist.get())
+
+    expected_scores = np.array([0.4, 0.5, 0.9], dtype=float)
+    self.assertAllClose(expected_scores, sorted_boxlist.get_field('scores'))
+
+
+class NonMaximumSuppressionTest(tf.test.TestCase):
+
+  def setUp(self):
+    self._boxes = np.array([[0, 0, 1, 1],
+                            [0, 0.1, 1, 1.1],
+                            [0, -0.1, 1, 0.9],
+                            [0, 10, 1, 11],
+                            [0, 10.1, 1, 11.1],
+                            [0, 100, 1, 101]],
+                           dtype=float)
+    self._boxlist = np_box_list.BoxList(self._boxes)
+
+  def test_with_no_scores_field(self):
+    boxlist = np_box_list.BoxList(self._boxes)
+    max_output_size = 3
+    iou_threshold = 0.5
+
+    with self.assertRaises(ValueError):
+      np_box_list_ops.non_max_suppression(
+          boxlist, max_output_size, iou_threshold)
+
+  def test_nms_disabled_max_output_size_equals_three(self):
+    boxlist = np_box_list.BoxList(self._boxes)
+    boxlist.add_field('scores',
+                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
+    max_output_size = 3
+    iou_threshold = 1.  # No NMS
+
+    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_select_from_three_clusters(self):
+    boxlist = np_box_list.BoxList(self._boxes)
+    boxlist.add_field('scores',
+                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
+    max_output_size = 3
+    iou_threshold = 0.5
+
+    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_select_at_most_two_from_three_clusters(self):
+    boxlist = np_box_list.BoxList(self._boxes)
+    boxlist.add_field('scores',
+                      np.array([.9, .75, .6, .95, .5, .3], dtype=float))
+    max_output_size = 2
+    iou_threshold = 0.5
+
+    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_select_at_most_thirty_from_three_clusters(self):
+    boxlist = np_box_list.BoxList(self._boxes)
+    boxlist.add_field('scores',
+                      np.array([.9, .75, .6, .95, .5, .3], dtype=float))
+    max_output_size = 30
+    iou_threshold = 0.5
+
+    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_select_from_ten_indentical_boxes(self):
+    boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    boxlist.add_field('scores', np.array(10 * [0.8]))
+    iou_threshold = .5
+    max_output_size = 3
+    expected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_different_iou_threshold(self):
+    boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300],
+                      [200, 200, 210, 250]],
+                     dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6]))
+    max_output_size = 4
+
+    iou_threshold = .4
+    expected_boxes = np.array([[0, 0, 20, 100],
+                               [200, 200, 210, 300],],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+    iou_threshold = .5
+    expected_boxes = np.array([[0, 0, 20, 100], [200, 200, 210, 300],
+                               [200, 200, 210, 250]],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+    iou_threshold = .8
+    expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
+                               [200, 200, 210, 300], [200, 200, 210, 250]],
+                              dtype=float)
+    nms_boxlist = np_box_list_ops.non_max_suppression(
+        boxlist, max_output_size, iou_threshold)
+    self.assertAllClose(nms_boxlist.get(), expected_boxes)
+
+  def test_multiclass_nms(self):
+    boxlist = np_box_list.BoxList(
+        np.array(
+            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
+            dtype=np.float32))
+    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
+                       [0.7, -0.7, 0.6, 0.2, -0.9],
+                       [0.4, 0.34, -0.9, 0.2, 0.31]],
+                      dtype=np.float32)
+    boxlist.add_field('scores', scores)
+    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
+        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)
+
+    scores_clean = boxlist_clean.get_field('scores')
+    classes_clean = boxlist_clean.get_field('classes')
+    boxes = boxlist_clean.get()
+    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
+    expected_classes = np.array([0, 2, 1, 4])
+    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
+                               [0.4, 0.2, 0.8, 0.8],
+                               [0.6, 0.0, 1.0, 1.0],
+                               [0.6, 0.0, 1.0, 1.0]],
+                              dtype=np.float32)
+    self.assertAllClose(scores_clean, expected_scores)
+    self.assertAllClose(classes_clean, expected_classes)
+    self.assertAllClose(boxes, expected_boxes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_test.py
new file mode 100644
index 00000000..2b98d1f9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_list_test.py
@@ -0,0 +1,135 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.np_box_list_test."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_box_list
+
+
+class BoxListTest(tf.test.TestCase):
+
+  def test_invalid_box_data(self):
+    with self.assertRaises(ValueError):
+      np_box_list.BoxList([0, 0, 1, 1])
+
+    with self.assertRaises(ValueError):
+      np_box_list.BoxList(np.array([[0, 0, 1, 1]], dtype=int))
+
+    with self.assertRaises(ValueError):
+      np_box_list.BoxList(np.array([0, 1, 1, 3, 4], dtype=float))
+
+    with self.assertRaises(ValueError):
+      np_box_list.BoxList(np.array([[0, 1, 1, 3], [3, 1, 1, 5]], dtype=float))
+
+  def test_has_field_with_existed_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    self.assertTrue(boxlist.has_field('boxes'))
+
+  def test_has_field_with_nonexisted_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    self.assertFalse(boxlist.has_field('scores'))
+
+  def test_get_field_with_existed_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    self.assertTrue(np.allclose(boxlist.get_field('boxes'), boxes))
+
+  def test_get_field_with_nonexited_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    with self.assertRaises(ValueError):
+      boxlist.get_field('scores')
+
+
+class AddExtraFieldTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    self.boxlist = np_box_list.BoxList(boxes)
+
+  def test_add_already_existed_field(self):
+    with self.assertRaises(ValueError):
+      self.boxlist.add_field('boxes', np.array([[0, 0, 0, 1, 0]], dtype=float))
+
+  def test_add_invalid_field_data(self):
+    with self.assertRaises(ValueError):
+      self.boxlist.add_field('scores', np.array([0.5, 0.7], dtype=float))
+    with self.assertRaises(ValueError):
+      self.boxlist.add_field('scores',
+                             np.array([0.5, 0.7, 0.9, 0.1], dtype=float))
+
+  def test_add_single_dimensional_field_data(self):
+    boxlist = self.boxlist
+    scores = np.array([0.5, 0.7, 0.9], dtype=float)
+    boxlist.add_field('scores', scores)
+    self.assertTrue(np.allclose(scores, self.boxlist.get_field('scores')))
+
+  def test_add_multi_dimensional_field_data(self):
+    boxlist = self.boxlist
+    labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
+                      dtype=int)
+    boxlist.add_field('labels', labels)
+    self.assertTrue(np.allclose(labels, self.boxlist.get_field('labels')))
+
+  def test_get_extra_fields(self):
+    boxlist = self.boxlist
+    self.assertItemsEqual(boxlist.get_extra_fields(), [])
+
+    scores = np.array([0.5, 0.7, 0.9], dtype=float)
+    boxlist.add_field('scores', scores)
+    self.assertItemsEqual(boxlist.get_extra_fields(), ['scores'])
+
+    labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
+                      dtype=int)
+    boxlist.add_field('labels', labels)
+    self.assertItemsEqual(boxlist.get_extra_fields(), ['scores', 'labels'])
+
+  def test_get_coordinates(self):
+    y_min, x_min, y_max, x_max = self.boxlist.get_coordinates()
+
+    expected_y_min = np.array([3.0, 14.0, 0.0], dtype=float)
+    expected_x_min = np.array([4.0, 14.0, 0.0], dtype=float)
+    expected_y_max = np.array([6.0, 15.0, 20.0], dtype=float)
+    expected_x_max = np.array([8.0, 15.0, 20.0], dtype=float)
+
+    self.assertTrue(np.allclose(y_min, expected_y_min))
+    self.assertTrue(np.allclose(x_min, expected_x_min))
+    self.assertTrue(np.allclose(y_max, expected_y_max))
+    self.assertTrue(np.allclose(x_max, expected_x_max))
+
+  def test_num_boxes(self):
+    boxes = np.array([[0., 0., 100., 100.], [10., 30., 50., 70.]], dtype=float)
+    boxlist = np_box_list.BoxList(boxes)
+    expected_num_boxes = 2
+    self.assertEquals(boxlist.num_boxes(), expected_num_boxes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list.py
new file mode 100644
index 00000000..28cf50c2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list.py
@@ -0,0 +1,63 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Numpy BoxMaskList classes and functions."""
+
+import numpy as np
+from object_detection.utils import np_box_list
+
+
+class BoxMaskList(np_box_list.BoxList):
+  """Convenience wrapper for BoxList with masks.
+
+  BoxMaskList extends the np_box_list.BoxList to contain masks as well.
+  In particular, its constructor receives both boxes and masks. Note that the
+  masks correspond to the full image.
+  """
+
+  def __init__(self, box_data, mask_data):
+    """Constructs box collection.
+
+    Args:
+      box_data: a numpy array of shape [N, 4] representing box coordinates
+      mask_data: a numpy array of shape [N, height, width] representing masks
+        with values are in {0,1}. The masks correspond to the full
+        image. The height and the width will be equal to image height and width.
+
+    Raises:
+      ValueError: if bbox data is not a numpy array
+      ValueError: if invalid dimensions for bbox data
+      ValueError: if mask data is not a numpy array
+      ValueError: if invalid dimension for mask data
+    """
+    super(BoxMaskList, self).__init__(box_data)
+    if not isinstance(mask_data, np.ndarray):
+      raise ValueError('Mask data must be a numpy array.')
+    if len(mask_data.shape) != 3:
+      raise ValueError('Invalid dimensions for mask data.')
+    if mask_data.dtype != np.uint8:
+      raise ValueError('Invalid data type for mask data: uint8 is required.')
+    if mask_data.shape[0] != box_data.shape[0]:
+      raise ValueError('There should be the same number of boxes and masks.')
+    self.data['masks'] = mask_data
+
+  def get_masks(self):
+    """Convenience function for accessing masks.
+
+    Returns:
+      a numpy array of shape [N, height, width] representing masks
+    """
+    return self.get_field('masks')
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops.py
new file mode 100644
index 00000000..ebaa8631
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops.py
@@ -0,0 +1,399 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Operations for np_box_mask_list.BoxMaskList.
+
+Example box operations that are supported:
+  * Areas: compute bounding box areas
+  * IOU: pairwise intersection-over-union scores
+"""
+import numpy as np
+
+from object_detection.utils import np_box_list_ops
+from object_detection.utils import np_box_mask_list
+from object_detection.utils import np_mask_ops
+
+
+def box_list_to_box_mask_list(boxlist):
+  """Converts a BoxList containing 'masks' into a BoxMaskList.
+
+  Args:
+    boxlist: An np_box_list.BoxList object.
+
+  Returns:
+    An np_box_mask_list.BoxMaskList object.
+
+  Raises:
+    ValueError: If boxlist does not contain `masks` as a field.
+  """
+  if not boxlist.has_field('masks'):
+    raise ValueError('boxlist does not contain mask field.')
+  box_mask_list = np_box_mask_list.BoxMaskList(
+      box_data=boxlist.get(),
+      mask_data=boxlist.get_field('masks'))
+  extra_fields = boxlist.get_extra_fields()
+  for key in extra_fields:
+    if key != 'masks':
+      box_mask_list.data[key] = boxlist.get_field(key)
+  return box_mask_list
+
+
+def area(box_mask_list):
+  """Computes area of masks.
+
+  Args:
+    box_mask_list: np_box_mask_list.BoxMaskList holding N boxes and masks
+
+  Returns:
+    a numpy array with shape [N*1] representing mask areas
+  """
+  return np_mask_ops.area(box_mask_list.get_masks())
+
+
+def intersection(box_mask_list1, box_mask_list2):
+  """Compute pairwise intersection areas between masks.
+
+  Args:
+    box_mask_list1: BoxMaskList holding N boxes and masks
+    box_mask_list2: BoxMaskList holding M boxes and masks
+
+  Returns:
+    a numpy array with shape [N*M] representing pairwise intersection area
+  """
+  return np_mask_ops.intersection(box_mask_list1.get_masks(),
+                                  box_mask_list2.get_masks())
+
+
+def iou(box_mask_list1, box_mask_list2):
+  """Computes pairwise intersection-over-union between box and mask collections.
+
+  Args:
+    box_mask_list1: BoxMaskList holding N boxes and masks
+    box_mask_list2: BoxMaskList holding M boxes and masks
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise iou scores.
+  """
+  return np_mask_ops.iou(box_mask_list1.get_masks(),
+                         box_mask_list2.get_masks())
+
+
+def ioa(box_mask_list1, box_mask_list2):
+  """Computes pairwise intersection-over-area between box and mask collections.
+
+  Intersection-over-area (ioa) between two masks mask1 and mask2 is defined as
+  their intersection area over mask2's area. Note that ioa is not symmetric,
+  that is, IOA(mask1, mask2) != IOA(mask2, mask1).
+
+  Args:
+    box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks
+    box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise ioa scores.
+  """
+  return np_mask_ops.ioa(box_mask_list1.get_masks(), box_mask_list2.get_masks())
+
+
+def gather(box_mask_list, indices, fields=None):
+  """Gather boxes from np_box_mask_list.BoxMaskList according to indices.
+
+  By default, gather returns boxes corresponding to the input index list, as
+  well as all additional fields stored in the box_mask_list (indexing into the
+  first dimension).  However one can optionally only gather from a
+  subset of fields.
+
+  Args:
+    box_mask_list: np_box_mask_list.BoxMaskList holding N boxes
+    indices: a 1-d numpy array of type int_
+    fields: (optional) list of fields to also gather from.  If None (default),
+        all fields are gathered from.  Pass an empty fields list to only gather
+        the box coordinates.
+
+  Returns:
+    subbox_mask_list: a np_box_mask_list.BoxMaskList corresponding to the subset
+        of the input box_mask_list specified by indices
+
+  Raises:
+    ValueError: if specified field is not contained in box_mask_list or if the
+        indices are not of type int_
+  """
+  if fields is not None:
+    if 'masks' not in fields:
+      fields.append('masks')
+  return box_list_to_box_mask_list(
+      np_box_list_ops.gather(
+          boxlist=box_mask_list, indices=indices, fields=fields))
+
+
+def sort_by_field(box_mask_list, field,
+                  order=np_box_list_ops.SortOrder.DESCEND):
+  """Sort boxes and associated fields according to a scalar field.
+
+  A common use case is reordering the boxes according to descending scores.
+
+  Args:
+    box_mask_list: BoxMaskList holding N boxes.
+    field: A BoxMaskList field for sorting and reordering the BoxMaskList.
+    order: (Optional) 'descend' or 'ascend'. Default is descend.
+
+  Returns:
+    sorted_box_mask_list: A sorted BoxMaskList with the field in the specified
+      order.
+  """
+  return box_list_to_box_mask_list(
+      np_box_list_ops.sort_by_field(
+          boxlist=box_mask_list, field=field, order=order))
+
+
+def non_max_suppression(box_mask_list,
+                        max_output_size=10000,
+                        iou_threshold=1.0,
+                        score_threshold=-10.0):
+  """Non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes. In each iteration, the detected bounding box with
+  highest score in the available pool is selected.
+
+  Args:
+    box_mask_list: np_box_mask_list.BoxMaskList holding N boxes.  Must contain
+      a 'scores' field representing detection scores. All scores belong to the
+      same class.
+    max_output_size: maximum number of retained boxes
+    iou_threshold: intersection over union threshold.
+    score_threshold: minimum score threshold. Remove the boxes with scores
+                     less than this value. Default value is set to -10. A very
+                     low threshold to pass pretty much all the boxes, unless
+                     the user sets a different score threshold.
+
+  Returns:
+    an np_box_mask_list.BoxMaskList holding M boxes where M <= max_output_size
+
+  Raises:
+    ValueError: if 'scores' field does not exist
+    ValueError: if threshold is not in [0, 1]
+    ValueError: if max_output_size < 0
+  """
+  if not box_mask_list.has_field('scores'):
+    raise ValueError('Field scores does not exist')
+  if iou_threshold < 0. or iou_threshold > 1.0:
+    raise ValueError('IOU threshold must be in [0, 1]')
+  if max_output_size < 0:
+    raise ValueError('max_output_size must be bigger than 0.')
+
+  box_mask_list = filter_scores_greater_than(box_mask_list, score_threshold)
+  if box_mask_list.num_boxes() == 0:
+    return box_mask_list
+
+  box_mask_list = sort_by_field(box_mask_list, 'scores')
+
+  # Prevent further computation if NMS is disabled.
+  if iou_threshold == 1.0:
+    if box_mask_list.num_boxes() > max_output_size:
+      selected_indices = np.arange(max_output_size)
+      return gather(box_mask_list, selected_indices)
+    else:
+      return box_mask_list
+
+  masks = box_mask_list.get_masks()
+  num_masks = box_mask_list.num_boxes()
+
+  # is_index_valid is True only for all remaining valid boxes,
+  is_index_valid = np.full(num_masks, 1, dtype=bool)
+  selected_indices = []
+  num_output = 0
+  for i in range(num_masks):
+    if num_output < max_output_size:
+      if is_index_valid[i]:
+        num_output += 1
+        selected_indices.append(i)
+        is_index_valid[i] = False
+        valid_indices = np.where(is_index_valid)[0]
+        if valid_indices.size == 0:
+          break
+
+        intersect_over_union = np_mask_ops.iou(
+            np.expand_dims(masks[i], axis=0), masks[valid_indices])
+        intersect_over_union = np.squeeze(intersect_over_union, axis=0)
+        is_index_valid[valid_indices] = np.logical_and(
+            is_index_valid[valid_indices],
+            intersect_over_union <= iou_threshold)
+  return gather(box_mask_list, np.array(selected_indices))
+
+
+def multi_class_non_max_suppression(box_mask_list, score_thresh, iou_thresh,
+                                    max_output_size):
+  """Multi-class version of non maximum suppression.
+
+  This op greedily selects a subset of detection bounding boxes, pruning
+  away boxes that have high IOU (intersection over union) overlap (> thresh)
+  with already selected boxes.  It operates independently for each class for
+  which scores are provided (via the scores field of the input box_list),
+  pruning boxes with score less than a provided threshold prior to
+  applying NMS.
+
+  Args:
+    box_mask_list: np_box_mask_list.BoxMaskList holding N boxes.  Must contain a
+      'scores' field representing detection scores.  This scores field is a
+      tensor that can be 1 dimensional (in the case of a single class) or
+      2-dimensional, in which case we assume that it takes the
+      shape [num_boxes, num_classes]. We further assume that this rank is known
+      statically and that scores.shape[1] is also known (i.e., the number of
+      classes is fixed and known at graph construction time).
+    score_thresh: scalar threshold for score (low scoring boxes are removed).
+    iou_thresh: scalar threshold for IOU (boxes that that high IOU overlap
+      with previously selected boxes are removed).
+    max_output_size: maximum number of retained boxes per class.
+
+  Returns:
+    a box_mask_list holding M boxes with a rank-1 scores field representing
+      corresponding scores for each box with scores sorted in decreasing order
+      and a rank-1 classes field representing a class label for each box.
+  Raises:
+    ValueError: if iou_thresh is not in [0, 1] or if input box_mask_list does
+      not have a valid scores field.
+  """
+  if not 0 <= iou_thresh <= 1.0:
+    raise ValueError('thresh must be between 0 and 1')
+  if not isinstance(box_mask_list, np_box_mask_list.BoxMaskList):
+    raise ValueError('box_mask_list must be a box_mask_list')
+  if not box_mask_list.has_field('scores'):
+    raise ValueError('input box_mask_list must have \'scores\' field')
+  scores = box_mask_list.get_field('scores')
+  if len(scores.shape) == 1:
+    scores = np.reshape(scores, [-1, 1])
+  elif len(scores.shape) == 2:
+    if scores.shape[1] is None:
+      raise ValueError('scores field must have statically defined second '
+                       'dimension')
+  else:
+    raise ValueError('scores field must be of rank 1 or 2')
+
+  num_boxes = box_mask_list.num_boxes()
+  num_scores = scores.shape[0]
+  num_classes = scores.shape[1]
+
+  if num_boxes != num_scores:
+    raise ValueError('Incorrect scores field length: actual vs expected.')
+
+  selected_boxes_list = []
+  for class_idx in range(num_classes):
+    box_mask_list_and_class_scores = np_box_mask_list.BoxMaskList(
+        box_data=box_mask_list.get(),
+        mask_data=box_mask_list.get_masks())
+    class_scores = np.reshape(scores[0:num_scores, class_idx], [-1])
+    box_mask_list_and_class_scores.add_field('scores', class_scores)
+    box_mask_list_filt = filter_scores_greater_than(
+        box_mask_list_and_class_scores, score_thresh)
+    nms_result = non_max_suppression(
+        box_mask_list_filt,
+        max_output_size=max_output_size,
+        iou_threshold=iou_thresh,
+        score_threshold=score_thresh)
+    nms_result.add_field(
+        'classes',
+        np.zeros_like(nms_result.get_field('scores')) + class_idx)
+    selected_boxes_list.append(nms_result)
+  selected_boxes = np_box_list_ops.concatenate(selected_boxes_list)
+  sorted_boxes = np_box_list_ops.sort_by_field(selected_boxes, 'scores')
+  return box_list_to_box_mask_list(boxlist=sorted_boxes)
+
+
+def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0):
+  """Prunes the boxes in list1 that overlap less than thresh with list2.
+
+  For each mask in box_mask_list1, we want its IOA to be more than minoverlap
+  with at least one of the masks in box_mask_list2. If it does not, we remove
+  it. If the masks are not full size image, we do the pruning based on boxes.
+
+  Args:
+    box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks.
+    box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks.
+    minoverlap: Minimum required overlap between boxes, to count them as
+                overlapping.
+
+  Returns:
+    A pruned box_mask_list with size [N', 4].
+  """
+  intersection_over_area = ioa(box_mask_list2, box_mask_list1)  # [M, N] tensor
+  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
+  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
+  keep_inds = np.nonzero(keep_bool)[0]
+  new_box_mask_list1 = gather(box_mask_list1, keep_inds)
+  return new_box_mask_list1
+
+
+def concatenate(box_mask_lists, fields=None):
+  """Concatenate list of box_mask_lists.
+
+  This op concatenates a list of input box_mask_lists into a larger
+  box_mask_list.  It also
+  handles concatenation of box_mask_list fields as long as the field tensor
+  shapes are equal except for the first dimension.
+
+  Args:
+    box_mask_lists: list of np_box_mask_list.BoxMaskList objects
+    fields: optional list of fields to also concatenate.  By default, all
+      fields from the first BoxMaskList in the list are included in the
+      concatenation.
+
+  Returns:
+    a box_mask_list with number of boxes equal to
+      sum([box_mask_list.num_boxes() for box_mask_list in box_mask_list])
+  Raises:
+    ValueError: if box_mask_lists is invalid (i.e., is not a list, is empty, or
+      contains non box_mask_list objects), or if requested fields are not
+      contained in all box_mask_lists
+  """
+  if fields is not None:
+    if 'masks' not in fields:
+      fields.append('masks')
+  return box_list_to_box_mask_list(
+      np_box_list_ops.concatenate(boxlists=box_mask_lists, fields=fields))
+
+
+def filter_scores_greater_than(box_mask_list, thresh):
+  """Filter to keep only boxes and masks with score exceeding a given threshold.
+
+  This op keeps the collection of boxes and masks whose corresponding scores are
+  greater than the input threshold.
+
+  Args:
+    box_mask_list: BoxMaskList holding N boxes and masks.  Must contain a
+      'scores' field representing detection scores.
+    thresh: scalar threshold
+
+  Returns:
+    a BoxMaskList holding M boxes and masks where M <= N
+
+  Raises:
+    ValueError: if box_mask_list not a np_box_mask_list.BoxMaskList object or
+      if it does not have a scores field
+  """
+  if not isinstance(box_mask_list, np_box_mask_list.BoxMaskList):
+    raise ValueError('box_mask_list must be a BoxMaskList')
+  if not box_mask_list.has_field('scores'):
+    raise ValueError('input box_mask_list must have \'scores\' field')
+  scores = box_mask_list.get_field('scores')
+  if len(scores.shape) > 2:
+    raise ValueError('Scores should have rank 1 or 2')
+  if len(scores.shape) == 2 and scores.shape[1] != 1:
+    raise ValueError('Scores should have rank 1 or have shape '
+                     'consistent with [None, 1]')
+  high_score_indices = np.reshape(np.where(np.greater(scores, thresh)),
+                                  [-1]).astype(np.int32)
+  return gather(box_mask_list, high_score_indices)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops_test.py
new file mode 100644
index 00000000..e2b99fee
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_ops_test.py
@@ -0,0 +1,191 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.np_box_mask_list_ops."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_box_mask_list
+from object_detection.utils import np_box_mask_list_ops
+
+
+class AreaRelatedTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
+                      dtype=float)
+    masks1_0 = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks1_1 = np.array([[1, 1, 1, 1, 1, 1, 1, 1],
+                         [1, 1, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks1 = np.stack([masks1_0, masks1_1])
+    boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                       [0.0, 0.0, 20.0, 20.0]],
+                      dtype=float)
+    masks2_0 = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2_1 = np.array([[1, 1, 1, 1, 1, 1, 1, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2_2 = np.array([[1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2 = np.stack([masks2_0, masks2_1, masks2_2])
+    self.box_mask_list1 = np_box_mask_list.BoxMaskList(
+        box_data=boxes1, mask_data=masks1)
+    self.box_mask_list2 = np_box_mask_list.BoxMaskList(
+        box_data=boxes2, mask_data=masks2)
+
+  def test_area(self):
+    areas = np_box_mask_list_ops.area(self.box_mask_list1)
+    expected_areas = np.array([8.0, 10.0], dtype=float)
+    self.assertAllClose(expected_areas, areas)
+
+  def test_intersection(self):
+    intersection = np_box_mask_list_ops.intersection(self.box_mask_list1,
+                                                     self.box_mask_list2)
+    expected_intersection = np.array([[8.0, 0.0, 8.0], [0.0, 9.0, 7.0]],
+                                     dtype=float)
+    self.assertAllClose(intersection, expected_intersection)
+
+  def test_iou(self):
+    iou = np_box_mask_list_ops.iou(self.box_mask_list1, self.box_mask_list2)
+    expected_iou = np.array(
+        [[1.0, 0.0, 8.0 / 25.0], [0.0, 9.0 / 16.0, 7.0 / 28.0]], dtype=float)
+    self.assertAllClose(iou, expected_iou)
+
+  def test_ioa(self):
+    ioa21 = np_box_mask_list_ops.ioa(self.box_mask_list1, self.box_mask_list2)
+    expected_ioa21 = np.array([[1.0, 0.0, 8.0/25.0],
+                               [0.0, 9.0/15.0, 7.0/25.0]],
+                              dtype=np.float32)
+    self.assertAllClose(ioa21, expected_ioa21)
+
+
+class NonMaximumSuppressionTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes1 = np.array(
+        [[4.0, 3.0, 7.0, 6.0], [5.0, 6.0, 10.0, 10.0]], dtype=float)
+    boxes2 = np.array(
+        [[3.0, 4.0, 6.0, 8.0], [5.0, 6.0, 10.0, 10.0], [1.0, 1.0, 10.0, 10.0]],
+        dtype=float)
+    masks1 = np.array(
+        [[[0, 1, 0], [1, 1, 0], [0, 0, 0]], [[0, 1, 1], [0, 1, 1], [0, 1, 1]]],
+        dtype=np.uint8)
+    masks2 = np.array(
+        [[[0, 1, 0], [1, 1, 1], [0, 0, 0]], [[0, 1, 0], [0, 0, 1], [0, 1, 1]],
+         [[0, 1, 1], [0, 1, 1], [0, 1, 1]]],
+        dtype=np.uint8)
+    self.boxes1 = boxes1
+    self.boxes2 = boxes2
+    self.masks1 = masks1
+    self.masks2 = masks2
+
+  def test_with_no_scores_field(self):
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=self.boxes1, mask_data=self.masks1)
+    max_output_size = 3
+    iou_threshold = 0.5
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list_ops.non_max_suppression(
+          box_mask_list, max_output_size, iou_threshold)
+
+  def test_nms_disabled_max_output_size_equals_one(self):
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=self.boxes2, mask_data=self.masks2)
+    box_mask_list.add_field('scores',
+                            np.array([.9, .75, .6], dtype=float))
+    max_output_size = 1
+    iou_threshold = 1.  # No NMS
+    expected_boxes = np.array([[3.0, 4.0, 6.0, 8.0]], dtype=float)
+    expected_masks = np.array(
+        [[[0, 1, 0], [1, 1, 1], [0, 0, 0]]], dtype=np.uint8)
+    nms_box_mask_list = np_box_mask_list_ops.non_max_suppression(
+        box_mask_list, max_output_size, iou_threshold)
+    self.assertAllClose(nms_box_mask_list.get(), expected_boxes)
+    self.assertAllClose(nms_box_mask_list.get_masks(), expected_masks)
+
+  def test_multiclass_nms(self):
+    boxes = np.array(
+        [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
+        dtype=np.float32)
+    mask0 = np.array([[0, 0, 0, 0, 0],
+                      [0, 0, 1, 1, 0],
+                      [0, 0, 1, 1, 0],
+                      [0, 0, 1, 1, 0],
+                      [0, 0, 0, 0, 0]],
+                     dtype=np.uint8)
+    mask1 = np.array([[0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0],
+                      [0, 1, 1, 1, 0],
+                      [0, 1, 1, 1, 0],
+                      [0, 0, 0, 0, 0]],
+                     dtype=np.uint8)
+    mask2 = np.array([[0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0],
+                      [1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1]],
+                     dtype=np.uint8)
+    masks = np.stack([mask0, mask1, mask2])
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=masks)
+    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
+                       [0.7, -0.7, 0.6, 0.2, -0.9],
+                       [0.4, 0.34, -0.9, 0.2, 0.31]],
+                      dtype=np.float32)
+    box_mask_list.add_field('scores', scores)
+    box_mask_list_clean = np_box_mask_list_ops.multi_class_non_max_suppression(
+        box_mask_list, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)
+
+    scores_clean = box_mask_list_clean.get_field('scores')
+    classes_clean = box_mask_list_clean.get_field('classes')
+    boxes = box_mask_list_clean.get()
+    masks = box_mask_list_clean.get_masks()
+    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
+    expected_classes = np.array([0, 2, 1, 4])
+    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
+                               [0.4, 0.2, 0.8, 0.8],
+                               [0.6, 0.0, 1.0, 1.0],
+                               [0.6, 0.0, 1.0, 1.0]],
+                              dtype=np.float32)
+    self.assertAllClose(scores_clean, expected_scores)
+    self.assertAllClose(classes_clean, expected_classes)
+    self.assertAllClose(boxes, expected_boxes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_test.py
new file mode 100644
index 00000000..033825b6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_mask_list_test.py
@@ -0,0 +1,182 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.np_box_mask_list_test."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_box_mask_list
+
+
+class BoxMaskListTest(tf.test.TestCase):
+
+  def test_invalid_box_mask_data(self):
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=[0, 0, 1, 1],
+          mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 0, 1, 1]], dtype=int),
+          mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([0, 1, 1, 3, 4], dtype=float),
+          mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 1, 1, 3], [3, 1, 1, 5]], dtype=float),
+          mask_data=np.zeros([2, 3, 3], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
+          mask_data=np.zeros([3, 5, 5], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
+          mask_data=np.zeros([2, 5], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
+          mask_data=np.zeros([2, 5, 5, 5], dtype=np.uint8))
+
+    with self.assertRaises(ValueError):
+      np_box_mask_list.BoxMaskList(
+          box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
+          mask_data=np.zeros([2, 5, 5], dtype=np.int32))
+
+  def test_has_field_with_existed_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=np.zeros([3, 5, 5], dtype=np.uint8))
+    self.assertTrue(box_mask_list.has_field('boxes'))
+    self.assertTrue(box_mask_list.has_field('masks'))
+
+  def test_has_field_with_nonexisted_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=np.zeros([3, 3, 3], dtype=np.uint8))
+    self.assertFalse(box_mask_list.has_field('scores'))
+
+  def test_get_field_with_existed_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    masks = np.zeros([3, 3, 3], dtype=np.uint8)
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=masks)
+    self.assertTrue(np.allclose(box_mask_list.get_field('boxes'), boxes))
+    self.assertTrue(np.allclose(box_mask_list.get_field('masks'), masks))
+
+  def test_get_field_with_nonexited_field(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    masks = np.zeros([3, 3, 3], dtype=np.uint8)
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=masks)
+    with self.assertRaises(ValueError):
+      box_mask_list.get_field('scores')
+
+
+class AddExtraFieldTest(tf.test.TestCase):
+
+  def setUp(self):
+    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                      [0.0, 0.0, 20.0, 20.0]],
+                     dtype=float)
+    masks = np.zeros([3, 3, 3], dtype=np.uint8)
+    self.box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=masks)
+
+  def test_add_already_existed_field_bbox(self):
+    with self.assertRaises(ValueError):
+      self.box_mask_list.add_field('boxes',
+                                   np.array([[0, 0, 0, 1, 0]], dtype=float))
+
+  def test_add_already_existed_field_mask(self):
+    with self.assertRaises(ValueError):
+      self.box_mask_list.add_field('masks',
+                                   np.zeros([3, 3, 3], dtype=np.uint8))
+
+  def test_add_invalid_field_data(self):
+    with self.assertRaises(ValueError):
+      self.box_mask_list.add_field('scores', np.array([0.5, 0.7], dtype=float))
+    with self.assertRaises(ValueError):
+      self.box_mask_list.add_field('scores',
+                                   np.array([0.5, 0.7, 0.9, 0.1], dtype=float))
+
+  def test_add_single_dimensional_field_data(self):
+    box_mask_list = self.box_mask_list
+    scores = np.array([0.5, 0.7, 0.9], dtype=float)
+    box_mask_list.add_field('scores', scores)
+    self.assertTrue(np.allclose(scores, self.box_mask_list.get_field('scores')))
+
+  def test_add_multi_dimensional_field_data(self):
+    box_mask_list = self.box_mask_list
+    labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
+                      dtype=int)
+    box_mask_list.add_field('labels', labels)
+    self.assertTrue(np.allclose(labels, self.box_mask_list.get_field('labels')))
+
+  def test_get_extra_fields(self):
+    box_mask_list = self.box_mask_list
+    self.assertItemsEqual(box_mask_list.get_extra_fields(), ['masks'])
+
+    scores = np.array([0.5, 0.7, 0.9], dtype=float)
+    box_mask_list.add_field('scores', scores)
+    self.assertItemsEqual(box_mask_list.get_extra_fields(), ['masks', 'scores'])
+
+    labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
+                      dtype=int)
+    box_mask_list.add_field('labels', labels)
+    self.assertItemsEqual(box_mask_list.get_extra_fields(),
+                          ['masks', 'scores', 'labels'])
+
+  def test_get_coordinates(self):
+    y_min, x_min, y_max, x_max = self.box_mask_list.get_coordinates()
+
+    expected_y_min = np.array([3.0, 14.0, 0.0], dtype=float)
+    expected_x_min = np.array([4.0, 14.0, 0.0], dtype=float)
+    expected_y_max = np.array([6.0, 15.0, 20.0], dtype=float)
+    expected_x_max = np.array([8.0, 15.0, 20.0], dtype=float)
+
+    self.assertTrue(np.allclose(y_min, expected_y_min))
+    self.assertTrue(np.allclose(x_min, expected_x_min))
+    self.assertTrue(np.allclose(y_max, expected_y_max))
+    self.assertTrue(np.allclose(x_max, expected_x_max))
+
+  def test_num_boxes(self):
+    boxes = np.array([[0., 0., 100., 100.], [10., 30., 50., 70.]], dtype=float)
+    masks = np.zeros([2, 5, 5], dtype=np.uint8)
+    box_mask_list = np_box_mask_list.BoxMaskList(
+        box_data=boxes, mask_data=masks)
+    expected_num_boxes = 2
+    self.assertEquals(box_mask_list.num_boxes(), expected_num_boxes)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops.py
new file mode 100644
index 00000000..b4b46a75
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops.py
@@ -0,0 +1,97 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Operations for [N, 4] numpy arrays representing bounding boxes.
+
+Example box operations that are supported:
+  * Areas: compute bounding box areas
+  * IOU: pairwise intersection-over-union scores
+"""
+import numpy as np
+
+
+def area(boxes):
+  """Computes area of boxes.
+
+  Args:
+    boxes: Numpy array with shape [N, 4] holding N boxes
+
+  Returns:
+    a numpy array with shape [N*1] representing box areas
+  """
+  return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+
+
+def intersection(boxes1, boxes2):
+  """Compute pairwise intersection areas between boxes.
+
+  Args:
+    boxes1: a numpy array with shape [N, 4] holding N boxes
+    boxes2: a numpy array with shape [M, 4] holding M boxes
+
+  Returns:
+    a numpy array with shape [N*M] representing pairwise intersection area
+  """
+  [y_min1, x_min1, y_max1, x_max1] = np.split(boxes1, 4, axis=1)
+  [y_min2, x_min2, y_max2, x_max2] = np.split(boxes2, 4, axis=1)
+
+  all_pairs_min_ymax = np.minimum(y_max1, np.transpose(y_max2))
+  all_pairs_max_ymin = np.maximum(y_min1, np.transpose(y_min2))
+  intersect_heights = np.maximum(
+      np.zeros(all_pairs_max_ymin.shape),
+      all_pairs_min_ymax - all_pairs_max_ymin)
+  all_pairs_min_xmax = np.minimum(x_max1, np.transpose(x_max2))
+  all_pairs_max_xmin = np.maximum(x_min1, np.transpose(x_min2))
+  intersect_widths = np.maximum(
+      np.zeros(all_pairs_max_xmin.shape),
+      all_pairs_min_xmax - all_pairs_max_xmin)
+  return intersect_heights * intersect_widths
+
+
+def iou(boxes1, boxes2):
+  """Computes pairwise intersection-over-union between box collections.
+
+  Args:
+    boxes1: a numpy array with shape [N, 4] holding N boxes.
+    boxes2: a numpy array with shape [M, 4] holding N boxes.
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise iou scores.
+  """
+  intersect = intersection(boxes1, boxes2)
+  area1 = area(boxes1)
+  area2 = area(boxes2)
+  union = np.expand_dims(area1, axis=1) + np.expand_dims(
+      area2, axis=0) - intersect
+  return intersect / union
+
+
+def ioa(boxes1, boxes2):
+  """Computes pairwise intersection-over-area between box collections.
+
+  Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
+  their intersection area over box2's area. Note that ioa is not symmetric,
+  that is, IOA(box1, box2) != IOA(box2, box1).
+
+  Args:
+    boxes1: a numpy array with shape [N, 4] holding N boxes.
+    boxes2: a numpy array with shape [M, 4] holding N boxes.
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise ioa scores.
+  """
+  intersect = intersection(boxes1, boxes2)
+  areas = np.expand_dims(area(boxes2), axis=0)
+  return intersect / areas
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops_test.py
new file mode 100644
index 00000000..730f3d20
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_box_ops_test.py
@@ -0,0 +1,68 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.np_box_ops."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_box_ops
+
+
+class BoxOpsTests(tf.test.TestCase):
+
+  def setUp(self):
+    boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
+                      dtype=float)
+    boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                       [0.0, 0.0, 20.0, 20.0]],
+                      dtype=float)
+    self.boxes1 = boxes1
+    self.boxes2 = boxes2
+
+  def testArea(self):
+    areas = np_box_ops.area(self.boxes1)
+    expected_areas = np.array([6.0, 5.0], dtype=float)
+    self.assertAllClose(expected_areas, areas)
+
+  def testIntersection(self):
+    intersection = np_box_ops.intersection(self.boxes1, self.boxes2)
+    expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]],
+                                     dtype=float)
+    self.assertAllClose(intersection, expected_intersection)
+
+  def testIOU(self):
+    iou = np_box_ops.iou(self.boxes1, self.boxes2)
+    expected_iou = np.array([[2.0 / 16.0, 0.0, 6.0 / 400.0],
+                             [1.0 / 16.0, 0.0, 5.0 / 400.0]],
+                            dtype=float)
+    self.assertAllClose(iou, expected_iou)
+
+  def testIOA(self):
+    boxes1 = np.array([[0.25, 0.25, 0.75, 0.75],
+                       [0.0, 0.0, 0.5, 0.75]],
+                      dtype=np.float32)
+    boxes2 = np.array([[0.5, 0.25, 1.0, 1.0],
+                       [0.0, 0.0, 1.0, 1.0]],
+                      dtype=np.float32)
+    ioa21 = np_box_ops.ioa(boxes2, boxes1)
+    expected_ioa21 = np.array([[0.5, 0.0],
+                               [1.0, 1.0]],
+                              dtype=np.float32)
+    self.assertAllClose(ioa21, expected_ioa21)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops.py
new file mode 100644
index 00000000..b7918b4e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops.py
@@ -0,0 +1,119 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Operations for [N, height, width] numpy arrays representing masks.
+
+Example mask operations that are supported:
+  * Areas: compute mask areas
+  * IOU: pairwise intersection-over-union scores
+"""
+import numpy as np
+
+EPSILON = 1e-7
+
+
+def area(masks):
+  """Computes area of masks.
+
+  Args:
+    masks: Numpy array with shape [N, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+
+  Returns:
+    a numpy array with shape [N*1] representing mask areas.
+
+  Raises:
+    ValueError: If masks.dtype is not np.uint8
+  """
+  if masks.dtype != np.uint8:
+    raise ValueError('Masks type should be np.uint8')
+  return np.sum(masks, axis=(1, 2), dtype=np.float32)
+
+
+def intersection(masks1, masks2):
+  """Compute pairwise intersection areas between masks.
+
+  Args:
+    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+    masks2: a numpy array with shape [M, height, width] holding M masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+
+  Returns:
+    a numpy array with shape [N*M] representing pairwise intersection area.
+
+  Raises:
+    ValueError: If masks1 and masks2 are not of type np.uint8.
+  """
+  if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
+    raise ValueError('masks1 and masks2 should be of type np.uint8')
+  n = masks1.shape[0]
+  m = masks2.shape[0]
+  answer = np.zeros([n, m], dtype=np.float32)
+  for i in np.arange(n):
+    for j in np.arange(m):
+      answer[i, j] = np.sum(np.minimum(masks1[i], masks2[j]), dtype=np.float32)
+  return answer
+
+
+def iou(masks1, masks2):
+  """Computes pairwise intersection-over-union between mask collections.
+
+  Args:
+    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+    masks2: a numpy array with shape [M, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise iou scores.
+
+  Raises:
+    ValueError: If masks1 and masks2 are not of type np.uint8.
+  """
+  if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
+    raise ValueError('masks1 and masks2 should be of type np.uint8')
+  intersect = intersection(masks1, masks2)
+  area1 = area(masks1)
+  area2 = area(masks2)
+  union = np.expand_dims(area1, axis=1) + np.expand_dims(
+      area2, axis=0) - intersect
+  return intersect / np.maximum(union, EPSILON)
+
+
+def ioa(masks1, masks2):
+  """Computes pairwise intersection-over-area between box collections.
+
+  Intersection-over-area (ioa) between two masks, mask1 and mask2 is defined as
+  their intersection area over mask2's area. Note that ioa is not symmetric,
+  that is, IOA(mask1, mask2) != IOA(mask2, mask1).
+
+  Args:
+    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+    masks2: a numpy array with shape [M, height, width] holding N masks. Masks
+      values are of type np.uint8 and values are in {0,1}.
+
+  Returns:
+    a numpy array with shape [N, M] representing pairwise ioa scores.
+
+  Raises:
+    ValueError: If masks1 and masks2 are not of type np.uint8.
+  """
+  if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
+    raise ValueError('masks1 and masks2 should be of type np.uint8')
+  intersect = intersection(masks1, masks2)
+  areas = np.expand_dims(area(masks2), axis=0)
+  return intersect / (areas + EPSILON)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops_test.py
new file mode 100644
index 00000000..b65e5830
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/np_mask_ops_test.py
@@ -0,0 +1,88 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.np_mask_ops."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import np_mask_ops
+
+
+class MaskOpsTests(tf.test.TestCase):
+
+  def setUp(self):
+    masks1_0 = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks1_1 = np.array([[1, 1, 1, 1, 1, 1, 1, 1],
+                         [1, 1, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks1 = np.stack([masks1_0, masks1_1])
+    masks2_0 = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0],
+                         [1, 1, 1, 1, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2_1 = np.array([[1, 1, 1, 1, 1, 1, 1, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2_2 = np.array([[1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0]],
+                        dtype=np.uint8)
+    masks2 = np.stack([masks2_0, masks2_1, masks2_2])
+    self.masks1 = masks1
+    self.masks2 = masks2
+
+  def testArea(self):
+    areas = np_mask_ops.area(self.masks1)
+    expected_areas = np.array([8.0, 10.0], dtype=np.float32)
+    self.assertAllClose(expected_areas, areas)
+
+  def testIntersection(self):
+    intersection = np_mask_ops.intersection(self.masks1, self.masks2)
+    expected_intersection = np.array(
+        [[8.0, 0.0, 8.0], [0.0, 9.0, 7.0]], dtype=np.float32)
+    self.assertAllClose(intersection, expected_intersection)
+
+  def testIOU(self):
+    iou = np_mask_ops.iou(self.masks1, self.masks2)
+    expected_iou = np.array(
+        [[1.0, 0.0, 8.0/25.0], [0.0, 9.0 / 16.0, 7.0 / 28.0]], dtype=np.float32)
+    self.assertAllClose(iou, expected_iou)
+
+  def testIOA(self):
+    ioa21 = np_mask_ops.ioa(self.masks1, self.masks2)
+    expected_ioa21 = np.array([[1.0, 0.0, 8.0/25.0],
+                               [0.0, 9.0/15.0, 7.0/25.0]],
+                              dtype=np.float32)
+    self.assertAllClose(ioa21, expected_ioa21)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation.py
new file mode 100644
index 00000000..474a8667
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation.py
@@ -0,0 +1,1060 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""object_detection_evaluation module.
+
+ObjectDetectionEvaluation is a class which manages ground truth information of a
+object detection dataset, and computes frequently used detection metrics such as
+Precision, Recall, CorLoc of the provided detection results.
+It supports the following operations:
+1) Add ground truth information of images sequentially.
+2) Add detection result of images sequentially.
+3) Evaluate detection metrics on already inserted detection results.
+4) Write evaluation result into a pickle file for future processing or
+   visualization.
+
+Note: This module operates on numpy boxes and box lists.
+"""
+
+from abc import ABCMeta
+from abc import abstractmethod
+import collections
+import logging
+import unicodedata
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.utils import label_map_util
+from object_detection.utils import metrics
+from object_detection.utils import per_image_evaluation
+
+
+class DetectionEvaluator(object):
+  """Interface for object detection evalution classes.
+
+  Example usage of the Evaluator:
+  ------------------------------
+  evaluator = DetectionEvaluator(categories)
+
+  # Detections and groundtruth for image 1.
+  evaluator.add_single_groundtruth_image_info(...)
+  evaluator.add_single_detected_image_info(...)
+
+  # Detections and groundtruth for image 2.
+  evaluator.add_single_groundtruth_image_info(...)
+  evaluator.add_single_detected_image_info(...)
+
+  metrics_dict = evaluator.evaluate()
+  """
+  __metaclass__ = ABCMeta
+
+  def __init__(self, categories):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+    """
+    self._categories = categories
+
+  @abstractmethod
+  def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary of groundtruth numpy arrays required
+        for evaluations.
+    """
+    pass
+
+  @abstractmethod
+  def add_single_detected_image_info(self, image_id, detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary of detection numpy arrays required
+        for evaluation.
+    """
+    pass
+
+  def get_estimator_eval_metric_ops(self, eval_dict):
+    """Returns dict of metrics to use with `tf.estimator.EstimatorSpec`.
+
+    Note that this must only be implemented if performing evaluation with a
+    `tf.estimator.Estimator`.
+
+    Args:
+      eval_dict: A dictionary that holds tensors for evaluating an object
+        detection model, returned from
+        eval_util.result_dict_for_single_example().
+
+    Returns:
+      A dictionary of metric names to tuple of value_op and update_op that can
+      be used as eval metric ops in `tf.estimator.EstimatorSpec`.
+    """
+    pass
+
+  @abstractmethod
+  def evaluate(self):
+    """Evaluates detections and returns a dictionary of metrics."""
+    pass
+
+  @abstractmethod
+  def clear(self):
+    """Clears the state to prepare for a fresh evaluation."""
+    pass
+
+
+class ObjectDetectionEvaluator(DetectionEvaluator):
+  """A class to evaluate detections."""
+
+  def __init__(self,
+               categories,
+               matching_iou_threshold=0.5,
+               evaluate_corlocs=False,
+               evaluate_precision_recall=False,
+               metric_prefix=None,
+               use_weighted_mean_ap=False,
+               evaluate_masks=False,
+               group_of_weight=0.0):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+      matching_iou_threshold: IOU threshold to use for matching groundtruth
+        boxes to detection boxes.
+      evaluate_corlocs: (optional) boolean which determines if corloc scores
+        are to be returned or not.
+      evaluate_precision_recall: (optional) boolean which determines if
+        precision and recall values are to be returned or not.
+      metric_prefix: (optional) string prefix for metric name; if None, no
+        prefix is used.
+      use_weighted_mean_ap: (optional) boolean which determines if the mean
+        average precision is computed directly from the scores and tp_fp_labels
+        of all classes.
+      evaluate_masks: If False, evaluation will be performed based on boxes.
+        If True, mask evaluation will be performed instead.
+      group_of_weight: Weight of group-of boxes.If set to 0, detections of the
+        correct class within a group-of box are ignored. If weight is > 0, then
+        if at least one detection falls within a group-of box with
+        matching_iou_threshold, weight group_of_weight is added to true
+        positives. Consequently, if no detection falls within a group-of box,
+        weight group_of_weight is added to false negatives.
+
+    Raises:
+      ValueError: If the category ids are not 1-indexed.
+    """
+    super(ObjectDetectionEvaluator, self).__init__(categories)
+    self._num_classes = max([cat['id'] for cat in categories])
+    if min(cat['id'] for cat in categories) < 1:
+      raise ValueError('Classes should be 1-indexed.')
+    self._matching_iou_threshold = matching_iou_threshold
+    self._use_weighted_mean_ap = use_weighted_mean_ap
+    self._label_id_offset = 1
+    self._evaluate_masks = evaluate_masks
+    self._group_of_weight = group_of_weight
+    self._evaluation = ObjectDetectionEvaluation(
+        num_groundtruth_classes=self._num_classes,
+        matching_iou_threshold=self._matching_iou_threshold,
+        use_weighted_mean_ap=self._use_weighted_mean_ap,
+        label_id_offset=self._label_id_offset,
+        group_of_weight=self._group_of_weight)
+    self._image_ids = set([])
+    self._evaluate_corlocs = evaluate_corlocs
+    self._evaluate_precision_recall = evaluate_precision_recall
+    self._metric_prefix = (metric_prefix + '_') if metric_prefix else ''
+    self._expected_keys = set([
+        standard_fields.InputDataFields.key,
+        standard_fields.InputDataFields.groundtruth_boxes,
+        standard_fields.InputDataFields.groundtruth_classes,
+        standard_fields.InputDataFields.groundtruth_difficult,
+        standard_fields.InputDataFields.groundtruth_instance_masks,
+        standard_fields.DetectionResultFields.detection_boxes,
+        standard_fields.DetectionResultFields.detection_scores,
+        standard_fields.DetectionResultFields.detection_classes,
+        standard_fields.DetectionResultFields.detection_masks
+    ])
+    self._build_metric_names()
+
+  def _build_metric_names(self):
+    """Builds a list with metric names."""
+
+    self._metric_names = [
+        self._metric_prefix + 'Precision/mAP@{}IOU'.format(
+            self._matching_iou_threshold)
+    ]
+    if self._evaluate_corlocs:
+      self._metric_names.append(
+          self._metric_prefix +
+          'Precision/meanCorLoc@{}IOU'.format(self._matching_iou_threshold))
+
+    category_index = label_map_util.create_category_index(self._categories)
+    for idx in range(self._num_classes):
+      if idx + self._label_id_offset in category_index:
+        category_name = category_index[idx + self._label_id_offset]['name']
+        try:
+          category_name = unicode(category_name, 'utf-8')
+        except TypeError:
+          pass
+        category_name = unicodedata.normalize('NFKD', category_name).encode(
+            'ascii', 'ignore')
+        self._metric_names.append(
+            self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format(
+                self._matching_iou_threshold, category_name))
+        if self._evaluate_corlocs:
+          self._metric_names.append(
+              self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'
+              .format(self._matching_iou_threshold, category_name))
+
+  def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
+          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
+          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
+          of shape [num_boxes] containing 1-indexed groundtruth classes for the
+          boxes.
+        standard_fields.InputDataFields.groundtruth_difficult: Optional length
+          M numpy boolean array denoting whether a ground truth box is a
+          difficult instance or not. This field is optional to support the case
+          that no boxes are difficult.
+        standard_fields.InputDataFields.groundtruth_instance_masks: Optional
+          numpy array of shape [num_boxes, height, width] with values in {0, 1}.
+
+    Raises:
+      ValueError: On adding groundtruth for an image more than once. Will also
+        raise error if instance masks are not in groundtruth dictionary.
+    """
+    if image_id in self._image_ids:
+      raise ValueError('Image with id {} already added.'.format(image_id))
+
+    groundtruth_classes = (
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
+        self._label_id_offset)
+    # If the key is not present in the groundtruth_dict or the array is empty
+    # (unless there are no annotations for the groundtruth on this image)
+    # use values from the dictionary or insert None otherwise.
+    if (standard_fields.InputDataFields.groundtruth_difficult in
+        groundtruth_dict.keys() and
+        (groundtruth_dict[standard_fields.InputDataFields.groundtruth_difficult]
+         .size or not groundtruth_classes.size)):
+      groundtruth_difficult = groundtruth_dict[
+          standard_fields.InputDataFields.groundtruth_difficult]
+    else:
+      groundtruth_difficult = None
+      if not len(self._image_ids) % 1000:
+        logging.warn(
+            'image %s does not have groundtruth difficult flag specified',
+            image_id)
+    groundtruth_masks = None
+    if self._evaluate_masks:
+      if (standard_fields.InputDataFields.groundtruth_instance_masks not in
+          groundtruth_dict):
+        raise ValueError('Instance masks not in groundtruth dictionary.')
+      groundtruth_masks = groundtruth_dict[
+          standard_fields.InputDataFields.groundtruth_instance_masks]
+    self._evaluation.add_single_ground_truth_image_info(
+        image_key=image_id,
+        groundtruth_boxes=groundtruth_dict[
+            standard_fields.InputDataFields.groundtruth_boxes],
+        groundtruth_class_labels=groundtruth_classes,
+        groundtruth_is_difficult_list=groundtruth_difficult,
+        groundtruth_masks=groundtruth_masks)
+    self._image_ids.update([image_id])
+
+  def add_single_detected_image_info(self, image_id, detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary containing -
+        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
+          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
+          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.DetectionResultFields.detection_scores: float32 numpy
+          array of shape [num_boxes] containing detection scores for the boxes.
+        standard_fields.DetectionResultFields.detection_classes: integer numpy
+          array of shape [num_boxes] containing 1-indexed detection classes for
+          the boxes.
+        standard_fields.DetectionResultFields.detection_masks: uint8 numpy
+          array of shape [num_boxes, height, width] containing `num_boxes` masks
+          of values ranging between 0 and 1.
+
+    Raises:
+      ValueError: If detection masks are not in detections dictionary.
+    """
+    detection_classes = (
+        detections_dict[standard_fields.DetectionResultFields.detection_classes]
+        - self._label_id_offset)
+    detection_masks = None
+    if self._evaluate_masks:
+      if (standard_fields.DetectionResultFields.detection_masks not in
+          detections_dict):
+        raise ValueError('Detection masks not in detections dictionary.')
+      detection_masks = detections_dict[
+          standard_fields.DetectionResultFields.detection_masks]
+    self._evaluation.add_single_detected_image_info(
+        image_key=image_id,
+        detected_boxes=detections_dict[
+            standard_fields.DetectionResultFields.detection_boxes],
+        detected_scores=detections_dict[
+            standard_fields.DetectionResultFields.detection_scores],
+        detected_class_labels=detection_classes,
+        detected_masks=detection_masks)
+
+  def evaluate(self):
+    """Compute evaluation result.
+
+    Returns:
+      A dictionary of metrics with the following fields -
+
+      1. summary_metrics:
+        '<prefix if not empty>_Precision/mAP@<matching_iou_threshold>IOU': mean
+        average precision at the specified IOU threshold.
+
+      2. per_category_ap: category specific results with keys of the form
+        '<prefix if not empty>_PerformanceByCategory/
+        mAP@<matching_iou_threshold>IOU/category'.
+    """
+    (per_class_ap, mean_ap, per_class_precision, per_class_recall,
+     per_class_corloc, mean_corloc) = (
+         self._evaluation.evaluate())
+    pascal_metrics = {self._metric_names[0]: mean_ap}
+    if self._evaluate_corlocs:
+      pascal_metrics[self._metric_names[1]] = mean_corloc
+    category_index = label_map_util.create_category_index(self._categories)
+    for idx in range(per_class_ap.size):
+      if idx + self._label_id_offset in category_index:
+        category_name = category_index[idx + self._label_id_offset]['name']
+        try:
+          category_name = unicode(category_name, 'utf-8')
+        except TypeError:
+          pass
+        category_name = unicodedata.normalize(
+            'NFKD', category_name).encode('ascii', 'ignore')
+        display_name = (
+            self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format(
+                self._matching_iou_threshold, category_name))
+        pascal_metrics[display_name] = per_class_ap[idx]
+
+        # Optionally add precision and recall values
+        if self._evaluate_precision_recall:
+          display_name = (
+              self._metric_prefix +
+              'PerformanceByCategory/Precision@{}IOU/{}'.format(
+                  self._matching_iou_threshold, category_name))
+          pascal_metrics[display_name] = per_class_precision[idx]
+          display_name = (
+              self._metric_prefix +
+              'PerformanceByCategory/Recall@{}IOU/{}'.format(
+                  self._matching_iou_threshold, category_name))
+          pascal_metrics[display_name] = per_class_recall[idx]
+
+        # Optionally add CorLoc metrics.classes
+        if self._evaluate_corlocs:
+          display_name = (
+              self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'
+              .format(self._matching_iou_threshold, category_name))
+          pascal_metrics[display_name] = per_class_corloc[idx]
+
+    return pascal_metrics
+
+  def clear(self):
+    """Clears the state to prepare for a fresh evaluation."""
+    self._evaluation = ObjectDetectionEvaluation(
+        num_groundtruth_classes=self._num_classes,
+        matching_iou_threshold=self._matching_iou_threshold,
+        use_weighted_mean_ap=self._use_weighted_mean_ap,
+        label_id_offset=self._label_id_offset)
+    self._image_ids.clear()
+
+  def get_estimator_eval_metric_ops(self, eval_dict):
+    """Returns dict of metrics to use with `tf.estimator.EstimatorSpec`.
+
+    Note that this must only be implemented if performing evaluation with a
+    `tf.estimator.Estimator`.
+
+    Args:
+      eval_dict: A dictionary that holds tensors for evaluating an object
+        detection model, returned from
+        eval_util.result_dict_for_single_example(). It must contain
+        standard_fields.InputDataFields.key.
+
+    Returns:
+      A dictionary of metric names to tuple of value_op and update_op that can
+      be used as eval metric ops in `tf.estimator.EstimatorSpec`.
+    """
+    # remove unexpected fields
+    eval_dict_filtered = dict()
+    for key, value in eval_dict.items():
+      if key in self._expected_keys:
+        eval_dict_filtered[key] = value
+
+    eval_dict_keys = eval_dict_filtered.keys()
+
+    def update_op(image_id, *eval_dict_batched_as_list):
+      """Update operation that adds batch of images to ObjectDetectionEvaluator.
+
+      Args:
+        image_id: image id (single id or an array)
+        *eval_dict_batched_as_list: the values of the dictionary of tensors.
+      """
+      if np.isscalar(image_id):
+        single_example_dict = dict(
+            zip(eval_dict_keys, eval_dict_batched_as_list))
+        self.add_single_ground_truth_image_info(image_id, single_example_dict)
+        self.add_single_detected_image_info(image_id, single_example_dict)
+      else:
+        for unzipped_tuple in zip(*eval_dict_batched_as_list):
+          single_example_dict = dict(zip(eval_dict_keys, unzipped_tuple))
+          image_id = single_example_dict[standard_fields.InputDataFields.key]
+          self.add_single_ground_truth_image_info(image_id, single_example_dict)
+          self.add_single_detected_image_info(image_id, single_example_dict)
+
+    args = [eval_dict_filtered[standard_fields.InputDataFields.key]]
+    args.extend(eval_dict_filtered.values())
+    update_op = tf.py_func(update_op, args, [])
+
+    def first_value_func():
+      self._metrics = self.evaluate()
+      self.clear()
+      return np.float32(self._metrics[self._metric_names[0]])
+
+    def value_func_factory(metric_name):
+
+      def value_func():
+        return np.float32(self._metrics[metric_name])
+
+      return value_func
+
+    # Ensure that the metrics are only evaluated once.
+    first_value_op = tf.py_func(first_value_func, [], tf.float32)
+    eval_metric_ops = {self._metric_names[0]: (first_value_op, update_op)}
+    with tf.control_dependencies([first_value_op]):
+      for metric_name in self._metric_names[1:]:
+        eval_metric_ops[metric_name] = (tf.py_func(
+            value_func_factory(metric_name), [], np.float32), update_op)
+    return eval_metric_ops
+
+
+class PascalDetectionEvaluator(ObjectDetectionEvaluator):
+  """A class to evaluate detections using PASCAL metrics."""
+
+  def __init__(self, categories, matching_iou_threshold=0.5):
+    super(PascalDetectionEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold=matching_iou_threshold,
+        evaluate_corlocs=False,
+        metric_prefix='PascalBoxes',
+        use_weighted_mean_ap=False)
+
+
+class WeightedPascalDetectionEvaluator(ObjectDetectionEvaluator):
+  """A class to evaluate detections using weighted PASCAL metrics.
+
+  Weighted PASCAL metrics computes the mean average precision as the average
+  precision given the scores and tp_fp_labels of all classes. In comparison,
+  PASCAL metrics computes the mean average precision as the mean of the
+  per-class average precisions.
+
+  This definition is very similar to the mean of the per-class average
+  precisions weighted by class frequency. However, they are typically not the
+  same as the average precision is not a linear function of the scores and
+  tp_fp_labels.
+  """
+
+  def __init__(self, categories, matching_iou_threshold=0.5):
+    super(WeightedPascalDetectionEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold=matching_iou_threshold,
+        evaluate_corlocs=False,
+        metric_prefix='WeightedPascalBoxes',
+        use_weighted_mean_ap=True)
+
+
+class PascalInstanceSegmentationEvaluator(ObjectDetectionEvaluator):
+  """A class to evaluate instance masks using PASCAL metrics."""
+
+  def __init__(self, categories, matching_iou_threshold=0.5):
+    super(PascalInstanceSegmentationEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold=matching_iou_threshold,
+        evaluate_corlocs=False,
+        metric_prefix='PascalMasks',
+        use_weighted_mean_ap=False,
+        evaluate_masks=True)
+
+
+class WeightedPascalInstanceSegmentationEvaluator(ObjectDetectionEvaluator):
+  """A class to evaluate instance masks using weighted PASCAL metrics.
+
+  Weighted PASCAL metrics computes the mean average precision as the average
+  precision given the scores and tp_fp_labels of all classes. In comparison,
+  PASCAL metrics computes the mean average precision as the mean of the
+  per-class average precisions.
+
+  This definition is very similar to the mean of the per-class average
+  precisions weighted by class frequency. However, they are typically not the
+  same as the average precision is not a linear function of the scores and
+  tp_fp_labels.
+  """
+
+  def __init__(self, categories, matching_iou_threshold=0.5):
+    super(WeightedPascalInstanceSegmentationEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold=matching_iou_threshold,
+        evaluate_corlocs=False,
+        metric_prefix='WeightedPascalMasks',
+        use_weighted_mean_ap=True,
+        evaluate_masks=True)
+
+
+class OpenImagesDetectionEvaluator(ObjectDetectionEvaluator):
+  """A class to evaluate detections using Open Images V2 metrics.
+
+    Open Images V2 introduce group_of type of bounding boxes and this metric
+    handles those boxes appropriately.
+  """
+
+  def __init__(self,
+               categories,
+               matching_iou_threshold=0.5,
+               evaluate_corlocs=False,
+               metric_prefix='OpenImagesV2',
+               group_of_weight=0.0):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+      matching_iou_threshold: IOU threshold to use for matching groundtruth
+        boxes to detection boxes.
+      evaluate_corlocs: if True, additionally evaluates and returns CorLoc.
+      metric_prefix: Prefix name of the metric.
+      group_of_weight: Weight of the group-of bounding box. If set to 0 (default
+        for Open Images V2 detection protocol), detections of the correct class
+        within a group-of box are ignored. If weight is > 0, then if at least
+        one detection falls within a group-of box with matching_iou_threshold,
+        weight group_of_weight is added to true positives. Consequently, if no
+        detection falls within a group-of box, weight group_of_weight is added
+        to false negatives.
+    """
+    super(OpenImagesDetectionEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold,
+        evaluate_corlocs,
+        metric_prefix=metric_prefix,
+        group_of_weight=group_of_weight)
+    self._expected_keys = set([
+        standard_fields.InputDataFields.key,
+        standard_fields.InputDataFields.groundtruth_boxes,
+        standard_fields.InputDataFields.groundtruth_classes,
+        standard_fields.InputDataFields.groundtruth_group_of,
+        standard_fields.DetectionResultFields.detection_boxes,
+        standard_fields.DetectionResultFields.detection_scores,
+        standard_fields.DetectionResultFields.detection_classes,
+    ])
+
+  def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
+          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
+          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
+          of shape [num_boxes] containing 1-indexed groundtruth classes for the
+          boxes.
+        standard_fields.InputDataFields.groundtruth_group_of: Optional length
+          M numpy boolean array denoting whether a groundtruth box contains a
+          group of instances.
+
+    Raises:
+      ValueError: On adding groundtruth for an image more than once.
+    """
+    if image_id in self._image_ids:
+      raise ValueError('Image with id {} already added.'.format(image_id))
+
+    groundtruth_classes = (
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
+        self._label_id_offset)
+    # If the key is not present in the groundtruth_dict or the array is empty
+    # (unless there are no annotations for the groundtruth on this image)
+    # use values from the dictionary or insert None otherwise.
+    if (standard_fields.InputDataFields.groundtruth_group_of in
+        groundtruth_dict.keys() and
+        (groundtruth_dict[standard_fields.InputDataFields.groundtruth_group_of]
+         .size or not groundtruth_classes.size)):
+      groundtruth_group_of = groundtruth_dict[
+          standard_fields.InputDataFields.groundtruth_group_of]
+    else:
+      groundtruth_group_of = None
+      if not len(self._image_ids) % 1000:
+        logging.warn(
+            'image %s does not have groundtruth group_of flag specified',
+            image_id)
+    self._evaluation.add_single_ground_truth_image_info(
+        image_id,
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_boxes],
+        groundtruth_classes,
+        groundtruth_is_difficult_list=None,
+        groundtruth_is_group_of_list=groundtruth_group_of)
+    self._image_ids.update([image_id])
+
+
+class OpenImagesDetectionChallengeEvaluator(OpenImagesDetectionEvaluator):
+  """A class implements Open Images Challenge Detection metrics.
+
+    Open Images Challenge Detection metric has two major changes in comparison
+    with Open Images V2 detection metric:
+    - a custom weight might be specified for detecting an object contained in
+    a group-of box.
+    - verified image-level labels should be explicitelly provided for
+    evaluation: in case in image has neither positive nor negative image level
+    label of class c, all detections of this class on this image will be
+    ignored.
+  """
+
+  def __init__(self,
+               categories,
+               matching_iou_threshold=0.5,
+               evaluate_corlocs=False,
+               group_of_weight=1.0):
+    """Constructor.
+
+    Args:
+      categories: A list of dicts, each of which has the following keys -
+        'id': (required) an integer id uniquely identifying this category.
+        'name': (required) string representing category name e.g., 'cat', 'dog'.
+      matching_iou_threshold: IOU threshold to use for matching groundtruth
+        boxes to detection boxes.
+      evaluate_corlocs: if True, additionally evaluates and returns CorLoc.
+      group_of_weight: weight of a group-of box. If set to 0, detections of the
+        correct class within a group-of box are ignored. If weight is > 0
+        (default for Open Images Detection Challenge 2018), then if at least one
+        detection falls within a group-of box with matching_iou_threshold,
+        weight group_of_weight is added to true positives. Consequently, if no
+        detection falls within a group-of box, weight group_of_weight is added
+        to false negatives.
+    """
+    super(OpenImagesDetectionChallengeEvaluator, self).__init__(
+        categories,
+        matching_iou_threshold,
+        evaluate_corlocs,
+        metric_prefix='OpenImagesChallenge2018',
+        group_of_weight=group_of_weight)
+
+    self._evaluatable_labels = {}
+    self._expected_keys = set([
+        standard_fields.InputDataFields.key,
+        standard_fields.InputDataFields.groundtruth_boxes,
+        standard_fields.InputDataFields.groundtruth_classes,
+        standard_fields.InputDataFields.groundtruth_group_of,
+        standard_fields.InputDataFields.groundtruth_image_classes,
+        standard_fields.DetectionResultFields.detection_boxes,
+        standard_fields.DetectionResultFields.detection_scores,
+        standard_fields.DetectionResultFields.detection_classes,
+    ])
+
+  def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
+          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
+          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
+          of shape [num_boxes] containing 1-indexed groundtruth classes for the
+          boxes.
+        standard_fields.InputDataFields.groundtruth_image_classes: integer 1D
+          numpy array containing all classes for which labels are verified.
+        standard_fields.InputDataFields.groundtruth_group_of: Optional length
+          M numpy boolean array denoting whether a groundtruth box contains a
+          group of instances.
+
+    Raises:
+      ValueError: On adding groundtruth for an image more than once.
+    """
+    super(OpenImagesDetectionChallengeEvaluator,
+          self).add_single_ground_truth_image_info(image_id, groundtruth_dict)
+    groundtruth_classes = (
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
+        self._label_id_offset)
+    self._evaluatable_labels[image_id] = np.unique(
+        np.concatenate(((groundtruth_dict.get(
+            standard_fields.InputDataFields.groundtruth_image_classes,
+            np.array([], dtype=int)) - self._label_id_offset),
+                        groundtruth_classes)))
+
+  def add_single_detected_image_info(self, image_id, detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary containing -
+        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
+          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
+          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
+        standard_fields.DetectionResultFields.detection_scores: float32 numpy
+          array of shape [num_boxes] containing detection scores for the boxes.
+        standard_fields.DetectionResultFields.detection_classes: integer numpy
+          array of shape [num_boxes] containing 1-indexed detection classes for
+          the boxes.
+
+    Raises:
+      ValueError: If detection masks are not in detections dictionary.
+    """
+    if image_id not in self._image_ids:
+      # Since for the correct work of evaluator it is assumed that groundtruth
+      # is inserted first we make sure to break the code if is it not the case.
+      self._image_ids.update([image_id])
+      self._evaluatable_labels[image_id] = np.array([])
+
+    detection_classes = (
+        detections_dict[standard_fields.DetectionResultFields.detection_classes]
+        - self._label_id_offset)
+    allowed_classes = np.where(
+        np.isin(detection_classes, self._evaluatable_labels[image_id]))
+    detection_classes = detection_classes[allowed_classes]
+    detected_boxes = detections_dict[
+        standard_fields.DetectionResultFields.detection_boxes][allowed_classes]
+    detected_scores = detections_dict[
+        standard_fields.DetectionResultFields.detection_scores][allowed_classes]
+
+    self._evaluation.add_single_detected_image_info(
+        image_key=image_id,
+        detected_boxes=detected_boxes,
+        detected_scores=detected_scores,
+        detected_class_labels=detection_classes)
+
+  def clear(self):
+    """Clears stored data."""
+
+    super(OpenImagesDetectionChallengeEvaluator, self).clear()
+    self._evaluatable_labels.clear()
+
+
+ObjectDetectionEvalMetrics = collections.namedtuple(
+    'ObjectDetectionEvalMetrics', [
+        'average_precisions', 'mean_ap', 'precisions', 'recalls', 'corlocs',
+        'mean_corloc'
+    ])
+
+
+class ObjectDetectionEvaluation(object):
+  """Internal implementation of Pascal object detection metrics."""
+
+  def __init__(self,
+               num_groundtruth_classes,
+               matching_iou_threshold=0.5,
+               nms_iou_threshold=1.0,
+               nms_max_output_boxes=10000,
+               use_weighted_mean_ap=False,
+               label_id_offset=0,
+               group_of_weight=0.0,
+               per_image_eval_class=per_image_evaluation.PerImageEvaluation):
+    """Constructor.
+
+    Args:
+      num_groundtruth_classes: Number of ground-truth classes.
+      matching_iou_threshold: IOU threshold used for matching detected boxes
+        to ground-truth boxes.
+      nms_iou_threshold: IOU threshold used for non-maximum suppression.
+      nms_max_output_boxes: Maximum number of boxes returned by non-maximum
+        suppression.
+      use_weighted_mean_ap: (optional) boolean which determines if the mean
+        average precision is computed directly from the scores and tp_fp_labels
+        of all classes.
+      label_id_offset: The label id offset.
+      group_of_weight: Weight of group-of boxes.If set to 0, detections of the
+        correct class within a group-of box are ignored. If weight is > 0, then
+        if at least one detection falls within a group-of box with
+        matching_iou_threshold, weight group_of_weight is added to true
+        positives. Consequently, if no detection falls within a group-of box,
+        weight group_of_weight is added to false negatives.
+      per_image_eval_class: The class that contains functions for computing
+        per image metrics.
+
+    Raises:
+      ValueError: if num_groundtruth_classes is smaller than 1.
+    """
+    if num_groundtruth_classes < 1:
+      raise ValueError('Need at least 1 groundtruth class for evaluation.')
+
+    self.per_image_eval = per_image_eval_class(
+        num_groundtruth_classes=num_groundtruth_classes,
+        matching_iou_threshold=matching_iou_threshold,
+        nms_iou_threshold=nms_iou_threshold,
+        nms_max_output_boxes=nms_max_output_boxes,
+        group_of_weight=group_of_weight)
+    self.group_of_weight = group_of_weight
+    self.num_class = num_groundtruth_classes
+    self.use_weighted_mean_ap = use_weighted_mean_ap
+    self.label_id_offset = label_id_offset
+
+    self.groundtruth_boxes = {}
+    self.groundtruth_class_labels = {}
+    self.groundtruth_masks = {}
+    self.groundtruth_is_difficult_list = {}
+    self.groundtruth_is_group_of_list = {}
+    self.num_gt_instances_per_class = np.zeros(self.num_class, dtype=float)
+    self.num_gt_imgs_per_class = np.zeros(self.num_class, dtype=int)
+
+    self._initialize_detections()
+
+  def _initialize_detections(self):
+    """Initializes internal data structures."""
+    self.detection_keys = set()
+    self.scores_per_class = [[] for _ in range(self.num_class)]
+    self.tp_fp_labels_per_class = [[] for _ in range(self.num_class)]
+    self.num_images_correctly_detected_per_class = np.zeros(self.num_class)
+    self.average_precision_per_class = np.empty(self.num_class, dtype=float)
+    self.average_precision_per_class.fill(np.nan)
+    self.precisions_per_class = [np.nan] * self.num_class
+    self.recalls_per_class = [np.nan] * self.num_class
+
+    self.corloc_per_class = np.ones(self.num_class, dtype=float)
+
+  def clear_detections(self):
+    self._initialize_detections()
+
+  def add_single_ground_truth_image_info(self,
+                                         image_key,
+                                         groundtruth_boxes,
+                                         groundtruth_class_labels,
+                                         groundtruth_is_difficult_list=None,
+                                         groundtruth_is_group_of_list=None,
+                                         groundtruth_masks=None):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_key: A unique string/integer identifier for the image.
+      groundtruth_boxes: float32 numpy array of shape [num_boxes, 4]
+        containing `num_boxes` groundtruth boxes of the format
+        [ymin, xmin, ymax, xmax] in absolute image coordinates.
+      groundtruth_class_labels: integer numpy array of shape [num_boxes]
+        containing 0-indexed groundtruth classes for the boxes.
+      groundtruth_is_difficult_list: A length M numpy boolean array denoting
+        whether a ground truth box is a difficult instance or not. To support
+        the case that no boxes are difficult, it is by default set as None.
+      groundtruth_is_group_of_list: A length M numpy boolean array denoting
+          whether a ground truth box is a group-of box or not. To support
+          the case that no boxes are groups-of, it is by default set as None.
+      groundtruth_masks: uint8 numpy array of shape
+        [num_boxes, height, width] containing `num_boxes` groundtruth masks.
+        The mask values range from 0 to 1.
+    """
+    if image_key in self.groundtruth_boxes:
+      logging.warn(
+          'image %s has already been added to the ground truth database.',
+          image_key)
+      return
+
+    self.groundtruth_boxes[image_key] = groundtruth_boxes
+    self.groundtruth_class_labels[image_key] = groundtruth_class_labels
+    self.groundtruth_masks[image_key] = groundtruth_masks
+    if groundtruth_is_difficult_list is None:
+      num_boxes = groundtruth_boxes.shape[0]
+      groundtruth_is_difficult_list = np.zeros(num_boxes, dtype=bool)
+    self.groundtruth_is_difficult_list[
+        image_key] = groundtruth_is_difficult_list.astype(dtype=bool)
+    if groundtruth_is_group_of_list is None:
+      num_boxes = groundtruth_boxes.shape[0]
+      groundtruth_is_group_of_list = np.zeros(num_boxes, dtype=bool)
+    self.groundtruth_is_group_of_list[
+        image_key] = groundtruth_is_group_of_list.astype(dtype=bool)
+
+    self._update_ground_truth_statistics(
+        groundtruth_class_labels,
+        groundtruth_is_difficult_list.astype(dtype=bool),
+        groundtruth_is_group_of_list.astype(dtype=bool))
+
+  def add_single_detected_image_info(self, image_key, detected_boxes,
+                                     detected_scores, detected_class_labels,
+                                     detected_masks=None):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_key: A unique string/integer identifier for the image.
+      detected_boxes: float32 numpy array of shape [num_boxes, 4]
+        containing `num_boxes` detection boxes of the format
+        [ymin, xmin, ymax, xmax] in absolute image coordinates.
+      detected_scores: float32 numpy array of shape [num_boxes] containing
+        detection scores for the boxes.
+      detected_class_labels: integer numpy array of shape [num_boxes] containing
+        0-indexed detection classes for the boxes.
+      detected_masks: np.uint8 numpy array of shape [num_boxes, height, width]
+        containing `num_boxes` detection masks with values ranging
+        between 0 and 1.
+
+    Raises:
+      ValueError: if the number of boxes, scores and class labels differ in
+        length.
+    """
+    if (len(detected_boxes) != len(detected_scores) or
+        len(detected_boxes) != len(detected_class_labels)):
+      raise ValueError('detected_boxes, detected_scores and '
+                       'detected_class_labels should all have same lengths. Got'
+                       '[%d, %d, %d]' % len(detected_boxes),
+                       len(detected_scores), len(detected_class_labels))
+
+    if image_key in self.detection_keys:
+      logging.warn(
+          'image %s has already been added to the detection result database',
+          image_key)
+      return
+
+    self.detection_keys.add(image_key)
+    if image_key in self.groundtruth_boxes:
+      groundtruth_boxes = self.groundtruth_boxes[image_key]
+      groundtruth_class_labels = self.groundtruth_class_labels[image_key]
+      # Masks are popped instead of look up. The reason is that we do not want
+      # to keep all masks in memory which can cause memory overflow.
+      groundtruth_masks = self.groundtruth_masks.pop(
+          image_key)
+      groundtruth_is_difficult_list = self.groundtruth_is_difficult_list[
+          image_key]
+      groundtruth_is_group_of_list = self.groundtruth_is_group_of_list[
+          image_key]
+    else:
+      groundtruth_boxes = np.empty(shape=[0, 4], dtype=float)
+      groundtruth_class_labels = np.array([], dtype=int)
+      if detected_masks is None:
+        groundtruth_masks = None
+      else:
+        groundtruth_masks = np.empty(shape=[0, 1, 1], dtype=float)
+      groundtruth_is_difficult_list = np.array([], dtype=bool)
+      groundtruth_is_group_of_list = np.array([], dtype=bool)
+    scores, tp_fp_labels, is_class_correctly_detected_in_image = (
+        self.per_image_eval.compute_object_detection_metrics(
+            detected_boxes=detected_boxes,
+            detected_scores=detected_scores,
+            detected_class_labels=detected_class_labels,
+            groundtruth_boxes=groundtruth_boxes,
+            groundtruth_class_labels=groundtruth_class_labels,
+            groundtruth_is_difficult_list=groundtruth_is_difficult_list,
+            groundtruth_is_group_of_list=groundtruth_is_group_of_list,
+            detected_masks=detected_masks,
+            groundtruth_masks=groundtruth_masks))
+
+    for i in range(self.num_class):
+      if scores[i].shape[0] > 0:
+        self.scores_per_class[i].append(scores[i])
+        self.tp_fp_labels_per_class[i].append(tp_fp_labels[i])
+    (self.num_images_correctly_detected_per_class
+    ) += is_class_correctly_detected_in_image
+
+  def _update_ground_truth_statistics(self, groundtruth_class_labels,
+                                      groundtruth_is_difficult_list,
+                                      groundtruth_is_group_of_list):
+    """Update grouth truth statitistics.
+
+    1. Difficult boxes are ignored when counting the number of ground truth
+    instances as done in Pascal VOC devkit.
+    2. Difficult boxes are treated as normal boxes when computing CorLoc related
+    statitistics.
+
+    Args:
+      groundtruth_class_labels: An integer numpy array of length M,
+          representing M class labels of object instances in ground truth
+      groundtruth_is_difficult_list: A boolean numpy array of length M denoting
+          whether a ground truth box is a difficult instance or not
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box is a group-of box or not
+    """
+    for class_index in range(self.num_class):
+      num_gt_instances = np.sum(groundtruth_class_labels[
+          ~groundtruth_is_difficult_list
+          & ~groundtruth_is_group_of_list] == class_index)
+      num_groupof_gt_instances = self.group_of_weight * np.sum(
+          groundtruth_class_labels[groundtruth_is_group_of_list] == class_index)
+      self.num_gt_instances_per_class[
+          class_index] += num_gt_instances + num_groupof_gt_instances
+      if np.any(groundtruth_class_labels == class_index):
+        self.num_gt_imgs_per_class[class_index] += 1
+
+  def evaluate(self):
+    """Compute evaluation result.
+
+    Returns:
+      A named tuple with the following fields -
+        average_precision: float numpy array of average precision for
+            each class.
+        mean_ap: mean average precision of all classes, float scalar
+        precisions: List of precisions, each precision is a float numpy
+            array
+        recalls: List of recalls, each recall is a float numpy array
+        corloc: numpy float array
+        mean_corloc: Mean CorLoc score for each class, float scalar
+    """
+    if (self.num_gt_instances_per_class == 0).any():
+      logging.warn(
+          'The following classes have no ground truth examples: %s',
+          np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0)) +
+          self.label_id_offset)
+
+    if self.use_weighted_mean_ap:
+      all_scores = np.array([], dtype=float)
+      all_tp_fp_labels = np.array([], dtype=bool)
+    for class_index in range(self.num_class):
+      if self.num_gt_instances_per_class[class_index] == 0:
+        continue
+      if not self.scores_per_class[class_index]:
+        scores = np.array([], dtype=float)
+        tp_fp_labels = np.array([], dtype=float)
+      else:
+        scores = np.concatenate(self.scores_per_class[class_index])
+        tp_fp_labels = np.concatenate(self.tp_fp_labels_per_class[class_index])
+      if self.use_weighted_mean_ap:
+        all_scores = np.append(all_scores, scores)
+        all_tp_fp_labels = np.append(all_tp_fp_labels, tp_fp_labels)
+      precision, recall = metrics.compute_precision_recall(
+          scores, tp_fp_labels, self.num_gt_instances_per_class[class_index])
+
+      self.precisions_per_class[class_index] = precision
+      self.recalls_per_class[class_index] = recall
+      average_precision = metrics.compute_average_precision(precision, recall)
+      self.average_precision_per_class[class_index] = average_precision
+      logging.info('average_precision: %f', average_precision)
+
+    self.corloc_per_class = metrics.compute_cor_loc(
+        self.num_gt_imgs_per_class,
+        self.num_images_correctly_detected_per_class)
+
+    if self.use_weighted_mean_ap:
+      num_gt_instances = np.sum(self.num_gt_instances_per_class)
+      precision, recall = metrics.compute_precision_recall(
+          all_scores, all_tp_fp_labels, num_gt_instances)
+      mean_ap = metrics.compute_average_precision(precision, recall)
+    else:
+      mean_ap = np.nanmean(self.average_precision_per_class)
+    mean_corloc = np.nanmean(self.corloc_per_class)
+    return ObjectDetectionEvalMetrics(
+        self.average_precision_per_class, mean_ap, self.precisions_per_class,
+        self.recalls_per_class, self.corloc_per_class, mean_corloc)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation_test.py
new file mode 100644
index 00000000..a625c99d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/object_detection_evaluation_test.py
@@ -0,0 +1,824 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.object_detection_evaluation."""
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+from object_detection import eval_util
+from object_detection.core import standard_fields
+from object_detection.utils import object_detection_evaluation
+
+
+class OpenImagesV2EvaluationTest(tf.test.TestCase):
+
+  def test_returns_correct_metric_values(self):
+    categories = [{
+        'id': 1,
+        'name': 'cat'
+    }, {
+        'id': 2,
+        'name': 'dog'
+    }, {
+        'id': 3,
+        'name': 'elephant'
+    }]
+
+    oiv2_evaluator = object_detection_evaluation.OpenImagesDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key1, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes1,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels1,
+        standard_fields.InputDataFields.groundtruth_group_of:
+            np.array([], dtype=bool)
+    })
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array(
+        [[10, 10, 11, 11], [500, 500, 510, 510], [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_group_of_list2 = np.array([False, True, False], dtype=bool)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key2, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes2,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels2,
+        standard_fields.InputDataFields.groundtruth_group_of:
+            groundtruth_is_group_of_list2
+    })
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key3, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes3,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels3
+    })
+    # Add detections
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    oiv2_evaluator.add_single_detected_image_info(image_key, {
+        standard_fields.DetectionResultFields.detection_boxes:
+            detected_boxes,
+        standard_fields.DetectionResultFields.detection_scores:
+            detected_scores,
+        standard_fields.DetectionResultFields.detection_classes:
+            detected_class_labels
+    })
+    metrics = oiv2_evaluator.evaluate()
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
+    self.assertAlmostEqual(metrics['OpenImagesV2_Precision/mAP@0.5IOU'],
+                           0.05555555)
+    oiv2_evaluator.clear()
+    self.assertFalse(oiv2_evaluator._image_ids)
+
+
+class OpenImagesDetectionChallengeEvaluatorTest(tf.test.TestCase):
+
+  def test_returns_correct_metric_values(self):
+    categories = [{
+        'id': 1,
+        'name': 'cat'
+    }, {
+        'id': 2,
+        'name': 'dog'
+    }, {
+        'id': 3,
+        'name': 'elephant'
+    }]
+    oivchallenge_evaluator = (
+        object_detection_evaluation.OpenImagesDetectionChallengeEvaluator(
+            categories, group_of_weight=0.5))
+
+    image_key = 'img1'
+    groundtruth_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], dtype=float)
+    groundtruth_class_labels = np.array([1, 3, 1], dtype=int)
+    groundtruth_is_group_of_list = np.array([False, False, True], dtype=bool)
+    groundtruth_verified_labels = np.array([1, 2, 3], dtype=int)
+    oivchallenge_evaluator.add_single_ground_truth_image_info(
+        image_key, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels,
+            standard_fields.InputDataFields.groundtruth_group_of:
+                groundtruth_is_group_of_list,
+            standard_fields.InputDataFields.groundtruth_image_classes:
+                groundtruth_verified_labels,
+        })
+    image_key = 'img2'
+    groundtruth_boxes = np.array(
+        [[10, 10, 11, 11], [500, 500, 510, 510], [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_group_of_list = np.array([False, False, True], dtype=bool)
+    oivchallenge_evaluator.add_single_ground_truth_image_info(
+        image_key, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels,
+            standard_fields.InputDataFields.groundtruth_group_of:
+                groundtruth_is_group_of_list
+        })
+    image_key = 'img3'
+    groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels = np.array([2], dtype=int)
+    oivchallenge_evaluator.add_single_ground_truth_image_info(
+        image_key, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels
+        })
+    image_key = 'img1'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120]], dtype=float)
+    detected_class_labels = np.array([2, 2], dtype=int)
+    detected_scores = np.array([0.7, 0.8], dtype=float)
+    oivchallenge_evaluator.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_boxes,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_labels
+        })
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220],
+         [10, 10, 11, 11]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 2, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.5, 0.9], dtype=float)
+    oivchallenge_evaluator.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_boxes,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_labels
+        })
+    image_key = 'img3'
+    detected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    detected_class_labels = np.array([2], dtype=int)
+    detected_scores = np.array([0.5], dtype=float)
+    oivchallenge_evaluator.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_boxes,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_labels
+        })
+    metrics = oivchallenge_evaluator.evaluate()
+
+    self.assertAlmostEqual(
+        metrics['OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/dog'],
+        0.3333333333)
+    self.assertAlmostEqual(
+        metrics[
+            'OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/elephant'],
+        0.333333333333)
+    self.assertAlmostEqual(
+        metrics['OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/cat'],
+        0.142857142857)
+    self.assertAlmostEqual(
+        metrics['OpenImagesChallenge2018_Precision/mAP@0.5IOU'], 0.269841269)
+
+    oivchallenge_evaluator.clear()
+    self.assertFalse(oivchallenge_evaluator._image_ids)
+
+
+class PascalEvaluationTest(tf.test.TestCase):
+
+  def test_returns_correct_metric_values_on_boxes(self):
+    categories = [{'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'},
+                  {'id': 3, 'name': 'elephant'}]
+    #  Add groundtruth
+    pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1,
+         standard_fields.InputDataFields.groundtruth_difficult:
+         np.array([], dtype=bool)})
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2,
+         standard_fields.InputDataFields.groundtruth_difficult:
+         groundtruth_is_difficult_list2})
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key3,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels3})
+
+    # Add detections
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    pascal_evaluator.add_single_detected_image_info(
+        image_key,
+        {standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
+         standard_fields.DetectionResultFields.detection_scores:
+         detected_scores,
+         standard_fields.DetectionResultFields.detection_classes:
+         detected_class_labels})
+
+    metrics = pascal_evaluator.evaluate()
+    self.assertAlmostEqual(
+        metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
+    self.assertAlmostEqual(metrics['PascalBoxes_Precision/mAP@0.5IOU'],
+                           0.05555555)
+    pascal_evaluator.clear()
+    self.assertFalse(pascal_evaluator._image_ids)
+
+  def test_returns_correct_metric_values_on_masks(self):
+    categories = [{'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'},
+                  {'id': 3, 'name': 'elephant'}]
+    #  Add groundtruth
+    pascal_evaluator = (
+        object_detection_evaluation.PascalInstanceSegmentationEvaluator(
+            categories))
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    groundtruth_masks_1_0 = np.array([[1, 0, 0, 0],
+                                      [1, 0, 0, 0],
+                                      [1, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_1_1 = np.array([[0, 0, 1, 0],
+                                      [0, 0, 1, 0],
+                                      [0, 0, 1, 0]], dtype=np.uint8)
+    groundtruth_masks_1_2 = np.array([[0, 1, 0, 0],
+                                      [0, 1, 0, 0],
+                                      [0, 1, 0, 0]], dtype=np.uint8)
+    groundtruth_masks1 = np.stack(
+        [groundtruth_masks_1_0, groundtruth_masks_1_1, groundtruth_masks_1_2],
+        axis=0)
+
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key1, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes1,
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+                groundtruth_masks1,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels1,
+            standard_fields.InputDataFields.groundtruth_difficult:
+                np.array([], dtype=bool)
+        })
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    groundtruth_masks_2_0 = np.array([[1, 1, 1, 1],
+                                      [0, 0, 0, 0],
+                                      [0, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_2_1 = np.array([[0, 0, 0, 0],
+                                      [1, 1, 1, 1],
+                                      [0, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_2_2 = np.array([[0, 0, 0, 0],
+                                      [0, 0, 0, 0],
+                                      [1, 1, 1, 1]], dtype=np.uint8)
+    groundtruth_masks2 = np.stack(
+        [groundtruth_masks_2_0, groundtruth_masks_2_1, groundtruth_masks_2_2],
+        axis=0)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key2, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes2,
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+                groundtruth_masks2,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels2,
+            standard_fields.InputDataFields.groundtruth_difficult:
+                groundtruth_is_difficult_list2
+        })
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    groundtruth_masks_3_0 = np.array([[1, 1, 1, 1],
+                                      [1, 1, 1, 1],
+                                      [1, 1, 1, 1]], dtype=np.uint8)
+    groundtruth_masks3 = np.stack([groundtruth_masks_3_0], axis=0)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key3, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_boxes3,
+            standard_fields.InputDataFields.groundtruth_instance_masks:
+                groundtruth_masks3,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_labels3
+        })
+
+    # Add detections
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    detected_masks_0 = np.array([[1, 1, 1, 1],
+                                 [0, 0, 1, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_1 = np.array([[1, 0, 0, 0],
+                                 [1, 1, 0, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_2 = np.array([[0, 1, 0, 0],
+                                 [0, 1, 1, 0],
+                                 [0, 1, 0, 0]], dtype=np.uint8)
+    detected_masks = np.stack(
+        [detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
+
+    pascal_evaluator.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_boxes,
+            standard_fields.DetectionResultFields.detection_masks:
+                detected_masks,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_labels
+        })
+
+    metrics = pascal_evaluator.evaluate()
+
+    self.assertAlmostEqual(
+        metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
+    self.assertAlmostEqual(metrics['PascalMasks_Precision/mAP@0.5IOU'],
+                           0.05555555)
+    pascal_evaluator.clear()
+    self.assertFalse(pascal_evaluator._image_ids)
+
+  def test_value_error_on_duplicate_images(self):
+    categories = [{'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'},
+                  {'id': 3, 'name': 'elephant'}]
+    #  Add groundtruth
+    pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    with self.assertRaises(ValueError):
+      pascal_evaluator.add_single_ground_truth_image_info(
+          image_key1,
+          {standard_fields.InputDataFields.groundtruth_boxes:
+           groundtruth_boxes1,
+           standard_fields.InputDataFields.groundtruth_classes:
+           groundtruth_class_labels1})
+
+
+class WeightedPascalEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    self.categories = [{'id': 1, 'name': 'cat'},
+                       {'id': 2, 'name': 'dog'},
+                       {'id': 3, 'name': 'elephant'}]
+
+  def create_and_add_common_ground_truth(self):
+    #  Add groundtruth
+    self.wp_eval = (
+        object_detection_evaluation.WeightedPascalDetectionEvaluator(
+            self.categories))
+
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    # add 'img2' separately
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key3,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels3})
+
+  def add_common_detected(self):
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    self.wp_eval.add_single_detected_image_info(
+        image_key,
+        {standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
+         standard_fields.DetectionResultFields.detection_scores:
+         detected_scores,
+         standard_fields.DetectionResultFields.detection_classes:
+         detected_class_labels})
+
+  def test_returns_correct_metric_values(self):
+    self.create_and_add_common_ground_truth()
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2
+        })
+    self.add_common_detected()
+
+    metrics = self.wp_eval.evaluate()
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 4)
+    self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
+                                   'Precision/mAP@0.5IOU'],
+                           1. / (4 + 1 + 2) / 3)
+    self.wp_eval.clear()
+    self.assertFalse(self.wp_eval._image_ids)
+
+  def test_returns_correct_metric_values_with_difficult_list(self):
+    self.create_and_add_common_ground_truth()
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2,
+         standard_fields.InputDataFields.groundtruth_difficult:
+         groundtruth_is_difficult_list2
+        })
+    self.add_common_detected()
+
+    metrics = self.wp_eval.evaluate()
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 3)
+    self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
+                                   'Precision/mAP@0.5IOU'],
+                           1. / (3 + 1 + 2) / 3)
+    self.wp_eval.clear()
+    self.assertFalse(self.wp_eval._image_ids)
+
+  def test_value_error_on_duplicate_images(self):
+    #  Add groundtruth
+    self.wp_eval = (
+        object_detection_evaluation.WeightedPascalDetectionEvaluator(
+            self.categories))
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    with self.assertRaises(ValueError):
+      self.wp_eval.add_single_ground_truth_image_info(
+          image_key1,
+          {standard_fields.InputDataFields.groundtruth_boxes:
+           groundtruth_boxes1,
+           standard_fields.InputDataFields.groundtruth_classes:
+           groundtruth_class_labels1})
+
+
+class ObjectDetectionEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 3
+    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
+        num_groundtruth_classes)
+
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
+    self.od_eval.add_single_ground_truth_image_info(
+        image_key1, groundtruth_boxes1, groundtruth_class_labels1)
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
+    self.od_eval.add_single_ground_truth_image_info(
+        image_key2, groundtruth_boxes2, groundtruth_class_labels2,
+        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)
+
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([1], dtype=int)
+    self.od_eval.add_single_ground_truth_image_info(
+        image_key3, groundtruth_boxes3, groundtruth_class_labels3)
+
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([0, 0, 2], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    self.od_eval.add_single_detected_image_info(
+        image_key, detected_boxes, detected_scores, detected_class_labels)
+
+  def test_value_error_on_zero_classes(self):
+    with self.assertRaises(ValueError):
+      object_detection_evaluation.ObjectDetectionEvaluation(
+          num_groundtruth_classes=0)
+
+  def test_add_single_ground_truth_image_info(self):
+    expected_num_gt_instances_per_class = np.array([3, 1, 1], dtype=int)
+    expected_num_gt_imgs_per_class = np.array([2, 1, 2], dtype=int)
+    self.assertTrue(np.array_equal(expected_num_gt_instances_per_class,
+                                   self.od_eval.num_gt_instances_per_class))
+    self.assertTrue(np.array_equal(expected_num_gt_imgs_per_class,
+                                   self.od_eval.num_gt_imgs_per_class))
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    self.assertTrue(np.allclose(self.od_eval.groundtruth_boxes['img2'],
+                                groundtruth_boxes2))
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    self.assertTrue(np.allclose(
+        self.od_eval.groundtruth_is_difficult_list['img2'],
+        groundtruth_is_difficult_list2))
+    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
+    self.assertTrue(
+        np.allclose(self.od_eval.groundtruth_is_group_of_list['img2'],
+                    groundtruth_is_group_of_list2))
+
+    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
+    self.assertTrue(np.array_equal(self.od_eval.groundtruth_class_labels[
+        'img1'], groundtruth_class_labels1))
+
+  def test_add_single_detected_image_info(self):
+    expected_scores_per_class = [[np.array([0.8, 0.7], dtype=float)], [],
+                                 [np.array([0.9], dtype=float)]]
+    expected_tp_fp_labels_per_class = [[np.array([0, 1], dtype=bool)], [],
+                                       [np.array([0], dtype=bool)]]
+    expected_num_images_correctly_detected_per_class = np.array([0, 0, 0],
+                                                                dtype=int)
+    for i in range(self.od_eval.num_class):
+      for j in range(len(expected_scores_per_class[i])):
+        self.assertTrue(np.allclose(expected_scores_per_class[i][j],
+                                    self.od_eval.scores_per_class[i][j]))
+        self.assertTrue(np.array_equal(expected_tp_fp_labels_per_class[i][
+            j], self.od_eval.tp_fp_labels_per_class[i][j]))
+    self.assertTrue(np.array_equal(
+        expected_num_images_correctly_detected_per_class,
+        self.od_eval.num_images_correctly_detected_per_class))
+
+  def test_evaluate(self):
+    (average_precision_per_class, mean_ap, precisions_per_class,
+     recalls_per_class, corloc_per_class,
+     mean_corloc) = self.od_eval.evaluate()
+    expected_precisions_per_class = [np.array([0, 0.5], dtype=float),
+                                     np.array([], dtype=float),
+                                     np.array([0], dtype=float)]
+    expected_recalls_per_class = [
+        np.array([0, 1. / 3.], dtype=float), np.array([], dtype=float),
+        np.array([0], dtype=float)
+    ]
+    expected_average_precision_per_class = np.array([1. / 6., 0, 0],
+                                                    dtype=float)
+    expected_corloc_per_class = np.array([0, np.divide(0, 0), 0], dtype=float)
+    expected_mean_ap = 1. / 18
+    expected_mean_corloc = 0.0
+    for i in range(self.od_eval.num_class):
+      self.assertTrue(np.allclose(expected_precisions_per_class[i],
+                                  precisions_per_class[i]))
+      self.assertTrue(np.allclose(expected_recalls_per_class[i],
+                                  recalls_per_class[i]))
+    self.assertTrue(np.allclose(expected_average_precision_per_class,
+                                average_precision_per_class))
+    self.assertTrue(np.allclose(expected_corloc_per_class, corloc_per_class))
+    self.assertAlmostEqual(expected_mean_ap, mean_ap)
+    self.assertAlmostEqual(expected_mean_corloc, mean_corloc)
+
+
+class ObjectDetectionEvaluatorTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    self.categories = [{
+        'id': 1,
+        'name': 'person'
+    }, {
+        'id': 2,
+        'name': 'dog'
+    }, {
+        'id': 3,
+        'name': 'cat'
+    }]
+    self.od_eval = object_detection_evaluation.ObjectDetectionEvaluator(
+        categories=self.categories)
+
+  def _make_evaluation_dict(self,
+                            resized_groundtruth_masks=False,
+                            batch_size=1,
+                            max_gt_boxes=None,
+                            scale_to_absolute=False):
+    input_data_fields = standard_fields.InputDataFields
+    detection_fields = standard_fields.DetectionResultFields
+
+    image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
+    if batch_size == 1:
+      key = tf.constant('image1')
+    else:
+      key = tf.constant([str(i) for i in range(batch_size)])
+    detection_boxes = tf.concat([
+        tf.tile(
+            tf.constant([[[0., 0., 1., 1.]]]), multiples=[batch_size - 1, 1, 1
+                                                         ]),
+        tf.constant([[[0., 0., 0.5, 0.5]]])
+    ],
+                                axis=0)
+    detection_scores = tf.concat([
+        tf.tile(tf.constant([[0.5]]), multiples=[batch_size - 1, 1]),
+        tf.constant([[0.8]])
+    ],
+                                 axis=0)
+    detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
+    detection_masks = tf.tile(
+        tf.ones(shape=[1, 2, 20, 20], dtype=tf.float32),
+        multiples=[batch_size, 1, 1, 1])
+    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
+    groundtruth_classes = tf.constant([1])
+    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
+    num_detections = tf.ones([batch_size])
+    if resized_groundtruth_masks:
+      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
+
+    if batch_size > 1:
+      groundtruth_boxes = tf.tile(
+          tf.expand_dims(groundtruth_boxes, 0), multiples=[batch_size, 1, 1])
+      groundtruth_classes = tf.tile(
+          tf.expand_dims(groundtruth_classes, 0), multiples=[batch_size, 1])
+      groundtruth_instance_masks = tf.tile(
+          tf.expand_dims(groundtruth_instance_masks, 0),
+          multiples=[batch_size, 1, 1, 1])
+
+    detections = {
+        detection_fields.detection_boxes: detection_boxes,
+        detection_fields.detection_scores: detection_scores,
+        detection_fields.detection_classes: detection_classes,
+        detection_fields.detection_masks: detection_masks,
+        detection_fields.num_detections: num_detections
+    }
+    groundtruth = {
+        input_data_fields.groundtruth_boxes:
+            groundtruth_boxes,
+        input_data_fields.groundtruth_classes:
+            groundtruth_classes,
+        input_data_fields.groundtruth_instance_masks:
+            groundtruth_instance_masks,
+    }
+    if batch_size > 1:
+      return eval_util.result_dict_for_batched_example(
+          image,
+          key,
+          detections,
+          groundtruth,
+          scale_to_absolute=scale_to_absolute,
+          max_gt_boxes=max_gt_boxes)
+    else:
+      return eval_util.result_dict_for_single_example(
+          image,
+          key,
+          detections,
+          groundtruth,
+          scale_to_absolute=scale_to_absolute)
+
+  @parameterized.parameters({
+      'batch_size': 1,
+      'expected_map': 0,
+      'max_gt_boxes': None,
+      'scale_to_absolute': True
+  }, {
+      'batch_size': 8,
+      'expected_map': 0.765625,
+      'max_gt_boxes': [1],
+      'scale_to_absolute': True
+  }, {
+      'batch_size': 1,
+      'expected_map': 0,
+      'max_gt_boxes': None,
+      'scale_to_absolute': False
+  }, {
+      'batch_size': 8,
+      'expected_map': 0.765625,
+      'max_gt_boxes': [1],
+      'scale_to_absolute': False
+  })
+  def test_get_estimator_eval_metric_ops(self,
+                                         batch_size=1,
+                                         expected_map=1,
+                                         max_gt_boxes=None,
+                                         scale_to_absolute=False):
+
+    eval_dict = self._make_evaluation_dict(
+        batch_size=batch_size,
+        max_gt_boxes=max_gt_boxes,
+        scale_to_absolute=scale_to_absolute)
+    tf.logging.info('eval_dict: {}'.format(eval_dict))
+    metric_ops = self.od_eval.get_estimator_eval_metric_ops(eval_dict)
+    _, update_op = metric_ops['Precision/mAP@0.5IOU']
+
+    with self.test_session() as sess:
+      metrics = {}
+      for key, (value_op, _) in metric_ops.iteritems():
+        metrics[key] = value_op
+      sess.run(update_op)
+      metrics = sess.run(metrics)
+      self.assertAlmostEqual(expected_map, metrics['Precision/mAP@0.5IOU'])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops.py
new file mode 100644
index 00000000..15c2a0c7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops.py
@@ -0,0 +1,1097 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A module for helper tensorflow ops."""
+import collections
+import math
+import numpy as np
+import six
+
+import tensorflow as tf
+
+from object_detection.core import standard_fields as fields
+from object_detection.utils import shape_utils
+from object_detection.utils import static_shape
+
+
+def expanded_shape(orig_shape, start_dim, num_dims):
+  """Inserts multiple ones into a shape vector.
+
+  Inserts an all-1 vector of length num_dims at position start_dim into a shape.
+  Can be combined with tf.reshape to generalize tf.expand_dims.
+
+  Args:
+    orig_shape: the shape into which the all-1 vector is added (int32 vector)
+    start_dim: insertion position (int scalar)
+    num_dims: length of the inserted all-1 vector (int scalar)
+  Returns:
+    An int32 vector of length tf.size(orig_shape) + num_dims.
+  """
+  with tf.name_scope('ExpandedShape'):
+    start_dim = tf.expand_dims(start_dim, 0)  # scalar to rank-1
+    before = tf.slice(orig_shape, [0], start_dim)
+    add_shape = tf.ones(tf.reshape(num_dims, [1]), dtype=tf.int32)
+    after = tf.slice(orig_shape, start_dim, [-1])
+    new_shape = tf.concat([before, add_shape, after], 0)
+    return new_shape
+
+
+def normalized_to_image_coordinates(normalized_boxes, image_shape,
+                                    parallel_iterations=32):
+  """Converts a batch of boxes from normal to image coordinates.
+
+  Args:
+    normalized_boxes: a float32 tensor of shape [None, num_boxes, 4] in
+      normalized coordinates.
+    image_shape: a float32 tensor of shape [4] containing the image shape.
+    parallel_iterations: parallelism for the map_fn op.
+
+  Returns:
+    absolute_boxes: a float32 tensor of shape [None, num_boxes, 4] containing
+      the boxes in image coordinates.
+  """
+  x_scale = tf.cast(image_shape[2], tf.float32)
+  y_scale = tf.cast(image_shape[1], tf.float32)
+  def _to_absolute_coordinates(normalized_boxes):
+    y_min, x_min, y_max, x_max = tf.split(
+        value=normalized_boxes, num_or_size_splits=4, axis=1)
+    y_min = y_scale * y_min
+    y_max = y_scale * y_max
+    x_min = x_scale * x_min
+    x_max = x_scale * x_max
+    scaled_boxes = tf.concat([y_min, x_min, y_max, x_max], 1)
+    return scaled_boxes
+
+  absolute_boxes = shape_utils.static_or_dynamic_map_fn(
+      _to_absolute_coordinates,
+      elems=(normalized_boxes),
+      dtype=tf.float32,
+      parallel_iterations=parallel_iterations,
+      back_prop=True)
+  return absolute_boxes
+
+
+def meshgrid(x, y):
+  """Tiles the contents of x and y into a pair of grids.
+
+  Multidimensional analog of numpy.meshgrid, giving the same behavior if x and y
+  are vectors. Generally, this will give:
+
+  xgrid(i1, ..., i_m, j_1, ..., j_n) = x(j_1, ..., j_n)
+  ygrid(i1, ..., i_m, j_1, ..., j_n) = y(i_1, ..., i_m)
+
+  Keep in mind that the order of the arguments and outputs is reverse relative
+  to the order of the indices they go into, done for compatibility with numpy.
+  The output tensors have the same shapes.  Specifically:
+
+  xgrid.get_shape() = y.get_shape().concatenate(x.get_shape())
+  ygrid.get_shape() = y.get_shape().concatenate(x.get_shape())
+
+  Args:
+    x: A tensor of arbitrary shape and rank. xgrid will contain these values
+       varying in its last dimensions.
+    y: A tensor of arbitrary shape and rank. ygrid will contain these values
+       varying in its first dimensions.
+  Returns:
+    A tuple of tensors (xgrid, ygrid).
+  """
+  with tf.name_scope('Meshgrid'):
+    x = tf.convert_to_tensor(x)
+    y = tf.convert_to_tensor(y)
+    x_exp_shape = expanded_shape(tf.shape(x), 0, tf.rank(y))
+    y_exp_shape = expanded_shape(tf.shape(y), tf.rank(y), tf.rank(x))
+
+    xgrid = tf.tile(tf.reshape(x, x_exp_shape), y_exp_shape)
+    ygrid = tf.tile(tf.reshape(y, y_exp_shape), x_exp_shape)
+    new_shape = y.get_shape().concatenate(x.get_shape())
+    xgrid.set_shape(new_shape)
+    ygrid.set_shape(new_shape)
+
+    return xgrid, ygrid
+
+
+def fixed_padding(inputs, kernel_size, rate=1):
+  """Pads the input along the spatial dimensions independently of input size.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
+                 Should be a positive integer.
+    rate: An integer, rate for atrous convolution.
+
+  Returns:
+    output: A tensor of size [batch, height_out, width_out, channels] with the
+      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+  """
+  kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+  pad_total = kernel_size_effective - 1
+  pad_beg = pad_total // 2
+  pad_end = pad_total - pad_beg
+  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
+                                  [pad_beg, pad_end], [0, 0]])
+  return padded_inputs
+
+
+def pad_to_multiple(tensor, multiple):
+  """Returns the tensor zero padded to the specified multiple.
+
+  Appends 0s to the end of the first and second dimension (height and width) of
+  the tensor until both dimensions are a multiple of the input argument
+  'multiple'. E.g. given an input tensor of shape [1, 3, 5, 1] and an input
+  multiple of 4, PadToMultiple will append 0s so that the resulting tensor will
+  be of shape [1, 4, 8, 1].
+
+  Args:
+    tensor: rank 4 float32 tensor, where
+            tensor -> [batch_size, height, width, channels].
+    multiple: the multiple to pad to.
+
+  Returns:
+    padded_tensor: the tensor zero padded to the specified multiple.
+  """
+  if multiple == 1:
+    return tensor
+
+  tensor_shape = tensor.get_shape()
+  batch_size = static_shape.get_batch_size(tensor_shape)
+  tensor_height = static_shape.get_height(tensor_shape)
+  tensor_width = static_shape.get_width(tensor_shape)
+  tensor_depth = static_shape.get_depth(tensor_shape)
+
+  if batch_size is None:
+    batch_size = tf.shape(tensor)[0]
+
+  if tensor_height is None:
+    tensor_height = tf.shape(tensor)[1]
+    padded_tensor_height = tf.to_int32(
+        tf.ceil(tf.to_float(tensor_height) / tf.to_float(multiple))) * multiple
+  else:
+    padded_tensor_height = int(
+        math.ceil(float(tensor_height) / multiple) * multiple)
+
+  if tensor_width is None:
+    tensor_width = tf.shape(tensor)[2]
+    padded_tensor_width = tf.to_int32(
+        tf.ceil(tf.to_float(tensor_width) / tf.to_float(multiple))) * multiple
+  else:
+    padded_tensor_width = int(
+        math.ceil(float(tensor_width) / multiple) * multiple)
+
+  if tensor_depth is None:
+    tensor_depth = tf.shape(tensor)[3]
+
+  # Use tf.concat instead of tf.pad to preserve static shape
+  if padded_tensor_height != tensor_height:
+    height_pad = tf.zeros([
+        batch_size, padded_tensor_height - tensor_height, tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, height_pad], 1)
+  if padded_tensor_width != tensor_width:
+    width_pad = tf.zeros([
+        batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, width_pad], 2)
+
+  return tensor
+
+
+def padded_one_hot_encoding(indices, depth, left_pad):
+  """Returns a zero padded one-hot tensor.
+
+  This function converts a sparse representation of indices (e.g., [4]) to a
+  zero padded one-hot representation (e.g., [0, 0, 0, 0, 1] with depth = 4 and
+  left_pad = 1). If `indices` is empty, the result will simply be a tensor of
+  shape (0, depth + left_pad). If depth = 0, then this function just returns
+  `None`.
+
+  Args:
+    indices: an integer tensor of shape [num_indices].
+    depth: depth for the one-hot tensor (integer).
+    left_pad: number of zeros to left pad the one-hot tensor with (integer).
+
+  Returns:
+    padded_onehot: a tensor with shape (num_indices, depth + left_pad). Returns
+      `None` if the depth is zero.
+
+  Raises:
+    ValueError: if `indices` does not have rank 1 or if `left_pad` or `depth are
+      either negative or non-integers.
+
+  TODO(rathodv): add runtime checks for depth and indices.
+  """
+  if depth < 0 or not isinstance(depth, six.integer_types):
+    raise ValueError('`depth` must be a non-negative integer.')
+  if left_pad < 0 or not isinstance(left_pad, six.integer_types):
+    raise ValueError('`left_pad` must be a non-negative integer.')
+  if depth == 0:
+    return None
+
+  rank = len(indices.get_shape().as_list())
+  if rank != 1:
+    raise ValueError('`indices` must have rank 1, but has rank=%s' % rank)
+
+  def one_hot_and_pad():
+    one_hot = tf.cast(tf.one_hot(tf.cast(indices, tf.int64), depth,
+                                 on_value=1, off_value=0), tf.float32)
+    return tf.pad(one_hot, [[0, 0], [left_pad, 0]], mode='CONSTANT')
+  result = tf.cond(tf.greater(tf.size(indices), 0), one_hot_and_pad,
+                   lambda: tf.zeros((depth + left_pad, 0)))
+  return tf.reshape(result, [-1, depth + left_pad])
+
+
+def dense_to_sparse_boxes(dense_locations, dense_num_boxes, num_classes):
+  """Converts bounding boxes from dense to sparse form.
+
+  Args:
+    dense_locations:  a [max_num_boxes, 4] tensor in which only the first k rows
+      are valid bounding box location coordinates, where k is the sum of
+      elements in dense_num_boxes.
+    dense_num_boxes: a [max_num_classes] tensor indicating the counts of
+       various bounding box classes e.g. [1, 0, 0, 2] means that the first
+       bounding box is of class 0 and the second and third bounding boxes are
+       of class 3. The sum of elements in this tensor is the number of valid
+       bounding boxes.
+    num_classes: number of classes
+
+  Returns:
+    box_locations: a [num_boxes, 4] tensor containing only valid bounding
+       boxes (i.e. the first num_boxes rows of dense_locations)
+    box_classes: a [num_boxes] tensor containing the classes of each bounding
+       box (e.g. dense_num_boxes = [1, 0, 0, 2] => box_classes = [0, 3, 3]
+  """
+
+  num_valid_boxes = tf.reduce_sum(dense_num_boxes)
+  box_locations = tf.slice(dense_locations,
+                           tf.constant([0, 0]), tf.stack([num_valid_boxes, 4]))
+  tiled_classes = [tf.tile([i], tf.expand_dims(dense_num_boxes[i], 0))
+                   for i in range(num_classes)]
+  box_classes = tf.concat(tiled_classes, 0)
+  box_locations.set_shape([None, 4])
+  return box_locations, box_classes
+
+
+def indices_to_dense_vector(indices,
+                            size,
+                            indices_value=1.,
+                            default_value=0,
+                            dtype=tf.float32):
+  """Creates dense vector with indices set to specific value and rest to zeros.
+
+  This function exists because it is unclear if it is safe to use
+    tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
+  with indices which are not ordered.
+  This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
+
+  Args:
+    indices: 1d Tensor with integer indices which are to be set to
+        indices_values.
+    size: scalar with size (integer) of output Tensor.
+    indices_value: values of elements specified by indices in the output vector
+    default_value: values of other elements in the output vector.
+    dtype: data type.
+
+  Returns:
+    dense 1D Tensor of shape [size] with indices set to indices_values and the
+        rest set to default_value.
+  """
+  size = tf.to_int32(size)
+  zeros = tf.ones([size], dtype=dtype) * default_value
+  values = tf.ones_like(indices, dtype=dtype) * indices_value
+
+  return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)],
+                           [zeros, values])
+
+
+def reduce_sum_trailing_dimensions(tensor, ndims):
+  """Computes sum across all dimensions following first `ndims` dimensions."""
+  return tf.reduce_sum(tensor, axis=tuple(range(ndims, tensor.shape.ndims)))
+
+
+def retain_groundtruth(tensor_dict, valid_indices):
+  """Retains groundtruth by valid indices.
+
+  Args:
+    tensor_dict: a dictionary of following groundtruth tensors -
+      fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_classes
+      fields.InputDataFields.groundtruth_keypoints
+      fields.InputDataFields.groundtruth_instance_masks
+      fields.InputDataFields.groundtruth_is_crowd
+      fields.InputDataFields.groundtruth_area
+      fields.InputDataFields.groundtruth_label_types
+      fields.InputDataFields.groundtruth_difficult
+    valid_indices: a tensor with valid indices for the box-level groundtruth.
+
+  Returns:
+    a dictionary of tensors containing only the groundtruth for valid_indices.
+
+  Raises:
+    ValueError: If the shape of valid_indices is invalid.
+    ValueError: field fields.InputDataFields.groundtruth_boxes is
+      not present in tensor_dict.
+  """
+  input_shape = valid_indices.get_shape().as_list()
+  if not (len(input_shape) == 1 or
+          (len(input_shape) == 2 and input_shape[1] == 1)):
+    raise ValueError('The shape of valid_indices is invalid.')
+  valid_indices = tf.reshape(valid_indices, [-1])
+  valid_dict = {}
+  if fields.InputDataFields.groundtruth_boxes in tensor_dict:
+    # Prevents reshape failure when num_boxes is 0.
+    num_boxes = tf.maximum(tf.shape(
+        tensor_dict[fields.InputDataFields.groundtruth_boxes])[0], 1)
+    for key in tensor_dict:
+      if key in [fields.InputDataFields.groundtruth_boxes,
+                 fields.InputDataFields.groundtruth_classes,
+                 fields.InputDataFields.groundtruth_keypoints,
+                 fields.InputDataFields.groundtruth_instance_masks]:
+        valid_dict[key] = tf.gather(tensor_dict[key], valid_indices)
+      # Input decoder returns empty tensor when these fields are not provided.
+      # Needs to reshape into [num_boxes, -1] for tf.gather() to work.
+      elif key in [fields.InputDataFields.groundtruth_is_crowd,
+                   fields.InputDataFields.groundtruth_area,
+                   fields.InputDataFields.groundtruth_difficult,
+                   fields.InputDataFields.groundtruth_label_types]:
+        valid_dict[key] = tf.reshape(
+            tf.gather(tf.reshape(tensor_dict[key], [num_boxes, -1]),
+                      valid_indices), [-1])
+      # Fields that are not associated with boxes.
+      else:
+        valid_dict[key] = tensor_dict[key]
+  else:
+    raise ValueError('%s not present in input tensor dict.' % (
+        fields.InputDataFields.groundtruth_boxes))
+  return valid_dict
+
+
+def retain_groundtruth_with_positive_classes(tensor_dict):
+  """Retains only groundtruth with positive class ids.
+
+  Args:
+    tensor_dict: a dictionary of following groundtruth tensors -
+      fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_classes
+      fields.InputDataFields.groundtruth_keypoints
+      fields.InputDataFields.groundtruth_instance_masks
+      fields.InputDataFields.groundtruth_is_crowd
+      fields.InputDataFields.groundtruth_area
+      fields.InputDataFields.groundtruth_label_types
+      fields.InputDataFields.groundtruth_difficult
+
+  Returns:
+    a dictionary of tensors containing only the groundtruth with positive
+    classes.
+
+  Raises:
+    ValueError: If groundtruth_classes tensor is not in tensor_dict.
+  """
+  if fields.InputDataFields.groundtruth_classes not in tensor_dict:
+    raise ValueError('`groundtruth classes` not in tensor_dict.')
+  keep_indices = tf.where(tf.greater(
+      tensor_dict[fields.InputDataFields.groundtruth_classes], 0))
+  return retain_groundtruth(tensor_dict, keep_indices)
+
+
+def replace_nan_groundtruth_label_scores_with_ones(label_scores):
+  """Replaces nan label scores with 1.0.
+
+  Args:
+    label_scores: a tensor containing object annoation label scores.
+
+  Returns:
+    a tensor where NaN label scores have been replaced by ones.
+  """
+  return tf.where(
+      tf.is_nan(label_scores), tf.ones(tf.shape(label_scores)), label_scores)
+
+
+def filter_groundtruth_with_crowd_boxes(tensor_dict):
+  """Filters out groundtruth with boxes corresponding to crowd.
+
+  Args:
+    tensor_dict: a dictionary of following groundtruth tensors -
+      fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_classes
+      fields.InputDataFields.groundtruth_keypoints
+      fields.InputDataFields.groundtruth_instance_masks
+      fields.InputDataFields.groundtruth_is_crowd
+      fields.InputDataFields.groundtruth_area
+      fields.InputDataFields.groundtruth_label_types
+
+  Returns:
+    a dictionary of tensors containing only the groundtruth that have bounding
+    boxes.
+  """
+  if fields.InputDataFields.groundtruth_is_crowd in tensor_dict:
+    is_crowd = tensor_dict[fields.InputDataFields.groundtruth_is_crowd]
+    is_not_crowd = tf.logical_not(is_crowd)
+    is_not_crowd_indices = tf.where(is_not_crowd)
+    tensor_dict = retain_groundtruth(tensor_dict, is_not_crowd_indices)
+  return tensor_dict
+
+
+def filter_groundtruth_with_nan_box_coordinates(tensor_dict):
+  """Filters out groundtruth with no bounding boxes.
+
+  Args:
+    tensor_dict: a dictionary of following groundtruth tensors -
+      fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_classes
+      fields.InputDataFields.groundtruth_keypoints
+      fields.InputDataFields.groundtruth_instance_masks
+      fields.InputDataFields.groundtruth_is_crowd
+      fields.InputDataFields.groundtruth_area
+      fields.InputDataFields.groundtruth_label_types
+
+  Returns:
+    a dictionary of tensors containing only the groundtruth that have bounding
+    boxes.
+  """
+  groundtruth_boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
+  nan_indicator_vector = tf.greater(tf.reduce_sum(tf.to_int32(
+      tf.is_nan(groundtruth_boxes)), reduction_indices=[1]), 0)
+  valid_indicator_vector = tf.logical_not(nan_indicator_vector)
+  valid_indices = tf.where(valid_indicator_vector)
+
+  return retain_groundtruth(tensor_dict, valid_indices)
+
+
+def normalize_to_target(inputs,
+                        target_norm_value,
+                        dim,
+                        epsilon=1e-7,
+                        trainable=True,
+                        scope='NormalizeToTarget',
+                        summarize=True):
+  """L2 normalizes the inputs across the specified dimension to a target norm.
+
+  This op implements the L2 Normalization layer introduced in
+  Liu, Wei, et al. "SSD: Single Shot MultiBox Detector."
+  and Liu, Wei, Andrew Rabinovich, and Alexander C. Berg.
+  "Parsenet: Looking wider to see better." and is useful for bringing
+  activations from multiple layers in a convnet to a standard scale.
+
+  Note that the rank of `inputs` must be known and the dimension to which
+  normalization is to be applied should be statically defined.
+
+  TODO(jonathanhuang): Add option to scale by L2 norm of the entire input.
+
+  Args:
+    inputs: A `Tensor` of arbitrary size.
+    target_norm_value: A float value that specifies an initial target norm or
+      a list of floats (whose length must be equal to the depth along the
+      dimension to be normalized) specifying a per-dimension multiplier
+      after normalization.
+    dim: The dimension along which the input is normalized.
+    epsilon: A small value to add to the inputs to avoid dividing by zero.
+    trainable: Whether the norm is trainable or not
+    scope: Optional scope for variable_scope.
+    summarize: Whether or not to add a tensorflow summary for the op.
+
+  Returns:
+    The input tensor normalized to the specified target norm.
+
+  Raises:
+    ValueError: If dim is smaller than the number of dimensions in 'inputs'.
+    ValueError: If target_norm_value is not a float or a list of floats with
+      length equal to the depth along the dimension to be normalized.
+  """
+  with tf.variable_scope(scope, 'NormalizeToTarget', [inputs]):
+    if not inputs.get_shape():
+      raise ValueError('The input rank must be known.')
+    input_shape = inputs.get_shape().as_list()
+    input_rank = len(input_shape)
+    if dim < 0 or dim >= input_rank:
+      raise ValueError(
+          'dim must be non-negative but smaller than the input rank.')
+    if not input_shape[dim]:
+      raise ValueError('input shape should be statically defined along '
+                       'the specified dimension.')
+    depth = input_shape[dim]
+    if not (isinstance(target_norm_value, float) or
+            (isinstance(target_norm_value, list) and
+             len(target_norm_value) == depth) and
+            all([isinstance(val, float) for val in target_norm_value])):
+      raise ValueError('target_norm_value must be a float or a list of floats '
+                       'with length equal to the depth along the dimension to '
+                       'be normalized.')
+    if isinstance(target_norm_value, float):
+      initial_norm = depth * [target_norm_value]
+    else:
+      initial_norm = target_norm_value
+    target_norm = tf.contrib.framework.model_variable(
+        name='weights', dtype=tf.float32,
+        initializer=tf.constant(initial_norm, dtype=tf.float32),
+        trainable=trainable)
+    if summarize:
+      mean = tf.reduce_mean(target_norm)
+      mean = tf.Print(mean, ['NormalizeToTarget:', mean])
+      tf.summary.scalar(tf.get_variable_scope().name, mean)
+    lengths = epsilon + tf.sqrt(tf.reduce_sum(tf.square(inputs), dim, True))
+    mult_shape = input_rank*[1]
+    mult_shape[dim] = depth
+    return tf.reshape(target_norm, mult_shape) * tf.truediv(inputs, lengths)
+
+
+def batch_position_sensitive_crop_regions(images,
+                                          boxes,
+                                          crop_size,
+                                          num_spatial_bins,
+                                          global_pool,
+                                          parallel_iterations=64):
+  """Position sensitive crop with batches of images and boxes.
+
+  This op is exactly like `position_sensitive_crop_regions` below but operates
+  on batches of images and boxes. See `position_sensitive_crop_regions` function
+  below for the operation applied per batch element.
+
+  Args:
+    images: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
+      `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+      A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
+      Both `image_height` and `image_width` need to be positive.
+    boxes: A `Tensor` of type `float32`.
+      A 3-D tensor of shape `[batch, num_boxes, 4]`. Each box is specified in
+      normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value
+      of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so
+      as the `[0, 1]` interval of normalized image height is mapped to
+      `[0, image_height - 1] in image height coordinates. We do allow y1 > y2,
+      in which case the sampled crop is an up-down flipped version of the
+      original image. The width dimension is treated similarly.
+    crop_size: See `position_sensitive_crop_regions` below.
+    num_spatial_bins: See `position_sensitive_crop_regions` below.
+    global_pool: See `position_sensitive_crop_regions` below.
+    parallel_iterations: Number of batch items to process in parallel.
+
+  Returns:
+  """
+  def _position_sensitive_crop_fn(inputs):
+    images, boxes = inputs
+    return position_sensitive_crop_regions(
+        images,
+        boxes,
+        crop_size=crop_size,
+        num_spatial_bins=num_spatial_bins,
+        global_pool=global_pool)
+
+  return shape_utils.static_or_dynamic_map_fn(
+      _position_sensitive_crop_fn,
+      elems=[images, boxes],
+      dtype=tf.float32,
+      parallel_iterations=parallel_iterations)
+
+
+def position_sensitive_crop_regions(image,
+                                    boxes,
+                                    crop_size,
+                                    num_spatial_bins,
+                                    global_pool):
+  """Position-sensitive crop and pool rectangular regions from a feature grid.
+
+  The output crops are split into `spatial_bins_y` vertical bins
+  and `spatial_bins_x` horizontal bins. For each intersection of a vertical
+  and a horizontal bin the output values are gathered by performing
+  `tf.image.crop_and_resize` (bilinear resampling) on a a separate subset of
+  channels of the image. This reduces `depth` by a factor of
+  `(spatial_bins_y * spatial_bins_x)`.
+
+  When global_pool is True, this function implements a differentiable version
+  of position-sensitive RoI pooling used in
+  [R-FCN detection system](https://arxiv.org/abs/1605.06409).
+
+  When global_pool is False, this function implements a differentiable version
+  of position-sensitive assembling operation used in
+  [instance FCN](https://arxiv.org/abs/1603.08678).
+
+  Args:
+    image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
+      `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+      A 3-D tensor of shape `[image_height, image_width, depth]`.
+      Both `image_height` and `image_width` need to be positive.
+    boxes: A `Tensor` of type `float32`.
+      A 2-D tensor of shape `[num_boxes, 4]`. Each box is specified in
+      normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value
+      of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so
+      as the `[0, 1]` interval of normalized image height is mapped to
+      `[0, image_height - 1] in image height coordinates. We do allow y1 > y2,
+      in which case the sampled crop is an up-down flipped version of the
+      original image. The width dimension is treated similarly.
+    crop_size: A list of two integers `[crop_height, crop_width]`. All
+      cropped image patches are resized to this size. The aspect ratio of the
+      image content is not preserved. Both `crop_height` and `crop_width` need
+      to be positive.
+    num_spatial_bins: A list of two integers `[spatial_bins_y, spatial_bins_x]`.
+      Represents the number of position-sensitive bins in y and x directions.
+      Both values should be >= 1. `crop_height` should be divisible by
+      `spatial_bins_y`, and similarly for width.
+      The number of image channels should be divisible by
+      (spatial_bins_y * spatial_bins_x).
+      Suggested value from R-FCN paper: [3, 3].
+    global_pool: A boolean variable.
+      If True, we perform average global pooling on the features assembled from
+        the position-sensitive score maps.
+      If False, we keep the position-pooled features without global pooling
+        over the spatial coordinates.
+      Note that using global_pool=True is equivalent to but more efficient than
+        running the function with global_pool=False and then performing global
+        average pooling.
+
+  Returns:
+    position_sensitive_features: A 4-D tensor of shape
+      `[num_boxes, K, K, crop_channels]`,
+      where `crop_channels = depth / (spatial_bins_y * spatial_bins_x)`,
+      where K = 1 when global_pool is True (Average-pooled cropped regions),
+      and K = crop_size when global_pool is False.
+  Raises:
+    ValueError: Raised in four situations:
+      `num_spatial_bins` is not >= 1;
+      `num_spatial_bins` does not divide `crop_size`;
+      `(spatial_bins_y*spatial_bins_x)` does not divide `depth`;
+      `bin_crop_size` is not square when global_pool=False due to the
+        constraint in function space_to_depth.
+  """
+  total_bins = 1
+  bin_crop_size = []
+
+  for (num_bins, crop_dim) in zip(num_spatial_bins, crop_size):
+    if num_bins < 1:
+      raise ValueError('num_spatial_bins should be >= 1')
+
+    if crop_dim % num_bins != 0:
+      raise ValueError('crop_size should be divisible by num_spatial_bins')
+
+    total_bins *= num_bins
+    bin_crop_size.append(crop_dim // num_bins)
+
+  if not global_pool and bin_crop_size[0] != bin_crop_size[1]:
+    raise ValueError('Only support square bin crop size for now.')
+
+  ymin, xmin, ymax, xmax = tf.unstack(boxes, axis=1)
+  spatial_bins_y, spatial_bins_x = num_spatial_bins
+
+  # Split each box into spatial_bins_y * spatial_bins_x bins.
+  position_sensitive_boxes = []
+  for bin_y in range(spatial_bins_y):
+    step_y = (ymax - ymin) / spatial_bins_y
+    for bin_x in range(spatial_bins_x):
+      step_x = (xmax - xmin) / spatial_bins_x
+      box_coordinates = [ymin + bin_y * step_y,
+                         xmin + bin_x * step_x,
+                         ymin + (bin_y + 1) * step_y,
+                         xmin + (bin_x + 1) * step_x,
+                        ]
+      position_sensitive_boxes.append(tf.stack(box_coordinates, axis=1))
+
+  image_splits = tf.split(value=image, num_or_size_splits=total_bins, axis=2)
+
+  image_crops = []
+  for (split, box) in zip(image_splits, position_sensitive_boxes):
+    if split.shape.is_fully_defined() and box.shape.is_fully_defined():
+      crop = tf.squeeze(
+          matmul_crop_and_resize(
+              tf.expand_dims(split, axis=0), tf.expand_dims(box, axis=0),
+              bin_crop_size),
+          axis=0)
+    else:
+      crop = tf.image.crop_and_resize(
+          tf.expand_dims(split, 0), box,
+          tf.zeros(tf.shape(boxes)[0], dtype=tf.int32), bin_crop_size)
+    image_crops.append(crop)
+
+  if global_pool:
+    # Average over all bins.
+    position_sensitive_features = tf.add_n(image_crops) / len(image_crops)
+    # Then average over spatial positions within the bins.
+    position_sensitive_features = tf.reduce_mean(
+        position_sensitive_features, [1, 2], keep_dims=True)
+  else:
+    # Reorder height/width to depth channel.
+    block_size = bin_crop_size[0]
+    if block_size >= 2:
+      image_crops = [tf.space_to_depth(
+          crop, block_size=block_size) for crop in image_crops]
+
+    # Pack image_crops so that first dimension is for position-senstive boxes.
+    position_sensitive_features = tf.stack(image_crops, axis=0)
+
+    # Unroll the position-sensitive boxes to spatial positions.
+    position_sensitive_features = tf.squeeze(
+        tf.batch_to_space_nd(position_sensitive_features,
+                             block_shape=[1] + num_spatial_bins,
+                             crops=tf.zeros((3, 2), dtype=tf.int32)),
+        squeeze_dims=[0])
+
+    # Reorder back the depth channel.
+    if block_size >= 2:
+      position_sensitive_features = tf.depth_to_space(
+          position_sensitive_features, block_size=block_size)
+
+  return position_sensitive_features
+
+
+def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
+                                     image_width):
+  """Transforms the box masks back to full image masks.
+
+  Embeds masks in bounding boxes of larger masks whose shapes correspond to
+  image shape.
+
+  Args:
+    box_masks: A tf.float32 tensor of size [num_masks, mask_height, mask_width].
+    boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
+           corners. Row i contains [ymin, xmin, ymax, xmax] of the box
+           corresponding to mask i. Note that the box corners are in
+           normalized coordinates.
+    image_height: Image height. The output mask will have the same height as
+                  the image height.
+    image_width: Image width. The output mask will have the same width as the
+                 image width.
+
+  Returns:
+    A tf.float32 tensor of size [num_masks, image_height, image_width].
+  """
+  # TODO(rathodv): Make this a public function.
+  def reframe_box_masks_to_image_masks_default():
+    """The default function when there are more than 0 box masks."""
+    def transform_boxes_relative_to_boxes(boxes, reference_boxes):
+      boxes = tf.reshape(boxes, [-1, 2, 2])
+      min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)
+      max_corner = tf.expand_dims(reference_boxes[:, 2:4], 1)
+      transformed_boxes = (boxes - min_corner) / (max_corner - min_corner)
+      return tf.reshape(transformed_boxes, [-1, 4])
+
+    box_masks_expanded = tf.expand_dims(box_masks, axis=3)
+    num_boxes = tf.shape(box_masks_expanded)[0]
+    unit_boxes = tf.concat(
+        [tf.zeros([num_boxes, 2]), tf.ones([num_boxes, 2])], axis=1)
+    reverse_boxes = transform_boxes_relative_to_boxes(unit_boxes, boxes)
+    return tf.image.crop_and_resize(
+        image=box_masks_expanded,
+        boxes=reverse_boxes,
+        box_ind=tf.range(num_boxes),
+        crop_size=[image_height, image_width],
+        extrapolation_value=0.0)
+  image_masks = tf.cond(
+      tf.shape(box_masks)[0] > 0,
+      reframe_box_masks_to_image_masks_default,
+      lambda: tf.zeros([0, image_height, image_width, 1], dtype=tf.float32))
+  return tf.squeeze(image_masks, axis=3)
+
+
+def merge_boxes_with_multiple_labels(boxes,
+                                     classes,
+                                     confidences,
+                                     num_classes,
+                                     quantization_bins=10000):
+  """Merges boxes with same coordinates and returns K-hot encoded classes.
+
+  Args:
+    boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only
+      normalized coordinates are allowed.
+    classes: A tf.int32 tensor with shape [N] holding class indices.
+      The class index starts at 0.
+    confidences: A tf.float32 tensor with shape [N] holding class confidences.
+    num_classes: total number of classes to use for K-hot encoding.
+    quantization_bins: the number of bins used to quantize the box coordinate.
+
+  Returns:
+    merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
+      where N' <= N.
+    class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
+      K-hot encodings for the merged boxes.
+    confidence_encodings: A tf.float32 tensor with shape [N', num_classes]
+      holding encodings of confidences for the merged boxes.
+    merged_box_indices: A tf.int32 tensor with shape [N'] holding original
+      indices of the boxes.
+  """
+  boxes_shape = tf.shape(boxes)
+  classes_shape = tf.shape(classes)
+  confidences_shape = tf.shape(confidences)
+  box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension(
+      boxes_shape, classes_shape)
+  box_confidence_shape_assert = (
+      shape_utils.assert_shape_equal_along_first_dimension(
+          boxes_shape, confidences_shape))
+  box_dimension_assert = tf.assert_equal(boxes_shape[1], 4)
+  box_normalized_assert = shape_utils.assert_box_normalized(boxes)
+
+  with tf.control_dependencies(
+      [box_class_shape_assert, box_confidence_shape_assert,
+       box_dimension_assert, box_normalized_assert]):
+    quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1))
+    ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1)
+    hashcodes = (
+        ymin +
+        xmin * quantization_bins +
+        ymax * quantization_bins * quantization_bins +
+        xmax * quantization_bins * quantization_bins * quantization_bins)
+    unique_hashcodes, unique_indices = tf.unique(hashcodes)
+    num_boxes = tf.shape(boxes)[0]
+    num_unique_boxes = tf.shape(unique_hashcodes)[0]
+    merged_box_indices = tf.unsorted_segment_min(
+        tf.range(num_boxes), unique_indices, num_unique_boxes)
+    merged_boxes = tf.gather(boxes, merged_box_indices)
+
+    def map_box_encodings(i):
+      """Produces box K-hot and score encodings for each class index."""
+      box_mask = tf.equal(
+          unique_indices, i * tf.ones(num_boxes, dtype=tf.int32))
+      box_mask = tf.reshape(box_mask, [-1])
+      box_indices = tf.boolean_mask(classes, box_mask)
+      box_confidences = tf.boolean_mask(confidences, box_mask)
+      box_class_encodings = tf.sparse_to_dense(
+          box_indices, [num_classes], 1, validate_indices=False)
+      box_confidence_encodings = tf.sparse_to_dense(
+          box_indices, [num_classes], box_confidences, validate_indices=False)
+      return box_class_encodings, box_confidence_encodings
+
+    class_encodings, confidence_encodings = tf.map_fn(
+        map_box_encodings,
+        tf.range(num_unique_boxes),
+        back_prop=False,
+        dtype=(tf.int32, tf.float32))
+
+    merged_boxes = tf.reshape(merged_boxes, [-1, 4])
+    class_encodings = tf.reshape(class_encodings, [-1, num_classes])
+    confidence_encodings = tf.reshape(confidence_encodings, [-1, num_classes])
+    merged_box_indices = tf.reshape(merged_box_indices, [-1])
+    return (merged_boxes, class_encodings, confidence_encodings,
+            merged_box_indices)
+
+
+def nearest_neighbor_upsampling(input_tensor, scale=None, height_scale=None,
+                                width_scale=None):
+  """Nearest neighbor upsampling implementation.
+
+  Nearest neighbor upsampling function that maps input tensor with shape
+  [batch_size, height, width, channels] to [batch_size, height * scale
+  , width * scale, channels]. This implementation only uses reshape and
+  broadcasting to make it TPU compatible.
+
+  Args:
+    input_tensor: A float32 tensor of size [batch, height_in, width_in,
+      channels].
+    scale: An integer multiple to scale resolution of input data in both height
+      and width dimensions.
+    height_scale: An integer multiple to scale the height of input image. This
+      option when provided overrides `scale` option.
+    width_scale: An integer multiple to scale the width of input image. This
+      option when provided overrides `scale` option.
+  Returns:
+    data_up: A float32 tensor of size
+      [batch, height_in*scale, width_in*scale, channels].
+
+  Raises:
+    ValueError: If both scale and height_scale or if both scale and width_scale
+      are None.
+  """
+  if not scale and (height_scale is None or width_scale is None):
+    raise ValueError('Provide either `scale` or `height_scale` and'
+                     ' `width_scale`.')
+  with tf.name_scope('nearest_neighbor_upsampling'):
+    h_scale = scale if height_scale is None else height_scale
+    w_scale = scale if width_scale is None else width_scale
+    (batch_size, height, width,
+     channels) = shape_utils.combined_static_and_dynamic_shape(input_tensor)
+    output_tensor = tf.reshape(
+        input_tensor, [batch_size, height, 1, width, 1, channels]) * tf.ones(
+            [1, 1, h_scale, 1, w_scale, 1], dtype=input_tensor.dtype)
+    return tf.reshape(output_tensor,
+                      [batch_size, height * h_scale, width * w_scale, channels])
+
+
+def matmul_gather_on_zeroth_axis(params, indices, scope=None):
+  """Matrix multiplication based implementation of tf.gather on zeroth axis.
+
+  TODO(rathodv, jonathanhuang): enable sparse matmul option.
+
+  Args:
+    params: A float32 Tensor. The tensor from which to gather values.
+      Must be at least rank 1.
+    indices: A Tensor. Must be one of the following types: int32, int64.
+      Must be in range [0, params.shape[0])
+    scope: A name for the operation (optional).
+
+  Returns:
+    A Tensor. Has the same type as params. Values from params gathered
+    from indices given by indices, with shape indices.shape + params.shape[1:].
+  """
+  with tf.name_scope(scope, 'MatMulGather'):
+    params_shape = shape_utils.combined_static_and_dynamic_shape(params)
+    indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
+    params2d = tf.reshape(params, [params_shape[0], -1])
+    indicator_matrix = tf.one_hot(indices, params_shape[0])
+    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
+    return tf.reshape(gathered_result_flattened,
+                      tf.stack(indices_shape + params_shape[1:]))
+
+
+def matmul_crop_and_resize(image, boxes, crop_size, scope=None):
+  """Matrix multiplication based implementation of the crop and resize op.
+
+  Extracts crops from the input image tensor and bilinearly resizes them
+  (possibly with aspect ratio change) to a common output size specified by
+  crop_size. This is more general than the crop_to_bounding_box op which
+  extracts a fixed size slice from the input image and does not allow
+  resizing or aspect ratio change.
+
+  Returns a tensor with crops from the input image at positions defined at
+  the bounding box locations in boxes. The cropped boxes are all resized
+  (with bilinear interpolation) to a fixed size = `[crop_height, crop_width]`.
+  The result is a 5-D tensor `[batch, num_boxes, crop_height, crop_width,
+  depth]`.
+
+  Running time complexity:
+    O((# channels) * (# boxes) * (crop_size)^2 * M), where M is the number
+  of pixels of the longer edge of the image.
+
+  Note that this operation is meant to replicate the behavior of the standard
+  tf.image.crop_and_resize operation but there are a few differences.
+  Specifically:
+    1) The extrapolation value (the values that are interpolated from outside
+      the bounds of the image window) is always zero
+    2) Only XLA supported operations are used (e.g., matrix multiplication).
+    3) There is no `box_indices` argument --- to run this op on multiple images,
+      one must currently call this op independently on each image.
+    4) All shapes and the `crop_size` parameter are assumed to be statically
+      defined.  Moreover, the number of boxes must be strictly nonzero.
+
+  Args:
+    image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
+      `int16`, `int32`, `int64`, `half`, 'bfloat16', `float32`, `float64`.
+      A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
+      Both `image_height` and `image_width` need to be positive.
+    boxes: A `Tensor` of type `float32` or 'bfloat16'.
+      A 3-D tensor of shape `[batch, num_boxes, 4]`. The boxes are specified in
+      normalized coordinates and are of the form `[y1, x1, y2, x2]`. A
+      normalized coordinate value of `y` is mapped to the image coordinate at
+      `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image
+      height is mapped to `[0, image_height - 1] in image height coordinates.
+      We do allow y1 > y2, in which case the sampled crop is an up-down flipped
+      version of the original image. The width dimension is treated similarly.
+      Normalized coordinates outside the `[0, 1]` range are allowed, in which
+      case we use `extrapolation_value` to extrapolate the input image values.
+    crop_size: A list of two integers `[crop_height, crop_width]`. All
+      cropped image patches are resized to this size. The aspect ratio of the
+      image content is not preserved. Both `crop_height` and `crop_width` need
+      to be positive.
+    scope: A name for the operation (optional).
+
+  Returns:
+    A 5-D tensor of shape `[batch, num_boxes, crop_height, crop_width, depth]`
+
+  Raises:
+    ValueError: if image tensor does not have shape
+      `[batch, image_height, image_width, depth]` and all dimensions statically
+      defined.
+    ValueError: if boxes tensor does not have shape `[batch, num_boxes, 4]`
+      where num_boxes > 0.
+    ValueError: if crop_size is not a list of two positive integers
+  """
+  img_shape = image.shape.as_list()
+  boxes_shape = boxes.shape.as_list()
+  _, img_height, img_width, _ = img_shape
+  if not isinstance(crop_size, list) or len(crop_size) != 2:
+    raise ValueError('`crop_size` must be a list of length 2')
+  dimensions = img_shape + crop_size + boxes_shape
+  if not all([isinstance(dim, int) for dim in dimensions]):
+    raise ValueError('all input shapes must be statically defined')
+  if len(boxes_shape) != 3 or boxes_shape[2] != 4:
+    raise ValueError('`boxes` should have shape `[batch, num_boxes, 4]`')
+  if len(img_shape) != 4:
+    raise ValueError('image should have shape '
+                     '`[batch, image_height, image_width, depth]`')
+  num_crops = boxes_shape[0]
+  if not num_crops > 0:
+    raise ValueError('number of boxes must be > 0')
+  if not (crop_size[0] > 0 and crop_size[1] > 0):
+    raise ValueError('`crop_size` must be a list of two positive integers.')
+
+  def _lin_space_weights(num, img_size):
+    if num > 1:
+      start_weights = tf.linspace(img_size - 1.0, 0.0, num)
+      stop_weights = img_size - 1 - start_weights
+    else:
+      start_weights = tf.constant(num * [.5 * (img_size - 1)], dtype=tf.float32)
+      stop_weights = tf.constant(num * [.5 * (img_size - 1)], dtype=tf.float32)
+    return (start_weights, stop_weights)
+
+  with tf.name_scope(scope, 'MatMulCropAndResize'):
+    y1_weights, y2_weights = _lin_space_weights(crop_size[0], img_height)
+    x1_weights, x2_weights = _lin_space_weights(crop_size[1], img_width)
+    y1_weights = tf.cast(y1_weights, boxes.dtype)
+    y2_weights = tf.cast(y2_weights, boxes.dtype)
+    x1_weights = tf.cast(x1_weights, boxes.dtype)
+    x2_weights = tf.cast(x2_weights, boxes.dtype)
+    [y1, x1, y2, x2] = tf.unstack(boxes, axis=2)
+
+    # Pixel centers of input image and grid points along height and width
+    image_idx_h = tf.constant(
+        np.reshape(np.arange(img_height), (1, 1, 1, img_height)),
+        dtype=boxes.dtype)
+    image_idx_w = tf.constant(
+        np.reshape(np.arange(img_width), (1, 1, 1, img_width)),
+        dtype=boxes.dtype)
+    grid_pos_h = tf.expand_dims(
+        tf.einsum('ab,c->abc', y1, y1_weights) + tf.einsum(
+            'ab,c->abc', y2, y2_weights),
+        axis=3)
+    grid_pos_w = tf.expand_dims(
+        tf.einsum('ab,c->abc', x1, x1_weights) + tf.einsum(
+            'ab,c->abc', x2, x2_weights),
+        axis=3)
+
+    # Create kernel matrices of pairwise kernel evaluations between pixel
+    # centers of image and grid points.
+    kernel_h = tf.nn.relu(1 - tf.abs(image_idx_h - grid_pos_h))
+    kernel_w = tf.nn.relu(1 - tf.abs(image_idx_w - grid_pos_w))
+
+    # Compute matrix multiplication between the spatial dimensions of the image
+    # and height-wise kernel using einsum.
+    intermediate_image = tf.einsum('abci,aiop->abcop', kernel_h, image)
+    # Compute matrix multiplication between the spatial dimensions of the
+    # intermediate_image and width-wise kernel using einsum.
+    return tf.einsum('abno,abcop->abcnp', kernel_w, intermediate_image)
+
+
+def native_crop_and_resize(image, boxes, crop_size, scope=None):
+  """Same as `matmul_crop_and_resize` but uses tf.image.crop_and_resize."""
+  def get_box_inds(proposals):
+    proposals_shape = proposals.get_shape().as_list()
+    if any(dim is None for dim in proposals_shape):
+      proposals_shape = tf.shape(proposals)
+    ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32)
+    multiplier = tf.expand_dims(
+        tf.range(start=0, limit=proposals_shape[0]), 1)
+    return tf.reshape(ones_mat * multiplier, [-1])
+
+  with tf.name_scope(scope, 'CropAndResize'):
+    cropped_regions = tf.image.crop_and_resize(
+        image, tf.reshape(boxes, [-1] + boxes.shape.as_list()[2:]),
+        get_box_inds(boxes), crop_size)
+    final_shape = tf.concat([tf.shape(boxes)[:2],
+                             tf.shape(cropped_regions)[1:]], axis=0)
+    return tf.reshape(cropped_regions, final_shape)
+
+
+
+
+
+EqualizationLossConfig = collections.namedtuple('EqualizationLossConfig',
+                                                ['weight', 'exclude_prefixes'])
+
+
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops_test.py
new file mode 100644
index 00000000..5c03b619
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/ops_test.py
@@ -0,0 +1,1477 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.ops."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import standard_fields as fields
+from object_detection.utils import ops
+from object_detection.utils import test_case
+
+slim = tf.contrib.slim
+
+
+class NormalizedToImageCoordinatesTest(tf.test.TestCase):
+
+  def test_normalized_to_image_coordinates(self):
+    normalized_boxes = tf.placeholder(tf.float32, shape=(None, 1, 4))
+    normalized_boxes_np = np.array([[[0.0, 0.0, 1.0, 1.0]],
+                                    [[0.5, 0.5, 1.0, 1.0]]])
+    image_shape = tf.convert_to_tensor([1, 4, 4, 3], dtype=tf.int32)
+    absolute_boxes = ops.normalized_to_image_coordinates(normalized_boxes,
+                                                         image_shape,
+                                                         parallel_iterations=2)
+
+    expected_boxes = np.array([[[0, 0, 4, 4]],
+                               [[2, 2, 4, 4]]])
+    with self.test_session() as sess:
+      absolute_boxes = sess.run(absolute_boxes,
+                                feed_dict={normalized_boxes:
+                                           normalized_boxes_np})
+
+    self.assertAllEqual(absolute_boxes, expected_boxes)
+
+
+class ReduceSumTrailingDimensions(tf.test.TestCase):
+
+  def test_reduce_sum_trailing_dimensions(self):
+    input_tensor = tf.placeholder(tf.float32, shape=[None, None, None])
+    reduced_tensor = ops.reduce_sum_trailing_dimensions(input_tensor, ndims=2)
+    with self.test_session() as sess:
+      reduced_np = sess.run(reduced_tensor,
+                            feed_dict={input_tensor: np.ones((2, 2, 2),
+                                                             np.float32)})
+    self.assertAllClose(reduced_np, 2 * np.ones((2, 2), np.float32))
+
+
+class MeshgridTest(tf.test.TestCase):
+
+  def test_meshgrid_numpy_comparison(self):
+    """Tests meshgrid op with vectors, for which it should match numpy."""
+    x = np.arange(4)
+    y = np.arange(6)
+    exp_xgrid, exp_ygrid = np.meshgrid(x, y)
+    xgrid, ygrid = ops.meshgrid(x, y)
+    with self.test_session() as sess:
+      xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
+      self.assertAllEqual(xgrid_output, exp_xgrid)
+      self.assertAllEqual(ygrid_output, exp_ygrid)
+
+  def test_meshgrid_multidimensional(self):
+    np.random.seed(18)
+    x = np.random.rand(4, 1, 2).astype(np.float32)
+    y = np.random.rand(2, 3).astype(np.float32)
+
+    xgrid, ygrid = ops.meshgrid(x, y)
+
+    grid_shape = list(y.shape) + list(x.shape)
+    self.assertEqual(xgrid.get_shape().as_list(), grid_shape)
+    self.assertEqual(ygrid.get_shape().as_list(), grid_shape)
+    with self.test_session() as sess:
+      xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
+
+    # Check the shape of the output grids
+    self.assertEqual(xgrid_output.shape, tuple(grid_shape))
+    self.assertEqual(ygrid_output.shape, tuple(grid_shape))
+
+    # Check a few elements
+    test_elements = [((3, 0, 0), (1, 2)),
+                     ((2, 0, 1), (0, 0)),
+                     ((0, 0, 0), (1, 1))]
+    for xind, yind in test_elements:
+      # These are float equality tests, but the meshgrid op should not introduce
+      # rounding.
+      self.assertEqual(xgrid_output[yind + xind], x[xind])
+      self.assertEqual(ygrid_output[yind + xind], y[yind])
+
+
+class OpsTestFixedPadding(tf.test.TestCase):
+
+  def test_3x3_kernel(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.fixed_padding(tensor, 3)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
+
+  def test_5x5_kernel(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.fixed_padding(tensor, 5)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
+
+  def test_3x3_atrous_kernel(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.fixed_padding(tensor, 3, 2)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
+
+
+class OpsTestPadToMultiple(tf.test.TestCase):
+
+  def test_zero_padding(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 1)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
+
+  def test_no_padding(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 2)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
+
+  def test_non_square_padding(self):
+    tensor = tf.constant([[[[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 2)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
+
+  def test_padding(self):
+    tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 4)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
+
+
+class OpsTestPaddedOneHotEncoding(tf.test.TestCase):
+
+  def test_correct_one_hot_tensor_with_no_pad(self):
+    indices = tf.constant([1, 2, 3, 5])
+    one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=0)
+    expected_tensor = np.array([[0, 1, 0, 0, 0, 0],
+                                [0, 0, 1, 0, 0, 0],
+                                [0, 0, 0, 1, 0, 0],
+                                [0, 0, 0, 0, 0, 1]], np.float32)
+    with self.test_session() as sess:
+      out_one_hot_tensor = sess.run(one_hot_tensor)
+      self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
+                          atol=1e-10)
+
+  def test_correct_one_hot_tensor_with_pad_one(self):
+    indices = tf.constant([1, 2, 3, 5])
+    one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=1)
+    expected_tensor = np.array([[0, 0, 1, 0, 0, 0, 0],
+                                [0, 0, 0, 1, 0, 0, 0],
+                                [0, 0, 0, 0, 1, 0, 0],
+                                [0, 0, 0, 0, 0, 0, 1]], np.float32)
+    with self.test_session() as sess:
+      out_one_hot_tensor = sess.run(one_hot_tensor)
+      self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
+                          atol=1e-10)
+
+  def test_correct_one_hot_tensor_with_pad_three(self):
+    indices = tf.constant([1, 2, 3, 5])
+    one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=3)
+    expected_tensor = np.array([[0, 0, 0, 0, 1, 0, 0, 0, 0],
+                                [0, 0, 0, 0, 0, 1, 0, 0, 0],
+                                [0, 0, 0, 0, 0, 0, 1, 0, 0],
+                                [0, 0, 0, 0, 0, 0, 0, 0, 1]], np.float32)
+    with self.test_session() as sess:
+      out_one_hot_tensor = sess.run(one_hot_tensor)
+      self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
+                          atol=1e-10)
+
+  def test_correct_padded_one_hot_tensor_with_empty_indices(self):
+    depth = 6
+    pad = 2
+    indices = tf.constant([])
+    one_hot_tensor = ops.padded_one_hot_encoding(
+        indices, depth=depth, left_pad=pad)
+    expected_tensor = np.zeros((0, depth + pad))
+    with self.test_session() as sess:
+      out_one_hot_tensor = sess.run(one_hot_tensor)
+      self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
+                          atol=1e-10)
+
+  def test_return_none_on_zero_depth(self):
+    indices = tf.constant([1, 2, 3, 4, 5])
+    one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=0, left_pad=2)
+    self.assertEqual(one_hot_tensor, None)
+
+  def test_raise_value_error_on_rank_two_input(self):
+    indices = tf.constant(1.0, shape=(2, 3))
+    with self.assertRaises(ValueError):
+      ops.padded_one_hot_encoding(indices, depth=6, left_pad=2)
+
+  def test_raise_value_error_on_negative_pad(self):
+    indices = tf.constant(1.0, shape=(2, 3))
+    with self.assertRaises(ValueError):
+      ops.padded_one_hot_encoding(indices, depth=6, left_pad=-1)
+
+  def test_raise_value_error_on_float_pad(self):
+    indices = tf.constant(1.0, shape=(2, 3))
+    with self.assertRaises(ValueError):
+      ops.padded_one_hot_encoding(indices, depth=6, left_pad=0.1)
+
+  def test_raise_value_error_on_float_depth(self):
+    indices = tf.constant(1.0, shape=(2, 3))
+    with self.assertRaises(ValueError):
+      ops.padded_one_hot_encoding(indices, depth=0.1, left_pad=2)
+
+
+class OpsDenseToSparseBoxesTest(tf.test.TestCase):
+
+  def test_return_all_boxes_when_all_input_boxes_are_valid(self):
+    num_classes = 4
+    num_valid_boxes = 3
+    code_size = 4
+    dense_location_placeholder = tf.placeholder(tf.float32,
+                                                shape=(num_valid_boxes,
+                                                       code_size))
+    dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
+    box_locations, box_classes = ops.dense_to_sparse_boxes(
+        dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
+    feed_dict = {dense_location_placeholder: np.random.uniform(
+        size=[num_valid_boxes, code_size]),
+                 dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
+                                                       dtype=np.int32)}
+
+    expected_box_locations = feed_dict[dense_location_placeholder]
+    expected_box_classses = np.array([0, 3, 3])
+    with self.test_session() as sess:
+      box_locations, box_classes = sess.run([box_locations, box_classes],
+                                            feed_dict=feed_dict)
+
+    self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
+                        atol=1e-6)
+    self.assertAllEqual(box_classes, expected_box_classses)
+
+  def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
+    num_classes = 4
+    num_valid_boxes = 3
+    num_boxes = 10
+    code_size = 4
+
+    dense_location_placeholder = tf.placeholder(tf.float32, shape=(num_boxes,
+                                                                   code_size))
+    dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
+    box_locations, box_classes = ops.dense_to_sparse_boxes(
+        dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
+    feed_dict = {dense_location_placeholder: np.random.uniform(
+        size=[num_boxes, code_size]),
+                 dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
+                                                       dtype=np.int32)}
+
+    expected_box_locations = (feed_dict[dense_location_placeholder]
+                              [:num_valid_boxes])
+    expected_box_classses = np.array([0, 3, 3])
+    with self.test_session() as sess:
+      box_locations, box_classes = sess.run([box_locations, box_classes],
+                                            feed_dict=feed_dict)
+
+    self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
+                        atol=1e-6)
+    self.assertAllEqual(box_classes, expected_box_classses)
+
+
+class OpsTestIndicesToDenseVector(tf.test.TestCase):
+
+  def test_indices_to_dense_vector(self):
+    size = 10000
+    num_indices = np.random.randint(size)
+    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
+
+    expected_output = np.zeros(size, dtype=np.float32)
+    expected_output[rand_indices] = 1.
+
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
+
+    with self.test_session() as sess:
+      output = sess.run(indicator)
+      self.assertAllEqual(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+  def test_indices_to_dense_vector_size_at_inference(self):
+    size = 5000
+    num_indices = 250
+    all_indices = np.arange(size)
+    rand_indices = np.random.permutation(all_indices)[0:num_indices]
+
+    expected_output = np.zeros(size, dtype=np.float32)
+    expected_output[rand_indices] = 1.
+
+    tf_all_indices = tf.placeholder(tf.int32)
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(tf_rand_indices,
+                                            tf.shape(tf_all_indices)[0])
+    feed_dict = {tf_all_indices: all_indices}
+
+    with self.test_session() as sess:
+      output = sess.run(indicator, feed_dict=feed_dict)
+      self.assertAllEqual(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+  def test_indices_to_dense_vector_int(self):
+    size = 500
+    num_indices = 25
+    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
+
+    expected_output = np.zeros(size, dtype=np.int64)
+    expected_output[rand_indices] = 1
+
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(
+        tf_rand_indices, size, 1, dtype=tf.int64)
+
+    with self.test_session() as sess:
+      output = sess.run(indicator)
+      self.assertAllEqual(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+  def test_indices_to_dense_vector_custom_values(self):
+    size = 100
+    num_indices = 10
+    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
+    indices_value = np.random.rand(1)
+    default_value = np.random.rand(1)
+
+    expected_output = np.float32(np.ones(size) * default_value)
+    expected_output[rand_indices] = indices_value
+
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(
+        tf_rand_indices,
+        size,
+        indices_value=indices_value,
+        default_value=default_value)
+
+    with self.test_session() as sess:
+      output = sess.run(indicator)
+      self.assertAllClose(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+  def test_indices_to_dense_vector_all_indices_as_input(self):
+    size = 500
+    num_indices = 500
+    rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
+
+    expected_output = np.ones(size, dtype=np.float32)
+
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
+
+    with self.test_session() as sess:
+      output = sess.run(indicator)
+      self.assertAllEqual(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+  def test_indices_to_dense_vector_empty_indices_as_input(self):
+    size = 500
+    rand_indices = []
+
+    expected_output = np.zeros(size, dtype=np.float32)
+
+    tf_rand_indices = tf.constant(rand_indices)
+    indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
+
+    with self.test_session() as sess:
+      output = sess.run(indicator)
+      self.assertAllEqual(output, expected_output)
+      self.assertEqual(output.dtype, expected_output.dtype)
+
+
+class GroundtruthFilterTest(tf.test.TestCase):
+
+  def test_filter_groundtruth(self):
+    input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+    input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    input_classes = tf.placeholder(tf.int32, shape=(None,))
+    input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
+    input_area = tf.placeholder(tf.float32, shape=(None,))
+    input_difficult = tf.placeholder(tf.float32, shape=(None,))
+    input_label_types = tf.placeholder(tf.string, shape=(None,))
+    valid_indices = tf.placeholder(tf.int32, shape=(None,))
+    input_tensors = {
+        fields.InputDataFields.image: input_image,
+        fields.InputDataFields.groundtruth_boxes: input_boxes,
+        fields.InputDataFields.groundtruth_classes: input_classes,
+        fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
+        fields.InputDataFields.groundtruth_area: input_area,
+        fields.InputDataFields.groundtruth_difficult: input_difficult,
+        fields.InputDataFields.groundtruth_label_types: input_label_types
+    }
+    output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
+
+    image_tensor = np.random.rand(224, 224, 3)
+    feed_dict = {
+        input_image: image_tensor,
+        input_boxes:
+        np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
+        input_classes:
+        np.array([1, 2], dtype=np.int32),
+        input_is_crowd:
+        np.array([False, True], dtype=np.bool),
+        input_area:
+        np.array([32, 48], dtype=np.float32),
+        input_difficult:
+        np.array([True, False], dtype=np.bool),
+        input_label_types:
+        np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.string_),
+        valid_indices:
+        np.array([0], dtype=np.int32)
+    }
+    expected_tensors = {
+        fields.InputDataFields.image:
+        image_tensor,
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False],
+        fields.InputDataFields.groundtruth_area:
+        [32],
+        fields.InputDataFields.groundtruth_difficult:
+        [True],
+        fields.InputDataFields.groundtruth_label_types:
+        ['APPROPRIATE']
+    }
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
+      for key in [fields.InputDataFields.image,
+                  fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd,
+                  fields.InputDataFields.groundtruth_label_types]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+  def test_filter_with_missing_fields(self):
+    input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    input_classes = tf.placeholder(tf.int32, shape=(None,))
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes: input_boxes,
+        fields.InputDataFields.groundtruth_classes: input_classes
+    }
+    valid_indices = tf.placeholder(tf.int32, shape=(None,))
+
+    feed_dict = {
+        input_boxes:
+        np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
+        input_classes:
+        np.array([1, 2], dtype=np.int32),
+        valid_indices:
+        np.array([0], dtype=np.int32)
+    }
+    expected_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1]
+    }
+
+    output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
+      for key in [fields.InputDataFields.groundtruth_boxes]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+  def test_filter_with_empty_fields(self):
+    input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    input_classes = tf.placeholder(tf.int32, shape=(None,))
+    input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
+    input_area = tf.placeholder(tf.float32, shape=(None,))
+    input_difficult = tf.placeholder(tf.float32, shape=(None,))
+    valid_indices = tf.placeholder(tf.int32, shape=(None,))
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes: input_boxes,
+        fields.InputDataFields.groundtruth_classes: input_classes,
+        fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
+        fields.InputDataFields.groundtruth_area: input_area,
+        fields.InputDataFields.groundtruth_difficult: input_difficult
+    }
+    output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
+
+    feed_dict = {
+        input_boxes:
+        np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
+        input_classes:
+        np.array([1, 2], dtype=np.int32),
+        input_is_crowd:
+        np.array([False, True], dtype=np.bool),
+        input_area:
+        np.array([], dtype=np.float32),
+        input_difficult:
+        np.array([], dtype=np.float32),
+        valid_indices:
+        np.array([0], dtype=np.int32)
+    }
+    expected_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False],
+        fields.InputDataFields.groundtruth_area:
+        [],
+        fields.InputDataFields.groundtruth_difficult:
+        []
+    }
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
+      for key in [fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+  def test_filter_with_empty_groundtruth_boxes(self):
+    input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    input_classes = tf.placeholder(tf.int32, shape=(None,))
+    input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
+    input_area = tf.placeholder(tf.float32, shape=(None,))
+    input_difficult = tf.placeholder(tf.float32, shape=(None,))
+    valid_indices = tf.placeholder(tf.int32, shape=(None,))
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes: input_boxes,
+        fields.InputDataFields.groundtruth_classes: input_classes,
+        fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
+        fields.InputDataFields.groundtruth_area: input_area,
+        fields.InputDataFields.groundtruth_difficult: input_difficult
+    }
+    output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
+
+    feed_dict = {
+        input_boxes:
+        np.array([], dtype=np.float).reshape(0, 4),
+        input_classes:
+        np.array([], dtype=np.int32),
+        input_is_crowd:
+        np.array([], dtype=np.bool),
+        input_area:
+        np.array([], dtype=np.float32),
+        input_difficult:
+        np.array([], dtype=np.float32),
+        valid_indices:
+        np.array([], dtype=np.int32)
+    }
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
+      for key in input_tensors:
+        if key == fields.InputDataFields.groundtruth_boxes:
+          self.assertAllEqual([0, 4], output_tensors[key].shape)
+        else:
+          self.assertAllEqual([0], output_tensors[key].shape)
+
+
+class RetainGroundTruthWithPositiveClasses(tf.test.TestCase):
+
+  def test_filter_groundtruth_with_positive_classes(self):
+    input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
+    input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
+    input_classes = tf.placeholder(tf.int32, shape=(None,))
+    input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
+    input_area = tf.placeholder(tf.float32, shape=(None,))
+    input_difficult = tf.placeholder(tf.float32, shape=(None,))
+    input_label_types = tf.placeholder(tf.string, shape=(None,))
+    valid_indices = tf.placeholder(tf.int32, shape=(None,))
+    input_tensors = {
+        fields.InputDataFields.image: input_image,
+        fields.InputDataFields.groundtruth_boxes: input_boxes,
+        fields.InputDataFields.groundtruth_classes: input_classes,
+        fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
+        fields.InputDataFields.groundtruth_area: input_area,
+        fields.InputDataFields.groundtruth_difficult: input_difficult,
+        fields.InputDataFields.groundtruth_label_types: input_label_types
+    }
+    output_tensors = ops.retain_groundtruth_with_positive_classes(input_tensors)
+
+    image_tensor = np.random.rand(224, 224, 3)
+    feed_dict = {
+        input_image: image_tensor,
+        input_boxes:
+        np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
+        input_classes:
+        np.array([1, 0], dtype=np.int32),
+        input_is_crowd:
+        np.array([False, True], dtype=np.bool),
+        input_area:
+        np.array([32, 48], dtype=np.float32),
+        input_difficult:
+        np.array([True, False], dtype=np.bool),
+        input_label_types:
+        np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.string_),
+        valid_indices:
+        np.array([0], dtype=np.int32)
+    }
+    expected_tensors = {
+        fields.InputDataFields.image:
+        image_tensor,
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False],
+        fields.InputDataFields.groundtruth_area:
+        [32],
+        fields.InputDataFields.groundtruth_difficult:
+        [True],
+        fields.InputDataFields.groundtruth_label_types:
+        ['APPROPRIATE']
+    }
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
+      for key in [fields.InputDataFields.image,
+                  fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd,
+                  fields.InputDataFields.groundtruth_label_types]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+
+class ReplaceNaNGroundtruthLabelScoresWithOnes(tf.test.TestCase):
+
+  def test_replace_nan_groundtruth_label_scores_with_ones(self):
+    label_scores = tf.constant([np.nan, 1.0, np.nan])
+    output_tensor = ops.replace_nan_groundtruth_label_scores_with_ones(
+        label_scores)
+    expected_tensor = [1.0, 1.0, 1.0]
+    with self.test_session():
+      output_tensor = output_tensor.eval()
+      self.assertAllClose(expected_tensor, output_tensor)
+
+  def test_input_equals_output_when_no_nans(self):
+    input_label_scores = [0.5, 1.0, 1.0]
+    label_scores_tensor = tf.constant(input_label_scores)
+    output_label_scores = ops.replace_nan_groundtruth_label_scores_with_ones(
+        label_scores_tensor)
+    with self.test_session():
+      output_label_scores = output_label_scores.eval()
+      self.assertAllClose(input_label_scores, output_label_scores)
+
+
+class GroundtruthFilterWithCrowdBoxesTest(tf.test.TestCase):
+
+  def test_filter_groundtruth_with_crowd_boxes(self):
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.1, 0.2, 0.6, 0.8], [0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1, 2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [True, False],
+        fields.InputDataFields.groundtruth_area:
+        [100.0, 238.7]
+    }
+
+    expected_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False],
+        fields.InputDataFields.groundtruth_area:
+        [238.7]
+    }
+
+    output_tensors = ops.filter_groundtruth_with_crowd_boxes(
+        input_tensors)
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors)
+      for key in [fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+
+class GroundtruthFilterWithNanBoxTest(tf.test.TestCase):
+
+  def test_filter_groundtruth_with_nan_box_coordinates(self):
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[np.nan, np.nan, np.nan, np.nan], [0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1, 2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False, True],
+        fields.InputDataFields.groundtruth_area:
+        [100.0, 238.7]
+    }
+
+    expected_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [True],
+        fields.InputDataFields.groundtruth_area:
+        [238.7]
+    }
+
+    output_tensors = ops.filter_groundtruth_with_nan_box_coordinates(
+        input_tensors)
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors)
+      for key in [fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+
+class OpsTestNormalizeToTarget(tf.test.TestCase):
+
+  def test_create_normalize_to_target(self):
+    inputs = tf.random_uniform([5, 10, 12, 3])
+    target_norm_value = 4.0
+    dim = 3
+    with self.test_session():
+      output = ops.normalize_to_target(inputs, target_norm_value, dim)
+      self.assertEqual(output.op.name, 'NormalizeToTarget/mul')
+      var_name = tf.contrib.framework.get_variables()[0].name
+      self.assertEqual(var_name, 'NormalizeToTarget/weights:0')
+
+  def test_invalid_dim(self):
+    inputs = tf.random_uniform([5, 10, 12, 3])
+    target_norm_value = 4.0
+    dim = 10
+    with self.assertRaisesRegexp(
+        ValueError,
+        'dim must be non-negative but smaller than the input rank.'):
+      ops.normalize_to_target(inputs, target_norm_value, dim)
+
+  def test_invalid_target_norm_values(self):
+    inputs = tf.random_uniform([5, 10, 12, 3])
+    target_norm_value = [4.0, 4.0]
+    dim = 3
+    with self.assertRaisesRegexp(
+        ValueError, 'target_norm_value must be a float or a list of floats'):
+      ops.normalize_to_target(inputs, target_norm_value, dim)
+
+  def test_correct_output_shape(self):
+    inputs = tf.random_uniform([5, 10, 12, 3])
+    target_norm_value = 4.0
+    dim = 3
+    with self.test_session():
+      output = ops.normalize_to_target(inputs, target_norm_value, dim)
+      self.assertEqual(output.get_shape().as_list(),
+                       inputs.get_shape().as_list())
+
+  def test_correct_initial_output_values(self):
+    inputs = tf.constant([[[[3, 4], [7, 24]],
+                           [[5, -12], [-1, 0]]]], tf.float32)
+    target_norm_value = 10.0
+    dim = 3
+    expected_output = [[[[30/5.0, 40/5.0], [70/25.0, 240/25.0]],
+                        [[50/13.0, -120/13.0], [-10, 0]]]]
+    with self.test_session() as sess:
+      normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
+                                                  dim)
+      sess.run(tf.global_variables_initializer())
+      output = normalized_inputs.eval()
+      self.assertAllClose(output, expected_output)
+
+  def test_multiple_target_norm_values(self):
+    inputs = tf.constant([[[[3, 4], [7, 24]],
+                           [[5, -12], [-1, 0]]]], tf.float32)
+    target_norm_value = [10.0, 20.0]
+    dim = 3
+    expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]],
+                        [[50/13.0, -240/13.0], [-10, 0]]]]
+    with self.test_session() as sess:
+      normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
+                                                  dim)
+      sess.run(tf.global_variables_initializer())
+      output = normalized_inputs.eval()
+      self.assertAllClose(output, expected_output)
+
+
+class OpsTestPositionSensitiveCropRegions(tf.test.TestCase):
+
+  def test_position_sensitive(self):
+    num_spatial_bins = [3, 2]
+    image_shape = [3, 2, 6]
+
+    # First channel is 1's, second channel is 2's, etc.
+    image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
+                        shape=image_shape)
+    boxes = tf.random_uniform((2, 4))
+
+    # The result for both boxes should be [[1, 2], [3, 4], [5, 6]]
+    # before averaging.
+    expected_output = np.array([3.5, 3.5]).reshape([2, 1, 1, 1])
+
+    for crop_size_mult in range(1, 3):
+      crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
+      ps_crop_and_pool = ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=True)
+
+      with self.test_session() as sess:
+        output = sess.run(ps_crop_and_pool)
+        self.assertAllClose(output, expected_output)
+
+  def test_position_sensitive_with_equal_channels(self):
+    num_spatial_bins = [2, 2]
+    image_shape = [3, 3, 4]
+    crop_size = [2, 2]
+
+    image = tf.constant(range(1, 3 * 3 + 1), dtype=tf.float32,
+                        shape=[3, 3, 1])
+    tiled_image = tf.tile(image, [1, 1, image_shape[2]])
+    boxes = tf.random_uniform((3, 4))
+    box_ind = tf.constant([0, 0, 0], dtype=tf.int32)
+
+    # All channels are equal so position-sensitive crop and resize should
+    # work as the usual crop and resize for just one channel.
+    crop = tf.image.crop_and_resize(tf.expand_dims(image, axis=0), boxes,
+                                    box_ind, crop_size)
+    crop_and_pool = tf.reduce_mean(crop, [1, 2], keep_dims=True)
+
+    ps_crop_and_pool = ops.position_sensitive_crop_regions(
+        tiled_image,
+        boxes,
+        crop_size,
+        num_spatial_bins,
+        global_pool=True)
+
+    with self.test_session() as sess:
+      expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
+      self.assertAllClose(output, expected_output)
+
+  def test_raise_value_error_on_num_bins_less_than_one(self):
+    num_spatial_bins = [1, -1]
+    image_shape = [1, 1, 2]
+    crop_size = [2, 2]
+
+    image = tf.constant(1, dtype=tf.float32, shape=image_shape)
+    boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
+
+    with self.assertRaisesRegexp(ValueError, 'num_spatial_bins should be >= 1'):
+      ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=True)
+
+  def test_raise_value_error_on_non_divisible_crop_size(self):
+    num_spatial_bins = [2, 3]
+    image_shape = [1, 1, 6]
+    crop_size = [3, 2]
+
+    image = tf.constant(1, dtype=tf.float32, shape=image_shape)
+    boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
+
+    with self.assertRaisesRegexp(
+        ValueError, 'crop_size should be divisible by num_spatial_bins'):
+      ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=True)
+
+  def test_raise_value_error_on_non_divisible_num_channels(self):
+    num_spatial_bins = [2, 2]
+    image_shape = [1, 1, 5]
+    crop_size = [2, 2]
+
+    image = tf.constant(1, dtype=tf.float32, shape=image_shape)
+    boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
+
+    with self.assertRaisesRegexp(
+        ValueError, 'Dimension size must be evenly divisible by 4 but is 5'):
+      ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=True)
+
+  def test_position_sensitive_with_global_pool_false(self):
+    num_spatial_bins = [3, 2]
+    image_shape = [3, 2, 6]
+    num_boxes = 2
+
+    # First channel is 1's, second channel is 2's, etc.
+    image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
+                        shape=image_shape)
+    boxes = tf.random_uniform((num_boxes, 4))
+
+    expected_output = []
+
+    # Expected output, when crop_size = [3, 2].
+    expected_output.append(np.expand_dims(
+        np.tile(np.array([[1, 2],
+                          [3, 4],
+                          [5, 6]]), (num_boxes, 1, 1)),
+        axis=-1))
+
+    # Expected output, when crop_size = [6, 4].
+    expected_output.append(np.expand_dims(
+        np.tile(np.array([[1, 1, 2, 2],
+                          [1, 1, 2, 2],
+                          [3, 3, 4, 4],
+                          [3, 3, 4, 4],
+                          [5, 5, 6, 6],
+                          [5, 5, 6, 6]]), (num_boxes, 1, 1)),
+        axis=-1))
+
+    for crop_size_mult in range(1, 3):
+      crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
+      ps_crop = ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=False)
+      with self.test_session() as sess:
+        output = sess.run(ps_crop)
+
+      self.assertAllEqual(output, expected_output[crop_size_mult - 1])
+
+  def test_position_sensitive_with_global_pool_false_and_do_global_pool(self):
+    num_spatial_bins = [3, 2]
+    image_shape = [3, 2, 6]
+    num_boxes = 2
+
+    # First channel is 1's, second channel is 2's, etc.
+    image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
+                        shape=image_shape)
+    boxes = tf.random_uniform((num_boxes, 4))
+
+    expected_output = []
+
+    # Expected output, when crop_size = [3, 2].
+    expected_output.append(np.mean(
+        np.expand_dims(
+            np.tile(np.array([[1, 2],
+                              [3, 4],
+                              [5, 6]]), (num_boxes, 1, 1)),
+            axis=-1),
+        axis=(1, 2), keepdims=True))
+
+    # Expected output, when crop_size = [6, 4].
+    expected_output.append(np.mean(
+        np.expand_dims(
+            np.tile(np.array([[1, 1, 2, 2],
+                              [1, 1, 2, 2],
+                              [3, 3, 4, 4],
+                              [3, 3, 4, 4],
+                              [5, 5, 6, 6],
+                              [5, 5, 6, 6]]), (num_boxes, 1, 1)),
+            axis=-1),
+        axis=(1, 2), keepdims=True))
+
+    for crop_size_mult in range(1, 3):
+      crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
+
+      # Perform global_pooling after running the function with
+      # global_pool=False.
+      ps_crop = ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=False)
+      ps_crop_and_pool = tf.reduce_mean(
+          ps_crop, reduction_indices=(1, 2), keep_dims=True)
+
+      with self.test_session() as sess:
+        output = sess.run(ps_crop_and_pool)
+
+      self.assertAllEqual(output, expected_output[crop_size_mult - 1])
+
+  def test_raise_value_error_on_non_square_block_size(self):
+    num_spatial_bins = [3, 2]
+    image_shape = [3, 2, 6]
+    crop_size = [6, 2]
+
+    image = tf.constant(1, dtype=tf.float32, shape=image_shape)
+    boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
+
+    with self.assertRaisesRegexp(
+        ValueError, 'Only support square bin crop size for now.'):
+      ops.position_sensitive_crop_regions(
+          image, boxes, crop_size, num_spatial_bins, global_pool=False)
+
+
+class OpsTestBatchPositionSensitiveCropRegions(tf.test.TestCase):
+
+  def test_position_sensitive_with_single_bin(self):
+    num_spatial_bins = [1, 1]
+    image_shape = [2, 3, 3, 4]
+    crop_size = [2, 2]
+
+    image = tf.random_uniform(image_shape)
+    boxes = tf.random_uniform((2, 3, 4))
+    box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
+
+    # When a single bin is used, position-sensitive crop and pool should be
+    # the same as non-position sensitive crop and pool.
+    crop = tf.image.crop_and_resize(image, tf.reshape(boxes, [-1, 4]), box_ind,
+                                    crop_size)
+    crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True)
+    crop_and_pool = tf.reshape(crop_and_pool, [2, 3, 1, 1, 4])
+
+    ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
+        image, boxes, crop_size, num_spatial_bins, global_pool=True)
+
+    with self.test_session() as sess:
+      expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
+      self.assertAllClose(output, expected_output)
+
+  def test_position_sensitive_with_global_pool_false_and_known_boxes(self):
+    num_spatial_bins = [2, 2]
+    image_shape = [2, 2, 2, 4]
+    crop_size = [2, 2]
+
+    images = tf.constant(range(1, 2 * 2 * 4  + 1) * 2, dtype=tf.float32,
+                         shape=image_shape)
+
+    # First box contains whole image, and second box contains only first row.
+    boxes = tf.constant(np.array([[[0., 0., 1., 1.]],
+                                  [[0., 0., 0.5, 1.]]]), dtype=tf.float32)
+    # box_ind = tf.constant([0, 1], dtype=tf.int32)
+
+    expected_output = []
+
+    # Expected output, when the box containing whole image.
+    expected_output.append(
+        np.reshape(np.array([[4, 7],
+                             [10, 13]]),
+                   (1, 2, 2, 1))
+    )
+
+    # Expected output, when the box containing only first row.
+    expected_output.append(
+        np.reshape(np.array([[3, 6],
+                             [7, 10]]),
+                   (1, 2, 2, 1))
+    )
+    expected_output = np.stack(expected_output, axis=0)
+
+    ps_crop = ops.batch_position_sensitive_crop_regions(
+        images, boxes, crop_size, num_spatial_bins, global_pool=False)
+
+    with self.test_session() as sess:
+      output = sess.run(ps_crop)
+      self.assertAllEqual(output, expected_output)
+
+  def test_position_sensitive_with_global_pool_false_and_single_bin(self):
+    num_spatial_bins = [1, 1]
+    image_shape = [2, 3, 3, 4]
+    crop_size = [1, 1]
+
+    images = tf.random_uniform(image_shape)
+    boxes = tf.random_uniform((2, 3, 4))
+    # box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
+
+    # Since single_bin is used and crop_size = [1, 1] (i.e., no crop resize),
+    # the outputs are the same whatever the global_pool value is.
+    ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
+        images, boxes, crop_size, num_spatial_bins, global_pool=True)
+    ps_crop = ops.batch_position_sensitive_crop_regions(
+        images, boxes, crop_size, num_spatial_bins, global_pool=False)
+
+    with self.test_session() as sess:
+      pooled_output, unpooled_output = sess.run((ps_crop_and_pool, ps_crop))
+      self.assertAllClose(pooled_output, unpooled_output)
+
+
+class ReframeBoxMasksToImageMasksTest(tf.test.TestCase):
+
+  def testZeroImageOnEmptyMask(self):
+    box_masks = tf.constant([[[0, 0],
+                              [0, 0]]], dtype=tf.float32)
+    boxes = tf.constant([[0.0, 0.0, 1.0, 1.0]], dtype=tf.float32)
+    image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
+                                                       image_height=4,
+                                                       image_width=4)
+    np_expected_image_masks = np.array([[[0, 0, 0, 0],
+                                         [0, 0, 0, 0],
+                                         [0, 0, 0, 0],
+                                         [0, 0, 0, 0]]], dtype=np.float32)
+    with self.test_session() as sess:
+      np_image_masks = sess.run(image_masks)
+      self.assertAllClose(np_image_masks, np_expected_image_masks)
+
+  def testZeroBoxMasks(self):
+    box_masks = tf.zeros([0, 3, 3], dtype=tf.float32)
+    boxes = tf.zeros([0, 4], dtype=tf.float32)
+    image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
+                                                       image_height=4,
+                                                       image_width=4)
+    with self.test_session() as sess:
+      np_image_masks = sess.run(image_masks)
+      self.assertAllEqual(np_image_masks.shape, np.array([0, 4, 4]))
+
+  def testMaskIsCenteredInImageWhenBoxIsCentered(self):
+    box_masks = tf.constant([[[1, 1],
+                              [1, 1]]], dtype=tf.float32)
+    boxes = tf.constant([[0.25, 0.25, 0.75, 0.75]], dtype=tf.float32)
+    image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
+                                                       image_height=4,
+                                                       image_width=4)
+    np_expected_image_masks = np.array([[[0, 0, 0, 0],
+                                         [0, 1, 1, 0],
+                                         [0, 1, 1, 0],
+                                         [0, 0, 0, 0]]], dtype=np.float32)
+    with self.test_session() as sess:
+      np_image_masks = sess.run(image_masks)
+      self.assertAllClose(np_image_masks, np_expected_image_masks)
+
+  def testMaskOffCenterRemainsOffCenterInImage(self):
+    box_masks = tf.constant([[[1, 0],
+                              [0, 1]]], dtype=tf.float32)
+    boxes = tf.constant([[0.25, 0.5, 0.75, 1.0]], dtype=tf.float32)
+    image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
+                                                       image_height=4,
+                                                       image_width=4)
+    np_expected_image_masks = np.array([[[0, 0, 0, 0],
+                                         [0, 0, 0.6111111, 0.16666669],
+                                         [0, 0, 0.3888889, 0.83333337],
+                                         [0, 0, 0, 0]]], dtype=np.float32)
+    with self.test_session() as sess:
+      np_image_masks = sess.run(image_masks)
+      self.assertAllClose(np_image_masks, np_expected_image_masks)
+
+
+class MergeBoxesWithMultipleLabelsTest(tf.test.TestCase):
+
+  def testMergeBoxesWithMultipleLabels(self):
+    boxes = tf.constant(
+        [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+         [0.25, 0.25, 0.75, 0.75]],
+        dtype=tf.float32)
+    class_indices = tf.constant([0, 4, 2], dtype=tf.int32)
+    class_confidences = tf.constant([0.8, 0.2, 0.1], dtype=tf.float32)
+    num_classes = 5
+    merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
+        ops.merge_boxes_with_multiple_labels(
+            boxes, class_indices, class_confidences, num_classes))
+    expected_merged_boxes = np.array(
+        [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=np.float32)
+    expected_merged_classes = np.array(
+        [[1, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=np.int32)
+    expected_merged_confidences = np.array(
+        [[0.8, 0, 0.1, 0, 0], [0, 0, 0, 0, 0.2]], dtype=np.float32)
+    expected_merged_box_indices = np.array([0, 1], dtype=np.int32)
+    with self.test_session() as sess:
+      (np_merged_boxes, np_merged_classes, np_merged_confidences,
+       np_merged_box_indices) = sess.run(
+           [merged_boxes, merged_classes, merged_confidences,
+            merged_box_indices])
+      self.assertAllClose(np_merged_boxes, expected_merged_boxes)
+      self.assertAllClose(np_merged_classes, expected_merged_classes)
+      self.assertAllClose(np_merged_confidences, expected_merged_confidences)
+      self.assertAllClose(np_merged_box_indices, expected_merged_box_indices)
+
+  def testMergeBoxesWithMultipleLabelsCornerCase(self):
+    boxes = tf.constant(
+        [[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1],
+         [1, 1, 1, 1], [1, 0, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1]],
+        dtype=tf.float32)
+    class_indices = tf.constant([0, 1, 2, 3, 2, 1, 0, 3], dtype=tf.int32)
+    class_confidences = tf.constant([0.1, 0.9, 0.2, 0.8, 0.3, 0.7, 0.4, 0.6],
+                                    dtype=tf.float32)
+    num_classes = 4
+    merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
+        ops.merge_boxes_with_multiple_labels(
+            boxes, class_indices, class_confidences, num_classes))
+    expected_merged_boxes = np.array(
+        [[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1]],
+        dtype=np.float32)
+    expected_merged_classes = np.array(
+        [[1, 0, 0, 1], [1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1]],
+        dtype=np.int32)
+    expected_merged_confidences = np.array(
+        [[0.1, 0, 0, 0.6], [0.4, 0.9, 0, 0],
+         [0, 0.7, 0.2, 0], [0, 0, 0.3, 0.8]], dtype=np.float32)
+    expected_merged_box_indices = np.array([0, 1, 2, 3], dtype=np.int32)
+    with self.test_session() as sess:
+      (np_merged_boxes, np_merged_classes, np_merged_confidences,
+       np_merged_box_indices) = sess.run(
+           [merged_boxes, merged_classes, merged_confidences,
+            merged_box_indices])
+      self.assertAllClose(np_merged_boxes, expected_merged_boxes)
+      self.assertAllClose(np_merged_classes, expected_merged_classes)
+      self.assertAllClose(np_merged_confidences, expected_merged_confidences)
+      self.assertAllClose(np_merged_box_indices, expected_merged_box_indices)
+
+  def testMergeBoxesWithEmptyInputs(self):
+    boxes = tf.zeros([0, 4], dtype=tf.float32)
+    class_indices = tf.constant([], dtype=tf.int32)
+    class_confidences = tf.constant([], dtype=tf.float32)
+    num_classes = 5
+    merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
+        ops.merge_boxes_with_multiple_labels(
+            boxes, class_indices, class_confidences, num_classes))
+    with self.test_session() as sess:
+      (np_merged_boxes, np_merged_classes, np_merged_confidences,
+       np_merged_box_indices) = sess.run(
+           [merged_boxes, merged_classes, merged_confidences,
+            merged_box_indices])
+      self.assertAllEqual(np_merged_boxes.shape, [0, 4])
+      self.assertAllEqual(np_merged_classes.shape, [0, 5])
+      self.assertAllEqual(np_merged_confidences.shape, [0, 5])
+      self.assertAllEqual(np_merged_box_indices.shape, [0])
+
+
+class NearestNeighborUpsamplingTest(test_case.TestCase):
+
+  def test_upsampling_with_single_scale(self):
+
+    def graph_fn(inputs):
+      custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
+      return custom_op_output
+    inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
+    custom_op_output = self.execute(graph_fn, [inputs])
+
+    expected_output = [[[[0], [0], [1], [1]],
+                        [[0], [0], [1], [1]],
+                        [[2], [2], [3], [3]],
+                        [[2], [2], [3], [3]]]]
+    self.assertAllClose(custom_op_output, expected_output)
+
+  def test_upsampling_with_separate_height_width_scales(self):
+
+    def graph_fn(inputs):
+      custom_op_output = ops.nearest_neighbor_upsampling(inputs,
+                                                         height_scale=2,
+                                                         width_scale=3)
+      return custom_op_output
+    inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
+    custom_op_output = self.execute(graph_fn, [inputs])
+
+    expected_output = [[[[0], [0], [0], [1], [1], [1]],
+                        [[0], [0], [0], [1], [1], [1]],
+                        [[2], [2], [2], [3], [3], [3]],
+                        [[2], [2], [2], [3], [3], [3]]]]
+    self.assertAllClose(custom_op_output, expected_output)
+
+
+class MatmulGatherOnZerothAxis(test_case.TestCase):
+
+  def test_gather_2d(self):
+
+    def graph_fn(params, indices):
+      return ops.matmul_gather_on_zeroth_axis(params, indices)
+
+    params = np.array([[1, 2, 3, 4],
+                       [5, 6, 7, 8],
+                       [9, 10, 11, 12],
+                       [0, 1, 0, 0]], dtype=np.float32)
+    indices = np.array([2, 2, 1], dtype=np.int32)
+    expected_output = np.array([[9, 10, 11, 12], [9, 10, 11, 12], [5, 6, 7, 8]])
+    gather_output = self.execute(graph_fn, [params, indices])
+    self.assertAllClose(gather_output, expected_output)
+
+  def test_gather_3d(self):
+
+    def graph_fn(params, indices):
+      return ops.matmul_gather_on_zeroth_axis(params, indices)
+
+    params = np.array([[[1, 2], [3, 4]],
+                       [[5, 6], [7, 8]],
+                       [[9, 10], [11, 12]],
+                       [[0, 1], [0, 0]]], dtype=np.float32)
+    indices = np.array([0, 3, 1], dtype=np.int32)
+    expected_output = np.array([[[1, 2], [3, 4]],
+                                [[0, 1], [0, 0]],
+                                [[5, 6], [7, 8]]])
+    gather_output = self.execute(graph_fn, [params, indices])
+    self.assertAllClose(gather_output, expected_output)
+
+  def test_gather_with_many_indices(self):
+
+    def graph_fn(params, indices):
+      return ops.matmul_gather_on_zeroth_axis(params, indices)
+
+    params = np.array([[1, 2, 3, 4],
+                       [5, 6, 7, 8],
+                       [9, 10, 11, 12],
+                       [0, 1, 0, 0]], dtype=np.float32)
+    indices = np.array([0, 0, 0, 0, 0, 0], dtype=np.int32)
+    expected_output = np.array(6*[[1, 2, 3, 4]])
+    gather_output = self.execute(graph_fn, [params, indices])
+    self.assertAllClose(gather_output, expected_output)
+
+  def test_gather_with_dynamic_shape_input(self):
+    params_placeholder = tf.placeholder(tf.float32, shape=[None, 4])
+    indices_placeholder = tf.placeholder(tf.int32, shape=[None])
+    gather_result = ops.matmul_gather_on_zeroth_axis(
+        params_placeholder, indices_placeholder)
+    params = np.array([[1, 2, 3, 4],
+                       [5, 6, 7, 8],
+                       [9, 10, 11, 12],
+                       [0, 1, 0, 0]], dtype=np.float32)
+    indices = np.array([0, 0, 0, 0, 0, 0])
+    expected_output = np.array(6*[[1, 2, 3, 4]])
+    with self.test_session() as sess:
+      gather_output = sess.run(gather_result, feed_dict={
+          params_placeholder: params, indices_placeholder: indices})
+      self.assertAllClose(gather_output, expected_output)
+
+
+class OpsTestMatMulCropAndResize(test_case.TestCase):
+
+  def testMatMulCropAndResize2x2To1x1(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
+
+    image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
+    expected_output = [[[[[2.5]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize2x2To1x1Flipped(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
+
+    image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
+    boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
+    expected_output = [[[[[2.5]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize2x2To3x3(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
+
+    image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
+    expected_output = [[[[[1.0], [1.5], [2.0]],
+                         [[2.0], [2.5], [3.0]],
+                         [[3.0], [3.5], [4.0]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize2x2To3x3Flipped(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
+
+    image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
+    boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
+    expected_output = [[[[[4.0], [3.5], [3.0]],
+                         [[3.0], [2.5], [2.0]],
+                         [[2.0], [1.5], [1.0]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize3x3To2x2(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+    image = np.array([[[[1], [2], [3]],
+                       [[4], [5], [6]],
+                       [[7], [8], [9]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1],
+                       [0, 0, .5, .5]]], dtype=np.float32)
+    expected_output = [[[[[1], [3]], [[7], [9]]],
+                        [[[1], [2]], [[4], [5]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize3x3To2x2_2Channels(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+    image = np.array([[[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1],
+                       [0, 0, .5, .5]]], dtype=np.float32)
+    expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
+                        [[[1, 0], [2, 1]], [[4, 3], [5, 4]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testBatchMatMulCropAndResize3x3To2x2_2Channels(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+    image = np.array([[[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]],
+                      [[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1],
+                       [0, 0, .5, .5]],
+                      [[1, 1, 0, 0],
+                       [.5, .5, 0, 0]]], dtype=np.float32)
+    expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
+                        [[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
+                       [[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
+                        [[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testMatMulCropAndResize3x3To2x2Flipped(self):
+
+    def graph_fn(image, boxes):
+      return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+    image = np.array([[[[1], [2], [3]],
+                       [[4], [5], [6]],
+                       [[7], [8], [9]]]], dtype=np.float32)
+    boxes = np.array([[[1, 1, 0, 0],
+                       [.5, .5, 0, 0]]], dtype=np.float32)
+    expected_output = [[[[[9], [7]], [[3], [1]]],
+                        [[[5], [4]], [[2], [1]]]]]
+    crop_output = self.execute(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+  def testInvalidInputShape(self):
+    image = tf.constant([[[1], [2]], [[3], [4]]], dtype=tf.float32)
+    boxes = tf.constant([[-1, -1, 1, 1]], dtype=tf.float32)
+    crop_size = [4, 4]
+    with self.assertRaises(ValueError):
+      _ = ops.matmul_crop_and_resize(image, boxes, crop_size)
+
+
+class OpsTestCropAndResize(test_case.TestCase):
+
+  def testBatchCropAndResize3x3To2x2_2Channels(self):
+
+    def graph_fn(image, boxes):
+      return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+    image = np.array([[[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]],
+                      [[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
+    boxes = np.array([[[0, 0, 1, 1],
+                       [0, 0, .5, .5]],
+                      [[1, 1, 0, 0],
+                       [.5, .5, 0, 0]]], dtype=np.float32)
+    expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
+                        [[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
+                       [[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
+                        [[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
+    crop_output = self.execute_cpu(graph_fn, [image, boxes])
+    self.assertAllClose(crop_output, expected_output)
+
+
+
+
+
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation.py
new file mode 100644
index 00000000..0bc1533f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation.py
@@ -0,0 +1,584 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Evaluate Object Detection result on a single image.
+
+Annotate each detected result as true positives or false positive according to
+a predefined IOU ratio. Non Maximum Supression is used by default. Multi class
+detection is supported by default.
+Based on the settings, per image evaluation is either performed on boxes or
+on object masks.
+"""
+import numpy as np
+
+from object_detection.utils import np_box_list
+from object_detection.utils import np_box_list_ops
+from object_detection.utils import np_box_mask_list
+from object_detection.utils import np_box_mask_list_ops
+
+
+class PerImageEvaluation(object):
+  """Evaluate detection result of a single image."""
+
+  def __init__(self,
+               num_groundtruth_classes,
+               matching_iou_threshold=0.5,
+               nms_iou_threshold=0.3,
+               nms_max_output_boxes=50,
+               group_of_weight=0.0):
+    """Initialized PerImageEvaluation by evaluation parameters.
+
+    Args:
+      num_groundtruth_classes: Number of ground truth object classes
+      matching_iou_threshold: A ratio of area intersection to union, which is
+          the threshold to consider whether a detection is true positive or not
+      nms_iou_threshold: IOU threshold used in Non Maximum Suppression.
+      nms_max_output_boxes: Number of maximum output boxes in NMS.
+      group_of_weight: Weight of the group-of boxes.
+    """
+    self.matching_iou_threshold = matching_iou_threshold
+    self.nms_iou_threshold = nms_iou_threshold
+    self.nms_max_output_boxes = nms_max_output_boxes
+    self.num_groundtruth_classes = num_groundtruth_classes
+    self.group_of_weight = group_of_weight
+
+  def compute_object_detection_metrics(
+      self, detected_boxes, detected_scores, detected_class_labels,
+      groundtruth_boxes, groundtruth_class_labels,
+      groundtruth_is_difficult_list, groundtruth_is_group_of_list,
+      detected_masks=None, groundtruth_masks=None):
+    """Evaluates detections as being tp, fp or weighted from a single image.
+
+    The evaluation is done in two stages:
+     1. All detections are matched to non group-of boxes; true positives are
+        determined and detections matched to difficult boxes are ignored.
+     2. Detections that are determined as false positives are matched against
+        group-of boxes and weighted if matched.
+
+    Args:
+      detected_boxes: A float numpy array of shape [N, 4], representing N
+          regions of detected object regions.
+          Each row is of the format [y_min, x_min, y_max, x_max]
+      detected_scores: A float numpy array of shape [N, 1], representing
+          the confidence scores of the detected N object instances.
+      detected_class_labels: A integer numpy array of shape [N, 1], repreneting
+          the class labels of the detected N object instances.
+      groundtruth_boxes: A float numpy array of shape [M, 4], representing M
+          regions of object instances in ground truth
+      groundtruth_class_labels: An integer numpy array of shape [M, 1],
+          representing M class labels of object instances in ground truth
+      groundtruth_is_difficult_list: A boolean numpy array of length M denoting
+          whether a ground truth box is a difficult instance or not
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag
+      detected_masks: (optional) A uint8 numpy array of shape
+        [N, height, width]. If not None, the metrics will be computed based
+        on masks.
+      groundtruth_masks: (optional) A uint8 numpy array of shape
+        [M, height, width].
+
+    Returns:
+      scores: A list of C float numpy arrays. Each numpy array is of
+          shape [K, 1], representing K scores detected with object class
+          label c
+      tp_fp_labels: A list of C boolean numpy arrays. Each numpy array
+          is of shape [K, 1], representing K True/False positive label of
+          object instances detected with class label c
+      is_class_correctly_detected_in_image: a numpy integer array of
+          shape [C, 1], indicating whether the correponding class has a least
+          one instance being correctly detected in the image
+    """
+    detected_boxes, detected_scores, detected_class_labels, detected_masks = (
+        self._remove_invalid_boxes(detected_boxes, detected_scores,
+                                   detected_class_labels, detected_masks))
+    scores, tp_fp_labels = self._compute_tp_fp(
+        detected_boxes=detected_boxes,
+        detected_scores=detected_scores,
+        detected_class_labels=detected_class_labels,
+        groundtruth_boxes=groundtruth_boxes,
+        groundtruth_class_labels=groundtruth_class_labels,
+        groundtruth_is_difficult_list=groundtruth_is_difficult_list,
+        groundtruth_is_group_of_list=groundtruth_is_group_of_list,
+        detected_masks=detected_masks,
+        groundtruth_masks=groundtruth_masks)
+
+    is_class_correctly_detected_in_image = self._compute_cor_loc(
+        detected_boxes=detected_boxes,
+        detected_scores=detected_scores,
+        detected_class_labels=detected_class_labels,
+        groundtruth_boxes=groundtruth_boxes,
+        groundtruth_class_labels=groundtruth_class_labels,
+        detected_masks=detected_masks,
+        groundtruth_masks=groundtruth_masks)
+
+    return scores, tp_fp_labels, is_class_correctly_detected_in_image
+
+  def _compute_cor_loc(self, detected_boxes, detected_scores,
+                       detected_class_labels, groundtruth_boxes,
+                       groundtruth_class_labels, detected_masks=None,
+                       groundtruth_masks=None):
+    """Compute CorLoc score for object detection result.
+
+    Args:
+      detected_boxes: A float numpy array of shape [N, 4], representing N
+          regions of detected object regions.
+          Each row is of the format [y_min, x_min, y_max, x_max]
+      detected_scores: A float numpy array of shape [N, 1], representing
+          the confidence scores of the detected N object instances.
+      detected_class_labels: A integer numpy array of shape [N, 1], repreneting
+          the class labels of the detected N object instances.
+      groundtruth_boxes: A float numpy array of shape [M, 4], representing M
+          regions of object instances in ground truth
+      groundtruth_class_labels: An integer numpy array of shape [M, 1],
+          representing M class labels of object instances in ground truth
+      detected_masks: (optional) A uint8 numpy array of shape
+        [N, height, width]. If not None, the scores will be computed based
+        on masks.
+      groundtruth_masks: (optional) A uint8 numpy array of shape
+        [M, height, width].
+
+    Returns:
+      is_class_correctly_detected_in_image: a numpy integer array of
+          shape [C, 1], indicating whether the correponding class has a least
+          one instance being correctly detected in the image
+
+    Raises:
+      ValueError: If detected masks is not None but groundtruth masks are None,
+        or the other way around.
+    """
+    if (detected_masks is not None and
+        groundtruth_masks is None) or (detected_masks is None and
+                                       groundtruth_masks is not None):
+      raise ValueError(
+          'If `detected_masks` is provided, then `groundtruth_masks` should '
+          'also be provided.'
+      )
+
+    is_class_correctly_detected_in_image = np.zeros(
+        self.num_groundtruth_classes, dtype=int)
+    for i in range(self.num_groundtruth_classes):
+      (gt_boxes_at_ith_class, gt_masks_at_ith_class,
+       detected_boxes_at_ith_class, detected_scores_at_ith_class,
+       detected_masks_at_ith_class) = self._get_ith_class_arrays(
+           detected_boxes, detected_scores, detected_masks,
+           detected_class_labels, groundtruth_boxes, groundtruth_masks,
+           groundtruth_class_labels, i)
+      is_class_correctly_detected_in_image[i] = (
+          self._compute_is_class_correctly_detected_in_image(
+              detected_boxes=detected_boxes_at_ith_class,
+              detected_scores=detected_scores_at_ith_class,
+              groundtruth_boxes=gt_boxes_at_ith_class,
+              detected_masks=detected_masks_at_ith_class,
+              groundtruth_masks=gt_masks_at_ith_class))
+
+    return is_class_correctly_detected_in_image
+
+  def _compute_is_class_correctly_detected_in_image(
+      self, detected_boxes, detected_scores, groundtruth_boxes,
+      detected_masks=None, groundtruth_masks=None):
+    """Compute CorLoc score for a single class.
+
+    Args:
+      detected_boxes: A numpy array of shape [N, 4] representing detected box
+          coordinates
+      detected_scores: A 1-d numpy array of length N representing classification
+          score
+      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
+          box coordinates
+      detected_masks: (optional) A np.uint8 numpy array of shape
+        [N, height, width]. If not None, the scores will be computed based
+        on masks.
+      groundtruth_masks: (optional) A np.uint8 numpy array of shape
+        [M, height, width].
+
+    Returns:
+      is_class_correctly_detected_in_image: An integer 1 or 0 denoting whether a
+          class is correctly detected in the image or not
+    """
+    if detected_boxes.size > 0:
+      if groundtruth_boxes.size > 0:
+        max_score_id = np.argmax(detected_scores)
+        mask_mode = False
+        if detected_masks is not None and groundtruth_masks is not None:
+          mask_mode = True
+        if mask_mode:
+          detected_boxlist = np_box_mask_list.BoxMaskList(
+              box_data=np.expand_dims(detected_boxes[max_score_id], axis=0),
+              mask_data=np.expand_dims(detected_masks[max_score_id], axis=0))
+          gt_boxlist = np_box_mask_list.BoxMaskList(
+              box_data=groundtruth_boxes, mask_data=groundtruth_masks)
+          iou = np_box_mask_list_ops.iou(detected_boxlist, gt_boxlist)
+        else:
+          detected_boxlist = np_box_list.BoxList(
+              np.expand_dims(detected_boxes[max_score_id, :], axis=0))
+          gt_boxlist = np_box_list.BoxList(groundtruth_boxes)
+          iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist)
+        if np.max(iou) >= self.matching_iou_threshold:
+          return 1
+    return 0
+
+  def _compute_tp_fp(self, detected_boxes, detected_scores,
+                     detected_class_labels, groundtruth_boxes,
+                     groundtruth_class_labels, groundtruth_is_difficult_list,
+                     groundtruth_is_group_of_list,
+                     detected_masks=None, groundtruth_masks=None):
+    """Labels true/false positives of detections of an image across all classes.
+
+    Args:
+      detected_boxes: A float numpy array of shape [N, 4], representing N
+          regions of detected object regions.
+          Each row is of the format [y_min, x_min, y_max, x_max]
+      detected_scores: A float numpy array of shape [N, 1], representing
+          the confidence scores of the detected N object instances.
+      detected_class_labels: A integer numpy array of shape [N, 1], repreneting
+          the class labels of the detected N object instances.
+      groundtruth_boxes: A float numpy array of shape [M, 4], representing M
+          regions of object instances in ground truth
+      groundtruth_class_labels: An integer numpy array of shape [M, 1],
+          representing M class labels of object instances in ground truth
+      groundtruth_is_difficult_list: A boolean numpy array of length M denoting
+          whether a ground truth box is a difficult instance or not
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag
+      detected_masks: (optional) A np.uint8 numpy array of shape
+        [N, height, width]. If not None, the scores will be computed based
+        on masks.
+      groundtruth_masks: (optional) A np.uint8 numpy array of shape
+        [M, height, width].
+
+    Returns:
+      result_scores: A list of float numpy arrays. Each numpy array is of
+          shape [K, 1], representing K scores detected with object class
+          label c
+      result_tp_fp_labels: A list of boolean numpy array. Each numpy array is of
+          shape [K, 1], representing K True/False positive label of object
+          instances detected with class label c
+
+    Raises:
+      ValueError: If detected masks is not None but groundtruth masks are None,
+        or the other way around.
+    """
+    if detected_masks is not None and groundtruth_masks is None:
+      raise ValueError(
+          'Detected masks is available but groundtruth masks is not.')
+    if detected_masks is None and groundtruth_masks is not None:
+      raise ValueError(
+          'Groundtruth masks is available but detected masks is not.')
+
+    result_scores = []
+    result_tp_fp_labels = []
+    for i in range(self.num_groundtruth_classes):
+      groundtruth_is_difficult_list_at_ith_class = (
+          groundtruth_is_difficult_list[groundtruth_class_labels == i])
+      groundtruth_is_group_of_list_at_ith_class = (
+          groundtruth_is_group_of_list[groundtruth_class_labels == i])
+      (gt_boxes_at_ith_class, gt_masks_at_ith_class,
+       detected_boxes_at_ith_class, detected_scores_at_ith_class,
+       detected_masks_at_ith_class) = self._get_ith_class_arrays(
+           detected_boxes, detected_scores, detected_masks,
+           detected_class_labels, groundtruth_boxes, groundtruth_masks,
+           groundtruth_class_labels, i)
+      scores, tp_fp_labels = self._compute_tp_fp_for_single_class(
+          detected_boxes=detected_boxes_at_ith_class,
+          detected_scores=detected_scores_at_ith_class,
+          groundtruth_boxes=gt_boxes_at_ith_class,
+          groundtruth_is_difficult_list=
+          groundtruth_is_difficult_list_at_ith_class,
+          groundtruth_is_group_of_list=
+          groundtruth_is_group_of_list_at_ith_class,
+          detected_masks=detected_masks_at_ith_class,
+          groundtruth_masks=gt_masks_at_ith_class)
+      result_scores.append(scores)
+      result_tp_fp_labels.append(tp_fp_labels)
+    return result_scores, result_tp_fp_labels
+
+  def _get_overlaps_and_scores_mask_mode(
+      self, detected_boxes, detected_scores, detected_masks, groundtruth_boxes,
+      groundtruth_masks, groundtruth_is_group_of_list):
+    """Computes overlaps and scores between detected and groudntruth masks.
+
+    Args:
+      detected_boxes: A numpy array of shape [N, 4] representing detected box
+          coordinates
+      detected_scores: A 1-d numpy array of length N representing classification
+          score
+      detected_masks: A uint8 numpy array of shape [N, height, width]. If not
+          None, the scores will be computed based on masks.
+      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
+          box coordinates
+      groundtruth_masks: A uint8 numpy array of shape [M, height, width].
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag. If a groundtruth box
+          is group-of box, every detection matching this box is ignored.
+
+    Returns:
+      iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
+          gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
+      ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
+          gt_group_of_boxlist.num_boxes() == 0 it will be None.
+      scores: The score of the detected boxlist.
+      num_boxes: Number of non-maximum suppressed detected boxes.
+    """
+    detected_boxlist = np_box_mask_list.BoxMaskList(
+        box_data=detected_boxes, mask_data=detected_masks)
+    detected_boxlist.add_field('scores', detected_scores)
+    detected_boxlist = np_box_mask_list_ops.non_max_suppression(
+        detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
+    gt_non_group_of_boxlist = np_box_mask_list.BoxMaskList(
+        box_data=groundtruth_boxes[~groundtruth_is_group_of_list],
+        mask_data=groundtruth_masks[~groundtruth_is_group_of_list])
+    gt_group_of_boxlist = np_box_mask_list.BoxMaskList(
+        box_data=groundtruth_boxes[groundtruth_is_group_of_list],
+        mask_data=groundtruth_masks[groundtruth_is_group_of_list])
+    iou = np_box_mask_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
+    ioa = np.transpose(
+        np_box_mask_list_ops.ioa(gt_group_of_boxlist, detected_boxlist))
+    scores = detected_boxlist.get_field('scores')
+    num_boxes = detected_boxlist.num_boxes()
+    return iou, ioa, scores, num_boxes
+
+  def _get_overlaps_and_scores_box_mode(
+      self,
+      detected_boxes,
+      detected_scores,
+      groundtruth_boxes,
+      groundtruth_is_group_of_list):
+    """Computes overlaps and scores between detected and groudntruth boxes.
+
+    Args:
+      detected_boxes: A numpy array of shape [N, 4] representing detected box
+          coordinates
+      detected_scores: A 1-d numpy array of length N representing classification
+          score
+      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
+          box coordinates
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag. If a groundtruth box
+          is group-of box, every detection matching this box is ignored.
+
+    Returns:
+      iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
+          gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
+      ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
+          gt_group_of_boxlist.num_boxes() == 0 it will be None.
+      scores: The score of the detected boxlist.
+      num_boxes: Number of non-maximum suppressed detected boxes.
+    """
+    detected_boxlist = np_box_list.BoxList(detected_boxes)
+    detected_boxlist.add_field('scores', detected_scores)
+    detected_boxlist = np_box_list_ops.non_max_suppression(
+        detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
+    gt_non_group_of_boxlist = np_box_list.BoxList(
+        groundtruth_boxes[~groundtruth_is_group_of_list])
+    gt_group_of_boxlist = np_box_list.BoxList(
+        groundtruth_boxes[groundtruth_is_group_of_list])
+    iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
+    ioa = np.transpose(
+        np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist))
+    scores = detected_boxlist.get_field('scores')
+    num_boxes = detected_boxlist.num_boxes()
+    return iou, ioa, scores, num_boxes
+
+  def _compute_tp_fp_for_single_class(
+      self, detected_boxes, detected_scores, groundtruth_boxes,
+      groundtruth_is_difficult_list, groundtruth_is_group_of_list,
+      detected_masks=None, groundtruth_masks=None):
+    """Labels boxes detected with the same class from the same image as tp/fp.
+
+    Args:
+      detected_boxes: A numpy array of shape [N, 4] representing detected box
+          coordinates
+      detected_scores: A 1-d numpy array of length N representing classification
+          score
+      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
+          box coordinates
+      groundtruth_is_difficult_list: A boolean numpy array of length M denoting
+          whether a ground truth box is a difficult instance or not. If a
+          groundtruth box is difficult, every detection matching this box
+          is ignored.
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag. If a groundtruth box
+          is group-of box, every detection matching this box is ignored.
+      detected_masks: (optional) A uint8 numpy array of shape
+        [N, height, width]. If not None, the scores will be computed based
+        on masks.
+      groundtruth_masks: (optional) A uint8 numpy array of shape
+        [M, height, width].
+
+    Returns:
+      Two arrays of the same size, containing all boxes that were evaluated as
+      being true positives or false positives; if a box matched to a difficult
+      box or to a group-of box, it is ignored.
+
+      scores: A numpy array representing the detection scores.
+      tp_fp_labels: a boolean numpy array indicating whether a detection is a
+          true positive.
+    """
+    if detected_boxes.size == 0:
+      return np.array([], dtype=float), np.array([], dtype=bool)
+
+    mask_mode = False
+    if detected_masks is not None and groundtruth_masks is not None:
+      mask_mode = True
+
+    if mask_mode:
+      (iou, ioa, scores,
+       num_detected_boxes) = self._get_overlaps_and_scores_mask_mode(
+           detected_boxes=detected_boxes,
+           detected_scores=detected_scores,
+           detected_masks=detected_masks,
+           groundtruth_boxes=groundtruth_boxes,
+           groundtruth_masks=groundtruth_masks,
+           groundtruth_is_group_of_list=groundtruth_is_group_of_list)
+    else:
+      (iou, ioa, scores,
+       num_detected_boxes) = self._get_overlaps_and_scores_box_mode(
+           detected_boxes=detected_boxes,
+           detected_scores=detected_scores,
+           groundtruth_boxes=groundtruth_boxes,
+           groundtruth_is_group_of_list=groundtruth_is_group_of_list)
+
+    if groundtruth_boxes.size == 0:
+      return scores, np.zeros(num_detected_boxes, dtype=bool)
+
+    tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool)
+    is_matched_to_difficult_box = np.zeros(num_detected_boxes, dtype=bool)
+    is_matched_to_group_of_box = np.zeros(num_detected_boxes, dtype=bool)
+
+    # The evaluation is done in two stages:
+    # 1. All detections are matched to non group-of boxes; true positives are
+    #    determined and detections matched to difficult boxes are ignored.
+    # 2. Detections that are determined as false positives are matched against
+    #    group-of boxes and scored with weight w per ground truth box is
+    # matched.
+
+    # Tp-fp evaluation for non-group of boxes (if any).
+    if iou.shape[1] > 0:
+      groundtruth_nongroup_of_is_difficult_list = groundtruth_is_difficult_list[
+          ~groundtruth_is_group_of_list]
+      max_overlap_gt_ids = np.argmax(iou, axis=1)
+      is_gt_box_detected = np.zeros(iou.shape[1], dtype=bool)
+      for i in range(num_detected_boxes):
+        gt_id = max_overlap_gt_ids[i]
+        if iou[i, gt_id] >= self.matching_iou_threshold:
+          if not groundtruth_nongroup_of_is_difficult_list[gt_id]:
+            if not is_gt_box_detected[gt_id]:
+              tp_fp_labels[i] = True
+              is_gt_box_detected[gt_id] = True
+          else:
+            is_matched_to_difficult_box[i] = True
+
+    scores_group_of = np.zeros(ioa.shape[1], dtype=float)
+    tp_fp_labels_group_of = self.group_of_weight * np.ones(
+        ioa.shape[1], dtype=float)
+    # Tp-fp evaluation for group of boxes.
+    if ioa.shape[1] > 0:
+      max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1)
+      for i in range(num_detected_boxes):
+        gt_id = max_overlap_group_of_gt_ids[i]
+        if (not tp_fp_labels[i] and not is_matched_to_difficult_box[i] and
+            ioa[i, gt_id] >= self.matching_iou_threshold):
+          is_matched_to_group_of_box[i] = True
+          scores_group_of[gt_id] = max(scores_group_of[gt_id], scores[i])
+      selector = np.where((scores_group_of > 0) & (tp_fp_labels_group_of > 0))
+      scores_group_of = scores_group_of[selector]
+      tp_fp_labels_group_of = tp_fp_labels_group_of[selector]
+
+    return np.concatenate(
+        (scores[~is_matched_to_difficult_box
+                & ~is_matched_to_group_of_box],
+         scores_group_of)), np.concatenate(
+             (tp_fp_labels[~is_matched_to_difficult_box
+                           & ~is_matched_to_group_of_box].astype(float),
+              tp_fp_labels_group_of))
+
+  def _get_ith_class_arrays(self, detected_boxes, detected_scores,
+                            detected_masks, detected_class_labels,
+                            groundtruth_boxes, groundtruth_masks,
+                            groundtruth_class_labels, class_index):
+    """Returns numpy arrays belonging to class with index `class_index`.
+
+    Args:
+      detected_boxes: A numpy array containing detected boxes.
+      detected_scores: A numpy array containing detected scores.
+      detected_masks: A numpy array containing detected masks.
+      detected_class_labels: A numpy array containing detected class labels.
+      groundtruth_boxes: A numpy array containing groundtruth boxes.
+      groundtruth_masks: A numpy array containing groundtruth masks.
+      groundtruth_class_labels: A numpy array containing groundtruth class
+        labels.
+      class_index: An integer index.
+
+    Returns:
+      gt_boxes_at_ith_class: A numpy array containing groundtruth boxes labeled
+        as ith class.
+      gt_masks_at_ith_class: A numpy array containing groundtruth masks labeled
+        as ith class.
+      detected_boxes_at_ith_class: A numpy array containing detected boxes
+        corresponding to the ith class.
+      detected_scores_at_ith_class: A numpy array containing detected scores
+        corresponding to the ith class.
+      detected_masks_at_ith_class: A numpy array containing detected masks
+        corresponding to the ith class.
+    """
+    selected_groundtruth = (groundtruth_class_labels == class_index)
+    gt_boxes_at_ith_class = groundtruth_boxes[selected_groundtruth]
+    if groundtruth_masks is not None:
+      gt_masks_at_ith_class = groundtruth_masks[selected_groundtruth]
+    else:
+      gt_masks_at_ith_class = None
+    selected_detections = (detected_class_labels == class_index)
+    detected_boxes_at_ith_class = detected_boxes[selected_detections]
+    detected_scores_at_ith_class = detected_scores[selected_detections]
+    if detected_masks is not None:
+      detected_masks_at_ith_class = detected_masks[selected_detections]
+    else:
+      detected_masks_at_ith_class = None
+    return (gt_boxes_at_ith_class, gt_masks_at_ith_class,
+            detected_boxes_at_ith_class, detected_scores_at_ith_class,
+            detected_masks_at_ith_class)
+
+  def _remove_invalid_boxes(self, detected_boxes, detected_scores,
+                            detected_class_labels, detected_masks=None):
+    """Removes entries with invalid boxes.
+
+    A box is invalid if either its xmax is smaller than its xmin, or its ymax
+    is smaller than its ymin.
+
+    Args:
+      detected_boxes: A float numpy array of size [num_boxes, 4] containing box
+        coordinates in [ymin, xmin, ymax, xmax] format.
+      detected_scores: A float numpy array of size [num_boxes].
+      detected_class_labels: A int32 numpy array of size [num_boxes].
+      detected_masks: A uint8 numpy array of size [num_boxes, height, width].
+
+    Returns:
+      valid_detected_boxes: A float numpy array of size [num_valid_boxes, 4]
+        containing box coordinates in [ymin, xmin, ymax, xmax] format.
+      valid_detected_scores: A float numpy array of size [num_valid_boxes].
+      valid_detected_class_labels: A int32 numpy array of size
+        [num_valid_boxes].
+      valid_detected_masks: A uint8 numpy array of size
+        [num_valid_boxes, height, width].
+    """
+    valid_indices = np.logical_and(detected_boxes[:, 0] < detected_boxes[:, 2],
+                                   detected_boxes[:, 1] < detected_boxes[:, 3])
+    detected_boxes = detected_boxes[valid_indices]
+    detected_scores = detected_scores[valid_indices]
+    detected_class_labels = detected_class_labels[valid_indices]
+    if detected_masks is not None:
+      detected_masks = detected_masks[valid_indices]
+    return [
+        detected_boxes, detected_scores, detected_class_labels, detected_masks
+    ]
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation_test.py
new file mode 100644
index 00000000..a870843f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_evaluation_test.py
@@ -0,0 +1,561 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.per_image_evaluation."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import per_image_evaluation
+
+
+class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 1
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    self.eval = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
+        nms_max_output_boxes)
+
+    self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                   dtype=float)
+    self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
+    detected_masks_0 = np.array([[0, 1, 1, 0],
+                                 [0, 0, 1, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_1 = np.array([[1, 0, 0, 0],
+                                 [1, 1, 0, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_2 = np.array([[0, 0, 0, 0],
+                                 [0, 1, 1, 0],
+                                 [0, 1, 0, 0]], dtype=np.uint8)
+    self.detected_masks = np.stack(
+        [detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
+    self.groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 10, 10]],
+                                      dtype=float)
+    groundtruth_masks_0 = np.array([[1, 1, 0, 0],
+                                    [1, 1, 0, 0],
+                                    [0, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_1 = np.array([[0, 0, 0, 1],
+                                    [0, 0, 0, 1],
+                                    [0, 0, 0, 1]], dtype=np.uint8)
+    self.groundtruth_masks = np.stack(
+        [groundtruth_masks_0, groundtruth_masks_1], axis=0)
+
+  def test_match_to_gt_box_0(self):
+    groundtruth_groundtruth_is_difficult_list = np.array([False, True],
+                                                         dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_to_gt_mask_0(self):
+    groundtruth_groundtruth_is_difficult_list = np.array([False, True],
+                                                         dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_match_to_gt_box_1(self):
+    groundtruth_groundtruth_is_difficult_list = np.array([True, False],
+                                                         dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_to_gt_mask_1(self):
+    groundtruth_groundtruth_is_difficult_list = np.array([True, False],
+                                                         dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+    expected_scores = np.array([0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 1
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    self.eval = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
+        nms_max_output_boxes)
+
+    self.detected_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
+    self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    detected_masks_0 = np.array([[0, 1, 1, 0],
+                                 [0, 0, 1, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_1 = np.array([[1, 0, 0, 0],
+                                 [1, 1, 0, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_2 = np.array([[0, 0, 0, 0],
+                                 [0, 1, 1, 0],
+                                 [0, 1, 0, 0]], dtype=np.uint8)
+    self.detected_masks = np.stack(
+        [detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
+
+    self.groundtruth_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
+    groundtruth_masks_0 = np.array([[1, 0, 0, 0],
+                                    [1, 0, 0, 0],
+                                    [1, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_1 = np.array([[0, 0, 1, 0],
+                                    [0, 0, 1, 0],
+                                    [0, 0, 1, 0]], dtype=np.uint8)
+    groundtruth_masks_2 = np.array([[0, 1, 0, 0],
+                                    [0, 1, 0, 0],
+                                    [0, 1, 0, 0]], dtype=np.uint8)
+    self.groundtruth_masks = np.stack(
+        [groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
+
+  def test_match_to_non_group_of_and_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, True, True], dtype=bool)
+    expected_scores = np.array([0.8], dtype=float)
+    expected_tp_fp_labels = np.array([True], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_to_non_group_of_and_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, True, True], dtype=bool)
+    expected_scores = np.array([0.6], dtype=float)
+    expected_tp_fp_labels = np.array([True], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_match_two_to_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [True, False, True], dtype=bool)
+    expected_scores = np.array([0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_two_to_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [True, False, True], dtype=bool)
+    expected_scores = np.array([0.8], dtype=float)
+    expected_tp_fp_labels = np.array([True], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+class SingleClassTpFpWithGroupOfBoxesTestWeighted(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 1
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    self.group_of_weight = 0.5
+    self.eval = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
+        nms_max_output_boxes, self.group_of_weight)
+
+    self.detected_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
+    self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    detected_masks_0 = np.array(
+        [[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_1 = np.array(
+        [[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_2 = np.array(
+        [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], dtype=np.uint8)
+    self.detected_masks = np.stack(
+        [detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
+
+    self.groundtruth_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
+    groundtruth_masks_0 = np.array(
+        [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks_1 = np.array(
+        [[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]], dtype=np.uint8)
+    groundtruth_masks_2 = np.array(
+        [[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8)
+    self.groundtruth_masks = np.stack(
+        [groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
+
+  def test_match_to_non_group_of_and_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, True, True], dtype=bool)
+    expected_scores = np.array([0.8, 0.6], dtype=float)
+    expected_tp_fp_labels = np.array([1.0, self.group_of_weight], dtype=float)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_to_non_group_of_and_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, True, True], dtype=bool)
+    expected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array(
+        [1.0, self.group_of_weight, self.group_of_weight], dtype=float)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+
+    tf.logging.info(
+        "test_mask_match_to_non_group_of_and_group_of_box {} {}".format(
+            tp_fp_labels, expected_tp_fp_labels))
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_match_two_to_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [True, False, True], dtype=bool)
+    expected_scores = np.array([0.5, 0.8], dtype=float)
+    expected_tp_fp_labels = np.array([0.0, self.group_of_weight], dtype=float)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+
+    tf.logging.info("test_match_two_to_group_of_box {} {}".format(
+        tp_fp_labels, expected_tp_fp_labels))
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_match_two_to_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [True, False, True], dtype=bool)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array(
+        [1.0, self.group_of_weight, self.group_of_weight], dtype=float)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=self.groundtruth_masks)
+
+    tf.logging.info("test_mask_match_two_to_group_of_box {} {}".format(
+        tp_fp_labels, expected_tp_fp_labels))
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 1
+    matching_iou_threshold_high_iou = 0.5
+    matching_iou_threshold_low_iou = 0.1
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    self.eval_high_iou = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold_high_iou,
+        nms_iou_threshold, nms_max_output_boxes)
+
+    self.eval_low_iou = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold_low_iou,
+        nms_iou_threshold, nms_max_output_boxes)
+
+    self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                   dtype=float)
+    self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
+    detected_masks_0 = np.array([[0, 1, 1, 0],
+                                 [0, 0, 1, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_1 = np.array([[1, 0, 0, 0],
+                                 [1, 1, 0, 0],
+                                 [0, 0, 0, 0]], dtype=np.uint8)
+    detected_masks_2 = np.array([[0, 0, 0, 0],
+                                 [0, 1, 1, 0],
+                                 [0, 1, 0, 0]], dtype=np.uint8)
+    self.detected_masks = np.stack(
+        [detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
+
+  def test_no_true_positives(self):
+    groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_no_true_positives(self):
+    groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
+    groundtruth_masks_0 = np.array([[1, 1, 1, 1],
+                                    [1, 1, 1, 1],
+                                    [1, 1, 1, 1]], dtype=np.uint8)
+    groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=groundtruth_masks)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_one_true_positives_with_large_iou_threshold(self):
+    groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_mask_one_true_positives_with_large_iou_threshold(self):
+    groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_masks_0 = np.array([[1, 0, 0, 0],
+                                    [1, 1, 0, 0],
+                                    [0, 0, 0, 0]], dtype=np.uint8)
+    groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes,
+        self.detected_scores,
+        groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list,
+        detected_masks=self.detected_masks,
+        groundtruth_masks=groundtruth_masks)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_one_true_positives_with_very_small_iou_threshold(self):
+    groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval_low_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_two_true_positives_with_large_iou_threshold(self):
+    groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False, True, True], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+class MultiClassesTpFpTest(tf.test.TestCase):
+
+  def test_tp_fp(self):
+    num_groundtruth_classes = 3
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
+                                                    matching_iou_threshold,
+                                                    nms_iou_threshold,
+                                                    nms_max_output_boxes)
+    detected_boxes = np.array([[0, 0, 1, 1], [10, 10, 5, 5], [0, 0, 2, 2],
+                               [5, 10, 10, 5], [10, 5, 5, 10], [0, 0, 3, 3]],
+                              dtype=float)
+    detected_scores = np.array([0.8, 0.1, 0.8, 0.9, 0.7, 0.8], dtype=float)
+    detected_class_labels = np.array([0, 1, 1, 2, 0, 2], dtype=int)
+    groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
+    groundtruth_class_labels = np.array([0, 2], dtype=int)
+    groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=float)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
+    scores, tp_fp_labels, _ = eval1.compute_object_detection_metrics(
+        detected_boxes, detected_scores, detected_class_labels,
+        groundtruth_boxes, groundtruth_class_labels,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    expected_scores = [np.array([0.8], dtype=float)] * 3
+    expected_tp_fp_labels = [np.array([True]), np.array([False]), np.array([True
+                                                                           ])]
+    for i in range(len(expected_scores)):
+      self.assertTrue(np.allclose(expected_scores[i], scores[i]))
+      self.assertTrue(np.array_equal(expected_tp_fp_labels[i], tp_fp_labels[i]))
+
+
+class CorLocTest(tf.test.TestCase):
+
+  def test_compute_corloc_with_normal_iou_threshold(self):
+    num_groundtruth_classes = 3
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
+                                                    matching_iou_threshold,
+                                                    nms_iou_threshold,
+                                                    nms_max_output_boxes)
+    detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
+                               [0, 0, 5, 5]], dtype=float)
+    detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
+    detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
+    groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
+                                 dtype=float)
+    groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
+
+    is_class_correctly_detected_in_image = eval1._compute_cor_loc(
+        detected_boxes, detected_scores, detected_class_labels,
+        groundtruth_boxes, groundtruth_class_labels)
+    expected_result = np.array([1, 0, 1], dtype=int)
+    self.assertTrue(np.array_equal(expected_result,
+                                   is_class_correctly_detected_in_image))
+
+  def test_compute_corloc_with_very_large_iou_threshold(self):
+    num_groundtruth_classes = 3
+    matching_iou_threshold = 0.9
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
+                                                    matching_iou_threshold,
+                                                    nms_iou_threshold,
+                                                    nms_max_output_boxes)
+    detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
+                               [0, 0, 5, 5]], dtype=float)
+    detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
+    detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
+    groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
+                                 dtype=float)
+    groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
+
+    is_class_correctly_detected_in_image = eval1._compute_cor_loc(
+        detected_boxes, detected_scores, detected_class_labels,
+        groundtruth_boxes, groundtruth_class_labels)
+    expected_result = np.array([1, 0, 0], dtype=int)
+    self.assertTrue(np.array_equal(expected_result,
+                                   is_class_correctly_detected_in_image))
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation.py
new file mode 100644
index 00000000..0e204968
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation.py
@@ -0,0 +1,224 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Evaluates Visual Relations Detection(VRD) result evaluation on an image.
+
+Annotate each VRD result as true positives or false positive according to
+a predefined IOU ratio. Multi-class detection is supported by default.
+Based on the settings, per image evaluation is performed either on phrase
+detection subtask or on relation detection subtask.
+"""
+import numpy as np
+
+from object_detection.utils import np_box_list
+from object_detection.utils import np_box_list_ops
+
+
+class PerImageVRDEvaluation(object):
+  """Evaluate vrd result of a single image."""
+
+  def __init__(self, matching_iou_threshold=0.5):
+    """Initialized PerImageVRDEvaluation by evaluation parameters.
+
+    Args:
+      matching_iou_threshold: A ratio of area intersection to union, which is
+          the threshold to consider whether a detection is true positive or not;
+          in phrase detection subtask.
+    """
+    self.matching_iou_threshold = matching_iou_threshold
+
+  def compute_detection_tp_fp(self, detected_box_tuples, detected_scores,
+                              detected_class_tuples, groundtruth_box_tuples,
+                              groundtruth_class_tuples):
+    """Evaluates VRD as being tp, fp from a single image.
+
+    Args:
+      detected_box_tuples: A numpy array of structures with shape [N,],
+          representing N tuples, each tuple containing the same number of named
+          bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max].
+      detected_scores: A float numpy array of shape [N,], representing
+          the confidence scores of the detected N object instances.
+      detected_class_tuples: A numpy array of structures shape [N,],
+          representing the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+      groundtruth_box_tuples: A float numpy array of structures with the shape
+          [M,], representing M tuples, each tuple containing the same number
+          of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max].
+      groundtruth_class_tuples: A numpy array of structures shape [M,],
+          representing  the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+
+    Returns:
+      scores: A single numpy array with shape [N,], representing N scores
+          detected with object class, sorted in descentent order.
+      tp_fp_labels: A single boolean numpy array of shape [N,], representing N
+          True/False positive label, one label per tuple. The labels are sorted
+          so that the order of the labels matches the order of the scores.
+      result_mapping: A numpy array with shape [N,] with original index of each
+          entry.
+    """
+
+    scores, tp_fp_labels, result_mapping = self._compute_tp_fp(
+        detected_box_tuples=detected_box_tuples,
+        detected_scores=detected_scores,
+        detected_class_tuples=detected_class_tuples,
+        groundtruth_box_tuples=groundtruth_box_tuples,
+        groundtruth_class_tuples=groundtruth_class_tuples)
+
+    return scores, tp_fp_labels, result_mapping
+
+  def _compute_tp_fp(self, detected_box_tuples, detected_scores,
+                     detected_class_tuples, groundtruth_box_tuples,
+                     groundtruth_class_tuples):
+    """Labels as true/false positives detection tuples across all classes.
+
+    Args:
+      detected_box_tuples: A numpy array of structures with shape [N,],
+          representing N tuples, each tuple containing the same number of named
+          bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+      detected_scores: A float numpy array of shape [N,], representing
+          the confidence scores of the detected N object instances.
+      detected_class_tuples: A numpy array of structures shape [N,],
+          representing the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+      groundtruth_box_tuples: A float numpy array of structures with the shape
+          [M,], representing M tuples, each tuple containing the same number
+          of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+      groundtruth_class_tuples: A numpy array of structures shape [M,],
+          representing  the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+
+    Returns:
+      scores: A single numpy array with shape [N,], representing N scores
+          detected with object class, sorted in descentent order.
+      tp_fp_labels: A single boolean numpy array of shape [N,], representing N
+          True/False positive label, one label per tuple. The labels are sorted
+          so that the order of the labels matches the order of the scores.
+      result_mapping: A numpy array with shape [N,] with original index of each
+          entry.
+    """
+    unique_gt_tuples = np.unique(
+        np.concatenate((groundtruth_class_tuples, detected_class_tuples)))
+    result_scores = []
+    result_tp_fp_labels = []
+    result_mapping = []
+
+    for unique_tuple in unique_gt_tuples:
+      detections_selector = (detected_class_tuples == unique_tuple)
+      gt_selector = (groundtruth_class_tuples == unique_tuple)
+
+      selector_mapping = np.where(detections_selector)[0]
+
+      detection_scores_per_tuple = detected_scores[detections_selector]
+      detection_box_per_tuple = detected_box_tuples[detections_selector]
+
+      sorted_indices = np.argsort(detection_scores_per_tuple)
+      sorted_indices = sorted_indices[::-1]
+
+      tp_fp_labels = self._compute_tp_fp_for_single_class(
+          detected_box_tuples=detection_box_per_tuple[sorted_indices],
+          groundtruth_box_tuples=groundtruth_box_tuples[gt_selector])
+      result_scores.append(detection_scores_per_tuple[sorted_indices])
+      result_tp_fp_labels.append(tp_fp_labels)
+      result_mapping.append(selector_mapping[sorted_indices])
+
+    if result_scores:
+      result_scores = np.concatenate(result_scores)
+      result_tp_fp_labels = np.concatenate(result_tp_fp_labels)
+      result_mapping = np.concatenate(result_mapping)
+    else:
+      result_scores = np.array([], dtype=float)
+      result_tp_fp_labels = np.array([], dtype=bool)
+      result_mapping = np.array([], dtype=int)
+
+    sorted_indices = np.argsort(result_scores)
+    sorted_indices = sorted_indices[::-1]
+
+    return result_scores[sorted_indices], result_tp_fp_labels[
+        sorted_indices], result_mapping[sorted_indices]
+
+  def _get_overlaps_and_scores_relation_tuples(self, detected_box_tuples,
+                                               groundtruth_box_tuples):
+    """Computes overlaps and scores between detected and groundtruth tuples.
+
+    Both detections and groundtruth boxes have the same class tuples.
+
+    Args:
+      detected_box_tuples: A numpy array of structures with shape [N,],
+          representing N tuples, each tuple containing the same number of named
+          bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+      groundtruth_box_tuples: A float numpy array of structures with the shape
+          [M,], representing M tuples, each tuple containing the same number
+          of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+
+    Returns:
+      result_iou: A float numpy array of size
+        [num_detected_tuples, num_gt_box_tuples].
+    """
+
+    result_iou = np.ones(
+        (detected_box_tuples.shape[0], groundtruth_box_tuples.shape[0]),
+        dtype=float)
+    for field in detected_box_tuples.dtype.fields:
+      detected_boxlist_field = np_box_list.BoxList(detected_box_tuples[field])
+      gt_boxlist_field = np_box_list.BoxList(groundtruth_box_tuples[field])
+      iou_field = np_box_list_ops.iou(detected_boxlist_field, gt_boxlist_field)
+      result_iou = np.minimum(iou_field, result_iou)
+    return result_iou
+
+  def _compute_tp_fp_for_single_class(self, detected_box_tuples,
+                                      groundtruth_box_tuples):
+    """Labels boxes detected with the same class from the same image as tp/fp.
+
+    Detection boxes are expected to be already sorted by score.
+    Args:
+      detected_box_tuples: A numpy array of structures with shape [N,],
+          representing N tuples, each tuple containing the same number of named
+          bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+      groundtruth_box_tuples: A float numpy array of structures with the shape
+          [M,], representing M tuples, each tuple containing the same number
+          of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max]
+
+    Returns:
+      tp_fp_labels: a boolean numpy array indicating whether a detection is a
+          true positive.
+    """
+    if detected_box_tuples.size == 0:
+      return np.array([], dtype=bool)
+
+    min_iou = self._get_overlaps_and_scores_relation_tuples(
+        detected_box_tuples, groundtruth_box_tuples)
+
+    num_detected_tuples = detected_box_tuples.shape[0]
+    tp_fp_labels = np.zeros(num_detected_tuples, dtype=bool)
+
+    if min_iou.shape[1] > 0:
+      max_overlap_gt_ids = np.argmax(min_iou, axis=1)
+      is_gt_tuple_detected = np.zeros(min_iou.shape[1], dtype=bool)
+      for i in range(num_detected_tuples):
+        gt_id = max_overlap_gt_ids[i]
+        if min_iou[i, gt_id] >= self.matching_iou_threshold:
+          if not is_gt_tuple_detected[gt_id]:
+            tp_fp_labels[i] = True
+            is_gt_tuple_detected[gt_id] = True
+
+    return tp_fp_labels
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation_test.py
new file mode 100644
index 00000000..b81c5631
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/per_image_vrd_evaluation_test.py
@@ -0,0 +1,94 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for object_detection.utils.per_image_vrd_evaluation."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import per_image_vrd_evaluation
+
+
+class SingleClassPerImageVrdEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    matching_iou_threshold = 0.5
+    self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
+        matching_iou_threshold)
+    box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
+
+    self.detected_box_tuples = np.array(
+        [([0, 0, 1.1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2]),
+         ([1, 1, 2, 2], [0, 0, 1.1, 1])],
+        dtype=box_data_type)
+    self.detected_scores = np.array([0.8, 0.2, 0.1], dtype=float)
+    self.groundtruth_box_tuples = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=box_data_type)
+
+  def test_tp_fp_eval(self):
+    tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_box_tuples, self.groundtruth_box_tuples)
+    expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_tp_fp_eval_empty_gt(self):
+    box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
+
+    tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_box_tuples, np.array([], dtype=box_data_type))
+    expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+class MultiClassPerImageVrdEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    matching_iou_threshold = 0.5
+    self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
+        matching_iou_threshold)
+    box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
+    label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'),
+                                ('relation', 'i4')])
+
+    self.detected_box_tuples = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1.1, 1], [1, 1, 2, 2]),
+         ([1, 1, 2, 2], [0, 0, 1.1, 1]), ([0, 0, 1, 1], [3, 4, 5, 6])],
+        dtype=box_data_type)
+    self.detected_class_tuples = np.array(
+        [(1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 4, 5)], dtype=label_data_type)
+    self.detected_scores = np.array([0.2, 0.8, 0.1, 0.5], dtype=float)
+
+    self.groundtruth_box_tuples = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2]), ([1, 1, 2, 2], [0, 0, 1.1, 1]),
+         ([0, 0, 1, 1], [3, 4, 5, 5.5])],
+        dtype=box_data_type)
+    self.groundtruth_class_tuples = np.array(
+        [(1, 2, 3), (1, 7, 3), (1, 4, 5)], dtype=label_data_type)
+
+  def test_tp_fp_eval(self):
+    scores, tp_fp_labels, mapping = self.eval.compute_detection_tp_fp(
+        self.detected_box_tuples, self.detected_scores,
+        self.detected_class_tuples, self.groundtruth_box_tuples,
+        self.groundtruth_class_tuples)
+
+    expected_scores = np.array([0.8, 0.5, 0.2, 0.1], dtype=float)
+    expected_tp_fp_labels = np.array([True, True, False, False], dtype=bool)
+    expected_mapping = np.array([1, 3, 0, 2])
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+    self.assertTrue(np.allclose(expected_mapping, mapping))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils.py
new file mode 100644
index 00000000..b8b30fd9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils.py
@@ -0,0 +1,367 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utils used to manipulate tensor shapes."""
+
+import tensorflow as tf
+
+from object_detection.utils import static_shape
+
+
+def _is_tensor(t):
+  """Returns a boolean indicating whether the input is a tensor.
+
+  Args:
+    t: the input to be tested.
+
+  Returns:
+    a boolean that indicates whether t is a tensor.
+  """
+  return isinstance(t, (tf.Tensor, tf.SparseTensor, tf.Variable))
+
+
+def _set_dim_0(t, d0):
+  """Sets the 0-th dimension of the input tensor.
+
+  Args:
+    t: the input tensor, assuming the rank is at least 1.
+    d0: an integer indicating the 0-th dimension of the input tensor.
+
+  Returns:
+    the tensor t with the 0-th dimension set.
+  """
+  t_shape = t.get_shape().as_list()
+  t_shape[0] = d0
+  t.set_shape(t_shape)
+  return t
+
+
+def pad_tensor(t, length):
+  """Pads the input tensor with 0s along the first dimension up to the length.
+
+  Args:
+    t: the input tensor, assuming the rank is at least 1.
+    length: a tensor of shape [1]  or an integer, indicating the first dimension
+      of the input tensor t after padding, assuming length <= t.shape[0].
+
+  Returns:
+    padded_t: the padded tensor, whose first dimension is length. If the length
+      is an integer, the first dimension of padded_t is set to length
+      statically.
+  """
+  t_rank = tf.rank(t)
+  t_shape = tf.shape(t)
+  t_d0 = t_shape[0]
+  pad_d0 = tf.expand_dims(length - t_d0, 0)
+  pad_shape = tf.cond(
+      tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
+      lambda: tf.expand_dims(length - t_d0, 0))
+  padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
+  if not _is_tensor(length):
+    padded_t = _set_dim_0(padded_t, length)
+  return padded_t
+
+
+def clip_tensor(t, length):
+  """Clips the input tensor along the first dimension up to the length.
+
+  Args:
+    t: the input tensor, assuming the rank is at least 1.
+    length: a tensor of shape [1]  or an integer, indicating the first dimension
+      of the input tensor t after clipping, assuming length <= t.shape[0].
+
+  Returns:
+    clipped_t: the clipped tensor, whose first dimension is length. If the
+      length is an integer, the first dimension of clipped_t is set to length
+      statically.
+  """
+  clipped_t = tf.gather(t, tf.range(length))
+  if not _is_tensor(length):
+    clipped_t = _set_dim_0(clipped_t, length)
+  return clipped_t
+
+
+def pad_or_clip_tensor(t, length):
+  """Pad or clip the input tensor along the first dimension.
+
+  Args:
+    t: the input tensor, assuming the rank is at least 1.
+    length: a tensor of shape [1]  or an integer, indicating the first dimension
+      of the input tensor t after processing.
+
+  Returns:
+    processed_t: the processed tensor, whose first dimension is length. If the
+      length is an integer, the first dimension of the processed tensor is set
+      to length statically.
+  """
+  return pad_or_clip_nd(t, [length] + t.shape.as_list()[1:])
+
+
+def pad_or_clip_nd(tensor, output_shape):
+  """Pad or Clip given tensor to the output shape.
+
+  Args:
+    tensor: Input tensor to pad or clip.
+    output_shape: A list of integers / scalar tensors (or None for dynamic dim)
+      representing the size to pad or clip each dimension of the input tensor.
+
+  Returns:
+    Input tensor padded and clipped to the output shape.
+  """
+  tensor_shape = tf.shape(tensor)
+  clip_size = [
+      tf.where(tensor_shape[i] - shape > 0, shape, -1)
+      if shape is not None else -1 for i, shape in enumerate(output_shape)
+  ]
+  clipped_tensor = tf.slice(
+      tensor,
+      begin=tf.zeros(len(clip_size), dtype=tf.int32),
+      size=clip_size)
+
+  # Pad tensor if the shape of clipped tensor is smaller than the expected
+  # shape.
+  clipped_tensor_shape = tf.shape(clipped_tensor)
+  trailing_paddings = [
+      shape - clipped_tensor_shape[i] if shape is not None else 0
+      for i, shape in enumerate(output_shape)
+  ]
+  paddings = tf.stack(
+      [
+          tf.zeros(len(trailing_paddings), dtype=tf.int32),
+          trailing_paddings
+      ],
+      axis=1)
+  padded_tensor = tf.pad(clipped_tensor, paddings=paddings)
+  output_static_shape = [
+      dim if not isinstance(dim, tf.Tensor) else None for dim in output_shape
+  ]
+  padded_tensor.set_shape(output_static_shape)
+  return padded_tensor
+
+
+def combined_static_and_dynamic_shape(tensor):
+  """Returns a list containing static and dynamic values for the dimensions.
+
+  Returns a list of static and dynamic values for shape dimensions. This is
+  useful to preserve static shapes when available in reshape operation.
+
+  Args:
+    tensor: A tensor of any type.
+
+  Returns:
+    A list of size tensor.shape.ndims containing integers or a scalar tensor.
+  """
+  static_tensor_shape = tensor.shape.as_list()
+  dynamic_tensor_shape = tf.shape(tensor)
+  combined_shape = []
+  for index, dim in enumerate(static_tensor_shape):
+    if dim is not None:
+      combined_shape.append(dim)
+    else:
+      combined_shape.append(dynamic_tensor_shape[index])
+  return combined_shape
+
+
+def static_or_dynamic_map_fn(fn, elems, dtype=None,
+                             parallel_iterations=32, back_prop=True):
+  """Runs map_fn as a (static) for loop when possible.
+
+  This function rewrites the map_fn as an explicit unstack input -> for loop
+  over function calls -> stack result combination.  This allows our graphs to
+  be acyclic when the batch size is static.
+  For comparison, see https://www.tensorflow.org/api_docs/python/tf/map_fn.
+
+  Note that `static_or_dynamic_map_fn` currently is not *fully* interchangeable
+  with the default tf.map_fn function as it does not accept nested inputs (only
+  Tensors or lists of Tensors).  Likewise, the output of `fn` can only be a
+  Tensor or list of Tensors.
+
+  TODO(jonathanhuang): make this function fully interchangeable with tf.map_fn.
+
+  Args:
+    fn: The callable to be performed. It accepts one argument, which will have
+      the same structure as elems. Its output must have the
+      same structure as elems.
+    elems: A tensor or list of tensors, each of which will
+      be unpacked along their first dimension. The sequence of the
+      resulting slices will be applied to fn.
+    dtype:  (optional) The output type(s) of fn. If fn returns a structure of
+      Tensors differing from the structure of elems, then dtype is not optional
+      and must have the same structure as the output of fn.
+    parallel_iterations: (optional) number of batch items to process in
+      parallel.  This flag is only used if the native tf.map_fn is used
+      and defaults to 32 instead of 10 (unlike the standard tf.map_fn default).
+    back_prop: (optional) True enables support for back propagation.
+      This flag is only used if the native tf.map_fn is used.
+
+  Returns:
+    A tensor or sequence of tensors. Each tensor packs the
+    results of applying fn to tensors unpacked from elems along the first
+    dimension, from first to last.
+  Raises:
+    ValueError: if `elems` a Tensor or a list of Tensors.
+    ValueError: if `fn` does not return a Tensor or list of Tensors
+  """
+  if isinstance(elems, list):
+    for elem in elems:
+      if not isinstance(elem, tf.Tensor):
+        raise ValueError('`elems` must be a Tensor or list of Tensors.')
+
+    elem_shapes = [elem.shape.as_list() for elem in elems]
+    # Fall back on tf.map_fn if shapes of each entry of `elems` are None or fail
+    # to all be the same size along the batch dimension.
+    for elem_shape in elem_shapes:
+      if (not elem_shape or not elem_shape[0]
+          or elem_shape[0] != elem_shapes[0][0]):
+        return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
+    arg_tuples = zip(*[tf.unstack(elem) for elem in elems])
+    outputs = [fn(arg_tuple) for arg_tuple in arg_tuples]
+  else:
+    if not isinstance(elems, tf.Tensor):
+      raise ValueError('`elems` must be a Tensor or list of Tensors.')
+    elems_shape = elems.shape.as_list()
+    if not elems_shape or not elems_shape[0]:
+      return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
+    outputs = [fn(arg) for arg in tf.unstack(elems)]
+  # Stack `outputs`, which is a list of Tensors or list of lists of Tensors
+  if all([isinstance(output, tf.Tensor) for output in outputs]):
+    return tf.stack(outputs)
+  else:
+    if all([isinstance(output, list) for output in outputs]):
+      if all([all(
+          [isinstance(entry, tf.Tensor) for entry in output_list])
+              for output_list in outputs]):
+        return [tf.stack(output_tuple) for output_tuple in zip(*outputs)]
+  raise ValueError('`fn` should return a Tensor or a list of Tensors.')
+
+
+def check_min_image_dim(min_dim, image_tensor):
+  """Checks that the image width/height are greater than some number.
+
+  This function is used to check that the width and height of an image are above
+  a certain value. If the image shape is static, this function will perform the
+  check at graph construction time. Otherwise, if the image shape varies, an
+  Assertion control dependency will be added to the graph.
+
+  Args:
+    min_dim: The minimum number of pixels along the width and height of the
+             image.
+    image_tensor: The image tensor to check size for.
+
+  Returns:
+    If `image_tensor` has dynamic size, return `image_tensor` with a Assert
+    control dependency. Otherwise returns image_tensor.
+
+  Raises:
+    ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
+  """
+  image_shape = image_tensor.get_shape()
+  image_height = static_shape.get_height(image_shape)
+  image_width = static_shape.get_width(image_shape)
+  if image_height is None or image_width is None:
+    shape_assert = tf.Assert(
+        tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
+                       tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
+        ['image size must be >= {} in both height and width.'.format(min_dim)])
+    with tf.control_dependencies([shape_assert]):
+      return tf.identity(image_tensor)
+
+  if image_height < min_dim or image_width < min_dim:
+    raise ValueError(
+        'image size must be >= %d in both height and width; image dim = %d,%d' %
+        (min_dim, image_height, image_width))
+
+  return image_tensor
+
+
+def assert_shape_equal(shape_a, shape_b):
+  """Asserts that shape_a and shape_b are equal.
+
+  If the shapes are static, raises a ValueError when the shapes
+  mismatch.
+
+  If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
+  mismatch.
+
+  Args:
+    shape_a: a list containing shape of the first tensor.
+    shape_b: a list containing shape of the second tensor.
+
+  Returns:
+    Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
+    when the shapes are dynamic.
+
+  Raises:
+    ValueError: When shapes are both static and unequal.
+  """
+  if (all(isinstance(dim, int) for dim in shape_a) and
+      all(isinstance(dim, int) for dim in shape_b)):
+    if shape_a != shape_b:
+      raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
+    else: return tf.no_op()
+  else:
+    return tf.assert_equal(shape_a, shape_b)
+
+
+def assert_shape_equal_along_first_dimension(shape_a, shape_b):
+  """Asserts that shape_a and shape_b are the same along the 0th-dimension.
+
+  If the shapes are static, raises a ValueError when the shapes
+  mismatch.
+
+  If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
+  mismatch.
+
+  Args:
+    shape_a: a list containing shape of the first tensor.
+    shape_b: a list containing shape of the second tensor.
+
+  Returns:
+    Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
+    when the shapes are dynamic.
+
+  Raises:
+    ValueError: When shapes are both static and unequal.
+  """
+  if isinstance(shape_a[0], int) and isinstance(shape_b[0], int):
+    if shape_a[0] != shape_b[0]:
+      raise ValueError('Unequal first dimension {}, {}'.format(
+          shape_a[0], shape_b[0]))
+    else: return tf.no_op()
+  else:
+    return tf.assert_equal(shape_a[0], shape_b[0])
+
+
+def assert_box_normalized(boxes, maximum_normalized_coordinate=1.1):
+  """Asserts the input box tensor is normalized.
+
+  Args:
+    boxes: a tensor of shape [N, 4] where N is the number of boxes.
+    maximum_normalized_coordinate: Maximum coordinate value to be considered
+      as normalized, default to 1.1.
+
+  Returns:
+    a tf.Assert op which fails when the input box tensor is not normalized.
+
+  Raises:
+    ValueError: When the input box tensor is not normalized.
+  """
+  box_minimum = tf.reduce_min(boxes)
+  box_maximum = tf.reduce_max(boxes)
+  return tf.Assert(
+      tf.logical_and(
+          tf.less_equal(box_maximum, maximum_normalized_coordinate),
+          tf.greater_equal(box_minimum, 0)),
+      [boxes])
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils_test.py
new file mode 100644
index 00000000..b2b33456
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/shape_utils_test.py
@@ -0,0 +1,337 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.shape_utils."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import shape_utils
+
+
+class UtilTest(tf.test.TestCase):
+
+  def test_pad_tensor_using_integer_input(self):
+    t1 = tf.constant([1], dtype=tf.int32)
+    pad_t1 = shape_utils.pad_tensor(t1, 2)
+    t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
+    pad_t2 = shape_utils.pad_tensor(t2, 2)
+
+    self.assertEqual(2, pad_t1.get_shape()[0])
+    self.assertEqual(2, pad_t2.get_shape()[0])
+
+    with self.test_session() as sess:
+      pad_t1_result, pad_t2_result = sess.run([pad_t1, pad_t2])
+      self.assertAllEqual([1, 0], pad_t1_result)
+      self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
+
+  def test_pad_tensor_using_tensor_input(self):
+    t1 = tf.constant([1], dtype=tf.int32)
+    pad_t1 = shape_utils.pad_tensor(t1, tf.constant(2))
+    t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
+    pad_t2 = shape_utils.pad_tensor(t2, tf.constant(2))
+
+    with self.test_session() as sess:
+      pad_t1_result, pad_t2_result = sess.run([pad_t1, pad_t2])
+      self.assertAllEqual([1, 0], pad_t1_result)
+      self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
+
+  def test_clip_tensor_using_integer_input(self):
+    t1 = tf.constant([1, 2, 3], dtype=tf.int32)
+    clip_t1 = shape_utils.clip_tensor(t1, 2)
+    t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
+    clip_t2 = shape_utils.clip_tensor(t2, 2)
+
+    self.assertEqual(2, clip_t1.get_shape()[0])
+    self.assertEqual(2, clip_t2.get_shape()[0])
+
+    with self.test_session() as sess:
+      clip_t1_result, clip_t2_result = sess.run([clip_t1, clip_t2])
+      self.assertAllEqual([1, 2], clip_t1_result)
+      self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
+
+  def test_clip_tensor_using_tensor_input(self):
+    t1 = tf.constant([1, 2, 3], dtype=tf.int32)
+    clip_t1 = shape_utils.clip_tensor(t1, tf.constant(2))
+    t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
+    clip_t2 = shape_utils.clip_tensor(t2, tf.constant(2))
+
+    with self.test_session() as sess:
+      clip_t1_result, clip_t2_result = sess.run([clip_t1, clip_t2])
+      self.assertAllEqual([1, 2], clip_t1_result)
+      self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
+
+  def test_pad_or_clip_tensor_using_integer_input(self):
+    t1 = tf.constant([1], dtype=tf.int32)
+    tt1 = shape_utils.pad_or_clip_tensor(t1, 2)
+    t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
+    tt2 = shape_utils.pad_or_clip_tensor(t2, 2)
+
+    t3 = tf.constant([1, 2, 3], dtype=tf.int32)
+    tt3 = shape_utils.clip_tensor(t3, 2)
+    t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
+    tt4 = shape_utils.clip_tensor(t4, 2)
+
+    self.assertEqual(2, tt1.get_shape()[0])
+    self.assertEqual(2, tt2.get_shape()[0])
+    self.assertEqual(2, tt3.get_shape()[0])
+    self.assertEqual(2, tt4.get_shape()[0])
+
+    with self.test_session() as sess:
+      tt1_result, tt2_result, tt3_result, tt4_result = sess.run(
+          [tt1, tt2, tt3, tt4])
+      self.assertAllEqual([1, 0], tt1_result)
+      self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
+      self.assertAllEqual([1, 2], tt3_result)
+      self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
+
+  def test_pad_or_clip_tensor_using_tensor_input(self):
+    t1 = tf.constant([1], dtype=tf.int32)
+    tt1 = shape_utils.pad_or_clip_tensor(t1, tf.constant(2))
+    t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
+    tt2 = shape_utils.pad_or_clip_tensor(t2, tf.constant(2))
+
+    t3 = tf.constant([1, 2, 3], dtype=tf.int32)
+    tt3 = shape_utils.clip_tensor(t3, tf.constant(2))
+    t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
+    tt4 = shape_utils.clip_tensor(t4, tf.constant(2))
+
+    with self.test_session() as sess:
+      tt1_result, tt2_result, tt3_result, tt4_result = sess.run(
+          [tt1, tt2, tt3, tt4])
+      self.assertAllEqual([1, 0], tt1_result)
+      self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
+      self.assertAllEqual([1, 2], tt3_result)
+      self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
+
+  def test_combines_static_dynamic_shape(self):
+    tensor = tf.placeholder(tf.float32, shape=(None, 2, 3))
+    combined_shape = shape_utils.combined_static_and_dynamic_shape(
+        tensor)
+    self.assertTrue(tf.contrib.framework.is_tensor(combined_shape[0]))
+    self.assertListEqual(combined_shape[1:], [2, 3])
+
+  def test_pad_or_clip_nd_tensor(self):
+    tensor_placeholder = tf.placeholder(tf.float32, [None, 5, 4, 7])
+    output_tensor = shape_utils.pad_or_clip_nd(
+        tensor_placeholder, [None, 3, 5, tf.constant(6)])
+
+    self.assertAllEqual(output_tensor.shape.as_list(), [None, 3, 5, None])
+
+    with self.test_session() as sess:
+      output_tensor_np = sess.run(
+          output_tensor,
+          feed_dict={
+              tensor_placeholder: np.random.rand(2, 5, 4, 7),
+          })
+
+    self.assertAllEqual(output_tensor_np.shape, [2, 3, 5, 6])
+
+
+class StaticOrDynamicMapFnTest(tf.test.TestCase):
+
+  def test_with_dynamic_shape(self):
+    def fn(input_tensor):
+      return tf.reduce_sum(input_tensor)
+    input_tensor = tf.placeholder(tf.float32, shape=(None, 2))
+    map_fn_output = shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
+
+    op_names = [op.name for op in tf.get_default_graph().get_operations()]
+    self.assertTrue(any(['map' == op_name[:3] for op_name in op_names]))
+
+    with self.test_session() as sess:
+      result1 = sess.run(
+          map_fn_output, feed_dict={
+              input_tensor: [[1, 2], [3, 1], [0, 4]]})
+      result2 = sess.run(
+          map_fn_output, feed_dict={
+              input_tensor: [[-1, 1], [0, 9]]})
+      self.assertAllEqual(result1, [3, 4, 4])
+      self.assertAllEqual(result2, [0, 9])
+
+  def test_with_static_shape(self):
+    def fn(input_tensor):
+      return tf.reduce_sum(input_tensor)
+    input_tensor = tf.constant([[1, 2], [3, 1], [0, 4]], dtype=tf.float32)
+    map_fn_output = shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
+
+    op_names = [op.name for op in tf.get_default_graph().get_operations()]
+    self.assertTrue(all(['map' != op_name[:3] for op_name in op_names]))
+
+    with self.test_session() as sess:
+      result = sess.run(map_fn_output)
+      self.assertAllEqual(result, [3, 4, 4])
+
+  def test_with_multiple_dynamic_shapes(self):
+    def fn(elems):
+      input_tensor, scalar_index_tensor = elems
+      return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
+
+    input_tensor = tf.placeholder(tf.float32, shape=(None, 3))
+    scalar_index_tensor = tf.placeholder(tf.int32, shape=(None, 1))
+    map_fn_output = shape_utils.static_or_dynamic_map_fn(
+        fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
+
+    op_names = [op.name for op in tf.get_default_graph().get_operations()]
+    self.assertTrue(any(['map' == op_name[:3] for op_name in op_names]))
+
+    with self.test_session() as sess:
+      result1 = sess.run(
+          map_fn_output, feed_dict={
+              input_tensor: [[1, 2, 3], [4, 5, -1], [0, 6, 9]],
+              scalar_index_tensor: [[0], [2], [1]],
+          })
+      result2 = sess.run(
+          map_fn_output, feed_dict={
+              input_tensor: [[-1, 1, 0], [3, 9, 30]],
+              scalar_index_tensor: [[1], [0]]
+          })
+      self.assertAllEqual(result1, [1, -1, 6])
+      self.assertAllEqual(result2, [1, 3])
+
+  def test_with_multiple_static_shapes(self):
+    def fn(elems):
+      input_tensor, scalar_index_tensor = elems
+      return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
+
+    input_tensor = tf.constant([[1, 2, 3], [4, 5, -1], [0, 6, 9]],
+                               dtype=tf.float32)
+    scalar_index_tensor = tf.constant([[0], [2], [1]], dtype=tf.int32)
+    map_fn_output = shape_utils.static_or_dynamic_map_fn(
+        fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
+
+    op_names = [op.name for op in tf.get_default_graph().get_operations()]
+    self.assertTrue(all(['map' != op_name[:3] for op_name in op_names]))
+
+    with self.test_session() as sess:
+      result = sess.run(map_fn_output)
+      self.assertAllEqual(result, [1, -1, 6])
+
+  def test_fails_with_nested_input(self):
+    def fn(input_tensor):
+      return input_tensor
+    input_tensor1 = tf.constant([1])
+    input_tensor2 = tf.constant([2])
+    with self.assertRaisesRegexp(
+        ValueError, '`elems` must be a Tensor or list of Tensors.'):
+      shape_utils.static_or_dynamic_map_fn(
+          fn, [input_tensor1, [input_tensor2]], dtype=tf.float32)
+
+
+class CheckMinImageShapeTest(tf.test.TestCase):
+
+  def test_check_min_image_dim_static_shape(self):
+    input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
+    _ = shape_utils.check_min_image_dim(33, input_tensor)
+
+    with self.assertRaisesRegexp(
+        ValueError, 'image size must be >= 64 in both height and width.'):
+      _ = shape_utils.check_min_image_dim(64, input_tensor)
+
+  def test_check_min_image_dim_dynamic_shape(self):
+    input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+    image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)
+
+    with self.test_session() as sess:
+      sess.run(image_tensor,
+               feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(image_tensor,
+                 feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])})
+
+
+class AssertShapeEqualTest(tf.test.TestCase):
+
+  def test_unequal_static_shape_raises_exception(self):
+    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
+    shape_b = tf.constant(np.zeros([4, 2, 3, 1]))
+    with self.assertRaisesRegexp(
+        ValueError, 'Unequal shapes'):
+      shape_utils.assert_shape_equal(
+          shape_utils.combined_static_and_dynamic_shape(shape_a),
+          shape_utils.combined_static_and_dynamic_shape(shape_b))
+
+  def test_equal_static_shape_succeeds(self):
+    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
+    shape_b = tf.constant(np.zeros([4, 2, 2, 1]))
+    with self.test_session() as sess:
+      op = shape_utils.assert_shape_equal(
+          shape_utils.combined_static_and_dynamic_shape(shape_a),
+          shape_utils.combined_static_and_dynamic_shape(shape_b))
+      sess.run(op)
+
+  def test_unequal_dynamic_shape_raises_tf_assert(self):
+    tensor_a = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+    tensor_b = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+    op = shape_utils.assert_shape_equal(
+        shape_utils.combined_static_and_dynamic_shape(tensor_a),
+        shape_utils.combined_static_and_dynamic_shape(tensor_b))
+    with self.test_session() as sess:
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
+                                tensor_b: np.zeros([1, 4, 4, 3])})
+
+  def test_equal_dynamic_shape_succeeds(self):
+    tensor_a = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+    tensor_b = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+    op = shape_utils.assert_shape_equal(
+        shape_utils.combined_static_and_dynamic_shape(tensor_a),
+        shape_utils.combined_static_and_dynamic_shape(tensor_b))
+    with self.test_session() as sess:
+      sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
+                              tensor_b: np.zeros([1, 2, 2, 3])})
+
+  def test_unequal_static_shape_along_first_dim_raises_exception(self):
+    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
+    shape_b = tf.constant(np.zeros([6, 2, 3, 1]))
+    with self.assertRaisesRegexp(
+        ValueError, 'Unequal first dimension'):
+      shape_utils.assert_shape_equal_along_first_dimension(
+          shape_utils.combined_static_and_dynamic_shape(shape_a),
+          shape_utils.combined_static_and_dynamic_shape(shape_b))
+
+  def test_equal_static_shape_along_first_dim_succeeds(self):
+    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
+    shape_b = tf.constant(np.zeros([4, 7, 2]))
+    with self.test_session() as sess:
+      op = shape_utils.assert_shape_equal_along_first_dimension(
+          shape_utils.combined_static_and_dynamic_shape(shape_a),
+          shape_utils.combined_static_and_dynamic_shape(shape_b))
+      sess.run(op)
+
+  def test_unequal_dynamic_shape_along_first_dim_raises_tf_assert(self):
+    tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
+    tensor_b = tf.placeholder(tf.float32, shape=[None, None, 3])
+    op = shape_utils.assert_shape_equal_along_first_dimension(
+        shape_utils.combined_static_and_dynamic_shape(tensor_a),
+        shape_utils.combined_static_and_dynamic_shape(tensor_b))
+    with self.test_session() as sess:
+      with self.assertRaises(tf.errors.InvalidArgumentError):
+        sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
+                                tensor_b: np.zeros([2, 4, 3])})
+
+  def test_equal_dynamic_shape_along_first_dim_succeeds(self):
+    tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
+    tensor_b = tf.placeholder(tf.float32, shape=[None])
+    op = shape_utils.assert_shape_equal_along_first_dimension(
+        shape_utils.combined_static_and_dynamic_shape(tensor_a),
+        shape_utils.combined_static_and_dynamic_shape(tensor_b))
+    with self.test_session() as sess:
+      sess.run(op, feed_dict={tensor_a: np.zeros([5, 2, 2, 3]),
+                              tensor_b: np.zeros([5])})
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape.py
new file mode 100644
index 00000000..8e4e522f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape.py
@@ -0,0 +1,71 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Helper functions to access TensorShape values.
+
+The rank 4 tensor_shape must be of the form [batch_size, height, width, depth].
+"""
+
+
+def get_batch_size(tensor_shape):
+  """Returns batch size from the tensor shape.
+
+  Args:
+    tensor_shape: A rank 4 TensorShape.
+
+  Returns:
+    An integer representing the batch size of the tensor.
+  """
+  tensor_shape.assert_has_rank(rank=4)
+  return tensor_shape[0].value
+
+
+def get_height(tensor_shape):
+  """Returns height from the tensor shape.
+
+  Args:
+    tensor_shape: A rank 4 TensorShape.
+
+  Returns:
+    An integer representing the height of the tensor.
+  """
+  tensor_shape.assert_has_rank(rank=4)
+  return tensor_shape[1].value
+
+
+def get_width(tensor_shape):
+  """Returns width from the tensor shape.
+
+  Args:
+    tensor_shape: A rank 4 TensorShape.
+
+  Returns:
+    An integer representing the width of the tensor.
+  """
+  tensor_shape.assert_has_rank(rank=4)
+  return tensor_shape[2].value
+
+
+def get_depth(tensor_shape):
+  """Returns depth from the tensor shape.
+
+  Args:
+    tensor_shape: A rank 4 TensorShape.
+
+  Returns:
+    An integer representing the depth of the tensor.
+  """
+  tensor_shape.assert_has_rank(rank=4)
+  return tensor_shape[3].value
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape_test.py
new file mode 100644
index 00000000..99307e93
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/static_shape_test.py
@@ -0,0 +1,50 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.static_shape."""
+
+import tensorflow as tf
+
+from object_detection.utils import static_shape
+
+
+class StaticShapeTest(tf.test.TestCase):
+
+  def test_return_correct_batchSize(self):
+    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
+    self.assertEqual(32, static_shape.get_batch_size(tensor_shape))
+
+  def test_return_correct_height(self):
+    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
+    self.assertEqual(299, static_shape.get_height(tensor_shape))
+
+  def test_return_correct_width(self):
+    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
+    self.assertEqual(384, static_shape.get_width(tensor_shape))
+
+  def test_return_correct_depth(self):
+    tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
+    self.assertEqual(3, static_shape.get_depth(tensor_shape))
+
+  def test_die_on_tensor_shape_with_rank_three(self):
+    tensor_shape = tf.TensorShape(dims=[32, 299, 384])
+    with self.assertRaises(ValueError):
+      static_shape.get_batch_size(tensor_shape)
+      static_shape.get_height(tensor_shape)
+      static_shape.get_width(tensor_shape)
+      static_shape.get_depth(tensor_shape)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_case.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_case.py
new file mode 100644
index 00000000..1f34fa5a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_case.py
@@ -0,0 +1,104 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A convenience wrapper around tf.test.TestCase to enable TPU tests."""
+
+import os
+import tensorflow as tf
+from tensorflow.contrib import tpu
+
+flags = tf.app.flags
+
+flags.DEFINE_bool('tpu_test', False, 'Whether to configure test for TPU.')
+FLAGS = flags.FLAGS
+
+
+
+
+class TestCase(tf.test.TestCase):
+  """Extends tf.test.TestCase to optionally allow running tests on TPU."""
+
+  def execute_tpu(self, graph_fn, inputs):
+    """Constructs the graph, executes it on TPU and returns the result.
+
+    Args:
+      graph_fn: a callable that constructs the tensorflow graph to test. The
+        arguments of this function should correspond to `inputs`.
+      inputs: a list of numpy arrays to feed input to the computation graph.
+
+    Returns:
+      A list of numpy arrays or a scalar returned from executing the tensorflow
+      graph.
+    """
+    with self.test_session(graph=tf.Graph()) as sess:
+      placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
+      tpu_computation = tpu.rewrite(graph_fn, placeholders)
+      sess.run(tpu.initialize_system())
+      sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
+                tf.local_variables_initializer()])
+      materialized_results = sess.run(tpu_computation,
+                                      feed_dict=dict(zip(placeholders, inputs)))
+      sess.run(tpu.shutdown_system())
+      if (hasattr(materialized_results, '__len__') and
+          len(materialized_results) == 1 and
+          (isinstance(materialized_results, list) or
+           isinstance(materialized_results, tuple))):
+        materialized_results = materialized_results[0]
+    return materialized_results
+
+  def execute_cpu(self, graph_fn, inputs):
+    """Constructs the graph, executes it on CPU and returns the result.
+
+    Args:
+      graph_fn: a callable that constructs the tensorflow graph to test. The
+        arguments of this function should correspond to `inputs`.
+      inputs: a list of numpy arrays to feed input to the computation graph.
+
+    Returns:
+      A list of numpy arrays or a scalar returned from executing the tensorflow
+      graph.
+    """
+    with self.test_session(graph=tf.Graph()) as sess:
+      placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
+      results = graph_fn(*placeholders)
+      sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
+                tf.local_variables_initializer()])
+      materialized_results = sess.run(results, feed_dict=dict(zip(placeholders,
+                                                                  inputs)))
+
+      if (hasattr(materialized_results, '__len__') and
+          len(materialized_results) == 1 and
+          (isinstance(materialized_results, list) or
+           isinstance(materialized_results, tuple))):
+        materialized_results = materialized_results[0]
+    return materialized_results
+
+  def execute(self, graph_fn, inputs):
+    """Constructs the graph, creates a test session and returns the results.
+
+    The graph is executed either on TPU or CPU based on the `tpu_test` flag.
+
+    Args:
+      graph_fn: a callable that constructs the tensorflow graph to test. The
+        arguments of this function should correspond to `inputs`.
+      inputs: a list of numpy arrays to feed input to the computation graph.
+
+    Returns:
+      A list of numpy arrays or a scalar returned from executing the tensorflow
+      graph.
+    """
+    if FLAGS.tpu_test:
+      return self.execute_tpu(graph_fn, inputs)
+    else:
+      return self.execute_cpu(graph_fn, inputs)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils.py
new file mode 100644
index 00000000..cee7d9aa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils.py
@@ -0,0 +1,229 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Contains functions which are convenient for unit testing."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import anchor_generator
+from object_detection.core import box_coder
+from object_detection.core import box_list
+from object_detection.core import box_predictor
+from object_detection.core import matcher
+from object_detection.utils import shape_utils
+
+# Default size (both width and height) used for testing mask predictions.
+DEFAULT_MASK_SIZE = 5
+
+
+class MockBoxCoder(box_coder.BoxCoder):
+  """Simple `difference` BoxCoder."""
+
+  @property
+  def code_size(self):
+    return 4
+
+  def _encode(self, boxes, anchors):
+    return boxes.get() - anchors.get()
+
+  def _decode(self, rel_codes, anchors):
+    return box_list.BoxList(rel_codes + anchors.get())
+
+
+class MockMaskHead(object):
+  """Simple maskhead that returns all zeros as mask predictions."""
+
+  def __init__(self, num_classes):
+    self._num_classes = num_classes
+
+  def predict(self, features):
+    batch_size = tf.shape(features)[0]
+    return tf.zeros((batch_size, 1, self._num_classes, DEFAULT_MASK_SIZE,
+                     DEFAULT_MASK_SIZE),
+                    dtype=tf.float32)
+
+
+class MockBoxPredictor(box_predictor.BoxPredictor):
+  """Simple box predictor that ignores inputs and outputs all zeros."""
+
+  def __init__(self, is_training, num_classes, add_background_class=True):
+    super(MockBoxPredictor, self).__init__(is_training, num_classes)
+    self._add_background_class = add_background_class
+
+  def _predict(self, image_features, num_predictions_per_location):
+    image_feature = image_features[0]
+    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
+        image_feature)
+    batch_size = combined_feature_shape[0]
+    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
+    code_size = 4
+    zero = tf.reduce_sum(0 * image_feature)
+    num_class_slots = self.num_classes
+    if self._add_background_class:
+      num_class_slots = num_class_slots + 1
+    box_encodings = zero + tf.zeros(
+        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
+    class_predictions_with_background = zero + tf.zeros(
+        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
+    predictions_dict = {
+        box_predictor.BOX_ENCODINGS:
+            box_encodings,
+        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
+            class_predictions_with_background
+    }
+    return predictions_dict
+
+
+class MockKerasBoxPredictor(box_predictor.KerasBoxPredictor):
+  """Simple box predictor that ignores inputs and outputs all zeros."""
+
+  def __init__(self, is_training, num_classes, add_background_class=True):
+    super(MockKerasBoxPredictor, self).__init__(
+        is_training, num_classes, False, False)
+    self._add_background_class = add_background_class
+
+  def _predict(self, image_features, **kwargs):
+    image_feature = image_features[0]
+    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
+        image_feature)
+    batch_size = combined_feature_shape[0]
+    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
+    code_size = 4
+    zero = tf.reduce_sum(0 * image_feature)
+    num_class_slots = self.num_classes
+    if self._add_background_class:
+      num_class_slots = num_class_slots + 1
+    box_encodings = zero + tf.zeros(
+        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
+    class_predictions_with_background = zero + tf.zeros(
+        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
+    predictions_dict = {
+        box_predictor.BOX_ENCODINGS:
+            box_encodings,
+        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
+            class_predictions_with_background
+    }
+    return predictions_dict
+
+
+class MockAnchorGenerator(anchor_generator.AnchorGenerator):
+  """Mock anchor generator."""
+
+  def name_scope(self):
+    return 'MockAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    return [1]
+
+  def _generate(self, feature_map_shape_list):
+    num_anchors = sum([shape[0] * shape[1] for shape in feature_map_shape_list])
+    return box_list.BoxList(tf.zeros((num_anchors, 4), dtype=tf.float32))
+
+
+class MockMatcher(matcher.Matcher):
+  """Simple matcher that matches first anchor to first groundtruth box."""
+
+  def _match(self, similarity_matrix, valid_rows):
+    return tf.constant([0, -1, -1, -1], dtype=tf.int32)
+
+
+def create_diagonal_gradient_image(height, width, depth):
+  """Creates pyramid image. Useful for testing.
+
+  For example, pyramid_image(5, 6, 1) looks like:
+  # [[[ 5.  4.  3.  2.  1.  0.]
+  #   [ 6.  5.  4.  3.  2.  1.]
+  #   [ 7.  6.  5.  4.  3.  2.]
+  #   [ 8.  7.  6.  5.  4.  3.]
+  #   [ 9.  8.  7.  6.  5.  4.]]]
+
+  Args:
+    height: height of image
+    width: width of image
+    depth: depth of image
+
+  Returns:
+    pyramid image
+  """
+  row = np.arange(height)
+  col = np.arange(width)[::-1]
+  image_layer = np.expand_dims(row, 1) + col
+  image_layer = np.expand_dims(image_layer, 2)
+
+  image = image_layer
+  for i in range(1, depth):
+    image = np.concatenate((image, image_layer * pow(10, i)), 2)
+
+  return image.astype(np.float32)
+
+
+def create_random_boxes(num_boxes, max_height, max_width):
+  """Creates random bounding boxes of specific maximum height and width.
+
+  Args:
+    num_boxes: number of boxes.
+    max_height: maximum height of boxes.
+    max_width: maximum width of boxes.
+
+  Returns:
+    boxes: numpy array of shape [num_boxes, 4]. Each row is in form
+        [y_min, x_min, y_max, x_max].
+  """
+
+  y_1 = np.random.uniform(size=(1, num_boxes)) * max_height
+  y_2 = np.random.uniform(size=(1, num_boxes)) * max_height
+  x_1 = np.random.uniform(size=(1, num_boxes)) * max_width
+  x_2 = np.random.uniform(size=(1, num_boxes)) * max_width
+
+  boxes = np.zeros(shape=(num_boxes, 4))
+  boxes[:, 0] = np.minimum(y_1, y_2)
+  boxes[:, 1] = np.minimum(x_1, x_2)
+  boxes[:, 2] = np.maximum(y_1, y_2)
+  boxes[:, 3] = np.maximum(x_1, x_2)
+
+  return boxes.astype(np.float32)
+
+
+def first_rows_close_as_set(a, b, k=None, rtol=1e-6, atol=1e-6):
+  """Checks if first K entries of two lists are close, up to permutation.
+
+  Inputs to this assert are lists of items which can be compared via
+  numpy.allclose(...) and can be sorted.
+
+  Args:
+    a: list of items which can be compared via numpy.allclose(...) and are
+      sortable.
+    b: list of items which can be compared via numpy.allclose(...) and are
+      sortable.
+    k: a non-negative integer.  If not provided, k is set to be len(a).
+    rtol: relative tolerance.
+    atol: absolute tolerance.
+
+  Returns:
+    boolean, True if input lists a and b have the same length and
+    the first k entries of the inputs satisfy numpy.allclose() after
+    sorting entries.
+  """
+  if not isinstance(a, list) or not isinstance(b, list) or len(a) != len(b):
+    return False
+  if not k:
+    k = len(a)
+  k = min(k, len(a))
+  a_sorted = sorted(a[:k])
+  b_sorted = sorted(b[:k])
+  return all([
+      np.allclose(entry_a, entry_b, rtol, atol)
+      for (entry_a, entry_b) in zip(a_sorted, b_sorted)
+  ])
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils_test.py
new file mode 100644
index 00000000..ea173c7a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/test_utils_test.py
@@ -0,0 +1,89 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.test_utils."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.utils import test_utils
+
+
+class TestUtilsTest(tf.test.TestCase):
+
+  def test_diagonal_gradient_image(self):
+    """Tests if a good pyramid image is created."""
+    pyramid_image = test_utils.create_diagonal_gradient_image(3, 4, 2)
+
+    # Test which is easy to understand.
+    expected_first_channel = np.array([[3, 2, 1, 0],
+                                       [4, 3, 2, 1],
+                                       [5, 4, 3, 2]], dtype=np.float32)
+    self.assertAllEqual(np.squeeze(pyramid_image[:, :, 0]),
+                        expected_first_channel)
+
+    # Actual test.
+    expected_image = np.array([[[3, 30],
+                                [2, 20],
+                                [1, 10],
+                                [0, 0]],
+                               [[4, 40],
+                                [3, 30],
+                                [2, 20],
+                                [1, 10]],
+                               [[5, 50],
+                                [4, 40],
+                                [3, 30],
+                                [2, 20]]], dtype=np.float32)
+
+    self.assertAllEqual(pyramid_image, expected_image)
+
+  def test_random_boxes(self):
+    """Tests if valid random boxes are created."""
+    num_boxes = 1000
+    max_height = 3
+    max_width = 5
+    boxes = test_utils.create_random_boxes(num_boxes,
+                                           max_height,
+                                           max_width)
+
+    true_column = np.ones(shape=(num_boxes)) == 1
+    self.assertAllEqual(boxes[:, 0] < boxes[:, 2], true_column)
+    self.assertAllEqual(boxes[:, 1] < boxes[:, 3], true_column)
+
+    self.assertTrue(boxes[:, 0].min() >= 0)
+    self.assertTrue(boxes[:, 1].min() >= 0)
+    self.assertTrue(boxes[:, 2].max() <= max_height)
+    self.assertTrue(boxes[:, 3].max() <= max_width)
+
+  def test_first_rows_close_as_set(self):
+    a = [1, 2, 3, 0, 0]
+    b = [3, 2, 1, 0, 0]
+    k = 3
+    self.assertTrue(test_utils.first_rows_close_as_set(a, b, k))
+
+    a = [[1, 2], [1, 4], [0, 0]]
+    b = [[1, 4 + 1e-9], [1, 2], [0, 0]]
+    k = 2
+    self.assertTrue(test_utils.first_rows_close_as_set(a, b, k))
+
+    a = [[1, 2], [1, 4], [0, 0]]
+    b = [[1, 4 + 1e-9], [2, 2], [0, 0]]
+    k = 2
+    self.assertFalse(test_utils.first_rows_close_as_set(a, b, k))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper.py
new file mode 100644
index 00000000..141228cd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper.py
@@ -0,0 +1,147 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Helper functions for manipulating collections of variables during training.
+"""
+import logging
+import re
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+# TODO(derekjchow): Consider replacing with tf.contrib.filter_variables in
+# tensorflow/contrib/framework/python/ops/variables.py
+def filter_variables(variables, filter_regex_list, invert=False):
+  """Filters out the variables matching the filter_regex.
+
+  Filter out the variables whose name matches the any of the regular
+  expressions in filter_regex_list and returns the remaining variables.
+  Optionally, if invert=True, the complement set is returned.
+
+  Args:
+    variables: a list of tensorflow variables.
+    filter_regex_list: a list of string regular expressions.
+    invert: (boolean).  If True, returns the complement of the filter set; that
+      is, all variables matching filter_regex are kept and all others discarded.
+
+  Returns:
+    a list of filtered variables.
+  """
+  kept_vars = []
+  variables_to_ignore_patterns = list(filter(None, filter_regex_list))
+  for var in variables:
+    add = True
+    for pattern in variables_to_ignore_patterns:
+      if re.match(pattern, var.op.name):
+        add = False
+        break
+    if add != invert:
+      kept_vars.append(var)
+  return kept_vars
+
+
+def multiply_gradients_matching_regex(grads_and_vars, regex_list, multiplier):
+  """Multiply gradients whose variable names match a regular expression.
+
+  Args:
+    grads_and_vars: A list of gradient to variable pairs (tuples).
+    regex_list: A list of string regular expressions.
+    multiplier: A (float) multiplier to apply to each gradient matching the
+      regular expression.
+
+  Returns:
+    grads_and_vars: A list of gradient to variable pairs (tuples).
+  """
+  variables = [pair[1] for pair in grads_and_vars]
+  matching_vars = filter_variables(variables, regex_list, invert=True)
+  for var in matching_vars:
+    logging.info('Applying multiplier %f to variable [%s]',
+                 multiplier, var.op.name)
+  grad_multipliers = {var: float(multiplier) for var in matching_vars}
+  return slim.learning.multiply_gradients(grads_and_vars,
+                                          grad_multipliers)
+
+
+def freeze_gradients_matching_regex(grads_and_vars, regex_list):
+  """Freeze gradients whose variable names match a regular expression.
+
+  Args:
+    grads_and_vars: A list of gradient to variable pairs (tuples).
+    regex_list: A list of string regular expressions.
+
+  Returns:
+    grads_and_vars: A list of gradient to variable pairs (tuples) that do not
+      contain the variables and gradients matching the regex.
+  """
+  variables = [pair[1] for pair in grads_and_vars]
+  matching_vars = filter_variables(variables, regex_list, invert=True)
+  kept_grads_and_vars = [pair for pair in grads_and_vars
+                         if pair[1] not in matching_vars]
+  for var in matching_vars:
+    logging.info('Freezing variable [%s]', var.op.name)
+  return kept_grads_and_vars
+
+
+def get_variables_available_in_checkpoint(variables,
+                                          checkpoint_path,
+                                          include_global_step=True):
+  """Returns the subset of variables available in the checkpoint.
+
+  Inspects given checkpoint and returns the subset of variables that are
+  available in it.
+
+  TODO(rathodv): force input and output to be a dictionary.
+
+  Args:
+    variables: a list or dictionary of variables to find in checkpoint.
+    checkpoint_path: path to the checkpoint to restore variables from.
+    include_global_step: whether to include `global_step` variable, if it
+      exists. Default True.
+
+  Returns:
+    A list or dictionary of variables.
+  Raises:
+    ValueError: if `variables` is not a list or dict.
+  """
+  if isinstance(variables, list):
+    variable_names_map = {variable.op.name: variable for variable in variables}
+  elif isinstance(variables, dict):
+    variable_names_map = variables
+  else:
+    raise ValueError('`variables` is expected to be a list or dict.')
+  ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path)
+  ckpt_vars_to_shape_map = ckpt_reader.get_variable_to_shape_map()
+  if not include_global_step:
+    ckpt_vars_to_shape_map.pop(tf.GraphKeys.GLOBAL_STEP, None)
+  vars_in_ckpt = {}
+  for variable_name, variable in sorted(variable_names_map.items()):
+    if variable_name in ckpt_vars_to_shape_map:
+      if ckpt_vars_to_shape_map[variable_name] == variable.shape.as_list():
+        vars_in_ckpt[variable_name] = variable
+      else:
+        logging.warning('Variable [%s] is available in checkpoint, but has an '
+                        'incompatible shape with model variable. Checkpoint '
+                        'shape: [%s], model variable shape: [%s]. This '
+                        'variable will not be initialized from the checkpoint.',
+                        variable_name, ckpt_vars_to_shape_map[variable_name],
+                        variable.shape.as_list())
+    else:
+      logging.warning('Variable [%s] is not available in checkpoint',
+                      variable_name)
+  if isinstance(variables, list):
+    return vars_in_ckpt.values()
+  return vars_in_ckpt
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper_test.py
new file mode 100644
index 00000000..94585d71
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/variables_helper_test.py
@@ -0,0 +1,221 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.variables_helper."""
+import os
+
+import tensorflow as tf
+
+from object_detection.utils import variables_helper
+
+
+class FilterVariablesTest(tf.test.TestCase):
+
+  def _create_variables(self):
+    return [tf.Variable(1.0, name='FeatureExtractor/InceptionV3/weights'),
+            tf.Variable(1.0, name='FeatureExtractor/InceptionV3/biases'),
+            tf.Variable(1.0, name='StackProposalGenerator/weights'),
+            tf.Variable(1.0, name='StackProposalGenerator/biases')]
+
+  def test_return_all_variables_when_empty_regex(self):
+    variables = self._create_variables()
+    out_variables = variables_helper.filter_variables(variables, [''])
+    self.assertItemsEqual(out_variables, variables)
+
+  def test_return_variables_which_do_not_match_single_regex(self):
+    variables = self._create_variables()
+    out_variables = variables_helper.filter_variables(variables,
+                                                      ['FeatureExtractor/.*'])
+    self.assertItemsEqual(out_variables, variables[2:])
+
+  def test_return_variables_which_do_not_match_any_regex_in_list(self):
+    variables = self._create_variables()
+    out_variables = variables_helper.filter_variables(variables, [
+        'FeatureExtractor.*biases', 'StackProposalGenerator.*biases'
+    ])
+    self.assertItemsEqual(out_variables, [variables[0], variables[2]])
+
+  def test_return_variables_matching_empty_regex_list(self):
+    variables = self._create_variables()
+    out_variables = variables_helper.filter_variables(
+        variables, [''], invert=True)
+    self.assertItemsEqual(out_variables, [])
+
+  def test_return_variables_matching_some_regex_in_list(self):
+    variables = self._create_variables()
+    out_variables = variables_helper.filter_variables(
+        variables,
+        ['FeatureExtractor.*biases', 'StackProposalGenerator.*biases'],
+        invert=True)
+    self.assertItemsEqual(out_variables, [variables[1], variables[3]])
+
+
+class MultiplyGradientsMatchingRegexTest(tf.test.TestCase):
+
+  def _create_grads_and_vars(self):
+    return [(tf.constant(1.0),
+             tf.Variable(1.0, name='FeatureExtractor/InceptionV3/weights')),
+            (tf.constant(2.0),
+             tf.Variable(2.0, name='FeatureExtractor/InceptionV3/biases')),
+            (tf.constant(3.0),
+             tf.Variable(3.0, name='StackProposalGenerator/weights')),
+            (tf.constant(4.0),
+             tf.Variable(4.0, name='StackProposalGenerator/biases'))]
+
+  def test_multiply_all_feature_extractor_variables(self):
+    grads_and_vars = self._create_grads_and_vars()
+    regex_list = ['FeatureExtractor/.*']
+    multiplier = 0.0
+    grads_and_vars = variables_helper.multiply_gradients_matching_regex(
+        grads_and_vars, regex_list, multiplier)
+    exp_output = [(0.0, 1.0), (0.0, 2.0), (3.0, 3.0), (4.0, 4.0)]
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      output = sess.run(grads_and_vars)
+      self.assertItemsEqual(output, exp_output)
+
+  def test_multiply_all_bias_variables(self):
+    grads_and_vars = self._create_grads_and_vars()
+    regex_list = ['.*/biases']
+    multiplier = 0.0
+    grads_and_vars = variables_helper.multiply_gradients_matching_regex(
+        grads_and_vars, regex_list, multiplier)
+    exp_output = [(1.0, 1.0), (0.0, 2.0), (3.0, 3.0), (0.0, 4.0)]
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      output = sess.run(grads_and_vars)
+      self.assertItemsEqual(output, exp_output)
+
+
+class FreezeGradientsMatchingRegexTest(tf.test.TestCase):
+
+  def _create_grads_and_vars(self):
+    return [(tf.constant(1.0),
+             tf.Variable(1.0, name='FeatureExtractor/InceptionV3/weights')),
+            (tf.constant(2.0),
+             tf.Variable(2.0, name='FeatureExtractor/InceptionV3/biases')),
+            (tf.constant(3.0),
+             tf.Variable(3.0, name='StackProposalGenerator/weights')),
+            (tf.constant(4.0),
+             tf.Variable(4.0, name='StackProposalGenerator/biases'))]
+
+  def test_freeze_all_feature_extractor_variables(self):
+    grads_and_vars = self._create_grads_and_vars()
+    regex_list = ['FeatureExtractor/.*']
+    grads_and_vars = variables_helper.freeze_gradients_matching_regex(
+        grads_and_vars, regex_list)
+    exp_output = [(3.0, 3.0), (4.0, 4.0)]
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      output = sess.run(grads_and_vars)
+      self.assertItemsEqual(output, exp_output)
+
+
+class GetVariablesAvailableInCheckpointTest(tf.test.TestCase):
+
+  def test_return_all_variables_from_checkpoint(self):
+    with tf.Graph().as_default():
+      variables = [
+          tf.Variable(1.0, name='weights'),
+          tf.Variable(1.0, name='biases')
+      ]
+      checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver(variables)
+      with self.test_session() as sess:
+        sess.run(init_op)
+        saver.save(sess, checkpoint_path)
+      out_variables = variables_helper.get_variables_available_in_checkpoint(
+          variables, checkpoint_path)
+    self.assertItemsEqual(out_variables, variables)
+
+  def test_return_variables_available_in_checkpoint(self):
+    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
+    with tf.Graph().as_default():
+      weight_variable = tf.Variable(1.0, name='weights')
+      global_step = tf.train.get_or_create_global_step()
+      graph1_variables = [
+          weight_variable,
+          global_step
+      ]
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver(graph1_variables)
+      with self.test_session() as sess:
+        sess.run(init_op)
+        saver.save(sess, checkpoint_path)
+
+    with tf.Graph().as_default():
+      graph2_variables = graph1_variables + [tf.Variable(1.0, name='biases')]
+      out_variables = variables_helper.get_variables_available_in_checkpoint(
+          graph2_variables, checkpoint_path, include_global_step=False)
+    self.assertItemsEqual(out_variables, [weight_variable])
+
+  def test_return_variables_available_an_checkpoint_with_dict_inputs(self):
+    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
+    with tf.Graph().as_default():
+      graph1_variables = [
+          tf.Variable(1.0, name='ckpt_weights'),
+      ]
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver(graph1_variables)
+      with self.test_session() as sess:
+        sess.run(init_op)
+        saver.save(sess, checkpoint_path)
+
+    with tf.Graph().as_default():
+      graph2_variables_dict = {
+          'ckpt_weights': tf.Variable(1.0, name='weights'),
+          'ckpt_biases': tf.Variable(1.0, name='biases')
+      }
+      out_variables = variables_helper.get_variables_available_in_checkpoint(
+          graph2_variables_dict, checkpoint_path)
+
+    self.assertTrue(isinstance(out_variables, dict))
+    self.assertItemsEqual(out_variables.keys(), ['ckpt_weights'])
+    self.assertTrue(out_variables['ckpt_weights'].op.name == 'weights')
+
+  def test_return_variables_with_correct_sizes(self):
+    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
+    with tf.Graph().as_default():
+      bias_variable = tf.Variable(3.0, name='biases')
+      global_step = tf.train.get_or_create_global_step()
+      graph1_variables = [
+          tf.Variable([[1.0, 2.0], [3.0, 4.0]], name='weights'),
+          bias_variable,
+          global_step
+      ]
+      init_op = tf.global_variables_initializer()
+      saver = tf.train.Saver(graph1_variables)
+      with self.test_session() as sess:
+        sess.run(init_op)
+        saver.save(sess, checkpoint_path)
+
+    with tf.Graph().as_default():
+      graph2_variables = [
+          tf.Variable([1.0, 2.0], name='weights'),  # New variable shape.
+          bias_variable,
+          global_step
+      ]
+
+    out_variables = variables_helper.get_variables_available_in_checkpoint(
+        graph2_variables, checkpoint_path, include_global_step=True)
+    self.assertItemsEqual(out_variables, [bias_variable, global_step])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/visualization_utils.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/visualization_utils.py
new file mode 100644
index 00000000..33226a1c
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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A set of functions that are used for visualization.
+
+These functions often receive an image, perform some visualization on the image.
+The functions do not return a value, instead they modify the image itself.
+
+"""
+import abc
+import collections
+import functools
+# Set headless-friendly backend.
+import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements
+import matplotlib.pyplot as plt  # pylint: disable=g-import-not-at-top
+import numpy as np
+import PIL.Image as Image
+import PIL.ImageColor as ImageColor
+import PIL.ImageDraw as ImageDraw
+import PIL.ImageFont as ImageFont
+import six
+import tensorflow as tf
+
+from object_detection.core import standard_fields as fields
+from object_detection.utils import shape_utils
+
+_TITLE_LEFT_MARGIN = 10
+_TITLE_TOP_MARGIN = 10
+STANDARD_COLORS = [
+    'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
+    'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
+    'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
+    'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
+    'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
+    'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
+    'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
+    'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
+    'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
+    'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
+    'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
+    'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
+    'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
+    'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
+    'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
+    'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
+    'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
+    'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
+    'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
+    'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
+    'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
+    'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
+    'WhiteSmoke', 'Yellow', 'YellowGreen'
+]
+
+
+def save_image_array_as_png(image, output_path):
+  """Saves an image (represented as a numpy array) to PNG.
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    output_path: path to which image should be written.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  with tf.gfile.Open(output_path, 'w') as fid:
+    image_pil.save(fid, 'PNG')
+
+
+def encode_image_array_as_png_str(image):
+  """Encodes a numpy array into a PNG string.
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+
+  Returns:
+    PNG encoded image string.
+  """
+  image_pil = Image.fromarray(np.uint8(image))
+  output = six.BytesIO()
+  image_pil.save(output, format='PNG')
+  png_string = output.getvalue()
+  output.close()
+  return png_string
+
+
+def draw_bounding_box_on_image_array(image,
+                                     ymin,
+                                     xmin,
+                                     ymax,
+                                     xmax,
+                                     color='red',
+                                     thickness=4,
+                                     display_str_list=(),
+                                     use_normalized_coordinates=True):
+  """Adds a bounding box to an image (numpy array).
+
+  Bounding box coordinates can be specified in either absolute (pixel) or
+  normalized coordinates by setting the use_normalized_coordinates argument.
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    ymin: ymin of bounding box.
+    xmin: xmin of bounding box.
+    ymax: ymax of bounding box.
+    xmax: xmax of bounding box.
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list: list of strings to display in box
+                      (each to be shown on its own line).
+    use_normalized_coordinates: If True (default), treat coordinates
+      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
+      coordinates as absolute.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
+                             thickness, display_str_list,
+                             use_normalized_coordinates)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_bounding_box_on_image(image,
+                               ymin,
+                               xmin,
+                               ymax,
+                               xmax,
+                               color='red',
+                               thickness=4,
+                               display_str_list=(),
+                               use_normalized_coordinates=True):
+  """Adds a bounding box to an image.
+
+  Bounding box coordinates can be specified in either absolute (pixel) or
+  normalized coordinates by setting the use_normalized_coordinates argument.
+
+  Each string in display_str_list is displayed on a separate line above the
+  bounding box in black text on a rectangle filled with the input 'color'.
+  If the top of the bounding box extends to the edge of the image, the strings
+  are displayed below the bounding box.
+
+  Args:
+    image: a PIL.Image object.
+    ymin: ymin of bounding box.
+    xmin: xmin of bounding box.
+    ymax: ymax of bounding box.
+    xmax: xmax of bounding box.
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list: list of strings to display in box
+                      (each to be shown on its own line).
+    use_normalized_coordinates: If True (default), treat coordinates
+      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
+      coordinates as absolute.
+  """
+  draw = ImageDraw.Draw(image)
+  im_width, im_height = image.size
+  if use_normalized_coordinates:
+    (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
+                                  ymin * im_height, ymax * im_height)
+  else:
+    (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
+  draw.line([(left, top), (left, bottom), (right, bottom),
+             (right, top), (left, top)], width=thickness, fill=color)
+  try:
+    font = ImageFont.truetype('arial.ttf', 24)
+  except IOError:
+    font = ImageFont.load_default()
+
+  # If the total height of the display strings added to the top of the bounding
+  # box exceeds the top of the image, stack the strings below the bounding box
+  # instead of above.
+  display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
+  # Each display_str has a top and bottom margin of 0.05x.
+  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
+
+  if top > total_display_str_height:
+    text_bottom = top
+  else:
+    text_bottom = bottom + total_display_str_height
+  # Reverse list and print from bottom to top.
+  for display_str in display_str_list[::-1]:
+    text_width, text_height = font.getsize(display_str)
+    margin = np.ceil(0.05 * text_height)
+    draw.rectangle(
+        [(left, text_bottom - text_height - 2 * margin), (left + text_width,
+                                                          text_bottom)],
+        fill=color)
+    draw.text(
+        (left + margin, text_bottom - text_height - margin),
+        display_str,
+        fill='black',
+        font=font)
+    text_bottom -= text_height - 2 * margin
+
+
+def draw_bounding_boxes_on_image_array(image,
+                                       boxes,
+                                       color='red',
+                                       thickness=4,
+                                       display_str_list_list=()):
+  """Draws bounding boxes on image (numpy array).
+
+  Args:
+    image: a numpy array object.
+    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
+           The coordinates are in normalized format between [0, 1].
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list_list: list of list of strings.
+                           a list of strings for each bounding box.
+                           The reason to pass a list of strings for a
+                           bounding box is that it might contain
+                           multiple labels.
+
+  Raises:
+    ValueError: if boxes is not a [N, 4] array
+  """
+  image_pil = Image.fromarray(image)
+  draw_bounding_boxes_on_image(image_pil, boxes, color, thickness,
+                               display_str_list_list)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_bounding_boxes_on_image(image,
+                                 boxes,
+                                 color='red',
+                                 thickness=4,
+                                 display_str_list_list=()):
+  """Draws bounding boxes on image.
+
+  Args:
+    image: a PIL.Image object.
+    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
+           The coordinates are in normalized format between [0, 1].
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list_list: list of list of strings.
+                           a list of strings for each bounding box.
+                           The reason to pass a list of strings for a
+                           bounding box is that it might contain
+                           multiple labels.
+
+  Raises:
+    ValueError: if boxes is not a [N, 4] array
+  """
+  boxes_shape = boxes.shape
+  if not boxes_shape:
+    return
+  if len(boxes_shape) != 2 or boxes_shape[1] != 4:
+    raise ValueError('Input must be of size [N, 4]')
+  for i in range(boxes_shape[0]):
+    display_str_list = ()
+    if display_str_list_list:
+      display_str_list = display_str_list_list[i]
+    draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
+                               boxes[i, 3], color, thickness, display_str_list)
+
+
+def _visualize_boxes(image, boxes, classes, scores, category_index, **kwargs):
+  return visualize_boxes_and_labels_on_image_array(
+      image, boxes, classes, scores, category_index=category_index, **kwargs)
+
+
+def _visualize_boxes_and_masks(image, boxes, classes, scores, masks,
+                               category_index, **kwargs):
+  return visualize_boxes_and_labels_on_image_array(
+      image,
+      boxes,
+      classes,
+      scores,
+      category_index=category_index,
+      instance_masks=masks,
+      **kwargs)
+
+
+def _visualize_boxes_and_keypoints(image, boxes, classes, scores, keypoints,
+                                   category_index, **kwargs):
+  return visualize_boxes_and_labels_on_image_array(
+      image,
+      boxes,
+      classes,
+      scores,
+      category_index=category_index,
+      keypoints=keypoints,
+      **kwargs)
+
+
+def _visualize_boxes_and_masks_and_keypoints(
+    image, boxes, classes, scores, masks, keypoints, category_index, **kwargs):
+  return visualize_boxes_and_labels_on_image_array(
+      image,
+      boxes,
+      classes,
+      scores,
+      category_index=category_index,
+      instance_masks=masks,
+      keypoints=keypoints,
+      **kwargs)
+
+
+def _resize_original_image(image, image_shape):
+  image = tf.expand_dims(image, 0)
+  image = tf.image.resize_images(
+      image,
+      image_shape,
+      method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+      align_corners=True)
+  return tf.cast(tf.squeeze(image, 0), tf.uint8)
+
+
+def draw_bounding_boxes_on_image_tensors(images,
+                                         boxes,
+                                         classes,
+                                         scores,
+                                         category_index,
+                                         original_image_spatial_shape=None,
+                                         true_image_shape=None,
+                                         instance_masks=None,
+                                         keypoints=None,
+                                         max_boxes_to_draw=20,
+                                         min_score_thresh=0.2,
+                                         use_normalized_coordinates=True):
+  """Draws bounding boxes, masks, and keypoints on batch of image tensors.
+
+  Args:
+    images: A 4D uint8 image tensor of shape [N, H, W, C]. If C > 3, additional
+      channels will be ignored. If C = 1, then we convert the images to RGB
+      images.
+    boxes: [N, max_detections, 4] float32 tensor of detection boxes.
+    classes: [N, max_detections] int tensor of detection classes. Note that
+      classes are 1-indexed.
+    scores: [N, max_detections] float32 tensor of detection scores.
+    category_index: a dict that maps integer ids to category dicts. e.g.
+      {1: {1: 'dog'}, 2: {2: 'cat'}, ...}
+    original_image_spatial_shape: [N, 2] tensor containing the spatial size of
+      the original image.
+    true_image_shape: [N, 3] tensor containing the spatial size of unpadded
+      original_image.
+    instance_masks: A 4D uint8 tensor of shape [N, max_detection, H, W] with
+      instance masks.
+    keypoints: A 4D float32 tensor of shape [N, max_detection, num_keypoints, 2]
+      with keypoints.
+    max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20.
+    min_score_thresh: Minimum score threshold for visualization. Default 0.2.
+    use_normalized_coordinates: Whether to assume boxes and kepoints are in
+      normalized coordinates (as opposed to absolute coordiantes).
+      Default is True.
+
+  Returns:
+    4D image tensor of type uint8, with boxes drawn on top.
+  """
+  # Additional channels are being ignored.
+  if images.shape[3] > 3:
+    images = images[:, :, :, 0:3]
+  elif images.shape[3] == 1:
+    images = tf.image.grayscale_to_rgb(images)
+  visualization_keyword_args = {
+      'use_normalized_coordinates': use_normalized_coordinates,
+      'max_boxes_to_draw': max_boxes_to_draw,
+      'min_score_thresh': min_score_thresh,
+      'agnostic_mode': False,
+      'line_thickness': 4
+  }
+  if true_image_shape is None:
+    true_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 3])
+  else:
+    true_shapes = true_image_shape
+  if original_image_spatial_shape is None:
+    original_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 2])
+  else:
+    original_shapes = original_image_spatial_shape
+
+  if instance_masks is not None and keypoints is None:
+    visualize_boxes_fn = functools.partial(
+        _visualize_boxes_and_masks,
+        category_index=category_index,
+        **visualization_keyword_args)
+    elems = [
+        true_shapes, original_shapes, images, boxes, classes, scores,
+        instance_masks
+    ]
+  elif instance_masks is None and keypoints is not None:
+    visualize_boxes_fn = functools.partial(
+        _visualize_boxes_and_keypoints,
+        category_index=category_index,
+        **visualization_keyword_args)
+    elems = [
+        true_shapes, original_shapes, images, boxes, classes, scores, keypoints
+    ]
+  elif instance_masks is not None and keypoints is not None:
+    visualize_boxes_fn = functools.partial(
+        _visualize_boxes_and_masks_and_keypoints,
+        category_index=category_index,
+        **visualization_keyword_args)
+    elems = [
+        true_shapes, original_shapes, images, boxes, classes, scores,
+        instance_masks, keypoints
+    ]
+  else:
+    visualize_boxes_fn = functools.partial(
+        _visualize_boxes,
+        category_index=category_index,
+        **visualization_keyword_args)
+    elems = [
+        true_shapes, original_shapes, images, boxes, classes, scores
+    ]
+
+  def draw_boxes(image_and_detections):
+    """Draws boxes on image."""
+    true_shape = image_and_detections[0]
+    original_shape = image_and_detections[1]
+    if true_image_shape is not None:
+      image = shape_utils.pad_or_clip_nd(image_and_detections[2],
+                                         [true_shape[0], true_shape[1], 3])
+    if original_image_spatial_shape is not None:
+      image_and_detections[2] = _resize_original_image(image, original_shape)
+
+    image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections[2:],
+                                  tf.uint8)
+    return image_with_boxes
+
+  images = tf.map_fn(draw_boxes, elems, dtype=tf.uint8, back_prop=False)
+  return images
+
+
+def draw_side_by_side_evaluation_image(eval_dict,
+                                       category_index,
+                                       max_boxes_to_draw=20,
+                                       min_score_thresh=0.2,
+                                       use_normalized_coordinates=True):
+  """Creates a side-by-side image with detections and groundtruth.
+
+  Bounding boxes (and instance masks, if available) are visualized on both
+  subimages.
+
+  Args:
+    eval_dict: The evaluation dictionary returned by
+      eval_util.result_dict_for_batched_example() or
+      eval_util.result_dict_for_single_example().
+    category_index: A category index (dictionary) produced from a labelmap.
+    max_boxes_to_draw: The maximum number of boxes to draw for detections.
+    min_score_thresh: The minimum score threshold for showing detections.
+    use_normalized_coordinates: Whether to assume boxes and kepoints are in
+      normalized coordinates (as opposed to absolute coordiantes).
+      Default is True.
+
+  Returns:
+    A list of [1, H, 2 * W, C] uint8 tensor. The subimage on the left
+      corresponds to detections, while the subimage on the right corresponds to
+      groundtruth.
+  """
+  detection_fields = fields.DetectionResultFields()
+  input_data_fields = fields.InputDataFields()
+
+  images_with_detections_list = []
+
+  # Add the batch dimension if the eval_dict is for single example.
+  if len(eval_dict[detection_fields.detection_classes].shape) == 1:
+    for key in eval_dict:
+      if key != input_data_fields.original_image:
+        eval_dict[key] = tf.expand_dims(eval_dict[key], 0)
+
+  for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
+    instance_masks = None
+    if detection_fields.detection_masks in eval_dict:
+      instance_masks = tf.cast(
+          tf.expand_dims(
+              eval_dict[detection_fields.detection_masks][indx], axis=0),
+          tf.uint8)
+    keypoints = None
+    if detection_fields.detection_keypoints in eval_dict:
+      keypoints = tf.expand_dims(
+          eval_dict[detection_fields.detection_keypoints][indx], axis=0)
+    groundtruth_instance_masks = None
+    if input_data_fields.groundtruth_instance_masks in eval_dict:
+      groundtruth_instance_masks = tf.cast(
+          tf.expand_dims(
+              eval_dict[input_data_fields.groundtruth_instance_masks][indx],
+              axis=0), tf.uint8)
+
+    images_with_detections = draw_bounding_boxes_on_image_tensors(
+        tf.expand_dims(
+            eval_dict[input_data_fields.original_image][indx], axis=0),
+        tf.expand_dims(
+            eval_dict[detection_fields.detection_boxes][indx], axis=0),
+        tf.expand_dims(
+            eval_dict[detection_fields.detection_classes][indx], axis=0),
+        tf.expand_dims(
+            eval_dict[detection_fields.detection_scores][indx], axis=0),
+        category_index,
+        original_image_spatial_shape=tf.expand_dims(
+            eval_dict[input_data_fields.original_image_spatial_shape][indx],
+            axis=0),
+        true_image_shape=tf.expand_dims(
+            eval_dict[input_data_fields.true_image_shape][indx], axis=0),
+        instance_masks=instance_masks,
+        keypoints=keypoints,
+        max_boxes_to_draw=max_boxes_to_draw,
+        min_score_thresh=min_score_thresh,
+        use_normalized_coordinates=use_normalized_coordinates)
+    images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
+        tf.expand_dims(
+            eval_dict[input_data_fields.original_image][indx], axis=0),
+        tf.expand_dims(
+            eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0),
+        tf.expand_dims(
+            eval_dict[input_data_fields.groundtruth_classes][indx], axis=0),
+        tf.expand_dims(
+            tf.ones_like(
+                eval_dict[input_data_fields.groundtruth_classes][indx],
+                dtype=tf.float32),
+            axis=0),
+        category_index,
+        original_image_spatial_shape=tf.expand_dims(
+            eval_dict[input_data_fields.original_image_spatial_shape][indx],
+            axis=0),
+        true_image_shape=tf.expand_dims(
+            eval_dict[input_data_fields.true_image_shape][indx], axis=0),
+        instance_masks=groundtruth_instance_masks,
+        keypoints=None,
+        max_boxes_to_draw=None,
+        min_score_thresh=0.0,
+        use_normalized_coordinates=use_normalized_coordinates)
+    images_with_detections_list.append(
+        tf.concat([images_with_detections, images_with_groundtruth], axis=2))
+  return images_with_detections_list
+
+
+def draw_keypoints_on_image_array(image,
+                                  keypoints,
+                                  color='red',
+                                  radius=2,
+                                  use_normalized_coordinates=True):
+  """Draws keypoints on an image (numpy array).
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    keypoints: a numpy array with shape [num_keypoints, 2].
+    color: color to draw the keypoints with. Default is red.
+    radius: keypoint radius. Default value is 2.
+    use_normalized_coordinates: if True (default), treat keypoint values as
+      relative to the image.  Otherwise treat them as absolute.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  draw_keypoints_on_image(image_pil, keypoints, color, radius,
+                          use_normalized_coordinates)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_keypoints_on_image(image,
+                            keypoints,
+                            color='red',
+                            radius=2,
+                            use_normalized_coordinates=True):
+  """Draws keypoints on an image.
+
+  Args:
+    image: a PIL.Image object.
+    keypoints: a numpy array with shape [num_keypoints, 2].
+    color: color to draw the keypoints with. Default is red.
+    radius: keypoint radius. Default value is 2.
+    use_normalized_coordinates: if True (default), treat keypoint values as
+      relative to the image.  Otherwise treat them as absolute.
+  """
+  draw = ImageDraw.Draw(image)
+  im_width, im_height = image.size
+  keypoints_x = [k[1] for k in keypoints]
+  keypoints_y = [k[0] for k in keypoints]
+  if use_normalized_coordinates:
+    keypoints_x = tuple([im_width * x for x in keypoints_x])
+    keypoints_y = tuple([im_height * y for y in keypoints_y])
+  for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
+    draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
+                  (keypoint_x + radius, keypoint_y + radius)],
+                 outline=color, fill=color)
+
+
+def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
+  """Draws mask on an image.
+
+  Args:
+    image: uint8 numpy array with shape (img_height, img_height, 3)
+    mask: a uint8 numpy array of shape (img_height, img_height) with
+      values between either 0 or 1.
+    color: color to draw the keypoints with. Default is red.
+    alpha: transparency value between 0 and 1. (default: 0.4)
+
+  Raises:
+    ValueError: On incorrect data type for image or masks.
+  """
+  if image.dtype != np.uint8:
+    raise ValueError('`image` not of type np.uint8')
+  if mask.dtype != np.uint8:
+    raise ValueError('`mask` not of type np.uint8')
+  if np.any(np.logical_and(mask != 1, mask != 0)):
+    raise ValueError('`mask` elements should be in [0, 1]')
+  if image.shape[:2] != mask.shape:
+    raise ValueError('The image has spatial dimensions %s but the mask has '
+                     'dimensions %s' % (image.shape[:2], mask.shape))
+  rgb = ImageColor.getrgb(color)
+  pil_image = Image.fromarray(image)
+
+  solid_color = np.expand_dims(
+      np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
+  pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
+  pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
+  pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
+  np.copyto(image, np.array(pil_image.convert('RGB')))
+
+
+def visualize_boxes_and_labels_on_image_array(
+    image,
+    boxes,
+    classes,
+    scores,
+    category_index,
+    instance_masks=None,
+    instance_boundaries=None,
+    keypoints=None,
+    use_normalized_coordinates=False,
+    max_boxes_to_draw=20,
+    min_score_thresh=.5,
+    agnostic_mode=False,
+    line_thickness=4,
+    groundtruth_box_visualization_color='black',
+    skip_scores=False,
+    skip_labels=False):
+  """Overlay labeled boxes on an image with formatted scores and label names.
+
+  This function groups boxes that correspond to the same location
+  and creates a display string for each detection and overlays these
+  on the image. Note that this function modifies the image in place, and returns
+  that same image.
+
+  Args:
+    image: uint8 numpy array with shape (img_height, img_width, 3)
+    boxes: a numpy array of shape [N, 4]
+    classes: a numpy array of shape [N]. Note that class indices are 1-based,
+      and match the keys in the label map.
+    scores: a numpy array of shape [N] or None.  If scores=None, then
+      this function assumes that the boxes to be plotted are groundtruth
+      boxes and plot all boxes as black with no classes or scores.
+    category_index: a dict containing category dictionaries (each holding
+      category index `id` and category name `name`) keyed by category indices.
+    instance_masks: a numpy array of shape [N, image_height, image_width] with
+      values ranging between 0 and 1, can be None.
+    instance_boundaries: a numpy array of shape [N, image_height, image_width]
+      with values ranging between 0 and 1, can be None.
+    keypoints: a numpy array of shape [N, num_keypoints, 2], can
+      be None
+    use_normalized_coordinates: whether boxes is to be interpreted as
+      normalized coordinates or not.
+    max_boxes_to_draw: maximum number of boxes to visualize.  If None, draw
+      all boxes.
+    min_score_thresh: minimum score threshold for a box to be visualized
+    agnostic_mode: boolean (default: False) controlling whether to evaluate in
+      class-agnostic mode or not.  This mode will display scores but ignore
+      classes.
+    line_thickness: integer (default: 4) controlling line width of the boxes.
+    groundtruth_box_visualization_color: box color for visualizing groundtruth
+      boxes
+    skip_scores: whether to skip score when drawing a single detection
+    skip_labels: whether to skip label when drawing a single detection
+
+  Returns:
+    uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
+  """
+  # Create a display string (and color) for every box location, group any boxes
+  # that correspond to the same location.
+  box_to_display_str_map = collections.defaultdict(list)
+  box_to_color_map = collections.defaultdict(str)
+  box_to_instance_masks_map = {}
+  box_to_instance_boundaries_map = {}
+  box_to_keypoints_map = collections.defaultdict(list)
+  if not max_boxes_to_draw:
+    max_boxes_to_draw = boxes.shape[0]
+  for i in range(min(max_boxes_to_draw, boxes.shape[0])):
+    if scores is None or scores[i] > min_score_thresh:
+      box = tuple(boxes[i].tolist())
+      if instance_masks is not None:
+        box_to_instance_masks_map[box] = instance_masks[i]
+      if instance_boundaries is not None:
+        box_to_instance_boundaries_map[box] = instance_boundaries[i]
+      if keypoints is not None:
+        box_to_keypoints_map[box].extend(keypoints[i])
+      if scores is None:
+        box_to_color_map[box] = groundtruth_box_visualization_color
+      else:
+        display_str = ''
+        if not skip_labels:
+          if not agnostic_mode:
+            if classes[i] in category_index.keys():
+              class_name = category_index[classes[i]]['name']
+            else:
+              class_name = 'N/A'
+            display_str = str(class_name)
+        if not skip_scores:
+          if not display_str:
+            display_str = '{}%'.format(int(100*scores[i]))
+          else:
+            display_str = '{}: {}%'.format(display_str, int(100*scores[i]))
+        box_to_display_str_map[box].append(display_str)
+        if agnostic_mode:
+          box_to_color_map[box] = 'DarkOrange'
+        else:
+          box_to_color_map[box] = STANDARD_COLORS[
+              classes[i] % len(STANDARD_COLORS)]
+
+  # Draw all boxes onto image.
+  for box, color in box_to_color_map.items():
+    ymin, xmin, ymax, xmax = box
+    if instance_masks is not None:
+      draw_mask_on_image_array(
+          image,
+          box_to_instance_masks_map[box],
+          color=color
+      )
+    if instance_boundaries is not None:
+      draw_mask_on_image_array(
+          image,
+          box_to_instance_boundaries_map[box],
+          color='red',
+          alpha=1.0
+      )
+    draw_bounding_box_on_image_array(
+        image,
+        ymin,
+        xmin,
+        ymax,
+        xmax,
+        color=color,
+        thickness=line_thickness,
+        display_str_list=box_to_display_str_map[box],
+        use_normalized_coordinates=use_normalized_coordinates)
+    if keypoints is not None:
+      draw_keypoints_on_image_array(
+          image,
+          box_to_keypoints_map[box],
+          color=color,
+          radius=line_thickness / 2,
+          use_normalized_coordinates=use_normalized_coordinates)
+
+  return image
+
+
+def add_cdf_image_summary(values, name):
+  """Adds a tf.summary.image for a CDF plot of the values.
+
+  Normalizes `values` such that they sum to 1, plots the cumulative distribution
+  function and creates a tf image summary.
+
+  Args:
+    values: a 1-D float32 tensor containing the values.
+    name: name for the image summary.
+  """
+  def cdf_plot(values):
+    """Numpy function to plot CDF."""
+    normalized_values = values / np.sum(values)
+    sorted_values = np.sort(normalized_values)
+    cumulative_values = np.cumsum(sorted_values)
+    fraction_of_examples = (np.arange(cumulative_values.size, dtype=np.float32)
+                            / cumulative_values.size)
+    fig = plt.figure(frameon=False)
+    ax = fig.add_subplot('111')
+    ax.plot(fraction_of_examples, cumulative_values)
+    ax.set_ylabel('cumulative normalized values')
+    ax.set_xlabel('fraction of examples')
+    fig.canvas.draw()
+    width, height = fig.get_size_inches() * fig.get_dpi()
+    image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+        1, int(height), int(width), 3)
+    return image
+  cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
+  tf.summary.image(name, cdf_plot)
+
+
+def add_hist_image_summary(values, bins, name):
+  """Adds a tf.summary.image for a histogram plot of the values.
+
+  Plots the histogram of values and creates a tf image summary.
+
+  Args:
+    values: a 1-D float32 tensor containing the values.
+    bins: bin edges which will be directly passed to np.histogram.
+    name: name for the image summary.
+  """
+
+  def hist_plot(values, bins):
+    """Numpy function to plot hist."""
+    fig = plt.figure(frameon=False)
+    ax = fig.add_subplot('111')
+    y, x = np.histogram(values, bins=bins)
+    ax.plot(x[:-1], y)
+    ax.set_ylabel('count')
+    ax.set_xlabel('value')
+    fig.canvas.draw()
+    width, height = fig.get_size_inches() * fig.get_dpi()
+    image = np.fromstring(
+        fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+            1, int(height), int(width), 3)
+    return image
+  hist_plot = tf.py_func(hist_plot, [values, bins], tf.uint8)
+  tf.summary.image(name, hist_plot)
+
+
+class EvalMetricOpsVisualization(object):
+  """Abstract base class responsible for visualizations during evaluation.
+
+  Currently, summary images are not run during evaluation. One way to produce
+  evaluation images in Tensorboard is to provide tf.summary.image strings as
+  `value_ops` in tf.estimator.EstimatorSpec's `eval_metric_ops`. This class is
+  responsible for accruing images (with overlaid detections and groundtruth)
+  and returning a dictionary that can be passed to `eval_metric_ops`.
+  """
+  __metaclass__ = abc.ABCMeta
+
+  def __init__(self,
+               category_index,
+               max_examples_to_draw=5,
+               max_boxes_to_draw=20,
+               min_score_thresh=0.2,
+               use_normalized_coordinates=True,
+               summary_name_prefix='evaluation_image'):
+    """Creates an EvalMetricOpsVisualization.
+
+    Args:
+      category_index: A category index (dictionary) produced from a labelmap.
+      max_examples_to_draw: The maximum number of example summaries to produce.
+      max_boxes_to_draw: The maximum number of boxes to draw for detections.
+      min_score_thresh: The minimum score threshold for showing detections.
+      use_normalized_coordinates: Whether to assume boxes and kepoints are in
+        normalized coordinates (as opposed to absolute coordiantes).
+        Default is True.
+      summary_name_prefix: A string prefix for each image summary.
+    """
+
+    self._category_index = category_index
+    self._max_examples_to_draw = max_examples_to_draw
+    self._max_boxes_to_draw = max_boxes_to_draw
+    self._min_score_thresh = min_score_thresh
+    self._use_normalized_coordinates = use_normalized_coordinates
+    self._summary_name_prefix = summary_name_prefix
+    self._images = []
+
+  def clear(self):
+    self._images = []
+
+  def add_images(self, images):
+    """Store a list of images, each with shape [1, H, W, C]."""
+    if len(self._images) >= self._max_examples_to_draw:
+      return
+
+    # Store images and clip list if necessary.
+    self._images.extend(images)
+    if len(self._images) > self._max_examples_to_draw:
+      self._images[self._max_examples_to_draw:] = []
+
+  def get_estimator_eval_metric_ops(self, eval_dict):
+    """Returns metric ops for use in tf.estimator.EstimatorSpec.
+
+    Args:
+      eval_dict: A dictionary that holds an image, groundtruth, and detections
+        for a batched example. Note that, we use only the first example for
+        visualization. See eval_util.result_dict_for_batched_example() for a
+        convenient method for constructing such a dictionary. The dictionary
+        contains
+        fields.InputDataFields.original_image: [batch_size, H, W, 3] image.
+        fields.InputDataFields.original_image_spatial_shape: [batch_size, 2]
+          tensor containing the size of the original image.
+        fields.InputDataFields.true_image_shape: [batch_size, 3]
+          tensor containing the spatial size of the upadded original image.
+        fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4]
+          float32 tensor with groundtruth boxes in range [0.0, 1.0].
+        fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes]
+          int64 tensor with 1-indexed groundtruth classes.
+        fields.InputDataFields.groundtruth_instance_masks - (optional)
+          [batch_size, num_boxes, H, W] int64 tensor with instance masks.
+        fields.DetectionResultFields.detection_boxes - [batch_size,
+          max_num_boxes, 4] float32 tensor with detection boxes in range [0.0,
+          1.0].
+        fields.DetectionResultFields.detection_classes - [batch_size,
+          max_num_boxes] int64 tensor with 1-indexed detection classes.
+        fields.DetectionResultFields.detection_scores - [batch_size,
+          max_num_boxes] float32 tensor with detection scores.
+        fields.DetectionResultFields.detection_masks - (optional) [batch_size,
+          max_num_boxes, H, W] float32 tensor of binarized masks.
+        fields.DetectionResultFields.detection_keypoints - (optional)
+          [batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with
+          keypoints.
+
+    Returns:
+      A dictionary of image summary names to tuple of (value_op, update_op). The
+      `update_op` is the same for all items in the dictionary, and is
+      responsible for saving a single side-by-side image with detections and
+      groundtruth. Each `value_op` holds the tf.summary.image string for a given
+      image.
+    """
+    if self._max_examples_to_draw == 0:
+      return {}
+    images = self.images_from_evaluation_dict(eval_dict)
+
+    def get_images():
+      """Returns a list of images, padded to self._max_images_to_draw."""
+      images = self._images
+      while len(images) < self._max_examples_to_draw:
+        images.append(np.array(0, dtype=np.uint8))
+      self.clear()
+      return images
+
+    def image_summary_or_default_string(summary_name, image):
+      """Returns image summaries for non-padded elements."""
+      return tf.cond(
+          tf.equal(tf.size(tf.shape(image)), 4),
+          lambda: tf.summary.image(summary_name, image),
+          lambda: tf.constant(''))
+
+    update_op = tf.py_func(self.add_images, [[images[0]]], [])
+    image_tensors = tf.py_func(
+        get_images, [], [tf.uint8] * self._max_examples_to_draw)
+    eval_metric_ops = {}
+    for i, image in enumerate(image_tensors):
+      summary_name = self._summary_name_prefix + '/' + str(i)
+      value_op = image_summary_or_default_string(summary_name, image)
+      eval_metric_ops[summary_name] = (value_op, update_op)
+    return eval_metric_ops
+
+  @abc.abstractmethod
+  def images_from_evaluation_dict(self, eval_dict):
+    """Converts evaluation dictionary into a list of image tensors.
+
+    To be overridden by implementations.
+
+    Args:
+      eval_dict: A dictionary with all the necessary information for producing
+        visualizations.
+
+    Returns:
+      A list of [1, H, W, C] uint8 tensors.
+    """
+    raise NotImplementedError
+
+
+class VisualizeSingleFrameDetections(EvalMetricOpsVisualization):
+  """Class responsible for single-frame object detection visualizations."""
+
+  def __init__(self,
+               category_index,
+               max_examples_to_draw=5,
+               max_boxes_to_draw=20,
+               min_score_thresh=0.2,
+               use_normalized_coordinates=True,
+               summary_name_prefix='Detections_Left_Groundtruth_Right'):
+    super(VisualizeSingleFrameDetections, self).__init__(
+        category_index=category_index,
+        max_examples_to_draw=max_examples_to_draw,
+        max_boxes_to_draw=max_boxes_to_draw,
+        min_score_thresh=min_score_thresh,
+        use_normalized_coordinates=use_normalized_coordinates,
+        summary_name_prefix=summary_name_prefix)
+
+  def images_from_evaluation_dict(self, eval_dict):
+    return draw_side_by_side_evaluation_image(
+        eval_dict, self._category_index, self._max_boxes_to_draw,
+        self._min_score_thresh, self._use_normalized_coordinates)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/visualization_utils_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/visualization_utils_test.py
new file mode 100644
index 00000000..4799cd44
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/visualization_utils_test.py
@@ -0,0 +1,372 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for object_detection.utils.visualization_utils."""
+import logging
+import os
+
+import numpy as np
+import PIL.Image as Image
+import tensorflow as tf
+
+from object_detection.core import standard_fields as fields
+from object_detection.utils import visualization_utils
+
+_TESTDATA_PATH = 'object_detection/test_images'
+
+
+class VisualizationUtilsTest(tf.test.TestCase):
+
+  def create_colorful_test_image(self):
+    """This function creates an image that can be used to test vis functions.
+
+    It makes an image composed of four colored rectangles.
+
+    Returns:
+      colorful test numpy array image.
+    """
+    ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)
+    ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)
+    ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)
+    imr = np.concatenate((ch255, ch128, ch128), axis=2)
+    img = np.concatenate((ch255, ch255, ch0), axis=2)
+    imb = np.concatenate((ch255, ch0, ch255), axis=2)
+    imw = np.concatenate((ch128, ch128, ch128), axis=2)
+    imu = np.concatenate((imr, img), axis=1)
+    imd = np.concatenate((imb, imw), axis=1)
+    image = np.concatenate((imu, imd), axis=0)
+    return image
+
+  def create_test_image_with_five_channels(self):
+    return np.full([100, 200, 5], 255, dtype=np.uint8)
+
+  def create_test_grayscale_image(self):
+    return np.full([100, 200, 1], 255, dtype=np.uint8)
+
+  def test_draw_bounding_box_on_image(self):
+    test_image = self.create_colorful_test_image()
+    test_image = Image.fromarray(test_image)
+    width_original, height_original = test_image.size
+    ymin = 0.25
+    ymax = 0.75
+    xmin = 0.4
+    xmax = 0.6
+
+    visualization_utils.draw_bounding_box_on_image(test_image, ymin, xmin, ymax,
+                                                   xmax)
+    width_final, height_final = test_image.size
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_bounding_box_on_image_array(self):
+    test_image = self.create_colorful_test_image()
+    width_original = test_image.shape[0]
+    height_original = test_image.shape[1]
+    ymin = 0.25
+    ymax = 0.75
+    xmin = 0.4
+    xmax = 0.6
+
+    visualization_utils.draw_bounding_box_on_image_array(
+        test_image, ymin, xmin, ymax, xmax)
+    width_final = test_image.shape[0]
+    height_final = test_image.shape[1]
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_bounding_boxes_on_image(self):
+    test_image = self.create_colorful_test_image()
+    test_image = Image.fromarray(test_image)
+    width_original, height_original = test_image.size
+    boxes = np.array([[0.25, 0.75, 0.4, 0.6],
+                      [0.1, 0.1, 0.9, 0.9]])
+
+    visualization_utils.draw_bounding_boxes_on_image(test_image, boxes)
+    width_final, height_final = test_image.size
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_bounding_boxes_on_image_array(self):
+    test_image = self.create_colorful_test_image()
+    width_original = test_image.shape[0]
+    height_original = test_image.shape[1]
+    boxes = np.array([[0.25, 0.75, 0.4, 0.6],
+                      [0.1, 0.1, 0.9, 0.9]])
+
+    visualization_utils.draw_bounding_boxes_on_image_array(test_image, boxes)
+    width_final = test_image.shape[0]
+    height_final = test_image.shape[1]
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_bounding_boxes_on_image_tensors(self):
+    """Tests that bounding box utility produces reasonable results."""
+    category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}}
+
+    fname = os.path.join(_TESTDATA_PATH, 'image1.jpg')
+    image_np = np.array(Image.open(fname))
+    images_np = np.stack((image_np, image_np), axis=0)
+    original_image_shape = [[636, 512], [636, 512]]
+
+    with tf.Graph().as_default():
+      images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
+      image_shape = tf.constant(original_image_shape, dtype=tf.int32)
+      boxes = tf.constant([[[0.4, 0.25, 0.75, 0.75], [0.5, 0.3, 0.6, 0.9]],
+                           [[0.25, 0.25, 0.75, 0.75], [0.1, 0.3, 0.6, 1.0]]])
+      classes = tf.constant([[1, 1], [1, 2]], dtype=tf.int64)
+      scores = tf.constant([[0.8, 0.1], [0.6, 0.5]])
+      images_with_boxes = (
+          visualization_utils.draw_bounding_boxes_on_image_tensors(
+              images_tensor,
+              boxes,
+              classes,
+              scores,
+              category_index,
+              original_image_spatial_shape=image_shape,
+              true_image_shape=image_shape,
+              min_score_thresh=0.2))
+
+      with self.test_session() as sess:
+        sess.run(tf.global_variables_initializer())
+
+        # Write output images for visualization.
+        images_with_boxes_np = sess.run(images_with_boxes)
+        self.assertEqual(images_np.shape[0], images_with_boxes_np.shape[0])
+        self.assertEqual(images_np.shape[3], images_with_boxes_np.shape[3])
+        self.assertEqual(
+            tuple(original_image_shape[0]), images_with_boxes_np.shape[1:3])
+        for i in range(images_with_boxes_np.shape[0]):
+          img_name = 'image_' + str(i) + '.png'
+          output_file = os.path.join(self.get_temp_dir(), img_name)
+          logging.info('Writing output image %d to %s', i, output_file)
+          image_pil = Image.fromarray(images_with_boxes_np[i, ...])
+          image_pil.save(output_file)
+
+  def test_draw_bounding_boxes_on_image_tensors_with_additional_channels(self):
+    """Tests the case where input image tensor has more than 3 channels."""
+    category_index = {1: {'id': 1, 'name': 'dog'}}
+    image_np = self.create_test_image_with_five_channels()
+    images_np = np.stack((image_np, image_np), axis=0)
+
+    with tf.Graph().as_default():
+      images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
+      boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4])
+      classes = tf.constant(0, dtype=tf.int64, shape=[2, 0])
+      scores = tf.constant(0, dtype=tf.float32, shape=[2, 0])
+      images_with_boxes = (
+          visualization_utils.draw_bounding_boxes_on_image_tensors(
+              images_tensor,
+              boxes,
+              classes,
+              scores,
+              category_index,
+              min_score_thresh=0.2))
+
+      with self.test_session() as sess:
+        sess.run(tf.global_variables_initializer())
+
+        final_images_np = sess.run(images_with_boxes)
+        self.assertEqual((2, 100, 200, 3), final_images_np.shape)
+
+  def test_draw_bounding_boxes_on_image_tensors_grayscale(self):
+    """Tests the case where input image tensor has one channel."""
+    category_index = {1: {'id': 1, 'name': 'dog'}}
+    image_np = self.create_test_grayscale_image()
+    images_np = np.stack((image_np, image_np), axis=0)
+
+    with tf.Graph().as_default():
+      images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
+      image_shape = tf.constant([[100, 200], [100, 200]], dtype=tf.int32)
+      boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4])
+      classes = tf.constant(0, dtype=tf.int64, shape=[2, 0])
+      scores = tf.constant(0, dtype=tf.float32, shape=[2, 0])
+      images_with_boxes = (
+          visualization_utils.draw_bounding_boxes_on_image_tensors(
+              images_tensor,
+              boxes,
+              classes,
+              scores,
+              category_index,
+              original_image_spatial_shape=image_shape,
+              true_image_shape=image_shape,
+              min_score_thresh=0.2))
+
+      with self.test_session() as sess:
+        sess.run(tf.global_variables_initializer())
+
+        final_images_np = sess.run(images_with_boxes)
+        self.assertEqual((2, 100, 200, 3), final_images_np.shape)
+
+  def test_draw_keypoints_on_image(self):
+    test_image = self.create_colorful_test_image()
+    test_image = Image.fromarray(test_image)
+    width_original, height_original = test_image.size
+    keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]]
+
+    visualization_utils.draw_keypoints_on_image(test_image, keypoints)
+    width_final, height_final = test_image.size
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_keypoints_on_image_array(self):
+    test_image = self.create_colorful_test_image()
+    width_original = test_image.shape[0]
+    height_original = test_image.shape[1]
+    keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]]
+
+    visualization_utils.draw_keypoints_on_image_array(test_image, keypoints)
+    width_final = test_image.shape[0]
+    height_final = test_image.shape[1]
+
+    self.assertEqual(width_original, width_final)
+    self.assertEqual(height_original, height_final)
+
+  def test_draw_mask_on_image_array(self):
+    test_image = np.asarray([[[0, 0, 0], [0, 0, 0]],
+                             [[0, 0, 0], [0, 0, 0]]], dtype=np.uint8)
+    mask = np.asarray([[0, 1],
+                       [1, 1]], dtype=np.uint8)
+    expected_result = np.asarray([[[0, 0, 0], [0, 0, 127]],
+                                  [[0, 0, 127], [0, 0, 127]]], dtype=np.uint8)
+    visualization_utils.draw_mask_on_image_array(test_image, mask,
+                                                 color='Blue', alpha=.5)
+    self.assertAllEqual(test_image, expected_result)
+
+  def test_add_cdf_image_summary(self):
+    values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
+    visualization_utils.add_cdf_image_summary(values, 'PositiveAnchorLoss')
+    cdf_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
+    with self.test_session():
+      cdf_image_summary.eval()
+
+  def test_add_hist_image_summary(self):
+    values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
+    bins = [0.01 * i for i in range(101)]
+    visualization_utils.add_hist_image_summary(values, bins,
+                                               'ScoresDistribution')
+    hist_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
+    with self.test_session():
+      hist_image_summary.eval()
+
+  def test_eval_metric_ops(self):
+    category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}}
+    max_examples_to_draw = 4
+    metric_op_base = 'Detections_Left_Groundtruth_Right'
+    eval_metric_ops = visualization_utils.VisualizeSingleFrameDetections(
+        category_index,
+        max_examples_to_draw=max_examples_to_draw,
+        summary_name_prefix=metric_op_base)
+    original_image = tf.placeholder(tf.uint8, [4, None, None, 3])
+    original_image_spatial_shape = tf.placeholder(tf.int32, [4, 2])
+    true_image_shape = tf.placeholder(tf.int32, [4, 3])
+    detection_boxes = tf.random_uniform([4, 20, 4],
+                                        minval=0.0,
+                                        maxval=1.0,
+                                        dtype=tf.float32)
+    detection_classes = tf.random_uniform([4, 20],
+                                          minval=1,
+                                          maxval=3,
+                                          dtype=tf.int64)
+    detection_scores = tf.random_uniform([4, 20],
+                                         minval=0.,
+                                         maxval=1.,
+                                         dtype=tf.float32)
+    groundtruth_boxes = tf.random_uniform([4, 8, 4],
+                                          minval=0.0,
+                                          maxval=1.0,
+                                          dtype=tf.float32)
+    groundtruth_classes = tf.random_uniform([4, 8],
+                                            minval=1,
+                                            maxval=3,
+                                            dtype=tf.int64)
+    eval_dict = {
+        fields.DetectionResultFields.detection_boxes:
+            detection_boxes,
+        fields.DetectionResultFields.detection_classes:
+            detection_classes,
+        fields.DetectionResultFields.detection_scores:
+            detection_scores,
+        fields.InputDataFields.original_image:
+            original_image,
+        fields.InputDataFields.original_image_spatial_shape: (
+            original_image_spatial_shape),
+        fields.InputDataFields.true_image_shape: (true_image_shape),
+        fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes,
+        fields.InputDataFields.groundtruth_classes:
+            groundtruth_classes
+    }
+    metric_ops = eval_metric_ops.get_estimator_eval_metric_ops(eval_dict)
+    _, update_op = metric_ops[metric_ops.keys()[0]]
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      value_ops = {}
+      for key, (value_op, _) in metric_ops.iteritems():
+        value_ops[key] = value_op
+
+      # First run enough update steps to surpass `max_examples_to_draw`.
+      for i in range(max_examples_to_draw):
+        # Use a unique image shape on each eval image.
+        sess.run(
+            update_op,
+            feed_dict={
+                original_image:
+                    np.random.randint(
+                        low=0,
+                        high=256,
+                        size=(4, 6 + i, 7 + i, 3),
+                        dtype=np.uint8),
+                original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
+                                               [6 + i, 7 + i], [6 + i, 7 + i]],
+                true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
+                                   [6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
+            })
+      value_ops_out = sess.run(value_ops)
+      for key, value_op in value_ops_out.iteritems():
+        self.assertNotEqual('', value_op)
+
+      # Now run fewer update steps than `max_examples_to_draw`. A single value
+      # op will be the empty string, since not enough image summaries can be
+      # produced.
+      for i in range(max_examples_to_draw - 1):
+        # Use a unique image shape on each eval image.
+        sess.run(
+            update_op,
+            feed_dict={
+                original_image:
+                    np.random.randint(
+                        low=0,
+                        high=256,
+                        size=(4, 6 + i, 7 + i, 3),
+                        dtype=np.uint8),
+                original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
+                                               [6 + i, 7 + i], [6 + i, 7 + i]],
+                true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
+                                   [6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
+            })
+      value_ops_out = sess.run(value_ops)
+      self.assertEqual(
+          '',
+          value_ops_out[metric_op_base + '/' + str(max_examples_to_draw - 1)])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation.py
new file mode 100644
index 00000000..f11f35ea
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation.py
@@ -0,0 +1,581 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Evaluator class for Visual Relations Detection.
+
+VRDDetectionEvaluator is a class which manages ground truth information of a
+visual relations detection (vrd) dataset, and computes frequently used detection
+metrics such as Precision, Recall, Recall@k, of the provided vrd detection
+results.
+It supports the following operations:
+1) Adding ground truth information of images sequentially.
+2) Adding detection results of images sequentially.
+3) Evaluating detection metrics on already inserted detection results.
+
+Note1: groundtruth should be inserted before evaluation.
+Note2: This module operates on numpy boxes and box lists.
+"""
+
+from abc import abstractmethod
+import collections
+import logging
+import numpy as np
+
+from object_detection.core import standard_fields
+from object_detection.utils import metrics
+from object_detection.utils import object_detection_evaluation
+from object_detection.utils import per_image_vrd_evaluation
+
+# Below standard input numpy datatypes are defined:
+# box_data_type - datatype of the groundtruth visual relations box annotations;
+# this datatype consists of two named boxes: subject bounding box and object
+# bounding box. Each box is of the format [y_min, x_min, y_max, x_max], each
+# coordinate being of type float32.
+# label_data_type - corresponding datatype of the visual relations label
+# annotaions; it consists of three numerical class labels: subject class label,
+# object class label and relation class label, each class label being of type
+# int32.
+vrd_box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
+single_box_data_type = np.dtype([('box', 'f4', (4,))])
+label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'), ('relation',
+                                                                  'i4')])
+
+
+class VRDDetectionEvaluator(object_detection_evaluation.DetectionEvaluator):
+  """A class to evaluate VRD detections.
+
+  This class serves as a base class for VRD evaluation in two settings:
+  - phrase detection
+  - relation detection.
+  """
+
+  def __init__(self, matching_iou_threshold=0.5, metric_prefix=None):
+    """Constructor.
+
+    Args:
+      matching_iou_threshold: IOU threshold to use for matching groundtruth
+        boxes to detection boxes.
+      metric_prefix: (optional) string prefix for metric name; if None, no
+        prefix is used.
+
+    """
+    super(VRDDetectionEvaluator, self).__init__([])
+    self._matching_iou_threshold = matching_iou_threshold
+    self._evaluation = _VRDDetectionEvaluation(
+        matching_iou_threshold=self._matching_iou_threshold)
+    self._image_ids = set([])
+    self._metric_prefix = (metric_prefix + '_') if metric_prefix else ''
+    self._evaluatable_labels = {}
+    self._negative_labels = {}
+
+  @abstractmethod
+  def _process_groundtruth_boxes(self, groundtruth_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    Phrase detection and Relation detection subclasses re-implement this method
+    depending on the task.
+
+    Args:
+      groundtruth_box_tuples:  A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
+        datatype vrd_box_data_type, single_box_data_type above).
+    """
+    raise NotImplementedError(
+        '_process_groundtruth_boxes method should be implemented in subclasses'
+        'of VRDDetectionEvaluator.')
+
+  @abstractmethod
+  def _process_detection_boxes(self, detections_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    Phrase detection and Relation detection subclasses re-implement this method
+    depending on the task.
+
+    Args:
+      detections_box_tuples:  A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
+        datatype vrd_box_data_type, single_box_data_type above).
+    """
+    raise NotImplementedError(
+        '_process_detection_boxes method should be implemented in subclasses'
+        'of VRDDetectionEvaluator.')
+
+  def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      groundtruth_dict: A dictionary containing -
+        standard_fields.InputDataFields.groundtruth_boxes: A numpy array
+          of structures with the shape [M, 1], representing M tuples, each tuple
+          containing the same number of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max] (see
+          datatype vrd_box_data_type, single_box_data_type above).
+        standard_fields.InputDataFields.groundtruth_classes: A numpy array of
+          structures shape [M, 1], representing  the class labels of the
+          corresponding bounding boxes and possibly additional classes (see
+          datatype label_data_type above).
+        standard_fields.InputDataFields.groundtruth_image_classes: numpy array
+          of shape [K] containing verified labels.
+    Raises:
+      ValueError: On adding groundtruth for an image more than once.
+    """
+    if image_id in self._image_ids:
+      raise ValueError('Image with id {} already added.'.format(image_id))
+
+    groundtruth_class_tuples = (
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes])
+    groundtruth_box_tuples = (
+        groundtruth_dict[standard_fields.InputDataFields.groundtruth_boxes])
+
+    self._evaluation.add_single_ground_truth_image_info(
+        image_key=image_id,
+        groundtruth_box_tuples=self._process_groundtruth_boxes(
+            groundtruth_box_tuples),
+        groundtruth_class_tuples=groundtruth_class_tuples)
+    self._image_ids.update([image_id])
+    all_classes = []
+    for field in groundtruth_box_tuples.dtype.fields:
+      all_classes.append(groundtruth_class_tuples[field])
+    groudtruth_positive_classes = np.unique(np.concatenate(all_classes))
+    verified_labels = groundtruth_dict.get(
+        standard_fields.InputDataFields.groundtruth_image_classes,
+        np.array([], dtype=int))
+    self._evaluatable_labels[image_id] = np.unique(
+        np.concatenate((verified_labels, groudtruth_positive_classes)))
+
+    self._negative_labels[image_id] = np.setdiff1d(verified_labels,
+                                                   groudtruth_positive_classes)
+
+  def add_single_detected_image_info(self, image_id, detections_dict):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_id: A unique string/integer identifier for the image.
+      detections_dict: A dictionary containing -
+        standard_fields.DetectionResultFields.detection_boxes: A numpy array of
+          structures with shape [N, 1], representing N tuples, each tuple
+          containing the same number of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max] (as an example
+          see datatype vrd_box_data_type, single_box_data_type above).
+        standard_fields.DetectionResultFields.detection_scores: float32 numpy
+          array of shape [N] containing detection scores for the boxes.
+        standard_fields.DetectionResultFields.detection_classes: A numpy array
+          of structures shape [N, 1], representing the class labels of the
+          corresponding bounding boxes and possibly additional classes (see
+          datatype label_data_type above).
+    """
+    if image_id not in self._image_ids:
+      logging.warn('No groundtruth for the image with id %s.', image_id)
+      # Since for the correct work of evaluator it is assumed that groundtruth
+      # is inserted first we make sure to break the code if is it not the case.
+      self._image_ids.update([image_id])
+      self._negative_labels[image_id] = np.array([])
+      self._evaluatable_labels[image_id] = np.array([])
+
+    num_detections = detections_dict[
+        standard_fields.DetectionResultFields.detection_boxes].shape[0]
+    detection_class_tuples = detections_dict[
+        standard_fields.DetectionResultFields.detection_classes]
+    detection_box_tuples = detections_dict[
+        standard_fields.DetectionResultFields.detection_boxes]
+    negative_selector = np.zeros(num_detections, dtype=bool)
+    selector = np.ones(num_detections, dtype=bool)
+    # Only check boxable labels
+    for field in detection_box_tuples.dtype.fields:
+      # Verify if one of the labels is negative (this is sure FP)
+      negative_selector |= np.isin(detection_class_tuples[field],
+                                   self._negative_labels[image_id])
+      # Verify if all labels are verified
+      selector &= np.isin(detection_class_tuples[field],
+                          self._evaluatable_labels[image_id])
+    selector |= negative_selector
+    self._evaluation.add_single_detected_image_info(
+        image_key=image_id,
+        detected_box_tuples=self._process_detection_boxes(
+            detection_box_tuples[selector]),
+        detected_scores=detections_dict[
+            standard_fields.DetectionResultFields.detection_scores][selector],
+        detected_class_tuples=detection_class_tuples[selector])
+
+  def evaluate(self, relationships=None):
+    """Compute evaluation result.
+
+    Args:
+      relationships: A dictionary of numerical label-text label mapping; if
+        specified, returns per-relationship AP.
+
+    Returns:
+      A dictionary of metrics with the following fields -
+
+      summary_metrics:
+        'weightedAP@<matching_iou_threshold>IOU' : weighted average precision
+        at the specified IOU threshold.
+        'AP@<matching_iou_threshold>IOU/<relationship>' : AP per relationship.
+        'mAP@<matching_iou_threshold>IOU': mean average precision at the
+        specified IOU threshold.
+        'Recall@50@<matching_iou_threshold>IOU': recall@50 at the specified IOU
+        threshold.
+        'Recall@100@<matching_iou_threshold>IOU': recall@100 at the specified
+        IOU threshold.
+      if relationships is specified, returns <relationship> in AP metrics as
+      readable names, otherwise the names correspond to class numbers.
+    """
+    (weighted_average_precision, mean_average_precision, average_precisions, _,
+     _, recall_50, recall_100, _, _) = (
+         self._evaluation.evaluate())
+
+    vrd_metrics = {
+        (self._metric_prefix + 'weightedAP@{}IOU'.format(
+            self._matching_iou_threshold)):
+            weighted_average_precision,
+        self._metric_prefix + 'mAP@{}IOU'.format(self._matching_iou_threshold):
+            mean_average_precision,
+        self._metric_prefix + 'Recall@50@{}IOU'.format(
+            self._matching_iou_threshold):
+            recall_50,
+        self._metric_prefix + 'Recall@100@{}IOU'.format(
+            self._matching_iou_threshold):
+            recall_100,
+    }
+    if relationships:
+      for key, average_precision in average_precisions.iteritems():
+        vrd_metrics[self._metric_prefix + 'AP@{}IOU/{}'.format(
+            self._matching_iou_threshold,
+            relationships[key])] = average_precision
+    else:
+      for key, average_precision in average_precisions.iteritems():
+        vrd_metrics[self._metric_prefix + 'AP@{}IOU/{}'.format(
+            self._matching_iou_threshold, key)] = average_precision
+
+    return vrd_metrics
+
+  def clear(self):
+    """Clears the state to prepare for a fresh evaluation."""
+    self._evaluation = _VRDDetectionEvaluation(
+        matching_iou_threshold=self._matching_iou_threshold)
+    self._image_ids.clear()
+    self._negative_labels.clear()
+    self._evaluatable_labels.clear()
+
+
+class VRDRelationDetectionEvaluator(VRDDetectionEvaluator):
+  """A class to evaluate VRD detections in relations setting.
+
+  Expected groundtruth box datatype is vrd_box_data_type, expected groudtruth
+  labels datatype is label_data_type.
+  Expected detection box datatype is vrd_box_data_type, expected detection
+  labels
+  datatype is label_data_type.
+  """
+
+  def __init__(self, matching_iou_threshold=0.5):
+    super(VRDRelationDetectionEvaluator, self).__init__(
+        matching_iou_threshold=matching_iou_threshold,
+        metric_prefix='VRDMetric_Relationships')
+
+  def _process_groundtruth_boxes(self, groundtruth_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    Args:
+      groundtruth_box_tuples: A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max].
+
+    Returns:
+      Unchanged input.
+    """
+
+    return groundtruth_box_tuples
+
+  def _process_detection_boxes(self, detections_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    Phrase detection and Relation detection subclasses re-implement this method
+    depending on the task.
+
+    Args:
+      detections_box_tuples:  A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
+        datatype vrd_box_data_type, single_box_data_type above).
+    Returns:
+      Unchanged input.
+    """
+    return detections_box_tuples
+
+
+class VRDPhraseDetectionEvaluator(VRDDetectionEvaluator):
+  """A class to evaluate VRD detections in phrase setting.
+
+  Expected groundtruth box datatype is vrd_box_data_type, expected groudtruth
+  labels datatype is label_data_type.
+  Expected detection box datatype is single_box_data_type, expected detection
+  labels datatype is label_data_type.
+  """
+
+  def __init__(self, matching_iou_threshold=0.5):
+    super(VRDPhraseDetectionEvaluator, self).__init__(
+        matching_iou_threshold=matching_iou_threshold,
+        metric_prefix='VRDMetric_Phrases')
+
+  def _process_groundtruth_boxes(self, groundtruth_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    In case of phrase evaluation task, evaluation expects exactly one bounding
+    box containing all objects in the phrase. This bounding box is computed
+    as an enclosing box of all groundtruth boxes of a phrase.
+
+    Args:
+      groundtruth_box_tuples: A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max]. See
+        vrd_box_data_type for an example of structure.
+
+    Returns:
+      result: A numpy array of structures with the shape [M, 1], each
+        structure containing exactly one named bounding box. i-th output
+        structure corresponds to the result of processing i-th input structure,
+        where the named bounding box is computed as an enclosing bounding box
+        of all bounding boxes of the i-th input structure.
+    """
+    first_box_key = groundtruth_box_tuples.dtype.fields.keys()[0]
+    miny = groundtruth_box_tuples[first_box_key][:, 0]
+    minx = groundtruth_box_tuples[first_box_key][:, 1]
+    maxy = groundtruth_box_tuples[first_box_key][:, 2]
+    maxx = groundtruth_box_tuples[first_box_key][:, 3]
+    for fields in groundtruth_box_tuples.dtype.fields:
+      miny = np.minimum(groundtruth_box_tuples[fields][:, 0], miny)
+      minx = np.minimum(groundtruth_box_tuples[fields][:, 1], minx)
+      maxy = np.maximum(groundtruth_box_tuples[fields][:, 2], maxy)
+      maxx = np.maximum(groundtruth_box_tuples[fields][:, 3], maxx)
+    data_result = []
+    for i in range(groundtruth_box_tuples.shape[0]):
+      data_result.append(([miny[i], minx[i], maxy[i], maxx[i]],))
+    result = np.array(data_result, dtype=[('box', 'f4', (4,))])
+    return result
+
+  def _process_detection_boxes(self, detections_box_tuples):
+    """Pre-processes boxes before adding them to the VRDDetectionEvaluation.
+
+    In case of phrase evaluation task, evaluation expects exactly one bounding
+    box containing all objects in the phrase. This bounding box is computed
+    as an enclosing box of all groundtruth boxes of a phrase.
+
+    Args:
+      detections_box_tuples: A numpy array of structures with the shape
+        [M, 1], each structure containing the same number of named bounding
+        boxes. Each box is of the format [y_min, x_min, y_max, x_max]. See
+        vrd_box_data_type for an example of this structure.
+
+    Returns:
+      result: A numpy array of structures with the shape [M, 1], each
+        structure containing exactly one named bounding box. i-th output
+        structure corresponds to the result of processing i-th input structure,
+        where the named bounding box is computed as an enclosing bounding box
+        of all bounding boxes of the i-th input structure.
+    """
+    first_box_key = detections_box_tuples.dtype.fields.keys()[0]
+    miny = detections_box_tuples[first_box_key][:, 0]
+    minx = detections_box_tuples[first_box_key][:, 1]
+    maxy = detections_box_tuples[first_box_key][:, 2]
+    maxx = detections_box_tuples[first_box_key][:, 3]
+    for fields in detections_box_tuples.dtype.fields:
+      miny = np.minimum(detections_box_tuples[fields][:, 0], miny)
+      minx = np.minimum(detections_box_tuples[fields][:, 1], minx)
+      maxy = np.maximum(detections_box_tuples[fields][:, 2], maxy)
+      maxx = np.maximum(detections_box_tuples[fields][:, 3], maxx)
+    data_result = []
+    for i in range(detections_box_tuples.shape[0]):
+      data_result.append(([miny[i], minx[i], maxy[i], maxx[i]],))
+    result = np.array(data_result, dtype=[('box', 'f4', (4,))])
+    return result
+
+
+VRDDetectionEvalMetrics = collections.namedtuple('VRDDetectionEvalMetrics', [
+    'weighted_average_precision', 'mean_average_precision',
+    'average_precisions', 'precisions', 'recalls', 'recall_50', 'recall_100',
+    'median_rank_50', 'median_rank_100'
+])
+
+
+class _VRDDetectionEvaluation(object):
+  """Performs metric computation for the VRD task. This class is internal.
+  """
+
+  def __init__(self, matching_iou_threshold=0.5):
+    """Constructor.
+
+    Args:
+      matching_iou_threshold: IOU threshold to use for matching groundtruth
+        boxes to detection boxes.
+    """
+    self._per_image_eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
+        matching_iou_threshold=matching_iou_threshold)
+
+    self._groundtruth_box_tuples = {}
+    self._groundtruth_class_tuples = {}
+    self._num_gt_instances = 0
+    self._num_gt_imgs = 0
+    self._num_gt_instances_per_relationship = {}
+
+    self.clear_detections()
+
+  def clear_detections(self):
+    """Clears detections."""
+    self._detection_keys = set()
+    self._scores = []
+    self._relation_field_values = []
+    self._tp_fp_labels = []
+    self._average_precisions = {}
+    self._precisions = []
+    self._recalls = []
+
+  def add_single_ground_truth_image_info(
+      self, image_key, groundtruth_box_tuples, groundtruth_class_tuples):
+    """Adds groundtruth for a single image to be used for evaluation.
+
+    Args:
+      image_key: A unique string/integer identifier for the image.
+      groundtruth_box_tuples: A numpy array of structures with the shape
+          [M, 1], representing M tuples, each tuple containing the same number
+          of named bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max].
+      groundtruth_class_tuples: A numpy array of structures shape [M, 1],
+          representing  the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+    """
+    if image_key in self._groundtruth_box_tuples:
+      logging.warn(
+          'image %s has already been added to the ground truth database.',
+          image_key)
+      return
+
+    self._groundtruth_box_tuples[image_key] = groundtruth_box_tuples
+    self._groundtruth_class_tuples[image_key] = groundtruth_class_tuples
+
+    self._update_groundtruth_statistics(groundtruth_class_tuples)
+
+  def add_single_detected_image_info(self, image_key, detected_box_tuples,
+                                     detected_scores, detected_class_tuples):
+    """Adds detections for a single image to be used for evaluation.
+
+    Args:
+      image_key: A unique string/integer identifier for the image.
+      detected_box_tuples: A numpy array of structures with shape [N, 1],
+          representing N tuples, each tuple containing the same number of named
+          bounding boxes.
+          Each box is of the format [y_min, x_min, y_max, x_max].
+      detected_scores: A float numpy array of shape [N, 1], representing
+          the confidence scores of the detected N object instances.
+      detected_class_tuples: A numpy array of structures shape [N, 1],
+          representing the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+    """
+    self._detection_keys.add(image_key)
+    if image_key in self._groundtruth_box_tuples:
+      groundtruth_box_tuples = self._groundtruth_box_tuples[image_key]
+      groundtruth_class_tuples = self._groundtruth_class_tuples[image_key]
+    else:
+      groundtruth_box_tuples = np.empty(
+          shape=[0, 4], dtype=detected_box_tuples.dtype)
+      groundtruth_class_tuples = np.array([], dtype=detected_class_tuples.dtype)
+
+    scores, tp_fp_labels, mapping = (
+        self._per_image_eval.compute_detection_tp_fp(
+            detected_box_tuples=detected_box_tuples,
+            detected_scores=detected_scores,
+            detected_class_tuples=detected_class_tuples,
+            groundtruth_box_tuples=groundtruth_box_tuples,
+            groundtruth_class_tuples=groundtruth_class_tuples))
+
+    self._scores += [scores]
+    self._tp_fp_labels += [tp_fp_labels]
+    self._relation_field_values += [detected_class_tuples[mapping]['relation']]
+
+  def _update_groundtruth_statistics(self, groundtruth_class_tuples):
+    """Updates grouth truth statistics.
+
+    Args:
+      groundtruth_class_tuples: A numpy array of structures shape [M, 1],
+          representing  the class labels of the corresponding bounding boxes and
+          possibly additional classes.
+    """
+    self._num_gt_instances += groundtruth_class_tuples.shape[0]
+    self._num_gt_imgs += 1
+    for relation_field_value in np.unique(groundtruth_class_tuples['relation']):
+      if relation_field_value not in self._num_gt_instances_per_relationship:
+        self._num_gt_instances_per_relationship[relation_field_value] = 0
+      self._num_gt_instances_per_relationship[relation_field_value] += np.sum(
+          groundtruth_class_tuples['relation'] == relation_field_value)
+
+  def evaluate(self):
+    """Computes evaluation result.
+
+    Returns:
+      A named tuple with the following fields -
+        average_precision: a float number corresponding to average precision.
+        precisions: an array of precisions.
+        recalls: an array of recalls.
+        recall@50: recall computed on 50 top-scoring samples.
+        recall@100: recall computed on 100 top-scoring samples.
+        median_rank@50: median rank computed on 50 top-scoring samples.
+        median_rank@100: median rank computed on 100 top-scoring samples.
+    """
+    if self._num_gt_instances == 0:
+      logging.warn('No ground truth instances')
+
+    if not self._scores:
+      scores = np.array([], dtype=float)
+      tp_fp_labels = np.array([], dtype=bool)
+    else:
+      scores = np.concatenate(self._scores)
+      tp_fp_labels = np.concatenate(self._tp_fp_labels)
+      relation_field_values = np.concatenate(self._relation_field_values)
+
+    for relation_field_value, _ in (
+        self._num_gt_instances_per_relationship.iteritems()):
+      precisions, recalls = metrics.compute_precision_recall(
+          scores[relation_field_values == relation_field_value],
+          tp_fp_labels[relation_field_values == relation_field_value],
+          self._num_gt_instances_per_relationship[relation_field_value])
+      self._average_precisions[
+          relation_field_value] = metrics.compute_average_precision(
+              precisions, recalls)
+
+    self._mean_average_precision = np.mean(self._average_precisions.values())
+
+    self._precisions, self._recalls = metrics.compute_precision_recall(
+        scores, tp_fp_labels, self._num_gt_instances)
+    self._weighted_average_precision = metrics.compute_average_precision(
+        self._precisions, self._recalls)
+
+    self._recall_50 = (
+        metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
+                                    50))
+    self._median_rank_50 = (
+        metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
+    self._recall_100 = (
+        metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
+                                    100))
+    self._median_rank_100 = (
+        metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
+
+    return VRDDetectionEvalMetrics(
+        self._weighted_average_precision, self._mean_average_precision,
+        self._average_precisions, self._precisions, self._recalls,
+        self._recall_50, self._recall_100, self._median_rank_50,
+        self._median_rank_100)
diff --git a/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation_test.py b/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation_test.py
new file mode 100644
index 00000000..f833521b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/object_detection/utils/vrd_evaluation_test.py
@@ -0,0 +1,257 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow_models.object_detection.utils.vrd_evaluation."""
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import standard_fields
+from object_detection.utils import vrd_evaluation
+
+
+class VRDRelationDetectionEvaluatorTest(tf.test.TestCase):
+
+  def test_vrdrelation_evaluator(self):
+    self.vrd_eval = vrd_evaluation.VRDRelationDetectionEvaluator()
+
+    image_key1 = 'img1'
+    groundtruth_box_tuples1 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 2, 2, 3])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples1 = np.array(
+        [(1, 2, 3), (1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    groundtruth_verified_labels1 = np.array([1, 2, 3, 4, 5], dtype=int)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key1, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples1,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples1,
+            standard_fields.InputDataFields.groundtruth_image_classes:
+                groundtruth_verified_labels1
+        })
+
+    image_key2 = 'img2'
+    groundtruth_box_tuples2 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples2 = np.array(
+        [(1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key2, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples2,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples2,
+        })
+
+    image_key3 = 'img3'
+    groundtruth_box_tuples3 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples3 = np.array(
+        [(1, 2, 4)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key3, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples3,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples3,
+        })
+
+    image_key = 'img1'
+    detected_box_tuples = np.array(
+        [([0, 0.3, 1, 1], [1.1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2]),
+         ([0.5, 0, 1, 1], [1, 1, 3, 3])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    detected_class_tuples = np.array(
+        [(1, 2, 5), (1, 2, 3), (1, 6, 3)], dtype=vrd_evaluation.label_data_type)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    self.vrd_eval.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_box_tuples,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_tuples
+        })
+
+    metrics = self.vrd_eval.evaluate()
+
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_weightedAP@0.5IOU'],
+                           0.25)
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_mAP@0.5IOU'],
+                           0.1666666666666666)
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_AP@0.5IOU/3'],
+                           0.3333333333333333)
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_AP@0.5IOU/4'], 0)
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_Recall@50@0.5IOU'],
+                           0.25)
+    self.assertAlmostEqual(metrics['VRDMetric_Relationships_Recall@100@0.5IOU'],
+                           0.25)
+    self.vrd_eval.clear()
+    self.assertFalse(self.vrd_eval._image_ids)
+
+
+class VRDPhraseDetectionEvaluatorTest(tf.test.TestCase):
+
+  def test_vrdphrase_evaluator(self):
+    self.vrd_eval = vrd_evaluation.VRDPhraseDetectionEvaluator()
+
+    image_key1 = 'img1'
+    groundtruth_box_tuples1 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 2, 2, 3])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples1 = np.array(
+        [(1, 2, 3), (1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    groundtruth_verified_labels1 = np.array([1, 2, 3, 4, 5], dtype=int)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key1, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples1,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples1,
+            standard_fields.InputDataFields.groundtruth_image_classes:
+                groundtruth_verified_labels1
+        })
+
+    image_key2 = 'img2'
+    groundtruth_box_tuples2 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples2 = np.array(
+        [(1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key2, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples2,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples2,
+        })
+
+    image_key3 = 'img3'
+    groundtruth_box_tuples3 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples3 = np.array(
+        [(1, 2, 4)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key3, {
+            standard_fields.InputDataFields.groundtruth_boxes:
+                groundtruth_box_tuples3,
+            standard_fields.InputDataFields.groundtruth_classes:
+                groundtruth_class_tuples3,
+        })
+
+    image_key = 'img1'
+    detected_box_tuples = np.array(
+        [([0, 0.3, 0.5, 0.5], [0.3, 0.3, 1.0, 1.0]),
+         ([0, 0, 1.2, 1.2], [0.0, 0.0, 2.0, 2.0]),
+         ([0.5, 0, 1, 1], [1, 1, 3, 3])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    detected_class_tuples = np.array(
+        [(1, 2, 5), (1, 2, 3), (1, 6, 3)], dtype=vrd_evaluation.label_data_type)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    self.vrd_eval.add_single_detected_image_info(
+        image_key, {
+            standard_fields.DetectionResultFields.detection_boxes:
+                detected_box_tuples,
+            standard_fields.DetectionResultFields.detection_scores:
+                detected_scores,
+            standard_fields.DetectionResultFields.detection_classes:
+                detected_class_tuples
+        })
+
+    metrics = self.vrd_eval.evaluate()
+
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_weightedAP@0.5IOU'], 0.25)
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_mAP@0.5IOU'],
+                           0.1666666666666666)
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_AP@0.5IOU/3'],
+                           0.3333333333333333)
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_AP@0.5IOU/4'], 0)
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_Recall@50@0.5IOU'], 0.25)
+    self.assertAlmostEqual(metrics['VRDMetric_Phrases_Recall@100@0.5IOU'], 0.25)
+
+    self.vrd_eval.clear()
+    self.assertFalse(self.vrd_eval._image_ids)
+
+
+class VRDDetectionEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+
+    self.vrd_eval = vrd_evaluation._VRDDetectionEvaluation(
+        matching_iou_threshold=0.5)
+
+    image_key1 = 'img1'
+    groundtruth_box_tuples1 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 2, 2, 3])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples1 = np.array(
+        [(1, 2, 3), (1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key1, groundtruth_box_tuples1, groundtruth_class_tuples1)
+
+    image_key2 = 'img2'
+    groundtruth_box_tuples2 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples2 = np.array(
+        [(1, 4, 3)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key2, groundtruth_box_tuples2, groundtruth_class_tuples2)
+
+    image_key3 = 'img3'
+    groundtruth_box_tuples3 = np.array(
+        [([0, 0, 1, 1], [1, 1, 2, 2])], dtype=vrd_evaluation.vrd_box_data_type)
+    groundtruth_class_tuples3 = np.array(
+        [(1, 2, 4)], dtype=vrd_evaluation.label_data_type)
+    self.vrd_eval.add_single_ground_truth_image_info(
+        image_key3, groundtruth_box_tuples3, groundtruth_class_tuples3)
+
+    image_key = 'img1'
+    detected_box_tuples = np.array(
+        [([0, 0.3, 1, 1], [1.1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2])],
+        dtype=vrd_evaluation.vrd_box_data_type)
+    detected_class_tuples = np.array(
+        [(1, 2, 3), (1, 2, 3)], dtype=vrd_evaluation.label_data_type)
+    detected_scores = np.array([0.7, 0.8], dtype=float)
+    self.vrd_eval.add_single_detected_image_info(
+        image_key, detected_box_tuples, detected_scores, detected_class_tuples)
+
+    metrics = self.vrd_eval.evaluate()
+    expected_weighted_average_precision = 0.25
+    expected_mean_average_precision = 0.16666666666666
+    expected_precision = np.array([1., 0.5], dtype=float)
+    expected_recall = np.array([0.25, 0.25], dtype=float)
+    expected_recall_50 = 0.25
+    expected_recall_100 = 0.25
+    expected_median_rank_50 = 0
+    expected_median_rank_100 = 0
+
+    self.assertAlmostEqual(expected_weighted_average_precision,
+                           metrics.weighted_average_precision)
+    self.assertAlmostEqual(expected_mean_average_precision,
+                           metrics.mean_average_precision)
+    self.assertAlmostEqual(expected_mean_average_precision,
+                           metrics.mean_average_precision)
+
+    self.assertAllClose(expected_precision, metrics.precisions)
+    self.assertAllClose(expected_recall, metrics.recalls)
+    self.assertAlmostEqual(expected_recall_50, metrics.recall_50)
+    self.assertAlmostEqual(expected_recall_100, metrics.recall_100)
+    self.assertAlmostEqual(expected_median_rank_50, metrics.median_rank_50)
+    self.assertAlmostEqual(expected_median_rank_100, metrics.median_rank_100)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/BUILD b/TensorFlow/Detection/SSD/models/research/slim/BUILD
new file mode 100644
index 00000000..df22311c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/BUILD
@@ -0,0 +1,820 @@
+# Description:
+#   Contains files for loading, training and evaluating TF-Slim-based models.
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "dataset_utils",
+    srcs = ["datasets/dataset_utils.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+sh_binary(
+    name = "download_and_convert_imagenet",
+    srcs = ["datasets/download_and_convert_imagenet.sh"],
+    data = [
+        "datasets/download_imagenet.sh",
+        "datasets/imagenet_2012_validation_synset_labels.txt",
+        "datasets/imagenet_lsvrc_2015_synsets.txt",
+        "datasets/imagenet_metadata.txt",
+        "datasets/preprocess_imagenet_validation_data.py",
+        "datasets/process_bounding_boxes.py",
+        ":build_imagenet_data",
+    ],
+)
+
+py_binary(
+    name = "build_imagenet_data",
+    srcs = ["datasets/build_imagenet_data.py"],
+    deps = [
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "download_and_convert_cifar10",
+    srcs = ["datasets/download_and_convert_cifar10.py"],
+    deps = [
+        ":dataset_utils",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "download_and_convert_flowers",
+    srcs = ["datasets/download_and_convert_flowers.py"],
+    deps = [
+        ":dataset_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "download_and_convert_mnist",
+    srcs = ["datasets/download_and_convert_mnist.py"],
+    deps = [
+        ":dataset_utils",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_binary(
+    name = "download_and_convert_data",
+    srcs = ["download_and_convert_data.py"],
+    deps = [
+        ":download_and_convert_cifar10",
+        ":download_and_convert_flowers",
+        ":download_and_convert_mnist",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "cifar10",
+    srcs = ["datasets/cifar10.py"],
+    deps = [
+        ":dataset_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "flowers",
+    srcs = ["datasets/flowers.py"],
+    deps = [
+        ":dataset_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "imagenet",
+    srcs = ["datasets/imagenet.py"],
+    deps = [
+        ":dataset_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "mnist",
+    srcs = ["datasets/mnist.py"],
+    deps = [
+        ":dataset_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "dataset_factory",
+    srcs = ["datasets/dataset_factory.py"],
+    deps = [
+        ":cifar10",
+        ":flowers",
+        ":imagenet",
+        ":mnist",
+    ],
+)
+
+py_library(
+    name = "model_deploy",
+    srcs = ["deployment/model_deploy.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "model_deploy_test",
+    srcs = ["deployment/model_deploy_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":model_deploy",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "cifarnet_preprocessing",
+    srcs = ["preprocessing/cifarnet_preprocessing.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_preprocessing",
+    srcs = ["preprocessing/inception_preprocessing.py"],
+    deps = [
+        # "//tensorflow",
+        # "//tensorflow/python:control_flow_ops",
+    ],
+)
+
+py_library(
+    name = "lenet_preprocessing",
+    srcs = ["preprocessing/lenet_preprocessing.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "vgg_preprocessing",
+    srcs = ["preprocessing/vgg_preprocessing.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "preprocessing_factory",
+    srcs = ["preprocessing/preprocessing_factory.py"],
+    deps = [
+        ":cifarnet_preprocessing",
+        ":inception_preprocessing",
+        ":lenet_preprocessing",
+        ":vgg_preprocessing",
+        # "//tensorflow",
+    ],
+)
+
+# Typical networks definitions.
+
+py_library(
+    name = "nets",
+    deps = [
+        ":alexnet",
+        ":cifarnet",
+        ":cyclegan",
+        ":i3d",
+        ":inception",
+        ":lenet",
+        ":mobilenet",
+        ":nasnet",
+        ":overfeat",
+        ":pix2pix",
+        ":pnasnet",
+        ":resnet_v1",
+        ":resnet_v2",
+        ":s3dg",
+        ":vgg",
+    ],
+)
+
+py_library(
+    name = "alexnet",
+    srcs = ["nets/alexnet.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "alexnet_test",
+    size = "medium",
+    srcs = ["nets/alexnet_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":alexnet",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "cifarnet",
+    srcs = ["nets/cifarnet.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "cyclegan",
+    srcs = ["nets/cyclegan.py"],
+    deps = [
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "cyclegan_test",
+    srcs = ["nets/cyclegan_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":cyclegan",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "dcgan",
+    srcs = ["nets/dcgan.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "dcgan_test",
+    srcs = ["nets/dcgan_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":dcgan",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "i3d",
+    srcs = ["nets/i3d.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":i3d_utils",
+        ":s3dg",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "i3d_test",
+    size = "large",
+    srcs = ["nets/i3d_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":i3d",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "i3d_utils",
+    srcs = ["nets/i3d_utils.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception",
+    srcs = ["nets/inception.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_resnet_v2",
+        ":inception_v1",
+        ":inception_v2",
+        ":inception_v3",
+        ":inception_v4",
+    ],
+)
+
+py_library(
+    name = "inception_utils",
+    srcs = ["nets/inception_utils.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_v1",
+    srcs = ["nets/inception_v1.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_v2",
+    srcs = ["nets/inception_v2.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_v3",
+    srcs = ["nets/inception_v3.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_v4",
+    srcs = ["nets/inception_v4.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "inception_resnet_v2",
+    srcs = ["nets/inception_resnet_v2.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "inception_v1_test",
+    size = "large",
+    srcs = ["nets/inception_v1_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "inception_v2_test",
+    size = "large",
+    srcs = ["nets/inception_v2_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "inception_v3_test",
+    size = "large",
+    srcs = ["nets/inception_v3_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "inception_v4_test",
+    size = "large",
+    srcs = ["nets/inception_v4_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "inception_resnet_v2_test",
+    size = "large",
+    srcs = ["nets/inception_resnet_v2_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "lenet",
+    srcs = ["nets/lenet.py"],
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "mobilenet_v1",
+    srcs = ["nets/mobilenet_v1.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "mobilenet_v2",
+    srcs = glob(["nets/mobilenet/*.py"]),
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "mobilenet_v2_test",
+    srcs = ["nets/mobilenet/mobilenet_v2_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":mobilenet",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "mobilenet",
+    deps = [
+        ":mobilenet_v1",
+        ":mobilenet_v2",
+    ],
+)
+
+py_test(
+    name = "mobilenet_v1_test",
+    size = "large",
+    srcs = ["nets/mobilenet_v1_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":mobilenet_v1",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_binary(
+    name = "mobilenet_v1_train",
+    srcs = ["nets/mobilenet_v1_train.py"],
+    deps = [
+        ":dataset_factory",
+        ":mobilenet_v1",
+        ":preprocessing_factory",
+        # "//tensorflow",
+    ],
+)
+
+py_binary(
+    name = "mobilenet_v1_eval",
+    srcs = ["nets/mobilenet_v1_eval.py"],
+    deps = [
+        ":dataset_factory",
+        ":mobilenet_v1",
+        ":preprocessing_factory",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "nasnet_utils",
+    srcs = ["nets/nasnet/nasnet_utils.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "nasnet",
+    srcs = ["nets/nasnet/nasnet.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nasnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "nasnet_utils_test",
+    size = "medium",
+    srcs = ["nets/nasnet/nasnet_utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nasnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "nasnet_test",
+    size = "large",
+    srcs = ["nets/nasnet/nasnet_test.py"],
+    shard_count = 10,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nasnet",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "pnasnet",
+    srcs = ["nets/nasnet/pnasnet.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nasnet",
+        ":nasnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "pnasnet_test",
+    size = "large",
+    srcs = ["nets/nasnet/pnasnet_test.py"],
+    shard_count = 4,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pnasnet",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "overfeat",
+    srcs = ["nets/overfeat.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "overfeat_test",
+    size = "medium",
+    srcs = ["nets/overfeat_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":overfeat",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "pix2pix",
+    srcs = ["nets/pix2pix.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "pix2pix_test",
+    srcs = ["nets/pix2pix_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pix2pix",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "resnet_utils",
+    srcs = ["nets/resnet_utils.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "resnet_v1",
+    srcs = ["nets/resnet_v1.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "resnet_v1_test",
+    size = "medium",
+    srcs = ["nets/resnet_v1_test.py"],
+    shard_count = 2,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+        ":resnet_v1",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "resnet_v2",
+    srcs = ["nets/resnet_v2.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "resnet_v2_test",
+    size = "medium",
+    srcs = ["nets/resnet_v2_test.py"],
+    shard_count = 2,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+        ":resnet_v2",
+        # "//numpy",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "s3dg",
+    srcs = ["nets/s3dg.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":i3d_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "s3dg_test",
+    size = "large",
+    srcs = ["nets/s3dg_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":s3dg",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "vgg",
+    srcs = ["nets/vgg.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "vgg_test",
+    size = "medium",
+    srcs = ["nets/vgg_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":vgg",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "nets_factory",
+    srcs = ["nets/nets_factory.py"],
+    deps = [
+        ":nets",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "nets_factory_test",
+    size = "large",
+    srcs = ["nets/nets_factory_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nets_factory",
+        # "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "train_image_classifier_lib",
+    srcs = ["train_image_classifier.py"],
+    deps = [
+        ":dataset_factory",
+        ":model_deploy",
+        ":nets_factory",
+        ":preprocessing_factory",
+        # "//tensorflow",
+    ],
+)
+
+py_binary(
+    name = "train_image_classifier",
+    srcs = ["train_image_classifier.py"],
+    # WARNING: not supported in bazel; will be commented out by copybara.
+    # paropts = ["--compress"],
+    deps = [
+        ":train_image_classifier_lib",
+    ],
+)
+
+py_library(
+    name = "eval_image_classifier_lib",
+    srcs = ["eval_image_classifier.py"],
+    deps = [
+        ":dataset_factory",
+        ":nets_factory",
+        ":preprocessing_factory",
+        # "//tensorflow",
+    ],
+)
+
+py_binary(
+    name = "eval_image_classifier",
+    srcs = ["eval_image_classifier.py"],
+    deps = [
+        ":eval_image_classifier_lib",
+    ],
+)
+
+py_binary(
+    name = "export_inference_graph",
+    srcs = ["export_inference_graph.py"],
+    # WARNING: not supported in bazel; will be commented out by copybara.
+    # paropts = ["--compress"],
+    deps = [
+        ":dataset_factory",
+        ":nets_factory",
+        # "//tensorflow",
+        # "//tensorflow/python:platform",
+    ],
+)
+
+py_test(
+    name = "export_inference_graph_test",
+    size = "medium",
+    srcs = ["export_inference_graph_test.py"],
+    srcs_version = "PY2AND3",
+    tags = [
+        "manual",
+    ],
+    deps = [
+        ":export_inference_graph",
+        # "//tensorflow",
+        # "//tensorflow/python:platform",
+    ],
+)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/README.md b/TensorFlow/Detection/SSD/models/research/slim/README.md
new file mode 100644
index 00000000..08bc48b0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/README.md
@@ -0,0 +1,530 @@
+# TensorFlow-Slim image classification model library
+
+[TF-slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim)
+is a new lightweight high-level API of TensorFlow (`tensorflow.contrib.slim`)
+for defining, training and evaluating complex
+models. This directory contains
+code for training and evaluating several widely used Convolutional Neural
+Network (CNN) image classification models using TF-slim.
+It contains scripts that will allow
+you to train models from scratch or fine-tune them from pre-trained network
+weights. It also contains code for downloading standard image datasets,
+converting them
+to TensorFlow's native TFRecord format and reading them in using TF-Slim's
+data reading and queueing utilities. You can easily train any model on any of
+these datasets, as we demonstrate below. We've also included a
+[jupyter notebook](https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb),
+which provides working examples of how to use TF-Slim for image classification.
+For developing or modifying your own models, see also the [main TF-Slim page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
+
+## Contacts
+
+Maintainers of TF-slim:
+
+* Nathan Silberman,
+  github: [nathansilberman](https://github.com/nathansilberman)
+* Sergio Guadarrama, github: [sguada](https://github.com/sguada)
+
+## Citation
+"TensorFlow-Slim image classification model library"
+N. Silberman and S. Guadarrama, 2016.
+https://github.com/tensorflow/models/tree/master/research/slim
+
+## Table of contents
+
+<a href="#Install">Installation and setup</a><br>
+<a href='#Data'>Preparing the datasets</a><br>
+<a href='#Pretrained'>Using pre-trained models</a><br>
+<a href='#Training'>Training from scratch</a><br>
+<a href='#Tuning'>Fine tuning to a new task</a><br>
+<a href='#Eval'>Evaluating performance</a><br>
+<a href='#Export'>Exporting Inference Graph</a><br>
+<a href='#Troubleshooting'>Troubleshooting</a><br>
+
+# Installation
+<a id='Install'></a>
+
+In this section, we describe the steps required to install the appropriate
+prerequisite packages.
+
+## Installing latest version of TF-slim
+
+TF-Slim is available as `tf.contrib.slim` via TensorFlow 1.0. To test that your
+installation is working, execute the following command; it should run without
+raising any errors.
+
+```
+python -c "import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once"
+```
+
+## Installing the TF-slim image models library
+
+To use TF-Slim for image classification, you also have to install
+the [TF-Slim image models library](https://github.com/tensorflow/models/tree/master/research/slim),
+which is not part of the core TF library.
+To do this, check out the
+[tensorflow/models](https://github.com/tensorflow/models/) repository as follows:
+
+```bash
+cd $HOME/workspace
+git clone https://github.com/tensorflow/models/
+```
+
+This will put the TF-Slim image models library in `$HOME/workspace/models/research/slim`.
+(It will also create a directory called
+[models/inception](https://github.com/tensorflow/models/tree/master/research/inception),
+which contains an older version of slim; you can safely ignore this.)
+
+To verify that this has worked, execute the following commands; it should run
+without raising any errors.
+
+```
+cd $HOME/workspace/models/research/slim
+python -c "from nets import cifarnet; mynet = cifarnet.cifarnet"
+```
+
+
+# Preparing the datasets
+<a id='Data'></a>
+
+As part of this library, we've included scripts to download several popular
+image datasets (listed below) and convert them to slim format.
+
+Dataset | Training Set Size | Testing Set Size | Number of Classes | Comments
+:------:|:---------------:|:---------------------:|:-----------:|:-----------:
+Flowers|2500 | 2500 | 5 | Various sizes (source: Flickr)
+[Cifar10](https://www.cs.toronto.edu/~kriz/cifar.html) | 60k| 10k | 10 |32x32 color
+[MNIST](http://yann.lecun.com/exdb/mnist/)| 60k | 10k | 10 | 28x28 gray
+[ImageNet](http://www.image-net.org/challenges/LSVRC/2012/)|1.2M| 50k | 1000 | Various sizes
+
+## Downloading and converting to TFRecord format
+
+For each dataset, we'll need to download the raw data and convert it to
+TensorFlow's native
+[TFRecord](https://www.tensorflow.org/versions/r0.10/api_docs/python/python_io.html#tfrecords-format-details)
+format. Each TFRecord contains a
+[TF-Example](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/core/example/example.proto)
+protocol buffer. Below we demonstrate how to do this for the Flowers dataset.
+
+```shell
+$ DATA_DIR=/tmp/data/flowers
+$ python download_and_convert_data.py \
+    --dataset_name=flowers \
+    --dataset_dir="${DATA_DIR}"
+```
+
+When the script finishes you will find several TFRecord files created:
+
+```shell
+$ ls ${DATA_DIR}
+flowers_train-00000-of-00005.tfrecord
+...
+flowers_train-00004-of-00005.tfrecord
+flowers_validation-00000-of-00005.tfrecord
+...
+flowers_validation-00004-of-00005.tfrecord
+labels.txt
+```
+
+These represent the training and validation data, sharded over 5 files each.
+You will also find the `$DATA_DIR/labels.txt` file which contains the mapping
+from integer labels to class names.
+
+You can use the same script to create the mnist and cifar10 datasets.
+However, for ImageNet, you have to follow the instructions
+[here](https://github.com/tensorflow/models/blob/master/research/inception/README.md#getting-started).
+Note that you first have to sign up for an account at image-net.org.
+Also, the download can take several hours, and could use up to 500GB.
+
+
+## Creating a TF-Slim Dataset Descriptor.
+
+Once the TFRecord files have been created, you can easily define a Slim
+[Dataset](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/contrib/slim/python/slim/data/dataset.py),
+which stores pointers to the data file, as well as various other pieces of
+metadata, such as the class labels, the train/test split, and how to parse the
+TFExample protos. We have included the TF-Slim Dataset descriptors
+for
+[Cifar10](https://github.com/tensorflow/models/blob/master/research/slim/datasets/cifar10.py),
+[ImageNet](https://github.com/tensorflow/models/blob/master/research/slim/datasets/imagenet.py),
+[Flowers](https://github.com/tensorflow/models/blob/master/research/slim/datasets/flowers.py),
+and
+[MNIST](https://github.com/tensorflow/models/blob/master/research/slim/datasets/mnist.py).
+An example of how to load data using a TF-Slim dataset descriptor using a
+TF-Slim
+[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py)
+is found below:
+
+```python
+import tensorflow as tf
+from datasets import flowers
+
+slim = tf.contrib.slim
+
+# Selects the 'validation' dataset.
+dataset = flowers.get_split('validation', DATA_DIR)
+
+# Creates a TF-Slim DataProvider which reads the dataset in the background
+# during both training and testing.
+provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
+[image, label] = provider.get(['image', 'label'])
+```
+## An automated script for processing ImageNet data.
+
+Training a model with the ImageNet dataset is a common request. To facilitate
+working with the ImageNet dataset, we provide an automated script for
+downloading and processing the ImageNet dataset into the native TFRecord
+format.
+
+The TFRecord format consists of a set of sharded files where each entry is a serialized `tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG encoded) as well as metadata such as label and bounding box information.
+
+We provide a single [script](datasets/download_and_preprocess_imagenet.sh) for
+downloading and converting ImageNet data to TFRecord format. Downloading and
+preprocessing the data may take several hours (up to half a day) depending on
+your network and computer speed. Please be patient.
+
+To begin, you will need to sign up for an account with [ImageNet]
+(http://image-net.org) to gain access to the data. Look for the sign up page,
+create an account and request an access key to download the data.
+
+After you have `USERNAME` and `PASSWORD`, you are ready to run our script. Make
+sure that your hard disk has at least 500 GB of free space for downloading and
+storing the data. Here we select `DATA_DIR=$HOME/imagenet-data` as such a
+location but feel free to edit accordingly.
+
+When you run the below script, please enter *USERNAME* and *PASSWORD* when
+prompted. This will occur at the very beginning. Once these values are entered,
+you will not need to interact with the script again.
+
+```shell
+# location of where to place the ImageNet data
+DATA_DIR=$HOME/imagenet-data
+
+# build the preprocessing script.
+bazel build slim/download_and_preprocess_imagenet
+
+# run it
+bazel-bin/slim/download_and_preprocess_imagenet "${DATA_DIR}"
+```
+
+The final line of the output script should read:
+
+```shell
+2016-02-17 14:30:17.287989: Finished writing all 1281167 images in data set.
+```
+
+When the script finishes you will find 1024 and 128 training and validation
+files in the `DATA_DIR`. The files will match the patterns `train-????-of-1024`
+and `validation-?????-of-00128`, respectively.
+
+[Congratulations!](https://www.youtube.com/watch?v=9bZkp7q19f0) You are now
+ready to train or evaluate with the ImageNet data set.
+
+# Pre-trained Models
+<a id='Pretrained'></a>
+
+Neural nets work best when they have many parameters, making them powerful
+function approximators.
+However, this  means they must be trained on very large datasets. Because
+training models from scratch can be a very computationally intensive process
+requiring days or even weeks, we provide various pre-trained models,
+as listed below. These CNNs have been trained on the
+[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/)
+image classification dataset.
+
+In the table below, we list each model, the corresponding
+TensorFlow model file, the link to the model checkpoint, and the top 1 and top 5
+accuracy (on the imagenet test set).
+Note that the VGG and ResNet V1 parameters have been converted from their original
+caffe formats
+([here](https://github.com/BVLC/caffe/wiki/Model-Zoo#models-used-by-the-vgg-team-in-ilsvrc-2014)
+and
+[here](https://github.com/KaimingHe/deep-residual-networks)),
+whereas the Inception and ResNet V2 parameters have been trained internally at
+Google. Also be aware that these accuracies were computed by evaluating using a
+single image crop. Some academic papers report higher accuracy by using multiple
+crops at multiple scales.
+
+Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
+:----:|:------------:|:----------:|:-------:|:--------:|
+[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
+[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
+[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
+[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
+[Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2_2016_08_30.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
+[ResNet V1 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
+[ResNet V1 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|
+[ResNet V1 152](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)|76.8|93.2|
+[ResNet V2 50](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz)|75.6|92.8|
+[ResNet V2 101](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)|77.0|93.7|
+[ResNet V2 152](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz)|77.8|94.1|
+[ResNet V2 200](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[TBA]()|79.9\*|95.2\*|
+[VGG 16](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_16_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)|71.5|89.8|
+[VGG 19](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)|71.1|89.8|
+[MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)|70.9|89.9|
+[MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz)|59.1|81.9|
+[MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz)|41.5|66.3|
+[MobileNet_v2_1.4_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.4_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 74.9 | 92.5|
+[MobileNet_v2_1.0_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.0_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 71.9 | 91.0
+[NASNet-A_Mobile_224](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_mobile_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz)|74.0|91.6|
+[NASNet-A_Large_331](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_large_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz)|82.7|96.2|
+[PNASNet-5_Large_331](https://arxiv.org/abs/1712.00559)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py)|[pnasnet-5_large_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_large_2017_12_13.tar.gz)|82.9|96.2|
+[PNASNet-5_Mobile_224](https://arxiv.org/abs/1712.00559)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py)|[pnasnet-5_mobile_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_mobile_2017_12_13.tar.gz)|74.2|91.9|
+
+^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
+`--preprocessing_name inception --eval_image_size 299` when using
+`eval_image_classifier.py`). Performance numbers for ResNet V2 models are
+reported on the ImageNet validation set.
+
+(#) More information and details about the NASNet architectures are available at this [README](nets/nasnet/README.md)
+
+All 16 float MobileNet V1 models reported in the [MobileNet Paper](https://arxiv.org/abs/1704.04861) and all
+16 quantized [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) compatible MobileNet V1 models can be found
+[here](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet_v1.md).
+
+(^#) More details on MobileNetV2 models can be found [here](nets/mobilenet/README.md).
+
+(\*): Results quoted from the [paper](https://arxiv.org/abs/1603.05027).
+
+Here is an example of how to download the Inception V3 checkpoint:
+
+```shell
+$ CHECKPOINT_DIR=/tmp/checkpoints
+$ mkdir ${CHECKPOINT_DIR}
+$ wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
+$ tar -xvf inception_v3_2016_08_28.tar.gz
+$ mv inception_v3.ckpt ${CHECKPOINT_DIR}
+$ rm inception_v3_2016_08_28.tar.gz
+```
+
+
+
+# Training a model from scratch.
+<a id='Training'></a>
+
+We provide an easy way to train a model from scratch using any TF-Slim dataset.
+The following example demonstrates how to train Inception V3 using the default
+parameters on the ImageNet dataset.
+
+```shell
+DATASET_DIR=/tmp/imagenet
+TRAIN_DIR=/tmp/train_logs
+python train_image_classifier.py \
+    --train_dir=${TRAIN_DIR} \
+    --dataset_name=imagenet \
+    --dataset_split_name=train \
+    --dataset_dir=${DATASET_DIR} \
+    --model_name=inception_v3
+```
+
+This process may take several days, depending on your hardware setup.
+For convenience, we provide a way to train a model on multiple GPUs,
+and/or multiple CPUs, either synchrononously or asynchronously.
+See [model_deploy](https://github.com/tensorflow/models/blob/master/research/slim/deployment/model_deploy.py)
+for details.
+
+### TensorBoard
+
+To visualize the losses and other metrics during training, you can use
+[TensorBoard](https://github.com/tensorflow/tensorboard)
+by running the command below.
+
+```shell
+tensorboard --logdir=${TRAIN_DIR}
+```
+
+Once TensorBoard is running, navigate your web browser to http://localhost:6006.
+
+# Fine-tuning a model from an existing checkpoint
+<a id='Tuning'></a>
+
+Rather than training from scratch, we'll often want to start from a pre-trained
+model and fine-tune it.
+To indicate a checkpoint from which to fine-tune, we'll call training with
+the `--checkpoint_path` flag and assign it an absolute path to a checkpoint
+file.
+
+When fine-tuning a model, we need to be careful about restoring checkpoint
+weights. In particular, when we fine-tune a model on a new task with a different
+number of output labels, we wont be able restore the final logits (classifier)
+layer. For this, we'll use the `--checkpoint_exclude_scopes` flag. This flag
+hinders certain variables from being loaded. When fine-tuning on a
+classification task using a different number of classes than the trained model,
+the new model will have a final 'logits' layer whose dimensions differ from the
+pre-trained model. For example, if fine-tuning an ImageNet-trained model on
+Flowers, the pre-trained logits layer will have dimensions `[2048 x 1001]` but
+our new logits layer will have dimensions `[2048 x 5]`. Consequently, this
+flag indicates to TF-Slim to avoid loading these weights from the checkpoint.
+
+Keep in mind that warm-starting from a checkpoint affects the model's weights
+only during the initialization of the model. Once a model has started training,
+a new checkpoint will be created in `${TRAIN_DIR}`. If the fine-tuning
+training is stopped and restarted, this new checkpoint will be the one from
+which weights are restored and not the `${checkpoint_path}$`. Consequently,
+the flags `--checkpoint_path` and `--checkpoint_exclude_scopes` are only used
+during the `0-`th global step (model initialization). Typically for fine-tuning
+one only want train a sub-set of layers, so the flag `--trainable_scopes` allows
+to specify which subsets of layers should trained, the rest would remain frozen.
+
+Below we give an example of
+[fine-tuning inception-v3 on flowers](https://github.com/tensorflow/models/blob/master/research/slim/scripts/finetune_inception_v3_on_flowers.sh),
+inception_v3  was trained on ImageNet with 1000 class labels, but the flowers
+dataset only have 5 classes. Since the dataset is quite small we will only train
+the new layers.
+
+
+```shell
+$ DATASET_DIR=/tmp/flowers
+$ TRAIN_DIR=/tmp/flowers-models/inception_v3
+$ CHECKPOINT_PATH=/tmp/my_checkpoints/inception_v3.ckpt
+$ python train_image_classifier.py \
+    --train_dir=${TRAIN_DIR} \
+    --dataset_dir=${DATASET_DIR} \
+    --dataset_name=flowers \
+    --dataset_split_name=train \
+    --model_name=inception_v3 \
+    --checkpoint_path=${CHECKPOINT_PATH} \
+    --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
+    --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits
+```
+
+
+
+# Evaluating performance of a model
+<a id='Eval'></a>
+
+To evaluate the performance of a model (whether pretrained or your own),
+you can use the eval_image_classifier.py script, as shown below.
+
+Below we give an example of downloading the pretrained inception model and
+evaluating it on the imagenet dataset.
+
+```shell
+CHECKPOINT_FILE = ${CHECKPOINT_DIR}/inception_v3.ckpt  # Example
+$ python eval_image_classifier.py \
+    --alsologtostderr \
+    --checkpoint_path=${CHECKPOINT_FILE} \
+    --dataset_dir=${DATASET_DIR} \
+    --dataset_name=imagenet \
+    --dataset_split_name=validation \
+    --model_name=inception_v3
+```
+
+See the [evaluation module example](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim#evaluation-loop)
+for an example of how to evaluate a model at multiple checkpoints during or after the training.
+
+# Exporting the Inference Graph
+<a id='Export'></a>
+
+Saves out a GraphDef containing the architecture of the model.
+
+To use it with a model name defined by slim, run:
+
+```shell
+$ python export_inference_graph.py \
+  --alsologtostderr \
+  --model_name=inception_v3 \
+  --output_file=/tmp/inception_v3_inf_graph.pb
+
+$ python export_inference_graph.py \
+  --alsologtostderr \
+  --model_name=mobilenet_v1 \
+  --image_size=224 \
+  --output_file=/tmp/mobilenet_v1_224.pb
+```
+
+## Freezing the exported Graph
+If you then want to use the resulting model with your own or pretrained
+checkpoints as part of a mobile model, you can run freeze_graph to get a graph
+def with the variables inlined as constants using:
+
+```shell
+bazel build tensorflow/python/tools:freeze_graph
+
+bazel-bin/tensorflow/python/tools/freeze_graph \
+  --input_graph=/tmp/inception_v3_inf_graph.pb \
+  --input_checkpoint=/tmp/checkpoints/inception_v3.ckpt \
+  --input_binary=true --output_graph=/tmp/frozen_inception_v3.pb \
+  --output_node_names=InceptionV3/Predictions/Reshape_1
+```
+
+The output node names will vary depending on the model, but you can inspect and
+estimate them using the summarize_graph tool:
+
+```shell
+bazel build tensorflow/tools/graph_transforms:summarize_graph
+
+bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
+  --in_graph=/tmp/inception_v3_inf_graph.pb
+```
+
+## Run label image in C++
+
+To run the resulting graph in C++, you can look at the label_image sample code:
+
+```shell
+bazel build tensorflow/examples/label_image:label_image
+
+bazel-bin/tensorflow/examples/label_image/label_image \
+  --image=${HOME}/Pictures/flowers.jpg \
+  --input_layer=input \
+  --output_layer=InceptionV3/Predictions/Reshape_1 \
+  --graph=/tmp/frozen_inception_v3.pb \
+  --labels=/tmp/imagenet_slim_labels.txt \
+  --input_mean=0 \
+  --input_std=255
+```
+
+
+# Troubleshooting
+<a id='Troubleshooting'></a>
+
+#### The model runs out of CPU memory.
+
+See
+[Model Runs out of CPU memory](https://github.com/tensorflow/models/tree/master/research/inception#the-model-runs-out-of-cpu-memory).
+
+#### The model runs out of GPU memory.
+
+See
+[Adjusting Memory Demands](https://github.com/tensorflow/models/tree/master/research/inception#adjusting-memory-demands).
+
+#### The model training results in NaN's.
+
+See
+[Model Resulting in NaNs](https://github.com/tensorflow/models/tree/master/research/inception#the-model-training-results-in-nans).
+
+#### The ResNet and VGG Models have 1000 classes but the ImageNet dataset has 1001
+
+The ImageNet dataset provided has an empty background class which can be used
+to fine-tune the model to other tasks. If you try training or fine-tuning the
+VGG or ResNet models using the ImageNet dataset, you might encounter the
+following error:
+
+```bash
+InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [1001] rhs shape= [1000]
+```
+This is due to the fact that the VGG and ResNet V1 final layers have only 1000
+outputs rather than 1001.
+
+To fix this issue, you can set the `--labels_offset=1` flag. This results in
+the ImageNet labels being shifted down by one:
+
+
+#### I wish to train a model with a different image size.
+
+The preprocessing functions all take `height` and `width` as parameters. You
+can change the default values using the following snippet:
+
+```python
+image_preprocessing_fn = preprocessing_factory.get_preprocessing(
+    preprocessing_name,
+    height=MY_NEW_HEIGHT,
+    width=MY_NEW_WIDTH,
+    is_training=True)
+```
+
+#### What hardware specification are these hyper-parameters targeted for?
+
+See
+[Hardware Specifications](https://github.com/tensorflow/models/tree/master/research/inception#what-hardware-specification-are-these-hyper-parameters-targeted-for).
diff --git a/TensorFlow/Detection/SSD/models/research/slim/WORKSPACE b/TensorFlow/Detection/SSD/models/research/slim/WORKSPACE
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/slim/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/build_imagenet_data.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/build_imagenet_data.py
new file mode 100644
index 00000000..d0218061
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/build_imagenet_data.py
@@ -0,0 +1,704 @@
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Converts ImageNet data to TFRecords file format with Example protos.
+
+The raw ImageNet data set is expected to reside in JPEG files located in the
+following directory structure.
+
+  data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
+  data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
+  ...
+
+where 'n01440764' is the unique synset label associated with
+these images.
+
+The training data set consists of 1000 sub-directories (i.e. labels)
+each containing 1200 JPEG images for a total of 1.2M JPEG images.
+
+The evaluation data set consists of 1000 sub-directories (i.e. labels)
+each containing 50 JPEG images for a total of 50K JPEG images.
+
+This TensorFlow script converts the training and evaluation data into
+a sharded data set consisting of 1024 and 128 TFRecord files, respectively.
+
+  train_directory/train-00000-of-01024
+  train_directory/train-00001-of-01024
+  ...
+  train_directory/train-00127-of-01024
+
+and
+
+  validation_directory/validation-00000-of-00128
+  validation_directory/validation-00001-of-00128
+  ...
+  validation_directory/validation-00127-of-00128
+
+Each validation TFRecord file contains ~390 records. Each training TFREcord
+file contains ~1250 records. Each record within the TFRecord file is a
+serialized Example proto. The Example proto contains the following fields:
+
+  image/encoded: string containing JPEG encoded image in RGB colorspace
+  image/height: integer, image height in pixels
+  image/width: integer, image width in pixels
+  image/colorspace: string, specifying the colorspace, always 'RGB'
+  image/channels: integer, specifying the number of channels, always 3
+  image/format: string, specifying the format, always'JPEG'
+
+  image/filename: string containing the basename of the image file
+            e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
+  image/class/label: integer specifying the index in a classification layer.
+    The label ranges from [1, 1000] where 0 is not used.
+  image/class/synset: string specifying the unique ID of the label,
+    e.g. 'n01440764'
+  image/class/text: string specifying the human-readable version of the label
+    e.g. 'red fox, Vulpes vulpes'
+
+  image/object/bbox/xmin: list of integers specifying the 0+ human annotated
+    bounding boxes
+  image/object/bbox/xmax: list of integers specifying the 0+ human annotated
+    bounding boxes
+  image/object/bbox/ymin: list of integers specifying the 0+ human annotated
+    bounding boxes
+  image/object/bbox/ymax: list of integers specifying the 0+ human annotated
+    bounding boxes
+  image/object/bbox/label: integer specifying the index in a classification
+    layer. The label ranges from [1, 1000] where 0 is not used. Note this is
+    always identical to the image label.
+
+Note that the length of xmin is identical to the length of xmax, ymin and ymax
+for each example.
+
+Running this script using 16 threads may take around ~2.5 hours on a HP Z420.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os
+import random
+import sys
+import threading
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+
+tf.app.flags.DEFINE_string('train_directory', '/tmp/',
+                           'Training data directory')
+tf.app.flags.DEFINE_string('validation_directory', '/tmp/',
+                           'Validation data directory')
+tf.app.flags.DEFINE_string('output_directory', '/tmp/',
+                           'Output data directory')
+
+tf.app.flags.DEFINE_integer('train_shards', 1024,
+                            'Number of shards in training TFRecord files.')
+tf.app.flags.DEFINE_integer('validation_shards', 128,
+                            'Number of shards in validation TFRecord files.')
+
+tf.app.flags.DEFINE_integer('num_threads', 8,
+                            'Number of threads to preprocess the images.')
+
+# The labels file contains a list of valid labels are held in this file.
+# Assumes that the file contains entries as such:
+#   n01440764
+#   n01443537
+#   n01484850
+# where each line corresponds to a label expressed as a synset. We map
+# each synset contained in the file to an integer (based on the alphabetical
+# ordering). See below for details.
+tf.app.flags.DEFINE_string('labels_file',
+                           'imagenet_lsvrc_2015_synsets.txt',
+                           'Labels file')
+
+# This file containing mapping from synset to human-readable label.
+# Assumes each line of the file looks like:
+#
+#   n02119247    black fox
+#   n02119359    silver fox
+#   n02119477    red fox, Vulpes fulva
+#
+# where each line corresponds to a unique mapping. Note that each line is
+# formatted as <synset>\t<human readable label>.
+tf.app.flags.DEFINE_string('imagenet_metadata_file',
+                           'imagenet_metadata.txt',
+                           'ImageNet metadata file')
+
+# This file is the output of process_bounding_box.py
+# Assumes each line of the file looks like:
+#
+#   n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
+#
+# where each line corresponds to one bounding box annotation associated
+# with an image. Each line can be parsed as:
+#
+#   <JPEG file name>, <xmin>, <ymin>, <xmax>, <ymax>
+#
+# Note that there might exist mulitple bounding box annotations associated
+# with an image file.
+tf.app.flags.DEFINE_string('bounding_box_file',
+                           './imagenet_2012_bounding_boxes.csv',
+                           'Bounding box file')
+
+FLAGS = tf.app.flags.FLAGS
+
+
+def _int64_feature(value):
+  """Wrapper for inserting int64 features into Example proto."""
+  if not isinstance(value, list):
+    value = [value]
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+
+def _float_feature(value):
+  """Wrapper for inserting float features into Example proto."""
+  if not isinstance(value, list):
+    value = [value]
+  return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+
+def _bytes_feature(value):
+  """Wrapper for inserting bytes features into Example proto."""
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
+                        height, width):
+  """Build an Example proto for an example.
+
+  Args:
+    filename: string, path to an image file, e.g., '/path/to/example.JPG'
+    image_buffer: string, JPEG encoding of RGB image
+    label: integer, identifier for the ground truth for the network
+    synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'
+    human: string, human-readable label, e.g., 'red fox, Vulpes vulpes'
+    bbox: list of bounding boxes; each box is a list of integers
+      specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong to
+      the same label as the image label.
+    height: integer, image height in pixels
+    width: integer, image width in pixels
+  Returns:
+    Example proto
+  """
+  xmin = []
+  ymin = []
+  xmax = []
+  ymax = []
+  for b in bbox:
+    assert len(b) == 4
+    # pylint: disable=expression-not-assigned
+    [l.append(point) for l, point in zip([xmin, ymin, xmax, ymax], b)]
+    # pylint: enable=expression-not-assigned
+
+  colorspace = 'RGB'
+  channels = 3
+  image_format = 'JPEG'
+
+  example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': _int64_feature(height),
+      'image/width': _int64_feature(width),
+      'image/colorspace': _bytes_feature(colorspace),
+      'image/channels': _int64_feature(channels),
+      'image/class/label': _int64_feature(label),
+      'image/class/synset': _bytes_feature(synset),
+      'image/class/text': _bytes_feature(human),
+      'image/object/bbox/xmin': _float_feature(xmin),
+      'image/object/bbox/xmax': _float_feature(xmax),
+      'image/object/bbox/ymin': _float_feature(ymin),
+      'image/object/bbox/ymax': _float_feature(ymax),
+      'image/object/bbox/label': _int64_feature([label] * len(xmin)),
+      'image/format': _bytes_feature(image_format),
+      'image/filename': _bytes_feature(os.path.basename(filename)),
+      'image/encoded': _bytes_feature(image_buffer)}))
+  return example
+
+
+class ImageCoder(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Create a single Session to run all image coding calls.
+    self._sess = tf.Session()
+
+    # Initializes function that converts PNG to JPEG data.
+    self._png_data = tf.placeholder(dtype=tf.string)
+    image = tf.image.decode_png(self._png_data, channels=3)
+    self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that converts CMYK JPEG data to RGB JPEG data.
+    self._cmyk_data = tf.placeholder(dtype=tf.string)
+    image = tf.image.decode_jpeg(self._cmyk_data, channels=0)
+    self._cmyk_to_rgb = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that decodes RGB JPEG data.
+    self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
+    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
+
+  def png_to_jpeg(self, image_data):
+    return self._sess.run(self._png_to_jpeg,
+                          feed_dict={self._png_data: image_data})
+
+  def cmyk_to_rgb(self, image_data):
+    return self._sess.run(self._cmyk_to_rgb,
+                          feed_dict={self._cmyk_data: image_data})
+
+  def decode_jpeg(self, image_data):
+    image = self._sess.run(self._decode_jpeg,
+                           feed_dict={self._decode_jpeg_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _is_png(filename):
+  """Determine if a file contains a PNG format image.
+
+  Args:
+    filename: string, path of the image file.
+
+  Returns:
+    boolean indicating if the image is a PNG.
+  """
+  # File list from:
+  # https://groups.google.com/forum/embed/?place=forum/torch7#!topic/torch7/fOSTXHIESSU
+  return 'n02105855_2933.JPEG' in filename
+
+
+def _is_cmyk(filename):
+  """Determine if file contains a CMYK JPEG format image.
+
+  Args:
+    filename: string, path of the image file.
+
+  Returns:
+    boolean indicating if the image is a JPEG encoded with CMYK color space.
+  """
+  # File list from:
+  # https://github.com/cytsai/ilsvrc-cmyk-image-list
+  blacklist = ['n01739381_1309.JPEG', 'n02077923_14822.JPEG',
+               'n02447366_23489.JPEG', 'n02492035_15739.JPEG',
+               'n02747177_10752.JPEG', 'n03018349_4028.JPEG',
+               'n03062245_4620.JPEG', 'n03347037_9675.JPEG',
+               'n03467068_12171.JPEG', 'n03529860_11437.JPEG',
+               'n03544143_17228.JPEG', 'n03633091_5218.JPEG',
+               'n03710637_5125.JPEG', 'n03961711_5286.JPEG',
+               'n04033995_2932.JPEG', 'n04258138_17003.JPEG',
+               'n04264628_27969.JPEG', 'n04336792_7448.JPEG',
+               'n04371774_5854.JPEG', 'n04596742_4225.JPEG',
+               'n07583066_647.JPEG', 'n13037406_4650.JPEG']
+  return filename.split('/')[-1] in blacklist
+
+
+def _process_image(filename, coder):
+  """Process a single image file.
+
+  Args:
+    filename: string, path to an image file e.g., '/path/to/example.JPG'.
+    coder: instance of ImageCoder to provide TensorFlow image coding utils.
+  Returns:
+    image_buffer: string, JPEG encoding of RGB image.
+    height: integer, image height in pixels.
+    width: integer, image width in pixels.
+  """
+  # Read the image file.
+  image_data = tf.gfile.FastGFile(filename, 'r').read()
+
+  # Clean the dirty data.
+  if _is_png(filename):
+    # 1 image is a PNG.
+    print('Converting PNG to JPEG for %s' % filename)
+    image_data = coder.png_to_jpeg(image_data)
+  elif _is_cmyk(filename):
+    # 22 JPEG images are in CMYK colorspace.
+    print('Converting CMYK to RGB for %s' % filename)
+    image_data = coder.cmyk_to_rgb(image_data)
+
+  # Decode the RGB JPEG.
+  image = coder.decode_jpeg(image_data)
+
+  # Check that image converted to RGB
+  assert len(image.shape) == 3
+  height = image.shape[0]
+  width = image.shape[1]
+  assert image.shape[2] == 3
+
+  return image_data, height, width
+
+
+def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
+                               synsets, labels, humans, bboxes, num_shards):
+  """Processes and saves list of images as TFRecord in 1 thread.
+
+  Args:
+    coder: instance of ImageCoder to provide TensorFlow image coding utils.
+    thread_index: integer, unique batch to run index is within [0, len(ranges)).
+    ranges: list of pairs of integers specifying ranges of each batches to
+      analyze in parallel.
+    name: string, unique identifier specifying the data set
+    filenames: list of strings; each string is a path to an image file
+    synsets: list of strings; each string is a unique WordNet ID
+    labels: list of integer; each integer identifies the ground truth
+    humans: list of strings; each string is a human-readable label
+    bboxes: list of bounding boxes for each image. Note that each entry in this
+      list might contain from 0+ entries corresponding to the number of bounding
+      box annotations for the image.
+    num_shards: integer number of shards for this data set.
+  """
+  # Each thread produces N shards where N = int(num_shards / num_threads).
+  # For instance, if num_shards = 128, and the num_threads = 2, then the first
+  # thread would produce shards [0, 64).
+  num_threads = len(ranges)
+  assert not num_shards % num_threads
+  num_shards_per_batch = int(num_shards / num_threads)
+
+  shard_ranges = np.linspace(ranges[thread_index][0],
+                             ranges[thread_index][1],
+                             num_shards_per_batch + 1).astype(int)
+  num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
+
+  counter = 0
+  for s in xrange(num_shards_per_batch):
+    # Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
+    shard = thread_index * num_shards_per_batch + s
+    output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
+    output_file = os.path.join(FLAGS.output_directory, output_filename)
+    writer = tf.python_io.TFRecordWriter(output_file)
+
+    shard_counter = 0
+    files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
+    for i in files_in_shard:
+      filename = filenames[i]
+      label = labels[i]
+      synset = synsets[i]
+      human = humans[i]
+      bbox = bboxes[i]
+
+      image_buffer, height, width = _process_image(filename, coder)
+
+      example = _convert_to_example(filename, image_buffer, label,
+                                    synset, human, bbox,
+                                    height, width)
+      writer.write(example.SerializeToString())
+      shard_counter += 1
+      counter += 1
+
+      if not counter % 1000:
+        print('%s [thread %d]: Processed %d of %d images in thread batch.' %
+              (datetime.now(), thread_index, counter, num_files_in_thread))
+        sys.stdout.flush()
+
+    writer.close()
+    print('%s [thread %d]: Wrote %d images to %s' %
+          (datetime.now(), thread_index, shard_counter, output_file))
+    sys.stdout.flush()
+    shard_counter = 0
+  print('%s [thread %d]: Wrote %d images to %d shards.' %
+        (datetime.now(), thread_index, counter, num_files_in_thread))
+  sys.stdout.flush()
+
+
+def _process_image_files(name, filenames, synsets, labels, humans,
+                         bboxes, num_shards):
+  """Process and save list of images as TFRecord of Example protos.
+
+  Args:
+    name: string, unique identifier specifying the data set
+    filenames: list of strings; each string is a path to an image file
+    synsets: list of strings; each string is a unique WordNet ID
+    labels: list of integer; each integer identifies the ground truth
+    humans: list of strings; each string is a human-readable label
+    bboxes: list of bounding boxes for each image. Note that each entry in this
+      list might contain from 0+ entries corresponding to the number of bounding
+      box annotations for the image.
+    num_shards: integer number of shards for this data set.
+  """
+  assert len(filenames) == len(synsets)
+  assert len(filenames) == len(labels)
+  assert len(filenames) == len(humans)
+  assert len(filenames) == len(bboxes)
+
+  # Break all images into batches with a [ranges[i][0], ranges[i][1]].
+  spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
+  ranges = []
+  threads = []
+  for i in xrange(len(spacing) - 1):
+    ranges.append([spacing[i], spacing[i+1]])
+
+  # Launch a thread for each batch.
+  print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
+  sys.stdout.flush()
+
+  # Create a mechanism for monitoring when all threads are finished.
+  coord = tf.train.Coordinator()
+
+  # Create a generic TensorFlow-based utility for converting all image codings.
+  coder = ImageCoder()
+
+  threads = []
+  for thread_index in xrange(len(ranges)):
+    args = (coder, thread_index, ranges, name, filenames,
+            synsets, labels, humans, bboxes, num_shards)
+    t = threading.Thread(target=_process_image_files_batch, args=args)
+    t.start()
+    threads.append(t)
+
+  # Wait for all the threads to terminate.
+  coord.join(threads)
+  print('%s: Finished writing all %d images in data set.' %
+        (datetime.now(), len(filenames)))
+  sys.stdout.flush()
+
+
+def _find_image_files(data_dir, labels_file):
+  """Build a list of all images files and labels in the data set.
+
+  Args:
+    data_dir: string, path to the root directory of images.
+
+      Assumes that the ImageNet data set resides in JPEG files located in
+      the following directory structure.
+
+        data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
+        data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
+
+      where 'n01440764' is the unique synset label associated with these images.
+
+    labels_file: string, path to the labels file.
+
+      The list of valid labels are held in this file. Assumes that the file
+      contains entries as such:
+        n01440764
+        n01443537
+        n01484850
+      where each line corresponds to a label expressed as a synset. We map
+      each synset contained in the file to an integer (based on the alphabetical
+      ordering) starting with the integer 1 corresponding to the synset
+      contained in the first line.
+
+      The reason we start the integer labels at 1 is to reserve label 0 as an
+      unused background class.
+
+  Returns:
+    filenames: list of strings; each string is a path to an image file.
+    synsets: list of strings; each string is a unique WordNet ID.
+    labels: list of integer; each integer identifies the ground truth.
+  """
+  print('Determining list of input files and labels from %s.' % data_dir)
+  challenge_synsets = [l.strip() for l in
+                       tf.gfile.FastGFile(labels_file, 'r').readlines()]
+
+  labels = []
+  filenames = []
+  synsets = []
+
+  # Leave label index 0 empty as a background class.
+  label_index = 1
+
+  # Construct the list of JPEG files and labels.
+  for synset in challenge_synsets:
+    jpeg_file_path = '%s/%s/*.JPEG' % (data_dir, synset)
+    matching_files = tf.gfile.Glob(jpeg_file_path)
+
+    labels.extend([label_index] * len(matching_files))
+    synsets.extend([synset] * len(matching_files))
+    filenames.extend(matching_files)
+
+    if not label_index % 100:
+      print('Finished finding files in %d of %d classes.' % (
+          label_index, len(challenge_synsets)))
+    label_index += 1
+
+  # Shuffle the ordering of all image files in order to guarantee
+  # random ordering of the images with respect to label in the
+  # saved TFRecord files. Make the randomization repeatable.
+  shuffled_index = range(len(filenames))
+  random.seed(12345)
+  random.shuffle(shuffled_index)
+
+  filenames = [filenames[i] for i in shuffled_index]
+  synsets = [synsets[i] for i in shuffled_index]
+  labels = [labels[i] for i in shuffled_index]
+
+  print('Found %d JPEG files across %d labels inside %s.' %
+        (len(filenames), len(challenge_synsets), data_dir))
+  return filenames, synsets, labels
+
+
+def _find_human_readable_labels(synsets, synset_to_human):
+  """Build a list of human-readable labels.
+
+  Args:
+    synsets: list of strings; each string is a unique WordNet ID.
+    synset_to_human: dict of synset to human labels, e.g.,
+      'n02119022' --> 'red fox, Vulpes vulpes'
+
+  Returns:
+    List of human-readable strings corresponding to each synset.
+  """
+  humans = []
+  for s in synsets:
+    assert s in synset_to_human, ('Failed to find: %s' % s)
+    humans.append(synset_to_human[s])
+  return humans
+
+
+def _find_image_bounding_boxes(filenames, image_to_bboxes):
+  """Find the bounding boxes for a given image file.
+
+  Args:
+    filenames: list of strings; each string is a path to an image file.
+    image_to_bboxes: dictionary mapping image file names to a list of
+      bounding boxes. This list contains 0+ bounding boxes.
+  Returns:
+    List of bounding boxes for each image. Note that each entry in this
+    list might contain from 0+ entries corresponding to the number of bounding
+    box annotations for the image.
+  """
+  num_image_bbox = 0
+  bboxes = []
+  for f in filenames:
+    basename = os.path.basename(f)
+    if basename in image_to_bboxes:
+      bboxes.append(image_to_bboxes[basename])
+      num_image_bbox += 1
+    else:
+      bboxes.append([])
+  print('Found %d images with bboxes out of %d images' % (
+      num_image_bbox, len(filenames)))
+  return bboxes
+
+
+def _process_dataset(name, directory, num_shards, synset_to_human,
+                     image_to_bboxes):
+  """Process a complete data set and save it as a TFRecord.
+
+  Args:
+    name: string, unique identifier specifying the data set.
+    directory: string, root path to the data set.
+    num_shards: integer number of shards for this data set.
+    synset_to_human: dict of synset to human labels, e.g.,
+      'n02119022' --> 'red fox, Vulpes vulpes'
+    image_to_bboxes: dictionary mapping image file names to a list of
+      bounding boxes. This list contains 0+ bounding boxes.
+  """
+  filenames, synsets, labels = _find_image_files(directory, FLAGS.labels_file)
+  humans = _find_human_readable_labels(synsets, synset_to_human)
+  bboxes = _find_image_bounding_boxes(filenames, image_to_bboxes)
+  _process_image_files(name, filenames, synsets, labels,
+                       humans, bboxes, num_shards)
+
+
+def _build_synset_lookup(imagenet_metadata_file):
+  """Build lookup for synset to human-readable label.
+
+  Args:
+    imagenet_metadata_file: string, path to file containing mapping from
+      synset to human-readable label.
+
+      Assumes each line of the file looks like:
+
+        n02119247    black fox
+        n02119359    silver fox
+        n02119477    red fox, Vulpes fulva
+
+      where each line corresponds to a unique mapping. Note that each line is
+      formatted as <synset>\t<human readable label>.
+
+  Returns:
+    Dictionary of synset to human labels, such as:
+      'n02119022' --> 'red fox, Vulpes vulpes'
+  """
+  lines = tf.gfile.FastGFile(imagenet_metadata_file, 'r').readlines()
+  synset_to_human = {}
+  for l in lines:
+    if l:
+      parts = l.strip().split('\t')
+      assert len(parts) == 2
+      synset = parts[0]
+      human = parts[1]
+      synset_to_human[synset] = human
+  return synset_to_human
+
+
+def _build_bounding_box_lookup(bounding_box_file):
+  """Build a lookup from image file to bounding boxes.
+
+  Args:
+    bounding_box_file: string, path to file with bounding boxes annotations.
+
+      Assumes each line of the file looks like:
+
+        n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
+
+      where each line corresponds to one bounding box annotation associated
+      with an image. Each line can be parsed as:
+
+        <JPEG file name>, <xmin>, <ymin>, <xmax>, <ymax>
+
+      Note that there might exist mulitple bounding box annotations associated
+      with an image file. This file is the output of process_bounding_boxes.py.
+
+  Returns:
+    Dictionary mapping image file names to a list of bounding boxes. This list
+    contains 0+ bounding boxes.
+  """
+  lines = tf.gfile.FastGFile(bounding_box_file, 'r').readlines()
+  images_to_bboxes = {}
+  num_bbox = 0
+  num_image = 0
+  for l in lines:
+    if l:
+      parts = l.split(',')
+      assert len(parts) == 5, ('Failed to parse: %s' % l)
+      filename = parts[0]
+      xmin = float(parts[1])
+      ymin = float(parts[2])
+      xmax = float(parts[3])
+      ymax = float(parts[4])
+      box = [xmin, ymin, xmax, ymax]
+
+      if filename not in images_to_bboxes:
+        images_to_bboxes[filename] = []
+        num_image += 1
+      images_to_bboxes[filename].append(box)
+      num_bbox += 1
+
+  print('Successfully read %d bounding boxes '
+        'across %d images.' % (num_bbox, num_image))
+  return images_to_bboxes
+
+
+def main(unused_argv):
+  assert not FLAGS.train_shards % FLAGS.num_threads, (
+      'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards')
+  assert not FLAGS.validation_shards % FLAGS.num_threads, (
+      'Please make the FLAGS.num_threads commensurate with '
+      'FLAGS.validation_shards')
+  print('Saving results to %s' % FLAGS.output_directory)
+
+  # Build a map from synset to human-readable label.
+  synset_to_human = _build_synset_lookup(FLAGS.imagenet_metadata_file)
+  image_to_bboxes = _build_bounding_box_lookup(FLAGS.bounding_box_file)
+
+  # Run it!
+  _process_dataset('validation', FLAGS.validation_directory,
+                   FLAGS.validation_shards, synset_to_human, image_to_bboxes)
+  _process_dataset('train', FLAGS.train_directory, FLAGS.train_shards,
+                   synset_to_human, image_to_bboxes)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/cifar10.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/cifar10.py
new file mode 100644
index 00000000..e0d29684
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/cifar10.py
@@ -0,0 +1,98 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides data for the Cifar10 dataset.
+
+The dataset scripts used to create the dataset can be found at:
+tensorflow/models/research/slim/datasets/download_and_convert_cifar10.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+slim = tf.contrib.slim
+
+_FILE_PATTERN = 'cifar10_%s.tfrecord'
+
+SPLITS_TO_SIZES = {'train': 50000, 'test': 10000}
+
+_NUM_CLASSES = 10
+
+_ITEMS_TO_DESCRIPTIONS = {
+    'image': 'A [32 x 32 x 3] color image.',
+    'label': 'A single integer between 0 and 9',
+}
+
+
+def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
+  """Gets a dataset tuple with instructions for reading cifar10.
+
+  Args:
+    split_name: A train/test split name.
+    dataset_dir: The base directory of the dataset sources.
+    file_pattern: The file pattern to use when matching the dataset sources.
+      It is assumed that the pattern contains a '%s' string so that the split
+      name can be inserted.
+    reader: The TensorFlow reader type.
+
+  Returns:
+    A `Dataset` namedtuple.
+
+  Raises:
+    ValueError: if `split_name` is not a valid train/test split.
+  """
+  if split_name not in SPLITS_TO_SIZES:
+    raise ValueError('split name %s was not recognized.' % split_name)
+
+  if not file_pattern:
+    file_pattern = _FILE_PATTERN
+  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
+
+  # Allowing None in the signature so that dataset_factory can use the default.
+  if not reader:
+    reader = tf.TFRecordReader
+
+  keys_to_features = {
+      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
+      'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
+      'image/class/label': tf.FixedLenFeature(
+          [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
+  }
+
+  items_to_handlers = {
+      'image': slim.tfexample_decoder.Image(shape=[32, 32, 3]),
+      'label': slim.tfexample_decoder.Tensor('image/class/label'),
+  }
+
+  decoder = slim.tfexample_decoder.TFExampleDecoder(
+      keys_to_features, items_to_handlers)
+
+  labels_to_names = None
+  if dataset_utils.has_labels(dataset_dir):
+    labels_to_names = dataset_utils.read_label_file(dataset_dir)
+
+  return slim.dataset.Dataset(
+      data_sources=file_pattern,
+      reader=reader,
+      decoder=decoder,
+      num_samples=SPLITS_TO_SIZES[split_name],
+      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
+      num_classes=_NUM_CLASSES,
+      labels_to_names=labels_to_names)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_factory.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_factory.py
new file mode 100644
index 00000000..141079a6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_factory.py
@@ -0,0 +1,57 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A factory-pattern class which returns classification image/label pairs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datasets import cifar10
+from datasets import flowers
+from datasets import imagenet
+from datasets import mnist
+
+datasets_map = {
+    'cifar10': cifar10,
+    'flowers': flowers,
+    'imagenet': imagenet,
+    'mnist': mnist,
+}
+
+
+def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None):
+  """Given a dataset name and a split_name returns a Dataset.
+
+  Args:
+    name: String, the name of the dataset.
+    split_name: A train/test split name.
+    dataset_dir: The directory where the dataset files are stored.
+    file_pattern: The file pattern to use for matching the dataset source files.
+    reader: The subclass of tf.ReaderBase. If left as `None`, then the default
+      reader defined by each dataset is used.
+
+  Returns:
+    A `Dataset` class.
+
+  Raises:
+    ValueError: If the dataset `name` is unknown.
+  """
+  if name not in datasets_map:
+    raise ValueError('Name of dataset unknown %s' % name)
+  return datasets_map[name].get_split(
+      split_name,
+      dataset_dir,
+      file_pattern,
+      reader)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_utils.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_utils.py
new file mode 100644
index 00000000..fdaefca3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/dataset_utils.py
@@ -0,0 +1,150 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains utilities for downloading and converting datasets."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import tarfile
+
+from six.moves import urllib
+import tensorflow as tf
+
+LABELS_FILENAME = 'labels.txt'
+
+
+def int64_feature(values):
+  """Returns a TF-Feature of int64s.
+
+  Args:
+    values: A scalar or list of values.
+
+  Returns:
+    A TF-Feature.
+  """
+  if not isinstance(values, (tuple, list)):
+    values = [values]
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
+
+
+def bytes_feature(values):
+  """Returns a TF-Feature of bytes.
+
+  Args:
+    values: A string.
+
+  Returns:
+    A TF-Feature.
+  """
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
+
+
+def float_feature(values):
+  """Returns a TF-Feature of floats.
+
+  Args:
+    values: A scalar of list of values.
+
+  Returns:
+    A TF-Feature.
+  """
+  if not isinstance(values, (tuple, list)):
+    values = [values]
+  return tf.train.Feature(float_list=tf.train.FloatList(value=values))
+
+
+def image_to_tfexample(image_data, image_format, height, width, class_id):
+  return tf.train.Example(features=tf.train.Features(feature={
+      'image/encoded': bytes_feature(image_data),
+      'image/format': bytes_feature(image_format),
+      'image/class/label': int64_feature(class_id),
+      'image/height': int64_feature(height),
+      'image/width': int64_feature(width),
+  }))
+
+
+def download_and_uncompress_tarball(tarball_url, dataset_dir):
+  """Downloads the `tarball_url` and uncompresses it locally.
+
+  Args:
+    tarball_url: The URL of a tarball file.
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  filename = tarball_url.split('/')[-1]
+  filepath = os.path.join(dataset_dir, filename)
+
+  def _progress(count, block_size, total_size):
+    sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+        filename, float(count * block_size) / float(total_size) * 100.0))
+    sys.stdout.flush()
+  filepath, _ = urllib.request.urlretrieve(tarball_url, filepath, _progress)
+  print()
+  statinfo = os.stat(filepath)
+  print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+  tarfile.open(filepath, 'r:gz').extractall(dataset_dir)
+
+
+def write_label_file(labels_to_class_names, dataset_dir,
+                     filename=LABELS_FILENAME):
+  """Writes a file with the list of class names.
+
+  Args:
+    labels_to_class_names: A map of (integer) labels to class names.
+    dataset_dir: The directory in which the labels file should be written.
+    filename: The filename where the class names are written.
+  """
+  labels_filename = os.path.join(dataset_dir, filename)
+  with tf.gfile.Open(labels_filename, 'w') as f:
+    for label in labels_to_class_names:
+      class_name = labels_to_class_names[label]
+      f.write('%d:%s\n' % (label, class_name))
+
+
+def has_labels(dataset_dir, filename=LABELS_FILENAME):
+  """Specifies whether or not the dataset directory contains a label map file.
+
+  Args:
+    dataset_dir: The directory in which the labels file is found.
+    filename: The filename where the class names are written.
+
+  Returns:
+    `True` if the labels file exists and `False` otherwise.
+  """
+  return tf.gfile.Exists(os.path.join(dataset_dir, filename))
+
+
+def read_label_file(dataset_dir, filename=LABELS_FILENAME):
+  """Reads the labels file and returns a mapping from ID to class name.
+
+  Args:
+    dataset_dir: The directory in which the labels file is found.
+    filename: The filename where the class names are written.
+
+  Returns:
+    A map from a label (integer) to class name.
+  """
+  labels_filename = os.path.join(dataset_dir, filename)
+  with tf.gfile.Open(labels_filename, 'rb') as f:
+    lines = f.read().decode()
+  lines = lines.split('\n')
+  lines = filter(None, lines)
+
+  labels_to_class_names = {}
+  for line in lines:
+    index = line.index(':')
+    labels_to_class_names[int(line[:index])] = line[index+1:]
+  return labels_to_class_names
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_cifar10.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_cifar10.py
new file mode 100644
index 00000000..f23618e7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_cifar10.py
@@ -0,0 +1,198 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Downloads and converts cifar10 data to TFRecords of TF-Example protos.
+
+This module downloads the cifar10 data, uncompresses it, reads the files
+that make up the cifar10 data and creates two TFRecord datasets: one for train
+and one for test. Each TFRecord dataset is comprised of a set of TF-Example
+protocol buffers, each of which contain a single image and label.
+
+The script should take several minutes to run.
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import tarfile
+
+import numpy as np
+from six.moves import cPickle
+from six.moves import urllib
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+# The URL where the CIFAR data can be downloaded.
+_DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
+
+# The number of training files.
+_NUM_TRAIN_FILES = 5
+
+# The height and width of each image.
+_IMAGE_SIZE = 32
+
+# The names of the classes.
+_CLASS_NAMES = [
+    'airplane',
+    'automobile',
+    'bird',
+    'cat',
+    'deer',
+    'dog',
+    'frog',
+    'horse',
+    'ship',
+    'truck',
+]
+
+
+def _add_to_tfrecord(filename, tfrecord_writer, offset=0):
+  """Loads data from the cifar10 pickle files and writes files to a TFRecord.
+
+  Args:
+    filename: The filename of the cifar10 pickle file.
+    tfrecord_writer: The TFRecord writer to use for writing.
+    offset: An offset into the absolute number of images previously written.
+
+  Returns:
+    The new offset.
+  """
+  with tf.gfile.Open(filename, 'rb') as f:
+    if sys.version_info < (3,):
+      data = cPickle.load(f)
+    else:
+      data = cPickle.load(f, encoding='bytes')
+
+  images = data[b'data']
+  num_images = images.shape[0]
+
+  images = images.reshape((num_images, 3, 32, 32))
+  labels = data[b'labels']
+
+  with tf.Graph().as_default():
+    image_placeholder = tf.placeholder(dtype=tf.uint8)
+    encoded_image = tf.image.encode_png(image_placeholder)
+
+    with tf.Session('') as sess:
+
+      for j in range(num_images):
+        sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
+            filename, offset + j + 1, offset + num_images))
+        sys.stdout.flush()
+
+        image = np.squeeze(images[j]).transpose((1, 2, 0))
+        label = labels[j]
+
+        png_string = sess.run(encoded_image,
+                              feed_dict={image_placeholder: image})
+
+        example = dataset_utils.image_to_tfexample(
+            png_string, b'png', _IMAGE_SIZE, _IMAGE_SIZE, label)
+        tfrecord_writer.write(example.SerializeToString())
+
+  return offset + num_images
+
+
+def _get_output_filename(dataset_dir, split_name):
+  """Creates the output filename.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+    split_name: The name of the train/test split.
+
+  Returns:
+    An absolute file path.
+  """
+  return '%s/cifar10_%s.tfrecord' % (dataset_dir, split_name)
+
+
+def _download_and_uncompress_dataset(dataset_dir):
+  """Downloads cifar10 and uncompresses it locally.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  filename = _DATA_URL.split('/')[-1]
+  filepath = os.path.join(dataset_dir, filename)
+
+  if not os.path.exists(filepath):
+    def _progress(count, block_size, total_size):
+      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+          filename, float(count * block_size) / float(total_size) * 100.0))
+      sys.stdout.flush()
+    filepath, _ = urllib.request.urlretrieve(_DATA_URL, filepath, _progress)
+    print()
+    statinfo = os.stat(filepath)
+    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)
+
+
+def _clean_up_temporary_files(dataset_dir):
+  """Removes temporary files used to create the dataset.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  filename = _DATA_URL.split('/')[-1]
+  filepath = os.path.join(dataset_dir, filename)
+  tf.gfile.Remove(filepath)
+
+  tmp_dir = os.path.join(dataset_dir, 'cifar-10-batches-py')
+  tf.gfile.DeleteRecursively(tmp_dir)
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  training_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
+
+  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
+
+  # First, process the training data:
+  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
+    offset = 0
+    for i in range(_NUM_TRAIN_FILES):
+      filename = os.path.join(dataset_dir,
+                              'cifar-10-batches-py',
+                              'data_batch_%d' % (i + 1))  # 1-indexed.
+      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)
+
+  # Next, process the testing data:
+  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
+    filename = os.path.join(dataset_dir,
+                            'cifar-10-batches-py',
+                            'test_batch')
+    _add_to_tfrecord(filename, tfrecord_writer)
+
+  # Finally, write the labels file:
+  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
+
+  _clean_up_temporary_files(dataset_dir)
+  print('\nFinished converting the Cifar10 dataset!')
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_flowers.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_flowers.py
new file mode 100644
index 00000000..93e5c414
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_flowers.py
@@ -0,0 +1,211 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Downloads and converts Flowers data to TFRecords of TF-Example protos.
+
+This module downloads the Flowers data, uncompresses it, reads the files
+that make up the Flowers data and creates two TFRecord datasets: one for train
+and one for test. Each TFRecord dataset is comprised of a set of TF-Example
+protocol buffers, each of which contain a single image and label.
+
+The script should take about a minute to run.
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import os
+import random
+import sys
+
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+# The URL where the Flowers data can be downloaded.
+_DATA_URL = 'http://download.tensorflow.org/example_images/flower_photos.tgz'
+
+# The number of images in the validation set.
+_NUM_VALIDATION = 350
+
+# Seed for repeatability.
+_RANDOM_SEED = 0
+
+# The number of shards per dataset split.
+_NUM_SHARDS = 5
+
+
+class ImageReader(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Initializes function that decodes RGB JPEG data.
+    self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
+    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
+
+  def read_image_dims(self, sess, image_data):
+    image = self.decode_jpeg(sess, image_data)
+    return image.shape[0], image.shape[1]
+
+  def decode_jpeg(self, sess, image_data):
+    image = sess.run(self._decode_jpeg,
+                     feed_dict={self._decode_jpeg_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _get_filenames_and_classes(dataset_dir):
+  """Returns a list of filenames and inferred class names.
+
+  Args:
+    dataset_dir: A directory containing a set of subdirectories representing
+      class names. Each subdirectory should contain PNG or JPG encoded images.
+
+  Returns:
+    A list of image file paths, relative to `dataset_dir` and the list of
+    subdirectories, representing class names.
+  """
+  flower_root = os.path.join(dataset_dir, 'flower_photos')
+  directories = []
+  class_names = []
+  for filename in os.listdir(flower_root):
+    path = os.path.join(flower_root, filename)
+    if os.path.isdir(path):
+      directories.append(path)
+      class_names.append(filename)
+
+  photo_filenames = []
+  for directory in directories:
+    for filename in os.listdir(directory):
+      path = os.path.join(directory, filename)
+      photo_filenames.append(path)
+
+  return photo_filenames, sorted(class_names)
+
+
+def _get_dataset_filename(dataset_dir, split_name, shard_id):
+  output_filename = 'flowers_%s_%05d-of-%05d.tfrecord' % (
+      split_name, shard_id, _NUM_SHARDS)
+  return os.path.join(dataset_dir, output_filename)
+
+
+def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
+  """Converts the given filenames to a TFRecord dataset.
+
+  Args:
+    split_name: The name of the dataset, either 'train' or 'validation'.
+    filenames: A list of absolute paths to png or jpg images.
+    class_names_to_ids: A dictionary from class names (strings) to ids
+      (integers).
+    dataset_dir: The directory where the converted datasets are stored.
+  """
+  assert split_name in ['train', 'validation']
+
+  num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))
+
+  with tf.Graph().as_default():
+    image_reader = ImageReader()
+
+    with tf.Session('') as sess:
+
+      for shard_id in range(_NUM_SHARDS):
+        output_filename = _get_dataset_filename(
+            dataset_dir, split_name, shard_id)
+
+        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
+          start_ndx = shard_id * num_per_shard
+          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
+          for i in range(start_ndx, end_ndx):
+            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
+                i+1, len(filenames), shard_id))
+            sys.stdout.flush()
+
+            # Read the filename:
+            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
+            height, width = image_reader.read_image_dims(sess, image_data)
+
+            class_name = os.path.basename(os.path.dirname(filenames[i]))
+            class_id = class_names_to_ids[class_name]
+
+            example = dataset_utils.image_to_tfexample(
+                image_data, b'jpg', height, width, class_id)
+            tfrecord_writer.write(example.SerializeToString())
+
+  sys.stdout.write('\n')
+  sys.stdout.flush()
+
+
+def _clean_up_temporary_files(dataset_dir):
+  """Removes temporary files used to create the dataset.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  filename = _DATA_URL.split('/')[-1]
+  filepath = os.path.join(dataset_dir, filename)
+  tf.gfile.Remove(filepath)
+
+  tmp_dir = os.path.join(dataset_dir, 'flower_photos')
+  tf.gfile.DeleteRecursively(tmp_dir)
+
+
+def _dataset_exists(dataset_dir):
+  for split_name in ['train', 'validation']:
+    for shard_id in range(_NUM_SHARDS):
+      output_filename = _get_dataset_filename(
+          dataset_dir, split_name, shard_id)
+      if not tf.gfile.Exists(output_filename):
+        return False
+  return True
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  if _dataset_exists(dataset_dir):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
+  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
+  class_names_to_ids = dict(zip(class_names, range(len(class_names))))
+
+  # Divide into train and test:
+  random.seed(_RANDOM_SEED)
+  random.shuffle(photo_filenames)
+  training_filenames = photo_filenames[_NUM_VALIDATION:]
+  validation_filenames = photo_filenames[:_NUM_VALIDATION]
+
+  # First, convert the training and validation sets.
+  _convert_dataset('train', training_filenames, class_names_to_ids,
+                   dataset_dir)
+  _convert_dataset('validation', validation_filenames, class_names_to_ids,
+                   dataset_dir)
+
+  # Finally, write the labels file:
+  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
+
+  _clean_up_temporary_files(dataset_dir)
+  print('\nFinished converting the Flowers dataset!')
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_imagenet.sh b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_imagenet.sh
new file mode 100755
index 00000000..b4b3866e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_imagenet.sh
@@ -0,0 +1,103 @@
+#!/bin/bash
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download and preprocess ImageNet Challenge 2012
+# training and validation data set.
+#
+# The final output of this script are sharded TFRecord files containing
+# serialized Example protocol buffers. See build_imagenet_data.py for
+# details of how the Example protocol buffers contain the ImageNet data.
+#
+# The final output of this script appears as such:
+#
+#   data_dir/train-00000-of-01024
+#   data_dir/train-00001-of-01024
+#    ...
+#   data_dir/train-00127-of-01024
+#
+# and
+#
+#   data_dir/validation-00000-of-00128
+#   data_dir/validation-00001-of-00128
+#   ...
+#   data_dir/validation-00127-of-00128
+#
+# Note that this script may take several hours to run to completion. The
+# conversion of the ImageNet data to TFRecords alone takes 2-3 hours depending
+# on the speed of your machine. Please be patient.
+#
+# **IMPORTANT**
+# To download the raw images, the user must create an account with image-net.org
+# and generate a username and access_key. The latter two are required for
+# downloading the raw images.
+#
+# usage:
+#  cd research/slim
+#  bazel build :download_and_convert_imagenet
+#  ./bazel-bin/download_and_convert_imagenet.sh [data-dir]
+set -e
+
+if [ -z "$1" ]; then
+  echo "usage download_and_convert_imagenet.sh [data dir]"
+  exit
+fi
+
+# Create the output and temporary directories.
+DATA_DIR="${1%/}"
+SCRATCH_DIR="${DATA_DIR}/raw-data/"
+mkdir -p "${DATA_DIR}"
+mkdir -p "${SCRATCH_DIR}"
+WORK_DIR="$0.runfiles/__main__"
+
+# Download the ImageNet data.
+LABELS_FILE="${WORK_DIR}/datasets/imagenet_lsvrc_2015_synsets.txt"
+DOWNLOAD_SCRIPT="${WORK_DIR}/datasets/download_imagenet.sh"
+"${DOWNLOAD_SCRIPT}" "${SCRATCH_DIR}" "${LABELS_FILE}"
+
+# Note the locations of the train and validation data.
+TRAIN_DIRECTORY="${SCRATCH_DIR}train/"
+VALIDATION_DIRECTORY="${SCRATCH_DIR}validation/"
+
+# Preprocess the validation data by moving the images into the appropriate
+# sub-directory based on the label (synset) of the image.
+echo "Organizing the validation data into sub-directories."
+PREPROCESS_VAL_SCRIPT="${WORK_DIR}/datasets/preprocess_imagenet_validation_data.py"
+VAL_LABELS_FILE="${WORK_DIR}/datasets/imagenet_2012_validation_synset_labels.txt"
+
+"${PREPROCESS_VAL_SCRIPT}" "${VALIDATION_DIRECTORY}" "${VAL_LABELS_FILE}"
+
+# Convert the XML files for bounding box annotations into a single CSV.
+echo "Extracting bounding box information from XML."
+BOUNDING_BOX_SCRIPT="${WORK_DIR}/datasets/process_bounding_boxes.py"
+BOUNDING_BOX_FILE="${SCRATCH_DIR}/imagenet_2012_bounding_boxes.csv"
+BOUNDING_BOX_DIR="${SCRATCH_DIR}bounding_boxes/"
+
+"${BOUNDING_BOX_SCRIPT}" "${BOUNDING_BOX_DIR}" "${LABELS_FILE}" \
+ | sort >"${BOUNDING_BOX_FILE}"
+echo "Finished downloading and preprocessing the ImageNet data."
+
+# Build the TFRecords version of the ImageNet data.
+BUILD_SCRIPT="${WORK_DIR}/build_imagenet_data"
+OUTPUT_DIRECTORY="${DATA_DIR}"
+IMAGENET_METADATA_FILE="${WORK_DIR}/datasets/imagenet_metadata.txt"
+
+"${BUILD_SCRIPT}" \
+  --train_directory="${TRAIN_DIRECTORY}" \
+  --validation_directory="${VALIDATION_DIRECTORY}" \
+  --output_directory="${OUTPUT_DIRECTORY}" \
+  --imagenet_metadata_file="${IMAGENET_METADATA_FILE}" \
+  --labels_file="${LABELS_FILE}" \
+  --bounding_box_file="${BOUNDING_BOX_FILE}"
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_mnist.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_mnist.py
new file mode 100644
index 00000000..d6ae8743
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_and_convert_mnist.py
@@ -0,0 +1,221 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Downloads and converts MNIST data to TFRecords of TF-Example protos.
+
+This module downloads the MNIST data, uncompresses it, reads the files
+that make up the MNIST data and creates two TFRecord datasets: one for train
+and one for test. Each TFRecord dataset is comprised of a set of TF-Example
+protocol buffers, each of which contain a single image and label.
+
+The script should take about a minute to run.
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gzip
+import os
+import sys
+
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+# The URLs where the MNIST data can be downloaded.
+_DATA_URL = 'http://yann.lecun.com/exdb/mnist/'
+_TRAIN_DATA_FILENAME = 'train-images-idx3-ubyte.gz'
+_TRAIN_LABELS_FILENAME = 'train-labels-idx1-ubyte.gz'
+_TEST_DATA_FILENAME = 't10k-images-idx3-ubyte.gz'
+_TEST_LABELS_FILENAME = 't10k-labels-idx1-ubyte.gz'
+
+_IMAGE_SIZE = 28
+_NUM_CHANNELS = 1
+
+# The names of the classes.
+_CLASS_NAMES = [
+    'zero',
+    'one',
+    'two',
+    'three',
+    'four',
+    'five',
+    'size',
+    'seven',
+    'eight',
+    'nine',
+]
+
+
+def _extract_images(filename, num_images):
+  """Extract the images into a numpy array.
+
+  Args:
+    filename: The path to an MNIST images file.
+    num_images: The number of images in the file.
+
+  Returns:
+    A numpy array of shape [number_of_images, height, width, channels].
+  """
+  print('Extracting images from: ', filename)
+  with gzip.open(filename) as bytestream:
+    bytestream.read(16)
+    buf = bytestream.read(
+        _IMAGE_SIZE * _IMAGE_SIZE * num_images * _NUM_CHANNELS)
+    data = np.frombuffer(buf, dtype=np.uint8)
+    data = data.reshape(num_images, _IMAGE_SIZE, _IMAGE_SIZE, _NUM_CHANNELS)
+  return data
+
+
+def _extract_labels(filename, num_labels):
+  """Extract the labels into a vector of int64 label IDs.
+
+  Args:
+    filename: The path to an MNIST labels file.
+    num_labels: The number of labels in the file.
+
+  Returns:
+    A numpy array of shape [number_of_labels]
+  """
+  print('Extracting labels from: ', filename)
+  with gzip.open(filename) as bytestream:
+    bytestream.read(8)
+    buf = bytestream.read(1 * num_labels)
+    labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
+  return labels
+
+
+def _add_to_tfrecord(data_filename, labels_filename, num_images,
+                     tfrecord_writer):
+  """Loads data from the binary MNIST files and writes files to a TFRecord.
+
+  Args:
+    data_filename: The filename of the MNIST images.
+    labels_filename: The filename of the MNIST labels.
+    num_images: The number of images in the dataset.
+    tfrecord_writer: The TFRecord writer to use for writing.
+  """
+  images = _extract_images(data_filename, num_images)
+  labels = _extract_labels(labels_filename, num_images)
+
+  shape = (_IMAGE_SIZE, _IMAGE_SIZE, _NUM_CHANNELS)
+  with tf.Graph().as_default():
+    image = tf.placeholder(dtype=tf.uint8, shape=shape)
+    encoded_png = tf.image.encode_png(image)
+
+    with tf.Session('') as sess:
+      for j in range(num_images):
+        sys.stdout.write('\r>> Converting image %d/%d' % (j + 1, num_images))
+        sys.stdout.flush()
+
+        png_string = sess.run(encoded_png, feed_dict={image: images[j]})
+
+        example = dataset_utils.image_to_tfexample(
+            png_string, 'png'.encode(), _IMAGE_SIZE, _IMAGE_SIZE, labels[j])
+        tfrecord_writer.write(example.SerializeToString())
+
+
+def _get_output_filename(dataset_dir, split_name):
+  """Creates the output filename.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+    split_name: The name of the train/test split.
+
+  Returns:
+    An absolute file path.
+  """
+  return '%s/mnist_%s.tfrecord' % (dataset_dir, split_name)
+
+
+def _download_dataset(dataset_dir):
+  """Downloads MNIST locally.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  for filename in [_TRAIN_DATA_FILENAME,
+                   _TRAIN_LABELS_FILENAME,
+                   _TEST_DATA_FILENAME,
+                   _TEST_LABELS_FILENAME]:
+    filepath = os.path.join(dataset_dir, filename)
+
+    if not os.path.exists(filepath):
+      print('Downloading file %s...' % filename)
+      def _progress(count, block_size, total_size):
+        sys.stdout.write('\r>> Downloading %.1f%%' % (
+            float(count * block_size) / float(total_size) * 100.0))
+        sys.stdout.flush()
+      filepath, _ = urllib.request.urlretrieve(_DATA_URL + filename,
+                                               filepath,
+                                               _progress)
+      print()
+      with tf.gfile.GFile(filepath) as f:
+        size = f.size()
+      print('Successfully downloaded', filename, size, 'bytes.')
+
+
+def _clean_up_temporary_files(dataset_dir):
+  """Removes temporary files used to create the dataset.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  for filename in [_TRAIN_DATA_FILENAME,
+                   _TRAIN_LABELS_FILENAME,
+                   _TEST_DATA_FILENAME,
+                   _TEST_LABELS_FILENAME]:
+    filepath = os.path.join(dataset_dir, filename)
+    tf.gfile.Remove(filepath)
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  training_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
+
+  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  _download_dataset(dataset_dir)
+
+  # First, process the training data:
+  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
+    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
+    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
+    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)
+
+  # Next, process the testing data:
+  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
+    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
+    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
+    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)
+
+  # Finally, write the labels file:
+  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
+
+  _clean_up_temporary_files(dataset_dir)
+  print('\nFinished converting the MNIST dataset!')
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/download_imagenet.sh b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_imagenet.sh
new file mode 100755
index 00000000..c780e179
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/download_imagenet.sh
@@ -0,0 +1,99 @@
+#!/bin/bash
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download ImageNet Challenge 2012 training and validation data set.
+#
+# Downloads and decompresses raw images and bounding boxes.
+#
+# **IMPORTANT**
+# To download the raw images, the user must create an account with image-net.org
+# and generate a username and access_key. The latter two are required for
+# downloading the raw images.
+#
+# usage:
+#  ./download_imagenet.sh [dirname]
+set -e
+
+if [ "x$IMAGENET_ACCESS_KEY" == x -o "x$IMAGENET_USERNAME" == x ]; then
+  cat <<END
+In order to download the imagenet data, you have to create an account with
+image-net.org. This will get you a username and an access key. You can set the
+IMAGENET_USERNAME and IMAGENET_ACCESS_KEY environment variables, or you can
+enter the credentials here.
+END
+  read -p "Username: " IMAGENET_USERNAME
+  read -p "Access key: " IMAGENET_ACCESS_KEY
+fi
+
+OUTDIR="${1:-./imagenet-data}"
+SYNSETS_FILE="${2:-./synsets.txt}"
+
+echo "Saving downloaded files to $OUTDIR"
+mkdir -p "${OUTDIR}"
+CURRENT_DIR=$(pwd)
+BBOX_DIR="${OUTDIR}bounding_boxes"
+mkdir -p "${BBOX_DIR}"
+cd "${OUTDIR}"
+
+# Download and process all of the ImageNet bounding boxes.
+BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nnoupb"
+
+# See here for details: http://www.image-net.org/download-bboxes
+BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"
+BBOX_TAR_BALL="${BBOX_DIR}/annotations.tar.gz"
+echo "Downloading bounding box annotations."
+wget "${BOUNDING_BOX_ANNOTATIONS}" -O "${BBOX_TAR_BALL}"
+echo "Uncompressing bounding box annotations ..."
+tar xzf "${BBOX_TAR_BALL}" -C "${BBOX_DIR}"
+
+LABELS_ANNOTATED="${BBOX_DIR}/*"
+NUM_XML=$(ls -1 ${LABELS_ANNOTATED} | wc -l)
+echo "Identified ${NUM_XML} bounding box annotations."
+
+# Download and uncompress all images from the ImageNet 2012 validation dataset.
+VALIDATION_TARBALL="ILSVRC2012_img_val.tar"
+OUTPUT_PATH="${OUTDIR}validation/"
+mkdir -p "${OUTPUT_PATH}"
+cd "${OUTDIR}/.."
+echo "Downloading ${VALIDATION_TARBALL} to ${OUTPUT_PATH}."
+wget -nd -c "${BASE_URL}/${VALIDATION_TARBALL}"
+tar xf "${VALIDATION_TARBALL}" -C "${OUTPUT_PATH}"
+
+# Download all images from the ImageNet 2012 train dataset.
+TRAIN_TARBALL="ILSVRC2012_img_train.tar"
+OUTPUT_PATH="${OUTDIR}train/"
+mkdir -p "${OUTPUT_PATH}"
+cd "${OUTDIR}/.."
+echo "Downloading ${TRAIN_TARBALL} to ${OUTPUT_PATH}."
+wget -nd -c "${BASE_URL}/${TRAIN_TARBALL}"
+
+# Un-compress the individual tar-files within the train tar-file.
+echo "Uncompressing individual train tar-balls in the training data."
+
+while read SYNSET; do
+  echo "Processing: ${SYNSET}"
+
+  # Create a directory and delete anything there.
+  mkdir -p "${OUTPUT_PATH}/${SYNSET}"
+  rm -rf "${OUTPUT_PATH}/${SYNSET}/*"
+
+  # Uncompress into the directory.
+  tar xf "${TRAIN_TARBALL}" "${SYNSET}.tar"
+  tar xf "${SYNSET}.tar" -C "${OUTPUT_PATH}/${SYNSET}/"
+  rm -f "${SYNSET}.tar"
+
+  echo "Finished processing: ${SYNSET}"
+done < "${SYNSETS_FILE}"
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/flowers.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/flowers.py
new file mode 100644
index 00000000..a2e4b2e0
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/flowers.py
@@ -0,0 +1,98 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides data for the flowers dataset.
+
+The dataset scripts used to create the dataset can be found at:
+tensorflow/models/research/slim/datasets/download_and_convert_flowers.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+slim = tf.contrib.slim
+
+_FILE_PATTERN = 'flowers_%s_*.tfrecord'
+
+SPLITS_TO_SIZES = {'train': 3320, 'validation': 350}
+
+_NUM_CLASSES = 5
+
+_ITEMS_TO_DESCRIPTIONS = {
+    'image': 'A color image of varying size.',
+    'label': 'A single integer between 0 and 4',
+}
+
+
+def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
+  """Gets a dataset tuple with instructions for reading flowers.
+
+  Args:
+    split_name: A train/validation split name.
+    dataset_dir: The base directory of the dataset sources.
+    file_pattern: The file pattern to use when matching the dataset sources.
+      It is assumed that the pattern contains a '%s' string so that the split
+      name can be inserted.
+    reader: The TensorFlow reader type.
+
+  Returns:
+    A `Dataset` namedtuple.
+
+  Raises:
+    ValueError: if `split_name` is not a valid train/validation split.
+  """
+  if split_name not in SPLITS_TO_SIZES:
+    raise ValueError('split name %s was not recognized.' % split_name)
+
+  if not file_pattern:
+    file_pattern = _FILE_PATTERN
+  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
+
+  # Allowing None in the signature so that dataset_factory can use the default.
+  if reader is None:
+    reader = tf.TFRecordReader
+
+  keys_to_features = {
+      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
+      'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
+      'image/class/label': tf.FixedLenFeature(
+          [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
+  }
+
+  items_to_handlers = {
+      'image': slim.tfexample_decoder.Image(),
+      'label': slim.tfexample_decoder.Tensor('image/class/label'),
+  }
+
+  decoder = slim.tfexample_decoder.TFExampleDecoder(
+      keys_to_features, items_to_handlers)
+
+  labels_to_names = None
+  if dataset_utils.has_labels(dataset_dir):
+    labels_to_names = dataset_utils.read_label_file(dataset_dir)
+
+  return slim.dataset.Dataset(
+      data_sources=file_pattern,
+      reader=reader,
+      decoder=decoder,
+      num_samples=SPLITS_TO_SIZES[split_name],
+      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
+      num_classes=_NUM_CLASSES,
+      labels_to_names=labels_to_names)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet.py
new file mode 100644
index 00000000..a505953a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet.py
@@ -0,0 +1,198 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides data for the ImageNet ILSVRC 2012 Dataset plus some bounding boxes.
+
+Some images have one or more bounding boxes associated with the label of the
+image. See details here: http://image-net.org/download-bboxes
+
+ImageNet is based upon WordNet 3.0. To uniquely identify a synset, we use
+"WordNet ID" (wnid), which is a concatenation of POS ( i.e. part of speech )
+and SYNSET OFFSET of WordNet. For more information, please refer to the
+WordNet documentation[http://wordnet.princeton.edu/wordnet/documentation/].
+
+"There are bounding boxes for over 3000 popular synsets available.
+For each synset, there are on average 150 images with bounding boxes."
+
+WARNING: Don't use for object detection, in this case all the bounding boxes
+of the image belong to just one class.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+from six.moves import urllib
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+slim = tf.contrib.slim
+
+# TODO(nsilberman): Add tfrecord file type once the script is updated.
+_FILE_PATTERN = '%s-*'
+
+_SPLITS_TO_SIZES = {
+    'train': 1281167,
+    'validation': 50000,
+}
+
+_ITEMS_TO_DESCRIPTIONS = {
+    'image': 'A color image of varying height and width.',
+    'label': 'The label id of the image, integer between 0 and 999',
+    'label_text': 'The text of the label.',
+    'object/bbox': 'A list of bounding boxes.',
+    'object/label': 'A list of labels, one per each object.',
+}
+
+_NUM_CLASSES = 1001
+
+# If set to false, will not try to set label_to_names in dataset
+# by reading them from labels.txt or github.
+LOAD_READABLE_NAMES = True
+
+
+def create_readable_names_for_imagenet_labels():
+  """Create a dict mapping label id to human readable string.
+
+  Returns:
+      labels_to_names: dictionary where keys are integers from to 1000
+      and values are human-readable names.
+
+  We retrieve a synset file, which contains a list of valid synset labels used
+  by ILSVRC competition. There is one synset one per line, eg.
+          #   n01440764
+          #   n01443537
+  We also retrieve a synset_to_human_file, which contains a mapping from synsets
+  to human-readable names for every synset in Imagenet. These are stored in a
+  tsv format, as follows:
+          #   n02119247    black fox
+          #   n02119359    silver fox
+  We assign each synset (in alphabetical order) an integer, starting from 1
+  (since 0 is reserved for the background class).
+
+  Code is based on
+  https://github.com/tensorflow/models/blob/master/research/inception/inception/data/build_imagenet_data.py#L463
+  """
+
+  # pylint: disable=g-line-too-long
+  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/'
+  synset_url = '{}/imagenet_lsvrc_2015_synsets.txt'.format(base_url)
+  synset_to_human_url = '{}/imagenet_metadata.txt'.format(base_url)
+
+  filename, _ = urllib.request.urlretrieve(synset_url)
+  synset_list = [s.strip() for s in open(filename).readlines()]
+  num_synsets_in_ilsvrc = len(synset_list)
+  assert num_synsets_in_ilsvrc == 1000
+
+  filename, _ = urllib.request.urlretrieve(synset_to_human_url)
+  synset_to_human_list = open(filename).readlines()
+  num_synsets_in_all_imagenet = len(synset_to_human_list)
+  assert num_synsets_in_all_imagenet == 21842
+
+  synset_to_human = {}
+  for s in synset_to_human_list:
+    parts = s.strip().split('\t')
+    assert len(parts) == 2
+    synset = parts[0]
+    human = parts[1]
+    synset_to_human[synset] = human
+
+  label_index = 1
+  labels_to_names = {0: 'background'}
+  for synset in synset_list:
+    name = synset_to_human[synset]
+    labels_to_names[label_index] = name
+    label_index += 1
+
+  return labels_to_names
+
+
+def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
+  """Gets a dataset tuple with instructions for reading ImageNet.
+
+  Args:
+    split_name: A train/test split name.
+    dataset_dir: The base directory of the dataset sources.
+    file_pattern: The file pattern to use when matching the dataset sources.
+      It is assumed that the pattern contains a '%s' string so that the split
+      name can be inserted.
+    reader: The TensorFlow reader type.
+
+  Returns:
+    A `Dataset` namedtuple.
+
+  Raises:
+    ValueError: if `split_name` is not a valid train/test split.
+  """
+  if split_name not in _SPLITS_TO_SIZES:
+    raise ValueError('split name %s was not recognized.' % split_name)
+
+  if not file_pattern:
+    file_pattern = _FILE_PATTERN
+  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
+
+  # Allowing None in the signature so that dataset_factory can use the default.
+  if reader is None:
+    reader = tf.TFRecordReader
+
+  keys_to_features = {
+      'image/encoded': tf.FixedLenFeature(
+          (), tf.string, default_value=''),
+      'image/format': tf.FixedLenFeature(
+          (), tf.string, default_value='jpeg'),
+      'image/class/label': tf.FixedLenFeature(
+          [], dtype=tf.int64, default_value=-1),
+      'image/class/text': tf.FixedLenFeature(
+          [], dtype=tf.string, default_value=''),
+      'image/object/bbox/xmin': tf.VarLenFeature(
+          dtype=tf.float32),
+      'image/object/bbox/ymin': tf.VarLenFeature(
+          dtype=tf.float32),
+      'image/object/bbox/xmax': tf.VarLenFeature(
+          dtype=tf.float32),
+      'image/object/bbox/ymax': tf.VarLenFeature(
+          dtype=tf.float32),
+      'image/object/class/label': tf.VarLenFeature(
+          dtype=tf.int64),
+  }
+
+  items_to_handlers = {
+      'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
+      'label': slim.tfexample_decoder.Tensor('image/class/label'),
+      'label_text': slim.tfexample_decoder.Tensor('image/class/text'),
+      'object/bbox': slim.tfexample_decoder.BoundingBox(
+          ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
+      'object/label': slim.tfexample_decoder.Tensor('image/object/class/label'),
+  }
+
+  decoder = slim.tfexample_decoder.TFExampleDecoder(
+      keys_to_features, items_to_handlers)
+
+  labels_to_names = None
+  if LOAD_READABLE_NAMES:
+    if dataset_utils.has_labels(dataset_dir):
+      labels_to_names = dataset_utils.read_label_file(dataset_dir)
+    else:
+      labels_to_names = create_readable_names_for_imagenet_labels()
+      dataset_utils.write_label_file(labels_to_names, dataset_dir)
+
+  return slim.dataset.Dataset(
+      data_sources=file_pattern,
+      reader=reader,
+      decoder=decoder,
+      num_samples=_SPLITS_TO_SIZES[split_name],
+      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
+      num_classes=_NUM_CLASSES,
+      labels_to_names=labels_to_names)
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/imagenet_2012_validation_synset_labels.txt b/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_2012_validation_synset_labels.txt
similarity index 100%
rename from TensorFlow/Classification/imagenet/data_preprocessing/imagenet_2012_validation_synset_labels.txt
rename to TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_2012_validation_synset_labels.txt
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/imagenet_lsvrc_2015_synsets.txt b/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
similarity index 100%
rename from TensorFlow/Classification/imagenet/data_preprocessing/imagenet_lsvrc_2015_synsets.txt
rename to TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/imagenet_metadata.txt b/TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_metadata.txt
similarity index 100%
rename from TensorFlow/Classification/imagenet/data_preprocessing/imagenet_metadata.txt
rename to TensorFlow/Detection/SSD/models/research/slim/datasets/imagenet_metadata.txt
diff --git a/TensorFlow/Detection/SSD/models/research/slim/datasets/mnist.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/mnist.py
new file mode 100644
index 00000000..b1c81eb7
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/mnist.py
@@ -0,0 +1,98 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides data for the MNIST dataset.
+
+The dataset scripts used to create the dataset can be found at:
+tensorflow/models/research/slim/datasets/download_and_convert_mnist.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tensorflow as tf
+
+from datasets import dataset_utils
+
+slim = tf.contrib.slim
+
+_FILE_PATTERN = 'mnist_%s.tfrecord'
+
+_SPLITS_TO_SIZES = {'train': 60000, 'test': 10000}
+
+_NUM_CLASSES = 10
+
+_ITEMS_TO_DESCRIPTIONS = {
+    'image': 'A [28 x 28 x 1] grayscale image.',
+    'label': 'A single integer between 0 and 9',
+}
+
+
+def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
+  """Gets a dataset tuple with instructions for reading MNIST.
+
+  Args:
+    split_name: A train/test split name.
+    dataset_dir: The base directory of the dataset sources.
+    file_pattern: The file pattern to use when matching the dataset sources.
+      It is assumed that the pattern contains a '%s' string so that the split
+      name can be inserted.
+    reader: The TensorFlow reader type.
+
+  Returns:
+    A `Dataset` namedtuple.
+
+  Raises:
+    ValueError: if `split_name` is not a valid train/test split.
+  """
+  if split_name not in _SPLITS_TO_SIZES:
+    raise ValueError('split name %s was not recognized.' % split_name)
+
+  if not file_pattern:
+    file_pattern = _FILE_PATTERN
+  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
+
+  # Allowing None in the signature so that dataset_factory can use the default.
+  if reader is None:
+    reader = tf.TFRecordReader
+
+  keys_to_features = {
+      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
+      'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
+      'image/class/label': tf.FixedLenFeature(
+          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
+  }
+
+  items_to_handlers = {
+      'image': slim.tfexample_decoder.Image(shape=[28, 28, 1], channels=1),
+      'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
+  }
+
+  decoder = slim.tfexample_decoder.TFExampleDecoder(
+      keys_to_features, items_to_handlers)
+
+  labels_to_names = None
+  if dataset_utils.has_labels(dataset_dir):
+    labels_to_names = dataset_utils.read_label_file(dataset_dir)
+
+  return slim.dataset.Dataset(
+      data_sources=file_pattern,
+      reader=reader,
+      decoder=decoder,
+      num_samples=_SPLITS_TO_SIZES[split_name],
+      num_classes=_NUM_CLASSES,
+      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
+      labels_to_names=labels_to_names)
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/preprocess_imagenet_validation_data.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/preprocess_imagenet_validation_data.py
similarity index 92%
rename from TensorFlow/Classification/imagenet/data_preprocessing/preprocess_imagenet_validation_data.py
rename to TensorFlow/Detection/SSD/models/research/slim/datasets/preprocess_imagenet_validation_data.py
index 8308277a..ba3b1b0f 100755
--- a/TensorFlow/Classification/imagenet/data_preprocessing/preprocess_imagenet_validation_data.py
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/preprocess_imagenet_validation_data.py
@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
+r"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
 
 Associate the ImageNet 2012 Challenge validation data set with labels.
 
@@ -49,9 +49,10 @@ from __future__ import division
 from __future__ import print_function
 
 import os
-import os.path
 import sys
 
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
 
 if __name__ == '__main__':
   if len(sys.argv) < 3:
@@ -72,11 +73,11 @@ if __name__ == '__main__':
     os.makedirs(labeled_data_dir)
 
   # Move all of the image to the appropriate sub-directory.
-  for i in range(len(labels)):
+  for i in xrange(len(labels)):
     basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1)
     original_filename = os.path.join(data_dir, basename)
     if not os.path.exists(original_filename):
-      print('Failed to find: ' % original_filename)
+      print('Failed to find: ', original_filename)
       sys.exit(-1)
     new_filename = os.path.join(data_dir, labels[i], basename)
     os.rename(original_filename, new_filename)
diff --git a/TensorFlow/Classification/imagenet/data_preprocessing/process_bounding_boxes.py b/TensorFlow/Detection/SSD/models/research/slim/datasets/process_bounding_boxes.py
similarity index 98%
rename from TensorFlow/Classification/imagenet/data_preprocessing/process_bounding_boxes.py
rename to TensorFlow/Detection/SSD/models/research/slim/datasets/process_bounding_boxes.py
index c1a97444..78c899e2 100755
--- a/TensorFlow/Classification/imagenet/data_preprocessing/process_bounding_boxes.py
+++ b/TensorFlow/Detection/SSD/models/research/slim/datasets/process_bounding_boxes.py
@@ -85,6 +85,7 @@ import glob
 import os.path
 import sys
 import xml.etree.ElementTree as ET
+from six.moves import xrange  # pylint: disable=redefined-builtin
 
 
 class BoundingBox(object):
@@ -128,7 +129,7 @@ def ProcessXMLAnnotation(xml_file):
   num_boxes = FindNumberBoundingBoxes(root)
   boxes = []
 
-  for index in range(num_boxes):
+  for index in xrange(num_boxes):
     box = BoundingBox()
     # Grab the 'index' annotation.
     box.xmin = GetInt('xmin', root, index)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/deployment/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/deployment/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/deployment/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy.py b/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy.py
new file mode 100644
index 00000000..1918d510
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy.py
@@ -0,0 +1,677 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Deploy Slim models across multiple clones and replicas.
+
+# TODO(sguada) docstring paragraph by (a) motivating the need for the file and
+# (b) defining clones.
+
+# TODO(sguada) describe the high-level components of model deployment.
+# E.g. "each model deployment is composed of several parts: a DeploymentConfig,
+# which captures A, B and C, an input_fn which loads data.. etc
+
+To easily train a model on multiple GPUs or across multiple machines this
+module provides a set of helper functions: `create_clones`,
+`optimize_clones` and `deploy`.
+
+Usage:
+
+  g = tf.Graph()
+
+  # Set up DeploymentConfig
+  config = model_deploy.DeploymentConfig(num_clones=2, clone_on_cpu=True)
+
+  # Create the global step on the device storing the variables.
+  with tf.device(config.variables_device()):
+    global_step = slim.create_global_step()
+
+  # Define the inputs
+  with tf.device(config.inputs_device()):
+    images, labels = LoadData(...)
+    inputs_queue = slim.data.prefetch_queue((images, labels))
+
+  # Define the optimizer.
+  with tf.device(config.optimizer_device()):
+    optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)
+
+  # Define the model including the loss.
+  def model_fn(inputs_queue):
+    images, labels = inputs_queue.dequeue()
+    predictions = CreateNetwork(images)
+    slim.losses.log_loss(predictions, labels)
+
+  model_dp = model_deploy.deploy(config, model_fn, [inputs_queue],
+                                 optimizer=optimizer)
+
+  # Run training.
+  slim.learning.train(model_dp.train_op, my_log_dir,
+                      summary_op=model_dp.summary_op)
+
+The Clone namedtuple holds together the values associated with each call to
+model_fn:
+  * outputs: The return values of the calls to `model_fn()`.
+  * scope: The scope used to create the clone.
+  * device: The device used to create the clone.
+
+DeployedModel namedtuple, holds together the values needed to train multiple
+clones:
+  * train_op: An operation that run the optimizer training op and include
+    all the update ops created by `model_fn`. Present only if an optimizer
+    was specified.
+  * summary_op: An operation that run the summaries created by `model_fn`
+    and process_gradients.
+  * total_loss: A `Tensor` that contains the sum of all losses created by
+    `model_fn` plus the regularization losses.
+  * clones: List of `Clone` tuples returned by `create_clones()`.
+
+DeploymentConfig parameters:
+  * num_clones: Number of model clones to deploy in each replica.
+  * clone_on_cpu: True if clones should be placed on CPU.
+  * replica_id: Integer.  Index of the replica for which the model is
+      deployed.  Usually 0 for the chief replica.
+  * num_replicas: Number of replicas to use.
+  * num_ps_tasks: Number of tasks for the `ps` job. 0 to not use replicas.
+  * worker_job_name: A name for the worker job.
+  * ps_job_name: A name for the parameter server job.
+
+TODO(sguada):
+  - describe side effect to the graph.
+  - what happens to summaries and update_ops.
+  - which graph collections are altered.
+  - write a tutorial on how to use this.
+  - analyze the possibility of calling deploy more than once.
+
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+__all__ = ['create_clones',
+           'deploy',
+           'optimize_clones',
+           'DeployedModel',
+           'DeploymentConfig',
+           'Clone',
+          ]
+
+
+# Namedtuple used to represent a clone during deployment.
+Clone = collections.namedtuple('Clone',
+                               ['outputs',  # Whatever model_fn() returned.
+                                'scope',  # The scope used to create it.
+                                'device',  # The device used to create.
+                               ])
+
+# Namedtuple used to represent a DeployedModel, returned by deploy().
+DeployedModel = collections.namedtuple('DeployedModel',
+                                       ['train_op',  # The `train_op`
+                                        'summary_op',  # The `summary_op`
+                                        'total_loss',  # The loss `Tensor`
+                                        'clones',  # A list of `Clones` tuples.
+                                       ])
+
+# Default parameters for DeploymentConfig
+_deployment_params = {'num_clones': 1,
+                      'clone_on_cpu': False,
+                      'replica_id': 0,
+                      'num_replicas': 1,
+                      'num_ps_tasks': 0,
+                      'worker_job_name': 'worker',
+                      'ps_job_name': 'ps'}
+
+
+def create_clones(config, model_fn, args=None, kwargs=None):
+  """Creates multiple clones according to config using a `model_fn`.
+
+  The returned values of `model_fn(*args, **kwargs)` are collected along with
+  the scope and device used to created it in a namedtuple
+  `Clone(outputs, scope, device)`
+
+  Note: it is assumed that any loss created by `model_fn` is collected at
+  the tf.GraphKeys.LOSSES collection.
+
+  To recover the losses, summaries or update_ops created by the clone use:
+  ```python
+    losses = tf.get_collection(tf.GraphKeys.LOSSES, clone.scope)
+    summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, clone.scope)
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, clone.scope)
+  ```
+
+  The deployment options are specified by the config object and support
+  deploying one or several clones on different GPUs and one or several replicas
+  of such clones.
+
+  The argument `model_fn` is called `config.num_clones` times to create the
+  model clones as `model_fn(*args, **kwargs)`.
+
+  If `config` specifies deployment on multiple replicas then the default
+  tensorflow device is set appropriatly for each call to `model_fn` and for the
+  slim variable creation functions: model and global variables will be created
+  on the `ps` device, the clone operations will be on the `worker` device.
+
+  Args:
+    config: A DeploymentConfig object.
+    model_fn: A callable. Called as `model_fn(*args, **kwargs)`
+    args: Optional list of arguments to pass to `model_fn`.
+    kwargs: Optional list of keyword arguments to pass to `model_fn`.
+
+  Returns:
+    A list of namedtuples `Clone`.
+  """
+  clones = []
+  args = args or []
+  kwargs = kwargs or {}
+  with slim.arg_scope([slim.model_variable, slim.variable],
+                      device=config.variables_device()):
+    # Create clones.
+    for i in range(0, config.num_clones):
+      with tf.name_scope(config.clone_scope(i)) as clone_scope:
+        clone_device = config.clone_device(i)
+        with tf.device(clone_device):
+          with tf.variable_scope(tf.get_variable_scope(),
+                                 reuse=True if i > 0 else None):
+            outputs = model_fn(*args, **kwargs)
+          clones.append(Clone(outputs, clone_scope, clone_device))
+  return clones
+
+
+def _gather_clone_loss(clone, num_clones, regularization_losses):
+  """Gather the loss for a single clone.
+
+  Args:
+    clone: A Clone namedtuple.
+    num_clones: The number of clones being deployed.
+    regularization_losses: Possibly empty list of regularization_losses
+      to add to the clone losses.
+
+  Returns:
+    A tensor for the total loss for the clone.  Can be None.
+  """
+  # The return value.
+  sum_loss = None
+  # Individual components of the loss that will need summaries.
+  clone_loss = None
+  regularization_loss = None
+  # Compute and aggregate losses on the clone device.
+  with tf.device(clone.device):
+    all_losses = []
+    clone_losses = tf.get_collection(tf.GraphKeys.LOSSES, clone.scope)
+    if clone_losses:
+      clone_loss = tf.add_n(clone_losses, name='clone_loss')
+      if num_clones > 1:
+        clone_loss = tf.div(clone_loss, 1.0 * num_clones,
+                            name='scaled_clone_loss')
+      all_losses.append(clone_loss)
+    if regularization_losses:
+      regularization_loss = tf.add_n(regularization_losses,
+                                     name='regularization_loss')
+      all_losses.append(regularization_loss)
+    if all_losses:
+      sum_loss = tf.add_n(all_losses)
+  # Add the summaries out of the clone device block.
+  if clone_loss is not None:
+    tf.summary.scalar('/'.join(filter(None,
+                                      ['Losses', clone.scope, 'clone_loss'])),
+                      clone_loss)
+  if regularization_loss is not None:
+    tf.summary.scalar('Losses/regularization_loss', regularization_loss)
+  return sum_loss
+
+
+def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
+                    **kwargs):
+  """Compute losses and gradients for a single clone.
+
+  Args:
+    optimizer: A tf.Optimizer  object.
+    clone: A Clone namedtuple.
+    num_clones: The number of clones being deployed.
+    regularization_losses: Possibly empty list of regularization_losses
+      to add to the clone losses.
+    **kwargs: Dict of kwarg to pass to compute_gradients().
+
+  Returns:
+    A tuple (clone_loss, clone_grads_and_vars).
+      - clone_loss: A tensor for the total loss for the clone.  Can be None.
+      - clone_grads_and_vars: List of (gradient, variable) for the clone.
+        Can be empty.
+  """
+  sum_loss = _gather_clone_loss(clone, num_clones, regularization_losses)
+  clone_grad = None
+  if sum_loss is not None:
+    with tf.device(clone.device):
+      clone_grad = optimizer.compute_gradients(sum_loss, **kwargs)
+  return sum_loss, clone_grad
+
+
+def optimize_clones(clones, optimizer,
+                    regularization_losses=None,
+                    **kwargs):
+  """Compute clone losses and gradients for the given list of `Clones`.
+
+  Note: The regularization_losses are added to the first clone losses.
+
+  Args:
+   clones: List of `Clones` created by `create_clones()`.
+   optimizer: An `Optimizer` object.
+   regularization_losses: Optional list of regularization losses. If None it
+     will gather them from tf.GraphKeys.REGULARIZATION_LOSSES. Pass `[]` to
+     exclude them.
+   **kwargs: Optional list of keyword arguments to pass to `compute_gradients`.
+
+  Returns:
+   A tuple (total_loss, grads_and_vars).
+     - total_loss: A Tensor containing the average of the clone losses including
+       the regularization loss.
+     - grads_and_vars: A List of tuples (gradient, variable) containing the sum
+       of the gradients for each variable.
+
+  """
+  grads_and_vars = []
+  clones_losses = []
+  num_clones = len(clones)
+  if regularization_losses is None:
+    regularization_losses = tf.get_collection(
+        tf.GraphKeys.REGULARIZATION_LOSSES)
+  for clone in clones:
+    with tf.name_scope(clone.scope):
+      clone_loss, clone_grad = _optimize_clone(
+          optimizer, clone, num_clones, regularization_losses, **kwargs)
+      if clone_loss is not None:
+        clones_losses.append(clone_loss)
+        grads_and_vars.append(clone_grad)
+      # Only use regularization_losses for the first clone
+      regularization_losses = None
+  # Compute the total_loss summing all the clones_losses.
+  total_loss = tf.add_n(clones_losses, name='total_loss')
+  # Sum the gradients across clones.
+  grads_and_vars = _sum_clones_gradients(grads_and_vars)
+  return total_loss, grads_and_vars
+
+
+def deploy(config,
+           model_fn,
+           args=None,
+           kwargs=None,
+           optimizer=None,
+           summarize_gradients=False):
+  """Deploys a Slim-constructed model across multiple clones.
+
+  The deployment options are specified by the config object and support
+  deploying one or several clones on different GPUs and one or several replicas
+  of such clones.
+
+  The argument `model_fn` is called `config.num_clones` times to create the
+  model clones as `model_fn(*args, **kwargs)`.
+
+  The optional argument `optimizer` is an `Optimizer` object.  If not `None`,
+  the deployed model is configured for training with that optimizer.
+
+  If `config` specifies deployment on multiple replicas then the default
+  tensorflow device is set appropriatly for each call to `model_fn` and for the
+  slim variable creation functions: model and global variables will be created
+  on the `ps` device, the clone operations will be on the `worker` device.
+
+  Args:
+    config: A `DeploymentConfig` object.
+    model_fn: A callable. Called as `model_fn(*args, **kwargs)`
+    args: Optional list of arguments to pass to `model_fn`.
+    kwargs: Optional list of keyword arguments to pass to `model_fn`.
+    optimizer: Optional `Optimizer` object.  If passed the model is deployed
+      for training with that optimizer.
+    summarize_gradients: Whether or not add summaries to the gradients.
+
+  Returns:
+    A `DeployedModel` namedtuple.
+
+  """
+  # Gather initial summaries.
+  summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
+
+  # Create Clones.
+  clones = create_clones(config, model_fn, args, kwargs)
+  first_clone = clones[0]
+
+  # Gather update_ops from the first clone. These contain, for example,
+  # the updates for the batch_norm variables created by model_fn.
+  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone.scope)
+
+  train_op = None
+  total_loss = None
+  with tf.device(config.optimizer_device()):
+    if optimizer:
+      # Place the global step on the device storing the variables.
+      with tf.device(config.variables_device()):
+        global_step = slim.get_or_create_global_step()
+
+      # Compute the gradients for the clones.
+      total_loss, clones_gradients = optimize_clones(clones, optimizer)
+
+      if clones_gradients:
+        if summarize_gradients:
+          # Add summaries to the gradients.
+          summaries |= set(_add_gradients_summaries(clones_gradients))
+
+        # Create gradient updates.
+        grad_updates = optimizer.apply_gradients(clones_gradients,
+                                                 global_step=global_step)
+        update_ops.append(grad_updates)
+
+        update_op = tf.group(*update_ops)
+        with tf.control_dependencies([update_op]):
+          train_op = tf.identity(total_loss, name='train_op')
+    else:
+      clones_losses = []
+      regularization_losses = tf.get_collection(
+          tf.GraphKeys.REGULARIZATION_LOSSES)
+      for clone in clones:
+        with tf.name_scope(clone.scope):
+          clone_loss = _gather_clone_loss(clone, len(clones),
+                                          regularization_losses)
+          if clone_loss is not None:
+            clones_losses.append(clone_loss)
+          # Only use regularization_losses for the first clone
+          regularization_losses = None
+      if clones_losses:
+        total_loss = tf.add_n(clones_losses, name='total_loss')
+
+    # Add the summaries from the first clone. These contain the summaries
+    # created by model_fn and either optimize_clones() or _gather_clone_loss().
+    summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
+                                       first_clone.scope))
+
+    if total_loss is not None:
+      # Add total_loss to summary.
+      summaries.add(tf.summary.scalar('total_loss', total_loss))
+
+    if summaries:
+      # Merge all summaries together.
+      summary_op = tf.summary.merge(list(summaries), name='summary_op')
+    else:
+      summary_op = None
+
+  return DeployedModel(train_op, summary_op, total_loss, clones)
+
+
+def _sum_clones_gradients(clone_grads):
+  """Calculate the sum gradient for each shared variable across all clones.
+
+  This function assumes that the clone_grads has been scaled appropriately by
+  1 / num_clones.
+
+  Args:
+    clone_grads: A List of List of tuples (gradient, variable), one list per
+    `Clone`.
+
+  Returns:
+     List of tuples of (gradient, variable) where the gradient has been summed
+     across all clones.
+  """
+  sum_grads = []
+  for grad_and_vars in zip(*clone_grads):
+    # Note that each grad_and_vars looks like the following:
+    #   ((grad_var0_clone0, var0), ... (grad_varN_cloneN, varN))
+    grads = []
+    var = grad_and_vars[0][1]
+    for g, v in grad_and_vars:
+      assert v == var
+      if g is not None:
+        grads.append(g)
+    if grads:
+      if len(grads) > 1:
+        sum_grad = tf.add_n(grads, name=var.op.name + '/sum_grads')
+      else:
+        sum_grad = grads[0]
+      sum_grads.append((sum_grad, var))
+  return sum_grads
+
+
+def _add_gradients_summaries(grads_and_vars):
+  """Add histogram summaries to gradients.
+
+  Note: The summaries are also added to the SUMMARIES collection.
+
+  Args:
+    grads_and_vars: A list of gradient to variable pairs (tuples).
+
+  Returns:
+    The _list_ of the added summaries for grads_and_vars.
+  """
+  summaries = []
+  for grad, var in grads_and_vars:
+    if grad is not None:
+      if isinstance(grad, tf.IndexedSlices):
+        grad_values = grad.values
+      else:
+        grad_values = grad
+      summaries.append(tf.summary.histogram(var.op.name + ':gradient',
+                                            grad_values))
+      summaries.append(tf.summary.histogram(var.op.name + ':gradient_norm',
+                                            tf.global_norm([grad_values])))
+    else:
+      tf.logging.info('Var %s has no gradient', var.op.name)
+  return summaries
+
+
+class DeploymentConfig(object):
+  """Configuration for deploying a model with `deploy()`.
+
+  You can pass an instance of this class to `deploy()` to specify exactly
+  how to deploy the model to build.  If you do not pass one, an instance built
+  from the default deployment_hparams will be used.
+  """
+
+  def __init__(self,
+               num_clones=1,
+               clone_on_cpu=False,
+               replica_id=0,
+               num_replicas=1,
+               num_ps_tasks=0,
+               worker_job_name='worker',
+               ps_job_name='ps'):
+    """Create a DeploymentConfig.
+
+    The config describes how to deploy a model across multiple clones and
+    replicas.  The model will be replicated `num_clones` times in each replica.
+    If `clone_on_cpu` is True, each clone will placed on CPU.
+
+    If `num_replicas` is 1, the model is deployed via a single process.  In that
+    case `worker_device`, `num_ps_tasks`, and `ps_device` are ignored.
+
+    If `num_replicas` is greater than 1, then `worker_device` and `ps_device`
+    must specify TensorFlow devices for the `worker` and `ps` jobs and
+    `num_ps_tasks` must be positive.
+
+    Args:
+      num_clones: Number of model clones to deploy in each replica.
+      clone_on_cpu: If True clones would be placed on CPU.
+      replica_id: Integer.  Index of the replica for which the model is
+        deployed.  Usually 0 for the chief replica.
+      num_replicas: Number of replicas to use.
+      num_ps_tasks: Number of tasks for the `ps` job. 0 to not use replicas.
+      worker_job_name: A name for the worker job.
+      ps_job_name: A name for the parameter server job.
+
+    Raises:
+      ValueError: If the arguments are invalid.
+    """
+    if num_replicas > 1:
+      if num_ps_tasks < 1:
+        raise ValueError('When using replicas num_ps_tasks must be positive')
+    if num_replicas > 1 or num_ps_tasks > 0:
+      if not worker_job_name:
+        raise ValueError('Must specify worker_job_name when using replicas')
+      if not ps_job_name:
+        raise ValueError('Must specify ps_job_name when using parameter server')
+    if replica_id >= num_replicas:
+      raise ValueError('replica_id must be less than num_replicas')
+    self._num_clones = num_clones
+    self._clone_on_cpu = clone_on_cpu
+    self._replica_id = replica_id
+    self._num_replicas = num_replicas
+    self._num_ps_tasks = num_ps_tasks
+    self._ps_device = '/job:' + ps_job_name if num_ps_tasks > 0 else ''
+    self._worker_device = '/job:' + worker_job_name if num_ps_tasks > 0 else ''
+
+  @property
+  def num_clones(self):
+    return self._num_clones
+
+  @property
+  def clone_on_cpu(self):
+    return self._clone_on_cpu
+
+  @property
+  def replica_id(self):
+    return self._replica_id
+
+  @property
+  def num_replicas(self):
+    return self._num_replicas
+
+  @property
+  def num_ps_tasks(self):
+    return self._num_ps_tasks
+
+  @property
+  def ps_device(self):
+    return self._ps_device
+
+  @property
+  def worker_device(self):
+    return self._worker_device
+
+  def caching_device(self):
+    """Returns the device to use for caching variables.
+
+    Variables are cached on the worker CPU when using replicas.
+
+    Returns:
+      A device string or None if the variables do not need to be cached.
+    """
+    if self._num_ps_tasks > 0:
+      return lambda op: op.device
+    else:
+      return None
+
+  def clone_device(self, clone_index):
+    """Device used to create the clone and all the ops inside the clone.
+
+    Args:
+      clone_index: Int, representing the clone_index.
+
+    Returns:
+      A value suitable for `tf.device()`.
+
+    Raises:
+      ValueError: if `clone_index` is greater or equal to the number of clones".
+    """
+    if clone_index >= self._num_clones:
+      raise ValueError('clone_index must be less than num_clones')
+    device = ''
+    if self._num_ps_tasks > 0:
+      device += self._worker_device
+    if self._clone_on_cpu:
+      device += '/device:CPU:0'
+    else:
+      device += '/device:GPU:%d' % clone_index
+    return device
+
+  def clone_scope(self, clone_index):
+    """Name scope to create the clone.
+
+    Args:
+      clone_index: Int, representing the clone_index.
+
+    Returns:
+      A name_scope suitable for `tf.name_scope()`.
+
+    Raises:
+      ValueError: if `clone_index` is greater or equal to the number of clones".
+    """
+    if clone_index >= self._num_clones:
+      raise ValueError('clone_index must be less than num_clones')
+    scope = ''
+    if self._num_clones > 1:
+      scope = 'clone_%d' % clone_index
+    return scope
+
+  def optimizer_device(self):
+    """Device to use with the optimizer.
+
+    Returns:
+      A value suitable for `tf.device()`.
+    """
+    if self._num_ps_tasks > 0 or self._num_clones > 0:
+      return self._worker_device + '/device:CPU:0'
+    else:
+      return ''
+
+  def inputs_device(self):
+    """Device to use to build the inputs.
+
+    Returns:
+      A value suitable for `tf.device()`.
+    """
+    device = ''
+    if self._num_ps_tasks > 0:
+      device += self._worker_device
+    device += '/device:CPU:0'
+    return device
+
+  def variables_device(self):
+    """Returns the device to use for variables created inside the clone.
+
+    Returns:
+      A value suitable for `tf.device()`.
+    """
+    device = ''
+    if self._num_ps_tasks > 0:
+      device += self._ps_device
+    device += '/device:CPU:0'
+
+    class _PSDeviceChooser(object):
+      """Slim device chooser for variables when using PS."""
+
+      def __init__(self, device, tasks):
+        self._device = device
+        self._tasks = tasks
+        self._task = 0
+
+      def choose(self, op):
+        if op.device:
+          return op.device
+        node_def = op if isinstance(op, tf.NodeDef) else op.node_def
+        if node_def.op.startswith('Variable'):
+          t = self._task
+          self._task = (self._task + 1) % self._tasks
+          d = '%s/task:%d' % (self._device, t)
+          return d
+        else:
+          return op.device
+
+    if not self._num_ps_tasks:
+      return device
+    else:
+      chooser = _PSDeviceChooser(device, self._num_ps_tasks)
+      return chooser.choose
diff --git a/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy_test.py b/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy_test.py
new file mode 100644
index 00000000..780b691d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/deployment/model_deploy_test.py
@@ -0,0 +1,572 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for model_deploy."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from deployment import model_deploy
+
+slim = tf.contrib.slim
+
+
+class DeploymentConfigTest(tf.test.TestCase):
+
+  def testDefaults(self):
+    deploy_config = model_deploy.DeploymentConfig()
+
+    self.assertEqual(slim.get_variables(), [])
+    self.assertEqual(deploy_config.caching_device(), None)
+    self.assertDeviceEqual(deploy_config.clone_device(0), 'GPU:0')
+    self.assertEqual(deploy_config.clone_scope(0), '')
+    self.assertDeviceEqual(deploy_config.optimizer_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.variables_device(), 'CPU:0')
+
+  def testCPUonly(self):
+    deploy_config = model_deploy.DeploymentConfig(clone_on_cpu=True)
+
+    self.assertEqual(deploy_config.caching_device(), None)
+    self.assertDeviceEqual(deploy_config.clone_device(0), 'CPU:0')
+    self.assertEqual(deploy_config.clone_scope(0), '')
+    self.assertDeviceEqual(deploy_config.optimizer_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.variables_device(), 'CPU:0')
+
+  def testMultiGPU(self):
+    deploy_config = model_deploy.DeploymentConfig(num_clones=2)
+
+    self.assertEqual(deploy_config.caching_device(), None)
+    self.assertDeviceEqual(deploy_config.clone_device(0), 'GPU:0')
+    self.assertDeviceEqual(deploy_config.clone_device(1), 'GPU:1')
+    self.assertEqual(deploy_config.clone_scope(0), 'clone_0')
+    self.assertEqual(deploy_config.clone_scope(1), 'clone_1')
+    self.assertDeviceEqual(deploy_config.optimizer_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(), 'CPU:0')
+    self.assertDeviceEqual(deploy_config.variables_device(), 'CPU:0')
+
+  def testPS(self):
+    deploy_config = model_deploy.DeploymentConfig(num_clones=1, num_ps_tasks=1)
+
+    self.assertDeviceEqual(deploy_config.clone_device(0),
+                           '/job:worker/device:GPU:0')
+    self.assertEqual(deploy_config.clone_scope(0), '')
+    self.assertDeviceEqual(deploy_config.optimizer_device(),
+                           '/job:worker/device:CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(),
+                           '/job:worker/device:CPU:0')
+    with tf.device(deploy_config.variables_device()):
+      a = tf.Variable(0)
+      b = tf.Variable(0)
+      c = tf.no_op()
+      d = slim.variable('a', [],
+                        caching_device=deploy_config.caching_device())
+    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
+    self.assertDeviceEqual(a.device, a.value().device)
+    self.assertDeviceEqual(b.device, '/job:ps/task:0/device:CPU:0')
+    self.assertDeviceEqual(b.device, b.value().device)
+    self.assertDeviceEqual(c.device, '')
+    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
+    self.assertDeviceEqual(d.value().device, '')
+
+  def testMultiGPUPS(self):
+    deploy_config = model_deploy.DeploymentConfig(num_clones=2, num_ps_tasks=1)
+
+    self.assertEqual(deploy_config.caching_device()(tf.no_op()), '')
+    self.assertDeviceEqual(deploy_config.clone_device(0),
+                           '/job:worker/device:GPU:0')
+    self.assertDeviceEqual(deploy_config.clone_device(1),
+                           '/job:worker/device:GPU:1')
+    self.assertEqual(deploy_config.clone_scope(0), 'clone_0')
+    self.assertEqual(deploy_config.clone_scope(1), 'clone_1')
+    self.assertDeviceEqual(deploy_config.optimizer_device(),
+                           '/job:worker/device:CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(),
+                           '/job:worker/device:CPU:0')
+
+  def testReplicasPS(self):
+    deploy_config = model_deploy.DeploymentConfig(num_replicas=2,
+                                                  num_ps_tasks=2)
+
+    self.assertDeviceEqual(deploy_config.clone_device(0),
+                           '/job:worker/device:GPU:0')
+    self.assertEqual(deploy_config.clone_scope(0), '')
+    self.assertDeviceEqual(deploy_config.optimizer_device(),
+                           '/job:worker/device:CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(),
+                           '/job:worker/device:CPU:0')
+
+  def testReplicasMultiGPUPS(self):
+    deploy_config = model_deploy.DeploymentConfig(num_replicas=2,
+                                                  num_clones=2,
+                                                  num_ps_tasks=2)
+    self.assertDeviceEqual(deploy_config.clone_device(0),
+                           '/job:worker/device:GPU:0')
+    self.assertDeviceEqual(deploy_config.clone_device(1),
+                           '/job:worker/device:GPU:1')
+    self.assertEqual(deploy_config.clone_scope(0), 'clone_0')
+    self.assertEqual(deploy_config.clone_scope(1), 'clone_1')
+    self.assertDeviceEqual(deploy_config.optimizer_device(),
+                           '/job:worker/device:CPU:0')
+    self.assertDeviceEqual(deploy_config.inputs_device(),
+                           '/job:worker/device:CPU:0')
+
+  def testVariablesPS(self):
+    deploy_config = model_deploy.DeploymentConfig(num_ps_tasks=2)
+
+    with tf.device(deploy_config.variables_device()):
+      a = tf.Variable(0)
+      b = tf.Variable(0)
+      c = tf.no_op()
+      d = slim.variable('a', [],
+                        caching_device=deploy_config.caching_device())
+
+    self.assertDeviceEqual(a.device, '/job:ps/task:0/device:CPU:0')
+    self.assertDeviceEqual(a.device, a.value().device)
+    self.assertDeviceEqual(b.device, '/job:ps/task:1/device:CPU:0')
+    self.assertDeviceEqual(b.device, b.value().device)
+    self.assertDeviceEqual(c.device, '')
+    self.assertDeviceEqual(d.device, '/job:ps/task:0/device:CPU:0')
+    self.assertDeviceEqual(d.value().device, '')
+
+
+def LogisticClassifier(inputs, labels, scope=None, reuse=None):
+  with tf.variable_scope(scope, 'LogisticClassifier', [inputs, labels],
+                         reuse=reuse):
+    predictions = slim.fully_connected(inputs, 1, activation_fn=tf.sigmoid,
+                                       scope='fully_connected')
+    slim.losses.log_loss(predictions, labels)
+    return predictions
+
+
+def BatchNormClassifier(inputs, labels, scope=None, reuse=None):
+  with tf.variable_scope(scope, 'BatchNormClassifier', [inputs, labels],
+                         reuse=reuse):
+    inputs = slim.batch_norm(inputs, decay=0.1, fused=True)
+    predictions = slim.fully_connected(inputs, 1,
+                                       activation_fn=tf.sigmoid,
+                                       scope='fully_connected')
+    slim.losses.log_loss(predictions, labels)
+    return predictions
+
+
+class CreatecloneTest(tf.test.TestCase):
+
+  def setUp(self):
+    # Create an easy training set:
+    np.random.seed(0)
+
+    self._inputs = np.zeros((16, 4))
+    self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32)
+    self._logdir = self.get_temp_dir()
+
+    for i in range(16):
+      j = int(2 * self._labels[i] + np.random.randint(0, 2))
+      self._inputs[i, j] = 1
+
+  def testCreateLogisticClassifier(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = LogisticClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      clone = clones[0]
+      self.assertEqual(len(slim.get_variables()), 2)
+      for v in slim.get_variables():
+        self.assertDeviceEqual(v.device, 'CPU:0')
+        self.assertDeviceEqual(v.value().device, 'CPU:0')
+      self.assertEqual(clone.outputs.op.name,
+                       'LogisticClassifier/fully_connected/Sigmoid')
+      self.assertEqual(clone.scope, '')
+      self.assertDeviceEqual(clone.device, 'GPU:0')
+      self.assertEqual(len(slim.losses.get_losses()), 1)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(update_ops, [])
+
+  def testCreateSingleclone(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      clone = clones[0]
+      self.assertEqual(len(slim.get_variables()), 5)
+      for v in slim.get_variables():
+        self.assertDeviceEqual(v.device, 'CPU:0')
+        self.assertDeviceEqual(v.value().device, 'CPU:0')
+      self.assertEqual(clone.outputs.op.name,
+                       'BatchNormClassifier/fully_connected/Sigmoid')
+      self.assertEqual(clone.scope, '')
+      self.assertDeviceEqual(clone.device, 'GPU:0')
+      self.assertEqual(len(slim.losses.get_losses()), 1)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), 2)
+
+  def testCreateMulticlone(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      num_clones = 4
+      deploy_config = model_deploy.DeploymentConfig(num_clones=num_clones)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      for v in slim.get_variables():
+        self.assertDeviceEqual(v.device, 'CPU:0')
+        self.assertDeviceEqual(v.value().device, 'CPU:0')
+      self.assertEqual(len(clones), num_clones)
+      for i, clone in enumerate(clones):
+        self.assertEqual(
+            clone.outputs.op.name,
+            'clone_%d/BatchNormClassifier/fully_connected/Sigmoid' % i)
+        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, clone.scope)
+        self.assertEqual(len(update_ops), 2)
+        self.assertEqual(clone.scope, 'clone_%d/' % i)
+        self.assertDeviceEqual(clone.device, 'GPU:%d' % i)
+
+  def testCreateOnecloneWithPS(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
+                                                    num_ps_tasks=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(clones), 1)
+      clone = clones[0]
+      self.assertEqual(clone.outputs.op.name,
+                       'BatchNormClassifier/fully_connected/Sigmoid')
+      self.assertDeviceEqual(clone.device, '/job:worker/device:GPU:0')
+      self.assertEqual(clone.scope, '')
+      self.assertEqual(len(slim.get_variables()), 5)
+      for v in slim.get_variables():
+        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
+        self.assertDeviceEqual(v.device, v.value().device)
+
+  def testCreateMulticloneWithPS(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=2,
+                                                    num_ps_tasks=2)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      for i, v in enumerate(slim.get_variables()):
+        t = i % 2
+        self.assertDeviceEqual(v.device, '/job:ps/task:%d/device:CPU:0' % t)
+        self.assertDeviceEqual(v.device, v.value().device)
+      self.assertEqual(len(clones), 2)
+      for i, clone in enumerate(clones):
+        self.assertEqual(
+            clone.outputs.op.name,
+            'clone_%d/BatchNormClassifier/fully_connected/Sigmoid' % i)
+        self.assertEqual(clone.scope, 'clone_%d/' % i)
+        self.assertDeviceEqual(clone.device, '/job:worker/device:GPU:%d' % i)
+
+
+class OptimizeclonesTest(tf.test.TestCase):
+
+  def setUp(self):
+    # Create an easy training set:
+    np.random.seed(0)
+
+    self._inputs = np.zeros((16, 4))
+    self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32)
+    self._logdir = self.get_temp_dir()
+
+    for i in range(16):
+      j = int(2 * self._labels[i] + np.random.randint(0, 2))
+      self._inputs[i, j] = 1
+
+  def testCreateLogisticClassifier(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = LogisticClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(slim.get_variables()), 2)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(update_ops, [])
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
+                                                                optimizer)
+      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
+      self.assertEqual(total_loss.op.name, 'total_loss')
+      for g, v in grads_and_vars:
+        self.assertDeviceEqual(g.device, 'GPU:0')
+        self.assertDeviceEqual(v.device, 'CPU:0')
+
+  def testCreateSingleclone(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), 2)
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
+                                                                optimizer)
+      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
+      self.assertEqual(total_loss.op.name, 'total_loss')
+      for g, v in grads_and_vars:
+        self.assertDeviceEqual(g.device, 'GPU:0')
+        self.assertDeviceEqual(v.device, 'CPU:0')
+
+  def testCreateMulticlone(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      clone_args = (tf_inputs, tf_labels)
+      num_clones = 4
+      deploy_config = model_deploy.DeploymentConfig(num_clones=num_clones)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), num_clones * 2)
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
+                                                                optimizer)
+      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
+      self.assertEqual(total_loss.op.name, 'total_loss')
+      for g, v in grads_and_vars:
+        self.assertDeviceEqual(g.device, '')
+        self.assertDeviceEqual(v.device, 'CPU:0')
+
+  def testCreateMulticloneCPU(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      model_args = (tf_inputs, tf_labels)
+      num_clones = 4
+      deploy_config = model_deploy.DeploymentConfig(num_clones=num_clones,
+                                                    clone_on_cpu=True)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, model_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), num_clones * 2)
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
+                                                                optimizer)
+      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
+      self.assertEqual(total_loss.op.name, 'total_loss')
+      for g, v in grads_and_vars:
+        self.assertDeviceEqual(g.device, '')
+        self.assertDeviceEqual(v.device, 'CPU:0')
+
+  def testCreateOnecloneWithPS(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      model_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
+                                                    num_ps_tasks=1)
+
+      self.assertEqual(slim.get_variables(), [])
+      clones = model_deploy.create_clones(deploy_config, model_fn, model_args)
+      self.assertEqual(len(slim.get_variables()), 5)
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), 2)
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
+                                                                optimizer)
+      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
+      self.assertEqual(total_loss.op.name, 'total_loss')
+      for g, v in grads_and_vars:
+        self.assertDeviceEqual(g.device, '/job:worker/device:GPU:0')
+        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
+
+
+class DeployTest(tf.test.TestCase):
+
+  def setUp(self):
+    # Create an easy training set:
+    np.random.seed(0)
+
+    self._inputs = np.zeros((16, 4))
+    self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32)
+    self._logdir = self.get_temp_dir()
+
+    for i in range(16):
+      j = int(2 * self._labels[i] + np.random.randint(0, 2))
+      self._inputs[i, j] = 1
+
+  def _addBesselsCorrection(self, sample_size, expected_var):
+    correction_factor = sample_size / (sample_size - 1)
+    expected_var *= correction_factor
+    return expected_var
+
+  def testLocalTrainOp(self):
+    g = tf.Graph()
+    with g.as_default():
+      tf.set_random_seed(0)
+      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
+      tf_labels = tf.constant(self._labels, dtype=tf.float32)
+
+      model_fn = BatchNormClassifier
+      model_args = (tf_inputs, tf_labels)
+      deploy_config = model_deploy.DeploymentConfig(num_clones=2,
+                                                    clone_on_cpu=True)
+
+      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
+
+      self.assertEqual(slim.get_variables(), [])
+      model = model_deploy.deploy(deploy_config, model_fn, model_args,
+                                  optimizer=optimizer)
+
+      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+      self.assertEqual(len(update_ops), 4)
+      self.assertEqual(len(model.clones), 2)
+      self.assertEqual(model.total_loss.op.name, 'total_loss')
+      self.assertEqual(model.summary_op.op.name, 'summary_op/summary_op')
+      self.assertEqual(model.train_op.op.name, 'train_op')
+
+      with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        moving_mean = tf.contrib.framework.get_variables_by_name(
+            'moving_mean')[0]
+        moving_variance = tf.contrib.framework.get_variables_by_name(
+            'moving_variance')[0]
+        initial_loss = sess.run(model.total_loss)
+        initial_mean, initial_variance = sess.run([moving_mean,
+                                                   moving_variance])
+        self.assertAllClose(initial_mean, [0.0, 0.0, 0.0, 0.0])
+        self.assertAllClose(initial_variance, [1.0, 1.0, 1.0, 1.0])
+        for _ in range(10):
+          sess.run(model.train_op)
+        final_loss = sess.run(model.total_loss)
+        self.assertLess(final_loss, initial_loss / 5.0)
+
+        final_mean, final_variance = sess.run([moving_mean,
+                                               moving_variance])
+        expected_mean = np.array([0.125, 0.25, 0.375, 0.25])
+        expected_var = np.array([0.109375, 0.1875, 0.234375, 0.1875])
+        expected_var = self._addBesselsCorrection(16, expected_var)
+        self.assertAllClose(final_mean, expected_mean)
+        self.assertAllClose(final_variance, expected_var)
+
+  def testNoSummariesOnGPU(self):
+    with tf.Graph().as_default():
+      deploy_config = model_deploy.DeploymentConfig(num_clones=2)
+
+      # clone function creates a fully_connected layer with a regularizer loss.
+      def ModelFn():
+        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
+        reg = tf.contrib.layers.l2_regularizer(0.001)
+        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)
+
+      model = model_deploy.deploy(
+          deploy_config, ModelFn,
+          optimizer=tf.train.GradientDescentOptimizer(1.0))
+      # The model summary op should have a few summary inputs and all of them
+      # should be on the CPU.
+      self.assertTrue(model.summary_op.op.inputs)
+      for inp in  model.summary_op.op.inputs:
+        self.assertEqual('/device:CPU:0', inp.device)
+
+  def testNoSummariesOnGPUForEvals(self):
+    with tf.Graph().as_default():
+      deploy_config = model_deploy.DeploymentConfig(num_clones=2)
+
+      # clone function creates a fully_connected layer with a regularizer loss.
+      def ModelFn():
+        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
+        reg = tf.contrib.layers.l2_regularizer(0.001)
+        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)
+
+      # No optimizer here, it's an eval.
+      model = model_deploy.deploy(deploy_config, ModelFn)
+      # The model summary op should have a few summary inputs and all of them
+      # should be on the CPU.
+      self.assertTrue(model.summary_op.op.inputs)
+      for inp in  model.summary_op.op.inputs:
+        self.assertEqual('/device:CPU:0', inp.device)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/download_and_convert_data.py b/TensorFlow/Detection/SSD/models/research/slim/download_and_convert_data.py
new file mode 100644
index 00000000..924a3a46
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/download_and_convert_data.py
@@ -0,0 +1,73 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Downloads and converts a particular dataset.
+
+Usage:
+```shell
+
+$ python download_and_convert_data.py \
+    --dataset_name=mnist \
+    --dataset_dir=/tmp/mnist
+
+$ python download_and_convert_data.py \
+    --dataset_name=cifar10 \
+    --dataset_dir=/tmp/cifar10
+
+$ python download_and_convert_data.py \
+    --dataset_name=flowers \
+    --dataset_dir=/tmp/flowers
+```
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from datasets import download_and_convert_cifar10
+from datasets import download_and_convert_flowers
+from datasets import download_and_convert_mnist
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The name of the dataset to convert, one of "cifar10", "flowers", "mnist".')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir',
+    None,
+    'The directory where the output TFRecords and temporary files are saved.')
+
+
+def main(_):
+  if not FLAGS.dataset_name:
+    raise ValueError('You must supply the dataset name with --dataset_name')
+  if not FLAGS.dataset_dir:
+    raise ValueError('You must supply the dataset directory with --dataset_dir')
+
+  if FLAGS.dataset_name == 'cifar10':
+    download_and_convert_cifar10.run(FLAGS.dataset_dir)
+  elif FLAGS.dataset_name == 'flowers':
+    download_and_convert_flowers.run(FLAGS.dataset_dir)
+  elif FLAGS.dataset_name == 'mnist':
+    download_and_convert_mnist.run(FLAGS.dataset_dir)
+  else:
+    raise ValueError(
+        'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/eval_image_classifier.py b/TensorFlow/Detection/SSD/models/research/slim/eval_image_classifier.py
new file mode 100644
index 00000000..8a42641f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/eval_image_classifier.py
@@ -0,0 +1,197 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Generic evaluation script that evaluates a model using a given dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import tensorflow as tf
+
+from datasets import dataset_factory
+from nets import nets_factory
+from preprocessing import preprocessing_factory
+
+slim = tf.contrib.slim
+
+tf.app.flags.DEFINE_integer(
+    'batch_size', 100, 'The number of samples in each batch.')
+
+tf.app.flags.DEFINE_integer(
+    'max_num_batches', None,
+    'Max number of batches to evaluate by default use all.')
+
+tf.app.flags.DEFINE_string(
+    'master', '', 'The address of the TensorFlow master to use.')
+
+tf.app.flags.DEFINE_string(
+    'checkpoint_path', '/tmp/tfmodel/',
+    'The directory where the model was written to or an absolute path to a '
+    'checkpoint file.')
+
+tf.app.flags.DEFINE_string(
+    'eval_dir', '/tmp/tfmodel/', 'Directory where the results are saved to.')
+
+tf.app.flags.DEFINE_integer(
+    'num_preprocessing_threads', 4,
+    'The number of threads used to create the batches.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_name', 'imagenet', 'The name of the dataset to load.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_split_name', 'test', 'The name of the train/test split.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', None, 'The directory where the dataset files are stored.')
+
+tf.app.flags.DEFINE_integer(
+    'labels_offset', 0,
+    'An offset for the labels in the dataset. This flag is primarily used to '
+    'evaluate the VGG and ResNet architectures which do not use a background '
+    'class for the ImageNet dataset.')
+
+tf.app.flags.DEFINE_string(
+    'model_name', 'inception_v3', 'The name of the architecture to evaluate.')
+
+tf.app.flags.DEFINE_string(
+    'preprocessing_name', None, 'The name of the preprocessing to use. If left '
+    'as `None`, then the model_name flag is used.')
+
+tf.app.flags.DEFINE_float(
+    'moving_average_decay', None,
+    'The decay to use for the moving average.'
+    'If left as None, then moving averages are not used.')
+
+tf.app.flags.DEFINE_integer(
+    'eval_image_size', None, 'Eval image size')
+
+tf.app.flags.DEFINE_bool(
+    'quantize', False, 'whether to use quantized graph or not.')
+
+FLAGS = tf.app.flags.FLAGS
+
+
+def main(_):
+  if not FLAGS.dataset_dir:
+    raise ValueError('You must supply the dataset directory with --dataset_dir')
+
+  tf.logging.set_verbosity(tf.logging.INFO)
+  with tf.Graph().as_default():
+    tf_global_step = slim.get_or_create_global_step()
+
+    ######################
+    # Select the dataset #
+    ######################
+    dataset = dataset_factory.get_dataset(
+        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
+
+    ####################
+    # Select the model #
+    ####################
+    network_fn = nets_factory.get_network_fn(
+        FLAGS.model_name,
+        num_classes=(dataset.num_classes - FLAGS.labels_offset),
+        is_training=False)
+
+    ##############################################################
+    # Create a dataset provider that loads data from the dataset #
+    ##############################################################
+    provider = slim.dataset_data_provider.DatasetDataProvider(
+        dataset,
+        shuffle=False,
+        common_queue_capacity=2 * FLAGS.batch_size,
+        common_queue_min=FLAGS.batch_size)
+    [image, label] = provider.get(['image', 'label'])
+    label -= FLAGS.labels_offset
+
+    #####################################
+    # Select the preprocessing function #
+    #####################################
+    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
+    image_preprocessing_fn = preprocessing_factory.get_preprocessing(
+        preprocessing_name,
+        is_training=False)
+
+    eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
+
+    image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
+
+    images, labels = tf.train.batch(
+        [image, label],
+        batch_size=FLAGS.batch_size,
+        num_threads=FLAGS.num_preprocessing_threads,
+        capacity=5 * FLAGS.batch_size)
+
+    ####################
+    # Define the model #
+    ####################
+    logits, _ = network_fn(images)
+
+    if FLAGS.quantize:
+      tf.contrib.quantize.create_eval_graph()
+
+    if FLAGS.moving_average_decay:
+      variable_averages = tf.train.ExponentialMovingAverage(
+          FLAGS.moving_average_decay, tf_global_step)
+      variables_to_restore = variable_averages.variables_to_restore(
+          slim.get_model_variables())
+      variables_to_restore[tf_global_step.op.name] = tf_global_step
+    else:
+      variables_to_restore = slim.get_variables_to_restore()
+
+    predictions = tf.argmax(logits, 1)
+    labels = tf.squeeze(labels)
+
+    # Define the metrics:
+    names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
+        'Accuracy': slim.metrics.streaming_accuracy(predictions, labels),
+        'Recall_5': slim.metrics.streaming_recall_at_k(
+            logits, labels, 5),
+    })
+
+    # Print the summaries to screen.
+    for name, value in names_to_values.items():
+      summary_name = 'eval/%s' % name
+      op = tf.summary.scalar(summary_name, value, collections=[])
+      op = tf.Print(op, [value], summary_name)
+      tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
+
+    # TODO(sguada) use num_epochs=1
+    if FLAGS.max_num_batches:
+      num_batches = FLAGS.max_num_batches
+    else:
+      # This ensures that we make a single pass over all of the data.
+      num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))
+
+    if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
+      checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
+    else:
+      checkpoint_path = FLAGS.checkpoint_path
+
+    tf.logging.info('Evaluating %s' % checkpoint_path)
+
+    slim.evaluation.evaluate_once(
+        master=FLAGS.master,
+        checkpoint_path=checkpoint_path,
+        logdir=FLAGS.eval_dir,
+        num_evals=num_batches,
+        eval_op=list(names_to_updates.values()),
+        variables_to_restore=variables_to_restore)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph.py b/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph.py
new file mode 100644
index 00000000..864b7a51
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph.py
@@ -0,0 +1,156 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Saves out a GraphDef containing the architecture of the model.
+
+To use it, run something like this, with a model name defined by slim:
+
+bazel build tensorflow_models/research/slim:export_inference_graph
+bazel-bin/tensorflow_models/research/slim/export_inference_graph \
+--model_name=inception_v3 --output_file=/tmp/inception_v3_inf_graph.pb
+
+If you then want to use the resulting model with your own or pretrained
+checkpoints as part of a mobile model, you can run freeze_graph to get a graph
+def with the variables inlined as constants using:
+
+bazel build tensorflow/python/tools:freeze_graph
+bazel-bin/tensorflow/python/tools/freeze_graph \
+--input_graph=/tmp/inception_v3_inf_graph.pb \
+--input_checkpoint=/tmp/checkpoints/inception_v3.ckpt \
+--input_binary=true --output_graph=/tmp/frozen_inception_v3.pb \
+--output_node_names=InceptionV3/Predictions/Reshape_1
+
+The output node names will vary depending on the model, but you can inspect and
+estimate them using the summarize_graph tool:
+
+bazel build tensorflow/tools/graph_transforms:summarize_graph
+bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
+--in_graph=/tmp/inception_v3_inf_graph.pb
+
+To run the resulting graph in C++, you can look at the label_image sample code:
+
+bazel build tensorflow/examples/label_image:label_image
+bazel-bin/tensorflow/examples/label_image/label_image \
+--image=${HOME}/Pictures/flowers.jpg \
+--input_layer=input \
+--output_layer=InceptionV3/Predictions/Reshape_1 \
+--graph=/tmp/frozen_inception_v3.pb \
+--labels=/tmp/imagenet_slim_labels.txt \
+--input_mean=0 \
+--input_std=255
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+
+import tensorflow as tf
+
+from tensorflow.python.platform import gfile
+from datasets import dataset_factory
+from nets import nets_factory
+
+
+slim = tf.contrib.slim
+
+tf.app.flags.DEFINE_string(
+    'model_name', 'inception_v3', 'The name of the architecture to save.')
+
+tf.app.flags.DEFINE_boolean(
+    'is_training', False,
+    'Whether to save out a training-focused version of the model.')
+
+tf.app.flags.DEFINE_integer(
+    'image_size', None,
+    'The image size to use, otherwise use the model default_image_size.')
+
+tf.app.flags.DEFINE_integer(
+    'batch_size', None,
+    'Batch size for the exported model. Defaulted to "None" so batch size can '
+    'be specified at model runtime.')
+
+tf.app.flags.DEFINE_string('dataset_name', 'imagenet',
+                           'The name of the dataset to use with the model.')
+
+tf.app.flags.DEFINE_integer(
+    'labels_offset', 0,
+    'An offset for the labels in the dataset. This flag is primarily used to '
+    'evaluate the VGG and ResNet architectures which do not use a background '
+    'class for the ImageNet dataset.')
+
+tf.app.flags.DEFINE_string(
+    'output_file', '', 'Where to save the resulting file to.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', '', 'Directory to save intermediate dataset files to')
+
+tf.app.flags.DEFINE_bool(
+    'quantize', False, 'whether to use quantized graph or not.')
+
+tf.app.flags.DEFINE_bool(
+    'is_video_model', False, 'whether to use 5-D inputs for video model.')
+
+tf.app.flags.DEFINE_integer(
+    'num_frames', None,
+    'The number of frames to use. Only used if is_video_model is True.')
+
+tf.app.flags.DEFINE_bool('write_text_graphdef', False,
+                         'Whether to write a text version of graphdef.')
+
+FLAGS = tf.app.flags.FLAGS
+
+
+def main(_):
+  if not FLAGS.output_file:
+    raise ValueError('You must supply the path to save to with --output_file')
+  if FLAGS.is_video_model and not FLAGS.num_frames:
+    raise ValueError(
+        'Number of frames must be specified for video models with --num_frames')
+  tf.logging.set_verbosity(tf.logging.INFO)
+  with tf.Graph().as_default() as graph:
+    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
+                                          FLAGS.dataset_dir)
+    network_fn = nets_factory.get_network_fn(
+        FLAGS.model_name,
+        num_classes=(dataset.num_classes - FLAGS.labels_offset),
+        is_training=FLAGS.is_training)
+    image_size = FLAGS.image_size or network_fn.default_image_size
+    if FLAGS.is_video_model:
+      input_shape = [FLAGS.batch_size, FLAGS.num_frames,
+                     image_size, image_size, 3]
+    else:
+      input_shape = [FLAGS.batch_size, image_size, image_size, 3]
+    placeholder = tf.placeholder(name='input', dtype=tf.float32,
+                                 shape=input_shape)
+    network_fn(placeholder)
+
+    if FLAGS.quantize:
+      tf.contrib.quantize.create_eval_graph()
+
+    graph_def = graph.as_graph_def()
+    if FLAGS.write_text_graphdef:
+      tf.io.write_graph(
+          graph_def,
+          os.path.dirname(FLAGS.output_file),
+          os.path.basename(FLAGS.output_file),
+          as_text=True)
+    else:
+      with gfile.GFile(FLAGS.output_file, 'wb') as f:
+        f.write(graph_def.SerializeToString())
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph_test.py b/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph_test.py
new file mode 100644
index 00000000..42474f22
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/export_inference_graph_test.py
@@ -0,0 +1,44 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for export_inference_graph."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+
+import tensorflow as tf
+
+from tensorflow.python.platform import gfile
+import export_inference_graph
+
+
+class ExportInferenceGraphTest(tf.test.TestCase):
+
+  def testExportInferenceGraph(self):
+    tmpdir = self.get_temp_dir()
+    output_file = os.path.join(tmpdir, 'inception_v3.pb')
+    flags = tf.app.flags.FLAGS
+    flags.output_file = output_file
+    flags.model_name = 'inception_v3'
+    flags.dataset_dir = tmpdir
+    export_inference_graph.main(None)
+    self.assertTrue(gfile.Exists(output_file))
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/nets/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet.py
new file mode 100644
index 00000000..e860a6cb
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet.py
@@ -0,0 +1,138 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains a model definition for AlexNet.
+
+This work was first described in:
+  ImageNet Classification with Deep Convolutional Neural Networks
+  Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
+
+and later refined in:
+  One weird trick for parallelizing convolutional neural networks
+  Alex Krizhevsky, 2014
+
+Here we provide the implementation proposed in "One weird trick" and not
+"ImageNet Classification", as per the paper, the LRN layers have been removed.
+
+Usage:
+  with slim.arg_scope(alexnet.alexnet_v2_arg_scope()):
+    outputs, end_points = alexnet.alexnet_v2(inputs)
+
+@@alexnet_v2
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def alexnet_v2_arg_scope(weight_decay=0.0005):
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      activation_fn=tf.nn.relu,
+                      biases_initializer=tf.constant_initializer(0.1),
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope([slim.conv2d], padding='SAME'):
+      with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
+        return arg_sc
+
+
+def alexnet_v2(inputs,
+               num_classes=1000,
+               is_training=True,
+               dropout_keep_prob=0.5,
+               spatial_squeeze=True,
+               scope='alexnet_v2',
+               global_pool=False):
+  """AlexNet version 2.
+
+  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
+  Parameters from:
+  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
+  layers-imagenet-1gpu.cfg
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 224x224 or set
+        global_pool=True. To use in fully convolutional mode, set
+        spatial_squeeze to false.
+        The LRN layers have been removed and change the initializers from
+        random_normal_initializer to xavier_initializer.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: the number of predicted classes. If 0 or None, the logits layer
+    is omitted and the input features to the logits layer are returned instead.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      logits. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original AlexNet.)
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the non-dropped-out input to the logits layer (if num_classes is 0
+      or None).
+    end_points: a dict of tensors with intermediate activations.
+  """
+  with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d.
+    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+                        outputs_collections=[end_points_collection]):
+      net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
+                        scope='conv1')
+      net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
+      net = slim.conv2d(net, 192, [5, 5], scope='conv2')
+      net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
+      net = slim.conv2d(net, 384, [3, 3], scope='conv3')
+      net = slim.conv2d(net, 384, [3, 3], scope='conv4')
+      net = slim.conv2d(net, 256, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
+
+      # Use conv2d instead of fully_connected layers.
+      with slim.arg_scope([slim.conv2d],
+                          weights_initializer=trunc_normal(0.005),
+                          biases_initializer=tf.constant_initializer(0.1)):
+        net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
+                          scope='fc6')
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout6')
+        net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+        # Convert end_points_collection into a end_point dict.
+        end_points = slim.utils.convert_collection_to_dict(
+            end_points_collection)
+        if global_pool:
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+          end_points['global_pool'] = net
+        if num_classes:
+          net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                             scope='dropout7')
+          net = slim.conv2d(net, num_classes, [1, 1],
+                            activation_fn=None,
+                            normalizer_fn=None,
+                            biases_initializer=tf.zeros_initializer(),
+                            scope='fc8')
+          if spatial_squeeze:
+            net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+          end_points[sc.name + '/fc8'] = net
+      return net, end_points
+alexnet_v2.default_image_size = 224
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet_test.py
new file mode 100644
index 00000000..6f85d33f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/alexnet_test.py
@@ -0,0 +1,180 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.alexnet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import alexnet
+
+slim = tf.contrib.slim
+
+
+class AlexnetV2Test(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = alexnet.alexnet_v2(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'alexnet_v2/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 300, 400
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 4, 7, num_classes])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False,
+                                     global_pool=True)
+      self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = alexnet.alexnet_v2(inputs, num_classes)
+      expected_names = ['alexnet_v2/conv1',
+                        'alexnet_v2/pool1',
+                        'alexnet_v2/conv2',
+                        'alexnet_v2/pool2',
+                        'alexnet_v2/conv3',
+                        'alexnet_v2/conv4',
+                        'alexnet_v2/conv5',
+                        'alexnet_v2/pool5',
+                        'alexnet_v2/fc6',
+                        'alexnet_v2/fc7',
+                        'alexnet_v2/fc8'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = alexnet.alexnet_v2(inputs, num_classes)
+      expected_names = ['alexnet_v2/conv1',
+                        'alexnet_v2/pool1',
+                        'alexnet_v2/conv2',
+                        'alexnet_v2/pool2',
+                        'alexnet_v2/conv3',
+                        'alexnet_v2/conv4',
+                        'alexnet_v2/conv5',
+                        'alexnet_v2/pool5',
+                        'alexnet_v2/fc6',
+                        'alexnet_v2/fc7'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('alexnet_v2/fc7'))
+      self.assertListEqual(net.get_shape().as_list(),
+                           [batch_size, 1, 1, 4096])
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      alexnet.alexnet_v2(inputs, num_classes)
+      expected_names = ['alexnet_v2/conv1/weights',
+                        'alexnet_v2/conv1/biases',
+                        'alexnet_v2/conv2/weights',
+                        'alexnet_v2/conv2/biases',
+                        'alexnet_v2/conv3/weights',
+                        'alexnet_v2/conv3/biases',
+                        'alexnet_v2/conv4/weights',
+                        'alexnet_v2/conv4/biases',
+                        'alexnet_v2/conv5/weights',
+                        'alexnet_v2/conv5/biases',
+                        'alexnet_v2/fc6/weights',
+                        'alexnet_v2/fc6/biases',
+                        'alexnet_v2/fc7/weights',
+                        'alexnet_v2/fc7/biases',
+                        'alexnet_v2/fc8/weights',
+                        'alexnet_v2/fc8/biases',
+                       ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 224, 224
+    eval_height, eval_width = 300, 400
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = alexnet.alexnet_v2(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False,
+                                     spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 4, 7, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 224, 224
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = alexnet.alexnet_v2(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/cifarnet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/cifarnet.py
new file mode 100644
index 00000000..97ed944b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/cifarnet.py
@@ -0,0 +1,117 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains a variant of the CIFAR-10 model definition."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(stddev=stddev)
+
+
+def cifarnet(images, num_classes=10, is_training=False,
+             dropout_keep_prob=0.5,
+             prediction_fn=slim.softmax,
+             scope='CifarNet'):
+  """Creates a variant of the CifarNet model.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+
+        logits = cifarnet.cifarnet(images, is_training=False)
+        probabilities = tf.nn.softmax(logits)
+        predictions = tf.argmax(logits, 1)
+
+  Args:
+    images: A batch of `Tensors` of size [batch_size, height, width, channels].
+    num_classes: the number of classes in the dataset. If 0 or None, the logits
+      layer is omitted and the input features to the logits layer are returned
+      instead.
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    prediction_fn: a function to get predictions out of logits.
+    scope: Optional variable_scope.
+
+  Returns:
+    net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the input to the logits layer if num_classes
+      is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+  """
+  end_points = {}
+
+  with tf.variable_scope(scope, 'CifarNet', [images]):
+    net = slim.conv2d(images, 64, [5, 5], scope='conv1')
+    end_points['conv1'] = net
+    net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
+    end_points['pool1'] = net
+    net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1')
+    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
+    end_points['conv2'] = net
+    net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2')
+    net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
+    end_points['pool2'] = net
+    net = slim.flatten(net)
+    end_points['Flatten'] = net
+    net = slim.fully_connected(net, 384, scope='fc3')
+    end_points['fc3'] = net
+    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                       scope='dropout3')
+    net = slim.fully_connected(net, 192, scope='fc4')
+    end_points['fc4'] = net
+    if not num_classes:
+      return net, end_points
+    logits = slim.fully_connected(net, num_classes,
+                                  biases_initializer=tf.zeros_initializer(),
+                                  weights_initializer=trunc_normal(1/192.0),
+                                  weights_regularizer=None,
+                                  activation_fn=None,
+                                  scope='logits')
+
+    end_points['Logits'] = logits
+    end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+
+  return logits, end_points
+cifarnet.default_image_size = 32
+
+
+def cifarnet_arg_scope(weight_decay=0.004):
+  """Defines the default cifarnet argument scope.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+
+  Returns:
+    An `arg_scope` to use for the inception v3 model.
+  """
+  with slim.arg_scope(
+      [slim.conv2d],
+      weights_initializer=tf.truncated_normal_initializer(stddev=5e-2),
+      activation_fn=tf.nn.relu):
+    with slim.arg_scope(
+        [slim.fully_connected],
+        biases_initializer=tf.constant_initializer(0.1),
+        weights_initializer=trunc_normal(0.04),
+        weights_regularizer=slim.l2_regularizer(weight_decay),
+        activation_fn=tf.nn.relu) as sc:
+      return sc
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan.py b/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan.py
new file mode 100644
index 00000000..4f9936fa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan.py
@@ -0,0 +1,278 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines the CycleGAN generator and discriminator networks."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+layers = tf.contrib.layers
+
+
+def cyclegan_arg_scope(instance_norm_center=True,
+                       instance_norm_scale=True,
+                       instance_norm_epsilon=0.001,
+                       weights_init_stddev=0.02,
+                       weight_decay=0.0):
+  """Returns a default argument scope for all generators and discriminators.
+
+  Args:
+    instance_norm_center: Whether instance normalization applies centering.
+    instance_norm_scale: Whether instance normalization applies scaling.
+    instance_norm_epsilon: Small float added to the variance in the instance
+      normalization to avoid dividing by zero.
+    weights_init_stddev: Standard deviation of the random values to initialize
+      the convolution kernels with.
+    weight_decay: Magnitude of weight decay applied to all convolution kernel
+      variables of the generator.
+
+  Returns:
+    An arg-scope.
+  """
+  instance_norm_params = {
+      'center': instance_norm_center,
+      'scale': instance_norm_scale,
+      'epsilon': instance_norm_epsilon,
+  }
+
+  weights_regularizer = None
+  if weight_decay and weight_decay > 0.0:
+    weights_regularizer = layers.l2_regularizer(weight_decay)
+
+  with tf.contrib.framework.arg_scope(
+      [layers.conv2d],
+      normalizer_fn=layers.instance_norm,
+      normalizer_params=instance_norm_params,
+      weights_initializer=tf.random_normal_initializer(0, weights_init_stddev),
+      weights_regularizer=weights_regularizer) as sc:
+    return sc
+
+
+def cyclegan_upsample(net, num_outputs, stride, method='conv2d_transpose',
+                      pad_mode='REFLECT', align_corners=False):
+  """Upsamples the given inputs.
+
+  Args:
+    net: A Tensor of size [batch_size, height, width, filters].
+    num_outputs: The number of output filters.
+    stride: A list of 2 scalars or a 1x2 Tensor indicating the scale,
+      relative to the inputs, of the output dimensions. For example, if kernel
+      size is [2, 3], then the output height and width will be twice and three
+      times the input size.
+    method: The upsampling method: 'nn_upsample_conv', 'bilinear_upsample_conv',
+      or 'conv2d_transpose'.
+    pad_mode: mode for tf.pad, one of "CONSTANT", "REFLECT", or "SYMMETRIC".
+    align_corners: option for method, 'bilinear_upsample_conv'. If true, the
+      centers of the 4 corner pixels of the input and output tensors are
+      aligned, preserving the values at the corner pixels.
+
+  Returns:
+    A Tensor which was upsampled using the specified method.
+
+  Raises:
+    ValueError: if `method` is not recognized.
+  """
+  with tf.variable_scope('upconv'):
+    net_shape = tf.shape(net)
+    height = net_shape[1]
+    width = net_shape[2]
+
+    # Reflection pad by 1 in spatial dimensions (axes 1, 2 = h, w) to make a 3x3
+    # 'valid' convolution produce an output with the same dimension as the
+    # input.
+    spatial_pad_1 = np.array([[0, 0], [1, 1], [1, 1], [0, 0]])
+
+    if method == 'nn_upsample_conv':
+      net = tf.image.resize_nearest_neighbor(
+          net, [stride[0] * height, stride[1] * width])
+      net = tf.pad(net, spatial_pad_1, pad_mode)
+      net = layers.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
+    elif method == 'bilinear_upsample_conv':
+      net = tf.image.resize_bilinear(
+          net, [stride[0] * height, stride[1] * width],
+          align_corners=align_corners)
+      net = tf.pad(net, spatial_pad_1, pad_mode)
+      net = layers.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
+    elif method == 'conv2d_transpose':
+      # This corrects 1 pixel offset for images with even width and height.
+      # conv2d is left aligned and conv2d_transpose is right aligned for even
+      # sized images (while doing 'SAME' padding).
+      # Note: This doesn't reflect actual model in paper.
+      net = layers.conv2d_transpose(
+          net, num_outputs, kernel_size=[3, 3], stride=stride, padding='valid')
+      net = net[:, 1:, 1:, :]
+    else:
+      raise ValueError('Unknown method: [%s]' % method)
+
+    return net
+
+
+def _dynamic_or_static_shape(tensor):
+  shape = tf.shape(tensor)
+  static_shape = tf.contrib.util.constant_value(shape)
+  return static_shape if static_shape is not None else shape
+
+
+def cyclegan_generator_resnet(images,
+                              arg_scope_fn=cyclegan_arg_scope,
+                              num_resnet_blocks=6,
+                              num_filters=64,
+                              upsample_fn=cyclegan_upsample,
+                              kernel_size=3,
+                              tanh_linear_slope=0.0,
+                              is_training=False):
+  """Defines the cyclegan resnet network architecture.
+
+  As closely as possible following
+  https://github.com/junyanz/CycleGAN/blob/master/models/architectures.lua#L232
+
+  FYI: This network requires input height and width to be divisible by 4 in
+  order to generate an output with shape equal to input shape. Assertions will
+  catch this if input dimensions are known at graph construction time, but
+  there's no protection if unknown at graph construction time (you'll see an
+  error).
+
+  Args:
+    images: Input image tensor of shape [batch_size, h, w, 3].
+    arg_scope_fn: Function to create the global arg_scope for the network.
+    num_resnet_blocks: Number of ResNet blocks in the middle of the generator.
+    num_filters: Number of filters of the first hidden layer.
+    upsample_fn: Upsampling function for the decoder part of the generator.
+    kernel_size: Size w or list/tuple [h, w] of the filter kernels for all inner
+      layers.
+    tanh_linear_slope: Slope of the linear function to add to the tanh over the
+      logits.
+    is_training: Whether the network is created in training mode or inference
+      only mode. Not actually needed, just for compliance with other generator
+      network functions.
+
+  Returns:
+    A `Tensor` representing the model output and a dictionary of model end
+      points.
+
+  Raises:
+    ValueError: If the input height or width is known at graph construction time
+      and not a multiple of 4.
+  """
+  # Neither dropout nor batch norm -> dont need is_training
+  del is_training
+
+  end_points = {}
+
+  input_size = images.shape.as_list()
+  height, width = input_size[1], input_size[2]
+  if height and height % 4 != 0:
+    raise ValueError('The input height must be a multiple of 4.')
+  if width and width % 4 != 0:
+    raise ValueError('The input width must be a multiple of 4.')
+  num_outputs = input_size[3]
+
+  if not isinstance(kernel_size, (list, tuple)):
+    kernel_size = [kernel_size, kernel_size]
+
+  kernel_height = kernel_size[0]
+  kernel_width = kernel_size[1]
+  pad_top = (kernel_height - 1) // 2
+  pad_bottom = kernel_height // 2
+  pad_left = (kernel_width - 1) // 2
+  pad_right = kernel_width // 2
+  paddings = np.array(
+      [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]],
+      dtype=np.int32)
+  spatial_pad_3 = np.array([[0, 0], [3, 3], [3, 3], [0, 0]])
+
+  with tf.contrib.framework.arg_scope(arg_scope_fn()):
+
+    ###########
+    # Encoder #
+    ###########
+    with tf.variable_scope('input'):
+      # 7x7 input stage
+      net = tf.pad(images, spatial_pad_3, 'REFLECT')
+      net = layers.conv2d(net, num_filters, kernel_size=[7, 7], padding='VALID')
+      end_points['encoder_0'] = net
+
+    with tf.variable_scope('encoder'):
+      with tf.contrib.framework.arg_scope(
+          [layers.conv2d],
+          kernel_size=kernel_size,
+          stride=2,
+          activation_fn=tf.nn.relu,
+          padding='VALID'):
+
+        net = tf.pad(net, paddings, 'REFLECT')
+        net = layers.conv2d(net, num_filters * 2)
+        end_points['encoder_1'] = net
+        net = tf.pad(net, paddings, 'REFLECT')
+        net = layers.conv2d(net, num_filters * 4)
+        end_points['encoder_2'] = net
+
+    ###################
+    # Residual Blocks #
+    ###################
+    with tf.variable_scope('residual_blocks'):
+      with tf.contrib.framework.arg_scope(
+          [layers.conv2d],
+          kernel_size=kernel_size,
+          stride=1,
+          activation_fn=tf.nn.relu,
+          padding='VALID'):
+        for block_id in xrange(num_resnet_blocks):
+          with tf.variable_scope('block_{}'.format(block_id)):
+            res_net = tf.pad(net, paddings, 'REFLECT')
+            res_net = layers.conv2d(res_net, num_filters * 4)
+            res_net = tf.pad(res_net, paddings, 'REFLECT')
+            res_net = layers.conv2d(res_net, num_filters * 4,
+                                    activation_fn=None)
+            net += res_net
+
+            end_points['resnet_block_%d' % block_id] = net
+
+    ###########
+    # Decoder #
+    ###########
+    with tf.variable_scope('decoder'):
+
+      with tf.contrib.framework.arg_scope(
+          [layers.conv2d],
+          kernel_size=kernel_size,
+          stride=1,
+          activation_fn=tf.nn.relu):
+
+        with tf.variable_scope('decoder1'):
+          net = upsample_fn(net, num_outputs=num_filters * 2, stride=[2, 2])
+        end_points['decoder1'] = net
+
+        with tf.variable_scope('decoder2'):
+          net = upsample_fn(net, num_outputs=num_filters, stride=[2, 2])
+        end_points['decoder2'] = net
+
+    with tf.variable_scope('output'):
+      net = tf.pad(net, spatial_pad_3, 'REFLECT')
+      logits = layers.conv2d(
+          net,
+          num_outputs, [7, 7],
+          activation_fn=None,
+          normalizer_fn=None,
+          padding='valid')
+      logits = tf.reshape(logits, _dynamic_or_static_shape(images))
+
+      end_points['logits'] = logits
+      end_points['predictions'] = tf.tanh(logits) + logits * tanh_linear_slope
+
+  return end_points['predictions'], end_points
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan_test.py
new file mode 100644
index 00000000..395773ea
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/cyclegan_test.py
@@ -0,0 +1,112 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.contrib.slim.nets.cyclegan."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import cyclegan
+
+
+# TODO(joelshor): Add a test to check generator endpoints.
+class CycleganTest(tf.test.TestCase):
+
+  def test_generator_inference(self):
+    """Check one inference step."""
+    img_batch = tf.zeros([2, 32, 32, 3])
+    model_output, _ = cyclegan.cyclegan_generator_resnet(img_batch)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(model_output)
+
+  def _test_generator_graph_helper(self, shape):
+    """Check that generator can take small and non-square inputs."""
+    output_imgs, _ = cyclegan.cyclegan_generator_resnet(tf.ones(shape))
+    self.assertAllEqual(shape, output_imgs.shape.as_list())
+
+  def test_generator_graph_small(self):
+    self._test_generator_graph_helper([4, 32, 32, 3])
+
+  def test_generator_graph_medium(self):
+    self._test_generator_graph_helper([3, 128, 128, 3])
+
+  def test_generator_graph_nonsquare(self):
+    self._test_generator_graph_helper([2, 80, 400, 3])
+
+  def test_generator_unknown_batch_dim(self):
+    """Check that generator can take unknown batch dimension inputs."""
+    img = tf.placeholder(tf.float32, shape=[None, 32, None, 3])
+    output_imgs, _ = cyclegan.cyclegan_generator_resnet(img)
+
+    self.assertAllEqual([None, 32, None, 3], output_imgs.shape.as_list())
+
+  def _input_and_output_same_shape_helper(self, kernel_size):
+    img_batch = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
+    output_img_batch, _ = cyclegan.cyclegan_generator_resnet(
+        img_batch, kernel_size=kernel_size)
+
+    self.assertAllEqual(img_batch.shape.as_list(),
+                        output_img_batch.shape.as_list())
+
+  def input_and_output_same_shape_kernel3(self):
+    self._input_and_output_same_shape_helper(3)
+
+  def input_and_output_same_shape_kernel4(self):
+    self._input_and_output_same_shape_helper(4)
+
+  def input_and_output_same_shape_kernel5(self):
+    self._input_and_output_same_shape_helper(5)
+
+  def input_and_output_same_shape_kernel6(self):
+    self._input_and_output_same_shape_helper(6)
+
+  def _error_if_height_not_multiple_of_four_helper(self, height):
+    self.assertRaisesRegexp(
+        ValueError,
+        'The input height must be a multiple of 4.',
+        cyclegan.cyclegan_generator_resnet,
+        tf.placeholder(tf.float32, shape=[None, height, 32, 3]))
+
+  def test_error_if_height_not_multiple_of_four_height29(self):
+    self._error_if_height_not_multiple_of_four_helper(29)
+
+  def test_error_if_height_not_multiple_of_four_height30(self):
+    self._error_if_height_not_multiple_of_four_helper(30)
+
+  def test_error_if_height_not_multiple_of_four_height31(self):
+    self._error_if_height_not_multiple_of_four_helper(31)
+
+  def _error_if_width_not_multiple_of_four_helper(self, width):
+    self.assertRaisesRegexp(
+        ValueError,
+        'The input width must be a multiple of 4.',
+        cyclegan.cyclegan_generator_resnet,
+        tf.placeholder(tf.float32, shape=[None, 32, width, 3]))
+
+  def test_error_if_width_not_multiple_of_four_width29(self):
+    self._error_if_width_not_multiple_of_four_helper(29)
+
+  def test_error_if_width_not_multiple_of_four_width30(self):
+    self._error_if_width_not_multiple_of_four_helper(30)
+
+  def test_error_if_width_not_multiple_of_four_width31(self):
+    self._error_if_width_not_multiple_of_four_helper(31)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan.py b/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan.py
new file mode 100644
index 00000000..b13ba1e6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan.py
@@ -0,0 +1,202 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DCGAN generator and discriminator from https://arxiv.org/abs/1511.06434."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from math import log
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def _validate_image_inputs(inputs):
+  inputs.get_shape().assert_has_rank(4)
+  inputs.get_shape()[1:3].assert_is_fully_defined()
+  if inputs.get_shape()[1] != inputs.get_shape()[2]:
+    raise ValueError('Input tensor does not have equal width and height: ',
+                     inputs.get_shape()[1:3])
+  width = inputs.get_shape().as_list()[1]
+  if log(width, 2) != int(log(width, 2)):
+    raise ValueError('Input tensor `width` is not a power of 2: ', width)
+
+
+# TODO(joelshor): Use fused batch norm by default. Investigate why some GAN
+# setups need the gradient of gradient FusedBatchNormGrad.
+def discriminator(inputs,
+                  depth=64,
+                  is_training=True,
+                  reuse=None,
+                  scope='Discriminator',
+                  fused_batch_norm=False):
+  """Discriminator network for DCGAN.
+
+  Construct discriminator network from inputs to the final endpoint.
+
+  Args:
+    inputs: A tensor of size [batch_size, height, width, channels]. Must be
+      floating point.
+    depth: Number of channels in first convolution layer.
+    is_training: Whether the network is for training or not.
+    reuse: Whether or not the network variables should be reused. `scope`
+      must be given to be reused.
+    scope: Optional variable_scope.
+    fused_batch_norm: If `True`, use a faster, fused implementation of
+      batch norm.
+
+  Returns:
+    logits: The pre-softmax activations, a tensor of size [batch_size, 1]
+    end_points: a dictionary from components of the network to their activation.
+
+  Raises:
+    ValueError: If the input image shape is not 4-dimensional, if the spatial
+      dimensions aren't defined at graph construction time, if the spatial
+      dimensions aren't square, or if the spatial dimensions aren't a power of
+      two.
+  """
+
+  normalizer_fn = slim.batch_norm
+  normalizer_fn_args = {
+      'is_training': is_training,
+      'zero_debias_moving_mean': True,
+      'fused': fused_batch_norm,
+  }
+
+  _validate_image_inputs(inputs)
+  inp_shape = inputs.get_shape().as_list()[1]
+
+  end_points = {}
+  with tf.variable_scope(scope, values=[inputs], reuse=reuse) as scope:
+    with slim.arg_scope([normalizer_fn], **normalizer_fn_args):
+      with slim.arg_scope([slim.conv2d],
+                          stride=2,
+                          kernel_size=4,
+                          activation_fn=tf.nn.leaky_relu):
+        net = inputs
+        for i in xrange(int(log(inp_shape, 2))):
+          scope = 'conv%i' % (i + 1)
+          current_depth = depth * 2**i
+          normalizer_fn_ = None if i == 0 else normalizer_fn
+          net = slim.conv2d(
+              net, current_depth, normalizer_fn=normalizer_fn_, scope=scope)
+          end_points[scope] = net
+
+        logits = slim.conv2d(net, 1, kernel_size=1, stride=1, padding='VALID',
+                             normalizer_fn=None, activation_fn=None)
+        logits = tf.reshape(logits, [-1, 1])
+        end_points['logits'] = logits
+
+        return logits, end_points
+
+
+# TODO(joelshor): Use fused batch norm by default. Investigate why some GAN
+# setups need the gradient of gradient FusedBatchNormGrad.
+def generator(inputs,
+              depth=64,
+              final_size=32,
+              num_outputs=3,
+              is_training=True,
+              reuse=None,
+              scope='Generator',
+              fused_batch_norm=False):
+  """Generator network for DCGAN.
+
+  Construct generator network from inputs to the final endpoint.
+
+  Args:
+    inputs: A tensor with any size N. [batch_size, N]
+    depth: Number of channels in last deconvolution layer.
+    final_size: The shape of the final output.
+    num_outputs: Number of output features. For images, this is the number of
+      channels.
+    is_training: whether is training or not.
+    reuse: Whether or not the network has its variables should be reused. scope
+      must be given to be reused.
+    scope: Optional variable_scope.
+    fused_batch_norm: If `True`, use a faster, fused implementation of
+      batch norm.
+
+  Returns:
+    logits: the pre-softmax activations, a tensor of size
+      [batch_size, 32, 32, channels]
+    end_points: a dictionary from components of the network to their activation.
+
+  Raises:
+    ValueError: If `inputs` is not 2-dimensional.
+    ValueError: If `final_size` isn't a power of 2 or is less than 8.
+  """
+  normalizer_fn = slim.batch_norm
+  normalizer_fn_args = {
+      'is_training': is_training,
+      'zero_debias_moving_mean': True,
+      'fused': fused_batch_norm,
+  }
+
+  inputs.get_shape().assert_has_rank(2)
+  if log(final_size, 2) != int(log(final_size, 2)):
+    raise ValueError('`final_size` (%i) must be a power of 2.' % final_size)
+  if final_size < 8:
+    raise ValueError('`final_size` (%i) must be greater than 8.' % final_size)
+
+  end_points = {}
+  num_layers = int(log(final_size, 2)) - 1
+  with tf.variable_scope(scope, values=[inputs], reuse=reuse) as scope:
+    with slim.arg_scope([normalizer_fn], **normalizer_fn_args):
+      with slim.arg_scope([slim.conv2d_transpose],
+                          normalizer_fn=normalizer_fn,
+                          stride=2,
+                          kernel_size=4):
+        net = tf.expand_dims(tf.expand_dims(inputs, 1), 1)
+
+        # First upscaling is different because it takes the input vector.
+        current_depth = depth * 2 ** (num_layers - 1)
+        scope = 'deconv1'
+        net = slim.conv2d_transpose(
+            net, current_depth, stride=1, padding='VALID', scope=scope)
+        end_points[scope] = net
+
+        for i in xrange(2, num_layers):
+          scope = 'deconv%i' % (i)
+          current_depth = depth * 2 ** (num_layers - i)
+          net = slim.conv2d_transpose(net, current_depth, scope=scope)
+          end_points[scope] = net
+
+        # Last layer has different normalizer and activation.
+        scope = 'deconv%i' % (num_layers)
+        net = slim.conv2d_transpose(
+            net, depth, normalizer_fn=None, activation_fn=None, scope=scope)
+        end_points[scope] = net
+
+        # Convert to proper channels.
+        scope = 'logits'
+        logits = slim.conv2d(
+            net,
+            num_outputs,
+            normalizer_fn=None,
+            activation_fn=None,
+            kernel_size=1,
+            stride=1,
+            padding='VALID',
+            scope=scope)
+        end_points[scope] = logits
+
+        logits.get_shape().assert_has_rank(4)
+        logits.get_shape().assert_is_compatible_with(
+            [None, final_size, final_size, num_outputs])
+
+        return logits, end_points
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan_test.py
new file mode 100644
index 00000000..343de628
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/dcgan_test.py
@@ -0,0 +1,120 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for dcgan."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+from nets import dcgan
+
+
+class DCGANTest(tf.test.TestCase):
+
+  def test_generator_run(self):
+    tf.set_random_seed(1234)
+    noise = tf.random_normal([100, 64])
+    image, _ = dcgan.generator(noise)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      image.eval()
+
+  def test_generator_graph(self):
+    tf.set_random_seed(1234)
+    # Check graph construction for a number of image size/depths and batch
+    # sizes.
+    for i, batch_size in zip(xrange(3, 7), xrange(3, 8)):
+      tf.reset_default_graph()
+      final_size = 2 ** i
+      noise = tf.random_normal([batch_size, 64])
+      image, end_points = dcgan.generator(
+          noise,
+          depth=32,
+          final_size=final_size)
+
+      self.assertAllEqual([batch_size, final_size, final_size, 3],
+                          image.shape.as_list())
+
+      expected_names = ['deconv%i' % j for j in xrange(1, i)] + ['logits']
+      self.assertSetEqual(set(expected_names), set(end_points.keys()))
+
+      # Check layer depths.
+      for j in range(1, i):
+        layer = end_points['deconv%i' % j]
+        self.assertEqual(32 * 2**(i-j-1), layer.get_shape().as_list()[-1])
+
+  def test_generator_invalid_input(self):
+    wrong_dim_input = tf.zeros([5, 32, 32])
+    with self.assertRaises(ValueError):
+      dcgan.generator(wrong_dim_input)
+
+    correct_input = tf.zeros([3, 2])
+    with self.assertRaisesRegexp(ValueError, 'must be a power of 2'):
+      dcgan.generator(correct_input, final_size=30)
+
+    with self.assertRaisesRegexp(ValueError, 'must be greater than 8'):
+      dcgan.generator(correct_input, final_size=4)
+
+  def test_discriminator_run(self):
+    image = tf.random_uniform([5, 32, 32, 3], -1, 1)
+    output, _ = dcgan.discriminator(image)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output.eval()
+
+  def test_discriminator_graph(self):
+    # Check graph construction for a number of image size/depths and batch
+    # sizes.
+    for i, batch_size in zip(xrange(1, 6), xrange(3, 8)):
+      tf.reset_default_graph()
+      img_w = 2 ** i
+      image = tf.random_uniform([batch_size, img_w, img_w, 3], -1, 1)
+      output, end_points = dcgan.discriminator(
+          image,
+          depth=32)
+
+      self.assertAllEqual([batch_size, 1], output.get_shape().as_list())
+
+      expected_names = ['conv%i' % j for j in xrange(1, i+1)] + ['logits']
+      self.assertSetEqual(set(expected_names), set(end_points.keys()))
+
+      # Check layer depths.
+      for j in range(1, i+1):
+        layer = end_points['conv%i' % j]
+        self.assertEqual(32 * 2**(j-1), layer.get_shape().as_list()[-1])
+
+  def test_discriminator_invalid_input(self):
+    wrong_dim_img = tf.zeros([5, 32, 32])
+    with self.assertRaises(ValueError):
+      dcgan.discriminator(wrong_dim_img)
+
+    spatially_undefined_shape = tf.placeholder(tf.float32, [5, 32, None, 3])
+    with self.assertRaises(ValueError):
+      dcgan.discriminator(spatially_undefined_shape)
+
+    not_square = tf.zeros([5, 32, 16, 3])
+    with self.assertRaisesRegexp(ValueError, 'not have equal width and height'):
+      dcgan.discriminator(not_square)
+
+    not_power_2 = tf.zeros([5, 30, 30, 3])
+    with self.assertRaisesRegexp(ValueError, 'not a power of 2'):
+      dcgan.discriminator(not_power_2)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/i3d.py b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d.py
new file mode 100644
index 00000000..97fe4f2e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d.py
@@ -0,0 +1,177 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for Inflated 3D Inception V1 (I3D).
+
+The network architecture is proposed by:
+  Joao Carreira and Andrew Zisserman,
+  Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset.
+  https://arxiv.org/abs/1705.07750
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import i3d_utils
+from nets import s3dg
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+conv3d_spatiotemporal = i3d_utils.conv3d_spatiotemporal
+
+
+def i3d_arg_scope(weight_decay=1e-7,
+                  batch_norm_decay=0.999,
+                  batch_norm_epsilon=0.001,
+                  use_renorm=False,
+                  separable_conv3d=False):
+  """Defines default arg_scope for I3D.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+    use_renorm: Whether to use batch renormalization or not.
+    separable_conv3d: Whether to use separable 3d Convs.
+
+  Returns:
+    sc: An arg_scope to use for the models.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      # Turns off fused batch norm.
+      'fused': False,
+      'renorm': use_renorm,
+      # collection containing the moving mean and moving variance.
+      'variables_collections': {
+          'beta': None,
+          'gamma': None,
+          'moving_mean': ['moving_vars'],
+          'moving_variance': ['moving_vars'],
+      }
+  }
+
+  with slim.arg_scope(
+      [slim.conv3d, conv3d_spatiotemporal],
+      weights_regularizer=slim.l2_regularizer(weight_decay),
+      activation_fn=tf.nn.relu,
+      normalizer_fn=slim.batch_norm,
+      normalizer_params=batch_norm_params):
+    with slim.arg_scope(
+        [conv3d_spatiotemporal], separable=separable_conv3d) as sc:
+      return sc
+
+
+def i3d_base(inputs, final_endpoint='Mixed_5c',
+             scope='InceptionV1'):
+  """Defines the I3D base architecture.
+
+  Note that we use the names as defined in Inception V1 to facilitate checkpoint
+  conversion from an image-trained Inception V1 checkpoint to I3D checkpoint.
+
+  Args:
+    inputs: A 5-D float tensor of size [batch_size, num_frames, height, width,
+      channels].
+    final_endpoint: Specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
+    scope: Optional variable_scope.
+
+  Returns:
+    A dictionary from components of the network to the corresponding activation.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values.
+  """
+
+  return s3dg.s3dg_base(
+      inputs,
+      first_temporal_kernel_size=7,
+      temporal_conv_startat='Conv2d_2c_3x3',
+      gating_startat=None,
+      final_endpoint=final_endpoint,
+      min_depth=16,
+      depth_multiplier=1.0,
+      data_format='NDHWC',
+      scope=scope)
+
+
+def i3d(inputs,
+        num_classes=1000,
+        dropout_keep_prob=0.8,
+        is_training=True,
+        prediction_fn=slim.softmax,
+        spatial_squeeze=True,
+        reuse=None,
+        scope='InceptionV1'):
+  """Defines the I3D architecture.
+
+  The default image size used to train this network is 224x224.
+
+  Args:
+    inputs: A 5-D float tensor of size [batch_size, num_frames, height, width,
+      channels].
+    num_classes: number of predicted classes.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    is_training: whether is training or not.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+
+  Returns:
+    logits: the pre-softmax activations, a tensor of size
+      [batch_size, num_classes]
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+  """
+  # Final pooling and prediction
+  with tf.variable_scope(
+      scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
+    with slim.arg_scope(
+        [slim.batch_norm, slim.dropout], is_training=is_training):
+      net, end_points = i3d_base(inputs, scope=scope)
+      with tf.variable_scope('Logits'):
+        kernel_size = i3d_utils.reduced_kernel_size_3d(net, [2, 7, 7])
+        net = slim.avg_pool3d(
+            net, kernel_size, stride=1, scope='AvgPool_0a_7x7')
+        net = slim.dropout(net, dropout_keep_prob, scope='Dropout_0b')
+        logits = slim.conv3d(
+            net,
+            num_classes, [1, 1, 1],
+            activation_fn=None,
+            normalizer_fn=None,
+            scope='Conv2d_0c_1x1')
+        # Temporal average pooling.
+        logits = tf.reduce_mean(logits, axis=1)
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+
+        end_points['Logits'] = logits
+        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+
+
+i3d.default_image_size = 224
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_test.py
new file mode 100644
index 00000000..ec3bb56b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_test.py
@@ -0,0 +1,149 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for networks.i3d."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import i3d
+
+
+class I3DTest(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    logits, end_points = i3d.i3d(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_6c, end_points = i3d.i3d_base(inputs)
+    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_6c.get_shape().as_list(),
+                         [batch_size, 8, 7, 7, 1024])
+    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
+                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
+                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
+                          'Mixed_5b', 'Mixed_5c']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
+                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
+                 'Mixed_5c']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+        out_tensor, end_points = i3d.i3d_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV1/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points)
+
+  def testBuildAndCheckAllEndPointsUptoMixed5c(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    _, end_points = i3d.i3d_base(inputs,
+                                 final_endpoint='Mixed_5c')
+    endpoints_shapes = {'Conv2d_1a_7x7': [5, 32, 112, 112, 64],
+                        'MaxPool_2a_3x3': [5, 32, 56, 56, 64],
+                        'Conv2d_2b_1x1': [5, 32, 56, 56, 64],
+                        'Conv2d_2c_3x3': [5, 32, 56, 56, 192],
+                        'MaxPool_3a_3x3': [5, 32, 28, 28, 192],
+                        'Mixed_3b': [5, 32, 28, 28, 256],
+                        'Mixed_3c': [5, 32, 28, 28, 480],
+                        'MaxPool_4a_3x3': [5, 16, 14, 14, 480],
+                        'Mixed_4b': [5, 16, 14, 14, 512],
+                        'Mixed_4c': [5, 16, 14, 14, 512],
+                        'Mixed_4d': [5, 16, 14, 14, 512],
+                        'Mixed_4e': [5, 16, 14, 14, 528],
+                        'Mixed_4f': [5, 16, 14, 14, 832],
+                        'MaxPool_5a_2x2': [5, 8, 7, 7, 832],
+                        'Mixed_5b': [5, 8, 7, 7, 832],
+                        'Mixed_5c': [5, 8, 7, 7, 1024]}
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 112, 112
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_5c, _ = i3d.i3d_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 8, 4, 4, 1024])
+
+  def testTenFrames(self):
+    batch_size = 5
+    num_frames = 10
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_5c, _ = i3d.i3d_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 2, 7, 7, 1024])
+
+  def testEvaluation(self):
+    batch_size = 2
+    num_frames = 64
+    height, width = 224, 224
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    logits, _ = i3d.i3d(eval_inputs, num_classes,
+                        is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_utils.py b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_utils.py
new file mode 100644
index 00000000..2b9911e8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/i3d_utils.py
@@ -0,0 +1,287 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for building I3D network models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+
+# Orignaly, add_arg_scope = slim.add_arg_scope and layers = slim, now switch to
+# more update-to-date tf.contrib.* API.
+add_arg_scope = tf.contrib.framework.add_arg_scope
+layers = tf.contrib.layers
+
+
+def center_initializer():
+  """Centering Initializer for I3D.
+
+  This initializer allows identity mapping for temporal convolution at the
+  initialization, which is critical for a desired convergence behavior
+  for training a seprable I3D model.
+
+  The centering behavior of this initializer requires an odd-sized kernel,
+  typically set to 3.
+
+  Returns:
+    A weight initializer op used in temporal convolutional layers.
+
+  Raises:
+    ValueError: Input tensor data type has to be tf.float32.
+    ValueError: If input tensor is not a 5-D tensor.
+    ValueError: If input and output channel dimensions are different.
+    ValueError: If spatial kernel sizes are not 1.
+    ValueError: If temporal kernel size is even.
+  """
+
+  def _initializer(shape, dtype=tf.float32, partition_info=None):  # pylint: disable=unused-argument
+    """Initializer op."""
+
+    if dtype != tf.float32 and dtype != tf.bfloat16:
+      raise ValueError(
+          'Input tensor data type has to be tf.float32 or tf.bfloat16.')
+    if len(shape) != 5:
+      raise ValueError('Input tensor has to be 5-D.')
+    if shape[3] != shape[4]:
+      raise ValueError('Input and output channel dimensions must be the same.')
+    if shape[1] != 1 or shape[2] != 1:
+      raise ValueError('Spatial kernel sizes must be 1 (pointwise conv).')
+    if shape[0] % 2 == 0:
+      raise ValueError('Temporal kernel size has to be odd.')
+
+    center_pos = int(shape[0] / 2)
+    init_mat = np.zeros(
+        [shape[0], shape[1], shape[2], shape[3], shape[4]], dtype=np.float32)
+    for i in range(0, shape[3]):
+      init_mat[center_pos, 0, 0, i, i] = 1.0
+
+    init_op = tf.constant(init_mat, dtype=dtype)
+    return init_op
+
+  return _initializer
+
+
+@add_arg_scope
+def conv3d_spatiotemporal(inputs,
+                          num_outputs,
+                          kernel_size,
+                          stride=1,
+                          padding='SAME',
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          normalizer_params=None,
+                          weights_regularizer=None,
+                          separable=False,
+                          data_format='NDHWC',
+                          scope=''):
+  """A wrapper for conv3d to model spatiotemporal representations.
+
+  This allows switching between original 3D convolution and separable 3D
+  convolutions for spatial and temporal features respectively. On Kinetics,
+  seprable 3D convolutions yields better classification performance.
+
+  Args:
+    inputs: a 5-D tensor  `[batch_size, depth, height, width, channels]`.
+    num_outputs: integer, the number of output filters.
+    kernel_size: a list of length 3
+      `[kernel_depth, kernel_height, kernel_width]` of the filters. Can be an
+      int if all values are the same.
+    stride: a list of length 3 `[stride_depth, stride_height, stride_width]`.
+      Can be an int if all strides are the same.
+    padding: one of `VALID` or `SAME`.
+    activation_fn: activation function.
+    normalizer_fn: normalization function to use instead of `biases`.
+    normalizer_params: dictionary of normalization function parameters.
+    weights_regularizer: Optional regularizer for the weights.
+    separable: If `True`, use separable spatiotemporal convolutions.
+    data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
+      The data format of the input and output data. With the default format
+      "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+      in_width, in_channels]. Alternatively, the format could be "NCDHW", the
+      data storage order is:
+      [batch, in_channels, in_depth, in_height, in_width].
+    scope: scope for `variable_scope`.
+
+  Returns:
+    A tensor representing the output of the (separable) conv3d operation.
+
+  """
+  assert len(kernel_size) == 3
+  if separable and kernel_size[0] != 1:
+    spatial_kernel_size = [1, kernel_size[1], kernel_size[2]]
+    temporal_kernel_size = [kernel_size[0], 1, 1]
+    if isinstance(stride, list) and len(stride) == 3:
+      spatial_stride = [1, stride[1], stride[2]]
+      temporal_stride = [stride[0], 1, 1]
+    else:
+      spatial_stride = [1, stride, stride]
+      temporal_stride = [stride, 1, 1]
+    net = layers.conv3d(
+        inputs,
+        num_outputs,
+        spatial_kernel_size,
+        stride=spatial_stride,
+        padding=padding,
+        activation_fn=activation_fn,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params,
+        weights_regularizer=weights_regularizer,
+        data_format=data_format,
+        scope=scope)
+    net = layers.conv3d(
+        net,
+        num_outputs,
+        temporal_kernel_size,
+        stride=temporal_stride,
+        padding=padding,
+        scope=scope + '/temporal',
+        activation_fn=activation_fn,
+        normalizer_fn=None,
+        data_format=data_format,
+        weights_initializer=center_initializer())
+    return net
+  else:
+    return layers.conv3d(
+        inputs,
+        num_outputs,
+        kernel_size,
+        stride=stride,
+        padding=padding,
+        activation_fn=activation_fn,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params,
+        weights_regularizer=weights_regularizer,
+        data_format=data_format,
+        scope=scope)
+
+
+@add_arg_scope
+def inception_block_v1_3d(inputs,
+                          num_outputs_0_0a,
+                          num_outputs_1_0a,
+                          num_outputs_1_0b,
+                          num_outputs_2_0a,
+                          num_outputs_2_0b,
+                          num_outputs_3_0b,
+                          temporal_kernel_size=3,
+                          self_gating_fn=None,
+                          data_format='NDHWC',
+                          scope=''):
+  """A 3D Inception v1 block.
+
+  This allows use of separable 3D convolutions and self-gating, as
+  described in:
+  Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu and Kevin Murphy,
+    Rethinking Spatiotemporal Feature Learning For Video Understanding.
+    https://arxiv.org/abs/1712.04851.
+
+  Args:
+    inputs: a 5-D tensor  `[batch_size, depth, height, width, channels]`.
+    num_outputs_0_0a: integer, the number of output filters for Branch 0,
+      operation Conv2d_0a_1x1.
+    num_outputs_1_0a: integer, the number of output filters for Branch 1,
+      operation Conv2d_0a_1x1.
+    num_outputs_1_0b: integer, the number of output filters for Branch 1,
+      operation Conv2d_0b_3x3.
+    num_outputs_2_0a: integer, the number of output filters for Branch 2,
+      operation Conv2d_0a_1x1.
+    num_outputs_2_0b: integer, the number of output filters for Branch 2,
+      operation Conv2d_0b_3x3.
+    num_outputs_3_0b: integer, the number of output filters for Branch 3,
+      operation Conv2d_0b_1x1.
+    temporal_kernel_size: integer, the size of the temporal convolutional
+      filters in the conv3d_spatiotemporal blocks.
+    self_gating_fn: function which optionally performs self-gating.
+      Must have two arguments, `inputs` and `scope`, and return one output
+      tensor the same size as `inputs`. If `None`, no self-gating is
+      applied.
+    data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
+      The data format of the input and output data. With the default format
+      "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+      in_width, in_channels]. Alternatively, the format could be "NCDHW", the
+      data storage order is:
+      [batch, in_channels, in_depth, in_height, in_width].
+    scope: scope for `variable_scope`.
+
+  Returns:
+    A 5-D tensor `[batch_size, depth, height, width, out_channels]`, where
+    `out_channels = num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b
+    + num_outputs_3_0b`.
+
+  """
+  use_gating = self_gating_fn is not None
+
+  with tf.variable_scope(scope):
+    with tf.variable_scope('Branch_0'):
+      branch_0 = layers.conv3d(
+          inputs, num_outputs_0_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
+      if use_gating:
+        branch_0 = self_gating_fn(branch_0, scope='Conv2d_0a_1x1')
+    with tf.variable_scope('Branch_1'):
+      branch_1 = layers.conv3d(
+          inputs, num_outputs_1_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
+      branch_1 = conv3d_spatiotemporal(
+          branch_1, num_outputs_1_0b, [temporal_kernel_size, 3, 3],
+          scope='Conv2d_0b_3x3')
+      if use_gating:
+        branch_1 = self_gating_fn(branch_1, scope='Conv2d_0b_3x3')
+    with tf.variable_scope('Branch_2'):
+      branch_2 = layers.conv3d(
+          inputs, num_outputs_2_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
+      branch_2 = conv3d_spatiotemporal(
+          branch_2, num_outputs_2_0b, [temporal_kernel_size, 3, 3],
+          scope='Conv2d_0b_3x3')
+      if use_gating:
+        branch_2 = self_gating_fn(branch_2, scope='Conv2d_0b_3x3')
+    with tf.variable_scope('Branch_3'):
+      branch_3 = layers.max_pool3d(inputs, [3, 3, 3], scope='MaxPool_0a_3x3')
+      branch_3 = layers.conv3d(
+          branch_3, num_outputs_3_0b, [1, 1, 1], scope='Conv2d_0b_1x1')
+      if use_gating:
+        branch_3 = self_gating_fn(branch_3, scope='Conv2d_0b_1x1')
+    index_c = data_format.index('C')
+    assert 1 <= index_c <= 4, 'Cannot identify channel dimension.'
+    output = tf.concat([branch_0, branch_1, branch_2, branch_3], index_c)
+  return output
+
+
+def reduced_kernel_size_3d(input_tensor, kernel_size):
+  """Define kernel size which is automatically reduced for small input.
+
+  If the shape of the input images is unknown at graph construction time this
+  function assumes that the input images are large enough.
+
+  Args:
+    input_tensor: input tensor of size
+      [batch_size, time, height, width, channels].
+    kernel_size: desired kernel size of length 3, corresponding to time,
+      height and width.
+
+  Returns:
+    a tensor with the kernel size.
+  """
+  assert len(kernel_size) == 3
+  shape = input_tensor.get_shape().as_list()
+  assert len(shape) == 5
+  if None in shape[1:4]:
+    kernel_size_out = kernel_size
+  else:
+    kernel_size_out = [min(shape[1], kernel_size[0]),
+                       min(shape[2], kernel_size[1]),
+                       min(shape[3], kernel_size[2])]
+  return kernel_size_out
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception.py
new file mode 100644
index 00000000..b69cd2aa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception.py
@@ -0,0 +1,37 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Brings all inception models under one namespace."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import
+from nets.inception_resnet_v2 import inception_resnet_v2
+from nets.inception_resnet_v2 import inception_resnet_v2_arg_scope
+from nets.inception_resnet_v2 import inception_resnet_v2_base
+from nets.inception_v1 import inception_v1
+from nets.inception_v1 import inception_v1_arg_scope
+from nets.inception_v1 import inception_v1_base
+from nets.inception_v2 import inception_v2
+from nets.inception_v2 import inception_v2_arg_scope
+from nets.inception_v2 import inception_v2_base
+from nets.inception_v3 import inception_v3
+from nets.inception_v3 import inception_v3_arg_scope
+from nets.inception_v3 import inception_v3_base
+from nets.inception_v4 import inception_v4
+from nets.inception_v4 import inception_v4_arg_scope
+from nets.inception_v4 import inception_v4_base
+# pylint: enable=unused-import
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2.py
new file mode 100644
index 00000000..f14c08ef
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2.py
@@ -0,0 +1,406 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition of the Inception Resnet V2 architecture.
+
+As described in http://arxiv.org/abs/1602.07261.
+
+  Inception-v4, Inception-ResNet and the Impact of Residual Connections
+    on Learning
+  Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+  """Builds the 35x35 resnet block."""
+  with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
+      tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
+    with tf.variable_scope('Branch_1'):
+      tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
+      tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
+    with tf.variable_scope('Branch_2'):
+      tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
+      tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
+      tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
+    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
+    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                     activation_fn=None, scope='Conv2d_1x1')
+    scaled_up = up * scale
+    if activation_fn == tf.nn.relu6:
+      # Use clip_by_value to simulate bandpass activation.
+      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)
+
+    net += scaled_up
+    if activation_fn:
+      net = activation_fn(net)
+  return net
+
+
+def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+  """Builds the 17x17 resnet block."""
+  with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
+      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
+    with tf.variable_scope('Branch_1'):
+      tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
+      tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
+                                  scope='Conv2d_0b_1x7')
+      tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
+                                  scope='Conv2d_0c_7x1')
+    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
+    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                     activation_fn=None, scope='Conv2d_1x1')
+
+    scaled_up = up * scale
+    if activation_fn == tf.nn.relu6:
+      # Use clip_by_value to simulate bandpass activation.
+      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)
+
+    net += scaled_up
+    if activation_fn:
+      net = activation_fn(net)
+  return net
+
+
+def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+  """Builds the 8x8 resnet block."""
+  with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
+      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
+    with tf.variable_scope('Branch_1'):
+      tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
+      tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
+                                  scope='Conv2d_0b_1x3')
+      tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
+                                  scope='Conv2d_0c_3x1')
+    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
+    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                     activation_fn=None, scope='Conv2d_1x1')
+
+    scaled_up = up * scale
+    if activation_fn == tf.nn.relu6:
+      # Use clip_by_value to simulate bandpass activation.
+      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)
+
+    net += scaled_up
+    if activation_fn:
+      net = activation_fn(net)
+  return net
+
+
+def inception_resnet_v2_base(inputs,
+                             final_endpoint='Conv2d_7b_1x1',
+                             output_stride=16,
+                             align_feature_maps=False,
+                             scope=None,
+                             activation_fn=tf.nn.relu):
+  """Inception model from  http://arxiv.org/abs/1602.07261.
+
+  Constructs an Inception Resnet v2 network from inputs to the given final
+  endpoint. This method can construct the network up to the final inception
+  block Conv2d_7b_1x1.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    final_endpoint: specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
+      'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+    output_stride: A scalar that specifies the requested ratio of input to
+      output spatial resolution. Only supports 8 and 16.
+    align_feature_maps: When true, changes all the VALID paddings in the network
+      to SAME padding so that the feature maps are aligned.
+    scope: Optional variable_scope.
+    activation_fn: Activation function for block scopes.
+
+  Returns:
+    tensor_out: output tensor corresponding to the final_endpoint.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+      or if the output_stride is not 8 or 16, or if the output_stride is 8 and
+      we request an end point after 'PreAuxLogits'.
+  """
+  if output_stride != 8 and output_stride != 16:
+    raise ValueError('output_stride must be 8 or 16.')
+
+  padding = 'SAME' if align_feature_maps else 'VALID'
+
+  end_points = {}
+
+  def add_and_check_final(name, net):
+    end_points[name] = net
+    return name == final_endpoint
+
+  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
+    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                        stride=1, padding='SAME'):
+      # 149 x 149 x 32
+      net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding,
+                        scope='Conv2d_1a_3x3')
+      if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
+
+      # 147 x 147 x 32
+      net = slim.conv2d(net, 32, 3, padding=padding,
+                        scope='Conv2d_2a_3x3')
+      if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
+      # 147 x 147 x 64
+      net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
+      if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
+      # 73 x 73 x 64
+      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
+                            scope='MaxPool_3a_3x3')
+      if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
+      # 73 x 73 x 80
+      net = slim.conv2d(net, 80, 1, padding=padding,
+                        scope='Conv2d_3b_1x1')
+      if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
+      # 71 x 71 x 192
+      net = slim.conv2d(net, 192, 3, padding=padding,
+                        scope='Conv2d_4a_3x3')
+      if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
+      # 35 x 35 x 192
+      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
+                            scope='MaxPool_5a_3x3')
+      if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points
+
+      # 35 x 35 x 320
+      with tf.variable_scope('Mixed_5b'):
+        with tf.variable_scope('Branch_0'):
+          tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+          tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
+          tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
+                                      scope='Conv2d_0b_5x5')
+        with tf.variable_scope('Branch_2'):
+          tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
+          tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
+                                      scope='Conv2d_0b_3x3')
+          tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
+                                      scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
+                                       scope='AvgPool_0a_3x3')
+          tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
+                                     scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
+
+      if add_and_check_final('Mixed_5b', net): return net, end_points
+      # TODO(alemi): Register intermediate endpoints
+      net = slim.repeat(net, 10, block35, scale=0.17,
+                        activation_fn=activation_fn)
+
+      # 17 x 17 x 1088 if output_stride == 8,
+      # 33 x 33 x 1088 if output_stride == 16
+      use_atrous = output_stride == 8
+
+      with tf.variable_scope('Mixed_6a'):
+        with tf.variable_scope('Branch_0'):
+          tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
+                                   padding=padding,
+                                   scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+          tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
+                                      scope='Conv2d_0b_3x3')
+          tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
+                                      stride=1 if use_atrous else 2,
+                                      padding=padding,
+                                      scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_2'):
+          tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
+                                       padding=padding,
+                                       scope='MaxPool_1a_3x3')
+        net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
+
+      if add_and_check_final('Mixed_6a', net): return net, end_points
+
+      # TODO(alemi): register intermediate endpoints
+      with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
+        net = slim.repeat(net, 20, block17, scale=0.10,
+                          activation_fn=activation_fn)
+      if add_and_check_final('PreAuxLogits', net): return net, end_points
+
+      if output_stride == 8:
+        # TODO(gpapan): Properly support output_stride for the rest of the net.
+        raise ValueError('output_stride==8 is only supported up to the '
+                         'PreAuxlogits end_point for now.')
+
+      # 8 x 8 x 2080
+      with tf.variable_scope('Mixed_7a'):
+        with tf.variable_scope('Branch_0'):
+          tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+          tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
+                                     padding=padding,
+                                     scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+          tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
+                                      padding=padding,
+                                      scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_2'):
+          tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+          tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
+                                      scope='Conv2d_0b_3x3')
+          tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
+                                      padding=padding,
+                                      scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_3'):
+          tower_pool = slim.max_pool2d(net, 3, stride=2,
+                                       padding=padding,
+                                       scope='MaxPool_1a_3x3')
+        net = tf.concat(
+            [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
+
+      if add_and_check_final('Mixed_7a', net): return net, end_points
+
+      # TODO(alemi): register intermediate endpoints
+      net = slim.repeat(net, 9, block8, scale=0.20, activation_fn=activation_fn)
+      net = block8(net, activation_fn=None)
+
+      # 8 x 8 x 1536
+      net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
+      if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points
+
+    raise ValueError('final_endpoint (%s) not recognized', final_endpoint)
+
+
+def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
+                        dropout_keep_prob=0.8,
+                        reuse=None,
+                        scope='InceptionResnetV2',
+                        create_aux_logits=True,
+                        activation_fn=tf.nn.relu):
+  """Creates the Inception Resnet V2 model.
+
+  Args:
+    inputs: a 4-D tensor of size [batch_size, height, width, 3].
+      Dimension batch_size may be undefined. If create_aux_logits is false,
+      also height and width may be undefined.
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before  dropout)
+      are returned instead.
+    is_training: whether is training or not.
+    dropout_keep_prob: float, the fraction to keep before final layer.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    create_aux_logits: Whether to include the auxilliary logits.
+    activation_fn: Activation function for conv2d.
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the non-dropped-out input to the logits layer (if num_classes is 0 or
+      None).
+    end_points: the set of end_points from the inception model.
+  """
+  end_points = {}
+
+  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs],
+                         reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+
+      net, end_points = inception_resnet_v2_base(inputs, scope=scope,
+                                                 activation_fn=activation_fn)
+
+      if create_aux_logits and num_classes:
+        with tf.variable_scope('AuxLogits'):
+          aux = end_points['PreAuxLogits']
+          aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
+                                scope='Conv2d_1a_3x3')
+          aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
+          aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
+                            padding='VALID', scope='Conv2d_2a_5x5')
+          aux = slim.flatten(aux)
+          aux = slim.fully_connected(aux, num_classes, activation_fn=None,
+                                     scope='Logits')
+          end_points['AuxLogits'] = aux
+
+      with tf.variable_scope('Logits'):
+        # TODO(sguada,arnoegw): Consider adding a parameter global_pool which
+        # can be set to False to disable pooling here (as in resnet_*()).
+        kernel_size = net.get_shape()[1:3]
+        if kernel_size.is_fully_defined():
+          net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+                                scope='AvgPool_1a_8x8')
+        else:
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+        if not num_classes:
+          return net, end_points
+        net = slim.flatten(net)
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='Dropout')
+        end_points['PreLogitsFlatten'] = net
+        logits = slim.fully_connected(net, num_classes, activation_fn=None,
+                                      scope='Logits')
+        end_points['Logits'] = logits
+        end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
+
+    return logits, end_points
+inception_resnet_v2.default_image_size = 299
+
+
+def inception_resnet_v2_arg_scope(
+    weight_decay=0.00004,
+    batch_norm_decay=0.9997,
+    batch_norm_epsilon=0.001,
+    activation_fn=tf.nn.relu,
+    batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
+    batch_norm_scale=False):
+  """Returns the scope with the default parameters for inception_resnet_v2.
+
+  Args:
+    weight_decay: the weight decay for weights variables.
+    batch_norm_decay: decay for the moving average of batch_norm momentums.
+    batch_norm_epsilon: small float added to variance to avoid dividing by zero.
+    activation_fn: Activation function for conv2d.
+    batch_norm_updates_collections: Collection for the update ops for
+      batch norm.
+    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
+      activations in the batch normalization layer.
+
+  Returns:
+    a arg_scope with the parameters needed for inception_resnet_v2.
+  """
+  # Set weight_decay for weights in conv2d and fully_connected layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay),
+                      biases_regularizer=slim.l2_regularizer(weight_decay)):
+
+    batch_norm_params = {
+        'decay': batch_norm_decay,
+        'epsilon': batch_norm_epsilon,
+        'updates_collections': batch_norm_updates_collections,
+        'fused': None,  # Use fused batch norm if possible.
+        'scale': batch_norm_scale,
+    }
+    # Set activation_fn and parameters for batch_norm.
+    with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,
+                        normalizer_fn=slim.batch_norm,
+                        normalizer_params=batch_norm_params) as scope:
+      return scope
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2_test.py
new file mode 100644
index 00000000..833ada5e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_resnet_v2_test.py
@@ -0,0 +1,334 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.inception_resnet_v2."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception
+
+
+class InceptionTest(tf.test.TestCase):
+
+  def testBuildLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, endpoints = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue('AuxLogits' in endpoints)
+      auxlogits = endpoints['AuxLogits']
+      self.assertTrue(
+          auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits'))
+      self.assertListEqual(auxlogits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testBuildWithoutAuxLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, endpoints = inception.inception_resnet_v2(inputs, num_classes,
+                                                        create_aux_logits=False)
+      self.assertTrue('AuxLogits' not in endpoints)
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testBuildNoClasses(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, endpoints = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue('AuxLogits' not in endpoints)
+      self.assertTrue('Logits' not in endpoints)
+      self.assertTrue(
+          net.op.name.startswith('InceptionResnetV2/Logits/AvgPool'))
+      self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1536])
+
+  def testBuildEndPoints(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue('Logits' in end_points)
+      logits = end_points['Logits']
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      self.assertTrue('AuxLogits' in end_points)
+      aux_logits = end_points['AuxLogits']
+      self.assertListEqual(aux_logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_7b_1x1']
+      self.assertListEqual(pre_pool.get_shape().as_list(),
+                           [batch_size, 8, 8, 1536])
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_resnet_v2_base(inputs)
+    self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1'))
+    self.assertListEqual(net.get_shape().as_list(),
+                         [batch_size, 8, 8, 1536])
+    expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+                          'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
+                          'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a',
+                          'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    height, width = 299, 299
+    endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+                 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
+                 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a',
+                 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = inception.inception_resnet_v2_base(
+            inputs, final_endpoint=endpoint)
+        if endpoint != 'PreAuxLogits':
+          self.assertTrue(out_tensor.op.name.startswith(
+              'InceptionResnetV2/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+
+  def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_resnet_v2_base(
+        inputs, final_endpoint='PreAuxLogits')
+    endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
+                        'Conv2d_2a_3x3': [5, 147, 147, 32],
+                        'Conv2d_2b_3x3': [5, 147, 147, 64],
+                        'MaxPool_3a_3x3': [5, 73, 73, 64],
+                        'Conv2d_3b_1x1': [5, 73, 73, 80],
+                        'Conv2d_4a_3x3': [5, 71, 71, 192],
+                        'MaxPool_5a_3x3': [5, 35, 35, 192],
+                        'Mixed_5b': [5, 35, 35, 320],
+                        'Mixed_6a': [5, 17, 17, 1088],
+                        'PreAuxLogits': [5, 17, 17, 1088]
+                       }
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_resnet_v2_base(
+        inputs, final_endpoint='PreAuxLogits', align_feature_maps=True)
+    endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32],
+                        'Conv2d_2a_3x3': [5, 150, 150, 32],
+                        'Conv2d_2b_3x3': [5, 150, 150, 64],
+                        'MaxPool_3a_3x3': [5, 75, 75, 64],
+                        'Conv2d_3b_1x1': [5, 75, 75, 80],
+                        'Conv2d_4a_3x3': [5, 75, 75, 192],
+                        'MaxPool_5a_3x3': [5, 38, 38, 192],
+                        'Mixed_5b': [5, 38, 38, 320],
+                        'Mixed_6a': [5, 19, 19, 1088],
+                        'PreAuxLogits': [5, 19, 19, 1088]
+                       }
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_resnet_v2_base(
+        inputs, final_endpoint='PreAuxLogits', output_stride=8)
+    endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
+                        'Conv2d_2a_3x3': [5, 147, 147, 32],
+                        'Conv2d_2b_3x3': [5, 147, 147, 64],
+                        'MaxPool_3a_3x3': [5, 73, 73, 64],
+                        'Conv2d_3b_1x1': [5, 73, 73, 80],
+                        'Conv2d_4a_3x3': [5, 71, 71, 192],
+                        'MaxPool_5a_3x3': [5, 35, 35, 192],
+                        'Mixed_5b': [5, 35, 35, 320],
+                        'Mixed_6a': [5, 33, 33, 1088],
+                        'PreAuxLogits': [5, 33, 33, 1088]
+                       }
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testVariablesSetDevice(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      # Force all Variables to reside on the device.
+      with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
+        inception.inception_resnet_v2(inputs, num_classes)
+      with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
+        inception.inception_resnet_v2(inputs, num_classes)
+      for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
+        self.assertDeviceEqual(v.device, '/cpu:0')
+      for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
+        self.assertDeviceEqual(v.device, '/gpu:0')
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 150, 150
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_7b_1x1']
+      self.assertListEqual(pre_pool.get_shape().as_list(),
+                           [batch_size, 3, 3, 1536])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 330, 400
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_7b_1x1']
+      self.assertListEqual(pre_pool.get_shape().as_list(),
+                           [batch_size, 8, 11, 1536])
+
+  def testGlobalPoolUnknownImageShape(self):
+    batch_size = 1
+    height, width = 330, 400
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
+      logits, end_points = inception.inception_resnet_v2(
+          inputs, num_classes, create_aux_logits=False)
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_7b_1x1']
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      logits_out, pre_pool_out = sess.run([logits, pre_pool],
+                                          {inputs: images.eval()})
+      self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
+      self.assertTupleEqual(pre_pool_out.shape, (batch_size, 8, 11, 1536))
+
+  def testUnknownBatchSize(self):
+    batch_size = 1
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+      logits, _ = inception.inception_resnet_v2(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [None, num_classes])
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session() as sess:
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = inception.inception_resnet_v2(eval_inputs,
+                                                num_classes,
+                                                is_training=False)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 150, 150
+    num_classes = 1000
+    with self.test_session() as sess:
+      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+      inception.inception_resnet_v2(train_inputs, num_classes)
+      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+      logits, _ = inception.inception_resnet_v2(eval_inputs,
+                                                num_classes,
+                                                is_training=False,
+                                                reuse=True)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testNoBatchNormScaleByDefault(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with tf.contrib.slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
+      inception.inception_resnet_v2(inputs, num_classes, is_training=False)
+
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
+
+  def testBatchNormScale(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with tf.contrib.slim.arg_scope(
+        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
+      inception.inception_resnet_v2(inputs, num_classes, is_training=False)
+
+    gamma_names = set(
+        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
+    self.assertGreater(len(gamma_names), 0)
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
+      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_utils.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_utils.py
new file mode 100644
index 00000000..a8c8fc14
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_utils.py
@@ -0,0 +1,82 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains common code shared by all inception models.
+
+Usage of arg scope:
+  with slim.arg_scope(inception_arg_scope()):
+    logits, end_points = inception.inception_v3(images, num_classes,
+                                                is_training=is_training)
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def inception_arg_scope(weight_decay=0.00004,
+                        use_batch_norm=True,
+                        batch_norm_decay=0.9997,
+                        batch_norm_epsilon=0.001,
+                        activation_fn=tf.nn.relu,
+                        batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
+                        batch_norm_scale=False):
+  """Defines the default arg scope for inception models.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+    activation_fn: Activation function for conv2d.
+    batch_norm_updates_collections: Collection for the update ops for
+      batch norm.
+    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
+      activations in the batch normalization layer.
+
+  Returns:
+    An `arg_scope` to use for the inception models.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      # collection containing update_ops.
+      'updates_collections': batch_norm_updates_collections,
+      # use fused batch norm if possible.
+      'fused': None,
+      'scale': batch_norm_scale,
+  }
+  if use_batch_norm:
+    normalizer_fn = slim.batch_norm
+    normalizer_params = batch_norm_params
+  else:
+    normalizer_fn = None
+    normalizer_params = {}
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope(
+        [slim.conv2d],
+        weights_initializer=slim.variance_scaling_initializer(),
+        activation_fn=activation_fn,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params) as sc:
+      return sc
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1.py
new file mode 100644
index 00000000..d9871659
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1.py
@@ -0,0 +1,329 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for inception v1 classification network."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def inception_v1_base(inputs,
+                      final_endpoint='Mixed_5c',
+                      scope='InceptionV1'):
+  """Defines the Inception V1 base architecture.
+
+  This architecture is defined in:
+    Going deeper with convolutions
+    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
+    http://arxiv.org/pdf/1409.4842v1.pdf.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    final_endpoint: specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
+    scope: Optional variable_scope.
+
+  Returns:
+    A dictionary from components of the network to the corresponding activation.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values.
+  """
+  end_points = {}
+  with tf.variable_scope(scope, 'InceptionV1', [inputs]):
+    with slim.arg_scope(
+        [slim.conv2d, slim.fully_connected],
+        weights_initializer=trunc_normal(0.01)):
+      with slim.arg_scope([slim.conv2d, slim.max_pool2d],
+                          stride=1, padding='SAME'):
+        end_point = 'Conv2d_1a_7x7'
+        net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+        end_point = 'MaxPool_2a_3x3'
+        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+        end_point = 'Conv2d_2b_1x1'
+        net = slim.conv2d(net, 64, [1, 1], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+        end_point = 'Conv2d_2c_3x3'
+        net = slim.conv2d(net, 192, [3, 3], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+        end_point = 'MaxPool_3a_3x3'
+        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_3b'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_3c'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'MaxPool_4a_3x3'
+        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_4b'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_4c'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_4d'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_4e'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_4f'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'MaxPool_5a_2x2'
+        net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_5b'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+
+        end_point = 'Mixed_5c'
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
+            branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
+          with tf.variable_scope('Branch_1'):
+            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
+            branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_2'):
+            branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
+            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
+          with tf.variable_scope('Branch_3'):
+            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+          net = tf.concat(
+              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if final_endpoint == end_point: return net, end_points
+    raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v1(inputs,
+                 num_classes=1000,
+                 is_training=True,
+                 dropout_keep_prob=0.8,
+                 prediction_fn=slim.softmax,
+                 spatial_squeeze=True,
+                 reuse=None,
+                 scope='InceptionV1',
+                 global_pool=False):
+  """Defines the Inception V1 architecture.
+
+  This architecture is defined in:
+
+    Going deeper with convolutions
+    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
+    http://arxiv.org/pdf/1409.4842v1.pdf.
+
+  The default image size used to train this network is 224x224.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is of
+        shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    global_pool: Optional boolean flag to control the avgpooling before the
+      logits layer. If false or unset, pooling is done with a fixed window
+      that reduces default-sized inputs to 1x1, while larger inputs lead to
+      larger outputs. If true, any input size is pooled down to 1x1.
+
+  Returns:
+    net: a Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the non-dropped-out input to the logits layer
+      if num_classes is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+  """
+  # Final pooling and prediction
+  with tf.variable_scope(scope, 'InceptionV1', [inputs], reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+      net, end_points = inception_v1_base(inputs, scope=scope)
+      with tf.variable_scope('Logits'):
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+          end_points['global_pool'] = net
+        else:
+          # Pooling with a fixed kernel size.
+          net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7')
+          end_points['AvgPool_0a_7x7'] = net
+        if not num_classes:
+          return net, end_points
+        net = slim.dropout(net, dropout_keep_prob, scope='Dropout_0b')
+        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                             normalizer_fn=None, scope='Conv2d_0c_1x1')
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+
+        end_points['Logits'] = logits
+        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+inception_v1.default_image_size = 224
+
+inception_v1_arg_scope = inception_utils.inception_arg_scope
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1_test.py
new file mode 100644
index 00000000..03862577
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v1_test.py
@@ -0,0 +1,265 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for nets.inception_v1."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import inception
+
+slim = tf.contrib.slim
+
+
+class InceptionV1Test(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v1(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith(
+        'InceptionV1/Logits/SpatialSqueeze'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_v1(inputs, num_classes)
+    self.assertTrue(net.op.name.startswith('InceptionV1/Logits/AvgPool'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
+    self.assertFalse('Logits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    mixed_6c, end_points = inception.inception_v1_base(inputs)
+    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_6c.get_shape().as_list(),
+                         [batch_size, 7, 7, 1024])
+    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
+                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
+                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
+                          'Mixed_5b', 'Mixed_5c']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    height, width = 224, 224
+    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
+                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
+                 'Mixed_5c']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = inception.inception_v1_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV1/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+
+  def testBuildAndCheckAllEndPointsUptoMixed5c(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v1_base(inputs,
+                                                final_endpoint='Mixed_5c')
+    endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
+                        'MaxPool_2a_3x3': [5, 56, 56, 64],
+                        'Conv2d_2b_1x1': [5, 56, 56, 64],
+                        'Conv2d_2c_3x3': [5, 56, 56, 192],
+                        'MaxPool_3a_3x3': [5, 28, 28, 192],
+                        'Mixed_3b': [5, 28, 28, 256],
+                        'Mixed_3c': [5, 28, 28, 480],
+                        'MaxPool_4a_3x3': [5, 14, 14, 480],
+                        'Mixed_4b': [5, 14, 14, 512],
+                        'Mixed_4c': [5, 14, 14, 512],
+                        'Mixed_4d': [5, 14, 14, 512],
+                        'Mixed_4e': [5, 14, 14, 528],
+                        'Mixed_4f': [5, 14, 14, 832],
+                        'MaxPool_5a_2x2': [5, 7, 7, 832],
+                        'Mixed_5b': [5, 7, 7, 832],
+                        'Mixed_5c': [5, 7, 7, 1024]}
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testModelHasExpectedNumberOfParameters(self):
+    batch_size = 5
+    height, width = 224, 224
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope(inception.inception_v1_arg_scope()):
+      inception.inception_v1_base(inputs)
+    total_params, _ = slim.model_analyzer.analyze_vars(
+        slim.get_model_variables())
+    self.assertAlmostEqual(5607184, total_params)
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 112, 112
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    mixed_5c, _ = inception.inception_v1_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 4, 4, 1024])
+
+  def testUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v1(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_5c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
+
+  def testGlobalPoolUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 1
+    height, width = 250, 300
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v1(inputs, num_classes,
+                                                  global_pool=True)
+      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_5c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
+
+  def testUnknowBatchSize(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+    logits, _ = inception.inception_v1(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, num_classes])
+    images = tf.random_uniform((batch_size, height, width, 3))
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, _ = inception.inception_v1(eval_inputs, num_classes,
+                                       is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+
+    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+    inception.inception_v1(train_inputs, num_classes)
+    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+    logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testLogitsNotSqueezed(self):
+    num_classes = 25
+    images = tf.random_uniform([1, 224, 224, 3])
+    logits, _ = inception.inception_v1(images,
+                                       num_classes=num_classes,
+                                       spatial_squeeze=False)
+
+    with self.test_session() as sess:
+      tf.global_variables_initializer().run()
+      logits_out = sess.run(logits)
+      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
+
+  def testNoBatchNormScaleByDefault(self):
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(inception.inception_v1_arg_scope()):
+      inception.inception_v1(inputs, num_classes, is_training=False)
+
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
+
+  def testBatchNormScale(self):
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(
+        inception.inception_v1_arg_scope(batch_norm_scale=True)):
+      inception.inception_v1(inputs, num_classes, is_training=False)
+
+    gamma_names = set(
+        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
+    self.assertGreater(len(gamma_names), 0)
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
+      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2.py
new file mode 100644
index 00000000..66290b4d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2.py
@@ -0,0 +1,572 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for inception v2 classification network."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def inception_v2_base(inputs,
+                      final_endpoint='Mixed_5c',
+                      min_depth=16,
+                      depth_multiplier=1.0,
+                      use_separable_conv=True,
+                      data_format='NHWC',
+                      scope=None):
+  """Inception v2 (6a2).
+
+  Constructs an Inception v2 network from inputs to the given final endpoint.
+  This method can construct the network up to the layer inception(5b) as
+  described in http://arxiv.org/abs/1502.03167.
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    final_endpoint: specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a',
+      'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b',
+      'Mixed_5c'].
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    use_separable_conv: Use a separable convolution for the first layer
+      Conv2d_1a_7x7. If this is False, use a normal convolution instead.
+    data_format: Data format of the activations ('NHWC' or 'NCHW').
+    scope: Optional variable_scope.
+
+  Returns:
+    tensor_out: output tensor corresponding to the final_endpoint.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+                or depth_multiplier <= 0
+  """
+
+  # end_points will collect relevant activations for external use, for example
+  # summaries or losses.
+  end_points = {}
+
+  # Used to find thinned depths for each layer.
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+  depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+  if data_format != 'NHWC' and data_format != 'NCHW':
+    raise ValueError('data_format must be either NHWC or NCHW.')
+  if data_format == 'NCHW' and use_separable_conv:
+    raise ValueError(
+        'separable convolution only supports NHWC layout. NCHW data format can'
+        ' only be used when use_separable_conv is False.'
+    )
+
+  concat_dim = 3 if data_format == 'NHWC' else 1
+  with tf.variable_scope(scope, 'InceptionV2', [inputs]):
+    with slim.arg_scope(
+        [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+        stride=1,
+        padding='SAME',
+        data_format=data_format):
+
+      # Note that sizes in the comments below assume an input spatial size of
+      # 224x224, however, the inputs can be of any size greater 32x32.
+
+      # 224 x 224 x 3
+      end_point = 'Conv2d_1a_7x7'
+
+      if use_separable_conv:
+        # depthwise_multiplier here is different from depth_multiplier.
+        # depthwise_multiplier determines the output channels of the initial
+        # depthwise conv (see docs for tf.nn.separable_conv2d), while
+        # depth_multiplier controls the # channels of the subsequent 1x1
+        # convolution. Must have
+        #   in_channels * depthwise_multipler <= out_channels
+        # so that the separable convolution is not overparameterized.
+        depthwise_multiplier = min(int(depth(64) / 3), 8)
+        net = slim.separable_conv2d(
+            inputs, depth(64), [7, 7],
+            depth_multiplier=depthwise_multiplier,
+            stride=2,
+            padding='SAME',
+            weights_initializer=trunc_normal(1.0),
+            scope=end_point)
+      else:
+        # Use a normal convolution instead of a separable convolution.
+        net = slim.conv2d(
+            inputs,
+            depth(64), [7, 7],
+            stride=2,
+            weights_initializer=trunc_normal(1.0),
+            scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 112 x 112 x 64
+      end_point = 'MaxPool_2a_3x3'
+      net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 56 x 56 x 64
+      end_point = 'Conv2d_2b_1x1'
+      net = slim.conv2d(net, depth(64), [1, 1], scope=end_point,
+                        weights_initializer=trunc_normal(0.1))
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 56 x 56 x 64
+      end_point = 'Conv2d_2c_3x3'
+      net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 56 x 56 x 192
+      end_point = 'MaxPool_3a_3x3'
+      net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 28 x 28 x 192
+      # Inception module.
+      end_point = 'Mixed_3b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(64), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(32), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 28 x 28 x 256
+      end_point = 'Mixed_3c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 28 x 28 x 320
+      end_point = 'Mixed_4a'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(
+              net, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2,
+                                 scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(
+              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
+          branch_1 = slim.conv2d(
+              branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.max_pool2d(
+              net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
+        net = tf.concat(axis=concat_dim, values=[branch_0, branch_1, branch_2])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 14 x 14 x 576
+      end_point = 'Mixed_4b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(64), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(
+              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(96), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 14 x 14 x 576
+      end_point = 'Mixed_4c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(96), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(128), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(96), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 14 x 14 x 576
+      end_point = 'Mixed_4d'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(160), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(96), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 14 x 14 x 576
+      end_point = 'Mixed_4e'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(192), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(160), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(96), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 14 x 14 x 576
+      end_point = 'Mixed_5a'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(
+              net, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2,
+                                 scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(192), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(256), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2,
+                                 scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
+                                     scope='MaxPool_1a_3x3')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 7 x 7 x 1024
+      end_point = 'Mixed_5b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(192), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(160), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+      # 7 x 7 x 1024
+      end_point = 'Mixed_5c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(
+              net, depth(192), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(
+              net, depth(192), [1, 1],
+              weights_initializer=trunc_normal(0.09),
+              scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(128), [1, 1],
+              weights_initializer=trunc_normal(0.1),
+              scope='Conv2d_0b_1x1')
+        net = tf.concat(
+            axis=concat_dim, values=[branch_0, branch_1, branch_2, branch_3])
+        end_points[end_point] = net
+        if end_point == final_endpoint: return net, end_points
+    raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v2(inputs,
+                 num_classes=1000,
+                 is_training=True,
+                 dropout_keep_prob=0.8,
+                 min_depth=16,
+                 depth_multiplier=1.0,
+                 prediction_fn=slim.softmax,
+                 spatial_squeeze=True,
+                 reuse=None,
+                 scope='InceptionV2',
+                 global_pool=False):
+  """Inception v2 model for classification.
+
+  Constructs an Inception v2 network for classification as described in
+  http://arxiv.org/abs/1502.03167.
+
+  The default image size used to train this network is 224x224.
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is of
+        shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    global_pool: Optional boolean flag to control the avgpooling before the
+      logits layer. If false or unset, pooling is done with a fixed window
+      that reduces default-sized inputs to 1x1, while larger inputs lead to
+      larger outputs. If true, any input size is pooled down to 1x1.
+
+  Returns:
+    net: a Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the non-dropped-out input to the logits layer
+      if num_classes is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+                or depth_multiplier <= 0
+  """
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+
+  # Final pooling and prediction
+  with tf.variable_scope(scope, 'InceptionV2', [inputs], reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+      net, end_points = inception_v2_base(
+          inputs, scope=scope, min_depth=min_depth,
+          depth_multiplier=depth_multiplier)
+      with tf.variable_scope('Logits'):
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+          end_points['global_pool'] = net
+        else:
+          # Pooling with a fixed kernel size.
+          kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
+          net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+                                scope='AvgPool_1a_{}x{}'.format(*kernel_size))
+          end_points['AvgPool_1a'] = net
+        if not num_classes:
+          return net, end_points
+        # 1 x 1 x 1024
+        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
+        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                             normalizer_fn=None, scope='Conv2d_1c_1x1')
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+      end_points['Logits'] = logits
+      end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+inception_v2.default_image_size = 224
+
+
+def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
+  """Define kernel size which is automatically reduced for small input.
+
+  If the shape of the input images is unknown at graph construction time this
+  function assumes that the input images are is large enough.
+
+  Args:
+    input_tensor: input tensor of size [batch_size, height, width, channels].
+    kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
+
+  Returns:
+    a tensor with the kernel size.
+
+  TODO(jrru): Make this function work with unknown shapes. Theoretically, this
+  can be done with the code below. Problems are two-fold: (1) If the shape was
+  known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot
+  handle tensors that define the kernel size.
+      shape = tf.shape(input_tensor)
+      return = tf.stack([tf.minimum(shape[1], kernel_size[0]),
+                         tf.minimum(shape[2], kernel_size[1])])
+
+  """
+  shape = input_tensor.get_shape().as_list()
+  if shape[1] is None or shape[2] is None:
+    kernel_size_out = kernel_size
+  else:
+    kernel_size_out = [min(shape[1], kernel_size[0]),
+                       min(shape[2], kernel_size[1])]
+  return kernel_size_out
+
+
+inception_v2_arg_scope = inception_utils.inception_arg_scope
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2_test.py
new file mode 100644
index 00000000..0bf03fd3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v2_test.py
@@ -0,0 +1,379 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for nets.inception_v2."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import inception
+
+slim = tf.contrib.slim
+
+
+class InceptionV2Test(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v2(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith(
+        'InceptionV2/Logits/SpatialSqueeze'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_v2(inputs, num_classes)
+    self.assertTrue(net.op.name.startswith('InceptionV2/Logits/AvgPool'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
+    self.assertFalse('Logits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    mixed_5c, end_points = inception.inception_v2_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 7, 7, 1024])
+    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
+                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
+                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
+                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
+                          'MaxPool_3a_3x3']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    height, width = 224, 224
+    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+                 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+                 'Mixed_5a', 'Mixed_5b', 'Mixed_5c']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = inception.inception_v2_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV2/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+
+  def testBuildAndCheckAllEndPointsUptoMixed5c(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v2_base(inputs,
+                                                final_endpoint='Mixed_5c')
+    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
+                        'Mixed_3c': [batch_size, 28, 28, 320],
+                        'Mixed_4a': [batch_size, 14, 14, 576],
+                        'Mixed_4b': [batch_size, 14, 14, 576],
+                        'Mixed_4c': [batch_size, 14, 14, 576],
+                        'Mixed_4d': [batch_size, 14, 14, 576],
+                        'Mixed_4e': [batch_size, 14, 14, 576],
+                        'Mixed_5a': [batch_size, 7, 7, 1024],
+                        'Mixed_5b': [batch_size, 7, 7, 1024],
+                        'Mixed_5c': [batch_size, 7, 7, 1024],
+                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
+                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
+                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
+                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
+                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testModelHasExpectedNumberOfParameters(self):
+    batch_size = 5
+    height, width = 224, 224
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope(inception.inception_v2_arg_scope()):
+      inception.inception_v2_base(inputs)
+    total_params, _ = slim.model_analyzer.analyze_vars(
+        slim.get_model_variables())
+    self.assertAlmostEqual(10173112, total_params)
+
+  def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v2(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys()
+                     if key.startswith('Mixed') or key.startswith('Conv')]
+
+    _, end_points_with_multiplier = inception.inception_v2(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=0.5)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(0.5 * original_depth, new_depth)
+
+  def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v2(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys()
+                     if key.startswith('Mixed') or key.startswith('Conv')]
+
+    _, end_points_with_multiplier = inception.inception_v2(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=2.0)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(2.0 * original_depth, new_depth)
+
+  def testRaiseValueErrorWithInvalidDepthMultiplier(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1)
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0)
+
+  def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v2_base(inputs)
+
+    endpoint_keys = [
+        key for key in end_points.keys()
+        if key.startswith('Mixed') or key.startswith('Conv')
+    ]
+
+    _, end_points_with_replacement = inception.inception_v2_base(
+        inputs, use_separable_conv=False)
+
+    # The endpoint shapes must be equal to the original shape even when the
+    # separable convolution is replaced with a normal convolution.
+    for key in endpoint_keys:
+      original_shape = end_points[key].get_shape().as_list()
+      self.assertTrue(key in end_points_with_replacement)
+      new_shape = end_points_with_replacement[key].get_shape().as_list()
+      self.assertListEqual(original_shape, new_shape)
+
+  def testBuildEndPointsNCHWDataFormat(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v2_base(inputs)
+
+    endpoint_keys = [
+        key for key in end_points.keys()
+        if key.startswith('Mixed') or key.startswith('Conv')
+    ]
+
+    inputs_in_nchw = tf.random_uniform((batch_size, 3, height, width))
+    _, end_points_with_replacement = inception.inception_v2_base(
+        inputs_in_nchw, use_separable_conv=False, data_format='NCHW')
+
+    # With the 'NCHW' data format, all endpoint activations have a transposed
+    # shape from the original shape with the 'NHWC' layout.
+    for key in endpoint_keys:
+      transposed_original_shape = tf.transpose(
+          end_points[key], [0, 3, 1, 2]).get_shape().as_list()
+      self.assertTrue(key in end_points_with_replacement)
+      new_shape = end_points_with_replacement[key].get_shape().as_list()
+      self.assertListEqual(transposed_original_shape, new_shape)
+
+  def testBuildErrorsForDataFormats(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+
+    # 'NCWH' data format is not supported.
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v2_base(inputs, data_format='NCWH')
+
+    # 'NCHW' data format is not supported for separable convolution.
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v2_base(inputs, data_format='NCHW')
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 112, 112
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v2(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    pre_pool = end_points['Mixed_5c']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 4, 4, 1024])
+
+  def testUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v2(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_5c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
+
+  def testGlobalPoolUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 1
+    height, width = 250, 300
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v2(inputs, num_classes,
+                                                  global_pool=True)
+      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_5c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
+
+  def testUnknowBatchSize(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+    logits, _ = inception.inception_v2(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, num_classes])
+    images = tf.random_uniform((batch_size, height, width, 3))
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, _ = inception.inception_v2(eval_inputs, num_classes,
+                                       is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 150, 150
+    num_classes = 1000
+
+    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+    inception.inception_v2(train_inputs, num_classes)
+    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+    logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testLogitsNotSqueezed(self):
+    num_classes = 25
+    images = tf.random_uniform([1, 224, 224, 3])
+    logits, _ = inception.inception_v2(images,
+                                       num_classes=num_classes,
+                                       spatial_squeeze=False)
+
+    with self.test_session() as sess:
+      tf.global_variables_initializer().run()
+      logits_out = sess.run(logits)
+      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
+
+  def testNoBatchNormScaleByDefault(self):
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(inception.inception_v2_arg_scope()):
+      inception.inception_v2(inputs, num_classes, is_training=False)
+
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
+
+  def testBatchNormScale(self):
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(
+        inception.inception_v2_arg_scope(batch_norm_scale=True)):
+      inception.inception_v2(inputs, num_classes, is_training=False)
+
+    gamma_names = set(
+        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
+    self.assertGreater(len(gamma_names), 0)
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
+      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3.py
new file mode 100644
index 00000000..12217791
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3.py
@@ -0,0 +1,579 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for inception v3 classification network."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def inception_v3_base(inputs,
+                      final_endpoint='Mixed_7c',
+                      min_depth=16,
+                      depth_multiplier=1.0,
+                      scope=None):
+  """Inception model from http://arxiv.org/abs/1512.00567.
+
+  Constructs an Inception v3 network from inputs to the given final endpoint.
+  This method can construct the network up to the final inception block
+  Mixed_7c.
+
+  Note that the names of the layers in the paper do not correspond to the names
+  of the endpoints registered by this function although they build the same
+  network.
+
+  Here is a mapping from the old_names to the new names:
+  Old name          | New name
+  =======================================
+  conv0             | Conv2d_1a_3x3
+  conv1             | Conv2d_2a_3x3
+  conv2             | Conv2d_2b_3x3
+  pool1             | MaxPool_3a_3x3
+  conv3             | Conv2d_3b_1x1
+  conv4             | Conv2d_4a_3x3
+  pool2             | MaxPool_5a_3x3
+  mixed_35x35x256a  | Mixed_5b
+  mixed_35x35x288a  | Mixed_5c
+  mixed_35x35x288b  | Mixed_5d
+  mixed_17x17x768a  | Mixed_6a
+  mixed_17x17x768b  | Mixed_6b
+  mixed_17x17x768c  | Mixed_6c
+  mixed_17x17x768d  | Mixed_6d
+  mixed_17x17x768e  | Mixed_6e
+  mixed_8x8x1280a   | Mixed_7a
+  mixed_8x8x2048a   | Mixed_7b
+  mixed_8x8x2048b   | Mixed_7c
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    final_endpoint: specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
+      'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
+      'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    scope: Optional variable_scope.
+
+  Returns:
+    tensor_out: output tensor corresponding to the final_endpoint.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+                or depth_multiplier <= 0
+  """
+  # end_points will collect relevant activations for external use, for example
+  # summaries or losses.
+  end_points = {}
+
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+  depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+  with tf.variable_scope(scope, 'InceptionV3', [inputs]):
+    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                        stride=1, padding='VALID'):
+      # 299 x 299 x 3
+      end_point = 'Conv2d_1a_3x3'
+      net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 149 x 149 x 32
+      end_point = 'Conv2d_2a_3x3'
+      net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 147 x 147 x 32
+      end_point = 'Conv2d_2b_3x3'
+      net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 147 x 147 x 64
+      end_point = 'MaxPool_3a_3x3'
+      net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 73 x 73 x 64
+      end_point = 'Conv2d_3b_1x1'
+      net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 73 x 73 x 80.
+      end_point = 'Conv2d_4a_3x3'
+      net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 71 x 71 x 192.
+      end_point = 'MaxPool_5a_3x3'
+      net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # 35 x 35 x 192.
+
+    # Inception blocks
+    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                        stride=1, padding='SAME'):
+      # mixed: 35 x 35 x 256.
+      end_point = 'Mixed_5b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+                                 scope='Conv2d_0b_5x5')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_1: 35 x 35 x 288.
+      end_point = 'Mixed_5c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+                                 scope='Conv_1_0c_5x5')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(64), [1, 1],
+                                 scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_2: 35 x 35 x 288.
+      end_point = 'Mixed_5d'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+                                 scope='Conv2d_0b_5x5')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+                                 scope='Conv2d_0c_3x3')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_3: 17 x 17 x 768.
+      end_point = 'Mixed_6a'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,
+                                 padding='VALID', scope='Conv2d_1a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
+                                 scope='Conv2d_0b_3x3')
+          branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2,
+                                 padding='VALID', scope='Conv2d_1a_1x1')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+                                     scope='MaxPool_1a_3x3')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed4: 17 x 17 x 768.
+      end_point = 'Mixed_6b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],
+                                 scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+                                 scope='Conv2d_0c_7x1')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
+                                 scope='Conv2d_0b_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],
+                                 scope='Conv2d_0c_1x7')
+          branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
+                                 scope='Conv2d_0d_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+                                 scope='Conv2d_0e_1x7')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_5: 17 x 17 x 768.
+      end_point = 'Mixed_6c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
+                                 scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+                                 scope='Conv2d_0c_7x1')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+                                 scope='Conv2d_0b_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
+                                 scope='Conv2d_0c_1x7')
+          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+                                 scope='Conv2d_0d_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+                                 scope='Conv2d_0e_1x7')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # mixed_6: 17 x 17 x 768.
+      end_point = 'Mixed_6d'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
+                                 scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+                                 scope='Conv2d_0c_7x1')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+                                 scope='Conv2d_0b_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
+                                 scope='Conv2d_0c_1x7')
+          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+                                 scope='Conv2d_0d_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+                                 scope='Conv2d_0e_1x7')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_7: 17 x 17 x 768.
+      end_point = 'Mixed_6e'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
+                                 scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+                                 scope='Conv2d_0c_7x1')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
+                                 scope='Conv2d_0b_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+                                 scope='Conv2d_0c_1x7')
+          branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
+                                 scope='Conv2d_0d_7x1')
+          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+                                 scope='Conv2d_0e_1x7')
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+                                 scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_8: 8 x 8 x 1280.
+      end_point = 'Mixed_7a'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+          branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2,
+                                 padding='VALID', scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
+                                 scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+                                 scope='Conv2d_0c_7x1')
+          branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2,
+                                 padding='VALID', scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+                                     scope='MaxPool_1a_3x3')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+      # mixed_9: 8 x 8 x 2048.
+      end_point = 'Mixed_7b'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = tf.concat(axis=3, values=[
+              slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
+              slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(
+              branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
+          branch_2 = tf.concat(axis=3, values=[
+              slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
+              slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+
+      # mixed_10: 8 x 8 x 2048.
+      end_point = 'Mixed_7c'
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = tf.concat(axis=3, values=[
+              slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
+              slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
+        with tf.variable_scope('Branch_2'):
+          branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
+          branch_2 = slim.conv2d(
+              branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
+          branch_2 = tf.concat(axis=3, values=[
+              slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
+              slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+        with tf.variable_scope('Branch_3'):
+          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+          branch_3 = slim.conv2d(
+              branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
+        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+      end_points[end_point] = net
+      if end_point == final_endpoint: return net, end_points
+    raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v3(inputs,
+                 num_classes=1000,
+                 is_training=True,
+                 dropout_keep_prob=0.8,
+                 min_depth=16,
+                 depth_multiplier=1.0,
+                 prediction_fn=slim.softmax,
+                 spatial_squeeze=True,
+                 reuse=None,
+                 create_aux_logits=True,
+                 scope='InceptionV3',
+                 global_pool=False):
+  """Inception model from http://arxiv.org/abs/1512.00567.
+
+  "Rethinking the Inception Architecture for Computer Vision"
+
+  Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
+  Zbigniew Wojna.
+
+  With the default arguments this method constructs the exact model defined in
+  the paper. However, one can experiment with variations of the inception_v3
+  network by changing arguments dropout_keep_prob, min_depth and
+  depth_multiplier.
+
+  The default image size used to train this network is 299x299.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is of
+        shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    create_aux_logits: Whether to create the auxiliary logits.
+    scope: Optional variable_scope.
+    global_pool: Optional boolean flag to control the avgpooling before the
+      logits layer. If false or unset, pooling is done with a fixed window
+      that reduces default-sized inputs to 1x1, while larger inputs lead to
+      larger outputs. If true, any input size is pooled down to 1x1.
+
+  Returns:
+    net: a Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the non-dropped-out input to the logits layer
+      if num_classes is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+
+  Raises:
+    ValueError: if 'depth_multiplier' is less than or equal to zero.
+  """
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+  depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+  with tf.variable_scope(scope, 'InceptionV3', [inputs], reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+      net, end_points = inception_v3_base(
+          inputs, scope=scope, min_depth=min_depth,
+          depth_multiplier=depth_multiplier)
+
+      # Auxiliary Head logits
+      if create_aux_logits and num_classes:
+        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                            stride=1, padding='SAME'):
+          aux_logits = end_points['Mixed_6e']
+          with tf.variable_scope('AuxLogits'):
+            aux_logits = slim.avg_pool2d(
+                aux_logits, [5, 5], stride=3, padding='VALID',
+                scope='AvgPool_1a_5x5')
+            aux_logits = slim.conv2d(aux_logits, depth(128), [1, 1],
+                                     scope='Conv2d_1b_1x1')
+
+            # Shape of feature map before the final layer.
+            kernel_size = _reduced_kernel_size_for_small_input(
+                aux_logits, [5, 5])
+            aux_logits = slim.conv2d(
+                aux_logits, depth(768), kernel_size,
+                weights_initializer=trunc_normal(0.01),
+                padding='VALID', scope='Conv2d_2a_{}x{}'.format(*kernel_size))
+            aux_logits = slim.conv2d(
+                aux_logits, num_classes, [1, 1], activation_fn=None,
+                normalizer_fn=None, weights_initializer=trunc_normal(0.001),
+                scope='Conv2d_2b_1x1')
+            if spatial_squeeze:
+              aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
+            end_points['AuxLogits'] = aux_logits
+
+      # Final pooling and prediction
+      with tf.variable_scope('Logits'):
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='GlobalPool')
+          end_points['global_pool'] = net
+        else:
+          # Pooling with a fixed kernel size.
+          kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
+          net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+                                scope='AvgPool_1a_{}x{}'.format(*kernel_size))
+          end_points['AvgPool_1a'] = net
+        if not num_classes:
+          return net, end_points
+        # 1 x 1 x 2048
+        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
+        end_points['PreLogits'] = net
+        # 2048
+        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                             normalizer_fn=None, scope='Conv2d_1c_1x1')
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+        # 1000
+      end_points['Logits'] = logits
+      end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+inception_v3.default_image_size = 299
+
+
+def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
+  """Define kernel size which is automatically reduced for small input.
+
+  If the shape of the input images is unknown at graph construction time this
+  function assumes that the input images are is large enough.
+
+  Args:
+    input_tensor: input tensor of size [batch_size, height, width, channels].
+    kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
+
+  Returns:
+    a tensor with the kernel size.
+
+  TODO(jrru): Make this function work with unknown shapes. Theoretically, this
+  can be done with the code below. Problems are two-fold: (1) If the shape was
+  known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot
+  handle tensors that define the kernel size.
+      shape = tf.shape(input_tensor)
+      return = tf.stack([tf.minimum(shape[1], kernel_size[0]),
+                         tf.minimum(shape[2], kernel_size[1])])
+
+  """
+  shape = input_tensor.get_shape().as_list()
+  if shape[1] is None or shape[2] is None:
+    kernel_size_out = kernel_size
+  else:
+    kernel_size_out = [min(shape[1], kernel_size[0]),
+                       min(shape[2], kernel_size[1])]
+  return kernel_size_out
+
+
+inception_v3_arg_scope = inception_utils.inception_arg_scope
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3_test.py
new file mode 100644
index 00000000..3d81aab3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v3_test.py
@@ -0,0 +1,346 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for nets.inception_v1."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import inception
+
+slim = tf.contrib.slim
+
+
+class InceptionV3Test(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v3(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith(
+        'InceptionV3/Logits/SpatialSqueeze'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = None
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_v3(inputs, num_classes)
+    self.assertTrue(net.op.name.startswith('InceptionV3/Logits/AvgPool'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 2048])
+    self.assertFalse('Logits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    final_endpoint, end_points = inception.inception_v3_base(inputs)
+    self.assertTrue(final_endpoint.op.name.startswith(
+        'InceptionV3/Mixed_7c'))
+    self.assertListEqual(final_endpoint.get_shape().as_list(),
+                         [batch_size, 8, 8, 2048])
+    expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+                          'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
+                          'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+                          'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
+                          'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    height, width = 299, 299
+    endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+                 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
+                 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+                 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
+                 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
+
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = inception.inception_v3_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV3/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+
+  def testBuildAndCheckAllEndPointsUptoMixed7c(self):
+    batch_size = 5
+    height, width = 299, 299
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v3_base(
+        inputs, final_endpoint='Mixed_7c')
+    endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
+                        'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
+                        'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
+                        'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
+                        'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
+                        'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
+                        'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
+                        'Mixed_5b': [batch_size, 35, 35, 256],
+                        'Mixed_5c': [batch_size, 35, 35, 288],
+                        'Mixed_5d': [batch_size, 35, 35, 288],
+                        'Mixed_6a': [batch_size, 17, 17, 768],
+                        'Mixed_6b': [batch_size, 17, 17, 768],
+                        'Mixed_6c': [batch_size, 17, 17, 768],
+                        'Mixed_6d': [batch_size, 17, 17, 768],
+                        'Mixed_6e': [batch_size, 17, 17, 768],
+                        'Mixed_7a': [batch_size, 8, 8, 1280],
+                        'Mixed_7b': [batch_size, 8, 8, 2048],
+                        'Mixed_7c': [batch_size, 8, 8, 2048]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testModelHasExpectedNumberOfParameters(self):
+    batch_size = 5
+    height, width = 299, 299
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope(inception.inception_v3_arg_scope()):
+      inception.inception_v3_base(inputs)
+    total_params, _ = slim.model_analyzer.analyze_vars(
+        slim.get_model_variables())
+    self.assertAlmostEqual(21802784, total_params)
+
+  def testBuildEndPoints(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v3(inputs, num_classes)
+    self.assertTrue('Logits' in end_points)
+    logits = end_points['Logits']
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('AuxLogits' in end_points)
+    aux_logits = end_points['AuxLogits']
+    self.assertListEqual(aux_logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Mixed_7c' in end_points)
+    pre_pool = end_points['Mixed_7c']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 8, 8, 2048])
+    self.assertTrue('PreLogits' in end_points)
+    pre_logits = end_points['PreLogits']
+    self.assertListEqual(pre_logits.get_shape().as_list(),
+                         [batch_size, 1, 1, 2048])
+
+  def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v3(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys()
+                     if key.startswith('Mixed') or key.startswith('Conv')]
+
+    _, end_points_with_multiplier = inception.inception_v3(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=0.5)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(0.5 * original_depth, new_depth)
+
+  def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v3(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys()
+                     if key.startswith('Mixed') or key.startswith('Conv')]
+
+    _, end_points_with_multiplier = inception.inception_v3(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=2.0)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(2.0 * original_depth, new_depth)
+
+  def testRaiseValueErrorWithInvalidDepthMultiplier(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
+    with self.assertRaises(ValueError):
+      _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0)
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 150, 150
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v3(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    pre_pool = end_points['Mixed_7c']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 3, 3, 2048])
+
+  def testUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 2
+    height, width = 299, 299
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v3(inputs, num_classes)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_7c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
+
+  def testGlobalPoolUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 1
+    height, width = 330, 400
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = inception.inception_v3(inputs, num_classes,
+                                                  global_pool=True)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_7c']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 11, 2048])
+
+  def testUnknowBatchSize(self):
+    batch_size = 1
+    height, width = 299, 299
+    num_classes = 1000
+
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+    logits, _ = inception.inception_v3(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, num_classes])
+    images = tf.random_uniform((batch_size, height, width, 3))
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 299, 299
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, _ = inception.inception_v3(eval_inputs, num_classes,
+                                       is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 150, 150
+    num_classes = 1000
+
+    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+    inception.inception_v3(train_inputs, num_classes)
+    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+    logits, _ = inception.inception_v3(eval_inputs, num_classes,
+                                       is_training=False, reuse=True)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testLogitsNotSqueezed(self):
+    num_classes = 25
+    images = tf.random_uniform([1, 299, 299, 3])
+    logits, _ = inception.inception_v3(images,
+                                       num_classes=num_classes,
+                                       spatial_squeeze=False)
+
+    with self.test_session() as sess:
+      tf.global_variables_initializer().run()
+      logits_out = sess.run(logits)
+      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
+
+  def testNoBatchNormScaleByDefault(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(inception.inception_v3_arg_scope()):
+      inception.inception_v3(inputs, num_classes, is_training=False)
+
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
+
+  def testBatchNormScale(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(
+        inception.inception_v3_arg_scope(batch_norm_scale=True)):
+      inception.inception_v3(inputs, num_classes, is_training=False)
+
+    gamma_names = set(
+        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
+    self.assertGreater(len(gamma_names), 0)
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
+      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4.py
new file mode 100644
index 00000000..bab406a8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4.py
@@ -0,0 +1,337 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition of the Inception V4 architecture.
+
+As described in http://arxiv.org/abs/1602.07261.
+
+  Inception-v4, Inception-ResNet and the Impact of Residual Connections
+    on Learning
+  Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+
+
+def block_inception_a(inputs, scope=None, reuse=None):
+  """Builds Inception-A block for Inception v4 network."""
+  # By default use stride=1 and SAME padding
+  with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+                      stride=1, padding='SAME'):
+    with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
+      with tf.variable_scope('Branch_0'):
+        branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
+      with tf.variable_scope('Branch_1'):
+        branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
+        branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
+      with tf.variable_scope('Branch_2'):
+        branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
+        branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
+        branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
+      with tf.variable_scope('Branch_3'):
+        branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+        branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def block_reduction_a(inputs, scope=None, reuse=None):
+  """Builds Reduction-A block for Inception v4 network."""
+  # By default use stride=1 and SAME padding
+  with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+                      stride=1, padding='SAME'):
+    with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
+      with tf.variable_scope('Branch_0'):
+        branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID',
+                               scope='Conv2d_1a_3x3')
+      with tf.variable_scope('Branch_1'):
+        branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+        branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
+        branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
+                               padding='VALID', scope='Conv2d_1a_3x3')
+      with tf.variable_scope('Branch_2'):
+        branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
+                                   scope='MaxPool_1a_3x3')
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_b(inputs, scope=None, reuse=None):
+  """Builds Inception-B block for Inception v4 network."""
+  # By default use stride=1 and SAME padding
+  with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+                      stride=1, padding='SAME'):
+    with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
+      with tf.variable_scope('Branch_0'):
+        branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+      with tf.variable_scope('Branch_1'):
+        branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+        branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
+        branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
+      with tf.variable_scope('Branch_2'):
+        branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+        branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
+        branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
+        branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
+        branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
+      with tf.variable_scope('Branch_3'):
+        branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+        branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def block_reduction_b(inputs, scope=None, reuse=None):
+  """Builds Reduction-B block for Inception v4 network."""
+  # By default use stride=1 and SAME padding
+  with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+                      stride=1, padding='SAME'):
+    with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
+      with tf.variable_scope('Branch_0'):
+        branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+        branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
+                               padding='VALID', scope='Conv2d_1a_3x3')
+      with tf.variable_scope('Branch_1'):
+        branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
+        branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
+        branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
+        branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
+                               padding='VALID', scope='Conv2d_1a_3x3')
+      with tf.variable_scope('Branch_2'):
+        branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
+                                   scope='MaxPool_1a_3x3')
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_c(inputs, scope=None, reuse=None):
+  """Builds Inception-C block for Inception v4 network."""
+  # By default use stride=1 and SAME padding
+  with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+                      stride=1, padding='SAME'):
+    with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
+      with tf.variable_scope('Branch_0'):
+        branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
+      with tf.variable_scope('Branch_1'):
+        branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+        branch_1 = tf.concat(axis=3, values=[
+            slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
+            slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
+      with tf.variable_scope('Branch_2'):
+        branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+        branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
+        branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
+        branch_2 = tf.concat(axis=3, values=[
+            slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
+            slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
+      with tf.variable_scope('Branch_3'):
+        branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+        branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
+  """Creates the Inception V4 network up to the given final endpoint.
+
+  Args:
+    inputs: a 4-D tensor of size [batch_size, height, width, 3].
+    final_endpoint: specifies the endpoint to construct the network up to.
+      It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+      'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+      'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
+      'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
+      'Mixed_7d']
+    scope: Optional variable_scope.
+
+  Returns:
+    logits: the logits outputs of the model.
+    end_points: the set of end_points from the inception model.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+  """
+  end_points = {}
+
+  def add_and_check_final(name, net):
+    end_points[name] = net
+    return name == final_endpoint
+
+  with tf.variable_scope(scope, 'InceptionV4', [inputs]):
+    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                        stride=1, padding='SAME'):
+      # 299 x 299 x 3
+      net = slim.conv2d(inputs, 32, [3, 3], stride=2,
+                        padding='VALID', scope='Conv2d_1a_3x3')
+      if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
+      # 149 x 149 x 32
+      net = slim.conv2d(net, 32, [3, 3], padding='VALID',
+                        scope='Conv2d_2a_3x3')
+      if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
+      # 147 x 147 x 32
+      net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
+      if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
+      # 147 x 147 x 64
+      with tf.variable_scope('Mixed_3a'):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+                                     scope='MaxPool_0a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
+                                 scope='Conv2d_0a_3x3')
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
+        if add_and_check_final('Mixed_3a', net): return net, end_points
+
+      # 73 x 73 x 160
+      with tf.variable_scope('Mixed_4a'):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+          branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
+                                 scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+          branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
+          branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
+          branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
+                                 scope='Conv2d_1a_3x3')
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
+        if add_and_check_final('Mixed_4a', net): return net, end_points
+
+      # 71 x 71 x 192
+      with tf.variable_scope('Mixed_5a'):
+        with tf.variable_scope('Branch_0'):
+          branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID',
+                                 scope='Conv2d_1a_3x3')
+        with tf.variable_scope('Branch_1'):
+          branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+                                     scope='MaxPool_1a_3x3')
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
+        if add_and_check_final('Mixed_5a', net): return net, end_points
+
+      # 35 x 35 x 384
+      # 4 x Inception-A blocks
+      for idx in range(4):
+        block_scope = 'Mixed_5' + chr(ord('b') + idx)
+        net = block_inception_a(net, block_scope)
+        if add_and_check_final(block_scope, net): return net, end_points
+
+      # 35 x 35 x 384
+      # Reduction-A block
+      net = block_reduction_a(net, 'Mixed_6a')
+      if add_and_check_final('Mixed_6a', net): return net, end_points
+
+      # 17 x 17 x 1024
+      # 7 x Inception-B blocks
+      for idx in range(7):
+        block_scope = 'Mixed_6' + chr(ord('b') + idx)
+        net = block_inception_b(net, block_scope)
+        if add_and_check_final(block_scope, net): return net, end_points
+
+      # 17 x 17 x 1024
+      # Reduction-B block
+      net = block_reduction_b(net, 'Mixed_7a')
+      if add_and_check_final('Mixed_7a', net): return net, end_points
+
+      # 8 x 8 x 1536
+      # 3 x Inception-C blocks
+      for idx in range(3):
+        block_scope = 'Mixed_7' + chr(ord('b') + idx)
+        net = block_inception_c(net, block_scope)
+        if add_and_check_final(block_scope, net): return net, end_points
+  raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v4(inputs, num_classes=1001, is_training=True,
+                 dropout_keep_prob=0.8,
+                 reuse=None,
+                 scope='InceptionV4',
+                 create_aux_logits=True):
+  """Creates the Inception V4 model.
+
+  Args:
+    inputs: a 4-D tensor of size [batch_size, height, width, 3].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    is_training: whether is training or not.
+    dropout_keep_prob: float, the fraction to keep before final layer.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    create_aux_logits: Whether to include the auxiliary logits.
+
+  Returns:
+    net: a Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the non-dropped input to the logits layer
+      if num_classes is 0 or None.
+    end_points: the set of end_points from the inception model.
+  """
+  end_points = {}
+  with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+      net, end_points = inception_v4_base(inputs, scope=scope)
+
+      with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                          stride=1, padding='SAME'):
+        # Auxiliary Head logits
+        if create_aux_logits and num_classes:
+          with tf.variable_scope('AuxLogits'):
+            # 17 x 17 x 1024
+            aux_logits = end_points['Mixed_6h']
+            aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
+                                         padding='VALID',
+                                         scope='AvgPool_1a_5x5')
+            aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
+                                     scope='Conv2d_1b_1x1')
+            aux_logits = slim.conv2d(aux_logits, 768,
+                                     aux_logits.get_shape()[1:3],
+                                     padding='VALID', scope='Conv2d_2a')
+            aux_logits = slim.flatten(aux_logits)
+            aux_logits = slim.fully_connected(aux_logits, num_classes,
+                                              activation_fn=None,
+                                              scope='Aux_logits')
+            end_points['AuxLogits'] = aux_logits
+
+        # Final pooling and prediction
+        # TODO(sguada,arnoegw): Consider adding a parameter global_pool which
+        # can be set to False to disable pooling here (as in resnet_*()).
+        with tf.variable_scope('Logits'):
+          # 8 x 8 x 1536
+          kernel_size = net.get_shape()[1:3]
+          if kernel_size.is_fully_defined():
+            net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+                                  scope='AvgPool_1a')
+          else:
+            net = tf.reduce_mean(net, [1, 2], keep_dims=True,
+                                 name='global_pool')
+          end_points['global_pool'] = net
+          if not num_classes:
+            return net, end_points
+          # 1 x 1 x 1536
+          net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
+          net = slim.flatten(net, scope='PreLogitsFlatten')
+          end_points['PreLogitsFlatten'] = net
+          # 1536
+          logits = slim.fully_connected(net, num_classes, activation_fn=None,
+                                        scope='Logits')
+          end_points['Logits'] = logits
+          end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
+    return logits, end_points
+inception_v4.default_image_size = 299
+
+
+inception_v4_arg_scope = inception_utils.inception_arg_scope
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4_test.py
new file mode 100644
index 00000000..224933f8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/inception_v4_test.py
@@ -0,0 +1,283 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.inception_v4."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception
+
+
+class InceptionTest(tf.test.TestCase):
+
+  def testBuildLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v4(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue(predictions.op.name.startswith(
+        'InceptionV4/Logits/Predictions'))
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_v4(inputs, num_classes)
+    self.assertTrue(net.op.name.startswith('InceptionV4/Logits/AvgPool'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1536])
+    self.assertFalse('Logits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+
+  def testBuildWithoutAuxLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, endpoints = inception.inception_v4(inputs, num_classes,
+                                               create_aux_logits=False)
+    self.assertFalse('AuxLogits' in endpoints)
+    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testAllEndPointsShapes(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = inception.inception_v4(inputs, num_classes)
+    endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
+                        'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
+                        'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
+                        'Mixed_3a': [batch_size, 73, 73, 160],
+                        'Mixed_4a': [batch_size, 71, 71, 192],
+                        'Mixed_5a': [batch_size, 35, 35, 384],
+                        # 4 x Inception-A blocks
+                        'Mixed_5b': [batch_size, 35, 35, 384],
+                        'Mixed_5c': [batch_size, 35, 35, 384],
+                        'Mixed_5d': [batch_size, 35, 35, 384],
+                        'Mixed_5e': [batch_size, 35, 35, 384],
+                        # Reduction-A block
+                        'Mixed_6a': [batch_size, 17, 17, 1024],
+                        # 7 x Inception-B blocks
+                        'Mixed_6b': [batch_size, 17, 17, 1024],
+                        'Mixed_6c': [batch_size, 17, 17, 1024],
+                        'Mixed_6d': [batch_size, 17, 17, 1024],
+                        'Mixed_6e': [batch_size, 17, 17, 1024],
+                        'Mixed_6f': [batch_size, 17, 17, 1024],
+                        'Mixed_6g': [batch_size, 17, 17, 1024],
+                        'Mixed_6h': [batch_size, 17, 17, 1024],
+                        # Reduction-A block
+                        'Mixed_7a': [batch_size, 8, 8, 1536],
+                        # 3 x Inception-C blocks
+                        'Mixed_7b': [batch_size, 8, 8, 1536],
+                        'Mixed_7c': [batch_size, 8, 8, 1536],
+                        'Mixed_7d': [batch_size, 8, 8, 1536],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'global_pool': [batch_size, 1, 1, 1536],
+                        'PreLogitsFlatten': [batch_size, 1536],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 299, 299
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = inception.inception_v4_base(inputs)
+    self.assertTrue(net.op.name.startswith(
+        'InceptionV4/Mixed_7d'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
+    expected_endpoints = [
+        'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
+        'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
+        'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
+        'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+    for name, op in end_points.items():
+      self.assertTrue(op.name.startswith('InceptionV4/' + name))
+
+  def testBuildOnlyUpToFinalEndpoint(self):
+    batch_size = 5
+    height, width = 299, 299
+    all_endpoints = [
+        'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
+        'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
+        'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
+        'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
+    for index, endpoint in enumerate(all_endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = inception.inception_v4_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV4/' + endpoint))
+        self.assertItemsEqual(all_endpoints[:index+1], end_points.keys())
+
+  def testVariablesSetDevice(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    # Force all Variables to reside on the device.
+    with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
+      inception.inception_v4(inputs, num_classes)
+    with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
+      inception.inception_v4(inputs, num_classes)
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
+      self.assertDeviceEqual(v.device, '/cpu:0')
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
+      self.assertDeviceEqual(v.device, '/gpu:0')
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 150, 150
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v4(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    pre_pool = end_points['Mixed_7d']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 3, 3, 1536])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 350, 400
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = inception.inception_v4(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    pre_pool = end_points['Mixed_7d']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 9, 11, 1536])
+
+  def testGlobalPoolUnknownImageShape(self):
+    batch_size = 1
+    height, width = 350, 400
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
+      logits, end_points = inception.inception_v4(
+          inputs, num_classes, create_aux_logits=False)
+      self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Mixed_7d']
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      logits_out, pre_pool_out = sess.run([logits, pre_pool],
+                                          {inputs: images.eval()})
+      self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
+      self.assertTupleEqual(pre_pool_out.shape, (batch_size, 9, 11, 1536))
+
+  def testUnknownBatchSize(self):
+    batch_size = 1
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+      logits, _ = inception.inception_v4(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [None, num_classes])
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session() as sess:
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = inception.inception_v4(eval_inputs,
+                                         num_classes,
+                                         is_training=False)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 150, 150
+    num_classes = 1000
+    with self.test_session() as sess:
+      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+      inception.inception_v4(train_inputs, num_classes)
+      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+      logits, _ = inception.inception_v4(eval_inputs,
+                                         num_classes,
+                                         is_training=False,
+                                         reuse=True)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testNoBatchNormScaleByDefault(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with tf.contrib.slim.arg_scope(inception.inception_v4_arg_scope()):
+      inception.inception_v4(inputs, num_classes, is_training=False)
+
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
+
+  def testBatchNormScale(self):
+    height, width = 299, 299
+    num_classes = 1000
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with tf.contrib.slim.arg_scope(
+        inception.inception_v4_arg_scope(batch_norm_scale=True)):
+      inception.inception_v4(inputs, num_classes, is_training=False)
+
+    gamma_names = set(
+        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
+    self.assertGreater(len(gamma_names), 0)
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
+      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/lenet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/lenet.py
new file mode 100644
index 00000000..c79dbfac
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/lenet.py
@@ -0,0 +1,97 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains a variant of the LeNet model definition."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def lenet(images, num_classes=10, is_training=False,
+          dropout_keep_prob=0.5,
+          prediction_fn=slim.softmax,
+          scope='LeNet'):
+  """Creates a variant of the LeNet model.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+
+        logits = lenet.lenet(images, is_training=False)
+        probabilities = tf.nn.softmax(logits)
+        predictions = tf.argmax(logits, 1)
+
+  Args:
+    images: A batch of `Tensors` of size [batch_size, height, width, channels].
+    num_classes: the number of classes in the dataset. If 0 or None, the logits
+      layer is omitted and the input features to the logits layer are returned
+      instead.
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    prediction_fn: a function to get predictions out of logits.
+    scope: Optional variable_scope.
+
+  Returns:
+     net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the inon-dropped-out nput to the logits layer
+      if num_classes is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+  """
+  end_points = {}
+
+  with tf.variable_scope(scope, 'LeNet', [images]):
+    net = end_points['conv1'] = slim.conv2d(images, 32, [5, 5], scope='conv1')
+    net = end_points['pool1'] = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
+    net = end_points['conv2'] = slim.conv2d(net, 64, [5, 5], scope='conv2')
+    net = end_points['pool2'] = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
+    net = slim.flatten(net)
+    end_points['Flatten'] = net
+
+    net = end_points['fc3'] = slim.fully_connected(net, 1024, scope='fc3')
+    if not num_classes:
+      return net, end_points
+    net = end_points['dropout3'] = slim.dropout(
+        net, dropout_keep_prob, is_training=is_training, scope='dropout3')
+    logits = end_points['Logits'] = slim.fully_connected(
+        net, num_classes, activation_fn=None, scope='fc4')
+
+  end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+
+  return logits, end_points
+lenet.default_image_size = 28
+
+
+def lenet_arg_scope(weight_decay=0.0):
+  """Defines the default lenet argument scope.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+
+  Returns:
+    An `arg_scope` to use for the inception v3 model.
+  """
+  with slim.arg_scope(
+      [slim.conv2d, slim.fully_connected],
+      weights_regularizer=slim.l2_regularizer(weight_decay),
+      weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
+      activation_fn=tf.nn.relu) as sc:
+    return sc
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/README.md b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/README.md
new file mode 100644
index 00000000..e4699d6a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/README.md
@@ -0,0 +1,73 @@
+# MobileNetV2
+This folder contains building code for MobileNetV2, based on
+[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
+
+# Performance
+## Latency
+This is the timing of [MobileNetV1](../mobilenet_v1.md) vs MobileNetV2 using
+TF-Lite on the large core of Pixel 1 phone.
+
+![mnet_v1_vs_v2_pixel1_latency.png](mnet_v1_vs_v2_pixel1_latency.png)
+
+## MACs
+MACs, also sometimes known as MADDs - the number of  multiply-accumulates needed
+to compute an inference on a single image is a common metric to measure the efficiency of the model.
+
+Below is the graph comparing V2 vs a few selected networks. The size
+of each blob represents the number of parameters. Note for [ShuffleNet](https://arxiv.org/abs/1707.01083) there
+are no published size numbers. We estimate it to be comparable to MobileNetV2 numbers.
+
+![madds_top1_accuracy](madds_top1_accuracy.png)
+
+# Pretrained models
+## Imagenet  Checkpoints
+
+ Classification Checkpoint | MACs (M)| Parameters (M)| Top 1 Accuracy| Top 5 Accuracy | Mobile CPU  (ms) Pixel 1
+---------------------------|---------|---------------|---------|----|-------------
+| [mobilenet_v2_1.4_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 582 | 6.06 | 75.0 | 92.5 | 138.0
+| [mobilenet_v2_1.3_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.3_224.tgz) | 509 | 5.34 | 74.4 | 92.1 | 123.0
+| [mobilenet_v2_1.0_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 300 | 3.47 | 71.8 | 91.0 | 73.8
+| [mobilenet_v2_1.0_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_192.tgz) | 221 | 3.47 | 70.7 | 90.1 | 55.1
+| [mobilenet_v2_1.0_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_160.tgz) | 154 | 3.47 | 68.8 | 89.0 | 40.2
+| [mobilenet_v2_1.0_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_128.tgz) | 99 | 3.47 | 65.3 | 86.9 | 27.6
+| [mobilenet_v2_1.0_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_96.tgz) | 56 | 3.47 | 60.3 | 83.2 | 17.6
+| [mobilenet_v2_0.75_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_224.tgz) | 209 | 2.61 | 69.8 | 89.6 | 55.8
+| [mobilenet_v2_0.75_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_192.tgz) | 153 | 2.61 | 68.7 | 88.9 | 41.6
+| [mobilenet_v2_0.75_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_160.tgz) | 107 | 2.61 | 66.4 | 87.3 | 30.4
+| [mobilenet_v2_0.75_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_128.tgz) | 69 | 2.61 | 63.2 | 85.3 | 21.9
+| [mobilenet_v2_0.75_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_96.tgz) | 39 | 2.61 | 58.8 | 81.6 | 14.2
+| [mobilenet_v2_0.5_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_224.tgz) | 97 | 1.95 | 65.4 | 86.4 | 28.7
+| [mobilenet_v2_0.5_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_192.tgz) | 71 | 1.95 | 63.9 | 85.4 | 21.1
+| [mobilenet_v2_0.5_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_160.tgz) | 50 | 1.95 | 61.0 | 83.2 | 14.9
+| [mobilenet_v2_0.5_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_128.tgz) | 32 | 1.95 | 57.7 | 80.8 | 9.9
+| [mobilenet_v2_0.5_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_96.tgz) | 18 | 1.95 | 51.2 | 75.8 | 6.4
+| [mobilenet_v2_0.35_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_224.tgz) | 59 | 1.66 | 60.3 | 82.9 | 19.7
+| [mobilenet_v2_0.35_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_192.tgz) | 43 | 1.66 | 58.2 | 81.2 | 14.6
+| [mobilenet_v2_0.35_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_160.tgz) | 30 | 1.66 | 55.7 | 79.1 | 10.5
+| [mobilenet_v2_0.35_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_128.tgz) | 20 | 1.66 | 50.8 | 75.0 | 6.9
+| [mobilenet_v2_0.35_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_96.tgz) | 11 | 1.66 | 45.5 | 70.4 | 4.5
+
+# Training
+The numbers above can be reproduced using slim's `train_image_classifier`.
+Below is the set of parameters that achieves 72.0% for full size MobileNetV2, after about 700K when trained on 8 GPU.
+If trained on a single GPU the full convergence is after 5.5M steps. Also note that learning rate and
+num_epochs_per_decay both need to be adjusted depending on how many GPUs are being
+used due to slim's internal averaging.
+
+```bash
+--model_name="mobilenet_v2"
+--learning_rate=0.045 * NUM_GPUS   #slim internally averages clones so we compensate
+--preprocessing_name="inception_v2"
+--label_smoothing=0.1
+--moving_average_decay=0.9999
+--batch_size= 96
+--num_clones = NUM_GPUS # you can use any number here between 1 and 8 depending on your hardware setup.
+--learning_rate_decay_factor=0.98
+--num_epochs_per_decay = 2.5 / NUM_GPUS # train_image_classifier does per clone epochs
+```
+
+# Example
+
+
+See this [ipython notebook](mobilenet_example.ipynb) or open and run the network directly in [Colaboratory](https://colab.research.google.com/github/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_example.ipynb).
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/conv_blocks.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/conv_blocks.py
new file mode 100644
index 00000000..a07d7e5b
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/conv_blocks.py
@@ -0,0 +1,358 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Convolution blocks for mobilenet."""
+import contextlib
+import functools
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def _fixed_padding(inputs, kernel_size, rate=1):
+  """Pads the input along the spatial dimensions independently of input size.
+
+  Pads the input such that if it was used in a convolution with 'VALID' padding,
+  the output would have the same dimensions as if the unpadded input was used
+  in a convolution with 'SAME' padding.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
+    rate: An integer, rate for atrous convolution.
+
+  Returns:
+    output: A tensor of size [batch, height_out, width_out, channels] with the
+      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+  """
+  kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
+                           kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
+  pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
+  pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
+  pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
+  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+                                  [pad_beg[1], pad_end[1]], [0, 0]])
+  return padded_inputs
+
+
+def _make_divisible(v, divisor, min_value=None):
+  if min_value is None:
+    min_value = divisor
+  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+  # Make sure that round down does not go down by more than 10%.
+  if new_v < 0.9 * v:
+    new_v += divisor
+  return new_v
+
+
+def _split_divisible(num, num_ways, divisible_by=8):
+  """Evenly splits num, num_ways so each piece is a multiple of divisible_by."""
+  assert num % divisible_by == 0
+  assert num / num_ways >= divisible_by
+  # Note: want to round down, we adjust each split to match the total.
+  base = num // num_ways // divisible_by * divisible_by
+  result = []
+  accumulated = 0
+  for i in range(num_ways):
+    r = base
+    while accumulated + r < num * (i + 1) / num_ways:
+      r += divisible_by
+    result.append(r)
+    accumulated += r
+  assert accumulated == num
+  return result
+
+
+@contextlib.contextmanager
+def _v1_compatible_scope_naming(scope):
+  if scope is None:  # Create uniqified separable blocks.
+    with tf.variable_scope(None, default_name='separable') as s, \
+         tf.name_scope(s.original_name_scope):
+      yield ''
+  else:
+    # We use scope_depthwise, scope_pointwise for compatibility with V1 ckpts.
+    # which provide numbered scopes.
+    scope += '_'
+    yield scope
+
+
+@slim.add_arg_scope
+def split_separable_conv2d(input_tensor,
+                           num_outputs,
+                           scope=None,
+                           normalizer_fn=None,
+                           stride=1,
+                           rate=1,
+                           endpoints=None,
+                           use_explicit_padding=False):
+  """Separable mobilenet V1 style convolution.
+
+  Depthwise convolution, with default non-linearity,
+  followed by 1x1 depthwise convolution.  This is similar to
+  slim.separable_conv2d, but differs in tha it applies batch
+  normalization and non-linearity to depthwise. This  matches
+  the basic building of Mobilenet Paper
+  (https://arxiv.org/abs/1704.04861)
+
+  Args:
+    input_tensor: input
+    num_outputs: number of outputs
+    scope: optional name of the scope. Note if provided it will use
+    scope_depthwise for deptwhise, and scope_pointwise for pointwise.
+    normalizer_fn: which normalizer function to use for depthwise/pointwise
+    stride: stride
+    rate: output rate (also known as dilation rate)
+    endpoints: optional, if provided, will export additional tensors to it.
+    use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+      inputs so that the output dimensions are the same as if 'SAME' padding
+      were used.
+
+  Returns:
+    output tesnor
+  """
+
+  with _v1_compatible_scope_naming(scope) as scope:
+    dw_scope = scope + 'depthwise'
+    endpoints = endpoints if endpoints is not None else {}
+    kernel_size = [3, 3]
+    padding = 'SAME'
+    if use_explicit_padding:
+      padding = 'VALID'
+      input_tensor = _fixed_padding(input_tensor, kernel_size, rate)
+    net = slim.separable_conv2d(
+        input_tensor,
+        None,
+        kernel_size,
+        depth_multiplier=1,
+        stride=stride,
+        rate=rate,
+        normalizer_fn=normalizer_fn,
+        padding=padding,
+        scope=dw_scope)
+
+    endpoints[dw_scope] = net
+
+    pw_scope = scope + 'pointwise'
+    net = slim.conv2d(
+        net,
+        num_outputs, [1, 1],
+        stride=1,
+        normalizer_fn=normalizer_fn,
+        scope=pw_scope)
+    endpoints[pw_scope] = net
+  return net
+
+
+def expand_input_by_factor(n, divisible_by=8):
+  return lambda num_inputs, **_: _make_divisible(num_inputs * n, divisible_by)
+
+
+@slim.add_arg_scope
+def expanded_conv(input_tensor,
+                  num_outputs,
+                  expansion_size=expand_input_by_factor(6),
+                  stride=1,
+                  rate=1,
+                  kernel_size=(3, 3),
+                  residual=True,
+                  normalizer_fn=None,
+                  project_activation_fn=tf.identity,
+                  split_projection=1,
+                  split_expansion=1,
+                  expansion_transform=None,
+                  depthwise_location='expansion',
+                  depthwise_channel_multiplier=1,
+                  endpoints=None,
+                  use_explicit_padding=False,
+                  padding='SAME',
+                  scope=None):
+  """Depthwise Convolution Block with expansion.
+
+  Builds a composite convolution that has the following structure
+  expansion (1x1) -> depthwise (kernel_size) -> projection (1x1)
+
+  Args:
+    input_tensor: input
+    num_outputs: number of outputs in the final layer.
+    expansion_size: the size of expansion, could be a constant or a callable.
+      If latter it will be provided 'num_inputs' as an input. For forward
+      compatibility it should accept arbitrary keyword arguments.
+      Default will expand the input by factor of 6.
+    stride: depthwise stride
+    rate: depthwise rate
+    kernel_size: depthwise kernel
+    residual: whether to include residual connection between input
+      and output.
+    normalizer_fn: batchnorm or otherwise
+    project_activation_fn: activation function for the project layer
+    split_projection: how many ways to split projection operator
+      (that is conv expansion->bottleneck)
+    split_expansion: how many ways to split expansion op
+      (that is conv bottleneck->expansion) ops will keep depth divisible
+      by this value.
+    expansion_transform: Optional function that takes expansion
+      as a single input and returns output.
+    depthwise_location: where to put depthwise covnvolutions supported
+      values None, 'input', 'output', 'expansion'
+    depthwise_channel_multiplier: depthwise channel multiplier:
+    each input will replicated (with different filters)
+    that many times. So if input had c channels,
+    output will have c x depthwise_channel_multpilier.
+    endpoints: An optional dictionary into which intermediate endpoints are
+      placed. The keys "expansion_output", "depthwise_output",
+      "projection_output" and "expansion_transform" are always populated, even
+      if the corresponding functions are not invoked.
+    use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+      inputs so that the output dimensions are the same as if 'SAME' padding
+      were used.
+    padding: Padding type to use if `use_explicit_padding` is not set.
+    scope: optional scope.
+
+  Returns:
+    Tensor of depth num_outputs
+
+  Raises:
+    TypeError: on inval
+  """
+  with tf.variable_scope(scope, default_name='expanded_conv') as s, \
+       tf.name_scope(s.original_name_scope):
+    prev_depth = input_tensor.get_shape().as_list()[3]
+    if  depthwise_location not in [None, 'input', 'output', 'expansion']:
+      raise TypeError('%r is unknown value for depthwise_location' %
+                      depthwise_location)
+    if use_explicit_padding:
+      if padding != 'SAME':
+        raise TypeError('`use_explicit_padding` should only be used with '
+                        '"SAME" padding.')
+      padding = 'VALID'
+    depthwise_func = functools.partial(
+        slim.separable_conv2d,
+        num_outputs=None,
+        kernel_size=kernel_size,
+        depth_multiplier=depthwise_channel_multiplier,
+        stride=stride,
+        rate=rate,
+        normalizer_fn=normalizer_fn,
+        padding=padding,
+        scope='depthwise')
+    # b1 -> b2 * r -> b2
+    #   i -> (o * r) (bottleneck) -> o
+    input_tensor = tf.identity(input_tensor, 'input')
+    net = input_tensor
+
+    if depthwise_location == 'input':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net, activation_fn=None)
+
+    if callable(expansion_size):
+      inner_size = expansion_size(num_inputs=prev_depth)
+    else:
+      inner_size = expansion_size
+
+    if inner_size > net.shape[3]:
+      net = split_conv(
+          net,
+          inner_size,
+          num_ways=split_expansion,
+          scope='expand',
+          stride=1,
+          normalizer_fn=normalizer_fn)
+      net = tf.identity(net, 'expansion_output')
+    if endpoints is not None:
+      endpoints['expansion_output'] = net
+
+    if depthwise_location == 'expansion':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net)
+
+    net = tf.identity(net, name='depthwise_output')
+    if endpoints is not None:
+      endpoints['depthwise_output'] = net
+    if expansion_transform:
+      net = expansion_transform(expansion_tensor=net, input_tensor=input_tensor)
+    # Note in contrast with expansion, we always have
+    # projection to produce the desired output size.
+    net = split_conv(
+        net,
+        num_outputs,
+        num_ways=split_projection,
+        stride=1,
+        scope='project',
+        normalizer_fn=normalizer_fn,
+        activation_fn=project_activation_fn)
+    if endpoints is not None:
+      endpoints['projection_output'] = net
+    if depthwise_location == 'output':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net, activation_fn=None)
+
+    if callable(residual):  # custom residual
+      net = residual(input_tensor=input_tensor, output_tensor=net)
+    elif (residual and
+          # stride check enforces that we don't add residuals when spatial
+          # dimensions are None
+          stride == 1 and
+          # Depth matches
+          net.get_shape().as_list()[3] ==
+          input_tensor.get_shape().as_list()[3]):
+      net += input_tensor
+    return tf.identity(net, name='output')
+
+
+def split_conv(input_tensor,
+               num_outputs,
+               num_ways,
+               scope,
+               divisible_by=8,
+               **kwargs):
+  """Creates a split convolution.
+
+  Split convolution splits the input and output into
+  'num_blocks' blocks of approximately the same size each,
+  and only connects $i$-th input to $i$ output.
+
+  Args:
+    input_tensor: input tensor
+    num_outputs: number of output filters
+    num_ways: num blocks to split by.
+    scope: scope for all the operators.
+    divisible_by: make sure that every part is divisiable by this.
+    **kwargs: will be passed directly into conv2d operator
+  Returns:
+    tensor
+  """
+  b = input_tensor.get_shape().as_list()[3]
+
+  if num_ways == 1 or min(b // num_ways,
+                          num_outputs // num_ways) < divisible_by:
+    # Don't do any splitting if we end up with less than 8 filters
+    # on either side.
+    return slim.conv2d(input_tensor, num_outputs, [1, 1], scope=scope, **kwargs)
+
+  outs = []
+  input_splits = _split_divisible(b, num_ways, divisible_by=divisible_by)
+  output_splits = _split_divisible(
+      num_outputs, num_ways, divisible_by=divisible_by)
+  inputs = tf.split(input_tensor, input_splits, axis=3, name='split_' + scope)
+  base = scope
+  for i, (input_tensor, out_size) in enumerate(zip(inputs, output_splits)):
+    scope = base + '_part_%d' % (i,)
+    n = slim.conv2d(input_tensor, out_size, [1, 1], scope=scope, **kwargs)
+    n = tf.identity(n, scope + '_output')
+    outs.append(n)
+  return tf.concat(outs, 3, name=scope + '_concat')
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet.py
new file mode 100644
index 00000000..f71e73f8
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet.py
@@ -0,0 +1,467 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Mobilenet Base Class."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import collections
+import contextlib
+import copy
+import os
+
+import tensorflow as tf
+
+
+slim = tf.contrib.slim
+
+
+@slim.add_arg_scope
+def apply_activation(x, name=None, activation_fn=None):
+  return activation_fn(x, name=name) if activation_fn else x
+
+
+def _fixed_padding(inputs, kernel_size, rate=1):
+  """Pads the input along the spatial dimensions independently of input size.
+
+  Pads the input such that if it was used in a convolution with 'VALID' padding,
+  the output would have the same dimensions as if the unpadded input was used
+  in a convolution with 'SAME' padding.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
+    rate: An integer, rate for atrous convolution.
+
+  Returns:
+    output: A tensor of size [batch, height_out, width_out, channels] with the
+      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+  """
+  kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
+                           kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
+  pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
+  pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
+  pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
+  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+                                  [pad_beg[1], pad_end[1]], [0, 0]])
+  return padded_inputs
+
+
+def _make_divisible(v, divisor, min_value=None):
+  if min_value is None:
+    min_value = divisor
+  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+  # Make sure that round down does not go down by more than 10%.
+  if new_v < 0.9 * v:
+    new_v += divisor
+  return new_v
+
+
+@contextlib.contextmanager
+def _set_arg_scope_defaults(defaults):
+  """Sets arg scope defaults for all items present in defaults.
+
+  Args:
+    defaults: dictionary/list of pairs, containing a mapping from
+    function to a dictionary of default args.
+
+  Yields:
+    context manager where all defaults are set.
+  """
+  if hasattr(defaults, 'items'):
+    items = list(defaults.items())
+  else:
+    items = defaults
+  if not items:
+    yield
+  else:
+    func, default_arg = items[0]
+    with slim.arg_scope(func, **default_arg):
+      with _set_arg_scope_defaults(items[1:]):
+        yield
+
+
+@slim.add_arg_scope
+def depth_multiplier(output_params,
+                     multiplier,
+                     divisible_by=8,
+                     min_depth=8,
+                     **unused_kwargs):
+  if 'num_outputs' not in output_params:
+    return
+  d = output_params['num_outputs']
+  output_params['num_outputs'] = _make_divisible(d * multiplier, divisible_by,
+                                                 min_depth)
+
+
+_Op = collections.namedtuple('Op', ['op', 'params', 'multiplier_func'])
+
+
+def op(opfunc, **params):
+  multiplier = params.pop('multiplier_transorm', depth_multiplier)
+  return _Op(opfunc, params=params, multiplier_func=multiplier)
+
+
+class NoOpScope(object):
+  """No-op context manager."""
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exc_type, exc_value, traceback):
+    return False
+
+
+def safe_arg_scope(funcs, **kwargs):
+  """Returns `slim.arg_scope` with all None arguments removed.
+
+  Arguments:
+    funcs: Functions to pass to `arg_scope`.
+    **kwargs: Arguments to pass to `arg_scope`.
+
+  Returns:
+    arg_scope or No-op context manager.
+
+  Note: can be useful if None value should be interpreted as "do not overwrite
+    this parameter value".
+  """
+  filtered_args = {name: value for name, value in kwargs.items()
+                   if value is not None}
+  if filtered_args:
+    return slim.arg_scope(funcs, **filtered_args)
+  else:
+    return NoOpScope()
+
+
+@slim.add_arg_scope
+def mobilenet_base(  # pylint: disable=invalid-name
+    inputs,
+    conv_defs,
+    multiplier=1.0,
+    final_endpoint=None,
+    output_stride=None,
+    use_explicit_padding=False,
+    scope=None,
+    is_training=False):
+  """Mobilenet base network.
+
+  Constructs a network from inputs to the given final endpoint. By default
+  the network is constructed in inference mode. To create network
+  in training mode use:
+
+  with slim.arg_scope(mobilenet.training_scope()):
+     logits, endpoints = mobilenet_base(...)
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    conv_defs: A list of op(...) layers specifying the net architecture.
+    multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    final_endpoint: The name of last layer, for early termination for
+    for V1-based networks: last layer is "layer_14", for V2: "layer_20"
+    output_stride: An integer that specifies the requested ratio of input to
+      output spatial resolution. If not None, then we invoke atrous convolution
+      if necessary to prevent the network from reducing the spatial resolution
+      of the activation maps. Allowed values are 1 or any even number, excluding
+      zero. Typical values are 8 (accurate fully convolutional mode), 16
+      (fast fully convolutional mode), and 32 (classification mode).
+
+      NOTE- output_stride relies on all consequent operators to support dilated
+      operators via "rate" parameter. This might require wrapping non-conv
+      operators to operate properly.
+
+    use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+      inputs so that the output dimensions are the same as if 'SAME' padding
+      were used.
+    scope: optional variable scope.
+    is_training: How to setup batch_norm and other ops. Note: most of the time
+      this does not need be set directly. Use mobilenet.training_scope() to set
+      up training instead. This parameter is here for backward compatibility
+      only. It is safe to set it to the value matching
+      training_scope(is_training=...). It is also safe to explicitly set
+      it to False, even if there is outer training_scope set to to training.
+      (The network will be built in inference mode). If this is set to None,
+      no arg_scope is added for slim.batch_norm's is_training parameter.
+
+  Returns:
+    tensor_out: output tensor.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: depth_multiplier <= 0, or the target output_stride is not
+                allowed.
+  """
+  if multiplier <= 0:
+    raise ValueError('multiplier is not greater than zero.')
+
+  # Set conv defs defaults and overrides.
+  conv_defs_defaults = conv_defs.get('defaults', {})
+  conv_defs_overrides = conv_defs.get('overrides', {})
+  if use_explicit_padding:
+    conv_defs_overrides = copy.deepcopy(conv_defs_overrides)
+    conv_defs_overrides[
+        (slim.conv2d, slim.separable_conv2d)] = {'padding': 'VALID'}
+
+  if output_stride is not None:
+    if output_stride == 0 or (output_stride > 1 and output_stride % 2):
+      raise ValueError('Output stride must be None, 1 or a multiple of 2.')
+
+  # a) Set the tensorflow scope
+  # b) set padding to default: note we might consider removing this
+  # since it is also set by mobilenet_scope
+  # c) set all defaults
+  # d) set all extra overrides.
+  with _scope_all(scope, default_scope='Mobilenet'), \
+      safe_arg_scope([slim.batch_norm], is_training=is_training), \
+      _set_arg_scope_defaults(conv_defs_defaults), \
+      _set_arg_scope_defaults(conv_defs_overrides):
+    # The current_stride variable keeps track of the output stride of the
+    # activations, i.e., the running product of convolution strides up to the
+    # current network layer. This allows us to invoke atrous convolution
+    # whenever applying the next convolution would result in the activations
+    # having output stride larger than the target output_stride.
+    current_stride = 1
+
+    # The atrous convolution rate parameter.
+    rate = 1
+
+    net = inputs
+    # Insert default parameters before the base scope which includes
+    # any custom overrides set in mobilenet.
+    end_points = {}
+    scopes = {}
+    for i, opdef in enumerate(conv_defs['spec']):
+      params = dict(opdef.params)
+      opdef.multiplier_func(params, multiplier)
+      stride = params.get('stride', 1)
+      if output_stride is not None and current_stride == output_stride:
+        # If we have reached the target output_stride, then we need to employ
+        # atrous convolution with stride=1 and multiply the atrous rate by the
+        # current unit's stride for use in subsequent layers.
+        layer_stride = 1
+        layer_rate = rate
+        rate *= stride
+      else:
+        layer_stride = stride
+        layer_rate = 1
+        current_stride *= stride
+      # Update params.
+      params['stride'] = layer_stride
+      # Only insert rate to params if rate > 1.
+      if layer_rate > 1:
+        params['rate'] = layer_rate
+      # Set padding
+      if use_explicit_padding:
+        if 'kernel_size' in params:
+          net = _fixed_padding(net, params['kernel_size'], layer_rate)
+        else:
+          params['use_explicit_padding'] = True
+
+      end_point = 'layer_%d' % (i + 1)
+      try:
+        net = opdef.op(net, **params)
+      except Exception:
+        print('Failed to create op %i: %r params: %r' % (i, opdef, params))
+        raise
+      end_points[end_point] = net
+      scope = os.path.dirname(net.name)
+      scopes[scope] = end_point
+      if final_endpoint is not None and end_point == final_endpoint:
+        break
+
+    # Add all tensors that end with 'output' to
+    # endpoints
+    for t in net.graph.get_operations():
+      scope = os.path.dirname(t.name)
+      bn = os.path.basename(t.name)
+      if scope in scopes and t.name.endswith('output'):
+        end_points[scopes[scope] + '/' + bn] = t.outputs[0]
+    return net, end_points
+
+
+@contextlib.contextmanager
+def _scope_all(scope, default_scope=None):
+  with tf.variable_scope(scope, default_name=default_scope) as s,\
+       tf.name_scope(s.original_name_scope):
+    yield s
+
+
+@slim.add_arg_scope
+def mobilenet(inputs,
+              num_classes=1001,
+              prediction_fn=slim.softmax,
+              reuse=None,
+              scope='Mobilenet',
+              base_only=False,
+              **mobilenet_args):
+  """Mobilenet model for classification, supports both V1 and V2.
+
+  Note: default mode is inference, use mobilenet.training_scope to create
+  training network.
+
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    prediction_fn: a function to get predictions out of logits
+      (default softmax).
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    base_only: if True will only create the base of the network (no pooling
+    and no logits).
+    **mobilenet_args: passed to mobilenet_base verbatim.
+      - conv_defs: list of conv defs
+      - multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+      - output_stride: will ensure that the last layer has at most total stride.
+      If the architecture calls for more stride than that provided
+      (e.g. output_stride=16, but the architecture has 5 stride=2 operators),
+      it will replace output_stride with fractional convolutions using Atrous
+      Convolutions.
+
+  Returns:
+    logits: the pre-softmax activations, a tensor of size
+      [batch_size, num_classes]
+    end_points: a dictionary from components of the network to the corresponding
+      activation tensor.
+
+  Raises:
+    ValueError: Input rank is invalid.
+  """
+  is_training = mobilenet_args.get('is_training', False)
+  input_shape = inputs.get_shape().as_list()
+  if len(input_shape) != 4:
+    raise ValueError('Expected rank 4 input, was: %d' % len(input_shape))
+
+  with tf.variable_scope(scope, 'Mobilenet', reuse=reuse) as scope:
+    inputs = tf.identity(inputs, 'input')
+    net, end_points = mobilenet_base(inputs, scope=scope, **mobilenet_args)
+    if base_only:
+      return net, end_points
+
+    net = tf.identity(net, name='embedding')
+
+    with tf.variable_scope('Logits'):
+      net = global_pool(net)
+      end_points['global_pool'] = net
+      if not num_classes:
+        return net, end_points
+      net = slim.dropout(net, scope='Dropout', is_training=is_training)
+      # 1 x 1 x num_classes
+      # Note: legacy scope name.
+      logits = slim.conv2d(
+          net,
+          num_classes, [1, 1],
+          activation_fn=None,
+          normalizer_fn=None,
+          biases_initializer=tf.zeros_initializer(),
+          scope='Conv2d_1c_1x1')
+
+      logits = tf.squeeze(logits, [1, 2])
+
+      logits = tf.identity(logits, name='output')
+    end_points['Logits'] = logits
+    if prediction_fn:
+      end_points['Predictions'] = prediction_fn(logits, 'Predictions')
+  return logits, end_points
+
+
+def global_pool(input_tensor, pool_op=tf.nn.avg_pool):
+  """Applies avg pool to produce 1x1 output.
+
+  NOTE: This function is funcitonally equivalenet to reduce_mean, but it has
+  baked in average pool which has better support across hardware.
+
+  Args:
+    input_tensor: input tensor
+    pool_op: pooling op (avg pool is default)
+  Returns:
+    a tensor batch_size x 1 x 1 x depth.
+  """
+  shape = input_tensor.get_shape().as_list()
+  if shape[1] is None or shape[2] is None:
+    kernel_size = tf.convert_to_tensor(
+        [1, tf.shape(input_tensor)[1],
+         tf.shape(input_tensor)[2], 1])
+  else:
+    kernel_size = [1, shape[1], shape[2], 1]
+  output = pool_op(
+      input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID')
+  # Recover output shape, for unknown shape.
+  output.set_shape([None, 1, 1, None])
+  return output
+
+
+def training_scope(is_training=True,
+                   weight_decay=0.00004,
+                   stddev=0.09,
+                   dropout_keep_prob=0.8,
+                   bn_decay=0.997):
+  """Defines Mobilenet training scope.
+
+  Usage:
+     with tf.contrib.slim.arg_scope(mobilenet.training_scope()):
+       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+     # the network created will be trainble with dropout/batch norm
+     # initialized appropriately.
+  Args:
+    is_training: if set to False this will ensure that all customizations are
+      set to non-training mode. This might be helpful for code that is reused
+      across both training/evaluation, but most of the time training_scope with
+      value False is not needed. If this is set to None, the parameters is not
+      added to the batch_norm arg_scope.
+
+    weight_decay: The weight decay to use for regularizing the model.
+    stddev: Standard deviation for initialization, if negative uses xavier.
+    dropout_keep_prob: dropout keep probability (not set if equals to None).
+    bn_decay: decay for the batch norm moving averages (not set if equals to
+      None).
+
+  Returns:
+    An argument scope to use via arg_scope.
+  """
+  # Note: do not introduce parameters that would change the inference
+  # model here (for example whether to use bias), modify conv_def instead.
+  batch_norm_params = {
+      'decay': bn_decay,
+      'is_training': is_training
+  }
+  if stddev < 0:
+    weight_intitializer = slim.initializers.xavier_initializer()
+  else:
+    weight_intitializer = tf.truncated_normal_initializer(stddev=stddev)
+
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope(
+      [slim.conv2d, slim.fully_connected, slim.separable_conv2d],
+      weights_initializer=weight_intitializer,
+      normalizer_fn=slim.batch_norm), \
+      slim.arg_scope([mobilenet_base, mobilenet], is_training=is_training),\
+      safe_arg_scope([slim.batch_norm], **batch_norm_params), \
+      safe_arg_scope([slim.dropout], is_training=is_training,
+                     keep_prob=dropout_keep_prob), \
+      slim.arg_scope([slim.conv2d], \
+                     weights_regularizer=slim.l2_regularizer(weight_decay)), \
+      slim.arg_scope([slim.separable_conv2d], weights_regularizer=None) as s:
+    return s
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_example.ipynb b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_example.ipynb
new file mode 100644
index 00000000..a17880e9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_example.ipynb
@@ -0,0 +1,445 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "toc",
+        "id": "aUVxY7xOGD1G"
+      },
+      "source": [
+        "\u003e[Prerequisites (downloading tensorflow_models and checkpoints)](#scrollTo=T_cETKXHDTXu)\n",
+        "\n",
+        "\u003e[Checkpoint based inference](#scrollTo=fxMe7_pkk_Vo)\n",
+        "\n",
+        "\u003e[Frozen inference](#scrollTo=PlwvpK3ElBk6)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "T_cETKXHDTXu"
+      },
+      "source": [
+        "# Prerequisites (downloading tensorflow_models and checkpoints)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 125,
+          "output_extras": [
+            {}
+          ]
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 31129,
+          "status": "ok",
+          "timestamp": 1521483961674,
+          "user": {
+            "displayName": "Mark Sandler",
+            "photoUrl": "//lh5.googleusercontent.com/-CjnV3zpGrlw/AAAAAAAAAAI/AAAAAAAABRU/dfjRy_tzX5M/s50-c-k-no/photo.jpg",
+            "userId": "108034853522252017283"
+          },
+          "user_tz": 420
+        },
+        "id": "zo5GyseklSVH",
+        "outputId": "e12a8a80-c0d2-4ebc-9230-b170f11d236e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'models'...\n",
+            "remote: Counting objects: 13504, done.\u001b[K\n",
+            "remote: Total 13504 (delta 2), reused 2 (delta 2), pack-reused 13501\u001b[K\n",
+            "Receiving objects: 100% (13504/13504), 422.07 MiB | 37.42 MiB/s, done.\n",
+            "Resolving deltas: 100% (7635/7635), done.\n",
+            "Checking out files: 100% (1946/1946), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!git clone https://github.com/tensorflow/models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "cellView": "both",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 35,
+          "output_extras": [
+            {}
+          ]
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 4504,
+          "status": "ok",
+          "timestamp": 1521493157017,
+          "user": {
+            "displayName": "Mark Sandler",
+            "photoUrl": "//lh5.googleusercontent.com/-CjnV3zpGrlw/AAAAAAAAAAI/AAAAAAAABRU/dfjRy_tzX5M/s50-c-k-no/photo.jpg",
+            "userId": "108034853522252017283"
+          },
+          "user_tz": 420
+        },
+        "id": "obaW6O8bz3mA",
+        "outputId": "79b3fb23-caa7-4683-9575-ba7c2f55ebdb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Successfully downloaded the checkpoint. It is available as mobilenet_v2_1.0_224.ckpt\n"
+          ]
+        }
+      ],
+      "source": [
+        "from __future__ import print_function\n",
+        "from IPython import display \n",
+        "checkpoint_name = 'mobilenet_v2_1.0_224' #@param\n",
+        "url = 'https://storage.googleapis.com/mobilenet_v2/checkpoints/' + checkpoint_name + '.tgz'\n",
+        "print('Downloading from ', url)\n",
+        "!wget {url}\n",
+        "print('Unpacking')\n",
+        "!tar -xvf {base_name}.tgz\n",
+        "checkpoint = base_name + '.ckpt'\n",
+        "\n",
+        "display.clear_output()\n",
+        "print('Successfully downloaded checkpoint from ', url,\n",
+        "      '. It is available as', checkpoint)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 215,
+          "output_extras": [
+            {}
+          ]
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1486,
+          "status": "ok",
+          "timestamp": 1521485010457,
+          "user": {
+            "displayName": "Mark Sandler",
+            "photoUrl": "//lh5.googleusercontent.com/-CjnV3zpGrlw/AAAAAAAAAAI/AAAAAAAABRU/dfjRy_tzX5M/s50-c-k-no/photo.jpg",
+            "userId": "108034853522252017283"
+          },
+          "user_tz": 420
+        },
+        "id": "qZDfLegf3hpw",
+        "outputId": "334ed084-b90e-4bd0-bd5e-125434a9a30f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2018-03-19 18:43:29--  https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG\n",
+            "Resolving upload.wikimedia.org (upload.wikimedia.org)... 208.80.154.240, 2620:0:860:ed1a::2:b\n",
+            "Connecting to upload.wikimedia.org (upload.wikimedia.org)|208.80.154.240|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 116068 (113K) [image/jpeg]\n",
+            "Saving to: ‘panda.jpg’\n",
+            "\n",
+            "panda.jpg           100%[===================\u003e] 113.35K  --.-KB/s    in 0.03s   \n",
+            "\n",
+            "2018-03-19 18:43:30 (3.18 MB/s) - ‘panda.jpg’ saved [116068/116068]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG -O panda.jpg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "g0H2RDadndug"
+      },
+      "outputs": [],
+      "source": [
+        "# setup path\n",
+        "import sys\n",
+        "sys.path.append('/content/models/research/slim')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "fxMe7_pkk_Vo"
+      },
+      "source": [
+        "# Checkpoint based inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "GrQemT66CxXt"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "from nets.mobilenet import mobilenet_v2\n",
+        "\n",
+        "tf.reset_default_graph()\n",
+        "\n",
+        "# For simplicity we just decode jpeg inside tensorflow.\n",
+        "# But one can provide any input obviously.\n",
+        "file_input = tf.placeholder(tf.string, ())\n",
+        "\n",
+        "image = tf.image.decode_jpeg(tf.read_file(file_input))\n",
+        "\n",
+        "images = tf.expand_dims(image, 0)\n",
+        "images = tf.cast(images, tf.float32) / 128.  - 1\n",
+        "images.set_shape((None, None, None, 3))\n",
+        "images = tf.image.resize_images(images, (224, 224))\n",
+        "\n",
+        "# Note: arg_scope is optional for inference.\n",
+        "with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope(is_training=False)):\n",
+        "  logits, endpoints = mobilenet_v2.mobilenet(images)\n",
+        "  \n",
+        "# Restore using exponential moving average since it produces (1.5-2%) higher \n",
+        "# accuracy\n",
+        "ema = tf.train.ExponentialMovingAverage(0.999)\n",
+        "vars = ema.variables_to_restore()\n",
+        "\n",
+        "saver = tf.train.Saver(vars)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 666,
+          "output_extras": [
+            {},
+            {}
+          ]
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1422,
+          "status": "ok",
+          "timestamp": 1521493723379,
+          "user": {
+            "displayName": "Mark Sandler",
+            "photoUrl": "//lh5.googleusercontent.com/-CjnV3zpGrlw/AAAAAAAAAAI/AAAAAAAABRU/dfjRy_tzX5M/s50-c-k-no/photo.jpg",
+            "userId": "108034853522252017283"
+          },
+          "user_tz": 420
+        },
+        "id": "TJbLYo_FCxXy",
+        "outputId": "17a4fbaa-dec0-4997-b233-15ba69806083"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/jpeg": 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54Iz3x27ep101Y31BRK1LEc4o73PWxTPFUQQbBLHwGSQDk47gqxBH+uglVUzy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rd3iiO7ErJMqkyqOrHA6/oKXrZNo8k/imwuWeKSWWbEscyY42/Trg9feoaOYP4faeIrI8LG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\nsvj4Ejqg8oXv9K9VEKf/2Q==\n",
+            "text/plain": [
+              "\u003cIPython.core.display.Image object\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Restoring parameters from mobilenet_v2_1.0_224.ckpt\n",
+            "Top 1 prediction:  389 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 0.90984344\n"
+          ]
+        }
+      ],
+      "source": [
+        "from IPython import display\n",
+        "import pylab\n",
+        "from datasets import imagenet\n",
+        "import PIL\n",
+        "display.display(display.Image('panda.jpg'))\n",
+        "\n",
+        "with tf.Session() as sess:\n",
+        "  saver.restore(sess,  checkpoint)\n",
+        "  x = endpoints['Predictions'].eval(feed_dict={file_input: 'panda.jpg'})\n",
+        "label_map = imagenet.create_readable_names_for_imagenet_labels()  \n",
+        "print(\"Top 1 prediction: \", x.argmax(),label_map[x.argmax()], x.max())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "PlwvpK3ElBk6"
+      },
+      "source": [
+        "# Frozen inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "o0BIbQUUlVrf"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "img = np.array(PIL.Image.open('panda.jpg').resize((224, 224))).astype(np.float) / 128 - 1\n",
+        "gd = tf.GraphDef.FromString(open(base_name + '_frozen.pb', 'rb').read())\n",
+        "inp, predictions = tf.import_graph_def(gd,  return_elements = ['input:0', 'MobilenetV2/Predictions/Reshape_1:0'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 35,
+          "output_extras": [
+            {}
+          ]
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1350,
+          "status": "ok",
+          "timestamp": 1521493472822,
+          "user": {
+            "displayName": "Mark Sandler",
+            "photoUrl": "//lh5.googleusercontent.com/-CjnV3zpGrlw/AAAAAAAAAAI/AAAAAAAABRU/dfjRy_tzX5M/s50-c-k-no/photo.jpg",
+            "userId": "108034853522252017283"
+          },
+          "user_tz": 420
+        },
+        "id": "qSU2h5NRlN7V",
+        "outputId": "4fb09105-b729-45c3-b5ef-83c8da30a215"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Top 1 Prediction:  389 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 0.9220208\n"
+          ]
+        }
+      ],
+      "source": [
+        "with tf.Session(graph=inp.graph):\n",
+        "  x = predictions.eval(feed_dict={inp: img.reshape(1, 224,224, 3)})\n",
+        "\n",
+        "label_map = imagenet.create_readable_names_for_imagenet_labels()  \n",
+        "print(\"Top 1 Prediction: \", x.argmax(),label_map[x.argmax()], x.max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "colab_type": "code",
+        "id": "CU8dJF8kCo6X"
+      },
+      "outputs": [],
+      "source": [
+        ""
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "T_cETKXHDTXu"
+      ],
+      "default_view": {},
+      "name": "Mobilenet Example.ipynb",
+      "provenance": [
+        {
+          "file_id": "1ylt6hB0JlXmWU9Bm6O1zGKVPgc2csZf5",
+          "timestamp": 1521507068201
+        }
+      ],
+      "version": "0.3.2",
+      "views": {}
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2.py
new file mode 100644
index 00000000..5bf4a13d
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2.py
@@ -0,0 +1,216 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implementation of Mobilenet V2.
+
+Architecture: https://arxiv.org/abs/1801.04381
+
+The base model gives 72.2% accuracy on ImageNet, with 300MMadds,
+3.4 M parameters.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import functools
+
+import tensorflow as tf
+
+from nets.mobilenet import conv_blocks as ops
+from nets.mobilenet import mobilenet as lib
+
+slim = tf.contrib.slim
+op = lib.op
+
+expand_input = ops.expand_input_by_factor
+
+# pyformat: disable
+# Architecture: https://arxiv.org/abs/1801.04381
+V2_DEF = dict(
+    defaults={
+        # Note: these parameters of batch norm affect the architecture
+        # that's why they are here and not in training_scope.
+        (slim.batch_norm,): {'center': True, 'scale': True},
+        (slim.conv2d, slim.fully_connected, slim.separable_conv2d): {
+            'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6
+        },
+        (ops.expanded_conv,): {
+            'expansion_size': expand_input(6),
+            'split_expansion': 1,
+            'normalizer_fn': slim.batch_norm,
+            'residual': True
+        },
+        (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'}
+    },
+    spec=[
+        op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]),
+        op(ops.expanded_conv,
+           expansion_size=expand_input(1, divisible_by=1),
+           num_outputs=16),
+        op(ops.expanded_conv, stride=2, num_outputs=24),
+        op(ops.expanded_conv, stride=1, num_outputs=24),
+        op(ops.expanded_conv, stride=2, num_outputs=32),
+        op(ops.expanded_conv, stride=1, num_outputs=32),
+        op(ops.expanded_conv, stride=1, num_outputs=32),
+        op(ops.expanded_conv, stride=2, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=2, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=320),
+        op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280)
+    ],
+)
+# pyformat: enable
+
+
+@slim.add_arg_scope
+def mobilenet(input_tensor,
+              num_classes=1001,
+              depth_multiplier=1.0,
+              scope='MobilenetV2',
+              conv_defs=None,
+              finegrain_classification_mode=False,
+              min_depth=None,
+              divisible_by=None,
+              activation_fn=None,
+              **kwargs):
+  """Creates mobilenet V2 network.
+
+  Inference mode is created by default. To create training use training_scope
+  below.
+
+  with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+     logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+  Args:
+    input_tensor: The input tensor
+    num_classes: number of classes
+    depth_multiplier: The multiplier applied to scale number of
+    channels in each layer. Note: this is called depth multiplier in the
+    paper but the name is kept for consistency with slim's model builder.
+    scope: Scope of the operator
+    conv_defs: Allows to override default conv def.
+    finegrain_classification_mode: When set to True, the model
+    will keep the last layer large even for small multipliers. Following
+    https://arxiv.org/abs/1801.04381
+    suggests that it improves performance for ImageNet-type of problems.
+      *Note* ignored if final_endpoint makes the builder exit earlier.
+    min_depth: If provided, will ensure that all layers will have that
+    many channels after application of depth multiplier.
+    divisible_by: If provided will ensure that all layers # channels
+    will be divisible by this number.
+    activation_fn: Activation function to use, defaults to tf.nn.relu6 if not
+      specified.
+    **kwargs: passed directly to mobilenet.mobilenet:
+      prediction_fn- what prediction function to use.
+      reuse-: whether to reuse variables (if reuse set to true, scope
+      must be given).
+  Returns:
+    logits/endpoints pair
+
+  Raises:
+    ValueError: On invalid arguments
+  """
+  if conv_defs is None:
+    conv_defs = V2_DEF
+  if 'multiplier' in kwargs:
+    raise ValueError('mobilenetv2 doesn\'t support generic '
+                     'multiplier parameter use "depth_multiplier" instead.')
+  if finegrain_classification_mode:
+    conv_defs = copy.deepcopy(conv_defs)
+    if depth_multiplier < 1:
+      conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier
+  if activation_fn:
+    conv_defs = copy.deepcopy(conv_defs)
+    defaults = conv_defs['defaults']
+    conv_defaults = (
+        defaults[(slim.conv2d, slim.fully_connected, slim.separable_conv2d)])
+    conv_defaults['activation_fn'] = activation_fn
+
+  depth_args = {}
+  # NB: do not set depth_args unless they are provided to avoid overriding
+  # whatever default depth_multiplier might have thanks to arg_scope.
+  if min_depth is not None:
+    depth_args['min_depth'] = min_depth
+  if divisible_by is not None:
+    depth_args['divisible_by'] = divisible_by
+
+  with slim.arg_scope((lib.depth_multiplier,), **depth_args):
+    return lib.mobilenet(
+        input_tensor,
+        num_classes=num_classes,
+        conv_defs=conv_defs,
+        scope=scope,
+        multiplier=depth_multiplier,
+        **kwargs)
+
+mobilenet.default_image_size = 224
+
+
+def wrapped_partial(func, *args, **kwargs):
+  partial_func = functools.partial(func, *args, **kwargs)
+  functools.update_wrapper(partial_func, func)
+  return partial_func
+
+
+# Wrappers for mobilenet v2 with depth-multipliers. Be noticed that
+# 'finegrain_classification_mode' is set to True, which means the embedding
+# layer will not be shrinked when given a depth-multiplier < 1.0.
+mobilenet_v2_140 = wrapped_partial(mobilenet, depth_multiplier=1.4)
+mobilenet_v2_050 = wrapped_partial(mobilenet, depth_multiplier=0.50,
+                                   finegrain_classification_mode=True)
+mobilenet_v2_035 = wrapped_partial(mobilenet, depth_multiplier=0.35,
+                                   finegrain_classification_mode=True)
+
+
+@slim.add_arg_scope
+def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
+  """Creates base of the mobilenet (no pooling and no logits) ."""
+  return mobilenet(input_tensor,
+                   depth_multiplier=depth_multiplier,
+                   base_only=True, **kwargs)
+
+
+def training_scope(**kwargs):
+  """Defines MobilenetV2 training scope.
+
+  Usage:
+     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+  with slim.
+
+  Args:
+    **kwargs: Passed to mobilenet.training_scope. The following parameters
+    are supported:
+      weight_decay- The weight decay to use for regularizing the model.
+      stddev-  Standard deviation for initialization, if negative uses xavier.
+      dropout_keep_prob- dropout keep probability
+      bn_decay- decay for the batch norm moving averages.
+
+  Returns:
+    An `arg_scope` to use for the mobilenet v2 model.
+  """
+  return lib.training_scope(**kwargs)
+
+
+__all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF']
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2_test.py
new file mode 100644
index 00000000..7ce1993c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet/mobilenet_v2_test.py
@@ -0,0 +1,189 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for mobilenet_v2."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import copy
+import tensorflow as tf
+from nets.mobilenet import conv_blocks as ops
+from nets.mobilenet import mobilenet
+from nets.mobilenet import mobilenet_v2
+
+
+slim = tf.contrib.slim
+
+
+def find_ops(optype):
+  """Find ops of a given type in graphdef or a graph.
+
+  Args:
+    optype: operation type (e.g. Conv2D)
+  Returns:
+     List of operations.
+  """
+  gd = tf.get_default_graph()
+  return [var for var in gd.get_operations() if var.type == optype]
+
+
+class MobilenetV2Test(tf.test.TestCase):
+
+  def setUp(self):
+    tf.reset_default_graph()
+
+  def testCreation(self):
+    spec = dict(mobilenet_v2.V2_DEF)
+    _, ep = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
+    num_convs = len(find_ops('Conv2D'))
+
+    # This is mostly a sanity test. No deep reason for these particular
+    # constants.
+    #
+    # All but first 2 and last one have  two convolutions, and there is one
+    # extra conv that is not in the spec. (logits)
+    self.assertEqual(num_convs, len(spec['spec']) * 2 - 2)
+    # Check that depthwise are exposed.
+    for i in range(2, 17):
+      self.assertIn('layer_%d/depthwise_output' % i, ep)
+
+  def testCreationNoClasses(self):
+    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
+    net, ep = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec,
+        num_classes=None)
+    self.assertIs(net, ep['global_pool'])
+
+  def testImageSizes(self):
+    for input_size, output_size in [(224, 7), (192, 6), (160, 5),
+                                    (128, 4), (96, 3)]:
+      tf.reset_default_graph()
+      _, ep = mobilenet_v2.mobilenet(
+          tf.placeholder(tf.float32, (10, input_size, input_size, 3)))
+
+      self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3],
+                       [output_size] * 2)
+
+  def testWithSplits(self):
+    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
+    spec['overrides'] = {
+        (ops.expanded_conv,): dict(split_expansion=2),
+    }
+    _, _ = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
+    num_convs = len(find_ops('Conv2D'))
+    # All but 3 op has 3 conv operatore, the remainign 3 have one
+    # and there is one unaccounted.
+    self.assertEqual(num_convs, len(spec['spec']) * 3 - 5)
+
+  def testWithOutputStride8(self):
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=8,
+        scope='MobilenetV2')
+    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28])
+
+  def testDivisibleBy(self):
+    tf.reset_default_graph()
+    mobilenet_v2.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        divisible_by=16,
+        min_depth=32)
+    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+    s = set(s)
+    self.assertSameElements([32, 64, 96, 160, 192, 320, 384, 576, 960, 1280,
+                             1001], s)
+
+  def testDivisibleByWithArgScope(self):
+    tf.reset_default_graph()
+    # Verifies that depth_multiplier arg scope actually works
+    # if no default min_depth is provided.
+    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
+      mobilenet_v2.mobilenet(
+          tf.placeholder(tf.float32, (10, 224, 224, 2)),
+          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
+      s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+      s = set(s)
+      self.assertSameElements(s, [32, 192, 128, 1001])
+
+  def testFineGrained(self):
+    tf.reset_default_graph()
+    # Verifies that depth_multiplier arg scope actually works
+    # if no default min_depth is provided.
+
+    mobilenet_v2.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 2)),
+        conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01,
+        finegrain_classification_mode=True)
+    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+    s = set(s)
+    # All convolutions will be 8->48, except for the last one.
+    self.assertSameElements(s, [8, 48, 1001, 1280])
+
+  def testMobilenetBase(self):
+    tf.reset_default_graph()
+    # Verifies that mobilenet_base returns pre-pooling layer.
+    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
+      net, _ = mobilenet_v2.mobilenet_base(
+          tf.placeholder(tf.float32, (10, 224, 224, 16)),
+          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
+      self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128])
+
+  def testWithOutputStride16(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=16)
+    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14])
+
+  def testWithOutputStride8AndExplicitPadding(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=8,
+        use_explicit_padding=True,
+        scope='MobilenetV2')
+    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28])
+
+  def testWithOutputStride16AndExplicitPadding(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=16,
+        use_explicit_padding=True)
+    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14])
+
+  def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
+    sc = mobilenet.training_scope(is_training=None)
+    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
+        slim.batch_norm)])
+
+  def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
+    sc = mobilenet.training_scope(is_training=False)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet.training_scope(is_training=True)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet.training_scope()
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.md b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.md
new file mode 100644
index 00000000..94e91a58
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.md
@@ -0,0 +1,136 @@
+# Mobilenet_v2
+For Mobilenet V2 see this file [mobilenet/README.md]
+
+# MobileNet_v1
+
+[MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with [TensorFlow Mobile](https://www.tensorflow.org/mobile/).
+
+MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
+
+![alt text](mobilenet_v1.png "MobileNet Graph")
+
+# Pre-trained Models
+
+Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. These MobileNet models have been trained on the
+[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/)
+image classification dataset. Accuracies were computed by evaluating using a single image crop.
+
+Model  | Million MACs | Million Parameters | Top-1 Accuracy| Top-5 Accuracy |
+:----:|:------------:|:----------:|:-------:|:-------:|
+[MobileNet_v1_1.0_224](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz)|569|4.24|70.9|89.9|
+[MobileNet_v1_1.0_192](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_192.tgz)|418|4.24|70.0|89.2|
+[MobileNet_v1_1.0_160](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_160.tgz)|291|4.24|68.0|87.7|
+[MobileNet_v1_1.0_128](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_128.tgz)|186|4.24|65.2|85.8|
+[MobileNet_v1_0.75_224](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_224.tgz)|317|2.59|68.4|88.2|
+[MobileNet_v1_0.75_192](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_192.tgz)|233|2.59|67.2|87.3|
+[MobileNet_v1_0.75_160](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160.tgz)|162|2.59|65.3|86.0|
+[MobileNet_v1_0.75_128](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_128.tgz)|104|2.59|62.1|83.9|
+[MobileNet_v1_0.50_224](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_224.tgz)|150|1.34|63.3|84.9|
+[MobileNet_v1_0.50_192](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_192.tgz)|110|1.34|61.7|83.6|
+[MobileNet_v1_0.50_160](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_160.tgz)|77|1.34|59.1|81.9|
+[MobileNet_v1_0.50_128](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_128.tgz)|49|1.34|56.3|79.4|
+[MobileNet_v1_0.25_224](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_224.tgz)|41|0.47|49.8|74.2|
+[MobileNet_v1_0.25_192](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192.tgz)|34|0.47|47.7|72.3|
+[MobileNet_v1_0.25_160](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_160.tgz)|21|0.47|45.5|70.3|
+[MobileNet_v1_0.25_128](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128.tgz)|14|0.47|41.5|66.3|
+[MobileNet_v1_1.0_224_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz)|569|4.24|70.1|88.9|
+[MobileNet_v1_1.0_192_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_192_quant.tgz)|418|4.24|69.2|88.3|
+[MobileNet_v1_1.0_160_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_160_quant.tgz)|291|4.24|67.2|86.7|
+[MobileNet_v1_1.0_128_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_128_quant.tgz)|186|4.24|63.4|84.2|
+[MobileNet_v1_0.75_224_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_224_quant.tgz)|317|2.59|66.8|87.0|
+[MobileNet_v1_0.75_192_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_192_quant.tgz)|233|2.59|66.1|86.4|
+[MobileNet_v1_0.75_160_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160_quant.tgz)|162|2.59|62.3|83.8|
+[MobileNet_v1_0.75_128_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_128_quant.tgz)|104|2.59|55.8|78.8|
+[MobileNet_v1_0.50_224_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_224_quant.tgz)|150|1.34|60.7|83.2|
+[MobileNet_v1_0.50_192_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_192_quant.tgz)|110|1.34|60.0|82.2|
+[MobileNet_v1_0.50_160_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_160_quant.tgz)|77|1.34|57.7|80.4|
+[MobileNet_v1_0.50_128_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_128_quant.tgz)|49|1.34|54.5|77.7|
+[MobileNet_v1_0.25_224_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_224_quant.tgz)|41|0.47|48.0|72.8|
+[MobileNet_v1_0.25_192_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192_quant.tgz)|34|0.47|46.0|71.2|
+[MobileNet_v1_0.25_160_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_160_quant.tgz)|21|0.47|43.4|68.5|
+[MobileNet_v1_0.25_128_quant](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128_quant.tgz)|14|0.47|39.5|64.4|
+
+Revisions to models:
+* July 12, 2018: Update to TFLite models that fixes an accuracy issue resolved by making conversion support weights with narrow_range. We now report validation on the actual TensorFlow Lite model rather than the emulated quantization number of TensorFlow.
+* August 2, 2018: Update to TFLite models that fixes an accuracy issue resolved by making sure the numerics of quantization match TF quantized training accurately.
+
+The linked model tar files contain the following:
+* Trained model checkpoints
+* Eval graph text protos (to be easily viewed)
+* Frozen trained models
+* Info file containing input and output information
+* Converted [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) flatbuffer model
+
+Note that quantized model GraphDefs are still float models, they just have FakeQuantization
+operation embedded to simulate quantization. These are converted by [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/)
+to be fully quantized. The final effect of quantization can be seen by comparing the frozen fake
+quantized graph to the size of the TFLite flatbuffer, i.e. The TFLite flatbuffer is about 1/4
+the size.
+For more information on the quantization techniques used here, see
+[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize).
+
+Here is an example of how to download the MobileNet_v1_1.0_224 checkpoint:
+
+```shell
+$ CHECKPOINT_DIR=/tmp/checkpoints
+$ mkdir ${CHECKPOINT_DIR}
+$ wget http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz
+$ tar -xvf mobilenet_v1_1.0_224.tgz
+$ mv mobilenet_v1_1.0_224.ckpt.* ${CHECKPOINT_DIR}
+```
+
+# MobileNet V1 scripts
+
+This package contains scripts for training floating point and eight-bit fixed
+point TensorFlow models.
+
+Quantization tools used are described in [contrib/quantize](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize).
+
+Conversion to fully quantized models for mobile can be done through [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/).
+
+## Usage
+
+### Build for GPU
+
+```
+$ bazel build -c opt --config=cuda mobilenet_v1_{eval,train}
+```
+
+### Running
+
+#### Float Training and Eval
+
+Train:
+
+```
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints"
+```
+
+Eval:
+
+```
+$ ./bazel-bin/mobilenet_v1_eval --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints"
+```
+
+#### Quantized Training and Eval
+
+Train from preexisting float checkpoint:
+
+```
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" \
+  --quantize=True --fine_tune_checkpoint=float/checkpoint/path
+```
+
+Train from scratch:
+
+```
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" --quantize=True
+```
+
+Eval:
+
+```
+$ ./bazel-bin/mobilenet_v1_eval --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" --quantize=True
+```
+
+The resulting float and quantized models can be run on-device via [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/).
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.py
new file mode 100644
index 00000000..413ba200
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1.py
@@ -0,0 +1,476 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""MobileNet v1.
+
+MobileNet is a general architecture and can be used for multiple use cases.
+Depending on the use case, it can use different input layer size and different
+head (for example: embeddings, localization and classification).
+
+As described in https://arxiv.org/abs/1704.04861.
+
+  MobileNets: Efficient Convolutional Neural Networks for
+    Mobile Vision Applications
+  Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang,
+    Tobias Weyand, Marco Andreetto, Hartwig Adam
+
+100% Mobilenet V1 (base) with input size 224x224:
+
+See mobilenet_v1()
+
+Layer                                                     params           macs
+--------------------------------------------------------------------------------
+MobilenetV1/Conv2d_0/Conv2D:                                 864      10,838,016
+MobilenetV1/Conv2d_1_depthwise/depthwise:                    288       3,612,672
+MobilenetV1/Conv2d_1_pointwise/Conv2D:                     2,048      25,690,112
+MobilenetV1/Conv2d_2_depthwise/depthwise:                    576       1,806,336
+MobilenetV1/Conv2d_2_pointwise/Conv2D:                     8,192      25,690,112
+MobilenetV1/Conv2d_3_depthwise/depthwise:                  1,152       3,612,672
+MobilenetV1/Conv2d_3_pointwise/Conv2D:                    16,384      51,380,224
+MobilenetV1/Conv2d_4_depthwise/depthwise:                  1,152         903,168
+MobilenetV1/Conv2d_4_pointwise/Conv2D:                    32,768      25,690,112
+MobilenetV1/Conv2d_5_depthwise/depthwise:                  2,304       1,806,336
+MobilenetV1/Conv2d_5_pointwise/Conv2D:                    65,536      51,380,224
+MobilenetV1/Conv2d_6_depthwise/depthwise:                  2,304         451,584
+MobilenetV1/Conv2d_6_pointwise/Conv2D:                   131,072      25,690,112
+MobilenetV1/Conv2d_7_depthwise/depthwise:                  4,608         903,168
+MobilenetV1/Conv2d_7_pointwise/Conv2D:                   262,144      51,380,224
+MobilenetV1/Conv2d_8_depthwise/depthwise:                  4,608         903,168
+MobilenetV1/Conv2d_8_pointwise/Conv2D:                   262,144      51,380,224
+MobilenetV1/Conv2d_9_depthwise/depthwise:                  4,608         903,168
+MobilenetV1/Conv2d_9_pointwise/Conv2D:                   262,144      51,380,224
+MobilenetV1/Conv2d_10_depthwise/depthwise:                 4,608         903,168
+MobilenetV1/Conv2d_10_pointwise/Conv2D:                  262,144      51,380,224
+MobilenetV1/Conv2d_11_depthwise/depthwise:                 4,608         903,168
+MobilenetV1/Conv2d_11_pointwise/Conv2D:                  262,144      51,380,224
+MobilenetV1/Conv2d_12_depthwise/depthwise:                 4,608         225,792
+MobilenetV1/Conv2d_12_pointwise/Conv2D:                  524,288      25,690,112
+MobilenetV1/Conv2d_13_depthwise/depthwise:                 9,216         451,584
+MobilenetV1/Conv2d_13_pointwise/Conv2D:                1,048,576      51,380,224
+--------------------------------------------------------------------------------
+Total:                                                 3,185,088     567,716,352
+
+
+75% Mobilenet V1 (base) with input size 128x128:
+
+See mobilenet_v1_075()
+
+Layer                                                     params           macs
+--------------------------------------------------------------------------------
+MobilenetV1/Conv2d_0/Conv2D:                                 648       2,654,208
+MobilenetV1/Conv2d_1_depthwise/depthwise:                    216         884,736
+MobilenetV1/Conv2d_1_pointwise/Conv2D:                     1,152       4,718,592
+MobilenetV1/Conv2d_2_depthwise/depthwise:                    432         442,368
+MobilenetV1/Conv2d_2_pointwise/Conv2D:                     4,608       4,718,592
+MobilenetV1/Conv2d_3_depthwise/depthwise:                    864         884,736
+MobilenetV1/Conv2d_3_pointwise/Conv2D:                     9,216       9,437,184
+MobilenetV1/Conv2d_4_depthwise/depthwise:                    864         221,184
+MobilenetV1/Conv2d_4_pointwise/Conv2D:                    18,432       4,718,592
+MobilenetV1/Conv2d_5_depthwise/depthwise:                  1,728         442,368
+MobilenetV1/Conv2d_5_pointwise/Conv2D:                    36,864       9,437,184
+MobilenetV1/Conv2d_6_depthwise/depthwise:                  1,728         110,592
+MobilenetV1/Conv2d_6_pointwise/Conv2D:                    73,728       4,718,592
+MobilenetV1/Conv2d_7_depthwise/depthwise:                  3,456         221,184
+MobilenetV1/Conv2d_7_pointwise/Conv2D:                   147,456       9,437,184
+MobilenetV1/Conv2d_8_depthwise/depthwise:                  3,456         221,184
+MobilenetV1/Conv2d_8_pointwise/Conv2D:                   147,456       9,437,184
+MobilenetV1/Conv2d_9_depthwise/depthwise:                  3,456         221,184
+MobilenetV1/Conv2d_9_pointwise/Conv2D:                   147,456       9,437,184
+MobilenetV1/Conv2d_10_depthwise/depthwise:                 3,456         221,184
+MobilenetV1/Conv2d_10_pointwise/Conv2D:                  147,456       9,437,184
+MobilenetV1/Conv2d_11_depthwise/depthwise:                 3,456         221,184
+MobilenetV1/Conv2d_11_pointwise/Conv2D:                  147,456       9,437,184
+MobilenetV1/Conv2d_12_depthwise/depthwise:                 3,456          55,296
+MobilenetV1/Conv2d_12_pointwise/Conv2D:                  294,912       4,718,592
+MobilenetV1/Conv2d_13_depthwise/depthwise:                 6,912         110,592
+MobilenetV1/Conv2d_13_pointwise/Conv2D:                  589,824       9,437,184
+--------------------------------------------------------------------------------
+Total:                                                 1,800,144     106,002,432
+
+"""
+
+# Tensorflow mandates these.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import namedtuple
+import functools
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+# Conv and DepthSepConv namedtuple define layers of the MobileNet architecture
+# Conv defines 3x3 convolution layers
+# DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution.
+# stride is the stride of the convolution
+# depth is the number of channels or filters in a layer
+Conv = namedtuple('Conv', ['kernel', 'stride', 'depth'])
+DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth'])
+
+# MOBILENETV1_CONV_DEFS specifies the MobileNet body
+MOBILENETV1_CONV_DEFS = [
+    Conv(kernel=[3, 3], stride=2, depth=32),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=64),
+    DepthSepConv(kernel=[3, 3], stride=2, depth=128),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=128),
+    DepthSepConv(kernel=[3, 3], stride=2, depth=256),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=256),
+    DepthSepConv(kernel=[3, 3], stride=2, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=512),
+    DepthSepConv(kernel=[3, 3], stride=2, depth=1024),
+    DepthSepConv(kernel=[3, 3], stride=1, depth=1024)
+]
+
+
+def _fixed_padding(inputs, kernel_size, rate=1):
+  """Pads the input along the spatial dimensions independently of input size.
+
+  Pads the input such that if it was used in a convolution with 'VALID' padding,
+  the output would have the same dimensions as if the unpadded input was used
+  in a convolution with 'SAME' padding.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
+    rate: An integer, rate for atrous convolution.
+
+  Returns:
+    output: A tensor of size [batch, height_out, width_out, channels] with the
+      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+  """
+  kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
+                           kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
+  pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
+  pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
+  pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
+  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+                                  [pad_beg[1], pad_end[1]], [0, 0]])
+  return padded_inputs
+
+
+def mobilenet_v1_base(inputs,
+                      final_endpoint='Conv2d_13_pointwise',
+                      min_depth=8,
+                      depth_multiplier=1.0,
+                      conv_defs=None,
+                      output_stride=None,
+                      use_explicit_padding=False,
+                      scope=None):
+  """Mobilenet v1.
+
+  Constructs a Mobilenet v1 network from inputs to the given final endpoint.
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    final_endpoint: specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise',
+      'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise,
+      'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise',
+      'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise',
+      'Conv2d_12_pointwise', 'Conv2d_13_pointwise'].
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    conv_defs: A list of ConvDef namedtuples specifying the net architecture.
+    output_stride: An integer that specifies the requested ratio of input to
+      output spatial resolution. If not None, then we invoke atrous convolution
+      if necessary to prevent the network from reducing the spatial resolution
+      of the activation maps. Allowed values are 8 (accurate fully convolutional
+      mode), 16 (fast fully convolutional mode), 32 (classification mode).
+    use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+      inputs so that the output dimensions are the same as if 'SAME' padding
+      were used.
+    scope: Optional variable_scope.
+
+  Returns:
+    tensor_out: output tensor corresponding to the final_endpoint.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values,
+                or depth_multiplier <= 0, or the target output_stride is not
+                allowed.
+  """
+  depth = lambda d: max(int(d * depth_multiplier), min_depth)
+  end_points = {}
+
+  # Used to find thinned depths for each layer.
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+
+  if conv_defs is None:
+    conv_defs = MOBILENETV1_CONV_DEFS
+
+  if output_stride is not None and output_stride not in [8, 16, 32]:
+    raise ValueError('Only allowed output_stride values are 8, 16, 32.')
+
+  padding = 'SAME'
+  if use_explicit_padding:
+    padding = 'VALID'
+  with tf.variable_scope(scope, 'MobilenetV1', [inputs]):
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding=padding):
+      # The current_stride variable keeps track of the output stride of the
+      # activations, i.e., the running product of convolution strides up to the
+      # current network layer. This allows us to invoke atrous convolution
+      # whenever applying the next convolution would result in the activations
+      # having output stride larger than the target output_stride.
+      current_stride = 1
+
+      # The atrous convolution rate parameter.
+      rate = 1
+
+      net = inputs
+      for i, conv_def in enumerate(conv_defs):
+        end_point_base = 'Conv2d_%d' % i
+
+        if output_stride is not None and current_stride == output_stride:
+          # If we have reached the target output_stride, then we need to employ
+          # atrous convolution with stride=1 and multiply the atrous rate by the
+          # current unit's stride for use in subsequent layers.
+          layer_stride = 1
+          layer_rate = rate
+          rate *= conv_def.stride
+        else:
+          layer_stride = conv_def.stride
+          layer_rate = 1
+          current_stride *= conv_def.stride
+
+        if isinstance(conv_def, Conv):
+          end_point = end_point_base
+          if use_explicit_padding:
+            net = _fixed_padding(net, conv_def.kernel)
+          net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel,
+                            stride=conv_def.stride,
+                            scope=end_point)
+          end_points[end_point] = net
+          if end_point == final_endpoint:
+            return net, end_points
+
+        elif isinstance(conv_def, DepthSepConv):
+          end_point = end_point_base + '_depthwise'
+
+          # By passing filters=None
+          # separable_conv2d produces only a depthwise convolution layer
+          if use_explicit_padding:
+            net = _fixed_padding(net, conv_def.kernel, layer_rate)
+          net = slim.separable_conv2d(net, None, conv_def.kernel,
+                                      depth_multiplier=1,
+                                      stride=layer_stride,
+                                      rate=layer_rate,
+                                      scope=end_point)
+
+          end_points[end_point] = net
+          if end_point == final_endpoint:
+            return net, end_points
+
+          end_point = end_point_base + '_pointwise'
+
+          net = slim.conv2d(net, depth(conv_def.depth), [1, 1],
+                            stride=1,
+                            scope=end_point)
+
+          end_points[end_point] = net
+          if end_point == final_endpoint:
+            return net, end_points
+        else:
+          raise ValueError('Unknown convolution type %s for layer %d'
+                           % (conv_def.ltype, i))
+  raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def mobilenet_v1(inputs,
+                 num_classes=1000,
+                 dropout_keep_prob=0.999,
+                 is_training=True,
+                 min_depth=8,
+                 depth_multiplier=1.0,
+                 conv_defs=None,
+                 prediction_fn=tf.contrib.layers.softmax,
+                 spatial_squeeze=True,
+                 reuse=None,
+                 scope='MobilenetV1',
+                 global_pool=False):
+  """Mobilenet v1 model for classification.
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer
+      is omitted and the input features to the logits layer (before dropout)
+      are returned instead.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    is_training: whether is training or not.
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    conv_defs: A list of ConvDef namedtuples specifying the net architecture.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+    global_pool: Optional boolean flag to control the avgpooling before the
+      logits layer. If false or unset, pooling is done with a fixed window
+      that reduces default-sized inputs to 1x1, while larger inputs lead to
+      larger outputs. If true, any input size is pooled down to 1x1.
+
+  Returns:
+    net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
+      is a non-zero integer, or the non-dropped-out input to the logits layer
+      if num_classes is 0 or None.
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+
+  Raises:
+    ValueError: Input rank is invalid.
+  """
+  input_shape = inputs.get_shape().as_list()
+  if len(input_shape) != 4:
+    raise ValueError('Invalid input tensor rank, expected 4, was: %d' %
+                     len(input_shape))
+
+  with tf.variable_scope(scope, 'MobilenetV1', [inputs], reuse=reuse) as scope:
+    with slim.arg_scope([slim.batch_norm, slim.dropout],
+                        is_training=is_training):
+      net, end_points = mobilenet_v1_base(inputs, scope=scope,
+                                          min_depth=min_depth,
+                                          depth_multiplier=depth_multiplier,
+                                          conv_defs=conv_defs)
+      with tf.variable_scope('Logits'):
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+          end_points['global_pool'] = net
+        else:
+          # Pooling with a fixed kernel size.
+          kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
+          net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+                                scope='AvgPool_1a')
+          end_points['AvgPool_1a'] = net
+        if not num_classes:
+          return net, end_points
+        # 1 x 1 x 1024
+        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
+        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                             normalizer_fn=None, scope='Conv2d_1c_1x1')
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+      end_points['Logits'] = logits
+      if prediction_fn:
+        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+
+mobilenet_v1.default_image_size = 224
+
+
+def wrapped_partial(func, *args, **kwargs):
+  partial_func = functools.partial(func, *args, **kwargs)
+  functools.update_wrapper(partial_func, func)
+  return partial_func
+
+
+mobilenet_v1_075 = wrapped_partial(mobilenet_v1, depth_multiplier=0.75)
+mobilenet_v1_050 = wrapped_partial(mobilenet_v1, depth_multiplier=0.50)
+mobilenet_v1_025 = wrapped_partial(mobilenet_v1, depth_multiplier=0.25)
+
+
+def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
+  """Define kernel size which is automatically reduced for small input.
+
+  If the shape of the input images is unknown at graph construction time this
+  function assumes that the input images are large enough.
+
+  Args:
+    input_tensor: input tensor of size [batch_size, height, width, channels].
+    kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
+
+  Returns:
+    a tensor with the kernel size.
+  """
+  shape = input_tensor.get_shape().as_list()
+  if shape[1] is None or shape[2] is None:
+    kernel_size_out = kernel_size
+  else:
+    kernel_size_out = [min(shape[1], kernel_size[0]),
+                       min(shape[2], kernel_size[1])]
+  return kernel_size_out
+
+
+def mobilenet_v1_arg_scope(
+    is_training=True,
+    weight_decay=0.00004,
+    stddev=0.09,
+    regularize_depthwise=False,
+    batch_norm_decay=0.9997,
+    batch_norm_epsilon=0.001,
+    batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
+    normalizer_fn=slim.batch_norm):
+  """Defines the default MobilenetV1 arg scope.
+
+  Args:
+    is_training: Whether or not we're training the model. If this is set to
+      None, the parameter is not added to the batch_norm arg_scope.
+    weight_decay: The weight decay to use for regularizing the model.
+    stddev: The standard deviation of the trunctated normal weight initializer.
+    regularize_depthwise: Whether or not apply regularization on depthwise.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+    batch_norm_updates_collections: Collection for the update ops for
+      batch norm.
+    normalizer_fn: Normalization function to apply after convolution.
+
+  Returns:
+    An `arg_scope` to use for the mobilenet v1 model.
+  """
+  batch_norm_params = {
+      'center': True,
+      'scale': True,
+      'decay': batch_norm_decay,
+      'epsilon': batch_norm_epsilon,
+      'updates_collections': batch_norm_updates_collections,
+  }
+  if is_training is not None:
+    batch_norm_params['is_training'] = is_training
+
+  # Set weight_decay for weights in Conv and DepthSepConv layers.
+  weights_init = tf.truncated_normal_initializer(stddev=stddev)
+  regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
+  if regularize_depthwise:
+    depthwise_regularizer = regularizer
+  else:
+    depthwise_regularizer = None
+  with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                      weights_initializer=weights_init,
+                      activation_fn=tf.nn.relu6, normalizer_fn=normalizer_fn):
+    with slim.arg_scope([slim.batch_norm], **batch_norm_params):
+      with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer):
+        with slim.arg_scope([slim.separable_conv2d],
+                            weights_regularizer=depthwise_regularizer) as sc:
+          return sc
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_eval.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_eval.py
new file mode 100644
index 00000000..5b42a382
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_eval.py
@@ -0,0 +1,152 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Validate mobilenet_v1 with options for quantization."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import tensorflow as tf
+
+from datasets import dataset_factory
+from nets import mobilenet_v1
+from preprocessing import preprocessing_factory
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+
+flags.DEFINE_string('master', '', 'Session master')
+flags.DEFINE_integer('batch_size', 250, 'Batch size')
+flags.DEFINE_integer('num_classes', 1001, 'Number of classes to distinguish')
+flags.DEFINE_integer('num_examples', 50000, 'Number of examples to evaluate')
+flags.DEFINE_integer('image_size', 224, 'Input image resolution')
+flags.DEFINE_float('depth_multiplier', 1.0, 'Depth multiplier for mobilenet')
+flags.DEFINE_bool('quantize', False, 'Quantize training')
+flags.DEFINE_string('checkpoint_dir', '', 'The directory for checkpoints')
+flags.DEFINE_string('eval_dir', '', 'Directory for writing eval event logs')
+flags.DEFINE_string('dataset_dir', '', 'Location of dataset')
+
+FLAGS = flags.FLAGS
+
+
+def imagenet_input(is_training):
+  """Data reader for imagenet.
+
+  Reads in imagenet data and performs pre-processing on the images.
+
+  Args:
+     is_training: bool specifying if train or validation dataset is needed.
+  Returns:
+     A batch of images and labels.
+  """
+  if is_training:
+    dataset = dataset_factory.get_dataset('imagenet', 'train',
+                                          FLAGS.dataset_dir)
+  else:
+    dataset = dataset_factory.get_dataset('imagenet', 'validation',
+                                          FLAGS.dataset_dir)
+
+  provider = slim.dataset_data_provider.DatasetDataProvider(
+      dataset,
+      shuffle=is_training,
+      common_queue_capacity=2 * FLAGS.batch_size,
+      common_queue_min=FLAGS.batch_size)
+  [image, label] = provider.get(['image', 'label'])
+
+  image_preprocessing_fn = preprocessing_factory.get_preprocessing(
+      'mobilenet_v1', is_training=is_training)
+
+  image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
+
+  images, labels = tf.train.batch(
+      tensors=[image, label],
+      batch_size=FLAGS.batch_size,
+      num_threads=4,
+      capacity=5 * FLAGS.batch_size)
+  return images, labels
+
+
+def metrics(logits, labels):
+  """Specify the metrics for eval.
+
+  Args:
+    logits: Logits output from the graph.
+    labels: Ground truth labels for inputs.
+
+  Returns:
+     Eval Op for the graph.
+  """
+  labels = tf.squeeze(labels)
+  names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
+      'Accuracy': tf.metrics.accuracy(tf.argmax(logits, 1), labels),
+      'Recall_5': tf.metrics.recall_at_k(labels, logits, 5),
+  })
+  for name, value in names_to_values.iteritems():
+    slim.summaries.add_scalar_summary(
+        value, name, prefix='eval', print_summary=True)
+  return names_to_updates.values()
+
+
+def build_model():
+  """Build the mobilenet_v1 model for evaluation.
+
+  Returns:
+    g: graph with rewrites after insertion of quantization ops and batch norm
+    folding.
+    eval_ops: eval ops for inference.
+    variables_to_restore: List of variables to restore from checkpoint.
+  """
+  g = tf.Graph()
+  with g.as_default():
+    inputs, labels = imagenet_input(is_training=False)
+
+    scope = mobilenet_v1.mobilenet_v1_arg_scope(
+        is_training=False, weight_decay=0.0)
+    with slim.arg_scope(scope):
+      logits, _ = mobilenet_v1.mobilenet_v1(
+          inputs,
+          is_training=False,
+          depth_multiplier=FLAGS.depth_multiplier,
+          num_classes=FLAGS.num_classes)
+
+    if FLAGS.quantize:
+      tf.contrib.quantize.create_eval_graph()
+
+    eval_ops = metrics(logits, labels)
+
+  return g, eval_ops
+
+
+def eval_model():
+  """Evaluates mobilenet_v1."""
+  g, eval_ops = build_model()
+  with g.as_default():
+    num_batches = math.ceil(FLAGS.num_examples / float(FLAGS.batch_size))
+    slim.evaluation.evaluate_once(
+        FLAGS.master,
+        FLAGS.checkpoint_dir,
+        logdir=FLAGS.eval_dir,
+        num_evals=num_batches,
+        eval_op=eval_ops)
+
+
+def main(unused_arg):
+  eval_model()
+
+
+if __name__ == '__main__':
+  tf.app.run(main)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_test.py
new file mode 100644
index 00000000..669a3ae3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_test.py
@@ -0,0 +1,534 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Tests for MobileNet v1."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+class MobilenetV1Test(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith(
+        'MobilenetV1/Logits/SpatialSqueeze'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+    self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
+    self.assertFalse('Logits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
+    self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
+    self.assertListEqual(net.get_shape().as_list(),
+                         [batch_size, 7, 7, 1024])
+    expected_endpoints = ['Conv2d_0',
+                          'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+                          'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+                          'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
+                          'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
+                          'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
+                          'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
+                          'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
+                          'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
+                          'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
+                          'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
+                          'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
+                          'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
+                          'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpoint(self):
+    batch_size = 5
+    height, width = 224, 224
+    endpoints = ['Conv2d_0',
+                 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+                 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+                 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
+                 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
+                 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
+                 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
+                 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
+                 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
+                 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
+                 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
+                 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
+                 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
+                 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, height, width, 3))
+        out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
+            inputs, final_endpoint=endpoint)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'MobilenetV1/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+
+  def testBuildCustomNetworkUsingConvDefs(self):
+    batch_size = 5
+    height, width = 224, 224
+    conv_defs = [
+        mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
+        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
+        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
+        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
+    ]
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    net, end_points = mobilenet_v1.mobilenet_v1_base(
+        inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
+    self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
+    self.assertListEqual(net.get_shape().as_list(),
+                         [batch_size, 56, 56, 512])
+    expected_endpoints = ['Conv2d_0',
+                          'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+                          'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+                          'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildAndCheckAllEndPointsUptoConv2d_13(self):
+    batch_size = 5
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                        normalizer_fn=slim.batch_norm):
+      _, end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, final_endpoint='Conv2d_13_pointwise')
+      _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, final_endpoint='Conv2d_13_pointwise',
+          use_explicit_padding=True)
+    endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+                        'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+                        'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+                        'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+                        'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+                        'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
+                        'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_12_depthwise': [batch_size, 7, 7, 512],
+                        'Conv2d_12_pointwise': [batch_size, 7, 7, 1024],
+                        'Conv2d_13_depthwise': [batch_size, 7, 7, 1024],
+                        'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+    self.assertItemsEqual(endpoints_shapes.keys(),
+                          explicit_padding_end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in explicit_padding_end_points)
+      self.assertListEqual(
+          explicit_padding_end_points[endpoint_name].get_shape().as_list(),
+          expected_shape)
+
+  def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self):
+    batch_size = 5
+    height, width = 224, 224
+    output_stride = 16
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                        normalizer_fn=slim.batch_norm):
+      _, end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, output_stride=output_stride,
+          final_endpoint='Conv2d_13_pointwise')
+      _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, output_stride=output_stride,
+          final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
+    endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+                        'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+                        'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+                        'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+                        'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+                        'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
+                        'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
+                        'Conv2d_12_depthwise': [batch_size, 14, 14, 512],
+                        'Conv2d_12_pointwise': [batch_size, 14, 14, 1024],
+                        'Conv2d_13_depthwise': [batch_size, 14, 14, 1024],
+                        'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+    self.assertItemsEqual(endpoints_shapes.keys(),
+                          explicit_padding_end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in explicit_padding_end_points)
+      self.assertListEqual(
+          explicit_padding_end_points[endpoint_name].get_shape().as_list(),
+          expected_shape)
+
+  def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
+    batch_size = 5
+    height, width = 224, 224
+    output_stride = 8
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                        normalizer_fn=slim.batch_norm):
+      _, end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, output_stride=output_stride,
+          final_endpoint='Conv2d_13_pointwise')
+      _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, output_stride=output_stride,
+          final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
+    endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+                        'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+                        'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+                        'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+                        'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+                        'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+                        'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+                        'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+                        'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+                        'Conv2d_6_depthwise': [batch_size, 28, 28, 256],
+                        'Conv2d_6_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_7_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_7_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_8_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_8_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_9_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_9_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_10_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_10_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_11_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_11_pointwise': [batch_size, 28, 28, 512],
+                        'Conv2d_12_depthwise': [batch_size, 28, 28, 512],
+                        'Conv2d_12_pointwise': [batch_size, 28, 28, 1024],
+                        'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
+                        'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+    self.assertItemsEqual(endpoints_shapes.keys(),
+                          explicit_padding_end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in explicit_padding_end_points)
+      self.assertListEqual(
+          explicit_padding_end_points[endpoint_name].get_shape().as_list(),
+          expected_shape)
+
+  def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
+    batch_size = 5
+    height, width = 128, 128
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                        normalizer_fn=slim.batch_norm):
+      _, end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75)
+      _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
+          inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75,
+          use_explicit_padding=True)
+    # For the Conv2d_0 layer FaceNet has depth=16
+    endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24],
+                        'Conv2d_1_depthwise': [batch_size, 64, 64, 24],
+                        'Conv2d_1_pointwise': [batch_size, 64, 64, 48],
+                        'Conv2d_2_depthwise': [batch_size, 32, 32, 48],
+                        'Conv2d_2_pointwise': [batch_size, 32, 32, 96],
+                        'Conv2d_3_depthwise': [batch_size, 32, 32, 96],
+                        'Conv2d_3_pointwise': [batch_size, 32, 32, 96],
+                        'Conv2d_4_depthwise': [batch_size, 16, 16, 96],
+                        'Conv2d_4_pointwise': [batch_size, 16, 16, 192],
+                        'Conv2d_5_depthwise': [batch_size, 16, 16, 192],
+                        'Conv2d_5_pointwise': [batch_size, 16, 16, 192],
+                        'Conv2d_6_depthwise': [batch_size, 8, 8, 192],
+                        'Conv2d_6_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_7_depthwise': [batch_size, 8, 8, 384],
+                        'Conv2d_7_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_8_depthwise': [batch_size, 8, 8, 384],
+                        'Conv2d_8_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_9_depthwise': [batch_size, 8, 8, 384],
+                        'Conv2d_9_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_10_depthwise': [batch_size, 8, 8, 384],
+                        'Conv2d_10_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_11_depthwise': [batch_size, 8, 8, 384],
+                        'Conv2d_11_pointwise': [batch_size, 8, 8, 384],
+                        'Conv2d_12_depthwise': [batch_size, 4, 4, 384],
+                        'Conv2d_12_pointwise': [batch_size, 4, 4, 768],
+                        'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
+                        'Conv2d_13_pointwise': [batch_size, 4, 4, 768]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+    self.assertItemsEqual(endpoints_shapes.keys(),
+                          explicit_padding_end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.items():
+      self.assertTrue(endpoint_name in explicit_padding_end_points)
+      self.assertListEqual(
+          explicit_padding_end_points[endpoint_name].get_shape().as_list(),
+          expected_shape)
+
+  def testModelHasExpectedNumberOfParameters(self):
+    batch_size = 5
+    height, width = 224, 224
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+                        normalizer_fn=slim.batch_norm):
+      mobilenet_v1.mobilenet_v1_base(inputs)
+      total_params, _ = slim.model_analyzer.analyze_vars(
+          slim.get_model_variables())
+      self.assertAlmostEqual(3217920, total_params)
+
+  def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
+
+    _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=0.5)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(0.5 * original_depth, new_depth)
+
+  def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+
+    endpoint_keys = [key for key in end_points.keys()
+                     if key.startswith('Mixed') or key.startswith('Conv')]
+
+    _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
+        inputs, num_classes, scope='depth_multiplied_net',
+        depth_multiplier=2.0)
+
+    for key in endpoint_keys:
+      original_depth = end_points[key].get_shape().as_list()[3]
+      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+      self.assertEqual(2.0 * original_depth, new_depth)
+
+  def testRaiseValueErrorWithInvalidDepthMultiplier(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    with self.assertRaises(ValueError):
+      _ = mobilenet_v1.mobilenet_v1(
+          inputs, num_classes, depth_multiplier=-0.1)
+    with self.assertRaises(ValueError):
+      _ = mobilenet_v1.mobilenet_v1(
+          inputs, num_classes, depth_multiplier=0.0)
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    height, width = 112, 112
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    pre_pool = end_points['Conv2d_13_pointwise']
+    self.assertListEqual(pre_pool.get_shape().as_list(),
+                         [batch_size, 4, 4, 1024])
+
+  def testUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_13_pointwise']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
+
+  def testGlobalPoolUnknownImageShape(self):
+    tf.reset_default_graph()
+    batch_size = 1
+    height, width = 250, 300
+    num_classes = 1000
+    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
+      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes,
+                                                     global_pool=True)
+      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      pre_pool = end_points['Conv2d_13_pointwise']
+      feed_dict = {inputs: input_np}
+      tf.global_variables_initializer().run()
+      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
+      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
+
+  def testUnknowBatchSize(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+    logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, num_classes])
+    images = tf.random_uniform((batch_size, height, width, 3))
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+    logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
+                                          is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 5
+    eval_batch_size = 2
+    height, width = 150, 150
+    num_classes = 1000
+
+    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+    mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
+    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+    logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
+                                          reuse=True)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (eval_batch_size,))
+
+  def testLogitsNotSqueezed(self):
+    num_classes = 25
+    images = tf.random_uniform([1, 224, 224, 3])
+    logits, _ = mobilenet_v1.mobilenet_v1(images,
+                                          num_classes=num_classes,
+                                          spatial_squeeze=False)
+
+    with self.test_session() as sess:
+      tf.global_variables_initializer().run()
+      logits_out = sess.run(logits)
+      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
+
+  def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
+    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)
+    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
+        slim.batch_norm)])
+
+  def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
+    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet_v1.mobilenet_v1_arg_scope()
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_train.py b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_train.py
new file mode 100644
index 00000000..f8328aa3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/mobilenet_v1_train.py
@@ -0,0 +1,212 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Build and train mobilenet_v1 with options for quantization."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from datasets import dataset_factory
+from nets import mobilenet_v1
+from preprocessing import preprocessing_factory
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+
+flags.DEFINE_string('master', '', 'Session master')
+flags.DEFINE_integer('task', 0, 'Task')
+flags.DEFINE_integer('ps_tasks', 0, 'Number of ps')
+flags.DEFINE_integer('batch_size', 64, 'Batch size')
+flags.DEFINE_integer('num_classes', 1001, 'Number of classes to distinguish')
+flags.DEFINE_integer('number_of_steps', None,
+                     'Number of training steps to perform before stopping')
+flags.DEFINE_integer('image_size', 224, 'Input image resolution')
+flags.DEFINE_float('depth_multiplier', 1.0, 'Depth multiplier for mobilenet')
+flags.DEFINE_bool('quantize', False, 'Quantize training')
+flags.DEFINE_string('fine_tune_checkpoint', '',
+                    'Checkpoint from which to start finetuning.')
+flags.DEFINE_string('checkpoint_dir', '',
+                    'Directory for writing training checkpoints and logs')
+flags.DEFINE_string('dataset_dir', '', 'Location of dataset')
+flags.DEFINE_integer('log_every_n_steps', 100, 'Number of steps per log')
+flags.DEFINE_integer('save_summaries_secs', 100,
+                     'How often to save summaries, secs')
+flags.DEFINE_integer('save_interval_secs', 100,
+                     'How often to save checkpoints, secs')
+
+FLAGS = flags.FLAGS
+
+_LEARNING_RATE_DECAY_FACTOR = 0.94
+
+
+def get_learning_rate():
+  if FLAGS.fine_tune_checkpoint:
+    # If we are fine tuning a checkpoint we need to start at a lower learning
+    # rate since we are farther along on training.
+    return 1e-4
+  else:
+    return 0.045
+
+
+def get_quant_delay():
+  if FLAGS.fine_tune_checkpoint:
+    # We can start quantizing immediately if we are finetuning.
+    return 0
+  else:
+    # We need to wait for the model to train a bit before we quantize if we are
+    # training from scratch.
+    return 250000
+
+
+def imagenet_input(is_training):
+  """Data reader for imagenet.
+
+  Reads in imagenet data and performs pre-processing on the images.
+
+  Args:
+     is_training: bool specifying if train or validation dataset is needed.
+  Returns:
+     A batch of images and labels.
+  """
+  if is_training:
+    dataset = dataset_factory.get_dataset('imagenet', 'train',
+                                          FLAGS.dataset_dir)
+  else:
+    dataset = dataset_factory.get_dataset('imagenet', 'validation',
+                                          FLAGS.dataset_dir)
+
+  provider = slim.dataset_data_provider.DatasetDataProvider(
+      dataset,
+      shuffle=is_training,
+      common_queue_capacity=2 * FLAGS.batch_size,
+      common_queue_min=FLAGS.batch_size)
+  [image, label] = provider.get(['image', 'label'])
+
+  image_preprocessing_fn = preprocessing_factory.get_preprocessing(
+      'mobilenet_v1', is_training=is_training)
+
+  image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
+
+  images, labels = tf.train.batch(
+      [image, label],
+      batch_size=FLAGS.batch_size,
+      num_threads=4,
+      capacity=5 * FLAGS.batch_size)
+  labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
+  return images, labels
+
+
+def build_model():
+  """Builds graph for model to train with rewrites for quantization.
+
+  Returns:
+    g: Graph with fake quantization ops and batch norm folding suitable for
+    training quantized weights.
+    train_tensor: Train op for execution during training.
+  """
+  g = tf.Graph()
+  with g.as_default(), tf.device(
+      tf.train.replica_device_setter(FLAGS.ps_tasks)):
+    inputs, labels = imagenet_input(is_training=True)
+    with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)):
+      logits, _ = mobilenet_v1.mobilenet_v1(
+          inputs,
+          is_training=True,
+          depth_multiplier=FLAGS.depth_multiplier,
+          num_classes=FLAGS.num_classes)
+
+    tf.losses.softmax_cross_entropy(labels, logits)
+
+    # Call rewriter to produce graph with fake quant ops and folded batch norms
+    # quant_delay delays start of quantization till quant_delay steps, allowing
+    # for better model accuracy.
+    if FLAGS.quantize:
+      tf.contrib.quantize.create_training_graph(quant_delay=get_quant_delay())
+
+    total_loss = tf.losses.get_total_loss(name='total_loss')
+    # Configure the learning rate using an exponential decay.
+    num_epochs_per_decay = 2.5
+    imagenet_size = 1271167
+    decay_steps = int(imagenet_size / FLAGS.batch_size * num_epochs_per_decay)
+
+    learning_rate = tf.train.exponential_decay(
+        get_learning_rate(),
+        tf.train.get_or_create_global_step(),
+        decay_steps,
+        _LEARNING_RATE_DECAY_FACTOR,
+        staircase=True)
+    opt = tf.train.GradientDescentOptimizer(learning_rate)
+
+    train_tensor = slim.learning.create_train_op(
+        total_loss,
+        optimizer=opt)
+
+  slim.summaries.add_scalar_summary(total_loss, 'total_loss', 'losses')
+  slim.summaries.add_scalar_summary(learning_rate, 'learning_rate', 'training')
+  return g, train_tensor
+
+
+def get_checkpoint_init_fn():
+  """Returns the checkpoint init_fn if the checkpoint is provided."""
+  if FLAGS.fine_tune_checkpoint:
+    variables_to_restore = slim.get_variables_to_restore()
+    global_step_reset = tf.assign(tf.train.get_or_create_global_step(), 0)
+    # When restoring from a floating point model, the min/max values for
+    # quantized weights and activations are not present.
+    # We instruct slim to ignore variables that are missing during restoration
+    # by setting ignore_missing_vars=True
+    slim_init_fn = slim.assign_from_checkpoint_fn(
+        FLAGS.fine_tune_checkpoint,
+        variables_to_restore,
+        ignore_missing_vars=True)
+
+    def init_fn(sess):
+      slim_init_fn(sess)
+      # If we are restoring from a floating point model, we need to initialize
+      # the global step to zero for the exponential decay to result in
+      # reasonable learning rates.
+      sess.run(global_step_reset)
+    return init_fn
+  else:
+    return None
+
+
+def train_model():
+  """Trains mobilenet_v1."""
+  g, train_tensor = build_model()
+  with g.as_default():
+    slim.learning.train(
+        train_tensor,
+        FLAGS.checkpoint_dir,
+        is_chief=(FLAGS.task == 0),
+        master=FLAGS.master,
+        log_every_n_steps=FLAGS.log_every_n_steps,
+        graph=g,
+        number_of_steps=FLAGS.number_of_steps,
+        save_summaries_secs=FLAGS.save_summaries_secs,
+        save_interval_secs=FLAGS.save_interval_secs,
+        init_fn=get_checkpoint_init_fn(),
+        global_step=tf.train.get_global_step())
+
+
+def main(unused_arg):
+  train_model()
+
+
+if __name__ == '__main__':
+  tf.app.run(main)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/README.md b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/README.md
new file mode 100644
index 00000000..3955ad18
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/README.md
@@ -0,0 +1,64 @@
+# TensorFlow-Slim NASNet-A Implementation/Checkpoints
+This directory contains the code for the NASNet-A model from the paper
+[Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) by Zoph et al.
+In nasnet.py there are three different configurations of NASNet-A that are implementented. One of the models is the NASNet-A built for CIFAR-10 and the
+other two are variants of NASNet-A trained on ImageNet, which are listed below.
+
+# Pre-Trained Models
+Two NASNet-A checkpoints are available that have been trained on the
+[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/)
+image classification dataset. Accuracies were computed by evaluating using a single image crop.
+
+Model Checkpoint | Million MACs | Million Parameters | Top-1 Accuracy| Top-5 Accuracy |
+:----:|:------------:|:----------:|:-------:|:-------:|
+[NASNet-A_Mobile_224](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz)|564|5.3|74.0|91.6|
+[NASNet-A_Large_331](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz)|23800|88.9|82.7|96.2|
+
+
+Here is an example of how to download the NASNet-A_Mobile_224 checkpoint. The way to download the NASNet-A_Large_331 is the same.
+
+```shell
+CHECKPOINT_DIR=/tmp/checkpoints
+mkdir ${CHECKPOINT_DIR}
+cd ${CHECKPOINT_DIR}
+wget https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz
+tar -xvf nasnet-a_mobile_04_10_2017.tar.gz
+rm nasnet-a_mobile_04_10_2017.tar.gz
+```
+More information on integrating NASNet Models into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/research/slim/README.md).
+
+To get started running models on-device go to [TensorFlow Mobile](https://www.tensorflow.org/mobile/).
+
+## Sample Commands for using NASNet-A Mobile and Large Checkpoints for Inference
+-------
+Run eval with the NASNet-A mobile ImageNet model
+
+```shell
+DATASET_DIR=/tmp/imagenet
+EVAL_DIR=/tmp/tfmodel/eval
+CHECKPOINT_DIR=/tmp/checkpoints/model.ckpt
+python tensorflow_models/research/slim/eval_image_classifier \
+--checkpoint_path=${CHECKPOINT_DIR} \
+--eval_dir=${EVAL_DIR} \
+--dataset_dir=${DATASET_DIR} \
+--dataset_name=imagenet \
+--dataset_split_name=validation \
+--model_name=nasnet_mobile \
+--eval_image_size=224
+```
+
+Run eval with the NASNet-A large ImageNet model
+
+```shell
+DATASET_DIR=/tmp/imagenet
+EVAL_DIR=/tmp/tfmodel/eval
+CHECKPOINT_DIR=/tmp/checkpoints/model.ckpt
+python tensorflow_models/research/slim/eval_image_classifier \
+--checkpoint_path=${CHECKPOINT_DIR} \
+--eval_dir=${EVAL_DIR} \
+--dataset_dir=${DATASET_DIR} \
+--dataset_name=imagenet \
+--dataset_split_name=validation \
+--model_name=nasnet_large \
+--eval_image_size=331
+```
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet.py
new file mode 100644
index 00000000..a5dc0dc1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet.py
@@ -0,0 +1,547 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for the NASNet classification networks.
+
+Paper: https://arxiv.org/abs/1707.07012
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import tensorflow as tf
+
+from nets.nasnet import nasnet_utils
+
+arg_scope = tf.contrib.framework.arg_scope
+slim = tf.contrib.slim
+
+
+# Notes for training NASNet Cifar Model
+# -------------------------------------
+# batch_size: 32
+# learning rate: 0.025
+# cosine (single period) learning rate decay
+# auxiliary head loss weighting: 0.4
+# clip global norm of all gradients by 5
+def cifar_config():
+  return tf.contrib.training.HParams(
+      stem_multiplier=3.0,
+      drop_path_keep_prob=0.6,
+      num_cells=18,
+      use_aux_head=1,
+      num_conv_filters=32,
+      dense_dropout_keep_prob=1.0,
+      filter_scaling_rate=2.0,
+      num_reduction_layers=2,
+      data_format='NHWC',
+      skip_reduction_layer_input=0,
+      # 600 epochs with a batch size of 32
+      # This is used for the drop path probabilities since it needs to increase
+      # the drop out probability over the course of training.
+      total_training_steps=937500,
+      use_bounded_activation=False,
+  )
+
+
+# Notes for training large NASNet model on ImageNet
+# -------------------------------------
+# batch size (per replica): 16
+# learning rate: 0.015 * 100
+# learning rate decay factor: 0.97
+# num epochs per decay: 2.4
+# sync sgd with 100 replicas
+# auxiliary head loss weighting: 0.4
+# label smoothing: 0.1
+# clip global norm of all gradients by 10
+def large_imagenet_config():
+  return tf.contrib.training.HParams(
+      stem_multiplier=3.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=18,
+      filter_scaling_rate=2.0,
+      num_conv_filters=168,
+      drop_path_keep_prob=0.7,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      data_format='NHWC',
+      skip_reduction_layer_input=1,
+      total_training_steps=250000,
+      use_bounded_activation=False,
+  )
+
+
+# Notes for training the mobile NASNet ImageNet model
+# -------------------------------------
+# batch size (per replica): 32
+# learning rate: 0.04 * 50
+# learning rate scaling factor: 0.97
+# num epochs per decay: 2.4
+# sync sgd with 50 replicas
+# auxiliary head weighting: 0.4
+# label smoothing: 0.1
+# clip global norm of all gradients by 10
+def mobile_imagenet_config():
+  return tf.contrib.training.HParams(
+      stem_multiplier=1.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=12,
+      filter_scaling_rate=2.0,
+      drop_path_keep_prob=1.0,
+      num_conv_filters=44,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      data_format='NHWC',
+      skip_reduction_layer_input=0,
+      total_training_steps=250000,
+      use_bounded_activation=False,
+  )
+
+
+def _update_hparams(hparams, is_training):
+  """Update hparams for given is_training option."""
+  if not is_training:
+    hparams.set_hparam('drop_path_keep_prob', 1.0)
+
+
+def nasnet_cifar_arg_scope(weight_decay=5e-4,
+                           batch_norm_decay=0.9,
+                           batch_norm_epsilon=1e-5):
+  """Defines the default arg scope for the NASNet-A Cifar model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+
+  Returns:
+    An `arg_scope` to use for the NASNet Cifar Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
+  weights_initializer = tf.contrib.layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+                 weights_regularizer=weights_regularizer,
+                 weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected],
+                   activation_fn=None, scope='FC'):
+      with arg_scope([slim.conv2d, slim.separable_conv2d],
+                     activation_fn=None, biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def nasnet_mobile_arg_scope(weight_decay=4e-5,
+                            batch_norm_decay=0.9997,
+                            batch_norm_epsilon=1e-3):
+  """Defines the default arg scope for the NASNet-A Mobile ImageNet model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+
+  Returns:
+    An `arg_scope` to use for the NASNet Mobile Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
+  weights_initializer = tf.contrib.layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+                 weights_regularizer=weights_regularizer,
+                 weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected],
+                   activation_fn=None, scope='FC'):
+      with arg_scope([slim.conv2d, slim.separable_conv2d],
+                     activation_fn=None, biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def nasnet_large_arg_scope(weight_decay=5e-5,
+                           batch_norm_decay=0.9997,
+                           batch_norm_epsilon=1e-3):
+  """Defines the default arg scope for the NASNet-A Large ImageNet model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+
+  Returns:
+    An `arg_scope` to use for the NASNet Large Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
+  weights_initializer = tf.contrib.layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+                 weights_regularizer=weights_regularizer,
+                 weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected],
+                   activation_fn=None, scope='FC'):
+      with arg_scope([slim.conv2d, slim.separable_conv2d],
+                     activation_fn=None, biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def _build_aux_head(net, end_points, num_classes, hparams, scope):
+  """Auxiliary head used for all models across all datasets."""
+  activation_fn = tf.nn.relu6 if hparams.use_bounded_activation else tf.nn.relu
+  with tf.variable_scope(scope):
+    aux_logits = tf.identity(net)
+    with tf.variable_scope('aux_logits'):
+      aux_logits = slim.avg_pool2d(
+          aux_logits, [5, 5], stride=3, padding='VALID')
+      aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='proj')
+      aux_logits = slim.batch_norm(aux_logits, scope='aux_bn0')
+      aux_logits = activation_fn(aux_logits)
+      # Shape of feature map before the final layer.
+      shape = aux_logits.shape
+      if hparams.data_format == 'NHWC':
+        shape = shape[1:3]
+      else:
+        shape = shape[2:4]
+      aux_logits = slim.conv2d(aux_logits, 768, shape, padding='VALID')
+      aux_logits = slim.batch_norm(aux_logits, scope='aux_bn1')
+      aux_logits = activation_fn(aux_logits)
+      aux_logits = tf.contrib.layers.flatten(aux_logits)
+      aux_logits = slim.fully_connected(aux_logits, num_classes)
+      end_points['AuxLogits'] = aux_logits
+
+
+def _imagenet_stem(inputs, hparams, stem_cell, current_step=None):
+  """Stem used for models trained on ImageNet."""
+  num_stem_cells = 2
+
+  # 149 x 149 x 32
+  num_stem_filters = int(32 * hparams.stem_multiplier)
+  net = slim.conv2d(
+      inputs, num_stem_filters, [3, 3], stride=2, scope='conv0',
+      padding='VALID')
+  net = slim.batch_norm(net, scope='conv0_bn')
+
+  # Run the reduction cells
+  cell_outputs = [None, net]
+  filter_scaling = 1.0 / (hparams.filter_scaling_rate**num_stem_cells)
+  for cell_num in range(num_stem_cells):
+    net = stem_cell(
+        net,
+        scope='cell_stem_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=2,
+        prev_layer=cell_outputs[-2],
+        cell_num=cell_num,
+        current_step=current_step)
+    cell_outputs.append(net)
+    filter_scaling *= hparams.filter_scaling_rate
+  return net, cell_outputs
+
+
+def _cifar_stem(inputs, hparams):
+  """Stem used for models trained on Cifar."""
+  num_stem_filters = int(hparams.num_conv_filters * hparams.stem_multiplier)
+  net = slim.conv2d(
+      inputs,
+      num_stem_filters,
+      3,
+      scope='l1_stem_3x3')
+  net = slim.batch_norm(net, scope='l1_stem_bn')
+  return net, [None, net]
+
+
+def build_nasnet_cifar(images, num_classes,
+                       is_training=True,
+                       config=None,
+                       current_step=None):
+  """Build NASNet model for the Cifar Dataset."""
+  hparams = cifar_config() if config is None else copy.deepcopy(config)
+  _update_hparams(hparams, is_training)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  if hparams.data_format == 'NCHW':
+    images = tf.transpose(images, [0, 3, 1, 2])
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+                 is_training=is_training):
+    with arg_scope([slim.avg_pool2d,
+                    slim.max_pool2d,
+                    slim.conv2d,
+                    slim.batch_norm,
+                    slim.separable_conv2d,
+                    nasnet_utils.factorized_reduction,
+                    nasnet_utils.global_avg_pool,
+                    nasnet_utils.get_channel_index,
+                    nasnet_utils.get_channel_dim],
+                   data_format=hparams.data_format):
+      return _build_nasnet_base(images,
+                                normal_cell=normal_cell,
+                                reduction_cell=reduction_cell,
+                                num_classes=num_classes,
+                                hparams=hparams,
+                                is_training=is_training,
+                                stem_type='cifar',
+                                current_step=current_step)
+build_nasnet_cifar.default_image_size = 32
+
+
+def build_nasnet_mobile(images, num_classes,
+                        is_training=True,
+                        final_endpoint=None,
+                        config=None,
+                        current_step=None):
+  """Build NASNet Mobile model for the ImageNet Dataset."""
+  hparams = (mobile_imagenet_config() if config is None
+             else copy.deepcopy(config))
+  _update_hparams(hparams, is_training)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  if hparams.data_format == 'NCHW':
+    images = tf.transpose(images, [0, 3, 1, 2])
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+  # If ImageNet, then add an additional two for the stem cells
+  total_num_cells += 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+                 is_training=is_training):
+    with arg_scope([slim.avg_pool2d,
+                    slim.max_pool2d,
+                    slim.conv2d,
+                    slim.batch_norm,
+                    slim.separable_conv2d,
+                    nasnet_utils.factorized_reduction,
+                    nasnet_utils.global_avg_pool,
+                    nasnet_utils.get_channel_index,
+                    nasnet_utils.get_channel_dim],
+                   data_format=hparams.data_format):
+      return _build_nasnet_base(images,
+                                normal_cell=normal_cell,
+                                reduction_cell=reduction_cell,
+                                num_classes=num_classes,
+                                hparams=hparams,
+                                is_training=is_training,
+                                stem_type='imagenet',
+                                final_endpoint=final_endpoint,
+                                current_step=current_step)
+build_nasnet_mobile.default_image_size = 224
+
+
+def build_nasnet_large(images, num_classes,
+                       is_training=True,
+                       final_endpoint=None,
+                       config=None,
+                       current_step=None):
+  """Build NASNet Large model for the ImageNet Dataset."""
+  hparams = (large_imagenet_config() if config is None
+             else copy.deepcopy(config))
+  _update_hparams(hparams, is_training)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  if hparams.data_format == 'NCHW':
+    images = tf.transpose(images, [0, 3, 1, 2])
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+  # If ImageNet, then add an additional two for the stem cells
+  total_num_cells += 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob,
+      total_num_cells, hparams.total_training_steps,
+      hparams.use_bounded_activation)
+  with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+                 is_training=is_training):
+    with arg_scope([slim.avg_pool2d,
+                    slim.max_pool2d,
+                    slim.conv2d,
+                    slim.batch_norm,
+                    slim.separable_conv2d,
+                    nasnet_utils.factorized_reduction,
+                    nasnet_utils.global_avg_pool,
+                    nasnet_utils.get_channel_index,
+                    nasnet_utils.get_channel_dim],
+                   data_format=hparams.data_format):
+      return _build_nasnet_base(images,
+                                normal_cell=normal_cell,
+                                reduction_cell=reduction_cell,
+                                num_classes=num_classes,
+                                hparams=hparams,
+                                is_training=is_training,
+                                stem_type='imagenet',
+                                final_endpoint=final_endpoint,
+                                current_step=current_step)
+build_nasnet_large.default_image_size = 331
+
+
+def _build_nasnet_base(images,
+                       normal_cell,
+                       reduction_cell,
+                       num_classes,
+                       hparams,
+                       is_training,
+                       stem_type,
+                       final_endpoint=None,
+                       current_step=None):
+  """Constructs a NASNet image model."""
+
+  end_points = {}
+  def add_and_check_endpoint(endpoint_name, net):
+    end_points[endpoint_name] = net
+    return final_endpoint and (endpoint_name == final_endpoint)
+
+  # Find where to place the reduction cells or stride normal cells
+  reduction_indices = nasnet_utils.calc_reduction_layers(
+      hparams.num_cells, hparams.num_reduction_layers)
+  stem_cell = reduction_cell
+
+  if stem_type == 'imagenet':
+    stem = lambda: _imagenet_stem(images, hparams, stem_cell)
+  elif stem_type == 'cifar':
+    stem = lambda: _cifar_stem(images, hparams)
+  else:
+    raise ValueError('Unknown stem_type: ', stem_type)
+  net, cell_outputs = stem()
+  if add_and_check_endpoint('Stem', net): return net, end_points
+
+  # Setup for building in the auxiliary head.
+  aux_head_cell_idxes = []
+  if len(reduction_indices) >= 2:
+    aux_head_cell_idxes.append(reduction_indices[1] - 1)
+
+  # Run the cells
+  filter_scaling = 1.0
+  # true_cell_num accounts for the stem cells
+  true_cell_num = 2 if stem_type == 'imagenet' else 0
+  activation_fn = tf.nn.relu6 if hparams.use_bounded_activation else tf.nn.relu
+  for cell_num in range(hparams.num_cells):
+    stride = 1
+    if hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    if cell_num in reduction_indices:
+      filter_scaling *= hparams.filter_scaling_rate
+      net = reduction_cell(
+          net,
+          scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
+          filter_scaling=filter_scaling,
+          stride=2,
+          prev_layer=cell_outputs[-2],
+          cell_num=true_cell_num,
+          current_step=current_step)
+      if add_and_check_endpoint(
+          'Reduction_Cell_{}'.format(reduction_indices.index(cell_num)), net):
+        return net, end_points
+      true_cell_num += 1
+      cell_outputs.append(net)
+    if not hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    net = normal_cell(
+        net,
+        scope='cell_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=stride,
+        prev_layer=prev_layer,
+        cell_num=true_cell_num,
+        current_step=current_step)
+
+    if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
+      return net, end_points
+    true_cell_num += 1
+    if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
+        num_classes and is_training):
+      aux_net = activation_fn(net)
+      _build_aux_head(aux_net, end_points, num_classes, hparams,
+                      scope='aux_{}'.format(cell_num))
+    cell_outputs.append(net)
+
+  # Final softmax layer
+  with tf.variable_scope('final_layer'):
+    net = activation_fn(net)
+    net = nasnet_utils.global_avg_pool(net)
+    if add_and_check_endpoint('global_pool', net) or not num_classes:
+      return net, end_points
+    net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
+    logits = slim.fully_connected(net, num_classes)
+
+    if add_and_check_endpoint('Logits', logits):
+      return net, end_points
+
+    predictions = tf.nn.softmax(logits, name='predictions')
+    if add_and_check_endpoint('Predictions', predictions):
+      return net, end_points
+  return logits, end_points
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_test.py
new file mode 100644
index 00000000..d4383545
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_test.py
@@ -0,0 +1,410 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nasnet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets.nasnet import nasnet
+
+slim = tf.contrib.slim
+
+
+class NASNetTest(tf.test.TestCase):
+
+  def testBuildLogitsCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      net, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    self.assertFalse('AuxLogits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 768])
+
+  def testBuildPreLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      net, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    self.assertFalse('AuxLogits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1056])
+
+  def testBuildPreLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      net, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    self.assertFalse('AuxLogits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 4032])
+
+  def testAllEndPointsShapesCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      _, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 32, 32, 96],
+                        'Cell_0': [batch_size, 32, 32, 192],
+                        'Cell_1': [batch_size, 32, 32, 192],
+                        'Cell_2': [batch_size, 32, 32, 192],
+                        'Cell_3': [batch_size, 32, 32, 192],
+                        'Cell_4': [batch_size, 32, 32, 192],
+                        'Cell_5': [batch_size, 32, 32, 192],
+                        'Cell_6': [batch_size, 16, 16, 384],
+                        'Cell_7': [batch_size, 16, 16, 384],
+                        'Cell_8': [batch_size, 16, 16, 384],
+                        'Cell_9': [batch_size, 16, 16, 384],
+                        'Cell_10': [batch_size, 16, 16, 384],
+                        'Cell_11': [batch_size, 16, 16, 384],
+                        'Cell_12': [batch_size, 8, 8, 768],
+                        'Cell_13': [batch_size, 8, 8, 768],
+                        'Cell_14': [batch_size, 8, 8, 768],
+                        'Cell_15': [batch_size, 8, 8, 768],
+                        'Cell_16': [batch_size, 8, 8, 768],
+                        'Cell_17': [batch_size, 8, 8, 768],
+                        'Reduction_Cell_0': [batch_size, 16, 16, 256],
+                        'Reduction_Cell_1': [batch_size, 8, 8, 512],
+                        'global_pool': [batch_size, 768],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testNoAuxHeadCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    for use_aux_head in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      tf.train.create_global_step()
+      config = nasnet.cifar_config()
+      config.set_hparam('use_aux_head', int(use_aux_head))
+      with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+        _, end_points = nasnet.build_nasnet_cifar(inputs, num_classes,
+                                                  config=config)
+      self.assertEqual('AuxLogits' in end_points, use_aux_head)
+
+  def testAllEndPointsShapesMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 28, 28, 88],
+                        'Cell_0': [batch_size, 28, 28, 264],
+                        'Cell_1': [batch_size, 28, 28, 264],
+                        'Cell_2': [batch_size, 28, 28, 264],
+                        'Cell_3': [batch_size, 28, 28, 264],
+                        'Cell_4': [batch_size, 14, 14, 528],
+                        'Cell_5': [batch_size, 14, 14, 528],
+                        'Cell_6': [batch_size, 14, 14, 528],
+                        'Cell_7': [batch_size, 14, 14, 528],
+                        'Cell_8': [batch_size, 7, 7, 1056],
+                        'Cell_9': [batch_size, 7, 7, 1056],
+                        'Cell_10': [batch_size, 7, 7, 1056],
+                        'Cell_11': [batch_size, 7, 7, 1056],
+                        'Reduction_Cell_0': [batch_size, 14, 14, 352],
+                        'Reduction_Cell_1': [batch_size, 7, 7, 704],
+                        'global_pool': [batch_size, 1056],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testNoAuxHeadMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    for use_aux_head in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      tf.train.create_global_step()
+      config = nasnet.mobile_imagenet_config()
+      config.set_hparam('use_aux_head', int(use_aux_head))
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes,
+                                                   config=config)
+      self.assertEqual('AuxLogits' in end_points, use_aux_head)
+
+  def testAllEndPointsShapesLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      _, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 42, 42, 336],
+                        'Cell_0': [batch_size, 42, 42, 1008],
+                        'Cell_1': [batch_size, 42, 42, 1008],
+                        'Cell_2': [batch_size, 42, 42, 1008],
+                        'Cell_3': [batch_size, 42, 42, 1008],
+                        'Cell_4': [batch_size, 42, 42, 1008],
+                        'Cell_5': [batch_size, 42, 42, 1008],
+                        'Cell_6': [batch_size, 21, 21, 2016],
+                        'Cell_7': [batch_size, 21, 21, 2016],
+                        'Cell_8': [batch_size, 21, 21, 2016],
+                        'Cell_9': [batch_size, 21, 21, 2016],
+                        'Cell_10': [batch_size, 21, 21, 2016],
+                        'Cell_11': [batch_size, 21, 21, 2016],
+                        'Cell_12': [batch_size, 11, 11, 4032],
+                        'Cell_13': [batch_size, 11, 11, 4032],
+                        'Cell_14': [batch_size, 11, 11, 4032],
+                        'Cell_15': [batch_size, 11, 11, 4032],
+                        'Cell_16': [batch_size, 11, 11, 4032],
+                        'Cell_17': [batch_size, 11, 11, 4032],
+                        'Reduction_Cell_0': [batch_size, 21, 21, 1344],
+                        'Reduction_Cell_1': [batch_size, 11, 11, 2688],
+                        'global_pool': [batch_size, 4032],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testNoAuxHeadLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    for use_aux_head in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      tf.train.create_global_step()
+      config = nasnet.large_imagenet_config()
+      config.set_hparam('use_aux_head', int(use_aux_head))
+      with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+        _, end_points = nasnet.build_nasnet_large(inputs, num_classes,
+                                                  config=config)
+      self.assertEqual('AuxLogits' in end_points, use_aux_head)
+
+  def testVariablesSetDeviceMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    # Force all Variables to reside on the device.
+    with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        nasnet.build_nasnet_mobile(inputs, num_classes)
+    with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        nasnet.build_nasnet_mobile(inputs, num_classes)
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
+      self.assertDeviceEqual(v.device, '/cpu:0')
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
+      self.assertDeviceEqual(v.device, '/gpu:0')
+
+  def testUnknownBatchSizeMobileModel(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        logits, _ = nasnet.build_nasnet_mobile(inputs, num_classes)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [None, num_classes])
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEquals(output.shape, (batch_size, num_classes))
+
+  def testEvaluationMobileModel(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session() as sess:
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        logits, _ = nasnet.build_nasnet_mobile(eval_inputs,
+                                               num_classes,
+                                               is_training=False)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+  def testOverrideHParamsCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    config = nasnet.cifar_config()
+    config.set_hparam('data_format', 'NCHW')
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      _, end_points = nasnet.build_nasnet_cifar(
+          inputs, num_classes, config=config)
+    self.assertListEqual(
+        end_points['Stem'].shape.as_list(), [batch_size, 96, 32, 32])
+
+  def testOverrideHParamsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    config = nasnet.mobile_imagenet_config()
+    config.set_hparam('data_format', 'NCHW')
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      _, end_points = nasnet.build_nasnet_mobile(
+          inputs, num_classes, config=config)
+    self.assertListEqual(
+        end_points['Stem'].shape.as_list(), [batch_size, 88, 28, 28])
+
+  def testOverrideHParamsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    config = nasnet.large_imagenet_config()
+    config.set_hparam('data_format', 'NCHW')
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      _, end_points = nasnet.build_nasnet_large(
+          inputs, num_classes, config=config)
+    self.assertListEqual(
+        end_points['Stem'].shape.as_list(), [batch_size, 336, 42, 42])
+
+  def testCurrentStepCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    global_step = tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_cifar(inputs,
+                                                     num_classes,
+                                                     current_step=global_step)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testUseBoundedAcitvationCifarModel(self):
+    batch_size = 1
+    height, width = 32, 32
+    num_classes = 10
+    for use_bounded_activation in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      config = nasnet.cifar_config()
+      config.set_hparam('use_bounded_activation', use_bounded_activation)
+      with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+        _, _ = nasnet.build_nasnet_cifar(
+            inputs, num_classes, config=config)
+      for node in tf.get_default_graph().as_graph_def().node:
+        if node.op.startswith('Relu'):
+          self.assertEqual(node.op == 'Relu6', use_bounded_activation)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils.py
new file mode 100644
index 00000000..caa39b91
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils.py
@@ -0,0 +1,524 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A custom module for some common operations used by NASNet.
+
+Functions exposed in this file:
+- calc_reduction_layers
+- get_channel_index
+- get_channel_dim
+- global_avg_pool
+- factorized_reduction
+- drop_path
+
+Classes exposed in this file:
+- NasNetABaseCell
+- NasNetANormalCell
+- NasNetAReductionCell
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+
+arg_scope = tf.contrib.framework.arg_scope
+slim = tf.contrib.slim
+
+DATA_FORMAT_NCHW = 'NCHW'
+DATA_FORMAT_NHWC = 'NHWC'
+INVALID = 'null'
+# The cap for tf.clip_by_value, it's hinted from the activation distribution
+# that the majority of activation values are in the range [-6, 6].
+CLIP_BY_VALUE_CAP = 6
+
+
+def calc_reduction_layers(num_cells, num_reduction_layers):
+  """Figure out what layers should have reductions."""
+  reduction_layers = []
+  for pool_num in range(1, num_reduction_layers + 1):
+    layer_num = (float(pool_num) / (num_reduction_layers + 1)) * num_cells
+    layer_num = int(layer_num)
+    reduction_layers.append(layer_num)
+  return reduction_layers
+
+
+@tf.contrib.framework.add_arg_scope
+def get_channel_index(data_format=INVALID):
+  assert data_format != INVALID
+  axis = 3 if data_format == 'NHWC' else 1
+  return axis
+
+
+@tf.contrib.framework.add_arg_scope
+def get_channel_dim(shape, data_format=INVALID):
+  assert data_format != INVALID
+  assert len(shape) == 4
+  if data_format == 'NHWC':
+    return int(shape[3])
+  elif data_format == 'NCHW':
+    return int(shape[1])
+  else:
+    raise ValueError('Not a valid data_format', data_format)
+
+
+@tf.contrib.framework.add_arg_scope
+def global_avg_pool(x, data_format=INVALID):
+  """Average pool away the height and width spatial dimensions of x."""
+  assert data_format != INVALID
+  assert data_format in ['NHWC', 'NCHW']
+  assert x.shape.ndims == 4
+  if data_format == 'NHWC':
+    return tf.reduce_mean(x, [1, 2])
+  else:
+    return tf.reduce_mean(x, [2, 3])
+
+
+@tf.contrib.framework.add_arg_scope
+def factorized_reduction(net, output_filters, stride, data_format=INVALID):
+  """Reduces the shape of net without information loss due to striding."""
+  assert data_format != INVALID
+  if stride == 1:
+    net = slim.conv2d(net, output_filters, 1, scope='path_conv')
+    net = slim.batch_norm(net, scope='path_bn')
+    return net
+  if data_format == 'NHWC':
+    stride_spec = [1, stride, stride, 1]
+  else:
+    stride_spec = [1, 1, stride, stride]
+
+  # Skip path 1
+  path1 = tf.nn.avg_pool(
+      net, [1, 1, 1, 1], stride_spec, 'VALID', data_format=data_format)
+  path1 = slim.conv2d(path1, int(output_filters / 2), 1, scope='path1_conv')
+
+  # Skip path 2
+  # First pad with 0's on the right and bottom, then shift the filter to
+  # include those 0's that were added.
+  if data_format == 'NHWC':
+    pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]
+    path2 = tf.pad(net, pad_arr)[:, 1:, 1:, :]
+    concat_axis = 3
+  else:
+    pad_arr = [[0, 0], [0, 0], [0, 1], [0, 1]]
+    path2 = tf.pad(net, pad_arr)[:, :, 1:, 1:]
+    concat_axis = 1
+
+  path2 = tf.nn.avg_pool(
+      path2, [1, 1, 1, 1], stride_spec, 'VALID', data_format=data_format)
+
+  # If odd number of filters, add an additional one to the second path.
+  final_filter_size = int(output_filters / 2) + int(output_filters % 2)
+  path2 = slim.conv2d(path2, final_filter_size, 1, scope='path2_conv')
+
+  # Concat and apply BN
+  final_path = tf.concat(values=[path1, path2], axis=concat_axis)
+  final_path = slim.batch_norm(final_path, scope='final_path_bn')
+  return final_path
+
+
+@tf.contrib.framework.add_arg_scope
+def drop_path(net, keep_prob, is_training=True):
+  """Drops out a whole example hiddenstate with the specified probability."""
+  if is_training:
+    batch_size = tf.shape(net)[0]
+    noise_shape = [batch_size, 1, 1, 1]
+    random_tensor = keep_prob
+    random_tensor += tf.random_uniform(noise_shape, dtype=tf.float32)
+    binary_tensor = tf.cast(tf.floor(random_tensor), net.dtype)
+    keep_prob_inv = tf.cast(1.0 / keep_prob, net.dtype)
+    net = net * keep_prob_inv * binary_tensor
+
+  return net
+
+
+def _operation_to_filter_shape(operation):
+  splitted_operation = operation.split('x')
+  filter_shape = int(splitted_operation[0][-1])
+  assert filter_shape == int(
+      splitted_operation[1][0]), 'Rectangular filters not supported.'
+  return filter_shape
+
+
+def _operation_to_num_layers(operation):
+  splitted_operation = operation.split('_')
+  if 'x' in splitted_operation[-1]:
+    return 1
+  return int(splitted_operation[-1])
+
+
+def _operation_to_info(operation):
+  """Takes in operation name and returns meta information.
+
+  An example would be 'separable_3x3_4' -> (3, 4).
+
+  Args:
+    operation: String that corresponds to convolution operation.
+
+  Returns:
+    Tuple of (filter shape, num layers).
+  """
+  num_layers = _operation_to_num_layers(operation)
+  filter_shape = _operation_to_filter_shape(operation)
+  return num_layers, filter_shape
+
+
+def _stacked_separable_conv(net, stride, operation, filter_size,
+                            use_bounded_activation):
+  """Takes in an operations and parses it to the correct sep operation."""
+  num_layers, kernel_size = _operation_to_info(operation)
+  activation_fn = tf.nn.relu6 if use_bounded_activation else tf.nn.relu
+  for layer_num in range(num_layers - 1):
+    net = activation_fn(net)
+    net = slim.separable_conv2d(
+        net,
+        filter_size,
+        kernel_size,
+        depth_multiplier=1,
+        scope='separable_{0}x{0}_{1}'.format(kernel_size, layer_num + 1),
+        stride=stride)
+    net = slim.batch_norm(
+        net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, layer_num + 1))
+    stride = 1
+  net = activation_fn(net)
+  net = slim.separable_conv2d(
+      net,
+      filter_size,
+      kernel_size,
+      depth_multiplier=1,
+      scope='separable_{0}x{0}_{1}'.format(kernel_size, num_layers),
+      stride=stride)
+  net = slim.batch_norm(
+      net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, num_layers))
+  return net
+
+
+def _operation_to_pooling_type(operation):
+  """Takes in the operation string and returns the pooling type."""
+  splitted_operation = operation.split('_')
+  return splitted_operation[0]
+
+
+def _operation_to_pooling_shape(operation):
+  """Takes in the operation string and returns the pooling kernel shape."""
+  splitted_operation = operation.split('_')
+  shape = splitted_operation[-1]
+  assert 'x' in shape
+  filter_height, filter_width = shape.split('x')
+  assert filter_height == filter_width
+  return int(filter_height)
+
+
+def _operation_to_pooling_info(operation):
+  """Parses the pooling operation string to return its type and shape."""
+  pooling_type = _operation_to_pooling_type(operation)
+  pooling_shape = _operation_to_pooling_shape(operation)
+  return pooling_type, pooling_shape
+
+
+def _pooling(net, stride, operation, use_bounded_activation):
+  """Parses operation and performs the correct pooling operation on net."""
+  padding = 'SAME'
+  pooling_type, pooling_shape = _operation_to_pooling_info(operation)
+  if use_bounded_activation:
+    net = tf.nn.relu6(net)
+  if pooling_type == 'avg':
+    net = slim.avg_pool2d(net, pooling_shape, stride=stride, padding=padding)
+  elif pooling_type == 'max':
+    net = slim.max_pool2d(net, pooling_shape, stride=stride, padding=padding)
+  else:
+    raise NotImplementedError('Unimplemented pooling type: ', pooling_type)
+  return net
+
+
+class NasNetABaseCell(object):
+  """NASNet Cell class that is used as a 'layer' in image architectures.
+
+  Args:
+    num_conv_filters: The number of filters for each convolution operation.
+    operations: List of operations that are performed in the NASNet Cell in
+      order.
+    used_hiddenstates: Binary array that signals if the hiddenstate was used
+      within the cell. This is used to determine what outputs of the cell
+      should be concatenated together.
+    hiddenstate_indices: Determines what hiddenstates should be combined
+      together with the specified operations to create the NASNet cell.
+    use_bounded_activation: Whether or not to use bounded activations. Bounded
+      activations better lend themselves to quantized inference.
+  """
+
+  def __init__(self, num_conv_filters, operations, used_hiddenstates,
+               hiddenstate_indices, drop_path_keep_prob, total_num_cells,
+               total_training_steps, use_bounded_activation=False):
+    self._num_conv_filters = num_conv_filters
+    self._operations = operations
+    self._used_hiddenstates = used_hiddenstates
+    self._hiddenstate_indices = hiddenstate_indices
+    self._drop_path_keep_prob = drop_path_keep_prob
+    self._total_num_cells = total_num_cells
+    self._total_training_steps = total_training_steps
+    self._use_bounded_activation = use_bounded_activation
+
+  def _reduce_prev_layer(self, prev_layer, curr_layer):
+    """Matches dimension of prev_layer to the curr_layer."""
+    # Set the prev layer to the current layer if it is none
+    if prev_layer is None:
+      return curr_layer
+    curr_num_filters = self._filter_size
+    prev_num_filters = get_channel_dim(prev_layer.shape)
+    curr_filter_shape = int(curr_layer.shape[2])
+    prev_filter_shape = int(prev_layer.shape[2])
+    activation_fn = tf.nn.relu6 if self._use_bounded_activation else tf.nn.relu
+    if curr_filter_shape != prev_filter_shape:
+      prev_layer = activation_fn(prev_layer)
+      prev_layer = factorized_reduction(
+          prev_layer, curr_num_filters, stride=2)
+    elif curr_num_filters != prev_num_filters:
+      prev_layer = activation_fn(prev_layer)
+      prev_layer = slim.conv2d(
+          prev_layer, curr_num_filters, 1, scope='prev_1x1')
+      prev_layer = slim.batch_norm(prev_layer, scope='prev_bn')
+    return prev_layer
+
+  def _cell_base(self, net, prev_layer):
+    """Runs the beginning of the conv cell before the predicted ops are run."""
+    num_filters = self._filter_size
+
+    # Check to be sure prev layer stuff is setup correctly
+    prev_layer = self._reduce_prev_layer(prev_layer, net)
+
+    net = tf.nn.relu6(net) if self._use_bounded_activation else tf.nn.relu(net)
+    net = slim.conv2d(net, num_filters, 1, scope='1x1')
+    net = slim.batch_norm(net, scope='beginning_bn')
+    # num_or_size_splits=1
+    net = [net]
+    net.append(prev_layer)
+    return net
+
+  def __call__(self, net, scope=None, filter_scaling=1, stride=1,
+               prev_layer=None, cell_num=-1, current_step=None):
+    """Runs the conv cell."""
+    self._cell_num = cell_num
+    self._filter_scaling = filter_scaling
+    self._filter_size = int(self._num_conv_filters * filter_scaling)
+
+    i = 0
+    with tf.variable_scope(scope):
+      net = self._cell_base(net, prev_layer)
+      for iteration in range(5):
+        with tf.variable_scope('comb_iter_{}'.format(iteration)):
+          left_hiddenstate_idx, right_hiddenstate_idx = (
+              self._hiddenstate_indices[i],
+              self._hiddenstate_indices[i + 1])
+          original_input_left = left_hiddenstate_idx < 2
+          original_input_right = right_hiddenstate_idx < 2
+          h1 = net[left_hiddenstate_idx]
+          h2 = net[right_hiddenstate_idx]
+
+          operation_left = self._operations[i]
+          operation_right = self._operations[i+1]
+          i += 2
+          # Apply conv operations
+          with tf.variable_scope('left'):
+            h1 = self._apply_conv_operation(h1, operation_left,
+                                            stride, original_input_left,
+                                            current_step)
+          with tf.variable_scope('right'):
+            h2 = self._apply_conv_operation(h2, operation_right,
+                                            stride, original_input_right,
+                                            current_step)
+
+          # Combine hidden states using 'add'.
+          with tf.variable_scope('combine'):
+            h = h1 + h2
+            if self._use_bounded_activation:
+              h = tf.nn.relu6(h)
+
+          # Add hiddenstate to the list of hiddenstates we can choose from
+          net.append(h)
+
+      with tf.variable_scope('cell_output'):
+        net = self._combine_unused_states(net)
+
+      return net
+
+  def _apply_conv_operation(self, net, operation,
+                            stride, is_from_original_input, current_step):
+    """Applies the predicted conv operation to net."""
+    # Dont stride if this is not one of the original hiddenstates
+    if stride > 1 and not is_from_original_input:
+      stride = 1
+    input_filters = get_channel_dim(net.shape)
+    filter_size = self._filter_size
+    if 'separable' in operation:
+      net = _stacked_separable_conv(net, stride, operation, filter_size,
+                                    self._use_bounded_activation)
+      if self._use_bounded_activation:
+        net = tf.clip_by_value(net, -CLIP_BY_VALUE_CAP, CLIP_BY_VALUE_CAP)
+    elif operation in ['none']:
+      if self._use_bounded_activation:
+        net = tf.nn.relu6(net)
+      # Check if a stride is needed, then use a strided 1x1 here
+      if stride > 1 or (input_filters != filter_size):
+        if not self._use_bounded_activation:
+          net = tf.nn.relu(net)
+        net = slim.conv2d(net, filter_size, 1, stride=stride, scope='1x1')
+        net = slim.batch_norm(net, scope='bn_1')
+        if self._use_bounded_activation:
+          net = tf.clip_by_value(net, -CLIP_BY_VALUE_CAP, CLIP_BY_VALUE_CAP)
+    elif 'pool' in operation:
+      net = _pooling(net, stride, operation, self._use_bounded_activation)
+      if input_filters != filter_size:
+        net = slim.conv2d(net, filter_size, 1, stride=1, scope='1x1')
+        net = slim.batch_norm(net, scope='bn_1')
+      if self._use_bounded_activation:
+        net = tf.clip_by_value(net, -CLIP_BY_VALUE_CAP, CLIP_BY_VALUE_CAP)
+    else:
+      raise ValueError('Unimplemented operation', operation)
+
+    if operation != 'none':
+      net = self._apply_drop_path(net, current_step=current_step)
+    return net
+
+  def _combine_unused_states(self, net):
+    """Concatenate the unused hidden states of the cell."""
+    used_hiddenstates = self._used_hiddenstates
+
+    final_height = int(net[-1].shape[2])
+    final_num_filters = get_channel_dim(net[-1].shape)
+    assert len(used_hiddenstates) == len(net)
+    for idx, used_h in enumerate(used_hiddenstates):
+      curr_height = int(net[idx].shape[2])
+      curr_num_filters = get_channel_dim(net[idx].shape)
+
+      # Determine if a reduction should be applied to make the number of
+      # filters match.
+      should_reduce = final_num_filters != curr_num_filters
+      should_reduce = (final_height != curr_height) or should_reduce
+      should_reduce = should_reduce and not used_h
+      if should_reduce:
+        stride = 2 if final_height != curr_height else 1
+        with tf.variable_scope('reduction_{}'.format(idx)):
+          net[idx] = factorized_reduction(
+              net[idx], final_num_filters, stride)
+
+    states_to_combine = (
+        [h for h, is_used in zip(net, used_hiddenstates) if not is_used])
+
+    # Return the concat of all the states
+    concat_axis = get_channel_index()
+    net = tf.concat(values=states_to_combine, axis=concat_axis)
+    return net
+
+  @tf.contrib.framework.add_arg_scope  # No public API. For internal use only.
+  def _apply_drop_path(self, net, current_step=None,
+                       use_summaries=False, drop_connect_version='v3'):
+    """Apply drop_path regularization.
+
+    Args:
+      net: the Tensor that gets drop_path regularization applied.
+      current_step: a float32 Tensor with the current global_step value,
+        to be divided by hparams.total_training_steps. Usually None, which
+        defaults to tf.train.get_or_create_global_step() properly casted.
+      use_summaries: a Python boolean. If set to False, no summaries are output.
+      drop_connect_version: one of 'v1', 'v2', 'v3', controlling whether
+        the dropout rate is scaled by current_step (v1), layer (v2), or
+        both (v3, the default).
+
+    Returns:
+      The dropped-out value of `net`.
+    """
+    drop_path_keep_prob = self._drop_path_keep_prob
+    if drop_path_keep_prob < 1.0:
+      assert drop_connect_version in ['v1', 'v2', 'v3']
+      if drop_connect_version in ['v2', 'v3']:
+        # Scale keep prob by layer number
+        assert self._cell_num != -1
+        # The added 2 is for the reduction cells
+        num_cells = self._total_num_cells
+        layer_ratio = (self._cell_num + 1)/float(num_cells)
+        if use_summaries:
+          with tf.device('/cpu:0'):
+            tf.summary.scalar('layer_ratio', layer_ratio)
+        drop_path_keep_prob = 1 - layer_ratio * (1 - drop_path_keep_prob)
+      if drop_connect_version in ['v1', 'v3']:
+        # Decrease the keep probability over time
+        if current_step is None:
+          current_step = tf.train.get_or_create_global_step()
+        current_step = tf.cast(current_step, tf.float32)
+        drop_path_burn_in_steps = self._total_training_steps
+        current_ratio = current_step / drop_path_burn_in_steps
+        current_ratio = tf.minimum(1.0, current_ratio)
+        if use_summaries:
+          with tf.device('/cpu:0'):
+            tf.summary.scalar('current_ratio', current_ratio)
+        drop_path_keep_prob = (1 - current_ratio * (1 - drop_path_keep_prob))
+      if use_summaries:
+        with tf.device('/cpu:0'):
+          tf.summary.scalar('drop_path_keep_prob', drop_path_keep_prob)
+      net = drop_path(net, drop_path_keep_prob)
+    return net
+
+
+class NasNetANormalCell(NasNetABaseCell):
+  """NASNetA Normal Cell."""
+
+  def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
+               total_training_steps, use_bounded_activation=False):
+    operations = ['separable_5x5_2',
+                  'separable_3x3_2',
+                  'separable_5x5_2',
+                  'separable_3x3_2',
+                  'avg_pool_3x3',
+                  'none',
+                  'avg_pool_3x3',
+                  'avg_pool_3x3',
+                  'separable_3x3_2',
+                  'none']
+    used_hiddenstates = [1, 0, 0, 0, 0, 0, 0]
+    hiddenstate_indices = [0, 1, 1, 1, 0, 1, 1, 1, 0, 0]
+    super(NasNetANormalCell, self).__init__(num_conv_filters, operations,
+                                            used_hiddenstates,
+                                            hiddenstate_indices,
+                                            drop_path_keep_prob,
+                                            total_num_cells,
+                                            total_training_steps,
+                                            use_bounded_activation)
+
+
+class NasNetAReductionCell(NasNetABaseCell):
+  """NASNetA Reduction Cell."""
+
+  def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
+               total_training_steps, use_bounded_activation=False):
+    operations = ['separable_5x5_2',
+                  'separable_7x7_2',
+                  'max_pool_3x3',
+                  'separable_7x7_2',
+                  'avg_pool_3x3',
+                  'separable_5x5_2',
+                  'none',
+                  'avg_pool_3x3',
+                  'separable_3x3_2',
+                  'max_pool_3x3']
+    used_hiddenstates = [1, 1, 1, 0, 0, 0, 0]
+    hiddenstate_indices = [0, 1, 0, 1, 0, 1, 3, 2, 2, 0]
+    super(NasNetAReductionCell, self).__init__(num_conv_filters, operations,
+                                               used_hiddenstates,
+                                               hiddenstate_indices,
+                                               drop_path_keep_prob,
+                                               total_num_cells,
+                                               total_training_steps,
+                                               use_bounded_activation)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils_test.py
new file mode 100644
index 00000000..60bf9029
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/nasnet_utils_test.py
@@ -0,0 +1,62 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.nasnet.nasnet_utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets.nasnet import nasnet_utils
+
+
+class NasnetUtilsTest(tf.test.TestCase):
+
+  def testCalcReductionLayers(self):
+    num_cells = 18
+    num_reduction_layers = 2
+    reduction_layers = nasnet_utils.calc_reduction_layers(
+        num_cells, num_reduction_layers)
+    self.assertEqual(len(reduction_layers), 2)
+    self.assertEqual(reduction_layers[0], 6)
+    self.assertEqual(reduction_layers[1], 12)
+
+  def testGetChannelIndex(self):
+    data_formats = ['NHWC', 'NCHW']
+    for data_format in data_formats:
+      index = nasnet_utils.get_channel_index(data_format)
+      correct_index = 3 if data_format == 'NHWC' else 1
+      self.assertEqual(index, correct_index)
+
+  def testGetChannelDim(self):
+    data_formats = ['NHWC', 'NCHW']
+    shape = [10, 20, 30, 40]
+    for data_format in data_formats:
+      dim = nasnet_utils.get_channel_dim(shape, data_format)
+      correct_dim = shape[3] if data_format == 'NHWC' else shape[1]
+      self.assertEqual(dim, correct_dim)
+
+  def testGlobalAvgPool(self):
+    data_formats = ['NHWC', 'NCHW']
+    inputs = tf.placeholder(tf.float32, (5, 10, 20, 10))
+    for data_format in data_formats:
+      output = nasnet_utils.global_avg_pool(
+          inputs, data_format)
+      self.assertEqual(output.shape, [5, 10])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet.py
new file mode 100644
index 00000000..8e8427fe
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet.py
@@ -0,0 +1,280 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for the PNASNet classification networks.
+
+Paper: https://arxiv.org/abs/1712.00559
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import tensorflow as tf
+
+from nets.nasnet import nasnet
+from nets.nasnet import nasnet_utils
+
+arg_scope = tf.contrib.framework.arg_scope
+slim = tf.contrib.slim
+
+
+def large_imagenet_config():
+  """Large ImageNet configuration based on PNASNet-5."""
+  return tf.contrib.training.HParams(
+      stem_multiplier=3.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=12,
+      filter_scaling_rate=2.0,
+      num_conv_filters=216,
+      drop_path_keep_prob=0.6,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      data_format='NHWC',
+      skip_reduction_layer_input=1,
+      total_training_steps=250000,
+      use_bounded_activation=False,
+  )
+
+
+def mobile_imagenet_config():
+  """Mobile ImageNet configuration based on PNASNet-5."""
+  return tf.contrib.training.HParams(
+      stem_multiplier=1.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=9,
+      filter_scaling_rate=2.0,
+      num_conv_filters=54,
+      drop_path_keep_prob=1.0,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      data_format='NHWC',
+      skip_reduction_layer_input=1,
+      total_training_steps=250000,
+      use_bounded_activation=False,
+  )
+
+
+def pnasnet_large_arg_scope(weight_decay=4e-5, batch_norm_decay=0.9997,
+                            batch_norm_epsilon=0.001):
+  """Default arg scope for the PNASNet Large ImageNet model."""
+  return nasnet.nasnet_large_arg_scope(
+      weight_decay, batch_norm_decay, batch_norm_epsilon)
+
+
+def pnasnet_mobile_arg_scope(weight_decay=4e-5,
+                             batch_norm_decay=0.9997,
+                             batch_norm_epsilon=0.001):
+  """Default arg scope for the PNASNet Mobile ImageNet model."""
+  return nasnet.nasnet_mobile_arg_scope(weight_decay, batch_norm_decay,
+                                        batch_norm_epsilon)
+
+
+def _build_pnasnet_base(images,
+                        normal_cell,
+                        num_classes,
+                        hparams,
+                        is_training,
+                        final_endpoint=None):
+  """Constructs a PNASNet image model."""
+
+  end_points = {}
+
+  def add_and_check_endpoint(endpoint_name, net):
+    end_points[endpoint_name] = net
+    return final_endpoint and (endpoint_name == final_endpoint)
+
+  # Find where to place the reduction cells or stride normal cells
+  reduction_indices = nasnet_utils.calc_reduction_layers(
+      hparams.num_cells, hparams.num_reduction_layers)
+
+  # pylint: disable=protected-access
+  stem = lambda: nasnet._imagenet_stem(images, hparams, normal_cell)
+  # pylint: enable=protected-access
+  net, cell_outputs = stem()
+  if add_and_check_endpoint('Stem', net):
+    return net, end_points
+
+  # Setup for building in the auxiliary head.
+  aux_head_cell_idxes = []
+  if len(reduction_indices) >= 2:
+    aux_head_cell_idxes.append(reduction_indices[1] - 1)
+
+  # Run the cells
+  filter_scaling = 1.0
+  # true_cell_num accounts for the stem cells
+  true_cell_num = 2
+  activation_fn = tf.nn.relu6 if hparams.use_bounded_activation else tf.nn.relu
+  for cell_num in range(hparams.num_cells):
+    is_reduction = cell_num in reduction_indices
+    stride = 2 if is_reduction else 1
+    if is_reduction: filter_scaling *= hparams.filter_scaling_rate
+    if hparams.skip_reduction_layer_input or not is_reduction:
+      prev_layer = cell_outputs[-2]
+    net = normal_cell(
+        net,
+        scope='cell_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=stride,
+        prev_layer=prev_layer,
+        cell_num=true_cell_num)
+    if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
+      return net, end_points
+    true_cell_num += 1
+    cell_outputs.append(net)
+
+    if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
+        num_classes and is_training):
+      aux_net = activation_fn(net)
+      # pylint: disable=protected-access
+      nasnet._build_aux_head(aux_net, end_points, num_classes, hparams,
+                             scope='aux_{}'.format(cell_num))
+      # pylint: enable=protected-access
+
+  # Final softmax layer
+  with tf.variable_scope('final_layer'):
+    net = activation_fn(net)
+    net = nasnet_utils.global_avg_pool(net)
+    if add_and_check_endpoint('global_pool', net) or not num_classes:
+      return net, end_points
+    net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
+    logits = slim.fully_connected(net, num_classes)
+
+    if add_and_check_endpoint('Logits', logits):
+      return net, end_points
+
+    predictions = tf.nn.softmax(logits, name='predictions')
+    if add_and_check_endpoint('Predictions', predictions):
+      return net, end_points
+  return logits, end_points
+
+
+def build_pnasnet_large(images,
+                        num_classes,
+                        is_training=True,
+                        final_endpoint=None,
+                        config=None):
+  """Build PNASNet Large model for the ImageNet Dataset."""
+  hparams = copy.deepcopy(config) if config else large_imagenet_config()
+  # pylint: disable=protected-access
+  nasnet._update_hparams(hparams, is_training)
+  # pylint: enable=protected-access
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  if hparams.data_format == 'NCHW':
+    images = tf.transpose(images, [0, 3, 1, 2])
+
+  # Calculate the total number of cells in the network.
+  # There is no distinction between reduction and normal cells in PNAS so the
+  # total number of cells is equal to the number normal cells plus the number
+  # of stem cells (two by default).
+  total_num_cells = hparams.num_cells + 2
+
+  normal_cell = PNasNetNormalCell(hparams.num_conv_filters,
+                                  hparams.drop_path_keep_prob, total_num_cells,
+                                  hparams.total_training_steps,
+                                  hparams.use_bounded_activation)
+  with arg_scope(
+      [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+      is_training=is_training):
+    with arg_scope([slim.avg_pool2d, slim.max_pool2d, slim.conv2d,
+                    slim.batch_norm, slim.separable_conv2d,
+                    nasnet_utils.factorized_reduction,
+                    nasnet_utils.global_avg_pool,
+                    nasnet_utils.get_channel_index,
+                    nasnet_utils.get_channel_dim],
+                   data_format=hparams.data_format):
+      return _build_pnasnet_base(
+          images,
+          normal_cell=normal_cell,
+          num_classes=num_classes,
+          hparams=hparams,
+          is_training=is_training,
+          final_endpoint=final_endpoint)
+build_pnasnet_large.default_image_size = 331
+
+
+def build_pnasnet_mobile(images,
+                         num_classes,
+                         is_training=True,
+                         final_endpoint=None,
+                         config=None):
+  """Build PNASNet Mobile model for the ImageNet Dataset."""
+  hparams = copy.deepcopy(config) if config else mobile_imagenet_config()
+  # pylint: disable=protected-access
+  nasnet._update_hparams(hparams, is_training)
+  # pylint: enable=protected-access
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  if hparams.data_format == 'NCHW':
+    images = tf.transpose(images, [0, 3, 1, 2])
+
+  # Calculate the total number of cells in the network.
+  # There is no distinction between reduction and normal cells in PNAS so the
+  # total number of cells is equal to the number normal cells plus the number
+  # of stem cells (two by default).
+  total_num_cells = hparams.num_cells + 2
+
+  normal_cell = PNasNetNormalCell(hparams.num_conv_filters,
+                                  hparams.drop_path_keep_prob, total_num_cells,
+                                  hparams.total_training_steps,
+                                  hparams.use_bounded_activation)
+  with arg_scope(
+      [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+      is_training=is_training):
+    with arg_scope(
+        [
+            slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
+            slim.separable_conv2d, nasnet_utils.factorized_reduction,
+            nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
+            nasnet_utils.get_channel_dim
+        ],
+        data_format=hparams.data_format):
+      return _build_pnasnet_base(
+          images,
+          normal_cell=normal_cell,
+          num_classes=num_classes,
+          hparams=hparams,
+          is_training=is_training,
+          final_endpoint=final_endpoint)
+
+
+build_pnasnet_mobile.default_image_size = 224
+
+
+class PNasNetNormalCell(nasnet_utils.NasNetABaseCell):
+  """PNASNet Normal Cell."""
+
+  def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
+               total_training_steps, use_bounded_activation=False):
+    # Configuration for the PNASNet-5 model.
+    operations = [
+        'separable_5x5_2', 'max_pool_3x3', 'separable_7x7_2', 'max_pool_3x3',
+        'separable_5x5_2', 'separable_3x3_2', 'separable_3x3_2', 'max_pool_3x3',
+        'separable_3x3_2', 'none'
+    ]
+    used_hiddenstates = [1, 1, 0, 0, 0, 0, 0]
+    hiddenstate_indices = [1, 1, 0, 0, 0, 0, 4, 0, 1, 0]
+
+    super(PNasNetNormalCell, self).__init__(
+        num_conv_filters, operations, used_hiddenstates, hiddenstate_indices,
+        drop_path_keep_prob, total_num_cells, total_training_steps,
+        use_bounded_activation)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet_test.py
new file mode 100644
index 00000000..2cbd8162
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nasnet/pnasnet_test.py
@@ -0,0 +1,256 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.pnasnet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets.nasnet import pnasnet
+
+slim = tf.contrib.slim
+
+
+class PNASNetTest(tf.test.TestCase):
+
+  def testBuildLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+      logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+      logits, end_points = pnasnet.build_pnasnet_mobile(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildNonExistingLayerLargeModel(self):
+    """Tests that the model is built correctly without unnecessary layers."""
+    inputs = tf.random_uniform((5, 331, 331, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+      pnasnet.build_pnasnet_large(inputs, 1000)
+    vars_names = [x.op.name for x in tf.trainable_variables()]
+    self.assertIn('cell_stem_0/1x1/weights', vars_names)
+    self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names)
+
+  def testBuildNonExistingLayerMobileModel(self):
+    """Tests that the model is built correctly without unnecessary layers."""
+    inputs = tf.random_uniform((5, 224, 224, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+      pnasnet.build_pnasnet_mobile(inputs, 1000)
+    vars_names = [x.op.name for x in tf.trainable_variables()]
+    self.assertIn('cell_stem_0/1x1/weights', vars_names)
+    self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names)
+
+  def testBuildPreLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+      net, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
+    self.assertFalse('AuxLogits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 4320])
+
+  def testBuildPreLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+      net, end_points = pnasnet.build_pnasnet_mobile(inputs, num_classes)
+    self.assertFalse('AuxLogits' in end_points)
+    self.assertFalse('Predictions' in end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1080])
+
+  def testAllEndPointsShapesLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+      _, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
+
+    endpoints_shapes = {'Stem': [batch_size, 42, 42, 540],
+                        'Cell_0': [batch_size, 42, 42, 1080],
+                        'Cell_1': [batch_size, 42, 42, 1080],
+                        'Cell_2': [batch_size, 42, 42, 1080],
+                        'Cell_3': [batch_size, 42, 42, 1080],
+                        'Cell_4': [batch_size, 21, 21, 2160],
+                        'Cell_5': [batch_size, 21, 21, 2160],
+                        'Cell_6': [batch_size, 21, 21, 2160],
+                        'Cell_7': [batch_size, 21, 21, 2160],
+                        'Cell_8': [batch_size, 11, 11, 4320],
+                        'Cell_9': [batch_size, 11, 11, 4320],
+                        'Cell_10': [batch_size, 11, 11, 4320],
+                        'Cell_11': [batch_size, 11, 11, 4320],
+                        'global_pool': [batch_size, 4320],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, 1000],
+                        'Predictions': [batch_size, 1000],
+                        'Logits': [batch_size, 1000],
+                       }
+    self.assertEqual(len(end_points), 17)
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertIn(endpoint_name, end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testAllEndPointsShapesMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+      _, end_points = pnasnet.build_pnasnet_mobile(inputs, num_classes)
+
+    endpoints_shapes = {
+        'Stem': [batch_size, 28, 28, 135],
+        'Cell_0': [batch_size, 28, 28, 270],
+        'Cell_1': [batch_size, 28, 28, 270],
+        'Cell_2': [batch_size, 28, 28, 270],
+        'Cell_3': [batch_size, 14, 14, 540],
+        'Cell_4': [batch_size, 14, 14, 540],
+        'Cell_5': [batch_size, 14, 14, 540],
+        'Cell_6': [batch_size, 7, 7, 1080],
+        'Cell_7': [batch_size, 7, 7, 1080],
+        'Cell_8': [batch_size, 7, 7, 1080],
+        'global_pool': [batch_size, 1080],
+        # Logits and predictions
+        'AuxLogits': [batch_size, num_classes],
+        'Predictions': [batch_size, num_classes],
+        'Logits': [batch_size, num_classes],
+    }
+    self.assertEqual(len(end_points), 14)
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertIn(endpoint_name, end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testNoAuxHeadLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    for use_aux_head in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      tf.train.create_global_step()
+      config = pnasnet.large_imagenet_config()
+      config.set_hparam('use_aux_head', int(use_aux_head))
+      with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+        _, end_points = pnasnet.build_pnasnet_large(inputs, num_classes,
+                                                    config=config)
+      self.assertEqual('AuxLogits' in end_points, use_aux_head)
+
+  def testNoAuxHeadMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    for use_aux_head in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      tf.train.create_global_step()
+      config = pnasnet.mobile_imagenet_config()
+      config.set_hparam('use_aux_head', int(use_aux_head))
+      with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+        _, end_points = pnasnet.build_pnasnet_mobile(
+            inputs, num_classes, config=config)
+      self.assertEqual('AuxLogits' in end_points, use_aux_head)
+
+  def testOverrideHParamsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    config = pnasnet.large_imagenet_config()
+    config.set_hparam('data_format', 'NCHW')
+    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
+      _, end_points = pnasnet.build_pnasnet_large(
+          inputs, num_classes, config=config)
+    self.assertListEqual(
+        end_points['Stem'].shape.as_list(), [batch_size, 540, 42, 42])
+
+  def testOverrideHParamsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    config = pnasnet.mobile_imagenet_config()
+    config.set_hparam('data_format', 'NCHW')
+    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+      _, end_points = pnasnet.build_pnasnet_mobile(
+          inputs, num_classes, config=config)
+    self.assertListEqual(end_points['Stem'].shape.as_list(),
+                         [batch_size, 135, 28, 28])
+
+  def testUseBoundedAcitvationMobileModel(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+    for use_bounded_activation in (True, False):
+      tf.reset_default_graph()
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      config = pnasnet.mobile_imagenet_config()
+      config.set_hparam('use_bounded_activation', use_bounded_activation)
+      with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
+        _, _ = pnasnet.build_pnasnet_mobile(
+            inputs, num_classes, config=config)
+      for node in tf.get_default_graph().as_graph_def().node:
+        if node.op.startswith('Relu'):
+          self.assertEqual(node.op == 'Relu6', use_bounded_activation)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory.py
new file mode 100644
index 00000000..c64afcbe
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory.py
@@ -0,0 +1,159 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains a factory for building various models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import functools
+
+import tensorflow as tf
+
+from nets import alexnet
+from nets import cifarnet
+from nets import i3d
+from nets import inception
+from nets import lenet
+from nets import mobilenet_v1
+from nets import overfeat
+from nets import resnet_v1
+from nets import resnet_v2
+from nets import s3dg
+from nets import vgg
+from nets.mobilenet import mobilenet_v2
+from nets.nasnet import nasnet
+from nets.nasnet import pnasnet
+
+
+slim = tf.contrib.slim
+
+networks_map = {'alexnet_v2': alexnet.alexnet_v2,
+                'cifarnet': cifarnet.cifarnet,
+                'overfeat': overfeat.overfeat,
+                'vgg_a': vgg.vgg_a,
+                'vgg_16': vgg.vgg_16,
+                'vgg_19': vgg.vgg_19,
+                'inception_v1': inception.inception_v1,
+                'inception_v2': inception.inception_v2,
+                'inception_v3': inception.inception_v3,
+                'inception_v4': inception.inception_v4,
+                'inception_resnet_v2': inception.inception_resnet_v2,
+                'i3d': i3d.i3d,
+                's3dg': s3dg.s3dg,
+                'lenet': lenet.lenet,
+                'resnet_v1_50': resnet_v1.resnet_v1_50,
+                'resnet_v1_101': resnet_v1.resnet_v1_101,
+                'resnet_v1_152': resnet_v1.resnet_v1_152,
+                'resnet_v1_200': resnet_v1.resnet_v1_200,
+                'resnet_v2_50': resnet_v2.resnet_v2_50,
+                'resnet_v2_101': resnet_v2.resnet_v2_101,
+                'resnet_v2_152': resnet_v2.resnet_v2_152,
+                'resnet_v2_200': resnet_v2.resnet_v2_200,
+                'mobilenet_v1': mobilenet_v1.mobilenet_v1,
+                'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_075,
+                'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_050,
+                'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_025,
+                'mobilenet_v2': mobilenet_v2.mobilenet,
+                'mobilenet_v2_140': mobilenet_v2.mobilenet_v2_140,
+                'mobilenet_v2_035': mobilenet_v2.mobilenet_v2_035,
+                'nasnet_cifar': nasnet.build_nasnet_cifar,
+                'nasnet_mobile': nasnet.build_nasnet_mobile,
+                'nasnet_large': nasnet.build_nasnet_large,
+                'pnasnet_large': pnasnet.build_pnasnet_large,
+                'pnasnet_mobile': pnasnet.build_pnasnet_mobile,
+               }
+
+arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
+                  'cifarnet': cifarnet.cifarnet_arg_scope,
+                  'overfeat': overfeat.overfeat_arg_scope,
+                  'vgg_a': vgg.vgg_arg_scope,
+                  'vgg_16': vgg.vgg_arg_scope,
+                  'vgg_19': vgg.vgg_arg_scope,
+                  'inception_v1': inception.inception_v3_arg_scope,
+                  'inception_v2': inception.inception_v3_arg_scope,
+                  'inception_v3': inception.inception_v3_arg_scope,
+                  'inception_v4': inception.inception_v4_arg_scope,
+                  'inception_resnet_v2':
+                  inception.inception_resnet_v2_arg_scope,
+                  'i3d': i3d.i3d_arg_scope,
+                  's3dg': s3dg.s3dg_arg_scope,
+                  'lenet': lenet.lenet_arg_scope,
+                  'resnet_v1_50': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_101': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_152': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_200': resnet_v1.resnet_arg_scope,
+                  'resnet_v2_50': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_101': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_152': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_200': resnet_v2.resnet_arg_scope,
+                  'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v2': mobilenet_v2.training_scope,
+                  'mobilenet_v2_035': mobilenet_v2.training_scope,
+                  'mobilenet_v2_140': mobilenet_v2.training_scope,
+                  'nasnet_cifar': nasnet.nasnet_cifar_arg_scope,
+                  'nasnet_mobile': nasnet.nasnet_mobile_arg_scope,
+                  'nasnet_large': nasnet.nasnet_large_arg_scope,
+                  'pnasnet_large': pnasnet.pnasnet_large_arg_scope,
+                  'pnasnet_mobile': pnasnet.pnasnet_mobile_arg_scope,
+                 }
+
+
+def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
+  """Returns a network_fn such as `logits, end_points = network_fn(images)`.
+
+  Args:
+    name: The name of the network.
+    num_classes: The number of classes to use for classification. If 0 or None,
+      the logits layer is omitted and its input features are returned instead.
+    weight_decay: The l2 coefficient for the model weights.
+    is_training: `True` if the model is being used for training and `False`
+      otherwise.
+
+  Returns:
+    network_fn: A function that applies the model to a batch of images. It has
+      the following signature:
+          net, end_points = network_fn(images)
+      The `images` input is a tensor of shape [batch_size, height, width, 3]
+      with height = width = network_fn.default_image_size. (The permissibility
+      and treatment of other sizes depends on the network_fn.)
+      The returned `end_points` are a dictionary of intermediate activations.
+      The returned `net` is the topmost layer, depending on `num_classes`:
+      If `num_classes` was a non-zero integer, `net` is a logits tensor
+      of shape [batch_size, num_classes].
+      If `num_classes` was 0 or `None`, `net` is a tensor with the input
+      to the logits layer of shape [batch_size, 1, 1, num_features] or
+      [batch_size, num_features]. Dropout has not been applied to this
+      (even if the network's original classification does); it remains for
+      the caller to do this or not.
+
+  Raises:
+    ValueError: If network `name` is not recognized.
+  """
+  if name not in networks_map:
+    raise ValueError('Name of network unknown %s' % name)
+  func = networks_map[name]
+  @functools.wraps(func)
+  def network_fn(images, **kwargs):
+    arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
+    with slim.arg_scope(arg_scope):
+      return func(images, num_classes=num_classes, is_training=is_training,
+                  **kwargs)
+  if hasattr(func, 'default_image_size'):
+    network_fn.default_image_size = func.default_image_size
+
+  return network_fn
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory_test.py
new file mode 100644
index 00000000..e111fc22
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/nets_factory_test.py
@@ -0,0 +1,81 @@
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for slim.inception."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import tensorflow as tf
+
+from nets import nets_factory
+
+
+class NetworksTest(tf.test.TestCase):
+
+  def testGetNetworkFnFirstHalf(self):
+    batch_size = 5
+    num_classes = 1000
+    for net in list(nets_factory.networks_map.keys())[:10]:
+      with tf.Graph().as_default() as g, self.test_session(g):
+        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
+        # Most networks use 224 as their default_image_size
+        image_size = getattr(net_fn, 'default_image_size', 224)
+        if net not in ['i3d', 's3dg']:
+          inputs = tf.random_uniform(
+              (batch_size, image_size, image_size, 3))
+          logits, end_points = net_fn(inputs)
+          self.assertTrue(isinstance(logits, tf.Tensor))
+          self.assertTrue(isinstance(end_points, dict))
+          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
+          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
+
+  def testGetNetworkFnSecondHalf(self):
+    batch_size = 5
+    num_classes = 1000
+    for net in list(nets_factory.networks_map.keys())[10:]:
+      with tf.Graph().as_default() as g, self.test_session(g):
+        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
+        # Most networks use 224 as their default_image_size
+        image_size = getattr(net_fn, 'default_image_size', 224)
+        if net not in ['i3d', 's3dg']:
+          inputs = tf.random_uniform(
+              (batch_size, image_size, image_size, 3))
+          logits, end_points = net_fn(inputs)
+          self.assertTrue(isinstance(logits, tf.Tensor))
+          self.assertTrue(isinstance(end_points, dict))
+          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
+          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
+
+  def testGetNetworkFnVideoModels(self):
+    batch_size = 5
+    num_classes = 400
+    for net in ['i3d', 's3dg']:
+      with tf.Graph().as_default() as g, self.test_session(g):
+        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
+        # Most networks use 224 as their default_image_size
+        image_size = getattr(net_fn, 'default_image_size', 224) // 2
+        inputs = tf.random_uniform(
+            (batch_size, 10, image_size, image_size, 3))
+        logits, end_points = net_fn(inputs)
+        self.assertTrue(isinstance(logits, tf.Tensor))
+        self.assertTrue(isinstance(end_points, dict))
+        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
+        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat.py b/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat.py
new file mode 100644
index 00000000..069f550e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat.py
@@ -0,0 +1,131 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the model definition for the OverFeat network.
+
+The definition for the network was obtained from:
+  OverFeat: Integrated Recognition, Localization and Detection using
+  Convolutional Networks
+  Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
+  Yann LeCun, 2014
+  http://arxiv.org/abs/1312.6229
+
+Usage:
+  with slim.arg_scope(overfeat.overfeat_arg_scope()):
+    outputs, end_points = overfeat.overfeat(inputs)
+
+@@overfeat
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def overfeat_arg_scope(weight_decay=0.0005):
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      activation_fn=tf.nn.relu,
+                      weights_regularizer=slim.l2_regularizer(weight_decay),
+                      biases_initializer=tf.zeros_initializer()):
+    with slim.arg_scope([slim.conv2d], padding='SAME'):
+      with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
+        return arg_sc
+
+
+def overfeat(inputs,
+             num_classes=1000,
+             is_training=True,
+             dropout_keep_prob=0.5,
+             spatial_squeeze=True,
+             scope='overfeat',
+             global_pool=False):
+  """Contains the model definition for the OverFeat network.
+
+  The definition for the network was obtained from:
+    OverFeat: Integrated Recognition, Localization and Detection using
+    Convolutional Networks
+    Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
+    Yann LeCun, 2014
+    http://arxiv.org/abs/1312.6229
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 231x231. To use in fully
+        convolutional mode, set spatial_squeeze to false.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      outputs. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original OverFeat.)
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the non-dropped-out input to the logits layer (if num_classes is 0 or
+      None).
+    end_points: a dict of tensors with intermediate activations.
+  """
+  with tf.variable_scope(scope, 'overfeat', [inputs]) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d
+    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+                        outputs_collections=end_points_collection):
+      net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
+                        scope='conv1')
+      net = slim.max_pool2d(net, [2, 2], scope='pool1')
+      net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
+      net = slim.max_pool2d(net, [2, 2], scope='pool2')
+      net = slim.conv2d(net, 512, [3, 3], scope='conv3')
+      net = slim.conv2d(net, 1024, [3, 3], scope='conv4')
+      net = slim.conv2d(net, 1024, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
+      # Use conv2d instead of fully_connected layers.
+      with slim.arg_scope([slim.conv2d],
+                          weights_initializer=trunc_normal(0.005),
+                          biases_initializer=tf.constant_initializer(0.1)):
+        net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout6')
+        net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+        # Convert end_points_collection into a end_point dict.
+        end_points = slim.utils.convert_collection_to_dict(
+            end_points_collection)
+        if global_pool:
+          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+          end_points['global_pool'] = net
+        if num_classes:
+          net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                             scope='dropout7')
+          net = slim.conv2d(net, num_classes, [1, 1],
+                            activation_fn=None,
+                            normalizer_fn=None,
+                            biases_initializer=tf.zeros_initializer(),
+                            scope='fc8')
+          if spatial_squeeze:
+            net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+          end_points[sc.name + '/fc8'] = net
+      return net, end_points
+overfeat.default_image_size = 231
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat_test.py
new file mode 100644
index 00000000..dab0039e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/overfeat_test.py
@@ -0,0 +1,178 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.overfeat."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import overfeat
+
+slim = tf.contrib.slim
+
+
+class OverFeatTest(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 281, 281
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 281, 281
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False,
+                                    global_pool=True)
+      self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = overfeat.overfeat(inputs, num_classes)
+      expected_names = ['overfeat/conv1',
+                        'overfeat/pool1',
+                        'overfeat/conv2',
+                        'overfeat/pool2',
+                        'overfeat/conv3',
+                        'overfeat/conv4',
+                        'overfeat/conv5',
+                        'overfeat/pool5',
+                        'overfeat/fc6',
+                        'overfeat/fc7',
+                        'overfeat/fc8'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = overfeat.overfeat(inputs, num_classes)
+      expected_names = ['overfeat/conv1',
+                        'overfeat/pool1',
+                        'overfeat/conv2',
+                        'overfeat/pool2',
+                        'overfeat/conv3',
+                        'overfeat/conv4',
+                        'overfeat/conv5',
+                        'overfeat/pool5',
+                        'overfeat/fc6',
+                        'overfeat/fc7'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('overfeat/fc7'))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      overfeat.overfeat(inputs, num_classes)
+      expected_names = ['overfeat/conv1/weights',
+                        'overfeat/conv1/biases',
+                        'overfeat/conv2/weights',
+                        'overfeat/conv2/biases',
+                        'overfeat/conv3/weights',
+                        'overfeat/conv3/biases',
+                        'overfeat/conv4/weights',
+                        'overfeat/conv4/biases',
+                        'overfeat/conv5/weights',
+                        'overfeat/conv5/biases',
+                        'overfeat/fc6/weights',
+                        'overfeat/fc6/biases',
+                        'overfeat/fc7/weights',
+                        'overfeat/fc7/biases',
+                        'overfeat/fc8/weights',
+                        'overfeat/fc8/biases',
+                       ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 231, 231
+    eval_height, eval_width = 281, 281
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = overfeat.overfeat(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = overfeat.overfeat(eval_inputs, is_training=False,
+                                    spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 231, 231
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix.py b/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix.py
new file mode 100644
index 00000000..8c3a2e99
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix.py
@@ -0,0 +1,295 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Implementation of the Image-to-Image Translation model.
+
+This network represents a port of the following work:
+
+  Image-to-Image Translation with Conditional Adversarial Networks
+  Phillip Isola, Jun-Yan Zhu, Tinghui Zhou and Alexei A. Efros
+  Arxiv, 2017
+  https://phillipi.github.io/pix2pix/
+
+A reference implementation written in Lua can be found at:
+https://github.com/phillipi/pix2pix/blob/master/models.lua
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import functools
+
+import tensorflow as tf
+
+layers = tf.contrib.layers
+
+
+def pix2pix_arg_scope():
+  """Returns a default argument scope for isola_net.
+
+  Returns:
+    An arg scope.
+  """
+  # These parameters come from the online port, which don't necessarily match
+  # those in the paper.
+  # TODO(nsilberman): confirm these values with Philip.
+  instance_norm_params = {
+      'center': True,
+      'scale': True,
+      'epsilon': 0.00001,
+  }
+
+  with tf.contrib.framework.arg_scope(
+      [layers.conv2d, layers.conv2d_transpose],
+      normalizer_fn=layers.instance_norm,
+      normalizer_params=instance_norm_params,
+      weights_initializer=tf.random_normal_initializer(0, 0.02)) as sc:
+    return sc
+
+
+def upsample(net, num_outputs, kernel_size, method='nn_upsample_conv'):
+  """Upsamples the given inputs.
+
+  Args:
+    net: A `Tensor` of size [batch_size, height, width, filters].
+    num_outputs: The number of output filters.
+    kernel_size: A list of 2 scalars or a 1x2 `Tensor` indicating the scale,
+      relative to the inputs, of the output dimensions. For example, if kernel
+      size is [2, 3], then the output height and width will be twice and three
+      times the input size.
+    method: The upsampling method.
+
+  Returns:
+    An `Tensor` which was upsampled using the specified method.
+
+  Raises:
+    ValueError: if `method` is not recognized.
+  """
+  net_shape = tf.shape(net)
+  height = net_shape[1]
+  width = net_shape[2]
+
+  if method == 'nn_upsample_conv':
+    net = tf.image.resize_nearest_neighbor(
+        net, [kernel_size[0] * height, kernel_size[1] * width])
+    net = layers.conv2d(net, num_outputs, [4, 4], activation_fn=None)
+  elif method == 'conv2d_transpose':
+    net = layers.conv2d_transpose(
+        net, num_outputs, [4, 4], stride=kernel_size, activation_fn=None)
+  else:
+    raise ValueError('Unknown method: [%s]' % method)
+
+  return net
+
+
+class Block(
+    collections.namedtuple('Block', ['num_filters', 'decoder_keep_prob'])):
+  """Represents a single block of encoder and decoder processing.
+
+  The Image-to-Image translation paper works a bit differently than the original
+  U-Net model. In particular, each block represents a single operation in the
+  encoder which is concatenated with the corresponding decoder representation.
+  A dropout layer follows the concatenation and convolution of the concatenated
+  features.
+  """
+  pass
+
+
+def _default_generator_blocks():
+  """Returns the default generator block definitions.
+
+  Returns:
+    A list of generator blocks.
+  """
+  return [
+      Block(64, 0.5),
+      Block(128, 0.5),
+      Block(256, 0.5),
+      Block(512, 0),
+      Block(512, 0),
+      Block(512, 0),
+      Block(512, 0),
+  ]
+
+
+def pix2pix_generator(net,
+                      num_outputs,
+                      blocks=None,
+                      upsample_method='nn_upsample_conv',
+                      is_training=False):  # pylint: disable=unused-argument
+  """Defines the network architecture.
+
+  Args:
+    net: A `Tensor` of size [batch, height, width, channels]. Note that the
+      generator currently requires square inputs (e.g. height=width).
+    num_outputs: The number of (per-pixel) outputs.
+    blocks: A list of generator blocks or `None` to use the default generator
+      definition.
+    upsample_method: The method of upsampling images, one of 'nn_upsample_conv'
+      or 'conv2d_transpose'
+    is_training: Whether or not we're in training or testing mode.
+
+  Returns:
+    A `Tensor` representing the model output and a dictionary of model end
+      points.
+
+  Raises:
+    ValueError: if the input heights do not match their widths.
+  """
+  end_points = {}
+
+  blocks = blocks or _default_generator_blocks()
+
+  input_size = net.get_shape().as_list()
+
+  input_size[3] = num_outputs
+
+  upsample_fn = functools.partial(upsample, method=upsample_method)
+
+  encoder_activations = []
+
+  ###########
+  # Encoder #
+  ###########
+  with tf.variable_scope('encoder'):
+    with tf.contrib.framework.arg_scope(
+        [layers.conv2d],
+        kernel_size=[4, 4],
+        stride=2,
+        activation_fn=tf.nn.leaky_relu):
+
+      for block_id, block in enumerate(blocks):
+        # No normalizer for the first encoder layers as per 'Image-to-Image',
+        # Section 5.1.1
+        if block_id == 0:
+          # First layer doesn't use normalizer_fn
+          net = layers.conv2d(net, block.num_filters, normalizer_fn=None)
+        elif block_id < len(blocks) - 1:
+          net = layers.conv2d(net, block.num_filters)
+        else:
+          # Last layer doesn't use activation_fn nor normalizer_fn
+          net = layers.conv2d(
+              net, block.num_filters, activation_fn=None, normalizer_fn=None)
+
+        encoder_activations.append(net)
+        end_points['encoder%d' % block_id] = net
+
+  ###########
+  # Decoder #
+  ###########
+  reversed_blocks = list(blocks)
+  reversed_blocks.reverse()
+
+  with tf.variable_scope('decoder'):
+    # Dropout is used at both train and test time as per 'Image-to-Image',
+    # Section 2.1 (last paragraph).
+    with tf.contrib.framework.arg_scope([layers.dropout], is_training=True):
+
+      for block_id, block in enumerate(reversed_blocks):
+        if block_id > 0:
+          net = tf.concat([net, encoder_activations[-block_id - 1]], axis=3)
+
+        # The Relu comes BEFORE the upsample op:
+        net = tf.nn.relu(net)
+        net = upsample_fn(net, block.num_filters, [2, 2])
+        if block.decoder_keep_prob > 0:
+          net = layers.dropout(net, keep_prob=block.decoder_keep_prob)
+        end_points['decoder%d' % block_id] = net
+
+  with tf.variable_scope('output'):
+    # Explicitly set the normalizer_fn to None to override any default value
+    # that may come from an arg_scope, such as pix2pix_arg_scope.
+    logits = layers.conv2d(
+        net, num_outputs, [4, 4], activation_fn=None, normalizer_fn=None)
+    logits = tf.reshape(logits, input_size)
+
+    end_points['logits'] = logits
+    end_points['predictions'] = tf.tanh(logits)
+
+  return logits, end_points
+
+
+def pix2pix_discriminator(net, num_filters, padding=2, pad_mode='REFLECT',
+                          activation_fn=tf.nn.leaky_relu, is_training=False):
+  """Creates the Image2Image Translation Discriminator.
+
+  Args:
+    net: A `Tensor` of size [batch_size, height, width, channels] representing
+      the input.
+    num_filters: A list of the filters in the discriminator. The length of the
+      list determines the number of layers in the discriminator.
+    padding: Amount of reflection padding applied before each convolution.
+    pad_mode: mode for tf.pad, one of "CONSTANT", "REFLECT", or "SYMMETRIC".
+    activation_fn: activation fn for layers.conv2d.
+    is_training: Whether or not the model is training or testing.
+
+  Returns:
+    A logits `Tensor` of size [batch_size, N, N, 1] where N is the number of
+    'patches' we're attempting to discriminate and a dictionary of model end
+    points.
+  """
+  del is_training
+  end_points = {}
+
+  num_layers = len(num_filters)
+
+  def padded(net, scope):
+    if padding:
+      with tf.variable_scope(scope):
+        spatial_pad = tf.constant(
+            [[0, 0], [padding, padding], [padding, padding], [0, 0]],
+            dtype=tf.int32)
+        return tf.pad(net, spatial_pad, pad_mode)
+    else:
+      return net
+
+  with tf.contrib.framework.arg_scope(
+      [layers.conv2d],
+      kernel_size=[4, 4],
+      stride=2,
+      padding='valid',
+      activation_fn=activation_fn):
+
+    # No normalization on the input layer.
+    net = layers.conv2d(
+        padded(net, 'conv0'), num_filters[0], normalizer_fn=None, scope='conv0')
+
+    end_points['conv0'] = net
+
+    for i in range(1, num_layers - 1):
+      net = layers.conv2d(
+          padded(net, 'conv%d' % i), num_filters[i], scope='conv%d' % i)
+      end_points['conv%d' % i] = net
+
+    # Stride 1 on the last layer.
+    net = layers.conv2d(
+        padded(net, 'conv%d' % (num_layers - 1)),
+        num_filters[-1],
+        stride=1,
+        scope='conv%d' % (num_layers - 1))
+    end_points['conv%d' % (num_layers - 1)] = net
+
+    # 1-dim logits, stride 1, no activation, no normalization.
+    logits = layers.conv2d(
+        padded(net, 'conv%d' % num_layers),
+        1,
+        stride=1,
+        activation_fn=None,
+        normalizer_fn=None,
+        scope='conv%d' % num_layers)
+    end_points['logits'] = logits
+    end_points['predictions'] = tf.sigmoid(logits)
+  return logits, end_points
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix_test.py
new file mode 100644
index 00000000..ab5acb5c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/pix2pix_test.py
@@ -0,0 +1,156 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Tests for pix2pix."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from nets import pix2pix
+
+
+class GeneratorTest(tf.test.TestCase):
+
+  def _reduced_default_blocks(self):
+    """Returns the default blocks, scaled down to make test run faster."""
+    return [pix2pix.Block(b.num_filters // 32, b.decoder_keep_prob)
+            for b in pix2pix._default_generator_blocks()]
+
+  def test_output_size_nn_upsample_conv(self):
+    batch_size = 2
+    height, width = 256, 256
+    num_outputs = 4
+
+    images = tf.ones((batch_size, height, width, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      logits, _ = pix2pix.pix2pix_generator(
+          images, num_outputs, blocks=self._reduced_default_blocks(),
+          upsample_method='nn_upsample_conv')
+
+    with self.test_session() as session:
+      session.run(tf.global_variables_initializer())
+      np_outputs = session.run(logits)
+      self.assertListEqual([batch_size, height, width, num_outputs],
+                           list(np_outputs.shape))
+
+  def test_output_size_conv2d_transpose(self):
+    batch_size = 2
+    height, width = 256, 256
+    num_outputs = 4
+
+    images = tf.ones((batch_size, height, width, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      logits, _ = pix2pix.pix2pix_generator(
+          images, num_outputs, blocks=self._reduced_default_blocks(),
+          upsample_method='conv2d_transpose')
+
+    with self.test_session() as session:
+      session.run(tf.global_variables_initializer())
+      np_outputs = session.run(logits)
+      self.assertListEqual([batch_size, height, width, num_outputs],
+                           list(np_outputs.shape))
+
+  def test_block_number_dictates_number_of_layers(self):
+    batch_size = 2
+    height, width = 256, 256
+    num_outputs = 4
+
+    images = tf.ones((batch_size, height, width, 3))
+    blocks = [
+        pix2pix.Block(64, 0.5),
+        pix2pix.Block(128, 0),
+    ]
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      _, end_points = pix2pix.pix2pix_generator(
+          images, num_outputs, blocks)
+
+    num_encoder_layers = 0
+    num_decoder_layers = 0
+    for end_point in end_points:
+      if end_point.startswith('encoder'):
+        num_encoder_layers += 1
+      elif end_point.startswith('decoder'):
+        num_decoder_layers += 1
+
+    self.assertEqual(num_encoder_layers, len(blocks))
+    self.assertEqual(num_decoder_layers, len(blocks))
+
+
+class DiscriminatorTest(tf.test.TestCase):
+
+  def _layer_output_size(self, input_size, kernel_size=4, stride=2, pad=2):
+    return (input_size + pad * 2 - kernel_size) // stride + 1
+
+  def test_four_layers(self):
+    batch_size = 2
+    input_size = 256
+
+    output_size = self._layer_output_size(input_size)
+    output_size = self._layer_output_size(output_size)
+    output_size = self._layer_output_size(output_size)
+    output_size = self._layer_output_size(output_size, stride=1)
+    output_size = self._layer_output_size(output_size, stride=1)
+
+    images = tf.ones((batch_size, input_size, input_size, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      logits, end_points = pix2pix.pix2pix_discriminator(
+          images, num_filters=[64, 128, 256, 512])
+    self.assertListEqual([batch_size, output_size, output_size, 1],
+                         logits.shape.as_list())
+    self.assertListEqual([batch_size, output_size, output_size, 1],
+                         end_points['predictions'].shape.as_list())
+
+  def test_four_layers_no_padding(self):
+    batch_size = 2
+    input_size = 256
+
+    output_size = self._layer_output_size(input_size, pad=0)
+    output_size = self._layer_output_size(output_size, pad=0)
+    output_size = self._layer_output_size(output_size, pad=0)
+    output_size = self._layer_output_size(output_size, stride=1, pad=0)
+    output_size = self._layer_output_size(output_size, stride=1, pad=0)
+
+    images = tf.ones((batch_size, input_size, input_size, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      logits, end_points = pix2pix.pix2pix_discriminator(
+          images, num_filters=[64, 128, 256, 512], padding=0)
+    self.assertListEqual([batch_size, output_size, output_size, 1],
+                         logits.shape.as_list())
+    self.assertListEqual([batch_size, output_size, output_size, 1],
+                         end_points['predictions'].shape.as_list())
+
+  def test_four_layers_wrog_paddig(self):
+    batch_size = 2
+    input_size = 256
+
+    images = tf.ones((batch_size, input_size, input_size, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      with self.assertRaises(TypeError):
+        pix2pix.pix2pix_discriminator(
+            images, num_filters=[64, 128, 256, 512], padding=1.5)
+
+  def test_four_layers_negative_padding(self):
+    batch_size = 2
+    input_size = 256
+
+    images = tf.ones((batch_size, input_size, input_size, 3))
+    with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
+      with self.assertRaises(ValueError):
+        pix2pix.pix2pix_discriminator(
+            images, num_filters=[64, 128, 256, 512], padding=-1)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_utils.py b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_utils.py
new file mode 100644
index 00000000..b4fc0e0f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_utils.py
@@ -0,0 +1,275 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains building blocks for various versions of Residual Networks.
+
+Residual networks (ResNets) were proposed in:
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015
+
+More variants were introduced in:
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016
+
+We can obtain different ResNet variants by changing the network depth, width,
+and form of residual unit. This module implements the infrastructure for
+building them. Concrete ResNet units and full ResNet networks are implemented in
+the accompanying resnet_v1.py and resnet_v2.py modules.
+
+Compared to https://github.com/KaimingHe/deep-residual-networks, in the current
+implementation we subsample the output activations in the last residual unit of
+each block, instead of subsampling the input activations in the first residual
+unit of each block. The two implementations give identical results but our
+implementation is more memory efficient.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+  """A named tuple describing a ResNet block.
+
+  Its parts are:
+    scope: The scope of the `Block`.
+    unit_fn: The ResNet unit function which takes as input a `Tensor` and
+      returns another `Tensor` with the output of the ResNet unit.
+    args: A list of length equal to the number of units in the `Block`. The list
+      contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+      block to serve as argument to unit_fn.
+  """
+
+
+def subsample(inputs, factor, scope=None):
+  """Subsamples the input along the spatial dimensions.
+
+  Args:
+    inputs: A `Tensor` of size [batch, height_in, width_in, channels].
+    factor: The subsampling factor.
+    scope: Optional variable_scope.
+
+  Returns:
+    output: A `Tensor` of size [batch, height_out, width_out, channels] with the
+      input, either intact (if factor == 1) or subsampled (if factor > 1).
+  """
+  if factor == 1:
+    return inputs
+  else:
+    return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):
+  """Strided 2-D convolution with 'SAME' padding.
+
+  When stride > 1, then we do explicit zero-padding, followed by conv2d with
+  'VALID' padding.
+
+  Note that
+
+     net = conv2d_same(inputs, num_outputs, 3, stride=stride)
+
+  is equivalent to
+
+     net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')
+     net = subsample(net, factor=stride)
+
+  whereas
+
+     net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')
+
+  is different when the input's height or width is even, which is why we add the
+  current function. For more details, see ResnetUtilsTest.testConv2DSameEven().
+
+  Args:
+    inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
+    num_outputs: An integer, the number of output filters.
+    kernel_size: An int with the kernel_size of the filters.
+    stride: An integer, the output stride.
+    rate: An integer, rate for atrous convolution.
+    scope: Scope.
+
+  Returns:
+    output: A 4-D tensor of size [batch, height_out, width_out, channels] with
+      the convolution output.
+  """
+  if stride == 1:
+    return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate,
+                       padding='SAME', scope=scope)
+  else:
+    kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+    pad_total = kernel_size_effective - 1
+    pad_beg = pad_total // 2
+    pad_end = pad_total - pad_beg
+    inputs = tf.pad(inputs,
+                    [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
+    return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride,
+                       rate=rate, padding='VALID', scope=scope)
+
+
+@slim.add_arg_scope
+def stack_blocks_dense(net, blocks, output_stride=None,
+                       store_non_strided_activations=False,
+                       outputs_collections=None):
+  """Stacks ResNet `Blocks` and controls output feature density.
+
+  First, this function creates scopes for the ResNet in the form of
+  'block_name/unit_1', 'block_name/unit_2', etc.
+
+  Second, this function allows the user to explicitly control the ResNet
+  output_stride, which is the ratio of the input to output spatial resolution.
+  This is useful for dense prediction tasks such as semantic segmentation or
+  object detection.
+
+  Most ResNets consist of 4 ResNet blocks and subsample the activations by a
+  factor of 2 when transitioning between consecutive ResNet blocks. This results
+  to a nominal ResNet output_stride equal to 8. If we set the output_stride to
+  half the nominal network stride (e.g., output_stride=4), then we compute
+  responses twice.
+
+  Control of the output feature density is implemented by atrous convolution.
+
+  Args:
+    net: A `Tensor` of size [batch, height, width, channels].
+    blocks: A list of length equal to the number of ResNet `Blocks`. Each
+      element is a ResNet `Block` object describing the units in the `Block`.
+    output_stride: If `None`, then the output will be computed at the nominal
+      network stride. If output_stride is not `None`, it specifies the requested
+      ratio of input to output spatial resolution, which needs to be equal to
+      the product of unit strides from the start up to some level of the ResNet.
+      For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
+      then valid values for the output_stride are 1, 2, 6, 24 or None (which
+      is equivalent to output_stride=24).
+    store_non_strided_activations: If True, we compute non-strided (undecimated)
+      activations at the last unit of each block and store them in the
+      `outputs_collections` before subsampling them. This gives us access to
+      higher resolution intermediate activations which are useful in some
+      dense prediction problems but increases 4x the computation and memory cost
+      at the last unit of each block.
+    outputs_collections: Collection to add the ResNet block outputs.
+
+  Returns:
+    net: Output tensor with stride equal to the specified output_stride.
+
+  Raises:
+    ValueError: If the target output_stride is not valid.
+  """
+  # The current_stride variable keeps track of the effective stride of the
+  # activations. This allows us to invoke atrous convolution whenever applying
+  # the next residual unit would result in the activations having stride larger
+  # than the target output_stride.
+  current_stride = 1
+
+  # The atrous convolution rate parameter.
+  rate = 1
+
+  for block in blocks:
+    with tf.variable_scope(block.scope, 'block', [net]) as sc:
+      block_stride = 1
+      for i, unit in enumerate(block.args):
+        if store_non_strided_activations and i == len(block.args) - 1:
+          # Move stride from the block's last unit to the end of the block.
+          block_stride = unit.get('stride', 1)
+          unit = dict(unit, stride=1)
+
+        with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
+          # If we have reached the target output_stride, then we need to employ
+          # atrous convolution with stride=1 and multiply the atrous rate by the
+          # current unit's stride for use in subsequent layers.
+          if output_stride is not None and current_stride == output_stride:
+            net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
+            rate *= unit.get('stride', 1)
+
+          else:
+            net = block.unit_fn(net, rate=1, **unit)
+            current_stride *= unit.get('stride', 1)
+            if output_stride is not None and current_stride > output_stride:
+              raise ValueError('The target output_stride cannot be reached.')
+
+      # Collect activations at the block's end before performing subsampling.
+      net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)
+
+      # Subsampling of the block's output activations.
+      if output_stride is not None and current_stride == output_stride:
+        rate *= block_stride
+      else:
+        net = subsample(net, block_stride)
+        current_stride *= block_stride
+        if output_stride is not None and current_stride > output_stride:
+          raise ValueError('The target output_stride cannot be reached.')
+
+  if output_stride is not None and current_stride != output_stride:
+    raise ValueError('The target output_stride cannot be reached.')
+
+  return net
+
+
+def resnet_arg_scope(weight_decay=0.0001,
+                     batch_norm_decay=0.997,
+                     batch_norm_epsilon=1e-5,
+                     batch_norm_scale=True,
+                     activation_fn=tf.nn.relu,
+                     use_batch_norm=True,
+                     batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS):
+  """Defines the default ResNet arg scope.
+
+  TODO(gpapan): The batch-normalization related default values above are
+    appropriate for use in conjunction with the reference ResNet models
+    released at https://github.com/KaimingHe/deep-residual-networks. When
+    training ResNets from scratch, they might need to be tuned.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: The moving average decay when estimating layer activation
+      statistics in batch normalization.
+    batch_norm_epsilon: Small constant to prevent division by zero when
+      normalizing activations by their variance in batch normalization.
+    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
+      activations in the batch normalization layer.
+    activation_fn: The activation function which is used in ResNet.
+    use_batch_norm: Whether or not to use batch normalization.
+    batch_norm_updates_collections: Collection for the update ops for
+      batch norm.
+
+  Returns:
+    An `arg_scope` to use for the resnet models.
+  """
+  batch_norm_params = {
+      'decay': batch_norm_decay,
+      'epsilon': batch_norm_epsilon,
+      'scale': batch_norm_scale,
+      'updates_collections': batch_norm_updates_collections,
+      'fused': None,  # Use fused batch norm if possible.
+  }
+
+  with slim.arg_scope(
+      [slim.conv2d],
+      weights_regularizer=slim.l2_regularizer(weight_decay),
+      weights_initializer=slim.variance_scaling_initializer(),
+      activation_fn=activation_fn,
+      normalizer_fn=slim.batch_norm if use_batch_norm else None,
+      normalizer_params=batch_norm_params):
+    with slim.arg_scope([slim.batch_norm], **batch_norm_params):
+      # The following implies padding='SAME' for pool1, which makes feature
+      # alignment easier for dense prediction tasks. This is also used in
+      # https://github.com/facebook/fb.resnet.torch. However the accompanying
+      # code of 'Deep Residual Learning for Image Recognition' uses
+      # padding='VALID' for pool1. You can switch to that choice by setting
+      # slim.arg_scope([slim.max_pool2d], padding='VALID').
+      with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
+        return arg_sc
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1.py b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1.py
new file mode 100644
index 00000000..95e1a11c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1.py
@@ -0,0 +1,375 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains definitions for the original form of Residual Networks.
+
+The 'v1' residual networks (ResNets) implemented in this module were proposed
+by:
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Deep Residual Learning for Image Recognition. arXiv:1512.03385
+
+Other variants were introduced in:
+[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
+
+The networks defined in this module utilize the bottleneck building block of
+[1] with projection shortcuts only for increasing depths. They employ batch
+normalization *after* every weight layer. This is the architecture used by
+MSRA in the Imagenet and MSCOCO 2016 competition models ResNet-101 and
+ResNet-152. See [2; Fig. 1a] for a comparison between the current 'v1'
+architecture and the alternative 'v2' architecture of [2] which uses batch
+normalization *before* every weight layer in the so-called full pre-activation
+units.
+
+Typical use:
+
+   from tensorflow.contrib.slim.nets import resnet_v1
+
+ResNet-101 for image classification into 1000 classes:
+
+   # inputs has shape [batch, 224, 224, 3]
+   with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+      net, end_points = resnet_v1.resnet_v1_101(inputs, 1000, is_training=False)
+
+ResNet-101 for semantic segmentation into 21 classes:
+
+   # inputs has shape [batch, 513, 513, 3]
+   with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+      net, end_points = resnet_v1.resnet_v1_101(inputs,
+                                                21,
+                                                is_training=False,
+                                                global_pool=False,
+                                                output_stride=16)
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import resnet_utils
+
+
+resnet_arg_scope = resnet_utils.resnet_arg_scope
+slim = tf.contrib.slim
+
+
+class NoOpScope(object):
+  """No-op context manager."""
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exc_type, exc_value, traceback):
+    return False
+
+
+@slim.add_arg_scope
+def bottleneck(inputs,
+               depth,
+               depth_bottleneck,
+               stride,
+               rate=1,
+               outputs_collections=None,
+               scope=None,
+               use_bounded_activations=False):
+  """Bottleneck residual unit variant with BN after convolutions.
+
+  This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
+  its definition. Note that we use here the bottleneck variant which has an
+  extra bottleneck layer.
+
+  When putting together two consecutive ResNet blocks that use this unit, one
+  should use stride = 2 in the last unit of the first block.
+
+  Args:
+    inputs: A tensor of size [batch, height, width, channels].
+    depth: The depth of the ResNet unit output.
+    depth_bottleneck: The depth of the bottleneck layers.
+    stride: The ResNet unit's stride. Determines the amount of downsampling of
+      the units output compared to its input.
+    rate: An integer, rate for atrous convolution.
+    outputs_collections: Collection to add the ResNet unit output.
+    scope: Optional variable_scope.
+    use_bounded_activations: Whether or not to use bounded activations. Bounded
+      activations better lend themselves to quantized inference.
+
+  Returns:
+    The ResNet unit's output.
+  """
+  with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
+    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
+    if depth == depth_in:
+      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
+    else:
+      shortcut = slim.conv2d(
+          inputs,
+          depth, [1, 1],
+          stride=stride,
+          activation_fn=tf.nn.relu6 if use_bounded_activations else None,
+          scope='shortcut')
+
+    residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
+                           scope='conv1')
+    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
+                                        rate=rate, scope='conv2')
+    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
+                           activation_fn=None, scope='conv3')
+
+    if use_bounded_activations:
+      # Use clip_by_value to simulate bandpass activation.
+      residual = tf.clip_by_value(residual, -6.0, 6.0)
+      output = tf.nn.relu6(shortcut + residual)
+    else:
+      output = tf.nn.relu(shortcut + residual)
+
+    return slim.utils.collect_named_outputs(outputs_collections,
+                                            sc.name,
+                                            output)
+
+
+def resnet_v1(inputs,
+              blocks,
+              num_classes=None,
+              is_training=True,
+              global_pool=True,
+              output_stride=None,
+              include_root_block=True,
+              spatial_squeeze=True,
+              store_non_strided_activations=False,
+              reuse=None,
+              scope=None):
+  """Generator for v1 ResNet models.
+
+  This function generates a family of ResNet v1 models. See the resnet_v1_*()
+  methods for specific model instantiations, obtained by selecting different
+  block instantiations that produce ResNets of various depths.
+
+  Training for image classification on Imagenet is usually done with [224, 224]
+  inputs, resulting in [7, 7] feature maps at the output of the last ResNet
+  block for the ResNets defined in [1] that have nominal stride equal to 32.
+  However, for dense prediction tasks we advise that one uses inputs with
+  spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
+  this case the feature maps at the ResNet output will have spatial shape
+  [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
+  and corners exactly aligned with the input image corners, which greatly
+  facilitates alignment of the features to the image. Using as input [225, 225]
+  images results in [8, 8] feature maps at the output of the last ResNet block.
+
+  For dense prediction tasks, the ResNet needs to run in fully-convolutional
+  (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
+  have nominal stride equal to 32 and a good choice in FCN mode is to use
+  output_stride=16 in order to increase the density of the computed features at
+  small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    blocks: A list of length equal to the number of ResNet blocks. Each element
+      is a resnet_utils.Block object describing the units in the block.
+    num_classes: Number of predicted classes for classification tasks.
+      If 0 or None, we return the features before the logit layer.
+    is_training: whether batch_norm layers are in training mode. If this is set
+      to None, the callers can specify slim.batch_norm's is_training parameter
+      from an outer slim.arg_scope.
+    global_pool: If True, we perform global average pooling before computing the
+      logits. Set to True for image classification, False for dense prediction.
+    output_stride: If None, then the output will be computed at the nominal
+      network stride. If output_stride is not None, it specifies the requested
+      ratio of input to output spatial resolution.
+    include_root_block: If True, include the initial convolution followed by
+      max-pooling, if False excludes it.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+        To use this parameter, the input images must be smaller than 300x300
+        pixels, in which case the output logit layer does not contain spatial
+        information and can be removed.
+    store_non_strided_activations: If True, we compute non-strided (undecimated)
+      activations at the last unit of each block and store them in the
+      `outputs_collections` before subsampling them. This gives us access to
+      higher resolution intermediate activations which are useful in some
+      dense prediction problems but increases 4x the computation and memory cost
+      at the last unit of each block.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+
+  Returns:
+    net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
+      If global_pool is False, then height_out and width_out are reduced by a
+      factor of output_stride compared to the respective height_in and width_in,
+      else both height_out and width_out equal one. If num_classes is 0 or None,
+      then net is the output of the last ResNet block, potentially after global
+      average pooling. If num_classes a non-zero integer, net contains the
+      pre-softmax activations.
+    end_points: A dictionary from components of the network to the corresponding
+      activation.
+
+  Raises:
+    ValueError: If the target output_stride is not valid.
+  """
+  with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    with slim.arg_scope([slim.conv2d, bottleneck,
+                         resnet_utils.stack_blocks_dense],
+                        outputs_collections=end_points_collection):
+      with (slim.arg_scope([slim.batch_norm], is_training=is_training)
+            if is_training is not None else NoOpScope()):
+        net = inputs
+        if include_root_block:
+          if output_stride is not None:
+            if output_stride % 4 != 0:
+              raise ValueError('The output_stride needs to be a multiple of 4.')
+            output_stride /= 4
+          net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
+          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
+        net = resnet_utils.stack_blocks_dense(net, blocks, output_stride,
+                                              store_non_strided_activations)
+        # Convert end_points_collection into a dictionary of end_points.
+        end_points = slim.utils.convert_collection_to_dict(
+            end_points_collection)
+
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
+          end_points['global_pool'] = net
+        if num_classes:
+          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                            normalizer_fn=None, scope='logits')
+          end_points[sc.name + '/logits'] = net
+          if spatial_squeeze:
+            net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+            end_points[sc.name + '/spatial_squeeze'] = net
+          end_points['predictions'] = slim.softmax(net, scope='predictions')
+        return net, end_points
+resnet_v1.default_image_size = 224
+
+
+def resnet_v1_block(scope, base_depth, num_units, stride):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return resnet_utils.Block(scope, bottleneck, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride
+  }])
+
+
+def resnet_v1_50(inputs,
+                 num_classes=None,
+                 is_training=True,
+                 global_pool=True,
+                 output_stride=None,
+                 spatial_squeeze=True,
+                 store_non_strided_activations=False,
+                 reuse=None,
+                 scope='resnet_v1_50'):
+  """ResNet-50 model of [1]. See resnet_v1() for arg and return description."""
+  blocks = [
+      resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
+      resnet_v1_block('block3', base_depth=256, num_units=6, stride=2),
+      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v1(inputs, blocks, num_classes, is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   store_non_strided_activations=store_non_strided_activations,
+                   reuse=reuse, scope=scope)
+resnet_v1_50.default_image_size = resnet_v1.default_image_size
+
+
+def resnet_v1_101(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  spatial_squeeze=True,
+                  store_non_strided_activations=False,
+                  reuse=None,
+                  scope='resnet_v1_101'):
+  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
+  blocks = [
+      resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
+      resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
+      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v1(inputs, blocks, num_classes, is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   store_non_strided_activations=store_non_strided_activations,
+                   reuse=reuse, scope=scope)
+resnet_v1_101.default_image_size = resnet_v1.default_image_size
+
+
+def resnet_v1_152(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  store_non_strided_activations=False,
+                  spatial_squeeze=True,
+                  reuse=None,
+                  scope='resnet_v1_152'):
+  """ResNet-152 model of [1]. See resnet_v1() for arg and return description."""
+  blocks = [
+      resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
+      resnet_v1_block('block3', base_depth=256, num_units=36, stride=2),
+      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v1(inputs, blocks, num_classes, is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   store_non_strided_activations=store_non_strided_activations,
+                   reuse=reuse, scope=scope)
+resnet_v1_152.default_image_size = resnet_v1.default_image_size
+
+
+def resnet_v1_200(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  store_non_strided_activations=False,
+                  spatial_squeeze=True,
+                  reuse=None,
+                  scope='resnet_v1_200'):
+  """ResNet-200 model of [2]. See resnet_v1() for arg and return description."""
+  blocks = [
+      resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v1_block('block2', base_depth=128, num_units=24, stride=2),
+      resnet_v1_block('block3', base_depth=256, num_units=36, stride=2),
+      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v1(inputs, blocks, num_classes, is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   store_non_strided_activations=store_non_strided_activations,
+                   reuse=reuse, scope=scope)
+resnet_v1_200.default_image_size = resnet_v1.default_image_size
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1_test.py
new file mode 100644
index 00000000..c40e7f88
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v1_test.py
@@ -0,0 +1,555 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.resnet_v1."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import resnet_utils
+from nets import resnet_v1
+
+slim = tf.contrib.slim
+
+
+def create_test_input(batch_size, height, width, channels):
+  """Create test input tensor.
+
+  Args:
+    batch_size: The number of images per batch or `None` if unknown.
+    height: The height of each image or `None` if unknown.
+    width: The width of each image or `None` if unknown.
+    channels: The number of channels per image or `None` if unknown.
+
+  Returns:
+    Either a placeholder `Tensor` of dimension
+      [batch_size, height, width, channels] if any of the inputs are `None` or a
+    constant `Tensor` with the mesh grid values along the spatial dimensions.
+  """
+  if None in [batch_size, height, width, channels]:
+    return tf.placeholder(tf.float32, (batch_size, height, width, channels))
+  else:
+    return tf.to_float(
+        np.tile(
+            np.reshape(
+                np.reshape(np.arange(height), [height, 1]) +
+                np.reshape(np.arange(width), [1, width]),
+                [1, height, width, 1]),
+            [batch_size, 1, 1, channels]))
+
+
+class ResnetUtilsTest(tf.test.TestCase):
+
+  def testSubsampleThreeByThree(self):
+    x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
+    x = resnet_utils.subsample(x, 2)
+    expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
+    with self.test_session():
+      self.assertAllClose(x.eval(), expected.eval())
+
+  def testSubsampleFourByFour(self):
+    x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
+    x = resnet_utils.subsample(x, 2)
+    expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
+    with self.test_session():
+      self.assertAllClose(x.eval(), expected.eval())
+
+  def testConv2DSameEven(self):
+    n, n2 = 4, 2
+
+    # Input image.
+    x = create_test_input(1, n, n, 1)
+
+    # Convolution kernel.
+    w = create_test_input(1, 3, 3, 1)
+    w = tf.reshape(w, [3, 3, 1, 1])
+
+    tf.get_variable('Conv/weights', initializer=w)
+    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
+    tf.get_variable_scope().reuse_variables()
+
+    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
+    y1_expected = tf.to_float([[14, 28, 43, 26],
+                               [28, 48, 66, 37],
+                               [43, 66, 84, 46],
+                               [26, 37, 46, 22]])
+    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
+
+    y2 = resnet_utils.subsample(y1, 2)
+    y2_expected = tf.to_float([[14, 43],
+                               [43, 84]])
+    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
+
+    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
+    y3_expected = y2_expected
+
+    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
+    y4_expected = tf.to_float([[48, 37],
+                               [37, 22]])
+    y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      self.assertAllClose(y1.eval(), y1_expected.eval())
+      self.assertAllClose(y2.eval(), y2_expected.eval())
+      self.assertAllClose(y3.eval(), y3_expected.eval())
+      self.assertAllClose(y4.eval(), y4_expected.eval())
+
+  def testConv2DSameOdd(self):
+    n, n2 = 5, 3
+
+    # Input image.
+    x = create_test_input(1, n, n, 1)
+
+    # Convolution kernel.
+    w = create_test_input(1, 3, 3, 1)
+    w = tf.reshape(w, [3, 3, 1, 1])
+
+    tf.get_variable('Conv/weights', initializer=w)
+    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
+    tf.get_variable_scope().reuse_variables()
+
+    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
+    y1_expected = tf.to_float([[14, 28, 43, 58, 34],
+                               [28, 48, 66, 84, 46],
+                               [43, 66, 84, 102, 55],
+                               [58, 84, 102, 120, 64],
+                               [34, 46, 55, 64, 30]])
+    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
+
+    y2 = resnet_utils.subsample(y1, 2)
+    y2_expected = tf.to_float([[14, 43, 34],
+                               [43, 84, 55],
+                               [34, 55, 30]])
+    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
+
+    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
+    y3_expected = y2_expected
+
+    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
+    y4_expected = y2_expected
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      self.assertAllClose(y1.eval(), y1_expected.eval())
+      self.assertAllClose(y2.eval(), y2_expected.eval())
+      self.assertAllClose(y3.eval(), y3_expected.eval())
+      self.assertAllClose(y4.eval(), y4_expected.eval())
+
+  def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
+    """A plain ResNet without extra layers before or after the ResNet blocks."""
+    with tf.variable_scope(scope, values=[inputs]):
+      with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
+        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
+        end_points = slim.utils.convert_collection_to_dict('end_points')
+        return net, end_points
+
+  def testEndPointsV1(self):
+    """Test the end points of a tiny v1 bottleneck network."""
+    blocks = [
+        resnet_v1.resnet_v1_block(
+            'block1', base_depth=1, num_units=2, stride=2),
+        resnet_v1.resnet_v1_block(
+            'block2', base_depth=2, num_units=2, stride=1),
+    ]
+    inputs = create_test_input(2, 32, 16, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
+    expected = [
+        'tiny/block1/unit_1/bottleneck_v1/shortcut',
+        'tiny/block1/unit_1/bottleneck_v1/conv1',
+        'tiny/block1/unit_1/bottleneck_v1/conv2',
+        'tiny/block1/unit_1/bottleneck_v1/conv3',
+        'tiny/block1/unit_2/bottleneck_v1/conv1',
+        'tiny/block1/unit_2/bottleneck_v1/conv2',
+        'tiny/block1/unit_2/bottleneck_v1/conv3',
+        'tiny/block2/unit_1/bottleneck_v1/shortcut',
+        'tiny/block2/unit_1/bottleneck_v1/conv1',
+        'tiny/block2/unit_1/bottleneck_v1/conv2',
+        'tiny/block2/unit_1/bottleneck_v1/conv3',
+        'tiny/block2/unit_2/bottleneck_v1/conv1',
+        'tiny/block2/unit_2/bottleneck_v1/conv2',
+        'tiny/block2/unit_2/bottleneck_v1/conv3']
+    self.assertItemsEqual(expected, end_points.keys())
+
+  def _stack_blocks_nondense(self, net, blocks):
+    """A simplified ResNet Block stacker without output stride control."""
+    for block in blocks:
+      with tf.variable_scope(block.scope, 'block', [net]):
+        for i, unit in enumerate(block.args):
+          with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
+            net = block.unit_fn(net, rate=1, **unit)
+    return net
+
+  def testAtrousValuesBottleneck(self):
+    """Verify the values of dense feature extraction by atrous convolution.
+
+    Make sure that dense feature extraction by stack_blocks_dense() followed by
+    subsampling gives identical results to feature extraction at the nominal
+    network output stride using the simple self._stack_blocks_nondense() above.
+    """
+    block = resnet_v1.resnet_v1_block
+    blocks = [
+        block('block1', base_depth=1, num_units=2, stride=2),
+        block('block2', base_depth=2, num_units=2, stride=2),
+        block('block3', base_depth=4, num_units=2, stride=2),
+        block('block4', base_depth=8, num_units=2, stride=1),
+    ]
+    nominal_stride = 8
+
+    # Test both odd and even input dimensions.
+    height = 30
+    width = 31
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      with slim.arg_scope([slim.batch_norm], is_training=False):
+        for output_stride in [1, 2, 4, 8, None]:
+          with tf.Graph().as_default():
+            with self.test_session() as sess:
+              tf.set_random_seed(0)
+              inputs = create_test_input(1, height, width, 3)
+              # Dense feature extraction followed by subsampling.
+              output = resnet_utils.stack_blocks_dense(inputs,
+                                                       blocks,
+                                                       output_stride)
+              if output_stride is None:
+                factor = 1
+              else:
+                factor = nominal_stride // output_stride
+
+              output = resnet_utils.subsample(output, factor)
+              # Make the two networks use the same weights.
+              tf.get_variable_scope().reuse_variables()
+              # Feature extraction at the nominal network rate.
+              expected = self._stack_blocks_nondense(inputs, blocks)
+              sess.run(tf.global_variables_initializer())
+              output, expected = sess.run([output, expected])
+              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
+
+  def testStridingLastUnitVsSubsampleBlockEnd(self):
+    """Compares subsampling at the block's last unit or block's end.
+
+    Makes sure that the final output is the same when we use a stride at the
+    last unit of a block vs. we subsample activations at the end of a block.
+    """
+    block = resnet_v1.resnet_v1_block
+
+    blocks = [
+        block('block1', base_depth=1, num_units=2, stride=2),
+        block('block2', base_depth=2, num_units=2, stride=2),
+        block('block3', base_depth=4, num_units=2, stride=2),
+        block('block4', base_depth=8, num_units=2, stride=1),
+    ]
+
+    # Test both odd and even input dimensions.
+    height = 30
+    width = 31
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      with slim.arg_scope([slim.batch_norm], is_training=False):
+        for output_stride in [1, 2, 4, 8, None]:
+          with tf.Graph().as_default():
+            with self.test_session() as sess:
+              tf.set_random_seed(0)
+              inputs = create_test_input(1, height, width, 3)
+
+              # Subsampling at the last unit of the block.
+              output = resnet_utils.stack_blocks_dense(
+                  inputs, blocks, output_stride,
+                  store_non_strided_activations=False,
+                  outputs_collections='output')
+              output_end_points = slim.utils.convert_collection_to_dict(
+                  'output')
+
+              # Make the two networks use the same weights.
+              tf.get_variable_scope().reuse_variables()
+
+              # Subsample activations at the end of the blocks.
+              expected = resnet_utils.stack_blocks_dense(
+                  inputs, blocks, output_stride,
+                  store_non_strided_activations=True,
+                  outputs_collections='expected')
+              expected_end_points = slim.utils.convert_collection_to_dict(
+                  'expected')
+
+              sess.run(tf.global_variables_initializer())
+
+              # Make sure that the final output is the same.
+              output, expected = sess.run([output, expected])
+              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
+
+              # Make sure that intermediate block activations in
+              # output_end_points are subsampled versions of the corresponding
+              # ones in expected_end_points.
+              for i, block in enumerate(blocks[:-1:]):
+                output = output_end_points[block.scope]
+                expected = expected_end_points[block.scope]
+                atrous_activated = (output_stride is not None and
+                                    2 ** i >= output_stride)
+                if not atrous_activated:
+                  expected = resnet_utils.subsample(expected, 2)
+                output, expected = sess.run([output, expected])
+                self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
+
+
+class ResnetCompleteNetworkTest(tf.test.TestCase):
+  """Tests with complete small ResNet v1 networks."""
+
+  def _resnet_small(self,
+                    inputs,
+                    num_classes=None,
+                    is_training=True,
+                    global_pool=True,
+                    output_stride=None,
+                    include_root_block=True,
+                    spatial_squeeze=True,
+                    reuse=None,
+                    scope='resnet_v1_small'):
+    """A shallow and thin ResNet v1 for faster tests."""
+    block = resnet_v1.resnet_v1_block
+    blocks = [
+        block('block1', base_depth=1, num_units=3, stride=2),
+        block('block2', base_depth=2, num_units=3, stride=2),
+        block('block3', base_depth=4, num_units=3, stride=2),
+        block('block4', base_depth=8, num_units=2, stride=1),
+    ]
+    return resnet_v1.resnet_v1(inputs, blocks, num_classes,
+                               is_training=is_training,
+                               global_pool=global_pool,
+                               output_stride=output_stride,
+                               include_root_block=include_root_block,
+                               spatial_squeeze=spatial_squeeze,
+                               reuse=reuse,
+                               scope=scope)
+
+  def testClassificationEndPoints(self):
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      logits, end_points = self._resnet_small(inputs, num_classes,
+                                              global_pool=global_pool,
+                                              spatial_squeeze=False,
+                                              scope='resnet')
+    self.assertTrue(logits.op.name.startswith('resnet/logits'))
+    self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
+    self.assertTrue('predictions' in end_points)
+    self.assertListEqual(end_points['predictions'].get_shape().as_list(),
+                         [2, 1, 1, num_classes])
+    self.assertTrue('global_pool' in end_points)
+    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
+                         [2, 1, 1, 32])
+
+  def testClassificationEndPointsWithNoBatchNormArgscope(self):
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      logits, end_points = self._resnet_small(inputs, num_classes,
+                                              global_pool=global_pool,
+                                              spatial_squeeze=False,
+                                              is_training=None,
+                                              scope='resnet')
+    self.assertTrue(logits.op.name.startswith('resnet/logits'))
+    self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
+    self.assertTrue('predictions' in end_points)
+    self.assertListEqual(end_points['predictions'].get_shape().as_list(),
+                         [2, 1, 1, num_classes])
+    self.assertTrue('global_pool' in end_points)
+    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
+                         [2, 1, 1, 32])
+
+  def testEndpointNames(self):
+    # Like ResnetUtilsTest.testEndPointsV1(), but for the public API.
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         scope='resnet')
+    expected = ['resnet/conv1']
+    for block in range(1, 5):
+      for unit in range(1, 4 if block < 4 else 3):
+        for conv in range(1, 4):
+          expected.append('resnet/block%d/unit_%d/bottleneck_v1/conv%d' %
+                          (block, unit, conv))
+        expected.append('resnet/block%d/unit_%d/bottleneck_v1' % (block, unit))
+      expected.append('resnet/block%d/unit_1/bottleneck_v1/shortcut' % block)
+      expected.append('resnet/block%d' % block)
+    expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
+                     'predictions'])
+    self.assertItemsEqual(end_points.keys(), expected)
+
+  def testClassificationShapes(self):
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 28, 28, 4],
+          'resnet/block2': [2, 14, 14, 8],
+          'resnet/block3': [2, 7, 7, 16],
+          'resnet/block4': [2, 7, 7, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    inputs = create_test_input(2, 321, 321, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 41, 41, 4],
+          'resnet/block2': [2, 21, 21, 8],
+          'resnet/block3': [2, 11, 11, 16],
+          'resnet/block4': [2, 11, 11, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testRootlessFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    inputs = create_test_input(2, 128, 128, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         include_root_block=False,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 64, 64, 4],
+          'resnet/block2': [2, 32, 32, 8],
+          'resnet/block3': [2, 16, 16, 16],
+          'resnet/block4': [2, 16, 16, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testAtrousFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    output_stride = 8
+    inputs = create_test_input(2, 321, 321, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs,
+                                         num_classes,
+                                         global_pool=global_pool,
+                                         output_stride=output_stride,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 41, 41, 4],
+          'resnet/block2': [2, 41, 41, 8],
+          'resnet/block3': [2, 41, 41, 16],
+          'resnet/block4': [2, 41, 41, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testAtrousFullyConvolutionalValues(self):
+    """Verify dense feature extraction with atrous convolution."""
+    nominal_stride = 32
+    for output_stride in [4, 8, 16, 32, None]:
+      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+        with tf.Graph().as_default():
+          with self.test_session() as sess:
+            tf.set_random_seed(0)
+            inputs = create_test_input(2, 81, 81, 3)
+            # Dense feature extraction followed by subsampling.
+            output, _ = self._resnet_small(inputs, None, is_training=False,
+                                           global_pool=False,
+                                           output_stride=output_stride)
+            if output_stride is None:
+              factor = 1
+            else:
+              factor = nominal_stride // output_stride
+            output = resnet_utils.subsample(output, factor)
+            # Make the two networks use the same weights.
+            tf.get_variable_scope().reuse_variables()
+            # Feature extraction at the nominal network rate.
+            expected, _ = self._resnet_small(inputs, None, is_training=False,
+                                             global_pool=False)
+            sess.run(tf.global_variables_initializer())
+            self.assertAllClose(output.eval(), expected.eval(),
+                                atol=1e-4, rtol=1e-4)
+
+  def testUnknownBatchSize(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(None, height, width, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      logits, _ = self._resnet_small(inputs, num_classes,
+                                     global_pool=global_pool,
+                                     spatial_squeeze=False,
+                                     scope='resnet')
+    self.assertTrue(logits.op.name.startswith('resnet/logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, 1, 1, num_classes])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 1, 1, num_classes))
+
+  def testFullyConvolutionalUnknownHeightWidth(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = False
+    inputs = create_test_input(batch, None, None, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [batch, None, None, 32])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(output, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 3, 3, 32))
+
+  def testAtrousFullyConvolutionalUnknownHeightWidth(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = False
+    output_stride = 8
+    inputs = create_test_input(batch, None, None, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      output, _ = self._resnet_small(inputs,
+                                     None,
+                                     global_pool=global_pool,
+                                     output_stride=output_stride)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [batch, None, None, 32])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(output, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 9, 9, 32))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2.py b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2.py
new file mode 100644
index 00000000..c719c1bd
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2.py
@@ -0,0 +1,337 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains definitions for the preactivation form of Residual Networks.
+
+Residual networks (ResNets) were originally proposed in:
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Deep Residual Learning for Image Recognition. arXiv:1512.03385
+
+The full preactivation 'v2' ResNet variant implemented in this module was
+introduced by:
+[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
+
+The key difference of the full preactivation 'v2' variant compared to the
+'v1' variant in [1] is the use of batch normalization before every weight layer.
+
+Typical use:
+
+   from tensorflow.contrib.slim.nets import resnet_v2
+
+ResNet-101 for image classification into 1000 classes:
+
+   # inputs has shape [batch, 224, 224, 3]
+   with slim.arg_scope(resnet_v2.resnet_arg_scope()):
+      net, end_points = resnet_v2.resnet_v2_101(inputs, 1000, is_training=False)
+
+ResNet-101 for semantic segmentation into 21 classes:
+
+   # inputs has shape [batch, 513, 513, 3]
+   with slim.arg_scope(resnet_v2.resnet_arg_scope()):
+      net, end_points = resnet_v2.resnet_v2_101(inputs,
+                                                21,
+                                                is_training=False,
+                                                global_pool=False,
+                                                output_stride=16)
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import resnet_utils
+
+slim = tf.contrib.slim
+resnet_arg_scope = resnet_utils.resnet_arg_scope
+
+
+@slim.add_arg_scope
+def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
+               outputs_collections=None, scope=None):
+  """Bottleneck residual unit variant with BN before convolutions.
+
+  This is the full preactivation residual unit variant proposed in [2]. See
+  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
+  variant which has an extra bottleneck layer.
+
+  When putting together two consecutive ResNet blocks that use this unit, one
+  should use stride = 2 in the last unit of the first block.
+
+  Args:
+    inputs: A tensor of size [batch, height, width, channels].
+    depth: The depth of the ResNet unit output.
+    depth_bottleneck: The depth of the bottleneck layers.
+    stride: The ResNet unit's stride. Determines the amount of downsampling of
+      the units output compared to its input.
+    rate: An integer, rate for atrous convolution.
+    outputs_collections: Collection to add the ResNet unit output.
+    scope: Optional variable_scope.
+
+  Returns:
+    The ResNet unit's output.
+  """
+  with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
+    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
+    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
+    if depth == depth_in:
+      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
+    else:
+      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
+                             normalizer_fn=None, activation_fn=None,
+                             scope='shortcut')
+
+    residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,
+                           scope='conv1')
+    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
+                                        rate=rate, scope='conv2')
+    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
+                           normalizer_fn=None, activation_fn=None,
+                           scope='conv3')
+
+    output = shortcut + residual
+
+    return slim.utils.collect_named_outputs(outputs_collections,
+                                            sc.name,
+                                            output)
+
+
+def resnet_v2(inputs,
+              blocks,
+              num_classes=None,
+              is_training=True,
+              global_pool=True,
+              output_stride=None,
+              include_root_block=True,
+              spatial_squeeze=True,
+              reuse=None,
+              scope=None):
+  """Generator for v2 (preactivation) ResNet models.
+
+  This function generates a family of ResNet v2 models. See the resnet_v2_*()
+  methods for specific model instantiations, obtained by selecting different
+  block instantiations that produce ResNets of various depths.
+
+  Training for image classification on Imagenet is usually done with [224, 224]
+  inputs, resulting in [7, 7] feature maps at the output of the last ResNet
+  block for the ResNets defined in [1] that have nominal stride equal to 32.
+  However, for dense prediction tasks we advise that one uses inputs with
+  spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
+  this case the feature maps at the ResNet output will have spatial shape
+  [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
+  and corners exactly aligned with the input image corners, which greatly
+  facilitates alignment of the features to the image. Using as input [225, 225]
+  images results in [8, 8] feature maps at the output of the last ResNet block.
+
+  For dense prediction tasks, the ResNet needs to run in fully-convolutional
+  (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
+  have nominal stride equal to 32 and a good choice in FCN mode is to use
+  output_stride=16 in order to increase the density of the computed features at
+  small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
+
+  Args:
+    inputs: A tensor of size [batch, height_in, width_in, channels].
+    blocks: A list of length equal to the number of ResNet blocks. Each element
+      is a resnet_utils.Block object describing the units in the block.
+    num_classes: Number of predicted classes for classification tasks.
+      If 0 or None, we return the features before the logit layer.
+    is_training: whether batch_norm layers are in training mode.
+    global_pool: If True, we perform global average pooling before computing the
+      logits. Set to True for image classification, False for dense prediction.
+    output_stride: If None, then the output will be computed at the nominal
+      network stride. If output_stride is not None, it specifies the requested
+      ratio of input to output spatial resolution.
+    include_root_block: If True, include the initial convolution followed by
+      max-pooling, if False excludes it. If excluded, `inputs` should be the
+      results of an activation-less convolution.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+        To use this parameter, the input images must be smaller than 300x300
+        pixels, in which case the output logit layer does not contain spatial
+        information and can be removed.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+
+
+  Returns:
+    net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
+      If global_pool is False, then height_out and width_out are reduced by a
+      factor of output_stride compared to the respective height_in and width_in,
+      else both height_out and width_out equal one. If num_classes is 0 or None,
+      then net is the output of the last ResNet block, potentially after global
+      average pooling. If num_classes is a non-zero integer, net contains the
+      pre-softmax activations.
+    end_points: A dictionary from components of the network to the corresponding
+      activation.
+
+  Raises:
+    ValueError: If the target output_stride is not valid.
+  """
+  with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    with slim.arg_scope([slim.conv2d, bottleneck,
+                         resnet_utils.stack_blocks_dense],
+                        outputs_collections=end_points_collection):
+      with slim.arg_scope([slim.batch_norm], is_training=is_training):
+        net = inputs
+        if include_root_block:
+          if output_stride is not None:
+            if output_stride % 4 != 0:
+              raise ValueError('The output_stride needs to be a multiple of 4.')
+            output_stride /= 4
+          # We do not include batch normalization or activation functions in
+          # conv1 because the first ResNet unit will perform these. Cf.
+          # Appendix of [2].
+          with slim.arg_scope([slim.conv2d],
+                              activation_fn=None, normalizer_fn=None):
+            net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
+          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
+        net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
+        # This is needed because the pre-activation variant does not have batch
+        # normalization or activation functions in the residual unit output. See
+        # Appendix of [2].
+        net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
+        # Convert end_points_collection into a dictionary of end_points.
+        end_points = slim.utils.convert_collection_to_dict(
+            end_points_collection)
+
+        if global_pool:
+          # Global average pooling.
+          net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
+          end_points['global_pool'] = net
+        if num_classes:
+          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+                            normalizer_fn=None, scope='logits')
+          end_points[sc.name + '/logits'] = net
+          if spatial_squeeze:
+            net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+            end_points[sc.name + '/spatial_squeeze'] = net
+          end_points['predictions'] = slim.softmax(net, scope='predictions')
+        return net, end_points
+resnet_v2.default_image_size = 224
+
+
+def resnet_v2_block(scope, base_depth, num_units, stride):
+  """Helper function for creating a resnet_v2 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v2 bottleneck block.
+  """
+  return resnet_utils.Block(scope, bottleneck, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride
+  }])
+resnet_v2.default_image_size = 224
+
+
+def resnet_v2_50(inputs,
+                 num_classes=None,
+                 is_training=True,
+                 global_pool=True,
+                 output_stride=None,
+                 spatial_squeeze=True,
+                 reuse=None,
+                 scope='resnet_v2_50'):
+  """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
+  blocks = [
+      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+      resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
+      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
+resnet_v2_50.default_image_size = resnet_v2.default_image_size
+
+
+def resnet_v2_101(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  spatial_squeeze=True,
+                  reuse=None,
+                  scope='resnet_v2_101'):
+  """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
+  blocks = [
+      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
+      resnet_v2_block('block3', base_depth=256, num_units=23, stride=2),
+      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
+resnet_v2_101.default_image_size = resnet_v2.default_image_size
+
+
+def resnet_v2_152(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  spatial_squeeze=True,
+                  reuse=None,
+                  scope='resnet_v2_152'):
+  """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""
+  blocks = [
+      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v2_block('block2', base_depth=128, num_units=8, stride=2),
+      resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),
+      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
+resnet_v2_152.default_image_size = resnet_v2.default_image_size
+
+
+def resnet_v2_200(inputs,
+                  num_classes=None,
+                  is_training=True,
+                  global_pool=True,
+                  output_stride=None,
+                  spatial_squeeze=True,
+                  reuse=None,
+                  scope='resnet_v2_200'):
+  """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""
+  blocks = [
+      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
+      resnet_v2_block('block2', base_depth=128, num_units=24, stride=2),
+      resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),
+      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
+  ]
+  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
+                   global_pool=global_pool, output_stride=output_stride,
+                   include_root_block=True, spatial_squeeze=spatial_squeeze,
+                   reuse=reuse, scope=scope)
+resnet_v2_200.default_image_size = resnet_v2.default_image_size
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2_test.py
new file mode 100644
index 00000000..891816d1
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/resnet_v2_test.py
@@ -0,0 +1,475 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.resnet_v2."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from nets import resnet_utils
+from nets import resnet_v2
+
+slim = tf.contrib.slim
+
+
+def create_test_input(batch_size, height, width, channels):
+  """Create test input tensor.
+
+  Args:
+    batch_size: The number of images per batch or `None` if unknown.
+    height: The height of each image or `None` if unknown.
+    width: The width of each image or `None` if unknown.
+    channels: The number of channels per image or `None` if unknown.
+
+  Returns:
+    Either a placeholder `Tensor` of dimension
+      [batch_size, height, width, channels] if any of the inputs are `None` or a
+    constant `Tensor` with the mesh grid values along the spatial dimensions.
+  """
+  if None in [batch_size, height, width, channels]:
+    return tf.placeholder(tf.float32, (batch_size, height, width, channels))
+  else:
+    return tf.to_float(
+        np.tile(
+            np.reshape(
+                np.reshape(np.arange(height), [height, 1]) +
+                np.reshape(np.arange(width), [1, width]),
+                [1, height, width, 1]),
+            [batch_size, 1, 1, channels]))
+
+
+class ResnetUtilsTest(tf.test.TestCase):
+
+  def testSubsampleThreeByThree(self):
+    x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
+    x = resnet_utils.subsample(x, 2)
+    expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
+    with self.test_session():
+      self.assertAllClose(x.eval(), expected.eval())
+
+  def testSubsampleFourByFour(self):
+    x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
+    x = resnet_utils.subsample(x, 2)
+    expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
+    with self.test_session():
+      self.assertAllClose(x.eval(), expected.eval())
+
+  def testConv2DSameEven(self):
+    n, n2 = 4, 2
+
+    # Input image.
+    x = create_test_input(1, n, n, 1)
+
+    # Convolution kernel.
+    w = create_test_input(1, 3, 3, 1)
+    w = tf.reshape(w, [3, 3, 1, 1])
+
+    tf.get_variable('Conv/weights', initializer=w)
+    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
+    tf.get_variable_scope().reuse_variables()
+
+    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
+    y1_expected = tf.to_float([[14, 28, 43, 26],
+                               [28, 48, 66, 37],
+                               [43, 66, 84, 46],
+                               [26, 37, 46, 22]])
+    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
+
+    y2 = resnet_utils.subsample(y1, 2)
+    y2_expected = tf.to_float([[14, 43],
+                               [43, 84]])
+    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
+
+    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
+    y3_expected = y2_expected
+
+    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
+    y4_expected = tf.to_float([[48, 37],
+                               [37, 22]])
+    y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      self.assertAllClose(y1.eval(), y1_expected.eval())
+      self.assertAllClose(y2.eval(), y2_expected.eval())
+      self.assertAllClose(y3.eval(), y3_expected.eval())
+      self.assertAllClose(y4.eval(), y4_expected.eval())
+
+  def testConv2DSameOdd(self):
+    n, n2 = 5, 3
+
+    # Input image.
+    x = create_test_input(1, n, n, 1)
+
+    # Convolution kernel.
+    w = create_test_input(1, 3, 3, 1)
+    w = tf.reshape(w, [3, 3, 1, 1])
+
+    tf.get_variable('Conv/weights', initializer=w)
+    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
+    tf.get_variable_scope().reuse_variables()
+
+    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
+    y1_expected = tf.to_float([[14, 28, 43, 58, 34],
+                               [28, 48, 66, 84, 46],
+                               [43, 66, 84, 102, 55],
+                               [58, 84, 102, 120, 64],
+                               [34, 46, 55, 64, 30]])
+    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
+
+    y2 = resnet_utils.subsample(y1, 2)
+    y2_expected = tf.to_float([[14, 43, 34],
+                               [43, 84, 55],
+                               [34, 55, 30]])
+    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
+
+    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
+    y3_expected = y2_expected
+
+    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
+    y4_expected = y2_expected
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      self.assertAllClose(y1.eval(), y1_expected.eval())
+      self.assertAllClose(y2.eval(), y2_expected.eval())
+      self.assertAllClose(y3.eval(), y3_expected.eval())
+      self.assertAllClose(y4.eval(), y4_expected.eval())
+
+  def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
+    """A plain ResNet without extra layers before or after the ResNet blocks."""
+    with tf.variable_scope(scope, values=[inputs]):
+      with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
+        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
+        end_points = slim.utils.convert_collection_to_dict('end_points')
+        return net, end_points
+
+  def testEndPointsV2(self):
+    """Test the end points of a tiny v2 bottleneck network."""
+    blocks = [
+        resnet_v2.resnet_v2_block(
+            'block1', base_depth=1, num_units=2, stride=2),
+        resnet_v2.resnet_v2_block(
+            'block2', base_depth=2, num_units=2, stride=1),
+    ]
+    inputs = create_test_input(2, 32, 16, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
+    expected = [
+        'tiny/block1/unit_1/bottleneck_v2/shortcut',
+        'tiny/block1/unit_1/bottleneck_v2/conv1',
+        'tiny/block1/unit_1/bottleneck_v2/conv2',
+        'tiny/block1/unit_1/bottleneck_v2/conv3',
+        'tiny/block1/unit_2/bottleneck_v2/conv1',
+        'tiny/block1/unit_2/bottleneck_v2/conv2',
+        'tiny/block1/unit_2/bottleneck_v2/conv3',
+        'tiny/block2/unit_1/bottleneck_v2/shortcut',
+        'tiny/block2/unit_1/bottleneck_v2/conv1',
+        'tiny/block2/unit_1/bottleneck_v2/conv2',
+        'tiny/block2/unit_1/bottleneck_v2/conv3',
+        'tiny/block2/unit_2/bottleneck_v2/conv1',
+        'tiny/block2/unit_2/bottleneck_v2/conv2',
+        'tiny/block2/unit_2/bottleneck_v2/conv3']
+    self.assertItemsEqual(expected, end_points.keys())
+
+  def _stack_blocks_nondense(self, net, blocks):
+    """A simplified ResNet Block stacker without output stride control."""
+    for block in blocks:
+      with tf.variable_scope(block.scope, 'block', [net]):
+        for i, unit in enumerate(block.args):
+          with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
+            net = block.unit_fn(net, rate=1, **unit)
+    return net
+
+  def testAtrousValuesBottleneck(self):
+    """Verify the values of dense feature extraction by atrous convolution.
+
+    Make sure that dense feature extraction by stack_blocks_dense() followed by
+    subsampling gives identical results to feature extraction at the nominal
+    network output stride using the simple self._stack_blocks_nondense() above.
+    """
+    block = resnet_v2.resnet_v2_block
+    blocks = [
+        block('block1', base_depth=1, num_units=2, stride=2),
+        block('block2', base_depth=2, num_units=2, stride=2),
+        block('block3', base_depth=4, num_units=2, stride=2),
+        block('block4', base_depth=8, num_units=2, stride=1),
+    ]
+    nominal_stride = 8
+
+    # Test both odd and even input dimensions.
+    height = 30
+    width = 31
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      with slim.arg_scope([slim.batch_norm], is_training=False):
+        for output_stride in [1, 2, 4, 8, None]:
+          with tf.Graph().as_default():
+            with self.test_session() as sess:
+              tf.set_random_seed(0)
+              inputs = create_test_input(1, height, width, 3)
+              # Dense feature extraction followed by subsampling.
+              output = resnet_utils.stack_blocks_dense(inputs,
+                                                       blocks,
+                                                       output_stride)
+              if output_stride is None:
+                factor = 1
+              else:
+                factor = nominal_stride // output_stride
+
+              output = resnet_utils.subsample(output, factor)
+              # Make the two networks use the same weights.
+              tf.get_variable_scope().reuse_variables()
+              # Feature extraction at the nominal network rate.
+              expected = self._stack_blocks_nondense(inputs, blocks)
+              sess.run(tf.global_variables_initializer())
+              output, expected = sess.run([output, expected])
+              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
+
+
+class ResnetCompleteNetworkTest(tf.test.TestCase):
+  """Tests with complete small ResNet v2 networks."""
+
+  def _resnet_small(self,
+                    inputs,
+                    num_classes=None,
+                    is_training=True,
+                    global_pool=True,
+                    output_stride=None,
+                    include_root_block=True,
+                    spatial_squeeze=True,
+                    reuse=None,
+                    scope='resnet_v2_small'):
+    """A shallow and thin ResNet v2 for faster tests."""
+    block = resnet_v2.resnet_v2_block
+    blocks = [
+        block('block1', base_depth=1, num_units=3, stride=2),
+        block('block2', base_depth=2, num_units=3, stride=2),
+        block('block3', base_depth=4, num_units=3, stride=2),
+        block('block4', base_depth=8, num_units=2, stride=1),
+    ]
+    return resnet_v2.resnet_v2(inputs, blocks, num_classes,
+                               is_training=is_training,
+                               global_pool=global_pool,
+                               output_stride=output_stride,
+                               include_root_block=include_root_block,
+                               spatial_squeeze=spatial_squeeze,
+                               reuse=reuse,
+                               scope=scope)
+
+  def testClassificationEndPoints(self):
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      logits, end_points = self._resnet_small(inputs, num_classes,
+                                              global_pool=global_pool,
+                                              spatial_squeeze=False,
+                                              scope='resnet')
+    self.assertTrue(logits.op.name.startswith('resnet/logits'))
+    self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
+    self.assertTrue('predictions' in end_points)
+    self.assertListEqual(end_points['predictions'].get_shape().as_list(),
+                         [2, 1, 1, num_classes])
+    self.assertTrue('global_pool' in end_points)
+    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
+                         [2, 1, 1, 32])
+
+  def testEndpointNames(self):
+    # Like ResnetUtilsTest.testEndPointsV2(), but for the public API.
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         scope='resnet')
+    expected = ['resnet/conv1']
+    for block in range(1, 5):
+      for unit in range(1, 4 if block < 4 else 3):
+        for conv in range(1, 4):
+          expected.append('resnet/block%d/unit_%d/bottleneck_v2/conv%d' %
+                          (block, unit, conv))
+        expected.append('resnet/block%d/unit_%d/bottleneck_v2' % (block, unit))
+      expected.append('resnet/block%d/unit_1/bottleneck_v2/shortcut' % block)
+      expected.append('resnet/block%d' % block)
+    expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
+                     'predictions'])
+    self.assertItemsEqual(end_points.keys(), expected)
+
+  def testClassificationShapes(self):
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 28, 28, 4],
+          'resnet/block2': [2, 14, 14, 8],
+          'resnet/block3': [2, 7, 7, 16],
+          'resnet/block4': [2, 7, 7, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    inputs = create_test_input(2, 321, 321, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 41, 41, 4],
+          'resnet/block2': [2, 21, 21, 8],
+          'resnet/block3': [2, 11, 11, 16],
+          'resnet/block4': [2, 11, 11, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testRootlessFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    inputs = create_test_input(2, 128, 128, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         include_root_block=False,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 64, 64, 4],
+          'resnet/block2': [2, 32, 32, 8],
+          'resnet/block3': [2, 16, 16, 16],
+          'resnet/block4': [2, 16, 16, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testAtrousFullyConvolutionalEndpointShapes(self):
+    global_pool = False
+    num_classes = 10
+    output_stride = 8
+    inputs = create_test_input(2, 321, 321, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs,
+                                         num_classes,
+                                         global_pool=global_pool,
+                                         output_stride=output_stride,
+                                         spatial_squeeze=False,
+                                         scope='resnet')
+      endpoint_to_shape = {
+          'resnet/block1': [2, 41, 41, 4],
+          'resnet/block2': [2, 41, 41, 8],
+          'resnet/block3': [2, 41, 41, 16],
+          'resnet/block4': [2, 41, 41, 32]}
+      for endpoint in endpoint_to_shape:
+        shape = endpoint_to_shape[endpoint]
+        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
+
+  def testAtrousFullyConvolutionalValues(self):
+    """Verify dense feature extraction with atrous convolution."""
+    nominal_stride = 32
+    for output_stride in [4, 8, 16, 32, None]:
+      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+        with tf.Graph().as_default():
+          with self.test_session() as sess:
+            tf.set_random_seed(0)
+            inputs = create_test_input(2, 81, 81, 3)
+            # Dense feature extraction followed by subsampling.
+            output, _ = self._resnet_small(inputs, None,
+                                           is_training=False,
+                                           global_pool=False,
+                                           output_stride=output_stride)
+            if output_stride is None:
+              factor = 1
+            else:
+              factor = nominal_stride // output_stride
+            output = resnet_utils.subsample(output, factor)
+            # Make the two networks use the same weights.
+            tf.get_variable_scope().reuse_variables()
+            # Feature extraction at the nominal network rate.
+            expected, _ = self._resnet_small(inputs, None,
+                                             is_training=False,
+                                             global_pool=False)
+            sess.run(tf.global_variables_initializer())
+            self.assertAllClose(output.eval(), expected.eval(),
+                                atol=1e-4, rtol=1e-4)
+
+  def testUnknownBatchSize(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(None, height, width, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      logits, _ = self._resnet_small(inputs, num_classes,
+                                     global_pool=global_pool,
+                                     spatial_squeeze=False,
+                                     scope='resnet')
+    self.assertTrue(logits.op.name.startswith('resnet/logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [None, 1, 1, num_classes])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 1, 1, num_classes))
+
+  def testFullyConvolutionalUnknownHeightWidth(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = False
+    inputs = create_test_input(batch, None, None, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      output, _ = self._resnet_small(inputs, None,
+                                     global_pool=global_pool)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [batch, None, None, 32])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(output, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 3, 3, 32))
+
+  def testAtrousFullyConvolutionalUnknownHeightWidth(self):
+    batch = 2
+    height, width = 65, 65
+    global_pool = False
+    output_stride = 8
+    inputs = create_test_input(batch, None, None, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      output, _ = self._resnet_small(inputs,
+                                     None,
+                                     global_pool=global_pool,
+                                     output_stride=output_stride)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [batch, None, None, 32])
+    images = create_test_input(batch, height, width, 3)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(output, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch, 9, 9, 32))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg.py b/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg.py
new file mode 100644
index 00000000..a13375e2
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg.py
@@ -0,0 +1,599 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for Gated Separable 3D network (S3D-G).
+
+The network architecture is proposed by:
+  Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu and Kevin Murphy,
+  Rethinking Spatiotemporal Feature Learning For Video Understanding.
+  https://arxiv.org/abs/1712.04851.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import i3d_utils
+
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+conv3d_spatiotemporal = i3d_utils.conv3d_spatiotemporal
+inception_block_v1_3d = i3d_utils.inception_block_v1_3d
+
+# Orignaly, arg_scope = slim.arg_scope and layers = slim, now switch to more
+# update-to-date tf.contrib.* API.
+arg_scope = tf.contrib.framework.arg_scope
+layers = tf.contrib.layers
+
+
+def s3dg_arg_scope(weight_decay=1e-7,
+                   batch_norm_decay=0.999,
+                   batch_norm_epsilon=0.001):
+  """Defines default arg_scope for S3D-G.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+
+  Returns:
+    sc: An arg_scope to use for the models.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      # Turns off fused batch norm.
+      'fused': False,
+      # collection containing the moving mean and moving variance.
+      'variables_collections': {
+          'beta': None,
+          'gamma': None,
+          'moving_mean': ['moving_vars'],
+          'moving_variance': ['moving_vars'],
+      }
+  }
+
+  with arg_scope(
+      [layers.conv3d, conv3d_spatiotemporal],
+      weights_regularizer=layers.l2_regularizer(weight_decay),
+      activation_fn=tf.nn.relu,
+      normalizer_fn=layers.batch_norm,
+      normalizer_params=batch_norm_params):
+    with arg_scope([conv3d_spatiotemporal], separable=True) as sc:
+      return sc
+
+
+def self_gating(input_tensor, scope, data_format='NDHWC'):
+  """Feature gating as used in S3D-G.
+
+  Transforms the input features by aggregating features from all
+  spatial and temporal locations, and applying gating conditioned
+  on the aggregated features. More details can be found at:
+  https://arxiv.org/abs/1712.04851
+
+  Args:
+    input_tensor: A 5-D float tensor of size [batch_size, num_frames,
+      height, width, channels].
+    scope: scope for `variable_scope`.
+    data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
+      The data format of the input and output data. With the default format
+      "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+      in_width, in_channels]. Alternatively, the format could be "NCDHW", the
+      data storage order is:
+      [batch, in_channels, in_depth, in_height, in_width].
+
+  Returns:
+    A tensor with the same shape as input_tensor.
+  """
+
+  index_c = data_format.index('C')
+  index_d = data_format.index('D')
+  index_h = data_format.index('H')
+  index_w = data_format.index('W')
+  input_shape = input_tensor.get_shape().as_list()
+  t = input_shape[index_d]
+  w = input_shape[index_w]
+  h = input_shape[index_h]
+  num_channels = input_shape[index_c]
+
+  spatiotemporal_average = layers.avg_pool3d(
+      input_tensor, [t, w, h],
+      stride=1,
+      data_format=data_format,
+      scope=scope + '/self_gating/avg_pool3d')
+
+  weights = layers.conv3d(
+      spatiotemporal_average,
+      num_channels, [1, 1, 1],
+      activation_fn=None,
+      normalizer_fn=None,
+      biases_initializer=None,
+      data_format=data_format,
+      weights_initializer=trunc_normal(0.01),
+      scope=scope + '/self_gating/transformer_W')
+
+  tile_multiples = [1, t, w, h]
+  tile_multiples.insert(index_c, 1)
+  weights = tf.tile(weights, tile_multiples)
+  weights = tf.nn.sigmoid(weights)
+
+  return tf.multiply(weights, input_tensor)
+
+
+def s3dg_base(inputs,
+              first_temporal_kernel_size=3,
+              temporal_conv_startat='Conv2d_2c_3x3',
+              gating_startat='Conv2d_2c_3x3',
+              final_endpoint='Mixed_5c',
+              min_depth=16,
+              depth_multiplier=1.0,
+              data_format='NDHWC',
+              scope='InceptionV1'):
+  """Defines the I3D/S3DG base architecture.
+
+  Note that we use the names as defined in Inception V1 to facilitate checkpoint
+  conversion from an image-trained Inception V1 checkpoint to I3D checkpoint.
+
+  Args:
+    inputs: A 5-D float tensor of size [batch_size, num_frames, height, width,
+      channels].
+    first_temporal_kernel_size: Specifies the temporal kernel size for the first
+      conv3d filter. A larger value slows down the model but provides little
+      accuracy improvement. The default is 7 in the original I3D and S3D-G but 3
+      gives better performance. Must be set to one of 1, 3, 5 or 7.
+    temporal_conv_startat: Specifies the first conv block to use 3D or separable
+      3D convs rather than 2D convs (implemented as [1, k, k] 3D conv). This is
+      used to construct the inverted pyramid models. 'Conv2d_2c_3x3' is the
+      first valid block to use separable 3D convs. If provided block name is
+      not present, all valid blocks will use separable 3D convs. Note that
+      'Conv2d_1a_7x7' cannot be made into a separable 3D conv, but can be made
+      into a 2D or 3D conv using the `first_temporal_kernel_size` option.
+    gating_startat: Specifies the first conv block to use self gating.
+      'Conv2d_2c_3x3' is the first valid block to use self gating. If provided
+      block name is not present, all valid blocks will use separable 3D convs.
+    final_endpoint: Specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
+      The data format of the input and output data. With the default format
+      "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+      in_width, in_channels]. Alternatively, the format could be "NCDHW", the
+      data storage order is:
+      [batch, in_channels, in_depth, in_height, in_width].
+    scope: Optional variable_scope.
+
+  Returns:
+    A dictionary from components of the network to the corresponding activation.
+
+  Raises:
+    ValueError: if final_endpoint is not set to one of the predefined values, or
+      if depth_multiplier <= 0.
+  """
+
+  assert data_format in ['NDHWC', 'NCDHW']
+  end_points = {}
+  t = 1
+  # For inverted pyramid models, we start with gating switched off.
+  use_gating = False
+  self_gating_fn = None
+  def gating_fn(inputs, scope):
+    return self_gating(inputs, scope, data_format=data_format)
+
+  if depth_multiplier <= 0:
+    raise ValueError('depth_multiplier is not greater than zero.')
+  depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+  with tf.variable_scope(scope, 'InceptionV1', [inputs]):
+    with arg_scope([layers.conv3d], weights_initializer=trunc_normal(0.01)):
+      with arg_scope(
+          [layers.conv3d, layers.max_pool3d, conv3d_spatiotemporal],
+          stride=1,
+          data_format=data_format,
+          padding='SAME'):
+        # batch_size x 32 x 112 x 112 x 64
+        end_point = 'Conv2d_1a_7x7'
+        if first_temporal_kernel_size not in [1, 3, 5, 7]:
+          raise ValueError(
+              'first_temporal_kernel_size can only be 1, 3, 5 or 7.')
+        # Separable conv is slow when used at first conv layer.
+        net = conv3d_spatiotemporal(
+            inputs,
+            depth(64), [first_temporal_kernel_size, 7, 7],
+            stride=2,
+            separable=False,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+        # batch_size x 32 x 56 x 56 x 64
+        end_point = 'MaxPool_2a_3x3'
+        net = layers.max_pool3d(
+            net, [1, 3, 3], stride=[1, 2, 2], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+        # batch_size x 32 x 56 x 56 x 64
+        end_point = 'Conv2d_2b_1x1'
+        net = layers.conv3d(net, depth(64), [1, 1, 1], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+        # batch_size x 32 x 56 x 56 x 192
+        end_point = 'Conv2d_2c_3x3'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = conv3d_spatiotemporal(net, depth(192), [t, 3, 3], scope=end_point)
+        if use_gating:
+          net = self_gating(net, scope=end_point, data_format=data_format)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+        # batch_size x 32 x 28 x 28 x 192
+        end_point = 'MaxPool_3a_3x3'
+        net = layers.max_pool3d(
+            net, [1, 3, 3], stride=[1, 2, 2], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 32 x 28 x 28 x 256
+        end_point = 'Mixed_3b'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(64),
+            num_outputs_1_0a=depth(96),
+            num_outputs_1_0b=depth(128),
+            num_outputs_2_0a=depth(16),
+            num_outputs_2_0b=depth(32),
+            num_outputs_3_0b=depth(32),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        end_point = 'Mixed_3c'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(128),
+            num_outputs_1_0a=depth(128),
+            num_outputs_1_0b=depth(192),
+            num_outputs_2_0a=depth(32),
+            num_outputs_2_0b=depth(96),
+            num_outputs_3_0b=depth(64),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        end_point = 'MaxPool_4a_3x3'
+        net = layers.max_pool3d(
+            net, [3, 3, 3], stride=[2, 2, 2], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 16 x 14 x 14 x 512
+        end_point = 'Mixed_4b'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(192),
+            num_outputs_1_0a=depth(96),
+            num_outputs_1_0b=depth(208),
+            num_outputs_2_0a=depth(16),
+            num_outputs_2_0b=depth(48),
+            num_outputs_3_0b=depth(64),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 16 x 14 x 14 x 512
+        end_point = 'Mixed_4c'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(160),
+            num_outputs_1_0a=depth(112),
+            num_outputs_1_0b=depth(224),
+            num_outputs_2_0a=depth(24),
+            num_outputs_2_0b=depth(64),
+            num_outputs_3_0b=depth(64),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 16 x 14 x 14 x 512
+        end_point = 'Mixed_4d'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(128),
+            num_outputs_1_0a=depth(128),
+            num_outputs_1_0b=depth(256),
+            num_outputs_2_0a=depth(24),
+            num_outputs_2_0b=depth(64),
+            num_outputs_3_0b=depth(64),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 16 x 14 x 14 x 528
+        end_point = 'Mixed_4e'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(112),
+            num_outputs_1_0a=depth(144),
+            num_outputs_1_0b=depth(288),
+            num_outputs_2_0a=depth(32),
+            num_outputs_2_0b=depth(64),
+            num_outputs_3_0b=depth(64),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 16 x 14 x 14 x 832
+        end_point = 'Mixed_4f'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(256),
+            num_outputs_1_0a=depth(160),
+            num_outputs_1_0b=depth(320),
+            num_outputs_2_0a=depth(32),
+            num_outputs_2_0b=depth(128),
+            num_outputs_3_0b=depth(128),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        end_point = 'MaxPool_5a_2x2'
+        net = layers.max_pool3d(
+            net, [2, 2, 2], stride=[2, 2, 2], scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 8 x 7 x 7 x 832
+        end_point = 'Mixed_5b'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(256),
+            num_outputs_1_0a=depth(160),
+            num_outputs_1_0b=depth(320),
+            num_outputs_2_0a=depth(32),
+            num_outputs_2_0b=depth(128),
+            num_outputs_3_0b=depth(128),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+
+        # batch_size x 8 x 7 x 7 x 1024
+        end_point = 'Mixed_5c'
+        if temporal_conv_startat == end_point:
+          t = 3
+        if gating_startat == end_point:
+          use_gating = True
+          self_gating_fn = gating_fn
+        net = inception_block_v1_3d(
+            net,
+            num_outputs_0_0a=depth(384),
+            num_outputs_1_0a=depth(192),
+            num_outputs_1_0b=depth(384),
+            num_outputs_2_0a=depth(48),
+            num_outputs_2_0b=depth(128),
+            num_outputs_3_0b=depth(128),
+            temporal_kernel_size=t,
+            self_gating_fn=self_gating_fn,
+            data_format=data_format,
+            scope=end_point)
+        end_points[end_point] = net
+        if final_endpoint == end_point:
+          return net, end_points
+    raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def s3dg(inputs,
+         num_classes=1000,
+         first_temporal_kernel_size=3,
+         temporal_conv_startat='Conv2d_2c_3x3',
+         gating_startat='Conv2d_2c_3x3',
+         final_endpoint='Mixed_5c',
+         min_depth=16,
+         depth_multiplier=1.0,
+         dropout_keep_prob=0.8,
+         is_training=True,
+         prediction_fn=layers.softmax,
+         spatial_squeeze=True,
+         reuse=None,
+         data_format='NDHWC',
+         scope='InceptionV1'):
+  """Defines the S3D-G architecture.
+
+  The default image size used to train this network is 224x224.
+
+  Args:
+    inputs: A 5-D float tensor of size [batch_size, num_frames, height, width,
+      channels].
+    num_classes: number of predicted classes.
+    first_temporal_kernel_size: Specifies the temporal kernel size for the first
+      conv3d filter. A larger value slows down the model but provides little
+      accuracy improvement. Must be set to one of 1, 3, 5 or 7.
+    temporal_conv_startat: Specifies the first conv block to use separable 3D
+      convs rather than 2D convs (implemented as [1, k, k] 3D conv). This is
+      used to construct the inverted pyramid models. 'Conv2d_2c_3x3' is the
+      first valid block to use separable 3D convs. If provided block name is
+      not present, all valid blocks will use separable 3D convs.
+    gating_startat: Specifies the first conv block to use self gating.
+      'Conv2d_2c_3x3' is the first valid block to use self gating. If provided
+      block name is not present, all valid blocks will use separable 3D convs.
+    final_endpoint: Specifies the endpoint to construct the network up to. It
+      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
+    min_depth: Minimum depth value (number of channels) for all convolution ops.
+      Enforced when depth_multiplier < 1, and not an active constraint when
+      depth_multiplier >= 1.
+    depth_multiplier: Float multiplier for the depth (number of channels)
+      for all convolution ops. The value must be greater than zero. Typical
+      usage will be to set this value in (0, 1) to reduce the number of
+      parameters or computation cost of the model.
+    dropout_keep_prob: the percentage of activation values that are retained.
+    is_training: whether is training or not.
+    prediction_fn: a function to get predictions out of logits.
+    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
+      The data format of the input and output data. With the default format
+      "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+      in_width, in_channels]. Alternatively, the format could be "NCDHW", the
+      data storage order is:
+      [batch, in_channels, in_depth, in_height, in_width].
+    scope: Optional variable_scope.
+
+  Returns:
+    logits: the pre-softmax activations, a tensor of size
+      [batch_size, num_classes]
+    end_points: a dictionary from components of the network to the corresponding
+      activation.
+  """
+  assert data_format in ['NDHWC', 'NCDHW']
+  # Final pooling and prediction
+  with tf.variable_scope(
+      scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
+    with arg_scope(
+        [layers.batch_norm, layers.dropout], is_training=is_training):
+      net, end_points = s3dg_base(
+          inputs,
+          first_temporal_kernel_size=first_temporal_kernel_size,
+          temporal_conv_startat=temporal_conv_startat,
+          gating_startat=gating_startat,
+          final_endpoint=final_endpoint,
+          min_depth=min_depth,
+          depth_multiplier=depth_multiplier,
+          data_format=data_format,
+          scope=scope)
+      with tf.variable_scope('Logits'):
+        if data_format.startswith('NC'):
+          net = tf.transpose(net, [0, 2, 3, 4, 1])
+        kernel_size = i3d_utils.reduced_kernel_size_3d(net, [2, 7, 7])
+        net = layers.avg_pool3d(
+            net,
+            kernel_size,
+            stride=1,
+            data_format='NDHWC',
+            scope='AvgPool_0a_7x7')
+        net = layers.dropout(net, dropout_keep_prob, scope='Dropout_0b')
+        logits = layers.conv3d(
+            net,
+            num_classes, [1, 1, 1],
+            activation_fn=None,
+            normalizer_fn=None,
+            data_format='NDHWC',
+            scope='Conv2d_0c_1x1')
+        # Temporal average pooling.
+        logits = tf.reduce_mean(logits, axis=1)
+        if spatial_squeeze:
+          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+
+        end_points['Logits'] = logits
+        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+  return logits, end_points
+
+
+s3dg.default_image_size = 224
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg_test.py
new file mode 100644
index 00000000..1758797e
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/s3dg_test.py
@@ -0,0 +1,150 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for networks.s3dg."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import s3dg
+
+
+class S3DGTest(tf.test.TestCase):
+
+  def testBuildClassificationNetwork(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+    num_classes = 1000
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    logits, end_points = s3dg.s3dg(inputs, num_classes)
+    self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertTrue('Predictions' in end_points)
+    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildBaseNetwork(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_6c, end_points = s3dg.s3dg_base(inputs)
+    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_6c.get_shape().as_list(),
+                         [batch_size, 8, 7, 7, 1024])
+    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
+                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
+                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
+                          'Mixed_5b', 'Mixed_5c']
+    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+  def testBuildOnlyUptoFinalEndpointNoGating(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
+                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
+                 'Mixed_5c']
+    for index, endpoint in enumerate(endpoints):
+      with tf.Graph().as_default():
+        inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+        out_tensor, end_points = s3dg.s3dg_base(
+            inputs, final_endpoint=endpoint, gating_startat=None)
+        print(endpoint, out_tensor.op.name)
+        self.assertTrue(out_tensor.op.name.startswith(
+            'InceptionV1/' + endpoint))
+        self.assertItemsEqual(endpoints[:index+1], end_points)
+
+  def testBuildAndCheckAllEndPointsUptoMixed5c(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    _, end_points = s3dg.s3dg_base(inputs,
+                                   final_endpoint='Mixed_5c')
+    endpoints_shapes = {'Conv2d_1a_7x7': [5, 32, 112, 112, 64],
+                        'MaxPool_2a_3x3': [5, 32, 56, 56, 64],
+                        'Conv2d_2b_1x1': [5, 32, 56, 56, 64],
+                        'Conv2d_2c_3x3': [5, 32, 56, 56, 192],
+                        'MaxPool_3a_3x3': [5, 32, 28, 28, 192],
+                        'Mixed_3b': [5, 32, 28, 28, 256],
+                        'Mixed_3c': [5, 32, 28, 28, 480],
+                        'MaxPool_4a_3x3': [5, 16, 14, 14, 480],
+                        'Mixed_4b': [5, 16, 14, 14, 512],
+                        'Mixed_4c': [5, 16, 14, 14, 512],
+                        'Mixed_4d': [5, 16, 14, 14, 512],
+                        'Mixed_4e': [5, 16, 14, 14, 528],
+                        'Mixed_4f': [5, 16, 14, 14, 832],
+                        'MaxPool_5a_2x2': [5, 8, 7, 7, 832],
+                        'Mixed_5b': [5, 8, 7, 7, 832],
+                        'Mixed_5c': [5, 8, 7, 7, 1024]}
+
+    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+      self.assertTrue(endpoint_name in end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testHalfSizeImages(self):
+    batch_size = 5
+    num_frames = 64
+    height, width = 112, 112
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_5c, _ = s3dg.s3dg_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 8, 4, 4, 1024])
+
+  def testTenFrames(self):
+    batch_size = 5
+    num_frames = 10
+    height, width = 224, 224
+
+    inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    mixed_5c, _ = s3dg.s3dg_base(inputs)
+    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
+    self.assertListEqual(mixed_5c.get_shape().as_list(),
+                         [batch_size, 2, 7, 7, 1024])
+
+  def testEvaluation(self):
+    batch_size = 2
+    num_frames = 64
+    height, width = 224, 224
+    num_classes = 1000
+
+    eval_inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
+    logits, _ = s3dg.s3dg(eval_inputs, num_classes,
+                          is_training=False)
+    predictions = tf.argmax(logits, 1)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEquals(output.shape, (batch_size,))
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/vgg.py b/TensorFlow/Detection/SSD/models/research/slim/nets/vgg.py
new file mode 100644
index 00000000..33a66305
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/vgg.py
@@ -0,0 +1,302 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains model definitions for versions of the Oxford VGG network.
+
+These model definitions were introduced in the following technical report:
+
+  Very Deep Convolutional Networks For Large-Scale Image Recognition
+  Karen Simonyan and Andrew Zisserman
+  arXiv technical report, 2015
+  PDF: http://arxiv.org/pdf/1409.1556.pdf
+  ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
+  CC-BY-4.0
+
+More information can be obtained from the VGG website:
+www.robots.ox.ac.uk/~vgg/research/very_deep/
+
+Usage:
+  with slim.arg_scope(vgg.vgg_arg_scope()):
+    outputs, end_points = vgg.vgg_a(inputs)
+
+  with slim.arg_scope(vgg.vgg_arg_scope()):
+    outputs, end_points = vgg.vgg_16(inputs)
+
+@@vgg_a
+@@vgg_16
+@@vgg_19
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def vgg_arg_scope(weight_decay=0.0005):
+  """Defines the VGG arg scope.
+
+  Args:
+    weight_decay: The l2 regularization coefficient.
+
+  Returns:
+    An arg_scope.
+  """
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      activation_fn=tf.nn.relu,
+                      weights_regularizer=slim.l2_regularizer(weight_decay),
+                      biases_initializer=tf.zeros_initializer()):
+    with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
+      return arg_sc
+
+
+def vgg_a(inputs,
+          num_classes=1000,
+          is_training=True,
+          dropout_keep_prob=0.5,
+          spatial_squeeze=True,
+          scope='vgg_a',
+          fc_conv_padding='VALID',
+          global_pool=False):
+  """Oxford Net VGG 11-Layers version A Example.
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 224x224.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      outputs. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+    fc_conv_padding: the type of padding to use for the fully connected layer
+      that is implemented as a convolutional layer. Use 'SAME' padding if you
+      are applying the network in a fully convolutional manner and want to
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the input to the logits layer (if num_classes is 0 or None).
+    end_points: a dict of tensors with intermediate activations.
+  """
+  with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d.
+    with slim.arg_scope([slim.conv2d, slim.max_pool2d],
+                        outputs_collections=end_points_collection):
+      net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
+      net = slim.max_pool2d(net, [2, 2], scope='pool1')
+      net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
+      net = slim.max_pool2d(net, [2, 2], scope='pool2')
+      net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
+      net = slim.max_pool2d(net, [2, 2], scope='pool3')
+      net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
+      net = slim.max_pool2d(net, [2, 2], scope='pool4')
+      net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
+      # Use conv2d instead of fully_connected layers.
+      net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                         scope='dropout6')
+      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+      # Convert end_points_collection into a end_point dict.
+      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+        end_points[sc.name + '/fc8'] = net
+      return net, end_points
+vgg_a.default_image_size = 224
+
+
+def vgg_16(inputs,
+           num_classes=1000,
+           is_training=True,
+           dropout_keep_prob=0.5,
+           spatial_squeeze=True,
+           scope='vgg_16',
+           fc_conv_padding='VALID',
+           global_pool=False):
+  """Oxford Net VGG 16-Layers version D Example.
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 224x224.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      outputs. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+    fc_conv_padding: the type of padding to use for the fully connected layer
+      that is implemented as a convolutional layer. Use 'SAME' padding if you
+      are applying the network in a fully convolutional manner and want to
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the input to the logits layer (if num_classes is 0 or None).
+    end_points: a dict of tensors with intermediate activations.
+  """
+  with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d.
+    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+                        outputs_collections=end_points_collection):
+      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+      net = slim.max_pool2d(net, [2, 2], scope='pool1')
+      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+      net = slim.max_pool2d(net, [2, 2], scope='pool2')
+      net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
+      net = slim.max_pool2d(net, [2, 2], scope='pool3')
+      net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
+      net = slim.max_pool2d(net, [2, 2], scope='pool4')
+      net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
+      # Use conv2d instead of fully_connected layers.
+      net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                         scope='dropout6')
+      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+      # Convert end_points_collection into a end_point dict.
+      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+        end_points[sc.name + '/fc8'] = net
+      return net, end_points
+vgg_16.default_image_size = 224
+
+
+def vgg_19(inputs,
+           num_classes=1000,
+           is_training=True,
+           dropout_keep_prob=0.5,
+           spatial_squeeze=True,
+           scope='vgg_19',
+           fc_conv_padding='VALID',
+           global_pool=False):
+  """Oxford Net VGG 19-Layers version E Example.
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 224x224.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      outputs. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+    fc_conv_padding: the type of padding to use for the fully connected layer
+      that is implemented as a convolutional layer. Use 'SAME' padding if you
+      are applying the network in a fully convolutional manner and want to
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
+
+  Returns:
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the non-dropped-out input to the logits layer (if num_classes is 0 or
+      None).
+    end_points: a dict of tensors with intermediate activations.
+  """
+  with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
+    end_points_collection = sc.original_name_scope + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d.
+    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+                        outputs_collections=end_points_collection):
+      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+      net = slim.max_pool2d(net, [2, 2], scope='pool1')
+      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+      net = slim.max_pool2d(net, [2, 2], scope='pool2')
+      net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
+      net = slim.max_pool2d(net, [2, 2], scope='pool3')
+      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
+      net = slim.max_pool2d(net, [2, 2], scope='pool4')
+      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
+      # Use conv2d instead of fully_connected layers.
+      net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                         scope='dropout6')
+      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+      # Convert end_points_collection into a end_point dict.
+      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+        end_points[sc.name + '/fc8'] = net
+      return net, end_points
+vgg_19.default_image_size = 224
+
+# Alias
+vgg_d = vgg_16
+vgg_e = vgg_19
diff --git a/TensorFlow/Detection/SSD/models/research/slim/nets/vgg_test.py b/TensorFlow/Detection/SSD/models/research/slim/nets/vgg_test.py
new file mode 100644
index 00000000..6760c368
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/nets/vgg_test.py
@@ -0,0 +1,583 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for slim.nets.vgg."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import vgg
+
+slim = tf.contrib.slim
+
+
+class VGGATest(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False,
+                            global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = vgg.vgg_a(inputs, num_classes)
+      expected_names = ['vgg_a/conv1/conv1_1',
+                        'vgg_a/pool1',
+                        'vgg_a/conv2/conv2_1',
+                        'vgg_a/pool2',
+                        'vgg_a/conv3/conv3_1',
+                        'vgg_a/conv3/conv3_2',
+                        'vgg_a/pool3',
+                        'vgg_a/conv4/conv4_1',
+                        'vgg_a/conv4/conv4_2',
+                        'vgg_a/pool4',
+                        'vgg_a/conv5/conv5_1',
+                        'vgg_a/conv5/conv5_2',
+                        'vgg_a/pool5',
+                        'vgg_a/fc6',
+                        'vgg_a/fc7',
+                        'vgg_a/fc8'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_a(inputs, num_classes)
+      expected_names = ['vgg_a/conv1/conv1_1',
+                        'vgg_a/pool1',
+                        'vgg_a/conv2/conv2_1',
+                        'vgg_a/pool2',
+                        'vgg_a/conv3/conv3_1',
+                        'vgg_a/conv3/conv3_2',
+                        'vgg_a/pool3',
+                        'vgg_a/conv4/conv4_1',
+                        'vgg_a/conv4/conv4_2',
+                        'vgg_a/pool4',
+                        'vgg_a/conv5/conv5_1',
+                        'vgg_a/conv5/conv5_2',
+                        'vgg_a/pool5',
+                        'vgg_a/fc6',
+                        'vgg_a/fc7',
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_a/fc7'))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      vgg.vgg_a(inputs, num_classes)
+      expected_names = ['vgg_a/conv1/conv1_1/weights',
+                        'vgg_a/conv1/conv1_1/biases',
+                        'vgg_a/conv2/conv2_1/weights',
+                        'vgg_a/conv2/conv2_1/biases',
+                        'vgg_a/conv3/conv3_1/weights',
+                        'vgg_a/conv3/conv3_1/biases',
+                        'vgg_a/conv3/conv3_2/weights',
+                        'vgg_a/conv3/conv3_2/biases',
+                        'vgg_a/conv4/conv4_1/weights',
+                        'vgg_a/conv4/conv4_1/biases',
+                        'vgg_a/conv4/conv4_2/weights',
+                        'vgg_a/conv4/conv4_2/biases',
+                        'vgg_a/conv5/conv5_1/weights',
+                        'vgg_a/conv5/conv5_1/biases',
+                        'vgg_a/conv5/conv5_2/weights',
+                        'vgg_a/conv5/conv5_2/biases',
+                        'vgg_a/fc6/weights',
+                        'vgg_a/fc6/biases',
+                        'vgg_a/fc7/weights',
+                        'vgg_a/fc7/biases',
+                        'vgg_a/fc8/weights',
+                        'vgg_a/fc8/biases',
+                       ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 224, 224
+    eval_height, eval_width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = vgg.vgg_a(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
+                            spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 224, 224
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+
+class VGG16Test(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False,
+                             global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = vgg.vgg_16(inputs, num_classes)
+      expected_names = ['vgg_16/conv1/conv1_1',
+                        'vgg_16/conv1/conv1_2',
+                        'vgg_16/pool1',
+                        'vgg_16/conv2/conv2_1',
+                        'vgg_16/conv2/conv2_2',
+                        'vgg_16/pool2',
+                        'vgg_16/conv3/conv3_1',
+                        'vgg_16/conv3/conv3_2',
+                        'vgg_16/conv3/conv3_3',
+                        'vgg_16/pool3',
+                        'vgg_16/conv4/conv4_1',
+                        'vgg_16/conv4/conv4_2',
+                        'vgg_16/conv4/conv4_3',
+                        'vgg_16/pool4',
+                        'vgg_16/conv5/conv5_1',
+                        'vgg_16/conv5/conv5_2',
+                        'vgg_16/conv5/conv5_3',
+                        'vgg_16/pool5',
+                        'vgg_16/fc6',
+                        'vgg_16/fc7',
+                        'vgg_16/fc8'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_16(inputs, num_classes)
+      expected_names = ['vgg_16/conv1/conv1_1',
+                        'vgg_16/conv1/conv1_2',
+                        'vgg_16/pool1',
+                        'vgg_16/conv2/conv2_1',
+                        'vgg_16/conv2/conv2_2',
+                        'vgg_16/pool2',
+                        'vgg_16/conv3/conv3_1',
+                        'vgg_16/conv3/conv3_2',
+                        'vgg_16/conv3/conv3_3',
+                        'vgg_16/pool3',
+                        'vgg_16/conv4/conv4_1',
+                        'vgg_16/conv4/conv4_2',
+                        'vgg_16/conv4/conv4_3',
+                        'vgg_16/pool4',
+                        'vgg_16/conv5/conv5_1',
+                        'vgg_16/conv5/conv5_2',
+                        'vgg_16/conv5/conv5_3',
+                        'vgg_16/pool5',
+                        'vgg_16/fc6',
+                        'vgg_16/fc7',
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_16/fc7'))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      vgg.vgg_16(inputs, num_classes)
+      expected_names = ['vgg_16/conv1/conv1_1/weights',
+                        'vgg_16/conv1/conv1_1/biases',
+                        'vgg_16/conv1/conv1_2/weights',
+                        'vgg_16/conv1/conv1_2/biases',
+                        'vgg_16/conv2/conv2_1/weights',
+                        'vgg_16/conv2/conv2_1/biases',
+                        'vgg_16/conv2/conv2_2/weights',
+                        'vgg_16/conv2/conv2_2/biases',
+                        'vgg_16/conv3/conv3_1/weights',
+                        'vgg_16/conv3/conv3_1/biases',
+                        'vgg_16/conv3/conv3_2/weights',
+                        'vgg_16/conv3/conv3_2/biases',
+                        'vgg_16/conv3/conv3_3/weights',
+                        'vgg_16/conv3/conv3_3/biases',
+                        'vgg_16/conv4/conv4_1/weights',
+                        'vgg_16/conv4/conv4_1/biases',
+                        'vgg_16/conv4/conv4_2/weights',
+                        'vgg_16/conv4/conv4_2/biases',
+                        'vgg_16/conv4/conv4_3/weights',
+                        'vgg_16/conv4/conv4_3/biases',
+                        'vgg_16/conv5/conv5_1/weights',
+                        'vgg_16/conv5/conv5_1/biases',
+                        'vgg_16/conv5/conv5_2/weights',
+                        'vgg_16/conv5/conv5_2/biases',
+                        'vgg_16/conv5/conv5_3/weights',
+                        'vgg_16/conv5/conv5_3/biases',
+                        'vgg_16/fc6/weights',
+                        'vgg_16/fc6/biases',
+                        'vgg_16/fc7/weights',
+                        'vgg_16/fc7/biases',
+                        'vgg_16/fc8/weights',
+                        'vgg_16/fc8/biases',
+                       ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 224, 224
+    eval_height, eval_width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = vgg.vgg_16(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = vgg.vgg_16(eval_inputs, is_training=False,
+                             spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 224, 224
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+
+class VGG19Test(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False,
+                             global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1',
+          'vgg_19/conv1/conv1_2',
+          'vgg_19/pool1',
+          'vgg_19/conv2/conv2_1',
+          'vgg_19/conv2/conv2_2',
+          'vgg_19/pool2',
+          'vgg_19/conv3/conv3_1',
+          'vgg_19/conv3/conv3_2',
+          'vgg_19/conv3/conv3_3',
+          'vgg_19/conv3/conv3_4',
+          'vgg_19/pool3',
+          'vgg_19/conv4/conv4_1',
+          'vgg_19/conv4/conv4_2',
+          'vgg_19/conv4/conv4_3',
+          'vgg_19/conv4/conv4_4',
+          'vgg_19/pool4',
+          'vgg_19/conv5/conv5_1',
+          'vgg_19/conv5/conv5_2',
+          'vgg_19/conv5/conv5_3',
+          'vgg_19/conv5/conv5_4',
+          'vgg_19/pool5',
+          'vgg_19/fc6',
+          'vgg_19/fc7',
+          'vgg_19/fc8'
+      ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1',
+          'vgg_19/conv1/conv1_2',
+          'vgg_19/pool1',
+          'vgg_19/conv2/conv2_1',
+          'vgg_19/conv2/conv2_2',
+          'vgg_19/pool2',
+          'vgg_19/conv3/conv3_1',
+          'vgg_19/conv3/conv3_2',
+          'vgg_19/conv3/conv3_3',
+          'vgg_19/conv3/conv3_4',
+          'vgg_19/pool3',
+          'vgg_19/conv4/conv4_1',
+          'vgg_19/conv4/conv4_2',
+          'vgg_19/conv4/conv4_3',
+          'vgg_19/conv4/conv4_4',
+          'vgg_19/pool4',
+          'vgg_19/conv5/conv5_1',
+          'vgg_19/conv5/conv5_2',
+          'vgg_19/conv5/conv5_3',
+          'vgg_19/conv5/conv5_4',
+          'vgg_19/pool5',
+          'vgg_19/fc6',
+          'vgg_19/fc7',
+      ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_19/fc7'))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1/weights',
+          'vgg_19/conv1/conv1_1/biases',
+          'vgg_19/conv1/conv1_2/weights',
+          'vgg_19/conv1/conv1_2/biases',
+          'vgg_19/conv2/conv2_1/weights',
+          'vgg_19/conv2/conv2_1/biases',
+          'vgg_19/conv2/conv2_2/weights',
+          'vgg_19/conv2/conv2_2/biases',
+          'vgg_19/conv3/conv3_1/weights',
+          'vgg_19/conv3/conv3_1/biases',
+          'vgg_19/conv3/conv3_2/weights',
+          'vgg_19/conv3/conv3_2/biases',
+          'vgg_19/conv3/conv3_3/weights',
+          'vgg_19/conv3/conv3_3/biases',
+          'vgg_19/conv3/conv3_4/weights',
+          'vgg_19/conv3/conv3_4/biases',
+          'vgg_19/conv4/conv4_1/weights',
+          'vgg_19/conv4/conv4_1/biases',
+          'vgg_19/conv4/conv4_2/weights',
+          'vgg_19/conv4/conv4_2/biases',
+          'vgg_19/conv4/conv4_3/weights',
+          'vgg_19/conv4/conv4_3/biases',
+          'vgg_19/conv4/conv4_4/weights',
+          'vgg_19/conv4/conv4_4/biases',
+          'vgg_19/conv5/conv5_1/weights',
+          'vgg_19/conv5/conv5_1/biases',
+          'vgg_19/conv5/conv5_2/weights',
+          'vgg_19/conv5/conv5_2/biases',
+          'vgg_19/conv5/conv5_3/weights',
+          'vgg_19/conv5/conv5_3/biases',
+          'vgg_19/conv5/conv5_4/weights',
+          'vgg_19/conv5/conv5_4/biases',
+          'vgg_19/fc6/weights',
+          'vgg_19/fc6/biases',
+          'vgg_19/fc7/weights',
+          'vgg_19/fc7/biases',
+          'vgg_19/fc8/weights',
+          'vgg_19/fc8/biases',
+      ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 224, 224
+    eval_height, eval_width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = vgg.vgg_19(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = vgg.vgg_19(eval_inputs, is_training=False,
+                             spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 224, 224
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/__init__.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/__init__.py
@@ -0,0 +1 @@
+
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/cifarnet_preprocessing.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/cifarnet_preprocessing.py
new file mode 100644
index 00000000..0b5a88fa
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/cifarnet_preprocessing.py
@@ -0,0 +1,128 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides utilities to preprocess images in CIFAR-10.
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+_PADDING = 4
+
+slim = tf.contrib.slim
+
+
+def preprocess_for_train(image,
+                         output_height,
+                         output_width,
+                         padding=_PADDING,
+                         add_image_summaries=True):
+  """Preprocesses the given image for training.
+
+  Note that the actual resizing scale is sampled from
+    [`resize_size_min`, `resize_size_max`].
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    padding: The amound of padding before and after each dimension of the image.
+    add_image_summaries: Enable image summaries.
+
+  Returns:
+    A preprocessed image.
+  """
+  if add_image_summaries:
+    tf.summary.image('image', tf.expand_dims(image, 0))
+
+  # Transform the image to floats.
+  image = tf.to_float(image)
+  if padding > 0:
+    image = tf.pad(image, [[padding, padding], [padding, padding], [0, 0]])
+  # Randomly crop a [height, width] section of the image.
+  distorted_image = tf.random_crop(image,
+                                   [output_height, output_width, 3])
+
+  # Randomly flip the image horizontally.
+  distorted_image = tf.image.random_flip_left_right(distorted_image)
+
+  if add_image_summaries:
+    tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))
+
+  # Because these operations are not commutative, consider randomizing
+  # the order their operation.
+  distorted_image = tf.image.random_brightness(distorted_image,
+                                               max_delta=63)
+  distorted_image = tf.image.random_contrast(distorted_image,
+                                             lower=0.2, upper=1.8)
+  # Subtract off the mean and divide by the variance of the pixels.
+  return tf.image.per_image_standardization(distorted_image)
+
+
+def preprocess_for_eval(image, output_height, output_width,
+                        add_image_summaries=True):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    add_image_summaries: Enable image summaries.
+
+  Returns:
+    A preprocessed image.
+  """
+  if add_image_summaries:
+    tf.summary.image('image', tf.expand_dims(image, 0))
+  # Transform the image to floats.
+  image = tf.to_float(image)
+
+  # Resize and crop if needed.
+  resized_image = tf.image.resize_image_with_crop_or_pad(image,
+                                                         output_width,
+                                                         output_height)
+  if add_image_summaries:
+    tf.summary.image('resized_image', tf.expand_dims(resized_image, 0))
+
+  # Subtract off the mean and divide by the variance of the pixels.
+  return tf.image.per_image_standardization(resized_image)
+
+
+def preprocess_image(image, output_height, output_width, is_training=False,
+                     add_image_summaries=True):
+  """Preprocesses the given image.
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    is_training: `True` if we're preprocessing the image for training and
+      `False` otherwise.
+    add_image_summaries: Enable image summaries.
+
+  Returns:
+    A preprocessed image.
+  """
+  if is_training:
+    return preprocess_for_train(
+        image, output_height, output_width,
+        add_image_summaries=add_image_summaries)
+  else:
+    return preprocess_for_eval(
+        image, output_height, output_width,
+        add_image_summaries=add_image_summaries)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/inception_preprocessing.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/inception_preprocessing.py
new file mode 100644
index 00000000..846b81b3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/inception_preprocessing.py
@@ -0,0 +1,318 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides utilities to preprocess images for the Inception networks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from tensorflow.python.ops import control_flow_ops
+
+
+def apply_with_random_selector(x, func, num_cases):
+  """Computes func(x, sel), with sel sampled from [0...num_cases-1].
+
+  Args:
+    x: input Tensor.
+    func: Python function to apply.
+    num_cases: Python int32, number of cases to sample sel from.
+
+  Returns:
+    The result of func(x, sel), where func receives the value of the
+    selector as a python integer, but sel is sampled dynamically.
+  """
+  sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
+  # Pass the real x only to one of the func calls.
+  return control_flow_ops.merge([
+      func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
+      for case in range(num_cases)])[0]
+
+
+def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
+  """Distort the color of a Tensor image.
+
+  Each color distortion is non-commutative and thus ordering of the color ops
+  matters. Ideally we would randomly permute the ordering of the color ops.
+  Rather then adding that level of complication, we select a distinct ordering
+  of color ops for each preprocessing thread.
+
+  Args:
+    image: 3-D Tensor containing single image in [0, 1].
+    color_ordering: Python int, a type of distortion (valid values: 0-3).
+    fast_mode: Avoids slower ops (random_hue and random_contrast)
+    scope: Optional scope for name_scope.
+  Returns:
+    3-D Tensor color-distorted image on range [0, 1]
+  Raises:
+    ValueError: if color_ordering not in [0, 3]
+  """
+  with tf.name_scope(scope, 'distort_color', [image]):
+    if fast_mode:
+      if color_ordering == 0:
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+      else:
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+    else:
+      if color_ordering == 0:
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+        image = tf.image.random_hue(image, max_delta=0.2)
+        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+      elif color_ordering == 1:
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+        image = tf.image.random_hue(image, max_delta=0.2)
+      elif color_ordering == 2:
+        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+        image = tf.image.random_hue(image, max_delta=0.2)
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+      elif color_ordering == 3:
+        image = tf.image.random_hue(image, max_delta=0.2)
+        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+        image = tf.image.random_brightness(image, max_delta=32. / 255.)
+      else:
+        raise ValueError('color_ordering must be in [0, 3]')
+
+    # The random_* ops do not necessarily clamp.
+    return tf.clip_by_value(image, 0.0, 1.0)
+
+
+def distorted_bounding_box_crop(image,
+                                bbox,
+                                min_object_covered=0.1,
+                                aspect_ratio_range=(0.75, 1.33),
+                                area_range=(0.05, 1.0),
+                                max_attempts=100,
+                                scope=None):
+  """Generates cropped_image using a one of the bboxes randomly distorted.
+
+  See `tf.image.sample_distorted_bounding_box` for more documentation.
+
+  Args:
+    image: 3-D Tensor of image (it will be converted to floats in [0, 1]).
+    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
+      where each coordinate is [0, 1) and the coordinates are arranged
+      as [ymin, xmin, ymax, xmax]. If num_boxes is 0 then it would use the whole
+      image.
+    min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
+      area of the image must contain at least this fraction of any bounding box
+      supplied.
+    aspect_ratio_range: An optional list of `floats`. The cropped area of the
+      image must have an aspect ratio = width / height within this range.
+    area_range: An optional list of `floats`. The cropped area of the image
+      must contain a fraction of the supplied image within in this range.
+    max_attempts: An optional `int`. Number of attempts at generating a cropped
+      region of the image of the specified constraints. After `max_attempts`
+      failures, return the entire image.
+    scope: Optional scope for name_scope.
+  Returns:
+    A tuple, a 3-D Tensor cropped_image and the distorted bbox
+  """
+  with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
+    # Each bounding box has shape [1, num_boxes, box coords] and
+    # the coordinates are ordered [ymin, xmin, ymax, xmax].
+
+    # A large fraction of image datasets contain a human-annotated bounding
+    # box delineating the region of the image containing the object of interest.
+    # We choose to create a new bounding box for the object which is a randomly
+    # distorted version of the human-annotated bounding box that obeys an
+    # allowed range of aspect ratios, sizes and overlap with the human-annotated
+    # bounding box. If no box is supplied, then we assume the bounding box is
+    # the entire image.
+    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bbox,
+        min_object_covered=min_object_covered,
+        aspect_ratio_range=aspect_ratio_range,
+        area_range=area_range,
+        max_attempts=max_attempts,
+        use_image_if_no_bounding_boxes=True)
+    bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
+
+    # Crop the image to the specified bounding box.
+    cropped_image = tf.slice(image, bbox_begin, bbox_size)
+    return cropped_image, distort_bbox
+
+
+def preprocess_for_train(image, height, width, bbox,
+                         fast_mode=True,
+                         scope=None,
+                         add_image_summaries=True):
+  """Distort one image for training a network.
+
+  Distorting images provides a useful technique for augmenting the data
+  set during training in order to make the network invariant to aspects
+  of the image that do not effect the label.
+
+  Additionally it would create image_summaries to display the different
+  transformations applied to the image.
+
+  Args:
+    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
+      [0, 1], otherwise it would converted to tf.float32 assuming that the range
+      is [0, MAX], where MAX is largest positive representable number for
+      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
+    height: integer
+    width: integer
+    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
+      where each coordinate is [0, 1) and the coordinates are arranged
+      as [ymin, xmin, ymax, xmax].
+    fast_mode: Optional boolean, if True avoids slower transformations (i.e.
+      bi-cubic resizing, random_hue or random_contrast).
+    scope: Optional scope for name_scope.
+    add_image_summaries: Enable image summaries.
+  Returns:
+    3-D float Tensor of distorted image used for training with range [-1, 1].
+  """
+  with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):
+    if bbox is None:
+      bbox = tf.constant([0.0, 0.0, 1.0, 1.0],
+                         dtype=tf.float32,
+                         shape=[1, 1, 4])
+    if image.dtype != tf.float32:
+      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+    # Each bounding box has shape [1, num_boxes, box coords] and
+    # the coordinates are ordered [ymin, xmin, ymax, xmax].
+    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                  bbox)
+    if add_image_summaries:
+      tf.summary.image('image_with_bounding_boxes', image_with_box)
+
+    distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
+    # Restore the shape since the dynamic slice based upon the bbox_size loses
+    # the third dimension.
+    distorted_image.set_shape([None, None, 3])
+    image_with_distorted_box = tf.image.draw_bounding_boxes(
+        tf.expand_dims(image, 0), distorted_bbox)
+    if add_image_summaries:
+      tf.summary.image('images_with_distorted_bounding_box',
+                       image_with_distorted_box)
+
+    # This resizing operation may distort the images because the aspect
+    # ratio is not respected. We select a resize method in a round robin
+    # fashion based on the thread number.
+    # Note that ResizeMethod contains 4 enumerated resizing methods.
+
+    # We select only 1 case for fast_mode bilinear.
+    num_resize_cases = 1 if fast_mode else 4
+    distorted_image = apply_with_random_selector(
+        distorted_image,
+        lambda x, method: tf.image.resize_images(x, [height, width], method),
+        num_cases=num_resize_cases)
+
+    if add_image_summaries:
+      tf.summary.image('cropped_resized_image',
+                       tf.expand_dims(distorted_image, 0))
+
+    # Randomly flip the image horizontally.
+    distorted_image = tf.image.random_flip_left_right(distorted_image)
+
+    # Randomly distort the colors. There are 1 or 4 ways to do it.
+    num_distort_cases = 1 if fast_mode else 4
+    distorted_image = apply_with_random_selector(
+        distorted_image,
+        lambda x, ordering: distort_color(x, ordering, fast_mode),
+        num_cases=num_distort_cases)
+
+    if add_image_summaries:
+      tf.summary.image('final_distorted_image',
+                       tf.expand_dims(distorted_image, 0))
+    distorted_image = tf.subtract(distorted_image, 0.5)
+    distorted_image = tf.multiply(distorted_image, 2.0)
+    return distorted_image
+
+
+def preprocess_for_eval(image, height, width,
+                        central_fraction=0.875, scope=None):
+  """Prepare one image for evaluation.
+
+  If height and width are specified it would output an image with that size by
+  applying resize_bilinear.
+
+  If central_fraction is specified it would crop the central fraction of the
+  input image.
+
+  Args:
+    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
+      [0, 1], otherwise it would converted to tf.float32 assuming that the range
+      is [0, MAX], where MAX is largest positive representable number for
+      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
+    height: integer
+    width: integer
+    central_fraction: Optional Float, fraction of the image to crop.
+    scope: Optional scope for name_scope.
+  Returns:
+    3-D float Tensor of prepared image.
+  """
+  with tf.name_scope(scope, 'eval_image', [image, height, width]):
+    if image.dtype != tf.float32:
+      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+    # Crop the central region of the image with an area containing 87.5% of
+    # the original image.
+    if central_fraction:
+      image = tf.image.central_crop(image, central_fraction=central_fraction)
+
+    if height and width:
+      # Resize the image to the specified height and width.
+      image = tf.expand_dims(image, 0)
+      image = tf.image.resize_bilinear(image, [height, width],
+                                       align_corners=False)
+      image = tf.squeeze(image, [0])
+    image = tf.subtract(image, 0.5)
+    image = tf.multiply(image, 2.0)
+    return image
+
+
+def preprocess_image(image, height, width,
+                     is_training=False,
+                     bbox=None,
+                     fast_mode=True,
+                     add_image_summaries=True):
+  """Pre-process one image for training or evaluation.
+
+  Args:
+    image: 3-D Tensor [height, width, channels] with the image. If dtype is
+      tf.float32 then the range should be [0, 1], otherwise it would converted
+      to tf.float32 assuming that the range is [0, MAX], where MAX is largest
+      positive representable number for int(8/16/32) data type (see
+      `tf.image.convert_image_dtype` for details).
+    height: integer, image expected height.
+    width: integer, image expected width.
+    is_training: Boolean. If true it would transform an image for train,
+      otherwise it would transform it for evaluation.
+    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
+      where each coordinate is [0, 1) and the coordinates are arranged as
+      [ymin, xmin, ymax, xmax].
+    fast_mode: Optional boolean, if True avoids slower transformations.
+    add_image_summaries: Enable image summaries.
+
+  Returns:
+    3-D float Tensor containing an appropriately scaled image
+
+  Raises:
+    ValueError: if user does not provide bounding box
+  """
+  if is_training:
+    return preprocess_for_train(image, height, width, bbox, fast_mode,
+                                add_image_summaries=add_image_summaries)
+  else:
+    return preprocess_for_eval(image, height, width)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/lenet_preprocessing.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/lenet_preprocessing.py
new file mode 100644
index 00000000..ac5e71af
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/lenet_preprocessing.py
@@ -0,0 +1,44 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides utilities for preprocessing."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def preprocess_image(image, output_height, output_width, is_training):
+  """Preprocesses the given image.
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    is_training: `True` if we're preprocessing the image for training and
+      `False` otherwise.
+
+  Returns:
+    A preprocessed image.
+  """
+  image = tf.to_float(image)
+  image = tf.image.resize_image_with_crop_or_pad(
+      image, output_width, output_height)
+  image = tf.subtract(image, 128.0)
+  image = tf.div(image, 128.0)
+  return image
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/preprocessing_factory.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/preprocessing_factory.py
new file mode 100644
index 00000000..a805e920
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/preprocessing_factory.py
@@ -0,0 +1,85 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains a factory for building various models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from preprocessing import cifarnet_preprocessing
+from preprocessing import inception_preprocessing
+from preprocessing import lenet_preprocessing
+from preprocessing import vgg_preprocessing
+
+slim = tf.contrib.slim
+
+
+def get_preprocessing(name, is_training=False):
+  """Returns preprocessing_fn(image, height, width, **kwargs).
+
+  Args:
+    name: The name of the preprocessing function.
+    is_training: `True` if the model is being used for training and `False`
+      otherwise.
+
+  Returns:
+    preprocessing_fn: A function that preprocessing a single image (pre-batch).
+      It has the following signature:
+        image = preprocessing_fn(image, output_height, output_width, ...).
+
+  Raises:
+    ValueError: If Preprocessing `name` is not recognized.
+  """
+  preprocessing_fn_map = {
+      'cifarnet': cifarnet_preprocessing,
+      'inception': inception_preprocessing,
+      'inception_v1': inception_preprocessing,
+      'inception_v2': inception_preprocessing,
+      'inception_v3': inception_preprocessing,
+      'inception_v4': inception_preprocessing,
+      'inception_resnet_v2': inception_preprocessing,
+      'lenet': lenet_preprocessing,
+      'mobilenet_v1': inception_preprocessing,
+      'mobilenet_v2': inception_preprocessing,
+      'mobilenet_v2_035': inception_preprocessing,
+      'mobilenet_v2_140': inception_preprocessing,
+      'nasnet_mobile': inception_preprocessing,
+      'nasnet_large': inception_preprocessing,
+      'pnasnet_mobile': inception_preprocessing,
+      'pnasnet_large': inception_preprocessing,
+      'resnet_v1_50': vgg_preprocessing,
+      'resnet_v1_101': vgg_preprocessing,
+      'resnet_v1_152': vgg_preprocessing,
+      'resnet_v1_200': vgg_preprocessing,
+      'resnet_v2_50': vgg_preprocessing,
+      'resnet_v2_101': vgg_preprocessing,
+      'resnet_v2_152': vgg_preprocessing,
+      'resnet_v2_200': vgg_preprocessing,
+      'vgg': vgg_preprocessing,
+      'vgg_a': vgg_preprocessing,
+      'vgg_16': vgg_preprocessing,
+      'vgg_19': vgg_preprocessing,
+  }
+
+  if name not in preprocessing_fn_map:
+    raise ValueError('Preprocessing name [%s] was not recognized' % name)
+
+  def preprocessing_fn(image, output_height, output_width, **kwargs):
+    return preprocessing_fn_map[name].preprocess_image(
+        image, output_height, output_width, is_training=is_training, **kwargs)
+
+  return preprocessing_fn
diff --git a/TensorFlow/Detection/SSD/models/research/slim/preprocessing/vgg_preprocessing.py b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/vgg_preprocessing.py
new file mode 100644
index 00000000..3bd50598
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/preprocessing/vgg_preprocessing.py
@@ -0,0 +1,365 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Provides utilities to preprocess images.
+
+The preprocessing steps for VGG were introduced in the following technical
+report:
+
+  Very Deep Convolutional Networks For Large-Scale Image Recognition
+  Karen Simonyan and Andrew Zisserman
+  arXiv technical report, 2015
+  PDF: http://arxiv.org/pdf/1409.1556.pdf
+  ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
+  CC-BY-4.0
+
+More information can be obtained from the VGG website:
+www.robots.ox.ac.uk/~vgg/research/very_deep/
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+_R_MEAN = 123.68
+_G_MEAN = 116.78
+_B_MEAN = 103.94
+
+_RESIZE_SIDE_MIN = 256
+_RESIZE_SIDE_MAX = 512
+
+
+def _crop(image, offset_height, offset_width, crop_height, crop_width):
+  """Crops the given image using the provided offsets and sizes.
+
+  Note that the method doesn't assume we know the input image size but it does
+  assume we know the input image rank.
+
+  Args:
+    image: an image of shape [height, width, channels].
+    offset_height: a scalar tensor indicating the height offset.
+    offset_width: a scalar tensor indicating the width offset.
+    crop_height: the height of the cropped image.
+    crop_width: the width of the cropped image.
+
+  Returns:
+    the cropped (and resized) image.
+
+  Raises:
+    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
+      less than the crop size.
+  """
+  original_shape = tf.shape(image)
+
+  rank_assertion = tf.Assert(
+      tf.equal(tf.rank(image), 3),
+      ['Rank of image must be equal to 3.'])
+  with tf.control_dependencies([rank_assertion]):
+    cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])
+
+  size_assertion = tf.Assert(
+      tf.logical_and(
+          tf.greater_equal(original_shape[0], crop_height),
+          tf.greater_equal(original_shape[1], crop_width)),
+      ['Crop size greater than the image size.'])
+
+  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
+
+  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
+  # define the crop size.
+  with tf.control_dependencies([size_assertion]):
+    image = tf.slice(image, offsets, cropped_shape)
+  return tf.reshape(image, cropped_shape)
+
+
+def _random_crop(image_list, crop_height, crop_width):
+  """Crops the given list of images.
+
+  The function applies the same crop to each image in the list. This can be
+  effectively applied when there are multiple image inputs of the same
+  dimension such as:
+
+    image, depths, normals = _random_crop([image, depths, normals], 120, 150)
+
+  Args:
+    image_list: a list of image tensors of the same dimension but possibly
+      varying channel.
+    crop_height: the new height.
+    crop_width: the new width.
+
+  Returns:
+    the image_list with cropped images.
+
+  Raises:
+    ValueError: if there are multiple image inputs provided with different size
+      or the images are smaller than the crop dimensions.
+  """
+  if not image_list:
+    raise ValueError('Empty image_list.')
+
+  # Compute the rank assertions.
+  rank_assertions = []
+  for i in range(len(image_list)):
+    image_rank = tf.rank(image_list[i])
+    rank_assert = tf.Assert(
+        tf.equal(image_rank, 3),
+        ['Wrong rank for tensor  %s [expected] [actual]',
+         image_list[i].name, 3, image_rank])
+    rank_assertions.append(rank_assert)
+
+  with tf.control_dependencies([rank_assertions[0]]):
+    image_shape = tf.shape(image_list[0])
+  image_height = image_shape[0]
+  image_width = image_shape[1]
+  crop_size_assert = tf.Assert(
+      tf.logical_and(
+          tf.greater_equal(image_height, crop_height),
+          tf.greater_equal(image_width, crop_width)),
+      ['Crop size greater than the image size.'])
+
+  asserts = [rank_assertions[0], crop_size_assert]
+
+  for i in range(1, len(image_list)):
+    image = image_list[i]
+    asserts.append(rank_assertions[i])
+    with tf.control_dependencies([rank_assertions[i]]):
+      shape = tf.shape(image)
+    height = shape[0]
+    width = shape[1]
+
+    height_assert = tf.Assert(
+        tf.equal(height, image_height),
+        ['Wrong height for tensor %s [expected][actual]',
+         image.name, height, image_height])
+    width_assert = tf.Assert(
+        tf.equal(width, image_width),
+        ['Wrong width for tensor %s [expected][actual]',
+         image.name, width, image_width])
+    asserts.extend([height_assert, width_assert])
+
+  # Create a random bounding box.
+  #
+  # Use tf.random_uniform and not numpy.random.rand as doing the former would
+  # generate random numbers at graph eval time, unlike the latter which
+  # generates random numbers at graph definition time.
+  with tf.control_dependencies(asserts):
+    max_offset_height = tf.reshape(image_height - crop_height + 1, [])
+  with tf.control_dependencies(asserts):
+    max_offset_width = tf.reshape(image_width - crop_width + 1, [])
+  offset_height = tf.random_uniform(
+      [], maxval=max_offset_height, dtype=tf.int32)
+  offset_width = tf.random_uniform(
+      [], maxval=max_offset_width, dtype=tf.int32)
+
+  return [_crop(image, offset_height, offset_width,
+                crop_height, crop_width) for image in image_list]
+
+
+def _central_crop(image_list, crop_height, crop_width):
+  """Performs central crops of the given image list.
+
+  Args:
+    image_list: a list of image tensors of the same dimension but possibly
+      varying channel.
+    crop_height: the height of the image following the crop.
+    crop_width: the width of the image following the crop.
+
+  Returns:
+    the list of cropped images.
+  """
+  outputs = []
+  for image in image_list:
+    image_height = tf.shape(image)[0]
+    image_width = tf.shape(image)[1]
+
+    offset_height = (image_height - crop_height) / 2
+    offset_width = (image_width - crop_width) / 2
+
+    outputs.append(_crop(image, offset_height, offset_width,
+                         crop_height, crop_width))
+  return outputs
+
+
+def _mean_image_subtraction(image, means):
+  """Subtracts the given means from each image channel.
+
+  For example:
+    means = [123.68, 116.779, 103.939]
+    image = _mean_image_subtraction(image, means)
+
+  Note that the rank of `image` must be known.
+
+  Args:
+    image: a tensor of size [height, width, C].
+    means: a C-vector of values to subtract from each channel.
+
+  Returns:
+    the centered image.
+
+  Raises:
+    ValueError: If the rank of `image` is unknown, if `image` has a rank other
+      than three or if the number of channels in `image` doesn't match the
+      number of values in `means`.
+  """
+  if image.get_shape().ndims != 3:
+    raise ValueError('Input must be of size [height, width, C>0]')
+  num_channels = image.get_shape().as_list()[-1]
+  if len(means) != num_channels:
+    raise ValueError('len(means) must match the number of channels')
+
+  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
+  for i in range(num_channels):
+    channels[i] -= means[i]
+  return tf.concat(axis=2, values=channels)
+
+
+def _smallest_size_at_least(height, width, smallest_side):
+  """Computes new shape with the smallest side equal to `smallest_side`.
+
+  Computes new shape with the smallest side equal to `smallest_side` while
+  preserving the original aspect ratio.
+
+  Args:
+    height: an int32 scalar tensor indicating the current height.
+    width: an int32 scalar tensor indicating the current width.
+    smallest_side: A python integer or scalar `Tensor` indicating the size of
+      the smallest side after resize.
+
+  Returns:
+    new_height: an int32 scalar tensor indicating the new height.
+    new_width: and int32 scalar tensor indicating the new width.
+  """
+  smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
+
+  height = tf.to_float(height)
+  width = tf.to_float(width)
+  smallest_side = tf.to_float(smallest_side)
+
+  scale = tf.cond(tf.greater(height, width),
+                  lambda: smallest_side / width,
+                  lambda: smallest_side / height)
+  new_height = tf.to_int32(tf.rint(height * scale))
+  new_width = tf.to_int32(tf.rint(width * scale))
+  return new_height, new_width
+
+
+def _aspect_preserving_resize(image, smallest_side):
+  """Resize images preserving the original aspect ratio.
+
+  Args:
+    image: A 3-D image `Tensor`.
+    smallest_side: A python integer or scalar `Tensor` indicating the size of
+      the smallest side after resize.
+
+  Returns:
+    resized_image: A 3-D tensor containing the resized image.
+  """
+  smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
+
+  shape = tf.shape(image)
+  height = shape[0]
+  width = shape[1]
+  new_height, new_width = _smallest_size_at_least(height, width, smallest_side)
+  image = tf.expand_dims(image, 0)
+  resized_image = tf.image.resize_bilinear(image, [new_height, new_width],
+                                           align_corners=False)
+  resized_image = tf.squeeze(resized_image)
+  resized_image.set_shape([None, None, 3])
+  return resized_image
+
+
+def preprocess_for_train(image,
+                         output_height,
+                         output_width,
+                         resize_side_min=_RESIZE_SIDE_MIN,
+                         resize_side_max=_RESIZE_SIDE_MAX):
+  """Preprocesses the given image for training.
+
+  Note that the actual resizing scale is sampled from
+    [`resize_size_min`, `resize_size_max`].
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    resize_side_min: The lower bound for the smallest side of the image for
+      aspect-preserving resizing.
+    resize_side_max: The upper bound for the smallest side of the image for
+      aspect-preserving resizing.
+
+  Returns:
+    A preprocessed image.
+  """
+  resize_side = tf.random_uniform(
+      [], minval=resize_side_min, maxval=resize_side_max+1, dtype=tf.int32)
+
+  image = _aspect_preserving_resize(image, resize_side)
+  image = _random_crop([image], output_height, output_width)[0]
+  image.set_shape([output_height, output_width, 3])
+  image = tf.to_float(image)
+  image = tf.image.random_flip_left_right(image)
+  return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
+
+
+def preprocess_for_eval(image, output_height, output_width, resize_side):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    resize_side: The smallest side of the image for aspect-preserving resizing.
+
+  Returns:
+    A preprocessed image.
+  """
+  image = _aspect_preserving_resize(image, resize_side)
+  image = _central_crop([image], output_height, output_width)[0]
+  image.set_shape([output_height, output_width, 3])
+  image = tf.to_float(image)
+  return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
+
+
+def preprocess_image(image, output_height, output_width, is_training=False,
+                     resize_side_min=_RESIZE_SIDE_MIN,
+                     resize_side_max=_RESIZE_SIDE_MAX):
+  """Preprocesses the given image.
+
+  Args:
+    image: A `Tensor` representing an image of arbitrary size.
+    output_height: The height of the image after preprocessing.
+    output_width: The width of the image after preprocessing.
+    is_training: `True` if we're preprocessing the image for training and
+      `False` otherwise.
+    resize_side_min: The lower bound for the smallest side of the image for
+      aspect-preserving resizing. If `is_training` is `False`, then this value
+      is used for rescaling.
+    resize_side_max: The upper bound for the smallest side of the image for
+      aspect-preserving resizing. If `is_training` is `False`, this value is
+      ignored. Otherwise, the resize side is sampled from
+        [resize_size_min, resize_size_max].
+
+  Returns:
+    A preprocessed image.
+  """
+  if is_training:
+    return preprocess_for_train(image, output_height, output_width,
+                                resize_side_min, resize_side_max)
+  else:
+    return preprocess_for_eval(image, output_height, output_width,
+                               resize_side_min)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/export_mobilenet.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/export_mobilenet.sh
new file mode 100755
index 00000000..bddabe15
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/export_mobilenet.sh
@@ -0,0 +1,132 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# This script prepares the various different versions of MobileNet models for
+# use in a mobile application. If you don't specify your own trained checkpoint
+# file, it will download pretrained checkpoints for ImageNet. You'll also need
+# to have a copy of the TensorFlow source code to run some of the commands,
+# by default it will be looked for in ./tensorflow, but you can set the
+# TENSORFLOW_PATH environment variable before calling the script if your source
+# is in a different location.
+# The main slim/nets/mobilenet_v1.md description has more details about the
+# model, but the main points are that it comes in four size versions, 1.0, 0.75,
+# 0.50, and 0.25, which controls the number of parameters and so the file size
+# of the model, and the input image size, which can be 224, 192, 160, or 128
+# pixels, and affects the amount of computation needed, and the latency.
+# Here's an example generating a frozen model from pretrained weights:
+#
+
+set -e
+
+print_usage () {
+  echo "Creates a frozen mobilenet model suitable for mobile use"
+  echo "Usage:"
+  echo "$0 <mobilenet version> <input size> [checkpoint path]"
+}
+
+MOBILENET_VERSION=$1
+IMAGE_SIZE=$2
+CHECKPOINT=$3
+
+if [[ ${MOBILENET_VERSION} = "1.0" ]]; then
+   SLIM_NAME=mobilenet_v1
+elif [[ ${MOBILENET_VERSION} = "0.75" ]]; then
+   SLIM_NAME=mobilenet_v1_075
+elif [[ ${MOBILENET_VERSION} = "0.50" ]]; then
+   SLIM_NAME=mobilenet_v1_050
+elif [[ ${MOBILENET_VERSION} = "0.25" ]]; then
+   SLIM_NAME=mobilenet_v1_025
+else
+  echo "Bad mobilenet version, should be one of 1.0, 0.75, 0.50, or 0.25"
+  print_usage
+  exit 1
+fi
+
+if [[ ${IMAGE_SIZE} -ne "224" ]] && [[ ${IMAGE_SIZE} -ne "192" ]] && [[ ${IMAGE_SIZE} -ne "160" ]] && [[ ${IMAGE_SIZE} -ne "128" ]]; then
+  echo "Bad input image size, should be one of 224, 192, 160, or 128"
+  print_usage
+  exit 1
+fi
+
+if [[ ${TENSORFLOW_PATH} -eq "" ]]; then
+   TENSORFLOW_PATH=../tensorflow
+fi
+
+if [[ ! -d ${TENSORFLOW_PATH} ]]; then
+   echo "TensorFlow source folder not found. You should download the source and then set"
+   echo "the TENSORFLOW_PATH environment variable to point to it, like this:"
+   echo "export TENSORFLOW_PATH=/my/path/to/tensorflow"
+   print_usage
+   exit 1
+fi
+
+MODEL_FOLDER=/tmp/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}
+if [[ -d ${MODEL_FOLDER} ]]; then
+  echo "Model folder ${MODEL_FOLDER} already exists!"
+  echo "If you want to overwrite it, then 'rm -rf ${MODEL_FOLDER}' first."
+  print_usage
+  exit 1
+fi
+mkdir ${MODEL_FOLDER}
+
+if [[ ${CHECKPOINT} = "" ]]; then
+  echo "*******"
+  echo "Downloading pretrained weights"
+  echo "*******"
+  curl "http://download.tensorflow.org/models/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}_2017_06_14.tar.gz" \
+    -o ${MODEL_FOLDER}/checkpoints.tar.gz
+  tar xzf ${MODEL_FOLDER}/checkpoints.tar.gz --directory ${MODEL_FOLDER}
+  CHECKPOINT=${MODEL_FOLDER}/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}.ckpt
+fi
+
+echo "*******"
+echo "Exporting graph architecture to ${MODEL_FOLDER}/unfrozen_graph.pb"
+echo "*******"
+bazel run slim:export_inference_graph -- \
+  --model_name=${SLIM_NAME} --image_size=${IMAGE_SIZE} --logtostderr \
+  --output_file=${MODEL_FOLDER}/unfrozen_graph.pb --dataset_dir=${MODEL_FOLDER}
+
+cd ../tensorflow
+
+echo "*******"
+echo "Freezing graph to ${MODEL_FOLDER}/frozen_graph.pb"
+echo "*******"
+bazel run tensorflow/python/tools:freeze_graph -- \
+  --input_graph=${MODEL_FOLDER}/unfrozen_graph.pb \
+  --input_checkpoint=${CHECKPOINT} \
+  --input_binary=true --output_graph=${MODEL_FOLDER}/frozen_graph.pb \
+  --output_node_names=MobilenetV1/Predictions/Reshape_1
+
+echo "Quantizing weights to ${MODEL_FOLDER}/quantized_graph.pb"
+bazel run tensorflow/tools/graph_transforms:transform_graph -- \
+  --in_graph=${MODEL_FOLDER}/frozen_graph.pb \
+  --out_graph=${MODEL_FOLDER}/quantized_graph.pb \
+  --inputs=input --outputs=MobilenetV1/Predictions/Reshape_1 \
+  --transforms='fold_constants fold_batch_norms quantize_weights'
+
+echo "*******"
+echo "Running label_image using the graph"
+echo "*******"
+bazel build tensorflow/examples/label_image:label_image
+bazel-bin/tensorflow/examples/label_image/label_image \
+  --input_layer=input --output_layer=MobilenetV1/Predictions/Reshape_1 \
+  --graph=${MODEL_FOLDER}/quantized_graph.pb --input_mean=-127 --input_std=127 \
+  --image=tensorflow/examples/label_image/data/grace_hopper.jpg \
+  --input_width=${IMAGE_SIZE} --input_height=${IMAGE_SIZE} --labels=${MODEL_FOLDER}/labels.txt
+
+echo "*******"
+echo "Saved graphs to ${MODEL_FOLDER}/frozen_graph.pb and ${MODEL_FOLDER}/quantized_graph.pb"
+echo "*******"
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_resnet_v2_on_flowers.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_resnet_v2_on_flowers.sh
new file mode 100644
index 00000000..ad003ba3
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_resnet_v2_on_flowers.sh
@@ -0,0 +1,109 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes an Inception Resnet V2 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_inception_resnet_v2_on_flowers.sh
+set -e
+
+# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
+MODEL_NAME=inception_resnet_v2
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/${MODEL_NAME}
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt ]; then
+  wget http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
+  tar -xvf inception_resnet_v2_2016_08_30.tar.gz
+  mv inception_resnet_v2.ckpt ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt
+  rm inception_resnet_v2_2016_08_30.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME} \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt \
+  --checkpoint_exclude_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
+  --trainable_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=10 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME}
+
+# Fine-tune all the new layers for 500 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME} \
+  --checkpoint_path=${TRAIN_DIR} \
+  --max_number_of_steps=500 \
+  --batch_size=32 \
+  --learning_rate=0.0001 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=10 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME}
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v1_on_flowers.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v1_on_flowers.sh
new file mode 100644
index 00000000..fee482ab
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v1_on_flowers.sh
@@ -0,0 +1,104 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes an InceptionV1 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_inception_v1_on_flowers.sh
+set -e
+
+# Where the pre-trained InceptionV1 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/inception_v1
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt ]; then
+  wget http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz
+  tar -xvf inception_v1_2016_08_28.tar.gz
+  mv inception_v1.ckpt ${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt
+  rm inception_v1_2016_08_28.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 2000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v1 \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt \
+  --checkpoint_exclude_scopes=InceptionV1/Logits \
+  --trainable_scopes=InceptionV1/Logits \
+  --max_number_of_steps=3000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v1
+
+# Fine-tune all the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --checkpoint_path=${TRAIN_DIR} \
+  --model_name=inception_v1 \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.001 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v1
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v3_on_flowers.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v3_on_flowers.sh
new file mode 100644
index 00000000..39986638
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_inception_v3_on_flowers.sh
@@ -0,0 +1,106 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes an InceptionV3 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_inception_v3_on_flowers.sh
+set -e
+
+# Where the pre-trained InceptionV3 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/inception_v3
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt ]; then
+  wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
+  tar -xvf inception_v3_2016_08_28.tar.gz
+  mv inception_v3.ckpt ${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt
+  rm inception_v3_2016_08_28.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v3 \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt \
+  --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
+  --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v3
+
+# Fine-tune all the new layers for 500 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v3 \
+  --checkpoint_path=${TRAIN_DIR} \
+  --max_number_of_steps=500 \
+  --batch_size=32 \
+  --learning_rate=0.0001 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=10 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=inception_v3
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_resnet_v1_50_on_flowers.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_resnet_v1_50_on_flowers.sh
new file mode 100644
index 00000000..00d9043c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/finetune_resnet_v1_50_on_flowers.sh
@@ -0,0 +1,104 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes a ResNetV1-50 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_resnet_v1_50_on_flowers.sh
+set -e
+
+# Where the pre-trained ResNetV1-50 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/resnet_v1_50
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt ]; then
+  wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
+  tar -xvf resnet_v1_50_2016_08_28.tar.gz
+  mv resnet_v1_50.ckpt ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt
+  rm resnet_v1_50_2016_08_28.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 3000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50 \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt \
+  --checkpoint_exclude_scopes=resnet_v1_50/logits \
+  --trainable_scopes=resnet_v1_50/logits \
+  --max_number_of_steps=3000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50
+
+# Fine-tune all the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --checkpoint_path=${TRAIN_DIR} \
+  --model_name=resnet_v1_50 \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.001 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/train_cifarnet_on_cifar10.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/train_cifarnet_on_cifar10.sh
new file mode 100644
index 00000000..4613ad18
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/train_cifarnet_on_cifar10.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the Cifar10 dataset
+# 2. Trains a CifarNet model on the Cifar10 training set.
+# 3. Evaluates the model on the Cifar10 testing set.
+#
+# Usage:
+# cd slim
+# ./scripts/train_cifarnet_on_cifar10.sh
+set -e
+
+# Where the checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/cifarnet-model
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/cifar10
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=cifar10 \
+  --dataset_dir=${DATASET_DIR}
+
+# Run training.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=cifar10 \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=cifarnet \
+  --preprocessing_name=cifarnet \
+  --max_number_of_steps=100000 \
+  --batch_size=128 \
+  --save_interval_secs=120 \
+  --save_summaries_secs=120 \
+  --log_every_n_steps=100 \
+  --optimizer=sgd \
+  --learning_rate=0.1 \
+  --learning_rate_decay_factor=0.1 \
+  --num_epochs_per_decay=200 \
+  --weight_decay=0.004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=cifar10 \
+  --dataset_split_name=test \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=cifarnet
diff --git a/TensorFlow/Detection/SSD/models/research/slim/scripts/train_lenet_on_mnist.sh b/TensorFlow/Detection/SSD/models/research/slim/scripts/train_lenet_on_mnist.sh
new file mode 100644
index 00000000..1d588eb5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/scripts/train_lenet_on_mnist.sh
@@ -0,0 +1,63 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# This script performs the following operations:
+# 1. Downloads the MNIST dataset
+# 2. Trains a LeNet model on the MNIST training set.
+# 3. Evaluates the model on the MNIST testing set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/train_lenet_on_mnist.sh
+set -e
+
+# Where the checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/lenet-model
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/mnist
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=mnist \
+  --dataset_dir=${DATASET_DIR}
+
+# Run training.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=mnist \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=lenet \
+  --preprocessing_name=lenet \
+  --max_number_of_steps=20000 \
+  --batch_size=50 \
+  --learning_rate=0.01 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=sgd \
+  --learning_rate_decay_type=fixed \
+  --weight_decay=0
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=mnist \
+  --dataset_split_name=test \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=lenet
diff --git a/TensorFlow/Detection/SSD/models/research/slim/setup.py b/TensorFlow/Detection/SSD/models/research/slim/setup.py
new file mode 100644
index 00000000..3ec7ecd6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/setup.py
@@ -0,0 +1,27 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Setup script for slim."""
+
+from setuptools import find_packages
+from setuptools import setup
+
+
+setup(
+    name='slim',
+    version='0.1',
+    include_package_data=True,
+    packages=find_packages(),
+    description='tf-slim',
+)
diff --git a/TensorFlow/Detection/SSD/models/research/slim/slim_walkthrough.ipynb b/TensorFlow/Detection/SSD/models/research/slim/slim_walkthrough.ipynb
new file mode 100644
index 00000000..262281a5
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/slim_walkthrough.ipynb
@@ -0,0 +1,1141 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TF-Slim Walkthrough\n",
+    "\n",
+    "This notebook will walk you through the basics of using TF-Slim to define, train and evaluate neural networks on various tasks. It assumes a basic knowledge of neural networks. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Table of contents\n",
+    "\n",
+    "<a href=\"#Install\">Installation and setup</a><br>\n",
+    "<a href='#MLP'>Creating your first neural network with TF-Slim</a><br>\n",
+    "<a href='#ReadingTFSlimDatasets'>Reading Data with TF-Slim</a><br>\n",
+    "<a href='#CNN'>Training a convolutional neural network (CNN)</a><br>\n",
+    "<a href='#Pretained'>Using pre-trained models</a><br>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and setup\n",
+    "<a id='Install'></a>\n",
+    "\n",
+    "Since the stable release of TF 1.0, the latest version of slim has been available as `tf.contrib.slim`.\n",
+    "To test that your installation is working, execute the following command; it should run without raising any errors.\n",
+    "\n",
+    "```\n",
+    "python -c \"import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once\"\n",
+    "```\n",
+    "\n",
+    "Although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/research/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to  TF_MODELS/research/slim **before** running this notebook, so that these files are in your python path.\n",
+    "\n",
+    "To check you've got these two steps to work, just execute the cell below. If it complains about unknown modules, restart the notebook after moving to the TF-Slim models directory.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import\n",
+    "from __future__ import division\n",
+    "from __future__ import print_function\n",
+    "\n",
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import time\n",
+    "\n",
+    "from datasets import dataset_utils\n",
+    "\n",
+    "# Main slim library\n",
+    "from tensorflow.contrib import slim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating your first neural network with TF-Slim\n",
+    "<a id='MLP'></a>\n",
+    "\n",
+    "Below we give some code to create a simple multilayer perceptron (MLP)  which can be used\n",
+    "for regression problems. The model has 2 hidden layers.\n",
+    "The output is a single node. \n",
+    "When this function is called, it will create various nodes, and silently add them to whichever global TF graph is currently in scope. When a node which corresponds to a layer with adjustable parameters (eg., a fully connected layer) is created, additional parameter variable nodes are silently created, and added to the graph. (We will discuss how to train the parameters later.)\n",
+    "\n",
+    "We use variable scope to put all the nodes under a common name,\n",
+    "so that the graph has some hierarchical structure.\n",
+    "This is useful when we want to visualize the TF graph in tensorboard, or if we want to query related\n",
+    "variables. \n",
+    "The fully connected layers all use the same L2 weight decay and ReLu activations, as specified by **arg_scope**. (However, the final layer overrides these defaults, and uses an identity activation function.)\n",
+    "\n",
+    "We also illustrate how to add a dropout layer after the first fully connected layer (FC1). Note that at test time, \n",
+    "we do not drop out nodes, but instead use the average activations; hence we need to know whether the model is being\n",
+    "constructed for training or testing, since the computational graph will be different in the two cases\n",
+    "(although the variables, storing the model parameters, will be shared, since they have the same name/scope)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def regression_model(inputs, is_training=True, scope=\"deep_regression\"):\n",
+    "    \"\"\"Creates the regression model.\n",
+    "\n",
+    "    Args:\n",
+    "        inputs: A node that yields a `Tensor` of size [batch_size, dimensions].\n",
+    "        is_training: Whether or not we're currently training the model.\n",
+    "        scope: An optional variable_op scope for the model.\n",
+    "\n",
+    "    Returns:\n",
+    "        predictions: 1-D `Tensor` of shape [batch_size] of responses.\n",
+    "        end_points: A dict of end points representing the hidden layers.\n",
+    "    \"\"\"\n",
+    "    with tf.variable_scope(scope, 'deep_regression', [inputs]):\n",
+    "        end_points = {}\n",
+    "        # Set the default weight _regularizer and acvitation for each fully_connected layer.\n",
+    "        with slim.arg_scope([slim.fully_connected],\n",
+    "                            activation_fn=tf.nn.relu,\n",
+    "                            weights_regularizer=slim.l2_regularizer(0.01)):\n",
+    "\n",
+    "            # Creates a fully connected layer from the inputs with 32 hidden units.\n",
+    "            net = slim.fully_connected(inputs, 32, scope='fc1')\n",
+    "            end_points['fc1'] = net\n",
+    "\n",
+    "            # Adds a dropout layer to prevent over-fitting.\n",
+    "            net = slim.dropout(net, 0.8, is_training=is_training)\n",
+    "\n",
+    "            # Adds another fully connected layer with 16 hidden units.\n",
+    "            net = slim.fully_connected(net, 16, scope='fc2')\n",
+    "            end_points['fc2'] = net\n",
+    "\n",
+    "            # Creates a fully-connected layer with a single hidden unit. Note that the\n",
+    "            # layer is made linear by setting activation_fn=None.\n",
+    "            predictions = slim.fully_connected(net, 1, activation_fn=None, scope='prediction')\n",
+    "            end_points['out'] = predictions\n",
+    "\n",
+    "            return predictions, end_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's create the model and examine its structure.\n",
+    "\n",
+    "We create a TF graph and call regression_model(), which adds nodes (tensors) to the graph. We then examine their shape, and print the names of all the model variables which have been implicitly created inside of each layer. We see that the names of the variables follow the scopes that we specified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with tf.Graph().as_default():\n",
+    "    # Dummy placeholders for arbitrary number of 1d inputs and outputs\n",
+    "    inputs = tf.placeholder(tf.float32, shape=(None, 1))\n",
+    "    outputs = tf.placeholder(tf.float32, shape=(None, 1))\n",
+    "\n",
+    "    # Build model\n",
+    "    predictions, end_points = regression_model(inputs)\n",
+    "\n",
+    "    # Print name and shape of each tensor.\n",
+    "    print(\"Layers\")\n",
+    "    for k, v in end_points.items():\n",
+    "        print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n",
+    "\n",
+    "    # Print name and shape of parameter nodes  (values not yet initialized)\n",
+    "    print(\"\\n\")\n",
+    "    print(\"Parameters\")\n",
+    "    for v in slim.get_model_variables():\n",
+    "        print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's create some 1d regression data .\n",
+    "\n",
+    "We will train and test the model on some noisy observations of a nonlinear function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def produce_batch(batch_size, noise=0.3):\n",
+    "    xs = np.random.random(size=[batch_size, 1]) * 10\n",
+    "    ys = np.sin(xs) + 5 + np.random.normal(size=[batch_size, 1], scale=noise)\n",
+    "    return [xs.astype(np.float32), ys.astype(np.float32)]\n",
+    "\n",
+    "x_train, y_train = produce_batch(200)\n",
+    "x_test, y_test = produce_batch(200)\n",
+    "plt.scatter(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's fit the model to the data\n",
+    "\n",
+    "The user has to specify the loss function and the optimizer, and slim does the rest.\n",
+    "In particular,  the slim.learning.train function does the following:\n",
+    "\n",
+    "- For each iteration, evaluate the train_op, which updates the parameters using the optimizer applied to the current minibatch. Also, update the global_step.\n",
+    "- Occasionally store the model checkpoint in the specified directory. This is useful in case your machine crashes  - then you can simply restart from the specified checkpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def convert_data_to_tensors(x, y):\n",
+    "    inputs = tf.constant(x)\n",
+    "    inputs.set_shape([None, 1])\n",
+    "    \n",
+    "    outputs = tf.constant(y)\n",
+    "    outputs.set_shape([None, 1])\n",
+    "    return inputs, outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# The following snippet trains the regression model using a mean_squared_error loss.\n",
+    "ckpt_dir = '/tmp/regression_model/'\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    tf.logging.set_verbosity(tf.logging.INFO)\n",
+    "    \n",
+    "    inputs, targets = convert_data_to_tensors(x_train, y_train)\n",
+    "\n",
+    "    # Make the model.\n",
+    "    predictions, nodes = regression_model(inputs, is_training=True)\n",
+    "\n",
+    "    # Add the loss function to the graph.\n",
+    "    loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
+    "    \n",
+    "    # The total loss is the user's loss plus any regularization losses.\n",
+    "    total_loss = slim.losses.get_total_loss()\n",
+    "\n",
+    "    # Specify the optimizer and create the train op:\n",
+    "    optimizer = tf.train.AdamOptimizer(learning_rate=0.005)\n",
+    "    train_op = slim.learning.create_train_op(total_loss, optimizer) \n",
+    "\n",
+    "    # Run the training inside a session.\n",
+    "    final_loss = slim.learning.train(\n",
+    "        train_op,\n",
+    "        logdir=ckpt_dir,\n",
+    "        number_of_steps=5000,\n",
+    "        save_summaries_secs=5,\n",
+    "        log_every_n_steps=500)\n",
+    "  \n",
+    "print(\"Finished training. Last batch loss:\", final_loss)\n",
+    "print(\"Checkpoint saved in %s\" % ckpt_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training with multiple loss functions.\n",
+    "\n",
+    "Sometimes we have multiple objectives we want to simultaneously optimize.\n",
+    "In slim, it is easy to add more losses, as we show below. (We do not optimize the total loss in this example,\n",
+    "but we show how to compute it.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with tf.Graph().as_default():\n",
+    "    inputs, targets = convert_data_to_tensors(x_train, y_train)\n",
+    "    predictions, end_points = regression_model(inputs, is_training=True)\n",
+    "\n",
+    "    # Add multiple loss nodes.\n",
+    "    mean_squared_error_loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
+    "    absolute_difference_loss = slim.losses.absolute_difference(predictions, targets)\n",
+    "\n",
+    "    # The following two ways to compute the total loss are equivalent\n",
+    "    regularization_loss = tf.add_n(slim.losses.get_regularization_losses())\n",
+    "    total_loss1 = mean_squared_error_loss + absolute_difference_loss + regularization_loss\n",
+    "\n",
+    "    # Regularization Loss is included in the total loss by default.\n",
+    "    # This is good for training, but not for testing.\n",
+    "    total_loss2 = slim.losses.get_total_loss(add_regularization_losses=True)\n",
+    "    \n",
+    "    init_op = tf.global_variables_initializer()\n",
+    "    \n",
+    "    with tf.Session() as sess:\n",
+    "        sess.run(init_op) # Will initialize the parameters with random weights.\n",
+    "        \n",
+    "        total_loss1, total_loss2 = sess.run([total_loss1, total_loss2])\n",
+    "        \n",
+    "        print('Total Loss1: %f' % total_loss1)\n",
+    "        print('Total Loss2: %f' % total_loss2)\n",
+    "\n",
+    "        print('Regularization Losses:')\n",
+    "        for loss in slim.losses.get_regularization_losses():\n",
+    "            print(loss)\n",
+    "\n",
+    "        print('Loss Functions:')\n",
+    "        for loss in slim.losses.get_losses():\n",
+    "            print(loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's load the saved model and use it for prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with tf.Graph().as_default():\n",
+    "    inputs, targets = convert_data_to_tensors(x_test, y_test)\n",
+    "  \n",
+    "    # Create the model structure. (Parameters will be loaded below.)\n",
+    "    predictions, end_points = regression_model(inputs, is_training=False)\n",
+    "\n",
+    "    # Make a session which restores the old parameters from a checkpoint.\n",
+    "    sv = tf.train.Supervisor(logdir=ckpt_dir)\n",
+    "    with sv.managed_session() as sess:\n",
+    "        inputs, predictions, targets = sess.run([inputs, predictions, targets])\n",
+    "\n",
+    "plt.scatter(inputs, targets, c='r');\n",
+    "plt.scatter(inputs, predictions, c='b');\n",
+    "plt.title('red=true, blue=predicted')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's compute various evaluation metrics on the test set.\n",
+    "\n",
+    "In TF-Slim termiology, losses are optimized, but metrics (which may not be differentiable, e.g., precision and recall) are just measured. As an illustration, the code below computes mean squared error and mean absolute error metrics on the test set.\n",
+    "\n",
+    "Each metric declaration creates several local variables (which must be initialized via tf.initialize_local_variables()) and returns both a value_op and an update_op. When evaluated, the value_op returns the current value of the metric. The update_op loads a new batch of data, runs the model, obtains the predictions and accumulates the metric statistics appropriately before returning the current value of the metric. We store these value nodes and update nodes in 2 dictionaries.\n",
+    "\n",
+    "After creating the metric nodes, we can pass them to slim.evaluation.evaluation, which repeatedly evaluates these nodes the specified number of times. (This allows us to compute the evaluation in a streaming fashion across minibatches, which is usefulf for large datasets.) Finally, we print the final value of each metric.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with tf.Graph().as_default():\n",
+    "    inputs, targets = convert_data_to_tensors(x_test, y_test)\n",
+    "    predictions, end_points = regression_model(inputs, is_training=False)\n",
+    "\n",
+    "    # Specify metrics to evaluate:\n",
+    "    names_to_value_nodes, names_to_update_nodes = slim.metrics.aggregate_metric_map({\n",
+    "      'Mean Squared Error': slim.metrics.streaming_mean_squared_error(predictions, targets),\n",
+    "      'Mean Absolute Error': slim.metrics.streaming_mean_absolute_error(predictions, targets)\n",
+    "    })\n",
+    "\n",
+    "    # Make a session which restores the old graph parameters, and then run eval.\n",
+    "    sv = tf.train.Supervisor(logdir=ckpt_dir)\n",
+    "    with sv.managed_session() as sess:\n",
+    "        metric_values = slim.evaluation.evaluation(\n",
+    "            sess,\n",
+    "            num_evals=1, # Single pass over data\n",
+    "            eval_op=names_to_update_nodes.values(),\n",
+    "            final_op=names_to_value_nodes.values())\n",
+    "\n",
+    "    names_to_values = dict(zip(names_to_value_nodes.keys(), metric_values))\n",
+    "    for key, value in names_to_values.items():\n",
+    "      print('%s: %f' % (key, value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading Data with TF-Slim\n",
+    "<a id='ReadingTFSlimDatasets'></a>\n",
+    "\n",
+    "Reading data with TF-Slim has two main components: A\n",
+    "[Dataset](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset.py) and a \n",
+    "[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py). The former is a descriptor of a dataset, while the latter performs the actions necessary for actually reading the data. Lets look at each one in detail:\n",
+    "\n",
+    "\n",
+    "## Dataset\n",
+    "A TF-Slim\n",
+    "[Dataset](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset.py)\n",
+    "contains descriptive information about a dataset necessary for reading it, such as the list of data files and how to decode them. It also contains metadata including class labels, the size of the train/test splits and descriptions of the tensors that the dataset provides. For example, some datasets contain images with labels. Others augment this data with bounding box annotations, etc. The Dataset object allows us to write generic code using the same API, regardless of the data content and encoding type.\n",
+    "\n",
+    "TF-Slim's Dataset works especially well when the data is stored as a (possibly sharded)\n",
+    "[TFRecords file](https://www.tensorflow.org/versions/r0.10/how_tos/reading_data/index.html#file-formats), where each record contains a [tf.train.Example protocol buffer](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/core/example/example.proto).\n",
+    "TF-Slim uses a consistent convention for naming the keys and values inside each Example record. \n",
+    "\n",
+    "## DatasetDataProvider\n",
+    "\n",
+    "A\n",
+    "[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py) is a class which actually reads the data from a dataset. It is highly configurable to read the data in various ways that may make a big impact on the efficiency of your training process. For example, it can be single or multi-threaded. If your data is sharded across many files, it can read each files serially, or from every file simultaneously.\n",
+    "\n",
+    "## Demo: The Flowers Dataset\n",
+    "\n",
+    "For convenience, we've include scripts to convert several common image datasets into TFRecord format and have provided\n",
+    "the Dataset descriptor files necessary for reading them. We demonstrate how easy it is to use these dataset via the Flowers dataset below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Flowers Dataset\n",
+    "<a id='DownloadFlowers'></a>\n",
+    "\n",
+    "We've made available a tarball of the Flowers dataset which has already been converted to TFRecord format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from datasets import dataset_utils\n",
+    "\n",
+    "url = \"http://download.tensorflow.org/data/flowers.tar.gz\"\n",
+    "flowers_data_dir = '/tmp/flowers'\n",
+    "\n",
+    "if not tf.gfile.Exists(flowers_data_dir):\n",
+    "    tf.gfile.MakeDirs(flowers_data_dir)\n",
+    "\n",
+    "dataset_utils.download_and_uncompress_tarball(url, flowers_data_dir) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display some of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import flowers\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "\n",
+    "with tf.Graph().as_default(): \n",
+    "    dataset = flowers.get_split('train', flowers_data_dir)\n",
+    "    data_provider = slim.dataset_data_provider.DatasetDataProvider(\n",
+    "        dataset, common_queue_capacity=32, common_queue_min=1)\n",
+    "    image, label = data_provider.get(['image', 'label'])\n",
+    "    \n",
+    "    with tf.Session() as sess:    \n",
+    "        with slim.queues.QueueRunners(sess):\n",
+    "            for i in range(4):\n",
+    "                np_image, np_label = sess.run([image, label])\n",
+    "                height, width, _ = np_image.shape\n",
+    "                class_name = name = dataset.labels_to_names[np_label]\n",
+    "                \n",
+    "                plt.figure()\n",
+    "                plt.imshow(np_image)\n",
+    "                plt.title('%s, %d x %d' % (name, height, width))\n",
+    "                plt.axis('off')\n",
+    "                plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional neural nets (CNNs).\n",
+    "<a id='CNN'></a>\n",
+    "\n",
+    "In this section, we show how to train an image classifier using a simple CNN.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the model.\n",
+    "\n",
+    "Below we define a simple CNN. Note that the output layer is linear function - we will apply softmax transformation externally to the model, either in the loss function (for training), or in the prediction function (during testing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def my_cnn(images, num_classes, is_training):  # is_training is not used...\n",
+    "    with slim.arg_scope([slim.max_pool2d], kernel_size=[3, 3], stride=2):\n",
+    "        net = slim.conv2d(images, 64, [5, 5])\n",
+    "        net = slim.max_pool2d(net)\n",
+    "        net = slim.conv2d(net, 64, [5, 5])\n",
+    "        net = slim.max_pool2d(net)\n",
+    "        net = slim.flatten(net)\n",
+    "        net = slim.fully_connected(net, 192)\n",
+    "        net = slim.fully_connected(net, num_classes, activation_fn=None)       \n",
+    "        return net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply the model to some randomly generated images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    # The model can handle any input size because the first layer is convolutional.\n",
+    "    # The size of the model is determined when image_node is first passed into the my_cnn function.\n",
+    "    # Once the variables are initialized, the size of all the weight matrices is fixed.\n",
+    "    # Because of the fully connected layers, this means that all subsequent images must have the same\n",
+    "    # input size as the first image.\n",
+    "    batch_size, height, width, channels = 3, 28, 28, 3\n",
+    "    images = tf.random_uniform([batch_size, height, width, channels], maxval=1)\n",
+    "    \n",
+    "    # Create the model.\n",
+    "    num_classes = 10\n",
+    "    logits = my_cnn(images, num_classes, is_training=True)\n",
+    "    probabilities = tf.nn.softmax(logits)\n",
+    "  \n",
+    "    # Initialize all the variables (including parameters) randomly.\n",
+    "    init_op = tf.global_variables_initializer()\n",
+    "  \n",
+    "    with tf.Session() as sess:\n",
+    "        # Run the init_op, evaluate the model outputs and print the results:\n",
+    "        sess.run(init_op)\n",
+    "        probabilities = sess.run(probabilities)\n",
+    "        \n",
+    "print('Probabilities Shape:')\n",
+    "print(probabilities.shape)  # batch_size x num_classes \n",
+    "\n",
+    "print('\\nProbabilities:')\n",
+    "print(probabilities)\n",
+    "\n",
+    "print('\\nSumming across all classes (Should equal 1):')\n",
+    "print(np.sum(probabilities, 1)) # Each row sums to 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model on the Flowers dataset.\n",
+    "\n",
+    "Before starting, make sure you've run the code to <a href=\"#DownloadFlowers\">Download the Flowers</a> dataset. Now, we'll get a sense of what it looks like to use TF-Slim's training functions found in\n",
+    "[learning.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/learning.py). First, we'll create a function, `load_batch`, that loads batches of dataset from a dataset. Next, we'll train a model for a single step (just to demonstrate the API), and evaluate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from preprocessing import inception_preprocessing\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "\n",
+    "\n",
+    "def load_batch(dataset, batch_size=32, height=299, width=299, is_training=False):\n",
+    "    \"\"\"Loads a single batch of data.\n",
+    "    \n",
+    "    Args:\n",
+    "      dataset: The dataset to load.\n",
+    "      batch_size: The number of images in the batch.\n",
+    "      height: The size of each image after preprocessing.\n",
+    "      width: The size of each image after preprocessing.\n",
+    "      is_training: Whether or not we're currently training or evaluating.\n",
+    "    \n",
+    "    Returns:\n",
+    "      images: A Tensor of size [batch_size, height, width, 3], image samples that have been preprocessed.\n",
+    "      images_raw: A Tensor of size [batch_size, height, width, 3], image samples that can be used for visualization.\n",
+    "      labels: A Tensor of size [batch_size], whose values range between 0 and dataset.num_classes.\n",
+    "    \"\"\"\n",
+    "    data_provider = slim.dataset_data_provider.DatasetDataProvider(\n",
+    "        dataset, common_queue_capacity=32,\n",
+    "        common_queue_min=8)\n",
+    "    image_raw, label = data_provider.get(['image', 'label'])\n",
+    "    \n",
+    "    # Preprocess image for usage by Inception.\n",
+    "    image = inception_preprocessing.preprocess_image(image_raw, height, width, is_training=is_training)\n",
+    "    \n",
+    "    # Preprocess the image for display purposes.\n",
+    "    image_raw = tf.expand_dims(image_raw, 0)\n",
+    "    image_raw = tf.image.resize_images(image_raw, [height, width])\n",
+    "    image_raw = tf.squeeze(image_raw)\n",
+    "\n",
+    "    # Batch it up.\n",
+    "    images, images_raw, labels = tf.train.batch(\n",
+    "          [image, image_raw, label],\n",
+    "          batch_size=batch_size,\n",
+    "          num_threads=1,\n",
+    "          capacity=2 * batch_size)\n",
+    "    \n",
+    "    return images, images_raw, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import flowers\n",
+    "\n",
+    "# This might take a few minutes.\n",
+    "train_dir = '/tmp/tfslim_model/'\n",
+    "print('Will save model to %s' % train_dir)\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    tf.logging.set_verbosity(tf.logging.INFO)\n",
+    "\n",
+    "    dataset = flowers.get_split('train', flowers_data_dir)\n",
+    "    images, _, labels = load_batch(dataset)\n",
+    "  \n",
+    "    # Create the model:\n",
+    "    logits = my_cnn(images, num_classes=dataset.num_classes, is_training=True)\n",
+    " \n",
+    "    # Specify the loss function:\n",
+    "    one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)\n",
+    "    slim.losses.softmax_cross_entropy(logits, one_hot_labels)\n",
+    "    total_loss = slim.losses.get_total_loss()\n",
+    "\n",
+    "    # Create some summaries to visualize the training process:\n",
+    "    tf.summary.scalar('losses/Total Loss', total_loss)\n",
+    "  \n",
+    "    # Specify the optimizer and create the train op:\n",
+    "    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)\n",
+    "    train_op = slim.learning.create_train_op(total_loss, optimizer)\n",
+    "\n",
+    "    # Run the training:\n",
+    "    final_loss = slim.learning.train(\n",
+    "      train_op,\n",
+    "      logdir=train_dir,\n",
+    "      number_of_steps=1, # For speed, we just do 1 epoch\n",
+    "      save_summaries_secs=1)\n",
+    "  \n",
+    "    print('Finished training. Final batch loss %d' % final_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate some metrics.\n",
+    "\n",
+    "As we discussed above, we can compute various metrics besides the loss.\n",
+    "Below we show how to compute prediction accuracy of the trained model, as well as top-5 classification accuracy. (The difference between evaluation and evaluation_loop is that the latter writes the results to a log directory, so they can be viewed in tensorboard.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import flowers\n",
+    "\n",
+    "# This might take a few minutes.\n",
+    "with tf.Graph().as_default():\n",
+    "    tf.logging.set_verbosity(tf.logging.DEBUG)\n",
+    "    \n",
+    "    dataset = flowers.get_split('train', flowers_data_dir)\n",
+    "    images, _, labels = load_batch(dataset)\n",
+    "    \n",
+    "    logits = my_cnn(images, num_classes=dataset.num_classes, is_training=False)\n",
+    "    predictions = tf.argmax(logits, 1)\n",
+    "    \n",
+    "    # Define the metrics:\n",
+    "    names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({\n",
+    "        'eval/Accuracy': slim.metrics.streaming_accuracy(predictions, labels),\n",
+    "        'eval/Recall@5': slim.metrics.streaming_recall_at_k(logits, labels, 5),\n",
+    "    })\n",
+    "\n",
+    "    print('Running evaluation Loop...')\n",
+    "    checkpoint_path = tf.train.latest_checkpoint(train_dir)\n",
+    "    metric_values = slim.evaluation.evaluate_once(\n",
+    "        master='',\n",
+    "        checkpoint_path=checkpoint_path,\n",
+    "        logdir=train_dir,\n",
+    "        eval_op=names_to_updates.values(),\n",
+    "        final_op=names_to_values.values())\n",
+    "\n",
+    "    names_to_values = dict(zip(names_to_values.keys(), metric_values))\n",
+    "    for name in names_to_values:\n",
+    "        print('%s: %f' % (name, names_to_values[name]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using pre-trained models\n",
+    "<a id='Pretrained'></a>\n",
+    "\n",
+    "Neural nets work best when they have many parameters, making them very flexible function approximators.\n",
+    "However, this  means they must be trained on big datasets. Since this process is slow, we provide various pre-trained models - see the list [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).\n",
+    "\n",
+    "\n",
+    "You can either use these models as-is, or you can perform \"surgery\" on them, to modify them for some other task. For example, it is common to \"chop off\" the final pre-softmax layer, and replace it with a new set of weights corresponding to some new set of labels. You can then quickly fine tune the new model on a small new dataset. We illustrate this below, using inception-v1 as the base model. While models like Inception V3 are more powerful, Inception V1 is used for speed purposes.\n",
+    "\n",
+    "Take into account that VGG and ResNet final layers have only 1000 outputs rather than 1001. The ImageNet dataset provied has an empty background class which can be used to fine-tune the model to other tasks. VGG and ResNet models provided here don't use that class. We provide two examples of using pretrained models: Inception V1 and VGG-19 models to highlight this difference.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Inception V1 checkpoint\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import dataset_utils\n",
+    "\n",
+    "url = \"http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz\"\n",
+    "checkpoints_dir = '/tmp/checkpoints'\n",
+    "\n",
+    "if not tf.gfile.Exists(checkpoints_dir):\n",
+    "    tf.gfile.MakeDirs(checkpoints_dir)\n",
+    "\n",
+    "dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Apply Pre-trained Inception V1 model to Images.\n",
+    "\n",
+    "We have to convert each image to the size expected by the model checkpoint.\n",
+    "There is no easy way to determine this size from the checkpoint itself.\n",
+    "So we use a preprocessor to enforce this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "try:\n",
+    "    import urllib2 as urllib\n",
+    "except ImportError:\n",
+    "    import urllib.request as urllib\n",
+    "\n",
+    "from datasets import imagenet\n",
+    "from nets import inception\n",
+    "from preprocessing import inception_preprocessing\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "\n",
+    "image_size = inception.inception_v1.default_image_size\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    url = 'https://upload.wikimedia.org/wikipedia/commons/7/70/EnglishCockerSpaniel_simon.jpg'\n",
+    "    image_string = urllib.urlopen(url).read()\n",
+    "    image = tf.image.decode_jpeg(image_string, channels=3)\n",
+    "    processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n",
+    "    processed_images  = tf.expand_dims(processed_image, 0)\n",
+    "    \n",
+    "    # Create the model, use the default arg scope to configure the batch norm parameters.\n",
+    "    with slim.arg_scope(inception.inception_v1_arg_scope()):\n",
+    "        logits, _ = inception.inception_v1(processed_images, num_classes=1001, is_training=False)\n",
+    "    probabilities = tf.nn.softmax(logits)\n",
+    "    \n",
+    "    init_fn = slim.assign_from_checkpoint_fn(\n",
+    "        os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n",
+    "        slim.get_model_variables('InceptionV1'))\n",
+    "    \n",
+    "    with tf.Session() as sess:\n",
+    "        init_fn(sess)\n",
+    "        np_image, probabilities = sess.run([image, probabilities])\n",
+    "        probabilities = probabilities[0, 0:]\n",
+    "        sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]\n",
+    "        \n",
+    "    plt.figure()\n",
+    "    plt.imshow(np_image.astype(np.uint8))\n",
+    "    plt.axis('off')\n",
+    "    plt.show()\n",
+    "\n",
+    "    names = imagenet.create_readable_names_for_imagenet_labels()\n",
+    "    for i in range(5):\n",
+    "        index = sorted_inds[i]\n",
+    "        print('Probability %0.2f%% => [%s]' % (probabilities[index] * 100, names[index]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the VGG-16 checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import dataset_utils\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "url = \"http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz\"\n",
+    "checkpoints_dir = '/tmp/checkpoints'\n",
+    "\n",
+    "if not tf.gfile.Exists(checkpoints_dir):\n",
+    "    tf.gfile.MakeDirs(checkpoints_dir)\n",
+    "\n",
+    "dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Apply Pre-trained VGG-16 model to Images.\n",
+    "\n",
+    "We have to convert each image to the size expected by the model checkpoint.\n",
+    "There is no easy way to determine this size from the checkpoint itself.\n",
+    "So we use a preprocessor to enforce this. Pay attention to the difference caused by 1000 classes instead of 1001."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "try:\n",
+    "    import urllib2\n",
+    "except ImportError:\n",
+    "    import urllib.request as urllib\n",
+    "\n",
+    "from datasets import imagenet\n",
+    "from nets import vgg\n",
+    "from preprocessing import vgg_preprocessing\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "\n",
+    "image_size = vgg.vgg_16.default_image_size\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    url = 'https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg'\n",
+    "    image_string = urllib.urlopen(url).read()\n",
+    "    image = tf.image.decode_jpeg(image_string, channels=3)\n",
+    "    processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n",
+    "    processed_images  = tf.expand_dims(processed_image, 0)\n",
+    "    \n",
+    "    # Create the model, use the default arg scope to configure the batch norm parameters.\n",
+    "    with slim.arg_scope(vgg.vgg_arg_scope()):\n",
+    "        # 1000 classes instead of 1001.\n",
+    "        logits, _ = vgg.vgg_16(processed_images, num_classes=1000, is_training=False)\n",
+    "    probabilities = tf.nn.softmax(logits)\n",
+    "    \n",
+    "    init_fn = slim.assign_from_checkpoint_fn(\n",
+    "        os.path.join(checkpoints_dir, 'vgg_16.ckpt'),\n",
+    "        slim.get_model_variables('vgg_16'))\n",
+    "    \n",
+    "    with tf.Session() as sess:\n",
+    "        init_fn(sess)\n",
+    "        np_image, probabilities = sess.run([image, probabilities])\n",
+    "        probabilities = probabilities[0, 0:]\n",
+    "        sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]\n",
+    "        \n",
+    "    plt.figure()\n",
+    "    plt.imshow(np_image.astype(np.uint8))\n",
+    "    plt.axis('off')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    names = imagenet.create_readable_names_for_imagenet_labels()\n",
+    "    for i in range(5):\n",
+    "        index = sorted_inds[i]\n",
+    "        # Shift the index of a class name by one. \n",
+    "        print('Probability %0.2f%% => [%s]' % (probabilities[index] * 100, names[index+1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fine-tune the model on a different set of labels.\n",
+    "\n",
+    "We will fine tune the inception model on the Flowers dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Note that this may take several minutes.\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "from datasets import flowers\n",
+    "from nets import inception\n",
+    "from preprocessing import inception_preprocessing\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "image_size = inception.inception_v1.default_image_size\n",
+    "\n",
+    "\n",
+    "def get_init_fn():\n",
+    "    \"\"\"Returns a function run by the chief worker to warm-start the training.\"\"\"\n",
+    "    checkpoint_exclude_scopes=[\"InceptionV1/Logits\", \"InceptionV1/AuxLogits\"]\n",
+    "    \n",
+    "    exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\n",
+    "\n",
+    "    variables_to_restore = []\n",
+    "    for var in slim.get_model_variables():\n",
+    "        for exclusion in exclusions:\n",
+    "            if var.op.name.startswith(exclusion):\n",
+    "                break\n",
+    "        else:\n",
+    "            variables_to_restore.append(var)\n",
+    "\n",
+    "    return slim.assign_from_checkpoint_fn(\n",
+    "      os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n",
+    "      variables_to_restore)\n",
+    "\n",
+    "\n",
+    "train_dir = '/tmp/inception_finetuned/'\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    tf.logging.set_verbosity(tf.logging.INFO)\n",
+    "    \n",
+    "    dataset = flowers.get_split('train', flowers_data_dir)\n",
+    "    images, _, labels = load_batch(dataset, height=image_size, width=image_size)\n",
+    "    \n",
+    "    # Create the model, use the default arg scope to configure the batch norm parameters.\n",
+    "    with slim.arg_scope(inception.inception_v1_arg_scope()):\n",
+    "        logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)\n",
+    "        \n",
+    "    # Specify the loss function:\n",
+    "    one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)\n",
+    "    slim.losses.softmax_cross_entropy(logits, one_hot_labels)\n",
+    "    total_loss = slim.losses.get_total_loss()\n",
+    "\n",
+    "    # Create some summaries to visualize the training process:\n",
+    "    tf.summary.scalar('losses/Total Loss', total_loss)\n",
+    "  \n",
+    "    # Specify the optimizer and create the train op:\n",
+    "    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)\n",
+    "    train_op = slim.learning.create_train_op(total_loss, optimizer)\n",
+    "    \n",
+    "    # Run the training:\n",
+    "    final_loss = slim.learning.train(\n",
+    "        train_op,\n",
+    "        logdir=train_dir,\n",
+    "        init_fn=get_init_fn(),\n",
+    "        number_of_steps=2)\n",
+    "        \n",
+    "  \n",
+    "print('Finished training. Last batch loss %f' % final_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply fine tuned model to some images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from datasets import flowers\n",
+    "from nets import inception\n",
+    "\n",
+    "from tensorflow.contrib import slim\n",
+    "\n",
+    "image_size = inception.inception_v1.default_image_size\n",
+    "batch_size = 3\n",
+    "\n",
+    "with tf.Graph().as_default():\n",
+    "    tf.logging.set_verbosity(tf.logging.INFO)\n",
+    "    \n",
+    "    dataset = flowers.get_split('train', flowers_data_dir)\n",
+    "    images, images_raw, labels = load_batch(dataset, height=image_size, width=image_size)\n",
+    "    \n",
+    "    # Create the model, use the default arg scope to configure the batch norm parameters.\n",
+    "    with slim.arg_scope(inception.inception_v1_arg_scope()):\n",
+    "        logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)\n",
+    "\n",
+    "    probabilities = tf.nn.softmax(logits)\n",
+    "    \n",
+    "    checkpoint_path = tf.train.latest_checkpoint(train_dir)\n",
+    "    init_fn = slim.assign_from_checkpoint_fn(\n",
+    "      checkpoint_path,\n",
+    "      slim.get_variables_to_restore())\n",
+    "    \n",
+    "    with tf.Session() as sess:\n",
+    "        with slim.queues.QueueRunners(sess):\n",
+    "            sess.run(tf.initialize_local_variables())\n",
+    "            init_fn(sess)\n",
+    "            np_probabilities, np_images_raw, np_labels = sess.run([probabilities, images_raw, labels])\n",
+    "    \n",
+    "            for i in range(batch_size): \n",
+    "                image = np_images_raw[i, :, :, :]\n",
+    "                true_label = np_labels[i]\n",
+    "                predicted_label = np.argmax(np_probabilities[i, :])\n",
+    "                predicted_name = dataset.labels_to_names[predicted_label]\n",
+    "                true_name = dataset.labels_to_names[true_label]\n",
+    "                \n",
+    "                plt.figure()\n",
+    "                plt.imshow(image.astype(np.uint8))\n",
+    "                plt.title('Ground Truth: [%s], Prediction [%s]' % (true_name, predicted_name))\n",
+    "                plt.axis('off')\n",
+    "                plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/TensorFlow/Detection/SSD/models/research/slim/train_image_classifier.py b/TensorFlow/Detection/SSD/models/research/slim/train_image_classifier.py
new file mode 100644
index 00000000..a704f4c9
--- /dev/null
+++ b/TensorFlow/Detection/SSD/models/research/slim/train_image_classifier.py
@@ -0,0 +1,590 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Generic training script that trains a model using a given dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from datasets import dataset_factory
+from deployment import model_deploy
+from nets import nets_factory
+from preprocessing import preprocessing_factory
+
+slim = tf.contrib.slim
+
+tf.app.flags.DEFINE_string(
+    'master', '', 'The address of the TensorFlow master to use.')
+
+tf.app.flags.DEFINE_string(
+    'train_dir', '/tmp/tfmodel/',
+    'Directory where checkpoints and event logs are written to.')
+
+tf.app.flags.DEFINE_integer('num_clones', 1,
+                            'Number of model clones to deploy. Note For '
+                            'historical reasons loss from all clones averaged '
+                            'out and learning rate decay happen per clone '
+                            'epochs')
+
+tf.app.flags.DEFINE_boolean('clone_on_cpu', False,
+                            'Use CPUs to deploy clones.')
+
+tf.app.flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas.')
+
+tf.app.flags.DEFINE_integer(
+    'num_ps_tasks', 0,
+    'The number of parameter servers. If the value is 0, then the parameters '
+    'are handled locally by the worker.')
+
+tf.app.flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+tf.app.flags.DEFINE_integer(
+    'num_preprocessing_threads', 4,
+    'The number of threads used to create the batches.')
+
+tf.app.flags.DEFINE_integer(
+    'log_every_n_steps', 10,
+    'The frequency with which logs are print.')
+
+tf.app.flags.DEFINE_integer(
+    'save_summaries_secs', 600,
+    'The frequency with which summaries are saved, in seconds.')
+
+tf.app.flags.DEFINE_integer(
+    'save_interval_secs', 600,
+    'The frequency with which the model is saved, in seconds.')
+
+tf.app.flags.DEFINE_integer(
+    'task', 0, 'Task id of the replica running the training.')
+
+######################
+# Optimization Flags #
+######################
+
+tf.app.flags.DEFINE_float(
+    'weight_decay', 0.00004, 'The weight decay on the model weights.')
+
+tf.app.flags.DEFINE_string(
+    'optimizer', 'rmsprop',
+    'The name of the optimizer, one of "adadelta", "adagrad", "adam",'
+    '"ftrl", "momentum", "sgd" or "rmsprop".')
+
+tf.app.flags.DEFINE_float(
+    'adadelta_rho', 0.95,
+    'The decay rate for adadelta.')
+
+tf.app.flags.DEFINE_float(
+    'adagrad_initial_accumulator_value', 0.1,
+    'Starting value for the AdaGrad accumulators.')
+
+tf.app.flags.DEFINE_float(
+    'adam_beta1', 0.9,
+    'The exponential decay rate for the 1st moment estimates.')
+
+tf.app.flags.DEFINE_float(
+    'adam_beta2', 0.999,
+    'The exponential decay rate for the 2nd moment estimates.')
+
+tf.app.flags.DEFINE_float('opt_epsilon', 1.0, 'Epsilon term for the optimizer.')
+
+tf.app.flags.DEFINE_float('ftrl_learning_rate_power', -0.5,
+                          'The learning rate power.')
+
+tf.app.flags.DEFINE_float(
+    'ftrl_initial_accumulator_value', 0.1,
+    'Starting value for the FTRL accumulators.')
+
+tf.app.flags.DEFINE_float(
+    'ftrl_l1', 0.0, 'The FTRL l1 regularization strength.')
+
+tf.app.flags.DEFINE_float(
+    'ftrl_l2', 0.0, 'The FTRL l2 regularization strength.')
+
+tf.app.flags.DEFINE_float(
+    'momentum', 0.9,
+    'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
+
+tf.app.flags.DEFINE_float('rmsprop_momentum', 0.9, 'Momentum.')
+
+tf.app.flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')
+
+tf.app.flags.DEFINE_integer(
+    'quantize_delay', -1,
+    'Number of steps to start quantized training. Set to -1 would disable '
+    'quantized training.')
+
+#######################
+# Learning Rate Flags #
+#######################
+
+tf.app.flags.DEFINE_string(
+    'learning_rate_decay_type',
+    'exponential',
+    'Specifies how the learning rate is decayed. One of "fixed", "exponential",'
+    ' or "polynomial"')
+
+tf.app.flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
+
+tf.app.flags.DEFINE_float(
+    'end_learning_rate', 0.0001,
+    'The minimal end learning rate used by a polynomial decay learning rate.')
+
+tf.app.flags.DEFINE_float(
+    'label_smoothing', 0.0, 'The amount of label smoothing.')
+
+tf.app.flags.DEFINE_float(
+    'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')
+
+tf.app.flags.DEFINE_float(
+    'num_epochs_per_decay', 2.0,
+    'Number of epochs after which learning rate decays. Note: this flag counts '
+    'epochs per clone but aggregates per sync replicas. So 1.0 means that '
+    'each clone will go over full epoch individually, but replicas will go '
+    'once across all replicas.')
+
+tf.app.flags.DEFINE_bool(
+    'sync_replicas', False,
+    'Whether or not to synchronize the replicas during training.')
+
+tf.app.flags.DEFINE_integer(
+    'replicas_to_aggregate', 1,
+    'The Number of gradients to collect before updating params.')
+
+tf.app.flags.DEFINE_float(
+    'moving_average_decay', None,
+    'The decay to use for the moving average.'
+    'If left as None, then moving averages are not used.')
+
+#######################
+# Dataset Flags #
+#######################
+
+tf.app.flags.DEFINE_string(
+    'dataset_name', 'imagenet', 'The name of the dataset to load.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_split_name', 'train', 'The name of the train/test split.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', None, 'The directory where the dataset files are stored.')
+
+tf.app.flags.DEFINE_integer(
+    'labels_offset', 0,
+    'An offset for the labels in the dataset. This flag is primarily used to '
+    'evaluate the VGG and ResNet architectures which do not use a background '
+    'class for the ImageNet dataset.')
+
+tf.app.flags.DEFINE_string(
+    'model_name', 'inception_v3', 'The name of the architecture to train.')
+
+tf.app.flags.DEFINE_string(
+    'preprocessing_name', None, 'The name of the preprocessing to use. If left '
+    'as `None`, then the model_name flag is used.')
+
+tf.app.flags.DEFINE_integer(
+    'batch_size', 32, 'The number of samples in each batch.')
+
+tf.app.flags.DEFINE_integer(
+    'train_image_size', None, 'Train image size')
+
+tf.app.flags.DEFINE_integer('max_number_of_steps', None,
+                            'The maximum number of training steps.')
+
+#####################
+# Fine-Tuning Flags #
+#####################
+
+tf.app.flags.DEFINE_string(
+    'checkpoint_path', None,
+    'The path to a checkpoint from which to fine-tune.')
+
+tf.app.flags.DEFINE_string(
+    'checkpoint_exclude_scopes', None,
+    'Comma-separated list of scopes of variables to exclude when restoring '
+    'from a checkpoint.')
+
+tf.app.flags.DEFINE_string(
+    'trainable_scopes', None,
+    'Comma-separated list of scopes to filter the set of variables to train.'
+    'By default, None would train all the variables.')
+
+tf.app.flags.DEFINE_boolean(
+    'ignore_missing_vars', False,
+    'When restoring a checkpoint would ignore missing variables.')
+
+FLAGS = tf.app.flags.FLAGS
+
+
+def _configure_learning_rate(num_samples_per_epoch, global_step):
+  """Configures the learning rate.
+
+  Args:
+    num_samples_per_epoch: The number of samples in each epoch of training.
+    global_step: The global_step tensor.
+
+  Returns:
+    A `Tensor` representing the learning rate.
+
+  Raises:
+    ValueError: if
+  """
+  # Note: when num_clones is > 1, this will actually have each clone to go
+  # over each epoch FLAGS.num_epochs_per_decay times. This is different
+  # behavior from sync replicas and is expected to produce different results.
+  decay_steps = int(num_samples_per_epoch * FLAGS.num_epochs_per_decay /
+                    FLAGS.batch_size)
+
+  if FLAGS.sync_replicas:
+    decay_steps /= FLAGS.replicas_to_aggregate
+
+  if FLAGS.learning_rate_decay_type == 'exponential':
+    return tf.train.exponential_decay(FLAGS.learning_rate,
+                                      global_step,
+                                      decay_steps,
+                                      FLAGS.learning_rate_decay_factor,
+                                      staircase=True,
+                                      name='exponential_decay_learning_rate')
+  elif FLAGS.learning_rate_decay_type == 'fixed':
+    return tf.constant(FLAGS.learning_rate, name='fixed_learning_rate')
+  elif FLAGS.learning_rate_decay_type == 'polynomial':
+    return tf.train.polynomial_decay(FLAGS.learning_rate,
+                                     global_step,
+                                     decay_steps,
+                                     FLAGS.end_learning_rate,
+                                     power=1.0,
+                                     cycle=False,
+                                     name='polynomial_decay_learning_rate')
+  else:
+    raise ValueError('learning_rate_decay_type [%s] was not recognized' %
+                     FLAGS.learning_rate_decay_type)
+
+
+def _configure_optimizer(learning_rate):
+  """Configures the optimizer used for training.
+
+  Args:
+    learning_rate: A scalar or `Tensor` learning rate.
+
+  Returns:
+    An instance of an optimizer.
+
+  Raises:
+    ValueError: if FLAGS.optimizer is not recognized.
+  """
+  if FLAGS.optimizer == 'adadelta':
+    optimizer = tf.train.AdadeltaOptimizer(
+        learning_rate,
+        rho=FLAGS.adadelta_rho,
+        epsilon=FLAGS.opt_epsilon)
+  elif FLAGS.optimizer == 'adagrad':
+    optimizer = tf.train.AdagradOptimizer(
+        learning_rate,
+        initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)
+  elif FLAGS.optimizer == 'adam':
+    optimizer = tf.train.AdamOptimizer(
+        learning_rate,
+        beta1=FLAGS.adam_beta1,
+        beta2=FLAGS.adam_beta2,
+        epsilon=FLAGS.opt_epsilon)
+  elif FLAGS.optimizer == 'ftrl':
+    optimizer = tf.train.FtrlOptimizer(
+        learning_rate,
+        learning_rate_power=FLAGS.ftrl_learning_rate_power,
+        initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,
+        l1_regularization_strength=FLAGS.ftrl_l1,
+        l2_regularization_strength=FLAGS.ftrl_l2)
+  elif FLAGS.optimizer == 'momentum':
+    optimizer = tf.train.MomentumOptimizer(
+        learning_rate,
+        momentum=FLAGS.momentum,
+        name='Momentum')
+  elif FLAGS.optimizer == 'rmsprop':
+    optimizer = tf.train.RMSPropOptimizer(
+        learning_rate,
+        decay=FLAGS.rmsprop_decay,
+        momentum=FLAGS.rmsprop_momentum,
+        epsilon=FLAGS.opt_epsilon)
+  elif FLAGS.optimizer == 'sgd':
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+  else:
+    raise ValueError('Optimizer [%s] was not recognized' % FLAGS.optimizer)
+  return optimizer
+
+
+def _get_init_fn():
+  """Returns a function run by the chief worker to warm-start the training.
+
+  Note that the init_fn is only run when initializing the model during the very
+  first global step.
+
+  Returns:
+    An init function run by the supervisor.
+  """
+  if FLAGS.checkpoint_path is None:
+    return None
+
+  # Warn the user if a checkpoint exists in the train_dir. Then we'll be
+  # ignoring the checkpoint anyway.
+  if tf.train.latest_checkpoint(FLAGS.train_dir):
+    tf.logging.info(
+        'Ignoring --checkpoint_path because a checkpoint already exists in %s'
+        % FLAGS.train_dir)
+    return None
+
+  exclusions = []
+  if FLAGS.checkpoint_exclude_scopes:
+    exclusions = [scope.strip()
+                  for scope in FLAGS.checkpoint_exclude_scopes.split(',')]
+
+  # TODO(sguada) variables.filter_variables()
+  variables_to_restore = []
+  for var in slim.get_model_variables():
+    for exclusion in exclusions:
+      if var.op.name.startswith(exclusion):
+        break
+    else:
+      variables_to_restore.append(var)
+
+  if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
+    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
+  else:
+    checkpoint_path = FLAGS.checkpoint_path
+
+  tf.logging.info('Fine-tuning from %s' % checkpoint_path)
+
+  return slim.assign_from_checkpoint_fn(
+      checkpoint_path,
+      variables_to_restore,
+      ignore_missing_vars=FLAGS.ignore_missing_vars)
+
+
+def _get_variables_to_train():
+  """Returns a list of variables to train.
+
+  Returns:
+    A list of variables to train by the optimizer.
+  """
+  if FLAGS.trainable_scopes is None:
+    return tf.trainable_variables()
+  else:
+    scopes = [scope.strip() for scope in FLAGS.trainable_scopes.split(',')]
+
+  variables_to_train = []
+  for scope in scopes:
+    variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
+    variables_to_train.extend(variables)
+  return variables_to_train
+
+
+def main(_):
+  if not FLAGS.dataset_dir:
+    raise ValueError('You must supply the dataset directory with --dataset_dir')
+
+  tf.logging.set_verbosity(tf.logging.INFO)
+  with tf.Graph().as_default():
+    #######################
+    # Config model_deploy #
+    #######################
+    deploy_config = model_deploy.DeploymentConfig(
+        num_clones=FLAGS.num_clones,
+        clone_on_cpu=FLAGS.clone_on_cpu,
+        replica_id=FLAGS.task,
+        num_replicas=FLAGS.worker_replicas,
+        num_ps_tasks=FLAGS.num_ps_tasks)
+
+    # Create global_step
+    with tf.device(deploy_config.variables_device()):
+      global_step = slim.create_global_step()
+
+    ######################
+    # Select the dataset #
+    ######################
+    dataset = dataset_factory.get_dataset(
+        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
+
+    ######################
+    # Select the network #
+    ######################
+    network_fn = nets_factory.get_network_fn(
+        FLAGS.model_name,
+        num_classes=(dataset.num_classes - FLAGS.labels_offset),
+        weight_decay=FLAGS.weight_decay,
+        is_training=True)
+
+    #####################################
+    # Select the preprocessing function #
+    #####################################
+    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
+    image_preprocessing_fn = preprocessing_factory.get_preprocessing(
+        preprocessing_name,
+        is_training=True)
+
+    ##############################################################
+    # Create a dataset provider that loads data from the dataset #
+    ##############################################################
+    with tf.device(deploy_config.inputs_device()):
+      provider = slim.dataset_data_provider.DatasetDataProvider(
+          dataset,
+          num_readers=FLAGS.num_readers,
+          common_queue_capacity=20 * FLAGS.batch_size,
+          common_queue_min=10 * FLAGS.batch_size)
+      [image, label] = provider.get(['image', 'label'])
+      label -= FLAGS.labels_offset
+
+      train_image_size = FLAGS.train_image_size or network_fn.default_image_size
+
+      image = image_preprocessing_fn(image, train_image_size, train_image_size)
+
+      images, labels = tf.train.batch(
+          [image, label],
+          batch_size=FLAGS.batch_size,
+          num_threads=FLAGS.num_preprocessing_threads,
+          capacity=5 * FLAGS.batch_size)
+      labels = slim.one_hot_encoding(
+          labels, dataset.num_classes - FLAGS.labels_offset)
+      batch_queue = slim.prefetch_queue.prefetch_queue(
+          [images, labels], capacity=2 * deploy_config.num_clones)
+
+    ####################
+    # Define the model #
+    ####################
+    def clone_fn(batch_queue):
+      """Allows data parallelism by creating multiple clones of network_fn."""
+      images, labels = batch_queue.dequeue()
+      logits, end_points = network_fn(images)
+
+      #############################
+      # Specify the loss function #
+      #############################
+      if 'AuxLogits' in end_points:
+        slim.losses.softmax_cross_entropy(
+            end_points['AuxLogits'], labels,
+            label_smoothing=FLAGS.label_smoothing, weights=0.4,
+            scope='aux_loss')
+      slim.losses.softmax_cross_entropy(
+          logits, labels, label_smoothing=FLAGS.label_smoothing, weights=1.0)
+      return end_points
+
+    # Gather initial summaries.
+    summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
+
+    clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
+    first_clone_scope = deploy_config.clone_scope(0)
+    # Gather update_ops from the first clone. These contain, for example,
+    # the updates for the batch_norm variables created by network_fn.
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
+
+    # Add summaries for end_points.
+    end_points = clones[0].outputs
+    for end_point in end_points:
+      x = end_points[end_point]
+      summaries.add(tf.summary.histogram('activations/' + end_point, x))
+      summaries.add(tf.summary.scalar('sparsity/' + end_point,
+                                      tf.nn.zero_fraction(x)))
+
+    # Add summaries for losses.
+    for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
+      summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
+
+    # Add summaries for variables.
+    for variable in slim.get_model_variables():
+      summaries.add(tf.summary.histogram(variable.op.name, variable))
+
+    #################################
+    # Configure the moving averages #
+    #################################
+    if FLAGS.moving_average_decay:
+      moving_average_variables = slim.get_model_variables()
+      variable_averages = tf.train.ExponentialMovingAverage(
+          FLAGS.moving_average_decay, global_step)
+    else:
+      moving_average_variables, variable_averages = None, None
+
+    if FLAGS.quantize_delay >= 0:
+      tf.contrib.quantize.create_training_graph(
+          quant_delay=FLAGS.quantize_delay)
+
+    #########################################
+    # Configure the optimization procedure. #
+    #########################################
+    with tf.device(deploy_config.optimizer_device()):
+      learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
+      optimizer = _configure_optimizer(learning_rate)
+      summaries.add(tf.summary.scalar('learning_rate', learning_rate))
+
+    if FLAGS.sync_replicas:
+      # If sync_replicas is enabled, the averaging will be done in the chief
+      # queue runner.
+      optimizer = tf.train.SyncReplicasOptimizer(
+          opt=optimizer,
+          replicas_to_aggregate=FLAGS.replicas_to_aggregate,
+          total_num_replicas=FLAGS.worker_replicas,
+          variable_averages=variable_averages,
+          variables_to_average=moving_average_variables)
+    elif FLAGS.moving_average_decay:
+      # Update ops executed locally by trainer.
+      update_ops.append(variable_averages.apply(moving_average_variables))
+
+    # Variables to train.
+    variables_to_train = _get_variables_to_train()
+
+    #  and returns a train_tensor and summary_op
+    total_loss, clones_gradients = model_deploy.optimize_clones(
+        clones,
+        optimizer,
+        var_list=variables_to_train)
+    # Add total_loss to summary.
+    summaries.add(tf.summary.scalar('total_loss', total_loss))
+
+    # Create gradient updates.
+    grad_updates = optimizer.apply_gradients(clones_gradients,
+                                             global_step=global_step)
+    update_ops.append(grad_updates)
+
+    update_op = tf.group(*update_ops)
+    with tf.control_dependencies([update_op]):
+      train_tensor = tf.identity(total_loss, name='train_op')
+
+    # Add the summaries from the first clone. These contain the summaries
+    # created by model_fn and either optimize_clones() or _gather_clone_loss().
+    summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
+                                       first_clone_scope))
+
+    # Merge all summaries together.
+    summary_op = tf.summary.merge(list(summaries), name='summary_op')
+
+    ###########################
+    # Kicks off the training. #
+    ###########################
+    slim.learning.train(
+        train_tensor,
+        logdir=FLAGS.train_dir,
+        master=FLAGS.master,
+        is_chief=(FLAGS.task == 0),
+        init_fn=_get_init_fn(),
+        summary_op=summary_op,
+        number_of_steps=FLAGS.max_number_of_steps,
+        log_every_n_steps=FLAGS.log_every_n_steps,
+        save_summaries_secs=FLAGS.save_summaries_secs,
+        save_interval_secs=FLAGS.save_interval_secs,
+        sync_optimizer=optimizer if FLAGS.sync_replicas else None)
+
+
+if __name__ == '__main__':
+  tf.app.run()
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_1epoch.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_1epoch.sh
new file mode 100644
index 00000000..40cb4ebe
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_1epoch.sh
@@ -0,0 +1,23 @@
+TARGET_mAP=${TARGET_mAP:-0.0020408058}
+TARGET_loss=${TARGET_loss:-2.2808013}
+TOLERANCE=${TOLERANCE:-0.1}
+
+PRECISION=${PRECISION:-FP16}
+
+TRAIN_LOG=$(bash examples/SSD320_${PRECISION}_8GPU.sh /results/SSD320_${PRECISION}_8GPU --num_train_steps $((12500/27)) 2>&1 | tee /dev/tty)
+
+mAP=$( echo $TRAIN_LOG | sed -n 's|.*DetectionBoxes_Precision/mAP = \([^,]*\),.*|\1|p' | tail -n1)
+loss=$(echo $TRAIN_LOG | sed -n 's|.*Loss for final step: \(.*\)\.|\1|p' | tail -n1)
+
+mAP_error=$( python -c "print(abs($TARGET_mAP  - $mAP)/$mAP)")
+loss_error=$(python -c "print(abs($TARGET_loss - $loss)/$loss)")
+
+if [[ $mAP_error < $TOLERANCE && $loss_error < $TOLERANCE ]]
+then
+    echo PASS
+else
+    echo expected: mAP=$TARGET_mAP loss=$TARGET_loss
+    echo got:      mAP=$mAP        loss=$loss
+    echo FAIL
+    exit 1
+fi
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP16_1epoch.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP16_1epoch.sh
new file mode 100644
index 00000000..b442a96f
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP16_1epoch.sh
@@ -0,0 +1 @@
+PRECISION=FP16 bash qa/testing_DGX1V_8GPU_1epoch.sh
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP32_1epoch.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP32_1epoch.sh
new file mode 100644
index 00000000..a15ed484
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_8GPU_FP32_1epoch.sh
@@ -0,0 +1 @@
+PRECISION=FP32 bash qa/testing_DGX1V_8GPU_1epoch.sh
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh
new file mode 100644
index 00000000..8c388041
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp16.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+BASELINES=(193.6 135.2 171.5 188.3 187 187.6 191.4)
+
+for i in `seq 0 6`
+do
+        echo "Testing mixed precision inference speed on batch size = $((2 ** $i))"
+        bash examples/SSD320_FP16_inference.sh --batch_size $((2 ** $i)) > tmp 2> /dev/null
+        echo -n "img/s: "; tail -n 1 tmp | awk '{print $3}'; echo "expected img/s: ${BASELINES[$i]}"; echo -n "relative error: "; err=`tail -n 1 tmp | awk -v BASELINE=${BASELINES[$i]} '{print sqrt(($3 - BASELINE)^2)/$3}'`; echo $err
+        rm tmp
+        if [[ $err > 0.1 ]]; then echo "FAILED" && exit 1; else echo "PASSED"; fi
+done
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh
new file mode 100644
index 00000000..3934621c
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_inference_benchmark_fp32.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+BASELINES=(97.4 134 163 175.1 175.4 174.5 177.6)
+
+for i in `seq 0 6`
+do
+        echo "Testing single precision inference speed on batch size = $((2 ** $i))"
+        bash examples/SSD320_FP16_inference.sh --batch_size $((2 ** $i)) > tmp 2> /dev/null
+        echo -n "img/s: "; tail -n 1 tmp | awk '{print $3}'; echo "expected img/s: ${BASELINES[$i]}"; echo -n "relative error: "; err=`tail -n 1 tmp | awk -v BASELINE=${BASELINES[$i]} '{print sqrt(($3 - BASELINE)^2)/$3}'`; echo $err
+        rm tmp
+        if [[ $err > 0.1 ]]; then echo "FAILED" && exit 1; else echo "PASSED"; fi
+done
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh
new file mode 100644
index 00000000..562e819a
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp16.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+BASELINES=(125 430 750)
+
+i=0
+for GPUS in 1 4 8
+do
+        echo "Testing mixed precision training speed on $GPUS GPUs"
+        bash examples/SSD320_FP16_${GPUS}_BENCHMARK.sh > tmp 2> /dev/null
+        echo -n "img/s: "; tail -n 1 tmp | awk '{print $7}'; echo "expected img/s: ${BASELINES[$i]}"; echo -n "relative error: "; err=`tail -n 1 tmp | awk -v BASELINE=${BASELINES[$i]} '{print sqrt(($7 - BASELINE)^2)/$7}'`; echo $err
+        rm tmp
+        if [[ $err > 0.1 ]]; then echo "FAILED" && exit 1; else echo "PASSED"; fi
+        i=$(($i + 1))
+done
diff --git a/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh b/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh
new file mode 100644
index 00000000..0d0d60af
--- /dev/null
+++ b/TensorFlow/Detection/SSD/qa/testing_DGX1V_training_benchmark_fp32.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+BASELINES=(87 330 569)
+
+i=0
+for GPUS in 1 4 8
+do
+        echo "Testing single precision training speed on $GPUS GPUs"
+        bash examples/SSD320_FP32_${GPUS}_BENCHMARK.sh > tmp 2> /dev/null
+        echo -n "img/s: "; tail -n 1 tmp | awk '{print $7}'; echo "expected img/s: ${BASELINES[$i]}"; echo -n "relative error: "; err=`tail -n 1 tmp | awk -v BASELINE=${BASELINES[$i]} '{print sqrt(($7 - BASELINE)^2)/$7}'`; echo $err
+        rm tmp
+        if [[ $err > 0.1 ]]; then echo "FAILED" && exit 1; else echo "PASSED"; fi
+        i=$(($i + 1))
+done
diff --git a/TensorFlow/Detection/SSD/requirements.txt b/TensorFlow/Detection/SSD/requirements.txt
new file mode 100644
index 00000000..1f8449e6
--- /dev/null
+++ b/TensorFlow/Detection/SSD/requirements.txt
@@ -0,0 +1,3 @@
+pillow==5.4.1
+pycocotools==2.0.0
+contextlib2
diff --git a/TensorFlow/LanguageModeling/BERT/.dockerignore b/TensorFlow/LanguageModeling/BERT/.dockerignore
new file mode 100644
index 00000000..6d814bae
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/.dockerignore
@@ -0,0 +1,6 @@
+.idea/
+.git/
+__pycache__/
+results/
+data/*/*/
+data/*/*.zip
diff --git a/TensorFlow/LanguageModeling/BERT/.gitignore b/TensorFlow/LanguageModeling/BERT/.gitignore
new file mode 100644
index 00000000..7b74e350
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/.gitignore
@@ -0,0 +1,121 @@
+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+#Data
+data/*/*/
+data/*/*.zip
+
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/TensorFlow/LanguageModeling/BERT/CONTRIBUTING.md b/TensorFlow/LanguageModeling/BERT/CONTRIBUTING.md
new file mode 100644
index 00000000..124b4b32
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/CONTRIBUTING.md
@@ -0,0 +1,31 @@
+# How to Contribute
+
+BERT needs to maintain permanent compatibility with the pre-trained model files,
+so we do not plan to make any major changes to this library (other than what was
+promised in the README). However, we can accept small patches related to
+re-factoring and documentation. To submit contributes, there are just a few
+small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution;
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to <https://cla.developers.google.com/> to see
+your current agreements on file or to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
+
+## Community Guidelines
+
+This project follows
+[Google's Open Source Community Guidelines](https://opensource.google.com/conduct/).
diff --git a/TensorFlow/LanguageModeling/BERT/Dockerfile b/TensorFlow/LanguageModeling/BERT/Dockerfile
new file mode 100644
index 00000000..0db49f6e
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/Dockerfile
@@ -0,0 +1,16 @@
+ARG FROM_IMAGE_NAME=nvcr.io/nvidia/tensorflow:19.03-py3
+FROM ${FROM_IMAGE_NAME}
+
+RUN apt-get update && apt-get install -y pbzip2 pv bzip2
+
+RUN pip install toposort networkx pytest nltk tqdm html2text progressbar
+
+WORKDIR /workspace
+RUN git clone https://github.com/openai/gradient-checkpointing.git
+RUN git clone https://github.com/attardi/wikiextractor.git
+RUN git clone https://github.com/soskek/bookcorpus.git
+
+WORKDIR /workspace/bert
+COPY . .
+
+ENV PYTHONPATH=/workspace/bert
diff --git a/TensorFlow/LanguageModeling/BERT/LICENSE b/TensorFlow/LanguageModeling/BERT/LICENSE
new file mode 100644
index 00000000..d6456956
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/TensorFlow/LanguageModeling/BERT/README.md b/TensorFlow/LanguageModeling/BERT/README.md
new file mode 100644
index 00000000..81713ec4
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/README.md
@@ -0,0 +1,512 @@
+# Bert For TensorFlow
+
+This repository provides a script and recipe to train BERT to achieve state of the art accuracy, and is tested and maintained by NVIDIA.
+
+
+## Table Of Contents:
+* [The model](#the-model)
+  * [Default configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+  * [Command line options](#command-line-options)
+  * [Getting the data](#getting-the-data)
+  * [Training process](#training-process)
+  * [Pre-training](#pre-training)
+  * [Fine tuning](#fine-tuning)
+  * [Enabling mixed precision](#enabling-mixed-precision)
+  * [Inference process](#inference-process)
+* [Benchmarking](#benchmarking)
+  * [Training performance benchmark](#training-performance-benchmark)
+  * [Inference performance benchmark](#inference-performance-benchmark)
+* [Results](#results)
+  * [Training accuracy results](#training-accuracy-results)
+  * [Training stability test](#training-stability-test)
+  * [Training performance results](#training-performance-results)
+      * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g)
+      * [NVIDIA DGX-1 (8x V100 32G)](#nvidia-dgx-1-8x-v100-32g)
+      * [NVIDIA DGX-2 (16x V100 32G)](#nvidia-dgx-2-16x-v100-32g)
+  * [Inference performance results](#inference-performance-results)
+      * [NVIDIA DGX-1 16G (1x V100 16G)](#nvidia-dgx-1-16g-1x-v100-16g)
+      * [NVIDIA DGX-1 32G (1x V100 32G)](#nvidia-dgx-1-32g-1x-v100-32g)
+      * [NVIDIA DGX-2 32G (1x V100 32G)](#nvidia-dgx-2-32g-1x-v100-32g)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+## The model
+
+BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's BERT 19.03 is an optimized version of [Google's official implementation](https://github.com/google-research/bert), leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy. 
+
+
+The repository also contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pretraining and fine tuning for Question Answering. The major differences between the official implementation of the paper and our version of BERT are as follows:
+- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
+- Scripts to download dataset for 
+    - Pretraining - [Wikipedia](https://dumps.wikimedia.org/),  [BookCorpus](http://yknzhu.wixsite.com/mbweb)
+    - Fine Tuning - [SQuaD](https://rajpurkar.github.io/SQuAD-explorer/) (Stanford Question Answering Dataset), Pretrained Weights from Google
+- Custom fused CUDA kernels for faster computations
+- Multi-GPU/Multi-Node support using Horovod
+
+
+The following performance optimizations were implemented in this model:
+- [XLA](https://www.tensorflow.org/xla) support (experimental).
+
+
+These techniques and optimizations improve model performance and reduce training time, allowing you to perform various NLP tasks with no additional effort.
+
+
+Other publicly available implementations of BERT include:
+1. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT)
+2. [codertimo](https://github.com/codertimo/BERT-pytorch)
+
+
+This model trains with mixed precision tensor cores on Volta, therefore researchers can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
+
+### Default configuration
+
+BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT.
+
+| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |
+|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|
+|BERTBASE |12 encoder| 768| 12|4 x  768|512|110M|
+|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|
+
+## Setup
+The following section list the requirements in order to start training the BERT model.
+
+### Requirements
+This repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies.  Aside from these dependencies, ensure you have the following components:
+- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+- [TensorFlow 19.03-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container
+- [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+
+For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:
+- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+- [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)
+
+## Quick start guide
+To pretrain or fine tune your model for Question Answering using mixed precision with tensor cores or using FP32, perform the following steps using the default parameters of the BERT model.
+
+### 1. Clone the repository.
+
+```bash
+git clone https://github.com/NVIDIA/DeepLearningExamples
+cd DeepLearningExamples/TensorFlow/LanguageModeling/BERT
+```
+
+### 2. Build the BERT TensorFlow NGC container.
+
+```bash
+bash scripts/docker/build.sh
+```
+
+### 3. Download and preprocess the dataset.
+This repository provides scripts to download, verify and extract the SQuaD dataset+pretrained weights for fine tuning as well as Wikipedia + BookCorpus dataset for pretraining.
+
+To download, verify, and extract required datasets: 
+
+```bash
+bash scripts/data_download.sh  
+```
+
+The script launches a docker container with current directory mounted and downloads datasets to `data/` folder on the host. 
+
+### 4. Start an interactive session in the NGC container to run training/inference.
+After you build the container image and download the data, you can start an interactive CLI session as follows:  
+
+```bash
+bash scripts/docker/launch.sh
+```
+
+The `launch.sh` script assumes that the datasets are in the following locations by default after downloading data. 
+- SQuaD v1.1 - `data/squad/v1.1`
+- BERT - `data/pretrained_models_google/uncased_L-24_H-1024_A-16`
+- Wikipedia - `data/wikipedia_corpus/final_tfrecords_sharded`
+- BookCorpus -  `data/bookcorpus/final_tfrecords_sharded`
+
+
+### 5. Start pre-training.
+BERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pretraining on Wikipedia+Book Corpus from the [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pretraining language representations on any corpus of choice.
+
+From within the container, you can use the following script to run pre-training.
+```bash
+bash scripts/run_pretraining.sh <train_batch_size_per_gpu> <eval_batch_size> <learning_rate_per_gpu> <precision> <num_gpus> <warmup_steps> <train_steps> <save_checkpoint_steps> <create_logfile>
+```
+
+For FP16 training with XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_pretraining.sh 14 8 5e-5 fp16_xla 8 5000 2285000 5000 true
+```
+
+For FP32 training without XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_pretraining.sh 6 6 2e-5 fp32 8 2000 5333333 5000 true
+```
+
+### 6. Start fine tuning.
+The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art Question Answering system. From within the container, you can use the following script to run fine-training for SQuaD.
+
+```bash
+bash scripts/run_squad.sh <batch_size_per_gpu> <learning_rate_per_gpu> <precision> <use_xla> <num_gpus> <checkpoint>
+```
+
+For FP16 training with XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_squad.sh 10 5e-6 fp16 true 8 /bert/bert_model.ckpt
+```
+
+For FP32 training without XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_squad.sh 5 5e-6 fp32 false 8 /bert/bert_model.ckpt
+```
+
+### 7. Start validation/evaluation.
+The `run_squad_inference.sh` script runs inference on a checkpoint fine tuned for SQuaD and evaluates the goodness of predictions on the basis of exact match and F1 score.
+
+```bash
+bash scripts/run_squad_inference.sh <init_checkpoint> <batch_size> <precision> <use_xla>
+```
+
+For FP16 inference with XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_squad_inference.sh /results/model.ckpt 8 fp16 true 
+```
+
+For FP32 inference without XLA using a DGX-1 V100 32G, run:
+```bash
+bash scripts/run_squad_inference.sh /results/model.ckpt 8 fp32 false
+```
+
+## Details
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+### Command line options
+To see the full list of available options and their descriptions, use the -h or --help command line option, for example: 
+```bash
+python run_pretraining.py --help
+python run_squad.py --help 
+```
+
+Aside from options to set hyperparameters, the relevant options to control the behaviour of the `run_pretraining.py` script are: 
+```bash
+  --[no]amp: Whether to enable AMP ops.(default: 'false')
+  --[no]amp_fastmath: Whether to enable AMP fasthmath ops.(default: 'false')
+  --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.
+  --[no]do_eval: Whether to run evaluation on the dev set.(default: 'false')
+  --[no]do_train: Whether to run training.(default: 'false')
+  --eval_batch_size: Total batch size for evaluation.(default: '8')(an integer)
+  --[no]fastmath: Whether to enable loss scaler for fasthmath ops.(default: 'false')
+  --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false')
+  --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model).
+  --input_file: Input TF example files (can be a glob or comma separated).
+  --iterations_per_loop: How many steps to make in each estimator call.(default: '1000')
+```
+
+Aside from options to set hyperparameters, some relevant options to control the behaviour of the run_squad.py script are: 
+```bash
+  --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.
+  --[no]do_predict: Whether to run evaluation on the dev set. (default: 'false')
+  --[no]do_train: Whether to run training. (default: 'false')
+  --learning_rate: The initial learning rate for Adam.(default: '5e-06')(a number)
+  --max_answer_length: The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another.(default: '30')(an integer)
+  --max_query_length: The maximum number of tokens for the question. Questions longer than this will be truncated to this length.(default: '64')(an integer)
+  --max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '384')(an integer)
+  --predict_batch_size: Total batch size for predictions.(default: '8')(an integer)
+  --train_batch_size: Total batch size for training.(default: '8')(an integer)
+  --[no]use_fp16: Whether to use fp32 or fp16 arithmetic on GPU.(default: 'false')
+  --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'false')
+  --[no]verbose_logging: If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation.(default: 'false')
+  --[no]version_2_with_negative: If true, the SQuAD examples contain some that do not have an answer.(default: 'false')
+```
+
+### Getting the data
+For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as Book Corpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/20190301/enwiki-20190301-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. The next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data.
+
+We can use a pre-trained BERT model for other fine tuning tasks like Question Answering. We use SQuaD for this task. SQuaD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuaD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. 
+
+### Training process
+The training process consists of two steps: pre-training and fine tuning.
+
+#### Pre-training
+Pre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining.sh`.
+
+
+The `run_pretraining.sh` script runs a job on a single node  that trains the BERT-large model from scratch using the Wikipedia and Book corpus datasets as training data. By default, the training script:
+- Runs on 8 GPUs with training batch size of 14 and evaluation batch size of 8 per GPU.
+- Has FP16 precision enabled.
+- Is XLA enabled.
+- Trains with default learning rate of 1e-4 for 1144000 steps with 10000 warm-up steps.
+- Saves a checkpoint every 5000 iterations.
+- Creates a log file containing all the output.
+- Evaluates the model at the end of training. To skip evaluation, modify `--do_eval` to `False`.
+
+These parameters will train Wikipedia + BooksCorpus to reasonable accuracy on a DGX1 with 32GB V100 cards. If you want to match google’s best results from the BERT paper, you should either train for twice as many steps (2,288,000 steps) on a DGX1, or train on 16 GPUs on a DGX2. The DGX2 having 16 GPUs will be able to fit a batch size twice as large as a DGX1 (224 vs 112), hence the DGX2 can finish in half as many steps. 
+
+
+For example:
+```bash
+run_pretraining.sh <node_type> <training_batch_size> <eval_batch_size> <learning-rate> <precision> <num_gpus> <warmup_steps> <training_steps> <save_checkpoint_steps> <create_logfile>
+```
+
+Where:
+- <training_batch_size> per-gpu batch size used for training. Batch size varies with <precision>, larger batch sizes run more efficiently, but require more memory.
+
+- <eval_batch_size> per-gpu batch size used for evaluation after training.<learning_rate> Default rate of 1e-4 is good for global batch size 256.
+
+- <precision> Type of math in your model, can be either fp32, fp16, fp16_xla, fastmath, amp_fm, amp_fm_xla, amp or amp_xla. The options mean:
+
+    - fp32 32 bit IEEE single precision floats.
+
+    - fp16 Hand-coded mixed precision 16 and 32 bit floats.
+
+    - fp16 Hand-coded mixed precision floats, JIT compiled with XLA.
+
+    - fastmath Matmuls done by tensor cores in mixed precision, the rest is done in FP32.
+
+    - amp_fm Alternative FastMath implementation that works by manipulating TensorFlow’s compute graph.
+
+    - amp_fm_xla The amp_fm flag plus XLA JIT compilation.
+
+    - amp Automatic rewrite of TensorFlow compute graph to take advantage of 16 bit arithmetic whenever that is safe.
+
+    - amp_xla The amp flag plus XLA JIT compilation.
+
+- <num_gpus> Number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node.
+
+- <warmup_steps> Number of warm-up steps at the start of training.
+
+- <training_steps> Total number of training steps.
+
+- <save_checkpoint_steps> Controls how often checkpoints are saved. Default is 5000 steps.
+
+- <create_logfile> Flag indicating if output should be written to a logfile or not (acceptable values are ‘true’ or ‘false’, true indicates output should be saved to a logfile.)
+
+
+For example:
+```bash
+bert_tf/scripts/run_pretraining.sh 14 8 1e-4 fp16_xla 16 10000 1144000 5000 true
+```
+
+Trains BERT-large from scratch on a single DGX-2 using FP16 arithmetic. This will take around 156 hours / 6.5 days. Checkpoints are written out every 5000 steps and all printouts are saved to a logfile.
+
+#### Fine tuning
+Fine tuning is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad.sh`.
+
+The `run_squad.sh` script trains a model and performs evaluation on the SQuaD v1.1 dataset. By default, the training script: 
+- Uses 8 GPUs and batch size of 10 on each GPU.
+- Has FP16 precision enabled.
+- Is XLA enabled.
+- Runs for 2 epochs.
+- Saves a checkpoint every 1000 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).
+- Evaluation is done at the end of training. To skip evaluation, modify `--do_predict` to `False`.
+
+This script outputs checkpoints to the `/results` directory, by default, inside the container. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The training log contains information about:
+- Loss for the final step
+- Training and evaluation performance
+- F1 and exact match score on the Dev Set of SQuaD after evaluation. 
+
+The summary after training is printed in the following format:
+```bash
+I0312 23:10:45.137036 140287431493376 run_squad.py:1332] 0 Total Training Time = 3007.00 Training Time W/O start up overhead = 2855.92 Sentences processed = 175176
+I0312 23:10:45.137243 140287431493376 run_squad.py:1333] 0 Training Performance = 61.3378 sentences/sec
+I0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840
+I0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec
+{"exact_match": 83.69914853358561, "f1": 90.8477003317459}
+```
+
+Multi-gpu training is enabled with the Horovod TensorFlow module. The following example runs training on 8 GPUs: 
+```bash
+mpi_command="mpirun -np 8 -H localhost:8 \
+    --allow-run-as-root -bind-to none -map-by slot \
+    -x NCCL_DEBUG=INFO \
+    -x LD_LIBRARY_PATH \
+    -x PATH -mca pml ob1 -mca btl ^openib" \
+     python run_squad.py --horovod
+```
+
+### Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network.  Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.  Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously required two steps:
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values. 
+This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code.  AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.
+
+In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
+
+For information about:
+- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
+- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+
+### Inference process
+Inference on a fine tuned Question Answering system is performed using the `run_squad.py` script along with parameters defined in the `scripts/run_squad_inference.sh`. Inference is supported on single GPU at this moment.
+
+The `run_squad_inference.sh` script trains a model and performs evaluation on the SQuaD v1.1 dataset. By default, the inferencing script: 
+- Has FP16 precision enabled
+- Is XLA enabled
+- Evaluates the latest checkpoint present in `/results` with a batch size of 8
+
+This script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuaD's `evaluate-v1.1.py`. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. 
+
+The output log contains information about:
+- Evaluation performance
+- F1 and exact match score on the Dev Set of SQuaD after evaluation. 
+
+The summary after inference is printed in the following format:
+```bash
+I0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840
+I0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance = 82.2095 sentences/sec
+{"exact_match": 83.69914853358561, "f1": 90.8477003317459}
+```
+
+## Benchmarking
+The following section shows how to run benchmarks measuring the model performance in training and inference modes.
+
+Benchmarking can be performed for both training and inference. Both scripts run the BERT model for fine tuning. You can specify whether benchmarking is performed in FP16 or FP32 by specifying it as an argument to the benchmarking scripts. 
+
+Both of these benchmarking scripts enable you to run a number of epochs and extract performance numbers.
+
+### Training performance benchmark
+Training benchmarking can be performed by running the script: 
+```bash
+scripts/finetune_train_benchmark.sh squad <fp16/fp32> <use_xla> <num_gpu> <batch_size/gpu> <lr> 
+```
+
+### Inference performance benchmark
+Inference benchmarking can be performed by running the script: 
+```bash
+scripts/finetune_inference_benchmark.sh squad <fp16/fp32> <use_xla> <batch_size> <path-to-checkpoint> 
+```
+
+## Results
+The following sections provide details on how we achieved our performance and accuracy in training and inference for Question Answering fine tuning.
+### Training accuracy results
+Our results were obtained by running the `run_squad.py`  training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs.
+
+
+| **Number of GPUs** | **Batch size per GPU** | **Training time with FP16 (Hrs)** | **Training time with FP32 (Hrs)** |
+|:---:|:---:|:----:|:----:|
+| 8 | 4 |0.51|0.77|
+
+#### Training stability test
+The following tables compare `F1` scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively.  The runs showcase consistent convergence on all 5 seeds with very little deviation.
+
+| **FP16, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |
+|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+|F1         |91.16|90.69|90.99|90.94|91.17|90.99|0.196|
+|Exact match|84.2 |83.68|84.14|83.95|84.34|84.06|0.255|
+
+| **FP32, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |
+|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+|F1         |90.67|90.8 |90.94|90.83|90.93|90.83|0.11 |
+|Exact match|83.56|83.96|83.99|83.95|84.12|83.92|0.21 |
+
+
+### Training performance results
+Our results were obtained by running batch sizes up to 3x GPUs on a 16GB V100 and up to 10x GPUs on a 32G V100 with mixed precision.
+
+#### NVIDIA DGX-1 (8x V100 16G)
+Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in tokens per second) were averaged over an entire training epoch.
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|:---:|:---:|:------:|:-----:|:----:|:----:|:----:|
+| 1 | 2 | 7.41 |11.86|1.6 |1.0 |1.0 |
+| 4 | 2 |23.699|35.34|1.49|3.2 |2.98|
+| 8 | 2 |44.29 |64.96|1.47|5.98|5.48|
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|:---:|:---:|:-----:|:-----:|:---:|:---:|:----:|
+| 1 | 3 |  -  |14.86| - | - |1.0 |
+| 4 | 3 |  -  |44.17| - | - |2.97|
+| 8 | 3 |  -  |78.89| - | - |5.3 |
+
+Note: The respective values for FP32 runs that use a batch size of 3 are not available due to out of memory errors that arise. Batch size of 3 is only available on using FP16.
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+#### NVIDIA DGX-1 (8x V100 32G)
+Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epochs.
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|---|---|-----|-----|----|----|----|
+| 1 | 4 | 8.55|18.14|2.12|1.0 |1.0 |
+| 4 | 4 |32.13|52.85|1.64|3.76|2.91|
+| 8 | 4 |62.83|95.28|1.51|7.35|5.25|
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|---|---|-----|-------|---|---|----|
+| 1 | 10|  -  | 27.69 | - | - |1.0 |
+| 4 | 10|  -  | 85.193| - | - |3.07|
+| 8 | 10|  -  |142.69 | - | - |5.1 |
+
+
+Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. 
+
+#### NVIDIA DGX-2 (16x V100 32G)
+Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|---|---|------|------|----|-----|----|
+|  1| 4 |  8.80| 17.43|1.98| 1.0 |1.0 |
+|  4| 4 | 33.22| 56.87|1.71| 3.78|3.26|
+|  8| 4 | 64.46|100.58|1.56| 7.33|5.77|
+| 16| 4 |117.83|162.29|1.38|13.39|9.31|
+
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** |
+|---|---|---|------|---|---|----|
+|  1| 10| - | 28.72| - | - |1.0 |
+|  4| 10| - | 92.73| - | - |3.22|
+|  8| 10| - |155.78| - | - |5.42|
+| 16| 10| - |228.75| - | - |7.96|
+
+
+Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. 
+### Inference performance results
+
+#### NVIDIA DGX-1 16G (1x V100 16G)
+Our results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |
+|---|---|-----|------|----|
+| 1 | 8 |41.04|112.55|2.74|
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+
+#### NVIDIA DGX-1 32G (1x V100 32G)
+Our results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |
+|---|---|-----|------|----|
+| 1 | 8 |36.78|118.54|3.22|
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+#### NVIDIA DGX-2 32G (1x V100 32G)
+Our results were obtained by running the `scripts/run_squad_inference.sh` training script in the TensorFlow 19.03-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs. Performance numbers (in sentences per second) were averaged over an entire training epoch.
+
+| **Number of GPUs** | **Batch size per GPU** | **FP32 sentences/sec** | **FP16 sentences/sec** | **Speedup** |
+|---|---|-----|------|----|
+| 1 | 8 |33.95|108.45|3.19|
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+## Changelog
+March 2019
+- Initial release
+
+## Known issues
+There are no known issues with this model.
+
+
diff --git a/TensorFlow/LanguageModeling/BERT/__init__.py b/TensorFlow/LanguageModeling/BERT/__init__.py
new file mode 100644
index 00000000..effb57b1
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/__init__.py
@@ -0,0 +1,15 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
diff --git a/TensorFlow/LanguageModeling/BERT/create_pretraining_data.py b/TensorFlow/LanguageModeling/BERT/create_pretraining_data.py
new file mode 100644
index 00000000..8bcbe274
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/create_pretraining_data.py
@@ -0,0 +1,442 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Create masked LM/next sentence masked_lm TF examples for BERT."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import random
+import tokenization
+import tensorflow as tf
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string("input_file", None,
+                    "Input raw text file (or comma-separated list of files).")
+
+flags.DEFINE_string(
+    "output_file", None,
+    "Output TF example file (or comma-separated list of files).")
+
+flags.DEFINE_string("vocab_file", None,
+                    "The vocabulary file that the BERT model was trained on.")
+
+flags.DEFINE_bool(
+    "do_lower_case", True,
+    "Whether to lower case the input text. Should be True for uncased "
+    "models and False for cased models.")
+
+flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")
+
+flags.DEFINE_integer("max_predictions_per_seq", 20,
+                     "Maximum number of masked LM predictions per sequence.")
+
+flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
+
+flags.DEFINE_integer(
+    "dupe_factor", 10,
+    "Number of times to duplicate the input data (with different masks).")
+
+flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
+
+flags.DEFINE_float(
+    "short_seq_prob", 0.1,
+    "Probability of creating sequences which are shorter than the "
+    "maximum length.")
+
+
+class TrainingInstance(object):
+  """A single training instance (sentence pair)."""
+
+  def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
+               is_random_next):
+    self.tokens = tokens
+    self.segment_ids = segment_ids
+    self.is_random_next = is_random_next
+    self.masked_lm_positions = masked_lm_positions
+    self.masked_lm_labels = masked_lm_labels
+
+  def __str__(self):
+    s = ""
+    s += "tokens: %s\n" % (" ".join(
+        [tokenization.printable_text(x) for x in self.tokens]))
+    s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
+    s += "is_random_next: %s\n" % self.is_random_next
+    s += "masked_lm_positions: %s\n" % (" ".join(
+        [str(x) for x in self.masked_lm_positions]))
+    s += "masked_lm_labels: %s\n" % (" ".join(
+        [tokenization.printable_text(x) for x in self.masked_lm_labels]))
+    s += "\n"
+    return s
+
+  def __repr__(self):
+    return self.__str__()
+
+
+def write_instance_to_example_files(instances, tokenizer, max_seq_length,
+                                    max_predictions_per_seq, output_files):
+  """Create TF example files from `TrainingInstance`s."""
+  writers = []
+  for output_file in output_files:
+    writers.append(tf.python_io.TFRecordWriter(output_file))
+
+  writer_index = 0
+
+  total_written = 0
+  for (inst_index, instance) in enumerate(instances):
+    input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
+    input_mask = [1] * len(input_ids)
+    segment_ids = list(instance.segment_ids)
+    assert len(input_ids) <= max_seq_length
+
+    while len(input_ids) < max_seq_length:
+      input_ids.append(0)
+      input_mask.append(0)
+      segment_ids.append(0)
+
+    assert len(input_ids) == max_seq_length
+    assert len(input_mask) == max_seq_length
+    assert len(segment_ids) == max_seq_length
+
+    masked_lm_positions = list(instance.masked_lm_positions)
+    masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
+    masked_lm_weights = [1.0] * len(masked_lm_ids)
+
+    while len(masked_lm_positions) < max_predictions_per_seq:
+      masked_lm_positions.append(0)
+      masked_lm_ids.append(0)
+      masked_lm_weights.append(0.0)
+
+    next_sentence_label = 1 if instance.is_random_next else 0
+
+    features = collections.OrderedDict()
+    features["input_ids"] = create_int_feature(input_ids)
+    features["input_mask"] = create_int_feature(input_mask)
+    features["segment_ids"] = create_int_feature(segment_ids)
+    features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
+    features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
+    features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
+    features["next_sentence_labels"] = create_int_feature([next_sentence_label])
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    writers[writer_index].write(tf_example.SerializeToString())
+    writer_index = (writer_index + 1) % len(writers)
+
+    total_written += 1
+
+    if inst_index < 20:
+      tf.logging.info("*** Example ***")
+      tf.logging.info("tokens: %s" % " ".join(
+          [tokenization.printable_text(x) for x in instance.tokens]))
+
+      for feature_name in features.keys():
+        feature = features[feature_name]
+        values = []
+        if feature.int64_list.value:
+          values = feature.int64_list.value
+        elif feature.float_list.value:
+          values = feature.float_list.value
+        tf.logging.info(
+            "%s: %s" % (feature_name, " ".join([str(x) for x in values])))
+
+  for writer in writers:
+    writer.close()
+
+  tf.logging.info("Wrote %d total instances", total_written)
+
+
+def create_int_feature(values):
+  feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
+  return feature
+
+
+def create_float_feature(values):
+  feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
+  return feature
+
+
+def create_training_instances(input_files, tokenizer, max_seq_length,
+                              dupe_factor, short_seq_prob, masked_lm_prob,
+                              max_predictions_per_seq, rng):
+  """Create `TrainingInstance`s from raw text."""
+  all_documents = [[]]
+
+  # Input file format:
+  # (1) One sentence per line. These should ideally be actual sentences, not
+  # entire paragraphs or arbitrary spans of text. (Because we use the
+  # sentence boundaries for the "next sentence prediction" task).
+  # (2) Blank lines between documents. Document boundaries are needed so
+  # that the "next sentence prediction" task doesn't span between documents.
+  for input_file in input_files:
+    with tf.gfile.GFile(input_file, "r") as reader:
+      while True:
+        line = tokenization.convert_to_unicode(reader.readline())
+        if not line:
+          break
+        line = line.strip()
+
+        # Empty lines are used as document delimiters
+        if not line:
+          all_documents.append([])
+        tokens = tokenizer.tokenize(line)
+        if tokens:
+          all_documents[-1].append(tokens)
+
+  # Remove empty documents
+  all_documents = [x for x in all_documents if x]
+  rng.shuffle(all_documents)
+
+  vocab_words = list(tokenizer.vocab.keys())
+  instances = []
+  for _ in range(dupe_factor):
+    for document_index in range(len(all_documents)):
+      instances.extend(
+          create_instances_from_document(
+              all_documents, document_index, max_seq_length, short_seq_prob,
+              masked_lm_prob, max_predictions_per_seq, vocab_words, rng))
+
+  rng.shuffle(instances)
+  return instances
+
+
+def create_instances_from_document(
+    all_documents, document_index, max_seq_length, short_seq_prob,
+    masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
+  """Creates `TrainingInstance`s for a single document."""
+  document = all_documents[document_index]
+
+  # Account for [CLS], [SEP], [SEP]
+  max_num_tokens = max_seq_length - 3
+
+  # We *usually* want to fill up the entire sequence since we are padding
+  # to `max_seq_length` anyways, so short sequences are generally wasted
+  # computation. However, we *sometimes*
+  # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
+  # sequences to minimize the mismatch between pre-training and fine-tuning.
+  # The `target_seq_length` is just a rough target however, whereas
+  # `max_seq_length` is a hard limit.
+  target_seq_length = max_num_tokens
+  if rng.random() < short_seq_prob:
+    target_seq_length = rng.randint(2, max_num_tokens)
+
+  # We DON'T just concatenate all of the tokens from a document into a long
+  # sequence and choose an arbitrary split point because this would make the
+  # next sentence prediction task too easy. Instead, we split the input into
+  # segments "A" and "B" based on the actual "sentences" provided by the user
+  # input.
+  instances = []
+  current_chunk = []
+  current_length = 0
+  i = 0
+  while i < len(document):
+    segment = document[i]
+    current_chunk.append(segment)
+    current_length += len(segment)
+    if i == len(document) - 1 or current_length >= target_seq_length:
+      if current_chunk:
+        # `a_end` is how many segments from `current_chunk` go into the `A`
+        # (first) sentence.
+        a_end = 1
+        if len(current_chunk) >= 2:
+          a_end = rng.randint(1, len(current_chunk) - 1)
+
+        tokens_a = []
+        for j in range(a_end):
+          tokens_a.extend(current_chunk[j])
+
+        tokens_b = []
+        # Random next
+        is_random_next = False
+        if len(current_chunk) == 1 or rng.random() < 0.5:
+          is_random_next = True
+          target_b_length = target_seq_length - len(tokens_a)
+
+          # This should rarely go for more than one iteration for large
+          # corpora. However, just to be careful, we try to make sure that
+          # the random document is not the same as the document
+          # we're processing.
+          for _ in range(10):
+            random_document_index = rng.randint(0, len(all_documents) - 1)
+            if random_document_index != document_index:
+              break
+
+          random_document = all_documents[random_document_index]
+          random_start = rng.randint(0, len(random_document) - 1)
+          for j in range(random_start, len(random_document)):
+            tokens_b.extend(random_document[j])
+            if len(tokens_b) >= target_b_length:
+              break
+          # We didn't actually use these segments so we "put them back" so
+          # they don't go to waste.
+          num_unused_segments = len(current_chunk) - a_end
+          i -= num_unused_segments
+        # Actual next
+        else:
+          is_random_next = False
+          for j in range(a_end, len(current_chunk)):
+            tokens_b.extend(current_chunk[j])
+        truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
+
+        assert len(tokens_a) >= 1
+        assert len(tokens_b) >= 1
+
+        tokens = []
+        segment_ids = []
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in tokens_a:
+          tokens.append(token)
+          segment_ids.append(0)
+
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        for token in tokens_b:
+          tokens.append(token)
+          segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        (tokens, masked_lm_positions,
+         masked_lm_labels) = create_masked_lm_predictions(
+             tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
+        instance = TrainingInstance(
+            tokens=tokens,
+            segment_ids=segment_ids,
+            is_random_next=is_random_next,
+            masked_lm_positions=masked_lm_positions,
+            masked_lm_labels=masked_lm_labels)
+        instances.append(instance)
+      current_chunk = []
+      current_length = 0
+    i += 1
+
+  return instances
+
+
+MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
+                                          ["index", "label"])
+
+
+def create_masked_lm_predictions(tokens, masked_lm_prob,
+                                 max_predictions_per_seq, vocab_words, rng):
+  """Creates the predictions for the masked LM objective."""
+
+  cand_indexes = []
+  for (i, token) in enumerate(tokens):
+    if token == "[CLS]" or token == "[SEP]":
+      continue
+    cand_indexes.append(i)
+
+  rng.shuffle(cand_indexes)
+
+  output_tokens = list(tokens)
+
+  num_to_predict = min(max_predictions_per_seq,
+                       max(1, int(round(len(tokens) * masked_lm_prob))))
+
+  masked_lms = []
+  covered_indexes = set()
+  for index in cand_indexes:
+    if len(masked_lms) >= num_to_predict:
+      break
+    if index in covered_indexes:
+      continue
+    covered_indexes.add(index)
+
+    masked_token = None
+    # 80% of the time, replace with [MASK]
+    if rng.random() < 0.8:
+      masked_token = "[MASK]"
+    else:
+      # 10% of the time, keep original
+      if rng.random() < 0.5:
+        masked_token = tokens[index]
+      # 10% of the time, replace with random word
+      else:
+        masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
+
+    output_tokens[index] = masked_token
+
+    masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
+
+  masked_lms = sorted(masked_lms, key=lambda x: x.index)
+
+  masked_lm_positions = []
+  masked_lm_labels = []
+  for p in masked_lms:
+    masked_lm_positions.append(p.index)
+    masked_lm_labels.append(p.label)
+
+  return (output_tokens, masked_lm_positions, masked_lm_labels)
+
+
+def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
+  """Truncates a pair of sequences to a maximum sequence length."""
+  while True:
+    total_length = len(tokens_a) + len(tokens_b)
+    if total_length <= max_num_tokens:
+      break
+
+    trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
+    assert len(trunc_tokens) >= 1
+
+    # We want to sometimes truncate from the front and sometimes from the
+    # back to add more randomness and avoid biases.
+    if rng.random() < 0.5:
+      del trunc_tokens[0]
+    else:
+      trunc_tokens.pop()
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  tokenizer = tokenization.FullTokenizer(
+      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
+
+  input_files = []
+  for input_pattern in FLAGS.input_file.split(","):
+    input_files.extend(tf.gfile.Glob(input_pattern))
+
+  tf.logging.info("*** Reading from input files ***")
+  for input_file in input_files:
+    tf.logging.info("  %s", input_file)
+
+  rng = random.Random(FLAGS.random_seed)
+  instances = create_training_instances(
+      input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor,
+      FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq,
+      rng)
+
+  output_files = FLAGS.output_file.split(",")
+  tf.logging.info("*** Writing to output files ***")
+  for output_file in output_files:
+    tf.logging.info("  %s", output_file)
+
+  write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
+                                  FLAGS.max_predictions_per_seq, output_files)
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("input_file")
+  flags.mark_flag_as_required("output_file")
+  flags.mark_flag_as_required("vocab_file")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/data/README.md b/TensorFlow/LanguageModeling/BERT/data/README.md
new file mode 100644
index 00000000..d2ec8d02
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/README.md
@@ -0,0 +1,30 @@
+Steps to reproduce datasets from web
+
+1) Build the container
+  * docker build -t bert_prep .
+2) Run the container interactively
+  * nvidia-docker run -it --ipc=host bert_prep
+  * Optional: Mount data volumes
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/download
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/extracted_articles
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/raw_data
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/intermediate_files
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/final_text_file_single
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/final_text_files_sharded
+    * -v yourpath:/workspace/bert/data/wikipedia_corpus/final_tfrecords_sharded
+    * -v yourpath:/workspace/bert/data/bookcorpus/download
+    * -v yourpath:/workspace/bert/data/bookcorpus/final_text_file_single
+    * -v yourpath:/workspace/bert/data/bookcorpus/final_text_files_sharded
+    * -v yourpath:/workspace/bert/data/bookcorpus/final_tfrecords_sharded
+  * Optional: Select visible GPUs
+    * -e CUDA_VISIBLE_DEVICES=0
+
+** Inside of the container starting here**
+3) Download pretrained weights (they contain vocab files for preprocessing)
+  * cd data/pretrained_models_google && python3 download_models.py
+4) "One-click" Wikipedia data download and prep (provides tfrecords)
+  * Set your configuration in data/wikipedia_corpus/config.sh
+  * cd /data/wikipedia_corpus && ./run_preprocessing.sh
+5) "One-click" BookCorpus data download and prep (provided tfrecords)
+  * Set your configuration in data/wikipedia_corpus/config.sh
+  * cd /data/bookcorpus && ./run_preprocessing.sh
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py
new file mode 100644
index 00000000..0b297b1d
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py
@@ -0,0 +1,15 @@
+# NVIDIA
+
+import glob
+import os
+
+output_file = os.environ['WORKING_DIR'] + '/intermediate_files/bookcorpus.txt'
+download_path = os.environ['WORKING_DIR'] + '/download/'
+
+with open(output_file, "w") as ofile:
+  for filename in glob.glob(download_path + '*.txt', recursive=True):
+    with open(filename, mode='r', encoding="utf-8-sig") as file:
+      for line in file:
+        if line.strip() != "":
+          ofile.write(line.strip() + " ")
+    ofile.write("\n\n ")
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh
new file mode 100755
index 00000000..e14a1acd
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh
@@ -0,0 +1,27 @@
+#! /bin/bash
+
+set -e
+
+USE_BERT_LARGE=true
+MAX_SEQUENCE_LENGTH=512
+MAX_PREDICTIONS_PER_SEQUENCE=80
+MASKED_LM_PROB=0.15
+SEED=12345
+DUPE_FACTOR=5
+DO_LOWER_CASE="True"
+N_LINES_PER_SHARD_APPROX=396000   # Default=396000 creates 256 shards
+
+N_PROCS_PREPROCESS=4    # Adjust this based on memory requirements and available number of cores
+export WORKING_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+
+BERT_BASE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-12_H-768_A-12"
+BERT_LARGE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-24_H-1024_A-16"
+
+if [ "$USE_BERT_LARGE" = true ] ; then
+  VOCAB_FILE="${BERT_LARGE_DIR}/vocab.txt"
+else
+  VOCAB_FILE="${BERT_BASE_DIR}/vocab.txt"
+fi
+
+OUTPUT_DIR="${WORKING_DIR}/final_tfrecords_sharded/bert_large_bookcorpus_seq_${MAX_SEQUENCE_LENGTH}_pred_${MAX_PREDICTIONS_PER_SEQUENCE}"
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py
new file mode 100644
index 00000000..194f15b7
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py
@@ -0,0 +1,18 @@
+# NVIDIA
+
+import glob
+import os
+import random
+import shutil
+
+input_dir = os.environ['WORKING_DIR'] + '/final_text_files_sharded/'
+output_dir = os.environ['WORKING_DIR'] + '/test_set_text_files/'
+
+random.seed(13254)
+n_shards_to_keep = 3
+
+file_glob = glob.glob(input_dir + '/*', recursive=False)
+file_glob = random.sample(file_glob, n_shards_to_keep)
+
+for filename in file_glob:
+  shutil.copy(filename, output_dir) 
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh
new file mode 100755
index 00000000..34fa09e9
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh
@@ -0,0 +1,10 @@
+#! /bin/bash
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+# Convert test set sharded text files into tfrecords that are ready for BERT pretraining
+echo "Creating test set tfrecords for each text shard"
+mkdir -p ${WORKING_DIR}/test_set_text_files
+mkdir -p ${WORKING_DIR}/test_set_tfrecords
+python3 ${WORKING_DIR}/create_pseudo_test_set.py
+. ${WORKING_DIR}/preprocessing_test_set_xargs_wrapper.sh ${N_PROCS_PREPROCESS}
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh
new file mode 100755
index 00000000..0169c533
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh
@@ -0,0 +1,23 @@
+#! /bin/bash
+
+SHARD_INDEX=${1}
+INPUT_FILE="${WORKING_DIR}/final_text_files_sharded/bookcorpus.segmented.part.${SHARD_INDEX}.txt"
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+OUTPUT_DIR=${WORKING_DIR}/final_tfrecords_sharded
+mkdir -p ${OUTPUT_DIR}
+
+OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}"
+
+python /workspace/bert/create_pretraining_data.py \
+  --input_file=${INPUT_FILE} \
+  --output_file=${OUTPUT_FILE} \
+  --vocab_file=${VOCAB_FILE} \
+  --do_lower_case=${DO_LOWER_CASE} \
+  --max_seq_length=${MAX_SEQUENCE_LENGTH} \
+  --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \
+  --masked_lm_prob=${MASKED_LM_PROB} \
+  --random_seed=${SEED} \
+  --dupe_factor=${DUPE_FACTOR}
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh
new file mode 100755
index 00000000..58145640
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh
@@ -0,0 +1,28 @@
+#! /bin/bash
+
+INPUT_FILE=${1}
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+OUTPUT_DIR=${WORKING_DIR}/test_set_tfrecords
+mkdir -p ${OUTPUT_DIR}
+
+#SHARD_INDEX=$(( echo ${INPUT_FILE} | egrep -o [0-9]+ ))
+SHARD_INDEX=$( eval echo ${INPUT_FILE} | sed -e s/[^0-9]//g )
+OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}"
+
+SEED=13254
+
+echo "Shard index ${SHARD_INDEX}"
+
+python /workspace/bert/create_pretraining_data.py \
+  --input_file=${INPUT_FILE} \
+  --output_file=${OUTPUT_FILE} \
+  --vocab_file=${VOCAB_FILE} \
+  --do_lower_case=${DO_LOWER_CASE} \
+  --max_seq_length=${MAX_SEQUENCE_LENGTH} \
+  --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \
+  --masked_lm_prob=${MASKED_LM_PROB} \
+  --random_seed=${SEED} \
+  --dupe_factor=${DUPE_FACTOR}
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh
new file mode 100755
index 00000000..f41516b7
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh
@@ -0,0 +1,12 @@
+#! /bin/bash
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+SHARD_COUNT=0
+rm -rf /workspace/bert/data/bookcorpus/xarg_list.txt
+touch /workspace/bert/data/bookcorpus/xarg_list.txt
+for file in /workspace/bert/data/bookcorpus/test_set_text_files/*; do
+  echo ${file} >> /workspace/bert/data/bookcorpus/xarg_list.txt
+done
+
+xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/bookcorpus/xarg_list.txt /workspace/bert/data/bookcorpus/preprocessing_test_set.sh
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh
new file mode 100755
index 00000000..387069ef
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh
@@ -0,0 +1,13 @@
+#! /bin/bash
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+SHARD_COUNT=0
+rm -rf /workspace/bert/data/bookcorpus/xarg_list.txt
+touch /workspace/bert/data/bookcorpus/xarg_list.txt
+for file in /workspace/bert/data/bookcorpus/final_text_files_sharded/*; do
+  echo ${SHARD_COUNT} >> /workspace/bert/data/bookcorpus/xarg_list.txt
+  SHARD_COUNT=$((SHARD_COUNT+1))
+done
+
+xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/bookcorpus/xarg_list.txt /workspace/bert/data/bookcorpus/preprocessing.sh
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh
new file mode 100755
index 00000000..f660e22c
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh
@@ -0,0 +1,28 @@
+#! /bin/bash
+
+source /workspace/bert/data/bookcorpus/config.sh
+
+# Download books
+mkdir -p download
+python3 /workspace/bookcorpus/download_files.py --list /workspace/bookcorpus/url_list.jsonl --out ${WORKING_DIR}/download --trash-bad-count
+
+# Clean and prep (one book per line)
+mkdir -p ${WORKING_DIR}/intermediate_files
+python3 ${WORKING_DIR}/clean_and_merge_text.py
+
+# Split books into one-sentence-per-line format for use with BERT scripts
+echo "Applying sentence segmentation to get one sentence per line"
+mkdir -p ${WORKING_DIR}/final_text_file_single
+python3 ${WORKING_DIR}/sentence_segmentation_nltk.py
+# Note: NLTK can be replaced with Spacy, although it is slower (2 variations provided)
+
+# Shard finalized text so that it has a chance of fitting in memory when creating pretraining data into tfrecords (choose appropriate number of shards for distributed training)
+echo "Shard text files - size is approximate to prevent splitting a book across shards"
+mkdir -p ${WORKING_DIR}/final_text_files_sharded
+python3 ${WORKING_DIR}/shard_text_input_file.py
+
+# Convert sharded text files into tfrecords that are ready for BERT pretraining
+echo "Creating tfrecords for each text shard"
+mkdir -p ${WORKING_DIR}/final_tfrecords_sharded
+. ${WORKING_DIR}/preprocessing_xargs_wrapper.sh ${N_PROCS_PREPROCESS}
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py
new file mode 100644
index 00000000..038205b6
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py
@@ -0,0 +1,20 @@
+# NVIDIA
+
+import nltk
+import os
+
+nltk.download('punkt')
+
+input_file = os.environ['WORKING_DIR'] + '/intermediate_files/bookcorpus.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_text_file_single/bookcorpus.segmented.nltk.txt'
+
+doc_seperator = "\n"
+
+with open(input_file) as ifile:
+  with open(output_file, "w") as ofile:
+    for line in ifile:
+      if line != "\n":
+        sent_list = nltk.tokenize.sent_tokenize(line)
+        for sent in sent_list:
+          ofile.write(sent + "\n")
+        ofile.write(doc_seperator)
diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py
new file mode 100644
index 00000000..5efe0be4
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py
@@ -0,0 +1,41 @@
+# NVIDIA
+
+import os
+
+input_file = os.environ['WORKING_DIR'] + '/final_text_file_single/bookcorpus.segmented.nltk.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_text_files_sharded/bookcorpus.segmented.part.'
+
+doc_seperator = "\n"
+
+line_buffer = []
+shard_size = 396000 # Approximate, will split at next article break
+line_counter = 0
+shard_index = 0
+
+ifile_lines = 0
+with open(input_file) as ifile:
+  for line in ifile:
+    ifile_lines += 1
+
+print("Input file contains", ifile_lines, "lines.")
+
+iline_counter = 1
+with open(input_file) as ifile:
+  for line in ifile:
+    if line_counter < shard_size and iline_counter < ifile_lines:
+      line_buffer.append(line)
+      line_counter += 1
+      iline_counter += 1
+    elif line_counter >= shard_size and line != "\n" and iline_counter < ifile_lines:
+      line_buffer.append(line)
+      line_counter += 1
+      iline_counter += 1
+    else:
+       with open(output_file + str(shard_index) + ".txt", "w") as ofile:
+         for oline in line_buffer:
+           ofile.write(oline)
+         line_buffer = []
+         line_counter = 0
+         shard_index += 1
+
+    
diff --git a/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py b/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py
new file mode 100644
index 00000000..e671c194
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py
@@ -0,0 +1,123 @@
+# NVIDIA
+
+import hashlib
+import urllib.request
+import zipfile
+
+# Download urls
+model_urls = {
+  'bert_base_uncased' : ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip', 'uncased_L-12_H-768_A-12.zip'),
+  'bert_large_uncased' : ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip', 'uncased_L-24_H-1024_A-16.zip'),
+  'bert_base_cased' : ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip', 'cased_L-12_H-768_A-12.zip'),
+  'bert_large_cased' : ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip', 'cased_L-24_H-1024_A-16.zip'),
+  'bert_base_multilingual_cased' : ('https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip', 'multi_cased_L-12_H-768_A-12.zip'),
+  'bert_large_multilingual_uncased' : ('https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip', 'multilingual_L-12_H-768_A-12.zip'),
+  'bert_base_chinese' : ('https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip', 'chinese_L-12_H-768_A-12.zip')
+}
+
+# SHA256sum verification for file download integrity (and checking for changes from the download source over time)
+bert_base_uncased_sha = {
+  'bert_config.json' : '7b4e5f53efbd058c67cda0aacfafb340113ea1b5797d9ce6ee411704ba21fcbc',
+  'bert_model.ckpt.data-00000-of-00001' : '58580dc5e0bf0ae0d2efd51d0e8272b2f808857f0a43a88aaf7549da6d7a8a84',
+  'bert_model.ckpt.index' : '04c1323086e2f1c5b7c0759d8d3e484afbb0ab45f51793daab9f647113a0117b',
+  'bert_model.ckpt.meta' : 'dd5682170a10c3ea0280c2e9b9a45fee894eb62da649bbdea37b38b0ded5f60e',
+  'vocab.txt' : '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3',
+}
+
+bert_large_uncased_sha = {
+  'bert_config.json' : 'bfa42236d269e2aeb3a6d30412a33d15dbe8ea597e2b01dc9518c63cc6efafcb',
+  'bert_model.ckpt.data-00000-of-00001' : 'bc6b3363e3be458c99ecf64b7f472d2b7c67534fd8f564c0556a678f90f4eea1',
+  'bert_model.ckpt.index' : '68b52f2205ffc64dc627d1120cf399c1ef1cbc35ea5021d1afc889ffe2ce2093',
+  'bert_model.ckpt.meta' : '6fcce8ff7628f229a885a593625e3d5ff9687542d5ef128d9beb1b0c05edc4a1',
+  'vocab.txt' : '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3',
+}
+
+bert_base_cased_sha = {
+  'bert_config.json' : 'f11dfb757bea16339a33e1bf327b0aade6e57fd9c29dc6b84f7ddb20682f48bc',
+  'bert_model.ckpt.data-00000-of-00001' : '734d5a1b68bf98d4e9cb6b6692725d00842a1937af73902e51776905d8f760ea',
+  'bert_model.ckpt.index' : '517d6ef5c41fc2ca1f595276d6fccf5521810d57f5a74e32616151557790f7b1',
+  'bert_model.ckpt.meta' : '5f8a9771ff25dadd61582abb4e3a748215a10a6b55947cbb66d0f0ba1694be98',
+  'vocab.txt' : 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02',
+}
+
+bert_large_cased_sha = {
+  'bert_config.json' : '7adb2125c8225da495656c982fd1c5f64ba8f20ad020838571a3f8a954c2df57',
+  'bert_model.ckpt.data-00000-of-00001' : '6ff33640f40d472f7a16af0c17b1179ca9dcc0373155fb05335b6a4dd1657ef0',
+  'bert_model.ckpt.index' : 'ef42a53f577fbe07381f4161b13c7cab4f4fc3b167cec6a9ae382c53d18049cf',
+  'bert_model.ckpt.meta' : 'd2ddff3ed33b80091eac95171e94149736ea74eb645e575d942ec4a5e01a40a1',
+  'vocab.txt' : 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02',
+}
+
+bert_base_multilingual_cased_sha = {
+  'bert_config.json' : 'e76c3964bc14a8bb37a5530cdc802699d2f4a6fddfab0611e153aa2528f234f0',
+  'bert_model.ckpt.data-00000-of-00001' : '55b8a2df41f69c60c5180e50a7c31b7cdf6238909390c4ddf05fbc0d37aa1ac5',
+  'bert_model.ckpt.index' : '7d8509c2a62b4e300feb55f8e5f1eef41638f4998dd4d887736f42d4f6a34b37',
+  'bert_model.ckpt.meta' : '95e5f1997e8831f1c31e5cf530f1a2e99f121e9cd20887f2dce6fe9e3343e3fa',
+  'vocab.txt' : 'fe0fda7c425b48c516fc8f160d594c8022a0808447475c1a7c6d6479763f310c',
+}
+
+bert_large_multilingual_uncased_sha = {
+  'bert_config.json' : '49063bb061390211d2fdd108cada1ed86faa5f90b80c8f6fdddf406afa4c4624',
+  'bert_model.ckpt.data-00000-of-00001' : '3cd83912ebeb0efe2abf35c9f1d5a515d8e80295e61c49b75c8853f756658429',
+  'bert_model.ckpt.index' : '87c372c1a3b1dc7effaaa9103c80a81b3cbab04c7933ced224eec3b8ad2cc8e7',
+  'bert_model.ckpt.meta' : '27f504f34f02acaa6b0f60d65195ec3e3f9505ac14601c6a32b421d0c8413a29',
+  'vocab.txt' : '87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f',
+}
+
+bert_base_chinese_sha = {
+  'bert_config.json' : '7aaad0335058e2640bcb2c2e9a932b1cd9da200c46ea7b8957d54431f201c015',
+  'bert_model.ckpt.data-00000-of-00001' : '756699356b78ad0ef1ca9ba6528297bcb3dd1aef5feadd31f4775d7c7fc989ba',
+  'bert_model.ckpt.index' : '46315546e05ce62327b3e2cd1bed22836adcb2ff29735ec87721396edb21b82e',
+  'bert_model.ckpt.meta' : 'c0f8d51e1ab986604bc2b25d6ec0af7fd21ff94cf67081996ec3f3bf5d823047',
+  'vocab.txt' : '45bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c',
+}
+
+# Relate SHA to urls for loop below
+model_sha = {
+  'bert_base_uncased' : bert_base_uncased_sha,
+  'bert_large_uncased' : bert_large_uncased_sha,
+  'bert_base_cased' : bert_base_cased_sha,
+  'bert_large_cased' : bert_large_cased_sha,
+  'bert_base_multilingual_cased' : bert_base_multilingual_cased_sha,
+  'bert_large_multilingual_uncased' : bert_large_multilingual_uncased_sha,
+  'bert_base_chinese' : bert_base_chinese_sha
+}
+
+# Helper to get sha256sum of a file
+def sha256sum(filename):
+  h  = hashlib.sha256()
+  b  = bytearray(128*1024)
+  mv = memoryview(b)
+  with open(filename, 'rb', buffering=0) as f:
+    for n in iter(lambda : f.readinto(mv), 0):
+      h.update(mv[:n])
+  return h.hexdigest()
+
+# Iterate over urls: download, unzip, verify sha256sum
+found_mismatch_sha = False
+for model in model_urls:
+  url = model_urls[model][0]
+  file = model_urls[model][1]
+
+  print("Downloading", url)
+  response = urllib.request.urlopen(url)
+  with open(file, "wb") as handle:
+    handle.write(response.read())
+
+  print("Unzipping", file)
+  zip = zipfile.ZipFile(file, 'r')
+  zip.extractall()
+  zip.close()
+
+  sha_dict = model_sha[model]
+  for extracted_file in sha_dict:
+    sha = sha_dict[extracted_file]
+    if sha != sha256sum(file[:-4] + "/" + extracted_file):
+      found_mismatch_sha = True
+      print("SHA256sum does not match on file:", extracted_file, "from download url:", url)
+    else:
+      print(file[:-4] + "/" + extracted_file, "\t", "verified")
+
+if not found_mismatch_sha:
+  print("All downloads pass sha256sum verification.")
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh b/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh
new file mode 100755
index 00000000..249778f5
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh
@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+
+echo "Downloading dataset for squad..."
+
+# Download SQuAD
+
+v1="v1.1"
+mkdir $v1
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json -O $v1/train-v1.1.json
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json -O $v1/dev-v1.1.json
+wget https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/ -O $v1/evaluate-v1.1.py
+
+EXP_TRAIN_v1='981b29407e0affa3b1b156f72073b945  -'
+EXP_DEV_v1='3e85deb501d4e538b6bc56f786231552  -'
+EXP_EVAL_v1='afb04912d18ff20696f7f88eed49bea9  -'
+CALC_TRAIN_v1=`cat ${v1}/train-v1.1.json |md5sum`
+CALC_DEV_v1=`cat ${v1}/dev-v1.1.json |md5sum`
+CALC_EVAL_v1=`cat ${v1}/evaluate-v1.1.py |md5sum`
+
+v2="v2.0"
+mkdir $v2
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O $v2/train-v2.0.json
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O $v2/dev-v2.0.json
+wget https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/ -O $v2/evaluate-v2.0.py
+
+EXP_TRAIN_v2='62108c273c268d70893182d5cf8df740  -'
+EXP_DEV_v2='246adae8b7002f8679c027697b0b7cf8  -'
+EXP_EVAL_v2='ff23213bed5516ea4a6d9edb6cd7d627  -'
+
+CALC_TRAIN_v2=`cat ${v2}/train-v2.0.json |md5sum`
+CALC_DEV_v2=`cat ${v2}/dev-v2.0.json |md5sum`
+CALC_EVAL_v2=`cat ${v2}/evaluate-v2.0.py |md5sum`
+
+echo "Squad data download done!"
+
+echo "Verifying Dataset...."
+
+if [ "$EXP_TRAIN_v1" != "$CALC_TRAIN_v1" ]; then
+    echo "train-v1.1.json is corrupted! md5sum doesn't match"
+fi
+
+if [ "$EXP_DEV_v1" != "$CALC_DEV_v1" ]; then
+    echo "dev-v1.1.json is corrupted! md5sum doesn't match"
+fi
+if [ "$EXP_EVAL_v1" != "$CALC_EVAL_v1" ]; then
+    echo "evaluate-v1.1.py is corrupted! md5sum doesn't match"
+fi
+
+
+if [ "$EXP_TRAIN_v2" != "$CALC_TRAIN_v2" ]; then
+    echo "train-v2.0.json is corrupted! md5sum doesn't match"
+fi
+if [ "$EXP_DEV_v2" != "$CALC_DEV_v2" ]; then
+    echo "dev-v2.0.json is corrupted! md5sum doesn't match"
+fi
+if [ "$EXP_EVAL_v2" != "$CALC_EVAL_v2" ]; then
+    echo "evaluate-v2.0.py is corrupted! md5sum doesn't match"
+fi
+
+echo "Complete!"
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh
new file mode 100755
index 00000000..88065ffc
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh
@@ -0,0 +1,28 @@
+#! /bin/bash
+
+set -e
+
+USE_BERT_LARGE=true
+MAX_SEQUENCE_LENGTH=512
+MAX_PREDICTIONS_PER_SEQUENCE=80
+MASKED_LM_PROB=0.15
+SEED=12345
+DUPE_FACTOR=5
+DO_LOWER_CASE="True"
+N_LINES_PER_SHARD_APPROX=396000   # Default=396000 creates 256 shards
+
+N_PROCS_PREPROCESS=4    # Adjust this based on memory requirements and available number of cores
+export WORKING_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+
+WIKI_DUMP="ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/20190301/enwiki-20190301-pages-articles-multistream.xml.bz2"
+BERT_BASE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-12_H-768_A-12"
+BERT_LARGE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-24_H-1024_A-16"
+
+if [ "$USE_BERT_LARGE" = true ] ; then
+  VOCAB_FILE="${BERT_LARGE_DIR}/vocab.txt"
+else
+  VOCAB_FILE="${BERT_BASE_DIR}/vocab.txt"
+fi
+
+OUTPUT_DIR="${WORKING_DIR}/final_tfrecords_sharded/bert_large_wikipedia_seq_${MAX_SEQUENCE_LENGTH}_pred_${MAX_PREDICTIONS_PER_SEQUENCE}"
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py
new file mode 100644
index 00000000..194f15b7
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py
@@ -0,0 +1,18 @@
+# NVIDIA
+
+import glob
+import os
+import random
+import shutil
+
+input_dir = os.environ['WORKING_DIR'] + '/final_text_files_sharded/'
+output_dir = os.environ['WORKING_DIR'] + '/test_set_text_files/'
+
+random.seed(13254)
+n_shards_to_keep = 3
+
+file_glob = glob.glob(input_dir + '/*', recursive=False)
+file_glob = random.sample(file_glob, n_shards_to_keep)
+
+for filename in file_glob:
+  shutil.copy(filename, output_dir) 
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh
new file mode 100755
index 00000000..ffe355c7
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh
@@ -0,0 +1,10 @@
+#! /bin/bash
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+# Convert test set sharded text files into tfrecords that are ready for BERT pretraining
+echo "Creating test set tfrecords for each text shard"
+mkdir -p ${WORKING_DIR}/test_set_text_files
+mkdir -p ${WORKING_DIR}/test_set_tfrecords
+python3 ${WORKING_DIR}/create_pseudo_test_set.py
+. ${WORKING_DIR}/preprocessing_test_set_xargs_wrapper.sh ${N_PROCS_PREPROCESS}
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh
new file mode 100755
index 00000000..4d10a3c4
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh
@@ -0,0 +1,23 @@
+#! /bin/bash
+
+SHARD_INDEX=${1}
+INPUT_FILE="${WORKING_DIR}/final_text_files_sharded/wikipedia.segmented.part.${SHARD_INDEX}.txt"
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+OUTPUT_DIR=${WORKING_DIR}/final_tfrecords_sharded
+mkdir -p ${OUTPUT_DIR}
+
+OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}"
+
+python /workspace/bert/create_pretraining_data.py \
+  --input_file=${INPUT_FILE} \
+  --output_file=${OUTPUT_FILE} \
+  --vocab_file=${VOCAB_FILE} \
+  --do_lower_case=${DO_LOWER_CASE} \
+  --max_seq_length=${MAX_SEQUENCE_LENGTH} \
+  --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \
+  --masked_lm_prob=${MASKED_LM_PROB} \
+  --random_seed=${SEED} \
+  --dupe_factor=${DUPE_FACTOR}
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh
new file mode 100755
index 00000000..214ef1a2
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh
@@ -0,0 +1,28 @@
+#! /bin/bash
+
+INPUT_FILE=${1}
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+OUTPUT_DIR=${WORKING_DIR}/test_set_tfrecords
+mkdir -p ${OUTPUT_DIR}
+
+#SHARD_INDEX=$(( echo ${INPUT_FILE} | egrep -o [0-9]+ ))
+SHARD_INDEX=$( eval echo ${INPUT_FILE} | sed -e s/[^0-9]//g )
+OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}"
+
+SEED=13254
+
+echo "Shard index ${SHARD_INDEX}"
+
+python /workspace/bert/create_pretraining_data.py \
+  --input_file=${INPUT_FILE} \
+  --output_file=${OUTPUT_FILE} \
+  --vocab_file=${VOCAB_FILE} \
+  --do_lower_case=${DO_LOWER_CASE} \
+  --max_seq_length=${MAX_SEQUENCE_LENGTH} \
+  --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \
+  --masked_lm_prob=${MASKED_LM_PROB} \
+  --random_seed=${SEED} \
+  --dupe_factor=${DUPE_FACTOR}
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh
new file mode 100755
index 00000000..8d61469a
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh
@@ -0,0 +1,12 @@
+#! /bin/bash
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+SHARD_COUNT=0
+rm -rf /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+touch /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+for file in /workspace/bert/data/wikipedia_corpus/test_set_text_files/*; do
+  echo ${file} >> /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+done
+
+xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/wikipedia_corpus/xarg_list.txt /workspace/bert/data/wikipedia_corpus/preprocessing_test_set.sh
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh
new file mode 100755
index 00000000..6e52bc75
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh
@@ -0,0 +1,13 @@
+#! /bin/bash
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+SHARD_COUNT=0
+rm -rf /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+touch /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+for file in /workspace/bert/data/wikipedia_corpus/final_text_files_sharded/*; do
+  echo ${SHARD_COUNT} >> /workspace/bert/data/wikipedia_corpus/xarg_list.txt
+  SHARD_COUNT=$((SHARD_COUNT+1))
+done
+
+xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/wikipedia_corpus/xarg_list.txt /workspace/bert/data/wikipedia_corpus/preprocessing.sh
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py
new file mode 100644
index 00000000..69ec57e2
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py
@@ -0,0 +1,30 @@
+# NVIDIA
+
+import glob
+import os
+
+output_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt'
+
+with open(output_file, "w") as ofile:
+  for dirname in glob.glob('extracted_articles/*/', recursive=False):
+    for filename in glob.glob(dirname + 'wiki_*', recursive=True):
+      print(filename)
+      article_lines = []
+      article_open = False
+      
+      with open(filename, "r") as file:
+        for line in file:
+          if "<doc id=" in line:
+            article_open = True
+          elif "</doc>" in line:
+            article_open = False
+            for oline in article_lines[1:]:
+              if oline != "\n":
+                ofile.write(oline.rstrip() + " ")
+            ofile.write("\n\n")
+            article_lines = []
+          else:
+            if article_open:
+              article_lines.append(line)
+            
+
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh
new file mode 100755
index 00000000..4736359b
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh
@@ -0,0 +1,49 @@
+#! /bin/bash
+
+source /workspace/bert/data/wikipedia_corpus/config.sh
+
+# Note: There are several directories created to make it clear what has been performed at each stage of preprocessing. The intermediate files may be useful if you want to further clean/prepare/augment the data for your own applications.
+# NLTK was chosen as the default over spaCy simply due to speed of sentence segmentation on the large files.
+
+# Download Wikipedia dump file
+mkdir -p ${WORKING_DIR}/download
+
+# Not using --noclobber since it emits an error if exists (incompatible with bash 'set -e')
+echo "Downloading Wikidump"
+if [ ! -f ${WORKING_DIR}/download/wikidump.xml.bz2 ]; then
+  cd ${WORKING_DIR}/download && wget -O wikidump.xml.bz2 ${WIKI_DUMP}
+fi
+
+# Extract dump
+echo "Extracting Wikidump"
+mkdir -p ${WORKING_DIR}/raw_data
+#cd ${WORKING_DIR}/raw_data && pv ${WORKING_DIR}/download/wikidump.xml.bz2 | pbzip2 -kdc > ${WORKING_DIR}/raw_data/wikidump.xml
+cd ${WORKING_DIR}/raw_data && pv ${WORKING_DIR}/download/wikidump.xml.bz2 | bunzip2 -kdc > ${WORKING_DIR}/raw_data/wikidump.xml
+#cd ${WORKING_DIR}/raw_data && bunzip2 -kdc ${WORKING_DIR}/download/wikidump.xml.bz2 > ${WORKING_DIR}/raw_data/wikidump.xml
+ 
+# Wikiextractor.py - Creates lots of folders/files in "doc format"
+echo "Running Wikiextractor"
+mkdir -p ${WORKING_DIR}/extracted_articles
+/workspace/wikiextractor/WikiExtractor.py ${WORKING_DIR}/raw_data/wikidump.xml -b 1000M --processes ${N_PROCS_PREPROCESS} -o ${WORKING_DIR}/extracted_articles
+
+# Remove XML Tags and extraneous titles (since they are not sentences)
+# Also clean to remove lines between paragraphs within article and use space-separated articles
+echo "Cleaning and formatting files (one article per line)"
+mkdir -p ${WORKING_DIR}/intermediate_files
+python3 ${WORKING_DIR}/remove_tags_and_clean.py
+
+# Split articles into one-sentence-per-line format for use with BERT scripts
+echo "Applying sentence segmentation to get one sentence per line"
+mkdir -p ${WORKING_DIR}/final_text_file_single
+python3 ${WORKING_DIR}/wiki_sentence_segmentation_nltk.py
+# Note: NLTK can be replaced with Spacy, although it is slower (2 variations provided)
+
+# Shard finalized text so that it has a chance of fitting in memory when creating pretraining data into tfrecords (choose appropriate number of shards for distributed training)
+echo "Shard text files - size is approximate to prevent splitting an article across shards"
+mkdir -p ${WORKING_DIR}/final_text_files_sharded
+python3 ${WORKING_DIR}/shard_text_input_file.py
+
+# Convert sharded text files into tfrecords that are ready for BERT pretraining
+echo "Creating tfrecords for each text shard"
+mkdir -p ${WORKING_DIR}/final_tfrecords_sharded
+. ${WORKING_DIR}/preprocessing_xargs_wrapper.sh ${N_PROCS_PREPROCESS}
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py
new file mode 100644
index 00000000..dad10935
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py
@@ -0,0 +1,39 @@
+# NVIDIA
+
+import os
+
+input_file = os.environ['WORKING_DIR'] + '/final_text_file_single/wikipedia.segmented.nltk.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_text_files_sharded/wikipedia.segmented.part.'
+
+doc_seperator = "\n"
+
+line_buffer = []
+shard_size = 396000 # Approximate, will split at next article break
+line_counter = 0
+shard_index = 0
+
+ifile_lines = 0
+with open(input_file) as ifile:
+  for line in ifile:
+    ifile_lines += 1
+
+print("Input file contains", ifile_lines, "lines.")
+
+iline_counter = 1
+with open(input_file) as ifile:
+  for line in ifile:
+    if line_counter < shard_size and iline_counter < ifile_lines:
+      line_buffer.append(line)
+      line_counter += 1
+      iline_counter += 1
+    elif line_counter >= shard_size and line != "\n" and iline_counter < ifile_lines:
+      line_buffer.append(line)
+      line_counter += 1
+      iline_counter += 1
+    else:
+       with open(output_file + str(shard_index) + ".txt", "w") as ofile:
+         for oline in line_buffer:
+           ofile.write(oline)
+         line_buffer = []
+         line_counter = 0
+         shard_index += 1
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py
new file mode 100644
index 00000000..58381def
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py
@@ -0,0 +1,20 @@
+# NVIDIA
+
+import nltk
+import os
+
+nltk.download('punkt')
+
+input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_text_file_single/wikipedia.segmented.nltk.txt'
+
+doc_seperator = "\n"
+
+with open(input_file) as ifile:
+  with open(output_file, "w") as ofile:
+    for line in ifile:
+      if line != "\n":
+        sent_list = nltk.tokenize.sent_tokenize(line)
+        for sent in sent_list:
+          ofile.write(sent + "\n")
+        ofile.write(doc_seperator)
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py
new file mode 100644
index 00000000..69a061b4
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py
@@ -0,0 +1,22 @@
+# NVIDIA
+
+import os
+import spacy
+
+#spacy.prefer_gpu()
+spacy.require_gpu()
+
+input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_test_file_single/wikipedia.segmented.txt'
+
+nlp = spacy.load('en_core_web_sm')
+
+doc_seperator = "\n"
+
+with open(input_file) as ifile:
+  with open(output_file, "w") as ofile:
+    for line in ifile:
+      if line != "\n":
+        doc = nlp(line)
+        for sent in doc.sents:
+          ofile.write(sent.text + "\n")
diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py
new file mode 100644
index 00000000..ca8b83b0
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py
@@ -0,0 +1,33 @@
+# NVIDIA
+
+import os
+import spacy
+
+#spacy.prefer_gpu()
+spacy.require_gpu()
+
+input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt'
+output_file = os.environ['WORKING_DIR'] + '/final_test_file_single/wikipedia.segmented.txt'
+
+nlp = spacy.load('en_core_web_sm')
+
+doc_seperator = "\n"
+
+file_mem = []
+
+print("Reading file into memory.")
+with open(input_file) as ifile:
+  for line in ifile:
+    if line != "\n":
+      file_mem.append(line)
+
+print("File read.")
+print("Starting nlp.pipe")
+docs = nlp.pipe(file_mem, batch_size=1000)
+
+print("Starting to write output")
+with open(output_file, "w") as ofile:
+  for item in docs:
+    for sent in item.sents:
+      if sent.text != "\n":
+        ofile.write(sent.text + "\n")
diff --git a/TensorFlow/LanguageModeling/BERT/extract_features.py b/TensorFlow/LanguageModeling/BERT/extract_features.py
new file mode 100644
index 00000000..60e3830a
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/extract_features.py
@@ -0,0 +1,419 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Extract pre-computed feature vectors from BERT."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import codecs
+import collections
+import json
+import re
+
+import modeling
+import tokenization
+import tensorflow as tf
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string("input_file", None, "")
+
+flags.DEFINE_string("output_file", None, "")
+
+flags.DEFINE_string("layers", "-1,-2,-3,-4", "")
+
+flags.DEFINE_string(
+    "bert_config_file", None,
+    "The config json file corresponding to the pre-trained BERT model. "
+    "This specifies the model architecture.")
+
+flags.DEFINE_integer(
+    "max_seq_length", 128,
+    "The maximum total input sequence length after WordPiece tokenization. "
+    "Sequences longer than this will be truncated, and sequences shorter "
+    "than this will be padded.")
+
+flags.DEFINE_string(
+    "init_checkpoint", None,
+    "Initial checkpoint (usually from a pre-trained BERT model).")
+
+flags.DEFINE_string("vocab_file", None,
+                    "The vocabulary file that the BERT model was trained on.")
+
+flags.DEFINE_bool(
+    "do_lower_case", True,
+    "Whether to lower case the input text. Should be True for uncased "
+    "models and False for cased models.")
+
+flags.DEFINE_integer("batch_size", 32, "Batch size for predictions.")
+
+flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
+
+flags.DEFINE_string("master", None,
+                    "If using a TPU, the address of the master.")
+
+flags.DEFINE_integer(
+    "num_tpu_cores", 8,
+    "Only used if `use_tpu` is True. Total number of TPU cores to use.")
+
+flags.DEFINE_bool(
+    "use_one_hot_embeddings", False,
+    "If True, tf.one_hot will be used for embedding lookups, otherwise "
+    "tf.nn.embedding_lookup will be used. On TPUs, this should be True "
+    "since it is much faster.")
+
+
+class InputExample(object):
+
+  def __init__(self, unique_id, text_a, text_b):
+    self.unique_id = unique_id
+    self.text_a = text_a
+    self.text_b = text_b
+
+
+class InputFeatures(object):
+  """A single set of features of data."""
+
+  def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
+    self.unique_id = unique_id
+    self.tokens = tokens
+    self.input_ids = input_ids
+    self.input_mask = input_mask
+    self.input_type_ids = input_type_ids
+
+
+def input_fn_builder(features, seq_length):
+  """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+  all_unique_ids = []
+  all_input_ids = []
+  all_input_mask = []
+  all_input_type_ids = []
+
+  for feature in features:
+    all_unique_ids.append(feature.unique_id)
+    all_input_ids.append(feature.input_ids)
+    all_input_mask.append(feature.input_mask)
+    all_input_type_ids.append(feature.input_type_ids)
+
+  def input_fn(params):
+    """The actual input function."""
+    batch_size = params["batch_size"]
+
+    num_examples = len(features)
+
+    # This is for demo purposes and does NOT scale to large data sets. We do
+    # not use Dataset.from_generator() because that uses tf.py_func which is
+    # not TPU compatible. The right way to load data is with TFRecordReader.
+    d = tf.data.Dataset.from_tensor_slices({
+        "unique_ids":
+            tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
+        "input_ids":
+            tf.constant(
+                all_input_ids, shape=[num_examples, seq_length],
+                dtype=tf.int32),
+        "input_mask":
+            tf.constant(
+                all_input_mask,
+                shape=[num_examples, seq_length],
+                dtype=tf.int32),
+        "input_type_ids":
+            tf.constant(
+                all_input_type_ids,
+                shape=[num_examples, seq_length],
+                dtype=tf.int32),
+    })
+
+    d = d.batch(batch_size=batch_size, drop_remainder=False)
+    return d
+
+  return input_fn
+
+
+def model_fn_builder(bert_config, init_checkpoint, layer_indexes, use_tpu,
+                     use_one_hot_embeddings):
+  """Returns `model_fn` closure for TPUEstimator."""
+
+  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
+    """The `model_fn` for TPUEstimator."""
+
+    unique_ids = features["unique_ids"]
+    input_ids = features["input_ids"]
+    input_mask = features["input_mask"]
+    input_type_ids = features["input_type_ids"]
+
+    model = modeling.BertModel(
+        config=bert_config,
+        is_training=False,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        token_type_ids=input_type_ids,
+        use_one_hot_embeddings=use_one_hot_embeddings)
+
+    if mode != tf.estimator.ModeKeys.PREDICT:
+      raise ValueError("Only PREDICT modes are supported: %s" % (mode))
+
+    tvars = tf.trainable_variables()
+    scaffold_fn = None
+    (assignment_map,
+     initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
+         tvars, init_checkpoint)
+    if use_tpu:
+
+      def tpu_scaffold():
+        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+        return tf.train.Scaffold()
+
+      scaffold_fn = tpu_scaffold
+    else:
+      tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+    tf.logging.info("**** Trainable Variables ****")
+    for var in tvars:
+      init_string = ""
+      if var.name in initialized_variable_names:
+        init_string = ", *INIT_FROM_CKPT*"
+      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
+                      init_string)
+
+    all_layers = model.get_all_encoder_layers()
+
+    predictions = {
+        "unique_id": unique_ids,
+    }
+
+    for (i, layer_index) in enumerate(layer_indexes):
+      predictions["layer_output_%d" % i] = all_layers[layer_index]
+
+    output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+        mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
+    return output_spec
+
+  return model_fn
+
+
+def convert_examples_to_features(examples, seq_length, tokenizer):
+  """Loads a data file into a list of `InputBatch`s."""
+
+  features = []
+  for (ex_index, example) in enumerate(examples):
+    tokens_a = tokenizer.tokenize(example.text_a)
+
+    tokens_b = None
+    if example.text_b:
+      tokens_b = tokenizer.tokenize(example.text_b)
+
+    if tokens_b:
+      # Modifies `tokens_a` and `tokens_b` in place so that the total
+      # length is less than the specified length.
+      # Account for [CLS], [SEP], [SEP] with "- 3"
+      _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
+    else:
+      # Account for [CLS] and [SEP] with "- 2"
+      if len(tokens_a) > seq_length - 2:
+        tokens_a = tokens_a[0:(seq_length - 2)]
+
+    # The convention in BERT is:
+    # (a) For sequence pairs:
+    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
+    # (b) For single sequences:
+    #  tokens:   [CLS] the dog is hairy . [SEP]
+    #  type_ids: 0     0   0   0  0     0 0
+    #
+    # Where "type_ids" are used to indicate whether this is the first
+    # sequence or the second sequence. The embedding vectors for `type=0` and
+    # `type=1` were learned during pre-training and are added to the wordpiece
+    # embedding vector (and position vector). This is not *strictly* necessary
+    # since the [SEP] token unambiguously separates the sequences, but it makes
+    # it easier for the model to learn the concept of sequences.
+    #
+    # For classification tasks, the first vector (corresponding to [CLS]) is
+    # used as as the "sentence vector". Note that this only makes sense because
+    # the entire model is fine-tuned.
+    tokens = []
+    input_type_ids = []
+    tokens.append("[CLS]")
+    input_type_ids.append(0)
+    for token in tokens_a:
+      tokens.append(token)
+      input_type_ids.append(0)
+    tokens.append("[SEP]")
+    input_type_ids.append(0)
+
+    if tokens_b:
+      for token in tokens_b:
+        tokens.append(token)
+        input_type_ids.append(1)
+      tokens.append("[SEP]")
+      input_type_ids.append(1)
+
+    input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+    # The mask has 1 for real tokens and 0 for padding tokens. Only real
+    # tokens are attended to.
+    input_mask = [1] * len(input_ids)
+
+    # Zero-pad up to the sequence length.
+    while len(input_ids) < seq_length:
+      input_ids.append(0)
+      input_mask.append(0)
+      input_type_ids.append(0)
+
+    assert len(input_ids) == seq_length
+    assert len(input_mask) == seq_length
+    assert len(input_type_ids) == seq_length
+
+    if ex_index < 5:
+      tf.logging.info("*** Example ***")
+      tf.logging.info("unique_id: %s" % (example.unique_id))
+      tf.logging.info("tokens: %s" % " ".join(
+          [tokenization.printable_text(x) for x in tokens]))
+      tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+      tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+      tf.logging.info(
+          "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
+
+    features.append(
+        InputFeatures(
+            unique_id=example.unique_id,
+            tokens=tokens,
+            input_ids=input_ids,
+            input_mask=input_mask,
+            input_type_ids=input_type_ids))
+  return features
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+  """Truncates a sequence pair in place to the maximum length."""
+
+  # This is a simple heuristic which will always truncate the longer sequence
+  # one token at a time. This makes more sense than truncating an equal percent
+  # of tokens from each, since if one sequence is very short then each token
+  # that's truncated likely contains more information than a longer sequence.
+  while True:
+    total_length = len(tokens_a) + len(tokens_b)
+    if total_length <= max_length:
+      break
+    if len(tokens_a) > len(tokens_b):
+      tokens_a.pop()
+    else:
+      tokens_b.pop()
+
+
+def read_examples(input_file):
+  """Read a list of `InputExample`s from an input file."""
+  examples = []
+  unique_id = 0
+  with tf.gfile.GFile(input_file, "r") as reader:
+    while True:
+      line = tokenization.convert_to_unicode(reader.readline())
+      if not line:
+        break
+      line = line.strip()
+      text_a = None
+      text_b = None
+      m = re.match(r"^(.*) \|\|\| (.*)$", line)
+      if m is None:
+        text_a = line
+      else:
+        text_a = m.group(1)
+        text_b = m.group(2)
+      examples.append(
+          InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
+      unique_id += 1
+  return examples
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  layer_indexes = [int(x) for x in FLAGS.layers.split(",")]
+
+  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
+
+  tokenizer = tokenization.FullTokenizer(
+      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
+
+  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      master=FLAGS.master,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          num_shards=FLAGS.num_tpu_cores,
+          per_host_input_for_training=is_per_host))
+
+  examples = read_examples(FLAGS.input_file)
+
+  features = convert_examples_to_features(
+      examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer)
+
+  unique_id_to_feature = {}
+  for feature in features:
+    unique_id_to_feature[feature.unique_id] = feature
+
+  model_fn = model_fn_builder(
+      bert_config=bert_config,
+      init_checkpoint=FLAGS.init_checkpoint,
+      layer_indexes=layer_indexes,
+      use_tpu=FLAGS.use_tpu,
+      use_one_hot_embeddings=FLAGS.use_one_hot_embeddings)
+
+  # If TPU is not available, this will fall back to normal Estimator on CPU
+  # or GPU.
+  estimator = tf.contrib.tpu.TPUEstimator(
+      use_tpu=FLAGS.use_tpu,
+      model_fn=model_fn,
+      config=run_config,
+      predict_batch_size=FLAGS.batch_size)
+
+  input_fn = input_fn_builder(
+      features=features, seq_length=FLAGS.max_seq_length)
+
+  with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file,
+                                               "w")) as writer:
+    for result in estimator.predict(input_fn, yield_single_examples=True):
+      unique_id = int(result["unique_id"])
+      feature = unique_id_to_feature[unique_id]
+      output_json = collections.OrderedDict()
+      output_json["linex_index"] = unique_id
+      all_features = []
+      for (i, token) in enumerate(feature.tokens):
+        all_layers = []
+        for (j, layer_index) in enumerate(layer_indexes):
+          layer_output = result["layer_output_%d" % j]
+          layers = collections.OrderedDict()
+          layers["index"] = layer_index
+          layers["values"] = [
+              round(float(x), 6) for x in layer_output[i:(i + 1)].flat
+          ]
+          all_layers.append(layers)
+        features = collections.OrderedDict()
+        features["token"] = token
+        features["layers"] = all_layers
+        all_features.append(features)
+      output_json["features"] = all_features
+      writer.write(json.dumps(output_json) + "\n")
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("input_file")
+  flags.mark_flag_as_required("vocab_file")
+  flags.mark_flag_as_required("bert_config_file")
+  flags.mark_flag_as_required("init_checkpoint")
+  flags.mark_flag_as_required("output_file")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/gpu_environment.py b/TensorFlow/LanguageModeling/BERT/gpu_environment.py
new file mode 100644
index 00000000..948c3fa4
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/gpu_environment.py
@@ -0,0 +1,36 @@
+# coding=utf-8
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+import numpy as np
+
+def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
+                                    initializer=None, regularizer=None,
+                                    trainable=True,
+                                    *args, **kwargs):
+    """Custom variable getter that forces trainable variables to be stored in
+       float32 precision and then casts them to the training precision.
+    """
+    storage_dtype = tf.float32 if trainable else dtype
+    variable = getter(name, shape, dtype=storage_dtype,
+                      initializer=initializer, regularizer=regularizer,
+                      trainable=trainable,
+                      *args, **kwargs)
+    if trainable and dtype != tf.float32:
+        variable = tf.cast(variable, dtype)
+    return variable
+
+def get_custom_getter(compute_type):
+    return float32_variable_storage_getter if compute_type == tf.float16 else None
diff --git a/TensorFlow/LanguageModeling/BERT/modeling.py b/TensorFlow/LanguageModeling/BERT/modeling.py
new file mode 100644
index 00000000..828a9387
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/modeling.py
@@ -0,0 +1,990 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The main BERT model and related functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import copy
+import json
+import math
+import re
+import numpy as np
+import six
+import tensorflow as tf
+
+from gpu_environment import get_custom_getter
+
+class BertConfig(object):
+  """Configuration for `BertModel`."""
+
+  def __init__(self,
+               vocab_size,
+               hidden_size=768,
+               num_hidden_layers=12,
+               num_attention_heads=12,
+               intermediate_size=3072,
+               hidden_act="gelu",
+               hidden_dropout_prob=0.1,
+               attention_probs_dropout_prob=0.1,
+               max_position_embeddings=512,
+               type_vocab_size=16,
+               initializer_range=0.02):
+    """Constructs BertConfig.
+
+    Args:
+      vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
+      hidden_size: Size of the encoder layers and the pooler layer.
+      num_hidden_layers: Number of hidden layers in the Transformer encoder.
+      num_attention_heads: Number of attention heads for each attention layer in
+        the Transformer encoder.
+      intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+        layer in the Transformer encoder.
+      hidden_act: The non-linear activation function (function or string) in the
+        encoder and pooler.
+      hidden_dropout_prob: The dropout probability for all fully connected
+        layers in the embeddings, encoder, and pooler.
+      attention_probs_dropout_prob: The dropout ratio for the attention
+        probabilities.
+      max_position_embeddings: The maximum sequence length that this model might
+        ever be used with. Typically set this to something large just in case
+        (e.g., 512 or 1024 or 2048).
+      type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+        `BertModel`.
+      initializer_range: The stdev of the truncated_normal_initializer for
+        initializing all weight matrices.
+    """
+    self.vocab_size = vocab_size
+    self.hidden_size = hidden_size
+    self.num_hidden_layers = num_hidden_layers
+    self.num_attention_heads = num_attention_heads
+    self.hidden_act = hidden_act
+    self.intermediate_size = intermediate_size
+    self.hidden_dropout_prob = hidden_dropout_prob
+    self.attention_probs_dropout_prob = attention_probs_dropout_prob
+    self.max_position_embeddings = max_position_embeddings
+    self.type_vocab_size = type_vocab_size
+    self.initializer_range = initializer_range
+
+  @classmethod
+  def from_dict(cls, json_object):
+    """Constructs a `BertConfig` from a Python dictionary of parameters."""
+    config = BertConfig(vocab_size=None)
+    for (key, value) in six.iteritems(json_object):
+      config.__dict__[key] = value
+    return config
+
+  @classmethod
+  def from_json_file(cls, json_file):
+    """Constructs a `BertConfig` from a json file of parameters."""
+    with tf.gfile.GFile(json_file, "r") as reader:
+      text = reader.read()
+    return cls.from_dict(json.loads(text))
+
+  def to_dict(self):
+    """Serializes this instance to a Python dictionary."""
+    output = copy.deepcopy(self.__dict__)
+    return output
+
+  def to_json_string(self):
+    """Serializes this instance to a JSON string."""
+    return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+
+class BertModel(object):
+  """BERT model ("Bidirectional Encoder Representations from Transformers").
+
+  Example usage:
+
+  ```python
+  # Already been converted into WordPiece token ids
+  input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
+  input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
+  token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
+
+  config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
+    num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+
+  model = modeling.BertModel(config=config, is_training=True,
+    input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
+
+  label_embeddings = tf.get_variable(...)
+  pooled_output = model.get_pooled_output()
+  logits = tf.matmul(pooled_output, label_embeddings)
+  ...
+  ```
+  """
+
+  def __init__(self,
+               config,
+               is_training,
+               input_ids,
+               input_mask=None,
+               token_type_ids=None,
+               use_one_hot_embeddings=False,
+               scope=None,
+               compute_type=tf.float32):
+    """Constructor for BertModel.
+
+    Args:
+      config: `BertConfig` instance.
+      is_training: bool. true for training model, false for eval model. Controls
+        whether dropout will be applied.
+      input_ids: int32 Tensor of shape [batch_size, seq_length].
+      input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
+      token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
+      use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
+        embeddings or tf.embedding_lookup() for the word embeddings. On the TPU,
+        it is much faster if this is True, on the CPU or GPU, it is faster if
+        this is False.
+      scope: (optional) variable scope. Defaults to "bert".
+
+    Raises:
+      ValueError: The config is invalid or one of the input tensor shapes
+        is invalid.
+    """
+    config = copy.deepcopy(config)
+    if not is_training:
+      config.hidden_dropout_prob = 0.0
+      config.attention_probs_dropout_prob = 0.0
+
+    input_shape = get_shape_list(input_ids, expected_rank=2)
+    batch_size = input_shape[0]
+    seq_length = input_shape[1]
+
+    if input_mask is None:
+      input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
+
+    if token_type_ids is None:
+      token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
+
+    with tf.variable_scope(scope, default_name="bert", custom_getter=get_custom_getter(compute_type)):
+      with tf.variable_scope("embeddings"):
+        # Perform embedding lookup on the word ids.
+        (self.embedding_output, self.embedding_table) = embedding_lookup(
+            input_ids=input_ids,
+            vocab_size=config.vocab_size,
+            embedding_size=config.hidden_size,
+            initializer_range=config.initializer_range,
+            word_embedding_name="word_embeddings",
+            use_one_hot_embeddings=use_one_hot_embeddings)
+
+        # Add positional embeddings and token type embeddings, then layer
+        # normalize and perform dropout.
+        self.embedding_output = embedding_postprocessor(
+            input_tensor=self.embedding_output,
+            use_token_type=True,
+            token_type_ids=token_type_ids,
+            token_type_vocab_size=config.type_vocab_size,
+            token_type_embedding_name="token_type_embeddings",
+            use_position_embeddings=True,
+            position_embedding_name="position_embeddings",
+            initializer_range=config.initializer_range,
+            max_position_embeddings=config.max_position_embeddings,
+            dropout_prob=config.hidden_dropout_prob)
+
+      with tf.variable_scope("encoder"):
+        # This converts a 2D mask of shape [batch_size, seq_length] to a 3D
+        # mask of shape [batch_size, seq_length, seq_length] which is used
+        # for the attention scores.
+        attention_mask = create_attention_mask_from_input_mask(
+            input_ids, input_mask)
+
+        # Run the stacked transformer.
+        # `sequence_output` shape = [batch_size, seq_length, hidden_size].
+        self.all_encoder_layers = transformer_model(
+            input_tensor=tf.saturate_cast(self.embedding_output, compute_type),
+            attention_mask=attention_mask,
+            hidden_size=config.hidden_size,
+            num_hidden_layers=config.num_hidden_layers,
+            num_attention_heads=config.num_attention_heads,
+            intermediate_size=config.intermediate_size,
+            intermediate_act_fn=get_activation(config.hidden_act),
+            hidden_dropout_prob=config.hidden_dropout_prob,
+            attention_probs_dropout_prob=config.attention_probs_dropout_prob,
+            initializer_range=config.initializer_range,
+            do_return_all_layers=True)
+
+      self.sequence_output = tf.cast(self.all_encoder_layers[-1], tf.float32)
+      # The "pooler" converts the encoded sequence tensor of shape
+      # [batch_size, seq_length, hidden_size] to a tensor of shape
+      # [batch_size, hidden_size]. This is necessary for segment-level
+      # (or segment-pair-level) classification tasks where we need a fixed
+      # dimensional representation of the segment.
+      with tf.variable_scope("pooler"):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token. We assume that this has been pre-trained
+        first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
+        self.pooled_output = tf.layers.dense(
+            first_token_tensor,
+            config.hidden_size,
+            activation=tf.tanh,
+            kernel_initializer=create_initializer(config.initializer_range))
+
+  def get_pooled_output(self):
+    return self.pooled_output
+
+  def get_sequence_output(self):
+    """Gets final hidden layer of encoder.
+
+    Returns:
+      float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
+      to the final hidden of the transformer encoder.
+    """
+    return self.sequence_output
+
+  def get_all_encoder_layers(self):
+    return self.all_encoder_layers
+
+  def get_embedding_output(self):
+    """Gets output of the embedding lookup (i.e., input to the transformer).
+
+    Returns:
+      float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
+      to the output of the embedding layer, after summing the word
+      embeddings with the positional embeddings and the token type embeddings,
+      then performing layer normalization. This is the input to the transformer.
+    """
+    return self.embedding_output
+
+  def get_embedding_table(self):
+    return self.embedding_table
+
+
+def gelu(x):
+  """Gaussian Error Linear Unit.
+
+  This is a smoother version of the RELU.
+  Original paper: https://arxiv.org/abs/1606.08415
+  Args:
+    x: float Tensor to perform activation.
+
+  Returns:
+    `x` with the GELU activation applied.
+  """
+  cdf = 0.5 * (1.0 + tf.tanh(
+      (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
+  return x * cdf
+
+
+def get_activation(activation_string):
+  """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.
+
+  Args:
+    activation_string: String name of the activation function.
+
+  Returns:
+    A Python function corresponding to the activation function. If
+    `activation_string` is None, empty, or "linear", this will return None.
+    If `activation_string` is not a string, it will return `activation_string`.
+
+  Raises:
+    ValueError: The `activation_string` does not correspond to a known
+      activation.
+  """
+
+  # We assume that anything that"s not a string is already an activation
+  # function, so we just return it.
+  if not isinstance(activation_string, six.string_types):
+    return activation_string
+
+  if not activation_string:
+    return None
+
+  act = activation_string.lower()
+  if act == "linear":
+    return None
+  elif act == "relu":
+    return tf.nn.relu
+  elif act == "gelu":
+    return gelu
+  elif act == "tanh":
+    return tf.tanh
+  else:
+    raise ValueError("Unsupported activation: %s" % act)
+
+
+def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
+  """Compute the union of the current variables and checkpoint variables."""
+  assignment_map = {}
+  initialized_variable_names = {}
+
+  name_to_variable = collections.OrderedDict()
+  for var in tvars:
+    name = var.name
+    m = re.match("^(.*):\\d+$", name)
+    if m is not None:
+      name = m.group(1)
+    name_to_variable[name] = var
+
+  init_vars = tf.train.list_variables(init_checkpoint)
+
+  assignment_map = collections.OrderedDict()
+  for x in init_vars:
+    (name, var) = (x[0], x[1])
+    if name not in name_to_variable:
+      continue
+    assignment_map[name] = name
+    initialized_variable_names[name] = 1
+    initialized_variable_names[name + ":0"] = 1
+
+  return (assignment_map, initialized_variable_names)
+
+
+def dropout(input_tensor, dropout_prob):
+  """Perform dropout.
+
+  Args:
+    input_tensor: float Tensor.
+    dropout_prob: Python float. The probability of dropping out a value (NOT of
+      *keeping* a dimension as in `tf.nn.dropout`).
+
+  Returns:
+    A version of `input_tensor` with dropout applied.
+  """
+  if dropout_prob is None or dropout_prob == 0.0:
+    return input_tensor
+
+  output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)
+  return output
+
+
+def layer_norm(input_tensor, name=None):
+  """Run layer normalization on the last dimension of the tensor."""
+  return tf.contrib.layers.layer_norm(
+      inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
+
+
+def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
+  """Runs layer normalization followed by dropout."""
+  output_tensor = layer_norm(input_tensor, name)
+  output_tensor = dropout(output_tensor, dropout_prob)
+  return output_tensor
+
+
+def create_initializer(initializer_range=0.02):
+  """Creates a `truncated_normal_initializer` with the given range."""
+  return tf.truncated_normal_initializer(stddev=initializer_range)
+
+
+def embedding_lookup(input_ids,
+                     vocab_size,
+                     embedding_size=128,
+                     initializer_range=0.02,
+                     word_embedding_name="word_embeddings",
+                     use_one_hot_embeddings=False):
+  """Looks up words embeddings for id tensor.
+
+  Args:
+    input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
+      ids.
+    vocab_size: int. Size of the embedding vocabulary.
+    embedding_size: int. Width of the word embeddings.
+    initializer_range: float. Embedding initialization range.
+    word_embedding_name: string. Name of the embedding table.
+    use_one_hot_embeddings: bool. If True, use one-hot method for word
+      embeddings. If False, use `tf.gather()`.
+
+  Returns:
+    float Tensor of shape [batch_size, seq_length, embedding_size].
+  """
+  # This function assumes that the input is of shape [batch_size, seq_length,
+  # num_inputs].
+  #
+  # If the input is a 2D tensor of shape [batch_size, seq_length], we
+  # reshape to [batch_size, seq_length, 1].
+  if input_ids.shape.ndims == 2:
+    input_ids = tf.expand_dims(input_ids, axis=[-1])
+
+  embedding_table = tf.get_variable(
+      name=word_embedding_name,
+      shape=[vocab_size, embedding_size],
+      initializer=create_initializer(initializer_range))
+
+  flat_input_ids = tf.reshape(input_ids, [-1])
+  if use_one_hot_embeddings:
+    one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
+    output = tf.matmul(one_hot_input_ids, embedding_table)
+  else:
+    output = tf.gather(embedding_table, flat_input_ids)
+
+  input_shape = get_shape_list(input_ids)
+
+  output = tf.reshape(output,
+                      input_shape[0:-1] + [input_shape[-1] * embedding_size])
+  return (output, embedding_table)
+
+
+def embedding_postprocessor(input_tensor,
+                            use_token_type=False,
+                            token_type_ids=None,
+                            token_type_vocab_size=16,
+                            token_type_embedding_name="token_type_embeddings",
+                            use_position_embeddings=True,
+                            position_embedding_name="position_embeddings",
+                            initializer_range=0.02,
+                            max_position_embeddings=512,
+                            dropout_prob=0.1):
+  """Performs various post-processing on a word embedding tensor.
+
+  Args:
+    input_tensor: float Tensor of shape [batch_size, seq_length,
+      embedding_size].
+    use_token_type: bool. Whether to add embeddings for `token_type_ids`.
+    token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
+      Must be specified if `use_token_type` is True.
+    token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
+    token_type_embedding_name: string. The name of the embedding table variable
+      for token type ids.
+    use_position_embeddings: bool. Whether to add position embeddings for the
+      position of each token in the sequence.
+    position_embedding_name: string. The name of the embedding table variable
+      for positional embeddings.
+    initializer_range: float. Range of the weight initialization.
+    max_position_embeddings: int. Maximum sequence length that might ever be
+      used with this model. This can be longer than the sequence length of
+      input_tensor, but cannot be shorter.
+    dropout_prob: float. Dropout probability applied to the final output tensor.
+
+  Returns:
+    float tensor with same shape as `input_tensor`.
+
+  Raises:
+    ValueError: One of the tensor shapes or input values is invalid.
+  """
+  input_shape = get_shape_list(input_tensor, expected_rank=3)
+  batch_size = input_shape[0]
+  seq_length = input_shape[1]
+  width = input_shape[2]
+
+  output = input_tensor
+
+  if use_token_type:
+    if token_type_ids is None:
+      raise ValueError("`token_type_ids` must be specified if"
+                       "`use_token_type` is True.")
+    token_type_table = tf.get_variable(
+        name=token_type_embedding_name,
+        shape=[token_type_vocab_size, width],
+        initializer=create_initializer(initializer_range))
+    # This vocab will be small so we always do one-hot here, since it is always
+    # faster for a small vocabulary.
+    flat_token_type_ids = tf.reshape(token_type_ids, [-1])
+    one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
+    token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
+    token_type_embeddings = tf.reshape(token_type_embeddings,
+                                       [batch_size, seq_length, width])
+    output += token_type_embeddings
+
+  if use_position_embeddings:
+    assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
+    with tf.control_dependencies([assert_op]):
+      full_position_embeddings = tf.get_variable(
+          name=position_embedding_name,
+          shape=[max_position_embeddings, width],
+          initializer=create_initializer(initializer_range))
+      # Since the position embedding table is a learned variable, we create it
+      # using a (long) sequence length `max_position_embeddings`. The actual
+      # sequence length might be shorter than this, for faster training of
+      # tasks that do not have long sequences.
+      #
+      # So `full_position_embeddings` is effectively an embedding table
+      # for position [0, 1, 2, ..., max_position_embeddings-1], and the current
+      # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
+      # perform a slice.
+      position_embeddings = tf.slice(full_position_embeddings, [0, 0],
+                                     [seq_length, -1])
+      num_dims = len(output.shape.as_list())
+
+      # Only the last two dimensions are relevant (`seq_length` and `width`), so
+      # we broadcast among the first dimensions, which is typically just
+      # the batch size.
+      position_broadcast_shape = []
+      for _ in range(num_dims - 2):
+        position_broadcast_shape.append(1)
+      position_broadcast_shape.extend([seq_length, width])
+      position_embeddings = tf.reshape(position_embeddings,
+                                       position_broadcast_shape)
+      output += position_embeddings
+
+  output = layer_norm_and_dropout(output, dropout_prob)
+  return output
+
+
+def create_attention_mask_from_input_mask(from_tensor, to_mask):
+  """Create 3D attention mask from a 2D tensor mask.
+
+  Args:
+    from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
+    to_mask: int32 Tensor of shape [batch_size, to_seq_length].
+
+  Returns:
+    float Tensor of shape [batch_size, from_seq_length, to_seq_length].
+  """
+  from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
+  batch_size = from_shape[0]
+  from_seq_length = from_shape[1]
+
+  to_shape = get_shape_list(to_mask, expected_rank=2)
+  to_seq_length = to_shape[1]
+
+  to_mask = tf.cast(
+      tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)
+
+  # We don't assume that `from_tensor` is a mask (although it could be). We
+  # don't actually care if we attend *from* padding tokens (only *to* padding)
+  # tokens so we create a tensor of all ones.
+  #
+  # `broadcast_ones` = [batch_size, from_seq_length, 1]
+  broadcast_ones = tf.ones(
+      shape=[batch_size, from_seq_length, 1], dtype=tf.float32)
+
+  # Here we broadcast along two dimensions to create the mask.
+  mask = broadcast_ones * to_mask
+
+  return mask
+
+
+def attention_layer(from_tensor,
+                    to_tensor,
+                    attention_mask=None,
+                    num_attention_heads=1,
+                    size_per_head=512,
+                    query_act=None,
+                    key_act=None,
+                    value_act=None,
+                    attention_probs_dropout_prob=0.0,
+                    initializer_range=0.02,
+                    do_return_2d_tensor=False,
+                    batch_size=None,
+                    from_seq_length=None,
+                    to_seq_length=None):
+  """Performs multi-headed attention from `from_tensor` to `to_tensor`.
+
+  This is an implementation of multi-headed attention based on "Attention
+  is all you Need". If `from_tensor` and `to_tensor` are the same, then
+  this is self-attention. Each timestep in `from_tensor` attends to the
+  corresponding sequence in `to_tensor`, and returns a fixed-with vector.
+
+  This function first projects `from_tensor` into a "query" tensor and
+  `to_tensor` into "key" and "value" tensors. These are (effectively) a list
+  of tensors of length `num_attention_heads`, where each tensor is of shape
+  [batch_size, seq_length, size_per_head].
+
+  Then, the query and key tensors are dot-producted and scaled. These are
+  softmaxed to obtain attention probabilities. The value tensors are then
+  interpolated by these probabilities, then concatenated back to a single
+  tensor and returned.
+
+  In practice, the multi-headed attention are done with transposes and
+  reshapes rather than actual separate tensors.
+
+  Args:
+    from_tensor: float Tensor of shape [batch_size, from_seq_length,
+      from_width].
+    to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
+    attention_mask: (optional) int32 Tensor of shape [batch_size,
+      from_seq_length, to_seq_length]. The values should be 1 or 0. The
+      attention scores will effectively be set to -infinity for any positions in
+      the mask that are 0, and will be unchanged for positions that are 1.
+    num_attention_heads: int. Number of attention heads.
+    size_per_head: int. Size of each attention head.
+    query_act: (optional) Activation function for the query transform.
+    key_act: (optional) Activation function for the key transform.
+    value_act: (optional) Activation function for the value transform.
+    attention_probs_dropout_prob: (optional) float. Dropout probability of the
+      attention probabilities.
+    initializer_range: float. Range of the weight initializer.
+    do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
+      * from_seq_length, num_attention_heads * size_per_head]. If False, the
+      output will be of shape [batch_size, from_seq_length, num_attention_heads
+      * size_per_head].
+    batch_size: (Optional) int. If the input is 2D, this might be the batch size
+      of the 3D version of the `from_tensor` and `to_tensor`.
+    from_seq_length: (Optional) If the input is 2D, this might be the seq length
+      of the 3D version of the `from_tensor`.
+    to_seq_length: (Optional) If the input is 2D, this might be the seq length
+      of the 3D version of the `to_tensor`.
+
+  Returns:
+    float Tensor of shape [batch_size, from_seq_length,
+      num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
+      true, this will be of shape [batch_size * from_seq_length,
+      num_attention_heads * size_per_head]).
+
+  Raises:
+    ValueError: Any of the arguments or tensor shapes are invalid.
+  """
+
+  def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
+                           seq_length, width):
+    output_tensor = tf.reshape(
+        input_tensor, [batch_size, seq_length, num_attention_heads, width])
+
+    output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
+    return output_tensor
+
+  from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
+  to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
+
+  if len(from_shape) != len(to_shape):
+    raise ValueError(
+        "The rank of `from_tensor` must match the rank of `to_tensor`.")
+
+  if len(from_shape) == 3:
+    batch_size = from_shape[0]
+    from_seq_length = from_shape[1]
+    to_seq_length = to_shape[1]
+  elif len(from_shape) == 2:
+    if (batch_size is None or from_seq_length is None or to_seq_length is None):
+      raise ValueError(
+          "When passing in rank 2 tensors to attention_layer, the values "
+          "for `batch_size`, `from_seq_length`, and `to_seq_length` "
+          "must all be specified.")
+
+  # Scalar dimensions referenced here:
+  #   B = batch size (number of sequences)
+  #   F = `from_tensor` sequence length
+  #   T = `to_tensor` sequence length
+  #   N = `num_attention_heads`
+  #   H = `size_per_head`
+
+  from_tensor_2d = reshape_to_matrix(from_tensor)
+  to_tensor_2d = reshape_to_matrix(to_tensor)
+
+  # `query_layer` = [B*F, N*H]
+  query_layer = tf.layers.dense(
+      from_tensor_2d,
+      num_attention_heads * size_per_head,
+      activation=query_act,
+      name="query",
+      kernel_initializer=create_initializer(initializer_range))
+
+  # `key_layer` = [B*T, N*H]
+  key_layer = tf.layers.dense(
+      to_tensor_2d,
+      num_attention_heads * size_per_head,
+      activation=key_act,
+      name="key",
+      kernel_initializer=create_initializer(initializer_range))
+
+  # `value_layer` = [B*T, N*H]
+  value_layer = tf.layers.dense(
+      to_tensor_2d,
+      num_attention_heads * size_per_head,
+      activation=value_act,
+      name="value",
+      kernel_initializer=create_initializer(initializer_range))
+
+  # `query_layer` = [B, N, F, H]
+  query_layer = transpose_for_scores(query_layer, batch_size,
+                                     num_attention_heads, from_seq_length,
+                                     size_per_head)
+
+  # `key_layer` = [B, N, T, H]
+  key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
+                                   to_seq_length, size_per_head)
+
+  # Take the dot product between "query" and "key" to get the raw
+  # attention scores.
+  # `attention_scores` = [B, N, F, T]
+  attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+  attention_scores = tf.multiply(attention_scores,
+                                 1.0 / math.sqrt(float(size_per_head)))
+
+  if attention_mask is not None:
+    # `attention_mask` = [B, 1, F, T]
+    attention_mask = tf.expand_dims(attention_mask, axis=[1])
+
+    # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+    # masked positions, this operation will create a tensor which is 0.0 for
+    # positions we want to attend and -10000.0 for masked positions.
+    adder = (1.0 - tf.cast(attention_mask, attention_scores.dtype)) * -10000.0
+
+    # Since we are adding it to the raw scores before the softmax, this is
+    # effectively the same as removing these entirely.
+    attention_scores += adder
+
+  # Normalize the attention scores to probabilities.
+  # `attention_probs` = [B, N, F, T]
+  attention_probs = tf.nn.softmax(attention_scores)
+
+  # This is actually dropping out entire tokens to attend to, which might
+  # seem a bit unusual, but is taken from the original Transformer paper.
+  attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
+
+  # `value_layer` = [B, T, N, H]
+  value_layer = tf.reshape(
+      value_layer,
+      [batch_size, to_seq_length, num_attention_heads, size_per_head])
+
+  # `value_layer` = [B, N, T, H]
+  value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
+
+  # `context_layer` = [B, N, F, H]
+  context_layer = tf.matmul(attention_probs, value_layer)
+
+  # `context_layer` = [B, F, N, H]
+  context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
+
+  if do_return_2d_tensor:
+    # `context_layer` = [B*F, N*H]
+    context_layer = tf.reshape(
+        context_layer,
+        [batch_size * from_seq_length, num_attention_heads * size_per_head])
+  else:
+    # `context_layer` = [B, F, N*H]
+    context_layer = tf.reshape(
+        context_layer,
+        [batch_size, from_seq_length, num_attention_heads * size_per_head])
+
+  return context_layer
+
+
+def transformer_model(input_tensor,
+                      attention_mask=None,
+                      hidden_size=768,
+                      num_hidden_layers=12,
+                      num_attention_heads=12,
+                      intermediate_size=3072,
+                      intermediate_act_fn=gelu,
+                      hidden_dropout_prob=0.1,
+                      attention_probs_dropout_prob=0.1,
+                      initializer_range=0.02,
+                      do_return_all_layers=False):
+  """Multi-headed, multi-layer Transformer from "Attention is All You Need".
+
+  This is almost an exact implementation of the original Transformer encoder.
+
+  See the original paper:
+  https://arxiv.org/abs/1706.03762
+
+  Also see:
+  https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
+
+  Args:
+    input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
+    attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
+      seq_length], with 1 for positions that can be attended to and 0 in
+      positions that should not be.
+    hidden_size: int. Hidden size of the Transformer.
+    num_hidden_layers: int. Number of layers (blocks) in the Transformer.
+    num_attention_heads: int. Number of attention heads in the Transformer.
+    intermediate_size: int. The size of the "intermediate" (a.k.a., feed
+      forward) layer.
+    intermediate_act_fn: function. The non-linear activation function to apply
+      to the output of the intermediate/feed-forward layer.
+    hidden_dropout_prob: float. Dropout probability for the hidden layers.
+    attention_probs_dropout_prob: float. Dropout probability of the attention
+      probabilities.
+    initializer_range: float. Range of the initializer (stddev of truncated
+      normal).
+    do_return_all_layers: Whether to also return all layers or just the final
+      layer.
+
+  Returns:
+    float Tensor of shape [batch_size, seq_length, hidden_size], the final
+    hidden layer of the Transformer.
+
+  Raises:
+    ValueError: A Tensor shape or parameter is invalid.
+  """
+  if hidden_size % num_attention_heads != 0:
+    raise ValueError(
+        "The hidden size (%d) is not a multiple of the number of attention "
+        "heads (%d)" % (hidden_size, num_attention_heads))
+
+  attention_head_size = int(hidden_size / num_attention_heads)
+  input_shape = get_shape_list(input_tensor, expected_rank=3)
+  batch_size = input_shape[0]
+  seq_length = input_shape[1]
+  input_width = input_shape[2]
+
+  # The Transformer performs sum residuals on all layers so the input needs
+  # to be the same as the hidden size.
+  if input_width != hidden_size:
+    raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
+                     (input_width, hidden_size))
+
+  # We keep the representation as a 2D tensor to avoid re-shaping it back and
+  # forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
+  # the GPU/CPU but may not be free on the TPU, so we want to minimize them to
+  # help the optimizer.
+  prev_output = reshape_to_matrix(input_tensor)
+
+  all_layer_outputs = []
+  for layer_idx in range(num_hidden_layers):
+    with tf.variable_scope("layer_%d" % layer_idx):
+      layer_input = prev_output
+
+      with tf.variable_scope("attention"):
+        attention_heads = []
+        with tf.variable_scope("self"):
+          attention_head = attention_layer(
+              from_tensor=layer_input,
+              to_tensor=layer_input,
+              attention_mask=attention_mask,
+              num_attention_heads=num_attention_heads,
+              size_per_head=attention_head_size,
+              attention_probs_dropout_prob=attention_probs_dropout_prob,
+              initializer_range=initializer_range,
+              do_return_2d_tensor=True,
+              batch_size=batch_size,
+              from_seq_length=seq_length,
+              to_seq_length=seq_length)
+          attention_heads.append(attention_head)
+
+        attention_output = None
+        if len(attention_heads) == 1:
+          attention_output = attention_heads[0]
+        else:
+          # In the case where we have other sequences, we just concatenate
+          # them to the self-attention head before the projection.
+          attention_output = tf.concat(attention_heads, axis=-1)
+
+        # Run a linear projection of `hidden_size` then add a residual
+        # with `layer_input`.
+        with tf.variable_scope("output"):
+          attention_output = tf.layers.dense(
+              attention_output,
+              hidden_size,
+              kernel_initializer=create_initializer(initializer_range))
+          attention_output = dropout(attention_output, hidden_dropout_prob)
+          attention_output = layer_norm(attention_output + layer_input)
+
+      # The activation is only applied to the "intermediate" hidden layer.
+      with tf.variable_scope("intermediate"):
+        intermediate_output = tf.layers.dense(
+            attention_output,
+            intermediate_size,
+            activation=intermediate_act_fn,
+            kernel_initializer=create_initializer(initializer_range))
+
+      # Down-project back to `hidden_size` then add the residual.
+      with tf.variable_scope("output"):
+        layer_output = tf.layers.dense(
+            intermediate_output,
+            hidden_size,
+            kernel_initializer=create_initializer(initializer_range))
+        layer_output = dropout(layer_output, hidden_dropout_prob)
+        layer_output = layer_norm(layer_output + attention_output)
+        prev_output = layer_output
+        all_layer_outputs.append(layer_output)
+
+  if do_return_all_layers:
+    final_outputs = []
+    for layer_output in all_layer_outputs:
+      final_output = reshape_from_matrix(layer_output, input_shape)
+      final_outputs.append(final_output)
+    return final_outputs
+  else:
+    final_output = reshape_from_matrix(prev_output, input_shape)
+    return final_output
+
+
+def get_shape_list(tensor, expected_rank=None, name=None):
+  """Returns a list of the shape of tensor, preferring static dimensions.
+
+  Args:
+    tensor: A tf.Tensor object to find the shape of.
+    expected_rank: (optional) int. The expected rank of `tensor`. If this is
+      specified and the `tensor` has a different rank, and exception will be
+      thrown.
+    name: Optional name of the tensor for the error message.
+
+  Returns:
+    A list of dimensions of the shape of tensor. All static dimensions will
+    be returned as python integers, and dynamic dimensions will be returned
+    as tf.Tensor scalars.
+  """
+  if name is None:
+    name = tensor.name
+
+  if expected_rank is not None:
+    assert_rank(tensor, expected_rank, name)
+
+  shape = tensor.shape.as_list()
+
+  non_static_indexes = []
+  for (index, dim) in enumerate(shape):
+    if dim is None:
+      non_static_indexes.append(index)
+
+  if not non_static_indexes:
+    return shape
+
+  dyn_shape = tf.shape(tensor)
+  for index in non_static_indexes:
+    shape[index] = dyn_shape[index]
+  return shape
+
+
+def reshape_to_matrix(input_tensor):
+  """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
+  ndims = input_tensor.shape.ndims
+  if ndims < 2:
+    raise ValueError("Input tensor must have at least rank 2. Shape = %s" %
+                     (input_tensor.shape))
+  if ndims == 2:
+    return input_tensor
+
+  width = input_tensor.shape[-1]
+  output_tensor = tf.reshape(input_tensor, [-1, width])
+  return output_tensor
+
+
+def reshape_from_matrix(output_tensor, orig_shape_list):
+  """Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
+  if len(orig_shape_list) == 2:
+    return output_tensor
+
+  output_shape = get_shape_list(output_tensor)
+
+  orig_dims = orig_shape_list[0:-1]
+  width = output_shape[-1]
+
+  return tf.reshape(output_tensor, orig_dims + [width])
+
+
+def assert_rank(tensor, expected_rank, name=None):
+  """Raises an exception if the tensor rank is not of the expected rank.
+
+  Args:
+    tensor: A tf.Tensor to check the rank of.
+    expected_rank: Python integer or list of integers, expected rank.
+    name: Optional name of the tensor for the error message.
+
+  Raises:
+    ValueError: If the expected shape doesn't match the actual shape.
+  """
+  if name is None:
+    name = tensor.name
+
+  expected_rank_dict = {}
+  if isinstance(expected_rank, six.integer_types):
+    expected_rank_dict[expected_rank] = True
+  else:
+    for x in expected_rank:
+      expected_rank_dict[x] = True
+
+  actual_rank = tensor.shape.ndims
+  if actual_rank not in expected_rank_dict:
+    scope_name = tf.get_variable_scope().name
+    raise ValueError(
+        "For the tensor `%s` in scope `%s`, the actual rank "
+        "`%d` (shape = %s) is not equal to the expected rank `%s`" %
+        (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))
diff --git a/TensorFlow/LanguageModeling/BERT/modeling_test.py b/TensorFlow/LanguageModeling/BERT/modeling_test.py
new file mode 100644
index 00000000..817ad2db
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/modeling_test.py
@@ -0,0 +1,277 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import random
+import re
+
+import modeling
+import six
+import tensorflow as tf
+
+
+class BertModelTest(tf.test.TestCase):
+
+  class BertModelTester(object):
+
+    def __init__(self,
+                 parent,
+                 batch_size=13,
+                 seq_length=7,
+                 is_training=True,
+                 use_input_mask=True,
+                 use_token_type_ids=True,
+                 vocab_size=99,
+                 hidden_size=32,
+                 num_hidden_layers=5,
+                 num_attention_heads=4,
+                 intermediate_size=37,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=16,
+                 initializer_range=0.02,
+                 scope=None):
+      self.parent = parent
+      self.batch_size = batch_size
+      self.seq_length = seq_length
+      self.is_training = is_training
+      self.use_input_mask = use_input_mask
+      self.use_token_type_ids = use_token_type_ids
+      self.vocab_size = vocab_size
+      self.hidden_size = hidden_size
+      self.num_hidden_layers = num_hidden_layers
+      self.num_attention_heads = num_attention_heads
+      self.intermediate_size = intermediate_size
+      self.hidden_act = hidden_act
+      self.hidden_dropout_prob = hidden_dropout_prob
+      self.attention_probs_dropout_prob = attention_probs_dropout_prob
+      self.max_position_embeddings = max_position_embeddings
+      self.type_vocab_size = type_vocab_size
+      self.initializer_range = initializer_range
+      self.scope = scope
+
+    def create_model(self):
+      input_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length],
+                                           self.vocab_size)
+
+      input_mask = None
+      if self.use_input_mask:
+        input_mask = BertModelTest.ids_tensor(
+            [self.batch_size, self.seq_length], vocab_size=2)
+
+      token_type_ids = None
+      if self.use_token_type_ids:
+        token_type_ids = BertModelTest.ids_tensor(
+            [self.batch_size, self.seq_length], self.type_vocab_size)
+
+      config = modeling.BertConfig(
+          vocab_size=self.vocab_size,
+          hidden_size=self.hidden_size,
+          num_hidden_layers=self.num_hidden_layers,
+          num_attention_heads=self.num_attention_heads,
+          intermediate_size=self.intermediate_size,
+          hidden_act=self.hidden_act,
+          hidden_dropout_prob=self.hidden_dropout_prob,
+          attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+          max_position_embeddings=self.max_position_embeddings,
+          type_vocab_size=self.type_vocab_size,
+          initializer_range=self.initializer_range)
+
+      model = modeling.BertModel(
+          config=config,
+          is_training=self.is_training,
+          input_ids=input_ids,
+          input_mask=input_mask,
+          token_type_ids=token_type_ids,
+          scope=self.scope)
+
+      outputs = {
+          "embedding_output": model.get_embedding_output(),
+          "sequence_output": model.get_sequence_output(),
+          "pooled_output": model.get_pooled_output(),
+          "all_encoder_layers": model.get_all_encoder_layers(),
+      }
+      return outputs
+
+    def check_output(self, result):
+      self.parent.assertAllEqual(
+          result["embedding_output"].shape,
+          [self.batch_size, self.seq_length, self.hidden_size])
+
+      self.parent.assertAllEqual(
+          result["sequence_output"].shape,
+          [self.batch_size, self.seq_length, self.hidden_size])
+
+      self.parent.assertAllEqual(result["pooled_output"].shape,
+                                 [self.batch_size, self.hidden_size])
+
+  def test_default(self):
+    self.run_tester(BertModelTest.BertModelTester(self))
+
+  def test_config_to_json_string(self):
+    config = modeling.BertConfig(vocab_size=99, hidden_size=37)
+    obj = json.loads(config.to_json_string())
+    self.assertEqual(obj["vocab_size"], 99)
+    self.assertEqual(obj["hidden_size"], 37)
+
+  def run_tester(self, tester):
+    with self.test_session() as sess:
+      ops = tester.create_model()
+      init_op = tf.group(tf.global_variables_initializer(),
+                         tf.local_variables_initializer())
+      sess.run(init_op)
+      output_result = sess.run(ops)
+      tester.check_output(output_result)
+
+      self.assert_all_tensors_reachable(sess, [init_op, ops])
+
+  @classmethod
+  def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
+    """Creates a random int32 tensor of the shape within the vocab size."""
+    if rng is None:
+      rng = random.Random()
+
+    total_dims = 1
+    for dim in shape:
+      total_dims *= dim
+
+    values = []
+    for _ in range(total_dims):
+      values.append(rng.randint(0, vocab_size - 1))
+
+    return tf.constant(value=values, dtype=tf.int32, shape=shape, name=name)
+
+  def assert_all_tensors_reachable(self, sess, outputs):
+    """Checks that all the tensors in the graph are reachable from outputs."""
+    graph = sess.graph
+
+    ignore_strings = [
+        "^.*/assert_less_equal/.*$",
+        "^.*/dilation_rate$",
+        "^.*/Tensordot/concat$",
+        "^.*/Tensordot/concat/axis$",
+        "^testing/.*$",
+    ]
+
+    ignore_regexes = [re.compile(x) for x in ignore_strings]
+
+    unreachable = self.get_unreachable_ops(graph, outputs)
+    filtered_unreachable = []
+    for x in unreachable:
+      do_ignore = False
+      for r in ignore_regexes:
+        m = r.match(x.name)
+        if m is not None:
+          do_ignore = True
+      if do_ignore:
+        continue
+      filtered_unreachable.append(x)
+    unreachable = filtered_unreachable
+
+    self.assertEqual(
+        len(unreachable), 0, "The following ops are unreachable: %s" %
+        (" ".join([x.name for x in unreachable])))
+
+  @classmethod
+  def get_unreachable_ops(cls, graph, outputs):
+    """Finds all of the tensors in graph that are unreachable from outputs."""
+    outputs = cls.flatten_recursive(outputs)
+    output_to_op = collections.defaultdict(list)
+    op_to_all = collections.defaultdict(list)
+    assign_out_to_in = collections.defaultdict(list)
+
+    for op in graph.get_operations():
+      for x in op.inputs:
+        op_to_all[op.name].append(x.name)
+      for y in op.outputs:
+        output_to_op[y.name].append(op.name)
+        op_to_all[op.name].append(y.name)
+      if str(op.type) == "Assign":
+        for y in op.outputs:
+          for x in op.inputs:
+            assign_out_to_in[y.name].append(x.name)
+
+    assign_groups = collections.defaultdict(list)
+    for out_name in assign_out_to_in.keys():
+      name_group = assign_out_to_in[out_name]
+      for n1 in name_group:
+        assign_groups[n1].append(out_name)
+        for n2 in name_group:
+          if n1 != n2:
+            assign_groups[n1].append(n2)
+
+    seen_tensors = {}
+    stack = [x.name for x in outputs]
+    while stack:
+      name = stack.pop()
+      if name in seen_tensors:
+        continue
+      seen_tensors[name] = True
+
+      if name in output_to_op:
+        for op_name in output_to_op[name]:
+          if op_name in op_to_all:
+            for input_name in op_to_all[op_name]:
+              if input_name not in stack:
+                stack.append(input_name)
+
+      expanded_names = []
+      if name in assign_groups:
+        for assign_name in assign_groups[name]:
+          expanded_names.append(assign_name)
+
+      for expanded_name in expanded_names:
+        if expanded_name not in stack:
+          stack.append(expanded_name)
+
+    unreachable_ops = []
+    for op in graph.get_operations():
+      is_unreachable = False
+      all_names = [x.name for x in op.inputs] + [x.name for x in op.outputs]
+      for name in all_names:
+        if name not in seen_tensors:
+          is_unreachable = True
+      if is_unreachable:
+        unreachable_ops.append(op)
+    return unreachable_ops
+
+  @classmethod
+  def flatten_recursive(cls, item):
+    """Flattens (potentially nested) a tuple/dictionary/list to a list."""
+    output = []
+    if isinstance(item, list):
+      output.extend(item)
+    elif isinstance(item, tuple):
+      output.extend(list(item))
+    elif isinstance(item, dict):
+      for (_, v) in six.iteritems(item):
+        output.append(v)
+    else:
+      return [item]
+
+    flat_output = []
+    for x in output:
+      flat_output.extend(cls.flatten_recursive(x))
+    return flat_output
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/TensorFlow/LanguageModeling/BERT/multilingual.md b/TensorFlow/LanguageModeling/BERT/multilingual.md
new file mode 100644
index 00000000..f1480ffd
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/multilingual.md
@@ -0,0 +1,305 @@
+## Models
+
+There are two multilingual models currently available. We do not plan to release
+more single-language models, but we may release `BERT-Large` versions of these
+two in the future:
+
+*   **[`BERT-Base, Multilingual Cased (New, recommended)`](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip)**:
+    104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
+*   **[`BERT-Base, Multilingual Uncased (Orig, not recommended)`](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip)**:
+    102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
+*   **[`BERT-Base, Chinese`](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)**:
+    Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M
+    parameters
+
+**The `Multilingual Cased (New)` model also fixes normalization issues in many
+languages, so it is recommended in languages with non-Latin alphabets (and is
+often better for most languages with Latin alphabets). When using this model,
+make sure to pass `--do_lower_case=false` to `run_pretraining.py` and other
+scripts.**
+
+See the [list of languages](#list-of-languages) that the Multilingual model
+supports. The Multilingual model does include Chinese (and English), but if your
+fine-tuning data is Chinese-only, then the Chinese model will likely produce
+better results.
+
+## Results
+
+To evaluate these systems, we use the
+[XNLI dataset](https://github.com/facebookresearch/XNLI) dataset, which is a
+version of [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) where the
+dev and test sets have been translated (by humans) into 15 languages. Note that
+the training set was *machine* translated (we used the translations provided by
+XNLI, not Google NMT). For clarity, we only report on 6 languages below:
+
+<!-- mdformat off(no table) -->
+
+| System                            | English  | Chinese  | Spanish  | German   | Arabic   | Urdu     |
+| --------------------------------- | -------- | -------- | -------- | -------- | -------- | -------- |
+| XNLI Baseline - Translate Train   | 73.7     | 67.0     | 68.8     | 66.5     | 65.8     | 56.6     |
+| XNLI Baseline - Translate Test    | 73.7     | 68.3     | 70.7     | 68.7     | 66.8     | 59.3     |
+| BERT - Translate Train Cased      | **81.9** | **76.6** | **77.8** | **75.9** | **70.7** | 61.6     |
+| BERT - Translate Train Uncased    | 81.4     | 74.2     | 77.3     | 75.2     | 70.5     | 61.7     |
+| BERT - Translate Test Uncased     | 81.4     | 70.1     | 74.9     | 74.4     | 70.4     | **62.1** |
+| BERT - Zero Shot Uncased          | 81.4     | 63.8     | 74.3     | 70.5     | 62.1     | 58.3     |
+
+<!-- mdformat on -->
+
+The first two rows are baselines from the XNLI paper and the last three rows are
+our results with BERT.
+
+**Translate Train** means that the MultiNLI training set was machine translated
+from English into the foreign language. So training and evaluation were both
+done in the foreign language. Unfortunately, training was done on
+machine-translated data, so it is impossible to quantify how much of the lower
+accuracy (compared to English) is due to the quality of the machine translation
+vs. the quality of the pre-trained model.
+
+**Translate Test** means that the XNLI test set was machine translated from the
+foreign language into English. So training and evaluation were both done on
+English. However, test evaluation was done on machine-translated English, so the
+accuracy depends on the quality of the machine translation system.
+
+**Zero Shot** means that the Multilingual BERT system was fine-tuned on English
+MultiNLI, and then evaluated on the foreign language XNLI test. In this case,
+machine translation was not involved at all in either the pre-training or
+fine-tuning.
+
+Note that the English result is worse than the 84.2 MultiNLI baseline because
+this training used Multilingual BERT rather than English-only BERT. This implies
+that for high-resource languages, the Multilingual model is somewhat worse than
+a single-language model. However, it is not feasible for us to train and
+maintain dozens of single-language model. Therefore, if your goal is to maximize
+performance with a language other than English or Chinese, you might find it
+beneficial to run pre-training for additional steps starting from our
+Multilingual model on data from your language of interest.
+
+Here is a comparison of training Chinese models with the Multilingual
+`BERT-Base` and Chinese-only `BERT-Base`:
+
+System                  | Chinese
+----------------------- | -------
+XNLI Baseline           | 67.0
+BERT Multilingual Model | 74.2
+BERT Chinese-only Model | 77.2
+
+Similar to English, the single-language model does 3% better than the
+Multilingual model.
+
+## Fine-tuning Example
+
+The multilingual model does **not** require any special consideration or API
+changes. We did update the implementation of `BasicTokenizer` in
+`tokenization.py` to support Chinese character tokenization, so please update if
+you forked it. However, we did not change the tokenization API.
+
+To test the new models, we did modify `run_classifier.py` to add support for the
+[XNLI dataset](https://github.com/facebookresearch/XNLI). This is a 15-language
+version of MultiNLI where the dev/test sets have been human-translated, and the
+training set has been machine-translated.
+
+To run the fine-tuning code, please download the
+[XNLI dev/test set](https://s3.amazonaws.com/xnli/XNLI-1.0.zip) and the
+[XNLI machine-translated training set](https://s3.amazonaws.com/xnli/XNLI-MT-1.0.zip)
+and then unpack both .zip files into some directory `$XNLI_DIR`.
+
+To run fine-tuning on XNLI. The language is hard-coded into `run_classifier.py`
+(Chinese by default), so please modify `XnliProcessor` if you want to run on
+another language.
+
+This is a large dataset, so this will training will take a few hours on a GPU
+(or about 30 minutes on a Cloud TPU). To run an experiment quickly for
+debugging, just set `num_train_epochs` to a small value like `0.1`.
+
+```shell
+export BERT_BASE_DIR=/path/to/bert/chinese_L-12_H-768_A-12 # or multilingual_L-12_H-768_A-12
+export XNLI_DIR=/path/to/xnli
+
+python run_classifier.py \
+  --task_name=XNLI \
+  --do_train=true \
+  --do_eval=true \
+  --data_dir=$XNLI_DIR \
+  --vocab_file=$BERT_BASE_DIR/vocab.txt \
+  --bert_config_file=$BERT_BASE_DIR/bert_config.json \
+  --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \
+  --max_seq_length=128 \
+  --train_batch_size=32 \
+  --learning_rate=5e-5 \
+  --num_train_epochs=2.0 \
+  --output_dir=/tmp/xnli_output/
+```
+
+With the Chinese-only model, the results should look something like this:
+
+```
+ ***** Eval results *****
+eval_accuracy = 0.774116
+eval_loss = 0.83554
+global_step = 24543
+loss = 0.74603
+```
+
+## Details
+
+### Data Source and Sampling
+
+The languages chosen were the
+[top 100 languages with the largest Wikipedias](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
+The entire Wikipedia dump for each language (excluding user and talk pages) was
+taken as the training data for each language
+
+However, the size of the Wikipedia for a given language varies greatly, and
+therefore low-resource languages may be "under-represented" in terms of the
+neural network model (under the assumption that languages are "competing" for
+limited model capacity to some extent).
+
+However, the size of a Wikipedia also correlates with the number of speakers of
+a language, and we also don't want to overfit the model by performing thousands
+of epochs over a tiny Wikipedia for a particular language.
+
+To balance these two factors, we performed exponentially smoothed weighting of
+the data during pre-training data creation (and WordPiece vocab creation). In
+other words, let's say that the probability of a language is *P(L)*, e.g.,
+*P(English) = 0.21* means that after concatenating all of the Wikipedias
+together, 21% of our data is English. We exponentiate each probability by some
+factor *S* and then re-normalize, and sample from that distribution. In our case
+we use *S=0.7*. So, high-resource languages like English will be under-sampled,
+and low-resource languages like Icelandic will be over-sampled. E.g., in the
+original distribution English would be sampled 1000x more than Icelandic, but
+after smoothing it's only sampled 100x more.
+
+### Tokenization
+
+For tokenization, we use a 110k shared WordPiece vocabulary. The word counts are
+weighted the same way as the data, so low-resource languages are upweighted by
+some factor. We intentionally do *not* use any marker to denote the input
+language (so that zero-shot training can work).
+
+Because Chinese (and Japanese Kanji and Korean Hanja) does not have whitespace
+characters, we add spaces around every character in the
+[CJK Unicode range](https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_\(Unicode_block\))
+before applying WordPiece. This means that Chinese is effectively
+character-tokenized. Note that the CJK Unicode block only includes
+Chinese-origin characters and does *not* include Hangul Korean or
+Katakana/Hiragana Japanese, which are tokenized with whitespace+WordPiece like
+all other languages.
+
+For all other languages, we apply the
+[same recipe as English](https://github.com/google-research/bert#tokenization):
+(a) lower casing+accent removal, (b) punctuation splitting, (c) whitespace
+tokenization. We understand that accent markers have substantial meaning in some
+languages, but felt that the benefits of reducing the effective vocabulary make
+up for this. Generally the strong contextual models of BERT should make up for
+any ambiguity introduced by stripping accent markers.
+
+### List of Languages
+
+The multilingual model supports the following languages. These languages were
+chosen because they are the top 100 languages with the largest Wikipedias:
+
+*   Afrikaans
+*   Albanian
+*   Arabic
+*   Aragonese
+*   Armenian
+*   Asturian
+*   Azerbaijani
+*   Bashkir
+*   Basque
+*   Bavarian
+*   Belarusian
+*   Bengali
+*   Bishnupriya Manipuri
+*   Bosnian
+*   Breton
+*   Bulgarian
+*   Burmese
+*   Catalan
+*   Cebuano
+*   Chechen
+*   Chinese (Simplified)
+*   Chinese (Traditional)
+*   Chuvash
+*   Croatian
+*   Czech
+*   Danish
+*   Dutch
+*   English
+*   Estonian
+*   Finnish
+*   French
+*   Galician
+*   Georgian
+*   German
+*   Greek
+*   Gujarati
+*   Haitian
+*   Hebrew
+*   Hindi
+*   Hungarian
+*   Icelandic
+*   Ido
+*   Indonesian
+*   Irish
+*   Italian
+*   Japanese
+*   Javanese
+*   Kannada
+*   Kazakh
+*   Kirghiz
+*   Korean
+*   Latin
+*   Latvian
+*   Lithuanian
+*   Lombard
+*   Low Saxon
+*   Luxembourgish
+*   Macedonian
+*   Malagasy
+*   Malay
+*   Malayalam
+*   Marathi
+*   Minangkabau
+*   Nepali
+*   Newar
+*   Norwegian (Bokmal)
+*   Norwegian (Nynorsk)
+*   Occitan
+*   Persian (Farsi)
+*   Piedmontese
+*   Polish
+*   Portuguese
+*   Punjabi
+*   Romanian
+*   Russian
+*   Scots
+*   Serbian
+*   Serbo-Croatian
+*   Sicilian
+*   Slovak
+*   Slovenian
+*   South Azerbaijani
+*   Spanish
+*   Sundanese
+*   Swahili
+*   Swedish
+*   Tagalog
+*   Tajik
+*   Tamil
+*   Tatar
+*   Telugu
+*   Turkish
+*   Ukrainian
+*   Urdu
+*   Uzbek
+*   Vietnamese
+*   Volapük
+*   Waray-Waray
+*   Welsh
+*   West Frisian
+*   Western Punjabi
+*   Yoruba
+
+The **Multilingual Cased (New)** release contains additionally **Thai** and
+**Mongolian**, which were not included in the original release.
diff --git a/TensorFlow/LanguageModeling/BERT/optimization.py b/TensorFlow/LanguageModeling/BERT/optimization.py
new file mode 100644
index 00000000..d2772236
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/optimization.py
@@ -0,0 +1,197 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Functions and classes related to optimization (weight updates)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import tensorflow as tf
+
+
+def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu, hvd=None, use_fp16=False, fastmath=False, amp=False, amp_fastmath=False):
+  """Creates an optimizer training op."""
+  global_step = tf.train.get_or_create_global_step()
+
+  # avoid step change in learning rate at end of warmup phase
+  decayed_learning_rate_at_crossover_point = init_lr * (1.0-float(num_warmup_steps)/float(num_train_steps))
+  adjusted_init_lr = init_lr * (init_lr / decayed_learning_rate_at_crossover_point)
+  print('decayed_learning_rate_at_crossover_point = %e, adjusted_init_lr = %e' % (decayed_learning_rate_at_crossover_point, adjusted_init_lr))
+
+  learning_rate = tf.constant(value=adjusted_init_lr, shape=[], dtype=tf.float32)
+
+  # Implements linear decay of the learning rate.
+  learning_rate = tf.train.polynomial_decay(
+      learning_rate,
+      global_step,
+      num_train_steps,
+      end_learning_rate=0.0,
+      power=1.0,
+      cycle=False)
+
+  # Implements linear warmup. I.e., if global_step < num_warmup_steps, the
+  # learning rate will be `global_step/num_warmup_steps * init_lr`.
+  if num_warmup_steps:
+    global_steps_int = tf.cast(global_step, tf.int32)
+    warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
+
+    global_steps_float = tf.cast(global_steps_int, tf.float32)
+    warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
+
+    warmup_percent_done = global_steps_float / warmup_steps_float
+    warmup_learning_rate = init_lr * warmup_percent_done
+
+    is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
+    learning_rate = (
+        (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)
+
+  # It is recommended that you use this optimizer for fine tuning, since this
+  # is how the model was trained (note that the Adam m/v variables are NOT
+  # loaded from init_checkpoint.)
+  optimizer = AdamWeightDecayOptimizer(
+      learning_rate=learning_rate,
+      weight_decay_rate=0.01,
+      beta_1=0.9,
+      beta_2=0.999,
+      epsilon=1e-6,
+      exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
+
+  if use_tpu:
+    optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
+  else:
+    if hvd is not None:
+      from horovod.tensorflow.compression import Compression
+      optimizer = hvd.DistributedOptimizer(optimizer, sparse_as_dense=True, compression=Compression.none)
+    if use_fp16 or fastmath or amp or amp_fastmath:
+      loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(init_loss_scale=2**32, incr_every_n_steps=1000, decr_every_n_nan_or_inf=2, decr_ratio=0.5)
+      optimizer = tf.contrib.mixed_precision.LossScaleOptimizer(optimizer, loss_scale_manager)
+
+  tvars = tf.trainable_variables()
+  grads_and_vars = optimizer.compute_gradients(loss, tvars)
+  grads_and_vars = [(g,v) for g,v in grads_and_vars if g is not None]
+  grads, tvars = list(zip(*grads_and_vars))
+  all_are_finite = tf.reduce_all([tf.reduce_all(tf.is_finite(g)) for g in grads]) if use_fp16 or fastmath or amp or amp_fastmath else tf.constant(True, dtype=tf.bool)
+
+  # This is how the model was pre-trained.
+  # ensure global norm is a finite number 
+  # to prevent clip_by_global_norm from having a hizzy fit.
+  (clipped_grads, _) = tf.clip_by_global_norm(
+        grads, clip_norm=1.0, 
+        use_norm=tf.cond(
+            all_are_finite,
+            lambda: tf.global_norm(grads),
+            lambda: tf.constant(1.0)))
+
+  train_op = optimizer.apply_gradients(
+      list(zip(clipped_grads, tvars)), global_step=global_step)
+
+  # Normally the global step update is done inside of `apply_gradients`.
+  # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
+  # a different optimizer, you should probably take this line out.
+  new_global_step = tf.cond(all_are_finite, lambda: global_step+1, lambda: global_step)
+  new_global_step = tf.identity(new_global_step, name='step_update')
+  train_op = tf.group(train_op, [global_step.assign(new_global_step)])
+  return train_op
+
+
+class AdamWeightDecayOptimizer(tf.train.Optimizer):
+  """A basic Adam optimizer that includes "correct" L2 weight decay."""
+
+  def __init__(self,
+               learning_rate,
+               weight_decay_rate=0.0,
+               beta_1=0.9,
+               beta_2=0.999,
+               epsilon=1e-6,
+               exclude_from_weight_decay=None,
+               name="AdamWeightDecayOptimizer"):
+    """Constructs a AdamWeightDecayOptimizer."""
+    super(AdamWeightDecayOptimizer, self).__init__(False, name)
+
+    self.learning_rate = tf.identity(learning_rate, name='learning_rate')
+    self.weight_decay_rate = weight_decay_rate
+    self.beta_1 = beta_1
+    self.beta_2 = beta_2
+    self.epsilon = epsilon
+    self.exclude_from_weight_decay = exclude_from_weight_decay
+
+  def apply_gradients(self, grads_and_vars, global_step=None, name=None):
+    """See base class."""
+    assignments = []
+    for (grad, param) in grads_and_vars:
+      if grad is None or param is None:
+        continue
+
+      param_name = self._get_variable_name(param.name)
+
+      m = tf.get_variable(
+          name=param_name + "/adam_m",
+          shape=param.shape.as_list(),
+          dtype=tf.float32,
+          trainable=False,
+          initializer=tf.zeros_initializer())
+      v = tf.get_variable(
+          name=param_name + "/adam_v",
+          shape=param.shape.as_list(),
+          dtype=tf.float32,
+          trainable=False,
+          initializer=tf.zeros_initializer())
+
+      # Standard Adam update.
+      next_m = (
+          tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
+      next_v = (
+          tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
+                                                    tf.square(grad)))
+
+      update = next_m / (tf.sqrt(next_v) + self.epsilon)
+
+      # Just adding the square of the weights to the loss function is *not*
+      # the correct way of using L2 regularization/weight decay with Adam,
+      # since that will interact with the m and v parameters in strange ways.
+      #
+      # Instead we want ot decay the weights in a manner that doesn't interact
+      # with the m/v parameters. This is equivalent to adding the square
+      # of the weights to the loss with plain (non-momentum) SGD.
+      if self._do_use_weight_decay(param_name):
+        update += self.weight_decay_rate * param
+
+      update_with_lr = self.learning_rate * update
+
+      next_param = param - update_with_lr
+
+      assignments.extend(
+          [param.assign(next_param),
+           m.assign(next_m),
+           v.assign(next_v)])
+    return tf.group(*assignments, name=name)
+
+  def _do_use_weight_decay(self, param_name):
+    """Whether to use L2 weight decay for `param_name`."""
+    if not self.weight_decay_rate:
+      return False
+    if self.exclude_from_weight_decay:
+      for r in self.exclude_from_weight_decay:
+        if re.search(r, param_name) is not None:
+          return False
+    return True
+
+  def _get_variable_name(self, param_name):
+    """Get the variable name from the tensor name."""
+    m = re.match("^(.*):\\d+$", param_name)
+    if m is not None:
+      param_name = m.group(1)
+    return param_name
diff --git a/TensorFlow/LanguageModeling/BERT/optimization_test.py b/TensorFlow/LanguageModeling/BERT/optimization_test.py
new file mode 100644
index 00000000..4f2dcf13
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/optimization_test.py
@@ -0,0 +1,48 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import optimization
+import tensorflow as tf
+
+
+class OptimizationTest(tf.test.TestCase):
+
+  def test_adam(self):
+    with self.test_session() as sess:
+      w = tf.get_variable(
+          "w",
+          shape=[3],
+          initializer=tf.constant_initializer([0.1, -0.2, -0.1]))
+      x = tf.constant([0.4, 0.2, -0.5])
+      loss = tf.reduce_mean(tf.square(x - w))
+      tvars = tf.trainable_variables()
+      grads = tf.gradients(loss, tvars)
+      global_step = tf.train.get_or_create_global_step()
+      optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2)
+      train_op = optimizer.apply_gradients(zip(grads, tvars), global_step)
+      init_op = tf.group(tf.global_variables_initializer(),
+                         tf.local_variables_initializer())
+      sess.run(init_op)
+      for _ in range(100):
+        sess.run(train_op)
+      w_np = sess.run(w)
+      self.assertAllClose(w_np.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/TensorFlow/LanguageModeling/BERT/predicting_movie_reviews_with_bert_on_tf_hub.ipynb b/TensorFlow/LanguageModeling/BERT/predicting_movie_reviews_with_bert_on_tf_hub.ipynb
new file mode 100644
index 00000000..466857f3
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/predicting_movie_reviews_with_bert_on_tf_hub.ipynb
@@ -0,0 +1,1231 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Predicting Movie Reviews with BERT on TF Hub.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "j0a4mTk9o1Qg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Copyright 2019 Google Inc.\n",
+        "\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "\n",
+        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+        "\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dCpvgG0vwXAZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#Predicting Movie Review Sentiment with BERT on TF Hub"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xiYrZKaHwV81",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "If you’ve been following Natural Language Processing over the past year, you’ve probably heard of BERT: Bidirectional Encoder Representations from Transformers. It’s a neural network architecture designed by Google researchers that’s totally transformed what’s state-of-the-art for NLP tasks, like text classification, translation, summarization, and question answering.\n",
+        "\n",
+        "Now that BERT's been added to [TF Hub](https://www.tensorflow.org/hub) as a loadable module, it's easy(ish) to add into existing Tensorflow text pipelines. In an existing pipeline, BERT can replace text embedding layers like ELMO and GloVE. Alternatively, [finetuning](http://wiki.fast.ai/index.php/Fine_tuning) BERT can provide both an accuracy boost and faster training time in many cases.\n",
+        "\n",
+        "Here, we'll train a model to predict whether an IMDB movie review is positive or negative using BERT in Tensorflow with tf hub. Some code was adapted from [this colab notebook](https://colab.sandbox.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb). Let's get started!"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hsZvic2YxnTz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "import pandas as pd\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_hub as hub\n",
+        "from datetime import datetime"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "cp5wfXDx5SPH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In addition to the standard libraries we imported above, we'll need to install BERT's python package."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jviywGyWyKsA",
+        "colab_type": "code",
+        "outputId": "166f3005-d219-404f-b201-2a0b75480360",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install bert-tensorflow"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: bert-tensorflow in /usr/local/lib/python3.6/dist-packages (1.0.1)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from bert-tensorflow) (1.11.0)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hhbGEfwgdEtw",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import bert\n",
+        "from bert import run_classifier\n",
+        "from bert import optimization\n",
+        "from bert import tokenization"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "KVB3eOcjxxm1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Below, we'll set an output directory location to store our model output and checkpoints. This can be a local directory, in which case you'd set OUTPUT_DIR to the name of the directory you'd like to create. If you're running this code in Google's hosted Colab, the directory won't persist after the Colab session ends.\n",
+        "\n",
+        "Alternatively, if you're a GCP user, you can store output in a GCP bucket. To do that, set a directory name in OUTPUT_DIR and the name of the GCP bucket in the BUCKET field.\n",
+        "\n",
+        "Set DO_DELETE to rewrite the OUTPUT_DIR if it exists. Otherwise, Tensorflow will load existing model checkpoints from that directory (if they exist)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "US_EAnICvP7f",
+        "colab_type": "code",
+        "outputId": "7780a032-31d4-4794-e6aa-664a5d2ae7dd",
+        "cellView": "form",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Set the output directory for saving model file\n",
+        "# Optionally, set a GCP bucket location\n",
+        "\n",
+        "OUTPUT_DIR = 'OUTPUT_DIR_NAME'#@param {type:\"string\"}\n",
+        "#@markdown Whether or not to clear/delete the directory and create a new one\n",
+        "DO_DELETE = False #@param {type:\"boolean\"}\n",
+        "#@markdown Set USE_BUCKET and BUCKET if you want to (optionally) store model output on GCP bucket.\n",
+        "USE_BUCKET = True #@param {type:\"boolean\"}\n",
+        "BUCKET = 'BUCKET_NAME' #@param {type:\"string\"}\n",
+        "\n",
+        "if USE_BUCKET:\n",
+        "  OUTPUT_DIR = 'gs://{}/{}'.format(BUCKET, OUTPUT_DIR)\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user()\n",
+        "\n",
+        "if DO_DELETE:\n",
+        "  try:\n",
+        "    tf.gfile.DeleteRecursively(OUTPUT_DIR)\n",
+        "  except:\n",
+        "    # Doesn't matter if the directory didn't exist\n",
+        "    pass\n",
+        "tf.gfile.MakeDirs(OUTPUT_DIR)\n",
+        "print('***** Model output directory: {} *****'.format(OUTPUT_DIR))\n"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "***** Model output directory: gs://bert-tfhub/aclImdb_v1 *****\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pmFYvkylMwXn",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#Data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MC_w8SRqN0fr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's download the dataset, hosted by Stanford. The code below, which downloads, extracts, and imports the IMDB Large Movie Review Dataset, is borrowed from [this Tensorflow tutorial](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fom_ff20gyy6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from tensorflow import keras\n",
+        "import os\n",
+        "import re\n",
+        "\n",
+        "# Load all files from a directory in a DataFrame.\n",
+        "def load_directory_data(directory):\n",
+        "  data = {}\n",
+        "  data[\"sentence\"] = []\n",
+        "  data[\"sentiment\"] = []\n",
+        "  for file_path in os.listdir(directory):\n",
+        "    with tf.gfile.GFile(os.path.join(directory, file_path), \"r\") as f:\n",
+        "      data[\"sentence\"].append(f.read())\n",
+        "      data[\"sentiment\"].append(re.match(\"\\d+_(\\d+)\\.txt\", file_path).group(1))\n",
+        "  return pd.DataFrame.from_dict(data)\n",
+        "\n",
+        "# Merge positive and negative examples, add a polarity column and shuffle.\n",
+        "def load_dataset(directory):\n",
+        "  pos_df = load_directory_data(os.path.join(directory, \"pos\"))\n",
+        "  neg_df = load_directory_data(os.path.join(directory, \"neg\"))\n",
+        "  pos_df[\"polarity\"] = 1\n",
+        "  neg_df[\"polarity\"] = 0\n",
+        "  return pd.concat([pos_df, neg_df]).sample(frac=1).reset_index(drop=True)\n",
+        "\n",
+        "# Download and process the dataset files.\n",
+        "def download_and_load_datasets(force_download=False):\n",
+        "  dataset = tf.keras.utils.get_file(\n",
+        "      fname=\"aclImdb.tar.gz\", \n",
+        "      origin=\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\", \n",
+        "      extract=True)\n",
+        "  \n",
+        "  train_df = load_dataset(os.path.join(os.path.dirname(dataset), \n",
+        "                                       \"aclImdb\", \"train\"))\n",
+        "  test_df = load_dataset(os.path.join(os.path.dirname(dataset), \n",
+        "                                      \"aclImdb\", \"test\"))\n",
+        "  \n",
+        "  return train_df, test_df\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2abfwdn-g135",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train, test = download_and_load_datasets()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XA8WHJgzhIZf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To keep training fast, we'll take a sample of 5000 train and test examples, respectively."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lw_F488eixTV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train = train.sample(5000)\n",
+        "test = test.sample(5000)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "prRQM8pDi8xI",
+        "colab_type": "code",
+        "outputId": "34445cb8-2be0-4379-fdbc-7794091f6049",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "train.columns"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['sentence', 'sentiment', 'polarity'], dtype='object')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sfRnHSz3iSXz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For us, our input data is the 'sentence' column and our label is the 'polarity' column (0, 1 for negative and positive, respecitvely)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IuMOGwFui4it",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "DATA_COLUMN = 'sentence'\n",
+        "LABEL_COLUMN = 'polarity'\n",
+        "# label_list is the list of labels, i.e. True, False or 0, 1 or 'dog', 'cat'\n",
+        "label_list = [0, 1]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "V399W0rqNJ-Z",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#Data Preprocessing\n",
+        "We'll need to transform our data into a format BERT understands. This involves two steps. First, we create  `InputExample`'s using the constructor provided in the BERT library.\n",
+        "\n",
+        "- `text_a` is the text we want to classify, which in this case, is the `Request` field in our Dataframe. \n",
+        "- `text_b` is used if we're training a model to understand the relationship between sentences (i.e. is `text_b` a translation of `text_a`? Is `text_b` an answer to the question asked by `text_a`?). This doesn't apply to our task, so we can leave `text_b` blank.\n",
+        "- `label` is the label for our example, i.e. True, False"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "p9gEt5SmM6i6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use the InputExample class from BERT's run_classifier code to create examples from the data\n",
+        "train_InputExamples = train.apply(lambda x: bert.run_classifier.InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this example\n",
+        "                                                                   text_a = x[DATA_COLUMN], \n",
+        "                                                                   text_b = None, \n",
+        "                                                                   label = x[LABEL_COLUMN]), axis = 1)\n",
+        "\n",
+        "test_InputExamples = test.apply(lambda x: bert.run_classifier.InputExample(guid=None, \n",
+        "                                                                   text_a = x[DATA_COLUMN], \n",
+        "                                                                   text_b = None, \n",
+        "                                                                   label = x[LABEL_COLUMN]), axis = 1)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "SCZWZtKxObjh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we need to preprocess our data so that it matches the data BERT was trained on. For this, we'll need to do a couple of things (but don't worry--this is also included in the Python library):\n",
+        "\n",
+        "\n",
+        "1. Lowercase our text (if we're using a BERT lowercase model)\n",
+        "2. Tokenize it (i.e. \"sally says hi\" -> [\"sally\", \"says\", \"hi\"])\n",
+        "3. Break words into WordPieces (i.e. \"calling\" -> [\"call\", \"##ing\"])\n",
+        "4. Map our words to indexes using a vocab file that BERT provides\n",
+        "5. Add special \"CLS\" and \"SEP\" tokens (see the [readme](https://github.com/google-research/bert))\n",
+        "6. Append \"index\" and \"segment\" tokens to each input (see the [BERT paper](https://arxiv.org/pdf/1810.04805.pdf))\n",
+        "\n",
+        "Happily, we don't have to worry about most of these details.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qMWiDtpyQSoU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To start, we'll need to load a vocabulary file and lowercasing information directly from the BERT tf hub module:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IhJSe0QHNG7U",
+        "colab_type": "code",
+        "outputId": "20b28cc7-3cb3-4ce6-bfff-a7847ce3bbaa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# This is a path to an uncased (all lowercase) version of BERT\n",
+        "BERT_MODEL_HUB = \"https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1\"\n",
+        "\n",
+        "def create_tokenizer_from_hub_module():\n",
+        "  \"\"\"Get the vocab file and casing info from the Hub module.\"\"\"\n",
+        "  with tf.Graph().as_default():\n",
+        "    bert_module = hub.Module(BERT_MODEL_HUB)\n",
+        "    tokenization_info = bert_module(signature=\"tokenization_info\", as_dict=True)\n",
+        "    with tf.Session() as sess:\n",
+        "      vocab_file, do_lower_case = sess.run([tokenization_info[\"vocab_file\"],\n",
+        "                                            tokenization_info[\"do_lower_case\"]])\n",
+        "      \n",
+        "  return bert.tokenization.FullTokenizer(\n",
+        "      vocab_file=vocab_file, do_lower_case=do_lower_case)\n",
+        "\n",
+        "tokenizer = create_tokenizer_from_hub_module()"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z4oFkhpZBDKm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Great--we just learned that the BERT model we're using expects lowercase data (that's what stored in tokenization_info[\"do_lower_case\"]) and we also loaded BERT's vocab file. We also created a tokenizer, which breaks words into word pieces:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dsBo6RCtQmwx",
+        "colab_type": "code",
+        "outputId": "9af8c917-90ec-4fe9-897b-79dc89ca88e1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "tokenizer.tokenize(\"This here's an example of using the BERT tokenizer\")"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['this',\n",
+              " 'here',\n",
+              " \"'\",\n",
+              " 's',\n",
+              " 'an',\n",
+              " 'example',\n",
+              " 'of',\n",
+              " 'using',\n",
+              " 'the',\n",
+              " 'bert',\n",
+              " 'token',\n",
+              " '##izer']"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0OEzfFIt6GIc",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Using our tokenizer, we'll call `run_classifier.convert_examples_to_features` on our InputExamples to convert them into features BERT understands."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LL5W8gEGRTAf",
+        "colab_type": "code",
+        "outputId": "65001dda-155b-48fc-b5fc-1e4cabc8dfbf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1261
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# We'll set sequences to be at most 128 tokens long.\n",
+        "MAX_SEQ_LENGTH = 128\n",
+        "# Convert our train and test features to InputFeatures that BERT understands.\n",
+        "train_features = bert.run_classifier.convert_examples_to_features(train_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)\n",
+        "test_features = bert.run_classifier.convert_examples_to_features(test_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Writing example 0 of 5000\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] i ' m watching this on the sci - fi channel right now . it ' s so horrible i can ' t stop watching it ! i ' m a video ##grapher and this movie makes me sad . i feel bad for anyone associated with this movie . some of the camera work is good . most is very questionable . there are a few decent actors in the flick . too bad they ' re surrounded by what must have been the director ' s relatives . that ' s the only way they could have been qualified to be in a movie ! music was a little better than the acting . if you get around to watching this i hope it [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1045 1005 1049 3666 2023 2006 1996 16596 1011 10882 3149 2157 2085 1012 2009 1005 1055 2061 9202 1045 2064 1005 1056 2644 3666 2009 999 1045 1005 1049 1037 2678 18657 1998 2023 3185 3084 2033 6517 1012 1045 2514 2919 2005 3087 3378 2007 2023 3185 1012 2070 1997 1996 4950 2147 2003 2204 1012 2087 2003 2200 21068 1012 2045 2024 1037 2261 11519 5889 1999 1996 17312 1012 2205 2919 2027 1005 2128 5129 2011 2054 2442 2031 2042 1996 2472 1005 1055 9064 1012 2008 1005 1055 1996 2069 2126 2027 2071 2031 2042 4591 2000 2022 1999 1037 3185 999 2189 2001 1037 2210 2488 2084 1996 3772 1012 2065 2017 2131 2105 2000 3666 2023 1045 3246 2009 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] i have been a fan of pushing dai ##sies since the very beginning . it is wonderful ##ly thought up , and bryan fuller has the most remarkable ideas for this show . < br / > < br / > it is unbelievable on how much tv has been needing a creative , original show like pushing dai ##sies . it is a huge relief to see a show , that is unlike the rest , where as , if you compared it to some of the newer shows , such as scrub ##s and house , you would see the similarities , and it does get ted ##ious at moments to see shows so close in identity . < br / > < br [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1045 2031 2042 1037 5470 1997 6183 18765 14625 2144 1996 2200 2927 1012 2009 2003 6919 2135 2245 2039 1010 1998 8527 12548 2038 1996 2087 9487 4784 2005 2023 2265 1012 1026 7987 1013 1028 1026 7987 1013 1028 2009 2003 23653 2006 2129 2172 2694 2038 2042 11303 1037 5541 1010 2434 2265 2066 6183 18765 14625 1012 2009 2003 1037 4121 4335 2000 2156 1037 2265 1010 2008 2003 4406 1996 2717 1010 2073 2004 1010 2065 2017 4102 2009 2000 2070 1997 1996 10947 3065 1010 2107 2004 18157 2015 1998 2160 1010 2017 2052 2156 1996 12319 1010 1998 2009 2515 2131 6945 6313 2012 5312 2000 2156 3065 2061 2485 1999 4767 1012 1026 7987 1013 1028 1026 7987 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 1 (id = 1)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] this movie starts out promising ##ly , with an early scene in which frank morgan advises against gary cooper ' s marriage to his daughter , anita louise . frank morgan , playing an una ##bas ##hed gold - digger , loudly complain ##s to cooper about his perceived pen ##ury at the hands of his family - including his daughter , anita louise . i am a fan of all 3 actors . frank morgan is ( to my mind ) a hollywood treasure , cooper a legend , and louise a very lovely , versatile and under - appreciated actress seldom seen in the leading role . i also have nothing against teresa wright , and while not blessed with great range , she [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 2023 3185 4627 2041 10015 2135 1010 2007 2019 2220 3496 1999 2029 3581 5253 25453 2114 5639 6201 1005 1055 3510 2000 2010 2684 1010 12918 8227 1012 3581 5253 1010 2652 2019 14477 22083 9072 2751 1011 28661 1010 9928 17612 2015 2000 6201 2055 2010 8690 7279 13098 2012 1996 2398 1997 2010 2155 1011 2164 2010 2684 1010 12918 8227 1012 1045 2572 1037 5470 1997 2035 1017 5889 1012 3581 5253 2003 1006 2000 2026 2568 1007 1037 5365 8813 1010 6201 1037 5722 1010 1998 8227 1037 2200 8403 1010 22979 1998 2104 1011 12315 3883 15839 2464 1999 1996 2877 2535 1012 1045 2036 2031 2498 2114 12409 6119 1010 1998 2096 2025 10190 2007 2307 2846 1010 2016 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] i was over ##taken by the emotion . un ##for ##get ##table rendering of a wartime story which is unknown to most people . the performances were fault ##less and outstanding . [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1045 2001 2058 25310 2011 1996 7603 1012 4895 29278 18150 10880 14259 1997 1037 12498 2466 2029 2003 4242 2000 2087 2111 1012 1996 4616 2020 6346 3238 1998 5151 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 1 (id = 1)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] soldier blue is a movie with pre ##tension ##s : pre ##tension ##s to be some sort of profound statement on man ' s inhuman ##ity to man , on the white man ' s exploitation of and brutality towards indigenous peoples ; a biting , un ##fl ##in ##ching and sar ##don ##ic commentary on the horrors of vietnam . well , sorry , but it fails mis ##era ##bly to be any of those things . what soldier blue actually is is per ##nic ##ious , tri ##te , badly made , dish ##ones ##t rubbish . < br / > < br / > another reviewer here hit the nail on the head in saying that it appears to be a hybrid of [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 5268 2630 2003 1037 3185 2007 3653 29048 2015 1024 3653 29048 2015 2000 2022 2070 4066 1997 13769 4861 2006 2158 1005 1055 29582 3012 2000 2158 1010 2006 1996 2317 2158 1005 1055 14427 1997 1998 24083 2875 6284 7243 1025 1037 12344 1010 4895 10258 2378 8450 1998 18906 5280 2594 8570 2006 1996 22812 1997 5148 1012 2092 1010 3374 1010 2021 2009 11896 28616 6906 6321 2000 2022 2151 1997 2216 2477 1012 2054 5268 2630 2941 2003 2003 2566 8713 6313 1010 13012 2618 1010 6649 2081 1010 9841 21821 2102 29132 1012 1026 7987 1013 1028 1026 7987 1013 1028 2178 12027 2182 2718 1996 13774 2006 1996 2132 1999 3038 2008 2009 3544 2000 2022 1037 8893 1997 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:Writing example 0 of 5000\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] i just watched this today on tv . it was on abc ' s sunday afternoon movie . < br / > < br / > this wasn ' t a very good movie , but for a low budget independent film like this , it was okay . there is some suspense in it , but there are so many bad qualities that really bring the movie down . the script is pretty lame , and the plot elements aren ' t very realistic , such as the way a 911 operator would laugh and hang up when someone is reporting a murder . i don ' t know what the writer was thinking when they came up with that idea , but it isn [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1045 2074 3427 2023 2651 2006 2694 1012 2009 2001 2006 5925 1005 1055 4465 5027 3185 1012 1026 7987 1013 1028 1026 7987 1013 1028 2023 2347 1005 1056 1037 2200 2204 3185 1010 2021 2005 1037 2659 5166 2981 2143 2066 2023 1010 2009 2001 3100 1012 2045 2003 2070 23873 1999 2009 1010 2021 2045 2024 2061 2116 2919 11647 2008 2428 3288 1996 3185 2091 1012 1996 5896 2003 3492 20342 1010 1998 1996 5436 3787 4995 1005 1056 2200 12689 1010 2107 2004 1996 2126 1037 19989 6872 2052 4756 1998 6865 2039 2043 2619 2003 7316 1037 4028 1012 1045 2123 1005 1056 2113 2054 1996 3213 2001 3241 2043 2027 2234 2039 2007 2008 2801 1010 2021 2009 3475 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] from hardly alien sounding lasers , to an elementary school style shuttle crash , \" night ##be ##ast \" is better classified as a far ##cic ##al mix of fake blood and bare chest . the almost pornographic style of the film seems to be a failed attempt to recover from a lack of co ##hesive or effective story . the acting however is not nearly as beast ##ly , many of the young , aspiring , actors ad ##mir ##ably showcase a hidden talent . particularly don lei ##fer ##t and jamie ze ##mare ##l , who shed a well needed sha ##rd of light on this otherwise terrible film . night ##be ##ast would have never shown up on set had he known the [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 2013 6684 7344 9391 23965 1010 2000 2019 4732 2082 2806 10382 5823 1010 1000 2305 4783 14083 1000 2003 2488 6219 2004 1037 2521 19053 2389 4666 1997 8275 2668 1998 6436 3108 1012 1996 2471 26932 2806 1997 1996 2143 3849 2000 2022 1037 3478 3535 2000 8980 2013 1037 3768 1997 2522 21579 2030 4621 2466 1012 1996 3772 2174 2003 2025 3053 2004 6841 2135 1010 2116 1997 1996 2402 1010 22344 1010 5889 4748 14503 8231 13398 1037 5023 5848 1012 3391 2123 26947 7512 2102 1998 6175 27838 24376 2140 1010 2040 8328 1037 2092 2734 21146 4103 1997 2422 2006 2023 4728 6659 2143 1012 2305 4783 14083 2052 2031 2196 3491 2039 2006 2275 2018 2002 2124 1996 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] here we have the in ##imi ##table charlie chaplin for ##sa ##king his slap ##stick past to tackle the serious subject of anti - semi ##tism , and into ##ler ##ance in general . he portrays two characters - the sweet , innocent jewish barber - a war veteran , and the ravi ##ng and ruthless dictator , aden ##oid h ##yn ##kel . the jewish ghetto in this country is not safe for long , due to the w ##him ##s of h ##yn ##kel and his armed thugs , who routinely rough up its residents , or leave them alone , dependent upon his mood that day or week . the barber is among them , but is befriended by his former commanding officer [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 2182 2057 2031 1996 1999 27605 10880 4918 23331 2005 3736 6834 2010 14308 21354 2627 2000 11147 1996 3809 3395 1997 3424 1011 4100 17456 1010 1998 2046 3917 6651 1999 2236 1012 2002 17509 2048 3494 1011 1996 4086 1010 7036 3644 13362 1011 1037 2162 8003 1010 1998 1996 16806 3070 1998 18101 21237 1010 16298 9314 1044 6038 11705 1012 1996 3644 17276 1999 2023 2406 2003 2025 3647 2005 2146 1010 2349 2000 1996 1059 14341 2015 1997 1044 6038 11705 1998 2010 4273 24106 1010 2040 19974 5931 2039 2049 3901 1010 2030 2681 2068 2894 1010 7790 2588 2010 6888 2008 2154 2030 2733 1012 1996 13362 2003 2426 2068 1010 2021 2003 23386 2011 2010 2280 7991 2961 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 1 (id = 1)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] i really hated this movie and it ' s the first movie written by stephen king that i didn ' t finish . i was truly disappointed , it was the worst crap i ' ve ever seen . what were you thinking making three hours out of it ? it may have a quite good story , but actors ? no . suspense ? no . romance ? no . horror ? no . it didn ' t have anything . < br / > < br / > it ' s got this strange , crazy science man with einstein - hair , the classic thing . not real at all . and a man keep getting younger all the time . it seems [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1045 2428 6283 2023 3185 1998 2009 1005 1055 1996 2034 3185 2517 2011 4459 2332 2008 1045 2134 1005 1056 3926 1012 1045 2001 5621 9364 1010 2009 2001 1996 5409 10231 1045 1005 2310 2412 2464 1012 2054 2020 2017 3241 2437 2093 2847 2041 1997 2009 1029 2009 2089 2031 1037 3243 2204 2466 1010 2021 5889 1029 2053 1012 23873 1029 2053 1012 7472 1029 2053 1012 5469 1029 2053 1012 2009 2134 1005 1056 2031 2505 1012 1026 7987 1013 1028 1026 7987 1013 1028 2009 1005 1055 2288 2023 4326 1010 4689 2671 2158 2007 15313 1011 2606 1010 1996 4438 2518 1012 2025 2613 2012 2035 1012 1998 1037 2158 2562 2893 3920 2035 1996 2051 1012 2009 3849 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: None\n",
+            "INFO:tensorflow:tokens: [CLS] story chinese tall story tells the story of righteous monk trip ##ita ##ka , who , along with his guardians monkey , sandy and pigs ##y make their journey west on a quest to recover ancient sutra ##s , finally , they reach the final leg of their journey in sha ##che city but all is not as it seems when the city is attacked by evil tree demons . monkey tries his best to battle them but is overwhelmed , knowing his master is in grave danger , he uses his trust ##y golden staff to thrust trip ##ita ##ka to safety . < br / > < br / > the monk ends up being knocked out when he land and when he wakes [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 2466 2822 4206 2466 4136 1996 2466 1997 19556 8284 4440 6590 2912 1010 2040 1010 2247 2007 2010 14240 10608 1010 7525 1998 14695 2100 2191 2037 4990 2225 2006 1037 8795 2000 8980 3418 26567 2015 1010 2633 1010 2027 3362 1996 2345 4190 1997 2037 4990 1999 21146 5403 2103 2021 2035 2003 2025 2004 2009 3849 2043 1996 2103 2003 4457 2011 4763 3392 7942 1012 10608 5363 2010 2190 2000 2645 2068 2021 2003 13394 1010 4209 2010 3040 2003 1999 6542 5473 1010 2002 3594 2010 3404 2100 3585 3095 2000 7400 4440 6590 2912 2000 3808 1012 1026 7987 1013 1028 1026 7987 1013 1028 1996 8284 4515 2039 2108 6573 2041 2043 2002 2455 1998 2043 2002 17507 102\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 1 (id = 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ccp5trMwRtmr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#Creating a model\n",
+        "\n",
+        "Now that we've prepared our data, let's focus on building a model. `create_model` does just this below. First, it loads the BERT tf hub module again (this time to extract the computation graph). Next, it creates a single new layer that will be trained to adapt BERT to our sentiment task (i.e. classifying whether a movie review is positive or negative). This strategy of using a mostly trained model is called [fine-tuning](http://wiki.fast.ai/index.php/Fine_tuning)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6o2a5ZIvRcJq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_model(is_predicting, input_ids, input_mask, segment_ids, labels,\n",
+        "                 num_labels):\n",
+        "  \"\"\"Creates a classification model.\"\"\"\n",
+        "\n",
+        "  bert_module = hub.Module(\n",
+        "      BERT_MODEL_HUB,\n",
+        "      trainable=True)\n",
+        "  bert_inputs = dict(\n",
+        "      input_ids=input_ids,\n",
+        "      input_mask=input_mask,\n",
+        "      segment_ids=segment_ids)\n",
+        "  bert_outputs = bert_module(\n",
+        "      inputs=bert_inputs,\n",
+        "      signature=\"tokens\",\n",
+        "      as_dict=True)\n",
+        "\n",
+        "  # Use \"pooled_output\" for classification tasks on an entire sentence.\n",
+        "  # Use \"sequence_outputs\" for token-level output.\n",
+        "  output_layer = bert_outputs[\"pooled_output\"]\n",
+        "\n",
+        "  hidden_size = output_layer.shape[-1].value\n",
+        "\n",
+        "  # Create our own layer to tune for politeness data.\n",
+        "  output_weights = tf.get_variable(\n",
+        "      \"output_weights\", [num_labels, hidden_size],\n",
+        "      initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
+        "\n",
+        "  output_bias = tf.get_variable(\n",
+        "      \"output_bias\", [num_labels], initializer=tf.zeros_initializer())\n",
+        "\n",
+        "  with tf.variable_scope(\"loss\"):\n",
+        "\n",
+        "    # Dropout helps prevent overfitting\n",
+        "    output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)\n",
+        "\n",
+        "    logits = tf.matmul(output_layer, output_weights, transpose_b=True)\n",
+        "    logits = tf.nn.bias_add(logits, output_bias)\n",
+        "    log_probs = tf.nn.log_softmax(logits, axis=-1)\n",
+        "\n",
+        "    # Convert labels into one-hot encoding\n",
+        "    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)\n",
+        "\n",
+        "    predicted_labels = tf.squeeze(tf.argmax(log_probs, axis=-1, output_type=tf.int32))\n",
+        "    # If we're predicting, we want predicted labels and the probabiltiies.\n",
+        "    if is_predicting:\n",
+        "      return (predicted_labels, log_probs)\n",
+        "\n",
+        "    # If we're train/eval, compute loss between predicted and actual label\n",
+        "    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)\n",
+        "    loss = tf.reduce_mean(per_example_loss)\n",
+        "    return (loss, predicted_labels, log_probs)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qpE0ZIDOCQzE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next we'll wrap our model function in a `model_fn_builder` function that adapts our model to work for training, evaluation, and prediction."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FnH-AnOQ9KKW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# model_fn_builder actually creates our model function\n",
+        "# using the passed parameters for num_labels, learning_rate, etc.\n",
+        "def model_fn_builder(num_labels, learning_rate, num_train_steps,\n",
+        "                     num_warmup_steps):\n",
+        "  \"\"\"Returns `model_fn` closure for TPUEstimator.\"\"\"\n",
+        "  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument\n",
+        "    \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n",
+        "\n",
+        "    input_ids = features[\"input_ids\"]\n",
+        "    input_mask = features[\"input_mask\"]\n",
+        "    segment_ids = features[\"segment_ids\"]\n",
+        "    label_ids = features[\"label_ids\"]\n",
+        "\n",
+        "    is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\n",
+        "    \n",
+        "    # TRAIN and EVAL\n",
+        "    if not is_predicting:\n",
+        "\n",
+        "      (loss, predicted_labels, log_probs) = create_model(\n",
+        "        is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n",
+        "\n",
+        "      train_op = bert.optimization.create_optimizer(\n",
+        "          loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)\n",
+        "\n",
+        "      # Calculate evaluation metrics. \n",
+        "      def metric_fn(label_ids, predicted_labels):\n",
+        "        accuracy = tf.metrics.accuracy(label_ids, predicted_labels)\n",
+        "        f1_score = tf.contrib.metrics.f1_score(\n",
+        "            label_ids,\n",
+        "            predicted_labels)\n",
+        "        auc = tf.metrics.auc(\n",
+        "            label_ids,\n",
+        "            predicted_labels)\n",
+        "        recall = tf.metrics.recall(\n",
+        "            label_ids,\n",
+        "            predicted_labels)\n",
+        "        precision = tf.metrics.precision(\n",
+        "            label_ids,\n",
+        "            predicted_labels) \n",
+        "        true_pos = tf.metrics.true_positives(\n",
+        "            label_ids,\n",
+        "            predicted_labels)\n",
+        "        true_neg = tf.metrics.true_negatives(\n",
+        "            label_ids,\n",
+        "            predicted_labels)   \n",
+        "        false_pos = tf.metrics.false_positives(\n",
+        "            label_ids,\n",
+        "            predicted_labels)  \n",
+        "        false_neg = tf.metrics.false_negatives(\n",
+        "            label_ids,\n",
+        "            predicted_labels)\n",
+        "        return {\n",
+        "            \"eval_accuracy\": accuracy,\n",
+        "            \"f1_score\": f1_score,\n",
+        "            \"auc\": auc,\n",
+        "            \"precision\": precision,\n",
+        "            \"recall\": recall,\n",
+        "            \"true_positives\": true_pos,\n",
+        "            \"true_negatives\": true_neg,\n",
+        "            \"false_positives\": false_pos,\n",
+        "            \"false_negatives\": false_neg\n",
+        "        }\n",
+        "\n",
+        "      eval_metrics = metric_fn(label_ids, predicted_labels)\n",
+        "\n",
+        "      if mode == tf.estimator.ModeKeys.TRAIN:\n",
+        "        return tf.estimator.EstimatorSpec(mode=mode,\n",
+        "          loss=loss,\n",
+        "          train_op=train_op)\n",
+        "      else:\n",
+        "          return tf.estimator.EstimatorSpec(mode=mode,\n",
+        "            loss=loss,\n",
+        "            eval_metric_ops=eval_metrics)\n",
+        "    else:\n",
+        "      (predicted_labels, log_probs) = create_model(\n",
+        "        is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n",
+        "\n",
+        "      predictions = {\n",
+        "          'probabilities': log_probs,\n",
+        "          'labels': predicted_labels\n",
+        "      }\n",
+        "      return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n",
+        "\n",
+        "  # Return the actual model function in the closure\n",
+        "  return model_fn\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "OjwJ4bTeWXD8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Compute train and warmup steps from batch size\n",
+        "# These hyperparameters are copied from this colab notebook (https://colab.sandbox.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb)\n",
+        "BATCH_SIZE = 32\n",
+        "LEARNING_RATE = 2e-5\n",
+        "NUM_TRAIN_EPOCHS = 3.0\n",
+        "# Warmup is a period of time where hte learning rate \n",
+        "# is small and gradually increases--usually helps training.\n",
+        "WARMUP_PROPORTION = 0.1\n",
+        "# Model configs\n",
+        "SAVE_CHECKPOINTS_STEPS = 500\n",
+        "SAVE_SUMMARY_STEPS = 100"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "emHf9GhfWBZ_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Compute # train and warmup steps from batch size\n",
+        "num_train_steps = int(len(train_features) / BATCH_SIZE * NUM_TRAIN_EPOCHS)\n",
+        "num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "oEJldMr3WYZa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Specify outpit directory and number of checkpoint steps to save\n",
+        "run_config = tf.estimator.RunConfig(\n",
+        "    model_dir=OUTPUT_DIR,\n",
+        "    save_summary_steps=SAVE_SUMMARY_STEPS,\n",
+        "    save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "q_WebpS1X97v",
+        "colab_type": "code",
+        "outputId": "1648932a-7391-49d3-8af7-52d514e226e8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 156
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "model_fn = model_fn_builder(\n",
+        "  num_labels=len(label_list),\n",
+        "  learning_rate=LEARNING_RATE,\n",
+        "  num_train_steps=num_train_steps,\n",
+        "  num_warmup_steps=num_warmup_steps)\n",
+        "\n",
+        "estimator = tf.estimator.Estimator(\n",
+        "  model_fn=model_fn,\n",
+        "  config=run_config,\n",
+        "  params={\"batch_size\": BATCH_SIZE})\n"
+      ],
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Using config: {'_model_dir': 'gs://bert-tfhub/aclImdb_v1', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 500, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true\n",
+            "graph_options {\n",
+            "  rewrite_options {\n",
+            "    meta_optimizer_iterations: ONE\n",
+            "  }\n",
+            "}\n",
+            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fcedb507be0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NOO3RfG1DYLo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next we create an input builder function that takes our training feature set (`train_features`) and produces a generator. This is a pretty standard design pattern for working with Tensorflow [Estimators](https://www.tensorflow.org/guide/estimators)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Pv2bAlOX_-K",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for training. drop_remainder = True for using TPUs.\n",
+        "train_input_fn = bert.run_classifier.input_fn_builder(\n",
+        "    features=train_features,\n",
+        "    seq_length=MAX_SEQ_LENGTH,\n",
+        "    is_training=True,\n",
+        "    drop_remainder=False)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "t6Nukby2EB6-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now we train our model! For me, using a Colab notebook running on Google's GPUs, my training time was about 14 minutes."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "nucD4gluYJmK",
+        "colab_type": "code",
+        "outputId": "5d728e72-4631-42bf-c48d-3f51d4b968ce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print(f'Beginning Training!')\n",
+        "current_time = datetime.now()\n",
+        "estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)\n",
+        "print(\"Training took time \", datetime.now() - current_time)"
+      ],
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Beginning Training!\n",
+            "INFO:tensorflow:Skipping training since max_steps has already saved.\n",
+            "Training took time  0:00:00.759709\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CmbLTVniARy3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's use our test data to see how well our model did:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JIhejfpyJ8Bx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "test_input_fn = run_classifier.input_fn_builder(\n",
+        "    features=test_features,\n",
+        "    seq_length=MAX_SEQ_LENGTH,\n",
+        "    is_training=False,\n",
+        "    drop_remainder=False)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "PPVEXhNjYXC-",
+        "colab_type": "code",
+        "outputId": "dd5482cd-c558-465f-c854-ec11a0175316",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 445
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "estimator.evaluate(input_fn=test_input_fn, steps=None)"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Calling model_fn.\n",
+            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gradients_impl.py:110: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
+            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Done calling model_fn.\n",
+            "INFO:tensorflow:Starting evaluation at 2019-02-12T21:04:20Z\n",
+            "INFO:tensorflow:Graph was finalized.\n",
+            "INFO:tensorflow:Restoring parameters from gs://bert-tfhub/aclImdb_v1/model.ckpt-468\n",
+            "INFO:tensorflow:Running local_init_op.\n",
+            "INFO:tensorflow:Done running local_init_op.\n",
+            "INFO:tensorflow:Finished evaluation at 2019-02-12-21:06:05\n",
+            "INFO:tensorflow:Saving dict for global step 468: auc = 0.86659324, eval_accuracy = 0.8664, f1_score = 0.8659711, false_negatives = 375.0, false_positives = 293.0, global_step = 468, loss = 0.51870537, precision = 0.880457, recall = 0.8519542, true_negatives = 2174.0, true_positives = 2158.0\n",
+            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 468: gs://bert-tfhub/aclImdb_v1/model.ckpt-468\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'auc': 0.86659324,\n",
+              " 'eval_accuracy': 0.8664,\n",
+              " 'f1_score': 0.8659711,\n",
+              " 'false_negatives': 375.0,\n",
+              " 'false_positives': 293.0,\n",
+              " 'global_step': 468,\n",
+              " 'loss': 0.51870537,\n",
+              " 'precision': 0.880457,\n",
+              " 'recall': 0.8519542,\n",
+              " 'true_negatives': 2174.0,\n",
+              " 'true_positives': 2158.0}"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ueKsULteiz1B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's write code to make predictions on new sentences:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OsrbTD2EJTVl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def getPrediction(in_sentences):\n",
+        "  labels = [\"Negative\", \"Positive\"]\n",
+        "  input_examples = [run_classifier.InputExample(guid=\"\", text_a = x, text_b = None, label = 0) for x in in_sentences] # here, \"\" is just a dummy label\n",
+        "  input_features = run_classifier.convert_examples_to_features(input_examples, label_list, MAX_SEQ_LENGTH, tokenizer)\n",
+        "  predict_input_fn = run_classifier.input_fn_builder(features=input_features, seq_length=MAX_SEQ_LENGTH, is_training=False, drop_remainder=False)\n",
+        "  predictions = estimator.predict(predict_input_fn)\n",
+        "  return [(sentence, prediction['probabilities'], labels[prediction['labels']]) for sentence, prediction in zip(in_sentences, predictions)]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-thbodgih_VJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pred_sentences = [\n",
+        "  \"That movie was absolutely awful\",\n",
+        "  \"The acting was a bit lacking\",\n",
+        "  \"The film was creative and surprising\",\n",
+        "  \"Absolutely fantastic!\"\n",
+        "]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "QrZmvZySKQTm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 649
+        },
+        "outputId": "3891fafb-a460-4eb8-fa6c-335a5bbc10e5"
+      },
+      "cell_type": "code",
+      "source": [
+        "predictions = getPrediction(pred_sentences)"
+      ],
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Writing example 0 of 4\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: \n",
+            "INFO:tensorflow:tokens: [CLS] that movie was absolutely awful [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 2008 3185 2001 7078 9643 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: \n",
+            "INFO:tensorflow:tokens: [CLS] the acting was a bit lacking [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1996 3772 2001 1037 2978 11158 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: \n",
+            "INFO:tensorflow:tokens: [CLS] the film was creative and surprising [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 1996 2143 2001 5541 1998 11341 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:*** Example ***\n",
+            "INFO:tensorflow:guid: \n",
+            "INFO:tensorflow:tokens: [CLS] absolutely fantastic ! [SEP]\n",
+            "INFO:tensorflow:input_ids: 101 7078 10392 999 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:input_mask: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            "INFO:tensorflow:label: 0 (id = 0)\n",
+            "INFO:tensorflow:Calling model_fn.\n",
+            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n",
+            "INFO:tensorflow:Done calling model_fn.\n",
+            "INFO:tensorflow:Graph was finalized.\n",
+            "INFO:tensorflow:Restoring parameters from gs://bert-tfhub/aclImdb_v1/model.ckpt-468\n",
+            "INFO:tensorflow:Running local_init_op.\n",
+            "INFO:tensorflow:Done running local_init_op.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MXkRiEBUqN3n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Voila! We have a sentiment classifier!"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ERkTE8-7oQLZ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "26c33224-dc2c-4b3d-f7b4-ac3ef0a58b27"
+      },
+      "cell_type": "code",
+      "source": [
+        "predictions"
+      ],
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('That movie was absolutely awful',\n",
+              "  array([-4.9142293e-03, -5.3180690e+00], dtype=float32),\n",
+              "  'Negative'),\n",
+              " ('The acting was a bit lacking',\n",
+              "  array([-0.03325794, -3.4200459 ], dtype=float32),\n",
+              "  'Negative'),\n",
+              " ('The film was creative and surprising',\n",
+              "  array([-5.3589125e+00, -4.7171740e-03], dtype=float32),\n",
+              "  'Positive'),\n",
+              " ('Absolutely fantastic!',\n",
+              "  array([-5.0434084 , -0.00647258], dtype=float32),\n",
+              "  'Positive')]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 73
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/TensorFlow/LanguageModeling/BERT/requirements.txt b/TensorFlow/LanguageModeling/BERT/requirements.txt
new file mode 100644
index 00000000..357b5ea0
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow >= 1.11.0   # CPU Version of TensorFlow.
+# tensorflow-gpu  >= 1.11.0  # GPU version of TensorFlow.
diff --git a/TensorFlow/LanguageModeling/BERT/run_classifier.py b/TensorFlow/LanguageModeling/BERT/run_classifier.py
new file mode 100644
index 00000000..812e92f8
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/run_classifier.py
@@ -0,0 +1,991 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""BERT finetuning runner."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import csv
+import os
+import modeling
+import optimization
+import tokenization
+import tensorflow as tf
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    "data_dir", None,
+    "The input data dir. Should contain the .tsv files (or other data files) "
+    "for the task.")
+
+flags.DEFINE_string(
+    "bert_config_file", None,
+    "The config json file corresponding to the pre-trained BERT model. "
+    "This specifies the model architecture.")
+
+flags.DEFINE_string("task_name", None, "The name of the task to train.")
+
+flags.DEFINE_string("vocab_file", None,
+                    "The vocabulary file that the BERT model was trained on.")
+
+flags.DEFINE_string(
+    "output_dir", None,
+    "The output directory where the model checkpoints will be written.")
+
+## Other parameters
+
+flags.DEFINE_string(
+    "init_checkpoint", None,
+    "Initial checkpoint (usually from a pre-trained BERT model).")
+
+flags.DEFINE_bool(
+    "do_lower_case", True,
+    "Whether to lower case the input text. Should be True for uncased "
+    "models and False for cased models.")
+
+flags.DEFINE_integer(
+    "max_seq_length", 128,
+    "The maximum total input sequence length after WordPiece tokenization. "
+    "Sequences longer than this will be truncated, and sequences shorter "
+    "than this will be padded.")
+
+flags.DEFINE_bool("do_train", False, "Whether to run training.")
+
+flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
+
+flags.DEFINE_bool(
+    "do_predict", False,
+    "Whether to run the model in inference mode on the test set.")
+
+flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
+
+flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
+
+flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
+
+flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
+
+flags.DEFINE_float("num_train_epochs", 3.0,
+                   "Total number of training epochs to perform.")
+
+flags.DEFINE_float(
+    "warmup_proportion", 0.1,
+    "Proportion of training to perform linear learning rate warmup for. "
+    "E.g., 0.1 = 10% of training.")
+
+flags.DEFINE_integer("save_checkpoints_steps", 1000,
+                     "How often to save the model checkpoint.")
+
+flags.DEFINE_integer("iterations_per_loop", 1000,
+                     "How many steps to make in each estimator call.")
+
+flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
+
+tf.flags.DEFINE_string(
+    "tpu_name", None,
+    "The Cloud TPU to use for training. This should be either the name "
+    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
+    "url.")
+
+tf.flags.DEFINE_string(
+    "tpu_zone", None,
+    "[Optional] GCE zone where the Cloud TPU is located in. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string(
+    "gcp_project", None,
+    "[Optional] Project name for the Cloud TPU-enabled project. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
+
+flags.DEFINE_integer(
+    "num_tpu_cores", 8,
+    "Only used if `use_tpu` is True. Total number of TPU cores to use.")
+
+flags.DEFINE_bool("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.")
+
+flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.")
+
+
+class InputExample(object):
+  """A single training/test example for simple sequence classification."""
+
+  def __init__(self, guid, text_a, text_b=None, label=None):
+    """Constructs a InputExample.
+
+    Args:
+      guid: Unique id for the example.
+      text_a: string. The untokenized text of the first sequence. For single
+        sequence tasks, only this sequence must be specified.
+      text_b: (Optional) string. The untokenized text of the second sequence.
+        Only must be specified for sequence pair tasks.
+      label: (Optional) string. The label of the example. This should be
+        specified for train and dev examples, but not for test examples.
+    """
+    self.guid = guid
+    self.text_a = text_a
+    self.text_b = text_b
+    self.label = label
+
+
+class PaddingInputExample(object):
+  """Fake example so the num input examples is a multiple of the batch size.
+
+  When running eval/predict on the TPU, we need to pad the number of examples
+  to be a multiple of the batch size, because the TPU requires a fixed batch
+  size. The alternative is to drop the last batch, which is bad because it means
+  the entire output data won't be generated.
+
+  We use this class instead of `None` because treating `None` as padding
+  battches could cause silent errors.
+  """
+
+
+class InputFeatures(object):
+  """A single set of features of data."""
+
+  def __init__(self,
+               input_ids,
+               input_mask,
+               segment_ids,
+               label_id,
+               is_real_example=True):
+    self.input_ids = input_ids
+    self.input_mask = input_mask
+    self.segment_ids = segment_ids
+    self.label_id = label_id
+    self.is_real_example = is_real_example
+
+
+class DataProcessor(object):
+  """Base class for data converters for sequence classification data sets."""
+
+  def get_train_examples(self, data_dir):
+    """Gets a collection of `InputExample`s for the train set."""
+    raise NotImplementedError()
+
+  def get_dev_examples(self, data_dir):
+    """Gets a collection of `InputExample`s for the dev set."""
+    raise NotImplementedError()
+
+  def get_test_examples(self, data_dir):
+    """Gets a collection of `InputExample`s for prediction."""
+    raise NotImplementedError()
+
+  def get_labels(self):
+    """Gets the list of labels for this data set."""
+    raise NotImplementedError()
+
+  @classmethod
+  def _read_tsv(cls, input_file, quotechar=None):
+    """Reads a tab separated value file."""
+    with tf.gfile.Open(input_file, "r") as f:
+      reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+      lines = []
+      for line in reader:
+        lines.append(line)
+      return lines
+
+
+class XnliProcessor(DataProcessor):
+  """Processor for the XNLI data set."""
+
+  def __init__(self):
+    self.language = "zh"
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    lines = self._read_tsv(
+        os.path.join(data_dir, "multinli",
+                     "multinli.train.%s.tsv" % self.language))
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
+      guid = "train-%d" % (i)
+      text_a = tokenization.convert_to_unicode(line[0])
+      text_b = tokenization.convert_to_unicode(line[1])
+      label = tokenization.convert_to_unicode(line[2])
+      if label == tokenization.convert_to_unicode("contradictory"):
+        label = tokenization.convert_to_unicode("contradiction")
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
+      guid = "dev-%d" % (i)
+      language = tokenization.convert_to_unicode(line[0])
+      if language != tokenization.convert_to_unicode(self.language):
+        continue
+      text_a = tokenization.convert_to_unicode(line[6])
+      text_b = tokenization.convert_to_unicode(line[7])
+      label = tokenization.convert_to_unicode(line[1])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+
+  def get_labels(self):
+    """See base class."""
+    return ["contradiction", "entailment", "neutral"]
+
+
+class MnliProcessor(DataProcessor):
+  """Processor for the MultiNLI data set (GLUE version)."""
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
+        "dev_matched")
+
+  def get_test_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
+
+  def get_labels(self):
+    """See base class."""
+    return ["contradiction", "entailment", "neutral"]
+
+  def _create_examples(self, lines, set_type):
+    """Creates examples for the training and dev sets."""
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
+      guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
+      text_a = tokenization.convert_to_unicode(line[8])
+      text_b = tokenization.convert_to_unicode(line[9])
+      if set_type == "test":
+        label = "contradiction"
+      else:
+        label = tokenization.convert_to_unicode(line[-1])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+
+
+class MrpcProcessor(DataProcessor):
+  """Processor for the MRPC data set (GLUE version)."""
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+  def get_test_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+  def get_labels(self):
+    """See base class."""
+    return ["0", "1"]
+
+  def _create_examples(self, lines, set_type):
+    """Creates examples for the training and dev sets."""
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
+      guid = "%s-%s" % (set_type, i)
+      text_a = tokenization.convert_to_unicode(line[3])
+      text_b = tokenization.convert_to_unicode(line[4])
+      if set_type == "test":
+        label = "0"
+      else:
+        label = tokenization.convert_to_unicode(line[0])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+
+
+class ColaProcessor(DataProcessor):
+  """Processor for the CoLA data set (GLUE version)."""
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+  def get_test_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+  def get_labels(self):
+    """See base class."""
+    return ["0", "1"]
+
+  def _create_examples(self, lines, set_type):
+    """Creates examples for the training and dev sets."""
+    examples = []
+    for (i, line) in enumerate(lines):
+      # Only the test set has a header
+      if set_type == "test" and i == 0:
+        continue
+      guid = "%s-%s" % (set_type, i)
+      if set_type == "test":
+        text_a = tokenization.convert_to_unicode(line[1])
+        label = "0"
+      else:
+        text_a = tokenization.convert_to_unicode(line[3])
+        label = tokenization.convert_to_unicode(line[1])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+    return examples
+
+
+def convert_single_example(ex_index, example, label_list, max_seq_length,
+                           tokenizer):
+  """Converts a single `InputExample` into a single `InputFeatures`."""
+
+  if isinstance(example, PaddingInputExample):
+    return InputFeatures(
+        input_ids=[0] * max_seq_length,
+        input_mask=[0] * max_seq_length,
+        segment_ids=[0] * max_seq_length,
+        label_id=0,
+        is_real_example=False)
+
+  label_map = {}
+  for (i, label) in enumerate(label_list):
+    label_map[label] = i
+
+  tokens_a = tokenizer.tokenize(example.text_a)
+  tokens_b = None
+  if example.text_b:
+    tokens_b = tokenizer.tokenize(example.text_b)
+
+  if tokens_b:
+    # Modifies `tokens_a` and `tokens_b` in place so that the total
+    # length is less than the specified length.
+    # Account for [CLS], [SEP], [SEP] with "- 3"
+    _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+  else:
+    # Account for [CLS] and [SEP] with "- 2"
+    if len(tokens_a) > max_seq_length - 2:
+      tokens_a = tokens_a[0:(max_seq_length - 2)]
+
+  # The convention in BERT is:
+  # (a) For sequence pairs:
+  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+  #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
+  # (b) For single sequences:
+  #  tokens:   [CLS] the dog is hairy . [SEP]
+  #  type_ids: 0     0   0   0  0     0 0
+  #
+  # Where "type_ids" are used to indicate whether this is the first
+  # sequence or the second sequence. The embedding vectors for `type=0` and
+  # `type=1` were learned during pre-training and are added to the wordpiece
+  # embedding vector (and position vector). This is not *strictly* necessary
+  # since the [SEP] token unambiguously separates the sequences, but it makes
+  # it easier for the model to learn the concept of sequences.
+  #
+  # For classification tasks, the first vector (corresponding to [CLS]) is
+  # used as the "sentence vector". Note that this only makes sense because
+  # the entire model is fine-tuned.
+  tokens = []
+  segment_ids = []
+  tokens.append("[CLS]")
+  segment_ids.append(0)
+  for token in tokens_a:
+    tokens.append(token)
+    segment_ids.append(0)
+  tokens.append("[SEP]")
+  segment_ids.append(0)
+
+  if tokens_b:
+    for token in tokens_b:
+      tokens.append(token)
+      segment_ids.append(1)
+    tokens.append("[SEP]")
+    segment_ids.append(1)
+
+  input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+  # The mask has 1 for real tokens and 0 for padding tokens. Only real
+  # tokens are attended to.
+  input_mask = [1] * len(input_ids)
+
+  # Zero-pad up to the sequence length.
+  while len(input_ids) < max_seq_length:
+    input_ids.append(0)
+    input_mask.append(0)
+    segment_ids.append(0)
+
+  assert len(input_ids) == max_seq_length
+  assert len(input_mask) == max_seq_length
+  assert len(segment_ids) == max_seq_length
+
+  label_id = label_map[example.label]
+  if ex_index < 5:
+    tf.logging.info("*** Example ***")
+    tf.logging.info("guid: %s" % (example.guid))
+    tf.logging.info("tokens: %s" % " ".join(
+        [tokenization.printable_text(x) for x in tokens]))
+    tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+    tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+    tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+    tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
+
+  feature = InputFeatures(
+      input_ids=input_ids,
+      input_mask=input_mask,
+      segment_ids=segment_ids,
+      label_id=label_id,
+      is_real_example=True)
+  return feature
+
+
+def file_based_convert_examples_to_features(
+    examples, label_list, max_seq_length, tokenizer, output_file):
+  """Convert a set of `InputExample`s to a TFRecord file."""
+
+  writer = tf.python_io.TFRecordWriter(output_file)
+
+  for (ex_index, example) in enumerate(examples):
+    if ex_index % 10000 == 0:
+      tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
+
+    feature = convert_single_example(ex_index, example, label_list,
+                                     max_seq_length, tokenizer)
+
+    def create_int_feature(values):
+      f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
+      return f
+
+    features = collections.OrderedDict()
+    features["input_ids"] = create_int_feature(feature.input_ids)
+    features["input_mask"] = create_int_feature(feature.input_mask)
+    features["segment_ids"] = create_int_feature(feature.segment_ids)
+    features["label_ids"] = create_int_feature([feature.label_id])
+    features["is_real_example"] = create_int_feature(
+        [int(feature.is_real_example)])
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+    writer.write(tf_example.SerializeToString())
+  writer.close()
+
+
+def file_based_input_fn_builder(input_file, seq_length, is_training,
+                                drop_remainder):
+  """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+  name_to_features = {
+      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
+      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
+      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
+      "label_ids": tf.FixedLenFeature([], tf.int64),
+      "is_real_example": tf.FixedLenFeature([], tf.int64),
+  }
+
+  def _decode_record(record, name_to_features):
+    """Decodes a record to a TensorFlow example."""
+    example = tf.parse_single_example(record, name_to_features)
+
+    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
+    # So cast all int64 to int32.
+    for name in list(example.keys()):
+      t = example[name]
+      if t.dtype == tf.int64:
+        t = tf.to_int32(t)
+      example[name] = t
+
+    return example
+
+  def input_fn(params):
+    """The actual input function."""
+    batch_size = params["batch_size"]
+
+    # For training, we want a lot of parallel reading and shuffling.
+    # For eval, we want no shuffling and parallel reading doesn't matter.
+    d = tf.data.TFRecordDataset(input_file)
+    if is_training:
+      d = d.repeat()
+      d = d.shuffle(buffer_size=100)
+
+    d = d.apply(
+        tf.contrib.data.map_and_batch(
+            lambda record: _decode_record(record, name_to_features),
+            batch_size=batch_size,
+            drop_remainder=drop_remainder))
+
+    return d
+
+  return input_fn
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+  """Truncates a sequence pair in place to the maximum length."""
+
+  # This is a simple heuristic which will always truncate the longer sequence
+  # one token at a time. This makes more sense than truncating an equal percent
+  # of tokens from each, since if one sequence is very short then each token
+  # that's truncated likely contains more information than a longer sequence.
+  while True:
+    total_length = len(tokens_a) + len(tokens_b)
+    if total_length <= max_length:
+      break
+    if len(tokens_a) > len(tokens_b):
+      tokens_a.pop()
+    else:
+      tokens_b.pop()
+
+
+def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
+                 labels, num_labels, use_one_hot_embeddings):
+  """Creates a classification model."""
+  model = modeling.BertModel(
+      config=bert_config,
+      is_training=is_training,
+      input_ids=input_ids,
+      input_mask=input_mask,
+      token_type_ids=segment_ids,
+      use_one_hot_embeddings=use_one_hot_embeddings,
+      compute_type=tf.float16 if FLAGS.use_fp16 else tf.float32)
+
+  # In the demo, we are doing a simple classification task on the entire
+  # segment.
+  #
+  # If you want to use the token-level output, use model.get_sequence_output()
+  # instead.
+  output_layer = model.get_pooled_output()
+
+  hidden_size = output_layer.shape[-1].value
+
+  output_weights = tf.get_variable(
+      "output_weights", [num_labels, hidden_size],
+      initializer=tf.truncated_normal_initializer(stddev=0.02))
+
+  output_bias = tf.get_variable(
+      "output_bias", [num_labels], initializer=tf.zeros_initializer())
+
+  with tf.variable_scope("loss"):
+    if is_training:
+      # I.e., 0.1 dropout
+      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
+
+    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
+    logits = tf.nn.bias_add(logits, output_bias)
+    probabilities = tf.nn.softmax(logits, axis=-1)
+    log_probs = tf.nn.log_softmax(logits, axis=-1)
+
+    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
+
+    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
+    loss = tf.reduce_mean(per_example_loss)
+
+    return (loss, per_example_loss, logits, probabilities)
+
+
+def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
+                     num_train_steps, num_warmup_steps, use_tpu,
+                     use_one_hot_embeddings):
+  """Returns `model_fn` closure for TPUEstimator."""
+
+  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
+    """The `model_fn` for TPUEstimator."""
+
+    tf.logging.info("*** Features ***")
+    for name in sorted(features.keys()):
+      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
+
+    input_ids = features["input_ids"]
+    input_mask = features["input_mask"]
+    segment_ids = features["segment_ids"]
+    label_ids = features["label_ids"]
+    is_real_example = None
+    if "is_real_example" in features:
+      is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
+    else:
+      is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
+
+    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+    (total_loss, per_example_loss, logits, probabilities) = create_model(
+        bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
+        num_labels, use_one_hot_embeddings)
+
+    tvars = tf.trainable_variables()
+    initialized_variable_names = {}
+    scaffold_fn = None
+    if init_checkpoint:
+      (assignment_map, initialized_variable_names
+      ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+      if use_tpu:
+
+        def tpu_scaffold():
+          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+          return tf.train.Scaffold()
+
+        scaffold_fn = tpu_scaffold
+      else:
+        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+    tf.logging.info("**** Trainable Variables ****")
+    for var in tvars:
+      init_string = ""
+      if var.name in initialized_variable_names:
+        init_string = ", *INIT_FROM_CKPT*"
+      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
+                      init_string)
+
+    output_spec = None
+    if mode == tf.estimator.ModeKeys.TRAIN:
+
+      train_op = optimization.create_optimizer(
+          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu,
+          None, FLAGS.use_fp16)
+
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          train_op=train_op,
+          scaffold_fn=scaffold_fn)
+    elif mode == tf.estimator.ModeKeys.EVAL:
+
+      def metric_fn(per_example_loss, label_ids, logits, is_real_example):
+        predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
+        accuracy = tf.metrics.accuracy(
+            labels=label_ids, predictions=predictions, weights=is_real_example)
+        loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
+        return {
+            "eval_accuracy": accuracy,
+            "eval_loss": loss,
+        }
+
+      eval_metrics = (metric_fn,
+                      [per_example_loss, label_ids, logits, is_real_example])
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          eval_metrics=eval_metrics,
+          scaffold_fn=scaffold_fn)
+    else:
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          predictions={"probabilities": probabilities},
+          scaffold_fn=scaffold_fn)
+    return output_spec
+
+  return model_fn
+
+
+# This function is not used by this file but is still used by the Colab and
+# people who depend on it.
+def input_fn_builder(features, seq_length, is_training, drop_remainder):
+  """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+  all_input_ids = []
+  all_input_mask = []
+  all_segment_ids = []
+  all_label_ids = []
+
+  for feature in features:
+    all_input_ids.append(feature.input_ids)
+    all_input_mask.append(feature.input_mask)
+    all_segment_ids.append(feature.segment_ids)
+    all_label_ids.append(feature.label_id)
+
+  def input_fn(params):
+    """The actual input function."""
+    batch_size = params["batch_size"]
+
+    num_examples = len(features)
+
+    # This is for demo purposes and does NOT scale to large data sets. We do
+    # not use Dataset.from_generator() because that uses tf.py_func which is
+    # not TPU compatible. The right way to load data is with TFRecordReader.
+    d = tf.data.Dataset.from_tensor_slices({
+        "input_ids":
+            tf.constant(
+                all_input_ids, shape=[num_examples, seq_length],
+                dtype=tf.int32),
+        "input_mask":
+            tf.constant(
+                all_input_mask,
+                shape=[num_examples, seq_length],
+                dtype=tf.int32),
+        "segment_ids":
+            tf.constant(
+                all_segment_ids,
+                shape=[num_examples, seq_length],
+                dtype=tf.int32),
+        "label_ids":
+            tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
+    })
+
+    if is_training:
+      d = d.repeat()
+      d = d.shuffle(buffer_size=100)
+
+    d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
+    return d
+
+  return input_fn
+
+
+# This function is not used by this file but is still used by the Colab and
+# people who depend on it.
+def convert_examples_to_features(examples, label_list, max_seq_length,
+                                 tokenizer):
+  """Convert a set of `InputExample`s to a list of `InputFeatures`."""
+
+  features = []
+  for (ex_index, example) in enumerate(examples):
+    if ex_index % 10000 == 0:
+      tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
+
+    feature = convert_single_example(ex_index, example, label_list,
+                                     max_seq_length, tokenizer)
+
+    features.append(feature)
+  return features
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  processors = {
+      "cola": ColaProcessor,
+      "mnli": MnliProcessor,
+      "mrpc": MrpcProcessor,
+      "xnli": XnliProcessor,
+  }
+
+  tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
+                                                FLAGS.init_checkpoint)
+
+  if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
+    raise ValueError(
+        "At least one of `do_train`, `do_eval` or `do_predict' must be True.")
+
+  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
+
+  if FLAGS.max_seq_length > bert_config.max_position_embeddings:
+    raise ValueError(
+        "Cannot use sequence length %d because the BERT model "
+        "was only trained up to sequence length %d" %
+        (FLAGS.max_seq_length, bert_config.max_position_embeddings))
+
+  tf.gfile.MakeDirs(FLAGS.output_dir)
+
+  task_name = FLAGS.task_name.lower()
+
+  if task_name not in processors:
+    raise ValueError("Task not found: %s" % (task_name))
+
+  processor = processors[task_name]()
+
+  label_list = processor.get_labels()
+
+  tokenizer = tokenization.FullTokenizer(
+      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
+
+  tpu_cluster_resolver = None
+  if FLAGS.use_tpu and FLAGS.tpu_name:
+    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
+        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
+
+  config = tf.ConfigProto()
+  if FLAGS.use_xla: 
+    config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      cluster=tpu_cluster_resolver,
+      master=FLAGS.master,
+      model_dir=FLAGS.output_dir,
+      session_config=config,
+      save_checkpoints_steps=FLAGS.save_checkpoints_steps,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations_per_loop,
+          num_shards=FLAGS.num_tpu_cores,
+          per_host_input_for_training=is_per_host))
+
+  train_examples = None
+  num_train_steps = None
+  num_warmup_steps = None
+  if FLAGS.do_train:
+    train_examples = processor.get_train_examples(FLAGS.data_dir)
+    num_train_steps = int(
+        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
+    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
+
+  model_fn = model_fn_builder(
+      bert_config=bert_config,
+      num_labels=len(label_list),
+      init_checkpoint=FLAGS.init_checkpoint,
+      learning_rate=FLAGS.learning_rate,
+      num_train_steps=num_train_steps,
+      num_warmup_steps=num_warmup_steps,
+      use_tpu=FLAGS.use_tpu,
+      use_one_hot_embeddings=FLAGS.use_tpu)
+
+  # If TPU is not available, this will fall back to normal Estimator on CPU
+  # or GPU.
+  estimator = tf.contrib.tpu.TPUEstimator(
+      use_tpu=FLAGS.use_tpu,
+      model_fn=model_fn,
+      config=run_config,
+      train_batch_size=FLAGS.train_batch_size,
+      eval_batch_size=FLAGS.eval_batch_size,
+      predict_batch_size=FLAGS.predict_batch_size)
+
+  if FLAGS.do_train:
+    train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
+    file_based_convert_examples_to_features(
+        train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
+    tf.logging.info("***** Running training *****")
+    tf.logging.info("  Num examples = %d", len(train_examples))
+    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
+    tf.logging.info("  Num steps = %d", num_train_steps)
+    train_input_fn = file_based_input_fn_builder(
+        input_file=train_file,
+        seq_length=FLAGS.max_seq_length,
+        is_training=True,
+        drop_remainder=True)
+    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
+
+  if FLAGS.do_eval:
+    eval_examples = processor.get_dev_examples(FLAGS.data_dir)
+    num_actual_eval_examples = len(eval_examples)
+    if FLAGS.use_tpu:
+      # TPU requires a fixed batch size for all batches, therefore the number
+      # of examples must be a multiple of the batch size, or else examples
+      # will get dropped. So we pad with fake examples which are ignored
+      # later on. These do NOT count towards the metric (all tf.metrics
+      # support a per-instance weight, and these get a weight of 0.0).
+      while len(eval_examples) % FLAGS.eval_batch_size != 0:
+        eval_examples.append(PaddingInputExample())
+
+    eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
+    file_based_convert_examples_to_features(
+        eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
+
+    tf.logging.info("***** Running evaluation *****")
+    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
+                    len(eval_examples), num_actual_eval_examples,
+                    len(eval_examples) - num_actual_eval_examples)
+    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
+
+    # This tells the estimator to run through the entire set.
+    eval_steps = None
+    # However, if running eval on the TPU, you will need to specify the
+    # number of steps.
+    if FLAGS.use_tpu:
+      assert len(eval_examples) % FLAGS.eval_batch_size == 0
+      eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
+
+    eval_drop_remainder = True if FLAGS.use_tpu else False
+    eval_input_fn = file_based_input_fn_builder(
+        input_file=eval_file,
+        seq_length=FLAGS.max_seq_length,
+        is_training=False,
+        drop_remainder=eval_drop_remainder)
+
+    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
+
+    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
+    with tf.gfile.GFile(output_eval_file, "w") as writer:
+      tf.logging.info("***** Eval results *****")
+      for key in sorted(result.keys()):
+        tf.logging.info("  %s = %s", key, str(result[key]))
+        writer.write("%s = %s\n" % (key, str(result[key])))
+
+  if FLAGS.do_predict:
+    predict_examples = processor.get_test_examples(FLAGS.data_dir)
+    num_actual_predict_examples = len(predict_examples)
+    if FLAGS.use_tpu:
+      # TPU requires a fixed batch size for all batches, therefore the number
+      # of examples must be a multiple of the batch size, or else examples
+      # will get dropped. So we pad with fake examples which are ignored
+      # later on.
+      while len(predict_examples) % FLAGS.predict_batch_size != 0:
+        predict_examples.append(PaddingInputExample())
+
+    predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
+    file_based_convert_examples_to_features(predict_examples, label_list,
+                                            FLAGS.max_seq_length, tokenizer,
+                                            predict_file)
+
+    tf.logging.info("***** Running prediction*****")
+    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
+                    len(predict_examples), num_actual_predict_examples,
+                    len(predict_examples) - num_actual_predict_examples)
+    tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
+
+    predict_drop_remainder = True if FLAGS.use_tpu else False
+    predict_input_fn = file_based_input_fn_builder(
+        input_file=predict_file,
+        seq_length=FLAGS.max_seq_length,
+        is_training=False,
+        drop_remainder=predict_drop_remainder)
+
+    result = estimator.predict(input_fn=predict_input_fn)
+
+    output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
+    with tf.gfile.GFile(output_predict_file, "w") as writer:
+      num_written_lines = 0
+      tf.logging.info("***** Predict results *****")
+      for (i, prediction) in enumerate(result):
+        probabilities = prediction["probabilities"]
+        if i >= num_actual_predict_examples:
+          break
+        output_line = "\t".join(
+            str(class_probability)
+            for class_probability in probabilities) + "\n"
+        writer.write(output_line)
+        num_written_lines += 1
+    assert num_written_lines == num_actual_predict_examples
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("data_dir")
+  flags.mark_flag_as_required("task_name")
+  flags.mark_flag_as_required("vocab_file")
+  flags.mark_flag_as_required("bert_config_file")
+  flags.mark_flag_as_required("output_dir")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/run_classifier_with_tfhub.py b/TensorFlow/LanguageModeling/BERT/run_classifier_with_tfhub.py
new file mode 100644
index 00000000..edea6bb1
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/run_classifier_with_tfhub.py
@@ -0,0 +1,275 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""BERT finetuning runner with TF-Hub."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import optimization
+import run_classifier
+import tokenization
+import tensorflow as tf
+import tensorflow_hub as hub
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+    "bert_hub_module_handle", None,
+    "Handle for the BERT TF-Hub module.")
+
+
+def create_model(is_training, input_ids, input_mask, segment_ids, labels,
+                 num_labels):
+  """Creates a classification model."""
+  tags = set()
+  if is_training:
+    tags.add("train")
+  bert_module = hub.Module(
+      FLAGS.bert_hub_module_handle,
+      tags=tags,
+      trainable=True)
+  bert_inputs = dict(
+      input_ids=input_ids,
+      input_mask=input_mask,
+      segment_ids=segment_ids)
+  bert_outputs = bert_module(
+      inputs=bert_inputs,
+      signature="tokens",
+      as_dict=True)
+
+  # In the demo, we are doing a simple classification task on the entire
+  # segment.
+  #
+  # If you want to use the token-level output, use
+  # bert_outputs["sequence_output"] instead.
+  output_layer = bert_outputs["pooled_output"]
+
+  hidden_size = output_layer.shape[-1].value
+
+  output_weights = tf.get_variable(
+      "output_weights", [num_labels, hidden_size],
+      initializer=tf.truncated_normal_initializer(stddev=0.02))
+
+  output_bias = tf.get_variable(
+      "output_bias", [num_labels], initializer=tf.zeros_initializer())
+
+  with tf.variable_scope("loss"):
+    if is_training:
+      # I.e., 0.1 dropout
+      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
+
+    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
+    logits = tf.nn.bias_add(logits, output_bias)
+    log_probs = tf.nn.log_softmax(logits, axis=-1)
+
+    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
+
+    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
+    loss = tf.reduce_mean(per_example_loss)
+
+    return (loss, per_example_loss, logits)
+
+
+def model_fn_builder(num_labels, learning_rate, num_train_steps,
+                     num_warmup_steps, use_tpu):
+  """Returns `model_fn` closure for TPUEstimator."""
+
+  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
+    """The `model_fn` for TPUEstimator."""
+
+    tf.logging.info("*** Features ***")
+    for name in sorted(features.keys()):
+      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
+
+    input_ids = features["input_ids"]
+    input_mask = features["input_mask"]
+    segment_ids = features["segment_ids"]
+    label_ids = features["label_ids"]
+
+    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+    (total_loss, per_example_loss, logits) = create_model(
+        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels)
+
+    output_spec = None
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      train_op = optimization.create_optimizer(
+          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
+
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          train_op=train_op)
+    elif mode == tf.estimator.ModeKeys.EVAL:
+
+      def metric_fn(per_example_loss, label_ids, logits):
+        predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
+        accuracy = tf.metrics.accuracy(label_ids, predictions)
+        loss = tf.metrics.mean(per_example_loss)
+        return {
+            "eval_accuracy": accuracy,
+            "eval_loss": loss,
+        }
+
+      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          eval_metrics=eval_metrics)
+    else:
+      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
+
+    return output_spec
+
+  return model_fn
+
+
+def create_tokenizer_from_hub_module():
+  """Get the vocab file and casing info from the Hub module."""
+  with tf.Graph().as_default():
+    bert_module = hub.Module(FLAGS.bert_hub_module_handle)
+    tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
+    with tf.Session() as sess:
+      vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
+                                            tokenization_info["do_lower_case"]])
+  return tokenization.FullTokenizer(
+      vocab_file=vocab_file, do_lower_case=do_lower_case)
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  processors = {
+      "cola": run_classifier.ColaProcessor,
+      "mnli": run_classifier.MnliProcessor,
+      "mrpc": run_classifier.MrpcProcessor,
+  }
+
+  if not FLAGS.do_train and not FLAGS.do_eval:
+    raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+
+  tf.gfile.MakeDirs(FLAGS.output_dir)
+
+  task_name = FLAGS.task_name.lower()
+
+  if task_name not in processors:
+    raise ValueError("Task not found: %s" % (task_name))
+
+  processor = processors[task_name]()
+
+  label_list = processor.get_labels()
+
+  tokenizer = create_tokenizer_from_hub_module()
+
+  tpu_cluster_resolver = None
+  if FLAGS.use_tpu and FLAGS.tpu_name:
+    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
+        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
+
+  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      cluster=tpu_cluster_resolver,
+      master=FLAGS.master,
+      model_dir=FLAGS.output_dir,
+      save_checkpoints_steps=FLAGS.save_checkpoints_steps,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations_per_loop,
+          num_shards=FLAGS.num_tpu_cores,
+          per_host_input_for_training=is_per_host))
+
+  train_examples = None
+  num_train_steps = None
+  num_warmup_steps = None
+  if FLAGS.do_train:
+    train_examples = processor.get_train_examples(FLAGS.data_dir)
+    num_train_steps = int(
+        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
+    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
+
+  model_fn = model_fn_builder(
+      num_labels=len(label_list),
+      learning_rate=FLAGS.learning_rate,
+      num_train_steps=num_train_steps,
+      num_warmup_steps=num_warmup_steps,
+      use_tpu=FLAGS.use_tpu)
+
+  # If TPU is not available, this will fall back to normal Estimator on CPU
+  # or GPU.
+  estimator = tf.contrib.tpu.TPUEstimator(
+      use_tpu=FLAGS.use_tpu,
+      model_fn=model_fn,
+      config=run_config,
+      train_batch_size=FLAGS.train_batch_size,
+      eval_batch_size=FLAGS.eval_batch_size)
+
+  if FLAGS.do_train:
+    train_features = run_classifier.convert_examples_to_features(
+        train_examples, label_list, FLAGS.max_seq_length, tokenizer)
+    tf.logging.info("***** Running training *****")
+    tf.logging.info("  Num examples = %d", len(train_examples))
+    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
+    tf.logging.info("  Num steps = %d", num_train_steps)
+    train_input_fn = run_classifier.input_fn_builder(
+        features=train_features,
+        seq_length=FLAGS.max_seq_length,
+        is_training=True,
+        drop_remainder=True)
+    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
+
+  if FLAGS.do_eval:
+    eval_examples = processor.get_dev_examples(FLAGS.data_dir)
+    eval_features = run_classifier.convert_examples_to_features(
+        eval_examples, label_list, FLAGS.max_seq_length, tokenizer)
+
+    tf.logging.info("***** Running evaluation *****")
+    tf.logging.info("  Num examples = %d", len(eval_examples))
+    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
+
+    # This tells the estimator to run through the entire set.
+    eval_steps = None
+    # However, if running eval on the TPU, you will need to specify the
+    # number of steps.
+    if FLAGS.use_tpu:
+      # Eval will be slightly WRONG on the TPU because it will truncate
+      # the last batch.
+      eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
+
+    eval_drop_remainder = True if FLAGS.use_tpu else False
+    eval_input_fn = run_classifier.input_fn_builder(
+        features=eval_features,
+        seq_length=FLAGS.max_seq_length,
+        is_training=False,
+        drop_remainder=eval_drop_remainder)
+
+    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
+
+    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
+    with tf.gfile.GFile(output_eval_file, "w") as writer:
+      tf.logging.info("***** Eval results *****")
+      for key in sorted(result.keys()):
+        tf.logging.info("  %s = %s", key, str(result[key]))
+        writer.write("%s = %s\n" % (key, str(result[key])))
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("data_dir")
+  flags.mark_flag_as_required("task_name")
+  flags.mark_flag_as_required("bert_hub_module_handle")
+  flags.mark_flag_as_required("output_dir")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/run_pretraining.py b/TensorFlow/LanguageModeling/BERT/run_pretraining.py
new file mode 100644
index 00000000..1ec3d33a
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/run_pretraining.py
@@ -0,0 +1,612 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Run masked LM/next sentence masked_lm pre-training for BERT."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import modeling
+import optimization
+import tensorflow as tf
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    "bert_config_file", None,
+    "The config json file corresponding to the pre-trained BERT model. "
+    "This specifies the model architecture.")
+
+flags.DEFINE_string(
+    "input_file", None,
+    "Input TF example files (can be a glob or comma separated).")
+
+flags.DEFINE_string(
+    "output_dir", None,
+    "The output directory where the model checkpoints will be written.")
+
+## Other parameters
+flags.DEFINE_string(
+    "init_checkpoint", None,
+    "Initial checkpoint (usually from a pre-trained BERT model).")
+
+flags.DEFINE_integer(
+    "max_seq_length", 512,
+    "The maximum total input sequence length after WordPiece tokenization. "
+    "Sequences longer than this will be truncated, and sequences shorter "
+    "than this will be padded. Must match data generation.")
+
+flags.DEFINE_integer(
+    "max_predictions_per_seq", 80,
+    "Maximum number of masked LM predictions per sequence. "
+    "Must match data generation.")
+
+flags.DEFINE_bool("do_train", False, "Whether to run training.")
+
+flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
+
+flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
+
+flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
+
+flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
+
+flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
+
+flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
+
+flags.DEFINE_integer("save_checkpoints_steps", 1000,
+                     "How often to save the model checkpoint.")
+
+flags.DEFINE_integer("iterations_per_loop", 1000,
+                     "How many steps to make in each estimator call.")
+
+flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
+
+flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
+
+tf.flags.DEFINE_string(
+    "tpu_name", None,
+    "The Cloud TPU to use for training. This should be either the name "
+    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
+    "url.")
+
+tf.flags.DEFINE_string(
+    "tpu_zone", None,
+    "[Optional] GCE zone where the Cloud TPU is located in. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string(
+    "gcp_project", None,
+    "[Optional] Project name for the Cloud TPU-enabled project. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
+
+flags.DEFINE_integer(
+    "num_tpu_cores", 8,
+    "Only used if `use_tpu` is True. Total number of TPU cores to use.")
+
+flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
+
+flags.DEFINE_bool("report_loss", False, "Whether to report total loss during training.")
+
+flags.DEFINE_bool("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.")
+
+flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.")
+
+flags.DEFINE_bool("fastmath", False, "Whether to enable loss scaler for fasthmath ops.")
+
+flags.DEFINE_bool("amp", False, "Whether to enable AMP ops.")
+
+flags.DEFINE_bool("amp_fastmath", False, "Whether to enable AMP fasthmath ops.")
+
+# report samples/sec, total loss and learning rate during training
+class _LogSessionRunHook(tf.train.SessionRunHook):
+  def __init__(self, global_batch_size, display_every=10, hvd_rank=-1):
+    self.global_batch_size = global_batch_size
+    self.display_every = display_every
+    self.hvd_rank = hvd_rank
+  def after_create_session(self, session, coord):
+    if FLAGS.use_fp16 or FLAGS.fastmath or FLAGS.amp or FLAGS.amp_fastmath:
+      print('  Step samples/sec   MLM Loss  NSP Loss  Loss  Learning-rate  Loss-scaler')
+    else:
+      print('  Step samples/sec   MLM Loss  NSP Loss  Loss  Learning-rate')
+    self.elapsed_secs = 0.
+    self.count = 0
+  def before_run(self, run_context):
+    self.t0 = time.time()
+    if FLAGS.use_fp16 or FLAGS.fastmath or FLAGS.amp or FLAGS.amp_fastmath:
+      return tf.train.SessionRunArgs(
+          fetches=['step_update:0', 'total_loss:0',
+                   'learning_rate:0', 'nsp_loss:0',
+                   'mlm_loss:0', 'loss_scale:0'])
+    else:
+      return tf.train.SessionRunArgs(
+          fetches=['step_update:0', 'total_loss:0',
+                   'learning_rate:0', 'nsp_loss:0',
+                   'mlm_loss:0'])
+  def after_run(self, run_context, run_values):
+    self.elapsed_secs += time.time() - self.t0
+    self.count += 1
+    if FLAGS.use_fp16 or FLAGS.fastmath or FLAGS.amp or FLAGS.amp_fastmath:
+      global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results
+    else:
+      global_step, total_loss, lr, nsp_loss, mlm_loss = run_values.results
+    print_step = global_step + 1 # One-based index for printing.
+    if print_step == 1 or print_step % self.display_every == 0:
+        dt = self.elapsed_secs / self.count
+        img_per_sec = self.global_batch_size / dt
+        if self.hvd_rank >= 0:
+          if FLAGS.use_fp16 or FLAGS.fastmath or FLAGS.amp or FLAGS.amp_fastmath:
+            print('%2d :: %6i %11.1f %10.4e %10.4e %6.3f     %6.4e  %6.4e' %
+                  (self.hvd_rank, print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr, loss_scaler))
+          else:
+            print('%2d :: %6i %11.1f %10.4e %10.4e %6.3f     %6.4e' %
+                  (self.hvd_rank, print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr))
+        else:
+          if FLAGS.use_fp16 or FLAGS.fastmath or FLAGS.amp or FLAGS.amp_fastmath:
+            print('%6i %11.1f %10.4e %10.4e %6.3f     %6.4e  %6.4e' %
+                  (print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr, loss_scaler))
+          else:
+            print('%6i %11.1f %10.4e %10.4e %6.3f     %6.4e' %
+                  (print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr))
+        self.elapsed_secs = 0.
+        self.count = 0
+
+def model_fn_builder(bert_config, init_checkpoint, learning_rate,
+                     num_train_steps, num_warmup_steps, use_tpu,
+                     use_one_hot_embeddings, hvd=None):
+  """Returns `model_fn` closure for TPUEstimator."""
+
+  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
+    """The `model_fn` for TPUEstimator."""
+
+    tf.logging.info("*** Features ***")
+    for name in sorted(features.keys()):
+      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
+
+    input_ids = features["input_ids"]
+    input_mask = features["input_mask"]
+    segment_ids = features["segment_ids"]
+    masked_lm_positions = features["masked_lm_positions"]
+    masked_lm_ids = features["masked_lm_ids"]
+    masked_lm_weights = features["masked_lm_weights"]
+    next_sentence_labels = features["next_sentence_labels"]
+
+    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+    model = modeling.BertModel(
+        config=bert_config,
+        is_training=is_training,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        token_type_ids=segment_ids,
+        use_one_hot_embeddings=use_one_hot_embeddings,
+        compute_type=tf.float16 if FLAGS.use_fp16 else tf.float32)
+
+    (masked_lm_loss,
+     masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
+         bert_config, model.get_sequence_output(), model.get_embedding_table(),
+         masked_lm_positions, masked_lm_ids, masked_lm_weights)
+
+    (next_sentence_loss, next_sentence_example_loss,
+     next_sentence_log_probs) = get_next_sentence_output(
+         bert_config, model.get_pooled_output(), next_sentence_labels)
+
+    masked_lm_loss = tf.identity(masked_lm_loss, name="mlm_loss")
+    next_sentence_loss = tf.identity(next_sentence_loss, name="nsp_loss")
+    total_loss = masked_lm_loss + next_sentence_loss
+    total_loss = tf.identity(total_loss, name='total_loss')
+
+    tvars = tf.trainable_variables()
+
+    initialized_variable_names = {}
+    scaffold_fn = None
+    if init_checkpoint and (hvd is None or hvd.rank() == 0):
+      (assignment_map, initialized_variable_names
+      ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+      if use_tpu:
+
+        def tpu_scaffold():
+          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+          return tf.train.Scaffold()
+
+        scaffold_fn = tpu_scaffold
+      else:
+        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+    tf.logging.info("**** Trainable Variables ****")
+    for var in tvars:
+      init_string = ""
+      if var.name in initialized_variable_names:
+        init_string = ", *INIT_FROM_CKPT*"
+      tf.logging.info("  %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape,
+                      init_string)
+
+    output_spec = None
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      train_op = optimization.create_optimizer(
+          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu,
+          hvd, FLAGS.use_fp16, FLAGS.fastmath, FLAGS.amp, FLAGS.amp_fastmath)
+
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          train_op=train_op,
+          scaffold_fn=scaffold_fn)
+    elif mode == tf.estimator.ModeKeys.EVAL:
+
+      def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
+                    masked_lm_weights, next_sentence_example_loss,
+                    next_sentence_log_probs, next_sentence_labels):
+        """Computes the loss and accuracy of the model."""
+        masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
+                                         [-1, masked_lm_log_probs.shape[-1]])
+        masked_lm_predictions = tf.argmax(
+            masked_lm_log_probs, axis=-1, output_type=tf.int32)
+        masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
+        masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
+        masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
+        masked_lm_accuracy = tf.metrics.accuracy(
+            labels=masked_lm_ids,
+            predictions=masked_lm_predictions,
+            weights=masked_lm_weights)
+        masked_lm_mean_loss = tf.metrics.mean(
+            values=masked_lm_example_loss, weights=masked_lm_weights)
+
+        next_sentence_log_probs = tf.reshape(
+            next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
+        next_sentence_predictions = tf.argmax(
+            next_sentence_log_probs, axis=-1, output_type=tf.int32)
+        next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
+        next_sentence_accuracy = tf.metrics.accuracy(
+            labels=next_sentence_labels, predictions=next_sentence_predictions)
+        next_sentence_mean_loss = tf.metrics.mean(
+            values=next_sentence_example_loss)
+
+        return {
+            "masked_lm_accuracy": masked_lm_accuracy,
+            "masked_lm_loss": masked_lm_mean_loss,
+            "next_sentence_accuracy": next_sentence_accuracy,
+            "next_sentence_loss": next_sentence_mean_loss,
+        }
+
+      eval_metrics = (metric_fn, [
+          masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
+          masked_lm_weights, next_sentence_example_loss,
+          next_sentence_log_probs, next_sentence_labels
+      ])
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          eval_metrics=eval_metrics,
+          scaffold_fn=scaffold_fn)
+    else:
+      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
+
+    return output_spec
+
+  return model_fn
+
+
+def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
+                         label_ids, label_weights):
+  """Get loss and log probs for the masked LM."""
+  input_tensor = gather_indexes(input_tensor, positions)
+
+  with tf.variable_scope("cls/predictions"):
+    # We apply one more non-linear transformation before the output layer.
+    # This matrix is not used after pre-training.
+    with tf.variable_scope("transform"):
+      input_tensor = tf.layers.dense(
+          input_tensor,
+          units=bert_config.hidden_size,
+          activation=modeling.get_activation(bert_config.hidden_act),
+          kernel_initializer=modeling.create_initializer(
+              bert_config.initializer_range))
+      input_tensor = modeling.layer_norm(input_tensor)
+
+    # The output weights are the same as the input embeddings, but there is
+    # an output-only bias for each token.
+    output_bias = tf.get_variable(
+        "output_bias",
+        shape=[bert_config.vocab_size],
+        initializer=tf.zeros_initializer())
+    logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
+    logits = tf.nn.bias_add(logits, output_bias)
+    log_probs = tf.nn.log_softmax(logits, axis=-1)
+
+    label_ids = tf.reshape(label_ids, [-1])
+    label_weights = tf.reshape(label_weights, [-1])
+
+    one_hot_labels = tf.one_hot(
+        label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
+
+    # The `positions` tensor might be zero-padded (if the sequence is too
+    # short to have the maximum number of predictions). The `label_weights`
+    # tensor has a value of 1.0 for every real prediction and 0.0 for the
+    # padding predictions.
+    per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
+    numerator = tf.reduce_sum(label_weights * per_example_loss)
+    denominator = tf.reduce_sum(label_weights) + 1e-5
+    loss = numerator / denominator
+
+  return (loss, per_example_loss, log_probs)
+
+
+def get_next_sentence_output(bert_config, input_tensor, labels):
+  """Get loss and log probs for the next sentence prediction."""
+
+  # Simple binary classification. Note that 0 is "next sentence" and 1 is
+  # "random sentence". This weight matrix is not used after pre-training.
+  with tf.variable_scope("cls/seq_relationship"):
+    output_weights = tf.get_variable(
+        "output_weights",
+        shape=[2, bert_config.hidden_size],
+        initializer=modeling.create_initializer(bert_config.initializer_range))
+    output_bias = tf.get_variable(
+        "output_bias", shape=[2], initializer=tf.zeros_initializer())
+
+    logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
+    logits = tf.nn.bias_add(logits, output_bias)
+    log_probs = tf.nn.log_softmax(logits, axis=-1)
+    labels = tf.reshape(labels, [-1])
+    one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
+    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
+    loss = tf.reduce_mean(per_example_loss)
+    return (loss, per_example_loss, log_probs)
+
+
+def gather_indexes(sequence_tensor, positions):
+  """Gathers the vectors at the specific positions over a minibatch."""
+  sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
+  batch_size = sequence_shape[0]
+  seq_length = sequence_shape[1]
+  width = sequence_shape[2]
+
+  flat_offsets = tf.reshape(
+      tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
+  flat_positions = tf.reshape(positions + flat_offsets, [-1])
+  flat_sequence_tensor = tf.reshape(sequence_tensor,
+                                    [batch_size * seq_length, width])
+  output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
+  return output_tensor
+
+
+def input_fn_builder(input_files,
+                     max_seq_length,
+                     max_predictions_per_seq,
+                     is_training,
+                     num_cpu_threads=4,
+                     hvd=None):
+  """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+  def input_fn(params):
+    """The actual input function."""
+    batch_size = params["batch_size"]
+
+    name_to_features = {
+        "input_ids":
+            tf.FixedLenFeature([max_seq_length], tf.int64),
+        "input_mask":
+            tf.FixedLenFeature([max_seq_length], tf.int64),
+        "segment_ids":
+            tf.FixedLenFeature([max_seq_length], tf.int64),
+        "masked_lm_positions":
+            tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
+        "masked_lm_ids":
+            tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
+        "masked_lm_weights":
+            tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
+        "next_sentence_labels":
+            tf.FixedLenFeature([1], tf.int64),
+    }
+
+    # For training, we want a lot of parallel reading and shuffling.
+    # For eval, we want no shuffling and parallel reading doesn't matter.
+    if is_training:
+      d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
+      if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
+      d = d.repeat()
+      d = d.shuffle(buffer_size=len(input_files))
+
+      # `cycle_length` is the number of parallel files that get read.
+      cycle_length = min(num_cpu_threads, len(input_files))
+
+      # `sloppy` mode means that the interleaving is not exact. This adds
+      # even more randomness to the training pipeline.
+      d = d.apply(
+          tf.contrib.data.parallel_interleave(
+              tf.data.TFRecordDataset,
+              sloppy=is_training,
+              cycle_length=cycle_length))
+      d = d.shuffle(buffer_size=100)
+    else:
+      d = tf.data.TFRecordDataset(input_files)
+      # Since we evaluate for a fixed number of steps we don't want to encounter
+      # out-of-range exceptions.
+      d = d.repeat()
+
+    # We must `drop_remainder` on training because the TPU requires fixed
+    # size dimensions. For eval, we assume we are evaluating on the CPU or GPU
+    # and we *don't* want to drop the remainder, otherwise we wont cover
+    # every sample.
+    d = d.apply(
+        tf.contrib.data.map_and_batch(
+            lambda record: _decode_record(record, name_to_features),
+            batch_size=batch_size,
+            num_parallel_batches=num_cpu_threads,
+            drop_remainder=True))
+    return d
+
+  return input_fn
+
+
+def _decode_record(record, name_to_features):
+  """Decodes a record to a TensorFlow example."""
+  example = tf.parse_single_example(record, name_to_features)
+
+  # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
+  # So cast all int64 to int32.
+  for name in list(example.keys()):
+    t = example[name]
+    if t.dtype == tf.int64:
+      t = tf.to_int32(t)
+    example[name] = t
+
+  return example
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  if not FLAGS.do_train and not FLAGS.do_eval:
+    raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+
+  if FLAGS.fastmath and FLAGS.amp:
+    raise ValueError("Only one of fastmath or amp must be True.")
+
+  if FLAGS.fastmath and FLAGS.amp_fastmath:
+    raise ValueError("Only one of fastmath or amp_fastmath must be True.")
+
+  if FLAGS.amp and FLAGS.amp_fastmath:
+    raise ValueError("Only one of amp or amp_fastmath must be True.")
+
+  if FLAGS.fastmath:
+    os.environ["TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32"] = "1"
+    os.environ["TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32"] = "1"
+    os.environ["TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32"] = "1"
+  elif FLAGS.amp:
+    os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
+  elif FLAGS.amp_fastmath:
+    os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
+    os.environ["TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_LEVEL"] = "TENSOR_CORES_ONLY"
+
+  if FLAGS.horovod:
+    import horovod.tensorflow as hvd
+    hvd.init()
+
+  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
+
+  tf.gfile.MakeDirs(FLAGS.output_dir)
+
+  input_files = []
+  for input_pattern in FLAGS.input_file.split(","):
+    input_files.extend(tf.gfile.Glob(input_pattern))
+
+  tf.logging.info("*** Input Files ***")
+  for input_file in input_files:
+    tf.logging.info("  %s" % input_file)
+
+  tpu_cluster_resolver = None
+  if FLAGS.use_tpu and FLAGS.tpu_name:
+    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
+        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
+
+  config = tf.ConfigProto()
+  if FLAGS.horovod: 
+    config.gpu_options.visible_device_list = str(hvd.local_rank())
+  if FLAGS.use_xla: 
+    config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      cluster=tpu_cluster_resolver,
+      master=FLAGS.master,
+      model_dir=FLAGS.output_dir,
+      session_config=config,
+      save_checkpoints_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations_per_loop,
+          num_shards=FLAGS.num_tpu_cores,
+          per_host_input_for_training=is_per_host),
+      # This variable controls how often estimator reports examples/sec.
+      # Default value is every 100 steps.
+      # When --report_loss is True, we set to very large value to prevent
+      # default info reporting from estimator.
+      # Ideally we should set it to None, but that does not work.
+      log_step_count_steps=10000 if FLAGS.report_loss else 100)
+
+  model_fn = model_fn_builder(
+      bert_config=bert_config,
+      init_checkpoint=FLAGS.init_checkpoint,
+      learning_rate=FLAGS.learning_rate,
+      num_train_steps=FLAGS.num_train_steps,
+      num_warmup_steps=FLAGS.num_warmup_steps,
+      use_tpu=FLAGS.use_tpu,
+      use_one_hot_embeddings=FLAGS.use_tpu,
+      hvd=None if not FLAGS.horovod else hvd)
+
+  training_hooks = []
+  if FLAGS.horovod and hvd.size() > 1:
+    training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
+  if FLAGS.report_loss:
+    global_batch_size = FLAGS.train_batch_size if not FLAGS.horovod else FLAGS.train_batch_size*hvd.size()
+    training_hooks.append(_LogSessionRunHook(global_batch_size,1,-1 if not FLAGS.horovod else hvd.rank()))
+
+  # If TPU is not available, this will fall back to normal Estimator on CPU
+  # or GPU.
+  estimator = tf.contrib.tpu.TPUEstimator(
+      use_tpu=FLAGS.use_tpu,
+      model_fn=model_fn,
+      config=run_config,
+      train_batch_size=FLAGS.train_batch_size,
+      eval_batch_size=FLAGS.eval_batch_size)
+
+  if FLAGS.do_train:
+    tf.logging.info("***** Running training *****")
+    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
+    train_input_fn = input_fn_builder(
+        input_files=input_files,
+        max_seq_length=FLAGS.max_seq_length,
+        max_predictions_per_seq=FLAGS.max_predictions_per_seq,
+        is_training=True,
+        hvd=None if not FLAGS.horovod else hvd)
+    estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=FLAGS.num_train_steps)
+
+  if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0):
+    tf.logging.info("***** Running evaluation *****")
+    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
+
+    eval_input_fn = input_fn_builder(
+        input_files=input_files,
+        max_seq_length=FLAGS.max_seq_length,
+        max_predictions_per_seq=FLAGS.max_predictions_per_seq,
+        is_training=False,
+        hvd=None if not FLAGS.horovod else hvd)
+
+    result = estimator.evaluate(
+        input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
+
+    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
+    with tf.gfile.GFile(output_eval_file, "w") as writer:
+      tf.logging.info("***** Eval results *****")
+      for key in sorted(result.keys()):
+        tf.logging.info("  %s = %s", key, str(result[key]))
+        writer.write("%s = %s\n" % (key, str(result[key])))
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("input_file")
+  flags.mark_flag_as_required("bert_config_file")
+  flags.mark_flag_as_required("output_dir")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/run_squad.py b/TensorFlow/LanguageModeling/BERT/run_squad.py
new file mode 100644
index 00000000..207c5cce
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/run_squad.py
@@ -0,0 +1,1393 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+
+flags = tf.flags
+
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    "bert_config_file", None,
+    "The config json file corresponding to the pre-trained BERT model. "
+    "This specifies the model architecture.")
+
+flags.DEFINE_string("vocab_file", None,
+                    "The vocabulary file that the BERT model was trained on.")
+
+flags.DEFINE_string(
+    "output_dir", None,
+    "The output directory where the model checkpoints will be written.")
+
+## Other parameters
+flags.DEFINE_string("train_file", None,
+                    "SQuAD json for training. E.g., train-v1.1.json")
+
+flags.DEFINE_string(
+    "predict_file", None,
+    "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
+
+flags.DEFINE_string(
+    "init_checkpoint", None,
+    "Initial checkpoint (usually from a pre-trained BERT model).")
+
+flags.DEFINE_bool(
+    "do_lower_case", True,
+    "Whether to lower case the input text. Should be True for uncased "
+    "models and False for cased models.")
+
+flags.DEFINE_integer(
+    "max_seq_length", 384,
+    "The maximum total input sequence length after WordPiece tokenization. "
+    "Sequences longer than this will be truncated, and sequences shorter "
+    "than this will be padded.")
+
+flags.DEFINE_integer(
+    "doc_stride", 128,
+    "When splitting up a long document into chunks, how much stride to "
+    "take between chunks.")
+
+flags.DEFINE_integer(
+    "max_query_length", 64,
+    "The maximum number of tokens for the question. Questions longer than "
+    "this will be truncated to this length.")
+
+flags.DEFINE_bool("do_train", False, "Whether to run training.")
+
+flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
+
+flags.DEFINE_integer("train_batch_size", 8, "Total batch size for training.")
+
+flags.DEFINE_integer("predict_batch_size", 8,
+                     "Total batch size for predictions.")
+
+flags.DEFINE_float("learning_rate", 5e-6, "The initial learning rate for Adam.")
+
+flags.DEFINE_float("num_train_epochs", 3.0,
+                   "Total number of training epochs to perform.")
+
+flags.DEFINE_float(
+    "warmup_proportion", 0.1,
+    "Proportion of training to perform linear learning rate warmup for. "
+    "E.g., 0.1 = 10% of training.")
+
+flags.DEFINE_integer("save_checkpoints_steps", 1000,
+                     "How often to save the model checkpoint.")
+
+flags.DEFINE_integer("iterations_per_loop", 1000,
+                     "How many steps to make in each estimator call.")
+
+flags.DEFINE_integer(
+    "n_best_size", 20,
+    "The total number of n-best predictions to generate in the "
+    "nbest_predictions.json output file.")
+
+flags.DEFINE_integer(
+    "max_answer_length", 30,
+    "The maximum length of an answer that can be generated. This is needed "
+    "because the start and end predictions are not conditioned on one another.")
+
+flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
+
+tf.flags.DEFINE_string(
+    "tpu_name", None,
+    "The Cloud TPU to use for training. This should be either the name "
+    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
+    "url.")
+
+tf.flags.DEFINE_string(
+    "tpu_zone", None,
+    "[Optional] GCE zone where the Cloud TPU is located in. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string(
+    "gcp_project", None,
+    "[Optional] Project name for the Cloud TPU-enabled project. If not "
+    "specified, we will attempt to automatically detect the GCE project from "
+    "metadata.")
+
+tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
+
+flags.DEFINE_integer(
+    "num_tpu_cores", 8,
+    "Only used if `use_tpu` is True. Total number of TPU cores to use.")
+
+flags.DEFINE_bool(
+    "verbose_logging", False,
+    "If true, all of the warnings related to data processing will be printed. "
+    "A number of warnings are expected for a normal SQuAD evaluation.")
+
+flags.DEFINE_bool(
+    "version_2_with_negative", False,
+    "If true, the SQuAD examples contain some that do not have an answer.")
+
+flags.DEFINE_float(
+    "null_score_diff_threshold", 0.0,
+    "If null_score - best_non_null is greater than the threshold predict null.")
+
+flags.DEFINE_bool("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.")
+
+flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.")
+
+# report samples/sec, total loss and learning rate during training
+class _LogEvalRunHook(tf.train.SessionRunHook):
+  def __init__(self, global_batch_size, hvd_rank=-1):
+    self.global_batch_size = global_batch_size
+    self.hvd_rank = hvd_rank
+    self.total_time = 0.0
+    self.count = 0
+
+  def before_run(self, run_context):
+    self.t0 = time.time()
+
+  def after_run(self, run_context, run_values):
+    elapsed_secs = time.time() - self.t0
+    self.total_time += elapsed_secs
+    self.count += 1
+
+# report samples/sec, total loss and learning rate during training
+class _LogTrainRunHook(tf.train.SessionRunHook):
+  def __init__(self, global_batch_size, hvd_rank=-1):
+    self.global_batch_size = global_batch_size
+    self.hvd_rank = hvd_rank
+    self.total_time = 0.0
+    self.count = 0
+
+  def before_run(self, run_context):
+    self.t0 = time.time()
+    return tf.train.SessionRunArgs(
+        fetches=['step_update:0'])
+  def after_run(self, run_context, run_values):
+    elapsed_secs = time.time() - self.t0
+    self.total_time += elapsed_secs
+    self.count += 1
+
+class SquadExample(object):
+  """A single training/test example for simple sequence classification.
+
+     For examples without an answer, the start and end position are -1.
+  """
+
+  def __init__(self,
+               qas_id,
+               question_text,
+               doc_tokens,
+               orig_answer_text=None,
+               start_position=None,
+               end_position=None,
+               is_impossible=False):
+    self.qas_id = qas_id
+    self.question_text = question_text
+    self.doc_tokens = doc_tokens
+    self.orig_answer_text = orig_answer_text
+    self.start_position = start_position
+    self.end_position = end_position
+    self.is_impossible = is_impossible
+
+  def __str__(self):
+    return self.__repr__()
+
+  def __repr__(self):
+    s = ""
+    s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+    s += ", question_text: %s" % (
+        tokenization.printable_text(self.question_text))
+    s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+    if self.start_position:
+      s += ", start_position: %d" % (self.start_position)
+    if self.start_position:
+      s += ", end_position: %d" % (self.end_position)
+    if self.start_position:
+      s += ", is_impossible: %r" % (self.is_impossible)
+    return s
+
+
+class InputFeatures(object):
+  """A single set of features of data."""
+
+  def __init__(self,
+               unique_id,
+               example_index,
+               doc_span_index,
+               tokens,
+               token_to_orig_map,
+               token_is_max_context,
+               input_ids,
+               input_mask,
+               segment_ids,
+               start_position=None,
+               end_position=None,
+               is_impossible=None):
+    self.unique_id = unique_id
+    self.example_index = example_index
+    self.doc_span_index = doc_span_index
+    self.tokens = tokens
+    self.token_to_orig_map = token_to_orig_map
+    self.token_is_max_context = token_is_max_context
+    self.input_ids = input_ids
+    self.input_mask = input_mask
+    self.segment_ids = segment_ids
+    self.start_position = start_position
+    self.end_position = end_position
+    self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training):
+  """Read a SQuAD json file into a list of SquadExample."""
+  with tf.gfile.Open(input_file, "r") as reader:
+    input_data = json.load(reader)["data"]
+
+  def is_whitespace(c):
+    if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+      return True
+    return False
+
+  examples = []
+  for entry in input_data:
+    for paragraph in entry["paragraphs"]:
+      paragraph_text = paragraph["context"]
+      doc_tokens = []
+      char_to_word_offset = []
+      prev_is_whitespace = True
+      for c in paragraph_text:
+        if is_whitespace(c):
+          prev_is_whitespace = True
+        else:
+          if prev_is_whitespace:
+            doc_tokens.append(c)
+          else:
+            doc_tokens[-1] += c
+          prev_is_whitespace = False
+        char_to_word_offset.append(len(doc_tokens) - 1)
+
+      for qa in paragraph["qas"]:
+        qas_id = qa["id"]
+        question_text = qa["question"]
+        start_position = None
+        end_position = None
+        orig_answer_text = None
+        is_impossible = False
+        if is_training:
+
+          if FLAGS.version_2_with_negative:
+            is_impossible = qa["is_impossible"]
+          if (len(qa["answers"]) != 1) and (not is_impossible):
+            raise ValueError(
+                "For training, each question should have exactly 1 answer.")
+          if not is_impossible:
+            answer = qa["answers"][0]
+            orig_answer_text = answer["text"]
+            answer_offset = answer["answer_start"]
+            answer_length = len(orig_answer_text)
+            start_position = char_to_word_offset[answer_offset]
+            end_position = char_to_word_offset[answer_offset + answer_length -
+                                               1]
+            # Only add answers where the text can be exactly recovered from the
+            # document. If this CAN'T happen it's likely due to weird Unicode
+            # stuff so we will just skip the example.
+            #
+            # Note that this means for training mode, every example is NOT
+            # guaranteed to be preserved.
+            actual_text = " ".join(
+                doc_tokens[start_position:(end_position + 1)])
+            cleaned_answer_text = " ".join(
+                tokenization.whitespace_tokenize(orig_answer_text))
+            if actual_text.find(cleaned_answer_text) == -1:
+              tf.logging.warning("Could not find answer: '%s' vs. '%s'",
+                                 actual_text, cleaned_answer_text)
+              continue
+          else:
+            start_position = -1
+            end_position = -1
+            orig_answer_text = ""
+
+        example = SquadExample(
+            qas_id=qas_id,
+            question_text=question_text,
+            doc_tokens=doc_tokens,
+            orig_answer_text=orig_answer_text,
+            start_position=start_position,
+            end_position=end_position,
+            is_impossible=is_impossible)
+        examples.append(example)
+
+  return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                 doc_stride, max_query_length, is_training,
+                                 output_fn):
+  """Loads a data file into a list of `InputBatch`s."""
+
+  unique_id = 1000000000
+
+  for (example_index, example) in enumerate(examples):
+    query_tokens = tokenizer.tokenize(example.question_text)
+
+    if len(query_tokens) > max_query_length:
+      query_tokens = query_tokens[0:max_query_length]
+
+    tok_to_orig_index = []
+    orig_to_tok_index = []
+    all_doc_tokens = []
+    for (i, token) in enumerate(example.doc_tokens):
+      orig_to_tok_index.append(len(all_doc_tokens))
+      sub_tokens = tokenizer.tokenize(token)
+      for sub_token in sub_tokens:
+        tok_to_orig_index.append(i)
+        all_doc_tokens.append(sub_token)
+
+    tok_start_position = None
+    tok_end_position = None
+    if is_training and example.is_impossible:
+      tok_start_position = -1
+      tok_end_position = -1
+    if is_training and not example.is_impossible:
+      tok_start_position = orig_to_tok_index[example.start_position]
+      if example.end_position < len(example.doc_tokens) - 1:
+        tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+      else:
+        tok_end_position = len(all_doc_tokens) - 1
+      (tok_start_position, tok_end_position) = _improve_answer_span(
+          all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+          example.orig_answer_text)
+
+    # The -3 accounts for [CLS], [SEP] and [SEP]
+    max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+    # We can have documents that are longer than the maximum sequence length.
+    # To deal with this we do a sliding window approach, where we take chunks
+    # of the up to our max length with a stride of `doc_stride`.
+    _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+        "DocSpan", ["start", "length"])
+    doc_spans = []
+    start_offset = 0
+    while start_offset < len(all_doc_tokens):
+      length = len(all_doc_tokens) - start_offset
+      if length > max_tokens_for_doc:
+        length = max_tokens_for_doc
+      doc_spans.append(_DocSpan(start=start_offset, length=length))
+      if start_offset + length == len(all_doc_tokens):
+        break
+      start_offset += min(length, doc_stride)
+
+    for (doc_span_index, doc_span) in enumerate(doc_spans):
+      tokens = []
+      token_to_orig_map = {}
+      token_is_max_context = {}
+      segment_ids = []
+      tokens.append("[CLS]")
+      segment_ids.append(0)
+      for token in query_tokens:
+        tokens.append(token)
+        segment_ids.append(0)
+      tokens.append("[SEP]")
+      segment_ids.append(0)
+
+      for i in range(doc_span.length):
+        split_token_index = doc_span.start + i
+        token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
+
+        is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+                                               split_token_index)
+        token_is_max_context[len(tokens)] = is_max_context
+        tokens.append(all_doc_tokens[split_token_index])
+        segment_ids.append(1)
+      tokens.append("[SEP]")
+      segment_ids.append(1)
+
+      input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+      # The mask has 1 for real tokens and 0 for padding tokens. Only real
+      # tokens are attended to.
+      input_mask = [1] * len(input_ids)
+
+      # Zero-pad up to the sequence length.
+      while len(input_ids) < max_seq_length:
+        input_ids.append(0)
+        input_mask.append(0)
+        segment_ids.append(0)
+
+      assert len(input_ids) == max_seq_length
+      assert len(input_mask) == max_seq_length
+      assert len(segment_ids) == max_seq_length
+
+      start_position = None
+      end_position = None
+      if is_training and not example.is_impossible:
+        # For training, if our document chunk does not contain an annotation
+        # we throw it out, since there is nothing to predict.
+        doc_start = doc_span.start
+        doc_end = doc_span.start + doc_span.length - 1
+        out_of_span = False
+        if not (tok_start_position >= doc_start and
+                tok_end_position <= doc_end):
+          out_of_span = True
+        if out_of_span:
+          start_position = 0
+          end_position = 0
+        else:
+          doc_offset = len(query_tokens) + 2
+          start_position = tok_start_position - doc_start + doc_offset
+          end_position = tok_end_position - doc_start + doc_offset
+
+      if is_training and example.is_impossible:
+        start_position = 0
+        end_position = 0
+
+      if example_index < 20:
+        tf.logging.info("*** Example ***")
+        tf.logging.info("unique_id: %s" % (unique_id))
+        tf.logging.info("example_index: %s" % (example_index))
+        tf.logging.info("doc_span_index: %s" % (doc_span_index))
+        tf.logging.info("tokens: %s" % " ".join(
+            [tokenization.printable_text(x) for x in tokens]))
+        tf.logging.info("token_to_orig_map: %s" % " ".join(
+            ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
+        tf.logging.info("token_is_max_context: %s" % " ".join([
+            "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
+        ]))
+        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+        tf.logging.info(
+            "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+        tf.logging.info(
+            "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+        if is_training and example.is_impossible:
+          tf.logging.info("impossible example")
+        if is_training and not example.is_impossible:
+          answer_text = " ".join(tokens[start_position:(end_position + 1)])
+          tf.logging.info("start_position: %d" % (start_position))
+          tf.logging.info("end_position: %d" % (end_position))
+          tf.logging.info(
+              "answer: %s" % (tokenization.printable_text(answer_text)))
+
+      feature = InputFeatures(
+          unique_id=unique_id,
+          example_index=example_index,
+          doc_span_index=doc_span_index,
+          tokens=tokens,
+          token_to_orig_map=token_to_orig_map,
+          token_is_max_context=token_is_max_context,
+          input_ids=input_ids,
+          input_mask=input_mask,
+          segment_ids=segment_ids,
+          start_position=start_position,
+          end_position=end_position,
+          is_impossible=example.is_impossible)
+
+      # Run callback
+      output_fn(feature)
+
+      unique_id += 1
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+                         orig_answer_text):
+  """Returns tokenized answer spans that better match the annotated answer."""
+
+  # The SQuAD annotations are character based. We first project them to
+  # whitespace-tokenized words. But then after WordPiece tokenization, we can
+  # often find a "better match". For example:
+  #
+  #   Question: What year was John Smith born?
+  #   Context: The leader was John Smith (1895-1943).
+  #   Answer: 1895
+  #
+  # The original whitespace-tokenized answer will be "(1895-1943).". However
+  # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+  # the exact answer, 1895.
+  #
+  # However, this is not always possible. Consider the following:
+  #
+  #   Question: What country is the top exporter of electornics?
+  #   Context: The Japanese electronics industry is the lagest in the world.
+  #   Answer: Japan
+  #
+  # In this case, the annotator chose "Japan" as a character sub-span of
+  # the word "Japanese". Since our WordPiece tokenizer does not split
+  # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+  # in SQuAD, but does happen.
+  tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+  for new_start in range(input_start, input_end + 1):
+    for new_end in range(input_end, new_start - 1, -1):
+      text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+      if text_span == tok_answer_text:
+        return (new_start, new_end)
+
+  return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+  """Check if this is the 'max context' doc span for the token."""
+
+  # Because of the sliding window approach taken to scoring documents, a single
+  # token can appear in multiple documents. E.g.
+  #  Doc: the man went to the store and bought a gallon of milk
+  #  Span A: the man went to the
+  #  Span B: to the store and bought
+  #  Span C: and bought a gallon of
+  #  ...
+  #
+  # Now the word 'bought' will have two scores from spans B and C. We only
+  # want to consider the score with "maximum context", which we define as
+  # the *minimum* of its left and right context (the *sum* of left and
+  # right context will always be the same, of course).
+  #
+  # In the example the maximum context for 'bought' would be span C since
+  # it has 1 left context and 3 right context, while span B has 4 left context
+  # and 0 right context.
+  best_score = None
+  best_span_index = None
+  for (span_index, doc_span) in enumerate(doc_spans):
+    end = doc_span.start + doc_span.length - 1
+    if position < doc_span.start:
+      continue
+    if position > end:
+      continue
+    num_left_context = position - doc_span.start
+    num_right_context = end - position
+    score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
+    if best_score is None or score > best_score:
+      best_score = score
+      best_span_index = span_index
+
+  return cur_span_index == best_span_index
+
+
+def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
+                 use_one_hot_embeddings):
+  """Creates a classification model."""
+  model = modeling.BertModel(
+      config=bert_config,
+      is_training=is_training,
+      input_ids=input_ids,
+      input_mask=input_mask,
+      token_type_ids=segment_ids,
+      use_one_hot_embeddings=use_one_hot_embeddings,
+      compute_type=tf.float16 if FLAGS.use_fp16 else tf.float32)
+
+  final_hidden = model.get_sequence_output()
+
+  final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
+  batch_size = final_hidden_shape[0]
+  seq_length = final_hidden_shape[1]
+  hidden_size = final_hidden_shape[2]
+
+  output_weights = tf.get_variable(
+      "cls/squad/output_weights", [2, hidden_size],
+      initializer=tf.truncated_normal_initializer(stddev=0.02))
+
+  output_bias = tf.get_variable(
+      "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
+
+  final_hidden_matrix = tf.reshape(final_hidden,
+                                   [batch_size * seq_length, hidden_size])
+  logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
+  logits = tf.nn.bias_add(logits, output_bias)
+
+  logits = tf.reshape(logits, [batch_size, seq_length, 2])
+  logits = tf.transpose(logits, [2, 0, 1])
+
+  unstacked_logits = tf.unstack(logits, axis=0)
+
+  (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
+
+  return (start_logits, end_logits)
+
+
+def model_fn_builder(bert_config, init_checkpoint, learning_rate,
+                     num_train_steps, num_warmup_steps, use_tpu,
+                     use_one_hot_embeddings, hvd=None, use_fp16=False):
+  """Returns `model_fn` closure for TPUEstimator."""
+
+  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
+    """The `model_fn` for TPUEstimator."""
+
+    tf.logging.info("*** Features ***")
+    for name in sorted(features.keys()):
+      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
+
+    unique_ids = features["unique_ids"]
+    input_ids = features["input_ids"]
+    input_mask = features["input_mask"]
+    segment_ids = features["segment_ids"]
+
+    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+    (start_logits, end_logits) = create_model(
+        bert_config=bert_config,
+        is_training=is_training,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        segment_ids=segment_ids,
+        use_one_hot_embeddings=use_one_hot_embeddings)
+
+    tvars = tf.trainable_variables()
+
+    initialized_variable_names = {}
+    scaffold_fn = None
+    if init_checkpoint:
+      (assignment_map, initialized_variable_names
+      ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+      if use_tpu:
+
+        def tpu_scaffold():
+          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+          return tf.train.Scaffold()
+
+        scaffold_fn = tpu_scaffold
+      else:
+        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+    tf.logging.info("**** Trainable Variables ****")
+    for var in tvars:
+      init_string = ""
+      if var.name in initialized_variable_names:
+        init_string = ", *INIT_FROM_CKPT*"
+      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
+                      init_string)
+
+    output_spec = None
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      seq_length = modeling.get_shape_list(input_ids)[1]
+
+      def compute_loss(logits, positions):
+        one_hot_positions = tf.one_hot(
+            positions, depth=seq_length, dtype=tf.float32)
+        log_probs = tf.nn.log_softmax(logits, axis=-1)
+        loss = -tf.reduce_mean(
+            tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
+        return loss
+
+      start_positions = features["start_positions"]
+      end_positions = features["end_positions"]
+
+      start_loss = compute_loss(start_logits, start_positions)
+      end_loss = compute_loss(end_logits, end_positions)
+
+      total_loss = (start_loss + end_loss) / 2.0
+
+      train_op = optimization.create_optimizer(
+          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, hvd, amp=use_fp16)
+
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          train_op=train_op,
+          scaffold_fn=scaffold_fn)
+    elif mode == tf.estimator.ModeKeys.PREDICT:
+      predictions = {
+          "unique_ids": unique_ids,
+          "start_logits": start_logits,
+          "end_logits": end_logits,
+      }
+      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
+    else:
+      raise ValueError(
+          "Only TRAIN and PREDICT modes are supported: %s" % (mode))
+
+    return output_spec
+
+  return model_fn
+
+
+def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
+  """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+  name_to_features = {
+      "unique_ids": tf.FixedLenFeature([], tf.int64),
+      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
+      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
+      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
+  }
+
+  if is_training:
+    name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
+    name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
+
+  def _decode_record(record, name_to_features):
+    """Decodes a record to a TensorFlow example."""
+    example = tf.parse_single_example(record, name_to_features)
+
+    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
+    # So cast all int64 to int32.
+    for name in list(example.keys()):
+      t = example[name]
+      if t.dtype == tf.int64:
+        t = tf.to_int32(t)
+      example[name] = t
+
+    return example
+
+  def input_fn(params):
+    """The actual input function."""
+    batch_size = params["batch_size"]
+
+    # For training, we want a lot of parallel reading and shuffling.
+    # For eval, we want no shuffling and parallel reading doesn't matter.
+    d = tf.data.TFRecordDataset(input_file)
+    if is_training:
+      d = d.repeat()
+      d = d.shuffle(buffer_size=100)
+
+    d = d.apply(
+        tf.contrib.data.map_and_batch(
+            lambda record: _decode_record(record, name_to_features),
+            batch_size=batch_size,
+            drop_remainder=drop_remainder))
+
+    return d
+
+  return input_fn
+
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case, output_prediction_file,
+                      output_nbest_file, output_null_log_odds_file):
+  """Write final predictions to the json file and log-odds of null if needed."""
+  tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
+  tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
+
+  example_index_to_features = collections.defaultdict(list)
+  for feature in all_features:
+    example_index_to_features[feature.example_index].append(feature)
+
+  unique_id_to_result = {}
+  for result in all_results:
+    unique_id_to_result[result.unique_id] = result
+
+  _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+      "PrelimPrediction",
+      ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+  all_predictions = collections.OrderedDict()
+  all_nbest_json = collections.OrderedDict()
+  scores_diff_json = collections.OrderedDict()
+
+  for (example_index, example) in enumerate(all_examples):
+    features = example_index_to_features[example_index]
+
+    prelim_predictions = []
+    # keep track of the minimum score of null start+end of position 0
+    score_null = 1000000  # large and positive
+    min_null_feature_index = 0  # the paragraph slice with min mull score
+    null_start_logit = 0  # the start logit at the slice with min null score
+    null_end_logit = 0  # the end logit at the slice with min null score
+    for (feature_index, feature) in enumerate(features):
+      result = unique_id_to_result[feature.unique_id]
+      start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+      end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+      # if we could have irrelevant answers, get the min score of irrelevant
+      if FLAGS.version_2_with_negative:
+        feature_null_score = result.start_logits[0] + result.end_logits[0]
+        if feature_null_score < score_null:
+          score_null = feature_null_score
+          min_null_feature_index = feature_index
+          null_start_logit = result.start_logits[0]
+          null_end_logit = result.end_logits[0]
+      for start_index in start_indexes:
+        for end_index in end_indexes:
+          # We could hypothetically create invalid predictions, e.g., predict
+          # that the start of the span is in the question. We throw out all
+          # invalid predictions.
+          if start_index >= len(feature.tokens):
+            continue
+          if end_index >= len(feature.tokens):
+            continue
+          if start_index not in feature.token_to_orig_map:
+            continue
+          if end_index not in feature.token_to_orig_map:
+            continue
+          if not feature.token_is_max_context.get(start_index, False):
+            continue
+          if end_index < start_index:
+            continue
+          length = end_index - start_index + 1
+          if length > max_answer_length:
+            continue
+          prelim_predictions.append(
+              _PrelimPrediction(
+                  feature_index=feature_index,
+                  start_index=start_index,
+                  end_index=end_index,
+                  start_logit=result.start_logits[start_index],
+                  end_logit=result.end_logits[end_index]))
+
+    if FLAGS.version_2_with_negative:
+      prelim_predictions.append(
+          _PrelimPrediction(
+              feature_index=min_null_feature_index,
+              start_index=0,
+              end_index=0,
+              start_logit=null_start_logit,
+              end_logit=null_end_logit))
+    prelim_predictions = sorted(
+        prelim_predictions,
+        key=lambda x: (x.start_logit + x.end_logit),
+        reverse=True)
+
+    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+    seen_predictions = {}
+    nbest = []
+    for pred in prelim_predictions:
+      if len(nbest) >= n_best_size:
+        break
+      feature = features[pred.feature_index]
+      if pred.start_index > 0:  # this is a non-null prediction
+        tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+        orig_doc_start = feature.token_to_orig_map[pred.start_index]
+        orig_doc_end = feature.token_to_orig_map[pred.end_index]
+        orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+        tok_text = " ".join(tok_tokens)
+
+        # De-tokenize WordPieces that have been split off.
+        tok_text = tok_text.replace(" ##", "")
+        tok_text = tok_text.replace("##", "")
+
+        # Clean whitespace
+        tok_text = tok_text.strip()
+        tok_text = " ".join(tok_text.split())
+        orig_text = " ".join(orig_tokens)
+
+        final_text = get_final_text(tok_text, orig_text, do_lower_case)
+        if final_text in seen_predictions:
+          continue
+
+        seen_predictions[final_text] = True
+      else:
+        final_text = ""
+        seen_predictions[final_text] = True
+
+      nbest.append(
+          _NbestPrediction(
+              text=final_text,
+              start_logit=pred.start_logit,
+              end_logit=pred.end_logit))
+
+    # if we didn't inlude the empty option in the n-best, inlcude it
+    if FLAGS.version_2_with_negative:
+      if "" not in seen_predictions:
+        nbest.append(
+            _NbestPrediction(
+                text="", start_logit=null_start_logit,
+                end_logit=null_end_logit))
+    # In very rare edge cases we could have no valid predictions. So we
+    # just create a nonce prediction in this case to avoid failure.
+    if not nbest:
+      nbest.append(
+          _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+    assert len(nbest) >= 1
+
+    total_scores = []
+    best_non_null_entry = None
+    for entry in nbest:
+      total_scores.append(entry.start_logit + entry.end_logit)
+      if not best_non_null_entry:
+        if entry.text:
+          best_non_null_entry = entry
+
+    probs = _compute_softmax(total_scores)
+
+    nbest_json = []
+    for (i, entry) in enumerate(nbest):
+      output = collections.OrderedDict()
+      output["text"] = entry.text
+      output["probability"] = probs[i]
+      output["start_logit"] = entry.start_logit
+      output["end_logit"] = entry.end_logit
+      nbest_json.append(output)
+
+    assert len(nbest_json) >= 1
+
+    if not FLAGS.version_2_with_negative:
+      all_predictions[example.qas_id] = nbest_json[0]["text"]
+    else:
+      # predict "" iff the null score - the score of best non-null > threshold
+      score_diff = score_null - best_non_null_entry.start_logit - (
+          best_non_null_entry.end_logit)
+      scores_diff_json[example.qas_id] = score_diff
+      if score_diff > FLAGS.null_score_diff_threshold:
+        all_predictions[example.qas_id] = ""
+      else:
+        all_predictions[example.qas_id] = best_non_null_entry.text
+
+    all_nbest_json[example.qas_id] = nbest_json
+
+  with tf.gfile.GFile(output_prediction_file, "w") as writer:
+    writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+  with tf.gfile.GFile(output_nbest_file, "w") as writer:
+    writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+  if FLAGS.version_2_with_negative:
+    with tf.gfile.GFile(output_null_log_odds_file, "w") as writer:
+      writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case):
+  """Project the tokenized prediction back to the original text."""
+
+  # When we created the data, we kept track of the alignment between original
+  # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+  # now `orig_text` contains the span of our original text corresponding to the
+  # span that we predicted.
+  #
+  # However, `orig_text` may contain extra characters that we don't want in
+  # our prediction.
+  #
+  # For example, let's say:
+  #   pred_text = steve smith
+  #   orig_text = Steve Smith's
+  #
+  # We don't want to return `orig_text` because it contains the extra "'s".
+  #
+  # We don't want to return `pred_text` because it's already been normalized
+  # (the SQuAD eval script also does punctuation stripping/lower casing but
+  # our tokenizer does additional normalization like stripping accent
+  # characters).
+  #
+  # What we really want to return is "Steve Smith".
+  #
+  # Therefore, we have to apply a semi-complicated alignment heruistic between
+  # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+  # can fail in certain cases in which case we just return `orig_text`.
+
+  def _strip_spaces(text):
+    ns_chars = []
+    ns_to_s_map = collections.OrderedDict()
+    for (i, c) in enumerate(text):
+      if c == " ":
+        continue
+      ns_to_s_map[len(ns_chars)] = i
+      ns_chars.append(c)
+    ns_text = "".join(ns_chars)
+    return (ns_text, ns_to_s_map)
+
+  # We first tokenize `orig_text`, strip whitespace from the result
+  # and `pred_text`, and check if they are the same length. If they are
+  # NOT the same length, the heuristic has failed. If they are the same
+  # length, we assume the characters are one-to-one aligned.
+  tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+  tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+  start_position = tok_text.find(pred_text)
+  if start_position == -1:
+    if FLAGS.verbose_logging:
+      tf.logging.info(
+          "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+    return orig_text
+  end_position = start_position + len(pred_text) - 1
+
+  (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+  (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+  if len(orig_ns_text) != len(tok_ns_text):
+    if FLAGS.verbose_logging:
+      tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
+                      orig_ns_text, tok_ns_text)
+    return orig_text
+
+  # We then project the characters in `pred_text` back to `orig_text` using
+  # the character-to-character alignment.
+  tok_s_to_ns_map = {}
+  for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+    tok_s_to_ns_map[tok_index] = i
+
+  orig_start_position = None
+  if start_position in tok_s_to_ns_map:
+    ns_start_position = tok_s_to_ns_map[start_position]
+    if ns_start_position in orig_ns_to_s_map:
+      orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+  if orig_start_position is None:
+    if FLAGS.verbose_logging:
+      tf.logging.info("Couldn't map start position")
+    return orig_text
+
+  orig_end_position = None
+  if end_position in tok_s_to_ns_map:
+    ns_end_position = tok_s_to_ns_map[end_position]
+    if ns_end_position in orig_ns_to_s_map:
+      orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+  if orig_end_position is None:
+    if FLAGS.verbose_logging:
+      tf.logging.info("Couldn't map end position")
+    return orig_text
+
+  output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+  return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+  """Get the n-best logits from a list."""
+  index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+  best_indexes = []
+  for i in range(len(index_and_score)):
+    if i >= n_best_size:
+      break
+    best_indexes.append(index_and_score[i][0])
+  return best_indexes
+
+
+def _compute_softmax(scores):
+  """Compute softmax probability over raw logits."""
+  if not scores:
+    return []
+
+  max_score = None
+  for score in scores:
+    if max_score is None or score > max_score:
+      max_score = score
+
+  exp_scores = []
+  total_sum = 0.0
+  for score in scores:
+    x = math.exp(score - max_score)
+    exp_scores.append(x)
+    total_sum += x
+
+  probs = []
+  for score in exp_scores:
+    probs.append(score / total_sum)
+  return probs
+
+
+class FeatureWriter(object):
+  """Writes InputFeature to TF example file."""
+
+  def __init__(self, filename, is_training):
+    self.filename = filename
+    self.is_training = is_training
+    self.num_features = 0
+    self._writer = tf.python_io.TFRecordWriter(filename)
+
+  def process_feature(self, feature):
+    """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
+    self.num_features += 1
+
+    def create_int_feature(values):
+      feature = tf.train.Feature(
+          int64_list=tf.train.Int64List(value=list(values)))
+      return feature
+
+    features = collections.OrderedDict()
+    features["unique_ids"] = create_int_feature([feature.unique_id])
+    features["input_ids"] = create_int_feature(feature.input_ids)
+    features["input_mask"] = create_int_feature(feature.input_mask)
+    features["segment_ids"] = create_int_feature(feature.segment_ids)
+
+    if self.is_training:
+      features["start_positions"] = create_int_feature([feature.start_position])
+      features["end_positions"] = create_int_feature([feature.end_position])
+      impossible = 0
+      if feature.is_impossible:
+        impossible = 1
+      features["is_impossible"] = create_int_feature([impossible])
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+    self._writer.write(tf_example.SerializeToString())
+
+  def close(self):
+    self._writer.close()
+
+
+def validate_flags_or_throw(bert_config):
+  """Validate the input FLAGS or throw an exception."""
+  tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
+                                                FLAGS.init_checkpoint)
+
+  if not FLAGS.do_train and not FLAGS.do_predict:
+    raise ValueError("At least one of `do_train` or `do_predict` must be True.")
+
+  if FLAGS.do_train:
+    if not FLAGS.train_file:
+      raise ValueError(
+          "If `do_train` is True, then `train_file` must be specified.")
+  if FLAGS.do_predict:
+    if not FLAGS.predict_file:
+      raise ValueError(
+          "If `do_predict` is True, then `predict_file` must be specified.")
+
+  if FLAGS.max_seq_length > bert_config.max_position_embeddings:
+    raise ValueError(
+        "Cannot use sequence length %d because the BERT model "
+        "was only trained up to sequence length %d" %
+        (FLAGS.max_seq_length, bert_config.max_position_embeddings))
+
+  if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
+    raise ValueError(
+        "The max_seq_length (%d) must be greater than max_query_length "
+        "(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
+
+  validate_flags_or_throw(bert_config)
+
+  tf.gfile.MakeDirs(FLAGS.output_dir)
+
+  tokenizer = tokenization.FullTokenizer(
+      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
+
+  tpu_cluster_resolver = None
+
+  master_process = True
+  training_hooks = []
+  global_batch_size = FLAGS.train_batch_size
+  hvd_rank = 0
+
+  if FLAGS.use_tpu and FLAGS.tpu_name:
+    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
+        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
+
+  config = tf.ConfigProto()
+  learning_rate = FLAGS.learning_rate
+  if FLAGS.horovod:
+
+      tf.logging.info("Multi-GPU training with TF Horovod")
+      tf.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank())
+      global_batch_size = FLAGS.train_batch_size * hvd.size()
+      learning_rate = learning_rate * hvd.size()
+      master_process = (hvd.rank() == 0)
+      hvd_rank = hvd.rank()
+      config.gpu_options.allow_growth = True
+      config.gpu_options.visible_device_list = str(hvd.local_rank())
+      if hvd.size() > 1:
+          training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
+  if FLAGS.use_xla:
+    config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      cluster=tpu_cluster_resolver,
+      master=FLAGS.master,
+      model_dir=FLAGS.output_dir,
+      session_config=config,
+      save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else None,
+      keep_checkpoint_max=1,
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations_per_loop,
+          num_shards=FLAGS.num_tpu_cores,
+          per_host_input_for_training=is_per_host))
+
+  train_examples = None
+  num_train_steps = None
+  num_warmup_steps = None
+  training_hooks.append(_LogTrainRunHook(global_batch_size, hvd_rank))
+
+  if FLAGS.do_train:
+    train_examples = read_squad_examples(
+        input_file=FLAGS.train_file, is_training=True)
+    num_train_steps = int(
+        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
+    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
+
+    # Pre-shuffle the input to avoid having to make a very large shuffle
+    # buffer in in the `input_fn`.
+    rng = random.Random(12345)
+    rng.shuffle(train_examples)
+
+    start_index = 0 
+    end_index = len(train_examples)
+    tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")]
+
+    if FLAGS.horovod:
+      tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(hvd.size())]
+      num_examples_per_rank = len(train_examples) // hvd.size()
+      remainder = len(train_examples) % hvd.size()
+      if hvd.rank() < remainder:
+        start_index = hvd.rank() * (num_examples_per_rank+1)
+        end_index = start_index + num_examples_per_rank + 1
+      else:
+        start_index = hvd.rank() * num_examples_per_rank + remainder
+        end_index = start_index + (num_examples_per_rank)
+
+
+  model_fn = model_fn_builder(
+      bert_config=bert_config,
+      init_checkpoint=FLAGS.init_checkpoint,
+      learning_rate=FLAGS.learning_rate,
+      num_train_steps=num_train_steps,
+      num_warmup_steps=num_warmup_steps,
+      use_tpu=FLAGS.use_tpu,
+      use_one_hot_embeddings=FLAGS.use_tpu,
+      hvd=None if not FLAGS.horovod else hvd,
+      use_fp16=FLAGS.use_fp16)
+
+  # If TPU is not available, this will fall back to normal Estimator on CPU
+  # or GPU.
+  estimator = tf.contrib.tpu.TPUEstimator(
+      use_tpu=FLAGS.use_tpu,
+      model_fn=model_fn,
+      config=run_config,
+      train_batch_size=FLAGS.train_batch_size,
+      predict_batch_size=FLAGS.predict_batch_size)
+
+  if FLAGS.do_train:
+
+    # We write to a temporary file to avoid storing very large constant tensors
+    # in memory.
+    train_writer = FeatureWriter(
+        filename=tmp_filenames[hvd_rank],
+        is_training=True)
+    convert_examples_to_features(
+        examples=train_examples,
+        tokenizer=tokenizer,
+        max_seq_length=FLAGS.max_seq_length,
+        doc_stride=FLAGS.doc_stride,
+        max_query_length=FLAGS.max_query_length,
+        is_training=True,
+        output_fn=train_writer.process_feature)
+    train_writer.close()
+
+    tf.logging.info("***** Running training *****")
+    tf.logging.info("  Num orig examples = %d", end_index - start_index)
+    tf.logging.info("  Num split examples = %d", train_writer.num_features)
+    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
+    tf.logging.info("  Num steps = %d", num_train_steps)
+    del train_examples
+
+    train_input_fn = input_fn_builder(
+        input_file=train_writer.filename,
+        seq_length=FLAGS.max_seq_length,
+        is_training=True,
+        drop_remainder=True,
+        hvd=None if not FLAGS.horovod else hvd)
+
+    train_start_time = time.time()
+    estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=num_train_steps)
+    train_time_elapsed = time.time() - train_start_time
+    train_time_wo_startup = training_hooks[-1].total_time
+    avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_wo_startup
+
+    if master_process:
+        tf.logging.info("-----------------------------")
+        tf.logging.info("%d Total Training Time = %0.2f Training Time W/O start up overhead = %0.2f "
+                        "Sentences processed = %d", hvd_rank, train_time_elapsed, train_time_wo_startup,
+                        num_train_steps * global_batch_size)
+        tf.logging.info("%d Training Performance = %0.4f sentences/sec", hvd_rank, avg_sentences_per_second)
+        tf.logging.info("-----------------------------")
+
+  if FLAGS.do_predict:
+    eval_examples = read_squad_examples(
+        input_file=FLAGS.predict_file, is_training=False)
+
+    eval_writer = FeatureWriter(
+        filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
+        is_training=False)
+    eval_features = []
+
+    def append_feature(feature):
+      eval_features.append(feature)
+      eval_writer.process_feature(feature)
+
+    convert_examples_to_features(
+        examples=eval_examples,
+        tokenizer=tokenizer,
+        max_seq_length=FLAGS.max_seq_length,
+        doc_stride=FLAGS.doc_stride,
+        max_query_length=FLAGS.max_query_length,
+        is_training=False,
+        output_fn=append_feature)
+    eval_writer.close()
+
+    tf.logging.info("***** Running predictions *****")
+    tf.logging.info("  Num orig examples = %d", len(eval_examples))
+    tf.logging.info("  Num split examples = %d", len(eval_features))
+    tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
+
+    all_results = []
+
+    predict_input_fn = input_fn_builder(
+        input_file=eval_writer.filename,
+        seq_length=FLAGS.max_seq_length,
+        is_training=False,
+        drop_remainder=False)
+
+    # If running eval on the TPU, you will need to specify the number of
+    # steps.
+    all_results = []
+    eval_hooks = [_LogEvalRunHook(FLAGS.predict_batch_size)]
+    eval_start_time = time.time()
+    for result in estimator.predict(
+        predict_input_fn, yield_single_examples=True, hooks=eval_hooks):
+      if len(all_results) % 1000 == 0:
+        tf.logging.info("Processing example: %d" % (len(all_results)))
+      unique_id = int(result["unique_ids"])
+      start_logits = [float(x) for x in result["start_logits"].flat]
+      end_logits = [float(x) for x in result["end_logits"].flat]
+      all_results.append(
+          RawResult(
+              unique_id=unique_id,
+              start_logits=start_logits,
+              end_logits=end_logits))
+
+    eval_time_elapsed = time.time() - eval_start_time
+    eval_time_wo_startup = eval_hooks[-1].total_time
+    num_sentences = eval_hooks[-1].count * FLAGS.predict_batch_size
+    avg_sentences_per_second = num_sentences * 1.0 / eval_time_wo_startup
+
+    tf.logging.info("-----------------------------")
+    tf.logging.info("%d Total Inference Time = %0.2f Inference Time W/O start up overhead = %0.2f "
+                    "Sentences processed = %d", hvd_rank, eval_time_elapsed, eval_time_wo_startup,
+                    num_sentences)
+    tf.logging.info("%d Inference Performance = %0.4f sentences/sec", hvd_rank, avg_sentences_per_second)
+    tf.logging.info("-----------------------------")
+
+    output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
+    output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
+    output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
+
+    write_predictions(eval_examples, eval_features, all_results,
+                      FLAGS.n_best_size, FLAGS.max_answer_length,
+                      FLAGS.do_lower_case, output_prediction_file,
+                      output_nbest_file, output_null_log_odds_file)
+
+
+if __name__ == "__main__":
+  flags.mark_flag_as_required("vocab_file")
+  flags.mark_flag_as_required("bert_config_file")
+  flags.mark_flag_as_required("output_dir")
+  tf.app.run()
diff --git a/TensorFlow/LanguageModeling/BERT/sample_text.txt b/TensorFlow/LanguageModeling/BERT/sample_text.txt
new file mode 100644
index 00000000..a4281206
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/sample_text.txt
@@ -0,0 +1,33 @@
+This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত
+Text should be one-sentence-per-line, with empty lines between documents.
+This sample text is public domain and was randomly selected from Project Guttenberg.
+
+The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors.
+Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity.
+Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them.
+"Cass" Beard had risen early that morning, but not with a view to discovery.
+A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets.
+The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency.
+This was nearly opposite.
+Mr. Cassius crossed the highway, and stopped suddenly.
+Something glittered in the nearest red pool before him.
+Gold, surely!
+But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring.
+Looking at it more attentively, he saw that it bore the inscription, "May to Cass."
+Like most of his fellow gold-seekers, Cass was superstitious.
+
+The fountain of classic wisdom, Hypatia herself.
+As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge.
+From my youth I felt in me a soul above the matter-entangled herd.
+She revealed to me the glorious fact, that I am a spark of Divinity itself.
+A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's.
+There is a philosophic pleasure in opening one's treasures to the modest young.
+Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street.
+Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide;
+but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind.
+Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now.
+His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert;
+while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts.
+At last they reached the quay at the opposite end of the street;
+and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers.
+He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him.
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh b/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh
new file mode 100755
index 00000000..a3b28714
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+docker run --runtime=nvidia -v $PWD:/workspace/bert \
+    --rm --shm-size=1g --ulimit memlock=-1 \
+    --ulimit stack=67108864 --ipc=host -t -i \
+    bert bash -c "bash scripts/data_download_helper.sh"
\ No newline at end of file
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh b/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh
new file mode 100755
index 00000000..abb913f7
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+
+# Download SQUAD
+cd /workspace/bert/data/squad && . squad_download.sh
+
+# Download pretrained_models
+cd /workspace/bert/data/pretrained_models_google && python3 download_models.py
+
+# WIKI Download, set config in data_generators/wikipedia_corpus/config.sh
+cd /workspace/bert/data/wikipedia_corpus && . run_preprocessing.sh
+
+cd /workspace/bert/data/bookcorpus && . run_preprocessing.sh
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/docker/build.sh b/TensorFlow/LanguageModeling/BERT/scripts/docker/build.sh
new file mode 100644
index 00000000..c365f7ab
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/docker/build.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build . --rm -t bert
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/docker/launch.sh b/TensorFlow/LanguageModeling/BERT/scripts/docker/launch.sh
new file mode 100755
index 00000000..0bd0f2d6
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/docker/launch.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+docker run -it --rm \
+  --runtime=nvidia \
+  -p 8888:8888 \
+  --shm-size=1g \
+  --ulimit memlock=-1 \
+  --ulimit stack=67108864 \
+  -v $PWD:/workspace/bert \
+  -v $PWD/results:/results \
+  bert bash
+
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh b/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh
new file mode 100755
index 00000000..b5fe7e28
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+
+export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16
+
+task=${1:-"squad"}
+precision=${2:-"fp16"}
+use_xla=${3:-"true"}
+batch_size=${4:-"8"}
+init_checkpoint=${5:-"$BERT_DIR/bert_model.ckpt"}
+
+
+if [ "$task" = "squad" ] ; then
+    export SQUAD_DIR=data/squad/v1.1
+
+    LOGFILE="/results/${task}_inference_benchmark.log"
+
+    use_fp16=""
+    if [ "$precision" = "fp16" ] ; then
+            echo "fp16 activated!"
+            export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1
+            use_fp16="--fast_math"
+    elif [ "$precision" = "fast_math" ] ; then
+            echo "fastmath activated!"
+            export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=1
+            export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=1
+            export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=1
+            export TF_USE_DEFAULT_LOSS_SCALING=1
+    fi
+
+    if [ "$use_xla" = "true" ] ; then
+        use_xla_tag="--use_xla"
+        echo "XLA activated"
+    else
+        use_xla_tag=""
+    fi
+
+
+    python run_squad.py \
+    --vocab_file=$BERT_DIR/vocab.txt \
+    --bert_config_file=$BERT_DIR/bert_config.json \
+    --init_checkpoint=$init_checkpoint \
+    --do_predict=True \
+    --predict_file=$SQUAD_DIR/dev-v1.1.json \
+    --predict_batch_size=$batch_size \
+    --max_seq_length=384 \
+    --doc_stride=128 \
+    --output_dir=/results \
+    "$use_fp16" \
+    $use_xla_tag &> $LOGFILE
+
+    perf=`cat $LOGFILE | grep -F 'Inference Performance' | awk -F'= ' '{print $2}'`
+    echo "Inference performance is $perf"
+
+else
+
+    echo "Benchmarking for " $task "currently not supported. Sorry!"
+
+fi
\ No newline at end of file
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh b/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh
new file mode 100755
index 00000000..67f1a029
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+
+export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16
+
+task=${1:-"squad"}
+precision=${2:-"fp16"}
+use_xla=${3:-"true"}
+num_gpu=${4:-"8"}
+batch_size=${5:-"8"}
+learning_rate=${6:-"5e-6"}
+
+if [ "$task" = "squad" ] ; then
+    export SQUAD_DIR=data/squad/v1.1
+
+    epochs="2.0"
+    use_fp16=""
+    LOGFILE="/results/${task}_training_benchmark.log"
+    if [ "$precision" = "fp16" ] ; then
+            export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1
+            use_fp16="--fast_math"
+    elif [ "$precision" = "fast_math" ] ; then
+            export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=1
+            export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=1
+            export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=1
+            export TF_USE_DEFAULT_LOSS_SCALING=1
+    fi
+
+
+    if [ "$use_xla" = "true" ] ; then
+        use_xla_tag="--use_xla"
+    else
+        use_xla_tag=""
+    fi
+
+    if [ "$num_gpu" = "0" ] ; then
+        mpi_command=""
+        use_hvd=""
+    else
+        mpi_command="mpirun -np $num_gpu -H localhost:$num_gpu \
+        --allow-run-as-root -bind-to none -map-by slot \
+        -x NCCL_DEBUG=INFO \
+        -x LD_LIBRARY_PATH \
+        -x PATH -mca pml ob1 -mca btl ^openib"
+        use_hvd="--horovod"
+    fi
+
+
+    $mpi_command python run_squad.py \
+    --vocab_file=$BERT_DIR/vocab.txt \
+    --bert_config_file=$BERT_DIR/bert_config.json \
+    --init_checkpoint=$BERT_DIR/bert_model.ckpt \
+    --do_train=True \
+    --train_file=$SQUAD_DIR/train-v1.1.json \
+    --train_batch_size=$batch_size \
+    --learning_rate=$learning_rate \
+    --num_train_epochs=$epochs \
+    --max_seq_length=384 \
+    --doc_stride=128 \
+    --output_dir=/results \
+    "$use_hvd" \
+    "$use_fp16" \
+    $use_xla_tag &> $LOGFILE
+
+    perf=`cat $LOGFILE | grep -F 'Training Performance' | awk -F'= ' '{print $2}'`
+    echo "Training performance is $perf"
+
+else
+
+    echo "Benchmarking for " $task "currently not supported. Sorry!"
+
+fi
\ No newline at end of file
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run.sub b/TensorFlow/LanguageModeling/BERT/scripts/run.sub
new file mode 100755
index 00000000..5bebc0b5
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/run.sub
@@ -0,0 +1,184 @@
+#!/bin/bash
+#SBATCH -p mlperf		# partition
+#SBATCH -N 1       		# number of nodes
+#SBATCH -t 12:00:00		# wall time
+#SBATCH -J image_classification	# job name
+#SBATCH --exclusive   		# exclusive node access
+#SBATCH --mem=0   		# all mem avail
+#SBATCH --mail-type=FAIL        # only send email on failure
+#SBATCH --ntasks-per-node=8	# n tasks per machine (one task per gpu)
+#SBATCH --threads-per-core=2	# HT is on
+#SBATCH --cores-per-socket=20	# 20 cores on each socket 
+#SBATCH --overcommit
+
+hostname
+#DGXIBDEVICES=$(eval ls /dev/infiniband/ | tr " " "\n" | awk '{printf "--device=/dev/infiniband/%s ",$1}' | sed s'/.$//')
+printf "DGXIBDEVICES=%s\n" "$DGXIBDEVICES"
+printf "VOLS=%s\n" "$VOLS"
+printf "EXTRA_PARAMS=%s\n" "$EXTRA_PARAMS"
+
+cd $CODEDIR
+
+VOLS+=" -v $CHKPTDIR/$SLURM_JOB_ID:/checkpoints"
+
+mkdir -p $CHKPTDIR/$SLURM_JOB_ID
+
+## DO NOT CHANGE ANYTHING BELOW -- DL params are in run_and_time.sh and config_<system>.sh files 
+
+DEBUG=1  # 1 = Print verbose messages for debugging
+
+## Pre-warming the containers ##
+hosts=( `scontrol show hostname |tr "\n" " "` )
+pids=(); for hostn in ${hosts[@]}; do
+  timeout -k 600s 600s \
+  srun -N 1 -n 1 -w $hostn \
+    docker pull $CONT &
+  pids+=($!);
+   pids+=($!); rets+=($?);
+done
+wait "${pids[@]}"
+success=0; for s in ${rets[@]}; do ((success+=s)); done ; if [ $success -ne 0 ]; then echo "ERR: Container pull failed"; exit $success ; fi
+
+IBDEVICES=${IBDEVICES:-$DGXIBDEVICES}
+
+## Check whether we are running in a slurm env
+INSLURM=1
+if [[ -z "$SLURM_JOB_ID" ]]; then
+  INSLURM=0
+  export SLURM_JOB_ID="${DATESTAMP}"
+  export SLURM_NNODES=1
+fi
+if [[ -z "SLURM_JOB_ID" || $SLURM_NNODES -eq 1 ]]; then
+  # don't need IB if not multi-node
+  export IBDEVICES=""
+fi
+
+# Create results directory
+LOGFILE_BASE="${LOGDIR}/${DATESTAMP}"
+mkdir -p $(dirname "${LOGFILE_BASE}")
+
+export CONTNAME="mpi_${SLURM_JOB_ID}"
+export DOCKEREXEC="nvidia-docker run --rm --net=host --uts=host --ipc=host --ulimit stack=67108864 --ulimit memlock=-1 --security-opt seccomp=unconfined  $IBDEVICES"
+MPICMD="mpirun --allow-run-as-root --tag-output --bind-to none -np $((SLURM_NNODES*DGXNGPU)) -x EXTRA_PARAMS=\"${EXTRA_PARAMS}\" -x HOROVOD_MPI_THREADS_DISABLE=1 -x NCCL_LL_THRESHOLD=0 -x NCCL_DEBUG=INFO -x NCCL_NET_GDR_READ=1 -x NCCL_SOCKET_IFNAME=^docker0,bond0,lo $BIND ./run_pretraining.sh"
+echo $MPICMD
+
+mkdir -m 777 -p $LOGDIR
+echo $MPICMD | tee -a $LOGDIR/$DATESTAMP.log 
+echo "slurm job id" $SLURM_JOB_ID &> $LOGDIR/$DATESTAMP.log 
+
+MASTER_IP=`getent hosts \`hostname\` | cut -d ' ' -f1`
+SSH=''
+SRUN=''
+if [[ $INSLURM -eq 0 ]]; then
+  export hosts=( `hostname` )
+else
+  export hosts=( `scontrol show hostname |tr "\n" " "` )
+  SSH='ssh -q -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no $hostn'
+  SRUN='srun -N 1 -n 1 -w $hostn'
+fi
+unique_hosts=( $(echo "${hosts[@]}" | tr ' ' '\n' | sort -u | tr '\n' ' ' ) )
+export MASTER_HOST=${hosts[0]}
+
+VARS="-e OMPI_MCA_mca_base_param_files=/dev/shm/mpi/${SLURM_JOB_ID}/mca_params.conf -e EXTRA_PARAMS -e GPUS -e BATCHSIZE -e CONT -e DGXSYSTEM=$DGXSYSTEM -e MASTER_HOST -e MASTER_IP -e SLURM_JOB_NUM_NODES -e SLURM_NNODES -e SLURM_NTASKS_PER_NODE -w /workspace/bert"
+
+RUNSLEEPCMD=""
+
+[[ "${PULL}" -eq "1" ]] && docker pull $CONT
+
+## Setting up MPI
+# MPI support files - in /dev/shm/mpi/<jobid>
+# 1. Copy user keys to /dev/shm/mpi/<jobid>
+# 2. Create mca_params.conf
+# 3. Create sshentry.sh to support lauching into containers on worker nodes
+# 4. Create mpi_hosts file
+# 5. Copy standard ssh
+
+if [[ $SLURM_NNODES -ne "1" ]]; then
+
+  # Make keys and copy
+  echo
+
+  [[ $DEBUG == 1 ]] && echo "Setting up ssh keys and config"
+
+  mkdir -p ${HOME}/.ssh/sbatch/${SLURM_JOB_ID}
+  ssh-keygen -t rsa -b 2048 -n "" -f "${HOME}/.ssh/sbatch/${SLURM_JOB_ID}/sshkey.rsa" -C "mxnet_${SLURM_JOB_ID}_"  &>/dev/null
+  echo command=\"/dev/shm/mpi/${SLURM_JOB_ID}/sshentry.sh\",no-port-forwarding,no-agent-forwarding,no-X11-forwarding $(cat ${HOME}/.ssh/sbatch/${SLURM_JOB_ID}/sshkey.rsa.pub) >> ${HOME}/.ssh/authorized_keys
+  chmod 600 ~/.ssh/authorized_keys
+
+  [[ $DEBUG == 1 ]] && echo "Copy keys: srun -n $SLURM_JOB_NUM_NODES mkdir /dev/shm/mpi && cp -R ${HOME}/.ssh/sbatch/${SLURM_JOB_ID} /dev/shm/mpi && chmod 700 /dev/shm/mpi/${SLURM_JOB_ID}" 
+
+  srun  -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 bash -c "mkdir -p /dev/shm/mpi/${SLURM_JOB_ID}; cp -R ${HOME}/.ssh/sbatch/${SLURM_JOB_ID} /dev/shm/mpi; chmod 700 /dev/shm/mpi/${SLURM_JOB_ID}"
+
+  sleep 2 # Making copy
+
+  [[ $DEBUG == 1 ]] && ls /dev/shm
+
+  # Create mpi config file
+  srun  -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 tee /dev/shm/mpi/${SLURM_JOB_ID}/mca_params.conf <<EOF
+plm_rsh_agent = /usr/bin/ssh
+plm_rsh_args = -i /dev/shm/mpi/${SLURM_JOB_ID}/sshkey.rsa -oStrictHostKeyChecking=no -oUserKnownHostsFile=/dev/null -oLogLevel=ERROR -l ${USER}
+orte_default_hostfile = /dev/shm/mpi/${SLURM_JOB_ID}/mpi_hosts
+btl_openib_warn_default_gid_prefix = 0
+mpi_warn_on_fork = 0
+allow_run_as_root = 1
+EOF
+
+  [[ $DEBUG == 1 ]] && echo "::mca_params.conf=" && cat /dev/shm/mpi/${SLURM_JOB_ID}/mca_params.conf
+
+  # Create ssh helper script that transfers an ssh into a compute node into the running container on that node
+  srun -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 tee /dev/shm/mpi/${SLURM_JOB_ID}/sshentry.sh <<EOF
+#!/bin/bash
+echo "::sshentry: entered \$(hostname)"
+[[ -f $CONTNAME ]] && "::worker container not found error" && exit 1
+echo "::sshentry: running \$SSH_ORIGINAL_COMMAND"
+exec docker exec $CONTNAME /bin/bash -c "\$SSH_ORIGINAL_COMMAND"
+EOF
+
+  [[ $DEBUG == 1 ]] && echo "::sshentry=" && cat /dev/shm/mpi/${SLURM_JOB_ID}/sshentry.sh
+
+  # Create mpi hostlist
+  for h in ${hosts[@]}; do
+     echo "$h slots=${SLURM_NTASKS_PER_NODE}" >> /dev/shm/mpi/${SLURM_JOB_ID}/mpi_hosts
+  done
+
+  [[ $DEBUG == 1 ]] && echo '::mpi-host file=' && cat /dev/shm/mpi/${SLURM_JOB_ID}/mpi_hosts
+
+  srun -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 bash -c "cp $(which ssh) /dev/shm/mpi/${SLURM_JOB_ID}/.;  chmod 755 /dev/shm/mpi/${SLURM_JOB_ID}/mca_params.conf;  chmod 755 /dev/shm/mpi/${SLURM_JOB_ID}/sshentry.sh"
+
+  # Check that ssh/mpi dir has correct number of files
+  [[ $(ls /dev/shm/mpi/${SLURM_JOB_ID} | wc -w) -lt 5 ]]  && echo "ERR: /dev/shm/mpi/${SLURM_JOB_ID} doesn't exist or missing ssh/mpi files" && exit $?
+
+fi
+
+# Container launch
+if [[ $INSLURM -eq 1 ]]; then
+
+  # Launch containers behind srun
+
+  [[ $DEBUG == 1 ]] && echo "" && echo ":Launch containers:  srun  -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 $DOCKEREXEC --name $CONTNAME $VOLS $VARS $CONT bash -c 'sleep infinity'"
+  srun  -n $SLURM_JOB_NUM_NODES --ntasks-per-node=1 $DOCKEREXEC --name $CONTNAME $VOLS $VARS $CONT bash -c 'sleep infinity' & rv=$?
+else
+  $DOCKEREXEC --name $CONTNAME $VOLS $VARS $CONT bash -c 'sleep infinity' & rv=$?
+fi
+[[ $rv -ne 0 ]] && echo "ERR: Launch sleep containers failed." && exit $rv
+echo "sleep 60 while we pull our container, good golly!"
+sleep 60
+
+# Run benchmarks
+echo "sleep again for 20"
+sleep 20
+export EXTRA_PARAMS
+
+(
+# Launching app
+echo 
+echo "Launching user script on master node:"
+  hostn=$MASTER_HOST
+  $(eval echo $SSH) docker exec $VARS $CONTNAME $MPICMD ; rv=$?
+  [[ $rv -ne 0 ]] && echo "ERR: User script failed." && exit $rv
+) |& tee ${LOGFILE_BASE}_$nrun.log
+
+# Clean up (note: on SLURM we skip this, as the epilogue will take care of it)
+if [[ $INSLURM -eq 0 ]]; then
+  docker rm -f $CONTNAME
+fi
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh
new file mode 100755
index 00000000..8cd2c519
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh
@@ -0,0 +1,107 @@
+#! /bin/bash
+
+echo "Container nvidia build = " $NVIDIA_BUILD_ID
+
+WIKI_DIR=/workspace/bert/data/wikipedia_corpus/final_tfrecords_sharded
+BOOKS_DIR=/workspace/bert/data/bookcorpus/final_tfrecords_sharded
+BERT_CONFIG=/workspace/bert/data/pretrained_models_google/uncased_L-24_H-1024_A-16/bert_config.json
+RESULTS_DIR=/results
+CHECKPOINTS_DIR=/checkpoints
+
+if [ ! -d "$WIKI_DIR" ] ; then
+   echo "Error! $WIKI_DIR directory missing. Please mount wikipedia dataset."
+   exit -1
+else
+   SOURCES="$WIKI_DIR/*"
+fi
+if [ ! -d "$BOOKS_DIR" ] ; then
+   echo "Warning! $BOOKS_DIR directory missing. Training will proceed without book corpus."
+else
+   SOURCES+=" $BOOKS_DIR/*"
+fi
+if [ ! -d "$RESULTS_DIR" ] ; then
+   echo "Error! $RESULTS_DIR directory missing."
+   exit -1
+fi
+if [ ! -d "$CHECKPOINTS_DIR" ] ; then
+   echo "Warning! $CHECKPOINTS_DIR directory missing."
+   echo "Checkpoints will be written to $RESULTS_DIR instead."
+   CHECKPOINTS_DIR=$RESULTS_DIR
+fi
+if [ ! -f "$BERT_CONFIG" ] ; then
+   echo "Error! BERT large configuration file not found at $BERT_CONFIG"
+   exit -1
+fi
+
+train_batch_size=${1:-14}
+eval_batch_size=${2:-8}
+learning_rate=${3:-"1e-4"}
+precision=${4:-"fp16_xla"}
+num_gpus=${5:-8}
+warmup_steps=${6:-"10000"}
+train_steps=${7:-1144000}
+save_checkpoint_steps=${8:-5000}
+create_logfile=${9:-"true"}
+
+PREC=""
+if [ "$precision" = "fp16" ] ; then
+   PREC="--use_fp16"
+elif [ "$precision" = "fp16_xla" ] ; then
+   PREC="--use_fp16 --use_xla"
+elif [ "$precision" = "fp32" ] ; then
+   PREC=""
+elif [ "$precision" = "fastmath" ] ; then
+   PREC="--fast_math"
+elif [ "$precision" = "amp_fm" ] ; then
+   PREC="--amp_fastmath"
+elif [ "$precision" = "amp_fm_xla" ] ; then
+   PREC="--amp_fastmath --use_xla"
+elif [ "$precision" = "amp" ] ; then
+   PREC="--amp"
+elif [ "$precision" = "amp_xla" ] ; then
+   PREC="--amp --use_xla"
+else
+   echo "Unknown <precision> argument"
+   exit -2
+fi
+
+echo $SOURCES
+INPUT_FILES=$(eval ls $SOURCES | tr " " "\n" | awk '{printf "%s,",$1}' | sed s'/.$//')
+CMD="python3 /workspace/bert/run_pretraining.py"
+CMD+=" --input_file=$INPUT_FILES"
+CMD+=" --output_dir=$CHECKPOINTS_DIR"
+CMD+=" --bert_config_file=$BERT_CONFIG"
+CMD+=" --do_train=True"
+CMD+=" --do_eval=True"
+CMD+=" --train_batch_size=$train_batch_size"
+CMD+=" --eval_batch_size=$eval_batch_size"
+CMD+=" --max_seq_length=512"
+CMD+=" --max_predictions_per_seq=80"
+CMD+=" --num_train_steps=$train_steps"
+CMD+=" --num_warmup_steps=$warmup_steps"
+CMD+=" --save_checkpoint_steps=$save_checkpoint_steps"
+CMD+=" --learning_rate=$learning_rate"
+CMD+=" --report_loss"
+CMD+=" --horovod $PREC"
+
+if [ $num_gpus > 1 ] ; then
+   CMD="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket $CMD"
+fi
+
+if [ "$create_logfile" = "true" ] ; then
+  export GBS=$(expr $train_batch_size \* $num_gpus)
+  printf -v TAG "tf_bert_1n_%s_gbs%d" "$precision" $GBS
+  DATESTAMP=`date +'%y%m%d%H%M%S'`
+  LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log
+  printf "Logs written to %s\n" "$LOGFILE"
+fi
+
+set -x
+if [ -z "$LOGFILE" ] ; then
+   $CMD
+else
+   (
+     $CMD
+   ) |& tee $LOGFILE
+fi
+set +x
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh
new file mode 100755
index 00000000..9f80e552
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh
@@ -0,0 +1,63 @@
+#!/usr/bin/env bash
+
+export SQUAD_DIR=data/squad/v1.1
+export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16
+
+
+echo "Container nvidia build = " $NVIDIA_BUILD_ID
+
+batch_size=${1:-"8"}
+learning_rate=${2:-"5e-6"}
+precision=${3:-"fp16"}
+use_xla=${4:-"true"}
+num_gpu=${5:-"8"}
+init_checkpoint=${6:-"$BERT_DIR/bert_model.ckpt"}
+epochs="2.0"
+
+use_fp16=""
+if [ "$precision" = "fp16" ] ; then
+        echo "fp16 activated!"
+        export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1
+        use_fp16="--use_fp16"
+fi
+
+if [ "$use_xla" = "true" ] ; then
+    use_xla_tag="--use_xla"
+    echo "XLA activated"
+else
+    use_xla_tag=""
+fi
+
+if [ "$num_gpu" = "0" ] ; then
+    mpi_command=""
+    use_hvd=""
+else
+    mpi_command="mpirun -np $num_gpu -H localhost:$num_gpu \
+    --allow-run-as-root -bind-to none -map-by slot \
+    -x NCCL_DEBUG=INFO \
+    -x LD_LIBRARY_PATH \
+    -x PATH -mca pml ob1 -mca btl ^openib"
+    use_hvd="--horovod"
+fi
+
+
+    $mpi_command python run_squad.py \
+    --vocab_file=$BERT_DIR/vocab.txt \
+    --bert_config_file=$BERT_DIR/bert_config.json \
+    --init_checkpoint=$init_checkpoint \
+    --do_train=True \
+    --train_file=$SQUAD_DIR/train-v1.1.json \
+    --do_predict=True \
+    --predict_file=$SQUAD_DIR/dev-v1.1.json \
+    --train_batch_size=$batch_size \
+    --learning_rate=$learning_rate \
+    --num_train_epochs=$epochs \
+    --max_seq_length=384 \
+    --doc_stride=128 \
+    --save_checkpoints_steps 1000 \
+    --output_dir=/results \
+    "$use_hvd" \
+    "$use_fp16" \
+    $use_xla_tag 
+
+python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json /results/predictions.json
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh
new file mode 100755
index 00000000..a83125fb
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+export SQUAD_DIR=data/squad/v1.1
+export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16
+
+echo "Container nvidia build = " $NVIDIA_BUILD_ID
+
+init_checkpoint=${1:-"/results/model.ckpt"}
+batch_size=${2:-"8"}
+precision=${3:-"fp16"}
+use_xla=${4:-"true"}
+
+use_fp16=""
+if [ "$precision" = "fp16" ] ; then
+        echo "fp16 activated!"
+        export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1
+        use_fp16="--fast_math"
+elif [ "$precision" = "fast_math" ] ; then
+        echo "fastmath activated!"
+        export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=1
+        export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=1
+        export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=1
+        export TF_USE_DEFAULT_LOSS_SCALING=1
+fi
+
+if [ "$use_xla" = "true" ] ; then
+    use_xla_tag="--use_xla"
+    echo "XLA activated"
+else
+    use_xla_tag=""
+fi
+
+
+python run_squad.py \
+--vocab_file=$BERT_DIR/vocab.txt \
+--bert_config_file=$BERT_DIR/bert_config.json \
+--init_checkpoint=$init_checkpoint \
+--do_predict=True \
+--predict_file=$SQUAD_DIR/dev-v1.1.json \
+--max_seq_length=384 \
+--doc_stride=128 \
+--predict_batch_size=$batch_size \
+--output_dir=/results \
+"$use_fp16" \
+$use_xla_tag
+
+python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json /results/predictions.json
diff --git a/TensorFlow/LanguageModeling/BERT/scripts/start_pretraining.sh b/TensorFlow/LanguageModeling/BERT/scripts/start_pretraining.sh
new file mode 100755
index 00000000..e2753871
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/scripts/start_pretraining.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+
+node_type=${1:-"dgx1"}
+num_nodes=${2:-1}
+partition=${3:-"default"}
+wall_time=${4:-"12:00:00"}
+job_name=${5:-"tf_bert"}
+root_dir=${6:-"$PWD"}
+train_batch_size=${7:-4}
+eval_batch_size=${8:-4}
+train_steps=${9:-1000000}
+warmup_steps=${10:-10000}
+learning_rate=${11:-1e-4}
+precision=${12:-"fp16_xla"}
+save_checkpoint_steps=${13:-5000}
+results_dir=${14:-"$root_dir/results"}
+checkpoints_dir=${15:-"$root_dir/checkpoints"}
+
+CONT=${CONT:-"gitlab-master.nvidia.com:5005/dl/dgx/tensorflow:19.03-py3-devel"}
+
+BENCHMARK=${BENCHMARK:-"bert"}
+BENCHMARK_NAME="bert"
+
+if [ "$node_type" = "dgx1" ] ; then
+   echo "Running on dgx1 systems"
+   DGXSYSTEM="DGX1"
+   DGXNGPU=8
+   DGXSOCKETCORES=20
+   DGXNSOCKET=2
+   DGXHT=2
+   DGXIBDEVICES='--device=/dev/infiniband --device=/dev/infiniband/rdma_cm --device=/dev/infiniband/ucm3 --device=/dev/infiniband/ucm2 --device=/dev/infiniband/ucm1 --device=/dev/infiniband/ucm0 --device=/dev/infiniband/uverbs3 --device=/dev/infiniband/uverbs2 --device=/dev/infiniband/uverbs1 --device=/dev/infiniband/uverbs0 --device=/dev/infiniband/issm3 --device=/dev/infiniband/umad3 --device=/dev/infiniband/issm2 --device=/dev/infiniband/umad2 --device=/dev/infiniband/issm1 --device=/dev/infiniband/umad1 --device=/dev/infiniband/issm0 --device=/dev/infiniband/umad0'
+elif [ "$node_type" = "dgx2h" ] ; then
+   echo "Running on dgx2h systems"
+   DGXSYSTEM="DGX2H"
+   DGXNGPU=16
+   DGXSOCKETCORES=24
+   DGXNSOCKET=2
+   DGXHT=2         # HT is on is 2, HT off is 1
+   DGXIBDEVICES='--device=/dev/infiniband/rdma_cm --device=/dev/infiniband/ucm10 --device=/dev/infiniband/ucm9 --device=/dev/infiniband/ucm8 --device=/dev/infiniband/ucm7 --device=/dev/infiniband/ucm4 --device=/dev/infiniband/ucm3 --device=/dev/infiniband/ucm2 --device=/dev/infiniband/ucm1 --device=/dev/infiniband/uverbs10 --device=/dev/infiniband/uverbs9 --device=/dev/infiniband/uverbs8 --device=/dev/infiniband/uverbs7 --device=/dev/infiniband/uverbs4 --device=/dev/infiniband/uverbs3 --device=/dev/infiniband/uverbs2 --device=/dev/infiniband/uverbs1 --device=/dev/infiniband/issm10 --device=/dev/infiniband/umad10 --device=/dev/infiniband/issm9 --device=/dev/infiniband/umad9 --device=/dev/infiniband/issm8 --device=/dev/infiniband/umad8 --device=/dev/infiniband/issm7 --device=/dev/infiniband/umad7 --device=/dev/infiniband/issm4 --device=/dev/infiniband/umad4 --device=/dev/infiniband/issm3 --device=/dev/infiniband/umad3 --device=/dev/infiniband/issm2 --device=/dev/infiniband/umad2 --device=/dev/infiniband/issm1 --device=/dev/infiniband/umad1'
+else
+   echo "Unknown <node_type>, must be either dgx1 or dgx2"
+   exit -1
+fi
+
+printf -v EXTRA_PARAMS "%d %d %e %s 1 %d %d %d false" $train_batch_size $eval_batch_size $learning_rate "$precision" $warmup_steps $train_steps $save_checkpoint_steps
+
+export ROOTDIR=$root_dir
+export WIKI_DIR=${WIKI_DIR:-$CODEDIR/data/wikipedia_corpus/final_tfrecords_sharded}
+export BOOKS_DIR=${BOOKS_DIR:-$CODEDIR/data/bookcorpus/final_tfrecords_sharded}
+
+VOLS="-v $ROOTDIR:/workspace/bert"
+VOLS+=" -v $WIKI_DIR:/workspace/bert/data/wikipedia_corpus/final_tfrecord_sharded"
+VOLS+=" -v $BOOKS_DIR:/workspace/bert/data/bookcorpus/final_tfrecord_sharded"
+VOLS+=" -v $results_dir:/results"
+VOLS+=" -v $checkpoints_dir:/checkpoints"
+
+export VOLS
+export CONT
+export DGXSYSTEM
+export DGXNGPU
+export DGXIBDEVICES
+export EXTRA_PARAMS
+
+set -x
+cd $CODEDIR
+pwd 
+
+PART=""
+if [ "$partition" != "default" ] ; then
+   printf -v PART "%s" "-p $partition"
+fi
+
+export GBS=$(expr $num_nodes \* $batch_size \* $DGXNGPU)
+printf -v TAG "%s_%dn_%s_gbs%d" "$job_name" $num_nodes "$precision" $GBS
+export DATESTAMP=`date +'%y%m%d%H%M%S'`
+
+sbatch $PART \
+	-N $num_nodes \
+       	-t $wall_time \
+       	-J $job_name \
+	--exclusive \
+        --mem=0 \
+	--mail-type=FAIL \
+	--ntasks-per-node=$DGXNGPU \
+	--threads-per-core=$DGXHT \
+	--cores-per-socket=$DGXSOCKETCORES \
+	--output=$LOGDIR/$TAG.$DATESTAMP.log \
+	$CODEDIR/scripts/run.sub 
+set +x
diff --git a/TensorFlow/LanguageModeling/BERT/tokenization.py b/TensorFlow/LanguageModeling/BERT/tokenization.py
new file mode 100644
index 00000000..dc476a69
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/tokenization.py
@@ -0,0 +1,399 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import unicodedata
+import six
+import tensorflow as tf
+
+
+def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
+  """Checks whether the casing config is consistent with the checkpoint name."""
+
+  # The casing has to be passed in by the user and there is no explicit check
+  # as to whether it matches the checkpoint. The casing information probably
+  # should have been stored in the bert_config.json file, but it's not, so
+  # we have to heuristically detect it to validate.
+
+  if not init_checkpoint:
+    return
+
+  m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
+  if m is None:
+    return
+
+  model_name = m.group(1)
+
+  lower_models = [
+      "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
+      "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
+  ]
+
+  cased_models = [
+      "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
+      "multi_cased_L-12_H-768_A-12"
+  ]
+
+  is_bad_config = False
+  if model_name in lower_models and not do_lower_case:
+    is_bad_config = True
+    actual_flag = "False"
+    case_name = "lowercased"
+    opposite_flag = "True"
+
+  if model_name in cased_models and do_lower_case:
+    is_bad_config = True
+    actual_flag = "True"
+    case_name = "cased"
+    opposite_flag = "False"
+
+  if is_bad_config:
+    raise ValueError(
+        "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
+        "However, `%s` seems to be a %s model, so you "
+        "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
+        "how the model was pre-training. If this error is wrong, please "
+        "just comment out this check." % (actual_flag, init_checkpoint,
+                                          model_name, case_name, opposite_flag))
+
+
+def convert_to_unicode(text):
+  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text.decode("utf-8", "ignore")
+    elif isinstance(text, unicode):
+      return text
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def printable_text(text):
+  """Returns text encoded in a way suitable for print or `tf.logging`."""
+
+  # These functions want `str` for both Python2 and Python3, but in one case
+  # it's a Unicode string and in the other it's a byte string.
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, unicode):
+      return text.encode("utf-8")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def load_vocab(vocab_file):
+  """Loads a vocabulary file into a dictionary."""
+  vocab = collections.OrderedDict()
+  index = 0
+  with tf.gfile.GFile(vocab_file, "r") as reader:
+    while True:
+      token = convert_to_unicode(reader.readline())
+      if not token:
+        break
+      token = token.strip()
+      vocab[token] = index
+      index += 1
+  return vocab
+
+
+def convert_by_vocab(vocab, items):
+  """Converts a sequence of [tokens|ids] using the vocab."""
+  output = []
+  for item in items:
+    output.append(vocab[item])
+  return output
+
+
+def convert_tokens_to_ids(vocab, tokens):
+  return convert_by_vocab(vocab, tokens)
+
+
+def convert_ids_to_tokens(inv_vocab, ids):
+  return convert_by_vocab(inv_vocab, ids)
+
+
+def whitespace_tokenize(text):
+  """Runs basic whitespace cleaning and splitting on a piece of text."""
+  text = text.strip()
+  if not text:
+    return []
+  tokens = text.split()
+  return tokens
+
+
+class FullTokenizer(object):
+  """Runs end-to-end tokenziation."""
+
+  def __init__(self, vocab_file, do_lower_case=True):
+    self.vocab = load_vocab(vocab_file)
+    self.inv_vocab = {v: k for k, v in self.vocab.items()}
+    self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+  def tokenize(self, text):
+    split_tokens = []
+    for token in self.basic_tokenizer.tokenize(text):
+      for sub_token in self.wordpiece_tokenizer.tokenize(token):
+        split_tokens.append(sub_token)
+
+    return split_tokens
+
+  def convert_tokens_to_ids(self, tokens):
+    return convert_by_vocab(self.vocab, tokens)
+
+  def convert_ids_to_tokens(self, ids):
+    return convert_by_vocab(self.inv_vocab, ids)
+
+
+class BasicTokenizer(object):
+  """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+  def __init__(self, do_lower_case=True):
+    """Constructs a BasicTokenizer.
+
+    Args:
+      do_lower_case: Whether to lower case the input.
+    """
+    self.do_lower_case = do_lower_case
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text."""
+    text = convert_to_unicode(text)
+    text = self._clean_text(text)
+
+    # This was added on November 1st, 2018 for the multilingual and Chinese
+    # models. This is also applied to the English models now, but it doesn't
+    # matter since the English models were not trained on any Chinese data
+    # and generally don't have any Chinese data in them (there are Chinese
+    # characters in the vocabulary because Wikipedia does have some Chinese
+    # words in the English Wikipedia.).
+    text = self._tokenize_chinese_chars(text)
+
+    orig_tokens = whitespace_tokenize(text)
+    split_tokens = []
+    for token in orig_tokens:
+      if self.do_lower_case:
+        token = token.lower()
+        token = self._run_strip_accents(token)
+      split_tokens.extend(self._run_split_on_punc(token))
+
+    output_tokens = whitespace_tokenize(" ".join(split_tokens))
+    return output_tokens
+
+  def _run_strip_accents(self, text):
+    """Strips accents from a piece of text."""
+    text = unicodedata.normalize("NFD", text)
+    output = []
+    for char in text:
+      cat = unicodedata.category(char)
+      if cat == "Mn":
+        continue
+      output.append(char)
+    return "".join(output)
+
+  def _run_split_on_punc(self, text):
+    """Splits punctuation on a piece of text."""
+    chars = list(text)
+    i = 0
+    start_new_word = True
+    output = []
+    while i < len(chars):
+      char = chars[i]
+      if _is_punctuation(char):
+        output.append([char])
+        start_new_word = True
+      else:
+        if start_new_word:
+          output.append([])
+        start_new_word = False
+        output[-1].append(char)
+      i += 1
+
+    return ["".join(x) for x in output]
+
+  def _tokenize_chinese_chars(self, text):
+    """Adds whitespace around any CJK character."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if self._is_chinese_char(cp):
+        output.append(" ")
+        output.append(char)
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+  def _is_chinese_char(self, cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+        (cp >= 0x3400 and cp <= 0x4DBF) or  #
+        (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+        (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+        (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+        (cp >= 0x2B820 and cp <= 0x2CEAF) or
+        (cp >= 0xF900 and cp <= 0xFAFF) or  #
+        (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+      return True
+
+    return False
+
+  def _clean_text(self, text):
+    """Performs invalid character removal and whitespace cleanup on text."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if cp == 0 or cp == 0xfffd or _is_control(char):
+        continue
+      if _is_whitespace(char):
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+
+class WordpieceTokenizer(object):
+  """Runs WordPiece tokenziation."""
+
+  def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
+    self.vocab = vocab
+    self.unk_token = unk_token
+    self.max_input_chars_per_word = max_input_chars_per_word
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text into its word pieces.
+
+    This uses a greedy longest-match-first algorithm to perform tokenization
+    using the given vocabulary.
+
+    For example:
+      input = "unaffable"
+      output = ["un", "##aff", "##able"]
+
+    Args:
+      text: A single token or whitespace separated tokens. This should have
+        already been passed through `BasicTokenizer.
+
+    Returns:
+      A list of wordpiece tokens.
+    """
+
+    text = convert_to_unicode(text)
+
+    output_tokens = []
+    for token in whitespace_tokenize(text):
+      chars = list(token)
+      if len(chars) > self.max_input_chars_per_word:
+        output_tokens.append(self.unk_token)
+        continue
+
+      is_bad = False
+      start = 0
+      sub_tokens = []
+      while start < len(chars):
+        end = len(chars)
+        cur_substr = None
+        while start < end:
+          substr = "".join(chars[start:end])
+          if start > 0:
+            substr = "##" + substr
+          if substr in self.vocab:
+            cur_substr = substr
+            break
+          end -= 1
+        if cur_substr is None:
+          is_bad = True
+          break
+        sub_tokens.append(cur_substr)
+        start = end
+
+      if is_bad:
+        output_tokens.append(self.unk_token)
+      else:
+        output_tokens.extend(sub_tokens)
+    return output_tokens
+
+
+def _is_whitespace(char):
+  """Checks whether `chars` is a whitespace character."""
+  # \t, \n, and \r are technically contorl characters but we treat them
+  # as whitespace since they are generally considered as such.
+  if char == " " or char == "\t" or char == "\n" or char == "\r":
+    return True
+  cat = unicodedata.category(char)
+  if cat == "Zs":
+    return True
+  return False
+
+
+def _is_control(char):
+  """Checks whether `chars` is a control character."""
+  # These are technically control characters but we count them as whitespace
+  # characters.
+  if char == "\t" or char == "\n" or char == "\r":
+    return False
+  cat = unicodedata.category(char)
+  if cat.startswith("C"):
+    return True
+  return False
+
+
+def _is_punctuation(char):
+  """Checks whether `chars` is a punctuation character."""
+  cp = ord(char)
+  # We treat all non-letter/number ASCII as punctuation.
+  # Characters such as "^", "$", and "`" are not in the Unicode
+  # Punctuation class but we treat them as punctuation anyways, for
+  # consistency.
+  if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+      (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+    return True
+  cat = unicodedata.category(char)
+  if cat.startswith("P"):
+    return True
+  return False
diff --git a/TensorFlow/LanguageModeling/BERT/tokenization_test.py b/TensorFlow/LanguageModeling/BERT/tokenization_test.py
new file mode 100644
index 00000000..e85a644d
--- /dev/null
+++ b/TensorFlow/LanguageModeling/BERT/tokenization_test.py
@@ -0,0 +1,136 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tempfile
+import tokenization
+import six
+import tensorflow as tf
+
+
+class TokenizationTest(tf.test.TestCase):
+
+  def test_full_tokenizer(self):
+    vocab_tokens = [
+        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+        "##ing", ","
+    ]
+    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
+      if six.PY2:
+        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+      else:
+        vocab_writer.write("".join(
+            [x + "\n" for x in vocab_tokens]).encode("utf-8"))
+
+      vocab_file = vocab_writer.name
+
+    tokenizer = tokenization.FullTokenizer(vocab_file)
+    os.unlink(vocab_file)
+
+    tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
+    self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
+
+    self.assertAllEqual(
+        tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
+
+  def test_chinese(self):
+    tokenizer = tokenization.BasicTokenizer()
+
+    self.assertAllEqual(
+        tokenizer.tokenize(u"ah\u535A\u63A8zz"),
+        [u"ah", u"\u535A", u"\u63A8", u"zz"])
+
+  def test_basic_tokenizer_lower(self):
+    tokenizer = tokenization.BasicTokenizer(do_lower_case=True)
+
+    self.assertAllEqual(
+        tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+        ["hello", "!", "how", "are", "you", "?"])
+    self.assertAllEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
+
+  def test_basic_tokenizer_no_lower(self):
+    tokenizer = tokenization.BasicTokenizer(do_lower_case=False)
+
+    self.assertAllEqual(
+        tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+        ["HeLLo", "!", "how", "Are", "yoU", "?"])
+
+  def test_wordpiece_tokenizer(self):
+    vocab_tokens = [
+        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+        "##ing"
+    ]
+
+    vocab = {}
+    for (i, token) in enumerate(vocab_tokens):
+      vocab[token] = i
+    tokenizer = tokenization.WordpieceTokenizer(vocab=vocab)
+
+    self.assertAllEqual(tokenizer.tokenize(""), [])
+
+    self.assertAllEqual(
+        tokenizer.tokenize("unwanted running"),
+        ["un", "##want", "##ed", "runn", "##ing"])
+
+    self.assertAllEqual(
+        tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
+
+  def test_convert_tokens_to_ids(self):
+    vocab_tokens = [
+        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+        "##ing"
+    ]
+
+    vocab = {}
+    for (i, token) in enumerate(vocab_tokens):
+      vocab[token] = i
+
+    self.assertAllEqual(
+        tokenization.convert_tokens_to_ids(
+            vocab, ["un", "##want", "##ed", "runn", "##ing"]), [7, 4, 5, 8, 9])
+
+  def test_is_whitespace(self):
+    self.assertTrue(tokenization._is_whitespace(u" "))
+    self.assertTrue(tokenization._is_whitespace(u"\t"))
+    self.assertTrue(tokenization._is_whitespace(u"\r"))
+    self.assertTrue(tokenization._is_whitespace(u"\n"))
+    self.assertTrue(tokenization._is_whitespace(u"\u00A0"))
+
+    self.assertFalse(tokenization._is_whitespace(u"A"))
+    self.assertFalse(tokenization._is_whitespace(u"-"))
+
+  def test_is_control(self):
+    self.assertTrue(tokenization._is_control(u"\u0005"))
+
+    self.assertFalse(tokenization._is_control(u"A"))
+    self.assertFalse(tokenization._is_control(u" "))
+    self.assertFalse(tokenization._is_control(u"\t"))
+    self.assertFalse(tokenization._is_control(u"\r"))
+
+  def test_is_punctuation(self):
+    self.assertTrue(tokenization._is_punctuation(u"-"))
+    self.assertTrue(tokenization._is_punctuation(u"$"))
+    self.assertTrue(tokenization._is_punctuation(u"`"))
+    self.assertTrue(tokenization._is_punctuation(u"."))
+
+    self.assertFalse(tokenization._is_punctuation(u"A"))
+    self.assertFalse(tokenization._is_punctuation(u" "))
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/TensorFlow/OpenSeq2Seq b/TensorFlow/OpenSeq2Seq
deleted file mode 160000
index 938d0c40..00000000
--- a/TensorFlow/OpenSeq2Seq
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 938d0c406ecfa92874c8b48eb71c6c7b48231566
diff --git a/TensorFlow/Recommendation/NCF/.dockerignore b/TensorFlow/Recommendation/NCF/.dockerignore
new file mode 100644
index 00000000..8fce6030
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/.dockerignore
@@ -0,0 +1 @@
+data/
diff --git a/TensorFlow/Recommendation/NCF/.gitignore b/TensorFlow/Recommendation/NCF/.gitignore
new file mode 100644
index 00000000..8fce6030
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/.gitignore
@@ -0,0 +1 @@
+data/
diff --git a/TensorFlow/Recommendation/NCF/Dockerfile b/TensorFlow/Recommendation/NCF/Dockerfile
new file mode 100644
index 00000000..e296a6d9
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/Dockerfile
@@ -0,0 +1,24 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FROM nvcr.io/nvidia/tensorflow:19.03-py3
+
+RUN apt-get update && \
+    apt-get install -y unzip
+
+ADD requirements.txt .
+RUN pip install -r requirements.txt
+
+ADD . /workspace/recommendation
+WORKDIR /workspace/recommendation
diff --git a/TensorFlow/Recommendation/NCF/LICENSE.md b/TensorFlow/Recommendation/NCF/LICENSE.md
new file mode 100644
index 00000000..b9829fb9
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/LICENSE.md
@@ -0,0 +1,203 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
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+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
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+   
+   Copyright 2018 NVIDIA Corporation
+
+   Copyright 2018 The MLPerf Authors
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/TensorFlow/Recommendation/NCF/NOTICE b/TensorFlow/Recommendation/NCF/NOTICE
new file mode 100644
index 00000000..f61b74c9
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/NOTICE
@@ -0,0 +1,4 @@
+NCF TensorFlow 
+
+This repository includes software from https://github.com/mlperf/results and 
+https://github.com/mlperf/training licensed under the Apache License, Version 2.0 (the "License")
diff --git a/TensorFlow/Recommendation/NCF/README.md b/TensorFlow/Recommendation/NCF/README.md
new file mode 100644
index 00000000..2e1677d1
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/README.md
@@ -0,0 +1,531 @@
+# Neural Collaborative Filtering (NCF) for TensorFlow
+
+This repository provides a script and recipe to train Neural Collaborative Filtering to achieve state of the art
+accuracy, and is tested and maintained by NVIDIA.
+
+## Table Of Contents
+* [The Model](#the-model)
+  * [Default Configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick Start Guide](#quick-start-guide)
+* [Details](#details)
+  * [Command Line Arguments](#command-line-arguments)
+  * [Getting the Data](#getting-the-data)
+  * [Other Datasets](#other-datasets)
+  * [Training Process](#training-process)
+  * [Enabling Mixed Precision](#enabling-mixed-precision)
+  * [Evaluation Process](#evaluation-process)
+* [Benchmarking](#benchmarking)
+  * [Performance Benchmark](#performance-benchmark)
+* [Results](#results)
+  * [Training Accuracy Results](#training-accuracy-results)
+  * [Training Performance Results](#training-performance-results)
+  * [Inference Performance Results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known Issues](#known-issues)
+
+## The Model
+
+The Neural Collaborative Filtering (NCF) model is a neural network that provides collaborative filtering based on
+implicit feedback, specifically, it provides product recommendations based on user and item interactions.  The training
+data for this model should contain a sequence of user ID, item ID pairs indicating that the specified user has
+interacted with, for example, was given a rating to or clicked on, the specified item. NCF was first described by
+Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua in the [Neural Collaborative Filtering
+paper](https://arxiv.org/abs/1708.05031).
+
+The implementation in this repository focuses on the NeuMF instantiation of the NCF architecture. We modified it to use
+dropout in the FullyConnected layers. This reduces overfitting and increases the final accuracy. Training the other two
+instantiations of NCF (GMF and MLP) is not supported.
+
+Contrary to the original paper, we benchmark the model on the larger [ml-20m
+dataset](https://grouplens.org/datasets/movielens/20m/) instead of using the smaller
+[ml-1m dataset](https://grouplens.org/datasets/movielens/1m/) as we think this is more realistic of production type
+environments. However, using the ml-1m dataset is also supported.
+
+This model takes advantage of the mixed precision tensor cores found on Volta GPUs, demonstrating the reduction in
+training time possible by leveraging tensor cores. On a single GPU configuration, training times can be improved close
+to 1.6x through the usage of tensor cores.
+
+This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
+
+### Default Configuration
+
+The model takes in a sequence of user ID and item ID pairs as inputs, then feeds them separately into a matrix
+factorization step (where the embeddings are multiplied) and a multilayer perceptron (MLP) network.
+
+The outputs of the matrix factorization and the MLP network are then combined and fed into a single dense layer which
+predicts whether the input user is likely to interact with the input item. The architecture of the MLP network is shown
+below.
+
+<p align="center">
+   <img width="70%" src="./img/ncf_diagram.png" />
+   <br>
+   Figure 1. The architecture of a Neural Collaborative Filtering model. Taken from the <a href="https://arxiv.org/abs/1708.05031">Neural Collaborative Filtering paper</a>.
+</p>
+
+This implementation is implemented with the following features:
+
+- model-parallel multi-gpu training with Horovod
+- mixed precision training with TF-AMP (TensorFlow-Automatic Mixed Precision), which enables mixed precision training
+  without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an
+  environmental variable
+- fast negative sample generation and data preprocessing with CuPy
+    - Before each training epoch, the training data is augmented with randomly generated negatives samples. A “shortcut” is
+      enabled by default where the script does not verify that the randomly generated samples are actually negative samples.
+      We have found that this shortcut has a low impact on model accuracy while considerably improving the speed and memory
+      footprint of the data augmentation stage of training. 
+    - Note: The negative samples generated for the test set are always verified regardless if the shortcut is enabled or
+      not.
+
+## Setup
+
+The following section lists the requirements in order to start training the NCF model.
+
+### Requirements
+
+This repository contains a `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies.
+Aside from these dependencies, ensure you have the following components:
+
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [TensorFlow 19.03-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
+* [NVIDIA Volta based GPU](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
+
+For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud
+Documentation and the Deep Learning Documentation:
+
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
+* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+* Running [TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)
+
+## Quick Start Guide
+
+To train your model using mixed precision with tensor cores or using FP32, perform the following steps using the default
+parameters of the NCF model on the ml-20m dataset.
+
+### 1. Clone this repository
+
+```bash
+git clone https://github.com/NVIDIA/DeepLearningExamples
+cd DeepLearningExamples/TensorFlow/Recommendation/NCF
+```
+
+### 2. Build the NCF TensorFlow NGC container.
+
+After Docker is correctly set up, you can build the NCF image with:
+
+```bash
+docker build . -t nvidia_ncf
+``` 
+
+### 3. Launch the NCF TensorFlow Docker container.
+
+```bash
+mkdir data
+docker run --runtime=nvidia -it --rm --ipc=host -v ${PWD}/data:/data nvidia_ncf bash
+```
+
+This will launch the container and mount the ./data directory as a volume to the /data directory inside the container.
+Any datasets and experiment results (logs, checkpoints etc.) saved to /data will be accessible in the ./data directory
+on the host.
+
+### 4. Download and preprocess the dataset.
+
+Preprocessing consists of downloading the data, filtering out users that have less than 20 ratings (by default), sorting
+the data and dropping the duplicates. No data augmentation techniques are used in the preprocessing stage.
+
+To download and preprocess the ml-20m dataset, run:
+
+```bash
+./prepare_dataset.sh
+```
+
+##### ml-1m
+
+To download and preprocess the ml-1m dataset, run:
+
+```bash
+./prepare_dataset.sh ml-1m
+```
+
+This will store the preprocessed training and evaluation data in the `/data` directory, so that it can be later used to
+train the model (by passing the appropriate `--data` argument to the `ncf.py` script).
+
+### 5. Start training.
+
+After the Docker container is launched, the training with the default hyper-parameters can be started with:
+
+```bash
+numgpu=4
+datadir=/data/cache/ml-20m
+mpirun -np $numgpu \
+    --allow-run-as-root \
+    python ncf.py --data $datadir
+```
+
+After the training is complete, the model parameters that provide the best evaluation accuracy are saved to the
+directory passed to the `--checkpoint-dir` argument. By default, this will be in the `/data/checkpoints/` directory.
+
+### 6. Start validation/evaluation.
+
+To run evaluation on a specific checkpoint, simply run the following command:
+
+```bash
+checkpoint=/data/checkpoints/model.ckpt
+python ncf.py --data /data/cache/ml-20m --mode test --checkpoint-dir $checkpoint
+```
+
+Note: TensorFlow checkpoints consist of 3 files each with a `*.ckpt` prefix.
+
+## Details
+
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+### Command Line Arguments
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for
+example: 
+
+```bash
+python ncf.py --help
+```
+
+Aside from options to set hyperparameters, the relevant options to control the behaviour of the script are: 
+
+```
+--data DATA           path to test and training data files
+-e EPOCHS, --epochs EPOCHS
+                      number of epochs to train for
+-b BATCH_SIZE, --batch-size BATCH_SIZE
+                      number of examples for each iteration
+--valid-users-per-batch VALID_USERS_PER_BATCH
+                      Number of users tested in each evaluation batch
+-n NEGATIVE_SAMPLES, --negative-samples NEGATIVE_SAMPLES
+                      number of negative examples per interaction
+-k TOPK, --topk TOPK  rank for test examples to be considered a hit
+--fp16                enable half-precision computations using automatic
+                      mixed precision (only available in supported
+                      containers)
+--xla                 enable TensorFlow XLA (Accelerated Linear Algebra)
+--valid-negative VALID_NEGATIVE
+                      Number of negative samples for each positive test
+                      example
+--loss-scale LOSS_SCALE
+                      Loss scale value to use when manually enabling mixed precision training
+--checkpoint-dir CHECKPOINT_DIR
+                      Path to store the result checkpoint file for training, or to read from for evaluation
+--mode {train,test}   Passing "test" will only run a single evaluation,
+                      otherwise full training will be performed
+--no-neg-trick        do not use negative sample generation shortcut to
+                      speed up preprocessing (will increase GPU memory
+                      consumption)
+--eval-after EVAL_AFTER
+                      Perform evaluations only after this many epochs
+--verbose             Log the performance and accuracy after every epoch
+
+```
+
+### Getting the Data
+
+For each user, the test dataset is generated by removing one movie the user has
+interacted with. For each removed movie, the data is augmented with a large
+number of movies (corresponding to the `--valid-negative option`) that the user
+has not interacted with.
+
+The repository contains the `prepare_dataset.sh` download script which will
+automatically call `download_dataset.sh` to download the desired dataset, and
+then preprocess the training and test datasets. By default, data will be
+downloaded to the `/data` directory.
+
+##### Other Datasets
+
+This implementation is tuned for the ml-20m and ml-1m datasets.  Using other
+datasets might require tuning some hyperparameters (for example, learning rate,
+beta1, beta2).
+
+If you'd like to use your custom dataset, you can do so by adding support for
+it in the `prepare_dataset.sh` and `download_dataset.sh` scripts. The required
+format of the data is a CSV file which should follow the pattern outlined
+below:
+
+```
+userId, movieId
+1,2
+1,10
+...
+```
+
+The CSV file may contain additional columns with extra features such as ratings
+and timestamps, but only the `userId` and `movieId` columns are required.
+
+The performance of the model depends on the dataset size. Generally, the model
+should scale better for datasets containing more data points. For a smaller
+dataset, you might experience slower performance as fixed cost operations that
+do not scale with input size will have a larger impact. Furthermore, it will be
+difficult for the model to converge.
+
+### Training Process
+
+The training can be launched with the `ncf.py` script. This script will train the
+NCF model for a number of epochs specified by the `--epochs` argument, which has
+a default value of 40.
+
+During training, the script will begin logging after the number of epochs
+specified by the `--eval-after` option. Once a new accuracy record has been set,
+the script will output a line like the one below:
+
+```
+New Best Epoch: 09, Train Time: 11.4197, Eval Time: 0.7425, HR: 0.9518, NDCG: 0.7341
+```
+
+If the `--verbose` option is set, then a line like the one below will be output
+at the end of each epoch:
+
+```
+Epoch: 08, Train Time: 2.6491, Eval Time: 0.1602, HR: 0.9566, NDCG: 0.7406
+```
+
+The evaluation metrics are HR (hit rate), and NDCG (normalized discounted
+cumulative gain). In the evaluation set, each user will be assigned one item
+that they have actually interacted with, and a number (by default 99) of items
+that they have not interacted with. For each user, the evaluation process will
+rank each of the items assigned to that user based on the user’s likeliness to
+interact with the items. The hit rate measures the percentage of users for
+which the item that they have interacted with is ranked within the top `k` items,
+where `k` is a number (by default 10) specified by the `-k` option. NDCG has a
+similar meaning, except the rank of the positive item is taken into account.
+Typically, HR is used as the primary evaluation metric.
+
+At the end of training, output similar to the following provides statistics
+regarding the training and evaluation throughputs, as well as the model
+accuracies:
+
+```
+Minimum Train Time per Epoch: 2.0085
+Average Train Time per Epoch: 2.0847
+Average Train Throughput:     47654877.9464
+Minimum Eval Time per Epoch:  0.1199
+Average Eval Time per Epoch:  0.1372
+Average Eval Throughput:      1030575.0538
+First Epoch to hit:           9
+Time to Train:                26.1703
+Best HR:                      0.9594
+Best Epoch:                   13
+```
+
+Additionally, the model parameters that give the best accuracy in validation
+will be stored at the directory pointed to by the `--checkpoint-dir` argument.
+
+Multiple GPUs can be used for training through Horovod. The number of GPUs can
+be controlled by the `-np` parameter passed to `mpirun`.
+
+##### Enabling Mixed Precision
+
+[Mixed Precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing
+operations in half-precision format, while storing information in single-precision to retain as much information as
+possible. Mixed precision is enabled in TensorFlow by using a custom variable getter that casts variables to
+half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small
+gradient magnitudes in backpropagation, a [loss
+scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included
+when applying gradients. In TensorFlow, loss scaling can be easily applied by using
+[LossScaleOptimizer](https://www.tensorflow.org/api_docs/python/tf/contrib/mixed_precision/LossScaleOptimizer) . The
+scaling value to be used can be
+[dynamic](https://www.tensorflow.org/api_docs/python/tf/contrib/mixed_precision/ExponentialUpdateLossScaleManager) or
+[fixed](https://www.tensorflow.org/api_docs/python/tf/contrib/mixed_precision/FixedLossScaleManager)
+
+Enabling mixed precision is now easier than ever with support for AMP in TensorFlow. TF-AMP is an extension of
+TensorFlow that enables mixed precision without any code changes. It accomplishes this by automatically rewriting all
+computation graphs with the necessary operations to enable mixed precision training and loss scaling. Currently, TF-AMP
+is only available through NVIDIA’s TensorFlow Docker container.
+
+TF-AMP is controlled by the `TF_ENABLE_AUTO_MIXED_PRECISION=1` environment variable; when set, TensorFlow will rewrite
+all graphs to perform computations in half-precision format and loss scaling will automatically be applied. 
+
+To enable mixed precision training using TF-AMP, the environment variable can be set prior to running `ncf.py`.
+Alternatively, `ncf.py` can be run with the `--fp16` flag.
+
+**Note:**  The `--fp16` flag sets the environment variable to the correct value
+for mixed precision training inside the script, for example:
+
+```
+# Note that the --fp16 flag maps to the amp variable in code
+if args.amp:
+    os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1" 
+```
+
+For more information about:
+* How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper
+  and the [Training With Mixed Precision documentation](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html).
+* How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)
+  from the TensorFlow User Guide.
+
+### Evaluation Process
+
+The evaluation process can be run by the ncf.py script as well. By passing the
+`--mode=test argument`, the script will run evaluation once using the TensorFlow
+checkpoint specified by the `--checkpoint-dir` file.
+
+
+The script will then output a line like the one below which describes the model accuracy:
+
+```
+Eval Time = 1.1829, HR@10 = 0.9574, NDCG@10 = 0.7420
+```
+
+## Benchmarking
+
+The following section shows how to run benchmarks measuring the model
+performance in training and inference modes.
+
+### Performance Benchmark
+
+To benchmark the training and inference performance, run: 
+
+```
+numgpu=4
+datadir=/data/cache/ml-20m
+mpirun -np $numgpu \
+    --allow-run-as-root \
+    python ncf.py --data $datadir
+```
+
+By default, the `ncf.py` script outputs metrics describing the following:
+
+* Training speed and throughput
+* Evaluation speed and throughput
+
+## Results
+
+The following sections provide details on how we achieved our performance and accuracy in training and inference.
+
+## Training Accuracy Results
+
+Our results were obtained by running the `ncf.py` training script in the
+TensorFlow 19.03-py3 NGC container on a NVIDIA DGX-1 with 8x V100 16G GPUs.
+Results for mixed precision were obtained using the `--fp16` flag.
+
+For each configuration, the `ncf.py` script was run 5 times each with different
+initial random seeds.  The maximum hit rate achieved among all 5 runs is
+recorded to demonstrate the maximum accuracy the model can achieve.
+
+| **Number of GPUs** | **Maximum HR achieved, FP16** | **Maximum HR achieved, FP32** |
+|:---:|:--------:|:-------:|
+| 1 | 0.9585 | 0.9592 |
+| 4 | 0.9589 | 0.9591 |
+| 8 | 0.9597 | 0.9598 |
+
+## Training Performance Results
+
+### NVIDIA DGX-1 (8x V100 16G)
+
+Our results were obtained by running the `ncf.py` training script in the
+TensorFlow 19.03-py3 NGC container on a NVIDIA DGX-1 with 8x V100 16GB GPUs
+with a consistent global batch size of 1048576 samples. Additionally, for
+multiple GPU configurations, a strong scaling strategy is used where the global
+batch size remains constant, as opposed to the more traditional weak scaling
+strategy where the local batch size is kept constant and the global batch size
+increases.  Strong scaling is required due to the model’s inability to converge
+at larger batch sizes. Results for mixed precision were obtained using the `--fp16` flag.
+
+For each configuration, the `ncf.py` script was run 5 times each with different
+initial random seeds.  The average training throughput among all 5 runs is
+recorded to demonstrate the expected training performance the model can
+achieve.
+
+| **Number of GPUs** | **FP16 items/sec** | **FP32 items/sec** | **FP16/FP32 speedup** |
+|:---:|:-------------:|:-----------:|:-----:|
+| 1 | 14,913,842 | 9,255,160 | 1.61x |
+| 4 | 39,507,815 | 29,632,703 | 1.33x |
+| 8 | 59,462,515 | 49,636,357 | 1.20x |
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+The performance was measured by the wall clock time over one training epoch.
+The number of samples in the epoch (roughly 100 million samples), was then
+divided by the average training duration to obtain the items per second metric.
+
+Those results can be improved when [XLA](https://www.tensorflow.org/xla) is used 
+in conjunction with mixed precision, delivering up to 2.6x speedup over FP32 on a single GPU (~24.3M items/sec). 
+However XLA is still considered experimental.
+
+### NVIDIA DGX-1 (8x V100 32G)
+
+Our results were obtained by running the `ncf.py` training script in the
+TensorFlow 19.03-py3 NGC container on a NVIDIA DGX-1 with 8x V100 32G GPUs with
+a consistent global batch size of 1048576 samples. Strong scaling is required
+due to the model’s inability to converge at larger batch sizes.
+
+For each configuration, the `ncf.py` script was run 5 times each with different
+initial random seeds.  The average training throughput among all 5 runs is
+recorded to demonstrate the expected training performance the model can
+achieve.
+
+| **Number of GPUs** | **FP16 items/sec** | **FP32 items/sec** | **FP16/FP32 speedup** |
+|:---:|:-------------:|:-----------:|:-----:|
+| 1 | 14,150,737 | 8,936,983 | 1.58x |
+| 4 | 37,770,501 | 28,848,636 | 1.31x |
+| 8 | 55,563,205 | 47,057,615 | 1.18x |
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
+
+The performance was measured by the wall clock time over one training epoch.
+The number of samples in the epoch (roughly 100 million samples), was then
+divided by the average training duration to obtain the items per second metric.
+
+## Inference Performance Results
+
+Our results were obtained by running the `ncf.py` training script in the
+TensorFlow 19.03-py3 NGC container on a NVIDIA DGX-1 with 1x V100 16G GPUs.
+
+For each configuration, the `ncf.py` script was run 5 times each with different
+initial random seeds.  The average inference throughput among all 5 runs is
+recorded to demonstrate the expected inference performance the model can
+achieve.
+
+| **Number of GPUs** | **FP16 items/sec** | **FP32 items/sec** | **FP16/FP32 speedup** |
+|:---:|:-------------:|:-----------:|:-----:|
+| 1 | 29,248,168 | 19,718,807 | 1.48x |
+| 4 | 88,255,971 | 66,625,422 | 1.32x |
+| 8 | 119,159,304 | 100,117,608 | 1.19x |
+
+## Changelog
+
+March 2019
+* Initial Release
+
+## Known Issues 
+
+### Multi-GPU Scaling Efficiency
+
+Currently, this model does not exhibit good scaling efficiency when scaling to
+4 and 8 GPUs.  Since we could not find hyper-parameters that could hit the
+target accuracy for batch size of over 1 million samples, we elected to use a
+strong scaling strategy which generally has worse scaling efficiency compared
+to a more common weak scaling strategy. Additionally, we believe that the small
+dataset size does not facilitate great scaling. However, the training scripts
+allow the use of custom datasets provided they are in the correct format.
+
+### Scaling beyond 8 GPUs
+
+Neural Collaborative Filtering (NCF) is a relatively lightweight model that
+trains quickly with this relatively smaller dataset, ml-20m. Because of the
+smaller dataset, the high ratio of communication to computation makes it
+difficult to efficiently use more than 8 GPUs. Typically, this is not an issue
+because when using 8 GPUs with FP16 precision the training is sufficiently
+fast. However, if you would like to scale the training to 16 GPUs and beyond,
+you might try modifying the model so that the communication to computation
+ratio facilitates better scaling. This could be done, for example, by finding
+hyper-parameters that enable using a larger global batch size.
+
+### Preprocessing Out-of-Memory with 16GB GPUs
+
+When running on GPUs with 16GB of memory, ensure the `--no-neg-trick` flag is
+**not** set. Otherwise, the data augmentation stage of training will consume
+too much GPU memory, causing TensorFlow to raise an out-of-memory error.
+
+This flag, when it is not set, reduces memory consumption in the negative
+samples generation phase of training by telling the script not to verify that
+the randomly generated samples are actually negative samples (verification
+still occurs for negative samples generated for the test set). Therefore, there
+is no need to keep the data structures used to verify negative samples in
+memory during training.
diff --git a/TensorFlow/Recommendation/NCF/convert.py b/TensorFlow/Recommendation/NCF/convert.py
new file mode 100644
index 00000000..918a1a3b
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/convert.py
@@ -0,0 +1,88 @@
+# Copyright (c) 2018, deepakn94, codyaustun, robieta. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# -----------------------------------------------------------------------
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from argparse import ArgumentParser
+import pandas as pd
+from load import implicit_load
+
+from logger.logger import LOGGER
+from logger import tags
+
+import tensorflow as tf
+
+MIN_RATINGS = 20
+USER_COLUMN = 'user_id'
+ITEM_COLUMN = 'item_id'
+
+LOGGER.model = 'ncf'
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('--path', type=str, default='/data/ml-20m/ratings.csv',
+                        help='Path to reviews CSV file from MovieLens')
+    parser.add_argument('--output', type=str, default='/data',
+                        help='Output directory for train and test files')
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+
+    print("Loading raw data from {}".format(args.path))
+    df = implicit_load(args.path, sort=False)
+
+    print("Filtering out users with less than {} ratings".format(MIN_RATINGS))
+    grouped = df.groupby(USER_COLUMN)
+    LOGGER.log(key=tags.PREPROC_HP_MIN_RATINGS, value=MIN_RATINGS)
+    df = grouped.filter(lambda x: len(x) >= MIN_RATINGS)
+
+    print("Mapping original user and item IDs to new sequential IDs")
+    df[USER_COLUMN] = pd.factorize(df[USER_COLUMN])[0]
+    df[ITEM_COLUMN] = pd.factorize(df[ITEM_COLUMN])[0]
+
+    print("Creating list of items for each user")
+    # Need to sort before popping to get last item
+    df.sort_values(by='timestamp', inplace=True)
+
+    # clean up data
+    del df['rating'], df['timestamp']
+    df = df.drop_duplicates() # assuming it keeps order
+
+    # now we have filtered and sorted by time data, we can split test data out
+    grouped_sorted = df.groupby(USER_COLUMN, group_keys=False)
+    test_data = grouped_sorted.tail(1).sort_values(by='user_id')
+    # need to pop for each group
+    train_data = grouped_sorted.apply(lambda x: x.iloc[:-1])
+    train_data = train_data.sort_values([USER_COLUMN, ITEM_COLUMN])
+
+    train_data.to_pickle(args.output + '/train_ratings.pickle')
+    test_data.to_pickle(args.output + '/test_ratings.pickle')
+
+if __name__ == '__main__':
+    main()
diff --git a/TensorFlow/Recommendation/NCF/download_dataset.sh b/TensorFlow/Recommendation/NCF/download_dataset.sh
new file mode 100755
index 00000000..526f6ac7
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/download_dataset.sh
@@ -0,0 +1,25 @@
+DATASET_NAME=$1
+RAW_DATADIR=$2
+
+function download_20m {
+	echo "Download ml-20m"
+	curl -O http://files.grouplens.org/datasets/movielens/ml-20m.zip
+	mv ml-20m.zip ${RAW_DATADIR}
+}
+
+function download_1m {
+	echo "Downloading ml-1m"
+	curl -O http://files.grouplens.org/datasets/movielens/ml-1m.zip
+	mv ml-1m.zip ${RAW_DATADIR}
+}
+
+if [[ ${DATASET_NAME} == "ml-1m" ]]
+then
+	download_1m
+elif [[ ${DATASET_NAME} == "ml-20m" ]]
+then
+    download_20m
+else
+	echo "Unsupported dataset name: $DATASET_NAME"
+	exit 1
+fi
diff --git a/TensorFlow/Recommendation/NCF/img/dgx1v16_curve.png b/TensorFlow/Recommendation/NCF/img/dgx1v16_curve.png
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diff --git a/TensorFlow/Recommendation/NCF/input_pipeline.py b/TensorFlow/Recommendation/NCF/input_pipeline.py
new file mode 100644
index 00000000..39f3909d
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/input_pipeline.py
@@ -0,0 +1,172 @@
+import numpy as np
+import tensorflow as tf
+import cupy as cp
+
+def generate_negatives(neg_users, true_mat, item_range, sort=False, use_trick=False):
+    """ 
+    Generate negative samples for data augmentation
+    """
+    neg_u = []
+    neg_i = []
+
+    # If using the shortcut, generate negative items without checking if the associated
+    # user has interacted with it. Speeds up training significantly with very low impact
+    # on accuracy.
+    if use_trick:
+        neg_items = cp.random.randint(0, high=item_range, size=neg_users.shape[0])
+        return neg_users, neg_items
+
+    # Otherwise, generate negative items, check if associated user has interacted with it,
+    # then generate a new one if true
+    while len(neg_users) > 0:
+        neg_items = cp.random.randint(0, high=item_range, size=neg_users.shape[0])
+        neg_mask = true_mat[neg_users, neg_items]
+        neg_u.append(neg_users[neg_mask])
+        neg_i.append(neg_items[neg_mask])
+
+        neg_users = neg_users[cp.logical_not(neg_mask)]
+
+    neg_users = cp.concatenate(neg_u)
+    neg_items = cp.concatenate(neg_i)
+
+    if sort == False:
+        return neg_users, neg_items
+
+    sorted_users = cp.sort(neg_users)
+    sort_indices = cp.argsort(neg_users)
+
+    return sorted_users, neg_items[sort_indices]
+
+class DataGenerator():
+    """
+    Class to handle data augmentation
+    """
+    def __init__(self,
+                 seed,
+                 hvd_rank,
+                 num_users,                 # type: int
+                 num_items,                 # type: int
+                 neg_mat,                   # type: np.ndarray
+                 train_users,               # type: np.ndarray
+                 train_items,               # type: np.ndarray
+                 train_labels,              # type: np.ndarray
+                 train_batch_size,          # type: int
+                 train_negative_samples,    # type: int
+                 pos_eval_users,            # type: np.ndarray
+                 pos_eval_items,            # type: np.ndarray
+                 eval_users_per_batch,      # type: int
+                 eval_negative_samples,     # type: int
+                 use_neg_trick=False,       # type: bool
+                ):
+        # Check input data
+        if train_users.shape != train_items.shape:
+            raise ValueError(
+                "Train shapes mismatch! {} Users vs {} Items!".format(
+                    train_users.shape, train_items.shape))
+        if pos_eval_users.shape != pos_eval_items.shape:
+            raise ValueError(
+                "Eval shapes mismatch! {} Users vs {} Items!".format(
+                    pos_eval_users.shape, pos_eval_items.shape))
+        
+        np.random.seed(seed)
+        cp.random.seed(seed)
+        # Use GPU assigned to the horovod rank
+        self.hvd_rank = hvd_rank
+        cp.cuda.Device(self.hvd_rank).use()
+
+        self.num_users = num_users
+        self.num_items = num_items
+        self._neg_mat = neg_mat
+        self._train_users = cp.array(train_users)
+        self._train_items = cp.array(train_items)
+        self._train_labels = cp.array(train_labels)
+        self.train_batch_size = train_batch_size
+        self._train_negative_samples = train_negative_samples
+        self._pos_eval_users = pos_eval_users
+        self._pos_eval_items = pos_eval_items
+        self.eval_users_per_batch = eval_users_per_batch
+        self._eval_negative_samples = eval_negative_samples
+        self.use_neg_trick = use_neg_trick
+
+        # Eval data
+        self.eval_users = None
+        self.eval_items = None
+        self.dup_mask = None
+
+        # Training data
+        self.train_users_batches = None
+        self.train_items_batches = None
+        self.train_labels_batches = None
+
+    # Augment test data with negative samples
+    def prepare_eval_data(self):
+        pos_eval_users = cp.array(self._pos_eval_users)
+        pos_eval_items = cp.array(self._pos_eval_items)
+
+        neg_mat = cp.array(self._neg_mat)
+
+        neg_eval_users_base = cp.repeat(pos_eval_users, self._eval_negative_samples)
+
+        # Generate negative samples
+        test_u_neg, test_i_neg = generate_negatives(
+            neg_eval_users_base, neg_mat, self.num_items, True
+        )
+        test_u_neg = test_u_neg.reshape((-1, self._eval_negative_samples)).get()
+        test_i_neg = test_i_neg.reshape((-1, self._eval_negative_samples)).get()
+
+        test_users = self._pos_eval_users.reshape((-1, 1))
+        test_items = self._pos_eval_items.reshape((-1, 1))
+        # Combine positive and negative samples
+        test_users = np.concatenate((test_u_neg, test_users), axis=1)
+        test_items = np.concatenate((test_i_neg, test_items), axis=1)
+
+        # Generate duplicate mask
+        ## Stable sort indices by incrementing all values with fractional position
+        indices = np.arange(test_users.shape[1]).reshape((1, -1)).repeat(test_users.shape[0], axis=0)
+        summed_items = np.add(test_items, indices/test_users.shape[1])
+        sorted_indices = np.argsort(summed_items, axis=1)
+        sorted_order = np.argsort(sorted_indices, axis=1)
+        sorted_items = np.sort(test_items, axis=1)
+        ## Generate duplicate mask
+        dup_mask = np.equal(sorted_items[:,0:-1], sorted_items[:,1:])
+        dup_mask = np.concatenate((dup_mask, np.zeros((test_users.shape[0], 1))), axis=1)
+        r_indices = np.arange(test_users.shape[0]).reshape((-1, 1)).repeat(test_users.shape[1], axis=1)
+        dup_mask = dup_mask[r_indices, sorted_order].astype(np.float32)
+
+        # Reshape all to (-1) and split into chunks
+        batch_size = self.eval_users_per_batch * test_users.shape[1]
+        split_indices = np.arange(batch_size, test_users.shape[0]*test_users.shape[1], batch_size)
+        self.eval_users = np.split(test_users.reshape(-1), split_indices)
+        self.eval_items = np.split(test_items.reshape(-1), split_indices)
+        self.dup_mask = np.split(dup_mask.reshape(-1), split_indices)
+
+        # Free GPU memory to make space for Tensorflow
+        cp.get_default_memory_pool().free_all_blocks()
+
+    # Augment training data with negative samples
+    def prepare_train_data(self):
+        batch_size = self.train_batch_size
+
+        is_neg = cp.logical_not(self._train_labels)
+
+        # Do not store verification matrix if using the negatives generation shortcut
+        neg_mat = None if self.use_neg_trick else cp.array(self._neg_mat)
+
+        # If there are no negative samples in the local portion of the training data, do nothing
+        any_neg = cp.any(is_neg)
+        if any_neg:
+            self._train_users[is_neg], self._train_items[is_neg] = generate_negatives(
+                self._train_users[is_neg], neg_mat, self.num_items, use_trick=self.use_neg_trick
+            )
+
+        shuffled_order = cp.random.permutation(self._train_users.shape[0])
+        self._train_users = self._train_users[shuffled_order]
+        self._train_items = self._train_items[shuffled_order]
+        self._train_labels = self._train_labels[shuffled_order]
+        is_neg = cp.logical_not(self._train_labels)
+        
+        # Manually create batches
+        split_indices = np.arange(batch_size, self._train_users.shape[0], batch_size)
+        self.train_users_batches = np.split(self._train_users, split_indices)
+        self.train_items_batches = np.split(self._train_items, split_indices)
+        self.train_labels_batches = np.split(self._train_labels, split_indices)
diff --git a/TensorFlow/Recommendation/NCF/load.py b/TensorFlow/Recommendation/NCF/load.py
new file mode 100644
index 00000000..618f02be
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/load.py
@@ -0,0 +1,70 @@
+# Copyright (c) 2018, deepakn94. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import namedtuple
+
+import pandas as pd
+
+
+RatingData = namedtuple('RatingData',
+                        ['items', 'users', 'ratings', 'min_date', 'max_date'])
+
+
+def describe_ratings(ratings):
+    info = RatingData(items=len(ratings['item_id'].unique()),
+                      users=len(ratings['user_id'].unique()),
+                      ratings=len(ratings),
+                      min_date=ratings['timestamp'].min(),
+                      max_date=ratings['timestamp'].max())
+    print("{ratings} ratings on {items} items from {users} users"
+          " from {min_date} to {max_date}"
+          .format(**(info._asdict())))
+    return info
+
+
+def process_movielens(ratings, sort=True):
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    if sort:
+        ratings.sort_values(by='timestamp', inplace=True)
+    describe_ratings(ratings)
+    return ratings
+
+
+def load_ml_1m(filename, sort=True):
+    names = ['user_id', 'item_id', 'rating', 'timestamp']
+    ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
+    return process_movielens(ratings, sort=sort)
+
+
+def load_ml_20m(filename, sort=True):
+    ratings = pd.read_csv(filename)
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    names = {'userId': 'user_id', 'movieId': 'item_id'}
+    ratings.rename(columns=names, inplace=True)
+    return process_movielens(ratings, sort=sort)
+
+
+DATASETS = [k.replace('load_', '') for k in locals().keys() if "load_" in k]
+
+
+def get_dataset_name(filename):
+    for dataset in DATASETS:
+        if dataset in filename.replace('-', '_').lower():
+            return dataset
+    raise NotImplementedError
+
+
+def implicit_load(filename, sort=True):
+    func = globals()["load_" + get_dataset_name(filename)]
+    return func(filename, sort=sort)
diff --git a/TensorFlow/Recommendation/NCF/logger/analyzer.py b/TensorFlow/Recommendation/NCF/logger/analyzer.py
new file mode 100644
index 00000000..6be19503
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/logger/analyzer.py
@@ -0,0 +1,131 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+from collections import defaultdict
+import json
+
+from logger import logger as nvl
+from logger.parser import NVLogParser
+from logger import tags
+
+
+def collect_by_scope(loglines):
+
+    # dict to gather run scope results
+    run_stats = dict()
+    epoch_stats = dict()
+    iteration_stats = dict()
+
+    # TODO: check if there is only one tag per run scope
+    # gather all lines with run_scope events & variables
+    run_events = dict((l.tag, l) for l in loglines if l.scope == nvl.RUN_SCOPE)
+
+    # gather all variable tags
+    run_variables = dict(k for k in run_events.items() if k[1].value is not None)
+
+    # find all time block names
+    timed_blocks = [k[:-6] for k in run_events if k.endswith('_start')]
+
+    # measure times for the run scope
+    for prefix in timed_blocks:
+        # only when both start & stop are found
+        # TODO: assert when not paired
+        if prefix + "_start" in run_events and prefix + "_stop" in run_events:
+            start = run_events[prefix + "_start"].timestamp
+            stop = run_events[prefix + "_stop"].timestamp
+            run_stats[prefix + "_time"] = stop - start
+
+    # collect all variables - even nested
+    for k in run_variables:
+        e = run_events[k]
+        if isinstance(e.value, dict):
+            for d in e.value.keys():
+                run_stats[k + "_" + d] = e.value[d]
+        else:
+            run_stats[k] = e.value
+
+    # find epochs
+    epochs = sorted(list({int(l.epoch) for l in loglines if int(l.epoch) >= 0}))
+    epoch_stats['x'] = epochs
+
+    # gather eval_accuracy
+    eval_accuracy_dup = [l.value for l in loglines if l.tag == tags.EVAL_ACCURACY]
+    eval_accuracy = list({l['value']:l for l in eval_accuracy_dup})
+    epoch_stats['eval_accuracy'] = eval_accuracy
+
+    # gather it_per_sec
+    eval_it_per_sec = [l.value for l in loglines if l.tag == tags.PERF_IT_PER_SEC]
+    #eval_it_per_sec = list({l['value']:l for l in eval_it_per_sec_dup})
+    epoch_stats['it_per_sec'] = eval_it_per_sec
+
+
+    # gather all epoch-iter tuples
+    # TODO: l.iteration is always set to -1 in parser.py
+    all_iterations = {(int(l.epoch), int(l.iteration)) for l in loglines if int(l.iteration) >= 0}
+
+    # group by epoch
+    collected_iterations = defaultdict(list)
+    for el in all_iterations:
+        collected_iterations[el[0]].append(el[1])
+
+    # convert to list of lists
+    iterations = [sorted(collected_iterations[k]) for k in sorted(collected_iterations.keys())]
+    iteration_stats['x'] = iterations
+
+    # gather all epoch-iter-loss triples
+    all_loss_dicts = [l.value for l in loglines if l.tag == tags.TRAIN_ITERATION_LOSS]
+    all_loss = {(l['epoch'], l['iteration'], l['value']) for l in all_loss_dicts}
+
+    # group by epoch
+    collected_loss = defaultdict(list)
+    for el in all_loss:
+        collected_loss[el[0]].append(el[2])
+
+    # convert to list of lists
+    iterations_loss = [sorted(collected_loss[k]) for k in sorted(collected_loss.keys())]
+    iteration_stats['loss'] = iterations_loss
+
+    # find epoch events and variables
+    epoch_events = [l for l in loglines if l.scope == nvl.EPOCH_SCOPE]
+    epoch_event_names = {l.tag for l in epoch_events}
+    epoch_timed_blocks = {k[:-6] for k in epoch_event_names if k.endswith('_start')}
+    epoch_variables = {l.tag for l in epoch_events if l.value is not None}
+
+    # TODO: WIP
+
+
+    return {"run" : run_stats, "epoch": epoch_stats, "iter" : iteration_stats}
+
+
+def analyze(input_path, output_path=None):
+    parser = NVLogParser()
+    loglines, errors, worker_loglines = parser.parse_file(input_path)
+
+    stats = collect_by_scope(worker_loglines['(0)'])
+
+    if not output_path:
+        print(json.dumps(stats, indent=4))
+    else:
+        with open(output_path, 'w') as f:
+            json.dump(obj=stats, fp=f, indent=4)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        print('usage: analyzer.py FILENAME')
+        print('       tests analyzing on the file.')
+        sys.exit(1)
+
+    analyze(input_path=sys.argv[1], output_path=None)
+
diff --git a/TensorFlow/Recommendation/NCF/logger/autologging.py b/TensorFlow/Recommendation/NCF/logger/autologging.py
new file mode 100644
index 00000000..a831a5b1
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/logger/autologging.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import subprocess
+import xml.etree.ElementTree as ET
+
+from logger.logger import LOGGER
+
+#TODO: print CUDA version, container version etc
+
+def log_hardware():
+    # TODO: asserts - what if you cannot launch those commands?
+    # number of CPU threads
+    cpu_info_command = 'cat /proc/cpuinfo'
+    cpu_info = subprocess.run(cpu_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    cpu_num_index = len(cpu_info) - cpu_info[::-1].index(b'processor') + 1
+    cpu_num = int(cpu_info[cpu_num_index]) + 1
+
+    # CPU name
+    cpu_name_begin_index = cpu_info.index(b'name')
+    cpu_name_end_index = cpu_info.index(b'stepping')
+    cpu_name = b' '.join(cpu_info[cpu_name_begin_index + 2:cpu_name_end_index]).decode('utf-8')
+
+    LOGGER.log(key='cpu_info', value={"num": cpu_num, "name": cpu_name}, stack_offset=1)
+
+    # RAM memory
+    ram_info_command = 'free -m -h'
+    ram_info = subprocess.run(ram_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    ram_index = ram_info.index(b'Mem:') + 1
+    ram = ram_info[ram_index].decode('utf-8')
+
+    LOGGER.log(key='mem_info', value={"ram": ram}, stack_offset=1)
+
+    # GPU
+    nvidia_smi_command = 'nvidia-smi -q -x'
+    nvidia_smi_output = subprocess.run(nvidia_smi_command.split(), stdout=subprocess.PIPE).stdout
+    nvidia_smi = ET.fromstring(nvidia_smi_output)
+    gpus = nvidia_smi.findall('gpu')
+    ver = nvidia_smi.findall('driver_version')
+
+    LOGGER.log(key="gpu_info",
+                 stack_offset=1,
+                 value={
+                      "driver_version": ver[0].text,
+                      "num": len(gpus),
+                      "name": [g.find('product_name').text for g in gpus],
+                      "mem": [g.find('fb_memory_usage').find('total').text for g in gpus]})
+
+def log_args(args):
+    LOGGER.log(key='args', value=vars(args), stack_offset=1)
diff --git a/TensorFlow/Recommendation/NCF/logger/logger.py b/TensorFlow/Recommendation/NCF/logger/logger.py
new file mode 100644
index 00000000..2caac6c8
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/logger/logger.py
@@ -0,0 +1,175 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import time
+import json
+import logging
+import os
+import inspect
+import sys
+import re
+from contextlib import contextmanager
+import functools
+
+
+NVLOGGER_VERSION='0.1.0'
+NVLOGGER_TOKEN= ':::NVLOG'
+NVLOGGER_NAME="nv_dl_logger"
+NVLOGGER_FILE_NAME="nv_dl_logger"
+
+RUN_SCOPE = 0
+EPOCH_SCOPE = 1
+TRAIN_ITER_SCOPE = 2
+EVAL_ITER_SCOPE = 3
+
+LOGGING_SCOPE = {
+    RUN_SCOPE,
+    EPOCH_SCOPE,
+    TRAIN_ITER_SCOPE,
+    EVAL_ITER_SCOPE
+}
+
+
+def get_caller(stack_index=2, root_dir=None):
+    caller = inspect.getframeinfo(inspect.stack()[stack_index][0])
+
+    # Trim the file names for readability.
+    filename = caller.filename
+    if root_dir is not None:
+        filename = re.sub("^" + root_dir + "/", "", filename)
+    return "%s:%d" % (filename, caller.lineno)
+
+
+class NVLogger(object):
+    __instance = None
+    token = NVLOGGER_TOKEN
+    version = NVLOGGER_VERSION
+    stack_offset = 0
+    extra_print = False
+    model = "NN"
+    root_dir = None
+    worker = [0]
+    prefix = ''
+    log_file = None
+    file_handler = None
+
+    @staticmethod
+    def get_instance():
+        if NVLogger.__instance is None:
+            NVLogger()
+
+        return NVLogger.__instance
+
+    def set_worker(self, worker):
+        if worker is None:
+            self.prefix = ''
+            self.worker = [0]
+        else:
+            self.prefix = json.dumps(worker)
+            self.worker = list(worker)
+
+    def set_file(self, file_name=None):
+
+        if file_name is None:
+            self.log_file = os.getenv(NVLOGGER_FILE_NAME)
+        else:
+            self.log_file = file_name
+
+        if self.log_file:
+            self.file_handler = logging.FileHandler(self.log_file)
+            self.file_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.file_handler)
+            self.stream_handler.setLevel(logging.INFO)
+        else:
+            self.stream_handler.setLevel(logging.DEBUG)
+
+    def __init__(self):
+
+        if NVLogger.__instance is None:
+            NVLogger.__instance = self
+        else:
+            raise Exception("This class is a singleton!")
+
+        self.logger = logging.getLogger(NVLOGGER_NAME)
+        self.logger.setLevel(logging.DEBUG)
+
+        self.stream_handler = logging.StreamHandler(stream=sys.stdout)
+        self.stream_handler.setLevel(logging.DEBUG)
+        self.logger.addHandler(self.stream_handler)
+
+    def print_vars(self, variables, forced=False, stack_offset=0):
+        if isinstance(variables, dict):
+            for v in variables.keys():
+                self.log(key=v, value=variables[v], forced=forced, stack_offset=stack_offset+1)
+
+    def print_vars2(self, key, variables, forced=False, stack_offset=0):
+        if isinstance(variables, dict):
+            self.log(key=key, value=variables, forced=forced, stack_offset=stack_offset+1)
+
+    def log(self, key, value=None, forced=False, stack_offset=0):
+
+        # only the 0-worker will log
+        if not forced and self.worker != 0:
+            pass
+
+        if value is None:
+            msg = key
+        else:
+            str_json = json.dumps(value)
+            msg = '{key}: {value}'.format(key=key, value=str_json)
+
+        call_site = get_caller(2 + self.stack_offset + stack_offset, root_dir=self.root_dir)
+        now = time.time()
+
+        message = msg
+        if self.extra_print:
+            print()
+
+        self.logger.debug(message)
+
+
+LOGGER = NVLogger.get_instance()
+
+@contextmanager
+def timed_block(prefix, value=None, logger=LOGGER, forced=False, stack_offset=2):
+    """ This function helps with timed blocks
+        ----
+        Parameters:
+        prefix - one of items from TIMED_BLOCKS; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    if logger is None:
+        pass
+    logger.log(key=prefix + "_start", value=value, forced=forced, stack_offset=stack_offset)
+    yield logger
+    logger.log(key=prefix + "_stop", forced=forced, stack_offset=stack_offset)
+
+
+def timed_function(prefix, variable=None, forced=False):
+    """ This decorator helps with timed functions
+        ----
+        Parameters:
+        prefix - one of items from TIME_BLOCK; the action to be timed
+        logger - NVLogger object
+        forced - if True then the events are always logged (even if it should be skipped)
+    """
+    def timed_function_decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            logger = kwargs.get('logger', LOGGER)
+            value = kwargs.get(variable, next(iter(args), None))
+            with timed_block(prefix=prefix, logger=logger, value=value, forced=forced, stack_offset=3):
+                    func(*args, **kwargs)
+        return wrapper
+    return timed_function_decorator
diff --git a/TensorFlow/Recommendation/NCF/logger/parser.py b/TensorFlow/Recommendation/NCF/logger/parser.py
new file mode 100644
index 00000000..37bbe15e
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/logger/parser.py
@@ -0,0 +1,223 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import collections
+import json
+import re
+import sys
+from collections import defaultdict
+
+from logger import tags
+import logger.logger as nvl
+
+
+LogLine = collections.namedtuple('LogLine', [
+    'full_string',  # the complete line as a string
+    'worker',       # the worker id
+    'token',        # the token, i.e. ':::NVLOG'
+    'version_str',  # the version string, e.g. 'v0.1.0'
+    'model',        # the model, e.g. 'ncf'
+    'timestamp',    # seconds as a float, e.g. 1234.567
+    'filename',     # the which generated the log line, e.g. './convert.py'
+    'lineno',       # the line in the file which generated the log line, e.g. 119
+    'tag',          # the string tag
+    'value',        # the parsed value associated with the tag, or None if no value
+    'epoch',        # the epoch number of -1 if none
+    'iteration',    # the interation number of -1 if none
+    'scope'         # run, epoch, iteration, eval_iteration
+])
+
+
+def get_dict_value(x):
+    if isinstance(x, dict):
+        return x
+    return {"value": x}
+
+
+def get_value(x):
+    if isinstance(x, dict):
+        if "value" in x:
+            return x.get("value")
+        else:
+            return x
+    return x
+
+
+def get_named_value(x, name):
+    if isinstance(x, dict):
+        if name in x:
+            return x.get(name)
+        else:
+            return None
+    return x
+
+
+class NVLogParser(object):
+
+    def __init__(self, token=nvl.NVLOGGER_TOKEN, version=nvl.NVLOGGER_VERSION):
+
+        self.epoch = defaultdict(lambda: -1)
+        self.iteration = defaultdict(lambda: -1)
+        self.scope = defaultdict(lambda: 0)
+
+        self.version = version
+        self.token = token
+        self.line_pattern = (
+            '^'
+            '([\d]?)'                    # optional worker id (0)
+            '(' + token + ')'            # mandatory token (1)
+            'v([\d]+\.[\d+]\.[\d+])[ ]'  # mandatory version (2)
+            '([A-Za-z0-9_]+)[ ]'         # mandatory model (3)
+            '([\d\.]+)[ ]'               # mandatory timestamp (4)
+            '\(([^: ]+)'                 # mandatory file (5)
+            ':(\d+)\)[ ]'                # mandatory lineno (6)
+            '([A-Za-z0-9_]+)[ ]?'        # mandatory tag (7)
+            '(:\s+(.+))?'                # optional value (8)
+            '$'
+        )
+        # print(self.line_pattern)
+
+        self.line_regex = re.compile(self.line_pattern, re.X)
+
+    def string_to_logline(self, string):
+
+        m = self.line_regex.match(string)
+
+        if m is None:
+            raise ValueError('does not match regex')
+
+        args = [
+            m.group(0),  # full string
+        ]
+
+        # by default
+        worker = m.group(1)
+        if worker == "":
+            worker = "(0)"
+
+        args.append(worker)
+
+        args.append(m.group(2))  # token
+        args.append(m.group(3))  # version
+        args.append(m.group(4))  # model
+
+        try:
+            ts = float(m.group(5))      # parse timestamp
+            args.append(ts)
+        except ValueError:
+            raise ValueError('timestamp format incorrect')
+
+        args.append(m.group(6))         # file name
+
+        try:
+            lineno = int(m.group(7))    # may raise error
+            args.append(lineno)
+        except ValueError:
+            raise ValueError('line number format incorrect')
+
+        tag = m.group(8)
+        args.append(tag)         # tag
+
+        # 9th is ignored
+
+        value = m.group(10)
+
+        if value is not None:
+            j = json.loads(value)
+            args.append(j)
+        else:
+            # no Value
+            args.append(None)
+
+        # update processing state
+        if tag == tags.TRAIN_EPOCH_START or tag == tags.TRAIN_EPOCH:
+            self.epoch[worker] = get_named_value(value, tags.VALUE_EPOCH)
+            self.scope[worker] = nvl.EPOCH_SCOPE
+            self.iteration[worker] = -1
+
+        if tag == tags.TRAIN_EPOCH_STOP:
+            self.scope[worker] = nvl.RUN_SCOPE
+
+        if tag == tags.TRAIN_ITER_START:
+            self.iteration[worker] = get_named_value(value, tags.VALUE_ITERATION)
+            self.scope[worker] = nvl.TRAIN_ITER_SCOPE
+
+        if tag == tags.TRAIN_ITER_STOP:
+            self.scope[worker] = nvl.EPOCH_SCOPE
+
+        if tag == tags.PERF_IT_PER_SEC:
+            self.scope[worker] = nvl.EPOCH_SCOPE
+
+        if tag == tags.PERF_TIME_TO_TRAIN:
+            self.scope[worker] = nvl.RUN_SCOPE
+
+        args.append(self.epoch[worker])
+        args.append(self.iteration[worker])
+        args.append(self.scope[worker])
+
+        return LogLine(*args)
+
+    def parse_generator(self, gen):
+        worker_loglines = defaultdict(list)
+        loglines = []
+        failed = []
+
+        # state init for parsing
+        self.epoch.clear()
+        self.iteration.clear()
+        self.scope.clear()
+
+        for line in gen:
+            line = line.strip()
+            if line.find(self.token) == -1:
+                continue
+            try:
+                ll = self.string_to_logline(line)
+                worker_loglines[ll.worker].append(ll)
+                loglines.append(ll)
+            except ValueError as e:
+                failed.append((line, str(e)))
+
+        return loglines, failed, worker_loglines
+
+    def parse_file(self, filename):
+        with open(filename) as f:
+            return self.parse_generator(f)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        print('usage: parser.py FILENAME')
+        print('       tests parsing on the file.')
+        sys.exit(1)
+
+    filename = sys.argv[1]
+    parser = NVLogParser()
+    loglines, errors, worker_loglines = parser.parse_file(filename)
+
+    print('Parsed {} log lines with {} errors.'.format(len(loglines), len(errors)))
+    print('Found workers: {}.'.format(list(worker_loglines.keys())))
+
+    if len(errors) > 0:
+        print('Lines which failed to parse:')
+        for line, error in errors:
+            print('  Following line failed: {}'.format(error))
+            print(line)
+
+    if len(loglines) > 0:
+        print('Lines which where parsed sucessfully:')
+        for line in loglines:
+            print(line.full_string, " ---> ", line.epoch, line.iteration, line.scope)
+
diff --git a/TensorFlow/Recommendation/NCF/logger/tags.py b/TensorFlow/Recommendation/NCF/logger/tags.py
new file mode 100644
index 00000000..45edc4eb
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/logger/tags.py
@@ -0,0 +1,242 @@
+# Copyright 2018 MLBenchmark Group. All Rights Reserved.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Common values reported
+
+VALUE_EPOCH = "epoch"
+VALUE_ITERATION = "iteration"
+VALUE_ACCURACY = "accuracy"
+VALUE_BLEU = "bleu"
+VALUE_TOP1 = "top1"
+VALUE_TOP5 = "top5"
+VALUE_BBOX_MAP = "bbox_map"
+VALUE_MASK_MAP = "mask_map"
+VALUE_BCE = "binary_cross_entropy"
+
+
+# Timed blocks (used with timed_function & timed_block
+# For each there should be *_start and *_stop tags defined
+
+RUN_BLOCK = "run"
+SETUP_BLOCK = "setup"
+PREPROC_BLOCK = "preproc"
+
+TRAIN_BLOCK = "train"
+TRAIN_PREPROC_BLOCK = "train_preproc"
+TRAIN_EPOCH_BLOCK = "train_epoch"
+TRAIN_EPOCH_PREPROC_BLOCK = "train_epoch_preproc"
+TRAIN_CHECKPOINT_BLOCK = "train_checkpoint"
+TRAIN_ITER_BLOCK = "train_iteration"
+
+EVAL_BLOCK = "eval"
+EVAL_ITER_BLOCK = "eval_iteration"
+
+TIMED_BLOCKS = {
+    RUN_BLOCK,
+    SETUP_BLOCK,
+    PREPROC_BLOCK,
+    TRAIN_BLOCK,
+    TRAIN_PREPROC_BLOCK,
+    TRAIN_EPOCH_BLOCK,
+    TRAIN_EPOCH_PREPROC_BLOCK,
+    TRAIN_CHECKPOINT_BLOCK,
+    TRAIN_ITER_BLOCK,
+    EVAL_BLOCK,
+    EVAL_ITER_BLOCK,
+}
+
+
+# Events
+
+RUN_INIT = "run_init"
+
+SETUP_START = "setup_start"
+SETUP_STOP = "setup_stop"
+
+PREPROC_START = "preproc_start"
+PREPROC_STOP = "preproc_stop"
+
+RUN_START = "run_start"
+RUN_STOP = "run_stop"
+RUN_FINAL = "run_final"
+
+TRAIN_CHECKPOINT_START = "train_checkpoint_start"
+TRAIN_CHECKPOINT_STOP = "train_checkpoint_stop"
+
+TRAIN_PREPROC_START = "train_preproc_start"
+TRAIN_PREPROC_STOP = "train_preproc_stop"
+
+TRAIN_EPOCH_PREPROC_START = "train_epoch_preproc_start"
+TRAIN_EPOCH_PREPROC_STOP = "train_epoch_preproc_stop"
+
+TRAIN_ITER_START = "train_iter_start"
+TRAIN_ITER_STOP = "train_iter_stop"
+
+TRAIN_EPOCH_START = "train_epoch_start"
+TRAIN_EPOCH_STOP = "train_epoch_stop"
+
+
+# MLPerf specific tags
+
+RUN_CLEAR_CACHES = "run_clear_caches"
+
+PREPROC_NUM_TRAIN_EXAMPLES = "preproc_num_train_examples"
+PREPROC_NUM_EVAL_EXAMPLES = "preproc_num_eval_examples"
+PREPROC_TOKENIZE_TRAINING = "preproc_tokenize_training"
+PREPROC_TOKENIZE_EVAL = "preproc_tokenize_eval"
+PREPROC_VOCAB_SIZE = "preproc_vocab_size"
+
+RUN_SET_RANDOM_SEED = "run_set_random_seed"
+
+INPUT_SIZE = "input_size"
+INPUT_BATCH_SIZE = "input_batch_size"
+INPUT_ORDER = "input_order"
+INPUT_SHARD = "input_shard"
+INPUT_BN_SPAN = "input_bn_span"
+
+INPUT_CENTRAL_CROP = "input_central_crop"
+INPUT_CROP_USES_BBOXES = "input_crop_uses_bboxes"
+INPUT_DISTORTED_CROP_MIN_OBJ_COV = "input_distorted_crop_min_object_covered"
+INPUT_DISTORTED_CROP_RATIO_RANGE = "input_distorted_crop_aspect_ratio_range"
+INPUT_DISTORTED_CROP_AREA_RANGE = "input_distorted_crop_area_range"
+INPUT_DISTORTED_CROP_MAX_ATTEMPTS = "input_distorted_crop_max_attempts"
+INPUT_MEAN_SUBTRACTION = "input_mean_subtraction"
+INPUT_RANDOM_FLIP = "input_random_flip"
+
+INPUT_RESIZE = "input_resize"
+INPUT_RESIZE_ASPECT_PRESERVING = "input_resize_aspect_preserving"
+
+
+# Opt
+
+OPT_NAME = "opt_name"
+
+OPT_LR = "opt_learning_rate"
+OPT_MOMENTUM = "opt_momentum"
+
+OPT_WEIGHT_DECAY = "opt_weight_decay"
+
+OPT_HP_ADAM_BETA1 = "opt_hp_Adam_beta1"
+OPT_HP_ADAM_BETA2 = "opt_hp_Adam_beta2"
+OPT_HP_ADAM_EPSILON = "opt_hp_Adam_epsilon"
+
+OPT_LR_WARMUP_STEPS = "opt_learning_rate_warmup_steps"
+
+
+#  Train
+
+TRAIN_LOOP = "train_loop"
+TRAIN_EPOCH = "train_epoch"
+TRAIN_CHECKPOINT = "train_checkpoint"
+TRAIN_LOSS = "train_loss"
+TRAIN_ITERATION_LOSS = "train_iteration_loss"
+
+
+# Eval
+
+EVAL_START = "eval_start"
+EVAL_SIZE = "eval_size"
+EVAL_TARGET = "eval_target"
+EVAL_ACCURACY = "eval_accuracy"
+EVAL_STOP = "eval_stop"
+
+
+# Perf
+
+PERF_IT_PER_SEC = "perf_it_per_sec"
+PERF_TIME_TO_TRAIN = "time_to_train"
+
+EVAL_ITERATION_ACCURACY = "eval_iteration_accuracy"
+
+
+# Model
+
+MODEL_HP_LOSS_FN = "model_hp_loss_fn"
+
+MODEL_HP_INITIAL_SHAPE = "model_hp_initial_shape"
+MODEL_HP_FINAL_SHAPE = "model_hp_final_shape"
+
+MODEL_L2_REGULARIZATION = "model_l2_regularization"
+MODEL_EXCLUDE_BN_FROM_L2 = "model_exclude_bn_from_l2"
+
+MODEL_HP_RELU = "model_hp_relu"
+MODEL_HP_CONV2D_FIXED_PADDING = "model_hp_conv2d_fixed_padding"
+MODEL_HP_BATCH_NORM = "model_hp_batch_norm"
+MODEL_HP_DENSE = "model_hp_dense"
+
+
+# GNMT specific
+
+MODEL_HP_LOSS_SMOOTHING = "model_hp_loss_smoothing"
+MODEL_HP_NUM_LAYERS = "model_hp_num_layers"
+MODEL_HP_HIDDEN_SIZE = "model_hp_hidden_size"
+MODEL_HP_DROPOUT = "model_hp_dropout"
+
+EVAL_HP_BEAM_SIZE = "eval_hp_beam_size"
+TRAIN_HP_MAX_SEQ_LEN = "train_hp_max_sequence_length"
+EVAL_HP_MAX_SEQ_LEN = "eval_hp_max_sequence_length"
+EVAL_HP_LEN_NORM_CONST = "eval_hp_length_normalization_constant"
+EVAL_HP_LEN_NORM_FACTOR = "eval_hp_length_normalization_factor"
+EVAL_HP_COV_PENALTY_FACTOR = "eval_hp_coverage_penalty_factor"
+
+
+# NCF specific
+
+PREPROC_HP_MIN_RATINGS = "preproc_hp_min_ratings"
+PREPROC_HP_NUM_EVAL = "preproc_hp_num_eval"
+PREPROC_HP_SAMPLE_EVAL_REPLACEMENT = "preproc_hp_sample_eval_replacement"
+
+INPUT_HP_NUM_NEG = "input_hp_num_neg"
+INPUT_HP_SAMPLE_TRAIN_REPLACEMENT = "input_hp_sample_train_replacement"
+INPUT_STEP_TRAIN_NEG_GEN = "input_step_train_neg_gen"
+INPUT_STEP_EVAL_NEG_GEN = "input_step_eval_neg_gen"
+
+EVAL_HP_NUM_USERS = "eval_hp_num_users"
+EVAL_HP_NUM_NEG = "eval_hp_num_neg"
+
+MODEL_HP_MF_DIM = "model_hp_mf_dim"
+MODEL_HP_MLP_LAYER_SIZES = "model_hp_mlp_layer_sizes"
+
+
+# RESNET specific
+
+EVAL_EPOCH_OFFSET = "eval_offset"
+
+MODEL_HP_INITIAL_MAX_POOL = "model_hp_initial_max_pool"
+MODEL_HP_BEGIN_BLOCK = "model_hp_begin_block"
+MODEL_HP_END_BLOCK = "model_hp_end_block"
+MODEL_HP_BLOCK_TYPE = "model_hp_block_type"
+MODEL_HP_PROJECTION_SHORTCUT = "model_hp_projection_shortcut"
+MODEL_HP_SHORTCUT_ADD = "model_hp_shorcut_add"
+MODEL_HP_RESNET_TOPOLOGY = "model_hp_resnet_topology"
+
+
+# Transformer specific
+
+INPUT_MAX_LENGTH = "input_max_length"
+
+MODEL_HP_INITIALIZER_GAIN = "model_hp_initializer_gain"
+MODEL_HP_VOCAB_SIZE = "model_hp_vocab_size"
+MODEL_HP_NUM_HIDDEN_LAYERS = "model_hp_hidden_layers"
+MODEL_HP_EMBEDDING_SHARED_WEIGHTS = "model_hp_embedding_shared_weights"
+MODEL_HP_ATTENTION_DENSE = "model_hp_attention_dense"
+MODEL_HP_ATTENTION_DROPOUT = "model_hp_attention_dropout"
+MODEL_HP_FFN_OUTPUT_DENSE = "model_hp_ffn_output_dense"
+MODEL_HP_FFN_FILTER_DENSE = "model_hp_ffn_filter_dense"
+MODEL_HP_RELU_DROPOUT = "model_hp_relu_dropout"
+MODEL_HP_LAYER_POSTPROCESS_DROPOUT = "model_hp_layer_postprocess_dropout"
+MODEL_HP_NORM = "model_hp_norm"
+MODEL_HP_SEQ_BEAM_SEARCH = "model_hp_sequence_beam_search"
+
diff --git a/TensorFlow/Recommendation/NCF/ncf.py b/TensorFlow/Recommendation/NCF/ncf.py
new file mode 100644
index 00000000..9b371aad
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/ncf.py
@@ -0,0 +1,459 @@
+# Copyright (c) 2018, deepakn94, codyaustun, robieta. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# -----------------------------------------------------------------------
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import time
+from argparse import ArgumentParser
+
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import cupy as cp
+import horovod.tensorflow as hvd
+
+from mpi4py import MPI
+
+from neumf import ncf_model_ops
+from input_pipeline import DataGenerator
+
+from logger.logger import LOGGER
+from logger.autologging import log_args
+
+def parse_args():
+    """
+    Parse command line arguments.
+    """
+    parser = ArgumentParser(description="Train a Neural Collaborative"
+                                        " Filtering model")
+    parser.add_argument('--data', type=str,
+                        help='path to test and training data files')
+    parser.add_argument('-e', '--epochs', type=int, default=40,
+                        help='number of epochs to train for')
+    parser.add_argument('-b', '--batch-size', type=int, default=1048576,
+                        help='number of examples for each iteration')
+    parser.add_argument('--valid-users-per-batch', type=int, default=5000,
+                        help='Number of users tested in each evaluation batch')
+    parser.add_argument('-f', '--factors', type=int, default=64,
+                        help='number of predictive factors')
+    parser.add_argument('--layers', nargs='+', type=int,
+                        default=[256, 256, 128, 64],
+                        help='size of hidden layers for MLP')
+    parser.add_argument('-n', '--negative-samples', type=int, default=4,
+                        help='number of negative examples per interaction')
+    parser.add_argument('-l', '--learning-rate', type=float, default=0.0045,
+                        help='learning rate for optimizer')
+    parser.add_argument('-k', '--topk', type=int, default=10,
+                        help='rank for test examples to be considered a hit')
+    parser.add_argument('--seed', '-s', type=int, default=0,
+                        help='manually set random seed for random number generation')
+    parser.add_argument('--target', '-t', type=float, default=0.9562,
+                        help='stop training early at target')
+    parser.add_argument('--fp16', action='store_true', dest='amp',
+                        help='enable half-precision computations using automatic mixed precision \
+                              (only available in supported containers)')
+    parser.add_argument('--manual-fp16', action='store_true', dest='fp16',
+                        help='manually enable mixed precision using code changes')
+    parser.add_argument('--xla', action='store_true',
+                        help='enable TensorFlow XLA (Accelerated Linear Algebra)')
+    parser.add_argument('--valid-negative', type=int, default=100,
+                        help='Number of negative samples for each positive test example')
+    parser.add_argument('--beta1', '-b1', type=float, default=0.25,
+                        help='beta1 for Adam')
+    parser.add_argument('--beta2', '-b2', type=float, default=0.5,
+                        help='beta2 for Adam')
+    parser.add_argument('--eps', type=float, default=1e-8,
+                        help='epsilon for Adam')
+    parser.add_argument('--dropout', type=float, default=0.5,
+                        help='Dropout probability, if equal to 0 will not use dropout at all')
+    parser.add_argument('--loss-scale', default=8192, type=int,
+                        help='Loss scale value to use when manually enabling mixed precision')
+    parser.add_argument('--checkpoint-dir', default='/data/checkpoints/', type=str,
+                        help='Path to the store the result checkpoint file for training, \
+                              or to read from for evaluation')
+    parser.add_argument('--mode', choices=['train', 'test'], default='train', type=str,
+                        help='Passing "test" will only run a single evaluation, \
+                              otherwise full training will be performed')
+    parser.add_argument('--no-neg-trick', action='store_true', dest='no_neg_trick',
+                        help='do not use negative sample generation shortcut to speed up data \
+                              augmentation (will increase GPU memory consumption)')
+    parser.add_argument('--eval-after', type=int, default=8,
+                        help='Perform evaluations only after this many epochs')
+    parser.add_argument('--verbose', action='store_true',
+                        help='Log the performance and accuracy after every epoch')
+
+    return parser.parse_args()
+
+def hvd_init():
+    """
+    Initialize Horovod
+    """
+    # Reduce logging
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+    tf.logging.set_verbosity(tf.logging.ERROR)
+    # Initialize horovod
+    hvd.init()
+
+    if hvd.rank() == 0:
+        print('PY', sys.version)
+        print('TF', tf.__version__)
+
+def get_local_train_data(pos_train_users, pos_train_items, negative_samples):
+    """
+    For distributed, split up the train data and only keep the local portion
+    """
+    num_pos_samples = pos_train_users.shape[0]
+    # Create the entire train set
+    all_train_users = np.tile(pos_train_users, negative_samples+1)
+    all_train_items = np.tile(pos_train_items, negative_samples+1)
+    all_train_labels = np.zeros_like(all_train_users, dtype=np.float32)
+    all_train_labels[:num_pos_samples] = 1.0
+
+    # Get local training set
+    split_size = all_train_users.shape[0] // hvd.size() + 1
+    split_indices = np.arange(split_size, all_train_users.shape[0], split_size)
+    all_train_users_splits = np.split(all_train_users, split_indices)
+    all_train_items_splits = np.split(all_train_items, split_indices)
+    all_train_labels_splits = np.split(all_train_labels, split_indices)
+    assert len(all_train_users_splits) == hvd.size()
+    local_train_users = all_train_users_splits[hvd.rank()]
+    local_train_items = all_train_items_splits[hvd.rank()]
+    local_train_labels = all_train_labels_splits[hvd.rank()]
+
+    return local_train_users, local_train_items, local_train_labels
+
+def get_local_test_data(pos_test_users, pos_test_items):
+    """
+    For distributed, split up the test data and only keep the local portion
+    """
+    split_size = pos_test_users.shape[0] // hvd.size() + 1
+    split_indices = np.arange(split_size, pos_test_users.shape[0], split_size)
+    test_users_splits = np.split(pos_test_users, split_indices)
+    test_items_splits = np.split(pos_test_items, split_indices)
+    assert len(test_users_splits) == hvd.size()
+    local_test_users = test_users_splits[hvd.rank()]
+    local_test_items = test_items_splits[hvd.rank()]
+
+    return local_test_users, local_test_items
+
+def main():
+    """
+    Run training/evaluation
+    """
+    script_start = time.time()
+    hvd_init()
+    mpi_comm = MPI.COMM_WORLD
+    args = parse_args()
+
+    if hvd.rank() == 0:
+        log_args(args)
+
+    if args.seed is not None:
+        tf.random.set_random_seed(args.seed)
+        np.random.seed(args.seed)
+        cp.random.seed(args.seed)
+
+    if args.amp:
+        os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1"
+    if "TF_ENABLE_AUTO_MIXED_PRECISION" in os.environ \
+       and os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] == "1":
+        args.fp16 = False
+
+    # directory to store/read final checkpoint
+    if args.mode == 'train' and hvd.rank() == 0:
+        print("Saving best checkpoint to {}".format(args.checkpoint_dir))
+    elif hvd.rank() == 0:
+        print("Reading checkpoint: {}".format(args.checkpoint_dir))
+    if not os.path.exists(args.checkpoint_dir) and args.checkpoint_dir != '':
+        os.makedirs(args.checkpoint_dir, exist_ok=True)
+    final_checkpoint_path = os.path.join(args.checkpoint_dir, 'model.ckpt')
+
+    # Load converted data and get statistics
+    train_df = pd.read_pickle(args.data+'/train_ratings.pickle')
+    test_df = pd.read_pickle(args.data+'/test_ratings.pickle')
+    nb_users, nb_items = train_df.max() + 1
+
+    # Extract train and test feature tensors from dataframe
+    pos_train_users = train_df.iloc[:, 0].values.astype(np.int32)
+    pos_train_items = train_df.iloc[:, 1].values.astype(np.int32)
+    pos_test_users = test_df.iloc[:, 0].values.astype(np.int32)
+    pos_test_items = test_df.iloc[:, 1].values.astype(np.int32)
+    # Negatives indicator for negatives generation
+    neg_mat = np.ones((nb_users, nb_items), dtype=np.bool)
+    neg_mat[pos_train_users, pos_train_items] = 0
+
+    # Get the local training/test data
+    train_users, train_items, train_labels = get_local_train_data(
+        pos_train_users, pos_train_items, args.negative_samples
+    )
+    test_users, test_items = get_local_test_data(
+        pos_test_users, pos_test_items
+    )
+
+    # Create and run Data Generator in a separate thread
+    data_generator = DataGenerator(
+        args.seed,
+        hvd.rank(),
+        nb_users,
+        nb_items,
+        neg_mat,
+        train_users,
+        train_items,
+        train_labels,
+        args.batch_size // hvd.size(),
+        args.negative_samples,
+        test_users,
+        test_items,
+        args.valid_users_per_batch,
+        args.valid_negative,
+        use_neg_trick=False if args.no_neg_trick else True
+        )
+
+    # Create tensorflow session and saver
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.gpu_options.visible_device_list = str(hvd.local_rank())
+    if args.xla:
+        config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+    sess = tf.Session(config=config)
+
+    # Input tensors
+    users = tf.placeholder(tf.int32, shape=(None,))
+    items = tf.placeholder(tf.int32, shape=(None,))
+    labels = tf.placeholder(tf.int32, shape=(None,))
+    is_dup = tf.placeholder(tf.float32, shape=(None,))
+    dropout = tf.placeholder_with_default(args.dropout, shape=())
+    # Model ops and saver
+    hit_rate, ndcg, eval_op, train_op = ncf_model_ops(
+        users,
+        items,
+        labels,
+        is_dup,
+        params={
+            'fp16': args.fp16,
+            'val_batch_size': args.valid_negative+1,
+            'top_k': args.topk,
+            'learning_rate': args.learning_rate,
+            'beta_1': args.beta1,
+            'beta_2': args.beta2,
+            'epsilon': args.eps,
+            'num_users': nb_users,
+            'num_items': nb_items,
+            'num_factors': args.factors,
+            'mf_reg': 0,
+            'layer_sizes': args.layers,
+            'layer_regs': [0. for i in args.layers],
+            'dropout': dropout,
+            'sigmoid': True,
+            'loss_scale': args.loss_scale
+        },
+        eval_only=False if args.mode == 'train' else True
+    )
+    saver = tf.train.Saver()
+
+    # Accuracy metric tensors
+    hr_sum = tf.get_default_graph().get_tensor_by_name('neumf/hit_rate/total:0')
+    hr_cnt = tf.get_default_graph().get_tensor_by_name('neumf/hit_rate/count:0')
+    ndcg_sum = tf.get_default_graph().get_tensor_by_name('neumf/ndcg/total:0')
+    ndcg_cnt = tf.get_default_graph().get_tensor_by_name('neumf/ndcg/count:0')
+
+    # Prepare evaluation data
+    data_generator.prepare_eval_data()
+
+    # If test mode, run one eval
+    if args.mode == 'test':
+        saver.restore(sess, args.checkpoint_dir)
+        eval_start = time.time()
+        sess.run(tf.local_variables_initializer())
+        for user_batch, item_batch, dup_batch \
+            in zip(data_generator.eval_users, data_generator.eval_items, data_generator.dup_mask):
+            sess.run(
+                eval_op,
+                feed_dict={
+                    users: user_batch,
+                    items: item_batch,
+                    is_dup:dup_batch, dropout: 0.0
+                }
+            )
+        eval_duration = time.time() - eval_start
+
+        # Report results
+        hit_rate_sum = sess.run(hvd.allreduce(hr_sum, average=False))
+        hit_rate_cnt = sess.run(hvd.allreduce(hr_cnt, average=False))
+        ndcg_sum = sess.run(hvd.allreduce(ndcg_sum, average=False))
+        ndcg_cnt = sess.run(hvd.allreduce(ndcg_cnt, average=False))
+
+        hit_rate = hit_rate_sum / hit_rate_cnt
+        ndcg = ndcg_sum / ndcg_cnt
+
+        if hvd.rank() == 0:
+            LOGGER.log("Eval Time: {:.4f}, HR: {:.4f}, NDCG: {:.4f}"
+                       .format(eval_duration, hit_rate, ndcg))
+        return
+
+    # Performance Metrics
+    train_times = list()
+    eval_times = list()
+    # Accuracy Metrics
+    first_to_target = None
+    time_to_train = 0.0
+    best_hr = 0
+    best_epoch = 0
+    # Manual initialize weights
+    sess.run(tf.global_variables_initializer())
+    # Buffers for global metrics
+    global_hr_sum = np.ones(1)
+    global_hr_count = np.ones(1)
+    global_ndcg_sum = np.ones(1)
+    global_ndcg_count = np.ones(1)
+    # Buffers for local metrics
+    local_hr_sum = np.ones(1)
+    local_hr_count = np.ones(1)
+    local_ndcg_sum = np.ones(1)
+    local_ndcg_count = np.ones(1)
+
+    # Begin training
+    begin_train = time.time()
+    if hvd.rank() == 0:
+        LOGGER.log("Begin Training. Setup Time: {}".format(begin_train - script_start))
+    for epoch in range(args.epochs):
+        # Train for one epoch
+        train_start = time.time()
+        data_generator.prepare_train_data()
+        for user_batch, item_batch, label_batch \
+            in zip(data_generator.train_users_batches,
+                   data_generator.train_items_batches,
+                   data_generator.train_labels_batches):
+            sess.run(
+                train_op,
+                feed_dict={
+                    users: user_batch.get(),
+                    items: item_batch.get(),
+                    labels: label_batch.get()
+                }
+            )
+        train_duration = time.time() - train_start
+        ## Only log "warm" epochs
+        if epoch >= 1:
+            train_times.append(train_duration)
+        # Evaluate
+        if epoch > args.eval_after:
+            eval_start = time.time()
+            sess.run(tf.local_variables_initializer())
+            for user_batch, item_batch, dup_batch \
+                in zip(data_generator.eval_users,
+                       data_generator.eval_items,
+                       data_generator.dup_mask):
+                sess.run(
+                    eval_op,
+                    feed_dict={
+                        users: user_batch,
+                        items: item_batch,
+                        is_dup: dup_batch,
+                        dropout: 0.0
+                    }
+                )
+            # Compute local metrics
+            local_hr_sum[0] = sess.run(hr_sum)
+            local_hr_count[0] = sess.run(hr_cnt)
+            local_ndcg_sum[0] = sess.run(ndcg_sum)
+            local_ndcg_count[0] = sess.run(ndcg_cnt)
+            # Reduce metrics across all workers
+            mpi_comm.Reduce(local_hr_count, global_hr_count)
+            mpi_comm.Reduce(local_hr_sum, global_hr_sum)
+            mpi_comm.Reduce(local_ndcg_count, global_ndcg_count)
+            mpi_comm.Reduce(local_ndcg_sum, global_ndcg_sum)
+            # Calculate metrics
+            hit_rate = global_hr_sum[0] / global_hr_count[0]
+            ndcg = global_ndcg_sum[0] / global_ndcg_count[0]
+
+            eval_duration = time.time() - eval_start
+            ## Only log "warm" epochs
+            if epoch >= 1:
+                eval_times.append(eval_duration)
+
+            if hvd.rank() == 0:
+                if args.verbose:
+                    log_string = "Epoch: {:02d}, Train Time: {:.4f}, Eval Time: {:.4f}, HR: {:.4f}, NDCG: {:.4f}"
+                    LOGGER.log(
+                        log_string.format(
+                            epoch,
+                            train_duration,
+                            eval_duration,
+                            hit_rate,
+                            ndcg
+                        )
+                    )
+
+                # Update summary metrics
+                if hit_rate > args.target and first_to_target is None:
+                    first_to_target = epoch
+                    time_to_train = time.time() - begin_train
+                if hit_rate > best_hr:
+                    best_hr = hit_rate
+                    best_epoch = epoch
+                    if not args.verbose:
+                        log_string = "New Best Epoch: {:02d}, Train Time: {:.4f}, Eval Time: {:.4f}, HR: {:.4f}, NDCG: {:.4f}"
+                        LOGGER.log(
+                            log_string.format(
+                                epoch,
+                                train_duration,
+                                eval_duration,
+                                hit_rate,
+                                ndcg
+                            )
+                        )
+                    # Save, if meets target
+                    if hit_rate > args.target:
+                        saver.save(sess, final_checkpoint_path)
+
+    # Final Summary
+    if hvd.rank() == 0:
+        train_times = np.array(train_times)
+        train_throughputs = pos_train_users.shape[0]*(args.negative_samples+1) / train_times
+        eval_times = np.array(eval_times)
+        eval_throughputs = pos_test_users.shape[0]*(args.valid_negative+1) / eval_times
+        LOGGER.log(' ')
+        LOGGER.log('Minimum Train Time per Epoch: {:.4f}'.format(np.min(train_times)))
+        LOGGER.log('Average Train Time per Epoch: {:.4f}'.format(np.mean(train_times)))
+        LOGGER.log('Average Train Throughput:     {:.4f}'.format(np.mean(train_throughputs)))
+        LOGGER.log('Minimum Eval Time per Epoch:  {:.4f}'.format(np.min(eval_times)))
+        LOGGER.log('Average Eval Time per Epoch:  {:.4f}'.format(np.mean(eval_times)))
+        LOGGER.log('Average Eval Throughput:      {:.4f}'.format(np.mean(eval_throughputs)))
+        LOGGER.log('First Epoch to hit:           {}'.format(first_to_target))
+        LOGGER.log('Time to Train:                {:.4f}'.format(time_to_train))
+        LOGGER.log('Best HR:                      {:.4f}'.format(best_hr))
+        LOGGER.log('Best Epoch:                   {}'.format(best_epoch))
+
+    sess.close()
+    return
+
+if __name__ == '__main__':
+    main()
diff --git a/TensorFlow/Recommendation/NCF/neumf.py b/TensorFlow/Recommendation/NCF/neumf.py
new file mode 100644
index 00000000..258f9010
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/neumf.py
@@ -0,0 +1,240 @@
+# Copyright (c) 2018. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# -----------------------------------------------------------------------
+#
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
+                                    initializer=None, regularizer=None,
+                                    trainable=True,
+                                    *args, **kwargs):
+    """
+    Custom variable getter that forces trainable variables to be stored in
+    float32 precision and then casts them to the half-precision
+    """
+    storage_dtype = tf.float32 if trainable else dtype
+    variable = getter(name, shape, dtype=storage_dtype,
+                      initializer=initializer, regularizer=regularizer,
+                      trainable=trainable,
+                      *args, **kwargs)
+    if trainable and dtype != tf.float32:
+        variable = tf.cast(variable, dtype)
+    return variable
+
+def neural_mf(users,
+              items,
+              model_dtype,
+              nb_users,
+              nb_items,
+              mf_dim,
+              mf_reg,
+              mlp_layer_sizes,
+              mlp_layer_regs,
+              dropout_rate,
+              sigmoid=False):
+    """
+    Constructs the model graph
+    """
+    # Check params
+    if len(mlp_layer_sizes) != len(mlp_layer_regs):
+        raise RuntimeError('u dummy, layer_sized != layer_regs')
+    if mlp_layer_sizes[0] % 2 != 0:
+        raise RuntimeError('u dummy, mlp_layer_sizes[0] % 2 != 0')
+    nb_mlp_layers = len(mlp_layer_sizes)
+
+    # Embeddings
+    user_embed = tf.get_variable(
+        "user_embeddings",
+        shape=[nb_users, mf_dim + mlp_layer_sizes[0] // 2],
+        initializer=tf.initializers.random_normal(mean=0.0, stddev=0.01))
+    item_embed = tf.get_variable(
+        "item_embeddings",
+        shape=[nb_items, mf_dim + mlp_layer_sizes[0] // 2],
+        initializer=tf.initializers.random_normal(mean=0.0, stddev=0.01))
+    # Matrix Factorization Embeddings
+    xmfu = tf.nn.embedding_lookup(user_embed[:, :mf_dim], users, partition_strategy='div')
+    xmfi = tf.nn.embedding_lookup(item_embed[:, :mf_dim], items, partition_strategy='div')
+    # MLP Network Embeddings
+    xmlpu = tf.nn.embedding_lookup(user_embed[:, mf_dim:], users, partition_strategy='div')
+    xmlpi = tf.nn.embedding_lookup(item_embed[:, mf_dim:], items, partition_strategy='div')
+    # Enforce model to use fp16 data types when manually enabling mixed precision
+    # (Tensorfow ops will use automatically use the data type of the first input)
+    if model_dtype == tf.float16:
+        xmfu = tf.cast(xmfu, model_dtype)
+        xmfi = tf.cast(xmfi, model_dtype)
+        xmlpu = tf.cast(xmlpu, model_dtype)
+        xmlpi = tf.cast(xmlpi, model_dtype)
+
+    # Matrix Factorization
+    xmf = tf.math.multiply(xmfu, xmfi)
+
+    # MLP Layers
+    xmlp = tf.concat((xmlpu, xmlpi), 1)
+    for i in range(1, nb_mlp_layers):
+        xmlp = tf.layers.Dense(
+            mlp_layer_sizes[i],
+            activation=tf.nn.relu,
+            kernel_initializer=tf.glorot_uniform_initializer()
+        ).apply(xmlp)
+        xmlp = tf.layers.Dropout(rate=dropout_rate).apply(xmlp)
+
+    # Final fully-connected layer
+    logits = tf.concat((xmf, xmlp), 1)
+    logits = tf.layers.Dense(
+        1,
+        kernel_initializer=tf.keras.initializers.lecun_uniform()
+    ).apply(logits)
+
+    if sigmoid:
+        logits = tf.math.sigmoid(logits)
+
+    # Cast model outputs back to float32 if manually enabling mixed precision for loss calculation
+    if model_dtype == tf.float16:
+        logits = tf.cast(logits, tf.float32)
+
+    return logits
+
+def compute_eval_metrics(logits, dup_mask, val_batch_size, K):
+    """
+    Constructs the graph to compute Hit Rate and NDCG
+    """
+    # Replace duplicate (uid, iid) pairs with -inf
+    logits = logits * (1. - dup_mask)
+    logits = logits + (dup_mask * logits.dtype.min)
+    # Reshape tensors so that each row corresponds with a user
+    logits_by_user = tf.reshape(logits, [-1, val_batch_size])
+    dup_mask_by_user = tf.cast(tf.reshape(logits, [-1, val_batch_size]), tf.bool)
+    # Get the topk items for each user
+    top_item_indices = tf.math.top_k(logits_by_user, K)[1]
+    # Check that the positive sample (last index) is in the top K
+    is_positive = tf.cast(tf.equal(top_item_indices, val_batch_size-1), tf.int32)
+    found_positive = tf.reduce_sum(is_positive, axis=1)
+    # Extract the rankings of the positive samples
+    positive_ranks = tf.reduce_sum(is_positive * tf.expand_dims(tf.range(K), 0), axis=1)
+    dcg = tf.log(2.) / tf.log(tf.cast(positive_ranks, tf.float32) + 2)
+    dcg *= tf.cast(found_positive, dcg.dtype)
+
+    return found_positive, dcg
+
+def ncf_model_ops(users,
+                  items,
+                  labels,
+                  dup_mask,
+                  params,
+                  eval_only=False):
+    """
+    Constructs the training and evaluation graphs
+    """
+    # Validation params
+    val_batch_size = params['val_batch_size']
+    K = params['top_k']
+    # Training params
+    learning_rate = params['learning_rate']
+    beta_1 = params['beta_1']
+    beta_2 = params['beta_2']
+    epsilon = params['epsilon']
+    # Model params
+    fp16 = params['fp16']
+    nb_users = params['num_users']
+    nb_items = params['num_items']
+    mf_dim = params['num_factors']
+    mf_reg = params['mf_reg']
+    mlp_layer_sizes = params['layer_sizes']
+    mlp_layer_regs = params['layer_regs']
+    dropout = params['dropout']
+    sigmoid = False #params['sigmoid']
+    loss_scale = params['loss_scale']
+
+    is_training = True
+    model_dtype = tf.float16 if fp16 else tf.float32
+
+    # If manually enabling mixed precision, use the custom variable getter
+    custom_getter = None if not fp16 else float32_variable_storage_getter
+    # Allow soft device placement
+    with tf.device(None), \
+         tf.variable_scope('neumf', custom_getter=custom_getter):
+        # Model graph
+        logits = neural_mf(
+            users,
+            items,
+            model_dtype,
+            nb_users,
+            nb_items,
+            mf_dim,
+            mf_reg,
+            mlp_layer_sizes,
+            mlp_layer_regs,
+            dropout,
+            sigmoid
+        )
+        logits = tf.squeeze(logits)
+
+        # Evaluation Ops
+        found_positive, dcg = compute_eval_metrics(logits, dup_mask, val_batch_size, K)
+        # Metrics
+        hit_rate = tf.metrics.mean(found_positive, name='hit_rate')
+        ndcg = tf.metrics.mean(dcg, name='ndcg')
+
+        eval_op = tf.group(hit_rate[1], ndcg[1])
+
+        if eval_only:
+            return hit_rate[0], ndcg[0], eval_op, None
+
+        # Labels
+        labels = tf.reshape(labels, [-1, 1])
+        logits = tf.reshape(logits, [-1, 1])
+
+        # Use adaptive momentum optimizer
+        optimizer = tf.train.AdamOptimizer(
+            learning_rate=learning_rate,
+            beta1=beta_1, beta2=beta_2,
+            epsilon=epsilon)
+
+        loss = tf.losses.sigmoid_cross_entropy(
+            labels,
+            logits,
+            reduction=tf.losses.Reduction.MEAN)
+
+        # Apply loss scaling if manually enabling mixed precision
+        if fp16:
+            if loss_scale is None:
+                loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(2**32, 1000)
+            else:
+                loss_scale_manager = tf.contrib.mixed_precision.FixedLossScaleManager(loss_scale)
+            optimizer = tf.contrib.mixed_precision.LossScaleOptimizer(optimizer, loss_scale_manager)
+
+        # Horovod wrapper for distributed training
+        optimizer = hvd.DistributedOptimizer(optimizer)
+
+        # Update ops
+        global_step = tf.train.get_global_step()
+        train_op = optimizer.minimize(loss, global_step=global_step)
+
+        return hit_rate[0], ndcg[0], eval_op, train_op
diff --git a/TensorFlow/Recommendation/NCF/prepare_dataset.sh b/TensorFlow/Recommendation/NCF/prepare_dataset.sh
new file mode 100755
index 00000000..5023186f
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/prepare_dataset.sh
@@ -0,0 +1,58 @@
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+#!/bin/bash
+set -e
+
+DATASET_NAME=${1:-'ml-20m'}
+RAW_DATADIR='/data'
+CACHED_DATADIR='/data/cache/'${DATASET_NAME}
+
+# you can add another option to this case in order to support other datasets
+case ${DATASET_NAME} in
+    'ml-20m')
+        ZIP_PATH=${RAW_DATADIR}/'ml-20m.zip'
+        RATINGS_PATH=${RAW_DATADIR}'/ml-20m/ratings.csv'
+        ;;
+    'ml-1m')
+        ZIP_PATH=${RAW_DATADIR}/'ml-1m.zip'
+        RATINGS_PATH=${RAW_DATADIR}'/ml-1m/ratings.dat'
+        ;;
+        *)
+        echo "Unsupported dataset name: $DATASET_NAME"
+        exit 1
+esac
+
+mkdir -p ${RAW_DATADIR}
+mkdir -p ${CACHED_DATADIR}
+rm -f log
+
+if [ ! -f ${ZIP_PATH} ]; then
+    echo 'Dataset not found, downloading...'
+    ./download_dataset.sh ${DATASET_NAME} ${RAW_DATADIR}
+fi
+
+if [ ! -f ${RATINGS_PATH} ]; then
+    unzip -u ${ZIP_PATH}  -d ${RAW_DATADIR}
+fi
+
+if [ ! -f ${CACHED_DATADIR}/train_ratings.pickle ]; then
+    echo "preprocessing ${RATINGS_PATH} and save to disk"
+    t0=$(date +%s)
+    python convert.py --path ${RATINGS_PATH} --output ${CACHED_DATADIR}
+    t1=$(date +%s)
+    delta=$(( $t1 - $t0 ))
+    echo "Finish preprocessing in $delta seconds"
+else
+    echo 'Using cached preprocessed data'
+fi
+
+echo "Dataset $DATASET_NAME successfully prepared at: $CACHED_DATADIR"
diff --git a/TensorFlow/Recommendation/NCF/requirements.txt b/TensorFlow/Recommendation/NCF/requirements.txt
new file mode 100644
index 00000000..a04e0d93
--- /dev/null
+++ b/TensorFlow/Recommendation/NCF/requirements.txt
@@ -0,0 +1,2 @@
+pandas
+cupy
diff --git a/TensorFlow/Segmentation/UNet_Industrial/.gitignore b/TensorFlow/Segmentation/UNet_Industrial/.gitignore
new file mode 100644
index 00000000..3f60e3ed
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/.gitignore
@@ -0,0 +1,107 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.idea/
+.vscode/
+*.ckpt
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/Dockerfile b/TensorFlow/Segmentation/UNet_Industrial/Dockerfile
new file mode 100644
index 00000000..d528ee57
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/Dockerfile
@@ -0,0 +1,26 @@
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+FROM nvcr.io/nvidia/tensorflow:19.03-py3
+
+LABEL version="1.0" maintainer="Jonathan DEKHTIAR <jonathan.dekhtiar@nvidia.com>"
+
+ADD . /workspace/unet_industrial
+WORKDIR /workspace/unet_industrial
+
+RUN pip install dllogger/
diff --git a/TensorFlow/Segmentation/UNet_Industrial/LICENSE b/TensorFlow/Segmentation/UNet_Industrial/LICENSE
new file mode 100644
index 00000000..faa07e60
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
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\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/NOTICE b/TensorFlow/Segmentation/UNet_Industrial/NOTICE
new file mode 100644
index 00000000..39dffd49
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/NOTICE
@@ -0,0 +1,5 @@
+UNet for the product inspection in TensorFlow
+
+This repository includes two scripts for the DAGM2007 dataset preprocessing 
+from https://github.com/DEKHTIARJonathan/DAGM2007_TF_Utils
+licensed under the MIT License.
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/README.md b/TensorFlow/Segmentation/UNet_Industrial/README.md
new file mode 100644
index 00000000..34387ff8
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/README.md
@@ -0,0 +1,506 @@
+# U-Net Industrial Defect Segmentation for TensorFlow
+
+This repository provides a script and recipe to train U-Net Industrial to achieve state of the art
+accuracy on the dataset DAGM2007, and is tested and maintained by NVIDIA.
+
+
+# Table of Contents
+
+* [The model](#the-model)
+    * [Default Configuration](#default-configuration)
+* [Setup](#setup)
+    * [Requirements](#requirements)
+* [Quick start guide](#quick-start-guide)
+* [Details](#details)
+    * [Command line options](#command-line-options)
+    * [Getting the data](#getting-the-data)
+    * [Training process](#training-process)
+    * [Enabling mixed precision](#enabling-mixed-precision)
+* [Benchmarking](#benchmarking)
+    * [Training performance benchmark](#training-performance-benchmark)
+    * [Inference performance benchmark](#inference-performance-benchmark)
+* [Results](#results)
+    * [Training accuracy results](#training-accuracy-results)
+    * [Training performance results](#training-performance-results)
+    * [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+# The model
+
+This U-Net model is adapted from the original version of the [U-Net model](https://arxiv.org/abs/1505.04597) which is
+a convolutional auto-encoder for 2D image segmentation. U-Net was first introduced by
+Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:
+[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597).
+
+This work proposes a modified version of U-Net, called `TinyUNet` which performs efficiently and with very high accuracy
+on the industrial anomaly dataset [DAGM2007](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).
+*TinyUNet*, like the original *U-Net* is composed of two parts:
+- an encoding sub-network (left-side)
+- a decoding sub-network (right-side).
+
+It repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling
+layer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D
+layer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.
+
+`TinyUNet` has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a
+small dataset like DAGM2007. The complete architecture is presented in the figure below:
+
+![UnetModel](images/unet.png)
+
+This model trains with mixed precision tensor cores on Volta, therefore researchers can get results much faster than
+training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent
+accuracy and performance over time.
+
+The following features were implemented in this model:
+* Data-parallel multi-GPU training with Horovod.
+* Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision
+training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled
+by an environmental variable.
+* Tensor Core operations to maximize throughput using NVIDIA Volta GPUs.
+* Loss auto-scaling for Tensor Cores (mixed precision) training
+
+The following performance optimizations were implemented in this model:
+* [XLA](https://www.tensorflow.org/xla) support (experimental)
+
+## Default Configuration
+
+This model trains in 2500 epochs, under the following setup:
+
+* Global Batch Size: 16
+
+* Optimizer RMSProp:
+    * decay: 0.9
+    * momentum: 0.8
+    * centered: True
+
+* Learning Rate Schedule: Exponential Step Decay
+    * decay: 0.8
+    * steps: 500
+    * initial learning rate: 1e-4
+
+* Weight Initialization: He Uniform Distribution (introduced by [Kaiming He et al. in 2015](https://arxiv.org/abs/1502.01852) to address issues related ReLU activations in deep neural networks)
+
+* Loss Function: Adaptive
+    * When DICE Loss < 0.3, Loss = Binary Cross Entropy
+    * Else, Loss = DICE Loss
+
+* Data Augmentation
+    * Random Horizontal Flip (50% chance)
+    * Random Rotation 90°
+
+* Activation Functions:
+    * ReLU is used for all layers
+    * Sigmoid is used at the output to ensure that the ouputs are between [0, 1]
+
+* Weight decay: 1e-5
+
+# Setup
+
+The following section list the requirements in order to start training the U-Net model
+(only the `TinyUnet` model is proposed here).
+
+## Requirements
+This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies.  
+Aside from these dependencies, ensure you have the following components:
+
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+
+* [TensorFlow 19.03-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
+* (optional) NVIDIA Volta GPU (see section below) - for best training performance using mixed precision
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:
+- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html),
+- [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
+- [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running).
+
+
+# Quick start guide
+
+To train your model using mixed precision with tensor cores or using FP32, perform the following steps using the
+default configuration of the U-Net model (only `TinyUNet` has been made available here) on the DAGM2007 dataset.
+
+##### Clone the repository
+
+```bash
+git clone https://github.com/NVIDIA/DeepLearningExamples
+cd DeepLearningExamples/TensorFlow/Segmentation/UNetIndustrial
+```
+
+##### Download and preprocess the dataset: DAGM2007
+
+In order to download the dataset. You can execute the following:
+
+```bash
+./download_and_preprocess_dagm2007.sh /path/to/dataset/directory/
+```
+
+**Important Information:** Some files of the dataset require an account to be downloaded, the script will invite you to download them manually and put them in the correct directory.
+
+##### Build and start the docker container based on the TensorFlow NGC container.
+
+```bash
+# Build the docker container
+docker build . --rm -t unet_industrial:latest
+
+# start the container with nvidia-docker
+nvidia-docker run -it --rm \
+    --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \
+    -v /path/to/dataset:/data/dagm2007/ \
+    -v /path/to/results:/results \
+    unet_industrial:latest
+```
+
+##### Run training  
+
+To run training for a default configuration (as described in Default configuration, for example 1/4/8 GPUs,
+FP32/TF-AMP), launch one of the scripts in the `./scripts` directory called
+`./scripts/UNet_{FP32, AMP}_{1, 4, 8}GPU.sh`
+
+Each of the scripts requires three parameters:
+* path to the result directory of the model as the first argument
+* path to the dataset as a second argument
+* class ID from DAGM used (between 1-10)
+
+For example:
+
+```bash
+cd scripts/
+./UNet_FP32_1GPU.sh <path to result repository> <path to dataset> <DAGM2007 classID (1-10)>
+```
+
+##### Run evaluation
+
+Model evaluation on a checkpoint can be launched by running  one of the scripts in the `./scripts` directory
+called `./scripts/UNet_{FP32, AMP}_EVAL.sh`.
+
+Each of the scripts requires three parameters:
+* path to the result directory of the model as the first argument
+* path to the dataset as a second argument
+* class ID from DAGM used (between 1-10)
+
+For example:
+
+```bash
+cd scripts/
+./UNet_FP32_EVAL.sh <path to result repository> <path to dataset> <DAGM2007 classID (1-10)>
+```
+
+# Details
+
+The following sections provide greater details of the dataset, running training and inference, and the training results.
+
+## Command line options
+
+To see the full list of available options and their descriptions, use the -h or --help command line option, for example:
+
+```bash
+python main.py --help
+```
+
+The following mandatory flags must be used to tune different aspects of the training:
+
+general
+-------
+`--exec_mode=train_and_evaluate` Which execution mode to run the model into.
+
+`--iter_unit=batch` Will the model be run for X batches or X epochs ?
+
+`--num_iter=2500` Number of iterations to run.
+
+`--batch_size=16` Size of each minibatch per GPU.
+
+`--results_dir=/path/to/results` Directory in which to write training logs, summaries and checkpoints.
+
+`--data_dir=/path/to/dataset` Directory which contains the DAGM2007 dataset.
+
+`--dataset_name=DAGM2007` Name of the dataset used in this run (only DAGM2007 is supported atm).
+
+`--dataset_classID=1` ClassID to consider to train or evaluate the network (used for DAGM).
+
+model
+-----
+
+`--use_tf_amp` Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores.
+
+`--use_xla` Enable TensorFlow XLA to maximise performance.
+
+`--use_auto_loss_scaling` Use AutoLossScaling in TF-AMP
+
+## Getting the data
+
+The U-Net model was trained with the [Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007)](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html) dataset.
+
+> The competition is inspired by problems from industrial image processing. In order to satisfy their customers' needs, companies have to guarantee the quality of their products, which can often be achieved only by inspection of the finished product. Automatic visual defect detection has the potential to reduce the cost of quality assurance significantly.
+>
+> The competitors have to design a stand-alone algorithm which is able to detect miscellaneous defects on various background textures.
+>
+> The particular challenge of this contest is that the algorithm must learn, without human intervention, to discern defects automatically from a weakly labeled (i.e., labels are not exact to the pixel level) training set, the exact characteristics of which are unknown at development time. During the competition, the programs have to be trained on new data without any human guidance.
+
+**Source:** https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html
+
+### Data description
+
+> The provided data is artificially generated, but similar to real world problems. It consists of multiple data sets, each consisting of 1000 images showing the background texture without defects, and of 150 images with one labeled defect each on the background texture. The images in a single data set are very similar, but each data set is generated by a different texture model and defect model.
+
+> Not all deviations from the texture are necessarily defects. The algorithm will need to use the weak labels provided during the training phase to learn the properties that characterize a defect.
+
+> Below are two sample images from two data sets. In both examples, the left images are without defects; the right ones contain a scratch-shaped defect which appears as a thin dark line, and a diffuse darker area, respectively. The defects are weakly labeled by a surrounding ellipse, shown in red.
+
+![DAGM_Examples](images/dagm2007.png)
+
+The DAGM2007 challenge comes in propose two different challenges:
+- A development set: public and available for download from
+[here](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).
+The number of classes and sub-challenges for the development set is 6.
+- A competition set: which requires an account to be downloaded from [here](https://hci.iwr.uni-heidelberg.de/node/3616).
+The number of classes and sub-challenges for the competition set is 10.
+
+### Challenge description
+
+The challenge consists in designing a single model with a set of predefined hyper-parameters which will not change
+across the 10 different classes or sub-challenges of the competition set.
+
+The performance shall be measured on the competition set which is normalized and more complex that the public dataset
+while offering the most unbiased evaluation method.
+
+## Training Process
+
+### Laplace Smoothing
+
+We use this technique in the DICE loss to improve the training efficiency. This technique consists in replacing the
+epsilon parameter (used to avoid dividing by zero and very small: +/- 1e-7) by 1. You can find more information on:
+[https://en.wikipedia.org/wiki/Additive_smoothing](https://en.wikipedia.org/wiki/Additive_smoothing)
+
+### Adaptive Loss
+
+The DICE Loss is not able to provide a meaningful gradient at initialisation. This leads to a model instability which
+often push the model to diverge. Nonetheless, once the model starts to converge, DICE loss is able to very efficiently
+fully train the model. Therefore, we implemented an *adaptive loss* which is composed of two sub-losses:
+
+- Binary Cross-Entropy (BCE)
+- DICE Loss
+
+The model is trained with the BCE loss until the DICE Loss reach a experimentally defined threshold (0.3).
+Thereafter, DICE loss is used to finish training.
+
+### Weak Labelling
+
+This dataset is referred as weakly labelled. That means that the segmentation labels are not given at the pixel level
+but rather in an approximate fashion.
+
+## Enabling mixed precision
+
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing
+operations in half-precision format, while storing minimal information in single-precision to retain as much
+information as possible in critical parts of the network.  Since the introduction of
+[tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training
+speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically
+intense model architectures.  Using
+[mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously
+required two steps:
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+
+This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision
+methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing
+TensorFlow model code.  AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow
+framework code makes all necessary model changes internally.
+
+In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,
+and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with
+the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to
+perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all
+computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
+
+For information about:
+- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and
+[Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
+- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
+- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
+
+# Benchmarking
+
+The following sections shows how to run benchmarks measuring the model performance in training and inference modes.
+
+## Training performance benchmark
+
+To benchmark the inference performance, you can run one of the scripts in the `./scripts/benchmarking/` directory
+called `./scripts/benchmarking/DGX1v_trainbench_{FP32, AMP}_{1, 4, 8}GPU.sh`.
+
+Each of the scripts requires three parameters:
+* path to the result directory of the model as the first argument
+* path to the dataset as a second argument
+* class ID from DAGM used (between 1-10)
+
+For example:
+
+```bash
+cd scripts/benchmarking/
+./DGX1v_trainbench_FP32_1GPU.sh <path to result repository> <path to dataset> <DAGM2007 classID (1-10)>
+```
+
+## Inference performance benchmark
+
+To benchmark the training performance, you can run one of the scripts in the `./scripts/benchmarking/` directory
+called `./scripts/benchmarking/DGX1v_trainbench_{FP32, AMP}_{1, 4, 8}GPU.sh`.
+
+Each of the scripts requires three parameters:
+* path to the result directory of the model as the first argument
+* path to the dataset as a second argument
+* class ID from DAGM used (between 1-10)
+
+For example:
+
+```bash
+cd scripts/benchmarking/
+./DGX1v_evalbench_FP16_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+```
+
+# Results
+
+The following sections provide details on the achieved results in training accuracy, performance and inference performance.
+
+## Training accuracy results
+
+Our results were obtained by running the `./scripts/UNet_{FP32, AMP}_{1, 4, 8}GPU.sh` training
+script in the Tensorflow:19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+
+### Threshold = 0.75
+
+| # DAGM Class ID | Precision                       | IoU (Intersection over Union) | TPR (True Positive Rate) | TNR (True Negative Rate) |
+|-----------------|---------------------------------|-------------------------------|--------------------------|--------------------------|
+| 1               | FP32                            | 0.968                         | 100.00                   | 97.88                    |
+| 1               | Automatic Mixed Precision (AMP) | 0.966                         | 100.00                   | 97.55                    |
+| 2               | FP32                            | 0.976                         | 100.00                   | 99.62                    |
+| 2               | Automatic Mixed Precision (AMP) | 0.976                         | 100.00                   | 99.66                    |
+| 3               | FP32                            | 0.979                         | 100.00                   | 99.64                    |
+| 3               | Automatic Mixed Precision (AMP) | 0.979                         | 100.00                   | 99.63                    |
+| 4               | FP32                            | 0.965                         | 100.00                   | 97.43                    |
+| 4               | Automatic Mixed Precision (AMP) | 0.960                         | 100.00                   | 96.85                    |
+| 5               | FP32                            | 0.992                         | 100.00                   | 99.88                    |
+| 5               | Automatic Mixed Precision (AMP) | 0.991                         | 100.00                   | 99.78                    |
+| 6               | FP32                            | 0.982                         | 100.00                   | 98.78                    |
+| 6               | Automatic Mixed Precision (AMP) | 0.979                         | 100.00                   | 98.39                    |
+| 7               | FP32                            | 0.980                         | 100.00                   | 99.58                    |
+| 7               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.56                    |
+| 8               | FP32                            | 0.970                         | 100.00                   | 99.85                    |
+| 8               | Automatic Mixed Precision (AMP) | 0.971                         | 100.00                   | 100.00                   |
+| 9               | FP32                            | 0.988                         | 99.82                    | 99.81                    |
+| 9               | Automatic Mixed Precision (AMP) | 0.988                         | 99.87                    | 99.81                    |
+| 10              | FP32                            | 0.979                         | 100.00                   | 99.43                    |
+| 10              | Automatic Mixed Precision (AMP) | 0.982                         | 100.00                   | 99.70                    |
+
+### Threshold = 0.85
+
+| # DAGM Class ID | Precision                       | IoU (Intersection over Union) | TPR (True Positive Rate) | TNR (True Negative Rate) |
+|-----------------|---------------------------------|-------------------------------|--------------------------|--------------------------|
+| 1               | FP32                            | 0.968                         | 100.00                   | 97.88                    |
+| 1               | Automatic Mixed Precision (AMP) | 0.966                         | 100.00                   | 97.56                    |
+| 2               | FP32                            | 0.976                         | 100.00                   | 99.62                    |
+| 2               | Automatic Mixed Precision (AMP) | 0.976                         | 100.00                   | 99.66                    |
+| 3               | FP32                            | 0.979                         | 100.00                   | 99.64                    |
+| 3               | Automatic Mixed Precision (AMP) | 0.977                         | 100.00                   | 99.33                    |
+| 4               | FP32                            | 0.966                         | 100.00                   | 97.44                    |
+| 4               | Automatic Mixed Precision (AMP) | 0.960                         | 100.00                   | 96.85                    |
+| 5               | FP32                            | 0.992                         | 100.00                   | 99.88                    |
+| 5               | Automatic Mixed Precision (AMP) | 0.991                         | 100.00                   | 99.78                    |
+| 6               | FP32                            | 0.982                         | 100.00                   | 98.80                    |
+| 6               | Automatic Mixed Precision (AMP) | 0.979                         | 100.00                   | 98.41                    |
+| 7               | FP32                            | 0.980                         | 100.00                   | 99.60                    |
+| 7               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.58                    |
+| 8               | FP32                            | 0.970                         | 100.00                   | 99.85                    |
+| 8               | Automatic Mixed Precision (AMP) | 0.971                         | 100.00                   | 100.00                   |
+| 9               | FP32                            | 0.988                         | 99.82                    | 99.81                    |
+| 9               | Automatic Mixed Precision (AMP) | 0.988                         | 99.87                    | 99.81                    |
+| 10              | FP32                            | 0.980                         | 100.00                   | 99.45                    |
+| 10              | Automatic Mixed Precision (AMP) | 0.982                         | 100.00                   | 99.71                    |
+
+### Threshold = 0.95
+
+| # DAGM Class ID | Precision                       | IoU (Intersection over Union) | TPR (True Positive Rate) | TNR (True Negative Rate) |
+|-----------------|---------------------------------|-------------------------------|--------------------------|--------------------------|
+| 1               | FP32                            | 0.969                         | 100.00                   | 97.93                    |
+| 1               | Automatic Mixed Precision (AMP) | 0.966                         | 100.00                   | 97.58                    |
+| 2               | FP32                            | 0.976                         | 100.00                   | 99.64                    |
+| 2               | Automatic Mixed Precision (AMP) | 0.976                         | 100.00                   | 99.66                    |
+| 3               | FP32                            | 0.979                         | 100.00                   | 99.64                    |
+| 3               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.64                    |
+| 4               | FP32                            | 0.966                         | 100.00                   | 97.52                    |
+| 4               | Automatic Mixed Precision (AMP) | 0.961                         | 100.00                   | 96.88                    |
+| 5               | FP32                            | 0.992                         | 100.00                   | 99.88                    |
+| 5               | Automatic Mixed Precision (AMP) | 0.991                         | 100.00                   | 99.78                    |
+| 6               | FP32                            | 0.982                         | 100.00                   | 98.83                    |
+| 6               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 98.58                    |
+| 7               | FP32                            | 0.981                         | 100.00                   | 99.65                    |
+| 7               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.59                    |
+| 8               | FP32                            | 0.970                         | 100.00                   | 99.88                    |
+| 8               | Automatic Mixed Precision (AMP) | 0.971                         | 100.00                   | 100.00                   |
+| 9               | FP32                            | 0.988                         | 99.82                    | 99.83                    |
+| 9               | Automatic Mixed Precision (AMP) | 0.988                         | 99.87                    | 99.82                    |
+| 10              | FP32                            | 0.980                         | 100.00                   | 99.54                    |
+| 10              | Automatic Mixed Precision (AMP) | 0.982                         | 100.00                   | 99.72                    |
+
+### Threshold = 0.99
+
+| # DAGM Class ID | Precision                       | IoU (Intersection over Union) | TPR (True Positive Rate) | TNR (True Negative Rate) |
+|-----------------|---------------------------------|-------------------------------|--------------------------|--------------------------|
+| 1               | FP32                            | 0.969                         | 100.00                   | 97.95                    |
+| 1               | Automatic Mixed Precision (AMP) | 0.967                         | 100.00                   | 97.66                    |
+| 2               | FP32                            | 0.976                         | 100.00                   | 99.64                    |
+| 2               | Automatic Mixed Precision (AMP) | 0.976                         | 100.00                   | 99.71                    |
+| 3               | FP32                            | 0.979                         | 100.00                   | 99.64                    |
+| 3               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.66                    |
+| 4               | FP32                            | 0.967                         | 100.00                   | 97.55                    |
+| 4               | Automatic Mixed Precision (AMP) | 0.961                         | 100.00                   | 96.89                    |
+| 5               | FP32                            | 0.992                         | 100.00                   | 99.88                    |
+| 5               | Automatic Mixed Precision (AMP) | 0.991                         | 100.00                   | 99.78                    |
+| 6               | FP32                            | 0.983                         | 100.00                   | 98.88                    |
+| 6               | Automatic Mixed Precision (AMP) | 0.981                         | 100.00                   | 98.65                    |
+| 7               | FP32                            | 0.981                         | 100.00                   | 99.67                    |
+| 7               | Automatic Mixed Precision (AMP) | 0.980                         | 100.00                   | 99.62                    |
+| 8               | FP32                            | 0.970                         | 100.00                   | 99.92                    |
+| 8               | Automatic Mixed Precision (AMP) | 0.971                         | 100.00                   | 100.00                   |
+| 9               | FP32                            | 0.988                         | 99.82                    | 99.84                    |
+| 9               | Automatic Mixed Precision (AMP) | 0.988                         | 99.87                    | 99.82                    |
+| 10              | FP32                            | 0.981                         | 100.00                   | 99.62                    |
+| 10              | Automatic Mixed Precision (AMP) | 0.982                         | 100.00                   | 99.78                    |
+
+## Training performance results
+
+<!-- Spreedsheet to Markdown: https://thisdavej.com/copy-table-in-excel-and-paste-as-a-markdown-table/ -->
+
+Our results were obtained by running the scripts
+`./scripts/benchmarking/DGX1v_trainbench_{FP16, FP32, FP32AMP, FP32FM}_{1, 4, 8}GPU.sh` training script in the
+TensorFlow 19.03-py3 NGC container on an NVIDIA DGX-1 with 8 V100 16G GPUs.
+
+
+| # GPUs | Precision                       | Training (Imgs/sec) | Training Time | Speedup |
+|--------|---------------------------------|---------------------|---------------|---------|
+| 1      | FP32                            | 89                  | 7m44          | 1.00    |
+| 1      | Automatic Mixed Precision (AMP) | 104                 | 6m40          | 1.17    |
+| 4      | FP32                            | 261                 | 2m48          | 1.00    |
+| 4      | Automatic Mixed Precision (AMP) | 302                 | 2m27          | 1.16    |
+| 8      | FP32                            | 445                 | 1m44          | 1.00    |
+| 8      | Automatic Mixed Precision (AMP) | 491                 | 1m36          | 1.10    |
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+## Inference performance results
+
+Our results were obtained by running the aforementioned scripts in the TensorFlow 
+19.03-py3 NGC container on an NVIDIA DGX-1 server with 8 V100 16G GPUs.
+
+| # GPUs | Precision                       | Training (Imgs/sec) | Speedup |
+|--------|---------------------------------|---------------------|---------|
+| 1      | FP32                            | 228                 | 1.00    |
+| 1      | Automatic Mixed Precision (AMP) | 301                 | 1.32    |
+
+To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above.
+
+# Changelog
+1. **March 18, 2019:** Initial release
+
+# Known issues
+There are no known issues with this model.
diff --git a/TensorFlow/Segmentation/UNet_Industrial/datasets/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/datasets/__init__.py
new file mode 100644
index 00000000..21a29033
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/datasets/__init__.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from datasets import core
+
+from datasets.dagm2007 import DAGM2007_Dataset
+
+known_datasets = {cls.dataset_name: cls for cls in core.BaseDataset.__subclasses__()}
+
+__all__ = ['known_datasets']
diff --git a/TensorFlow/Segmentation/UNet_Industrial/datasets/core.py b/TensorFlow/Segmentation/UNet_Industrial/datasets/core.py
new file mode 100644
index 00000000..58c96b02
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/datasets/core.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from __future__ import print_function
+
+import os
+from abc import ABC, abstractmethod
+
+import math
+
+import tensorflow as tf
+
+__all__ = ["BaseDataset"]
+
+
+class BaseDataset(ABC):
+
+    authorized_normalization_methods = [None, "zero_centered", "zero_one"]
+
+    def __init__(self, data_dir):
+        self.data_dir = data_dir
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError("The dataset directory `%s` does not exist." % data_dir)
+
+    @staticmethod
+    def _count_steps(iter_unit, num_samples, num_iter, global_batch_size):
+
+        if iter_unit not in ["batch", "epoch"]:
+            raise ValueError("Invalid `iter_unit` value: %s" % iter_unit)
+
+        if iter_unit == 'epoch':
+            num_steps = (num_samples // global_batch_size) * num_iter
+            num_epochs = num_iter
+
+        else:
+            num_steps = num_iter
+            num_epochs = math.ceil(num_steps / (num_samples // global_batch_size))
+
+        return num_steps, num_epochs
+
+    @abstractmethod
+    def dataset_name(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_dataset_runtime_specs(self, training, iter_unit, num_iter, global_batch_size):
+        # return filenames, num_samples, num_steps, num_epochs
+        raise NotImplementedError
+
+    @abstractmethod
+    def dataset_fn(
+        self,
+        batch_size,
+        training,
+        input_shape,
+        mask_shape,
+        num_threads,
+        use_gpu_prefetch,
+        normalize_data_method,
+        only_defective_images,
+        augment_data,
+        seed=None
+    ):
+
+        if normalize_data_method not in BaseDataset.authorized_normalization_methods:
+            raise ValueError(
+                'Unknown `normalize_data_method`: %s - Authorized: %s' %
+                (normalize_data_method, BaseDataset.authorized_normalization_methods)
+            )
+
+    def synth_dataset_fn(
+        self,
+        batch_size,
+        training,
+        input_shape,
+        mask_shape,
+        num_threads,
+        use_gpu_prefetch,
+        normalize_data_method,
+        only_defective_images,
+        augment_data,
+        seed=None
+    ):
+
+        if normalize_data_method not in BaseDataset.authorized_normalization_methods:
+            raise ValueError(
+                'Unknown `normalize_data_method`: %s - Authorized: %s' %
+                (normalize_data_method, BaseDataset.authorized_normalization_methods)
+            )
+
+        input_shape = [batch_size] + list(input_shape)
+        mask_shape = [batch_size] + list(mask_shape)
+
+        # Convert the inputs to a Dataset
+        if normalize_data_method is None:
+            mean_val = 127.5
+
+        elif normalize_data_method == "zero_centered":
+            mean_val = 0
+
+        else:
+            mean_val = 0.5
+
+        inputs = tf.truncated_normal(
+            input_shape, dtype=tf.float32, mean=mean_val, stddev=1, seed=seed, name='synth_inputs'
+        )
+        masks = tf.truncated_normal(mask_shape, dtype=tf.float32, mean=0.01, stddev=0.1, seed=seed, name='synth_masks')
+        labels = tf.random_uniform([batch_size], minval=0, maxval=1, dtype=tf.int32, name='synthetic_labels')
+
+        dataset = tf.data.Dataset.from_tensors(((inputs, masks), labels))
+
+        dataset = dataset.cache()
+        dataset = dataset.repeat()
+
+        dataset = dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
+
+        if use_gpu_prefetch:
+            dataset.apply(tf.data.experimental.prefetch_to_device(device="/gpu:0", buffer_size=batch_size * 8))
+
+        return dataset
diff --git a/TensorFlow/Segmentation/UNet_Industrial/datasets/dagm2007.py b/TensorFlow/Segmentation/UNet_Industrial/datasets/dagm2007.py
new file mode 100644
index 00000000..0d20b84d
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/datasets/dagm2007.py
@@ -0,0 +1,453 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+##############################################################################
+# Copyright (c) Jonathan Dekhtiar - contact@jonathandekhtiar.eu
+# All Rights Reserved.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+##############################################################################
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import os
+import glob
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from datasets.core import BaseDataset
+
+from utils import hvd_utils
+
+from dllogger.logger import LOGGER
+
+__all__ = ['DAGM2007_Dataset']
+
+
+class DAGM2007_Dataset(BaseDataset):
+
+    dataset_name = "DAGM2007"
+
+    def __init__(self, data_dir, class_id):
+
+        if class_id is None:
+            raise ValueError("The parameter `class_id` cannot be set to None")
+
+        data_dir = os.path.join(data_dir, "raw_images/private/Class%d" % class_id)
+
+        super(DAGM2007_Dataset, self).__init__(data_dir)
+
+    def _get_data_dirs(self, training):
+
+        if training:
+            csv_file = os.path.join(self.data_dir, "train_list.csv")
+            image_dir = os.path.join(self.data_dir, "Train")
+
+        else:
+            csv_file = os.path.join(self.data_dir, "test_list.csv")
+            image_dir = os.path.join(self.data_dir, "Test")
+
+        return image_dir, csv_file
+
+    def get_dataset_runtime_specs(self, training, iter_unit, num_iter, global_batch_size):
+
+        image_dir, _ = self._get_data_dirs(training=training)
+
+        filenames = glob.glob(os.path.join(image_dir, "*.PNG"))
+        num_samples = len(filenames)
+
+        num_steps, num_epochs = DAGM2007_Dataset._count_steps(
+            iter_unit=iter_unit, num_samples=num_samples, num_iter=num_iter, global_batch_size=global_batch_size
+        )
+
+        return filenames, num_samples, num_steps, num_epochs
+
+    def dataset_fn(
+        self,
+        batch_size,
+        training,
+        input_shape,
+        mask_shape,
+        num_threads,
+        use_gpu_prefetch,
+        normalize_data_method,
+        only_defective_images,
+        augment_data,
+        seed=None
+    ):
+
+        super(DAGM2007_Dataset, self).dataset_fn(
+            batch_size=batch_size,
+            training=training,
+            input_shape=input_shape,
+            mask_shape=mask_shape,
+            num_threads=num_threads,
+            use_gpu_prefetch=use_gpu_prefetch,
+            normalize_data_method=normalize_data_method,  # [None, "zero_centered", "zero_one"]
+            only_defective_images=only_defective_images,
+            augment_data=augment_data,
+            seed=seed
+        )
+
+        shuffle_buffer_size = 10000
+
+        def decode_csv(line):
+
+            input_image_name, image_mask_name, label = tf.decode_csv(
+                line, record_defaults=[[""], [""], [0]], field_delim=','
+            )
+
+            def decode_image(filepath, resize_shape, normalize_data_method):
+                image_content = tf.read_file(filepath)
+
+                # image = tf.image.decode_image(image_content, channels=resize_shape[-1])
+                image = tf.image.decode_png(contents=image_content, channels=resize_shape[-1], dtype=tf.uint8)
+
+                image = tf.image.resize_images(
+                    image,
+                    size=resize_shape[:2],
+                    method=tf.image.ResizeMethod.BILINEAR,  # [BILINEAR, NEAREST_NEIGHBOR, BICUBIC, AREA]
+                    align_corners=False,
+                    preserve_aspect_ratio=True
+                )
+
+                image.set_shape(resize_shape)
+                image = tf.cast(image, tf.float32)
+
+                if normalize_data_method == "zero_centered":
+                    image = tf.divide(image, 127.5) - 1
+
+                elif normalize_data_method == "zero_one":
+                    image = tf.divide(image, 255.0)
+
+                return image
+
+            input_image = decode_image(
+                filepath=tf.strings.join([image_dir, input_image_name], separator='/'),
+                resize_shape=input_shape,
+                normalize_data_method=normalize_data_method,
+            )
+
+            mask_image = tf.cond(
+                tf.equal(image_mask_name, ""),
+                true_fn=lambda: tf.zeros(mask_shape, dtype=tf.float32),
+                false_fn=lambda: decode_image(
+                    filepath=tf.strings.join([mask_image_dir, image_mask_name], separator='/'),
+                    resize_shape=mask_shape,
+                    normalize_data_method="zero_one",
+                ),
+            )
+
+            if augment_data:
+
+                if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                    LOGGER.log("Using data augmentation ...")
+
+                #input_image = tf.image.per_image_standardization(input_image)
+
+                horizontal_flip = tf.random_uniform(shape=(), seed=seed) > 0.5
+                input_image = tf.cond(
+                    horizontal_flip, lambda: tf.image.flip_left_right(input_image), lambda: input_image
+                )
+                mask_image = tf.cond(horizontal_flip, lambda: tf.image.flip_left_right(mask_image), lambda: mask_image)
+
+                n_rots = tf.random_uniform(shape=(), dtype=tf.int32, minval=0, maxval=3, seed=seed)
+                input_image = tf.image.rot90(input_image, k=n_rots)
+                mask_image = tf.image.rot90(mask_image, k=n_rots)
+
+            label = tf.cast(label, tf.int32)
+
+            return (input_image, mask_image), label
+
+        image_dir, csv_file = self._get_data_dirs(training=training)
+
+        mask_image_dir = os.path.join(image_dir, "Label")
+
+        dataset = tf.data.TextLineDataset(csv_file)
+
+        dataset = dataset.skip(1)  # Skip CSV Header
+
+        if only_defective_images:
+            dataset = dataset.filter(lambda line: tf.not_equal(tf.strings.substr(line, -1, 1), "0"))
+
+        dataset = dataset.cache()
+
+        if training:
+
+            dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=shuffle_buffer_size, seed=seed))
+
+            if hvd_utils.is_using_hvd():
+                dataset = dataset.shard(hvd.size(), hvd.rank())
+
+        else:
+            dataset = dataset.repeat()
+
+        dataset = dataset.apply(
+            tf.data.experimental.map_and_batch(
+                map_func=decode_csv,
+                num_parallel_calls=num_threads,
+                batch_size=batch_size,
+                drop_remainder=True,
+            )
+        )
+
+        dataset = dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
+
+        if use_gpu_prefetch:
+            dataset.apply(tf.data.experimental.prefetch_to_device(device="/gpu:0", buffer_size=batch_size * 8))
+
+        return dataset
+
+
+if __name__ == "__main__":
+    '''
+    Data Loading Benchmark Usage:
+
+    # Real Data - Training
+    python -m datasets.dagm2007 \
+        --data_dir="/data/dagm2007/" \
+        --batch_size=64 \
+        --warmup_steps=200 \
+        --benchmark_steps=2000 \
+        --training \
+        --class_id=1
+
+    # Real Data - Inference
+    python -m datasets.dagm2007 \
+        --data_dir="/data/dagm2007/" \
+        --batch_size=64 \
+        --warmup_steps=200 \
+        --benchmark_steps=2000 \
+        --class_id=1
+
+    # --------------- #
+
+    # Synthetic Data - Training
+    python -m datasets.dagm2007 \
+        --data_dir="/data/dagm2007/" \
+        --batch_size=64 \
+        --warmup_steps=200 \
+        --benchmark_steps=2000 \
+        --class_id=1 \
+        --training \
+        --use_synthetic_data
+
+    # Synthetic Data - Inference
+    python -m datasets.dagm2007 \
+        --data_dir="/data/dagm2007/" \
+        --batch_size=64 \
+        --warmup_steps=200 \
+        --benchmark_steps=2000 \
+        --class_id=1 \
+        --use_synthetic_data
+
+    # --------------- #
+    '''
+
+    import time
+    import argparse
+
+    import numpy as np
+
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+    tf.logging.set_verbosity(tf.logging.INFO)
+
+    parser = argparse.ArgumentParser(description="DAGM2007_data_loader_benchmark")
+
+    parser.add_argument(
+        '--data_dir', required=True, type=str, help="Directory path which contains the preprocessed DAGM 2007 dataset"
+    )
+
+    parser.add_argument(
+        '--batch_size', default=64, type=int, required=True, help="""Batch size used to measure performance."""
+    )
+
+    parser.add_argument(
+        '--warmup_steps',
+        default=200,
+        type=int,
+        required=True,
+        help="""Number of steps considered as warmup and not taken into account for performance measurements."""
+    )
+
+    parser.add_argument(
+        '--benchmark_steps',
+        default=200,
+        type=int,
+        required=True,
+        help="""Number of steps considered as warmup and not taken into account for performance measurements."""
+    )
+
+    parser.add_argument(
+        '--class_id',
+        default=1,
+        choices=range(1, 11),  # between 1 and 10
+        type=int,
+        required=True,
+        help="""Class ID used for benchmark."""
+    )
+
+    parser.add_argument("--training", default=False, action="store_true", help="Benchmark in training mode")
+
+    parser.add_argument("--use_synthetic_data", default=False, action="store_true", help="Use synthetic dataset")
+
+    FLAGS, unknown_args = parser.parse_known_args()
+
+    if len(unknown_args) > 0:
+
+        for bad_arg in unknown_args:
+            print("ERROR: Unknown command line arg: %s" % bad_arg)
+
+        raise ValueError("Invalid command line arg(s)")
+
+    BURNIN_STEPS = FLAGS.warmup_steps
+    TOTAL_STEPS = FLAGS.warmup_steps + FLAGS.benchmark_steps
+
+    dataset = DAGM2007_Dataset(data_dir=FLAGS.data_dir, class_id=FLAGS.class_id)
+
+    _filenames, _num_samples, _num_steps, _num_epochs = dataset.get_dataset_runtime_specs(
+        training=FLAGS.training, iter_unit="batch", num_iter=TOTAL_STEPS, global_batch_size=FLAGS.batch_size
+    )
+
+    tf.logging.info("[*] Executing Benchmark in %s mode" % ("training" if FLAGS.training else "inference"))
+    tf.logging.info("[*] Benchmark using %s data" % ("synthetic" if FLAGS.use_synthetic_data else "real"))
+
+    print()
+    tf.logging.info("[*] num_samples: %d" % _num_samples)
+    tf.logging.info("[*] num_steps: %d" % _num_steps)
+    tf.logging.info("[*] num_epochs: %d" % _num_epochs)
+
+    time.sleep(4)
+
+    if not FLAGS.use_synthetic_data:
+        # Build the data input
+        dataset = dataset.dataset_fn(
+            batch_size=FLAGS.batch_size,
+            training=FLAGS.training,
+            input_shape=(512, 512, 1),
+            mask_shape=(512, 512, 1),
+            num_threads=64,
+            use_gpu_prefetch=True,
+            seed=None
+        )
+
+    else:
+        # Build the data input
+        dataset = dataset.synth_dataset_fn(
+            batch_size=FLAGS.batch_size,
+            training=FLAGS.training,
+            input_shape=(512, 512, 1),
+            mask_shape=(512, 512, 1),
+            num_threads=64,
+            use_gpu_prefetch=True,
+            seed=None
+        )
+
+    dataset_iterator = dataset.make_initializable_iterator()
+
+    (input_images, mask_images), labels = dataset_iterator.get_next()
+
+    print("Input Image Shape: %s" % (input_images.get_shape()))
+    print("Mask Image Shape: %s" % (mask_images.get_shape()))
+    print("Label Shape: %s" % (labels.get_shape()))
+
+    input_images = tf.image.resize_image_with_crop_or_pad(input_images, target_height=512, target_width=512)
+
+    with tf.device("/gpu:0"):
+
+        input_images = tf.identity(input_images)
+        mask_images = tf.identity(mask_images)
+        labels = tf.identity(labels)
+
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.log_device_placement = True
+
+    with tf.Session(config=config) as sess:
+
+        sess.run(dataset_iterator.initializer)
+
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.global_variables_initializer())
+
+        total_files_processed = 0
+
+        img_per_sec_arr = []
+        processing_time_arr = []
+
+        processing_start_time = time.time()
+
+        for step in range(TOTAL_STEPS):
+
+            start_time = time.time()
+
+            img_batch, mask_batch, lbl_batch = sess.run([input_images, mask_images, labels])
+
+            batch_size = img_batch.shape[0]
+            total_files_processed += batch_size
+
+            elapsed_time = (time.time() - start_time) * 1000
+            imgs_per_sec = (batch_size / elapsed_time) * 1000
+
+            if (step + 1) > BURNIN_STEPS:
+                processing_time_arr.append(elapsed_time)
+                img_per_sec_arr.append(imgs_per_sec)
+
+            if (step + 1) % 20 == 0 or (step + 1) == TOTAL_STEPS:
+
+                print(
+                    "[STEP %04d] # Files: %03d - Time: %03d msecs - Speed: %6d img/s" %
+                    (step + 1, batch_size, elapsed_time, imgs_per_sec)
+                )
+
+        processing_time = time.time() - processing_start_time
+
+        avg_processing_speed = np.mean(img_per_sec_arr)
+
+        print("\n###################################################################")
+        print("*** Data Loading Performance Metrics ***\n")
+        print("\t=> Number of Steps: %d" % (step + 1))
+        print("\t=> Batch Size: %d" % FLAGS.batch_size)
+        print("\t=> Files Processed: %d" % total_files_processed)
+        print("\t=> Total Execution Time: %d secs" % processing_time)
+        print("\t=> Median Time per step: %3d msecs" % np.median(processing_time_arr))
+        print("\t=> Median Processing Speed: %d images/secs" % np.median(img_per_sec_arr))
+        print("\t=> Median Processing Time: %.2f msecs/image" % (1 / float(np.median(img_per_sec_arr)) * 1000))
+
+        print("\n*** Debug Shape Information:")
+        print(
+            "\t[*] Batch Shape: %s - Max Val: %.2f - Min Val: %.2f - Mean: %.2f - Stddev: %.2f" % (
+                str(img_batch.shape), np.max(img_batch), np.min(img_batch), float(np.mean(img_batch)),
+                float(np.std(img_batch))
+            )
+        )
+        print(
+            "\t[*] Mask Shape: %s - Max Val: %.2f - Min Val: %.2f - Mean: %.2f - Stddev: %.2f" % (
+                str(mask_batch.shape), np.max(mask_batch), np.min(mask_batch), float(np.mean(mask_batch)),
+                float(np.std(mask_batch))
+            )
+        )
+        print(
+            "\t[*] Label Shape: %s - Max Val: %.2f - Min Val: %.2f" %
+            (str(lbl_batch.shape), np.max(lbl_batch), np.min(lbl_batch))
+        )
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/LICENSE b/TensorFlow/Segmentation/UNet_Industrial/dllogger/LICENSE
new file mode 100644
index 00000000..93144ec8
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/LICENSE
@@ -0,0 +1,12 @@
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/README.md b/TensorFlow/Segmentation/UNet_Industrial/dllogger/README.md
new file mode 100644
index 00000000..87da84a5
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/README.md
@@ -0,0 +1,22 @@
+# Tools for logging DL training
+DLLogger is a tool to generate logs during Deep Learning training.
+
+## Installation
+```
+git clone https://gitlab-master.nvidia.com/dl/JoC/DLLogger.git
+pip install DLLogger/.
+```
+
+## Usage
+You can use DLLogger with the simplest `LOGGER.log()` API:
+```
+from logger.logger import LOGGER
+from logger import tags
+
+LOGGER.model = 'ResNet'
+LOGGER.log(key=tags.INPUT_BATCH_SIZE, value=128)
+```
+For the more advanced usage, please refer to the `dummy_run.py` example.
+
+## Tags
+All available tags are listed in the `logger/tags.py` file.
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/__init__.py
new file mode 100644
index 00000000..b7b6a0c9
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .logger import LOGGER, StdOutBackend, MLPerfBackend, JsonBackend, CompactBackend, Scope, AverageMeter, StandardMeter
+from . import tags
+
+__all__ = [LOGGER, StdOutBackend, MLPerfBackend, JsonBackend, CompactBackend, Scope, AverageMeter, StandardMeter, tags]
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/autologging.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/autologging.py
new file mode 100644
index 00000000..a23999e0
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/autologging.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Common values reported
+
+
+import subprocess
+import xml.etree.ElementTree as ET
+
+#TODO: print CUDA version, container version etc
+
+def log_hardware(logger):
+    # TODO: asserts - what if you cannot launch those commands?
+    # number of CPU threads
+    cpu_info_command = 'cat /proc/cpuinfo'
+    cpu_info = subprocess.run(cpu_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    cpu_num_index = len(cpu_info) - cpu_info[::-1].index(b'processor') + 1
+    cpu_num = int(cpu_info[cpu_num_index]) + 1
+
+    # CPU name
+    cpu_name_begin_index = cpu_info.index(b'name')
+    cpu_name_end_index = cpu_info.index(b'stepping')
+    cpu_name = b' '.join(cpu_info[cpu_name_begin_index + 2:cpu_name_end_index]).decode('utf-8')
+
+    logger.log(key='cpu_info', value={"num": cpu_num, "name": cpu_name})
+
+    # RAM memory
+    ram_info_command = 'free -m -h'
+    ram_info = subprocess.run(ram_info_command.split(), stdout=subprocess.PIPE).stdout.split()
+    ram_index = ram_info.index(b'Mem:') + 1
+    ram = ram_info[ram_index].decode('utf-8')
+
+    logger.log(key='mem_info', value={"ram": ram})
+
+    # GPU
+    nvidia_smi_command = 'nvidia-smi -q -x'
+    nvidia_smi_output = subprocess.run(nvidia_smi_command.split(), stdout=subprocess.PIPE).stdout
+    nvidia_smi = ET.fromstring(nvidia_smi_output)
+    gpus = nvidia_smi.findall('gpu')
+    ver = nvidia_smi.findall('driver_version')
+
+    logger.log(key="gpu_info",
+                 value={
+                      "driver_version": ver[0].text,
+                      "num": len(gpus),
+                      "name": [g.find('product_name').text for g in gpus],
+                      "mem": [g.find('fb_memory_usage').find('total').text for g in gpus]})
+
+def log_args(logger, args):
+    logger.log(key='args', value=vars(args))
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/logger.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/logger.py
new file mode 100644
index 00000000..ed4621e0
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/logger.py
@@ -0,0 +1,519 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import time
+import json
+import logging
+import inspect
+import sys
+from contextlib import contextmanager
+import functools
+from collections import OrderedDict
+import datetime
+
+from . import autologging
+
+NVLOGGER_NAME = 'nv_dl_logger'
+NVLOGGER_VERSION = '0.3.1'
+NVLOGGER_TOKEN = ':::NVLOG'
+
+MLPERF_NAME = 'mlperf_logger'
+MLPERF_VERSION = '0.5.0'
+MLPERF_TOKEN = ':::MLP'
+
+COMPACT_NAME = 'compact_logger'
+
+DEFAULT_JSON_FILENAME = 'nvlog.json'
+
+class Scope:
+    RUN = 0
+    EPOCH = 1
+    TRAIN_ITER = 2
+
+
+class Level:
+    CRITICAL = 5
+    ERROR = 4
+    WARNING = 3
+    INFO = 2
+    DEBUG = 1
+
+
+_data = OrderedDict([
+    ('model', None),
+    ('epoch', -1),
+    ('iteration', -1),
+    ('total_iteration', -1),
+    ('metrics', OrderedDict()),
+    ('timed_blocks', OrderedDict()),
+    ('current_scope', Scope.RUN)
+    ])
+
+def get_caller(root_dir=None):
+    stack_files = [s.filename.split('/')[-1] for s in inspect.stack()]
+    stack_index = 0
+    while stack_index < len(stack_files) and stack_files[stack_index] != 'logger.py':
+        stack_index += 1
+    while (stack_index < len(stack_files) and 
+            stack_files[stack_index] in ['logger.py', 'autologging.py', 'contextlib.py']):
+        stack_index += 1
+
+    caller = inspect.stack()[stack_index]
+
+    return "%s:%d" % (stack_files[stack_index], caller.lineno)
+
+class StandardMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.value = None
+
+    def record(self, value):
+        self.value = value
+
+    def get_value(self):
+        return self.value
+
+    def get_last(self):
+        return self.value
+
+class AverageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.count = 0
+        self.value = 0
+        self.last = 0
+
+    def record(self, value, n = 1):
+        self.last = value
+        self.count += n
+        self.value += value * n
+
+    def get_value(self):
+        return self.value / self.count
+
+    def get_last(self):
+        return self.last
+
+class JsonBackend(object):
+
+    def __init__(self, log_file=DEFAULT_JSON_FILENAME, logging_scope=Scope.TRAIN_ITER,
+            iteration_interval=1):
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.json_log = OrderedDict([
+            ('run', OrderedDict()),
+            ('epoch', OrderedDict()),
+            ('iter', OrderedDict()),
+            ('event', OrderedDict()),
+            ])
+        
+        self.json_log['epoch']['x'] = []
+        if self.logging_scope == Scope.TRAIN_ITER:
+            self.json_log['iter']['x'] = [[]]
+
+    def register_metric(self, key, metric_scope):
+        if (metric_scope == Scope.TRAIN_ITER and
+                self.logging_scope == Scope.TRAIN_ITER):
+            if not key in self.json_log['iter'].keys():
+                self.json_log['iter'][key] = [[]]
+        if metric_scope == Scope.EPOCH:
+            if not key in self.json_log['epoch'].keys():
+                self.json_log['epoch'][key] = []
+
+    def log(self, key, value):
+        if _data['current_scope'] == Scope.RUN:
+            self.json_log['run'][key] = value
+        elif _data['current_scope'] == Scope.EPOCH: 
+            pass
+        elif _data['current_scope'] == Scope.TRAIN_ITER:
+            pass
+        else:
+            raise ValueError('log function for scope "', _data['current_scope'], 
+                    '" not implemented')
+
+    def log_event(self, key, value):
+        if not key in self.json_log['event'].keys():
+            self.json_log['event'][key] = []
+        entry = OrderedDict()
+        entry['epoch'] = _data['epoch']
+        entry['iter'] = _data['iteration']
+        entry['timestamp'] = time.time()
+        if value:
+            entry['value'] = value
+        self.json_log['event'][key].append(str(entry))
+
+    def log_iteration_summary(self):
+        if (self.logging_scope == Scope.TRAIN_ITER and 
+                _data['total_iteration'] % self.iteration_interval == 0):
+            for key, m in _data['metrics'].items():
+                if m.metric_scope == Scope.TRAIN_ITER:
+                    self.json_log['iter'][key][-1].append(str(m.get_last()))
+
+            # log x for iteration number
+            self.json_log['iter']['x'][-1].append(_data['iteration'])
+
+
+    def dump_json(self):
+        if self.log_file is None:
+            print(json.dumps(self.json_log, indent=4))
+        else:
+            with open(self.log_file, 'w') as f:
+                json.dump(self.json_log, fp=f, indent=4)
+
+    def log_epoch_summary(self):
+        for key, m in _data['metrics'].items():
+            if m.metric_scope == Scope.EPOCH:
+                self.json_log['epoch'][key].append(str(m.get_value()))
+            elif (m.metric_scope == Scope.TRAIN_ITER and 
+                    self.logging_scope == Scope.TRAIN_ITER):
+                # create new sublists for each iter metric in the next epoch
+                self.json_log['iter'][key].append([])
+        
+        # log x for epoch number
+        self.json_log['epoch']['x'].append(_data['epoch'])
+
+        # create new sublist for iter's x in the next epoch
+        if self.logging_scope == Scope.TRAIN_ITER:
+            self.json_log['iter']['x'].append([])
+
+        self.dump_json()
+
+    def timed_block_start(self, name):
+        pass
+
+    def timed_block_stop(self, name):
+        pass
+
+    def finish(self):
+        self.dump_json()
+
+class _ParentStdOutBackend(object):
+
+    def __init__(self, name, token, version, log_file, logging_scope, iteration_interval):
+
+        self.root_dir = None
+        self.worker = [0]
+        self.prefix = ''
+
+        self.name = name
+        self.token = token
+        self.version = version
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.logger = logging.getLogger(self.name)
+        self.logger.setLevel(logging.DEBUG)
+        self.logger.handlers = []
+
+        if (self.log_file is None):
+            self.stream_handler = logging.StreamHandler(stream=sys.stdout)
+            self.stream_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.stream_handler)
+        else:
+            self.file_handler = logging.FileHandler(self.log_file, mode='w')
+            self.file_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.file_handler)
+
+    def register_metric(self, key, meter=None, metric_scope=Scope.EPOCH):
+        pass
+
+    def log_epoch_summary(self):
+        pass
+
+    def log_iteration_summary(self):
+        pass
+
+    def log(self, key, value):
+        if _data['current_scope'] > self.logging_scope:
+            pass
+        elif (_data['current_scope'] == Scope.TRAIN_ITER and 
+                _data['total_iteration'] % self.iteration_interval != 0):
+            pass
+        else:
+            self.log_stdout(key, value)
+
+    def log_event(self, key, value):
+        self.log_stdout(key, value)
+        
+    def log_stdout(self, key, value=None, forced=False):
+        # TODO: worker 0 
+        # only the 0-worker will log
+        #if not forced and self.worker != 0:
+        #    pass
+
+        if value is None:
+            msg = key
+        else:
+            str_json = json.dumps(str(value))
+            msg = '{key}: {value}'.format(key=key, value=str_json)
+
+        call_site = get_caller(root_dir=self.root_dir)
+        now = time.time()
+
+        message = '{prefix}{token}v{ver} {model} {secs:.9f} ({call_site}) {msg}'.format(
+            prefix=self.prefix, token=self.token, ver=self.version, secs=now, 
+            model=_data['model'],
+            call_site=call_site, msg=msg)
+
+        self.logger.debug(message)
+
+    def timed_block_start(self, name):
+        self.log_stdout(key=name + "_start")
+
+    def timed_block_stop(self, name):
+        self.log_stdout(key=name + "_stop")
+
+    def finish(self):
+        pass
+
+class StdOutBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=Scope.TRAIN_ITER, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=NVLOGGER_NAME, token=NVLOGGER_TOKEN, 
+                version=NVLOGGER_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+        
+class MLPerfBackend(_ParentStdOutBackend):
+
+    def __init__(self, log_file=None, logging_scope=Scope.TRAIN_ITER, iteration_interval=1):
+        _ParentStdOutBackend.__init__(self, name=MLPERF_NAME, token=MLPERF_TOKEN, 
+                version=MLPERF_VERSION, log_file=log_file, logging_scope=logging_scope, 
+                iteration_interval=iteration_interval)
+
+class CompactBackend(object):
+
+    def __init__(self, log_file=None, logging_scope=Scope.TRAIN_ITER, iteration_interval=1):
+        self.log_file = log_file
+        self.logging_scope = logging_scope
+        self.iteration_interval = iteration_interval
+
+        self.logger = logging.getLogger(COMPACT_NAME)
+        self.logger.setLevel(logging.DEBUG)
+        self.logger.handlers = []
+
+        if (self.log_file is None):
+            self.stream_handler = logging.StreamHandler(stream=sys.stdout)
+            self.stream_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.stream_handler)
+        else:
+            self.file_handler = logging.FileHandler(self.log_file, mode='w')
+            self.file_handler.setLevel(logging.DEBUG)
+            self.logger.addHandler(self.file_handler)
+    
+    def register_metric(self, key, meter=None, metric_scope=Scope.EPOCH):
+        pass
+    
+    def timestamp_prefix(self):
+        return datetime.datetime.now().strftime('[%Y-%m-%d %H:%M:%S]')
+
+    def log(self, key, value):
+        if _data['current_scope'] == Scope.RUN:
+            self.log_event(key, value)
+    
+    def log_event(self, key, value):
+        msg = self.timestamp_prefix() + ' ' + str(key)
+        if value is not None:
+            msg += ": " + str(value)
+        self.logger.debug(msg)
+    
+    def log_epoch_summary(self):
+        if self.logging_scope >= Scope.EPOCH:
+            summary = self.timestamp_prefix() + ' Epoch {:<4} '.format(str(_data['epoch']) + ':')
+            for key, m in _data['metrics'].items():
+                if m.metric_scope >= Scope.EPOCH:
+                    summary += str(key) + ": " + str(m.get_value()) + ", "
+            self.logger.debug(summary)
+
+    def log_iteration_summary(self):
+        if self.logging_scope >= Scope.TRAIN_ITER and _data['total_iteration'] % self.iteration_interval == 0:
+            summary = self.timestamp_prefix() + ' Iter {:<5} '.format(str(_data['iteration']) + ':')
+            for key, m in _data['metrics'].items():
+                if m.metric_scope == Scope.TRAIN_ITER:
+                    summary += str(key) + ": " + str(m.get_last()) + ", "
+            self.logger.debug(summary)
+ 
+    def timed_block_start(self, name):
+        pass
+
+    def timed_block_stop(self, name):
+        pass
+
+    def finish(self):
+        pass
+
+class _Logger(object):
+    def __init__(self):
+
+        self.backends = [
+                CompactBackend(),
+                JsonBackend()
+                ]
+
+        self.level = Level.INFO
+   
+    def set_model_name(self, name):
+        _data['model'] = name
+
+
+    def set_backends(self, backends):
+        self.backends = backends
+        
+    def register_metric(self, key, meter=None, metric_scope=Scope.EPOCH):
+        if meter is None:
+            meter = StandardMeter()
+        #TODO: move to argument of Meter?
+        meter.metric_scope = metric_scope
+        _data['metrics'][key] = meter
+        for b in self.backends:
+            b.register_metric(key, metric_scope)
+
+    def log(self, key, value=None, forced=False, level=Level.INFO):
+        if level < self.level:
+            return
+
+        if _data['current_scope'] == Scope.TRAIN_ITER or _data['current_scope'] == Scope.EPOCH:
+            if key in _data['metrics'].keys():
+                if _data['metrics'][key].metric_scope == _data['current_scope']:
+                    _data['metrics'][key].record(value)
+        for b in self.backends:
+            b.log(key, value)
+
+    def debug(self, *args, **kwargs):
+        self.log(*args, level=Level.DEBUG, **kwargs)
+
+    def info(self, *args, **kwargs):
+        self.log(*args, level=Level.INFO, **kwargs)
+
+    def warning(self, *args, **kwargs):
+        self.log(*args, level=Level.WARNING, **kwargs)
+
+    def error(self, *args, **kwargs):
+        self.log(*args, level=Level.ERROR, **kwargs)
+
+    def critical(self, *args, **kwargs):
+        self.log(*args, level=Level.CRITICAL, **kwargs)
+
+    def log_event(self, key, value=None):
+        for b in self.backends:
+            b.log_event(key, value)
+    
+    def timed_block_start(self, name):
+        if not name in _data['timed_blocks']:
+            _data['timed_blocks'][name] = OrderedDict()
+        _data['timed_blocks'][name]['start'] = time.time()
+        for b in self.backends:
+            b.timed_block_start(name)
+    
+    def timed_block_stop(self, name):
+        if not name in _data['timed_blocks']:
+            raise ValueError('timed_block_stop called before timed_block_start for ' + name)
+        _data['timed_blocks'][name]['stop'] = time.time()
+        delta = _data['timed_blocks'][name]['stop'] - _data['timed_blocks'][name]['start']
+        self.log(name + '_time', delta)
+        for b in self.backends:
+            b.timed_block_stop(name)
+
+    def iteration_start(self):
+        _data['current_scope'] = Scope.TRAIN_ITER
+        _data['iteration'] += 1
+        _data['total_iteration'] += 1
+
+
+    def iteration_stop(self):
+        for b in self.backends:
+            b.log_iteration_summary()
+        _data['current_scope'] = Scope.EPOCH
+
+    def epoch_start(self):
+        _data['current_scope'] = Scope.EPOCH 
+        _data['epoch'] += 1
+        _data['iteration'] = -1
+
+        for n, m in _data['metrics'].items():
+            if m.metric_scope == Scope.TRAIN_ITER:
+                m.reset()
+
+    def epoch_stop(self):
+        for b in self.backends:
+            b.log_epoch_summary()
+        _data['current_scope'] = Scope.RUN
+
+    def finish(self):
+        for b in self.backends:
+            b.finish()
+
+    def iteration_generator_wrapper(self, gen):
+        for g in gen:
+            self.iteration_start()
+            yield g
+            self.iteration_stop()
+
+    def epoch_generator_wrapper(self, gen):
+        for g in gen:
+            self.epoch_start()
+            yield g
+            self.epoch_stop()
+
+    @contextmanager
+    def timed_block(self, prefix, value=None, forced=False):
+        """ This function helps with timed blocks
+            ----
+            Parameters:
+            prefix - one of items from TIMED_BLOCKS; the action to be timed
+            logger - NVLogger object
+            forced - if True then the events are always logged (even if it should be skipped)
+        """
+        self.timed_block_start(prefix)
+        yield self
+        self.timed_block_stop(prefix)
+
+    def log_hardware(self):
+        autologging.log_hardware(self)
+
+    def log_args(self, args):
+        autologging.log_args(self, args)
+
+    def timed_function(self, prefix, variable=None, forced=False):
+        """ This decorator helps with timed functions
+            ----
+            Parameters:
+            prefix - one of items from TIME_BLOCK; the action to be timed
+            logger - NVLogger object
+            forced - if True then the events are always logged (even if it should be skipped)
+        """
+
+        def timed_function_decorator(func):
+            @functools.wraps(func)
+            def wrapper(*args, **kwargs):
+                value = kwargs.get(variable, next(iter(args), None))
+                with self.timed_block(prefix=prefix, value=value, forced=forced):
+                    func(*args, **kwargs)
+
+            return wrapper
+
+        return timed_function_decorator
+
+
+LOGGER = _Logger()
+
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/tags.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/tags.py
new file mode 100644
index 00000000..d4661720
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dllogger/tags.py
@@ -0,0 +1,255 @@
+# Copyright 2018 MLBenchmark Group. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Common values reported
+
+VALUE_EPOCH = "epoch"
+VALUE_ITERATION = "iteration"
+VALUE_ACCURACY = "accuracy"
+VALUE_BLEU = "bleu"
+VALUE_TOP1 = "top1"
+VALUE_TOP5 = "top5"
+VALUE_BBOX_MAP = "bbox_map"
+VALUE_MASK_MAP = "mask_map"
+VALUE_BCE = "binary_cross_entropy"
+
+
+# Timed blocks (used with timed_function & timed_block
+# For each there should be *_start and *_stop tags defined
+
+RUN_BLOCK = "run"
+SETUP_BLOCK = "setup"
+PREPROC_BLOCK = "preproc"
+
+TRAIN_BLOCK = "train"
+TRAIN_PREPROC_BLOCK = "train_preproc"
+TRAIN_EPOCH_BLOCK = "train_epoch"
+TRAIN_EPOCH_PREPROC_BLOCK = "train_epoch_preproc"
+TRAIN_CHECKPOINT_BLOCK = "train_checkpoint"
+TRAIN_ITER_BLOCK = "train_iteration"
+
+EVAL_BLOCK = "eval"
+EVAL_ITER_BLOCK = "eval_iteration"
+
+#TODO: to remove?
+TIMED_BLOCKS = {
+    RUN_BLOCK,
+    SETUP_BLOCK,
+    PREPROC_BLOCK,
+    TRAIN_BLOCK,
+    TRAIN_PREPROC_BLOCK,
+    TRAIN_EPOCH_BLOCK,
+    TRAIN_EPOCH_PREPROC_BLOCK,
+    TRAIN_CHECKPOINT_BLOCK,
+    TRAIN_ITER_BLOCK,
+    EVAL_BLOCK,
+    EVAL_ITER_BLOCK,
+}
+
+
+# Events
+
+RUN_INIT = "run_init"
+
+SETUP_START = "setup_start"
+SETUP_STOP = "setup_stop"
+
+PREPROC_START = "preproc_start"
+PREPROC_STOP = "preproc_stop"
+
+RUN_START = "run_start"
+RUN_STOP = "run_stop"
+RUN_FINAL = "run_final"
+
+TRAIN_CHECKPOINT_START = "train_checkpoint_start"
+TRAIN_CHECKPOINT_STOP = "train_checkpoint_stop"
+
+TRAIN_PREPROC_START = "train_preproc_start"
+TRAIN_PREPROC_STOP = "train_preproc_stop"
+
+TRAIN_EPOCH_PREPROC_START = "train_epoch_preproc_start"
+TRAIN_EPOCH_PREPROC_STOP = "train_epoch_preproc_stop"
+
+TRAIN_ITER_START = "train_iter_start"
+TRAIN_ITER_STOP = "train_iter_stop"
+
+TRAIN_EPOCH_START = "train_epoch_start"
+TRAIN_EPOCH_STOP = "train_epoch_stop"
+
+
+# MLPerf specific tags
+
+RUN_CLEAR_CACHES = "run_clear_caches"
+
+PREPROC_NUM_TRAIN_EXAMPLES = "preproc_num_train_examples"
+PREPROC_NUM_EVAL_EXAMPLES = "preproc_num_eval_examples"
+PREPROC_TOKENIZE_TRAINING = "preproc_tokenize_training"
+PREPROC_TOKENIZE_EVAL = "preproc_tokenize_eval"
+PREPROC_VOCAB_SIZE = "preproc_vocab_size"
+
+RUN_SET_RANDOM_SEED = "run_set_random_seed"
+
+INPUT_SIZE = "input_size"
+INPUT_BATCH_SIZE = "input_batch_size"
+INPUT_ORDER = "input_order"
+INPUT_SHARD = "input_shard"
+INPUT_BN_SPAN = "input_bn_span"
+
+INPUT_CENTRAL_CROP = "input_central_crop"
+INPUT_CROP_USES_BBOXES = "input_crop_uses_bboxes"
+INPUT_DISTORTED_CROP_MIN_OBJ_COV = "input_distorted_crop_min_object_covered"
+INPUT_DISTORTED_CROP_RATIO_RANGE = "input_distorted_crop_aspect_ratio_range"
+INPUT_DISTORTED_CROP_AREA_RANGE = "input_distorted_crop_area_range"
+INPUT_DISTORTED_CROP_MAX_ATTEMPTS = "input_distorted_crop_max_attempts"
+INPUT_MEAN_SUBTRACTION = "input_mean_subtraction"
+INPUT_RANDOM_FLIP = "input_random_flip"
+
+INPUT_RESIZE = "input_resize"
+INPUT_RESIZE_ASPECT_PRESERVING = "input_resize_aspect_preserving"
+
+
+# Opt
+
+OPT_NAME = "opt_name"
+
+OPT_LR = "opt_learning_rate"
+OPT_MOMENTUM = "opt_momentum"
+
+OPT_WEIGHT_DECAY = "opt_weight_decay"
+
+OPT_HP_ADAM_BETA1 = "opt_hp_Adam_beta1"
+OPT_HP_ADAM_BETA2 = "opt_hp_Adam_beta2"
+OPT_HP_ADAM_EPSILON = "opt_hp_Adam_epsilon"
+
+OPT_LR_WARMUP_STEPS = "opt_learning_rate_warmup_steps"
+
+
+#  Train
+
+TRAIN_LOOP = "train_loop"
+TRAIN_EPOCH = "train_epoch"
+TRAIN_CHECKPOINT = "train_checkpoint"
+TRAIN_LOSS = "train_loss"
+TRAIN_ITERATION_LOSS = "train_iteration_loss"
+
+
+# Eval
+
+EVAL_START = "eval_start"
+EVAL_SIZE = "eval_size"
+EVAL_TARGET = "eval_target"
+EVAL_ACCURACY = "eval_accuracy"
+EVAL_STOP = "eval_stop"
+
+
+# Perf
+
+PERF_IT_PER_SEC = "perf_it_per_sec"
+PERF_TIME_TO_TRAIN = "time_to_train"
+
+EVAL_ITERATION_ACCURACY = "eval_iteration_accuracy"
+
+
+# Model
+
+MODEL_HP_LOSS_FN = "model_hp_loss_fn"
+
+MODEL_HP_INITIAL_SHAPE = "model_hp_initial_shape"
+MODEL_HP_FINAL_SHAPE = "model_hp_final_shape"
+
+MODEL_L2_REGULARIZATION = "model_l2_regularization"
+MODEL_EXCLUDE_BN_FROM_L2 = "model_exclude_bn_from_l2"
+
+MODEL_HP_RELU = "model_hp_relu"
+MODEL_HP_CONV2D_FIXED_PADDING = "model_hp_conv2d_fixed_padding"
+MODEL_HP_BATCH_NORM = "model_hp_batch_norm"
+MODEL_HP_DENSE = "model_hp_dense"
+
+
+# GNMT specific
+
+MODEL_HP_LOSS_SMOOTHING = "model_hp_loss_smoothing"
+MODEL_HP_NUM_LAYERS = "model_hp_num_layers"
+MODEL_HP_HIDDEN_SIZE = "model_hp_hidden_size"
+MODEL_HP_DROPOUT = "model_hp_dropout"
+
+EVAL_HP_BEAM_SIZE = "eval_hp_beam_size"
+TRAIN_HP_MAX_SEQ_LEN = "train_hp_max_sequence_length"
+EVAL_HP_MAX_SEQ_LEN = "eval_hp_max_sequence_length"
+EVAL_HP_LEN_NORM_CONST = "eval_hp_length_normalization_constant"
+EVAL_HP_LEN_NORM_FACTOR = "eval_hp_length_normalization_factor"
+EVAL_HP_COV_PENALTY_FACTOR = "eval_hp_coverage_penalty_factor"
+
+
+# NCF specific
+
+PREPROC_HP_MIN_RATINGS = "preproc_hp_min_ratings"
+PREPROC_HP_NUM_EVAL = "preproc_hp_num_eval"
+PREPROC_HP_SAMPLE_EVAL_REPLACEMENT = "preproc_hp_sample_eval_replacement"
+
+INPUT_HP_NUM_NEG = "input_hp_num_neg"
+INPUT_HP_SAMPLE_TRAIN_REPLACEMENT = "input_hp_sample_train_replacement"
+INPUT_STEP_TRAIN_NEG_GEN = "input_step_train_neg_gen"
+INPUT_STEP_EVAL_NEG_GEN = "input_step_eval_neg_gen"
+
+EVAL_HP_NUM_USERS = "eval_hp_num_users"
+EVAL_HP_NUM_NEG = "eval_hp_num_neg"
+
+MODEL_HP_MF_DIM = "model_hp_mf_dim"
+MODEL_HP_MLP_LAYER_SIZES = "model_hp_mlp_layer_sizes"
+
+
+# RESNET specific
+
+EVAL_EPOCH_OFFSET = "eval_offset"
+
+MODEL_HP_INITIAL_MAX_POOL = "model_hp_initial_max_pool"
+MODEL_HP_BEGIN_BLOCK = "model_hp_begin_block"
+MODEL_HP_END_BLOCK = "model_hp_end_block"
+MODEL_HP_BLOCK_TYPE = "model_hp_block_type"
+MODEL_HP_PROJECTION_SHORTCUT = "model_hp_projection_shortcut"
+MODEL_HP_SHORTCUT_ADD = "model_hp_shorcut_add"
+MODEL_HP_RESNET_TOPOLOGY = "model_hp_resnet_topology"
+
+
+# Transformer specific
+
+INPUT_MAX_LENGTH = "input_max_length"
+
+MODEL_HP_INITIALIZER_GAIN = "model_hp_initializer_gain"
+MODEL_HP_VOCAB_SIZE = "model_hp_vocab_size"
+MODEL_HP_NUM_HIDDEN_LAYERS = "model_hp_hidden_layers"
+MODEL_HP_EMBEDDING_SHARED_WEIGHTS = "model_hp_embedding_shared_weights"
+MODEL_HP_ATTENTION_DENSE = "model_hp_attention_dense"
+MODEL_HP_ATTENTION_DROPOUT = "model_hp_attention_dropout"
+MODEL_HP_FFN_OUTPUT_DENSE = "model_hp_ffn_output_dense"
+MODEL_HP_FFN_FILTER_DENSE = "model_hp_ffn_filter_dense"
+MODEL_HP_RELU_DROPOUT = "model_hp_relu_dropout"
+MODEL_HP_LAYER_POSTPROCESS_DROPOUT = "model_hp_layer_postprocess_dropout"
+MODEL_HP_NORM = "model_hp_norm"
+MODEL_HP_SEQ_BEAM_SEARCH = "model_hp_sequence_beam_search"
+
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/dummy_run.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dummy_run.py
new file mode 100644
index 00000000..3ac614c6
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/dummy_run.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dllogger import LOGGER, CompactBackend, StdOutBackend, MLPerfBackend, JsonBackend, Scope, AverageMeter, tags
+from argparse import ArgumentParser
+import random
+
+@LOGGER.timed_function("train")
+def train():
+    for i in range(0, 10):
+    #for i in LOGGER.epoch_generator_wrapper(range(0, 10)):
+        LOGGER.epoch_start()
+        LOGGER.log("epoch_nr", i)
+        LOGGER.log("epochs2", 2 * i)
+        train_epoch(i)
+        LOGGER.epoch_stop()
+
+
+@LOGGER.timed_function("train_epoch", "epoch")
+def train_epoch(epoch):
+    for i in range(epoch*30, (epoch+1)*30, 2):
+    #for i in LOGGER.iteration_generator_wrapper(range(epoch*10, (epoch+1)*10, 2)):
+        LOGGER.iteration_start()
+        LOGGER.log("loss", i*epoch)
+        LOGGER.iteration_stop()
+    if epoch % 3 == 1:
+        with LOGGER.timed_block("eval"):
+            LOGGER.log("accuracy", i * epoch)
+            LOGGER.log_event(key="ep divisible by 3", value=epoch)
+
+
+def main():
+    LOGGER.set_model_name('ResNet')
+    LOGGER.set_backends([
+            StdOutBackend(log_file='std.out',
+                logging_scope=Scope.TRAIN_ITER),
+            CompactBackend(log_file=None,
+                logging_scope=Scope.TRAIN_ITER, iteration_interval=5),
+            JsonBackend(log_file='dummy.json',
+                logging_scope=Scope.TRAIN_ITER, iteration_interval=4)
+            ])
+
+    parser = ArgumentParser()
+    parser.add_argument('--dummy', type=str, default='default_dummy_value')
+    args = parser.parse_args()
+
+    LOGGER.log_hardware()
+    LOGGER.log_args(args)
+
+    LOGGER.log(tags.RUN_INIT)
+    LOGGER.register_metric('loss', meter=AverageMeter(), metric_scope=Scope.TRAIN_ITER)
+    LOGGER.register_metric('epoch_nr', metric_scope=Scope.EPOCH)
+    LOGGER.register_metric('epochs2')
+
+    with LOGGER.timed_block(tags.SETUP_BLOCK):
+        print("This is setup.")
+
+    with LOGGER.timed_block(tags.PREPROC_BLOCK):
+        print("This is preprocessing.")
+
+    with LOGGER.timed_block(tags.RUN_BLOCK):
+        print("This is run.")
+        train()
+        print("This is the end.")
+
+    LOGGER.log(tags.RUN_FINAL)
+
+    LOGGER.finish()
+
+def main2():
+    LOGGER.set_backends([
+            CompactBackend(log_file=None,
+                logging_scope=Scope.TRAIN_ITER),
+            StdOutBackend(log_file='std.out',
+                logging_scope=Scope.EPOCH, iteration_interval=4),
+            JsonBackend(log_file='dummy.json',
+                logging_scope=Scope.TRAIN_ITER, iteration_interval=1)
+            ])
+    LOGGER.log_hardware()
+
+    data_x = range(0,10)
+    data_y = [3.*x + 2. for x in data_x]
+
+    data = list(zip(data_x, data_y))
+
+    LOGGER.register_metric('l', AverageMeter(), metric_scope=Scope.TRAIN_ITER)
+    LOGGER.register_metric('a', metric_scope=Scope.TRAIN_ITER)
+    LOGGER.register_metric('b', metric_scope=Scope.TRAIN_ITER)
+
+    LOGGER.info('RUN_INIT')
+
+    model_a = 1.
+    model_b = 0.
+
+    def model(ma, mb, x):
+        return ma*x+mb
+
+    def loss(y, t):
+        return (y-t)**2
+
+    def update_a(ma, mb, x, t):
+        return ma - 0.001 * 2*x*(ma*x+mb-t)
+
+    def update_b(ma, mb, x, t):
+        return mb - 0.001 * 2*(ma*x+mb-t)
+
+    for e in range(0, 5):
+        LOGGER.epoch_start()
+        for (x, t) in data:
+            LOGGER.iteration_start()
+            y = model(model_a, model_b, x)
+            model_a = update_a(model_a, model_b, x, t)
+            model_b = update_b(model_a, model_b, x, t)
+            l = loss(y, t)
+            LOGGER.info('b', model_b)
+            LOGGER.debug('a', model_a)
+            LOGGER.warning('l', l)
+            #LOGGER.log('a', model_a)
+            LOGGER.iteration_stop()
+        LOGGER.epoch_stop()
+
+    #for e in LOGGER.epoch_generator_wrapper(range(0, 10)):
+    #    for (x, t) in LOGGER.iteration_generator_wrapper(random.sample(data, len(data))):
+    #        y = model(model_a, model_b, x)
+    #        model_a = update_a(model_a, model_b, x, t)
+    #        model_b = update_b(model_a, model_b, x, t)
+    #        l = loss(y, t)
+    #        LOGGER.debug('a', model_a)
+    #        LOGGER.info('b', model_b)
+    #        LOGGER.warning('l', l)
+
+
+    LOGGER.finish()
+
+    print("FINAL: {}*x+{}".format(model_a, model_b))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/TensorFlow/Segmentation/UNet_Industrial/dllogger/setup.py b/TensorFlow/Segmentation/UNet_Industrial/dllogger/setup.py
new file mode 100644
index 00000000..0a5856f2
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/dllogger/setup.py
@@ -0,0 +1,37 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import setuptools
+
+with open("README.md", "r") as f:
+  long_description = f.read()
+
+setuptools.setup(
+    name="DLLogger",
+    version="0.3.1",
+    author="Lukasz Mazurek",
+    author_email="lmazurek@nvidia.com",
+    description="Tools for logging DL training.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/nvlmazurek/DLLogger",
+    packages=['dllogger'],
+    classifiers=[
+      "Programming Language :: Python :: 2",
+      "Programming Language :: Python :: 3",
+      "License :: BSD",
+      "Operating System :: OS Independent",
+    ],
+    license="BSD",
+)
diff --git a/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007.sh b/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007.sh
new file mode 100755
index 00000000..55da98c0
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007.sh
@@ -0,0 +1,151 @@
+#!/bin/bash
+
+##############################################################################
+# Copyright (c) Jonathan Dekhtiar - contact@jonathandekhtiar.eu
+# All Rights Reserved.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+##############################################################################
+
+# Usage: ./download_and_preprocess_dagm2007.sh /path/to/dataset/directory/
+
+if [[ ! "$BASH_VERSION" ]] ; then
+    echo "Please do not use sh to run this script ($0), just execute it directly" 1>&2
+    exit 1
+fi
+
+if [[ -z "$1" ]]
+  then
+    echo -e "Error: Argument is missing. No dataset directory received."
+    echo -e "Usage: '$0 /path/to/dataset/directory/'"
+    exit 1
+fi
+
+DATASET_DIR=$(realpath -s $1)
+
+ZIP_FILES_DIR=${DATASET_DIR}/zip_files
+RAW_IMAGES_DIR=${DATASET_DIR}/raw_images
+
+PUBLIC_ZIP_FILES_DIR=${ZIP_FILES_DIR}/public
+PUBLIC_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/public
+
+if [[ ! -e ${PUBLIC_ZIP_FILES_DIR} ]]; then
+    echo "creating ${PUBLIC_ZIP_FILES_DIR} ..."
+    mkdir -p ${PUBLIC_ZIP_FILES_DIR}
+fi
+
+if [[ ! -e ${PUBLIC_RAW_IMAGES_DIR} ]]; then
+    echo "creating ${PUBLIC_RAW_IMAGES_DIR} ..."
+    mkdir -p ${PUBLIC_RAW_IMAGES_DIR}
+fi
+
+PRIVATE_ZIP_FILES_DIR=${ZIP_FILES_DIR}/private
+PRIVATE_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/private
+
+if [[ ! -e ${PRIVATE_ZIP_FILES_DIR} ]]; then
+    echo "creating ${PRIVATE_ZIP_FILES_DIR} ..."
+    mkdir -p ${PRIVATE_ZIP_FILES_DIR}
+fi
+
+if [[ ! -e ${PRIVATE_RAW_IMAGES_DIR} ]]; then
+    echo "creating ${PRIVATE_RAW_IMAGES_DIR} ..."
+    mkdir -p ${PRIVATE_RAW_IMAGES_DIR}
+fi
+
+echo -e "\n################################################"
+echo -e "Processing Public Dataset"
+echo -e "################################################\n"
+
+sleep 2
+
+BASE_PUBLIC_URL="https://resources.mpi-inf.mpg.de/conference/dagm/2007"
+
+declare -a arr=(
+    "Class1.zip"
+    "Class1_def.zip"
+    "Class2.zip"
+    "Class2_def.zip"
+    "Class3.zip"
+    "Class3_def.zip"
+    "Class4.zip"
+    "Class4_def.zip"
+    "Class5.zip"
+    "Class5_def.zip"
+    "Class6.zip"
+    "Class6_def.zip"
+)
+
+for file in "${arr[@]}"
+do
+    if [[ ! -e ${PUBLIC_ZIP_FILES_DIR}/${file} ]]; then
+        echo -e "Downloading File: $BASE_PUBLIC_URL/$file ..."
+        wget -N ${BASE_PUBLIC_URL}/${file} -O ${PUBLIC_ZIP_FILES_DIR}/${file}
+    fi
+
+    # Unzip without overwriting
+    unzip -n ${PUBLIC_ZIP_FILES_DIR}/${file} -d ${PUBLIC_RAW_IMAGES_DIR}
+
+done
+
+chmod -R 744 ${PUBLIC_ZIP_FILES_DIR}
+chmod -R 744 ${PUBLIC_RAW_IMAGES_DIR}
+
+echo -e "\n################################################"
+echo -e "Processing Private Dataset"
+echo -e "################################################\n"
+
+sleep 2
+
+declare -a arr=(
+    "Class1.zip"
+    "Class2.zip"
+    "Class3.zip"
+    "Class4.zip"
+    "Class5.zip"
+    "Class6.zip"
+    "Class7.zip"
+    "Class8.zip"
+    "Class9.zip"
+    "Class10.zip"
+)
+
+for file in "${arr[@]}"
+do
+    if [[ ! -e ${PRIVATE_ZIP_FILES_DIR}/${file} ]]; then
+        echo -e "\nError! File not found: '${PRIVATE_ZIP_FILES_DIR}/${file}'"
+        echo -e "Instructions:"
+        echo -e "    1. Create an account on 'https://hci.iwr.uni-heidelberg.de/node/3616'"
+        echo -e "    2. Download the missing file(s) to: '${PRIVATE_ZIP_FILES_DIR}'\n"
+        exit 1
+    fi
+
+    # Unzip without overwriting
+    unzip -n ${PRIVATE_ZIP_FILES_DIR}/${file} -d ${PRIVATE_RAW_IMAGES_DIR}
+
+done
+
+chmod -R 744 ${PRIVATE_ZIP_FILES_DIR}
+chmod -R 744 ${PRIVATE_RAW_IMAGES_DIR}
+
+echo -e "\n################################################"
+echo -e "Dataset Preprocessing Starting ..."
+echo -e "################################################\n"
+
+sleep 2
+
+echo "Cleaning non useful files ..."
+
+rm -rf ${DATASET_DIR}/**/Thumbs.db
+rm -rf ${DATASET_DIR}/**/**/Thumbs.db
+rm -rf ${DATASET_DIR}/**/**/**/Thumbs.db
+rm -rf ${DATASET_DIR}/**/**/**/**/Thumbs.db
+rm -rf ${DATASET_DIR}/**/**/**/**/**/Thumbs.db
+
+echo "Generating file listing for private dataset ..."
+
+python preprocess_dagm2007.py --data_dir=${PRIVATE_RAW_IMAGES_DIR}
+
+echo -e "\n################################################"
+echo -e "Dataset Processing Finished with success"
+echo -e "################################################\n"
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/images/dagm2007.png b/TensorFlow/Segmentation/UNet_Industrial/images/dagm2007.png
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diff --git a/TensorFlow/Segmentation/UNet_Industrial/main.py b/TensorFlow/Segmentation/UNet_Industrial/main.py
new file mode 100755
index 00000000..f05a0bfe
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/main.py
@@ -0,0 +1,162 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import os
+
+import warnings
+warnings.simplefilter("ignore")
+
+import tensorflow as tf
+
+import horovod.tensorflow as hvd
+from utils import hvd_utils
+
+from runtime import Runner
+
+from utils.cmdline_helper import parse_cmdline
+
+if __name__ == "__main__":
+
+    tf.logging.set_verbosity(tf.logging.ERROR)
+
+    FLAGS = parse_cmdline()
+
+    RUNNING_CONFIG = tf.contrib.training.HParams(
+        exec_mode=FLAGS.exec_mode,
+
+        # ======= Directory HParams ======= #
+        log_dir=os.path.join(FLAGS.results_dir, "logs"),
+        model_dir=os.path.join(FLAGS.results_dir, "checkpoints"),
+        summaries_dir=os.path.join(FLAGS.results_dir, "summaries"),
+        sample_dir=os.path.join(FLAGS.results_dir, "samples"),
+        data_dir=FLAGS.data_dir,
+        dataset_name=FLAGS.dataset_name,
+        dataset_hparams=dict(),
+
+        # ========= Model HParams ========= #
+        unet_variant=FLAGS.unet_variant,
+        activation_fn=FLAGS.activation_fn,
+        input_format='NHWC',
+        compute_format=FLAGS.data_format,
+        input_shape=(512, 512),
+        mask_shape=(512, 512),
+        n_channels=1,
+        input_normalization_method="zero_one",
+
+        # ======== Runtime HParams ======== #
+        use_tf_amp=FLAGS.use_tf_amp,
+        use_xla=FLAGS.use_xla,
+
+        # ======= Training HParams ======== #
+        iter_unit=FLAGS.iter_unit,
+        num_iter=FLAGS.num_iter,
+        warmup_steps=FLAGS.warmup_step,
+        batch_size=FLAGS.batch_size,
+        learning_rate=FLAGS.learning_rate,
+        learning_rate_decay_factor=FLAGS.learning_rate_decay_factor,
+        learning_rate_decay_steps=FLAGS.learning_rate_decay_steps,
+        rmsprop_decay=FLAGS.rmsprop_decay,
+        rmsprop_momentum=FLAGS.rmsprop_momentum,
+        weight_decay=FLAGS.weight_decay,
+        use_auto_loss_scaling=FLAGS.use_auto_loss_scaling,
+        loss_fn_name=FLAGS.loss_fn_name,
+        augment_data=FLAGS.augment_data,
+        weight_init_method=FLAGS.weight_init_method,
+
+        # ======== Debug Flags ======== #
+        # 0: No debug
+        # 1: Layer Creation Debug Info
+        # 2: Layer + Var Creation Debug Info
+        debug_verbosity=FLAGS.debug_verbosity,
+        log_every_n_steps=FLAGS.display_every,
+        seed=FLAGS.seed,
+    )
+
+    # ===================================
+
+    if RUNNING_CONFIG.dataset_name == "DAGM2007":
+        RUNNING_CONFIG.dataset_hparams["class_id"] = FLAGS.dataset_classID
+
+    runner = Runner(
+        input_format=RUNNING_CONFIG.input_format,
+        compute_format=RUNNING_CONFIG.compute_format,
+        n_channels=RUNNING_CONFIG.n_channels,
+        model_variant=RUNNING_CONFIG.unet_variant,
+        activation_fn=RUNNING_CONFIG.activation_fn,
+        input_shape=RUNNING_CONFIG.input_shape,
+        mask_shape=RUNNING_CONFIG.mask_shape,
+        input_normalization_method=RUNNING_CONFIG.input_normalization_method,
+
+        # Training HParams
+        augment_data=RUNNING_CONFIG.augment_data,
+        loss_fn_name=RUNNING_CONFIG.loss_fn_name,
+        weight_init_method=RUNNING_CONFIG.weight_init_method,
+
+        #  Runtime HParams
+        use_tf_amp=RUNNING_CONFIG.use_tf_amp,
+        use_xla=RUNNING_CONFIG.use_xla,
+
+        # Directory Params
+        log_dir=RUNNING_CONFIG.log_dir,
+        model_dir=RUNNING_CONFIG.model_dir,
+        sample_dir=RUNNING_CONFIG.sample_dir,
+        data_dir=RUNNING_CONFIG.data_dir,
+        dataset_name=RUNNING_CONFIG.dataset_name,
+        dataset_hparams=RUNNING_CONFIG.dataset_hparams,
+
+        # Debug Params
+        debug_verbosity=RUNNING_CONFIG.debug_verbosity,
+        log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,
+        seed=RUNNING_CONFIG.seed
+    )
+
+    if RUNNING_CONFIG.exec_mode in ["train", "train_and_evaluate", "training_benchmark"]:
+
+        runner.train(
+            iter_unit=RUNNING_CONFIG.iter_unit,
+            num_iter=RUNNING_CONFIG.num_iter,
+            batch_size=RUNNING_CONFIG.batch_size,
+            warmup_steps=RUNNING_CONFIG.warmup_steps,
+            weight_decay=RUNNING_CONFIG.weight_decay,
+            learning_rate=RUNNING_CONFIG.learning_rate,
+            learning_rate_decay_factor=RUNNING_CONFIG.learning_rate_decay_factor,
+            learning_rate_decay_steps=RUNNING_CONFIG.learning_rate_decay_steps,
+            rmsprop_decay=RUNNING_CONFIG.rmsprop_decay,
+            rmsprop_momentum=RUNNING_CONFIG.rmsprop_momentum,
+            use_auto_loss_scaling=FLAGS.use_auto_loss_scaling,
+            augment_data=RUNNING_CONFIG.augment_data,
+            is_benchmark=RUNNING_CONFIG.exec_mode == 'training_benchmark'
+        )
+
+    if RUNNING_CONFIG.exec_mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:
+
+        if RUNNING_CONFIG.exec_mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
+            raise NotImplementedError("Only single GPU inference is implemented.")
+
+        elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
+
+            runner.evaluate(
+                iter_unit=RUNNING_CONFIG.iter_unit if RUNNING_CONFIG.exec_mode != "train_and_evaluate" else "epoch",
+                num_iter=RUNNING_CONFIG.num_iter if RUNNING_CONFIG.exec_mode != "train_and_evaluate" else 1,
+                warmup_steps=RUNNING_CONFIG.warmup_steps,
+                batch_size=RUNNING_CONFIG.batch_size,
+                is_benchmark=RUNNING_CONFIG.exec_mode == 'inference_benchmark'
+            )
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/model/__init__.py
new file mode 100644
index 00000000..f1da12ec
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/__init__.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from model import layers
+from model import blocks
+
+from model import unet
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/__init__.py
new file mode 100644
index 00000000..af056bde
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/__init__.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from model.blocks.activation_blck import activation_block
+from model.blocks.activation_blck import authorized_activation_fn
+
+from model.blocks.unet_downsample import downsample_unet_block
+from model.blocks.unet_upsample import upsample_unet_block
+
+from model.blocks.unet_bottleneck import bottleneck_unet_block
+
+from model.blocks.unet_io_blocks import input_unet_block
+from model.blocks.unet_io_blocks import output_unet_block
+
+__all__ = [
+    'activation_block',
+    'authorized_activation_fn',
+    'upsample_unet_block',
+    'upsample_unet_block',
+    'bottleneck_unet_block',
+    'input_unet_block',
+    'output_unet_block',
+]
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/activation_blck.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/activation_blck.py
new file mode 100644
index 00000000..674323b5
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/activation_blck.py
@@ -0,0 +1,59 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+
+__all__ = [
+    "authorized_activation_fn",
+    "activation_block",
+]
+
+authorized_activation_fn = ["relu", "leaky_relu", "prelu_shared", "prelu_not_shared", "selu", "crelu", "elu"]
+
+
+def activation_block(inputs, act_fn, trainable=True, block_name='activation'):
+
+    with tf.variable_scope(block_name):
+
+        if act_fn == "relu":
+            return layers.relu(inputs)
+
+        if act_fn == "leaky_relu":
+            return layers.leaky_relu(inputs, alpha=0.2)
+
+        if act_fn == "prelu_shared":
+            return layers.prelu(inputs, channel_shared=True, trainable=trainable)
+
+        if act_fn == "prelu_not_shared":
+            return layers.prelu(inputs, channel_shared=False, trainable=trainable)
+
+        if act_fn == "selu":
+            return layers.selu(inputs)
+
+        if act_fn == "crelu":
+            return layers.crelu(inputs)
+
+        if act_fn == "elu":
+            return layers.elu(inputs)
+
+        raise ValueError("Unknown activation function: %s - Authorized: %s" % (act_fn, authorized_activation_fn))
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_bottleneck.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_bottleneck.py
new file mode 100644
index 00000000..429e4d71
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_bottleneck.py
@@ -0,0 +1,86 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+from model import blocks
+
+__all__ = ["bottleneck_unet_block"]
+
+
+def bottleneck_unet_block(
+    inputs, filters, data_format='NCHW', is_training=True, conv2d_hparams=None, block_name='bottleneck_block'
+):
+
+    with tf.variable_scope(block_name):
+
+        net = layers.conv2d(
+            inputs,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act1'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act2'
+        )
+
+        net = layers.deconv2d(
+            net,
+            n_channels=filters / 2,
+            kernel_size=(2, 2),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act3'
+        )
+
+        return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_downsample.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_downsample.py
new file mode 100644
index 00000000..7031b223
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_downsample.py
@@ -0,0 +1,85 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+from model import blocks
+
+__all__ = ["downsample_unet_block"]
+
+
+def downsample_unet_block(
+    inputs, filters, data_format='NCHW', is_training=True, conv2d_hparams=None, block_name='downsample_block'
+):
+
+    if not isinstance(conv2d_hparams, tf.contrib.training.HParams):
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    with tf.variable_scope(block_name):
+
+        net = layers.conv2d(
+            inputs,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act1'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act2'
+        )
+
+        outputs = layers.max_pooling2d(
+            inputs=net,
+            pool_size=(2, 2),
+            strides=(2, 2),
+            padding='valid',
+            data_format=data_format,
+            name="max_pooling2d"
+        )
+
+        return outputs, net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_io_blocks.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_io_blocks.py
new file mode 100644
index 00000000..3e1c632f
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_io_blocks.py
@@ -0,0 +1,144 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+from model import blocks
+
+__all__ = ["input_unet_block", "output_unet_block"]
+
+
+def input_unet_block(
+    inputs, filters, data_format='NCHW', is_training=True, conv2d_hparams=None, block_name='input_block'
+):
+
+    with tf.variable_scope(block_name):
+
+        net = layers.conv2d(
+            inputs,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act1'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act2'
+        )
+
+        outputs = layers.max_pooling2d(
+            inputs=net,
+            pool_size=(2, 2),
+            strides=(2, 2),
+            padding='valid',
+            data_format=data_format,
+            name="max_pooling2d"
+        )
+
+        return outputs, net
+
+
+def output_unet_block(
+    inputs,
+    residual_input,
+    filters,
+    n_output_channels,
+    data_format='NCHW',
+    is_training=True,
+    conv2d_hparams=None,
+    block_name='output_block'
+):
+
+    with tf.variable_scope(block_name):
+
+        net = layers.concat([inputs, residual_input], axis=1 if data_format == 'NCHW' else 3)
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act1'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act2'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=n_output_channels,
+            kernel_size=(1, 1),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_upsample.py b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_upsample.py
new file mode 100644
index 00000000..6850332f
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/blocks/unet_upsample.py
@@ -0,0 +1,103 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+from model import blocks
+
+__all__ = ["upsample_unet_block"]
+
+
+def upsample_unet_block(
+    inputs,
+    residual_input,
+    filters,
+    data_format='NCHW',
+    is_training=True,
+    conv2d_hparams=None,
+    block_name='upsample_block'
+):
+
+    if not isinstance(conv2d_hparams, tf.contrib.training.HParams):
+        raise ValueError("The paramater `conv2d_hparams` is not of type `HParams`")
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if not isinstance(residual_input, tf.Tensor):
+        raise ValueError("`residual_input` should be a Tensorflow Tensor")
+
+    with tf.variable_scope(block_name):
+
+        net = layers.concat([inputs, residual_input], axis=1 if data_format == 'NCHW' else 3)
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act1'
+        )
+
+        net = layers.conv2d(
+            net,
+            n_channels=filters / 2,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act2'
+        )
+
+        net = layers.deconv2d(
+            net,
+            n_channels=filters / 2,
+            kernel_size=(2, 2),
+            padding='same',
+            data_format=data_format,
+            use_bias=True,
+            trainable=is_training,
+            kernel_initializer=conv2d_hparams.kernel_initializer,
+            bias_initializer=conv2d_hparams.bias_initializer,
+        )
+
+        net = blocks.activation_block(
+            inputs=net, act_fn=conv2d_hparams.activation_fn, trainable=is_training, block_name='act3'
+        )
+
+        return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/__init__.py
new file mode 100644
index 00000000..a7816ce0
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/__init__.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from model.layers.utils import _log_hparams
+
+from model.layers.activation import crelu
+from model.layers.activation import elu
+from model.layers.activation import leaky_relu
+from model.layers.activation import prelu
+from model.layers.activation import relu
+from model.layers.activation import relu6
+from model.layers.activation import selu
+from model.layers.activation import sigmoid
+from model.layers.activation import softmax
+from model.layers.activation import tanh
+
+from model.layers.conv2d import conv2d
+from model.layers.deconv2d import deconv2d
+
+from model.layers.dense import dense
+
+from model.layers.drop_layers import dropout
+
+from model.layers.math_ops import reduce_mean
+
+from model.layers.normalization import batch_norm
+
+from model.layers.padding import pad
+
+from model.layers.pooling import average_pooling2d
+from model.layers.pooling import max_pooling2d
+
+from model.layers.array_ops import concat
+from model.layers.array_ops import flatten
+from model.layers.array_ops import reshape
+from model.layers.array_ops import squeeze
+from model.layers.array_ops import upscale_2d
+
+__all__ = [
+
+    # activation layers
+    'crelu',
+    'elu',
+    'leaky_relu',
+    'prelu',
+    'relu',
+    'relu6',
+    'selu',
+    'sigmoid',
+    'softmax',
+    'tanh',
+
+    # array ops
+    'concat',
+    'flatten',
+    'reshape',
+    'squeeze',
+    'upscale_2d',
+
+    # conv layers
+    'conv2d',
+
+    # deconv layers
+    'deconv2d',
+
+    # dense layers
+    'dense',
+
+    # drop layers
+    'dropout',
+
+    # math_ops layers
+    'reduce_mean',
+
+    # normalization layers
+    'batch_norm',
+
+    # padding layers
+    'pad',
+
+    # pooling layers
+    'average_pooling2d',
+    'max_pooling2d',
+]
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/activation.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/activation.py
new file mode 100644
index 00000000..bdb6b19d
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/activation.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['crelu', 'elu', 'leaky_relu', 'prelu', 'relu', 'relu6', 'selu', 'sigmoid', 'softmax', 'tanh']
+
+
+def crelu(features, name='crelu', axis=-1):
+
+    net = tf.nn.crelu(features, name=name, axis=axis)
+
+    _log_hparams(classname='CReLU', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def elu(features, name='elu'):
+
+    net = tf.nn.elu(features, name=name)
+
+    _log_hparams(classname='ELU', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def leaky_relu(features, alpha=0.2, name='leaky_relu'):
+
+    net = tf.nn.leaky_relu(features, alpha=alpha, name=name)
+
+    _log_hparams(
+        classname='LeakyReLU', layername=net.name, alpha=alpha, out_shape=str(net.get_shape()), out_dtype=net.dtype
+    )
+
+    return net
+
+
+def prelu(inputs, channel_shared=False, trainable=True, name='prelu'):
+
+    def parametric_relu(_x):
+
+        if channel_shared:
+            w_shape = (1, )
+
+        else:
+            w_shape = int(_x.get_shape()[-1])
+
+        alphas = tf.get_variable(
+            'alpha', w_shape, trainable=trainable, initializer=tf.initializers.truncated_normal(mean=-1.0, stddev=0.2)
+        )
+
+        alphas = tf.nn.sigmoid(alphas, name="constraining_alpha_var_in_0_1")
+
+        return tf.maximum(_x, _x * alphas)
+
+    with tf.variable_scope(name):
+        net = parametric_relu(inputs)
+
+    _log_hparams(
+        classname='PReLU',
+        layername=net.name,
+        channel_shared=channel_shared,
+        trainable=trainable,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
+
+
+def relu(inputs, name='relu'):
+
+    net = tf.nn.relu(inputs, name=name)
+
+    _log_hparams(classname='ReLU', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def relu6(inputs, name='relu6'):
+
+    net = tf.nn.relu6(inputs, name=name)
+
+    _log_hparams(classname='ReLU6', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def selu(features, name='selu'):
+
+    net = tf.nn.selu(features, name=name)
+
+    _log_hparams(classname='SELU', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def sigmoid(x, name='sigmoid'):
+
+    net = tf.math.sigmoid(x, name=name)
+
+    _log_hparams(classname='Sigmoid', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def softmax(inputs, axis=None, name="softmax"):
+
+    net = tf.nn.softmax(
+        inputs,
+        axis=axis,
+        name=name,
+    )
+
+    _log_hparams(
+        classname='Softmax', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype
+    )
+
+    return net
+
+
+def tanh(inputs, name='tanh'):
+
+    net = tf.math.tanh(inputs, name=name)
+
+    _log_hparams(classname='TanH', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/array_ops.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/array_ops.py
new file mode 100644
index 00000000..1c33588c
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/array_ops.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['concat', 'flatten', 'reshape', 'squeeze', 'upscale_2d']
+
+
+def concat(values, axis, name='concat'):
+
+    net = tf.concat(values=values, axis=axis, name=name)
+
+    _log_hparams(classname='Concat', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def flatten(inputs, name='flatten'):
+
+    net = tf.layers.flatten(inputs, name=name)
+
+    _log_hparams(classname='Flatten', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
+
+    return net
+
+
+def reshape(tensor, shape, name='reshape'):
+
+    net = tf.reshape(tensor, shape=shape, name=name)
+
+    _log_hparams(
+        classname='Reshape', layername=net.name, shape=shape, out_shape=str(net.get_shape()), out_dtype=net.dtype
+    )
+
+    return net
+
+
+def squeeze(tensor, axis, name='squeeze'):
+
+    net = tf.squeeze(tensor, axis=axis, name=name)
+
+    _log_hparams(
+        classname='Squeeze', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype
+    )
+
+    return net
+
+
+def upscale_2d(inputs, size, is_scale=True, method=0, align_corners=True, data_format='NHWC', name='upsample2d_layer'):
+
+    if not isinstance(size, (list, tuple)) and len(size) == 2:
+        raise AssertionError()
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format received: `%s` (allowed: `NHWC`, `NCHW`)" % data_format)
+
+    input_shape = inputs.get_shape()
+
+    if len(inputs.get_shape()) == 3:
+        if is_scale:
+            size_h = size[0] * int(inputs.get_shape()[0])
+            size_w = size[1] * int(inputs.get_shape()[1])
+            _size = [size_h, size_w]
+        else:
+            _size = size
+
+    elif len(inputs.get_shape()) == 4:
+        if data_format == 'NCHW':
+            inputs = tf.transpose(inputs, [0, 2, 3, 1])  # NCHW => NHWC
+
+        if is_scale:
+            size_h = size[0] * int(inputs.get_shape()[1])
+            size_w = size[1] * int(inputs.get_shape()[2])
+            _size = [size_h, size_w]
+        else:
+            _size = size
+
+    else:
+        raise Exception("Do not support shape %s" % str(inputs.get_shape()))
+
+    with tf.variable_scope(name):
+        net = tf.image.resize_images(inputs, size=_size, method=method, align_corners=align_corners)
+
+    if data_format == 'NCHW' and len(inputs.get_shape()) == 4:
+        net = tf.transpose(net, [0, 3, 1, 2])  # NHWC => NCHW
+
+    _log_hparams(
+        classname='Upscale2D',
+        layername=net.name,
+        size=size,
+        is_scale=is_scale,
+        method=method,
+        align_corners=align_corners,
+        data_format=data_format,
+        input_shape=str(input_shape),
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/conv2d.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/conv2d.py
new file mode 100644
index 00000000..3ce4a1ce
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/conv2d.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['conv2d']
+
+
+def conv2d(
+    inputs,
+    n_channels=8,
+    kernel_size=(3, 3),
+    strides=(1, 1),
+    padding='VALID',
+    data_format='NHWC',
+    dilation_rate=(1, 1),
+    use_bias=True,
+    kernel_initializer=tf.variance_scaling_initializer(),
+    bias_initializer=tf.zeros_initializer(),
+    trainable=True
+):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.upper() not in ['SAME', 'VALID']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['SAME', 'VALID'])" % padding.upper())
+
+    net = tf.layers.conv2d(
+        inputs,
+        filters=n_channels,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        dilation_rate=dilation_rate,
+        data_format='channels_last' if data_format == 'NHWC' else 'channels_first',
+        use_bias=use_bias,
+        kernel_initializer=kernel_initializer,
+        bias_initializer=bias_initializer,
+        trainable=trainable,
+        activation=None
+    )
+
+    _log_hparams(
+        classname='Conv2D',
+        layername=net.name,
+        n_channels=n_channels,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        dilation_rate=dilation_rate,
+        use_bias=use_bias,
+        trainable=trainable,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/deconv2d.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/deconv2d.py
new file mode 100644
index 00000000..a62a2bfe
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/deconv2d.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model import layers
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['deconv2d']
+
+
+def deconv2d(
+    inputs,
+    n_channels=8,
+    kernel_size=(3, 3),
+    padding='VALID',
+    data_format='NHWC',
+    use_bias=True,
+    kernel_initializer=tf.variance_scaling_initializer(),
+    bias_initializer=tf.zeros_initializer(),
+    trainable=True,
+    use_upscale_conv=True
+):
+
+    padding = padding.upper()  # Enforce capital letters for the padding mode
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding not in ['SAME', 'VALID']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['SAME', 'VALID'])" % padding)
+
+    with tf.variable_scope("deconv2d"):
+
+        if use_upscale_conv:
+
+            layer = layers.upscale_2d(
+                inputs,
+                size=(2, 2),
+                method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,  # [BILINEAR, NEAREST_NEIGHBOR, BICUBIC, AREA]
+                align_corners=True,
+                is_scale=True,
+                data_format=data_format
+            )
+
+            layer = layers.conv2d(
+                layer,
+                n_channels=n_channels,
+                kernel_size=kernel_size,
+                strides=(1, 1),
+                padding=padding,
+                data_format=data_format,
+                use_bias=use_bias,
+                trainable=trainable,
+                kernel_initializer=kernel_initializer,
+                bias_initializer=bias_initializer
+            )
+
+        else:
+
+            input_shape = inputs.get_shape()
+
+            layer = tf.layers.conv2d_transpose(
+                inputs=inputs,
+                filters=n_channels,
+                kernel_size=kernel_size,
+                strides=(2, 2),
+                padding=padding,
+                data_format='channels_first' if data_format == "NCHW" else "channels_last",
+                use_bias=use_bias,
+                trainable=trainable,
+                kernel_initializer=kernel_initializer,
+                bias_initializer=bias_initializer
+            )
+
+            _log_hparams(
+                classname='Conv2DTranspose',
+                layername=layer.name,
+                n_channels=n_channels,
+                kernel_size=kernel_size,
+                strides=(2, 2),
+                padding=padding,
+                data_format=data_format,
+                use_bias=use_bias,
+                trainable=trainable,
+                input_shape=str(input_shape),
+                out_shape=str(layer.get_shape()),
+                out_dtype=layer.dtype
+            )
+
+    return layer
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/dense.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/dense.py
new file mode 100644
index 00000000..c1dea57e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/dense.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['dense']
+
+
+def dense(
+    inputs,
+    units,
+    use_bias=True,
+    trainable=True,
+    kernel_initializer=tf.variance_scaling_initializer(),
+    bias_initializer=tf.zeros_initializer()
+):
+
+    net = tf.layers.dense(
+        inputs,
+        units=units,
+        activation=None,
+        use_bias=use_bias,
+        kernel_initializer=kernel_initializer,
+        bias_initializer=bias_initializer,
+        trainable=trainable
+    )
+
+    _log_hparams(
+        classname='Dense',
+        layername=net.name,
+        units=units,
+        use_bias=use_bias,
+        trainable=trainable,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/drop_layers.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/drop_layers.py
new file mode 100644
index 00000000..b3be676e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/drop_layers.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['dropout']
+
+
+def dropout(inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None):
+
+    layer = tf.keras.layers.Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
+    net = layer.apply(inputs, training=training)
+
+    _log_hparams(
+        classname='Dropout',
+        layername=net.name,
+        noise_shape=noise_shape,
+        training=training,
+        seed=seed,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/math_ops.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/math_ops.py
new file mode 100644
index 00000000..ae30c25b
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/math_ops.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['reduce_mean']
+
+
+def reduce_mean(inputs, keepdims=None, data_format='channels_last', name='spatial_mean'):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    axes = [1, 2] if data_format == 'NHWC' else [2, 3]
+
+    net = tf.math.reduce_mean(inputs, axis=axes, keepdims=keepdims, name=name)
+
+    _log_hparams(
+        classname='ReduceMean',
+        layername=net.name,
+        axis=axes,
+        keepdims=keepdims,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/normalization.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/normalization.py
new file mode 100644
index 00000000..040607ff
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/normalization.py
@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import inspect
+
+import tensorflow as tf
+
+from model.layers import _log_hparams
+
+__all__ = ['batch_norm']
+
+
+def batch_norm(
+    inputs,
+    decay=0.999,
+    epsilon=0.001,
+    scale=False,
+    center=True,
+    is_training=True,
+    data_format='NHWC',
+    param_initializers=None
+):
+    """Adds a Batch Normalization layer."""
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if param_initializers is not None:
+
+        for key, initializer in param_initializers.items():
+
+            if key not in ['beta', 'gamma', 'moving_mean', 'moving_variance']:
+                raise ValueError("Unknown key received: `%s`" % key)
+
+            if inspect.isclass(initializer):
+                initializer = initializer()
+                setattr(param_initializers, key, initializer)
+
+            if initializer.__class__.__module__ != 'tensorflow.python.ops.init_ops':
+                raise ValueError("The object `%s` is not a Tensor initializer" % str(initializer))
+
+    input_shape = inputs.get_shape()
+    input_rank = input_shape.ndims
+    input_channels = input_shape[1]
+
+    if input_rank == 2:
+
+        if data_format == 'NCHW':
+            new_shape = [-1, input_channels, 1, 1]
+        else:
+            new_shape = [-1, 1, 1, input_channels]
+
+        inputs = tf.reshape(inputs, new_shape)
+
+    net = tf.contrib.layers.batch_norm(
+        inputs,
+        decay=decay,
+        scale=scale,
+        epsilon=epsilon,
+        is_training=is_training,
+        trainable=is_training,
+        fused=True,
+        data_format=data_format,
+        center=center,
+        param_initializers=param_initializers
+    )
+
+    if input_rank == 2:
+        net = tf.reshape(net, [-1, input_channels])
+
+    _log_hparams(
+        classname='BatchNorm',
+        layername=net.name,
+        data_format=data_format,
+        is_training=is_training,
+        decay=decay,
+        epsilon=epsilon,
+        scale=scale,
+        center=center,
+        fused=True,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/padding.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/padding.py
new file mode 100644
index 00000000..8a23b9cc
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/padding.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['pad']
+
+
+def pad(inputs, paddings, mode='CONSTANT', name='padding', constant_values=0):
+
+    if mode.upper() not in ['CONSTANT', 'REFLECT', 'SYMMETRIC']:
+        raise ValueError("Unknown padding mode: `%s` (accepted: ['CONSTANT', 'REFLECT', 'SYMMETRIC'])" % mode)
+
+    net = tf.pad(inputs, paddings=paddings, mode=mode, name=name, constant_values=constant_values)
+
+    _log_hparams(
+        classname='Padding',
+        layername=net.name,
+        paddings=paddings,
+        mode=mode,
+        constant_values=constant_values,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/pooling.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/pooling.py
new file mode 100644
index 00000000..03bf63d7
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/pooling.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+from model.layers.utils import _log_hparams
+
+__all__ = ['average_pooling2d', 'max_pooling2d']
+
+
+def average_pooling2d(inputs, pool_size=(2, 2), strides=None, padding='valid', data_format=None, name="avg_pooling2d"):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.lower() not in ['same', 'valid']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['same', 'valid'])" % padding)
+    '''
+    net = tf.keras.layers.AveragePooling2D(
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name,
+    )(inputs)
+    '''
+
+    net = tf.layers.average_pooling2d(
+        inputs,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name
+    )
+
+    _log_hparams(
+        classname='AveragePooling2D',
+        layername=net.name,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        out_shape=str(net.get_shape())
+    )
+
+    return net
+
+
+def max_pooling2d(inputs, pool_size=(2, 2), strides=None, padding='valid', data_format=None, name="max_pooling2d"):
+
+    if data_format not in ['NHWC', 'NCHW']:
+        raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
+
+    if padding.lower() not in ['same', 'valid']:
+        raise ValueError("Unknown padding: `%s` (accepted: ['same', 'valid'])" % padding)
+    '''
+    net = tf.keras.layers.MaxPool2D(
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name,
+    )(inputs)
+    '''
+
+    net = tf.layers.max_pooling2d(
+        inputs,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format='channels_first' if data_format == 'NCHW' else 'channels_last',
+        name=name
+    )
+
+    _log_hparams(
+        classname='MaxPooling2D',
+        layername=net.name,
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        out_shape=str(net.get_shape()),
+        out_dtype=net.dtype
+    )
+
+    return net
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/layers/utils.py b/TensorFlow/Segmentation/UNet_Industrial/model/layers/utils.py
new file mode 100644
index 00000000..e86a5bc1
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/layers/utils.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from dllogger.logger import LOGGER
+
+import horovod.tensorflow as hvd
+
+from utils import hvd_utils
+
+__all__ = ["_log_hparams"]
+
+
+def _log_hparams(classname, layername, **kwargs):
+
+    log_msg = "%s: `%s`" % (classname, layername)
+
+    for arg, val in sorted(kwargs.items()):
+        log_msg += "\n\t[*] {}: {}".format(arg, val)
+
+    log_msg += "\n"
+
+    if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+        LOGGER.log(log_msg)
diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/unet.py b/TensorFlow/Segmentation/UNet_Industrial/model/unet.py
new file mode 100644
index 00000000..63da11f3
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/model/unet.py
@@ -0,0 +1,502 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from model import layers
+from model import blocks
+
+from utils import hvd_utils
+
+from utils import losses
+from utils import metrics
+
+from utils import image_processing
+
+from dllogger.logger import LOGGER
+
+__all__ = ["UNet_v1"]
+
+
+class UNet_v1(object):
+
+    authorized_weight_init_methods = [
+        "he_normal",
+        "he_uniform",
+        "glorot_normal",
+        "glorot_uniform",
+        "orthogonal",
+    ]
+
+    authorized_models_variants = [
+        "original",
+        "tinyUNet",
+    ]
+
+    def __init__(
+        self,
+        model_name,
+        compute_format,
+        input_format,
+        n_output_channels,
+        unet_variant,
+        activation_fn,
+        weight_init_method,
+    ):
+
+        if unet_variant == "original":  # Total Params: 36,950,273
+            input_filters = 64
+            unet_block_filters = [128, 256, 512]
+            bottleneck_filters = 1024
+            output_filters = 64
+
+        elif unet_variant == "tinyUNet":  # Total Params: 1,824,945
+            input_filters = 32
+            unet_block_filters = [32, 64, 128]
+            bottleneck_filters = 256
+            output_filters = 32
+
+        else:
+            raise ValueError(
+                "Unknown `UNet` variant: %s. Authorized: %s" % (unet_variant, UNet_v1.authorized_models_variants)
+            )
+
+        if activation_fn not in blocks.authorized_activation_fn:
+            raise ValueError(
+                "Unknown activation function: %s - Authorised: %s" % (activation_fn, blocks.authorized_activation_fn)
+            )
+
+        self.model_hparams = tf.contrib.training.HParams(
+            compute_format=compute_format,
+            input_format=input_format,
+            input_filters=input_filters,
+            unet_block_filters=unet_block_filters,
+            bottleneck_filters=bottleneck_filters,
+            output_filters=output_filters,
+            n_output_channels=n_output_channels,
+            model_name=model_name,
+        )
+
+        self.conv2d_hparams = tf.contrib.training.HParams(
+            kernel_initializer=None, bias_initializer=tf.initializers.constant(0.0), activation_fn=activation_fn
+        )
+
+        if weight_init_method == "he_normal":
+            self.conv2d_hparams.kernel_initializer = tf.initializers.variance_scaling(
+                scale=2.0, distribution='truncated_normal', mode='fan_in'
+            )
+
+        elif weight_init_method == "he_uniform":
+            self.conv2d_hparams.kernel_initializer = tf.initializers.variance_scaling(
+                scale=2.0, distribution='uniform', mode='fan_in'
+            )
+
+        elif weight_init_method == "glorot_normal":
+            self.conv2d_hparams.kernel_initializer = tf.initializers.variance_scaling(
+                scale=1.0, distribution='truncated_normal', mode='fan_avg'
+            )
+
+        elif weight_init_method == "glorot_uniform":
+            self.conv2d_hparams.kernel_initializer = tf.initializers.variance_scaling(
+                scale=1.0, distribution='uniform', mode='fan_avg'
+            )
+
+        elif weight_init_method == "orthogonal":
+            self.conv2d_hparams.kernel_initializer = tf.initializers.orthogonal(gain=1.0)
+
+        else:
+            raise ValueError(
+                "Unknown weight init method: %s - Authorized: %s" %
+                (weight_init_method, UNet_v1.authorized_weight_init_methods)
+            )
+
+    def __call__(self, features, labels, mode, params):
+
+        if "debug_verbosity" not in params.keys():
+            raise RuntimeError("Parameter `debug_verbosity` is missing...")
+
+        if mode == tf.estimator.ModeKeys.TRAIN:
+
+            if "rmsprop_decay" not in params.keys():
+                raise RuntimeError("Parameter `rmsprop_decay` is missing...")
+
+            if "rmsprop_momentum" not in params.keys():
+                raise RuntimeError("Parameter `rmsprop_momentum` is missing...")
+
+            if "learning_rate" not in params.keys():
+                raise RuntimeError("Parameter `learning_rate` is missing...")
+
+            if "learning_rate_decay_steps" not in params.keys():
+                raise RuntimeError("Parameter `learning_rate` is missing...")
+
+            if "learning_rate_decay_factor" not in params.keys():
+                raise RuntimeError("Parameter `learning_rate` is missing...")
+
+            if "weight_decay" not in params.keys():
+                raise RuntimeError("Parameter `weight_decay` is missing...")
+
+            if "loss_fn_name" not in params.keys():
+                raise RuntimeError("Parameter `loss_fn_name` is missing...")
+
+        input_image, mask_image = features
+
+        with tf.device("/gpu:0"):
+
+            tf.identity(input_image, name="input_image_ref")
+            tf.identity(mask_image, name="mask_image_ref")
+            tf.identity(labels, name="labels_ref")
+
+            y_pred, y_pred_logits = self.build_model(
+                input_image,
+                training=mode == tf.estimator.ModeKeys.TRAIN,
+                reuse=False,
+                debug_verbosity=params["debug_verbosity"]
+            )
+
+            all_trainable_vars = tf.reduce_sum([tf.reduce_prod(v.shape) for v in tf.trainable_variables()])
+            tf.identity(all_trainable_vars, name='trainable_parameters_count_ref')
+
+            if mode == tf.estimator.ModeKeys.PREDICT:
+                predictions = {'logits': y_pred}
+                return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+
+            if mode == tf.estimator.ModeKeys.EVAL:
+                eval_metrics = dict()
+
+            # ==================== Samples ==================== #
+
+            image_uint8 = tf.cast((input_image + 1) * 127.5, dtype=tf.uint8)
+            input_image_jpeg = tf.image.encode_jpeg(image_uint8[0], format='grayscale', quality=100)
+            tf.identity(input_image_jpeg, name="input_image_jpeg_ref")
+
+            for threshold in [None, 0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99]:
+                binarize_img, binarize_img_jpeg = image_processing.binarize_output(y_pred[0], threshold=threshold)
+
+                tf.identity(binarize_img_jpeg, name="output_sample_ths_%s_ref" % threshold)
+                tf.summary.image('output_sample_ths_%s' % threshold, binarize_img, 10)
+
+            # ==============+ Evaluation Metrics ==================== #
+
+            with tf.name_scope("IoU_Metrics"):
+
+                for threshold in [0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99]:
+
+                    iou_score = metrics.iou_score(y_pred=y_pred, y_true=mask_image, threshold=threshold)
+
+                    tf.identity(iou_score, name='iou_score_ths_%s_ref' % threshold)
+                    tf.summary.scalar('iou_score_ths_%s' % threshold, iou_score)
+
+                    if mode == tf.estimator.ModeKeys.EVAL:
+                        eval_metrics["IoU_THS_%s" % threshold] = tf.metrics.mean(iou_score)
+
+            labels = tf.cast(labels, tf.float32)
+            labels_preds = tf.reduce_max(y_pred, axis=(1, 2, 3))
+
+            with tf.variable_scope("Confusion_Matrix") as scope:
+
+                tp, update_tp = tf.metrics.true_positives_at_thresholds(
+                    labels=labels,
+                    predictions=labels_preds,
+                    thresholds=[0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99],
+                )
+
+                tn, update_tn = tf.metrics.true_negatives_at_thresholds(
+                    labels=labels,
+                    predictions=labels_preds,
+                    thresholds=[0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99],
+                )
+
+                fp, update_fp = tf.metrics.false_positives_at_thresholds(
+                    labels=labels,
+                    predictions=labels_preds,
+                    thresholds=[0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99],
+                )
+
+                fn, update_fn = tf.metrics.false_negatives_at_thresholds(
+                    labels=labels,
+                    predictions=labels_preds,
+                    thresholds=[0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99],
+                )
+
+                if mode == tf.estimator.ModeKeys.TRAIN:
+                    local_vars = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=scope.name)
+                    confusion_matrix_reset_op = tf.initializers.variables(local_vars, name='reset_op')
+
+                    with tf.control_dependencies([confusion_matrix_reset_op]):
+                        with tf.control_dependencies([update_tp, update_tn, update_fp, update_fn]):
+                            tp = tf.identity(tp)
+                            tn = tf.identity(tn)
+                            fp = tf.identity(fp)
+                            fn = tf.identity(fn)
+
+                else:
+                    eval_metrics["Confusion_Matrix_TP"] = tp, update_tp
+                    eval_metrics["Confusion_Matrix_TN"] = tn, update_tn
+                    eval_metrics["Confusion_Matrix_FP"] = fp, update_fp
+                    eval_metrics["Confusion_Matrix_FN"] = fn, update_fn
+
+                tf.identity(tp, name='true_positives_ref')  # Confusion_Matrix/true_positives_ref:0
+                tf.identity(tn, name='true_negatives_ref')  # Confusion_Matrix/true_negatives_ref:0
+                tf.identity(fp, name='false_positives_ref')  # Confusion_Matrix/false_positives_ref:0
+                tf.identity(fn, name='false_negatives_ref')  # Confusion_Matrix/false_negatives_ref:0
+
+                tf.summary.scalar('true_positives', tp[3])  # For Ths = 0.5
+                tf.summary.scalar('true_negatives', tn[3])  # For Ths = 0.5
+                tf.summary.scalar('false_positives', fp[3])  # For Ths = 0.5
+                tf.summary.scalar('false_negatives', fn[3])  # For Ths = 0.5
+
+            binarized_mask, binarized_mask_jpeg = image_processing.binarize_output(mask_image[0], threshold=0.5)
+            tf.identity(binarized_mask_jpeg, name="mask_sample_ref")
+            tf.summary.image('sample_mask', binarized_mask, 10)
+
+            ##########################
+
+            mask_max_val = tf.reduce_max(mask_image)
+            tf.identity(mask_max_val, name='mask_max_val_ref')
+
+            mask_min_val = tf.reduce_min(mask_image)
+            tf.identity(mask_min_val, name='mask_min_val_ref')
+
+            mask_mean_val = tf.reduce_mean(mask_image)
+            tf.identity(mask_mean_val, name='mask_mean_val_ref')
+
+            mask_std_val = tf.math.reduce_std(mask_image)
+            tf.identity(mask_std_val, name='mask_std_val_ref')
+
+            ##########################
+
+            output_max_val = tf.reduce_max(y_pred)
+            tf.identity(output_max_val, name='output_max_val_ref')
+
+            output_min_val = tf.reduce_min(y_pred)
+            tf.identity(output_min_val, name='output_min_val_ref')
+
+            output_mean_val = tf.reduce_mean(y_pred)
+            tf.identity(output_mean_val, name='output_mean_val_ref')
+
+            output_std_val = tf.math.reduce_std(y_pred)
+            tf.identity(output_std_val, name='output_std_val_ref')
+
+            with tf.variable_scope("losses"):
+
+                # ==============+ Reconstruction Loss ==================== #
+
+                if params["loss_fn_name"] == "x-entropy":
+                    reconstruction_loss = losses.reconstruction_x_entropy(y_pred=y_pred, y_true=mask_image)
+
+                elif params["loss_fn_name"] == "l2_loss":
+                    reconstruction_loss = losses.reconstruction_l2loss(y_pred=y_pred, y_true=mask_image)
+
+                elif params["loss_fn_name"] == "dice_sorensen":
+                    reconstruction_loss = 1 - losses.dice_coe(y_pred=y_pred, y_true=mask_image, loss_type='sorensen')
+
+                elif params["loss_fn_name"] == "dice_jaccard":
+                    reconstruction_loss = 1 - losses.dice_coe(y_pred=y_pred, y_true=mask_image, loss_type='jaccard')
+
+                elif params["loss_fn_name"] == "adaptive_loss":
+                    reconstruction_loss = losses.adaptive_loss(
+                        y_pred=y_pred,
+                        y_pred_logits=y_pred_logits,
+                        y_true=mask_image,
+                        switch_at_threshold=0.3,
+                        loss_type='sorensen'
+                    )
+
+                else:
+                    raise ValueError("Unknown loss function received: %s" % params["loss_fn_name"])
+
+                tf.identity(reconstruction_loss, name='reconstruction_loss_ref')
+                tf.summary.scalar('reconstruction_loss', reconstruction_loss)
+
+                if mode == tf.estimator.ModeKeys.TRAIN:
+
+                    # ============== Regularization Loss ==================== #
+
+                    l2_loss = losses.regularization_l2loss(weight_decay=params["weight_decay"])
+
+                    tf.identity(l2_loss, name='l2_loss_ref')
+                    tf.summary.scalar('l2_loss', l2_loss)
+
+                    total_loss = tf.add(reconstruction_loss, l2_loss, name="total_loss")
+
+                else:
+                    total_loss = reconstruction_loss
+
+                tf.identity(total_loss, name='total_loss_ref')
+                tf.summary.scalar('total_loss', total_loss)
+
+            if mode == tf.estimator.ModeKeys.TRAIN:
+
+                with tf.variable_scope("optimizers"):
+
+                    # Update Global Step
+                    global_step = tf.train.get_or_create_global_step()
+                    tf.identity(global_step, name="global_step_ref")
+
+                    learning_rate = tf.train.exponential_decay(
+                        learning_rate=params["learning_rate"],
+                        decay_steps=params["learning_rate_decay_steps"],
+                        decay_rate=params["learning_rate_decay_factor"],
+                        global_step=global_step,
+                        staircase=True
+                    )
+
+                    tf.identity(learning_rate, name="learning_rate_ref")
+                    tf.summary.scalar('learning_rate_ref', learning_rate)
+
+                    opt = tf.train.RMSPropOptimizer(
+                        learning_rate=learning_rate,
+                        use_locking=False,
+                        centered=True,
+                        decay=params["rmsprop_decay"],
+                        momentum=params["rmsprop_momentum"],
+                    )
+
+                    if hvd_utils.is_using_hvd():
+                        opt = hvd.DistributedOptimizer(opt, device_dense='/gpu:0')
+
+                    if params["apply_manual_loss_scaling"]:
+
+                        if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                            LOGGER.log("Applying manual Loss Scaling ...")
+
+                        loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(
+                            init_loss_scale=2**32,  # 4,294,967,296
+                            incr_every_n_steps=1000
+                        )
+                        opt = tf.contrib.mixed_precision.LossScaleOptimizer(opt, loss_scale_manager)
+
+                    deterministic = True
+                    gate_gradients = (tf.train.Optimizer.GATE_OP if deterministic else tf.train.Optimizer.GATE_NONE)
+
+                    backprop_op = opt.minimize(total_loss, gate_gradients=gate_gradients, global_step=global_step)
+
+                    train_op = tf.group(backprop_op, tf.get_collection(tf.GraphKeys.UPDATE_OPS))
+
+                    return tf.estimator.EstimatorSpec(
+                        mode,
+                        loss=total_loss,
+                        train_op=train_op,
+                    )
+
+            elif mode == tf.estimator.ModeKeys.EVAL:
+
+                return tf.estimator.EstimatorSpec(
+                    mode, loss=total_loss, eval_metric_ops=eval_metrics, predictions={"output": y_pred}
+                )
+
+            else:
+                raise NotImplementedError('Unknown mode {}'.format(mode))
+
+    def build_model(self, inputs, training=True, reuse=False, debug_verbosity=0):
+        """
+        U-Net: Convolutional Networks for Biomedical Image Segmentation
+        https://arxiv.org/pdf/1505.04597
+        """
+
+        skip_connections = []
+
+        with tf.variable_scope(self.model_hparams.model_name, reuse=reuse):
+
+            with tf.variable_scope("input_reshape"):
+
+                with tf.variable_scope("initial_zero_padding"):
+                    inputs = tf.image.resize_image_with_crop_or_pad(inputs, target_height=512, target_width=512)
+
+                if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
+                    # Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
+                    # This provides a large performance boost on GPU. See
+                    # https://www.tensorflow.org/performance/performance_guide#data_formats
+
+                    # Reshape inputs: NHWC => NCHW
+                    net = tf.transpose(inputs, [0, 3, 1, 2])
+
+                elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
+
+                    # Reshape inputs: NCHW => NHWC
+                    net = tf.transpose(inputs, [0, 2, 3, 1])
+
+                else:
+                    net = inputs
+
+            # net, out = input_block(net, filters=64)
+
+            net, out = blocks.input_unet_block(
+                net,
+                filters=self.model_hparams.input_filters,
+                data_format=self.model_hparams.compute_format,
+                is_training=training,
+                conv2d_hparams=self.conv2d_hparams
+            )
+
+            skip_connections.append(out)
+
+            for idx, filters in enumerate(self.model_hparams.unet_block_filters):
+                # net, out = downsample_block(net, filters=filters, idx=idx)
+
+                net, skip_connect = blocks.downsample_unet_block(
+                    net,
+                    filters=filters,
+                    data_format=self.model_hparams.compute_format,
+                    is_training=training,
+                    conv2d_hparams=self.conv2d_hparams,
+                    block_name="downsample_block_%d" % (idx + 1)
+                )
+
+                skip_connections.append(skip_connect)
+
+            net = blocks.bottleneck_unet_block(
+                net,
+                filters=self.model_hparams.bottleneck_filters,
+                data_format=self.model_hparams.compute_format,
+                is_training=training,
+                conv2d_hparams=self.conv2d_hparams,
+            )
+
+            for idx, filters in enumerate(reversed(self.model_hparams.unet_block_filters)):
+                net = blocks.upsample_unet_block(
+                    net,
+                    residual_input=skip_connections.pop(),
+                    filters=filters,
+                    data_format=self.model_hparams.compute_format,
+                    is_training=training,
+                    conv2d_hparams=self.conv2d_hparams,
+                    block_name='upsample_block_%d' % (idx + 1)
+                )
+
+            logits = blocks.output_unet_block(
+                inputs=net,
+                residual_input=skip_connections.pop(),
+                filters=self.model_hparams.output_filters,
+                n_output_channels=self.model_hparams.n_output_channels,
+                data_format=self.model_hparams.compute_format,
+                is_training=training,
+                conv2d_hparams=self.conv2d_hparams,
+                block_name='ouputs_block'
+            )
+
+            if self.model_hparams.compute_format == "NCHW":
+                logits = tf.transpose(logits, [0, 2, 3, 1])
+
+            outputs = layers.sigmoid(logits)
+
+            return outputs, logits
diff --git a/TensorFlow/Segmentation/UNet_Industrial/preprocess_dagm2007.py b/TensorFlow/Segmentation/UNet_Industrial/preprocess_dagm2007.py
new file mode 100644
index 00000000..758524d7
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/preprocess_dagm2007.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+##############################################################################
+# Copyright (c) Jonathan Dekhtiar - contact@jonathandekhtiar.eu
+# All Rights Reserved.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+##############################################################################
+
+import os
+import glob
+import ntpath
+
+import argparse
+
+from collections import defaultdict
+
+parser = argparse.ArgumentParser(description="DAGM2007_preprocessing")
+
+parser.add_argument('--data_dir', required=True, type=str, help="Path to DAGM 2007 private dataset")
+
+DEFECTIVE_COUNT = defaultdict(lambda: defaultdict(int))
+
+EXPECTED_DEFECTIVE_SAMPLES_PER_CLASS = {
+    "Train": {
+        1: 79,
+        2: 66,
+        3: 66,
+        4: 82,
+        5: 70,
+        6: 83,
+        7: 150,
+        8: 150,
+        9: 150,
+        10: 150,
+    },
+    "Test": {
+        1: 71,
+        2: 84,
+        3: 84,
+        4: 68,
+        5: 80,
+        6: 67,
+        7: 150,
+        8: 150,
+        9: 150,
+        10: 150,
+    }
+}
+
+if __name__ == "__main__":
+
+    FLAGS, unknown_args = parser.parse_known_args()
+
+    if len(unknown_args) > 0:
+
+        for bad_arg in unknown_args:
+            print("ERROR: Unknown command line arg: %s" % bad_arg)
+
+        raise ValueError("Invalid command line arg(s)")
+
+    if not os.path.exists(FLAGS.data_dir):
+        raise ValueError('The dataset directory received `%s` does not exists' % FLAGS.data_dir)
+
+    for challenge_id in range(10):
+        challenge_name = "Class%d" % (challenge_id + 1)
+        challenge_folder_path = os.path.join(FLAGS.data_dir, challenge_name)
+
+        print("[DAGM Preprocessing] Parsing Class ID: %02d ..." % (challenge_id + 1))
+
+        if not os.path.exists(challenge_folder_path):
+            raise ValueError('The folder `%s` does not exists' % challenge_folder_path)
+
+        for data_set in ["Train", "Test"]:
+
+            challenge_set_folder_path = os.path.join(challenge_folder_path, data_set)
+
+            if not os.path.exists(challenge_set_folder_path):
+                raise ValueError('The folder `%s` does not exists' % challenge_set_folder_path)
+
+            with open(os.path.join(challenge_folder_path, "%s_list.csv" % data_set.lower()), 'w') as data_list_file:
+                data_list_file.write('image_filepath,lbl_image_filepath,is_defective\n')
+
+                files = glob.glob(os.path.join(challenge_set_folder_path, "*.PNG"))
+
+                for file in files:
+                    filepath, fullname = ntpath.split(file)
+
+                    filename, extension = os.path.splitext(os.path.basename(fullname))
+
+                    lbl_filename = "%s_label.PNG" % filename
+                    lbl_filepath = os.path.join(filepath, "Label", lbl_filename)
+
+                    if os.path.exists(lbl_filepath):
+                        defective = True
+                    else:
+                        defective = False
+                        lbl_filename = ""
+
+                    if defective:
+                        DEFECTIVE_COUNT[data_set][challenge_id + 1] += 1
+
+                    data_list_file.write('%s,%s,%d\n' % (fullname, lbl_filename, defective))
+
+                if DEFECTIVE_COUNT[data_set][challenge_id +
+                                             1] != EXPECTED_DEFECTIVE_SAMPLES_PER_CLASS[data_set][challenge_id + 1]:
+                    raise RuntimeError(
+                        "There should be `%d` defective samples instead of `%d` in challenge (%s): %d" % (
+                            DEFECTIVE_COUNT[data_set][challenge_id + 1],
+                            EXPECTED_DEFECTIVE_SAMPLES_PER_CLASS[data_set][challenge_id + 1], data_set, challenge_id + 1
+                        )
+                    )
diff --git a/TensorFlow/Segmentation/UNet_Industrial/runtime/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/runtime/__init__.py
new file mode 100644
index 00000000..85bada5e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/runtime/__init__.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from runtime.runner import Runner
\ No newline at end of file
diff --git a/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py b/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py
new file mode 100644
index 00000000..2f497554
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py
@@ -0,0 +1,634 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from __future__ import print_function
+
+import os
+import multiprocessing
+import operator
+import random
+
+import numpy as np
+
+import tensorflow as tf
+import horovod.tensorflow as hvd
+
+from datasets import known_datasets
+
+from model.unet import UNet_v1
+
+from utils import hvd_utils
+
+from utils.hooks import ProfilerHook
+
+from dllogger.logger import LOGGER
+import dllogger.logger as dllg
+
+__all__ = [
+    'Runner',
+]
+
+
+class Runner(object):
+
+    def __init__(
+        self,
+
+        # Model Params
+        input_format,  # NCHW or NHWC
+        compute_format,  # NCHW or NHWC
+        n_channels,
+        activation_fn,
+        weight_init_method,
+        model_variant,
+        input_shape,
+        mask_shape,
+        input_normalization_method,
+
+        # Training HParams
+        augment_data,
+        loss_fn_name,
+
+        #  Runtime HParams
+        use_tf_amp,
+        use_xla,
+
+        # Directory Params
+        model_dir=None,
+        log_dir=None,
+        sample_dir=None,
+        data_dir=None,
+        dataset_name=None,
+        dataset_hparams=None,
+
+        # Debug Params
+        log_every_n_steps=1,
+        debug_verbosity=0,
+        seed=None
+    ):
+
+        if dataset_hparams is None:
+            dataset_hparams = dict()
+
+        if compute_format not in ["NHWC", 'NCHW']:
+            raise ValueError("Unknown `compute_format` received: %s (allowed: ['NHWC', 'NCHW'])" % compute_format)
+
+        if input_format not in ["NHWC", 'NCHW']:
+            raise ValueError("Unknown `input_format` received: %s (allowed: ['NHWC', 'NCHW'])" % input_format)
+
+        if n_channels not in [1, 3]:
+            raise ValueError("Unsupported number of channels: %d (allowed: 1 (grayscale) and 3 (color))" % n_channels)
+
+        if data_dir is not None and not os.path.exists(data_dir):
+            raise ValueError("The `data_dir` received does not exists: %s" % data_dir)
+
+        LOGGER.set_model_name('UNet_TF')
+
+        LOGGER.set_backends(
+            [
+                dllg.JsonBackend(
+                    log_file=os.path.join(model_dir, 'dlloger_out.json'),
+                    logging_scope=dllg.Scope.TRAIN_ITER,
+                    iteration_interval=log_every_n_steps
+                ),
+                dllg.StdOutBackend(
+                    log_file=None, logging_scope=dllg.Scope.TRAIN_ITER, iteration_interval=log_every_n_steps
+                )
+            ]
+        )
+
+        if hvd_utils.is_using_hvd():
+            hvd.init()
+
+            if hvd.local_rank() == 0:
+                LOGGER.log("Horovod successfully initialized ...")
+
+            tf_seed = 2 * (seed + hvd.rank()) if seed is not None else None
+
+        else:
+            tf_seed = 2 * seed if seed is not None else None
+
+        # ============================================
+        # Optimisation Flags - Do not remove
+        # ============================================
+
+        os.environ['CUDA_CACHE_DISABLE'] = '0'
+
+        os.environ['HOROVOD_GPU_ALLREDUCE'] = 'NCCL'
+
+        # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+        os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
+        os.environ['TF_GPU_THREAD_COUNT'] = '1' if not hvd_utils.is_using_hvd() else str(hvd.size())
+
+        os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
+
+        os.environ['TF_ADJUST_HUE_FUSED'] = '1'
+        os.environ['TF_ADJUST_SATURATION_FUSED'] = '1'
+        os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
+
+        os.environ['TF_SYNC_ON_FINISH'] = '0'
+        os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'
+        os.environ['TF_DISABLE_NVTX_RANGES'] = '1'
+
+        # =================================================
+
+        self.use_xla = use_xla
+
+        if use_tf_amp:
+
+            if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                LOGGER.log("TF AMP is activated - Experimental Feature")
+
+            os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
+
+        # =================================================
+
+        model_hparams = tf.contrib.training.HParams(
+            # Model Params
+            input_format=input_format,
+            compute_format=compute_format,
+            input_shape=input_shape,
+            mask_shape=mask_shape,
+            n_channels=n_channels,
+            activation_fn=activation_fn,
+            weight_init_method=weight_init_method,
+            model_variant=model_variant,
+            input_normalization_method=input_normalization_method,
+
+            # Training HParams
+            augment_data=augment_data,
+            loss_fn_name=loss_fn_name,
+
+            # Runtime Params
+            use_tf_amp=use_tf_amp,
+
+            # Debug Params
+            log_every_n_steps=log_every_n_steps,
+            debug_verbosity=debug_verbosity,
+            seed=tf_seed
+        )
+
+        run_config_additional = tf.contrib.training.HParams(
+            dataset_hparams=dataset_hparams,
+            model_dir=model_dir if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
+            log_dir=log_dir if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
+            sample_dir=sample_dir if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
+            data_dir=data_dir,
+            num_preprocessing_threads=32,
+        )
+
+        if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
+            try:
+                os.makedirs(sample_dir)
+            except FileExistsError:
+                pass
+
+        self.run_hparams = Runner._build_hparams(model_hparams, run_config_additional)
+
+        if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+            LOGGER.log('Defining Model Estimator ...\n')
+
+        self._model = UNet_v1(
+            model_name="UNet_v1",
+            input_format=self.run_hparams.input_format,
+            compute_format=self.run_hparams.compute_format,
+            n_output_channels=1,
+            unet_variant=self.run_hparams.model_variant,
+            weight_init_method=self.run_hparams.weight_init_method,
+            activation_fn=self.run_hparams.activation_fn
+        )
+
+        if self.run_hparams.seed is not None:
+
+            if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                LOGGER.log("Deterministic Run - Seed: %d\n" % seed)
+
+            tf.set_random_seed(self.run_hparams.seed)
+            np.random.seed(self.run_hparams.seed)
+            random.seed(self.run_hparams.seed)
+
+        if dataset_name not in known_datasets.keys():
+            raise RuntimeError(
+                "The dataset `%s` is unknown, allowed values: %s ..." % (dataset_name, list(known_datasets.keys()))
+            )
+
+        self.dataset = known_datasets[dataset_name](data_dir=data_dir, **self.run_hparams.dataset_hparams)
+
+        self.num_gpus = 1 if not hvd_utils.is_using_hvd() else hvd.size()
+
+    @staticmethod
+    def _build_hparams(*args):
+
+        hparams = tf.contrib.training.HParams()
+
+        for _hparams in args:
+            if not isinstance(_hparams, tf.contrib.training.HParams):
+                raise ValueError("Non valid HParams argument object detected:", _hparams)
+
+            for key, val in _hparams.values().items():
+                try:
+                    hparams.add_hparam(name=key, value=val)
+
+                except ValueError:
+                    LOGGER.log(
+                        "the parameter `{}` already exists - existing value: {} and duplicated value: {}".format(
+                            key, hparams.get(key), val
+                        )
+                    )
+
+        return hparams
+
+    @staticmethod
+    def _get_global_batch_size(worker_batch_size):
+
+        if hvd_utils.is_using_hvd():
+            return worker_batch_size * hvd.size()
+        else:
+            return worker_batch_size
+
+    @staticmethod
+    def _get_session_config(mode, use_xla):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        config = tf.ConfigProto()
+
+        config.allow_soft_placement = True
+        config.log_device_placement = False
+
+        config.gpu_options.allow_growth = True
+        # config.gpu_options.per_process_gpu_memory_fraction=0.7
+
+        if hvd_utils.is_using_hvd():
+            config.gpu_options.visible_device_list = str(hvd.local_rank())
+
+        if use_xla:  # Only working on single GPU
+            LOGGER.log("XLA is activated - Experimental Feature")
+            config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
+
+        config.gpu_options.force_gpu_compatible = True  # Force pinned memory
+
+        # TODO: Provide correct session configuration for both
+        # variations with comments explaining why specific options were used
+
+        if mode == 'train':
+            config.intra_op_parallelism_threads = 1  # Avoid pool of Eigen threads
+
+            if hvd_utils.is_using_hvd():
+                config.inter_op_parallelism_threads = max(2, (multiprocessing.cpu_count() // hvd.size()) - 2)
+            else:
+                config.inter_op_parallelism_threads = 4
+
+        return config
+
+    @staticmethod
+    def _get_run_config(mode, model_dir, use_xla, seed=None):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        if seed is not None:
+            if hvd_utils.is_using_hvd():
+                tf_random_seed = 2 * (seed + hvd.rank())
+            else:
+                tf_random_seed = 2 * seed
+        else:
+            tf_random_seed = None
+
+        config = tf.estimator.RunConfig(
+            model_dir=model_dir,
+            tf_random_seed=tf_random_seed,
+            save_summary_steps=10 if mode == "train" else 1e9,  # disabled
+            save_checkpoints_steps=None,
+            save_checkpoints_secs=None,
+            session_config=Runner._get_session_config(mode=mode, use_xla=use_xla),
+            keep_checkpoint_max=5,
+            keep_checkpoint_every_n_hours=1e6,  # disabled
+            log_step_count_steps=1e9,
+            train_distribute=None,
+            device_fn=None,
+            protocol=None,
+            eval_distribute=None,
+            experimental_distribute=None
+        )
+
+        if mode == 'train':
+            if hvd_utils.is_using_hvd():
+                config = config.replace(
+                    save_checkpoints_steps=1000 if hvd.rank() == 0 else None, keep_checkpoint_every_n_hours=3
+                )
+            else:
+                config = config.replace(save_checkpoints_steps=1000, keep_checkpoint_every_n_hours=3)
+
+        return config
+
+    def _get_estimator(self, mode, run_params, use_xla):
+
+        if mode not in ["train", 'validation', 'benchmark']:
+            raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode)
+
+        run_config = Runner._get_run_config(
+            mode=mode, model_dir=self.run_hparams.model_dir, use_xla=use_xla, seed=self.run_hparams.seed
+        )
+
+        return tf.estimator.Estimator(
+            model_fn=self._model, model_dir=self.run_hparams.model_dir, config=run_config, params=run_params
+        )
+
+    def train(
+        self,
+        iter_unit,
+        num_iter,
+        batch_size,
+        weight_decay,
+        learning_rate,
+        learning_rate_decay_factor,
+        learning_rate_decay_steps,
+        rmsprop_decay,
+        rmsprop_momentum,
+        use_auto_loss_scaling,
+        augment_data,
+        warmup_steps=50,
+        is_benchmark=False
+    ):
+
+        if iter_unit not in ["epoch", "batch"]:
+            raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
+
+        if self.run_hparams.data_dir is None and not is_benchmark:
+            raise ValueError('`data_dir` must be specified for training!')
+
+        if self.run_hparams.use_tf_amp:
+            if use_auto_loss_scaling:
+
+                if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                    LOGGER.log("TF Loss Auto Scaling is activated - Experimental Feature")
+
+                os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1"
+                apply_manual_loss_scaling = False
+
+            else:
+                os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "0"
+                apply_manual_loss_scaling = True
+        else:
+            apply_manual_loss_scaling = False
+
+        if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+            LOGGER.log('Defining Model Estimator ...\n')
+
+        global_batch_size = batch_size * self.num_gpus
+
+        if self.run_hparams.data_dir is not None:
+            filenames, num_samples, num_steps, num_epochs = self.dataset.get_dataset_runtime_specs(
+                training=True, iter_unit=iter_unit, num_iter=num_iter, global_batch_size=global_batch_size
+            )
+
+            steps_per_epoch = int(num_steps / num_epochs)
+
+        else:
+            num_epochs = 1
+            num_steps = num_iter
+            steps_per_epoch = 625
+
+        training_hooks = []
+
+        if hvd_utils.is_using_hvd():
+            training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
+
+        if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+            training_hooks.append(
+                ProfilerHook(
+                    global_batch_size=global_batch_size,
+                    log_every=self.run_hparams.log_every_n_steps,
+                    warmup_steps=warmup_steps,
+                    is_training=True,
+                    sample_dir=self.run_hparams.sample_dir
+                )
+            )
+
+            LOGGER.log('Starting Model Training ...\n')
+
+            LOGGER.log("=> Epochs: %d" % num_epochs)
+            LOGGER.log("=> Total Steps: %d" % num_steps)
+            LOGGER.log("=> Steps per Epoch: %d" % steps_per_epoch)
+            LOGGER.log("=> Weight Decay Factor: %.1e" % weight_decay)
+            LOGGER.log("=> Learning Rate: %.1e" % learning_rate)
+            LOGGER.log("=> Learning Rate Decay Factor: %.2f" % learning_rate_decay_factor)
+            LOGGER.log("=> Learning Rate Decay Steps: %d" % learning_rate_decay_steps)
+            LOGGER.log("=> RMSProp - Decay: %.1f" % rmsprop_decay)
+            LOGGER.log("=> RMSProp - Momentum: %.1f" % rmsprop_momentum)
+            LOGGER.log("=> Loss Function Name: %s" % self.run_hparams.loss_fn_name)
+
+            if self.run_hparams.use_tf_amp:
+                LOGGER.log("=> Use Auto Loss Scaling: %s" % use_auto_loss_scaling)
+
+            LOGGER.log("=> # GPUs: %d" % self.num_gpus)
+            LOGGER.log("=> GPU Batch Size: %d" % batch_size)
+            LOGGER.log("=> Global Batch Size: %d" % global_batch_size)
+            LOGGER.log("=> Total Files to Processed: %d\n" % (num_steps * global_batch_size))
+
+        estimator_params = {
+            'batch_size': batch_size,
+            'steps_per_epoch': steps_per_epoch,
+            'learning_rate': learning_rate,
+            'learning_rate_decay_steps': learning_rate_decay_steps,
+            'learning_rate_decay_factor': learning_rate_decay_factor,
+            'rmsprop_decay': rmsprop_decay,
+            'rmsprop_momentum': rmsprop_momentum,
+            'weight_decay': weight_decay,
+            'apply_manual_loss_scaling': apply_manual_loss_scaling,
+            'loss_fn_name': self.run_hparams.loss_fn_name,
+            'debug_verbosity': self.run_hparams.debug_verbosity,
+        }
+
+        def training_data_fn():
+
+            if not is_benchmark or self.run_hparams.data_dir is not None:
+
+                return self.dataset.dataset_fn(
+                    batch_size=batch_size,
+                    training=True,
+                    only_defective_images=True,
+                    augment_data=augment_data,
+                    input_shape=list(self.run_hparams.input_shape) + [self.run_hparams.n_channels],
+                    mask_shape=list(self.run_hparams.mask_shape) + [self.run_hparams.n_channels],
+                    num_threads=64,
+                    use_gpu_prefetch=True,
+                    normalize_data_method="zero_centered",
+                    seed=self.run_hparams.seed
+                )
+
+            else:
+                if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+                    LOGGER.log("Using Synthetic Data ...")
+
+                return self.dataset.synth_dataset_fn(
+                    batch_size=batch_size,
+                    training=True,
+                    input_shape=list(self.run_hparams.input_shape) + [self.run_hparams.n_channels],
+                    mask_shape=list(self.run_hparams.mask_shape) + [self.run_hparams.n_channels],
+                    num_threads=64,
+                    use_gpu_prefetch=True,
+                    normalize_data_method="zero_centered",
+                    only_defective_images=True,
+                    augment_data=augment_data,
+                    seed=self.run_hparams.seed
+                )
+
+        model = self._get_estimator(mode='train', run_params=estimator_params, use_xla=self.use_xla)
+
+        try:
+            model.train(
+                input_fn=training_data_fn,
+                steps=num_steps,
+                hooks=training_hooks,
+            )
+        except KeyboardInterrupt:
+            print("Keyboard interrupt")
+
+        if not hvd_utils.is_using_hvd() or hvd.local_rank() == 0:
+            LOGGER.log('Ending Model Training ...')
+
+    def evaluate(self, iter_unit, num_iter, batch_size, warmup_steps=50, is_benchmark=False):
+
+        if iter_unit not in ["epoch", "batch"]:
+            raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
+
+        if self.run_hparams.data_dir is None and not is_benchmark:
+            raise ValueError('`data_dir` must be specified for evaluation!')
+
+        if hvd_utils.is_using_hvd() and hvd.rank() != 0:
+            raise RuntimeError('Multi-GPU inference is not supported')
+
+        LOGGER.log('Defining Model Estimator ...\n')
+
+        if self.run_hparams.data_dir is not None:
+            filenames, num_samples, num_steps, num_epochs = self.dataset.get_dataset_runtime_specs(
+                training=False, iter_unit=iter_unit, num_iter=num_iter, global_batch_size=batch_size
+            )
+
+            steps_per_epoch = num_steps / num_epochs
+
+        else:
+            num_epochs = 1
+            num_steps = num_iter
+            steps_per_epoch = num_steps
+
+        evaluation_hooks = [
+            ProfilerHook(
+                global_batch_size=batch_size,
+                log_every=self.run_hparams.log_every_n_steps,
+                warmup_steps=warmup_steps,
+                is_training=False,
+                sample_dir=None
+            )
+        ]
+
+        LOGGER.log('Starting Model Evaluation ...\n')
+
+        LOGGER.log("=> Epochs: %d" % num_epochs)
+        LOGGER.log("=> Total Steps: %d" % num_steps)
+        LOGGER.log("=> Steps per Epoch: %d" % steps_per_epoch)
+        LOGGER.log("=> GPU Batch Size: %d" % batch_size)
+        LOGGER.log("=> Total Files to Processed: %d\n" % (num_steps * batch_size))
+
+        estimator_params = {
+            'batch_size': batch_size,
+            'steps_per_epoch': steps_per_epoch,
+            'loss_fn_name': self.run_hparams.loss_fn_name,
+            'debug_verbosity': self.run_hparams.debug_verbosity,
+        }
+
+        def evaluation_data_fn():
+
+            if not is_benchmark or self.run_hparams.data_dir is not None:
+
+                return self.dataset.dataset_fn(
+                    batch_size=batch_size,
+                    training=False,
+                    input_shape=list(self.run_hparams.input_shape) + [self.run_hparams.n_channels],
+                    mask_shape=list(self.run_hparams.mask_shape) + [self.run_hparams.n_channels],
+                    num_threads=64,
+                    use_gpu_prefetch=True,
+                    normalize_data_method="zero_centered",
+                    only_defective_images=False,
+                    augment_data=False,
+                    seed=self.run_hparams.seed
+                )
+
+            else:
+                LOGGER.log("Using Synthetic Data ...")
+
+                return self.dataset.synth_dataset_fn(
+                    batch_size=batch_size,
+                    training=False,
+                    input_shape=list(self.run_hparams.input_shape) + [self.run_hparams.n_channels],
+                    mask_shape=list(self.run_hparams.mask_shape) + [self.run_hparams.n_channels],
+                    num_threads=64,
+                    use_gpu_prefetch=True,
+                    normalize_data_method="zero_centered",
+                    only_defective_images=False,
+                    augment_data=False,
+                    seed=self.run_hparams.seed
+                )
+
+        model = self._get_estimator(mode='validation', run_params=estimator_params, use_xla=self.use_xla)
+
+        try:
+            eval_results = model.evaluate(
+                input_fn=evaluation_data_fn,
+                steps=num_steps,
+                hooks=evaluation_hooks,
+            )
+
+            LOGGER.log('Ending Model Evaluation ...')
+
+            LOGGER.log('###################################\n\nEvaluation Results:\n')
+
+            for key, val in sorted(eval_results.items(), key=operator.itemgetter(0)):
+
+                if any(val in key for val in ["loss", "global_step", "Confusion_Matrix"]):
+                    continue
+
+                LOGGER.log('%s: %.3f' % (key, float(val)))
+
+            fns = eval_results["Confusion_Matrix_FN"]
+            fps = eval_results["Confusion_Matrix_FP"]
+            tns = eval_results["Confusion_Matrix_TN"]
+            tps = eval_results["Confusion_Matrix_TP"]
+
+            positives = np.add(tps, fns)
+            negatives = np.add(tns, fps)
+
+            tpr = np.divide(tps, positives)
+            tnr = np.divide(tns, negatives)
+
+            LOGGER.log('TP', tps)
+            LOGGER.log('FN', fns)
+            LOGGER.log('TN', tns)
+            LOGGER.log('FP', tps)
+            LOGGER.log('TPR', tpr)
+            LOGGER.log('TNR', tnr)
+
+        except KeyboardInterrupt:
+            print("Keyboard interrupt")
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_1GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_1GPU.sh
new file mode 100755
index 00000000..285b86cc
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_1GPU.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32-AMP on 1 GPU using 16 batch size (16 per GPU)
+# Usage ./UNet_FP32AMP_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+python ../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='train_and_evaluate' \
+    --iter_unit='batch' \
+    --num_iter=2500 \
+    --batch_size=16 \
+    --warmup_step=10 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --use_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_4GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_4GPU.sh
new file mode 100755
index 00000000..0df8d365
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_4GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32-AMP on 4 GPUs using 16 batch size (4 per GPU)
+# Usage ./UNet_FP32AMP_4GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+mpirun \
+    -np 4 \
+    -H localhost:4 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='train_and_evaluate' \
+        --iter_unit='batch' \
+        --num_iter=2500 \
+        --batch_size=4 \
+        --warmup_step=10 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --use_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_8GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_8GPU.sh
new file mode 100755
index 00000000..e9e885a3
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_8GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32-AMP on 8 GPUs using 16 batch size (2 per GPU)
+# Usage ./UNet_FP32AMP_8GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+mpirun \
+    -np 8 \
+    -H localhost:8 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='train_and_evaluate' \
+        --iter_unit='batch' \
+        --num_iter=2500 \
+        --batch_size=2 \
+        --warmup_step=10 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --use_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_EVAL.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_EVAL.sh
new file mode 100755
index 00000000..e24caa58
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_AMP_EVAL.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet evaluation in FP32-AMP on 1 GPUs using 16 batch size
+# Usage ./UNet_FP32AMP_EVAL.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+python ../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='evaluate' \
+    --iter_unit='epoch' \
+    --num_iter=1 \
+    --batch_size=16 \
+    --warmup_step=10 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --use_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=50 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_1GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_1GPU.sh
new file mode 100755
index 00000000..94a36bdd
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_1GPU.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32 on 1 GPU using 16 batch size (16 per GPU)
+# Usage ./UNet_FP32_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+python ../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='train_and_evaluate' \
+    --iter_unit='batch' \
+    --num_iter=2500 \
+    --batch_size=16 \
+    --warmup_step=10 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --nouse_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_4GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_4GPU.sh
new file mode 100755
index 00000000..b4af96ba
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_4GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32 on 4 GPUs using 16 batch size (4 per GPU)
+# Usage ./UNet_FP32_4GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+mpirun \
+    -np 4 \
+    -H localhost:4 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='train_and_evaluate' \
+        --iter_unit='batch' \
+        --num_iter=2500 \
+        --batch_size=4 \
+        --warmup_step=10 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --nouse_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_8GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_8GPU.sh
new file mode 100755
index 00000000..91a4c9a2
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_8GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training in FP32 on 8 GPUs using 16 batch size (2 per GPU)
+# Usage ./UNet_FP32_8GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+mpirun \
+    -np 8 \
+    -H localhost:8 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='train_and_evaluate' \
+        --iter_unit='batch' \
+        --num_iter=2500 \
+        --batch_size=2 \
+        --warmup_step=10 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --nouse_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_EVAL.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_EVAL.sh
new file mode 100755
index 00000000..4c304249
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/UNet_FP32_EVAL.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet evaluation in FP32 on 1 GPUs using 16 batch size
+# Usage ./UNet_FP32_EVAL.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../dllogger/
+
+python ../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='evaluate' \
+    --iter_unit='epoch' \
+    --num_iter=1 \
+    --batch_size=16 \
+    --warmup_step=10 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --nouse_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=50 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_AMP.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_AMP.sh
new file mode 100755
index 00000000..90d0018e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_AMP.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet evaluation benchmark in FP32-AMP on 1 GPUs using 16 batch size
+# Usage ./DGX1v_evalbench_FP32AMP.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+python ../../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='inference_benchmark' \
+    --iter_unit='batch' \
+    --num_iter=1500 \
+    --batch_size=16 \
+    --warmup_step=500 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --use_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_FP32.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_FP32.sh
new file mode 100755
index 00000000..3894512e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_evalbench_FP32.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet evaluation benchmark in FP32 on 1 GPUs using 16 batch size
+# Usage ./DGX1v_evalbench_FP32.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+python ../../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='inference_benchmark' \
+    --iter_unit='batch' \
+    --num_iter=1500 \
+    --batch_size=16 \
+    --warmup_step=500 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --nouse_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_1GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_1GPU.sh
new file mode 100755
index 00000000..5a3fe2a7
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_1GPU.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32-AMP on 1 GPU using 16 batch size (16 per GPU)
+# Usage ./DGX1v_trainbench_FP32AMP_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+python ../../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='training_benchmark' \
+    --iter_unit='batch' \
+    --num_iter=1500 \
+    --batch_size=16 \
+    --warmup_step=500 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --use_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_4GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_4GPU.sh
new file mode 100755
index 00000000..652ab2af
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_4GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32-AMP on 4 GPUs using 16 batch size (4 per GPU)
+# Usage ./DGX1v_trainbench_FP32AMP_4GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+mpirun \
+    -np 4 \
+    -H localhost:4 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='training_benchmark' \
+        --iter_unit='batch' \
+        --num_iter=1500 \
+        --batch_size=4 \
+        --warmup_step=500 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --use_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_8GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_8GPU.sh
new file mode 100755
index 00000000..816fdb0a
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_AMP_8GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32-AMP on 8 GPUs using 16 batch size (2 per GPU)
+# Usage ./DGX1v_trainbench_FP32AMP_8GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+mpirun \
+    -np 8 \
+    -H localhost:8 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='training_benchmark' \
+        --iter_unit='batch' \
+        --num_iter=1500 \
+        --batch_size=2 \
+        --warmup_step=500 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --use_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_1GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_1GPU.sh
new file mode 100755
index 00000000..f96a03d1
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_1GPU.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32 on 1 GPU using 16 batch size (16 per GPU)
+# Usage ./DGX1v_trainbench_FP32_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+python ../../main.py \
+    --unet_variant='tinyUNet' \
+    --activation_fn='relu' \
+    --exec_mode='training_benchmark' \
+    --iter_unit='batch' \
+    --num_iter=1500 \
+    --batch_size=16 \
+    --warmup_step=500 \
+    --results_dir="${1}" \
+    --data_dir="${2}" \
+    --dataset_name='DAGM2007' \
+    --dataset_classID="${3}" \
+    --data_format='NCHW' \
+    --use_auto_loss_scaling \
+    --nouse_tf_amp \
+    --nouse_xla \
+    --learning_rate=1e-4 \
+    --learning_rate_decay_factor=0.8 \
+    --learning_rate_decay_steps=500 \
+    --rmsprop_decay=0.9 \
+    --rmsprop_momentum=0.8 \
+    --loss_fn_name='adaptive_loss' \
+    --weight_decay=1e-5 \
+    --weight_init_method='he_uniform' \
+    --augment_data \
+    --display_every=250 \
+    --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_4GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_4GPU.sh
new file mode 100755
index 00000000..bb1e0141
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_4GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32 on 4 GPUs using 16 batch size (4 per GPU)
+# Usage ./DGX1v_trainbench_FP32_4GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+mpirun \
+    -np 4 \
+    -H localhost:4 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='training_benchmark' \
+        --iter_unit='batch' \
+        --num_iter=1500 \
+        --batch_size=4 \
+        --warmup_step=500 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --nouse_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_8GPU.sh b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_8GPU.sh
new file mode 100755
index 00000000..741bb132
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking/DGX1v_trainbench_FP32_8GPU.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches UNet training benchmark in FP32 on 8 GPUs using 16 batch size (2 per GPU)
+# Usage ./DGX1v_trainbench_FP32_8GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
+
+pip install ../../dllogger/
+
+mpirun \
+    -np 8 \
+    -H localhost:8 \
+    -bind-to none \
+    -map-by slot \
+    -x NCCL_DEBUG=VERSION \
+    -x LD_LIBRARY_PATH \
+    -x PATH \
+    -mca pml ob1 -mca btl ^openib \
+    --allow-run-as-root \
+    python ../../main.py \
+        --unet_variant='tinyUNet' \
+        --activation_fn='relu' \
+        --exec_mode='training_benchmark' \
+        --iter_unit='batch' \
+        --num_iter=1500 \
+        --batch_size=2 \
+        --warmup_step=500 \
+        --results_dir="${1}" \
+        --data_dir="${2}" \
+        --dataset_name='DAGM2007' \
+        --dataset_classID="${3}" \
+        --data_format='NCHW' \
+        --use_auto_loss_scaling \
+        --nouse_tf_amp \
+        --nouse_xla \
+        --learning_rate=1e-4 \
+        --learning_rate_decay_factor=0.8 \
+        --learning_rate_decay_steps=500 \
+        --rmsprop_decay=0.9 \
+        --rmsprop_momentum=0.8 \
+        --loss_fn_name='adaptive_loss' \
+        --weight_decay=1e-5 \
+        --weight_init_method='he_uniform' \
+        --augment_data \
+        --display_every=250 \
+        --debug_verbosity=0
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/utils/__init__.py
new file mode 100644
index 00000000..f7651e14
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/__init__.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from utils import hooks
+
+from utils import cmdline_helper
+from utils import hvd_utils
+from utils import image_processing
+from utils import losses
+from utils import metrics
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/cmdline_helper.py b/TensorFlow/Segmentation/UNet_Industrial/utils/cmdline_helper.py
new file mode 100644
index 00000000..14746e81
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/cmdline_helper.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import argparse
+
+from datasets import known_datasets
+from model.unet import UNet_v1
+from model.blocks.activation_blck import authorized_activation_fn
+
+
+def _add_bool_argument(parser, name=None, default=False, required=False, help=None):
+
+    if not isinstance(default, bool):
+        raise ValueError()
+
+    feature_parser = parser.add_mutually_exclusive_group(required=required)
+
+    feature_parser.add_argument('--' + name, dest=name, action='store_true', help=help, default=default)
+    feature_parser.add_argument('--no' + name, dest=name, action='store_false')
+    feature_parser.set_defaults(name=default)
+
+
+def parse_cmdline():
+
+    p = argparse.ArgumentParser(description="JoC-UNet_v1-TF")
+
+    p.add_argument(
+        '--unet_variant',
+        default="tinyUNet",
+        choices=UNet_v1.authorized_models_variants,
+        type=str,
+        required=False,
+        help="""Which model size is used. This parameter control directly the size and the number of parameters"""
+    )
+
+    p.add_argument(
+        '--activation_fn',
+        choices=authorized_activation_fn,
+        type=str,
+        default="relu",
+        required=False,
+        help="""Which activation function is used after the convolution layers"""
+    )
+
+    p.add_argument(
+        '--exec_mode',
+        choices=['train', 'train_and_evaluate', 'evaluate', 'training_benchmark', 'inference_benchmark'],
+        type=str,
+        required=True,
+        help="""Which execution mode to run the model into"""
+    )
+
+    p.add_argument(
+        '--iter_unit',
+        choices=['epoch', 'batch'],
+        type=str,
+        required=True,
+        help="""Will the model be run for X batches or X epochs ?"""
+    )
+
+    p.add_argument('--num_iter', type=int, required=True, help="""Number of iterations to run.""")
+
+    p.add_argument('--batch_size', type=int, required=True, help="""Size of each minibatch per GPU.""")
+
+    p.add_argument(
+        '--warmup_step',
+        default=200,
+        type=int,
+        required=False,
+        help="""Number of steps considered as warmup and not taken into account for performance measurements."""
+    )
+
+    p.add_argument(
+        '--results_dir',
+        type=str,
+        required=True,
+        help="""Directory in which to write training logs, summaries and checkpoints."""
+    )
+
+    p.add_argument('--data_dir', required=False, default=None, type=str, help="Path to dataset directory")
+
+    p.add_argument(
+        '--dataset_name',
+        choices=list(known_datasets.keys()),
+        type=str,
+        required=True,
+        help="""Name of the dataset used in this run (only DAGM2007 is supported atm.)"""
+    )
+
+    p.add_argument(
+        '--dataset_classID',
+        default=None,
+        type=int,
+        required=False,
+        help="""ClassID to consider to train or evaluate the network (used for DAGM)."""
+    )
+
+    p.add_argument(
+        '--data_format',
+        choices=['NHWC', 'NCHW'],
+        type=str,
+        default="NCHW",
+        required=False,
+        help="""Which Tensor format is used for computation inside the mode"""
+    )
+
+    _add_bool_argument(
+        parser=p,
+        name="use_tf_amp",
+        default=False,
+        required=False,
+        help="Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores"
+    )
+
+    _add_bool_argument(
+        parser=p, name="use_xla", default=False, required=False, help="Enable Tensorflow XLA to maximise performance."
+    )
+
+    p.add_argument(
+        '--weight_init_method',
+        choices=UNet_v1.authorized_weight_init_methods,
+        default="he_normal",
+        type=str,
+        required=False,
+        help="""Which initialisation method is used to randomly intialize the model during training"""
+    )
+
+    p.add_argument('--learning_rate', default=1e-5, type=float, required=False, help="""Learning rate value.""")
+
+    p.add_argument(
+        '--learning_rate_decay_factor',
+        default=0.75,
+        type=float,
+        required=False,
+        help="""Decay factor to decrease the learning rate."""
+    )
+
+    p.add_argument(
+        '--learning_rate_decay_steps',
+        default=500,
+        type=int,
+        required=False,
+        help="""Decay factor to decrease the learning rate."""
+    )
+
+    p.add_argument('--rmsprop_decay', default=0.9, type=float, required=False, help="""RMSProp - Decay value.""")
+
+    p.add_argument('--rmsprop_momentum', default=0.8, type=float, required=False, help="""RMSProp - Momentum value.""")
+
+    p.add_argument('--weight_decay', default=1e-4, type=float, required=False, help="""Weight Decay scale factor""")
+
+    _add_bool_argument(
+        parser=p, name="use_auto_loss_scaling", default=False, required=False, help="Use AutoLossScaling with TF-AMP"
+    )
+
+    p.add_argument(
+        '--loss_fn_name',
+        type=str,
+        default="adaptive_loss",
+        required=False,
+        help="""Loss function Name to use to train the network"""
+    )
+
+    _add_bool_argument(
+        parser=p, name="augment_data", default=True, required=False, help="Choose whether to use data augmentation"
+    )
+
+    p.add_argument(
+        '--display_every',
+        type=int,
+        default=50,
+        required=False,
+        help="""How often (in batches) to print out debug information."""
+    )
+
+    p.add_argument(
+        '--debug_verbosity',
+        choices=[0, 1, 2],
+        default=0,
+        type=int,
+        required=False,
+        help="""Verbosity Level: 0 minimum, 1 with layer creation debug info, 2 with layer + var creation debug info."""
+    )
+
+    p.add_argument('--seed', type=int, default=None, help="""Random seed.""")
+
+    FLAGS, unknown_args = p.parse_known_args()
+
+    if len(unknown_args) > 0:
+
+        for bad_arg in unknown_args:
+            print("ERROR: Unknown command line arg: %s" % bad_arg)
+
+        raise ValueError("Invalid command line arg(s)")
+
+    return FLAGS
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/__init__.py b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/__init__.py
new file mode 100644
index 00000000..0fc4d7c3
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/__init__.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+from utils.hooks.profiler_hook import *
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py
new file mode 100644
index 00000000..6339908c
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py
@@ -0,0 +1,210 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import os
+import time
+import operator
+
+import numpy as np
+import tensorflow as tf
+
+from dllogger.logger import LOGGER
+
+__all__ = ["ProfilerHook"]
+
+
+class ProfilerHook(tf.train.SessionRunHook):
+
+    def __init__(self, global_batch_size, sample_dir, log_every=10, warmup_steps=20, is_training=True):
+
+        self._warmup_steps = warmup_steps
+        self._global_batch_size = global_batch_size
+        self._current_step = 0
+
+        self._log_every = log_every
+
+        self._t0 = None
+        self._start_training_time = None
+
+        self._is_training = is_training
+
+        self._sample_dir = sample_dir
+
+        self._processing_speed_arr = list()
+
+    @staticmethod
+    def moving_average(a, n=4):
+        if len(a) < n:
+            return [np.mean(a)]
+
+        ret = np.cumsum(a, dtype=float)
+        ret[n:] = ret[n:] - ret[:-n]
+        return ret[n - 1:] / n
+
+    def begin(self):
+        LOGGER.log_hardware()
+
+    def after_create_session(self, session, coord):
+
+        params_count = tf.get_default_graph().get_tensor_by_name("trainable_parameters_count_ref:0")
+        _params_count = session.run(params_count)
+
+        LOGGER.log("# Total Trainable Parameters:", int(_params_count))
+
+        self._start_training_time = time.time()
+
+    def before_run(self, run_context):
+
+        self._current_step += 1
+
+        request_fetches = dict()
+
+        if self._current_step % self._log_every == 0:
+
+            additional_fetches = {
+                'total_loss': tf.get_default_graph().get_tensor_by_name("losses/total_loss_ref:0"),
+                'iou_scores': dict(),
+                'confusion_matrix': dict()
+            }
+
+            if self._is_training:
+
+                additional_fetches["weight_decay"] = tf.get_default_graph().get_tensor_by_name("losses/l2_loss_ref:0")
+
+                additional_fetches["reconstruction_loss"] = tf.get_default_graph(
+                ).get_tensor_by_name("losses/reconstruction_loss_ref:0")
+
+                additional_fetches["learning_rate"] = tf.get_default_graph(
+                ).get_tensor_by_name("optimizers/learning_rate_ref:0")
+
+            # ==================== Samples ==================== #
+
+            if self._sample_dir is not None:
+                additional_fetches["samples"] = {}
+
+                additional_fetches["samples"]["input_image"] = tf.get_default_graph(
+                ).get_tensor_by_name("input_image_jpeg_ref:0")
+
+                additional_fetches["samples"]["mask"] = tf.get_default_graph().get_tensor_by_name("mask_sample_ref:0")
+
+                for threshold in [None, 0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99]:
+
+                    additional_fetches["samples"][str(threshold)] = tf.get_default_graph().get_tensor_by_name(
+                        "output_sample_ths_%s_ref:0" % threshold
+                    )
+
+            # ==================== Evaluation Metrics ==================== #
+
+            for threshold in [0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99]:
+
+                if threshold is not None:
+                    additional_fetches["iou_scores"][str(threshold)] = tf.get_default_graph().get_tensor_by_name(
+                        "IoU_Metrics/iou_score_ths_%s_ref:0" % threshold
+                    )
+
+            additional_fetches["confusion_matrix"]["tp"] = tf.get_default_graph(
+            ).get_tensor_by_name("Confusion_Matrix/true_positives_ref:0")
+
+            additional_fetches["confusion_matrix"]["tn"] = tf.get_default_graph(
+            ).get_tensor_by_name("Confusion_Matrix/true_negatives_ref:0")
+
+            additional_fetches["confusion_matrix"]["fp"] = tf.get_default_graph(
+            ).get_tensor_by_name("Confusion_Matrix/false_positives_ref:0")
+
+            additional_fetches["confusion_matrix"]["fn"] = tf.get_default_graph(
+            ).get_tensor_by_name("Confusion_Matrix/false_negatives_ref:0")
+
+            # Update `request_fetches` dict
+            request_fetches.update(additional_fetches)
+
+            print("\n######### START: %d ##############" % self._current_step)
+
+        self._t0 = time.time()
+
+        return tf.train.SessionRunArgs(fetches=request_fetches)
+
+    def after_run(self, run_context, run_values):
+
+        batch_time = time.time() - self._t0
+        imgs_per_sec = int(self._global_batch_size / batch_time)
+
+        is_log_step = self._current_step % self._log_every == 0
+
+        if is_log_step:
+
+            if self._current_step > self._warmup_steps:
+                imgs_per_sec = float(ProfilerHook.moving_average(self._processing_speed_arr, n=30)[-1])
+
+            LOGGER.log("iteration", int(self._current_step))
+            LOGGER.log("total_ips", float(imgs_per_sec))
+
+            if self._is_training:
+                LOGGER.log("weight_decay", float(run_values.results["weight_decay"]))
+                LOGGER.log("reconstruction_loss", float(run_values.results["reconstruction_loss"]))
+                LOGGER.log("total_loss", float(run_values.results["total_loss"]))
+                LOGGER.log("learning_rate", float(run_values.results["learning_rate"]))
+
+            for key, val in sorted(run_values.results["iou_scores"].items(), key=operator.itemgetter(0)):
+                LOGGER.log("iou_score - THS %s" % key, float(val))
+
+            LOGGER.log("True Positives:", run_values.results["confusion_matrix"]["tp"])
+            LOGGER.log("True Negatives:", run_values.results["confusion_matrix"]["tn"])
+            LOGGER.log("False Positives:", run_values.results["confusion_matrix"]["fp"])
+            LOGGER.log("False Negatives:", run_values.results["confusion_matrix"]["fn"])
+
+            if self._sample_dir is not None:
+
+                for key in sorted(run_values.results["samples"].keys(), key=operator.itemgetter(0)):
+
+                    with open(
+                        os.path.join(self._sample_dir, "sample_step_%04d_ths_%s.jpeg" % (self._current_step, key)), 'wb'
+                    ) as fd:
+                        fd.write(run_values.results["samples"][key])
+
+                    with open(
+                        os.path.join(self._sample_dir, "sample_step_%04d_mask.jpeg" % self._current_step), 'wb'
+                    ) as fd:
+                        fd.write(run_values.results["samples"]["mask"])
+
+            print("######### STOP: %d ##############" % self._current_step)
+
+        elif self._current_step > self._warmup_steps:
+            # Do not store speed for log step due to additional fetches
+            self._processing_speed_arr.append(imgs_per_sec)
+
+    def end(self, session):
+
+        try:
+            avg_processing_speed = float(ProfilerHook.moving_average(self._processing_speed_arr, n=100)[-1])
+        except:
+            avg_processing_speed = float(np.mean(self._processing_speed_arr))
+
+        total_processing_time = time.time() - self._start_training_time
+
+        total_processing_hours, rem = divmod(total_processing_time, 3600)
+        total_processing_minutes, total_processing_seconds = divmod(rem, 60)
+
+        LOGGER.log(
+            "Final Summary:\n"
+            "\t[*] Average Imgs/sec: %d\n"
+            "\t[*] Total Processing Time: %dh %02dm %02ds\n" %
+            (avg_processing_speed, total_processing_hours, total_processing_minutes, total_processing_seconds)
+        )
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/hvd_utils.py b/TensorFlow/Segmentation/UNet_Industrial/utils/hvd_utils.py
new file mode 100644
index 00000000..db8d8e72
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/hvd_utils.py
@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import os
+
+__all__ = ["is_using_hvd"]
+
+
+def is_using_hvd():
+    env_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"]
+
+    if all([var in os.environ for var in env_vars]):
+        return True
+    else:
+        return False
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/image_processing.py b/TensorFlow/Segmentation/UNet_Industrial/utils/image_processing.py
new file mode 100644
index 00000000..7f3b74aa
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/image_processing.py
@@ -0,0 +1,40 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+__all__ = ["binarize_output"]
+
+
+def binarize_output(image, threshold=None):
+
+    if threshold is not None:
+        image = tf.cast(image > threshold, dtype=tf.uint8)
+        image = image * 255
+    else:
+        image = tf.cast(image * 255, dtype=tf.uint8)
+
+    encoded_image = tf.image.encode_jpeg(image, format='grayscale', quality=100)
+
+    if image.get_shape().rank == 3:
+        image = tf.expand_dims(image, axis=0)
+
+    return image, encoded_image
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/losses.py b/TensorFlow/Segmentation/UNet_Industrial/utils/losses.py
new file mode 100644
index 00000000..275c02e8
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/losses.py
@@ -0,0 +1,110 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+__all__ = ["regularization_l2loss", "reconstruction_l2loss", "reconstruction_x_entropy", "adaptive_loss"]
+
+
+def regularization_l2loss(weight_decay):
+
+    def loss_filter_fn(name):
+        """we don't need to compute L2 loss for BN"""
+
+        return all(
+            [tensor_name not in name.lower() for tensor_name in ["batchnorm", "batch_norm", "batch_normalization"]]
+        )
+
+    filtered_params = [tf.cast(v, tf.float32) for v in tf.trainable_variables() if loss_filter_fn(v.name)]
+
+    if len(filtered_params) != 0:
+
+        l2_loss_per_vars = [tf.nn.l2_loss(v) for v in filtered_params]
+        l2_loss = tf.multiply(tf.add_n(l2_loss_per_vars), weight_decay)
+
+    else:
+        l2_loss = tf.zeros(shape=(), dtype=tf.float32)
+
+    return l2_loss
+
+
+def reconstruction_l2loss(y_pred, y_true):
+    reconstruction_err = tf.subtract(y_pred, y_true)
+    return tf.reduce_mean(tf.nn.l2_loss(reconstruction_err), name='reconstruction_loss_l2_loss')
+
+
+def reconstruction_x_entropy(y_pred, y_true, from_logits=False):
+    return tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true=y_true, y_pred=y_pred, from_logits=from_logits))
+
+
+def dice_coe(y_pred, y_true, loss_type='jaccard', smooth=1.):
+    """Soft dice (Sørensen or Jaccard) coefficient for comparing the similarity
+    of two batch of data, usually be used for binary image segmentation
+    i.e. labels are binary. The coefficient between 0 to 1, 1 means totally match.
+
+    Parameters
+    -----------
+    y_true : Tensor
+        A distribution with shape: [batch_size, ....], (any dimensions).
+    y_pred : Tensor
+        The target distribution, format the same with `output`.
+    loss_type : str
+        ``jaccard`` or ``sorensen``, default is ``jaccard``.
+    smooth : float
+        This small value will be added to the numerator and denominator.
+            - If both output and target are empty, it makes sure dice is 1.
+            - If either output or target are empty (all pixels are background),
+            dice = ```smooth/(small_value + smooth)``,
+            then if smooth is very small, dice close to 0 (even the image values lower than the threshold),
+            so in this case, higher smooth can have a higher dice.
+
+    References
+    -----------
+    - `Wiki-Dice <https://en.wikipedia.org/wiki/Sørensen–Dice_coefficient>`__
+
+    """
+
+    y_true_f = tf.layers.flatten(y_true)
+    y_pred_f = tf.layers.flatten(y_pred)
+
+    intersection = tf.reduce_sum(y_true_f * y_pred_f)
+
+    if loss_type == 'jaccard':
+        union = tf.reduce_sum(tf.square(y_pred_f)) + tf.reduce_sum(tf.square(y_true_f))
+
+    elif loss_type == 'sorensen':
+        union = tf.reduce_sum(y_pred_f) + tf.reduce_sum(y_true_f)
+
+    else:
+        raise ValueError("Unknown `loss_type`: %s" % loss_type)
+
+    return (2. * intersection + smooth) / (union + smooth)
+
+
+def adaptive_loss(y_pred, y_pred_logits, y_true, switch_at_threshold=0.3, loss_type='jaccard'):
+
+    dice_loss = 1 - dice_coe(y_pred=y_pred, y_true=y_true, loss_type=loss_type, smooth=1.)
+
+    return tf.cond(
+        dice_loss < switch_at_threshold,
+        true_fn=lambda: dice_loss,
+        false_fn=lambda: reconstruction_x_entropy(y_pred=y_pred_logits, y_true=y_true, from_logits=True)
+    )
diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/metrics.py b/TensorFlow/Segmentation/UNet_Industrial/utils/metrics.py
new file mode 100644
index 00000000..951c8f5e
--- /dev/null
+++ b/TensorFlow/Segmentation/UNet_Industrial/utils/metrics.py
@@ -0,0 +1,41 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==============================================================================
+
+import tensorflow as tf
+
+__all__ = [
+    "iou_score",
+]
+
+
+def iou_score(y_pred, y_true, threshold, eps=1e-5):
+
+    y_true = tf.cast(y_true > threshold, dtype=tf.float32)
+    y_pred = tf.cast(y_pred > threshold, dtype=tf.float32)
+
+    intersection = y_true * y_pred
+    intersection = tf.reduce_sum(intersection, axis=(1, 2, 3))
+
+    numerator = 2.0 * intersection + eps
+
+    divisor = tf.reduce_sum(y_true, axis=(1, 2, 3)) + tf.reduce_sum(y_pred, axis=(1, 2, 3)) + eps
+
+    return tf.reduce_mean(numerator / divisor)
diff --git a/TensorFlow/Translation/GNMT/.dockerignore b/TensorFlow/Translation/GNMT/.dockerignore
new file mode 100644
index 00000000..e824184f
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/.dockerignore
@@ -0,0 +1,5 @@
+__pycache__
+*.log
+/results
+/data
+.DS_Store
diff --git a/TensorFlow/Translation/GNMT/.gitignore b/TensorFlow/Translation/GNMT/.gitignore
new file mode 100644
index 00000000..e824184f
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/.gitignore
@@ -0,0 +1,5 @@
+__pycache__
+*.log
+/results
+/data
+.DS_Store
diff --git a/TensorFlow/Translation/GNMT/Dockerfile b/TensorFlow/Translation/GNMT/Dockerfile
new file mode 100644
index 00000000..e30e5c71
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/Dockerfile
@@ -0,0 +1,23 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+FROM nvcr.io/nvidia/tensorflow:19.03-py3
+
+
+COPY . /workspace/gnmt
+WORKDIR /workspace/gnmt
+
+RUN pip install -r requirements.txt
+
+ENTRYPOINT ["/bin/sh", "-c"]
+CMD ["bash"]
diff --git a/TensorFlow/Translation/GNMT/NOTICE b/TensorFlow/Translation/GNMT/NOTICE
new file mode 100644
index 00000000..f54d61e9
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/NOTICE
@@ -0,0 +1,4 @@
+GNMT TensorFlow
+
+This product includes software from https://github.com/mlperf/results
+licensed under the Apache License, Version 2.0 (the "License")
\ No newline at end of file
diff --git a/TensorFlow/Translation/GNMT/README.md b/TensorFlow/Translation/GNMT/README.md
new file mode 100644
index 00000000..b74ab28f
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/README.md
@@ -0,0 +1,464 @@
+# GNMT v2 For TensorFlow
+
+This repository provides a script and recipe to train GNMT v2 to achieve
+state of the art accuracy, and is tested and maintained by NVIDIA.
+
+## Table Of Contents
+
+* [The model](#the-model)
+  * [Default configuration](#default-configuration)
+* [Setup](#setup)
+  * [Requirements](#requirements)
+* [Quick Start Guide](#quick-start-guide)
+* [Details](#details)
+  * [Command line arguments](#command-line-arguments)
+  * [Getting the data](#getting-the-data)
+  * [Training process](#training-process)
+  * [Enabling mixed precision](#enabling-mixed-precision)
+  * [Inference process](#inference-process)
+* [Results](#results)
+  * [Training accuracy results](#training-accuracy-results)
+    * [Training stability test](#training-stability-test)
+  * [Training performance results](#training-performance-results)
+  * [Inference performance results](#inference-performance-results)
+* [Changelog](#changelog)
+* [Known issues](#known-issues)
+
+## The model
+The GNMT v2 model is similar to the one discussed in the [Google's Neural Machine
+Translation System: Bridging the Gap between Human and Machine
+Translation](https://arxiv.org/abs/1609.08144) paper.
+
+The most important difference between the two models is in the attention
+mechanism. In our model, the output from the first LSTM layer of the decoder
+goes into the attention module, then the re-weighted context is concatenated
+with inputs to all subsequent LSTM layers in the decoder at the current
+timestep.
+
+The same attention mechanism is also implemented in the default
+GNMT-like models from
+[TensorFlow Neural Machine Translation Tutorial](https://github.com/tensorflow/nmt)
+and
+[NVIDIA OpenSeq2Seq Toolkit](https://github.com/NVIDIA/OpenSeq2Seq).
+
+## Default configuration
+
+The following features were implemented in this model:
+
+* general:
+  * encoder and decoder are using shared embeddings
+  * data-parallel multi-gpu training
+  * dynamic loss scaling with backoff for Tensor Cores (mixed precision) training
+  * trained with label smoothing loss (smoothing factor 0.1)
+* encoder:
+  * 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are
+    unidirectional
+  * with residual connections starting from 3rd layer
+  * dropout is applied on input to all LSTM layers, probability of dropout is
+    set to 0.2
+  * hidden state of LSTM layers is initialized with zeros
+  * weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1)
+    distribution
+* decoder:
+  * 4-layer unidirectional LSTM with hidden size 1024 and fully-connected
+    classifier
+  * with residual connections starting from 3rd layer
+  * dropout is applied on input to all LSTM layers, probability of dropout is
+    set to 0.2
+  * hidden state of LSTM layers is initialized with the last hidden state from
+    encoder
+  * weights and bias of LSTM layers is initialized with uniform(-0.1, 0.1)
+    distribution
+  * weights and bias of fully-connected classifier is initialized with
+    uniform(-0.1, 0.1) distribution
+* attention:
+  * normalized Bahdanau attention
+  * output from first LSTM layer of decoder goes into attention,
+  then re-weighted context is concatenated with the input to all subsequent
+  LSTM layers of the decoder at the current timestep
+  * linear transform of keys and queries is initialized with uniform(-0.1, 0.1),
+  normalization scalar is initialized with 1.0 / sqrt(1024),
+    normalization bias is initialized with zero
+* inference:
+  * beam search with default beam size of 5
+  * with coverage penalty and length normalization, coverage penalty factor is
+    set to 0.1, length normalization factor is set to 0.6 and length
+    normalization constant is set to 5.0
+  * de-tokenized BLEU computed by [SacreBLEU](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu)
+  * [motivation](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu#motivation) for choosing SacreBLEU
+
+When comparing the BLEU score, there are various tokenization approaches and
+BLEU calculation methodologies; therefore, ensure you align similar metrics.
+
+Code from this repository can be used to train a larger, 8-layer GNMT v2 model.
+Our experiments show that a 4-layer model is significantly faster to train and
+yields comparable accuracy on the public
+[WMT16 English-German](http://www.statmt.org/wmt16/translation-task.html)
+dataset. The number of LSTM layers is controlled by the `--num_layers` parameter
+in the `nmt.py` script.
+
+# Setup
+
+The following section list the requirements in order to start training the GNMT
+v2 model.
+
+## Requirements
+
+This repository contains `Dockerfile` which extends the TensorFlow NGC container
+and encapsulates some dependencies.  Aside from these dependencies, ensure you
+have the following components:
+
+* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
+* [TensorFlow 19.03-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
+* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
+
+For more information about how to get started with NGC containers, see the
+following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
+DGX Documentation:
+
+* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html),
+* [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry),
+* [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running).
+
+# Quick Start Guide
+To train your model using mixed precision with Tensor Cores or using FP32,
+perform the following steps using the default parameters of the GNMT v2 model
+on the WMT16 English German dataset.
+
+### 1. Clone the repository.
+```
+git clone https://github.com/NVIDIA/DeepLearningExamples
+cd DeepLearningExamples/TensorFlow/Translation/GNMT
+```
+
+### 2. Build the GNMT v2 container.
+```
+bash scripts/docker/build.sh
+```
+
+### 3. Start an interactive session in the container to run training/inference.
+```
+bash scripts/docker/interactive.sh
+```
+
+### 4. Download and preprocess the dataset.
+Data will be downloaded to the `data` directory (on the host). The `data`
+directory is mounted to the `/workspace/gnmt/data` location in the Docker
+container.
+```
+bash scripts/wmt16_en_de.sh
+```
+
+### 5. Start training.
+All results and logs are saved to the `results` directory (on the host) or to
+the `/workspace/gnmt/results` directory (in the container). The training script
+saves checkpoint after every training epoch and after every 2000 training steps
+within each epoch. You can modify the results directory using `--output_dir`
+argument.
+
+
+To launch mixed precision training on 1 GPU, run:
+
+```
+python nmt.py --output_dir=results --batch_size=192 --learning_rate=8e-4
+```
+
+To launch mixed precision training on 8 GPUs, run:
+
+```
+python nmt.py --output_dir=results --batch_size=1536 --num_gpus=8 --learning_rate=2e-3
+```
+
+To launch FP32 training on 1 GPU, run:
+
+```
+python nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 --use_amp=false
+```
+
+To launch FP32 training on 8 GPUs, run:
+
+```
+python nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 --use_amp=false
+```
+
+### 6. Start evaluation.
+The training process automatically runs evaluation and outputs the BLEU score
+after each training epoch. Additionally, after the training is done, you can
+manually run inference on test dataset with the checkpoint saved during the
+training.
+
+To launch mixed precision inference on 1 GPU, run:
+
+```
+python nmt.py --output_dir=results --infer_batch_size=128 --mode=infer
+```
+
+To launch FP32 inference on 1 GPU, run:
+
+```
+python nmt.py --output_dir=results --infer_batch_size=128 --use_amp=false --mode=infer
+```
+
+# Details
+The following sections provide greater details of the dataset, running training
+and inference, and the training results.
+
+## Command line arguments
+To see the full list of available options and their descriptions, use the `-h`
+or `--help` command line option, for example:
+
+```
+python nmt.py --help
+```
+
+To summarize, the most useful arguments are as follows:
+
+```
+  --learning_rate LEARNING_RATE
+                        Learning rate.
+  --max_train_epochs MAX_TRAIN_EPOCHS
+                        Max number of epochs.
+  --data_dir DATA_DIR   Training/eval data directory.
+  --output_dir OUTPUT_DIR
+                        Store log/model files.
+  --batch_size BATCH_SIZE
+                        Total batch size.
+  --log_step_count_steps LOG_STEP_COUNT_STEPS
+                        The frequency, in number of global steps, that the
+                        global step and the loss will be logged during training
+  --num_gpus NUM_GPUS   Number of gpus in each worker.
+  --random_seed RANDOM_SEED
+                        Random seed (>0, set a specific seed).
+  --ckpt CKPT           Checkpoint file to load a model for inference.
+                        (defaults to newest checkpoint)
+  --infer_batch_size INFER_BATCH_SIZE
+                        Batch size for inference mode.
+  --beam_width BEAM_WIDTH
+                        beam width when using beam search decoder. If 0,
+                        use standard decoder with greedy helper.
+  --use_amp USE_AMP     use_amp for training and inference
+  --mode {train_and_eval,infer}
+```
+
+## Getting the data
+The GNMT v2 model was trained on the
+[WMT16 English-German](http://www.statmt.org/wmt16/translation-task.html)
+dataset and *newstest2014* is used as a testing dataset.
+
+This repository contains the `scripts/wmt16_en_de.sh` download script which
+automatically downloads and preprocesses the training and test datasets. By
+default, data is downloaded to the `data` directory.
+
+Our download script is very similar to the `wmt16_en_de.sh` script from the
+[tensorflow/nmt](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/wmt16_en_de.sh)
+repository. Our download script contains an extra preprocessing step, which
+discards all pairs of sentences which can't be decoded by *latin-1* encoder.
+The `scripts/wmt16_en_de.sh` script uses the
+[subword-nmt](https://github.com/rsennrich/subword-nmt)
+package to segment text into subword units (Byte Pair Encodings - [BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding)). By default, the script builds
+the shared vocabulary of 32,000 tokens.
+
+In order to test with other datasets, scripts need to be customized accordingly.
+
+## Training process
+The training configuration can be launched by running the `nmt.py` script.
+By default, the training script saves the checkpoint after every training epoch
+and after every 2000 training steps within each epoch.
+Results are stored in the `results` directory.
+
+The training script launches data-parallel training on multiple GPUs. We have
+tested reliance on up to 8 GPUs on a single node.
+
+After each training epoch, the script runs an evaluation and outputs a BLEU
+score on the test dataset (*newstest2014*). BLEU is computed by the
+[SacreBLEU](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu)
+package. Logs from the training and evaluation are saved to the `results`
+directory.
+
+The training script automatically runs testing after each training epoch. The
+results from the testing are printed to the standard output and saved to the
+log files.
+
+The summary after each training epoch is printed in the following format:
+
+```
+training time for epoch 1: 29.37 mins (2918.36 sent/sec, 139640.48 tokens/sec)
+[...]
+bleu is 20.50000
+eval time for epoch 1: 1.57 mins (78.48 sent/sec, 4283.88 tokens/sec)
+```
+The BLEU score is computed on the test dataset.
+Performance is reported in total sentences per second and in total tokens per
+second. The performance result is averaged over an entire training epoch and
+summed over all GPUs participating in the training.
+
+
+To view all available options for training, run `python nmt.py --help`.
+
+## Enabling mixed precision
+[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant
+computational speedup by performing operations in half-precision format, while
+storing minimal information in single-precision to retain as much information
+as possible in critical parts of the network. Since the introduction of [tensor
+cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing
+architectures, significant training speedups are experienced by switching to
+mixed precision - up to 3x overall speedup on the most arithmetically intense
+model architectures.  Using [mixed precision
+training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)
+previously required two steps:
+
+1. Porting the model to use the FP16 data type where appropriate.
+2. Manually adding loss scaling to preserve small gradient values.
+
+This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow
+to enable the full [mixed precision
+methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow)
+in your existing TensorFlow model code. AMP enables mixed precision training on
+Volta and Turing GPUs automatically. The TensorFlow framework code makes all
+necessary model changes internally.
+
+In TF-AMP, the computational graph is optimized to use as few casts as
+necessary and maximize the use of FP16, and the loss scaling is automatically
+applied inside of supported optimizers. AMP can be configured to work with the
+existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a
+single environment variable to perform only the automatic mixed-precision
+optimization. It accomplishes this by automatically rewriting all computation
+graphs with the necessary operations to enable mixed precision training and
+automatic loss scaling.
+
+For information about:
+
+* How to train using mixed precision, see the [Mixed Precision
+  Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed
+  Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)
+  documentation.
+* How to access and enable AMP for TensorFlow, see [Using
+  TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)
+  from the TensorFlow User Guide.
+* Techniques used for mixed precision training, see the [Mixed-Precision
+  Training of Deep Neural
+  Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)
+  blog.
+
+## Inference process
+Inference can be run by launching the `nmt.py` script, although, it requires a
+pre-trained model checkpoint and tokenized input.
+
+The script, `nmt.py`, supports batched inference (`--mode=infer` flag). By
+default, it launches beam search with beam size of 5, coverage penalty term and
+length normalization term. Greedy decoding can be enabled by setting the
+`--beam_width=1` flag for the `nmt.py` inference script. To control the
+batch size use the `--infer_batch_size` flag.
+
+To view all available options for inference, run `python nmt.py --help`.
+
+# Results
+
+The following sections provide details on how we achieved our performance and
+accuracy in training and inference.
+
+## Training accuracy results
+Our results were obtained by running the `nmt.py` script in the
+tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+
+Commands to launch the training:
+
+```
+for 1 GPUs in mixed precision:
+python nmt.py --output_dir=results --batch_size=192 --learning_rate=8e-4
+
+for 8 GPUs in mixed precision:
+python nmt.py --output_dir=results --batch_size=1536 --num_gpus=8 --learning_rate=2e-3
+
+for 1 GPUs in FP32:
+python nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 --use_amp=false
+
+for 8 GPUs in FP32:
+python nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 --use_amp=false
+```
+
+| **Number of GPUs** | **Mixed precision batch size/GPU** | **FP32 batch size/GPU** |**Mixed precision BLEU** | **FP32 BLEU** | **Mixed precision training time** | **FP32 training time** |
+| --- | --- | --- | ----- | ----- | -------- | -------- |
+|  1  | 192 | 128 | 24.58 | 24.64 | 789 min  | 1375 min |
+|  8  | 192 | 128 | 24.46 | 24.51 | 184 min  | 262 min  |
+
+
+In the following plot, the BLEU scores after each training epoch for different
+configurations are displayed.
+
+![BLEUScore](./img/bleu_score.png)
+
+### Training stability test
+The GNMT v2 model was trained for 6 epochs, starting from 6 different initial
+random seeds. After each training epoch, the model was evaluated on the test
+dataset and the BLEU score was recorded. The training was performed in the
+tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with 8 Tesla V100 16G GPUs.
+
+In the following table, the BLEU scores after each training epoch for different
+initial random seeds are displayed.
+
+| **Epoch** | **Average** | **Standard deviation** | **Minimum** | **Maximum** | **Median** |
+| --- | ------ | ----- | ------ | ------ | ------ |
+|  1  | 19.943 | 0.240 | 19.670 | 20.290 | 19.855 |
+|  2  | 21.750 | 0.197 | 21.550 | 22.110 | 21.690 |
+|  3  | 22.408 | 0.150 | 22.160 | 22.630 | 22.430 |
+|  4  | 23.057 | 0.219 | 22.770 | 23.440 | 22.985 |
+|  5  | 23.897 | 0.142 | 23.700 | 24.080 | 23.915 |
+|  6  | 24.243 | 0.174 | 24.030 | 24.460 | 24.235 |
+
+
+## Training performance results
+Our results were obtained by running the `nmt.py` script in the
+tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
+Performance numbers (in tokens per second) were averaged over an entire
+training epoch.
+
+| **Number of GPUs** | **Mixed precision batch size/GPU** | **FP32 batch size/GPU** | **Mixed precision tokens/s** | **FP32 tokens/s** | **Mixed precision speedup** | **Mixed precision multi-gpu weak scaling** | **FP32 multi-gpu weak scaling** |
+| --- | --- | --- | ------- | ------ | ---- | ---- | ---- |
+|  1  | 192 | 128 | 22 297  | 12 767 | 1.74 | 1.00 | 1.00 |
+|  8  | 192 | 128 | 133 992 | 83 337 | 1.61 | 6.01 | 6.53 |
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide)
+outlined above.
+
+## Inference performance results
+Our results were obtained by running the `scripts/translate.py` script in the
+tensorflow-19.03-py3 NGC container on NVIDIA DGX-1 with a single V100 16G GPUs.
+The benchmark requires a checkpoint from a fully trained model.
+
+To launch the inference benchmark in mixed precision on 1 GPU, run:
+
+```
+python scripts/inference_benchmark.py --output_dir=/path/to/trained/model --beam_width 1,2,5,10 --infer_batch_size 32,128,512
+```
+
+To launch the inference benchmark in FP32 on 1 GPU, run:
+
+```
+python scripts/inference_benchmark.py --output_dir=/path/to/trained/model --beam_width 1,2,5,10 --infer_batch_size 32,128,512 --use_amp=false
+```
+
+| **Batch size** | **Beam size** | **Mixed precision BLEU** | **FP32 BLEU** | **Mixed precision tokens/s** | **FP32 tokens/s** |
+| ---- | ----- | ------- | ------- | ---------|-------- |
+|  32  |   1   |  23.43  |  23.47  |  8180    |  7555   |
+|  32  |   2   |  24.14  |  24.13  |  6870    |  6359   |
+|  32  |   5   |  24.43  |  24.47  |  4729    |  3991   |
+|  32  |   10  |  24.29  |  24.29  |  2344    |  2495   |
+|  128 |   1   |  23.43  |  23.47  |  19343   |  14810  |
+|  128 |   2   |  24.15  |  24.13  |  10267   |  9956   |
+|  128 |   5   |  24.45  |  24.47  |  7009    |  5224   |
+|  128 |   10  |  24.30  |  24.29  |  3873    |  2855   |
+|  512 |   1   |  23.45  |  23.47  |  32325   |  20782  |
+|  512 |   2   |  24.17  |  24.13  |  18917   |  13702  |
+|  512 |   5   |  24.46  |  24.47  |  8789    |  6036   |
+|  512 |   10  |  24.29  |  24.29  |  4684    |  3149   |
+
+To achieve these same results, follow the [Quick Start Guide](#quick-start-guide)
+outlined above.
+
+# Changelog
+1. Mar 18, 2019
+  * Initial release
+
+# Known issues
+There are no known issues in this release.
diff --git a/TensorFlow/Translation/GNMT/attention_wrapper.py b/TensorFlow/Translation/GNMT/attention_wrapper.py
new file mode 100644
index 00000000..d584ebdb
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/attention_wrapper.py
@@ -0,0 +1,1075 @@
+# Copyright 2018 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A powerful dynamic attention wrapper object."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from utils import math_utils
+from utils import misc_utils
+
+from tensorflow.contrib.framework.python.framework import tensor_util
+from tensorflow.python.framework import function
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import rnn_cell_impl
+
+_zero_state_tensors = rnn_cell_impl._zero_state_tensors  # pylint: disable=protected-access
+nest = tf.contrib.framework.nest
+
+
+class AttentionMechanism(object):
+
+  @property
+  def alignments_size(self):
+    raise NotImplementedError
+
+  @property
+  def state_size(self):
+    raise NotImplementedError
+
+
+def _prepare_memory(memory, memory_sequence_length, check_inner_dims_defined):
+  """Convert to tensor and possibly mask `memory`.
+
+  Args:
+    memory: `Tensor`, shaped `[batch_size, max_time, ...]`.
+    memory_sequence_length: `int32` `Tensor`, shaped `[batch_size]`.
+    check_inner_dims_defined: Python boolean.  If `True`, the `memory`
+      argument's shape is checked to ensure all but the two outermost
+      dimensions are fully defined.
+
+  Returns:
+    A (possibly masked), checked, new `memory`.
+
+  Raises:
+    ValueError: If `check_inner_dims_defined` is `True` and not
+      `memory.shape[2:].is_fully_defined()`.
+  """
+  memory = nest.map_structure(
+      lambda m: tf.convert_to_tensor(m, name="memory"), memory)
+  if memory_sequence_length is not None:
+    memory_sequence_length = tf.convert_to_tensor(
+        memory_sequence_length, name="memory_sequence_length")
+  if check_inner_dims_defined:
+    def _check_dims(m):
+      if not m.get_shape()[2:].is_fully_defined():
+        raise ValueError("Expected memory %s to have fully defined inner dims, "
+                         "but saw shape: %s" % (m.name, m.get_shape()))
+    nest.map_structure(_check_dims, memory)
+  if memory_sequence_length is None:
+    seq_len_mask = None
+  else:
+    seq_len_mask = tf.sequence_mask(
+        memory_sequence_length,
+        maxlen=tf.shape(nest.flatten(memory)[0])[1],
+        dtype=nest.flatten(memory)[0].dtype)
+    seq_len_batch_size = (
+        memory_sequence_length.shape[0].value
+        or tf.shape(memory_sequence_length)[0])
+  def _maybe_mask(m, seq_len_mask):
+    rank = m.get_shape().ndims
+    rank = rank if rank is not None else tf.rank(m)
+    extra_ones = tf.ones(rank - 2, dtype=tf.int32)
+    m_batch_size = m.shape[0].value or tf.shape(m)[0]
+    if memory_sequence_length is not None:
+      seq_len_mask = tf.reshape(
+          seq_len_mask,
+          tf.concat((tf.shape(seq_len_mask), extra_ones), 0))
+      return m * seq_len_mask
+    else:
+      return m
+  return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask), memory)
+
+
+def _maybe_mask_score(score, memory_sequence_length, score_mask_value):
+  if memory_sequence_length is None:
+    return score
+  if score_mask_value is None:
+    score_mask_value = tf.as_dtype(score.dtype).as_numpy_dtype(-np.inf)
+  score_mask = tf.sequence_mask(
+      memory_sequence_length, maxlen=tf.shape(score)[1])
+  score_mask_values = score_mask_value * tf.ones_like(score)
+  return tf.where(score_mask, score, score_mask_values)
+
+
+class _BaseAttentionMechanism(AttentionMechanism):
+  """A base AttentionMechanism class providing common functionality.
+
+  Common functionality includes:
+    1. Storing the query and memory layers.
+    2. Preprocessing and storing the memory.
+  """
+
+  def __init__(self,
+               query_layer,
+               memory,
+               probability_fn,
+               memory_sequence_length=None,
+               memory_layer=None,
+               check_inner_dims_defined=True,
+               score_mask_value=None,
+               name=None):
+    """Construct base AttentionMechanism class.
+
+    Args:
+      query_layer: Callable.  Instance of `tf.layers.Layer`.  The layer's depth
+        must match the depth of `memory_layer`.  If `query_layer` is not
+        provided, the shape of `query` must match that of `memory_layer`.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      probability_fn: A `callable`.  Converts the score and previous alignments
+        to probabilities. Its signature should be:
+        `probabilities = probability_fn(score, state)`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      memory_layer: Instance of `tf.layers.Layer` (may be None).  The layer's
+        depth must match the depth of `query_layer`.
+        If `memory_layer` is not provided, the shape of `memory` must match
+        that of `query_layer`.
+      check_inner_dims_defined: Python boolean.  If `True`, the `memory`
+        argument's shape is checked to ensure all but the two outermost
+        dimensions are fully defined.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      name: Name to use when creating ops.
+    """
+    if (query_layer is not None
+        and not isinstance(query_layer, tf.layers.Layer)):
+      raise TypeError(
+          "query_layer is not a Layer: %s" % type(query_layer).__name__)
+    if (memory_layer is not None
+        and not isinstance(memory_layer, tf.layers.Layer)):
+      raise TypeError(
+          "memory_layer is not a Layer: %s" % type(memory_layer).__name__)
+    self._query_layer = query_layer
+    self._memory_layer = memory_layer
+    self.dtype = memory_layer.dtype
+    if not callable(probability_fn):
+      raise TypeError("probability_fn must be callable, saw type: %s" %
+                      type(probability_fn).__name__)
+    self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
+        probability_fn(
+            _maybe_mask_score(score, memory_sequence_length, score_mask_value),
+            prev))
+    with tf.name_scope(
+        name, "BaseAttentionMechanismInit", nest.flatten(memory)):
+      self._values = _prepare_memory(
+          memory, memory_sequence_length,
+          check_inner_dims_defined=check_inner_dims_defined)
+      self._keys = (
+          self.memory_layer(self._values) if self.memory_layer  # pylint: disable=not-callable
+          else self._values)
+      self._batch_size = (
+          self._keys.shape[0].value or tf.shape(self._keys)[0])
+      self._alignments_size = (self._keys.shape[1].value or
+                               tf.shape(self._keys)[1])
+
+  def build(self, query_shape):
+    self._query_layer.build((None, query_shape[-1]))
+    # memory_layer is built in the constructor.
+    self.built = True
+
+  @property
+  def memory_layer(self):
+    return self._memory_layer
+
+  @property
+  def query_layer(self):
+    return self._query_layer
+
+  @property
+  def values(self):
+    return self._values
+
+  @property
+  def keys(self):
+    return self._keys
+
+  @property
+  def batch_size(self):
+    return self._batch_size
+
+  @property
+  def alignments_size(self):
+    return self._alignments_size
+
+  @property
+  def state_size(self):
+    return self._alignments_size
+
+  def initial_alignments(self, batch_size, dtype):
+    """Creates the initial alignment values for the `AttentionWrapper` class.
+
+    This is important for AttentionMechanisms that use the previous alignment
+    to calculate the alignment at the next time step (e.g. monotonic attention).
+
+    The default behavior is to return a tensor of all zeros.
+
+    Args:
+      batch_size: `int32` scalar, the batch_size.
+      dtype: The `dtype`.
+
+    Returns:
+      A `dtype` tensor shaped `[batch_size, alignments_size]`
+      (`alignments_size` is the values' `max_time`).
+    """
+    max_time = self._alignments_size
+    return _zero_state_tensors(max_time, batch_size, dtype)
+
+  def initial_state(self, batch_size, dtype):
+    """Creates the initial state values for the `AttentionWrapper` class.
+
+    This is important for AttentionMechanisms that use the previous alignment
+    to calculate the alignment at the next time step (e.g. monotonic attention).
+
+    The default behavior is to return the same output as initial_alignments.
+
+    Args:
+      batch_size: `int32` scalar, the batch_size.
+      dtype: The `dtype`.
+
+    Returns:
+      A structure of all-zero tensors with shapes as described by `state_size`.
+    """
+    return self.initial_alignments(batch_size, dtype)
+
+
+def _bahdanau_score(processed_query, keys, normalize, v, g, b):
+  """Implements Bahdanau-style (additive) scoring function.
+
+  This attention has two forms.  The first is Bhandanau attention,
+  as described in:
+
+  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
+  "Neural Machine Translation by Jointly Learning to Align and Translate."
+  ICLR 2015. https://arxiv.org/abs/1409.0473
+
+  The second is the normalized form.  This form is inspired by the
+  weight normalization article:
+
+  Tim Salimans, Diederik P. Kingma.
+  "Weight Normalization: A Simple Reparameterization to Accelerate
+   Training of Deep Neural Networks."
+  https://arxiv.org/abs/1602.07868
+
+  To enable the second form, set `normalize=True`.
+
+  Args:
+    processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
+    keys: Processed memory, shape `[batch_size, max_time, num_units]`.
+    normalize: Whether to normalize the score function.
+
+  Returns:
+    A `[batch_size, max_time]` tensor of unnormalized score values.
+  """
+  # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
+  processed_query = tf.expand_dims(processed_query, 1)
+
+  if normalize:
+    # normed_v = g * v / ||v||
+    def NormalizedAttenFwd(keys, processed_query, g, v, b):
+      """normalized atten."""
+      normed_v = g * v * tf.rsqrt(
+          tf.reduce_sum(tf.square(v)))
+      batch = tf.shape(keys)[0]
+      max_time = tf.shape(keys)[1]
+      units = tf.shape(keys)[-1]
+
+      # [batch, time, dim]
+      activation = tf.tanh(keys + processed_query + b)
+      # [batch * time, dim]
+      activation = tf.reshape(activation, [batch * max_time, units])
+
+      # [dim, 1]
+      v = tf.expand_dims(normed_v, -1)
+      # [batch * time, 1]  -> [batch * time]
+      y = tf.squeeze(tf.matmul(activation, v), axis=1)
+      y = tf.reshape(y, [batch, max_time])
+      return y
+
+    use_xla = os.environ["use_xla"] == "true"
+    def NormalizedAtten(keys, processed_query, g, v, b):
+      return NormalizedAttenFwd(keys, processed_query, g, v, b)
+
+    fn = NormalizedAtten
+    if os.environ["use_defun"] == "true":
+      fn = function.Defun(compiled=use_xla)(fn)
+    res = fn(keys, processed_query, g, v, b)
+    res.set_shape((None, keys.shape[1]))
+    return res
+  else:
+    def _Atten(keys, processed_query, v):
+      """atten."""
+      batch = tf.shape(keys)[0]
+      max_time = tf.shape(keys)[1]
+      units = tf.shape(keys)[-1]
+
+      activation = tf.tanh(keys + processed_query)
+      activation = tf.reshape(activation, [batch * max_time, units])
+
+      v = tf.expand_dims(v, -1)
+      y = tf.squeeze(tf.matmul(activation, v), axis=1)
+      y = tf.reshape(y, [batch, max_time])
+      return y
+    fn = _Atten
+    if os.environ["use_defun"] == "true":
+      fn = function.Defun()(fn)
+    return fn(keys, processed_query, v)
+
+
+class BahdanauAttention(_BaseAttentionMechanism):
+  """Implements Bahdanau-style (additive) attention.
+
+  This attention has two forms.  The first is Bahdanau attention,
+  as described in:
+
+  Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
+  "Neural Machine Translation by Jointly Learning to Align and Translate."
+  ICLR 2015. https://arxiv.org/abs/1409.0473
+
+  The second is the normalized form.  This form is inspired by the
+  weight normalization article:
+
+  Tim Salimans, Diederik P. Kingma.
+  "Weight Normalization: A Simple Reparameterization to Accelerate
+   Training of Deep Neural Networks."
+  https://arxiv.org/abs/1602.07868
+
+  To enable the second form, construct the object with parameter
+  `normalize=True`.
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               normalize=False,
+               probability_fn=None,
+               score_mask_value=None,
+               dtype=None,
+               name="BahdanauAttention"):
+    """Construct the Attention mechanism.
+
+    Args:
+      num_units: The depth of the query mechanism.
+      memory: The memory to query; usually the output of an RNN encoder.  This
+        tensor should be shaped `[batch_size, max_time, ...]`.
+      memory_sequence_length (optional): Sequence lengths for the batch entries
+        in memory.  If provided, the memory tensor rows are masked with zeros
+        for values past the respective sequence lengths.
+      normalize: Python boolean.  Whether to normalize the energy term.
+      probability_fn: (optional) A `callable`.  Converts the score to
+        probabilities.  The default is `tf.nn.softmax`. Other options include
+        `tf.contrib.seq2seq.hardmax` and `tf.contrib.sparsemax.sparsemax`.
+        Its signature should be: `probabilities = probability_fn(score)`.
+      score_mask_value: (optional): The mask value for score before passing into
+        `probability_fn`. The default is -inf. Only used if
+        `memory_sequence_length` is not None.
+      dtype: The data type for the query and memory layers of the attention
+        mechanism.
+      name: Name to use when creating ops.
+    """
+    if probability_fn is None:
+      probability_fn = tf.nn.softmax
+    if dtype is None:
+      dtype = tf.float32
+    wrapped_probability_fn = lambda score, _: probability_fn(score)
+    super(BahdanauAttention, self).__init__(
+        query_layer=tf.layers.Dense(
+            num_units, name="query_layer", use_bias=False, dtype=dtype),
+        memory_layer=tf.layers.Dense(
+            num_units, name="memory_layer", use_bias=False, dtype=dtype),
+        memory=memory,
+        probability_fn=wrapped_probability_fn,
+        memory_sequence_length=memory_sequence_length,
+        score_mask_value=score_mask_value,
+        name=name)
+    self._num_units = num_units
+    self._normalize = normalize
+    self._name = name
+
+    self.v = tf.get_variable("attention_v", [num_units], dtype=dtype)
+    # TODO(jamesqin): I have no idea why the following is happening.
+    # If not doing read_value(), later in backprop it would complain about
+    # AddN op is fed with "resource" dtype input, which shouldn't be happening.
+    if isinstance(self.v, resource_variable_ops.ResourceVariable):
+      self.v = self.v.read_value()
+    self.g, self.b = None, None
+    if self._normalize:
+      # Scalar used in weight normalization
+      self.g = tf.get_variable(
+          "attention_g", dtype=dtype,
+          initializer=tf.constant_initializer(math.sqrt((1. / num_units))),
+          shape=())
+      # Same as above self.g
+      if isinstance(self.g, resource_variable_ops.ResourceVariable):
+        self.g = self.g.read_value()
+      # Bias added prior to the nonlinearity
+      self.b = tf.get_variable(
+          "attention_b", [num_units], dtype=dtype,
+          initializer=tf.zeros_initializer())
+      # Same as above self.g
+      if isinstance(self.b, resource_variable_ops.ResourceVariable):
+        self.b = self.b.read_value()
+
+  def __call__(self, query, state):
+    """Score the query based on the keys and values.
+
+    Args:
+      query: Tensor of dtype matching `self.values` and shape
+        `[batch_size, query_depth]`.
+      state: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]`
+        (`alignments_size` is memory's `max_time`).
+
+    Returns:
+      alignments: Tensor of dtype matching `self.values` and shape
+        `[batch_size, alignments_size]` (`alignments_size` is memory's
+        `max_time`).
+    """
+    query_dim = query.shape[-1].value
+    assert query_dim
+    def _compute_alignments(query, state):
+      with tf.variable_scope(None, "bahdanau_attention", [query]):
+        # TODO(jamesqin): figure out the shape implications of Defun.
+        query.set_shape((None, query_dim))
+        processed_query = self.query_layer(query) if self.query_layer else query
+        score = _bahdanau_score(processed_query, self._keys, self._normalize,
+                                self.v, self.g, self.b)
+      alignments = self._probability_fn(score, state)
+      next_state = alignments
+      return alignments, next_state
+    return _compute_alignments(query, state)
+
+
+class AttentionWrapperState(
+    collections.namedtuple("AttentionWrapperState",
+                           ("cell_state", "attention", "time", "alignments",
+                            "alignment_history", "attention_state"))):
+  """`namedtuple` storing the state of a `AttentionWrapper`.
+
+  Contains:
+
+    - `cell_state`: The state of the wrapped `RNNCell` at the previous time
+      step.
+    - `attention`: The attention emitted at the previous time step.
+    - `time`: int32 scalar containing the current time step.
+    - `alignments`: A single or tuple of `Tensor`(s) containing the alignments
+       emitted at the previous time step for each attention mechanism.
+    - `alignment_history`: (if enabled) a single or tuple of `TensorArray`(s)
+       containing alignment matrices from all time steps for each attention
+       mechanism. Call `stack()` on each to convert to a `Tensor`.
+    - `attention_state`: A single or tuple of nested objects
+       containing attention mechanism state for each attention mechanism.
+       The objects may contain Tensors or TensorArrays.
+  """
+
+  def clone(self, **kwargs):
+    """Clone this object, overriding components provided by kwargs.
+
+    The new state fields' shape must match original state fields' shape. This
+    will be validated, and original fields' shape will be propagated to new
+    fields.
+
+    Example:
+
+    ```python
+    initial_state = attention_wrapper.zero_state(dtype=..., batch_size=...)
+    initial_state = initial_state.clone(cell_state=encoder_state)
+    ```
+
+    Args:
+      **kwargs: Any properties of the state object to replace in the returned
+        `AttentionWrapperState`.
+
+    Returns:
+      A new `AttentionWrapperState` whose properties are the same as
+      this one, except any overridden properties as provided in `kwargs`.
+    """
+    def with_same_shape(old, new):
+      """Check and set new tensor's shape."""
+      xla_compile = (os.environ["xla_compile"] == "true")
+      if not xla_compile:
+        if isinstance(old, tf.Tensor) and isinstance(new, tf.Tensor):
+          return tensor_util.with_same_shape(old, new)
+      return new
+
+    return nest.map_structure(
+        with_same_shape,
+        self,
+        super(AttentionWrapperState, self)._replace(**kwargs))
+
+
+def _compute_attention(attention_mechanism, cell_output, attention_state,
+                       attention_layer):
+  """Computes the attention and alignments for a given attention_mechanism."""
+  alignments, next_attention_state = attention_mechanism(
+      cell_output, state=attention_state)
+
+  # Context is the inner product of alignments and values along the
+  # memory time dimension.
+  # alignments shape is
+  #   [batch_size, 1, memory_time]
+  # attention_mechanism.values shape is
+  #   [batch_size, memory_time, memory_size]
+  # the batched matmul is over memory_time, so the output shape is
+  #   [batch_size, 1, memory_size].
+  # we then squeeze out the singleton dim.
+  expanded_alignments = tf.expand_dims(alignments, 1)
+  context = math_utils.BatchMatMul(expanded_alignments,
+                                   attention_mechanism.values)
+  context = tf.squeeze(context, [1])
+
+  if attention_layer is not None:
+    attention = attention_layer(tf.concat([cell_output, context], 1))
+  else:
+    attention = context
+
+  return attention, alignments, next_attention_state
+
+
+class AttentionWrapper(tf.nn.rnn_cell.RNNCell):
+  """Wraps another `RNNCell` with attention.
+  """
+
+  def __init__(self,
+               cell,
+               attention_mechanism,
+               attention_layer_size=None,
+               alignment_history=False,
+               cell_input_fn=None,
+               output_attention=True,
+               initial_cell_state=None,
+               name=None,
+               attention_layer=None):
+    """Construct the `AttentionWrapper`.
+
+    **NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
+    `AttentionWrapper`, then you must ensure that:
+
+    - The encoder output has been tiled to `beam_width` via
+      `tf.contrib.seq2seq.tile_batch` (NOT `tf.tile`).
+    - The `batch_size` argument passed to the `zero_state` method of this
+      wrapper is equal to `true_batch_size * beam_width`.
+    - The initial state created with `zero_state` above contains a
+      `cell_state` value containing properly tiled final state from the
+      encoder.
+
+    An example:
+
+    ```
+    tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
+        encoder_outputs, multiplier=beam_width)
+    tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
+        encoder_final_state, multiplier=beam_width)
+    tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
+        sequence_length, multiplier=beam_width)
+    attention_mechanism = MyFavoriteAttentionMechanism(
+        num_units=attention_depth,
+        memory=tiled_inputs,
+        memory_sequence_length=tiled_sequence_length)
+    attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
+    decoder_initial_state = attention_cell.zero_state(
+        dtype, batch_size=true_batch_size * beam_width)
+    decoder_initial_state = decoder_initial_state.clone(
+        cell_state=tiled_encoder_final_state)
+    ```
+
+    Args:
+      cell: An instance of `RNNCell`.
+      attention_mechanism: A list of `AttentionMechanism` instances or a single
+        instance.
+      attention_layer_size: A list of Python integers or a single Python
+        integer, the depth of the attention (output) layer(s). If None
+        (default), use the context as attention at each time step. Otherwise,
+        feed the context and cell output into the attention layer to generate
+        attention at each time step. If attention_mechanism is a list,
+        attention_layer_size must be a list of the same length. If
+        attention_layer is set, this must be None.
+      alignment_history: Python boolean, whether to store alignment history
+        from all time steps in the final output state (currently stored as a
+        time major `TensorArray` on which you must call `stack()`).
+      cell_input_fn: (optional) A `callable`.  The default is:
+        `lambda inputs, attention: tf.concat([inputs, attention], -1)`.
+      output_attention: Python bool.  If `True` (default), the output at each
+        time step is the attention value.  This is the behavior of Luong-style
+        attention mechanisms.  If `False`, the output at each time step is
+        the output of `cell`.  This is the behavior of Bhadanau-style
+        attention mechanisms.  In both cases, the `attention` tensor is
+        propagated to the next time step via the state and is used there.
+        This flag only controls whether the attention mechanism is propagated
+        up to the next cell in an RNN stack or to the top RNN output.
+      initial_cell_state: The initial state value to use for the cell when
+        the user calls `zero_state()`.  Note that if this value is provided
+        now, and the user uses a `batch_size` argument of `zero_state` which
+        does not match the batch size of `initial_cell_state`, proper
+        behavior is not guaranteed.
+      name: Name to use when creating ops.
+      attention_layer: A list of `tf.layers.Layer` instances or a
+        single `tf.layers.Layer` instance taking the context and cell output as
+        inputs to generate attention at each time step. If None (default), use
+        the context as attention at each time step. If attention_mechanism is a
+        list, attention_layer must be a list of the same length. If
+        attention_layers_size is set, this must be None.
+
+    Raises:
+      TypeError: `attention_layer_size` is not None and (`attention_mechanism`
+        is a list but `attention_layer_size` is not; or vice versa).
+      ValueError: if `attention_layer_size` is not None, `attention_mechanism`
+        is a list, and its length does not match that of `attention_layer_size`;
+        if `attention_layer_size` and `attention_layer` are set simultaneously.
+    """
+    super(AttentionWrapper, self).__init__(name=name)
+    rnn_cell_impl.assert_like_rnncell("cell", cell)
+    if isinstance(attention_mechanism, (list, tuple)):
+      self._is_multi = True
+      attention_mechanisms = attention_mechanism
+      for attention_mechanism in attention_mechanisms:
+        if not isinstance(attention_mechanism, AttentionMechanism):
+          raise TypeError(
+              "attention_mechanism must contain only instances of "
+              "AttentionMechanism, saw type: %s"
+              % type(attention_mechanism).__name__)
+    else:
+      self._is_multi = False
+      if not isinstance(attention_mechanism, AttentionMechanism):
+        raise TypeError(
+            "attention_mechanism must be an AttentionMechanism or list of "
+            "multiple AttentionMechanism instances, saw type: %s"
+            % type(attention_mechanism).__name__)
+      attention_mechanisms = (attention_mechanism,)
+
+    if cell_input_fn is None:
+      cell_input_fn = (
+          lambda inputs, attention: tf.concat([inputs, attention], -1))
+    else:
+      if not callable(cell_input_fn):
+        raise TypeError(
+            "cell_input_fn must be callable, saw type: %s"
+            % type(cell_input_fn).__name__)
+
+    if attention_layer_size is not None and attention_layer is not None:
+      raise ValueError("Only one of attention_layer_size and attention_layer "
+                       "should be set")
+
+    if attention_layer_size is not None:
+      attention_layer_sizes = tuple(
+          attention_layer_size
+          if isinstance(attention_layer_size, (list, tuple))
+          else (attention_layer_size,))
+      if len(attention_layer_sizes) != len(attention_mechanisms):
+        raise ValueError(
+            "If provided, attention_layer_size must contain exactly one "
+            "integer per attention_mechanism, saw: %d vs %d"
+            % (len(attention_layer_sizes), len(attention_mechanisms)))
+      self._attention_layers = tuple(
+          tf.layers.Dense(
+              attention_layer_size,
+              name="attention_layer",
+              use_bias=False,
+              dtype=attention_mechanisms[i].dtype)
+          for i, attention_layer_size in enumerate(attention_layer_sizes))
+      self._attention_layer_size = sum(attention_layer_sizes)
+    elif attention_layer is not None:
+      self._attention_layers = tuple(
+          attention_layer
+          if isinstance(attention_layer, (list, tuple))
+          else (attention_layer,))
+      if len(self._attention_layers) != len(attention_mechanisms):
+        raise ValueError(
+            "If provided, attention_layer must contain exactly one "
+            "layer per attention_mechanism, saw: %d vs %d"
+            % (len(self._attention_layers), len(attention_mechanisms)))
+      self._attention_layer_size = sum(
+          layer.compute_output_shape(
+              [None,
+               cell.output_size + mechanism.values.shape[-1].value])[-1].value
+          for layer, mechanism in zip(
+              self._attention_layers, attention_mechanisms))
+    else:
+      self._attention_layers = None
+      self._attention_layer_size = sum(
+          attention_mechanism.values.get_shape()[-1].value
+          for attention_mechanism in attention_mechanisms)
+
+    self._cell = cell
+    self._attention_mechanisms = attention_mechanisms
+    self._cell_input_fn = cell_input_fn
+    self._output_attention = output_attention
+    self._alignment_history = alignment_history
+    with tf.name_scope(name, "AttentionWrapperInit"):
+      if initial_cell_state is None:
+        self._initial_cell_state = None
+      else:
+        final_state_tensor = nest.flatten(initial_cell_state)[-1]
+        state_batch_size = (
+            final_state_tensor.shape[0].value
+            or tf.shape(final_state_tensor)[0])
+        error_message = (
+            "When constructing AttentionWrapper %s: " % self._base_name +
+            "Non-matching batch sizes between the memory "
+            "(encoder output) and initial_cell_state.  Are you using "
+            "the BeamSearchDecoder?  You may need to tile your initial state "
+            "via the tf.contrib.seq2seq.tile_batch function with argument "
+            "multiple=beam_width.")
+        with tf.control_dependencies(
+            self._batch_size_checks(state_batch_size, error_message)):
+          self._initial_cell_state = nest.map_structure(
+              lambda s: tf.identity(s, name="check_initial_cell_state"),
+              initial_cell_state)
+
+
+  def force_build(self, cell_input_shape, query_shape):
+    def _build_cell(cell, input_shape):
+      if isinstance(cell, tf.nn.rnn_cell.DropoutWrapper):
+        _build_cell(cell._cell, input_shape)
+      elif isinstance(cell, tf.nn.rnn_cell.ResidualWrapper):
+        _build_cell(cell._cell, input_shape)
+      else:
+        cell.build(input_shape)
+
+    _build_cell(self._cell,
+                (cell_input_shape[-1] + self._attention_layer_size))
+
+    for am in self._attention_mechanisms:
+      am.build(query_shape)
+
+    self.built = True
+
+  def _batch_size_checks(self, batch_size, error_message):
+    return []
+
+  def _item_or_tuple(self, seq):
+    """Returns `seq` as tuple or the singular element.
+
+    Which is returned is determined by how the AttentionMechanism(s) were passed
+    to the constructor.
+
+    Args:
+      seq: A non-empty sequence of items or generator.
+
+    Returns:
+       Either the values in the sequence as a tuple if AttentionMechanism(s)
+       were passed to the constructor as a sequence or the singular element.
+    """
+    t = tuple(seq)
+    if self._is_multi:
+      return t
+    else:
+      return t[0]
+
+  @property
+  def output_size(self):
+    if self._output_attention:
+      return self._attention_layer_size
+    else:
+      return self._cell.output_size
+
+  @property
+  def state_size(self):
+    """The `state_size` property of `AttentionWrapper`.
+
+    Returns:
+      An `AttentionWrapperState` tuple containing shapes used by this object.
+    """
+    return AttentionWrapperState(
+        cell_state=self._cell.state_size,
+        time=tf.TensorShape([]),
+        attention=self._attention_layer_size,
+        alignments=self._item_or_tuple(
+            a.alignments_size for a in self._attention_mechanisms),
+        attention_state=self._item_or_tuple(
+            a.state_size for a in self._attention_mechanisms),
+        alignment_history=self._item_or_tuple(
+            a.alignments_size if self._alignment_history else ()
+            for a in self._attention_mechanisms))  # sometimes a TensorArray
+
+  def zero_state(self, batch_size, dtype):
+    """Return an initial (zero) state tuple for this `AttentionWrapper`.
+
+    **NOTE** Please see the initializer documentation for details of how
+    to call `zero_state` if using an `AttentionWrapper` with a
+    `BeamSearchDecoder`.
+
+    Args:
+      batch_size: `0D` integer tensor: the batch size.
+      dtype: The internal state data type.
+
+    Returns:
+      An `AttentionWrapperState` tuple containing zeroed out tensors and,
+      possibly, empty `TensorArray` objects.
+
+    Raises:
+      ValueError: (or, possibly at runtime, InvalidArgument), if
+        `batch_size` does not match the output size of the encoder passed
+        to the wrapper object at initialization time.
+    """
+    with tf.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
+      if self._initial_cell_state is not None:
+        cell_state = self._initial_cell_state
+      else:
+        cell_state = self._cell.zero_state(batch_size, dtype)
+      error_message = (
+          "When calling zero_state of AttentionWrapper %s: " % self._base_name +
+          "Non-matching batch sizes between the memory "
+          "(encoder output) and the requested batch size.  Are you using "
+          "the BeamSearchDecoder?  If so, make sure your encoder output has "
+          "been tiled to beam_width via tf.contrib.seq2seq.tile_batch, and "
+          "the batch_size= argument passed to zero_state is "
+          "batch_size * beam_width.")
+      with tf.control_dependencies(
+          self._batch_size_checks(batch_size, error_message)):
+        cell_state = nest.map_structure(
+            lambda s: tf.identity(s, name="checked_cell_state"),
+            cell_state)
+      initial_alignments = [
+          attention_mechanism.initial_alignments(batch_size, dtype)
+          for attention_mechanism in self._attention_mechanisms]
+      return AttentionWrapperState(
+          cell_state=cell_state,
+          time=tf.zeros([], dtype=tf.int64),
+          attention=_zero_state_tensors(self._attention_layer_size, batch_size,
+                                        dtype),
+          alignments=self._item_or_tuple(initial_alignments),
+          attention_state=self._item_or_tuple(
+              attention_mechanism.initial_state(batch_size, dtype)
+              for attention_mechanism in self._attention_mechanisms),
+          alignment_history=self._item_or_tuple(
+              tf.TensorArray(
+                  dtype,
+                  size=0,
+                  dynamic_size=True,
+                  element_shape=alignment.shape)
+              if self._alignment_history else ()
+              for alignment in initial_alignments))
+
+  def call(self, inputs, state):
+    """Perform a step of attention-wrapped RNN.
+
+    - Step 1: Mix the `inputs` and previous step's `attention` output via
+      `cell_input_fn`.
+    - Step 2: Call the wrapped `cell` with this input and its previous state.
+    - Step 3: Score the cell's output with `attention_mechanism`.
+    - Step 4: Calculate the alignments by passing the score through the
+      `normalizer`.
+    - Step 5: Calculate the context vector as the inner product between the
+      alignments and the attention_mechanism's values (memory).
+    - Step 6: Calculate the attention output by concatenating the cell output
+      and context through the attention layer (a linear layer with
+      `attention_layer_size` outputs).
+
+    Args:
+      inputs: (Possibly nested tuple of) Tensor, the input at this time step.
+      state: An instance of `AttentionWrapperState` containing
+        tensors from the previous time step.
+
+    Returns:
+      A tuple `(attention_or_cell_output, next_state)`, where:
+
+      - `attention_or_cell_output` depending on `output_attention`.
+      - `next_state` is an instance of `AttentionWrapperState`
+         containing the state calculated at this time step.
+
+    Raises:
+      TypeError: If `state` is not an instance of `AttentionWrapperState`.
+    """
+    if not isinstance(state, AttentionWrapperState):
+      raise TypeError("Expected state to be instance of AttentionWrapperState. "
+                      "Received type %s instead."  % type(state))
+
+    # Step 1: Calculate the true inputs to the cell based on the
+    # previous attention value.
+    cell_inputs = self._cell_input_fn(inputs, state.attention)
+    cell_state = state.cell_state
+    cell_output, next_cell_state = self._cell(cell_inputs, cell_state)
+
+    cell_batch_size = (
+        cell_output.shape[0].value or tf.shape(cell_output)[0])
+    error_message = (
+        "When applying AttentionWrapper %s: " % self.name +
+        "Non-matching batch sizes between the memory "
+        "(encoder output) and the query (decoder output).  Are you using "
+        "the BeamSearchDecoder?  You may need to tile your memory input via "
+        "the tf.contrib.seq2seq.tile_batch function with argument "
+        "multiple=beam_width.")
+    with tf.control_dependencies(
+        self._batch_size_checks(cell_batch_size, error_message)):
+      cell_output = tf.identity(
+          cell_output, name="checked_cell_output")
+
+    if self._is_multi:
+      previous_attention_state = state.attention_state
+      previous_alignment_history = state.alignment_history
+    else:
+      previous_attention_state = [state.attention_state]
+      previous_alignment_history = [state.alignment_history]
+
+    all_alignments = []
+    all_attentions = []
+    all_attention_states = []
+    maybe_all_histories = []
+    for i, attention_mechanism in enumerate(self._attention_mechanisms):
+      attention, alignments, next_attention_state = _compute_attention(
+          attention_mechanism, cell_output, previous_attention_state[i],
+          self._attention_layers[i] if self._attention_layers else None)
+      alignment_history = previous_alignment_history[i].write(
+          state.time, alignments) if self._alignment_history else ()
+
+      all_attention_states.append(next_attention_state)
+      all_alignments.append(alignments)
+      all_attentions.append(attention)
+      maybe_all_histories.append(alignment_history)
+
+    attention = tf.concat(all_attentions, 1)
+    next_state = AttentionWrapperState(
+        time=state.time + 1,
+        cell_state=next_cell_state,
+        attention=attention,
+        attention_state=self._item_or_tuple(all_attention_states),
+        alignments=self._item_or_tuple(all_alignments),
+        alignment_history=self._item_or_tuple(maybe_all_histories))
+
+    if self._output_attention:
+      return attention, next_state
+    else:
+      return cell_output, next_state
+
+
+class BahdanauAttentionFusedLayer(object):
+  """Fused attention layer using Bahdanau attention.
+
+  Only used during training.
+  """
+
+  def __init__(self,
+               num_units,
+               memory,
+               memory_sequence_length=None,
+               dtype=None,
+               name="BahdanauAttention"):
+
+    self.v = tf.get_variable("attention_v", [num_units], dtype=dtype)
+    # TODO(jamesqin): I have no idea why the following is happening.
+    # If not doing read_value(), later in backprop it would complain about
+    # AddN op is fed with "resource" dtype input, which shouldn't be happening.
+    if isinstance(self.v, resource_variable_ops.ResourceVariable):
+      self.v = self.v.read_value()
+    # Scalar used in weight normalization
+    self.g = tf.get_variable(
+        "attention_g", dtype=dtype,
+        initializer=tf.constant_initializer(math.sqrt((1. / num_units))),
+        shape=())
+    if isinstance(self.g, resource_variable_ops.ResourceVariable):
+      self.g = self.g.read_value()
+
+    # Bias added prior to the nonlinearity
+    self.b = tf.get_variable(
+        "attention_b", [num_units], dtype=dtype,
+        initializer=tf.zeros_initializer())
+    if isinstance(self.b, resource_variable_ops.ResourceVariable):
+      self.b = self.b.read_value()
+
+    self.query_layer = tf.layers.Dense(
+        num_units, name="query_layer", use_bias=False, dtype=dtype)
+    self.memory_layer = tf.layers.Dense(
+        num_units, name="memory_layer", use_bias=False, dtype=dtype)
+
+    def _mask_score(score, memory_sequence_length):
+      # score is [batch, query_max_t, memory_t]
+      if memory_sequence_length is None:
+        return score
+      # [batch, 1]
+      memory_sequence_length = tf.expand_dims(memory_sequence_length, -1)
+
+      query_max_t = tf.shape(score)[1]
+      # [batch, query_t]
+      memory_sequence_length = tf.broadcast_to(
+          memory_sequence_length,
+          [tf.shape(memory_sequence_length)[0], query_max_t])
+      # [batch, query_t, memory_t]
+      score_mask = tf.sequence_mask(
+          memory_sequence_length, maxlen=tf.shape(score)[-1])
+
+      score_mask_value = tf.as_dtype(score.dtype).as_numpy_dtype(-np.inf)
+      score_mask_values = score_mask_value * tf.ones_like(score)
+      return tf.where(score_mask, score, score_mask_values)
+
+    # score: [batch, query_max_t, memory_t]
+    # memory_seq_length: [batch]
+    self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
+        tf.nn.softmax(_mask_score(score, memory_sequence_length)))
+
+    with tf.name_scope(name, "BaseAttentionMechanismInit", [memory]):
+      # Mask padded memory.
+      # [batch, memory_t, units]
+      self.values = _prepare_memory(
+          memory, memory_sequence_length, check_inner_dims_defined=True)
+      # [batch, memory_t, units]
+      self.keys = self.memory_layer(self.values)
+
+  def __call__(self, queries):
+    """Invoke the layer.
+
+    Args:
+      queries: [batch, queryt_t, query_size]
+    Returns:
+      attention: [batch, query_t, num_units]
+    """
+
+    # Compute score for all queries, returns [batch, query_t, memory_t].
+    # The var scope naming is chosen to build inference graph correctly.
+    with tf.variable_scope(
+        "multi_rnn_cell/cell_0_attention/attention/bahdanau_attention",
+        [queries]):
+      # [batch, query_t, query_size] -> [batch, query_t, num_units]
+      processed_queries = self.query_layer(queries)
+
+      # [batch, memory_t, num_units] -> [batch, 1, memory_t, num_units]
+      keys = tf.expand_dims(self.keys, axis=1)
+      # [batch, query_t, num_units] -> [batch, query_t, 1, num_units]
+      processed_queries = tf.expand_dims(processed_queries, axis=2)
+      # [batch, 1, memory_t, units] + [batch, query_t, 1, units] + [units] ->
+      # [batch, query_t, memory_t, units]
+      activation = tf.tanh(keys + processed_queries + self.b)
+
+      # [units, 1]
+      normed_v = self.g * self.v * tf.rsqrt(tf.reduce_sum(tf.square(self.v)))
+      v = tf.expand_dims(normed_v, -1)
+      # [batch, query_t, memory_t, units] * [units, 1] ->
+      # [batch, query_t, memory_t, 1]
+      # [batch, query_t, memory_t, 1] --(squeeze)--> [batch, query_t, memory_t]
+      score = tf.squeeze(tf.tensordot(activation, v, axes=1), axis=-1)
+
+    # Compute alignment
+    # bahdanau attention doesn't use the attention state in prob func (softmax)
+    unused_state = None
+    # [batch, query_t, memory_t]
+    alignments = self._probability_fn(score, unused_state)
+    # Note: slow batched matmul in fp16
+    # [batch, query_t, memory_t] * [ batch, memory_t, units] ->
+    # [batch, query_t, units]
+    attention = math_utils.BatchMatMul(alignments, self.values)
+    return attention
diff --git a/TensorFlow/Translation/GNMT/beam_search_decoder.py b/TensorFlow/Translation/GNMT/beam_search_decoder.py
new file mode 100644
index 00000000..09d2e695
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/beam_search_decoder.py
@@ -0,0 +1,1119 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A decoder that performs beam search."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import numpy as np
+import tensorflow as tf
+
+import attention_wrapper
+
+__all__ = [
+    "BeamSearchDecoderOutput",
+    "BeamSearchDecoderState",
+    "BeamSearchDecoder",
+    "FinalBeamSearchDecoderOutput",
+    "tile_batch",
+]
+
+
+class BeamSearchDecoderState(
+    collections.namedtuple("BeamSearchDecoderState",
+                           ("cell_state", "log_probs", "finished", "lengths",
+                            "accumulated_attention_probs"))):
+  pass
+
+
+class BeamSearchDecoderOutput(
+    collections.namedtuple("BeamSearchDecoderOutput",
+                           ("scores", "predicted_ids", "parent_ids"))):
+  pass
+
+
+class FinalBeamSearchDecoderOutput(
+    collections.namedtuple("FinalBeamDecoderOutput",
+                           ["predicted_ids", "beam_search_decoder_output"])):
+  """Final outputs returned by the beam search after all decoding is finished.
+
+  Args:
+    predicted_ids: The final prediction. A tensor of shape
+      `[batch_size, T, beam_width]` (or `[T, batch_size, beam_width]` if
+      `output_time_major` is True). Beams are ordered from best to worst.
+    beam_search_decoder_output: An instance of `BeamSearchDecoderOutput` that
+      describes the state of the beam search.
+  """
+  pass
+
+
+def _tile_batch(t, multiplier):
+  """Core single-tensor implementation of tile_batch."""
+  t = tf.convert_to_tensor(t, name="t")
+  shape_t = tf.shape(t)
+  if t.shape.ndims is None or t.shape.ndims < 1:
+    raise ValueError("t must have statically known rank")
+  tiling = [1] * (t.shape.ndims + 1)
+  tiling[1] = multiplier
+  tiled_static_batch_size = (
+      t.shape[0].value * multiplier if t.shape[0].value is not None else None)
+  tiled = tf.tile(tf.expand_dims(t, 1), tiling)
+  tiled = tf.reshape(
+      tiled, tf.concat(([shape_t[0] * multiplier], shape_t[1:]), 0))
+  tiled.set_shape(
+      tf.TensorShape([tiled_static_batch_size]).concatenate(
+          t.shape[1:]))
+  return tiled
+
+
+def tile_batch(t, multiplier, name=None):
+  """Tile the batch dimension of a (possibly nested structure of) tensor(s) t.
+
+  For each tensor t in a (possibly nested structure) of tensors,
+  this function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed of
+  minibatch entries `t[0], ..., t[batch_size - 1]` and tiles it to have a shape
+  `[batch_size * multiplier, s0, s1, ...]` composed of minibatch entries
+  `t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is repeated
+  `multiplier` times.
+
+  Args:
+    t: `Tensor` shaped `[batch_size, ...]`.
+    multiplier: Python int.
+    name: Name scope for any created operations.
+
+  Returns:
+    A (possibly nested structure of) `Tensor` shaped
+    `[batch_size * multiplier, ...]`.
+
+  Raises:
+    ValueError: if tensor(s) `t` do not have a statically known rank or
+    the rank is < 1.
+  """
+  flat_t = tf.contrib.framework.nest.flatten(t)
+  with tf.name_scope(name, "tile_batch", flat_t + [multiplier]):
+    return tf.contrib.framework.nest.map_structure(
+        lambda t_: _tile_batch(t_, multiplier), t)
+
+
+def gather_tree_from_array(t, parent_ids, sequence_length):
+  """Calculates the full beams for `TensorArray`s.
+
+  Args:
+    t: A stacked `TensorArray` of size `max_time` that contains `Tensor`s of
+      shape `[batch_size, beam_width, s]` or `[batch_size * beam_width, s]`
+      where `s` is the depth shape.
+    parent_ids: The parent ids of shape `[max_time, batch_size, beam_width]`.
+    sequence_length: The sequence length of shape `[batch_size, beam_width]`.
+
+  Returns:
+    A `Tensor` which is a stacked `TensorArray` of the same size and type as
+    `t` and where beams are sorted in each `Tensor` according to `parent_ids`.
+  """
+  max_time = parent_ids.shape[0].value or tf.shape(parent_ids)[0]
+  batch_size = parent_ids.shape[1].value or tf.shape(parent_ids)[1]
+  beam_width = parent_ids.shape[2].value or tf.shape(parent_ids)[2]
+
+  # Generate beam ids that will be reordered by gather_tree.
+  beam_ids = tf.expand_dims(
+      tf.expand_dims(tf.range(beam_width), 0), 0)
+  beam_ids = tf.tile(beam_ids, [max_time, batch_size, 1])
+
+  max_sequence_lengths = tf.to_int32(tf.reduce_max(sequence_length, axis=1))
+  sorted_beam_ids = tf.contrib.seq2seq.gather_tree(
+      step_ids=beam_ids,
+      parent_ids=parent_ids,
+      max_sequence_lengths=max_sequence_lengths,
+      end_token=beam_width + 1)
+
+  # For out of range steps, simply copy the same beam.
+  in_bound_steps = tf.transpose(
+      tf.sequence_mask(sequence_length, maxlen=max_time),
+      perm=[2, 0, 1])
+  sorted_beam_ids = tf.where(
+      in_bound_steps, x=sorted_beam_ids, y=beam_ids)
+
+  # Generate indices for gather_nd.
+  time_ind = tf.tile(tf.reshape(
+      tf.range(max_time), [-1, 1, 1]), [1, batch_size, beam_width])
+  batch_ind = tf.tile(tf.reshape(
+      tf.range(batch_size), [-1, 1, 1]), [1, max_time, beam_width])
+  batch_ind = tf.transpose(batch_ind, perm=[1, 0, 2])
+  indices = tf.stack([time_ind, batch_ind, sorted_beam_ids], -1)
+
+  # Gather from a tensor with collapsed additional dimensions.
+  gather_from = t
+  final_shape = tf.shape(gather_from)
+  gather_from = tf.reshape(
+      gather_from, [max_time, batch_size, beam_width, -1])
+  ordered = tf.gather_nd(gather_from, indices)
+  ordered = tf.reshape(ordered, final_shape)
+
+  return ordered
+
+
+def _check_maybe(t):
+  if t.shape.ndims is None:
+    raise ValueError(
+        "Expected tensor (%s) to have known rank, but ndims == None." % t)
+
+
+def _check_static_batch_beam_maybe(shape, batch_size, beam_width):
+  """Raises an exception if dimensions are known statically and can not be
+  reshaped to [batch_size, beam_size, -1].
+  """
+  reshaped_shape = tf.TensorShape([batch_size, beam_width, None])
+  if (batch_size is not None and shape[0].value is not None
+      and (shape[0] != batch_size * beam_width
+           or (shape.ndims >= 2 and shape[1].value is not None
+               and (shape[0] != batch_size or shape[1] != beam_width)))):
+    tf.logging.warn("TensorArray reordering expects elements to be "
+                    "reshapable to %s which is incompatible with the "
+                    "current shape %s. Consider setting "
+                    "reorder_tensor_arrays to False to disable TensorArray "
+                    "reordering during the beam search."
+                    % (reshaped_shape, shape))
+    return False
+  return True
+
+
+def _check_batch_beam(t, batch_size, beam_width):
+  """Returns an Assert operation checking that the elements of the stacked
+  TensorArray can be reshaped to [batch_size, beam_size, -1]. At this point,
+  the TensorArray elements have a known rank of at least 1.
+  """
+  error_message = ("TensorArray reordering expects elements to be "
+                   "reshapable to [batch_size, beam_size, -1] which is "
+                   "incompatible with the dynamic shape of %s elements. "
+                   "Consider setting reorder_tensor_arrays to False to disable "
+                   "TensorArray reordering during the beam search."
+                   % (t.name))
+  rank = t.shape.ndims
+  shape = tf.shape(t)
+  if rank == 2:
+    condition = tf.equal(shape[1], batch_size * beam_width)
+  else:
+    condition = tf.logical_or(
+        tf.equal(shape[1], batch_size * beam_width),
+        tf.logical_and(
+            tf.equal(shape[1], batch_size),
+            tf.equal(shape[2], beam_width)))
+  return tf.Assert(condition, [error_message])
+
+
+class BeamSearchDecoder(tf.contrib.seq2seq.Decoder):
+  """BeamSearch sampling decoder.
+
+    **NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
+    `AttentionWrapper`, then you must ensure that:
+
+    - The encoder output has been tiled to `beam_width` via
+      `tf.contrib.seq2seq.tile_batch` (NOT `tf.tile`).
+    - The `batch_size` argument passed to the `zero_state` method of this
+      wrapper is equal to `true_batch_size * beam_width`.
+    - The initial state created with `zero_state` above contains a
+      `cell_state` value containing properly tiled final state from the
+      encoder.
+
+    An example:
+
+    ```
+    tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
+        encoder_outputs, multiplier=beam_width)
+    tiled_encoder_final_state = tf.contrib.seq2seq.tile_batch(
+        encoder_final_state, multiplier=beam_width)
+    tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
+        sequence_length, multiplier=beam_width)
+    attention_mechanism = MyFavoriteAttentionMechanism(
+        num_units=attention_depth,
+        memory=tiled_inputs,
+        memory_sequence_length=tiled_sequence_length)
+    attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
+    decoder_initial_state = attention_cell.zero_state(
+        dtype, batch_size=true_batch_size * beam_width)
+    decoder_initial_state = decoder_initial_state.clone(
+        cell_state=tiled_encoder_final_state)
+    ```
+
+    Meanwhile, with `AttentionWrapper`, coverage penalty is suggested to use
+    when computing scores(https://arxiv.org/pdf/1609.08144.pdf). It encourages
+    the translation to cover all inputs.
+  """
+
+  def __init__(self,
+               cell,
+               embedding,
+               start_tokens,
+               end_token,
+               initial_state,
+               beam_width,
+               output_layer=None,
+               length_penalty_weight=0.0,
+               coverage_penalty_weight=0.0,
+               reorder_tensor_arrays=True):
+    """Initialize the BeamSearchDecoder.
+
+    Args:
+      cell: An `RNNCell` instance.
+      embedding: A callable that takes a vector tensor of `ids` (argmax ids),
+        or the `params` argument for `embedding_lookup`.
+      start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
+      end_token: `int32` scalar, the token that marks end of decoding.
+      initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
+      beam_width:  Python integer, the number of beams.
+      output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
+        `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
+        to storing the result or sampling.
+      length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
+      coverage_penalty_weight: Float weight to penalize the coverage of source
+        sentence. Disabled with 0.0.
+      reorder_tensor_arrays: If `True`, `TensorArray`s' elements within the cell
+        state will be reordered according to the beam search path. If the
+        `TensorArray` can be reordered, the stacked form will be returned.
+        Otherwise, the `TensorArray` will be returned as is. Set this flag to
+        `False` if the cell state contains `TensorArray`s that are not amenable
+        to reordering.
+
+    Raises:
+      TypeError: if `cell` is not an instance of `RNNCell`,
+        or `output_layer` is not an instance of `tf.layers.Layer`.
+      ValueError: If `start_tokens` is not a vector or
+        `end_token` is not a scalar.
+    """
+    if (output_layer is not None and
+        not isinstance(output_layer, tf.layers.Layer)):
+      raise TypeError(
+          "output_layer must be a Layer, received: %s" % type(output_layer))
+    self._cell = cell
+    self._output_layer = output_layer
+    self._reorder_tensor_arrays = reorder_tensor_arrays
+
+    if callable(embedding):
+      self._embedding_fn = embedding
+    else:
+      self._embedding_fn = (
+          lambda ids: tf.nn.embedding_lookup(embedding, ids))
+
+    self._start_tokens = tf.convert_to_tensor(
+        start_tokens, dtype=tf.int32, name="start_tokens")
+    if self._start_tokens.get_shape().ndims != 1:
+      raise ValueError("start_tokens must be a vector")
+    self._end_token = tf.convert_to_tensor(
+        end_token, dtype=tf.int32, name="end_token")
+    if self._end_token.get_shape().ndims != 0:
+      raise ValueError("end_token must be a scalar")
+
+    self._batch_size = tf.size(start_tokens)
+    self._beam_width = beam_width
+    self._length_penalty_weight = length_penalty_weight
+    self._coverage_penalty_weight = coverage_penalty_weight
+    self._initial_cell_state = tf.contrib.framework.nest.map_structure(
+        self._maybe_split_batch_beams, initial_state, self._cell.state_size)
+    self._start_tokens = tf.tile(
+        tf.expand_dims(self._start_tokens, 1), [1, self._beam_width])
+    self._start_inputs = self._embedding_fn(self._start_tokens)
+
+    self._finished = tf.one_hot(
+        tf.zeros([self._batch_size], dtype=tf.int32),
+        depth=self._beam_width,
+        on_value=False,
+        off_value=True,
+        dtype=tf.bool)
+
+  @property
+  def batch_size(self):
+    return self._batch_size
+
+  def _rnn_output_size(self):
+    size = self._cell.output_size
+    if self._output_layer is None:
+      return size
+    else:
+      # To use layer's compute_output_shape, we need to convert the
+      # RNNCell's output_size entries into shapes with an unknown
+      # batch size.  We then pass this through the layer's
+      # compute_output_shape and read off all but the first (batch)
+      # dimensions to get the output size of the rnn with the layer
+      # applied to the top.
+      output_shape_with_unknown_batch = tf.contrib.framework.nest.map_structure(
+          lambda s: tf.TensorShape([None]).concatenate(s), size)
+      layer_output_shape = self._output_layer.compute_output_shape(
+          output_shape_with_unknown_batch)
+      return tf.contrib.framework.nest.map_structure(
+          lambda s: s[1:], layer_output_shape)
+
+  @property
+  def tracks_own_finished(self):
+    """The BeamSearchDecoder shuffles its beams and their finished state.
+
+    For this reason, it conflicts with the `dynamic_decode` function's
+    tracking of finished states.  Setting this property to true avoids
+    early stopping of decoding due to mismanagement of the finished state
+    in `dynamic_decode`.
+
+    Returns:
+      `True`.
+    """
+    return True
+
+  @property
+  def output_size(self):
+    # Return the cell output and the id
+    return BeamSearchDecoderOutput(
+        scores=tf.TensorShape([self._beam_width]),
+        predicted_ids=tf.TensorShape([self._beam_width]),
+        parent_ids=tf.TensorShape([self._beam_width]))
+
+  @property
+  def output_dtype(self):
+    # Assume the dtype of the cell is the output_size structure
+    # containing the input_state's first component's dtype.
+    # Return that structure and int32 (the id)
+    dtype = tf.contrib.framework.nest.flatten(self._initial_cell_state)[0].dtype
+    return BeamSearchDecoderOutput(
+        scores=tf.contrib.framework.nest.map_structure(
+            lambda _: dtype, self._rnn_output_size()),
+        predicted_ids=tf.int32,
+        parent_ids=tf.int32)
+
+  def initialize(self, name=None):
+    """Initialize the decoder.
+
+    Args:
+      name: Name scope for any created operations.
+
+    Returns:
+      `(finished, start_inputs, initial_state)`.
+    """
+    finished, start_inputs = self._finished, self._start_inputs
+
+    dtype = tf.contrib.framework.nest.flatten(self._initial_cell_state)[0].dtype
+    log_probs = tf.one_hot(  # shape(batch_sz, beam_sz)
+        tf.zeros([self._batch_size], dtype=tf.int32),
+        depth=self._beam_width,
+        on_value=tf.convert_to_tensor(0.0, dtype=dtype),
+        off_value=tf.convert_to_tensor(-np.Inf, dtype=dtype),
+        dtype=dtype)
+    init_attention_probs = get_attention_probs(
+        self._initial_cell_state, self._coverage_penalty_weight)
+    if init_attention_probs is None:
+      init_attention_probs = ()
+
+    initial_state = BeamSearchDecoderState(
+        cell_state=self._initial_cell_state,
+        log_probs=log_probs,
+        finished=finished,
+        lengths=tf.zeros(
+            [self._batch_size, self._beam_width], dtype=tf.int64),
+        accumulated_attention_probs=init_attention_probs)
+
+    return (finished, start_inputs, initial_state)
+
+  def finalize(self, outputs, final_state, sequence_lengths):
+    """Finalize and return the predicted_ids.
+
+    Args:
+      outputs: An instance of BeamSearchDecoderOutput.
+      final_state: An instance of BeamSearchDecoderState. Passed through to the
+        output.
+      sequence_lengths: An `int64` tensor shaped `[batch_size, beam_width]`.
+        The sequence lengths determined for each beam during decode.
+        **NOTE** These are ignored; the updated sequence lengths are stored in
+        `final_state.lengths`.
+
+    Returns:
+      outputs: An instance of `FinalBeamSearchDecoderOutput` where the
+        predicted_ids are the result of calling _gather_tree.
+      final_state: The same input instance of `BeamSearchDecoderState`.
+    """
+    del sequence_lengths
+    # Get max_sequence_length across all beams for each batch.
+    max_sequence_lengths = tf.to_int32(
+        tf.reduce_max(final_state.lengths, axis=1))
+    predicted_ids = tf.contrib.seq2seq.gather_tree(
+        outputs.predicted_ids,
+        outputs.parent_ids,
+        max_sequence_lengths=max_sequence_lengths,
+        end_token=self._end_token)
+    if self._reorder_tensor_arrays:
+      final_state = final_state._replace(
+          cell_state=tf.contrib.framework.nest.map_structure(
+              lambda t: self._maybe_sort_array_beams(
+                  t, outputs.parent_ids, final_state.lengths),
+              final_state.cell_state))
+    outputs = FinalBeamSearchDecoderOutput(
+        beam_search_decoder_output=outputs, predicted_ids=predicted_ids)
+    return outputs, final_state
+
+  def _merge_batch_beams(self, t, s=None):
+    """Merges the tensor from a batch of beams into a batch by beams.
+
+    More exactly, t is a tensor of dimension [batch_size, beam_width, s]. We
+    reshape this into [batch_size*beam_width, s]
+
+    Args:
+      t: Tensor of dimension [batch_size, beam_width, s]
+      s: (Possibly known) depth shape.
+
+    Returns:
+      A reshaped version of t with dimension [batch_size * beam_width, s].
+    """
+    if isinstance(s, tf.Tensor):
+      s = tf.contrib.util.constant_value(s)
+      if isinstance(s, tf.TensorShape):
+        return s
+      else:
+        s = tf.TensorShape(s)
+    else:
+      s = tf.TensorShape(s)
+    t_shape = tf.shape(t)
+    static_batch_size = tf.contrib.util.constant_value(self._batch_size)
+    batch_size_beam_width = (
+        None
+        if static_batch_size is None else static_batch_size * self._beam_width)
+    reshaped_t = tf.reshape(
+        t,
+        tf.concat(([self._batch_size * self._beam_width], t_shape[2:]), 0))
+    reshaped_t.set_shape(
+        (tf.TensorShape([batch_size_beam_width]).concatenate(s)))
+    return reshaped_t
+
+  def _split_batch_beams(self, t, s=None):
+    """Splits the tensor from a batch by beams into a batch of beams.
+
+    More exactly, t is a tensor of dimension [batch_size*beam_width, s]. We
+    reshape this into [batch_size, beam_width, s]
+
+    Args:
+      t: Tensor of dimension [batch_size*beam_width, s].
+      s: (Possibly known) depth shape.
+
+    Returns:
+      A reshaped version of t with dimension [batch_size, beam_width, s].
+
+    Raises:
+      ValueError: If, after reshaping, the new tensor is not shaped
+        `[batch_size, beam_width, s]` (assuming batch_size and beam_width
+        are known statically).
+    """
+    if isinstance(s, tf.Tensor):
+      s = tf.TensorShape(tf.contrib.util.constant_value(s))
+    else:
+      s = tf.TensorShape(s)
+    t_shape = tf.shape(t)
+    reshaped_t = tf.reshape(
+        t,
+        tf.concat(([self._batch_size, self._beam_width], t_shape[1:]), 0))
+    static_batch_size = tf.contrib.util.constant_value(self._batch_size)
+    expected_reshaped_shape = tf.TensorShape(
+        [static_batch_size, self._beam_width]).concatenate(s)
+    if not reshaped_t.shape.is_compatible_with(expected_reshaped_shape):
+      raise ValueError("Unexpected behavior when reshaping between beam width "
+                       "and batch size.  The reshaped tensor has shape: %s.  "
+                       "We expected it to have shape "
+                       "(batch_size, beam_width, depth) == %s.  Perhaps you "
+                       "forgot to create a zero_state with "
+                       "batch_size=encoder_batch_size * beam_width?" %
+                       (reshaped_t.shape, expected_reshaped_shape))
+    reshaped_t.set_shape(expected_reshaped_shape)
+    return reshaped_t
+
+  def _maybe_split_batch_beams(self, t, s):
+    """Maybe splits the tensor from a batch by beams into a batch of beams.
+
+    We do this so that we can use nest and not run into problems with shapes.
+
+    Args:
+      t: `Tensor`, either scalar or shaped `[batch_size * beam_width] + s`.
+      s: `Tensor`, Python int, or `TensorShape`.
+
+    Returns:
+      If `t` is a matrix or higher order tensor, then the return value is
+      `t` reshaped to `[batch_size, beam_width] + s`.  Otherwise `t` is
+      returned unchanged.
+
+    Raises:
+      ValueError: If the rank of `t` is not statically known.
+    """
+    if isinstance(t, tf.TensorArray):
+      return t
+    _check_maybe(t)
+    if t.shape.ndims >= 1:
+      return self._split_batch_beams(t, s)
+    else:
+      return t
+
+  def _maybe_merge_batch_beams(self, t, s):
+    """Splits the tensor from a batch by beams into a batch of beams.
+
+    More exactly, `t` is a tensor of dimension `[batch_size * beam_width] + s`,
+    then we reshape it to `[batch_size, beam_width] + s`.
+
+    Args:
+      t: `Tensor` of dimension `[batch_size * beam_width] + s`.
+      s: `Tensor`, Python int, or `TensorShape`.
+
+    Returns:
+      A reshaped version of t with shape `[batch_size, beam_width] + s`.
+
+    Raises:
+      ValueError:  If the rank of `t` is not statically known.
+    """
+    if isinstance(t, tf.TensorArray):
+      return t
+    _check_maybe(t)
+    if t.shape.ndims >= 2:
+      return self._merge_batch_beams(t, s)
+    else:
+      return t
+
+  def _maybe_sort_array_beams(self, t, parent_ids, sequence_length):
+    """Maybe sorts beams within a `TensorArray`.
+
+    Args:
+      t: A `TensorArray` of size `max_time` that contains `Tensor`s of shape
+        `[batch_size, beam_width, s]` or `[batch_size * beam_width, s]` where
+        `s` is the depth shape.
+      parent_ids: The parent ids of shape `[max_time, batch_size, beam_width]`.
+      sequence_length: The sequence length of shape `[batch_size, beam_width]`.
+
+    Returns:
+      A `TensorArray` where beams are sorted in each `Tensor` or `t` itself if
+      it is not a `TensorArray` or does not meet shape requirements.
+    """
+    if not isinstance(t, tf.TensorArray):
+      return t
+    # pylint: disable=protected-access
+    if (not t._infer_shape or not t._element_shape
+        or t._element_shape[0].ndims is None
+        or t._element_shape[0].ndims < 1):
+      shape = (
+          t._element_shape[0] if t._infer_shape and t._element_shape
+          else tf.TensorShape(None))
+      tf.logging.warn("The TensorArray %s in the cell state is not amenable to "
+                      "sorting based on the beam search result. For a "
+                      "TensorArray to be sorted, its elements shape must be "
+                      "defined and have at least a rank of 1, but saw shape: %s"
+                      % (t.handle.name, shape))
+      return t
+    shape = t._element_shape[0]
+    # pylint: enable=protected-access
+    if not _check_static_batch_beam_maybe(
+        shape, tf.contrib.util.constant_value(self._batch_size),
+        self._beam_width):
+      return t
+    t = t.stack()
+    with tf.control_dependencies(
+        [_check_batch_beam(t, self._batch_size, self._beam_width)]):
+      return gather_tree_from_array(t, parent_ids, sequence_length)
+
+  def step(self, time, inputs, state, name=None):
+    """Perform a decoding step.
+
+    Args:
+      time: scalar `int32` tensor.
+      inputs: A (structure of) input tensors.
+      state: A (structure of) state tensors and TensorArrays.
+      name: Name scope for any created operations.
+
+    Returns:
+      `(outputs, next_state, next_inputs, finished)`.
+    """
+    batch_size = self._batch_size
+    beam_width = self._beam_width
+    end_token = self._end_token
+    length_penalty_weight = self._length_penalty_weight
+    coverage_penalty_weight = self._coverage_penalty_weight
+
+    with tf.name_scope(name, "BeamSearchDecoderStep", (time, inputs, state)):
+      cell_state = state.cell_state
+      inputs = tf.contrib.framework.nest.map_structure(
+          lambda inp: self._merge_batch_beams(inp, s=inp.shape[2:]), inputs)
+      cell_state = tf.contrib.framework.nest.map_structure(
+          self._maybe_merge_batch_beams, cell_state, self._cell.state_size)
+      cell_outputs, next_cell_state = self._cell(inputs, cell_state)
+      cell_outputs = tf.contrib.framework.nest.map_structure(
+          lambda out: self._split_batch_beams(out, out.shape[1:]), cell_outputs)
+      next_cell_state = tf.contrib.framework.nest.map_structure(
+          self._maybe_split_batch_beams, next_cell_state, self._cell.state_size)
+
+      if self._output_layer is not None:
+        cell_outputs = self._output_layer(cell_outputs)
+
+      beam_search_output, beam_search_state = _beam_search_step(
+          time=time,
+          logits=cell_outputs,
+          next_cell_state=next_cell_state,
+          beam_state=state,
+          batch_size=batch_size,
+          beam_width=beam_width,
+          end_token=end_token,
+          length_penalty_weight=length_penalty_weight,
+          coverage_penalty_weight=coverage_penalty_weight)
+
+      finished = beam_search_state.finished
+      sample_ids = beam_search_output.predicted_ids
+      next_inputs = tf.cond(
+          tf.reduce_all(finished), lambda: self._start_inputs,
+          lambda: self._embedding_fn(sample_ids))
+
+    return (beam_search_output, beam_search_state, next_inputs, finished)
+
+
+def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
+                      beam_width, end_token, length_penalty_weight,
+                      coverage_penalty_weight):
+  """Performs a single step of Beam Search Decoding.
+
+  Args:
+    time: Beam search time step, should start at 0. At time 0 we assume
+      that all beams are equal and consider only the first beam for
+      continuations.
+    logits: Logits at the current time step. A tensor of shape
+      `[batch_size, beam_width, vocab_size]`
+    next_cell_state: The next state from the cell, e.g. an instance of
+      AttentionWrapperState if the cell is attentional.
+    beam_state: Current state of the beam search.
+      An instance of `BeamSearchDecoderState`.
+    batch_size: The batch size for this input.
+    beam_width: Python int.  The size of the beams.
+    end_token: The int32 end token.
+    length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
+    coverage_penalty_weight: Float weight to penalize the coverage of source
+      sentence. Disabled with 0.0.
+
+  Returns:
+    A new beam state.
+  """
+  static_batch_size = tf.contrib.util.constant_value(batch_size)
+
+  # Calculate the current lengths of the predictions
+  prediction_lengths = beam_state.lengths
+  previously_finished = beam_state.finished
+  not_finished = tf.logical_not(previously_finished)
+
+  # Calculate the total log probs for the new hypotheses
+  # Final Shape: [batch_size, beam_width, vocab_size]
+  step_log_probs = tf.nn.log_softmax(logits)
+  step_log_probs = _mask_probs(step_log_probs, end_token, previously_finished)
+  total_probs = tf.expand_dims(beam_state.log_probs, 2) + step_log_probs
+
+  # Calculate the continuation lengths by adding to all continuing beams.
+  vocab_size = logits.shape[-1].value or tf.shape(logits)[-1]
+  lengths_to_add = tf.one_hot(
+      indices=tf.fill([batch_size, beam_width], end_token),
+      depth=vocab_size,
+      on_value=np.int64(0),
+      off_value=np.int64(1),
+      dtype=tf.int64)
+  add_mask = tf.to_int64(not_finished)
+  lengths_to_add *= tf.expand_dims(add_mask, 2)
+  new_prediction_lengths = (
+      lengths_to_add + tf.expand_dims(prediction_lengths, 2))
+
+  # Calculate the accumulated attention probabilities if coverage penalty is
+  # enabled.
+  accumulated_attention_probs = None
+  attention_probs = get_attention_probs(
+      next_cell_state, coverage_penalty_weight)
+  if attention_probs is not None:
+    attention_probs *= tf.expand_dims(tf.to_float(not_finished), 2)
+    accumulated_attention_probs = (
+        beam_state.accumulated_attention_probs + attention_probs)
+
+  batch_finished = tf.reduce_all(
+      previously_finished, axis=1, keepdims=True)
+  any_batch_finished = tf.reduce_any(batch_finished)
+  batch_finished = tf.tile(tf.expand_dims(batch_finished, 2),
+                           [1, beam_width, vocab_size])
+  def _normalized_scores():
+    return _get_scores(
+        log_probs=total_probs,
+        sequence_lengths=new_prediction_lengths,
+        length_penalty_weight=length_penalty_weight,
+        coverage_penalty_weight=coverage_penalty_weight,
+        finished=batch_finished,
+        accumulated_attention_probs=accumulated_attention_probs)
+
+  # Normalize the scores of finished batches.
+  scores = tf.cond(any_batch_finished, _normalized_scores, lambda: total_probs)
+
+  time = tf.convert_to_tensor(time, name="time")
+  # During the first time step we only consider the initial beam
+  scores_flat = tf.reshape(scores, [batch_size, -1])
+
+  # Pick the next beams according to the specified successors function
+  next_beam_size = tf.convert_to_tensor(
+      beam_width, dtype=tf.int32, name="beam_width")
+  next_beam_scores, word_indices = tf.nn.top_k(scores_flat, k=next_beam_size)
+
+  next_beam_scores.set_shape([static_batch_size, beam_width])
+  word_indices.set_shape([static_batch_size, beam_width])
+
+  # Pick out the probs, beam_ids, and states according to the chosen predictions
+  next_beam_probs = _tensor_gather_helper(
+      gather_indices=word_indices,
+      gather_from=total_probs,
+      batch_size=batch_size,
+      range_size=beam_width * vocab_size,
+      gather_shape=[-1],
+      name="next_beam_probs")
+  # Note: just doing the following
+  #   tf.to_int32(word_indices % vocab_size,
+  #       name="next_beam_word_ids")
+  # would be a lot cleaner but for reasons unclear, that hides the results of
+  # the op which prevents capturing it with tfdbg debug ops.
+  raw_next_word_ids = tf.mod(
+      word_indices, vocab_size, name="next_beam_word_ids")
+  next_word_ids = tf.to_int32(raw_next_word_ids)
+  next_beam_ids = tf.to_int32(
+      word_indices / vocab_size, name="next_beam_parent_ids")
+
+  # Append new ids to current predictions
+  previously_finished = _tensor_gather_helper(
+      gather_indices=next_beam_ids,
+      gather_from=previously_finished,
+      batch_size=batch_size,
+      range_size=beam_width,
+      gather_shape=[-1])
+  next_finished = tf.logical_or(
+      previously_finished,
+      tf.equal(next_word_ids, end_token),
+      name="next_beam_finished")
+
+  # Calculate the length of the next predictions.
+  # 1. Finished beams remain unchanged.
+  # 2. Beams that are now finished (EOS predicted) have their length
+  #    increased by 1.
+  # 3. Beams that are not yet finished have their length increased by 1.
+  lengths_to_add = tf.to_int64(tf.logical_not(previously_finished))
+  next_prediction_len = _tensor_gather_helper(
+      gather_indices=next_beam_ids,
+      gather_from=beam_state.lengths,
+      batch_size=batch_size,
+      range_size=beam_width,
+      gather_shape=[-1])
+  next_prediction_len += lengths_to_add
+  next_accumulated_attention_probs = ()
+  if accumulated_attention_probs is not None:
+    next_accumulated_attention_probs = _tensor_gather_helper(
+        gather_indices=next_beam_ids,
+        gather_from=accumulated_attention_probs,
+        batch_size=batch_size,
+        range_size=beam_width,
+        gather_shape=[batch_size * beam_width, -1],
+        name="next_accumulated_attention_probs")
+
+  # Pick out the cell_states according to the next_beam_ids. We use a
+  # different gather_shape here because the cell_state tensors, i.e.
+  # the tensors that would be gathered from, all have dimension
+  # greater than two and we need to preserve those dimensions.
+  # pylint: disable=g-long-lambda
+  next_cell_state = tf.contrib.framework.nest.map_structure(
+      lambda gather_from: _maybe_tensor_gather_helper(
+          gather_indices=next_beam_ids,
+          gather_from=gather_from,
+          batch_size=batch_size,
+          range_size=beam_width,
+          gather_shape=[batch_size * beam_width, -1]),
+      next_cell_state)
+  # pylint: enable=g-long-lambda
+
+  next_state = BeamSearchDecoderState(
+      cell_state=next_cell_state,
+      log_probs=next_beam_probs,
+      lengths=next_prediction_len,
+      finished=next_finished,
+      accumulated_attention_probs=next_accumulated_attention_probs)
+
+  output = BeamSearchDecoderOutput(
+      scores=next_beam_scores,
+      predicted_ids=next_word_ids,
+      parent_ids=next_beam_ids)
+
+  return output, next_state
+
+
+def get_attention_probs(next_cell_state, coverage_penalty_weight):
+  """Get attention probabilities from the cell state.
+
+  Args:
+    next_cell_state: The next state from the cell, e.g. an instance of
+      AttentionWrapperState if the cell is attentional.
+    coverage_penalty_weight: Float weight to penalize the coverage of source
+      sentence. Disabled with 0.0.
+
+  Returns:
+    The attention probabilities with shape `[batch_size, beam_width, max_time]`
+    if coverage penalty is enabled. Otherwise, returns None.
+
+  Raises:
+    ValueError: If no cell is attentional but coverage penalty is enabled.
+  """
+  if coverage_penalty_weight == 0.0:
+    return None
+
+  # Attention probabilities of each attention layer. Each with shape
+  # `[batch_size, beam_width, max_time]`.
+  probs_per_attn_layer = []
+  if isinstance(next_cell_state, attention_wrapper.AttentionWrapperState):
+    probs_per_attn_layer = [attention_probs_from_attn_state(next_cell_state)]
+  elif isinstance(next_cell_state, tuple):
+    for state in next_cell_state:
+      if isinstance(state, attention_wrapper.AttentionWrapperState):
+        probs_per_attn_layer.append(attention_probs_from_attn_state(state))
+
+  if not probs_per_attn_layer:
+    raise ValueError(
+        "coverage_penalty_weight must be 0.0 if no cell is attentional.")
+
+  if len(probs_per_attn_layer) == 1:
+    attention_probs = probs_per_attn_layer[0]
+  else:
+    # Calculate the average attention probabilities from all attention layers.
+    attention_probs = [
+        tf.expand_dims(prob, -1) for prob in probs_per_attn_layer]
+    attention_probs = tf.concat(attention_probs, -1)
+    attention_probs = tf.reduce_mean(attention_probs, -1)
+
+  return attention_probs
+
+
+def _get_scores(log_probs, sequence_lengths, length_penalty_weight,
+                coverage_penalty_weight, finished, accumulated_attention_probs):
+  """Calculates scores for beam search hypotheses.
+
+  Args:
+    log_probs: The log probabilities with shape
+      `[batch_size, beam_width, vocab_size]`.
+    sequence_lengths: The array of sequence lengths.
+    length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
+    coverage_penalty_weight: Float weight to penalize the coverage of source
+      sentence. Disabled with 0.0.
+    finished: A boolean tensor of shape `[batch_size, beam_width, vocab_size]`
+      that specifies which elements in the beam are finished already.
+    accumulated_attention_probs: Accumulated attention probabilities up to the
+      current time step, with shape `[batch_size, beam_width, max_time]` if
+      coverage_penalty_weight is not 0.0.
+
+  Returns:
+    The scores normalized by the length_penalty and coverage_penalty.
+
+  Raises:
+    ValueError: accumulated_attention_probs is None when coverage penalty is
+      enabled.
+  """
+  length_penalty_ = _length_penalty(
+      sequence_lengths=sequence_lengths, penalty_factor=length_penalty_weight)
+
+  coverage_penalty_weight = tf.convert_to_tensor(
+      coverage_penalty_weight, name="coverage_penalty_weight")
+  if coverage_penalty_weight.shape.ndims != 0:
+    raise ValueError("coverage_penalty_weight should be a scalar, "
+                     "but saw shape: %s" % coverage_penalty_weight.shape)
+
+  if accumulated_attention_probs is None:
+    raise ValueError(
+        "accumulated_attention_probs can be None only if coverage penalty is "
+        "disabled.")
+
+  # Add source sequence length mask before computing coverage penalty.
+  accumulated_attention_probs = tf.where(
+      tf.equal(accumulated_attention_probs, 0.0),
+      tf.ones_like(accumulated_attention_probs),
+      accumulated_attention_probs)
+
+  # coverage penalty =
+  #     sum over `max_time` {log(min(accumulated_attention_probs, 1.0))}
+  coverage_penalty = tf.reduce_sum(
+      tf.log(tf.minimum(accumulated_attention_probs, 1.0)), 2)
+  # Apply coverage penalty to finished predictions.
+  weighted_coverage_penalty = coverage_penalty * coverage_penalty_weight
+  # Reshape from [batch_size, beam_width] to [batch_size, beam_width, 1]
+  weighted_coverage_penalty = tf.expand_dims(
+      weighted_coverage_penalty, 2)
+
+  # Normalize the scores of finished predictions.
+  return tf.where(
+      finished, log_probs / length_penalty_ + weighted_coverage_penalty,
+      log_probs)
+
+
+def attention_probs_from_attn_state(attention_state):
+  """Calculates the average attention probabilities.
+
+  Args:
+    attention_state: An instance of `AttentionWrapperState`.
+
+  Returns:
+    The attention probabilities in the given AttentionWrapperState.
+    If there're multiple attention mechanisms, return the average value from
+    all attention mechanisms.
+  """
+  # Attention probabilities over time steps, with shape
+  # `[batch_size, beam_width, max_time]`.
+  attention_probs = attention_state.alignments
+  if isinstance(attention_probs, tuple):
+    attention_probs = [
+        tf.expand_dims(prob, -1) for prob in attention_probs]
+    attention_probs = tf.concat(attention_probs, -1)
+    attention_probs = tf.reduce_mean(attention_probs, -1)
+  return attention_probs
+
+
+def _length_penalty(sequence_lengths, penalty_factor):
+  """Calculates the length penalty. See https://arxiv.org/abs/1609.08144.
+
+  Returns the length penalty tensor:
+  ```
+  [(5+sequence_lengths)/6]**penalty_factor
+  ```
+  where all operations are performed element-wise.
+
+  Args:
+    sequence_lengths: `Tensor`, the sequence lengths of each hypotheses.
+    penalty_factor: A scalar that weights the length penalty.
+
+  Returns:
+    If the penalty is `0`, returns the scalar `1.0`.  Otherwise returns
+    the length penalty factor, a tensor with the same shape as
+    `sequence_lengths`.
+  """
+  penalty_factor = tf.convert_to_tensor(penalty_factor, name="penalty_factor")
+  penalty_factor.set_shape(())  # penalty should be a scalar.
+  static_penalty = tf.contrib.util.constant_value(penalty_factor)
+  if static_penalty is not None and static_penalty == 0:
+    return 1.0
+  return tf.div((5. + tf.to_float(sequence_lengths))
+                **penalty_factor, (5. + 1.)**penalty_factor)
+
+
+def _mask_probs(probs, eos_token, finished):
+  """Masks log probabilities.
+
+  The result is that finished beams allocate all probability mass to eos and
+  unfinished beams remain unchanged.
+
+  Args:
+    probs: Log probabilities of shape `[batch_size, beam_width, vocab_size]`
+    eos_token: An int32 id corresponding to the EOS token to allocate
+      probability to.
+    finished: A boolean tensor of shape `[batch_size, beam_width]` that
+      specifies which elements in the beam are finished already.
+
+  Returns:
+    A tensor of shape `[batch_size, beam_width, vocab_size]`, where unfinished
+    beams stay unchanged and finished beams are replaced with a tensor with all
+    probability on the EOS token.
+  """
+  vocab_size = tf.shape(probs)[2]
+  # All finished examples are replaced with a vector that has all
+  # probability on EOS
+  finished_row = tf.one_hot(
+      eos_token,
+      vocab_size,
+      dtype=probs.dtype,
+      on_value=tf.convert_to_tensor(0., dtype=probs.dtype),
+      off_value=probs.dtype.min)
+  finished_probs = tf.tile(
+      tf.reshape(finished_row, [1, 1, -1]),
+      tf.concat([tf.shape(finished), [1]], 0))
+  finished_mask = tf.tile(
+      tf.expand_dims(finished, 2), [1, 1, vocab_size])
+
+  return tf.where(finished_mask, finished_probs, probs)
+
+
+def _maybe_tensor_gather_helper(gather_indices, gather_from, batch_size,
+                                range_size, gather_shape):
+  """Maybe applies _tensor_gather_helper.
+
+  This applies _tensor_gather_helper when the gather_from dims is at least as
+  big as the length of gather_shape. This is used in conjunction with nest so
+  that we don't apply _tensor_gather_helper to inapplicable values like scalars.
+
+  Args:
+    gather_indices: The tensor indices that we use to gather.
+    gather_from: The tensor that we are gathering from.
+    batch_size: The batch size.
+    range_size: The number of values in each range. Likely equal to beam_width.
+    gather_shape: What we should reshape gather_from to in order to preserve the
+      correct values. An example is when gather_from is the attention from an
+      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
+      There, we want to preserve the attention_size elements, so gather_shape is
+      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
+      attention_size as desired.
+
+  Returns:
+    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
+      or the original tensor if its dimensions are too small.
+  """
+  if isinstance(gather_from, tf.TensorArray):
+    return gather_from
+  _check_maybe(gather_from)
+  if gather_from.shape.ndims >= len(gather_shape):
+    return _tensor_gather_helper(
+        gather_indices=gather_indices,
+        gather_from=gather_from,
+        batch_size=batch_size,
+        range_size=range_size,
+        gather_shape=gather_shape)
+  else:
+    return gather_from
+
+
+def _tensor_gather_helper(gather_indices,
+                          gather_from,
+                          batch_size,
+                          range_size,
+                          gather_shape,
+                          name=None):
+  """Helper for gathering the right indices from the tensor.
+
+  This works by reshaping gather_from to gather_shape (e.g. [-1]) and then
+  gathering from that according to the gather_indices, which are offset by
+  the right amounts in order to preserve the batch order.
+
+  Args:
+    gather_indices: The tensor indices that we use to gather.
+    gather_from: The tensor that we are gathering from.
+    batch_size: The input batch size.
+    range_size: The number of values in each range. Likely equal to beam_width.
+    gather_shape: What we should reshape gather_from to in order to preserve the
+      correct values. An example is when gather_from is the attention from an
+      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
+      There, we want to preserve the attention_size elements, so gather_shape is
+      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
+      attention_size as desired.
+    name: The tensor name for set of operations. By default this is
+      'tensor_gather_helper'. The final output is named 'output'.
+
+  Returns:
+    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
+  """
+  with tf.name_scope(name, "tensor_gather_helper"):
+    range_ = tf.expand_dims(tf.range(batch_size) * range_size, 1)
+    gather_indices = tf.reshape(gather_indices + range_, [-1])
+    output = tf.gather(
+        tf.reshape(gather_from, gather_shape), gather_indices)
+    final_shape = tf.shape(gather_from)[:1 + len(gather_shape)]
+    static_batch_size = tf.contrib.util.constant_value(batch_size)
+    final_static_shape = (
+        tf.TensorShape([static_batch_size]).concatenate(
+            gather_from.shape[1:1 + len(gather_shape)]))
+    output = tf.reshape(output, final_shape, name="output")
+    output.set_shape(final_static_shape)
+    return output
diff --git a/TensorFlow/Translation/GNMT/benchmark_hooks.py b/TensorFlow/Translation/GNMT/benchmark_hooks.py
new file mode 100644
index 00000000..1d49450e
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/benchmark_hooks.py
@@ -0,0 +1,41 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+
+__all__ = ['BenchmarkHook']
+
+
+class BenchmarkHook(tf.train.SessionRunHook):
+
+  def __init__(self, global_batch_size, warmup_steps=5):
+    self.warmup_steps = warmup_steps
+    self.global_batch_size = global_batch_size
+    self.iter_times = []
+
+  def before_run(self, run_context):
+    self.t0 = time.time()
+
+  def after_run(self, run_context, run_values):
+    batch_time = time.time() - self.t0
+    self.iter_times.append(batch_time)
+
+  def get_average_speed(self):
+    avg_time = np.mean(self.iter_times[self.warmup_steps:])
+    speed = self.global_batch_size / avg_time
+    return speed
diff --git a/TensorFlow/Translation/GNMT/block_lstm.py b/TensorFlow/Translation/GNMT/block_lstm.py
new file mode 100644
index 00000000..3b0c784c
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/block_lstm.py
@@ -0,0 +1,308 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""LSTM Block Cell ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import tensorflow as tf
+
+from tensorflow.contrib.rnn.ops import gen_lstm_ops
+from tensorflow.python.framework import function
+from tensorflow.python.layers import base as base_layer
+
+
+class LSTMBlockWrapper(base_layer.Layer):
+  """This is a helper class that provides housekeeping for LSTM cells.
+
+  This may be useful for alternative LSTM and similar type of cells.
+  The subclasses must implement `_call_cell` method and `num_units` property.
+  """
+
+  @abc.abstractproperty
+  def num_units(self):
+    """Number of units in this cell (output dimension)."""
+    pass
+
+  @abc.abstractmethod
+  def _call_cell(self, inputs, initial_cell_state, initial_output, dtype,
+                 sequence_length):
+    """Run this LSTM on inputs, starting from the given state.
+
+    This method must be implemented by subclasses and does the actual work
+    of calling the cell.
+
+    Args:
+      inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`
+      initial_cell_state: initial value for cell state, shape `[batch_size,
+        self._num_units]`
+      initial_output: initial value of cell output, shape `[batch_size,
+        self._num_units]`
+      dtype: The data type for the initial state and expected output.
+      sequence_length: Specifies the length of each sequence in inputs. An int32
+        or int64 vector (tensor) size [batch_size], values in [0, time_len) or
+          None.
+
+    Returns:
+      A pair containing:
+
+      - State: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
+      - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
+    """
+    pass
+
+  def call(self, inputs, initial_state=None, dtype=None, sequence_length=None,
+           mask_output=False):
+    """Run this LSTM on inputs, starting from the given state.
+
+    Args:
+      inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`.
+      initial_state: a tuple `(initial_cell_state, initial_output)` with tensors
+        of shape `[batch_size, self._num_units]`. If this is not provided, the
+        cell is expected to create a zero initial state of type `dtype`.
+      dtype: The data type for the initial state and expected output. Required
+        if `initial_state` is not provided or RNN state has a heterogeneous
+        dtype.
+      sequence_length: Specifies the length of each sequence in inputs. An
+        `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0,
+        time_len).`
+        Defaults to `time_len` for each element.
+      mask_output: ...
+
+    Returns:
+      A pair containing:
+
+      - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
+        or a list of time_len tensors of shape `[batch_size, output_size]`,
+        to match the type of the `inputs`.
+      - Final state: a tuple `(cell_state, output)` matching `initial_state`.
+
+    Raises:
+      ValueError: in case of shape mismatches
+    """
+    is_list = isinstance(inputs, list)
+    if is_list:
+      inputs = tf.stack(inputs)
+    inputs_shape = inputs.get_shape().with_rank(3)
+    if not inputs_shape[2]:
+      raise ValueError("Expecting inputs_shape[2] to be set: %s" % inputs_shape)
+    batch_size = inputs_shape[1].value
+    if batch_size is None:
+      batch_size = tf.shape(inputs)[1]
+    time_len = inputs_shape[0].value
+    if time_len is None:
+      time_len = tf.shape(inputs)[0]
+
+    # Provide default values for initial_state and dtype
+    if initial_state is None:
+      if dtype is None:
+        raise ValueError("Either initial_state or dtype needs to be specified")
+      z = tf.zeros(
+          tf.stack([batch_size, self.num_units]), dtype=dtype)
+      initial_state = z, z
+    else:
+      if len(initial_state) != 2:
+        raise ValueError(
+            "Expecting initial_state to be a tuple with length 2 or None")
+      if dtype is None:
+        dtype = initial_state[0].dtype
+
+    # create the actual cell
+    if sequence_length is not None:
+      sequence_length = tf.convert_to_tensor(sequence_length)
+    initial_cell_state, initial_output = initial_state  # pylint: disable=unpacking-non-sequence
+    cell_states, outputs = self._call_cell(
+        inputs, initial_cell_state, initial_output, dtype, sequence_length)
+
+    if sequence_length is not None:
+      if mask_output:
+        # Mask out the part beyond sequence_length.
+        # In MLPerf we don't do it b.c output is masked when computing loss.
+        # And in inference we don't use this layer.
+        mask = tf.transpose(
+            tf.sequence_mask(sequence_length, time_len, dtype=dtype),
+            [1, 0])
+        mask = tf.tile(
+            tf.expand_dims(mask, axis=-1), [1, 1, self.num_units])
+        outputs *= mask
+      """
+      # Prepend initial states to cell_states and outputs for indexing to work
+      # correctly,since we want to access the last valid state at
+      # sequence_length - 1, which can even be -1, corresponding to the
+      # initial state.
+      mod_cell_states = tf.concat(
+          [tf.expand_dims(initial_cell_state, axis=0), cell_states], 0)
+      mod_outputs = tf.concat(
+          [tf.expand_dims(initial_output, axis=0), outputs], 0)
+      final_cell_state = self._gather_states(mod_cell_states,
+                                             sequence_length, batch_size)
+      final_output = self._gather_states(mod_outputs, sequence_length,
+                                         batch_size)
+      """
+      # sequence_length can't be zero in our impl, pass sequence_length -1 for
+      # indices.
+      mod_cell_states = cell_states
+      mod_outputs = outputs
+      final_cell_state = self._gather_states(mod_cell_states,
+                                             sequence_length - 1, batch_size)
+      final_output = self._gather_states(mod_outputs, sequence_length - 1,
+                                         batch_size)
+    else:
+      # No sequence_lengths used: final state is the last state
+      final_cell_state = cell_states[-1]
+      final_output = outputs[-1]
+
+    if is_list:
+      # Input was a list, so return a list
+      outputs = tf.unstack(outputs)
+
+    final_state = tf.nn.rnn_cell.LSTMStateTuple(final_cell_state, final_output)
+    return outputs, final_state
+
+  def _gather_states(self, data, indices, batch_size):
+    """Produce `out`, s.t. out(i, j) = data(indices(i), i, j)."""
+    gather_indices = tf.stack([indices, tf.range(batch_size)], axis=1)
+    # TODO(jamesqin): ScatterNd doesn't support fp16 on GPU.
+    return tf.gather_nd(data, gather_indices)
+
+
+class LSTMBlockFusedCell(LSTMBlockWrapper):
+  """FusedRNNCell implementation of LSTM.
+
+  This is an extremely efficient LSTM implementation, that uses a single TF op
+  for the entire LSTM. It should be both faster and more memory-efficient than
+  LSTMBlockCell defined above.
+
+  The implementation is based on: http://arxiv.org/abs/1409.2329.
+
+  We add forget_bias (default: 1) to the biases of the forget gate in order to
+  reduce the scale of forgetting in the beginning of the training.
+
+  The variable naming is consistent with `rnn_cell_impl.LSTMCell`.
+  """
+
+  def __init__(self,
+               num_units,
+               forget_bias=1.0,
+               cell_clip=None,
+               use_peephole=False,
+               reuse=None,
+               dtype=None,
+               name="lstm_cell"):
+    """Initialize the LSTM cell.
+
+    Args:
+      num_units: int, The number of units in the LSTM cell.
+      forget_bias: float, The bias added to forget gates (see above).
+      cell_clip: clip the cell to this value. Default is no cell clipping.
+      use_peephole: Whether to use peephole connections or not.
+      reuse: (optional) boolean describing whether to reuse variables in an
+        existing scope.  If not `True`, and the existing scope already has the
+        given variables, an error is raised.
+      dtype: the dtype of variables of this layer.
+      name: String, the name of the layer. Layers with the same name will
+        share weights, but to avoid mistakes we require reuse=True in such
+        cases.  By default this is "lstm_cell", for variable-name compatibility
+        with `tf.nn.rnn_cell.LSTMCell`.
+    """
+    super(LSTMBlockFusedCell, self).__init__(
+        _reuse=reuse, name=name, dtype=dtype)
+    self._num_units = num_units
+    self._forget_bias = forget_bias
+    self._cell_clip = cell_clip if cell_clip is not None else -1
+    self._use_peephole = use_peephole
+
+    # Inputs must be 3-dimensional.
+    self.input_spec = base_layer.InputSpec(ndim=3)
+
+  @property
+  def num_units(self):
+    """Number of units in this cell (output dimension)."""
+    return self._num_units
+
+  def build(self, input_shape):
+    input_size = input_shape[2].value
+    self._kernel = self.add_variable(
+        "kernel", [input_size + self._num_units, self._num_units * 4])
+    self._bias = self.add_variable(
+        "bias", [self._num_units * 4],
+        initializer=tf.constant_initializer(0.0))
+    if self._use_peephole:
+      self._w_i_diag = self.add_variable("w_i_diag", [self._num_units])
+      self._w_f_diag = self.add_variable("w_f_diag", [self._num_units])
+      self._w_o_diag = self.add_variable("w_o_diag", [self._num_units])
+
+    self.built = True
+
+  def _call_cell(self,
+                 inputs,
+                 initial_cell_state=None,
+                 initial_output=None,
+                 dtype=None,
+                 sequence_length=None):
+    """Run this LSTM on inputs, starting from the given state.
+
+    Args:
+      inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`
+      initial_cell_state: initial value for cell state, shape `[batch_size,
+        self._num_units]`
+      initial_output: initial value of cell output, shape `[batch_size,
+        self._num_units]`
+      dtype: The data type for the initial state and expected output.
+      sequence_length: Specifies the length of each sequence in inputs. An
+        `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0,
+        time_len)` or None.
+
+    Returns:
+      A pair containing:
+
+      - Cell state (cs): A `3-D` tensor of shape `[time_len, batch_size,
+                         output_size]`
+      - Output (h): A `3-D` tensor of shape `[time_len, batch_size,
+                    output_size]`
+    """
+
+    inputs_shape = inputs.get_shape().with_rank(3)
+    time_len = inputs_shape[0].value
+    if time_len is None:
+      time_len = tf.shape(inputs)[0]
+
+    if self._use_peephole:
+      wci = self._w_i_diag
+      wco = self._w_o_diag
+      wcf = self._w_f_diag
+    else:
+      wci = wcf = wco = tf.zeros([self._num_units], dtype=dtype)
+
+    if sequence_length is None:
+      max_seq_len = tf.to_int64(time_len)
+    else:
+      max_seq_len = tf.to_int64(tf.reduce_max(sequence_length))
+
+    _, cs, _, _, _, _, h = gen_lstm_ops.block_lstm(
+        seq_len_max=max_seq_len,
+        x=inputs,
+        cs_prev=initial_cell_state,
+        h_prev=initial_output,
+        w=self._kernel,
+        wci=wci,
+        wcf=wcf,
+        wco=wco,
+        b=self._bias,
+        forget_bias=self._forget_bias,
+        cell_clip=self._cell_clip,
+        use_peephole=self._use_peephole)
+    return cs, h
diff --git a/TensorFlow/Translation/GNMT/estimator.py b/TensorFlow/Translation/GNMT/estimator.py
new file mode 100644
index 00000000..0179aa58
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/estimator.py
@@ -0,0 +1,829 @@
+# Copyright 2018 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Estimator functions supporting running on TPU."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import codecs
+import os
+import subprocess
+import time
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.contrib.compiler import xla
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.framework import ops as tf_ops
+from tensorflow.python.ops import lookup_ops
+from tensorflow.python.util import tf_contextlib
+
+import gnmt_model
+import model_helper
+from utils import iterator_utils
+from utils import misc_utils
+from utils import nmt_utils
+from utils import vocab_utils
+from variable_mgr import variable_mgr
+from variable_mgr import variable_mgr_util
+
+from benchmark_hooks import BenchmarkHook
+
+
+def _get_custom_getter():
+  """Returns a custom getter that this class's methods must be called under.
+
+  All methods of this class must be called under a variable scope that was
+  passed this custom getter. Example:
+
+  ```python
+  network = ConvNetBuilder(...)
+  with tf.variable_scope('cg', custom_getter=network.get_custom_getter()):
+    network.conv(...)
+    # Call more methods of network here
+  ```
+
+  Currently, this custom getter only does anything if self.use_tf_layers is
+  True. In that case, it causes variables to be stored as dtype
+  self.variable_type, then casted to the requested dtype, instead of directly
+  storing the variable as the requested dtype.
+  """
+
+  def inner_custom_getter(getter, *args, **kwargs):
+    """Custom getter that forces variables to have type self.variable_type."""
+    cast_to_float16 = False
+    requested_dtype = kwargs["dtype"]
+    if requested_dtype == tf.float16:
+      # Only change the variable dtype if doing so does not decrease variable
+      # precision.
+      kwargs["dtype"] = tf.float32
+      cast_to_float16 = True
+    var = getter(*args, **kwargs)
+    with tf_ops.init_scope():
+      # This if statement is needed to guard the cast, because batch norm
+      # assigns directly to the return value of this custom getter. The cast
+      # makes the return value not a variable so it cannot be assigned. Batch
+      # norm variables are always in fp32 so this if statement is never
+      # triggered for them.
+      if cast_to_float16:
+        var = tf.cast(var, tf.float16)
+    return var
+
+  return inner_custom_getter
+
+
+@tf_contextlib.contextmanager
+def mixed_precision_scope():
+  with tf.variable_scope("", custom_getter=_get_custom_getter()) as varscope:
+    yield varscope
+
+
+def maybe_xla_compile(hparams, fn, *args):
+  pure_fn = lambda: fn(*args)
+  if hparams and hparams.xla_compile:
+    return xla.compile(pure_fn)
+  else:
+    return pure_fn()
+
+
+class ModelFnFactory(object):
+  """docstring."""
+
+  def __init__(self, hparams):
+    self.hparams = hparams
+
+  def build_graph_dist_strategy(self, features, labels, mode, params):
+    """Model function."""
+    del labels, params
+    misc_utils.print_out("Running dist_strategy mode_fn")
+
+    hparams = self.hparams
+
+    # Create a GNMT model for training.
+    # assert (hparams.encoder_type == "gnmt" or
+    #        hparams.attention_architecture in ["gnmt", "gnmt_v2"])
+    with mixed_precision_scope():
+      model = gnmt_model.GNMTModel(hparams, mode=mode, features=features)
+      if mode == tf.contrib.learn.ModeKeys.INFER:
+        sample_ids = model.sample_id
+        reverse_target_vocab_table = lookup_ops.index_to_string_table_from_file(
+            hparams.tgt_vocab_file, default_value=vocab_utils.UNK)
+        sample_words = reverse_target_vocab_table.lookup(
+            tf.to_int64(sample_ids))
+        # make sure outputs is of shape [batch_size, time] or [beam_width,
+        # batch_size, time] when using beam search.
+        if hparams.time_major:
+          sample_words = tf.transpose(sample_words)
+        elif sample_words.shape.ndims == 3:
+          # beam search output in [batch_size, time, beam_width] shape.
+          sample_words = tf.transpose(sample_words, [2, 0, 1])
+        predictions = {"predictions": sample_words}
+        # return loss, vars, grads, predictions, train_op, scaffold
+        return None, None, None, predictions, None, None
+      elif mode == tf.contrib.learn.ModeKeys.TRAIN:
+        loss = model.train_loss
+        train_op = model.update
+        return loss, model.params, model.grads, None, train_op, None
+      else:
+        raise ValueError("Unknown mode in model_fn: %s" % mode)
+
+  def _create_loss_scale_vars(self):
+    """docstring."""
+    # Create loss scale vars if necessary
+    hparams = self.hparams
+    loss_scale, loss_scale_normal_steps = None, None
+    if hparams.use_fp16:
+      loss_scale = tf.get_variable(
+          "loss_scale",
+          initializer=float(hparams.fp16_loss_scale),
+          dtype=tf.float32,
+          trainable=False)
+      if hparams.enable_auto_loss_scale:
+        loss_scale_normal_steps = tf.get_variable(
+            "loss_scale_normal_steps", initializer=0, trainable=False)
+
+    return loss_scale, loss_scale_normal_steps
+
+  def _shard_inputs(self, features, num_towers):
+    """docstring."""
+    if num_towers == 1:
+      return [features]
+
+    source = features["source"]
+    target_input = features["target_input"]
+    target_output = features["target_output"]
+    source_seq_length = features["source_sequence_length"]
+    target_seq_length = features["target_sequence_length"]
+
+    # Compute each split sizes.
+    global_batch_size = tf.size(source_seq_length)
+    tower_batch_size = tf.cast(global_batch_size / num_towers, dtype=tf.int32)
+
+    split_sizes = [tower_batch_size] * (num_towers - 1)
+    split_sizes.append(global_batch_size - (num_towers - 1) * tower_batch_size)
+
+    sources = tf.split(source, split_sizes, axis=0)
+    target_inputs = tf.split(target_input, split_sizes, axis=0)
+    target_outputs = tf.split(target_output, split_sizes, axis=0)
+    source_sequence_lengths = tf.split(source_seq_length, split_sizes)
+    target_sequence_lengths = tf.split(target_seq_length, split_sizes)
+
+    tower_features = []
+    for i in range(num_towers):
+      tower_features.append({
+          "source": sources[i],
+          "target_input": target_inputs[i],
+          "target_output": target_outputs[i],
+          "source_sequence_length": source_sequence_lengths[i],
+          "target_sequence_length": target_sequence_lengths[i]
+      })
+
+    return tower_features
+
+  def get_optimizer(self, hparams, learning_rate):
+    """docstring."""
+    if hparams.optimizer == "sgd":
+      opt = tf.train.GradientDescentOptimizer(learning_rate)
+    elif hparams.optimizer == "adam":
+      opt = tf.train.AdamOptimizer(learning_rate)
+    else:
+      raise ValueError("Unknown optimizer type %s" % hparams.optimizer)
+    return opt
+
+  def _compute_tower_grads(self, tower_loss, tower_params, learning_rate, use_fp16=False,
+                           loss_scale=None, colocate_gradients_with_ops=True):
+    """docstring."""
+    if use_fp16:
+      assert loss_scale
+      scaled_loss = tf.multiply(
+          tower_loss,
+          tf.convert_to_tensor(loss_scale, dtype=tower_loss.dtype),
+          name="scaling_loss")
+    else:
+      scaled_loss = tower_loss
+
+    opt = self.get_optimizer(self.hparams, learning_rate)
+    grads_and_vars = opt.compute_gradients(scaled_loss, tower_params,
+            colocate_gradients_with_ops=self.hparams.colocate_gradients_with_ops)
+    grads = [x for (x, _) in grads_and_vars]
+    assert grads
+    for g in grads:
+      assert g.dtype == tf.float32, "grad.dtype isn't fp32: %s" % g.name
+    # Downscale grads
+    for var, grad in zip(tower_params, grads):
+      if grad is None:
+        misc_utils.print_out("%s gradient is None!" % var.name)
+
+    if use_fp16:
+      grads = [
+          grad * tf.reciprocal(loss_scale) for grad in grads
+      ]
+    return tower_params, grads, opt
+
+  def _get_variable_mgr(self, hparams):
+    """docstring."""
+    assert not hparams.use_dist_strategy
+
+    # A hack to create a container object that later get passed to VariableMgr
+    # __init__() as the ill-designed `benchmark_cnn` argument.
+    class Config(object):
+      pass
+
+    config = Config()
+    config.params = Config()
+    params = config.params
+    # This is num_gpus per worker, a.k.a the number of towers.
+    params.num_gpus = hparams.num_gpus
+    # TODO(jamesqin): make more robust
+    params.use_resource_vars = hparams.use_resource_vars
+    params.use_fp16 = hparams.use_fp16
+    params.compact_gradient_transfer = hparams.compact_gradient_transfer
+    # For nmt, only strong consistency
+    params.variable_consistency = "strong"
+    params.all_reduce_spec = hparams.all_reduce_spec
+    params.gpu_indices = hparams.gpu_indices
+    params.agg_small_grads_max_bytes = hparams.agg_small_grads_max_bytes
+    params.agg_small_grads_max_group = hparams.agg_small_grads_max_group
+    params.hierarchical_copy = hparams.hierarchical_copy
+    params.network_topology = hparams.network_topology
+    params.local_parameter_device = hparams.local_parameter_device
+    params.gradient_repacking = hparams.gradient_repacking
+    params.allreduce_merge_scope = hparams.allreduce_merge_scope
+
+    config.enable_auto_loss_scale = hparams.enable_auto_loss_scale
+    if hparams.num_gpus > 0:
+      config.raw_devices = ["gpu:%i" % i for i in range(hparams.num_gpus)]
+    else:
+      config.raw_devices = ["cpu:0"]
+    config.devices = config.raw_devices
+
+    return variable_mgr.VariableMgrLocalReplicated(
+        config, config.params.all_reduce_spec,
+        config.params.agg_small_grads_max_bytes,
+        config.params.agg_small_grads_max_group,
+        config.params.allreduce_merge_scope)
+
+  def _print_varinfo(self, var_params, tower_id):
+    # Print trainable variables
+    misc_utils.print_out("# Trainable variables for tower: %d" % tower_id)
+    misc_utils.print_out(
+        "Format: <name>, <shape>, <dtype>, <(soft) device placement>")
+    for param in var_params:
+      misc_utils.print_out(
+          "  %s, %s, %s, %s" % (param.name, str(param.get_shape()),
+                                param.dtype.name, param.op.device))
+    misc_utils.print_out("Total params size: %.2f GB" % (4. * np.sum([
+        p.get_shape().num_elements()
+        for p in var_params
+        if p.get_shape().is_fully_defined()
+    ]) / 2**30))
+
+  def build_graph(self, features, labels, mode, params):
+    """docstring."""
+    del labels, params
+    misc_utils.print_out("Running fast mode_fn")
+
+    hparams = self.hparams
+
+    # Create global_step
+    tf.train.get_or_create_global_step()
+
+    if mode == tf.contrib.learn.ModeKeys.INFER:
+      # Doing inference only on one GPU
+      inf_hparams = tf.contrib.training.HParams(**hparams.values())
+      inf_hparams.set_hparam("num_gpus", 1)
+      # Inference is done in fp32 and in the same way as that of dist_strategy.
+      inf_hparams.set_hparam("use_fp16", False)
+
+      misc_utils.print_out("inference hparmas:")
+      misc_utils.print_hparams(inf_hparams)
+
+      # Create variable_mgr
+      var_mgr = self._get_variable_mgr(inf_hparams)
+
+      with mixed_precision_scope(), tf.device("gpu:0"), tf.name_scope(
+          "tower_0"), var_mgr.create_outer_variable_scope(0):
+        model = gnmt_model.GNMTModel(inf_hparams, mode=mode, features=features)
+        sample_ids = model.sample_id
+        reverse_target_vocab_table = lookup_ops.index_to_string_table_from_file(
+            inf_hparams.tgt_vocab_file, default_value=vocab_utils.UNK)
+        sample_words = reverse_target_vocab_table.lookup(
+            tf.to_int64(sample_ids))
+        # make sure outputs is of shape [batch_size, time] or [beam_width,
+        # batch_size, time] when using beam search.
+        if inf_hparams.time_major:
+          sample_words = tf.transpose(sample_words)
+        elif sample_words.shape.ndims == 3:
+          # beam search output in [batch_size, time, beam_width] shape.
+          sample_words = tf.transpose(sample_words, [2, 0, 1])
+        predictions = {"predictions": sample_words}
+        # return loss, vars, grads, predictions, train_op, scaffold
+        return None, None, None, predictions, None, None
+    elif mode == tf.contrib.learn.ModeKeys.TRAIN:
+      num_towers = hparams.num_gpus
+      # Shard inputs
+      tower_features = self._shard_inputs(features, num_towers)
+      # Create loss scale vars if necessary
+      loss_scale, loss_scale_normal_steps = self._create_loss_scale_vars()
+
+      # Create variable_mgr
+      var_mgr = self._get_variable_mgr(hparams)
+
+      # Build per-tower fprop and bprop
+      devices = var_mgr.get_devices()
+      tower_gradvars = []
+      tower_scopes = []
+      var_scopes = []
+      train_losses = []
+      learning_rates = []
+      batch_sizes = []
+      opts = []
+
+      def fprop_and_bprop(tid):
+        """docstring."""
+        model = gnmt_model.GNMTModel(
+            hparams, mode=mode, features=tower_features[tid])
+        # sync training.
+        assert model.learning_rate is not None
+        # The following handles shouldn't be built in when doing manual
+        assert model.grad_norm is None
+        assert model.update is None
+        tower_loss = model.train_loss
+        # Only check loss numerics if in fp16
+        if hparams.use_fp16 and hparams.check_tower_loss_numerics:
+          tower_loss = tf.check_numerics(
+              tower_loss, "tower_%d has Inf/NaN loss" % tid)
+        # Cast to fp32, otherwise would easily overflow.
+        tower_loss = tf.to_float(tower_loss)
+        var_params, grads, opt = self._compute_tower_grads(
+            tower_loss,
+            var_mgr.trainable_variables_on_device(tid, tid),
+            model.learning_rate,
+            use_fp16=hparams.use_fp16,
+            loss_scale=loss_scale,
+            colocate_gradients_with_ops=hparams.colocate_gradients_with_ops)
+        self._print_varinfo(var_params, tid)
+        res = [model.train_loss, model.learning_rate, model.batch_size]
+        res.extend(grads)
+        opts.append(opt)
+        return res
+
+      def unpack_fprop_and_bprop_output(output):
+        train_loss = output[0]
+        learning_rate = output[1]
+        batch_size = output[2]
+        grads = output[3:]
+        return train_loss, learning_rate, batch_size, grads
+
+      with mixed_precision_scope():
+        for tid in range(num_towers):
+          with tf.device(devices[tid % len(devices)]), tf.name_scope(
+              "tower_%s" % tid) as scope:
+            tower_scopes.append(scope)
+            with var_mgr.create_outer_variable_scope(tid) as var_scope:
+              var_scopes.append(var_scope)
+
+              outputs = maybe_xla_compile(hparams, fprop_and_bprop, tid)
+              (train_loss, learning_rate, batch_size,
+               grads) = unpack_fprop_and_bprop_output(outputs)
+              train_losses.append(train_loss)
+              learning_rates.append(learning_rate)
+              batch_sizes.append(batch_size)
+              var_params = var_mgr.trainable_variables_on_device(tid, tid)
+              tower_gradvars.append(list(zip(grads, var_params)))
+
+      # Add summaries
+      if hparams.show_metrics:
+        tf.summary.scalar("learning_rate", learning_rates[0])
+        if loss_scale:
+          tf.summary.scalar("loss_scale", loss_scale)
+          if hparams.enable_auto_loss_scale:
+            tf.summary.scalar("loss_scale_normal_steps",
+                              loss_scale_normal_steps)
+      misc_utils.print_out("Finish building fprop and per-tower bprop.")
+      # Aggregate gradients
+      # The following compute the aggregated grads for each tower, stored in
+      # opaque grad_states structure.
+      apply_grads_devices, grad_states = var_mgr.preprocess_device_grads(
+          tower_gradvars)
+      master_grads = None
+      master_params = None
+      update_ops = []
+      for i, device in enumerate(apply_grads_devices):
+        with tf.device(device), tf.name_scope(tower_scopes[i]):
+          # Get per-tower grads.
+          with tf.name_scope("get_gradients_to_apply"):
+            avg_gradvars = var_mgr.get_gradients_to_apply(i, grad_states)
+          avg_grads = [gv[0] for gv in avg_gradvars]
+
+          # gradients post-processing
+          with tf.name_scope("clip_gradients"):
+            if hparams.clip_grads:
+              clipped_grads, grad_norm = model_helper.gradient_clip(
+                  avg_grads, max_gradient_norm=hparams.max_gradient_norm)
+              # summary the grad on the 1st tower
+              if i == 0 and hparams.show_metrics:
+                tf.summary.scalar("grad_norm", grad_norm)
+                tf.summary.scalar("clipped_grad_norm",
+                                  tf.global_norm(clipped_grads))
+            else:
+              clipped_grads = avg_grads
+            if i == 0:
+              master_grads = clipped_grads
+
+          # Build apply-gradients ops
+          clipped_gradvars = list(
+              zip(clipped_grads, [gv[1] for gv in avg_gradvars]))
+          if i == 0:
+            master_params = [gv[1] for gv in avg_gradvars]
+          with tf.name_scope("append_gradient_ops"):
+            loss_scale_params = variable_mgr_util.AutoLossScaleParams(
+                enable_auto_loss_scale=hparams.enable_auto_loss_scale,
+                loss_scale=loss_scale,
+                loss_scale_normal_steps=loss_scale_normal_steps,
+                inc_loss_scale_every_n=hparams.fp16_inc_loss_scale_every_n,
+                is_chief=True)
+            opt = opts[i]
+            var_mgr.append_apply_gradients_ops(grad_states, opt,
+                                               clipped_gradvars, update_ops,
+                                               loss_scale_params)
+      misc_utils.print_out("Finish building grad aggregation.")
+
+      assert len(update_ops) == num_towers
+      train_op = tf.group(update_ops)
+      with tf.control_dependencies([train_op]):
+        global_step = tf.train.get_global_step()
+        train_op = global_step.assign_add(1)
+
+      # Compute loss on the first gpu
+      # TODO(jamesqin): optimize it?
+      with tf.device("gpu:0"):
+        loss = misc_utils.weighted_avg(train_losses, batch_sizes)
+
+      # Create local init_ops
+      # TODO(jamesqin): handle resource variables!
+      # At present if not using mirror strategy, not using resource vars.
+      local_init_ops = []
+      local_init_op = tf.local_variables_initializer()
+      with tf.control_dependencies([local_init_op]):
+        local_init_ops.append(var_mgr.get_post_init_ops())
+      local_init_ops.extend([local_init_op, tf.tables_initializer()])
+
+      saveable_vars = var_mgr.savable_variables()
+      # Add saveables for cudnn vars in master tower.
+      saveable_objects = tf.get_collection(tf.GraphKeys.SAVEABLE_OBJECTS)
+      saveable_objects = [x for x in saveable_objects if "v0" in x.name]
+
+      misc_utils.print_out("Saveable vars(%d): " % len(saveable_vars))
+      for mv in saveable_vars:
+        misc_utils.print_out(mv.name)
+
+      misc_utils.print_out(
+          "All global trainable vars(%d): " % len(tf.trainable_variables()))
+      for tv in tf.trainable_variables():
+        misc_utils.print_out(tv.name)
+
+      misc_utils.print_out(
+          "All global vars(%d): " % len(tf.global_variables()))
+      for gv in tf.global_variables():
+        misc_utils.print_out(gv.name)
+
+      misc_utils.print_out(
+          "master backproped params(%d): " % len(master_params))
+      for mp in master_params:
+        misc_utils.print_out(mp.name)
+
+      # Note the cudnn vars are skipped the init check. :(
+      scaffold = tf.train.Scaffold(
+          ready_op=tf.report_uninitialized_variables(saveable_vars),
+          ready_for_local_init_op=tf.report_uninitialized_variables(
+              saveable_vars),
+          local_init_op=tf.group(*local_init_ops),
+          saver=tf.train.Saver(saveable_vars + saveable_objects, save_relative_paths=True))
+
+      misc_utils.print_out("Finish building model_fn")
+      # return loss, vars, grads, predictions, train_op, scaffold
+      return loss, master_params, master_grads, None, train_op, scaffold
+
+
+def make_model_fn(hparams):
+  """Construct a GNMT model function for training."""
+  factory = ModelFnFactory(hparams)
+
+  if hparams.use_dist_strategy:
+    def fn(features, labels, mode, params):
+      """docstring."""
+      (loss, _, _, predictions, train_op,
+       _) = factory.build_graph_dist_strategy(features, labels, mode, params)
+      if mode == tf.contrib.learn.ModeKeys.INFER:
+        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+      else:
+        if hparams.use_tpu:
+          return tf.contrib.tpu.TPUEstimatorSpec(
+              mode=mode, loss=loss, train_op=train_op)
+        else:
+          return tf.estimator.EstimatorSpec(mode=mode, loss=loss,
+                                            train_op=train_op)
+    return fn
+  else:
+    build_fn = factory.build_graph
+    def fn(features, labels, mode, params):
+      """docstring."""
+      (loss, _, _, predictions, train_op, scaffold) = build_fn(
+          features, labels, mode, params)
+      if mode == tf.contrib.learn.ModeKeys.INFER:
+        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+      else:
+        return tf.estimator.EstimatorSpec(mode=mode, loss=loss,
+                                          scaffold=scaffold,
+                                          train_op=train_op)
+    return fn
+
+
+def make_input_fn(hparams, mode):
+  """Construct a input function for training."""
+
+  def _input_fn(params):
+    """Input function."""
+    del params
+
+    if mode == tf.contrib.learn.ModeKeys.TRAIN:
+      src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
+      tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
+    else:
+      src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
+      tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
+    src_vocab_file = hparams.src_vocab_file
+    tgt_vocab_file = hparams.tgt_vocab_file
+    src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
+        src_vocab_file, tgt_vocab_file, hparams.share_vocab)
+    src_dataset = tf.data.TextLineDataset(src_file)
+    tgt_dataset = tf.data.TextLineDataset(tgt_file)
+
+    if mode == tf.contrib.learn.ModeKeys.TRAIN:
+      # Run one epoch and stop if running train_and_eval.
+      if hparams.mode == "train_and_eval":
+        # In this mode input pipeline is restarted every epoch, so choose a
+        # different random_seed.
+        num_repeat = 1
+        random_seed = hparams.random_seed + int(time.time()) % 100
+      else:
+        num_repeat = 8
+        random_seed = hparams.random_seed
+      return iterator_utils.get_iterator(
+          src_dataset,
+          tgt_dataset,
+          src_vocab_table,
+          tgt_vocab_table,
+          batch_size=hparams.batch_size,
+          sos=hparams.sos,
+          eos=hparams.eos,
+          random_seed=random_seed,
+          num_buckets=hparams.num_buckets,
+          src_max_len=hparams.src_max_len,
+          tgt_max_len=hparams.tgt_max_len,
+          output_buffer_size=None,
+          skip_count=None,
+          num_shards=1,  # flags.num_workers
+          shard_index=0,  # flags.jobid
+          reshuffle_each_iteration=True,
+          use_char_encode=hparams.use_char_encode,
+          num_repeat=num_repeat,
+          filter_oversized_sequences=True
+      )  # need to update get_effective_train_epoch_size() if this flag flips.
+    else:
+      return iterator_utils.get_infer_iterator(
+          src_dataset,
+          src_vocab_table,
+          batch_size=hparams.infer_batch_size,
+          eos=hparams.eos,
+          src_max_len=hparams.src_max_len,
+          use_char_encode=hparams.use_char_encode)
+
+  def _synthetic_input_fn(params):
+    """Fake inputs for debugging and benchmarking."""
+    del params
+    batch_size = hparams.batch_size
+    src_max_len = hparams.src_max_len
+    tgt_max_len = hparams.tgt_max_len
+    features = {
+        "source":
+            tf.random_uniform(
+                dtype=tf.int32,
+                minval=1,
+                maxval=10,
+                seed=1,
+                shape=(batch_size, src_max_len)),
+        "target_input":
+            tf.random_uniform(
+                dtype=tf.int32,
+                minval=1,
+                maxval=10,
+                seed=2,
+                shape=(batch_size, tgt_max_len)),
+        "target_output":
+            tf.random_uniform(
+                dtype=tf.int32,
+                minval=1,
+                maxval=10,
+                seed=3,
+                shape=(batch_size, tgt_max_len)),
+        "source_sequence_length":
+            tf.constant([src_max_len] * batch_size),
+        "target_sequence_length":
+            tf.constant([tgt_max_len] * batch_size)
+    }
+    return features
+
+  if hparams.use_synthetic_data:
+    return _synthetic_input_fn
+  else:
+    return _input_fn
+
+
+def get_distribution_strategy(num_gpus):
+  if num_gpus == 0:
+    return tf.contrib.distribute.OneDeviceStrategy("device:CPU:0")
+  elif num_gpus == 1:
+    return tf.contrib.distribute.OneDeviceStrategy("device:GPU:0")
+  else:
+    return tf.contrib.distribute.MirroredStrategy(num_gpus=num_gpus)
+
+
+def get_sacrebleu(trans_file, detokenizer_file):
+  """Detokenize the trans_file and get the sacrebleu score."""
+  assert tf.gfile.Exists(detokenizer_file)
+  local_detokenizer_file = "/tmp/detokenizer.perl"
+  if tf.gfile.Exists(local_detokenizer_file):
+    tf.gfile.Remove(local_detokenizer_file)
+  tf.gfile.Copy(detokenizer_file, local_detokenizer_file, overwrite=True)
+
+  assert tf.gfile.Exists(trans_file)
+  local_trans_file = "/tmp/newstest2014_out.tok.de"
+  if tf.gfile.Exists(local_trans_file):
+    tf.gfile.Remove(local_trans_file)
+  tf.gfile.Copy(trans_file, local_trans_file, overwrite=True)
+
+  detok_trans_path = "/tmp/newstest2014_out.detok.de"
+  if tf.gfile.Exists(detok_trans_path):
+    tf.gfile.Remove(detok_trans_path)
+
+  # Detokenize the trans_file.
+  cmd = "cat %s | perl %s -l de | cat > %s" % (
+      local_trans_file, local_detokenizer_file, detok_trans_path)
+  subprocess.run(cmd, shell=True)
+  assert tf.gfile.Exists(detok_trans_path)
+
+  # run sacrebleu
+  cmd = ("cat %s | sacrebleu -t wmt14/full -l en-de --score-only -lc --tokenize"
+         " intl") % (detok_trans_path)
+  sacrebleu = subprocess.run([cmd], stdout=subprocess.PIPE, shell=True)
+  score = sacrebleu.stdout.strip()
+  return float(score)
+
+
+def get_metrics(hparams, model_fn, ckpt=None):
+  """Run inference and compute metrics."""
+  pred_estimator = tf.estimator.Estimator(
+      model_fn=model_fn, model_dir=hparams.output_dir)
+
+  benchmark_hook = BenchmarkHook(hparams.infer_batch_size)
+
+  predictions = pred_estimator.predict(
+      make_input_fn(hparams, tf.contrib.learn.ModeKeys.INFER),
+      checkpoint_path=ckpt, hooks=[benchmark_hook])
+  translations = []
+  output_tokens = []
+  beam_id = 0
+  for prediction in predictions:
+    # get the top translation.
+    if beam_id == 0:
+      for sent_id in range(hparams.infer_batch_size):
+        if sent_id >= prediction["predictions"].shape[0]:
+          break
+        trans, output_length = nmt_utils.get_translation(
+            prediction["predictions"],
+            sent_id=sent_id,
+            tgt_eos=hparams.eos,
+            subword_option=hparams.subword_option)
+        translations.append(trans)
+        output_tokens.append(output_length)
+    beam_id += 1
+    if beam_id == hparams.beam_width:
+      beam_id = 0
+
+  trans_file = os.path.join(
+      hparams.output_dir, "newstest2014_out_{}.tok.de".format(
+          pred_estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)))
+  trans_dir = os.path.dirname(trans_file)
+  if not tf.gfile.Exists(trans_dir):
+    tf.gfile.MakeDirs(trans_dir)
+  tf.logging.info("Writing to file %s" % trans_file)
+  with codecs.getwriter("utf-8")(tf.gfile.GFile(trans_file,
+                                                mode="wb")) as trans_f:
+    trans_f.write("")  # Write empty string to ensure file is created.
+    for translation in translations:
+      trans_f.write((translation + b"\n").decode("utf-8"))
+
+  # Evaluation
+  output_dir = os.path.join(pred_estimator.model_dir, "eval")
+  tf.gfile.MakeDirs(output_dir)
+  summary_writer = tf.summary.FileWriter(output_dir)
+
+  ref_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
+  # Hardcoded.
+  metric = "bleu"
+  score = get_sacrebleu(trans_file, hparams.detokenizer_file)
+
+  misc_utils.print_out("bleu is %.5f" % score)
+  with tf.Graph().as_default():
+    summaries = []
+    summaries.append(tf.Summary.Value(tag=metric, simple_value=score))
+  tf_summary = tf.Summary(value=list(summaries))
+  summary_writer.add_summary(
+      tf_summary, pred_estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP))
+
+  summary_writer.close()
+  return score, benchmark_hook.get_average_speed(), sum(output_tokens)
+
+
+def train_fn(hparams):
+  """Train function."""
+  model_fn = make_model_fn(hparams)
+  input_fn = make_input_fn(hparams, tf.contrib.learn.ModeKeys.TRAIN)
+
+  log_step_count_steps = hparams.log_step_count_steps
+  save_checkpoints_steps = hparams.save_checkpoints_steps
+  if hparams.use_dist_strategy:
+    distribution_strategy = get_distribution_strategy(hparams.num_gpus)
+    config = tf.estimator.RunConfig(
+        train_distribute=distribution_strategy,
+        log_step_count_steps=log_step_count_steps,
+        keep_checkpoint_max=None,
+        save_checkpoints_steps=save_checkpoints_steps)
+  else:
+    sess_config = tf.ConfigProto(allow_soft_placement=True)
+    if hparams.use_autojit_xla:
+      sess_config.graph_options.optimizer_options.global_jit_level = (
+          tf.OptimizerOptions.ON_1)
+    if not hparams.use_pintohost_optimizer:
+      sess_config.graph_options.rewrite_options.pin_to_host_optimization = (
+          rewriter_config_pb2.RewriterConfig.OFF)
+    config = tf.estimator.RunConfig(
+        log_step_count_steps=log_step_count_steps,
+        session_config=sess_config,
+        keep_checkpoint_max=None,
+        save_checkpoints_steps=save_checkpoints_steps)
+
+  misc_utils.print_out("sess master is %s" % config.master)
+  estimator = tf.estimator.Estimator(
+      model_fn=model_fn, model_dir=hparams.output_dir, config=config)
+
+  benchmark_hook = BenchmarkHook(hparams.batch_size)
+  train_hooks = [benchmark_hook]
+  if hparams.profile:
+    train_hooks.append(tf.train.ProfilerHook(
+        output_dir=hparams.output_dir,
+        save_steps=hparams.profile_save_steps,
+        show_dataflow=True,
+        show_memory=True))
+
+  max_steps = hparams.debug_num_train_steps
+  estimator.train(
+      input_fn=input_fn,
+      max_steps=max_steps,
+      hooks=train_hooks,
+  )
+
+  return benchmark_hook.get_average_speed()
+
+
+def eval_fn(hparams, ckpt=None):
+  model_fn = make_model_fn(hparams)
+  return get_metrics(hparams, model_fn, ckpt)
diff --git a/TensorFlow/Translation/GNMT/gnmt_model.py b/TensorFlow/Translation/GNMT/gnmt_model.py
new file mode 100644
index 00000000..4c5f1ae6
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/gnmt_model.py
@@ -0,0 +1,715 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""GNMT attention sequence-to-sequence model with dynamic RNN support."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn
+import attention_wrapper
+import block_lstm
+import model
+import model_helper
+from utils import misc_utils as utils
+
+
+class GNMTModel(model.BaseModel):
+  """Sequence-to-sequence dynamic model with GNMT attention architecture.
+  """
+
+  def __init__(self,
+               hparams,
+               mode,
+               features,
+               scope=None,
+               extra_args=None):
+    self.is_gnmt_attention = (
+        hparams.attention_architecture in ["gnmt", "gnmt_v2"])
+
+    super(GNMTModel, self).__init__(
+        hparams=hparams,
+        mode=mode,
+        features=features,
+        scope=scope,
+        extra_args=extra_args)
+
+  def _prepare_beam_search_decoder_inputs(
+      self, beam_width, memory, source_sequence_length, encoder_state):
+    memory = tf.contrib.seq2seq.tile_batch(
+        memory, multiplier=beam_width)
+    source_sequence_length = tf.contrib.seq2seq.tile_batch(
+        source_sequence_length, multiplier=beam_width)
+    encoder_state = tf.contrib.seq2seq.tile_batch(
+        encoder_state, multiplier=beam_width)
+    batch_size = self.batch_size * beam_width
+    return memory, source_sequence_length, encoder_state, batch_size
+
+  def _build_encoder(self, hparams):
+    """Build a GNMT encoder."""
+    assert hparams.encoder_type == "gnmt"
+
+    # Build GNMT encoder.
+    num_bi_layers = 1
+    num_uni_layers = self.num_encoder_layers - num_bi_layers
+    utils.print_out("# Build a GNMT encoder")
+    utils.print_out("  num_bi_layers = %d" % num_bi_layers)
+    utils.print_out("  num_uni_layers = %d" % num_uni_layers)
+
+    # source is batch-majored
+    source = self.features["source"]
+    import sys
+    print('source.shape: %s' % source.shape, file=sys.stderr)
+    if self.time_major:
+      # Later rnn would use time-majored inputs
+      source = tf.transpose(source)
+
+    with tf.variable_scope("encoder"):
+      dtype = self.dtype
+
+      encoder_emb_inp = tf.cast(
+          self.encoder_emb_lookup_fn(self.embedding_encoder, source), dtype)
+
+      # Build 1st bidi layer.
+      bi_encoder_outputs, bi_encoder_state = self._build_encoder_layers_bidi(
+          encoder_emb_inp, self.features["source_sequence_length"], hparams,
+          dtype)
+
+      # Build all the rest unidi layers
+      encoder_state, encoder_outputs = self._build_encoder_layers_unidi(
+          bi_encoder_outputs, self.features["source_sequence_length"],
+          num_uni_layers, hparams, dtype)
+
+      # Pass all encoder states to the decoder
+      #   except the first bi-directional layer
+      encoder_state = (bi_encoder_state[1],) + (
+          (encoder_state,) if num_uni_layers == 1 else encoder_state)
+    return encoder_outputs, encoder_state
+
+  def _build_encoder_layers_bidi(self, inputs, sequence_length, hparams, dtype):
+    """docstring."""
+    if hparams.use_fused_lstm:
+      fn = self._build_bidi_rnn_fused
+    elif hparams.use_cudnn_lstm:
+      fn = self._build_bidi_rnn_cudnn
+    else:
+      fn = self._build_bidi_rnn_base
+    return fn(inputs, sequence_length, hparams, dtype)
+
+  def _build_bidi_rnn_fused(self, inputs, sequence_length, hparams, dtype):
+    if (not np.isclose(hparams.dropout, 0.) and
+        self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+      inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
+
+    fwd_cell = block_lstm.LSTMBlockFusedCell(
+        hparams.num_units, hparams.forget_bias, dtype=dtype)
+    fwd_encoder_outputs, (fwd_final_c, fwd_final_h) = fwd_cell(
+        inputs,
+        dtype=dtype,
+        sequence_length=sequence_length)
+
+    inputs_r = tf.reverse_sequence(
+        inputs, sequence_length, batch_axis=1, seq_axis=0)
+    bak_cell = block_lstm.LSTMBlockFusedCell(
+        hparams.num_units, hparams.forget_bias, dtype=dtype)
+    bak_encoder_outputs, (bak_final_c, bak_final_h) = bak_cell(
+        inputs_r,
+        dtype=dtype,
+        sequence_length=sequence_length)
+    bak_encoder_outputs = tf.reverse_sequence(
+        bak_encoder_outputs, sequence_length, batch_axis=1, seq_axis=0)
+    bi_encoder_outputs = tf.concat(
+        [fwd_encoder_outputs, bak_encoder_outputs], axis=-1)
+    fwd_state = tf.nn.rnn_cell.LSTMStateTuple(fwd_final_c, fwd_final_h)
+    bak_state = tf.nn.rnn_cell.LSTMStateTuple(bak_final_c, bak_final_h)
+    bi_encoder_state = (fwd_state, bak_state)
+
+    # mask aren't applied on outputs, but final states are post-masking.
+    return bi_encoder_outputs, bi_encoder_state
+
+  def _build_unidi_rnn_fused(self, inputs, state,
+                             sequence_length, hparams, dtype):
+    if (not np.isclose(hparams.dropout, 0.) and
+        self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+      inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
+
+    cell = block_lstm.LSTMBlockFusedCell(
+        hparams.num_units, hparams.forget_bias, dtype=dtype)
+    outputs, (final_c, final_h) = cell(
+        inputs,
+        state,
+        dtype=dtype,
+        sequence_length=sequence_length)
+
+    # mask aren't applied on outputs, but final states are post-masking.
+    return outputs, tf.nn.rnn_cell.LSTMStateTuple(final_c, final_h)
+
+  def _build_unidi_rnn_cudnn(self, inputs, state, sequence_length, dtype,
+                             hparams, num_layers, is_fwd):
+    # cudnn inputs only support time-major
+    if not self.time_major:
+      inputs = tf.transpose(inputs, axis=[1, 0, 2])
+
+    if num_layers == 1 and not np.isclose(hparams.dropout, 0.):
+      # Special case when drop is used and only one layer
+      dropout = 0.
+      inputs = tf.nn.dropout(inputs, keep_prob=1-dropout)
+    else:
+      dropout = hparams.dropout
+
+    # the outputs would be in time-majored
+    sequence_length = tf.transpose(sequence_length)
+
+    if not is_fwd:
+      inputs = tf.reverse_sequence(
+          inputs, sequence_length, batch_axis=1, seq_axis=0)
+    cell = tf.contrib.cudnn_rnn.CudnnLSTM(
+        num_layers=num_layers,
+        num_units=hparams.num_units,
+        direction=cudnn_rnn.CUDNN_RNN_UNIDIRECTION,
+        dtype=self.dtype,
+        dropout=dropout)
+    outputs, (h, c) = cell(inputs, initial_state=state)
+
+    """
+    # Mask outputs
+    # [batch, time]
+    mask = tf.sequence_mask(sequence_length, dtype=self.dtype)
+    # [time, batch]
+    mask = tf.transpose(mask)
+    outputs *= mask
+    """
+
+    if not is_fwd:
+      outputs = tf.reverse_sequence(
+          inputs, sequence_length, batch_axis=1, seq_axis=0)
+    # NOTICE! There's no way to get the "correct" masked cell state in cudnn
+    # rnn.
+    if num_layers == 1:
+      h = tf.squeeze(h, axis=0)
+      c = tf.squeeze(c, axis=0)
+      return outputs, tf.nn.rnn_cell.LSTMStateTuple(c=c, h=h)
+
+    # Split h and c to form a
+    h.set_shape((num_layers, None, hparams.num_units))
+    c.set_shape((num_layers, None, hparams.num_units))
+    hs = tf.unstack(h)
+    cs = tf.unstack(c)
+    # The cell passed to bidi-dyanmic-rnn is a MultiRNNCell consisting 2 regular
+    # LSTM, the state of each is a simple LSTMStateTuple. Thus the state of the
+    # MultiRNNCell is a tuple of LSTMStateTuple.
+    states = tuple(
+        tf.nn.rnn_cell.LSTMStateTuple(c=c, h=h) for h, c in zip(hs, cs))
+    # No need to transpose back
+    return outputs, states
+
+  def _build_encoder_cell(self, hparams, num_layers, num_residual_layers,
+                          dtype=None):
+    """Build a multi-layer RNN cell that can be used by encoder."""
+    return model_helper.create_rnn_cell(
+        unit_type=hparams.unit_type,
+        num_units=self.num_units,
+        num_layers=num_layers,
+        num_residual_layers=num_residual_layers,
+        forget_bias=hparams.forget_bias,
+        dropout=hparams.dropout,
+        mode=self.mode,
+        dtype=dtype,
+        single_cell_fn=self.single_cell_fn,
+        use_block_lstm=hparams.use_block_lstm)
+
+  def _build_bidi_rnn_base(self, inputs, sequence_length, hparams, dtype):
+    """Create and call biddirectional RNN cells."""
+    # num_residual_layers: Number of residual layers from top to bottom. For
+    # example, if `num_bi_layers=4` and `num_residual_layers=2`, the last 2
+    # RNN layers in each RNN cell will be wrapped with `ResidualWrapper`.
+
+    # Construct forward and backward cells
+    fw_cell = self._build_encoder_cell(hparams,
+                                       1,  # num_bi_layers,
+                                       0,  # num_bi_residual_layers,
+                                       dtype)
+    bw_cell = self._build_encoder_cell(hparams,
+                                       1,  # num_bi_layers,
+                                       0,  # num_bi_residual_layers,
+                                       dtype)
+    if hparams.use_dynamic_rnn:
+      bi_outputs, bi_state = tf.nn.bidirectional_dynamic_rnn(
+          fw_cell,
+          bw_cell,
+          inputs,
+          dtype=dtype,
+          sequence_length=sequence_length,
+          time_major=self.time_major,
+          swap_memory=True)
+    else:
+      bi_outputs, bi_state = tf.contrib.recurrent.bidirectional_functional_rnn(
+          fw_cell,
+          bw_cell,
+          inputs,
+          dtype=dtype,
+          sequence_length=sequence_length,
+          time_major=self.time_major,
+          use_tpu=False)
+    return tf.concat(bi_outputs, -1), bi_state
+
+  def _build_bidi_rnn_cudnn(self, inputs, sequence_length, hparams, dtype):
+    # Notice cudnn rnn dropout is applied between layers. (if 1 layer only then
+    # no dropout).
+    if not np.isclose(hparams.dropout, 0.):
+      inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
+    if not hparams.use_loose_bidi_cudnn_lstm:
+      fwd_outputs, fwd_states = self._build_unidi_rnn_cudnn(
+          inputs, None,  # initial_state
+          sequence_length, dtype, hparams,
+          1,  # num_layer
+          is_fwd=True)
+      bak_outputs, bak_states = self._build_unidi_rnn_cudnn(
+          inputs, None,  # initial_state
+          sequence_length, dtype, hparams,
+          1,  # num_layer
+          is_fwd=False)
+      bi_outputs = tf.concat([fwd_outputs, bak_outputs], axis=-1)
+      return bi_outputs, (fwd_states, bak_states)
+    else:
+      # Cudnn only accept time-majored inputs
+      if not self.time_major:
+        inputs = tf.transpose(inputs, axis=[1, 0, 2])
+      bi_outputs, (bi_h, bi_c) = tf.contrib.cudnn_rnn.CudnnLSTM(
+          num_layers=1,  # num_bi_layers,
+          num_units=hparams.num_units,
+          direction=cudnn_rnn.CUDNN_RNN_BIDIRECTION,
+          dropout=0.,  # one layer, dropout isn't applied anyway,
+          seed=hparams.random_seed,
+          dtype=self.dtype,
+          kernel_initializer=tf.get_variable_scope().initializer,
+          bias_initializer=tf.zeros_initializer())(inputs)
+      # state shape is [num_layers * num_dir, batch, dim]
+      bi_h.set_shape((2, None, hparams.num_units))
+      bi_c.set_shape((2, None, hparams.num_units))
+      fwd_h, bak_h = tf.unstack(bi_h)
+      fwd_c, bak_c = tf.unstack(bi_c)
+      # No need to transpose back
+      return bi_outputs, (tf.nn.rnn_cell.LSTMStateTuple(c=fwd_c, h=fwd_h),
+                          tf.nn.rnn_cell.LSTMStateTuple(c=bak_c, h=bak_h))
+
+  def _build_encoder_layers_unidi(self, inputs, sequence_length,
+                                  num_uni_layers, hparams, dtype):
+    """Build encoder layers all at once."""
+    encoder_outputs = None
+    encoder_state = tuple()
+
+    if hparams.use_fused_lstm:
+      for i in range(num_uni_layers):
+        if (not np.isclose(hparams.dropout, 0.) and
+            self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+          cell_inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
+        else:
+          cell_inputs = inputs
+
+        cell = block_lstm.LSTMBlockFusedCell(
+            hparams.num_units, hparams.forget_bias, dtype=dtype)
+        encoder_outputs, (final_c, final_h) = cell(
+            cell_inputs,
+            dtype=dtype,
+            sequence_length=sequence_length)
+        encoder_state += (tf.nn.rnn_cell.LSTMStateTuple(final_c, final_h),)
+        if i >= num_uni_layers - self.num_encoder_residual_layers:
+          # Add the pre-dropout inputs. Residual wrapper is applied after
+          # dropout wrapper.
+          encoder_outputs += inputs
+        inputs = encoder_outputs
+    elif hparams.use_cudnn_lstm:
+      # Single layer cudnn rnn, dropout isnt applied in the kernel
+      for i in range(num_uni_layers):
+        if (not np.isclose(hparams.dropout, 0.) and
+            self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+          inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
+
+        encoder_outputs, encoder_states = self._build_unidi_rnn_cudnn(
+            inputs,
+            None,  # initial_state
+            sequence_length,
+            dtype,
+            hparams,
+            1,  # num_layer
+            is_fwd=True)
+        encoder_state += (tf.nn.rnn_cell.LSTMStateTuple(encoder_states.c,
+                                                        encoder_states.h),)
+        if i >= num_uni_layers - self.num_encoder_residual_layers:
+          encoder_outputs += inputs
+        inputs = encoder_outputs
+    else:
+      uni_cell = model_helper.create_rnn_cell(
+          unit_type=hparams.unit_type,
+          num_units=hparams.num_units,
+          num_layers=num_uni_layers,
+          num_residual_layers=self.num_encoder_residual_layers,
+          forget_bias=hparams.forget_bias,
+          dropout=hparams.dropout,
+          dtype=dtype,
+          mode=self.mode,
+          single_cell_fn=self.single_cell_fn,
+          use_block_lstm=hparams.use_block_lstm)
+
+      if hparams.use_dynamic_rnn:
+        encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
+            uni_cell,
+            inputs,
+            dtype=dtype,
+            sequence_length=sequence_length,
+            time_major=self.time_major)
+      else:
+        encoder_outputs, encoder_state = tf.contrib.recurrent.functional_rnn(
+            uni_cell,
+            inputs,
+            dtype=dtype,
+            sequence_length=sequence_length,
+            time_major=self.time_major,
+            use_tpu=False)
+
+    return encoder_state, encoder_outputs
+
+  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
+                          source_sequence_length):
+    """Build a RNN cell with GNMT attention architecture."""
+    # GNMT attention
+    assert self.is_gnmt_attention
+    attention_option = hparams.attention
+    attention_architecture = hparams.attention_architecture
+    assert attention_option == "normed_bahdanau"
+    assert attention_architecture == "gnmt_v2"
+
+    num_units = hparams.num_units
+    infer_mode = hparams.infer_mode
+    dtype = tf.float16 if hparams.use_fp16 else tf.float32
+
+    if self.time_major:
+      memory = tf.transpose(encoder_outputs, [1, 0, 2])
+    else:
+      memory = encoder_outputs
+
+    if (self.mode == tf.contrib.learn.ModeKeys.INFER and
+        infer_mode == "beam_search"):
+      memory, source_sequence_length, encoder_state, batch_size = (
+          self._prepare_beam_search_decoder_inputs(
+              hparams.beam_width, memory, source_sequence_length,
+              encoder_state))
+    else:
+      batch_size = self.batch_size
+
+    attention_mechanism = model.create_attention_mechanism(
+        num_units, memory, source_sequence_length, dtype=dtype)
+
+    cell_list = model_helper._cell_list(  # pylint: disable=protected-access
+        unit_type=hparams.unit_type,
+        num_units=num_units,
+        num_layers=self.num_decoder_layers,
+        num_residual_layers=self.num_decoder_residual_layers,
+        forget_bias=hparams.forget_bias,
+        dropout=hparams.dropout,
+        mode=self.mode,
+        dtype=dtype,
+        single_cell_fn=self.single_cell_fn,
+        residual_fn=gnmt_residual_fn,
+        use_block_lstm=hparams.use_block_lstm)
+
+    # Only wrap the bottom layer with the attention mechanism.
+    attention_cell = cell_list.pop(0)
+
+    # Only generate alignment in greedy INFER mode.
+    alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
+                         infer_mode != "beam_search")
+    attention_cell = attention_wrapper.AttentionWrapper(
+        attention_cell,
+        attention_mechanism,
+        attention_layer_size=None,  # don't use attention layer.
+        output_attention=False,
+        alignment_history=alignment_history,
+        name="attention")
+    cell = GNMTAttentionMultiCell(attention_cell, cell_list)
+
+    if hparams.pass_hidden_state:
+      decoder_initial_state = tuple(
+        zs.clone(cell_state=es)
+        if isinstance(zs, attention_wrapper.AttentionWrapperState) else es
+        for zs, es in zip(
+            cell.zero_state(batch_size, dtype), encoder_state))
+    else:
+      decoder_initial_state = cell.zero_state(batch_size, dtype)
+
+    return cell, decoder_initial_state
+
+  def _build_decoder_cudnn(self, encoder_outputs, encoder_state, hparams):
+    pass
+    """
+    # Training
+    # Use dynamic_rnn to compute the 1st layer outputs and attention
+    # GNMT attention
+    with tf.variable_scope("decoder") as decoder_scope:
+
+      assert self.is_gnmt_attention
+      attention_option = hparams.attention
+      attention_architecture = hparams.attention_architecture
+      assert attention_option == "normed_bahdanau"
+      assert attention_architecture == "gnmt_v2"
+
+      num_units = hparams.num_units
+      infer_mode = hparams.infer_mode
+      dtype = tf.float16 if hparams.use_fp16 else tf.float32
+
+      if self.time_major:
+        memory = tf.transpose(encoder_outputs, [1, 0, 2])
+      else:
+        memory = encoder_outputs
+
+      source_sequence_length = self.features["source_sequence_length"]
+      if (self.mode == tf.contrib.learn.ModeKeys.INFER and
+          infer_mode == "beam_search"):
+        memory, source_sequence_length, encoder_state, batch_size = (
+            self._prepare_beam_search_decoder_inputs(
+                hparams.beam_width, memory, source_sequence_length,
+                encoder_state))
+      else:
+        batch_size = self.batch_size
+
+      attention_mechanism = model.create_attention_mechanism(
+          num_units, memory, source_sequence_length, dtype=dtype)
+
+      attention_cell = model_helper._cell_list(  # pylint: disable=protected-access
+          unit_type=hparams.unit_type,
+          num_units=num_units,
+          num_layers=1,  # just one layer
+          num_residual_layers=0,  # 1st layer has no residual connection.
+          forget_bias=hparams.forget_bias,
+          dropout=hparams.dropout,
+          mode=self.mode,
+          dtype=dtype,
+          single_cell_fn=self.single_cell_fn,
+          residual_fn=gnmt_residual_fn,
+          use_block_lstm=False)[0]
+      # Only generate alignment in greedy INFER mode.
+      alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
+                           infer_mode != "beam_search")
+      attention_cell = attention_wrapper.AttentionWrapper(
+          attention_cell,
+          attention_mechanism,
+          attention_layer_size=None,  # don't use attention layer.
+          output_attention=False,
+          alignment_history=alignment_history,
+          name="attention")
+      decoder_attention_cell_initial_state = attention_cell.zero_state(
+          batch_size, dtype).clone(cell_state=encoder_state[0])
+
+      # TODO(jamesqin): support frnn
+      # [batch, time]
+      target_input = self.features["target_input"]
+      if self.time_major:
+        # If using time_major mode, then target_input should be [time, batch]
+        # then the decoder_emb_inp would be [time, batch, dim]
+        target_input = tf.transpose(target_input)
+      decoder_emb_inp = tf.cast(
+          tf.nn.embedding_lookup(self.embedding_decoder, target_input),
+          self.dtype)
+
+      attention_cell_outputs, attention_cell_state = tf.nn.dynamic_rnn(
+          attention_cell,
+          decoder_emb_inp,
+          sequence_length=self.features["target_sequence_length"],
+          initial_state=decoder_attention_cell_initial_state,
+          dtype=self.dtype,
+          scope=decoder_scope,
+          parallel_iterations=hparams.parallel_iterations,
+          time_major=self.time_major)
+
+      attention = None
+      inputs = tf.concat([target_input, attention_cell_outputs], axis=-1)
+      initial_state = encoder_state[1:]
+      num_bi_layers = 1
+      num_unidi_decoder_layers = self.num_decoder_layers = num_bi_layers
+      # 3 layers of uni cudnn
+      for i in range(num_unidi_decoder_layers):
+        # Concat input with attention
+        if (not np.isclose(hparams.dropout, 0.) and
+            self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+          inputs = tf.nn.dropout(inputs, keep_prob=1 - hparams.dropout)
+
+        outputs, states = self._build_unidi_rnn_cudnn(
+            inputs,
+            initial_state[i],
+            self.features["target_sequence_length"],
+            self.dtype,
+            hparams,
+            1,  # num_layer
+            is_fwd=True)
+        if i >= num_unidi_decoder_layers - self.num_decoder_residual_layers:
+          outputs += inputs
+        inputs = outputs
+      pass
+      """
+
+  def _build_decoder_fused_for_training(self, encoder_outputs, initial_state,
+                                        decoder_emb_inp, hparams):
+    assert self.mode == tf.contrib.learn.ModeKeys.TRAIN
+    num_bi_layers = 1
+    num_unidi_decoder_layers = self.num_decoder_layers - num_bi_layers
+    assert num_unidi_decoder_layers == 3
+
+    # The 1st LSTM layer
+    if self.time_major:
+      batch = tf.shape(encoder_outputs)[1]
+      tgt_max_len = tf.shape(decoder_emb_inp)[0]
+      # [batch_size] -> scalar
+      initial_attention = tf.zeros(
+          shape=[tgt_max_len, batch, hparams.num_units], dtype=self.dtype)
+    else:
+      batch = tf.shape(encoder_outputs)[0]
+      tgt_max_len = tf.shape(decoder_emb_inp)[1]
+      initial_attention = tf.zeros(
+          shape=[batch, tgt_max_len, hparams.num_units], dtype=self.dtype)
+
+    # Concat with initial attention
+    dec_inp = tf.concat([decoder_emb_inp, initial_attention], axis=-1)
+
+    # [tgt_time, batch, units]
+    # var_scope naming chosen to agree with inference graph.
+    with tf.variable_scope("multi_rnn_cell/cell_0_attention/attention"):
+      outputs, _ = self._build_unidi_rnn_fused(
+          dec_inp,
+          initial_state[0],
+          self.features["target_sequence_length"],
+          hparams,
+          self.dtype)
+    # Get attention
+    # Fused attention layer has memory of shape [batch, src_time, ...]
+    if self.time_major:
+      memory = tf.transpose(encoder_outputs, [1, 0, 2])
+    else:
+      memory = encoder_outputs
+    fused_attention_layer = attention_wrapper.BahdanauAttentionFusedLayer(
+        hparams.num_units, memory,
+        memory_sequence_length=self.features["source_sequence_length"],
+        dtype=self.dtype)
+    # [batch, tgt_time, units]
+    if self.time_major:
+      queries = tf.transpose(outputs, [1, 0, 2])
+    else:
+      queries = outputs
+    fused_attention = fused_attention_layer(queries)
+
+    if self.time_major:
+      # [tgt_time, batch, units]
+      fused_attention = tf.transpose(fused_attention, [1, 0, 2])
+
+    # 2-4th layer
+    inputs = outputs
+    for i in range(num_unidi_decoder_layers):
+      # [tgt_time, batch, 2 * units]
+      concat_inputs = tf.concat([inputs, fused_attention], axis=-1)
+
+      # var_scope naming chosen to agree with inference graph.
+      with tf.variable_scope("multi_rnn_cell/cell_%d" % (i+1)):
+        outputs, _ = self._build_unidi_rnn_fused(
+            concat_inputs, initial_state[i + 1],
+            self.features["target_sequence_length"], hparams, self.dtype)
+      if i >= num_unidi_decoder_layers - self.num_decoder_residual_layers:
+        # gnmt_v2 attention adds the original inputs.
+        outputs += inputs
+      inputs = outputs
+    return outputs
+
+
+class GNMTAttentionMultiCell(tf.nn.rnn_cell.MultiRNNCell):
+  """A MultiCell with GNMT attention style."""
+
+  def __init__(self, attention_cell, cells):
+    """Creates a GNMTAttentionMultiCell.
+
+    Args:
+      attention_cell: An instance of AttentionWrapper.
+      cells: A list of RNNCell wrapped with AttentionInputWrapper.
+    """
+    cells = [attention_cell] + cells
+    super(GNMTAttentionMultiCell, self).__init__(cells, state_is_tuple=True)
+
+  def __call__(self, inputs, state, scope=None):
+    """Run the cell with bottom layer's attention copied to all upper layers."""
+    if not tf.contrib.framework.nest.is_sequence(state):
+      raise ValueError(
+          "Expected state to be a tuple of length %d, but received: %s"
+          % (len(self.state_size), state))
+
+    with tf.variable_scope(scope or "multi_rnn_cell"):
+      new_states = []
+
+      with tf.variable_scope("cell_0_attention"):
+        attention_cell = self._cells[0]
+        attention_state = state[0]
+        cur_inp, new_attention_state = attention_cell(inputs, attention_state)
+        new_states.append(new_attention_state)
+
+      for i in range(1, len(self._cells)):
+        with tf.variable_scope("cell_%d" % i):
+          cell = self._cells[i]
+          cur_state = state[i]
+
+          cur_inp = tf.concat([cur_inp, new_attention_state.attention], -1)
+          cur_inp, new_state = cell(cur_inp, cur_state)
+          new_states.append(new_state)
+
+    return cur_inp, tuple(new_states)
+
+
+def gnmt_residual_fn(inputs, outputs):
+  """Residual function that handles different inputs and outputs inner dims.
+
+  Args:
+    inputs: cell inputs, this is actual inputs concatenated with the attention
+      vector.
+    outputs: cell outputs
+
+  Returns:
+    outputs + actual inputs
+  """
+  def split_input(inp, out):
+    inp_dim = inp.get_shape().as_list()[-1]
+    out_dim = out.get_shape().as_list()[-1]
+    return tf.split(inp, [out_dim, inp_dim - out_dim], axis=-1)
+  actual_inputs, _ = tf.contrib.framework.nest.map_structure(
+      split_input, inputs, outputs)
+  def assert_shape_match(inp, out):
+    inp.get_shape().assert_is_compatible_with(out.get_shape())
+  tf.contrib.framework.nest.assert_same_structure(actual_inputs, outputs)
+  tf.contrib.framework.nest.map_structure(
+      assert_shape_match, actual_inputs, outputs)
+  return tf.contrib.framework.nest.map_structure(
+      lambda inp, out: inp + out, actual_inputs, outputs)
diff --git a/TensorFlow/Translation/GNMT/img/bleu_score.png b/TensorFlow/Translation/GNMT/img/bleu_score.png
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HcmV?d00001

diff --git a/TensorFlow/Translation/GNMT/model.py b/TensorFlow/Translation/GNMT/model.py
new file mode 100644
index 00000000..c0858d47
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/model.py
@@ -0,0 +1,657 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Basic sequence-to-sequence model with dynamic RNN support."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+import os
+
+import tensorflow as tf
+import numpy as np
+
+
+from tensorflow.python.framework import function
+from tensorflow.python.ops import math_ops
+
+import attention_wrapper
+import model_helper
+import beam_search_decoder
+from utils import iterator_utils
+from utils import math_utils
+from utils import misc_utils as utils
+from utils import vocab_utils
+
+utils.check_tensorflow_version()
+
+__all__ = ["BaseModel"]
+
+
+def create_attention_mechanism(
+    num_units, memory, source_sequence_length, dtype=None):
+  """Create attention mechanism based on the attention_option."""
+  # Mechanism
+  attention_mechanism = attention_wrapper.BahdanauAttention(
+      num_units,
+      memory,
+      memory_sequence_length=tf.to_int64(source_sequence_length),
+      normalize=True, dtype=dtype)
+  return attention_mechanism
+
+
+class BaseModel(object):
+  """Sequence-to-sequence base class.
+  """
+
+  def __init__(self, hparams, mode, features, scope=None, extra_args=None):
+    """Create the model.
+
+    Args:
+      hparams: Hyperparameter configurations.
+      mode: TRAIN | EVAL | INFER
+      features: a dict of input features.
+      scope: scope of the model.
+      extra_args: model_helper.ExtraArgs, for passing customizable functions.
+
+    """
+    self.hparams = hparams
+    # Set params
+    self._set_params_initializer(hparams, mode, features, scope, extra_args)
+
+    # Train graph
+    res = self.build_graph(hparams, scope=scope)
+    self._set_train_or_infer(res, hparams)
+
+  def _set_params_initializer(self,
+                              hparams,
+                              mode,
+                              features,
+                              scope,
+                              extra_args=None):
+    """Set various params for self and initialize."""
+    self.mode = mode
+    self.src_vocab_size = hparams.src_vocab_size
+    self.tgt_vocab_size = hparams.tgt_vocab_size
+    self.features = features
+    self.time_major = hparams.time_major
+
+    if hparams.use_char_encode:
+      assert (not self.time_major), ("Can't use time major for"
+                                     " char-level inputs.")
+
+    self.dtype = tf.float16 if hparams.use_fp16 else tf.float32
+
+    # extra_args: to make it flexible for adding external customizable code
+    self.single_cell_fn = None
+    if extra_args:
+      self.single_cell_fn = extra_args.single_cell_fn
+
+    # Set num units
+    self.num_units = hparams.num_units
+    # Set num layers
+    self.num_encoder_layers = hparams.num_encoder_layers
+    self.num_decoder_layers = hparams.num_decoder_layers
+    assert self.num_encoder_layers
+    assert self.num_decoder_layers
+
+    # Set num residual layers
+    if hasattr(hparams, "num_residual_layers"):  # compatible common_test_utils
+      self.num_encoder_residual_layers = hparams.num_residual_layers
+      self.num_decoder_residual_layers = hparams.num_residual_layers
+    else:
+      self.num_encoder_residual_layers = hparams.num_encoder_residual_layers
+      self.num_decoder_residual_layers = hparams.num_decoder_residual_layers
+
+    # Batch size
+    self.batch_size = tf.size(self.features["source_sequence_length"])
+
+    # Global step
+    global_step = tf.train.get_global_step()
+    if global_step is not None:
+      utils.print_out("global_step already created!")
+
+    self.global_step = tf.train.get_or_create_global_step()
+    utils.print_out("model.global_step.name: %s" % self.global_step.name)
+
+    # Initializer
+    self.random_seed = hparams.random_seed
+    initializer = model_helper.get_initializer(
+        hparams.init_op, self.random_seed, hparams.init_weight)
+    tf.get_variable_scope().set_initializer(initializer)
+
+    # Embeddings
+    self.encoder_emb_lookup_fn = tf.nn.embedding_lookup
+    self.init_embeddings(hparams, scope)
+
+  def _set_train_or_infer(self, res, hparams):
+    """Set up training."""
+    loss = res[1]
+    if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
+      self.train_loss = loss
+      self.word_count = tf.reduce_sum(
+          self.features["source_sequence_length"]) + tf.reduce_sum(
+              self.features["target_sequence_length"])
+    elif self.mode == tf.contrib.learn.ModeKeys.EVAL:
+      self.eval_loss = loss
+    elif self.mode == tf.contrib.learn.ModeKeys.INFER:
+      self.infer_logits = res[0]
+      self.infer_loss = loss
+      self.sample_id = res[2]
+
+    if self.mode != tf.contrib.learn.ModeKeys.INFER:
+      ## Count the number of predicted words for compute ppl.
+      self.predict_count = tf.reduce_sum(
+          self.features["target_sequence_length"])
+
+    # Gradients and SGD update operation for training the model.
+    # Arrange for the embedding vars to appear at the beginning.
+    # Only build bprop if running on GPU and using dist_strategy, in which
+    # case learning rate, grads and train_op are created in estimator model
+    # function.
+    with tf.name_scope("learning_rate"):
+      self.learning_rate = tf.constant(hparams.learning_rate)
+      # warm-up
+      self.learning_rate = self._get_learning_rate_warmup(hparams)
+      # decay
+      self.learning_rate = self._get_learning_rate_decay(hparams)
+
+    if (hparams.use_dist_strategy and
+        self.mode == tf.contrib.learn.ModeKeys.TRAIN):
+      # Gradients
+      params = tf.trainable_variables()
+      # Print trainable variables
+      utils.print_out("# Trainable variables")
+      utils.print_out(
+          "Format: <name>, <shape>, <dtype>, <(soft) device placement>")
+      for param in params:
+        utils.print_out(
+            "  %s, %s, %s, %s" % (param.name, str(param.get_shape()),
+                                  param.dtype.name, param.op.device))
+      utils.print_out("Total params size: %.2f GB" % (4. * np.sum([
+          p.get_shape().num_elements()
+          for p in params
+          if p.shape.is_fully_defined()
+      ]) / 2**30))
+
+      # Optimizer
+      if hparams.optimizer == "sgd":
+        opt = tf.train.GradientDescentOptimizer(self.learning_rate)
+      elif hparams.optimizer == "adam":
+        opt = tf.train.AdamOptimizer(self.learning_rate)
+      else:
+        raise ValueError("Unknown optimizer type %s" % hparams.optimizer)
+      assert opt is not None
+
+      grads_and_vars = opt.compute_gradients(
+          self.train_loss,
+          params,
+          colocate_gradients_with_ops=hparams.colocate_gradients_with_ops)
+      gradients = [x for (x, _) in grads_and_vars]
+
+      clipped_grads, grad_norm = model_helper.gradient_clip(
+          gradients, max_gradient_norm=hparams.max_gradient_norm)
+      self.grad_norm = grad_norm
+      self.params = params
+      self.grads = clipped_grads
+
+      self.update = opt.apply_gradients(
+          list(zip(clipped_grads, params)), global_step=self.global_step)
+    else:
+      self.grad_norm = None
+      self.update = None
+      self.params = None
+      self.grads = None
+
+  def _get_learning_rate_warmup(self, hparams):
+    """Get learning rate warmup."""
+    warmup_steps = hparams.warmup_steps
+    warmup_scheme = hparams.warmup_scheme
+    utils.print_out("  learning_rate=%g, warmup_steps=%d, warmup_scheme=%s" %
+                    (hparams.learning_rate, warmup_steps, warmup_scheme))
+    if not warmup_scheme:
+      return self.learning_rate
+
+    # Apply inverse decay if global steps less than warmup steps.
+    # Inspired by https://arxiv.org/pdf/1706.03762.pdf (Section 5.3)
+    # When step < warmup_steps,
+    #   learing_rate *= warmup_factor ** (warmup_steps - step)
+    if warmup_scheme == "t2t":
+      # 0.01^(1/warmup_steps): we start with a lr, 100 times smaller
+      warmup_factor = tf.exp(tf.log(0.01) / warmup_steps)
+      inv_decay = warmup_factor**(tf.to_float(warmup_steps - self.global_step))
+    else:
+      raise ValueError("Unknown warmup scheme %s" % warmup_scheme)
+
+    return tf.cond(
+        self.global_step < hparams.warmup_steps,
+        lambda: inv_decay * self.learning_rate,
+        lambda: self.learning_rate,
+        name="learning_rate_warump_cond")
+
+  def _get_decay_info(self, hparams):
+    """Return decay info based on decay_scheme."""
+    if hparams.decay_scheme in [
+        "luong5", "luong10", "luong234", "jamesqin1616"
+    ]:
+      epoch_size, _, _ = iterator_utils.get_effective_epoch_size(hparams)
+      num_train_steps = int(hparams.max_train_epochs * epoch_size / hparams.batch_size)
+      decay_factor = 0.5
+      if hparams.decay_scheme == "luong5":
+        start_decay_step = int(num_train_steps / 2)
+        decay_times = 5
+        remain_steps = num_train_steps - start_decay_step
+      elif hparams.decay_scheme == "luong10":
+        start_decay_step = int(num_train_steps / 2)
+        decay_times = 10
+        remain_steps = num_train_steps - start_decay_step
+      elif hparams.decay_scheme == "luong234":
+        start_decay_step = int(num_train_steps * 2 / 3)
+        decay_times = 4
+        remain_steps = num_train_steps - start_decay_step
+      elif hparams.decay_scheme == "jamesqin1616":
+        # dehao@ reported TPU setting max_epoch = 2 and use luong234.
+        # They start decay after 2 * 2/3 epochs for 4 times.
+        # If keep max_epochs = 8 then decay should start at 8 * 2/(3 * 4) epochs
+        # and for (4 *4 = 16) times.
+        decay_times = 16
+        start_decay_step = int(num_train_steps / 16.)
+        remain_steps = num_train_steps - start_decay_step
+      decay_steps = int(remain_steps / decay_times)
+    elif not hparams.decay_scheme:  # no decay
+      start_decay_step = num_train_steps
+      decay_steps = 0
+      decay_factor = 1.0
+    elif hparams.decay_scheme:
+      raise ValueError("Unknown decay scheme %s" % hparams.decay_scheme)
+    return start_decay_step, decay_steps, decay_factor
+
+  def _get_learning_rate_decay(self, hparams):
+    """Get learning rate decay."""
+    start_decay_step, decay_steps, decay_factor = self._get_decay_info(hparams)
+    utils.print_out("  decay_scheme=%s, start_decay_step=%d, decay_steps %d, "
+                    "decay_factor %g" % (hparams.decay_scheme, start_decay_step,
+                                         decay_steps, decay_factor))
+
+    return tf.cond(
+        self.global_step < start_decay_step,
+        lambda: self.learning_rate,
+        lambda: tf.train.exponential_decay(  # pylint: disable=g-long-lambda
+            self.learning_rate,
+            (self.global_step - start_decay_step),
+            decay_steps, decay_factor, staircase=True),
+        name="learning_rate_decay_cond")
+
+  def init_embeddings(self, hparams, scope):
+    """Init embeddings."""
+    self.embedding_encoder, self.embedding_decoder = (
+        model_helper.create_emb_for_encoder_and_decoder(
+            share_vocab=hparams.share_vocab,
+            src_vocab_size=self.src_vocab_size,
+            tgt_vocab_size=self.tgt_vocab_size,
+            src_embed_size=self.num_units,
+            tgt_embed_size=self.num_units,
+            dtype=self.dtype,
+            num_enc_partitions=hparams.num_enc_emb_partitions,
+            num_dec_partitions=hparams.num_dec_emb_partitions,
+            src_vocab_file=hparams.src_vocab_file,
+            tgt_vocab_file=hparams.tgt_vocab_file,
+            src_embed_file=hparams.src_embed_file,
+            tgt_embed_file=hparams.tgt_embed_file,
+            use_char_encode=hparams.use_char_encode,
+            scope=scope,
+        ))
+
+  def build_graph(self, hparams, scope=None):
+    """Subclass must implement this method.
+
+    Creates a sequence-to-sequence model with dynamic RNN decoder API.
+    Args:
+      hparams: Hyperparameter configurations.
+      scope: VariableScope for the created subgraph; default "dynamic_seq2seq".
+
+    Returns:
+      A tuple of the form (logits, loss_tuple, final_context_state, sample_id),
+      where:
+        logits: float32 Tensor [batch_size x num_decoder_symbols].
+        loss: loss = the total loss / batch_size.
+        final_context_state: the final state of decoder RNN.
+        sample_id: sampling indices.
+
+    Raises:
+      ValueError: if encoder_type differs from mono and bi, or
+        attention_option is not (luong | scaled_luong |
+        bahdanau | normed_bahdanau).
+    """
+    utils.print_out("# Creating %s graph ..." % self.mode)
+
+    # Projection
+    with tf.variable_scope(scope or "build_network"):
+      with tf.variable_scope("decoder/output_projection"):
+        self.output_layer = tf.layers.Dense(
+            self.tgt_vocab_size, use_bias=False, name="output_projection",
+            dtype=self.dtype)
+
+    with tf.variable_scope(scope or "dynamic_seq2seq", dtype=self.dtype):
+      # Encoder
+      if hparams.language_model:  # no encoder for language modeling
+        utils.print_out("  language modeling: no encoder")
+        self.encoder_outputs = None
+        encoder_state = None
+      else:
+        self.encoder_outputs, encoder_state = self._build_encoder(hparams)
+
+      ## Decoder
+      logits, sample_id = (
+          self._build_decoder(self.encoder_outputs, encoder_state, hparams))
+
+      ## Loss
+      if self.mode != tf.contrib.learn.ModeKeys.INFER:
+        loss = self._compute_loss(logits, hparams.label_smoothing)
+      else:
+        loss = tf.constant(0.0)
+
+    return logits, loss, sample_id
+
+  @abc.abstractmethod
+  def _build_encoder(self, hparams):
+    """Subclass must implement this.
+
+    Build and run an RNN encoder.
+
+    Args:
+      hparams: Hyperparameters configurations.
+
+    Returns:
+      A tuple of encoder_outputs and encoder_state.
+    """
+    pass
+
+  def _get_infer_maximum_iterations(self, hparams, source_sequence_length):
+    """Maximum decoding steps at inference time."""
+    if hparams.tgt_max_len_infer:
+      maximum_iterations = hparams.tgt_max_len_infer
+      utils.print_out("  decoding maximum_iterations %d" % maximum_iterations)
+    else:
+      # TODO(thangluong): add decoding_length_factor flag
+      decoding_length_factor = 2.0
+      max_encoder_length = tf.reduce_max(source_sequence_length)
+      maximum_iterations = tf.to_int32(
+          tf.round(tf.to_float(max_encoder_length) * decoding_length_factor))
+    return maximum_iterations
+
+  def _build_decoder(self, encoder_outputs, encoder_state, hparams):
+    """Build and run a RNN decoder with a final projection layer.
+
+    Args:
+      encoder_outputs: The outputs of encoder for every time step.
+      encoder_state: The final state of the encoder.
+      hparams: The Hyperparameters configurations.
+
+    Returns:
+      A tuple of final logits and final decoder state:
+        logits: size [time, batch_size, vocab_size] when time_major=True.
+    """
+
+    ## Decoder.
+    with tf.variable_scope("decoder") as decoder_scope:
+
+      ## Train or eval
+      if self.mode != tf.contrib.learn.ModeKeys.INFER:
+        # [batch, time]
+        target_input = self.features["target_input"]
+        if self.time_major:
+          # If using time_major mode, then target_input should be [time, batch]
+          # then the decoder_emb_inp would be [time, batch, dim]
+          target_input = tf.transpose(target_input)
+        decoder_emb_inp = tf.cast(
+            tf.nn.embedding_lookup(self.embedding_decoder, target_input),
+            self.dtype)
+
+        if not hparams.use_fused_lstm_dec:
+          cell, decoder_initial_state = self._build_decoder_cell(
+              hparams, encoder_outputs, encoder_state,
+              self.features["source_sequence_length"])
+
+          if hparams.use_dynamic_rnn:
+            final_rnn_outputs, _ = tf.nn.dynamic_rnn(
+                cell,
+                decoder_emb_inp,
+                sequence_length=self.features["target_sequence_length"],
+                initial_state=decoder_initial_state,
+                dtype=self.dtype,
+                scope=decoder_scope,
+                parallel_iterations=hparams.parallel_iterations,
+                time_major=self.time_major)
+          else:
+            final_rnn_outputs, _ = tf.contrib.recurrent.functional_rnn(
+                cell,
+                decoder_emb_inp,
+                sequence_length=tf.to_int32(
+                    self.features["target_sequence_length"]),
+                initial_state=decoder_initial_state,
+                dtype=self.dtype,
+                scope=decoder_scope,
+                time_major=self.time_major,
+                use_tpu=False)
+        else:
+          if hparams.pass_hidden_state:
+            decoder_initial_state = encoder_state
+          else:
+            decoder_initial_state = tuple((tf.nn.rnn_cell.LSTMStateTuple(
+              tf.zeros_like(s[0]), tf.zeros_like(s[1])) for s in encoder_state))
+          final_rnn_outputs = self._build_decoder_fused_for_training(
+              encoder_outputs, decoder_initial_state, decoder_emb_inp, self.hparams)
+
+        # We chose to apply the output_layer to all timesteps for speed:
+        #   10% improvements for small models & 20% for larger ones.
+        # If memory is a concern, we should apply output_layer per timestep.
+        logits = self.output_layer(final_rnn_outputs)
+        sample_id = None
+      ## Inference
+      else:
+        cell, decoder_initial_state = self._build_decoder_cell(
+            hparams, encoder_outputs, encoder_state,
+            self.features["source_sequence_length"])
+
+        assert hparams.infer_mode == "beam_search"
+        _, tgt_vocab_table = vocab_utils.create_vocab_tables(
+            hparams.src_vocab_file, hparams.tgt_vocab_file, hparams.share_vocab)
+        tgt_sos_id = tf.cast(
+            tgt_vocab_table.lookup(tf.constant(hparams.sos)), tf.int32)
+        tgt_eos_id = tf.cast(
+            tgt_vocab_table.lookup(tf.constant(hparams.eos)), tf.int32)
+        start_tokens = tf.fill([self.batch_size], tgt_sos_id)
+        end_token = tgt_eos_id
+        beam_width = hparams.beam_width
+        length_penalty_weight = hparams.length_penalty_weight
+        coverage_penalty_weight = hparams.coverage_penalty_weight
+
+        my_decoder = beam_search_decoder.BeamSearchDecoder(
+            cell=cell,
+            embedding=self.embedding_decoder,
+            start_tokens=start_tokens,
+            end_token=end_token,
+            initial_state=decoder_initial_state,
+            beam_width=beam_width,
+            output_layer=self.output_layer,
+            length_penalty_weight=length_penalty_weight,
+            coverage_penalty_weight=coverage_penalty_weight)
+
+        # maximum_iteration: The maximum decoding steps.
+        maximum_iterations = self._get_infer_maximum_iterations(
+            hparams, self.features["source_sequence_length"])
+
+        # Dynamic decoding
+        outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(
+            my_decoder,
+            maximum_iterations=maximum_iterations,
+            output_time_major=self.time_major,
+            swap_memory=True,
+            scope=decoder_scope)
+
+        logits = tf.no_op()
+        sample_id = outputs.predicted_ids
+
+    return logits, sample_id
+
+  def get_max_time(self, tensor):
+    time_axis = 0 if self.time_major else 1
+    return tensor.shape[time_axis].value or tf.shape(tensor)[time_axis]
+
+  @abc.abstractmethod
+  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
+                          source_sequence_length):
+    """Subclass must implement this.
+
+    Args:
+      hparams: Hyperparameters configurations.
+      encoder_outputs: The outputs of encoder for every time step.
+      encoder_state: The final state of the encoder.
+      source_sequence_length: sequence length of encoder_outputs.
+
+    Returns:
+      A tuple of a multi-layer RNN cell used by decoder and the initial state of
+      the decoder RNN.
+    """
+    pass
+
+  def _softmax_cross_entropy_loss(self, logits, labels, label_smoothing):
+    """Compute softmax loss or sampled softmax loss."""
+    use_defun = os.environ["use_defun"] == "true"
+    use_xla = os.environ["use_xla"] == "true"
+
+    # @function.Defun(noinline=True, compiled=use_xla)
+    def ComputePositiveCrossent(labels, logits):
+      crossent = math_utils.sparse_softmax_crossent_with_logits(
+          labels=labels, logits=logits)
+      return crossent
+    crossent = ComputePositiveCrossent(labels, logits)
+    assert crossent.dtype == tf.float32
+
+    def _safe_shape_div(x, y):
+      """Divides `x / y` assuming `x, y >= 0`, treating `0 / 0 = 0`."""
+      return x // tf.maximum(y, 1)
+
+    @function.Defun(tf.float32, tf.float32, compiled=use_xla)
+    def ReduceSumGrad(x, grad):
+      """docstring."""
+      input_shape = tf.shape(x)
+      # TODO(apassos) remove this once device placement for eager ops makes more
+      # sense.
+      with tf.colocate_with(input_shape):
+        output_shape_kept_dims = math_ops.reduced_shape(input_shape, -1)
+        tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
+      grad = tf.reshape(grad, output_shape_kept_dims)
+      return tf.tile(grad, tile_scaling)
+
+    def ReduceSum(x):
+      """docstring."""
+      return tf.reduce_sum(x, axis=-1)
+    if use_defun:
+      ReduceSum = function.Defun(
+          tf.float32,
+          compiled=use_xla,
+          noinline=True,
+          grad_func=ReduceSumGrad)(ReduceSum)
+
+    if abs(label_smoothing) > 1e-3:
+      # pylint:disable=invalid-name
+      def ComputeNegativeCrossentFwd(logits):
+        """docstring."""
+        # [time, batch, dim]
+        # [time, batch]
+        max_logits = tf.reduce_max(logits, axis=-1)
+        # [time, batch, dim]
+        shifted_logits = logits - tf.expand_dims(max_logits, axis=-1)
+        # Always compute loss in fp32
+        shifted_logits = tf.to_float(shifted_logits)
+        # [time, batch]
+        log_sum_exp = tf.log(ReduceSum(tf.exp(shifted_logits)))
+        # [time, batch, dim] - [time, batch, 1] --> reduce_sum(-1) -->
+        # [time, batch]
+        neg_crossent = ReduceSum(
+            shifted_logits - tf.expand_dims(log_sum_exp, axis=-1))
+        return neg_crossent
+
+      def ComputeNegativeCrossent(logits):
+        return ComputeNegativeCrossentFwd(logits)
+
+      if use_defun:
+        ComputeNegativeCrossent = function.Defun(
+            compiled=use_xla)(ComputeNegativeCrossent)
+
+      neg_crossent = ComputeNegativeCrossent(logits)
+      neg_crossent = tf.to_float(neg_crossent)
+      num_labels = logits.shape[-1].value
+      crossent = (1.0 - label_smoothing) * crossent - (
+          label_smoothing / tf.to_float(num_labels) * neg_crossent)
+      # pylint:enable=invalid-name
+
+    return crossent
+
+  def _compute_loss(self, logits, label_smoothing):
+    """Compute optimization loss."""
+    target_output = self.features["target_output"]
+    if self.time_major:
+      target_output = tf.transpose(target_output)
+    max_time = self.get_max_time(target_output)
+    self.batch_seq_len = max_time
+
+    crossent = self._softmax_cross_entropy_loss(
+        logits, target_output, label_smoothing)
+    assert crossent.dtype == tf.float32
+
+    target_weights = tf.sequence_mask(
+        self.features["target_sequence_length"], max_time, dtype=crossent.dtype)
+    if self.time_major:
+      # [time, batch] if time_major, since the crossent is [time, batch] in this
+      # case.
+      target_weights = tf.transpose(target_weights)
+
+    loss = tf.reduce_sum(crossent * target_weights) / tf.to_float(
+        self.batch_size)
+
+    return loss
+
+  def build_encoder_states(self, include_embeddings=False):
+    """Stack encoder states and return tensor [batch, length, layer, size]."""
+    assert self.mode == tf.contrib.learn.ModeKeys.INFER
+    if include_embeddings:
+      stack_state_list = tf.stack(
+          [self.encoder_emb_inp] + self.encoder_state_list, 2)
+    else:
+      stack_state_list = tf.stack(self.encoder_state_list, 2)
+
+    # transform from [length, batch, ...] -> [batch, length, ...]
+    if self.time_major:
+      stack_state_list = tf.transpose(stack_state_list, [1, 0, 2, 3])
+
+    return stack_state_list
diff --git a/TensorFlow/Translation/GNMT/model_helper.py b/TensorFlow/Translation/GNMT/model_helper.py
new file mode 100644
index 00000000..3a9697ee
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/model_helper.py
@@ -0,0 +1,484 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utility functions for building models."""
+from __future__ import print_function
+
+import collections
+import os
+import time
+import numpy as np
+import six
+import tensorflow as tf
+
+from utils import math_utils
+from utils import misc_utils as utils
+from utils import vocab_utils
+
+__all__ = [
+    "get_initializer", "create_emb_for_encoder_and_decoder", "create_rnn_cell",
+    "gradient_clip", "create_or_load_model", "load_model", "avg_checkpoints",
+]
+
+# If a vocab size is greater than this value, put the embedding on cpu instead
+VOCAB_SIZE_THRESHOLD_CPU = 50000
+
+
+def get_initializer(init_op, seed=None, init_weight=0):
+  """Create an initializer. init_weight is only for uniform."""
+  if init_op == "uniform":
+    assert init_weight
+    return tf.random_uniform_initializer(
+        -init_weight, init_weight, seed=seed)
+  elif init_op == "glorot_normal":
+    return tf.keras.initializers.glorot_normal(
+        seed=seed)
+  elif init_op == "glorot_uniform":
+    return tf.keras.initializers.glorot_uniform(
+        seed=seed)
+  elif init_op.isdigit():
+    # dtype is default fp32 for variables.
+    val = int(init_op)
+    return tf.constant_initializer(val)
+  else:
+    raise ValueError("Unknown init_op %s" % init_op)
+
+
+class ExtraArgs(collections.namedtuple(
+    "ExtraArgs", ("single_cell_fn", "model_device_fn",
+                  "attention_mechanism_fn", "encoder_emb_lookup_fn"))):
+  pass
+
+
+class TrainModel(
+    collections.namedtuple("TrainModel", ("graph", "model", "iterator",
+                                          "skip_count_placeholder"))):
+  pass
+
+
+def _get_embed_device(vocab_size):
+  """Decide on which device to place an embed matrix given its vocab size."""
+  if vocab_size > VOCAB_SIZE_THRESHOLD_CPU:
+    return "/cpu:0"
+  else:
+    return "/gpu:0"
+
+
+def _create_pretrained_emb_from_txt(
+    vocab_file, embed_file, num_trainable_tokens=3, dtype=tf.float32,
+    scope=None):
+  """Load pretrain embeding from embed_file, and return an embedding matrix.
+
+  Args:
+    vocab_file: Path to vocab file.
+    embed_file: Path to a Glove formmated embedding txt file.
+    num_trainable_tokens: Make the first n tokens in the vocab file as trainable
+      variables. Default is 3, which is "<unk>", "<s>" and "</s>".
+    dtype: data type.
+    scope: tf scope name.
+
+  Returns:
+    pretrained embedding table variable.
+  """
+  vocab, _ = vocab_utils.load_vocab(vocab_file)
+  trainable_tokens = vocab[:num_trainable_tokens]
+
+  utils.print_out("# Using pretrained embedding: %s." % embed_file)
+  utils.print_out("  with trainable tokens: ")
+
+  emb_dict, emb_size = vocab_utils.load_embed_txt(embed_file)
+  for token in trainable_tokens:
+    utils.print_out("    %s" % token)
+    if token not in emb_dict:
+      emb_dict[token] = [0.0] * emb_size
+
+  emb_mat = np.array(
+      [emb_dict[token] for token in vocab], dtype=dtype.as_numpy_dtype())
+  emb_mat = tf.constant(emb_mat)
+  emb_mat_const = tf.slice(emb_mat, [num_trainable_tokens, 0], [-1, -1])
+  with tf.variable_scope(scope or "pretrain_embeddings", dtype=dtype) as scope:
+    emb_mat_var = tf.get_variable(
+        "emb_mat_var", [num_trainable_tokens, emb_size])
+  return tf.concat([emb_mat_var, emb_mat_const], 0)
+
+
+def _create_or_load_embed(embed_name, vocab_file, embed_file,
+                          vocab_size, embed_size, dtype):
+  """Create a new or load an existing embedding matrix."""
+  if vocab_file and embed_file:
+    embedding = _create_pretrained_emb_from_txt(vocab_file, embed_file)
+  else:
+    embedding = tf.get_variable(
+        embed_name, [vocab_size, embed_size], dtype)
+  return embedding
+
+
+def create_emb_for_encoder_and_decoder(share_vocab,
+                                       src_vocab_size,
+                                       tgt_vocab_size,
+                                       src_embed_size,
+                                       tgt_embed_size,
+                                       dtype=tf.float32,
+                                       num_enc_partitions=0,
+                                       num_dec_partitions=0,
+                                       src_vocab_file=None,
+                                       tgt_vocab_file=None,
+                                       src_embed_file=None,
+                                       tgt_embed_file=None,
+                                       use_char_encode=False,
+                                       scope=None):
+  """Create embedding matrix for both encoder and decoder.
+
+  Args:
+    share_vocab: A boolean. Whether to share embedding matrix for both
+      encoder and decoder.
+    src_vocab_size: An integer. The source vocab size.
+    tgt_vocab_size: An integer. The target vocab size.
+    src_embed_size: An integer. The embedding dimension for the encoder's
+      embedding.
+    tgt_embed_size: An integer. The embedding dimension for the decoder's
+      embedding.
+    dtype: dtype of the embedding matrix. Default to float32.
+    num_enc_partitions: number of partitions used for the encoder's embedding
+      vars.
+    num_dec_partitions: number of partitions used for the decoder's embedding
+      vars.
+    src_vocab_file: A string. The source vocabulary file.
+    tgt_vocab_file: A string. The target vocabulary file.
+    src_embed_file: A string. The source embedding file.
+    tgt_embed_file: A string. The target embedding file.
+    use_char_encode: A boolean. If true, use char encoder.
+    scope: VariableScope for the created subgraph. Default to "embedding".
+
+  Returns:
+    embedding_encoder: Encoder's embedding matrix.
+    embedding_decoder: Decoder's embedding matrix.
+
+  Raises:
+    ValueError: if use share_vocab but source and target have different vocab
+      size.
+  """
+  if num_enc_partitions <= 1:
+    enc_partitioner = None
+  else:
+    # Note: num_partitions > 1 is required for distributed training due to
+    # embedding_lookup tries to colocate single partition-ed embedding variable
+    # with lookup ops. This may cause embedding variables being placed on worker
+    # jobs.
+    enc_partitioner = tf.fixed_size_partitioner(num_enc_partitions)
+
+  if num_dec_partitions <= 1:
+    dec_partitioner = None
+  else:
+    # Note: num_partitions > 1 is required for distributed training due to
+    # embedding_lookup tries to colocate single partition-ed embedding variable
+    # with lookup ops. This may cause embedding variables being placed on worker
+    # jobs.
+    dec_partitioner = tf.fixed_size_partitioner(num_dec_partitions)
+
+  if src_embed_file and enc_partitioner:
+    raise ValueError(
+        "Can't set num_enc_partitions > 1 when using pretrained encoder "
+        "embedding")
+
+  if tgt_embed_file and dec_partitioner:
+    raise ValueError(
+        "Can't set num_dec_partitions > 1 when using pretrained decdoer "
+        "embedding")
+
+  with tf.variable_scope(
+      scope or "embeddings", dtype=dtype, partitioner=enc_partitioner) as scope:
+    # Share embedding
+    if share_vocab:
+      if src_vocab_size != tgt_vocab_size:
+        raise ValueError("Share embedding but different src/tgt vocab sizes"
+                         " %d vs. %d" % (src_vocab_size, tgt_vocab_size))
+      assert src_embed_size == tgt_embed_size
+      utils.print_out("# Use the same embedding for source and target")
+      vocab_file = src_vocab_file or tgt_vocab_file
+      embed_file = src_embed_file or tgt_embed_file
+
+      embedding_encoder = _create_or_load_embed(
+          "embedding_share", vocab_file, embed_file,
+          src_vocab_size, src_embed_size, dtype)
+      embedding_decoder = embedding_encoder
+    else:
+      if not use_char_encode:
+        with tf.variable_scope("encoder", partitioner=enc_partitioner):
+          embedding_encoder = _create_or_load_embed(
+              "embedding_encoder", src_vocab_file, src_embed_file,
+              src_vocab_size, src_embed_size, dtype)
+      else:
+        embedding_encoder = None
+
+      with tf.variable_scope("decoder", partitioner=dec_partitioner):
+        embedding_decoder = _create_or_load_embed(
+            "embedding_decoder", tgt_vocab_file, tgt_embed_file,
+            tgt_vocab_size, tgt_embed_size, dtype)
+
+  return embedding_encoder, embedding_decoder
+
+
+def build_cell(cell, input_shape):
+  if isinstance(cell, tf.contrib.rnn.MultiRNNCell):
+    assert isinstance(input_shape, collections.Sequence)
+    for i, c in enumerate(cell._cells):
+      if i == 0:
+        c.build((None, input_shape))
+      else:
+        c.build((None, c.num_units))
+    return
+
+  if isinstance(cell, tf.nn.rnn_cell.DropoutWrapper):
+    build_cell(cell._cell, input_shape)
+  elif isinstance(cell, tf.nn.rnn_cell.ResidualWrapper):
+    build_cell(cell._cell, input_shape)
+  elif isinstance(cell, tf.nn.rnn_cell.LSTMCell):
+    cell.build(input_shape)
+  else:
+    raise ValueError("%s not supported" % type(cell))
+
+
+def _single_cell(unit_type, num_units, forget_bias, dropout, mode,
+                 dtype=None, residual_connection=False, residual_fn=None,
+                 use_block_lstm=False):
+  """Create an instance of a single RNN cell."""
+  # dropout (= 1 - keep_prob) is set to 0 during eval and infer
+  dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0
+
+  # Cell Type
+  if unit_type == "lstm":
+    utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
+    if not use_block_lstm:
+      single_cell = tf.nn.rnn_cell.LSTMCell(
+          num_units, dtype=dtype, forget_bias=forget_bias)
+    else:
+      single_cell = tf.contrib.rnn.LSTMBlockCell(
+          num_units, forget_bias=forget_bias)
+  elif unit_type == "gru":
+    utils.print_out("  GRU", new_line=False)
+    single_cell = tf.contrib.rnn.GRUCell(num_units)
+  elif unit_type == "layer_norm_lstm":
+    utils.print_out("  Layer Normalized LSTM, forget_bias=%g" % forget_bias,
+                    new_line=False)
+    single_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
+        num_units,
+        forget_bias=forget_bias,
+        layer_norm=True)
+  elif unit_type == "nas":
+    utils.print_out("  NASCell", new_line=False)
+    single_cell = tf.contrib.rnn.NASCell(num_units)
+  else:
+    raise ValueError("Unknown unit type %s!" % unit_type)
+
+  # Dropout (= 1 - keep_prob)
+  if dropout > 0.0:
+    single_cell = tf.contrib.rnn.DropoutWrapper(
+        cell=single_cell, input_keep_prob=(1.0 - dropout))
+    utils.print_out("  %s, dropout=%g " %(type(single_cell).__name__, dropout),
+                    new_line=False)
+
+  # Residual
+  if residual_connection:
+    single_cell = tf.contrib.rnn.ResidualWrapper(
+        single_cell, residual_fn=residual_fn)
+    utils.print_out("  %s" % type(single_cell).__name__, new_line=False)
+
+  return single_cell
+
+
+def _cell_list(unit_type, num_units, num_layers, num_residual_layers,
+               forget_bias, dropout, mode, dtype=None,
+               single_cell_fn=None, residual_fn=None, use_block_lstm=False):
+  """Create a list of RNN cells."""
+  if not single_cell_fn:
+    single_cell_fn = _single_cell
+
+  # Multi-GPU
+  cell_list = []
+  for i in range(num_layers):
+    utils.print_out("  cell %d" % i, new_line=False)
+    single_cell = single_cell_fn(
+        unit_type=unit_type,
+        num_units=num_units,
+        forget_bias=forget_bias,
+        dropout=dropout,
+        mode=mode,
+        dtype=dtype,
+        residual_connection=(i >= num_layers - num_residual_layers),
+        residual_fn=residual_fn,
+        use_block_lstm=use_block_lstm
+    )
+    utils.print_out("")
+    cell_list.append(single_cell)
+
+  return cell_list
+
+
+def create_rnn_cell(unit_type, num_units, num_layers, num_residual_layers,
+                    forget_bias, dropout, mode, dtype=None,
+                    single_cell_fn=None, use_block_lstm=False):
+  """Create multi-layer RNN cell.
+
+  Args:
+    unit_type: string representing the unit type, i.e. "lstm".
+    num_units: the depth of each unit.
+    num_layers: number of cells.
+    num_residual_layers: Number of residual layers from top to bottom. For
+      example, if `num_layers=4` and `num_residual_layers=2`, the last 2 RNN
+      cells in the returned list will be wrapped with `ResidualWrapper`.
+    forget_bias: the initial forget bias of the RNNCell(s).
+    dropout: floating point value between 0.0 and 1.0:
+      the probability of dropout.  this is ignored if `mode != TRAIN`.
+    mode: either tf.contrib.learn.TRAIN/EVAL/INFER
+    single_cell_fn: allow for adding customized cell.
+      When not specified, we default to model_helper._single_cell
+  Returns:
+    An `RNNCell` instance.
+  """
+  cell_list = _cell_list(unit_type=unit_type,
+                         num_units=num_units,
+                         num_layers=num_layers,
+                         num_residual_layers=num_residual_layers,
+                         forget_bias=forget_bias,
+                         dropout=dropout,
+                         mode=mode,
+                         dtype=dtype,
+                         single_cell_fn=single_cell_fn,
+                         use_block_lstm=use_block_lstm)
+
+  if len(cell_list) == 1:  # Single layer.
+    return cell_list[0]
+  else:  # Multi layers
+    return tf.contrib.rnn.MultiRNNCell(cell_list)
+
+
+def gradient_clip(gradients, max_gradient_norm):
+  """Clipping gradients of a model."""
+  clipped_gradients, gradient_norm = math_utils.clip_by_global_norm(
+      gradients, max_gradient_norm)
+
+  return clipped_gradients, gradient_norm
+
+
+def print_variables_in_ckpt(ckpt_path):
+  """Print a list of variables in a checkpoint together with their shapes."""
+  utils.print_out("# Variables in ckpt %s" % ckpt_path)
+  reader = tf.train.NewCheckpointReader(ckpt_path)
+  variable_map = reader.get_variable_to_shape_map()
+  for key in sorted(variable_map.keys()):
+    utils.print_out("  %s: %s" % (key, variable_map[key]))
+
+
+def load_model(model, ckpt_path, session, name):
+  """Load model from a checkpoint."""
+  start_time = time.time()
+  try:
+    model.saver.restore(session, ckpt_path)
+  except tf.errors.NotFoundError as e:
+    utils.print_out("Can't load checkpoint")
+    print_variables_in_ckpt(ckpt_path)
+    utils.print_out("%s" % str(e))
+
+  session.run(tf.tables_initializer())
+  utils.print_out(
+      "  loaded %s model parameters from %s, time %.2fs" %
+      (name, ckpt_path, time.time() - start_time))
+  return model
+
+
+def avg_checkpoints(model_dir, num_last_checkpoints, global_step_name):
+  """Average the last N checkpoints in the model_dir."""
+  checkpoint_state = tf.train.get_checkpoint_state(model_dir)
+  if not checkpoint_state:
+    utils.print_out("# No checkpoint file found in directory: %s" % model_dir)
+    return None
+
+  # Checkpoints are ordered from oldest to newest.
+  checkpoints = (
+      checkpoint_state.all_model_checkpoint_paths[-num_last_checkpoints:])
+
+  if len(checkpoints) < num_last_checkpoints:
+    utils.print_out(
+        "# Skipping averaging checkpoints because not enough checkpoints is "
+        "available.")
+    return None
+
+  avg_model_dir = os.path.join(model_dir, "avg_checkpoints")
+  if not tf.gfile.Exists(avg_model_dir):
+    utils.print_out(
+        "# Creating new directory %s for saving averaged checkpoints." %
+        avg_model_dir)
+    tf.gfile.MakeDirs(avg_model_dir)
+
+  utils.print_out("# Reading and averaging variables in checkpoints:")
+  var_list = tf.contrib.framework.list_variables(checkpoints[0])
+  var_values, var_dtypes = {}, {}
+  for (name, shape) in var_list:
+    if name != global_step_name:
+      var_values[name] = np.zeros(shape)
+
+  for checkpoint in checkpoints:
+    utils.print_out("    %s" % checkpoint)
+    reader = tf.contrib.framework.load_checkpoint(checkpoint)
+    for name in var_values:
+      tensor = reader.get_tensor(name)
+      var_dtypes[name] = tensor.dtype
+      var_values[name] += tensor
+
+  for name in var_values:
+    var_values[name] /= len(checkpoints)
+
+  # Build a graph with same variables in the checkpoints, and save the averaged
+  # variables into the avg_model_dir.
+  with tf.Graph().as_default():
+    tf_vars = [
+        tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[name])
+        for v in var_values
+    ]
+
+    placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
+    assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
+    saver = tf.train.Saver(tf.all_variables(), save_relative_paths=True)
+
+    with tf.Session() as sess:
+      sess.run(tf.initialize_all_variables())
+      for p, assign_op, (name, value) in zip(placeholders, assign_ops,
+                                             six.iteritems(var_values)):
+        sess.run(assign_op, {p: value})
+
+      # Use the built saver to save the averaged checkpoint. Only keep 1
+      # checkpoint and the best checkpoint will be moved to avg_best_metric_dir.
+      saver.save(
+          sess,
+          os.path.join(avg_model_dir, "translate.ckpt"))
+
+  return avg_model_dir
+
+
+def create_or_load_model(model, model_dir, session, name):
+  """Create translation model and initialize or load parameters in session."""
+  latest_ckpt = tf.train.latest_checkpoint(model_dir)
+  if latest_ckpt:
+    model = load_model(model, latest_ckpt, session, name)
+  else:
+    start_time = time.time()
+    session.run(tf.global_variables_initializer())
+    session.run(tf.tables_initializer())
+    utils.print_out("  created %s model with fresh parameters, time %.2fs" %
+                    (name, time.time() - start_time))
+
+  global_step = model.global_step.eval(session=session)
+  return model, global_step
diff --git a/TensorFlow/Translation/GNMT/nmt.py b/TensorFlow/Translation/GNMT/nmt.py
new file mode 100644
index 00000000..29b57a22
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/nmt.py
@@ -0,0 +1,1042 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""TensorFlow NMT model implementation."""
+from __future__ import print_function
+
+import argparse
+import os
+import random
+import sys
+
+# import matplotlib.image as mpimg
+import numpy as np
+import time
+import tensorflow as tf
+
+import estimator
+from utils import evaluation_utils
+from utils import iterator_utils
+from utils import misc_utils as utils
+from utils import vocab_utils
+from variable_mgr import constants
+
+utils.check_tensorflow_version()
+
+FLAGS = None
+
+
+# LINT.IfChange
+def add_arguments(parser):
+  """Build ArgumentParser."""
+  parser.register("type", "bool", lambda v: v.lower() == "true")
+
+  # network
+  parser.add_argument(
+      "--num_units", type=int, default=1024, help="Network size.")
+  parser.add_argument(
+      "--num_layers", type=int, default=4, help="Network depth.")
+  parser.add_argument("--num_encoder_layers", type=int, default=None,
+                      help="Encoder depth, equal to num_layers if None.")
+  parser.add_argument("--num_decoder_layers", type=int, default=None,
+                      help="Decoder depth, equal to num_layers if None.")
+  parser.add_argument(
+      "--encoder_type",
+      type=str,
+      default="gnmt",
+      help="""\
+      uni | bi | gnmt.
+      For bi, we build num_encoder_layers/2 bi-directional layers.
+      For gnmt, we build 1 bi-directional layer, and (num_encoder_layers - 1)
+        uni-directional layers.\
+      """)
+  parser.add_argument(
+      "--residual",
+      type="bool",
+      nargs="?",
+      const=True,
+      default=True,
+      help="Whether to add residual connections.")
+  parser.add_argument("--time_major", type="bool", nargs="?", const=True,
+                      default=True,
+                      help="Whether to use time-major mode for dynamic RNN.")
+  parser.add_argument("--num_embeddings_partitions", type=int, default=0,
+                      help="Number of partitions for embedding vars.")
+
+  # attention mechanisms
+  parser.add_argument(
+      "--attention",
+      type=str,
+      default="normed_bahdanau",
+      help="""\
+      luong | scaled_luong | bahdanau | normed_bahdanau or set to "" for no
+      attention\
+      """)
+  parser.add_argument(
+      "--attention_architecture",
+      type=str,
+      default="gnmt_v2",
+      help="""\
+      standard | gnmt | gnmt_v2.
+      standard: use top layer to compute attention.
+      gnmt: GNMT style of computing attention, use previous bottom layer to
+          compute attention.
+      gnmt_v2: similar to gnmt, but use current bottom layer to compute
+          attention.\
+      """)
+  parser.add_argument(
+      "--output_attention", type="bool", nargs="?", const=True,
+      default=True,
+      help="""\
+      Only used in standard attention_architecture. Whether use attention as
+      the cell output at each timestep.
+      .\
+      """)
+  parser.add_argument(
+      "--pass_hidden_state", type="bool", nargs="?", const=True,
+      default=True,
+      help="""\
+      Whether to pass encoder's hidden state to decoder when using an attention
+      based model.\
+      """)
+
+  # optimizer
+  parser.add_argument(
+      "--optimizer", type=str, default="adam", help="sgd | adam")
+  parser.add_argument(
+      "--learning_rate",
+      type=float,
+      default=5e-4,
+      help="Learning rate. Adam: 0.001 | 0.0001")
+  parser.add_argument("--warmup_steps", type=int, default=200,
+                      help="How many steps we inverse-decay learning.")
+  parser.add_argument("--warmup_scheme", type=str, default="t2t", help="""\
+      How to warmup learning rates. Options include:
+        t2t: Tensor2Tensor's way, start with lr 100 times smaller, then
+             exponentiate until the specified lr.\
+      """)
+  parser.add_argument(
+      "--decay_scheme", type=str, default="luong234", help="""\
+      How we decay learning rate. Options include:
+        luong234: after 2/3 num train steps, we start halving the learning rate
+          for 4 times before finishing.
+        luong5: after 1/2 num train steps, we start halving the learning rate
+          for 5 times before finishing.\
+        luong10: after 1/2 num train steps, we start halving the learning rate
+          for 10 times before finishing.\
+      """)
+
+  parser.add_argument(
+      "--max_train_epochs", type=int, default=6, help="Max number of epochs.")
+  parser.add_argument(
+      "--target_bleu", type=float, default=None, help="Target bleu.")
+  parser.add_argument("--colocate_gradients_with_ops", type="bool", nargs="?",
+                      const=True,
+                      default=True,
+                      help=("Whether try colocating gradients with "
+                            "corresponding op"))
+  parser.add_argument("--label_smoothing", type=float, default=0.1,
+                      help=("If nonzero, smooth the labels towards "
+                            "1/num_classes."))
+
+  # initializer
+  parser.add_argument("--init_op", type=str, default="uniform",
+                      help="uniform | glorot_normal | glorot_uniform")
+  parser.add_argument("--init_weight", type=float, default=0.1,
+                      help=("for uniform init_op, initialize weights "
+                            "between [-this, this]."))
+
+  # data
+  parser.add_argument(
+      "--src", type=str, default="en", help="Source suffix, e.g., en.")
+  parser.add_argument(
+      "--tgt", type=str, default="de", help="Target suffix, e.g., de.")
+  parser.add_argument(
+      "--data_dir", type=str, default="data/wmt16_de_en",
+      help="Training/eval data directory.")
+
+  parser.add_argument(
+      "--train_prefix",
+      type=str,
+      default="train.tok.clean.bpe.32000",
+      help="Train prefix, expect files with src/tgt suffixes.")
+  parser.add_argument(
+      "--test_prefix",
+      type=str,
+      default="newstest2014.tok.bpe.32000",
+      help="Test prefix, expect files with src/tgt suffixes.")
+
+  parser.add_argument(
+      "--output_dir", type=str, default="results",
+      help="Store log/model files.")
+
+  # Vocab
+  parser.add_argument(
+      "--vocab_prefix",
+      type=str,
+      default="vocab.bpe.32000",
+      help="""\
+      Vocab prefix, expect files with src/tgt suffixes.\
+      """)
+
+  parser.add_argument(
+      "--embed_prefix",
+      type=str,
+      default=None,
+      help="""\
+      Pretrained embedding prefix, expect files with src/tgt suffixes.
+      The embedding files should be Glove formatted txt files.\
+      """)
+  parser.add_argument("--sos", type=str, default="<s>",
+                      help="Start-of-sentence symbol.")
+  parser.add_argument("--eos", type=str, default="</s>",
+                      help="End-of-sentence symbol.")
+  parser.add_argument(
+      "--share_vocab",
+      type="bool",
+      nargs="?",
+      const=True,
+      default=True,
+      help="""\
+      Whether to use the source vocab and embeddings for both source and
+      target.\
+      """)
+  parser.add_argument("--check_special_token", type="bool", default=True,
+                      help="""\
+                      Whether check special sos, eos, unk tokens exist in the
+                      vocab files.\
+                      """)
+
+  # Sequence lengths
+  parser.add_argument(
+      "--src_max_len",
+      type=int,
+      default=50,
+      help="Max length of src sequences during training (including EOS).")
+  parser.add_argument(
+      "--tgt_max_len",
+      type=int,
+      default=50,
+      help="Max length of tgt sequences during training (including BOS).")
+  parser.add_argument("--src_max_len_infer", type=int, default=None,
+                      help="Max length of src sequences during inference (including EOS).")
+  parser.add_argument("--tgt_max_len_infer", type=int, default=80,
+                      help="""\
+      Max length of tgt sequences during inference (including BOS).  Also use to restrict the
+      maximum decoding length.\
+      """)
+
+  # Default settings works well (rarely need to change)
+  parser.add_argument("--unit_type", type=str, default="lstm",
+                      help="lstm | gru | layer_norm_lstm | nas")
+  parser.add_argument("--forget_bias", type=float, default=0.0,
+                      help="Forget bias for BasicLSTMCell.")
+  parser.add_argument("--dropout", type=float, default=0.2,
+                      help="Dropout rate (not keep_prob)")
+  parser.add_argument("--max_gradient_norm", type=float, default=5.0,
+                      help="Clip gradients to this norm.")
+  parser.add_argument("--batch_size", type=int, default=128, help="Total batch size.")
+
+  parser.add_argument(
+      "--num_buckets",
+      type=int,
+      default=5,
+      help="Put data into similar-length buckets (only for training).")
+
+  # SPM
+  parser.add_argument("--subword_option", type=str, default="bpe",
+                      choices=["", "bpe", "spm"],
+                      help="""\
+                      Set to bpe or spm to activate subword desegmentation.\
+                      """)
+
+  # Experimental encoding feature.
+  parser.add_argument("--use_char_encode", type="bool", default=False,
+                      help="""\
+                      Whether to split each word or bpe into character, and then
+                      generate the word-level representation from the character
+                      reprentation.
+                      """)
+
+  # Misc
+  parser.add_argument(
+      "--save_checkpoints_steps", type=int, default=2000,
+      help="save_checkpoints_steps")
+  parser.add_argument(
+      "--log_step_count_steps", type=int, default=10,
+      help=("The frequency, in number of global steps, that the global step"
+            "and the loss will be logged during training"))
+  parser.add_argument(
+      "--num_gpus", type=int, default=1, help="Number of gpus in each worker.")
+  parser.add_argument("--hparams_path", type=str, default=None,
+      help=("Path to standard hparams json file that overrides"
+            "hparams values from FLAGS."))
+  parser.add_argument(
+      "--random_seed",
+      type=int,
+      default=1,
+      help="Random seed (>0, set a specific seed).")
+  parser.add_argument("--language_model", type="bool", nargs="?",
+                      const=True, default=False,
+                      help="True to train a language model, ignoring encoder")
+
+  # Inference
+  parser.add_argument("--ckpt", type=str, default=None,
+                      help="Checkpoint file to load a model for inference. (defaults to newest checkpoint)")
+  parser.add_argument(
+      "--infer_batch_size",
+      type=int,
+      default=128,
+      help="Batch size for inference mode.")
+  parser.add_argument("--detokenizer_file", type=str,
+                      default=None,
+                      help=("""Detokenizer script file. Default: DATA_DIR/mosesdecoder/scripts/tokenizer/detokenizer.perl"""))
+
+  # Advanced inference arguments
+  parser.add_argument("--infer_mode", type=str, default="beam_search",
+                      choices=["greedy", "beam_search"],
+                      help="Which type of decoder to use during inference.")
+  parser.add_argument("--beam_width", type=int, default=5,
+                      help=("""\
+      beam width when using beam search decoder. If 0, use standard
+      decoder with greedy helper.\
+      """))
+  parser.add_argument(
+      "--length_penalty_weight",
+      type=float,
+      default=0.6,
+      help="Length penalty for beam search.")
+  parser.add_argument(
+      "--coverage_penalty_weight",
+      type=float,
+      default=0.1,
+      help="Coverage penalty for beam search.")
+
+  # Job info
+  parser.add_argument("--num_workers", type=int, default=1,
+                      help="Number of workers (inference only).")
+
+  parser.add_argument("--use_amp", type="bool", default=True,
+                      help="use_amp for training and inference")
+  parser.add_argument("--use_fastmath", type="bool", default=False,
+                      help="use_fastmath for training and inference")
+  parser.add_argument("--use_fp16", type="bool", default=False,
+                      help="use_fp16 for training and inference")
+  parser.add_argument(
+      "--fp16_loss_scale",
+      type=float,
+      default=128,
+      help="If fp16 is enabled, the loss is multiplied by this amount "
+      "right before gradients are computed, then each gradient "
+      "is divided by this amount. Mathematically, this has no "
+      "effect, but it helps avoid fp16 underflow. Set to 1 to "
+      "effectively disable.")
+  parser.add_argument(
+      "--enable_auto_loss_scale",
+      type="bool",
+      default=True,
+      help="If True and use_fp16 is True, automatically adjust the "
+      "loss scale during training.")
+  parser.add_argument(
+      "--fp16_inc_loss_scale_every_n",
+      type=int,
+      default=128,
+      help="If fp16 is enabled and enable_auto_loss_scale is "
+      "True, increase the loss scale every n steps.")
+  parser.add_argument(
+      "--check_tower_loss_numerics",
+      type="bool",
+      default=False,  # Set to false for xla.compile()
+      help="whether to check tower loss numerics")
+  parser.add_argument(
+      "--use_fp32_batch_matmul",
+      type="bool",
+      default=False,
+      help="Whether to use fp32 batch matmul")
+
+  # Performance
+  # XLA
+  parser.add_argument(
+      "--force_inputs_padding",
+      type="bool",
+      default=False,
+      help="Force padding input batch to src_max_len and tgt_max_len")
+  parser.add_argument(
+      "--use_xla",
+      type="bool",
+      default=False,
+      help="Use xla to compile a few selected locations, mostly Defuns.")
+  parser.add_argument(
+      "--xla_compile",
+      type="bool",
+      default=False,
+      help="Use xla.compile() for each tower's fwd and bak pass.")
+  parser.add_argument(
+      "--use_autojit_xla",
+      type="bool",
+      default=False,
+      help="Use auto jit xla.")
+  # GPU knobs
+  parser.add_argument(
+      "--use_pintohost_optimizer",
+      type="bool",
+      default=False,
+      help="whether to use PinToHost optimizer")
+  parser.add_argument(
+      "--use_cudnn_lstm",
+      type="bool",
+      default=False,
+      help="whether to use cudnn_lstm for encoder, non residual layers")
+  parser.add_argument(
+      "--use_loose_bidi_cudnn_lstm",
+      type="bool",
+      default=False,
+      help="whether to use loose bidi cudnn_lstm")
+  parser.add_argument(
+      "--use_fused_lstm",
+      type="bool",
+      default=True,
+      help="whether to use fused lstm and variant. If enabled, training will "
+      "use LSTMBlockFusedCell, infer will use LSTMBlockCell when appropriate.")
+  parser.add_argument(
+      "--use_fused_lstm_dec",
+      type="bool",
+      default=False,
+      help="whether to use fused lstm for decoder (training only).")
+  parser.add_argument(
+      "--gpu_indices",
+      type=str,
+      default="",
+      help="Indices of worker GPUs in ring order")
+  # Graph knobs
+  parser.add_argument("--parallel_iterations", type=int, default=10,
+                      help="number of parallel iterations in dynamic_rnn")
+  parser.add_argument("--use_dist_strategy", type="bool", default=False,
+                      help="whether to use distribution strategy")
+  parser.add_argument(
+      "--hierarchical_copy",
+      type="bool",
+      default=False,
+      help="Use hierarchical copies. Currently only optimized for "
+      "use on a DGX-1 with 8 GPUs and may perform poorly on "
+      "other hardware. Requires --num_gpus > 1, and only "
+      "recommended when --num_gpus=8")
+  parser.add_argument(
+      "--network_topology",
+      type=constants.NetworkTopology,
+      default=constants.NetworkTopology.DGX1,
+      choices=list(constants.NetworkTopology))
+  parser.add_argument(
+      "--use_block_lstm",
+      type="bool",
+      default=False,
+      help="whether to use block lstm")
+  parser.add_argument(
+      "--use_defun",
+      type="bool",
+      default=False,
+      help="whether to use Defun")
+
+  # Gradient tricks
+  parser.add_argument(
+      "--gradient_repacking",
+      type=int,
+      default=0,
+      help="Use gradient repacking. It"
+      "currently only works with replicated mode. At the end of"
+      "of each step, it repacks the gradients for more efficient"
+      "cross-device transportation. A non-zero value specifies"
+      "the number of split packs that will be formed.")
+  parser.add_argument(
+      "--compact_gradient_transfer",
+      type="bool",
+      default=True,
+      help="Compact gradient as much as possible for cross-device transfer and "
+      "aggregation.")
+  parser.add_argument(
+      "--all_reduce_spec",
+      type=str,
+      default="nccl",
+      help="A specification of the all_reduce algorithm to be used "
+      "for reducing gradients.  For more details, see "
+      "parse_all_reduce_spec in variable_mgr.py.  An "
+      "all_reduce_spec has BNF form:\n"
+      "int ::= positive whole number\n"
+      "g_int ::= int[KkMGT]?\n"
+      "alg_spec ::= alg | alg#int\n"
+      "range_spec ::= alg_spec | alg_spec/alg_spec\n"
+      "spec ::= range_spec | range_spec:g_int:range_spec\n"
+      "NOTE: not all syntactically correct constructs are "
+      "supported.\n\n"
+      "Examples:\n "
+      "\"xring\" == use one global ring reduction for all "
+      "tensors\n"
+      "\"pscpu\" == use CPU at worker 0 to reduce all tensors\n"
+      "\"nccl\" == use NCCL to locally reduce all tensors.  "
+      "Limited to 1 worker.\n"
+      "\"nccl/xring\" == locally (to one worker) reduce values "
+      "using NCCL then ring reduce across workers.\n"
+      "\"pscpu:32k:xring\" == use pscpu algorithm for tensors of "
+      "size up to 32kB, then xring for larger tensors.")
+  parser.add_argument(
+      "--agg_small_grads_max_bytes",
+      type=int,
+      default=0,
+      help="If > 0, try to aggregate tensors of less than this "
+      "number of bytes prior to all-reduce.")
+  parser.add_argument(
+      "--agg_small_grads_max_group",
+      type=int,
+      default=10,
+      help="When aggregating small tensors for all-reduce do not "
+      "aggregate more than this many into one new tensor.")
+  parser.add_argument(
+      "--allreduce_merge_scope",
+      type=int,
+      default=1,
+      help="Establish a name scope around this many "
+      "gradients prior to creating the all-reduce operations. "
+      "It may affect the ability of the backend to merge "
+      "parallel ops.")
+  # Other knobs
+  parser.add_argument(
+      "--local_parameter_device",
+      type=str,
+      default="gpu",
+      help="Device to use as parameter server: cpu or gpu. For "
+      "distributed training, it can affect where caching of "
+      "variables happens.")
+  parser.add_argument(
+      "--use_resource_vars",
+      type="bool",
+      default=False,
+      help="Use resource variables instead of normal variables. "
+      "Resource variables are slower, but this option is useful "
+      "for debugging their performance.")
+
+
+  parser.add_argument("--debug", type="bool", default=False,
+                      help="Debug train and eval")
+  parser.add_argument(
+      "--debug_num_train_steps", type=int, default=None, help="Num steps to train.")
+  parser.add_argument("--show_metrics", type="bool", default=True,
+                      help="whether to show detailed metrics")
+  parser.add_argument("--clip_grads", type="bool", default=True,
+                      help="whether to clip gradients")
+  parser.add_argument("--profile", type="bool", default=False,
+                      help="If generate profile")
+  parser.add_argument("--profile_save_steps", type=int, default=10,
+                      help="Save timeline every N steps.")
+
+  parser.add_argument("--use_dynamic_rnn", type="bool", default=True)
+  parser.add_argument("--use_synthetic_data", type="bool", default=False)
+  parser.add_argument(
+      "--mode", type=str, default="train_and_eval",
+      choices=["train_and_eval", "infer"])
+
+
+def create_hparams(flags):
+  """Create training hparams."""
+  return tf.contrib.training.HParams(
+      # Data
+      src=flags.src,
+      tgt=flags.tgt,
+      train_prefix=os.path.join(flags.data_dir, flags.train_prefix),
+      test_prefix=os.path.join(flags.data_dir, flags.test_prefix),
+      vocab_prefix=os.path.join(flags.data_dir, flags.vocab_prefix),
+      embed_prefix=flags.embed_prefix,
+      output_dir=flags.output_dir,
+
+      # Networks
+      num_units=flags.num_units,
+      num_encoder_layers=(flags.num_encoder_layers or flags.num_layers),
+      num_decoder_layers=(flags.num_decoder_layers or flags.num_layers),
+      dropout=flags.dropout,
+      unit_type=flags.unit_type,
+      encoder_type=flags.encoder_type,
+      residual=flags.residual,
+      time_major=flags.time_major,
+      num_embeddings_partitions=flags.num_embeddings_partitions,
+
+      # Attention mechanisms
+      attention=flags.attention,
+      attention_architecture=flags.attention_architecture,
+      output_attention=flags.output_attention,
+      pass_hidden_state=flags.pass_hidden_state,
+
+      # Train
+      optimizer=flags.optimizer,
+      max_train_epochs=flags.max_train_epochs,
+      target_bleu=flags.target_bleu,
+      label_smoothing=flags.label_smoothing,
+      batch_size=flags.batch_size,
+      init_op=flags.init_op,
+      init_weight=flags.init_weight,
+      max_gradient_norm=flags.max_gradient_norm,
+      learning_rate=flags.learning_rate,
+      warmup_steps=flags.warmup_steps,
+      warmup_scheme=flags.warmup_scheme,
+      decay_scheme=flags.decay_scheme,
+      colocate_gradients_with_ops=flags.colocate_gradients_with_ops,
+
+      # Data constraints
+      num_buckets=flags.num_buckets,
+      src_max_len=flags.src_max_len,
+      tgt_max_len=flags.tgt_max_len,
+
+      # Inference
+      src_max_len_infer=flags.src_max_len_infer,
+      tgt_max_len_infer=flags.tgt_max_len_infer,
+      ckpt=flags.ckpt,
+      infer_batch_size=flags.infer_batch_size,
+      detokenizer_file=flags.detokenizer_file if flags.detokenizer_file is not None \
+                       else os.path.join(flags.data_dir, 'mosesdecoder/scripts/tokenizer/detokenizer.perl'),
+
+      # Advanced inference arguments
+      infer_mode=flags.infer_mode,
+      beam_width=flags.beam_width,
+      length_penalty_weight=flags.length_penalty_weight,
+      coverage_penalty_weight=flags.coverage_penalty_weight,
+
+      # Vocab
+      sos=flags.sos if flags.sos else vocab_utils.SOS,
+      eos=flags.eos if flags.eos else vocab_utils.EOS,
+      subword_option=flags.subword_option,
+      check_special_token=flags.check_special_token,
+      use_char_encode=flags.use_char_encode,
+
+      # Misc
+      forget_bias=flags.forget_bias,
+      num_gpus=flags.num_gpus,
+      save_checkpoints_steps=flags.save_checkpoints_steps,
+      log_step_count_steps=flags.log_step_count_steps,
+      epoch_step=0,  # record where we were within an epoch.
+      share_vocab=flags.share_vocab,
+      random_seed=flags.random_seed,
+      language_model=flags.language_model,
+
+      use_amp=flags.use_amp,
+      use_fastmath=flags.use_fastmath,
+      use_fp16=flags.use_fp16,
+      fp16_loss_scale=flags.fp16_loss_scale,
+      enable_auto_loss_scale=flags.enable_auto_loss_scale,
+      fp16_inc_loss_scale_every_n=flags.fp16_inc_loss_scale_every_n,
+      check_tower_loss_numerics=flags.check_tower_loss_numerics,
+      use_fp32_batch_matmul=flags.use_fp32_batch_matmul,
+
+      # Performance
+      # GPU knbs
+      force_inputs_padding=flags.force_inputs_padding,
+      use_xla=flags.use_xla,
+      xla_compile=flags.xla_compile,
+      use_autojit_xla=flags.use_autojit_xla,
+      use_pintohost_optimizer=flags.use_pintohost_optimizer,
+      use_cudnn_lstm=flags.use_cudnn_lstm,
+      use_loose_bidi_cudnn_lstm=flags.use_loose_bidi_cudnn_lstm,
+      use_fused_lstm=flags.use_fused_lstm,
+      use_fused_lstm_dec=flags.use_fused_lstm_dec,
+      gpu_indices=flags.gpu_indices,
+      # Graph knobs
+      parallel_iterations=flags.parallel_iterations,
+      use_dynamic_rnn=flags.use_dynamic_rnn,
+      use_dist_strategy=flags.use_dist_strategy,
+      hierarchical_copy=flags.hierarchical_copy,
+      network_topology=flags.network_topology,
+      use_block_lstm=flags.use_block_lstm,
+      # Grad tricks
+      gradient_repacking=flags.gradient_repacking,
+      compact_gradient_transfer=flags.compact_gradient_transfer,
+      all_reduce_spec=flags.all_reduce_spec,
+      agg_small_grads_max_bytes=flags.agg_small_grads_max_bytes,
+      agg_small_grads_max_group=flags.agg_small_grads_max_group,
+      allreduce_merge_scope=flags.allreduce_merge_scope,
+      # Other knobs
+      local_parameter_device=("cpu" if flags.num_gpus ==0
+                              else flags.local_parameter_device),
+      use_resource_vars=flags.use_resource_vars,
+
+      debug=flags.debug,
+      debug_num_train_steps=flags.debug_num_train_steps,
+      clip_grads=flags.clip_grads,
+      profile=flags.profile,
+      profile_save_steps=flags.profile_save_steps,
+      show_metrics=flags.show_metrics,
+
+      use_synthetic_data=flags.use_synthetic_data,
+      mode=flags.mode,
+  )
+
+
+def _add_argument(hparams, key, value, update=True):
+  """Add an argument to hparams; if exists, change the value if update==True."""
+  if hasattr(hparams, key):
+    if update:
+      setattr(hparams, key, value)
+  else:
+    hparams.add_hparam(key, value)
+
+
+def extend_hparams(hparams):
+  """Add new arguments to hparams."""
+  # Sanity checks
+  if hparams.encoder_type == "bi" and hparams.num_encoder_layers % 2 != 0:
+    raise ValueError("For bi, num_encoder_layers %d should be even" %
+                     hparams.num_encoder_layers)
+  if (hparams.attention_architecture in ["gnmt"] and
+      hparams.num_encoder_layers < 2):
+    raise ValueError("For gnmt attention architecture, "
+                     "num_encoder_layers %d should be >= 2" %
+                     hparams.num_encoder_layers)
+  if hparams.subword_option and hparams.subword_option not in ["spm", "bpe"]:
+    raise ValueError("subword option must be either spm, or bpe")
+  if hparams.infer_mode == "beam_search" and hparams.beam_width <= 0:
+    raise ValueError("beam_width must greater than 0 when using beam_search"
+                     "decoder.")
+
+  # Different number of encoder / decoder layers
+  assert hparams.num_encoder_layers and hparams.num_decoder_layers
+  if hparams.num_encoder_layers != hparams.num_decoder_layers:
+    hparams.pass_hidden_state = False
+    utils.print_out("Num encoder layer %d is different from num decoder layer"
+                    " %d, so set pass_hidden_state to False" % (
+                        hparams.num_encoder_layers,
+                        hparams.num_decoder_layers))
+
+  # Set residual layers
+  num_encoder_residual_layers = 0
+  num_decoder_residual_layers = 0
+  if hparams.residual:
+    if hparams.num_encoder_layers > 1:
+      num_encoder_residual_layers = hparams.num_encoder_layers - 1
+    if hparams.num_decoder_layers > 1:
+      num_decoder_residual_layers = hparams.num_decoder_layers - 1
+
+    if hparams.encoder_type == "gnmt":
+      # The first unidirectional layer (after the bi-directional layer) in
+      # the GNMT encoder can't have residual connection due to the input is
+      # the concatenation of fw_cell and bw_cell's outputs.
+      num_encoder_residual_layers = hparams.num_encoder_layers - 2
+
+      # Compatible for GNMT models
+      if hparams.num_encoder_layers == hparams.num_decoder_layers:
+        num_decoder_residual_layers = num_encoder_residual_layers
+  _add_argument(hparams, "num_encoder_residual_layers",
+                num_encoder_residual_layers)
+  _add_argument(hparams, "num_decoder_residual_layers",
+                num_decoder_residual_layers)
+
+  # Language modeling
+  if hparams.language_model:
+    hparams.attention = ""
+    hparams.attention_architecture = ""
+    hparams.pass_hidden_state = False
+    hparams.share_vocab = True
+    hparams.src = hparams.tgt
+    utils.print_out("For language modeling, we turn off attention and "
+                    "pass_hidden_state; turn on share_vocab; set src to tgt.")
+
+  ## Vocab
+  # Get vocab file names first
+  if hparams.vocab_prefix:
+    src_vocab_file = hparams.vocab_prefix + "." + hparams.src
+    tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
+  else:
+    raise ValueError("hparams.vocab_prefix must be provided.")
+
+  # Source vocab
+  src_vocab_size, src_vocab_file = vocab_utils.check_vocab(
+      src_vocab_file,
+      hparams.output_dir,
+      check_special_token=hparams.check_special_token,
+      sos=hparams.sos,
+      eos=hparams.eos,
+      unk=vocab_utils.UNK)
+
+  # Target vocab
+  if hparams.share_vocab:
+    utils.print_out("  using source vocab for target")
+    tgt_vocab_file = src_vocab_file
+    tgt_vocab_size = src_vocab_size
+  else:
+    tgt_vocab_size, tgt_vocab_file = vocab_utils.check_vocab(
+        tgt_vocab_file,
+        hparams.output_dir,
+        check_special_token=hparams.check_special_token,
+        sos=hparams.sos,
+        eos=hparams.eos,
+        unk=vocab_utils.UNK)
+  _add_argument(hparams, "src_vocab_size", src_vocab_size)
+  _add_argument(hparams, "tgt_vocab_size", tgt_vocab_size)
+  _add_argument(hparams, "src_vocab_file", src_vocab_file)
+  _add_argument(hparams, "tgt_vocab_file", tgt_vocab_file)
+
+  # Num embedding partitions
+  _add_argument(
+      hparams, "num_enc_emb_partitions", hparams.num_embeddings_partitions)
+  _add_argument(
+      hparams, "num_dec_emb_partitions", hparams.num_embeddings_partitions)
+
+  # Pretrained Embeddings
+  _add_argument(hparams, "src_embed_file", "")
+  _add_argument(hparams, "tgt_embed_file", "")
+  if hparams.embed_prefix:
+    src_embed_file = hparams.embed_prefix + "." + hparams.src
+    tgt_embed_file = hparams.embed_prefix + "." + hparams.tgt
+
+    if tf.gfile.Exists(src_embed_file):
+      utils.print_out("  src_embed_file %s exist" % src_embed_file)
+      hparams.src_embed_file = src_embed_file
+
+      utils.print_out(
+          "For pretrained embeddings, set num_enc_emb_partitions to 1")
+      hparams.num_enc_emb_partitions = 1
+    else:
+      utils.print_out("  src_embed_file %s doesn't exist" % src_embed_file)
+
+    if tf.gfile.Exists(tgt_embed_file):
+      utils.print_out("  tgt_embed_file %s exist" % tgt_embed_file)
+      hparams.tgt_embed_file = tgt_embed_file
+
+      utils.print_out(
+          "For pretrained embeddings, set num_dec_emb_partitions to 1")
+      hparams.num_dec_emb_partitions = 1
+    else:
+      utils.print_out("  tgt_embed_file %s doesn't exist" % tgt_embed_file)
+
+  # Evaluation
+  metric = "bleu"
+  best_metric_dir = os.path.join(hparams.output_dir, "best_" + metric)
+  tf.gfile.MakeDirs(best_metric_dir)
+  _add_argument(hparams, "best_" + metric, 0, update=False)
+  _add_argument(hparams, "best_" + metric + "_dir", best_metric_dir)
+
+  return hparams
+
+
+def create_or_load_hparams(default_hparams, hparams_path):
+  """Create hparams or load hparams from output_dir."""
+  hparams = utils.maybe_parse_standard_hparams(default_hparams, hparams_path)
+  hparams = extend_hparams(hparams)
+  # Print HParams
+  utils.print_hparams(hparams)
+  return hparams
+
+
+def run_main(flags, default_hparams, estimator_fn):
+  """Run main."""
+  # Random
+  random_seed = flags.random_seed
+  if random_seed is not None and random_seed > 0:
+    utils.print_out("# Set random seed to %d" % random_seed)
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+    tf.set_random_seed(random_seed)
+
+  # Model output directory
+  output_dir = flags.output_dir
+  if output_dir and not tf.gfile.Exists(output_dir):
+    utils.print_out("# Creating output directory %s ..." % output_dir)
+    tf.gfile.MakeDirs(output_dir)
+
+  # Load hparams.
+  hparams = create_or_load_hparams(default_hparams, flags.hparams_path)
+
+  # Train or Evaluation
+  estimator_fn(hparams)
+  return hparams
+
+
+def main(unused_argv):
+  experiment_start = time.time()
+
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  if FLAGS.use_fp16 and FLAGS.use_dist_strategy:
+    raise ValueError("use_fp16 and use_dist_strategy aren't compatible")
+
+  if FLAGS.use_fp16 + FLAGS.use_amp + FLAGS.use_fastmath > 1:
+    raise ValueError("Only one of use_fp16, use_amp, use_fastmath can be set")
+
+  if FLAGS.use_amp:
+    utils.print_out('Enabling TF-AMP')
+
+    os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
+
+  if FLAGS.use_fastmath:
+    utils.print_out('Enabling FastMath')
+
+    os.environ["TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32"] = '1'
+    os.environ["TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32"] = '1'
+    os.environ["TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32"] = '1'
+
+  # Set up hacky envvars.
+  # Hack that affects Defun in attention_wrapper.py
+  active_xla_option_nums = np.sum([FLAGS.use_xla, FLAGS.use_autojit_xla,
+                                   FLAGS.xla_compile])
+  if active_xla_option_nums > 1:
+    raise ValueError(
+        "Only one of use_xla, xla_compile, use_autojit_xla can be set")
+
+  os.environ["use_xla"] = str(FLAGS.use_xla).lower()
+  if FLAGS.use_xla:
+    os.environ["use_defun"] = str(True).lower()
+  else:
+    os.environ["use_defun"] = str(FLAGS.use_defun).lower()
+  utils.print_out("use_defun is %s for attention" % os.environ["use_defun"])
+
+  # TODO(jamesqin): retire this config after Cuda9.1
+  os.environ["use_fp32_batch_matmul"] = ("true" if FLAGS.use_fp32_batch_matmul
+                                         else "false")
+  os.environ["xla_compile"] = "true" if FLAGS.xla_compile else "false"
+  os.environ["force_inputs_padding"] = (
+      "true" if FLAGS.force_inputs_padding else "false")
+
+  if FLAGS.mode == "train":
+    utils.print_out("Running training mode.")
+    default_hparams = create_hparams(FLAGS)
+    run_main(FLAGS, default_hparams, estimator.train_fn)
+  elif FLAGS.mode == "infer":
+    utils.print_out("Running inference mode.")
+    # Random
+    random_seed = FLAGS.random_seed
+    if random_seed is not None and random_seed > 0:
+      utils.print_out("# Set random seed to %d" % random_seed)
+      random.seed(random_seed)
+      np.random.seed(random_seed)
+      tf.set_random_seed(random_seed)
+
+    # Model output directory
+    output_dir = FLAGS.output_dir
+    if output_dir and not tf.gfile.Exists(output_dir):
+      utils.print_out("# Creating output directory %s ..." % output_dir)
+      tf.gfile.MakeDirs(output_dir)
+
+    # Load hparams.
+    default_hparams = create_hparams(FLAGS)
+    default_hparams.num_buckets = 1
+    # The estimator model_fn is written in a way allowing train hparams to be
+    # passed in infer mode.
+    hparams = create_or_load_hparams(default_hparams, FLAGS.hparams_path)
+    utils.print_out("infer_hparams:")
+    utils.print_hparams(hparams)
+
+    eval_sentences, eval_src_tokens, eval_tgt_tokens = iterator_utils.get_effective_epoch_size(hparams, train=False)
+
+    # Run evaluation when there's a new checkpoint
+    tf.logging.info("Starting to evaluate...")
+    eval_start = time.time()
+    bleu_score, eval_speed, eval_output_tokens = estimator.eval_fn(hparams, hparams.ckpt)
+    eval_end = time.time()
+    eval_delta = eval_end - eval_start
+    utils.print_out("eval time for ckpt: %.2f mins (%.2f sent/sec, %.2f tokens/sec)" %
+                    (eval_delta / 60., eval_speed, eval_speed * (eval_src_tokens + eval_output_tokens) / eval_sentences), f=sys.stderr)
+  else:
+    assert FLAGS.mode == "train_and_eval"
+    utils.print_out("Running train and eval mode.")
+
+    # Random
+    random_seed = FLAGS.random_seed
+    if random_seed is not None and random_seed > 0:
+      utils.print_out("# Set random seed to %d" % random_seed)
+      random.seed(random_seed)
+      np.random.seed(random_seed)
+      tf.set_random_seed(random_seed)
+
+    # Model output directory
+    output_dir = FLAGS.output_dir
+    if output_dir and not tf.gfile.Exists(output_dir):
+      utils.print_out("# Creating output directory %s ..." % output_dir)
+      tf.gfile.MakeDirs(output_dir)
+
+    # Load hparams.
+    default_hparams = create_hparams(FLAGS)
+
+    hparams = create_or_load_hparams(default_hparams, FLAGS.hparams_path)
+    utils.print_out("training hparams:")
+    utils.print_hparams(hparams)
+    with tf.gfile.GFile(os.path.join(output_dir, "train_hparams.txt"), "w") as f:
+      f.write(utils.serialize_hparams(hparams) + "\n")
+
+    # The estimator model_fn is written in a way allowing train hparams to be
+    # passed in infer mode.
+    infer_hparams = tf.contrib.training.HParams(**hparams.values())
+    infer_hparams.num_buckets = 1
+    utils.print_out("infer_hparams:")
+    utils.print_hparams(infer_hparams)
+    with tf.gfile.GFile(os.path.join(output_dir, "infer_hparams.txt"), "w") as f:
+      f.write(utils.serialize_hparams(infer_hparams) + "\n")
+
+    epochs = 0
+    should_stop = epochs >= FLAGS.max_train_epochs
+
+    train_sentences, train_src_tokens, train_tgt_tokens = iterator_utils.get_effective_epoch_size(hparams)
+    eval_sentences, eval_src_tokens, eval_tgt_tokens = iterator_utils.get_effective_epoch_size(hparams, train=False)
+
+    while not should_stop:
+      utils.print_out("Starting epoch %d" % epochs)
+      try:
+        train_start = time.time()
+        train_speed = estimator.train_fn(hparams)
+      except tf.errors.OutOfRangeError:
+        utils.print_out("training hits OutOfRangeError", f=sys.stderr)
+
+      train_end = time.time()
+      train_delta = train_end - train_start
+      utils.print_out("training time for epoch %d: %.2f mins (%.2f sent/sec, %.2f tokens/sec)" %
+                      (epochs + 1, train_delta / 60., train_speed, train_speed * (train_src_tokens + train_tgt_tokens) / train_sentences), f=sys.stderr)
+
+      # This is probably sub-optimal, doing eval per-epoch
+      eval_start = time.time()
+      bleu_score, eval_speed, eval_output_tokens = estimator.eval_fn(infer_hparams)
+      eval_end = time.time()
+      eval_delta = eval_end - eval_start
+      utils.print_out("eval time for epoch %d: %.2f mins (%.2f sent/sec, %.2f tokens/sec)" %
+                      (epochs + 1, eval_delta / 60., eval_speed, eval_speed * (eval_src_tokens + eval_output_tokens) / eval_sentences), f=sys.stderr)
+
+      if FLAGS.debug or (FLAGS.target_bleu is not None and bleu_score > FLAGS.target_bleu):
+        should_stop = True
+        utils.print_out(
+            "Stop job since target bleu is reached at epoch %d ." % epochs,
+            f=sys.stderr)
+
+      epochs += 1
+      if epochs >= FLAGS.max_train_epochs:
+        should_stop = True
+        utils.print_out("Stop job since max_train_epochs is reached.",
+                        f=sys.stderr)
+
+  experiment_end = time.time()
+  utils.print_out('Experiment took {} min'.format((experiment_end - experiment_start) / 60))
+
+
+if __name__ == "__main__":
+  nmt_parser = argparse.ArgumentParser()
+  add_arguments(nmt_parser)
+  FLAGS, unparsed = nmt_parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/TensorFlow/Translation/GNMT/requirements.txt b/TensorFlow/Translation/GNMT/requirements.txt
new file mode 100644
index 00000000..40f48e43
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/requirements.txt
@@ -0,0 +1 @@
+sacrebleu==1.2.10
diff --git a/TensorFlow/Translation/GNMT/scripts/docker/build.sh b/TensorFlow/Translation/GNMT/scripts/docker/build.sh
new file mode 100755
index 00000000..b0dd3b71
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/docker/build.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build . --rm -t gnmt_tf
diff --git a/TensorFlow/Translation/GNMT/scripts/docker/interactive.sh b/TensorFlow/Translation/GNMT/scripts/docker/interactive.sh
new file mode 100755
index 00000000..18d3de86
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/docker/interactive.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+nvidia-docker run -it --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v $PWD:/workspace/gnmt gnmt_tf bash
diff --git a/TensorFlow/Translation/GNMT/scripts/filter_dataset.py b/TensorFlow/Translation/GNMT/scripts/filter_dataset.py
new file mode 100644
index 00000000..155e4eb2
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/filter_dataset.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+from collections import Counter
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Clean dataset')
+    parser.add_argument('-f1', '--file1', help='file1')
+    parser.add_argument('-f2', '--file2', help='file2')
+    return parser.parse_args()
+
+
+def save_output(fname, data):
+    with open(fname, 'w') as f:
+        f.writelines(data)
+
+
+def main():
+    """
+    Discards all pairs of sentences which can't be decoded by latin-1 encoder.
+
+    It aims to filter out sentences with rare unicode glyphs and pairs which
+    are most likely not valid English-German sentences.
+
+    Examples of discarded sentences:
+
+        ✿★★★Hommage au king de la pop ★★★✿ ✿★★★Que son âme repos...
+
+        Для их осуществления нам, прежде всего, необходимо преодолеть
+        возражения рыночных фундаменталистов, которые хотят ликвидировать или
+        уменьшить роль МВФ.
+
+        practised as a scientist in various medical departments of the ⇗Medical
+        University of Hanover , the ⇗University of Ulm , and the ⇗RWTH Aachen
+        (rheumatology, pharmacology, physiology, pathology, microbiology,
+        immunology and electron-microscopy).
+
+        The same shift】 and press 【】 【alt out with a smaller diameter
+        circle.
+
+        Brought to you by ABMSUBS ♥leira(Coordinator/Translator)
+        ♥chibichan93(Timer/Typesetter) ♥ja...
+
+        Some examples: &0u - ☺ &0U - ☻ &tel - ☏ &PI - ¶ &SU - ☼ &cH- - ♥ &M2=♫
+        &sn - ﺵ SGML maps SGML to unicode.
+    """
+    args = parse_args()
+
+    c = Counter()
+    skipped = 0
+    valid = 0
+    data1 = []
+    data2 = []
+
+    with open(args.file1) as f1, open(args.file2) as f2:
+        for idx, lines in enumerate(zip(f1, f2)):
+            line1, line2 = lines
+            if idx % 100000 == 1:
+                print('Processed {} lines'.format(idx))
+            try:
+                line1.encode('latin1')
+                line2.encode('latin1')
+            except UnicodeEncodeError:
+                skipped += 1
+            else:
+                data1.append(line1)
+                data2.append(line2)
+                valid += 1
+                c.update(line1)
+
+    ratio = valid / (skipped + valid)
+    print('Skipped: {}, Valid: {}, Valid ratio {}'.format(skipped, valid, ratio))
+    print('Character frequency:', c)
+
+    save_output(args.file1, data1)
+    save_output(args.file2, data2)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/TensorFlow/Translation/GNMT/scripts/parse_training_log.py b/TensorFlow/Translation/GNMT/scripts/parse_training_log.py
new file mode 100644
index 00000000..21810209
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/parse_training_log.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import re
+import sys
+import json
+from pathlib import Path
+from subprocess import Popen, PIPE
+
+parser = argparse.ArgumentParser(description='Parse training logs')
+parser.add_argument('log', help='path to log file', type=Path)
+args = parser.parse_args()
+
+content = args.log.read_bytes()
+
+bleu = list(map(lambda x: float(x[0]), re.findall(rb'\nbleu is ((\d|.)+)', content)))
+
+training_speed = re.findall(rb'\ntraining time for epoch (\d+): ((\d|.)+) mins \(((\d|.)+) sent/sec, ((\d|.)+) tokens/sec\)', content)
+training_tokens = list(map(lambda x: float(x[5]), training_speed))
+training_sentences = list(map(lambda x: float(x[3]), training_speed))
+
+eval_speed = re.findall(rb'\neval time for epoch (\d+): ((\d|.)+) mins \(((\d|.)+) sent/sec, ((\d|.)+) tokens/sec\)', content)
+eval_tokens = list(map(lambda x: float(x[5]), eval_speed))
+eval_sentences = list(map(lambda x: float(x[3]), eval_speed))
+
+ret = {}
+ret['bleu'] = bleu
+ret['training_tokens_per_sec'] = training_tokens
+ret['training_sentences_per_sec'] = training_sentences
+ret['eval_tokens_per_sec'] = eval_tokens
+ret['eval_sentences_per_sec'] = eval_sentences
+
+print(json.dumps(ret))
diff --git a/TensorFlow/Translation/GNMT/scripts/translate.py b/TensorFlow/Translation/GNMT/scripts/translate.py
new file mode 100644
index 00000000..aee5720d
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/translate.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import re
+import sys
+from subprocess import Popen, PIPE
+
+parser = argparse.ArgumentParser(description='Translate')
+parser.add_argument('--executable', default='nmt.py', help='path to nmt.py')
+parser.add_argument('--infer_batch_size', metavar='B1,[B2,...]',
+                    default='64', help='batch sizes separated by comma')
+parser.add_argument('--beam_width', metavar='W1,[W2,...]',
+                    default='5', help='beam widths separated by comma')
+args, other_args = parser.parse_known_args()
+
+batch_sizes = list(map(int, args.infer_batch_size.split(',')))
+beam_widths = list(map(int, args.beam_width.split(',')))
+
+
+def pr(*args, column_len=14):
+  for arg in args:
+    if type(arg) is float:
+      arg = '{:.2f}'.format(arg)
+    arg = str(arg)
+
+    print('', arg.ljust(column_len), end=' |')
+  print()
+
+pr('batch size', 'beam width', 'bleu', 'sentences/sec', 'tokens/sec')
+
+for batch_size in batch_sizes:
+  for beam_width in beam_widths:
+    cmd = ['python', args.executable, '--beam_width', str(beam_width),
+           '--infer_batch_size', str(batch_size), '--mode', 'infer'] + other_args
+    proc = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    out, err = proc.communicate()
+
+    bleu_search_res = re.search(rb'\nbleu is ((\d|.)+)', out)
+    speed_search_res = re.search(
+      rb'\neval time for ckpt: ((\d|.)+) mins \(((\d|.)+) sent/sec, ((\d|.)+) tokens/sec\)', out)
+    if bleu_search_res is None or speed_search_res is None:
+      print('AN ERROR OCCURRED WHILE RUNNING:', cmd, file=sys.stderr)
+      print('-' * 20, 'STDOUT', '-' * 20, file=sys.stderr)
+      print(out.decode())
+      print('-' * 20, 'STDERR', '-' * 20, file=sys.stderr)
+      print(err.decode())
+      exit(1)
+
+    bleu = float(bleu_search_res.group(1))
+    sentences_per_sec, tokens_per_sec = map(float, speed_search_res.group(3, 5))
+
+    pr(batch_size, beam_width, bleu, sentences_per_sec, tokens_per_sec)
diff --git a/TensorFlow/Translation/GNMT/scripts/verify_dataset.sh b/TensorFlow/Translation/GNMT/scripts/verify_dataset.sh
new file mode 100644
index 00000000..1204fb4f
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/verify_dataset.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+
+DATASET_DIR=${1:-"data/wmt16_de_en"}
+
+ACTUAL_SRC_TRAIN=`cat ${DATASET_DIR}/train.tok.clean.bpe.32000.en |md5sum`
+EXPECTED_SRC_TRAIN='b7482095b787264a310d4933d197a134  -'
+if [[ $ACTUAL_SRC_TRAIN = $EXPECTED_SRC_TRAIN ]]; then
+  echo "OK: correct ${DATASET_DIR}/train.tok.clean.bpe.32000.en"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/train.tok.clean.bpe.32000.en"
+  echo "ERROR: expected $EXPECTED_SRC_TRAIN"
+  echo "ERROR: found $ACTUAL_SRC_TRAIN"
+fi
+
+ACTUAL_TGT_TRAIN=`cat ${DATASET_DIR}/train.tok.clean.bpe.32000.de |md5sum`
+EXPECTED_TGT_TRAIN='409064aedaef5b7c458ff19a7beda462  -'
+if [[ $ACTUAL_TGT_TRAIN = $EXPECTED_TGT_TRAIN ]]; then
+  echo "OK: correct ${DATASET_DIR}/train.tok.clean.bpe.32000.de"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/train.tok.clean.bpe.32000.de"
+  echo "ERROR: expected $EXPECTED_TGT_TRAIN"
+  echo "ERROR: found $ACTUAL_TGT_TRAIN"
+fi
+
+ACTUAL_SRC_VAL=`cat ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.en |md5sum`
+EXPECTED_SRC_VAL='704c4ba8c8b63df1f6678d32b91438b5  -'
+if [[ $ACTUAL_SRC_VAL = $EXPECTED_SRC_VAL ]]; then
+  echo "OK: correct ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.en"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.en"
+  echo "ERROR: expected $EXPECTED_SRC_VAL"
+  echo "ERROR: found $ACTUAL_SRC_VAL"
+fi
+
+ACTUAL_TGT_VAL=`cat ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.de |md5sum`
+EXPECTED_TGT_VAL='d27f5a64c839e20c5caa8b9d60075dde  -'
+if [[ $ACTUAL_TGT_VAL = $EXPECTED_TGT_VAL ]]; then
+  echo "OK: correct ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.de"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/newstest_dev.tok.clean.bpe.32000.de"
+  echo "ERROR: expected $EXPECTED_TGT_VAL"
+  echo "ERROR: found $ACTUAL_TGT_VAL"
+fi
+
+ACTUAL_SRC_TEST=`cat ${DATASET_DIR}/newstest2014.tok.bpe.32000.en |md5sum`
+EXPECTED_SRC_TEST='cb014e2509f86cd81d5a87c240c07464  -'
+if [[ $ACTUAL_SRC_TEST = $EXPECTED_SRC_TEST ]]; then
+  echo "OK: correct ${DATASET_DIR}/newstest2014.tok.bpe.32000.en"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/newstest2014.tok.bpe.32000.en"
+  echo "ERROR: expected $EXPECTED_SRC_TEST"
+  echo "ERROR: found $ACTUAL_SRC_TEST"
+fi
+
+ACTUAL_TGT_TEST=`cat ${DATASET_DIR}/newstest2014.tok.bpe.32000.de |md5sum`
+EXPECTED_TGT_TEST='d616740f6026dc493e66efdf9ac1cb04  -'
+if [[ $ACTUAL_TGT_TEST = $EXPECTED_TGT_TEST ]]; then
+  echo "OK: correct ${DATASET_DIR}/newstest2014.tok.bpe.32000.de"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/newstest2014.tok.bpe.32000.de"
+  echo "ERROR: expected $EXPECTED_TGT_TEST"
+  echo "ERROR: found $ACTUAL_TGT_TEST"
+fi
+
+ACTUAL_TGT_TEST_TARGET=`cat ${DATASET_DIR}/newstest2014.de |md5sum`
+EXPECTED_TGT_TEST_TARGET='f6c3818b477e4a25cad68b61cc883c17  -'
+if [[ $ACTUAL_TGT_TEST_TARGET = $EXPECTED_TGT_TEST_TARGET ]]; then
+  echo "OK: correct ${DATASET_DIR}/newstest2014.de"
+else
+  echo "ERROR: incorrect ${DATASET_DIR}/newstest2014.de"
+  echo "ERROR: expected $EXPECTED_TGT_TEST_TARGET"
+  echo "ERROR: found $ACTUAL_TGT_TEST_TARGET"
+fi
diff --git a/TensorFlow/Translation/GNMT/scripts/wmt16_en_de.sh b/TensorFlow/Translation/GNMT/scripts/wmt16_en_de.sh
new file mode 100755
index 00000000..6d670c12
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/scripts/wmt16_en_de.sh
@@ -0,0 +1,196 @@
+#!/usr/bin/env bash
+
+# Copyright 2017 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# -----------------------------------------------------------------------
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+
+export LANG=C.UTF-8
+export LC_ALL=C.UTF-8
+
+OUTPUT_DIR=${1:-"data/wmt16_de_en"}
+echo "Writing to ${OUTPUT_DIR}. To change this, set the OUTPUT_DIR environment variable."
+
+OUTPUT_DIR_DATA="${OUTPUT_DIR}/data"
+mkdir -p $OUTPUT_DIR_DATA
+
+echo "Downloading Europarl v7. This may take a while..."
+curl -o ${OUTPUT_DIR_DATA}/europarl-v7-de-en.tgz \
+  http://www.statmt.org/europarl/v7/de-en.tgz
+
+echo "Downloading Common Crawl corpus. This may take a while..."
+curl -o ${OUTPUT_DIR_DATA}/common-crawl.tgz \
+  http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
+
+echo "Downloading News Commentary v11. This may take a while..."
+curl -o ${OUTPUT_DIR_DATA}/nc-v11.tgz \
+  http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz
+
+echo "Downloading dev/test sets"
+curl -o ${OUTPUT_DIR_DATA}/dev.tgz \
+  http://data.statmt.org/wmt16/translation-task/dev.tgz
+curl -o ${OUTPUT_DIR_DATA}/test.tgz \
+  http://data.statmt.org/wmt16/translation-task/test.tgz
+
+# Extract everything
+echo "Extracting all files..."
+mkdir -p "${OUTPUT_DIR_DATA}/europarl-v7-de-en"
+tar -xvzf "${OUTPUT_DIR_DATA}/europarl-v7-de-en.tgz" -C "${OUTPUT_DIR_DATA}/europarl-v7-de-en"
+mkdir -p "${OUTPUT_DIR_DATA}/common-crawl"
+tar -xvzf "${OUTPUT_DIR_DATA}/common-crawl.tgz" -C "${OUTPUT_DIR_DATA}/common-crawl"
+mkdir -p "${OUTPUT_DIR_DATA}/nc-v11"
+tar -xvzf "${OUTPUT_DIR_DATA}/nc-v11.tgz" -C "${OUTPUT_DIR_DATA}/nc-v11"
+mkdir -p "${OUTPUT_DIR_DATA}/dev"
+tar -xvzf "${OUTPUT_DIR_DATA}/dev.tgz" -C "${OUTPUT_DIR_DATA}/dev"
+mkdir -p "${OUTPUT_DIR_DATA}/test"
+tar -xvzf "${OUTPUT_DIR_DATA}/test.tgz" -C "${OUTPUT_DIR_DATA}/test"
+
+# Concatenate Training data
+cat "${OUTPUT_DIR_DATA}/europarl-v7-de-en/europarl-v7.de-en.en" \
+  "${OUTPUT_DIR_DATA}/common-crawl/commoncrawl.de-en.en" \
+  "${OUTPUT_DIR_DATA}/nc-v11/training-parallel-nc-v11/news-commentary-v11.de-en.en" \
+  > "${OUTPUT_DIR}/train.en"
+wc -l "${OUTPUT_DIR}/train.en"
+
+cat "${OUTPUT_DIR_DATA}/europarl-v7-de-en/europarl-v7.de-en.de" \
+  "${OUTPUT_DIR_DATA}/common-crawl/commoncrawl.de-en.de" \
+  "${OUTPUT_DIR_DATA}/nc-v11/training-parallel-nc-v11/news-commentary-v11.de-en.de" \
+  > "${OUTPUT_DIR}/train.de"
+wc -l "${OUTPUT_DIR}/train.de"
+
+# Clone Moses
+if [ ! -d "${OUTPUT_DIR}/mosesdecoder" ]; then
+  echo "Cloning moses for data processing"
+  git clone https://github.com/moses-smt/mosesdecoder.git "${OUTPUT_DIR}/mosesdecoder"
+  cd ${OUTPUT_DIR}/mosesdecoder
+  git reset --hard 8c5eaa1a122236bbf927bde4ec610906fea599e6
+  cd -
+fi
+
+# Convert SGM files
+# Convert newstest2014 data into raw text format
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/dev/dev/newstest2014-deen-src.de.sgm \
+  > ${OUTPUT_DIR_DATA}/dev/dev/newstest2014.de
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/dev/dev/newstest2014-deen-ref.en.sgm \
+  > ${OUTPUT_DIR_DATA}/dev/dev/newstest2014.en
+
+# Convert newstest2015 data into raw text format
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/dev/dev/newstest2015-deen-src.de.sgm \
+  > ${OUTPUT_DIR_DATA}/dev/dev/newstest2015.de
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/dev/dev/newstest2015-deen-ref.en.sgm \
+  > ${OUTPUT_DIR_DATA}/dev/dev/newstest2015.en
+
+# Convert newstest2016 data into raw text format
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/test/test/newstest2016-deen-src.de.sgm \
+  > ${OUTPUT_DIR_DATA}/test/test/newstest2016.de
+${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+  < ${OUTPUT_DIR_DATA}/test/test/newstest2016-deen-ref.en.sgm \
+  > ${OUTPUT_DIR_DATA}/test/test/newstest2016.en
+
+# Copy dev/test data to output dir
+cp ${OUTPUT_DIR_DATA}/dev/dev/newstest20*.de ${OUTPUT_DIR}
+cp ${OUTPUT_DIR_DATA}/dev/dev/newstest20*.en ${OUTPUT_DIR}
+cp ${OUTPUT_DIR_DATA}/test/test/newstest20*.de ${OUTPUT_DIR}
+cp ${OUTPUT_DIR_DATA}/test/test/newstest20*.en ${OUTPUT_DIR}
+
+# Tokenize data
+for f in ${OUTPUT_DIR}/*.de; do
+  echo "Tokenizing $f..."
+  ${OUTPUT_DIR}/mosesdecoder/scripts/tokenizer/tokenizer.perl -q -l de -threads 8 < $f > ${f%.*}.tok.de
+done
+
+for f in ${OUTPUT_DIR}/*.en; do
+  echo "Tokenizing $f..."
+  ${OUTPUT_DIR}/mosesdecoder/scripts/tokenizer/tokenizer.perl -q -l en -threads 8 < $f > ${f%.*}.tok.en
+done
+
+# Clean all corpora
+for f in ${OUTPUT_DIR}/*.en; do
+  fbase=${f%.*}
+  echo "Cleaning ${fbase}..."
+  ${OUTPUT_DIR}/mosesdecoder/scripts/training/clean-corpus-n.perl $fbase de en "${fbase}.clean" 1 80
+done
+
+# Create dev dataset
+cat "${OUTPUT_DIR}/newstest2015.tok.clean.en" \
+   "${OUTPUT_DIR}/newstest2016.tok.clean.en" \
+   > "${OUTPUT_DIR}/newstest_dev.tok.clean.en"
+
+cat "${OUTPUT_DIR}/newstest2015.tok.clean.de" \
+   "${OUTPUT_DIR}/newstest2016.tok.clean.de" \
+   > "${OUTPUT_DIR}/newstest_dev.tok.clean.de"
+
+# Filter datasets
+python3 scripts/filter_dataset.py \
+   -f1 ${OUTPUT_DIR}/train.tok.clean.en \
+   -f2 ${OUTPUT_DIR}/train.tok.clean.de
+python3 scripts/filter_dataset.py \
+   -f1 ${OUTPUT_DIR}/newstest_dev.tok.clean.en \
+   -f2 ${OUTPUT_DIR}/newstest_dev.tok.clean.de
+
+# Generate Subword Units (BPE)
+# Clone Subword NMT
+if [ ! -d "${OUTPUT_DIR}/subword-nmt" ]; then
+  git clone https://github.com/rsennrich/subword-nmt.git "${OUTPUT_DIR}/subword-nmt"
+  cd ${OUTPUT_DIR}/subword-nmt
+  git reset --hard 48ba99e657591c329e0003f0c6e32e493fa959ef
+  cd -
+fi
+
+# Learn Shared BPE
+for merge_ops in 32000; do
+  echo "Learning BPE with merge_ops=${merge_ops}. This may take a while..."
+  cat "${OUTPUT_DIR}/train.tok.clean.de" "${OUTPUT_DIR}/train.tok.clean.en" | \
+    ${OUTPUT_DIR}/subword-nmt/learn_bpe.py -s $merge_ops > "${OUTPUT_DIR}/bpe.${merge_ops}"
+
+  echo "Apply BPE with merge_ops=${merge_ops} to tokenized files..."
+  for lang in en de; do
+    for f in ${OUTPUT_DIR}/*.tok.${lang} ${OUTPUT_DIR}/*.tok.clean.${lang}; do
+      outfile="${f%.*}.bpe.${merge_ops}.${lang}"
+      ${OUTPUT_DIR}/subword-nmt/apply_bpe.py -c "${OUTPUT_DIR}/bpe.${merge_ops}" < $f > "${outfile}"
+      echo ${outfile}
+    done
+  done
+
+  # Create vocabulary file for BPE
+  cat "${OUTPUT_DIR}/train.tok.clean.bpe.${merge_ops}.en" "${OUTPUT_DIR}/train.tok.clean.bpe.${merge_ops}.de" | \
+    ${OUTPUT_DIR}/subword-nmt/get_vocab.py | cut -f1 -d ' ' > "${OUTPUT_DIR}/vocab.bpe.${merge_ops}"
+
+done
+
+# Duplicate vocab file with language suffix
+cp "${OUTPUT_DIR}/vocab.bpe.32000" "${OUTPUT_DIR}/vocab.bpe.32000.en"
+cp "${OUTPUT_DIR}/vocab.bpe.32000" "${OUTPUT_DIR}/vocab.bpe.32000.de"
+
+echo "All done."
diff --git a/TensorFlow/Translation/GNMT/utils/__init__.py b/TensorFlow/Translation/GNMT/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Translation/GNMT/utils/evaluation_utils.py b/TensorFlow/Translation/GNMT/utils/evaluation_utils.py
new file mode 100644
index 00000000..4ede7fb1
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/evaluation_utils.py
@@ -0,0 +1,58 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Utility for evaluating various tasks, e.g., translation & summarization."""
+import codecs
+import os
+import re
+import subprocess
+
+import tensorflow as tf
+
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.training import checkpoint_management as cm
+
+
+def get_all_checkpoints(output_dir):
+  """docstring."""
+  ckpt = cm.get_checkpoint_state(output_dir, None)
+  res = []
+  if not ckpt:
+    return None
+  for path in ckpt.all_model_checkpoint_paths:
+    # Look for either a V2 path or a V1 path, with priority for V2.
+    v2_path = cm._prefix_to_checkpoint_path(path, saver_pb2.SaverDef.V2)
+    v1_path = cm._prefix_to_checkpoint_path(path, saver_pb2.SaverDef.V1)
+    if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
+        v1_path):
+      res.append(path)
+    else:
+      tf.logging.error("Couldn't match files for checkpoint %s", path)
+  return res
diff --git a/TensorFlow/Translation/GNMT/utils/iterator_utils.py b/TensorFlow/Translation/GNMT/utils/iterator_utils.py
new file mode 100644
index 00000000..2fb4b5e0
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/iterator_utils.py
@@ -0,0 +1,306 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""For loading data into NMT models."""
+from __future__ import print_function
+
+import os
+
+import tensorflow as tf
+
+from utils import vocab_utils
+
+
+def get_effective_epoch_size(hparams, train=True):
+  """Get training epoch size after filtering."""
+  if train:
+    src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
+    tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
+    src_max_len = hparams.src_max_len
+    tgt_max_len = hparams.tgt_max_len
+  else:
+    src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
+    tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
+    src_max_len = hparams.src_max_len_infer
+    tgt_max_len = None
+
+  if src_max_len is None:
+    src_max_len = float('inf')
+  if tgt_max_len is None:
+    tgt_max_len = float('inf')
+
+  srcf = tf.gfile.GFile(src_file, "r")
+  tgtf = tf.gfile.GFile(tgt_file, "r")
+
+  epoch_size = 0
+  src_tokens = 0
+  tgt_tokens = 0
+  for srcline, tgtline in zip(srcf, tgtf):
+    len_srcline = len(srcline.split())
+    len_tgtline = len(tgtline.split())
+    if (
+        len_srcline < src_max_len and
+        len_tgtline < tgt_max_len):
+      epoch_size += 1
+      src_tokens += len_srcline
+      tgt_tokens += len_tgtline
+  srcf.close()
+  tgtf.close()
+  return epoch_size, src_tokens, tgt_tokens
+
+
+# pylint: disable=g-long-lambda,line-too-long
+def get_iterator(src_dataset,
+                 tgt_dataset,
+                 src_vocab_table,
+                 tgt_vocab_table,
+                 batch_size,
+                 sos,
+                 eos,
+                 random_seed,
+                 num_buckets,
+                 src_max_len=None,
+                 tgt_max_len=None,
+                 num_parallel_calls=4,
+                 output_buffer_size=None,
+                 skip_count=None,
+                 num_shards=1,
+                 shard_index=0,
+                 reshuffle_each_iteration=True,
+                 use_char_encode=False,
+                 num_repeat=1,
+                 filter_oversized_sequences=False):
+  """Function that returns input dataset."""
+  if not output_buffer_size:
+    output_buffer_size = batch_size * 1000
+
+  if use_char_encode:
+    src_eos_id = vocab_utils.EOS_CHAR_ID
+  else:
+    src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
+
+  tgt_sos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(sos)), tf.int32)
+  tgt_eos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(eos)), tf.int32)
+
+  src_tgt_dataset = tf.data.Dataset.zip((src_dataset, tgt_dataset))
+
+  src_tgt_dataset = src_tgt_dataset.shard(num_shards, shard_index)
+
+  if skip_count is not None:
+    src_tgt_dataset = src_tgt_dataset.skip(skip_count)
+
+  src_tgt_dataset = src_tgt_dataset.shuffle(
+      output_buffer_size, random_seed,
+      reshuffle_each_iteration).repeat(num_repeat)
+
+  src_tgt_dataset = src_tgt_dataset.map(
+      lambda src, tgt: (tf.string_split([src]).values, tf.string_split([tgt]).values),
+      num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
+
+  # Filter zero length input sequences.
+  src_tgt_dataset = src_tgt_dataset.filter(
+      lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
+
+  # Filter oversized input sequences.
+  if filter_oversized_sequences:
+    src_tgt_dataset = src_tgt_dataset.filter(
+        lambda src, tgt: tf.logical_and(tf.size(src) < src_max_len,
+                                        tf.size(tgt) < tgt_max_len))
+
+  if src_max_len:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt: (src[:src_max_len], tgt),
+        num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
+  if tgt_max_len:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt: (src, tgt[:tgt_max_len]),
+        num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
+
+  # Convert the word strings to ids.  Word strings that are not in the
+  # vocab get the lookup table's default_value integer.
+  if use_char_encode:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt: (tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]),
+                          tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
+        num_parallel_calls=num_parallel_calls)
+  else:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt: (tf.cast(src_vocab_table.lookup(src), tf.int32),
+                          tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
+        num_parallel_calls=num_parallel_calls)
+
+  src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
+  # Create a tgt_input prefixed with <sos> and a tgt_output suffixed with <eos>.
+  src_tgt_dataset = src_tgt_dataset.map(
+      lambda src, tgt: (src,
+                        tf.concat(([tgt_sos_id], tgt), 0),
+                        tf.concat((tgt, [tgt_eos_id]), 0)),
+      num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
+  # Add in sequence lengths.
+  if use_char_encode:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt_in, tgt_out: (
+            src, tgt_in, tgt_out,
+            tf.to_int32(tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN),
+            tf.size(tgt_in)),
+        num_parallel_calls=num_parallel_calls)
+  else:
+    src_tgt_dataset = src_tgt_dataset.map(
+        lambda src, tgt_in, tgt_out: (
+            src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in)),
+        num_parallel_calls=num_parallel_calls)
+
+  src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
+
+  use_xla_compile = os.environ["xla_compile"] == "true"
+  force_inputs_padding = os.environ["force_inputs_padding"] == "true"
+  use_static_input_shape = use_xla_compile or force_inputs_padding
+
+  # Bucket by source sequence length (buckets for lengths 0-9, 10-19, ...)
+  def batching_func(x):
+    return x.padded_batch(
+        batch_size,
+        # The first three entries are the source and target line rows;
+        # these have unknown-length vectors.  The last two entries are
+        # the source and target row sizes; these are scalars.
+        padded_shapes=(
+            tf.TensorShape(
+                [src_max_len if use_static_input_shape else None]),  # src
+            tf.TensorShape(
+                [tgt_max_len if use_static_input_shape else None]),  # tgt_input
+            tf.TensorShape([tgt_max_len if use_static_input_shape else None
+                           ]),  # tgt_output
+            tf.TensorShape([]),  # src_len
+            tf.TensorShape([])),  # tgt_len
+        # Pad the source and target sequences with eos tokens.
+        # (Though notice we don't generally need to do this since
+        # later on we will be masking out calculations past the true sequence.
+        padding_values=(
+            src_eos_id,  # src
+            tgt_eos_id,  # tgt_input
+            tgt_eos_id,  # tgt_output
+            0,  # src_len -- unused
+            0),
+        drop_remainder=True)
+
+  if num_buckets > 1:
+
+    def key_func(unused_1, unused_2, unused_3, src_len, tgt_len):
+      """Calculate bucket_width by maximum source sequence length."""
+      # Pairs with length [0, bucket_width) go to bucket 0, length
+      # [bucket_width, 2 * bucket_width) go to bucket 1, etc.  Pairs with length
+      # over ((num_bucket-1) * bucket_width) words all go into the last bucket.
+      if src_max_len:
+        bucket_width = (src_max_len + num_buckets - 1) // num_buckets
+      else:
+        bucket_width = 10
+
+      # Bucket sentence pairs by the length of their source sentence and target
+      # sentence.
+      bucket_id = tf.maximum(src_len // bucket_width, tgt_len // bucket_width)
+      return tf.to_int64(tf.minimum(num_buckets, bucket_id))
+
+    def reduce_func(unused_key, windowed_data):
+      return batching_func(windowed_data)
+
+    batched_dataset = src_tgt_dataset.apply(
+        tf.contrib.data.group_by_window(
+            key_func=key_func, reduce_func=reduce_func, window_size=batch_size))
+  else:
+    batched_dataset = batching_func(src_tgt_dataset)
+
+
+# Make_one_shot_iterator is not applicable here since we have lookup table.
+# Instead return a tf.data.dataset and let TpuEstimator to initialize and make
+# iterator out of it.
+  batched_dataset = batched_dataset.map(
+      lambda src, tgt_in, tgt_out, source_size, tgt_in_size: (
+          {"source": src,
+           "target_input": tgt_in,
+           "target_output": tgt_out,
+           "source_sequence_length": source_size,
+           "target_sequence_length": tgt_in_size}))
+  return batched_dataset
+
+
+def get_infer_iterator(src_dataset,
+                       src_vocab_table,
+                       batch_size,
+                       eos,
+                       src_max_len=None,
+                       use_char_encode=False):
+  """Get dataset for inference."""
+  if use_char_encode:
+    src_eos_id = vocab_utils.EOS_CHAR_ID
+  else:
+    src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
+  src_dataset = src_dataset.map(lambda src: tf.string_split([src]).values)
+
+  if src_max_len:
+    src_dataset = src_dataset.map(lambda src: src[:src_max_len])
+
+  if use_char_encode:
+    # Convert the word strings to character ids
+    src_dataset = src_dataset.map(
+        lambda src: tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]))
+  else:
+    # Convert the word strings to ids
+    src_dataset = src_dataset.map(
+        lambda src: tf.cast(src_vocab_table.lookup(src), tf.int32))
+
+  # Add in the word counts.
+  if use_char_encode:
+    src_dataset = src_dataset.map(
+        lambda src: (src,
+                     tf.to_int32(
+                         tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN)))
+  else:
+    src_dataset = src_dataset.map(lambda src: (src, tf.size(src)))
+
+  def batching_func(x):
+    return x.padded_batch(
+        batch_size,
+        # The entry is the source line rows;
+        # this has unknown-length vectors.  The last entry is
+        # the source row size; this is a scalar.
+        padded_shapes=(
+            tf.TensorShape([None]),  # src
+            tf.TensorShape([])),  # src_len
+        # Pad the source sequences with eos tokens.
+        # (Though notice we don't generally need to do this since
+        # later on we will be masking out calculations past the true sequence.
+        padding_values=(
+            src_eos_id,  # src
+            0))  # src_len -- unused
+
+  batched_dataset = batching_func(src_dataset)
+  batched_dataset = batched_dataset.map(
+      lambda src_ids, src_seq_len: (
+          {"source": src_ids,
+           "source_sequence_length": src_seq_len}))
+  return batched_dataset
diff --git a/TensorFlow/Translation/GNMT/utils/math_utils.py b/TensorFlow/Translation/GNMT/utils/math_utils.py
new file mode 100644
index 00000000..599103e6
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/math_utils.py
@@ -0,0 +1,160 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Generally useful utility functions."""
+from __future__ import print_function
+
+import collections
+import six
+import os
+
+import tensorflow as tf
+
+from tensorflow.python.framework import function
+from tensorflow.python.ops import gen_nn_ops
+
+
+def sparse_softmax_crossent_with_logits(logits=None, labels=None, name=None):
+  """docstring."""
+  # TODO(jamesqin): merge with tf.nn.sparse_softmax_cross_entropy_with_logits
+  # Basically forks the tf lib function, only that the result isn't casted
+  # back to tf.float16 if the input is tf.float16
+  # TODO(jamesqin): implement a fused kernel to reduce memory footprint.
+
+  # Reshape logits and labels to rank 2.
+  with tf.name_scope(name, "SparseSoftmaxCrossEntropyWithLogits",
+                     [labels, logits]):
+    labels = tf.convert_to_tensor(labels)
+    logits = tf.convert_to_tensor(logits)
+    precise_logits = tf.cast(logits, tf.float32) if (tf.as_dtype(
+        logits.dtype) == tf.float16) else logits
+
+    # Store label shape for result later.
+    labels_static_shape = labels.get_shape()
+    labels_shape = tf.shape(labels)
+    static_shapes_fully_defined = (
+        labels_static_shape.is_fully_defined() and
+        logits.get_shape()[:-1].is_fully_defined())
+    if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0:
+      raise ValueError(
+          "Logits cannot be scalars - received shape %s." % logits.get_shape())
+    if logits.get_shape().ndims is not None and (
+        labels_static_shape.ndims is not None and
+        labels_static_shape.ndims != logits.get_shape().ndims - 1):
+      raise ValueError("Rank mismatch: Rank of labels (received %s) should "
+                       "equal rank of logits minus 1 (received %s)." %
+                       (labels_static_shape.ndims, logits.get_shape().ndims))
+    if (static_shapes_fully_defined and
+        labels_static_shape != logits.get_shape()[:-1]):
+      raise ValueError("Shape mismatch: The shape of labels (received %s) "
+                       "should equal the shape of logits except for the last "
+                       "dimension (received %s)." % (labels_static_shape,
+                                                     logits.get_shape()))
+    # Check if no reshapes are required.
+    if logits.get_shape().ndims == 2:
+      cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
+          precise_logits, labels, name=name)
+      # cost.dtype is always fp32
+      return cost
+
+    # Perform a check of the dynamic shapes if the static shapes are not fully
+    # defined.
+    shape_checks = []
+    if not static_shapes_fully_defined:
+      xla_compile = (os.environ["xla_compile"] == "true")
+      use_xla = (os.environ["use_xla"] == "true")
+      if not (xla_compile or use_xla):
+        # Assert isn't registered w/ GPU, not working w/ xla.compile()
+        shape_checks.append(
+            tf.assert_equal(
+                tf.shape(labels),
+                tf.shape(logits)[:-1]))
+    with tf.control_dependencies(shape_checks):
+      # Reshape logits to 2 dim, labels to 1 dim.
+      num_classes = tf.shape(logits)[tf.rank(logits) - 1]
+      precise_logits = tf.reshape(precise_logits, [-1, num_classes])
+      labels = tf.reshape(labels, [-1])
+      # The second output tensor contains the gradients.  We use it in
+      # _CrossEntropyGrad() in nn_grad but not here.
+      cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
+          precise_logits, labels, name=name)
+      cost = tf.reshape(cost, labels_shape)
+      cost.set_shape(labels_static_shape)
+      # cost is always fp32
+      return cost
+
+
+def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
+  """Custom version of tf.clip_by_global_norm that doesn't check numerics."""
+  if (not isinstance(t_list, collections.Sequence)
+      or isinstance(t_list, six.string_types)):
+    raise TypeError("t_list should be a sequence")
+  t_list = list(t_list)
+  if use_norm is None:
+    use_norm = tf.global_norm(t_list, name)
+
+  with tf.name_scope(name, "clip_by_global_norm", t_list + [clip_norm]) as name:
+    # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
+    scale = clip_norm * tf.minimum(
+        1.0 / use_norm,
+        tf.constant(1.0, dtype=use_norm.dtype) / clip_norm)
+
+    values = [
+        tf.convert_to_tensor(
+            t.values if isinstance(t, tf.IndexedSlices) else t,
+            name="t_%d" % i)
+        if t is not None else t
+        for i, t in enumerate(t_list)]
+
+    values_clipped = []
+    for i, v in enumerate(values):
+      if v is None:
+        values_clipped.append(None)
+      else:
+        with tf.colocate_with(v):
+          values_clipped.append(
+              tf.identity(v * scale, name="%s_%d" % (name, i)))
+
+    list_clipped = [
+        tf.IndexedSlices(c_v, t.indices, t.dense_shape)
+        if isinstance(t, tf.IndexedSlices)
+        else c_v
+        for (c_v, t) in zip(values_clipped, t_list)]
+
+  return list_clipped, use_norm
+
+
+def BatchMatMul(a, b):
+  use_fp32_batch_matmul = (os.environ["use_fp32_batch_matmul"] == "true")
+  xla_compile = (os.environ["xla_compile"] == "true")
+  if use_fp32_batch_matmul:
+    def DoFn(a, b):
+      dtype = a.dtype
+      a = tf.to_float(a)
+      b = tf.to_float(b)
+      return tf.cast(tf.matmul(a, b), dtype)
+    # If using xla_compile, the fwd and bak per tower are wrapped in xla_compile
+    if not xla_compile:
+      DoFn = function.Defun(noinline=True)(DoFn)
+      res = DoFn(a, b)
+      res.set_shape((None, None, b.shape[-1].value))
+    else:
+      # If xla_compile, leave to xla to handle the casts.
+      res = DoFn(a, b)
+  else:
+    res = tf.matmul(a, b)
+  return res
+
+
diff --git a/TensorFlow/Translation/GNMT/utils/misc_utils.py b/TensorFlow/Translation/GNMT/utils/misc_utils.py
new file mode 100644
index 00000000..f461b831
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/misc_utils.py
@@ -0,0 +1,179 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Generally useful utility functions."""
+from __future__ import print_function
+
+import codecs
+import collections
+import json
+import math
+import os
+import sys
+import time
+from distutils import version
+import tensorflow as tf
+
+
+def check_tensorflow_version():
+  # LINT.IfChange
+  min_tf_version = "1.3.0"
+  # LINT
+  if (version.LooseVersion(tf.__version__) <
+      version.LooseVersion(min_tf_version)):
+    raise EnvironmentError("Tensorflow version must >= %s" % min_tf_version)
+
+
+def weighted_avg(inputs, weights, force_fp32=False):
+  dtype = tf.float32 if force_fp32 else inputs[0].dtype
+  inputs = [tf.cast(x, dtype) for x in inputs]
+  weights = [tf.cast(x, dtype) for x in weights]
+  norm = tf.add_n([x * y for x, y in zip(inputs, weights)])
+  denorm = tf.add_n(weights)
+  return norm / denorm
+
+
+def safe_exp(value):
+  """Exponentiation with catching of overflow error."""
+  try:
+    ans = math.exp(value)
+  except OverflowError:
+    ans = float("inf")
+  return ans
+
+
+def print_time(s, start_time):
+  """Take a start time, print elapsed duration, and return a new time."""
+  print("%s, time %ds, %s." % (s, (time.time() - start_time), time.ctime()))
+  sys.stdout.flush()
+  return time.time()
+
+
+def print_out(s, f=None, new_line=True):
+  """Similar to print but with support to flush and output to a file."""
+  if isinstance(s, bytes):
+    s = s.decode("utf-8")
+
+  if f:
+    f.write(s)
+    if new_line:
+      f.write(u"\n")
+
+  # stdout
+  out_s = s.encode("utf-8")
+  if not isinstance(out_s, str):
+    out_s = out_s.decode("utf-8")
+  print(out_s, end="", file=sys.stdout)
+
+  if new_line:
+    sys.stdout.write("\n")
+  sys.stdout.flush()
+
+
+def print_hparams(hparams, skip_patterns=None, header=None):
+  """Print hparams, can skip keys based on pattern."""
+  if header: print_out("%s" % header)
+  values = hparams.values()
+  for key in sorted(values.keys()):
+    if not skip_patterns or all(
+        [skip_pattern not in key for skip_pattern in skip_patterns]):
+      print_out("  %s=%s" % (key, str(values[key])))
+
+
+def serialize_hparams(hparams):
+  """Print hparams, can skip keys based on pattern."""
+  values = hparams.values()
+  res = ""
+  for key in sorted(values.keys()):
+    res += "%s=%s\n" % (key, str(values[key]))
+  return res
+
+
+def load_hparams(model_dir):
+  """Load hparams from an existing model directory."""
+  hparams_file = os.path.join(model_dir, "hparams")
+  if tf.gfile.Exists(hparams_file):
+    print_out("# Loading hparams from %s" % hparams_file)
+    with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_file, "rb")) as f:
+      try:
+        hparams_values = json.load(f)
+        hparams = tf.contrib.training.HParams(**hparams_values)
+      except ValueError:
+        print_out("  can't load hparams file")
+        return None
+    return hparams
+  else:
+    return None
+
+
+def maybe_parse_standard_hparams(hparams, hparams_path):
+  """Override hparams values with existing standard hparams config."""
+  if hparams_path and tf.gfile.Exists(hparams_path):
+    print_out("# Loading standard hparams from %s" % hparams_path)
+    with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_path, "rb")) as f:
+      hparams.parse_json(f.read())
+  return hparams
+
+
+def save_hparams(output_dir, hparams):
+  """Save hparams."""
+  hparams_file = os.path.join(output_dir, "hparams")
+  print_out("  saving hparams to %s" % hparams_file)
+  with codecs.getwriter("utf-8")(tf.gfile.GFile(hparams_file, "wb")) as f:
+    f.write(hparams.to_json(indent=4, sort_keys=True))
+
+
+def debug_tensor(s, msg=None, summarize=10):
+  """Print the shape and value of a tensor at test time. Return a new tensor."""
+  if not msg:
+    msg = s.name
+  return tf.Print(s, [tf.shape(s), s], msg + " ", summarize=summarize)
+
+
+def add_summary(summary_writer, global_step, tag, value):
+  """Add a new summary to the current summary_writer."""
+  summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+  summary_writer.add_summary(summary, global_step)
+
+
+def format_text(words):
+  """Convert a sequence words into sentence."""
+  if (not hasattr(words, "__len__") and  # for numpy array
+      not isinstance(words, collections.Iterable)):
+    words = [words]
+  return b" ".join(words)
+
+
+def format_bpe_text(symbols, delimiter=b"@@"):
+  """Convert a sequence of bpe words into sentence."""
+  words = []
+  word = b""
+  if isinstance(symbols, str):
+    symbols = symbols.encode()
+  delimiter_len = len(delimiter)
+  for symbol in symbols:
+    if len(symbol) >= delimiter_len and symbol[-delimiter_len:] == delimiter:
+      word += symbol[:-delimiter_len]
+    else:  # end of a word
+      word += symbol
+      words.append(word)
+      word = b""
+  return b" ".join(words)
+
+
+def format_spm_text(symbols):
+  """Decode a text in SPM (https://github.com/google/sentencepiece) format."""
+  return u"".join(format_text(symbols).decode("utf-8").split()).replace(
+      u"\u2581", u" ").strip().encode("utf-8")
diff --git a/TensorFlow/Translation/GNMT/utils/nmt_utils.py b/TensorFlow/Translation/GNMT/utils/nmt_utils.py
new file mode 100644
index 00000000..3865e0f4
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/nmt_utils.py
@@ -0,0 +1,60 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Utility functions specifically for NMT."""
+from __future__ import print_function
+
+import codecs
+import time
+import numpy as np
+import tensorflow as tf
+
+from utils import misc_utils as utils
+
+__all__ = ["get_translation"]
+
+
+def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
+  """Given batch decoding outputs, select a sentence and turn to text."""
+  if tgt_eos: tgt_eos = tgt_eos.encode("utf-8")
+  # Select a sentence
+  output = nmt_outputs[sent_id, :].tolist()
+
+  # If there is an eos symbol in outputs, cut them at that point.
+  if tgt_eos and tgt_eos in output:
+    output = output[:output.index(tgt_eos)]
+
+  if subword_option == "bpe":  # BPE
+    translation = utils.format_bpe_text(output)
+  elif subword_option == "spm":  # SPM
+    translation = utils.format_spm_text(output)
+  else:
+    translation = utils.format_text(output)
+
+  return translation, len(output)
diff --git a/TensorFlow/Translation/GNMT/utils/vocab_utils.py b/TensorFlow/Translation/GNMT/utils/vocab_utils.py
new file mode 100644
index 00000000..0d63f8c7
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/utils/vocab_utils.py
@@ -0,0 +1,194 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utility to handle vocabularies."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import codecs
+import os
+import tensorflow as tf
+
+from tensorflow.python.ops import lookup_ops
+
+from utils import misc_utils as utils
+
+# word level special token
+UNK = "<unk>"
+SOS = "<s>"
+EOS = "</s>"
+UNK_ID = 0
+
+# char ids 0-255 come from utf-8 encoding bytes
+# assign 256-300 to special chars
+BOS_CHAR_ID = 256  # <begin sentence>
+EOS_CHAR_ID = 257  # <end sentence>
+BOW_CHAR_ID = 258  # <begin word>
+EOW_CHAR_ID = 259  # <end word>
+PAD_CHAR_ID = 260  # <padding>
+
+DEFAULT_CHAR_MAXLEN = 50  # max number of chars for each word.
+
+
+def _string_to_bytes(text, max_length):
+  """Given string and length, convert to byte seq of at most max_length.
+
+  This process mimics docqa/elmo's preprocessing:
+  https://github.com/allenai/document-qa/blob/master/docqa/elmo/data.py
+
+  Note that we make use of BOS_CHAR_ID and EOS_CHAR_ID in iterator_utils.py &
+  our usage differs from docqa/elmo.
+
+  Args:
+    text: tf.string tensor of shape []
+    max_length: max number of chars for each word.
+
+  Returns:
+    A tf.int32 tensor of the byte encoded text.
+  """
+  byte_ids = tf.to_int32(tf.decode_raw(text, tf.uint8))
+  byte_ids = byte_ids[:max_length - 2]
+  padding = tf.fill([max_length - tf.shape(byte_ids)[0] - 2], PAD_CHAR_ID)
+  byte_ids = tf.concat(
+      [[BOW_CHAR_ID], byte_ids, [EOW_CHAR_ID], padding], axis=0)
+  tf.logging.info(byte_ids)
+
+  byte_ids = tf.reshape(byte_ids, [max_length])
+  tf.logging.info(byte_ids.get_shape().as_list())
+  return byte_ids + 1
+
+
+def tokens_to_bytes(tokens):
+  """Given a sequence of strings, map to sequence of bytes.
+
+  Args:
+    tokens: A tf.string tensor
+
+  Returns:
+    A tensor of shape words.shape + [bytes_per_word] containing byte versions
+    of each word.
+  """
+  bytes_per_word = DEFAULT_CHAR_MAXLEN
+  with tf.device("/cpu:0"):
+    tf.assert_rank(tokens, 1)
+    shape = tf.shape(tokens)
+    tf.logging.info(tokens)
+    tokens_flat = tf.reshape(tokens, [-1])
+    as_bytes_flat = tf.map_fn(
+        fn=lambda x: _string_to_bytes(x, max_length=bytes_per_word),
+        elems=tokens_flat,
+        dtype=tf.int32,
+        back_prop=False)
+    tf.logging.info(as_bytes_flat)
+    as_bytes = tf.reshape(as_bytes_flat, [shape[0], bytes_per_word])
+  return as_bytes
+
+
+def load_vocab(vocab_file):
+  vocab = []
+  with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
+    vocab_size = 0
+    for word in f:
+      vocab_size += 1
+      vocab.append(word.strip())
+  return vocab, vocab_size
+
+
+def check_vocab(vocab_file, output_dir, check_special_token=True, sos=None,
+                eos=None, unk=None):
+  """Check if vocab_file doesn't exist, create from corpus_file."""
+  if tf.gfile.Exists(vocab_file):
+    utils.print_out("# Vocab file %s exists" % vocab_file)
+    vocab, vocab_size = load_vocab(vocab_file)
+    if check_special_token:
+      # Verify if the vocab starts with unk, sos, eos
+      # If not, prepend those tokens & generate a new vocab file
+      if not unk: unk = UNK
+      if not sos: sos = SOS
+      if not eos: eos = EOS
+      assert len(vocab) >= 3
+      if vocab[0] != unk or vocab[1] != sos or vocab[2] != eos:
+        utils.print_out("The first 3 vocab words [%s, %s, %s]"
+                        " are not [%s, %s, %s]" %
+                        (vocab[0], vocab[1], vocab[2], unk, sos, eos))
+        vocab = [unk, sos, eos] + vocab
+        vocab_size += 3
+        new_vocab_file = os.path.join(output_dir, os.path.basename(vocab_file))
+        with codecs.getwriter("utf-8")(
+            tf.gfile.GFile(new_vocab_file, "wb")) as f:
+          for word in vocab:
+            f.write("%s\n" % word)
+        vocab_file = new_vocab_file
+  else:
+    raise ValueError("vocab_file '%s' does not exist." % vocab_file)
+
+  vocab_size = len(vocab)
+  return vocab_size, vocab_file
+
+
+def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
+  """Creates vocab tables for src_vocab_file and tgt_vocab_file."""
+  src_vocab_table = lookup_ops.index_table_from_file(
+      src_vocab_file, default_value=UNK_ID)
+  if share_vocab:
+    tgt_vocab_table = src_vocab_table
+  else:
+    tgt_vocab_table = lookup_ops.index_table_from_file(
+        tgt_vocab_file, default_value=UNK_ID)
+  return src_vocab_table, tgt_vocab_table
+
+
+def load_embed_txt(embed_file):
+  """Load embed_file into a python dictionary.
+
+  Note: the embed_file should be a Glove/word2vec formatted txt file. Assuming
+  Here is an exampe assuming embed_size=5:
+
+  the -0.071549 0.093459 0.023738 -0.090339 0.056123
+  to 0.57346 0.5417 -0.23477 -0.3624 0.4037
+  and 0.20327 0.47348 0.050877 0.002103 0.060547
+
+  For word2vec format, the first line will be: <num_words> <emb_size>.
+
+  Args:
+    embed_file: file path to the embedding file.
+  Returns:
+    a dictionary that maps word to vector, and the size of embedding dimensions.
+  """
+  emb_dict = dict()
+  emb_size = None
+
+  is_first_line = True
+  with codecs.getreader("utf-8")(tf.gfile.GFile(embed_file, "rb")) as f:
+    for line in f:
+      tokens = line.rstrip().split(" ")
+      if is_first_line:
+        is_first_line = False
+        if len(tokens) == 2:  # header line
+          emb_size = int(tokens[1])
+          continue
+      word = tokens[0]
+      vec = list(map(float, tokens[1:]))
+      emb_dict[word] = vec
+      if emb_size:
+        if emb_size != len(vec):
+          utils.print_out(
+              "Ignoring %s since embeding size is inconsistent." % word)
+          del emb_dict[word]
+      else:
+        emb_size = len(vec)
+  return emb_dict, emb_size
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/BUILD b/TensorFlow/Translation/GNMT/variable_mgr/BUILD
new file mode 100644
index 00000000..d0e4120f
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/BUILD
@@ -0,0 +1,60 @@
+licenses(["notice"])  # Apache 2.0
+
+package(default_visibility = ["//visibility:public"])
+
+py_library(
+    name = "variable_mgr_util",
+    srcs = [
+        "variable_mgr_util.py",
+    ],
+    deps = [
+        "//third_party/py/tensorflow",
+        "//third_party/tensorflow/python:data_flow_ops",
+        "//third_party/tensorflow/python:framework_ops",
+    ],
+)
+
+py_library(
+    name = "allreduce",
+    srcs = [
+        "allreduce.py",
+    ],
+    deps = [
+        "//third_party/py/tensorflow",
+        "//third_party/tensorflow/contrib/all_reduce",
+        "//third_party/tensorflow/python:collective_ops",
+        "//third_party/tensorflow/python:nccl_ops",
+    ],
+)
+
+py_library(
+    name = "batch_allreduce",
+    srcs = [
+        "batch_allreduce.py",
+    ],
+    deps = [
+        ":allreduce",
+        ":constants",
+        "//third_party/py/tensorflow",
+    ],
+)
+
+py_library(
+    name = "variable_mgr",
+    srcs = [
+        "variable_mgr.py",
+    ],
+    deps = [
+        ":allreduce",
+        ":batch_allreduce",
+        ":variable_mgr_util",
+        "//third_party/py/tensorflow",
+    ],
+)
+
+py_library(
+    name = "constants",
+    srcs = [
+        "constants.py",
+    ],
+)
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/__init__.py b/TensorFlow/Translation/GNMT/variable_mgr/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/allreduce.py b/TensorFlow/Translation/GNMT/variable_mgr/allreduce.py
new file mode 100644
index 00000000..9bb6d4ca
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/allreduce.py
@@ -0,0 +1,640 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for allreduce."""
+
+from __future__ import print_function
+
+import collections as pycoll
+import re
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+from tensorflow.contrib.all_reduce.python import all_reduce
+from tensorflow.python.ops import collective_ops
+
+AllReduceSpecTuple = pycoll.namedtuple('AllReduceSpecTuple', 'alg shards limit')
+
+
+def parse_general_int(s):
+  """Parse integer with power-of-2 suffix eg. 32k."""
+  mo = re.match(r'(\d+)([KkMGT]?)$', s)
+  if mo:
+    i, suffix = mo.group(1, 2)
+    v = int(i)
+    if suffix:
+      if suffix == 'K' or suffix == 'k':
+        v *= 1024
+      elif suffix == 'M':
+        v *= (1024 * 1024)
+      elif suffix == 'G':
+        v *= (1024 * 1024 * 1024)
+      elif suffix == 'T':
+        v *= (1024 * 1024 * 1024 * 1024)
+      else:
+        raise ValueError('invalid integer string %s' % s)
+    return v
+  else:
+    v = int(s)
+  return v
+
+
+def parse_all_reduce_spec(all_reduce_spec):
+  """Parse all_reduce_spec.
+
+  Args:
+    all_reduce_spec: a string specifying a combination of all-reduce
+      algorithms to apply for gradient reduction.
+
+  Returns:
+    a list of AllReduceSpecTuple.
+
+  Raises:
+    ValueError: all_reduce_spec is not well-formed.
+
+  An all_reduce_spec has BNF form:
+     int ::= positive whole number
+     g_int ::= int[KkMGT]?
+     alg_spec ::= alg | alg#int
+     range_spec ::= alg_spec | alg_spec/alg_spec
+     spec ::= range_spec | range_spec:g_int:range_spec
+
+  Not all syntactically correct specifications are supported.
+  Examples of supported all_reduce_spec strings, with semantics explained:
+
+    'collective' == apply tf.collective_reduce operator to all tensors.
+    'collective#2' == apply tf.collective_reduce operator to all tensors,
+            requesting up to 2 simultaneous transfers at each node, if
+            feasible, by subdividing tensor by an additional factor of 2.
+    'xring' == apply ring all-reduce to all tensors
+    'xring#2' == apply ring all-reduce to all tensors, using two simultaneous
+            transfer rings, each operating on 1/2 of each tensor.
+    'nccl'  == apply NCCL all-reduce to all tensors (only works within
+            a single worker process where all devices are GPUs)
+    'nccl/xring' == apply NCCL all-reduce to all tensors within each worker
+            to produce at least one full-reduced (locally) value,
+            then apply ring all-reduce to one such value from each
+            worker, then apply NCCL broadcast to propagate those globally
+            reduced values back to every device within each worker.
+    'pscpu' == Shuffle reduce using worker CPUs as the gather devices: each
+            distributed tensor is reduced by copying all instances to
+            one of the worker CPUs, computing the reduction there, then
+            copying back to each participating device.  Tensor reductions
+            are assigned to specific CPUs round-robin.
+    'psgpu#4' == Arrange all GPUs across all workers into groups of 4.
+            Each distributed tensor is shuffle reduced against one
+            such group of 4 GPUs, selected round-robin.  That is, each
+            tensor is split across 4 shards for the reduction.
+    'pscpu:2k:pscpu#2:64k:xring' == Apply single-shard pscpu to
+            tensors of size <= 2048 elements, apply 2-shard pscpu to
+            tensors up to size 64k elements, apply xring to larger tensors.
+    'pscpu/pscpu#2' == Use shuffle gather to locally reduce each tensor on
+            the worker's CPU, then use 2-shard shuffle to reduce those
+            locally reduced tensors across workers (on the worker CPUs), then
+            scatter the globally reduced values locally from each worker CPU.
+  """
+  range_parts = all_reduce_spec.split(':') + ['-1']
+  if len(range_parts) % 2:
+    raise ValueError('all_reduce_spec not well formed: %s' % all_reduce_spec)
+  limit = 0
+  spec = []
+  alg = None
+  shards = 1
+  for i, range_part in enumerate(range_parts):
+    if i % 2 == 1:
+      try:
+        limit = parse_general_int(range_part)
+        spec.append(AllReduceSpecTuple(alg=alg, shards=shards, limit=limit))
+      except ValueError:
+        raise ValueError('all_reduce_spec (%s) contains non-integer range %s' %
+                         (all_reduce_spec, range_part))
+    else:
+      alg = range_part
+      alg_parts = range_part.split('#')
+      alg = alg_parts[0]
+      if len(alg_parts) > 1:
+        try:
+          shards = int(alg_parts[1])
+        except ValueError:
+          raise ValueError('all_reduce_spec (%s) contains non-integer '
+                           'shards %s' % all_reduce_spec, alg_parts[1])
+      else:
+        shards = 1
+      if alg not in [
+          'nccl', 'nccl/xring', 'nccl/rechd', 'nccl/pscpu', 'xring', 'pscpu',
+          'psgpu', 'pscpu/pscpu', 'collective'
+      ]:
+        raise ValueError('all_reduce_spec (%s) contains invalid alg %s' %
+                         (all_reduce_spec, alg))
+  return spec
+
+
+def build_all_reduce_device_prefixes(job_name, num_tasks):
+  """Build list of device prefix names for all_reduce.
+
+  Args:
+    job_name: 'worker', 'ps' or 'localhost'.
+    num_tasks: number of jobs across which device names should be generated.
+
+  Returns:
+     A list of device name prefix strings. Each element spells out the full
+     host name without adding the device.
+     e.g. '/job:worker/task:0'
+  """
+  if job_name != 'localhost':
+    return ['/job:%s/task:%d' % (job_name, d) for d in range(0, num_tasks)]
+  else:
+    assert num_tasks == 1
+    return ['/job:%s' % job_name]
+
+
+def group_device_names(devices, group_size):
+  """Group device names into groups of group_size.
+
+  Args:
+    devices: list of strings naming devices.
+    group_size: int >= 1
+
+  Returns:
+    list of lists of devices, where each inner list is group_size long,
+      and each device appears at least once in an inner list.  If
+      len(devices) % group_size = 0 then each device will appear
+      exactly once.
+
+  Raises:
+    ValueError: group_size > len(devices)
+  """
+  num_devices = len(devices)
+  if group_size > num_devices:
+    raise ValueError('only %d devices, but group_size=%d' % (num_devices,
+                                                             group_size))
+  num_groups = (
+      num_devices // group_size + (1 if (num_devices % group_size != 0) else 0))
+  groups = [[] for i in range(num_groups)]
+  for i in range(0, num_groups * group_size):
+    groups[i % num_groups].append(devices[i % num_devices])
+  return groups
+
+
+def split_grads_by_size(threshold_size, device_grads):
+  """Break gradients into two sets according to tensor size.
+
+  Args:
+    threshold_size: int size cutoff for small vs large tensor.
+    device_grads: List of lists of (gradient, variable) tuples.  The outer
+        list is over devices. The inner list is over individual gradients.
+
+  Returns:
+    small_grads: Subset of device_grads where shape is <= theshold_size
+       elements.
+    large_grads: Subset of device_grads where shape is > threshold_size
+       elements.
+  """
+  small_grads = []
+  large_grads = []
+  for dl in device_grads:
+    small_dl = []
+    large_dl = []
+    for (g, v) in dl:
+      tensor_size = g.get_shape().num_elements()
+      if tensor_size <= threshold_size:
+        small_dl.append([g, v])
+      else:
+        large_dl.append([g, v])
+    if small_dl:
+      small_grads.append(small_dl)
+    if large_dl:
+      large_grads.append(large_dl)
+  return small_grads, large_grads
+
+
+_instance_key = 1
+
+
+def new_collective_instance_key():
+  """Returns a new instance key for use in defining a collective op."""
+  global _instance_key
+  v = _instance_key
+  _instance_key += 1
+  return v
+
+
+_group_key = 1
+_group_key_table = dict()
+
+
+def collective_group_key(devices):
+  """Returns a group key for the set of devices.
+
+  Args:
+    devices: list of strings naming devices in a collective group.
+
+  Returns:
+    int key uniquely identifying the set of device names.
+  """
+  global _group_key
+  global _group_key_table
+  parsed = [tf.DeviceSpec.from_string(d) for d in devices]
+  names = sorted(['%s:%d' % (d.device_type, d.device_index) for d in parsed])
+  concat = ','.join(names)
+  if concat not in _group_key_table.keys():
+    new_key = _group_key
+    _group_key += 1
+    _group_key_table[concat] = new_key
+  rv = _group_key_table[concat]
+  return rv
+
+
+def build_collective_reduce(input_tensors, num_workers, num_shards,
+                            red_op='Add', un_op='Id'):
+  """Build a subgraph that does one full all-reduce, using the collective Op.
+
+  Args:
+    input_tensors: tensors within a single worker graph that are to be reduced
+      together; must be one per device.
+    num_workers: total number of workers with identical independent graphs that
+      will be doing this same reduction.  The reduction will actually include
+      the corresponding tensors at all these workers.
+    num_shards: number of shards into which to divide each per-tick chunk,
+      normally 1 but could be higher on multi-data-path architectures.
+    red_op: string naming the reduction op
+    un_op: string naming the unary final op
+
+  Returns:
+    An array of final tensors, one per device, computed by the full reduction.
+
+  Raises:
+    ValueError: There must be at least two tensors over all the workers.
+  """
+  group_size = len(input_tensors) * num_workers
+  if group_size < 2:
+    raise ValueError('num_workers * len(input_tensors) must be 2 or greater')
+  devices = [t.device for t in input_tensors]
+  num_devices = len(devices)
+  group_key = collective_group_key(devices)
+  instance_key = new_collective_instance_key()
+  out_tensors = []
+  if num_shards == 1:
+    subdiv_offsets = [0]
+  elif num_shards == 2:
+    if num_devices > 1:
+      subdiv_offsets = [0, -(num_devices // 2)]
+    else:
+      subdiv_offsets = [0]
+  else:
+    raise ValueError('Unsupported num_shards %d' % num_shards)
+  for d in range(num_devices):
+    with tf.device(devices[d]):
+      reduce_op = collective_ops.all_reduce(input_tensors[d],
+                                            group_size, group_key, instance_key,
+                                            red_op, un_op,
+                                            subdiv_offsets)
+      out_tensors.append(reduce_op)
+  return out_tensors
+
+
+def broadcast_send(t, shape, dtype, group_size, group_key, instance_key):
+  return collective_ops.broadcast_send(t, shape, dtype, group_size, group_key,
+                                       instance_key)
+
+
+def broadcast_recv(shape, dtype, group_size, group_key, instance_key):
+  return collective_ops.broadcast_recv(shape, dtype, group_size, group_key,
+                                       instance_key)
+
+
+def sum_grad_and_var_all_reduce(single_session,
+                                grad_and_vars,
+                                num_workers,
+                                alg,
+                                gpu_indices,
+                                aux_devices=None,
+                                num_shards=1):
+  """Apply all-reduce algorithm over specified gradient tensors."""
+  scaled_grads = [g for g, _ in grad_and_vars]
+  if alg == 'collective':
+    assert not single_session
+    summed_grads = build_collective_reduce(
+        scaled_grads, num_workers, num_shards, 'Add', 'Id')
+  else:
+    with tf.name_scope('allreduce'):
+      # Note that each grad_and_vars looks like the following:
+      #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+      if alg == 'nccl':
+        summed_grads = all_reduce.build_nccl_all_reduce(scaled_grads, tf.add)
+      elif alg == 'xring':
+        summed_grads = all_reduce.build_ring_all_reduce(
+            scaled_grads, num_workers, num_shards, gpu_indices, tf.add)
+      elif alg == 'nccl/xring':
+        summed_grads = all_reduce.build_nccl_then_ring(scaled_grads, num_shards,
+                                                       tf.add)
+      elif alg == 'nccl/rechd':
+        summed_grads = all_reduce.build_nccl_then_recursive_hd(
+            scaled_grads, tf.add)
+      elif alg == 'nccl/pscpu':
+        summed_grads = all_reduce.build_nccl_then_shuffle(
+            scaled_grads, aux_devices, tf.add, tf.add_n)
+      elif alg == 'pscpu/pscpu':
+        summed_grads = all_reduce.build_shuffle_then_shuffle(
+            scaled_grads,
+            aux_devices,
+            # TODO(tucker): devise a way of better specifying the device set
+            # for the second level.
+            [aux_devices[0]],
+            tf.add_n)
+      elif alg in ['pscpu', 'psgpu']:
+        summed_grads = all_reduce.build_shuffle_all_reduce(
+            scaled_grads, aux_devices, tf.add_n)
+      else:
+        raise ValueError('unsupported all_reduce alg: ', alg)
+
+  result = []
+  for (_, v), g in zip(grad_and_vars, summed_grads):
+    result.append([g, v])
+  return result
+
+
+def contains_any(haystack, needles):
+  """Tests if any needle is a substring of haystack.
+
+  Args:
+    haystack: a string
+    needles: list of strings
+
+  Returns:
+    True if any element of needles is a substring of haystack,
+      False otherwise.
+  """
+  for n in needles:
+    if n in haystack:
+      return True
+  return False
+
+
+def sum_gradients_all_reduce(single_session,
+                             dev_prefixes,
+                             tower_grads,
+                             num_workers,
+                             alg,
+                             num_shards,
+                             gpu_indices,
+                             agg_small_grads_max_bytes=0,
+                             agg_small_grads_max_group=10,
+                             allreduce_merge_scope=1):
+  """Apply all-reduce algorithm over specified gradient tensors.
+
+  Args:
+    single_session: true if reduction is applied to one graph across
+      all workers, false if ths application is to a single-worker graph only.
+    dev_prefixes: list of prefix strings to use to generate PS device names.
+    tower_grads: the gradients to reduce.
+    num_workers: number of worker processes across entire job.
+    alg: the all-reduce algorithm to apply.
+    num_shards: alg-specific sharding factor.
+    gpu_indices: indices of local GPUs in order usable for ring-reduce.
+    agg_small_grads_max_bytes: largest tensor eligible for aggregation,
+      in number of bytes.
+    agg_small_grads_max_group: largest permitted aggregation of small
+      tensors.
+    allreduce_merge_scope: size of groups into which to partition consecutive
+      gradients grouped under a common 'allreduce' name scope for application
+      of ScopedAllocator optimization.
+
+  Returns:
+    list of reduced tensors
+  """
+  alg_contains_shuffle = contains_any(alg, ['pscpu', 'psgpu'])
+  is_hierarchical = '/' in alg
+  if 'pscpu' in alg:
+    aux_devices = [prefix + '/cpu:0' for prefix in dev_prefixes]
+  elif 'psgpu' in alg:
+    aux_devices = [
+        prefix + '/gpu:%d' % i
+        for i in range(len(gpu_indices))
+        for prefix in dev_prefixes
+    ]
+  else:
+    aux_devices = ['/job:localhost/cpu:0']
+  aux_device_groups = group_device_names(
+      aux_devices,
+      num_shards if (alg != 'collective' and alg_contains_shuffle) else 1)
+  group_index = 0
+  if agg_small_grads_max_bytes > 0 and agg_small_grads_max_group > 0:
+    tower_grads, packing = pack_small_tensors(
+        tower_grads,
+        max_bytes=agg_small_grads_max_bytes,
+        max_group=agg_small_grads_max_group)
+  else:
+    packing = None
+  reduced_gv_list = []
+  gv = list(zip(*tower_grads))
+  merge_scope = allreduce_merge_scope if allreduce_merge_scope > 0 else 1
+  chunked_gv = [gv[x:x + merge_scope]
+                for x in xrange(0, len(gv), merge_scope)]
+  for chunk in chunked_gv:
+    with tf.name_scope('allreduce'):
+      for grad_and_vars in chunk:
+        reduced_gv_list.append(sum_grad_and_var_all_reduce(
+            single_session,
+            grad_and_vars, num_workers, alg, gpu_indices,
+            (aux_devices if is_hierarchical
+             else aux_device_groups[group_index]),
+            num_shards))
+        group_index = (group_index + 1) % len(aux_device_groups)
+  new_tower_grads = [list(x) for x in zip(*reduced_gv_list)]
+  if packing:
+    new_tower_grads = unpack_small_tensors(new_tower_grads, packing)
+  return new_tower_grads
+
+
+def extract_ranges(index_list, range_size_limit=32):
+  """Extract consecutive ranges and singles from index_list.
+
+  Args:
+    index_list: List of monotone increasing non-negative integers.
+    range_size_limit: Largest size range to return.  If a larger
+      consecutive range exists it will be returned as multiple
+      ranges.
+
+  Returns:
+   ranges, singles where ranges is a list of [first, last] pairs of
+     consecutive elements in index_list, and singles is all of the
+     other elements, in original order.
+  """
+  if not index_list:
+    return [], []
+  first = index_list[0]
+  last = first
+  ranges = []
+  singles = []
+  for i in index_list[1:]:
+    if i == last + 1 and (last - first) <= range_size_limit:
+      last = i
+    else:
+      if last > first:
+        ranges.append([first, last])
+      else:
+        singles.append(first)
+      first = i
+      last = i
+  if last > first:
+    ranges.append([first, last])
+  else:
+    singles.append(first)
+  return ranges, singles
+
+
+GradPackTuple = pycoll.namedtuple('GradPackTuple', 'indices vars shapes')
+
+
+def pack_range(key, packing, grad_vars, rng):
+  """Form the concatenation of a specified range of gradient tensors.
+
+  Args:
+    key: Value under which to store meta-data in packing that will be used
+      later to restore the grad_var list structure.
+    packing: Dict holding data describing packed ranges of small tensors.
+    grad_vars: List of (grad, var) pairs for one tower.
+    rng: A pair of integers giving the first, last indices of a consecutive
+      range of tensors to be packed.
+
+  Returns:
+    A tensor that is the concatenation of all the specified small tensors.
+  """
+  to_pack = grad_vars[rng[0]:rng[1] + 1]
+  members = []
+  variables = []
+  restore_shapes = []
+  with tf.name_scope('pack'):
+    for g, v in to_pack:
+      variables.append(v)
+      restore_shapes.append(g.shape)
+      with tf.device(g.device):
+        members.append(tf.reshape(g, [-1]))
+    packing[key] = GradPackTuple(
+        indices=range(rng[0], rng[1] + 1),
+        vars=variables,
+        shapes=restore_shapes)
+    with tf.device(members[0].device):
+      return tf.concat(members, 0)
+
+
+def unpack_grad_tuple(gv, gpt):
+  """Unpack a previously packed collection of gradient tensors.
+
+  Args:
+    gv: A (grad, var) pair to be unpacked.
+    gpt: A GradPackTuple describing the packing operation that produced gv.
+
+  Returns:
+    A list of (grad, var) pairs corresponding to the values that were
+     originally packed into gv, maybe following subsequent operations like
+     reduction.
+  """
+  elt_widths = [x.num_elements() for x in gpt.shapes]
+  with tf.device(gv[0][0].device):
+    with tf.name_scope('unpack'):
+      splits = tf.split(gv[0], elt_widths)
+      unpacked_gv = []
+      for idx, s in enumerate(splits):
+        unpacked_gv.append((tf.reshape(s, gpt.shapes[idx]), gpt.vars[idx]))
+  return unpacked_gv
+
+
+def pack_small_tensors(tower_grads, max_bytes=0, max_group=0):
+  """Concatenate small gradient tensors together for reduction.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples.
+    max_bytes: Int giving max number of bytes in a tensor that
+      may be considered small.
+    max_group: Int giving max number of small tensors that may be
+      concatenated into one new tensor.
+
+  Returns:
+    new_tower_grads, packing where new_tower_grads is identical to
+      tower_grads except that all feasible small_tensors have been removed
+      from their places and concatenated into larger tensors that are
+      now in the front of the list for each tower, and packing contains
+      the data necessary to restore the tower_grads structure.
+
+  Look through the first tower for gradients of the same type (float),
+  and small size, that are all sequential.  For each such group,
+  replace by a new tensor that is a flattened concatenation.  Note
+  that the corresponding variable will be absent, which doesn't matter
+  because it isn't used during all-reduce.
+
+  Requires:
+    Every gv_list in towers must have isomorphic structure including identical
+      tensor sizes and types.
+  """
+  small_indices = []
+  large_indices = []
+  for idx, (g, _) in enumerate(tower_grads[0]):
+    if g.dtype == tf.float32 and (4 * g.shape.num_elements()) <= max_bytes:
+      small_indices.append(idx)
+    else:
+      large_indices.append(idx)
+  small_ranges, small_singles = extract_ranges(
+      small_indices, range_size_limit=max_group)
+  large_indices = sorted(large_indices + small_singles)
+  num_gv = len(tower_grads[0])
+  packing = {}
+  if small_ranges:
+    new_tower_grads = []
+    for dev_idx, gv_list in enumerate(tower_grads):
+      assert len(gv_list) == num_gv
+      new_gv_list = []
+      for r in small_ranges:
+        key = '%d:%d' % (dev_idx, len(new_gv_list))
+        new_gv_list.append((pack_range(key, packing, gv_list, r),
+                            'packing_var_placeholder'))
+      for i in large_indices:
+        new_gv_list.append(gv_list[i])
+      new_tower_grads.append(new_gv_list)
+    return new_tower_grads, packing
+  else:
+    return tower_grads, None
+
+
+def unpack_small_tensors(tower_grads, packing):
+  """Undo the structure alterations to tower_grads done by pack_small_tensors.
+
+  Args:
+    tower_grads: List of List of (grad, var) tuples.
+    packing: A dict generated by pack_small_tensors describing the changes
+      it made to tower_grads.
+
+  Returns:
+    new_tower_grads: identical to tower_grads except that concatentations
+      of small tensors have been split apart and returned to their original
+      positions, paired with their original variables.
+  """
+  if not packing:
+    return tower_grads
+  new_tower_grads = []
+  num_devices = len(tower_grads)
+  num_packed = len(packing.keys()) // num_devices
+  for dev_idx, gv_list in enumerate(tower_grads):
+    new_gv_list = gv_list[num_packed:]
+    for i in xrange(0, num_packed):
+      k = '%d:%d' % (dev_idx, i)
+      gpt = packing[k]
+      gv = unpack_grad_tuple(gv_list[i], gpt)
+      for gi, idx in enumerate(gpt.indices):
+        assert idx == gpt.indices[gi]
+        new_gv_list.insert(idx, gv[gi])
+    new_tower_grads.append(new_gv_list)
+  return new_tower_grads
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/batch_allreduce.py b/TensorFlow/Translation/GNMT/variable_mgr/batch_allreduce.py
new file mode 100644
index 00000000..651e9f90
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/batch_allreduce.py
@@ -0,0 +1,612 @@
+# Copyright 2018 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains classes and functions for doing a single-machine batch all-reduce.
+
+An all-reduce is taking the reduction (typically a sum) of a list of tensors,
+each on a different device. The result must end up back on each device, which is
+where the word "all" comes from. In summary, each device starts with a single
+tensor, and ends up with the reduction of all tensors.
+
+A batch all-reduce is doing several independent all-reduces. When doing a batch
+all-reduce, care is taken to evenly distribute the reduction computations
+across devices and inter-device tensor transfers across device links.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# TODO(reedwm): Support distributed all-reduces in this file.
+# TODO(reedwm): Merge this code with allreduce.py, which contains some batch
+# all-reduce code that this file calls. allreduce.py also supports distributed
+# batch-reduce while this file only supports single-machine all-reduce.
+
+import abc
+from collections import namedtuple
+
+import six
+import tensorflow as tf
+
+from tensorflow.python.ops import gradients_impl
+
+from variable_mgr import allreduce
+from variable_mgr import constants
+
+
+def _all_reduce_using_copy(tensors_across_devices, use_mean):
+  """Does an all-reduce of a list of tensors by copying to the current device.
+
+  The tensors are copied to the current device and then reduced.
+
+  Args:
+    tensors_across_devices: A list of tensors, each on a different device.
+    use_mean: Whether to take the mean of the tensors instead of a sum:
+  Returns:
+    A reduced tensor on the current device.
+  """
+  assert tensors_across_devices
+  if isinstance(tensors_across_devices[0], tf.IndexedSlices):
+    reduced_tensor = gradients_impl._AggregateIndexedSlicesGradients(
+        tensors_across_devices)
+    if use_mean:
+      val = tf.multiply(reduced_tensor.values,
+                        float(1. / len(tensors_across_devices)))
+      reduced_tensor = tf.IndexedSlices(val, reduced_tensor.indices,
+                                        reduced_tensor.dense_shape)
+  else:
+    reduced_tensor = tf.add_n(tensors_across_devices)
+    if use_mean:
+      reduced_tensor *= 1. / len(tensors_across_devices)
+  return reduced_tensor
+
+
+@six.add_metaclass(abc.ABCMeta)
+class BatchAllReduceAlgorithm(object):
+  """Represents an algorithm for performing a batch all-reduce operation."""
+
+  def batch_all_reduce(self, all_device_tensors, num_splits, compact_tensors,
+                       defer_tensors):
+    """Performs a batch all-reduce.
+
+    The reduction done is a sum.
+
+    `all_device_tensors` is a list of list of tensors that will be batch
+    all-reduced. All tensors within a single inner list must be on the same
+    device. The nth element in each list, for any n, will be reduced together.
+    The return value is in the same form as `all_device_tensors`, except that
+    each tensor is reduced.
+
+    For example, if `all_device_tensors` is:
+    [[ A,  B  ],     # A and B are on GPU 0
+     [ C,  D  ]]     # C and D are on GPU 1
+
+    Then the return value will be:
+    [[ A+C,  B+D ],  # These two tensors are on GPU 0
+     [ A+C,  B+D ]]  # These two tensors are on GPU 1
+
+    Arguments:
+      all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]`
+        is a tensor where `i` is the device index and `j` is the tensor index.
+      num_splits: If not None, tensors will be concatenated and split into this
+        many pieces during the all-reduce, then split back into their original
+        shapes afterwards. Has no impact on correctness and can improve
+        performance. Requires all tensors to be the same type.
+      compact_tensors: If True, tensors are casted to fp16 before being all-
+        reduced. Improves performance, but hurts numerical stability.
+      defer_tensors: If True, every time the return value
+        `reduced_all_device_tensors` is evaluated, the result will be the
+        reduced tensors values of `all_device_tensors` from the previous session
+        run instead of the current session run, or zero on the first session
+        run. This can improve performance. When training neural networks,
+        deferring gradients often does not harm training, so this can be used to
+        improve performance.
+
+    Returns:
+      reduced_all_device_tensors: A list in the same form as
+        `all_device_tensors`, except each tensor has been reduced.
+      warmup_ops: A list of ops needed to be run once before the all-reduce can
+        occur.
+    """
+    # Before all-reducing tensors, we do several preprocessing functions that
+    # can speed up the all-reduce. We undo these functions after all-reducing
+    # the tensors.
+    warmup_ops = []
+    if num_splits:
+      packer = _TensorPacker(num_splits)
+      all_device_tensors = packer.concat_all_device_tensors(all_device_tensors)
+    # If enabled, we compact and defer tensors in between concatenating them
+    # and splitting them, because it is faster to do operations on a single
+    # concatenated tensor than on multiple smaller tensors.
+    if compact_tensors:
+      all_device_tensors_before_compact = all_device_tensors
+      all_device_tensors = _compact_all_device_tensors(all_device_tensors)
+    if defer_tensors:
+      all_device_tensors, put_ops, warmup_ops = _defer_all_device_tensors(
+          all_device_tensors)
+    if num_splits:
+      all_device_tensors = packer.split_all_device_tensors(all_device_tensors)
+
+    all_device_tensors = self._do_batch_all_reduce(all_device_tensors)
+
+    # Undo the preprocessing operations in opposite order as we applied them.
+    if num_splits:
+      all_device_tensors = packer.undo_split_all_device_tensors(
+          all_device_tensors)
+    # Note: There is no undo operation for deferring tensors. But we do need to
+    # call _add_put_op_control_deps at the end if we deferred the tensors.
+    if compact_tensors:
+      all_device_tensors = _undo_compact_all_device_tensors(
+          all_device_tensors, all_device_tensors_before_compact)
+    if num_splits:
+      all_device_tensors = packer.undo_concat_all_device_tensors(
+          all_device_tensors)
+
+    if defer_tensors:
+      all_device_tensors = _add_put_op_control_deps(all_device_tensors,
+                                                    num_splits, put_ops)
+    return all_device_tensors, warmup_ops
+
+  @abc.abstractmethod
+  def _do_batch_all_reduce(self, all_device_tensors):
+    """Performs a batch all-reduce.
+
+    Unlike `self.batch_all_reduce`, this does not do any preprocessing of the
+    tensors.
+
+    Args:
+      all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]`
+        is a tensor where `i` is the device index and `j` is the tensor index.
+    Returns:
+      reduced_all_device_tensors: A list in the same form as
+        `all_device_tensors`, except each tensor has been reduced.
+    """
+    pass
+
+
+class CopyToDeviceAlgorithm(BatchAllReduceAlgorithm):
+  """An algorithm that copies tensors to be reduced to a specific device."""
+
+  def __init__(self, devices_to_reduce_on, use_mean=False):
+    self._devices = devices_to_reduce_on
+    self._use_mean = use_mean
+
+  def _do_batch_all_reduce(self, all_device_tensors):
+    reduced_tensors = []
+    for i, tensors_across_devices in enumerate(zip(*all_device_tensors)):
+      with tf.device(self._devices[i % len(self._devices)]):
+        reduced_tensor = _all_reduce_using_copy(tensors_across_devices,
+                                                self._use_mean)
+        reduced_tensors.append(reduced_tensor)
+    # The tensors will be brought back to each device once they are used.
+    return [reduced_tensors] * len(all_device_tensors)
+
+
+class HierarchicalCopyAlgorithm(BatchAllReduceAlgorithm):
+  """An algorithm that uses hierarchical copies. This is only optimized for
+  eight devices connected in NetworkTopology.DGX1 or NetworkTopology.GCP_V100
+  topology.
+  """
+
+  def __init__(self, network_topology):
+    """Initializer for HierarchicalCopyAlgorithm.
+
+    Args:
+      network_topology: An instance of Enum class constants.NetworkTopology.
+    """
+    self._network_topology = network_topology
+
+  def _do_batch_all_reduce(self, all_device_tensors):
+    avail_devices = [device_tensors[0].device
+                     for device_tensors in all_device_tensors]
+    reduced_tensors = []
+    num_devices = len(avail_devices)
+    group_size = num_devices // 2
+    for i, tensors_across_devices in enumerate(zip(*all_device_tensors)):
+      group_0_main_device, group_1_main_device = self.__get_main_devices(
+          i, num_devices)
+      if group_0_main_device < group_size:
+        group_0_begin = 0
+        group_1_begin = group_size
+      else:
+        group_0_begin = group_size
+        group_1_begin = 0
+
+      # Reduce the first group.
+      group_0_tensors = tensors_across_devices[group_0_begin:
+                                               group_0_begin + group_size]
+      with tf.device(avail_devices[group_0_main_device]):
+        group_0_reduced_tensor = _all_reduce_using_copy(group_0_tensors, False)
+
+      # Reduce the second group.
+      group_1_tensors = tensors_across_devices[group_1_begin:
+                                               group_1_begin + group_size]
+      with tf.device(avail_devices[group_1_main_device]):
+        group_1_reduced_tensor = _all_reduce_using_copy(group_1_tensors, False)
+
+      # Reduce between the groups.
+      with tf.device(avail_devices[group_0_main_device]):
+        total_reduced_tensor = _all_reduce_using_copy(
+            [group_0_reduced_tensor, group_1_reduced_tensor], False)
+
+      # Broadcast the result back into the root of each group.
+      with tf.device(avail_devices[group_0_main_device]):
+        group_0_reduced_tensor_bcast = tf.identity(total_reduced_tensor)
+      with tf.device(avail_devices[group_1_main_device]):
+        group_1_reduced_tensor_bcast = tf.identity(total_reduced_tensor)
+
+      reduced_tensors_bcast = []
+      for j in range(len(tensors_across_devices)):
+        with tf.device(avail_devices[j]):
+          # Broadcast the result back to each member in the group from the root.
+          if (group_0_main_device < group_size) == (j < group_size):
+            src_device_tensor = group_0_reduced_tensor_bcast
+          else:
+            src_device_tensor = group_1_reduced_tensor_bcast
+          reduced_tensors_bcast.append(tf.identity(src_device_tensor))
+
+      reduced_tensors.append(reduced_tensors_bcast)
+
+    reduced_tensors = list(zip(*reduced_tensors))
+    return reduced_tensors
+
+  def __get_main_devices(self, tensor_index, num_devices):
+    """Returns the pair of main devices to use for initial reduction.
+
+    Args:
+      tensor_index: Index of the current tensor in the list of tensors to copy.
+      num_devices: Total number of devices.
+
+    Returns:
+      A tuple containing pair of main device indices for the initial
+      reduction. Then, the first element of the tuple should be used for the
+      final reduction.
+
+    Raises:
+      ValueError: Invalid input arguments.
+    """
+    if self._network_topology == constants.NetworkTopology.DGX1:
+      return tensor_index % num_devices, (tensor_index +
+                                          (num_devices // 2)) % num_devices
+    elif self._network_topology == constants.NetworkTopology.GCP_V100:
+      if num_devices != 8:
+        raise ValueError('HierarchicalCopy only supports eight devices in %s.' %
+                         self._network_topology)
+      # TODO(hinsu): Generalize main device indices to handle any other
+      # isomorphic connection graph that connects two cliques using connections
+      # other than 0-5 and 2-7.
+      main_device_pairs = [(0, 5), (2, 7), (5, 0), (7, 2)]
+      return main_device_pairs[tensor_index % len(main_device_pairs)]
+    else:
+      # TODO(reedwm): make this logic more general for arbitrary topology.
+      raise ValueError(
+          'HierarchicalCopy is not supported for %s network topology.' %
+          self._network_topology)
+
+
+class AllReduceSpecAlgorithm(BatchAllReduceAlgorithm):
+  """An algorithm that uses an all reduce spec."""
+
+  def __init__(self, all_reduce_spec, gpu_indices, agg_small_grads_max_bytes,
+               agg_small_grads_max_group):
+    spec = allreduce.parse_all_reduce_spec(all_reduce_spec)
+    if len(spec) != 1:
+      raise ValueError(
+          'Replicated mode does not support hybrid all-reduce strategies')
+    self._all_reduce_spec = spec[0]
+    self._gpu_indices = gpu_indices
+    self._agg_small_grads_max_bytes = agg_small_grads_max_bytes
+    self._agg_small_grads_max_group = agg_small_grads_max_group
+
+  def _do_batch_all_reduce(self, all_device_tensors):
+    # TODO(reedwm): Merge allreduce.sum_gradients_all_reduce with the other
+    # gradient aggregation code, since gradient aggregation is doing an all
+    # reduce. Currently, we do gradient repacking in two different places.
+    # TODO(reedwm): Change the allreduce code to reduce tensors instead of
+    # tower_grads.
+    tower_grads = [[(t, None) for t in device_tensors]
+                   for device_tensors in all_device_tensors]
+    aggregated_device_grads = allreduce.sum_gradients_all_reduce(
+        False,  # single_session
+        ['/job:localhost'],
+        tower_grads,
+        1,
+        self._all_reduce_spec.alg,
+        self._all_reduce_spec.shards,
+        self._gpu_indices,
+        agg_small_grads_max_bytes=self._agg_small_grads_max_bytes,
+        agg_small_grads_max_group=self._agg_small_grads_max_group)
+    return [[t for t, _ in grad_vars] for grad_vars in aggregated_device_grads]
+
+
+def algorithm_from_params(params):
+  """Returns a BatchAllReduceAlgorithm from a Params tuple."""
+  if params.all_reduce_spec:
+    if params.gpu_indices:
+      gpu_indices = [int(x) for x in params.gpu_indices.split(',')]
+    else:
+      gpu_indices = [x for x in range(params.num_gpus)]
+    return AllReduceSpecAlgorithm(params.all_reduce_spec, gpu_indices,
+                                  params.agg_small_grads_max_bytes,
+                                  params.agg_small_grads_max_group)
+  elif params.hierarchical_copy:
+    return HierarchicalCopyAlgorithm(params.network_topology)
+  else:
+    if params.local_parameter_device == 'gpu':
+      devices_to_reduce_on = ['/gpu:%d' % i for i in range(params.num_gpus)]
+    else:
+      devices_to_reduce_on = ['/cpu:0']
+    #### Made only for adam optimizer ####
+    return CopyToDeviceAlgorithm(devices_to_reduce_on, use_mean=True)
+
+
+def _apply_to_all_device_tensors(all_device_tensors, apply_func, colocate=True):
+  """Applies a function to each tensor in `all_device_tensors`.
+
+  A new list of lists of tensors is returned, where every tensor in
+  `all_device_tensors` has had `apply_func` called on it. `all_device_tensors`
+  is not modified.
+
+  Args:
+    all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]` is
+      a tensor where `i` is the device index and `j` is the tensor index.
+    apply_func: A function taking in three arguments: tensor, device_index,
+      tensor_index, and returning a modified tensor.
+      `tensor` is `all_device_tensors[device_index][tensor_index]`.
+    colocate: If True, apply_func will be run under context manager colocated
+      with it's input tensor.
+  Returns:
+    A list in the same form as `all_device_tensors`, except each tensor has had
+    `apply_func` called on it.
+  """
+  new_all_device_tensors = []
+  for device_index, device_tensors in enumerate(all_device_tensors):
+    new_device_tensors = []
+    for tensor_index, t in enumerate(device_tensors):
+      if colocate:
+        with tf.colocate_with(t):
+          new_t = apply_func(t, device_index, tensor_index)
+      else:
+        new_t = apply_func(t, device_index, tensor_index)
+      new_device_tensors.append(new_t)
+    new_all_device_tensors.append(new_device_tensors)
+  return new_all_device_tensors
+
+
+def _defer_tensor(tensor):
+  """Defers the retrieval of a tensor.
+
+  The tensor is put into a StagingArea, and the return value is the
+  retrieval of the tensor from the StagingArea. The effect is that the
+  tensor returned from this function is the tensor that was put in the
+  StagingArea for the previous Session.run() call.
+
+  Args:
+    tensor: The tensor to defer for one step.
+
+  Returns:
+    deferred_tensor: The tensor deferred for one step.
+    put_op: An op to put `tensor` in the StagingArea. Must be run every step
+      that `deferred_tensor` is run.
+    warmup_op: A warmup op that should be called before the first step. Puts
+      a zero tensor into the StagingArea.
+  """
+  tensor_stage = tf.contrib.staging.StagingArea([tensor.dtype], [tensor.shape])
+  put_op = tensor_stage.put([tensor])
+  warmup_op = tensor_stage.put([tf.zeros(tensor.shape, dtype=tensor.dtype)])
+
+  # Fetch the next tensor to use.
+  (tensor,) = tensor_stage.get()
+  return tensor, put_op, warmup_op
+
+
+def _defer_all_device_tensors(all_device_tensors):
+  """Defers every tensor in `all_device_tensors`."""
+  put_ops = [[] for _ in all_device_tensors]
+  warmup_ops = [[] for _ in all_device_tensors]
+  def apply_func(tensor, device_index, tensor_index):
+    del tensor_index
+    tensor, put_op, warmup_op = _defer_tensor(tensor)
+    put_ops[device_index].append(put_op)
+    warmup_ops[device_index].append(warmup_op)
+    return tensor
+  new_all_device_tensors = _apply_to_all_device_tensors(all_device_tensors,
+                                                        apply_func)
+  return new_all_device_tensors, put_ops, warmup_ops
+
+
+def _add_put_op_control_deps(all_device_tensors, num_splits, put_ops):
+  """Add control dependencies from `put_ops` to `all_device_tensors`.
+
+  This should only be called when deferred tensors are being used.
+
+  The control dependencies are added so that the put ops are run whenever
+  `all_device_tensors` is run. That way, the caller does not have to explicitly
+  run the put ops.
+
+  Args:
+    all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]` is
+      a tensor where `i` is the device index and `j` is the tensor index.
+    num_splits: The number of splits that were used for the all-reduce.
+    put_ops: A list of put ops from deferring the tensors.
+  Returns:
+    A list in the same form as `all_device_tensors`, except each tensor has a
+    control dependency on an op in `put_ops`.
+
+  """
+  def apply_func(tensor, device_index, tensor_index):
+    if num_splits == 0:
+      deps = [put_ops[device_index][tensor_index]]
+    else:
+      deps = put_ops[device_index]
+    assert len(deps) == 1
+    with tf.control_dependencies(deps):
+      return tf.identity(tensor, name='control_dependency')
+  return _apply_to_all_device_tensors(all_device_tensors, apply_func)
+
+
+def _compact_all_device_tensors(all_device_tensors):
+  """Compacts each tensor by casting to fp16."""
+  def apply_func(tensor, device_index, tensor_index):
+    del device_index, tensor_index
+    return tf.cast(tensor, tf.float16)
+  return _apply_to_all_device_tensors(all_device_tensors, apply_func)
+
+
+def _undo_compact_all_device_tensors(all_device_tensors,
+                                     orig_all_device_tensors):
+  """Uncompacts each tensor by casting to it's original dtype."""
+  def apply_func(tensor, device_index, tensor_index):
+    orig_tensor = orig_all_device_tensors[device_index][tensor_index]
+    with tf.colocate_with(orig_tensor):
+      return tf.cast(tensor, orig_tensor.dtype)
+  return _apply_to_all_device_tensors(all_device_tensors, apply_func,
+                                      colocate=False)
+
+
+class _TensorPacker(object):
+  """Packs and unpacks tensors into groups.
+
+  This class first concatenates a set of tensors, then split the concatenated
+  tensor into a small number of chunks. This is useful for all-reducing tensors,
+  as doing a small number of all-reduces on large tensors can be faster than
+  doing a large number of all-reduces on small tensors.
+  """
+
+  def __init__(self, num_splits):
+    """Initializes the _TensorPacker.
+
+    Args:
+      num_splits: The number of tensors to split the concatenated tensor into.
+        The batch all-reduce will consist of `num_splits` all-reduces.
+    """
+    assert num_splits > 0
+    self._num_splits = num_splits
+    self._next_method = 'concat'
+
+  _concat_tensor_state = namedtuple('_concat_tensor_state',
+                                    ['orig_shapes', 'orig_sizes'])
+
+  def _concat_tensors(self, device_tensors):
+    """Concatenate tensors into a single tensor."""
+    flat_tensors = [tf.reshape(t, [-1]) for t in device_tensors]
+    orig_shapes = [t.shape for t in device_tensors]
+    orig_sizes = [s.num_elements() for s in orig_shapes]
+    # All shapes must be fully defined.
+    assert None not in orig_sizes
+    concatenated_grad = tf.concat(flat_tensors, 0)
+    return concatenated_grad, self._concat_tensor_state(orig_shapes, orig_sizes)
+
+  def _split_tensors(self, concatenated_tensor):
+    """Splits concatenated tensor into `num_splits` pieces."""
+    # TODO(zhengxq): it is possible to optimize away the additional
+    # data movement by copying along the original tensor boundary.
+    # TODO(zhengxq): it is also possible to optimize away all the concat
+    # as well.
+    total_tensor_size = concatenated_tensor.shape.num_elements()
+    split_size = total_tensor_size // self._num_splits
+    split_size_last = total_tensor_size - split_size * (self._num_splits - 1)
+    split_sizes = [split_size] * (self._num_splits - 1) + [split_size_last]
+    tensor_packs = tf.split(concatenated_tensor, split_sizes)
+    return tensor_packs
+
+  def _undo_split_tensors(self, tensor_packs):
+    """Undoes self._split_tensors()."""
+    return tf.concat(tensor_packs, 0)
+
+  def _undo_concat_tensors(self, concatenated_tensor, concat_tensor_state):
+    """Undoes self._concat_tensors()."""
+    tensors_with_sizes = tf.split(concatenated_tensor,
+                                  concat_tensor_state.orig_sizes)
+    tensors_with_shapes = [
+        tf.reshape(grad, shape)
+        for grad, shape in zip(tensors_with_sizes,
+                               concat_tensor_state.orig_shapes)
+    ]
+    return tensors_with_shapes
+
+  def concat_all_device_tensors(self, all_device_tensors):
+    """For each device, concatenate the device's tensors into a single tensor.
+
+    Args:
+      all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]`
+        is a tensor where `i` is the device index and `j` is the tensor index.
+
+    Returns:
+      A list of list of tensors in a similar form as all_device_tensors, except
+      the tensors on each device have been concatenated. Each inner list
+      consists of a single concatenated tensor.
+    """
+    assert self._next_method == 'concat'
+    new_all_device_tensors = []
+    tensor_states = []
+    for device_tensors in all_device_tensors:
+      with tf.colocate_with(device_tensors[0]):
+        concat_tensor, tensor_state = self._concat_tensors(device_tensors)
+        new_all_device_tensors.append([concat_tensor])
+        tensor_states.append(tensor_state)
+    self._tensor_states = tensor_states
+    self._next_method = 'split'
+    return new_all_device_tensors
+
+  def split_all_device_tensors(self, all_device_tensors):
+    """Splits concatenated tensors into `num_splits` pieces.
+
+    `num_splits` is specified in the constructor.  In the case where the total
+    size of a concatenated tensor is not divisible by `num_splits`, the last
+    split tensor gets more elements.
+
+    Args:
+      all_device_tensors: A list of list of tensors. `all_device_tensors[i][j]`
+        is a tensor where `i` is the device index and `j` is the tensor index.
+        For each i, `all_device_tensors[i]` must be a list of length 1 of a
+        single concatenated tensor.
+
+    Returns:
+      A list of list of tensors in a similar form as all_device_tensors, except
+      the concatenated tensor on each device have been split. Each inner list
+      is a list of length `num_splits`.
+    """
+    assert self._next_method == 'split'
+    new_all_device_tensors = []
+    for [concat_tensor] in all_device_tensors:
+      with tf.colocate_with(concat_tensor):
+        new_all_device_tensors.append(self._split_tensors(concat_tensor))
+    self._orig_concat_all_device_tensors = all_device_tensors
+    self._next_method = 'undo_split'
+    return new_all_device_tensors
+
+  def undo_split_all_device_tensors(self, all_device_tensors):
+    """Undoes the effects of `split_all_device_tensors`."""
+    assert self._next_method == 'undo_split'
+    new_all_device_tensors = []
+    for i, device_tensors in enumerate(all_device_tensors):
+      [orig_tensor] = self._orig_concat_all_device_tensors[i]
+      with tf.colocate_with(orig_tensor):
+        new_all_device_tensors.append(
+            [self._undo_split_tensors(device_tensors)])
+    self._next_method = 'undo_concat'
+    return new_all_device_tensors
+
+  def undo_concat_all_device_tensors(self, all_device_tensors):
+    """Undoes the effects of `concat_all_device_tensors`."""
+    assert self._next_method == 'undo_concat'
+    new_all_device_tensors = []
+    for [concat_tensor], tensor_state in zip(all_device_tensors,
+                                             self._tensor_states):
+      with tf.colocate_with(concat_tensor):
+        new_all_device_tensors.append(self._undo_concat_tensors(concat_tensor,
+                                                                tensor_state))
+    self._next_method = None
+    return new_all_device_tensors
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/constants.py b/TensorFlow/Translation/GNMT/variable_mgr/constants.py
new file mode 100644
index 00000000..c92ee5bb
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/constants.py
@@ -0,0 +1,49 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Constants used in tf_cnn_benchmarks."""
+
+from enum import Enum
+
+
+class NetworkTopology(str, Enum):
+  """Network topology describes how multiple GPUs are inter-connected.
+  """
+  # DGX-1 uses hybrid cube mesh topology with the following device peer to peer
+  # matrix:
+  # DMA: 0 1 2 3 4 5 6 7
+  # 0:   Y Y Y Y Y N N N
+  # 1:   Y Y Y Y N Y N N
+  # 2:   Y Y Y Y N N Y N
+  # 3:   Y Y Y Y N N N Y
+  # 4:   Y N N N Y Y Y Y
+  # 5:   N Y N N Y Y Y Y
+  # 6:   N N Y N Y Y Y Y
+  # 7:   N N N Y Y Y Y Y
+  DGX1 = "dgx1"
+
+  # V100 in GCP are connected with the following device peer to peer matrix.
+  # In this topology, bandwidth of the connection depends on if it uses NVLink
+  # or PCIe link.
+  # DMA: 0 1 2 3 4 5 6 7
+  # 0:   Y Y Y Y N Y N N
+  # 1:   Y Y Y Y N N N N
+  # 2:   Y Y Y Y N N N Y
+  # 3:   Y Y Y Y N N N N
+  # 4:   N N N N Y Y Y Y
+  # 5:   Y N N N Y Y Y Y
+  # 6:   N N N N Y Y Y Y
+  # 7:   N N Y N Y Y Y Y
+  GCP_V100 = "gcp_v100"
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr.py b/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr.py
new file mode 100644
index 00000000..21ff9ca6
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr.py
@@ -0,0 +1,254 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines VariableMgr and subclasses used to manage variables.
+
+"""
+
+from __future__ import print_function
+
+import re
+
+import tensorflow as tf
+
+from utils import misc_utils
+from variable_mgr import allreduce
+from variable_mgr import batch_allreduce
+from variable_mgr import variable_mgr_util
+
+
+class VariableMgr(object):
+  """Abstract superclass for class used by BenchmarkCNN to control variables.
+
+    Functions on this class are used to control how variables are created and
+    managed, and how gradients are computed and applied.
+  """
+
+  def __init__(self, benchmark_cnn):
+    self.benchmark_cnn = benchmark_cnn
+    self.staging_delta_ops = []
+    self.use_resource_vars = benchmark_cnn.params.use_resource_vars
+
+    # A variable for automatic loss scaling.
+    self.grad_has_inf_nan = None
+
+  def each_tower_has_variables(self):
+    """Returns True if each GPU tower of the model has separate variables."""
+    assert False, 'Must be implemented in subclass'
+
+  def supports_staged_vars(self):
+    """Whether staged variable management is supported."""
+    return False
+
+  def create_outer_variable_scope(self, device_num):
+    """Create the tf.variable_scope around all model graph operations."""
+    del device_num  # unused by this implementation
+    assert False, 'Must be implemented in subclass'
+
+  def preprocess_device_grads(self, device_grads):
+    """Preprocess the device gradients prior to applying them.
+
+    Args:
+      device_grads: List of lists of (gradient, variable) tuples.
+        device_grads[t][g] = (gradient, variable), where t is the index of the
+        tower and g is the index of the gradient-variable pair.
+
+    Returns: a tuple of (apply_gradients_devices, gradient_state).
+      gradient_state is an opaque structure that should be passed to
+      get_gradients_to_apply() and append_apply_gradients_ops() (in that order).
+      apply_gradients_devices is a list of devices where the gradients will be
+      applied with get_gradients_to_apply() and append_apply_gradients_ops().
+    """
+    del device_grads  # unused by this implementation
+    assert False, 'Must be implemented in subclass'
+
+  def get_gradients_to_apply(self, device_num, gradient_state):
+    """Returns the [(gradient, variable)] list to apply for device_num.
+
+    Args:
+      device_num: indexes into apply_gradients_devices, which was returned by an
+        earlier call to preprocess_device_grads.
+      gradient_state: from previous call to apply_gradients_devices.
+    """
+    del device_num, gradient_state  # unused by this implementation
+    assert False, 'Must be implemented in subclass'
+
+  def append_apply_gradients_ops(self, gradient_state, opt, grads, training_ops,
+                                 loss_scale_params):
+    """Adds training ops for grads to 'training_ops'.
+
+
+
+    Args:
+      gradient_state: from previous call to apply_gradients_devices.
+      opt: the underlying optimizer
+      grads: [(grad, var)] to apply
+      training_ops: list to which to add ops
+      loss_scale_params: parameters for loss scaling.
+    """
+    del gradient_state  # unused by this implementation
+
+    def get_apply_gradients_ops_func():
+      """Returns the apply_gradients op."""
+      return [opt.apply_gradients(grads)]
+
+    variable_mgr_util.append_gradients_with_loss_scale(
+        training_ops, get_apply_gradients_ops_func, loss_scale_params,
+        self.grad_has_inf_nan)
+
+  def get_post_init_ops(self):
+    """Returns ops that should run post-initialization."""
+    return []
+
+  def get_devices(self):
+    """Returns devices to use for computation; includes replica selection."""
+    assert False, 'Must be implemented in subclass'
+
+  def savable_variables(self):
+    """Returns a list/dict of savable variables to pass to tf.train.Saver."""
+    return tf.global_variables()
+
+  def trainable_variables_on_device(self,
+                                    rel_device_num,
+                                    abs_device_num,
+                                    writable=False):
+    """Return the set of trainable variables on device.
+
+    Args:
+      rel_device_num: local worker device index.
+      abs_device_num: global graph device index.
+      writable: whether to get a reference to the underlying variable.
+
+    Returns:
+      The set of trainable variables on the specified device.
+    """
+    del rel_device_num, writable
+    if self.each_tower_has_variables():
+      params = [
+          v for v in tf.trainable_variables()
+          if v.name.startswith('v%s/' % abs_device_num)
+      ]
+    else:
+      params = tf.trainable_variables()
+    return params
+
+class VariableMgrLocalReplicated(VariableMgr):
+  """VariableMgr that implements the --replicated mode for local jobs.
+
+     Each GPU has its own copy of the variables. To apply gradients,
+     either a local all-reduce algorithm is applied or a regular
+     cross-device aggregation is used to replicate the combined
+     gradients to all towers.
+  """
+
+  def __init__(self, benchmark_cnn, all_reduce_spec,
+               agg_small_grads_max_bytes, agg_small_grads_max_group,
+               allreduce_merge_scope):
+    super(VariableMgrLocalReplicated, self).__init__(benchmark_cnn)
+    if all_reduce_spec:
+      spec = allreduce.parse_all_reduce_spec(all_reduce_spec)
+      if len(spec) != 1:
+        raise ValueError(
+            'replicated mode does not support hybrid all-reduce strategies')
+      self._all_reduce_spec = spec[0]
+    else:
+      self._all_reduce_spec = None
+    self._agg_small_grads_max_bytes = agg_small_grads_max_bytes
+    self._agg_small_grads_max_group = agg_small_grads_max_group
+    self._warmup_ops = []
+    self._allreduce_merge_scope = allreduce_merge_scope
+    self._gradient_put_ops = None
+
+  def each_tower_has_variables(self):
+    return True
+
+  def create_outer_variable_scope(self, device_num):
+    return tf.variable_scope('v%s' % device_num,
+                             use_resource=self.use_resource_vars)
+
+  def preprocess_device_grads(self, device_grads):
+    compact_grads = (self.benchmark_cnn.params.use_fp16 and
+                     self.benchmark_cnn.params.compact_gradient_transfer)
+    defer_grads = (self.benchmark_cnn.params.variable_consistency == 'relaxed')
+
+    grads_to_reduce = [[g for g, _ in grad_vars] for grad_vars in device_grads]
+    algorithm = batch_allreduce.algorithm_from_params(self.benchmark_cnn.params)
+    reduced_grads, self._warmup_ops = algorithm.batch_all_reduce(
+        grads_to_reduce, self.benchmark_cnn.params.gradient_repacking,
+        compact_grads, defer_grads)
+    assert not self._warmup_ops
+    if (self.benchmark_cnn.params.use_fp16 and
+        self.benchmark_cnn.enable_auto_loss_scale):
+      # Check for infs or nans
+      is_finite_list = []
+      with tf.name_scope('check_for_inf_and_nan'):
+        for tower_grads in reduced_grads:
+          with tf.colocate_with(tower_grads[0]):
+            # TODO(tanmingxing): Create fused op that takes in a list of tensors
+            # as input and returns scalar boolean True if there are any
+            # infs/nans.
+            is_finite_list.append(tf.reduce_all(
+                [tf.reduce_all(tf.is_finite(g)) for g in tower_grads]))
+        self.grad_has_inf_nan = tf.logical_not(tf.reduce_all(is_finite_list))
+    reduced_device_grads = [[
+        (g, v) for g, (_, v) in zip(grads, grad_vars)
+    ] for grads, grad_vars in zip(reduced_grads, device_grads)]
+    return self.benchmark_cnn.devices, reduced_device_grads
+
+  def get_gradients_to_apply(self, device_num, gradient_state):
+    device_grads = gradient_state
+    return device_grads[device_num]
+
+  def get_post_init_ops(self):
+    # Copy initialized values for variables on GPU 0 to other GPUs.
+    global_vars = tf.global_variables()
+    var_by_name = dict([(v.name, v) for v in global_vars])
+    post_init_ops = []
+    copy_froms = set()
+    skipped_vars = []
+    for v in global_vars:
+      split_name = v.name.split('/')
+      # TODO(b/62630508): use more specific prefix than v or v0.
+      if split_name[0] == 'v0' or not v.name.startswith('v'):
+        skipped_vars.append(v)
+        continue
+      # Only vars starts with "v[number]" are synced.
+      split_name[0] = 'v0'
+      copy_from = var_by_name['/'.join(split_name)]
+      copy_froms.add(copy_from)
+      post_init_ops.append(v.assign(copy_from.read_value()))
+    post_init_ops += self._warmup_ops
+    # If copy-froms is empty, then all vars are actually saved.
+    misc_utils.print_out('All copy-from vars(%d): ' % len(copy_froms))
+    for gv in copy_froms:
+      misc_utils.print_out(gv.name)
+    misc_utils.print_out('All skippped vars(%d): ' % len(skipped_vars))
+    for gv in skipped_vars:
+      misc_utils.print_out(gv.name)
+    assert len(skipped_vars) >= len(copy_froms)
+
+    return post_init_ops
+
+  def savable_variables(self):
+    """Return the set of variables used for saving/loading the model."""
+    params = []
+    for v in tf.global_variables():
+      split_name = v.name.split('/')
+      if split_name[0] == 'v0' or not v.name.startswith('v'):
+        params.append(v)
+    return params
+
+  def get_devices(self):
+    return self.benchmark_cnn.raw_devices
+
diff --git a/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr_util.py b/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr_util.py
new file mode 100644
index 00000000..62ebc5d8
--- /dev/null
+++ b/TensorFlow/Translation/GNMT/variable_mgr/variable_mgr_util.py
@@ -0,0 +1,534 @@
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for VariableMgr."""
+
+from __future__ import print_function
+
+import collections as pycoll
+import operator
+
+import tensorflow as tf
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import gradients_impl
+
+
+PS_SHADOW_VAR_PREFIX = 'ps_var'
+
+AutoLossScaleParams = pycoll.namedtuple(
+    'AutoLossScaleParams',
+    [
+        # If true, enable automatic loss scaling.
+        'enable_auto_loss_scale',
+        # The value to scale the loss before computing gradients.
+        'loss_scale',
+        # Number of normal steps with the current `loss_scale`.
+        'loss_scale_normal_steps',
+        # Increase loss scale every n steps.
+        'inc_loss_scale_every_n',
+        # If true, the current worker is chief. The current implementation
+        # relies on the chief to update loss_scale value, but in future, we
+        # might change this to ask the parameter server to update loss_scales
+        # for better performance.
+        # TODO(tanmingxing): remove this if loss_scale is updated in ps.
+        'is_chief',
+    ])
+
+
+def get_loss_scale_update_op(loss_scale, loss_scale_normal_steps,
+                             inc_loss_scale_every_n):
+  """Returns the update op for loss scaling variables.
+
+  We maintain the counter `loss_scale_normal_steps` to count the number of steps
+  we have been using the current `loss_scale`. In most cases, this function
+  increments `loss_scale_normal_steps`. However, if `loss_scale_normal_steps` is
+  greater than the threshold `inc_loss_scale_every_n`, we double `loss_scale`
+  and reset `loss_scale_normal_steps` to zero.
+
+  This op is only called if the gradients don't have any infs or nans. Instead,
+  if infs or nans occur in the gradients, we immeditately halve `loss_scale` and
+  reset `loss_scale_normal_steps` to zero.
+
+  Args:
+    loss_scale: a tf.Variable represneting the loss_scale value.
+    loss_scale_normal_steps: a tf.Variable representing the number of training
+      steps that have run since the loss_scale last changed.
+    inc_loss_scale_every_n: a Python integer threshold. `loss_scale` is
+      increased every `inc_loss_scale_every_n` steps, unless the gradients have
+      infs or nans.
+
+  Returns:
+    An op for updating `loss_scale` and `loss_scale_normal_steps`.
+  """
+
+  def increment_loss_scale_normal_steps_func():
+    return tf.group(loss_scale_normal_steps.assign_add(1))
+
+  def increase_loss_scale_func():
+    return tf.group(
+        tf.assign(loss_scale_normal_steps, 0),
+        tf.assign(loss_scale, loss_scale * 2))
+
+  # true_fn and false_fn must have the same type.
+  return tf.cond(loss_scale_normal_steps < inc_loss_scale_every_n,
+                 increment_loss_scale_normal_steps_func,
+                 increase_loss_scale_func)
+
+
+def append_gradients_with_loss_scale(training_ops, get_apply_gradients_ops_func,
+                                     loss_scale_params, grad_has_inf_nan):
+  """Selectively appends gradients update ops with loss scaling.
+
+  Args:
+    training_ops: a list of training ops to be executed.
+    get_apply_gradients_ops_func: a function that returns a list of ops for
+      applying gradients. Here, we must pass a function instead of the actual
+      list of ops; otherwise, those ops would be executed unconditionally due to
+      the semantics of tf.cond.
+    loss_scale_params: An AutoLossScaleParams tuple.
+    grad_has_inf_nan: Boolean tensor indicating whether the gradients have infs
+      or nans.
+  """
+  is_chief = loss_scale_params.is_chief
+  loss_scale = loss_scale_params.loss_scale
+  loss_scale_normal_steps = loss_scale_params.loss_scale_normal_steps
+  inc_loss_scale_every_n = loss_scale_params.inc_loss_scale_every_n
+  enable_auto_loss_scale = loss_scale_params.enable_auto_loss_scale
+
+  if loss_scale is None or not enable_auto_loss_scale or not is_chief:
+    training_ops.extend(get_apply_gradients_ops_func())
+  else:
+    # If nans/infs occurred, skip applying gradients and instead update
+    # loss_scale (halve loss_scale and reset loss_scale_normal_steps to zero).
+    def update_op_if_nan_or_inf():
+      """Update loss_scale and discard gradients if nans/infs occurred."""
+      return tf.group(
+          tf.assign(loss_scale, loss_scale / 2.),
+          tf.assign(loss_scale_normal_steps, 0))
+
+    # Otherwise, apply gradients, and update loss_scale and
+    # loss_scale_normal_steps.
+    def update_op_if_no_nan_or_inf():
+      """Apply gradients, and update loss scaling."""
+      return tf.group(
+          get_loss_scale_update_op(loss_scale, loss_scale_normal_steps,
+                                   inc_loss_scale_every_n),
+          *get_apply_gradients_ops_func())
+
+    # TODO(tanmingxing): Add support for independent and distributed all_reduce.
+    assert grad_has_inf_nan is not None
+    update_op = tf.cond(
+        grad_has_inf_nan,
+        update_op_if_nan_or_inf,
+        update_op_if_no_nan_or_inf,
+        name='cond_if_grad_has_inf_nan'
+    )
+    training_ops.append(update_op)
+
+
+# To be used with custom_getter on tf.get_variable.
+class OverrideCachingDevice(object):
+  """Variable getter which caches variables on the least loaded device.
+
+  Variables smaller than a certain threshold are cached on a single specific
+  device, as specified in the constructor. All other variables are load balanced
+  across a pool of devices, by caching each variable on the least loaded device.
+
+  Note that variable creation only happen when building the model graph on the
+  first device (see how it sets the 'reuse' parameter in
+  VariableMgr.*.create_outer_variable_scope()). That means, for all other
+  devices, the variable scope will reuse the variables created before, which
+  requires that we set the caching_device correctly as otherwise it may not be
+  able to find the previously created variable and will create a new one. This
+  requires when building the model graph on different devices, variables with
+  the same name should have same size.
+
+  TODO(laigd): consider adding tests or verification logic to enforce this, or
+  refactor it.
+  """
+
+  def __init__(self, devices, device_for_small_variables,
+               small_variable_size_threshold):
+    self.devices = devices
+    self.sizes = [0] * len(self.devices)
+    self.device_for_small_variables = device_for_small_variables
+    self.small_variable_size_threshold = small_variable_size_threshold
+
+  def __call__(self, getter, *args, **kwargs):
+    size = tf.TensorShape(kwargs['shape']).num_elements()
+    if size < self.small_variable_size_threshold:
+      device_name = self.device_for_small_variables
+    else:
+      device_index, _ = min(enumerate(self.sizes), key=operator.itemgetter(1))
+      device_name = self.devices[device_index]
+      self.sizes[device_index] += size
+
+    kwargs['caching_device'] = device_name
+    var = getter(*args, **kwargs)
+    return var
+
+
+# To be used with custom_getter on tf.get_variable. Ensures the created variable
+# is in LOCAL_VARIABLES and not GLOBAL_VARIBLES collection.
+class OverrideToLocalVariableIfNotPsVar(object):
+
+  # args and kwargs come from the custom_getter interface for Tensorflow
+  # variables, and matches tf.get_variable's signature, with the addition of
+  # 'getter' at the beginning.
+  def __call__(self, getter, name, *args, **kwargs):
+    if name.startswith(PS_SHADOW_VAR_PREFIX):
+      return getter(*args, **kwargs)
+
+    if 'collections' in kwargs:
+      collections = kwargs['collections']
+    if not collections:
+      collections = [tf.GraphKeys.GLOBAL_VARIABLES]
+    else:
+      collections = collections[:]
+    collections.remove(tf.GraphKeys.GLOBAL_VARIABLES)
+    collections.append(tf.GraphKeys.LOCAL_VARIABLES)
+    kwargs['collections'] = list(collections)
+    return getter(name, *args, **kwargs)
+
+
+class ParamServerDeviceSetter(object):
+  """Helper class to assign variables on the least loaded ps-device."""
+
+  def __init__(self, worker_device, ps_devices):
+    """Initializer for ParamServerDevicSetter.
+
+    Args:
+      worker_device: the device to use for computer ops.
+      ps_devices: a list of device to use for Variable ops. Each variable is
+      assigned to the least loaded device.
+    """
+    self.ps_devices = ps_devices
+    self.worker_device = worker_device
+    self.ps_sizes = [0] * len(self.ps_devices)
+
+  def __call__(self, op):
+    if op.device:
+      return op.device
+    if op.type not in ['Variable', 'VariableV2']:
+      return self.worker_device
+
+    device_index, _ = min(enumerate(self.ps_sizes), key=operator.itemgetter(1))
+    device_name = self.ps_devices[device_index]
+    var_size = op.outputs[0].get_shape().num_elements()
+    self.ps_sizes[device_index] += var_size
+
+    return device_name
+
+
+class StagedModelVariable(object):
+  """Staging variable wrapper that decouples reads and updates.
+
+  This class represents a variable through a staging buffer. Reads from this
+  variable directly gets from the staging buffer. Updates are stacked into
+  another staging buffer, and will be processed later.
+  """
+
+  def __init__(self, real_var, var_stage_get, variable_mgr):
+    """Initializer for the model variables through a staging buffer.
+
+    Args:
+      real_var: the underlying real variable.
+      var_stage_get: the read op from the staging buffer.
+      variable_mgr: the parent variable-manager.
+    """
+    self.real_var = real_var
+    self.var_stage_get = var_stage_get
+    self.variable_mgr = variable_mgr
+
+  def _value(self):
+    """The read access of this variable. The content from the staging buffer."""
+    return self.var_stage_get
+
+  def _ref(self):
+    """Return the underlying variable ref, required by tf.colocate_with."""
+    return self.real_var._ref()  # pylint: disable=protected-access
+
+  def read_value(self):
+    """Mimics tf.Variable.read_value()."""
+    return tf.identity(self.var_stage_get, name='read')
+
+  @property
+  def dtype(self):
+    """Return the non-reference dtype."""
+    return self.var_stage_get.dtype
+
+  def assign_sub(self, delta, name=None):
+    """Mimic the updates to the variable.
+
+    Args:
+      delta: is pushed into a staging buffer and will be pumped later.
+      name: currently ignored; names of ops and the StagingArea are
+            computed without using this pass name.
+    Returns:
+      The actual updates. The colocation constraint will be reapplied.
+    """
+    # This parameter is ignored: the StagingArea only supports setting
+    # the shared name, not the names of individual ops it uses.
+    del name
+
+    # colocate_with(None, True) clears the colocation constraints.
+    # Push the delta into a staging buffer.
+    with ops.colocate_with(None, True), tf.device(self.var_stage_get.device):
+      delta_staging_area = tf.contrib.staging.StagingArea(
+          [self.var_stage_get.dtype], shapes=[self.var_stage_get.shape])
+      delta_put_op = delta_staging_area.put([delta])
+      self.variable_mgr.staging_delta_ops.append(delta_put_op)
+      delta_get_op = delta_staging_area.get()[0]
+    # Return the actual updates. The colocation constraint will be reapplied.
+    return self.real_var.assign_sub(delta_get_op)
+
+  @staticmethod
+  # pylint: disable=bad-staticmethod-argument,invalid-name
+  def _TensorConversionFunction(self, dtype=None, name=None, as_ref=False):
+    """Utility function for converting a StagedModelVariable to a Tensor."""
+    del dtype, name  # unused: this function returns the cached ref or value.
+    if as_ref:
+      return self._ref()
+    else:
+      return self._value()
+
+
+ops.register_tensor_conversion_function(
+    StagedModelVariable, StagedModelVariable._TensorConversionFunction)  # pylint: disable=protected-access
+
+
+class StagedVariableGetter(object):
+  """A variable getter through staging buffers on devices.
+
+  Instead of a caching device, this getter tracks where the variable is used.
+  And on each device, it goes through a staging buffer.
+  """
+
+  def __init__(self, device_num, devices, cpu_device, variable_mgr):
+    """Initializer for StagedVariableGetter.
+
+    Args:
+      device_num: the current device index.
+      devices: a list of all the devices to build towers.
+      cpu_device: a cpu_device for this replica. If None, no cpu-caching is
+          done.
+      variable_mgr: the parent variable manager.
+    """
+    self.device_num = device_num
+    self.devices = devices
+    self.cpu_device = cpu_device
+    self.variable_mgr = variable_mgr
+
+  def __call__(self, getter, name, *args, **kwargs):
+    staging_ops = self.variable_mgr.staging_vars_on_devices[self.device_num]
+    if name in staging_ops:
+      put_op, get_op = staging_ops[name]
+      return get_op
+    real_var = getter(name, *args, **kwargs)
+    shape = kwargs['shape']
+    dtype = kwargs['dtype']
+    trainable = kwargs['trainable']
+    if self.cpu_device:
+      with tf.device(self.cpu_device):
+        # This helps copying the weights from the parameter to this server only
+        # once.
+        if name in self.variable_mgr.staged_vars_on_cpu:
+          cpu_var = self.variable_mgr.staged_vars_on_cpu[name]
+        else:
+          cpu_var = tf.identity(real_var)
+          self.variable_mgr.staged_vars_on_cpu[name] = cpu_var
+      var_to_stage = cpu_var
+    else:
+      var_to_stage = tf.identity(real_var)  # de-reference the variable.
+
+    with tf.device(self.devices[self.device_num]):
+      staging_area = tf.contrib.staging.StagingArea([dtype], shapes=[shape])
+      put_op = staging_area.put([var_to_stage])
+      get_op = staging_area.get()[0]
+      staging_ops[name] = (put_op, get_op)
+    if trainable:
+      # For trainable variables, they are managed separatedly through
+      # apply_gradients.
+      return get_op
+    else:
+      # For other shadow variables, the access is decoupled through a wrapper
+      # class.
+      return StagedModelVariable(real_var, get_op, self.variable_mgr)
+
+  def trainable_variables_on_device(self, rel_device_num, abs_device_num,
+                                    writable):
+    """Return the set of trainable variables on the specified device.
+
+    Args:
+      rel_device_num: local worker device index.
+      abs_device_num: global graph device index.
+      writable: whether the returned variables is writable or read-only.
+
+    Returns:
+      Return the set of trainable variables on the specified device.
+    """
+    del abs_device_num
+    params_refs = tf.trainable_variables()
+    if writable:
+      return params_refs
+    params = []
+    for param in params_refs:
+      var_name = param.name.split(':')[0]
+      _, var_get_op = self.variable_mgr.staging_vars_on_devices[rel_device_num][
+          var_name]
+      params.append(var_get_op)
+    return params
+
+
+def aggregate_gradients_using_copy_with_device_selection(
+    benchmark_cnn, tower_grads, use_mean, check_inf_nan):
+  """Aggregate gradients, controlling device for the aggregation.
+
+  Args:
+    benchmark_cnn: benchmark_cnn class.
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over towers. The inner list is over individual gradients.
+    use_mean: if True, mean is taken, else sum of gradients is taken.
+    check_inf_nan: If true, check grads for nans and infs.
+
+  Returns:
+    The tuple ([(average_gradient, variable),], has_nan_or_inf) where the
+      gradient has been averaged across all towers. The variable is chosen from
+      the first tower. The has_nan_or_inf indicates the grads has nan or inf.
+  """
+  if benchmark_cnn.local_parameter_device_flag == 'gpu':
+    avail_devices = benchmark_cnn.raw_devices
+  else:
+    avail_devices = [benchmark_cnn.param_server_device]
+  agg_grads = []
+  has_nan_or_inf_list = []
+  for i, single_grads in enumerate(zip(*tower_grads)):
+    with tf.device(avail_devices[i % len(avail_devices)]):
+      grad_and_var, has_nan_or_inf = aggregate_single_gradient_using_copy(
+          single_grads, use_mean, check_inf_nan)
+      agg_grads.append(grad_and_var)
+      has_nan_or_inf_list.append(has_nan_or_inf)
+  if check_inf_nan:
+    return agg_grads, tf.reduce_any(has_nan_or_inf_list)
+  else:
+    return agg_grads, None
+
+
+def aggregate_gradients_using_copy_with_variable_colocation(
+    tower_grads, use_mean, check_inf_nan):
+  """Aggregate gradients, colocating computation with the gradient's variable.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over towers. The inner list is over individual gradients. All variables
+      of the same gradient across towers must be the same (that is,
+      tower_grads[x][a][1] == tower_grads[y][a][1] for all indices x, y, and a)
+    use_mean: if True, mean is taken, else sum of gradients is taken.
+    check_inf_nan: If true, check grads for nans and infs.
+
+  Returns:
+    The tuple ([(average_gradient, variable),], has_nan_or_inf) where the
+      gradient has been averaged across all towers. The variable is chosen from
+      the first tower. The has_nan_or_inf indicates the grads has nan or inf.
+  """
+  agg_grads = []
+  has_nan_or_inf_list = []
+  for single_grads in zip(*tower_grads):
+    # Note that each single_grads looks like the following:
+    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+    var = single_grads[0][1]
+
+    for _, v in single_grads:
+      assert v == var
+
+    with tf.device(var.device):
+      grad_and_var, has_nan_or_inf = aggregate_single_gradient_using_copy(
+          single_grads, use_mean, check_inf_nan)
+      agg_grads.append(grad_and_var)
+      has_nan_or_inf_list.append(has_nan_or_inf)
+
+  if check_inf_nan:
+    return agg_grads, tf.reduce_any(has_nan_or_inf_list)
+  else:
+    return agg_grads, None
+
+
+def aggregate_gradients_using_copy(tower_grads, use_mean, check_inf_nan):
+  """Calculate the average gradient for each shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over towers. The inner list is over individual gradients.
+    use_mean: if True, mean is taken, else sum of gradients is taken.
+    check_inf_nan: check grads for nans and infs.
+
+  Returns:
+    The tuple ([(average_gradient, variable),], has_nan_or_inf) where the
+      gradient has been averaged across all towers. The variable is chosen from
+      the first tower. The has_nan_or_inf indicates the grads has nan or inf.
+  """
+  agg_grads = []
+  has_nan_or_inf_list = []
+
+  for single_grads in zip(*tower_grads):
+    grad_and_var, has_nan_or_inf = aggregate_single_gradient_using_copy(
+        single_grads, use_mean, check_inf_nan)
+    agg_grads.append(grad_and_var)
+    has_nan_or_inf_list.append(has_nan_or_inf)
+
+  if check_inf_nan:
+    return agg_grads, tf.reduce_any(has_nan_or_inf_list)
+  else:
+    return agg_grads, None
+
+
+def aggregate_single_gradient_using_copy(grad_and_vars, use_mean,
+                                         check_inf_nan):
+  """Calculate the average gradient for a shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
+      (gradient, variable) pair within the outer list represents the gradient
+      of the variable calculated for a single tower, and the number of pairs
+      equals the number of towers.
+    use_mean: if True, mean is taken, else sum of gradients is taken.
+    check_inf_nan: check grads for nans and infs.
+
+  Returns:
+    The tuple ([(average_gradient, variable),], has_nan_or_inf) where the
+      gradient has been averaged across all towers. The variable is chosen from
+      the first tower. The has_nan_or_inf indicates the grads has nan or inf.
+  """
+  grads = [g for g, _ in grad_and_vars]
+  if any(isinstance(g, tf.IndexedSlices) for g in grads):
+    # TODO(reedwm): All-reduce IndexedSlices more effectively.
+    grad = gradients_impl._AggregateIndexedSlicesGradients(grads)  # pylint: disable=protected-access
+  else:
+    grad = tf.add_n(grads)
+
+  if use_mean and len(grads) > 1:
+    grad = tf.scalar_mul(1.0 / len(grads), grad)
+
+  v = grad_and_vars[0][1]
+  if check_inf_nan:
+    with tf.name_scope('check_for_inf_and_nan'):
+      has_nan_or_inf = tf.logical_not(tf.reduce_all(tf.is_finite(grads)))
+    return (grad, v), has_nan_or_inf
+  else:
+    return (grad, v), None